diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..64dcd00 --- /dev/null +++ b/.gitignore @@ -0,0 +1,74 @@ +# Temporarily ignore CSVs until we choose which ones should be committed +*.csv +slurm +archives +output +__pycache__ +*.pyc +.ipynb_checkpoints +*.html + +# From GitHub .gitignore Python template + +# Ignore large files, download manually +# data/reference_files/ +# data/ +alignments +baseline_models/model_checkpoints/ +outputs/intermediate_outputs/ +outputs/output_scores/ +clustal/ +processed_data/ +make_website_data_folder.py +website_data/ + +# Ignore vespa output +vespa_run_directory + +# Ignoring slurm scripts that we use for scoring +o2_scripts/ +slurm_scripts/ + +# Ignoring HMM model files +# And temporary scripts directories +scripts/scoring_DMS_zero_shot/HMM_temp/ +scripts/scoring_DMS_zero_shot/gemme_tmp/ +**/gemme_tmp/* +**/vespa_tmp/* + + +# Optional: +baseline_models/bash_scripts/old/ +# Slurm script outputs +*.out +*.err +logs/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Jupyter Notebook +.ipynb_checkpoints \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..3c47051 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 OATML-Markslab, Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood van Niekerk + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..6b020ea --- /dev/null +++ b/README.md @@ -0,0 +1,226 @@ +# ProteinGym + +## Table of Contents + +- [Overview](#overview) +- [Fitness prediction performance](#fitness-prediction-performance) +- [Resources](#resources) +- [How to contribute?](#how-to-contribute) +- [Usage and reproducibility](#usage-and-reproducibility) +- [Acknowledgements](#acknowledgements) +- [Releases](#releases) +- [License](#license) +- [Reference](#reference) +- [Links](#links) + +## Overview + +ProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays and annotated human clinical variants curated to enable thorough comparisons of various mutation effect predictors in different regimes. Both the DMS assays and clinical variants are divided into 1) a substitution benchmark which currently consists of the experimental characterisation of ~2.7M missense variants across 217 DMS assays and 2,525 clinical proteins, and 2) an indel benchmark that includes ∼300k mutants across 74 DMS assays and 1,555 clinical proteins. + +Each processed file in each benchmark corresponds to a single DMS assay or clinical protein, and contains the following variables: +- mutant (str): describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N'). Present in the the ProteinGym substitution benchmark only (not indels). +- mutated_sequence (str): represents the full amino acid sequence for the mutated protein. +- DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein. This column is not present in the clinical files, since they are classified as benign/pathogenic, and do not have continuous scores +- DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit (pathogenic for clinical variants), 0 is not fit (benign for clinical variants)) + +Additionally, we provide two reference files for each benchmark that give further details on each assay and contain in particular: +- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category +- The target sequence (target_seq) used in the assay +- For the assays, details on how the DMS_score was created from the raw files and how it was binarized + +To download the benchmarks, please see `DMS benchmark - Substitutions` and `DMS benchmark - Indels` in the "Resources" section below. + +## Fitness prediction performance + +The [benchmarks](https://github.com/OATML-Markslab/ProteinGym/tree/main/benchmarks) folder provides detailed performance files for all baselines on the DMS and clinical benchmarks. + +We report the following metrics: +- For DMS benchmarks in the zero-shot setting: Spearman, NDCG, AUC, MCC and Top-K recall +- For DMS benchmarks in the supervised setting: Spearman and MSE +- For clinical benchmarks: AUC + +Metrics are aggregated as follows: +1. Aggregating by UniProt ID (to avoid biasing results towards proteins for which several DMS assays are available in ProteinGym) +2. Aggregating by different functional categories, and taking the mean across those categories. + +These files are named e.g. `DMS_substitutions_Spearman_DMS_level.csv`, `DMS_substitutions_Spearman_Uniprot_level` and `DMS_substitutions_Spearman_Uniprot_Selection_Type_level` respectively for these different steps. + +For other deep dives (performance split by taxa, MSA depth, mutational depth and more), these are all contained in the `benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv` folder (resp. DMS_indels/clinical_substitutions/clinical_indels & their supervised counterparts). These files are also what are hosted on the website. + +We also include, as on the website, a bootstrapped standard error of these aggregated metrics to reflect the variance in the final numbers with respect to the individual assays. + +To calculate the DMS substitution benchmark metrics: +1. Download the model scores from the website +2. Run `./scripts/scoring_DMS_zero_shot/performance_substitutions.sh` + +And for indels, follow step #1 and run `./scripts/scoring_DMS_zero_shot/performance_substitutions_indels.sh`. + +### ProteinGym benchmarks - Leaderboard + +The full ProteinGym benchmarks performance files are also accessible via our dedicated website: https://www.proteingym.org/. +It includes leaderboards for the substitution and indel benchmarks, as well as detailed DMS-level performance files for all baselines. +The current version of the substitution benchmark includes the following baselines: + +Model name | Model type | Reference +--- | --- | --- | +Site Independent | Alignment-based model | [Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.](https://www.nature.com/articles/nbt.3769) +EVmutation | Alignment-based model | [Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.](https://www.nature.com/articles/nbt.3769) +WaveNet | Alignment-based model | [Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.](https://www.nature.com/articles/s41467-021-22732-w) +DeepSequence | Alignment-based model | [Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.](https://www.nature.com/articles/s41592-018-0138-4) +GEMME | Alignment-based model | [Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.](https://pubmed.ncbi.nlm.nih.gov/31406981/) +EVE | Alignment-based model | [Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.](https://www.nature.com/articles/s41586-021-04043-8) +Unirep | Protein language model | [Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8](https://www.nature.com/articles/s41592-019-0598-1) +ESM-1b | Protein language model | Original model: [Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118](https://www.biorxiv.org/content/10.1101/622803v4); Extensions: [Brandes, N., Goldman, G., Wang, C.H. et al. Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet 55, 1512–1522 (2023).](https://doi.org/10.1038/s41588-023-01465-0) +ESM-1v | Protein language model | [Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.](https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html) +VESPA | Protein language model | [Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.](https://link.springer.com/article/10.1007/s00439-021-02411-y) +RITA | Protein language model | [Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.](https://arxiv.org/abs/2205.05789) +ProtGPT2 | Protein language model | [Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.](https://www.nature.com/articles/s41467-022-32007-7) +ProGen2 | Protein language model | [Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.](https://arxiv.org/abs/2206.13517) +MSA Transformer | Hybrid - Alignment & PLM |[Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.](http://proceedings.mlr.press/v139/rao21a.html) +Tranception | Hybrid - Alignment & PLM | [Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. ICML.](https://proceedings.mlr.press/v162/notin22a.html) +TranceptEVE | Hybrid - Alignment & PLM | [Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.](https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1) +CARP | Protein language model | [Yang, K.K., Fusi, N., Lu, A.X. (2022). Convolutions are competitive with transformers for protein sequence pretraining.](https://doi.org/10.1101/2022.05.19.492714) +MIF | Inverse folding | [Yang, K.K., Yeh, H., Zanichelli, N. (2022). Masked Inverse Folding with Sequence Transfer for Protein Representation Learning.](https://doi.org/10.1101/2022.05.25.493516) +ProteinMPNN | Inverse folding | [J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.](https://www.science.org/doi/10.1126/science.add2187) +ESM-IF1 | Inverse folding | [Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. ICML](https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html) +ProtSSN | Hybrid - Structure & PLM | [Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability.](https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1) +SaProt | Hybrid - Structure & PLM | [Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. ICLR](href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5) + +Except for the WaveNet model (which only uses alignments to recover a set of homologous protein sequences to train on, but then trains on non-aligned sequences), all alignment-based methods are unable to score indels given the fixed coordinate system they are trained on. Similarly, the masked-marginals procedure to generate the masked-marginals for ESM-1v and MSA Transformer requires the position to exist in the wild-type sequence. All the other model architectures listed above (eg., Tranception, RITA, ProGen2) are included in the indel benchmark. + +For clinical baselines, we used dbNSFP 4.4a as detailed in the manuscript appendix (and in `proteingym/clinical_benchmark_notebooks/clinical_subs_processing.ipynb`). + +## Resources + +To download and unzip the data, use the following template, replacing {VERSION} with the desired version number (e.g., "v1.1") and {FILENAME} with the specific file you want to download, as listed in the table below. The latest version is v1.1. +For example, you can download & unzip the zero-shot predictions for all baselines for all DMS substitution assays as follows: +``` +VERSION="v1.1" +FILENAME="DMS_ProteinGym_substitutions.zip" +curl -o ${FILENAME} https://marks.hms.harvard.edu/proteingym/ProteinGym_${VERSION}/${FILENAME} +unzip ${FILENAME} && rm ${FILENAME} +``` + +Data | Size (unzipped) | Filename +--- | --- | --- | +DMS benchmark - Substitutions | 1.0GB | DMS_ProteinGym_substitutions.zip +DMS benchmark - Indels | 200MB | DMS_ProteinGym_indels.zip +Zero-shot DMS Model scores - Substitutions | 31GB | zero_shot_substitutions_scores.zip +Zero-shot DMS Model scores - Indels | 5.2GB | zero_shot_indels_scores.zip +Supervised DMS Model performance - Substitutions | 2.7MB | DMS_supervised_substitutions_scores.zip +Supervised DMS Model performance - Indels | 0.9MB | DMS_supervised_indels_scores.zip +Multiple Sequence Alignments (MSAs) for DMS assays | 5.2GB | DMS_msa_files.zip +Redundancy-based sequence weights for DMS assays | 200MB | DMS_msa_weights.zip +Predicted 3D structures from inverse-folding models | 84MB | ProteinGym_AF2_structures.zip +Clinical benchmark - Substitutions | 123MB | clinical_ProteinGym_substitutions.zip +Clinical benchmark - Indels | 2.8MB | clinical_ProteinGym_indels.zip +Clinical MSAs | 17.8GB | clinical_msa_files.zip +Clinical MSA weights | 250MB | clinical_msa_weights.zip +Clinical Model scores - Substitutions | 0.9GB | zero_shot_clinical_substitutions_scores.zip +Clinical Model scores - Indels | 0.7GB | zero_shot_clinical_indels_scores.zip +CV folds - Substitutions - Singles | 50M | cv_folds_singles_substitutions.zip +CV folds - Substitutions - Multiples | 81M | cv_folds_multiples_substitutions.zip +CV folds - Indels | 19MB | cv_folds_indels.zip + +Then we also host the raw DMS assays (before preprocessing) + +Data | Size (unzipped) | Link +--- | --- | --- | +DMS benchmark: Substitutions (raw) | 500MB | substitutions_raw_DMS.zip +DMS benchmark: Indels (raw) | 450MB | indels_raw_DMS.zip +Clinical benchmark: Substitutions (raw) | 58MB | substitutions_raw_clinical.zip +Clinical benchmark: Indels (raw) | 12.4MB | indels_raw_clinical.zip + +## How to contribute? + +### New assays +If you would like to suggest new assays to be part of ProteinGym, please raise an issue on this repository with a `new_assay' label. The criteria we typically consider for inclusion are as follows: +1. The corresponding raw dataset needs to be publicly available +2. The assay needs to be protein-related (ie., exclude UTR, tRNA, promoter, etc.) +3. The dataset needs to have insufficient number of measurements +4. The assay needs to have a sufficiently high dynamic range +5. The assay has to be relevant to fitness prediction + +### New baselines +If you would like new baselines to be included in ProteinGym (ie., website, performance files, detailed scoring files), please follow the following steps: +1. Submit a PR to our repo with two things: + - A new subfolder under proteingym/baselines named with your new model name. This subfolder should include a python scoring script similar to [this script](https://github.com/OATML-Markslab/ProteinGym/blob/main/proteingym/baselines/rita/compute_fitness.py), as well as all code dependencies required for the scoring script to run properly + - An example bash script (e.g., under scripts/scoring_DMS_zero_shot) with all relevant hyperparameters for scoring, similar to [this script](https://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/scoring_RITA_substitutions.sh) +2. Raise an issue with a 'new model' label, providing instructions on how to download relevant model checkpoints for scoring, and reporting the performance of your model on the relevant benchmark using our performance scripts (e.g., [for zero-shot DMS benchmarks](https://github.com/OATML-Markslab/ProteinGym/blob/main/proteingym/performance_DMS_benchmarks.py)). Please note that our DMS performance scripts correct for various biases (e.g., number of assays per protein family and function groupings) and thus the resulting aggregated performance is not the same as the arithmetic average across assays. + +At this point we are only considering new baselines satisfying the following conditions: +1. The model is able to score all mutants in the relevant benchmark (to ensure all models are compared exactly on the same set of mutants everywhere); +2. The corresponding model is open source (we should be able to reproduce scores if needed). + +At this stage, we are only considering requests for which all model scores for all mutants in a given benchmark (substitution or indel) are provided by the requester; but we are planning on regularly scoring new baselines ourselves for methods with wide adoption by the community and/or suggestions with many upvotes. + +### Notes +12 December 2023: The code for training and evaluating supervised models is currently shared in https://github.com/OATML-Markslab/ProteinNPT. We are in the process of integrating the code into this repo. + +## Usage and reproducibility + +If you would like to compute all performance metrics for the various benchmarks, please follow the following steps: +1. Download locally all relevant files as per instructions above (see Resources) +2. Update the paths for all files downloaded in the prior step in the [config script](https://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/zero_shot_config.sh) +3. If adding a new model, adjust the [config.json](https://github.com/OATML-Markslab/ProteinGym/blob/main/config.json) file accordingly and add the model scores to the relevant path (e.g., [DMS_output_score_folder_subs](https://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/zero_shot_config.sh#L19)) +4. If focusing on DMS benchmarks, run the [merge script](https://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/merge_all_scores.sh). This will create a single file for each DMS assay, with scores for all model baselines +5. Run the relevant performance script (eg., [scripts/scoring_DMS_zero_shot/performance_substitutions.sh](https://github.com/OATML-Markslab/ProteinGym/blob/main/scripts/scoring_DMS_zero_shot/performance_substitutions.sh)) + +## Acknowledgements + +Our codebase leveraged code from the following repositories to compute baselines: + +Model | Repo +--- | --- +UniRep | https://github.com/churchlab/UniRep +UniRep | https://github.com/chloechsu/combining-evolutionary-and-assay-labelled-data +EVE | https://github.com/OATML-Markslab/EVE +GEMME | https://hub.docker.com/r/elodielaine/gemme +ESM | https://github.com/facebookresearch/esm +EVmutation | https://github.com/debbiemarkslab/EVcouplings +ProGen2 | https://github.com/salesforce/progen +HMMER | https://github.com/EddyRivasLab/hmmer +MSA Transformer | https://github.com/rmrao/msa-transformer +ProtGPT2 | https://huggingface.co/nferruz/ProtGPT2 +ProteinMPNN | https://github.com/dauparas/ProteinMPNN +RITA | https://github.com/lightonai/RITA +Tranception | https://github.com/OATML-Markslab/Tranception +VESPA | https://github.com/Rostlab/VESPA +CARP | https://github.com/microsoft/protein-sequence-models +MIF | https://github.com/microsoft/protein-sequence-models +Foldseek | https://github.com/steineggerlab/foldseek +ProtSSN | https://github.com/tyang816/ProtSSN +SaProt | https://github.com/westlake-repl/SaProt + +We would like to thank the GEMME team for providing model scores on an earlier version of the benchmark (ProteinGym v0.1), and the ProtSSN and SaProt teams for integrating their model in the ProteinGym repo. + +Special thanks the teams of experimentalists who developed and performed the assays that ProteinGym is built on. If you are using ProteinGym in your work, please consider citing the corresponding papers. To facilitate this, we have prepared a file (assays.bib) containing the bibtex entries for all these papers. + +## Releases + +1. [ProteinGym_v1.0](https://zenodo.org/records/13932633): Initial release. +2. [ProteinGym_v1.1](https://zenodo.org/records/13936340): Updates to reference file, and addition of ProtSSN and SaProt baselines. + +## License +This project is available under the MIT license found in the LICENSE file in this GitHub repository. + +## Reference +If you use ProteinGym in your work, please cite the following paper: +```bibtex +@inproceedings{NEURIPS2023_cac723e5, + author = {Notin, Pascal and Kollasch, Aaron and Ritter, Daniel and van Niekerk, Lood and Paul, Steffanie and Spinner, Han and Rollins, Nathan and Shaw, Ada and Orenbuch, Rose and Weitzman, Ruben and Frazer, Jonathan and Dias, Mafalda and Franceschi, Dinko and Gal, Yarin and Marks, Debora}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine}, + pages = {64331--64379}, + publisher = {Curran Associates, Inc.}, + title = {ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design}, + url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/cac723e5ff29f65e3fcbb0739ae91bee-Paper-Datasets_and_Benchmarks.pdf}, + volume = {36}, + year = {2023} +} +``` + +## Links +- Website: https://www.proteingym.org/ +- NeurIPS proceedings: [link to abstract](https://papers.nips.cc/paper_files/paper/2023/hash/cac723e5ff29f65e3fcbb0739ae91bee-Abstract-Datasets_and_Benchmarks.html) +- Preprint: [link to abstract](https://www.biorxiv.org/content/10.1101/2023.12.07.570727v1) diff --git a/assays.bib b/assays.bib new file mode 100644 index 0000000..a1fae35 --- /dev/null +++ b/assays.bib @@ -0,0 +1,3526 @@ +@article{sourisseau_deep_2019, + title = {Deep {Mutational} {Scanning} {Comprehensively} {Maps} {How} {Zika} {Envelope} {Protein} {Mutations} {Affect} {Viral} {Growth} and {Antibody} {Escape}}, + volume = {93}, + issn = {0022-538X, 1098-5514}, + url = {https://journals.asm.org/doi/10.1128/JVI.01291-19}, + doi = {10.1128/JVI.01291-19}, + abstract = {Zika virus has recently been shown to be associated with severe birth defects. The virus’s E protein mediates its ability to infect cells and is also the primary target of the antibodies that are elicited by natural infection and vaccines that are being developed against the virus. Therefore, determining the effects of mutations to this protein is important for understanding its function, its susceptibility to vaccine-mediated immunity, and its potential for future evolution. We completely mapped how amino acid mutations to the E protein affected the virus’s ability to grow in cells in the laboratory and escape from several antibodies. The resulting maps relate changes in the E protein’s sequence to changes in viral function and therefore provide a valuable complement to existing maps of the physical structure of the protein. + , + ABSTRACT + Functional constraints on viral proteins are often assessed by examining sequence conservation among natural strains, but this approach is relatively ineffective for Zika virus because all known sequences are highly similar. Here, we take an alternative approach to map functional constraints on Zika virus’s envelope (E) protein by using deep mutational scanning to measure how all amino acid mutations to the E protein affect viral growth in cell culture. The resulting sequence-function map is consistent with existing knowledge about E protein structure and function but also provides insight into mutation-level constraints in many regions of the protein that have not been well characterized in prior functional work. In addition, we extend our approach to completely map how mutations affect viral neutralization by two monoclonal antibodies, thereby precisely defining their functional epitopes. Overall, our study provides a valuable resource for understanding the effects of mutations to this important viral protein and also offers a roadmap for future work to map functional and antigenic selection to Zika virus at high resolution. + + IMPORTANCE + Zika virus has recently been shown to be associated with severe birth defects. The virus’s E protein mediates its ability to infect cells and is also the primary target of the antibodies that are elicited by natural infection and vaccines that are being developed against the virus. Therefore, determining the effects of mutations to this protein is important for understanding its function, its susceptibility to vaccine-mediated immunity, and its potential for future evolution. We completely mapped how amino acid mutations to the E protein affected the virus’s ability to grow in cells in the laboratory and escape from several antibodies. The resulting maps relate changes in the E protein’s sequence to changes in viral function and therefore provide a valuable complement to existing maps of the physical structure of the protein.}, + language = {en}, + number = {23}, + urldate = {2022-07-11}, + journal = {Journal of Virology}, + author = {Sourisseau, Marion and Lawrence, Daniel J. P. and Schwarz, Megan C. and Storrs, Carina H. and Veit, Ethan C. and Bloom, Jesse D. and Evans, Matthew J.}, + editor = {Pfeiffer, Julie K.}, + month = dec, + year = {2019}, + pages = {e01291--19}, + file = {Full Text:/Users/admin/Zotero/storage/D4BDUGQ6/Sourisseau et al. - 2019 - Deep Mutational Scanning Comprehensively Maps How .pdf:application/pdf}, +} + +@article{deng_deep_2012, + title = {Deep {Sequencing} of {Systematic} {Combinatorial} {Libraries} {Reveals} Beta-{Lactamase} {Sequence} {Constraints} at {High} {Resolution}}, + volume = {424}, + issn = {00222836}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0022283612007711}, + doi = {10.1016/j.jmb.2012.09.014}, + language = {en}, + number = {3-4}, + urldate = {2022-07-11}, + journal = {Journal of Molecular Biology}, + author = {Deng, Zhifeng and Huang, Wanzhi and Bakkalbasi, Erol and Brown, Nicholas G. and Adamski, Carolyn J. and Rice, Kacie and Muzny, Donna and Gibbs, Richard A. and Palzkill, Timothy}, + month = dec, + year = {2012}, + pages = {150--167}, + file = {Accepted Version:/Users/admin/Zotero/storage/X5RENMCM/Deng et al. - 2012 - Deep Sequencing of Systematic Combinatorial Librar.pdf:application/pdf}, +} + +@article{amorosi_massively_2021, + title = {Massively parallel characterization of {CYP2C9} variant enzyme activity and abundance}, + volume = {108}, + issn = {00029297}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S000292972100269X}, + doi = {10.1016/j.ajhg.2021.07.001}, + language = {en}, + number = {9}, + urldate = {2022-07-11}, + journal = {The American Journal of Human Genetics}, + author = {Amorosi, Clara J. and Chiasson, Melissa A. and McDonald, Matthew G. and Wong, Lai Hong and Sitko, Katherine A. and Boyle, Gabriel and Kowalski, John P. and Rettie, Allan E. and Fowler, Douglas M. and Dunham, Maitreya J.}, + month = sep, + year = {2021}, + pages = {1735--1751}, + file = {Full Text:/Users/admin/Zotero/storage/8DWGHK9I/Amorosi et al. - 2021 - Massively parallel characterization of CYP2C9 vari.pdf:application/pdf}, +} + +@article{chiasson_multiplexed_2020, + title = {Multiplexed measurement of variant abundance and activity reveals {VKOR} topology, active site and human variant impact}, + volume = {9}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/58026}, + doi = {10.7554/eLife.58026}, + abstract = {Vitamin K epoxide reductase (VKOR) drives the vitamin K cycle, activating vitamin K-dependent blood clotting factors. VKOR is also the target of the widely used anticoagulant drug, warfarin. Despite VKOR’s pivotal role in coagulation, its structure and active site remain poorly understood. In addition, VKOR variants can cause vitamin K-dependent clotting factor deficiency or alter warfarin response. Here, we used multiplexed, sequencing-based assays to measure the effects of 2,695 VKOR missense variants on abundance and 697 variants on activity in cultured human cells. The large-scale functional data, along with an evolutionary coupling analysis, supports a four transmembrane domain topology, with variants in transmembrane domains exhibiting strongly deleterious effects on abundance and activity. Functionally constrained regions of the protein define the active site, and we find that, of four conserved cysteines putatively critical for function, only three are absolutely required. Finally, 25\% of human VKOR missense variants show reduced abundance or activity, possibly conferring warfarin sensitivity or causing disease.}, + language = {en}, + urldate = {2022-07-11}, + journal = {eLife}, + author = {Chiasson, Melissa A and Rollins, Nathan J and Stephany, Jason J and Sitko, Katherine A and Matreyek, Kenneth A and Verby, Marta and Sun, Song and Roth, Frederick P and DeSloover, Daniel and Marks, Debora S and Rettie, Allan E and Fowler, Douglas M}, + month = sep, + year = {2020}, + pages = {e58026}, + file = {Full Text:/Users/admin/Zotero/storage/GWWNSVDK/Chiasson et al. - 2020 - Multiplexed measurement of variant abundance and a.pdf:application/pdf}, +} + +@article{wrenbeck_single-mutation_2017, + title = {Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded}, + volume = {8}, + issn = {2041-1723}, + url = {http://www.nature.com/articles/ncomms15695}, + doi = {10.1038/ncomms15695}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Nature Communications}, + author = {Wrenbeck, Emily E. and Azouz, Laura R. and Whitehead, Timothy A.}, + month = aug, + year = {2017}, + pages = {15695}, + file = {Full Text:/Users/admin/Zotero/storage/7WF3NED2/Wrenbeck et al. - 2017 - Single-mutation fitness landscapes for an enzyme o.pdf:application/pdf}, +} + +@article{brenan_phenotypic_2016, + title = {Phenotypic {Characterization} of a {Comprehensive} {Set} of {MAPK1} /{ERK2} {Missense} {Mutants}}, + volume = {17}, + issn = {22111247}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S2211124716313171}, + doi = {10.1016/j.celrep.2016.09.061}, + language = {en}, + number = {4}, + urldate = {2022-07-11}, + journal = {Cell Reports}, + author = {Brenan, Lisa and Andreev, Aleksandr and Cohen, Ofir and Pantel, Sasha and Kamburov, Atanas and Cacchiarelli, Davide and Persky, Nicole S. and Zhu, Cong and Bagul, Mukta and Goetz, Eva M. and Burgin, Alex B. and Garraway, Levi A. and Getz, Gad and Mikkelsen, Tarjei S. and Piccioni, Federica and Root, David E. and Johannessen, Cory M.}, + month = oct, + year = {2016}, + pages = {1171--1183}, + file = {Full Text:/Users/admin/Zotero/storage/W3B46C64/Brenan et al. - 2016 - Phenotypic Characterization of a Comprehensive Set.pdf:application/pdf}, +} + +@article{kozek_high-throughput_2020, + title = {High-throughput discovery of trafficking-deficient variants in the cardiac potassium channel {KV11}.1}, + volume = {17}, + issn = {15475271}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1547527120305427}, + doi = {10.1016/j.hrthm.2020.05.041}, + language = {en}, + number = {12}, + urldate = {2022-07-11}, + journal = {Heart Rhythm}, + author = {Kozek, Krystian A. and Glazer, Andrew M. and Ng, Chai-Ann and Blackwell, Daniel and Egly, Christian L. and Vanags, Loren R. and Blair, Marcia and Mitchell, Devyn and Matreyek, Kenneth A. and Fowler, Douglas M. and Knollmann, Bjorn C. and Vandenberg, Jamie I. and Roden, Dan M. and Kroncke, Brett M.}, + month = dec, + year = {2020}, + pages = {2180--2189}, + file = {Accepted Version:/Users/admin/Zotero/storage/JETSTCHG/Kozek et al. - 2020 - High-throughput discovery of trafficking-deficient.pdf:application/pdf}, +} + +@article{davidi_highly_2020, + title = {Highly active rubiscos discovered by systematic interrogation of natural sequence diversity}, + volume = {39}, + issn = {0261-4189, 1460-2075}, + url = {https://onlinelibrary.wiley.com/doi/10.15252/embj.2019104081}, + doi = {10.15252/embj.2019104081}, + language = {en}, + number = {18}, + urldate = {2022-07-11}, + journal = {The EMBO Journal}, + author = {Davidi, Dan and Shamshoum, Melina and Guo, Zhijun and Bar‐On, Yinon M and Prywes, Noam and Oz, Aia and Jablonska, Jagoda and Flamholz, Avi and Wernick, David G and Antonovsky, Niv and Pins, Benoit and Shachar, Lior and Hochhauser, Dina and Peleg, Yoav and Albeck, Shira and Sharon, Itai and Mueller‐Cajar, Oliver and Milo, Ron}, + month = sep, + year = {2020}, + file = {Full Text:/Users/admin/Zotero/storage/PYUPVUZK/Davidi et al. - 2020 - Highly active rubiscos discovered by systematic in.pdf:application/pdf}, +} + +@article{sarkisyan_local_2016, + title = {Local fitness landscape of the green fluorescent protein}, + volume = {533}, + issn = {0028-0836, 1476-4687}, + url = {http://www.nature.com/articles/nature17995}, + doi = {10.1038/nature17995}, + language = {en}, + number = {7603}, + urldate = {2022-07-11}, + journal = {Nature}, + author = {Sarkisyan, Karen S. and Bolotin, Dmitry A. and Meer, Margarita V. and Usmanova, Dinara R. and Mishin, Alexander S. and Sharonov, George V. and Ivankov, Dmitry N. and Bozhanova, Nina G. and Baranov, Mikhail S. and Soylemez, Onuralp and Bogatyreva, Natalya S. and Vlasov, Peter K. and Egorov, Evgeny S. and Logacheva, Maria D. and Kondrashov, Alexey S. and Chudakov, Dmitry M. and Putintseva, Ekaterina V. and Mamedov, Ilgar Z. and Tawfik, Dan S. and Lukyanov, Konstantin A. and Kondrashov, Fyodor A.}, + month = may, + year = {2016}, + pages = {397--401}, + file = {Accepted Version:/Users/admin/Zotero/storage/KPQ2DLXG/Sarkisyan et al. - 2016 - Local fitness landscape of the green fluorescent p.pdf:application/pdf}, +} + +@article{olson_comprehensive_2014, + title = {A {Comprehensive} {Biophysical} {Description} of {Pairwise} {Epistasis} throughout an {Entire} {Protein} {Domain}}, + volume = {24}, + issn = {09609822}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0960982214012688}, + doi = {10.1016/j.cub.2014.09.072}, + language = {en}, + number = {22}, + urldate = {2022-07-11}, + journal = {Current Biology}, + author = {Olson, C. Anders and Wu, Nicholas C. and Sun, Ren}, + month = nov, + year = {2014}, + pages = {2643--2651}, + file = {Full Text:/Users/admin/Zotero/storage/XHQR6TLB/Olson et al. - 2014 - A Comprehensive Biophysical Description of Pairwis.pdf:application/pdf}, +} + +@article{duenas-decamp_saturation_2016, + title = {Saturation {Mutagenesis} of the {HIV}-1 {Envelope} {CD4} {Binding} {Loop} {Reveals} {Residues} {Controlling} {Distinct} {Trimer} {Conformations}}, + volume = {12}, + issn = {1553-7374}, + url = {https://dx.plos.org/10.1371/journal.ppat.1005988}, + doi = {10.1371/journal.ppat.1005988}, + language = {en}, + number = {11}, + urldate = {2022-07-11}, + journal = {PLOS Pathogens}, + author = {Duenas-Decamp, Maria and Jiang, Li and Bolon, Daniel and Clapham, Paul R.}, + editor = {Desrosiers, Ronald C.}, + month = nov, + year = {2016}, + pages = {e1005988}, + file = {Full Text:/Users/admin/Zotero/storage/B948VI9X/Duenas-Decamp et al. - 2016 - Saturation Mutagenesis of the HIV-1 Envelope CD4 B.pdf:application/pdf}, +} + +@article{thompson_altered_2020, + title = {Altered expression of a quality control protease in {E}. coli reshapes the in vivo mutational landscape of a model enzyme}, + volume = {9}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/53476}, + doi = {10.7554/eLife.53476}, + abstract = {Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common + E. coli + expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3\% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.}, + language = {en}, + urldate = {2022-07-11}, + journal = {eLife}, + author = {Thompson, Samuel and Zhang, Yang and Ingle, Christine and Reynolds, Kimberly A and Kortemme, Tanja}, + month = jul, + year = {2020}, + pages = {e53476}, + file = {Full Text:/Users/admin/Zotero/storage/4U2CHYQR/Thompson et al. - 2020 - Altered expression of a quality control protease i.pdf:application/pdf}, +} + +@article{starita_activity-enhancing_2013, + title = {Activity-enhancing mutations in an {E3} ubiquitin ligase identified by high-throughput mutagenesis}, + volume = {110}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1303309110}, + doi = {10.1073/pnas.1303309110}, + abstract = {Significance + Ubiquitin is a 76 residue protein that is attached to target proteins as a posttranslational modification. This modification is dependent on the successive activity of three enzymes, designated E1, E2, and E3. We developed a high-throughput mutagenesis strategy to probe the mechanism of E3-catalyzed transfer of ubiquitin from the E2 to the target protein. By scoring the effect of nearly 100,000 mutations in an E3, we identified mutations that affect direct and allosteric interactions between the E3 and the E2. These results highlight the general utility of high-throughput mutagenesis in delineating the molecular basis of enzyme activity. + , + Although ubiquitination plays a critical role in virtually all cellular processes, mechanistic details of ubiquitin (Ub) transfer are still being defined. To identify the molecular determinants within E3 ligases that modulate activity, we scored each member of a library of nearly 100,000 protein variants of the murine ubiquitination factor E4B (Ube4b) U-box domain for auto-ubiquitination activity in the presence of the E2 UbcH5c. This assay identified mutations that enhance activity both in vitro and in cellular p53 degradation assays. The activity-enhancing mutations fall into two distinct mechanistic classes: One increases the U-box:E2-binding affinity, and the other allosterically stimulates the formation of catalytically active conformations of the E2∼Ub conjugate. The same mutations enhance E3 activity in the presence of another E2, Ube2w, implying a common allosteric mechanism, and therefore the general applicability of our observations to other E3s. A comparison of the E3 activity with the two different E2s identified an additional variant that exhibits E3:E2 specificity. Our results highlight the general utility of high-throughput mutagenesis in delineating the molecular basis of enzyme activity.}, + language = {en}, + number = {14}, + urldate = {2022-07-11}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Starita, Lea M. and Pruneda, Jonathan N. and Lo, Russell S. and Fowler, Douglas M. and Kim, Helen J. and Hiatt, Joseph B. and Shendure, Jay and Brzovic, Peter S. and Fields, Stanley and Klevit, Rachel E.}, + month = apr, + year = {2013}, + file = {Full Text:/Users/admin/Zotero/storage/FBVQ2HYR/Starita et al. - 2013 - Activity-enhancing mutations in an E3 ubiquitin li.pdf:application/pdf;Full Text:/Users/admin/Zotero/storage/SF4WML4G/Starita et al. - 2013 - Activity-enhancing mutations in an E3 ubiquitin li.pdf:application/pdf}, +} + +@article{araya_fundamental_2012, + title = {A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function}, + volume = {109}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1209751109}, + doi = {10.1073/pnas.1209751109}, + abstract = {The ability of a protein to carry out a given function results from fundamental physicochemical properties that include the protein’s structure, mechanism of action, and thermodynamic stability. Traditional approaches to study these properties have typically required the direct measurement of the property of interest, oftentimes a laborious undertaking. Although protein properties can be probed by mutagenesis, this approach has been limited by its low throughput. Recent technological developments have enabled the rapid quantification of a protein’s function, such as binding to a ligand, for numerous variants of that protein. Here, we measure the ability of 47,000 variants of a WW domain to bind to a peptide ligand and use these functional measurements to identify stabilizing mutations without directly assaying stability. Our approach is rooted in the well-established concept that protein function is closely related to stability. Protein function is generally reduced by destabilizing mutations, but this decrease can be rescued by stabilizing mutations. Based on this observation, we introduce partner potentiation, a metric that uses this rescue ability to identify stabilizing mutations, and identify 15 candidate stabilizing mutations in the WW domain. We tested six candidates by thermal denaturation and found two highly stabilizing mutations, one more stabilizing than any previously known mutation. Thus, physicochemical properties such as stability are latent within these large-scale protein functional data and can be revealed by systematic analysis. This approach should allow other protein properties to be discovered.}, + language = {en}, + number = {42}, + urldate = {2022-07-11}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Araya, Carlos L. and Fowler, Douglas M. and Chen, Wentao and Muniez, Ike and Kelly, Jeffery W. and Fields, Stanley}, + month = oct, + year = {2012}, + pages = {16858--16863}, + file = {Full Text:/Users/admin/Zotero/storage/KR46AVWQ/Araya et al. - 2012 - A fundamental protein property, thermodynamic stab.pdf:application/pdf}, +} + +@article{wu_high-throughput_2014, + title = {High-throughput profiling of influenza {A} virus hemagglutinin gene at single-nucleotide resolution}, + volume = {4}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/srep04942}, + doi = {10.1038/srep04942}, + abstract = {Abstract + Genetic research on influenza virus biology has been informed in large part by nucleotide variants present in seasonal or pandemic samples, or individual mutants generated in the laboratory, leaving a substantial part of the genome uncharacterized. Here, we have developed a single-nucleotide resolution genetic approach to interrogate the fitness effect of point mutations in 98\% of the amino acid positions in the influenza A virus hemagglutinin (HA) gene. Our HA fitness map provides a reference to identify indispensable regions to aid in drug and vaccine design as targeting these regions will increase the genetic barrier for the emergence of escape mutations. This study offers a new platform for studying genome dynamics, structure-function relationships, virus-host interactions and can further rational drug and vaccine design. Our approach can also be applied to any virus that can be genetically manipulated.}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Scientific Reports}, + author = {Wu, Nicholas C. and Young, Arthur P. and Al-Mawsawi, Laith Q. and Olson, C. Anders and Feng, Jun and Qi, Hangfei and Chen, Shu-Hwa and Lu, I.-Hsuan and Lin, Chung-Yen and Chin, Robert G. and Luan, Harding H. and Nguyen, Nguyen and Nelson, Stanley F. and Li, Xinmin and Wu, Ting-Ting and Sun, Ren}, + month = dec, + year = {2014}, + pages = {4942}, + file = {Full Text:/Users/admin/Zotero/storage/IPZQQNJQ/Wu et al. - 2014 - High-throughput profiling of influenza A virus hem.pdf:application/pdf}, +} + +@techreport{young_deep_2021, + type = {preprint}, + title = {Deep {Mutagenesis} of a {Transporter} for {Uptake} of a {Non}-{Native} {Substrate} {Identifies} {Conformationally} {Dynamic} {Regions}}, + url = {http://biorxiv.org/lookup/doi/10.1101/2021.04.19.440442}, + abstract = {Abstract + + The serotonin transporter, SERT, catalyzes serotonin reuptake at the synapse to terminate neurotransmission via an alternating access mechanism, and SERT inhibitors are the most widely prescribed antidepressants. Here, deep mutagenesis is used to determine the effects of nearly all amino acid substitutions on human SERT surface expression and transport of the fluorescent substrate analogue APP+, identifying many mutations that enhance APP+ import. Comprehensive simulations of the entire ion-coupled import process reveal that while binding of the native substrate, serotonin, reduces free energy barriers between conformational states to promote SERT dynamics, the conformational free energy landscape in the presence of APP+ instead resembles Na + + + bound-SERT, with a higher free energy barrier for transitioning to an inward-facing state. The deep mutational scan for SERT-catalyzed import of APP+ finds mutations that promote the necessary conformational changes that would otherwise be facilitated by the native substrate. Indeed, hundreds of gain-of-function mutations for APP+ import are found along the permeation pathway, most notably mutations that favor opening of a solvent-exposed intracellular vestibule. The mutagenesis data support the simulated mechanism in which the neurotransmitter and a symported sodium share a common cytosolic exit pathway to achieve coupling. Furthermore, the mutational landscape for SERT surface trafficking, which likely filters out misfolded sequences, reveals that residues along the permeation pathway are mutationally tolerant, providing plausible evolutionary pathways for changes in transporter properties while maintaining folded structure.}, + language = {en}, + urldate = {2022-07-11}, + institution = {Biochemistry}, + author = {Young, Heather J. and Chan, Matthew and Selvam, Balaji and Szymanski, Steven K. and Shukla, Diwakar and Procko, Erik}, + month = apr, + year = {2021}, + doi = {10.1101/2021.04.19.440442}, + file = {Submitted Version:/Users/admin/Zotero/storage/T69VH4DC/Young et al. - 2021 - Deep Mutagenesis of a Transporter for Uptake of a .pdf:application/pdf}, +} + +@article{jiang_balance_2016, + title = {A {Balance} between {Inhibitor} {Binding} and {Substrate} {Processing} {Confers} {Influenza} {Drug} {Resistance}}, + volume = {428}, + issn = {00222836}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0022283615006907}, + doi = {10.1016/j.jmb.2015.11.027}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Journal of Molecular Biology}, + author = {Jiang, Li and Liu, Ping and Bank, Claudia and Renzette, Nicholas and Prachanronarong, Kristina and Yilmaz, Lutfu S. and Caffrey, Daniel R. and Zeldovich, Konstantin B. and Schiffer, Celia A. and Kowalik, Timothy F. and Jensen, Jeffrey D. and Finberg, Robert W. and Wang, Jennifer P. and Bolon, Daniel N.A.}, + month = feb, + year = {2016}, + pages = {538--553}, + file = {Accepted Version:/Users/admin/Zotero/storage/TJGBH3YJ/Jiang et al. - 2016 - A Balance between Inhibitor Binding and Substrate .pdf:application/pdf}, +} + +@article{melamed_deep_2013, + title = {Deep mutational scanning of an {RRM} domain of the \textit{{Saccharomyces} cerevisiae} poly({A})-binding protein}, + volume = {19}, + issn = {1355-8382, 1469-9001}, + url = {http://rnajournal.cshlp.org/lookup/doi/10.1261/rna.040709.113}, + doi = {10.1261/rna.040709.113}, + abstract = {The RNA recognition motif (RRM) is the most common RNA-binding domain in eukaryotes. Differences in RRM sequences dictate, in part, both RNA and protein-binding specificities and affinities. We used a deep mutational scanning approach to study the sequence-function relationship of the RRM2 domain of the + Saccharomyces cerevisiae + poly(A)-binding protein (Pab1). By scoring the activity of more than 100,000 unique Pab1 variants, including 1246 with single amino acid substitutions, we delineated the mutational constraints on each residue. Clustering of residues with similar mutational patterns reveals three major classes, composed principally of RNA-binding residues, of hydrophobic core residues, and of the remaining residues. The first class also includes a highly conserved residue not involved in RNA binding, G150, which can be mutated to destabilize Pab1. A comparison of the mutational sensitivity of yeast Pab1 residues to their evolutionary conservation reveals that most residues tolerate more substitutions than are present in the natural sequences, although other residues that tolerate fewer substitutions may point to specialized functions in yeast. An analysis of ∼40,000 double mutants indicates a preference for a short distance between two mutations that display an epistatic interaction. As examples of interactions, the mutations N139T, N139S, and I157L suppress other mutations that interfere with RNA binding and protein stability. Overall, this study demonstrates that living cells can be subjected to a single assay to analyze hundreds of thousands of protein variants in parallel.}, + language = {en}, + number = {11}, + urldate = {2022-07-11}, + journal = {RNA}, + author = {Melamed, Daniel and Young, David L. and Gamble, Caitlin E. and Miller, Christina R. and Fields, Stanley}, + month = nov, + year = {2013}, + pages = {1537--1551}, + file = {Full Text:/Users/admin/Zotero/storage/AFZEI4G6/Melamed et al. - 2013 - Deep mutational scanning of an RRM domain of the .pdf:application/pdf}, +} + +@article{jia_massively_2021, + title = {Massively parallel functional testing of {MSH2} missense variants conferring {Lynch} syndrome risk}, + volume = {108}, + issn = {00029297}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0002929720304390}, + doi = {10.1016/j.ajhg.2020.12.003}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {The American Journal of Human Genetics}, + author = {Jia, Xiaoyan and Burugula, Bala Bharathi and Chen, Victor and Lemons, Rosemary M. and Jayakody, Sajini and Maksutova, Mariam and Kitzman, Jacob O.}, + month = jan, + year = {2021}, + pages = {163--175}, + file = {Full Text:/Users/admin/Zotero/storage/4J7YRE9U/Jia et al. - 2021 - Massively parallel functional testing of MSH2 miss.pdf:application/pdf}, +} + +@article{mavor_determination_2016, + title = {Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting}, + volume = {5}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/15802}, + doi = {10.7554/eLife.15802}, + abstract = {Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum. + , + The ability of an organism to grow and reproduce, that is, it’s “fitness”, is determined by how its genes interact with the environment. Yeast is a model organism in which researchers can control the exact mutations present in the yeast’s genes (its genotype) and the conditions in which the yeast cells live (their environment). This allows researchers to measure how a yeast cell’s genotype and environment affect its fitness. + Ubiquitin is a protein that many organisms depend on to manage cell stress by acting as a tag that targets other proteins for degradation. Essential proteins such as ubiquitin often remain unchanged by mutation over long periods of time. As a result, these proteins evolve very slowly. Like all proteins, ubiquitin is built from a chain of amino acid molecules linked together, and the ubiquitin proteins of yeast and humans are made of almost identical sequences of amino acids. + Although ubiquitin has barely changed its sequence over evolution, previous studies have shown that – under normal growth conditions in the laboratory – most amino acids in ubiquitin can be mutated without any loss of cell fitness. This led Mavor et al. to hypothesize that treating the yeast cells with chemicals that cause cell stress might lead to amino acids in ubiquitin becoming more sensitive to mutation. + To test this idea, a class of graduate students at the University of California, San Francisco grew yeast cells with different ubiquitin mutations together, and with different chemicals that induce cell stress, and measured their growth rates. Sequencing the ubiquitin gene in the thousands of tested yeast cells revealed that three of the chemicals cause a shared set of amino acids in ubiquitin to become more sensitive to mutation. + This result suggests that these amino acids are important for the stress response, possibly by altering the ability of yeast cells to target certain proteins for degradation. Conversely, another chemical causes yeast to become more tolerant to changes in the ubiquitin sequence. The experiments also link changes in particular amino acids in ubiquitin to specific stress responses. + Mavor et al. show that many of ubquitin’s amino acids are sensitive to mutation under different stress conditions, while others can be mutated to form different amino acids without effecting fitness. By testing the effects of other chemicals, future experiments could further characterize how the yeast’s genotype and environment interact.}, + language = {en}, + urldate = {2022-07-11}, + journal = {eLife}, + author = {Mavor, David and Barlow, Kyle and Thompson, Samuel and Barad, Benjamin A and Bonny, Alain R and Cario, Clinton L and Gaskins, Garrett and Liu, Zairan and Deming, Laura and Axen, Seth D and Caceres, Elena and Chen, Weilin and Cuesta, Adolfo and Gate, Rachel E and Green, Evan M and Hulce, Kaitlin R and Ji, Weiyue and Kenner, Lillian R and Mensa, Bruk and Morinishi, Leanna S and Moss, Steven M and Mravic, Marco and Muir, Ryan K and Niekamp, Stefan and Nnadi, Chimno I and Palovcak, Eugene and Poss, Erin M and Ross, Tyler D and Salcedo, Eugenia C and See, Stephanie K and Subramaniam, Meena and Wong, Allison W and Li, Jennifer and Thorn, Kurt S and Conchúir, Shane Ó and Roscoe, Benjamin P and Chow, Eric D and DeRisi, Joseph L and Kortemme, Tanja and Bolon, Daniel N and Fraser, James S}, + month = apr, + year = {2016}, + pages = {e15802}, + file = {Full Text:/Users/admin/Zotero/storage/H2575JWN/Mavor et al. - 2016 - Determination of ubiquitin fitness landscapes unde.pdf:application/pdf}, +} + +@article{glazer_deep_2020, + title = {Deep {Mutational} {Scan} of an \textit{{SCN5A}} {Voltage} {Sensor}}, + volume = {13}, + issn = {2574-8300}, + url = {https://www.ahajournals.org/doi/10.1161/CIRCGEN.119.002786}, + doi = {10.1161/CIRCGEN.119.002786}, + abstract = {Background: + Variants in ion channel genes have classically been studied in low throughput by patch clamping. Deep mutational scanning is a complementary approach that can simultaneously assess function of thousands of variants. + + + Methods: + + We have developed and validated a method to perform a deep mutational scan of variants in + SCN5A + , which encodes the major voltage-gated sodium channel in the heart. We created a library of nearly all possible variants in a 36 base region of + SCN5A + in the S4 voltage sensor of domain IV and stably integrated the library into HEK293T cells. + + + + Results: + In preliminary experiments, challenge with 3 drugs (veratridine, brevetoxin, and ouabain) could discriminate wild-type channels from gain- and loss-of-function pathogenic variants. High-throughput sequencing of the pre- and postdrug challenge pools was used to count the prevalence of each variant and identify variants with abnormal function. The deep mutational scan scores identified 40 putative gain-of-function and 33 putative loss-of-function variants. For 8 of 9 variants, patch clamping data were consistent with the scores. + + + Conclusions: + + These experiments demonstrate the accuracy of a high-throughput in vitro scan of + SCN5A + variant function, which can be used to identify deleterious variants in + SCN5A + and other ion channel genes.}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Circulation: Genomic and Precision Medicine}, + author = {Glazer, Andrew M. and Kroncke, Brett M. and Matreyek, Kenneth A. and Yang, Tao and Wada, Yuko and Shields, Tiffany and Salem, Joe-Elie and Fowler, Douglas M. and Roden, Dan M.}, + month = feb, + year = {2020}, + pages = {e002786}, + file = {Full Text:/Users/admin/Zotero/storage/AHYB9LRW/Glazer et al. - 2020 - Deep Mutational Scan of an SCN5A Voltage Se.pdf:application/pdf}, +} + +@article{kelsic_rna_2016, + title = {{RNA} {Structural} {Determinants} of {Optimal} {Codons} {Revealed} by {MAGE}-{Seq}}, + volume = {3}, + issn = {24054712}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S2405471216303684}, + doi = {10.1016/j.cels.2016.11.004}, + language = {en}, + number = {6}, + urldate = {2022-07-11}, + journal = {Cell Systems}, + author = {Kelsic, Eric D. and Chung, Hattie and Cohen, Niv and Park, Jimin and Wang, Harris H. and Kishony, Roy}, + month = dec, + year = {2016}, + pages = {563--571.e6}, + file = {Full Text:/Users/admin/Zotero/storage/IF39P5MD/Kelsic et al. - 2016 - RNA Structural Determinants of Optimal Codons Reve.pdf:application/pdf}, +} + +@article{rockah-shmuel_systematic_2015, + title = {Systematic {Mapping} of {Protein} {Mutational} {Space} by {Prolonged} {Drift} {Reveals} the {Deleterious} {Effects} of {Seemingly} {Neutral} {Mutations}}, + volume = {11}, + issn = {1553-7358}, + url = {https://dx.plos.org/10.1371/journal.pcbi.1004421}, + doi = {10.1371/journal.pcbi.1004421}, + language = {en}, + number = {8}, + urldate = {2022-07-11}, + journal = {PLOS Computational Biology}, + author = {Rockah-Shmuel, Liat and Tóth-Petróczy, Ágnes and Tawfik, Dan S.}, + editor = {Orengo, Christine A.}, + month = aug, + year = {2015}, + pages = {e1004421}, + file = {Full Text:/Users/admin/Zotero/storage/QVEEKDBN/Rockah-Shmuel et al. - 2015 - Systematic Mapping of Protein Mutational Space by .pdf:application/pdf}, +} + +@article{kitzman_massively_2015, + title = {Massively parallel single-amino-acid mutagenesis}, + volume = {12}, + issn = {1548-7091, 1548-7105}, + url = {http://www.nature.com/articles/nmeth.3223}, + doi = {10.1038/nmeth.3223}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Nature Methods}, + author = {Kitzman, Jacob O and Starita, Lea M and Lo, Russell S and Fields, Stanley and Shendure, Jay}, + month = mar, + year = {2015}, + pages = {203--206}, + file = {Accepted Version:/Users/admin/Zotero/storage/SSUEVZ32/Kitzman et al. - 2015 - Massively parallel single-amino-acid mutagenesis.pdf:application/pdf}, +} + +@article{aakre_evolving_2015, + title = {Evolving {New} {Protein}-{Protein} {Interaction} {Specificity} through {Promiscuous} {Intermediates}}, + volume = {163}, + issn = {00928674}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867415012726}, + doi = {10.1016/j.cell.2015.09.055}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Cell}, + author = {Aakre, Christopher D. and Herrou, Julien and Phung, Tuyen N. and Perchuk, Barrett S. and Crosson, Sean and Laub, Michael T.}, + month = oct, + year = {2015}, + pages = {594--606}, + file = {Full Text:/Users/admin/Zotero/storage/4IIQTP93/Aakre et al. - 2015 - Evolving New Protein-Protein Interaction Specifici.pdf:application/pdf}, +} + +@article{bolognesi_mutational_2019, + title = {The mutational landscape of a prion-like domain}, + volume = {10}, + issn = {2041-1723}, + url = {http://www.nature.com/articles/s41467-019-12101-z}, + doi = {10.1038/s41467-019-12101-z}, + abstract = {Abstract + Insoluble protein aggregates are the hallmarks of many neurodegenerative diseases. For example, aggregates of TDP-43 occur in nearly all cases of amyotrophic lateral sclerosis (ALS). However, whether aggregates cause cellular toxicity is still not clear, even in simpler cellular systems. We reasoned that deep mutagenesis might be a powerful approach to disentangle the relationship between aggregation and toxicity. We generated {\textgreater}50,000 mutations in the prion-like domain (PRD) of TDP-43 and quantified their toxicity in yeast cells. Surprisingly, mutations that increase hydrophobicity and aggregation strongly decrease toxicity. In contrast, toxic variants promote the formation of dynamic liquid-like condensates. Mutations have their strongest effects in a hotspot that genetic interactions reveal to be structured in vivo, illustrating how mutagenesis can probe the in vivo structures of unstructured proteins. Our results show that aggregation of TDP-43 is not harmful but protects cells, most likely by titrating the protein away from a toxic liquid-like phase.}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Nature Communications}, + author = {Bolognesi, Benedetta and Faure, Andre J. and Seuma, Mireia and Schmiedel, Jörn M. and Tartaglia, Gian Gaetano and Lehner, Ben}, + month = dec, + year = {2019}, + pages = {4162}, + file = {Full Text:/Users/admin/Zotero/storage/3J8H73B4/Bolognesi et al. - 2019 - The mutational landscape of a prion-like domain.pdf:application/pdf}, +} + +@techreport{flynn_comprehensive_2022, + type = {preprint}, + title = {Comprehensive fitness landscape of {SARS}-{CoV}-2 {M} $^{\textrm{pro}}$ reveals insights into viral resistance mechanisms}, + url = {http://biorxiv.org/lookup/doi/10.1101/2022.01.26.477860}, + abstract = {Abstract + + With the continual evolution of new strains of SARS-CoV-2 that are more virulent, transmissible, and able to evade current vaccines, there is an urgent need for effective anti-viral drugs. SARS-CoV-2 main protease (M + pro + ) is a leading target for drug design due to its conserved and indispensable role in the viral life cycle. Drugs targeting M + pro + appear promising but will elicit selection pressure for resistance. To understand resistance potential in M + pro + , we performed a comprehensive mutational scan of the protease that analyzed the function of all possible single amino acid changes. We developed three separate high-throughput assays of M + pro + function in yeast, based on either the ability of M + pro + variants to cleave at a defined cut-site or on the toxicity of their expression to yeast. We used deep sequencing to quantify the functional effects of each variant in each screen. The protein fitness landscapes from all three screens were strongly correlated, indicating that they captured the biophysical properties critical to M + pro + function. The fitness landscapes revealed a non-active site location on the surface that is extremely sensitive to mutation making it a favorable location to target with inhibitors. In addition, we found a network of critical amino acids that physically bridge the two active sites of the M + pro + dimer. The clinical variants of M + pro + were predominantly functional in our screens, indicating that M + pro + is under strong selection pressure in the human population. Our results provide predictions of mutations that will be readily accessible to M + pro + evolution and that are likely to contribute to drug resistance. This complete mutational guide of M + pro + can be used in the design of inhibitors with reduced potential of evolving viral resistance.}, + language = {en}, + urldate = {2022-07-11}, + institution = {Molecular Biology}, + author = {Flynn, Julia M. and Samant, Neha and Schneider-Nachum, Gily and Barkan, David T. and Yilmaz, Nese Kurt and Schiffer, Celia A. and Moquin, Stephanie A. and Dovala, Dustin and Bolon, Daniel N.A.}, + month = jan, + year = {2022}, + doi = {10.1101/2022.01.26.477860}, + file = {Submitted Version:/Users/admin/Zotero/storage/MR6KIKAK/Flynn et al. - 2022 - Comprehensive fitness landscape of SARS-CoV-2 M s.pdf:application/pdf}, +} + +@article{haddox_mapping_2018, + title = {Mapping mutational effects along the evolutionary landscape of {HIV} envelope}, + volume = {7}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/34420}, + doi = {10.7554/eLife.34420}, + abstract = {The immediate evolutionary space accessible to HIV is largely determined by how single amino acid mutations affect fitness. These mutational effects can shift as the virus evolves. However, the prevalence of such shifts in mutational effects remains unclear. Here, we quantify the effects on viral growth of all amino acid mutations to two HIV envelope (Env) proteins that differ at + + {\textgreater} + + 100 residues. Most mutations similarly affect both Envs, but the amino acid preferences of a minority of sites have clearly shifted. These shifted sites usually prefer a specific amino acid in one Env, but tolerate many amino acids in the other. Surprisingly, shifts are only slightly enriched at sites that have substituted between the Envs—and many occur at residues that do not even contact substitutions. Therefore, long-range epistasis can unpredictably shift Env’s mutational tolerance during HIV evolution, although the amino acid preferences of most sites are conserved between moderately diverged viral strains. + + , + The virus that causes AIDS, or HIV, has a protein called Env on its surface, which is essential for the virus to infect cells. Env can also be recognized by the immune system, which then targets the virus for destruction or blocks it from infecting cells. Unfortunately, Env evolves very quickly, which means that HIV can evade our defenses. However, there are limits to how much this protein can change, since it still needs to perform its essential role in helping viruses enter cells. + In the century since HIV first appeared in human populations, the virus has evolved considerably. There are now many HIV strains that infect people, and they bear Env proteins with substantially different sequences. However, it is not clear if these changes in sequence have resulted in Envs from distinct strains being able to tolerate different mutations. + To examine this question, Haddox et al. compared how the Envs from two strains of HIV react to modifications in their sequences. They created all possible individual mutations in the proteins, and the resulting collections of mutated viruses were then tested for their ability to infect cells in the laboratory. + Most mutations had similar effects in both Env proteins. This allowed Haddox et al. to identify portions of the protein that easily accommodate changes, and portions that must remain unchanged for viruses to remain infectious—at least in the laboratory. Some of these mutations are under different types of pressures when the virus faces the immune system, and those were identified using computational approaches. + However, some mutations were tolerated differently by the two Env proteins. Therefore, viral strains differ in how their Env proteins can evolve. The parts of Env that showed differences in mutational tolerance between the strains were not necessarily the parts that differ in sequence. This shows that changes in sequence in one part of the protein can modify how other portions evolve. + It remains to be determined whether changes in tolerance to mutations translate into differences in how the virus can escape immunity. This is an important question given that the rapid evolution of Env is a major obstacle to creating a vaccine for HIV.}, + language = {en}, + urldate = {2022-07-11}, + journal = {eLife}, + author = {Haddox, Hugh K and Dingens, Adam S and Hilton, Sarah K and Overbaugh, Julie and Bloom, Jesse D}, + month = mar, + year = {2018}, + pages = {e34420}, + file = {Full Text:/Users/admin/Zotero/storage/2JX4KQTR/Haddox et al. - 2018 - Mapping mutational effects along the evolutionary .pdf:application/pdf}, +} + +@article{stiffler_evolvability_2015, + title = {Evolvability as a {Function} of {Purifying} {Selection} in {TEM}-1 Beta-{Lactamase}}, + volume = {160}, + issn = {00928674}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867415000781}, + doi = {10.1016/j.cell.2015.01.035}, + language = {en}, + number = {5}, + urldate = {2022-07-11}, + journal = {Cell}, + author = {Stiffler, Michael A. and Hekstra, Doeke R. and Ranganathan, Rama}, + month = feb, + year = {2015}, + pages = {882--892}, + file = {Full Text:/Users/admin/Zotero/storage/D95TSRL8/Stiffler et al. - 2015 - Evolvability as a Function of Purifying Selection .pdf:application/pdf}, +} + +@article{tripathi_molecular_2016, + title = {Molecular {Determinants} of {Mutant} {Phenotypes}, {Inferred} from {Saturation} {Mutagenesis} {Data}}, + volume = {33}, + issn = {0737-4038, 1537-1719}, + url = {https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msw182}, + doi = {10.1093/molbev/msw182}, + language = {en}, + number = {11}, + urldate = {2022-07-11}, + journal = {Molecular Biology and Evolution}, + author = {Tripathi, Arti and Gupta, Kritika and Khare, Shruti and Jain, Pankaj C. and Patel, Siddharth and Kumar, Prasanth and Pulianmackal, Ajai J. and Aghera, Nilesh and Varadarajan, Raghavan}, + month = nov, + year = {2016}, + pages = {2960--2975}, + file = {Full Text:/Users/admin/Zotero/storage/UFKDKVYH/Tripathi et al. - 2016 - Molecular Determinants of Mutant Phenotypes, Infer.pdf:application/pdf}, +} + +@article{findlay_accurate_2018, + title = {Accurate classification of {BRCA1} variants with saturation genome editing}, + volume = {562}, + issn = {0028-0836, 1476-4687}, + url = {http://www.nature.com/articles/s41586-018-0461-z}, + doi = {10.1038/s41586-018-0461-z}, + language = {en}, + number = {7726}, + urldate = {2022-07-11}, + journal = {Nature}, + author = {Findlay, Gregory M. and Daza, Riza M. and Martin, Beth and Zhang, Melissa D. and Leith, Anh P. and Gasperini, Molly and Janizek, Joseph D. and Huang, Xingfan and Starita, Lea M. and Shendure, Jay}, + month = oct, + year = {2018}, + pages = {217--222}, + file = {Accepted Version:/Users/admin/Zotero/storage/QFJF3L2Y/Findlay et al. - 2018 - Accurate classification of BRCA1 variants with sat.pdf:application/pdf}, +} + +@article{lee_deep_2018, + title = {Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human {H3N2} influenza variants}, + volume = {115}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1806133115}, + doi = {10.1073/pnas.1806133115}, + abstract = {Significance + A key goal in the study of influenza virus evolution is to forecast which viral strains will persist and which ones will die out. Here we experimentally measure the effects of all amino acid mutations to the hemagglutinin protein from a human H3N2 influenza strain on viral growth in cell culture. We show that these measurements have utility for distinguishing among viral strains that do and do not succeed in nature. Overall, our work suggests that new high-throughput experimental approaches may be useful for understanding virus evolution in nature. + , + Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HA’s functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature—but only when the measurements are made on a viral strain similar to the ones being studied in nature.}, + language = {en}, + number = {35}, + urldate = {2022-07-11}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Lee, Juhye M. and Huddleston, John and Doud, Michael B. and Hooper, Kathryn A. and Wu, Nicholas C. and Bedford, Trevor and Bloom, Jesse D.}, + month = aug, + year = {2018}, + file = {Full Text:/Users/admin/Zotero/storage/HG3QHAPN/Lee et al. - 2018 - Deep mutational scanning of hemagglutinin helps pr.pdf:application/pdf}, +} + +@article{weile_framework_2017, + title = {A framework for exhaustively mapping functional missense variants}, + volume = {13}, + issn = {1744-4292, 1744-4292}, + url = {https://onlinelibrary.wiley.com/doi/10.15252/msb.20177908}, + doi = {10.15252/msb.20177908}, + language = {en}, + number = {12}, + urldate = {2022-07-11}, + journal = {Molecular Systems Biology}, + author = {Weile, Jochen and Sun, Song and Cote, Atina G and Knapp, Jennifer and Verby, Marta and Mellor, Joseph C and Wu, Yingzhou and Pons, Carles and Wong, Cassandra and Lieshout, Natascha and Yang, Fan and Tasan, Murat and Tan, Guihong and Yang, Shan and Fowler, Douglas M and Nussbaum, Robert and Bloom, Jesse D and Vidal, Marc and Hill, David E and Aloy, Patrick and Roth, Frederick P}, + month = dec, + year = {2017}, + pages = {957}, + file = {Full Text:/Users/admin/Zotero/storage/YYUXTHFT/Weile et al. - 2017 - A framework for exhaustively mapping functional mi.pdf:application/pdf;Full Text:/Users/admin/Zotero/storage/HCRUH6RQ/Weile et al. - 2017 - A framework for exhaustively mapping functional mi.pdf:application/pdf}, +} + +@article{qi_quantitative_2014, + title = {A {Quantitative} {High}-{Resolution} {Genetic} {Profile} {Rapidly} {Identifies} {Sequence} {Determinants} of {Hepatitis} {C} {Viral} {Fitness} and {Drug} {Sensitivity}}, + volume = {10}, + issn = {1553-7374}, + url = {https://dx.plos.org/10.1371/journal.ppat.1004064}, + doi = {10.1371/journal.ppat.1004064}, + language = {en}, + number = {4}, + urldate = {2022-07-11}, + journal = {PLoS Pathogens}, + author = {Qi, Hangfei and Olson, C. Anders and Wu, Nicholas C. and Ke, Ruian and Loverdo, Claude and Chu, Virginia and Truong, Shawna and Remenyi, Roland and Chen, Zugen and Du, Yushen and Su, Sheng-Yao and Al-Mawsawi, Laith Q. and Wu, Ting-Ting and Chen, Shu-Hua and Lin, Chung-Yen and Zhong, Weidong and Lloyd-Smith, James O. and Sun, Ren}, + editor = {Wilke, Claus O.}, + month = apr, + year = {2014}, + pages = {e1004064}, + file = {Full Text:/Users/admin/Zotero/storage/ZXNH72PK/Qi et al. - 2014 - A Quantitative High-Resolution Genetic Profile Rap.pdf:application/pdf}, +} + +@article{chan_correlation_2017, + title = {Correlation of fitness landscapes from three orthologous {TIM} barrels originates from sequence and structure constraints}, + volume = {8}, + issn = {2041-1723}, + url = {http://www.nature.com/articles/ncomms14614}, + doi = {10.1038/ncomms14614}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Nature Communications}, + author = {Chan, Yvonne H. and Venev, Sergey V. and Zeldovich, Konstantin B. and Matthews, C. Robert}, + month = apr, + year = {2017}, + pages = {14614}, + file = {Full Text:/Users/admin/Zotero/storage/A6E42JVX/Chan et al. - 2017 - Correlation of fitness landscapes from three ortho.pdf:application/pdf}, +} + +@article{melnikov_comprehensive_2014, + title = {Comprehensive mutational scanning of a kinase \textit{in vivo} reveals substrate-dependent fitness landscapes}, + volume = {42}, + issn = {0305-1048, 1362-4962}, + url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gku511}, + doi = {10.1093/nar/gku511}, + language = {en}, + number = {14}, + urldate = {2022-07-11}, + journal = {Nucleic Acids Research}, + author = {Melnikov, Alexandre and Rogov, Peter and Wang, Li and Gnirke, Andreas and Mikkelsen, Tarjei S.}, + month = aug, + year = {2014}, + pages = {e112--e112}, + file = {Full Text:/Users/admin/Zotero/storage/U454BG33/Melnikov et al. - 2014 - Comprehensive mutational scanning of a kinase i.pdf:application/pdf}, +} + +@article{nutschel_systematically_2020, + title = {Systematically {Scrutinizing} the {Impact} of {Substitution} {Sites} on {Thermostability} and {Detergent} {Tolerance} for \textit{{Bacillus} subtilis} {Lipase} {A}}, + volume = {60}, + issn = {1549-9596, 1549-960X}, + url = {https://pubs.acs.org/doi/10.1021/acs.jcim.9b00954}, + doi = {10.1021/acs.jcim.9b00954}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Journal of Chemical Information and Modeling}, + author = {Nutschel, Christina and Fulton, Alexander and Zimmermann, Olav and Schwaneberg, Ulrich and Jaeger, Karl-Erich and Gohlke, Holger}, + month = mar, + year = {2020}, + pages = {1568--1584}, +} + +@article{jacquier_capturing_2013, + title = {Capturing the mutational landscape of the beta-lactamase {TEM}-1}, + volume = {110}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1215206110}, + doi = {10.1073/pnas.1215206110}, + abstract = {Adaptation proceeds through the selection of mutations. The distribution of mutant fitness effect and the forces shaping this distribution are therefore keys to predict the evolutionary fate of organisms and their constituents such as enzymes. Here, by producing and sequencing a comprehensive collection of 10,000 mutants, we explore the mutational landscape of one enzyme involved in the spread of antibiotic resistance, the beta-lactamase TEM-1. We measured mutation impact on the enzyme activity through the estimation of amoxicillin minimum inhibitory concentration on a subset of 990 mutants carrying a unique missense mutation, representing 64\% of possible amino acid changes in that protein reachable by point mutation. We established that mutation type, solvent accessibility of residues, and the predicted effect of mutations on protein stability primarily determined alone or in combination changes in minimum inhibitory concentration of mutants. Moreover, we were able to capture the drastic modification of the mutational landscape induced by a single stabilizing point mutation (M182T) by a simple model of protein stability. This work thereby provides an integrated framework to study mutation effects and a tool to understand/define better the epistatic interactions.}, + language = {en}, + number = {32}, + urldate = {2022-07-11}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Jacquier, Hervé and Birgy, André and Le Nagard, Hervé and Mechulam, Yves and Schmitt, Emmanuelle and Glodt, Jérémy and Bercot, Beatrice and Petit, Emmanuelle and Poulain, Julie and Barnaud, Guilène and Gros, Pierre-Alexis and Tenaillon, Olivier}, + month = aug, + year = {2013}, + pages = {13067--13072}, + file = {Full Text:/Users/admin/Zotero/storage/KCXP8PUL/Jacquier et al. - 2013 - Capturing the mutational landscape of the beta-lac.pdf:application/pdf}, +} + +@article{mishra_systematic_2016, + title = {Systematic {Mutant} {Analyses} {Elucidate} {General} and {Client}-{Specific} {Aspects} of {Hsp90} {Function}}, + volume = {15}, + issn = {22111247}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S2211124716303175}, + doi = {10.1016/j.celrep.2016.03.046}, + language = {en}, + number = {3}, + urldate = {2022-07-11}, + journal = {Cell Reports}, + author = {Mishra, Parul and Flynn, Julia M. and Starr, Tyler N. and Bolon, Daniel N.A.}, + month = apr, + year = {2016}, + pages = {588--598}, + file = {Full Text:/Users/admin/Zotero/storage/N3VLLXTY/Mishra et al. - 2016 - Systematic Mutant Analyses Elucidate General and C.pdf:application/pdf}, +} + +@article{pokusaeva_experimental_2019, + title = {An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape}, + volume = {15}, + issn = {1553-7404}, + url = {https://dx.plos.org/10.1371/journal.pgen.1008079}, + doi = {10.1371/journal.pgen.1008079}, + language = {en}, + number = {4}, + urldate = {2022-07-11}, + journal = {PLOS Genetics}, + author = {Pokusaeva, Victoria O. and Usmanova, Dinara R. and Putintseva, Ekaterina V. and Espinar, Lorena and Sarkisyan, Karen S. and Mishin, Alexander S. and Bogatyreva, Natalya S. and Ivankov, Dmitry N. and Akopyan, Arseniy V. and Avvakumov, Sergey Ya. and Povolotskaya, Inna S. and Filion, Guillaume J. and Carey, Lucas B. and Kondrashov, Fyodor A.}, + editor = {Petrov, Dmitri A.}, + month = apr, + year = {2019}, + pages = {e1008079}, + file = {Full Text:/Users/admin/Zotero/storage/39VZ7AQN/Pokusaeva et al. - 2019 - An experimental assay of the interactions of amino.pdf:application/pdf}, +} + +@article{fernandes_functional_2016, + title = {Functional {Segregation} of {Overlapping} {Genes} in {HIV}}, + volume = {167}, + issn = {00928674}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867416316038}, + doi = {10.1016/j.cell.2016.11.031}, + language = {en}, + number = {7}, + urldate = {2022-07-11}, + journal = {Cell}, + author = {Fernandes, Jason D. and Faust, Tyler B. and Strauli, Nicolas B. and Smith, Cynthia and Crosby, David C. and Nakamura, Robert L. and Hernandez, Ryan D. and Frankel, Alan D.}, + month = dec, + year = {2016}, + pages = {1762--1773.e12}, + file = {Full Text:/Users/admin/Zotero/storage/M2JCUQLM/Fernandes et al. - 2016 - Functional Segregation of Overlapping Genes in HIV.pdf:application/pdf}, +} + +@techreport{sinai_generative_2021, + type = {preprint}, + title = {Generative {AAV} capsid diversification by latent interpolation}, + url = {http://biorxiv.org/lookup/doi/10.1101/2021.04.16.440236}, + abstract = {Summary + Adeno-associated virus (AAV) capsids have shown clinical promise as delivery vectors for gene therapy. However, the high prevalence of pre-existing immunity against natural capsids poses a challenge for widespread treatment. The generation of diverse capsids that are potentially more capable of immune evasion is challenging because introducing multiple mutations often breaks capsid assembly. Here we target a representative, immunologically relevant 28-amino-acid segment of the AAV2 capsid and show that a low-complexity Variational Auto-encoder (VAE) can interpolate in sequence space to produce diverse and novel capsids capable of packaging their own genomes. We first train the VAE on a 564-sample Multiple-Sequence Alignment (MSA) of dependo-parvoviruses, and then further augment this dataset by adding 22,704 samples from a deep mutational exploration (DME) on the target region. In both cases the VAE generated viable variants with many mutations, which we validated experimentally. We propose that this simple approach can be used to optimize and diversify other proteins, as well as other capsid traits of interest for gene delivery.}, + language = {en}, + urldate = {2022-07-11}, + institution = {Synthetic Biology}, + author = {Sinai, Sam and Jain, Nina and Church, George M and Kelsic, Eric D}, + month = apr, + year = {2021}, + doi = {10.1101/2021.04.16.440236}, + file = {Submitted Version:/Users/admin/Zotero/storage/TEMTVFT7/Sinai et al. - 2021 - Generative AAV capsid diversification by latent in.pdf:application/pdf}, +} + +@article{wu_functional_2015, + title = {Functional {Constraint} {Profiling} of a {Viral} {Protein} {Reveals} {Discordance} of {Evolutionary} {Conservation} and {Functionality}}, + volume = {11}, + issn = {1553-7404}, + url = {https://dx.plos.org/10.1371/journal.pgen.1005310}, + doi = {10.1371/journal.pgen.1005310}, + language = {en}, + number = {7}, + urldate = {2022-07-11}, + journal = {PLOS Genetics}, + author = {Wu, Nicholas C. and Olson, C. Anders and Du, Yushen and Le, Shuai and Tran, Kevin and Remenyi, Roland and Gong, Danyang and Al-Mawsawi, Laith Q. and Qi, Hangfei and Wu, Ting-Ting and Sun, Ren}, + editor = {Worobey, Michael}, + month = jul, + year = {2015}, + pages = {e1005310}, + file = {Full Text:/Users/admin/Zotero/storage/NNQX65KR/Wu et al. - 2015 - Functional Constraint Profiling of a Viral Protein.pdf:application/pdf}, +} + +@article{matreyek_integrating_2021, + title = {Integrating thousands of {PTEN} variant activity and abundance measurements reveals variant subgroups and new dominant negatives in cancers}, + volume = {13}, + issn = {1756-994X}, + url = {https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00984-x}, + doi = {10.1186/s13073-021-00984-x}, + abstract = {Abstract + + Background + PTEN is a multi-functional tumor suppressor protein regulating cell growth, immune signaling, neuronal function, and genome stability. Experimental characterization can help guide the clinical interpretation of the thousands of germline or somatic PTEN variants observed in patients. Two large-scale mutational datasets, one for PTEN variant intracellular abundance encompassing 4112 missense variants and one for lipid phosphatase activity encompassing 7244 variants, were recently published. The combined information from these datasets can reveal variant-specific phenotypes that may underlie various clinical presentations, but this has not been comprehensively examined, particularly for somatic PTEN variants observed in cancers. + + + Methods + Here, we add to these efforts by measuring the intracellular abundance of 764 new PTEN variants and refining abundance measurements for 3351 previously studied variants. We use this expanded and refined PTEN abundance dataset to explore the mutational patterns governing PTEN intracellular abundance, and then incorporate the phosphatase activity data to subdivide PTEN variants into four functionally distinct groups. + + + Results + This analysis revealed a set of highly abundant but lipid phosphatase defective variants that could act in a dominant-negative fashion to suppress PTEN activity. Two of these variants were, indeed, capable of dysregulating Akt signaling in cells harboring a WT PTEN allele. Both variants were observed in multiple breast or uterine tumors, demonstrating the disease relevance of these high abundance, inactive variants. + + + Conclusions + We show that multidimensional, large-scale variant functional data, when paired with public cancer genomics datasets and follow-up assays, can improve understanding of uncharacterized cancer-associated variants, and provide better insights into how they contribute to oncogenesis.}, + language = {en}, + number = {1}, + urldate = {2022-07-11}, + journal = {Genome Medicine}, + author = {Matreyek, Kenneth A. and Stephany, Jason J. and Ahler, Ethan and Fowler, Douglas M.}, + month = dec, + year = {2021}, + pages = {165}, + file = {Full Text:/Users/admin/Zotero/storage/8VXIZT8Q/Matreyek et al. - 2021 - Integrating thousands of PTEN variant activity and.pdf:application/pdf}, +} + +@article{mighell_saturation_2018, + title = {A {Saturation} {Mutagenesis} {Approach} to {Understanding} {PTEN} {Lipid} {Phosphatase} {Activity} and {Genotype}-{Phenotype} {Relationships}}, + volume = {102}, + issn = {00029297}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0002929718301071}, + doi = {10.1016/j.ajhg.2018.03.018}, + language = {en}, + number = {5}, + urldate = {2023-06-14}, + journal = {The American Journal of Human Genetics}, + author = {Mighell, Taylor L. and Evans-Dutson, Sara and O’Roak, Brian J.}, + month = may, + year = {2018}, + pages = {943--955}, + file = {Full Text:/Users/admin/Zotero/storage/9UEQ49C4/Mighell et al. - 2018 - A Saturation Mutagenesis Approach to Understanding.pdf:application/pdf}, +} + +@article{russ_evolution-based_2020, + title = {An evolution-based model for designing chorismate mutase enzymes}, + volume = {369}, + issn = {0036-8075, 1095-9203}, + url = {https://www.sciencemag.org/lookup/doi/10.1126/science.aba3304}, + doi = {10.1126/science.aba3304}, + abstract = {The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.}, + language = {en}, + number = {6502}, + urldate = {2023-06-14}, + journal = {Science}, + author = {Russ, William P. and Figliuzzi, Matteo and Stocker, Christian and Barrat-Charlaix, Pierre and Socolich, Michael and Kast, Peter and Hilvert, Donald and Monasson, Remi and Cocco, Simona and Weigt, Martin and Ranganathan, Rama}, + month = jul, + year = {2020}, + pages = {440--445}, +} + +@article{gonzalez_fitness_2019, + title = {Fitness {Effects} of {Single} {Amino} {Acid} {Insertions} and {Deletions} in {TEM}-1 Beta-{Lactamase}}, + volume = {431}, + issn = {00222836}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0022283619302372}, + doi = {10.1016/j.jmb.2019.04.030}, + language = {en}, + number = {12}, + urldate = {2023-06-14}, + journal = {Journal of Molecular Biology}, + author = {Gonzalez, Courtney E. and Roberts, Paul and Ostermeier, Marc}, + month = may, + year = {2019}, + pages = {2320--2330}, + file = {Accepted Version:/Users/admin/Zotero/storage/ZWGQXGBD/Gonzalez et al. - 2019 - Fitness Effects of Single Amino Acid Insertions an.pdf:application/pdf}, +} + +@article{macdonald_dimple_2023, + title = {{DIMPLE}: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology}, + volume = {24}, + issn = {1474-760X}, + shorttitle = {{DIMPLE}}, + url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02880-6}, + doi = {10.1186/s13059-023-02880-6}, + abstract = {Abstract + Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.}, + language = {en}, + number = {1}, + urldate = {2023-06-14}, + journal = {Genome Biology}, + author = {Macdonald, Christian B. and Nedrud, David and Grimes, Patrick Rockefeller and Trinidad, Donovan and Fraser, James S. and Coyote-Maestas, Willow}, + month = feb, + year = {2023}, + pages = {36}, + file = {Full Text:/Users/admin/Zotero/storage/3MMXFUJ4/Macdonald et al. - 2023 - DIMPLE deep insertion, deletion, and missense mut.pdf:application/pdf}, +} + +@article{jones_structural_2020, + title = {Structural and functional characterization of {G} protein–coupled receptors with deep mutational scanning}, + volume = {9}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/54895}, + doi = {10.7554/eLife.54895}, + abstract = {The {\textgreater}800 human G protein–coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state- and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here, we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G protein signal transduction. We tested 7800 of 7828 possible single amino acid substitutions to the beta-2 adrenergic receptor (Beta + 2 + AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for Beta + 2 + AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we identify residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Jones, Eric M and Lubock, Nathan B and Venkatakrishnan, Aj and Wang, Jeffrey and Tseng, Alex M and Paggi, Joseph M and Latorraca, Naomi R and Cancilla, Daniel and Satyadi, Megan and Davis, Jessica E and Babu, M Madan and Dror, Ron O and Kosuri, Sriram}, + month = oct, + year = {2020}, + pages = {e54895}, + file = {Full Text:/Users/admin/Zotero/storage/T2SD9K3D/Jones et al. - 2020 - Structural and functional characterization of G pr.pdf:application/pdf;Submitted Version:/Users/admin/Zotero/storage/WYWCXHH9/Jones et al. - 2020 - Structural and functional characterization of G pr.pdf:application/pdf}, +} + +@article{chen_comprehensive_2020, + title = {Comprehensive exploration of the translocation, stability and substrate recognition requirements in {VIM}-2 lactamase}, + volume = {9}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/56707}, + doi = {10.7554/eLife.56707}, + abstract = {Metallo-Beta-lactamases (MBLs) degrade a broad spectrum of Beta-lactam antibiotics, and are a major disseminating source for multidrug resistant bacteria. Despite many biochemical studies in diverse MBLs, molecular understanding of the roles of residues in the enzyme’s stability and function, and especially substrate specificity, is lacking. Here, we employ deep mutational scanning (DMS) to generate comprehensive single amino acid variant data on a major clinical MBL, VIM-2, by measuring the effect of thousands of VIM-2 mutants on the degradation of three representative classes of Beta-lactams (ampicillin, cefotaxime, and meropenem) and at two different temperatures (25°C and 37°C). We revealed residues responsible for expression and translocation, and mutations that increase resistance and/or alter substrate specificity. The distribution of specificity-altering mutations unveiled distinct molecular recognition of the three substrates. Moreover, these function-altering mutations are frequently observed among naturally occurring variants, suggesting that the enzymes have continuously evolved to become more potent resistance genes.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Chen, John Z and Fowler, Douglas M and Tokuriki, Nobuhiko}, + month = jun, + year = {2020}, + pages = {e56707}, + file = {Full Text:/Users/admin/Zotero/storage/LBP8NZRP/Chen et al. - 2020 - Comprehensive exploration of the translocation, st.pdf:application/pdf}, +} + +@article{kennouche_deep_2019, + title = {Deep mutational scanning of the \textit{{Neisseria} meningitidis} major pilin reveals the importance of pilus tip‐mediated adhesion}, + volume = {38}, + issn = {0261-4189, 1460-2075}, + url = {https://www.embopress.org/doi/10.15252/embj.2019102145}, + doi = {10.15252/embj.2019102145}, + language = {en}, + number = {22}, + urldate = {2023-06-14}, + journal = {The EMBO Journal}, + author = {Kennouche, Paul and Charles‐Orszag, Arthur and Nishiguchi, Daiki and Goussard, Sylvie and Imhaus, Anne‐Flore and Dupré, Mathieu and Chamot‐Rooke, Julia and Duménil, Guillaume}, + month = nov, + year = {2019}, + pages = {e102145}, + file = {Full Text:/Users/admin/Zotero/storage/K9LIAMBH/Kennouche et al. - 2019 - Deep mutational scanning of the Neisseria menin.pdf:application/pdf;Submitted Version:/Users/admin/Zotero/storage/9587BW8A/Kennouche et al. - 2019 - Deep mutational scanning of the Neisseria menin.pdf:application/pdf}, +} + +@article{haddox_experimental_2016, + title = {Experimental {Estimation} of the {Effects} of {All} {Amino}-{Acid} {Mutations} to {HIV}’s {Envelope} {Protein} on {Viral} {Replication} in {Cell} {Culture}}, + volume = {12}, + issn = {1553-7374}, + url = {https://dx.plos.org/10.1371/journal.ppat.1006114}, + doi = {10.1371/journal.ppat.1006114}, + language = {en}, + number = {12}, + urldate = {2023-06-14}, + journal = {PLOS Pathogens}, + author = {Haddox, Hugh K. and Dingens, Adam S. and Bloom, Jesse D.}, + editor = {Swanstrom, Ronald}, + month = dec, + year = {2016}, + pages = {e1006114}, + file = {Full Text:/Users/admin/Zotero/storage/MJ2VVEBN/Haddox et al. - 2016 - Experimental Estimation of the Effects of All Amin.pdf:application/pdf}, +} + +@article{roscoe_systematic_2014, + title = {Systematic {Exploration} of {Ubiquitin} {Sequence}, {E1} {Activation} {Efficiency}, and {Experimental} {Fitness} in {Yeast}}, + volume = {426}, + issn = {00222836}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0022283614002587}, + doi = {10.1016/j.jmb.2014.05.019}, + language = {en}, + number = {15}, + urldate = {2023-06-14}, + journal = {Journal of Molecular Biology}, + author = {Roscoe, Benjamin P. and Bolon, Daniel N.A.}, + month = jul, + year = {2014}, + pages = {2854--2870}, + file = {Accepted Version:/Users/admin/Zotero/storage/ZZGGND7E/Roscoe and Bolon - 2014 - Systematic Exploration of Ubiquitin Sequence, E1 A.pdf:application/pdf}, +} + +@article{klesmith_comprehensive_2015, + title = {Comprehensive {Sequence}-{Flux} {Mapping} of a {Levoglucosan} {Utilization} {Pathway} in \textit{{E}. coli}}, + volume = {4}, + issn = {2161-5063, 2161-5063}, + url = {https://pubs.acs.org/doi/10.1021/acssynbio.5b00131}, + doi = {10.1021/acssynbio.5b00131}, + language = {en}, + number = {11}, + urldate = {2023-06-14}, + journal = {ACS Synthetic Biology}, + author = {Klesmith, Justin R. and Bacik, John-Paul and Michalczyk, Ryszard and Whitehead, Timothy A.}, + month = nov, + year = {2015}, + pages = {1235--1243}, + file = {Submitted Version:/Users/admin/Zotero/storage/EPYWRHC2/Klesmith et al. - 2015 - Comprehensive Sequence-Flux Mapping of a Levogluco.pdf:application/pdf}, +} + +@article{gonzalez_somermeyer_heterogeneity_2022, + title = {Heterogeneity of the {GFP} fitness landscape and data-driven protein design}, + volume = {11}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/75842}, + doi = {10.7554/eLife.75842}, + abstract = {Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Gonzalez Somermeyer, Louisa and Fleiss, Aubin and Mishin, Alexander S and Bozhanova, Nina G and Igolkina, Anna A and Meiler, Jens and Alaball Pujol, Maria-Elisenda and Putintseva, Ekaterina V and Sarkisyan, Karen S and Kondrashov, Fyodor A}, + month = may, + year = {2022}, + pages = {e75842}, + file = {Full Text:/Users/admin/Zotero/storage/N7EZUGF7/Gonzalez Somermeyer et al. - 2022 - Heterogeneity of the GFP fitness landscape and dat.pdf:application/pdf}, +} + +@article{doud_accurate_2016, + title = {Accurate {Measurement} of the {Effects} of {All} {Amino}-{Acid} {Mutations} on {Influenza} {Hemagglutinin}}, + volume = {8}, + issn = {1999-4915}, + url = {http://www.mdpi.com/1999-4915/8/6/155}, + doi = {10.3390/v8060155}, + language = {en}, + number = {6}, + urldate = {2023-06-14}, + journal = {Viruses}, + author = {Doud, Michael and Bloom, Jesse}, + month = jun, + year = {2016}, + pages = {155}, + file = {Full Text:/Users/admin/Zotero/storage/Z3RRFCKV/Doud and Bloom - 2016 - Accurate Measurement of the Effects of All Amino-A.pdf:application/pdf}, +} + +@article{soh_comprehensive_2019, + title = {Comprehensive mapping of adaptation of the avian influenza polymerase protein {PB2} to humans}, + volume = {8}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/45079}, + doi = {10.7554/eLife.45079}, + abstract = {Viruses like influenza are infamous for their ability to adapt to new hosts. Retrospective studies of natural zoonoses and passaging in the lab have identified a modest number of host-adaptive mutations. However, it is unclear if these mutations represent all ways that influenza can adapt to a new host. Here we take a prospective approach to this question by completely mapping amino-acid mutations to the avian influenza virus polymerase protein PB2 that enhance growth in human cells. We identify numerous previously uncharacterized human-adaptive mutations. These mutations cluster on PB2’s surface, highlighting potential interfaces with host factors. Some previously uncharacterized adaptive mutations occur in avian-to-human transmission of H7N9 influenza, showing their importance for natural virus evolution. But other adaptive mutations do not occur in nature because they are inaccessible via single-nucleotide mutations. Overall, our work shows how selection at key molecular surfaces combines with evolutionary accessibility to shape viral host adaptation. + , + Viruses copy themselves by hijacking the cells of an infected host, but this comes with some limitations. Cells from different species have different molecular machinery and so viruses often have to specialize to a narrow group of species. This specialization consists largely of fine-tuning the way that viral proteins interact with host proteins. + For instance, in bird flu viruses, a protein known as PB2 does not interact well with the machinery in human cells. Because PB2 proteins form part of the viral polymerase (the structure that copies the viral genome), this prevents bird flu viruses from replicating efficiently in humans. Sometimes however, changes in the PB2 protein allow bird flu viruses to better replicate in humans, potentially leading to deadly flu pandemics. + To understand exactly how this happens, researchers have previously used two approaches: examining the changes that have happened in past flu viruses, and monitoring the evolution of bird flu viruses grown in human cells in the lab. However, these approaches can only look at a small number of the many possible genetic changes to the virus. This makes it hard to anticipate the new ways that flu might adapt to human cells in the future. + To overcome this problem, Soh et al. systematically created all of the single changes to the bird flu PB2, altering every element of the protein sequence one-by-one. They then tested which of the changes to PB2 helped the virus grow better in human cells. The modifications that made the viruses thrive were on the surface of the protein, suggesting that they might improve interaction with the cell machinery of the host. Some changes have been found in bird flu viruses that have recently jumped into humans in nature, although fortunately none of these viruses have yet spread widely to cause a pandemic. + Many factors affect the evolution of viruses, and their ability to infect new species. Understanding which changes in proteins help these microbes adapt to new hosts is an important element that scientists could consider to assess future risks of pandemics.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Soh, Yq Shirleen and Moncla, Louise H and Eguia, Rachel and Bedford, Trevor and Bloom, Jesse D}, + month = apr, + year = {2019}, + pages = {e45079}, + file = {Full Text:/Users/admin/Zotero/storage/U4C4J7MA/Soh et al. - 2019 - Comprehensive mapping of adaptation of the avian i.pdf:application/pdf}, +} + +@article{seuma_genetic_2021, + title = {The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial {Alzheimer}’s disease mutations}, + volume = {10}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/63364}, + doi = {10.7554/eLife.63364}, + abstract = {Plaques of the amyloid beta (Aß) peptide are a pathological hallmark of Alzheimer’s disease (AD), the most common form of dementia. Mutations in Aß also cause familial forms of AD (fAD). Here, we use deep mutational scanning to quantify the effects of {\textgreater}14,000 mutations on the aggregation of Aß. The resulting genetic landscape reveals mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the empirical nucleation scores accurately identify all known dominant fAD mutations in Aß, genetically validating that the mechanism of nucleation in a cell-based assay is likely to be very similar to the mechanism that causes the human disease. These results provide the first comprehensive atlas of how mutations alter the formation of any amyloid fibril and a resource for the interpretation of genetic variation in Aß. + , + Alzheimer’s disease is the most common form of dementia, affecting more than 50 million people worldwide. Despite more than 400 clinical trials, there are still no effective drugs that can prevent or treat the disease. A common target in Alzheimer’s disease trials is a small protein called amyloid beta. Amyloid beta proteins are ‘sticky’ molecules. In the brains of people with Alzheimer’s disease, they join to form first small aggregates and then long chains called fibrils, a process which is toxic to neurons. + Specific mutations in the gene for amyloid beta are known to cause rare, aggressive forms of Alzheimer’s disease that typically affect people in their fifties or sixties. But these are not the only mutations that can occur in amyloid beta. In principle, any part of the protein could undergo mutation. And given the size of the human population, it is likely that each of these mutations exists in someone alive today. + Seuma et al. reasoned that studying these mutations could help us understand the process by which amyloid beta forms new aggregates. Using an approach called deep mutational scanning, Seuma et al. mutated each point in the protein, one at a time. This produced more than 14,000 different versions of amyloid beta. Seuma et al. then measured how quickly these mutants were able to form aggregates by introducing them into yeast cells. + All the mutations known to cause early-onset Alzheimer’s disease accelerated amyloid beta aggregation in the yeast. But the results also revealed previously unknown properties that control how fast aggregation occurs. In addition, they highlighted a number of positions in the amyloid beta sequence that act as ‘gatekeepers’. In healthy brains, these gatekeepers prevent amyloid beta proteins from sticking together. When mutated, they drive the protein to form aggregates. + This comprehensive dataset will help researchers understand how proteins form toxic aggregates, which could in turn help them find ways to prevent this from happening. By providing an ‘atlas’ of all possible amyloid beta mutations, the dataset will also help clinicians interpret any new mutations they encounter in patients. By showing whether or not a mutation speeds up aggregation, the atlas will help clinicians predict whether that mutation increases the risk of Alzheimer’s disease.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Seuma, Mireia and Faure, Andre J and Badia, Marta and Lehner, Ben and Bolognesi, Benedetta}, + month = feb, + year = {2021}, + pages = {e63364}, + file = {Full Text:/Users/admin/Zotero/storage/8WFU6TK4/Seuma et al. - 2021 - The genetic landscape for amyloid beta fibril nucl.pdf:application/pdf}, +} + +@article{dandage_differential_2018, + title = {Differential strengths of molecular determinants guide environment specific mutational fates}, + volume = {14}, + issn = {1553-7404}, + url = {https://dx.plos.org/10.1371/journal.pgen.1007419}, + doi = {10.1371/journal.pgen.1007419}, + language = {en}, + number = {5}, + urldate = {2023-06-14}, + journal = {PLOS Genetics}, + author = {Dandage, Rohan and Pandey, Rajesh and Jayaraj, Gopal and Rai, Manish and Berger, David and Chakraborty, Kausik}, + editor = {Matic, Ivan}, + month = may, + year = {2018}, + pages = {e1007419}, + file = {Full Text:/Users/admin/Zotero/storage/GELATYV7/Dandage et al. - 2018 - Differential strengths of molecular determinants g.pdf:application/pdf}, +} + +@article{firnberg_comprehensive_2014, + title = {A {Comprehensive}, {High}-{Resolution} {Map} of a {Gene}’s {Fitness} {Landscape}}, + volume = {31}, + issn = {1537-1719, 0737-4038}, + url = {https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msu081}, + doi = {10.1093/molbev/msu081}, + language = {en}, + number = {6}, + urldate = {2023-06-14}, + journal = {Molecular Biology and Evolution}, + author = {Firnberg, Elad and Labonte, Jason W. and Gray, Jeffrey J. and Ostermeier, Marc}, + month = jun, + year = {2014}, + pages = {1581--1592}, + file = {Full Text:/Users/admin/Zotero/storage/9TTFDAL5/Firnberg et al. - 2014 - A Comprehensive, High-Resolution Map of a Gene’s F.pdf:application/pdf}, +} + +@article{adkar_protein_2012, + title = {Protein {Model} {Discrimination} {Using} {Mutational} {Sensitivity} {Derived} from {Deep} {Sequencing}}, + volume = {20}, + issn = {09692126}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0969212612000068}, + doi = {10.1016/j.str.2011.11.021}, + language = {en}, + number = {2}, + urldate = {2023-06-14}, + journal = {Structure}, + author = {Adkar, Bharat V. and Tripathi, Arti and Sahoo, Anusmita and Bajaj, Kanika and Goswami, Devrishi and Chakrabarti, Purbani and Swarnkar, Mohit K. and Gokhale, Rajesh S. and Varadarajan, Raghavan}, + month = feb, + year = {2012}, + pages = {371--381}, + file = {Full Text:/Users/admin/Zotero/storage/TIV665XI/Adkar et al. - 2012 - Protein Model Discrimination Using Mutational Sens.pdf:application/pdf}, +} + +@article{giacomelli_mutational_2018, + title = {Mutational processes shape the landscape of {TP53} mutations in human cancer}, + volume = {50}, + issn = {1061-4036, 1546-1718}, + url = {https://www.nature.com/articles/s41588-018-0204-y}, + doi = {10.1038/s41588-018-0204-y}, + language = {en}, + number = {10}, + urldate = {2023-06-14}, + journal = {Nature Genetics}, + author = {Giacomelli, Andrew O. and Yang, Xiaoping and Lintner, Robert E. and McFarland, James M. and Duby, Marc and Kim, Jaegil and Howard, Thomas P. and Takeda, David Y. and Ly, Seav Huong and Kim, Eejung and Gannon, Hugh S. and Hurhula, Brian and Sharpe, Ted and Goodale, Amy and Fritchman, Briana and Steelman, Scott and Vazquez, Francisca and Tsherniak, Aviad and Aguirre, Andrew J. and Doench, John G. and Piccioni, Federica and Roberts, Charles W. M. and Meyerson, Matthew and Getz, Gad and Johannessen, Cory M. and Root, David E. and Hahn, William C.}, + month = oct, + year = {2018}, + pages = {1381--1387}, + file = {Accepted Version:/Users/admin/Zotero/storage/EFU93FB6/Giacomelli et al. - 2018 - Mutational processes shape the landscape of TP53 m.pdf:application/pdf}, +} + +@article{suiter_massively_2020, + title = {Massively parallel variant characterization identifies \textit{{NUDT15}} alleles associated with thiopurine toxicity}, + volume = {117}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1915680117}, + doi = {10.1073/pnas.1915680117}, + abstract = {Significance + + Pharmacogenetics is a prototype of genomics-guided precision medicine. While there is a rapid expansion of novel pharmacogenetic variants discovered by genome sequencing, the lack of variant interpretation in a scalable fashion is a formidable barrier in this field. + NUDT15 + polymorphism is a major genetic cause for hematopoietic toxicity during thiopurine therapy. Motivated by the need to understand + NUDT15 + variant effects for clinical actions, we developed a massively parallel assay to preemptively characterize 91.8\% of all possible missense variants in + NUDT15 + . Our function-based variant classification accurately predicted thiopurine toxicity risk alleles in patients. These results vastly improved the ability to implement genotype-guided thiopurine therapy and illustrated the value and potential of a high-throughput variant effect screen in general. + + , + + As a prototype of genomics-guided precision medicine, individualized thiopurine dosing based on pharmacogenetics is a highly effective way to mitigate hematopoietic toxicity of this class of drugs. Recently, + NUDT15 + deficiency was identified as a genetic cause of thiopurine toxicity, and + NUDT15 + -informed preemptive dose reduction was quickly adopted in clinical settings. To exhaustively identify pharmacogenetic variants in this gene, we developed massively parallel NUDT15 function assays to determine the variants’ effect on protein abundance and thiopurine cytotoxicity. Of the 3,097 possible missense variants, we characterized the abundance of 2,922 variants and found 54 hotspot residues at which variants resulted in complete loss of protein stability. Analyzing 2,935 variants in the thiopurine cytotoxicity-based assay, we identified 17 additional residues where variants altered NUDT15 activity without affecting protein stability. We identified structural elements key to NUDT15 stability and/or catalytical activity with single amino acid resolution. Functional effects for + NUDT15 + variants accurately predicted toxicity risk alleles in patients treated with thiopurines with far superior sensitivity and specificity compared to bioinformatic prediction algorithms. In conclusion, our massively parallel variant function assays identified 1,152 deleterious + NUDT15 + variants, providing a comprehensive reference of variant function and vastly improving the ability to implement pharmacogenetics-guided thiopurine treatment individualization.}, + language = {en}, + number = {10}, + urldate = {2023-06-14}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Suiter, Chase C. and Moriyama, Takaya and Matreyek, Kenneth A. and Yang, Wentao and Scaletti, Emma Rose and Nishii, Rina and Yang, Wenjian and Hoshitsuki, Keito and Singh, Minu and Trehan, Amita and Parish, Chris and Smith, Colton and Li, Lie and Bhojwani, Deepa and Yuen, Liz Y. P. and Li, Chi-kong and Li, Chak-ho and Yang, Yung-li and Walker, Gareth J. and Goodhand, James R. and Kennedy, Nicholas A. and Klussmann, Federico Antillon and Bhatia, Smita and Relling, Mary V. and Kato, Motohiro and Hori, Hiroki and Bhatia, Prateek and Ahmad, Tariq and Yeoh, Allen E. J. and Stenmark, Pål and Fowler, Douglas M. and Yang, Jun J.}, + month = mar, + year = {2020}, + pages = {5394--5401}, + file = {Full Text:/Users/admin/Zotero/storage/CALTVXTU/Suiter et al. - 2020 - Massively parallel variant characterization identi.pdf:application/pdf}, +} + +@article{hobbs_saturation_2022, + title = {Saturation mutagenesis of a predicted ancestral {Syk}‐family kinase}, + volume = {31}, + issn = {0961-8368, 1469-896X}, + url = {https://onlinelibrary.wiley.com/doi/10.1002/pro.4411}, + doi = {10.1002/pro.4411}, + language = {en}, + number = {10}, + urldate = {2023-06-14}, + journal = {Protein Science}, + author = {Hobbs, Helen T. and Shah, Neel H. and Shoemaker, Sophie R. and Amacher, Jeanine F. and Marqusee, Susan and Kuriyan, John}, + month = oct, + year = {2022}, + file = {Full Text:/Users/admin/Zotero/storage/5TCADGH8/Hobbs et al. - 2022 - Saturation mutagenesis of a predicted ancestral Sy.pdf:application/pdf}, +} + +@techreport{gersing_comprehensive_2022, + type = {preprint}, + title = {A comprehensive map of human glucokinase variant activity}, + url = {http://biorxiv.org/lookup/doi/10.1101/2022.05.04.490571}, + abstract = {Abstract + + Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia (HH) or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. Here we exploited a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97\% of all possible missense and nonsense variants. Activity scores correlated with + in vitro + catalytic efficiency, fasting glucose levels in carriers of + GCK + variants and with evolutionary conservation. Hypoactive variants were concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shifted the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.}, + language = {en}, + urldate = {2023-06-14}, + institution = {Genetics}, + author = {Gersing, Sarah and Cagiada, Matteo and Gebbia, Marinella and Gjesing, Anette P. and Coté, Atina G. and Seesankar, Gireesh and Li, Roujia and Tabet, Daniel and Stein, Amelie and Gloyn, Anna L. and Hansen, Torben and Roth, Frederick P. and Lindorff-Larsen, Kresten and Hartmann-Petersen, Rasmus}, + month = may, + year = {2022}, + doi = {10.1101/2022.05.04.490571}, + file = {Submitted Version:/Users/admin/Zotero/storage/U2HI9MX9/Gersing et al. - 2022 - A comprehensive map of human glucokinase variant a.pdf:application/pdf}, +} + +@article{staller_high-throughput_2018, + title = {A {High}-{Throughput} {Mutational} {Scan} of an {Intrinsically} {Disordered} {Acidic} {Transcriptional} {Activation} {Domain}}, + volume = {6}, + issn = {24054712}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S2405471218300528}, + doi = {10.1016/j.cels.2018.01.015}, + language = {en}, + number = {4}, + urldate = {2023-06-14}, + journal = {Cell Systems}, + author = {Staller, Max V. and Holehouse, Alex S. and Swain-Lenz, Devjanee and Das, Rahul K. and Pappu, Rohit V. and Cohen, Barak A.}, + month = apr, + year = {2018}, + pages = {444--455.e6}, + file = {Full Text:/Users/admin/Zotero/storage/WTXI6TPC/Staller et al. - 2018 - A High-Throughput Mutational Scan of an Intrinsica.pdf:application/pdf}, +} + +@article{bandaru_deconstruction_2017, + title = {Deconstruction of the {Ras} switching cycle through saturation mutagenesis}, + volume = {6}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/27810}, + doi = {10.7554/eLife.27810}, + abstract = {Ras proteins are highly conserved signaling molecules that exhibit regulated, nucleotide-dependent switching between active and inactive states. The high conservation of Ras requires mechanistic explanation, especially given the general mutational tolerance of proteins. Here, we use deep mutational scanning, biochemical analysis and molecular simulations to understand constraints on Ras sequence. Ras exhibits global sensitivity to mutation when regulated by a GTPase activating protein and a nucleotide exchange factor. Removing the regulators shifts the distribution of mutational effects to be largely neutral, and reveals hotspots of activating mutations in residues that restrain Ras dynamics and promote the inactive state. Evolutionary analysis, combined with structural and mutational data, argue that Ras has co-evolved with its regulators in the vertebrate lineage. Overall, our results show that sequence conservation in Ras depends strongly on the biochemical network in which it operates, providing a framework for understanding the origin of global selection pressures on proteins.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Bandaru, Pradeep and Shah, Neel H and Bhattacharyya, Moitrayee and Barton, John P and Kondo, Yasushi and Cofsky, Joshua C and Gee, Christine L and Chakraborty, Arup K and Kortemme, Tanja and Ranganathan, Rama and Kuriyan, John}, + month = jul, + year = {2017}, + pages = {e27810}, + file = {Full Text:/Users/admin/Zotero/storage/GX5ZXSNL/Bandaru et al. - 2017 - Deconstruction of the Ras switching cycle through .pdf:application/pdf}, +} + +@article{bridgford_novel_2020, + title = {Novel drivers and modifiers of {MPL}-dependent oncogenic transformation identified by deep mutational scanning}, + volume = {135}, + issn = {0006-4971, 1528-0020}, + url = {https://ashpublications.org/blood/article/135/4/287/381157/Novel-drivers-and-modifiers-of-MPLdependent}, + doi = {10.1182/blood.2019002561}, + abstract = {Abstract + The single transmembrane domain (TMD) of the human thrombopoietin receptor (TpoR/myeloproliferative leukemia [MPL] protein), encoded by exon 10 of the MPL gene, is a hotspot for somatic mutations associated with myeloproliferative neoplasms (MPNs). Approximately 6\% and 14\% of JAK2 V617F− essential thrombocythemia and primary myelofibrosis patients, respectively, have “canonical” MPL exon 10 driver mutations W515L/K/R/A or S505N, which generate constitutively active receptors and consequent loss of Tpo dependence. Other “noncanonical” MPL exon 10 mutations have also been identified in patients, both alone and in combination with canonical mutations, but, in almost all cases, their functional consequences and relevance to disease are unknown. Here, we used a deep mutational scanning approach to evaluate all possible single amino acid substitutions in the human TpoR TMD for their ability to confer cytokine-independent growth in Ba/F3 cells. We identified all currently recognized driver mutations and 7 novel mutations that cause constitutive TpoR activation, and a much larger number of second-site mutations that enhance S505N-driven activation. We found examples of both of these categories in published and previously unpublished MPL exon 10 sequencing data from MPN patients, demonstrating that some, if not all, of the new mutations reported here represent likely drivers or modifiers of myeloproliferative disease.}, + language = {en}, + number = {4}, + urldate = {2023-06-14}, + journal = {Blood}, + author = {Bridgford, Jessica L. and Lee, Su Min and Lee, Christine M. M. and Guglielmelli, Paola and Rumi, Elisa and Pietra, Daniela and Wilcox, Stephen and Chhabra, Yash and Rubin, Alan F. and Cazzola, Mario and Vannucchi, Alessandro M. and Brooks, Andrew J. and Call, Matthew E. and Call, Melissa J.}, + month = jan, + year = {2020}, + pages = {287--292}, + file = {Full Text:/Users/admin/Zotero/storage/EAT9FPV2/Bridgford et al. - 2020 - Novel drivers and modifiers of MPL-dependent oncog.pdf:application/pdf}, +} + +@article{spencer_deep_2017, + title = {Deep mutational scanning of {S}. pyogenes {Cas9} reveals important functional domains}, + volume = {7}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-017-17081-y}, + doi = {10.1038/s41598-017-17081-y}, + abstract = {Abstract + + RNA-guided endonucleases (RGENs) have invigorated the field of site-specific nucleases. The success of + Streptococcus pyogenes + Cas9 (SpCas9) has led to the discovery of several other CRISPR-associated RGENs. As more RGENs become available, it will be necessary to refine their activity before they can be translated into the clinic. With this in mind, we sought to demonstrate how deep mutational scanning (DMS) could provide details about important functional regions in SpCas9 and speed engineering efforts. Consequently, we developed a nuclease screening platform which could distinguish active Cas9 mutants. We screened a library of 1.9 × 10 + 7 + with over 8500 possible non-synonymous mutations and inferred the effects of each mutation using DMS. We demonstrate that the RuvC and HNH domains are the least tolerant regions to mutation. In contrast, the Rec2 and PI domains tolerate mutation better than other regions. The mutation information defined in this work provides a foundation for further SpCas9 engineering. Together, our results demonstrate how DMS can be a powerful tool to uncover features important to RGEN function. Application of this approach to emerging RGENs should enhance their engineering and optimization for therapeutic and other applications.}, + language = {en}, + number = {1}, + urldate = {2023-06-14}, + journal = {Scientific Reports}, + author = {Spencer, Jeffrey M. and Zhang, Xiaoliu}, + month = dec, + year = {2017}, + pages = {16836}, + file = {Full Text:/Users/admin/Zotero/storage/BWJHTMKA/Spencer and Zhang - 2017 - Deep mutational scanning of S. pyogenes Cas9 revea.pdf:application/pdf}, +} + +@article{mclaughlin_jr_spatial_2012, + title = {The spatial architecture of protein function and adaptation}, + volume = {491}, + issn = {0028-0836, 1476-4687}, + url = {https://www.nature.com/articles/nature11500}, + doi = {10.1038/nature11500}, + language = {en}, + number = {7422}, + urldate = {2023-06-14}, + journal = {Nature}, + author = {McLaughlin, Jr., Richard N. and Poelwijk, Frank J. and Raman, Arjun and Gosal, Walraj S. and Ranganathan, Rama}, + month = nov, + year = {2012}, + pages = {138--142}, + file = {Accepted Version:/Users/admin/Zotero/storage/8SK84J8S/McLaughlin Jr et al. - 2012 - The spatial architecture of protein function and a.pdf:application/pdf}, +} + +@article{doud_site-specific_2015, + title = {Site-{Specific} {Amino} {Acid} {Preferences} {Are} {Mostly} {Conserved} in {Two} {Closely} {Related} {Protein} {Homologs}}, + volume = {32}, + issn = {0737-4038, 1537-1719}, + url = {https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msv167}, + doi = {10.1093/molbev/msv167}, + language = {en}, + number = {11}, + urldate = {2023-06-14}, + journal = {Molecular Biology and Evolution}, + author = {Doud, Michael B. and Ashenberg, Orr and Bloom, Jesse D.}, + month = nov, + year = {2015}, + pages = {2944--2960}, + file = {Full Text:/Users/admin/Zotero/storage/KVBHH6S5/Doud et al. - 2015 - Site-Specific Amino Acid Preferences Are Mostly Co.pdf:application/pdf}, +} + +@article{mattenberger_globally_2021, + title = {Globally defining the effects of mutations in a picornavirus capsid}, + volume = {10}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/64256}, + doi = {10.7554/eLife.64256}, + abstract = {The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis. Despite their importance, a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking. To address this limitation, we globally define the effects of mutations across the capsid of a human picornavirus. Using this resource, we identify structural and sequence determinants that accurately predict mutational fitness effects, refine evolutionary analyses, and define the sequence specificity of key capsid-encoded motifs. Furthermore, capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites, we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases. Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology, evolution, and host interactions. + , + A virus is made up of genetic material that is encased with a protective protein coat called the capsid. The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material. Inside the cell, the virus hitchhikes the infected cell’s machinery to grow or replicate its own genetic material. + Viral capsids are the main target of the host’s defence system, and therefore, continuously change in an attempt to escape the immune system by introducing alterations (known as mutations) into the genes encoding viral capsid proteins. Mutations occur randomly, and so while some changes to the viral capsid might confer an advantage, others may have no effect at all, or even weaken the virus. + To better understand the effect of capsid mutations on the virus’ ability to infect host cells, Mattenberger et al. studied the Coxsackievirus B3, which is linked to heart problems and acute heart failure in humans. The researchers analysed around 90\% of possible amino acid mutations (over 14,800 mutations) and correlated each mutation to how it influenced the virus’ ability to replicate in human cells grown in the laboratory. + Based on these results, Mattenberger et al. developed a computer model to predict how a particular mutation might affect the virus. The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks, such as building the capsid. It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein, which cuts the capsid proteins into the right size from a larger precursor. By looking for similar sequences in human genes, the researchers identified several ones that the virus may attack and inactivate to support its own replication. + These findings may help identify potential drug targets to develop new antiviral therapies. For example, proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment. They also highlight new proteins in human cells that could potentially block the virus in cells.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Mattenberger, Florian and Latorre, Victor and Tirosh, Omer and Stern, Adi and Geller, Ron}, + month = jan, + year = {2021}, + pages = {e64256}, + file = {Full Text:/Users/admin/Zotero/storage/Z7NP9QNK/Mattenberger et al. - 2021 - Globally defining the effects of mutations in a pi.pdf:application/pdf}, +} + +@article{matreyek_multiplex_2018, + title = {Multiplex assessment of protein variant abundance by massively parallel sequencing}, + volume = {50}, + issn = {1061-4036, 1546-1718}, + url = {https://www.nature.com/articles/s41588-018-0122-z}, + doi = {10.1038/s41588-018-0122-z}, + language = {en}, + number = {6}, + urldate = {2023-06-14}, + journal = {Nature Genetics}, + author = {Matreyek, Kenneth A. and Starita, Lea M. and Stephany, Jason J. and Martin, Beth and Chiasson, Melissa A. and Gray, Vanessa E. and Kircher, Martin and Khechaduri, Arineh and Dines, Jennifer N. and Hause, Ronald J. and Bhatia, Smita and Evans, William E. and Relling, Mary V. and Yang, Wenjian and Shendure, Jay and Fowler, Douglas M.}, + month = jun, + year = {2018}, + pages = {874--882}, + file = {Accepted Version:/Users/admin/Zotero/storage/IRF7FEAI/Matreyek et al. - 2018 - Multiplex assessment of protein variant abundance .pdf:application/pdf}, +} + +@article{flynn_comprehensive_2020, + title = {Comprehensive fitness maps of {Hsp90} show widespread environmental dependence}, + volume = {9}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/53810}, + doi = {10.7554/eLife.53810}, + abstract = {Gene-environment interactions have long been theorized to influence molecular evolution. However, the environmental dependence of most mutations remains unknown. Using deep mutational scanning, we engineered yeast with all 44,604 single codon changes encoding 14,160 amino acid variants in Hsp90 and quantified growth effects under standard conditions and under five stress conditions. To our knowledge, these are the largest determined comprehensive fitness maps of point mutants. The growth of many variants differed between conditions, indicating that environment can have a large impact on Hsp90 evolution. Multiple variants provided growth advantages under individual conditions; however, these variants tended to exhibit growth defects in other environments. The diversity of Hsp90 sequences observed in extant eukaryotes preferentially contains variants that supported robust growth under all tested conditions. Rather than favoring substitutions in individual conditions, the long-term selective pressure on Hsp90 may have been that of fluctuating environments, leading to robustness under a variety of conditions.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Flynn, Julia M and Rossouw, Ammeret and Cote-Hammarlof, Pamela and Fragata, Inês and Mavor, David and Hollins, Carl and Bank, Claudia and Bolon, Daniel Na}, + month = mar, + year = {2020}, + pages = {e53810}, + file = {Full Text:/Users/admin/Zotero/storage/CQWF9FY9/Flynn et al. - 2020 - Comprehensive fitness maps of Hsp90 show widesprea.pdf:application/pdf}, +} + +@article{ahler_combined_2019, + title = {A {Combined} {Approach} {Reveals} a {Regulatory} {Mechanism} {Coupling} {Src}’s {Kinase} {Activity}, {Localization}, and {Phosphotransferase}-{Independent} {Functions}}, + volume = {74}, + issn = {10972765}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1097276519300930}, + doi = {10.1016/j.molcel.2019.02.003}, + language = {en}, + number = {2}, + urldate = {2023-06-14}, + journal = {Molecular Cell}, + author = {Ahler, Ethan and Register, Ames C. and Chakraborty, Sujata and Fang, Linglan and Dieter, Emily M. and Sitko, Katherine A. and Vidadala, Rama Subba Rao and Trevillian, Bridget M. and Golkowski, Martin and Gelman, Hannah and Stephany, Jason J. and Rubin, Alan F. and Merritt, Ethan A. and Fowler, Douglas M. and Maly, Dustin J.}, + month = apr, + year = {2019}, + pages = {393--408.e20}, + file = {Full Text:/Users/admin/Zotero/storage/BAUMXZ8I/Ahler et al. - 2019 - A Combined Approach Reveals a Regulatory Mechanism.pdf:application/pdf}, +} + +@article{romero_dissecting_2015, + title = {Dissecting enzyme function with microfluidic-based deep mutational scanning}, + volume = {112}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.1422285112}, + doi = {10.1073/pnas.1422285112}, + abstract = {Significance + As powerful biological catalysts, enzymes can solve challenging problems that range from the industrial production of chemicals to the treatment of human disease. The ability to design new enzymes with tailor-made chemical functions would have a far-reaching impact. However, this important capability has been limited by our cursory understanding of enzyme catalysis. Here, we report a method that uses unbiased empirical analysis to dissect the molecular basis of enzyme function. By comprehensively mapping how changes in an enzyme’s amino acid sequence affect its activity, we obtain a detailed view of the interactions that shape the enzyme function landscape. Large, unbiased analyses of enzyme function allow the discovery of new biochemical mechanisms that will improve our ability to engineer custom biocatalysts. + , + Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme’s sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence–function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a high-temperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNA-sequencing technologies, enables high-throughput mapping of enzyme sequence space.}, + language = {en}, + number = {23}, + urldate = {2023-06-14}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Romero, Philip A. and Tran, Tuan M. and Abate, Adam R.}, + month = jun, + year = {2015}, + pages = {7159--7164}, + file = {Full Text:/Users/admin/Zotero/storage/7D996XKU/Romero et al. - 2015 - Dissecting enzyme function with microfluidic-based.pdf:application/pdf}, +} + +@article{faure_mapping_2022, + title = {Mapping the energetic and allosteric landscapes of protein binding domains}, + volume = {604}, + issn = {0028-0836, 1476-4687}, + url = {https://www.nature.com/articles/s41586-022-04586-4}, + doi = {10.1038/s41586-022-04586-4}, + language = {en}, + number = {7904}, + urldate = {2023-06-14}, + journal = {Nature}, + author = {Faure, Andre J. and Domingo, Júlia and Schmiedel, Jörn M. and Hidalgo-Carcedo, Cristina and Diss, Guillaume and Lehner, Ben}, + month = apr, + year = {2022}, + pages = {175--183}, + file = {Accepted Version:/Users/admin/Zotero/storage/H46C8P4R/Faure et al. - 2022 - Mapping the energetic and allosteric landscapes of.pdf:application/pdf}, +} + +@article{roscoe_analyses_2013, + title = {Analyses of the {Effects} of {All} {Ubiquitin} {Point} {Mutants} on {Yeast} {Growth} {Rate}}, + volume = {425}, + issn = {00222836}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0022283613000636}, + doi = {10.1016/j.jmb.2013.01.032}, + language = {en}, + number = {8}, + urldate = {2023-06-14}, + journal = {Journal of Molecular Biology}, + author = {Roscoe, Benjamin P. and Thayer, Kelly M. and Zeldovich, Konstantin B. and Fushman, David and Bolon, Daniel N.A.}, + month = apr, + year = {2013}, + pages = {1363--1377}, + file = {Accepted Version:/Users/admin/Zotero/storage/Z4CIGXLZ/Roscoe et al. - 2013 - Analyses of the Effects of All Ubiquitin Point Mut.pdf:application/pdf}, +} + +@article{newberry_robust_2020, + title = {Robust {Sequence} {Determinants} of alpha-{Synuclein} {Toxicity} in {Yeast} {Implicate} {Membrane} {Binding}}, + volume = {15}, + issn = {1554-8929, 1554-8937}, + url = {https://pubs.acs.org/doi/10.1021/acschembio.0c00339}, + doi = {10.1021/acschembio.0c00339}, + language = {en}, + number = {8}, + urldate = {2023-06-14}, + journal = {ACS Chemical Biology}, + author = {Newberry, Robert W. and Arhar, Taylor and Costello, Jean and Hartoularos, George C. and Maxwell, Alison M. and Naing, Zun Zar Chi and Pittman, Maureen and Reddy, Nishith R. and Schwarz, Daniel M. C. and Wassarman, Douglas R. and Wu, Taia S. and Barrero, Daniel and Caggiano, Christa and Catching, Adam and Cavazos, Taylor B. and Estes, Laurel S. and Faust, Bryan and Fink, Elissa A. and Goldman, Miriam A. and Gomez, Yessica K. and Gordon, M. Grace and Gunsalus, Laura M. and Hoppe, Nick and Jaime-Garza, Maru and Johnson, Matthew C. and Jones, Matthew G. and Kung, Andrew F. and Lopez, Kyle E. and Lumpe, Jared and Martyn, Calla and McCarthy, Elizabeth E. and Miller-Vedam, Lakshmi E. and Navarro, Erik J. and Palar, Aji and Pellegrino, Jenna and Saylor, Wren and Stephens, Christina A. and Strickland, Jack and Torosyan, Hayarpi and Wankowicz, Stephanie A. and Wong, Daniel R. and Wong, Garrett and Redding, Sy and Chow, Eric D. and DeGrado, William F. and Kampmann, Martin}, + month = aug, + year = {2020}, + pages = {2137--2153}, + file = {Accepted Version:/Users/admin/Zotero/storage/NQ4BLNN6/Newberry et al. - 2020 - Robust Sequence Determinants of alpha-Synuclein Toxici.pdf:application/pdf;Submitted Version:/Users/admin/Zotero/storage/7IFR9453/Newberry et al. - 2020 - Robust Sequence Determinants of alpha-Synuclein Toxici.pdf:application/pdf}, +} + +@article{wu_adaptation_2016, + title = {Adaptation in protein fitness landscapes is facilitated by indirect paths}, + volume = {5}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/16965}, + doi = {10.7554/eLife.16965}, + abstract = {The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 204 = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. + , + Proteins can evolve over time by changing their component parts, which are called amino acids. These changes usually happen one at a time and natural selection tends to preserve those changes that make the protein more efficient at its specific tasks, while discarding those that impair the protein’s activity. However the effect of each change depends on the protein as a whole, and so two changes that separately make the protein worse can make it much better if they occur together. This phenomenon is called epistasis and in some cases it can trap proteins in a sub-optimal form and prevent them from improving further. + Proteins are made from twenty different kinds of amino acid, and there are millions of different combinations of amino acids that could, in theory, make a protein of a given length. Studying protein evolution involves making variants of the same protein, each with just a few changes, and comparing how efficient, or “fit”, they are. Previous studies only measured the fitness of a few variants and showed that epistasis could block protein evolution by requiring the protein to lose some fitness before it could improve further. However, new techniques have now made it easier to study protein evolution by testing many more protein variants. + Wu, Dai et al. focused on four amino acids in part of a protein called GB1 and tested the efficiency of every possible combination of these four amino acids, a total of 160,000 (204) variants. Contrary to expectations, the results suggested that the protein could evolve quickly to maximise fitness despite there being epistasis between the four amino acids. Overcoming epistasis typically involved making a change to one amino acid that paved the way for further changes while avoiding the need to lose fitness. The original change could then be reversed once the epistasis was overcome. The complexity of this solution means it can only be seen by studying a large number of protein variants that represent many alternative sequences of protein changes. + Wu, Dai et al. conclude that proteins are able to achieve a higher level of fitness through evolution by exploring a large number of changes. There are many possible changes for each protein and it is this variety that, despite epistasis, allows proteins to become naturally optimised for the tasks that they perform. While the full complexity of protein evolution cannot be explored at the moment, as technology advances it will become possible to study more protein variants. Such advances would therefore hopefully allow researchers to discover even more about the natural mechanisms of protein evolution.}, + language = {en}, + urldate = {2023-06-14}, + journal = {eLife}, + author = {Wu, Nicholas C and Dai, Lei and Olson, C Anders and Lloyd-Smith, James O and Sun, Ren}, + month = jul, + year = {2016}, + pages = {e16965}, + file = {Full Text:/Users/admin/Zotero/storage/GYE5JDBN/Wu et al. - 2016 - Adaptation in protein fitness landscapes is facili.pdf:application/pdf}, +} + +@article{starr_deep_2020, + title = {Deep {Mutational} {Scanning} of {SARS}-{CoV}-2 {Receptor} {Binding} {Domain} {Reveals} {Constraints} on {Folding} and {ACE2} {Binding}}, + volume = {182}, + issn = {00928674}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867420310035}, + doi = {10.1016/j.cell.2020.08.012}, + language = {en}, + number = {5}, + urldate = {2023-06-14}, + journal = {Cell}, + author = {Starr, Tyler N. and Greaney, Allison J. and Hilton, Sarah K. and Ellis, Daniel and Crawford, Katharine H.D. and Dingens, Adam S. and Navarro, Mary Jane and Bowen, John E. and Tortorici, M. Alejandra and Walls, Alexandra C. and King, Neil P. and Veesler, David and Bloom, Jesse D.}, + month = sep, + year = {2020}, + pages = {1295--1310.e20}, + file = {Submitted Version:/Users/admin/Zotero/storage/TCMBL3SJ/Starr et al. - 2020 - Deep Mutational Scanning of SARS-CoV-2 Receptor Bi.pdf:application/pdf}, +} + +@techreport{brauer_comprehensive_2021, + type = {preprint}, + title = {Comprehensive {Fitness} {Landscape} of a {Multi}-{Geometry} {Protein} {Capsid} {Informs} {Machine} {Learning} {Models} of {Assembly}}, + url = {http://biorxiv.org/lookup/doi/10.1101/2021.12.21.473721}, + abstract = {Abstract + + Virus-like particles (VLPs) are non-infections viral-derived nanomaterials poised for biotechnological applications due to their well-defined, modular self-assembling architecture. Although progress has been made in understanding the complex effects that mutations may have on VLPs, nuanced understanding of the influence particle mutability has on quaternary structure has yet to be achieved. Here, we generate and compare the apparent fitness landscapes of two capsid geometries (T=3 and T=1 icosahedral) of the bacteriophage MS2 VLP. We find significant shifts in mutability at the symmetry interfaces of the T=1 capsid when compared to the wildtype T=3 assembly. Furthermore, we use the generated landscapes to benchmark the performance of + in silico + mutational scanning tools in capturing the effect of missense mutation on complex particle assembly. Finding that predicted stability effects correlated relatively poorly with assembly phenotype, we used a combination of + de novo + features in tandem with + in silico + results to train machine learning algorithms for the classification of variant effects on assembly. Our findings not only reveal ways that assembly geometry affects the mutable landscape of a self-assembled particle, but also establish a template for the generation of predictive mutational models of self-assembled capsids using minimal empirical training data.}, + language = {en}, + urldate = {2023-06-14}, + institution = {Bioengineering}, + author = {Brauer, Daniel D. and Santiago, Celine B. and Merz, Zoe N. and McCarthy, Esther and Tullman-Ercek, Danielle and Francis, Matthew B.}, + month = dec, + year = {2021}, + doi = {10.1101/2021.12.21.473721}, + file = {Submitted Version:/Users/admin/Zotero/storage/TRVZDGPY/Brauer et al. - 2021 - Comprehensive Fitness Landscape of a Multi-Geometr.pdf:application/pdf}, +} + +@article{linsky_novo_2020, + title = {De novo design of potent and resilient {hACE2} decoys to neutralize {SARS}-{CoV}-2}, + volume = {370}, + issn = {0036-8075, 1095-9203}, + url = {https://www.science.org/doi/10.1126/science.abe0075}, + doi = {10.1126/science.abe0075}, + abstract = {A decoy to neutralize SARS-CoV-2 + + Many efforts to develop therapies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are focused on the interaction between the spike protein, which decorates the surface of the virus, and its host receptor, human angiotensin-converting enzyme 2 (hACE2). Linsky + et al. + describe a de novo design strategy that allowed them to engineer decoy proteins that bind to the spike protein by replicating the hACE2 interface. The best decoy, CTC-445, bound with low nanomolar affinity, and selection of viral mutants that decrease binding is unlikely because this would also affect binding to hACE2. A bivalent version of CTC-445 bound even more tightly, neutralized SARS-CoV-2 infection of cells, and protected hamsters from a SARS-CoV-2 challenge. The stable decoy has the potential for respiratory therapeutic delivery. + + + Science + , this issue p. + 1208 + + , + Designed de novo protein decoys neutralize SARS-CoV-2 in vitro and in vivo and are resilient to viral mutational escape. + , + We developed a de novo protein design strategy to swiftly engineer decoys for neutralizing pathogens that exploit extracellular host proteins to infect the cell. Our pipeline allowed the design, validation, and optimization of de novo human angiotensin-converting enzyme 2 (hACE2) decoys to neutralize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The best monovalent decoy, CTC-445.2, bound with low nanomolar affinity and high specificity to the receptor-binding domain (RBD) of the spike protein. Cryo–electron microscopy (cryo-EM) showed that the design is accurate and can simultaneously bind to all three RBDs of a single spike protein. Because the decoy replicates the spike protein target interface in hACE2, it is intrinsically resilient to viral mutational escape. A bivalent decoy, CTC-445.2d, showed {\textasciitilde}10-fold improvement in binding. CTC-445.2d potently neutralized SARS-CoV-2 infection of cells in vitro, and a single intranasal prophylactic dose of decoy protected Syrian hamsters from a subsequent lethal SARS-CoV-2 challenge.}, + language = {en}, + number = {6521}, + urldate = {2023-06-14}, + journal = {Science}, + author = {Linsky, Thomas W. and Vergara, Renan and Codina, Nuria and Nelson, Jorgen W. and Walker, Matthew J. and Su, Wen and Barnes, Christopher O. and Hsiang, Tien-Ying and Esser-Nobis, Katharina and Yu, Kevin and Reneer, Z. Beau and Hou, Yixuan J. and Priya, Tanu and Mitsumoto, Masaya and Pong, Avery and Lau, Uland Y. and Mason, Marsha L. and Chen, Jerry and Chen, Alex and Berrocal, Tania and Peng, Hong and Clairmont, Nicole S. and Castellanos, Javier and Lin, Yu-Ru and Josephson-Day, Anna and Baric, Ralph S. and Fuller, Deborah H. and Walkey, Carl D. and Ross, Ted M. and Swanson, Ryan and Bjorkman, Pamela J. and Gale, Michael and Blancas-Mejia, Luis M. and Yen, Hui-Ling and Silva, Daniel-Adriano}, + month = dec, + year = {2020}, + pages = {1208--1214}, + file = {Full Text:/Users/admin/Zotero/storage/RDJMYBGK/Linsky et al. - 2020 - De novo design of potent and resilient hACE2 decoy.pdf:application/pdf}, +} + +@techreport{stadelmann_deep_2021, + type = {preprint}, + title = {A deep mutational scanning platform to characterize the fitness landscape of anti-{CRISPR} proteins}, + url = {http://biorxiv.org/lookup/doi/10.1101/2021.08.21.457204}, + abstract = {ABSTRACT + + Deep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in + Escherichia coli + combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of + Streptococcus pyogenes + Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and + in vitro + affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.}, + language = {en}, + urldate = {2023-06-14}, + institution = {Synthetic Biology}, + author = {Stadelmann, Tobias and Heid, Daniel and Jendrusch, Michael and Mathony, Jan and Rosset, Stéphane and Correia, Bruno E. and Niopek, Dominik}, + month = aug, + year = {2021}, + doi = {10.1101/2021.08.21.457204}, + file = {Submitted Version:/Users/admin/Zotero/storage/WPHVK96E/Stadelmann et al. - 2021 - A deep mutational scanning platform to characteriz.pdf:application/pdf}, +} + +@article{koch_optimization_2022, + title = {Optimization of the antimicrobial peptide {Bac7} by deep mutational scanning}, + volume = {20}, + issn = {1741-7007}, + url = {https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-022-01304-4}, + doi = {10.1186/s12915-022-01304-4}, + abstract = {Abstract + + Background + + Intracellularly active antimicrobial peptides are promising candidates for the development of antibiotics for human applications. However, drug development using peptides is challenging as, owing to their large size, an enormous sequence space is spanned. We built a high-throughput platform that incorporates rapid investigation of the sequence-activity relationship of peptides and enables rational optimization of their antimicrobial activity. The platform is based on deep mutational scanning of DNA-encoded peptides and employs highly parallelized bacterial self-screening coupled to next-generation sequencing as a readout for their antimicrobial activity. As a target, we used Bac7 + 1-23 + , a 23 amino acid residues long variant of bactenecin-7, a potent translational inhibitor and one of the best researched proline-rich antimicrobial peptides. + + + + Results + + Using the platform, we simultaneously determined the antimicrobial activity of {\textgreater}600,000 Bac7 + 1-23 + variants and explored their sequence-activity relationship. This dataset guided the design of a focused library of {\textasciitilde}160,000 variants and the identification of a lead candidate Bac7PS. Bac7PS showed high activity against multidrug-resistant clinical isolates of + E. coli, + and its activity was less dependent on SbmA, a transporter commonly used by proline-rich antimicrobial peptides to reach the cytosol and then inhibit translation. Furthermore, Bac7PS displayed strong ribosomal inhibition and low toxicity against eukaryotic cells and demonstrated good efficacy in a murine septicemia model induced by + E. coli + . + + + + Conclusion + We demonstrated that the presented platform can be used to establish the sequence-activity relationship of antimicrobial peptides, and showed its usefulness for hit-to-lead identification and optimization of antimicrobial drug candidates.}, + language = {en}, + number = {1}, + urldate = {2023-06-14}, + journal = {BMC Biology}, + author = {Koch, Philipp and Schmitt, Steven and Heynisch, Alexander and Gumpinger, Anja and Wüthrich, Irene and Gysin, Marina and Shcherbakov, Dimitri and Hobbie, Sven N. and Panke, Sven and Held, Martin}, + month = dec, + year = {2022}, + pages = {114}, + file = {Full Text:/Users/admin/Zotero/storage/8QZH39IE/Koch et al. - 2022 - Optimization of the antimicrobial peptide Bac7 by .pdf:application/pdf}, +} + +@article{kotler_systematic_2018, + title = {A {Systematic} p53 {Mutation} {Library} {Links} {Differential} {Functional} {Impact} to {Cancer} {Mutation} {Pattern} and {Evolutionary} {Conservation}}, + volume = {71}, + issn = {10972765}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1097276518304544}, + doi = {10.1016/j.molcel.2018.06.012}, + language = {en}, + number = {1}, + urldate = {2023-06-14}, + journal = {Molecular Cell}, + author = {Kotler, Eran and Shani, Odem and Goldfeld, Guy and Lotan-Pompan, Maya and Tarcic, Ohad and Gershoni, Anat and Hopf, Thomas A. and Marks, Debora S. and Oren, Moshe and Segal, Eran}, + month = jul, + year = {2018}, + pages = {178--190.e8}, + file = {Full Text:/Users/admin/Zotero/storage/7M4DXV8M/Kotler et al. - 2018 - A Systematic p53 Mutation Library Links Differenti.pdf:application/pdf}, +} + +@article{campbell_determinants_2022, + title = {Determinants of {Multiheme} {Cytochrome} {Extracellular} {Electron} {Transfer} {Uncovered} by {Systematic} {Peptide} {Insertion}}, + volume = {61}, + issn = {0006-2960, 1520-4995}, + url = {https://pubs.acs.org/doi/10.1021/acs.biochem.2c00148}, + doi = {10.1021/acs.biochem.2c00148}, + language = {en}, + number = {13}, + urldate = {2023-06-14}, + journal = {Biochemistry}, + author = {Campbell, Ian J. and Atkinson, Joshua T. and Carpenter, Matthew D. and Myerscough, Dru and Su, Lin and Ajo-Franklin, Caroline M. and Silberg, Jonathan J.}, + month = jul, + year = {2022}, + pages = {1337--1350}, + file = {Submitted Version:/Users/admin/Zotero/storage/CQJVV8DL/Campbell et al. - 2022 - Determinants of Multiheme Cytochrome Extracellular.pdf:application/pdf}, +} + +@article{ding_co-evolution_2022, + title = {Co-evolution of interacting proteins through non-contacting and non-specific mutations}, + volume = {6}, + issn = {2397-334X}, + url = {https://www.nature.com/articles/s41559-022-01688-0}, + doi = {10.1038/s41559-022-01688-0}, + language = {en}, + number = {5}, + urldate = {2023-06-14}, + journal = {Nature Ecology \& Evolution}, + author = {Ding, David and Green, Anna G. and Wang, Boyuan and Lite, Thuy-Lan Vo and Weinstein, Eli N. and Marks, Debora S. and Laub, Michael T.}, + month = mar, + year = {2022}, + pages = {590--603}, + file = {Accepted Version:/Users/admin/Zotero/storage/RIQVW8ID/Ding et al. - 2022 - Co-evolution of interacting proteins through non-c.pdf:application/pdf}, +} + +% pgv1 + +@article{tsuboyama_mega-scale_2023, + title = {Mega-scale experimental analysis of protein folding stability in biology and design}, + volume = {620}, + issn = {0028-0836, 1476-4687}, + url = {https://www.nature.com/articles/s41586-023-06328-6}, + doi = {10.1038/s41586-023-06328-6}, + abstract = {Abstract + + Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale + 1 + . However, the energetics driving folding are invisible in these structures and remain largely unknown + 2 + . The hidden thermodynamics of folding can drive disease + 3,4 + , shape protein evolution + 5–7 + and guide protein engineering + 8–10 + , and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40–72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.}, + language = {en}, + number = {7973}, + urldate = {2023-08-15}, + journal = {Nature}, + author = {Tsuboyama, Kotaro and Dauparas, Justas and Chen, Jonathan and Laine, Elodie and Mohseni Behbahani, Yasser and Weinstein, Jonathan J. and Mangan, Niall M. and Ovchinnikov, Sergey and Rocklin, Gabriel J.}, + month = aug, + year = {2023}, + pages = {434--444}, + file = {Full Text:/Users/admin/Zotero/storage/54IHFJGX/Tsuboyama et al. - 2023 - Mega-scale experimental analysis of protein foldin.pdf:application/pdf}, +} + + +@UNPUBLISHED{suphatrakul_functional_2023, + title = "Functional analysis of flavivirus replicase by deep mutational + scanning of dengue {NS5}", + author = "Suphatrakul, Amporn and Posiri, Pratsaneeyaporn and Srisuk, + Nittaya and Nantachokchawapan, Rapirat and Onnome, Suppachoke and + Mongkolsapaya, Juthathip and Siridechadilok, Bunpote", + abstract = "Flavivirus NS5 is multi-functional viral protein that play + critical roles in virus replication, evolution, and immune + antagonism against the hosts. Its error-prone replicase activity + copies viral RNA for progeny virus particles and shapes virus + evolution. Its methyltransferase activity and STAT2-targeting + activity compromise type-I interferon signalling, dampening + protective immune response during infection. It interacts with + several host factors to shape the host-cell environment for virus + replication. Thus, NS5 represents a critical target for both + vaccine and antiviral drug development. Here, we performed deep + mutational scanning (DMS) on the NS5 of dengue virus serotype 2 + in mammalian cells. In combination with available structural and + biochemical data, the comprehensive single amino-acid mutational + data corroborated key residues and interactions involved in + enzymatic functions of the replicase and suggested potential + plasticity in NS5 guanylyl transferase. Strikingly, we identified + that a set of strictly conserved residues in the motifs lining + the replicase active site could tolerate mutations, suggesting + additional roles of the priming loop in viral RNA synthesis and + possible strategies to modulate the error rate of viral replicase + activity through active-site engineering. Our DMS dataset and NS5 + libraries could provide a framework and a resource to investigate + molecular, evolutionary, and immunological aspects of NS5 + functions, with relevance to vaccine and antiviral drug + development. \#\#\# Competing Interest Statement B.S., A.S., + P.P., N.S., R.N., and S.O. have filed a patent application + related to this work.", + journal = "bioRxiv", + pages = "2023.03.07.531617", + month = mar, + year = 2023, + language = "en" +} + +@ARTICLE{weile_shifting_2021, + title = "Shifting landscapes of human {MTHFR} missense-variant effects", + author = "Weile, Jochen and Kishore, Nishka and Sun, Song and Maaieh, Ranim + and Verby, Marta and Li, Roujia and Fotiadou, Iosifina and + Kitaygorodsky, Julia and Wu, Yingzhou and Holenstein, Alexander + and B{\"u}rer, C{\'e}line and Blomgren, Linnea and Yang, Shan and + Nussbaum, Robert and Rozen, Rima and Watkins, David and Gebbia, + Marinella and Kozich, Viktor and Garton, Michael and Froese, D + Sean and Roth, Frederick P", + abstract = "Most rare clinical missense variants cannot currently be + classified as pathogenic or benign. Deficiency in human + 5,10-methylenetetrahydrofolate reductase (MTHFR), the most common + inherited disorder of folate metabolism, is caused primarily by + rare missense variants. Further complicating variant + interpretation, variant impacts often depend on environment. An + important example of this phenomenon is the MTHFR variant + p.Ala222Val (c.665C>T), which is carried by half of all humans + and has a phenotypic impact that depends on dietary folate. Here + we describe the results of 98,336 variant functional-impact + assays, covering nearly all possible MTHFR amino acid + substitutions in four folinate environments, each in the presence + and absence of p.Ala222Val. The resulting atlas of MTHFR variant + effects reveals many complex dependencies on both folinate and + p.Ala222Val. MTHFR atlas scores can distinguish pathogenic from + benign variants and, among individuals with severe MTHFR + deficiency, correlate with age of disease onset. Providing a + powerful tool for understanding structure-function relationships, + the atlas suggests a role for a disordered loop in retaining + cofactor at the active site and identifies variants that enable + escape of inhibition by S-adenosylmethionine. Thus, a model based + on eight MTHFR variant effect maps illustrates how shifting + landscapes of environment- and genetic-background-dependent + missense variation can inform our clinical, structural, and + functional understanding of MTHFR deficiency.", + journal = "American Journal of Human Genetics", + volume = 108, + number = 7, + pages = "1283--1300", + month = jul, + year = 2021, + keywords = "clinical variant interpretation; cystathionine beta synthase; + deep mutational scanning; folate; gene- environment interaction; + homocystinuria; methylenetetrahydrofolate reductase; molecular + dynamics; mthfr; variant effect mapping", + language = "en" +} + +@ARTICLE{xie_predicting_2023, + title = "Predicting the functional effect of compound heterozygous + genotypes from large scale variant effect maps", + author = "Xie, Michael J and Cromie, Gareth A and Owens, Katherine and + Timour, Martin S and Tang, Michelle and Kutz, J Nathan and + El-Hattab, Ayman W and McLaughlin, Richard N and Dudley, + Aim{\'e}e M", + abstract = "BACKGROUND: Pathogenic variants in PHGDH, PSAT1 , and PSPH cause + a set of rare, autosomal recessive diseases known as serine + biosynthesis defects. Serine biosynthesis defects present in a + broad phenotypic spectrum that includes, at the severe end, + Neu-Laxova syndrome, a lethal multiple congenital anomaly + disease, intermediately in the form of infantile serine + biosynthesis defects with severe neurological manifestations and + growth deficiency, and at the mild end, as childhood disease with + intellectual disability. However, because L-serine + supplementation, especially if started early, can ameliorate and + in some cases even prevent symptoms, knowledge of pathogenic + variants is highly actionable. METHODS: Recently, our laboratory + established a yeast-based assay for human PSAT1 function. We have + now applied it at scale to assay the functional impact of 1,914 + SNV-accessible amino acid substitutions. In addition to assaying + the functional impact of individual variants in yeast haploid + cells, we can assay pairwise combinations of PSAT1 alleles that + recapitulate human genotypes, including compound heterozygotes, + in yeast diploids. RESULTS: Results of our assays of individual + variants (in haploid yeast cells) agree well with clinical + interpretations and protein structure-function relationships, + supporting the use of our data as functional evidence under the + ACMG interpretation guidelines. Results from our diploid assay + successfully distinguish patient genotypes from those of healthy + carriers and agree well with disease severity. Finally, we + present a linear model that uses individual allele measurements + (in haploid yeast cells) to accurately predict the biallelic + function (in diploid yeast cells) of ~ 1.8 million allele + combinations corresponding to potential human genotypes. + CONCLUSIONS: Taken together, our work provides an example of how + large-scale functional assays in model systems can be powerfully + applied to the study of a rare disease.", + journal = "bioRxiv", + month = jan, + year = 2023, + language = "en" +} + +@ARTICLE{roychowdhury_microfluidic_2022, + title = "Microfluidic deep mutational scanning of the human executioner + caspases reveals differences in structure and regulation", + author = "Roychowdhury, Hridindu and Romero, Philip A", + abstract = "The human caspase family comprises 12 cysteine proteases that are + centrally involved in cell death and inflammation responses. The + members of this family have conserved sequences and structures, + highly similar enzymatic activities and substrate preferences, + and overlapping physiological roles. In this paper, we present a + deep mutational scan of the executioner caspases CASP3 and CASP7 + to dissect differences in their structure, function, and + regulation. Our approach leverages high-throughput microfluidic + screening to analyze hundreds of thousands of caspase variants in + tightly controlled in vitro reactions. The resulting data + provides a large-scale and unbiased view of the impact of amino + acid substitutions on the proteolytic activity of CASP3 and + CASP7. We use this data to pinpoint key functional differences + between CASP3 and CASP7, including a secondary internal cleavage + site, CASP7 Q196 that is not present in CASP3. Our results will + open avenues for inquiry in caspase function and regulation that + could potentially inform the development of future + caspase-specific therapeutics.", + journal = "Cell Death Discovery", + volume = 8, + number = 1, + pages = "7", + month = jan, + year = 2022, + language = "en" +} + +@ARTICLE{andrews_distinct_2020, + title = "Distinct patterns of mutational sensitivity for $\lambda$ + resistance and maltodextrin transport in Escherichia coli {LamB}", + author = "Andrews, Bryan and Fields, Stanley", + abstract = "Bacteria can evade cohabiting phages through mutations in phage + receptors, but these mutations may come at a cost if they disrupt + the receptor's native cellular function. To investigate the + relationship between these two conflicting activities, we + generated sequence-function maps of Escherichia coli LamB with + respect to sensitivity to phage $\lambda$ and transport of + maltodextrin. By comparing 413 missense mutations whose effect on + both traits could be analysed, we find that these two phenotypes + were correlated, implying that most mutations affect these + phenotypes through a common mechanism such as loss of protein + stability. However, individual mutations could be found that + specifically disrupt $\lambda$-sensitivity without affecting + maltodextrin transport. We identify and individually assay nine + such mutations, whose spatial positions implicate loop L6 of LamB + in $\lambda$ binding. Although missense mutations that lead to + $\lambda$-resistance are rare, they were approximately as likely + to be maltodextrin-utilizing (Mal+) as not (Mal-), implying that + E. coli can adapt to $\lambda$ while conserving the receptor's + native function. We propose that in order for E. coli and + $\lambda$ to stably cohabitate, selection for + $\lambda$-resistance and maltose transport must be spatially or + temporally separated.", + journal = "Microbial Genomics", + volume = 6, + number = 4, + month = apr, + year = 2020, + keywords = "Escherichia coli; LamB; evolution; nutrient transport; phage + $\lambda$", + language = "en" +} + +@ARTICLE{lo_functional_2023, + title = "The functional impact of 1,570 individual amino acid + substitutions in human {OTC}", + author = "Lo, Russell S and Cromie, Gareth A and Tang, Michelle and Teng, + Kevin and Owens, Katherine and Sirr, Amy and Kutz, J Nathan and + Morizono, Hiroki and Caldovic, Ljubica and Ah Mew, Nicholas and + Gropman, Andrea and Dudley, Aim{\'e}e M", + abstract = "Deleterious mutations in the X-linked gene encoding ornithine + transcarbamylase (OTC) cause the most common urea cycle disorder, + OTC deficiency. This rare but highly actionable disease can + present with severe neonatal onset in males or with later onset + in either sex. Individuals with neonatal onset appear normal at + birth but rapidly develop hyperammonemia, which can progress to + cerebral edema, coma, and death, outcomes ameliorated by rapid + diagnosis and treatment. Here, we develop a high-throughput + functional assay for human OTC and individually measure the + impact of 1,570 variants, 84\% of all SNV-accessible missense + mutations. Comparison to existing clinical significance calls, + demonstrated that our assay distinguishes known benign from + pathogenic variants and variants with neonatal onset from + late-onset disease presentation. This functional stratification + allowed us to identify score ranges corresponding to clinically + relevant levels of impairment of OTC activity. Examining the + results of our assay in the context of protein structure further + allowed us to identify a 13 amino acid domain, the SMG loop, + whose function appears to be required in human cells but not in + yeast. Finally, inclusion of our data as PS3 evidence under the + current ACMG guidelines, in a pilot reclassification of 34 + variants with complete loss of activity, would change the + classification of 22 from variants of unknown significance to + clinically actionable likely pathogenic variants. These results + illustrate how large-scale functional assays are especially + powerful when applied to rare genetic diseases.", + journal = "American Journal of Human Genetics", + volume = 110, + number = 5, + pages = "863--879", + month = may, + year = 2023, + keywords = "OTC deficiency; Oxford Nanopore sequencing; SNV + analysis/discovery; X-linked disease; metabolic disorder; model + organisms; multiplexed assays of variant effect; rare disease; + rare variants; urea cycle disorder; variant interpretation", + language = "en" +} + +@ARTICLE{gajula_high_2014, + title = "High-throughput mutagenesis reveals functional determinants for + {DNA} targeting by activation-induced deaminase", + author = "Gajula, Kiran S and Huwe, Peter J and Mo, Charlie Y and Crawford, + Daniel J and Stivers, James T and Radhakrishnan, Ravi and Kohli, + Rahul M", + abstract = "Antibody maturation is a critical immune process governed by the + enzyme activation-induced deaminase (AID), a member of the + AID/APOBEC DNA deaminase family. AID/APOBEC deaminases + preferentially target cytosine within distinct preferred sequence + motifs in DNA, with specificity largely conferred by a small 9-11 + residue protein loop that differs among family members. Here, we + aimed to determine the key functional characteristics of this + protein loop in AID and to thereby inform our understanding of + the mode of DNA engagement. To this end, we developed a + methodology (Sat-Sel-Seq) that couples saturation mutagenesis at + each position across the targeting loop, with iterative + functional selection and next-generation sequencing. This + high-throughput mutational analysis revealed dominant + characteristics for residues within the loop and additionally + yielded enzymatic variants that enhance deaminase activity. To + rationalize these functional requirements, we performed molecular + dynamics simulations that suggest that AID and its hyperactive + variants can engage DNA in multiple specific modes. These + findings align with AID's competing requirements for specificity + and flexibility to efficiently drive antibody maturation. Beyond + insights into the AID-DNA interface, our Sat-Sel-Seq approach + also serves to further expand the repertoire of techniques for + deep positional scanning and may find general utility for + high-throughput analysis of protein function.", + journal = "Nucleic Acids Research", + volume = 42, + number = 15, + pages = "9964--9975", + month = sep, + year = 2014, + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@UNPUBLISHED{chakraborty_profiling_2021, + title = "Profiling of the drug resistance of thousands of Src tyrosine + kinase mutants uncovers a regulatory network that couples + autoinhibition to catalytic domain dynamics", + author = "Chakraborty, Sujata and Ahler, Ethan and Simon, Jessica J and + Fang, Linglan and Potter, Zachary E and Sitko, Katherine A and + Stephany, Jason J and Guttman, Miklos and Fowler, Douglas M and + Maly, Dustin J", + abstract = "SUMMARYKinase inhibitors are effective cancer therapies but + resistance often limits clinical efficacy. Despite the + cataloguing of numerous resistance mutations, our understanding + of kinase inhibitor resistance is still incomplete. Here, we + comprehensively profiled the resistance of ∼3500 Src tyrosine + kinase mutants to four different ATP-competitive inhibitors. We + found that ATP-competitive inhibitor resistance mutations are + distributed throughout Src's catalytic domain. In addition to + inhibitor contact residues, residues that participate in + regulating Src's phosphotransferase activity were prone to the + development of resistance. Unexpectedly, we found that a + resistance-prone cluster of residues located on the top face of + the N-terminal lobe of Src's catalytic domain contributes to + autoinhibition by reducing catalytic domain dynamics, and + mutations in this cluster led to resistance by lowering inhibitor + affinity and promoting kinase hyperactivation. Together, our + studies demonstrate how drug resistance profiling can be used to + define potential resistance pathways and uncover new mechanisms + of kinase regulation.", + journal = "bioRxiv", + month = dec, + year = 2021 +} + +@UNPUBLISHED{weng_energetic_2022, + title = "The energetic and allosteric landscape for {KRAS} inhibition", + author = "Weng, Chenchun and Faure, Andre J and Lehner, Ben", + abstract = "Thousands of proteins have now been genetically-validated as + therapeutic targets in hundreds of human diseases. However, very + few have actually been successfully targeted and many are + considered `undruggable'. This is particularly true for proteins + that function via protein-protein interactions: direct inhibition + of binding interfaces is difficult, requiring the identification + of allosteric sites. However, most proteins have no known + allosteric sites and a comprehensive allosteric map does not + exist for any protein. Here we address this shortcoming by + charting multiple global atlases of inhibitory allosteric + communication in KRAS, a protein mutated in 1 in 10 human + cancers. We quantified the impact of >26,000 mutations on the + folding of KRAS and its binding to six interaction partners. + Genetic interactions in double mutants allowed us to perform + biophysical measurements at scale, inferring >22,000 causal free + energy changes, a similar number of measurements as the total + made for proteins to date. These energy landscapes quantify how + mutations tune the binding specificity of a signalling protein + and map the inhibitory allosteric sites for an important + therapeutic target. Allosteric propagation is particularly + effective across the central beta sheet of KRAS and multiple + surface pockets are genetically-validated as allosterically + active, including a distal pocket in the C-terminal lobe of the + protein. Allosteric mutations typically inhibit binding to all + tested effectors but they can also change the binding + specificity, revealing the regulatory, evolutionary and + therapeutic potential to tune pathway activation. Using the + approach described here it should be possible to rapidly and + comprehensively identify allosteric target sites in many + important proteins. \#\#\# Competing Interest Statement The + authors have declared no competing interest.", + journal = "bioRxiv", + pages = "2022.12.06.519122", + month = dec, + year = 2022, + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@UNPUBLISHED{ding_protein_2023, + title = "Protein design using structure-based residue preferences", + author = "Ding, David and Shaw, Ada and Sinai, Sam and Rollins, Nathan and + Prywes, Noam and Savage, David F and Laub, Michael T and Marks, + Debora S", + abstract = "Recent developments in protein design have adapted large neural + networks with up to 100s of millions of parameters to learn + complex sequence-function mappings. However, it is unclear which + dependencies between residues are critical for determining + protein function, and a better empirical understanding could + enable high quality models that are also more data- and + resource-efficient. Here, we observe that the per residue amino + acid preferences - without considering interactions between + mutations are sufficient to explain much, and sometimes virtually + all of the combinatorial mutation effects across 7 datasets (R2 ∼ + 78-98\%), including one generated here. These preference + parameters (20*N, where N is the number of mutated residues) can + be learned from as few as ∼5\textbackslash*20\textbackslash*N + observations to predict a much larger number (potentially up to + 20N) of combinatorial variant effects with high accuracy (Pearson + r > 0.8). We hypothesized that the local structural dependencies + surrounding a residue could be sufficient to learn these required + mutation preferences, and developed an unsupervised design + approach, which we term CoVES for `Combinatorial Variant Effects + from Structure'. We show that CoVES outperforms not just model + free sampling approaches but also complicated, high-capacity + autoregressive neural networks in generating functional and + diverse sequence variants for two example proteins. This simple, + biologically-rooted model can be an effective alternative to + high-capacity, out of domain models for the design of functional + proteins. \#\#\# Competing Interest Statement SS is employed by + Dyno Therapeutics. DSM is an advisor for Dyno Therapeutics, + Octant, Jura Bio, Tectonic Therapeutics, and Genentech, and a + co-founder of Seismic. NR is employed by Seismic. DFS is a + co-founder and scientific advisory board member of Scribe + Therapeutics. The remaining authors declare no competing + interests.", + journal = "bioRxiv", + pages = "2022.10.31.514613", + month = jun, + year = 2023, + language = "en" +} + +@ARTICLE{nguyen_molecular_2023, + title = "Molecular determinants of Hsp90 dependence of Src kinase revealed + by deep mutational scanning", + author = "Nguyen, Vanessa and Ahler, Ethan and Sitko, Katherine A and + Stephany, Jason J and Maly, Dustin J and Fowler, Douglas M", + abstract = "Hsp90 is a molecular chaperone involved in the refolding and + activation of numerous protein substrates referred to as clients. + While the molecular determinants of Hsp90 client specificity are + poorly understood and limited to a handful of client proteins, + strong clients are thought to be destabilized and + conformationally extended. Here, we measured the + phosphotransferase activity of 3929 variants of the tyrosine + kinase Src in both the presence and absence of an Hsp90 + inhibitor. We identified 84 previously unknown functionally + dependent client variants. Unexpectedly, many destabilized or + extended variants were not functionally dependent on Hsp90. + Instead, functionally dependent client variants were clustered in + the $\alpha$F pocket and $\beta$1-$\beta$2 strand regions of Src, + which have yet to be described in driving Hsp90 dependence. Hsp90 + dependence was also strongly correlated with kinase activity. We + found that a combination of activation, global extension, and + general conformational flexibility, primarily induced by variants + at the $\alpha$F pocket and $\beta$1-$\beta$2 strands, was + necessary to render Src functionally dependent on Hsp90. + Moreover, the degree of activation and flexibility required to + transform Src into a functionally dependent client varied with + variant location, suggesting that a combination of regulatory + domain disengagement and catalytic domain flexibility are + required for chaperone dependence. Thus, by studying the + chaperone dependence of a massive number of variants, we + highlight factors driving Hsp90 client specificity and propose a + model of chaperone-kinase interactions.", + journal = "Protein Science", + volume = 32, + number = 7, + pages = "e4656", + month = jul, + year = 2023, + keywords = "Hsp90; Hsp90 inhibitor; chaperone; client; src kinase; tyrosine + kinase", + language = "en" +} + +@ARTICLE{seuma_atlas_2022, + title = "An atlas of amyloid aggregation: the impact of substitutions, + insertions, deletions and truncations on amyloid beta fibril + nucleation", + author = "Seuma, Mireia and Lehner, Ben and Bolognesi, Benedetta", + abstract = "Multiplexed assays of variant effects (MAVEs) guide clinical + variant interpretation and reveal disease mechanisms. To date, + MAVEs have focussed on a single mutation type-amino acid (AA) + substitutions-despite the diversity of coding variants that cause + disease. Here we use Deep Indel Mutagenesis (DIM) to generate a + comprehensive atlas of diverse variant effects for a disease + protein, the amyloid beta (A$\beta$) peptide that aggregates in + Alzheimer's disease (AD) and is mutated in familial AD (fAD). The + atlas identifies known fAD mutations and reveals that many + variants beyond substitutions accelerate A$\beta$ aggregation and + are likely to be pathogenic. Truncations, substitutions, + insertions, single- and internal multi-AA deletions differ in + their propensity to enhance or impair aggregation, but likely + pathogenic variants from all classes are highly enriched in the + polar N-terminal region of A$\beta$. This comparative atlas + highlights the importance of including diverse mutation types in + MAVEs and provides important mechanistic insights into amyloid + nucleation.", + journal = "Nature Communications", + volume = 13, + number = 1, + pages = "7084", + month = nov, + year = 2022, + language = "en" +} + +@ARTICLE{ursu_massively_2022, + title = "Massively parallel phenotyping of coding variants in cancer with + Perturb-seq", + author = "Ursu, Oana and Neal, James T and Shea, Emily and Thakore, + Pratiksha I and Jerby-Arnon, Livnat and Nguyen, Lan and Dionne, + Danielle and Diaz, Celeste and Bauman, Julia and Mosaad, Mariam + Mounir and Fagre, Christian and Lo, April and McSharry, Maria and + Giacomelli, Andrew O and Ly, Seav Huong and Rozenblatt-Rosen, + Orit and Hahn, William C and Aguirre, Andrew J and Berger, Alice + H and Regev, Aviv and Boehm, Jesse S", + abstract = "Genome sequencing studies have identified millions of somatic + variants in cancer, but it remains challenging to predict the + phenotypic impact of most. Experimental approaches to distinguish + impactful variants often use phenotypic assays that report on + predefined gene-specific functional effects in bulk cell + populations. Here, we develop an approach to functionally assess + variant impact in single cells by pooled Perturb-seq. We measured + the impact of 200 TP53 and KRAS variants on RNA profiles in over + 300,000 single lung cancer cells, and used the profiles to + categorize variants into phenotypic subsets to distinguish + gain-of-function, loss-of-function and dominant negative + variants, which we validated by comparison with orthogonal + assays. We discovered that KRAS variants did not merely fit into + discrete functional categories, but spanned a continuum of + gain-of-function phenotypes, and that their functional impact + could not have been predicted solely by their frequency in + patient cohorts. Our work provides a scalable, gene-agnostic + method for coding variant impact phenotyping, with potential + applications in multiple disease settings.", + journal = "Nature Biotechnology", + volume = 40, + number = 6, + pages = "896--905", + month = jun, + year = 2022, + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@ARTICLE{wrenbeck_automated_2019, + title = "An Automated {Data-Driven} Pipeline for Improving Heterologous + Enzyme Expression", + author = "Wrenbeck, Emily E and Bedewitz, Matthew A and Klesmith, Justin R + and Noshin, Syeda and Barry, Cornelius S and Whitehead, Timothy A", + abstract = "Enzymes are the ultimate entities responsible for chemical + transformations in natural and engineered biosynthetic pathways. + However, many natural enzymes suffer from suboptimal functional + expression due to poor intrinsic protein stability. Further, + stability enhancing mutations often come at the cost of impaired + function. Here we demonstrate an automated protein engineering + strategy for stabilizing enzymes while retaining catalytic + function using deep mutational scanning coupled to + multiple-filter based screening and combinatorial mutagenesis. We + validated this strategy by improving the functional expression of + a Type III polyketide synthase from the Atropa belladonna + biosynthetic pathway for tropane alkaloids. The best variant had + a total of 8 mutations with over 25-fold improved activity over + wild-type in E. coli cell lysates, an improved melting + temperature of 11.5 $\pm$ 0.6 °C, and only minimal reduction in + catalytic efficiency. We show that the multiple-filter approach + maintains acceptable sensitivity with homology modeling + structures up to 4 {\AA} RMS. Our results highlight an automated + protein engineering tool for improving the stability and + solubility of difficult to express enzymes, which has impact for + biotechnological applications.", + journal = "ACS Synthetic Biology", + volume = 8, + number = 3, + pages = "474--481", + month = mar, + year = 2019, + keywords = "deep mutational scanning; enzyme stability; heterologous pathway + expression; high-throughput screening; polyketide synthase; + tropane alkaloids", + language = "en" +} + +@ARTICLE{hom_deep_2019, + title = "Deep Mutational Scan of the Highly Conserved Influenza A Virus + {M1} Matrix Protein Reveals Substantial Intrinsic Mutational + Tolerance", + author = "Hom, Nancy and Gentles, Lauren and Bloom, Jesse D and Lee, Kelly + K", + abstract = "Influenza A virus matrix protein M1 is involved in multiple + stages of the viral infectious cycle. Despite its functional + importance, our present understanding of this essential viral + protein is limited. The roles of a small subset of specific amino + acids have been reported, but a more comprehensive understanding + of the relationship between M1 sequence, structure, and virus + fitness remains elusive. In this study, we used deep mutational + scanning to measure the effect of every amino acid substitution + in M1 on viral replication in cell culture. The map of amino acid + mutational tolerance we have generated allows us to identify + sites that are functionally constrained in cell culture as well + as sites that are less constrained. Several sites that exhibit + low tolerance to mutation have been found to be critical for M1 + function and production of viable virions. Surprisingly, + significant portions of the M1 sequence, especially in the + C-terminal domain, whose structure is undetermined, were found to + be highly tolerant of amino acid variation, despite having + extremely low levels of sequence diversity among natural + influenza virus strains. This unexpected discrepancy indicates + that not all sites in M1 that exhibit high sequence conservation + in nature are under strong constraint during selection for viral + replication in cell culture.IMPORTANCE The M1 matrix protein is + critical for many stages of the influenza virus infection cycle. + Currently, we have an incomplete understanding of this highly + conserved protein's function and structure. Key regions of M1, + particularly in the C terminus of the protein, remain poorly + characterized. In this study, we used deep mutational scanning to + determine the extent of M1's tolerance to mutation. Surprisingly, + nearly two-thirds of the M1 sequence exhibits a high tolerance + for substitutions, contrary to the extremely low sequence + diversity observed across naturally occurring M1 isolates. Sites + with low mutational tolerance were also identified, suggesting + that they likely play critical functional roles and are under + selective pressure. These results reveal the intrinsic mutational + tolerance throughout M1 and shape future inquiries probing the + functions of this essential influenza A virus protein.", + journal = "Journal of Virology", + volume = 93, + number = 13, + month = jul, + year = 2019, + keywords = "M1 matrix; codon library; deep mutational scan; influenza A + virus; mutational tolerance; selection; viral evolution", + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@ARTICLE{gray_elucidating_2019, + title = "Elucidating the Molecular Determinants of {A$\beta$} Aggregation + with Deep Mutational Scanning", + author = "Gray, Vanessa E and Sitko, Katherine and Kameni, Floriane Z Ngako + and Williamson, Miriam and Stephany, Jason J and Hasle, Nicholas + and Fowler, Douglas M", + abstract = "Despite the importance of A$\beta$ aggregation in Alzheimer's + disease etiology, our understanding of the sequence determinants + of aggregation is sparse and largely derived from in vitro + studies. For example, in vitro proline and alanine scanning + mutagenesis of A$\beta$40 proposed core regions important for + aggregation. However, we lack even this limited mutagenesis data + for the more disease-relevant A$\beta$42 Thus, to better + understand the molecular determinants of A$\beta$42 aggregation + in a cell-based system, we combined a yeast DHFR aggregation + assay with deep mutational scanning. We measured the effect of + 791 of the 798 possible single amino acid substitutions on the + aggregation propensity of A$\beta$42 We found that ∼75\% of + substitutions, largely to hydrophobic residues, maintained or + increased aggregation. We identified 11 positions at which + substitutions, particularly to hydrophilic and charged amino + acids, disrupted A$\beta$ aggregation. These critical positions + were similar but not identical to critical positions identified + in previous A$\beta$ mutagenesis studies. Finally, we analyzed + our large-scale mutagenesis data in the context of different + A$\beta$ aggregate structural models, finding that the + mutagenesis data agreed best with models derived from fibrils + seeded using brain-derived A$\beta$ aggregates.", + journal = "G3", + volume = 9, + number = 11, + pages = "3683--3689", + month = nov, + year = 2019, + keywords = "Amyloid; Amyloid beta; Deep mutational scanning; Protein + aggregation", + language = "en" +} + +@ARTICLE{veerapandian_directed_2018, + title = "Directed Evolution of Reprogramming Factors by Cell Selection and + Sequencing", + author = "Veerapandian, Veeramohan and Ackermann, Jan Ole and Srivastava, + Yogesh and Malik, Vikas and Weng, Mingxi and Yang, Xiaoxiao and + Jauch, Ralf", + abstract = "Directed biomolecular evolution is widely used to tailor and + enhance enzymes, fluorescent proteins, and antibodies but has + hitherto not been applied in the reprogramming of mammalian + cells. Here, we describe a method termed directed evolution of + reprogramming factors by cell selection and sequencing + (DERBY-seq) to identify artificially enhanced and evolved + reprogramming transcription factors. DERBY-seq entails pooled + screens with libraries of positionally randomised genes, cell + selection based on phenotypic readouts, and genotyping by + amplicon sequencing for candidate identification. We benchmark + this approach using pluripotency reprogramming with libraries + based on the reprogramming factor SOX2 and the reprogramming + incompetent endodermal factor SOX17. We identified several SOX2 + variants outperforming the wild-type protein in three- and + four-factor cocktails. The most effective variants were + discovered from the SOX17 library, demonstrating that this factor + can be converted into a highly potent inducer of pluripotency + with a range of diverse modifications. We propose DERBY-seq as a + broad-based approach to discover reprogramming factors for any + donor/target cell combination applicable to direct lineage + reprogramming in vitro and in vivo.", + journal = "Stem Cell Reports", + volume = 11, + number = 2, + pages = "593--606", + month = aug, + year = 2018, + keywords = "OCT4; SOX17; SOX2; cellular reprogramming; deep mutational + scanning; directed evolution; protein engineering; synthetic + biology; synthetic transcription factors", + language = "en" +} + +@ARTICLE{thyagarajan_inherent_2014, + title = "The inherent mutational tolerance and antigenic evolvability of + influenza hemagglutinin", + author = "Thyagarajan, Bargavi and Bloom, Jesse D", + abstract = "Influenza is notable for its evolutionary capacity to escape + immunity targeting the viral hemagglutinin. We used deep + mutational scanning to examine the extent to which a high + inherent mutational tolerance contributes to this antigenic + evolvability. We created mutant viruses that incorporate most of + the $\approx$10(4) amino-acid mutations to hemagglutinin from + A/WSN/1933 (H1N1) influenza. After passaging these viruses in + tissue culture to select for functional variants, we used deep + sequencing to quantify mutation frequencies before and after + selection. These data enable us to infer the preference for each + amino acid at each site in hemagglutinin. These inferences are + consistent with existing knowledge about the protein's structure + and function, and can be used to create a model that describes + hemagglutinin's evolution far better than existing phylogenetic + models. We show that hemagglutinin has a high inherent tolerance + for mutations at antigenic sites, suggesting that this is one + factor contributing to influenza's antigenic evolution.", + journal = "Elife", + volume = 3, + month = jul, + year = 2014, + keywords = "antigenic evolution; deep mutational scanning; evolutionary + biology; evolvability; genomics; hemagglutinin; infectious + disease; influenza; microbiology; phylogenetics; viruses", + language = "en" +} + +@ARTICLE{ostermaier_functional_2014, + title = "Functional map of arrestin-1 at single amino acid resolution", + author = "Ostermaier, Martin K and Peterhans, Christian and Jaussi, Rolf + and Deupi, Xavier and Standfuss, J{\"o}rg", + abstract = "Arrestins function as adapter proteins that mediate G + protein-coupled receptor (GPCR) desensitization, internalization, + and additional rounds of signaling. Here we have compared binding + of the GPCR rhodopsin to 403 mutants of arrestin-1 covering its + complete sequence. This comprehensive and unbiased mutagenesis + approach provides a functional dimension to the crystal + structures of inactive, preactivated p44 and phosphopeptide-bound + arrestins and will guide our understanding of arrestin-GPCR + complexes. The presented functional map quantitatively connects + critical interactions in the polar core and along the C tail of + arrestin. A series of amino acids (Phe375, Phe377, Phe380, and + Arg382) anchor the C tail in a position that blocks binding of + the receptor. Interaction of phosphates in the rhodopsin C + terminus with Arg29 controls a C-tail exchange mechanism in which + the C tail of arrestin is released and exposes several charged + amino acids (Lys14, Lys15, Arg18, Lys20, Lys110, and Lys300) for + binding of the phosphorylated receptor C terminus. In addition to + this arrestin phosphosensor, our data reveal several patches of + amino acids in the finger (Gln69 and Asp73-Met75) and the lariat + loops (L249-S252 and Y254) that can act as direct binding + interfaces. A stretch of amino acids at the edge of the C domain + (Trp194-Ser199, Gly337-Gly340, Thr343, and Thr345) could act as + membrane anchor, binding interface for a second rhodopsin, or + rearrange closer to the central loops upon complex formation. We + discuss these interfaces in the context of experimentally guided + docking between the crystal structures of arrestin and + light-activated rhodopsin.", + journal = "Proceedings of the National Academy of Sciences", + volume = 111, + number = 5, + pages = "1825--1830", + month = feb, + year = 2014, + keywords = "cell signaling; membrane receptor; protein engineering; scanning + mutagenesis; visual system", + language = "en" +} + +@ARTICLE{bloom_experimentally_2014, + title = "An experimentally determined evolutionary model dramatically + improves phylogenetic fit", + author = "Bloom, Jesse D", + abstract = "All modern approaches to molecular phylogenetics require a + quantitative model for how genes evolve. Unfortunately, existing + evolutionary models do not realistically represent the + site-heterogeneous selection that governs actual sequence change. + Attempts to remedy this problem have involved augmenting these + models with a burgeoning number of free parameters. Here, I + demonstrate an alternative: Experimental determination of a + parameter-free evolutionary model via mutagenesis, functional + selection, and deep sequencing. Using this strategy, I create an + evolutionary model for influenza nucleoprotein that describes the + gene phylogeny far better than existing models with dozens or + even hundreds of free parameters. Emerging high-throughput + experimental strategies such as the one employed here provide + fundamentally new information that has the potential to transform + the sensitivity of phylogenetic and genetic analyses.", + journal = "Molecular Biology and Evolution", + volume = 31, + number = 8, + pages = "1956--1978", + month = aug, + year = 2014, + keywords = "codon model; deep mutational scanning; influenza; nucleoprotein; + phylogenetics; substitution model", + language = "en" +} + +@ARTICLE{hietpas_experimental_2011, + title = "Experimental illumination of a fitness landscape", + author = "Hietpas, Ryan T and Jensen, Jeffrey D and Bolon, Daniel N A", + abstract = "The genes of all organisms have been shaped by selective + pressures. The relationship between gene sequence and fitness has + tremendous implications for understanding both evolutionary + processes and functional constraints on the encoded proteins. + Here, we have exploited deep sequencing technology to + experimentally determine the fitness of all possible individual + point mutants under controlled conditions for a nine-amino acid + region of Hsp90. Over the past five decades, limited glimpses + into the relationship between gene sequence and function have + sparked a long debate regarding the distribution, relative + proportion, and evolutionary significance of deleterious, + neutral, and advantageous mutations. Our systematic experimental + measurement of fitness effects of Hsp90 mutants in yeast, + evaluated in the light of existing population genetic theory, are + remarkably consistent with a nearly neutral model of molecular + evolution.", + journal = "Proceedings of the National Academy of Sciences of the United States of America", + volume = 108, + number = 19, + pages = "7896--7901", + month = may, + year = 2011, + language = "en" +} + +@ARTICLE{gill_multiple_2023, + title = "Multiple mechanisms of self-association of chemokine receptors + {CXCR4} and {CCR5} demonstrated by deep mutagenesis", + author = "Gill, Kevin S and Mehta, Kritika and Heredia, Jeremiah D and + Krishnamurthy, Vishnu V and Zhang, Kai and Procko, Erik", + abstract = "Chemokine receptors are members of the rhodopsin-like class A + GPCRs whose signaling through G proteins drives the directional + movement of cells in response to a chemokine gradient. Chemokine + receptors CXCR4 and CCR5 have been extensively studied due to + their roles in white blood cell development and inflammation and + their status as coreceptors for HIV-1 infection, among other + functions. Both receptors form dimers or oligomers but the + function/s of self-associations are unclear. While CXCR4 has been + crystallized in a dimeric arrangement, available atomic + resolution structures of CCR5 are monomeric. To investigate the + dimerization interfaces of these chemokine receptors, we used a + bimolecular fluorescence complementation (BiFC)-based screen and + deep mutational scanning to find mutations that modify receptor + self-association. Many disruptive mutations promoted + self-associations nonspecifically, suggesting they aggregated in + the membrane. A mutationally intolerant region was found on CXCR4 + that matched the crystallographic dimer interface, supporting + this dimeric arrangement in living cells. A mutationally + intolerant region was also observed on the surface of CCR5 by + transmembrane helices 3 and 4. Mutations from the deep mutational + scan that reduce BiFC were validated and were localized in the + transmembrane domains as well as the C-terminal cytoplasmic tails + where they reduced lipid microdomain localization. The reduced + self-association mutants of CXCR4 had increased binding to the + ligand CXCL12 but diminished calcium signaling. There was no + change in syncytia formation with cells expressing HIV-1 Env. The + data highlight that multiple mechanisms are involved in + self-association of chemokine receptor chains.", + journal = "bioRxiv", + month = mar, + year = 2023, + language = "en" +} + +@ARTICLE{meier_deep_2023, + title = "Deep mutational scan of a drug efflux pump reveals its + structure-function landscape", + author = "Meier, Gianmarco and Thavarasah, Sujani and Ehrenbolger, Kai and + Hutter, Cedric A J and H{\"u}rlimann, Lea M and Barandun, Jonas + and Seeger, Markus A", + abstract = "Drug efflux is a common resistance mechanism found in bacteria + and cancer cells, but studies providing comprehensive functional + insights are scarce. In this study, we performed deep mutational + scanning (DMS) on the bacterial ABC transporter EfrCD to + determine the drug efflux activity profile of more than 1,430 + single variants. These systematic measurements revealed that the + introduction of negative charges at different locations within + the large substrate binding pocket results in strongly increased + efflux activity toward positively charged ethidium, whereas + additional aromatic residues did not display the same effect. + Data analysis in the context of an inward-facing cryogenic + electron microscopy structure of EfrCD uncovered a high-affinity + binding site, which releases bound drugs through a peristaltic + transport mechanism as the transporter transits to its + outward-facing conformation. Finally, we identified substitutions + resulting in rapid Hoechst influx without affecting the efflux + activity for ethidium and daunorubicin. Hence, single mutations + can convert EfrCD into a drug-specific ABC importer.", + journal = "Nature Chemical Biology", + volume = 19, + number = 4, + pages = "440--450", + month = apr, + year = 2023, + language = "en" +} + +@ARTICLE{tan_high_2023, + title = "High-throughput identification of prefusion-stabilizing mutations + in {SARS-CoV-2} spike", + author = "Tan, Timothy J C and Mou, Zongjun and Lei, Ruipeng and Ouyang, + Wenhao O and Yuan, Meng and Song, Ge and Andrabi, Raiees and + Wilson, Ian A and Kieffer, Collin and Dai, Xinghong and Matreyek, + Kenneth A and Wu, Nicholas C", + abstract = "Designing prefusion-stabilized SARS-CoV-2 spike is critical for + the effectiveness of COVID-19 vaccines. All COVID-19 vaccines in + the US encode spike with K986P/V987P mutations to stabilize its + prefusion conformation. However, contemporary methods on + engineering prefusion-stabilized spike immunogens involve tedious + experimental work and heavily rely on structural information. + Here, we establish a systematic and unbiased method of + identifying mutations that concomitantly improve expression and + stabilize the prefusion conformation of the SARS-CoV-2 spike. Our + method integrates a fluorescence-based fusion assay, mammalian + cell display technology, and deep mutational scanning. As a + proof-of-concept, we apply this method to a region in the S2 + domain that includes the first heptad repeat and central helix. + Our results reveal that besides K986P and V987P, several + mutations simultaneously improve expression and significantly + lower the fusogenicity of the spike. As prefusion stabilization + is a common challenge for viral immunogen design, this work will + help accelerate vaccine development against different viruses.", + journal = "Nature Communications", + volume = 14, + number = 1, + pages = "2003", + month = apr, + year = 2023, + language = "en" +} + +@ARTICLE{macrae_protein_2023, + title = "Protein-protein interactions in the Mla lipid transport system + probed by computational structure prediction and deep mutational + scanning", + author = "MacRae, Mark R and Puvanendran, Dhenesh and Haase, Max A B and + Coudray, Nicolas and Kolich, Ljuvica and Lam, Cherry and Baek, + Minkyung and Bhabha, Gira and Ekiert, Damian C", + abstract = "The outer membrane (OM) of Gram-negative bacteria is an + asymmetric bilayer that protects the cell from external + stressors, such as antibiotics. The Mla transport system is + implicated in the Maintenance of OM Lipid Asymmetry by mediating + retrograde phospholipid transport across the cell envelope. Mla + uses a shuttle-like mechanism to move lipids between the MlaFEDB + inner membrane complex and the MlaA-OmpF/C OM complex, via a + periplasmic lipid-binding protein, MlaC. MlaC binds to MlaD and + MlaA, but the underlying protein-protein interactions that + facilitate lipid transfer are not well understood. Here, we take + an unbiased deep mutational scanning approach to map the fitness + landscape of MlaC from Escherichia coli, which provides insights + into important functional sites. Combining this analysis with + AlphaFold2 structure predictions and binding experiments, we map + the MlaC-MlaA and MlaC-MlaD protein-protein interfaces. Our + results suggest that the MlaD and MlaA binding surfaces on MlaC + overlap to a large extent, leading to a model in which MlaC can + only bind one of these proteins at a time. Low-resolution + cryo-electron microscopy (cryo-EM) maps of MlaC bound to MlaFEDB + suggest that at least two MlaC molecules can bind to MlaD at + once, in a conformation consistent with AlphaFold2 predictions. + These data lead us to a model for MlaC interaction with its + binding partners and insights into lipid transfer steps that + underlie phospholipid transport between the bacterial inner and + OMs.", + journal = "Journal of Biological Chemistry", + volume = 299, + number = 6, + pages = "104744", + month = jun, + year = 2023, + keywords = "AlphaFold2; E. coli; Mla; bacterial cell envelope; deep + mutational scanning; lipid transfer; protein interaction", + language = "en" +} + +@ARTICLE{ghose_marginal_2023, + title = "Marginal specificity in protein interactions constrains evolution + of a paralogous family", + author = "Ghose, Dia A and Przydzial, Kaitlyn E and Mahoney, Emily M and + Keating, Amy E and Laub, Michael T", + abstract = "The evolution of novel functions in biology relies heavily on + gene duplication and divergence, creating large paralogous + protein families. Selective pressure to avoid detrimental + cross-talk often results in paralogs that exhibit exquisite + specificity for their interaction partners. But how robust or + sensitive is this specificity to mutation? Here, using deep + mutational scanning, we demonstrate that a paralogous family of + bacterial signaling proteins exhibits marginal specificity, such + that many individual substitutions give rise to substantial + cross-talk between normally insulated pathways. Our results + indicate that sequence space is locally crowded despite overall + sparseness, and we provide evidence that this crowding has + constrained the evolution of bacterial signaling proteins. These + findings underscore how evolution selects for ``good enough'' + rather than optimized phenotypes, leading to restrictions on the + subsequent evolution of paralogs.", + journal = "Proceedings of the National Academy of Sciences of the United States of America", + volume = 120, + number = 18, + pages = "e2221163120", + month = may, + year = 2023, + keywords = "gene duplication; paralogous proteins; protein evolution; + protein-protein interactions; signal transduction", + language = "en" +} + +@UNPUBLISHED{nguyen_genetic_2023, + title = "The genetic landscape of a metabolic interaction", + author = "Nguyen, Thuy N and Ingle, Christine and Thompson, Samuel and + Reynolds, Kimberly A", + abstract = "AbstractEnzyme abundance, catalytic activity, and ultimately + sequence are all shaped by the need of growing cells to maintain + metabolic flux while minimizing accumulation of deleterious + intermediates. To quantify how variation in the activity of one + enzyme constrains the biochemical parameters and sequence of + another, we focused on dihydrofolate reductase (DHFR) and + thymidylate synthase (TYMS), a pair of enzymes catalyzing + consecutive reactions in folate metabolism. We used deep + mutational scanning to quantify the growth rate effect of 2,696 + DHFR single mutations in 3 TYMS backgrounds and show that our + data are well-described by a relatively simple enzyme velocity to + growth rate model. From the data and model we estimate the + approximate effects of all single mutations on DHFR catalytic + power. Together our data provide a comprehensive view of + epistasis between mutations in a biochemically linked enzyme + pair, reveal the structural distribution of positions tuning DHFR + catalysis, and establish a foundation for the design of + multi-enzyme systems.", + journal = "bioRxiv", + month = may, + year = 2023 +} + +@UNPUBLISHED{clausen_mutational_2023, + title = "A mutational atlas for Parkin proteostasis", + author = "Clausen, Lene and Voutsinos, Vasileios and Cagiada, Matteo and + Johansson, Kristoffer E and Gr{\o}nb{\ae}k-Thygesen, Martin and + Nariya, Snehal and Powell, Rachel L and Have, Magnus K N and + Oestergaard, Vibe H and Stein, Amelie and Fowler, Douglas M and + Lindorff-Larsen, Kresten and Hartmann-Petersen, Rasmus", + abstract = "The delicate balance of protein homeostasis can be disturbed by + mutations that affect folding and stability of the encoded + protein. More than half of disease-causing missense variants are + thought to lead to protein degradation, but determining which and + the molecular mechanisms involved remain enigmatic. To examine + these issues, we selected the ubiquitin-protein ligase Parkin, + where known missense variants result in an autosomal recessive, + early onset Parkinsonism. We used the variant abundance by + massively parallel sequencing (VAMP-seq) approach to quantify the + abundance of Parkin missense variants in cultured human cells. + The resulting mutational map, covering 9219 out of the 9300 + possible single-site amino acid substitutions and nonsense Parkin + variants, show that most low abundance variants are located + within the structured domains of the protein, while the flexible + linker regions are more tolerant. The vast majority of low + abundance Parkin variants are degraded through the + ubiquitin-proteasome system and are stabilized at a lowered + temperature. The cellular abundance data correlate with + thermodynamic stability, evolutionary conservation, and show that + half of the known disease-linked variants are found at low + abundance. Systematic mapping of degradation signals (degrons) + shows that inherent primary degrons in Parkin largely overlap + with regions that are buried, and highly sensitive to mutations. + An exposed degron region proximal to the so-called ``activation + element'' is enhanced by substitutions to hydrophobic residues + and destroyed by introduction of hydrophilic residues. The data + provide examples of how missense variants may cause degradation + either via destabilization of the native protein, or by + introducing local signals for degradation. Combined with the + computational methods based on Parkin structure and conservation, + our abundance map sheds light on the mechanisms that cause loss + of function, and points to variants where function potentially + can be restored. \#\#\# Competing Interest Statement The authors + have declared no competing interest.", + journal = "bioRxiv", + pages = "2023.06.08.544160", + month = jun, + year = 2023, + language = "en" +} + +@UNPUBLISHED{vanella_understanding_2023, + title = "Understanding {Activity-Stability} Tradeoffs in Biocatalysts by + Enzyme Proximity Sequencing", + author = "Vanella, Rosario and K{\"u}ng, Christoph and Schoepfer, Alexandre + A and Doffini, Vanni and Ren, Jin and Nash, Michael A", + abstract = "Understanding the complex relationships between enzyme sequence, + folding stability and catalytic activity is crucial for + applications in industry and biomedicine. However, current enzyme + assay technologies are limited by an inability to simultaneously + resolve both stability and activity phenotypes and to couple + these to gene sequences at large scale. Here we developed Enzyme + Proximity Sequencing (EP-Seq), a deep mutational scanning method + that leverages peroxidase-mediated radical labeling with single + cell fidelity to dissect the effects of thousands of mutations on + stability and catalytic activity of oxidoreductase enzymes in a + single experiment. We used EP-Seq to analyze how 6,387 missense + mutations influence folding stability and catalytic activity in a + D-amino acid oxidase (DAOx) from R. gracilis . The resulting + datasets demonstrate activity-based constraints that limit + folding stability during natural evolution, and identify hotspots + distant from the active site as candidates for mutations that + improve catalytic activity without sacrificing stability. EP-Seq + can be extended to other enzyme classes and provides valuable + insights into biophysical principles governing enzyme structure + and function. \#\#\# Competing Interest Statement The authors + have declared no competing interest.", + journal = "bioRxiv", + pages = "2023.02.24.529916", + month = mar, + year = 2023, + language = "en" +} + +@ARTICLE{lei_mutational_2023, + title = "Mutational fitness landscape of human influenza {H3N2} + neuraminidase", + author = "Lei, Ruipeng and Hernandez Garcia, Andrea and Tan, Timothy J C + and Teo, Qi Wen and Wang, Yiquan and Zhang, Xiwen and Luo, + Shitong and Nair, Satish K and Peng, Jian and Wu, Nicholas C", + abstract = "Influenza neuraminidase (NA) has received increasing attention as + an effective vaccine target. However, its mutational tolerance is + not well characterized. Here, the fitness effects of >6,000 + mutations in human H3N2 NA are probed using deep mutational + scanning. Our result shows that while its antigenic regions have + high mutational tolerance, there are solvent-exposed regions with + low mutational tolerance. We also find that protein stability is + a major determinant of NA mutational fitness. The deep mutational + scanning result correlates well with mutational fitness inferred + from natural sequences using a protein language model, + substantiating the relevance of our findings to the natural + evolution of circulating strains. Additional analysis further + suggests that human H3N2 NA is far from running out of mutations + despite already evolving for >50 years. Overall, this study + advances our understanding of the evolutionary potential of NA + and the underlying biophysical constraints, which in turn provide + insights into NA-based vaccine design.", + journal = "Cell Reports", + volume = 42, + number = 1, + pages = "111951", + month = jan, + year = 2023, + keywords = "CP: Molecular biology; deep mutational scanning; evolution; + influenza; neuraminidase; protein language model; protein + stability; protein structure", + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@ARTICLE{loggerenberg_systematically_2023, + title = "Systematically testing human {HMBS} missense variants to reveal + mechanism and pathogenic variation", + author = "van Loggerenberg, Warren and Sowlati-Hashjin, Shahin and Weile, + Jochen and Hamilton, Rayna and Chawla, Aditya and Gebbia, + Marinella and Kishore, Nishka and Fr{\'e}sard, Laure and + Mustajoki, Sami and Pischik, Elena and Pierro, Elena Di and + Barbaro, Michela and Floderus, Ylva and Schmitt, Caroline and + Gouya, Laurent and Colavin, Alexandre and Nussbaum, Robert and + Friesema, Edith C H and Kauppinen, Raili and To-Figueras, Jordi + and Aarsand, Aasne K and Desnick, Robert J and Garton, Michael + and Roth, Frederick P", + abstract = "Defects in hydroxymethylbilane synthase (HMBS) can cause Acute + Intermittent Porphyria (AIP), an acute neurological disease. + Although sequencing-based diagnosis can be definitive, ~⅓ of + clinical HMBS variants are missense variants, and most + clinically-reported HMBS missense variants are designated as + ``variants of uncertain significance'' (VUS). Using saturation + mutagenesis, en masse selection, and sequencing, we applied a + multiplexed validated assay to both the erythroid-specific and + ubiquitous isoforms of HMBS, obtaining confident functional + impact scores for >84\% of all possible amino-acid substitutions. + The resulting variant effect maps generally agreed with + biochemical expectation. However, the maps showed variants at the + dimerization interface to be unexpectedly well tolerated, and + suggested residue roles in active site dynamics that were + supported by molecular dynamics simulations. Most importantly, + these HMBS variant effect maps can help discriminate pathogenic + from benign variants, proactively providing evidence even for + yet-to-be-observed clinical missense variants.", + journal = "bioRxiv", + month = feb, + year = 2023, + language = "en" +} + +% The entry below contains non-ASCII chars that could not be converted +% to a LaTeX equivalent. +@ARTICLE{weeks_fitness_2023, + title = "Fitness and Functional Landscapes of the E. coli {RNase} {III} + Gene rnc", + author = "Weeks, Ryan and Ostermeier, Marc", + abstract = "How protein properties such as protein activity and protein + essentiality affect the distribution of fitness effects (DFE) of + mutations are important questions in protein evolution. Deep + mutational scanning studies typically measure the effects of a + comprehensive set of mutations on either protein activity or + fitness. Our understanding of the underpinnings of the DFE would + be enhanced by a comprehensive study of both for the same gene. + Here, we compared the fitness effects and in vivo protein + activity effects of ∼4,500 missense mutations in the E. coli rnc + gene. This gene encodes RNase III, a global regulator enzyme that + cleaves diverse RNA substrates including precursor ribosomal RNA + and various mRNAs including its own 5' untranslated region + (5'UTR). We find that RNase III's ability to cleave dsRNA is the + most important determinant of the fitness effects of rnc + mutations. The DFE of RNase III was bimodal, with mutations + centered around neutral and deleterious effects, consistent with + previously reported DFE's of enzymes with a singular + physiological role. Fitness was buffered to small effects on + RNase III activity. The enzyme's RNase III domain, which contains + the RNase III signature motif and all active site residues, was + more sensitive to mutation than its dsRNA binding domain, which + is responsible for recognition and binding to dsRNA. Differential + effects on fitness and functional scores for mutations at highly + conserved residues G97, G99, and F188 suggest that these + positions may be important for RNase III cleavage specificity.", + journal = "Molecular Biology and Evolution", + volume = 40, + number = 3, + month = mar, + year = 2023, + keywords = "RNase III; fitness landscape; protein evolution", + language = "en" +} + +@ARTICLE{muhammad_high_2023, + title = "High-throughput functional mapping of variants in an arrhythmia + gene, {KCNE1} , reveals novel biology", + author = "Muhammad, Ayesha and Calandranis, Maria E and Li, Bian and Yang, + Tao and Blackwell, Daniel J and Harvey, M Lorena and Smith, + Jeremy E and Chew, Ashli E and Capra, John A and Matreyek, + Kenneth A and Fowler, Douglas M and Roden, Dan M and Glazer, + Andrew M", + abstract = "BACKGROUND: KCNE1 encodes a 129-residue cardiac potassium channel + (I Ks ) subunit. KCNE1 variants are associated with long QT + syndrome and atrial fibrillation. However, most variants have + insufficient evidence of clinical consequences and thus limited + clinical utility. RESULTS: Here, we demonstrate the power of + variant effect mapping, which couples saturation mutagenesis with + high-throughput sequencing, to ascertain the function of + thousands of protein coding KCNE1 variants. We comprehensively + assayed KCNE1 variant cell surface expression (2,554/2,709 + possible single amino acid variants) and function (2,539 + variants). We identified 470 loss-of-surface expression and 588 + loss-of-function variants. Out of the 588 loss-of-function + variants, only 155 had low cell surface expression. The latter + half of the protein is dispensable for protein trafficking but + essential for channel function. 22 of the 30 KCNE1 residues + (73\%) highly intolerant of variation were in predicted close + contact with binding partners KCNQ1 or calmodulin. Our data were + highly concordant with gold standard electrophysiological data + ($\rho$ = -0.65), population and patient cohorts (32/38 + concordant variants), and computational metrics ($\rho$ = -0.55). + Our data provide moderate-strength evidence for the ACMG/AMP + functional criteria for benign and pathogenic variants. + CONCLUSIONS: Comprehensive variant effect maps of KCNE1 can both + provide insight into I Ks channel biology and help reclassify + variants of uncertain significance.", + journal = "bioRxiv", + month = apr, + year = 2023, + language = "en" +} + +@ARTICLE{yee_full_2023, + title = "The full spectrum of {OCT1} ({SLC22A1}) mutations bridges + transporter biophysics to drug pharmacogenomics", + author = "Yee, Sook Wah and Macdonald, Christian and Mitrovic, Darko and + Zhou, Xujia and Koleske, Megan L and Yang, Jia and Silva, Dina + Buitrago and Grimes, Patrick Rockefeller and Trinidad, Donovan + and More, Swati S and Kachuri, Linda and Witte, John S and + Delemotte, Lucie and Giacomini, Kathleen M and Coyote-Maestas, + Willow", + abstract = "Membrane transporters play a fundamental role in the tissue + distribution of endogenous compounds and xenobiotics and are + major determinants of efficacy and side effects profiles. + Polymorphisms within these drug transporters result in + inter-individual variation in drug response, with some patients + not responding to the recommended dosage of drug whereas others + experience catastrophic side effects. For example, variants + within the major hepatic Human organic cation transporter OCT1 + (SLC22A1) can change endogenous organic cations and many + prescription drug levels. To understand how variants + mechanistically impact drug uptake, we systematically study how + all known and possible single missense and single amino acid + deletion variants impact expression and substrate uptake of OCT1. + We find that human variants primarily disrupt function via + folding rather than substrate uptake. Our study revealed that the + major determinants of folding reside in the first 300 amino + acids, including the first 6 transmembrane domains and the + extracellular domain (ECD) with a stabilizing and highly + conserved stabilizing helical motif making key interactions + between the ECD and transmembrane domains. Using the functional + data combined with computational approaches, we determine and + validate a structure-function model of OCT1s conformational + ensemble without experimental structures. Using this model and + molecular dynamic simulations of key mutants, we determine + biophysical mechanisms for how specific human variants alter + transport phenotypes. We identify differences in frequencies of + reduced function alleles across populations with East Asians vs + European populations having the lowest and highest frequency of + reduced function variants, respectively. Mining human population + databases reveals that reduced function alleles of OCT1 + identified in this study associate significantly with high LDL + cholesterol levels. Our general approach broadly applied could + transform the landscape of precision medicine by producing a + mechanistic basis for understanding the effects of human + mutations on disease and drug response.", + journal = "bioRxiv", + month = jun, + year = 2023, + language = "en" +} + +@ARTICLE{chen_deep_2023, + title = "Deep Mutational Scanning of an {Oxygen-Independent} Fluorescent + Protein {CreiLOV} for Comprehensive Profiling of Mutational and + Epistatic Effects", + author = "Chen, Yongcan and Hu, Ruyun and Li, Keyi and Zhang, Yating and + Fu, Lihao and Zhang, Jianzhi and Si, Tong", + abstract = "Oxygen-independent, flavin mononucleotide-based fluorescent + proteins (FbFPs) are promising alternatives to green fluorescent + protein in anaerobic contexts. Deep mutational scanning performs + systematic profiling of protein sequence-function relationships + but has not been applied to FbFPs. Focusing on CreiLOV from + Chlamydomonas reinhardtii, we created and analyzed two + comprehensive mutant collections: (1) single-residue, + site-saturation mutagenesis libraries covering all 118 residues; + and (2) a full combinatorial metagenesis library among 20 + mutations at 15 residues, where mutation and residue selection + was based on single-site mutagenesis results. Notably, the second + type of library is indispensable to study higher-order epistasis + but underrepresented in the literature. Using optimized FACS-seq + assays, 2,185 (>92.5\%) out of 2,360 possible single-site mutants + and 165,428 (>89.7\%) out of 184,320 possible combinatorial + mutants were reliably assigned with fitness values. We + constructed statistical and machine-learning models to analyze + the CreiLOV data set, enabling accurate fitness prediction of + higher-order mutants using lower-order mutagenesis data. In + addition, we successfully isolated CreiLOV variants with improved + fluorescence quantum yield and thermostability. This work + provides new empirical data and design rules to engineer + combinatorial protein variants.", + journal = "ACS Synthetic Biology", + volume = 12, + number = 5, + pages = "1461--1473", + month = may, + year = 2023, + keywords = "CreiLOV; FACS-Seq; deep mutational scanning; epistasis; protein + engineering", + language = "en" +} + +@ARTICLE{gersing_characterizing_2023, + title = "Characterizing glucokinase variant mechanisms using a multiplexed + abundance assay", + author = "Gersing, Sarah and Schulze, Thea K and Cagiada, Matteo and Stein, + Amelie and Roth, Frederick P and Lindorff-Larsen, Kresten and + Hartmann-Petersen, Rasmus", + abstract = "Amino acid substitutions can perturb protein activity in multiple + ways. Understanding their mechanistic basis may pinpoint how + residues contribute to protein function. Here, we characterize + the mechanisms of human glucokinase (GCK) variants, building on + our previous comprehensive study on GCK variant activity. We + assayed the abundance of 95\% of GCK missense and nonsense + variants, and found that 43\% of hypoactive variants have a + decreased cellular abundance. By combining our abundance scores + with predictions of protein thermodynamic stability, we identify + residues important for GCK metabolic stability and conformational + dynamics. These residues could be targeted to modulate GCK + activity, and thereby affect glucose homeostasis.", + journal = "bioRxiv", + month = may, + year = 2023, + language = "en" +} + +@ARTICLE{huttinger_deep_2021, + title = "Deep mutational scanning of the plasminogen activator inhibitor-1 + functional landscape", + author = "Huttinger, Zachary M and Haynes, Laura M and Yee, Andrew and + Kretz, Colin A and Holding, Matthew L and Siemieniak, David R and + Lawrence, Daniel A and Ginsburg, David", + abstract = "The serine protease inhibitor (SERPIN) plasminogen activator + inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic + system, inhibiting the serine proteases tissue- and + urokinase-type plasminogen activator (tPA and uPA, respectively). + Missense variants render PAI-1 non-functional through misfolding, + leading to its turnover as a protease substrate, or to a more + rapid transition to the latent/inactive state. Deep mutational + scanning was performed to evaluate the impact of amino acid + sequence variation on PAI-1 inhibition of uPA using an M13 + filamentous phage display system. Error prone PCR was used to + construct a mutagenized PAI-1 library encompassing ~ 70\% of + potential single amino acid substitutions. The relative effects + of 27\% of all possible missense variants on PAI-1 inhibition of + uPA were determined using high-throughput DNA sequencing. 826 + missense variants demonstrated conserved inhibitory activity + while 1137 resulted in loss of PAI-1 inhibitory function. The + least evolutionarily conserved regions of PAI-1 were also + identified as being the most tolerant of missense mutations. The + results of this screen confirm previous low-throughput mutational + studies, including those of the reactive center loop. These data + provide a powerful resource for explaining structure-function + relationships for PAI-1 and for the interpretation of human + genomic sequence variants.", + journal = "Scientific Reports", + volume = 11, + number = 1, + pages = "18827", + month = sep, + year = 2021, + language = "en" +} + +@article{kwon_structurefunction_2022, + title = {Structure–function analysis of the {SHOC2}–{MRAS}–{PP1C} holophosphatase complex}, + volume = {609}, + issn = {0028-0836, 1476-4687}, + url = {https://www.nature.com/articles/s41586-022-04928-2}, + doi = {10.1038/s41586-022-04928-2}, + language = {en}, + number = {7926}, + urldate = {2023-10-16}, + journal = {Nature}, + author = {Kwon, Jason J. and Hajian, Behnoush and Bian, Yuemin and Young, Lucy C. and Amor, Alvaro J. and Fuller, James R. and Fraley, Cara V. and Sykes, Abbey M. and So, Jonathan and Pan, Joshua and Baker, Laura and Lee, Sun Joo and Wheeler, Douglas B. and Mayhew, David L. and Persky, Nicole S. and Yang, Xiaoping and Root, David E. and Barsotti, Anthony M. and Stamford, Andrew W. and Perry, Charles K. and Burgin, Alex and McCormick, Frank and Lemke, Christopher T. and Hahn, William C. and Aguirre, Andrew J.}, + month = sep, + year = {2022}, + pages = {408--415}, + file = {Accepted Version:/Users/admin/Zotero/storage/R96FAMJM/Kwon et al. - 2022 - Structure–function analysis of the SHOC2–MRAS–PP1C.pdf:application/pdf}, +} + +@article{sun_proactive_2020, + title = {A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase}, + volume = {12}, + issn = {1756-994X}, + url = {https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-020-0711-1}, + doi = {10.1186/s13073-020-0711-1}, + abstract = {Abstract + + Background + + For the majority of rare clinical missense variants, pathogenicity status cannot currently be classified. Classical homocystinuria, characterized by elevated homocysteine in plasma and urine, is caused by variants in the cystathionine beta-synthase ( + CBS + ) gene, most of which are rare. With early detection, existing therapies are highly effective. + + + + Methods + + Damaging + CBS + variants can be detected based on their failure to restore growth in yeast cells lacking the yeast ortholog + CYS4 + . This assay has only been applied reactively, after first observing a variant in patients. Using saturation codon-mutagenesis, en masse growth selection, and sequencing, we generated a comprehensive, proactive map of CBS missense variant function. + + + + Results + + Our CBS variant effect map far exceeds the performance of computational predictors of disease variants. Map scores correlated strongly with both disease severity (Spearman’s + ϱ +  = 0.9) and human clinical response to vitamin B + 6 + ( + ϱ +  = 0.93). + + + + Conclusions + We demonstrate that highly multiplexed cell-based assays can yield proactive maps of variant function and patient response to therapy, even for rare variants not previously seen in the clinic.}, + language = {en}, + number = {1}, + urldate = {2023-10-16}, + journal = {Genome Medicine}, + author = {Sun, Song and Weile, Jochen and Verby, Marta and Wu, Yingzhou and Wang, Yang and Cote, Atina G. and Fotiadou, Iosifina and Kitaygorodsky, Julia and Vidal, Marc and Rine, Jasper and Ješina, Pavel and Kožich, Viktor and Roth, Frederick P.}, + month = dec, + year = {2020}, + pages = {13}, + file = {Full Text:/Users/admin/Zotero/storage/4KCD9FVJ/Sun et al. - 2020 - A proactive genotype-to-patient-phenotype map for .pdf:application/pdf}, +} + +@article{wan_characterizing_2019, + title = {Characterizing variants of unknown significance in rhodopsin: {A} functional genomics approach}, + volume = {40}, + issn = {1059-7794, 1098-1004}, + shorttitle = {Characterizing variants of unknown significance in rhodopsin}, + url = {https://onlinelibrary.wiley.com/doi/10.1002/humu.23762}, + doi = {10.1002/humu.23762}, + language = {en}, + number = {8}, + urldate = {2023-10-16}, + journal = {Human Mutation}, + author = {Wan, Aliete and Place, Emily and Pierce, Eric A. and Comander, Jason}, + month = aug, + year = {2019}, + pages = {1127--1144}, + file = {Full Text:/Users/admin/Zotero/storage/SSKD3J59/Wan et al. - 2019 - Characterizing variants of unknown significance in.pdf:application/pdf}, +} + +@article{chan_engineering_2020, + title = {Engineering human {ACE2} to optimize binding to the spike protein of {SARS} coronavirus 2}, + volume = {369}, + issn = {0036-8075, 1095-9203}, + url = {https://www.science.org/doi/10.1126/science.abc0870}, + doi = {10.1126/science.abc0870}, + abstract = {A decoy receptor for SARS-CoV-2 + + For severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter human cells, the spike protein on the surface of the virus must bind to the host receptor protein, angiotensin-converting enzyme 2 (ACE2). A soluble version of the receptor is being explored as a therapeutic. Chan + et al. + used deep mutagenesis to identify ACE2 mutants that bind more tightly to the spike protein and combined mutations to further increase binding affinity (see the Perspective by DeKosky). A promising variant was engineered to be a stable dimer that has a binding affinity for the spike protein; it is comparable with neutralizing antibodies and neutralized both SARS-CoV-2 and SARS-CoV-1 in a cell-based assay. In addition, the similarity to the natural receptor may limit the possibility for viral escape. + + + Science + , this issue p. + 1261 + ; see also p. + 1167 + + , + A variant of ACE2 based on deep mutagenesis far outcompetes the natural receptor in binding the SARS-CoV-2 spike protein. + , + The spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds angiotensin-converting enzyme 2 (ACE2) on host cells to initiate entry, and soluble ACE2 is a therapeutic candidate that neutralizes infection by acting as a decoy. By using deep mutagenesis, mutations in ACE2 that increase S binding are found across the interaction surface, in the asparagine 90–glycosylation motif and at buried sites. The mutational landscape provides a blueprint for understanding the specificity of the interaction between ACE2 and S and for engineering high-affinity decoy receptors. Combining mutations gives ACE2 variants with affinities that rival those of monoclonal antibodies. A stable dimeric variant shows potent SARS-CoV-2 and -1 neutralization in vitro. The engineered receptor is catalytically active, and its close similarity with the native receptor may limit the potential for viral escape.}, + language = {en}, + number = {6508}, + urldate = {2023-10-16}, + journal = {Science}, + author = {Chan, Kui K. and Dorosky, Danielle and Sharma, Preeti and Abbasi, Shawn A. and Dye, John M. and Kranz, David M. and Herbert, Andrew S. and Procko, Erik}, + month = sep, + year = {2020}, + pages = {1261--1265}, + file = {Full Text:/Users/admin/Zotero/storage/RHH3R5SV/Chan et al. - 2020 - Engineering human ACE2 to optimize binding to the .pdf:application/pdf}, +} + +@techreport{silverstein_systematic_2021, + type = {preprint}, + title = {A systematic genotype-phenotype map for missense variants in the human intellectual disability-associated gene \textit{{GDI1}}}, + url = {http://biorxiv.org/lookup/doi/10.1101/2021.10.06.463360}, + abstract = {Abstract + + Next generation sequencing has become a common tool in the diagnosis of genetic diseases. However, for the vast majority of genetic variants that are discovered, a clinical interpretation is not available. Variant effect mapping allows the functional effects of many single amino acid variants to be characterized in parallel. Here, we combine multiplexed functional assays with machine learning to assess the effects of amino acid substitutions in the human intellectual disability-associated gene, + GDI1 + . We show that the resulting variant effect map can be used to discriminate pathogenic from benign variants. Our variant effect map recovers known biochemical and structural features of + GDI1 + and reveals additional aspects of + GDI1 + function. We explore how our functional assays can aid in the interpretation of novel + GDI1 + variants as they are discovered, and to re-classify previously observed variants of unknown significance.}, + language = {en}, + urldate = {2023-10-16}, + institution = {Genetics}, + author = {Silverstein, Rachel A. and Sun, Song and Verby, Marta and Weile, Jochen and Wu, Yingzhou and Gebbia, Marinella and Fotiadou, Iosifina and Kitaygorodsky, Julia and Roth, Frederick P.}, + month = oct, + year = {2021}, + doi = {10.1101/2021.10.06.463360}, + file = {Submitted Version:/Users/admin/Zotero/storage/4EYJCVVM/Silverstein et al. - 2021 - A systematic genotype-phenotype map for missense v.pdf:application/pdf}, +} + +@article{zinkus-boltz_phage-assisted_2019, + title = {A {Phage}-{Assisted} {Continuous} {Selection} {Approach} for {Deep} {Mutational} {Scanning} of {Protein}–{Protein} {Interactions}}, + volume = {14}, + issn = {1554-8929, 1554-8937}, + url = {https://pubs.acs.org/doi/10.1021/acschembio.9b00669}, + doi = {10.1021/acschembio.9b00669}, + language = {en}, + number = {12}, + urldate = {2023-10-16}, + journal = {ACS Chemical Biology}, + author = {Zinkus-Boltz, Julia and DeValk, Craig and Dickinson, Bryan C.}, + month = dec, + year = {2019}, + pages = {2757--2767}, + file = {Submitted Version:/Users/admin/Zotero/storage/XRNNITUF/Zinkus-Boltz et al. - 2019 - A Phage-Assisted Continuous Selection Approach for.pdf:application/pdf}, +} + +@article{klesmith_retargeting_2019, + title = {Retargeting {CD19} {Chimeric} {Antigen} {Receptor} {T} {Cells} via {Engineered} {CD19}-{Fusion} {Proteins}}, + volume = {16}, + issn = {1543-8384, 1543-8392}, + url = {https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.9b00418}, + doi = {10.1021/acs.molpharmaceut.9b00418}, + language = {en}, + number = {8}, + urldate = {2023-10-16}, + journal = {Molecular Pharmaceutics}, + author = {Klesmith, Justin R. and Su, Lihe and Wu, Lan and Schrack, Ian A. and Dufort, Fay J. and Birt, Alyssa and Ambrose, Christine and Hackel, Benjamin J. and Lobb, Roy R. and Rennert, Paul D.}, + month = aug, + year = {2019}, + pages = {3544--3558}, +} + +@article{elazar_mutational_2016, + title = {Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane}, + volume = {5}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/12125}, + doi = {10.7554/eLife.12125}, + abstract = {Insertion of helix-forming segments into the membrane and their association determines the structure, function, and expression levels of all plasma membrane proteins. However, systematic and reliable quantification of membrane-protein energetics has been challenging. We developed a deep mutational scanning method to monitor the effects of hundreds of point mutations on helix insertion and self-association within the bacterial inner membrane. The assay quantifies insertion energetics for all natural amino acids at 27 positions across the membrane, revealing that the hydrophobicity of biological membranes is significantly higher than appreciated. We further quantitate the contributions to membrane-protein insertion from positively charged residues at the cytoplasm-membrane interface and reveal large and unanticipated differences among these residues. Finally, we derive comprehensive mutational landscapes in the membrane domains of Glycophorin A and the ErbB2 oncogene, and find that insertion and self-association are strongly coupled in receptor homodimers. + , + Cells are defined by a thin membrane that separates the inside of the cell from the outside. The core of this membrane is hydrophobic, meaning that it repels water. Many signals and nutrients cannot pass through the membrane itself, but can pass through the proteins that span the membrane. Membrane proteins are therefore essential for living cells; yet even after decades of research, it remains unclear how proteins interact with the membrane and which features determine a protein’s stability in a biological membrane. + Since the early 1980s it was known that the bacterium E. coli could grow on a common antibiotic called ampicillin if it had enough of an antibiotic-degrading enzyme called β-lactamase anchored into its inner membrane. Now, Elazar et al. have used this enzyme to obtain detailed information on the interactions between a biological membrane and a membrane protein. First, hundreds of different mutations were introduced into the gene that encodes the enzyme to generate a population of bacteria that each had a slightly different membrane anchor. The mutant bacteria were then grown in the presence of the antibiotic, meaning that those mutants with a more stable membrane anchor were more likely to survive and grow than those with less stable anchors. + Elazar et al. then collected all the surviving bacteria, sequenced their DNA and measured how common the different mutations were in the final population. This approach was less labor-intensive and more accurate than traditional methods for monitoring membrane-anchored proteins, and the resulting large dataset was used to uncover which features affect a protein’s stability in a membrane. These results also showed that a biological membrane’s core is considerably more hydrophobic than was previously thought. + In addition to being hydrophobic, biological membranes have more negative charge in the side that faces into the cell. This means that membrane proteins with a positive charge in this region will be more stable, and Elazar et al. were able to use their new system to measure this effect for the first time. + Finally, membrane proteins do not only span the membrane; they also bind with other membrane proteins in order to carry out their roles. Elazar et al. used their system to look at the surfaces of human membrane proteins that interact with one another, and build a detailed map of the interaction surfaces, from which they derived accurate models of the membrane proteins. + Overall, these new findings could now be used to model the three-dimensional structures of membrane proteins and improve their stability. This in turn may help efforts to develop these proteins into more robust experimental tools and in the search for drugs that target membrane proteins.}, + language = {en}, + urldate = {2023-10-16}, + journal = {eLife}, + author = {Elazar, Assaf and Weinstein, Jonathan and Biran, Ido and Fridman, Yearit and Bibi, Eitan and Fleishman, Sarel Jacob}, + month = jan, + year = {2016}, + pages = {e12125}, + file = {Full Text:/Users/admin/Zotero/storage/UVHQ2HLP/Elazar et al. - 2016 - Mutational scanning reveals the determinants of pr.pdf:application/pdf}, +} + +@article{coyote-maestas_determinants_2022, + title = {Determinants of trafficking, conduction, and disease within a {K}+ channel revealed through multiparametric deep mutational scanning}, + volume = {11}, + issn = {2050-084X}, + url = {https://elifesciences.org/articles/76903}, + doi = {10.7554/eLife.76903}, + abstract = {A long-standing goal in protein science and clinical genetics is to develop quantitative models of sequence, structure, and function relationships to understand how mutations cause disease. Deep mutational scanning (DMS) is a promising strategy to map how amino acids contribute to protein structure and function and to advance clinical variant interpretation. Here, we introduce 7429 single-residue missense mutations into the inward rectifier K + + + channel Kir2.1 and determine how this affects folding, assembly, and trafficking, as well as regulation by allosteric ligands and ion conduction. Our data provide high-resolution information on a cotranslationally folded biogenic unit, trafficking and quality control signals, and segregated roles of different structural elements in fold stability and function. We show that Kir2.1 surface trafficking mutants are underrepresented in variant effect databases, which has implications for clinical practice. By comparing fitness scores with expert-reviewed variant effects, we can predict the pathogenicity of ‘variants of unknown significance’ and disease mechanisms of known pathogenic mutations. Our study in Kir2.1 provides a blueprint for how multiparametric DMS can help us understand the mechanistic basis of genetic disorders and the structure–function relationships of proteins.}, + language = {en}, + urldate = {2023-10-16}, + journal = {eLife}, + author = {Coyote-Maestas, Willow and Nedrud, David and He, Yungui and Schmidt, Daniel}, + month = may, + year = {2022}, + pages = {e76903}, + file = {Submitted Version:/Users/admin/Zotero/storage/AB4N657M/Coyote-Maestas et al. - 2022 - Determinants of trafficking, conduction, and disea.pdf:application/pdf}, +} + +@techreport{li_deep_2023, + type = {preprint}, + title = {Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza {A} virus {PB1} protein}, + url = {http://biorxiv.org/lookup/doi/10.1101/2023.08.27.554986}, + abstract = {Abstract + The influenza virus polymerase is central to influenza virus evolution. Adaptive mutations within the polymerase are often a prerequisite for efficient spread of novel animal-derived viruses in human populations. The polymerase also determines fidelity, and therefore the rate at which the virus will acquire mutations that lead to host range expansion, drug resistance, or antigenic drift. Despite its importance to viral replication and evolution, our understanding of the mutational effects and associated constraints on the influenza RNA-dependent RNA polymerase (RdRp) is relatively limited. We performed deep mutational scanning of the A/WSN/1933(H1N1) PB1, generating a library of 95.4\% of amino acid substitutions at 757 sites. After accuracy filters, we were able to measure replicative fitness for 13,354 (84\%) of all possible amino acid substitutions, and 16 were validated by results from pairwise competition assays. Functional and structural constraints were better revealed by individual sites involved in RNA or protein interactions than by major subdomains defined by sequence conservation. Mutational tolerance, as defined by site entropy, was correlated with evolutionary potential, as captured by diversity in available H1N1 sequences. Of 29 beneficial sites, many have either been identified in the natural evolution of PB1 or shown experimentally to have important impacts on replication and adaptation. Accessibility of amino acid substitutions by single nucleotide mutation was a key factor in determining whether mutations appeared in natural PB1 evolution. Our work provides a comprehensive map of mutational effects on a viral RdRp and a valuable resource for subsequent studies of influenza replication and evolution.}, + language = {en}, + urldate = {2023-10-16}, + institution = {Microbiology}, + author = {Li, Yuan and Arcos, Sarah and Sabsay, Kimberly R. and Te Velthuis, Aartjan J.W. and Lauring, Adam S.}, + month = aug, + year = {2023}, + doi = {10.1101/2023.08.27.554986}, + file = {Submitted Version:/Users/admin/Zotero/storage/8WM9KUK5/Li et al. - 2023 - Deep mutational scanning reveals the functional co.pdf:application/pdf}, +} + +@article{meitlis_multiplexed_2020, + title = {Multiplexed {Functional} {Assessment} of {Genetic} {Variants} in {CARD11}}, + volume = {107}, + issn = {00029297}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0002929720303736}, + doi = {10.1016/j.ajhg.2020.10.015}, + language = {en}, + number = {6}, + urldate = {2023-10-16}, + journal = {The American Journal of Human Genetics}, + author = {Meitlis, Iana and Allenspach, Eric J. and Bauman, Bradly M. and Phan, Isabelle Q. and Dabbah, Gina and Schmitt, Erica G. and Camp, Nathan D. and Torgerson, Troy R. and Nickerson, Deborah A. and Bamshad, Michael J. and Hagin, David and Luthers, Christopher R. and Stinson, Jeffrey R. and Gray, Jessica and Lundgren, Ingrid and Church, Joseph A. and Butte, Manish J. and Jordan, Mike B. and Aceves, Seema S. and Schwartz, Daniella M. and Milner, Joshua D. and Schuval, Susan and Skoda-Smith, Suzanne and Cooper, Megan A. and Starita, Lea M. and Rawlings, David J. and Snow, Andrew L. and James, Richard G.}, + month = dec, + year = {2020}, + pages = {1029--1043}, + file = {Full Text:/Users/admin/Zotero/storage/CFKJ6Y9I/Meitlis et al. - 2020 - Multiplexed Functional Assessment of Genetic Varia.pdf:application/pdf}, +} + +@article{uk_monogenic_diabetes_consortium_prospective_2016, + title = {Prospective functional classification of all possible missense variants in {PPARG}}, + volume = {48}, + issn = {1061-4036, 1546-1718}, + url = {https://www.nature.com/articles/ng.3700}, + doi = {10.1038/ng.3700}, + language = {en}, + number = {12}, + urldate = {2023-10-16}, + journal = {Nature Genetics}, + author = {{UK Monogenic Diabetes Consortium} and {Myocardial Infarction Genetics Consortium} and {UK Congenital Lipodystrophy Consortium} and Majithia, Amit R and Tsuda, Ben and Agostini, Maura and Gnanapradeepan, Keerthana and Rice, Robert and Peloso, Gina and Patel, Kashyap A and Zhang, Xiaolan and Broekema, Marjoleine F and Patterson, Nick and Duby, Marc and Sharpe, Ted and Kalkhoven, Eric and Rosen, Evan D and Barroso, Inês and Ellard, Sian and Kathiresan, Sekar and O'Rahilly, Stephen and Chatterjee, Krishna and Florez, Jose C and Mikkelsen, Tarjei and Savage, David B and Altshuler, David}, + month = dec, + year = {2016}, + pages = {1570--1575}, + file = {Accepted Version:/Users/admin/Zotero/storage/MEZTM6WW/UK Monogenic Diabetes Consortium et al. - 2016 - Prospective functional classification of all possi.pdf:application/pdf}, +} + +@techreport{estevam_conserved_2023, + type = {preprint}, + title = {Conserved regulatory motifs in the juxtamembrane domain and kinase {N}-lobe revealed through deep mutational scanning of the {MET} receptor tyrosine kinase domain}, + url = {http://biorxiv.org/lookup/doi/10.1101/2023.08.03.551866}, + abstract = {Abstract + MET is a receptor tyrosine kinase (RTK) responsible for initiating signaling pathways involved in development and wound repair. MET activation relies on ligand binding to the extracellular receptor, which prompts dimerization, intracellular phosphorylation, and recruitment of associated signaling proteins. Mutations, which are predominantly observed clinically in the intracellular juxtamembrane and kinase domains, can disrupt typical MET regulatory mechanisms. Understanding how juxtamembrane variants, such as exon 14 skipping (METΔEx14), and rare kinase domain mutations can increase signaling, often leading to cancer, remains a challenge. Here, we perform a parallel deep mutational scan (DMS) of MET intracellular kinase domain in two fusion protein backgrounds: wild type and METΔEx14. Our comparative approach has revealed a critical hydrophobic interaction between a juxtamembrane segment and the kinase ⍺C helix, pointing to differences in regulatory mechanisms between MET and other RTKs. Additionally, we have uncovered a β5 motif that acts as a structural pivot for kinase domain activation in MET and other TAM family of kinases. We also describe a number of previously unknown activating mutations, aiding the effort to annotate driver, passenger, and drug resistance mutations in the MET kinase domain.}, + language = {en}, + urldate = {2023-10-16}, + institution = {Molecular Biology}, + author = {Estevam, Gabriella O. and Linossi, Edmond M. and Macdonald, Christian B. and Espinoza, Carla A. and Michaud, Jennifer M. and Coyote-Maestas, Willow and Collisson, Eric A. and Jura, Natalia and Fraser, James S.}, + month = aug, + year = {2023}, + doi = {10.1101/2023.08.03.551866}, + file = {Full Text:/Users/admin/Zotero/storage/S7UXHT33/Estevam et al. - 2023 - Conserved regulatory motifs in the juxtamembrane d.pdf:application/pdf}, +} + +@article{miller_allosteric_2022, + title = {Allosteric inhibition of {PPM1D} serine/threonine phosphatase via an altered conformational state}, + volume = {13}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-022-30463-9}, + doi = {10.1038/s41467-022-30463-9}, + abstract = {Abstract + + PPM1D + encodes a serine/threonine phosphatase that regulates numerous pathways including the DNA damage response and p53. Activating mutations and amplification of + PPM1D + are found across numerous cancer types. GSK2830371 is a potent and selective allosteric inhibitor of PPM1D, but its mechanism of binding and inhibition of catalytic activity are unknown. Here we use computational, biochemical and functional genetic studies to elucidate the molecular basis of GSK2830371 activity. These data confirm that GSK2830371 binds an allosteric site of PPM1D with high affinity. By further incorporating data from hydrogen deuterium exchange mass spectrometry and sedimentation velocity analytical ultracentrifugation, we demonstrate that PPM1D exists in an equilibrium between two conformations that are defined by the movement of the flap domain, which is required for substrate recognition. A hinge region was identified that is critical for switching between the two conformations and was directly implicated in the high-affinity binding of GSK2830371 to PPM1D. We propose that the two conformations represent active and inactive forms of the protein reflected by the position of the flap, and that binding of GSK2830371 shifts the equilibrium to the inactive form. Finally, we found that C-terminal truncating mutations proximal to residue 400 result in destabilization of the protein via loss of a stabilizing N- and C-terminal interaction, consistent with the observation from human genetic data that nearly all + PPM1D + mutations in cancer are truncating and occur distal to residue 400. Taken together, our findings elucidate the mechanism by which binding of a small molecule to an allosteric site of PPM1D inhibits its activity and provides insights into the biology of PPM1D.}, + language = {en}, + number = {1}, + urldate = {2023-10-16}, + journal = {Nature Communications}, + author = {Miller, Peter G. and Sathappa, Murugappan and Moroco, Jamie A. and Jiang, Wei and Qian, Yue and Iqbal, Sumaiya and Guo, Qi and Giacomelli, Andrew O. and Shaw, Subrata and Vernier, Camille and Bajrami, Besnik and Yang, Xiaoping and Raffier, Cerise and Sperling, Adam S. and Gibson, Christopher J. and Kahn, Josephine and Jin, Cyrus and Ranaghan, Matthew and Caliman, Alisha and Brousseau, Merissa and Fischer, Eric S. and Lintner, Robert and Piccioni, Federica and Campbell, Arthur J. and Root, David E. and Garvie, Colin W. and Ebert, Benjamin L.}, + month = jun, + year = {2022}, + pages = {3778}, + file = {Full Text:/Users/admin/Zotero/storage/93RHZ3F3/Miller et al. - 2022 - Allosteric inhibition of PPM1D serinethreonine ph.pdf:application/pdf}, +} + +@article{erwood_saturation_2022, + title = {Saturation variant interpretation using {CRISPR} prime editing}, + volume = {40}, + issn = {1087-0156, 1546-1696}, + url = {https://www.nature.com/articles/s41587-021-01201-1}, + doi = {10.1038/s41587-021-01201-1}, + language = {en}, + number = {6}, + urldate = {2023-10-16}, + journal = {Nature Biotechnology}, + author = {Erwood, Steven and Bily, Teija M. I. and Lequyer, Jason and Yan, Joyce and Gulati, Nitya and Brewer, Reid A. and Zhou, Liangchi and Pelletier, Laurence and Ivakine, Evgueni A. and Cohn, Ronald D.}, + month = jun, + year = {2022}, + pages = {885--895}, + file = {Submitted Version:/Users/admin/Zotero/storage/Q5IRIPHG/Erwood et al. - 2022 - Saturation variant interpretation using CRISPR pri.pdf:application/pdf}, +} + +@phdthesis{jiang_exhaustive_2019, + title = {Exhaustive {Mapping} of {Missense} {Variation} in {Coronary} {Heart} {Disease}-related {Genes}}, + url = {https://hdl.handle.net/1807/98076}, + abstract = {Coronary heart disease (CHD) is a leading cause of death worldwide. A major risk factor of CHD is high low-density lipoprotein cholesterol (LDL-C), which is the defining feature of familial hypercholesterolemia, a genetic disorder characterized by mutations in genes involved in cholesterol uptake, such as LDLRAP1. The risk of CHD can be reduced by LDL-C-lowering medications called statins that potently inhibit HMG-CoA reductase (HMGCR), the enzyme that catalyzes the rate-limiting reaction of the mevalonate pathway. The study of genetic variation in CHD-related genes may contribute to early detection or prevention of CHD. I exhaustively mapped missense variant effects of two CHD-related genes, HMGCR and LDLRAP1. Proactive maps of human HMGCR may be able to model patient response to statins and identify the statin with the greatest likelihood of efficacy. Proactive maps of LDLRAP1 may identify pathogenic variation of autosomal recessive hypercholesterolemia (ARH) for early diagnosis of ARH.}, + school = {University of Toronto}, + author = {Jiang, Rosanna Junchen}, + month = nov, + year = {2019}, +} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Moving assays over from references.bib +@inproceedings{chen_hotprotein_2022, + title = {{HotProtein}: {A} {Novel} {Framework} for {Protein} {Thermostability} {Prediction} and {Editing}}, + shorttitle = {{HotProtein}}, + url = {https://openreview.net/forum?id=RtV_iEbWeGE}, + abstract = {The molecular basis of protein thermal stability is only partially understood and has major significance for drug and vaccine discovery. The lack of datasets and standardized benchmarks considerably limits learning-based discovery methods. We present \${\textbackslash}texttt\{HotProtein\}\$, a large-scale protein dataset with {\textbackslash}textit\{growth temperature\} annotations of thermostability, containing \$182\$K amino acid sequences and \$3\$K folded structures from \$230\$ different species with a wide temperature range \$-20{\textasciicircum}\{{\textbackslash}circ\}{\textbackslash}texttt\{C\}{\textbackslash}sim 120{\textasciicircum}\{{\textbackslash}circ\}{\textbackslash}texttt\{C\}\$. Due to functional domain differences and data scarcity within each species, existing methods fail to generalize well on our dataset. We address this problem through a novel learning framework, consisting of (\$1\$) Protein structure-aware pre-training (SAP) which leverages 3D information to enhance sequence-based pre-training; (\$2\$) Factorized sparse tuning (FST) that utilizes low-rank and sparse priors as an implicit regularization, together with feature augmentations. Extensive empirical studies demonstrate that our framework improves thermostability prediction compared to other deep learning models. Finally, we propose a novel editing algorithm to efficiently generate positive amino acid mutations that improve thermostability.}, + language = {en}, + urldate = {2023-06-05}, + author = {Chen, Tianlong and Gong, Chengyue and Diaz, Daniel Jesus and Chen, Xuxi and Wells, Jordan Tyler and Liu, Qiang and Wang, Zhangyang and Ellington, Andrew and Dimakis, Alex and Klivans, Adam}, + month = oct, + year = {2022}, + file = {Full Text PDF:/Users/pascalnotin/Zotero/storage/WIGJZS6V/Chen et al. - 2022 - HotProtein A Novel Framework for Protein Thermost.pdf:application/pdf}, +} diff --git a/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level.csv b/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level.csv new file mode 100644 index 0000000..ec86879 --- /dev/null +++ b/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level.csv @@ -0,0 +1,67 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,Tranception Embeddings +B1LPA6_ECOSM_Russ_2020_indels,0.547,0.569,0.793 +BLAT_ECOLX_Gonzalez_2019_indels,0.366,0.313,0.273 +CAPSD_AAV2S_Sinai_2021_designed_indels,0.216,0.205,0.25 +CAPSD_AAV2S_Sinai_2021_library_indels,0.434,0.414,0.299 +HIS7_YEAST_Pokusaeva_2019_indels,0.161,0.198,0.158 +PTEN_HUMAN_Mighell_2018_indels,0.274,0.467,0.365 +P53_HUMAN_Kotler_2018_indels,0.315,0.383,0.348 +KCNJ2_MOUSE_Macdonald_2022_indels,0.517,0.671,0.391 +Q8EG35_SHEON_Campbell_2022_indels,0.57,0.624,0.512 +A4_HUMAN_Seuma_2022_indels,0.262,0.36,0.249 +S22A1_HUMAN_Yee_2023_abundance_indels,0.42,0.613,0.44 +S22A1_HUMAN_Yee_2023_activity_indels,0.427,0.606,0.397 +AMFR_HUMAN_Rocklin_2023_4G3O_indels,0.267,0.467,0.848 +ARGR_ECOLI_Rocklin_2023_1AOY_indels,0.138,0.259,0.204 +BBC1_YEAST_Rocklin_2023_1TG0_indels,0.224,0.328,0.391 +BCHB_CHLTE_Rocklin_2023_2KRU_indels,0.194,0.503,0.449 +CATR_CHLRE_Rocklin_2023_2AMI_indels,0.192,0.362,0.247 +CBPA2_HUMAN_Rocklin_2023_1O6X_indels,0.131,0.292,0.346 +CBX4_HUMAN_Rocklin_2023_2K28_indels,0.246,0.423,0.409 +CSN4_MOUSE_Rocklin_2023_1UFM_indels,0.087,0.292,0.288 +CUE1_YEAST_Rocklin_2023_2MYX_indels,0.216,0.361,0.56 +DN7A_SACS2_Rocklin_2023_1JIC_indels,0.282,0.547,0.549 +DNJA1_HUMAN_Rocklin_2023_2LO1_indels,0.128,0.25,0.162 +DOCK1_MOUSE_Rocklin_2023_2M0Y_indels,0.281,0.491,0.353 +EPHB2_HUMAN_Rocklin_2023_1F0M_indels,0.125,0.299,0.307 +FECA_ECOLI_Rocklin_2023_2D1U_indels,0.488,0.508,0.592 +HCP_LAMBD_Rocklin_2023_2L6Q_indels,0.091,0.267,0.238 +HECD1_HUMAN_Rocklin_2023_3DKM_indels,0.63,0.568,0.645 +ILF3_HUMAN_Rocklin_2023_2L33_indels,0.135,0.304,0.245 +MAFG_MOUSE_Rocklin_2023_1K1V_indels,0.257,0.548,0.493 +MBD11_ARATH_Rocklin_2023_6ACV_indels,0.272,0.377,0.597 +MYO3_YEAST_Rocklin_2023_2BTT_indels,0.392,0.612,0.514 +NKX31_HUMAN_Rocklin_2023_2L9R_indels,0.153,0.367,0.156 +NUSA_ECOLI_Rocklin_2023_1WCL_indels,0.154,0.284,0.527 +NUSG_MYCTU_Rocklin_2023_2MI6_indels,0.22,0.388,0.327 +OBSCN_HUMAN_Rocklin_2023_1V1C_indels,0.178,0.282,0.342 +ODP2_GEOSE_Rocklin_2023_1W4G_indels,0.46,0.682,0.543 +OTU7A_HUMAN_Rocklin_2023_2L2D_indels,0.216,0.422,0.56 +PIN1_HUMAN_Rocklin_2023_1I6C_indels,0.113,0.389,0.269 +PITX2_HUMAN_Rocklin_2023_2L7M_indels,0.495,0.773,0.605 +PKN1_HUMAN_Rocklin_2023_1URF_indels,0.082,0.178,0.156 +POLG_PESV_Rocklin_2023_2MXD_indels,0.198,0.495,0.384 +PR40A_HUMAN_Rocklin_2023_1UZC_indels,0.168,0.312,0.279 +PSAE_SYNP2_Rocklin_2023_1PSE_indels,0.24,0.259,0.311 +RAD_ANTMA_Rocklin_2023_2CJJ_indels,0.193,0.33,0.294 +RCD1_ARATH_Rocklin_2023_5OAO_indels,0.251,0.359,0.424 +RD23A_HUMAN_Rocklin_2023_1IFY_indels,0.154,0.349,0.29 +RPC1_BP434_Rocklin_2023_1R69_indels,0.484,0.511,0.431 +RS15_GEOSE_Rocklin_2023_1A32_indels,0.203,0.355,0.331 +SAV1_MOUSE_Rocklin_2023_2YSB_indels,0.531,0.708,0.648 +SDA_BACSU_Rocklin_2023_1PV0_indels,0.113,0.197,0.184 +SOX30_HUMAN_Rocklin_2023_7JJK_indels,0.195,0.336,0.566 +SPG2_STRSG_Rocklin_2023_5UBS_indels,0.16,0.256,0.262 +SPTN1_CHICK_Rocklin_2023_1TUD_indels,0.237,0.332,0.471 +SQSTM_MOUSE_Rocklin_2023_2RRU_indels,0.225,0.559,0.442 +SR43C_ARATH_Rocklin_2023_2N88_indels,0.223,0.323,0.235 +SRBS1_HUMAN_Rocklin_2023_2O2W_indels,0.153,0.366,0.201 +TCRG1_MOUSE_Rocklin_2023_1E0L_indels,0.118,0.22,0.329 +THO1_YEAST_Rocklin_2023_2WQG_indels,0.233,0.421,0.623 +TNKS2_HUMAN_Rocklin_2023_5JRT_indels,0.22,0.405,0.477 +UBE4B_HUMAN_Rocklin_2023_3L1X_indels,0.348,0.431,0.533 +UBR5_HUMAN_Rocklin_2023_1I2T_indels,0.196,0.344,0.288 +VG08_BPP22_Rocklin_2023_2GP8_indels,0.187,0.291,0.394 +VILI_CHICK_Rocklin_2023_1YU5_indels,0.133,0.215,0.184 +VRPI_BPT7_Rocklin_2023_2WNM_indels,0.235,0.496,0.474 +YNZC_BACSU_Rocklin_2023_2JVD_indels,0.1,0.343,0.303 diff --git a/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level_fold_random_5.csv b/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level_fold_random_5.csv new file mode 100644 index 0000000..ec86879 --- /dev/null +++ b/benchmarks/DMS_supervised/indels/MSE/DMS_indels_MSE_DMS_level_fold_random_5.csv @@ -0,0 +1,67 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,Tranception Embeddings +B1LPA6_ECOSM_Russ_2020_indels,0.547,0.569,0.793 +BLAT_ECOLX_Gonzalez_2019_indels,0.366,0.313,0.273 +CAPSD_AAV2S_Sinai_2021_designed_indels,0.216,0.205,0.25 +CAPSD_AAV2S_Sinai_2021_library_indels,0.434,0.414,0.299 +HIS7_YEAST_Pokusaeva_2019_indels,0.161,0.198,0.158 +PTEN_HUMAN_Mighell_2018_indels,0.274,0.467,0.365 +P53_HUMAN_Kotler_2018_indels,0.315,0.383,0.348 +KCNJ2_MOUSE_Macdonald_2022_indels,0.517,0.671,0.391 +Q8EG35_SHEON_Campbell_2022_indels,0.57,0.624,0.512 +A4_HUMAN_Seuma_2022_indels,0.262,0.36,0.249 +S22A1_HUMAN_Yee_2023_abundance_indels,0.42,0.613,0.44 +S22A1_HUMAN_Yee_2023_activity_indels,0.427,0.606,0.397 +AMFR_HUMAN_Rocklin_2023_4G3O_indels,0.267,0.467,0.848 +ARGR_ECOLI_Rocklin_2023_1AOY_indels,0.138,0.259,0.204 +BBC1_YEAST_Rocklin_2023_1TG0_indels,0.224,0.328,0.391 +BCHB_CHLTE_Rocklin_2023_2KRU_indels,0.194,0.503,0.449 +CATR_CHLRE_Rocklin_2023_2AMI_indels,0.192,0.362,0.247 +CBPA2_HUMAN_Rocklin_2023_1O6X_indels,0.131,0.292,0.346 +CBX4_HUMAN_Rocklin_2023_2K28_indels,0.246,0.423,0.409 +CSN4_MOUSE_Rocklin_2023_1UFM_indels,0.087,0.292,0.288 +CUE1_YEAST_Rocklin_2023_2MYX_indels,0.216,0.361,0.56 +DN7A_SACS2_Rocklin_2023_1JIC_indels,0.282,0.547,0.549 +DNJA1_HUMAN_Rocklin_2023_2LO1_indels,0.128,0.25,0.162 +DOCK1_MOUSE_Rocklin_2023_2M0Y_indels,0.281,0.491,0.353 +EPHB2_HUMAN_Rocklin_2023_1F0M_indels,0.125,0.299,0.307 +FECA_ECOLI_Rocklin_2023_2D1U_indels,0.488,0.508,0.592 +HCP_LAMBD_Rocklin_2023_2L6Q_indels,0.091,0.267,0.238 +HECD1_HUMAN_Rocklin_2023_3DKM_indels,0.63,0.568,0.645 +ILF3_HUMAN_Rocklin_2023_2L33_indels,0.135,0.304,0.245 +MAFG_MOUSE_Rocklin_2023_1K1V_indels,0.257,0.548,0.493 +MBD11_ARATH_Rocklin_2023_6ACV_indels,0.272,0.377,0.597 +MYO3_YEAST_Rocklin_2023_2BTT_indels,0.392,0.612,0.514 +NKX31_HUMAN_Rocklin_2023_2L9R_indels,0.153,0.367,0.156 +NUSA_ECOLI_Rocklin_2023_1WCL_indels,0.154,0.284,0.527 +NUSG_MYCTU_Rocklin_2023_2MI6_indels,0.22,0.388,0.327 +OBSCN_HUMAN_Rocklin_2023_1V1C_indels,0.178,0.282,0.342 +ODP2_GEOSE_Rocklin_2023_1W4G_indels,0.46,0.682,0.543 +OTU7A_HUMAN_Rocklin_2023_2L2D_indels,0.216,0.422,0.56 +PIN1_HUMAN_Rocklin_2023_1I6C_indels,0.113,0.389,0.269 +PITX2_HUMAN_Rocklin_2023_2L7M_indels,0.495,0.773,0.605 +PKN1_HUMAN_Rocklin_2023_1URF_indels,0.082,0.178,0.156 +POLG_PESV_Rocklin_2023_2MXD_indels,0.198,0.495,0.384 +PR40A_HUMAN_Rocklin_2023_1UZC_indels,0.168,0.312,0.279 +PSAE_SYNP2_Rocklin_2023_1PSE_indels,0.24,0.259,0.311 +RAD_ANTMA_Rocklin_2023_2CJJ_indels,0.193,0.33,0.294 +RCD1_ARATH_Rocklin_2023_5OAO_indels,0.251,0.359,0.424 +RD23A_HUMAN_Rocklin_2023_1IFY_indels,0.154,0.349,0.29 +RPC1_BP434_Rocklin_2023_1R69_indels,0.484,0.511,0.431 +RS15_GEOSE_Rocklin_2023_1A32_indels,0.203,0.355,0.331 +SAV1_MOUSE_Rocklin_2023_2YSB_indels,0.531,0.708,0.648 +SDA_BACSU_Rocklin_2023_1PV0_indels,0.113,0.197,0.184 +SOX30_HUMAN_Rocklin_2023_7JJK_indels,0.195,0.336,0.566 +SPG2_STRSG_Rocklin_2023_5UBS_indels,0.16,0.256,0.262 +SPTN1_CHICK_Rocklin_2023_1TUD_indels,0.237,0.332,0.471 +SQSTM_MOUSE_Rocklin_2023_2RRU_indels,0.225,0.559,0.442 +SR43C_ARATH_Rocklin_2023_2N88_indels,0.223,0.323,0.235 +SRBS1_HUMAN_Rocklin_2023_2O2W_indels,0.153,0.366,0.201 +TCRG1_MOUSE_Rocklin_2023_1E0L_indels,0.118,0.22,0.329 +THO1_YEAST_Rocklin_2023_2WQG_indels,0.233,0.421,0.623 +TNKS2_HUMAN_Rocklin_2023_5JRT_indels,0.22,0.405,0.477 +UBE4B_HUMAN_Rocklin_2023_3L1X_indels,0.348,0.431,0.533 +UBR5_HUMAN_Rocklin_2023_1I2T_indels,0.196,0.344,0.288 +VG08_BPP22_Rocklin_2023_2GP8_indels,0.187,0.291,0.394 +VILI_CHICK_Rocklin_2023_1YU5_indels,0.133,0.215,0.184 +VRPI_BPT7_Rocklin_2023_2WNM_indels,0.235,0.496,0.474 +YNZC_BACSU_Rocklin_2023_2JVD_indels,0.1,0.343,0.303 diff --git a/benchmarks/DMS_supervised/indels/MSE/Summary_performance_DMS_indels_MSE.csv b/benchmarks/DMS_supervised/indels/MSE/Summary_performance_DMS_indels_MSE.csv new file mode 100644 index 0000000..328b4bc --- /dev/null +++ b/benchmarks/DMS_supervised/indels/MSE/Summary_performance_DMS_indels_MSE.csv @@ -0,0 +1,4 @@ +Model_rank,Model_name,Model type,Average_MSE,Bootstrap_standard_error_MSE,Average_MSE_fold_random_5,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,References,Model details +1,ESM-1v Embeddings,Embedding,0.365,0.0,0.365,0.416,N/A,0.468,0.347,0.229,0.248,0.301,0.25,0.262,0.234,0.268,0.253,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v Embeddings +2,Tranception Embeddings,Embedding,0.41,0.022,0.41,0.518,N/A,0.415,0.313,0.391,0.385,0.291,0.409,0.412,0.378,0.398,0.366,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception Embeddings +3,MSA Transformer Embeddings,Embedding,0.486,0.013,0.486,0.547,N/A,0.642,0.366,0.389,0.396,0.351,0.407,0.411,0.391,0.399,0.395,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer Embeddings diff --git a/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level.csv b/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level.csv new file mode 100644 index 0000000..ae37987 --- /dev/null +++ b/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level.csv @@ -0,0 +1,67 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,Tranception Embeddings +B1LPA6_ECOSM_Russ_2020_indels,0.591,0.581,0.536 +BLAT_ECOLX_Gonzalez_2019_indels,0.78,0.814,0.78 +CAPSD_AAV2S_Sinai_2021_designed_indels,0.834,0.832,0.828 +CAPSD_AAV2S_Sinai_2021_library_indels,0.656,0.655,0.708 +HIS7_YEAST_Pokusaeva_2019_indels,0.623,0.62,0.597 +PTEN_HUMAN_Mighell_2018_indels,0.821,0.767,0.789 +P53_HUMAN_Kotler_2018_indels,0.68,0.679,0.653 +KCNJ2_MOUSE_Macdonald_2022_indels,0.678,0.563,0.733 +Q8EG35_SHEON_Campbell_2022_indels,0.764,0.693,0.782 +A4_HUMAN_Seuma_2022_indels,0.816,0.746,0.813 +S22A1_HUMAN_Yee_2023_abundance_indels,0.779,0.664,0.773 +S22A1_HUMAN_Yee_2023_activity_indels,0.705,0.636,0.696 +AMFR_HUMAN_Rocklin_2023_4G3O_indels,0.757,0.636,0.621 +ARGR_ECOLI_Rocklin_2023_1AOY_indels,0.926,0.87,0.9 +BBC1_YEAST_Rocklin_2023_1TG0_indels,0.849,0.795,0.811 +BCHB_CHLTE_Rocklin_2023_2KRU_indels,0.881,0.718,0.776 +CATR_CHLRE_Rocklin_2023_2AMI_indels,0.914,0.847,0.89 +CBPA2_HUMAN_Rocklin_2023_1O6X_indels,0.937,0.847,0.846 +CBX4_HUMAN_Rocklin_2023_2K28_indels,0.874,0.786,0.782 +CSN4_MOUSE_Rocklin_2023_1UFM_indels,0.9,0.813,0.831 +CUE1_YEAST_Rocklin_2023_2MYX_indels,0.759,0.639,0.588 +DN7A_SACS2_Rocklin_2023_1JIC_indels,0.858,0.707,0.74 +DNJA1_HUMAN_Rocklin_2023_2LO1_indels,0.93,0.879,0.925 +DOCK1_MOUSE_Rocklin_2023_2M0Y_indels,0.836,0.719,0.823 +EPHB2_HUMAN_Rocklin_2023_1F0M_indels,0.907,0.828,0.81 +FECA_ECOLI_Rocklin_2023_2D1U_indels,0.729,0.679,0.677 +HCP_LAMBD_Rocklin_2023_2L6Q_indels,0.954,0.858,0.906 +HECD1_HUMAN_Rocklin_2023_3DKM_indels,0.708,0.721,0.673 +ILF3_HUMAN_Rocklin_2023_2L33_indels,0.938,0.858,0.893 +MAFG_MOUSE_Rocklin_2023_1K1V_indels,0.843,0.696,0.705 +MBD11_ARATH_Rocklin_2023_6ACV_indels,0.84,0.813,0.72 +MYO3_YEAST_Rocklin_2023_2BTT_indels,0.761,0.599,0.771 +NKX31_HUMAN_Rocklin_2023_2L9R_indels,0.853,0.746,0.86 +NUSA_ECOLI_Rocklin_2023_1WCL_indels,0.917,0.847,0.781 +NUSG_MYCTU_Rocklin_2023_2MI6_indels,0.831,0.712,0.768 +OBSCN_HUMAN_Rocklin_2023_1V1C_indels,0.913,0.867,0.85 +ODP2_GEOSE_Rocklin_2023_1W4G_indels,0.83,0.733,0.812 +OTU7A_HUMAN_Rocklin_2023_2L2D_indels,0.805,0.693,0.742 +PIN1_HUMAN_Rocklin_2023_1I6C_indels,0.839,0.754,0.793 +PITX2_HUMAN_Rocklin_2023_2L7M_indels,0.678,0.579,0.657 +PKN1_HUMAN_Rocklin_2023_1URF_indels,0.925,0.879,0.876 +POLG_PESV_Rocklin_2023_2MXD_indels,0.93,0.763,0.812 +PR40A_HUMAN_Rocklin_2023_1UZC_indels,0.893,0.827,0.843 +PSAE_SYNP2_Rocklin_2023_1PSE_indels,0.821,0.822,0.85 +RAD_ANTMA_Rocklin_2023_2CJJ_indels,0.87,0.799,0.847 +RCD1_ARATH_Rocklin_2023_5OAO_indels,0.861,0.798,0.771 +RD23A_HUMAN_Rocklin_2023_1IFY_indels,0.908,0.792,0.844 +RPC1_BP434_Rocklin_2023_1R69_indels,0.658,0.609,0.677 +RS15_GEOSE_Rocklin_2023_1A32_indels,0.93,0.838,0.846 +SAV1_MOUSE_Rocklin_2023_2YSB_indels,0.692,0.569,0.727 +SDA_BACSU_Rocklin_2023_1PV0_indels,0.946,0.907,0.92 +SOX30_HUMAN_Rocklin_2023_7JJK_indels,0.875,0.81,0.826 +SPG2_STRSG_Rocklin_2023_5UBS_indels,0.922,0.862,0.869 +SPTN1_CHICK_Rocklin_2023_1TUD_indels,0.865,0.806,0.81 +SQSTM_MOUSE_Rocklin_2023_2RRU_indels,0.859,0.675,0.773 +SR43C_ARATH_Rocklin_2023_2N88_indels,0.879,0.821,0.87 +SRBS1_HUMAN_Rocklin_2023_2O2W_indels,0.927,0.814,0.916 +TCRG1_MOUSE_Rocklin_2023_1E0L_indels,0.9,0.834,0.792 +THO1_YEAST_Rocklin_2023_2WQG_indels,0.833,0.687,0.698 +TNKS2_HUMAN_Rocklin_2023_5JRT_indels,0.852,0.746,0.726 +UBE4B_HUMAN_Rocklin_2023_3L1X_indels,0.733,0.647,0.584 +UBR5_HUMAN_Rocklin_2023_1I2T_indels,0.893,0.82,0.869 +VG08_BPP22_Rocklin_2023_2GP8_indels,0.851,0.824,0.78 +VILI_CHICK_Rocklin_2023_1YU5_indels,0.932,0.899,0.91 +VRPI_BPT7_Rocklin_2023_2WNM_indels,0.873,0.725,0.77 +YNZC_BACSU_Rocklin_2023_2JVD_indels,0.878,0.759,0.839 diff --git a/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level_fold_random_5.csv b/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level_fold_random_5.csv new file mode 100644 index 0000000..ae37987 --- /dev/null +++ b/benchmarks/DMS_supervised/indels/Spearman/DMS_indels_Spearman_DMS_level_fold_random_5.csv @@ -0,0 +1,67 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,Tranception Embeddings +B1LPA6_ECOSM_Russ_2020_indels,0.591,0.581,0.536 +BLAT_ECOLX_Gonzalez_2019_indels,0.78,0.814,0.78 +CAPSD_AAV2S_Sinai_2021_designed_indels,0.834,0.832,0.828 +CAPSD_AAV2S_Sinai_2021_library_indels,0.656,0.655,0.708 +HIS7_YEAST_Pokusaeva_2019_indels,0.623,0.62,0.597 +PTEN_HUMAN_Mighell_2018_indels,0.821,0.767,0.789 +P53_HUMAN_Kotler_2018_indels,0.68,0.679,0.653 +KCNJ2_MOUSE_Macdonald_2022_indels,0.678,0.563,0.733 +Q8EG35_SHEON_Campbell_2022_indels,0.764,0.693,0.782 +A4_HUMAN_Seuma_2022_indels,0.816,0.746,0.813 +S22A1_HUMAN_Yee_2023_abundance_indels,0.779,0.664,0.773 +S22A1_HUMAN_Yee_2023_activity_indels,0.705,0.636,0.696 +AMFR_HUMAN_Rocklin_2023_4G3O_indels,0.757,0.636,0.621 +ARGR_ECOLI_Rocklin_2023_1AOY_indels,0.926,0.87,0.9 +BBC1_YEAST_Rocklin_2023_1TG0_indels,0.849,0.795,0.811 +BCHB_CHLTE_Rocklin_2023_2KRU_indels,0.881,0.718,0.776 +CATR_CHLRE_Rocklin_2023_2AMI_indels,0.914,0.847,0.89 +CBPA2_HUMAN_Rocklin_2023_1O6X_indels,0.937,0.847,0.846 +CBX4_HUMAN_Rocklin_2023_2K28_indels,0.874,0.786,0.782 +CSN4_MOUSE_Rocklin_2023_1UFM_indels,0.9,0.813,0.831 +CUE1_YEAST_Rocklin_2023_2MYX_indels,0.759,0.639,0.588 +DN7A_SACS2_Rocklin_2023_1JIC_indels,0.858,0.707,0.74 +DNJA1_HUMAN_Rocklin_2023_2LO1_indels,0.93,0.879,0.925 +DOCK1_MOUSE_Rocklin_2023_2M0Y_indels,0.836,0.719,0.823 +EPHB2_HUMAN_Rocklin_2023_1F0M_indels,0.907,0.828,0.81 +FECA_ECOLI_Rocklin_2023_2D1U_indels,0.729,0.679,0.677 +HCP_LAMBD_Rocklin_2023_2L6Q_indels,0.954,0.858,0.906 +HECD1_HUMAN_Rocklin_2023_3DKM_indels,0.708,0.721,0.673 +ILF3_HUMAN_Rocklin_2023_2L33_indels,0.938,0.858,0.893 +MAFG_MOUSE_Rocklin_2023_1K1V_indels,0.843,0.696,0.705 +MBD11_ARATH_Rocklin_2023_6ACV_indels,0.84,0.813,0.72 +MYO3_YEAST_Rocklin_2023_2BTT_indels,0.761,0.599,0.771 +NKX31_HUMAN_Rocklin_2023_2L9R_indels,0.853,0.746,0.86 +NUSA_ECOLI_Rocklin_2023_1WCL_indels,0.917,0.847,0.781 +NUSG_MYCTU_Rocklin_2023_2MI6_indels,0.831,0.712,0.768 +OBSCN_HUMAN_Rocklin_2023_1V1C_indels,0.913,0.867,0.85 +ODP2_GEOSE_Rocklin_2023_1W4G_indels,0.83,0.733,0.812 +OTU7A_HUMAN_Rocklin_2023_2L2D_indels,0.805,0.693,0.742 +PIN1_HUMAN_Rocklin_2023_1I6C_indels,0.839,0.754,0.793 +PITX2_HUMAN_Rocklin_2023_2L7M_indels,0.678,0.579,0.657 +PKN1_HUMAN_Rocklin_2023_1URF_indels,0.925,0.879,0.876 +POLG_PESV_Rocklin_2023_2MXD_indels,0.93,0.763,0.812 +PR40A_HUMAN_Rocklin_2023_1UZC_indels,0.893,0.827,0.843 +PSAE_SYNP2_Rocklin_2023_1PSE_indels,0.821,0.822,0.85 +RAD_ANTMA_Rocklin_2023_2CJJ_indels,0.87,0.799,0.847 +RCD1_ARATH_Rocklin_2023_5OAO_indels,0.861,0.798,0.771 +RD23A_HUMAN_Rocklin_2023_1IFY_indels,0.908,0.792,0.844 +RPC1_BP434_Rocklin_2023_1R69_indels,0.658,0.609,0.677 +RS15_GEOSE_Rocklin_2023_1A32_indels,0.93,0.838,0.846 +SAV1_MOUSE_Rocklin_2023_2YSB_indels,0.692,0.569,0.727 +SDA_BACSU_Rocklin_2023_1PV0_indels,0.946,0.907,0.92 +SOX30_HUMAN_Rocklin_2023_7JJK_indels,0.875,0.81,0.826 +SPG2_STRSG_Rocklin_2023_5UBS_indels,0.922,0.862,0.869 +SPTN1_CHICK_Rocklin_2023_1TUD_indels,0.865,0.806,0.81 +SQSTM_MOUSE_Rocklin_2023_2RRU_indels,0.859,0.675,0.773 +SR43C_ARATH_Rocklin_2023_2N88_indels,0.879,0.821,0.87 +SRBS1_HUMAN_Rocklin_2023_2O2W_indels,0.927,0.814,0.916 +TCRG1_MOUSE_Rocklin_2023_1E0L_indels,0.9,0.834,0.792 +THO1_YEAST_Rocklin_2023_2WQG_indels,0.833,0.687,0.698 +TNKS2_HUMAN_Rocklin_2023_5JRT_indels,0.852,0.746,0.726 +UBE4B_HUMAN_Rocklin_2023_3L1X_indels,0.733,0.647,0.584 +UBR5_HUMAN_Rocklin_2023_1I2T_indels,0.893,0.82,0.869 +VG08_BPP22_Rocklin_2023_2GP8_indels,0.851,0.824,0.78 +VILI_CHICK_Rocklin_2023_1YU5_indels,0.932,0.899,0.91 +VRPI_BPT7_Rocklin_2023_2WNM_indels,0.873,0.725,0.77 +YNZC_BACSU_Rocklin_2023_2JVD_indels,0.878,0.759,0.839 diff --git a/benchmarks/DMS_supervised/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv b/benchmarks/DMS_supervised/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv new file mode 100644 index 0000000..e972c93 --- /dev/null +++ b/benchmarks/DMS_supervised/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv @@ -0,0 +1,4 @@ +Model_rank,Model_name,Model type,Average_Spearman,Bootstrap_standard_error_Spearman,Average_Spearman_fold_random_5,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,References,Model details +1,ESM-1v Embeddings,Embedding,0.752,0.0,0.752,0.705,N/A,0.728,0.718,0.856,0.843,0.747,0.838,0.821,0.842,0.848,0.835,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v Embeddings +2,Tranception Embeddings,Embedding,0.735,0.007,0.735,0.674,N/A,0.753,0.716,0.797,0.797,0.745,0.774,0.767,0.789,0.8,0.785,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception Embeddings +3,MSA Transformer Embeddings,Embedding,0.689,0.004,0.689,0.661,N/A,0.614,0.71,0.769,0.764,0.723,0.752,0.733,0.765,0.776,0.754,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer Embeddings diff --git a/benchmarks/DMS_supervised/substitutions/MSE/DMS_substitutions_MSE_DMS_level.csv b/benchmarks/DMS_supervised/substitutions/MSE/DMS_substitutions_MSE_DMS_level.csv new file mode 100644 index 0000000..6ddfd4a --- /dev/null +++ b/benchmarks/DMS_supervised/substitutions/MSE/DMS_substitutions_MSE_DMS_level.csv @@ -0,0 +1,218 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,DeepSequence + One-Hot Encodings,ESM-1v + One-Hot Encodings,MSA_Transformer + One-Hot Encodings,Tranception + One-Hot Encodings,TranceptEVE + One-Hot Encodings,One-Hot Encodings,ProteinNPT,Tranception Embeddings +A0A140D2T1_ZIKV_Sourisseau_2019,1.174,0.972,1.096,1.123,0.98,1.032,1.019,1.057,0.962,1.025 +A0A192B1T2_9HIV1_Haddox_2018,0.774,0.73,0.814,0.766,0.739,0.739,0.728,1.0,0.607,0.69 +A0A1I9GEU1_NEIME_Kennouche_2019,1.165,1.098,1.01,1.017,1.01,1.019,1.016,1.005,1.254,1.559 +A0A2Z5U3Z0_9INFA_Doud_2016,0.687,0.778,0.737,0.739,0.721,0.718,0.687,0.991,0.787,0.632 +A0A2Z5U3Z0_9INFA_Wu_2014,0.992,1.25,0.988,0.995,0.984,0.979,0.976,1.048,1.074,0.976 +A4D664_9INFA_Soh_2019,1.047,0.985,0.959,1.055,0.978,0.956,0.927,1.04,0.925,0.883 +A4GRB6_PSEAI_Chen_2020,0.442,0.399,0.559,0.559,0.532,0.584,0.533,0.978,0.314,0.503 +AACC1_PSEAI_Dandage_2018,0.785,0.715,0.836,0.767,0.771,0.817,0.782,0.98,0.826,0.902 +ADRB2_HUMAN_Jones_2020,0.753,0.831,0.784,0.761,0.808,0.783,0.767,1.008,0.77,0.767 +AMIE_PSEAE_Wrenbeck_2017,0.589,0.611,0.795,0.663,0.721,0.866,0.814,1.005,0.542,0.705 +LGK_LIPST_Klesmith_2015,0.698,0.675,0.801,0.754,0.731,0.844,0.775,0.996,0.563,0.778 +BLAT_ECOLX_Deng_2012,0.714,0.674,0.718,0.741,0.727,0.798,0.719,0.973,0.639,0.788 +BLAT_ECOLX_Firnberg_2014,0.468,0.396,0.487,0.578,0.543,0.673,0.512,0.964,0.352,0.595 +BLAT_ECOLX_Jacquier_2013,0.443,0.401,0.524,0.57,0.553,0.581,0.498,0.992,0.412,0.565 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+UBE4B_HUMAN_Rocklin_2023_3L1X,0.223,0.197,0.55,0.534,0.538,0.663,0.566,0.717,0.184,0.263 +UBR5_HUMAN_Rocklin_2023_1I2T,0.154,0.135,0.576,0.636,0.599,0.491,0.488,0.689,0.13,0.163 +VG08_BPP22_Rocklin_2023_2GP8,0.173,0.159,0.637,0.531,0.599,0.568,0.586,0.644,0.13,0.213 +VILI_CHICK_Rocklin_2023_1YU5,0.181,0.2,0.543,0.517,0.512,0.632,0.524,0.727,0.179,0.23 +VRPI_BPT7_Rocklin_2023_2WNM,0.155,0.178,0.71,0.594,0.682,0.757,0.752,0.663,0.137,0.211 +YAIA_ECOLI_Rocklin_2023_2KVT,0.227,0.198,0.722,0.723,0.71,0.723,0.69,0.716,0.181,0.278 +YNZC_BACSU_Rocklin_2023_2JVD,0.161,0.155,0.58,0.514,0.558,0.561,0.564,0.617,0.136,0.2 +POLG_DEN26_Suphatrakul_2023,0.916,0.705,0.887,0.999,0.755,0.928,0.908,0.966,0.37,0.647 +MET_HUMAN_Estevam_2023,0.506,0.53,0.607,0.591,0.555,0.627,0.591,0.923,0.407,0.456 +RDRP_I33A0_Li_2023,0.982,0.735,0.809,0.995,0.719,0.776,0.698,0.977,0.547,0.672 +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.901,0.905,0.901,0.897,0.917,0.932,0.909,0.977,0.842,0.876 +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.704,0.69,0.866,0.834,0.836,0.821,0.813,0.947,0.471,0.52 +ACE2_HUMAN_Chan_2020,0.706,0.896,0.829,0.843,0.819,0.909,0.897,0.841,0.455,0.567 +PPM1D_HUMAN_Miller_2022,0.654,0.739,0.735,0.651,0.738,0.734,0.694,0.95,0.56,0.623 +HMDH_HUMAN_Jiang_2019,0.643,0.916,0.848,0.864,0.913,0.929,0.886,0.968,0.528,0.6 +CBS_HUMAN_Sun_2020,0.842,0.866,0.856,0.879,0.863,0.91,0.877,0.977,0.779,0.832 +NPC1_HUMAN_Erwood_2022_HEK293T,0.633,0.45,0.53,0.734,0.415,0.556,0.483,0.868,0.359,0.44 +NPC1_HUMAN_Erwood_2022_RPE1,0.721,0.221,0.417,0.741,0.308,0.503,0.394,0.812,0.186,0.27 +BRCA2_HUMAN_Erwood_2022_HEK293T,0.421,1.014,0.822,0.924,0.922,0.822,0.827,0.916,1.117,0.623 +SHOC2_HUMAN_Kwon_2022,0.8,0.838,0.843,0.817,0.819,0.843,0.829,0.968,0.602,0.723 +RAF1_HUMAN_Zinkus-Boltz_2019,0.836,0.747,0.823,0.768,0.768,0.743,0.747,0.949,0.928,1.037 +OPSD_HUMAN_Wan_2019,0.569,0.428,0.603,0.623,0.562,0.661,0.63,0.846,0.421,0.624 +CAR11_HUMAN_Meitlis_2020_lof,0.516,0.868,0.828,0.679,0.83,0.829,0.82,0.842,0.455,0.462 +CAR11_HUMAN_Meitlis_2020_gof,0.72,0.992,0.933,0.878,0.927,0.927,0.929,0.907,0.689,0.669 +ERBB2_HUMAN_Elazar_2016,0.357,0.422,0.793,0.694,0.72,0.627,0.686,0.86,0.352,0.309 +GLPA_HUMAN_Elazar_2016,0.368,0.419,0.841,0.701,0.753,0.761,0.741,0.885,0.333,0.389 +LYAM1_HUMAN_Elazar_2016,0.582,0.483,0.734,0.664,0.608,0.676,0.69,0.773,0.521,0.606 +GDIA_HUMAN_Silverstein_2021,0.667,0.676,0.787,0.747,0.777,0.726,0.728,0.99,0.711,0.746 +PPARG_HUMAN_Majithia_2016,0.323,0.533,0.539,0.497,0.576,0.589,0.511,0.909,0.118,0.262 +CD19_HUMAN_Klesmith_2019_FMC_singles,0.785,0.948,0.911,0.945,0.903,0.923,0.909,0.927,0.552,0.671 diff --git a/benchmarks/DMS_supervised/substitutions/MSE/Summary_performance_DMS_substitutions_MSE.csv b/benchmarks/DMS_supervised/substitutions/MSE/Summary_performance_DMS_substitutions_MSE.csv new file mode 100644 index 0000000..a010bf3 --- /dev/null +++ b/benchmarks/DMS_supervised/substitutions/MSE/Summary_performance_DMS_substitutions_MSE.csv @@ -0,0 +1,11 @@ +Model_rank,Model_name,Model type,Average_MSE,Bootstrap_standard_error_MSE,Average_MSE_fold_random_5,Average_MSE_fold_modulo_5,Average_MSE_fold_contiguous_5,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,References,Model details +1,ProteinNPT,Embedding,0.683,0.0,0.459,0.771,0.82,0.703,1.016,0.578,0.752,0.368,0.516,0.738,0.659,0.602,0.605,0.618,0.672,"Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ProteinNPT Model +2,MSA Transformer Embeddings,Embedding,0.735,0.016,0.573,0.795,0.836,0.728,1.092,0.66,0.789,0.405,0.522,0.816,0.712,0.636,0.659,0.618,0.736,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer Embeddings +3,Tranception Embeddings,Embedding,0.769,0.027,0.503,0.833,0.972,0.814,1.08,0.639,0.788,0.525,0.635,0.781,0.757,0.724,0.712,0.703,0.717,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception Embeddings +4,ESM-1v Embeddings,Embedding,0.787,0.029,0.563,0.861,0.937,0.799,1.231,0.655,0.792,0.456,0.555,0.902,0.738,0.724,0.668,0.659,0.818,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v Embeddings +5,TranceptEVE + One-Hot Encodings,One-hot Encoding,0.87,0.016,0.743,0.914,0.953,0.793,1.199,0.78,0.825,0.756,0.765,0.889,0.845,0.849,0.791,0.837,0.822,"[1] Original model: Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",TranceptEVE + One-Hot Encodings +6,MSA_Transformer + One-Hot Encodings,One-hot Encoding,0.882,0.013,0.749,0.934,0.963,0.81,1.221,0.788,0.836,0.756,0.765,0.921,0.852,0.848,0.808,0.842,0.818,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer + One-Hot Encodings +7,DeepSequence + One-Hot Encodings,One-hot Encoding,0.891,0.015,0.767,0.94,0.967,0.83,1.14,0.832,0.86,0.793,0.794,0.927,0.877,0.88,0.824,0.853,0.881,"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5",DeepSequence + One-Hot Encodings +8,Tranception + One-Hot Encodings,One-hot Encoding,0.895,0.02,0.766,0.934,0.985,0.831,1.246,0.787,0.845,0.765,0.776,0.92,0.869,0.869,0.814,0.86,0.836,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception + One-Hot Encodings +9,ESM-1v + One-Hot Encodings,One-hot Encoding,0.897,0.017,0.764,0.949,0.977,0.843,1.192,0.795,0.87,0.783,0.768,0.968,0.879,0.868,0.819,0.864,0.907,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v + One-Hot Encodings +10,One-Hot Encodings,One-hot Encoding,1.061,0.017,0.898,1.125,1.158,1.022,1.306,0.986,1.04,0.949,1.004,1.076,1.027,1.03,1.004,1.062,0.991,"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5",One-Hot Encodings diff --git a/benchmarks/DMS_supervised/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv b/benchmarks/DMS_supervised/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv new file mode 100644 index 0000000..1c85f14 --- /dev/null +++ b/benchmarks/DMS_supervised/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv @@ -0,0 +1,218 @@ +DMS_id,ESM-1v Embeddings,MSA Transformer Embeddings,DeepSequence + One-Hot Encodings,ESM-1v + One-Hot Encodings,MSA_Transformer + One-Hot Encodings,Tranception + One-Hot Encodings,TranceptEVE + One-Hot Encodings,One-Hot Encodings,ProteinNPT,Tranception Embeddings +A0A140D2T1_ZIKV_Sourisseau_2019,0.111,0.455,0.125,-0.039,0.454,0.355,0.383,0.15,0.468,0.385 +A0A192B1T2_9HIV1_Haddox_2018,0.499,0.54,0.447,0.508,0.528,0.528,0.542,0.242,0.648,0.581 +A0A1I9GEU1_NEIME_Kennouche_2019,0.064,0.089,0.1,0.072,0.104,0.064,0.08,0.015,0.079,0.074 +A0A2Z5U3Z0_9INFA_Doud_2016,0.584,0.505,0.528,0.527,0.539,0.549,0.583,0.279,0.507,0.633 +A0A2Z5U3Z0_9INFA_Wu_2014,0.459,0.294,0.475,0.461,0.509,0.539,0.554,0.174,0.373,0.52 +A4D664_9INFA_Soh_2019,0.2,0.332,0.416,0.056,0.347,0.405,0.471,0.22,0.438,0.498 +A4GRB6_PSEAI_Chen_2020,0.739,0.767,0.683,0.669,0.687,0.676,0.697,0.336,0.81,0.728 +AACC1_PSEAI_Dandage_2018,0.524,0.588,0.448,0.51,0.511,0.493,0.517,0.181,0.52,0.487 +ADRB2_HUMAN_Jones_2020,0.548,0.512,0.525,0.539,0.512,0.545,0.548,0.102,0.548,0.573 +AMIE_PSEAE_Wrenbeck_2017,0.686,0.667,0.524,0.63,0.586,0.448,0.492,0.29,0.711,0.623 +LGK_LIPST_Klesmith_2015,0.589,0.616,0.501,0.546,0.568,0.535,0.547,0.129,0.692,0.594 +BLAT_ECOLX_Deng_2012,0.552,0.57,0.542,0.519,0.54,0.503,0.544,0.184,0.622,0.531 +BLAT_ECOLX_Firnberg_2014,0.77,0.803,0.752,0.671,0.711,0.654,0.743,0.262,0.832,0.707 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+RDRP_I33A0_Li_2023,0.121,0.486,0.399,0.094,0.5,0.488,0.537,0.425,0.633,0.572 +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.354,0.358,0.363,0.363,0.331,0.317,0.354,0.401,0.448,0.421 +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.449,0.445,0.321,0.331,0.347,0.376,0.383,0.541,0.571,0.574 +ACE2_HUMAN_Chan_2020,0.517,0.343,0.42,0.41,0.423,0.588,0.567,0.64,0.717,0.641 +PPM1D_HUMAN_Miller_2022,0.605,0.558,0.56,0.609,0.555,0.608,0.628,0.607,0.67,0.64 +HMDH_HUMAN_Jiang_2019,0.589,0.304,0.418,0.369,0.309,0.364,0.389,0.552,0.666,0.621 +CBS_HUMAN_Sun_2020,0.401,0.376,0.39,0.358,0.381,0.345,0.376,0.398,0.469,0.412 +NPC1_HUMAN_Erwood_2022_HEK293T,0.612,0.757,0.717,0.572,0.788,0.764,0.778,0.568,0.809,0.777 +NPC1_HUMAN_Erwood_2022_RPE1,0.575,0.88,0.748,0.478,0.83,0.637,0.735,0.486,0.861,0.823 +BRCA2_HUMAN_Erwood_2022_HEK293T,0.551,-0.067,0.501,0.354,0.132,0.478,0.491,0.526,0.124,0.502 +SHOC2_HUMAN_Kwon_2022,0.442,0.391,0.407,0.407,0.414,0.397,0.419,0.507,0.603,0.506 +RAF1_HUMAN_Zinkus-Boltz_2019,0.378,0.443,0.416,0.447,0.473,0.399,0.445,0.265,0.347,0.337 +OPSD_HUMAN_Wan_2019,0.659,0.716,0.594,0.564,0.629,0.591,0.577,0.53,0.735,0.666 +CAR11_HUMAN_Meitlis_2020_lof,0.703,0.404,0.505,0.595,0.431,0.487,0.5,0.712,0.755,0.74 +CAR11_HUMAN_Meitlis_2020_gof,0.503,0.2,0.413,0.439,0.336,0.395,0.399,0.572,0.592,0.557 +ERBB2_HUMAN_Elazar_2016,0.752,0.722,0.577,0.624,0.644,0.671,0.636,0.444,0.735,0.785 +GLPA_HUMAN_Elazar_2016,0.829,0.778,0.414,0.556,0.516,0.499,0.536,0.33,0.827,0.831 +LYAM1_HUMAN_Elazar_2016,0.675,0.732,0.556,0.594,0.654,0.586,0.574,0.49,0.72,0.678 +GDIA_HUMAN_Silverstein_2021,0.475,0.528,0.452,0.43,0.469,0.442,0.467,0.174,0.541,0.452 +PPARG_HUMAN_Majithia_2016,0.832,0.7,0.69,0.726,0.647,0.679,0.712,0.931,0.934,0.869 +CD19_HUMAN_Klesmith_2019_FMC_singles,0.417,0.298,0.299,0.219,0.371,0.306,0.322,0.501,0.624,0.494 diff --git a/benchmarks/DMS_supervised/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv b/benchmarks/DMS_supervised/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv new file mode 100644 index 0000000..c763c49 --- /dev/null +++ b/benchmarks/DMS_supervised/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv @@ -0,0 +1,11 @@ +Model_rank,Model_name,Model type,Average_Spearman,Bootstrap_standard_error_Spearman,Average_Spearman_fold_random_5,Average_Spearman_fold_modulo_5,Average_Spearman_fold_contiguous_5,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,References,Model details +1,ProteinNPT,Embedding,0.613,0.0,0.73,0.564,0.547,0.577,0.536,0.637,0.545,0.772,0.701,0.587,0.608,0.649,0.628,0.668,0.58,"Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ProteinNPT Model +2,Tranception Embeddings,Embedding,0.571,0.008,0.696,0.526,0.49,0.52,0.529,0.613,0.519,0.674,0.621,0.556,0.561,0.569,0.582,0.594,0.568,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception Embeddings +3,MSA Transformer Embeddings,Embedding,0.568,0.008,0.642,0.538,0.525,0.547,0.47,0.584,0.493,0.749,0.685,0.518,0.567,0.634,0.579,0.648,0.521,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer Embeddings +4,ESM-1v Embeddings,Embedding,0.542,0.013,0.639,0.506,0.481,0.487,0.45,0.587,0.468,0.717,0.653,0.465,0.541,0.565,0.579,0.617,0.433,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v Embeddings +5,TranceptEVE + One-Hot Encodings,One-hot Encoding,0.477,0.011,0.55,0.44,0.441,0.502,0.444,0.476,0.47,0.493,0.503,0.483,0.468,0.481,0.49,0.475,0.478,"[1] Original model: Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",TranceptEVE + One-Hot Encodings +6,Tranception + One-Hot Encodings,One-hot Encoding,0.458,0.011,0.535,0.419,0.419,0.475,0.416,0.476,0.448,0.473,0.49,0.455,0.445,0.457,0.472,0.453,0.456,"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",Tranception + One-Hot Encodings +7,MSA_Transformer + One-Hot Encodings,One-hot Encoding,0.453,0.011,0.536,0.412,0.41,0.48,0.393,0.463,0.437,0.491,0.5,0.441,0.448,0.482,0.459,0.468,0.448,"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",MSA Transformer + One-Hot Encodings +8,DeepSequence + One-Hot Encodings,One-hot Encoding,0.44,0.014,0.521,0.4,0.4,0.467,0.418,0.424,0.422,0.471,0.482,0.422,0.426,0.451,0.46,0.455,0.383,"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5",DeepSequence + One-Hot Encodings +9,ESM-1v + One-Hot Encodings,One-hot Encoding,0.417,0.014,0.514,0.368,0.367,0.421,0.363,0.452,0.383,0.463,0.496,0.338,0.4,0.426,0.444,0.452,0.292,"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems",ESM-1v + One-Hot Encodings +10,One-Hot Encodings,One-hot Encoding,0.224,0.015,0.579,0.027,0.064,0.213,0.212,0.226,0.194,0.273,0.246,0.204,0.227,0.236,0.217,0.233,0.238,"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5",One-Hot Encodings diff --git a/benchmarks/DMS_zero_shot/indels/AUC/DMS_indels_AUC_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/AUC/DMS_indels_AUC_DMS_level.csv new file mode 100644 index 0000000..ca673d4 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/AUC/DMS_indels_AUC_DMS_level.csv @@ -0,0 +1,67 @@ +DMS ID,Unirep,Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,Hidden Markov Model,Provean,Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A4_HUMAN_Seuma_2022_indels,0.822,0.879,0.854,0.854,0.848,0.843,0.9,0.87,0.875,0.869,0.862,0.88,0.896,0.849,0.85,0.884,0.861,0.815,0.892,0.858,0.862,0.26,0.835,2346,Stability,A4_HUMAN,Low,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,0.536,0.915,0.248,0.687,0.873,0.888,0.5,0.845,0.84,0.737,0.866,0.863,0.453,0.458,0.577,0.284,0.311,0.322,0.297,0.32,0.336,0.893,0.727,117,Stability,AMFR_HUMAN,Medium,Human +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,0.676,0.969,0.934,0.97,0.967,0.951,0.751,0.962,0.98,0.974,0.949,0.532,0.901,0.897,0.992,0.588,0.707,0.848,0.621,0.761,0.886,0.903,0.776,181,Stability,ARGR_ECOLI,Medium,Prokaryote +B1LPA6_ECOSM_Russ_2020_indels,0.676,0.77,0.678,0.717,0.701,0.71,0.73,0.721,0.757,0.744,0.717,0.7,0.606,0.613,0.611,0.699,0.701,0.684,0.694,0.708,0.691,0.743,0.756,3074,Activity,B1LPA6_ECOSM,Medium,Prokaryote +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,0.762,0.679,0.774,0.711,0.777,0.733,0.689,0.81,0.851,0.81,0.793,0.636,0.797,0.846,0.843,0.756,0.778,0.779,0.76,0.78,0.788,0.617,0.701,134,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU_indels,0.445,0.947,0.323,0.604,0.688,0.785,0.596,0.755,0.67,0.875,0.957,0.585,0.489,0.699,0.764,0.312,0.382,0.53,0.332,0.412,0.557,0.895,0.753,82,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Gonzalez_2019_indels,0.474,0.699,0.714,0.697,0.644,0.66,0.74,0.808,0.816,0.801,0.7,0.573,0.722,0.673,0.642,0.709,0.668,0.667,0.714,0.685,0.664,0.667,0.671,4751,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +CAPSD_AAV2S_Sinai_2021_designed_indels,0.28,0.852,0.654,0.689,0.795,0.826,0.272,0.267,0.304,0.265,0.764,0.587,0.676,0.69,0.874,0.892,0.864,0.883,0.893,0.899,0.902,0.812,0.871,225998,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAPSD_AAV2S_Sinai_2021_library_indels,0.375,0.479,0.54,0.611,0.612,0.634,0.439,0.448,0.459,0.451,0.664,0.58,0.547,0.595,0.716,0.766,0.754,0.761,0.764,0.755,0.792,0.565,0.668,24908,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,0.724,0.723,0.74,0.725,0.709,0.659,0.689,0.626,0.652,0.604,0.626,0.542,0.753,0.618,0.655,0.796,0.692,0.738,0.792,0.674,0.718,0.548,0.619,197,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,0.673,0.731,0.666,0.77,0.674,0.686,0.611,0.668,0.666,0.606,0.749,0.531,0.618,0.738,0.615,0.425,0.45,0.433,0.424,0.453,0.433,0.776,0.648,205,Stability,CBPA2_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,0.58,0.645,0.578,0.627,0.617,0.599,0.599,0.605,0.593,0.618,0.586,0.485,0.673,0.621,0.625,0.61,0.594,0.619,0.623,0.601,0.626,0.672,0.546,129,Stability,CBX4_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,0.81,0.898,0.849,0.843,0.909,0.954,0.712,0.929,0.943,0.935,0.926,0.695,0.775,0.884,0.913,0.369,0.472,0.515,0.38,0.498,0.553,0.958,0.724,195,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,0.825,0.939,0.868,0.778,0.812,0.734,0.649,0.731,0.67,0.749,0.92,0.649,0.746,0.726,0.759,0.239,0.24,0.258,0.256,0.25,0.276,0.819,0.922,140,Stability,CUE1_YEAST,Medium,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,0.656,0.905,0.618,0.636,0.556,0.594,0.531,0.552,0.547,0.589,0.682,0.595,0.608,0.555,0.525,0.491,0.485,0.422,0.52,0.506,0.445,0.734,0.901,136,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,0.734,0.626,0.614,0.696,0.734,0.672,0.717,0.66,0.681,0.65,0.702,0.636,0.766,0.774,0.765,0.663,0.703,0.701,0.675,0.715,0.712,0.616,0.532,174,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,0.702,0.815,0.821,0.834,0.843,0.81,0.855,0.857,0.839,0.829,0.837,0.64,0.834,0.853,0.86,0.701,0.739,0.75,0.72,0.758,0.769,0.716,0.738,154,Stability,DOCK1_MOUSE,High,Eukaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,0.601,0.81,0.811,0.801,0.82,0.803,0.858,0.831,0.874,0.853,0.843,0.749,0.774,0.779,0.799,0.517,0.594,0.662,0.558,0.63,0.701,0.79,0.658,185,Stability,EPHB2_HUMAN,High,Human +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,0.685,0.528,0.659,0.696,0.713,0.606,0.646,0.739,0.774,0.707,0.691,0.575,0.522,0.659,0.52,0.424,0.523,0.465,0.426,0.536,0.471,0.557,0.68,193,Stability,FECA_ECOLI,High,Eukaryote +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,0.692,0.884,0.829,0.856,0.888,0.894,0.808,0.769,0.758,0.773,0.932,0.763,0.807,0.753,0.9,0.397,0.382,0.779,0.393,0.368,0.807,0.822,0.869,148,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,0.638,0.862,0.752,0.821,0.849,0.891,0.788,0.877,0.907,0.878,0.87,0.516,0.585,0.796,0.534,0.573,0.705,0.555,0.569,0.725,0.545,0.913,0.719,154,Stability,HECD1_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019_indels,0.503,0.941,0.911,0.92,0.936,0.94,0.931,0.953,0.953,0.953,0.958,0.445,0.906,0.941,0.954,0.904,0.895,0.922,0.911,0.937,0.947,0.853,0.95,6102,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,0.483,0.796,0.714,0.755,0.643,0.657,0.746,0.638,0.711,0.764,0.743,0.584,0.608,0.805,0.788,0.481,0.645,0.694,0.499,0.676,0.722,0.666,0.539,193,Stability,ILF3_HUMAN,High,Human +KCNJ2_MOUSE_Macdonald_2022_indels,0.559,0.804,0.761,0.784,0.778,0.775,0.764,0.803,0.803,0.798,0.779,0.68,0.769,0.795,0.783,0.796,0.805,0.813,0.792,0.814,0.808,0.762,0.791,10501,Expression,KCNJ2_MOUSE,Medium,Eukaryote +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,0.889,0.816,0.59,0.89,0.886,0.892,0.83,0.863,0.871,0.86,0.861,0.587,0.564,0.823,0.899,0.506,0.579,0.639,0.507,0.597,0.671,0.754,0.535,115,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,0.627,0.65,0.581,0.553,0.764,0.772,0.692,0.866,0.848,0.743,0.739,0.588,0.515,0.58,0.556,0.492,0.522,0.494,0.509,0.534,0.512,0.638,0.758,131,Stability,MBD11_ARATH,Medium,Eukaryote +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,0.479,0.714,0.708,0.664,0.793,0.762,0.753,0.762,0.657,0.731,0.764,0.582,0.736,0.74,0.704,0.588,0.605,0.576,0.613,0.631,0.599,0.627,0.745,80,Stability,MYO3_YEAST,High,Eukaryote +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,0.878,0.934,0.96,0.935,0.922,0.922,0.906,0.911,0.954,0.896,0.897,0.634,0.985,0.874,0.888,0.936,0.876,0.896,0.94,0.876,0.888,0.88,0.755,178,Stability,NKX31_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,0.632,0.701,0.667,0.696,0.675,0.661,0.679,0.671,0.716,0.658,0.675,0.458,0.697,0.714,0.7,0.471,0.519,0.508,0.475,0.543,0.526,0.55,0.738,191,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,0.763,0.76,0.744,0.751,0.757,0.742,0.759,0.75,0.743,0.758,0.77,0.813,0.737,0.751,0.781,0.653,0.672,0.72,0.666,0.686,0.733,0.668,0.889,157,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,0.682,0.784,0.924,0.884,0.874,0.856,0.891,0.93,0.956,0.891,0.873,0.687,0.654,0.653,0.715,0.583,0.599,0.614,0.591,0.61,0.634,0.71,0.606,169,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,0.541,0.9,0.952,0.995,0.984,0.982,0.923,0.968,0.98,0.934,0.984,0.776,0.824,0.667,0.692,0.821,0.88,0.805,0.864,0.912,0.821,0.756,0.993,47,Stability,ODP2_GEOSE,High,Prokaryote +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,0.747,0.604,0.715,0.957,0.958,0.978,0.79,0.975,0.975,0.955,0.919,0.669,0.627,0.803,0.947,0.305,0.415,0.577,0.307,0.44,0.603,0.822,0.898,84,Stability,OTU7A_HUMAN,High,Human +P53_HUMAN_Kotler_2018_indels,0.531,0.5,0.667,0.69,0.66,0.597,0.723,0.684,0.662,0.697,0.658,0.501,0.748,0.763,0.664,0.689,0.735,0.643,0.699,0.754,0.661,0.745,0.623,341,OrganismalFitness,P53_HUMAN,Low,Human +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,0.901,0.941,0.915,0.956,0.932,0.955,0.97,0.879,0.93,0.942,0.889,0.925,0.917,0.965,0.975,0.647,0.88,0.968,0.699,0.924,0.98,0.904,0.868,106,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,0.89,0.867,0.902,0.925,0.915,0.926,0.874,0.892,0.874,0.936,0.897,0.771,0.895,0.903,0.914,0.645,0.634,0.626,0.719,0.7,0.707,0.763,0.779,117,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,0.609,0.882,0.882,0.887,0.883,0.9,0.895,0.929,0.929,0.926,0.924,0.674,0.794,0.888,0.885,0.613,0.837,0.856,0.645,0.861,0.868,0.724,0.762,187,Stability,PKN1_HUMAN,High,Human +POLG_PESV_Tsuboyama_2023_2MXD_indels,0.741,0.777,0.731,0.708,0.699,0.668,0.676,0.654,0.665,0.605,0.691,0.602,0.746,0.693,0.701,0.362,0.335,0.348,0.377,0.351,0.37,0.652,0.63,149,Stability,POLG_PESV,Medium,Virus +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,0.555,0.869,0.741,0.829,0.819,0.816,0.818,0.836,0.83,0.835,0.812,0.772,0.678,0.659,0.646,0.54,0.571,0.528,0.558,0.595,0.549,0.854,0.575,168,Stability,PR40A_HUMAN,Medium,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE_indels,0.524,0.533,0.618,0.547,0.586,0.575,0.643,0.702,0.481,0.646,0.703,0.583,0.63,0.692,0.43,0.621,0.743,0.561,0.628,0.753,0.55,0.621,0.641,175,Stability,PSAE_SYNP2,Medium,Prokaryote +PTEN_HUMAN_Mighell_2018_indels,0.321,0.895,0.85,0.841,0.787,0.795,0.904,0.82,0.88,0.851,0.734,0.551,0.895,0.907,0.832,0.894,0.897,0.827,0.897,0.915,0.856,0.888,0.638,314,Activity,PTEN_HUMAN,Medium,Human +Q8EG35_SHEON_Campbell_2022_indels,0.769,0.587,0.756,0.721,0.668,0.624,0.8,0.684,0.65,0.641,0.651,0.621,0.822,0.805,0.677,0.742,0.824,0.69,0.741,0.793,0.679,0.754,0.609,331,OrganismalFitness,Q8EG35_SHEON,Medium,Prokaryote +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,0.815,0.794,0.748,0.951,0.961,0.942,0.853,0.924,0.937,0.899,0.833,0.599,0.795,0.873,0.782,0.527,0.777,0.725,0.55,0.812,0.754,0.852,0.459,97,Stability,RAD_ANTMA,High,Eukaryote +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,0.598,0.888,0.536,0.538,0.488,0.682,0.501,0.598,0.593,0.547,0.684,0.594,0.487,0.558,0.62,0.327,0.335,0.358,0.323,0.335,0.361,0.836,0.679,124,Stability,RCD1_ARATH,Medium,Eukaryote +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,0.77,0.897,0.944,0.922,0.922,0.954,0.948,0.974,0.968,0.961,0.982,0.957,0.804,0.969,0.939,0.557,0.783,0.84,0.579,0.839,0.871,0.844,0.869,120,Stability,RD23A_HUMAN,High,Human +RPC1_BP434_Tsuboyama_2023_1R69_indels,0.895,0.736,0.892,0.889,0.872,0.867,0.905,0.873,0.869,0.89,0.707,0.642,0.839,0.893,0.884,0.791,0.851,0.887,0.798,0.855,0.896,0.589,0.704,164,Stability,RPC1_BP434,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32_indels,0.639,0.675,0.651,0.684,0.681,0.755,0.627,0.765,0.763,0.755,0.752,0.569,0.823,0.764,0.727,0.659,0.676,0.548,0.693,0.709,0.579,0.642,0.671,176,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance_indels,0.638,0.509,0.718,0.743,0.811,0.791,0.729,0.813,0.818,0.767,0.712,0.633,0.751,0.746,0.782,0.707,0.715,0.779,0.739,0.75,0.781,0.674,0.698,430,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity_indels,0.708,0.573,0.771,0.781,0.835,0.828,0.769,0.849,0.814,0.806,0.738,0.631,0.765,0.807,0.824,0.723,0.751,0.822,0.76,0.806,0.817,0.706,0.704,490,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,0.901,0.972,0.937,0.91,0.9,0.9,0.897,0.926,0.906,0.903,0.901,0.863,0.927,0.92,0.931,0.657,0.812,0.838,0.711,0.862,0.885,0.914,0.873,86,Stability,SAV1_MOUSE,High,Eukaryote +SDA_BACSU_Tsuboyama_2023_1PV0_indels,0.604,0.666,0.601,0.689,0.719,0.784,0.655,0.739,0.539,0.694,0.817,0.501,0.65,0.722,0.719,0.356,0.368,0.668,0.371,0.383,0.689,0.763,0.731,127,Stability,SDA_BACSU,Medium,Prokaryote +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,0.936,0.757,0.613,0.756,0.787,0.863,0.504,0.735,0.784,0.779,0.801,0.816,0.416,0.808,0.727,0.35,0.468,0.55,0.364,0.505,0.598,0.691,0.856,109,Stability,SOX30_HUMAN,High,Human +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,0.417,0.682,0.558,0.575,0.672,0.684,0.626,0.667,0.655,0.682,0.738,0.378,0.49,0.51,0.8,0.608,0.613,0.687,0.605,0.612,0.705,0.556,0.663,148,Stability,SPG2_STRSG,Medium,Prokaryote +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,0.716,0.54,0.756,0.803,0.778,0.842,0.737,0.885,0.828,0.858,0.81,0.597,0.818,0.787,0.824,0.675,0.722,0.725,0.688,0.74,0.744,0.621,0.609,129,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,0.641,0.739,0.811,0.856,0.87,0.875,0.834,0.83,0.891,0.879,0.861,0.56,0.719,0.841,0.86,0.545,0.674,0.788,0.561,0.718,0.82,0.681,0.924,111,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88_indels,0.511,0.665,0.762,0.61,0.693,0.643,0.586,0.608,0.706,0.713,0.684,0.752,0.657,0.455,0.656,0.746,0.675,0.783,0.755,0.681,0.784,0.641,0.652,135,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,0.821,0.885,0.804,0.866,0.861,0.855,0.841,0.868,0.888,0.871,0.821,0.762,0.86,0.862,0.902,0.889,0.891,0.925,0.889,0.891,0.923,0.72,0.756,154,Stability,SRBS1_HUMAN,High,Human +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,0.911,0.942,0.942,0.969,0.964,0.981,0.94,0.94,0.964,0.95,0.956,0.821,0.784,0.941,0.964,0.152,0.212,0.284,0.183,0.258,0.354,0.967,0.809,99,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG_indels,0.625,0.949,0.546,0.867,0.878,0.856,0.816,0.893,0.856,0.895,0.93,0.806,0.624,0.905,0.858,0.384,0.609,0.573,0.392,0.655,0.612,0.886,0.955,82,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels,0.435,0.917,0.668,0.818,0.903,0.861,0.746,0.82,0.851,0.828,0.837,0.682,0.522,0.657,0.751,0.55,0.626,0.676,0.564,0.633,0.68,0.926,0.695,171,Stability,TNKS2_HUMAN,High,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels,0.408,0.66,0.489,0.6,0.569,0.648,0.665,0.593,0.621,0.643,0.713,0.687,0.489,0.477,0.666,0.489,0.486,0.637,0.499,0.498,0.654,0.393,0.681,147,Stability,UBE4B_HUMAN,High,Human +UBR5_HUMAN_Tsuboyama_2023_1I2T_indels,0.641,0.788,0.764,0.727,0.751,0.761,0.774,0.743,0.793,0.781,0.808,0.673,0.746,0.766,0.771,0.509,0.597,0.611,0.519,0.623,0.633,0.687,0.623,156,Stability,UBR5_HUMAN,Medium,Human +VG08_BPP22_Tsuboyama_2023_2GP8_indels,0.796,0.932,0.825,0.938,0.897,0.987,0.771,0.878,0.818,0.788,0.952,0.356,0.751,0.948,0.976,0.634,0.711,0.946,0.634,0.745,0.977,0.859,0.729,101,Stability,VG08_BPP22,High,Virus +VILI_CHICK_Tsuboyama_2023_1YU5_indels,0.495,0.916,0.629,0.805,0.777,0.786,0.622,0.826,0.791,0.78,0.846,0.566,0.857,0.836,0.831,0.691,0.805,0.805,0.724,0.816,0.824,0.808,0.8,156,Stability,VILI_CHICK,High,Eukaryote +VRPI_BPT7_Tsuboyama_2023_2WNM_indels,0.704,0.826,0.562,0.71,0.8,0.675,0.721,0.698,0.696,0.7,0.736,0.538,0.644,0.602,0.673,0.394,0.384,0.375,0.396,0.384,0.374,0.708,0.857,154,Stability,VRPI_BPT7,Medium,Virus +YNZC_BACSU_Tsuboyama_2023_2JVD_indels,0.749,0.915,0.766,0.771,0.854,0.869,0.772,0.921,0.7,0.898,0.887,0.602,0.718,0.751,0.856,0.264,0.256,0.359,0.269,0.265,0.39,0.898,0.637,104,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_DMS_indels_AUC.csv b/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_DMS_indels_AUC.csv new file mode 100644 index 0000000..0b2c092 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_DMS_indels_AUC.csv @@ -0,0 +1,24 @@ +Model_rank,Model_name,Model type,Average_AUC,Bootstrap_standard_error_AUC,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Model details,References +1,Progen2 Base,Protein language model,0.778,0.0,0.817,,0.81,0.692,0.794,0.64,0.763,0.823,0.827,0.817,0.714,0.698,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +2,Progen2 M,Protein language model,0.776,0.007,0.797,,0.808,0.697,0.8,0.637,0.784,0.814,0.809,0.818,0.762,0.705,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +3,RITA L,Protein language model,0.773,0.019,0.774,,0.794,0.722,0.799,0.737,0.775,0.812,0.809,0.811,0.725,0.81,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +4,RITA XL,Protein language model,0.769,0.02,0.778,,0.783,0.71,0.804,0.723,0.787,0.809,0.814,0.807,0.741,0.804,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +5,Progen2 L,Protein language model,0.767,0.005,0.8,,0.782,0.69,0.797,0.641,0.775,0.815,0.815,0.807,0.761,0.686,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +6,Tranception M no retrieval,Protein language model,0.767,0.02,0.776,,0.77,0.765,0.756,0.752,0.733,0.78,0.773,0.779,0.701,0.755,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +7,RITA M,Protein language model,0.766,0.015,0.78,,0.764,0.736,0.784,0.731,0.75,0.81,0.807,0.785,0.718,0.792,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +8,Tranception L no retrieval,Protein language model,0.764,0.024,0.756,,0.782,0.746,0.772,0.77,0.74,0.795,0.774,0.789,0.708,0.822,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +9,Progen2 XL,Protein language model,0.757,0.02,0.73,,0.746,0.736,0.818,0.745,0.809,0.81,0.813,0.82,0.784,0.789,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +10,TranceptEVE M,Hybrid model,0.754,0.024,0.81,,0.782,0.799,0.625,0.813,0.564,0.712,0.684,0.644,0.623,0.588,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +11,Progen2 S,Protein language model,0.751,0.011,0.801,,0.746,0.71,0.747,0.66,0.722,0.777,0.78,0.75,0.702,0.706,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +12,TranceptEVE L,Hybrid model,0.751,0.025,0.788,,0.794,0.76,0.66,0.79,0.592,0.742,0.702,0.662,0.637,0.712,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +13,Tranception S no retrieval,Protein language model,0.747,0.018,0.755,,0.76,0.762,0.71,0.752,0.687,0.74,0.715,0.729,0.694,0.733,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,Hidden Markov Model,Alignment-based model,0.744,0.022,0.779,,0.718,0.742,0.737,0.564,0.781,0.719,0.743,0.753,0.725,0.72,Profile Hidden Markov model,HMMER: biosequence analysis using profile hidden Markov models +15,RITA S,Protein language model,0.741,0.015,0.766,,0.74,0.729,0.729,0.706,0.698,0.763,0.742,0.746,0.684,0.739,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +16,Tranception L,Hybrid model,0.741,0.024,0.778,,0.796,0.749,0.64,0.76,0.574,0.723,0.682,0.641,0.621,0.693,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +17,Tranception M,Hybrid model,0.733,0.025,0.783,,0.76,0.786,0.605,0.802,0.547,0.69,0.662,0.624,0.607,0.579,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +18,Provean,Alignment-based model,0.725,0.027,0.699,,0.744,0.724,0.733,0.742,0.732,0.73,0.704,0.746,0.745,0.76,Provean model,"Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one." +19,TranceptEVE S,Hybrid model,0.722,0.024,0.784,,0.766,0.779,0.562,0.807,0.52,0.639,0.615,0.578,0.585,0.571,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +20,Wavenet,Alignment-based model,0.72,0.037,0.746,,0.657,0.678,0.8,0.682,0.805,0.782,0.791,0.796,0.765,0.803,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +21,Tranception S,Hybrid model,0.711,0.025,0.772,,0.752,0.775,0.546,0.801,0.509,0.621,0.598,0.564,0.571,0.568,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +22,ProtGPT2,Protein language model,0.62,0.032,0.627,,0.657,0.545,0.653,0.655,0.611,0.672,0.693,0.639,0.592,0.581,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +23,Unirep,Protein language model,0.593,0.04,0.568,,0.598,0.521,0.682,0.56,0.645,0.687,0.661,0.689,0.612,0.693,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." diff --git a/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_all_models_indels_AUC.csv b/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_all_models_indels_AUC.csv new file mode 100644 index 0000000..629d82f --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/AUC/Summary_performance_all_models_indels_AUC.csv @@ -0,0 +1,22 @@ +Model_rank,Model_name,Model type,Average_AUC,Bootstrap_standard_error_AUC,Model details,References +1,TranceptEVE M,Hybrid model,0.799,0.0,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +2,Tranception M,Hybrid model,0.796,0.001,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +3,Tranception L,Hybrid model,0.775,0.009,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +4,TranceptEVE L,Hybrid model,0.775,0.01,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +5,Tranception S,Hybrid model,0.773,0.015,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,RITA (ensemble),Protein language model,0.761,0.014,Ensemble of the 4 RITA models,"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +7,Tranception L no retrieval,Protein language model,0.756,0.012,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +8,Progen2 (ensemble),Protein language model,0.748,0.053,Ensemble of the 5 Progen2 models," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +9,RITA M,Protein language model,0.747,0.018,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +10,RITA L,Protein language model,0.74,0.015,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +11,Wavenet,Alignment-based model,0.74,0.036,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +12,RITA XL,Protein language model,0.738,0.018,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +13,Progen2 Base,Protein language model,0.737,0.056,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +14,Progen2 XL,Protein language model,0.735,0.024,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +15,Progen2 L,Protein language model,0.728,0.058,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +16,Unirep evotuned,Hybrid model,0.728,0.033,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +17,Progen2 M,Protein language model,0.72,0.058,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +18,Progen2 S,Protein language model,0.714,0.053,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +19,RITA S,Protein language model,0.71,0.028,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +20,Unirep,Protein language model,0.576,0.045,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +21,ProtGPT2,Protein language model,0.575,0.059,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." diff --git a/benchmarks/DMS_zero_shot/indels/AUC/all_models_indels_AUC_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/AUC/all_models_indels_AUC_DMS_level.csv new file mode 100644 index 0000000..25a285f --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/AUC/all_models_indels_AUC_DMS_level.csv @@ -0,0 +1,9 @@ +,Tranception_L_no_retrieval,Tranception_S_retrieval,Tranception_M_retrieval,Tranception_L_retrieval,Wavenet,RITA_s,RITA_m,RITA_l,RITA_xl,RITA_ensemble,Progen2_small,Progen2_medium,Progen2_base,Progen2_large,Progen2_xlarge,Progen2_ensemble,Unirep,Unirep_evotune,ProtGPT2,TranceptEVE_L,TranceptEVE_M,number_mutants +A0A1J4YT16_9PROT_Davidi_2020,0.625,0.618,0.658,0.645,0.577,0.454,0.626,0.636,0.643,0.621,0.592,0.625,0.63,0.63,0.601,0.618,0.468,0.679,0.634,0.641,0.657,105 +B1LPA6_ECOSM_Russ_2020,0.691,0.752,0.759,0.744,0.73,0.686,0.736,0.706,0.707,0.72,0.684,0.694,0.728,0.708,0.695,0.711,0.679,0.758,0.69,0.749,0.766,3074 +BLAT_ECOLX_Gonzalez_indels_2019,0.642,0.718,0.687,0.663,0.757,0.714,0.697,0.644,0.66,0.703,0.74,0.808,0.816,0.802,0.7,0.831,0.526,0.639,0.573,0.664,0.686,4751 +CAPSD_AAV2S_Sinai_indels_2021,0.769,0.787,0.788,0.789,0.708,0.62,0.657,0.726,0.712,0.697,0.376,0.372,0.402,0.371,0.714,0.43,0.688,0.746,0.559,0.794,0.796,250907 +HIS7_YEAST_Pokusaeva_indels_2019,0.954,0.909,0.933,0.946,0.937,0.911,0.92,0.936,0.94,0.936,0.931,0.953,0.953,0.953,0.958,0.96,0.497,0.671,0.445,0.947,0.937,6102 +PTEN_HUMAN_Mighell_deletions_2018,0.832,0.893,0.916,0.861,0.889,0.85,0.841,0.787,0.795,0.883,0.904,0.82,0.88,0.851,0.734,0.895,0.679,0.82,0.551,0.856,0.915,314 +P53_HUMAN_Kotler_deletions_2018,0.782,0.732,0.833,0.775,0.581,0.735,0.752,0.746,0.713,0.768,0.774,0.764,0.753,0.78,0.742,0.792,0.496,0.782,0.571,0.776,0.839,341 +Average,0.756,0.773,0.796,0.775,0.74,0.71,0.747,0.74,0.738,0.761,0.714,0.72,0.737,0.728,0.735,0.748,0.576,0.728,0.575,0.775,0.799,37942 diff --git a/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.csv new file mode 100644 index 0000000..cfad26e --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.csv @@ -0,0 +1,67 @@ +DMS ID,Unirep,Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,Hidden Markov Model,Provean,Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A4_HUMAN_Seuma_2022_indels,0.585,0.619,0.55,0.614,0.592,0.628,0.664,0.647,0.633,0.633,0.618,0.626,0.656,0.635,0.604,0.637,0.642,0.58,0.64,0.632,0.607,-0.376,0.511,2346,Stability,A4_HUMAN,Low,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,0.137,0.462,-0.39,0.259,0.421,0.421,0.016,0.421,0.421,0.218,0.421,0.421,-0.106,-0.025,0.178,-0.228,-0.147,-0.147,-0.187,-0.147,-0.147,0.462,0.172,117,Stability,AMFR_HUMAN,Medium,Human +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,0.25,0.407,0.407,0.407,0.407,0.344,0.155,0.344,0.407,0.407,0.344,0.029,0.376,0.376,0.407,-0.002,0.187,0.281,0.061,0.218,0.344,0.407,0.218,181,Stability,ARGR_ECOLI,Medium,Prokaryote +B1LPA6_ECOSM_Russ_2020_indels,0.221,0.362,0.223,0.283,0.241,0.251,0.336,0.286,0.378,0.33,0.286,0.261,0.146,0.118,0.108,0.276,0.252,0.236,0.269,0.253,0.235,0.299,0.369,3074,Activity,B1LPA6_ECOSM,Medium,Prokaryote +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,0.309,0.034,0.377,0.137,0.343,0.24,0.24,0.343,0.446,0.309,0.377,0.206,0.48,0.412,0.446,0.377,0.412,0.412,0.377,0.412,0.412,0.171,0.171,134,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU_indels,-0.087,0.549,-0.202,0.145,0.26,0.318,0.145,0.376,0.26,0.434,0.491,0.087,0.029,0.26,0.318,-0.26,-0.145,0.087,-0.145,-0.145,0.087,0.549,0.26,82,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Gonzalez_2019_indels,-0.052,0.31,0.266,0.245,0.179,0.244,0.303,0.445,0.478,0.475,0.256,0.078,0.286,0.277,0.224,0.269,0.233,0.242,0.266,0.274,0.254,0.293,0.238,4751,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +CAPSD_AAV2S_Sinai_2021_designed_indels,-0.353,0.558,0.252,0.298,0.443,0.502,-0.353,-0.358,-0.303,-0.361,0.383,0.13,0.274,0.302,0.578,0.603,0.554,0.599,0.606,0.618,0.633,0.474,0.571,225998,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAPSD_AAV2S_Sinai_2021_library_indels,-0.118,0.033,0.038,0.103,0.097,0.114,-0.058,-0.041,-0.033,-0.039,0.141,0.083,0.038,0.09,0.195,0.245,0.219,0.223,0.239,0.238,0.247,0.082,0.187,24908,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,0.184,0.324,0.23,0.23,0.277,0.23,0.207,0.09,0.207,0.044,0.137,0.067,0.324,0.114,0.184,0.324,0.254,0.277,0.324,0.184,0.3,-0.05,0.114,197,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,0.18,0.288,0.245,0.331,0.18,0.202,0.137,0.115,0.094,0.051,0.331,0.072,0.072,0.309,0.137,-0.057,-0.014,0.008,-0.057,-0.014,-0.014,0.352,0.18,205,Stability,CBPA2_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,0.024,0.055,0.024,0.086,0.055,0.024,-0.069,-0.007,0.024,0.055,0.024,-0.007,0.149,0.086,0.118,0.18,0.149,0.18,0.18,0.149,0.211,0.18,0.055,129,Stability,CBX4_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,0.48,0.429,0.404,0.429,0.429,0.505,0.302,0.48,0.505,0.429,0.429,0.201,0.404,0.404,0.505,-0.13,-0.053,-0.003,-0.13,-0.003,0.048,0.505,0.251,195,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,0.372,0.328,0.372,0.284,0.284,0.241,0.197,0.197,0.153,0.197,0.372,0.197,0.197,0.284,0.372,-0.197,-0.197,-0.109,-0.153,-0.197,-0.109,0.328,0.284,140,Stability,CUE1_YEAST,Medium,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,0.148,0.207,0.148,0.03,0.089,0.03,0.03,0.089,0.03,0.03,0.089,0.148,0.148,0.03,0.03,0.03,-0.03,-0.089,0.03,0.03,-0.089,0.207,0.207,136,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,0.369,0.092,0.138,0.231,0.254,0.138,0.277,0.161,0.231,0.231,0.323,0.208,0.392,0.369,0.346,0.185,0.3,0.277,0.208,0.277,0.277,0.231,0.069,174,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,0.268,0.522,0.409,0.409,0.465,0.296,0.494,0.494,0.437,0.465,0.465,0.183,0.578,0.606,0.578,0.268,0.381,0.381,0.324,0.409,0.381,0.409,0.155,154,Stability,DOCK1_MOUSE,High,Eukaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,0.122,0.322,0.347,0.322,0.372,0.372,0.422,0.372,0.372,0.372,0.422,0.347,0.272,0.297,0.297,-0.003,0.022,0.147,0.072,0.047,0.222,0.347,0.147,185,Stability,EPHB2_HUMAN,High,Human +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,0.241,-0.032,0.15,0.15,0.21,-0.032,0.18,0.241,0.301,0.15,0.089,0.089,-0.002,0.18,0.028,-0.063,0.028,-0.063,-0.063,0.059,-0.032,-0.032,0.028,193,Stability,FECA_ECOLI,High,Eukaryote +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,0.207,0.461,0.429,0.429,0.493,0.493,0.334,0.334,0.334,0.302,0.493,0.302,0.429,0.366,0.461,-0.016,-0.048,0.334,-0.08,-0.08,0.334,0.27,0.334,148,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,0.25,0.426,0.25,0.367,0.367,0.426,0.397,0.455,0.485,0.455,0.426,0.044,0.044,0.338,0.015,0.132,0.22,0.044,0.073,0.22,0.044,0.544,0.213,154,Stability,HECD1_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019_indels,0.027,0.568,0.555,0.555,0.577,0.571,0.594,0.61,0.586,0.602,0.597,-0.087,0.542,0.574,0.596,0.525,0.528,0.547,0.535,0.572,0.577,0.467,0.585,6102,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,0.063,0.374,0.307,0.263,0.063,0.174,0.352,0.085,0.241,0.374,0.329,0.063,0.174,0.374,0.418,0.019,0.241,0.241,0.041,0.285,0.307,0.041,0.056,193,Stability,ILF3_HUMAN,High,Human +KCNJ2_MOUSE_Macdonald_2022_indels,0.063,0.385,0.313,0.315,0.317,0.303,0.327,0.373,0.377,0.374,0.316,0.225,0.316,0.367,0.336,0.362,0.396,0.4,0.359,0.406,0.392,0.349,0.342,10501,Expression,KCNJ2_MOUSE,Medium,Eukaryote +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,0.322,0.263,-0.032,0.263,0.263,0.263,0.263,0.263,0.263,0.263,0.263,-0.032,0.027,0.263,0.322,0.027,0.027,0.086,0.027,0.027,0.086,0.204,-0.062,115,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,0.179,0.148,0.148,0.022,0.463,0.432,0.306,0.59,0.495,0.306,0.243,0.116,0.022,0.179,0.116,-0.01,0.022,-0.041,-0.01,0.022,-0.01,0.243,0.463,131,Stability,MBD11_ARATH,Medium,Eukaryote +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,-0.052,0.314,0.262,0.262,0.367,0.367,0.314,0.367,0.105,0.314,0.314,0.262,0.262,0.367,0.314,0.105,0.157,0.157,0.052,0.262,0.157,0.367,0.419,80,Stability,MYO3_YEAST,High,Eukaryote +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,0.411,0.565,0.565,0.565,0.539,0.565,0.513,0.565,0.565,0.565,0.565,0.128,0.59,0.436,0.565,0.488,0.436,0.513,0.539,0.436,0.488,0.462,0.104,178,Stability,NKX31_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,0.193,0.193,0.054,0.193,0.124,0.078,0.216,0.078,0.286,0.078,0.078,-0.038,0.239,0.239,0.147,-0.038,0.008,0.054,-0.061,0.031,0.031,0.078,0.263,191,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,0.223,0.123,0.123,0.123,0.123,0.123,0.123,0.123,0.123,0.123,0.123,0.223,0.123,0.123,0.173,0.023,0.073,0.173,0.073,0.073,0.173,0.123,0.273,157,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,0.215,0.473,0.602,0.525,0.576,0.628,0.55,0.628,0.628,0.55,0.499,0.293,0.241,0.267,0.318,0.138,0.241,0.267,0.138,0.189,0.267,0.318,0.119,169,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,0.034,0.605,0.605,0.605,0.605,0.605,0.605,0.605,0.605,0.605,0.605,0.415,0.32,0.225,0.32,0.415,0.51,0.415,0.415,0.605,0.415,0.415,0.605,47,Stability,ODP2_GEOSE,High,Prokaryote +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,0.406,0.174,0.232,0.464,0.522,0.464,0.174,0.464,0.464,0.464,0.464,0.116,0.232,0.348,0.464,-0.174,0.0,0.116,-0.174,0.0,0.116,0.522,0.406,84,Stability,OTU7A_HUMAN,High,Human +P53_HUMAN_Kotler_2018_indels,0.056,-0.003,0.232,0.326,0.267,0.138,0.349,0.29,0.208,0.29,0.173,-0.026,0.361,0.419,0.243,0.29,0.419,0.22,0.29,0.443,0.267,0.372,0.126,341,OrganismalFitness,P53_HUMAN,Low,Human +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,0.586,0.628,0.503,0.628,0.503,0.586,0.586,0.419,0.545,0.586,0.419,0.586,0.545,0.586,0.628,0.042,0.461,0.586,0.084,0.545,0.628,0.503,0.461,106,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,0.375,0.33,0.33,0.419,0.375,0.375,0.33,0.375,0.33,0.419,0.419,0.286,0.33,0.375,0.375,0.107,0.152,0.063,0.196,0.196,0.196,0.196,0.233,117,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,0.162,0.634,0.516,0.539,0.563,0.516,0.539,0.587,0.634,0.61,0.634,0.209,0.492,0.539,0.563,0.115,0.469,0.469,0.162,0.492,0.469,0.209,0.256,187,Stability,PKN1_HUMAN,High,Human +POLG_PESV_Tsuboyama_2023_2MXD_indels,0.283,0.369,0.455,0.369,0.283,0.254,0.34,0.34,0.34,0.283,0.34,0.11,0.369,0.283,0.283,-0.148,-0.206,-0.148,-0.12,-0.148,-0.12,0.168,0.139,149,Stability,POLG_PESV,Medium,Virus +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,0.127,0.433,0.204,0.459,0.459,0.357,0.408,0.459,0.484,0.433,0.357,0.433,0.255,0.178,0.127,0.025,0.076,0.025,0.0,0.127,0.102,0.51,0.051,168,Stability,PR40A_HUMAN,Medium,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE_indels,0.064,0.178,0.247,0.109,0.109,0.018,0.247,0.339,-0.051,0.293,0.362,0.132,0.178,0.293,-0.143,0.224,0.431,0.041,0.247,0.431,0.041,0.109,0.293,175,Stability,PSAE_SYNP2,Medium,Prokaryote +PTEN_HUMAN_Mighell_2018_indels,-0.274,0.58,0.503,0.49,0.376,0.478,0.592,0.465,0.605,0.529,0.338,0.057,0.592,0.618,0.516,0.618,0.592,0.465,0.631,0.631,0.529,0.643,0.159,314,Activity,PTEN_HUMAN,Medium,Human +Q8EG35_SHEON_Campbell_2022_indels,0.39,0.148,0.366,0.305,0.269,0.184,0.426,0.269,0.269,0.233,0.233,0.136,0.45,0.499,0.269,0.39,0.462,0.233,0.378,0.39,0.269,0.414,0.196,331,OrganismalFitness,Q8EG35_SHEON,Medium,Prokaryote +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,0.511,0.381,0.295,0.684,0.684,0.727,0.511,0.684,0.684,0.597,0.425,0.122,0.468,0.511,0.381,0.036,0.381,0.295,0.079,0.511,0.381,0.641,-0.152,97,Stability,RAD_ANTMA,High,Eukaryote +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,0.198,0.475,0.119,0.0,0.04,0.238,0.0,0.0,0.079,0.04,0.158,0.119,0.0,0.079,0.158,-0.198,-0.158,-0.119,-0.198,-0.158,-0.119,0.357,0.198,124,Stability,RCD1_ARATH,Medium,Eukaryote +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,0.402,0.402,0.445,0.402,0.445,0.445,0.445,0.487,0.487,0.487,0.487,0.487,0.36,0.487,0.487,0.106,0.275,0.445,0.064,0.318,0.487,0.487,0.134,120,Stability,RD23A_HUMAN,High,Human +RPC1_BP434_Tsuboyama_2023_1R69_indels,0.389,0.265,0.389,0.389,0.389,0.42,0.389,0.42,0.327,0.42,0.202,0.14,0.389,0.389,0.389,0.296,0.327,0.358,0.296,0.327,0.358,0.016,0.358,164,Stability,RPC1_BP434,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32_indels,0.158,0.119,0.04,0.119,0.079,0.119,0.079,0.119,0.158,0.198,0.158,0.079,0.237,0.198,0.158,0.119,0.079,0.0,0.119,0.119,-0.04,0.079,0.115,176,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance_indels,0.228,0.033,0.282,0.304,0.369,0.348,0.282,0.358,0.369,0.304,0.217,0.163,0.304,0.293,0.326,0.25,0.293,0.348,0.282,0.337,0.369,0.326,0.25,430,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity_indels,0.346,0.115,0.423,0.389,0.508,0.474,0.423,0.517,0.431,0.44,0.295,0.226,0.363,0.491,0.457,0.312,0.406,0.465,0.38,0.465,0.465,0.354,0.26,490,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,0.771,0.817,0.677,0.631,0.631,0.631,0.631,0.677,0.631,0.631,0.631,0.49,0.677,0.631,0.677,0.21,0.444,0.49,0.257,0.49,0.537,0.817,0.631,86,Stability,SAV1_MOUSE,High,Eukaryote +SDA_BACSU_Tsuboyama_2023_1PV0_indels,0.116,0.186,0.151,0.359,0.29,0.428,0.186,0.29,0.116,0.22,0.498,-0.022,0.255,0.428,0.151,-0.23,-0.196,0.255,-0.23,-0.126,0.186,0.255,0.255,127,Stability,SDA_BACSU,Medium,Prokaryote +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,0.455,0.31,0.068,0.165,0.262,0.407,-0.028,0.165,0.31,0.213,0.262,0.262,-0.174,0.213,0.213,-0.125,-0.125,0.068,-0.125,-0.077,0.068,0.262,0.145,109,Stability,SOX30_HUMAN,High,Human +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,-0.155,0.296,0.07,0.099,0.324,0.268,0.24,0.24,0.211,0.211,0.324,-0.183,-0.014,-0.014,0.409,0.127,0.155,0.268,0.127,0.127,0.268,0.155,0.24,148,Stability,SPG2_STRSG,Medium,Prokaryote +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,0.29,0.071,0.352,0.477,0.446,0.477,0.258,0.633,0.508,0.539,0.415,0.04,0.508,0.508,0.539,0.227,0.383,0.383,0.227,0.352,0.383,0.071,0.258,129,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,0.15,0.202,0.304,0.202,0.304,0.304,0.304,0.304,0.355,0.304,0.355,0.048,0.253,0.304,0.304,-0.004,0.099,0.253,-0.004,0.202,0.304,0.202,0.279,111,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88_indels,-0.052,0.228,0.383,0.041,0.259,0.29,0.103,0.135,0.383,0.414,0.228,0.321,0.197,-0.052,0.135,0.321,0.321,0.446,0.383,0.29,0.383,0.228,0.197,135,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,0.501,0.637,0.339,0.501,0.447,0.474,0.474,0.529,0.583,0.529,0.366,0.339,0.447,0.501,0.637,0.529,0.529,0.637,0.529,0.529,0.637,0.23,0.204,154,Stability,SRBS1_HUMAN,High,Human +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,0.641,0.555,0.598,0.598,0.641,0.641,0.598,0.598,0.598,0.598,0.598,0.425,0.425,0.598,0.641,-0.482,-0.439,-0.309,-0.482,-0.309,-0.266,0.684,0.05,99,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG_indels,0.236,0.53,0.059,0.471,0.471,0.53,0.295,0.471,0.412,0.471,0.53,0.471,0.236,0.471,0.471,-0.059,0.059,0.0,-0.059,0.118,0.059,0.471,0.53,82,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels,-0.095,0.351,0.128,0.203,0.314,0.203,0.128,0.203,0.203,0.203,0.203,0.165,0.017,0.091,0.128,0.091,0.165,0.165,0.128,0.165,0.091,0.351,0.104,171,Stability,TNKS2_HUMAN,High,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels,-0.126,0.155,0.014,0.014,-0.021,0.014,0.12,0.014,0.085,0.014,0.155,0.225,0.014,-0.056,0.12,-0.021,-0.056,0.085,0.049,0.014,0.12,-0.126,0.043,147,Stability,UBE4B_HUMAN,High,Human 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+YNZC_BACSU_Tsuboyama_2023_2JVD_indels,0.382,0.582,0.382,0.341,0.502,0.462,0.341,0.582,0.221,0.582,0.582,0.181,0.422,0.422,0.582,-0.341,-0.382,-0.261,-0.341,-0.301,-0.181,0.542,0.301,104,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.html b/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.html new file mode 100644 index 0000000..718ff3b --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/DMS_indels_MCC_DMS_level.html @@ -0,0 +1,2083 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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scoreUnirepWavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LHidden Markov ModelProveanNumber of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A4_HUMAN_Seuma_2022_indels0.5850.6190.5500.6140.5920.6280.6640.6470.6330.6330.6180.6260.6560.6350.6040.6370.6420.5800.6400.6320.607-0.3760.5112346StabilityA4_HUMANLowHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O_indels0.1370.462-0.3900.2590.4210.4210.0160.4210.4210.2180.4210.421-0.106-0.0250.178-0.228-0.147-0.147-0.187-0.147-0.1470.4620.172117StabilityAMFR_HUMANMediumHuman
ARGR_ECOLI_Tsuboyama_2023_1AOY_indels0.2500.4070.4070.4070.4070.3440.1550.3440.4070.4070.3440.0290.3760.3760.407-0.0020.1870.2810.0610.2180.3440.4070.218181StabilityARGR_ECOLIMediumProkaryote
B1LPA6_ECOSM_Russ_2020_indels0.2210.3620.2230.2830.2410.2510.3360.2860.3780.3300.2860.2610.1460.1180.1080.2760.2520.2360.2690.2530.2350.2990.3693074ActivityB1LPA6_ECOSMMediumProkaryote
BBC1_YEAST_Tsuboyama_2023_1TG0_indels0.3090.0340.3770.1370.3430.2400.2400.3430.4460.3090.3770.2060.4800.4120.4460.3770.4120.4120.3770.4120.4120.1710.171134StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU_indels-0.0870.549-0.2020.1450.2600.3180.1450.3760.2600.4340.4910.0870.0290.2600.318-0.260-0.1450.087-0.145-0.1450.0870.5490.26082StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Gonzalez_2019_indels-0.0520.3100.2660.2450.1790.2440.3030.4450.4780.4750.2560.0780.2860.2770.2240.2690.2330.2420.2660.2740.2540.2930.2384751OrganismalFitnessBLAT_ECOLXHighProkaryote
CAPSD_AAV2S_Sinai_2021_designed_indels-0.3530.5580.2520.2980.4430.502-0.353-0.358-0.303-0.3610.3830.1300.2740.3020.5780.6030.5540.5990.6060.6180.6330.4740.571225998OrganismalFitnessCAPSD_AAV2SLowVirus
CAPSD_AAV2S_Sinai_2021_library_indels-0.1180.0330.0380.1030.0970.114-0.058-0.041-0.033-0.0390.1410.0830.0380.0900.1950.2450.2190.2230.2390.2380.2470.0820.18724908OrganismalFitnessCAPSD_AAV2SLowVirus
CATR_CHLRE_Tsuboyama_2023_2AMI_indels0.1840.3240.2300.2300.2770.2300.2070.0900.2070.0440.1370.0670.3240.1140.1840.3240.2540.2770.3240.1840.300-0.0500.114197StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels0.1800.2880.2450.3310.1800.2020.1370.1150.0940.0510.3310.0720.0720.3090.137-0.057-0.0140.008-0.057-0.014-0.0140.3520.180205StabilityCBPA2_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K28_indels0.0240.0550.0240.0860.0550.024-0.069-0.0070.0240.0550.024-0.0070.1490.0860.1180.1800.1490.1800.1800.1490.2110.1800.055129StabilityCBX4_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM_indels0.4800.4290.4040.4290.4290.5050.3020.4800.5050.4290.4290.2010.4040.4040.505-0.130-0.053-0.003-0.130-0.0030.0480.5050.251195StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX_indels0.3720.3280.3720.2840.2840.2410.1970.1970.1530.1970.3720.1970.1970.2840.372-0.197-0.197-0.109-0.153-0.197-0.1090.3280.284140StabilityCUE1_YEASTMediumEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC_indels0.1480.2070.1480.0300.0890.0300.0300.0890.0300.0300.0890.1480.1480.0300.0300.030-0.030-0.0890.0300.030-0.0890.2070.207136StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels0.3690.0920.1380.2310.2540.1380.2770.1610.2310.2310.3230.2080.3920.3690.3460.1850.3000.2770.2080.2770.2770.2310.069174StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels0.2680.5220.4090.4090.4650.2960.4940.4940.4370.4650.4650.1830.5780.6060.5780.2680.3810.3810.3240.4090.3810.4090.155154StabilityDOCK1_MOUSEHighEukaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels0.1220.3220.3470.3220.3720.3720.4220.3720.3720.3720.4220.3470.2720.2970.297-0.0030.0220.1470.0720.0470.2220.3470.147185StabilityEPHB2_HUMANHighHuman
FECA_ECOLI_Tsuboyama_2023_2D1U_indels0.241-0.0320.1500.1500.210-0.0320.1800.2410.3010.1500.0890.089-0.0020.1800.028-0.0630.028-0.063-0.0630.059-0.032-0.0320.028193StabilityFECA_ECOLIHighEukaryote
HCP_LAMBD_Tsuboyama_2023_2L6Q_indels0.2070.4610.4290.4290.4930.4930.3340.3340.3340.3020.4930.3020.4290.3660.461-0.016-0.0480.334-0.080-0.0800.3340.2700.334148StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM_indels0.2500.4260.2500.3670.3670.4260.3970.4550.4850.4550.4260.0440.0440.3380.0150.1320.2200.0440.0730.2200.0440.5440.213154StabilityHECD1_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_2019_indels0.0270.5680.5550.5550.5770.5710.5940.6100.5860.6020.597-0.0870.5420.5740.5960.5250.5280.5470.5350.5720.5770.4670.5856102OrganismalFitnessHIS7_YEASTMediumEukaryote
ILF3_HUMAN_Tsuboyama_2023_2L33_indels0.0630.3740.3070.2630.0630.1740.3520.0850.2410.3740.3290.0630.1740.3740.4180.0190.2410.2410.0410.2850.3070.0410.056193StabilityILF3_HUMANHighHuman
KCNJ2_MOUSE_Macdonald_2022_indels0.0630.3850.3130.3150.3170.3030.3270.3730.3770.3740.3160.2250.3160.3670.3360.3620.3960.4000.3590.4060.3920.3490.34210501ExpressionKCNJ2_MOUSEMediumEukaryote
MAFG_MOUSE_Tsuboyama_2023_1K1V_indels0.3220.263-0.0320.2630.2630.2630.2630.2630.2630.2630.263-0.0320.0270.2630.3220.0270.0270.0860.0270.0270.0860.204-0.062115StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV_indels0.1790.1480.1480.0220.4630.4320.3060.5900.4950.3060.2430.1160.0220.1790.116-0.0100.022-0.041-0.0100.022-0.0100.2430.463131StabilityMBD11_ARATHMediumEukaryote
MYO3_YEAST_Tsuboyama_2023_2BTT_indels-0.0520.3140.2620.2620.3670.3670.3140.3670.1050.3140.3140.2620.2620.3670.3140.1050.1570.1570.0520.2620.1570.3670.41980StabilityMYO3_YEASTHighEukaryote
NKX31_HUMAN_Tsuboyama_2023_2L9R_indels0.4110.5650.5650.5650.5390.5650.5130.5650.5650.5650.5650.1280.5900.4360.5650.4880.4360.5130.5390.4360.4880.4620.104178StabilityNKX31_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL_indels0.1930.1930.0540.1930.1240.0780.2160.0780.2860.0780.078-0.0380.2390.2390.147-0.0380.0080.054-0.0610.0310.0310.0780.263191StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI6_indels0.2230.1230.1230.1230.1230.1230.1230.1230.1230.1230.1230.2230.1230.1230.1730.0230.0730.1730.0730.0730.1730.1230.273157StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels0.2150.4730.6020.5250.5760.6280.5500.6280.6280.5500.4990.2930.2410.2670.3180.1380.2410.2670.1380.1890.2670.3180.119169StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G_indels0.0340.6050.6050.6050.6050.6050.6050.6050.6050.6050.6050.4150.3200.2250.3200.4150.5100.4150.4150.6050.4150.4150.60547StabilityODP2_GEOSEHighProkaryote
OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels0.4060.1740.2320.4640.5220.4640.1740.4640.4640.4640.4640.1160.2320.3480.464-0.1740.0000.116-0.1740.0000.1160.5220.40684StabilityOTU7A_HUMANHighHuman
P53_HUMAN_Kotler_2018_indels0.056-0.0030.2320.3260.2670.1380.3490.2900.2080.2900.173-0.0260.3610.4190.2430.2900.4190.2200.2900.4430.2670.3720.126341OrganismalFitnessP53_HUMANLowHuman
PIN1_HUMAN_Tsuboyama_2023_1I6C_indels0.5860.6280.5030.6280.5030.5860.5860.4190.5450.5860.4190.5860.5450.5860.6280.0420.4610.5860.0840.5450.6280.5030.461106StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M_indels0.3750.3300.3300.4190.3750.3750.3300.3750.3300.4190.4190.2860.3300.3750.3750.1070.1520.0630.1960.1960.1960.1960.233117StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF_indels0.1620.6340.5160.5390.5630.5160.5390.5870.6340.6100.6340.2090.4920.5390.5630.1150.4690.4690.1620.4920.4690.2090.256187StabilityPKN1_HUMANHighHuman
POLG_PESV_Tsuboyama_2023_2MXD_indels0.2830.3690.4550.3690.2830.2540.3400.3400.3400.2830.3400.1100.3690.2830.283-0.148-0.206-0.148-0.120-0.148-0.1200.1680.139149StabilityPOLG_PESVMediumVirus
PR40A_HUMAN_Tsuboyama_2023_1UZC_indels0.1270.4330.2040.4590.4590.3570.4080.4590.4840.4330.3570.4330.2550.1780.1270.0250.0760.0250.0000.1270.1020.5100.051168StabilityPR40A_HUMANMediumHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE_indels0.0640.1780.2470.1090.1090.0180.2470.339-0.0510.2930.3620.1320.1780.293-0.1430.2240.4310.0410.2470.4310.0410.1090.293175StabilityPSAE_SYNP2MediumProkaryote
PTEN_HUMAN_Mighell_2018_indels-0.2740.5800.5030.4900.3760.4780.5920.4650.6050.5290.3380.0570.5920.6180.5160.6180.5920.4650.6310.6310.5290.6430.159314ActivityPTEN_HUMANMediumHuman
Q8EG35_SHEON_Campbell_2022_indels0.3900.1480.3660.3050.2690.1840.4260.2690.2690.2330.2330.1360.4500.4990.2690.3900.4620.2330.3780.3900.2690.4140.196331OrganismalFitnessQ8EG35_SHEONMediumProkaryote
RAD_ANTMA_Tsuboyama_2023_2CJJ_indels0.5110.3810.2950.6840.6840.7270.5110.6840.6840.5970.4250.1220.4680.5110.3810.0360.3810.2950.0790.5110.3810.641-0.15297StabilityRAD_ANTMAHighEukaryote
RCD1_ARATH_Tsuboyama_2023_5OAO_indels0.1980.4750.1190.0000.0400.2380.0000.0000.0790.0400.1580.1190.0000.0790.158-0.198-0.158-0.119-0.198-0.158-0.1190.3570.198124StabilityRCD1_ARATHMediumEukaryote
RD23A_HUMAN_Tsuboyama_2023_1IFY_indels0.4020.4020.4450.4020.4450.4450.4450.4870.4870.4870.4870.4870.3600.4870.4870.1060.2750.4450.0640.3180.4870.4870.134120StabilityRD23A_HUMANHighHuman
RPC1_BP434_Tsuboyama_2023_1R69_indels0.3890.2650.3890.3890.3890.4200.3890.4200.3270.4200.2020.1400.3890.3890.3890.2960.3270.3580.2960.3270.3580.0160.358164StabilityRPC1_BP434HighVirus
RS15_GEOSE_Tsuboyama_2023_1A32_indels0.1580.1190.0400.1190.0790.1190.0790.1190.1580.1980.1580.0790.2370.1980.1580.1190.0790.0000.1190.119-0.0400.0790.115176StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance_indels0.2280.0330.2820.3040.3690.3480.2820.3580.3690.3040.2170.1630.3040.2930.3260.2500.2930.3480.2820.3370.3690.3260.250430ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity_indels0.3460.1150.4230.3890.5080.4740.4230.5170.4310.4400.2950.2260.3630.4910.4570.3120.4060.4650.3800.4650.4650.3540.260490ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB_indels0.7710.8170.6770.6310.6310.6310.6310.6770.6310.6310.6310.4900.6770.6310.6770.2100.4440.4900.2570.4900.5370.8170.63186StabilitySAV1_MOUSEHighEukaryote
SDA_BACSU_Tsuboyama_2023_1PV0_indels0.1160.1860.1510.3590.2900.4280.1860.2900.1160.2200.498-0.0220.2550.4280.151-0.230-0.1960.255-0.230-0.1260.1860.2550.255127StabilitySDA_BACSUMediumProkaryote
SOX30_HUMAN_Tsuboyama_2023_7JJK_indels0.4550.3100.0680.1650.2620.407-0.0280.1650.3100.2130.2620.262-0.1740.2130.213-0.125-0.1250.068-0.125-0.0770.0680.2620.145109StabilitySOX30_HUMANHighHuman
SPG2_STRSG_Tsuboyama_2023_5UBS_indels-0.1550.2960.0700.0990.3240.2680.2400.2400.2110.2110.324-0.183-0.014-0.0140.4090.1270.1550.2680.1270.1270.2680.1550.240148StabilitySPG2_STRSGMediumProkaryote
SPTN1_CHICK_Tsuboyama_2023_1TUD_indels0.2900.0710.3520.4770.4460.4770.2580.6330.5080.5390.4150.0400.5080.5080.5390.2270.3830.3830.2270.3520.3830.0710.258129StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels0.1500.2020.3040.2020.3040.3040.3040.3040.3550.3040.3550.0480.2530.3040.304-0.0040.0990.253-0.0040.2020.3040.2020.279111StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N88_indels-0.0520.2280.3830.0410.2590.2900.1030.1350.3830.4140.2280.3210.197-0.0520.1350.3210.3210.4460.3830.2900.3830.2280.197135StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels0.5010.6370.3390.5010.4470.4740.4740.5290.5830.5290.3660.3390.4470.5010.6370.5290.5290.6370.5290.5290.6370.2300.204154StabilitySRBS1_HUMANHighHuman
TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels0.6410.5550.5980.5980.6410.6410.5980.5980.5980.5980.5980.4250.4250.5980.641-0.482-0.439-0.309-0.482-0.309-0.2660.6840.05099StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG_indels0.2360.5300.0590.4710.4710.5300.2950.4710.4120.4710.5300.4710.2360.4710.471-0.0590.0590.000-0.0590.1180.0590.4710.53082StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels-0.0950.3510.1280.2030.3140.2030.1280.2030.2030.2030.2030.1650.0170.0910.1280.0910.1650.1650.1280.1650.0910.3510.104171StabilityTNKS2_HUMANHighHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels-0.1260.1550.0140.014-0.0210.0140.1200.0140.0850.0140.1550.2250.014-0.0560.120-0.021-0.0560.0850.0490.0140.120-0.1260.043147StabilityUBE4B_HUMANHighHuman
UBR5_HUMAN_Tsuboyama_2023_1I2T_indels0.2090.2730.2090.2090.2090.1770.3050.2410.2410.2730.2730.2090.2410.2410.241-0.0160.0160.048-0.0160.0160.0480.1120.106156StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP8_indels0.3340.6740.4610.6320.6320.6740.2060.5040.2910.2910.674-0.2620.3760.6740.6740.0780.2060.5470.1210.2910.6320.6740.334101StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU5_indels-0.0900.3800.1270.4160.3070.3070.2350.3800.3440.3070.2710.0540.4160.2710.3440.1630.2710.2710.1630.2710.2710.3800.235156StabilityVILI_CHICKHighEukaryote
VRPI_BPT7_Tsuboyama_2023_2WNM_indels0.3140.3660.1570.2880.4180.2620.3400.3400.2350.2880.3660.0000.2090.1050.183-0.131-0.131-0.209-0.105-0.157-0.1830.2090.564154StabilityVRPI_BPT7MediumVirus
YNZC_BACSU_Tsuboyama_2023_2JVD_indels0.3820.5820.3820.3410.5020.4620.3410.5820.2210.5820.5820.1810.4220.4220.582-0.341-0.382-0.261-0.341-0.301-0.1810.5420.301104StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.csv b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.csv new file mode 100644 index 0000000..dd203b5 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.csv @@ -0,0 +1,24 @@ +Model_rank,Model_name,Model type,Average_MCC,Bootstrap_standard_error_MCC,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Model details,References +1,Progen2 Base,Protein language model,0.365,0.0,0.471,,0.373,0.275,0.343,0.224,0.312,0.383,0.386,0.393,0.249,0.226,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +2,Tranception M no retrieval,Protein language model,0.361,0.031,0.409,,0.33,0.393,0.311,0.417,0.292,0.339,0.334,0.354,0.248,0.335,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +3,Hidden Markov Model,Alignment-based model,0.357,0.028,0.432,,0.338,0.365,0.292,0.091,0.344,0.29,0.299,0.341,0.28,0.269,Profile Hidden Markov model,HMMER: biosequence analysis using profile hidden Markov models +4,Progen2 M,Protein language model,0.356,0.015,0.423,,0.366,0.283,0.354,0.246,0.343,0.367,0.357,0.396,0.299,0.29,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +5,TranceptEVE M,Hybrid model,0.35,0.033,0.45,,0.372,0.421,0.157,0.501,0.083,0.266,0.245,0.196,0.141,0.11,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +6,Progen2 L,Protein language model,0.349,0.009,0.433,,0.339,0.28,0.343,0.241,0.319,0.372,0.372,0.368,0.301,0.231,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +7,RITA L,Protein language model,0.346,0.033,0.375,,0.343,0.312,0.355,0.376,0.329,0.368,0.357,0.389,0.257,0.414,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +8,RITA XL,Protein language model,0.341,0.03,0.401,,0.326,0.289,0.349,0.358,0.324,0.362,0.359,0.378,0.248,0.402,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +9,Tranception L no retrieval,Protein language model,0.341,0.035,0.36,,0.331,0.344,0.328,0.411,0.282,0.366,0.339,0.372,0.225,0.396,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +10,RITA M,Protein language model,0.337,0.023,0.387,,0.31,0.326,0.326,0.38,0.29,0.357,0.364,0.329,0.24,0.385,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +11,TranceptEVE L,Hybrid model,0.336,0.034,0.41,,0.38,0.361,0.194,0.438,0.105,0.302,0.268,0.207,0.142,0.244,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +12,Progen2 S,Protein language model,0.335,0.015,0.45,,0.304,0.293,0.294,0.269,0.286,0.316,0.335,0.318,0.245,0.234,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +13,Tranception S no retrieval,Protein language model,0.326,0.025,0.367,,0.31,0.359,0.269,0.391,0.236,0.311,0.272,0.316,0.228,0.321,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,Tranception M,Hybrid model,0.325,0.033,0.417,,0.344,0.406,0.132,0.482,0.06,0.241,0.226,0.166,0.117,0.089,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +15,Tranception L,Hybrid model,0.318,0.034,0.389,,0.374,0.331,0.177,0.404,0.089,0.284,0.246,0.188,0.138,0.216,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,RITA S,Protein language model,0.315,0.022,0.383,,0.298,0.313,0.269,0.309,0.242,0.306,0.28,0.305,0.206,0.339,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +17,Progen2 XL,Protein language model,0.31,0.028,0.306,,0.266,0.304,0.364,0.351,0.355,0.356,0.365,0.361,0.316,0.39,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +18,TranceptEVE S,Hybrid model,0.3,0.033,0.427,,0.32,0.378,0.074,0.451,0.032,0.165,0.158,0.1,0.086,0.089,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +19,Wavenet,Alignment-based model,0.295,0.047,0.352,,0.209,0.264,0.355,0.304,0.349,0.351,0.361,0.346,0.305,0.405,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +20,Tranception S,Hybrid model,0.287,0.034,0.402,,0.306,0.38,0.059,0.45,0.024,0.145,0.14,0.09,0.072,0.084,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +21,Provean,Alignment-based model,0.273,0.041,0.263,,0.296,0.305,0.229,0.339,0.242,0.225,0.18,0.249,0.274,0.351,Provean model,"Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one." +22,ProtGPT2,Protein language model,0.151,0.045,0.181,,0.194,0.042,0.186,0.236,0.135,0.205,0.239,0.176,0.109,0.066,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +23,Unirep,Protein language model,0.13,0.062,0.098,,0.146,0.037,0.239,0.135,0.194,0.238,0.226,0.252,0.135,0.215,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." diff --git a/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.html b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.html new file mode 100644 index 0000000..2b8da55 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_DMS_indels_MCC.html @@ -0,0 +1,531 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_MCCBootstrap_standard_error_MCCFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusModel detailsReferences
Model_rank
1TranceptEVE MHybrid model0.3870.0000.580NaN0.3730.4320.1640.5190.1260.2510.2550.2300.1460.082TranceptEVE Medium model (Tranception Medium & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
2TranceptEVE LHybrid model0.3800.0120.544NaN0.3820.3810.2140.4710.1440.3140.3020.2570.1420.214TranceptEVE Large model (Tranception Large & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
3Progen2 BaseProtein language model0.3750.0340.472NaN0.3740.2910.3650.2510.3170.4160.4070.4380.2360.240Progen2 base model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
4Progen2 MProtein language model0.3690.0390.423NaN0.3670.2960.3890.2670.3580.4140.3870.4500.3050.314Progen2 medium model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
5Tranception M no retrievalProtein language model0.3680.0390.409NaN0.3320.4110.3190.4470.2770.3660.3650.3500.2170.382Tranception Medium model (300M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
6Progen2 LProtein language model0.3590.0350.433NaN0.3400.2930.3710.2620.3250.4140.4030.4050.2870.278Progen2 large model (2.7B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
7Tranception L no retrievalProtein language model0.3520.0340.361NaN0.3320.3710.3450.4560.2750.4020.3780.3780.2160.426Tranception Large model (700M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
8Hidden Markov ModelAlignment-based model0.3510.0250.433NaN0.3380.3870.2450.1290.2930.2550.2800.2630.2460.271Profile Hidden Markov modelHMMER: biosequence analysis using profile hidden Markov models
9RITA XLProtein language model0.3500.0290.401NaN0.3260.3190.3550.4080.3310.3660.3680.3780.2380.468RITA xlarge model (1.2B params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
10RITA LProtein language model0.3480.0310.375NaN0.3440.3250.3490.3970.3250.3620.3590.3690.2440.466RITA large model (680M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
11Progen2 SProtein language model0.3460.0340.451NaN0.3060.2940.3340.2710.3000.3740.3730.3630.2680.249Progen2 small model (150M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
12RITA MProtein language model0.3390.0210.388NaN0.3110.3270.3320.3810.2730.3830.3780.3340.2180.419RITA medium model (300M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
13Tranception MHybrid model0.3340.0240.417NaN0.3460.4260.1450.5170.0960.2310.2400.2070.0970.077Tranception Medium model (300M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
14Tranception S no retrievalProtein language model0.3330.0330.368NaN0.3120.3730.2800.4140.2560.3120.2800.3320.2380.324Tranception Small model (85M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
15Tranception LHybrid model0.3290.0250.389NaN0.3750.3620.1900.4560.1060.2910.2780.2310.0960.187Tranception Large model (700M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
16Progen2 XLProtein language model0.3260.0170.307NaN0.2680.3330.3960.3980.3750.3930.3910.4110.3380.384Progen2 xlarge model (6.4B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
17RITA SProtein language model0.3240.0280.383NaN0.2990.3270.2870.3330.2640.3180.3050.3090.2260.360RITA small model (85M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
18TranceptEVE SHybrid model0.3030.0240.427NaN0.3220.3910.0700.4720.0570.1360.1420.1370.0710.043TranceptEVE Small model (Tranception Small & retrieved EVE model)Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
19Tranception SHybrid model0.2910.0220.403NaN0.3080.3920.0610.4710.0510.1250.1350.1230.0620.052Tranception Small model (85M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
20ProveanAlignment-based model0.2790.0240.263NaN0.2980.3140.2400.3540.2380.2470.2050.2560.2880.301Provean modelChoi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one.
21WavenetAlignment-based model0.2370.0360.353NaN0.2110.2940.0890.1580.1720.0720.0560.1650.1060.274Wavenet modelShin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.
22ProtGPT2Protein language model0.1560.0280.182NaN0.1960.0710.1740.2850.1280.1900.2290.1820.0910.046ProtGPT2 modelFerruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.
23UnirepProtein language model0.1350.0580.098NaN0.1470.0300.2660.1220.2100.2700.2410.2880.1520.236Unirep modelAlley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_all_models_indels_MCC.csv b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_all_models_indels_MCC.csv new file mode 100644 index 0000000..7fbf1b0 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/Summary_performance_all_models_indels_MCC.csv @@ -0,0 +1,22 @@ +Model_rank,Model_name,Model type,Average_MCC,Bootstrap_standard_error_MCC,Model details,References +1,TranceptEVE M,Hybrid model,0.431,0.0,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +2,Tranception M,Hybrid model,0.427,0.002,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +3,Tranception S,Hybrid model,0.396,0.018,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +4,TranceptEVE L,Hybrid model,0.383,0.018,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +5,Tranception L,Hybrid model,0.375,0.018,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,Tranception L no retrieval,Protein language model,0.365,0.018,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +7,RITA (ensemble),Protein language model,0.35,0.028,Ensemble of the 4 RITA models,"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +8,RITA XL,Protein language model,0.345,0.027,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +9,Progen2 (ensemble),Protein language model,0.341,0.091,Ensemble of the 5 Progen2 models," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +10,Wavenet,Alignment-based model,0.341,0.049,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +11,Unirep evotuned,Hybrid model,0.333,0.045,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +12,RITA M,Protein language model,0.33,0.03,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +13,Progen2 Base,Protein language model,0.323,0.093,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +14,RITA L,Protein language model,0.32,0.03,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +15,Progen2 L,Protein language model,0.306,0.097,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +16,Progen2 XL,Protein language model,0.303,0.037,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +17,RITA S,Protein language model,0.299,0.036,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +18,Progen2 M,Protein language model,0.297,0.096,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +19,Progen2 S,Protein language model,0.276,0.086,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +20,Unirep,Protein language model,0.107,0.062,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +21,ProtGPT2,Protein language model,0.089,0.078,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." diff --git a/benchmarks/DMS_zero_shot/indels/MCC/all_models_indels_MCC_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/MCC/all_models_indels_MCC_DMS_level.csv new file mode 100644 index 0000000..b2f95c3 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/MCC/all_models_indels_MCC_DMS_level.csv @@ -0,0 +1,9 @@ +,Tranception_L_no_retrieval,Tranception_S_retrieval,Tranception_M_retrieval,Tranception_L_retrieval,Wavenet,RITA_s,RITA_m,RITA_l,RITA_xl,RITA_ensemble,Progen2_small,Progen2_medium,Progen2_base,Progen2_large,Progen2_xlarge,Progen2_ensemble,Unirep,Unirep_evotune,ProtGPT2,TranceptEVE_L,TranceptEVE_M,number_mutants +A0A1J4YT16_9PROT_Davidi_2020,0.143,0.181,0.219,0.143,0.143,0.028,0.143,0.181,0.219,0.105,0.066,0.181,0.181,0.143,0.105,0.105,-0.048,0.295,0.143,0.181,0.219,105 +B1LPA6_ECOSM_Russ_2020,0.264,0.357,0.351,0.301,0.274,0.261,0.325,0.268,0.263,0.293,0.241,0.253,0.301,0.264,0.218,0.275,0.241,0.35,0.234,0.303,0.361,3074 +BLAT_ECOLX_Gonzalez_indels_2019,0.225,0.285,0.274,0.246,0.357,0.267,0.245,0.179,0.245,0.28,0.304,0.445,0.479,0.475,0.257,0.521,0.053,0.171,0.078,0.254,0.274,4751 +CAPSD_AAV2S_Sinai_indels_2021,0.432,0.451,0.452,0.465,0.371,0.176,0.224,0.331,0.355,0.281,-0.221,-0.226,-0.176,-0.227,0.291,-0.132,0.28,0.384,0.077,0.471,0.464,250907 +HIS7_YEAST_Pokusaeva_indels_2019,0.596,0.53,0.568,0.578,0.567,0.555,0.555,0.577,0.571,0.572,0.594,0.61,0.586,0.602,0.597,0.61,-0.027,0.239,-0.087,0.577,0.572,6102 +PTEN_HUMAN_Mighell_deletions_2018,0.516,0.618,0.631,0.541,0.554,0.503,0.49,0.376,0.478,0.605,0.592,0.465,0.605,0.529,0.338,0.631,0.274,0.478,0.057,0.529,0.631,314 +P53_HUMAN_Kotler_deletions_2018,0.379,0.353,0.496,0.353,0.121,0.302,0.328,0.328,0.289,0.315,0.353,0.353,0.289,0.353,0.315,0.379,-0.021,0.418,0.121,0.366,0.496,341 +Average,0.365,0.396,0.427,0.375,0.341,0.299,0.33,0.32,0.345,0.35,0.276,0.297,0.323,0.306,0.303,0.341,0.107,0.333,0.089,0.383,0.431,37942 diff --git a/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.csv new file mode 100644 index 0000000..1ed3b55 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.csv @@ -0,0 +1,67 @@ +DMS ID,Unirep,Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,Hidden Markov Model,Provean,Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A4_HUMAN_Seuma_2022_indels,0.729,0.758,0.781,0.708,0.689,0.696,0.778,0.765,0.767,0.779,0.751,0.759,0.793,0.654,0.702,0.793,0.701,0.705,0.791,0.696,0.735,0.416,0.769,2346,Stability,A4_HUMAN,Low,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,0.331,0.882,0.26,0.467,0.837,0.921,0.374,0.871,0.883,0.848,0.892,0.921,0.325,0.24,0.336,0.221,0.281,0.31,0.225,0.307,0.352,0.822,0.845,117,Stability,AMFR_HUMAN,Medium,Human +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,0.74,0.935,0.933,0.973,0.969,0.984,0.786,0.984,0.982,0.987,0.981,0.58,0.895,0.813,0.971,0.785,0.781,0.921,0.801,0.795,0.926,0.796,0.881,181,Stability,ARGR_ECOLI,Medium,Prokaryote +B1LPA6_ECOSM_Russ_2020_indels,0.702,0.75,0.697,0.707,0.707,0.711,0.735,0.751,0.722,0.733,0.726,0.684,0.669,0.716,0.693,0.718,0.73,0.704,0.71,0.724,0.713,0.75,0.686,3074,Activity,B1LPA6_ECOSM,Medium,Prokaryote +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,0.588,0.737,0.398,0.503,0.601,0.743,0.603,0.9,0.863,0.855,0.819,0.426,0.661,0.688,0.456,0.485,0.629,0.525,0.489,0.664,0.488,0.454,0.938,134,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU_indels,0.579,0.862,0.507,0.58,0.37,0.665,0.713,0.762,0.453,0.93,0.976,0.434,0.496,0.698,0.817,0.341,0.453,0.792,0.484,0.471,0.813,0.837,0.958,82,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Gonzalez_2019_indels,0.256,0.577,0.505,0.552,0.534,0.44,0.463,0.582,0.593,0.58,0.406,0.21,0.515,0.5,0.387,0.451,0.466,0.396,0.481,0.52,0.392,0.225,0.513,4751,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +CAPSD_AAV2S_Sinai_2021_designed_indels,0.512,0.886,0.784,0.8,0.894,0.918,0.468,0.462,0.49,0.459,0.858,0.716,0.804,0.808,0.937,0.924,0.916,0.929,0.923,0.929,0.936,0.898,0.904,225998,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAPSD_AAV2S_Sinai_2021_library_indels,0.472,0.467,0.551,0.597,0.573,0.613,0.472,0.479,0.483,0.481,0.642,0.539,0.55,0.589,0.641,0.695,0.699,0.709,0.697,0.69,0.749,0.466,0.491,24908,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,0.888,0.872,0.882,0.885,0.88,0.874,0.87,0.885,0.881,0.856,0.875,0.671,0.852,0.877,0.872,0.909,0.875,0.871,0.908,0.872,0.861,0.669,0.876,197,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,0.719,0.933,0.606,0.738,0.659,0.645,0.608,0.729,0.771,0.732,0.917,0.474,0.621,0.647,0.578,0.57,0.61,0.566,0.569,0.631,0.569,0.852,0.838,205,Stability,CBPA2_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,0.519,0.698,0.686,0.791,0.791,0.829,0.847,0.826,0.826,0.848,0.859,0.416,0.725,0.806,0.819,0.623,0.551,0.653,0.65,0.588,0.695,0.77,0.822,129,Stability,CBX4_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,0.478,0.959,0.62,0.535,0.888,0.959,0.422,0.966,0.935,0.965,0.965,0.618,0.48,0.866,0.862,0.437,0.819,0.843,0.413,0.797,0.836,0.897,0.932,195,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,0.583,0.887,0.673,0.596,0.591,0.457,0.427,0.461,0.473,0.529,0.799,0.418,0.504,0.462,0.401,0.325,0.352,0.33,0.325,0.354,0.345,0.593,0.976,140,Stability,CUE1_YEAST,Medium,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,0.525,0.808,0.474,0.666,0.623,0.606,0.702,0.59,0.576,0.757,0.755,0.415,0.558,0.455,0.562,0.384,0.392,0.445,0.398,0.377,0.404,0.591,0.96,136,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,0.863,0.811,0.949,0.949,0.967,0.951,0.961,0.958,0.983,0.965,0.967,0.7,0.955,0.952,0.971,0.928,0.954,0.945,0.93,0.954,0.943,0.758,0.836,174,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,0.62,0.862,0.914,0.926,0.891,0.915,0.849,0.924,0.853,0.875,0.952,0.633,0.667,0.664,0.753,0.643,0.656,0.705,0.656,0.663,0.782,0.595,0.921,154,Stability,DOCK1_MOUSE,High,Eukaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,0.518,0.907,0.972,0.977,0.979,0.973,0.97,0.979,0.981,0.975,0.972,0.65,0.957,0.978,0.971,0.757,0.787,0.822,0.783,0.825,0.823,0.705,0.841,185,Stability,EPHB2_HUMAN,High,Human +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,0.558,0.561,0.46,0.788,0.75,0.757,0.421,0.817,0.846,0.812,0.791,0.503,0.26,0.246,0.326,0.262,0.261,0.279,0.265,0.276,0.276,0.714,0.787,193,Stability,FECA_ECOLI,High,Eukaryote +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,0.564,0.927,0.754,0.893,0.867,0.845,0.839,0.645,0.762,0.787,0.909,0.661,0.649,0.616,0.887,0.43,0.503,0.883,0.426,0.421,0.885,0.844,0.933,148,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,0.665,0.888,0.749,0.889,0.881,0.887,0.654,0.841,0.81,0.82,0.822,0.5,0.518,0.637,0.586,0.647,0.674,0.617,0.641,0.665,0.586,0.791,0.786,154,Stability,HECD1_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019_indels,0.094,0.863,0.772,0.748,0.795,0.808,0.73,0.837,0.888,0.857,0.888,0.298,0.723,0.862,0.874,0.8,0.728,0.851,0.818,0.849,0.881,0.748,0.879,6102,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,0.488,0.85,0.816,0.852,0.853,0.862,0.836,0.856,0.836,0.852,0.834,0.871,0.585,0.84,0.833,0.453,0.777,0.799,0.491,0.776,0.808,0.891,0.832,193,Stability,ILF3_HUMAN,High,Human +KCNJ2_MOUSE_Macdonald_2022_indels,0.662,0.772,0.775,0.78,0.768,0.771,0.797,0.786,0.776,0.794,0.77,0.69,0.773,0.771,0.768,0.776,0.764,0.771,0.776,0.771,0.771,0.765,0.774,10501,Expression,KCNJ2_MOUSE,Medium,Eukaryote +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,0.626,0.694,0.518,0.751,0.821,0.841,0.688,0.802,0.834,0.755,0.725,0.512,0.432,0.7,0.714,0.498,0.706,0.713,0.5,0.706,0.678,0.532,0.599,115,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,0.494,0.872,0.507,0.612,0.749,0.655,0.734,0.91,0.907,0.852,0.922,0.802,0.553,0.551,0.447,0.618,0.622,0.618,0.631,0.624,0.636,0.737,0.918,131,Stability,MBD11_ARATH,Medium,Eukaryote +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,0.401,0.753,0.766,0.571,0.708,0.669,0.935,0.858,0.626,0.899,0.987,0.518,0.562,0.653,0.545,0.595,0.611,0.574,0.609,0.649,0.634,0.47,0.917,80,Stability,MYO3_YEAST,High,Eukaryote +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,0.908,0.778,0.837,0.879,0.85,0.836,0.884,0.805,0.917,0.711,0.789,0.723,0.907,0.826,0.715,0.907,0.91,0.828,0.908,0.904,0.81,0.825,0.826,178,Stability,NKX31_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,0.664,0.81,0.876,0.841,0.877,0.892,0.652,0.839,0.679,0.921,0.916,0.343,0.699,0.876,0.734,0.584,0.658,0.63,0.617,0.73,0.665,0.652,0.902,191,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,0.504,0.685,0.865,0.891,0.88,0.891,0.859,0.884,0.857,0.875,0.865,0.479,0.857,0.84,0.783,0.672,0.649,0.682,0.674,0.643,0.7,0.436,0.69,157,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,0.68,0.862,0.908,0.942,0.946,0.919,0.887,0.93,0.934,0.884,0.89,0.766,0.724,0.507,0.702,0.445,0.446,0.554,0.468,0.47,0.559,0.671,0.764,169,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,0.501,0.92,0.924,0.988,0.947,0.958,0.982,0.986,0.983,0.976,0.97,0.871,0.931,0.662,0.786,0.776,0.792,0.575,0.776,0.954,0.692,0.767,0.972,47,Stability,ODP2_GEOSE,High,Prokaryote +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,0.359,0.579,0.398,0.883,0.903,0.924,0.784,0.925,0.927,0.949,0.939,0.715,0.505,0.543,0.798,0.241,0.327,0.479,0.248,0.327,0.593,0.473,0.949,84,Stability,OTU7A_HUMAN,High,Human +P53_HUMAN_Kotler_2018_indels,0.291,0.447,0.558,0.408,0.456,0.693,0.533,0.692,0.616,0.661,0.653,0.19,0.66,0.566,0.681,0.576,0.543,0.629,0.614,0.501,0.616,0.548,0.653,341,OrganismalFitness,P53_HUMAN,Low,Human +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,0.766,0.853,0.844,0.894,0.874,0.846,0.833,0.837,0.842,0.84,0.846,0.851,0.689,0.921,0.849,0.681,0.967,0.858,0.666,0.965,0.85,0.931,0.838,106,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,0.89,0.798,0.94,0.907,0.888,0.91,0.886,0.934,0.969,0.922,0.967,0.71,0.922,0.921,0.937,0.68,0.701,0.75,0.722,0.773,0.782,0.67,0.903,117,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,0.311,0.857,0.905,0.907,0.92,0.915,0.895,0.909,0.905,0.928,0.921,0.769,0.442,0.884,0.895,0.698,0.894,0.9,0.719,0.893,0.9,0.855,0.916,187,Stability,PKN1_HUMAN,High,Human +POLG_PESV_Tsuboyama_2023_2MXD_indels,0.641,0.905,0.591,0.603,0.602,0.648,0.631,0.592,0.576,0.494,0.638,0.59,0.637,0.596,0.667,0.518,0.459,0.514,0.636,0.473,0.522,0.684,0.859,149,Stability,POLG_PESV,Medium,Virus +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,0.463,0.963,0.911,0.978,0.979,0.983,0.954,0.985,0.96,0.983,0.982,0.783,0.689,0.78,0.781,0.693,0.762,0.74,0.729,0.773,0.74,0.808,0.739,168,Stability,PR40A_HUMAN,Medium,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE_indels,0.371,0.508,0.41,0.271,0.375,0.647,0.458,0.631,0.374,0.599,0.636,0.63,0.447,0.753,0.591,0.514,0.628,0.548,0.476,0.679,0.626,0.749,0.602,175,Stability,PSAE_SYNP2,Medium,Prokaryote +PTEN_HUMAN_Mighell_2018_indels,0.325,0.872,0.799,0.874,0.87,0.799,0.863,0.854,0.892,0.837,0.837,0.375,0.877,0.891,0.907,0.869,0.904,0.908,0.867,0.888,0.9,0.849,0.897,314,Activity,PTEN_HUMAN,Medium,Human +Q8EG35_SHEON_Campbell_2022_indels,0.512,0.131,0.394,0.351,0.299,0.352,0.55,0.395,0.255,0.288,0.202,0.396,0.549,0.597,0.486,0.451,0.538,0.517,0.448,0.581,0.473,0.441,0.16,331,OrganismalFitness,Q8EG35_SHEON,Medium,Prokaryote +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,0.6,0.825,0.786,0.836,0.825,0.846,0.876,0.844,0.795,0.832,0.874,0.744,0.56,0.809,0.791,0.786,0.839,0.87,0.804,0.837,0.868,0.674,0.796,97,Stability,RAD_ANTMA,High,Eukaryote +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,0.409,0.842,0.484,0.595,0.504,0.642,0.331,0.696,0.651,0.653,0.9,0.462,0.386,0.413,0.487,0.448,0.372,0.346,0.438,0.382,0.348,0.774,0.772,124,Stability,RCD1_ARATH,Medium,Eukaryote +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,0.663,0.93,0.951,0.965,0.914,0.983,0.982,0.978,0.956,0.979,0.973,0.97,0.806,0.937,0.958,0.795,0.922,0.891,0.825,0.928,0.912,0.696,0.98,120,Stability,RD23A_HUMAN,High,Human +RPC1_BP434_Tsuboyama_2023_1R69_indels,0.863,0.687,0.783,0.82,0.818,0.832,0.837,0.851,0.829,0.847,0.847,0.554,0.563,0.803,0.871,0.558,0.71,0.809,0.526,0.739,0.834,0.523,0.768,164,Stability,RPC1_BP434,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32_indels,0.7,0.851,0.884,0.89,0.883,0.888,0.851,0.882,0.909,0.888,0.888,0.68,0.889,0.878,0.872,0.828,0.794,0.789,0.86,0.801,0.795,0.802,0.782,176,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance_indels,0.649,0.646,0.72,0.81,0.825,0.792,0.77,0.855,0.853,0.81,0.789,0.614,0.802,0.835,0.805,0.738,0.692,0.765,0.755,0.802,0.769,0.555,0.766,430,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity_indels,0.552,0.486,0.602,0.721,0.762,0.727,0.7,0.784,0.821,0.772,0.733,0.498,0.742,0.787,0.771,0.636,0.622,0.705,0.664,0.751,0.712,0.416,0.694,490,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,0.764,0.844,0.85,0.861,0.849,0.864,0.863,0.849,0.858,0.857,0.833,0.894,0.838,0.84,0.831,0.769,0.853,0.843,0.852,0.853,0.846,0.841,0.851,86,Stability,SAV1_MOUSE,High,Eukaryote +SDA_BACSU_Tsuboyama_2023_1PV0_indels,0.587,0.949,0.674,0.707,0.692,0.743,0.807,0.857,0.587,0.804,0.951,0.571,0.708,0.675,0.935,0.661,0.696,0.782,0.661,0.701,0.88,0.81,0.965,127,Stability,SDA_BACSU,Medium,Prokaryote +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,0.714,0.857,0.893,0.939,0.955,0.958,0.92,0.961,0.949,0.967,0.971,0.979,0.54,0.984,0.963,0.466,0.805,0.859,0.581,0.845,0.888,0.782,0.979,109,Stability,SOX30_HUMAN,High,Human +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,0.394,0.755,0.526,0.579,0.55,0.577,0.672,0.769,0.761,0.834,0.805,0.426,0.447,0.507,0.819,0.563,0.592,0.675,0.566,0.591,0.698,0.579,0.835,148,Stability,SPG2_STRSG,Medium,Prokaryote +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,0.584,0.684,0.891,0.824,0.933,0.88,0.932,0.927,0.795,0.948,0.939,0.428,0.714,0.688,0.823,0.653,0.682,0.766,0.706,0.665,0.796,0.491,0.479,129,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,0.526,0.691,0.767,0.918,0.919,0.922,0.736,0.908,0.951,0.929,0.909,0.591,0.445,0.858,0.937,0.483,0.834,0.81,0.549,0.835,0.849,0.44,0.955,111,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88_indels,0.517,0.79,0.758,0.723,0.729,0.636,0.534,0.439,0.599,0.436,0.581,0.53,0.709,0.39,0.599,0.774,0.606,0.731,0.791,0.609,0.74,0.67,0.758,135,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,0.692,0.918,0.935,0.904,0.935,0.915,0.933,0.918,0.913,0.937,0.934,0.809,0.873,0.941,0.868,0.921,0.92,0.892,0.924,0.929,0.906,0.922,0.927,154,Stability,SRBS1_HUMAN,High,Human +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,0.829,0.956,0.967,0.972,0.974,0.982,0.945,0.981,0.973,0.981,0.985,0.812,0.834,0.892,0.952,0.376,0.572,0.633,0.375,0.592,0.759,0.917,0.976,99,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG_indels,0.338,0.844,0.259,0.536,0.546,0.497,0.648,0.768,0.693,0.683,0.874,0.461,0.325,0.841,0.519,0.227,0.734,0.744,0.234,0.837,0.82,0.625,0.988,82,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels,0.431,0.599,0.702,0.881,0.844,0.881,0.899,0.906,0.901,0.898,0.906,0.467,0.454,0.433,0.728,0.481,0.495,0.654,0.504,0.447,0.712,0.645,0.831,171,Stability,TNKS2_HUMAN,High,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels,0.178,0.763,0.21,0.783,0.803,0.825,0.799,0.829,0.801,0.83,0.823,0.6,0.417,0.238,0.755,0.376,0.309,0.65,0.336,0.314,0.643,0.398,0.72,147,Stability,UBE4B_HUMAN,High,Human +UBR5_HUMAN_Tsuboyama_2023_1I2T_indels,0.608,0.88,0.945,0.917,0.955,0.952,0.941,0.953,0.94,0.954,0.956,0.54,0.878,0.909,0.831,0.6,0.79,0.702,0.596,0.794,0.75,0.904,0.844,156,Stability,UBR5_HUMAN,Medium,Human +VG08_BPP22_Tsuboyama_2023_2GP8_indels,0.943,0.908,0.783,0.903,0.871,0.936,0.935,0.923,0.915,0.917,0.907,0.613,0.858,0.904,0.931,0.727,0.864,0.913,0.697,0.871,0.908,0.929,0.843,101,Stability,VG08_BPP22,High,Virus +VILI_CHICK_Tsuboyama_2023_1YU5_indels,0.612,0.905,0.699,0.686,0.67,0.668,0.686,0.738,0.709,0.664,0.888,0.77,0.67,0.899,0.767,0.754,0.879,0.848,0.753,0.891,0.847,0.588,0.861,156,Stability,VILI_CHICK,High,Eukaryote +VRPI_BPT7_Tsuboyama_2023_2WNM_indels,0.74,0.864,0.589,0.722,0.749,0.704,0.761,0.685,0.636,0.645,0.798,0.613,0.664,0.67,0.718,0.69,0.701,0.655,0.652,0.686,0.654,0.741,0.876,154,Stability,VRPI_BPT7,Medium,Virus +YNZC_BACSU_Tsuboyama_2023_2JVD_indels,0.705,0.96,0.658,0.772,0.937,0.941,0.901,0.932,0.698,0.925,0.897,0.637,0.644,0.881,0.912,0.439,0.507,0.734,0.442,0.516,0.725,0.923,0.887,104,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.html b/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.html new file mode 100644 index 0000000..d138847 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/NDCG/DMS_indels_NDCG_DMS_level.html @@ -0,0 +1,2083 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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scoreUnirepWavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LHidden Markov ModelProveanNumber of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A4_HUMAN_Seuma_2022_indels0.7290.7580.7810.7080.6890.6960.7780.7650.7670.7790.7510.7590.7930.6540.7020.7930.7010.7050.7910.6960.7350.4160.7692346StabilityA4_HUMANLowHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O_indels0.3310.8820.2600.4670.8370.9210.3740.8710.8830.8480.8920.9210.3250.2400.3360.2210.2810.3100.2250.3070.3520.8220.845117StabilityAMFR_HUMANMediumHuman
ARGR_ECOLI_Tsuboyama_2023_1AOY_indels0.7400.9350.9330.9730.9690.9840.7860.9840.9820.9870.9810.5800.8950.8130.9710.7850.7810.9210.8010.7950.9260.7960.881181StabilityARGR_ECOLIMediumProkaryote
B1LPA6_ECOSM_Russ_2020_indels0.7020.7500.6970.7070.7070.7110.7350.7510.7220.7330.7260.6840.6690.7160.6930.7180.7300.7040.7100.7240.7130.7500.6863074ActivityB1LPA6_ECOSMMediumProkaryote
BBC1_YEAST_Tsuboyama_2023_1TG0_indels0.5880.7370.3980.5030.6010.7430.6030.9000.8630.8550.8190.4260.6610.6880.4560.4850.6290.5250.4890.6640.4880.4540.938134StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU_indels0.5790.8620.5070.5800.3700.6650.7130.7620.4530.9300.9760.4340.4960.6980.8170.3410.4530.7920.4840.4710.8130.8370.95882StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Gonzalez_2019_indels0.2560.5770.5050.5520.5340.4400.4630.5820.5930.5800.4060.2100.5150.5000.3870.4510.4660.3960.4810.5200.3920.2250.5134751OrganismalFitnessBLAT_ECOLXHighProkaryote
CAPSD_AAV2S_Sinai_2021_designed_indels0.5120.8860.7840.8000.8940.9180.4680.4620.4900.4590.8580.7160.8040.8080.9370.9240.9160.9290.9230.9290.9360.8980.904225998OrganismalFitnessCAPSD_AAV2SLowVirus
CAPSD_AAV2S_Sinai_2021_library_indels0.4720.4670.5510.5970.5730.6130.4720.4790.4830.4810.6420.5390.5500.5890.6410.6950.6990.7090.6970.6900.7490.4660.49124908OrganismalFitnessCAPSD_AAV2SLowVirus
CATR_CHLRE_Tsuboyama_2023_2AMI_indels0.8880.8720.8820.8850.8800.8740.8700.8850.8810.8560.8750.6710.8520.8770.8720.9090.8750.8710.9080.8720.8610.6690.876197StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels0.7190.9330.6060.7380.6590.6450.6080.7290.7710.7320.9170.4740.6210.6470.5780.5700.6100.5660.5690.6310.5690.8520.838205StabilityCBPA2_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K28_indels0.5190.6980.6860.7910.7910.8290.8470.8260.8260.8480.8590.4160.7250.8060.8190.6230.5510.6530.6500.5880.6950.7700.822129StabilityCBX4_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM_indels0.4780.9590.6200.5350.8880.9590.4220.9660.9350.9650.9650.6180.4800.8660.8620.4370.8190.8430.4130.7970.8360.8970.932195StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX_indels0.5830.8870.6730.5960.5910.4570.4270.4610.4730.5290.7990.4180.5040.4620.4010.3250.3520.3300.3250.3540.3450.5930.976140StabilityCUE1_YEASTMediumEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC_indels0.5250.8080.4740.6660.6230.6060.7020.5900.5760.7570.7550.4150.5580.4550.5620.3840.3920.4450.3980.3770.4040.5910.960136StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels0.8630.8110.9490.9490.9670.9510.9610.9580.9830.9650.9670.7000.9550.9520.9710.9280.9540.9450.9300.9540.9430.7580.836174StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels0.6200.8620.9140.9260.8910.9150.8490.9240.8530.8750.9520.6330.6670.6640.7530.6430.6560.7050.6560.6630.7820.5950.921154StabilityDOCK1_MOUSEHighEukaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels0.5180.9070.9720.9770.9790.9730.9700.9790.9810.9750.9720.6500.9570.9780.9710.7570.7870.8220.7830.8250.8230.7050.841185StabilityEPHB2_HUMANHighHuman
FECA_ECOLI_Tsuboyama_2023_2D1U_indels0.5580.5610.4600.7880.7500.7570.4210.8170.8460.8120.7910.5030.2600.2460.3260.2620.2610.2790.2650.2760.2760.7140.787193StabilityFECA_ECOLIHighEukaryote
HCP_LAMBD_Tsuboyama_2023_2L6Q_indels0.5640.9270.7540.8930.8670.8450.8390.6450.7620.7870.9090.6610.6490.6160.8870.4300.5030.8830.4260.4210.8850.8440.933148StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM_indels0.6650.8880.7490.8890.8810.8870.6540.8410.8100.8200.8220.5000.5180.6370.5860.6470.6740.6170.6410.6650.5860.7910.786154StabilityHECD1_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_2019_indels0.0940.8630.7720.7480.7950.8080.7300.8370.8880.8570.8880.2980.7230.8620.8740.8000.7280.8510.8180.8490.8810.7480.8796102OrganismalFitnessHIS7_YEASTMediumEukaryote
ILF3_HUMAN_Tsuboyama_2023_2L33_indels0.4880.8500.8160.8520.8530.8620.8360.8560.8360.8520.8340.8710.5850.8400.8330.4530.7770.7990.4910.7760.8080.8910.832193StabilityILF3_HUMANHighHuman
KCNJ2_MOUSE_Macdonald_2022_indels0.6620.7720.7750.7800.7680.7710.7970.7860.7760.7940.7700.6900.7730.7710.7680.7760.7640.7710.7760.7710.7710.7650.77410501ExpressionKCNJ2_MOUSEMediumEukaryote
MAFG_MOUSE_Tsuboyama_2023_1K1V_indels0.6260.6940.5180.7510.8210.8410.6880.8020.8340.7550.7250.5120.4320.7000.7140.4980.7060.7130.5000.7060.6780.5320.599115StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV_indels0.4940.8720.5070.6120.7490.6550.7340.9100.9070.8520.9220.8020.5530.5510.4470.6180.6220.6180.6310.6240.6360.7370.918131StabilityMBD11_ARATHMediumEukaryote
MYO3_YEAST_Tsuboyama_2023_2BTT_indels0.4010.7530.7660.5710.7080.6690.9350.8580.6260.8990.9870.5180.5620.6530.5450.5950.6110.5740.6090.6490.6340.4700.91780StabilityMYO3_YEASTHighEukaryote
NKX31_HUMAN_Tsuboyama_2023_2L9R_indels0.9080.7780.8370.8790.8500.8360.8840.8050.9170.7110.7890.7230.9070.8260.7150.9070.9100.8280.9080.9040.8100.8250.826178StabilityNKX31_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL_indels0.6640.8100.8760.8410.8770.8920.6520.8390.6790.9210.9160.3430.6990.8760.7340.5840.6580.6300.6170.7300.6650.6520.902191StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI6_indels0.5040.6850.8650.8910.8800.8910.8590.8840.8570.8750.8650.4790.8570.8400.7830.6720.6490.6820.6740.6430.7000.4360.690157StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels0.6800.8620.9080.9420.9460.9190.8870.9300.9340.8840.8900.7660.7240.5070.7020.4450.4460.5540.4680.4700.5590.6710.764169StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G_indels0.5010.9200.9240.9880.9470.9580.9820.9860.9830.9760.9700.8710.9310.6620.7860.7760.7920.5750.7760.9540.6920.7670.97247StabilityODP2_GEOSEHighProkaryote
OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels0.3590.5790.3980.8830.9030.9240.7840.9250.9270.9490.9390.7150.5050.5430.7980.2410.3270.4790.2480.3270.5930.4730.94984StabilityOTU7A_HUMANHighHuman
P53_HUMAN_Kotler_2018_indels0.2910.4470.5580.4080.4560.6930.5330.6920.6160.6610.6530.1900.6600.5660.6810.5760.5430.6290.6140.5010.6160.5480.653341OrganismalFitnessP53_HUMANLowHuman
PIN1_HUMAN_Tsuboyama_2023_1I6C_indels0.7660.8530.8440.8940.8740.8460.8330.8370.8420.8400.8460.8510.6890.9210.8490.6810.9670.8580.6660.9650.8500.9310.838106StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M_indels0.8900.7980.9400.9070.8880.9100.8860.9340.9690.9220.9670.7100.9220.9210.9370.6800.7010.7500.7220.7730.7820.6700.903117StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF_indels0.3110.8570.9050.9070.9200.9150.8950.9090.9050.9280.9210.7690.4420.8840.8950.6980.8940.9000.7190.8930.9000.8550.916187StabilityPKN1_HUMANHighHuman
POLG_PESV_Tsuboyama_2023_2MXD_indels0.6410.9050.5910.6030.6020.6480.6310.5920.5760.4940.6380.5900.6370.5960.6670.5180.4590.5140.6360.4730.5220.6840.859149StabilityPOLG_PESVMediumVirus
PR40A_HUMAN_Tsuboyama_2023_1UZC_indels0.4630.9630.9110.9780.9790.9830.9540.9850.9600.9830.9820.7830.6890.7800.7810.6930.7620.7400.7290.7730.7400.8080.739168StabilityPR40A_HUMANMediumHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE_indels0.3710.5080.4100.2710.3750.6470.4580.6310.3740.5990.6360.6300.4470.7530.5910.5140.6280.5480.4760.6790.6260.7490.602175StabilityPSAE_SYNP2MediumProkaryote
PTEN_HUMAN_Mighell_2018_indels0.3250.8720.7990.8740.8700.7990.8630.8540.8920.8370.8370.3750.8770.8910.9070.8690.9040.9080.8670.8880.9000.8490.897314ActivityPTEN_HUMANMediumHuman
Q8EG35_SHEON_Campbell_2022_indels0.5120.1310.3940.3510.2990.3520.5500.3950.2550.2880.2020.3960.5490.5970.4860.4510.5380.5170.4480.5810.4730.4410.160331OrganismalFitnessQ8EG35_SHEONMediumProkaryote
RAD_ANTMA_Tsuboyama_2023_2CJJ_indels0.6000.8250.7860.8360.8250.8460.8760.8440.7950.8320.8740.7440.5600.8090.7910.7860.8390.8700.8040.8370.8680.6740.79697StabilityRAD_ANTMAHighEukaryote
RCD1_ARATH_Tsuboyama_2023_5OAO_indels0.4090.8420.4840.5950.5040.6420.3310.6960.6510.6530.9000.4620.3860.4130.4870.4480.3720.3460.4380.3820.3480.7740.772124StabilityRCD1_ARATHMediumEukaryote
RD23A_HUMAN_Tsuboyama_2023_1IFY_indels0.6630.9300.9510.9650.9140.9830.9820.9780.9560.9790.9730.9700.8060.9370.9580.7950.9220.8910.8250.9280.9120.6960.980120StabilityRD23A_HUMANHighHuman
RPC1_BP434_Tsuboyama_2023_1R69_indels0.8630.6870.7830.8200.8180.8320.8370.8510.8290.8470.8470.5540.5630.8030.8710.5580.7100.8090.5260.7390.8340.5230.768164StabilityRPC1_BP434HighVirus
RS15_GEOSE_Tsuboyama_2023_1A32_indels0.7000.8510.8840.8900.8830.8880.8510.8820.9090.8880.8880.6800.8890.8780.8720.8280.7940.7890.8600.8010.7950.8020.782176StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance_indels0.6490.6460.7200.8100.8250.7920.7700.8550.8530.8100.7890.6140.8020.8350.8050.7380.6920.7650.7550.8020.7690.5550.766430ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity_indels0.5520.4860.6020.7210.7620.7270.7000.7840.8210.7720.7330.4980.7420.7870.7710.6360.6220.7050.6640.7510.7120.4160.694490ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB_indels0.7640.8440.8500.8610.8490.8640.8630.8490.8580.8570.8330.8940.8380.8400.8310.7690.8530.8430.8520.8530.8460.8410.85186StabilitySAV1_MOUSEHighEukaryote
SDA_BACSU_Tsuboyama_2023_1PV0_indels0.5870.9490.6740.7070.6920.7430.8070.8570.5870.8040.9510.5710.7080.6750.9350.6610.6960.7820.6610.7010.8800.8100.965127StabilitySDA_BACSUMediumProkaryote
SOX30_HUMAN_Tsuboyama_2023_7JJK_indels0.7140.8570.8930.9390.9550.9580.9200.9610.9490.9670.9710.9790.5400.9840.9630.4660.8050.8590.5810.8450.8880.7820.979109StabilitySOX30_HUMANHighHuman
SPG2_STRSG_Tsuboyama_2023_5UBS_indels0.3940.7550.5260.5790.5500.5770.6720.7690.7610.8340.8050.4260.4470.5070.8190.5630.5920.6750.5660.5910.6980.5790.835148StabilitySPG2_STRSGMediumProkaryote
SPTN1_CHICK_Tsuboyama_2023_1TUD_indels0.5840.6840.8910.8240.9330.8800.9320.9270.7950.9480.9390.4280.7140.6880.8230.6530.6820.7660.7060.6650.7960.4910.479129StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels0.5260.6910.7670.9180.9190.9220.7360.9080.9510.9290.9090.5910.4450.8580.9370.4830.8340.8100.5490.8350.8490.4400.955111StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N88_indels0.5170.7900.7580.7230.7290.6360.5340.4390.5990.4360.5810.5300.7090.3900.5990.7740.6060.7310.7910.6090.7400.6700.758135StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels0.6920.9180.9350.9040.9350.9150.9330.9180.9130.9370.9340.8090.8730.9410.8680.9210.9200.8920.9240.9290.9060.9220.927154StabilitySRBS1_HUMANHighHuman
TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels0.8290.9560.9670.9720.9740.9820.9450.9810.9730.9810.9850.8120.8340.8920.9520.3760.5720.6330.3750.5920.7590.9170.97699StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG_indels0.3380.8440.2590.5360.5460.4970.6480.7680.6930.6830.8740.4610.3250.8410.5190.2270.7340.7440.2340.8370.8200.6250.98882StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels0.4310.5990.7020.8810.8440.8810.8990.9060.9010.8980.9060.4670.4540.4330.7280.4810.4950.6540.5040.4470.7120.6450.831171StabilityTNKS2_HUMANHighHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels0.1780.7630.2100.7830.8030.8250.7990.8290.8010.8300.8230.6000.4170.2380.7550.3760.3090.6500.3360.3140.6430.3980.720147StabilityUBE4B_HUMANHighHuman
UBR5_HUMAN_Tsuboyama_2023_1I2T_indels0.6080.8800.9450.9170.9550.9520.9410.9530.9400.9540.9560.5400.8780.9090.8310.6000.7900.7020.5960.7940.7500.9040.844156StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP8_indels0.9430.9080.7830.9030.8710.9360.9350.9230.9150.9170.9070.6130.8580.9040.9310.7270.8640.9130.6970.8710.9080.9290.843101StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU5_indels0.6120.9050.6990.6860.6700.6680.6860.7380.7090.6640.8880.7700.6700.8990.7670.7540.8790.8480.7530.8910.8470.5880.861156StabilityVILI_CHICKHighEukaryote
VRPI_BPT7_Tsuboyama_2023_2WNM_indels0.7400.8640.5890.7220.7490.7040.7610.6850.6360.6450.7980.6130.6640.6700.7180.6900.7010.6550.6520.6860.6540.7410.876154StabilityVRPI_BPT7MediumVirus
YNZC_BACSU_Tsuboyama_2023_2JVD_indels0.7050.9600.6580.7720.9370.9410.9010.9320.6980.9250.8970.6370.6440.8810.9120.4390.5070.7340.4420.5160.7250.9230.887104StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.csv b/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.csv new file mode 100644 index 0000000..27ffbb1 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.csv @@ -0,0 +1,24 @@ +Model_rank,Model_name,Model type,Average_NDCG,Bootstrap_standard_error_NDCG,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Model details,References +1,Progen2 M,Protein language model,0.763,0.0,0.796,,0.82,0.595,0.84,0.642,0.789,0.861,0.878,0.815,0.775,0.694,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +2,Progen2 Base,Protein language model,0.751,0.01,0.812,,0.814,0.568,0.811,0.623,0.751,0.844,0.88,0.795,0.674,0.701,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +3,Progen2 XL,Protein language model,0.751,0.02,0.765,,0.78,0.58,0.878,0.718,0.834,0.872,0.882,0.864,0.784,0.808,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +4,Progen2 L,Protein language model,0.748,0.007,0.781,,0.802,0.571,0.839,0.637,0.791,0.854,0.87,0.802,0.793,0.693,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +5,Tranception L no retrieval,Protein language model,0.744,0.021,0.79,,0.786,0.643,0.755,0.724,0.73,0.767,0.79,0.686,0.739,0.811,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,Tranception M no retrieval,Protein language model,0.741,0.018,0.798,,0.803,0.645,0.719,0.64,0.695,0.747,0.744,0.698,0.704,0.715,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +7,Provean,Alignment-based model,0.74,0.021,0.759,,0.77,0.58,0.851,0.706,0.822,0.838,0.836,0.848,0.771,0.829,Provean model,"Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one." +8,RITA XL,Protein language model,0.74,0.017,0.746,,0.782,0.612,0.821,0.718,0.769,0.838,0.869,0.769,0.735,0.788,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +9,RITA L,Protein language model,0.736,0.019,0.78,,0.796,0.564,0.805,0.626,0.745,0.835,0.856,0.77,0.689,0.773,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +10,TranceptEVE L,Hybrid model,0.727,0.024,0.775,,0.77,0.641,0.722,0.731,0.685,0.749,0.742,0.703,0.679,0.774,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +11,TranceptEVE M,Hybrid model,0.726,0.022,0.788,,0.786,0.652,0.679,0.669,0.64,0.725,0.707,0.686,0.649,0.667,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +12,RITA M,Protein language model,0.726,0.02,0.767,,0.795,0.552,0.788,0.605,0.719,0.831,0.842,0.732,0.698,0.773,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +13,Tranception L,Hybrid model,0.721,0.024,0.772,,0.768,0.642,0.701,0.718,0.671,0.728,0.727,0.684,0.656,0.766,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,Progen2 S,Protein language model,0.718,0.017,0.766,,0.784,0.549,0.773,0.594,0.702,0.821,0.823,0.701,0.724,0.745,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +15,Tranception S no retrieval,Protein language model,0.706,0.017,0.763,,0.788,0.625,0.649,0.71,0.623,0.679,0.693,0.597,0.665,0.675,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,Wavenet,Alignment-based model,0.693,0.034,0.703,,0.709,0.539,0.823,0.627,0.818,0.791,0.802,0.811,0.75,0.828,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +17,Tranception M,Hybrid model,0.691,0.028,0.752,,0.728,0.616,0.667,0.684,0.628,0.704,0.695,0.67,0.62,0.674,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +18,TranceptEVE S,Hybrid model,0.688,0.022,0.747,,0.766,0.634,0.605,0.738,0.577,0.644,0.646,0.595,0.6,0.624,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +19,RITA S,Protein language model,0.687,0.02,0.699,,0.748,0.579,0.721,0.669,0.659,0.761,0.759,0.687,0.666,0.695,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +20,Tranception S,Hybrid model,0.677,0.024,0.741,,0.757,0.617,0.591,0.726,0.564,0.627,0.63,0.581,0.583,0.622,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +21,Hidden Markov Model,Alignment-based model,0.642,0.041,0.672,,0.66,0.529,0.708,0.549,0.74,0.666,0.728,0.66,0.668,0.734,Profile Hidden Markov model,HMMER: biosequence analysis using profile hidden Markov models +22,ProtGPT2,Protein language model,0.537,0.042,0.519,,0.652,0.344,0.634,0.526,0.575,0.645,0.671,0.589,0.525,0.61,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +23,Unirep,Protein language model,0.527,0.046,0.526,,0.656,0.329,0.599,0.504,0.558,0.599,0.571,0.559,0.553,0.707,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." diff --git a/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.html b/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.html new file mode 100644 index 0000000..5b2ad43 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/NDCG/Summary_performance_DMS_indels_NDCG.html @@ -0,0 +1,531 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_NDCGBootstrap_standard_error_NDCGFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusModel detailsReferences
Model_rank
1Progen2 MProtein language model0.7640.0260.798NaN0.8200.5950.8430.6420.7890.8660.8820.8150.7780.695Progen2 medium model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
2Progen2 XLProtein language model0.7540.0240.766NaN0.7860.5800.8820.7180.8380.8770.8860.8650.7920.811Progen2 xlarge model (6.4B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
3Progen2 BaseProtein language model0.7520.0290.810NaN0.8160.5680.8140.6230.7520.8490.8830.7980.6770.704Progen2 base model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
4Progen2 LProtein language model0.7500.0270.780NaN0.8060.5710.8430.6370.7940.8580.8740.8040.7960.696Progen2 large model (2.7B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
5TranceptEVE MHybrid model0.7480.0000.860NaN0.7860.6520.6940.6700.6610.7380.7150.7020.6820.676TranceptEVE Medium model (Tranception Medium & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
6TranceptEVE LHybrid model0.7480.0120.842NaN0.7700.6410.7370.7310.7050.7630.7560.7170.7070.779TranceptEVE Large model (Tranception Large & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
7Tranception L no retrievalProtein language model0.7470.0230.792NaN0.7880.6430.7650.7240.7380.7760.7950.6970.7540.811Tranception Large model (700M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
8ProveanAlignment-based model0.7450.0280.761NaN0.7740.5800.8650.7070.8340.8520.8450.8610.7870.842Provean modelChoi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one.
9Tranception M no retrievalProtein language model0.7430.0190.800NaN0.8040.6450.7230.6420.6980.7500.7450.7020.7110.712Tranception Medium model (300M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
10RITA XLProtein language model0.7410.0200.746NaN0.7820.6120.8240.7180.7680.8440.8730.7700.7380.789RITA xlarge model (1.2B params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
11RITA LProtein language model0.7380.0200.780NaN0.7980.5640.8090.6260.7460.8420.8600.7730.6920.776RITA large model (680M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
12RITA MProtein language model0.7270.0180.769NaN0.7950.5520.7930.6040.7210.8370.8470.7340.7040.773RITA medium model (300M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
13Tranception LHybrid model0.7260.0190.772NaN0.7690.6420.7210.7180.6890.7440.7420.7030.6770.771Tranception Large model (700M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
14Progen2 SProtein language model0.7210.0200.769NaN0.7880.5490.7770.5940.7020.8280.8290.7060.7250.743Progen2 small model (150M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
15Tranception S no retrievalProtein language model0.7080.0220.763NaN0.7920.6250.6510.7100.6240.6820.6940.6000.6660.675Tranception Small model (85M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
16Tranception MHybrid model0.6950.0170.753NaN0.7300.6160.6800.6840.6390.7180.7060.6830.6340.680Tranception Medium model (300M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
17TranceptEVE SHybrid model0.6900.0160.746NaN0.7660.6340.6150.7380.5830.6540.6530.6040.6080.631TranceptEVE Small model (Tranception Small & retrieved EVE model)Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
18RITA SProtein language model0.6870.0160.698NaN0.7480.5790.7230.6690.6620.7620.7610.6930.6650.695RITA small model (85M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
19Tranception SHybrid model0.6780.0160.740NaN0.7570.6170.5990.7260.5710.6360.6350.5880.5950.624Tranception Small model (85M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
20WavenetAlignment-based model0.6540.0290.713NaN0.7210.5390.6440.5260.7090.5920.4930.7440.6710.828Wavenet modelShin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.
21Hidden Markov ModelAlignment-based model0.6430.0310.672NaN0.6600.5290.7110.5490.7430.6700.7290.6640.6710.741Profile Hidden Markov modelHMMER: biosequence analysis using profile hidden Markov models
22ProtGPT2Protein language model0.5390.0260.519NaN0.6520.3440.6390.5260.5750.6540.6770.5950.5280.608ProtGPT2 modelFerruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.
23UnirepProtein language model0.5270.0350.526NaN0.6560.3290.5980.5040.5560.6000.5720.5580.5520.705Unirep modelAlley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.csv new file mode 100644 index 0000000..be2d26d --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.csv @@ -0,0 +1,67 @@ +DMS ID,Unirep,Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,Hidden Markov Model,Provean,Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A4_HUMAN_Seuma_2022_indels,0.523,0.512,0.472,0.451,0.438,0.429,0.539,0.478,0.494,0.481,0.462,0.552,0.555,0.442,0.444,0.536,0.466,0.416,0.544,0.459,0.466,-0.207,0.381,2346,Stability,A4_HUMAN,Low,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,0.026,0.701,-0.314,0.18,0.561,0.547,0.019,0.512,0.502,0.298,0.523,0.574,-0.044,-0.113,0.125,-0.309,-0.26,-0.27,-0.3,-0.257,-0.25,0.632,0.331,117,Stability,AMFR_HUMAN,Medium,Human +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,0.513,0.611,0.757,0.594,0.58,0.729,0.459,0.603,0.68,0.698,0.692,0.171,0.728,0.586,0.595,0.252,0.427,0.505,0.293,0.477,0.536,0.454,0.129,181,Stability,ARGR_ECOLI,Medium,Prokaryote +B1LPA6_ECOSM_Russ_2020_indels,0.275,0.438,0.294,0.363,0.337,0.36,0.384,0.38,0.415,0.412,0.379,0.316,0.172,0.219,0.223,0.33,0.344,0.326,0.316,0.357,0.34,0.398,0.372,3074,Activity,B1LPA6_ECOSM,Medium,Prokaryote +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,0.537,0.41,0.499,0.499,0.513,0.486,0.33,0.548,0.588,0.524,0.566,0.259,0.547,0.588,0.604,0.46,0.518,0.524,0.463,0.515,0.53,0.29,0.26,134,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU_indels,-0.055,0.647,0.025,0.165,0.085,0.184,0.195,0.262,0.086,0.382,0.539,0.21,0.18,0.276,0.263,-0.31,-0.287,-0.191,-0.274,-0.246,-0.147,0.47,0.283,82,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Gonzalez_2019_indels,-0.013,0.437,0.436,0.455,0.334,0.345,0.496,0.619,0.664,0.604,0.409,0.155,0.451,0.379,0.296,0.425,0.365,0.344,0.439,0.401,0.342,0.347,0.385,4751,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +CAPSD_AAV2S_Sinai_2021_designed_indels,-0.409,0.666,0.298,0.358,0.543,0.621,-0.457,-0.466,-0.402,-0.469,0.492,0.159,0.336,0.362,0.691,0.715,0.677,0.709,0.718,0.726,0.736,0.607,0.683,225998,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAPSD_AAV2S_Sinai_2021_library_indels,-0.125,-0.007,0.043,0.119,0.103,0.141,-0.117,-0.1,-0.084,-0.104,0.167,0.083,0.044,0.126,0.21,0.425,0.371,0.338,0.419,0.419,0.416,0.057,0.177,24908,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,0.466,0.439,0.505,0.425,0.421,0.334,0.416,0.337,0.346,0.287,0.338,0.072,0.521,0.242,0.355,0.468,0.274,0.368,0.473,0.261,0.353,0.145,0.239,197,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,0.412,0.375,0.494,0.589,0.449,0.445,0.244,0.41,0.452,0.379,0.493,0.018,0.455,0.609,0.461,-0.068,-0.041,-0.012,-0.052,-0.016,0.013,0.472,0.366,205,Stability,CBPA2_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,0.231,0.31,0.208,0.294,0.259,0.233,0.199,0.234,0.201,0.265,0.208,-0.017,0.338,0.287,0.297,0.261,0.259,0.3,0.279,0.271,0.305,0.38,0.073,129,Stability,CBX4_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,0.637,0.581,0.684,0.668,0.551,0.753,0.684,0.782,0.806,0.722,0.745,0.272,0.512,0.681,0.77,-0.194,0.047,0.158,-0.171,0.094,0.223,0.536,0.312,195,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,0.528,0.695,0.51,0.518,0.535,0.466,0.255,0.351,0.346,0.404,0.636,0.27,0.471,0.386,0.507,-0.183,-0.181,-0.153,-0.153,-0.164,-0.125,0.474,0.46,140,Stability,CUE1_YEAST,Medium,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,0.19,0.242,0.179,0.292,0.364,0.285,0.38,0.362,0.303,0.317,0.458,0.026,0.243,0.218,0.196,-0.117,-0.117,-0.135,-0.103,-0.103,-0.115,0.12,0.696,136,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,0.476,0.291,0.262,0.378,0.466,0.311,0.431,0.332,0.4,0.285,0.415,0.23,0.536,0.498,0.495,0.343,0.368,0.396,0.371,0.387,0.413,0.22,0.109,174,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,0.414,0.619,0.554,0.559,0.627,0.515,0.62,0.596,0.553,0.559,0.628,0.309,0.64,0.7,0.659,0.391,0.48,0.463,0.42,0.513,0.495,0.433,0.353,154,Stability,DOCK1_MOUSE,High,Eukaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,0.243,0.429,0.408,0.378,0.459,0.428,0.547,0.443,0.601,0.554,0.6,0.361,0.416,0.367,0.405,0.112,0.198,0.269,0.158,0.229,0.308,0.381,0.316,185,Stability,EPHB2_HUMAN,High,Human +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,0.426,0.187,0.334,0.387,0.411,0.376,0.35,0.494,0.576,0.505,0.429,0.104,0.204,0.286,0.029,-0.035,0.076,-0.058,-0.037,0.084,-0.058,0.226,0.444,193,Stability,FECA_ECOLI,High,Eukaryote +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,0.315,0.396,0.625,0.593,0.775,0.815,0.505,0.451,0.448,0.48,0.589,0.244,0.521,0.47,0.517,-0.096,-0.107,0.383,-0.108,-0.13,0.405,0.408,0.378,148,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,0.239,0.603,0.307,0.434,0.522,0.572,0.436,0.567,0.539,0.545,0.524,0.025,0.129,0.331,0.104,0.189,0.338,0.205,0.179,0.348,0.183,0.551,0.282,154,Stability,HECD1_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019_indels,0.002,0.687,0.638,0.656,0.677,0.684,0.669,0.702,0.706,0.703,0.713,-0.091,0.63,0.687,0.707,0.628,0.611,0.655,0.639,0.678,0.695,0.548,0.701,6102,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,0.03,0.503,0.425,0.518,0.31,0.346,0.583,0.403,0.565,0.618,0.527,0.052,0.258,0.672,0.632,-0.033,0.309,0.423,-0.003,0.369,0.479,0.291,0.216,193,Stability,ILF3_HUMAN,High,Human +KCNJ2_MOUSE_Macdonald_2022_indels,0.162,0.408,0.385,0.4,0.383,0.377,0.401,0.432,0.426,0.431,0.387,0.265,0.401,0.412,0.391,0.441,0.437,0.44,0.434,0.444,0.431,0.368,0.386,10501,Expression,KCNJ2_MOUSE,Medium,Eukaryote +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,0.412,0.049,-0.077,0.313,0.293,0.279,0.321,0.369,0.314,0.3,0.349,0.043,-0.014,0.167,0.274,0.083,0.124,0.196,0.075,0.141,0.23,-0.033,0.018,115,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,0.216,0.312,0.2,0.106,0.472,0.494,0.32,0.67,0.565,0.447,0.499,0.298,0.093,0.174,0.138,0.041,0.094,0.045,0.074,0.119,0.081,0.253,0.482,131,Stability,MBD11_ARATH,Medium,Eukaryote +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,0.096,0.354,0.39,0.283,0.535,0.47,0.616,0.533,0.397,0.484,0.548,0.067,0.544,0.525,0.412,0.286,0.295,0.242,0.343,0.358,0.285,0.147,0.519,80,Stability,MYO3_YEAST,High,Eukaryote +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,0.667,0.772,0.648,0.767,0.761,0.754,0.711,0.706,0.767,0.698,0.677,0.073,0.612,0.667,0.698,0.476,0.609,0.658,0.487,0.617,0.652,0.612,0.331,178,Stability,NKX31_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,0.128,0.34,0.219,0.315,0.279,0.241,0.253,0.227,0.287,0.213,0.226,-0.027,0.206,0.289,0.266,-0.076,0.033,0.002,-0.076,0.065,0.029,0.064,0.32,191,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,0.441,0.023,-0.125,-0.044,-0.101,-0.147,0.111,-0.039,-0.026,-0.083,-0.044,0.101,-0.161,-0.07,0.167,-0.156,-0.14,0.062,-0.129,-0.112,0.094,-0.031,0.17,157,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,0.505,0.505,0.807,0.678,0.691,0.714,0.78,0.815,0.845,0.733,0.696,0.39,0.367,0.437,0.584,0.149,0.25,0.33,0.162,0.274,0.373,0.451,0.152,169,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,0.142,0.677,0.762,0.833,0.857,0.873,0.821,0.881,0.863,0.838,0.85,0.331,0.52,0.444,0.492,0.204,0.337,0.258,0.269,0.383,0.284,0.629,0.686,47,Stability,ODP2_GEOSE,High,Prokaryote +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,0.468,0.221,0.367,0.336,0.388,0.429,0.303,0.482,0.587,0.468,0.546,0.286,0.251,0.462,0.501,-0.162,-0.078,0.155,-0.148,-0.042,0.174,0.36,0.519,84,Stability,OTU7A_HUMAN,High,Human +P53_HUMAN_Kotler_2018_indels,0.073,0.031,0.36,0.407,0.383,0.273,0.474,0.428,0.391,0.449,0.354,0.026,0.536,0.579,0.395,0.409,0.536,0.362,0.429,0.56,0.399,0.482,0.273,341,OrganismalFitness,P53_HUMAN,Low,Human +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,0.787,0.758,0.732,0.752,0.771,0.735,0.805,0.634,0.619,0.719,0.608,0.577,0.649,0.74,0.789,0.183,0.544,0.666,0.256,0.605,0.693,0.624,0.58,106,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,0.416,0.481,0.389,0.633,0.625,0.671,0.409,0.69,0.543,0.708,0.655,0.463,0.249,0.535,0.551,-0.063,0.16,0.131,0.013,0.239,0.227,0.339,0.436,117,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,0.099,0.727,0.734,0.779,0.729,0.733,0.795,0.809,0.855,0.787,0.784,0.295,0.497,0.751,0.699,0.215,0.693,0.675,0.27,0.731,0.69,0.44,0.392,187,Stability,PKN1_HUMAN,High,Human +POLG_PESV_Tsuboyama_2023_2MXD_indels,0.499,0.538,0.316,0.377,0.435,0.33,0.31,0.253,0.288,0.215,0.357,0.107,0.388,0.434,0.434,-0.192,-0.135,-0.159,-0.166,-0.103,-0.119,0.304,0.256,149,Stability,POLG_PESV,Medium,Virus +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,0.251,0.688,0.434,0.63,0.654,0.6,0.631,0.677,0.672,0.688,0.638,0.484,0.43,0.368,0.294,0.184,0.227,0.119,0.215,0.262,0.146,0.625,0.163,168,Stability,PR40A_HUMAN,Medium,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE_indels,-0.049,0.155,0.142,0.042,0.133,0.142,0.145,0.345,-0.089,0.286,0.397,0.256,0.175,0.315,-0.092,0.206,0.377,0.095,0.215,0.39,0.081,0.322,0.296,175,Stability,PSAE_SYNP2,Medium,Prokaryote +PTEN_HUMAN_Mighell_2018_indels,-0.325,0.697,0.612,0.575,0.504,0.523,0.696,0.552,0.64,0.59,0.402,0.056,0.68,0.7,0.563,0.661,0.678,0.546,0.67,0.708,0.602,0.668,0.237,314,Activity,PTEN_HUMAN,Medium,Human +Q8EG35_SHEON_Campbell_2022_indels,0.528,0.244,0.468,0.465,0.38,0.281,0.584,0.403,0.352,0.33,0.348,0.286,0.587,0.615,0.375,0.409,0.612,0.395,0.411,0.562,0.374,0.472,0.278,331,OrganismalFitness,Q8EG35_SHEON,Medium,Prokaryote +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,0.565,0.571,0.42,0.764,0.804,0.762,0.528,0.722,0.728,0.704,0.61,0.215,0.531,0.601,0.529,0.076,0.477,0.452,0.113,0.522,0.485,0.644,-0.069,97,Stability,RAD_ANTMA,High,Eukaryote +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,0.192,0.724,0.045,0.093,0.223,0.374,-0.013,0.306,0.297,0.23,0.504,0.096,0.073,0.054,0.192,-0.374,-0.423,-0.379,-0.368,-0.421,-0.368,0.643,0.168,124,Stability,RCD1_ARATH,Medium,Eukaryote +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,0.396,0.725,0.565,0.676,0.644,0.749,0.622,0.733,0.731,0.73,0.702,0.539,0.502,0.669,0.744,0.255,0.498,0.643,0.278,0.556,0.677,0.601,0.418,120,Stability,RD23A_HUMAN,High,Human +RPC1_BP434_Tsuboyama_2023_1R69_indels,0.607,0.349,0.637,0.658,0.59,0.627,0.658,0.644,0.602,0.675,0.35,0.161,0.565,0.622,0.678,0.305,0.408,0.508,0.312,0.417,0.53,0.133,0.297,164,Stability,RPC1_BP434,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32_indels,0.359,0.252,0.079,0.066,0.114,0.16,0.143,0.328,0.291,0.274,0.296,0.056,0.586,0.277,0.211,0.348,0.137,-0.031,0.388,0.172,0.01,0.086,0.114,176,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance_indels,0.203,0.056,0.311,0.362,0.528,0.49,0.324,0.496,0.494,0.43,0.33,0.208,0.342,0.404,0.429,0.272,0.364,0.428,0.323,0.408,0.425,0.236,0.269,430,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity_indels,0.304,0.118,0.402,0.453,0.556,0.541,0.436,0.597,0.543,0.51,0.435,0.21,0.405,0.489,0.503,0.332,0.394,0.487,0.383,0.476,0.481,0.324,0.334,490,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,0.712,0.764,0.639,0.65,0.629,0.629,0.652,0.7,0.644,0.668,0.645,0.575,0.72,0.687,0.706,0.315,0.547,0.608,0.408,0.619,0.674,0.631,0.585,86,Stability,SAV1_MOUSE,High,Eukaryote +SDA_BACSU_Tsuboyama_2023_1PV0_indels,0.181,0.31,0.264,0.354,0.421,0.572,0.205,0.327,0.073,0.281,0.513,0.068,0.35,0.457,0.395,-0.151,-0.144,0.322,-0.13,-0.114,0.361,0.581,0.353,127,Stability,SDA_BACSU,Medium,Prokaryote +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,0.61,0.502,0.469,0.631,0.615,0.699,0.405,0.63,0.59,0.611,0.682,0.425,0.038,0.594,0.605,-0.268,-0.039,0.247,-0.232,0.025,0.351,0.399,0.424,109,Stability,SOX30_HUMAN,High,Human +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,-0.107,0.41,0.215,0.211,0.455,0.434,0.313,0.444,0.38,0.47,0.573,-0.171,0.025,0.154,0.597,0.144,0.167,0.287,0.146,0.171,0.327,0.133,0.298,148,Stability,SPG2_STRSG,Medium,Prokaryote +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,0.358,-0.002,0.515,0.565,0.523,0.625,0.515,0.693,0.664,0.663,0.597,0.195,0.567,0.47,0.573,0.431,0.477,0.502,0.451,0.497,0.522,0.186,0.152,129,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,0.31,0.31,0.35,0.493,0.494,0.515,0.548,0.471,0.476,0.459,0.486,0.142,0.375,0.523,0.454,0.028,0.203,0.365,0.046,0.278,0.415,0.011,0.479,111,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88_indels,0.048,0.374,0.425,0.282,0.392,0.36,0.271,0.344,0.489,0.484,0.474,0.367,0.319,0.099,0.357,0.4,0.35,0.495,0.415,0.364,0.499,0.35,0.303,135,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,0.574,0.707,0.647,0.683,0.675,0.668,0.675,0.677,0.693,0.676,0.581,0.526,0.683,0.71,0.731,0.718,0.753,0.752,0.72,0.755,0.757,0.494,0.449,154,Stability,SRBS1_HUMAN,High,Human +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,0.66,0.823,0.645,0.659,0.69,0.758,0.552,0.657,0.636,0.649,0.708,0.412,0.347,0.618,0.698,-0.446,-0.302,-0.164,-0.397,-0.235,-0.064,0.822,0.335,99,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG_indels,0.416,0.663,0.253,0.607,0.652,0.666,0.631,0.788,0.714,0.759,0.737,0.425,0.335,0.693,0.664,-0.283,0.144,0.061,-0.255,0.216,0.128,0.371,0.684,82,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels,-0.025,0.218,0.315,0.29,0.217,0.266,0.382,0.333,0.282,0.367,0.27,0.119,0.136,0.193,0.291,-0.054,0.02,0.103,-0.053,0.025,0.103,0.163,0.312,171,Stability,TNKS2_HUMAN,High,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels,-0.011,0.22,0.126,0.162,0.152,0.239,0.348,0.187,0.248,0.239,0.335,0.267,0.151,0.085,0.325,-0.17,-0.17,0.114,-0.188,-0.181,0.127,-0.074,0.312,147,Stability,UBE4B_HUMAN,High,Human +UBR5_HUMAN_Tsuboyama_2023_1I2T_indels,0.223,0.385,0.481,0.389,0.414,0.431,0.424,0.377,0.488,0.51,0.521,0.358,0.429,0.481,0.446,-0.084,0.114,0.127,-0.067,0.156,0.162,0.241,0.253,156,Stability,UBR5_HUMAN,Medium,Human +VG08_BPP22_Tsuboyama_2023_2GP8_indels,0.519,0.726,0.528,0.731,0.688,0.84,0.372,0.577,0.425,0.401,0.711,-0.16,0.417,0.747,0.823,-0.029,0.078,0.562,-0.046,0.115,0.645,0.645,0.282,101,Stability,VG08_BPP22,High,Virus +VILI_CHICK_Tsuboyama_2023_1YU5_indels,0.118,0.681,0.244,0.439,0.366,0.297,0.601,0.793,0.744,0.76,0.618,0.368,0.575,0.612,0.775,0.262,0.489,0.655,0.288,0.512,0.687,0.002,0.346,156,Stability,VILI_CHICK,High,Eukaryote +VRPI_BPT7_Tsuboyama_2023_2WNM_indels,0.399,0.597,0.156,0.409,0.56,0.343,0.409,0.388,0.375,0.416,0.454,0.128,0.273,0.243,0.341,-0.187,-0.177,-0.233,-0.186,-0.185,-0.236,0.333,0.674,154,Stability,VRPI_BPT7,Medium,Virus +YNZC_BACSU_Tsuboyama_2023_2JVD_indels,0.492,0.728,0.541,0.548,0.66,0.683,0.495,0.746,0.355,0.665,0.703,0.327,0.438,0.517,0.653,-0.19,-0.171,0.004,-0.179,-0.148,0.055,0.674,0.262,104,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.html b/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.html new file mode 100644 index 0000000..62db53e --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Spearman/DMS_indels_Spearman_DMS_level.html @@ -0,0 +1,2083 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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scoreUnirepWavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LHidden Markov ModelProveanNumber of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A4_HUMAN_Seuma_2022_indels0.5230.5120.4720.4510.4380.4290.5390.4780.4940.4810.4620.5520.5550.4420.4440.5360.4660.4160.5440.4590.466-0.2070.3812346StabilityA4_HUMANLowHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O_indels0.0260.701-0.3140.1800.5610.5470.0190.5120.5020.2980.5230.574-0.044-0.1130.125-0.309-0.260-0.270-0.300-0.257-0.2500.6320.331117StabilityAMFR_HUMANMediumHuman
ARGR_ECOLI_Tsuboyama_2023_1AOY_indels0.5130.6110.7570.5940.5800.7290.4590.6030.6800.6980.6920.1710.7280.5860.5950.2520.4270.5050.2930.4770.5360.4540.129181StabilityARGR_ECOLIMediumProkaryote
B1LPA6_ECOSM_Russ_2020_indels0.2750.4380.2940.3630.3370.3600.3840.3800.4150.4120.3790.3160.1720.2190.2230.3300.3440.3260.3160.3570.3400.3980.3723074ActivityB1LPA6_ECOSMMediumProkaryote
BBC1_YEAST_Tsuboyama_2023_1TG0_indels0.5370.4100.4990.4990.5130.4860.3300.5480.5880.5240.5660.2590.5470.5880.6040.4600.5180.5240.4630.5150.5300.2900.260134StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU_indels-0.0550.6470.0250.1650.0850.1840.1950.2620.0860.3820.5390.2100.1800.2760.263-0.310-0.287-0.191-0.274-0.246-0.1470.4700.28382StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Gonzalez_2019_indels-0.0130.4370.4360.4550.3340.3450.4960.6190.6640.6040.4090.1550.4510.3790.2960.4250.3650.3440.4390.4010.3420.3470.3854751OrganismalFitnessBLAT_ECOLXHighProkaryote
CAPSD_AAV2S_Sinai_2021_designed_indels-0.4090.6660.2980.3580.5430.621-0.457-0.466-0.402-0.4690.4920.1590.3360.3620.6910.7150.6770.7090.7180.7260.7360.6070.683225998OrganismalFitnessCAPSD_AAV2SLowVirus
CAPSD_AAV2S_Sinai_2021_library_indels-0.125-0.0070.0430.1190.1030.141-0.117-0.100-0.084-0.1040.1670.0830.0440.1260.2100.4250.3710.3380.4190.4190.4160.0570.17724908OrganismalFitnessCAPSD_AAV2SLowVirus
CATR_CHLRE_Tsuboyama_2023_2AMI_indels0.4660.4390.5050.4250.4210.3340.4160.3370.3460.2870.3380.0720.5210.2420.3550.4680.2740.3680.4730.2610.3530.1450.239197StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels0.4120.3750.4940.5890.4490.4450.2440.4100.4520.3790.4930.0180.4550.6090.461-0.068-0.041-0.012-0.052-0.0160.0130.4720.366205StabilityCBPA2_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K28_indels0.2310.3100.2080.2940.2590.2330.1990.2340.2010.2650.208-0.0170.3380.2870.2970.2610.2590.3000.2790.2710.3050.3800.073129StabilityCBX4_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM_indels0.6370.5810.6840.6680.5510.7530.6840.7820.8060.7220.7450.2720.5120.6810.770-0.1940.0470.158-0.1710.0940.2230.5360.312195StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX_indels0.5280.6950.5100.5180.5350.4660.2550.3510.3460.4040.6360.2700.4710.3860.507-0.183-0.181-0.153-0.153-0.164-0.1250.4740.460140StabilityCUE1_YEASTMediumEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC_indels0.1900.2420.1790.2920.3640.2850.3800.3620.3030.3170.4580.0260.2430.2180.196-0.117-0.117-0.135-0.103-0.103-0.1150.1200.696136StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels0.4760.2910.2620.3780.4660.3110.4310.3320.4000.2850.4150.2300.5360.4980.4950.3430.3680.3960.3710.3870.4130.2200.109174StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels0.4140.6190.5540.5590.6270.5150.6200.5960.5530.5590.6280.3090.6400.7000.6590.3910.4800.4630.4200.5130.4950.4330.353154StabilityDOCK1_MOUSEHighEukaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels0.2430.4290.4080.3780.4590.4280.5470.4430.6010.5540.6000.3610.4160.3670.4050.1120.1980.2690.1580.2290.3080.3810.316185StabilityEPHB2_HUMANHighHuman
FECA_ECOLI_Tsuboyama_2023_2D1U_indels0.4260.1870.3340.3870.4110.3760.3500.4940.5760.5050.4290.1040.2040.2860.029-0.0350.076-0.058-0.0370.084-0.0580.2260.444193StabilityFECA_ECOLIHighEukaryote
HCP_LAMBD_Tsuboyama_2023_2L6Q_indels0.3150.3960.6250.5930.7750.8150.5050.4510.4480.4800.5890.2440.5210.4700.517-0.096-0.1070.383-0.108-0.1300.4050.4080.378148StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM_indels0.2390.6030.3070.4340.5220.5720.4360.5670.5390.5450.5240.0250.1290.3310.1040.1890.3380.2050.1790.3480.1830.5510.282154StabilityHECD1_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_2019_indels0.0020.6870.6380.6560.6770.6840.6690.7020.7060.7030.713-0.0910.6300.6870.7070.6280.6110.6550.6390.6780.6950.5480.7016102OrganismalFitnessHIS7_YEASTMediumEukaryote
ILF3_HUMAN_Tsuboyama_2023_2L33_indels0.0300.5030.4250.5180.3100.3460.5830.4030.5650.6180.5270.0520.2580.6720.632-0.0330.3090.423-0.0030.3690.4790.2910.216193StabilityILF3_HUMANHighHuman
KCNJ2_MOUSE_Macdonald_2022_indels0.1620.4080.3850.4000.3830.3770.4010.4320.4260.4310.3870.2650.4010.4120.3910.4410.4370.4400.4340.4440.4310.3680.38610501ExpressionKCNJ2_MOUSEMediumEukaryote
MAFG_MOUSE_Tsuboyama_2023_1K1V_indels0.4120.049-0.0770.3130.2930.2790.3210.3690.3140.3000.3490.043-0.0140.1670.2740.0830.1240.1960.0750.1410.230-0.0330.018115StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV_indels0.2160.3120.2000.1060.4720.4940.3200.6700.5650.4470.4990.2980.0930.1740.1380.0410.0940.0450.0740.1190.0810.2530.482131StabilityMBD11_ARATHMediumEukaryote
MYO3_YEAST_Tsuboyama_2023_2BTT_indels0.0960.3540.3900.2830.5350.4700.6160.5330.3970.4840.5480.0670.5440.5250.4120.2860.2950.2420.3430.3580.2850.1470.51980StabilityMYO3_YEASTHighEukaryote
NKX31_HUMAN_Tsuboyama_2023_2L9R_indels0.6670.7720.6480.7670.7610.7540.7110.7060.7670.6980.6770.0730.6120.6670.6980.4760.6090.6580.4870.6170.6520.6120.331178StabilityNKX31_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL_indels0.1280.3400.2190.3150.2790.2410.2530.2270.2870.2130.226-0.0270.2060.2890.266-0.0760.0330.002-0.0760.0650.0290.0640.320191StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI6_indels0.4410.023-0.125-0.044-0.101-0.1470.111-0.039-0.026-0.083-0.0440.101-0.161-0.0700.167-0.156-0.1400.062-0.129-0.1120.094-0.0310.170157StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels0.5050.5050.8070.6780.6910.7140.7800.8150.8450.7330.6960.3900.3670.4370.5840.1490.2500.3300.1620.2740.3730.4510.152169StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G_indels0.1420.6770.7620.8330.8570.8730.8210.8810.8630.8380.8500.3310.5200.4440.4920.2040.3370.2580.2690.3830.2840.6290.68647StabilityODP2_GEOSEHighProkaryote
OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels0.4680.2210.3670.3360.3880.4290.3030.4820.5870.4680.5460.2860.2510.4620.501-0.162-0.0780.155-0.148-0.0420.1740.3600.51984StabilityOTU7A_HUMANHighHuman
P53_HUMAN_Kotler_2018_indels0.0730.0310.3600.4070.3830.2730.4740.4280.3910.4490.3540.0260.5360.5790.3950.4090.5360.3620.4290.5600.3990.4820.273341OrganismalFitnessP53_HUMANLowHuman
PIN1_HUMAN_Tsuboyama_2023_1I6C_indels0.7870.7580.7320.7520.7710.7350.8050.6340.6190.7190.6080.5770.6490.7400.7890.1830.5440.6660.2560.6050.6930.6240.580106StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M_indels0.4160.4810.3890.6330.6250.6710.4090.6900.5430.7080.6550.4630.2490.5350.551-0.0630.1600.1310.0130.2390.2270.3390.436117StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF_indels0.0990.7270.7340.7790.7290.7330.7950.8090.8550.7870.7840.2950.4970.7510.6990.2150.6930.6750.2700.7310.6900.4400.392187StabilityPKN1_HUMANHighHuman
POLG_PESV_Tsuboyama_2023_2MXD_indels0.4990.5380.3160.3770.4350.3300.3100.2530.2880.2150.3570.1070.3880.4340.434-0.192-0.135-0.159-0.166-0.103-0.1190.3040.256149StabilityPOLG_PESVMediumVirus
PR40A_HUMAN_Tsuboyama_2023_1UZC_indels0.2510.6880.4340.6300.6540.6000.6310.6770.6720.6880.6380.4840.4300.3680.2940.1840.2270.1190.2150.2620.1460.6250.163168StabilityPR40A_HUMANMediumHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE_indels-0.0490.1550.1420.0420.1330.1420.1450.345-0.0890.2860.3970.2560.1750.315-0.0920.2060.3770.0950.2150.3900.0810.3220.296175StabilityPSAE_SYNP2MediumProkaryote
PTEN_HUMAN_Mighell_2018_indels-0.3250.6970.6120.5750.5040.5230.6960.5520.6400.5900.4020.0560.6800.7000.5630.6610.6780.5460.6700.7080.6020.6680.237314ActivityPTEN_HUMANMediumHuman
Q8EG35_SHEON_Campbell_2022_indels0.5280.2440.4680.4650.3800.2810.5840.4030.3520.3300.3480.2860.5870.6150.3750.4090.6120.3950.4110.5620.3740.4720.278331OrganismalFitnessQ8EG35_SHEONMediumProkaryote
RAD_ANTMA_Tsuboyama_2023_2CJJ_indels0.5650.5710.4200.7640.8040.7620.5280.7220.7280.7040.6100.2150.5310.6010.5290.0760.4770.4520.1130.5220.4850.644-0.06997StabilityRAD_ANTMAHighEukaryote
RCD1_ARATH_Tsuboyama_2023_5OAO_indels0.1920.7240.0450.0930.2230.374-0.0130.3060.2970.2300.5040.0960.0730.0540.192-0.374-0.423-0.379-0.368-0.421-0.3680.6430.168124StabilityRCD1_ARATHMediumEukaryote
RD23A_HUMAN_Tsuboyama_2023_1IFY_indels0.3960.7250.5650.6760.6440.7490.6220.7330.7310.7300.7020.5390.5020.6690.7440.2550.4980.6430.2780.5560.6770.6010.418120StabilityRD23A_HUMANHighHuman
RPC1_BP434_Tsuboyama_2023_1R69_indels0.6070.3490.6370.6580.5900.6270.6580.6440.6020.6750.3500.1610.5650.6220.6780.3050.4080.5080.3120.4170.5300.1330.297164StabilityRPC1_BP434HighVirus
RS15_GEOSE_Tsuboyama_2023_1A32_indels0.3590.2520.0790.0660.1140.1600.1430.3280.2910.2740.2960.0560.5860.2770.2110.3480.137-0.0310.3880.1720.0100.0860.114176StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance_indels0.2030.0560.3110.3620.5280.4900.3240.4960.4940.4300.3300.2080.3420.4040.4290.2720.3640.4280.3230.4080.4250.2360.269430ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity_indels0.3040.1180.4020.4530.5560.5410.4360.5970.5430.5100.4350.2100.4050.4890.5030.3320.3940.4870.3830.4760.4810.3240.334490ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB_indels0.7120.7640.6390.6500.6290.6290.6520.7000.6440.6680.6450.5750.7200.6870.7060.3150.5470.6080.4080.6190.6740.6310.58586StabilitySAV1_MOUSEHighEukaryote
SDA_BACSU_Tsuboyama_2023_1PV0_indels0.1810.3100.2640.3540.4210.5720.2050.3270.0730.2810.5130.0680.3500.4570.395-0.151-0.1440.322-0.130-0.1140.3610.5810.353127StabilitySDA_BACSUMediumProkaryote
SOX30_HUMAN_Tsuboyama_2023_7JJK_indels0.6100.5020.4690.6310.6150.6990.4050.6300.5900.6110.6820.4250.0380.5940.605-0.268-0.0390.247-0.2320.0250.3510.3990.424109StabilitySOX30_HUMANHighHuman
SPG2_STRSG_Tsuboyama_2023_5UBS_indels-0.1070.4100.2150.2110.4550.4340.3130.4440.3800.4700.573-0.1710.0250.1540.5970.1440.1670.2870.1460.1710.3270.1330.298148StabilitySPG2_STRSGMediumProkaryote
SPTN1_CHICK_Tsuboyama_2023_1TUD_indels0.358-0.0020.5150.5650.5230.6250.5150.6930.6640.6630.5970.1950.5670.4700.5730.4310.4770.5020.4510.4970.5220.1860.152129StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels0.3100.3100.3500.4930.4940.5150.5480.4710.4760.4590.4860.1420.3750.5230.4540.0280.2030.3650.0460.2780.4150.0110.479111StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N88_indels0.0480.3740.4250.2820.3920.3600.2710.3440.4890.4840.4740.3670.3190.0990.3570.4000.3500.4950.4150.3640.4990.3500.303135StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels0.5740.7070.6470.6830.6750.6680.6750.6770.6930.6760.5810.5260.6830.7100.7310.7180.7530.7520.7200.7550.7570.4940.449154StabilitySRBS1_HUMANHighHuman
TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels0.6600.8230.6450.6590.6900.7580.5520.6570.6360.6490.7080.4120.3470.6180.698-0.446-0.302-0.164-0.397-0.235-0.0640.8220.33599StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG_indels0.4160.6630.2530.6070.6520.6660.6310.7880.7140.7590.7370.4250.3350.6930.664-0.2830.1440.061-0.2550.2160.1280.3710.68482StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels-0.0250.2180.3150.2900.2170.2660.3820.3330.2820.3670.2700.1190.1360.1930.291-0.0540.0200.103-0.0530.0250.1030.1630.312171StabilityTNKS2_HUMANHighHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels-0.0110.2200.1260.1620.1520.2390.3480.1870.2480.2390.3350.2670.1510.0850.325-0.170-0.1700.114-0.188-0.1810.127-0.0740.312147StabilityUBE4B_HUMANHighHuman
UBR5_HUMAN_Tsuboyama_2023_1I2T_indels0.2230.3850.4810.3890.4140.4310.4240.3770.4880.5100.5210.3580.4290.4810.446-0.0840.1140.127-0.0670.1560.1620.2410.253156StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP8_indels0.5190.7260.5280.7310.6880.8400.3720.5770.4250.4010.711-0.1600.4170.7470.823-0.0290.0780.562-0.0460.1150.6450.6450.282101StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU5_indels0.1180.6810.2440.4390.3660.2970.6010.7930.7440.7600.6180.3680.5750.6120.7750.2620.4890.6550.2880.5120.6870.0020.346156StabilityVILI_CHICKHighEukaryote
VRPI_BPT7_Tsuboyama_2023_2WNM_indels0.3990.5970.1560.4090.5600.3430.4090.3880.3750.4160.4540.1280.2730.2430.341-0.187-0.177-0.233-0.186-0.185-0.2360.3330.674154StabilityVRPI_BPT7MediumVirus
YNZC_BACSU_Tsuboyama_2023_2JVD_indels0.4920.7280.5410.5480.6600.6830.4950.7460.3550.6650.7030.3270.4380.5170.653-0.190-0.1710.004-0.179-0.1480.0550.6740.262104StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv b/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv new file mode 100644 index 0000000..8132440 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.csv @@ -0,0 +1,24 @@ +Model_rank,Model_name,Model type,Average_Spearman,Bootstrap_standard_error_Spearman,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Model details,References +1,Progen2 M,Protein language model,0.465,0.0,0.51,,0.464,0.374,0.511,0.208,0.472,0.549,0.527,0.564,0.421,0.338,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +2,Progen2 Base,Protein language model,0.464,0.01,0.533,,0.46,0.374,0.489,0.214,0.426,0.556,0.551,0.551,0.331,0.316,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +3,RITA L,Protein language model,0.457,0.035,0.466,,0.456,0.419,0.488,0.381,0.457,0.509,0.51,0.51,0.35,0.562,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +4,Tranception M no retrieval,Protein language model,0.453,0.036,0.469,,0.408,0.501,0.434,0.422,0.39,0.487,0.48,0.46,0.334,0.46,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +5,RITA XL,Protein language model,0.449,0.038,0.475,,0.434,0.393,0.496,0.361,0.471,0.509,0.513,0.511,0.367,0.556,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +6,Progen2 L,Protein language model,0.449,0.011,0.504,,0.43,0.36,0.5,0.214,0.45,0.547,0.536,0.537,0.406,0.317,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +7,Tranception L no retrieval,Protein language model,0.437,0.041,0.43,,0.41,0.445,0.463,0.43,0.39,0.523,0.485,0.49,0.331,0.541,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +8,RITA M,Protein language model,0.436,0.03,0.464,,0.381,0.444,0.456,0.366,0.4,0.511,0.501,0.468,0.333,0.501,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +9,Progen2 XL,Protein language model,0.431,0.036,0.405,,0.359,0.431,0.531,0.382,0.511,0.531,0.524,0.561,0.453,0.465,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +10,TranceptEVE M,Hybrid model,0.424,0.046,0.514,,0.426,0.555,0.202,0.531,0.127,0.331,0.313,0.27,0.161,0.114,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +11,Progen2 S,Protein language model,0.424,0.025,0.505,,0.363,0.387,0.441,0.242,0.383,0.507,0.493,0.463,0.356,0.328,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +12,TranceptEVE L,Hybrid model,0.412,0.046,0.474,,0.428,0.477,0.269,0.48,0.162,0.402,0.354,0.306,0.184,0.3,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +13,Tranception S no retrieval,Protein language model,0.41,0.036,0.419,,0.372,0.479,0.372,0.427,0.345,0.412,0.384,0.42,0.321,0.392,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,RITA S,Protein language model,0.397,0.032,0.436,,0.348,0.414,0.39,0.334,0.338,0.448,0.429,0.408,0.304,0.405,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +15,Tranception L,Hybrid model,0.395,0.044,0.453,,0.434,0.456,0.238,0.434,0.135,0.372,0.326,0.274,0.16,0.264,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,Tranception M,Hybrid model,0.394,0.048,0.472,,0.4,0.53,0.174,0.509,0.102,0.299,0.284,0.237,0.139,0.098,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +17,Hidden Markov Model,Alignment-based model,0.389,0.046,0.463,,0.302,0.436,0.354,0.202,0.408,0.34,0.393,0.352,0.337,0.359,Profile Hidden Markov model,HMMER: biosequence analysis using profile hidden Markov models +18,Wavenet,Alignment-based model,0.368,0.067,0.418,,0.232,0.346,0.476,0.291,0.472,0.468,0.477,0.482,0.394,0.489,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +19,TranceptEVE S,Hybrid model,0.357,0.05,0.456,,0.378,0.497,0.096,0.514,0.062,0.194,0.181,0.163,0.113,0.062,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +20,Provean,Alignment-based model,0.347,0.047,0.314,,0.328,0.413,0.334,0.361,0.333,0.344,0.318,0.358,0.332,0.386,Provean model,"Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one." +21,Tranception S,Hybrid model,0.34,0.053,0.441,,0.357,0.488,0.074,0.505,0.046,0.169,0.158,0.14,0.094,0.062,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +22,ProtGPT2,Protein language model,0.191,0.054,0.194,,0.236,0.099,0.235,0.233,0.188,0.253,0.287,0.233,0.15,0.1,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +23,Unirep,Protein language model,0.169,0.06,0.085,,0.182,0.065,0.342,0.11,0.267,0.358,0.298,0.364,0.216,0.345,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." diff --git a/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.html b/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.html new file mode 100644 index 0000000..5ebba00 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Spearman/Summary_performance_DMS_indels_Spearman.html @@ -0,0 +1,531 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_SpearmanBootstrap_standard_error_SpearmanFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusModel detailsReferences
Model_rank
1TranceptEVE MHybrid model0.4670.0000.669NaN0.4280.5550.2160.5310.1560.3420.3230.2820.2080.126TranceptEVE Medium model (Tranception Medium & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
2Progen2 MProtein language model0.4670.0540.510NaN0.4660.3740.5170.2080.4750.5560.5340.5700.4270.335Progen2 medium model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
3Progen2 BaseProtein language model0.4660.0510.533NaN0.4620.3740.4940.2140.4270.5630.5580.5560.3330.312Progen2 base model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
4RITA LProtein language model0.4590.0390.466NaN0.4560.4190.4950.3820.4620.5170.5170.5160.3560.564RITA large model (680M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
5TranceptEVE LHybrid model0.4570.0130.640NaN0.4290.4770.2820.4810.1920.4130.3640.3180.2320.309TranceptEVE Large model (Tranception Large & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
6Tranception M no retrievalProtein language model0.4550.0450.470NaN0.4100.5010.4390.4220.3940.4920.4840.4660.3400.460Tranception Medium model (300M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
7RITA XLProtein language model0.4520.0380.475NaN0.4350.3930.5030.3610.4760.5170.5200.5180.3750.557RITA xlarge model (1.2B params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
8Progen2 LProtein language model0.4510.0490.505NaN0.4320.3600.5060.2140.4540.5540.5430.5420.4120.315Progen2 large model (2.7B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
9Tranception L no retrievalProtein language model0.4390.0410.431NaN0.4120.4450.4690.4300.3950.5290.4910.4950.3390.542Tranception Large model (700M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
10RITA MProtein language model0.4390.0320.464NaN0.3820.4440.4630.3660.4050.5190.5080.4740.3410.503RITA medium model (300M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
11Progen2 XLProtein language model0.4340.0290.406NaN0.3600.4310.5390.3820.5160.5390.5320.5680.4600.465Progen2 xlarge model (6.4B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
12Progen2 SProtein language model0.4250.0490.506NaN0.3650.3870.4440.2420.3810.5140.4990.4660.3550.325Progen2 small model (150M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
13Tranception S no retrievalProtein language model0.4120.0460.420NaN0.3730.4790.3760.4270.3470.4170.3880.4220.3270.393Tranception Small model (85M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
14Tranception LHybrid model0.3990.0320.454NaN0.4360.4560.2510.4340.1480.3840.3350.2870.1740.273Tranception Large model (700M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
15RITA SProtein language model0.3990.0360.437NaN0.3500.4140.3950.3340.3410.4550.4350.4140.3090.406RITA small model (85M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
16Tranception MHybrid model0.3980.0290.473NaN0.4020.5300.1880.5090.1160.3110.2940.2500.1530.110Tranception Medium model (300M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
17Hidden Markov ModelAlignment-based model0.3910.0290.464NaN0.3020.4360.3630.2020.4160.3500.4000.3620.3450.367Profile Hidden Markov modelHMMER: biosequence analysis using profile hidden Markov models
18TranceptEVE SHybrid model0.3610.0320.457NaN0.3800.4970.1110.5140.0750.2080.1920.1770.1280.074TranceptEVE Small model (Tranception Small & retrieved EVE model)Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
19ProveanAlignment-based model0.3510.0290.315NaN0.3290.4130.3470.3610.3440.3560.3280.3700.3430.396Provean modelChoi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one.
20Tranception SHybrid model0.3440.0310.442NaN0.3580.4880.0890.5050.0590.1830.1690.1540.1090.073Tranception Small model (85M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
21WavenetAlignment-based model0.2850.0460.419NaN0.2340.3460.1410.1700.2470.1080.0570.2500.1860.358Wavenet modelShin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.
22ProtGPT2Protein language model0.1940.0330.195NaN0.2380.0990.2420.2330.1920.2630.2950.2400.1550.104ProtGPT2 modelFerruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.
23UnirepProtein language model0.1690.0610.085NaN0.1840.0650.3440.1100.2680.3600.3000.3640.2180.348Unirep modelAlley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/Top_recall/DMS_indels_Top_recall_DMS_level.csv b/benchmarks/DMS_zero_shot/indels/Top_recall/DMS_indels_Top_recall_DMS_level.csv new file mode 100644 index 0000000..ffb8dfd --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Top_recall/DMS_indels_Top_recall_DMS_level.csv @@ -0,0 +1,67 @@ +DMS ID,Unirep,Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,Hidden Markov Model,Provean,Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A4_HUMAN_Seuma_2022_indels,0.243,0.179,0.179,0.145,0.153,0.132,0.157,0.17,0.149,0.17,0.153,0.191,0.183,0.145,0.145,0.191,0.166,0.204,0.179,0.157,0.191,0.0,0.153,2346,Stability,A4_HUMAN,Low,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,0.25,0.667,0.0,0.25,0.583,0.75,0.25,0.583,0.75,0.583,0.667,0.667,0.083,0.0,0.167,0.0,0.083,0.167,0.0,0.083,0.167,0.417,0.75,117,Stability,AMFR_HUMAN,Medium,Human +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,0.105,0.632,0.789,0.684,0.684,0.789,0.316,0.789,0.789,0.789,0.789,0.053,0.526,0.368,0.632,0.316,0.263,0.526,0.316,0.263,0.526,0.368,0.474,181,Stability,ARGR_ECOLI,Medium,Prokaryote +B1LPA6_ECOSM_Russ_2020_indels,0.237,0.24,0.157,0.151,0.157,0.169,0.194,0.215,0.203,0.197,0.194,0.172,0.16,0.215,0.16,0.24,0.255,0.209,0.237,0.246,0.218,0.302,0.182,3074,Activity,B1LPA6_ECOSM,Medium,Prokaryote +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,0.214,0.357,0.214,0.286,0.429,0.571,0.143,0.643,0.643,0.643,0.714,0.143,0.143,0.5,0.0,0.143,0.357,0.214,0.286,0.357,0.214,0.143,0.786,134,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU_indels,0.0,0.667,0.111,0.333,0.222,0.444,0.222,0.333,0.444,0.667,0.667,0.111,0.222,0.444,0.444,0.222,0.333,0.444,0.222,0.333,0.556,0.222,0.778,82,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Gonzalez_2019_indels,0.166,0.422,0.382,0.42,0.405,0.317,0.359,0.426,0.424,0.424,0.242,0.12,0.418,0.382,0.258,0.366,0.351,0.263,0.374,0.403,0.263,0.174,0.363,4751,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +CAPSD_AAV2S_Sinai_2021_designed_indels,0.013,0.309,0.133,0.139,0.239,0.34,0.011,0.01,0.013,0.01,0.212,0.114,0.152,0.147,0.349,0.328,0.325,0.35,0.333,0.327,0.356,0.316,0.314,225998,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAPSD_AAV2S_Sinai_2021_library_indels,0.02,0.0,0.11,0.186,0.161,0.209,0.037,0.043,0.048,0.051,0.242,0.103,0.113,0.158,0.269,0.27,0.306,0.341,0.275,0.263,0.378,0.001,0.044,24908,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,0.4,0.45,0.45,0.45,0.45,0.45,0.4,0.45,0.45,0.45,0.45,0.15,0.45,0.4,0.45,0.45,0.4,0.4,0.45,0.4,0.4,0.25,0.4,197,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,0.095,0.619,0.095,0.095,0.048,0.19,0.048,0.238,0.238,0.286,0.571,0.048,0.048,0.095,0.095,0.048,0.095,0.095,0.048,0.143,0.095,0.333,0.333,205,Stability,CBPA2_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,0.154,0.154,0.385,0.385,0.462,0.538,0.538,0.385,0.462,0.462,0.538,0.077,0.308,0.308,0.385,0.308,0.231,0.385,0.385,0.231,0.385,0.308,0.538,129,Stability,CBX4_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,0.05,0.6,0.25,0.2,0.6,0.55,0.05,0.65,0.6,0.65,0.6,0.2,0.15,0.45,0.5,0.25,0.35,0.4,0.25,0.35,0.4,0.45,0.55,195,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,0.357,0.714,0.357,0.214,0.286,0.0,0.071,0.071,0.071,0.071,0.571,0.071,0.143,0.143,0.071,0.0,0.0,0.0,0.0,0.0,0.0,0.286,0.857,140,Stability,CUE1_YEAST,Medium,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,0.214,0.5,0.0,0.286,0.214,0.286,0.143,0.071,0.214,0.286,0.286,0.071,0.071,0.0,0.071,0.0,0.0,0.0,0.071,0.0,0.0,0.143,0.643,136,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,0.722,0.444,0.667,0.722,0.722,0.667,0.722,0.722,0.778,0.722,0.722,0.278,0.667,0.722,0.778,0.556,0.611,0.611,0.556,0.611,0.611,0.333,0.5,174,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,0.062,0.438,0.438,0.438,0.562,0.562,0.438,0.5,0.438,0.375,0.562,0.25,0.25,0.188,0.375,0.25,0.25,0.375,0.25,0.25,0.375,0.188,0.562,154,Stability,DOCK1_MOUSE,High,Eukaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,0.211,0.632,0.632,0.789,0.737,0.737,0.632,0.632,0.684,0.684,0.684,0.211,0.632,0.789,0.684,0.368,0.421,0.421,0.421,0.474,0.421,0.316,0.421,185,Stability,EPHB2_HUMAN,High,Human +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,0.25,0.25,0.2,0.4,0.4,0.4,0.1,0.4,0.4,0.35,0.4,0.2,0.0,0.0,0.05,0.0,0.0,0.05,0.0,0.0,0.05,0.4,0.35,193,Stability,FECA_ECOLI,High,Eukaryote +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,0.2,0.467,0.333,0.467,0.4,0.6,0.267,0.267,0.333,0.267,0.333,0.267,0.133,0.133,0.333,0.067,0.133,0.333,0.067,0.067,0.4,0.4,0.533,148,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,0.312,0.562,0.375,0.625,0.625,0.625,0.188,0.688,0.5,0.5,0.438,0.312,0.0,0.125,0.125,0.062,0.25,0.125,0.062,0.25,0.125,0.438,0.438,154,Stability,HECD1_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019_indels,0.031,0.383,0.373,0.345,0.336,0.378,0.295,0.354,0.406,0.38,0.403,0.108,0.303,0.417,0.399,0.357,0.344,0.406,0.37,0.401,0.414,0.345,0.396,6102,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,0.1,0.35,0.3,0.25,0.25,0.3,0.25,0.25,0.25,0.25,0.25,0.65,0.1,0.25,0.25,0.1,0.3,0.25,0.15,0.3,0.25,0.55,0.25,193,Stability,ILF3_HUMAN,High,Human +KCNJ2_MOUSE_Macdonald_2022_indels,0.147,0.102,0.185,0.143,0.088,0.087,0.219,0.145,0.114,0.167,0.082,0.105,0.168,0.108,0.088,0.134,0.097,0.1,0.129,0.096,0.097,0.166,0.114,10501,Expression,KCNJ2_MOUSE,Medium,Eukaryote +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,0.5,0.5,0.25,0.5,0.583,0.583,0.417,0.5,0.583,0.417,0.417,0.25,0.333,0.5,0.5,0.167,0.333,0.417,0.167,0.333,0.417,0.333,0.25,115,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,0.143,0.286,0.143,0.071,0.071,0.143,0.143,0.429,0.286,0.286,0.357,0.214,0.214,0.071,0.0,0.143,0.071,0.071,0.214,0.071,0.0,0.143,0.286,131,Stability,MBD11_ARATH,Medium,Eukaryote +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,0.0,0.375,0.125,0.0,0.25,0.0,0.625,0.375,0.25,0.375,0.75,0.125,0.0,0.0,0.125,0.0,0.0,0.0,0.0,0.125,0.125,0.0,0.375,80,Stability,MYO3_YEAST,High,Eukaryote +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,0.333,0.5,0.333,0.333,0.167,0.222,0.222,0.167,0.333,0.167,0.167,0.278,0.444,0.167,0.111,0.444,0.389,0.333,0.5,0.278,0.278,0.5,0.222,178,Stability,NKX31_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,0.35,0.55,0.7,0.55,0.6,0.6,0.35,0.55,0.25,0.65,0.65,0.05,0.35,0.65,0.3,0.3,0.4,0.4,0.3,0.45,0.4,0.3,0.65,191,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,0.188,0.375,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.375,0.5,0.5,0.5,0.375,0.375,0.312,0.375,0.375,0.312,0.125,0.375,157,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,0.176,0.471,0.471,0.471,0.588,0.588,0.471,0.471,0.529,0.353,0.471,0.235,0.118,0.0,0.176,0.118,0.176,0.118,0.118,0.118,0.059,0.353,0.294,169,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,0.0,0.2,0.2,0.6,0.4,0.6,0.6,0.8,0.8,0.8,0.4,0.0,0.2,0.0,0.2,0.4,0.4,0.2,0.4,0.4,0.2,0.2,0.8,47,Stability,ODP2_GEOSE,High,Prokaryote +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,0.111,0.111,0.222,0.556,0.667,0.778,0.556,0.778,0.778,0.778,0.778,0.556,0.222,0.111,0.333,0.0,0.111,0.333,0.0,0.111,0.333,0.222,0.889,84,Stability,OTU7A_HUMAN,High,Human +P53_HUMAN_Kotler_2018_indels,0.114,0.171,0.229,0.171,0.143,0.343,0.229,0.314,0.257,0.286,0.371,0.029,0.257,0.343,0.343,0.114,0.4,0.257,0.2,0.286,0.314,0.257,0.257,341,OrganismalFitness,P53_HUMAN,Low,Human +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,0.364,0.455,0.364,0.273,0.455,0.364,0.182,0.273,0.455,0.273,0.455,0.455,0.273,0.364,0.364,0.545,0.636,0.455,0.455,0.636,0.455,0.364,0.364,106,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,0.667,0.417,0.833,0.667,0.667,0.75,0.667,0.75,0.833,0.75,0.75,0.583,0.667,0.75,0.75,0.417,0.333,0.417,0.5,0.417,0.417,0.333,0.583,117,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,0.158,0.316,0.368,0.316,0.368,0.316,0.368,0.316,0.368,0.421,0.368,0.211,0.211,0.263,0.316,0.211,0.368,0.368,0.263,0.316,0.368,0.263,0.421,187,Stability,PKN1_HUMAN,High,Human +POLG_PESV_Tsuboyama_2023_2MXD_indels,0.067,0.467,0.0,0.0,0.0,0.067,0.067,0.067,0.0,0.0,0.067,0.067,0.067,0.0,0.0,0.067,0.0,0.0,0.133,0.0,0.0,0.133,0.467,149,Stability,POLG_PESV,Medium,Virus +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,0.0,0.706,0.529,0.706,0.765,0.765,0.706,0.824,0.706,0.765,0.765,0.294,0.353,0.294,0.412,0.294,0.353,0.353,0.353,0.412,0.412,0.294,0.353,168,Stability,PR40A_HUMAN,Medium,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE_indels,0.111,0.0,0.111,0.0,0.0,0.222,0.056,0.056,0.0,0.056,0.0,0.222,0.167,0.278,0.0,0.222,0.278,0.056,0.167,0.278,0.056,0.278,0.0,175,Stability,PSAE_SYNP2,Medium,Prokaryote +PTEN_HUMAN_Mighell_2018_indels,0.0,0.125,0.219,0.125,0.188,0.281,0.25,0.281,0.312,0.094,0.219,0.0,0.25,0.219,0.219,0.188,0.281,0.219,0.156,0.125,0.156,0.219,0.281,314,Activity,PTEN_HUMAN,Medium,Human +Q8EG35_SHEON_Campbell_2022_indels,0.235,0.0,0.147,0.176,0.088,0.176,0.206,0.118,0.029,0.118,0.059,0.176,0.235,0.235,0.206,0.118,0.147,0.206,0.176,0.206,0.147,0.206,0.118,331,OrganismalFitness,Q8EG35_SHEON,Medium,Prokaryote +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,0.2,0.1,0.2,0.1,0.2,0.3,0.4,0.4,0.2,0.2,0.4,0.3,0.1,0.4,0.3,0.3,0.5,0.4,0.4,0.5,0.4,0.1,0.2,97,Stability,RAD_ANTMA,High,Eukaryote +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,0.077,0.615,0.0,0.077,0.0,0.154,0.077,0.308,0.154,0.231,0.538,0.077,0.0,0.0,0.231,0.0,0.0,0.0,0.0,0.0,0.0,0.308,0.385,124,Stability,RCD1_ARATH,Medium,Eukaryote +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,0.167,0.5,0.583,0.583,0.5,0.833,0.75,0.75,0.667,0.75,0.75,0.667,0.333,0.75,0.667,0.417,0.583,0.583,0.417,0.667,0.583,0.167,0.75,120,Stability,RD23A_HUMAN,High,Human +RPC1_BP434_Tsuboyama_2023_1R69_indels,0.647,0.353,0.412,0.588,0.471,0.471,0.588,0.471,0.471,0.471,0.471,0.176,0.235,0.529,0.588,0.118,0.294,0.412,0.118,0.353,0.471,0.118,0.412,164,Stability,RPC1_BP434,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32_indels,0.111,0.444,0.5,0.5,0.5,0.5,0.389,0.5,0.5,0.5,0.5,0.056,0.5,0.5,0.444,0.333,0.278,0.278,0.389,0.278,0.278,0.278,0.389,176,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance_indels,0.186,0.14,0.302,0.302,0.349,0.349,0.256,0.326,0.349,0.233,0.256,0.163,0.326,0.302,0.279,0.326,0.256,0.256,0.302,0.326,0.256,0.093,0.209,430,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity_indels,0.163,0.102,0.224,0.347,0.347,0.347,0.286,0.327,0.388,0.286,0.306,0.245,0.347,0.306,0.265,0.286,0.265,0.265,0.265,0.327,0.265,0.163,0.224,490,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,0.222,0.222,0.222,0.222,0.222,0.333,0.333,0.222,0.222,0.222,0.111,0.556,0.111,0.111,0.111,0.222,0.111,0.111,0.333,0.111,0.111,0.222,0.111,86,Stability,SAV1_MOUSE,High,Eukaryote +SDA_BACSU_Tsuboyama_2023_1PV0_indels,0.0,0.615,0.077,0.0,0.077,0.154,0.231,0.385,0.077,0.231,0.538,0.077,0.0,0.0,0.615,0.077,0.077,0.308,0.077,0.077,0.538,0.308,0.692,127,Stability,SDA_BACSU,Medium,Prokaryote +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,0.545,0.273,0.364,0.545,0.636,0.636,0.455,0.636,0.636,0.727,0.727,0.818,0.273,0.727,0.636,0.0,0.364,0.455,0.091,0.455,0.455,0.091,0.727,109,Stability,SOX30_HUMAN,High,Human +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,0.067,0.267,0.067,0.067,0.067,0.133,0.267,0.133,0.067,0.2,0.333,0.067,0.133,0.067,0.4,0.067,0.0,0.133,0.067,0.0,0.267,0.0,0.333,148,Stability,SPG2_STRSG,Medium,Prokaryote +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,0.231,0.154,0.692,0.385,0.692,0.462,0.615,0.615,0.538,0.769,0.769,0.077,0.154,0.154,0.462,0.077,0.154,0.308,0.077,0.077,0.385,0.077,0.077,129,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,0.083,0.333,0.583,0.833,0.75,0.833,0.417,0.667,0.75,0.667,0.667,0.167,0.167,0.667,0.75,0.083,0.5,0.5,0.083,0.5,0.667,0.167,0.833,111,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88_indels,0.071,0.429,0.286,0.357,0.214,0.214,0.214,0.143,0.214,0.071,0.214,0.286,0.214,0.0,0.214,0.143,0.214,0.214,0.214,0.143,0.214,0.357,0.357,135,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,0.125,0.5,0.5,0.438,0.438,0.375,0.5,0.375,0.312,0.438,0.438,0.438,0.312,0.5,0.438,0.312,0.5,0.5,0.312,0.5,0.5,0.438,0.375,154,Stability,SRBS1_HUMAN,High,Human +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,0.3,0.6,0.7,0.7,0.8,0.6,0.6,0.7,0.6,0.6,0.7,0.2,0.3,0.4,0.7,0.2,0.3,0.3,0.2,0.3,0.4,0.4,0.6,99,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG_indels,0.111,0.556,0.0,0.111,0.222,0.111,0.444,0.333,0.222,0.444,0.556,0.222,0.111,0.444,0.333,0.111,0.556,0.667,0.111,0.667,0.556,0.333,0.778,82,Stability,THO1_YEAST,High,Eukaryote 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scoreUnirepWavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LHidden Markov ModelProveanNumber of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A4_HUMAN_Seuma_2022_indels0.2430.1790.1790.1450.1530.1320.1570.1700.1490.1700.1530.1910.1830.1450.1450.1910.1660.2040.1790.1570.1910.0000.1532346StabilityA4_HUMANLowHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O_indels0.2500.6670.0000.2500.5830.7500.2500.5830.7500.5830.6670.6670.0830.0000.1670.0000.0830.1670.0000.0830.1670.4170.750117StabilityAMFR_HUMANMediumHuman
ARGR_ECOLI_Tsuboyama_2023_1AOY_indels0.1050.6320.7890.6840.6840.7890.3160.7890.7890.7890.7890.0530.5260.3680.6320.3160.2630.5260.3160.2630.5260.3680.474181StabilityARGR_ECOLIMediumProkaryote
B1LPA6_ECOSM_Russ_2020_indels0.2370.2400.1570.1510.1570.1690.1940.2150.2030.1970.1940.1720.1600.2150.1600.2400.2550.2090.2370.2460.2180.3020.1823074ActivityB1LPA6_ECOSMMediumProkaryote
BBC1_YEAST_Tsuboyama_2023_1TG0_indels0.2140.3570.2140.2860.4290.5710.1430.6430.6430.6430.7140.1430.1430.5000.0000.1430.3570.2140.2860.3570.2140.1430.786134StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU_indels0.0000.6670.1110.3330.2220.4440.2220.3330.4440.6670.6670.1110.2220.4440.4440.2220.3330.4440.2220.3330.5560.2220.77882StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Gonzalez_2019_indels0.1660.4220.3820.4200.4050.3170.3590.4260.4240.4240.2420.1200.4180.3820.2580.3660.3510.2630.3740.4030.2630.1740.3634751OrganismalFitnessBLAT_ECOLXHighProkaryote
CAPSD_AAV2S_Sinai_2021_designed_indels0.0130.3090.1330.1390.2390.3400.0110.0100.0130.0100.2120.1140.1520.1470.3490.3280.3250.3500.3330.3270.3560.3160.314225998OrganismalFitnessCAPSD_AAV2SLowVirus
CAPSD_AAV2S_Sinai_2021_library_indels0.0200.0000.1100.1860.1610.2090.0370.0430.0480.0510.2420.1030.1130.1580.2690.2700.3060.3410.2750.2630.3780.0010.04424908OrganismalFitnessCAPSD_AAV2SLowVirus
CATR_CHLRE_Tsuboyama_2023_2AMI_indels0.4000.4500.4500.4500.4500.4500.4000.4500.4500.4500.4500.1500.4500.4000.4500.4500.4000.4000.4500.4000.4000.2500.400197StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels0.0950.6190.0950.0950.0480.1900.0480.2380.2380.2860.5710.0480.0480.0950.0950.0480.0950.0950.0480.1430.0950.3330.333205StabilityCBPA2_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K28_indels0.1540.1540.3850.3850.4620.5380.5380.3850.4620.4620.5380.0770.3080.3080.3850.3080.2310.3850.3850.2310.3850.3080.538129StabilityCBX4_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM_indels0.0500.6000.2500.2000.6000.5500.0500.6500.6000.6500.6000.2000.1500.4500.5000.2500.3500.4000.2500.3500.4000.4500.550195StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX_indels0.3570.7140.3570.2140.2860.0000.0710.0710.0710.0710.5710.0710.1430.1430.0710.0000.0000.0000.0000.0000.0000.2860.857140StabilityCUE1_YEASTMediumEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC_indels0.2140.5000.0000.2860.2140.2860.1430.0710.2140.2860.2860.0710.0710.0000.0710.0000.0000.0000.0710.0000.0000.1430.643136StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels0.7220.4440.6670.7220.7220.6670.7220.7220.7780.7220.7220.2780.6670.7220.7780.5560.6110.6110.5560.6110.6110.3330.500174StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels0.0620.4380.4380.4380.5620.5620.4380.5000.4380.3750.5620.2500.2500.1880.3750.2500.2500.3750.2500.2500.3750.1880.562154StabilityDOCK1_MOUSEHighEukaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels0.2110.6320.6320.7890.7370.7370.6320.6320.6840.6840.6840.2110.6320.7890.6840.3680.4210.4210.4210.4740.4210.3160.421185StabilityEPHB2_HUMANHighHuman
FECA_ECOLI_Tsuboyama_2023_2D1U_indels0.2500.2500.2000.4000.4000.4000.1000.4000.4000.3500.4000.2000.0000.0000.0500.0000.0000.0500.0000.0000.0500.4000.350193StabilityFECA_ECOLIHighEukaryote
HCP_LAMBD_Tsuboyama_2023_2L6Q_indels0.2000.4670.3330.4670.4000.6000.2670.2670.3330.2670.3330.2670.1330.1330.3330.0670.1330.3330.0670.0670.4000.4000.533148StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM_indels0.3120.5620.3750.6250.6250.6250.1880.6880.5000.5000.4380.3120.0000.1250.1250.0620.2500.1250.0620.2500.1250.4380.438154StabilityHECD1_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_2019_indels0.0310.3830.3730.3450.3360.3780.2950.3540.4060.3800.4030.1080.3030.4170.3990.3570.3440.4060.3700.4010.4140.3450.3966102OrganismalFitnessHIS7_YEASTMediumEukaryote
ILF3_HUMAN_Tsuboyama_2023_2L33_indels0.1000.3500.3000.2500.2500.3000.2500.2500.2500.2500.2500.6500.1000.2500.2500.1000.3000.2500.1500.3000.2500.5500.250193StabilityILF3_HUMANHighHuman
KCNJ2_MOUSE_Macdonald_2022_indels0.1470.1020.1850.1430.0880.0870.2190.1450.1140.1670.0820.1050.1680.1080.0880.1340.0970.1000.1290.0960.0970.1660.11410501ExpressionKCNJ2_MOUSEMediumEukaryote
MAFG_MOUSE_Tsuboyama_2023_1K1V_indels0.5000.5000.2500.5000.5830.5830.4170.5000.5830.4170.4170.2500.3330.5000.5000.1670.3330.4170.1670.3330.4170.3330.250115StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV_indels0.1430.2860.1430.0710.0710.1430.1430.4290.2860.2860.3570.2140.2140.0710.0000.1430.0710.0710.2140.0710.0000.1430.286131StabilityMBD11_ARATHMediumEukaryote
MYO3_YEAST_Tsuboyama_2023_2BTT_indels0.0000.3750.1250.0000.2500.0000.6250.3750.2500.3750.7500.1250.0000.0000.1250.0000.0000.0000.0000.1250.1250.0000.37580StabilityMYO3_YEASTHighEukaryote
NKX31_HUMAN_Tsuboyama_2023_2L9R_indels0.3330.5000.3330.3330.1670.2220.2220.1670.3330.1670.1670.2780.4440.1670.1110.4440.3890.3330.5000.2780.2780.5000.222178StabilityNKX31_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL_indels0.3500.5500.7000.5500.6000.6000.3500.5500.2500.6500.6500.0500.3500.6500.3000.3000.4000.4000.3000.4500.4000.3000.650191StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI6_indels0.1880.3750.5000.5000.5000.5000.5000.5000.5000.5000.5000.3750.5000.5000.5000.3750.3750.3120.3750.3750.3120.1250.375157StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels0.1760.4710.4710.4710.5880.5880.4710.4710.5290.3530.4710.2350.1180.0000.1760.1180.1760.1180.1180.1180.0590.3530.294169StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G_indels0.0000.2000.2000.6000.4000.6000.6000.8000.8000.8000.4000.0000.2000.0000.2000.4000.4000.2000.4000.4000.2000.2000.80047StabilityODP2_GEOSEHighProkaryote
OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels0.1110.1110.2220.5560.6670.7780.5560.7780.7780.7780.7780.5560.2220.1110.3330.0000.1110.3330.0000.1110.3330.2220.88984StabilityOTU7A_HUMANHighHuman
P53_HUMAN_Kotler_2018_indels0.1140.1710.2290.1710.1430.3430.2290.3140.2570.2860.3710.0290.2570.3430.3430.1140.4000.2570.2000.2860.3140.2570.257341OrganismalFitnessP53_HUMANLowHuman
PIN1_HUMAN_Tsuboyama_2023_1I6C_indels0.3640.4550.3640.2730.4550.3640.1820.2730.4550.2730.4550.4550.2730.3640.3640.5450.6360.4550.4550.6360.4550.3640.364106StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M_indels0.6670.4170.8330.6670.6670.7500.6670.7500.8330.7500.7500.5830.6670.7500.7500.4170.3330.4170.5000.4170.4170.3330.583117StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF_indels0.1580.3160.3680.3160.3680.3160.3680.3160.3680.4210.3680.2110.2110.2630.3160.2110.3680.3680.2630.3160.3680.2630.421187StabilityPKN1_HUMANHighHuman
POLG_PESV_Tsuboyama_2023_2MXD_indels0.0670.4670.0000.0000.0000.0670.0670.0670.0000.0000.0670.0670.0670.0000.0000.0670.0000.0000.1330.0000.0000.1330.467149StabilityPOLG_PESVMediumVirus
PR40A_HUMAN_Tsuboyama_2023_1UZC_indels0.0000.7060.5290.7060.7650.7650.7060.8240.7060.7650.7650.2940.3530.2940.4120.2940.3530.3530.3530.4120.4120.2940.353168StabilityPR40A_HUMANMediumHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE_indels0.1110.0000.1110.0000.0000.2220.0560.0560.0000.0560.0000.2220.1670.2780.0000.2220.2780.0560.1670.2780.0560.2780.000175StabilityPSAE_SYNP2MediumProkaryote
PTEN_HUMAN_Mighell_2018_indels0.0000.1250.2190.1250.1880.2810.2500.2810.3120.0940.2190.0000.2500.2190.2190.1880.2810.2190.1560.1250.1560.2190.281314ActivityPTEN_HUMANMediumHuman
Q8EG35_SHEON_Campbell_2022_indels0.2350.0000.1470.1760.0880.1760.2060.1180.0290.1180.0590.1760.2350.2350.2060.1180.1470.2060.1760.2060.1470.2060.118331OrganismalFitnessQ8EG35_SHEONMediumProkaryote
RAD_ANTMA_Tsuboyama_2023_2CJJ_indels0.2000.1000.2000.1000.2000.3000.4000.4000.2000.2000.4000.3000.1000.4000.3000.3000.5000.4000.4000.5000.4000.1000.20097StabilityRAD_ANTMAHighEukaryote
RCD1_ARATH_Tsuboyama_2023_5OAO_indels0.0770.6150.0000.0770.0000.1540.0770.3080.1540.2310.5380.0770.0000.0000.2310.0000.0000.0000.0000.0000.0000.3080.385124StabilityRCD1_ARATHMediumEukaryote
RD23A_HUMAN_Tsuboyama_2023_1IFY_indels0.1670.5000.5830.5830.5000.8330.7500.7500.6670.7500.7500.6670.3330.7500.6670.4170.5830.5830.4170.6670.5830.1670.750120StabilityRD23A_HUMANHighHuman
RPC1_BP434_Tsuboyama_2023_1R69_indels0.6470.3530.4120.5880.4710.4710.5880.4710.4710.4710.4710.1760.2350.5290.5880.1180.2940.4120.1180.3530.4710.1180.412164StabilityRPC1_BP434HighVirus
RS15_GEOSE_Tsuboyama_2023_1A32_indels0.1110.4440.5000.5000.5000.5000.3890.5000.5000.5000.5000.0560.5000.5000.4440.3330.2780.2780.3890.2780.2780.2780.389176StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance_indels0.1860.1400.3020.3020.3490.3490.2560.3260.3490.2330.2560.1630.3260.3020.2790.3260.2560.2560.3020.3260.2560.0930.209430ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity_indels0.1630.1020.2240.3470.3470.3470.2860.3270.3880.2860.3060.2450.3470.3060.2650.2860.2650.2650.2650.3270.2650.1630.224490ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB_indels0.2220.2220.2220.2220.2220.3330.3330.2220.2220.2220.1110.5560.1110.1110.1110.2220.1110.1110.3330.1110.1110.2220.11186StabilitySAV1_MOUSEHighEukaryote
SDA_BACSU_Tsuboyama_2023_1PV0_indels0.0000.6150.0770.0000.0770.1540.2310.3850.0770.2310.5380.0770.0000.0000.6150.0770.0770.3080.0770.0770.5380.3080.692127StabilitySDA_BACSUMediumProkaryote
SOX30_HUMAN_Tsuboyama_2023_7JJK_indels0.5450.2730.3640.5450.6360.6360.4550.6360.6360.7270.7270.8180.2730.7270.6360.0000.3640.4550.0910.4550.4550.0910.727109StabilitySOX30_HUMANHighHuman
SPG2_STRSG_Tsuboyama_2023_5UBS_indels0.0670.2670.0670.0670.0670.1330.2670.1330.0670.2000.3330.0670.1330.0670.4000.0670.0000.1330.0670.0000.2670.0000.333148StabilitySPG2_STRSGMediumProkaryote
SPTN1_CHICK_Tsuboyama_2023_1TUD_indels0.2310.1540.6920.3850.6920.4620.6150.6150.5380.7690.7690.0770.1540.1540.4620.0770.1540.3080.0770.0770.3850.0770.077129StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels0.0830.3330.5830.8330.7500.8330.4170.6670.7500.6670.6670.1670.1670.6670.7500.0830.5000.5000.0830.5000.6670.1670.833111StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N88_indels0.0710.4290.2860.3570.2140.2140.2140.1430.2140.0710.2140.2860.2140.0000.2140.1430.2140.2140.2140.1430.2140.3570.357135StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels0.1250.5000.5000.4380.4380.3750.5000.3750.3120.4380.4380.4380.3120.5000.4380.3120.5000.5000.3120.5000.5000.4380.375154StabilitySRBS1_HUMANHighHuman
TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels0.3000.6000.7000.7000.8000.6000.6000.7000.6000.6000.7000.2000.3000.4000.7000.2000.3000.3000.2000.3000.4000.4000.60099StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG_indels0.1110.5560.0000.1110.2220.1110.4440.3330.2220.4440.5560.2220.1110.4440.3330.1110.5560.6670.1110.6670.5560.3330.77882StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels0.1670.3890.3330.7220.7220.6670.6110.6670.6110.6670.6670.1110.0000.2220.6110.1670.2220.4440.1670.2220.6110.4440.556171StabilityTNKS2_HUMANHighHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels0.0000.4670.0000.5330.5330.5330.4670.5330.5330.5330.5330.4000.1330.0670.4670.0670.0670.3330.0670.0670.3330.2000.467147StabilityUBE4B_HUMANHighHuman
UBR5_HUMAN_Tsuboyama_2023_1I2T_indels0.0620.5000.5620.5000.6250.5620.5620.6250.6250.6250.6250.1250.3750.5000.3750.2500.3750.3750.2500.4380.4380.6250.438156StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP8_indels0.3640.3640.4550.3640.4550.5450.1820.2730.1820.1820.1820.0000.1820.3640.3640.1820.2730.3640.2730.3640.2730.5450.182101StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU5_indels0.1250.6880.0000.0000.0000.0000.0000.1250.0000.0620.5000.1880.1880.5620.3750.1880.5000.4380.2500.5620.4380.3750.688156StabilityVILI_CHICKHighEukaryote
VRPI_BPT7_Tsuboyama_2023_2WNM_indels0.2500.1250.1250.2500.2500.1250.3120.1250.0620.1880.2500.0620.1250.1250.1880.1880.1880.1250.1880.1250.1250.1250.250154StabilityVRPI_BPT7MediumVirus
YNZC_BACSU_Tsuboyama_2023_2JVD_indels0.2730.5450.2730.2730.3640.3640.3640.4550.1820.3640.2730.2730.2730.3640.2730.0910.1820.2730.0910.1820.2730.3640.364104StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.csv b/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.csv new file mode 100644 index 0000000..a30d174 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.csv @@ -0,0 +1,24 @@ +Model_rank,Model_name,Model type,Average_Top_recall,Bootstrap_standard_error_Top_recall,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Model details,References +1,RITA XL,Protein language model,0.304,0.0,0.266,,0.218,0.298,0.436,0.25,0.375,0.461,0.513,0.337,0.375,0.347,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +2,Progen2 M,Protein language model,0.299,0.016,0.274,,0.236,0.248,0.439,0.17,0.376,0.467,0.49,0.401,0.381,0.205,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +3,Progen2 Base,Protein language model,0.294,0.015,0.301,,0.231,0.229,0.413,0.146,0.344,0.451,0.506,0.357,0.32,0.18,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +4,Progen2 XL,Protein language model,0.29,0.013,0.24,,0.169,0.26,0.491,0.25,0.42,0.505,0.516,0.488,0.388,0.255,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +5,Provean,Alignment-based model,0.28,0.02,0.229,,0.162,0.263,0.466,0.196,0.424,0.463,0.441,0.448,0.44,0.337,Provean model,"Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one." +6,RITA L,Protein language model,0.274,0.012,0.231,,0.218,0.234,0.412,0.165,0.335,0.45,0.475,0.358,0.306,0.296,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +7,Progen2 L,Protein language model,0.269,0.026,0.192,,0.2,0.248,0.435,0.162,0.36,0.468,0.481,0.372,0.413,0.19,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +8,RITA M,Protein language model,0.265,0.018,0.208,,0.222,0.255,0.377,0.16,0.307,0.417,0.439,0.292,0.324,0.305,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +9,Progen2 S,Protein language model,0.265,0.025,0.243,,0.238,0.223,0.357,0.137,0.262,0.428,0.419,0.3,0.3,0.24,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +10,Tranception M no retrieval,Protein language model,0.264,0.014,0.247,,0.205,0.306,0.299,0.214,0.242,0.349,0.334,0.276,0.286,0.217,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +11,Tranception L no retrieval,Protein language model,0.262,0.011,0.215,,0.184,0.303,0.349,0.266,0.299,0.374,0.38,0.302,0.322,0.297,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +12,TranceptEVE M,Hybrid model,0.259,0.017,0.233,,0.211,0.318,0.276,0.246,0.203,0.342,0.318,0.262,0.249,0.201,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +13,Tranception M,Hybrid model,0.256,0.016,0.267,,0.176,0.312,0.267,0.294,0.201,0.326,0.316,0.252,0.238,0.201,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,RITA S,Protein language model,0.255,0.018,0.2,,0.244,0.25,0.325,0.176,0.261,0.37,0.367,0.283,0.287,0.241,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +15,TranceptEVE L,Hybrid model,0.251,0.017,0.213,,0.176,0.301,0.315,0.291,0.256,0.351,0.342,0.283,0.288,0.273,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +16,Tranception L,Hybrid model,0.25,0.015,0.231,,0.178,0.296,0.297,0.269,0.23,0.348,0.336,0.269,0.258,0.263,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +17,Tranception S no retrieval,Protein language model,0.249,0.016,0.252,,0.247,0.269,0.226,0.191,0.201,0.259,0.274,0.175,0.268,0.146,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +18,TranceptEVE S,Hybrid model,0.234,0.018,0.219,,0.216,0.285,0.215,0.228,0.164,0.269,0.247,0.189,0.233,0.181,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +19,Wavenet,Alignment-based model,0.232,0.032,0.156,,0.121,0.226,0.426,0.168,0.428,0.383,0.401,0.408,0.39,0.322,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +20,Tranception S,Hybrid model,0.228,0.018,0.238,,0.23,0.251,0.193,0.201,0.155,0.239,0.228,0.161,0.223,0.154,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +21,Hidden Markov Model,Alignment-based model,0.217,0.037,0.228,,0.13,0.228,0.281,0.138,0.282,0.275,0.316,0.252,0.233,0.247,Profile Hidden Markov model,HMMER: biosequence analysis using profile hidden Markov models +22,ProtGPT2,Protein language model,0.155,0.029,0.139,,0.134,0.108,0.24,0.11,0.163,0.287,0.327,0.194,0.13,0.113,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +23,Unirep,Protein language model,0.154,0.034,0.133,,0.166,0.112,0.206,0.124,0.154,0.238,0.219,0.179,0.147,0.257,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." diff --git a/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.html b/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.html new file mode 100644 index 0000000..e0453d6 --- /dev/null +++ b/benchmarks/DMS_zero_shot/indels/Top_recall/Summary_performance_DMS_indels_Top_recall.html @@ -0,0 +1,531 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_Top_recallBootstrap_standard_error_Top_recallFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusModel detailsReferences
Model_rank
1RITA XLProtein language model0.3030.0380.260NaN0.2070.3010.4430.2530.3850.4630.5120.3460.3890.347RITA xlarge model (1.2B params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
2Progen2 MProtein language model0.2970.0360.277NaN0.2230.2500.4380.1740.3720.4690.4900.4090.3670.205Progen2 medium model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
3TranceptEVE LHybrid model0.2920.0120.348NaN0.1760.3040.3380.2940.2960.3680.3560.3160.3450.256TranceptEVE Large model (Tranception Large & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
4TranceptEVE MHybrid model0.2910.0000.354NaN0.1990.3210.2920.2490.2380.3480.3150.2950.2980.195TranceptEVE Medium model (Tranception Medium & retrieved EVE model)Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.
5Progen2 XLProtein language model0.2910.0330.242NaN0.1690.2510.5020.2350.4370.5080.5120.4990.4170.255Progen2 xlarge model (6.4B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
6Progen2 BaseProtein language model0.2900.0390.297NaN0.2310.2310.4020.1480.3360.4390.4920.3560.3020.180Progen2 base model (760M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
7ProveanAlignment-based model0.2850.0370.233NaN0.1620.2580.4860.1990.4410.4830.4460.4750.4630.358Provean modelChoi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one.
8RITA LProtein language model0.2750.0350.232NaN0.2180.2360.4130.1650.3330.4550.4760.3560.3090.306RITA large model (680M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
9Progen2 LProtein language model0.2710.0310.195NaN0.2000.2500.4390.1650.3700.4660.4780.3890.4090.190Progen2 large model (2.7B params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
10RITA MProtein language model0.2680.0330.210NaN0.2220.2510.3890.1510.3080.4370.4440.3080.3380.305RITA medium model (300M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
11Tranception L no retrievalProtein language model0.2660.0340.217NaN0.1840.3060.3570.2690.3160.3730.3880.3000.3490.287Tranception Large model (700M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
12Tranception M no retrievalProtein language model0.2650.0310.250NaN0.2050.3030.3020.2070.2470.3510.3290.2850.2940.217Tranception Medium model (300M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
13Progen2 SProtein language model0.2630.0380.245NaN0.2380.2130.3580.1290.2600.4300.4120.3060.3020.240Progen2 small model (150M params) Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517.
14Tranception MHybrid model0.2590.0300.271NaN0.1760.3030.2870.2780.2290.3350.3180.2840.2580.210Tranception Medium model (300M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
15Tranception LHybrid model0.2570.0300.229NaN0.1780.2980.3230.2720.2670.3600.3530.2930.3000.258Tranception Large model (700M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
16RITA SProtein language model0.2520.0350.195NaN0.2440.2470.3230.1670.2570.3710.3630.2820.2870.241RITA small model (85M params)Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.
17Tranception S no retrievalProtein language model0.2450.0370.255NaN0.2470.2600.2200.1840.1950.2530.2600.1730.2660.146Tranception Small model (85M params) without retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
18TranceptEVE SHybrid model0.2350.0250.216NaN0.2160.2750.2340.2200.1830.2840.2550.2130.2590.175TranceptEVE Small model (Tranception Small & retrieved EVE model)Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
19Tranception SHybrid model0.2310.0270.234NaN0.2300.2520.2090.2030.1730.2500.2320.1820.2470.154Tranception Small model (85M params) with retrievalNotin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
20Hidden Markov ModelAlignment-based model0.2170.0360.229NaN0.1300.2190.2890.1220.2960.2760.3110.2620.2530.256Profile Hidden Markov modelHMMER: biosequence analysis using profile hidden Markov models
21WavenetAlignment-based model0.1880.0300.155NaN0.1210.2270.2500.1270.3000.2000.1330.3220.2790.326Wavenet modelShin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.
22ProtGPT2Protein language model0.1540.0300.138NaN0.1340.1100.2370.1100.1640.2800.3270.1850.1300.113ProtGPT2 modelFerruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.
23UnirepProtein language model0.1530.0270.135NaN0.1660.1150.1970.1260.1420.2340.2120.1700.1390.257Unirep modelAlley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_DMS_level.csv b/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_DMS_level.csv new file mode 100644 index 0000000..30ab73d --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_DMS_level.csv @@ -0,0 +1,217 @@ +DMS ID,Site-Independent,EVmutation,DeepSequence (single),DeepSequence (ensemble),EVE (single),EVE (ensemble),Unirep,Unirep evotuned,MSA Transformer (single),MSA Transformer (ensemble),ESM-1b,ESM-1v (single),ESM-1v (ensemble),ESM2 (8M),ESM2 (35M),ESM2 (150M),ESM2 (650M),ESM2 (3B),ESM2 (15B),Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,GEMME,VESPA,VESPAl,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,CARP (38M),CARP (600K),CARP (640M),CARP 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+A0A247D711_LISMN_Stadelmann_2021,0.727,0.733,0.568,0.534,0.716,0.717,0.515,0.526,0.655,0.665,0.556,0.558,0.558,0.542,0.553,0.564,0.549,0.557,0.555,0.534,0.518,0.516,0.535,0.547,0.501,0.543,0.514,0.535,0.561,0.73,0.668,0.672,0.537,0.532,0.522,0.538,0.661,0.657,0.666,0.636,0.631,0.641,0.536,0.514,0.55,0.556,0.713,0.724,0.738,0.683,0.64,0.668,0.656,0.673,0.663,0.681,0.657,0.669,0.632,0.67,0.718,0.645,1653,Activity,A0A247D711_LISMN,High,Prokaryote +A0A2Z5U3Z0_9INFA_Doud_2016,0.745,0.744,0.751,0.769,0.782,0.783,0.499,0.758,0.755,0.755,0.559,0.769,0.786,0.495,0.504,0.529,0.766,0.759,0.758,0.708,0.742,0.777,0.768,0.782,0.721,0.769,0.765,0.757,0.762,0.774,0.76,0.715,0.54,0.741,0.771,0.77,0.767,0.783,0.777,0.787,0.797,0.796,0.502,0.5,0.673,0.507,0.723,0.751,0.739,0.591,0.758,0.752,0.781,0.777,0.778,0.778,0.784,0.776,0.788,0.785,0.605,0.579,10715,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A0A2Z5U3Z0_9INFA_Wu_2014,0.753,0.766,0.734,0.749,0.769,0.777,0.497,0.725,0.754,0.761,0.579,0.741,0.766,0.52,0.531,0.532,0.742,0.752,0.754,0.735,0.729,0.76,0.755,0.768,0.681,0.738,0.747,0.731,0.748,0.767,0.731,0.697,0.547,0.73,0.758,0.771,0.766,0.781,0.784,0.787,0.794,0.794,0.519,0.52,0.626,0.528,0.688,0.7,0.689,0.576,0.733,0.726,0.743,0.74,0.741,0.745,0.75,0.743,0.744,0.75,0.607,0.588,2350,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A4_HUMAN_Seuma_2022,0.714,0.707,0.735,0.722,0.673,0.676,0.693,0.577,0.673,0.679,0.666,0.666,0.711,0.709,0.7,0.703,0.736,0.758,0.688,0.632,0.672,0.645,0.666,0.659,0.718,0.654,0.667,0.664,0.662,0.757,0.622,0.588,0.766,0.713,0.639,0.692,0.74,0.695,0.743,0.739,0.716,0.728,0.703,0.699,0.73,0.696,0.65,0.697,0.405,0.527,0.739,0.734,0.726,0.72,0.722,0.703,0.73,0.687,0.698,0.725,0.722,0.709,14811,Stability,A4_HUMAN,Low,Human 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+ADRB2_HUMAN_Jones_2020,0.672,0.718,0.759,0.763,0.754,0.764,0.738,0.755,0.771,0.771,0.773,0.77,0.775,0.712,0.729,0.745,0.755,0.76,0.766,0.768,0.763,0.768,0.765,0.754,0.77,0.772,0.776,0.773,0.758,0.779,0.759,0.715,0.649,0.773,0.771,0.759,0.773,0.777,0.774,0.773,0.775,0.774,0.745,0.594,0.771,0.764,0.716,0.748,0.736,0.616,0.752,0.753,0.761,0.762,0.759,0.759,0.757,0.764,0.756,0.764,0.788,0.768,7800,Activity,ADRB2_HUMAN,Medium,Human +AICDA_HUMAN_Gajula_2014_3cycles,0.47,0.697,0.728,0.74,0.727,0.74,0.361,0.622,0.672,0.709,0.68,0.665,0.714,0.339,0.373,0.572,0.65,0.649,0.621,0.776,0.392,0.557,0.708,0.696,0.448,0.672,0.713,0.704,0.631,0.635,0.71,0.711,0.563,0.471,0.496,0.66,0.589,0.591,0.682,0.716,0.711,0.737,0.368,0.368,0.657,0.568,0.735,0.717,0.719,0.689,0.672,0.716,0.653,0.733,0.658,0.659,0.659,0.66,0.644,0.667,0.651,0.486,209,Activity,AICDA_HUMAN,Medium,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O,0.667,0.692,0.693,0.687,0.676,0.682,0.33,0.535,0.588,0.598,0.67,0.542,0.699,0.394,0.639,0.636,0.609,0.628,0.61,0.504,0.485,0.385,0.44,0.447,0.317,0.419,0.444,0.416,0.494,0.682,0.61,0.598,0.524,0.356,0.428,0.334,0.684,0.688,0.661,0.697,0.695,0.669,0.409,0.407,0.556,0.533,0.603,0.534,0.689,0.65,0.705,0.642,0.576,0.657,0.649,0.654,0.653,0.648,0.66,0.651,0.652,0.674,2972,Stability,AMFR_HUMAN,Medium,Human +AMIE_PSEAE_Wrenbeck_2017,0.624,0.75,0.779,0.788,0.769,0.763,0.534,0.741,0.713,0.825,0.79,0.833,0.862,0.653,0.693,0.702,0.78,0.858,0.837,0.759,0.794,0.794,0.796,0.8,0.8,0.808,0.797,0.802,0.788,0.814,0.824,0.794,0.635,0.821,0.764,0.735,0.814,0.765,0.745,0.823,0.787,0.775,0.668,0.528,0.735,0.695,0.683,0.735,0.709,0.608,0.783,0.768,0.771,0.774,0.769,0.779,0.764,0.772,0.785,0.783,0.808,0.696,6227,Activity,AMIE_PSEAE,High,Prokaryote +ANCSZ_Hobbs_2022,0.768,0.753,0.715,0.73,0.754,0.756,0.754,0.727,0.744,0.756,0.761,0.756,0.768,0.752,0.783,0.786,0.791,0.789,0.781,0.508,0.723,0.744,0.751,0.747,0.747,0.756,0.752,0.732,0.74,0.777,0.766,0.758,0.649,0.732,0.751,0.736,0.76,0.772,0.767,0.764,0.773,0.766,0.763,0.697,0.739,0.75,0.731,0.728,0.706,0.587,0.771,0.767,0.78,0.779,0.775,0.781,0.773,0.778,0.775,0.783,0.783,0.787,4670,Activity,ANCSZ,Medium,Eukaryote +ARGR_ECOLI_Tsuboyama_2023_1AOY,0.614,0.697,0.7,0.711,0.71,0.704,0.623,0.677,0.715,0.735,0.688,0.638,0.691,0.638,0.619,0.739,0.741,0.748,0.706,0.706,0.667,0.72,0.726,0.699,0.657,0.711,0.726,0.727,0.705,0.723,0.703,0.656,0.525,0.677,0.686,0.709,0.678,0.686,0.703,0.719,0.717,0.725,0.618,0.604,0.716,0.694,0.871,0.824,0.879,0.828,0.749,0.758,0.756,0.762,0.75,0.749,0.737,0.759,0.735,0.759,0.797,0.741,1287,Stability,ARGR_ECOLI,Medium,Prokaryote +B2L11_HUMAN_Dutta_2010_binding-Mcl-1,0.853,0.659,0.823,0.827,0.764,0.813,0.645,0.769,0.619,0.612,0.642,0.519,0.685,0.607,0.586,0.606,0.67,0.686,0.674,0.664,0.647,0.568,0.67,0.788,0.563,0.646,0.711,0.713,0.714,0.89,0.702,0.69,0.647,0.648,0.635,0.681,0.791,0.76,0.709,0.831,0.816,0.712,0.616,0.591,0.632,0.605,0.649,0.655,0.709,0.509,0.695,0.686,0.729,0.665,0.627,0.757,0.662,0.667,0.738,0.706,0.635,0.621,170,Binding,B2L11_HUMAN,Low,Human +BBC1_YEAST_Tsuboyama_2023_1TG0,0.605,0.688,0.722,0.725,0.715,0.723,0.607,0.65,0.724,0.735,0.736,0.724,0.739,0.693,0.724,0.716,0.754,0.744,0.768,0.685,0.687,0.688,0.706,0.691,0.56,0.711,0.718,0.709,0.678,0.688,0.698,0.684,0.645,0.684,0.636,0.653,0.7,0.675,0.687,0.729,0.71,0.721,0.592,0.577,0.671,0.617,0.801,0.748,0.857,0.824,0.743,0.752,0.756,0.764,0.757,0.758,0.758,0.754,0.764,0.758,0.789,0.807,2069,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU,0.698,0.731,0.7,0.705,0.704,0.711,0.563,0.637,0.704,0.772,0.714,0.677,0.717,0.591,0.603,0.662,0.766,0.778,0.732,0.725,0.569,0.693,0.623,0.707,0.608,0.733,0.655,0.716,0.737,0.827,0.796,0.77,0.524,0.634,0.66,0.734,0.734,0.734,0.759,0.717,0.718,0.739,0.563,0.563,0.609,0.598,0.784,0.759,0.845,0.79,0.788,0.792,0.797,0.805,0.798,0.807,0.78,0.794,0.789,0.797,0.769,0.648,1572,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Deng_2012,0.667,0.763,0.764,0.773,0.759,0.764,0.542,0.653,0.764,0.784,0.762,0.751,0.761,0.628,0.683,0.737,0.772,0.735,0.686,0.733,0.691,0.698,0.706,0.704,0.7,0.73,0.732,0.714,0.678,0.742,0.77,0.751,0.552,0.698,0.708,0.693,0.735,0.747,0.748,0.766,0.775,0.773,0.676,0.52,0.749,0.71,0.718,0.755,0.741,0.63,0.77,0.774,0.776,0.784,0.781,0.779,0.781,0.771,0.776,0.785,0.781,0.715,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Firnberg_2014,0.723,0.859,0.868,0.876,0.852,0.863,0.562,0.729,0.859,0.877,0.858,0.836,0.856,0.697,0.773,0.83,0.876,0.795,0.719,0.832,0.784,0.779,0.76,0.758,0.799,0.827,0.831,0.79,0.721,0.845,0.889,0.888,0.588,0.778,0.761,0.732,0.822,0.815,0.811,0.865,0.864,0.864,0.769,0.519,0.855,0.799,0.81,0.863,0.851,0.667,0.871,0.867,0.87,0.882,0.882,0.882,0.885,0.87,0.88,0.888,0.884,0.796,4783,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Jacquier_2013,0.727,0.853,0.857,0.869,0.855,0.868,0.551,0.716,0.843,0.863,0.835,0.833,0.843,0.682,0.755,0.819,0.858,0.801,0.749,0.822,0.781,0.768,0.759,0.758,0.784,0.816,0.811,0.784,0.729,0.791,0.866,0.851,0.577,0.757,0.766,0.747,0.807,0.816,0.821,0.86,0.867,0.871,0.739,0.524,0.837,0.783,0.771,0.83,0.813,0.655,0.837,0.842,0.845,0.847,0.845,0.847,0.855,0.842,0.84,0.854,0.862,0.79,989,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Stiffler_2015,0.728,0.862,0.875,0.882,0.857,0.868,0.563,0.73,0.863,0.881,0.865,0.846,0.867,0.704,0.782,0.839,0.883,0.802,0.724,0.837,0.793,0.786,0.757,0.761,0.809,0.835,0.838,0.794,0.724,0.851,0.897,0.895,0.587,0.787,0.758,0.729,0.83,0.815,0.812,0.871,0.868,0.869,0.776,0.518,0.863,0.807,0.816,0.867,0.854,0.675,0.875,0.866,0.872,0.882,0.882,0.884,0.886,0.873,0.882,0.89,0.891,0.805,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BRCA1_HUMAN_Findlay_2018,0.824,0.756,0.788,0.774,0.776,0.8,0.566,0.683,0.76,0.772,0.836,0.775,0.805,0.582,0.767,0.823,0.86,0.845,0.788,0.699,0.577,0.762,0.778,0.774,0.705,0.82,0.814,0.801,0.815,0.776,0.86,0.832,0.658,0.603,0.621,0.79,0.792,0.792,0.846,0.806,0.806,0.855,0.642,0.58,0.865,0.815,0.804,0.809,0.57,0.587,0.856,0.85,0.86,0.852,0.861,0.862,0.858,0.857,0.853,0.864,0.864,0.832,1837,OrganismalFitness,BRCA1_HUMAN,Low,Human +BRCA2_HUMAN_Erwood_2022_HEK293T,0.908,0.816,0.866,0.861,0.85,0.875,0.588,0.893,0.702,0.556,0.906,0.593,0.526,0.529,0.528,0.914,0.94,0.904,0.899,0.584,0.515,0.921,0.923,0.861,0.928,0.888,0.896,0.887,0.587,0.86,0.785,0.85,0.558,0.549,0.581,0.59,0.822,0.817,0.825,0.833,0.819,0.837,0.59,0.553,0.892,0.492,0.48,0.503,0.279,0.76,0.871,0.899,0.896,0.878,0.876,0.874,0.868,0.839,0.841,0.885,0.5,0.507,265,OrganismalFitness,BRCA2_HUMAN,,Human +C6KNH7_9INFA_Lee_2018,0.697,0.687,0.676,0.68,0.719,0.72,0.485,0.731,0.686,0.689,0.526,0.717,0.747,0.49,0.488,0.495,0.75,0.705,0.716,0.674,0.701,0.685,0.693,0.678,0.621,0.734,0.715,0.736,0.69,0.742,0.731,0.681,0.559,0.683,0.691,0.701,0.71,0.713,0.719,0.724,0.724,0.729,0.487,0.481,0.644,0.498,0.761,0.776,0.777,0.619,0.756,0.756,0.767,0.769,0.774,0.763,0.772,0.768,0.768,0.773,0.657,0.593,10754,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +CALM1_HUMAN_Weile_2017,0.594,0.631,0.63,0.627,0.63,0.631,0.588,0.61,0.635,0.644,0.646,0.629,0.65,0.58,0.602,0.602,0.615,0.623,0.627,0.63,0.599,0.629,0.636,0.644,0.631,0.662,0.66,0.676,0.684,0.641,0.619,0.58,0.553,0.621,0.648,0.674,0.621,0.639,0.653,0.634,0.638,0.639,0.619,0.58,0.658,0.643,0.552,0.58,0.592,0.548,0.598,0.598,0.599,0.593,0.6,0.594,0.596,0.602,0.593,0.6,0.659,0.639,1813,OrganismalFitness,CALM1_HUMAN,High,Human +CAPSD_AAV2S_Sinai_2021,0.698,0.67,0.665,0.688,0.67,0.662,0.703,0.724,0.66,0.679,0.588,0.609,0.61,0.643,0.659,0.61,0.649,0.594,0.553,0.63,0.606,0.641,0.648,0.65,0.614,0.61,0.644,0.614,0.717,0.719,0.586,0.582,0.56,0.612,0.644,0.757,0.686,0.689,0.74,0.668,0.67,0.716,0.558,0.594,0.644,0.564,0.713,0.703,0.673,0.666,0.614,0.61,0.603,0.615,0.604,0.605,0.607,0.602,0.613,0.609,0.668,0.629,42328,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAR11_HUMAN_Meitlis_2020_gof,0.551,0.464,0.464,0.467,0.459,0.466,0.591,0.403,0.519,0.527,0.522,0.589,0.548,0.545,0.556,0.653,0.57,0.57,0.583,0.545,0.564,0.475,0.511,0.525,0.365,0.478,0.498,0.476,0.518,0.451,0.512,0.514,0.593,0.524,0.471,0.425,0.518,0.492,0.45,0.488,0.475,0.442,0.557,0.516,0.517,0.623,0.589,0.524,0.592,0.548,0.597,0.593,0.595,0.574,0.576,0.581,0.575,0.584,0.589,0.587,0.654,0.643,2374,OrganismalFitness,CAR11_HUMAN,Low,Human +CAR11_HUMAN_Meitlis_2020_lof,0.752,0.711,0.689,0.679,0.713,0.721,0.541,0.509,0.654,0.652,0.761,0.77,0.765,0.524,0.552,0.74,0.783,0.794,0.802,0.604,0.572,0.698,0.676,0.658,0.605,0.686,0.69,0.727,0.598,0.707,0.75,0.722,0.7,0.49,0.675,0.644,0.678,0.707,0.684,0.711,0.717,0.697,0.539,0.512,0.746,0.661,0.725,0.757,0.742,0.582,0.789,0.785,0.787,0.786,0.781,0.792,0.779,0.787,0.795,0.793,0.803,0.716,2395,OrganismalFitness,CAR11_HUMAN,Low,Human +CAS9_STRP1_Spencer_2017_positive,0.58,0.594,0.587,0.589,0.59,0.593,0.516,0.549,0.593,0.595,0.588,0.532,0.533,0.528,0.544,0.578,0.594,0.597,0.6,0.504,0.519,0.537,0.585,0.56,0.517,0.599,0.592,0.589,0.534,0.596,0.603,0.593,0.513,0.514,0.521,0.582,0.584,0.584,0.593,0.595,0.595,0.598,0.528,0.512,0.592,0.551,0.551,0.592,0.513,0.517,0.59,0.591,0.591,0.592,0.591,0.592,0.59,0.59,0.591,0.593,0.587,0.556,8117,Activity,CAS9_STRP1,Medium,Prokaryote +CASP3_HUMAN_Roychowdhury_2020,0.694,0.776,0.814,0.818,0.822,0.825,0.532,0.735,0.825,0.833,0.799,0.805,0.818,0.637,0.792,0.828,0.824,0.783,0.751,0.813,0.61,0.748,0.757,0.779,0.756,0.781,0.785,0.757,0.784,0.809,0.804,0.773,0.621,0.537,0.739,0.765,0.768,0.782,0.797,0.821,0.816,0.824,0.709,0.492,0.813,0.784,0.707,0.78,0.751,0.638,0.781,0.787,0.791,0.787,0.791,0.785,0.797,0.785,0.787,0.797,0.833,0.762,1567,Activity,CASP3_HUMAN,High,Human +CASP7_HUMAN_Roychowdhury_2020,0.713,0.782,0.835,0.835,0.831,0.833,0.537,0.767,0.828,0.842,0.815,0.835,0.845,0.669,0.826,0.838,0.84,0.816,0.803,0.832,0.613,0.799,0.796,0.811,0.798,0.819,0.822,0.816,0.81,0.85,0.83,0.8,0.682,0.552,0.804,0.787,0.782,0.831,0.822,0.825,0.847,0.842,0.76,0.509,0.848,0.825,0.763,0.835,0.817,0.674,0.823,0.816,0.81,0.827,0.836,0.833,0.827,0.817,0.828,0.835,0.847,0.793,1680,Activity,CASP7_HUMAN,Medium,Human +CATR_CHLRE_Tsuboyama_2023_2AMI,0.796,0.813,0.85,0.853,0.86,0.863,0.87,0.818,0.81,0.791,0.847,0.852,0.864,0.798,0.869,0.87,0.866,0.837,0.862,0.521,0.844,0.835,0.841,0.836,0.857,0.852,0.835,0.817,0.829,0.866,0.846,0.848,0.734,0.851,0.836,0.853,0.862,0.853,0.869,0.856,0.854,0.864,0.748,0.732,0.691,0.758,0.76,0.744,0.873,0.799,0.868,0.866,0.878,0.872,0.883,0.868,0.869,0.862,0.865,0.874,0.851,0.874,1903,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X,0.854,0.892,0.904,0.906,0.891,0.899,0.748,0.832,0.918,0.921,0.911,0.893,0.888,0.785,0.86,0.898,0.897,0.905,0.898,0.912,0.769,0.778,0.866,0.867,0.79,0.852,0.851,0.852,0.884,0.902,0.899,0.868,0.516,0.767,0.834,0.869,0.877,0.881,0.895,0.903,0.9,0.908,0.772,0.663,0.828,0.843,0.905,0.893,0.959,0.929,0.929,0.928,0.93,0.927,0.933,0.931,0.93,0.928,0.926,0.932,0.902,0.951,2068,Stability,CBPA2_HUMAN,Medium,Human +CBS_HUMAN_Sun_2020,0.687,0.701,0.707,0.717,0.713,0.717,0.613,0.644,0.71,0.712,0.696,0.696,0.71,0.545,0.627,0.689,0.692,0.684,0.69,0.696,0.699,0.646,0.659,0.662,0.696,0.652,0.662,0.652,0.674,0.714,0.711,0.698,0.611,0.702,0.656,0.648,0.715,0.685,0.683,0.724,0.705,0.706,0.673,0.534,0.714,0.711,0.633,0.655,0.676,0.547,0.683,0.684,0.688,0.691,0.691,0.69,0.687,0.688,0.689,0.693,0.72,0.664,7217,OrganismalFitness,CBS_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28,0.752,0.773,0.844,0.865,0.811,0.847,0.339,0.819,0.812,0.808,0.812,0.829,0.828,0.344,0.878,0.855,0.844,0.801,0.802,0.828,0.753,0.803,0.797,0.797,0.783,0.793,0.799,0.767,0.793,0.815,0.845,0.84,0.719,0.714,0.75,0.765,0.802,0.799,0.81,0.844,0.856,0.859,0.816,0.317,0.682,0.786,0.54,0.561,0.796,0.783,0.853,0.809,0.85,0.838,0.861,0.839,0.839,0.871,0.835,0.852,0.826,0.888,2282,Stability,CBX4_HUMAN,High,Human +CCDB_ECOLI_Adkar_2012,0.705,0.749,0.757,0.777,0.767,0.775,0.47,0.619,0.706,0.715,0.745,0.701,0.749,0.484,0.477,0.723,0.761,0.746,0.629,0.756,0.514,0.471,0.431,0.571,0.396,0.538,0.484,0.5,0.713,0.743,0.817,0.807,0.558,0.489,0.507,0.657,0.743,0.729,0.75,0.763,0.753,0.771,0.497,0.468,0.747,0.495,0.65,0.741,0.67,0.648,0.743,0.728,0.733,0.743,0.743,0.743,0.743,0.755,0.768,0.751,0.746,0.61,1176,Activity,CCDB_ECOLI,High,Prokaryote +CCDB_ECOLI_Tripathi_2016,0.809,0.861,0.872,0.886,0.867,0.876,0.496,0.709,0.786,0.807,0.851,0.808,0.843,0.498,0.502,0.826,0.865,0.871,0.757,0.87,0.528,0.512,0.447,0.624,0.425,0.567,0.482,0.553,0.841,0.861,0.891,0.87,0.577,0.505,0.597,0.803,0.844,0.845,0.88,0.869,0.87,0.89,0.507,0.462,0.85,0.517,0.727,0.806,0.74,0.666,0.847,0.822,0.822,0.844,0.844,0.843,0.837,0.848,0.869,0.851,0.849,0.734,1663,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +CCR5_HUMAN_Gill_2023,0.634,0.637,0.638,0.639,0.634,0.638,0.649,0.644,0.662,0.668,0.675,0.664,0.67,0.651,0.67,0.674,0.668,0.662,0.664,0.67,0.675,0.673,0.657,0.659,0.675,0.67,0.665,0.673,0.66,0.674,0.644,0.619,0.557,0.675,0.676,0.678,0.678,0.682,0.685,0.658,0.658,0.659,0.668,0.59,0.676,0.67,0.633,0.647,0.638,0.585,0.65,0.647,0.652,0.652,0.649,0.652,0.657,0.657,0.657,0.657,0.668,0.671,6137,Binding,CCR5_HUMAN,High,Human +CD19_HUMAN_Klesmith_2019_FMC_singles,0.623,0.617,0.612,0.621,0.633,0.632,0.574,0.58,0.597,0.599,0.591,0.598,0.613,0.603,0.62,0.6,0.599,0.607,0.589,0.492,0.597,0.6,0.628,0.617,0.593,0.648,0.616,0.607,0.63,0.647,0.599,0.574,0.581,0.606,0.631,0.609,0.63,0.636,0.628,0.642,0.644,0.639,0.61,0.568,0.594,0.613,0.728,0.683,0.72,0.608,0.644,0.648,0.643,0.662,0.654,0.653,0.658,0.647,0.653,0.659,0.755,0.71,3761,Binding,CD19_HUMAN,Low,Human +CP2C9_HUMAN_Amorosi_2021_abundance,0.748,0.788,0.8,0.81,0.8,0.808,0.763,0.783,0.8,0.808,0.758,0.795,0.814,0.767,0.803,0.816,0.822,0.817,0.8,0.807,0.783,0.783,0.8,0.777,0.792,0.795,0.796,0.79,0.787,0.806,0.792,0.762,0.611,0.797,0.787,0.786,0.811,0.811,0.811,0.82,0.818,0.819,0.79,0.549,0.768,0.806,0.782,0.775,0.81,0.611,0.806,0.804,0.812,0.823,0.821,0.817,0.817,0.813,0.814,0.824,0.822,0.811,6370,Expression,CP2C9_HUMAN,High,Human +CP2C9_HUMAN_Amorosi_2021_activity,0.758,0.787,0.804,0.814,0.797,0.808,0.794,0.805,0.8,0.816,0.752,0.819,0.831,0.784,0.825,0.839,0.835,0.833,0.804,0.822,0.791,0.796,0.808,0.789,0.806,0.794,0.796,0.799,0.791,0.812,0.8,0.774,0.609,0.814,0.798,0.787,0.829,0.825,0.82,0.826,0.822,0.818,0.813,0.543,0.775,0.832,0.802,0.791,0.833,0.628,0.817,0.821,0.824,0.829,0.833,0.835,0.831,0.824,0.829,0.837,0.834,0.833,6142,Binding,CP2C9_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM,0.695,0.831,0.803,0.81,0.797,0.805,0.653,0.791,0.817,0.821,0.842,0.859,0.879,0.728,0.886,0.828,0.781,0.821,0.82,0.79,0.756,0.775,0.836,0.845,0.741,0.857,0.849,0.834,0.835,0.833,0.818,0.811,0.67,0.704,0.784,0.83,0.779,0.798,0.831,0.815,0.828,0.84,0.674,0.621,0.754,0.767,0.785,0.776,0.839,0.86,0.846,0.846,0.842,0.858,0.86,0.861,0.853,0.868,0.842,0.857,0.848,0.873,3295,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX,0.666,0.715,0.699,0.7,0.725,0.709,0.551,0.702,0.755,0.764,0.736,0.694,0.672,0.563,0.695,0.717,0.763,0.74,0.73,0.652,0.564,0.592,0.597,0.522,0.538,0.588,0.601,0.6,0.69,0.702,0.733,0.707,0.62,0.562,0.556,0.587,0.678,0.682,0.684,0.704,0.705,0.709,0.562,0.51,0.713,0.603,0.748,0.743,0.813,0.728,0.769,0.749,0.746,0.772,0.753,0.771,0.765,0.759,0.755,0.765,0.79,0.771,1580,Stability,CUE1_YEAST,Medium,Eukaryote +D7PM05_CLYGR_Somermeyer_2022,0.781,0.83,0.828,0.81,0.835,0.835,0.531,0.75,0.849,0.851,0.732,0.53,0.529,0.53,0.522,0.512,0.52,0.531,0.575,0.735,0.514,0.519,0.554,0.541,0.5,0.521,0.535,0.601,0.69,0.831,0.805,0.813,0.514,0.549,0.563,0.58,0.783,0.783,0.781,0.834,0.833,0.828,0.496,0.506,0.498,0.508,0.636,0.652,0.76,0.706,0.759,0.756,0.758,0.764,0.756,0.761,0.758,0.756,0.755,0.76,0.668,0.6,24515,Activity,D7PM05_CLYGR,Low,Eukaryote +DLG4_HUMAN_Faure_2021,0.877,0.822,0.834,0.816,0.834,0.838,0.904,0.853,0.791,0.795,0.781,0.801,0.834,0.909,0.921,0.905,0.822,0.764,0.741,0.854,0.812,0.818,0.809,0.791,0.834,0.832,0.809,0.804,0.768,0.839,0.855,0.848,0.777,0.816,0.852,0.817,0.861,0.888,0.867,0.854,0.865,0.849,0.827,0.613,0.708,0.793,0.839,0.741,0.868,0.668,0.768,0.7,0.798,0.774,0.786,0.77,0.782,0.77,0.779,0.778,0.783,0.913,6976,OrganismalFitness,DLG4_HUMAN,Low,Human +DLG4_RAT_McLaughlin_2012,0.83,0.771,0.789,0.801,0.84,0.848,0.818,0.794,0.824,0.847,0.806,0.869,0.88,0.796,0.883,0.884,0.857,0.823,0.796,0.8,0.764,0.769,0.767,0.752,0.789,0.795,0.781,0.755,0.756,0.834,0.848,0.833,0.685,0.759,0.763,0.724,0.821,0.816,0.804,0.855,0.853,0.854,0.847,0.552,0.777,0.834,0.791,0.709,0.814,0.593,0.791,0.76,0.801,0.771,0.804,0.79,0.775,0.797,0.803,0.81,0.82,0.865,1576,Binding,DLG4_RAT,Low,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC,0.574,0.58,0.601,0.61,0.601,0.625,0.424,0.668,0.66,0.672,0.496,0.539,0.531,0.502,0.567,0.581,0.63,0.647,0.681,0.604,0.44,0.5,0.493,0.508,0.498,0.533,0.5,0.511,0.59,0.647,0.722,0.699,0.522,0.469,0.467,0.486,0.593,0.589,0.593,0.614,0.606,0.608,0.522,0.451,0.6,0.53,0.787,0.782,0.837,0.783,0.683,0.675,0.738,0.73,0.72,0.718,0.709,0.689,0.682,0.716,0.759,0.723,1008,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1,0.928,0.937,0.937,0.937,0.944,0.943,0.887,0.916,0.938,0.945,0.946,0.937,0.949,0.95,0.955,0.955,0.946,0.935,0.953,0.937,0.862,0.906,0.924,0.914,0.929,0.922,0.93,0.896,0.929,0.938,0.92,0.909,0.864,0.928,0.929,0.943,0.954,0.955,0.96,0.948,0.95,0.953,0.846,0.582,0.711,0.85,0.822,0.766,0.928,0.933,0.932,0.936,0.934,0.948,0.945,0.95,0.948,0.944,0.949,0.946,0.926,0.946,2264,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,0.714,0.738,0.718,0.729,0.757,0.761,0.521,0.72,0.686,0.687,0.749,0.727,0.741,0.607,0.719,0.736,0.753,0.744,0.751,0.764,0.654,0.696,0.678,0.685,0.645,0.677,0.659,0.646,0.673,0.721,0.719,0.72,0.591,0.597,0.652,0.612,0.735,0.743,0.739,0.751,0.752,0.758,0.666,0.4,0.741,0.711,0.668,0.672,0.703,0.707,0.743,0.734,0.742,0.746,0.738,0.748,0.729,0.74,0.747,0.742,0.767,0.752,2915,Stability,DOCK1_MOUSE,High,Eukaryote +DYR_ECOLI_Nguyen_2023,0.757,0.851,0.863,0.869,0.868,0.872,0.497,0.834,0.861,0.855,0.874,0.879,0.887,0.564,0.854,0.874,0.886,0.886,0.878,0.873,0.739,0.805,0.732,0.778,0.828,0.831,0.843,0.855,0.817,0.876,0.886,0.873,0.624,0.842,0.758,0.747,0.865,0.811,0.821,0.88,0.856,0.86,0.839,0.499,0.877,0.855,0.665,0.793,0.748,0.576,0.841,0.817,0.826,0.833,0.836,0.837,0.843,0.843,0.859,0.849,0.861,0.808,2916,OrganismalFitness,DYR_ECOLI,High,Prokaryote +DYR_ECOLI_Thompson_2019,0.745,0.792,0.79,0.792,0.796,0.797,0.457,0.72,0.788,0.806,0.782,0.764,0.772,0.532,0.724,0.773,0.789,0.807,0.806,0.792,0.644,0.746,0.667,0.703,0.734,0.758,0.789,0.778,0.766,0.79,0.794,0.78,0.605,0.749,0.712,0.711,0.773,0.757,0.765,0.797,0.795,0.8,0.718,0.48,0.77,0.751,0.621,0.728,0.681,0.554,0.763,0.739,0.744,0.742,0.757,0.752,0.764,0.759,0.771,0.763,0.792,0.722,2363,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +ENV_HV1B9_DuenasDecamp_2016,0.747,0.744,0.688,0.742,0.765,0.761,0.503,0.704,0.732,0.751,0.722,0.791,0.777,0.533,0.515,0.496,0.47,0.507,0.539,0.751,0.753,0.74,0.768,0.771,0.707,0.783,0.769,0.746,0.747,0.767,0.768,0.757,0.674,0.767,0.767,0.771,0.774,0.776,0.772,0.772,0.773,0.774,0.719,0.451,0.748,0.759,0.736,0.713,0.731,0.651,0.643,0.695,0.704,0.676,0.655,0.653,0.667,0.659,0.644,0.682,0.607,0.587,375,OrganismalFitness,ENV_HV1B9,Medium,Virus +ENV_HV1BR_Haddox_2016,0.669,0.652,0.66,0.661,0.671,0.673,0.499,0.659,0.674,0.673,0.649,0.659,0.669,0.495,0.495,0.497,0.519,0.529,0.577,0.667,0.676,0.68,0.687,0.682,0.673,0.68,0.68,0.676,0.682,0.677,0.665,0.649,0.599,0.673,0.682,0.681,0.681,0.685,0.684,0.685,0.687,0.684,0.615,0.49,0.66,0.649,0.597,0.616,0.553,0.534,0.585,0.581,0.594,0.607,0.587,0.594,0.595,0.59,0.589,0.598,0.577,0.541,12863,OrganismalFitness,ENV_HV1BR,Medium,Virus +ENVZ_ECOLI_Ghose_2023,0.571,0.569,0.61,0.61,0.619,0.618,0.503,0.616,0.608,0.614,0.606,0.625,0.636,0.607,0.615,0.618,0.613,0.6,0.584,0.614,0.605,0.601,0.632,0.625,0.606,0.611,0.626,0.621,0.615,0.618,0.598,0.595,0.559,0.602,0.612,0.621,0.618,0.623,0.63,0.621,0.625,0.625,0.611,0.566,0.612,0.61,0.528,0.596,0.582,0.525,0.623,0.62,0.604,0.617,0.639,0.625,0.615,0.625,0.627,0.627,0.594,0.637,1121,Activity,ENVZ_ECOLI,High,Prokaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M,0.839,0.898,0.899,0.902,0.914,0.915,0.309,0.906,0.948,0.952,0.944,0.946,0.953,0.286,0.941,0.954,0.963,0.945,0.94,0.923,0.831,0.86,0.899,0.889,0.88,0.898,0.905,0.913,0.92,0.954,0.94,0.936,0.886,0.86,0.859,0.879,0.908,0.908,0.916,0.919,0.918,0.917,0.85,0.341,0.857,0.861,0.83,0.828,0.943,0.909,0.957,0.954,0.956,0.955,0.961,0.958,0.958,0.958,0.955,0.96,0.949,0.944,1960,Stability,EPHB2_HUMAN,High,Human +ERBB2_HUMAN_Elazar_2016,0.697,0.691,0.641,0.642,0.634,0.645,0.717,0.694,0.702,0.715,0.719,0.73,0.738,0.737,0.732,0.735,0.706,0.692,0.652,0.525,0.721,0.729,0.752,0.725,0.74,0.747,0.778,0.782,0.734,0.692,0.716,0.543,0.732,0.768,0.728,0.736,0.747,0.731,0.737,0.728,0.723,0.72,0.739,0.735,0.764,0.734,0.718,0.73,0.407,0.527,0.698,0.693,0.72,0.702,0.699,0.699,0.709,0.717,0.721,0.714,0.756,0.741,326,Expression,ERBB2_HUMAN,Low,Human +ESTA_BACSU_Nutschel_2020,0.637,0.694,0.702,0.711,0.696,0.694,0.577,0.665,0.669,0.707,0.659,0.645,0.662,0.576,0.621,0.629,0.645,0.641,0.664,0.654,0.548,0.591,0.634,0.648,0.628,0.62,0.653,0.634,0.696,0.69,0.724,0.711,0.534,0.579,0.63,0.641,0.655,0.665,0.673,0.7,0.703,0.703,0.588,0.552,0.637,0.623,0.783,0.743,0.774,0.681,0.677,0.664,0.673,0.666,0.682,0.67,0.666,0.677,0.675,0.679,0.691,0.594,2172,Stability,ESTA_BACSU,High,Prokaryote +F7YBW8_MESOW_Aakre_2015,0.537,0.668,0.668,0.689,0.683,0.684,0.517,0.633,0.668,0.672,0.689,0.669,0.674,0.475,0.513,0.531,0.671,0.665,0.687,0.67,0.479,0.457,0.458,0.503,0.555,0.559,0.501,0.633,0.677,0.659,0.698,0.687,0.518,0.498,0.46,0.685,0.537,0.515,0.684,0.664,0.658,0.691,0.488,0.479,0.599,0.492,0.498,0.616,0.528,0.519,0.687,0.692,0.68,0.7,0.696,0.697,0.686,0.692,0.695,0.694,0.626,0.458,9192,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +FECA_ECOLI_Tsuboyama_2023_2D1U,0.705,0.727,0.746,0.751,0.746,0.742,0.51,0.677,0.701,0.745,0.775,0.637,0.727,0.51,0.735,0.784,0.78,0.764,0.711,0.746,0.577,0.648,0.697,0.663,0.581,0.754,0.743,0.641,0.717,0.779,0.719,0.684,0.657,0.526,0.562,0.577,0.692,0.687,0.665,0.729,0.727,0.716,0.701,0.5,0.756,0.729,0.801,0.783,0.816,0.781,0.783,0.761,0.776,0.775,0.769,0.787,0.768,0.763,0.77,0.777,0.814,0.811,1886,Stability,FECA_ECOLI,High,Prokaryote +FKBP3_HUMAN_Tsuboyama_2023_2KFV,0.727,0.707,0.759,0.76,0.763,0.771,0.589,0.675,0.63,0.629,0.602,0.594,0.591,0.593,0.592,0.583,0.6,0.64,0.694,0.661,0.622,0.622,0.627,0.632,0.585,0.622,0.599,0.625,0.628,0.752,0.673,0.676,0.464,0.566,0.602,0.624,0.718,0.721,0.688,0.765,0.767,0.73,0.599,0.602,0.595,0.613,0.856,0.824,0.855,0.819,0.66,0.716,0.738,0.711,0.71,0.716,0.693,0.697,0.65,0.712,0.798,0.684,1237,Stability,FKBP3_HUMAN,Medium,Human +GAL4_YEAST_Kitzman_2015,0.646,0.711,0.712,0.748,0.733,0.749,0.677,0.496,0.745,0.771,0.782,0.74,0.743,0.685,0.712,0.759,0.795,0.793,0.788,0.749,0.672,0.691,0.695,0.695,0.691,0.716,0.741,0.727,0.771,0.792,0.792,0.763,0.636,0.655,0.669,0.67,0.757,0.752,0.754,0.742,0.739,0.737,0.708,0.653,0.777,0.731,0.649,0.763,0.67,0.568,0.773,0.769,0.767,0.767,0.779,0.778,0.77,0.768,0.767,0.777,0.767,0.719,1195,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +GCN4_YEAST_Staller_2018,0.632,0.633,0.629,0.63,0.628,0.628,0.584,0.611,0.631,0.631,0.627,0.644,0.647,0.662,0.644,0.638,0.649,0.638,0.631,0.599,0.574,0.581,0.572,0.572,0.563,0.551,0.532,0.559,0.577,0.622,0.637,0.634,0.519,0.573,0.571,0.641,0.635,0.635,0.647,0.635,0.635,0.645,0.549,0.589,0.572,0.556,0.568,0.591,0.612,0.587,0.624,0.62,0.62,0.617,0.623,0.622,0.621,0.621,0.623,0.622,0.624,0.627,2638,Binding,GCN4_YEAST,Low,Eukaryote +GDIA_HUMAN_Silverstein_2021,0.719,0.72,0.726,0.724,0.727,0.727,0.591,0.696,0.739,0.736,0.694,0.706,0.726,0.582,0.618,0.716,0.698,0.684,0.704,0.701,0.626,0.688,0.686,0.701,0.659,0.718,0.708,0.685,0.696,0.704,0.712,0.692,0.627,0.657,0.69,0.671,0.715,0.719,0.713,0.726,0.733,0.729,0.616,0.56,0.717,0.62,0.671,0.709,0.682,0.544,0.695,0.669,0.697,0.684,0.691,0.7,0.688,0.696,0.69,0.701,0.729,0.659,1154,OrganismalFitness,GDIA_HUMAN,Low,Human +GFP_AEQVI_Sarkisyan_2016,0.889,0.884,0.9,0.901,0.903,0.903,0.54,0.883,0.894,0.891,0.816,0.565,0.565,0.552,0.583,0.564,0.572,0.596,0.679,0.858,0.555,0.572,0.614,0.565,0.532,0.615,0.684,0.884,0.886,0.904,0.865,0.87,0.541,0.545,0.616,0.878,0.889,0.89,0.902,0.906,0.907,0.919,0.515,0.495,0.525,0.525,0.796,0.792,0.925,0.86,0.856,0.86,0.866,0.871,0.867,0.866,0.864,0.865,0.854,0.865,0.872,0.769,51714,Activity,GFP_AEQVI,Low,Eukaryote +GLPA_HUMAN_Elazar_2016,0.602,0.546,0.603,0.601,0.594,0.583,0.711,0.799,0.682,0.672,0.741,0.756,0.754,0.728,0.746,0.77,0.751,0.727,0.757,0.583,0.743,0.755,0.737,0.761,0.739,0.752,0.761,0.765,0.798,0.672,0.746,0.638,0.638,0.761,0.737,0.761,0.75,0.736,0.748,0.732,0.717,0.74,0.758,0.749,0.762,0.727,0.722,0.767,0.706,0.617,0.79,0.779,0.782,0.765,0.776,0.747,0.776,0.748,0.77,0.781,0.775,0.766,245,Expression,GLPA_HUMAN,Low,Human +GRB2_HUMAN_Faure_2021,0.727,0.787,0.784,0.801,0.799,0.802,0.777,0.748,0.796,0.768,0.792,0.747,0.781,0.797,0.832,0.847,0.858,0.792,0.816,0.799,0.807,0.79,0.786,0.752,0.801,0.785,0.753,0.793,0.751,0.78,0.741,0.741,0.757,0.801,0.786,0.727,0.79,0.784,0.745,0.812,0.812,0.796,0.826,0.673,0.789,0.816,0.879,0.79,0.89,0.785,0.845,0.86,0.855,0.856,0.86,0.849,0.849,0.859,0.848,0.861,0.806,0.837,63366,OrganismalFitness,GRB2_HUMAN,Medium,Human +HCP_LAMBD_Tsuboyama_2023_2L6Q,0.652,0.715,0.572,0.586,0.679,0.682,0.629,0.707,0.726,0.749,0.829,0.724,0.747,0.644,0.704,0.783,0.835,0.79,0.801,0.62,0.644,0.652,0.67,0.677,0.66,0.669,0.605,0.666,0.765,0.802,0.824,0.758,0.579,0.633,0.643,0.753,0.711,0.716,0.773,0.687,0.714,0.765,0.653,0.643,0.81,0.686,0.829,0.861,0.864,0.786,0.84,0.846,0.846,0.843,0.851,0.853,0.844,0.854,0.853,0.857,0.878,0.799,1040,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM,0.846,0.873,0.829,0.844,0.885,0.887,0.667,0.823,0.858,0.859,0.83,0.696,0.72,0.642,0.656,0.917,0.884,0.86,0.846,0.869,0.674,0.815,0.866,0.859,0.792,0.863,0.856,0.841,0.848,0.887,0.873,0.871,0.62,0.675,0.697,0.743,0.907,0.886,0.91,0.895,0.895,0.901,0.596,0.581,0.902,0.668,0.755,0.869,0.857,0.772,0.868,0.88,0.899,0.889,0.893,0.914,0.887,0.897,0.888,0.896,0.892,0.749,5586,Stability,HECD1_HUMAN,Medium,Human +HEM3_HUMAN_Loggerenberg_2023,0.712,0.707,0.701,0.702,0.71,0.709,0.56,0.566,0.716,0.722,0.694,0.688,0.696,0.569,0.69,0.702,0.694,0.713,0.72,0.562,0.669,0.688,0.692,0.7,0.676,0.694,0.691,0.693,0.709,0.72,0.715,0.711,0.547,0.689,0.694,0.703,0.713,0.719,0.731,0.715,0.719,0.726,0.66,0.573,0.69,0.685,0.666,0.684,0.659,0.58,0.678,0.669,0.676,0.681,0.688,0.686,0.686,0.686,0.683,0.688,0.716,0.709,5689,Activity,HEM3_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019,0.768,0.809,0.825,0.822,0.801,0.8,0.589,0.645,0.762,0.785,0.761,0.734,0.773,0.534,0.568,0.749,0.728,0.763,0.769,0.755,0.686,0.734,0.75,0.777,0.724,0.779,0.771,0.802,0.81,0.804,0.724,0.714,0.583,0.734,0.758,0.849,0.783,0.783,0.865,0.829,0.812,0.835,0.587,0.524,0.666,0.507,0.729,0.75,0.82,0.722,0.746,0.685,0.718,0.737,0.744,0.723,0.726,0.748,0.738,0.732,0.795,0.632,496137,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +HMDH_HUMAN_Jiang_2019,0.741,0.7,0.683,0.688,0.72,0.718,0.575,0.627,0.625,0.621,0.626,0.664,0.649,0.514,0.498,0.654,0.755,0.682,0.683,0.64,0.727,0.566,0.572,0.595,0.719,0.588,0.572,0.594,0.592,0.735,0.699,0.694,0.61,0.655,0.617,0.6,0.719,0.664,0.657,0.714,0.672,0.67,0.581,0.526,0.735,0.75,0.66,0.721,0.397,0.555,0.745,0.745,0.741,0.747,0.743,0.744,0.736,0.749,0.74,0.748,0.765,0.716,16853,OrganismalFitness,HMDH_HUMAN,Low,Human +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,0.656,0.674,0.68,0.68,0.677,0.676,0.606,0.684,0.713,0.717,0.677,0.706,0.725,0.482,0.55,0.619,0.622,0.644,0.643,0.508,0.681,0.683,0.694,0.687,0.663,0.673,0.699,0.7,0.703,0.692,0.711,0.685,0.611,0.664,0.66,0.666,0.677,0.674,0.677,0.685,0.683,0.686,0.508,0.485,0.691,0.693,0.523,0.631,0.542,0.493,0.614,0.625,0.614,0.62,0.619,0.615,0.627,0.617,0.611,0.621,0.683,0.651,2252,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Flynn_2019,0.758,0.772,0.796,0.803,0.795,0.795,0.62,0.749,0.804,0.796,0.772,0.79,0.807,0.515,0.57,0.634,0.709,0.765,0.767,0.786,0.78,0.79,0.781,0.794,0.788,0.798,0.806,0.781,0.811,0.806,0.826,0.814,0.647,0.778,0.781,0.781,0.795,0.799,0.798,0.807,0.811,0.81,0.596,0.451,0.773,0.725,0.627,0.726,0.574,0.538,0.712,0.705,0.714,0.722,0.712,0.716,0.719,0.716,0.705,0.719,0.78,0.74,13294,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Mishra_2016,0.882,0.815,0.891,0.9,0.897,0.899,0.794,0.812,0.837,0.839,0.859,0.886,0.893,0.688,0.78,0.819,0.873,0.878,0.867,0.842,0.85,0.843,0.839,0.866,0.866,0.859,0.872,0.831,0.858,0.893,0.903,0.895,0.717,0.845,0.844,0.851,0.881,0.88,0.882,0.898,0.898,0.899,0.849,0.455,0.843,0.88,0.623,0.753,0.645,0.533,0.855,0.838,0.829,0.86,0.86,0.853,0.859,0.865,0.855,0.862,0.842,0.841,4323,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HXK4_HUMAN_Gersing_2022_activity,0.762,0.78,0.759,0.766,0.761,0.766,0.618,0.709,0.779,0.788,0.748,0.758,0.771,0.59,0.649,0.767,0.776,0.775,0.76,0.522,0.746,0.75,0.743,0.732,0.749,0.756,0.746,0.747,0.722,0.776,0.77,0.744,0.601,0.754,0.734,0.702,0.77,0.762,0.743,0.777,0.775,0.767,0.664,0.51,0.775,0.756,0.684,0.738,0.704,0.588,0.771,0.768,0.76,0.766,0.766,0.772,0.768,0.772,0.771,0.774,0.78,0.751,8570,OrganismalFitness,HXK4_HUMAN,Medium,Human +HXK4_HUMAN_Gersing_2023_abundance,0.694,0.719,0.697,0.713,0.708,0.711,0.534,0.7,0.734,0.746,0.703,0.697,0.712,0.58,0.61,0.701,0.724,0.731,0.713,0.74,0.669,0.671,0.686,0.686,0.668,0.681,0.688,0.686,0.69,0.723,0.681,0.659,0.595,0.681,0.675,0.686,0.705,0.705,0.713,0.711,0.716,0.723,0.601,0.547,0.712,0.703,0.734,0.728,0.732,0.594,0.719,0.727,0.725,0.725,0.73,0.727,0.728,0.724,0.724,0.729,0.757,0.701,8396,Expression,HXK4_HUMAN,Medium,Human +I6TAH8_I68A0_Doud_2015,0.677,0.664,0.639,0.638,0.685,0.683,0.495,0.659,0.645,0.663,0.503,0.507,0.506,0.509,0.51,0.501,0.507,0.503,0.543,0.608,0.663,0.669,0.691,0.691,0.504,0.508,0.554,0.497,0.655,0.695,0.627,0.63,0.572,0.652,0.666,0.673,0.665,0.676,0.677,0.693,0.701,0.704,0.502,0.511,0.506,0.509,0.608,0.604,0.623,0.549,0.533,0.552,0.564,0.563,0.554,0.551,0.542,0.545,0.527,0.551,0.521,0.504,9462,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +IF1_ECOLI_Kelsic_2016,0.695,0.775,0.823,0.823,0.816,0.82,0.578,0.712,0.649,0.641,0.803,0.815,0.826,0.6,0.756,0.817,0.832,0.818,0.8,0.793,0.673,0.729,0.695,0.714,0.746,0.748,0.751,0.735,0.745,0.722,0.784,0.754,0.643,0.756,0.774,0.787,0.773,0.78,0.785,0.812,0.816,0.82,0.693,0.563,0.821,0.782,0.692,0.797,0.781,0.645,0.798,0.79,0.808,0.814,0.804,0.809,0.789,0.813,0.813,0.819,0.852,0.783,1367,OrganismalFitness,IF1_ECOLI,High,Prokaryote +ILF3_HUMAN_Tsuboyama_2023_2L33,0.718,0.758,0.827,0.821,0.811,0.814,0.604,0.741,0.797,0.813,0.747,0.757,0.772,0.718,0.775,0.838,0.722,0.71,0.649,0.837,0.621,0.646,0.685,0.726,0.735,0.672,0.751,0.696,0.697,0.82,0.773,0.758,0.529,0.614,0.67,0.738,0.747,0.757,0.787,0.792,0.783,0.821,0.755,0.62,0.745,0.813,0.834,0.79,0.851,0.752,0.693,0.67,0.727,0.705,0.726,0.758,0.733,0.708,0.779,0.741,0.737,0.783,1329,Stability,ILF3_HUMAN,High,Human +ISDH_STAAW_Tsuboyama_2023_2LHR,0.54,0.548,0.599,0.594,0.612,0.612,0.662,0.571,0.68,0.679,0.738,0.71,0.719,0.687,0.695,0.724,0.718,0.74,0.724,0.589,0.653,0.601,0.614,0.629,0.649,0.66,0.655,0.642,0.685,0.669,0.718,0.691,0.622,0.636,0.62,0.636,0.625,0.605,0.616,0.629,0.606,0.616,0.639,0.611,0.665,0.652,0.788,0.743,0.773,0.752,0.725,0.717,0.728,0.724,0.726,0.728,0.715,0.72,0.724,0.726,0.791,0.749,1944,Stability,ISDH_STAAW,High,Prokaryote +KCNE1_HUMAN_Muhammad_2023_expression,0.641,0.648,0.622,0.628,0.636,0.62,0.67,0.569,0.634,0.636,0.691,0.679,0.666,0.663,0.651,0.682,0.652,0.635,0.657,0.649,0.68,0.694,0.625,0.606,0.69,0.628,0.672,0.633,0.643,0.647,0.636,0.603,0.393,0.656,0.708,0.659,0.662,0.673,0.67,0.657,0.662,0.666,0.673,0.652,0.667,0.693,0.649,0.65,0.644,0.553,0.637,0.625,0.638,0.614,0.617,0.612,0.642,0.636,0.639,0.633,0.66,0.678,2339,Expression,KCNE1_HUMAN,Medium,Human +KCNE1_HUMAN_Muhammad_2023_function,0.693,0.743,0.739,0.75,0.735,0.756,0.599,0.75,0.752,0.752,0.667,0.728,0.786,0.588,0.567,0.797,0.759,0.721,0.684,0.607,0.62,0.638,0.719,0.77,0.629,0.811,0.793,0.796,0.791,0.74,0.793,0.749,0.495,0.575,0.68,0.816,0.722,0.741,0.812,0.756,0.77,0.821,0.59,0.586,0.766,0.577,0.582,0.712,0.583,0.538,0.766,0.766,0.757,0.76,0.764,0.755,0.768,0.769,0.77,0.773,0.819,0.603,2315,Activity,KCNE1_HUMAN,Medium,Human +KCNH2_HUMAN_Kozek_2020,0.738,0.73,0.671,0.67,0.636,0.635,0.755,0.588,0.667,0.678,0.644,0.607,0.609,0.619,0.593,0.608,0.623,0.625,0.622,0.739,0.741,0.766,0.751,0.729,0.762,0.748,0.736,0.739,0.735,0.758,0.693,0.63,0.678,0.738,0.767,0.749,0.75,0.782,0.771,0.739,0.767,0.759,0.728,0.599,0.59,0.652,0.605,0.477,0.426,0.547,0.798,0.719,0.766,0.748,0.738,0.739,0.76,0.746,0.749,0.762,0.648,0.738,200,Activity,KCNH2_HUMAN,Medium,Human +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.639,0.675,0.675,0.682,0.686,0.689,0.531,0.625,0.668,0.665,0.696,0.685,0.691,0.531,0.652,0.7,0.702,0.704,0.706,0.586,0.68,0.657,0.631,0.627,0.683,0.674,0.662,0.678,0.618,0.698,0.694,0.686,0.578,0.669,0.652,0.621,0.683,0.672,0.652,0.691,0.686,0.677,0.61,0.493,0.675,0.682,0.601,0.65,0.623,0.54,0.694,0.686,0.692,0.691,0.693,0.689,0.694,0.692,0.693,0.696,0.696,0.64,6963,Activity,KCNJ2_MOUSE,Medium,Eukaryote +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.677,0.655,0.614,0.62,0.654,0.655,0.557,0.602,0.624,0.624,0.639,0.623,0.63,0.59,0.669,0.663,0.652,0.651,0.658,0.609,0.604,0.606,0.607,0.622,0.614,0.621,0.619,0.622,0.621,0.666,0.632,0.617,0.575,0.602,0.613,0.614,0.642,0.65,0.656,0.646,0.654,0.658,0.625,0.534,0.615,0.637,0.623,0.612,0.622,0.567,0.637,0.63,0.641,0.637,0.644,0.635,0.646,0.637,0.634,0.641,0.649,0.655,6917,Expression,KCNJ2_MOUSE,Medium,Eukaryote +KKA2_KLEPN_Melnikov_2014,0.658,0.806,0.756,0.859,0.85,0.856,0.623,0.756,0.813,0.854,0.827,0.842,0.858,0.632,0.655,0.782,0.844,0.87,0.876,0.794,0.674,0.748,0.805,0.814,0.706,0.837,0.835,0.832,0.869,0.868,0.87,0.845,0.59,0.65,0.787,0.839,0.764,0.809,0.843,0.842,0.855,0.868,0.653,0.551,0.826,0.733,0.749,0.829,0.806,0.64,0.838,0.828,0.834,0.83,0.83,0.835,0.825,0.837,0.842,0.843,0.868,0.685,4960,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +LGK_LIPST_Klesmith_2015,0.64,0.706,0.73,0.734,0.73,0.737,0.56,0.717,0.728,0.772,0.715,0.756,0.771,0.588,0.654,0.685,0.75,0.782,0.779,0.742,0.639,0.693,0.722,0.736,0.677,0.734,0.751,0.729,0.765,0.755,0.756,0.718,0.565,0.649,0.69,0.767,0.68,0.696,0.755,0.73,0.736,0.759,0.584,0.53,0.732,0.676,0.689,0.743,0.735,0.57,0.747,0.742,0.747,0.746,0.747,0.752,0.751,0.749,0.751,0.755,0.699,0.662,7890,Activity,LGK_LIPST,Medium,Eukaryote +LYAM1_HUMAN_Elazar_2016,0.669,0.667,0.61,0.594,0.649,0.653,0.675,0.58,0.698,0.701,0.698,0.673,0.67,0.643,0.672,0.66,0.637,0.691,0.732,0.653,0.681,0.687,0.682,0.675,0.674,0.679,0.669,0.642,0.66,0.688,0.641,0.583,0.598,0.672,0.705,0.664,0.684,0.705,0.679,0.677,0.691,0.667,0.642,0.658,0.644,0.665,0.586,0.631,0.59,0.556,0.608,0.653,0.696,0.664,0.635,0.646,0.675,0.672,0.63,0.662,0.709,0.69,359,Expression,LYAM1_HUMAN,Medium,Human +MAFG_MOUSE_Tsuboyama_2023_1K1V,0.854,0.874,0.866,0.866,0.861,0.861,0.74,0.904,0.875,0.866,0.9,0.749,0.886,0.79,0.795,0.823,0.829,0.859,0.762,0.868,0.752,0.837,0.848,0.872,0.817,0.858,0.83,0.859,0.839,0.835,0.863,0.855,0.776,0.686,0.851,0.855,0.844,0.885,0.905,0.869,0.894,0.911,0.718,0.525,0.781,0.676,0.859,0.728,0.89,0.905,0.917,0.903,0.907,0.916,0.91,0.915,0.912,0.916,0.911,0.915,0.922,0.934,1429,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV,0.808,0.904,0.885,0.895,0.917,0.921,0.466,0.816,0.87,0.908,0.907,0.757,0.805,0.428,0.539,0.915,0.891,0.882,0.91,0.902,0.494,0.556,0.847,0.864,0.626,0.836,0.824,0.842,0.911,0.923,0.906,0.878,0.84,0.333,0.554,0.544,0.823,0.872,0.86,0.899,0.919,0.913,0.483,0.395,0.858,0.749,0.75,0.848,0.903,0.792,0.925,0.91,0.924,0.91,0.911,0.908,0.912,0.919,0.923,0.922,0.927,0.884,2116,Stability,MBD11_ARATH,Medium,Eukaryote +MET_HUMAN_Estevam_2023,0.789,0.845,0.839,0.856,0.868,0.869,0.827,0.825,0.846,0.855,0.869,0.847,0.853,0.789,0.826,0.843,0.871,0.876,0.875,0.87,0.832,0.819,0.797,0.811,0.834,0.829,0.825,0.808,0.786,0.849,0.86,0.833,0.677,0.798,0.814,0.828,0.826,0.84,0.855,0.855,0.859,0.863,0.842,0.723,0.859,0.863,0.728,0.768,0.815,0.624,0.838,0.845,0.842,0.844,0.852,0.847,0.85,0.852,0.848,0.854,0.864,0.837,5393,Activity,MET_HUMAN,Medium,Human +MK01_HUMAN_Brenan_2016,0.614,0.622,0.638,0.641,0.634,0.636,0.627,0.605,0.625,0.618,0.542,0.607,0.617,0.605,0.621,0.622,0.615,0.621,0.597,0.614,0.631,0.582,0.562,0.53,0.613,0.569,0.552,0.561,0.486,0.63,0.613,0.633,0.568,0.61,0.554,0.524,0.623,0.586,0.572,0.637,0.627,0.626,0.621,0.579,0.594,0.614,0.541,0.514,0.582,0.502,0.602,0.601,0.594,0.604,0.608,0.603,0.603,0.606,0.61,0.608,0.602,0.616,6809,OrganismalFitness,MK01_HUMAN,Medium,Human +MLAC_ECOLI_MacRae_2023,0.607,0.686,0.723,0.728,0.72,0.721,0.489,0.684,0.716,0.72,0.708,0.72,0.728,0.489,0.614,0.631,0.694,0.691,0.706,0.708,0.538,0.692,0.697,0.71,0.659,0.706,0.708,0.705,0.725,0.716,0.737,0.726,0.513,0.566,0.662,0.651,0.634,0.667,0.66,0.698,0.708,0.707,0.584,0.479,0.662,0.618,0.514,0.605,0.571,0.491,0.666,0.646,0.654,0.655,0.665,0.664,0.654,0.66,0.674,0.664,0.61,0.599,4007,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +MSH2_HUMAN_Jia_2020,0.827,0.861,0.833,0.841,0.852,0.854,0.696,0.794,0.861,0.87,0.809,0.839,0.857,0.703,0.824,0.858,0.814,0.775,0.678,0.843,0.762,0.792,0.775,0.762,0.788,0.804,0.815,0.804,0.785,0.859,0.828,0.804,0.697,0.753,0.795,0.773,0.821,0.837,0.829,0.849,0.856,0.856,0.761,0.599,0.843,0.811,0.784,0.815,0.562,0.594,0.78,0.755,0.769,0.785,0.796,0.776,0.771,0.779,0.802,0.795,0.865,0.842,16749,OrganismalFitness,MSH2_HUMAN,Medium,Human +MTH3_HAEAE_RockahShmuel_2015,0.706,0.8,0.836,0.842,0.83,0.835,0.686,0.823,0.834,0.845,0.811,0.854,0.856,0.631,0.697,0.717,0.775,0.822,0.835,0.84,0.68,0.747,0.818,0.838,0.764,0.836,0.855,0.843,0.862,0.83,0.847,0.828,0.671,0.692,0.764,0.832,0.722,0.763,0.824,0.815,0.829,0.847,0.692,0.52,0.756,0.692,0.713,0.786,0.777,0.6,0.786,0.77,0.781,0.788,0.783,0.79,0.777,0.785,0.784,0.79,0.801,0.707,1777,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +MTHR_HUMAN_Weile_2021,0.607,0.618,0.608,0.611,0.611,0.61,0.588,0.575,0.632,0.632,0.676,0.629,0.639,0.575,0.685,0.724,0.661,0.625,0.631,0.614,0.707,0.564,0.597,0.621,0.7,0.601,0.619,0.604,0.639,0.629,0.592,0.577,0.559,0.71,0.641,0.595,0.668,0.641,0.612,0.66,0.636,0.618,0.62,0.547,0.669,0.683,0.608,0.653,0.674,0.543,0.647,0.631,0.636,0.635,0.639,0.645,0.653,0.645,0.66,0.648,0.647,0.67,12464,OrganismalFitness,MTHR_HUMAN,Low,Human +MYO3_YEAST_Tsuboyama_2023_2BTT,0.584,0.635,0.689,0.7,0.711,0.727,0.574,0.623,0.732,0.726,0.783,0.76,0.761,0.592,0.726,0.815,0.821,0.774,0.645,0.675,0.675,0.618,0.665,0.657,0.676,0.647,0.699,0.664,0.684,0.65,0.665,0.613,0.58,0.721,0.674,0.644,0.704,0.677,0.659,0.723,0.719,0.711,0.566,0.526,0.72,0.654,0.715,0.737,0.77,0.696,0.794,0.779,0.803,0.788,0.793,0.794,0.785,0.791,0.786,0.794,0.805,0.777,3297,Stability,MYO3_YEAST,High,Eukaryote +NCAP_I34A1_Doud_2015,0.687,0.67,0.673,0.672,0.685,0.686,0.508,0.668,0.686,0.677,0.514,0.517,0.518,0.519,0.52,0.516,0.521,0.522,0.553,0.646,0.677,0.687,0.702,0.704,0.517,0.529,0.557,0.52,0.677,0.7,0.641,0.645,0.565,0.68,0.686,0.71,0.699,0.704,0.717,0.716,0.715,0.724,0.517,0.515,0.52,0.519,0.631,0.635,0.639,0.559,0.565,0.585,0.588,0.594,0.588,0.587,0.58,0.583,0.566,0.587,0.568,0.543,9462,OrganismalFitness,NCAP_I34A1,Medium,Virus +NKX31_HUMAN_Tsuboyama_2023_2L9R,0.689,0.8,0.859,0.858,0.87,0.86,0.816,0.861,0.867,0.873,0.864,0.874,0.87,0.857,0.824,0.868,0.858,0.833,0.856,0.846,0.858,0.842,0.844,0.842,0.841,0.835,0.852,0.84,0.861,0.876,0.828,0.814,0.847,0.817,0.833,0.853,0.841,0.851,0.866,0.867,0.869,0.875,0.77,0.717,0.705,0.751,0.823,0.721,0.872,0.868,0.879,0.882,0.885,0.888,0.889,0.879,0.885,0.883,0.878,0.886,0.866,0.873,2482,Stability,NKX31_HUMAN,High,Human +NPC1_HUMAN_Erwood_2022_HEK293T,0.863,0.876,0.844,0.848,0.88,0.878,0.605,0.761,0.898,0.891,0.871,0.671,0.742,0.592,0.716,0.818,0.873,0.876,0.874,0.554,0.617,0.73,0.707,0.707,0.727,0.758,0.835,0.739,0.759,0.889,0.861,0.803,0.544,0.603,0.787,0.799,0.836,0.859,0.867,0.871,0.879,0.885,0.652,0.551,0.874,0.799,0.752,0.871,0.515,0.601,0.875,0.862,0.867,0.867,0.867,0.858,0.87,0.882,0.872,0.877,0.864,0.712,637,Activity,NPC1_HUMAN,Low,Human +NPC1_HUMAN_Erwood_2022_RPE1,0.87,0.773,0.682,0.685,0.8,0.81,0.663,0.721,0.763,0.806,0.833,0.538,0.582,0.581,0.61,0.763,0.789,0.775,0.811,0.675,0.795,0.687,0.715,0.648,0.69,0.689,0.689,0.635,0.71,0.795,0.883,0.795,0.78,0.688,0.584,0.742,0.867,0.77,0.847,0.838,0.779,0.811,0.673,0.681,0.912,0.609,0.731,0.817,0.44,0.602,0.77,0.755,0.771,0.707,0.767,0.756,0.758,0.807,0.799,0.769,0.855,0.72,63,Activity,NPC1_HUMAN,Low,Human +NRAM_I33A0_Jiang_2016,0.802,0.775,0.727,0.717,0.799,0.795,0.53,0.718,0.809,0.814,0.474,0.598,0.745,0.45,0.444,0.505,0.615,0.752,0.795,0.686,0.769,0.802,0.781,0.777,0.549,0.774,0.809,0.747,0.81,0.833,0.729,0.732,0.456,0.747,0.767,0.772,0.803,0.822,0.823,0.82,0.828,0.823,0.428,0.432,0.425,0.423,0.725,0.706,0.731,0.592,0.636,0.648,0.653,0.677,0.663,0.679,0.666,0.66,0.634,0.662,0.644,0.567,298,OrganismalFitness,NRAM_I33A0,Low,Virus +NUD15_HUMAN_Suiter_2020,0.644,0.75,0.797,0.813,0.809,0.811,0.504,0.711,0.832,0.866,0.838,0.839,0.863,0.677,0.735,0.753,0.801,0.833,0.84,0.777,0.671,0.75,0.808,0.798,0.736,0.828,0.826,0.81,0.788,0.813,0.841,0.811,0.594,0.698,0.739,0.816,0.728,0.744,0.83,0.809,0.809,0.838,0.724,0.511,0.831,0.744,0.753,0.805,0.779,0.667,0.806,0.785,0.798,0.813,0.804,0.814,0.817,0.813,0.813,0.817,0.882,0.815,2844,Expression,NUD15_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL,0.692,0.804,0.802,0.796,0.814,0.821,0.636,0.73,0.85,0.821,0.793,0.654,0.667,0.656,0.698,0.701,0.744,0.767,0.758,0.798,0.705,0.796,0.81,0.812,0.657,0.762,0.729,0.788,0.807,0.839,0.865,0.894,0.587,0.682,0.703,0.707,0.758,0.746,0.756,0.832,0.81,0.825,0.679,0.609,0.683,0.67,0.851,0.828,0.881,0.844,0.888,0.885,0.884,0.897,0.898,0.907,0.89,0.889,0.896,0.897,0.852,0.78,2028,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6,0.752,0.76,0.767,0.774,0.783,0.774,0.653,0.758,0.782,0.776,0.749,0.722,0.746,0.693,0.796,0.763,0.784,0.789,0.788,0.761,0.682,0.705,0.7,0.727,0.73,0.722,0.737,0.699,0.733,0.803,0.741,0.739,0.66,0.657,0.669,0.745,0.743,0.744,0.771,0.763,0.765,0.775,0.681,0.559,0.674,0.743,0.815,0.789,0.886,0.862,0.798,0.788,0.777,0.798,0.794,0.792,0.796,0.8,0.798,0.797,0.762,0.808,1380,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C,0.758,0.86,0.916,0.928,0.917,0.923,0.644,0.809,0.917,0.915,0.921,0.869,0.887,0.649,0.9,0.923,0.928,0.924,0.919,0.916,0.909,0.899,0.898,0.887,0.854,0.902,0.893,0.882,0.877,0.909,0.927,0.922,0.758,0.618,0.67,0.762,0.874,0.86,0.869,0.927,0.917,0.92,0.735,0.484,0.73,0.787,0.837,0.787,0.916,0.918,0.943,0.937,0.944,0.94,0.947,0.944,0.941,0.94,0.947,0.945,0.932,0.89,3197,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G,0.372,0.393,0.643,0.562,0.637,0.636,0.404,0.518,0.558,0.564,0.5,0.432,0.426,0.448,0.415,0.532,0.569,0.514,0.571,0.589,0.559,0.555,0.546,0.56,0.554,0.561,0.552,0.564,0.581,0.642,0.504,0.493,0.614,0.453,0.49,0.479,0.566,0.56,0.56,0.61,0.6,0.618,0.599,0.452,0.632,0.625,0.694,0.678,0.681,0.626,0.525,0.505,0.508,0.529,0.501,0.528,0.52,0.523,0.512,0.518,0.608,0.551,1134,Stability,ODP2_GEOSE,High,Prokaryote +OPSD_HUMAN_Wan_2019,0.611,0.75,0.755,0.796,0.758,0.76,0.701,0.808,0.787,0.806,0.692,0.788,0.808,0.659,0.777,0.767,0.784,0.751,0.769,0.784,0.782,0.778,0.772,0.796,0.804,0.812,0.822,0.815,0.801,0.749,0.74,0.694,0.604,0.761,0.785,0.768,0.78,0.783,0.773,0.763,0.765,0.767,0.702,0.557,0.762,0.752,0.874,0.789,0.882,0.611,0.703,0.67,0.699,0.696,0.745,0.754,0.682,0.701,0.729,0.729,0.814,0.785,165,Expression,OPSD_HUMAN,High,Human +OTC_HUMAN_Lo_2023,0.757,0.786,0.748,0.769,0.778,0.78,0.547,0.71,0.791,0.793,0.773,0.779,0.785,0.552,0.69,0.739,0.753,0.749,0.763,0.782,0.713,0.731,0.768,0.782,0.735,0.783,0.769,0.777,0.798,0.806,0.745,0.716,0.533,0.703,0.761,0.798,0.753,0.787,0.815,0.786,0.798,0.807,0.619,0.524,0.752,0.72,0.83,0.797,0.837,0.687,0.755,0.766,0.764,0.781,0.766,0.775,0.77,0.766,0.763,0.773,0.799,0.734,1570,Activity,OTC_HUMAN,Medium,Human +OTU7A_HUMAN_Tsuboyama_2023_2L2D,0.52,0.593,0.582,0.59,0.602,0.604,0.599,0.574,0.56,0.564,0.747,0.862,0.858,0.593,0.835,0.847,0.726,0.741,0.728,0.579,0.578,0.665,0.691,0.713,0.731,0.731,0.759,0.732,0.615,0.671,0.815,0.793,0.562,0.566,0.639,0.734,0.596,0.602,0.688,0.612,0.603,0.636,0.688,0.577,0.836,0.812,0.829,0.842,0.865,0.787,0.769,0.717,0.731,0.762,0.773,0.782,0.735,0.764,0.782,0.766,0.854,0.855,635,Stability,OTU7A_HUMAN,High,Human +OXDA_RHOTO_Vanella_2023_activity,0.57,0.623,0.649,0.65,0.638,0.646,0.591,0.62,0.613,0.618,0.667,0.653,0.657,0.562,0.609,0.639,0.659,0.665,0.67,0.653,0.569,0.604,0.643,0.647,0.622,0.657,0.665,0.65,0.692,0.643,0.687,0.678,0.519,0.59,0.614,0.639,0.624,0.629,0.639,0.642,0.645,0.648,0.595,0.529,0.667,0.632,0.63,0.667,0.646,0.537,0.655,0.659,0.665,0.654,0.658,0.669,0.658,0.661,0.66,0.665,0.665,0.601,6396,Activity,OXDA_RHOTO,High,Eukaryote +OXDA_RHOTO_Vanella_2023_expression,0.635,0.669,0.681,0.681,0.676,0.677,0.652,0.637,0.689,0.694,0.705,0.7,0.699,0.661,0.679,0.694,0.699,0.719,0.709,0.646,0.638,0.668,0.67,0.662,0.67,0.674,0.694,0.683,0.697,0.693,0.701,0.678,0.552,0.67,0.654,0.67,0.684,0.678,0.682,0.688,0.684,0.684,0.676,0.624,0.698,0.676,0.715,0.708,0.675,0.55,0.701,0.69,0.688,0.694,0.696,0.688,0.699,0.696,0.695,0.7,0.736,0.694,6769,Expression,OXDA_RHOTO,High,Eukaryote +P53_HUMAN_Giacomelli_2018_Null_Etoposide,0.831,0.831,0.765,0.777,0.826,0.829,0.45,0.764,0.763,0.772,0.826,0.827,0.855,0.417,0.436,0.764,0.835,0.849,0.869,0.627,0.722,0.788,0.804,0.796,0.778,0.818,0.822,0.818,0.783,0.83,0.853,0.836,0.611,0.701,0.8,0.784,0.819,0.839,0.821,0.833,0.847,0.829,0.419,0.412,0.84,0.5,0.771,0.833,0.784,0.637,0.834,0.836,0.838,0.834,0.839,0.847,0.839,0.843,0.847,0.848,0.859,0.66,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_Null_Nutlin,0.67,0.694,0.649,0.654,0.688,0.689,0.455,0.623,0.645,0.647,0.726,0.713,0.738,0.418,0.426,0.629,0.685,0.697,0.711,0.56,0.636,0.712,0.718,0.707,0.68,0.731,0.738,0.749,0.66,0.706,0.713,0.678,0.598,0.606,0.713,0.677,0.669,0.705,0.684,0.682,0.708,0.688,0.425,0.422,0.719,0.464,0.68,0.723,0.689,0.586,0.692,0.694,0.696,0.699,0.699,0.704,0.694,0.698,0.702,0.703,0.717,0.577,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_WT_Nutlin,0.855,0.865,0.782,0.795,0.851,0.856,0.453,0.766,0.774,0.778,0.883,0.863,0.891,0.415,0.44,0.828,0.869,0.886,0.889,0.626,0.78,0.862,0.86,0.847,0.835,0.87,0.867,0.889,0.761,0.881,0.887,0.867,0.634,0.752,0.881,0.821,0.855,0.894,0.85,0.865,0.896,0.853,0.423,0.405,0.894,0.509,0.832,0.888,0.833,0.67,0.873,0.881,0.879,0.88,0.884,0.888,0.881,0.886,0.886,0.891,0.893,0.688,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Kotler_2018,0.851,0.828,0.768,0.805,0.806,0.819,0.544,0.772,0.828,0.836,0.838,0.81,0.854,0.527,0.544,0.85,0.889,0.9,0.907,0.747,0.731,0.766,0.786,0.782,0.761,0.789,0.782,0.789,0.791,0.831,0.86,0.837,0.532,0.724,0.776,0.746,0.838,0.832,0.82,0.837,0.833,0.829,0.527,0.502,0.868,0.608,0.765,0.835,0.775,0.643,0.869,0.879,0.875,0.872,0.874,0.878,0.888,0.875,0.884,0.887,0.906,0.706,1048,OrganismalFitness,P53_HUMAN,Low,Human +P84126_THETH_Chan_2017,0.79,0.827,0.844,0.852,0.818,0.827,0.661,0.785,0.86,0.847,0.818,0.808,0.832,0.634,0.809,0.817,0.831,0.838,0.811,0.846,0.737,0.785,0.781,0.813,0.792,0.828,0.837,0.819,0.87,0.801,0.817,0.773,0.699,0.769,0.775,0.798,0.795,0.798,0.811,0.821,0.822,0.831,0.754,0.488,0.797,0.774,0.661,0.727,0.768,0.56,0.795,0.782,0.748,0.796,0.799,0.777,0.787,0.799,0.802,0.802,0.835,0.828,1519,OrganismalFitness,P84126_THETH,Medium,Prokaryote +PA_I34A1_Wu_2015,0.772,0.773,0.761,0.766,0.782,0.783,0.535,0.694,0.589,0.589,0.526,0.537,0.569,0.518,0.52,0.521,0.525,0.525,0.681,0.667,0.736,0.755,0.777,0.778,0.607,0.71,0.733,0.725,0.728,0.805,0.689,0.693,0.566,0.722,0.747,0.777,0.782,0.793,0.796,0.804,0.808,0.803,0.522,0.515,0.521,0.519,0.627,0.616,0.599,0.54,0.581,0.584,0.583,0.594,0.584,0.591,0.583,0.588,0.568,0.59,0.612,0.577,1820,OrganismalFitness,PA_I34A1,Medium,Virus +PABP_YEAST_Melamed_2013,0.855,0.827,0.787,0.791,0.846,0.843,0.752,0.805,0.838,0.852,0.866,0.851,0.861,0.758,0.805,0.856,0.898,0.875,0.882,0.787,0.847,0.858,0.863,0.874,0.846,0.879,0.879,0.866,0.86,0.859,0.884,0.846,0.649,0.849,0.85,0.847,0.874,0.874,0.872,0.866,0.868,0.866,0.825,0.469,0.851,0.852,0.691,0.817,0.779,0.604,0.87,0.865,0.872,0.888,0.88,0.883,0.884,0.884,0.901,0.892,0.885,0.844,37708,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +PAI1_HUMAN_Huttinger_2021,0.693,0.693,0.686,0.693,0.695,0.702,0.526,0.656,0.705,0.711,0.708,0.693,0.704,0.545,0.688,0.705,0.715,0.662,0.636,0.704,0.57,0.686,0.677,0.668,0.67,0.69,0.693,0.681,0.665,0.705,0.692,0.676,0.561,0.583,0.678,0.681,0.683,0.693,0.697,0.702,0.706,0.706,0.669,0.533,0.705,0.695,0.69,0.723,0.703,0.576,0.712,0.712,0.712,0.718,0.714,0.716,0.715,0.717,0.713,0.72,0.728,0.692,5345,Activity,PAI1_HUMAN,,Human +PHOT_CHLRE_Chen_2023,0.592,0.708,0.858,0.846,0.66,0.652,0.821,0.77,0.85,0.855,0.819,0.873,0.888,0.878,0.911,0.852,0.87,0.851,0.876,0.819,0.836,0.835,0.79,0.841,0.754,0.8,0.788,0.822,0.791,0.783,0.781,0.74,0.647,0.774,0.744,0.795,0.786,0.769,0.793,0.696,0.71,0.7,0.808,0.687,0.796,0.805,0.599,0.747,0.83,0.7,0.797,0.765,0.77,0.769,0.772,0.779,0.778,0.781,0.786,0.779,0.865,0.884,167529,Activity,PHOT_CHLRE,High,Eukaryote +PIN1_HUMAN_Tsuboyama_2023_1I6C,0.608,0.662,0.858,0.837,0.829,0.85,0.8,0.753,0.851,0.872,0.85,0.799,0.858,0.837,0.832,0.846,0.848,0.761,0.79,0.837,0.722,0.859,0.834,0.825,0.789,0.857,0.869,0.852,0.859,0.873,0.879,0.861,0.799,0.766,0.834,0.859,0.806,0.85,0.884,0.856,0.872,0.888,0.8,0.381,0.836,0.777,0.786,0.808,0.871,0.862,0.846,0.824,0.752,0.782,0.789,0.808,0.812,0.835,0.821,0.817,0.889,0.895,802,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M,0.795,0.773,0.787,0.791,0.787,0.79,0.76,0.754,0.781,0.793,0.745,0.732,0.726,0.786,0.836,0.847,0.827,0.807,0.765,0.79,0.771,0.743,0.752,0.738,0.793,0.766,0.744,0.73,0.752,0.78,0.712,0.703,0.729,0.797,0.771,0.761,0.813,0.788,0.784,0.801,0.782,0.784,0.748,0.718,0.646,0.669,0.715,0.653,0.85,0.82,0.794,0.798,0.795,0.805,0.812,0.816,0.806,0.808,0.81,0.809,0.75,0.812,1824,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF,0.623,0.605,0.662,0.671,0.69,0.694,0.612,0.658,0.639,0.661,0.68,0.706,0.737,0.716,0.733,0.814,0.721,0.678,0.676,0.718,0.702,0.705,0.68,0.712,0.71,0.708,0.711,0.704,0.712,0.717,0.752,0.75,0.555,0.721,0.665,0.708,0.69,0.686,0.714,0.713,0.697,0.712,0.684,0.674,0.705,0.692,0.835,0.777,0.83,0.79,0.685,0.703,0.724,0.689,0.707,0.706,0.692,0.707,0.713,0.709,0.728,0.825,1301,Stability,PKN1_HUMAN,High,Human +POLG_CXB3N_Mattenberger_2021,0.709,0.687,0.681,0.699,0.726,0.731,0.478,0.672,0.749,0.749,0.651,0.468,0.516,0.457,0.469,0.595,0.702,0.704,0.717,0.677,0.664,0.694,0.689,0.688,0.561,0.691,0.688,0.683,0.694,0.743,0.694,0.66,0.501,0.524,0.631,0.677,0.667,0.688,0.706,0.688,0.709,0.727,0.465,0.465,0.677,0.472,0.596,0.673,0.559,0.523,0.673,0.683,0.685,0.687,0.684,0.686,0.685,0.684,0.69,0.689,0.558,0.536,15711,OrganismalFitness,POLG_CXB3N,Medium,Virus +POLG_DEN26_Suphatrakul_2023,0.76,0.794,0.633,0.633,0.772,0.772,0.485,0.722,0.848,0.85,0.669,0.507,0.512,0.481,0.513,0.547,0.587,0.645,0.69,0.715,0.732,0.741,0.733,0.732,0.725,0.754,0.756,0.747,0.745,0.816,0.809,0.771,0.522,0.484,0.577,0.743,0.729,0.709,0.781,0.745,0.714,0.79,0.478,0.473,0.709,0.504,0.69,0.771,0.572,0.561,0.632,0.624,0.631,0.643,0.626,0.643,0.633,0.637,0.627,0.64,0.592,0.524,16897,OrganismalFitness,POLG_DEN26,Low,Virus +POLG_HCVJF_Qi_2014,0.824,0.784,0.712,0.716,0.823,0.828,0.47,0.598,0.792,0.798,0.588,0.842,0.839,0.552,0.565,0.556,0.556,0.543,0.535,0.631,0.714,0.744,0.741,0.761,0.721,0.756,0.669,0.72,0.78,0.84,0.821,0.772,0.599,0.747,0.769,0.78,0.768,0.79,0.809,0.748,0.78,0.798,0.555,0.52,0.763,0.554,0.483,0.672,0.869,0.703,0.673,0.714,0.7,0.715,0.698,0.657,0.683,0.685,0.679,0.712,0.596,0.584,1630,OrganismalFitness,POLG_HCVJF,Medium,Virus +POLG_PESV_Tsuboyama_2023_2MXD,0.638,0.766,0.695,0.72,0.74,0.74,0.525,0.722,0.679,0.735,0.737,0.523,0.558,0.525,0.556,0.515,0.567,0.553,0.53,0.818,0.558,0.53,0.507,0.574,0.542,0.47,0.486,0.463,0.578,0.761,0.822,0.826,0.575,0.498,0.503,0.5,0.702,0.706,0.702,0.734,0.741,0.733,0.479,0.448,0.491,0.469,0.778,0.748,0.882,0.841,0.859,0.863,0.848,0.892,0.894,0.894,0.867,0.896,0.894,0.888,0.907,0.83,5130,Stability,POLG_PESV,Medium,Virus +PPARG_HUMAN_Majithia_2016,0.758,0.814,0.837,0.842,0.838,0.845,0.625,0.731,0.81,0.821,0.812,0.841,0.843,0.533,0.612,0.808,0.865,0.893,0.869,0.858,0.831,0.827,0.697,0.719,0.81,0.837,0.822,0.838,0.734,0.871,0.852,0.843,0.732,0.855,0.816,0.788,0.862,0.845,0.833,0.871,0.861,0.859,0.679,0.524,0.791,0.779,0.807,0.788,0.834,0.668,0.86,0.862,0.867,0.865,0.865,0.866,0.864,0.865,0.865,0.871,0.861,0.811,9576,Activity,PPARG_HUMAN,Medium,Human +PPM1D_HUMAN_Miller_2022,0.783,0.779,0.779,0.782,0.816,0.817,0.513,0.694,0.741,0.77,0.797,0.805,0.816,0.639,0.692,0.734,0.805,0.82,0.817,0.697,0.726,0.767,0.776,0.719,0.763,0.795,0.79,0.787,0.714,0.813,0.804,0.777,0.614,0.718,0.769,0.775,0.801,0.805,0.805,0.819,0.817,0.818,0.677,0.473,0.794,0.763,0.756,0.798,0.783,0.627,0.791,0.793,0.789,0.796,0.801,0.794,0.797,0.797,0.799,0.802,0.824,0.757,7889,OrganismalFitness,PPM1D_HUMAN,Low,Human +PR40A_HUMAN_Tsuboyama_2023_1UZC,0.845,0.882,0.926,0.921,0.939,0.94,0.771,0.771,0.936,0.939,0.913,0.85,0.881,0.783,0.779,0.951,0.945,0.921,0.912,0.904,0.829,0.885,0.886,0.898,0.853,0.888,0.907,0.898,0.912,0.946,0.944,0.942,0.856,0.828,0.8,0.824,0.917,0.92,0.909,0.932,0.943,0.933,0.718,0.611,0.635,0.719,0.798,0.664,0.902,0.913,0.945,0.947,0.947,0.949,0.954,0.954,0.949,0.953,0.953,0.954,0.94,0.937,2033,Stability,PR40A_HUMAN,Medium,Human +PRKN_HUMAN_Clausen_2023,0.839,0.84,0.835,0.827,0.848,0.844,0.593,0.757,0.797,0.807,0.816,0.839,0.854,0.615,0.652,0.689,0.769,0.861,0.865,0.753,0.652,0.803,0.828,0.819,0.74,0.839,0.838,0.835,0.799,0.846,0.832,0.809,0.652,0.628,0.801,0.817,0.816,0.842,0.848,0.842,0.855,0.86,0.659,0.54,0.821,0.69,0.845,0.831,0.873,0.656,0.788,0.791,0.793,0.81,0.8,0.805,0.807,0.806,0.8,0.808,0.866,0.768,8756,Expression,PRKN_HUMAN,Low,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE,0.859,0.864,0.856,0.855,0.86,0.857,0.712,0.785,0.824,0.824,0.904,0.871,0.88,0.811,0.842,0.895,0.909,0.888,0.853,0.806,0.706,0.656,0.794,0.739,0.793,0.809,0.388,0.802,0.834,0.846,0.853,0.825,0.554,0.717,0.755,0.78,0.845,0.841,0.82,0.866,0.866,0.843,0.748,0.65,0.864,0.784,0.828,0.854,0.907,0.873,0.902,0.896,0.898,0.903,0.901,0.907,0.895,0.901,0.901,0.905,0.909,0.906,1579,Stability,PSAE_PICP2,Medium,Prokaryote +PTEN_HUMAN_Matreyek_2021,0.681,0.695,0.681,0.691,0.695,0.7,0.579,0.683,0.714,0.736,0.721,0.704,0.73,0.587,0.635,0.727,0.732,0.642,0.654,0.739,0.595,0.73,0.712,0.702,0.633,0.679,0.67,0.696,0.657,0.737,0.698,0.695,0.567,0.636,0.717,0.68,0.688,0.73,0.711,0.706,0.731,0.725,0.622,0.526,0.719,0.669,0.736,0.734,0.736,0.611,0.719,0.714,0.729,0.718,0.717,0.723,0.725,0.726,0.721,0.727,0.748,0.725,5083,Expression,PTEN_HUMAN,Medium,Human +PTEN_HUMAN_Mighell_2018,0.815,0.826,0.827,0.829,0.844,0.848,0.619,0.734,0.828,0.833,0.805,0.816,0.833,0.639,0.772,0.85,0.832,0.695,0.683,0.831,0.73,0.774,0.72,0.693,0.77,0.696,0.689,0.714,0.66,0.842,0.819,0.813,0.541,0.75,0.767,0.702,0.829,0.813,0.781,0.844,0.844,0.846,0.747,0.481,0.832,0.815,0.771,0.762,0.805,0.625,0.816,0.791,0.811,0.818,0.826,0.825,0.816,0.816,0.826,0.828,0.845,0.828,7260,Activity,PTEN_HUMAN,Medium,Human +Q2N0S5_9HIV1_Haddox_2018,0.758,0.704,0.69,0.709,0.76,0.763,0.51,0.723,0.77,0.773,0.745,0.767,0.781,0.509,0.509,0.511,0.532,0.559,0.586,0.711,0.77,0.707,0.698,0.671,0.769,0.702,0.702,0.722,0.683,0.764,0.751,0.753,0.644,0.758,0.716,0.71,0.772,0.762,0.762,0.775,0.769,0.768,0.72,0.507,0.765,0.738,0.695,0.743,0.634,0.575,0.615,0.636,0.658,0.656,0.631,0.634,0.635,0.628,0.615,0.645,0.61,0.56,12729,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,0.671,0.658,0.665,0.666,0.669,0.667,0.545,0.608,0.639,0.645,0.605,0.636,0.646,0.605,0.596,0.671,0.657,0.63,0.663,0.651,0.615,0.619,0.615,0.593,0.637,0.636,0.624,0.642,0.633,0.638,0.679,0.666,0.551,0.61,0.623,0.576,0.647,0.656,0.634,0.667,0.671,0.662,0.699,0.609,0.642,0.653,0.643,0.664,0.655,0.59,0.639,0.645,0.659,0.656,0.652,0.652,0.648,0.646,0.647,0.654,0.647,0.717,3344,Binding,Q53Z42_HUMAN,Medium,Human +Q53Z42_HUMAN_McShan_2019_expression,0.771,0.755,0.772,0.779,0.784,0.784,0.471,0.721,0.784,0.791,0.719,0.747,0.766,0.533,0.545,0.745,0.785,0.785,0.792,0.787,0.718,0.752,0.752,0.746,0.747,0.765,0.754,0.767,0.784,0.785,0.784,0.755,0.582,0.721,0.737,0.728,0.773,0.784,0.787,0.788,0.794,0.795,0.657,0.539,0.777,0.746,0.734,0.761,0.739,0.604,0.777,0.781,0.786,0.783,0.792,0.786,0.784,0.787,0.792,0.793,0.771,0.812,3344,Expression,Q53Z42_HUMAN,Medium,Human +Q59976_STRSQ_Romero_2015,0.781,0.831,0.853,0.857,0.857,0.864,0.689,0.776,0.865,0.869,0.827,0.786,0.796,0.574,0.741,0.775,0.813,0.812,0.817,0.85,0.826,0.85,0.849,0.857,0.838,0.859,0.866,0.865,0.869,0.872,0.854,0.827,0.674,0.827,0.85,0.842,0.838,0.856,0.857,0.864,0.869,0.869,0.763,0.492,0.824,0.784,0.711,0.783,0.781,0.569,0.813,0.792,0.792,0.803,0.808,0.798,0.81,0.797,0.802,0.809,0.843,0.79,2999,Activity,Q59976_STRSQ,Medium,Prokaryote +Q6WV13_9MAXI_Somermeyer_2022,0.634,0.658,0.605,0.606,0.64,0.639,0.496,0.562,0.661,0.659,0.599,0.52,0.512,0.506,0.519,0.501,0.505,0.505,0.493,0.626,0.518,0.519,0.502,0.552,0.513,0.5,0.512,0.496,0.493,0.682,0.636,0.639,0.514,0.513,0.518,0.512,0.599,0.599,0.599,0.637,0.637,0.635,0.505,0.501,0.496,0.51,0.611,0.615,0.664,0.601,0.61,0.624,0.614,0.636,0.628,0.63,0.621,0.622,0.621,0.624,0.554,0.536,31401,Activity,Q6WV12_9MAXI,Low,Eukaryote +Q837P4_ENTFA_Meier_2023,0.705,0.712,0.718,0.719,0.725,0.736,0.709,0.675,0.711,0.719,0.765,0.741,0.755,0.682,0.716,0.718,0.724,0.744,0.729,0.476,0.733,0.712,0.721,0.734,0.742,0.749,0.76,0.706,0.754,0.738,0.738,0.718,0.561,0.721,0.747,0.729,0.719,0.754,0.749,0.744,0.752,0.752,0.704,0.677,0.755,0.72,0.63,0.725,0.685,0.551,0.726,0.714,0.718,0.736,0.74,0.748,0.741,0.711,0.736,0.737,0.772,0.72,697,Activity,Q837P4_ENTFA,Medium,Prokaryote +Q837P5_ENTFA_Meier_2023,0.565,0.677,0.682,0.698,0.664,0.668,0.573,0.601,0.613,0.618,0.638,0.646,0.638,0.526,0.578,0.629,0.653,0.691,0.671,0.636,0.657,0.676,0.701,0.698,0.639,0.684,0.69,0.735,0.714,0.644,0.665,0.658,0.559,0.68,0.703,0.741,0.643,0.674,0.714,0.679,0.686,0.699,0.611,0.557,0.653,0.6,0.665,0.642,0.676,0.563,0.636,0.641,0.648,0.629,0.657,0.65,0.63,0.647,0.637,0.648,0.64,0.629,747,Activity,Q837P5_ENTFA,Medium,Prokaryote +Q8WTC7_9CNID_Somermeyer_2022,0.63,0.671,0.603,0.602,0.652,0.649,0.495,0.648,0.638,0.641,0.592,0.471,0.463,0.466,0.454,0.459,0.465,0.461,0.484,0.601,0.476,0.492,0.516,0.489,0.482,0.469,0.487,0.606,0.613,0.665,0.646,0.654,0.477,0.452,0.49,0.639,0.596,0.601,0.642,0.641,0.646,0.66,0.472,0.473,0.46,0.46,0.51,0.543,0.628,0.571,0.604,0.598,0.603,0.609,0.598,0.606,0.605,0.599,0.599,0.603,0.537,0.447,33510,Activity,Q8WTC7_9CNID,Low,Eukaryote +R1AB_SARS2_Flynn_2022,0.835,0.829,0.618,0.633,0.846,0.848,0.487,0.675,0.496,0.496,0.575,0.497,0.49,0.513,0.499,0.555,0.567,0.792,0.824,0.663,0.636,0.667,0.674,0.676,0.643,0.643,0.623,0.634,0.642,0.825,0.801,0.745,0.486,0.607,0.637,0.63,0.711,0.735,0.734,0.823,0.834,0.832,0.501,0.486,0.549,0.495,0.752,0.733,0.777,0.626,0.65,0.636,0.659,0.675,0.665,0.661,0.662,0.66,0.641,0.664,0.636,0.589,5725,OrganismalFitness,R1AB_SARS2,Medium,Virus +RAD_ANTMA_Tsuboyama_2023_2CJJ,0.605,0.585,0.66,0.663,0.675,0.692,0.735,0.703,0.853,0.835,0.794,0.748,0.769,0.726,0.846,0.868,0.761,0.754,0.797,0.714,0.788,0.824,0.803,0.766,0.816,0.744,0.808,0.754,0.747,0.799,0.725,0.681,0.649,0.75,0.834,0.745,0.745,0.811,0.744,0.734,0.762,0.723,0.811,0.62,0.625,0.726,0.761,0.666,0.865,0.778,0.786,0.801,0.804,0.8,0.812,0.802,0.799,0.801,0.797,0.805,0.719,0.818,912,Stability,RAD_ANTMA,High,Eukaryote +RAF1_HUMAN_Zinkus-Boltz_2019,0.708,0.719,0.706,0.712,0.725,0.725,0.525,0.684,0.752,0.763,0.76,0.732,0.769,0.523,0.619,0.74,0.756,0.744,0.734,0.678,0.654,0.715,0.708,0.696,0.699,0.712,0.708,0.696,0.708,0.727,0.764,0.73,0.569,0.653,0.688,0.702,0.693,0.703,0.711,0.717,0.734,0.737,0.607,0.529,0.779,0.707,0.638,0.725,0.639,0.665,0.729,0.726,0.73,0.752,0.734,0.747,0.732,0.729,0.74,0.745,0.727,0.59,297,OrganismalFitness,RAF1_HUMAN,Low,Human +RASH_HUMAN_Bandaru_2017,0.798,0.811,0.827,0.842,0.839,0.844,0.689,0.772,0.823,0.837,0.757,0.787,0.811,0.807,0.849,0.835,0.85,0.817,0.735,0.859,0.819,0.805,0.805,0.791,0.809,0.791,0.791,0.767,0.71,0.815,0.789,0.779,0.648,0.787,0.803,0.76,0.821,0.83,0.808,0.839,0.844,0.846,0.838,0.662,0.72,0.822,0.711,0.641,0.807,0.644,0.798,0.794,0.799,0.796,0.806,0.793,0.813,0.796,0.805,0.812,0.747,0.827,3134,Activity,RASH_HUMAN,High,Human +RASK_HUMAN_Weng_2022_abundance,0.648,0.626,0.679,0.672,0.665,0.676,0.605,0.623,0.62,0.634,0.644,0.606,0.624,0.658,0.683,0.663,0.631,0.61,0.603,0.665,0.633,0.671,0.69,0.699,0.64,0.677,0.711,0.677,0.738,0.683,0.605,0.584,0.607,0.6,0.662,0.706,0.636,0.682,0.721,0.667,0.685,0.694,0.616,0.655,0.531,0.621,0.659,0.664,0.688,0.662,0.627,0.62,0.645,0.637,0.652,0.637,0.646,0.634,0.623,0.639,0.671,0.661,26012,Expression,RASK_HUMAN,High,Human +RASK_HUMAN_Weng_2022_binding-DARPin_K55,0.734,0.755,0.822,0.827,0.815,0.816,0.645,0.701,0.83,0.833,0.806,0.777,0.805,0.796,0.824,0.834,0.842,0.79,0.733,0.861,0.798,0.781,0.798,0.754,0.778,0.791,0.773,0.718,0.694,0.824,0.779,0.753,0.66,0.765,0.809,0.762,0.791,0.82,0.791,0.818,0.826,0.817,0.794,0.648,0.732,0.814,0.628,0.64,0.814,0.648,0.779,0.781,0.783,0.782,0.792,0.792,0.779,0.782,0.756,0.789,0.812,0.814,24873,Binding,RASK_HUMAN,High,Human +RBP1_HUMAN_Tsuboyama_2023_2KWH,0.584,0.542,0.656,0.66,0.659,0.657,0.646,0.65,0.634,0.642,0.728,0.718,0.713,0.669,0.708,0.759,0.781,0.684,0.682,0.633,0.694,0.577,0.656,0.654,0.653,0.565,0.591,0.673,0.631,0.701,0.704,0.676,0.644,0.655,0.698,0.708,0.666,0.679,0.678,0.666,0.675,0.666,0.667,0.636,0.709,0.714,0.754,0.778,0.796,0.73,0.755,0.751,0.756,0.755,0.759,0.754,0.749,0.762,0.752,0.758,0.783,0.769,1332,Stability,RBP1_HUMAN,High,Human +RCD1_ARATH_Tsuboyama_2023_5OAO,0.694,0.683,0.725,0.728,0.728,0.723,0.66,0.662,0.736,0.74,0.757,0.669,0.695,0.685,0.692,0.784,0.792,0.787,0.783,0.739,0.628,0.616,0.668,0.71,0.659,0.759,0.749,0.727,0.767,0.744,0.787,0.752,0.519,0.638,0.657,0.66,0.717,0.721,0.715,0.742,0.743,0.735,0.655,0.644,0.727,0.666,0.744,0.757,0.801,0.773,0.802,0.809,0.816,0.814,0.806,0.812,0.809,0.808,0.811,0.814,0.799,0.762,1261,Stability,RCD1_ARATH,Medium,Eukaryote +RCRO_LAMBD_Tsuboyama_2023_1ORC,0.665,0.773,0.805,0.8,0.788,0.806,0.533,0.64,0.764,0.767,0.737,0.649,0.697,0.591,0.654,0.66,0.786,0.786,0.803,0.801,0.506,0.524,0.549,0.541,0.414,0.522,0.439,0.482,0.798,0.799,0.782,0.771,0.518,0.525,0.495,0.778,0.713,0.708,0.795,0.789,0.8,0.823,0.452,0.545,0.698,0.548,0.78,0.806,0.866,0.807,0.79,0.781,0.771,0.782,0.782,0.784,0.773,0.781,0.792,0.785,0.815,0.71,2278,Stability,RCRO_LAMBD,High,Virus +RD23A_HUMAN_Tsuboyama_2023_1IFY,0.619,0.649,0.714,0.714,0.711,0.711,0.576,0.735,0.766,0.771,0.75,0.792,0.805,0.603,0.836,0.78,0.768,0.761,0.723,0.735,0.705,0.725,0.722,0.743,0.769,0.765,0.764,0.749,0.765,0.747,0.725,0.718,0.729,0.646,0.749,0.737,0.703,0.747,0.75,0.716,0.742,0.748,0.747,0.507,0.788,0.772,0.724,0.783,0.787,0.745,0.744,0.735,0.741,0.733,0.748,0.75,0.746,0.748,0.749,0.749,0.741,0.796,1019,Stability,RD23A_HUMAN,High,Human +RDRP_I33A0_Li_2023,0.667,0.699,0.727,0.732,0.779,0.781,0.524,0.728,0.791,0.794,0.612,0.547,0.554,0.535,0.54,0.59,0.698,0.724,0.785,0.768,0.734,0.768,0.778,0.789,0.588,0.723,0.716,0.712,0.751,0.796,0.766,0.729,0.568,0.73,0.761,0.781,0.75,0.768,0.779,0.79,0.799,0.808,0.535,0.522,0.612,0.536,0.626,0.646,0.622,0.54,0.681,0.664,0.674,0.674,0.683,0.679,0.675,0.686,0.686,0.687,0.595,0.569,12003,OrganismalFitness,RDRP_I33A0,Low,Virus +REV_HV1H2_Fernandes_2016,0.596,0.574,0.603,0.606,0.603,0.603,0.521,0.646,0.603,0.607,0.566,0.616,0.624,0.526,0.522,0.585,0.613,0.638,0.629,0.575,0.6,0.623,0.617,0.608,0.635,0.637,0.576,0.617,0.623,0.63,0.667,0.672,0.525,0.608,0.627,0.613,0.614,0.625,0.61,0.616,0.623,0.611,0.529,0.533,0.599,0.537,0.626,0.612,0.639,0.604,0.591,0.596,0.633,0.622,0.624,0.625,0.633,0.61,0.649,0.632,0.633,0.608,2147,OrganismalFitness,REV_HV1H2,Medium,Virus +RFAH_ECOLI_Tsuboyama_2023_2LCL,0.507,0.596,0.607,0.618,0.618,0.616,0.487,0.62,0.613,0.627,0.622,0.571,0.585,0.469,0.529,0.511,0.649,0.637,0.632,0.635,0.454,0.519,0.559,0.553,0.464,0.565,0.577,0.552,0.576,0.61,0.636,0.61,0.5,0.498,0.558,0.576,0.543,0.572,0.583,0.61,0.61,0.611,0.547,0.447,0.585,0.54,0.649,0.622,0.689,0.658,0.668,0.657,0.649,0.668,0.66,0.655,0.66,0.656,0.668,0.663,0.652,0.593,1326,Stability,RFAH_ECOLI,High,Prokaryote +RL20_AQUAE_Tsuboyama_2023_1GYZ,0.688,0.851,0.867,0.866,0.866,0.87,0.593,0.855,0.718,0.681,0.886,0.885,0.875,0.591,0.706,0.753,0.908,0.899,0.907,0.879,0.587,0.798,0.786,0.78,0.774,0.789,0.805,0.818,0.861,0.865,0.843,0.832,0.531,0.774,0.803,0.79,0.812,0.836,0.832,0.852,0.875,0.875,0.484,0.434,0.769,0.622,0.856,0.858,0.936,0.906,0.917,0.917,0.915,0.929,0.923,0.919,0.924,0.924,0.925,0.927,0.93,0.872,1461,Stability,RL20_AQUAE,High,Prokaryote +RL40A_YEAST_Mavor_2016,0.654,0.683,0.685,0.713,0.695,0.708,0.556,0.728,0.744,0.746,0.623,0.653,0.674,0.547,0.738,0.769,0.788,0.744,0.797,0.73,0.706,0.789,0.774,0.745,0.775,0.75,0.751,0.739,0.724,0.672,0.7,0.705,0.532,0.715,0.757,0.709,0.726,0.76,0.726,0.738,0.759,0.721,0.653,0.518,0.653,0.668,0.553,0.587,0.627,0.503,0.767,0.755,0.776,0.767,0.778,0.765,0.762,0.777,0.77,0.781,0.697,0.698,1253,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2013,0.66,0.695,0.708,0.742,0.714,0.728,0.53,0.684,0.767,0.769,0.614,0.654,0.692,0.54,0.73,0.773,0.814,0.753,0.806,0.748,0.709,0.799,0.777,0.755,0.783,0.759,0.769,0.754,0.739,0.696,0.745,0.756,0.536,0.714,0.775,0.716,0.731,0.778,0.734,0.75,0.777,0.737,0.68,0.522,0.659,0.66,0.542,0.567,0.628,0.511,0.802,0.787,0.806,0.793,0.804,0.797,0.787,0.813,0.803,0.814,0.698,0.71,1195,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2014,0.731,0.684,0.73,0.753,0.72,0.743,0.596,0.761,0.745,0.759,0.595,0.66,0.676,0.628,0.816,0.813,0.828,0.765,0.791,0.744,0.714,0.796,0.776,0.764,0.781,0.752,0.748,0.723,0.719,0.733,0.714,0.719,0.616,0.731,0.778,0.73,0.757,0.794,0.767,0.765,0.783,0.756,0.663,0.576,0.659,0.672,0.63,0.64,0.685,0.588,0.816,0.796,0.802,0.83,0.828,0.825,0.801,0.811,0.8,0.828,0.69,0.746,1380,Activity,RL40A_YEAST,Medium,Eukaryote +RNC_ECOLI_Weeks_2023,0.772,0.804,0.803,0.809,0.812,0.813,0.531,0.719,0.808,0.807,0.806,0.803,0.81,0.535,0.782,0.801,0.809,0.807,0.81,0.808,0.779,0.794,0.765,0.755,0.786,0.802,0.802,0.801,0.795,0.809,0.799,0.776,0.584,0.77,0.777,0.718,0.794,0.801,0.777,0.818,0.819,0.815,0.769,0.534,0.806,0.792,0.657,0.777,0.65,0.597,0.787,0.781,0.778,0.792,0.787,0.782,0.786,0.786,0.787,0.793,0.815,0.772,4277,Activity,RNC_ECOLI,Medium,Prokaryote +RPC1_BP434_Tsuboyama_2023_1R69,0.818,0.857,0.864,0.875,0.833,0.856,0.841,0.829,0.85,0.87,0.891,0.898,0.902,0.876,0.89,0.901,0.894,0.88,0.85,0.884,0.865,0.886,0.889,0.871,0.882,0.894,0.89,0.891,0.87,0.897,0.869,0.834,0.836,0.826,0.881,0.881,0.874,0.888,0.887,0.873,0.873,0.869,0.888,0.854,0.898,0.887,0.875,0.847,0.925,0.889,0.879,0.872,0.88,0.887,0.884,0.881,0.89,0.886,0.892,0.89,0.913,0.906,1459,Stability,RPC1_BP434,High,Virus +RPC1_LAMBD_Li_2019_high-expression,0.707,0.796,0.837,0.849,0.824,0.826,0.67,0.816,0.833,0.857,0.866,0.855,0.879,0.721,0.718,0.794,0.898,0.902,0.904,0.815,0.656,0.744,0.79,0.82,0.674,0.777,0.764,0.746,0.894,0.811,0.934,0.902,0.586,0.625,0.764,0.86,0.706,0.769,0.857,0.81,0.832,0.866,0.731,0.735,0.804,0.743,0.671,0.774,0.761,0.626,0.898,0.873,0.872,0.897,0.887,0.898,0.883,0.879,0.873,0.897,0.878,0.789,351,Activity,RPC1_LAMBD,High,Virus +RPC1_LAMBD_Li_2019_low-expression,0.612,0.699,0.736,0.746,0.724,0.718,0.563,0.757,0.716,0.736,0.755,0.728,0.736,0.619,0.62,0.671,0.761,0.803,0.808,0.7,0.605,0.678,0.68,0.713,0.583,0.686,0.661,0.653,0.802,0.712,0.829,0.817,0.54,0.563,0.663,0.738,0.61,0.657,0.727,0.702,0.716,0.743,0.636,0.621,0.67,0.641,0.634,0.681,0.7,0.636,0.769,0.752,0.767,0.777,0.758,0.772,0.776,0.767,0.758,0.776,0.765,0.653,351,Activity,RPC1_LAMBD,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32,0.698,0.672,0.685,0.682,0.672,0.675,0.587,0.62,0.71,0.71,0.734,0.709,0.701,0.636,0.657,0.758,0.714,0.702,0.67,0.663,0.601,0.652,0.667,0.658,0.657,0.672,0.7,0.679,0.686,0.701,0.726,0.723,0.548,0.711,0.659,0.662,0.725,0.687,0.69,0.703,0.679,0.673,0.665,0.62,0.719,0.683,0.801,0.723,0.817,0.768,0.71,0.709,0.715,0.713,0.709,0.708,0.719,0.726,0.71,0.719,0.72,0.821,1195,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance,0.775,0.791,0.802,0.806,0.813,0.82,0.709,0.779,0.831,0.831,0.822,0.835,0.857,0.766,0.791,0.802,0.837,0.826,0.812,0.52,0.767,0.823,0.837,0.826,0.786,0.839,0.848,0.83,0.81,0.819,0.81,0.741,0.682,0.785,0.828,0.825,0.806,0.843,0.845,0.834,0.844,0.843,0.771,0.651,0.814,0.802,0.733,0.789,0.788,0.594,0.813,0.806,0.807,0.817,0.827,0.821,0.819,0.822,0.817,0.827,0.837,0.812,9803,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity,0.773,0.81,0.835,0.843,0.838,0.845,0.714,0.821,0.844,0.843,0.837,0.848,0.877,0.769,0.789,0.789,0.854,0.833,0.822,0.521,0.793,0.854,0.861,0.849,0.815,0.86,0.865,0.854,0.831,0.852,0.856,0.802,0.691,0.805,0.858,0.846,0.817,0.86,0.857,0.856,0.87,0.866,0.775,0.636,0.837,0.8,0.728,0.805,0.788,0.587,0.828,0.812,0.809,0.828,0.837,0.826,0.827,0.837,0.837,0.839,0.828,0.804,10094,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB,0.537,0.586,0.693,0.677,0.692,0.687,0.702,0.788,0.693,0.701,0.762,0.765,0.778,0.593,0.739,0.757,0.765,0.786,0.732,0.723,0.771,0.756,0.75,0.766,0.776,0.786,0.768,0.771,0.769,0.785,0.764,0.733,0.703,0.736,0.778,0.773,0.714,0.758,0.746,0.698,0.719,0.719,0.768,0.459,0.787,0.774,0.647,0.658,0.709,0.767,0.752,0.693,0.638,0.704,0.699,0.716,0.713,0.742,0.735,0.714,0.803,0.813,965,Stability,SAV1_MOUSE,High,Eukaryote +SBI_STAAM_Tsuboyama_2023_2JVG,0.606,0.599,0.685,0.666,0.714,0.699,0.607,0.603,0.737,0.763,0.661,0.626,0.642,0.614,0.629,0.687,0.789,0.833,0.674,0.68,0.61,0.63,0.64,0.639,0.607,0.619,0.611,0.629,0.688,0.789,0.798,0.75,0.602,0.589,0.598,0.606,0.641,0.646,0.648,0.674,0.681,0.685,0.629,0.608,0.659,0.655,0.836,0.828,0.854,0.812,0.768,0.765,0.782,0.784,0.781,0.782,0.794,0.79,0.785,0.787,0.848,0.785,1025,Stability,SBI_STAAM,Medium,Prokaryote +SC6A4_HUMAN_Young_2021,0.71,0.745,0.728,0.734,0.771,0.78,0.686,0.766,0.8,0.805,0.788,0.782,0.787,0.588,0.675,0.77,0.792,0.793,0.78,0.795,0.764,0.771,0.762,0.761,0.771,0.774,0.773,0.776,0.771,0.788,0.745,0.693,0.683,0.772,0.77,0.759,0.783,0.788,0.781,0.793,0.796,0.791,0.762,0.564,0.79,0.782,0.746,0.778,0.762,0.598,0.778,0.783,0.788,0.791,0.793,0.788,0.788,0.79,0.783,0.793,0.808,0.782,11576,Activity,SC6A4_HUMAN,Medium,Human +SCIN_STAAR_Tsuboyama_2023_2QFF,0.535,0.56,0.633,0.63,0.654,0.641,0.552,0.53,0.632,0.63,0.62,0.618,0.607,0.585,0.625,0.613,0.638,0.67,0.675,0.634,0.538,0.563,0.549,0.555,0.564,0.576,0.56,0.578,0.585,0.629,0.638,0.602,0.49,0.522,0.534,0.565,0.566,0.564,0.574,0.626,0.62,0.628,0.592,0.566,0.623,0.607,0.763,0.758,0.763,0.733,0.692,0.699,0.699,0.72,0.686,0.715,0.689,0.688,0.667,0.702,0.797,0.733,1212,Stability,SCIN_STAAR,High,Prokaryote +SCN5A_HUMAN_Glazer_2019,0.537,0.543,0.558,0.558,0.553,0.559,0.548,0.51,0.569,0.583,0.589,0.613,0.567,0.623,0.591,0.582,0.594,0.569,0.554,0.579,0.546,0.557,0.567,0.567,0.536,0.547,0.548,0.543,0.58,0.545,0.588,0.582,0.532,0.534,0.525,0.532,0.537,0.53,0.529,0.553,0.552,0.556,0.542,0.585,0.574,0.585,0.529,0.561,0.527,0.504,0.573,0.564,0.574,0.563,0.543,0.566,0.578,0.572,0.575,0.57,0.608,0.574,224,OrganismalFitness,SCN5A_HUMAN,Medium,Human +SDA_BACSU_Tsuboyama_2023_1PV0,0.944,0.971,0.971,0.969,0.973,0.973,0.59,0.86,0.959,0.962,0.969,0.939,0.952,0.72,0.734,0.947,0.955,0.952,0.957,0.954,0.628,0.652,0.652,0.77,0.643,0.799,0.565,0.776,0.862,0.967,0.965,0.962,0.353,0.579,0.738,0.808,0.934,0.936,0.908,0.962,0.96,0.947,0.576,0.497,0.818,0.562,0.841,0.827,0.961,0.952,0.973,0.97,0.969,0.974,0.972,0.972,0.974,0.971,0.975,0.974,0.961,0.967,2770,Stability,SDA_BACSU,Medium,Prokaryote +SERC_HUMAN_Xie_2023,0.678,0.722,0.759,0.759,0.745,0.75,0.509,0.715,0.774,0.778,0.747,0.757,0.76,0.587,0.692,0.754,0.765,0.776,0.76,0.755,0.736,0.744,0.746,0.735,0.738,0.755,0.747,0.755,0.745,0.744,0.753,0.732,0.588,0.747,0.753,0.749,0.748,0.754,0.757,0.757,0.759,0.759,0.651,0.519,0.759,0.725,0.696,0.735,0.681,0.593,0.762,0.756,0.764,0.768,0.771,0.767,0.767,0.766,0.765,0.772,0.765,0.727,1914,OrganismalFitness,SERC_HUMAN,High,Human +SHOC2_HUMAN_Kwon_2022,0.599,0.664,0.689,0.69,0.685,0.69,0.606,0.679,0.711,0.711,0.696,0.689,0.702,0.613,0.618,0.627,0.711,0.695,0.648,0.694,0.624,0.679,0.693,0.677,0.626,0.693,0.692,0.698,0.678,0.704,0.704,0.677,0.574,0.621,0.651,0.687,0.627,0.648,0.682,0.682,0.682,0.698,0.623,0.611,0.713,0.639,0.65,0.681,0.638,0.545,0.685,0.682,0.691,0.693,0.697,0.699,0.689,0.695,0.69,0.697,0.656,0.639,10972,OrganismalFitness,SHOC2_HUMAN,Medium,Human +SOX30_HUMAN_Tsuboyama_2023_7JJK,0.592,0.618,0.628,0.627,0.639,0.634,0.694,0.584,0.596,0.613,0.62,0.659,0.663,0.644,0.687,0.661,0.66,0.625,0.624,0.615,0.576,0.587,0.586,0.599,0.631,0.632,0.596,0.626,0.599,0.611,0.535,0.517,0.609,0.561,0.63,0.62,0.593,0.609,0.615,0.635,0.631,0.631,0.63,0.572,0.598,0.671,0.756,0.671,0.768,0.724,0.674,0.691,0.674,0.672,0.668,0.693,0.67,0.671,0.665,0.68,0.664,0.737,1010,Stability,SOX30_HUMAN,High,Human +SPA_STAAU_Tsuboyama_2023_1LP1,0.748,0.788,0.799,0.788,0.822,0.822,0.411,0.796,0.744,0.744,0.7,0.434,0.456,0.436,0.438,0.427,0.457,0.447,0.468,0.794,0.386,0.48,0.586,0.546,0.428,0.447,0.504,0.674,0.733,0.811,0.736,0.75,0.476,0.461,0.471,0.505,0.744,0.738,0.737,0.82,0.818,0.812,0.387,0.418,0.411,0.445,0.753,0.72,0.871,0.785,0.761,0.756,0.743,0.766,0.753,0.755,0.75,0.745,0.757,0.755,0.768,0.687,2105,Stability,SPA_STAAU,Medium,Prokaryote +SPG1_STRSG_Olson_2014,0.608,0.634,0.497,0.507,0.618,0.627,0.489,0.529,0.451,0.555,0.671,0.629,0.606,0.65,0.626,0.652,0.68,0.649,0.688,0.661,0.637,0.616,0.619,0.612,0.631,0.623,0.614,0.626,0.692,0.634,0.754,0.746,0.536,0.629,0.599,0.65,0.636,0.611,0.654,0.631,0.605,0.656,0.473,0.436,0.624,0.531,0.682,0.644,0.711,0.567,0.717,0.696,0.709,0.733,0.733,0.716,0.745,0.754,0.744,0.739,0.716,0.708,536962,Binding,SPG1_STRSG,Low,Prokaryote +SPG1_STRSG_Wu_2016,0.517,0.605,0.612,0.621,0.614,0.622,0.604,0.541,0.723,0.715,0.693,0.677,0.662,0.645,0.684,0.692,0.708,0.738,0.761,0.653,0.609,0.589,0.58,0.607,0.573,0.643,0.622,0.6,0.6,0.631,0.702,0.664,0.493,0.619,0.567,0.605,0.625,0.611,0.627,0.64,0.63,0.633,0.572,0.508,0.617,0.561,0.604,0.594,0.725,0.636,0.718,0.7,0.696,0.73,0.699,0.693,0.732,0.711,0.734,0.715,0.73,0.68,149360,Binding,SPG1_STRSG,Medium,Prokaryote +SPG2_STRSG_Tsuboyama_2023_5UBS,0.767,0.811,0.804,0.823,0.84,0.839,0.704,0.705,0.818,0.793,0.785,0.752,0.744,0.719,0.756,0.769,0.792,0.789,0.81,0.8,0.736,0.757,0.744,0.739,0.705,0.771,0.712,0.772,0.796,0.866,0.842,0.835,0.416,0.722,0.766,0.778,0.697,0.718,0.743,0.833,0.836,0.842,0.647,0.564,0.66,0.642,0.761,0.715,0.902,0.837,0.838,0.839,0.851,0.85,0.835,0.853,0.848,0.861,0.847,0.85,0.853,0.827,1451,Stability,SPG2_STRSG,Medium,Prokaryote +SPIKE_SARS2_Starr_2020_binding,0.57,0.595,0.531,0.576,0.624,0.628,0.454,0.682,0.684,0.686,0.475,0.445,0.459,0.454,0.46,0.47,0.471,0.477,0.519,0.659,0.644,0.671,0.665,0.671,0.684,0.655,0.641,0.677,0.644,0.625,0.648,0.67,0.592,0.653,0.652,0.673,0.649,0.649,0.649,0.665,0.662,0.666,0.464,0.479,0.467,0.474,0.766,0.748,0.702,0.584,0.602,0.606,0.602,0.638,0.613,0.637,0.619,0.63,0.599,0.627,0.656,0.56,3802,Binding,SPIKE_SARS2,Medium,Virus +SPIKE_SARS2_Starr_2020_expression,0.607,0.664,0.592,0.651,0.743,0.741,0.476,0.682,0.721,0.735,0.511,0.469,0.484,0.473,0.481,0.492,0.493,0.502,0.538,0.68,0.662,0.693,0.694,0.705,0.696,0.689,0.666,0.703,0.666,0.694,0.654,0.675,0.589,0.672,0.677,0.69,0.681,0.686,0.685,0.732,0.737,0.746,0.49,0.497,0.494,0.502,0.782,0.77,0.736,0.597,0.658,0.668,0.656,0.705,0.68,0.699,0.681,0.691,0.671,0.694,0.735,0.621,3798,Expression,SPIKE_SARS2,Medium,Virus +SPTN1_CHICK_Tsuboyama_2023_1TUD,0.794,0.805,0.81,0.778,0.798,0.787,0.605,0.745,0.766,0.788,0.797,0.816,0.807,0.444,0.797,0.801,0.796,0.857,0.822,0.788,0.782,0.808,0.789,0.782,0.786,0.789,0.814,0.767,0.778,0.787,0.791,0.792,0.711,0.746,0.777,0.766,0.808,0.813,0.804,0.792,0.795,0.791,0.736,0.402,0.723,0.736,0.702,0.693,0.809,0.802,0.824,0.819,0.825,0.822,0.828,0.824,0.818,0.816,0.825,0.825,0.83,0.767,3201,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU,0.678,0.75,0.781,0.788,0.785,0.787,0.529,0.686,0.768,0.782,0.729,0.654,0.714,0.574,0.708,0.745,0.804,0.815,0.753,0.796,0.59,0.701,0.715,0.718,0.64,0.743,0.76,0.724,0.741,0.809,0.786,0.77,0.576,0.571,0.696,0.735,0.727,0.741,0.768,0.791,0.78,0.791,0.582,0.538,0.779,0.771,0.832,0.816,0.813,0.823,0.815,0.786,0.818,0.811,0.813,0.812,0.816,0.804,0.798,0.818,0.824,0.725,707,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88,0.82,0.856,0.869,0.869,0.873,0.87,0.382,0.779,0.844,0.831,0.878,0.842,0.851,0.347,0.853,0.878,0.863,0.877,0.864,0.843,0.419,0.682,0.687,0.762,0.76,0.801,0.825,0.819,0.823,0.865,0.866,0.848,0.686,0.504,0.575,0.678,0.853,0.851,0.855,0.868,0.868,0.865,0.794,0.321,0.871,0.825,0.567,0.81,0.859,0.864,0.867,0.864,0.861,0.874,0.878,0.867,0.862,0.862,0.87,0.87,0.917,0.891,1583,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W,0.711,0.786,0.866,0.866,0.886,0.883,0.788,0.697,0.893,0.893,0.837,0.859,0.876,0.767,0.867,0.881,0.888,0.842,0.867,0.852,0.866,0.846,0.853,0.839,0.845,0.842,0.849,0.841,0.832,0.864,0.887,0.868,0.809,0.825,0.844,0.841,0.858,0.875,0.875,0.878,0.889,0.887,0.69,0.574,0.786,0.769,0.786,0.778,0.823,0.842,0.896,0.886,0.892,0.889,0.897,0.894,0.896,0.889,0.897,0.899,0.887,0.847,1556,Stability,SRBS1_HUMAN,High,Human +SRC_HUMAN_Ahler_2019,0.825,0.825,0.796,0.797,0.82,0.825,0.833,0.78,0.837,0.848,0.813,0.861,0.875,0.775,0.797,0.806,0.856,0.833,0.814,0.815,0.78,0.769,0.766,0.74,0.792,0.804,0.785,0.766,0.715,0.847,0.865,0.855,0.77,0.764,0.771,0.721,0.816,0.819,0.806,0.831,0.832,0.827,0.855,0.749,0.82,0.845,0.697,0.684,0.754,0.562,0.826,0.81,0.813,0.825,0.831,0.826,0.833,0.834,0.832,0.836,0.826,0.797,3372,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,0.748,0.753,0.743,0.753,0.76,0.758,0.755,0.731,0.771,0.768,0.74,0.78,0.79,0.694,0.718,0.725,0.766,0.746,0.74,0.74,0.722,0.711,0.709,0.686,0.721,0.735,0.716,0.709,0.659,0.769,0.787,0.785,0.704,0.698,0.702,0.665,0.74,0.742,0.733,0.761,0.761,0.758,0.766,0.677,0.748,0.761,0.642,0.654,0.688,0.532,0.75,0.732,0.736,0.749,0.75,0.75,0.749,0.753,0.752,0.755,0.75,0.726,3637,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Nguyen_2022,0.725,0.734,0.733,0.738,0.738,0.736,0.737,0.721,0.695,0.732,0.732,0.764,0.777,0.674,0.696,0.706,0.755,0.738,0.731,0.726,0.705,0.696,0.699,0.678,0.709,0.723,0.705,0.698,0.655,0.749,0.769,0.76,0.7,0.689,0.693,0.657,0.722,0.724,0.715,0.741,0.741,0.737,0.757,0.658,0.74,0.756,0.627,0.642,0.671,0.537,0.741,0.723,0.726,0.738,0.741,0.739,0.741,0.745,0.741,0.745,0.739,0.703,3366,OrganismalFitness,SRC_HUMAN,Medium,Human +SUMO1_HUMAN_Weile_2017,0.718,0.703,0.735,0.747,0.778,0.773,0.58,0.755,0.757,0.723,0.753,0.78,0.8,0.645,0.8,0.818,0.802,0.717,0.686,0.808,0.631,0.743,0.769,0.758,0.786,0.732,0.777,0.757,0.684,0.767,0.77,0.763,0.628,0.657,0.788,0.689,0.743,0.805,0.736,0.763,0.809,0.763,0.803,0.548,0.716,0.815,0.808,0.751,0.814,0.757,0.79,0.764,0.797,0.792,0.79,0.802,0.783,0.796,0.786,0.801,0.777,0.817,1700,OrganismalFitness,SUMO1_HUMAN,High,Human +SYUA_HUMAN_Newberry_2020,0.61,0.647,0.668,0.676,0.671,0.676,0.667,0.719,0.703,0.697,0.774,0.775,0.772,0.669,0.7,0.699,0.702,0.748,0.753,0.753,0.683,0.735,0.698,0.697,0.654,0.717,0.731,0.689,0.687,0.755,0.736,0.712,0.579,0.694,0.755,0.72,0.658,0.724,0.692,0.679,0.717,0.697,0.665,0.617,0.719,0.697,0.495,0.634,0.519,0.472,0.648,0.64,0.638,0.657,0.687,0.659,0.678,0.671,0.692,0.67,0.574,0.544,2497,OrganismalFitness,SYUA_HUMAN,Medium,Human +TADBP_HUMAN_Bolognesi_2019,0.555,0.538,0.554,0.552,0.547,0.545,0.62,0.503,0.544,0.542,0.512,0.525,0.524,0.552,0.517,0.516,0.447,0.502,0.533,0.474,0.568,0.532,0.504,0.502,0.582,0.551,0.517,0.525,0.498,0.525,0.554,0.57,0.471,0.595,0.619,0.567,0.571,0.59,0.569,0.556,0.566,0.563,0.538,0.584,0.499,0.528,0.638,0.524,0.59,0.529,0.568,0.508,0.503,0.521,0.503,0.523,0.532,0.502,0.469,0.517,0.518,0.549,1196,OrganismalFitness,TADBP_HUMAN,Low,Human +TAT_HV1BR_Fernandes_2016,0.707,0.64,0.678,0.687,0.721,0.708,0.439,0.774,0.673,0.685,0.616,0.738,0.74,0.479,0.499,0.498,0.522,0.461,0.529,0.646,0.76,0.758,0.775,0.781,0.772,0.68,0.591,0.67,0.649,0.756,0.787,0.779,0.709,0.767,0.649,0.639,0.765,0.688,0.665,0.757,0.709,0.685,0.509,0.465,0.712,0.625,0.65,0.702,0.726,0.64,0.58,0.582,0.598,0.583,0.576,0.585,0.573,0.577,0.571,0.586,0.607,0.581,1577,OrganismalFitness,TAT_HV1BR,High,Virus +TCRG1_MOUSE_Tsuboyama_2023_1E0L,0.837,0.852,0.88,0.885,0.887,0.888,0.883,0.686,0.892,0.918,0.874,0.922,0.933,0.908,0.914,0.94,0.931,0.915,0.929,0.837,0.785,0.843,0.837,0.865,0.854,0.861,0.888,0.884,0.851,0.909,0.899,0.886,0.84,0.777,0.85,0.885,0.895,0.907,0.91,0.897,0.908,0.906,0.905,0.588,0.827,0.901,0.853,0.797,0.928,0.917,0.918,0.907,0.898,0.931,0.928,0.931,0.922,0.93,0.919,0.928,0.934,0.935,1058,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG,0.707,0.76,0.81,0.805,0.811,0.81,0.509,0.561,0.851,0.845,0.836,0.804,0.828,0.576,0.848,0.849,0.84,0.851,0.857,0.831,0.441,0.692,0.695,0.668,0.683,0.717,0.618,0.75,0.795,0.852,0.853,0.843,0.775,0.469,0.721,0.686,0.784,0.816,0.809,0.803,0.825,0.824,0.641,0.547,0.797,0.711,0.796,0.796,0.812,0.868,0.86,0.851,0.866,0.866,0.87,0.856,0.858,0.855,0.858,0.861,0.826,0.775,1279,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT,0.712,0.742,0.774,0.767,0.769,0.769,0.422,0.71,0.795,0.795,0.757,0.762,0.765,0.505,0.76,0.791,0.778,0.776,0.785,0.752,0.643,0.691,0.709,0.707,0.696,0.726,0.69,0.704,0.719,0.779,0.753,0.732,0.602,0.536,0.67,0.682,0.767,0.766,0.763,0.786,0.775,0.774,0.653,0.465,0.781,0.735,0.733,0.813,0.839,0.816,0.772,0.783,0.776,0.772,0.772,0.767,0.774,0.77,0.772,0.777,0.814,0.8,1479,Stability,TNKS2_HUMAN,High,Human +TPK1_HUMAN_Weile_2017,0.622,0.631,0.626,0.629,0.632,0.631,0.547,0.639,0.662,0.652,0.658,0.653,0.675,0.571,0.618,0.644,0.684,0.68,0.706,0.64,0.555,0.575,0.618,0.646,0.569,0.647,0.652,0.635,0.669,0.641,0.642,0.606,0.563,0.571,0.614,0.669,0.632,0.638,0.671,0.64,0.64,0.655,0.578,0.56,0.673,0.601,0.617,0.645,0.633,0.559,0.636,0.655,0.649,0.655,0.663,0.664,0.656,0.658,0.66,0.664,0.652,0.622,3181,OrganismalFitness,TPK1_HUMAN,Medium,Human +TPMT_HUMAN_Matreyek_2018,0.722,0.754,0.773,0.782,0.778,0.787,0.656,0.761,0.786,0.787,0.812,0.785,0.802,0.706,0.751,0.814,0.807,0.756,0.749,0.779,0.694,0.725,0.761,0.766,0.742,0.777,0.757,0.746,0.736,0.805,0.802,0.779,0.708,0.698,0.768,0.719,0.785,0.797,0.765,0.799,0.805,0.783,0.736,0.637,0.797,0.777,0.779,0.788,0.807,0.641,0.782,0.785,0.791,0.776,0.795,0.793,0.795,0.79,0.798,0.798,0.827,0.778,3648,Expression,TPMT_HUMAN,Medium,Human +TPOR_HUMAN_Bridgford_2020,0.723,0.695,0.677,0.655,0.658,0.667,0.765,0.752,0.718,0.721,0.739,0.732,0.739,0.694,0.748,0.704,0.685,0.736,0.678,0.677,0.681,0.737,0.714,0.726,0.749,0.634,0.806,0.78,0.708,0.741,0.751,0.63,0.747,0.756,0.73,0.751,0.776,0.768,0.773,0.75,0.739,0.757,0.709,0.712,0.717,0.738,0.578,0.69,0.681,0.511,0.702,0.676,0.722,0.701,0.684,0.699,0.739,0.643,0.672,0.699,0.749,0.752,562,OrganismalFitness,TPOR_HUMAN,Low,Human +TRPC_SACS2_Chan_2017,0.832,0.841,0.818,0.827,0.829,0.834,0.63,0.804,0.873,0.88,0.858,0.849,0.869,0.689,0.817,0.858,0.864,0.86,0.847,0.835,0.758,0.832,0.8,0.832,0.792,0.798,0.817,0.803,0.799,0.808,0.846,0.808,0.62,0.804,0.829,0.827,0.834,0.848,0.844,0.841,0.854,0.847,0.763,0.539,0.85,0.8,0.674,0.788,0.793,0.564,0.842,0.833,0.838,0.867,0.851,0.85,0.853,0.86,0.847,0.863,0.858,0.836,1519,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +TRPC_THEMA_Chan_2017,0.768,0.78,0.76,0.764,0.76,0.762,0.691,0.748,0.794,0.79,0.772,0.797,0.807,0.661,0.796,0.797,0.794,0.793,0.811,0.773,0.728,0.76,0.762,0.783,0.756,0.763,0.746,0.744,0.787,0.753,0.765,0.732,0.603,0.731,0.784,0.775,0.762,0.784,0.779,0.77,0.783,0.777,0.757,0.558,0.79,0.754,0.64,0.721,0.73,0.52,0.784,0.751,0.765,0.784,0.784,0.757,0.777,0.777,0.765,0.783,0.814,0.794,1519,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +UBC9_HUMAN_Weile_2017,0.697,0.771,0.796,0.801,0.788,0.794,0.477,0.72,0.787,0.791,0.731,0.773,0.79,0.498,0.499,0.716,0.756,0.786,0.803,0.816,0.625,0.727,0.746,0.732,0.734,0.76,0.759,0.747,0.745,0.766,0.757,0.745,0.551,0.63,0.727,0.74,0.678,0.752,0.762,0.749,0.792,0.803,0.648,0.493,0.733,0.774,0.684,0.695,0.754,0.643,0.736,0.727,0.72,0.735,0.73,0.731,0.735,0.739,0.741,0.738,0.715,0.736,2563,OrganismalFitness,UBC9_HUMAN,Medium,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X,0.703,0.811,0.829,0.828,0.834,0.833,0.628,0.78,0.81,0.824,0.815,0.845,0.837,0.751,0.81,0.854,0.843,0.847,0.858,0.818,0.66,0.792,0.795,0.801,0.784,0.803,0.787,0.789,0.807,0.83,0.863,0.852,0.813,0.612,0.615,0.757,0.772,0.769,0.784,0.833,0.831,0.823,0.763,0.484,0.72,0.726,0.805,0.763,0.845,0.755,0.826,0.843,0.838,0.845,0.845,0.84,0.839,0.843,0.842,0.843,0.861,0.813,3622,Stability,UBE4B_HUMAN,High,Human +UBE4B_MOUSE_Starita_2013,0.779,0.799,0.824,0.831,0.842,0.844,0.536,0.594,0.796,0.805,0.762,0.838,0.846,0.817,0.838,0.847,0.834,0.786,0.752,0.798,0.59,0.764,0.749,0.732,0.837,0.8,0.786,0.775,0.731,0.826,0.845,0.825,0.534,0.492,0.58,0.698,0.782,0.749,0.776,0.834,0.828,0.842,0.815,0.499,0.814,0.826,0.694,0.773,0.469,0.54,0.814,0.798,0.832,0.836,0.82,0.834,0.813,0.829,0.826,0.833,0.814,0.791,899,Activity,UBE4B_MOUSE,Low,Eukaryote +UBR5_HUMAN_Tsuboyama_2023_1I2T,0.735,0.832,0.832,0.834,0.837,0.845,0.601,0.665,0.798,0.816,0.813,0.742,0.791,0.627,0.635,0.587,0.588,0.84,0.835,0.795,0.754,0.749,0.777,0.804,0.765,0.784,0.795,0.789,0.801,0.815,0.784,0.717,0.669,0.668,0.758,0.742,0.76,0.786,0.784,0.845,0.846,0.845,0.555,0.552,0.707,0.589,0.784,0.716,0.854,0.82,0.837,0.853,0.858,0.86,0.86,0.864,0.852,0.858,0.849,0.858,0.871,0.826,1453,Stability,UBR5_HUMAN,Medium,Human +VG08_BPP22_Tsuboyama_2023_2GP8,0.583,0.623,0.591,0.609,0.612,0.609,0.618,0.467,0.688,0.681,0.831,0.785,0.79,0.674,0.761,0.808,0.844,0.817,0.85,0.566,0.701,0.765,0.747,0.813,0.715,0.736,0.729,0.752,0.815,0.766,0.782,0.715,0.487,0.652,0.76,0.78,0.659,0.7,0.753,0.655,0.684,0.709,0.737,0.637,0.781,0.752,0.742,0.783,0.792,0.759,0.84,0.821,0.842,0.844,0.842,0.86,0.822,0.84,0.857,0.849,0.819,0.809,723,Stability,VG08_BPP22,High,Virus +VILI_CHICK_Tsuboyama_2023_1YU5,0.687,0.772,0.869,0.894,0.896,0.897,0.552,0.811,0.857,0.874,0.914,0.886,0.896,0.695,0.618,0.904,0.912,0.88,0.817,0.862,0.668,0.757,0.842,0.812,0.793,0.826,0.812,0.792,0.843,0.88,0.919,0.913,0.692,0.683,0.791,0.789,0.812,0.84,0.831,0.894,0.898,0.888,0.626,0.66,0.79,0.795,0.816,0.793,0.922,0.861,0.92,0.921,0.925,0.925,0.919,0.927,0.919,0.922,0.924,0.925,0.909,0.891,2568,Stability,VILI_CHICK,High,Eukaryote +VKOR1_HUMAN_Chiasson_2020_abundance,0.704,0.711,0.712,0.723,0.734,0.736,0.601,0.71,0.764,0.772,0.738,0.742,0.753,0.604,0.707,0.754,0.762,0.759,0.757,0.75,0.626,0.636,0.705,0.732,0.672,0.743,0.74,0.724,0.726,0.752,0.715,0.666,0.554,0.601,0.664,0.764,0.727,0.738,0.784,0.747,0.754,0.779,0.595,0.57,0.749,0.679,0.734,0.744,0.762,0.644,0.779,0.765,0.788,0.795,0.789,0.782,0.783,0.772,0.78,0.789,0.777,0.742,2695,Expression,VKOR1_HUMAN,Medium,Human 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scoreSite-IndependentEVmutationDeepSequence (single)DeepSequence (ensemble)EVE (single)EVE (ensemble)UnirepUnirep evotunedMSA Transformer (single)MSA Transformer (ensemble)ESM-1bESM-1v (single)ESM-1v (ensemble)ESM2 (8M)ESM2 (35M)ESM2 (150M)ESM2 (650M)ESM2 (3B)ESM2 (15B)WavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLGEMMEVESPAVESPAlProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LCARP (38M)CARP (600K)CARP (640M)CARP (76M)MIFMIF-STESM-IF1ProteinMPNNProtSSN (k=10 h=512)ProtSSN (k=10 h=768)ProtSSN (k=10 h=1280)ProtSSN (k=20 h=512)ProtSSN (k=20 h=768)ProtSSN (k=20 h=1280)ProtSSN (k=30 h=512)ProtSSN (k=30 h=768)ProtSSN (k=30 h=1280)ProtSSN (ensemble)SaProt (650M)SaProt (35M)Number of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A0A140D2T1_ZIKV_Sourisseau_20190.6900.6740.5600.5440.6920.6980.4260.5320.7180.7210.4850.4670.4940.4540.4640.4600.5970.6780.6850.6120.6740.6510.6560.6510.6580.6690.6600.6530.6440.7150.6530.6400.4990.6480.6590.6330.6770.6760.6720.6750.6680.6820.4590.4530.5550.4590.6370.6420.6420.5650.6180.6170.6330.6420.6310.6280.6230.6280.6260.6310.5980.5569576OrganismalFitnessA0A140D2T1_ZIKVMediumVirus
A0A192B1T2_9HIV1_Haddox_20180.7610.7240.7270.7390.7760.7790.5020.7710.7760.7780.7440.7670.7780.5020.5100.5190.5390.5620.5780.7520.7670.7750.7750.7740.7690.7700.7540.7650.7670.7690.7960.7790.6810.7630.7640.7780.7740.7750.7790.7830.7820.7860.7210.4920.7670.7300.6850.7480.6290.5710.6080.6260.6380.6440.6160.6250.6270.6100.5990.6310.5920.55112577OrganismalFitnessA0A192B1T2_9HIV1MediumVirus
A0A1I9GEU1_NEIME_Kennouche_20190.4910.5180.5560.5470.5280.5280.4880.5370.5410.5370.5210.5210.5250.4770.4680.4900.5160.5120.5160.5380.4910.5180.5310.5400.5170.5370.5370.5390.5420.5240.5100.5040.5130.5210.5270.5500.5160.5210.5310.5280.5300.5380.4730.4700.5240.4750.5160.5320.5130.5170.5280.5240.5220.5170.5200.5290.5190.5300.5230.5250.5170.491922ActivityA0A1I9GEU1_NEIMEMediumProkaryote
A0A247D711_LISMN_Stadelmann_20210.7270.7330.5680.5340.7160.7170.5150.5260.6550.6650.5560.5580.5580.5420.5530.5640.5490.5570.5550.5340.5180.5160.5350.5470.5010.5430.5140.5350.5610.7300.6680.6720.5370.5320.5220.5380.6610.6570.6660.6360.6310.6410.5360.5140.5500.5560.7130.7240.7380.6830.6400.6680.6560.6730.6630.6810.6570.6690.6320.6700.7180.6451653ActivityA0A247D711_LISMNHighProkaryote
A0A2Z5U3Z0_9INFA_Doud_20160.7450.7440.7510.7690.7820.7830.4990.7580.7550.7550.5590.7690.7860.4950.5040.5290.7660.7590.7580.7080.7420.7770.7680.7820.7210.7690.7650.7570.7620.7740.7600.7150.5400.7410.7710.7700.7670.7830.7770.7870.7970.7960.5020.5000.6730.5070.7230.7510.7390.5910.7580.7520.7810.7770.7780.7780.7840.7760.7880.7850.6050.57910715OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A0A2Z5U3Z0_9INFA_Wu_20140.7530.7660.7340.7490.7690.7770.4970.7250.7540.7610.5790.7410.7660.5200.5310.5320.7420.7520.7540.7350.7290.7600.7550.7680.6810.7380.7470.7310.7480.7670.7310.6970.5470.7300.7580.7710.7660.7810.7840.7870.7940.7940.5190.5200.6260.5280.6880.7000.6890.5760.7330.7260.7430.7400.7410.7450.7500.7430.7440.7500.6070.5882350OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A4_HUMAN_Seuma_20220.7140.7070.7350.7220.6730.6760.6930.5770.6730.6790.6660.6660.7110.7090.7000.7030.7360.7580.6880.6320.6720.6450.6660.6590.7180.6540.6670.6640.6620.7570.6220.5880.7660.7130.6390.6920.7400.6950.7430.7390.7160.7280.7030.6990.7300.6960.6500.6970.4050.5270.7390.7340.7260.7200.7220.7030.7300.6870.6980.7250.7220.70914811StabilityA4_HUMANLowHuman
A4D664_9INFA_Soh_20190.7140.6810.7130.7110.7150.7140.5130.6920.6680.6630.5210.5120.5140.5100.5090.5130.5700.6030.6360.6350.6790.7050.7120.7080.5330.6430.6550.6340.6760.7330.6660.6580.5250.6860.6970.7110.6940.7010.7040.7300.7320.7410.5100.4990.5390.5010.6400.6330.5660.5590.5920.5890.5960.6090.6050.6030.6000.6000.5970.6020.5840.55114421OrganismalFitnessA4D664_9INFAMediumVirus
A4GRB6_PSEAI_Chen_20200.6730.7720.8550.8570.8380.8470.6970.7810.8470.8690.8750.8530.8670.7360.7910.8580.9050.8910.8380.8060.7160.7920.8050.8360.7850.8350.8450.8430.8740.8650.9080.8710.6270.7300.8020.8400.8060.8240.8560.8580.8610.8700.7670.5470.8650.8300.8450.8790.8370.7230.8900.8770.8750.8780.8850.8870.8890.8920.8930.8950.8870.7725004OrganismalFitnessA4GRB6_PSEAIHighProkaryote
AACC1_PSEAI_Dandage_20180.6420.7440.6650.7150.7520.7530.5960.6050.7530.7550.7070.7410.7480.6000.6210.6210.7480.7540.7600.7010.5970.6130.6350.6520.6370.7150.7040.7130.7240.7390.7570.7330.5090.6280.6220.7080.7200.7140.7360.7430.7410.7530.6090.5790.6820.6190.6320.6950.6800.5880.7480.7500.7470.7470.7520.7460.7490.7430.7490.7540.7350.6361801OrganismalFitnessAACC1_PSEAIHighProkaryote
ACE2_HUMAN_Chan_20200.6110.5880.5530.5620.5930.5860.4720.5020.5920.5990.6120.5550.5700.4830.5220.6030.6060.5620.5540.5950.4990.5270.5580.5890.4810.5450.5740.5720.6160.5760.5470.5340.5340.5010.5270.5260.5860.5650.5630.5800.5640.5640.4970.5160.6230.5280.6940.6600.6750.5590.6030.6250.6080.6070.6120.6210.6030.6020.6020.6110.6560.5912223BindingACE2_HUMANMediumHuman
ADRB2_HUMAN_Jones_20200.6720.7180.7590.7630.7540.7640.7380.7550.7710.7710.7730.7700.7750.7120.7290.7450.7550.7600.7660.7680.7630.7680.7650.7540.7700.7720.7760.7730.7580.7790.7590.7150.6490.7730.7710.7590.7730.7770.7740.7730.7750.7740.7450.5940.7710.7640.7160.7480.7360.6160.7520.7530.7610.7620.7590.7590.7570.7640.7560.7640.7880.7687800ActivityADRB2_HUMANMediumHuman
AICDA_HUMAN_Gajula_2014_3cycles0.4700.6970.7280.7400.7270.7400.3610.6220.6720.7090.6800.6650.7140.3390.3730.5720.6500.6490.6210.7760.3920.5570.7080.6960.4480.6720.7130.7040.6310.6350.7100.7110.5630.4710.4960.6600.5890.5910.6820.7160.7110.7370.3680.3680.6570.5680.7350.7170.7190.6890.6720.7160.6530.7330.6580.6590.6590.6600.6440.6670.6510.486209ActivityAICDA_HUMANMediumHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O0.6670.6920.6930.6870.6760.6820.3300.5350.5880.5980.6700.5420.6990.3940.6390.6360.6090.6280.6100.5040.4850.3850.4400.4470.3170.4190.4440.4160.4940.6820.6100.5980.5240.3560.4280.3340.6840.6880.6610.6970.6950.6690.4090.4070.5560.5330.6030.5340.6890.6500.7050.6420.5760.6570.6490.6540.6530.6480.6600.6510.6520.6742972StabilityAMFR_HUMANMediumHuman
AMIE_PSEAE_Wrenbeck_20170.6240.7500.7790.7880.7690.7630.5340.7410.7130.8250.7900.8330.8620.6530.6930.7020.7800.8580.8370.7590.7940.7940.7960.8000.8000.8080.7970.8020.7880.8140.8240.7940.6350.8210.7640.7350.8140.7650.7450.8230.7870.7750.6680.5280.7350.6950.6830.7350.7090.6080.7830.7680.7710.7740.7690.7790.7640.7720.7850.7830.8080.6966227ActivityAMIE_PSEAEHighProkaryote
ANCSZ_Hobbs_20220.7680.7530.7150.7300.7540.7560.7540.7270.7440.7560.7610.7560.7680.7520.7830.7860.7910.7890.7810.5080.7230.7440.7510.7470.7470.7560.7520.7320.7400.7770.7660.7580.6490.7320.7510.7360.7600.7720.7670.7640.7730.7660.7630.6970.7390.7500.7310.7280.7060.5870.7710.7670.7800.7790.7750.7810.7730.7780.7750.7830.7830.7874670ActivityANCSZMediumEukaryote
ARGR_ECOLI_Tsuboyama_2023_1AOY0.6140.6970.7000.7110.7100.7040.6230.6770.7150.7350.6880.6380.6910.6380.6190.7390.7410.7480.7060.7060.6670.7200.7260.6990.6570.7110.7260.7270.7050.7230.7030.6560.5250.6770.6860.7090.6780.6860.7030.7190.7170.7250.6180.6040.7160.6940.8710.8240.8790.8280.7490.7580.7560.7620.7500.7490.7370.7590.7350.7590.7970.7411287StabilityARGR_ECOLIMediumProkaryote
B2L11_HUMAN_Dutta_2010_binding-Mcl-10.8530.6590.8230.8270.7640.8130.6450.7690.6190.6120.6420.5190.6850.6070.5860.6060.6700.6860.6740.6640.6470.5680.6700.7880.5630.6460.7110.7130.7140.8900.7020.6900.6470.6480.6350.6810.7910.7600.7090.8310.8160.7120.6160.5910.6320.6050.6490.6550.7090.5090.6950.6860.7290.6650.6270.7570.6620.6670.7380.7060.6350.621170BindingB2L11_HUMANLowHuman
BBC1_YEAST_Tsuboyama_2023_1TG00.6050.6880.7220.7250.7150.7230.6070.6500.7240.7350.7360.7240.7390.6930.7240.7160.7540.7440.7680.6850.6870.6880.7060.6910.5600.7110.7180.7090.6780.6880.6980.6840.6450.6840.6360.6530.7000.6750.6870.7290.7100.7210.5920.5770.6710.6170.8010.7480.8570.8240.7430.7520.7560.7640.7570.7580.7580.7540.7640.7580.7890.8072069StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU0.6980.7310.7000.7050.7040.7110.5630.6370.7040.7720.7140.6770.7170.5910.6030.6620.7660.7780.7320.7250.5690.6930.6230.7070.6080.7330.6550.7160.7370.8270.7960.7700.5240.6340.6600.7340.7340.7340.7590.7170.7180.7390.5630.5630.6090.5980.7840.7590.8450.7900.7880.7920.7970.8050.7980.8070.7800.7940.7890.7970.7690.6481572StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Deng_20120.6670.7630.7640.7730.7590.7640.5420.6530.7640.7840.7620.7510.7610.6280.6830.7370.7720.7350.6860.7330.6910.6980.7060.7040.7000.7300.7320.7140.6780.7420.7700.7510.5520.6980.7080.6930.7350.7470.7480.7660.7750.7730.6760.5200.7490.7100.7180.7550.7410.6300.7700.7740.7760.7840.7810.7790.7810.7710.7760.7850.7810.7154996OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Firnberg_20140.7230.8590.8680.8760.8520.8630.5620.7290.8590.8770.8580.8360.8560.6970.7730.8300.8760.7950.7190.8320.7840.7790.7600.7580.7990.8270.8310.7900.7210.8450.8890.8880.5880.7780.7610.7320.8220.8150.8110.8650.8640.8640.7690.5190.8550.7990.8100.8630.8510.6670.8710.8670.8700.8820.8820.8820.8850.8700.8800.8880.8840.7964783OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Jacquier_20130.7270.8530.8570.8690.8550.8680.5510.7160.8430.8630.8350.8330.8430.6820.7550.8190.8580.8010.7490.8220.7810.7680.7590.7580.7840.8160.8110.7840.7290.7910.8660.8510.5770.7570.7660.7470.8070.8160.8210.8600.8670.8710.7390.5240.8370.7830.7710.8300.8130.6550.8370.8420.8450.8470.8450.8470.8550.8420.8400.8540.8620.790989OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Stiffler_20150.7280.8620.8750.8820.8570.8680.5630.7300.8630.8810.8650.8460.8670.7040.7820.8390.8830.8020.7240.8370.7930.7860.7570.7610.8090.8350.8380.7940.7240.8510.8970.8950.5870.7870.7580.7290.8300.8150.8120.8710.8680.8690.7760.5180.8630.8070.8160.8670.8540.6750.8750.8660.8720.8820.8820.8840.8860.8730.8820.8900.8910.8054996OrganismalFitnessBLAT_ECOLXHighProkaryote
BRCA1_HUMAN_Findlay_20180.8240.7560.7880.7740.7760.8000.5660.6830.7600.7720.8360.7750.8050.5820.7670.8230.8600.8450.7880.6990.5770.7620.7780.7740.7050.8200.8140.8010.8150.7760.8600.8320.6580.6030.6210.7900.7920.7920.8460.8060.8060.8550.6420.5800.8650.8150.8040.8090.5700.5870.8560.8500.8600.8520.8610.8620.8580.8570.8530.8640.8640.8321837OrganismalFitnessBRCA1_HUMANLowHuman
BRCA2_HUMAN_Erwood_2022_HEK293T0.9080.8160.8660.8610.8500.8750.5880.8930.7020.5560.9060.5930.5260.5290.5280.9140.9400.9040.8990.5840.5150.9210.9230.8610.9280.8880.8960.8870.5870.8600.7850.8500.5580.5490.5810.5900.8220.8170.8250.8330.8190.8370.5900.5530.8920.4920.4800.5030.2790.7600.8710.8990.8960.8780.8760.8740.8680.8390.8410.8850.5000.507265OrganismalFitnessBRCA2_HUMANNaNHuman
C6KNH7_9INFA_Lee_20180.6970.6870.6760.6800.7190.7200.4850.7310.6860.6890.5260.7170.7470.4900.4880.4950.7500.7050.7160.6740.7010.6850.6930.6780.6210.7340.7150.7360.6900.7420.7310.6810.5590.6830.6910.7010.7100.7130.7190.7240.7240.7290.4870.4810.6440.4980.7610.7760.7770.6190.7560.7560.7670.7690.7740.7630.7720.7680.7680.7730.6570.59310754OrganismalFitnessC6KNH7_9INFAMediumVirus
CALM1_HUMAN_Weile_20170.5940.6310.6300.6270.6300.6310.5880.6100.6350.6440.6460.6290.6500.5800.6020.6020.6150.6230.6270.6300.5990.6290.6360.6440.6310.6620.6600.6760.6840.6410.6190.5800.5530.6210.6480.6740.6210.6390.6530.6340.6380.6390.6190.5800.6580.6430.5520.5800.5920.5480.5980.5980.5990.5930.6000.5940.5960.6020.5930.6000.6590.6391813OrganismalFitnessCALM1_HUMANHighHuman
CAPSD_AAV2S_Sinai_20210.6980.6700.6650.6880.6700.6620.7030.7240.6600.6790.5880.6090.6100.6430.6590.6100.6490.5940.5530.6300.6060.6410.6480.6500.6140.6100.6440.6140.7170.7190.5860.5820.5600.6120.6440.7570.6860.6890.7400.6680.6700.7160.5580.5940.6440.5640.7130.7030.6730.6660.6140.6100.6030.6150.6040.6050.6070.6020.6130.6090.6680.62942328OrganismalFitnessCAPSD_AAV2SLowVirus
CAR11_HUMAN_Meitlis_2020_gof0.5510.4640.4640.4670.4590.4660.5910.4030.5190.5270.5220.5890.5480.5450.5560.6530.5700.5700.5830.5450.5640.4750.5110.5250.3650.4780.4980.4760.5180.4510.5120.5140.5930.5240.4710.4250.5180.4920.4500.4880.4750.4420.5570.5160.5170.6230.5890.5240.5920.5480.5970.5930.5950.5740.5760.5810.5750.5840.5890.5870.6540.6432374OrganismalFitnessCAR11_HUMANLowHuman
CAR11_HUMAN_Meitlis_2020_lof0.7520.7110.6890.6790.7130.7210.5410.5090.6540.6520.7610.7700.7650.5240.5520.7400.7830.7940.8020.6040.5720.6980.6760.6580.6050.6860.6900.7270.5980.7070.7500.7220.7000.4900.6750.6440.6780.7070.6840.7110.7170.6970.5390.5120.7460.6610.7250.7570.7420.5820.7890.7850.7870.7860.7810.7920.7790.7870.7950.7930.8030.7162395OrganismalFitnessCAR11_HUMANLowHuman
CAS9_STRP1_Spencer_2017_positive0.5800.5940.5870.5890.5900.5930.5160.5490.5930.5950.5880.5320.5330.5280.5440.5780.5940.5970.6000.5040.5190.5370.5850.5600.5170.5990.5920.5890.5340.5960.6030.5930.5130.5140.5210.5820.5840.5840.5930.5950.5950.5980.5280.5120.5920.5510.5510.5920.5130.5170.5900.5910.5910.5920.5910.5920.5900.5900.5910.5930.5870.5568117ActivityCAS9_STRP1MediumProkaryote
CASP3_HUMAN_Roychowdhury_20200.6940.7760.8140.8180.8220.8250.5320.7350.8250.8330.7990.8050.8180.6370.7920.8280.8240.7830.7510.8130.6100.7480.7570.7790.7560.7810.7850.7570.7840.8090.8040.7730.6210.5370.7390.7650.7680.7820.7970.8210.8160.8240.7090.4920.8130.7840.7070.7800.7510.6380.7810.7870.7910.7870.7910.7850.7970.7850.7870.7970.8330.7621567ActivityCASP3_HUMANHighHuman
CASP7_HUMAN_Roychowdhury_20200.7130.7820.8350.8350.8310.8330.5370.7670.8280.8420.8150.8350.8450.6690.8260.8380.8400.8160.8030.8320.6130.7990.7960.8110.7980.8190.8220.8160.8100.8500.8300.8000.6820.5520.8040.7870.7820.8310.8220.8250.8470.8420.7600.5090.8480.8250.7630.8350.8170.6740.8230.8160.8100.8270.8360.8330.8270.8170.8280.8350.8470.7931680ActivityCASP7_HUMANMediumHuman
CATR_CHLRE_Tsuboyama_2023_2AMI0.7960.8130.8500.8530.8600.8630.8700.8180.8100.7910.8470.8520.8640.7980.8690.8700.8660.8370.8620.5210.8440.8350.8410.8360.8570.8520.8350.8170.8290.8660.8460.8480.7340.8510.8360.8530.8620.8530.8690.8560.8540.8640.7480.7320.6910.7580.7600.7440.8730.7990.8680.8660.8780.8720.8830.8680.8690.8620.8650.8740.8510.8741903StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X0.8540.8920.9040.9060.8910.8990.7480.8320.9180.9210.9110.8930.8880.7850.8600.8980.8970.9050.8980.9120.7690.7780.8660.8670.7900.8520.8510.8520.8840.9020.8990.8680.5160.7670.8340.8690.8770.8810.8950.9030.9000.9080.7720.6630.8280.8430.9050.8930.9590.9290.9290.9280.9300.9270.9330.9310.9300.9280.9260.9320.9020.9512068StabilityCBPA2_HUMANMediumHuman
CBS_HUMAN_Sun_20200.6870.7010.7070.7170.7130.7170.6130.6440.7100.7120.6960.6960.7100.5450.6270.6890.6920.6840.6900.6960.6990.6460.6590.6620.6960.6520.6620.6520.6740.7140.7110.6980.6110.7020.6560.6480.7150.6850.6830.7240.7050.7060.6730.5340.7140.7110.6330.6550.6760.5470.6830.6840.6880.6910.6910.6900.6870.6880.6890.6930.7200.6647217OrganismalFitnessCBS_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K280.7520.7730.8440.8650.8110.8470.3390.8190.8120.8080.8120.8290.8280.3440.8780.8550.8440.8010.8020.8280.7530.8030.7970.7970.7830.7930.7990.7670.7930.8150.8450.8400.7190.7140.7500.7650.8020.7990.8100.8440.8560.8590.8160.3170.6820.7860.5400.5610.7960.7830.8530.8090.8500.8380.8610.8390.8390.8710.8350.8520.8260.8882282StabilityCBX4_HUMANHighHuman
CCDB_ECOLI_Adkar_20120.7050.7490.7570.7770.7670.7750.4700.6190.7060.7150.7450.7010.7490.4840.4770.7230.7610.7460.6290.7560.5140.4710.4310.5710.3960.5380.4840.5000.7130.7430.8170.8070.5580.4890.5070.6570.7430.7290.7500.7630.7530.7710.4970.4680.7470.4950.6500.7410.6700.6480.7430.7280.7330.7430.7430.7430.7430.7550.7680.7510.7460.6101176ActivityCCDB_ECOLIHighProkaryote
CCDB_ECOLI_Tripathi_20160.8090.8610.8720.8860.8670.8760.4960.7090.7860.8070.8510.8080.8430.4980.5020.8260.8650.8710.7570.8700.5280.5120.4470.6240.4250.5670.4820.5530.8410.8610.8910.8700.5770.5050.5970.8030.8440.8450.8800.8690.8700.8900.5070.4620.8500.5170.7270.8060.7400.6660.8470.8220.8220.8440.8440.8430.8370.8480.8690.8510.8490.7341663OrganismalFitnessCCDB_ECOLIHighProkaryote
CCR5_HUMAN_Gill_20230.6340.6370.6380.6390.6340.6380.6490.6440.6620.6680.6750.6640.6700.6510.6700.6740.6680.6620.6640.6700.6750.6730.6570.6590.6750.6700.6650.6730.6600.6740.6440.6190.5570.6750.6760.6780.6780.6820.6850.6580.6580.6590.6680.5900.6760.6700.6330.6470.6380.5850.6500.6470.6520.6520.6490.6520.6570.6570.6570.6570.6680.6716137BindingCCR5_HUMANHighHuman
CD19_HUMAN_Klesmith_2019_FMC_singles0.6230.6170.6120.6210.6330.6320.5740.5800.5970.5990.5910.5980.6130.6030.6200.6000.5990.6070.5890.4920.5970.6000.6280.6170.5930.6480.6160.6070.6300.6470.5990.5740.5810.6060.6310.6090.6300.6360.6280.6420.6440.6390.6100.5680.5940.6130.7280.6830.7200.6080.6440.6480.6430.6620.6540.6530.6580.6470.6530.6590.7550.7103761BindingCD19_HUMANLowHuman
CP2C9_HUMAN_Amorosi_2021_abundance0.7480.7880.8000.8100.8000.8080.7630.7830.8000.8080.7580.7950.8140.7670.8030.8160.8220.8170.8000.8070.7830.7830.8000.7770.7920.7950.7960.7900.7870.8060.7920.7620.6110.7970.7870.7860.8110.8110.8110.8200.8180.8190.7900.5490.7680.8060.7820.7750.8100.6110.8060.8040.8120.8230.8210.8170.8170.8130.8140.8240.8220.8116370ExpressionCP2C9_HUMANHighHuman
CP2C9_HUMAN_Amorosi_2021_activity0.7580.7870.8040.8140.7970.8080.7940.8050.8000.8160.7520.8190.8310.7840.8250.8390.8350.8330.8040.8220.7910.7960.8080.7890.8060.7940.7960.7990.7910.8120.8000.7740.6090.8140.7980.7870.8290.8250.8200.8260.8220.8180.8130.5430.7750.8320.8020.7910.8330.6280.8170.8210.8240.8290.8330.8350.8310.8240.8290.8370.8340.8336142BindingCP2C9_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM0.6950.8310.8030.8100.7970.8050.6530.7910.8170.8210.8420.8590.8790.7280.8860.8280.7810.8210.8200.7900.7560.7750.8360.8450.7410.8570.8490.8340.8350.8330.8180.8110.6700.7040.7840.8300.7790.7980.8310.8150.8280.8400.6740.6210.7540.7670.7850.7760.8390.8600.8460.8460.8420.8580.8600.8610.8530.8680.8420.8570.8480.8733295StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX0.6660.7150.6990.7000.7250.7090.5510.7020.7550.7640.7360.6940.6720.5630.6950.7170.7630.7400.7300.6520.5640.5920.5970.5220.5380.5880.6010.6000.6900.7020.7330.7070.6200.5620.5560.5870.6780.6820.6840.7040.7050.7090.5620.5100.7130.6030.7480.7430.8130.7280.7690.7490.7460.7720.7530.7710.7650.7590.7550.7650.7900.7711580StabilityCUE1_YEASTMediumEukaryote
D7PM05_CLYGR_Somermeyer_20220.7810.8300.8280.8100.8350.8350.5310.7500.8490.8510.7320.5300.5290.5300.5220.5120.5200.5310.5750.7350.5140.5190.5540.5410.5000.5210.5350.6010.6900.8310.8050.8130.5140.5490.5630.5800.7830.7830.7810.8340.8330.8280.4960.5060.4980.5080.6360.6520.7600.7060.7590.7560.7580.7640.7560.7610.7580.7560.7550.7600.6680.60024515ActivityD7PM05_CLYGRLowEukaryote
DLG4_HUMAN_Faure_20210.8770.8220.8340.8160.8340.8380.9040.8530.7910.7950.7810.8010.8340.9090.9210.9050.8220.7640.7410.8540.8120.8180.8090.7910.8340.8320.8090.8040.7680.8390.8550.8480.7770.8160.8520.8170.8610.8880.8670.8540.8650.8490.8270.6130.7080.7930.8390.7410.8680.6680.7680.7000.7980.7740.7860.7700.7820.7700.7790.7780.7830.9136976OrganismalFitnessDLG4_HUMANLowHuman
DLG4_RAT_McLaughlin_20120.8300.7710.7890.8010.8400.8480.8180.7940.8240.8470.8060.8690.8800.7960.8830.8840.8570.8230.7960.8000.7640.7690.7670.7520.7890.7950.7810.7550.7560.8340.8480.8330.6850.7590.7630.7240.8210.8160.8040.8550.8530.8540.8470.5520.7770.8340.7910.7090.8140.5930.7910.7600.8010.7710.8040.7900.7750.7970.8030.8100.8200.8651576BindingDLG4_RATLowEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC0.5740.5800.6010.6100.6010.6250.4240.6680.6600.6720.4960.5390.5310.5020.5670.5810.6300.6470.6810.6040.4400.5000.4930.5080.4980.5330.5000.5110.5900.6470.7220.6990.5220.4690.4670.4860.5930.5890.5930.6140.6060.6080.5220.4510.6000.5300.7870.7820.8370.7830.6830.6750.7380.7300.7200.7180.7090.6890.6820.7160.7590.7231008StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO10.9280.9370.9370.9370.9440.9430.8870.9160.9380.9450.9460.9370.9490.9500.9550.9550.9460.9350.9530.9370.8620.9060.9240.9140.9290.9220.9300.8960.9290.9380.9200.9090.8640.9280.9290.9430.9540.9550.9600.9480.9500.9530.8460.5820.7110.8500.8220.7660.9280.9330.9320.9360.9340.9480.9450.9500.9480.9440.9490.9460.9260.9462264StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y0.7140.7380.7180.7290.7570.7610.5210.7200.6860.6870.7490.7270.7410.6070.7190.7360.7530.7440.7510.7640.6540.6960.6780.6850.6450.6770.6590.6460.6730.7210.7190.7200.5910.5970.6520.6120.7350.7430.7390.7510.7520.7580.6660.4000.7410.7110.6680.6720.7030.7070.7430.7340.7420.7460.7380.7480.7290.7400.7470.7420.7670.7522915StabilityDOCK1_MOUSEHighEukaryote
DYR_ECOLI_Nguyen_20230.7570.8510.8630.8690.8680.8720.4970.8340.8610.8550.8740.8790.8870.5640.8540.8740.8860.8860.8780.8730.7390.8050.7320.7780.8280.8310.8430.8550.8170.8760.8860.8730.6240.8420.7580.7470.8650.8110.8210.8800.8560.8600.8390.4990.8770.8550.6650.7930.7480.5760.8410.8170.8260.8330.8360.8370.8430.8430.8590.8490.8610.8082916OrganismalFitnessDYR_ECOLIHighProkaryote
DYR_ECOLI_Thompson_20190.7450.7920.7900.7920.7960.7970.4570.7200.7880.8060.7820.7640.7720.5320.7240.7730.7890.8070.8060.7920.6440.7460.6670.7030.7340.7580.7890.7780.7660.7900.7940.7800.6050.7490.7120.7110.7730.7570.7650.7970.7950.8000.7180.4800.7700.7510.6210.7280.6810.5540.7630.7390.7440.7420.7570.7520.7640.7590.7710.7630.7920.7222363OrganismalFitnessDYR_ECOLIMediumProkaryote
ENV_HV1B9_DuenasDecamp_20160.7470.7440.6880.7420.7650.7610.5030.7040.7320.7510.7220.7910.7770.5330.5150.4960.4700.5070.5390.7510.7530.7400.7680.7710.7070.7830.7690.7460.7470.7670.7680.7570.6740.7670.7670.7710.7740.7760.7720.7720.7730.7740.7190.4510.7480.7590.7360.7130.7310.6510.6430.6950.7040.6760.6550.6530.6670.6590.6440.6820.6070.587375OrganismalFitnessENV_HV1B9MediumVirus
ENV_HV1BR_Haddox_20160.6690.6520.6600.6610.6710.6730.4990.6590.6740.6730.6490.6590.6690.4950.4950.4970.5190.5290.5770.6670.6760.6800.6870.6820.6730.6800.6800.6760.6820.6770.6650.6490.5990.6730.6820.6810.6810.6850.6840.6850.6870.6840.6150.4900.6600.6490.5970.6160.5530.5340.5850.5810.5940.6070.5870.5940.5950.5900.5890.5980.5770.54112863OrganismalFitnessENV_HV1BRMediumVirus
ENVZ_ECOLI_Ghose_20230.5710.5690.6100.6100.6190.6180.5030.6160.6080.6140.6060.6250.6360.6070.6150.6180.6130.6000.5840.6140.6050.6010.6320.6250.6060.6110.6260.6210.6150.6180.5980.5950.5590.6020.6120.6210.6180.6230.6300.6210.6250.6250.6110.5660.6120.6100.5280.5960.5820.5250.6230.6200.6040.6170.6390.6250.6150.6250.6270.6270.5940.6371121ActivityENVZ_ECOLIHighProkaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M0.8390.8980.8990.9020.9140.9150.3090.9060.9480.9520.9440.9460.9530.2860.9410.9540.9630.9450.9400.9230.8310.8600.8990.8890.8800.8980.9050.9130.9200.9540.9400.9360.8860.8600.8590.8790.9080.9080.9160.9190.9180.9170.8500.3410.8570.8610.8300.8280.9430.9090.9570.9540.9560.9550.9610.9580.9580.9580.9550.9600.9490.9441960StabilityEPHB2_HUMANHighHuman
ERBB2_HUMAN_Elazar_20160.6970.6910.6410.6420.6340.6450.7170.6940.7020.7150.7190.7300.7380.7370.7320.7350.7060.6920.6520.5250.7210.7290.7520.7250.7400.7470.7780.7820.7340.6920.7160.5430.7320.7680.7280.7360.7470.7310.7370.7280.7230.7200.7390.7350.7640.7340.7180.7300.4070.5270.6980.6930.7200.7020.6990.6990.7090.7170.7210.7140.7560.741326ExpressionERBB2_HUMANLowHuman
ESTA_BACSU_Nutschel_20200.6370.6940.7020.7110.6960.6940.5770.6650.6690.7070.6590.6450.6620.5760.6210.6290.6450.6410.6640.6540.5480.5910.6340.6480.6280.6200.6530.6340.6960.6900.7240.7110.5340.5790.6300.6410.6550.6650.6730.7000.7030.7030.5880.5520.6370.6230.7830.7430.7740.6810.6770.6640.6730.6660.6820.6700.6660.6770.6750.6790.6910.5942172StabilityESTA_BACSUHighProkaryote
F7YBW8_MESOW_Aakre_20150.5370.6680.6680.6890.6830.6840.5170.6330.6680.6720.6890.6690.6740.4750.5130.5310.6710.6650.6870.6700.4790.4570.4580.5030.5550.5590.5010.6330.6770.6590.6980.6870.5180.4980.4600.6850.5370.5150.6840.6640.6580.6910.4880.4790.5990.4920.4980.6160.5280.5190.6870.6920.6800.7000.6960.6970.6860.6920.6950.6940.6260.4589192OrganismalFitnessF7YBW8_MESOWHighProkaryote
FECA_ECOLI_Tsuboyama_2023_2D1U0.7050.7270.7460.7510.7460.7420.5100.6770.7010.7450.7750.6370.7270.5100.7350.7840.7800.7640.7110.7460.5770.6480.6970.6630.5810.7540.7430.6410.7170.7790.7190.6840.6570.5260.5620.5770.6920.6870.6650.7290.7270.7160.7010.5000.7560.7290.8010.7830.8160.7810.7830.7610.7760.7750.7690.7870.7680.7630.7700.7770.8140.8111886StabilityFECA_ECOLIHighProkaryote
FKBP3_HUMAN_Tsuboyama_2023_2KFV0.7270.7070.7590.7600.7630.7710.5890.6750.6300.6290.6020.5940.5910.5930.5920.5830.6000.6400.6940.6610.6220.6220.6270.6320.5850.6220.5990.6250.6280.7520.6730.6760.4640.5660.6020.6240.7180.7210.6880.7650.7670.7300.5990.6020.5950.6130.8560.8240.8550.8190.6600.7160.7380.7110.7100.7160.6930.6970.6500.7120.7980.6841237StabilityFKBP3_HUMANMediumHuman
GAL4_YEAST_Kitzman_20150.6460.7110.7120.7480.7330.7490.6770.4960.7450.7710.7820.7400.7430.6850.7120.7590.7950.7930.7880.7490.6720.6910.6950.6950.6910.7160.7410.7270.7710.7920.7920.7630.6360.6550.6690.6700.7570.7520.7540.7420.7390.7370.7080.6530.7770.7310.6490.7630.6700.5680.7730.7690.7670.7670.7790.7780.7700.7680.7670.7770.7670.7191195OrganismalFitnessGAL4_YEASTMediumEukaryote
GCN4_YEAST_Staller_20180.6320.6330.6290.6300.6280.6280.5840.6110.6310.6310.6270.6440.6470.6620.6440.6380.6490.6380.6310.5990.5740.5810.5720.5720.5630.5510.5320.5590.5770.6220.6370.6340.5190.5730.5710.6410.6350.6350.6470.6350.6350.6450.5490.5890.5720.5560.5680.5910.6120.5870.6240.6200.6200.6170.6230.6220.6210.6210.6230.6220.6240.6272638BindingGCN4_YEASTLowEukaryote
GDIA_HUMAN_Silverstein_20210.7190.7200.7260.7240.7270.7270.5910.6960.7390.7360.6940.7060.7260.5820.6180.7160.6980.6840.7040.7010.6260.6880.6860.7010.6590.7180.7080.6850.6960.7040.7120.6920.6270.6570.6900.6710.7150.7190.7130.7260.7330.7290.6160.5600.7170.6200.6710.7090.6820.5440.6950.6690.6970.6840.6910.7000.6880.6960.6900.7010.7290.6591154OrganismalFitnessGDIA_HUMANLowHuman
GFP_AEQVI_Sarkisyan_20160.8890.8840.9000.9010.9030.9030.5400.8830.8940.8910.8160.5650.5650.5520.5830.5640.5720.5960.6790.8580.5550.5720.6140.5650.5320.6150.6840.8840.8860.9040.8650.8700.5410.5450.6160.8780.8890.8900.9020.9060.9070.9190.5150.4950.5250.5250.7960.7920.9250.8600.8560.8600.8660.8710.8670.8660.8640.8650.8540.8650.8720.76951714ActivityGFP_AEQVILowEukaryote
GLPA_HUMAN_Elazar_20160.6020.5460.6030.6010.5940.5830.7110.7990.6820.6720.7410.7560.7540.7280.7460.7700.7510.7270.7570.5830.7430.7550.7370.7610.7390.7520.7610.7650.7980.6720.7460.6380.6380.7610.7370.7610.7500.7360.7480.7320.7170.7400.7580.7490.7620.7270.7220.7670.7060.6170.7900.7790.7820.7650.7760.7470.7760.7480.7700.7810.7750.766245ExpressionGLPA_HUMANLowHuman
GRB2_HUMAN_Faure_20210.7270.7870.7840.8010.7990.8020.7770.7480.7960.7680.7920.7470.7810.7970.8320.8470.8580.7920.8160.7990.8070.7900.7860.7520.8010.7850.7530.7930.7510.7800.7410.7410.7570.8010.7860.7270.7900.7840.7450.8120.8120.7960.8260.6730.7890.8160.8790.7900.8900.7850.8450.8600.8550.8560.8600.8490.8490.8590.8480.8610.8060.83763366OrganismalFitnessGRB2_HUMANMediumHuman
HCP_LAMBD_Tsuboyama_2023_2L6Q0.6520.7150.5720.5860.6790.6820.6290.7070.7260.7490.8290.7240.7470.6440.7040.7830.8350.7900.8010.6200.6440.6520.6700.6770.6600.6690.6050.6660.7650.8020.8240.7580.5790.6330.6430.7530.7110.7160.7730.6870.7140.7650.6530.6430.8100.6860.8290.8610.8640.7860.8400.8460.8460.8430.8510.8530.8440.8540.8530.8570.8780.7991040StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM0.8460.8730.8290.8440.8850.8870.6670.8230.8580.8590.8300.6960.7200.6420.6560.9170.8840.8600.8460.8690.6740.8150.8660.8590.7920.8630.8560.8410.8480.8870.8730.8710.6200.6750.6970.7430.9070.8860.9100.8950.8950.9010.5960.5810.9020.6680.7550.8690.8570.7720.8680.8800.8990.8890.8930.9140.8870.8970.8880.8960.8920.7495586StabilityHECD1_HUMANMediumHuman
HEM3_HUMAN_Loggerenberg_20230.7120.7070.7010.7020.7100.7090.5600.5660.7160.7220.6940.6880.6960.5690.6900.7020.6940.7130.7200.5620.6690.6880.6920.7000.6760.6940.6910.6930.7090.7200.7150.7110.5470.6890.6940.7030.7130.7190.7310.7150.7190.7260.6600.5730.6900.6850.6660.6840.6590.5800.6780.6690.6760.6810.6880.6860.6860.6860.6830.6880.7160.7095689ActivityHEM3_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_20190.7680.8090.8250.8220.8010.8000.5890.6450.7620.7850.7610.7340.7730.5340.5680.7490.7280.7630.7690.7550.6860.7340.7500.7770.7240.7790.7710.8020.8100.8040.7240.7140.5830.7340.7580.8490.7830.7830.8650.8290.8120.8350.5870.5240.6660.5070.7290.7500.8200.7220.7460.6850.7180.7370.7440.7230.7260.7480.7380.7320.7950.632496137OrganismalFitnessHIS7_YEASTMediumEukaryote
HMDH_HUMAN_Jiang_20190.7410.7000.6830.6880.7200.7180.5750.6270.6250.6210.6260.6640.6490.5140.4980.6540.7550.6820.6830.6400.7270.5660.5720.5950.7190.5880.5720.5940.5920.7350.6990.6940.6100.6550.6170.6000.7190.6640.6570.7140.6720.6700.5810.5260.7350.7500.6600.7210.3970.5550.7450.7450.7410.7470.7430.7440.7360.7490.7400.7480.7650.71616853OrganismalFitnessHMDH_HUMANLowHuman
HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O20.6560.6740.6800.6800.6770.6760.6060.6840.7130.7170.6770.7060.7250.4820.5500.6190.6220.6440.6430.5080.6810.6830.6940.6870.6630.6730.6990.7000.7030.6920.7110.6850.6110.6640.6600.6660.6770.6740.6770.6850.6830.6860.5080.4850.6910.6930.5230.6310.5420.4930.6140.6250.6140.6200.6190.6150.6270.6170.6110.6210.6830.6512252OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Flynn_20190.7580.7720.7960.8030.7950.7950.6200.7490.8040.7960.7720.7900.8070.5150.5700.6340.7090.7650.7670.7860.7800.7900.7810.7940.7880.7980.8060.7810.8110.8060.8260.8140.6470.7780.7810.7810.7950.7990.7980.8070.8110.8100.5960.4510.7730.7250.6270.7260.5740.5380.7120.7050.7140.7220.7120.7160.7190.7160.7050.7190.7800.74013294OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Mishra_20160.8820.8150.8910.9000.8970.8990.7940.8120.8370.8390.8590.8860.8930.6880.7800.8190.8730.8780.8670.8420.8500.8430.8390.8660.8660.8590.8720.8310.8580.8930.9030.8950.7170.8450.8440.8510.8810.8800.8820.8980.8980.8990.8490.4550.8430.8800.6230.7530.6450.5330.8550.8380.8290.8600.8600.8530.8590.8650.8550.8620.8420.8414323OrganismalFitnessHSP82_YEASTMediumEukaryote
HXK4_HUMAN_Gersing_2022_activity0.7620.7800.7590.7660.7610.7660.6180.7090.7790.7880.7480.7580.7710.5900.6490.7670.7760.7750.7600.5220.7460.7500.7430.7320.7490.7560.7460.7470.7220.7760.7700.7440.6010.7540.7340.7020.7700.7620.7430.7770.7750.7670.6640.5100.7750.7560.6840.7380.7040.5880.7710.7680.7600.7660.7660.7720.7680.7720.7710.7740.7800.7518570OrganismalFitnessHXK4_HUMANMediumHuman
HXK4_HUMAN_Gersing_2023_abundance0.6940.7190.6970.7130.7080.7110.5340.7000.7340.7460.7030.6970.7120.5800.6100.7010.7240.7310.7130.7400.6690.6710.6860.6860.6680.6810.6880.6860.6900.7230.6810.6590.5950.6810.6750.6860.7050.7050.7130.7110.7160.7230.6010.5470.7120.7030.7340.7280.7320.5940.7190.7270.7250.7250.7300.7270.7280.7240.7240.7290.7570.7018396ExpressionHXK4_HUMANMediumHuman
I6TAH8_I68A0_Doud_20150.6770.6640.6390.6380.6850.6830.4950.6590.6450.6630.5030.5070.5060.5090.5100.5010.5070.5030.5430.6080.6630.6690.6910.6910.5040.5080.5540.4970.6550.6950.6270.6300.5720.6520.6660.6730.6650.6760.6770.6930.7010.7040.5020.5110.5060.5090.6080.6040.6230.5490.5330.5520.5640.5630.5540.5510.5420.5450.5270.5510.5210.5049462OrganismalFitnessI6TAH8_I68A0MediumVirus
IF1_ECOLI_Kelsic_20160.6950.7750.8230.8230.8160.8200.5780.7120.6490.6410.8030.8150.8260.6000.7560.8170.8320.8180.8000.7930.6730.7290.6950.7140.7460.7480.7510.7350.7450.7220.7840.7540.6430.7560.7740.7870.7730.7800.7850.8120.8160.8200.6930.5630.8210.7820.6920.7970.7810.6450.7980.7900.8080.8140.8040.8090.7890.8130.8130.8190.8520.7831367OrganismalFitnessIF1_ECOLIHighProkaryote
ILF3_HUMAN_Tsuboyama_2023_2L330.7180.7580.8270.8210.8110.8140.6040.7410.7970.8130.7470.7570.7720.7180.7750.8380.7220.7100.6490.8370.6210.6460.6850.7260.7350.6720.7510.6960.6970.8200.7730.7580.5290.6140.6700.7380.7470.7570.7870.7920.7830.8210.7550.6200.7450.8130.8340.7900.8510.7520.6930.6700.7270.7050.7260.7580.7330.7080.7790.7410.7370.7831329StabilityILF3_HUMANHighHuman
ISDH_STAAW_Tsuboyama_2023_2LHR0.5400.5480.5990.5940.6120.6120.6620.5710.6800.6790.7380.7100.7190.6870.6950.7240.7180.7400.7240.5890.6530.6010.6140.6290.6490.6600.6550.6420.6850.6690.7180.6910.6220.6360.6200.6360.6250.6050.6160.6290.6060.6160.6390.6110.6650.6520.7880.7430.7730.7520.7250.7170.7280.7240.7260.7280.7150.7200.7240.7260.7910.7491944StabilityISDH_STAAWHighProkaryote
KCNE1_HUMAN_Muhammad_2023_expression0.6410.6480.6220.6280.6360.6200.6700.5690.6340.6360.6910.6790.6660.6630.6510.6820.6520.6350.6570.6490.6800.6940.6250.6060.6900.6280.6720.6330.6430.6470.6360.6030.3930.6560.7080.6590.6620.6730.6700.6570.6620.6660.6730.6520.6670.6930.6490.6500.6440.5530.6370.6250.6380.6140.6170.6120.6420.6360.6390.6330.6600.6782339ExpressionKCNE1_HUMANMediumHuman
KCNE1_HUMAN_Muhammad_2023_function0.6930.7430.7390.7500.7350.7560.5990.7500.7520.7520.6670.7280.7860.5880.5670.7970.7590.7210.6840.6070.6200.6380.7190.7700.6290.8110.7930.7960.7910.7400.7930.7490.4950.5750.6800.8160.7220.7410.8120.7560.7700.8210.5900.5860.7660.5770.5820.7120.5830.5380.7660.7660.7570.7600.7640.7550.7680.7690.7700.7730.8190.6032315ActivityKCNE1_HUMANMediumHuman
KCNH2_HUMAN_Kozek_20200.7380.7300.6710.6700.6360.6350.7550.5880.6670.6780.6440.6070.6090.6190.5930.6080.6230.6250.6220.7390.7410.7660.7510.7290.7620.7480.7360.7390.7350.7580.6930.6300.6780.7380.7670.7490.7500.7820.7710.7390.7670.7590.7280.5990.5900.6520.6050.4770.4260.5470.7980.7190.7660.7480.7380.7390.7600.7460.7490.7620.6480.738200ActivityKCNH2_HUMANMediumHuman
KCNJ2_MOUSE_Coyote-Maestas_2022_function0.6390.6750.6750.6820.6860.6890.5310.6250.6680.6650.6960.6850.6910.5310.6520.7000.7020.7040.7060.5860.6800.6570.6310.6270.6830.6740.6620.6780.6180.6980.6940.6860.5780.6690.6520.6210.6830.6720.6520.6910.6860.6770.6100.4930.6750.6820.6010.6500.6230.5400.6940.6860.6920.6910.6930.6890.6940.6920.6930.6960.6960.6406963ActivityKCNJ2_MOUSEMediumEukaryote
KCNJ2_MOUSE_Coyote-Maestas_2022_surface0.6770.6550.6140.6200.6540.6550.5570.6020.6240.6240.6390.6230.6300.5900.6690.6630.6520.6510.6580.6090.6040.6060.6070.6220.6140.6210.6190.6220.6210.6660.6320.6170.5750.6020.6130.6140.6420.6500.6560.6460.6540.6580.6250.5340.6150.6370.6230.6120.6220.5670.6370.6300.6410.6370.6440.6350.6460.6370.6340.6410.6490.6556917ExpressionKCNJ2_MOUSEMediumEukaryote
KKA2_KLEPN_Melnikov_20140.6580.8060.7560.8590.8500.8560.6230.7560.8130.8540.8270.8420.8580.6320.6550.7820.8440.8700.8760.7940.6740.7480.8050.8140.7060.8370.8350.8320.8690.8680.8700.8450.5900.6500.7870.8390.7640.8090.8430.8420.8550.8680.6530.5510.8260.7330.7490.8290.8060.6400.8380.8280.8340.8300.8300.8350.8250.8370.8420.8430.8680.6854960OrganismalFitnessKKA2_KLEPNHighProkaryote
LGK_LIPST_Klesmith_20150.6400.7060.7300.7340.7300.7370.5600.7170.7280.7720.7150.7560.7710.5880.6540.6850.7500.7820.7790.7420.6390.6930.7220.7360.6770.7340.7510.7290.7650.7550.7560.7180.5650.6490.6900.7670.6800.6960.7550.7300.7360.7590.5840.5300.7320.6760.6890.7430.7350.5700.7470.7420.7470.7460.7470.7520.7510.7490.7510.7550.6990.6627890ActivityLGK_LIPSTMediumEukaryote
LYAM1_HUMAN_Elazar_20160.6690.6670.6100.5940.6490.6530.6750.5800.6980.7010.6980.6730.6700.6430.6720.6600.6370.6910.7320.6530.6810.6870.6820.6750.6740.6790.6690.6420.6600.6880.6410.5830.5980.6720.7050.6640.6840.7050.6790.6770.6910.6670.6420.6580.6440.6650.5860.6310.5900.5560.6080.6530.6960.6640.6350.6460.6750.6720.6300.6620.7090.690359ExpressionLYAM1_HUMANMediumHuman
MAFG_MOUSE_Tsuboyama_2023_1K1V0.8540.8740.8660.8660.8610.8610.7400.9040.8750.8660.9000.7490.8860.7900.7950.8230.8290.8590.7620.8680.7520.8370.8480.8720.8170.8580.8300.8590.8390.8350.8630.8550.7760.6860.8510.8550.8440.8850.9050.8690.8940.9110.7180.5250.7810.6760.8590.7280.8900.9050.9170.9030.9070.9160.9100.9150.9120.9160.9110.9150.9220.9341429StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV0.8080.9040.8850.8950.9170.9210.4660.8160.8700.9080.9070.7570.8050.4280.5390.9150.8910.8820.9100.9020.4940.5560.8470.8640.6260.8360.8240.8420.9110.9230.9060.8780.8400.3330.5540.5440.8230.8720.8600.8990.9190.9130.4830.3950.8580.7490.7500.8480.9030.7920.9250.9100.9240.9100.9110.9080.9120.9190.9230.9220.9270.8842116StabilityMBD11_ARATHMediumEukaryote
MET_HUMAN_Estevam_20230.7890.8450.8390.8560.8680.8690.8270.8250.8460.8550.8690.8470.8530.7890.8260.8430.8710.8760.8750.8700.8320.8190.7970.8110.8340.8290.8250.8080.7860.8490.8600.8330.6770.7980.8140.8280.8260.8400.8550.8550.8590.8630.8420.7230.8590.8630.7280.7680.8150.6240.8380.8450.8420.8440.8520.8470.8500.8520.8480.8540.8640.8375393ActivityMET_HUMANMediumHuman
MK01_HUMAN_Brenan_20160.6140.6220.6380.6410.6340.6360.6270.6050.6250.6180.5420.6070.6170.6050.6210.6220.6150.6210.5970.6140.6310.5820.5620.5300.6130.5690.5520.5610.4860.6300.6130.6330.5680.6100.5540.5240.6230.5860.5720.6370.6270.6260.6210.5790.5940.6140.5410.5140.5820.5020.6020.6010.5940.6040.6080.6030.6030.6060.6100.6080.6020.6166809OrganismalFitnessMK01_HUMANMediumHuman
MLAC_ECOLI_MacRae_20230.6070.6860.7230.7280.7200.7210.4890.6840.7160.7200.7080.7200.7280.4890.6140.6310.6940.6910.7060.7080.5380.6920.6970.7100.6590.7060.7080.7050.7250.7160.7370.7260.5130.5660.6620.6510.6340.6670.6600.6980.7080.7070.5840.4790.6620.6180.5140.6050.5710.4910.6660.6460.6540.6550.6650.6640.6540.6600.6740.6640.6100.5994007OrganismalFitnessMLAC_ECOLIMediumProkaryote
MSH2_HUMAN_Jia_20200.8270.8610.8330.8410.8520.8540.6960.7940.8610.8700.8090.8390.8570.7030.8240.8580.8140.7750.6780.8430.7620.7920.7750.7620.7880.8040.8150.8040.7850.8590.8280.8040.6970.7530.7950.7730.8210.8370.8290.8490.8560.8560.7610.5990.8430.8110.7840.8150.5620.5940.7800.7550.7690.7850.7960.7760.7710.7790.8020.7950.8650.84216749OrganismalFitnessMSH2_HUMANMediumHuman
MTH3_HAEAE_RockahShmuel_20150.7060.8000.8360.8420.8300.8350.6860.8230.8340.8450.8110.8540.8560.6310.6970.7170.7750.8220.8350.8400.6800.7470.8180.8380.7640.8360.8550.8430.8620.8300.8470.8280.6710.6920.7640.8320.7220.7630.8240.8150.8290.8470.6920.5200.7560.6920.7130.7860.7770.6000.7860.7700.7810.7880.7830.7900.7770.7850.7840.7900.8010.7071777OrganismalFitnessMTH3_HAEAEMediumProkaryote
MTHR_HUMAN_Weile_20210.6070.6180.6080.6110.6110.6100.5880.5750.6320.6320.6760.6290.6390.5750.6850.7240.6610.6250.6310.6140.7070.5640.5970.6210.7000.6010.6190.6040.6390.6290.5920.5770.5590.7100.6410.5950.6680.6410.6120.6600.6360.6180.6200.5470.6690.6830.6080.6530.6740.5430.6470.6310.6360.6350.6390.6450.6530.6450.6600.6480.6470.67012464OrganismalFitnessMTHR_HUMANLowHuman
MYO3_YEAST_Tsuboyama_2023_2BTT0.5840.6350.6890.7000.7110.7270.5740.6230.7320.7260.7830.7600.7610.5920.7260.8150.8210.7740.6450.6750.6750.6180.6650.6570.6760.6470.6990.6640.6840.6500.6650.6130.5800.7210.6740.6440.7040.6770.6590.7230.7190.7110.5660.5260.7200.6540.7150.7370.7700.6960.7940.7790.8030.7880.7930.7940.7850.7910.7860.7940.8050.7773297StabilityMYO3_YEASTHighEukaryote
NCAP_I34A1_Doud_20150.6870.6700.6730.6720.6850.6860.5080.6680.6860.6770.5140.5170.5180.5190.5200.5160.5210.5220.5530.6460.6770.6870.7020.7040.5170.5290.5570.5200.6770.7000.6410.6450.5650.6800.6860.7100.6990.7040.7170.7160.7150.7240.5170.5150.5200.5190.6310.6350.6390.5590.5650.5850.5880.5940.5880.5870.5800.5830.5660.5870.5680.5439462OrganismalFitnessNCAP_I34A1MediumVirus
NKX31_HUMAN_Tsuboyama_2023_2L9R0.6890.8000.8590.8580.8700.8600.8160.8610.8670.8730.8640.8740.8700.8570.8240.8680.8580.8330.8560.8460.8580.8420.8440.8420.8410.8350.8520.8400.8610.8760.8280.8140.8470.8170.8330.8530.8410.8510.8660.8670.8690.8750.7700.7170.7050.7510.8230.7210.8720.8680.8790.8820.8850.8880.8890.8790.8850.8830.8780.8860.8660.8732482StabilityNKX31_HUMANHighHuman
NPC1_HUMAN_Erwood_2022_HEK293T0.8630.8760.8440.8480.8800.8780.6050.7610.8980.8910.8710.6710.7420.5920.7160.8180.8730.8760.8740.5540.6170.7300.7070.7070.7270.7580.8350.7390.7590.8890.8610.8030.5440.6030.7870.7990.8360.8590.8670.8710.8790.8850.6520.5510.8740.7990.7520.8710.5150.6010.8750.8620.8670.8670.8670.8580.8700.8820.8720.8770.8640.712637ActivityNPC1_HUMANLowHuman
NPC1_HUMAN_Erwood_2022_RPE10.8700.7730.6820.6850.8000.8100.6630.7210.7630.8060.8330.5380.5820.5810.6100.7630.7890.7750.8110.6750.7950.6870.7150.6480.6900.6890.6890.6350.7100.7950.8830.7950.7800.6880.5840.7420.8670.7700.8470.8380.7790.8110.6730.6810.9120.6090.7310.8170.4400.6020.7700.7550.7710.7070.7670.7560.7580.8070.7990.7690.8550.72063ActivityNPC1_HUMANLowHuman
NRAM_I33A0_Jiang_20160.8020.7750.7270.7170.7990.7950.5300.7180.8090.8140.4740.5980.7450.4500.4440.5050.6150.7520.7950.6860.7690.8020.7810.7770.5490.7740.8090.7470.8100.8330.7290.7320.4560.7470.7670.7720.8030.8220.8230.8200.8280.8230.4280.4320.4250.4230.7250.7060.7310.5920.6360.6480.6530.6770.6630.6790.6660.6600.6340.6620.6440.567298OrganismalFitnessNRAM_I33A0LowVirus
NUD15_HUMAN_Suiter_20200.6440.7500.7970.8130.8090.8110.5040.7110.8320.8660.8380.8390.8630.6770.7350.7530.8010.8330.8400.7770.6710.7500.8080.7980.7360.8280.8260.8100.7880.8130.8410.8110.5940.6980.7390.8160.7280.7440.8300.8090.8090.8380.7240.5110.8310.7440.7530.8050.7790.6670.8060.7850.7980.8130.8040.8140.8170.8130.8130.8170.8820.8152844ExpressionNUD15_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL0.6920.8040.8020.7960.8140.8210.6360.7300.8500.8210.7930.6540.6670.6560.6980.7010.7440.7670.7580.7980.7050.7960.8100.8120.6570.7620.7290.7880.8070.8390.8650.8940.5870.6820.7030.7070.7580.7460.7560.8320.8100.8250.6790.6090.6830.6700.8510.8280.8810.8440.8880.8850.8840.8970.8980.9070.8900.8890.8960.8970.8520.7802028StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI60.7520.7600.7670.7740.7830.7740.6530.7580.7820.7760.7490.7220.7460.6930.7960.7630.7840.7890.7880.7610.6820.7050.7000.7270.7300.7220.7370.6990.7330.8030.7410.7390.6600.6570.6690.7450.7430.7440.7710.7630.7650.7750.6810.5590.6740.7430.8150.7890.8860.8620.7980.7880.7770.7980.7940.7920.7960.8000.7980.7970.7620.8081380StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C0.7580.8600.9160.9280.9170.9230.6440.8090.9170.9150.9210.8690.8870.6490.9000.9230.9280.9240.9190.9160.9090.8990.8980.8870.8540.9020.8930.8820.8770.9090.9270.9220.7580.6180.6700.7620.8740.8600.8690.9270.9170.9200.7350.4840.7300.7870.8370.7870.9160.9180.9430.9370.9440.9400.9470.9440.9410.9400.9470.9450.9320.8903197StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G0.3720.3930.6430.5620.6370.6360.4040.5180.5580.5640.5000.4320.4260.4480.4150.5320.5690.5140.5710.5890.5590.5550.5460.5600.5540.5610.5520.5640.5810.6420.5040.4930.6140.4530.4900.4790.5660.5600.5600.6100.6000.6180.5990.4520.6320.6250.6940.6780.6810.6260.5250.5050.5080.5290.5010.5280.5200.5230.5120.5180.6080.5511134StabilityODP2_GEOSEHighProkaryote
OPSD_HUMAN_Wan_20190.6110.7500.7550.7960.7580.7600.7010.8080.7870.8060.6920.7880.8080.6590.7770.7670.7840.7510.7690.7840.7820.7780.7720.7960.8040.8120.8220.8150.8010.7490.7400.6940.6040.7610.7850.7680.7800.7830.7730.7630.7650.7670.7020.5570.7620.7520.8740.7890.8820.6110.7030.6700.6990.6960.7450.7540.6820.7010.7290.7290.8140.785165ExpressionOPSD_HUMANHighHuman
OTC_HUMAN_Lo_20230.7570.7860.7480.7690.7780.7800.5470.7100.7910.7930.7730.7790.7850.5520.6900.7390.7530.7490.7630.7820.7130.7310.7680.7820.7350.7830.7690.7770.7980.8060.7450.7160.5330.7030.7610.7980.7530.7870.8150.7860.7980.8070.6190.5240.7520.7200.8300.7970.8370.6870.7550.7660.7640.7810.7660.7750.7700.7660.7630.7730.7990.7341570ActivityOTC_HUMANMediumHuman
OTU7A_HUMAN_Tsuboyama_2023_2L2D0.5200.5930.5820.5900.6020.6040.5990.5740.5600.5640.7470.8620.8580.5930.8350.8470.7260.7410.7280.5790.5780.6650.6910.7130.7310.7310.7590.7320.6150.6710.8150.7930.5620.5660.6390.7340.5960.6020.6880.6120.6030.6360.6880.5770.8360.8120.8290.8420.8650.7870.7690.7170.7310.7620.7730.7820.7350.7640.7820.7660.8540.855635StabilityOTU7A_HUMANHighHuman
OXDA_RHOTO_Vanella_2023_activity0.5700.6230.6490.6500.6380.6460.5910.6200.6130.6180.6670.6530.6570.5620.6090.6390.6590.6650.6700.6530.5690.6040.6430.6470.6220.6570.6650.6500.6920.6430.6870.6780.5190.5900.6140.6390.6240.6290.6390.6420.6450.6480.5950.5290.6670.6320.6300.6670.6460.5370.6550.6590.6650.6540.6580.6690.6580.6610.6600.6650.6650.6016396ActivityOXDA_RHOTOHighEukaryote
OXDA_RHOTO_Vanella_2023_expression0.6350.6690.6810.6810.6760.6770.6520.6370.6890.6940.7050.7000.6990.6610.6790.6940.6990.7190.7090.6460.6380.6680.6700.6620.6700.6740.6940.6830.6970.6930.7010.6780.5520.6700.6540.6700.6840.6780.6820.6880.6840.6840.6760.6240.6980.6760.7150.7080.6750.5500.7010.6900.6880.6940.6960.6880.6990.6960.6950.7000.7360.6946769ExpressionOXDA_RHOTOHighEukaryote
P53_HUMAN_Giacomelli_2018_Null_Etoposide0.8310.8310.7650.7770.8260.8290.4500.7640.7630.7720.8260.8270.8550.4170.4360.7640.8350.8490.8690.6270.7220.7880.8040.7960.7780.8180.8220.8180.7830.8300.8530.8360.6110.7010.8000.7840.8190.8390.8210.8330.8470.8290.4190.4120.8400.5000.7710.8330.7840.6370.8340.8360.8380.8340.8390.8470.8390.8430.8470.8480.8590.6607467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_Null_Nutlin0.6700.6940.6490.6540.6880.6890.4550.6230.6450.6470.7260.7130.7380.4180.4260.6290.6850.6970.7110.5600.6360.7120.7180.7070.6800.7310.7380.7490.6600.7060.7130.6780.5980.6060.7130.6770.6690.7050.6840.6820.7080.6880.4250.4220.7190.4640.6800.7230.6890.5860.6920.6940.6960.6990.6990.7040.6940.6980.7020.7030.7170.5777467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_WT_Nutlin0.8550.8650.7820.7950.8510.8560.4530.7660.7740.7780.8830.8630.8910.4150.4400.8280.8690.8860.8890.6260.7800.8620.8600.8470.8350.8700.8670.8890.7610.8810.8870.8670.6340.7520.8810.8210.8550.8940.8500.8650.8960.8530.4230.4050.8940.5090.8320.8880.8330.6700.8730.8810.8790.8800.8840.8880.8810.8860.8860.8910.8930.6887467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Kotler_20180.8510.8280.7680.8050.8060.8190.5440.7720.8280.8360.8380.8100.8540.5270.5440.8500.8890.9000.9070.7470.7310.7660.7860.7820.7610.7890.7820.7890.7910.8310.8600.8370.5320.7240.7760.7460.8380.8320.8200.8370.8330.8290.5270.5020.8680.6080.7650.8350.7750.6430.8690.8790.8750.8720.8740.8780.8880.8750.8840.8870.9060.7061048OrganismalFitnessP53_HUMANLowHuman
P84126_THETH_Chan_20170.7900.8270.8440.8520.8180.8270.6610.7850.8600.8470.8180.8080.8320.6340.8090.8170.8310.8380.8110.8460.7370.7850.7810.8130.7920.8280.8370.8190.8700.8010.8170.7730.6990.7690.7750.7980.7950.7980.8110.8210.8220.8310.7540.4880.7970.7740.6610.7270.7680.5600.7950.7820.7480.7960.7990.7770.7870.7990.8020.8020.8350.8281519OrganismalFitnessP84126_THETHMediumProkaryote
PA_I34A1_Wu_20150.7720.7730.7610.7660.7820.7830.5350.6940.5890.5890.5260.5370.5690.5180.5200.5210.5250.5250.6810.6670.7360.7550.7770.7780.6070.7100.7330.7250.7280.8050.6890.6930.5660.7220.7470.7770.7820.7930.7960.8040.8080.8030.5220.5150.5210.5190.6270.6160.5990.5400.5810.5840.5830.5940.5840.5910.5830.5880.5680.5900.6120.5771820OrganismalFitnessPA_I34A1MediumVirus
PABP_YEAST_Melamed_20130.8550.8270.7870.7910.8460.8430.7520.8050.8380.8520.8660.8510.8610.7580.8050.8560.8980.8750.8820.7870.8470.8580.8630.8740.8460.8790.8790.8660.8600.8590.8840.8460.6490.8490.8500.8470.8740.8740.8720.8660.8680.8660.8250.4690.8510.8520.6910.8170.7790.6040.8700.8650.8720.8880.8800.8830.8840.8840.9010.8920.8850.84437708OrganismalFitnessPABP_YEASTMediumEukaryote
PAI1_HUMAN_Huttinger_20210.6930.6930.6860.6930.6950.7020.5260.6560.7050.7110.7080.6930.7040.5450.6880.7050.7150.6620.6360.7040.5700.6860.6770.6680.6700.6900.6930.6810.6650.7050.6920.6760.5610.5830.6780.6810.6830.6930.6970.7020.7060.7060.6690.5330.7050.6950.6900.7230.7030.5760.7120.7120.7120.7180.7140.7160.7150.7170.7130.7200.7280.6925345ActivityPAI1_HUMANNaNHuman
PHOT_CHLRE_Chen_20230.5920.7080.8580.8460.6600.6520.8210.7700.8500.8550.8190.8730.8880.8780.9110.8520.8700.8510.8760.8190.8360.8350.7900.8410.7540.8000.7880.8220.7910.7830.7810.7400.6470.7740.7440.7950.7860.7690.7930.6960.7100.7000.8080.6870.7960.8050.5990.7470.8300.7000.7970.7650.7700.7690.7720.7790.7780.7810.7860.7790.8650.884167529ActivityPHOT_CHLREHighEukaryote
PIN1_HUMAN_Tsuboyama_2023_1I6C0.6080.6620.8580.8370.8290.8500.8000.7530.8510.8720.8500.7990.8580.8370.8320.8460.8480.7610.7900.8370.7220.8590.8340.8250.7890.8570.8690.8520.8590.8730.8790.8610.7990.7660.8340.8590.8060.8500.8840.8560.8720.8880.8000.3810.8360.7770.7860.8080.8710.8620.8460.8240.7520.7820.7890.8080.8120.8350.8210.8170.8890.895802StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M0.7950.7730.7870.7910.7870.7900.7600.7540.7810.7930.7450.7320.7260.7860.8360.8470.8270.8070.7650.7900.7710.7430.7520.7380.7930.7660.7440.7300.7520.7800.7120.7030.7290.7970.7710.7610.8130.7880.7840.8010.7820.7840.7480.7180.6460.6690.7150.6530.8500.8200.7940.7980.7950.8050.8120.8160.8060.8080.8100.8090.7500.8121824StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF0.6230.6050.6620.6710.6900.6940.6120.6580.6390.6610.6800.7060.7370.7160.7330.8140.7210.6780.6760.7180.7020.7050.6800.7120.7100.7080.7110.7040.7120.7170.7520.7500.5550.7210.6650.7080.6900.6860.7140.7130.6970.7120.6840.6740.7050.6920.8350.7770.8300.7900.6850.7030.7240.6890.7070.7060.6920.7070.7130.7090.7280.8251301StabilityPKN1_HUMANHighHuman
POLG_CXB3N_Mattenberger_20210.7090.6870.6810.6990.7260.7310.4780.6720.7490.7490.6510.4680.5160.4570.4690.5950.7020.7040.7170.6770.6640.6940.6890.6880.5610.6910.6880.6830.6940.7430.6940.6600.5010.5240.6310.6770.6670.6880.7060.6880.7090.7270.4650.4650.6770.4720.5960.6730.5590.5230.6730.6830.6850.6870.6840.6860.6850.6840.6900.6890.5580.53615711OrganismalFitnessPOLG_CXB3NMediumVirus
POLG_DEN26_Suphatrakul_20230.7600.7940.6330.6330.7720.7720.4850.7220.8480.8500.6690.5070.5120.4810.5130.5470.5870.6450.6900.7150.7320.7410.7330.7320.7250.7540.7560.7470.7450.8160.8090.7710.5220.4840.5770.7430.7290.7090.7810.7450.7140.7900.4780.4730.7090.5040.6900.7710.5720.5610.6320.6240.6310.6430.6260.6430.6330.6370.6270.6400.5920.52416897OrganismalFitnessPOLG_DEN26LowVirus
POLG_HCVJF_Qi_20140.8240.7840.7120.7160.8230.8280.4700.5980.7920.7980.5880.8420.8390.5520.5650.5560.5560.5430.5350.6310.7140.7440.7410.7610.7210.7560.6690.7200.7800.8400.8210.7720.5990.7470.7690.7800.7680.7900.8090.7480.7800.7980.5550.5200.7630.5540.4830.6720.8690.7030.6730.7140.7000.7150.6980.6570.6830.6850.6790.7120.5960.5841630OrganismalFitnessPOLG_HCVJFMediumVirus
POLG_PESV_Tsuboyama_2023_2MXD0.6380.7660.6950.7200.7400.7400.5250.7220.6790.7350.7370.5230.5580.5250.5560.5150.5670.5530.5300.8180.5580.5300.5070.5740.5420.4700.4860.4630.5780.7610.8220.8260.5750.4980.5030.5000.7020.7060.7020.7340.7410.7330.4790.4480.4910.4690.7780.7480.8820.8410.8590.8630.8480.8920.8940.8940.8670.8960.8940.8880.9070.8305130StabilityPOLG_PESVMediumVirus
PPARG_HUMAN_Majithia_20160.7580.8140.8370.8420.8380.8450.6250.7310.8100.8210.8120.8410.8430.5330.6120.8080.8650.8930.8690.8580.8310.8270.6970.7190.8100.8370.8220.8380.7340.8710.8520.8430.7320.8550.8160.7880.8620.8450.8330.8710.8610.8590.6790.5240.7910.7790.8070.7880.8340.6680.8600.8620.8670.8650.8650.8660.8640.8650.8650.8710.8610.8119576ActivityPPARG_HUMANMediumHuman
PPM1D_HUMAN_Miller_20220.7830.7790.7790.7820.8160.8170.5130.6940.7410.7700.7970.8050.8160.6390.6920.7340.8050.8200.8170.6970.7260.7670.7760.7190.7630.7950.7900.7870.7140.8130.8040.7770.6140.7180.7690.7750.8010.8050.8050.8190.8170.8180.6770.4730.7940.7630.7560.7980.7830.6270.7910.7930.7890.7960.8010.7940.7970.7970.7990.8020.8240.7577889OrganismalFitnessPPM1D_HUMANLowHuman
PR40A_HUMAN_Tsuboyama_2023_1UZC0.8450.8820.9260.9210.9390.9400.7710.7710.9360.9390.9130.8500.8810.7830.7790.9510.9450.9210.9120.9040.8290.8850.8860.8980.8530.8880.9070.8980.9120.9460.9440.9420.8560.8280.8000.8240.9170.9200.9090.9320.9430.9330.7180.6110.6350.7190.7980.6640.9020.9130.9450.9470.9470.9490.9540.9540.9490.9530.9530.9540.9400.9372033StabilityPR40A_HUMANMediumHuman
PRKN_HUMAN_Clausen_20230.8390.8400.8350.8270.8480.8440.5930.7570.7970.8070.8160.8390.8540.6150.6520.6890.7690.8610.8650.7530.6520.8030.8280.8190.7400.8390.8380.8350.7990.8460.8320.8090.6520.6280.8010.8170.8160.8420.8480.8420.8550.8600.6590.5400.8210.6900.8450.8310.8730.6560.7880.7910.7930.8100.8000.8050.8070.8060.8000.8080.8660.7688756ExpressionPRKN_HUMANLowHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE0.8590.8640.8560.8550.8600.8570.7120.7850.8240.8240.9040.8710.8800.8110.8420.8950.9090.8880.8530.8060.7060.6560.7940.7390.7930.8090.3880.8020.8340.8460.8530.8250.5540.7170.7550.7800.8450.8410.8200.8660.8660.8430.7480.6500.8640.7840.8280.8540.9070.8730.9020.8960.8980.9030.9010.9070.8950.9010.9010.9050.9090.9061579StabilityPSAE_PICP2MediumProkaryote
PTEN_HUMAN_Matreyek_20210.6810.6950.6810.6910.6950.7000.5790.6830.7140.7360.7210.7040.7300.5870.6350.7270.7320.6420.6540.7390.5950.7300.7120.7020.6330.6790.6700.6960.6570.7370.6980.6950.5670.6360.7170.6800.6880.7300.7110.7060.7310.7250.6220.5260.7190.6690.7360.7340.7360.6110.7190.7140.7290.7180.7170.7230.7250.7260.7210.7270.7480.7255083ExpressionPTEN_HUMANMediumHuman
PTEN_HUMAN_Mighell_20180.8150.8260.8270.8290.8440.8480.6190.7340.8280.8330.8050.8160.8330.6390.7720.8500.8320.6950.6830.8310.7300.7740.7200.6930.7700.6960.6890.7140.6600.8420.8190.8130.5410.7500.7670.7020.8290.8130.7810.8440.8440.8460.7470.4810.8320.8150.7710.7620.8050.6250.8160.7910.8110.8180.8260.8250.8160.8160.8260.8280.8450.8287260ActivityPTEN_HUMANMediumHuman
Q2N0S5_9HIV1_Haddox_20180.7580.7040.6900.7090.7600.7630.5100.7230.7700.7730.7450.7670.7810.5090.5090.5110.5320.5590.5860.7110.7700.7070.6980.6710.7690.7020.7020.7220.6830.7640.7510.7530.6440.7580.7160.7100.7720.7620.7620.7750.7690.7680.7200.5070.7650.7380.6950.7430.6340.5750.6150.6360.6580.6560.6310.6340.6350.6280.6150.6450.6100.56012729OrganismalFitnessQ2N0S5_9HIV1MediumVirus
Q53Z42_HUMAN_McShan_2019_binding-TAPBPR0.6710.6580.6650.6660.6690.6670.5450.6080.6390.6450.6050.6360.6460.6050.5960.6710.6570.6300.6630.6510.6150.6190.6150.5930.6370.6360.6240.6420.6330.6380.6790.6660.5510.6100.6230.5760.6470.6560.6340.6670.6710.6620.6990.6090.6420.6530.6430.6640.6550.5900.6390.6450.6590.6560.6520.6520.6480.6460.6470.6540.6470.7173344BindingQ53Z42_HUMANMediumHuman
Q53Z42_HUMAN_McShan_2019_expression0.7710.7550.7720.7790.7840.7840.4710.7210.7840.7910.7190.7470.7660.5330.5450.7450.7850.7850.7920.7870.7180.7520.7520.7460.7470.7650.7540.7670.7840.7850.7840.7550.5820.7210.7370.7280.7730.7840.7870.7880.7940.7950.6570.5390.7770.7460.7340.7610.7390.6040.7770.7810.7860.7830.7920.7860.7840.7870.7920.7930.7710.8123344ExpressionQ53Z42_HUMANMediumHuman
Q59976_STRSQ_Romero_20150.7810.8310.8530.8570.8570.8640.6890.7760.8650.8690.8270.7860.7960.5740.7410.7750.8130.8120.8170.8500.8260.8500.8490.8570.8380.8590.8660.8650.8690.8720.8540.8270.6740.8270.8500.8420.8380.8560.8570.8640.8690.8690.7630.4920.8240.7840.7110.7830.7810.5690.8130.7920.7920.8030.8080.7980.8100.7970.8020.8090.8430.7902999ActivityQ59976_STRSQMediumProkaryote
Q6WV13_9MAXI_Somermeyer_20220.6340.6580.6050.6060.6400.6390.4960.5620.6610.6590.5990.5200.5120.5060.5190.5010.5050.5050.4930.6260.5180.5190.5020.5520.5130.5000.5120.4960.4930.6820.6360.6390.5140.5130.5180.5120.5990.5990.5990.6370.6370.6350.5050.5010.4960.5100.6110.6150.6640.6010.6100.6240.6140.6360.6280.6300.6210.6220.6210.6240.5540.53631401ActivityQ6WV12_9MAXILowEukaryote
Q837P4_ENTFA_Meier_20230.7050.7120.7180.7190.7250.7360.7090.6750.7110.7190.7650.7410.7550.6820.7160.7180.7240.7440.7290.4760.7330.7120.7210.7340.7420.7490.7600.7060.7540.7380.7380.7180.5610.7210.7470.7290.7190.7540.7490.7440.7520.7520.7040.6770.7550.7200.6300.7250.6850.5510.7260.7140.7180.7360.7400.7480.7410.7110.7360.7370.7720.720697ActivityQ837P4_ENTFAMediumProkaryote
Q837P5_ENTFA_Meier_20230.5650.6770.6820.6980.6640.6680.5730.6010.6130.6180.6380.6460.6380.5260.5780.6290.6530.6910.6710.6360.6570.6760.7010.6980.6390.6840.6900.7350.7140.6440.6650.6580.5590.6800.7030.7410.6430.6740.7140.6790.6860.6990.6110.5570.6530.6000.6650.6420.6760.5630.6360.6410.6480.6290.6570.6500.6300.6470.6370.6480.6400.629747ActivityQ837P5_ENTFAMediumProkaryote
Q8WTC7_9CNID_Somermeyer_20220.6300.6710.6030.6020.6520.6490.4950.6480.6380.6410.5920.4710.4630.4660.4540.4590.4650.4610.4840.6010.4760.4920.5160.4890.4820.4690.4870.6060.6130.6650.6460.6540.4770.4520.4900.6390.5960.6010.6420.6410.6460.6600.4720.4730.4600.4600.5100.5430.6280.5710.6040.5980.6030.6090.5980.6060.6050.5990.5990.6030.5370.44733510ActivityQ8WTC7_9CNIDLowEukaryote
R1AB_SARS2_Flynn_20220.8350.8290.6180.6330.8460.8480.4870.6750.4960.4960.5750.4970.4900.5130.4990.5550.5670.7920.8240.6630.6360.6670.6740.6760.6430.6430.6230.6340.6420.8250.8010.7450.4860.6070.6370.6300.7110.7350.7340.8230.8340.8320.5010.4860.5490.4950.7520.7330.7770.6260.6500.6360.6590.6750.6650.6610.6620.6600.6410.6640.6360.5895725OrganismalFitnessR1AB_SARS2MediumVirus
RAD_ANTMA_Tsuboyama_2023_2CJJ0.6050.5850.6600.6630.6750.6920.7350.7030.8530.8350.7940.7480.7690.7260.8460.8680.7610.7540.7970.7140.7880.8240.8030.7660.8160.7440.8080.7540.7470.7990.7250.6810.6490.7500.8340.7450.7450.8110.7440.7340.7620.7230.8110.6200.6250.7260.7610.6660.8650.7780.7860.8010.8040.8000.8120.8020.7990.8010.7970.8050.7190.818912StabilityRAD_ANTMAHighEukaryote
RAF1_HUMAN_Zinkus-Boltz_20190.7080.7190.7060.7120.7250.7250.5250.6840.7520.7630.7600.7320.7690.5230.6190.7400.7560.7440.7340.6780.6540.7150.7080.6960.6990.7120.7080.6960.7080.7270.7640.7300.5690.6530.6880.7020.6930.7030.7110.7170.7340.7370.6070.5290.7790.7070.6380.7250.6390.6650.7290.7260.7300.7520.7340.7470.7320.7290.7400.7450.7270.590297OrganismalFitnessRAF1_HUMANLowHuman
RASH_HUMAN_Bandaru_20170.7980.8110.8270.8420.8390.8440.6890.7720.8230.8370.7570.7870.8110.8070.8490.8350.8500.8170.7350.8590.8190.8050.8050.7910.8090.7910.7910.7670.7100.8150.7890.7790.6480.7870.8030.7600.8210.8300.8080.8390.8440.8460.8380.6620.7200.8220.7110.6410.8070.6440.7980.7940.7990.7960.8060.7930.8130.7960.8050.8120.7470.8273134ActivityRASH_HUMANHighHuman
RASK_HUMAN_Weng_2022_abundance0.6480.6260.6790.6720.6650.6760.6050.6230.6200.6340.6440.6060.6240.6580.6830.6630.6310.6100.6030.6650.6330.6710.6900.6990.6400.6770.7110.6770.7380.6830.6050.5840.6070.6000.6620.7060.6360.6820.7210.6670.6850.6940.6160.6550.5310.6210.6590.6640.6880.6620.6270.6200.6450.6370.6520.6370.6460.6340.6230.6390.6710.66126012ExpressionRASK_HUMANHighHuman
RASK_HUMAN_Weng_2022_binding-DARPin_K550.7340.7550.8220.8270.8150.8160.6450.7010.8300.8330.8060.7770.8050.7960.8240.8340.8420.7900.7330.8610.7980.7810.7980.7540.7780.7910.7730.7180.6940.8240.7790.7530.6600.7650.8090.7620.7910.8200.7910.8180.8260.8170.7940.6480.7320.8140.6280.6400.8140.6480.7790.7810.7830.7820.7920.7920.7790.7820.7560.7890.8120.81424873BindingRASK_HUMANHighHuman
RBP1_HUMAN_Tsuboyama_2023_2KWH0.5840.5420.6560.6600.6590.6570.6460.6500.6340.6420.7280.7180.7130.6690.7080.7590.7810.6840.6820.6330.6940.5770.6560.6540.6530.5650.5910.6730.6310.7010.7040.6760.6440.6550.6980.7080.6660.6790.6780.6660.6750.6660.6670.6360.7090.7140.7540.7780.7960.7300.7550.7510.7560.7550.7590.7540.7490.7620.7520.7580.7830.7691332StabilityRBP1_HUMANHighHuman
RCD1_ARATH_Tsuboyama_2023_5OAO0.6940.6830.7250.7280.7280.7230.6600.6620.7360.7400.7570.6690.6950.6850.6920.7840.7920.7870.7830.7390.6280.6160.6680.7100.6590.7590.7490.7270.7670.7440.7870.7520.5190.6380.6570.6600.7170.7210.7150.7420.7430.7350.6550.6440.7270.6660.7440.7570.8010.7730.8020.8090.8160.8140.8060.8120.8090.8080.8110.8140.7990.7621261StabilityRCD1_ARATHMediumEukaryote
RCRO_LAMBD_Tsuboyama_2023_1ORC0.6650.7730.8050.8000.7880.8060.5330.6400.7640.7670.7370.6490.6970.5910.6540.6600.7860.7860.8030.8010.5060.5240.5490.5410.4140.5220.4390.4820.7980.7990.7820.7710.5180.5250.4950.7780.7130.7080.7950.7890.8000.8230.4520.5450.6980.5480.7800.8060.8660.8070.7900.7810.7710.7820.7820.7840.7730.7810.7920.7850.8150.7102278StabilityRCRO_LAMBDHighVirus
RD23A_HUMAN_Tsuboyama_2023_1IFY0.6190.6490.7140.7140.7110.7110.5760.7350.7660.7710.7500.7920.8050.6030.8360.7800.7680.7610.7230.7350.7050.7250.7220.7430.7690.7650.7640.7490.7650.7470.7250.7180.7290.6460.7490.7370.7030.7470.7500.7160.7420.7480.7470.5070.7880.7720.7240.7830.7870.7450.7440.7350.7410.7330.7480.7500.7460.7480.7490.7490.7410.7961019StabilityRD23A_HUMANHighHuman
RDRP_I33A0_Li_20230.6670.6990.7270.7320.7790.7810.5240.7280.7910.7940.6120.5470.5540.5350.5400.5900.6980.7240.7850.7680.7340.7680.7780.7890.5880.7230.7160.7120.7510.7960.7660.7290.5680.7300.7610.7810.7500.7680.7790.7900.7990.8080.5350.5220.6120.5360.6260.6460.6220.5400.6810.6640.6740.6740.6830.6790.6750.6860.6860.6870.5950.56912003OrganismalFitnessRDRP_I33A0LowVirus
REV_HV1H2_Fernandes_20160.5960.5740.6030.6060.6030.6030.5210.6460.6030.6070.5660.6160.6240.5260.5220.5850.6130.6380.6290.5750.6000.6230.6170.6080.6350.6370.5760.6170.6230.6300.6670.6720.5250.6080.6270.6130.6140.6250.6100.6160.6230.6110.5290.5330.5990.5370.6260.6120.6390.6040.5910.5960.6330.6220.6240.6250.6330.6100.6490.6320.6330.6082147OrganismalFitnessREV_HV1H2MediumVirus
RFAH_ECOLI_Tsuboyama_2023_2LCL0.5070.5960.6070.6180.6180.6160.4870.6200.6130.6270.6220.5710.5850.4690.5290.5110.6490.6370.6320.6350.4540.5190.5590.5530.4640.5650.5770.5520.5760.6100.6360.6100.5000.4980.5580.5760.5430.5720.5830.6100.6100.6110.5470.4470.5850.5400.6490.6220.6890.6580.6680.6570.6490.6680.6600.6550.6600.6560.6680.6630.6520.5931326StabilityRFAH_ECOLIHighProkaryote
RL20_AQUAE_Tsuboyama_2023_1GYZ0.6880.8510.8670.8660.8660.8700.5930.8550.7180.6810.8860.8850.8750.5910.7060.7530.9080.8990.9070.8790.5870.7980.7860.7800.7740.7890.8050.8180.8610.8650.8430.8320.5310.7740.8030.7900.8120.8360.8320.8520.8750.8750.4840.4340.7690.6220.8560.8580.9360.9060.9170.9170.9150.9290.9230.9190.9240.9240.9250.9270.9300.8721461StabilityRL20_AQUAEHighProkaryote
RL40A_YEAST_Mavor_20160.6540.6830.6850.7130.6950.7080.5560.7280.7440.7460.6230.6530.6740.5470.7380.7690.7880.7440.7970.7300.7060.7890.7740.7450.7750.7500.7510.7390.7240.6720.7000.7050.5320.7150.7570.7090.7260.7600.7260.7380.7590.7210.6530.5180.6530.6680.5530.5870.6270.5030.7670.7550.7760.7670.7780.7650.7620.7770.7700.7810.6970.6981253OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20130.6600.6950.7080.7420.7140.7280.5300.6840.7670.7690.6140.6540.6920.5400.7300.7730.8140.7530.8060.7480.7090.7990.7770.7550.7830.7590.7690.7540.7390.6960.7450.7560.5360.7140.7750.7160.7310.7780.7340.7500.7770.7370.6800.5220.6590.6600.5420.5670.6280.5110.8020.7870.8060.7930.8040.7970.7870.8130.8030.8140.6980.7101195OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20140.7310.6840.7300.7530.7200.7430.5960.7610.7450.7590.5950.6600.6760.6280.8160.8130.8280.7650.7910.7440.7140.7960.7760.7640.7810.7520.7480.7230.7190.7330.7140.7190.6160.7310.7780.7300.7570.7940.7670.7650.7830.7560.6630.5760.6590.6720.6300.6400.6850.5880.8160.7960.8020.8300.8280.8250.8010.8110.8000.8280.6900.7461380ActivityRL40A_YEASTMediumEukaryote
RNC_ECOLI_Weeks_20230.7720.8040.8030.8090.8120.8130.5310.7190.8080.8070.8060.8030.8100.5350.7820.8010.8090.8070.8100.8080.7790.7940.7650.7550.7860.8020.8020.8010.7950.8090.7990.7760.5840.7700.7770.7180.7940.8010.7770.8180.8190.8150.7690.5340.8060.7920.6570.7770.6500.5970.7870.7810.7780.7920.7870.7820.7860.7860.7870.7930.8150.7724277ActivityRNC_ECOLIMediumProkaryote
RPC1_BP434_Tsuboyama_2023_1R690.8180.8570.8640.8750.8330.8560.8410.8290.8500.8700.8910.8980.9020.8760.8900.9010.8940.8800.8500.8840.8650.8860.8890.8710.8820.8940.8900.8910.8700.8970.8690.8340.8360.8260.8810.8810.8740.8880.8870.8730.8730.8690.8880.8540.8980.8870.8750.8470.9250.8890.8790.8720.8800.8870.8840.8810.8900.8860.8920.8900.9130.9061459StabilityRPC1_BP434HighVirus
RPC1_LAMBD_Li_2019_high-expression0.7070.7960.8370.8490.8240.8260.6700.8160.8330.8570.8660.8550.8790.7210.7180.7940.8980.9020.9040.8150.6560.7440.7900.8200.6740.7770.7640.7460.8940.8110.9340.9020.5860.6250.7640.8600.7060.7690.8570.8100.8320.8660.7310.7350.8040.7430.6710.7740.7610.6260.8980.8730.8720.8970.8870.8980.8830.8790.8730.8970.8780.789351ActivityRPC1_LAMBDHighVirus
RPC1_LAMBD_Li_2019_low-expression0.6120.6990.7360.7460.7240.7180.5630.7570.7160.7360.7550.7280.7360.6190.6200.6710.7610.8030.8080.7000.6050.6780.6800.7130.5830.6860.6610.6530.8020.7120.8290.8170.5400.5630.6630.7380.6100.6570.7270.7020.7160.7430.6360.6210.6700.6410.6340.6810.7000.6360.7690.7520.7670.7770.7580.7720.7760.7670.7580.7760.7650.653351ActivityRPC1_LAMBDHighVirus
RS15_GEOSE_Tsuboyama_2023_1A320.6980.6720.6850.6820.6720.6750.5870.6200.7100.7100.7340.7090.7010.6360.6570.7580.7140.7020.6700.6630.6010.6520.6670.6580.6570.6720.7000.6790.6860.7010.7260.7230.5480.7110.6590.6620.7250.6870.6900.7030.6790.6730.6650.6200.7190.6830.8010.7230.8170.7680.7100.7090.7150.7130.7090.7080.7190.7260.7100.7190.7200.8211195StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance0.7750.7910.8020.8060.8130.8200.7090.7790.8310.8310.8220.8350.8570.7660.7910.8020.8370.8260.8120.5200.7670.8230.8370.8260.7860.8390.8480.8300.8100.8190.8100.7410.6820.7850.8280.8250.8060.8430.8450.8340.8440.8430.7710.6510.8140.8020.7330.7890.7880.5940.8130.8060.8070.8170.8270.8210.8190.8220.8170.8270.8370.8129803ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity0.7730.8100.8350.8430.8380.8450.7140.8210.8440.8430.8370.8480.8770.7690.7890.7890.8540.8330.8220.5210.7930.8540.8610.8490.8150.8600.8650.8540.8310.8520.8560.8020.6910.8050.8580.8460.8170.8600.8570.8560.8700.8660.7750.6360.8370.8000.7280.8050.7880.5870.8280.8120.8090.8280.8370.8260.8270.8370.8370.8390.8280.80410094ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB0.5370.5860.6930.6770.6920.6870.7020.7880.6930.7010.7620.7650.7780.5930.7390.7570.7650.7860.7320.7230.7710.7560.7500.7660.7760.7860.7680.7710.7690.7850.7640.7330.7030.7360.7780.7730.7140.7580.7460.6980.7190.7190.7680.4590.7870.7740.6470.6580.7090.7670.7520.6930.6380.7040.6990.7160.7130.7420.7350.7140.8030.813965StabilitySAV1_MOUSEHighEukaryote
SBI_STAAM_Tsuboyama_2023_2JVG0.6060.5990.6850.6660.7140.6990.6070.6030.7370.7630.6610.6260.6420.6140.6290.6870.7890.8330.6740.6800.6100.6300.6400.6390.6070.6190.6110.6290.6880.7890.7980.7500.6020.5890.5980.6060.6410.6460.6480.6740.6810.6850.6290.6080.6590.6550.8360.8280.8540.8120.7680.7650.7820.7840.7810.7820.7940.7900.7850.7870.8480.7851025StabilitySBI_STAAMMediumProkaryote
SC6A4_HUMAN_Young_20210.7100.7450.7280.7340.7710.7800.6860.7660.8000.8050.7880.7820.7870.5880.6750.7700.7920.7930.7800.7950.7640.7710.7620.7610.7710.7740.7730.7760.7710.7880.7450.6930.6830.7720.7700.7590.7830.7880.7810.7930.7960.7910.7620.5640.7900.7820.7460.7780.7620.5980.7780.7830.7880.7910.7930.7880.7880.7900.7830.7930.8080.78211576ActivitySC6A4_HUMANMediumHuman
SCIN_STAAR_Tsuboyama_2023_2QFF0.5350.5600.6330.6300.6540.6410.5520.5300.6320.6300.6200.6180.6070.5850.6250.6130.6380.6700.6750.6340.5380.5630.5490.5550.5640.5760.5600.5780.5850.6290.6380.6020.4900.5220.5340.5650.5660.5640.5740.6260.6200.6280.5920.5660.6230.6070.7630.7580.7630.7330.6920.6990.6990.7200.6860.7150.6890.6880.6670.7020.7970.7331212StabilitySCIN_STAARHighProkaryote
SCN5A_HUMAN_Glazer_20190.5370.5430.5580.5580.5530.5590.5480.5100.5690.5830.5890.6130.5670.6230.5910.5820.5940.5690.5540.5790.5460.5570.5670.5670.5360.5470.5480.5430.5800.5450.5880.5820.5320.5340.5250.5320.5370.5300.5290.5530.5520.5560.5420.5850.5740.5850.5290.5610.5270.5040.5730.5640.5740.5630.5430.5660.5780.5720.5750.5700.6080.574224OrganismalFitnessSCN5A_HUMANMediumHuman
SDA_BACSU_Tsuboyama_2023_1PV00.9440.9710.9710.9690.9730.9730.5900.8600.9590.9620.9690.9390.9520.7200.7340.9470.9550.9520.9570.9540.6280.6520.6520.7700.6430.7990.5650.7760.8620.9670.9650.9620.3530.5790.7380.8080.9340.9360.9080.9620.9600.9470.5760.4970.8180.5620.8410.8270.9610.9520.9730.9700.9690.9740.9720.9720.9740.9710.9750.9740.9610.9672770StabilitySDA_BACSUMediumProkaryote
SERC_HUMAN_Xie_20230.6780.7220.7590.7590.7450.7500.5090.7150.7740.7780.7470.7570.7600.5870.6920.7540.7650.7760.7600.7550.7360.7440.7460.7350.7380.7550.7470.7550.7450.7440.7530.7320.5880.7470.7530.7490.7480.7540.7570.7570.7590.7590.6510.5190.7590.7250.6960.7350.6810.5930.7620.7560.7640.7680.7710.7670.7670.7660.7650.7720.7650.7271914OrganismalFitnessSERC_HUMANHighHuman
SHOC2_HUMAN_Kwon_20220.5990.6640.6890.6900.6850.6900.6060.6790.7110.7110.6960.6890.7020.6130.6180.6270.7110.6950.6480.6940.6240.6790.6930.6770.6260.6930.6920.6980.6780.7040.7040.6770.5740.6210.6510.6870.6270.6480.6820.6820.6820.6980.6230.6110.7130.6390.6500.6810.6380.5450.6850.6820.6910.6930.6970.6990.6890.6950.6900.6970.6560.63910972OrganismalFitnessSHOC2_HUMANMediumHuman
SOX30_HUMAN_Tsuboyama_2023_7JJK0.5920.6180.6280.6270.6390.6340.6940.5840.5960.6130.6200.6590.6630.6440.6870.6610.6600.6250.6240.6150.5760.5870.5860.5990.6310.6320.5960.6260.5990.6110.5350.5170.6090.5610.6300.6200.5930.6090.6150.6350.6310.6310.6300.5720.5980.6710.7560.6710.7680.7240.6740.6910.6740.6720.6680.6930.6700.6710.6650.6800.6640.7371010StabilitySOX30_HUMANHighHuman
SPA_STAAU_Tsuboyama_2023_1LP10.7480.7880.7990.7880.8220.8220.4110.7960.7440.7440.7000.4340.4560.4360.4380.4270.4570.4470.4680.7940.3860.4800.5860.5460.4280.4470.5040.6740.7330.8110.7360.7500.4760.4610.4710.5050.7440.7380.7370.8200.8180.8120.3870.4180.4110.4450.7530.7200.8710.7850.7610.7560.7430.7660.7530.7550.7500.7450.7570.7550.7680.6872105StabilitySPA_STAAUMediumProkaryote
SPG1_STRSG_Olson_20140.6080.6340.4970.5070.6180.6270.4890.5290.4510.5550.6710.6290.6060.6500.6260.6520.6800.6490.6880.6610.6370.6160.6190.6120.6310.6230.6140.6260.6920.6340.7540.7460.5360.6290.5990.6500.6360.6110.6540.6310.6050.6560.4730.4360.6240.5310.6820.6440.7110.5670.7170.6960.7090.7330.7330.7160.7450.7540.7440.7390.7160.708536962BindingSPG1_STRSGLowProkaryote
SPG1_STRSG_Wu_20160.5170.6050.6120.6210.6140.6220.6040.5410.7230.7150.6930.6770.6620.6450.6840.6920.7080.7380.7610.6530.6090.5890.5800.6070.5730.6430.6220.6000.6000.6310.7020.6640.4930.6190.5670.6050.6250.6110.6270.6400.6300.6330.5720.5080.6170.5610.6040.5940.7250.6360.7180.7000.6960.7300.6990.6930.7320.7110.7340.7150.7300.680149360BindingSPG1_STRSGMediumProkaryote
SPG2_STRSG_Tsuboyama_2023_5UBS0.7670.8110.8040.8230.8400.8390.7040.7050.8180.7930.7850.7520.7440.7190.7560.7690.7920.7890.8100.8000.7360.7570.7440.7390.7050.7710.7120.7720.7960.8660.8420.8350.4160.7220.7660.7780.6970.7180.7430.8330.8360.8420.6470.5640.6600.6420.7610.7150.9020.8370.8380.8390.8510.8500.8350.8530.8480.8610.8470.8500.8530.8271451StabilitySPG2_STRSGMediumProkaryote
SPIKE_SARS2_Starr_2020_binding0.5700.5950.5310.5760.6240.6280.4540.6820.6840.6860.4750.4450.4590.4540.4600.4700.4710.4770.5190.6590.6440.6710.6650.6710.6840.6550.6410.6770.6440.6250.6480.6700.5920.6530.6520.6730.6490.6490.6490.6650.6620.6660.4640.4790.4670.4740.7660.7480.7020.5840.6020.6060.6020.6380.6130.6370.6190.6300.5990.6270.6560.5603802BindingSPIKE_SARS2MediumVirus
SPIKE_SARS2_Starr_2020_expression0.6070.6640.5920.6510.7430.7410.4760.6820.7210.7350.5110.4690.4840.4730.4810.4920.4930.5020.5380.6800.6620.6930.6940.7050.6960.6890.6660.7030.6660.6940.6540.6750.5890.6720.6770.6900.6810.6860.6850.7320.7370.7460.4900.4970.4940.5020.7820.7700.7360.5970.6580.6680.6560.7050.6800.6990.6810.6910.6710.6940.7350.6213798ExpressionSPIKE_SARS2MediumVirus
SPTN1_CHICK_Tsuboyama_2023_1TUD0.7940.8050.8100.7780.7980.7870.6050.7450.7660.7880.7970.8160.8070.4440.7970.8010.7960.8570.8220.7880.7820.8080.7890.7820.7860.7890.8140.7670.7780.7870.7910.7920.7110.7460.7770.7660.8080.8130.8040.7920.7950.7910.7360.4020.7230.7360.7020.6930.8090.8020.8240.8190.8250.8220.8280.8240.8180.8160.8250.8250.8300.7673201StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU0.6780.7500.7810.7880.7850.7870.5290.6860.7680.7820.7290.6540.7140.5740.7080.7450.8040.8150.7530.7960.5900.7010.7150.7180.6400.7430.7600.7240.7410.8090.7860.7700.5760.5710.6960.7350.7270.7410.7680.7910.7800.7910.5820.5380.7790.7710.8320.8160.8130.8230.8150.7860.8180.8110.8130.8120.8160.8040.7980.8180.8240.725707StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N880.8200.8560.8690.8690.8730.8700.3820.7790.8440.8310.8780.8420.8510.3470.8530.8780.8630.8770.8640.8430.4190.6820.6870.7620.7600.8010.8250.8190.8230.8650.8660.8480.6860.5040.5750.6780.8530.8510.8550.8680.8680.8650.7940.3210.8710.8250.5670.8100.8590.8640.8670.8640.8610.8740.8780.8670.8620.8620.8700.8700.9170.8911583StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W0.7110.7860.8660.8660.8860.8830.7880.6970.8930.8930.8370.8590.8760.7670.8670.8810.8880.8420.8670.8520.8660.8460.8530.8390.8450.8420.8490.8410.8320.8640.8870.8680.8090.8250.8440.8410.8580.8750.8750.8780.8890.8870.6900.5740.7860.7690.7860.7780.8230.8420.8960.8860.8920.8890.8970.8940.8960.8890.8970.8990.8870.8471556StabilitySRBS1_HUMANHighHuman
SRC_HUMAN_Ahler_20190.8250.8250.7960.7970.8200.8250.8330.7800.8370.8480.8130.8610.8750.7750.7970.8060.8560.8330.8140.8150.7800.7690.7660.7400.7920.8040.7850.7660.7150.8470.8650.8550.7700.7640.7710.7210.8160.8190.8060.8310.8320.8270.8550.7490.8200.8450.6970.6840.7540.5620.8260.8100.8130.8250.8310.8260.8330.8340.8320.8360.8260.7973372ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM0.7480.7530.7430.7530.7600.7580.7550.7310.7710.7680.7400.7800.7900.6940.7180.7250.7660.7460.7400.7400.7220.7110.7090.6860.7210.7350.7160.7090.6590.7690.7870.7850.7040.6980.7020.6650.7400.7420.7330.7610.7610.7580.7660.6770.7480.7610.6420.6540.6880.5320.7500.7320.7360.7490.7500.7500.7490.7530.7520.7550.7500.7263637ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Nguyen_20220.7250.7340.7330.7380.7380.7360.7370.7210.6950.7320.7320.7640.7770.6740.6960.7060.7550.7380.7310.7260.7050.6960.6990.6780.7090.7230.7050.6980.6550.7490.7690.7600.7000.6890.6930.6570.7220.7240.7150.7410.7410.7370.7570.6580.7400.7560.6270.6420.6710.5370.7410.7230.7260.7380.7410.7390.7410.7450.7410.7450.7390.7033366OrganismalFitnessSRC_HUMANMediumHuman
SUMO1_HUMAN_Weile_20170.7180.7030.7350.7470.7780.7730.5800.7550.7570.7230.7530.7800.8000.6450.8000.8180.8020.7170.6860.8080.6310.7430.7690.7580.7860.7320.7770.7570.6840.7670.7700.7630.6280.6570.7880.6890.7430.8050.7360.7630.8090.7630.8030.5480.7160.8150.8080.7510.8140.7570.7900.7640.7970.7920.7900.8020.7830.7960.7860.8010.7770.8171700OrganismalFitnessSUMO1_HUMANHighHuman
SYUA_HUMAN_Newberry_20200.6100.6470.6680.6760.6710.6760.6670.7190.7030.6970.7740.7750.7720.6690.7000.6990.7020.7480.7530.7530.6830.7350.6980.6970.6540.7170.7310.6890.6870.7550.7360.7120.5790.6940.7550.7200.6580.7240.6920.6790.7170.6970.6650.6170.7190.6970.4950.6340.5190.4720.6480.6400.6380.6570.6870.6590.6780.6710.6920.6700.5740.5442497OrganismalFitnessSYUA_HUMANMediumHuman
TADBP_HUMAN_Bolognesi_20190.5550.5380.5540.5520.5470.5450.6200.5030.5440.5420.5120.5250.5240.5520.5170.5160.4470.5020.5330.4740.5680.5320.5040.5020.5820.5510.5170.5250.4980.5250.5540.5700.4710.5950.6190.5670.5710.5900.5690.5560.5660.5630.5380.5840.4990.5280.6380.5240.5900.5290.5680.5080.5030.5210.5030.5230.5320.5020.4690.5170.5180.5491196OrganismalFitnessTADBP_HUMANLowHuman
TAT_HV1BR_Fernandes_20160.7070.6400.6780.6870.7210.7080.4390.7740.6730.6850.6160.7380.7400.4790.4990.4980.5220.4610.5290.6460.7600.7580.7750.7810.7720.6800.5910.6700.6490.7560.7870.7790.7090.7670.6490.6390.7650.6880.6650.7570.7090.6850.5090.4650.7120.6250.6500.7020.7260.6400.5800.5820.5980.5830.5760.5850.5730.5770.5710.5860.6070.5811577OrganismalFitnessTAT_HV1BRHighVirus
TCRG1_MOUSE_Tsuboyama_2023_1E0L0.8370.8520.8800.8850.8870.8880.8830.6860.8920.9180.8740.9220.9330.9080.9140.9400.9310.9150.9290.8370.7850.8430.8370.8650.8540.8610.8880.8840.8510.9090.8990.8860.8400.7770.8500.8850.8950.9070.9100.8970.9080.9060.9050.5880.8270.9010.8530.7970.9280.9170.9180.9070.8980.9310.9280.9310.9220.9300.9190.9280.9340.9351058StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG0.7070.7600.8100.8050.8110.8100.5090.5610.8510.8450.8360.8040.8280.5760.8480.8490.8400.8510.8570.8310.4410.6920.6950.6680.6830.7170.6180.7500.7950.8520.8530.8430.7750.4690.7210.6860.7840.8160.8090.8030.8250.8240.6410.5470.7970.7110.7960.7960.8120.8680.8600.8510.8660.8660.8700.8560.8580.8550.8580.8610.8260.7751279StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT0.7120.7420.7740.7670.7690.7690.4220.7100.7950.7950.7570.7620.7650.5050.7600.7910.7780.7760.7850.7520.6430.6910.7090.7070.6960.7260.6900.7040.7190.7790.7530.7320.6020.5360.6700.6820.7670.7660.7630.7860.7750.7740.6530.4650.7810.7350.7330.8130.8390.8160.7720.7830.7760.7720.7720.7670.7740.7700.7720.7770.8140.8001479StabilityTNKS2_HUMANHighHuman
TPK1_HUMAN_Weile_20170.6220.6310.6260.6290.6320.6310.5470.6390.6620.6520.6580.6530.6750.5710.6180.6440.6840.6800.7060.6400.5550.5750.6180.6460.5690.6470.6520.6350.6690.6410.6420.6060.5630.5710.6140.6690.6320.6380.6710.6400.6400.6550.5780.5600.6730.6010.6170.6450.6330.5590.6360.6550.6490.6550.6630.6640.6560.6580.6600.6640.6520.6223181OrganismalFitnessTPK1_HUMANMediumHuman
TPMT_HUMAN_Matreyek_20180.7220.7540.7730.7820.7780.7870.6560.7610.7860.7870.8120.7850.8020.7060.7510.8140.8070.7560.7490.7790.6940.7250.7610.7660.7420.7770.7570.7460.7360.8050.8020.7790.7080.6980.7680.7190.7850.7970.7650.7990.8050.7830.7360.6370.7970.7770.7790.7880.8070.6410.7820.7850.7910.7760.7950.7930.7950.7900.7980.7980.8270.7783648ExpressionTPMT_HUMANMediumHuman
TPOR_HUMAN_Bridgford_20200.7230.6950.6770.6550.6580.6670.7650.7520.7180.7210.7390.7320.7390.6940.7480.7040.6850.7360.6780.6770.6810.7370.7140.7260.7490.6340.8060.7800.7080.7410.7510.6300.7470.7560.7300.7510.7760.7680.7730.7500.7390.7570.7090.7120.7170.7380.5780.6900.6810.5110.7020.6760.7220.7010.6840.6990.7390.6430.6720.6990.7490.752562OrganismalFitnessTPOR_HUMANLowHuman
TRPC_SACS2_Chan_20170.8320.8410.8180.8270.8290.8340.6300.8040.8730.8800.8580.8490.8690.6890.8170.8580.8640.8600.8470.8350.7580.8320.8000.8320.7920.7980.8170.8030.7990.8080.8460.8080.6200.8040.8290.8270.8340.8480.8440.8410.8540.8470.7630.5390.8500.8000.6740.7880.7930.5640.8420.8330.8380.8670.8510.8500.8530.8600.8470.8630.8580.8361519OrganismalFitnessTRPC_SACS2MediumProkaryote
TRPC_THEMA_Chan_20170.7680.7800.7600.7640.7600.7620.6910.7480.7940.7900.7720.7970.8070.6610.7960.7970.7940.7930.8110.7730.7280.7600.7620.7830.7560.7630.7460.7440.7870.7530.7650.7320.6030.7310.7840.7750.7620.7840.7790.7700.7830.7770.7570.5580.7900.7540.6400.7210.7300.5200.7840.7510.7650.7840.7840.7570.7770.7770.7650.7830.8140.7941519OrganismalFitnessTRPC_THEMAMediumProkaryote
UBC9_HUMAN_Weile_20170.6970.7710.7960.8010.7880.7940.4770.7200.7870.7910.7310.7730.7900.4980.4990.7160.7560.7860.8030.8160.6250.7270.7460.7320.7340.7600.7590.7470.7450.7660.7570.7450.5510.6300.7270.7400.6780.7520.7620.7490.7920.8030.6480.4930.7330.7740.6840.6950.7540.6430.7360.7270.7200.7350.7300.7310.7350.7390.7410.7380.7150.7362563OrganismalFitnessUBC9_HUMANMediumHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X0.7030.8110.8290.8280.8340.8330.6280.7800.8100.8240.8150.8450.8370.7510.8100.8540.8430.8470.8580.8180.6600.7920.7950.8010.7840.8030.7870.7890.8070.8300.8630.8520.8130.6120.6150.7570.7720.7690.7840.8330.8310.8230.7630.4840.7200.7260.8050.7630.8450.7550.8260.8430.8380.8450.8450.8400.8390.8430.8420.8430.8610.8133622StabilityUBE4B_HUMANHighHuman
UBE4B_MOUSE_Starita_20130.7790.7990.8240.8310.8420.8440.5360.5940.7960.8050.7620.8380.8460.8170.8380.8470.8340.7860.7520.7980.5900.7640.7490.7320.8370.8000.7860.7750.7310.8260.8450.8250.5340.4920.5800.6980.7820.7490.7760.8340.8280.8420.8150.4990.8140.8260.6940.7730.4690.5400.8140.7980.8320.8360.8200.8340.8130.8290.8260.8330.8140.791899ActivityUBE4B_MOUSELowEukaryote
UBR5_HUMAN_Tsuboyama_2023_1I2T0.7350.8320.8320.8340.8370.8450.6010.6650.7980.8160.8130.7420.7910.6270.6350.5870.5880.8400.8350.7950.7540.7490.7770.8040.7650.7840.7950.7890.8010.8150.7840.7170.6690.6680.7580.7420.7600.7860.7840.8450.8460.8450.5550.5520.7070.5890.7840.7160.8540.8200.8370.8530.8580.8600.8600.8640.8520.8580.8490.8580.8710.8261453StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP80.5830.6230.5910.6090.6120.6090.6180.4670.6880.6810.8310.7850.7900.6740.7610.8080.8440.8170.8500.5660.7010.7650.7470.8130.7150.7360.7290.7520.8150.7660.7820.7150.4870.6520.7600.7800.6590.7000.7530.6550.6840.7090.7370.6370.7810.7520.7420.7830.7920.7590.8400.8210.8420.8440.8420.8600.8220.8400.8570.8490.8190.809723StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU50.6870.7720.8690.8940.8960.8970.5520.8110.8570.8740.9140.8860.8960.6950.6180.9040.9120.8800.8170.8620.6680.7570.8420.8120.7930.8260.8120.7920.8430.8800.9190.9130.6920.6830.7910.7890.8120.8400.8310.8940.8980.8880.6260.6600.7900.7950.8160.7930.9220.8610.9200.9210.9250.9250.9190.9270.9190.9220.9240.9250.9090.8912568StabilityVILI_CHICKHighEukaryote
VKOR1_HUMAN_Chiasson_2020_abundance0.7040.7110.7120.7230.7340.7360.6010.7100.7640.7720.7380.7420.7530.6040.7070.7540.7620.7590.7570.7500.6260.6360.7050.7320.6720.7430.7400.7240.7260.7520.7150.6660.5540.6010.6640.7640.7270.7380.7840.7470.7540.7790.5950.5700.7490.6790.7340.7440.7620.6440.7790.7650.7880.7950.7890.7820.7830.7720.7800.7890.7770.7422695ExpressionVKOR1_HUMANMediumHuman
VKOR1_HUMAN_Chiasson_2020_activity0.7050.7200.7290.7390.7420.7510.5250.7200.7500.7570.7490.7550.7660.5340.6870.7120.7540.7600.7540.7470.5540.5680.6640.6840.5920.7300.7220.7170.7330.7480.7440.7180.6010.5340.5980.7210.7150.7210.7420.7470.7530.7580.5630.5400.7460.6460.6210.6880.7110.5410.7420.7090.7210.7300.7340.7270.7320.7320.7290.7360.7480.693697ActivityVKOR1_HUMANMediumHuman
VRPI_BPT7_Tsuboyama_2023_2WNM0.4220.5140.5870.6150.5500.5720.5480.6030.7500.7480.8010.7220.7330.6690.7550.8110.8600.8740.8130.6160.4880.5670.5750.6360.6130.6220.5700.6220.6640.5630.8090.7690.6380.5140.5140.5530.4680.4740.4560.5280.5370.4900.6560.5210.8190.7280.8330.8120.8400.8420.8540.8450.8620.8610.8570.8690.8580.8720.8530.8680.8780.8371047StabilityVRPI_BPT7MediumVirus
YAIA_ECOLI_Tsuboyama_2023_2KVT0.6500.8160.7950.8090.8070.8030.3800.7830.7810.8040.7980.6180.7330.4320.5740.7910.8130.8440.8510.8000.4590.4040.4350.6330.4310.4090.3730.4980.7710.8030.8540.8400.4710.3630.4510.7500.6770.6820.7660.7910.7880.8060.4590.3630.5820.4790.7090.6910.8130.7670.7940.8190.7940.8130.8080.8190.8180.8350.8440.8200.8050.7431890StabilityYAIA_ECOLIMediumProkaryote
YAP1_HUMAN_Araya_20120.7260.6740.7430.7420.7350.7380.6730.6540.5240.5340.6770.6490.6540.7160.7360.7340.7400.6980.6670.6070.5980.5970.5880.5920.6570.6230.6370.6120.5860.6770.7290.7400.5690.6600.6050.6180.7120.6720.6910.7280.7120.7310.7500.4540.6730.7500.6790.6850.7000.6030.7340.6990.7250.7210.7180.7130.7160.7290.7320.7280.6980.72010075BindingYAP1_HUMANLowHuman
YNZC_BACSU_Tsuboyama_2023_2JVD0.9040.9270.9550.9540.9480.9450.8190.8530.9390.9410.9690.9670.9710.8740.8830.9730.9740.9610.9700.9420.8480.9120.9290.9330.9230.9300.8860.9340.9520.9230.9420.9190.9110.8560.9070.8970.9220.9260.9230.9420.9380.9440.7320.6580.8260.7390.7750.8250.9800.9320.9710.9640.9700.9710.9700.9730.9700.9670.9710.9710.9590.9642300StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_Uniprot_Selection_Type_level.csv b/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_Uniprot_Selection_Type_level.csv new file mode 100644 index 0000000..91e9b18 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/AUC/DMS_substitutions_AUC_Uniprot_Selection_Type_level.csv @@ -0,0 +1,7 @@ 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+0.711,0.834,0.841,0.85,0.831,0.841,0.554,0.707,0.832,0.851,0.83,0.816,0.832,0.678,0.748,0.806,0.847,0.783,0.72,0.806,0.762,0.758,0.746,0.745,0.773,0.802,0.803,0.77,0.713,0.807,0.856,0.846,0.576,0.755,0.748,0.725,0.798,0.798,0.798,0.84,0.844,0.844,0.74,0.52,0.826,0.775,0.779,0.829,0.815,0.657,0.838,0.837,0.841,0.849,0.848,0.848,0.852,0.839,0.844,0.854,0.854,0.776,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.711,0.834,0.841,0.85,0.831,0.841,0.554,0.707,0.832,0.851,0.83,0.816,0.832,0.678,0.748,0.806,0.847,0.783,0.72,0.806,0.762,0.758,0.746,0.745,0.773,0.802,0.803,0.77,0.713,0.807,0.856,0.846,0.576,0.755,0.748,0.725,0.798,0.798,0.798,0.84,0.844,0.844,0.74,0.52,0.826,0.775,0.779,0.829,0.815,0.657,0.838,0.837,0.841,0.849,0.848,0.848,0.852,0.839,0.844,0.854,0.854,0.776,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.824,0.756,0.788,0.774,0.776,0.8,0.566,0.683,0.76,0.772,0.836,0.775,0.805,0.582,0.767,0.823,0.86,0.845,0.788,0.699,0.577,0.762,0.778,0.774,0.705,0.82,0.814,0.801,0.815,0.776,0.86,0.832,0.658,0.603,0.621,0.79,0.792,0.792,0.846,0.806,0.806,0.855,0.642,0.58,0.865,0.815,0.804,0.809,0.57,0.587,0.856,0.85,0.86,0.852,0.861,0.862,0.858,0.857,0.853,0.864,0.864,0.832,OrganismalFitness,BRCA1_HUMAN,Low,Human +0.908,0.816,0.866,0.861,0.85,0.875,0.588,0.893,0.702,0.556,0.906,0.593,0.526,0.529,0.528,0.914,0.94,0.904,0.899,0.584,0.515,0.921,0.923,0.861,0.928,0.888,0.896,0.887,0.587,0.86,0.785,0.85,0.558,0.549,0.581,0.59,0.822,0.817,0.825,0.833,0.819,0.837,0.59,0.553,0.892,0.492,0.48,0.503,0.279,0.76,0.871,0.899,0.896,0.878,0.876,0.874,0.868,0.839,0.841,0.885,0.5,0.507,OrganismalFitness,BRCA2_HUMAN,,Human +0.697,0.687,0.676,0.68,0.719,0.72,0.485,0.731,0.686,0.689,0.526,0.717,0.747,0.49,0.488,0.495,0.75,0.705,0.716,0.674,0.701,0.685,0.693,0.678,0.621,0.734,0.715,0.736,0.69,0.742,0.731,0.681,0.559,0.683,0.691,0.701,0.71,0.713,0.719,0.724,0.724,0.729,0.487,0.481,0.644,0.498,0.761,0.776,0.777,0.619,0.756,0.756,0.767,0.769,0.774,0.763,0.772,0.768,0.768,0.773,0.657,0.593,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +0.594,0.631,0.63,0.627,0.63,0.631,0.588,0.61,0.635,0.644,0.646,0.629,0.65,0.58,0.602,0.602,0.615,0.623,0.627,0.63,0.599,0.629,0.636,0.644,0.631,0.662,0.66,0.676,0.684,0.641,0.619,0.58,0.553,0.621,0.648,0.674,0.621,0.639,0.653,0.634,0.638,0.639,0.619,0.58,0.658,0.643,0.552,0.58,0.592,0.548,0.598,0.598,0.599,0.593,0.6,0.594,0.596,0.602,0.593,0.6,0.659,0.639,OrganismalFitness,CALM1_HUMAN,High,Human +0.698,0.67,0.665,0.688,0.67,0.662,0.703,0.724,0.66,0.679,0.588,0.609,0.61,0.643,0.659,0.61,0.649,0.594,0.553,0.63,0.606,0.641,0.648,0.65,0.614,0.61,0.644,0.614,0.717,0.719,0.586,0.582,0.56,0.612,0.644,0.757,0.686,0.689,0.74,0.668,0.67,0.716,0.558,0.594,0.644,0.564,0.713,0.703,0.673,0.666,0.614,0.61,0.603,0.615,0.604,0.605,0.607,0.602,0.613,0.609,0.668,0.629,OrganismalFitness,CAPSD_AAV2S,Low,Virus +0.652,0.588,0.576,0.573,0.586,0.594,0.566,0.456,0.586,0.59,0.642,0.68,0.657,0.534,0.554,0.696,0.676,0.682,0.692,0.574,0.568,0.586,0.594,0.592,0.485,0.582,0.594,0.601,0.558,0.579,0.631,0.618,0.646,0.507,0.573,0.534,0.598,0.599,0.567,0.599,0.596,0.57,0.548,0.514,0.632,0.642,0.657,0.641,0.667,0.565,0.693,0.689,0.691,0.68,0.678,0.686,0.677,0.686,0.692,0.69,0.728,0.68,OrganismalFitness,CAR11_HUMAN,Low,Human +0.652,0.588,0.576,0.573,0.586,0.594,0.566,0.456,0.586,0.59,0.642,0.68,0.657,0.534,0.554,0.696,0.676,0.682,0.692,0.574,0.568,0.586,0.594,0.592,0.485,0.582,0.594,0.601,0.558,0.579,0.631,0.618,0.646,0.507,0.573,0.534,0.598,0.599,0.567,0.599,0.596,0.57,0.548,0.514,0.632,0.642,0.657,0.641,0.667,0.565,0.693,0.689,0.691,0.68,0.678,0.686,0.677,0.686,0.692,0.69,0.728,0.68,OrganismalFitness,CAR11_HUMAN,Low,Human +0.58,0.594,0.587,0.589,0.59,0.593,0.516,0.549,0.593,0.595,0.588,0.532,0.533,0.528,0.544,0.578,0.594,0.597,0.6,0.504,0.519,0.537,0.585,0.56,0.517,0.599,0.592,0.589,0.534,0.596,0.603,0.593,0.513,0.514,0.521,0.582,0.584,0.584,0.593,0.595,0.595,0.598,0.528,0.512,0.592,0.551,0.551,0.592,0.513,0.517,0.59,0.591,0.591,0.592,0.591,0.592,0.59,0.59,0.591,0.593,0.587,0.556,Activity,CAS9_STRP1,Medium,Prokaryote +0.694,0.776,0.814,0.818,0.822,0.825,0.532,0.735,0.825,0.833,0.799,0.805,0.818,0.637,0.792,0.828,0.824,0.783,0.751,0.813,0.61,0.748,0.757,0.779,0.756,0.781,0.785,0.757,0.784,0.809,0.804,0.773,0.621,0.537,0.739,0.765,0.768,0.782,0.797,0.821,0.816,0.824,0.709,0.492,0.813,0.784,0.707,0.78,0.751,0.638,0.781,0.787,0.791,0.787,0.791,0.785,0.797,0.785,0.787,0.797,0.833,0.762,Activity,CASP3_HUMAN,High,Human +0.713,0.782,0.835,0.835,0.831,0.833,0.537,0.767,0.828,0.842,0.815,0.835,0.845,0.669,0.826,0.838,0.84,0.816,0.803,0.832,0.613,0.799,0.796,0.811,0.798,0.819,0.822,0.816,0.81,0.85,0.83,0.8,0.682,0.552,0.804,0.787,0.782,0.831,0.822,0.825,0.847,0.842,0.76,0.509,0.848,0.825,0.763,0.835,0.817,0.674,0.823,0.816,0.81,0.827,0.836,0.833,0.827,0.817,0.828,0.835,0.847,0.793,Activity,CASP7_HUMAN,Medium,Human +0.796,0.813,0.85,0.853,0.86,0.863,0.87,0.818,0.81,0.791,0.847,0.852,0.864,0.798,0.869,0.87,0.866,0.837,0.862,0.521,0.844,0.835,0.841,0.836,0.857,0.852,0.835,0.817,0.829,0.866,0.846,0.848,0.734,0.851,0.836,0.853,0.862,0.853,0.869,0.856,0.854,0.864,0.748,0.732,0.691,0.758,0.76,0.744,0.873,0.799,0.868,0.866,0.878,0.872,0.883,0.868,0.869,0.862,0.865,0.874,0.851,0.874,Stability,CATR_CHLRE,High,Eukaryote +0.854,0.892,0.904,0.906,0.891,0.899,0.748,0.832,0.918,0.921,0.911,0.893,0.888,0.785,0.86,0.898,0.897,0.905,0.898,0.912,0.769,0.778,0.866,0.867,0.79,0.852,0.851,0.852,0.884,0.902,0.899,0.868,0.516,0.767,0.834,0.869,0.877,0.881,0.895,0.903,0.9,0.908,0.772,0.663,0.828,0.843,0.905,0.893,0.959,0.929,0.929,0.928,0.93,0.927,0.933,0.931,0.93,0.928,0.926,0.932,0.902,0.951,Stability,CBPA2_HUMAN,Medium,Human +0.687,0.701,0.707,0.717,0.713,0.717,0.613,0.644,0.71,0.712,0.696,0.696,0.71,0.545,0.627,0.689,0.692,0.684,0.69,0.696,0.699,0.646,0.659,0.662,0.696,0.652,0.662,0.652,0.674,0.714,0.711,0.698,0.611,0.702,0.656,0.648,0.715,0.685,0.683,0.724,0.705,0.706,0.673,0.534,0.714,0.711,0.633,0.655,0.676,0.547,0.683,0.684,0.688,0.691,0.691,0.69,0.687,0.688,0.689,0.693,0.72,0.664,OrganismalFitness,CBS_HUMAN,Medium,Human +0.752,0.773,0.844,0.865,0.811,0.847,0.339,0.819,0.812,0.808,0.812,0.829,0.828,0.344,0.878,0.855,0.844,0.801,0.802,0.828,0.753,0.803,0.797,0.797,0.783,0.793,0.799,0.767,0.793,0.815,0.845,0.84,0.719,0.714,0.75,0.765,0.802,0.799,0.81,0.844,0.856,0.859,0.816,0.317,0.682,0.786,0.54,0.561,0.796,0.783,0.853,0.809,0.85,0.838,0.861,0.839,0.839,0.871,0.835,0.852,0.826,0.888,Stability,CBX4_HUMAN,High,Human +0.757,0.805,0.814,0.832,0.817,0.826,0.483,0.664,0.746,0.761,0.798,0.754,0.796,0.491,0.49,0.774,0.813,0.808,0.693,0.813,0.521,0.492,0.439,0.597,0.41,0.552,0.483,0.526,0.777,0.802,0.854,0.838,0.568,0.497,0.552,0.73,0.794,0.787,0.815,0.816,0.812,0.83,0.502,0.465,0.798,0.506,0.688,0.774,0.705,0.657,0.795,0.775,0.778,0.794,0.794,0.793,0.79,0.802,0.818,0.801,0.798,0.672,Activity,CCDB_ECOLI,High,Prokaryote +0.757,0.805,0.814,0.832,0.817,0.826,0.483,0.664,0.746,0.761,0.798,0.754,0.796,0.491,0.49,0.774,0.813,0.808,0.693,0.813,0.521,0.492,0.439,0.597,0.41,0.552,0.483,0.526,0.777,0.802,0.854,0.838,0.568,0.497,0.552,0.73,0.794,0.787,0.815,0.816,0.812,0.83,0.502,0.465,0.798,0.506,0.688,0.774,0.705,0.657,0.795,0.775,0.778,0.794,0.794,0.793,0.79,0.802,0.818,0.801,0.798,0.672,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +0.634,0.637,0.638,0.639,0.634,0.638,0.649,0.644,0.662,0.668,0.675,0.664,0.67,0.651,0.67,0.674,0.668,0.662,0.664,0.67,0.675,0.673,0.657,0.659,0.675,0.67,0.665,0.673,0.66,0.674,0.644,0.619,0.557,0.675,0.676,0.678,0.678,0.682,0.685,0.658,0.658,0.659,0.668,0.59,0.676,0.67,0.633,0.647,0.638,0.585,0.65,0.647,0.652,0.652,0.649,0.652,0.657,0.657,0.657,0.657,0.668,0.671,Binding,CCR5_HUMAN,High,Human +0.623,0.617,0.612,0.621,0.633,0.632,0.574,0.58,0.597,0.599,0.591,0.598,0.613,0.603,0.62,0.6,0.599,0.607,0.589,0.492,0.597,0.6,0.628,0.617,0.593,0.648,0.616,0.607,0.63,0.647,0.599,0.574,0.581,0.606,0.631,0.609,0.63,0.636,0.628,0.642,0.644,0.639,0.61,0.568,0.594,0.613,0.728,0.683,0.72,0.608,0.644,0.648,0.643,0.662,0.654,0.653,0.658,0.647,0.653,0.659,0.755,0.71,Binding,CD19_HUMAN,Low,Human +0.753,0.788,0.802,0.812,0.798,0.808,0.778,0.794,0.8,0.812,0.755,0.807,0.822,0.776,0.814,0.827,0.828,0.825,0.802,0.814,0.787,0.79,0.804,0.783,0.799,0.794,0.796,0.794,0.789,0.809,0.796,0.768,0.61,0.806,0.792,0.786,0.82,0.818,0.816,0.823,0.82,0.818,0.802,0.546,0.772,0.819,0.792,0.783,0.822,0.619,0.812,0.812,0.818,0.826,0.827,0.826,0.824,0.818,0.821,0.83,0.828,0.822,Expression,CP2C9_HUMAN,High,Human +0.753,0.788,0.802,0.812,0.798,0.808,0.778,0.794,0.8,0.812,0.755,0.807,0.822,0.776,0.814,0.827,0.828,0.825,0.802,0.814,0.787,0.79,0.804,0.783,0.799,0.794,0.796,0.794,0.789,0.809,0.796,0.768,0.61,0.806,0.792,0.786,0.82,0.818,0.816,0.823,0.82,0.818,0.802,0.546,0.772,0.819,0.792,0.783,0.822,0.619,0.812,0.812,0.818,0.826,0.827,0.826,0.824,0.818,0.821,0.83,0.828,0.822,Binding,CP2C9_HUMAN,High,Human +0.695,0.831,0.803,0.81,0.797,0.805,0.653,0.791,0.817,0.821,0.842,0.859,0.879,0.728,0.886,0.828,0.781,0.821,0.82,0.79,0.756,0.775,0.836,0.845,0.741,0.857,0.849,0.834,0.835,0.833,0.818,0.811,0.67,0.704,0.784,0.83,0.779,0.798,0.831,0.815,0.828,0.84,0.674,0.621,0.754,0.767,0.785,0.776,0.839,0.86,0.846,0.846,0.842,0.858,0.86,0.861,0.853,0.868,0.842,0.857,0.848,0.873,Stability,CSN4_MOUSE,Medium,Eukaryote +0.666,0.715,0.699,0.7,0.725,0.709,0.551,0.702,0.755,0.764,0.736,0.694,0.672,0.563,0.695,0.717,0.763,0.74,0.73,0.652,0.564,0.592,0.597,0.522,0.538,0.588,0.601,0.6,0.69,0.702,0.733,0.707,0.62,0.562,0.556,0.587,0.678,0.682,0.684,0.704,0.705,0.709,0.562,0.51,0.713,0.603,0.748,0.743,0.813,0.728,0.769,0.749,0.746,0.772,0.753,0.771,0.765,0.759,0.755,0.765,0.79,0.771,Stability,CUE1_YEAST,Medium,Eukaryote +0.781,0.83,0.828,0.81,0.835,0.835,0.531,0.75,0.849,0.851,0.732,0.53,0.529,0.53,0.522,0.512,0.52,0.531,0.575,0.735,0.514,0.519,0.554,0.541,0.5,0.521,0.535,0.601,0.69,0.831,0.805,0.813,0.514,0.549,0.563,0.58,0.783,0.783,0.781,0.834,0.833,0.828,0.496,0.506,0.498,0.508,0.636,0.652,0.76,0.706,0.759,0.756,0.758,0.764,0.756,0.761,0.758,0.756,0.755,0.76,0.668,0.6,Activity,D7PM05_CLYGR,Low,Eukaryote +0.877,0.822,0.834,0.816,0.834,0.838,0.904,0.853,0.791,0.795,0.781,0.801,0.834,0.909,0.921,0.905,0.822,0.764,0.741,0.854,0.812,0.818,0.809,0.791,0.834,0.832,0.809,0.804,0.768,0.839,0.855,0.848,0.777,0.816,0.852,0.817,0.861,0.888,0.867,0.854,0.865,0.849,0.827,0.613,0.708,0.793,0.839,0.741,0.868,0.668,0.768,0.7,0.798,0.774,0.786,0.77,0.782,0.77,0.779,0.778,0.783,0.913,OrganismalFitness,DLG4_HUMAN,Low,Human +0.83,0.771,0.789,0.801,0.84,0.848,0.818,0.794,0.824,0.847,0.806,0.869,0.88,0.796,0.883,0.884,0.857,0.823,0.796,0.8,0.764,0.769,0.767,0.752,0.789,0.795,0.781,0.755,0.756,0.834,0.848,0.833,0.685,0.759,0.763,0.724,0.821,0.816,0.804,0.855,0.853,0.854,0.847,0.552,0.777,0.834,0.791,0.709,0.814,0.593,0.791,0.76,0.801,0.771,0.804,0.79,0.775,0.797,0.803,0.81,0.82,0.865,Binding,DLG4_RAT,Low,Eukaryote +0.574,0.58,0.601,0.61,0.601,0.625,0.424,0.668,0.66,0.672,0.496,0.539,0.531,0.502,0.567,0.581,0.63,0.647,0.681,0.604,0.44,0.5,0.493,0.508,0.498,0.533,0.5,0.511,0.59,0.647,0.722,0.699,0.522,0.469,0.467,0.486,0.593,0.589,0.593,0.614,0.606,0.608,0.522,0.451,0.6,0.53,0.787,0.782,0.837,0.783,0.683,0.675,0.738,0.73,0.72,0.718,0.709,0.689,0.682,0.716,0.759,0.723,Stability,DN7A_SACS2,Medium,Prokaryote +0.928,0.937,0.937,0.937,0.944,0.943,0.887,0.916,0.938,0.945,0.946,0.937,0.949,0.95,0.955,0.955,0.946,0.935,0.953,0.937,0.862,0.906,0.924,0.914,0.929,0.922,0.93,0.896,0.929,0.938,0.92,0.909,0.864,0.928,0.929,0.943,0.954,0.955,0.96,0.948,0.95,0.953,0.846,0.582,0.711,0.85,0.822,0.766,0.928,0.933,0.932,0.936,0.934,0.948,0.945,0.95,0.948,0.944,0.949,0.946,0.926,0.946,Stability,DNJA1_HUMAN,High,Human +0.714,0.738,0.718,0.729,0.757,0.761,0.521,0.72,0.686,0.687,0.749,0.727,0.741,0.607,0.719,0.736,0.753,0.744,0.751,0.764,0.654,0.696,0.678,0.685,0.645,0.677,0.659,0.646,0.673,0.721,0.719,0.72,0.591,0.597,0.652,0.612,0.735,0.743,0.739,0.751,0.752,0.758,0.666,0.4,0.741,0.711,0.668,0.672,0.703,0.707,0.743,0.734,0.742,0.746,0.738,0.748,0.729,0.74,0.747,0.742,0.767,0.752,Stability,DOCK1_MOUSE,High,Eukaryote +0.751,0.822,0.826,0.83,0.832,0.834,0.477,0.777,0.824,0.83,0.828,0.822,0.83,0.548,0.789,0.824,0.838,0.846,0.842,0.832,0.692,0.776,0.7,0.74,0.781,0.794,0.816,0.816,0.792,0.833,0.84,0.826,0.614,0.796,0.735,0.729,0.819,0.784,0.793,0.838,0.826,0.83,0.778,0.49,0.824,0.803,0.643,0.76,0.714,0.565,0.802,0.778,0.785,0.788,0.796,0.794,0.804,0.801,0.815,0.806,0.826,0.765,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.751,0.822,0.826,0.83,0.832,0.834,0.477,0.777,0.824,0.83,0.828,0.822,0.83,0.548,0.789,0.824,0.838,0.846,0.842,0.832,0.692,0.776,0.7,0.74,0.781,0.794,0.816,0.816,0.792,0.833,0.84,0.826,0.614,0.796,0.735,0.729,0.819,0.784,0.793,0.838,0.826,0.83,0.778,0.49,0.824,0.803,0.643,0.76,0.714,0.565,0.802,0.778,0.785,0.788,0.796,0.794,0.804,0.801,0.815,0.806,0.826,0.765,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.751,0.822,0.826,0.83,0.832,0.834,0.477,0.777,0.824,0.83,0.828,0.822,0.83,0.548,0.789,0.824,0.838,0.846,0.842,0.832,0.692,0.776,0.7,0.74,0.781,0.794,0.816,0.816,0.792,0.833,0.84,0.826,0.614,0.796,0.735,0.729,0.819,0.784,0.793,0.838,0.826,0.83,0.778,0.49,0.824,0.803,0.643,0.76,0.714,0.565,0.802,0.778,0.785,0.788,0.796,0.794,0.804,0.801,0.815,0.806,0.826,0.765,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.751,0.822,0.826,0.83,0.832,0.834,0.477,0.777,0.824,0.83,0.828,0.822,0.83,0.548,0.789,0.824,0.838,0.846,0.842,0.832,0.692,0.776,0.7,0.74,0.781,0.794,0.816,0.816,0.792,0.833,0.84,0.826,0.614,0.796,0.735,0.729,0.819,0.784,0.793,0.838,0.826,0.83,0.778,0.49,0.824,0.803,0.643,0.76,0.714,0.565,0.802,0.778,0.785,0.788,0.796,0.794,0.804,0.801,0.815,0.806,0.826,0.765,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.571,0.569,0.61,0.61,0.619,0.618,0.503,0.616,0.608,0.614,0.606,0.625,0.636,0.607,0.615,0.618,0.613,0.6,0.584,0.614,0.605,0.601,0.632,0.625,0.606,0.611,0.626,0.621,0.615,0.618,0.598,0.595,0.559,0.602,0.612,0.621,0.618,0.623,0.63,0.621,0.625,0.625,0.611,0.566,0.612,0.61,0.528,0.596,0.582,0.525,0.623,0.62,0.604,0.617,0.639,0.625,0.615,0.625,0.627,0.627,0.594,0.637,Activity,ENVZ_ECOLI,High,Prokaryote +0.747,0.744,0.688,0.742,0.765,0.761,0.503,0.704,0.732,0.751,0.722,0.791,0.777,0.533,0.515,0.496,0.47,0.507,0.539,0.751,0.753,0.74,0.768,0.771,0.707,0.783,0.769,0.746,0.747,0.767,0.768,0.757,0.674,0.767,0.767,0.771,0.774,0.776,0.772,0.772,0.773,0.774,0.719,0.451,0.748,0.759,0.736,0.713,0.731,0.651,0.643,0.695,0.704,0.676,0.655,0.653,0.667,0.659,0.644,0.682,0.607,0.587,OrganismalFitness,ENV_HV1B9,Medium,Virus +0.669,0.652,0.66,0.661,0.671,0.673,0.499,0.659,0.674,0.673,0.649,0.659,0.669,0.495,0.495,0.497,0.519,0.529,0.577,0.667,0.676,0.68,0.687,0.682,0.673,0.68,0.68,0.676,0.682,0.677,0.665,0.649,0.599,0.673,0.682,0.681,0.681,0.685,0.684,0.685,0.687,0.684,0.615,0.49,0.66,0.649,0.597,0.616,0.553,0.534,0.585,0.581,0.594,0.607,0.587,0.594,0.595,0.59,0.589,0.598,0.577,0.541,OrganismalFitness,ENV_HV1BR,Medium,Virus +0.839,0.898,0.899,0.902,0.914,0.915,0.309,0.906,0.948,0.952,0.944,0.946,0.953,0.286,0.941,0.954,0.963,0.945,0.94,0.923,0.831,0.86,0.899,0.889,0.88,0.898,0.905,0.913,0.92,0.954,0.94,0.936,0.886,0.86,0.859,0.879,0.908,0.908,0.916,0.919,0.918,0.917,0.85,0.341,0.857,0.861,0.83,0.828,0.943,0.909,0.957,0.954,0.956,0.955,0.961,0.958,0.958,0.958,0.955,0.96,0.949,0.944,Stability,EPHB2_HUMAN,High,Human +0.697,0.691,0.641,0.642,0.634,0.645,0.717,0.694,0.702,0.715,0.719,0.73,0.738,0.737,0.732,0.735,0.706,0.692,0.652,0.525,0.721,0.729,0.752,0.725,0.74,0.747,0.778,0.782,0.734,0.692,0.716,0.543,0.732,0.768,0.728,0.736,0.747,0.731,0.737,0.728,0.723,0.72,0.739,0.735,0.764,0.734,0.718,0.73,0.407,0.527,0.698,0.693,0.72,0.702,0.699,0.699,0.709,0.717,0.721,0.714,0.756,0.741,Expression,ERBB2_HUMAN,Low,Human +0.637,0.694,0.702,0.711,0.696,0.694,0.577,0.665,0.669,0.707,0.659,0.645,0.662,0.576,0.621,0.629,0.645,0.641,0.664,0.654,0.548,0.591,0.634,0.648,0.628,0.62,0.653,0.634,0.696,0.69,0.724,0.711,0.534,0.579,0.63,0.641,0.655,0.665,0.673,0.7,0.703,0.703,0.588,0.552,0.637,0.623,0.783,0.743,0.774,0.681,0.677,0.664,0.673,0.666,0.682,0.67,0.666,0.677,0.675,0.679,0.691,0.594,Stability,ESTA_BACSU,High,Prokaryote +0.537,0.668,0.668,0.689,0.683,0.684,0.517,0.633,0.668,0.672,0.689,0.669,0.674,0.475,0.513,0.531,0.671,0.665,0.687,0.67,0.479,0.457,0.458,0.503,0.555,0.559,0.501,0.633,0.677,0.659,0.698,0.687,0.518,0.498,0.46,0.685,0.537,0.515,0.684,0.664,0.658,0.691,0.488,0.479,0.599,0.492,0.498,0.616,0.528,0.519,0.687,0.692,0.68,0.7,0.696,0.697,0.686,0.692,0.695,0.694,0.626,0.458,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.537,0.668,0.668,0.689,0.683,0.684,0.517,0.633,0.668,0.672,0.689,0.669,0.674,0.475,0.513,0.531,0.671,0.665,0.687,0.67,0.479,0.457,0.458,0.503,0.555,0.559,0.501,0.633,0.677,0.659,0.698,0.687,0.518,0.498,0.46,0.685,0.537,0.515,0.684,0.664,0.658,0.691,0.488,0.479,0.599,0.492,0.498,0.616,0.528,0.519,0.687,0.692,0.68,0.7,0.696,0.697,0.686,0.692,0.695,0.694,0.626,0.458,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.705,0.727,0.746,0.751,0.746,0.742,0.51,0.677,0.701,0.745,0.775,0.637,0.727,0.51,0.735,0.784,0.78,0.764,0.711,0.746,0.577,0.648,0.697,0.663,0.581,0.754,0.743,0.641,0.717,0.779,0.719,0.684,0.657,0.526,0.562,0.577,0.692,0.687,0.665,0.729,0.727,0.716,0.701,0.5,0.756,0.729,0.801,0.783,0.816,0.781,0.783,0.761,0.776,0.775,0.769,0.787,0.768,0.763,0.77,0.777,0.814,0.811,Stability,FECA_ECOLI,High,Prokaryote +0.727,0.707,0.759,0.76,0.763,0.771,0.589,0.675,0.63,0.629,0.602,0.594,0.591,0.593,0.592,0.583,0.6,0.64,0.694,0.661,0.622,0.622,0.627,0.632,0.585,0.622,0.599,0.625,0.628,0.752,0.673,0.676,0.464,0.566,0.602,0.624,0.718,0.721,0.688,0.765,0.767,0.73,0.599,0.602,0.595,0.613,0.856,0.824,0.855,0.819,0.66,0.716,0.738,0.711,0.71,0.716,0.693,0.697,0.65,0.712,0.798,0.684,Stability,FKBP3_HUMAN,Medium,Human +0.646,0.711,0.712,0.748,0.733,0.749,0.677,0.496,0.745,0.771,0.782,0.74,0.743,0.685,0.712,0.759,0.795,0.793,0.788,0.749,0.672,0.691,0.695,0.695,0.691,0.716,0.741,0.727,0.771,0.792,0.792,0.763,0.636,0.655,0.669,0.67,0.757,0.752,0.754,0.742,0.739,0.737,0.708,0.653,0.777,0.731,0.649,0.763,0.67,0.568,0.773,0.769,0.767,0.767,0.779,0.778,0.77,0.768,0.767,0.777,0.767,0.719,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +0.632,0.633,0.629,0.63,0.628,0.628,0.584,0.611,0.631,0.631,0.627,0.644,0.647,0.662,0.644,0.638,0.649,0.638,0.631,0.599,0.574,0.581,0.572,0.572,0.563,0.551,0.532,0.559,0.577,0.622,0.637,0.634,0.519,0.573,0.571,0.641,0.635,0.635,0.647,0.635,0.635,0.645,0.549,0.589,0.572,0.556,0.568,0.591,0.612,0.587,0.624,0.62,0.62,0.617,0.623,0.622,0.621,0.621,0.623,0.622,0.624,0.627,Binding,GCN4_YEAST,Low,Eukaryote +0.719,0.72,0.726,0.724,0.727,0.727,0.591,0.696,0.739,0.736,0.694,0.706,0.726,0.582,0.618,0.716,0.698,0.684,0.704,0.701,0.626,0.688,0.686,0.701,0.659,0.718,0.708,0.685,0.696,0.704,0.712,0.692,0.627,0.657,0.69,0.671,0.715,0.719,0.713,0.726,0.733,0.729,0.616,0.56,0.717,0.62,0.671,0.709,0.682,0.544,0.695,0.669,0.697,0.684,0.691,0.7,0.688,0.696,0.69,0.701,0.729,0.659,OrganismalFitness,GDIA_HUMAN,Low,Human +0.889,0.884,0.9,0.901,0.903,0.903,0.54,0.883,0.894,0.891,0.816,0.565,0.565,0.552,0.583,0.564,0.572,0.596,0.679,0.858,0.555,0.572,0.614,0.565,0.532,0.615,0.684,0.884,0.886,0.904,0.865,0.87,0.541,0.545,0.616,0.878,0.889,0.89,0.902,0.906,0.907,0.919,0.515,0.495,0.525,0.525,0.796,0.792,0.925,0.86,0.856,0.86,0.866,0.871,0.867,0.866,0.864,0.865,0.854,0.865,0.872,0.769,Activity,GFP_AEQVI,Low,Eukaryote +0.602,0.546,0.603,0.601,0.594,0.583,0.711,0.799,0.682,0.672,0.741,0.756,0.754,0.728,0.746,0.77,0.751,0.727,0.757,0.583,0.743,0.755,0.737,0.761,0.739,0.752,0.761,0.765,0.798,0.672,0.746,0.638,0.638,0.761,0.737,0.761,0.75,0.736,0.748,0.732,0.717,0.74,0.758,0.749,0.762,0.727,0.722,0.767,0.706,0.617,0.79,0.779,0.782,0.765,0.776,0.747,0.776,0.748,0.77,0.781,0.775,0.766,Expression,GLPA_HUMAN,Low,Human +0.727,0.787,0.784,0.801,0.799,0.802,0.777,0.748,0.796,0.768,0.792,0.747,0.781,0.797,0.832,0.847,0.858,0.792,0.816,0.799,0.807,0.79,0.786,0.752,0.801,0.785,0.753,0.793,0.751,0.78,0.741,0.741,0.757,0.801,0.786,0.727,0.79,0.784,0.745,0.812,0.812,0.796,0.826,0.673,0.789,0.816,0.879,0.79,0.89,0.785,0.845,0.86,0.855,0.856,0.86,0.849,0.849,0.859,0.848,0.861,0.806,0.837,OrganismalFitness,GRB2_HUMAN,Medium,Human +0.652,0.715,0.572,0.586,0.679,0.682,0.629,0.707,0.726,0.749,0.829,0.724,0.747,0.644,0.704,0.783,0.835,0.79,0.801,0.62,0.644,0.652,0.67,0.677,0.66,0.669,0.605,0.666,0.765,0.802,0.824,0.758,0.579,0.633,0.643,0.753,0.711,0.716,0.773,0.687,0.714,0.765,0.653,0.643,0.81,0.686,0.829,0.861,0.864,0.786,0.84,0.846,0.846,0.843,0.851,0.853,0.844,0.854,0.853,0.857,0.878,0.799,Stability,HCP_LAMBD,Medium,Virus +0.846,0.873,0.829,0.844,0.885,0.887,0.667,0.823,0.858,0.859,0.83,0.696,0.72,0.642,0.656,0.917,0.884,0.86,0.846,0.869,0.674,0.815,0.866,0.859,0.792,0.863,0.856,0.841,0.848,0.887,0.873,0.871,0.62,0.675,0.697,0.743,0.907,0.886,0.91,0.895,0.895,0.901,0.596,0.581,0.902,0.668,0.755,0.869,0.857,0.772,0.868,0.88,0.899,0.889,0.893,0.914,0.887,0.897,0.888,0.896,0.892,0.749,Stability,HECD1_HUMAN,Medium,Human +0.712,0.707,0.701,0.702,0.71,0.709,0.56,0.566,0.716,0.722,0.694,0.688,0.696,0.569,0.69,0.702,0.694,0.713,0.72,0.562,0.669,0.688,0.692,0.7,0.676,0.694,0.691,0.693,0.709,0.72,0.715,0.711,0.547,0.689,0.694,0.703,0.713,0.719,0.731,0.715,0.719,0.726,0.66,0.573,0.69,0.685,0.666,0.684,0.659,0.58,0.678,0.669,0.676,0.681,0.688,0.686,0.686,0.686,0.683,0.688,0.716,0.709,Activity,HEM3_HUMAN,Medium,Human +0.768,0.809,0.825,0.822,0.801,0.8,0.589,0.645,0.762,0.785,0.761,0.734,0.773,0.534,0.568,0.749,0.728,0.763,0.769,0.755,0.686,0.734,0.75,0.777,0.724,0.779,0.771,0.802,0.81,0.804,0.724,0.714,0.583,0.734,0.758,0.849,0.783,0.783,0.865,0.829,0.812,0.835,0.587,0.524,0.666,0.507,0.729,0.75,0.82,0.722,0.746,0.685,0.718,0.737,0.744,0.723,0.726,0.748,0.738,0.732,0.795,0.632,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +0.741,0.7,0.683,0.688,0.72,0.718,0.575,0.627,0.625,0.621,0.626,0.664,0.649,0.514,0.498,0.654,0.755,0.682,0.683,0.64,0.727,0.566,0.572,0.595,0.719,0.588,0.572,0.594,0.592,0.735,0.699,0.694,0.61,0.655,0.617,0.6,0.719,0.664,0.657,0.714,0.672,0.67,0.581,0.526,0.735,0.75,0.66,0.721,0.397,0.555,0.745,0.745,0.741,0.747,0.743,0.744,0.736,0.749,0.74,0.748,0.765,0.716,OrganismalFitness,HMDH_HUMAN,Low,Human +0.765,0.754,0.789,0.794,0.79,0.79,0.673,0.748,0.785,0.784,0.769,0.794,0.808,0.562,0.633,0.691,0.735,0.762,0.759,0.712,0.77,0.772,0.771,0.782,0.772,0.777,0.792,0.771,0.791,0.797,0.813,0.798,0.658,0.762,0.762,0.766,0.784,0.784,0.786,0.797,0.797,0.798,0.651,0.464,0.769,0.766,0.591,0.703,0.587,0.521,0.727,0.723,0.719,0.734,0.73,0.728,0.735,0.733,0.724,0.734,0.768,0.744,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.765,0.754,0.789,0.794,0.79,0.79,0.673,0.748,0.785,0.784,0.769,0.794,0.808,0.562,0.633,0.691,0.735,0.762,0.759,0.712,0.77,0.772,0.771,0.782,0.772,0.777,0.792,0.771,0.791,0.797,0.813,0.798,0.658,0.762,0.762,0.766,0.784,0.784,0.786,0.797,0.797,0.798,0.651,0.464,0.769,0.766,0.591,0.703,0.587,0.521,0.727,0.723,0.719,0.734,0.73,0.728,0.735,0.733,0.724,0.734,0.768,0.744,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.765,0.754,0.789,0.794,0.79,0.79,0.673,0.748,0.785,0.784,0.769,0.794,0.808,0.562,0.633,0.691,0.735,0.762,0.759,0.712,0.77,0.772,0.771,0.782,0.772,0.777,0.792,0.771,0.791,0.797,0.813,0.798,0.658,0.762,0.762,0.766,0.784,0.784,0.786,0.797,0.797,0.798,0.651,0.464,0.769,0.766,0.591,0.703,0.587,0.521,0.727,0.723,0.719,0.734,0.73,0.728,0.735,0.733,0.724,0.734,0.768,0.744,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.728,0.75,0.728,0.74,0.734,0.738,0.576,0.704,0.756,0.767,0.726,0.728,0.742,0.585,0.63,0.734,0.75,0.753,0.736,0.631,0.708,0.71,0.714,0.709,0.708,0.718,0.717,0.716,0.706,0.75,0.726,0.702,0.598,0.718,0.704,0.694,0.738,0.734,0.728,0.744,0.746,0.745,0.633,0.528,0.744,0.73,0.709,0.733,0.718,0.591,0.745,0.748,0.742,0.746,0.748,0.75,0.748,0.748,0.748,0.752,0.768,0.726,OrganismalFitness,HXK4_HUMAN,Medium,Human +0.728,0.75,0.728,0.74,0.734,0.738,0.576,0.704,0.756,0.767,0.726,0.728,0.742,0.585,0.63,0.734,0.75,0.753,0.736,0.631,0.708,0.71,0.714,0.709,0.708,0.718,0.717,0.716,0.706,0.75,0.726,0.702,0.598,0.718,0.704,0.694,0.738,0.734,0.728,0.744,0.746,0.745,0.633,0.528,0.744,0.73,0.709,0.733,0.718,0.591,0.745,0.748,0.742,0.746,0.748,0.75,0.748,0.748,0.748,0.752,0.768,0.726,Expression,HXK4_HUMAN,Medium,Human +0.677,0.664,0.639,0.638,0.685,0.683,0.495,0.659,0.645,0.663,0.503,0.507,0.506,0.509,0.51,0.501,0.507,0.503,0.543,0.608,0.663,0.669,0.691,0.691,0.504,0.508,0.554,0.497,0.655,0.695,0.627,0.63,0.572,0.652,0.666,0.673,0.665,0.676,0.677,0.693,0.701,0.704,0.502,0.511,0.506,0.509,0.608,0.604,0.623,0.549,0.533,0.552,0.564,0.563,0.554,0.551,0.542,0.545,0.527,0.551,0.521,0.504,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +0.695,0.775,0.823,0.823,0.816,0.82,0.578,0.712,0.649,0.641,0.803,0.815,0.826,0.6,0.756,0.817,0.832,0.818,0.8,0.793,0.673,0.729,0.695,0.714,0.746,0.748,0.751,0.735,0.745,0.722,0.784,0.754,0.643,0.756,0.774,0.787,0.773,0.78,0.785,0.812,0.816,0.82,0.693,0.563,0.821,0.782,0.692,0.797,0.781,0.645,0.798,0.79,0.808,0.814,0.804,0.809,0.789,0.813,0.813,0.819,0.852,0.783,OrganismalFitness,IF1_ECOLI,High,Prokaryote +0.718,0.758,0.827,0.821,0.811,0.814,0.604,0.741,0.797,0.813,0.747,0.757,0.772,0.718,0.775,0.838,0.722,0.71,0.649,0.837,0.621,0.646,0.685,0.726,0.735,0.672,0.751,0.696,0.697,0.82,0.773,0.758,0.529,0.614,0.67,0.738,0.747,0.757,0.787,0.792,0.783,0.821,0.755,0.62,0.745,0.813,0.834,0.79,0.851,0.752,0.693,0.67,0.727,0.705,0.726,0.758,0.733,0.708,0.779,0.741,0.737,0.783,Stability,ILF3_HUMAN,High,Human +0.54,0.548,0.599,0.594,0.612,0.612,0.662,0.571,0.68,0.679,0.738,0.71,0.719,0.687,0.695,0.724,0.718,0.74,0.724,0.589,0.653,0.601,0.614,0.629,0.649,0.66,0.655,0.642,0.685,0.669,0.718,0.691,0.622,0.636,0.62,0.636,0.625,0.605,0.616,0.629,0.606,0.616,0.639,0.611,0.665,0.652,0.788,0.743,0.773,0.752,0.725,0.717,0.728,0.724,0.726,0.728,0.715,0.72,0.724,0.726,0.791,0.749,Stability,ISDH_STAAW,High,Prokaryote +0.667,0.696,0.68,0.689,0.686,0.688,0.635,0.66,0.693,0.694,0.679,0.704,0.726,0.626,0.609,0.74,0.706,0.678,0.671,0.628,0.65,0.666,0.672,0.688,0.66,0.72,0.732,0.714,0.717,0.694,0.714,0.676,0.444,0.615,0.694,0.738,0.692,0.707,0.741,0.706,0.716,0.744,0.632,0.619,0.716,0.635,0.615,0.681,0.613,0.546,0.702,0.696,0.698,0.687,0.69,0.684,0.705,0.702,0.704,0.703,0.74,0.641,Expression,KCNE1_HUMAN,Medium,Human +0.667,0.696,0.68,0.689,0.686,0.688,0.635,0.66,0.693,0.694,0.679,0.704,0.726,0.626,0.609,0.74,0.706,0.678,0.671,0.628,0.65,0.666,0.672,0.688,0.66,0.72,0.732,0.714,0.717,0.694,0.714,0.676,0.444,0.615,0.694,0.738,0.692,0.707,0.741,0.706,0.716,0.744,0.632,0.619,0.716,0.635,0.615,0.681,0.613,0.546,0.702,0.696,0.698,0.687,0.69,0.684,0.705,0.702,0.704,0.703,0.74,0.641,Activity,KCNE1_HUMAN,Medium,Human +0.738,0.73,0.671,0.67,0.636,0.635,0.755,0.588,0.667,0.678,0.644,0.607,0.609,0.619,0.593,0.608,0.623,0.625,0.622,0.739,0.741,0.766,0.751,0.729,0.762,0.748,0.736,0.739,0.735,0.758,0.693,0.63,0.678,0.738,0.767,0.749,0.75,0.782,0.771,0.739,0.767,0.759,0.728,0.599,0.59,0.652,0.605,0.477,0.426,0.547,0.798,0.719,0.766,0.748,0.738,0.739,0.76,0.746,0.749,0.762,0.648,0.738,Activity,KCNH2_HUMAN,Medium,Human +0.658,0.665,0.645,0.651,0.67,0.672,0.544,0.613,0.646,0.645,0.668,0.654,0.66,0.56,0.661,0.682,0.677,0.678,0.682,0.597,0.642,0.632,0.619,0.624,0.649,0.648,0.641,0.65,0.619,0.682,0.663,0.652,0.576,0.636,0.633,0.617,0.663,0.661,0.654,0.668,0.67,0.668,0.617,0.514,0.645,0.66,0.612,0.631,0.622,0.554,0.666,0.658,0.666,0.664,0.668,0.662,0.67,0.664,0.664,0.668,0.672,0.648,Activity,KCNJ2_MOUSE,Medium,Eukaryote +0.658,0.665,0.645,0.651,0.67,0.672,0.544,0.613,0.646,0.645,0.668,0.654,0.66,0.56,0.661,0.682,0.677,0.678,0.682,0.597,0.642,0.632,0.619,0.624,0.649,0.648,0.641,0.65,0.619,0.682,0.663,0.652,0.576,0.636,0.633,0.617,0.663,0.661,0.654,0.668,0.67,0.668,0.617,0.514,0.645,0.66,0.612,0.631,0.622,0.554,0.666,0.658,0.666,0.664,0.668,0.662,0.67,0.664,0.664,0.668,0.672,0.648,Expression,KCNJ2_MOUSE,Medium,Eukaryote +0.658,0.806,0.756,0.859,0.85,0.856,0.623,0.756,0.813,0.854,0.827,0.842,0.858,0.632,0.655,0.782,0.844,0.87,0.876,0.794,0.674,0.748,0.805,0.814,0.706,0.837,0.835,0.832,0.869,0.868,0.87,0.845,0.59,0.65,0.787,0.839,0.764,0.809,0.843,0.842,0.855,0.868,0.653,0.551,0.826,0.733,0.749,0.829,0.806,0.64,0.838,0.828,0.834,0.83,0.83,0.835,0.825,0.837,0.842,0.843,0.868,0.685,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +0.64,0.706,0.73,0.734,0.73,0.737,0.56,0.717,0.728,0.772,0.715,0.756,0.771,0.588,0.654,0.685,0.75,0.782,0.779,0.742,0.639,0.693,0.722,0.736,0.677,0.734,0.751,0.729,0.765,0.755,0.756,0.718,0.565,0.649,0.69,0.767,0.68,0.696,0.755,0.73,0.736,0.759,0.584,0.53,0.732,0.676,0.689,0.743,0.735,0.57,0.747,0.742,0.747,0.746,0.747,0.752,0.751,0.749,0.751,0.755,0.699,0.662,Activity,LGK_LIPST,Medium,Eukaryote +0.669,0.667,0.61,0.594,0.649,0.653,0.675,0.58,0.698,0.701,0.698,0.673,0.67,0.643,0.672,0.66,0.637,0.691,0.732,0.653,0.681,0.687,0.682,0.675,0.674,0.679,0.669,0.642,0.66,0.688,0.641,0.583,0.598,0.672,0.705,0.664,0.684,0.705,0.679,0.677,0.691,0.667,0.642,0.658,0.644,0.665,0.586,0.631,0.59,0.556,0.608,0.653,0.696,0.664,0.635,0.646,0.675,0.672,0.63,0.662,0.709,0.69,Expression,LYAM1_HUMAN,Medium,Human +0.854,0.874,0.866,0.866,0.861,0.861,0.74,0.904,0.875,0.866,0.9,0.749,0.886,0.79,0.795,0.823,0.829,0.859,0.762,0.868,0.752,0.837,0.848,0.872,0.817,0.858,0.83,0.859,0.839,0.835,0.863,0.855,0.776,0.686,0.851,0.855,0.844,0.885,0.905,0.869,0.894,0.911,0.718,0.525,0.781,0.676,0.859,0.728,0.89,0.905,0.917,0.903,0.907,0.916,0.91,0.915,0.912,0.916,0.911,0.915,0.922,0.934,Stability,MAFG_MOUSE,Medium,Eukaryote +0.808,0.904,0.885,0.895,0.917,0.921,0.466,0.816,0.87,0.908,0.907,0.757,0.805,0.428,0.539,0.915,0.891,0.882,0.91,0.902,0.494,0.556,0.847,0.864,0.626,0.836,0.824,0.842,0.911,0.923,0.906,0.878,0.84,0.333,0.554,0.544,0.823,0.872,0.86,0.899,0.919,0.913,0.483,0.395,0.858,0.749,0.75,0.848,0.903,0.792,0.925,0.91,0.924,0.91,0.911,0.908,0.912,0.919,0.923,0.922,0.927,0.884,Stability,MBD11_ARATH,Medium,Eukaryote +0.789,0.845,0.839,0.856,0.868,0.869,0.827,0.825,0.846,0.855,0.869,0.847,0.853,0.789,0.826,0.843,0.871,0.876,0.875,0.87,0.832,0.819,0.797,0.811,0.834,0.829,0.825,0.808,0.786,0.849,0.86,0.833,0.677,0.798,0.814,0.828,0.826,0.84,0.855,0.855,0.859,0.863,0.842,0.723,0.859,0.863,0.728,0.768,0.815,0.624,0.838,0.845,0.842,0.844,0.852,0.847,0.85,0.852,0.848,0.854,0.864,0.837,Activity,MET_HUMAN,Medium,Human +0.614,0.622,0.638,0.641,0.634,0.636,0.627,0.605,0.625,0.618,0.542,0.607,0.617,0.605,0.621,0.622,0.615,0.621,0.597,0.614,0.631,0.582,0.562,0.53,0.613,0.569,0.552,0.561,0.486,0.63,0.613,0.633,0.568,0.61,0.554,0.524,0.623,0.586,0.572,0.637,0.627,0.626,0.621,0.579,0.594,0.614,0.541,0.514,0.582,0.502,0.602,0.601,0.594,0.604,0.608,0.603,0.603,0.606,0.61,0.608,0.602,0.616,OrganismalFitness,MK01_HUMAN,Medium,Human +0.607,0.686,0.723,0.728,0.72,0.721,0.489,0.684,0.716,0.72,0.708,0.72,0.728,0.489,0.614,0.631,0.694,0.691,0.706,0.708,0.538,0.692,0.697,0.71,0.659,0.706,0.708,0.705,0.725,0.716,0.737,0.726,0.513,0.566,0.662,0.651,0.634,0.667,0.66,0.698,0.708,0.707,0.584,0.479,0.662,0.618,0.514,0.605,0.571,0.491,0.666,0.646,0.654,0.655,0.665,0.664,0.654,0.66,0.674,0.664,0.61,0.599,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +0.827,0.861,0.833,0.841,0.852,0.854,0.696,0.794,0.861,0.87,0.809,0.839,0.857,0.703,0.824,0.858,0.814,0.775,0.678,0.843,0.762,0.792,0.775,0.762,0.788,0.804,0.815,0.804,0.785,0.859,0.828,0.804,0.697,0.753,0.795,0.773,0.821,0.837,0.829,0.849,0.856,0.856,0.761,0.599,0.843,0.811,0.784,0.815,0.562,0.594,0.78,0.755,0.769,0.785,0.796,0.776,0.771,0.779,0.802,0.795,0.865,0.842,OrganismalFitness,MSH2_HUMAN,Medium,Human +0.706,0.8,0.836,0.842,0.83,0.835,0.686,0.823,0.834,0.845,0.811,0.854,0.856,0.631,0.697,0.717,0.775,0.822,0.835,0.84,0.68,0.747,0.818,0.838,0.764,0.836,0.855,0.843,0.862,0.83,0.847,0.828,0.671,0.692,0.764,0.832,0.722,0.763,0.824,0.815,0.829,0.847,0.692,0.52,0.756,0.692,0.713,0.786,0.777,0.6,0.786,0.77,0.781,0.788,0.783,0.79,0.777,0.785,0.784,0.79,0.801,0.707,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +0.607,0.618,0.608,0.611,0.611,0.61,0.588,0.575,0.632,0.632,0.676,0.629,0.639,0.575,0.685,0.724,0.661,0.625,0.631,0.614,0.707,0.564,0.597,0.621,0.7,0.601,0.619,0.604,0.639,0.629,0.592,0.577,0.559,0.71,0.641,0.595,0.668,0.641,0.612,0.66,0.636,0.618,0.62,0.547,0.669,0.683,0.608,0.653,0.674,0.543,0.647,0.631,0.636,0.635,0.639,0.645,0.653,0.645,0.66,0.648,0.647,0.67,OrganismalFitness,MTHR_HUMAN,Low,Human +0.584,0.635,0.689,0.7,0.711,0.727,0.574,0.623,0.732,0.726,0.783,0.76,0.761,0.592,0.726,0.815,0.821,0.774,0.645,0.675,0.675,0.618,0.665,0.657,0.676,0.647,0.699,0.664,0.684,0.65,0.665,0.613,0.58,0.721,0.674,0.644,0.704,0.677,0.659,0.723,0.719,0.711,0.566,0.526,0.72,0.654,0.715,0.737,0.77,0.696,0.794,0.779,0.803,0.788,0.793,0.794,0.785,0.791,0.786,0.794,0.805,0.777,Stability,MYO3_YEAST,High,Eukaryote +0.687,0.67,0.673,0.672,0.685,0.686,0.508,0.668,0.686,0.677,0.514,0.517,0.518,0.519,0.52,0.516,0.521,0.522,0.553,0.646,0.677,0.687,0.702,0.704,0.517,0.529,0.557,0.52,0.677,0.7,0.641,0.645,0.565,0.68,0.686,0.71,0.699,0.704,0.717,0.716,0.715,0.724,0.517,0.515,0.52,0.519,0.631,0.635,0.639,0.559,0.565,0.585,0.588,0.594,0.588,0.587,0.58,0.583,0.566,0.587,0.568,0.543,OrganismalFitness,NCAP_I34A1,Medium,Virus +0.689,0.8,0.859,0.858,0.87,0.86,0.816,0.861,0.867,0.873,0.864,0.874,0.87,0.857,0.824,0.868,0.858,0.833,0.856,0.846,0.858,0.842,0.844,0.842,0.841,0.835,0.852,0.84,0.861,0.876,0.828,0.814,0.847,0.817,0.833,0.853,0.841,0.851,0.866,0.867,0.869,0.875,0.77,0.717,0.705,0.751,0.823,0.721,0.872,0.868,0.879,0.882,0.885,0.888,0.889,0.879,0.885,0.883,0.878,0.886,0.866,0.873,Stability,NKX31_HUMAN,High,Human +0.866,0.824,0.763,0.766,0.84,0.844,0.634,0.741,0.83,0.848,0.852,0.604,0.662,0.586,0.663,0.79,0.831,0.826,0.842,0.614,0.706,0.708,0.711,0.678,0.708,0.724,0.762,0.687,0.734,0.842,0.872,0.799,0.662,0.646,0.686,0.77,0.851,0.814,0.857,0.854,0.829,0.848,0.663,0.616,0.893,0.704,0.742,0.844,0.478,0.601,0.822,0.808,0.819,0.787,0.817,0.807,0.814,0.844,0.836,0.823,0.859,0.716,Activity,NPC1_HUMAN,Low,Human +0.866,0.824,0.763,0.766,0.84,0.844,0.634,0.741,0.83,0.848,0.852,0.604,0.662,0.586,0.663,0.79,0.831,0.826,0.842,0.614,0.706,0.708,0.711,0.678,0.708,0.724,0.762,0.687,0.734,0.842,0.872,0.799,0.662,0.646,0.686,0.77,0.851,0.814,0.857,0.854,0.829,0.848,0.663,0.616,0.893,0.704,0.742,0.844,0.478,0.601,0.822,0.808,0.819,0.787,0.817,0.807,0.814,0.844,0.836,0.823,0.859,0.716,Activity,NPC1_HUMAN,Low,Human +0.802,0.775,0.727,0.717,0.799,0.795,0.53,0.718,0.809,0.814,0.474,0.598,0.745,0.45,0.444,0.505,0.615,0.752,0.795,0.686,0.769,0.802,0.781,0.777,0.549,0.774,0.809,0.747,0.81,0.833,0.729,0.732,0.456,0.747,0.767,0.772,0.803,0.822,0.823,0.82,0.828,0.823,0.428,0.432,0.425,0.423,0.725,0.706,0.731,0.592,0.636,0.648,0.653,0.677,0.663,0.679,0.666,0.66,0.634,0.662,0.644,0.567,OrganismalFitness,NRAM_I33A0,Low,Virus +0.644,0.75,0.797,0.813,0.809,0.811,0.504,0.711,0.832,0.866,0.838,0.839,0.863,0.677,0.735,0.753,0.801,0.833,0.84,0.777,0.671,0.75,0.808,0.798,0.736,0.828,0.826,0.81,0.788,0.813,0.841,0.811,0.594,0.698,0.739,0.816,0.728,0.744,0.83,0.809,0.809,0.838,0.724,0.511,0.831,0.744,0.753,0.805,0.779,0.667,0.806,0.785,0.798,0.813,0.804,0.814,0.817,0.813,0.813,0.817,0.882,0.815,Expression,NUD15_HUMAN,High,Human +0.692,0.804,0.802,0.796,0.814,0.821,0.636,0.73,0.85,0.821,0.793,0.654,0.667,0.656,0.698,0.701,0.744,0.767,0.758,0.798,0.705,0.796,0.81,0.812,0.657,0.762,0.729,0.788,0.807,0.839,0.865,0.894,0.587,0.682,0.703,0.707,0.758,0.746,0.756,0.832,0.81,0.825,0.679,0.609,0.683,0.67,0.851,0.828,0.881,0.844,0.888,0.885,0.884,0.897,0.898,0.907,0.89,0.889,0.896,0.897,0.852,0.78,Stability,NUSA_ECOLI,High,Prokaryote +0.752,0.76,0.767,0.774,0.783,0.774,0.653,0.758,0.782,0.776,0.749,0.722,0.746,0.693,0.796,0.763,0.784,0.789,0.788,0.761,0.682,0.705,0.7,0.727,0.73,0.722,0.737,0.699,0.733,0.803,0.741,0.739,0.66,0.657,0.669,0.745,0.743,0.744,0.771,0.763,0.765,0.775,0.681,0.559,0.674,0.743,0.815,0.789,0.886,0.862,0.798,0.788,0.777,0.798,0.794,0.792,0.796,0.8,0.798,0.797,0.762,0.808,Stability,NUSG_MYCTU,High,Prokaryote +0.758,0.86,0.916,0.928,0.917,0.923,0.644,0.809,0.917,0.915,0.921,0.869,0.887,0.649,0.9,0.923,0.928,0.924,0.919,0.916,0.909,0.899,0.898,0.887,0.854,0.902,0.893,0.882,0.877,0.909,0.927,0.922,0.758,0.618,0.67,0.762,0.874,0.86,0.869,0.927,0.917,0.92,0.735,0.484,0.73,0.787,0.837,0.787,0.916,0.918,0.943,0.937,0.944,0.94,0.947,0.944,0.941,0.94,0.947,0.945,0.932,0.89,Stability,OBSCN_HUMAN,High,Human +0.372,0.393,0.643,0.562,0.637,0.636,0.404,0.518,0.558,0.564,0.5,0.432,0.426,0.448,0.415,0.532,0.569,0.514,0.571,0.589,0.559,0.555,0.546,0.56,0.554,0.561,0.552,0.564,0.581,0.642,0.504,0.493,0.614,0.453,0.49,0.479,0.566,0.56,0.56,0.61,0.6,0.618,0.599,0.452,0.632,0.625,0.694,0.678,0.681,0.626,0.525,0.505,0.508,0.529,0.501,0.528,0.52,0.523,0.512,0.518,0.608,0.551,Stability,ODP2_GEOSE,High,Prokaryote +0.611,0.75,0.755,0.796,0.758,0.76,0.701,0.808,0.787,0.806,0.692,0.788,0.808,0.659,0.777,0.767,0.784,0.751,0.769,0.784,0.782,0.778,0.772,0.796,0.804,0.812,0.822,0.815,0.801,0.749,0.74,0.694,0.604,0.761,0.785,0.768,0.78,0.783,0.773,0.763,0.765,0.767,0.702,0.557,0.762,0.752,0.874,0.789,0.882,0.611,0.703,0.67,0.699,0.696,0.745,0.754,0.682,0.701,0.729,0.729,0.814,0.785,Expression,OPSD_HUMAN,High,Human +0.757,0.786,0.748,0.769,0.778,0.78,0.547,0.71,0.791,0.793,0.773,0.779,0.785,0.552,0.69,0.739,0.753,0.749,0.763,0.782,0.713,0.731,0.768,0.782,0.735,0.783,0.769,0.777,0.798,0.806,0.745,0.716,0.533,0.703,0.761,0.798,0.753,0.787,0.815,0.786,0.798,0.807,0.619,0.524,0.752,0.72,0.83,0.797,0.837,0.687,0.755,0.766,0.764,0.781,0.766,0.775,0.77,0.766,0.763,0.773,0.799,0.734,Activity,OTC_HUMAN,Medium,Human +0.52,0.593,0.582,0.59,0.602,0.604,0.599,0.574,0.56,0.564,0.747,0.862,0.858,0.593,0.835,0.847,0.726,0.741,0.728,0.579,0.578,0.665,0.691,0.713,0.731,0.731,0.759,0.732,0.615,0.671,0.815,0.793,0.562,0.566,0.639,0.734,0.596,0.602,0.688,0.612,0.603,0.636,0.688,0.577,0.836,0.812,0.829,0.842,0.865,0.787,0.769,0.717,0.731,0.762,0.773,0.782,0.735,0.764,0.782,0.766,0.854,0.855,Stability,OTU7A_HUMAN,High,Human +0.602,0.646,0.665,0.666,0.657,0.662,0.621,0.629,0.651,0.656,0.686,0.676,0.678,0.612,0.644,0.666,0.679,0.692,0.69,0.65,0.603,0.636,0.657,0.655,0.646,0.666,0.68,0.667,0.694,0.668,0.694,0.678,0.536,0.63,0.634,0.655,0.654,0.654,0.661,0.665,0.665,0.666,0.636,0.576,0.682,0.654,0.672,0.688,0.661,0.544,0.678,0.674,0.676,0.674,0.677,0.678,0.678,0.678,0.678,0.682,0.7,0.648,Activity,OXDA_RHOTO,High,Eukaryote +0.602,0.646,0.665,0.666,0.657,0.662,0.621,0.629,0.651,0.656,0.686,0.676,0.678,0.612,0.644,0.666,0.679,0.692,0.69,0.65,0.603,0.636,0.657,0.655,0.646,0.666,0.68,0.667,0.694,0.668,0.694,0.678,0.536,0.63,0.634,0.655,0.654,0.654,0.661,0.665,0.665,0.666,0.636,0.576,0.682,0.654,0.672,0.688,0.661,0.544,0.678,0.674,0.676,0.674,0.677,0.678,0.678,0.678,0.678,0.682,0.7,0.648,Expression,OXDA_RHOTO,High,Eukaryote +0.802,0.804,0.741,0.758,0.793,0.798,0.476,0.731,0.752,0.758,0.818,0.803,0.834,0.444,0.462,0.768,0.82,0.833,0.844,0.64,0.717,0.782,0.792,0.783,0.764,0.802,0.802,0.811,0.749,0.812,0.828,0.804,0.594,0.696,0.792,0.757,0.795,0.818,0.794,0.804,0.821,0.8,0.448,0.435,0.83,0.52,0.762,0.82,0.77,0.634,0.817,0.822,0.822,0.821,0.824,0.829,0.826,0.826,0.83,0.832,0.844,0.658,OrganismalFitness,P53_HUMAN,Low,Human +0.802,0.804,0.741,0.758,0.793,0.798,0.476,0.731,0.752,0.758,0.818,0.803,0.834,0.444,0.462,0.768,0.82,0.833,0.844,0.64,0.717,0.782,0.792,0.783,0.764,0.802,0.802,0.811,0.749,0.812,0.828,0.804,0.594,0.696,0.792,0.757,0.795,0.818,0.794,0.804,0.821,0.8,0.448,0.435,0.83,0.52,0.762,0.82,0.77,0.634,0.817,0.822,0.822,0.821,0.824,0.829,0.826,0.826,0.83,0.832,0.844,0.658,OrganismalFitness,P53_HUMAN,Low,Human +0.802,0.804,0.741,0.758,0.793,0.798,0.476,0.731,0.752,0.758,0.818,0.803,0.834,0.444,0.462,0.768,0.82,0.833,0.844,0.64,0.717,0.782,0.792,0.783,0.764,0.802,0.802,0.811,0.749,0.812,0.828,0.804,0.594,0.696,0.792,0.757,0.795,0.818,0.794,0.804,0.821,0.8,0.448,0.435,0.83,0.52,0.762,0.82,0.77,0.634,0.817,0.822,0.822,0.821,0.824,0.829,0.826,0.826,0.83,0.832,0.844,0.658,OrganismalFitness,P53_HUMAN,Low,Human +0.802,0.804,0.741,0.758,0.793,0.798,0.476,0.731,0.752,0.758,0.818,0.803,0.834,0.444,0.462,0.768,0.82,0.833,0.844,0.64,0.717,0.782,0.792,0.783,0.764,0.802,0.802,0.811,0.749,0.812,0.828,0.804,0.594,0.696,0.792,0.757,0.795,0.818,0.794,0.804,0.821,0.8,0.448,0.435,0.83,0.52,0.762,0.82,0.77,0.634,0.817,0.822,0.822,0.821,0.824,0.829,0.826,0.826,0.83,0.832,0.844,0.658,OrganismalFitness,P53_HUMAN,Low,Human +0.79,0.827,0.844,0.852,0.818,0.827,0.661,0.785,0.86,0.847,0.818,0.808,0.832,0.634,0.809,0.817,0.831,0.838,0.811,0.846,0.737,0.785,0.781,0.813,0.792,0.828,0.837,0.819,0.87,0.801,0.817,0.773,0.699,0.769,0.775,0.798,0.795,0.798,0.811,0.821,0.822,0.831,0.754,0.488,0.797,0.774,0.661,0.727,0.768,0.56,0.795,0.782,0.748,0.796,0.799,0.777,0.787,0.799,0.802,0.802,0.835,0.828,OrganismalFitness,P84126_THETH,Medium,Prokaryote +0.855,0.827,0.787,0.791,0.846,0.843,0.752,0.805,0.838,0.852,0.866,0.851,0.861,0.758,0.805,0.856,0.898,0.875,0.882,0.787,0.847,0.858,0.863,0.874,0.846,0.879,0.879,0.866,0.86,0.859,0.884,0.846,0.649,0.849,0.85,0.847,0.874,0.874,0.872,0.866,0.868,0.866,0.825,0.469,0.851,0.852,0.691,0.817,0.779,0.604,0.87,0.865,0.872,0.888,0.88,0.883,0.884,0.884,0.901,0.892,0.885,0.844,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +0.693,0.693,0.686,0.693,0.695,0.702,0.526,0.656,0.705,0.711,0.708,0.693,0.704,0.545,0.688,0.705,0.715,0.662,0.636,0.704,0.57,0.686,0.677,0.668,0.67,0.69,0.693,0.681,0.665,0.705,0.692,0.676,0.561,0.583,0.678,0.681,0.683,0.693,0.697,0.702,0.706,0.706,0.669,0.533,0.705,0.695,0.69,0.723,0.703,0.576,0.712,0.712,0.712,0.718,0.714,0.716,0.715,0.717,0.713,0.72,0.728,0.692,Activity,PAI1_HUMAN,,Human +0.772,0.773,0.761,0.766,0.782,0.783,0.535,0.694,0.589,0.589,0.526,0.537,0.569,0.518,0.52,0.521,0.525,0.525,0.681,0.667,0.736,0.755,0.777,0.778,0.607,0.71,0.733,0.725,0.728,0.805,0.689,0.693,0.566,0.722,0.747,0.777,0.782,0.793,0.796,0.804,0.808,0.803,0.522,0.515,0.521,0.519,0.627,0.616,0.599,0.54,0.581,0.584,0.583,0.594,0.584,0.591,0.583,0.588,0.568,0.59,0.612,0.577,OrganismalFitness,PA_I34A1,Medium,Virus +0.592,0.708,0.858,0.846,0.66,0.652,0.821,0.77,0.85,0.855,0.819,0.873,0.888,0.878,0.911,0.852,0.87,0.851,0.876,0.819,0.836,0.835,0.79,0.841,0.754,0.8,0.788,0.822,0.791,0.783,0.781,0.74,0.647,0.774,0.744,0.795,0.786,0.769,0.793,0.696,0.71,0.7,0.808,0.687,0.796,0.805,0.599,0.747,0.83,0.7,0.797,0.765,0.77,0.769,0.772,0.779,0.778,0.781,0.786,0.779,0.865,0.884,Activity,PHOT_CHLRE,High,Eukaryote +0.608,0.662,0.858,0.837,0.829,0.85,0.8,0.753,0.851,0.872,0.85,0.799,0.858,0.837,0.832,0.846,0.848,0.761,0.79,0.837,0.722,0.859,0.834,0.825,0.789,0.857,0.869,0.852,0.859,0.873,0.879,0.861,0.799,0.766,0.834,0.859,0.806,0.85,0.884,0.856,0.872,0.888,0.8,0.381,0.836,0.777,0.786,0.808,0.871,0.862,0.846,0.824,0.752,0.782,0.789,0.808,0.812,0.835,0.821,0.817,0.889,0.895,Stability,PIN1_HUMAN,High,Human +0.795,0.773,0.787,0.791,0.787,0.79,0.76,0.754,0.781,0.793,0.745,0.732,0.726,0.786,0.836,0.847,0.827,0.807,0.765,0.79,0.771,0.743,0.752,0.738,0.793,0.766,0.744,0.73,0.752,0.78,0.712,0.703,0.729,0.797,0.771,0.761,0.813,0.788,0.784,0.801,0.782,0.784,0.748,0.718,0.646,0.669,0.715,0.653,0.85,0.82,0.794,0.798,0.795,0.805,0.812,0.816,0.806,0.808,0.81,0.809,0.75,0.812,Stability,PITX2_HUMAN,High,Human +0.623,0.605,0.662,0.671,0.69,0.694,0.612,0.658,0.639,0.661,0.68,0.706,0.737,0.716,0.733,0.814,0.721,0.678,0.676,0.718,0.702,0.705,0.68,0.712,0.71,0.708,0.711,0.704,0.712,0.717,0.752,0.75,0.555,0.721,0.665,0.708,0.69,0.686,0.714,0.713,0.697,0.712,0.684,0.674,0.705,0.692,0.835,0.777,0.83,0.79,0.685,0.703,0.724,0.689,0.707,0.706,0.692,0.707,0.713,0.709,0.728,0.825,Stability,PKN1_HUMAN,High,Human +0.709,0.687,0.681,0.699,0.726,0.731,0.478,0.672,0.749,0.749,0.651,0.468,0.516,0.457,0.469,0.595,0.702,0.704,0.717,0.677,0.664,0.694,0.689,0.688,0.561,0.691,0.688,0.683,0.694,0.743,0.694,0.66,0.501,0.524,0.631,0.677,0.667,0.688,0.706,0.688,0.709,0.727,0.465,0.465,0.677,0.472,0.596,0.673,0.559,0.523,0.673,0.683,0.685,0.687,0.684,0.686,0.685,0.684,0.69,0.689,0.558,0.536,OrganismalFitness,POLG_CXB3N,Medium,Virus +0.76,0.794,0.633,0.633,0.772,0.772,0.485,0.722,0.848,0.85,0.669,0.507,0.512,0.481,0.513,0.547,0.587,0.645,0.69,0.715,0.732,0.741,0.733,0.732,0.725,0.754,0.756,0.747,0.745,0.816,0.809,0.771,0.522,0.484,0.577,0.743,0.729,0.709,0.781,0.745,0.714,0.79,0.478,0.473,0.709,0.504,0.69,0.771,0.572,0.561,0.632,0.624,0.631,0.643,0.626,0.643,0.633,0.637,0.627,0.64,0.592,0.524,OrganismalFitness,POLG_DEN26,Low,Virus +0.824,0.784,0.712,0.716,0.823,0.828,0.47,0.598,0.792,0.798,0.588,0.842,0.839,0.552,0.565,0.556,0.556,0.543,0.535,0.631,0.714,0.744,0.741,0.761,0.721,0.756,0.669,0.72,0.78,0.84,0.821,0.772,0.599,0.747,0.769,0.78,0.768,0.79,0.809,0.748,0.78,0.798,0.555,0.52,0.763,0.554,0.483,0.672,0.869,0.703,0.673,0.714,0.7,0.715,0.698,0.657,0.683,0.685,0.679,0.712,0.596,0.584,OrganismalFitness,POLG_HCVJF,Medium,Virus +0.638,0.766,0.695,0.72,0.74,0.74,0.525,0.722,0.679,0.735,0.737,0.523,0.558,0.525,0.556,0.515,0.567,0.553,0.53,0.818,0.558,0.53,0.507,0.574,0.542,0.47,0.486,0.463,0.578,0.761,0.822,0.826,0.575,0.498,0.503,0.5,0.702,0.706,0.702,0.734,0.741,0.733,0.479,0.448,0.491,0.469,0.778,0.748,0.882,0.841,0.859,0.863,0.848,0.892,0.894,0.894,0.867,0.896,0.894,0.888,0.907,0.83,Stability,POLG_PESV,Medium,Virus +0.758,0.814,0.837,0.842,0.838,0.845,0.625,0.731,0.81,0.821,0.812,0.841,0.843,0.533,0.612,0.808,0.865,0.893,0.869,0.858,0.831,0.827,0.697,0.719,0.81,0.837,0.822,0.838,0.734,0.871,0.852,0.843,0.732,0.855,0.816,0.788,0.862,0.845,0.833,0.871,0.861,0.859,0.679,0.524,0.791,0.779,0.807,0.788,0.834,0.668,0.86,0.862,0.867,0.865,0.865,0.866,0.864,0.865,0.865,0.871,0.861,0.811,Activity,PPARG_HUMAN,Medium,Human +0.783,0.779,0.779,0.782,0.816,0.817,0.513,0.694,0.741,0.77,0.797,0.805,0.816,0.639,0.692,0.734,0.805,0.82,0.817,0.697,0.726,0.767,0.776,0.719,0.763,0.795,0.79,0.787,0.714,0.813,0.804,0.777,0.614,0.718,0.769,0.775,0.801,0.805,0.805,0.819,0.817,0.818,0.677,0.473,0.794,0.763,0.756,0.798,0.783,0.627,0.791,0.793,0.789,0.796,0.801,0.794,0.797,0.797,0.799,0.802,0.824,0.757,OrganismalFitness,PPM1D_HUMAN,Low,Human +0.845,0.882,0.926,0.921,0.939,0.94,0.771,0.771,0.936,0.939,0.913,0.85,0.881,0.783,0.779,0.951,0.945,0.921,0.912,0.904,0.829,0.885,0.886,0.898,0.853,0.888,0.907,0.898,0.912,0.946,0.944,0.942,0.856,0.828,0.8,0.824,0.917,0.92,0.909,0.932,0.943,0.933,0.718,0.611,0.635,0.719,0.798,0.664,0.902,0.913,0.945,0.947,0.947,0.949,0.954,0.954,0.949,0.953,0.953,0.954,0.94,0.937,Stability,PR40A_HUMAN,Medium,Human +0.839,0.84,0.835,0.827,0.848,0.844,0.593,0.757,0.797,0.807,0.816,0.839,0.854,0.615,0.652,0.689,0.769,0.861,0.865,0.753,0.652,0.803,0.828,0.819,0.74,0.839,0.838,0.835,0.799,0.846,0.832,0.809,0.652,0.628,0.801,0.817,0.816,0.842,0.848,0.842,0.855,0.86,0.659,0.54,0.821,0.69,0.845,0.831,0.873,0.656,0.788,0.791,0.793,0.81,0.8,0.805,0.807,0.806,0.8,0.808,0.866,0.768,Expression,PRKN_HUMAN,Low,Human +0.859,0.864,0.856,0.855,0.86,0.857,0.712,0.785,0.824,0.824,0.904,0.871,0.88,0.811,0.842,0.895,0.909,0.888,0.853,0.806,0.706,0.656,0.794,0.739,0.793,0.809,0.388,0.802,0.834,0.846,0.853,0.825,0.554,0.717,0.755,0.78,0.845,0.841,0.82,0.866,0.866,0.843,0.748,0.65,0.864,0.784,0.828,0.854,0.907,0.873,0.902,0.896,0.898,0.903,0.901,0.907,0.895,0.901,0.901,0.905,0.909,0.906,Stability,PSAE_PICP2,Medium,Prokaryote +0.748,0.76,0.754,0.76,0.77,0.774,0.599,0.708,0.771,0.784,0.763,0.76,0.782,0.613,0.704,0.788,0.782,0.668,0.669,0.785,0.662,0.752,0.716,0.698,0.702,0.688,0.68,0.705,0.659,0.79,0.758,0.754,0.554,0.693,0.742,0.691,0.758,0.772,0.746,0.775,0.788,0.786,0.684,0.504,0.776,0.742,0.754,0.748,0.77,0.618,0.768,0.752,0.77,0.768,0.772,0.774,0.77,0.771,0.774,0.778,0.796,0.776,Expression,PTEN_HUMAN,Medium,Human +0.748,0.76,0.754,0.76,0.77,0.774,0.599,0.708,0.771,0.784,0.763,0.76,0.782,0.613,0.704,0.788,0.782,0.668,0.669,0.785,0.662,0.752,0.716,0.698,0.702,0.688,0.68,0.705,0.659,0.79,0.758,0.754,0.554,0.693,0.742,0.691,0.758,0.772,0.746,0.775,0.788,0.786,0.684,0.504,0.776,0.742,0.754,0.748,0.77,0.618,0.768,0.752,0.77,0.768,0.772,0.774,0.77,0.771,0.774,0.778,0.796,0.776,Activity,PTEN_HUMAN,Medium,Human +0.758,0.704,0.69,0.709,0.76,0.763,0.51,0.723,0.77,0.773,0.745,0.767,0.781,0.509,0.509,0.511,0.532,0.559,0.586,0.711,0.77,0.707,0.698,0.671,0.769,0.702,0.702,0.722,0.683,0.764,0.751,0.753,0.644,0.758,0.716,0.71,0.772,0.762,0.762,0.775,0.769,0.768,0.72,0.507,0.765,0.738,0.695,0.743,0.634,0.575,0.615,0.636,0.658,0.656,0.631,0.634,0.635,0.628,0.615,0.645,0.61,0.56,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +0.721,0.706,0.718,0.722,0.726,0.726,0.508,0.664,0.712,0.718,0.662,0.692,0.706,0.569,0.57,0.708,0.721,0.708,0.728,0.719,0.666,0.686,0.684,0.67,0.692,0.7,0.689,0.704,0.708,0.712,0.732,0.71,0.566,0.666,0.68,0.652,0.71,0.72,0.71,0.728,0.732,0.728,0.678,0.574,0.71,0.7,0.688,0.712,0.697,0.597,0.708,0.713,0.722,0.72,0.722,0.719,0.716,0.716,0.72,0.724,0.709,0.764,Binding,Q53Z42_HUMAN,Medium,Human +0.721,0.706,0.718,0.722,0.726,0.726,0.508,0.664,0.712,0.718,0.662,0.692,0.706,0.569,0.57,0.708,0.721,0.708,0.728,0.719,0.666,0.686,0.684,0.67,0.692,0.7,0.689,0.704,0.708,0.712,0.732,0.71,0.566,0.666,0.68,0.652,0.71,0.72,0.71,0.728,0.732,0.728,0.678,0.574,0.71,0.7,0.688,0.712,0.697,0.597,0.708,0.713,0.722,0.72,0.722,0.719,0.716,0.716,0.72,0.724,0.709,0.764,Expression,Q53Z42_HUMAN,Medium,Human +0.781,0.831,0.853,0.857,0.857,0.864,0.689,0.776,0.865,0.869,0.827,0.786,0.796,0.574,0.741,0.775,0.813,0.812,0.817,0.85,0.826,0.85,0.849,0.857,0.838,0.859,0.866,0.865,0.869,0.872,0.854,0.827,0.674,0.827,0.85,0.842,0.838,0.856,0.857,0.864,0.869,0.869,0.763,0.492,0.824,0.784,0.711,0.783,0.781,0.569,0.813,0.792,0.792,0.803,0.808,0.798,0.81,0.797,0.802,0.809,0.843,0.79,Activity,Q59976_STRSQ,Medium,Prokaryote +0.634,0.658,0.605,0.606,0.64,0.639,0.496,0.562,0.661,0.659,0.599,0.52,0.512,0.506,0.519,0.501,0.505,0.505,0.493,0.626,0.518,0.519,0.502,0.552,0.513,0.5,0.512,0.496,0.493,0.682,0.636,0.639,0.514,0.513,0.518,0.512,0.599,0.599,0.599,0.637,0.637,0.635,0.505,0.501,0.496,0.51,0.611,0.615,0.664,0.601,0.61,0.624,0.614,0.636,0.628,0.63,0.621,0.622,0.621,0.624,0.554,0.536,Activity,Q6WV12_9MAXI,Low,Eukaryote +0.705,0.712,0.718,0.719,0.725,0.736,0.709,0.675,0.711,0.719,0.765,0.741,0.755,0.682,0.716,0.718,0.724,0.744,0.729,0.476,0.733,0.712,0.721,0.734,0.742,0.749,0.76,0.706,0.754,0.738,0.738,0.718,0.561,0.721,0.747,0.729,0.719,0.754,0.749,0.744,0.752,0.752,0.704,0.677,0.755,0.72,0.63,0.725,0.685,0.551,0.726,0.714,0.718,0.736,0.74,0.748,0.741,0.711,0.736,0.737,0.772,0.72,Activity,Q837P4_ENTFA,Medium,Prokaryote +0.565,0.677,0.682,0.698,0.664,0.668,0.573,0.601,0.613,0.618,0.638,0.646,0.638,0.526,0.578,0.629,0.653,0.691,0.671,0.636,0.657,0.676,0.701,0.698,0.639,0.684,0.69,0.735,0.714,0.644,0.665,0.658,0.559,0.68,0.703,0.741,0.643,0.674,0.714,0.679,0.686,0.699,0.611,0.557,0.653,0.6,0.665,0.642,0.676,0.563,0.636,0.641,0.648,0.629,0.657,0.65,0.63,0.647,0.637,0.648,0.64,0.629,Activity,Q837P5_ENTFA,Medium,Prokaryote +0.63,0.671,0.603,0.602,0.652,0.649,0.495,0.648,0.638,0.641,0.592,0.471,0.463,0.466,0.454,0.459,0.465,0.461,0.484,0.601,0.476,0.492,0.516,0.489,0.482,0.469,0.487,0.606,0.613,0.665,0.646,0.654,0.477,0.452,0.49,0.639,0.596,0.601,0.642,0.641,0.646,0.66,0.472,0.473,0.46,0.46,0.51,0.543,0.628,0.571,0.604,0.598,0.603,0.609,0.598,0.606,0.605,0.599,0.599,0.603,0.537,0.447,Activity,Q8WTC7_9CNID,Low,Eukaryote +0.835,0.829,0.618,0.633,0.846,0.848,0.487,0.675,0.496,0.496,0.575,0.497,0.49,0.513,0.499,0.555,0.567,0.792,0.824,0.663,0.636,0.667,0.674,0.676,0.643,0.643,0.623,0.634,0.642,0.825,0.801,0.745,0.486,0.607,0.637,0.63,0.711,0.735,0.734,0.823,0.834,0.832,0.501,0.486,0.549,0.495,0.752,0.733,0.777,0.626,0.65,0.636,0.659,0.675,0.665,0.661,0.662,0.66,0.641,0.664,0.636,0.589,OrganismalFitness,R1AB_SARS2,Medium,Virus +0.605,0.585,0.66,0.663,0.675,0.692,0.735,0.703,0.853,0.835,0.794,0.748,0.769,0.726,0.846,0.868,0.761,0.754,0.797,0.714,0.788,0.824,0.803,0.766,0.816,0.744,0.808,0.754,0.747,0.799,0.725,0.681,0.649,0.75,0.834,0.745,0.745,0.811,0.744,0.734,0.762,0.723,0.811,0.62,0.625,0.726,0.761,0.666,0.865,0.778,0.786,0.801,0.804,0.8,0.812,0.802,0.799,0.801,0.797,0.805,0.719,0.818,Stability,RAD_ANTMA,High,Eukaryote +0.708,0.719,0.706,0.712,0.725,0.725,0.525,0.684,0.752,0.763,0.76,0.732,0.769,0.523,0.619,0.74,0.756,0.744,0.734,0.678,0.654,0.715,0.708,0.696,0.699,0.712,0.708,0.696,0.708,0.727,0.764,0.73,0.569,0.653,0.688,0.702,0.693,0.703,0.711,0.717,0.734,0.737,0.607,0.529,0.779,0.707,0.638,0.725,0.639,0.665,0.729,0.726,0.73,0.752,0.734,0.747,0.732,0.729,0.74,0.745,0.727,0.59,OrganismalFitness,RAF1_HUMAN,Low,Human +0.798,0.811,0.827,0.842,0.839,0.844,0.689,0.772,0.823,0.837,0.757,0.787,0.811,0.807,0.849,0.835,0.85,0.817,0.735,0.859,0.819,0.805,0.805,0.791,0.809,0.791,0.791,0.767,0.71,0.815,0.789,0.779,0.648,0.787,0.803,0.76,0.821,0.83,0.808,0.839,0.844,0.846,0.838,0.662,0.72,0.822,0.711,0.641,0.807,0.644,0.798,0.794,0.799,0.796,0.806,0.793,0.813,0.796,0.805,0.812,0.747,0.827,Activity,RASH_HUMAN,High,Human +0.691,0.69,0.75,0.75,0.74,0.746,0.625,0.662,0.725,0.734,0.725,0.692,0.714,0.727,0.754,0.748,0.736,0.7,0.668,0.763,0.716,0.726,0.744,0.726,0.709,0.734,0.742,0.698,0.716,0.754,0.692,0.668,0.634,0.682,0.736,0.734,0.714,0.751,0.756,0.742,0.756,0.756,0.705,0.652,0.632,0.718,0.644,0.652,0.751,0.655,0.703,0.7,0.714,0.71,0.722,0.714,0.712,0.708,0.69,0.714,0.742,0.738,Expression,RASK_HUMAN,High,Human +0.691,0.69,0.75,0.75,0.74,0.746,0.625,0.662,0.725,0.734,0.725,0.692,0.714,0.727,0.754,0.748,0.736,0.7,0.668,0.763,0.716,0.726,0.744,0.726,0.709,0.734,0.742,0.698,0.716,0.754,0.692,0.668,0.634,0.682,0.736,0.734,0.714,0.751,0.756,0.742,0.756,0.756,0.705,0.652,0.632,0.718,0.644,0.652,0.751,0.655,0.703,0.7,0.714,0.71,0.722,0.714,0.712,0.708,0.69,0.714,0.742,0.738,Binding,RASK_HUMAN,High,Human +0.584,0.542,0.656,0.66,0.659,0.657,0.646,0.65,0.634,0.642,0.728,0.718,0.713,0.669,0.708,0.759,0.781,0.684,0.682,0.633,0.694,0.577,0.656,0.654,0.653,0.565,0.591,0.673,0.631,0.701,0.704,0.676,0.644,0.655,0.698,0.708,0.666,0.679,0.678,0.666,0.675,0.666,0.667,0.636,0.709,0.714,0.754,0.778,0.796,0.73,0.755,0.751,0.756,0.755,0.759,0.754,0.749,0.762,0.752,0.758,0.783,0.769,Stability,RBP1_HUMAN,High,Human +0.694,0.683,0.725,0.728,0.728,0.723,0.66,0.662,0.736,0.74,0.757,0.669,0.695,0.685,0.692,0.784,0.792,0.787,0.783,0.739,0.628,0.616,0.668,0.71,0.659,0.759,0.749,0.727,0.767,0.744,0.787,0.752,0.519,0.638,0.657,0.66,0.717,0.721,0.715,0.742,0.743,0.735,0.655,0.644,0.727,0.666,0.744,0.757,0.801,0.773,0.802,0.809,0.816,0.814,0.806,0.812,0.809,0.808,0.811,0.814,0.799,0.762,Stability,RCD1_ARATH,Medium,Eukaryote +0.665,0.773,0.805,0.8,0.788,0.806,0.533,0.64,0.764,0.767,0.737,0.649,0.697,0.591,0.654,0.66,0.786,0.786,0.803,0.801,0.506,0.524,0.549,0.541,0.414,0.522,0.439,0.482,0.798,0.799,0.782,0.771,0.518,0.525,0.495,0.778,0.713,0.708,0.795,0.789,0.8,0.823,0.452,0.545,0.698,0.548,0.78,0.806,0.866,0.807,0.79,0.781,0.771,0.782,0.782,0.784,0.773,0.781,0.792,0.785,0.815,0.71,Stability,RCRO_LAMBD,High,Virus +0.619,0.649,0.714,0.714,0.711,0.711,0.576,0.735,0.766,0.771,0.75,0.792,0.805,0.603,0.836,0.78,0.768,0.761,0.723,0.735,0.705,0.725,0.722,0.743,0.769,0.765,0.764,0.749,0.765,0.747,0.725,0.718,0.729,0.646,0.749,0.737,0.703,0.747,0.75,0.716,0.742,0.748,0.747,0.507,0.788,0.772,0.724,0.783,0.787,0.745,0.744,0.735,0.741,0.733,0.748,0.75,0.746,0.748,0.749,0.749,0.741,0.796,Stability,RD23A_HUMAN,High,Human +0.667,0.699,0.727,0.732,0.779,0.781,0.524,0.728,0.791,0.794,0.612,0.547,0.554,0.535,0.54,0.59,0.698,0.724,0.785,0.768,0.734,0.768,0.778,0.789,0.588,0.723,0.716,0.712,0.751,0.796,0.766,0.729,0.568,0.73,0.761,0.781,0.75,0.768,0.779,0.79,0.799,0.808,0.535,0.522,0.612,0.536,0.626,0.646,0.622,0.54,0.681,0.664,0.674,0.674,0.683,0.679,0.675,0.686,0.686,0.687,0.595,0.569,OrganismalFitness,RDRP_I33A0,Low,Virus +0.596,0.574,0.603,0.606,0.603,0.603,0.521,0.646,0.603,0.607,0.566,0.616,0.624,0.526,0.522,0.585,0.613,0.638,0.629,0.575,0.6,0.623,0.617,0.608,0.635,0.637,0.576,0.617,0.623,0.63,0.667,0.672,0.525,0.608,0.627,0.613,0.614,0.625,0.61,0.616,0.623,0.611,0.529,0.533,0.599,0.537,0.626,0.612,0.639,0.604,0.591,0.596,0.633,0.622,0.624,0.625,0.633,0.61,0.649,0.632,0.633,0.608,OrganismalFitness,REV_HV1H2,Medium,Virus +0.507,0.596,0.607,0.618,0.618,0.616,0.487,0.62,0.613,0.627,0.622,0.571,0.585,0.469,0.529,0.511,0.649,0.637,0.632,0.635,0.454,0.519,0.559,0.553,0.464,0.565,0.577,0.552,0.576,0.61,0.636,0.61,0.5,0.498,0.558,0.576,0.543,0.572,0.583,0.61,0.61,0.611,0.547,0.447,0.585,0.54,0.649,0.622,0.689,0.658,0.668,0.657,0.649,0.668,0.66,0.655,0.66,0.656,0.668,0.663,0.652,0.593,Stability,RFAH_ECOLI,High,Prokaryote +0.688,0.851,0.867,0.866,0.866,0.87,0.593,0.855,0.718,0.681,0.886,0.885,0.875,0.591,0.706,0.753,0.908,0.899,0.907,0.879,0.587,0.798,0.786,0.78,0.774,0.789,0.805,0.818,0.861,0.865,0.843,0.832,0.531,0.774,0.803,0.79,0.812,0.836,0.832,0.852,0.875,0.875,0.484,0.434,0.769,0.622,0.856,0.858,0.936,0.906,0.917,0.917,0.915,0.929,0.923,0.919,0.924,0.924,0.925,0.927,0.93,0.872,Stability,RL20_AQUAE,High,Prokaryote +0.682,0.687,0.708,0.736,0.71,0.726,0.561,0.724,0.752,0.758,0.611,0.656,0.681,0.572,0.761,0.785,0.81,0.754,0.798,0.741,0.71,0.795,0.776,0.755,0.78,0.754,0.756,0.739,0.727,0.7,0.72,0.727,0.561,0.72,0.77,0.718,0.738,0.777,0.742,0.751,0.773,0.738,0.665,0.539,0.657,0.667,0.575,0.598,0.647,0.534,0.795,0.779,0.795,0.797,0.803,0.796,0.783,0.8,0.791,0.808,0.695,0.718,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.682,0.687,0.708,0.736,0.71,0.726,0.561,0.724,0.752,0.758,0.611,0.656,0.681,0.572,0.761,0.785,0.81,0.754,0.798,0.741,0.71,0.795,0.776,0.755,0.78,0.754,0.756,0.739,0.727,0.7,0.72,0.727,0.561,0.72,0.77,0.718,0.738,0.777,0.742,0.751,0.773,0.738,0.665,0.539,0.657,0.667,0.575,0.598,0.647,0.534,0.795,0.779,0.795,0.797,0.803,0.796,0.783,0.8,0.791,0.808,0.695,0.718,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.682,0.687,0.708,0.736,0.71,0.726,0.561,0.724,0.752,0.758,0.611,0.656,0.681,0.572,0.761,0.785,0.81,0.754,0.798,0.741,0.71,0.795,0.776,0.755,0.78,0.754,0.756,0.739,0.727,0.7,0.72,0.727,0.561,0.72,0.77,0.718,0.738,0.777,0.742,0.751,0.773,0.738,0.665,0.539,0.657,0.667,0.575,0.598,0.647,0.534,0.795,0.779,0.795,0.797,0.803,0.796,0.783,0.8,0.791,0.808,0.695,0.718,Activity,RL40A_YEAST,Medium,Eukaryote +0.772,0.804,0.803,0.809,0.812,0.813,0.531,0.719,0.808,0.807,0.806,0.803,0.81,0.535,0.782,0.801,0.809,0.807,0.81,0.808,0.779,0.794,0.765,0.755,0.786,0.802,0.802,0.801,0.795,0.809,0.799,0.776,0.584,0.77,0.777,0.718,0.794,0.801,0.777,0.818,0.819,0.815,0.769,0.534,0.806,0.792,0.657,0.777,0.65,0.597,0.787,0.781,0.778,0.792,0.787,0.782,0.786,0.786,0.787,0.793,0.815,0.772,Activity,RNC_ECOLI,Medium,Prokaryote +0.818,0.857,0.864,0.875,0.833,0.856,0.841,0.829,0.85,0.87,0.891,0.898,0.902,0.876,0.89,0.901,0.894,0.88,0.85,0.884,0.865,0.886,0.889,0.871,0.882,0.894,0.89,0.891,0.87,0.897,0.869,0.834,0.836,0.826,0.881,0.881,0.874,0.888,0.887,0.873,0.873,0.869,0.888,0.854,0.898,0.887,0.875,0.847,0.925,0.889,0.879,0.872,0.88,0.887,0.884,0.881,0.89,0.886,0.892,0.89,0.913,0.906,Stability,RPC1_BP434,High,Virus +0.66,0.748,0.786,0.798,0.774,0.772,0.616,0.786,0.774,0.796,0.81,0.792,0.808,0.67,0.669,0.732,0.83,0.852,0.856,0.758,0.631,0.711,0.735,0.766,0.629,0.732,0.712,0.7,0.848,0.762,0.882,0.859,0.563,0.594,0.714,0.799,0.658,0.713,0.792,0.756,0.774,0.804,0.684,0.678,0.737,0.692,0.653,0.728,0.73,0.631,0.834,0.812,0.82,0.837,0.822,0.835,0.83,0.823,0.816,0.836,0.822,0.721,Activity,RPC1_LAMBD,High,Virus +0.66,0.748,0.786,0.798,0.774,0.772,0.616,0.786,0.774,0.796,0.81,0.792,0.808,0.67,0.669,0.732,0.83,0.852,0.856,0.758,0.631,0.711,0.735,0.766,0.629,0.732,0.712,0.7,0.848,0.762,0.882,0.859,0.563,0.594,0.714,0.799,0.658,0.713,0.792,0.756,0.774,0.804,0.684,0.678,0.737,0.692,0.653,0.728,0.73,0.631,0.834,0.812,0.82,0.837,0.822,0.835,0.83,0.823,0.816,0.836,0.822,0.721,Activity,RPC1_LAMBD,High,Virus +0.698,0.672,0.685,0.682,0.672,0.675,0.587,0.62,0.71,0.71,0.734,0.709,0.701,0.636,0.657,0.758,0.714,0.702,0.67,0.663,0.601,0.652,0.667,0.658,0.657,0.672,0.7,0.679,0.686,0.701,0.726,0.723,0.548,0.711,0.659,0.662,0.725,0.687,0.69,0.703,0.679,0.673,0.665,0.62,0.719,0.683,0.801,0.723,0.817,0.768,0.71,0.709,0.715,0.713,0.709,0.708,0.719,0.726,0.71,0.719,0.72,0.821,Stability,RS15_GEOSE,Medium,Prokaryote +0.774,0.8,0.818,0.824,0.825,0.832,0.712,0.8,0.837,0.837,0.829,0.841,0.867,0.768,0.79,0.796,0.845,0.829,0.817,0.52,0.78,0.838,0.849,0.837,0.8,0.849,0.856,0.842,0.82,0.835,0.833,0.772,0.686,0.795,0.843,0.835,0.812,0.851,0.851,0.845,0.857,0.854,0.773,0.644,0.825,0.801,0.73,0.797,0.788,0.59,0.82,0.809,0.808,0.822,0.832,0.823,0.823,0.829,0.827,0.833,0.832,0.808,Expression,S22A1_HUMAN,Medium,Human +0.774,0.8,0.818,0.824,0.825,0.832,0.712,0.8,0.837,0.837,0.829,0.841,0.867,0.768,0.79,0.796,0.845,0.829,0.817,0.52,0.78,0.838,0.849,0.837,0.8,0.849,0.856,0.842,0.82,0.835,0.833,0.772,0.686,0.795,0.843,0.835,0.812,0.851,0.851,0.845,0.857,0.854,0.773,0.644,0.825,0.801,0.73,0.797,0.788,0.59,0.82,0.809,0.808,0.822,0.832,0.823,0.823,0.829,0.827,0.833,0.832,0.808,Activity,S22A1_HUMAN,Medium,Human +0.537,0.586,0.693,0.677,0.692,0.687,0.702,0.788,0.693,0.701,0.762,0.765,0.778,0.593,0.739,0.757,0.765,0.786,0.732,0.723,0.771,0.756,0.75,0.766,0.776,0.786,0.768,0.771,0.769,0.785,0.764,0.733,0.703,0.736,0.778,0.773,0.714,0.758,0.746,0.698,0.719,0.719,0.768,0.459,0.787,0.774,0.647,0.658,0.709,0.767,0.752,0.693,0.638,0.704,0.699,0.716,0.713,0.742,0.735,0.714,0.803,0.813,Stability,SAV1_MOUSE,High,Eukaryote +0.606,0.599,0.685,0.666,0.714,0.699,0.607,0.603,0.737,0.763,0.661,0.626,0.642,0.614,0.629,0.687,0.789,0.833,0.674,0.68,0.61,0.63,0.64,0.639,0.607,0.619,0.611,0.629,0.688,0.789,0.798,0.75,0.602,0.589,0.598,0.606,0.641,0.646,0.648,0.674,0.681,0.685,0.629,0.608,0.659,0.655,0.836,0.828,0.854,0.812,0.768,0.765,0.782,0.784,0.781,0.782,0.794,0.79,0.785,0.787,0.848,0.785,Stability,SBI_STAAM,Medium,Prokaryote +0.71,0.745,0.728,0.734,0.771,0.78,0.686,0.766,0.8,0.805,0.788,0.782,0.787,0.588,0.675,0.77,0.792,0.793,0.78,0.795,0.764,0.771,0.762,0.761,0.771,0.774,0.773,0.776,0.771,0.788,0.745,0.693,0.683,0.772,0.77,0.759,0.783,0.788,0.781,0.793,0.796,0.791,0.762,0.564,0.79,0.782,0.746,0.778,0.762,0.598,0.778,0.783,0.788,0.791,0.793,0.788,0.788,0.79,0.783,0.793,0.808,0.782,Activity,SC6A4_HUMAN,Medium,Human +0.535,0.56,0.633,0.63,0.654,0.641,0.552,0.53,0.632,0.63,0.62,0.618,0.607,0.585,0.625,0.613,0.638,0.67,0.675,0.634,0.538,0.563,0.549,0.555,0.564,0.576,0.56,0.578,0.585,0.629,0.638,0.602,0.49,0.522,0.534,0.565,0.566,0.564,0.574,0.626,0.62,0.628,0.592,0.566,0.623,0.607,0.763,0.758,0.763,0.733,0.692,0.699,0.699,0.72,0.686,0.715,0.689,0.688,0.667,0.702,0.797,0.733,Stability,SCIN_STAAR,High,Prokaryote +0.537,0.543,0.558,0.558,0.553,0.559,0.548,0.51,0.569,0.583,0.589,0.613,0.567,0.623,0.591,0.582,0.594,0.569,0.554,0.579,0.546,0.557,0.567,0.567,0.536,0.547,0.548,0.543,0.58,0.545,0.588,0.582,0.532,0.534,0.525,0.532,0.537,0.53,0.529,0.553,0.552,0.556,0.542,0.585,0.574,0.585,0.529,0.561,0.527,0.504,0.573,0.564,0.574,0.563,0.543,0.566,0.578,0.572,0.575,0.57,0.608,0.574,OrganismalFitness,SCN5A_HUMAN,Medium,Human +0.944,0.971,0.971,0.969,0.973,0.973,0.59,0.86,0.959,0.962,0.969,0.939,0.952,0.72,0.734,0.947,0.955,0.952,0.957,0.954,0.628,0.652,0.652,0.77,0.643,0.799,0.565,0.776,0.862,0.967,0.965,0.962,0.353,0.579,0.738,0.808,0.934,0.936,0.908,0.962,0.96,0.947,0.576,0.497,0.818,0.562,0.841,0.827,0.961,0.952,0.973,0.97,0.969,0.974,0.972,0.972,0.974,0.971,0.975,0.974,0.961,0.967,Stability,SDA_BACSU,Medium,Prokaryote +0.678,0.722,0.759,0.759,0.745,0.75,0.509,0.715,0.774,0.778,0.747,0.757,0.76,0.587,0.692,0.754,0.765,0.776,0.76,0.755,0.736,0.744,0.746,0.735,0.738,0.755,0.747,0.755,0.745,0.744,0.753,0.732,0.588,0.747,0.753,0.749,0.748,0.754,0.757,0.757,0.759,0.759,0.651,0.519,0.759,0.725,0.696,0.735,0.681,0.593,0.762,0.756,0.764,0.768,0.771,0.767,0.767,0.766,0.765,0.772,0.765,0.727,OrganismalFitness,SERC_HUMAN,High,Human +0.599,0.664,0.689,0.69,0.685,0.69,0.606,0.679,0.711,0.711,0.696,0.689,0.702,0.613,0.618,0.627,0.711,0.695,0.648,0.694,0.624,0.679,0.693,0.677,0.626,0.693,0.692,0.698,0.678,0.704,0.704,0.677,0.574,0.621,0.651,0.687,0.627,0.648,0.682,0.682,0.682,0.698,0.623,0.611,0.713,0.639,0.65,0.681,0.638,0.545,0.685,0.682,0.691,0.693,0.697,0.699,0.689,0.695,0.69,0.697,0.656,0.639,OrganismalFitness,SHOC2_HUMAN,Medium,Human +0.592,0.618,0.628,0.627,0.639,0.634,0.694,0.584,0.596,0.613,0.62,0.659,0.663,0.644,0.687,0.661,0.66,0.625,0.624,0.615,0.576,0.587,0.586,0.599,0.631,0.632,0.596,0.626,0.599,0.611,0.535,0.517,0.609,0.561,0.63,0.62,0.593,0.609,0.615,0.635,0.631,0.631,0.63,0.572,0.598,0.671,0.756,0.671,0.768,0.724,0.674,0.691,0.674,0.672,0.668,0.693,0.67,0.671,0.665,0.68,0.664,0.737,Stability,SOX30_HUMAN,High,Human +0.748,0.788,0.799,0.788,0.822,0.822,0.411,0.796,0.744,0.744,0.7,0.434,0.456,0.436,0.438,0.427,0.457,0.447,0.468,0.794,0.386,0.48,0.586,0.546,0.428,0.447,0.504,0.674,0.733,0.811,0.736,0.75,0.476,0.461,0.471,0.505,0.744,0.738,0.737,0.82,0.818,0.812,0.387,0.418,0.411,0.445,0.753,0.72,0.871,0.785,0.761,0.756,0.743,0.766,0.753,0.755,0.75,0.745,0.757,0.755,0.768,0.687,Stability,SPA_STAAU,Medium,Prokaryote +0.562,0.619,0.554,0.564,0.616,0.624,0.546,0.535,0.587,0.635,0.682,0.653,0.634,0.648,0.655,0.672,0.694,0.694,0.724,0.657,0.623,0.602,0.599,0.609,0.602,0.633,0.618,0.613,0.646,0.633,0.728,0.705,0.514,0.624,0.583,0.628,0.631,0.611,0.641,0.636,0.617,0.645,0.522,0.472,0.62,0.546,0.643,0.619,0.718,0.601,0.718,0.698,0.702,0.732,0.716,0.704,0.738,0.732,0.739,0.727,0.723,0.694,Binding,SPG1_STRSG,Low,Prokaryote +0.562,0.619,0.554,0.564,0.616,0.624,0.546,0.535,0.587,0.635,0.682,0.653,0.634,0.648,0.655,0.672,0.694,0.694,0.724,0.657,0.623,0.602,0.599,0.609,0.602,0.633,0.618,0.613,0.646,0.633,0.728,0.705,0.514,0.624,0.583,0.628,0.631,0.611,0.641,0.636,0.617,0.645,0.522,0.472,0.62,0.546,0.643,0.619,0.718,0.601,0.718,0.698,0.702,0.732,0.716,0.704,0.738,0.732,0.739,0.727,0.723,0.694,Binding,SPG1_STRSG,Low,Prokaryote +0.562,0.619,0.554,0.564,0.616,0.624,0.546,0.535,0.587,0.635,0.682,0.653,0.634,0.648,0.655,0.672,0.694,0.694,0.724,0.657,0.623,0.602,0.599,0.609,0.602,0.633,0.618,0.613,0.646,0.633,0.728,0.705,0.514,0.624,0.583,0.628,0.631,0.611,0.641,0.636,0.617,0.645,0.522,0.472,0.62,0.546,0.643,0.619,0.718,0.601,0.718,0.698,0.702,0.732,0.716,0.704,0.738,0.732,0.739,0.727,0.723,0.694,Binding,SPG1_STRSG,Medium,Prokaryote +0.562,0.619,0.554,0.564,0.616,0.624,0.546,0.535,0.587,0.635,0.682,0.653,0.634,0.648,0.655,0.672,0.694,0.694,0.724,0.657,0.623,0.602,0.599,0.609,0.602,0.633,0.618,0.613,0.646,0.633,0.728,0.705,0.514,0.624,0.583,0.628,0.631,0.611,0.641,0.636,0.617,0.645,0.522,0.472,0.62,0.546,0.643,0.619,0.718,0.601,0.718,0.698,0.702,0.732,0.716,0.704,0.738,0.732,0.739,0.727,0.723,0.694,Binding,SPG1_STRSG,Medium,Prokaryote +0.767,0.811,0.804,0.823,0.84,0.839,0.704,0.705,0.818,0.793,0.785,0.752,0.744,0.719,0.756,0.769,0.792,0.789,0.81,0.8,0.736,0.757,0.744,0.739,0.705,0.771,0.712,0.772,0.796,0.866,0.842,0.835,0.416,0.722,0.766,0.778,0.697,0.718,0.743,0.833,0.836,0.842,0.647,0.564,0.66,0.642,0.761,0.715,0.902,0.837,0.838,0.839,0.851,0.85,0.835,0.853,0.848,0.861,0.847,0.85,0.853,0.827,Stability,SPG2_STRSG,Medium,Prokaryote +0.588,0.63,0.562,0.613,0.684,0.684,0.465,0.682,0.702,0.71,0.493,0.457,0.472,0.464,0.471,0.481,0.482,0.49,0.528,0.67,0.653,0.682,0.68,0.688,0.69,0.672,0.654,0.69,0.655,0.66,0.651,0.673,0.59,0.663,0.665,0.682,0.665,0.668,0.667,0.698,0.7,0.706,0.477,0.488,0.481,0.488,0.774,0.759,0.719,0.59,0.63,0.637,0.629,0.672,0.647,0.668,0.65,0.66,0.635,0.66,0.696,0.59,Binding,SPIKE_SARS2,Medium,Virus +0.588,0.63,0.562,0.613,0.684,0.684,0.465,0.682,0.702,0.71,0.493,0.457,0.472,0.464,0.471,0.481,0.482,0.49,0.528,0.67,0.653,0.682,0.68,0.688,0.69,0.672,0.654,0.69,0.655,0.66,0.651,0.673,0.59,0.663,0.665,0.682,0.665,0.668,0.667,0.698,0.7,0.706,0.477,0.488,0.481,0.488,0.774,0.759,0.719,0.59,0.63,0.637,0.629,0.672,0.647,0.668,0.65,0.66,0.635,0.66,0.696,0.59,Expression,SPIKE_SARS2,Medium,Virus +0.794,0.805,0.81,0.778,0.798,0.787,0.605,0.745,0.766,0.788,0.797,0.816,0.807,0.444,0.797,0.801,0.796,0.857,0.822,0.788,0.782,0.808,0.789,0.782,0.786,0.789,0.814,0.767,0.778,0.787,0.791,0.792,0.711,0.746,0.777,0.766,0.808,0.813,0.804,0.792,0.795,0.791,0.736,0.402,0.723,0.736,0.702,0.693,0.809,0.802,0.824,0.819,0.825,0.822,0.828,0.824,0.818,0.816,0.825,0.825,0.83,0.767,Stability,SPTN1_CHICK,High,Eukaryote +0.678,0.75,0.781,0.788,0.785,0.787,0.529,0.686,0.768,0.782,0.729,0.654,0.714,0.574,0.708,0.745,0.804,0.815,0.753,0.796,0.59,0.701,0.715,0.718,0.64,0.743,0.76,0.724,0.741,0.809,0.786,0.77,0.576,0.571,0.696,0.735,0.727,0.741,0.768,0.791,0.78,0.791,0.582,0.538,0.779,0.771,0.832,0.816,0.813,0.823,0.815,0.786,0.818,0.811,0.813,0.812,0.816,0.804,0.798,0.818,0.824,0.725,Stability,SQSTM_MOUSE,Medium,Eukaryote +0.82,0.856,0.869,0.869,0.873,0.87,0.382,0.779,0.844,0.831,0.878,0.842,0.851,0.347,0.853,0.878,0.863,0.877,0.864,0.843,0.419,0.682,0.687,0.762,0.76,0.801,0.825,0.819,0.823,0.865,0.866,0.848,0.686,0.504,0.575,0.678,0.853,0.851,0.855,0.868,0.868,0.865,0.794,0.321,0.871,0.825,0.567,0.81,0.859,0.864,0.867,0.864,0.861,0.874,0.878,0.867,0.862,0.862,0.87,0.87,0.917,0.891,Stability,SR43C_ARATH,High,Eukaryote +0.711,0.786,0.866,0.866,0.886,0.883,0.788,0.697,0.893,0.893,0.837,0.859,0.876,0.767,0.867,0.881,0.888,0.842,0.867,0.852,0.866,0.846,0.853,0.839,0.845,0.842,0.849,0.841,0.832,0.864,0.887,0.868,0.809,0.825,0.844,0.841,0.858,0.875,0.875,0.878,0.889,0.887,0.69,0.574,0.786,0.769,0.786,0.778,0.823,0.842,0.896,0.886,0.892,0.889,0.897,0.894,0.896,0.889,0.897,0.899,0.887,0.847,Stability,SRBS1_HUMAN,High,Human +0.766,0.771,0.757,0.763,0.773,0.773,0.775,0.744,0.768,0.783,0.762,0.802,0.814,0.714,0.737,0.746,0.792,0.772,0.762,0.76,0.736,0.725,0.725,0.701,0.741,0.754,0.735,0.724,0.676,0.788,0.807,0.8,0.725,0.717,0.722,0.681,0.759,0.762,0.751,0.778,0.778,0.774,0.793,0.695,0.769,0.787,0.655,0.66,0.704,0.544,0.772,0.755,0.758,0.771,0.774,0.772,0.774,0.777,0.775,0.779,0.772,0.742,Activity,SRC_HUMAN,Medium,Human +0.766,0.771,0.757,0.763,0.773,0.773,0.775,0.744,0.768,0.783,0.762,0.802,0.814,0.714,0.737,0.746,0.792,0.772,0.762,0.76,0.736,0.725,0.725,0.701,0.741,0.754,0.735,0.724,0.676,0.788,0.807,0.8,0.725,0.717,0.722,0.681,0.759,0.762,0.751,0.778,0.778,0.774,0.793,0.695,0.769,0.787,0.655,0.66,0.704,0.544,0.772,0.755,0.758,0.771,0.774,0.772,0.774,0.777,0.775,0.779,0.772,0.742,Activity,SRC_HUMAN,Medium,Human +0.766,0.771,0.757,0.763,0.773,0.773,0.775,0.744,0.768,0.783,0.762,0.802,0.814,0.714,0.737,0.746,0.792,0.772,0.762,0.76,0.736,0.725,0.725,0.701,0.741,0.754,0.735,0.724,0.676,0.788,0.807,0.8,0.725,0.717,0.722,0.681,0.759,0.762,0.751,0.778,0.778,0.774,0.793,0.695,0.769,0.787,0.655,0.66,0.704,0.544,0.772,0.755,0.758,0.771,0.774,0.772,0.774,0.777,0.775,0.779,0.772,0.742,OrganismalFitness,SRC_HUMAN,Medium,Human +0.718,0.703,0.735,0.747,0.778,0.773,0.58,0.755,0.757,0.723,0.753,0.78,0.8,0.645,0.8,0.818,0.802,0.717,0.686,0.808,0.631,0.743,0.769,0.758,0.786,0.732,0.777,0.757,0.684,0.767,0.77,0.763,0.628,0.657,0.788,0.689,0.743,0.805,0.736,0.763,0.809,0.763,0.803,0.548,0.716,0.815,0.808,0.751,0.814,0.757,0.79,0.764,0.797,0.792,0.79,0.802,0.783,0.796,0.786,0.801,0.777,0.817,OrganismalFitness,SUMO1_HUMAN,High,Human +0.61,0.647,0.668,0.676,0.671,0.676,0.667,0.719,0.703,0.697,0.774,0.775,0.772,0.669,0.7,0.699,0.702,0.748,0.753,0.753,0.683,0.735,0.698,0.697,0.654,0.717,0.731,0.689,0.687,0.755,0.736,0.712,0.579,0.694,0.755,0.72,0.658,0.724,0.692,0.679,0.717,0.697,0.665,0.617,0.719,0.697,0.495,0.634,0.519,0.472,0.648,0.64,0.638,0.657,0.687,0.659,0.678,0.671,0.692,0.67,0.574,0.544,OrganismalFitness,SYUA_HUMAN,Medium,Human +0.555,0.538,0.554,0.552,0.547,0.545,0.62,0.503,0.544,0.542,0.512,0.525,0.524,0.552,0.517,0.516,0.447,0.502,0.533,0.474,0.568,0.532,0.504,0.502,0.582,0.551,0.517,0.525,0.498,0.525,0.554,0.57,0.471,0.595,0.619,0.567,0.571,0.59,0.569,0.556,0.566,0.563,0.538,0.584,0.499,0.528,0.638,0.524,0.59,0.529,0.568,0.508,0.503,0.521,0.503,0.523,0.532,0.502,0.469,0.517,0.518,0.549,OrganismalFitness,TADBP_HUMAN,Low,Human +0.707,0.64,0.678,0.687,0.721,0.708,0.439,0.774,0.673,0.685,0.616,0.738,0.74,0.479,0.499,0.498,0.522,0.461,0.529,0.646,0.76,0.758,0.775,0.781,0.772,0.68,0.591,0.67,0.649,0.756,0.787,0.779,0.709,0.767,0.649,0.639,0.765,0.688,0.665,0.757,0.709,0.685,0.509,0.465,0.712,0.625,0.65,0.702,0.726,0.64,0.58,0.582,0.598,0.583,0.576,0.585,0.573,0.577,0.571,0.586,0.607,0.581,OrganismalFitness,TAT_HV1BR,High,Virus +0.837,0.852,0.88,0.885,0.887,0.888,0.883,0.686,0.892,0.918,0.874,0.922,0.933,0.908,0.914,0.94,0.931,0.915,0.929,0.837,0.785,0.843,0.837,0.865,0.854,0.861,0.888,0.884,0.851,0.909,0.899,0.886,0.84,0.777,0.85,0.885,0.895,0.907,0.91,0.897,0.908,0.906,0.905,0.588,0.827,0.901,0.853,0.797,0.928,0.917,0.918,0.907,0.898,0.931,0.928,0.931,0.922,0.93,0.919,0.928,0.934,0.935,Stability,TCRG1_MOUSE,Medium,Eukaryote +0.707,0.76,0.81,0.805,0.811,0.81,0.509,0.561,0.851,0.845,0.836,0.804,0.828,0.576,0.848,0.849,0.84,0.851,0.857,0.831,0.441,0.692,0.695,0.668,0.683,0.717,0.618,0.75,0.795,0.852,0.853,0.843,0.775,0.469,0.721,0.686,0.784,0.816,0.809,0.803,0.825,0.824,0.641,0.547,0.797,0.711,0.796,0.796,0.812,0.868,0.86,0.851,0.866,0.866,0.87,0.856,0.858,0.855,0.858,0.861,0.826,0.775,Stability,THO1_YEAST,High,Eukaryote +0.712,0.742,0.774,0.767,0.769,0.769,0.422,0.71,0.795,0.795,0.757,0.762,0.765,0.505,0.76,0.791,0.778,0.776,0.785,0.752,0.643,0.691,0.709,0.707,0.696,0.726,0.69,0.704,0.719,0.779,0.753,0.732,0.602,0.536,0.67,0.682,0.767,0.766,0.763,0.786,0.775,0.774,0.653,0.465,0.781,0.735,0.733,0.813,0.839,0.816,0.772,0.783,0.776,0.772,0.772,0.767,0.774,0.77,0.772,0.777,0.814,0.8,Stability,TNKS2_HUMAN,High,Human +0.622,0.631,0.626,0.629,0.632,0.631,0.547,0.639,0.662,0.652,0.658,0.653,0.675,0.571,0.618,0.644,0.684,0.68,0.706,0.64,0.555,0.575,0.618,0.646,0.569,0.647,0.652,0.635,0.669,0.641,0.642,0.606,0.563,0.571,0.614,0.669,0.632,0.638,0.671,0.64,0.64,0.655,0.578,0.56,0.673,0.601,0.617,0.645,0.633,0.559,0.636,0.655,0.649,0.655,0.663,0.664,0.656,0.658,0.66,0.664,0.652,0.622,OrganismalFitness,TPK1_HUMAN,Medium,Human +0.722,0.754,0.773,0.782,0.778,0.787,0.656,0.761,0.786,0.787,0.812,0.785,0.802,0.706,0.751,0.814,0.807,0.756,0.749,0.779,0.694,0.725,0.761,0.766,0.742,0.777,0.757,0.746,0.736,0.805,0.802,0.779,0.708,0.698,0.768,0.719,0.785,0.797,0.765,0.799,0.805,0.783,0.736,0.637,0.797,0.777,0.779,0.788,0.807,0.641,0.782,0.785,0.791,0.776,0.795,0.793,0.795,0.79,0.798,0.798,0.827,0.778,Expression,TPMT_HUMAN,Medium,Human +0.723,0.695,0.677,0.655,0.658,0.667,0.765,0.752,0.718,0.721,0.739,0.732,0.739,0.694,0.748,0.704,0.685,0.736,0.678,0.677,0.681,0.737,0.714,0.726,0.749,0.634,0.806,0.78,0.708,0.741,0.751,0.63,0.747,0.756,0.73,0.751,0.776,0.768,0.773,0.75,0.739,0.757,0.709,0.712,0.717,0.738,0.578,0.69,0.681,0.511,0.702,0.676,0.722,0.701,0.684,0.699,0.739,0.643,0.672,0.699,0.749,0.752,OrganismalFitness,TPOR_HUMAN,Low,Human +0.832,0.841,0.818,0.827,0.829,0.834,0.63,0.804,0.873,0.88,0.858,0.849,0.869,0.689,0.817,0.858,0.864,0.86,0.847,0.835,0.758,0.832,0.8,0.832,0.792,0.798,0.817,0.803,0.799,0.808,0.846,0.808,0.62,0.804,0.829,0.827,0.834,0.848,0.844,0.841,0.854,0.847,0.763,0.539,0.85,0.8,0.674,0.788,0.793,0.564,0.842,0.833,0.838,0.867,0.851,0.85,0.853,0.86,0.847,0.863,0.858,0.836,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +0.768,0.78,0.76,0.764,0.76,0.762,0.691,0.748,0.794,0.79,0.772,0.797,0.807,0.661,0.796,0.797,0.794,0.793,0.811,0.773,0.728,0.76,0.762,0.783,0.756,0.763,0.746,0.744,0.787,0.753,0.765,0.732,0.603,0.731,0.784,0.775,0.762,0.784,0.779,0.77,0.783,0.777,0.757,0.558,0.79,0.754,0.64,0.721,0.73,0.52,0.784,0.751,0.765,0.784,0.784,0.757,0.777,0.777,0.765,0.783,0.814,0.794,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +0.697,0.771,0.796,0.801,0.788,0.794,0.477,0.72,0.787,0.791,0.731,0.773,0.79,0.498,0.499,0.716,0.756,0.786,0.803,0.816,0.625,0.727,0.746,0.732,0.734,0.76,0.759,0.747,0.745,0.766,0.757,0.745,0.551,0.63,0.727,0.74,0.678,0.752,0.762,0.749,0.792,0.803,0.648,0.493,0.733,0.774,0.684,0.695,0.754,0.643,0.736,0.727,0.72,0.735,0.73,0.731,0.735,0.739,0.741,0.738,0.715,0.736,OrganismalFitness,UBC9_HUMAN,Medium,Human +0.703,0.811,0.829,0.828,0.834,0.833,0.628,0.78,0.81,0.824,0.815,0.845,0.837,0.751,0.81,0.854,0.843,0.847,0.858,0.818,0.66,0.792,0.795,0.801,0.784,0.803,0.787,0.789,0.807,0.83,0.863,0.852,0.813,0.612,0.615,0.757,0.772,0.769,0.784,0.833,0.831,0.823,0.763,0.484,0.72,0.726,0.805,0.763,0.845,0.755,0.826,0.843,0.838,0.845,0.845,0.84,0.839,0.843,0.842,0.843,0.861,0.813,Stability,UBE4B_HUMAN,High,Human +0.779,0.799,0.824,0.831,0.842,0.844,0.536,0.594,0.796,0.805,0.762,0.838,0.846,0.817,0.838,0.847,0.834,0.786,0.752,0.798,0.59,0.764,0.749,0.732,0.837,0.8,0.786,0.775,0.731,0.826,0.845,0.825,0.534,0.492,0.58,0.698,0.782,0.749,0.776,0.834,0.828,0.842,0.815,0.499,0.814,0.826,0.694,0.773,0.469,0.54,0.814,0.798,0.832,0.836,0.82,0.834,0.813,0.829,0.826,0.833,0.814,0.791,Activity,UBE4B_MOUSE,Low,Eukaryote +0.735,0.832,0.832,0.834,0.837,0.845,0.601,0.665,0.798,0.816,0.813,0.742,0.791,0.627,0.635,0.587,0.588,0.84,0.835,0.795,0.754,0.749,0.777,0.804,0.765,0.784,0.795,0.789,0.801,0.815,0.784,0.717,0.669,0.668,0.758,0.742,0.76,0.786,0.784,0.845,0.846,0.845,0.555,0.552,0.707,0.589,0.784,0.716,0.854,0.82,0.837,0.853,0.858,0.86,0.86,0.864,0.852,0.858,0.849,0.858,0.871,0.826,Stability,UBR5_HUMAN,Medium,Human +0.583,0.623,0.591,0.609,0.612,0.609,0.618,0.467,0.688,0.681,0.831,0.785,0.79,0.674,0.761,0.808,0.844,0.817,0.85,0.566,0.701,0.765,0.747,0.813,0.715,0.736,0.729,0.752,0.815,0.766,0.782,0.715,0.487,0.652,0.76,0.78,0.659,0.7,0.753,0.655,0.684,0.709,0.737,0.637,0.781,0.752,0.742,0.783,0.792,0.759,0.84,0.821,0.842,0.844,0.842,0.86,0.822,0.84,0.857,0.849,0.819,0.809,Stability,VG08_BPP22,High,Virus +0.687,0.772,0.869,0.894,0.896,0.897,0.552,0.811,0.857,0.874,0.914,0.886,0.896,0.695,0.618,0.904,0.912,0.88,0.817,0.862,0.668,0.757,0.842,0.812,0.793,0.826,0.812,0.792,0.843,0.88,0.919,0.913,0.692,0.683,0.791,0.789,0.812,0.84,0.831,0.894,0.898,0.888,0.626,0.66,0.79,0.795,0.816,0.793,0.922,0.861,0.92,0.921,0.925,0.925,0.919,0.927,0.919,0.922,0.924,0.925,0.909,0.891,Stability,VILI_CHICK,High,Eukaryote +0.704,0.716,0.72,0.731,0.738,0.744,0.563,0.715,0.757,0.764,0.744,0.748,0.76,0.569,0.697,0.733,0.758,0.76,0.756,0.748,0.59,0.602,0.684,0.708,0.632,0.736,0.731,0.72,0.73,0.75,0.73,0.692,0.578,0.568,0.631,0.742,0.721,0.73,0.763,0.747,0.754,0.768,0.579,0.555,0.748,0.663,0.678,0.716,0.736,0.592,0.76,0.737,0.754,0.762,0.762,0.754,0.758,0.752,0.754,0.762,0.762,0.718,Expression,VKOR1_HUMAN,Medium,Human +0.704,0.716,0.72,0.731,0.738,0.744,0.563,0.715,0.757,0.764,0.744,0.748,0.76,0.569,0.697,0.733,0.758,0.76,0.756,0.748,0.59,0.602,0.684,0.708,0.632,0.736,0.731,0.72,0.73,0.75,0.73,0.692,0.578,0.568,0.631,0.742,0.721,0.73,0.763,0.747,0.754,0.768,0.579,0.555,0.748,0.663,0.678,0.716,0.736,0.592,0.76,0.737,0.754,0.762,0.762,0.754,0.758,0.752,0.754,0.762,0.762,0.718,Activity,VKOR1_HUMAN,Medium,Human +0.422,0.514,0.587,0.615,0.55,0.572,0.548,0.603,0.75,0.748,0.801,0.722,0.733,0.669,0.755,0.811,0.86,0.874,0.813,0.616,0.488,0.567,0.575,0.636,0.613,0.622,0.57,0.622,0.664,0.563,0.809,0.769,0.638,0.514,0.514,0.553,0.468,0.474,0.456,0.528,0.537,0.49,0.656,0.521,0.819,0.728,0.833,0.812,0.84,0.842,0.854,0.845,0.862,0.861,0.857,0.869,0.858,0.872,0.853,0.868,0.878,0.837,Stability,VRPI_BPT7,Medium,Virus +0.65,0.816,0.795,0.809,0.807,0.803,0.38,0.783,0.781,0.804,0.798,0.618,0.733,0.432,0.574,0.791,0.813,0.844,0.851,0.8,0.459,0.404,0.435,0.633,0.431,0.409,0.373,0.498,0.771,0.803,0.854,0.84,0.471,0.363,0.451,0.75,0.677,0.682,0.766,0.791,0.788,0.806,0.459,0.363,0.582,0.479,0.709,0.691,0.813,0.767,0.794,0.819,0.794,0.813,0.808,0.819,0.818,0.835,0.844,0.82,0.805,0.743,Stability,YAIA_ECOLI,Medium,Prokaryote +0.726,0.674,0.743,0.742,0.735,0.738,0.673,0.654,0.524,0.534,0.677,0.649,0.654,0.716,0.736,0.734,0.74,0.698,0.667,0.607,0.598,0.597,0.588,0.592,0.657,0.623,0.637,0.612,0.586,0.677,0.729,0.74,0.569,0.66,0.605,0.618,0.712,0.672,0.691,0.728,0.712,0.731,0.75,0.454,0.673,0.75,0.679,0.685,0.7,0.603,0.734,0.699,0.725,0.721,0.718,0.713,0.716,0.729,0.732,0.728,0.698,0.72,Binding,YAP1_HUMAN,Low,Human +0.904,0.927,0.955,0.954,0.948,0.945,0.819,0.853,0.939,0.941,0.969,0.967,0.971,0.874,0.883,0.973,0.974,0.961,0.97,0.942,0.848,0.912,0.929,0.933,0.923,0.93,0.886,0.934,0.952,0.923,0.942,0.919,0.911,0.856,0.907,0.897,0.922,0.926,0.923,0.942,0.938,0.944,0.732,0.658,0.826,0.739,0.775,0.825,0.98,0.932,0.971,0.964,0.97,0.971,0.97,0.973,0.97,0.967,0.971,0.971,0.959,0.964,Stability,YNZC_BACSU,Medium,Prokaryote +0.702,0.73,0.736,0.742,0.751,0.755,0.594,0.699,0.743,0.751,0.735,0.721,0.737,0.61,0.674,0.722,0.744,0.741,0.738,0.72,0.67,0.701,0.709,0.714,0.691,0.719,0.715,0.719,0.729,0.762,0.761,0.741,0.608,0.665,0.694,0.713,0.736,0.741,0.749,0.759,0.761,0.763,0.646,0.545,0.714,0.677,0.706,0.725,0.736,0.658,0.754,0.748,0.753,0.758,0.757,0.758,0.756,0.757,0.757,0.762,0.761,0.725,,,, diff --git a/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.csv b/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.csv new file mode 100644 index 0000000..dfcbf9d --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.csv @@ -0,0 +1,63 @@ +Model_rank,Model_name,Model type,Average_AUC,Bootstrap_standard_error_AUC,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,TranceptEVE L,Hybrid - Alignment & PLM,0.751,0.0,0.764,0.701,0.75,0.756,0.783,0.746,0.762,0.772,0.765,0.782,0.755,0.746,0.751,0.683,0.703,0.688,0.739,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +2,SaProt (650M),Hybrid - Structure & PLM,0.751,0.005,0.747,0.711,0.766,0.703,0.826,0.721,0.75,0.796,0.768,0.788,0.777,0.671,0.755,0.705,0.679,0.665,0.719,SaProt (650M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +3,TranceptEVE M,Hybrid - Alignment & PLM,0.749,0.002,0.759,0.707,0.747,0.753,0.78,0.74,0.761,0.768,0.765,0.781,0.75,0.737,0.749,0.681,0.674,0.678,0.732,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +4,GEMME,Alignment-based model,0.749,0.004,0.759,0.704,0.737,0.752,0.792,0.744,0.762,0.775,0.762,0.783,0.758,0.754,0.754,0.675,0.693,0.694,0.77,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +5,TranceptEVE S,Hybrid - Alignment & PLM,0.748,0.002,0.756,0.712,0.741,0.75,0.779,0.745,0.756,0.767,0.764,0.777,0.751,0.733,0.746,0.68,0.675,0.678,0.731,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,ProtSSN (ensemble),Hybrid - Structure & PLM,0.747,0.003,0.752,0.702,0.739,0.722,0.817,0.723,0.755,0.786,0.765,0.792,0.768,0.699,0.755,0.688,0.687,0.658,0.697,ProtSSN (ensemble of 9 models),"Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +7,ProtSSN (k=20 h=1280),Hybrid - Structure & PLM,0.743,0.003,0.748,0.702,0.732,0.715,0.817,0.72,0.75,0.785,0.761,0.789,0.765,0.695,0.75,0.688,0.684,0.654,0.698,"ProtSSN (k=20, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +8,VESPA,Protein language model,0.742,0.003,0.756,0.695,0.724,0.747,0.79,0.735,0.757,0.775,0.753,0.78,0.765,0.743,0.751,0.638,0.711,0.678,0.701,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +9,ProtSSN (k=20 h=512),Hybrid - Structure & PLM,0.742,0.004,0.749,0.694,0.732,0.719,0.814,0.717,0.753,0.78,0.758,0.789,0.766,0.701,0.749,0.685,0.693,0.666,0.703,"ProtSSN (k=20, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +10,ProtSSN (k=20 h=768),Hybrid - Structure & PLM,0.741,0.004,0.748,0.691,0.734,0.716,0.814,0.715,0.75,0.782,0.76,0.789,0.763,0.692,0.748,0.689,0.682,0.65,0.7,"ProtSSN (k=20, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +11,ProtSSN (k=30 h=1280),Hybrid - Structure & PLM,0.741,0.004,0.746,0.698,0.732,0.714,0.813,0.718,0.747,0.783,0.759,0.788,0.764,0.687,0.747,0.689,0.681,0.65,0.693,"ProtSSN (k=30, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +12,ProtSSN (k=30 h=768),Hybrid - Structure & PLM,0.741,0.004,0.747,0.695,0.732,0.714,0.814,0.715,0.75,0.781,0.758,0.789,0.764,0.692,0.748,0.685,0.678,0.65,0.694,"ProtSSN (k=30, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +13,EVE (ensemble),Alignment-based model,0.741,0.003,0.753,0.702,0.723,0.748,0.777,0.729,0.754,0.765,0.755,0.772,0.754,0.732,0.741,0.676,0.672,0.672,0.721,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +14,ProtSSN (k=30 h=512),Hybrid - Structure & PLM,0.74,0.004,0.746,0.692,0.735,0.715,0.81,0.719,0.749,0.778,0.759,0.785,0.761,0.691,0.747,0.678,0.676,0.655,0.693,"ProtSSN (k=30, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +15,Tranception L,Hybrid - Alignment & PLM,0.739,0.002,0.752,0.688,0.747,0.742,0.764,0.736,0.745,0.76,0.755,0.771,0.731,0.733,0.737,0.673,0.711,0.688,0.736,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,ProtSSN (k=10 h=1280),Hybrid - Structure & PLM,0.739,0.004,0.742,0.696,0.733,0.714,0.809,0.719,0.746,0.776,0.757,0.784,0.757,0.694,0.744,0.668,0.665,0.64,0.686,"ProtSSN (k=10, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +17,EVE (single),Alignment-based model,0.737,0.002,0.748,0.696,0.721,0.745,0.774,0.726,0.751,0.763,0.75,0.77,0.752,0.729,0.738,0.672,0.669,0.669,0.723,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +18,ProtSSN (k=10 h=512),Hybrid - Structure & PLM,0.737,0.004,0.746,0.691,0.725,0.711,0.811,0.718,0.744,0.78,0.757,0.788,0.761,0.682,0.743,0.681,0.673,0.649,0.692,"ProtSSN (k=10, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +19,MSA Transformer (ensemble),Hybrid - Alignment & PLM,0.737,0.005,0.756,0.675,0.742,0.732,0.777,0.716,0.753,0.764,0.746,0.781,0.748,0.724,0.739,0.644,0.71,0.719,0.758,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +20,Tranception M,Hybrid - Alignment & PLM,0.733,0.003,0.741,0.694,0.74,0.736,0.756,0.727,0.74,0.748,0.751,0.766,0.718,0.719,0.732,0.665,0.647,0.654,0.709,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +21,ProtSSN (k=10 h=768),Hybrid - Structure & PLM,0.732,0.004,0.739,0.686,0.721,0.706,0.806,0.707,0.742,0.772,0.751,0.777,0.754,0.686,0.737,0.672,0.675,0.643,0.685,"ProtSSN (k=10, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +22,DeepSequence (ensemble),Alignment-based model,0.729,0.004,0.746,0.689,0.713,0.729,0.769,0.705,0.74,0.761,0.748,0.767,0.746,0.691,0.728,0.673,0.69,0.689,0.732,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +23,ESM-IF1,Inverse folding model,0.729,0.007,0.697,0.716,0.721,0.674,0.838,0.661,0.738,0.793,0.728,0.767,0.766,0.71,0.741,0.721,0.697,0.684,0.743,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +24,MSA Transformer (single),Hybrid - Alignment & PLM,0.729,0.004,0.746,0.666,0.733,0.729,0.769,0.707,0.746,0.756,0.742,0.774,0.737,0.716,0.731,0.639,0.71,0.712,0.754,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +25,ESM2 (650M),Protein language model,0.728,0.006,0.73,0.691,0.725,0.708,0.789,0.691,0.725,0.782,0.758,0.766,0.748,0.639,0.737,0.665,0.632,0.605,0.62,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +26,Tranception S,Hybrid - Alignment & PLM,0.728,0.003,0.734,0.7,0.728,0.73,0.749,0.733,0.731,0.742,0.745,0.758,0.715,0.712,0.724,0.666,0.654,0.659,0.711,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +27,ESM2 (3B),Protein language model,0.724,0.006,0.724,0.675,0.722,0.712,0.786,0.693,0.732,0.77,0.749,0.765,0.752,0.655,0.732,0.645,0.615,0.605,0.621,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +28,ESM-1v (ensemble),Protein language model,0.723,0.006,0.726,0.674,0.735,0.718,0.764,0.682,0.725,0.776,0.754,0.756,0.728,0.659,0.724,0.644,0.616,0.598,0.622,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +29,DeepSequence (single),Alignment-based model,0.723,0.004,0.741,0.68,0.704,0.721,0.768,0.705,0.733,0.757,0.745,0.763,0.741,0.68,0.721,0.666,0.669,0.682,0.73,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +30,SaProt (35M),Hybrid - Structure & PLM,0.722,0.005,0.702,0.702,0.736,0.662,0.807,0.683,0.716,0.771,0.748,0.764,0.727,0.626,0.728,0.72,0.627,0.613,0.657,SaProt (35M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +31,VESPAl,Protein language model,0.72,0.004,0.737,0.683,0.683,0.728,0.771,0.709,0.736,0.756,0.729,0.765,0.746,0.723,0.728,0.615,0.693,0.657,0.696,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +32,ESM2 (15B),Protein language model,0.72,0.006,0.717,0.668,0.725,0.715,0.775,0.694,0.731,0.761,0.742,0.759,0.741,0.676,0.726,0.641,0.637,0.609,0.64,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +33,ESM-1b,Protein language model,0.719,0.006,0.73,0.662,0.72,0.699,0.782,0.695,0.724,0.767,0.747,0.771,0.741,0.642,0.715,0.644,0.616,0.596,0.662,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +34,Progen2 XL,Protein language model,0.717,0.004,0.72,0.662,0.73,0.718,0.757,0.696,0.73,0.749,0.722,0.752,0.736,0.718,0.718,0.642,0.68,0.647,0.691,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +35,MIF-ST,Hybrid - Structure & PLM,0.717,0.006,0.709,0.674,0.738,0.701,0.762,0.705,0.72,0.746,0.721,0.724,0.741,0.715,0.736,0.675,0.706,0.673,0.711,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +36,EVmutation,Alignment-based model,0.716,0.003,0.737,0.666,0.707,0.727,0.741,0.713,0.737,0.723,0.728,0.746,0.729,0.707,0.711,0.673,0.683,0.674,0.757,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +37,ESM2 (150M),Protein language model,0.713,0.007,0.71,0.685,0.718,0.674,0.779,0.675,0.701,0.767,0.754,0.761,0.712,0.582,0.717,0.655,0.588,0.592,0.611,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +38,Progen2 M,Protein language model,0.711,0.004,0.713,0.666,0.735,0.715,0.726,0.678,0.716,0.737,0.735,0.73,0.699,0.681,0.709,0.61,0.596,0.588,0.599,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +39,Progen2 L,Protein language model,0.711,0.004,0.718,0.662,0.734,0.713,0.726,0.693,0.715,0.733,0.733,0.739,0.703,0.672,0.708,0.615,0.649,0.63,0.669,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +40,Progen2 Base,Protein language model,0.709,0.005,0.713,0.664,0.739,0.714,0.712,0.689,0.706,0.733,0.739,0.733,0.678,0.668,0.707,0.597,0.598,0.593,0.613,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +41,RITA XL,Protein language model,0.707,0.005,0.702,0.665,0.728,0.715,0.727,0.674,0.716,0.732,0.727,0.721,0.693,0.711,0.703,0.616,0.605,0.602,0.634,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +42,ESM-1v (single),Protein language model,0.707,0.007,0.713,0.652,0.723,0.705,0.744,0.663,0.708,0.763,0.738,0.74,0.714,0.642,0.709,0.624,0.62,0.596,0.619,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +43,Wavenet,Alignment-based model,0.707,0.006,0.709,0.673,0.691,0.706,0.757,0.664,0.723,0.75,0.723,0.737,0.729,0.685,0.703,0.648,0.654,0.631,0.669,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +44,Tranception L no retrieval,Protein language model,0.707,0.004,0.717,0.659,0.727,0.715,0.714,0.697,0.708,0.73,0.719,0.717,0.699,0.715,0.703,0.621,0.695,0.657,0.704,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +45,MIF,Inverse folding model,0.705,0.007,0.673,0.685,0.731,0.663,0.775,0.69,0.704,0.734,0.717,0.704,0.716,0.695,0.723,0.673,0.67,0.645,0.689,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +46,RITA L,Protein language model,0.703,0.005,0.698,0.66,0.729,0.711,0.718,0.676,0.709,0.724,0.727,0.72,0.675,0.704,0.698,0.609,0.589,0.585,0.62,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +47,CARP (640M),Protein language model,0.701,0.007,0.711,0.649,0.718,0.707,0.722,0.676,0.705,0.732,0.732,0.715,0.704,0.653,0.718,0.638,0.609,0.595,0.595,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +48,Site-Independent,Alignment-based model,0.696,0.005,0.699,0.684,0.687,0.714,0.695,0.73,0.708,0.67,0.71,0.713,0.674,0.698,0.69,0.657,0.625,0.643,0.721,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +49,RITA M,Protein language model,0.693,0.005,0.691,0.649,0.72,0.709,0.698,0.668,0.696,0.716,0.716,0.702,0.665,0.699,0.69,0.591,0.586,0.591,0.614,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +50,Unirep evotuned,Hybrid - Alignment & PLM,0.692,0.004,0.695,0.657,0.7,0.695,0.714,0.68,0.697,0.71,0.704,0.711,0.69,0.688,0.684,0.634,0.653,0.638,0.699,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +51,Tranception M no retrieval,Protein language model,0.691,0.005,0.687,0.656,0.721,0.702,0.687,0.661,0.695,0.705,0.71,0.694,0.666,0.681,0.685,0.596,0.581,0.583,0.611,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +52,Progen2 S,Protein language model,0.685,0.006,0.68,0.652,0.71,0.689,0.695,0.659,0.679,0.714,0.718,0.698,0.657,0.643,0.683,0.584,0.572,0.569,0.587,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +53,CARP (76M),Protein language model,0.678,0.007,0.682,0.656,0.701,0.653,0.697,0.64,0.664,0.718,0.714,0.698,0.654,0.584,0.687,0.628,0.561,0.564,0.568,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +54,ESM2 (35M),Protein language model,0.676,0.008,0.669,0.668,0.684,0.622,0.734,0.637,0.652,0.739,0.706,0.722,0.664,0.562,0.669,0.649,0.576,0.581,0.613,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +55,RITA S,Protein language model,0.667,0.006,0.659,0.652,0.684,0.68,0.66,0.651,0.668,0.679,0.687,0.659,0.629,0.682,0.659,0.583,0.575,0.576,0.598,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +56,Tranception S no retrieval,Protein language model,0.665,0.006,0.652,0.657,0.689,0.679,0.645,0.641,0.664,0.672,0.678,0.648,0.642,0.661,0.654,0.587,0.579,0.582,0.597,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +57,CARP (38M),Protein language model,0.653,0.007,0.655,0.652,0.671,0.623,0.661,0.612,0.633,0.688,0.679,0.664,0.628,0.567,0.656,0.61,0.557,0.571,0.578,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +58,ProteinMPNN,Inverse folding model,0.639,0.006,0.605,0.591,0.603,0.59,0.805,0.596,0.655,0.729,0.658,0.71,0.684,0.64,0.657,0.673,0.604,0.61,0.695,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +59,ESM2 (8M),Protein language model,0.624,0.008,0.607,0.65,0.647,0.577,0.638,0.611,0.601,0.636,0.635,0.635,0.601,0.546,0.611,0.593,0.566,0.573,0.599,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +60,Unirep,Protein language model,0.604,0.008,0.597,0.617,0.621,0.577,0.61,0.602,0.591,0.603,0.622,0.62,0.576,0.532,0.595,0.557,0.566,0.582,0.592,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +61,ProtGPT2,Protein language model,0.603,0.006,0.593,0.585,0.602,0.595,0.639,0.598,0.597,0.637,0.636,0.631,0.57,0.576,0.601,0.602,0.528,0.509,0.534,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +62,CARP (600K),Protein language model,0.557,0.009,0.555,0.551,0.596,0.531,0.549,0.552,0.545,0.548,0.568,0.539,0.531,0.526,0.556,0.533,0.524,0.548,0.557,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.html b/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.html new file mode 100644 index 0000000..2c4b1ea --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/AUC/Summary_performance_DMS_substitutions_AUC.html @@ -0,0 +1,1670 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_AUCBootstrap_standard_error_AUCFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusDepth_1Depth_2Depth_3Depth_4Depth_5+Model detailsReferences
Model_rank
1TranceptEVE LHybrid - Alignment & PLM0.7510.0000.7640.7010.7500.7560.7830.7460.7620.7720.7650.7820.7550.7460.7510.6830.7030.6880.739TranceptEVE Large model (Tranception Large & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
2SaProt (650M)Hybrid - Structure & PLM0.7510.0050.7470.7110.7660.7030.8260.7210.7500.7960.7680.7880.7770.6710.7550.7050.6790.6650.719SaProt (650M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
3TranceptEVE MHybrid - Alignment & PLM0.7490.0020.7590.7070.7470.7530.7800.7400.7610.7680.7650.7810.7500.7370.7490.6810.6740.6780.732TranceptEVE Medium model (Tranception Medium & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
4GEMMEAlignment-based model0.7490.0040.7590.7040.7370.7520.7920.7440.7620.7750.7620.7830.7580.7540.7540.6750.6930.6940.770GEMME model<a href='https://pubmed.ncbi.nlm.nih.gov/31406981/'>Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.</a>
5TranceptEVE SHybrid - Alignment & PLM0.7480.0020.7560.7120.7410.7500.7790.7450.7560.7670.7640.7770.7510.7330.7460.6800.6750.6780.731TranceptEVE Small model (Tranception Small & retrieved EVE model)<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
6ProtSSN (ensemble)Hybrid - Structure & PLM0.7470.0030.7520.7020.7390.7220.8170.7230.7550.7860.7650.7920.7680.6990.7550.6880.6870.6580.697ProtSSN (ensemble of 9 models)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
7ProtSSN (k=20 h=1280)Hybrid - Structure & PLM0.7430.0030.7480.7020.7320.7150.8170.7200.7500.7850.7610.7890.7650.6950.7500.6880.6840.6540.698ProtSSN (k=20, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
8VESPAProtein language model0.7420.0030.7560.6950.7240.7470.7900.7350.7570.7750.7530.7800.7650.7430.7510.6380.7110.6780.701VESPA model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
9ProtSSN (k=20 h=512)Hybrid - Structure & PLM0.7420.0040.7490.6940.7320.7190.8140.7170.7530.7800.7580.7890.7660.7010.7490.6850.6930.6660.703ProtSSN (k=20, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
10ProtSSN (k=20 h=768)Hybrid - Structure & PLM0.7410.0040.7480.6910.7340.7160.8140.7150.7500.7820.7600.7890.7630.6920.7480.6890.6820.6500.700ProtSSN (k=20, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
11ProtSSN (k=30 h=1280)Hybrid - Structure & PLM0.7410.0040.7460.6980.7320.7140.8130.7180.7470.7830.7590.7880.7640.6870.7470.6890.6810.6500.693ProtSSN (k=30, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
12ProtSSN (k=30 h=768)Hybrid - Structure & PLM0.7410.0040.7470.6950.7320.7140.8140.7150.7500.7810.7580.7890.7640.6920.7480.6850.6780.6500.694ProtSSN (k=30, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
13EVE (ensemble)Alignment-based model0.7410.0030.7530.7020.7230.7480.7770.7290.7540.7650.7550.7720.7540.7320.7410.6760.6720.6720.721EVE model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
14ProtSSN (k=30 h=512)Hybrid - Structure & PLM0.7400.0040.7460.6920.7350.7150.8100.7190.7490.7780.7590.7850.7610.6910.7470.6780.6760.6550.693ProtSSN (k=30, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
15Tranception LHybrid - Alignment & PLM0.7390.0020.7520.6880.7470.7420.7640.7360.7450.7600.7550.7710.7310.7330.7370.6730.7110.6880.736Tranception Large model (700M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
16ProtSSN (k=10 h=1280)Hybrid - Structure & PLM0.7390.0040.7420.6960.7330.7140.8090.7190.7460.7760.7570.7840.7570.6940.7440.6680.6650.6400.686ProtSSN (k=10, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
17EVE (single)Alignment-based model0.7370.0020.7480.6960.7210.7450.7740.7260.7510.7630.7500.7700.7520.7290.7380.6720.6690.6690.723EVE model (single seed)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
18ProtSSN (k=10 h=512)Hybrid - Structure & PLM0.7370.0040.7460.6910.7250.7110.8110.7180.7440.7800.7570.7880.7610.6820.7430.6810.6730.6490.692ProtSSN (k=10, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
19MSA Transformer (ensemble)Hybrid - Alignment & PLM0.7370.0050.7560.6750.7420.7320.7770.7160.7530.7640.7460.7810.7480.7240.7390.6440.7100.7190.758MSA Transformer (ensemble of 5 MSA samples)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
20Tranception MHybrid - Alignment & PLM0.7330.0030.7410.6940.7400.7360.7560.7270.7400.7480.7510.7660.7180.7190.7320.6650.6470.6540.709Tranception Medium model (300M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
21ProtSSN (k=10 h=768)Hybrid - Structure & PLM0.7320.0040.7390.6860.7210.7060.8060.7070.7420.7720.7510.7770.7540.6860.7370.6720.6750.6430.685ProtSSN (k=10, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
22DeepSequence (ensemble)Alignment-based model0.7290.0040.7460.6890.7130.7290.7690.7050.7400.7610.7480.7670.7460.6910.7280.6730.6900.6890.732DeepSequence model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
23ESM-IF1Inverse folding model0.7290.0070.6970.7160.7210.6740.8380.6610.7380.7930.7280.7670.7660.7100.7410.7210.6970.6840.743ESM-IF1 model<a href='https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html'>Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.</a>
24MSA Transformer (single)Hybrid - Alignment & PLM0.7290.0040.7460.6660.7330.7290.7690.7070.7460.7560.7420.7740.7370.7160.7310.6390.7100.7120.754MSA Transformer (single MSA sample)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
25ESM2 (650M)Protein language model0.7280.0060.7300.6910.7250.7080.7890.6910.7250.7820.7580.7660.7480.6390.7370.6650.6320.6050.620ESM2 model (650M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
26Tranception SHybrid - Alignment & PLM0.7280.0030.7340.7000.7280.7300.7490.7330.7310.7420.7450.7580.7150.7120.7240.6660.6540.6590.711Tranception Small model (85M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
27ESM2 (3B)Protein language model0.7240.0060.7240.6750.7220.7120.7860.6930.7320.7700.7490.7650.7520.6550.7320.6450.6150.6050.621ESM2 model (3B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
28ESM-1v (ensemble)Protein language model0.7230.0060.7260.6740.7350.7180.7640.6820.7250.7760.7540.7560.7280.6590.7240.6440.6160.5980.622ESM-1v (ensemble of 5 independently-trained models)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
29DeepSequence (single)Alignment-based model0.7230.0040.7410.6800.7040.7210.7680.7050.7330.7570.7450.7630.7410.6800.7210.6660.6690.6820.730DeepSequence model (single seed)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
30SaProt (35M)Hybrid - Structure & PLM0.7220.0050.7020.7020.7360.6620.8070.6830.7160.7710.7480.7640.7270.6260.7280.7200.6270.6130.657SaProt (35M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
31VESPAlProtein language model0.7200.0040.7370.6830.6830.7280.7710.7090.7360.7560.7290.7650.7460.7230.7280.6150.6930.6570.696VESPAl model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
32ESM2 (15B)Protein language model0.7200.0060.7170.6680.7250.7150.7750.6940.7310.7610.7420.7590.7410.6760.7260.6410.6370.6090.640ESM2 model (15B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
33ESM-1bProtein language model0.7190.0060.7300.6620.7200.6990.7820.6950.7240.7670.7470.7710.7410.6420.7150.6440.6160.5960.662ESM-1b (w/ Brandes et al. extensions)[1] Original model: <a href='https://www.biorxiv.org/content/10.1101/622803v4'>Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118.</a> [2] Extensions: <a href='https://www.biorxiv.org/content/10.1101/2022.08.25.505311v1'>Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.</a>
34Progen2 XLProtein language model0.7170.0040.7200.6620.7300.7180.7570.6960.7300.7490.7220.7520.7360.7180.7180.6420.6800.6470.691Progen2 xlarge model (6.4B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
35MIF-STHybrid - Structure & PLM0.7170.0060.7090.6740.7380.7010.7620.7050.7200.7460.7210.7240.7410.7150.7360.6750.7060.6730.711MIF-ST model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
36EVmutationAlignment-based model0.7160.0030.7370.6660.7070.7270.7410.7130.7370.7230.7280.7460.7290.7070.7110.6730.6830.6740.757EVmutation model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
37ESM2 (150M)Protein language model0.7130.0070.7100.6850.7180.6740.7790.6750.7010.7670.7540.7610.7120.5820.7170.6550.5880.5920.611ESM2 model (150M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
38Progen2 MProtein language model0.7110.0040.7130.6660.7350.7150.7260.6780.7160.7370.7350.7300.6990.6810.7090.6100.5960.5880.599Progen2 medium model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
39Progen2 LProtein language model0.7110.0040.7180.6620.7340.7130.7260.6930.7150.7330.7330.7390.7030.6720.7080.6150.6490.6300.669Progen2 large model (2.7B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
40Progen2 BaseProtein language model0.7090.0050.7130.6640.7390.7140.7120.6890.7060.7330.7390.7330.6780.6680.7070.5970.5980.5930.613Progen2 base model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
41RITA XLProtein language model0.7070.0050.7020.6650.7280.7150.7270.6740.7160.7320.7270.7210.6930.7110.7030.6160.6050.6020.634RITA xlarge model (1.2B params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
42ESM-1v (single)Protein language model0.7070.0070.7130.6520.7230.7050.7440.6630.7080.7630.7380.7400.7140.6420.7090.6240.6200.5960.619ESM-1v (single seed)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
43WavenetAlignment-based model0.7070.0060.7090.6730.6910.7060.7570.6640.7230.7500.7230.7370.7290.6850.7030.6480.6540.6310.669Wavenet model<a href='https://www.nature.com/articles/s41467-021-22732-w'>Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.</a>
44Tranception L no retrievalProtein language model0.7070.0040.7170.6590.7270.7150.7140.6970.7080.7300.7190.7170.6990.7150.7030.6210.6950.6570.704Tranception Large model (700M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
45MIFInverse folding model0.7050.0070.6730.6850.7310.6630.7750.6900.7040.7340.7170.7040.7160.6950.7230.6730.6700.6450.689MIF model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
46RITA LProtein language model0.7030.0050.6980.6600.7290.7110.7180.6760.7090.7240.7270.7200.6750.7040.6980.6090.5890.5850.620RITA large model (680M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
47CARP (640M)Protein language model0.7010.0070.7110.6490.7180.7070.7220.6760.7050.7320.7320.7150.7040.6530.7180.6380.6090.5950.595CARP model (640M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
48Site-IndependentAlignment-based model0.6960.0050.6990.6840.6870.7140.6950.7300.7080.6700.7100.7130.6740.6980.6900.6570.6250.6430.721Site-Independent model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
49RITA MProtein language model0.6930.0050.6910.6490.7200.7090.6980.6680.6960.7160.7160.7020.6650.6990.6900.5910.5860.5910.614RITA medium model (300M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
50Unirep evotunedHybrid - Alignment & PLM0.6920.0040.6950.6570.7000.6950.7140.6800.6970.7100.7040.7110.6900.6880.6840.6340.6530.6380.699Unirep model w/ evotuning<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
51Tranception M no retrievalProtein language model0.6910.0050.6870.6560.7210.7020.6870.6610.6950.7050.7100.6940.6660.6810.6850.5960.5810.5830.611Tranception Medium model (300M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
52Progen2 SProtein language model0.6850.0060.6800.6520.7100.6890.6950.6590.6790.7140.7180.6980.6570.6430.6830.5840.5720.5690.587Progen2 small model (150M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
53CARP (76M)Protein language model0.6780.0070.6820.6560.7010.6530.6970.6400.6640.7180.7140.6980.6540.5840.6870.6280.5610.5640.568CARP model (76M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
54ESM2 (35M)Protein language model0.6760.0080.6690.6680.6840.6220.7340.6370.6520.7390.7060.7220.6640.5620.6690.6490.5760.5810.613ESM2 model (35M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
55RITA SProtein language model0.6670.0060.6590.6520.6840.6800.6600.6510.6680.6790.6870.6590.6290.6820.6590.5830.5750.5760.598RITA small model (85M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
56Tranception S no retrievalProtein language model0.6650.0060.6520.6570.6890.6790.6450.6410.6640.6720.6780.6480.6420.6610.6540.5870.5790.5820.597Tranception Small model (85M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
57CARP (38M)Protein language model0.6530.0070.6550.6520.6710.6230.6610.6120.6330.6880.6790.6640.6280.5670.6560.6100.5570.5710.578CARP model (38M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
58ProteinMPNNInverse folding model0.6390.0060.6050.5910.6030.5900.8050.5960.6550.7290.6580.7100.6840.6400.6570.6730.6040.6100.695ProteinMPNN model<a href='https://www.science.org/doi/10.1126/science.add2187'>J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.</a>
59ESM2 (8M)Protein language model0.6240.0080.6070.6500.6470.5770.6380.6110.6010.6360.6350.6350.6010.5460.6110.5930.5660.5730.599ESM2 model (8M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
60UnirepProtein language model0.6040.0080.5970.6170.6210.5770.6100.6020.5910.6030.6220.6200.5760.5320.5950.5570.5660.5820.592Unirep model<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
61ProtGPT2Protein language model0.6030.0060.5930.5850.6020.5950.6390.5980.5970.6370.6360.6310.5700.5760.6010.6020.5280.5090.534ProtGPT2 model<a href='https://www.nature.com/articles/s41467-022-32007-7'>Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.</a>
62CARP (600K)Protein language model0.5570.0090.5550.5510.5960.5310.5490.5520.5450.5480.5680.5390.5310.5260.5560.5330.5240.5480.557CARP model (600K params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_DMS_level.csv b/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_DMS_level.csv new file mode 100644 index 0000000..3e3d4a5 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_DMS_level.csv @@ -0,0 +1,217 @@ +DMS ID,Site-Independent,EVmutation,DeepSequence (single),DeepSequence (ensemble),EVE (single),EVE (ensemble),Unirep,Unirep evotuned,MSA Transformer (single),MSA Transformer (ensemble),ESM-1b,ESM-1v (single),ESM-1v (ensemble),ESM2 (8M),ESM2 (35M),ESM2 (150M),ESM2 (650M),ESM2 (3B),ESM2 (15B),Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,GEMME,VESPA,VESPAl,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,CARP (38M),CARP (600K),CARP (640M),CARP (76M),MIF,MIF-ST,ESM-IF1,ProteinMPNN,ProtSSN (k=10 h=512),ProtSSN (k=10 h=768),ProtSSN (k=10 h=1280),ProtSSN (k=20 h=512),ProtSSN (k=20 h=768),ProtSSN (k=20 h=1280),ProtSSN (k=30 h=512),ProtSSN (k=30 h=768),ProtSSN (k=30 h=1280),ProtSSN (ensemble),SaProt (650M),SaProt (35M),Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A0A140D2T1_ZIKV_Sourisseau_2019,0.265,0.23,0.082,0.075,0.28,0.292,-0.124,0.063,0.334,0.335,-0.023,-0.041,0.005,-0.058,-0.045,-0.037,0.147,0.277,0.281,0.168,0.263,0.218,0.219,0.21,0.241,0.236,0.224,0.226,0.197,0.27,0.208,0.191,-0.005,0.217,0.234,0.193,0.264,0.263,0.248,0.258,0.254,0.271,-0.043,-0.061,0.097,-0.052,0.202,0.205,0.21,0.086,0.175,0.178,0.183,0.202,0.191,0.191,0.184,0.183,0.183,0.188,0.14,0.084,9576,OrganismalFitness,A0A140D2T1_ZIKV,Medium,Virus +A0A192B1T2_9HIV1_Haddox_2018,0.359,0.332,0.317,0.337,0.387,0.401,0.008,0.39,0.406,0.409,0.356,0.391,0.401,0.022,0.022,0.04,0.076,0.087,0.1,0.359,0.386,0.397,0.398,0.394,0.393,0.389,0.366,0.384,0.385,0.382,0.435,0.418,0.256,0.385,0.384,0.4,0.393,0.399,0.398,0.402,0.409,0.411,0.329,-0.002,0.378,0.332,0.284,0.359,0.187,0.102,0.167,0.19,0.205,0.208,0.168,0.186,0.187,0.163,0.145,0.197,0.119,0.063,12577,OrganismalFitness,A0A192B1T2_9HIV1,Medium,Virus +A0A1I9GEU1_NEIME_Kennouche_2019,-0.009,0.026,0.082,0.061,0.043,0.052,0.0,0.065,0.061,0.069,0.017,0.043,0.065,-0.026,-0.03,0.004,0.048,0.022,0.056,0.048,-0.004,0.022,0.052,0.043,0.03,0.061,0.052,0.069,0.065,0.03,0.0,-0.007,0.017,0.043,0.052,0.082,0.03,0.065,0.056,0.039,0.082,0.061,-0.013,-0.035,0.052,-0.03,0.022,0.052,0.035,0.056,0.065,0.052,0.065,0.056,0.065,0.078,0.039,0.069,0.061,0.082,0.043,0.004,922,Activity,A0A1I9GEU1_NEIME,Medium,Prokaryote +A0A247D711_LISMN_Stadelmann_2021,0.347,0.359,0.096,0.041,0.312,0.316,0.028,0.038,0.234,0.258,0.065,0.082,0.087,0.07,0.079,0.084,0.06,0.07,0.084,0.043,0.067,0.055,0.053,0.094,0.014,0.065,0.033,0.07,0.089,0.351,0.244,0.234,0.004,0.053,0.041,0.055,0.212,0.21,0.215,0.193,0.183,0.193,0.062,0.043,0.067,0.074,0.312,0.353,0.328,0.263,0.21,0.237,0.224,0.246,0.241,0.28,0.208,0.244,0.191,0.237,0.316,0.212,1653,Activity,A0A247D711_LISMN,High,Prokaryote +A0A2Z5U3Z0_9INFA_Doud_2016,0.357,0.355,0.359,0.39,0.417,0.413,-0.006,0.373,0.38,0.376,0.071,0.405,0.431,-0.022,-0.001,0.04,0.397,0.39,0.378,0.296,0.352,0.413,0.396,0.42,0.334,0.391,0.395,0.376,0.385,0.403,0.389,0.311,0.059,0.356,0.407,0.415,0.407,0.416,0.402,0.436,0.44,0.431,0.001,0.007,0.263,0.004,0.333,0.378,0.366,0.132,0.384,0.384,0.425,0.415,0.417,0.431,0.426,0.407,0.439,0.432,0.15,0.122,10715,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A0A2Z5U3Z0_9INFA_Wu_2014,0.413,0.423,0.365,0.375,0.413,0.431,-0.011,0.316,0.382,0.387,0.101,0.358,0.394,0.03,0.054,0.054,0.362,0.389,0.375,0.346,0.336,0.389,0.386,0.392,0.29,0.333,0.355,0.321,0.355,0.425,0.333,0.283,0.066,0.338,0.379,0.406,0.401,0.45,0.44,0.457,0.466,0.459,0.031,0.031,0.183,0.03,0.289,0.295,0.278,0.096,0.35,0.333,0.369,0.362,0.362,0.379,0.379,0.37,0.369,0.38,0.163,0.13,2350,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A4_HUMAN_Seuma_2022,0.363,0.323,0.35,0.323,0.246,0.243,0.292,0.11,0.246,0.25,0.234,0.218,0.295,0.338,0.307,0.305,0.359,0.371,0.269,0.185,0.217,0.171,0.204,0.198,0.28,0.192,0.212,0.202,0.201,0.388,0.191,0.128,0.412,0.28,0.161,0.236,0.333,0.266,0.361,0.332,0.288,0.31,0.316,0.311,0.333,0.304,0.232,0.315,-0.13,0.042,0.357,0.338,0.337,0.341,0.344,0.316,0.356,0.281,0.29,0.343,0.348,0.332,14811,Stability,A4_HUMAN,Low,Human +A4D664_9INFA_Soh_2019,0.344,0.252,0.341,0.335,0.326,0.323,0.015,0.267,0.254,0.245,0.024,0.019,0.025,0.019,0.02,0.02,0.103,0.139,0.196,0.194,0.269,0.301,0.319,0.313,0.05,0.208,0.224,0.197,0.258,0.365,0.24,0.237,0.024,0.269,0.284,0.314,0.279,0.293,0.296,0.349,0.356,0.367,0.015,0.002,0.047,0.005,0.204,0.199,0.09,0.081,0.154,0.151,0.145,0.163,0.153,0.163,0.158,0.168,0.16,0.158,0.112,0.064,14421,OrganismalFitness,A4D664_9INFA,Medium,Virus +A4GRB6_PSEAI_Chen_2020,0.304,0.469,0.554,0.557,0.514,0.536,0.29,0.427,0.526,0.58,0.585,0.546,0.572,0.354,0.441,0.551,0.639,0.614,0.513,0.465,0.336,0.448,0.463,0.525,0.441,0.514,0.53,0.527,0.593,0.568,0.643,0.573,0.206,0.34,0.464,0.521,0.46,0.493,0.539,0.547,0.556,0.567,0.412,0.08,0.56,0.504,0.546,0.597,0.536,0.325,0.618,0.603,0.596,0.59,0.597,0.607,0.617,0.617,0.616,0.615,0.61,0.417,5004,OrganismalFitness,A4GRB6_PSEAI,High,Prokaryote +AACC1_PSEAI_Dandage_2018,0.192,0.363,0.249,0.336,0.376,0.374,0.12,0.196,0.389,0.38,0.287,0.345,0.371,0.127,0.147,0.154,0.363,0.398,0.414,0.278,0.112,0.156,0.174,0.209,0.178,0.32,0.298,0.327,0.354,0.363,0.4,0.349,0.003,0.163,0.163,0.311,0.32,0.303,0.334,0.349,0.343,0.385,0.154,0.076,0.243,0.163,0.187,0.265,0.245,0.114,0.363,0.349,0.36,0.349,0.356,0.34,0.351,0.349,0.347,0.358,0.36,0.2,1801,OrganismalFitness,AACC1_PSEAI,High,Prokaryote +ACE2_HUMAN_Chan_2020,0.162,0.105,0.08,0.08,0.117,0.103,-0.028,0.013,0.161,0.17,0.18,0.087,0.126,-0.014,0.035,0.152,0.152,0.116,0.089,0.126,-0.005,0.033,0.094,0.141,-0.028,0.074,0.117,0.116,0.18,0.096,0.103,0.073,0.062,0.006,0.04,0.045,0.135,0.092,0.083,0.107,0.083,0.081,0.008,0.045,0.218,0.035,0.288,0.24,0.27,0.08,0.161,0.207,0.161,0.182,0.173,0.195,0.164,0.162,0.166,0.173,0.233,0.161,2223,Binding,ACE2_HUMAN,Medium,Human +ADRB2_HUMAN_Jones_2020,0.277,0.364,0.41,0.411,0.397,0.407,0.364,0.396,0.432,0.426,0.43,0.428,0.428,0.313,0.341,0.373,0.392,0.402,0.405,0.412,0.425,0.427,0.415,0.39,0.431,0.421,0.421,0.425,0.391,0.429,0.406,0.311,0.25,0.435,0.421,0.401,0.428,0.43,0.427,0.423,0.43,0.428,0.375,0.124,0.431,0.411,0.336,0.396,0.368,0.169,0.386,0.39,0.412,0.402,0.409,0.402,0.395,0.414,0.403,0.417,0.457,0.411,7800,Activity,ADRB2_HUMAN,Medium,Human +AICDA_HUMAN_Gajula_2014_3cycles,0.011,0.216,0.267,0.267,0.267,0.267,-0.169,0.139,0.164,0.216,0.216,0.216,0.267,-0.143,-0.143,0.011,0.19,0.19,0.113,0.395,-0.092,0.036,0.19,0.241,-0.066,0.139,0.293,0.216,0.19,0.164,0.293,0.293,0.036,-0.041,-0.015,0.216,0.036,0.113,0.19,0.216,0.241,0.216,-0.169,-0.169,0.164,0.036,0.293,0.241,0.267,0.139,0.216,0.241,0.216,0.216,0.164,0.216,0.216,0.216,0.19,0.19,0.19,-0.015,209,Activity,AICDA_HUMAN,Medium,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O,0.201,0.249,0.241,0.226,0.229,0.234,-0.227,0.013,0.085,0.096,0.188,0.07,0.271,-0.139,0.186,0.127,0.102,0.135,0.127,-0.015,-0.063,-0.157,-0.094,-0.087,-0.271,-0.124,-0.112,-0.176,0.005,0.203,0.125,0.128,0.007,-0.207,-0.108,-0.248,0.222,0.234,0.183,0.262,0.256,0.211,-0.16,-0.162,0.04,0.003,0.143,0.02,0.276,0.232,0.283,0.149,0.048,0.172,0.162,0.164,0.163,0.166,0.174,0.159,0.153,0.26,2972,Stability,AMFR_HUMAN,Medium,Human +AMIE_PSEAE_Wrenbeck_2017,0.207,0.391,0.424,0.448,0.421,0.396,0.035,0.363,0.314,0.494,0.466,0.496,0.546,0.214,0.282,0.299,0.437,0.557,0.493,0.394,0.452,0.449,0.444,0.454,0.453,0.485,0.455,0.453,0.433,0.47,0.487,0.455,0.243,0.499,0.389,0.351,0.474,0.395,0.36,0.494,0.432,0.415,0.233,0.04,0.358,0.281,0.291,0.374,0.324,0.167,0.448,0.41,0.418,0.435,0.422,0.432,0.413,0.426,0.453,0.435,0.475,0.286,6227,Activity,AMIE_PSEAE,High,Prokaryote +ANCSZ_Hobbs_2022,0.417,0.382,0.325,0.348,0.364,0.369,0.389,0.342,0.368,0.382,0.405,0.382,0.406,0.36,0.431,0.434,0.448,0.437,0.418,0.008,0.316,0.346,0.38,0.38,0.374,0.383,0.372,0.352,0.37,0.433,0.41,0.406,0.222,0.343,0.37,0.341,0.404,0.424,0.409,0.388,0.408,0.394,0.381,0.285,0.366,0.372,0.339,0.361,0.311,0.123,0.414,0.408,0.431,0.434,0.431,0.442,0.418,0.43,0.422,0.441,0.419,0.442,4670,Activity,ANCSZ,Medium,Eukaryote +ARGR_ECOLI_Tsuboyama_2023_1AOY,0.212,0.24,0.293,0.296,0.281,0.281,0.181,0.249,0.315,0.352,0.284,0.203,0.262,0.209,0.181,0.355,0.34,0.377,0.293,0.296,0.225,0.327,0.33,0.277,0.231,0.296,0.29,0.324,0.302,0.336,0.305,0.239,0.066,0.253,0.265,0.324,0.212,0.215,0.281,0.268,0.277,0.299,0.162,0.144,0.33,0.296,0.585,0.498,0.601,0.495,0.371,0.392,0.377,0.386,0.358,0.38,0.355,0.368,0.34,0.377,0.43,0.368,1287,Stability,ARGR_ECOLI,Medium,Prokaryote +B2L11_HUMAN_Dutta_2010_binding-Mcl-1,0.519,0.002,0.508,0.532,0.319,0.508,0.177,0.414,0.201,0.225,0.177,0.059,0.248,0.059,0.154,0.106,0.225,0.343,0.272,0.225,0.154,0.035,0.225,0.366,0.059,0.177,0.319,0.248,0.295,0.658,0.319,0.3,0.201,0.106,0.177,0.366,0.437,0.343,0.343,0.532,0.461,0.366,0.13,0.012,0.154,0.083,0.272,0.248,0.319,0.035,0.272,0.272,0.319,0.201,0.248,0.414,0.225,0.177,0.366,0.248,0.201,0.201,170,Binding,B2L11_HUMAN,Low,Human +BBC1_YEAST_Tsuboyama_2023_1TG0,0.189,0.239,0.379,0.361,0.375,0.381,0.185,0.282,0.365,0.382,0.433,0.369,0.41,0.324,0.359,0.359,0.439,0.371,0.44,0.29,0.332,0.352,0.381,0.34,0.123,0.375,0.408,0.369,0.299,0.294,0.352,0.329,0.266,0.299,0.206,0.268,0.328,0.263,0.309,0.408,0.359,0.382,0.098,0.108,0.21,0.127,0.468,0.367,0.572,0.514,0.442,0.421,0.448,0.442,0.442,0.446,0.435,0.442,0.442,0.444,0.45,0.483,2069,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU,0.327,0.307,0.281,0.296,0.284,0.302,0.098,0.159,0.309,0.391,0.33,0.248,0.289,0.118,0.149,0.251,0.406,0.414,0.324,0.342,0.088,0.299,0.154,0.302,0.136,0.363,0.21,0.33,0.34,0.5,0.472,0.401,0.024,0.164,0.23,0.35,0.355,0.36,0.36,0.296,0.309,0.34,0.078,0.085,0.169,0.146,0.421,0.37,0.518,0.431,0.454,0.452,0.464,0.472,0.459,0.487,0.459,0.449,0.454,0.462,0.378,0.21,1572,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Deng_2012,0.261,0.406,0.402,0.422,0.402,0.414,0.067,0.24,0.419,0.448,0.413,0.394,0.418,0.189,0.281,0.373,0.443,0.345,0.259,0.365,0.293,0.297,0.303,0.309,0.31,0.356,0.361,0.315,0.271,0.387,0.428,0.396,0.091,0.286,0.296,0.261,0.355,0.356,0.359,0.414,0.429,0.426,0.273,0.028,0.389,0.34,0.335,0.387,0.364,0.193,0.43,0.443,0.438,0.453,0.45,0.442,0.45,0.435,0.442,0.466,0.44,0.321,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Firnberg_2014,0.348,0.564,0.589,0.604,0.549,0.573,0.104,0.365,0.58,0.608,0.579,0.53,0.575,0.284,0.413,0.514,0.62,0.429,0.3,0.523,0.42,0.408,0.381,0.385,0.451,0.5,0.506,0.418,0.335,0.555,0.614,0.63,0.139,0.41,0.366,0.323,0.48,0.464,0.462,0.572,0.574,0.576,0.397,0.032,0.571,0.469,0.471,0.572,0.529,0.244,0.599,0.591,0.59,0.617,0.611,0.612,0.616,0.584,0.603,0.632,0.605,0.448,4783,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Jacquier_2013,0.357,0.551,0.551,0.587,0.567,0.579,0.07,0.345,0.571,0.587,0.523,0.523,0.535,0.24,0.393,0.499,0.579,0.466,0.369,0.515,0.422,0.393,0.377,0.393,0.426,0.527,0.47,0.418,0.345,0.446,0.575,0.571,0.114,0.385,0.397,0.381,0.47,0.494,0.499,0.563,0.583,0.596,0.341,0.058,0.519,0.43,0.41,0.519,0.499,0.236,0.547,0.519,0.539,0.559,0.555,0.535,0.567,0.547,0.519,0.559,0.559,0.438,989,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Stiffler_2015,0.35,0.564,0.601,0.614,0.551,0.586,0.102,0.363,0.594,0.62,0.591,0.549,0.595,0.298,0.424,0.526,0.632,0.436,0.306,0.528,0.433,0.419,0.374,0.392,0.466,0.511,0.526,0.427,0.339,0.565,0.627,0.638,0.137,0.432,0.369,0.326,0.49,0.462,0.465,0.585,0.58,0.586,0.411,0.032,0.581,0.483,0.48,0.576,0.53,0.247,0.6,0.582,0.594,0.608,0.608,0.611,0.61,0.591,0.604,0.63,0.616,0.462,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BRCA1_HUMAN_Findlay_2018,0.436,0.298,0.423,0.426,0.403,0.408,0.048,0.227,0.334,0.331,0.413,0.299,0.341,0.077,0.286,0.379,0.413,0.426,0.361,0.237,0.065,0.321,0.341,0.349,0.239,0.403,0.391,0.379,0.398,0.399,0.436,0.416,0.212,0.09,0.127,0.371,0.406,0.406,0.436,0.441,0.431,0.448,0.142,0.068,0.446,0.361,0.369,0.391,0.044,0.122,0.431,0.423,0.428,0.411,0.421,0.423,0.428,0.413,0.408,0.423,0.436,0.398,1837,OrganismalFitness,BRCA1_HUMAN,Low,Human +BRCA2_HUMAN_Erwood_2022_HEK293T,0.312,0.309,0.309,0.309,0.309,0.309,0.068,0.283,0.122,-0.012,0.309,0.095,0.015,0.015,0.015,0.283,0.309,0.309,0.309,0.041,0.015,0.309,0.309,0.309,0.309,0.283,0.309,0.309,0.068,0.309,0.256,0.285,-0.012,0.041,0.068,0.068,0.309,0.309,0.309,0.309,0.309,0.309,0.015,0.015,0.309,-0.012,-0.039,0.015,-0.2,0.175,0.256,0.283,0.283,0.309,0.309,0.309,0.309,0.309,0.309,0.309,-0.039,0.015,265,OrganismalFitness,BRCA2_HUMAN,,Human +C6KNH7_9INFA_Lee_2018,0.297,0.268,0.258,0.264,0.308,0.308,-0.011,0.333,0.267,0.272,0.024,0.32,0.37,-0.002,-0.007,-0.003,0.381,0.293,0.298,0.245,0.288,0.269,0.275,0.261,0.168,0.341,0.323,0.357,0.281,0.345,0.333,0.251,0.088,0.269,0.276,0.299,0.312,0.317,0.318,0.332,0.335,0.325,-0.009,-0.016,0.204,0.014,0.393,0.422,0.426,0.172,0.387,0.396,0.41,0.411,0.417,0.399,0.417,0.416,0.409,0.416,0.235,0.134,10754,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +CALM1_HUMAN_Weile_2017,0.135,0.201,0.197,0.204,0.186,0.192,0.124,0.135,0.197,0.221,0.232,0.19,0.234,0.115,0.153,0.153,0.166,0.166,0.168,0.188,0.131,0.184,0.215,0.21,0.188,0.243,0.239,0.25,0.276,0.206,0.166,0.098,0.062,0.173,0.226,0.27,0.175,0.197,0.226,0.204,0.215,0.204,0.164,0.115,0.256,0.212,0.06,0.124,0.137,0.067,0.124,0.144,0.151,0.155,0.153,0.14,0.142,0.133,0.137,0.151,0.252,0.192,1813,OrganismalFitness,CALM1_HUMAN,High,Human +CAPSD_AAV2S_Sinai_2021,0.234,0.195,0.188,0.211,0.194,0.188,0.285,0.285,0.179,0.202,0.122,0.146,0.148,0.201,0.217,0.144,0.201,0.111,0.044,0.163,0.145,0.198,0.206,0.202,0.159,0.149,0.188,0.152,0.3,0.274,0.118,0.11,0.102,0.149,0.203,0.36,0.213,0.223,0.318,0.194,0.197,0.263,0.092,0.136,0.21,0.096,0.313,0.301,0.24,0.221,0.151,0.148,0.131,0.151,0.139,0.139,0.143,0.139,0.15,0.145,0.214,0.167,42328,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAR11_HUMAN_Meitlis_2020_gof,-0.01,-0.026,-0.027,-0.03,-0.055,-0.062,0.09,-0.095,0.025,0.027,0.012,0.082,0.035,0.047,0.05,0.145,0.057,0.065,0.1,0.045,0.075,-0.042,0.012,0.005,-0.14,-0.052,-0.035,-0.032,0.02,-0.019,0.022,0.012,0.097,0.042,-0.042,-0.09,0.032,-0.022,-0.07,-0.02,-0.062,-0.092,0.06,0.015,0.002,0.125,0.102,0.015,0.07,0.04,0.105,0.095,0.09,0.08,0.085,0.075,0.072,0.09,0.095,0.095,0.137,0.13,2374,OrganismalFitness,CAR11_HUMAN,Low,Human +CAR11_HUMAN_Meitlis_2020_lof,0.365,0.311,0.299,0.284,0.294,0.299,0.075,0.011,0.199,0.189,0.361,0.41,0.395,0.02,0.085,0.381,0.426,0.436,0.443,0.128,0.123,0.269,0.246,0.21,0.155,0.256,0.241,0.296,0.113,0.314,0.363,0.325,0.277,-0.009,0.224,0.205,0.313,0.267,0.237,0.329,0.276,0.252,0.072,0.026,0.334,0.241,0.353,0.375,0.348,0.108,0.443,0.431,0.431,0.443,0.428,0.443,0.42,0.436,0.453,0.44,0.465,0.323,2395,OrganismalFitness,CAR11_HUMAN,Low,Human +CAS9_STRP1_Spencer_2017_positive,0.107,0.141,0.123,0.126,0.135,0.139,0.014,0.079,0.131,0.14,0.123,0.046,0.039,0.027,0.056,0.101,0.133,0.137,0.134,0.01,0.027,0.048,0.116,0.091,0.026,0.144,0.138,0.133,0.048,0.128,0.145,0.129,0.016,0.022,0.033,0.12,0.132,0.134,0.132,0.129,0.131,0.139,0.039,0.011,0.129,0.074,0.074,0.129,0.015,0.02,0.123,0.123,0.121,0.125,0.126,0.13,0.127,0.128,0.121,0.128,0.119,0.077,8117,Activity,CAS9_STRP1,Medium,Prokaryote +CASP3_HUMAN_Roychowdhury_2020,0.314,0.447,0.508,0.503,0.518,0.523,0.053,0.37,0.518,0.518,0.464,0.477,0.487,0.186,0.441,0.518,0.521,0.452,0.38,0.48,0.132,0.378,0.424,0.406,0.383,0.441,0.447,0.393,0.441,0.49,0.467,0.401,0.181,0.048,0.355,0.388,0.39,0.429,0.462,0.505,0.49,0.526,0.27,-0.029,0.48,0.424,0.316,0.429,0.395,0.188,0.459,0.457,0.459,0.462,0.482,0.47,0.467,0.447,0.457,0.487,0.531,0.372,1567,Activity,CASP3_HUMAN,High,Human +CASP7_HUMAN_Roychowdhury_2020,0.335,0.461,0.509,0.521,0.502,0.502,0.051,0.392,0.487,0.533,0.487,0.502,0.53,0.225,0.516,0.518,0.535,0.499,0.478,0.504,0.18,0.459,0.461,0.464,0.475,0.497,0.511,0.485,0.48,0.549,0.511,0.459,0.299,0.101,0.487,0.425,0.442,0.506,0.509,0.497,0.549,0.533,0.371,0.023,0.549,0.514,0.397,0.523,0.521,0.249,0.514,0.502,0.471,0.492,0.518,0.521,0.495,0.495,0.514,0.511,0.556,0.433,1680,Activity,CASP7_HUMAN,Medium,Human +CATR_CHLRE_Tsuboyama_2023_2AMI,0.47,0.473,0.567,0.563,0.58,0.578,0.607,0.487,0.449,0.422,0.498,0.538,0.563,0.481,0.594,0.586,0.557,0.508,0.573,0.088,0.506,0.515,0.51,0.508,0.54,0.519,0.498,0.46,0.477,0.576,0.536,0.544,0.357,0.527,0.502,0.529,0.569,0.542,0.561,0.58,0.563,0.576,0.395,0.38,0.294,0.424,0.393,0.386,0.576,0.439,0.565,0.544,0.578,0.557,0.584,0.563,0.557,0.536,0.555,0.559,0.512,0.565,1903,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X,0.555,0.605,0.633,0.634,0.638,0.65,0.375,0.569,0.673,0.685,0.648,0.615,0.611,0.404,0.561,0.642,0.636,0.633,0.607,0.673,0.4,0.406,0.592,0.571,0.449,0.573,0.582,0.557,0.611,0.667,0.658,0.595,0.019,0.404,0.513,0.611,0.576,0.603,0.623,0.65,0.64,0.65,0.397,0.279,0.513,0.522,0.677,0.625,0.805,0.71,0.698,0.725,0.692,0.71,0.737,0.714,0.712,0.71,0.706,0.725,0.631,0.776,2068,Stability,CBPA2_HUMAN,Medium,Human +CBS_HUMAN_Sun_2020,0.297,0.306,0.314,0.339,0.319,0.331,0.162,0.205,0.33,0.341,0.304,0.305,0.329,0.07,0.177,0.281,0.299,0.285,0.283,0.309,0.315,0.238,0.261,0.262,0.319,0.254,0.262,0.248,0.273,0.322,0.316,0.299,0.179,0.332,0.268,0.233,0.329,0.289,0.286,0.349,0.317,0.315,0.243,0.062,0.326,0.324,0.21,0.236,0.277,0.058,0.277,0.277,0.283,0.297,0.292,0.289,0.281,0.284,0.29,0.293,0.336,0.238,7217,OrganismalFitness,CBS_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28,0.389,0.45,0.493,0.505,0.463,0.507,-0.205,0.454,0.482,0.468,0.48,0.482,0.47,-0.21,0.545,0.514,0.503,0.428,0.442,0.5,0.312,0.41,0.377,0.394,0.372,0.361,0.366,0.31,0.394,0.489,0.507,0.502,0.349,0.277,0.305,0.31,0.415,0.41,0.396,0.496,0.517,0.503,0.465,-0.261,0.249,0.435,-0.002,0.067,0.452,0.421,0.528,0.458,0.535,0.526,0.545,0.515,0.519,0.552,0.519,0.526,0.493,0.615,2282,Stability,CBX4_HUMAN,High,Human +CCDB_ECOLI_Adkar_2012,0.298,0.349,0.389,0.413,0.389,0.413,-0.019,0.165,0.315,0.308,0.379,0.304,0.386,0.015,-0.012,0.349,0.4,0.369,0.213,0.393,0.015,0.012,-0.08,0.094,-0.145,0.077,0.036,-0.005,0.328,0.41,0.495,0.481,0.117,-0.022,0.005,0.253,0.383,0.345,0.366,0.403,0.366,0.396,0.036,-0.009,0.379,0.015,0.25,0.349,0.27,0.219,0.379,0.321,0.349,0.366,0.342,0.359,0.352,0.379,0.406,0.383,0.359,0.138,1176,Activity,CCDB_ECOLI,High,Prokaryote +CCDB_ECOLI_Tripathi_2016,0.326,0.384,0.414,0.436,0.402,0.414,0.026,0.217,0.314,0.341,0.393,0.357,0.378,0.023,0.029,0.357,0.393,0.423,0.311,0.411,0.038,0.032,-0.044,0.153,-0.074,0.093,0.011,0.059,0.384,0.387,0.448,0.439,0.069,0.02,0.105,0.332,0.384,0.372,0.417,0.393,0.393,0.414,0.026,-0.022,0.399,0.02,0.254,0.341,0.251,0.196,0.396,0.332,0.36,0.375,0.357,0.363,0.357,0.36,0.381,0.372,0.372,0.241,1663,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +CCR5_HUMAN_Gill_2023,0.208,0.215,0.206,0.203,0.203,0.21,0.221,0.227,0.239,0.263,0.268,0.258,0.268,0.228,0.255,0.263,0.256,0.241,0.258,0.268,0.271,0.257,0.232,0.23,0.271,0.255,0.255,0.273,0.233,0.259,0.216,0.182,0.086,0.272,0.268,0.264,0.261,0.27,0.28,0.234,0.233,0.227,0.267,0.121,0.268,0.264,0.19,0.23,0.203,0.112,0.246,0.234,0.246,0.25,0.245,0.24,0.252,0.254,0.256,0.256,0.271,0.26,6137,Binding,CCR5_HUMAN,High,Human +CD19_HUMAN_Klesmith_2019_FMC_singles,0.144,0.152,0.146,0.146,0.163,0.153,0.062,0.081,0.126,0.131,0.09,0.11,0.123,0.102,0.13,0.109,0.099,0.126,0.099,-0.018,0.103,0.109,0.14,0.126,0.094,0.159,0.131,0.095,0.155,0.168,0.099,0.073,0.078,0.119,0.147,0.126,0.136,0.135,0.132,0.164,0.161,0.156,0.11,0.07,0.094,0.11,0.264,0.209,0.252,0.132,0.164,0.182,0.169,0.193,0.176,0.169,0.179,0.176,0.182,0.193,0.305,0.26,3761,Binding,CD19_HUMAN,Low,Human +CP2C9_HUMAN_Amorosi_2021_abundance,0.384,0.442,0.455,0.465,0.446,0.469,0.396,0.441,0.446,0.465,0.38,0.453,0.472,0.41,0.475,0.477,0.498,0.488,0.447,0.483,0.449,0.445,0.464,0.425,0.447,0.437,0.441,0.449,0.423,0.46,0.446,0.399,0.175,0.451,0.438,0.422,0.478,0.464,0.463,0.483,0.484,0.486,0.452,0.05,0.383,0.473,0.435,0.408,0.473,0.159,0.479,0.47,0.483,0.505,0.493,0.498,0.49,0.479,0.486,0.505,0.484,0.478,6370,Expression,CP2C9_HUMAN,High,Human +CP2C9_HUMAN_Amorosi_2021_activity,0.371,0.438,0.446,0.459,0.434,0.463,0.437,0.456,0.452,0.48,0.367,0.479,0.497,0.414,0.503,0.511,0.513,0.5,0.463,0.495,0.445,0.455,0.461,0.437,0.45,0.439,0.442,0.46,0.439,0.471,0.448,0.402,0.181,0.484,0.44,0.426,0.5,0.489,0.484,0.495,0.484,0.482,0.48,0.045,0.408,0.503,0.465,0.44,0.505,0.186,0.483,0.489,0.499,0.505,0.513,0.515,0.502,0.493,0.504,0.514,0.508,0.504,6142,Binding,CP2C9_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM,0.255,0.474,0.392,0.409,0.356,0.369,0.213,0.367,0.421,0.434,0.455,0.455,0.502,0.305,0.494,0.418,0.365,0.436,0.429,0.378,0.328,0.375,0.455,0.465,0.342,0.464,0.459,0.453,0.442,0.448,0.428,0.419,0.259,0.274,0.364,0.433,0.347,0.359,0.421,0.417,0.421,0.457,0.233,0.167,0.306,0.329,0.345,0.329,0.426,0.467,0.468,0.459,0.445,0.471,0.479,0.471,0.47,0.476,0.438,0.478,0.456,0.482,3295,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX,0.252,0.308,0.277,0.282,0.31,0.292,0.072,0.32,0.366,0.376,0.368,0.297,0.265,0.105,0.32,0.361,0.434,0.361,0.373,0.257,0.115,0.141,0.158,0.062,0.085,0.153,0.148,0.148,0.232,0.295,0.305,0.271,0.189,0.113,0.11,0.135,0.257,0.247,0.247,0.315,0.315,0.315,0.108,0.032,0.31,0.151,0.396,0.381,0.454,0.346,0.404,0.373,0.381,0.409,0.404,0.419,0.416,0.416,0.411,0.404,0.449,0.404,1580,Stability,CUE1_YEAST,Medium,Eukaryote +D7PM05_CLYGR_Somermeyer_2022,0.426,0.5,0.5,0.465,0.506,0.503,0.049,0.378,0.538,0.543,0.353,0.048,0.042,0.045,0.032,0.022,0.032,0.046,0.109,0.356,0.032,0.033,0.082,0.06,-0.003,0.035,0.05,0.145,0.281,0.506,0.444,0.461,0.024,0.078,0.094,0.133,0.418,0.417,0.415,0.505,0.504,0.493,-0.002,0.01,0.001,0.02,0.209,0.228,0.389,0.292,0.386,0.386,0.384,0.392,0.381,0.392,0.383,0.376,0.377,0.385,0.235,0.147,24515,Activity,D7PM05_CLYGR,Low,Eukaryote +DLG4_HUMAN_Faure_2021,0.611,0.49,0.509,0.487,0.498,0.51,0.652,0.557,0.444,0.448,0.435,0.459,0.497,0.671,0.688,0.646,0.473,0.386,0.368,0.536,0.436,0.478,0.465,0.437,0.487,0.482,0.453,0.451,0.38,0.508,0.545,0.542,0.394,0.437,0.549,0.481,0.533,0.623,0.576,0.532,0.555,0.529,0.502,0.162,0.314,0.443,0.548,0.35,0.561,0.251,0.412,0.3,0.458,0.415,0.434,0.405,0.436,0.415,0.424,0.421,0.417,0.662,6976,OrganismalFitness,DLG4_HUMAN,Low,Human +DLG4_RAT_McLaughlin_2012,0.407,0.404,0.398,0.423,0.435,0.447,0.338,0.359,0.383,0.401,0.371,0.453,0.462,0.334,0.438,0.447,0.432,0.398,0.347,0.35,0.298,0.31,0.31,0.292,0.304,0.383,0.341,0.301,0.334,0.371,0.426,0.417,0.207,0.286,0.304,0.252,0.389,0.392,0.371,0.438,0.444,0.441,0.404,0.049,0.328,0.407,0.334,0.258,0.371,0.119,0.341,0.31,0.347,0.331,0.353,0.338,0.307,0.347,0.359,0.362,0.404,0.42,1576,Binding,DLG4_RAT,Low,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC,0.161,0.117,0.188,0.173,0.185,0.208,-0.093,0.268,0.252,0.248,-0.014,0.038,0.034,0.006,0.093,0.089,0.216,0.212,0.256,0.177,-0.069,-0.002,-0.01,0.034,0.03,0.058,-0.006,0.03,0.121,0.204,0.3,0.272,0.01,-0.022,-0.026,-0.014,0.157,0.157,0.161,0.2,0.181,0.173,0.034,-0.077,0.133,0.042,0.45,0.438,0.522,0.423,0.276,0.232,0.343,0.331,0.308,0.315,0.288,0.268,0.272,0.308,0.387,0.335,1008,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1,0.724,0.742,0.684,0.693,0.724,0.726,0.633,0.689,0.716,0.735,0.723,0.716,0.749,0.765,0.777,0.779,0.758,0.71,0.763,0.703,0.597,0.648,0.68,0.652,0.684,0.661,0.689,0.588,0.707,0.712,0.67,0.648,0.581,0.7,0.673,0.74,0.76,0.76,0.784,0.728,0.737,0.754,0.532,0.069,0.309,0.551,0.502,0.401,0.714,0.726,0.721,0.742,0.751,0.739,0.758,0.747,0.777,0.731,0.762,0.754,0.712,0.733,2264,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,0.321,0.337,0.321,0.327,0.36,0.359,0.046,0.317,0.268,0.28,0.309,0.332,0.354,0.184,0.337,0.359,0.35,0.272,0.335,0.342,0.241,0.298,0.262,0.272,0.247,0.268,0.214,0.21,0.208,0.319,0.261,0.274,0.153,0.214,0.249,0.227,0.372,0.386,0.352,0.35,0.37,0.354,0.224,-0.155,0.321,0.265,0.231,0.234,0.286,0.297,0.287,0.258,0.265,0.273,0.268,0.279,0.283,0.276,0.278,0.265,0.365,0.357,2915,Stability,DOCK1_MOUSE,High,Eukaryote +DYR_ECOLI_Nguyen_2023,0.291,0.42,0.427,0.42,0.432,0.434,0.001,0.361,0.402,0.407,0.42,0.405,0.42,0.056,0.402,0.412,0.426,0.429,0.429,0.41,0.309,0.37,0.276,0.323,0.4,0.412,0.399,0.417,0.372,0.42,0.424,0.415,0.164,0.395,0.297,0.313,0.417,0.387,0.39,0.436,0.424,0.42,0.388,0.006,0.424,0.397,0.186,0.36,0.291,0.078,0.382,0.37,0.363,0.387,0.378,0.392,0.388,0.383,0.392,0.392,0.405,0.319,2916,OrganismalFitness,DYR_ECOLI,High,Prokaryote +DYR_ECOLI_Thompson_2019,0.336,0.396,0.391,0.387,0.406,0.404,-0.048,0.286,0.396,0.422,0.404,0.341,0.358,0.047,0.292,0.373,0.398,0.426,0.422,0.407,0.235,0.323,0.24,0.293,0.314,0.393,0.409,0.389,0.363,0.396,0.389,0.378,0.159,0.314,0.297,0.312,0.35,0.358,0.373,0.4,0.42,0.411,0.292,-0.025,0.369,0.336,0.2,0.358,0.27,0.084,0.363,0.336,0.334,0.349,0.367,0.358,0.376,0.354,0.385,0.373,0.407,0.268,2363,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +ENV_HV1B9_DuenasDecamp_2016,0.298,0.321,0.286,0.298,0.357,0.321,0.004,0.263,0.286,0.286,0.31,0.38,0.357,0.039,0.075,0.051,-0.008,0.039,0.004,0.298,0.357,0.321,0.321,0.321,0.298,0.357,0.333,0.286,0.321,0.38,0.368,0.353,0.251,0.345,0.31,0.31,0.392,0.345,0.345,0.345,0.333,0.333,0.274,0.004,0.31,0.333,0.333,0.286,0.274,0.216,0.18,0.274,0.298,0.204,0.157,0.216,0.216,0.204,0.157,0.216,0.122,0.086,375,OrganismalFitness,ENV_HV1B9,Medium,Virus +ENV_HV1BR_Haddox_2016,0.251,0.241,0.235,0.237,0.251,0.256,-0.003,0.237,0.264,0.267,0.23,0.241,0.26,-0.006,-0.004,-0.001,0.027,0.043,0.107,0.251,0.271,0.269,0.275,0.279,0.256,0.272,0.268,0.263,0.271,0.277,0.248,0.223,0.158,0.26,0.27,0.27,0.277,0.279,0.274,0.274,0.277,0.27,0.163,-0.011,0.24,0.23,0.143,0.162,0.069,0.049,0.124,0.115,0.133,0.156,0.129,0.136,0.14,0.134,0.133,0.142,0.108,0.058,12863,OrganismalFitness,ENV_HV1BR,Medium,Virus +ENVZ_ECOLI_Ghose_2023,0.138,0.131,0.123,0.142,0.187,0.187,0.001,0.157,0.135,0.127,0.131,0.183,0.194,0.146,0.164,0.157,0.161,0.142,0.12,0.153,0.138,0.149,0.168,0.176,0.135,0.153,0.202,0.19,0.19,0.164,0.157,0.135,0.064,0.123,0.153,0.194,0.176,0.179,0.198,0.183,0.202,0.202,0.127,0.068,0.164,0.168,0.049,0.146,0.101,0.042,0.164,0.172,0.131,0.161,0.138,0.164,0.157,0.153,0.176,0.164,0.112,0.183,1121,Activity,ENVZ_ECOLI,High,Prokaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M,0.612,0.608,0.631,0.637,0.682,0.686,-0.261,0.655,0.749,0.753,0.755,0.745,0.773,-0.3,0.725,0.788,0.804,0.735,0.708,0.688,0.476,0.547,0.659,0.616,0.606,0.643,0.637,0.684,0.686,0.798,0.737,0.725,0.604,0.573,0.541,0.631,0.651,0.661,0.694,0.682,0.68,0.667,0.569,-0.253,0.553,0.541,0.492,0.537,0.741,0.657,0.794,0.778,0.788,0.776,0.8,0.796,0.802,0.794,0.786,0.802,0.751,0.745,1960,Stability,EPHB2_HUMAN,High,Human +ERBB2_HUMAN_Elazar_2016,0.351,0.301,0.191,0.203,0.203,0.24,0.338,0.338,0.326,0.351,0.363,0.338,0.4,0.351,0.351,0.388,0.326,0.351,0.289,0.043,0.351,0.326,0.375,0.351,0.363,0.351,0.449,0.437,0.388,0.375,0.314,0.05,0.4,0.363,0.338,0.351,0.4,0.4,0.338,0.363,0.363,0.351,0.388,0.363,0.412,0.4,0.314,0.363,-0.105,0.043,0.351,0.301,0.363,0.289,0.301,0.314,0.338,0.314,0.338,0.351,0.412,0.4,326,Expression,ERBB2_HUMAN,Low,Human +ESTA_BACSU_Nutschel_2020,0.216,0.286,0.307,0.314,0.29,0.288,0.13,0.237,0.248,0.305,0.237,0.218,0.248,0.113,0.18,0.2,0.224,0.235,0.27,0.231,0.054,0.137,0.209,0.227,0.181,0.205,0.227,0.205,0.301,0.279,0.355,0.298,0.064,0.113,0.191,0.202,0.238,0.244,0.238,0.312,0.301,0.31,0.126,0.1,0.207,0.185,0.425,0.367,0.426,0.272,0.272,0.259,0.253,0.242,0.262,0.248,0.25,0.273,0.272,0.266,0.318,0.152,2172,Stability,ESTA_BACSU,High,Prokaryote +F7YBW8_MESOW_Aakre_2015,0.047,0.227,0.239,0.258,0.257,0.258,0.013,0.183,0.232,0.241,0.25,0.228,0.237,-0.043,0.009,0.049,0.248,0.237,0.264,0.225,-0.029,-0.07,-0.066,-0.014,0.076,0.071,-0.009,0.19,0.238,0.211,0.27,0.263,0.04,-0.006,-0.07,0.244,0.046,0.011,0.252,0.237,0.227,0.264,-0.006,-0.023,0.134,-0.008,-0.007,0.159,0.037,0.023,0.268,0.264,0.248,0.273,0.275,0.271,0.258,0.265,0.266,0.269,0.181,-0.079,9192,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +FECA_ECOLI_Tsuboyama_2023_2D1U,0.351,0.359,0.357,0.366,0.374,0.364,0.037,0.245,0.292,0.353,0.396,0.198,0.326,0.039,0.34,0.451,0.425,0.376,0.29,0.353,0.12,0.226,0.264,0.247,0.101,0.391,0.41,0.209,0.315,0.381,0.366,0.345,0.268,0.06,0.086,0.09,0.302,0.304,0.222,0.326,0.349,0.306,0.29,-0.022,0.362,0.343,0.436,0.391,0.485,0.419,0.44,0.374,0.423,0.4,0.393,0.432,0.425,0.391,0.41,0.419,0.476,0.483,1886,Stability,FECA_ECOLI,High,Prokaryote +FKBP3_HUMAN_Tsuboyama_2023_2KFV,0.3,0.271,0.374,0.378,0.378,0.387,0.135,0.248,0.18,0.177,0.196,0.164,0.164,0.171,0.148,0.154,0.151,0.216,0.277,0.216,0.209,0.203,0.196,0.167,0.141,0.158,0.18,0.177,0.184,0.374,0.235,0.263,-0.033,0.109,0.167,0.196,0.274,0.284,0.251,0.361,0.371,0.348,0.154,0.164,0.174,0.2,0.555,0.491,0.53,0.468,0.232,0.31,0.329,0.284,0.281,0.303,0.274,0.258,0.232,0.297,0.445,0.258,1237,Stability,FKBP3_HUMAN,Medium,Human +GAL4_YEAST_Kitzman_2015,0.193,0.295,0.31,0.375,0.342,0.346,0.231,-0.007,0.342,0.426,0.426,0.335,0.353,0.255,0.27,0.382,0.466,0.451,0.441,0.346,0.204,0.241,0.284,0.284,0.237,0.291,0.375,0.331,0.397,0.451,0.451,0.399,0.186,0.171,0.201,0.197,0.342,0.351,0.375,0.331,0.313,0.321,0.288,0.193,0.419,0.339,0.186,0.368,0.259,0.102,0.404,0.415,0.39,0.397,0.426,0.386,0.397,0.393,0.408,0.419,0.422,0.306,1195,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +GCN4_YEAST_Staller_2018,0.192,0.175,0.178,0.184,0.16,0.158,0.119,0.157,0.18,0.175,0.169,0.212,0.216,0.246,0.215,0.201,0.215,0.187,0.169,0.166,0.113,0.136,0.107,0.105,0.096,0.089,0.061,0.095,0.122,0.14,0.183,0.186,0.033,0.114,0.116,0.231,0.195,0.199,0.228,0.193,0.19,0.215,0.037,0.084,0.087,0.046,0.105,0.125,0.172,0.16,0.154,0.145,0.143,0.139,0.151,0.149,0.143,0.149,0.158,0.148,0.171,0.183,2638,Binding,GCN4_YEAST,Low,Eukaryote +GDIA_HUMAN_Silverstein_2021,0.338,0.341,0.366,0.348,0.338,0.338,0.102,0.279,0.373,0.369,0.293,0.307,0.328,0.113,0.161,0.293,0.289,0.258,0.317,0.289,0.182,0.279,0.289,0.307,0.213,0.341,0.31,0.258,0.279,0.31,0.348,0.314,0.185,0.217,0.248,0.255,0.317,0.307,0.331,0.338,0.355,0.359,0.175,0.088,0.317,0.172,0.217,0.289,0.248,0.095,0.272,0.248,0.293,0.272,0.276,0.289,0.279,0.272,0.262,0.314,0.334,0.213,1154,OrganismalFitness,GDIA_HUMAN,Low,Human +GFP_AEQVI_Sarkisyan_2016,0.584,0.582,0.613,0.615,0.618,0.618,0.061,0.583,0.604,0.596,0.473,0.097,0.097,0.081,0.119,0.09,0.106,0.14,0.255,0.537,0.087,0.11,0.167,0.089,0.048,0.166,0.261,0.584,0.584,0.619,0.546,0.555,0.058,0.069,0.163,0.573,0.581,0.583,0.618,0.624,0.627,0.65,0.023,-0.006,0.036,0.036,0.456,0.445,0.67,0.535,0.529,0.537,0.546,0.554,0.546,0.545,0.542,0.543,0.525,0.541,0.562,0.402,51714,Activity,GFP_AEQVI,Low,Eukaryote +GLPA_HUMAN_Elazar_2016,0.114,-0.045,0.102,0.135,0.102,0.07,0.331,0.413,0.282,0.25,0.446,0.446,0.446,0.315,0.446,0.462,0.495,0.381,0.43,0.152,0.381,0.397,0.43,0.413,0.348,0.413,0.462,0.43,0.479,0.209,0.364,0.136,0.168,0.43,0.364,0.43,0.397,0.381,0.397,0.299,0.25,0.364,0.413,0.397,0.462,0.397,0.331,0.495,0.266,0.168,0.413,0.381,0.495,0.43,0.446,0.397,0.462,0.397,0.413,0.479,0.413,0.413,245,Expression,GLPA_HUMAN,Low,Human +GRB2_HUMAN_Faure_2021,0.314,0.392,0.384,0.411,0.404,0.406,0.403,0.338,0.405,0.365,0.39,0.337,0.378,0.43,0.464,0.474,0.496,0.396,0.429,0.407,0.419,0.396,0.4,0.344,0.419,0.397,0.341,0.409,0.342,0.376,0.308,0.308,0.361,0.418,0.385,0.31,0.39,0.381,0.331,0.421,0.421,0.397,0.451,0.235,0.399,0.435,0.54,0.401,0.548,0.396,0.484,0.518,0.507,0.505,0.511,0.494,0.496,0.512,0.491,0.514,0.421,0.468,63366,OrganismalFitness,GRB2_HUMAN,Medium,Human +HCP_LAMBD_Tsuboyama_2023_2L6Q,0.238,0.312,0.092,0.123,0.269,0.281,0.188,0.288,0.346,0.342,0.458,0.35,0.377,0.238,0.315,0.419,0.473,0.412,0.431,0.192,0.212,0.219,0.273,0.288,0.254,0.273,0.162,0.265,0.392,0.408,0.477,0.36,0.115,0.227,0.223,0.369,0.312,0.315,0.373,0.25,0.323,0.377,0.223,0.235,0.458,0.308,0.492,0.527,0.573,0.408,0.519,0.531,0.523,0.523,0.535,0.531,0.504,0.535,0.523,0.535,0.577,0.45,1040,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM,0.297,0.324,0.238,0.244,0.344,0.353,0.172,0.294,0.286,0.284,0.253,0.209,0.231,0.169,0.175,0.37,0.326,0.277,0.272,0.303,0.201,0.261,0.333,0.312,0.252,0.32,0.307,0.288,0.302,0.331,0.305,0.308,0.132,0.186,0.215,0.24,0.368,0.345,0.372,0.351,0.335,0.356,0.105,0.079,0.354,0.141,0.222,0.336,0.331,0.266,0.272,0.32,0.351,0.332,0.335,0.372,0.327,0.342,0.329,0.348,0.34,0.29,5586,Stability,HECD1_HUMAN,Medium,Human +HEM3_HUMAN_Loggerenberg_2023,0.298,0.313,0.295,0.304,0.313,0.311,0.1,0.096,0.32,0.33,0.311,0.283,0.306,0.105,0.273,0.299,0.31,0.321,0.337,0.115,0.239,0.292,0.307,0.308,0.254,0.297,0.295,0.305,0.321,0.318,0.313,0.312,0.061,0.277,0.291,0.313,0.306,0.328,0.347,0.307,0.325,0.344,0.22,0.106,0.292,0.26,0.228,0.28,0.224,0.115,0.274,0.268,0.271,0.288,0.294,0.285,0.297,0.292,0.293,0.301,0.339,0.303,5689,Activity,HEM3_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019,0.355,0.41,0.44,0.433,0.409,0.41,0.114,0.218,0.343,0.377,0.354,0.333,0.377,0.038,0.089,0.347,0.282,0.341,0.367,0.342,0.237,0.303,0.337,0.346,0.294,0.375,0.339,0.378,0.394,0.419,0.307,0.301,0.115,0.296,0.325,0.475,0.376,0.377,0.511,0.444,0.424,0.463,0.127,0.013,0.212,0.002,0.322,0.342,0.437,0.3,0.342,0.249,0.295,0.326,0.331,0.308,0.306,0.338,0.319,0.317,0.399,0.136,496137,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +HMDH_HUMAN_Jiang_2019,0.368,0.321,0.289,0.297,0.323,0.319,0.116,0.171,0.206,0.198,0.209,0.242,0.225,0.032,0.014,0.231,0.399,0.278,0.283,0.222,0.346,0.092,0.113,0.154,0.374,0.126,0.113,0.143,0.147,0.354,0.3,0.301,0.173,0.241,0.183,0.154,0.361,0.297,0.285,0.342,0.296,0.294,0.114,0.041,0.362,0.389,0.229,0.349,-0.175,0.074,0.378,0.376,0.367,0.378,0.379,0.373,0.37,0.382,0.374,0.382,0.42,0.327,16853,OrganismalFitness,HMDH_HUMAN,Low,Human +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,0.235,0.25,0.255,0.266,0.23,0.232,0.173,0.283,0.301,0.31,0.239,0.298,0.33,-0.003,0.1,0.17,0.171,0.216,0.214,-0.001,0.235,0.255,0.259,0.271,0.212,0.246,0.285,0.292,0.292,0.275,0.314,0.259,0.134,0.228,0.248,0.228,0.246,0.264,0.253,0.244,0.253,0.244,0.02,-0.01,0.282,0.285,0.035,0.186,0.06,-0.026,0.166,0.187,0.17,0.189,0.184,0.186,0.18,0.18,0.166,0.187,0.276,0.203,2252,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Flynn_2019,0.315,0.361,0.374,0.385,0.374,0.371,0.125,0.311,0.394,0.384,0.337,0.357,0.377,0.01,0.068,0.141,0.247,0.323,0.334,0.371,0.351,0.371,0.357,0.377,0.367,0.38,0.385,0.371,0.399,0.374,0.408,0.398,0.176,0.351,0.358,0.362,0.364,0.368,0.375,0.382,0.388,0.389,0.081,-0.056,0.35,0.27,0.16,0.295,0.09,0.05,0.245,0.248,0.262,0.264,0.254,0.259,0.265,0.255,0.242,0.258,0.355,0.295,13294,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Mishra_2016,0.394,0.4,0.405,0.405,0.397,0.394,0.27,0.309,0.374,0.381,0.394,0.411,0.416,0.187,0.275,0.305,0.368,0.369,0.37,0.376,0.38,0.366,0.368,0.395,0.385,0.385,0.401,0.366,0.391,0.399,0.401,0.396,0.238,0.363,0.378,0.386,0.403,0.413,0.41,0.403,0.408,0.41,0.358,-0.052,0.385,0.395,0.12,0.286,0.144,0.035,0.341,0.341,0.327,0.363,0.357,0.341,0.359,0.349,0.348,0.359,0.375,0.347,4323,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HXK4_HUMAN_Gersing_2022_activity,0.39,0.426,0.406,0.413,0.396,0.406,0.178,0.311,0.426,0.44,0.375,0.384,0.403,0.121,0.201,0.408,0.421,0.415,0.398,0.033,0.374,0.367,0.346,0.327,0.366,0.368,0.357,0.358,0.325,0.413,0.417,0.388,0.138,0.375,0.338,0.279,0.411,0.384,0.351,0.424,0.421,0.406,0.225,0.024,0.418,0.398,0.287,0.366,0.319,0.123,0.421,0.407,0.409,0.413,0.415,0.427,0.411,0.418,0.411,0.424,0.439,0.375,8570,OrganismalFitness,HXK4_HUMAN,Medium,Human +HXK4_HUMAN_Gersing_2023_abundance,0.282,0.309,0.287,0.303,0.301,0.305,0.045,0.282,0.347,0.363,0.295,0.289,0.308,0.106,0.148,0.294,0.32,0.336,0.317,0.348,0.244,0.249,0.261,0.27,0.241,0.264,0.283,0.281,0.275,0.323,0.287,0.25,0.152,0.265,0.258,0.265,0.302,0.304,0.308,0.317,0.317,0.324,0.135,0.075,0.302,0.29,0.325,0.335,0.334,0.11,0.33,0.332,0.331,0.333,0.334,0.337,0.328,0.327,0.321,0.332,0.37,0.286,8396,Expression,HXK4_HUMAN,Medium,Human +I6TAH8_I68A0_Doud_2015,0.287,0.24,0.213,0.208,0.274,0.277,-0.008,0.234,0.217,0.239,0.008,0.015,0.008,0.026,0.02,0.012,0.015,0.006,0.054,0.151,0.24,0.245,0.286,0.283,0.013,0.019,0.085,0.003,0.23,0.284,0.179,0.183,0.111,0.211,0.242,0.247,0.225,0.252,0.256,0.28,0.306,0.306,0.016,0.015,0.016,0.017,0.166,0.149,0.182,0.078,0.048,0.075,0.101,0.097,0.079,0.072,0.059,0.067,0.051,0.072,0.024,0.007,9462,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +IF1_ECOLI_Kelsic_2016,0.262,0.37,0.443,0.456,0.418,0.437,0.086,0.277,0.195,0.163,0.405,0.43,0.44,0.08,0.306,0.414,0.446,0.446,0.424,0.418,0.242,0.328,0.223,0.297,0.341,0.354,0.344,0.306,0.354,0.313,0.386,0.335,0.214,0.354,0.373,0.398,0.367,0.363,0.36,0.418,0.421,0.437,0.252,0.093,0.449,0.373,0.23,0.398,0.348,0.172,0.408,0.408,0.408,0.43,0.389,0.408,0.389,0.411,0.414,0.421,0.456,0.379,1367,OrganismalFitness,IF1_ECOLI,High,Prokaryote +ILF3_HUMAN_Tsuboyama_2023_2L33,0.295,0.387,0.425,0.418,0.415,0.422,0.176,0.271,0.38,0.404,0.313,0.331,0.352,0.264,0.366,0.422,0.26,0.246,0.18,0.432,0.127,0.187,0.274,0.302,0.317,0.222,0.324,0.243,0.267,0.411,0.352,0.316,0.025,0.173,0.232,0.313,0.302,0.345,0.359,0.39,0.387,0.415,0.299,0.176,0.306,0.38,0.415,0.366,0.432,0.331,0.267,0.229,0.288,0.25,0.278,0.331,0.292,0.25,0.341,0.324,0.292,0.362,1329,Stability,ILF3_HUMAN,High,Human +ISDH_STAAW_Tsuboyama_2023_2LHR,0.065,0.108,0.139,0.147,0.151,0.176,0.25,0.124,0.281,0.266,0.378,0.326,0.326,0.271,0.291,0.336,0.324,0.371,0.326,0.131,0.219,0.145,0.106,0.166,0.221,0.242,0.221,0.211,0.242,0.275,0.334,0.315,0.17,0.192,0.174,0.184,0.155,0.139,0.137,0.194,0.157,0.17,0.199,0.178,0.252,0.24,0.427,0.353,0.419,0.4,0.32,0.324,0.345,0.332,0.33,0.345,0.316,0.324,0.332,0.334,0.437,0.359,1944,Stability,ISDH_STAAW,High,Prokaryote +KCNE1_HUMAN_Muhammad_2023_expression,0.195,0.2,0.195,0.177,0.181,0.171,0.179,0.108,0.161,0.171,0.226,0.21,0.195,0.179,0.175,0.226,0.181,0.173,0.179,0.197,0.195,0.204,0.161,0.11,0.212,0.148,0.208,0.191,0.161,0.181,0.175,0.128,-0.087,0.189,0.226,0.163,0.177,0.185,0.212,0.191,0.191,0.208,0.206,0.167,0.204,0.228,0.193,0.2,0.189,0.063,0.153,0.169,0.163,0.155,0.169,0.15,0.179,0.153,0.163,0.163,0.204,0.193,2339,Expression,KCNE1_HUMAN,Medium,Human +KCNE1_HUMAN_Muhammad_2023_function,0.282,0.331,0.358,0.393,0.362,0.391,0.111,0.389,0.374,0.388,0.215,0.32,0.41,0.13,0.085,0.446,0.391,0.322,0.254,0.159,0.153,0.179,0.329,0.401,0.166,0.445,0.453,0.433,0.424,0.337,0.46,0.38,0.006,0.104,0.234,0.491,0.298,0.318,0.457,0.375,0.391,0.486,0.121,0.116,0.386,0.087,0.123,0.313,0.127,0.03,0.407,0.396,0.389,0.394,0.394,0.381,0.403,0.4,0.42,0.42,0.507,0.127,2315,Activity,KCNE1_HUMAN,Medium,Human +KCNH2_HUMAN_Kozek_2020,0.36,0.4,0.18,0.18,0.18,0.16,0.36,0.04,0.26,0.26,0.2,0.18,0.16,0.18,0.12,0.16,0.16,0.16,0.12,0.36,0.36,0.36,0.34,0.28,0.38,0.32,0.32,0.28,0.28,0.281,0.3,0.14,0.28,0.32,0.36,0.36,0.36,0.4,0.4,0.3,0.4,0.36,0.32,0.14,0.16,0.22,0.12,0.0,-0.12,0.06,0.4,0.3,0.4,0.34,0.3,0.34,0.38,0.34,0.4,0.36,0.18,0.36,200,Activity,KCNH2_HUMAN,Medium,Human +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.215,0.273,0.26,0.277,0.285,0.288,0.049,0.183,0.256,0.246,0.306,0.288,0.297,0.035,0.216,0.308,0.322,0.326,0.326,0.126,0.283,0.222,0.187,0.179,0.28,0.268,0.241,0.274,0.172,0.299,0.303,0.291,0.118,0.261,0.238,0.168,0.286,0.267,0.227,0.299,0.291,0.27,0.154,-0.009,0.267,0.297,0.153,0.227,0.185,0.062,0.318,0.304,0.308,0.301,0.307,0.293,0.311,0.309,0.301,0.313,0.297,0.215,6963,Activity,KCNJ2_MOUSE,Medium,Eukaryote +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.261,0.225,0.165,0.183,0.214,0.217,0.073,0.143,0.186,0.188,0.207,0.186,0.198,0.11,0.23,0.225,0.221,0.222,0.243,0.158,0.143,0.158,0.161,0.178,0.157,0.179,0.172,0.176,0.176,0.236,0.19,0.152,0.114,0.143,0.17,0.161,0.199,0.223,0.224,0.203,0.224,0.224,0.166,0.043,0.172,0.195,0.169,0.157,0.168,0.094,0.217,0.198,0.214,0.199,0.208,0.194,0.209,0.201,0.21,0.21,0.22,0.22,6917,Expression,KCNJ2_MOUSE,Medium,Eukaryote +KKA2_KLEPN_Melnikov_2014,0.221,0.452,0.366,0.516,0.499,0.508,0.148,0.38,0.449,0.509,0.471,0.488,0.512,0.165,0.199,0.404,0.486,0.531,0.556,0.394,0.213,0.331,0.418,0.467,0.268,0.488,0.486,0.473,0.526,0.523,0.548,0.499,0.113,0.184,0.407,0.495,0.346,0.429,0.487,0.495,0.511,0.526,0.191,0.069,0.467,0.324,0.362,0.486,0.439,0.185,0.479,0.475,0.478,0.478,0.476,0.482,0.476,0.482,0.488,0.491,0.53,0.239,4960,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +LGK_LIPST_Klesmith_2015,0.233,0.292,0.34,0.345,0.332,0.344,0.089,0.352,0.348,0.414,0.336,0.394,0.424,0.125,0.241,0.3,0.391,0.433,0.42,0.381,0.228,0.316,0.366,0.378,0.292,0.39,0.402,0.367,0.409,0.39,0.406,0.338,0.114,0.253,0.312,0.422,0.28,0.31,0.389,0.339,0.357,0.393,0.128,0.047,0.368,0.278,0.29,0.371,0.358,0.106,0.387,0.366,0.383,0.374,0.375,0.395,0.393,0.392,0.395,0.397,0.301,0.251,7890,Activity,LGK_LIPST,Medium,Eukaryote +LYAM1_HUMAN_Elazar_2016,0.26,0.26,0.148,0.159,0.215,0.226,0.26,0.137,0.304,0.327,0.315,0.26,0.293,0.193,0.237,0.193,0.226,0.26,0.349,0.193,0.237,0.26,0.304,0.282,0.215,0.304,0.271,0.182,0.182,0.289,0.204,0.144,0.137,0.282,0.315,0.215,0.282,0.304,0.282,0.204,0.249,0.204,0.215,0.215,0.237,0.249,0.103,0.148,0.115,0.025,0.182,0.249,0.26,0.226,0.193,0.204,0.237,0.271,0.193,0.249,0.293,0.226,359,Expression,LYAM1_HUMAN,Medium,Human +MAFG_MOUSE_Tsuboyama_2023_1K1V,0.422,0.536,0.481,0.481,0.498,0.486,0.327,0.649,0.581,0.563,0.643,0.309,0.584,0.416,0.395,0.436,0.457,0.498,0.339,0.575,0.365,0.557,0.546,0.569,0.498,0.528,0.469,0.531,0.472,0.43,0.548,0.522,0.383,0.247,0.513,0.525,0.454,0.566,0.599,0.522,0.599,0.64,0.265,0.038,0.36,0.236,0.516,0.301,0.548,0.566,0.652,0.634,0.628,0.64,0.637,0.634,0.637,0.649,0.661,0.64,0.593,0.64,1429,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV,0.518,0.657,0.614,0.628,0.675,0.681,-0.026,0.471,0.612,0.675,0.658,0.333,0.461,-0.066,0.104,0.692,0.612,0.605,0.677,0.65,0.04,0.1,0.544,0.592,0.221,0.476,0.444,0.512,0.667,0.692,0.662,0.615,0.558,-0.242,0.104,0.093,0.488,0.578,0.565,0.63,0.667,0.658,0.03,-0.106,0.578,0.34,0.399,0.541,0.664,0.446,0.698,0.673,0.675,0.681,0.66,0.662,0.671,0.692,0.703,0.688,0.673,0.609,2116,Stability,MBD11_ARATH,Medium,Eukaryote +MET_HUMAN_Estevam_2023,0.453,0.528,0.52,0.524,0.554,0.558,0.489,0.49,0.517,0.527,0.55,0.503,0.517,0.414,0.471,0.494,0.555,0.579,0.572,0.563,0.497,0.482,0.435,0.461,0.496,0.503,0.499,0.452,0.423,0.507,0.528,0.494,0.26,0.463,0.467,0.489,0.479,0.499,0.527,0.524,0.531,0.543,0.503,0.303,0.535,0.544,0.328,0.388,0.476,0.173,0.509,0.521,0.513,0.522,0.526,0.533,0.521,0.534,0.531,0.532,0.544,0.49,5393,Activity,MET_HUMAN,Medium,Human +MK01_HUMAN_Brenan_2016,0.152,0.19,0.209,0.212,0.193,0.197,0.173,0.149,0.195,0.171,0.069,0.156,0.171,0.132,0.173,0.171,0.166,0.178,0.155,0.172,0.206,0.131,0.099,0.048,0.161,0.104,0.081,0.087,-0.021,0.19,0.171,0.198,0.119,0.163,0.093,0.054,0.176,0.138,0.125,0.202,0.185,0.179,0.149,0.1,0.146,0.162,0.068,0.016,0.122,0.004,0.158,0.155,0.153,0.171,0.169,0.156,0.163,0.168,0.165,0.167,0.168,0.166,6809,OrganismalFitness,MK01_HUMAN,Medium,Human +MLAC_ECOLI_MacRae_2023,0.176,0.274,0.344,0.354,0.337,0.33,-0.022,0.271,0.346,0.359,0.302,0.343,0.354,-0.022,0.174,0.207,0.312,0.295,0.313,0.295,0.042,0.266,0.274,0.309,0.248,0.314,0.306,0.301,0.339,0.32,0.356,0.319,0.028,0.103,0.218,0.216,0.203,0.249,0.231,0.286,0.306,0.303,0.108,-0.05,0.244,0.174,0.027,0.154,0.112,-0.012,0.244,0.214,0.235,0.23,0.239,0.242,0.222,0.233,0.268,0.24,0.16,0.151,4007,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +MSH2_HUMAN_Jia_2020,0.308,0.343,0.326,0.335,0.335,0.338,0.177,0.276,0.342,0.348,0.308,0.328,0.34,0.184,0.287,0.338,0.324,0.268,0.181,0.335,0.245,0.302,0.283,0.277,0.28,0.315,0.33,0.306,0.289,0.34,0.327,0.309,0.176,0.235,0.29,0.277,0.299,0.326,0.326,0.337,0.343,0.34,0.222,0.095,0.332,0.283,0.258,0.317,0.044,0.089,0.279,0.262,0.272,0.279,0.297,0.275,0.277,0.275,0.298,0.288,0.343,0.306,16749,OrganismalFitness,MSH2_HUMAN,Medium,Human +MTH3_HAEAE_RockahShmuel_2015,0.259,0.419,0.447,0.453,0.458,0.442,0.215,0.414,0.435,0.442,0.38,0.455,0.45,0.153,0.21,0.236,0.336,0.393,0.435,0.429,0.202,0.298,0.393,0.429,0.323,0.437,0.445,0.447,0.46,0.45,0.455,0.429,0.215,0.212,0.331,0.427,0.261,0.329,0.419,0.396,0.419,0.458,0.223,0.026,0.321,0.212,0.287,0.37,0.357,0.119,0.36,0.334,0.36,0.354,0.37,0.378,0.357,0.362,0.354,0.365,0.378,0.215,1777,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +MTHR_HUMAN_Weile_2021,0.169,0.177,0.167,0.173,0.175,0.17,0.113,0.111,0.2,0.204,0.258,0.2,0.213,0.108,0.261,0.335,0.255,0.191,0.203,0.178,0.311,0.105,0.141,0.182,0.297,0.156,0.18,0.17,0.221,0.203,0.149,0.139,0.087,0.309,0.219,0.133,0.233,0.203,0.158,0.219,0.193,0.171,0.174,0.07,0.251,0.262,0.159,0.237,0.27,0.066,0.232,0.207,0.211,0.225,0.223,0.234,0.249,0.245,0.254,0.238,0.223,0.245,12464,OrganismalFitness,MTHR_HUMAN,Low,Human +MYO3_YEAST_Tsuboyama_2023_2BTT,0.156,0.169,0.255,0.261,0.262,0.298,0.104,0.207,0.33,0.321,0.394,0.376,0.39,0.136,0.323,0.442,0.44,0.393,0.206,0.244,0.249,0.18,0.244,0.213,0.244,0.176,0.251,0.19,0.222,0.189,0.225,0.13,0.114,0.309,0.216,0.221,0.287,0.26,0.246,0.302,0.298,0.283,0.1,-0.002,0.313,0.223,0.297,0.336,0.379,0.257,0.421,0.398,0.43,0.406,0.418,0.421,0.406,0.413,0.401,0.411,0.431,0.39,3297,Stability,MYO3_YEAST,High,Eukaryote +NCAP_I34A1_Doud_2015,0.29,0.24,0.248,0.25,0.283,0.282,0.012,0.241,0.275,0.252,0.029,0.026,0.025,0.037,0.034,0.026,0.04,0.034,0.089,0.215,0.266,0.264,0.293,0.288,0.03,0.058,0.098,0.055,0.256,0.294,0.199,0.211,0.105,0.259,0.272,0.295,0.281,0.301,0.321,0.325,0.324,0.339,0.035,0.019,0.029,0.035,0.196,0.196,0.198,0.081,0.102,0.128,0.133,0.149,0.135,0.129,0.116,0.134,0.107,0.13,0.096,0.066,9462,OrganismalFitness,NCAP_I34A1,Medium,Virus +NKX31_HUMAN_Tsuboyama_2023_2L9R,0.272,0.343,0.463,0.459,0.511,0.47,0.399,0.482,0.5,0.509,0.534,0.532,0.535,0.497,0.417,0.472,0.458,0.422,0.467,0.444,0.479,0.459,0.477,0.475,0.436,0.435,0.47,0.479,0.507,0.498,0.435,0.42,0.481,0.431,0.438,0.486,0.442,0.447,0.498,0.489,0.505,0.512,0.33,0.24,0.258,0.306,0.431,0.256,0.497,0.511,0.523,0.53,0.527,0.535,0.535,0.509,0.525,0.518,0.523,0.528,0.525,0.498,2482,Stability,NKX31_HUMAN,High,Human +NPC1_HUMAN_Erwood_2022_HEK293T,0.526,0.572,0.481,0.513,0.578,0.565,0.183,0.358,0.611,0.598,0.572,0.274,0.371,0.151,0.306,0.494,0.572,0.578,0.578,0.054,0.196,0.358,0.3,0.306,0.332,0.403,0.475,0.358,0.365,0.572,0.565,0.468,0.131,0.157,0.41,0.416,0.488,0.501,0.546,0.539,0.546,0.578,0.235,0.041,0.559,0.488,0.378,0.533,0.028,0.119,0.572,0.565,0.572,0.585,0.578,0.552,0.559,0.572,0.578,0.572,0.533,0.287,637,Activity,NPC1_HUMAN,Low,Human +NPC1_HUMAN_Erwood_2022_RPE1,0.651,0.397,0.079,0.142,0.397,0.46,0.269,0.333,0.333,0.397,0.46,0.015,0.142,0.079,0.206,0.524,0.46,0.397,0.397,0.206,0.524,0.333,0.397,0.142,0.333,0.333,0.269,0.206,0.206,0.333,0.651,0.426,0.397,0.269,0.079,0.333,0.587,0.397,0.524,0.587,0.397,0.46,0.333,0.206,0.651,0.142,0.269,0.397,0.142,0.142,0.397,0.46,0.46,0.397,0.46,0.46,0.397,0.397,0.397,0.46,0.587,0.333,63,Activity,NPC1_HUMAN,Low,Human +NRAM_I33A0_Jiang_2016,0.477,0.45,0.356,0.329,0.45,0.463,0.06,0.289,0.477,0.477,-0.02,0.114,0.369,-0.06,-0.034,0.06,0.154,0.396,0.45,0.302,0.342,0.477,0.396,0.409,0.06,0.409,0.45,0.329,0.423,0.53,0.289,0.342,0.007,0.356,0.383,0.396,0.436,0.503,0.544,0.503,0.544,0.477,-0.034,-0.034,-0.06,-0.074,0.356,0.289,0.369,0.154,0.248,0.195,0.181,0.221,0.154,0.235,0.221,0.208,0.154,0.168,0.221,0.128,298,OrganismalFitness,NRAM_I33A0,Low,Virus +NUD15_HUMAN_Suiter_2020,0.218,0.386,0.423,0.454,0.443,0.446,0.01,0.317,0.469,0.528,0.478,0.472,0.514,0.206,0.32,0.35,0.405,0.472,0.475,0.393,0.221,0.356,0.44,0.42,0.308,0.49,0.471,0.454,0.408,0.417,0.502,0.449,0.148,0.254,0.326,0.454,0.287,0.314,0.456,0.435,0.434,0.472,0.3,0.021,0.481,0.327,0.351,0.451,0.398,0.23,0.417,0.399,0.431,0.453,0.435,0.454,0.438,0.459,0.454,0.447,0.549,0.453,2844,Expression,NUD15_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL,0.352,0.417,0.455,0.449,0.486,0.484,0.204,0.324,0.512,0.453,0.411,0.23,0.24,0.232,0.291,0.287,0.356,0.403,0.388,0.423,0.246,0.427,0.464,0.461,0.263,0.386,0.39,0.386,0.441,0.486,0.526,0.618,0.129,0.244,0.322,0.307,0.364,0.372,0.386,0.496,0.464,0.486,0.257,0.179,0.267,0.248,0.528,0.49,0.591,0.512,0.61,0.597,0.61,0.628,0.644,0.654,0.634,0.616,0.64,0.642,0.536,0.455,2028,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6,0.353,0.326,0.338,0.341,0.369,0.356,0.225,0.372,0.36,0.347,0.356,0.298,0.32,0.289,0.396,0.35,0.375,0.399,0.402,0.375,0.234,0.289,0.286,0.304,0.344,0.28,0.283,0.243,0.32,0.427,0.341,0.346,0.2,0.216,0.234,0.344,0.335,0.326,0.372,0.338,0.347,0.356,0.237,0.115,0.216,0.311,0.439,0.39,0.571,0.531,0.405,0.363,0.35,0.399,0.412,0.396,0.399,0.415,0.412,0.399,0.338,0.399,1380,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C,0.342,0.402,0.508,0.521,0.505,0.512,0.199,0.396,0.523,0.526,0.533,0.482,0.498,0.198,0.515,0.541,0.545,0.522,0.533,0.511,0.526,0.519,0.518,0.526,0.425,0.503,0.499,0.491,0.472,0.486,0.531,0.514,0.316,0.133,0.223,0.318,0.465,0.441,0.439,0.515,0.511,0.511,0.268,-0.044,0.261,0.319,0.406,0.349,0.508,0.513,0.571,0.562,0.565,0.562,0.569,0.568,0.559,0.555,0.575,0.566,0.535,0.493,3197,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G,-0.298,-0.201,0.189,0.083,0.189,0.185,-0.171,0.03,0.083,0.083,0.002,-0.108,-0.097,-0.079,-0.139,0.026,0.097,0.034,0.083,0.129,0.108,0.051,0.041,0.069,0.044,0.069,0.079,0.09,0.108,0.189,-0.034,-0.063,0.146,-0.108,0.009,-0.005,0.097,0.097,0.101,0.161,0.168,0.189,0.15,-0.079,0.157,0.168,0.245,0.228,0.256,0.185,0.026,-0.005,0.012,0.041,-0.002,0.041,0.037,0.044,0.019,0.026,0.15,0.076,1134,Stability,ODP2_GEOSE,High,Prokaryote +OPSD_HUMAN_Wan_2019,0.139,0.382,0.358,0.503,0.406,0.382,0.309,0.43,0.43,0.43,0.212,0.406,0.43,0.188,0.479,0.43,0.406,0.358,0.43,0.503,0.479,0.455,0.455,0.455,0.503,0.455,0.503,0.455,0.43,0.333,0.382,0.261,0.188,0.406,0.43,0.382,0.455,0.43,0.406,0.43,0.358,0.382,0.261,0.042,0.406,0.406,0.624,0.406,0.576,0.212,0.333,0.309,0.309,0.261,0.43,0.406,0.236,0.309,0.382,0.358,0.503,0.406,165,Expression,OPSD_HUMAN,High,Human +OTC_HUMAN_Lo_2023,0.404,0.432,0.376,0.414,0.404,0.419,0.062,0.34,0.455,0.442,0.424,0.424,0.452,0.057,0.32,0.394,0.417,0.376,0.424,0.424,0.355,0.33,0.396,0.427,0.386,0.445,0.417,0.427,0.439,0.447,0.366,0.312,0.055,0.327,0.389,0.429,0.396,0.432,0.48,0.434,0.452,0.462,0.149,0.045,0.419,0.345,0.511,0.468,0.516,0.266,0.434,0.422,0.434,0.439,0.445,0.437,0.437,0.427,0.419,0.452,0.473,0.353,1570,Activity,OTC_HUMAN,Medium,Human +OTU7A_HUMAN_Tsuboyama_2023_2L2D,0.089,0.218,0.177,0.184,0.204,0.211,0.157,0.13,0.15,0.137,0.354,0.51,0.496,0.198,0.462,0.482,0.347,0.313,0.333,0.15,0.123,0.191,0.259,0.259,0.32,0.313,0.367,0.34,0.137,0.232,0.381,0.381,0.062,0.137,0.211,0.306,0.218,0.211,0.286,0.232,0.177,0.225,0.266,0.13,0.449,0.442,0.435,0.476,0.489,0.394,0.394,0.286,0.32,0.36,0.374,0.388,0.313,0.374,0.442,0.374,0.51,0.469,635,Stability,OTU7A_HUMAN,High,Human +OXDA_RHOTO_Vanella_2023_activity,0.06,0.082,0.085,0.094,0.08,0.085,0.056,0.074,0.068,0.072,0.105,0.098,0.086,0.04,0.072,0.074,0.088,0.101,0.109,0.094,0.045,0.071,0.101,0.098,0.076,0.1,0.109,0.089,0.121,0.085,0.123,0.106,0.011,0.056,0.074,0.088,0.082,0.089,0.089,0.085,0.085,0.08,0.072,0.028,0.097,0.082,0.074,0.105,0.083,0.036,0.098,0.098,0.095,0.095,0.092,0.106,0.094,0.095,0.088,0.098,0.1,0.059,6396,Activity,OXDA_RHOTO,High,Eukaryote +OXDA_RHOTO_Vanella_2023_expression,0.156,0.208,0.215,0.215,0.192,0.192,0.156,0.155,0.209,0.209,0.247,0.231,0.226,0.164,0.188,0.223,0.241,0.253,0.247,0.158,0.154,0.181,0.187,0.188,0.175,0.204,0.221,0.213,0.226,0.204,0.259,0.227,0.066,0.168,0.163,0.184,0.19,0.185,0.204,0.209,0.204,0.206,0.188,0.122,0.231,0.179,0.262,0.248,0.204,0.059,0.245,0.223,0.224,0.233,0.23,0.229,0.231,0.236,0.227,0.237,0.272,0.216,6769,Expression,OXDA_RHOTO,High,Eukaryote +P53_HUMAN_Giacomelli_2018_Null_Etoposide,0.418,0.42,0.383,0.395,0.429,0.439,-0.046,0.319,0.323,0.346,0.411,0.415,0.45,-0.095,-0.072,0.294,0.402,0.423,0.457,0.157,0.261,0.364,0.414,0.403,0.355,0.423,0.429,0.426,0.371,0.416,0.449,0.431,0.153,0.236,0.39,0.36,0.416,0.434,0.424,0.436,0.455,0.431,-0.09,-0.096,0.417,-0.009,0.338,0.42,0.358,0.169,0.407,0.407,0.409,0.405,0.414,0.431,0.406,0.415,0.419,0.42,0.437,0.179,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_Null_Nutlin,0.251,0.284,0.25,0.254,0.301,0.306,-0.067,0.181,0.206,0.21,0.349,0.341,0.363,-0.136,-0.118,0.165,0.278,0.305,0.338,0.09,0.194,0.343,0.357,0.344,0.289,0.369,0.374,0.386,0.245,0.304,0.301,0.268,0.164,0.142,0.354,0.255,0.257,0.306,0.297,0.283,0.335,0.299,-0.117,-0.115,0.349,-0.068,0.268,0.341,0.28,0.118,0.298,0.281,0.296,0.305,0.301,0.319,0.288,0.294,0.297,0.304,0.338,0.09,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_WT_Nutlin,0.394,0.399,0.363,0.38,0.392,0.4,-0.034,0.305,0.311,0.31,0.438,0.409,0.434,-0.086,-0.066,0.334,0.39,0.411,0.422,0.14,0.3,0.402,0.434,0.418,0.369,0.439,0.44,0.441,0.317,0.43,0.426,0.41,0.155,0.268,0.435,0.374,0.404,0.431,0.418,0.411,0.438,0.411,-0.081,-0.104,0.434,0.004,0.367,0.431,0.363,0.187,0.398,0.407,0.399,0.407,0.415,0.421,0.4,0.407,0.403,0.415,0.429,0.189,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Kotler_2018,0.542,0.519,0.429,0.507,0.487,0.515,0.056,0.386,0.499,0.511,0.476,0.472,0.542,0.052,0.037,0.53,0.604,0.619,0.631,0.359,0.317,0.402,0.441,0.429,0.386,0.433,0.421,0.445,0.429,0.476,0.55,0.515,0.056,0.336,0.421,0.344,0.519,0.484,0.464,0.53,0.495,0.511,0.052,0.002,0.573,0.153,0.39,0.507,0.394,0.181,0.588,0.612,0.604,0.588,0.585,0.588,0.608,0.573,0.616,0.612,0.639,0.27,1048,OrganismalFitness,P53_HUMAN,Low,Human +P84126_THETH_Chan_2017,0.484,0.516,0.534,0.534,0.489,0.521,0.272,0.407,0.539,0.545,0.481,0.489,0.531,0.193,0.484,0.492,0.524,0.486,0.463,0.524,0.383,0.436,0.434,0.486,0.481,0.518,0.51,0.518,0.563,0.486,0.471,0.401,0.322,0.423,0.436,0.471,0.492,0.494,0.516,0.518,0.524,0.537,0.404,-0.032,0.46,0.402,0.256,0.336,0.396,0.081,0.46,0.436,0.373,0.479,0.473,0.42,0.449,0.457,0.473,0.476,0.524,0.526,1519,OrganismalFitness,P84126_THETH,Medium,Prokaryote +PA_I34A1_Wu_2015,0.36,0.376,0.36,0.365,0.396,0.409,0.048,0.301,0.145,0.101,0.026,0.04,0.088,-0.002,0.024,0.026,0.024,0.018,0.275,0.248,0.336,0.382,0.4,0.407,0.171,0.297,0.332,0.336,0.327,0.516,0.284,0.264,0.086,0.308,0.347,0.396,0.433,0.44,0.435,0.462,0.455,0.433,0.024,0.031,0.037,0.013,0.18,0.178,0.149,0.046,0.132,0.141,0.147,0.149,0.134,0.152,0.134,0.132,0.101,0.143,0.167,0.121,1820,OrganismalFitness,PA_I34A1,Medium,Virus +PABP_YEAST_Melamed_2013,0.521,0.474,0.41,0.418,0.495,0.493,0.35,0.429,0.491,0.512,0.545,0.514,0.534,0.357,0.429,0.515,0.597,0.549,0.563,0.408,0.508,0.528,0.531,0.555,0.506,0.558,0.57,0.538,0.525,0.522,0.568,0.503,0.191,0.511,0.504,0.503,0.556,0.556,0.551,0.531,0.53,0.529,0.465,-0.048,0.518,0.517,0.274,0.461,0.393,0.144,0.542,0.532,0.545,0.57,0.558,0.563,0.565,0.561,0.594,0.581,0.574,0.502,37708,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +PAI1_HUMAN_Huttinger_2021,0.301,0.28,0.263,0.275,0.283,0.298,0.046,0.238,0.316,0.325,0.335,0.312,0.312,0.061,0.298,0.322,0.344,0.24,0.205,0.316,0.103,0.277,0.256,0.24,0.277,0.293,0.289,0.271,0.218,0.311,0.3,0.268,0.125,0.121,0.264,0.274,0.284,0.295,0.299,0.301,0.31,0.306,0.252,0.053,0.322,0.306,0.297,0.343,0.305,0.108,0.334,0.344,0.337,0.348,0.339,0.348,0.342,0.347,0.341,0.353,0.36,0.302,5345,Activity,PAI1_HUMAN,,Human +PHOT_CHLRE_Chen_2023,0.149,0.3,0.541,0.52,0.215,0.256,0.477,0.395,0.526,0.536,0.474,0.57,0.6,0.58,0.648,0.529,0.563,0.528,0.574,0.472,0.499,0.526,0.433,0.51,0.326,0.434,0.417,0.483,0.426,0.415,0.414,0.346,0.21,0.398,0.325,0.419,0.413,0.369,0.422,0.282,0.302,0.287,0.469,0.278,0.452,0.463,0.142,0.364,0.493,0.284,0.439,0.388,0.395,0.393,0.398,0.411,0.408,0.413,0.421,0.41,0.551,0.588,167529,Activity,PHOT_CHLRE,High,Eukaryote +PIN1_HUMAN_Tsuboyama_2023_1I6C,0.2,0.234,0.549,0.499,0.489,0.544,0.489,0.384,0.514,0.579,0.524,0.449,0.569,0.529,0.494,0.524,0.549,0.394,0.464,0.499,0.354,0.524,0.509,0.439,0.424,0.519,0.574,0.544,0.509,0.594,0.594,0.549,0.434,0.399,0.494,0.529,0.434,0.499,0.564,0.549,0.564,0.564,0.474,-0.17,0.529,0.414,0.399,0.444,0.594,0.569,0.534,0.494,0.414,0.389,0.389,0.444,0.424,0.504,0.454,0.444,0.603,0.653,802,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M,0.461,0.364,0.333,0.342,0.336,0.351,0.349,0.36,0.412,0.41,0.36,0.366,0.384,0.415,0.524,0.577,0.474,0.447,0.373,0.375,0.371,0.338,0.369,0.344,0.423,0.364,0.344,0.331,0.353,0.349,0.349,0.35,0.338,0.415,0.358,0.373,0.472,0.423,0.404,0.406,0.358,0.36,0.349,0.303,0.178,0.235,0.294,0.2,0.537,0.498,0.377,0.388,0.393,0.406,0.399,0.419,0.395,0.384,0.393,0.39,0.39,0.443,1824,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF,0.177,0.184,0.232,0.246,0.288,0.285,0.177,0.246,0.201,0.229,0.226,0.281,0.299,0.327,0.334,0.435,0.26,0.226,0.229,0.292,0.292,0.278,0.222,0.281,0.281,0.299,0.299,0.278,0.281,0.295,0.32,0.32,0.062,0.309,0.271,0.306,0.246,0.292,0.323,0.306,0.288,0.306,0.274,0.267,0.274,0.288,0.431,0.337,0.428,0.376,0.232,0.264,0.292,0.243,0.257,0.271,0.236,0.257,0.281,0.257,0.292,0.424,1301,Stability,PKN1_HUMAN,High,Human +POLG_CXB3N_Mattenberger_2021,0.301,0.281,0.26,0.299,0.34,0.348,-0.03,0.255,0.376,0.374,0.228,-0.036,0.001,-0.047,-0.03,0.124,0.317,0.324,0.346,0.259,0.25,0.283,0.272,0.264,0.074,0.287,0.277,0.261,0.273,0.365,0.295,0.229,-0.005,0.036,0.187,0.248,0.263,0.276,0.311,0.288,0.307,0.347,-0.035,-0.039,0.268,-0.03,0.147,0.268,0.077,0.04,0.259,0.286,0.289,0.291,0.286,0.288,0.286,0.285,0.294,0.29,0.068,0.051,15711,OrganismalFitness,POLG_CXB3N,Medium,Virus +POLG_DEN26_Suphatrakul_2023,0.357,0.454,0.184,0.185,0.413,0.418,-0.014,0.33,0.535,0.542,0.241,0.011,0.02,-0.009,0.025,0.059,0.122,0.206,0.274,0.312,0.334,0.351,0.344,0.344,0.327,0.367,0.371,0.363,0.352,0.484,0.468,0.408,0.029,-0.025,0.087,0.352,0.331,0.337,0.422,0.364,0.315,0.43,-0.028,-0.025,0.319,0.017,0.282,0.42,0.099,0.084,0.195,0.189,0.198,0.216,0.184,0.217,0.197,0.199,0.19,0.206,0.115,0.023,16897,OrganismalFitness,POLG_DEN26,Low,Virus +POLG_HCVJF_Qi_2014,0.448,0.432,0.336,0.346,0.46,0.472,-0.041,0.152,0.405,0.423,0.131,0.507,0.475,0.074,0.101,0.082,0.082,0.062,0.062,0.158,0.309,0.358,0.336,0.376,0.302,0.376,0.213,0.314,0.405,0.465,0.467,0.399,0.151,0.361,0.395,0.386,0.4,0.428,0.415,0.353,0.403,0.423,0.079,0.047,0.378,0.084,-0.035,0.26,0.578,0.311,0.245,0.292,0.282,0.297,0.299,0.227,0.269,0.299,0.267,0.309,0.119,0.143,1630,OrganismalFitness,POLG_HCVJF,Medium,Virus +POLG_PESV_Tsuboyama_2023_2MXD,0.194,0.352,0.217,0.263,0.295,0.297,0.053,0.269,0.222,0.256,0.267,0.041,0.089,0.048,0.066,0.032,0.089,0.064,0.048,0.39,0.079,0.06,0.023,0.116,0.076,-0.032,-0.003,-0.042,0.105,0.309,0.37,0.377,0.125,0.023,0.018,0.009,0.242,0.249,0.249,0.302,0.306,0.305,-0.025,-0.054,-0.012,-0.035,0.37,0.297,0.486,0.434,0.456,0.462,0.437,0.518,0.525,0.529,0.462,0.53,0.53,0.511,0.545,0.466,5130,Stability,POLG_PESV,Medium,Virus +PPARG_HUMAN_Majithia_2016,0.299,0.373,0.407,0.41,0.396,0.397,0.132,0.263,0.363,0.383,0.397,0.405,0.409,0.04,0.12,0.347,0.414,0.469,0.461,0.434,0.406,0.389,0.214,0.256,0.39,0.407,0.399,0.414,0.283,0.45,0.424,0.411,0.278,0.43,0.38,0.342,0.431,0.414,0.397,0.445,0.429,0.423,0.195,0.042,0.359,0.32,0.368,0.355,0.402,0.2,0.404,0.412,0.419,0.408,0.408,0.417,0.416,0.414,0.42,0.42,0.424,0.352,9576,Activity,PPARG_HUMAN,Medium,Human +PPM1D_HUMAN_Miller_2022,0.438,0.462,0.454,0.465,0.508,0.512,0.005,0.298,0.34,0.4,0.462,0.487,0.495,0.184,0.258,0.327,0.494,0.511,0.509,0.286,0.327,0.434,0.412,0.323,0.421,0.448,0.448,0.439,0.307,0.492,0.488,0.461,0.216,0.312,0.427,0.414,0.478,0.493,0.504,0.528,0.509,0.512,0.229,-0.034,0.465,0.397,0.389,0.464,0.437,0.197,0.467,0.463,0.46,0.469,0.483,0.46,0.477,0.476,0.479,0.482,0.52,0.392,7889,OrganismalFitness,PPM1D_HUMAN,Low,Human +PR40A_HUMAN_Tsuboyama_2023_1UZC,0.58,0.629,0.69,0.672,0.724,0.724,0.413,0.413,0.743,0.755,0.696,0.58,0.623,0.444,0.428,0.747,0.769,0.735,0.712,0.659,0.497,0.6,0.613,0.611,0.539,0.604,0.672,0.619,0.653,0.763,0.789,0.794,0.56,0.539,0.436,0.486,0.67,0.676,0.667,0.69,0.716,0.698,0.367,0.181,0.19,0.358,0.472,0.226,0.633,0.649,0.789,0.765,0.775,0.781,0.787,0.79,0.79,0.789,0.806,0.802,0.731,0.712,2033,Stability,PR40A_HUMAN,Medium,Human +PRKN_HUMAN_Clausen_2023,0.505,0.501,0.505,0.49,0.509,0.512,0.104,0.367,0.425,0.443,0.458,0.507,0.527,0.137,0.183,0.25,0.389,0.544,0.553,0.357,0.207,0.44,0.478,0.458,0.359,0.502,0.493,0.477,0.424,0.523,0.482,0.462,0.217,0.17,0.43,0.45,0.49,0.522,0.52,0.517,0.526,0.527,0.206,0.048,0.472,0.246,0.511,0.482,0.552,0.224,0.416,0.427,0.436,0.452,0.44,0.459,0.453,0.447,0.449,0.459,0.556,0.371,8756,Expression,PRKN_HUMAN,Low,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE,0.518,0.542,0.508,0.511,0.526,0.521,0.335,0.407,0.428,0.436,0.602,0.532,0.529,0.428,0.516,0.609,0.627,0.563,0.49,0.397,0.311,0.241,0.433,0.418,0.407,0.41,-0.129,0.449,0.459,0.471,0.49,0.459,0.161,0.332,0.394,0.399,0.511,0.498,0.459,0.558,0.568,0.495,0.423,0.286,0.586,0.441,0.532,0.576,0.638,0.539,0.617,0.583,0.602,0.599,0.591,0.625,0.591,0.599,0.612,0.607,0.622,0.625,1579,Stability,PSAE_PICP2,Medium,Prokaryote +PTEN_HUMAN_Matreyek_2021,0.298,0.304,0.314,0.304,0.315,0.32,0.105,0.263,0.327,0.368,0.335,0.31,0.355,0.117,0.2,0.38,0.359,0.195,0.21,0.348,0.128,0.339,0.308,0.298,0.216,0.264,0.241,0.28,0.224,0.342,0.318,0.323,0.114,0.195,0.328,0.272,0.271,0.346,0.314,0.324,0.354,0.339,0.179,0.033,0.344,0.255,0.369,0.348,0.375,0.157,0.35,0.346,0.357,0.354,0.344,0.362,0.358,0.365,0.351,0.365,0.402,0.366,5083,Expression,PTEN_HUMAN,Medium,Human +PTEN_HUMAN_Mighell_2018,0.399,0.42,0.396,0.396,0.417,0.418,0.112,0.3,0.418,0.423,0.395,0.393,0.412,0.144,0.301,0.418,0.43,0.239,0.225,0.417,0.257,0.356,0.276,0.257,0.343,0.234,0.245,0.274,0.212,0.417,0.391,0.39,0.058,0.3,0.346,0.257,0.411,0.401,0.368,0.417,0.431,0.431,0.28,-0.032,0.415,0.379,0.334,0.338,0.398,0.153,0.405,0.395,0.405,0.412,0.419,0.414,0.412,0.407,0.42,0.425,0.428,0.388,7260,Activity,PTEN_HUMAN,Medium,Human +Q2N0S5_9HIV1_Haddox_2018,0.361,0.321,0.279,0.304,0.378,0.376,0.024,0.327,0.396,0.401,0.35,0.391,0.414,0.034,0.03,0.024,0.054,0.078,0.104,0.303,0.394,0.301,0.289,0.244,0.392,0.287,0.284,0.327,0.26,0.379,0.378,0.369,0.222,0.371,0.318,0.295,0.39,0.384,0.379,0.397,0.394,0.387,0.34,0.02,0.385,0.345,0.292,0.348,0.199,0.106,0.166,0.207,0.23,0.226,0.188,0.193,0.194,0.185,0.167,0.218,0.151,0.079,12729,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,0.24,0.215,0.228,0.224,0.236,0.231,0.083,0.15,0.194,0.201,0.134,0.172,0.19,0.161,0.135,0.24,0.206,0.177,0.237,0.208,0.176,0.188,0.172,0.134,0.191,0.191,0.178,0.209,0.176,0.213,0.257,0.246,0.051,0.163,0.179,0.104,0.214,0.23,0.205,0.24,0.255,0.242,0.309,0.165,0.184,0.206,0.22,0.243,0.24,0.135,0.193,0.194,0.22,0.225,0.207,0.199,0.193,0.205,0.203,0.206,0.188,0.322,3344,Binding,Q53Z42_HUMAN,Medium,Human +Q53Z42_HUMAN_McShan_2019_expression,0.411,0.375,0.416,0.423,0.425,0.425,-0.034,0.319,0.408,0.423,0.319,0.365,0.387,0.059,0.069,0.382,0.435,0.408,0.44,0.428,0.333,0.371,0.381,0.35,0.368,0.403,0.389,0.423,0.416,0.431,0.418,0.375,0.108,0.326,0.355,0.343,0.419,0.42,0.435,0.452,0.456,0.44,0.247,0.045,0.413,0.357,0.356,0.407,0.361,0.144,0.438,0.438,0.444,0.443,0.453,0.438,0.429,0.442,0.456,0.45,0.4,0.472,3344,Expression,Q53Z42_HUMAN,Medium,Human +Q59976_STRSQ_Romero_2015,0.418,0.49,0.508,0.518,0.516,0.523,0.235,0.408,0.546,0.55,0.471,0.415,0.415,0.085,0.325,0.38,0.431,0.462,0.453,0.507,0.459,0.505,0.519,0.523,0.481,0.525,0.528,0.537,0.561,0.528,0.514,0.452,0.265,0.477,0.514,0.501,0.484,0.518,0.528,0.526,0.536,0.537,0.372,-0.004,0.456,0.398,0.317,0.411,0.407,0.093,0.456,0.422,0.409,0.429,0.435,0.426,0.433,0.419,0.438,0.433,0.488,0.415,2999,Activity,Q59976_STRSQ,Medium,Prokaryote +Q6WV13_9MAXI_Somermeyer_2022,0.175,0.194,0.137,0.14,0.18,0.18,-0.009,0.096,0.2,0.196,0.14,0.027,0.018,0.008,0.024,0.006,0.007,0.013,-0.011,0.161,0.026,0.024,0.002,0.071,0.021,-0.0,0.02,0.0,-0.001,0.233,0.167,0.171,0.026,0.015,0.025,0.02,0.13,0.132,0.137,0.173,0.173,0.172,0.006,0.005,-0.002,0.012,0.138,0.15,0.215,0.129,0.153,0.164,0.151,0.175,0.168,0.164,0.16,0.167,0.163,0.164,0.068,0.047,31401,Activity,Q6WV12_9MAXI,Low,Eukaryote +Q837P4_ENTFA_Meier_2023,0.257,0.303,0.28,0.252,0.28,0.326,0.315,0.24,0.269,0.297,0.355,0.32,0.338,0.274,0.309,0.309,0.286,0.355,0.315,-0.024,0.355,0.303,0.32,0.361,0.349,0.332,0.366,0.252,0.372,0.303,0.343,0.333,0.079,0.286,0.378,0.338,0.274,0.378,0.384,0.332,0.32,0.32,0.257,0.229,0.372,0.297,0.229,0.326,0.246,0.068,0.315,0.274,0.309,0.326,0.303,0.338,0.338,0.309,0.326,0.338,0.349,0.332,697,Activity,Q837P4_ENTFA,Medium,Prokaryote +Q837P5_ENTFA_Meier_2023,0.082,0.264,0.291,0.291,0.232,0.232,0.055,0.125,0.173,0.2,0.168,0.2,0.2,0.076,0.125,0.2,0.216,0.291,0.259,0.205,0.248,0.27,0.275,0.253,0.216,0.253,0.264,0.345,0.302,0.2,0.248,0.227,0.06,0.248,0.302,0.334,0.184,0.248,0.312,0.259,0.27,0.28,0.146,0.135,0.227,0.119,0.253,0.221,0.275,0.098,0.189,0.205,0.237,0.216,0.237,0.237,0.189,0.232,0.243,0.232,0.211,0.189,747,Activity,Q837P5_ENTFA,Medium,Prokaryote +Q8WTC7_9CNID_Somermeyer_2022,0.162,0.21,0.127,0.125,0.189,0.185,-0.004,0.187,0.169,0.173,0.111,-0.038,-0.048,-0.042,-0.059,-0.054,-0.048,-0.049,-0.023,0.129,-0.036,-0.005,0.022,-0.019,-0.024,-0.039,-0.02,0.138,0.144,0.203,0.187,0.194,-0.029,-0.064,-0.015,0.18,0.123,0.13,0.18,0.173,0.182,0.202,-0.035,-0.035,-0.053,-0.054,0.007,0.054,0.161,0.087,0.126,0.119,0.126,0.133,0.12,0.129,0.13,0.12,0.116,0.127,0.038,-0.07,33510,Activity,Q8WTC7_9CNID,Low,Eukaryote +R1AB_SARS2_Flynn_2022,0.477,0.427,0.185,0.221,0.517,0.514,-0.011,0.227,0.013,0.013,0.094,0.01,0.002,0.038,0.015,0.067,0.096,0.395,0.456,0.228,0.186,0.239,0.258,0.233,0.211,0.225,0.173,0.177,0.215,0.46,0.414,0.331,-0.026,0.152,0.19,0.174,0.308,0.339,0.33,0.463,0.484,0.48,0.006,-0.005,0.063,-0.003,0.337,0.31,0.367,0.162,0.197,0.156,0.216,0.216,0.201,0.201,0.204,0.2,0.173,0.205,0.17,0.097,5725,OrganismalFitness,R1AB_SARS2,Medium,Virus +RAD_ANTMA_Tsuboyama_2023_2CJJ,0.204,0.112,0.226,0.213,0.23,0.226,0.362,0.283,0.542,0.485,0.415,0.38,0.397,0.327,0.52,0.56,0.353,0.353,0.432,0.279,0.432,0.476,0.441,0.362,0.459,0.305,0.472,0.345,0.318,0.424,0.279,0.191,0.248,0.388,0.525,0.345,0.331,0.459,0.371,0.349,0.388,0.309,0.472,0.195,0.2,0.323,0.38,0.261,0.586,0.38,0.38,0.406,0.415,0.415,0.419,0.41,0.384,0.432,0.393,0.428,0.283,0.463,912,Stability,RAD_ANTMA,High,Eukaryote +RAF1_HUMAN_Zinkus-Boltz_2019,0.394,0.367,0.367,0.367,0.354,0.367,0.044,0.286,0.354,0.367,0.394,0.354,0.421,0.017,0.192,0.354,0.394,0.381,0.34,0.246,0.205,0.313,0.34,0.3,0.286,0.313,0.286,0.259,0.259,0.34,0.394,0.3,0.111,0.246,0.232,0.286,0.34,0.3,0.313,0.354,0.367,0.394,0.165,0.071,0.421,0.327,0.232,0.354,0.192,0.286,0.34,0.354,0.3,0.34,0.3,0.407,0.34,0.354,0.367,0.327,0.354,0.111,297,OrganismalFitness,RAF1_HUMAN,Low,Human +RASH_HUMAN_Bandaru_2017,0.397,0.442,0.439,0.467,0.454,0.465,0.288,0.376,0.464,0.479,0.368,0.41,0.442,0.417,0.464,0.443,0.489,0.453,0.317,0.497,0.451,0.403,0.403,0.388,0.414,0.4,0.407,0.347,0.277,0.422,0.399,0.378,0.199,0.375,0.407,0.338,0.449,0.45,0.425,0.455,0.461,0.475,0.48,0.186,0.278,0.454,0.289,0.177,0.414,0.198,0.419,0.415,0.43,0.429,0.422,0.422,0.436,0.421,0.432,0.449,0.349,0.444,3134,Activity,RASH_HUMAN,High,Human +RASK_HUMAN_Weng_2022_abundance,0.232,0.2,0.247,0.233,0.226,0.236,0.173,0.179,0.177,0.203,0.219,0.167,0.182,0.213,0.256,0.233,0.188,0.158,0.161,0.232,0.211,0.253,0.272,0.289,0.213,0.252,0.311,0.251,0.358,0.259,0.148,0.111,0.155,0.146,0.223,0.307,0.199,0.258,0.314,0.232,0.254,0.268,0.172,0.211,0.045,0.181,0.234,0.245,0.257,0.23,0.182,0.165,0.205,0.197,0.222,0.199,0.206,0.197,0.177,0.201,0.245,0.231,26012,Expression,RASK_HUMAN,High,Human +RASK_HUMAN_Weng_2022_binding-DARPin_K55,0.39,0.388,0.484,0.489,0.468,0.472,0.253,0.315,0.483,0.493,0.469,0.414,0.46,0.439,0.482,0.504,0.499,0.417,0.332,0.541,0.421,0.424,0.446,0.377,0.394,0.439,0.404,0.3,0.276,0.493,0.408,0.359,0.24,0.382,0.443,0.391,0.42,0.469,0.44,0.467,0.482,0.475,0.438,0.219,0.337,0.475,0.18,0.208,0.466,0.212,0.412,0.411,0.421,0.421,0.446,0.444,0.41,0.423,0.365,0.431,0.476,0.476,24873,Binding,RASK_HUMAN,High,Human +RBP1_HUMAN_Tsuboyama_2023_2KWH,0.165,0.072,0.264,0.264,0.267,0.267,0.231,0.243,0.21,0.21,0.363,0.318,0.318,0.255,0.321,0.378,0.426,0.3,0.273,0.18,0.282,0.06,0.228,0.213,0.207,0.111,0.117,0.279,0.192,0.339,0.318,0.288,0.264,0.204,0.309,0.312,0.255,0.27,0.279,0.264,0.264,0.264,0.255,0.186,0.327,0.333,0.333,0.378,0.462,0.345,0.378,0.378,0.411,0.384,0.393,0.399,0.387,0.393,0.381,0.39,0.432,0.417,1332,Stability,RBP1_HUMAN,High,Human +RCD1_ARATH_Tsuboyama_2023_5OAO,0.277,0.274,0.331,0.34,0.331,0.331,0.213,0.217,0.328,0.34,0.372,0.197,0.236,0.258,0.226,0.461,0.464,0.451,0.429,0.315,0.178,0.182,0.242,0.305,0.239,0.41,0.416,0.359,0.423,0.385,0.467,0.391,0.042,0.185,0.239,0.232,0.299,0.305,0.283,0.362,0.369,0.337,0.229,0.194,0.356,0.229,0.388,0.391,0.464,0.439,0.461,0.461,0.47,0.505,0.467,0.496,0.47,0.48,0.499,0.483,0.464,0.381,1261,Stability,RCD1_ARATH,Medium,Eukaryote +RCRO_LAMBD_Tsuboyama_2023_1ORC,0.286,0.414,0.49,0.471,0.45,0.474,0.074,0.23,0.406,0.414,0.4,0.256,0.334,0.146,0.228,0.205,0.42,0.432,0.432,0.488,0.018,0.065,0.095,0.091,-0.079,0.049,-0.06,-0.002,0.479,0.46,0.427,0.416,0.018,0.054,0.042,0.425,0.36,0.332,0.465,0.45,0.457,0.511,-0.109,0.053,0.234,0.042,0.367,0.442,0.543,0.432,0.427,0.411,0.414,0.404,0.406,0.409,0.411,0.409,0.418,0.418,0.453,0.32,2278,Stability,RCRO_LAMBD,High,Virus +RD23A_HUMAN_Tsuboyama_2023_1IFY,0.235,0.168,0.266,0.282,0.305,0.309,0.121,0.344,0.419,0.419,0.384,0.45,0.462,0.156,0.521,0.458,0.423,0.399,0.286,0.344,0.301,0.321,0.313,0.356,0.403,0.372,0.372,0.38,0.388,0.364,0.356,0.339,0.305,0.231,0.38,0.38,0.29,0.321,0.368,0.305,0.317,0.329,0.384,0.023,0.415,0.466,0.372,0.392,0.447,0.376,0.36,0.341,0.36,0.344,0.38,0.384,0.368,0.368,0.38,0.38,0.368,0.423,1019,Stability,RD23A_HUMAN,High,Human +RDRP_I33A0_Li_2023,0.221,0.252,0.281,0.286,0.362,0.367,0.042,0.289,0.381,0.385,0.146,0.072,0.079,0.064,0.058,0.111,0.278,0.292,0.368,0.33,0.286,0.331,0.341,0.355,0.125,0.267,0.263,0.259,0.307,0.357,0.336,0.292,0.086,0.278,0.321,0.347,0.317,0.349,0.358,0.368,0.38,0.392,0.052,0.035,0.15,0.054,0.17,0.191,0.181,0.041,0.233,0.215,0.227,0.229,0.24,0.242,0.229,0.247,0.253,0.248,0.13,0.096,12003,OrganismalFitness,RDRP_I33A0,Low,Virus +REV_HV1H2_Fernandes_2016,0.132,0.128,0.141,0.152,0.156,0.154,0.033,0.219,0.132,0.149,0.104,0.178,0.195,0.029,0.018,0.109,0.152,0.206,0.184,0.115,0.141,0.18,0.171,0.141,0.206,0.19,0.106,0.175,0.19,0.191,0.245,0.266,0.024,0.143,0.182,0.173,0.178,0.182,0.162,0.182,0.19,0.167,0.031,0.037,0.145,0.044,0.177,0.16,0.201,0.139,0.141,0.143,0.203,0.188,0.177,0.204,0.206,0.134,0.219,0.193,0.208,0.136,2147,OrganismalFitness,REV_HV1H2,Medium,Virus +RFAH_ECOLI_Tsuboyama_2023_2LCL,-0.026,0.176,0.24,0.264,0.222,0.231,-0.014,0.192,0.195,0.201,0.207,0.125,0.119,-0.053,0.023,0.005,0.24,0.261,0.204,0.222,-0.068,0.032,0.137,0.101,-0.053,0.116,0.152,0.125,0.149,0.164,0.27,0.247,0.014,0.002,0.113,0.152,0.053,0.14,0.131,0.192,0.179,0.195,0.053,-0.104,0.053,-0.014,0.173,0.137,0.252,0.234,0.279,0.261,0.252,0.267,0.234,0.255,0.255,0.276,0.255,0.264,0.198,0.104,1326,Stability,RFAH_ECOLI,High,Prokaryote +RL20_AQUAE_Tsuboyama_2023_1GYZ,0.353,0.499,0.536,0.536,0.514,0.525,0.19,0.536,0.308,0.255,0.567,0.564,0.539,0.128,0.308,0.351,0.601,0.581,0.612,0.556,0.157,0.488,0.446,0.44,0.415,0.438,0.438,0.471,0.547,0.525,0.502,0.487,0.044,0.415,0.429,0.415,0.466,0.485,0.466,0.514,0.522,0.514,-0.043,-0.113,0.381,0.207,0.589,0.544,0.665,0.595,0.637,0.623,0.615,0.657,0.643,0.626,0.646,0.632,0.626,0.646,0.663,0.544,1461,Stability,RL20_AQUAE,High,Prokaryote +RL40A_YEAST_Mavor_2016,0.167,0.253,0.257,0.302,0.264,0.298,0.095,0.298,0.333,0.322,0.171,0.202,0.229,0.023,0.343,0.377,0.405,0.308,0.425,0.333,0.253,0.415,0.384,0.322,0.367,0.312,0.329,0.322,0.288,0.222,0.291,0.31,0.016,0.277,0.339,0.295,0.312,0.333,0.295,0.346,0.343,0.291,0.216,-0.001,0.164,0.209,0.04,0.116,0.136,-0.005,0.374,0.357,0.388,0.377,0.374,0.381,0.353,0.384,0.384,0.388,0.247,0.26,1253,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2013,0.183,0.284,0.294,0.339,0.301,0.319,0.054,0.256,0.364,0.374,0.159,0.221,0.277,0.037,0.332,0.367,0.451,0.336,0.461,0.36,0.28,0.458,0.395,0.357,0.395,0.346,0.385,0.353,0.326,0.277,0.36,0.367,0.03,0.312,0.402,0.312,0.343,0.388,0.305,0.371,0.409,0.332,0.26,0.013,0.187,0.221,0.037,0.093,0.141,0.048,0.433,0.405,0.426,0.426,0.43,0.43,0.405,0.458,0.437,0.433,0.27,0.27,1195,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2014,0.246,0.216,0.286,0.332,0.279,0.303,0.116,0.332,0.299,0.309,0.113,0.199,0.209,0.126,0.396,0.386,0.426,0.313,0.399,0.289,0.253,0.409,0.356,0.329,0.396,0.313,0.319,0.259,0.259,0.283,0.273,0.263,0.123,0.316,0.359,0.289,0.329,0.376,0.329,0.336,0.366,0.316,0.186,0.08,0.163,0.223,0.146,0.176,0.213,0.113,0.432,0.372,0.409,0.442,0.416,0.426,0.389,0.406,0.416,0.436,0.233,0.309,1380,Activity,RL40A_YEAST,Medium,Eukaryote +RNC_ECOLI_Weeks_2023,0.425,0.49,0.484,0.5,0.502,0.494,0.032,0.319,0.478,0.488,0.478,0.478,0.489,0.046,0.419,0.452,0.486,0.492,0.499,0.483,0.425,0.461,0.406,0.376,0.45,0.472,0.462,0.473,0.459,0.496,0.48,0.457,0.138,0.426,0.421,0.331,0.459,0.471,0.435,0.506,0.517,0.502,0.397,0.049,0.467,0.447,0.227,0.425,0.2,0.138,0.462,0.453,0.419,0.453,0.453,0.454,0.46,0.46,0.462,0.464,0.492,0.408,4277,Activity,RNC_ECOLI,Medium,Prokaryote +RPC1_BP434_Tsuboyama_2023_1R69,0.557,0.576,0.549,0.571,0.491,0.533,0.538,0.524,0.557,0.576,0.623,0.667,0.67,0.629,0.661,0.678,0.623,0.598,0.505,0.596,0.609,0.62,0.637,0.579,0.629,0.626,0.626,0.626,0.587,0.664,0.615,0.555,0.502,0.516,0.623,0.631,0.596,0.642,0.642,0.596,0.585,0.593,0.626,0.59,0.664,0.642,0.615,0.557,0.711,0.607,0.607,0.579,0.601,0.612,0.62,0.604,0.612,0.607,0.626,0.612,0.694,0.683,1459,Stability,RPC1_BP434,High,Virus +RPC1_LAMBD_Li_2019_high-expression,0.287,0.424,0.473,0.498,0.461,0.436,0.188,0.387,0.411,0.461,0.473,0.498,0.536,0.275,0.275,0.399,0.536,0.523,0.548,0.411,0.225,0.337,0.449,0.424,0.213,0.337,0.362,0.3,0.523,0.387,0.622,0.523,0.089,0.138,0.362,0.511,0.275,0.362,0.486,0.424,0.449,0.523,0.287,0.287,0.424,0.312,0.188,0.349,0.3,0.138,0.573,0.498,0.511,0.56,0.548,0.573,0.523,0.511,0.536,0.56,0.486,0.349,351,Activity,RPC1_LAMBD,High,Virus +RPC1_LAMBD_Li_2019_low-expression,0.208,0.299,0.379,0.379,0.368,0.311,0.083,0.345,0.299,0.322,0.356,0.345,0.356,0.14,0.14,0.242,0.402,0.47,0.481,0.299,0.128,0.311,0.322,0.333,0.105,0.242,0.231,0.197,0.425,0.265,0.481,0.413,0.037,0.06,0.254,0.345,0.162,0.231,0.322,0.311,0.333,0.368,0.197,0.162,0.299,0.162,0.174,0.276,0.265,0.254,0.436,0.39,0.436,0.436,0.436,0.47,0.425,0.413,0.436,0.459,0.402,0.208,351,Activity,RPC1_LAMBD,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32,0.295,0.289,0.279,0.282,0.272,0.272,0.162,0.168,0.322,0.322,0.356,0.332,0.305,0.225,0.245,0.339,0.326,0.329,0.232,0.242,0.108,0.185,0.252,0.212,0.235,0.252,0.245,0.262,0.245,0.315,0.339,0.324,0.085,0.279,0.222,0.242,0.299,0.259,0.265,0.265,0.255,0.255,0.262,0.175,0.315,0.259,0.423,0.322,0.469,0.389,0.322,0.322,0.312,0.312,0.302,0.326,0.329,0.336,0.312,0.329,0.322,0.463,1195,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance,0.384,0.435,0.447,0.449,0.458,0.467,0.279,0.411,0.496,0.499,0.483,0.492,0.532,0.37,0.418,0.436,0.499,0.482,0.455,0.026,0.382,0.478,0.49,0.485,0.398,0.502,0.512,0.482,0.458,0.447,0.453,0.329,0.273,0.411,0.468,0.479,0.43,0.498,0.509,0.487,0.499,0.504,0.39,0.193,0.466,0.439,0.331,0.431,0.433,0.127,0.463,0.456,0.454,0.466,0.482,0.473,0.478,0.479,0.471,0.489,0.502,0.456,9803,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity,0.344,0.418,0.443,0.452,0.446,0.459,0.26,0.42,0.465,0.464,0.451,0.461,0.492,0.338,0.374,0.364,0.46,0.445,0.426,0.027,0.368,0.464,0.466,0.456,0.379,0.472,0.481,0.465,0.437,0.447,0.474,0.385,0.261,0.385,0.468,0.459,0.4,0.47,0.473,0.463,0.478,0.478,0.355,0.154,0.453,0.382,0.295,0.413,0.38,0.115,0.427,0.411,0.408,0.426,0.446,0.424,0.439,0.447,0.44,0.446,0.418,0.389,10094,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB,0.082,0.074,0.297,0.248,0.285,0.281,0.297,0.438,0.268,0.285,0.418,0.409,0.422,0.136,0.339,0.463,0.405,0.463,0.368,0.38,0.443,0.372,0.355,0.376,0.422,0.451,0.389,0.401,0.405,0.467,0.447,0.418,0.281,0.405,0.434,0.389,0.281,0.397,0.351,0.26,0.285,0.302,0.463,-0.072,0.463,0.451,0.19,0.206,0.289,0.397,0.438,0.281,0.181,0.285,0.264,0.322,0.339,0.434,0.372,0.347,0.455,0.48,965,Stability,SAV1_MOUSE,High,Eukaryote +SBI_STAAM_Tsuboyama_2023_2JVG,0.218,0.179,0.311,0.272,0.315,0.315,0.182,0.128,0.378,0.381,0.253,0.233,0.241,0.186,0.206,0.311,0.463,0.502,0.233,0.276,0.175,0.175,0.214,0.233,0.194,0.206,0.167,0.218,0.311,0.42,0.409,0.358,0.194,0.14,0.155,0.19,0.186,0.194,0.19,0.276,0.292,0.3,0.221,0.182,0.264,0.233,0.526,0.475,0.538,0.479,0.389,0.417,0.424,0.428,0.432,0.436,0.448,0.44,0.405,0.436,0.538,0.444,1025,Stability,SBI_STAAM,Medium,Prokaryote +SC6A4_HUMAN_Young_2021,0.359,0.4,0.352,0.372,0.438,0.452,0.273,0.405,0.483,0.49,0.454,0.467,0.467,0.135,0.255,0.44,0.477,0.478,0.449,0.47,0.43,0.418,0.399,0.394,0.435,0.419,0.43,0.428,0.421,0.456,0.384,0.317,0.283,0.436,0.416,0.386,0.459,0.455,0.432,0.475,0.475,0.461,0.419,0.1,0.461,0.453,0.383,0.43,0.406,0.132,0.455,0.454,0.468,0.47,0.478,0.469,0.475,0.477,0.466,0.482,0.491,0.452,11576,Activity,SC6A4_HUMAN,Medium,Human +SCIN_STAAR_Tsuboyama_2023_2QFF,0.051,0.084,0.167,0.18,0.236,0.213,0.097,0.012,0.183,0.2,0.18,0.173,0.17,0.127,0.186,0.167,0.19,0.262,0.262,0.17,0.054,0.087,0.084,0.091,0.107,0.111,0.101,0.13,0.134,0.18,0.177,0.109,-0.025,0.035,0.051,0.107,0.114,0.104,0.12,0.19,0.173,0.18,0.13,0.111,0.2,0.163,0.375,0.391,0.381,0.325,0.256,0.276,0.269,0.292,0.259,0.295,0.272,0.249,0.206,0.276,0.447,0.355,1212,Stability,SCIN_STAAR,High,Prokaryote +SCN5A_HUMAN_Glazer_2019,0.036,0.054,0.089,0.089,0.125,0.143,0.071,0.0,0.071,0.125,0.089,0.143,0.071,0.196,0.143,0.071,0.089,0.054,0.071,0.054,0.018,0.054,0.071,0.089,-0.018,0.018,0.018,0.036,0.107,0.027,0.161,0.143,0.036,0.0,0.0,0.089,-0.018,-0.018,-0.018,0.054,0.036,0.036,0.018,0.125,0.107,0.125,0.036,0.054,0.018,-0.018,0.089,0.054,0.071,0.036,0.054,0.089,0.143,0.107,0.036,0.089,0.125,0.018,224,OrganismalFitness,SCN5A_HUMAN,Medium,Human +SDA_BACSU_Tsuboyama_2023_1PV0,0.477,0.527,0.53,0.53,0.532,0.532,0.118,0.443,0.524,0.525,0.529,0.512,0.522,0.284,0.282,0.518,0.508,0.5,0.505,0.529,0.156,0.19,0.192,0.318,0.163,0.323,0.084,0.323,0.385,0.532,0.527,0.527,-0.24,0.103,0.287,0.342,0.488,0.496,0.46,0.512,0.512,0.508,0.096,-0.009,0.342,0.068,0.376,0.349,0.529,0.522,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.518,0.525,2770,Stability,SDA_BACSU,Medium,Prokaryote +SERC_HUMAN_Xie_2023,0.273,0.354,0.394,0.396,0.392,0.394,0.018,0.333,0.417,0.421,0.371,0.379,0.369,0.116,0.281,0.388,0.396,0.436,0.398,0.379,0.346,0.371,0.381,0.369,0.352,0.386,0.369,0.396,0.371,0.354,0.365,0.315,0.152,0.361,0.373,0.379,0.365,0.375,0.379,0.4,0.413,0.415,0.202,0.047,0.39,0.335,0.277,0.375,0.292,0.112,0.419,0.396,0.411,0.427,0.417,0.421,0.398,0.4,0.404,0.423,0.396,0.35,1914,OrganismalFitness,SERC_HUMAN,High,Human +SHOC2_HUMAN_Kwon_2022,0.133,0.265,0.294,0.29,0.285,0.296,0.129,0.26,0.314,0.319,0.298,0.278,0.304,0.144,0.152,0.166,0.323,0.295,0.213,0.294,0.161,0.282,0.289,0.262,0.166,0.296,0.3,0.304,0.27,0.308,0.303,0.254,0.099,0.15,0.234,0.277,0.152,0.201,0.258,0.26,0.275,0.303,0.156,0.149,0.317,0.191,0.216,0.277,0.202,0.064,0.295,0.296,0.303,0.308,0.306,0.306,0.29,0.307,0.296,0.307,0.217,0.187,10972,OrganismalFitness,SHOC2_HUMAN,Medium,Human +SOX30_HUMAN_Tsuboyama_2023_7JJK,0.17,0.21,0.25,0.253,0.242,0.238,0.289,0.158,0.166,0.202,0.174,0.234,0.218,0.21,0.25,0.226,0.222,0.17,0.186,0.158,0.103,0.115,0.111,0.131,0.19,0.182,0.103,0.194,0.103,0.17,0.04,0.026,0.158,0.079,0.162,0.166,0.147,0.158,0.154,0.234,0.23,0.214,0.186,0.127,0.147,0.246,0.38,0.253,0.412,0.356,0.293,0.277,0.242,0.277,0.273,0.281,0.257,0.269,0.246,0.285,0.25,0.364,1010,Stability,SOX30_HUMAN,High,Human +SPA_STAAU_Tsuboyama_2023_1LP1,0.373,0.44,0.464,0.445,0.493,0.498,-0.114,0.46,0.407,0.409,0.367,-0.068,-0.028,-0.055,-0.058,-0.076,-0.057,-0.068,-0.022,0.491,-0.155,-0.022,0.111,0.054,-0.085,-0.055,0.042,0.23,0.312,0.436,0.424,0.412,-0.02,-0.009,-0.024,0.008,0.335,0.327,0.318,0.489,0.487,0.481,-0.159,-0.117,-0.123,-0.074,0.362,0.306,0.563,0.434,0.472,0.455,0.445,0.47,0.451,0.455,0.472,0.46,0.46,0.47,0.409,0.286,2105,Stability,SPA_STAAU,Medium,Prokaryote +SPG1_STRSG_Olson_2014,0.169,0.186,-0.005,0.012,0.158,0.16,-0.006,0.044,-0.068,0.072,0.228,0.174,0.142,0.2,0.165,0.205,0.237,0.2,0.254,0.202,0.18,0.152,0.154,0.149,0.167,0.162,0.15,0.167,0.248,0.17,0.328,0.316,0.047,0.172,0.132,0.198,0.185,0.149,0.203,0.173,0.136,0.205,-0.029,-0.08,0.157,0.051,0.24,0.19,0.276,0.086,0.279,0.253,0.272,0.302,0.303,0.28,0.314,0.334,0.316,0.309,0.287,0.273,536962,Binding,SPG1_STRSG,Low,Prokaryote +SPG1_STRSG_Wu_2016,0.01,0.079,0.087,0.091,0.084,0.093,0.076,0.028,0.162,0.158,0.131,0.117,0.108,0.096,0.125,0.129,0.138,0.163,0.182,0.098,0.071,0.056,0.048,0.069,0.044,0.096,0.079,0.063,0.063,0.095,0.125,0.093,-0.004,0.08,0.04,0.061,0.09,0.078,0.086,0.103,0.094,0.092,0.05,0.005,0.078,0.045,0.058,0.054,0.145,0.089,0.141,0.124,0.118,0.149,0.125,0.119,0.143,0.136,0.149,0.136,0.148,0.115,149360,Binding,SPG1_STRSG,Medium,Prokaryote +SPG2_STRSG_Tsuboyama_2023_5UBS,0.442,0.458,0.461,0.483,0.555,0.519,0.312,0.301,0.489,0.414,0.417,0.384,0.354,0.326,0.376,0.403,0.436,0.425,0.489,0.425,0.343,0.376,0.378,0.354,0.298,0.381,0.301,0.403,0.45,0.571,0.525,0.507,-0.126,0.332,0.409,0.389,0.304,0.309,0.37,0.486,0.513,0.5,0.235,0.13,0.235,0.216,0.395,0.315,0.662,0.494,0.53,0.505,0.527,0.513,0.494,0.522,0.527,0.569,0.516,0.53,0.519,0.519,1451,Stability,SPG2_STRSG,Medium,Prokaryote +SPIKE_SARS2_Starr_2020_binding,0.111,0.135,0.051,0.116,0.153,0.166,-0.059,0.259,0.27,0.257,-0.011,-0.075,-0.045,-0.048,-0.048,-0.033,-0.024,-0.023,0.016,0.255,0.223,0.264,0.256,0.264,0.284,0.236,0.209,0.272,0.227,0.163,0.185,0.238,0.161,0.227,0.215,0.262,0.209,0.21,0.214,0.249,0.242,0.238,-0.053,-0.016,-0.024,-0.018,0.413,0.379,0.32,0.114,0.137,0.152,0.137,0.181,0.153,0.183,0.156,0.187,0.158,0.174,0.216,0.095,3802,Binding,SPIKE_SARS2,Medium,Virus +SPIKE_SARS2_Starr_2020_expression,0.161,0.223,0.122,0.215,0.375,0.352,-0.034,0.266,0.321,0.344,0.038,-0.032,-0.007,-0.022,-0.024,-0.006,0.001,0.019,0.055,0.271,0.24,0.288,0.293,0.308,0.289,0.277,0.236,0.302,0.249,0.267,0.207,0.269,0.144,0.25,0.241,0.274,0.259,0.27,0.273,0.339,0.352,0.367,-0.01,0.013,0.013,0.021,0.434,0.419,0.377,0.14,0.225,0.239,0.23,0.285,0.247,0.298,0.246,0.27,0.247,0.273,0.338,0.18,3798,Expression,SPIKE_SARS2,Medium,Virus +SPTN1_CHICK_Tsuboyama_2023_1TUD,0.499,0.446,0.433,0.402,0.426,0.412,0.14,0.378,0.4,0.422,0.402,0.485,0.445,-0.069,0.443,0.437,0.442,0.544,0.489,0.392,0.425,0.417,0.435,0.4,0.458,0.412,0.46,0.396,0.382,0.385,0.405,0.431,0.33,0.373,0.418,0.391,0.463,0.465,0.471,0.406,0.42,0.415,0.367,-0.148,0.335,0.352,0.303,0.248,0.472,0.44,0.433,0.432,0.463,0.455,0.451,0.448,0.433,0.423,0.435,0.433,0.5,0.415,3201,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU,0.324,0.409,0.42,0.437,0.426,0.437,0.064,0.233,0.369,0.414,0.318,0.233,0.324,0.143,0.318,0.386,0.437,0.494,0.369,0.431,0.143,0.296,0.369,0.296,0.182,0.392,0.42,0.347,0.358,0.426,0.414,0.378,0.075,0.149,0.284,0.397,0.358,0.324,0.42,0.471,0.386,0.437,0.16,0.115,0.454,0.403,0.528,0.494,0.528,0.528,0.477,0.431,0.516,0.494,0.482,0.477,0.488,0.471,0.482,0.494,0.482,0.341,707,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88,0.506,0.529,0.536,0.539,0.567,0.554,-0.141,0.37,0.546,0.544,0.572,0.564,0.559,-0.169,0.539,0.589,0.579,0.584,0.569,0.506,-0.111,0.319,0.231,0.375,0.347,0.42,0.458,0.483,0.435,0.564,0.577,0.574,0.263,0.049,0.15,0.319,0.534,0.519,0.511,0.562,0.559,0.526,0.466,-0.245,0.572,0.476,0.059,0.461,0.587,0.572,0.572,0.567,0.574,0.582,0.587,0.595,0.567,0.567,0.584,0.577,0.655,0.612,1583,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W,0.413,0.418,0.559,0.549,0.598,0.585,0.444,0.336,0.618,0.621,0.474,0.536,0.567,0.379,0.575,0.613,0.593,0.544,0.541,0.541,0.549,0.513,0.51,0.495,0.495,0.515,0.523,0.503,0.503,0.539,0.59,0.559,0.485,0.48,0.487,0.539,0.513,0.554,0.559,0.577,0.587,0.585,0.248,0.117,0.456,0.433,0.438,0.446,0.497,0.51,0.621,0.593,0.6,0.6,0.613,0.608,0.598,0.59,0.613,0.608,0.582,0.521,1556,Stability,SRBS1_HUMAN,High,Human +SRC_HUMAN_Ahler_2019,0.46,0.453,0.418,0.428,0.453,0.457,0.466,0.386,0.494,0.51,0.442,0.515,0.533,0.357,0.418,0.413,0.495,0.45,0.446,0.45,0.377,0.359,0.373,0.33,0.394,0.428,0.391,0.358,0.272,0.501,0.519,0.493,0.379,0.352,0.359,0.295,0.443,0.453,0.432,0.459,0.459,0.457,0.496,0.311,0.442,0.486,0.276,0.247,0.355,0.094,0.456,0.438,0.445,0.465,0.479,0.471,0.47,0.476,0.472,0.487,0.47,0.412,3372,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,0.385,0.378,0.38,0.383,0.391,0.384,0.387,0.333,0.417,0.407,0.361,0.423,0.439,0.268,0.335,0.332,0.425,0.352,0.376,0.362,0.329,0.304,0.322,0.277,0.328,0.357,0.316,0.313,0.22,0.411,0.438,0.435,0.299,0.282,0.292,0.226,0.362,0.358,0.345,0.38,0.378,0.384,0.41,0.238,0.36,0.393,0.219,0.224,0.29,0.035,0.393,0.372,0.373,0.396,0.407,0.41,0.393,0.412,0.396,0.414,0.389,0.346,3637,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Nguyen_2022,0.337,0.341,0.356,0.351,0.351,0.351,0.345,0.319,0.284,0.345,0.343,0.389,0.411,0.238,0.296,0.298,0.382,0.338,0.351,0.34,0.283,0.284,0.293,0.257,0.291,0.332,0.288,0.279,0.209,0.369,0.402,0.391,0.295,0.257,0.265,0.207,0.32,0.321,0.316,0.35,0.338,0.34,0.387,0.213,0.345,0.39,0.193,0.201,0.256,0.04,0.366,0.346,0.343,0.366,0.377,0.372,0.364,0.378,0.366,0.381,0.365,0.308,3366,OrganismalFitness,SRC_HUMAN,Medium,Human +SUMO1_HUMAN_Weile_2017,0.339,0.293,0.339,0.363,0.392,0.378,0.123,0.37,0.378,0.303,0.341,0.402,0.431,0.213,0.412,0.467,0.433,0.307,0.281,0.448,0.196,0.322,0.378,0.337,0.402,0.295,0.402,0.349,0.269,0.375,0.399,0.426,0.201,0.223,0.416,0.252,0.339,0.46,0.322,0.351,0.465,0.344,0.431,0.056,0.293,0.45,0.438,0.358,0.445,0.37,0.431,0.37,0.429,0.429,0.419,0.453,0.402,0.436,0.412,0.443,0.397,0.453,1700,OrganismalFitness,SUMO1_HUMAN,High,Human +SYUA_HUMAN_Newberry_2020,0.152,0.192,0.198,0.234,0.196,0.211,0.202,0.267,0.242,0.248,0.338,0.332,0.324,0.206,0.231,0.229,0.236,0.278,0.309,0.332,0.248,0.326,0.255,0.238,0.194,0.273,0.286,0.259,0.238,0.307,0.311,0.252,0.129,0.257,0.359,0.284,0.189,0.278,0.229,0.225,0.276,0.231,0.211,0.173,0.313,0.24,-0.006,0.206,0.051,-0.041,0.19,0.19,0.189,0.198,0.229,0.196,0.227,0.215,0.229,0.221,0.074,0.059,2497,OrganismalFitness,SYUA_HUMAN,Medium,Human +TADBP_HUMAN_Bolognesi_2019,0.09,0.04,0.077,0.08,0.074,0.07,0.154,0.023,0.06,0.054,0.01,0.03,0.023,0.054,0.02,0.023,-0.07,0.013,0.054,-0.047,0.077,0.037,-0.003,-0.01,0.104,0.07,0.043,-0.017,-0.03,0.03,0.107,0.142,-0.043,0.12,0.147,0.084,0.09,0.11,0.087,0.1,0.117,0.084,0.054,0.107,-0.007,0.03,0.197,0.023,0.12,0.047,0.09,-0.033,-0.013,0.003,0.007,0.027,0.033,0.003,-0.06,0.023,0.01,0.057,1196,OrganismalFitness,TADBP_HUMAN,Low,Human +TAT_HV1BR_Fernandes_2016,0.265,0.183,0.238,0.243,0.259,0.243,-0.076,0.396,0.227,0.257,0.159,0.338,0.322,-0.032,0.006,0.009,0.028,-0.057,0.044,0.186,0.393,0.415,0.417,0.423,0.36,0.257,0.12,0.229,0.199,0.314,0.401,0.403,0.319,0.371,0.175,0.227,0.374,0.254,0.229,0.341,0.27,0.257,0.02,-0.027,0.284,0.156,0.202,0.281,0.3,0.178,0.082,0.126,0.139,0.099,0.107,0.107,0.074,0.099,0.09,0.093,0.161,0.145,1577,OrganismalFitness,TAT_HV1BR,High,Virus +TCRG1_MOUSE_Tsuboyama_2023_1E0L,0.59,0.537,0.606,0.61,0.655,0.655,0.682,0.312,0.648,0.697,0.552,0.728,0.747,0.67,0.69,0.762,0.731,0.69,0.724,0.514,0.423,0.571,0.556,0.602,0.583,0.579,0.636,0.644,0.575,0.693,0.636,0.587,0.541,0.446,0.602,0.686,0.64,0.705,0.697,0.636,0.655,0.648,0.701,0.103,0.53,0.667,0.541,0.461,0.739,0.712,0.705,0.663,0.644,0.739,0.712,0.735,0.709,0.724,0.697,0.72,0.758,0.754,1058,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG,0.25,0.26,0.437,0.411,0.407,0.42,-0.002,0.122,0.535,0.525,0.512,0.42,0.483,0.099,0.525,0.532,0.496,0.515,0.545,0.496,-0.097,0.266,0.312,0.296,0.266,0.286,0.185,0.368,0.443,0.499,0.551,0.548,0.414,-0.042,0.339,0.306,0.397,0.463,0.473,0.411,0.489,0.483,0.188,0.076,0.424,0.306,0.414,0.401,0.443,0.568,0.542,0.529,0.568,0.587,0.571,0.555,0.555,0.558,0.548,0.561,0.479,0.404,1279,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT,0.346,0.335,0.443,0.438,0.424,0.421,-0.109,0.318,0.511,0.492,0.405,0.416,0.419,-0.014,0.394,0.456,0.473,0.432,0.47,0.405,0.227,0.316,0.34,0.321,0.351,0.381,0.289,0.332,0.346,0.486,0.454,0.435,0.167,0.067,0.278,0.302,0.397,0.394,0.397,0.462,0.421,0.427,0.153,-0.076,0.4,0.337,0.335,0.473,0.559,0.508,0.459,0.459,0.467,0.47,0.451,0.443,0.44,0.432,0.446,0.454,0.586,0.467,1479,Stability,TNKS2_HUMAN,High,Human +TPK1_HUMAN_Weile_2017,0.19,0.194,0.185,0.198,0.203,0.209,0.059,0.193,0.236,0.221,0.23,0.215,0.257,0.121,0.162,0.216,0.266,0.258,0.306,0.191,0.096,0.107,0.18,0.222,0.119,0.217,0.22,0.209,0.231,0.191,0.199,0.161,0.105,0.117,0.171,0.234,0.19,0.197,0.242,0.208,0.209,0.239,0.131,0.104,0.269,0.152,0.148,0.209,0.184,0.076,0.2,0.207,0.211,0.217,0.226,0.226,0.215,0.229,0.233,0.231,0.217,0.185,3181,OrganismalFitness,TPK1_HUMAN,Medium,Human +TPMT_HUMAN_Matreyek_2018,0.263,0.306,0.313,0.323,0.322,0.331,0.173,0.302,0.322,0.329,0.349,0.33,0.344,0.225,0.265,0.344,0.349,0.295,0.291,0.341,0.186,0.263,0.301,0.299,0.265,0.304,0.301,0.268,0.295,0.351,0.359,0.342,0.223,0.195,0.302,0.29,0.302,0.337,0.331,0.349,0.334,0.344,0.244,0.148,0.345,0.316,0.322,0.347,0.36,0.162,0.345,0.342,0.349,0.342,0.348,0.356,0.359,0.344,0.355,0.356,0.36,0.324,3648,Expression,TPMT_HUMAN,Medium,Human +TPOR_HUMAN_Bridgford_2020,0.273,0.222,0.23,0.188,0.162,0.213,0.299,0.316,0.265,0.29,0.29,0.265,0.282,0.247,0.307,0.265,0.179,0.282,0.205,0.213,0.213,0.299,0.239,0.265,0.307,0.128,0.35,0.41,0.239,0.358,0.282,0.133,0.324,0.307,0.29,0.333,0.299,0.299,0.367,0.316,0.307,0.35,0.247,0.247,0.213,0.265,0.119,0.23,0.213,0.0,0.205,0.196,0.273,0.23,0.205,0.273,0.29,0.111,0.188,0.239,0.324,0.299,562,OrganismalFitness,TPOR_HUMAN,Low,Human +TRPC_SACS2_Chan_2017,0.516,0.527,0.462,0.479,0.487,0.508,0.188,0.465,0.557,0.562,0.546,0.543,0.557,0.258,0.506,0.532,0.541,0.543,0.524,0.462,0.368,0.519,0.465,0.516,0.436,0.471,0.444,0.46,0.441,0.441,0.527,0.462,0.196,0.487,0.492,0.508,0.511,0.527,0.527,0.506,0.516,0.516,0.395,0.045,0.543,0.465,0.25,0.384,0.387,0.099,0.541,0.495,0.503,0.557,0.53,0.524,0.543,0.546,0.524,0.551,0.535,0.511,1519,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +TRPC_THEMA_Chan_2017,0.365,0.371,0.348,0.371,0.365,0.363,0.237,0.333,0.383,0.383,0.371,0.395,0.406,0.19,0.377,0.386,0.392,0.383,0.389,0.354,0.283,0.324,0.33,0.339,0.354,0.354,0.313,0.333,0.36,0.348,0.339,0.304,0.146,0.333,0.392,0.365,0.363,0.38,0.363,0.368,0.365,0.357,0.33,0.093,0.377,0.333,0.169,0.263,0.275,0.003,0.383,0.316,0.357,0.377,0.377,0.33,0.36,0.36,0.351,0.377,0.409,0.383,1519,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +UBC9_HUMAN_Weile_2017,0.304,0.407,0.457,0.474,0.438,0.457,-0.027,0.327,0.43,0.44,0.34,0.396,0.432,0.018,0.025,0.326,0.379,0.426,0.49,0.477,0.168,0.337,0.379,0.346,0.348,0.396,0.388,0.359,0.354,0.399,0.371,0.372,0.103,0.168,0.346,0.355,0.243,0.366,0.394,0.371,0.437,0.455,0.209,0.001,0.316,0.423,0.273,0.277,0.385,0.218,0.327,0.346,0.316,0.352,0.338,0.346,0.327,0.343,0.351,0.351,0.293,0.357,2563,OrganismalFitness,UBC9_HUMAN,Medium,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X,0.247,0.336,0.369,0.379,0.37,0.378,0.152,0.349,0.363,0.371,0.341,0.414,0.4,0.334,0.358,0.394,0.374,0.402,0.428,0.383,0.217,0.358,0.35,0.371,0.377,0.383,0.342,0.341,0.369,0.387,0.422,0.418,0.387,0.153,0.167,0.299,0.333,0.324,0.332,0.382,0.375,0.386,0.326,-0.02,0.252,0.259,0.369,0.292,0.394,0.305,0.35,0.385,0.369,0.37,0.374,0.366,0.374,0.375,0.385,0.374,0.411,0.39,3622,Stability,UBE4B_HUMAN,High,Human +UBE4B_MOUSE_Starita_2013,0.306,0.312,0.348,0.342,0.348,0.354,0.04,0.046,0.318,0.342,0.276,0.33,0.348,0.318,0.336,0.36,0.33,0.318,0.288,0.318,0.082,0.276,0.27,0.252,0.348,0.3,0.288,0.288,0.252,0.33,0.36,0.3,0.058,-0.009,0.118,0.203,0.312,0.288,0.306,0.342,0.36,0.379,0.306,-0.003,0.33,0.348,0.191,0.27,-0.057,0.034,0.318,0.33,0.354,0.354,0.342,0.36,0.33,0.36,0.342,0.354,0.33,0.288,899,Activity,UBE4B_MOUSE,Low,Eukaryote 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AICDA_HUMAN_Gajula_2014_3cycles0.0110.2160.2670.2670.2670.267-0.1690.1390.1640.2160.2160.2160.267-0.143-0.1430.0110.1900.1900.1130.395-0.0920.0360.1900.241-0.0660.1390.2930.2160.1900.1640.2930.2930.036-0.041-0.0150.2160.0360.1130.1900.2160.2410.216-0.169-0.1690.1640.0360.2930.2410.2670.1390.2160.2410.2160.2160.1640.2160.2160.2160.1900.1900.190-0.015209ActivityAICDA_HUMANMediumHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O0.2010.2490.2410.2260.2290.234-0.2270.0130.0850.0960.1880.0700.271-0.1390.1860.1270.1020.1350.127-0.015-0.063-0.157-0.094-0.087-0.271-0.124-0.112-0.1760.0050.2030.1250.1280.007-0.207-0.108-0.2480.2220.2340.1830.2620.2560.211-0.160-0.1620.0400.0030.1430.0200.2760.2320.2830.1490.0480.1720.1620.1640.1630.1660.1740.1590.1530.2602972StabilityAMFR_HUMANMediumHuman
AMIE_PSEAE_Wrenbeck_20170.2070.3910.4240.4480.4210.3960.0350.3630.3140.4940.4660.4960.5460.2140.2820.2990.4370.5570.4930.3940.4520.4490.4440.4540.4530.4850.4550.4530.4330.4700.4870.4550.2430.4990.3890.3510.4740.3950.3600.4940.4320.4150.2330.0400.3580.2810.2910.3740.3240.1670.4480.4100.4180.4350.4220.4320.4130.4260.4530.4350.4750.2866227ActivityAMIE_PSEAEHighProkaryote
ANCSZ_Hobbs_20220.4170.3820.3250.3480.3640.3690.3890.3420.3680.3820.4050.3820.4060.3600.4310.4340.4480.4370.4180.0080.3160.3460.3800.3800.3740.3830.3720.3520.3700.4330.4100.4060.2220.3430.3700.3410.4040.4240.4090.3880.4080.3940.3810.2850.3660.3720.3390.3610.3110.1230.4140.4080.4310.4340.4310.4420.4180.4300.4220.4410.4190.4424670ActivityANCSZMediumEukaryote
ARGR_ECOLI_Tsuboyama_2023_1AOY0.2120.2400.2930.2960.2810.2810.1810.2490.3150.3520.2840.2030.2620.2090.1810.3550.3400.3770.2930.2960.2250.3270.3300.2770.2310.2960.2900.3240.3020.3360.3050.2390.0660.2530.2650.3240.2120.2150.2810.2680.2770.2990.1620.1440.3300.2960.5850.4980.6010.4950.3710.3920.3770.3860.3580.3800.3550.3680.3400.3770.4300.3681287StabilityARGR_ECOLIMediumProkaryote
B2L11_HUMAN_Dutta_2010_binding-Mcl-10.5190.0020.5080.5320.3190.5080.1770.4140.2010.2250.1770.0590.2480.0590.1540.1060.2250.3430.2720.2250.1540.0350.2250.3660.0590.1770.3190.2480.2950.6580.3190.3000.2010.1060.1770.3660.4370.3430.3430.5320.4610.3660.1300.0120.1540.0830.2720.2480.3190.0350.2720.2720.3190.2010.2480.4140.2250.1770.3660.2480.2010.201170BindingB2L11_HUMANLowHuman
BBC1_YEAST_Tsuboyama_2023_1TG00.1890.2390.3790.3610.3750.3810.1850.2820.3650.3820.4330.3690.4100.3240.3590.3590.4390.3710.4400.2900.3320.3520.3810.3400.1230.3750.4080.3690.2990.2940.3520.3290.2660.2990.2060.2680.3280.2630.3090.4080.3590.3820.0980.1080.2100.1270.4680.3670.5720.5140.4420.4210.4480.4420.4420.4460.4350.4420.4420.4440.4500.4832069StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU0.3270.3070.2810.2960.2840.3020.0980.1590.3090.3910.3300.2480.2890.1180.1490.2510.4060.4140.3240.3420.0880.2990.1540.3020.1360.3630.2100.3300.3400.5000.4720.4010.0240.1640.2300.3500.3550.3600.3600.2960.3090.3400.0780.0850.1690.1460.4210.3700.5180.4310.4540.4520.4640.4720.4590.4870.4590.4490.4540.4620.3780.2101572StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Deng_20120.2610.4060.4020.4220.4020.4140.0670.2400.4190.4480.4130.3940.4180.1890.2810.3730.4430.3450.2590.3650.2930.2970.3030.3090.3100.3560.3610.3150.2710.3870.4280.3960.0910.2860.2960.2610.3550.3560.3590.4140.4290.4260.2730.0280.3890.3400.3350.3870.3640.1930.4300.4430.4380.4530.4500.4420.4500.4350.4420.4660.4400.3214996OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Firnberg_20140.3480.5640.5890.6040.5490.5730.1040.3650.5800.6080.5790.5300.5750.2840.4130.5140.6200.4290.3000.5230.4200.4080.3810.3850.4510.5000.5060.4180.3350.5550.6140.6300.1390.4100.3660.3230.4800.4640.4620.5720.5740.5760.3970.0320.5710.4690.4710.5720.5290.2440.5990.5910.5900.6170.6110.6120.6160.5840.6030.6320.6050.4484783OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Jacquier_20130.3570.5510.5510.5870.5670.5790.0700.3450.5710.5870.5230.5230.5350.2400.3930.4990.5790.4660.3690.5150.4220.3930.3770.3930.4260.5270.4700.4180.3450.4460.5750.5710.1140.3850.3970.3810.4700.4940.4990.5630.5830.5960.3410.0580.5190.4300.4100.5190.4990.2360.5470.5190.5390.5590.5550.5350.5670.5470.5190.5590.5590.438989OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Stiffler_20150.3500.5640.6010.6140.5510.5860.1020.3630.5940.6200.5910.5490.5950.2980.4240.5260.6320.4360.3060.5280.4330.4190.3740.3920.4660.5110.5260.4270.3390.5650.6270.6380.1370.4320.3690.3260.4900.4620.4650.5850.5800.5860.4110.0320.5810.4830.4800.5760.5300.2470.6000.5820.5940.6080.6080.6110.6100.5910.6040.6300.6160.4624996OrganismalFitnessBLAT_ECOLXHighProkaryote
BRCA1_HUMAN_Findlay_20180.4360.2980.4230.4260.4030.4080.0480.2270.3340.3310.4130.2990.3410.0770.2860.3790.4130.4260.3610.2370.0650.3210.3410.3490.2390.4030.3910.3790.3980.3990.4360.4160.2120.0900.1270.3710.4060.4060.4360.4410.4310.4480.1420.0680.4460.3610.3690.3910.0440.1220.4310.4230.4280.4110.4210.4230.4280.4130.4080.4230.4360.3981837OrganismalFitnessBRCA1_HUMANLowHuman
BRCA2_HUMAN_Erwood_2022_HEK293T0.3120.3090.3090.3090.3090.3090.0680.2830.122-0.0120.3090.0950.0150.0150.0150.2830.3090.3090.3090.0410.0150.3090.3090.3090.3090.2830.3090.3090.0680.3090.2560.285-0.0120.0410.0680.0680.3090.3090.3090.3090.3090.3090.0150.0150.309-0.012-0.0390.015-0.2000.1750.2560.2830.2830.3090.3090.3090.3090.3090.3090.309-0.0390.015265OrganismalFitnessBRCA2_HUMANNaNHuman
C6KNH7_9INFA_Lee_20180.2970.2680.2580.2640.3080.308-0.0110.3330.2670.2720.0240.3200.370-0.002-0.007-0.0030.3810.2930.2980.2450.2880.2690.2750.2610.1680.3410.3230.3570.2810.3450.3330.2510.0880.2690.2760.2990.3120.3170.3180.3320.3350.325-0.009-0.0160.2040.0140.3930.4220.4260.1720.3870.3960.4100.4110.4170.3990.4170.4160.4090.4160.2350.13410754OrganismalFitnessC6KNH7_9INFAMediumVirus
CALM1_HUMAN_Weile_20170.1350.2010.1970.2040.1860.1920.1240.1350.1970.2210.2320.1900.2340.1150.1530.1530.1660.1660.1680.1880.1310.1840.2150.2100.1880.2430.2390.2500.2760.2060.1660.0980.0620.1730.2260.2700.1750.1970.2260.2040.2150.2040.1640.1150.2560.2120.0600.1240.1370.0670.1240.1440.1510.1550.1530.1400.1420.1330.1370.1510.2520.1921813OrganismalFitnessCALM1_HUMANHighHuman
CAPSD_AAV2S_Sinai_20210.2340.1950.1880.2110.1940.1880.2850.2850.1790.2020.1220.1460.1480.2010.2170.1440.2010.1110.0440.1630.1450.1980.2060.2020.1590.1490.1880.1520.3000.2740.1180.1100.1020.1490.2030.3600.2130.2230.3180.1940.1970.2630.0920.1360.2100.0960.3130.3010.2400.2210.1510.1480.1310.1510.1390.1390.1430.1390.1500.1450.2140.16742328OrganismalFitnessCAPSD_AAV2SLowVirus
CAR11_HUMAN_Meitlis_2020_gof-0.010-0.026-0.027-0.030-0.055-0.0620.090-0.0950.0250.0270.0120.0820.0350.0470.0500.1450.0570.0650.1000.0450.075-0.0420.0120.005-0.140-0.052-0.035-0.0320.020-0.0190.0220.0120.0970.042-0.042-0.0900.032-0.022-0.070-0.020-0.062-0.0920.0600.0150.0020.1250.1020.0150.0700.0400.1050.0950.0900.0800.0850.0750.0720.0900.0950.0950.1370.1302374OrganismalFitnessCAR11_HUMANLowHuman
CAR11_HUMAN_Meitlis_2020_lof0.3650.3110.2990.2840.2940.2990.0750.0110.1990.1890.3610.4100.3950.0200.0850.3810.4260.4360.4430.1280.1230.2690.2460.2100.1550.2560.2410.2960.1130.3140.3630.3250.277-0.0090.2240.2050.3130.2670.2370.3290.2760.2520.0720.0260.3340.2410.3530.3750.3480.1080.4430.4310.4310.4430.4280.4430.4200.4360.4530.4400.4650.3232395OrganismalFitnessCAR11_HUMANLowHuman
CAS9_STRP1_Spencer_2017_positive0.1070.1410.1230.1260.1350.1390.0140.0790.1310.1400.1230.0460.0390.0270.0560.1010.1330.1370.1340.0100.0270.0480.1160.0910.0260.1440.1380.1330.0480.1280.1450.1290.0160.0220.0330.1200.1320.1340.1320.1290.1310.1390.0390.0110.1290.0740.0740.1290.0150.0200.1230.1230.1210.1250.1260.1300.1270.1280.1210.1280.1190.0778117ActivityCAS9_STRP1MediumProkaryote
CASP3_HUMAN_Roychowdhury_20200.3140.4470.5080.5030.5180.5230.0530.3700.5180.5180.4640.4770.4870.1860.4410.5180.5210.4520.3800.4800.1320.3780.4240.4060.3830.4410.4470.3930.4410.4900.4670.4010.1810.0480.3550.3880.3900.4290.4620.5050.4900.5260.270-0.0290.4800.4240.3160.4290.3950.1880.4590.4570.4590.4620.4820.4700.4670.4470.4570.4870.5310.3721567ActivityCASP3_HUMANHighHuman
CASP7_HUMAN_Roychowdhury_20200.3350.4610.5090.5210.5020.5020.0510.3920.4870.5330.4870.5020.5300.2250.5160.5180.5350.4990.4780.5040.1800.4590.4610.4640.4750.4970.5110.4850.4800.5490.5110.4590.2990.1010.4870.4250.4420.5060.5090.4970.5490.5330.3710.0230.5490.5140.3970.5230.5210.2490.5140.5020.4710.4920.5180.5210.4950.4950.5140.5110.5560.4331680ActivityCASP7_HUMANMediumHuman
CATR_CHLRE_Tsuboyama_2023_2AMI0.4700.4730.5670.5630.5800.5780.6070.4870.4490.4220.4980.5380.5630.4810.5940.5860.5570.5080.5730.0880.5060.5150.5100.5080.5400.5190.4980.4600.4770.5760.5360.5440.3570.5270.5020.5290.5690.5420.5610.5800.5630.5760.3950.3800.2940.4240.3930.3860.5760.4390.5650.5440.5780.5570.5840.5630.5570.5360.5550.5590.5120.5651903StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X0.5550.6050.6330.6340.6380.6500.3750.5690.6730.6850.6480.6150.6110.4040.5610.6420.6360.6330.6070.6730.4000.4060.5920.5710.4490.5730.5820.5570.6110.6670.6580.5950.0190.4040.5130.6110.5760.6030.6230.6500.6400.6500.3970.2790.5130.5220.6770.6250.8050.7100.6980.7250.6920.7100.7370.7140.7120.7100.7060.7250.6310.7762068StabilityCBPA2_HUMANMediumHuman
CBS_HUMAN_Sun_20200.2970.3060.3140.3390.3190.3310.1620.2050.3300.3410.3040.3050.3290.0700.1770.2810.2990.2850.2830.3090.3150.2380.2610.2620.3190.2540.2620.2480.2730.3220.3160.2990.1790.3320.2680.2330.3290.2890.2860.3490.3170.3150.2430.0620.3260.3240.2100.2360.2770.0580.2770.2770.2830.2970.2920.2890.2810.2840.2900.2930.3360.2387217OrganismalFitnessCBS_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K280.3890.4500.4930.5050.4630.507-0.2050.4540.4820.4680.4800.4820.470-0.2100.5450.5140.5030.4280.4420.5000.3120.4100.3770.3940.3720.3610.3660.3100.3940.4890.5070.5020.3490.2770.3050.3100.4150.4100.3960.4960.5170.5030.465-0.2610.2490.435-0.0020.0670.4520.4210.5280.4580.5350.5260.5450.5150.5190.5520.5190.5260.4930.6152282StabilityCBX4_HUMANHighHuman
CCDB_ECOLI_Adkar_20120.2980.3490.3890.4130.3890.413-0.0190.1650.3150.3080.3790.3040.3860.015-0.0120.3490.4000.3690.2130.3930.0150.012-0.0800.094-0.1450.0770.036-0.0050.3280.4100.4950.4810.117-0.0220.0050.2530.3830.3450.3660.4030.3660.3960.036-0.0090.3790.0150.2500.3490.2700.2190.3790.3210.3490.3660.3420.3590.3520.3790.4060.3830.3590.1381176ActivityCCDB_ECOLIHighProkaryote
CCDB_ECOLI_Tripathi_20160.3260.3840.4140.4360.4020.4140.0260.2170.3140.3410.3930.3570.3780.0230.0290.3570.3930.4230.3110.4110.0380.032-0.0440.153-0.0740.0930.0110.0590.3840.3870.4480.4390.0690.0200.1050.3320.3840.3720.4170.3930.3930.4140.026-0.0220.3990.0200.2540.3410.2510.1960.3960.3320.3600.3750.3570.3630.3570.3600.3810.3720.3720.2411663OrganismalFitnessCCDB_ECOLIHighProkaryote
CCR5_HUMAN_Gill_20230.2080.2150.2060.2030.2030.2100.2210.2270.2390.2630.2680.2580.2680.2280.2550.2630.2560.2410.2580.2680.2710.2570.2320.2300.2710.2550.2550.2730.2330.2590.2160.1820.0860.2720.2680.2640.2610.2700.2800.2340.2330.2270.2670.1210.2680.2640.1900.2300.2030.1120.2460.2340.2460.2500.2450.2400.2520.2540.2560.2560.2710.2606137BindingCCR5_HUMANHighHuman
CD19_HUMAN_Klesmith_2019_FMC_singles0.1440.1520.1460.1460.1630.1530.0620.0810.1260.1310.0900.1100.1230.1020.1300.1090.0990.1260.099-0.0180.1030.1090.1400.1260.0940.1590.1310.0950.1550.1680.0990.0730.0780.1190.1470.1260.1360.1350.1320.1640.1610.1560.1100.0700.0940.1100.2640.2090.2520.1320.1640.1820.1690.1930.1760.1690.1790.1760.1820.1930.3050.2603761BindingCD19_HUMANLowHuman
CP2C9_HUMAN_Amorosi_2021_abundance0.3840.4420.4550.4650.4460.4690.3960.4410.4460.4650.3800.4530.4720.4100.4750.4770.4980.4880.4470.4830.4490.4450.4640.4250.4470.4370.4410.4490.4230.4600.4460.3990.1750.4510.4380.4220.4780.4640.4630.4830.4840.4860.4520.0500.3830.4730.4350.4080.4730.1590.4790.4700.4830.5050.4930.4980.4900.4790.4860.5050.4840.4786370ExpressionCP2C9_HUMANHighHuman
CP2C9_HUMAN_Amorosi_2021_activity0.3710.4380.4460.4590.4340.4630.4370.4560.4520.4800.3670.4790.4970.4140.5030.5110.5130.5000.4630.4950.4450.4550.4610.4370.4500.4390.4420.4600.4390.4710.4480.4020.1810.4840.4400.4260.5000.4890.4840.4950.4840.4820.4800.0450.4080.5030.4650.4400.5050.1860.4830.4890.4990.5050.5130.5150.5020.4930.5040.5140.5080.5046142BindingCP2C9_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM0.2550.4740.3920.4090.3560.3690.2130.3670.4210.4340.4550.4550.5020.3050.4940.4180.3650.4360.4290.3780.3280.3750.4550.4650.3420.4640.4590.4530.4420.4480.4280.4190.2590.2740.3640.4330.3470.3590.4210.4170.4210.4570.2330.1670.3060.3290.3450.3290.4260.4670.4680.4590.4450.4710.4790.4710.4700.4760.4380.4780.4560.4823295StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX0.2520.3080.2770.2820.3100.2920.0720.3200.3660.3760.3680.2970.2650.1050.3200.3610.4340.3610.3730.2570.1150.1410.1580.0620.0850.1530.1480.1480.2320.2950.3050.2710.1890.1130.1100.1350.2570.2470.2470.3150.3150.3150.1080.0320.3100.1510.3960.3810.4540.3460.4040.3730.3810.4090.4040.4190.4160.4160.4110.4040.4490.4041580StabilityCUE1_YEASTMediumEukaryote
D7PM05_CLYGR_Somermeyer_20220.4260.5000.5000.4650.5060.5030.0490.3780.5380.5430.3530.0480.0420.0450.0320.0220.0320.0460.1090.3560.0320.0330.0820.060-0.0030.0350.0500.1450.2810.5060.4440.4610.0240.0780.0940.1330.4180.4170.4150.5050.5040.493-0.0020.0100.0010.0200.2090.2280.3890.2920.3860.3860.3840.3920.3810.3920.3830.3760.3770.3850.2350.14724515ActivityD7PM05_CLYGRLowEukaryote
DLG4_HUMAN_Faure_20210.6110.4900.5090.4870.4980.5100.6520.5570.4440.4480.4350.4590.4970.6710.6880.6460.4730.3860.3680.5360.4360.4780.4650.4370.4870.4820.4530.4510.3800.5080.5450.5420.3940.4370.5490.4810.5330.6230.5760.5320.5550.5290.5020.1620.3140.4430.5480.3500.5610.2510.4120.3000.4580.4150.4340.4050.4360.4150.4240.4210.4170.6626976OrganismalFitnessDLG4_HUMANLowHuman
DLG4_RAT_McLaughlin_20120.4070.4040.3980.4230.4350.4470.3380.3590.3830.4010.3710.4530.4620.3340.4380.4470.4320.3980.3470.3500.2980.3100.3100.2920.3040.3830.3410.3010.3340.3710.4260.4170.2070.2860.3040.2520.3890.3920.3710.4380.4440.4410.4040.0490.3280.4070.3340.2580.3710.1190.3410.3100.3470.3310.3530.3380.3070.3470.3590.3620.4040.4201576BindingDLG4_RATLowEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC0.1610.1170.1880.1730.1850.208-0.0930.2680.2520.248-0.0140.0380.0340.0060.0930.0890.2160.2120.2560.177-0.069-0.002-0.0100.0340.0300.058-0.0060.0300.1210.2040.3000.2720.010-0.022-0.026-0.0140.1570.1570.1610.2000.1810.1730.034-0.0770.1330.0420.4500.4380.5220.4230.2760.2320.3430.3310.3080.3150.2880.2680.2720.3080.3870.3351008StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO10.7240.7420.6840.6930.7240.7260.6330.6890.7160.7350.7230.7160.7490.7650.7770.7790.7580.7100.7630.7030.5970.6480.6800.6520.6840.6610.6890.5880.7070.7120.6700.6480.5810.7000.6730.7400.7600.7600.7840.7280.7370.7540.5320.0690.3090.5510.5020.4010.7140.7260.7210.7420.7510.7390.7580.7470.7770.7310.7620.7540.7120.7332264StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y0.3210.3370.3210.3270.3600.3590.0460.3170.2680.2800.3090.3320.3540.1840.3370.3590.3500.2720.3350.3420.2410.2980.2620.2720.2470.2680.2140.2100.2080.3190.2610.2740.1530.2140.2490.2270.3720.3860.3520.3500.3700.3540.224-0.1550.3210.2650.2310.2340.2860.2970.2870.2580.2650.2730.2680.2790.2830.2760.2780.2650.3650.3572915StabilityDOCK1_MOUSEHighEukaryote
DYR_ECOLI_Nguyen_20230.2910.4200.4270.4200.4320.4340.0010.3610.4020.4070.4200.4050.4200.0560.4020.4120.4260.4290.4290.4100.3090.3700.2760.3230.4000.4120.3990.4170.3720.4200.4240.4150.1640.3950.2970.3130.4170.3870.3900.4360.4240.4200.3880.0060.4240.3970.1860.3600.2910.0780.3820.3700.3630.3870.3780.3920.3880.3830.3920.3920.4050.3192916OrganismalFitnessDYR_ECOLIHighProkaryote
DYR_ECOLI_Thompson_20190.3360.3960.3910.3870.4060.404-0.0480.2860.3960.4220.4040.3410.3580.0470.2920.3730.3980.4260.4220.4070.2350.3230.2400.2930.3140.3930.4090.3890.3630.3960.3890.3780.1590.3140.2970.3120.3500.3580.3730.4000.4200.4110.292-0.0250.3690.3360.2000.3580.2700.0840.3630.3360.3340.3490.3670.3580.3760.3540.3850.3730.4070.2682363OrganismalFitnessDYR_ECOLIMediumProkaryote
ENV_HV1B9_DuenasDecamp_20160.2980.3210.2860.2980.3570.3210.0040.2630.2860.2860.3100.3800.3570.0390.0750.051-0.0080.0390.0040.2980.3570.3210.3210.3210.2980.3570.3330.2860.3210.3800.3680.3530.2510.3450.3100.3100.3920.3450.3450.3450.3330.3330.2740.0040.3100.3330.3330.2860.2740.2160.1800.2740.2980.2040.1570.2160.2160.2040.1570.2160.1220.086375OrganismalFitnessENV_HV1B9MediumVirus
ENV_HV1BR_Haddox_20160.2510.2410.2350.2370.2510.256-0.0030.2370.2640.2670.2300.2410.260-0.006-0.004-0.0010.0270.0430.1070.2510.2710.2690.2750.2790.2560.2720.2680.2630.2710.2770.2480.2230.1580.2600.2700.2700.2770.2790.2740.2740.2770.2700.163-0.0110.2400.2300.1430.1620.0690.0490.1240.1150.1330.1560.1290.1360.1400.1340.1330.1420.1080.05812863OrganismalFitnessENV_HV1BRMediumVirus
ENVZ_ECOLI_Ghose_20230.1380.1310.1230.1420.1870.1870.0010.1570.1350.1270.1310.1830.1940.1460.1640.1570.1610.1420.1200.1530.1380.1490.1680.1760.1350.1530.2020.1900.1900.1640.1570.1350.0640.1230.1530.1940.1760.1790.1980.1830.2020.2020.1270.0680.1640.1680.0490.1460.1010.0420.1640.1720.1310.1610.1380.1640.1570.1530.1760.1640.1120.1831121ActivityENVZ_ECOLIHighProkaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M0.6120.6080.6310.6370.6820.686-0.2610.6550.7490.7530.7550.7450.773-0.3000.7250.7880.8040.7350.7080.6880.4760.5470.6590.6160.6060.6430.6370.6840.6860.7980.7370.7250.6040.5730.5410.6310.6510.6610.6940.6820.6800.6670.569-0.2530.5530.5410.4920.5370.7410.6570.7940.7780.7880.7760.8000.7960.8020.7940.7860.8020.7510.7451960StabilityEPHB2_HUMANHighHuman
ERBB2_HUMAN_Elazar_20160.3510.3010.1910.2030.2030.2400.3380.3380.3260.3510.3630.3380.4000.3510.3510.3880.3260.3510.2890.0430.3510.3260.3750.3510.3630.3510.4490.4370.3880.3750.3140.0500.4000.3630.3380.3510.4000.4000.3380.3630.3630.3510.3880.3630.4120.4000.3140.363-0.1050.0430.3510.3010.3630.2890.3010.3140.3380.3140.3380.3510.4120.400326ExpressionERBB2_HUMANLowHuman
ESTA_BACSU_Nutschel_20200.2160.2860.3070.3140.2900.2880.1300.2370.2480.3050.2370.2180.2480.1130.1800.2000.2240.2350.2700.2310.0540.1370.2090.2270.1810.2050.2270.2050.3010.2790.3550.2980.0640.1130.1910.2020.2380.2440.2380.3120.3010.3100.1260.1000.2070.1850.4250.3670.4260.2720.2720.2590.2530.2420.2620.2480.2500.2730.2720.2660.3180.1522172StabilityESTA_BACSUHighProkaryote
F7YBW8_MESOW_Aakre_20150.0470.2270.2390.2580.2570.2580.0130.1830.2320.2410.2500.2280.237-0.0430.0090.0490.2480.2370.2640.225-0.029-0.070-0.066-0.0140.0760.071-0.0090.1900.2380.2110.2700.2630.040-0.006-0.0700.2440.0460.0110.2520.2370.2270.264-0.006-0.0230.134-0.008-0.0070.1590.0370.0230.2680.2640.2480.2730.2750.2710.2580.2650.2660.2690.181-0.0799192OrganismalFitnessF7YBW8_MESOWHighProkaryote
FECA_ECOLI_Tsuboyama_2023_2D1U0.3510.3590.3570.3660.3740.3640.0370.2450.2920.3530.3960.1980.3260.0390.3400.4510.4250.3760.2900.3530.1200.2260.2640.2470.1010.3910.4100.2090.3150.3810.3660.3450.2680.0600.0860.0900.3020.3040.2220.3260.3490.3060.290-0.0220.3620.3430.4360.3910.4850.4190.4400.3740.4230.4000.3930.4320.4250.3910.4100.4190.4760.4831886StabilityFECA_ECOLIHighProkaryote
FKBP3_HUMAN_Tsuboyama_2023_2KFV0.3000.2710.3740.3780.3780.3870.1350.2480.1800.1770.1960.1640.1640.1710.1480.1540.1510.2160.2770.2160.2090.2030.1960.1670.1410.1580.1800.1770.1840.3740.2350.263-0.0330.1090.1670.1960.2740.2840.2510.3610.3710.3480.1540.1640.1740.2000.5550.4910.5300.4680.2320.3100.3290.2840.2810.3030.2740.2580.2320.2970.4450.2581237StabilityFKBP3_HUMANMediumHuman
GAL4_YEAST_Kitzman_20150.1930.2950.3100.3750.3420.3460.231-0.0070.3420.4260.4260.3350.3530.2550.2700.3820.4660.4510.4410.3460.2040.2410.2840.2840.2370.2910.3750.3310.3970.4510.4510.3990.1860.1710.2010.1970.3420.3510.3750.3310.3130.3210.2880.1930.4190.3390.1860.3680.2590.1020.4040.4150.3900.3970.4260.3860.3970.3930.4080.4190.4220.3061195OrganismalFitnessGAL4_YEASTMediumEukaryote
GCN4_YEAST_Staller_20180.1920.1750.1780.1840.1600.1580.1190.1570.1800.1750.1690.2120.2160.2460.2150.2010.2150.1870.1690.1660.1130.1360.1070.1050.0960.0890.0610.0950.1220.1400.1830.1860.0330.1140.1160.2310.1950.1990.2280.1930.1900.2150.0370.0840.0870.0460.1050.1250.1720.1600.1540.1450.1430.1390.1510.1490.1430.1490.1580.1480.1710.1832638BindingGCN4_YEASTLowEukaryote
GDIA_HUMAN_Silverstein_20210.3380.3410.3660.3480.3380.3380.1020.2790.3730.3690.2930.3070.3280.1130.1610.2930.2890.2580.3170.2890.1820.2790.2890.3070.2130.3410.3100.2580.2790.3100.3480.3140.1850.2170.2480.2550.3170.3070.3310.3380.3550.3590.1750.0880.3170.1720.2170.2890.2480.0950.2720.2480.2930.2720.2760.2890.2790.2720.2620.3140.3340.2131154OrganismalFitnessGDIA_HUMANLowHuman
GFP_AEQVI_Sarkisyan_20160.5840.5820.6130.6150.6180.6180.0610.5830.6040.5960.4730.0970.0970.0810.1190.0900.1060.1400.2550.5370.0870.1100.1670.0890.0480.1660.2610.5840.5840.6190.5460.5550.0580.0690.1630.5730.5810.5830.6180.6240.6270.6500.023-0.0060.0360.0360.4560.4450.6700.5350.5290.5370.5460.5540.5460.5450.5420.5430.5250.5410.5620.40251714ActivityGFP_AEQVILowEukaryote
GLPA_HUMAN_Elazar_20160.114-0.0450.1020.1350.1020.0700.3310.4130.2820.2500.4460.4460.4460.3150.4460.4620.4950.3810.4300.1520.3810.3970.4300.4130.3480.4130.4620.4300.4790.2090.3640.1360.1680.4300.3640.4300.3970.3810.3970.2990.2500.3640.4130.3970.4620.3970.3310.4950.2660.1680.4130.3810.4950.4300.4460.3970.4620.3970.4130.4790.4130.413245ExpressionGLPA_HUMANLowHuman
GRB2_HUMAN_Faure_20210.3140.3920.3840.4110.4040.4060.4030.3380.4050.3650.3900.3370.3780.4300.4640.4740.4960.3960.4290.4070.4190.3960.4000.3440.4190.3970.3410.4090.3420.3760.3080.3080.3610.4180.3850.3100.3900.3810.3310.4210.4210.3970.4510.2350.3990.4350.5400.4010.5480.3960.4840.5180.5070.5050.5110.4940.4960.5120.4910.5140.4210.46863366OrganismalFitnessGRB2_HUMANMediumHuman
HCP_LAMBD_Tsuboyama_2023_2L6Q0.2380.3120.0920.1230.2690.2810.1880.2880.3460.3420.4580.3500.3770.2380.3150.4190.4730.4120.4310.1920.2120.2190.2730.2880.2540.2730.1620.2650.3920.4080.4770.3600.1150.2270.2230.3690.3120.3150.3730.2500.3230.3770.2230.2350.4580.3080.4920.5270.5730.4080.5190.5310.5230.5230.5350.5310.5040.5350.5230.5350.5770.4501040StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM0.2970.3240.2380.2440.3440.3530.1720.2940.2860.2840.2530.2090.2310.1690.1750.3700.3260.2770.2720.3030.2010.2610.3330.3120.2520.3200.3070.2880.3020.3310.3050.3080.1320.1860.2150.2400.3680.3450.3720.3510.3350.3560.1050.0790.3540.1410.2220.3360.3310.2660.2720.3200.3510.3320.3350.3720.3270.3420.3290.3480.3400.2905586StabilityHECD1_HUMANMediumHuman
HEM3_HUMAN_Loggerenberg_20230.2980.3130.2950.3040.3130.3110.1000.0960.3200.3300.3110.2830.3060.1050.2730.2990.3100.3210.3370.1150.2390.2920.3070.3080.2540.2970.2950.3050.3210.3180.3130.3120.0610.2770.2910.3130.3060.3280.3470.3070.3250.3440.2200.1060.2920.2600.2280.2800.2240.1150.2740.2680.2710.2880.2940.2850.2970.2920.2930.3010.3390.3035689ActivityHEM3_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_20190.3550.4100.4400.4330.4090.4100.1140.2180.3430.3770.3540.3330.3770.0380.0890.3470.2820.3410.3670.3420.2370.3030.3370.3460.2940.3750.3390.3780.3940.4190.3070.3010.1150.2960.3250.4750.3760.3770.5110.4440.4240.4630.1270.0130.2120.0020.3220.3420.4370.3000.3420.2490.2950.3260.3310.3080.3060.3380.3190.3170.3990.136496137OrganismalFitnessHIS7_YEASTMediumEukaryote
HMDH_HUMAN_Jiang_20190.3680.3210.2890.2970.3230.3190.1160.1710.2060.1980.2090.2420.2250.0320.0140.2310.3990.2780.2830.2220.3460.0920.1130.1540.3740.1260.1130.1430.1470.3540.3000.3010.1730.2410.1830.1540.3610.2970.2850.3420.2960.2940.1140.0410.3620.3890.2290.349-0.1750.0740.3780.3760.3670.3780.3790.3730.3700.3820.3740.3820.4200.32716853OrganismalFitnessHMDH_HUMANLowHuman
HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O20.2350.2500.2550.2660.2300.2320.1730.2830.3010.3100.2390.2980.330-0.0030.1000.1700.1710.2160.214-0.0010.2350.2550.2590.2710.2120.2460.2850.2920.2920.2750.3140.2590.1340.2280.2480.2280.2460.2640.2530.2440.2530.2440.020-0.0100.2820.2850.0350.1860.060-0.0260.1660.1870.1700.1890.1840.1860.1800.1800.1660.1870.2760.2032252OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Flynn_20190.3150.3610.3740.3850.3740.3710.1250.3110.3940.3840.3370.3570.3770.0100.0680.1410.2470.3230.3340.3710.3510.3710.3570.3770.3670.3800.3850.3710.3990.3740.4080.3980.1760.3510.3580.3620.3640.3680.3750.3820.3880.3890.081-0.0560.3500.2700.1600.2950.0900.0500.2450.2480.2620.2640.2540.2590.2650.2550.2420.2580.3550.29513294OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Mishra_20160.3940.4000.4050.4050.3970.3940.2700.3090.3740.3810.3940.4110.4160.1870.2750.3050.3680.3690.3700.3760.3800.3660.3680.3950.3850.3850.4010.3660.3910.3990.4010.3960.2380.3630.3780.3860.4030.4130.4100.4030.4080.4100.358-0.0520.3850.3950.1200.2860.1440.0350.3410.3410.3270.3630.3570.3410.3590.3490.3480.3590.3750.3474323OrganismalFitnessHSP82_YEASTMediumEukaryote
HXK4_HUMAN_Gersing_2022_activity0.3900.4260.4060.4130.3960.4060.1780.3110.4260.4400.3750.3840.4030.1210.2010.4080.4210.4150.3980.0330.3740.3670.3460.3270.3660.3680.3570.3580.3250.4130.4170.3880.1380.3750.3380.2790.4110.3840.3510.4240.4210.4060.2250.0240.4180.3980.2870.3660.3190.1230.4210.4070.4090.4130.4150.4270.4110.4180.4110.4240.4390.3758570OrganismalFitnessHXK4_HUMANMediumHuman
HXK4_HUMAN_Gersing_2023_abundance0.2820.3090.2870.3030.3010.3050.0450.2820.3470.3630.2950.2890.3080.1060.1480.2940.3200.3360.3170.3480.2440.2490.2610.2700.2410.2640.2830.2810.2750.3230.2870.2500.1520.2650.2580.2650.3020.3040.3080.3170.3170.3240.1350.0750.3020.2900.3250.3350.3340.1100.3300.3320.3310.3330.3340.3370.3280.3270.3210.3320.3700.2868396ExpressionHXK4_HUMANMediumHuman
I6TAH8_I68A0_Doud_20150.2870.2400.2130.2080.2740.277-0.0080.2340.2170.2390.0080.0150.0080.0260.0200.0120.0150.0060.0540.1510.2400.2450.2860.2830.0130.0190.0850.0030.2300.2840.1790.1830.1110.2110.2420.2470.2250.2520.2560.2800.3060.3060.0160.0150.0160.0170.1660.1490.1820.0780.0480.0750.1010.0970.0790.0720.0590.0670.0510.0720.0240.0079462OrganismalFitnessI6TAH8_I68A0MediumVirus
IF1_ECOLI_Kelsic_20160.2620.3700.4430.4560.4180.4370.0860.2770.1950.1630.4050.4300.4400.0800.3060.4140.4460.4460.4240.4180.2420.3280.2230.2970.3410.3540.3440.3060.3540.3130.3860.3350.2140.3540.3730.3980.3670.3630.3600.4180.4210.4370.2520.0930.4490.3730.2300.3980.3480.1720.4080.4080.4080.4300.3890.4080.3890.4110.4140.4210.4560.3791367OrganismalFitnessIF1_ECOLIHighProkaryote
ILF3_HUMAN_Tsuboyama_2023_2L330.2950.3870.4250.4180.4150.4220.1760.2710.3800.4040.3130.3310.3520.2640.3660.4220.2600.2460.1800.4320.1270.1870.2740.3020.3170.2220.3240.2430.2670.4110.3520.3160.0250.1730.2320.3130.3020.3450.3590.3900.3870.4150.2990.1760.3060.3800.4150.3660.4320.3310.2670.2290.2880.2500.2780.3310.2920.2500.3410.3240.2920.3621329StabilityILF3_HUMANHighHuman
ISDH_STAAW_Tsuboyama_2023_2LHR0.0650.1080.1390.1470.1510.1760.2500.1240.2810.2660.3780.3260.3260.2710.2910.3360.3240.3710.3260.1310.2190.1450.1060.1660.2210.2420.2210.2110.2420.2750.3340.3150.1700.1920.1740.1840.1550.1390.1370.1940.1570.1700.1990.1780.2520.2400.4270.3530.4190.4000.3200.3240.3450.3320.3300.3450.3160.3240.3320.3340.4370.3591944StabilityISDH_STAAWHighProkaryote
KCNE1_HUMAN_Muhammad_2023_expression0.1950.2000.1950.1770.1810.1710.1790.1080.1610.1710.2260.2100.1950.1790.1750.2260.1810.1730.1790.1970.1950.2040.1610.1100.2120.1480.2080.1910.1610.1810.1750.128-0.0870.1890.2260.1630.1770.1850.2120.1910.1910.2080.2060.1670.2040.2280.1930.2000.1890.0630.1530.1690.1630.1550.1690.1500.1790.1530.1630.1630.2040.1932339ExpressionKCNE1_HUMANMediumHuman
KCNE1_HUMAN_Muhammad_2023_function0.2820.3310.3580.3930.3620.3910.1110.3890.3740.3880.2150.3200.4100.1300.0850.4460.3910.3220.2540.1590.1530.1790.3290.4010.1660.4450.4530.4330.4240.3370.4600.3800.0060.1040.2340.4910.2980.3180.4570.3750.3910.4860.1210.1160.3860.0870.1230.3130.1270.0300.4070.3960.3890.3940.3940.3810.4030.4000.4200.4200.5070.1272315ActivityKCNE1_HUMANMediumHuman
KCNH2_HUMAN_Kozek_20200.3600.4000.1800.1800.1800.1600.3600.0400.2600.2600.2000.1800.1600.1800.1200.1600.1600.1600.1200.3600.3600.3600.3400.2800.3800.3200.3200.2800.2800.2810.3000.1400.2800.3200.3600.3600.3600.4000.4000.3000.4000.3600.3200.1400.1600.2200.1200.000-0.1200.0600.4000.3000.4000.3400.3000.3400.3800.3400.4000.3600.1800.360200ActivityKCNH2_HUMANMediumHuman
KCNJ2_MOUSE_Coyote-Maestas_2022_function0.2150.2730.2600.2770.2850.2880.0490.1830.2560.2460.3060.2880.2970.0350.2160.3080.3220.3260.3260.1260.2830.2220.1870.1790.2800.2680.2410.2740.1720.2990.3030.2910.1180.2610.2380.1680.2860.2670.2270.2990.2910.2700.154-0.0090.2670.2970.1530.2270.1850.0620.3180.3040.3080.3010.3070.2930.3110.3090.3010.3130.2970.2156963ActivityKCNJ2_MOUSEMediumEukaryote
KCNJ2_MOUSE_Coyote-Maestas_2022_surface0.2610.2250.1650.1830.2140.2170.0730.1430.1860.1880.2070.1860.1980.1100.2300.2250.2210.2220.2430.1580.1430.1580.1610.1780.1570.1790.1720.1760.1760.2360.1900.1520.1140.1430.1700.1610.1990.2230.2240.2030.2240.2240.1660.0430.1720.1950.1690.1570.1680.0940.2170.1980.2140.1990.2080.1940.2090.2010.2100.2100.2200.2206917ExpressionKCNJ2_MOUSEMediumEukaryote
KKA2_KLEPN_Melnikov_20140.2210.4520.3660.5160.4990.5080.1480.3800.4490.5090.4710.4880.5120.1650.1990.4040.4860.5310.5560.3940.2130.3310.4180.4670.2680.4880.4860.4730.5260.5230.5480.4990.1130.1840.4070.4950.3460.4290.4870.4950.5110.5260.1910.0690.4670.3240.3620.4860.4390.1850.4790.4750.4780.4780.4760.4820.4760.4820.4880.4910.5300.2394960OrganismalFitnessKKA2_KLEPNHighProkaryote
LGK_LIPST_Klesmith_20150.2330.2920.3400.3450.3320.3440.0890.3520.3480.4140.3360.3940.4240.1250.2410.3000.3910.4330.4200.3810.2280.3160.3660.3780.2920.3900.4020.3670.4090.3900.4060.3380.1140.2530.3120.4220.2800.3100.3890.3390.3570.3930.1280.0470.3680.2780.2900.3710.3580.1060.3870.3660.3830.3740.3750.3950.3930.3920.3950.3970.3010.2517890ActivityLGK_LIPSTMediumEukaryote
LYAM1_HUMAN_Elazar_20160.2600.2600.1480.1590.2150.2260.2600.1370.3040.3270.3150.2600.2930.1930.2370.1930.2260.2600.3490.1930.2370.2600.3040.2820.2150.3040.2710.1820.1820.2890.2040.1440.1370.2820.3150.2150.2820.3040.2820.2040.2490.2040.2150.2150.2370.2490.1030.1480.1150.0250.1820.2490.2600.2260.1930.2040.2370.2710.1930.2490.2930.226359ExpressionLYAM1_HUMANMediumHuman
MAFG_MOUSE_Tsuboyama_2023_1K1V0.4220.5360.4810.4810.4980.4860.3270.6490.5810.5630.6430.3090.5840.4160.3950.4360.4570.4980.3390.5750.3650.5570.5460.5690.4980.5280.4690.5310.4720.4300.5480.5220.3830.2470.5130.5250.4540.5660.5990.5220.5990.6400.2650.0380.3600.2360.5160.3010.5480.5660.6520.6340.6280.6400.6370.6340.6370.6490.6610.6400.5930.6401429StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV0.5180.6570.6140.6280.6750.681-0.0260.4710.6120.6750.6580.3330.461-0.0660.1040.6920.6120.6050.6770.6500.0400.1000.5440.5920.2210.4760.4440.5120.6670.6920.6620.6150.558-0.2420.1040.0930.4880.5780.5650.6300.6670.6580.030-0.1060.5780.3400.3990.5410.6640.4460.6980.6730.6750.6810.6600.6620.6710.6920.7030.6880.6730.6092116StabilityMBD11_ARATHMediumEukaryote
MET_HUMAN_Estevam_20230.4530.5280.5200.5240.5540.5580.4890.4900.5170.5270.5500.5030.5170.4140.4710.4940.5550.5790.5720.5630.4970.4820.4350.4610.4960.5030.4990.4520.4230.5070.5280.4940.2600.4630.4670.4890.4790.4990.5270.5240.5310.5430.5030.3030.5350.5440.3280.3880.4760.1730.5090.5210.5130.5220.5260.5330.5210.5340.5310.5320.5440.4905393ActivityMET_HUMANMediumHuman
MK01_HUMAN_Brenan_20160.1520.1900.2090.2120.1930.1970.1730.1490.1950.1710.0690.1560.1710.1320.1730.1710.1660.1780.1550.1720.2060.1310.0990.0480.1610.1040.0810.087-0.0210.1900.1710.1980.1190.1630.0930.0540.1760.1380.1250.2020.1850.1790.1490.1000.1460.1620.0680.0160.1220.0040.1580.1550.1530.1710.1690.1560.1630.1680.1650.1670.1680.1666809OrganismalFitnessMK01_HUMANMediumHuman
MLAC_ECOLI_MacRae_20230.1760.2740.3440.3540.3370.330-0.0220.2710.3460.3590.3020.3430.354-0.0220.1740.2070.3120.2950.3130.2950.0420.2660.2740.3090.2480.3140.3060.3010.3390.3200.3560.3190.0280.1030.2180.2160.2030.2490.2310.2860.3060.3030.108-0.0500.2440.1740.0270.1540.112-0.0120.2440.2140.2350.2300.2390.2420.2220.2330.2680.2400.1600.1514007OrganismalFitnessMLAC_ECOLIMediumProkaryote
MSH2_HUMAN_Jia_20200.3080.3430.3260.3350.3350.3380.1770.2760.3420.3480.3080.3280.3400.1840.2870.3380.3240.2680.1810.3350.2450.3020.2830.2770.2800.3150.3300.3060.2890.3400.3270.3090.1760.2350.2900.2770.2990.3260.3260.3370.3430.3400.2220.0950.3320.2830.2580.3170.0440.0890.2790.2620.2720.2790.2970.2750.2770.2750.2980.2880.3430.30616749OrganismalFitnessMSH2_HUMANMediumHuman
MTH3_HAEAE_RockahShmuel_20150.2590.4190.4470.4530.4580.4420.2150.4140.4350.4420.3800.4550.4500.1530.2100.2360.3360.3930.4350.4290.2020.2980.3930.4290.3230.4370.4450.4470.4600.4500.4550.4290.2150.2120.3310.4270.2610.3290.4190.3960.4190.4580.2230.0260.3210.2120.2870.3700.3570.1190.3600.3340.3600.3540.3700.3780.3570.3620.3540.3650.3780.2151777OrganismalFitnessMTH3_HAEAEMediumProkaryote
MTHR_HUMAN_Weile_20210.1690.1770.1670.1730.1750.1700.1130.1110.2000.2040.2580.2000.2130.1080.2610.3350.2550.1910.2030.1780.3110.1050.1410.1820.2970.1560.1800.1700.2210.2030.1490.1390.0870.3090.2190.1330.2330.2030.1580.2190.1930.1710.1740.0700.2510.2620.1590.2370.2700.0660.2320.2070.2110.2250.2230.2340.2490.2450.2540.2380.2230.24512464OrganismalFitnessMTHR_HUMANLowHuman
MYO3_YEAST_Tsuboyama_2023_2BTT0.1560.1690.2550.2610.2620.2980.1040.2070.3300.3210.3940.3760.3900.1360.3230.4420.4400.3930.2060.2440.2490.1800.2440.2130.2440.1760.2510.1900.2220.1890.2250.1300.1140.3090.2160.2210.2870.2600.2460.3020.2980.2830.100-0.0020.3130.2230.2970.3360.3790.2570.4210.3980.4300.4060.4180.4210.4060.4130.4010.4110.4310.3903297StabilityMYO3_YEASTHighEukaryote
NCAP_I34A1_Doud_20150.2900.2400.2480.2500.2830.2820.0120.2410.2750.2520.0290.0260.0250.0370.0340.0260.0400.0340.0890.2150.2660.2640.2930.2880.0300.0580.0980.0550.2560.2940.1990.2110.1050.2590.2720.2950.2810.3010.3210.3250.3240.3390.0350.0190.0290.0350.1960.1960.1980.0810.1020.1280.1330.1490.1350.1290.1160.1340.1070.1300.0960.0669462OrganismalFitnessNCAP_I34A1MediumVirus
NKX31_HUMAN_Tsuboyama_2023_2L9R0.2720.3430.4630.4590.5110.4700.3990.4820.5000.5090.5340.5320.5350.4970.4170.4720.4580.4220.4670.4440.4790.4590.4770.4750.4360.4350.4700.4790.5070.4980.4350.4200.4810.4310.4380.4860.4420.4470.4980.4890.5050.5120.3300.2400.2580.3060.4310.2560.4970.5110.5230.5300.5270.5350.5350.5090.5250.5180.5230.5280.5250.4982482StabilityNKX31_HUMANHighHuman
NPC1_HUMAN_Erwood_2022_HEK293T0.5260.5720.4810.5130.5780.5650.1830.3580.6110.5980.5720.2740.3710.1510.3060.4940.5720.5780.5780.0540.1960.3580.3000.3060.3320.4030.4750.3580.3650.5720.5650.4680.1310.1570.4100.4160.4880.5010.5460.5390.5460.5780.2350.0410.5590.4880.3780.5330.0280.1190.5720.5650.5720.5850.5780.5520.5590.5720.5780.5720.5330.287637ActivityNPC1_HUMANLowHuman
NPC1_HUMAN_Erwood_2022_RPE10.6510.3970.0790.1420.3970.4600.2690.3330.3330.3970.4600.0150.1420.0790.2060.5240.4600.3970.3970.2060.5240.3330.3970.1420.3330.3330.2690.2060.2060.3330.6510.4260.3970.2690.0790.3330.5870.3970.5240.5870.3970.4600.3330.2060.6510.1420.2690.3970.1420.1420.3970.4600.4600.3970.4600.4600.3970.3970.3970.4600.5870.33363ActivityNPC1_HUMANLowHuman
NRAM_I33A0_Jiang_20160.4770.4500.3560.3290.4500.4630.0600.2890.4770.477-0.0200.1140.369-0.060-0.0340.0600.1540.3960.4500.3020.3420.4770.3960.4090.0600.4090.4500.3290.4230.5300.2890.3420.0070.3560.3830.3960.4360.5030.5440.5030.5440.477-0.034-0.034-0.060-0.0740.3560.2890.3690.1540.2480.1950.1810.2210.1540.2350.2210.2080.1540.1680.2210.128298OrganismalFitnessNRAM_I33A0LowVirus
NUD15_HUMAN_Suiter_20200.2180.3860.4230.4540.4430.4460.0100.3170.4690.5280.4780.4720.5140.2060.3200.3500.4050.4720.4750.3930.2210.3560.4400.4200.3080.4900.4710.4540.4080.4170.5020.4490.1480.2540.3260.4540.2870.3140.4560.4350.4340.4720.3000.0210.4810.3270.3510.4510.3980.2300.4170.3990.4310.4530.4350.4540.4380.4590.4540.4470.5490.4532844ExpressionNUD15_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL0.3520.4170.4550.4490.4860.4840.2040.3240.5120.4530.4110.2300.2400.2320.2910.2870.3560.4030.3880.4230.2460.4270.4640.4610.2630.3860.3900.3860.4410.4860.5260.6180.1290.2440.3220.3070.3640.3720.3860.4960.4640.4860.2570.1790.2670.2480.5280.4900.5910.5120.6100.5970.6100.6280.6440.6540.6340.6160.6400.6420.5360.4552028StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI60.3530.3260.3380.3410.3690.3560.2250.3720.3600.3470.3560.2980.3200.2890.3960.3500.3750.3990.4020.3750.2340.2890.2860.3040.3440.2800.2830.2430.3200.4270.3410.3460.2000.2160.2340.3440.3350.3260.3720.3380.3470.3560.2370.1150.2160.3110.4390.3900.5710.5310.4050.3630.3500.3990.4120.3960.3990.4150.4120.3990.3380.3991380StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C0.3420.4020.5080.5210.5050.5120.1990.3960.5230.5260.5330.4820.4980.1980.5150.5410.5450.5220.5330.5110.5260.5190.5180.5260.4250.5030.4990.4910.4720.4860.5310.5140.3160.1330.2230.3180.4650.4410.4390.5150.5110.5110.268-0.0440.2610.3190.4060.3490.5080.5130.5710.5620.5650.5620.5690.5680.5590.5550.5750.5660.5350.4933197StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G-0.298-0.2010.1890.0830.1890.185-0.1710.0300.0830.0830.002-0.108-0.097-0.079-0.1390.0260.0970.0340.0830.1290.1080.0510.0410.0690.0440.0690.0790.0900.1080.189-0.034-0.0630.146-0.1080.009-0.0050.0970.0970.1010.1610.1680.1890.150-0.0790.1570.1680.2450.2280.2560.1850.026-0.0050.0120.041-0.0020.0410.0370.0440.0190.0260.1500.0761134StabilityODP2_GEOSEHighProkaryote
OPSD_HUMAN_Wan_20190.1390.3820.3580.5030.4060.3820.3090.4300.4300.4300.2120.4060.4300.1880.4790.4300.4060.3580.4300.5030.4790.4550.4550.4550.5030.4550.5030.4550.4300.3330.3820.2610.1880.4060.4300.3820.4550.4300.4060.4300.3580.3820.2610.0420.4060.4060.6240.4060.5760.2120.3330.3090.3090.2610.4300.4060.2360.3090.3820.3580.5030.406165ExpressionOPSD_HUMANHighHuman
OTC_HUMAN_Lo_20230.4040.4320.3760.4140.4040.4190.0620.3400.4550.4420.4240.4240.4520.0570.3200.3940.4170.3760.4240.4240.3550.3300.3960.4270.3860.4450.4170.4270.4390.4470.3660.3120.0550.3270.3890.4290.3960.4320.4800.4340.4520.4620.1490.0450.4190.3450.5110.4680.5160.2660.4340.4220.4340.4390.4450.4370.4370.4270.4190.4520.4730.3531570ActivityOTC_HUMANMediumHuman
OTU7A_HUMAN_Tsuboyama_2023_2L2D0.0890.2180.1770.1840.2040.2110.1570.1300.1500.1370.3540.5100.4960.1980.4620.4820.3470.3130.3330.1500.1230.1910.2590.2590.3200.3130.3670.3400.1370.2320.3810.3810.0620.1370.2110.3060.2180.2110.2860.2320.1770.2250.2660.1300.4490.4420.4350.4760.4890.3940.3940.2860.3200.3600.3740.3880.3130.3740.4420.3740.5100.469635StabilityOTU7A_HUMANHighHuman
OXDA_RHOTO_Vanella_2023_activity0.0600.0820.0850.0940.0800.0850.0560.0740.0680.0720.1050.0980.0860.0400.0720.0740.0880.1010.1090.0940.0450.0710.1010.0980.0760.1000.1090.0890.1210.0850.1230.1060.0110.0560.0740.0880.0820.0890.0890.0850.0850.0800.0720.0280.0970.0820.0740.1050.0830.0360.0980.0980.0950.0950.0920.1060.0940.0950.0880.0980.1000.0596396ActivityOXDA_RHOTOHighEukaryote
OXDA_RHOTO_Vanella_2023_expression0.1560.2080.2150.2150.1920.1920.1560.1550.2090.2090.2470.2310.2260.1640.1880.2230.2410.2530.2470.1580.1540.1810.1870.1880.1750.2040.2210.2130.2260.2040.2590.2270.0660.1680.1630.1840.1900.1850.2040.2090.2040.2060.1880.1220.2310.1790.2620.2480.2040.0590.2450.2230.2240.2330.2300.2290.2310.2360.2270.2370.2720.2166769ExpressionOXDA_RHOTOHighEukaryote
P53_HUMAN_Giacomelli_2018_Null_Etoposide0.4180.4200.3830.3950.4290.439-0.0460.3190.3230.3460.4110.4150.450-0.095-0.0720.2940.4020.4230.4570.1570.2610.3640.4140.4030.3550.4230.4290.4260.3710.4160.4490.4310.1530.2360.3900.3600.4160.4340.4240.4360.4550.431-0.090-0.0960.417-0.0090.3380.4200.3580.1690.4070.4070.4090.4050.4140.4310.4060.4150.4190.4200.4370.1797467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_Null_Nutlin0.2510.2840.2500.2540.3010.306-0.0670.1810.2060.2100.3490.3410.363-0.136-0.1180.1650.2780.3050.3380.0900.1940.3430.3570.3440.2890.3690.3740.3860.2450.3040.3010.2680.1640.1420.3540.2550.2570.3060.2970.2830.3350.299-0.117-0.1150.349-0.0680.2680.3410.2800.1180.2980.2810.2960.3050.3010.3190.2880.2940.2970.3040.3380.0907467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_WT_Nutlin0.3940.3990.3630.3800.3920.400-0.0340.3050.3110.3100.4380.4090.434-0.086-0.0660.3340.3900.4110.4220.1400.3000.4020.4340.4180.3690.4390.4400.4410.3170.4300.4260.4100.1550.2680.4350.3740.4040.4310.4180.4110.4380.411-0.081-0.1040.4340.0040.3670.4310.3630.1870.3980.4070.3990.4070.4150.4210.4000.4070.4030.4150.4290.1897467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Kotler_20180.5420.5190.4290.5070.4870.5150.0560.3860.4990.5110.4760.4720.5420.0520.0370.5300.6040.6190.6310.3590.3170.4020.4410.4290.3860.4330.4210.4450.4290.4760.5500.5150.0560.3360.4210.3440.5190.4840.4640.5300.4950.5110.0520.0020.5730.1530.3900.5070.3940.1810.5880.6120.6040.5880.5850.5880.6080.5730.6160.6120.6390.2701048OrganismalFitnessP53_HUMANLowHuman
P84126_THETH_Chan_20170.4840.5160.5340.5340.4890.5210.2720.4070.5390.5450.4810.4890.5310.1930.4840.4920.5240.4860.4630.5240.3830.4360.4340.4860.4810.5180.5100.5180.5630.4860.4710.4010.3220.4230.4360.4710.4920.4940.5160.5180.5240.5370.404-0.0320.4600.4020.2560.3360.3960.0810.4600.4360.3730.4790.4730.4200.4490.4570.4730.4760.5240.5261519OrganismalFitnessP84126_THETHMediumProkaryote
PA_I34A1_Wu_20150.3600.3760.3600.3650.3960.4090.0480.3010.1450.1010.0260.0400.088-0.0020.0240.0260.0240.0180.2750.2480.3360.3820.4000.4070.1710.2970.3320.3360.3270.5160.2840.2640.0860.3080.3470.3960.4330.4400.4350.4620.4550.4330.0240.0310.0370.0130.1800.1780.1490.0460.1320.1410.1470.1490.1340.1520.1340.1320.1010.1430.1670.1211820OrganismalFitnessPA_I34A1MediumVirus
PABP_YEAST_Melamed_20130.5210.4740.4100.4180.4950.4930.3500.4290.4910.5120.5450.5140.5340.3570.4290.5150.5970.5490.5630.4080.5080.5280.5310.5550.5060.5580.5700.5380.5250.5220.5680.5030.1910.5110.5040.5030.5560.5560.5510.5310.5300.5290.465-0.0480.5180.5170.2740.4610.3930.1440.5420.5320.5450.5700.5580.5630.5650.5610.5940.5810.5740.50237708OrganismalFitnessPABP_YEASTMediumEukaryote
PAI1_HUMAN_Huttinger_20210.3010.2800.2630.2750.2830.2980.0460.2380.3160.3250.3350.3120.3120.0610.2980.3220.3440.2400.2050.3160.1030.2770.2560.2400.2770.2930.2890.2710.2180.3110.3000.2680.1250.1210.2640.2740.2840.2950.2990.3010.3100.3060.2520.0530.3220.3060.2970.3430.3050.1080.3340.3440.3370.3480.3390.3480.3420.3470.3410.3530.3600.3025345ActivityPAI1_HUMANNaNHuman
PHOT_CHLRE_Chen_20230.1490.3000.5410.5200.2150.2560.4770.3950.5260.5360.4740.5700.6000.5800.6480.5290.5630.5280.5740.4720.4990.5260.4330.5100.3260.4340.4170.4830.4260.4150.4140.3460.2100.3980.3250.4190.4130.3690.4220.2820.3020.2870.4690.2780.4520.4630.1420.3640.4930.2840.4390.3880.3950.3930.3980.4110.4080.4130.4210.4100.5510.588167529ActivityPHOT_CHLREHighEukaryote
PIN1_HUMAN_Tsuboyama_2023_1I6C0.2000.2340.5490.4990.4890.5440.4890.3840.5140.5790.5240.4490.5690.5290.4940.5240.5490.3940.4640.4990.3540.5240.5090.4390.4240.5190.5740.5440.5090.5940.5940.5490.4340.3990.4940.5290.4340.4990.5640.5490.5640.5640.474-0.1700.5290.4140.3990.4440.5940.5690.5340.4940.4140.3890.3890.4440.4240.5040.4540.4440.6030.653802StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M0.4610.3640.3330.3420.3360.3510.3490.3600.4120.4100.3600.3660.3840.4150.5240.5770.4740.4470.3730.3750.3710.3380.3690.3440.4230.3640.3440.3310.3530.3490.3490.3500.3380.4150.3580.3730.4720.4230.4040.4060.3580.3600.3490.3030.1780.2350.2940.2000.5370.4980.3770.3880.3930.4060.3990.4190.3950.3840.3930.3900.3900.4431824StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF0.1770.1840.2320.2460.2880.2850.1770.2460.2010.2290.2260.2810.2990.3270.3340.4350.2600.2260.2290.2920.2920.2780.2220.2810.2810.2990.2990.2780.2810.2950.3200.3200.0620.3090.2710.3060.2460.2920.3230.3060.2880.3060.2740.2670.2740.2880.4310.3370.4280.3760.2320.2640.2920.2430.2570.2710.2360.2570.2810.2570.2920.4241301StabilityPKN1_HUMANHighHuman
POLG_CXB3N_Mattenberger_20210.3010.2810.2600.2990.3400.348-0.0300.2550.3760.3740.228-0.0360.001-0.047-0.0300.1240.3170.3240.3460.2590.2500.2830.2720.2640.0740.2870.2770.2610.2730.3650.2950.229-0.0050.0360.1870.2480.2630.2760.3110.2880.3070.347-0.035-0.0390.268-0.0300.1470.2680.0770.0400.2590.2860.2890.2910.2860.2880.2860.2850.2940.2900.0680.05115711OrganismalFitnessPOLG_CXB3NMediumVirus
POLG_DEN26_Suphatrakul_20230.3570.4540.1840.1850.4130.418-0.0140.3300.5350.5420.2410.0110.020-0.0090.0250.0590.1220.2060.2740.3120.3340.3510.3440.3440.3270.3670.3710.3630.3520.4840.4680.4080.029-0.0250.0870.3520.3310.3370.4220.3640.3150.430-0.028-0.0250.3190.0170.2820.4200.0990.0840.1950.1890.1980.2160.1840.2170.1970.1990.1900.2060.1150.02316897OrganismalFitnessPOLG_DEN26LowVirus
POLG_HCVJF_Qi_20140.4480.4320.3360.3460.4600.472-0.0410.1520.4050.4230.1310.5070.4750.0740.1010.0820.0820.0620.0620.1580.3090.3580.3360.3760.3020.3760.2130.3140.4050.4650.4670.3990.1510.3610.3950.3860.4000.4280.4150.3530.4030.4230.0790.0470.3780.084-0.0350.2600.5780.3110.2450.2920.2820.2970.2990.2270.2690.2990.2670.3090.1190.1431630OrganismalFitnessPOLG_HCVJFMediumVirus
POLG_PESV_Tsuboyama_2023_2MXD0.1940.3520.2170.2630.2950.2970.0530.2690.2220.2560.2670.0410.0890.0480.0660.0320.0890.0640.0480.3900.0790.0600.0230.1160.076-0.032-0.003-0.0420.1050.3090.3700.3770.1250.0230.0180.0090.2420.2490.2490.3020.3060.305-0.025-0.054-0.012-0.0350.3700.2970.4860.4340.4560.4620.4370.5180.5250.5290.4620.5300.5300.5110.5450.4665130StabilityPOLG_PESVMediumVirus
PPARG_HUMAN_Majithia_20160.2990.3730.4070.4100.3960.3970.1320.2630.3630.3830.3970.4050.4090.0400.1200.3470.4140.4690.4610.4340.4060.3890.2140.2560.3900.4070.3990.4140.2830.4500.4240.4110.2780.4300.3800.3420.4310.4140.3970.4450.4290.4230.1950.0420.3590.3200.3680.3550.4020.2000.4040.4120.4190.4080.4080.4170.4160.4140.4200.4200.4240.3529576ActivityPPARG_HUMANMediumHuman
PPM1D_HUMAN_Miller_20220.4380.4620.4540.4650.5080.5120.0050.2980.3400.4000.4620.4870.4950.1840.2580.3270.4940.5110.5090.2860.3270.4340.4120.3230.4210.4480.4480.4390.3070.4920.4880.4610.2160.3120.4270.4140.4780.4930.5040.5280.5090.5120.229-0.0340.4650.3970.3890.4640.4370.1970.4670.4630.4600.4690.4830.4600.4770.4760.4790.4820.5200.3927889OrganismalFitnessPPM1D_HUMANLowHuman
PR40A_HUMAN_Tsuboyama_2023_1UZC0.5800.6290.6900.6720.7240.7240.4130.4130.7430.7550.6960.5800.6230.4440.4280.7470.7690.7350.7120.6590.4970.6000.6130.6110.5390.6040.6720.6190.6530.7630.7890.7940.5600.5390.4360.4860.6700.6760.6670.6900.7160.6980.3670.1810.1900.3580.4720.2260.6330.6490.7890.7650.7750.7810.7870.7900.7900.7890.8060.8020.7310.7122033StabilityPR40A_HUMANMediumHuman
PRKN_HUMAN_Clausen_20230.5050.5010.5050.4900.5090.5120.1040.3670.4250.4430.4580.5070.5270.1370.1830.2500.3890.5440.5530.3570.2070.4400.4780.4580.3590.5020.4930.4770.4240.5230.4820.4620.2170.1700.4300.4500.4900.5220.5200.5170.5260.5270.2060.0480.4720.2460.5110.4820.5520.2240.4160.4270.4360.4520.4400.4590.4530.4470.4490.4590.5560.3718756ExpressionPRKN_HUMANLowHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE0.5180.5420.5080.5110.5260.5210.3350.4070.4280.4360.6020.5320.5290.4280.5160.6090.6270.5630.4900.3970.3110.2410.4330.4180.4070.410-0.1290.4490.4590.4710.4900.4590.1610.3320.3940.3990.5110.4980.4590.5580.5680.4950.4230.2860.5860.4410.5320.5760.6380.5390.6170.5830.6020.5990.5910.6250.5910.5990.6120.6070.6220.6251579StabilityPSAE_PICP2MediumProkaryote
PTEN_HUMAN_Matreyek_20210.2980.3040.3140.3040.3150.3200.1050.2630.3270.3680.3350.3100.3550.1170.2000.3800.3590.1950.2100.3480.1280.3390.3080.2980.2160.2640.2410.2800.2240.3420.3180.3230.1140.1950.3280.2720.2710.3460.3140.3240.3540.3390.1790.0330.3440.2550.3690.3480.3750.1570.3500.3460.3570.3540.3440.3620.3580.3650.3510.3650.4020.3665083ExpressionPTEN_HUMANMediumHuman
PTEN_HUMAN_Mighell_20180.3990.4200.3960.3960.4170.4180.1120.3000.4180.4230.3950.3930.4120.1440.3010.4180.4300.2390.2250.4170.2570.3560.2760.2570.3430.2340.2450.2740.2120.4170.3910.3900.0580.3000.3460.2570.4110.4010.3680.4170.4310.4310.280-0.0320.4150.3790.3340.3380.3980.1530.4050.3950.4050.4120.4190.4140.4120.4070.4200.4250.4280.3887260ActivityPTEN_HUMANMediumHuman
Q2N0S5_9HIV1_Haddox_20180.3610.3210.2790.3040.3780.3760.0240.3270.3960.4010.3500.3910.4140.0340.0300.0240.0540.0780.1040.3030.3940.3010.2890.2440.3920.2870.2840.3270.2600.3790.3780.3690.2220.3710.3180.2950.3900.3840.3790.3970.3940.3870.3400.0200.3850.3450.2920.3480.1990.1060.1660.2070.2300.2260.1880.1930.1940.1850.1670.2180.1510.07912729OrganismalFitnessQ2N0S5_9HIV1MediumVirus
Q53Z42_HUMAN_McShan_2019_binding-TAPBPR0.2400.2150.2280.2240.2360.2310.0830.1500.1940.2010.1340.1720.1900.1610.1350.2400.2060.1770.2370.2080.1760.1880.1720.1340.1910.1910.1780.2090.1760.2130.2570.2460.0510.1630.1790.1040.2140.2300.2050.2400.2550.2420.3090.1650.1840.2060.2200.2430.2400.1350.1930.1940.2200.2250.2070.1990.1930.2050.2030.2060.1880.3223344BindingQ53Z42_HUMANMediumHuman
Q53Z42_HUMAN_McShan_2019_expression0.4110.3750.4160.4230.4250.425-0.0340.3190.4080.4230.3190.3650.3870.0590.0690.3820.4350.4080.4400.4280.3330.3710.3810.3500.3680.4030.3890.4230.4160.4310.4180.3750.1080.3260.3550.3430.4190.4200.4350.4520.4560.4400.2470.0450.4130.3570.3560.4070.3610.1440.4380.4380.4440.4430.4530.4380.4290.4420.4560.4500.4000.4723344ExpressionQ53Z42_HUMANMediumHuman
Q59976_STRSQ_Romero_20150.4180.4900.5080.5180.5160.5230.2350.4080.5460.5500.4710.4150.4150.0850.3250.3800.4310.4620.4530.5070.4590.5050.5190.5230.4810.5250.5280.5370.5610.5280.5140.4520.2650.4770.5140.5010.4840.5180.5280.5260.5360.5370.372-0.0040.4560.3980.3170.4110.4070.0930.4560.4220.4090.4290.4350.4260.4330.4190.4380.4330.4880.4152999ActivityQ59976_STRSQMediumProkaryote
Q6WV13_9MAXI_Somermeyer_20220.1750.1940.1370.1400.1800.180-0.0090.0960.2000.1960.1400.0270.0180.0080.0240.0060.0070.013-0.0110.1610.0260.0240.0020.0710.021-0.0000.0200.000-0.0010.2330.1670.1710.0260.0150.0250.0200.1300.1320.1370.1730.1730.1720.0060.005-0.0020.0120.1380.1500.2150.1290.1530.1640.1510.1750.1680.1640.1600.1670.1630.1640.0680.04731401ActivityQ6WV12_9MAXILowEukaryote
Q837P4_ENTFA_Meier_20230.2570.3030.2800.2520.2800.3260.3150.2400.2690.2970.3550.3200.3380.2740.3090.3090.2860.3550.315-0.0240.3550.3030.3200.3610.3490.3320.3660.2520.3720.3030.3430.3330.0790.2860.3780.3380.2740.3780.3840.3320.3200.3200.2570.2290.3720.2970.2290.3260.2460.0680.3150.2740.3090.3260.3030.3380.3380.3090.3260.3380.3490.332697ActivityQ837P4_ENTFAMediumProkaryote
Q837P5_ENTFA_Meier_20230.0820.2640.2910.2910.2320.2320.0550.1250.1730.2000.1680.2000.2000.0760.1250.2000.2160.2910.2590.2050.2480.2700.2750.2530.2160.2530.2640.3450.3020.2000.2480.2270.0600.2480.3020.3340.1840.2480.3120.2590.2700.2800.1460.1350.2270.1190.2530.2210.2750.0980.1890.2050.2370.2160.2370.2370.1890.2320.2430.2320.2110.189747ActivityQ837P5_ENTFAMediumProkaryote
Q8WTC7_9CNID_Somermeyer_20220.1620.2100.1270.1250.1890.185-0.0040.1870.1690.1730.111-0.038-0.048-0.042-0.059-0.054-0.048-0.049-0.0230.129-0.036-0.0050.022-0.019-0.024-0.039-0.0200.1380.1440.2030.1870.194-0.029-0.064-0.0150.1800.1230.1300.1800.1730.1820.202-0.035-0.035-0.053-0.0540.0070.0540.1610.0870.1260.1190.1260.1330.1200.1290.1300.1200.1160.1270.038-0.07033510ActivityQ8WTC7_9CNIDLowEukaryote
R1AB_SARS2_Flynn_20220.4770.4270.1850.2210.5170.514-0.0110.2270.0130.0130.0940.0100.0020.0380.0150.0670.0960.3950.4560.2280.1860.2390.2580.2330.2110.2250.1730.1770.2150.4600.4140.331-0.0260.1520.1900.1740.3080.3390.3300.4630.4840.4800.006-0.0050.063-0.0030.3370.3100.3670.1620.1970.1560.2160.2160.2010.2010.2040.2000.1730.2050.1700.0975725OrganismalFitnessR1AB_SARS2MediumVirus
RAD_ANTMA_Tsuboyama_2023_2CJJ0.2040.1120.2260.2130.2300.2260.3620.2830.5420.4850.4150.3800.3970.3270.5200.5600.3530.3530.4320.2790.4320.4760.4410.3620.4590.3050.4720.3450.3180.4240.2790.1910.2480.3880.5250.3450.3310.4590.3710.3490.3880.3090.4720.1950.2000.3230.3800.2610.5860.3800.3800.4060.4150.4150.4190.4100.3840.4320.3930.4280.2830.463912StabilityRAD_ANTMAHighEukaryote
RAF1_HUMAN_Zinkus-Boltz_20190.3940.3670.3670.3670.3540.3670.0440.2860.3540.3670.3940.3540.4210.0170.1920.3540.3940.3810.3400.2460.2050.3130.3400.3000.2860.3130.2860.2590.2590.3400.3940.3000.1110.2460.2320.2860.3400.3000.3130.3540.3670.3940.1650.0710.4210.3270.2320.3540.1920.2860.3400.3540.3000.3400.3000.4070.3400.3540.3670.3270.3540.111297OrganismalFitnessRAF1_HUMANLowHuman
RASH_HUMAN_Bandaru_20170.3970.4420.4390.4670.4540.4650.2880.3760.4640.4790.3680.4100.4420.4170.4640.4430.4890.4530.3170.4970.4510.4030.4030.3880.4140.4000.4070.3470.2770.4220.3990.3780.1990.3750.4070.3380.4490.4500.4250.4550.4610.4750.4800.1860.2780.4540.2890.1770.4140.1980.4190.4150.4300.4290.4220.4220.4360.4210.4320.4490.3490.4443134ActivityRASH_HUMANHighHuman
RASK_HUMAN_Weng_2022_abundance0.2320.2000.2470.2330.2260.2360.1730.1790.1770.2030.2190.1670.1820.2130.2560.2330.1880.1580.1610.2320.2110.2530.2720.2890.2130.2520.3110.2510.3580.2590.1480.1110.1550.1460.2230.3070.1990.2580.3140.2320.2540.2680.1720.2110.0450.1810.2340.2450.2570.2300.1820.1650.2050.1970.2220.1990.2060.1970.1770.2010.2450.23126012ExpressionRASK_HUMANHighHuman
RASK_HUMAN_Weng_2022_binding-DARPin_K550.3900.3880.4840.4890.4680.4720.2530.3150.4830.4930.4690.4140.4600.4390.4820.5040.4990.4170.3320.5410.4210.4240.4460.3770.3940.4390.4040.3000.2760.4930.4080.3590.2400.3820.4430.3910.4200.4690.4400.4670.4820.4750.4380.2190.3370.4750.1800.2080.4660.2120.4120.4110.4210.4210.4460.4440.4100.4230.3650.4310.4760.47624873BindingRASK_HUMANHighHuman
RBP1_HUMAN_Tsuboyama_2023_2KWH0.1650.0720.2640.2640.2670.2670.2310.2430.2100.2100.3630.3180.3180.2550.3210.3780.4260.3000.2730.1800.2820.0600.2280.2130.2070.1110.1170.2790.1920.3390.3180.2880.2640.2040.3090.3120.2550.2700.2790.2640.2640.2640.2550.1860.3270.3330.3330.3780.4620.3450.3780.3780.4110.3840.3930.3990.3870.3930.3810.3900.4320.4171332StabilityRBP1_HUMANHighHuman
RCD1_ARATH_Tsuboyama_2023_5OAO0.2770.2740.3310.3400.3310.3310.2130.2170.3280.3400.3720.1970.2360.2580.2260.4610.4640.4510.4290.3150.1780.1820.2420.3050.2390.4100.4160.3590.4230.3850.4670.3910.0420.1850.2390.2320.2990.3050.2830.3620.3690.3370.2290.1940.3560.2290.3880.3910.4640.4390.4610.4610.4700.5050.4670.4960.4700.4800.4990.4830.4640.3811261StabilityRCD1_ARATHMediumEukaryote
RCRO_LAMBD_Tsuboyama_2023_1ORC0.2860.4140.4900.4710.4500.4740.0740.2300.4060.4140.4000.2560.3340.1460.2280.2050.4200.4320.4320.4880.0180.0650.0950.091-0.0790.049-0.060-0.0020.4790.4600.4270.4160.0180.0540.0420.4250.3600.3320.4650.4500.4570.511-0.1090.0530.2340.0420.3670.4420.5430.4320.4270.4110.4140.4040.4060.4090.4110.4090.4180.4180.4530.3202278StabilityRCRO_LAMBDHighVirus
RD23A_HUMAN_Tsuboyama_2023_1IFY0.2350.1680.2660.2820.3050.3090.1210.3440.4190.4190.3840.4500.4620.1560.5210.4580.4230.3990.2860.3440.3010.3210.3130.3560.4030.3720.3720.3800.3880.3640.3560.3390.3050.2310.3800.3800.2900.3210.3680.3050.3170.3290.3840.0230.4150.4660.3720.3920.4470.3760.3600.3410.3600.3440.3800.3840.3680.3680.3800.3800.3680.4231019StabilityRD23A_HUMANHighHuman
RDRP_I33A0_Li_20230.2210.2520.2810.2860.3620.3670.0420.2890.3810.3850.1460.0720.0790.0640.0580.1110.2780.2920.3680.3300.2860.3310.3410.3550.1250.2670.2630.2590.3070.3570.3360.2920.0860.2780.3210.3470.3170.3490.3580.3680.3800.3920.0520.0350.1500.0540.1700.1910.1810.0410.2330.2150.2270.2290.2400.2420.2290.2470.2530.2480.1300.09612003OrganismalFitnessRDRP_I33A0LowVirus
REV_HV1H2_Fernandes_20160.1320.1280.1410.1520.1560.1540.0330.2190.1320.1490.1040.1780.1950.0290.0180.1090.1520.2060.1840.1150.1410.1800.1710.1410.2060.1900.1060.1750.1900.1910.2450.2660.0240.1430.1820.1730.1780.1820.1620.1820.1900.1670.0310.0370.1450.0440.1770.1600.2010.1390.1410.1430.2030.1880.1770.2040.2060.1340.2190.1930.2080.1362147OrganismalFitnessREV_HV1H2MediumVirus
RFAH_ECOLI_Tsuboyama_2023_2LCL-0.0260.1760.2400.2640.2220.231-0.0140.1920.1950.2010.2070.1250.119-0.0530.0230.0050.2400.2610.2040.222-0.0680.0320.1370.101-0.0530.1160.1520.1250.1490.1640.2700.2470.0140.0020.1130.1520.0530.1400.1310.1920.1790.1950.053-0.1040.053-0.0140.1730.1370.2520.2340.2790.2610.2520.2670.2340.2550.2550.2760.2550.2640.1980.1041326StabilityRFAH_ECOLIHighProkaryote
RL20_AQUAE_Tsuboyama_2023_1GYZ0.3530.4990.5360.5360.5140.5250.1900.5360.3080.2550.5670.5640.5390.1280.3080.3510.6010.5810.6120.5560.1570.4880.4460.4400.4150.4380.4380.4710.5470.5250.5020.4870.0440.4150.4290.4150.4660.4850.4660.5140.5220.514-0.043-0.1130.3810.2070.5890.5440.6650.5950.6370.6230.6150.6570.6430.6260.6460.6320.6260.6460.6630.5441461StabilityRL20_AQUAEHighProkaryote
RL40A_YEAST_Mavor_20160.1670.2530.2570.3020.2640.2980.0950.2980.3330.3220.1710.2020.2290.0230.3430.3770.4050.3080.4250.3330.2530.4150.3840.3220.3670.3120.3290.3220.2880.2220.2910.3100.0160.2770.3390.2950.3120.3330.2950.3460.3430.2910.216-0.0010.1640.2090.0400.1160.136-0.0050.3740.3570.3880.3770.3740.3810.3530.3840.3840.3880.2470.2601253OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20130.1830.2840.2940.3390.3010.3190.0540.2560.3640.3740.1590.2210.2770.0370.3320.3670.4510.3360.4610.3600.2800.4580.3950.3570.3950.3460.3850.3530.3260.2770.3600.3670.0300.3120.4020.3120.3430.3880.3050.3710.4090.3320.2600.0130.1870.2210.0370.0930.1410.0480.4330.4050.4260.4260.4300.4300.4050.4580.4370.4330.2700.2701195OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20140.2460.2160.2860.3320.2790.3030.1160.3320.2990.3090.1130.1990.2090.1260.3960.3860.4260.3130.3990.2890.2530.4090.3560.3290.3960.3130.3190.2590.2590.2830.2730.2630.1230.3160.3590.2890.3290.3760.3290.3360.3660.3160.1860.0800.1630.2230.1460.1760.2130.1130.4320.3720.4090.4420.4160.4260.3890.4060.4160.4360.2330.3091380ActivityRL40A_YEASTMediumEukaryote
RNC_ECOLI_Weeks_20230.4250.4900.4840.5000.5020.4940.0320.3190.4780.4880.4780.4780.4890.0460.4190.4520.4860.4920.4990.4830.4250.4610.4060.3760.4500.4720.4620.4730.4590.4960.4800.4570.1380.4260.4210.3310.4590.4710.4350.5060.5170.5020.3970.0490.4670.4470.2270.4250.2000.1380.4620.4530.4190.4530.4530.4540.4600.4600.4620.4640.4920.4084277ActivityRNC_ECOLIMediumProkaryote
RPC1_BP434_Tsuboyama_2023_1R690.5570.5760.5490.5710.4910.5330.5380.5240.5570.5760.6230.6670.6700.6290.6610.6780.6230.5980.5050.5960.6090.6200.6370.5790.6290.6260.6260.6260.5870.6640.6150.5550.5020.5160.6230.6310.5960.6420.6420.5960.5850.5930.6260.5900.6640.6420.6150.5570.7110.6070.6070.5790.6010.6120.6200.6040.6120.6070.6260.6120.6940.6831459StabilityRPC1_BP434HighVirus
RPC1_LAMBD_Li_2019_high-expression0.2870.4240.4730.4980.4610.4360.1880.3870.4110.4610.4730.4980.5360.2750.2750.3990.5360.5230.5480.4110.2250.3370.4490.4240.2130.3370.3620.3000.5230.3870.6220.5230.0890.1380.3620.5110.2750.3620.4860.4240.4490.5230.2870.2870.4240.3120.1880.3490.3000.1380.5730.4980.5110.5600.5480.5730.5230.5110.5360.5600.4860.349351ActivityRPC1_LAMBDHighVirus
RPC1_LAMBD_Li_2019_low-expression0.2080.2990.3790.3790.3680.3110.0830.3450.2990.3220.3560.3450.3560.1400.1400.2420.4020.4700.4810.2990.1280.3110.3220.3330.1050.2420.2310.1970.4250.2650.4810.4130.0370.0600.2540.3450.1620.2310.3220.3110.3330.3680.1970.1620.2990.1620.1740.2760.2650.2540.4360.3900.4360.4360.4360.4700.4250.4130.4360.4590.4020.208351ActivityRPC1_LAMBDHighVirus
RS15_GEOSE_Tsuboyama_2023_1A320.2950.2890.2790.2820.2720.2720.1620.1680.3220.3220.3560.3320.3050.2250.2450.3390.3260.3290.2320.2420.1080.1850.2520.2120.2350.2520.2450.2620.2450.3150.3390.3240.0850.2790.2220.2420.2990.2590.2650.2650.2550.2550.2620.1750.3150.2590.4230.3220.4690.3890.3220.3220.3120.3120.3020.3260.3290.3360.3120.3290.3220.4631195StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance0.3840.4350.4470.4490.4580.4670.2790.4110.4960.4990.4830.4920.5320.3700.4180.4360.4990.4820.4550.0260.3820.4780.4900.4850.3980.5020.5120.4820.4580.4470.4530.3290.2730.4110.4680.4790.4300.4980.5090.4870.4990.5040.3900.1930.4660.4390.3310.4310.4330.1270.4630.4560.4540.4660.4820.4730.4780.4790.4710.4890.5020.4569803ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity0.3440.4180.4430.4520.4460.4590.2600.4200.4650.4640.4510.4610.4920.3380.3740.3640.4600.4450.4260.0270.3680.4640.4660.4560.3790.4720.4810.4650.4370.4470.4740.3850.2610.3850.4680.4590.4000.4700.4730.4630.4780.4780.3550.1540.4530.3820.2950.4130.3800.1150.4270.4110.4080.4260.4460.4240.4390.4470.4400.4460.4180.38910094ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB0.0820.0740.2970.2480.2850.2810.2970.4380.2680.2850.4180.4090.4220.1360.3390.4630.4050.4630.3680.3800.4430.3720.3550.3760.4220.4510.3890.4010.4050.4670.4470.4180.2810.4050.4340.3890.2810.3970.3510.2600.2850.3020.463-0.0720.4630.4510.1900.2060.2890.3970.4380.2810.1810.2850.2640.3220.3390.4340.3720.3470.4550.480965StabilitySAV1_MOUSEHighEukaryote
SBI_STAAM_Tsuboyama_2023_2JVG0.2180.1790.3110.2720.3150.3150.1820.1280.3780.3810.2530.2330.2410.1860.2060.3110.4630.5020.2330.2760.1750.1750.2140.2330.1940.2060.1670.2180.3110.4200.4090.3580.1940.1400.1550.1900.1860.1940.1900.2760.2920.3000.2210.1820.2640.2330.5260.4750.5380.4790.3890.4170.4240.4280.4320.4360.4480.4400.4050.4360.5380.4441025StabilitySBI_STAAMMediumProkaryote
SC6A4_HUMAN_Young_20210.3590.4000.3520.3720.4380.4520.2730.4050.4830.4900.4540.4670.4670.1350.2550.4400.4770.4780.4490.4700.4300.4180.3990.3940.4350.4190.4300.4280.4210.4560.3840.3170.2830.4360.4160.3860.4590.4550.4320.4750.4750.4610.4190.1000.4610.4530.3830.4300.4060.1320.4550.4540.4680.4700.4780.4690.4750.4770.4660.4820.4910.45211576ActivitySC6A4_HUMANMediumHuman
SCIN_STAAR_Tsuboyama_2023_2QFF0.0510.0840.1670.1800.2360.2130.0970.0120.1830.2000.1800.1730.1700.1270.1860.1670.1900.2620.2620.1700.0540.0870.0840.0910.1070.1110.1010.1300.1340.1800.1770.109-0.0250.0350.0510.1070.1140.1040.1200.1900.1730.1800.1300.1110.2000.1630.3750.3910.3810.3250.2560.2760.2690.2920.2590.2950.2720.2490.2060.2760.4470.3551212StabilitySCIN_STAARHighProkaryote
SCN5A_HUMAN_Glazer_20190.0360.0540.0890.0890.1250.1430.0710.0000.0710.1250.0890.1430.0710.1960.1430.0710.0890.0540.0710.0540.0180.0540.0710.089-0.0180.0180.0180.0360.1070.0270.1610.1430.0360.0000.0000.089-0.018-0.018-0.0180.0540.0360.0360.0180.1250.1070.1250.0360.0540.018-0.0180.0890.0540.0710.0360.0540.0890.1430.1070.0360.0890.1250.018224OrganismalFitnessSCN5A_HUMANMediumHuman
SDA_BACSU_Tsuboyama_2023_1PV00.4770.5270.5300.5300.5320.5320.1180.4430.5240.5250.5290.5120.5220.2840.2820.5180.5080.5000.5050.5290.1560.1900.1920.3180.1630.3230.0840.3230.3850.5320.5270.527-0.2400.1030.2870.3420.4880.4960.4600.5120.5120.5080.096-0.0090.3420.0680.3760.3490.5290.5220.5290.5290.5290.5290.5290.5290.5290.5290.5290.5290.5180.5252770StabilitySDA_BACSUMediumProkaryote
SERC_HUMAN_Xie_20230.2730.3540.3940.3960.3920.3940.0180.3330.4170.4210.3710.3790.3690.1160.2810.3880.3960.4360.3980.3790.3460.3710.3810.3690.3520.3860.3690.3960.3710.3540.3650.3150.1520.3610.3730.3790.3650.3750.3790.4000.4130.4150.2020.0470.3900.3350.2770.3750.2920.1120.4190.3960.4110.4270.4170.4210.3980.4000.4040.4230.3960.3501914OrganismalFitnessSERC_HUMANHighHuman
SHOC2_HUMAN_Kwon_20220.1330.2650.2940.2900.2850.2960.1290.2600.3140.3190.2980.2780.3040.1440.1520.1660.3230.2950.2130.2940.1610.2820.2890.2620.1660.2960.3000.3040.2700.3080.3030.2540.0990.1500.2340.2770.1520.2010.2580.2600.2750.3030.1560.1490.3170.1910.2160.2770.2020.0640.2950.2960.3030.3080.3060.3060.2900.3070.2960.3070.2170.18710972OrganismalFitnessSHOC2_HUMANMediumHuman
SOX30_HUMAN_Tsuboyama_2023_7JJK0.1700.2100.2500.2530.2420.2380.2890.1580.1660.2020.1740.2340.2180.2100.2500.2260.2220.1700.1860.1580.1030.1150.1110.1310.1900.1820.1030.1940.1030.1700.0400.0260.1580.0790.1620.1660.1470.1580.1540.2340.2300.2140.1860.1270.1470.2460.3800.2530.4120.3560.2930.2770.2420.2770.2730.2810.2570.2690.2460.2850.2500.3641010StabilitySOX30_HUMANHighHuman
SPA_STAAU_Tsuboyama_2023_1LP10.3730.4400.4640.4450.4930.498-0.1140.4600.4070.4090.367-0.068-0.028-0.055-0.058-0.076-0.057-0.068-0.0220.491-0.155-0.0220.1110.054-0.085-0.0550.0420.2300.3120.4360.4240.412-0.020-0.009-0.0240.0080.3350.3270.3180.4890.4870.481-0.159-0.117-0.123-0.0740.3620.3060.5630.4340.4720.4550.4450.4700.4510.4550.4720.4600.4600.4700.4090.2862105StabilitySPA_STAAUMediumProkaryote
SPG1_STRSG_Olson_20140.1690.186-0.0050.0120.1580.160-0.0060.044-0.0680.0720.2280.1740.1420.2000.1650.2050.2370.2000.2540.2020.1800.1520.1540.1490.1670.1620.1500.1670.2480.1700.3280.3160.0470.1720.1320.1980.1850.1490.2030.1730.1360.205-0.029-0.0800.1570.0510.2400.1900.2760.0860.2790.2530.2720.3020.3030.2800.3140.3340.3160.3090.2870.273536962BindingSPG1_STRSGLowProkaryote
SPG1_STRSG_Wu_20160.0100.0790.0870.0910.0840.0930.0760.0280.1620.1580.1310.1170.1080.0960.1250.1290.1380.1630.1820.0980.0710.0560.0480.0690.0440.0960.0790.0630.0630.0950.1250.093-0.0040.0800.0400.0610.0900.0780.0860.1030.0940.0920.0500.0050.0780.0450.0580.0540.1450.0890.1410.1240.1180.1490.1250.1190.1430.1360.1490.1360.1480.115149360BindingSPG1_STRSGMediumProkaryote
SPG2_STRSG_Tsuboyama_2023_5UBS0.4420.4580.4610.4830.5550.5190.3120.3010.4890.4140.4170.3840.3540.3260.3760.4030.4360.4250.4890.4250.3430.3760.3780.3540.2980.3810.3010.4030.4500.5710.5250.507-0.1260.3320.4090.3890.3040.3090.3700.4860.5130.5000.2350.1300.2350.2160.3950.3150.6620.4940.5300.5050.5270.5130.4940.5220.5270.5690.5160.5300.5190.5191451StabilitySPG2_STRSGMediumProkaryote
SPIKE_SARS2_Starr_2020_binding0.1110.1350.0510.1160.1530.166-0.0590.2590.2700.257-0.011-0.075-0.045-0.048-0.048-0.033-0.024-0.0230.0160.2550.2230.2640.2560.2640.2840.2360.2090.2720.2270.1630.1850.2380.1610.2270.2150.2620.2090.2100.2140.2490.2420.238-0.053-0.016-0.024-0.0180.4130.3790.3200.1140.1370.1520.1370.1810.1530.1830.1560.1870.1580.1740.2160.0953802BindingSPIKE_SARS2MediumVirus
SPIKE_SARS2_Starr_2020_expression0.1610.2230.1220.2150.3750.352-0.0340.2660.3210.3440.038-0.032-0.007-0.022-0.024-0.0060.0010.0190.0550.2710.2400.2880.2930.3080.2890.2770.2360.3020.2490.2670.2070.2690.1440.2500.2410.2740.2590.2700.2730.3390.3520.367-0.0100.0130.0130.0210.4340.4190.3770.1400.2250.2390.2300.2850.2470.2980.2460.2700.2470.2730.3380.1803798ExpressionSPIKE_SARS2MediumVirus
SPTN1_CHICK_Tsuboyama_2023_1TUD0.4990.4460.4330.4020.4260.4120.1400.3780.4000.4220.4020.4850.445-0.0690.4430.4370.4420.5440.4890.3920.4250.4170.4350.4000.4580.4120.4600.3960.3820.3850.4050.4310.3300.3730.4180.3910.4630.4650.4710.4060.4200.4150.367-0.1480.3350.3520.3030.2480.4720.4400.4330.4320.4630.4550.4510.4480.4330.4230.4350.4330.5000.4153201StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU0.3240.4090.4200.4370.4260.4370.0640.2330.3690.4140.3180.2330.3240.1430.3180.3860.4370.4940.3690.4310.1430.2960.3690.2960.1820.3920.4200.3470.3580.4260.4140.3780.0750.1490.2840.3970.3580.3240.4200.4710.3860.4370.1600.1150.4540.4030.5280.4940.5280.5280.4770.4310.5160.4940.4820.4770.4880.4710.4820.4940.4820.341707StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N880.5060.5290.5360.5390.5670.554-0.1410.3700.5460.5440.5720.5640.559-0.1690.5390.5890.5790.5840.5690.506-0.1110.3190.2310.3750.3470.4200.4580.4830.4350.5640.5770.5740.2630.0490.1500.3190.5340.5190.5110.5620.5590.5260.466-0.2450.5720.4760.0590.4610.5870.5720.5720.5670.5740.5820.5870.5950.5670.5670.5840.5770.6550.6121583StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W0.4130.4180.5590.5490.5980.5850.4440.3360.6180.6210.4740.5360.5670.3790.5750.6130.5930.5440.5410.5410.5490.5130.5100.4950.4950.5150.5230.5030.5030.5390.5900.5590.4850.4800.4870.5390.5130.5540.5590.5770.5870.5850.2480.1170.4560.4330.4380.4460.4970.5100.6210.5930.6000.6000.6130.6080.5980.5900.6130.6080.5820.5211556StabilitySRBS1_HUMANHighHuman
SRC_HUMAN_Ahler_20190.4600.4530.4180.4280.4530.4570.4660.3860.4940.5100.4420.5150.5330.3570.4180.4130.4950.4500.4460.4500.3770.3590.3730.3300.3940.4280.3910.3580.2720.5010.5190.4930.3790.3520.3590.2950.4430.4530.4320.4590.4590.4570.4960.3110.4420.4860.2760.2470.3550.0940.4560.4380.4450.4650.4790.4710.4700.4760.4720.4870.4700.4123372ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM0.3850.3780.3800.3830.3910.3840.3870.3330.4170.4070.3610.4230.4390.2680.3350.3320.4250.3520.3760.3620.3290.3040.3220.2770.3280.3570.3160.3130.2200.4110.4380.4350.2990.2820.2920.2260.3620.3580.3450.3800.3780.3840.4100.2380.3600.3930.2190.2240.2900.0350.3930.3720.3730.3960.4070.4100.3930.4120.3960.4140.3890.3463637ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Nguyen_20220.3370.3410.3560.3510.3510.3510.3450.3190.2840.3450.3430.3890.4110.2380.2960.2980.3820.3380.3510.3400.2830.2840.2930.2570.2910.3320.2880.2790.2090.3690.4020.3910.2950.2570.2650.2070.3200.3210.3160.3500.3380.3400.3870.2130.3450.3900.1930.2010.2560.0400.3660.3460.3430.3660.3770.3720.3640.3780.3660.3810.3650.3083366OrganismalFitnessSRC_HUMANMediumHuman
SUMO1_HUMAN_Weile_20170.3390.2930.3390.3630.3920.3780.1230.3700.3780.3030.3410.4020.4310.2130.4120.4670.4330.3070.2810.4480.1960.3220.3780.3370.4020.2950.4020.3490.2690.3750.3990.4260.2010.2230.4160.2520.3390.4600.3220.3510.4650.3440.4310.0560.2930.4500.4380.3580.4450.3700.4310.3700.4290.4290.4190.4530.4020.4360.4120.4430.3970.4531700OrganismalFitnessSUMO1_HUMANHighHuman
SYUA_HUMAN_Newberry_20200.1520.1920.1980.2340.1960.2110.2020.2670.2420.2480.3380.3320.3240.2060.2310.2290.2360.2780.3090.3320.2480.3260.2550.2380.1940.2730.2860.2590.2380.3070.3110.2520.1290.2570.3590.2840.1890.2780.2290.2250.2760.2310.2110.1730.3130.240-0.0060.2060.051-0.0410.1900.1900.1890.1980.2290.1960.2270.2150.2290.2210.0740.0592497OrganismalFitnessSYUA_HUMANMediumHuman
TADBP_HUMAN_Bolognesi_20190.0900.0400.0770.0800.0740.0700.1540.0230.0600.0540.0100.0300.0230.0540.0200.023-0.0700.0130.054-0.0470.0770.037-0.003-0.0100.1040.0700.043-0.017-0.0300.0300.1070.142-0.0430.1200.1470.0840.0900.1100.0870.1000.1170.0840.0540.107-0.0070.0300.1970.0230.1200.0470.090-0.033-0.0130.0030.0070.0270.0330.003-0.0600.0230.0100.0571196OrganismalFitnessTADBP_HUMANLowHuman
TAT_HV1BR_Fernandes_20160.2650.1830.2380.2430.2590.243-0.0760.3960.2270.2570.1590.3380.322-0.0320.0060.0090.028-0.0570.0440.1860.3930.4150.4170.4230.3600.2570.1200.2290.1990.3140.4010.4030.3190.3710.1750.2270.3740.2540.2290.3410.2700.2570.020-0.0270.2840.1560.2020.2810.3000.1780.0820.1260.1390.0990.1070.1070.0740.0990.0900.0930.1610.1451577OrganismalFitnessTAT_HV1BRHighVirus
TCRG1_MOUSE_Tsuboyama_2023_1E0L0.5900.5370.6060.6100.6550.6550.6820.3120.6480.6970.5520.7280.7470.6700.6900.7620.7310.6900.7240.5140.4230.5710.5560.6020.5830.5790.6360.6440.5750.6930.6360.5870.5410.4460.6020.6860.6400.7050.6970.6360.6550.6480.7010.1030.5300.6670.5410.4610.7390.7120.7050.6630.6440.7390.7120.7350.7090.7240.6970.7200.7580.7541058StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG0.2500.2600.4370.4110.4070.420-0.0020.1220.5350.5250.5120.4200.4830.0990.5250.5320.4960.5150.5450.496-0.0970.2660.3120.2960.2660.2860.1850.3680.4430.4990.5510.5480.414-0.0420.3390.3060.3970.4630.4730.4110.4890.4830.1880.0760.4240.3060.4140.4010.4430.5680.5420.5290.5680.5870.5710.5550.5550.5580.5480.5610.4790.4041279StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT0.3460.3350.4430.4380.4240.421-0.1090.3180.5110.4920.4050.4160.419-0.0140.3940.4560.4730.4320.4700.4050.2270.3160.3400.3210.3510.3810.2890.3320.3460.4860.4540.4350.1670.0670.2780.3020.3970.3940.3970.4620.4210.4270.153-0.0760.4000.3370.3350.4730.5590.5080.4590.4590.4670.4700.4510.4430.4400.4320.4460.4540.5860.4671479StabilityTNKS2_HUMANHighHuman
TPK1_HUMAN_Weile_20170.1900.1940.1850.1980.2030.2090.0590.1930.2360.2210.2300.2150.2570.1210.1620.2160.2660.2580.3060.1910.0960.1070.1800.2220.1190.2170.2200.2090.2310.1910.1990.1610.1050.1170.1710.2340.1900.1970.2420.2080.2090.2390.1310.1040.2690.1520.1480.2090.1840.0760.2000.2070.2110.2170.2260.2260.2150.2290.2330.2310.2170.1853181OrganismalFitnessTPK1_HUMANMediumHuman
TPMT_HUMAN_Matreyek_20180.2630.3060.3130.3230.3220.3310.1730.3020.3220.3290.3490.3300.3440.2250.2650.3440.3490.2950.2910.3410.1860.2630.3010.2990.2650.3040.3010.2680.2950.3510.3590.3420.2230.1950.3020.2900.3020.3370.3310.3490.3340.3440.2440.1480.3450.3160.3220.3470.3600.1620.3450.3420.3490.3420.3480.3560.3590.3440.3550.3560.3600.3243648ExpressionTPMT_HUMANMediumHuman
TPOR_HUMAN_Bridgford_20200.2730.2220.2300.1880.1620.2130.2990.3160.2650.2900.2900.2650.2820.2470.3070.2650.1790.2820.2050.2130.2130.2990.2390.2650.3070.1280.3500.4100.2390.3580.2820.1330.3240.3070.2900.3330.2990.2990.3670.3160.3070.3500.2470.2470.2130.2650.1190.2300.2130.0000.2050.1960.2730.2300.2050.2730.2900.1110.1880.2390.3240.299562OrganismalFitnessTPOR_HUMANLowHuman
TRPC_SACS2_Chan_20170.5160.5270.4620.4790.4870.5080.1880.4650.5570.5620.5460.5430.5570.2580.5060.5320.5410.5430.5240.4620.3680.5190.4650.5160.4360.4710.4440.4600.4410.4410.5270.4620.1960.4870.4920.5080.5110.5270.5270.5060.5160.5160.3950.0450.5430.4650.2500.3840.3870.0990.5410.4950.5030.5570.5300.5240.5430.5460.5240.5510.5350.5111519OrganismalFitnessTRPC_SACS2MediumProkaryote
TRPC_THEMA_Chan_20170.3650.3710.3480.3710.3650.3630.2370.3330.3830.3830.3710.3950.4060.1900.3770.3860.3920.3830.3890.3540.2830.3240.3300.3390.3540.3540.3130.3330.3600.3480.3390.3040.1460.3330.3920.3650.3630.3800.3630.3680.3650.3570.3300.0930.3770.3330.1690.2630.2750.0030.3830.3160.3570.3770.3770.3300.3600.3600.3510.3770.4090.3831519OrganismalFitnessTRPC_THEMAMediumProkaryote
UBC9_HUMAN_Weile_20170.3040.4070.4570.4740.4380.457-0.0270.3270.4300.4400.3400.3960.4320.0180.0250.3260.3790.4260.4900.4770.1680.3370.3790.3460.3480.3960.3880.3590.3540.3990.3710.3720.1030.1680.3460.3550.2430.3660.3940.3710.4370.4550.2090.0010.3160.4230.2730.2770.3850.2180.3270.3460.3160.3520.3380.3460.3270.3430.3510.3510.2930.3572563OrganismalFitnessUBC9_HUMANMediumHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X0.2470.3360.3690.3790.3700.3780.1520.3490.3630.3710.3410.4140.4000.3340.3580.3940.3740.4020.4280.3830.2170.3580.3500.3710.3770.3830.3420.3410.3690.3870.4220.4180.3870.1530.1670.2990.3330.3240.3320.3820.3750.3860.326-0.0200.2520.2590.3690.2920.3940.3050.3500.3850.3690.3700.3740.3660.3740.3750.3850.3740.4110.3903622StabilityUBE4B_HUMANHighHuman
UBE4B_MOUSE_Starita_20130.3060.3120.3480.3420.3480.3540.0400.0460.3180.3420.2760.3300.3480.3180.3360.3600.3300.3180.2880.3180.0820.2760.2700.2520.3480.3000.2880.2880.2520.3300.3600.3000.058-0.0090.1180.2030.3120.2880.3060.3420.3600.3790.306-0.0030.3300.3480.1910.270-0.0570.0340.3180.3300.3540.3540.3420.3600.3300.3600.3420.3540.3300.288899ActivityUBE4B_MOUSELowEukaryote
UBR5_HUMAN_Tsuboyama_2023_1I2T0.3740.4950.5280.5440.5420.5660.1420.2940.4320.4780.4920.3740.4540.1920.1870.1290.1010.5610.5220.4210.4040.3930.4290.4870.4430.4540.4620.4430.4510.5060.5280.3820.2640.2830.3760.3650.3740.4100.4180.5530.5530.5470.0930.0980.2910.1010.4450.2440.5880.4840.5330.5360.5470.5530.5610.5720.5360.5550.5470.5500.5860.5141453StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP80.1200.1920.1200.1480.1920.1980.187-0.0570.2640.2420.5190.4410.4630.2860.4190.4910.5190.4910.5460.1090.2970.4020.3580.4850.3200.3420.3310.3690.4470.4410.4300.2950.0480.2370.3580.3970.1980.2810.3420.2200.2480.2810.3470.2090.4410.3800.3860.4250.4300.3750.5300.4800.5080.5300.5080.5680.4740.5350.5630.5350.4970.480723StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU50.2710.3230.4320.4760.4950.4990.0650.4160.4410.4780.5500.4850.4900.2600.1580.5130.5170.4740.4200.4250.2340.3500.4290.3850.3790.4250.4110.3780.4410.4740.4990.4890.2830.2640.3430.3570.4090.4150.4090.5010.4940.4800.1630.2000.3600.3510.3870.3460.5390.4640.5450.5530.5550.5390.5460.5520.5530.5460.5530.5520.4990.5082568StabilityVILI_CHICKHighEukaryote
VKOR1_HUMAN_Chiasson_2020_abundance0.3230.3400.3510.3570.3870.3860.1240.3170.4460.4490.3960.3750.4110.1390.3070.3810.4430.4260.4240.3930.1750.1880.3320.3590.2360.4110.3840.3840.3420.3930.3450.2640.0890.1110.2210.4020.3310.3420.4520.3860.3960.4370.1080.0770.4050.2650.3710.3830.4090.1910.4370.4210.4700.4750.4780.4450.4640.4370.4580.4700.4610.3772695ExpressionVKOR1_HUMANMediumHuman
VKOR1_HUMAN_Chiasson_2020_activity0.3170.3110.3350.3590.3650.3650.0370.3110.3710.3830.3530.3830.3830.0370.2630.2810.3710.3530.3650.3710.0960.0550.2690.2930.1440.3170.3230.3170.3470.3350.3410.2870.1620.0610.1320.3290.3170.3110.3830.3530.3710.3710.0670.0310.3770.1980.1980.3170.3350.0490.3710.3110.3470.3350.3470.3470.3470.3530.3350.3590.3710.281697ActivityVKOR1_HUMANMediumHuman
VRPI_BPT7_Tsuboyama_2023_2WNM-0.0760.0110.1020.1440.0810.1100.0940.1100.3420.3510.3630.2640.2800.2140.3420.3840.4630.4960.4210.1350.0070.1390.1150.2220.1970.1850.1390.1890.2470.0690.3550.3380.2310.0400.0110.073-0.084-0.055-0.0800.0280.028-0.0430.2180.0360.4250.2930.4630.3920.4420.4420.4670.4460.4460.4460.4540.4540.4380.4580.4340.4670.4920.4171047StabilityVRPI_BPT7MediumVirus
YAIA_ECOLI_Tsuboyama_2023_2KVT0.2430.5110.4320.4550.4340.432-0.1440.4610.4970.5040.4830.1480.309-0.0910.1060.4320.5210.5440.5460.523-0.091-0.167-0.0910.188-0.099-0.142-0.1860.0170.3940.4490.5500.5240.004-0.197-0.0510.3960.2730.2670.4170.4040.3960.485-0.059-0.2120.068-0.0400.3260.2710.4320.3870.5230.5310.5290.5270.5290.5330.5250.5400.5250.5270.4590.3721890StabilityYAIA_ECOLIMediumProkaryote
YAP1_HUMAN_Araya_20120.3380.2600.3560.3590.3470.3580.2400.2500.0550.0670.2630.2080.2180.3420.3610.3690.3810.3000.2460.1490.1290.1340.1170.1140.2300.1710.2010.1570.1070.2450.3530.3780.0930.2300.1460.1510.3090.2590.2730.3420.3120.3370.380-0.0700.2540.3910.2850.2720.3270.1450.3700.3030.3430.3410.3510.3270.3390.3620.3670.3610.2900.34810075BindingYAP1_HUMANLowHuman
YNZC_BACSU_Tsuboyama_2023_2JVD0.4710.5500.5530.5530.5500.5530.3930.4480.5260.5220.5590.5530.5530.4360.4380.5500.5480.5500.5460.5420.4210.4860.5060.5060.5000.5000.4640.5000.5400.4970.5160.4930.5020.4340.4760.4460.5120.5100.5060.5200.5160.5140.2400.1860.3590.2640.3150.3810.5630.5340.5590.5500.5550.5570.5550.5610.5530.5570.5530.5550.5440.5552300StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_Uniprot_Selection_Type_level.csv b/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_Uniprot_Selection_Type_level.csv new file mode 100644 index 0000000..225ca8b --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/MCC/DMS_substitutions_MCC_Uniprot_Selection_Type_level.csv @@ -0,0 +1,7 @@ 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+0.189,0.239,0.379,0.361,0.375,0.381,0.185,0.282,0.365,0.382,0.433,0.369,0.41,0.324,0.359,0.359,0.439,0.371,0.44,0.29,0.332,0.352,0.381,0.34,0.123,0.375,0.408,0.369,0.299,0.294,0.352,0.329,0.266,0.299,0.206,0.268,0.328,0.263,0.309,0.408,0.359,0.382,0.098,0.108,0.21,0.127,0.468,0.367,0.572,0.514,0.442,0.421,0.448,0.442,0.442,0.446,0.435,0.442,0.442,0.444,0.45,0.483,Stability,BBC1_YEAST,High,Eukaryote +0.327,0.307,0.281,0.296,0.284,0.302,0.098,0.159,0.309,0.391,0.33,0.248,0.289,0.118,0.149,0.251,0.406,0.414,0.324,0.342,0.088,0.299,0.154,0.302,0.136,0.363,0.21,0.33,0.34,0.5,0.472,0.401,0.024,0.164,0.23,0.35,0.355,0.36,0.36,0.296,0.309,0.34,0.078,0.085,0.169,0.146,0.421,0.37,0.518,0.431,0.454,0.452,0.464,0.472,0.459,0.487,0.459,0.449,0.454,0.462,0.378,0.21,Stability,BCHB_CHLTE,Medium,Prokaryote +0.329,0.521,0.536,0.557,0.517,0.538,0.086,0.328,0.541,0.566,0.526,0.499,0.531,0.253,0.378,0.478,0.568,0.419,0.308,0.483,0.392,0.379,0.359,0.37,0.413,0.474,0.466,0.394,0.322,0.488,0.561,0.559,0.12,0.378,0.357,0.323,0.449,0.444,0.446,0.534,0.542,0.546,0.356,0.038,0.515,0.43,0.424,0.514,0.481,0.23,0.544,0.534,0.54,0.559,0.556,0.55,0.561,0.539,0.542,0.572,0.555,0.417,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.329,0.521,0.536,0.557,0.517,0.538,0.086,0.328,0.541,0.566,0.526,0.499,0.531,0.253,0.378,0.478,0.568,0.419,0.308,0.483,0.392,0.379,0.359,0.37,0.413,0.474,0.466,0.394,0.322,0.488,0.561,0.559,0.12,0.378,0.357,0.323,0.449,0.444,0.446,0.534,0.542,0.546,0.356,0.038,0.515,0.43,0.424,0.514,0.481,0.23,0.544,0.534,0.54,0.559,0.556,0.55,0.561,0.539,0.542,0.572,0.555,0.417,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.329,0.521,0.536,0.557,0.517,0.538,0.086,0.328,0.541,0.566,0.526,0.499,0.531,0.253,0.378,0.478,0.568,0.419,0.308,0.483,0.392,0.379,0.359,0.37,0.413,0.474,0.466,0.394,0.322,0.488,0.561,0.559,0.12,0.378,0.357,0.323,0.449,0.444,0.446,0.534,0.542,0.546,0.356,0.038,0.515,0.43,0.424,0.514,0.481,0.23,0.544,0.534,0.54,0.559,0.556,0.55,0.561,0.539,0.542,0.572,0.555,0.417,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.329,0.521,0.536,0.557,0.517,0.538,0.086,0.328,0.541,0.566,0.526,0.499,0.531,0.253,0.378,0.478,0.568,0.419,0.308,0.483,0.392,0.379,0.359,0.37,0.413,0.474,0.466,0.394,0.322,0.488,0.561,0.559,0.12,0.378,0.357,0.323,0.449,0.444,0.446,0.534,0.542,0.546,0.356,0.038,0.515,0.43,0.424,0.514,0.481,0.23,0.544,0.534,0.54,0.559,0.556,0.55,0.561,0.539,0.542,0.572,0.555,0.417,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.436,0.298,0.423,0.426,0.403,0.408,0.048,0.227,0.334,0.331,0.413,0.299,0.341,0.077,0.286,0.379,0.413,0.426,0.361,0.237,0.065,0.321,0.341,0.349,0.239,0.403,0.391,0.379,0.398,0.399,0.436,0.416,0.212,0.09,0.127,0.371,0.406,0.406,0.436,0.441,0.431,0.448,0.142,0.068,0.446,0.361,0.369,0.391,0.044,0.122,0.431,0.423,0.428,0.411,0.421,0.423,0.428,0.413,0.408,0.423,0.436,0.398,OrganismalFitness,BRCA1_HUMAN,Low,Human +0.312,0.309,0.309,0.309,0.309,0.309,0.068,0.283,0.122,-0.012,0.309,0.095,0.015,0.015,0.015,0.283,0.309,0.309,0.309,0.041,0.015,0.309,0.309,0.309,0.309,0.283,0.309,0.309,0.068,0.309,0.256,0.285,-0.012,0.041,0.068,0.068,0.309,0.309,0.309,0.309,0.309,0.309,0.015,0.015,0.309,-0.012,-0.039,0.015,-0.2,0.175,0.256,0.283,0.283,0.309,0.309,0.309,0.309,0.309,0.309,0.309,-0.039,0.015,OrganismalFitness,BRCA2_HUMAN,,Human +0.297,0.268,0.258,0.264,0.308,0.308,-0.011,0.333,0.267,0.272,0.024,0.32,0.37,-0.002,-0.007,-0.003,0.381,0.293,0.298,0.245,0.288,0.269,0.275,0.261,0.168,0.341,0.323,0.357,0.281,0.345,0.333,0.251,0.088,0.269,0.276,0.299,0.312,0.317,0.318,0.332,0.335,0.325,-0.009,-0.016,0.204,0.014,0.393,0.422,0.426,0.172,0.387,0.396,0.41,0.411,0.417,0.399,0.417,0.416,0.409,0.416,0.235,0.134,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +0.135,0.201,0.197,0.204,0.186,0.192,0.124,0.135,0.197,0.221,0.232,0.19,0.234,0.115,0.153,0.153,0.166,0.166,0.168,0.188,0.131,0.184,0.215,0.21,0.188,0.243,0.239,0.25,0.276,0.206,0.166,0.098,0.062,0.173,0.226,0.27,0.175,0.197,0.226,0.204,0.215,0.204,0.164,0.115,0.256,0.212,0.06,0.124,0.137,0.067,0.124,0.144,0.151,0.155,0.153,0.14,0.142,0.133,0.137,0.151,0.252,0.192,OrganismalFitness,CALM1_HUMAN,High,Human +0.234,0.195,0.188,0.211,0.194,0.188,0.285,0.285,0.179,0.202,0.122,0.146,0.148,0.201,0.217,0.144,0.201,0.111,0.044,0.163,0.145,0.198,0.206,0.202,0.159,0.149,0.188,0.152,0.3,0.274,0.118,0.11,0.102,0.149,0.203,0.36,0.213,0.223,0.318,0.194,0.197,0.263,0.092,0.136,0.21,0.096,0.313,0.301,0.24,0.221,0.151,0.148,0.131,0.151,0.139,0.139,0.143,0.139,0.15,0.145,0.214,0.167,OrganismalFitness,CAPSD_AAV2S,Low,Virus +0.178,0.142,0.136,0.127,0.12,0.118,0.082,-0.042,0.112,0.108,0.186,0.246,0.215,0.034,0.068,0.263,0.242,0.25,0.272,0.086,0.099,0.114,0.129,0.108,0.007,0.102,0.103,0.132,0.066,0.148,0.192,0.168,0.187,0.016,0.091,0.057,0.172,0.123,0.083,0.154,0.107,0.08,0.066,0.02,0.168,0.183,0.227,0.195,0.209,0.074,0.274,0.263,0.26,0.262,0.256,0.259,0.246,0.263,0.274,0.268,0.301,0.226,OrganismalFitness,CAR11_HUMAN,Low,Human +0.178,0.142,0.136,0.127,0.12,0.118,0.082,-0.042,0.112,0.108,0.186,0.246,0.215,0.034,0.068,0.263,0.242,0.25,0.272,0.086,0.099,0.114,0.129,0.108,0.007,0.102,0.103,0.132,0.066,0.148,0.192,0.168,0.187,0.016,0.091,0.057,0.172,0.123,0.083,0.154,0.107,0.08,0.066,0.02,0.168,0.183,0.227,0.195,0.209,0.074,0.274,0.263,0.26,0.262,0.256,0.259,0.246,0.263,0.274,0.268,0.301,0.226,OrganismalFitness,CAR11_HUMAN,Low,Human +0.107,0.141,0.123,0.126,0.135,0.139,0.014,0.079,0.131,0.14,0.123,0.046,0.039,0.027,0.056,0.101,0.133,0.137,0.134,0.01,0.027,0.048,0.116,0.091,0.026,0.144,0.138,0.133,0.048,0.128,0.145,0.129,0.016,0.022,0.033,0.12,0.132,0.134,0.132,0.129,0.131,0.139,0.039,0.011,0.129,0.074,0.074,0.129,0.015,0.02,0.123,0.123,0.121,0.125,0.126,0.13,0.127,0.128,0.121,0.128,0.119,0.077,Activity,CAS9_STRP1,Medium,Prokaryote +0.314,0.447,0.508,0.503,0.518,0.523,0.053,0.37,0.518,0.518,0.464,0.477,0.487,0.186,0.441,0.518,0.521,0.452,0.38,0.48,0.132,0.378,0.424,0.406,0.383,0.441,0.447,0.393,0.441,0.49,0.467,0.401,0.181,0.048,0.355,0.388,0.39,0.429,0.462,0.505,0.49,0.526,0.27,-0.029,0.48,0.424,0.316,0.429,0.395,0.188,0.459,0.457,0.459,0.462,0.482,0.47,0.467,0.447,0.457,0.487,0.531,0.372,Activity,CASP3_HUMAN,High,Human +0.335,0.461,0.509,0.521,0.502,0.502,0.051,0.392,0.487,0.533,0.487,0.502,0.53,0.225,0.516,0.518,0.535,0.499,0.478,0.504,0.18,0.459,0.461,0.464,0.475,0.497,0.511,0.485,0.48,0.549,0.511,0.459,0.299,0.101,0.487,0.425,0.442,0.506,0.509,0.497,0.549,0.533,0.371,0.023,0.549,0.514,0.397,0.523,0.521,0.249,0.514,0.502,0.471,0.492,0.518,0.521,0.495,0.495,0.514,0.511,0.556,0.433,Activity,CASP7_HUMAN,Medium,Human +0.47,0.473,0.567,0.563,0.58,0.578,0.607,0.487,0.449,0.422,0.498,0.538,0.563,0.481,0.594,0.586,0.557,0.508,0.573,0.088,0.506,0.515,0.51,0.508,0.54,0.519,0.498,0.46,0.477,0.576,0.536,0.544,0.357,0.527,0.502,0.529,0.569,0.542,0.561,0.58,0.563,0.576,0.395,0.38,0.294,0.424,0.393,0.386,0.576,0.439,0.565,0.544,0.578,0.557,0.584,0.563,0.557,0.536,0.555,0.559,0.512,0.565,Stability,CATR_CHLRE,High,Eukaryote +0.555,0.605,0.633,0.634,0.638,0.65,0.375,0.569,0.673,0.685,0.648,0.615,0.611,0.404,0.561,0.642,0.636,0.633,0.607,0.673,0.4,0.406,0.592,0.571,0.449,0.573,0.582,0.557,0.611,0.667,0.658,0.595,0.019,0.404,0.513,0.611,0.576,0.603,0.623,0.65,0.64,0.65,0.397,0.279,0.513,0.522,0.677,0.625,0.805,0.71,0.698,0.725,0.692,0.71,0.737,0.714,0.712,0.71,0.706,0.725,0.631,0.776,Stability,CBPA2_HUMAN,Medium,Human +0.297,0.306,0.314,0.339,0.319,0.331,0.162,0.205,0.33,0.341,0.304,0.305,0.329,0.07,0.177,0.281,0.299,0.285,0.283,0.309,0.315,0.238,0.261,0.262,0.319,0.254,0.262,0.248,0.273,0.322,0.316,0.299,0.179,0.332,0.268,0.233,0.329,0.289,0.286,0.349,0.317,0.315,0.243,0.062,0.326,0.324,0.21,0.236,0.277,0.058,0.277,0.277,0.283,0.297,0.292,0.289,0.281,0.284,0.29,0.293,0.336,0.238,OrganismalFitness,CBS_HUMAN,Medium,Human +0.389,0.45,0.493,0.505,0.463,0.507,-0.205,0.454,0.482,0.468,0.48,0.482,0.47,-0.21,0.545,0.514,0.503,0.428,0.442,0.5,0.312,0.41,0.377,0.394,0.372,0.361,0.366,0.31,0.394,0.489,0.507,0.502,0.349,0.277,0.305,0.31,0.415,0.41,0.396,0.496,0.517,0.503,0.465,-0.261,0.249,0.435,-0.002,0.067,0.452,0.421,0.528,0.458,0.535,0.526,0.545,0.515,0.519,0.552,0.519,0.526,0.493,0.615,Stability,CBX4_HUMAN,High,Human +0.312,0.366,0.401,0.424,0.396,0.414,0.003,0.191,0.314,0.324,0.386,0.33,0.382,0.019,0.008,0.353,0.396,0.396,0.262,0.402,0.026,0.022,-0.062,0.124,-0.109,0.085,0.024,0.027,0.356,0.398,0.472,0.46,0.093,-0.001,0.055,0.292,0.384,0.358,0.391,0.398,0.38,0.405,0.031,-0.016,0.389,0.018,0.252,0.345,0.26,0.208,0.388,0.326,0.354,0.37,0.35,0.361,0.354,0.37,0.394,0.378,0.366,0.19,Activity,CCDB_ECOLI,High,Prokaryote +0.312,0.366,0.401,0.424,0.396,0.414,0.003,0.191,0.314,0.324,0.386,0.33,0.382,0.019,0.008,0.353,0.396,0.396,0.262,0.402,0.026,0.022,-0.062,0.124,-0.109,0.085,0.024,0.027,0.356,0.398,0.472,0.46,0.093,-0.001,0.055,0.292,0.384,0.358,0.391,0.398,0.38,0.405,0.031,-0.016,0.389,0.018,0.252,0.345,0.26,0.208,0.388,0.326,0.354,0.37,0.35,0.361,0.354,0.37,0.394,0.378,0.366,0.19,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +0.208,0.215,0.206,0.203,0.203,0.21,0.221,0.227,0.239,0.263,0.268,0.258,0.268,0.228,0.255,0.263,0.256,0.241,0.258,0.268,0.271,0.257,0.232,0.23,0.271,0.255,0.255,0.273,0.233,0.259,0.216,0.182,0.086,0.272,0.268,0.264,0.261,0.27,0.28,0.234,0.233,0.227,0.267,0.121,0.268,0.264,0.19,0.23,0.203,0.112,0.246,0.234,0.246,0.25,0.245,0.24,0.252,0.254,0.256,0.256,0.271,0.26,Binding,CCR5_HUMAN,High,Human +0.144,0.152,0.146,0.146,0.163,0.153,0.062,0.081,0.126,0.131,0.09,0.11,0.123,0.102,0.13,0.109,0.099,0.126,0.099,-0.018,0.103,0.109,0.14,0.126,0.094,0.159,0.131,0.095,0.155,0.168,0.099,0.073,0.078,0.119,0.147,0.126,0.136,0.135,0.132,0.164,0.161,0.156,0.11,0.07,0.094,0.11,0.264,0.209,0.252,0.132,0.164,0.182,0.169,0.193,0.176,0.169,0.179,0.176,0.182,0.193,0.305,0.26,Binding,CD19_HUMAN,Low,Human +0.378,0.44,0.45,0.462,0.44,0.466,0.416,0.448,0.449,0.473,0.374,0.466,0.484,0.412,0.489,0.494,0.506,0.494,0.455,0.489,0.447,0.45,0.462,0.431,0.448,0.438,0.442,0.454,0.431,0.466,0.447,0.4,0.178,0.468,0.439,0.424,0.489,0.477,0.474,0.489,0.484,0.484,0.466,0.048,0.395,0.488,0.45,0.424,0.489,0.172,0.481,0.48,0.491,0.505,0.503,0.506,0.496,0.486,0.495,0.51,0.496,0.491,Expression,CP2C9_HUMAN,High,Human +0.378,0.44,0.45,0.462,0.44,0.466,0.416,0.448,0.449,0.473,0.374,0.466,0.484,0.412,0.489,0.494,0.506,0.494,0.455,0.489,0.447,0.45,0.462,0.431,0.448,0.438,0.442,0.454,0.431,0.466,0.447,0.4,0.178,0.468,0.439,0.424,0.489,0.477,0.474,0.489,0.484,0.484,0.466,0.048,0.395,0.488,0.45,0.424,0.489,0.172,0.481,0.48,0.491,0.505,0.503,0.506,0.496,0.486,0.495,0.51,0.496,0.491,Binding,CP2C9_HUMAN,High,Human +0.255,0.474,0.392,0.409,0.356,0.369,0.213,0.367,0.421,0.434,0.455,0.455,0.502,0.305,0.494,0.418,0.365,0.436,0.429,0.378,0.328,0.375,0.455,0.465,0.342,0.464,0.459,0.453,0.442,0.448,0.428,0.419,0.259,0.274,0.364,0.433,0.347,0.359,0.421,0.417,0.421,0.457,0.233,0.167,0.306,0.329,0.345,0.329,0.426,0.467,0.468,0.459,0.445,0.471,0.479,0.471,0.47,0.476,0.438,0.478,0.456,0.482,Stability,CSN4_MOUSE,Medium,Eukaryote +0.252,0.308,0.277,0.282,0.31,0.292,0.072,0.32,0.366,0.376,0.368,0.297,0.265,0.105,0.32,0.361,0.434,0.361,0.373,0.257,0.115,0.141,0.158,0.062,0.085,0.153,0.148,0.148,0.232,0.295,0.305,0.271,0.189,0.113,0.11,0.135,0.257,0.247,0.247,0.315,0.315,0.315,0.108,0.032,0.31,0.151,0.396,0.381,0.454,0.346,0.404,0.373,0.381,0.409,0.404,0.419,0.416,0.416,0.411,0.404,0.449,0.404,Stability,CUE1_YEAST,Medium,Eukaryote +0.426,0.5,0.5,0.465,0.506,0.503,0.049,0.378,0.538,0.543,0.353,0.048,0.042,0.045,0.032,0.022,0.032,0.046,0.109,0.356,0.032,0.033,0.082,0.06,-0.003,0.035,0.05,0.145,0.281,0.506,0.444,0.461,0.024,0.078,0.094,0.133,0.418,0.417,0.415,0.505,0.504,0.493,-0.002,0.01,0.001,0.02,0.209,0.228,0.389,0.292,0.386,0.386,0.384,0.392,0.381,0.392,0.383,0.376,0.377,0.385,0.235,0.147,Activity,D7PM05_CLYGR,Low,Eukaryote +0.611,0.49,0.509,0.487,0.498,0.51,0.652,0.557,0.444,0.448,0.435,0.459,0.497,0.671,0.688,0.646,0.473,0.386,0.368,0.536,0.436,0.478,0.465,0.437,0.487,0.482,0.453,0.451,0.38,0.508,0.545,0.542,0.394,0.437,0.549,0.481,0.533,0.623,0.576,0.532,0.555,0.529,0.502,0.162,0.314,0.443,0.548,0.35,0.561,0.251,0.412,0.3,0.458,0.415,0.434,0.405,0.436,0.415,0.424,0.421,0.417,0.662,OrganismalFitness,DLG4_HUMAN,Low,Human +0.407,0.404,0.398,0.423,0.435,0.447,0.338,0.359,0.383,0.401,0.371,0.453,0.462,0.334,0.438,0.447,0.432,0.398,0.347,0.35,0.298,0.31,0.31,0.292,0.304,0.383,0.341,0.301,0.334,0.371,0.426,0.417,0.207,0.286,0.304,0.252,0.389,0.392,0.371,0.438,0.444,0.441,0.404,0.049,0.328,0.407,0.334,0.258,0.371,0.119,0.341,0.31,0.347,0.331,0.353,0.338,0.307,0.347,0.359,0.362,0.404,0.42,Binding,DLG4_RAT,Low,Eukaryote +0.161,0.117,0.188,0.173,0.185,0.208,-0.093,0.268,0.252,0.248,-0.014,0.038,0.034,0.006,0.093,0.089,0.216,0.212,0.256,0.177,-0.069,-0.002,-0.01,0.034,0.03,0.058,-0.006,0.03,0.121,0.204,0.3,0.272,0.01,-0.022,-0.026,-0.014,0.157,0.157,0.161,0.2,0.181,0.173,0.034,-0.077,0.133,0.042,0.45,0.438,0.522,0.423,0.276,0.232,0.343,0.331,0.308,0.315,0.288,0.268,0.272,0.308,0.387,0.335,Stability,DN7A_SACS2,Medium,Prokaryote +0.724,0.742,0.684,0.693,0.724,0.726,0.633,0.689,0.716,0.735,0.723,0.716,0.749,0.765,0.777,0.779,0.758,0.71,0.763,0.703,0.597,0.648,0.68,0.652,0.684,0.661,0.689,0.588,0.707,0.712,0.67,0.648,0.581,0.7,0.673,0.74,0.76,0.76,0.784,0.728,0.737,0.754,0.532,0.069,0.309,0.551,0.502,0.401,0.714,0.726,0.721,0.742,0.751,0.739,0.758,0.747,0.777,0.731,0.762,0.754,0.712,0.733,Stability,DNJA1_HUMAN,High,Human +0.321,0.337,0.321,0.327,0.36,0.359,0.046,0.317,0.268,0.28,0.309,0.332,0.354,0.184,0.337,0.359,0.35,0.272,0.335,0.342,0.241,0.298,0.262,0.272,0.247,0.268,0.214,0.21,0.208,0.319,0.261,0.274,0.153,0.214,0.249,0.227,0.372,0.386,0.352,0.35,0.37,0.354,0.224,-0.155,0.321,0.265,0.231,0.234,0.286,0.297,0.287,0.258,0.265,0.273,0.268,0.279,0.283,0.276,0.278,0.265,0.365,0.357,Stability,DOCK1_MOUSE,High,Eukaryote +0.314,0.408,0.409,0.403,0.419,0.419,-0.024,0.324,0.399,0.414,0.412,0.373,0.389,0.052,0.347,0.392,0.412,0.428,0.426,0.408,0.272,0.347,0.258,0.308,0.357,0.402,0.404,0.403,0.368,0.408,0.406,0.396,0.162,0.355,0.297,0.312,0.383,0.372,0.382,0.418,0.422,0.416,0.34,-0.01,0.396,0.367,0.193,0.359,0.28,0.081,0.372,0.353,0.349,0.368,0.372,0.375,0.382,0.368,0.388,0.382,0.406,0.294,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.314,0.408,0.409,0.403,0.419,0.419,-0.024,0.324,0.399,0.414,0.412,0.373,0.389,0.052,0.347,0.392,0.412,0.428,0.426,0.408,0.272,0.347,0.258,0.308,0.357,0.402,0.404,0.403,0.368,0.408,0.406,0.396,0.162,0.355,0.297,0.312,0.383,0.372,0.382,0.418,0.422,0.416,0.34,-0.01,0.396,0.367,0.193,0.359,0.28,0.081,0.372,0.353,0.349,0.368,0.372,0.375,0.382,0.368,0.388,0.382,0.406,0.294,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.314,0.408,0.409,0.403,0.419,0.419,-0.024,0.324,0.399,0.414,0.412,0.373,0.389,0.052,0.347,0.392,0.412,0.428,0.426,0.408,0.272,0.347,0.258,0.308,0.357,0.402,0.404,0.403,0.368,0.408,0.406,0.396,0.162,0.355,0.297,0.312,0.383,0.372,0.382,0.418,0.422,0.416,0.34,-0.01,0.396,0.367,0.193,0.359,0.28,0.081,0.372,0.353,0.349,0.368,0.372,0.375,0.382,0.368,0.388,0.382,0.406,0.294,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.314,0.408,0.409,0.403,0.419,0.419,-0.024,0.324,0.399,0.414,0.412,0.373,0.389,0.052,0.347,0.392,0.412,0.428,0.426,0.408,0.272,0.347,0.258,0.308,0.357,0.402,0.404,0.403,0.368,0.408,0.406,0.396,0.162,0.355,0.297,0.312,0.383,0.372,0.382,0.418,0.422,0.416,0.34,-0.01,0.396,0.367,0.193,0.359,0.28,0.081,0.372,0.353,0.349,0.368,0.372,0.375,0.382,0.368,0.388,0.382,0.406,0.294,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.138,0.131,0.123,0.142,0.187,0.187,0.001,0.157,0.135,0.127,0.131,0.183,0.194,0.146,0.164,0.157,0.161,0.142,0.12,0.153,0.138,0.149,0.168,0.176,0.135,0.153,0.202,0.19,0.19,0.164,0.157,0.135,0.064,0.123,0.153,0.194,0.176,0.179,0.198,0.183,0.202,0.202,0.127,0.068,0.164,0.168,0.049,0.146,0.101,0.042,0.164,0.172,0.131,0.161,0.138,0.164,0.157,0.153,0.176,0.164,0.112,0.183,Activity,ENVZ_ECOLI,High,Prokaryote +0.298,0.321,0.286,0.298,0.357,0.321,0.004,0.263,0.286,0.286,0.31,0.38,0.357,0.039,0.075,0.051,-0.008,0.039,0.004,0.298,0.357,0.321,0.321,0.321,0.298,0.357,0.333,0.286,0.321,0.38,0.368,0.353,0.251,0.345,0.31,0.31,0.392,0.345,0.345,0.345,0.333,0.333,0.274,0.004,0.31,0.333,0.333,0.286,0.274,0.216,0.18,0.274,0.298,0.204,0.157,0.216,0.216,0.204,0.157,0.216,0.122,0.086,OrganismalFitness,ENV_HV1B9,Medium,Virus +0.251,0.241,0.235,0.237,0.251,0.256,-0.003,0.237,0.264,0.267,0.23,0.241,0.26,-0.006,-0.004,-0.001,0.027,0.043,0.107,0.251,0.271,0.269,0.275,0.279,0.256,0.272,0.268,0.263,0.271,0.277,0.248,0.223,0.158,0.26,0.27,0.27,0.277,0.279,0.274,0.274,0.277,0.27,0.163,-0.011,0.24,0.23,0.143,0.162,0.069,0.049,0.124,0.115,0.133,0.156,0.129,0.136,0.14,0.134,0.133,0.142,0.108,0.058,OrganismalFitness,ENV_HV1BR,Medium,Virus +0.612,0.608,0.631,0.637,0.682,0.686,-0.261,0.655,0.749,0.753,0.755,0.745,0.773,-0.3,0.725,0.788,0.804,0.735,0.708,0.688,0.476,0.547,0.659,0.616,0.606,0.643,0.637,0.684,0.686,0.798,0.737,0.725,0.604,0.573,0.541,0.631,0.651,0.661,0.694,0.682,0.68,0.667,0.569,-0.253,0.553,0.541,0.492,0.537,0.741,0.657,0.794,0.778,0.788,0.776,0.8,0.796,0.802,0.794,0.786,0.802,0.751,0.745,Stability,EPHB2_HUMAN,High,Human +0.351,0.301,0.191,0.203,0.203,0.24,0.338,0.338,0.326,0.351,0.363,0.338,0.4,0.351,0.351,0.388,0.326,0.351,0.289,0.043,0.351,0.326,0.375,0.351,0.363,0.351,0.449,0.437,0.388,0.375,0.314,0.05,0.4,0.363,0.338,0.351,0.4,0.4,0.338,0.363,0.363,0.351,0.388,0.363,0.412,0.4,0.314,0.363,-0.105,0.043,0.351,0.301,0.363,0.289,0.301,0.314,0.338,0.314,0.338,0.351,0.412,0.4,Expression,ERBB2_HUMAN,Low,Human +0.216,0.286,0.307,0.314,0.29,0.288,0.13,0.237,0.248,0.305,0.237,0.218,0.248,0.113,0.18,0.2,0.224,0.235,0.27,0.231,0.054,0.137,0.209,0.227,0.181,0.205,0.227,0.205,0.301,0.279,0.355,0.298,0.064,0.113,0.191,0.202,0.238,0.244,0.238,0.312,0.301,0.31,0.126,0.1,0.207,0.185,0.425,0.367,0.426,0.272,0.272,0.259,0.253,0.242,0.262,0.248,0.25,0.273,0.272,0.266,0.318,0.152,Stability,ESTA_BACSU,High,Prokaryote +0.047,0.227,0.239,0.258,0.257,0.258,0.013,0.183,0.232,0.241,0.25,0.228,0.237,-0.043,0.009,0.049,0.248,0.237,0.264,0.225,-0.029,-0.07,-0.066,-0.014,0.076,0.071,-0.009,0.19,0.238,0.211,0.27,0.263,0.04,-0.006,-0.07,0.244,0.046,0.011,0.252,0.237,0.227,0.264,-0.006,-0.023,0.134,-0.008,-0.007,0.159,0.037,0.023,0.268,0.264,0.248,0.273,0.275,0.271,0.258,0.265,0.266,0.269,0.181,-0.079,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.047,0.227,0.239,0.258,0.257,0.258,0.013,0.183,0.232,0.241,0.25,0.228,0.237,-0.043,0.009,0.049,0.248,0.237,0.264,0.225,-0.029,-0.07,-0.066,-0.014,0.076,0.071,-0.009,0.19,0.238,0.211,0.27,0.263,0.04,-0.006,-0.07,0.244,0.046,0.011,0.252,0.237,0.227,0.264,-0.006,-0.023,0.134,-0.008,-0.007,0.159,0.037,0.023,0.268,0.264,0.248,0.273,0.275,0.271,0.258,0.265,0.266,0.269,0.181,-0.079,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.351,0.359,0.357,0.366,0.374,0.364,0.037,0.245,0.292,0.353,0.396,0.198,0.326,0.039,0.34,0.451,0.425,0.376,0.29,0.353,0.12,0.226,0.264,0.247,0.101,0.391,0.41,0.209,0.315,0.381,0.366,0.345,0.268,0.06,0.086,0.09,0.302,0.304,0.222,0.326,0.349,0.306,0.29,-0.022,0.362,0.343,0.436,0.391,0.485,0.419,0.44,0.374,0.423,0.4,0.393,0.432,0.425,0.391,0.41,0.419,0.476,0.483,Stability,FECA_ECOLI,High,Prokaryote +0.3,0.271,0.374,0.378,0.378,0.387,0.135,0.248,0.18,0.177,0.196,0.164,0.164,0.171,0.148,0.154,0.151,0.216,0.277,0.216,0.209,0.203,0.196,0.167,0.141,0.158,0.18,0.177,0.184,0.374,0.235,0.263,-0.033,0.109,0.167,0.196,0.274,0.284,0.251,0.361,0.371,0.348,0.154,0.164,0.174,0.2,0.555,0.491,0.53,0.468,0.232,0.31,0.329,0.284,0.281,0.303,0.274,0.258,0.232,0.297,0.445,0.258,Stability,FKBP3_HUMAN,Medium,Human +0.193,0.295,0.31,0.375,0.342,0.346,0.231,-0.007,0.342,0.426,0.426,0.335,0.353,0.255,0.27,0.382,0.466,0.451,0.441,0.346,0.204,0.241,0.284,0.284,0.237,0.291,0.375,0.331,0.397,0.451,0.451,0.399,0.186,0.171,0.201,0.197,0.342,0.351,0.375,0.331,0.313,0.321,0.288,0.193,0.419,0.339,0.186,0.368,0.259,0.102,0.404,0.415,0.39,0.397,0.426,0.386,0.397,0.393,0.408,0.419,0.422,0.306,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +0.192,0.175,0.178,0.184,0.16,0.158,0.119,0.157,0.18,0.175,0.169,0.212,0.216,0.246,0.215,0.201,0.215,0.187,0.169,0.166,0.113,0.136,0.107,0.105,0.096,0.089,0.061,0.095,0.122,0.14,0.183,0.186,0.033,0.114,0.116,0.231,0.195,0.199,0.228,0.193,0.19,0.215,0.037,0.084,0.087,0.046,0.105,0.125,0.172,0.16,0.154,0.145,0.143,0.139,0.151,0.149,0.143,0.149,0.158,0.148,0.171,0.183,Binding,GCN4_YEAST,Low,Eukaryote +0.338,0.341,0.366,0.348,0.338,0.338,0.102,0.279,0.373,0.369,0.293,0.307,0.328,0.113,0.161,0.293,0.289,0.258,0.317,0.289,0.182,0.279,0.289,0.307,0.213,0.341,0.31,0.258,0.279,0.31,0.348,0.314,0.185,0.217,0.248,0.255,0.317,0.307,0.331,0.338,0.355,0.359,0.175,0.088,0.317,0.172,0.217,0.289,0.248,0.095,0.272,0.248,0.293,0.272,0.276,0.289,0.279,0.272,0.262,0.314,0.334,0.213,OrganismalFitness,GDIA_HUMAN,Low,Human +0.584,0.582,0.613,0.615,0.618,0.618,0.061,0.583,0.604,0.596,0.473,0.097,0.097,0.081,0.119,0.09,0.106,0.14,0.255,0.537,0.087,0.11,0.167,0.089,0.048,0.166,0.261,0.584,0.584,0.619,0.546,0.555,0.058,0.069,0.163,0.573,0.581,0.583,0.618,0.624,0.627,0.65,0.023,-0.006,0.036,0.036,0.456,0.445,0.67,0.535,0.529,0.537,0.546,0.554,0.546,0.545,0.542,0.543,0.525,0.541,0.562,0.402,Activity,GFP_AEQVI,Low,Eukaryote +0.114,-0.045,0.102,0.135,0.102,0.07,0.331,0.413,0.282,0.25,0.446,0.446,0.446,0.315,0.446,0.462,0.495,0.381,0.43,0.152,0.381,0.397,0.43,0.413,0.348,0.413,0.462,0.43,0.479,0.209,0.364,0.136,0.168,0.43,0.364,0.43,0.397,0.381,0.397,0.299,0.25,0.364,0.413,0.397,0.462,0.397,0.331,0.495,0.266,0.168,0.413,0.381,0.495,0.43,0.446,0.397,0.462,0.397,0.413,0.479,0.413,0.413,Expression,GLPA_HUMAN,Low,Human +0.314,0.392,0.384,0.411,0.404,0.406,0.403,0.338,0.405,0.365,0.39,0.337,0.378,0.43,0.464,0.474,0.496,0.396,0.429,0.407,0.419,0.396,0.4,0.344,0.419,0.397,0.341,0.409,0.342,0.376,0.308,0.308,0.361,0.418,0.385,0.31,0.39,0.381,0.331,0.421,0.421,0.397,0.451,0.235,0.399,0.435,0.54,0.401,0.548,0.396,0.484,0.518,0.507,0.505,0.511,0.494,0.496,0.512,0.491,0.514,0.421,0.468,OrganismalFitness,GRB2_HUMAN,Medium,Human +0.238,0.312,0.092,0.123,0.269,0.281,0.188,0.288,0.346,0.342,0.458,0.35,0.377,0.238,0.315,0.419,0.473,0.412,0.431,0.192,0.212,0.219,0.273,0.288,0.254,0.273,0.162,0.265,0.392,0.408,0.477,0.36,0.115,0.227,0.223,0.369,0.312,0.315,0.373,0.25,0.323,0.377,0.223,0.235,0.458,0.308,0.492,0.527,0.573,0.408,0.519,0.531,0.523,0.523,0.535,0.531,0.504,0.535,0.523,0.535,0.577,0.45,Stability,HCP_LAMBD,Medium,Virus +0.297,0.324,0.238,0.244,0.344,0.353,0.172,0.294,0.286,0.284,0.253,0.209,0.231,0.169,0.175,0.37,0.326,0.277,0.272,0.303,0.201,0.261,0.333,0.312,0.252,0.32,0.307,0.288,0.302,0.331,0.305,0.308,0.132,0.186,0.215,0.24,0.368,0.345,0.372,0.351,0.335,0.356,0.105,0.079,0.354,0.141,0.222,0.336,0.331,0.266,0.272,0.32,0.351,0.332,0.335,0.372,0.327,0.342,0.329,0.348,0.34,0.29,Stability,HECD1_HUMAN,Medium,Human +0.298,0.313,0.295,0.304,0.313,0.311,0.1,0.096,0.32,0.33,0.311,0.283,0.306,0.105,0.273,0.299,0.31,0.321,0.337,0.115,0.239,0.292,0.307,0.308,0.254,0.297,0.295,0.305,0.321,0.318,0.313,0.312,0.061,0.277,0.291,0.313,0.306,0.328,0.347,0.307,0.325,0.344,0.22,0.106,0.292,0.26,0.228,0.28,0.224,0.115,0.274,0.268,0.271,0.288,0.294,0.285,0.297,0.292,0.293,0.301,0.339,0.303,Activity,HEM3_HUMAN,Medium,Human +0.355,0.41,0.44,0.433,0.409,0.41,0.114,0.218,0.343,0.377,0.354,0.333,0.377,0.038,0.089,0.347,0.282,0.341,0.367,0.342,0.237,0.303,0.337,0.346,0.294,0.375,0.339,0.378,0.394,0.419,0.307,0.301,0.115,0.296,0.325,0.475,0.376,0.377,0.511,0.444,0.424,0.463,0.127,0.013,0.212,0.002,0.322,0.342,0.437,0.3,0.342,0.249,0.295,0.326,0.331,0.308,0.306,0.338,0.319,0.317,0.399,0.136,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +0.368,0.321,0.289,0.297,0.323,0.319,0.116,0.171,0.206,0.198,0.209,0.242,0.225,0.032,0.014,0.231,0.399,0.278,0.283,0.222,0.346,0.092,0.113,0.154,0.374,0.126,0.113,0.143,0.147,0.354,0.3,0.301,0.173,0.241,0.183,0.154,0.361,0.297,0.285,0.342,0.296,0.294,0.114,0.041,0.362,0.389,0.229,0.349,-0.175,0.074,0.378,0.376,0.367,0.378,0.379,0.373,0.37,0.382,0.374,0.382,0.42,0.327,OrganismalFitness,HMDH_HUMAN,Low,Human +0.315,0.337,0.345,0.352,0.334,0.332,0.189,0.301,0.356,0.358,0.323,0.355,0.374,0.065,0.148,0.205,0.262,0.303,0.306,0.249,0.322,0.331,0.328,0.348,0.321,0.337,0.357,0.343,0.361,0.349,0.374,0.351,0.183,0.314,0.328,0.325,0.338,0.348,0.346,0.343,0.35,0.348,0.153,-0.039,0.339,0.317,0.105,0.256,0.098,0.02,0.251,0.259,0.253,0.272,0.265,0.262,0.268,0.261,0.252,0.268,0.335,0.282,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.315,0.337,0.345,0.352,0.334,0.332,0.189,0.301,0.356,0.358,0.323,0.355,0.374,0.065,0.148,0.205,0.262,0.303,0.306,0.249,0.322,0.331,0.328,0.348,0.321,0.337,0.357,0.343,0.361,0.349,0.374,0.351,0.183,0.314,0.328,0.325,0.338,0.348,0.346,0.343,0.35,0.348,0.153,-0.039,0.339,0.317,0.105,0.256,0.098,0.02,0.251,0.259,0.253,0.272,0.265,0.262,0.268,0.261,0.252,0.268,0.335,0.282,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.315,0.337,0.345,0.352,0.334,0.332,0.189,0.301,0.356,0.358,0.323,0.355,0.374,0.065,0.148,0.205,0.262,0.303,0.306,0.249,0.322,0.331,0.328,0.348,0.321,0.337,0.357,0.343,0.361,0.349,0.374,0.351,0.183,0.314,0.328,0.325,0.338,0.348,0.346,0.343,0.35,0.348,0.153,-0.039,0.339,0.317,0.105,0.256,0.098,0.02,0.251,0.259,0.253,0.272,0.265,0.262,0.268,0.261,0.252,0.268,0.335,0.282,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.336,0.368,0.347,0.358,0.349,0.356,0.111,0.296,0.386,0.401,0.335,0.336,0.356,0.113,0.174,0.351,0.37,0.376,0.358,0.19,0.309,0.308,0.304,0.298,0.304,0.316,0.32,0.32,0.3,0.368,0.352,0.319,0.145,0.32,0.298,0.272,0.356,0.344,0.33,0.37,0.369,0.365,0.18,0.05,0.36,0.344,0.306,0.351,0.326,0.116,0.376,0.37,0.37,0.373,0.374,0.382,0.37,0.372,0.366,0.378,0.404,0.33,OrganismalFitness,HXK4_HUMAN,Medium,Human +0.336,0.368,0.347,0.358,0.349,0.356,0.111,0.296,0.386,0.401,0.335,0.336,0.356,0.113,0.174,0.351,0.37,0.376,0.358,0.19,0.309,0.308,0.304,0.298,0.304,0.316,0.32,0.32,0.3,0.368,0.352,0.319,0.145,0.32,0.298,0.272,0.356,0.344,0.33,0.37,0.369,0.365,0.18,0.05,0.36,0.344,0.306,0.351,0.326,0.116,0.376,0.37,0.37,0.373,0.374,0.382,0.37,0.372,0.366,0.378,0.404,0.33,Expression,HXK4_HUMAN,Medium,Human +0.287,0.24,0.213,0.208,0.274,0.277,-0.008,0.234,0.217,0.239,0.008,0.015,0.008,0.026,0.02,0.012,0.015,0.006,0.054,0.151,0.24,0.245,0.286,0.283,0.013,0.019,0.085,0.003,0.23,0.284,0.179,0.183,0.111,0.211,0.242,0.247,0.225,0.252,0.256,0.28,0.306,0.306,0.016,0.015,0.016,0.017,0.166,0.149,0.182,0.078,0.048,0.075,0.101,0.097,0.079,0.072,0.059,0.067,0.051,0.072,0.024,0.007,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +0.262,0.37,0.443,0.456,0.418,0.437,0.086,0.277,0.195,0.163,0.405,0.43,0.44,0.08,0.306,0.414,0.446,0.446,0.424,0.418,0.242,0.328,0.223,0.297,0.341,0.354,0.344,0.306,0.354,0.313,0.386,0.335,0.214,0.354,0.373,0.398,0.367,0.363,0.36,0.418,0.421,0.437,0.252,0.093,0.449,0.373,0.23,0.398,0.348,0.172,0.408,0.408,0.408,0.43,0.389,0.408,0.389,0.411,0.414,0.421,0.456,0.379,OrganismalFitness,IF1_ECOLI,High,Prokaryote +0.295,0.387,0.425,0.418,0.415,0.422,0.176,0.271,0.38,0.404,0.313,0.331,0.352,0.264,0.366,0.422,0.26,0.246,0.18,0.432,0.127,0.187,0.274,0.302,0.317,0.222,0.324,0.243,0.267,0.411,0.352,0.316,0.025,0.173,0.232,0.313,0.302,0.345,0.359,0.39,0.387,0.415,0.299,0.176,0.306,0.38,0.415,0.366,0.432,0.331,0.267,0.229,0.288,0.25,0.278,0.331,0.292,0.25,0.341,0.324,0.292,0.362,Stability,ILF3_HUMAN,High,Human +0.065,0.108,0.139,0.147,0.151,0.176,0.25,0.124,0.281,0.266,0.378,0.326,0.326,0.271,0.291,0.336,0.324,0.371,0.326,0.131,0.219,0.145,0.106,0.166,0.221,0.242,0.221,0.211,0.242,0.275,0.334,0.315,0.17,0.192,0.174,0.184,0.155,0.139,0.137,0.194,0.157,0.17,0.199,0.178,0.252,0.24,0.427,0.353,0.419,0.4,0.32,0.324,0.345,0.332,0.33,0.345,0.316,0.324,0.332,0.334,0.437,0.359,Stability,ISDH_STAAW,High,Prokaryote +0.238,0.266,0.276,0.285,0.271,0.281,0.145,0.248,0.268,0.28,0.22,0.265,0.302,0.154,0.13,0.336,0.286,0.248,0.216,0.178,0.174,0.192,0.245,0.256,0.189,0.296,0.33,0.312,0.292,0.259,0.318,0.254,-0.04,0.146,0.23,0.327,0.238,0.252,0.334,0.283,0.291,0.347,0.163,0.142,0.295,0.158,0.158,0.256,0.158,0.046,0.28,0.283,0.276,0.274,0.282,0.266,0.291,0.276,0.292,0.292,0.356,0.16,Expression,KCNE1_HUMAN,Medium,Human +0.238,0.266,0.276,0.285,0.271,0.281,0.145,0.248,0.268,0.28,0.22,0.265,0.302,0.154,0.13,0.336,0.286,0.248,0.216,0.178,0.174,0.192,0.245,0.256,0.189,0.296,0.33,0.312,0.292,0.259,0.318,0.254,-0.04,0.146,0.23,0.327,0.238,0.252,0.334,0.283,0.291,0.347,0.163,0.142,0.295,0.158,0.158,0.256,0.158,0.046,0.28,0.283,0.276,0.274,0.282,0.266,0.291,0.276,0.292,0.292,0.356,0.16,Activity,KCNE1_HUMAN,Medium,Human +0.36,0.4,0.18,0.18,0.18,0.16,0.36,0.04,0.26,0.26,0.2,0.18,0.16,0.18,0.12,0.16,0.16,0.16,0.12,0.36,0.36,0.36,0.34,0.28,0.38,0.32,0.32,0.28,0.28,0.281,0.3,0.14,0.28,0.32,0.36,0.36,0.36,0.4,0.4,0.3,0.4,0.36,0.32,0.14,0.16,0.22,0.12,0.0,-0.12,0.06,0.4,0.3,0.4,0.34,0.3,0.34,0.38,0.34,0.4,0.36,0.18,0.36,Activity,KCNH2_HUMAN,Medium,Human +0.238,0.249,0.213,0.23,0.25,0.252,0.061,0.163,0.221,0.217,0.256,0.237,0.248,0.073,0.223,0.266,0.272,0.274,0.284,0.142,0.213,0.19,0.174,0.178,0.219,0.224,0.206,0.225,0.174,0.267,0.246,0.221,0.116,0.202,0.204,0.164,0.242,0.245,0.226,0.251,0.258,0.247,0.16,0.017,0.22,0.246,0.161,0.192,0.176,0.078,0.268,0.251,0.261,0.25,0.258,0.244,0.26,0.255,0.256,0.262,0.258,0.218,Activity,KCNJ2_MOUSE,Medium,Eukaryote +0.238,0.249,0.213,0.23,0.25,0.252,0.061,0.163,0.221,0.217,0.256,0.237,0.248,0.073,0.223,0.266,0.272,0.274,0.284,0.142,0.213,0.19,0.174,0.178,0.219,0.224,0.206,0.225,0.174,0.267,0.246,0.221,0.116,0.202,0.204,0.164,0.242,0.245,0.226,0.251,0.258,0.247,0.16,0.017,0.22,0.246,0.161,0.192,0.176,0.078,0.268,0.251,0.261,0.25,0.258,0.244,0.26,0.255,0.256,0.262,0.258,0.218,Expression,KCNJ2_MOUSE,Medium,Eukaryote +0.221,0.452,0.366,0.516,0.499,0.508,0.148,0.38,0.449,0.509,0.471,0.488,0.512,0.165,0.199,0.404,0.486,0.531,0.556,0.394,0.213,0.331,0.418,0.467,0.268,0.488,0.486,0.473,0.526,0.523,0.548,0.499,0.113,0.184,0.407,0.495,0.346,0.429,0.487,0.495,0.511,0.526,0.191,0.069,0.467,0.324,0.362,0.486,0.439,0.185,0.479,0.475,0.478,0.478,0.476,0.482,0.476,0.482,0.488,0.491,0.53,0.239,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +0.233,0.292,0.34,0.345,0.332,0.344,0.089,0.352,0.348,0.414,0.336,0.394,0.424,0.125,0.241,0.3,0.391,0.433,0.42,0.381,0.228,0.316,0.366,0.378,0.292,0.39,0.402,0.367,0.409,0.39,0.406,0.338,0.114,0.253,0.312,0.422,0.28,0.31,0.389,0.339,0.357,0.393,0.128,0.047,0.368,0.278,0.29,0.371,0.358,0.106,0.387,0.366,0.383,0.374,0.375,0.395,0.393,0.392,0.395,0.397,0.301,0.251,Activity,LGK_LIPST,Medium,Eukaryote +0.26,0.26,0.148,0.159,0.215,0.226,0.26,0.137,0.304,0.327,0.315,0.26,0.293,0.193,0.237,0.193,0.226,0.26,0.349,0.193,0.237,0.26,0.304,0.282,0.215,0.304,0.271,0.182,0.182,0.289,0.204,0.144,0.137,0.282,0.315,0.215,0.282,0.304,0.282,0.204,0.249,0.204,0.215,0.215,0.237,0.249,0.103,0.148,0.115,0.025,0.182,0.249,0.26,0.226,0.193,0.204,0.237,0.271,0.193,0.249,0.293,0.226,Expression,LYAM1_HUMAN,Medium,Human +0.422,0.536,0.481,0.481,0.498,0.486,0.327,0.649,0.581,0.563,0.643,0.309,0.584,0.416,0.395,0.436,0.457,0.498,0.339,0.575,0.365,0.557,0.546,0.569,0.498,0.528,0.469,0.531,0.472,0.43,0.548,0.522,0.383,0.247,0.513,0.525,0.454,0.566,0.599,0.522,0.599,0.64,0.265,0.038,0.36,0.236,0.516,0.301,0.548,0.566,0.652,0.634,0.628,0.64,0.637,0.634,0.637,0.649,0.661,0.64,0.593,0.64,Stability,MAFG_MOUSE,Medium,Eukaryote +0.518,0.657,0.614,0.628,0.675,0.681,-0.026,0.471,0.612,0.675,0.658,0.333,0.461,-0.066,0.104,0.692,0.612,0.605,0.677,0.65,0.04,0.1,0.544,0.592,0.221,0.476,0.444,0.512,0.667,0.692,0.662,0.615,0.558,-0.242,0.104,0.093,0.488,0.578,0.565,0.63,0.667,0.658,0.03,-0.106,0.578,0.34,0.399,0.541,0.664,0.446,0.698,0.673,0.675,0.681,0.66,0.662,0.671,0.692,0.703,0.688,0.673,0.609,Stability,MBD11_ARATH,Medium,Eukaryote +0.453,0.528,0.52,0.524,0.554,0.558,0.489,0.49,0.517,0.527,0.55,0.503,0.517,0.414,0.471,0.494,0.555,0.579,0.572,0.563,0.497,0.482,0.435,0.461,0.496,0.503,0.499,0.452,0.423,0.507,0.528,0.494,0.26,0.463,0.467,0.489,0.479,0.499,0.527,0.524,0.531,0.543,0.503,0.303,0.535,0.544,0.328,0.388,0.476,0.173,0.509,0.521,0.513,0.522,0.526,0.533,0.521,0.534,0.531,0.532,0.544,0.49,Activity,MET_HUMAN,Medium,Human +0.152,0.19,0.209,0.212,0.193,0.197,0.173,0.149,0.195,0.171,0.069,0.156,0.171,0.132,0.173,0.171,0.166,0.178,0.155,0.172,0.206,0.131,0.099,0.048,0.161,0.104,0.081,0.087,-0.021,0.19,0.171,0.198,0.119,0.163,0.093,0.054,0.176,0.138,0.125,0.202,0.185,0.179,0.149,0.1,0.146,0.162,0.068,0.016,0.122,0.004,0.158,0.155,0.153,0.171,0.169,0.156,0.163,0.168,0.165,0.167,0.168,0.166,OrganismalFitness,MK01_HUMAN,Medium,Human +0.176,0.274,0.344,0.354,0.337,0.33,-0.022,0.271,0.346,0.359,0.302,0.343,0.354,-0.022,0.174,0.207,0.312,0.295,0.313,0.295,0.042,0.266,0.274,0.309,0.248,0.314,0.306,0.301,0.339,0.32,0.356,0.319,0.028,0.103,0.218,0.216,0.203,0.249,0.231,0.286,0.306,0.303,0.108,-0.05,0.244,0.174,0.027,0.154,0.112,-0.012,0.244,0.214,0.235,0.23,0.239,0.242,0.222,0.233,0.268,0.24,0.16,0.151,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +0.308,0.343,0.326,0.335,0.335,0.338,0.177,0.276,0.342,0.348,0.308,0.328,0.34,0.184,0.287,0.338,0.324,0.268,0.181,0.335,0.245,0.302,0.283,0.277,0.28,0.315,0.33,0.306,0.289,0.34,0.327,0.309,0.176,0.235,0.29,0.277,0.299,0.326,0.326,0.337,0.343,0.34,0.222,0.095,0.332,0.283,0.258,0.317,0.044,0.089,0.279,0.262,0.272,0.279,0.297,0.275,0.277,0.275,0.298,0.288,0.343,0.306,OrganismalFitness,MSH2_HUMAN,Medium,Human +0.259,0.419,0.447,0.453,0.458,0.442,0.215,0.414,0.435,0.442,0.38,0.455,0.45,0.153,0.21,0.236,0.336,0.393,0.435,0.429,0.202,0.298,0.393,0.429,0.323,0.437,0.445,0.447,0.46,0.45,0.455,0.429,0.215,0.212,0.331,0.427,0.261,0.329,0.419,0.396,0.419,0.458,0.223,0.026,0.321,0.212,0.287,0.37,0.357,0.119,0.36,0.334,0.36,0.354,0.37,0.378,0.357,0.362,0.354,0.365,0.378,0.215,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +0.169,0.177,0.167,0.173,0.175,0.17,0.113,0.111,0.2,0.204,0.258,0.2,0.213,0.108,0.261,0.335,0.255,0.191,0.203,0.178,0.311,0.105,0.141,0.182,0.297,0.156,0.18,0.17,0.221,0.203,0.149,0.139,0.087,0.309,0.219,0.133,0.233,0.203,0.158,0.219,0.193,0.171,0.174,0.07,0.251,0.262,0.159,0.237,0.27,0.066,0.232,0.207,0.211,0.225,0.223,0.234,0.249,0.245,0.254,0.238,0.223,0.245,OrganismalFitness,MTHR_HUMAN,Low,Human +0.156,0.169,0.255,0.261,0.262,0.298,0.104,0.207,0.33,0.321,0.394,0.376,0.39,0.136,0.323,0.442,0.44,0.393,0.206,0.244,0.249,0.18,0.244,0.213,0.244,0.176,0.251,0.19,0.222,0.189,0.225,0.13,0.114,0.309,0.216,0.221,0.287,0.26,0.246,0.302,0.298,0.283,0.1,-0.002,0.313,0.223,0.297,0.336,0.379,0.257,0.421,0.398,0.43,0.406,0.418,0.421,0.406,0.413,0.401,0.411,0.431,0.39,Stability,MYO3_YEAST,High,Eukaryote +0.29,0.24,0.248,0.25,0.283,0.282,0.012,0.241,0.275,0.252,0.029,0.026,0.025,0.037,0.034,0.026,0.04,0.034,0.089,0.215,0.266,0.264,0.293,0.288,0.03,0.058,0.098,0.055,0.256,0.294,0.199,0.211,0.105,0.259,0.272,0.295,0.281,0.301,0.321,0.325,0.324,0.339,0.035,0.019,0.029,0.035,0.196,0.196,0.198,0.081,0.102,0.128,0.133,0.149,0.135,0.129,0.116,0.134,0.107,0.13,0.096,0.066,OrganismalFitness,NCAP_I34A1,Medium,Virus +0.272,0.343,0.463,0.459,0.511,0.47,0.399,0.482,0.5,0.509,0.534,0.532,0.535,0.497,0.417,0.472,0.458,0.422,0.467,0.444,0.479,0.459,0.477,0.475,0.436,0.435,0.47,0.479,0.507,0.498,0.435,0.42,0.481,0.431,0.438,0.486,0.442,0.447,0.498,0.489,0.505,0.512,0.33,0.24,0.258,0.306,0.431,0.256,0.497,0.511,0.523,0.53,0.527,0.535,0.535,0.509,0.525,0.518,0.523,0.528,0.525,0.498,Stability,NKX31_HUMAN,High,Human +0.588,0.484,0.28,0.328,0.488,0.512,0.226,0.346,0.472,0.498,0.516,0.145,0.256,0.115,0.256,0.509,0.516,0.488,0.488,0.13,0.36,0.346,0.349,0.224,0.332,0.368,0.372,0.282,0.286,0.452,0.608,0.447,0.264,0.213,0.244,0.374,0.538,0.449,0.535,0.563,0.472,0.519,0.284,0.124,0.605,0.315,0.324,0.465,0.085,0.13,0.484,0.512,0.516,0.491,0.519,0.506,0.478,0.484,0.488,0.516,0.56,0.31,Activity,NPC1_HUMAN,Low,Human +0.588,0.484,0.28,0.328,0.488,0.512,0.226,0.346,0.472,0.498,0.516,0.145,0.256,0.115,0.256,0.509,0.516,0.488,0.488,0.13,0.36,0.346,0.349,0.224,0.332,0.368,0.372,0.282,0.286,0.452,0.608,0.447,0.264,0.213,0.244,0.374,0.538,0.449,0.535,0.563,0.472,0.519,0.284,0.124,0.605,0.315,0.324,0.465,0.085,0.13,0.484,0.512,0.516,0.491,0.519,0.506,0.478,0.484,0.488,0.516,0.56,0.31,Activity,NPC1_HUMAN,Low,Human +0.477,0.45,0.356,0.329,0.45,0.463,0.06,0.289,0.477,0.477,-0.02,0.114,0.369,-0.06,-0.034,0.06,0.154,0.396,0.45,0.302,0.342,0.477,0.396,0.409,0.06,0.409,0.45,0.329,0.423,0.53,0.289,0.342,0.007,0.356,0.383,0.396,0.436,0.503,0.544,0.503,0.544,0.477,-0.034,-0.034,-0.06,-0.074,0.356,0.289,0.369,0.154,0.248,0.195,0.181,0.221,0.154,0.235,0.221,0.208,0.154,0.168,0.221,0.128,OrganismalFitness,NRAM_I33A0,Low,Virus +0.218,0.386,0.423,0.454,0.443,0.446,0.01,0.317,0.469,0.528,0.478,0.472,0.514,0.206,0.32,0.35,0.405,0.472,0.475,0.393,0.221,0.356,0.44,0.42,0.308,0.49,0.471,0.454,0.408,0.417,0.502,0.449,0.148,0.254,0.326,0.454,0.287,0.314,0.456,0.435,0.434,0.472,0.3,0.021,0.481,0.327,0.351,0.451,0.398,0.23,0.417,0.399,0.431,0.453,0.435,0.454,0.438,0.459,0.454,0.447,0.549,0.453,Expression,NUD15_HUMAN,High,Human +0.352,0.417,0.455,0.449,0.486,0.484,0.204,0.324,0.512,0.453,0.411,0.23,0.24,0.232,0.291,0.287,0.356,0.403,0.388,0.423,0.246,0.427,0.464,0.461,0.263,0.386,0.39,0.386,0.441,0.486,0.526,0.618,0.129,0.244,0.322,0.307,0.364,0.372,0.386,0.496,0.464,0.486,0.257,0.179,0.267,0.248,0.528,0.49,0.591,0.512,0.61,0.597,0.61,0.628,0.644,0.654,0.634,0.616,0.64,0.642,0.536,0.455,Stability,NUSA_ECOLI,High,Prokaryote +0.353,0.326,0.338,0.341,0.369,0.356,0.225,0.372,0.36,0.347,0.356,0.298,0.32,0.289,0.396,0.35,0.375,0.399,0.402,0.375,0.234,0.289,0.286,0.304,0.344,0.28,0.283,0.243,0.32,0.427,0.341,0.346,0.2,0.216,0.234,0.344,0.335,0.326,0.372,0.338,0.347,0.356,0.237,0.115,0.216,0.311,0.439,0.39,0.571,0.531,0.405,0.363,0.35,0.399,0.412,0.396,0.399,0.415,0.412,0.399,0.338,0.399,Stability,NUSG_MYCTU,High,Prokaryote +0.342,0.402,0.508,0.521,0.505,0.512,0.199,0.396,0.523,0.526,0.533,0.482,0.498,0.198,0.515,0.541,0.545,0.522,0.533,0.511,0.526,0.519,0.518,0.526,0.425,0.503,0.499,0.491,0.472,0.486,0.531,0.514,0.316,0.133,0.223,0.318,0.465,0.441,0.439,0.515,0.511,0.511,0.268,-0.044,0.261,0.319,0.406,0.349,0.508,0.513,0.571,0.562,0.565,0.562,0.569,0.568,0.559,0.555,0.575,0.566,0.535,0.493,Stability,OBSCN_HUMAN,High,Human +-0.298,-0.201,0.189,0.083,0.189,0.185,-0.171,0.03,0.083,0.083,0.002,-0.108,-0.097,-0.079,-0.139,0.026,0.097,0.034,0.083,0.129,0.108,0.051,0.041,0.069,0.044,0.069,0.079,0.09,0.108,0.189,-0.034,-0.063,0.146,-0.108,0.009,-0.005,0.097,0.097,0.101,0.161,0.168,0.189,0.15,-0.079,0.157,0.168,0.245,0.228,0.256,0.185,0.026,-0.005,0.012,0.041,-0.002,0.041,0.037,0.044,0.019,0.026,0.15,0.076,Stability,ODP2_GEOSE,High,Prokaryote +0.139,0.382,0.358,0.503,0.406,0.382,0.309,0.43,0.43,0.43,0.212,0.406,0.43,0.188,0.479,0.43,0.406,0.358,0.43,0.503,0.479,0.455,0.455,0.455,0.503,0.455,0.503,0.455,0.43,0.333,0.382,0.261,0.188,0.406,0.43,0.382,0.455,0.43,0.406,0.43,0.358,0.382,0.261,0.042,0.406,0.406,0.624,0.406,0.576,0.212,0.333,0.309,0.309,0.261,0.43,0.406,0.236,0.309,0.382,0.358,0.503,0.406,Expression,OPSD_HUMAN,High,Human +0.404,0.432,0.376,0.414,0.404,0.419,0.062,0.34,0.455,0.442,0.424,0.424,0.452,0.057,0.32,0.394,0.417,0.376,0.424,0.424,0.355,0.33,0.396,0.427,0.386,0.445,0.417,0.427,0.439,0.447,0.366,0.312,0.055,0.327,0.389,0.429,0.396,0.432,0.48,0.434,0.452,0.462,0.149,0.045,0.419,0.345,0.511,0.468,0.516,0.266,0.434,0.422,0.434,0.439,0.445,0.437,0.437,0.427,0.419,0.452,0.473,0.353,Activity,OTC_HUMAN,Medium,Human +0.089,0.218,0.177,0.184,0.204,0.211,0.157,0.13,0.15,0.137,0.354,0.51,0.496,0.198,0.462,0.482,0.347,0.313,0.333,0.15,0.123,0.191,0.259,0.259,0.32,0.313,0.367,0.34,0.137,0.232,0.381,0.381,0.062,0.137,0.211,0.306,0.218,0.211,0.286,0.232,0.177,0.225,0.266,0.13,0.449,0.442,0.435,0.476,0.489,0.394,0.394,0.286,0.32,0.36,0.374,0.388,0.313,0.374,0.442,0.374,0.51,0.469,Stability,OTU7A_HUMAN,High,Human +0.108,0.145,0.15,0.154,0.136,0.138,0.106,0.114,0.138,0.14,0.176,0.164,0.156,0.102,0.13,0.148,0.164,0.177,0.178,0.126,0.1,0.126,0.144,0.143,0.126,0.152,0.165,0.151,0.174,0.144,0.191,0.166,0.038,0.112,0.118,0.136,0.136,0.137,0.146,0.147,0.144,0.143,0.13,0.075,0.164,0.13,0.168,0.176,0.144,0.048,0.172,0.16,0.16,0.164,0.161,0.168,0.162,0.165,0.158,0.167,0.186,0.138,Activity,OXDA_RHOTO,High,Eukaryote +0.108,0.145,0.15,0.154,0.136,0.138,0.106,0.114,0.138,0.14,0.176,0.164,0.156,0.102,0.13,0.148,0.164,0.177,0.178,0.126,0.1,0.126,0.144,0.143,0.126,0.152,0.165,0.151,0.174,0.144,0.191,0.166,0.038,0.112,0.118,0.136,0.136,0.137,0.146,0.147,0.144,0.143,0.13,0.075,0.164,0.13,0.168,0.176,0.144,0.048,0.172,0.16,0.16,0.164,0.161,0.168,0.162,0.165,0.158,0.167,0.186,0.138,Expression,OXDA_RHOTO,High,Eukaryote +0.401,0.406,0.356,0.384,0.402,0.415,-0.023,0.298,0.335,0.344,0.418,0.409,0.447,-0.066,-0.055,0.331,0.418,0.44,0.462,0.186,0.268,0.378,0.412,0.398,0.35,0.416,0.416,0.424,0.34,0.406,0.432,0.406,0.132,0.246,0.4,0.333,0.399,0.414,0.401,0.415,0.431,0.413,-0.059,-0.078,0.443,0.02,0.341,0.425,0.349,0.164,0.423,0.427,0.427,0.426,0.429,0.44,0.426,0.422,0.434,0.438,0.461,0.182,OrganismalFitness,P53_HUMAN,Low,Human +0.401,0.406,0.356,0.384,0.402,0.415,-0.023,0.298,0.335,0.344,0.418,0.409,0.447,-0.066,-0.055,0.331,0.418,0.44,0.462,0.186,0.268,0.378,0.412,0.398,0.35,0.416,0.416,0.424,0.34,0.406,0.432,0.406,0.132,0.246,0.4,0.333,0.399,0.414,0.401,0.415,0.431,0.413,-0.059,-0.078,0.443,0.02,0.341,0.425,0.349,0.164,0.423,0.427,0.427,0.426,0.429,0.44,0.426,0.422,0.434,0.438,0.461,0.182,OrganismalFitness,P53_HUMAN,Low,Human +0.401,0.406,0.356,0.384,0.402,0.415,-0.023,0.298,0.335,0.344,0.418,0.409,0.447,-0.066,-0.055,0.331,0.418,0.44,0.462,0.186,0.268,0.378,0.412,0.398,0.35,0.416,0.416,0.424,0.34,0.406,0.432,0.406,0.132,0.246,0.4,0.333,0.399,0.414,0.401,0.415,0.431,0.413,-0.059,-0.078,0.443,0.02,0.341,0.425,0.349,0.164,0.423,0.427,0.427,0.426,0.429,0.44,0.426,0.422,0.434,0.438,0.461,0.182,OrganismalFitness,P53_HUMAN,Low,Human +0.401,0.406,0.356,0.384,0.402,0.415,-0.023,0.298,0.335,0.344,0.418,0.409,0.447,-0.066,-0.055,0.331,0.418,0.44,0.462,0.186,0.268,0.378,0.412,0.398,0.35,0.416,0.416,0.424,0.34,0.406,0.432,0.406,0.132,0.246,0.4,0.333,0.399,0.414,0.401,0.415,0.431,0.413,-0.059,-0.078,0.443,0.02,0.341,0.425,0.349,0.164,0.423,0.427,0.427,0.426,0.429,0.44,0.426,0.422,0.434,0.438,0.461,0.182,OrganismalFitness,P53_HUMAN,Low,Human +0.484,0.516,0.534,0.534,0.489,0.521,0.272,0.407,0.539,0.545,0.481,0.489,0.531,0.193,0.484,0.492,0.524,0.486,0.463,0.524,0.383,0.436,0.434,0.486,0.481,0.518,0.51,0.518,0.563,0.486,0.471,0.401,0.322,0.423,0.436,0.471,0.492,0.494,0.516,0.518,0.524,0.537,0.404,-0.032,0.46,0.402,0.256,0.336,0.396,0.081,0.46,0.436,0.373,0.479,0.473,0.42,0.449,0.457,0.473,0.476,0.524,0.526,OrganismalFitness,P84126_THETH,Medium,Prokaryote +0.521,0.474,0.41,0.418,0.495,0.493,0.35,0.429,0.491,0.512,0.545,0.514,0.534,0.357,0.429,0.515,0.597,0.549,0.563,0.408,0.508,0.528,0.531,0.555,0.506,0.558,0.57,0.538,0.525,0.522,0.568,0.503,0.191,0.511,0.504,0.503,0.556,0.556,0.551,0.531,0.53,0.529,0.465,-0.048,0.518,0.517,0.274,0.461,0.393,0.144,0.542,0.532,0.545,0.57,0.558,0.563,0.565,0.561,0.594,0.581,0.574,0.502,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +0.301,0.28,0.263,0.275,0.283,0.298,0.046,0.238,0.316,0.325,0.335,0.312,0.312,0.061,0.298,0.322,0.344,0.24,0.205,0.316,0.103,0.277,0.256,0.24,0.277,0.293,0.289,0.271,0.218,0.311,0.3,0.268,0.125,0.121,0.264,0.274,0.284,0.295,0.299,0.301,0.31,0.306,0.252,0.053,0.322,0.306,0.297,0.343,0.305,0.108,0.334,0.344,0.337,0.348,0.339,0.348,0.342,0.347,0.341,0.353,0.36,0.302,Activity,PAI1_HUMAN,,Human +0.36,0.376,0.36,0.365,0.396,0.409,0.048,0.301,0.145,0.101,0.026,0.04,0.088,-0.002,0.024,0.026,0.024,0.018,0.275,0.248,0.336,0.382,0.4,0.407,0.171,0.297,0.332,0.336,0.327,0.516,0.284,0.264,0.086,0.308,0.347,0.396,0.433,0.44,0.435,0.462,0.455,0.433,0.024,0.031,0.037,0.013,0.18,0.178,0.149,0.046,0.132,0.141,0.147,0.149,0.134,0.152,0.134,0.132,0.101,0.143,0.167,0.121,OrganismalFitness,PA_I34A1,Medium,Virus +0.149,0.3,0.541,0.52,0.215,0.256,0.477,0.395,0.526,0.536,0.474,0.57,0.6,0.58,0.648,0.529,0.563,0.528,0.574,0.472,0.499,0.526,0.433,0.51,0.326,0.434,0.417,0.483,0.426,0.415,0.414,0.346,0.21,0.398,0.325,0.419,0.413,0.369,0.422,0.282,0.302,0.287,0.469,0.278,0.452,0.463,0.142,0.364,0.493,0.284,0.439,0.388,0.395,0.393,0.398,0.411,0.408,0.413,0.421,0.41,0.551,0.588,Activity,PHOT_CHLRE,High,Eukaryote +0.2,0.234,0.549,0.499,0.489,0.544,0.489,0.384,0.514,0.579,0.524,0.449,0.569,0.529,0.494,0.524,0.549,0.394,0.464,0.499,0.354,0.524,0.509,0.439,0.424,0.519,0.574,0.544,0.509,0.594,0.594,0.549,0.434,0.399,0.494,0.529,0.434,0.499,0.564,0.549,0.564,0.564,0.474,-0.17,0.529,0.414,0.399,0.444,0.594,0.569,0.534,0.494,0.414,0.389,0.389,0.444,0.424,0.504,0.454,0.444,0.603,0.653,Stability,PIN1_HUMAN,High,Human +0.461,0.364,0.333,0.342,0.336,0.351,0.349,0.36,0.412,0.41,0.36,0.366,0.384,0.415,0.524,0.577,0.474,0.447,0.373,0.375,0.371,0.338,0.369,0.344,0.423,0.364,0.344,0.331,0.353,0.349,0.349,0.35,0.338,0.415,0.358,0.373,0.472,0.423,0.404,0.406,0.358,0.36,0.349,0.303,0.178,0.235,0.294,0.2,0.537,0.498,0.377,0.388,0.393,0.406,0.399,0.419,0.395,0.384,0.393,0.39,0.39,0.443,Stability,PITX2_HUMAN,High,Human +0.177,0.184,0.232,0.246,0.288,0.285,0.177,0.246,0.201,0.229,0.226,0.281,0.299,0.327,0.334,0.435,0.26,0.226,0.229,0.292,0.292,0.278,0.222,0.281,0.281,0.299,0.299,0.278,0.281,0.295,0.32,0.32,0.062,0.309,0.271,0.306,0.246,0.292,0.323,0.306,0.288,0.306,0.274,0.267,0.274,0.288,0.431,0.337,0.428,0.376,0.232,0.264,0.292,0.243,0.257,0.271,0.236,0.257,0.281,0.257,0.292,0.424,Stability,PKN1_HUMAN,High,Human +0.301,0.281,0.26,0.299,0.34,0.348,-0.03,0.255,0.376,0.374,0.228,-0.036,0.001,-0.047,-0.03,0.124,0.317,0.324,0.346,0.259,0.25,0.283,0.272,0.264,0.074,0.287,0.277,0.261,0.273,0.365,0.295,0.229,-0.005,0.036,0.187,0.248,0.263,0.276,0.311,0.288,0.307,0.347,-0.035,-0.039,0.268,-0.03,0.147,0.268,0.077,0.04,0.259,0.286,0.289,0.291,0.286,0.288,0.286,0.285,0.294,0.29,0.068,0.051,OrganismalFitness,POLG_CXB3N,Medium,Virus +0.357,0.454,0.184,0.185,0.413,0.418,-0.014,0.33,0.535,0.542,0.241,0.011,0.02,-0.009,0.025,0.059,0.122,0.206,0.274,0.312,0.334,0.351,0.344,0.344,0.327,0.367,0.371,0.363,0.352,0.484,0.468,0.408,0.029,-0.025,0.087,0.352,0.331,0.337,0.422,0.364,0.315,0.43,-0.028,-0.025,0.319,0.017,0.282,0.42,0.099,0.084,0.195,0.189,0.198,0.216,0.184,0.217,0.197,0.199,0.19,0.206,0.115,0.023,OrganismalFitness,POLG_DEN26,Low,Virus +0.448,0.432,0.336,0.346,0.46,0.472,-0.041,0.152,0.405,0.423,0.131,0.507,0.475,0.074,0.101,0.082,0.082,0.062,0.062,0.158,0.309,0.358,0.336,0.376,0.302,0.376,0.213,0.314,0.405,0.465,0.467,0.399,0.151,0.361,0.395,0.386,0.4,0.428,0.415,0.353,0.403,0.423,0.079,0.047,0.378,0.084,-0.035,0.26,0.578,0.311,0.245,0.292,0.282,0.297,0.299,0.227,0.269,0.299,0.267,0.309,0.119,0.143,OrganismalFitness,POLG_HCVJF,Medium,Virus +0.194,0.352,0.217,0.263,0.295,0.297,0.053,0.269,0.222,0.256,0.267,0.041,0.089,0.048,0.066,0.032,0.089,0.064,0.048,0.39,0.079,0.06,0.023,0.116,0.076,-0.032,-0.003,-0.042,0.105,0.309,0.37,0.377,0.125,0.023,0.018,0.009,0.242,0.249,0.249,0.302,0.306,0.305,-0.025,-0.054,-0.012,-0.035,0.37,0.297,0.486,0.434,0.456,0.462,0.437,0.518,0.525,0.529,0.462,0.53,0.53,0.511,0.545,0.466,Stability,POLG_PESV,Medium,Virus +0.299,0.373,0.407,0.41,0.396,0.397,0.132,0.263,0.363,0.383,0.397,0.405,0.409,0.04,0.12,0.347,0.414,0.469,0.461,0.434,0.406,0.389,0.214,0.256,0.39,0.407,0.399,0.414,0.283,0.45,0.424,0.411,0.278,0.43,0.38,0.342,0.431,0.414,0.397,0.445,0.429,0.423,0.195,0.042,0.359,0.32,0.368,0.355,0.402,0.2,0.404,0.412,0.419,0.408,0.408,0.417,0.416,0.414,0.42,0.42,0.424,0.352,Activity,PPARG_HUMAN,Medium,Human +0.438,0.462,0.454,0.465,0.508,0.512,0.005,0.298,0.34,0.4,0.462,0.487,0.495,0.184,0.258,0.327,0.494,0.511,0.509,0.286,0.327,0.434,0.412,0.323,0.421,0.448,0.448,0.439,0.307,0.492,0.488,0.461,0.216,0.312,0.427,0.414,0.478,0.493,0.504,0.528,0.509,0.512,0.229,-0.034,0.465,0.397,0.389,0.464,0.437,0.197,0.467,0.463,0.46,0.469,0.483,0.46,0.477,0.476,0.479,0.482,0.52,0.392,OrganismalFitness,PPM1D_HUMAN,Low,Human +0.58,0.629,0.69,0.672,0.724,0.724,0.413,0.413,0.743,0.755,0.696,0.58,0.623,0.444,0.428,0.747,0.769,0.735,0.712,0.659,0.497,0.6,0.613,0.611,0.539,0.604,0.672,0.619,0.653,0.763,0.789,0.794,0.56,0.539,0.436,0.486,0.67,0.676,0.667,0.69,0.716,0.698,0.367,0.181,0.19,0.358,0.472,0.226,0.633,0.649,0.789,0.765,0.775,0.781,0.787,0.79,0.79,0.789,0.806,0.802,0.731,0.712,Stability,PR40A_HUMAN,Medium,Human +0.505,0.501,0.505,0.49,0.509,0.512,0.104,0.367,0.425,0.443,0.458,0.507,0.527,0.137,0.183,0.25,0.389,0.544,0.553,0.357,0.207,0.44,0.478,0.458,0.359,0.502,0.493,0.477,0.424,0.523,0.482,0.462,0.217,0.17,0.43,0.45,0.49,0.522,0.52,0.517,0.526,0.527,0.206,0.048,0.472,0.246,0.511,0.482,0.552,0.224,0.416,0.427,0.436,0.452,0.44,0.459,0.453,0.447,0.449,0.459,0.556,0.371,Expression,PRKN_HUMAN,Low,Human +0.518,0.542,0.508,0.511,0.526,0.521,0.335,0.407,0.428,0.436,0.602,0.532,0.529,0.428,0.516,0.609,0.627,0.563,0.49,0.397,0.311,0.241,0.433,0.418,0.407,0.41,-0.129,0.449,0.459,0.471,0.49,0.459,0.161,0.332,0.394,0.399,0.511,0.498,0.459,0.558,0.568,0.495,0.423,0.286,0.586,0.441,0.532,0.576,0.638,0.539,0.617,0.583,0.602,0.599,0.591,0.625,0.591,0.599,0.612,0.607,0.622,0.625,Stability,PSAE_PICP2,Medium,Prokaryote +0.349,0.362,0.355,0.35,0.366,0.369,0.108,0.282,0.372,0.395,0.365,0.352,0.383,0.13,0.25,0.399,0.394,0.217,0.218,0.382,0.192,0.348,0.292,0.277,0.28,0.249,0.243,0.277,0.218,0.38,0.355,0.357,0.086,0.248,0.337,0.264,0.341,0.374,0.341,0.37,0.392,0.385,0.23,0.001,0.379,0.317,0.352,0.343,0.386,0.155,0.378,0.37,0.381,0.383,0.381,0.388,0.385,0.386,0.385,0.395,0.415,0.377,Expression,PTEN_HUMAN,Medium,Human +0.349,0.362,0.355,0.35,0.366,0.369,0.108,0.282,0.372,0.395,0.365,0.352,0.383,0.13,0.25,0.399,0.394,0.217,0.218,0.382,0.192,0.348,0.292,0.277,0.28,0.249,0.243,0.277,0.218,0.38,0.355,0.357,0.086,0.248,0.337,0.264,0.341,0.374,0.341,0.37,0.392,0.385,0.23,0.001,0.379,0.317,0.352,0.343,0.386,0.155,0.378,0.37,0.381,0.383,0.381,0.388,0.385,0.386,0.385,0.395,0.415,0.377,Activity,PTEN_HUMAN,Medium,Human +0.361,0.321,0.279,0.304,0.378,0.376,0.024,0.327,0.396,0.401,0.35,0.391,0.414,0.034,0.03,0.024,0.054,0.078,0.104,0.303,0.394,0.301,0.289,0.244,0.392,0.287,0.284,0.327,0.26,0.379,0.378,0.369,0.222,0.371,0.318,0.295,0.39,0.384,0.379,0.397,0.394,0.387,0.34,0.02,0.385,0.345,0.292,0.348,0.199,0.106,0.166,0.207,0.23,0.226,0.188,0.193,0.194,0.185,0.167,0.218,0.151,0.079,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +0.326,0.295,0.322,0.324,0.33,0.328,0.024,0.234,0.301,0.312,0.226,0.268,0.288,0.11,0.102,0.311,0.32,0.292,0.338,0.318,0.254,0.279,0.276,0.242,0.279,0.297,0.284,0.316,0.296,0.322,0.338,0.31,0.08,0.244,0.267,0.224,0.316,0.325,0.32,0.346,0.356,0.341,0.278,0.105,0.298,0.282,0.288,0.325,0.3,0.14,0.316,0.316,0.332,0.334,0.33,0.318,0.311,0.324,0.33,0.328,0.294,0.397,Binding,Q53Z42_HUMAN,Medium,Human +0.326,0.295,0.322,0.324,0.33,0.328,0.024,0.234,0.301,0.312,0.226,0.268,0.288,0.11,0.102,0.311,0.32,0.292,0.338,0.318,0.254,0.279,0.276,0.242,0.279,0.297,0.284,0.316,0.296,0.322,0.338,0.31,0.08,0.244,0.267,0.224,0.316,0.325,0.32,0.346,0.356,0.341,0.278,0.105,0.298,0.282,0.288,0.325,0.3,0.14,0.316,0.316,0.332,0.334,0.33,0.318,0.311,0.324,0.33,0.328,0.294,0.397,Expression,Q53Z42_HUMAN,Medium,Human +0.418,0.49,0.508,0.518,0.516,0.523,0.235,0.408,0.546,0.55,0.471,0.415,0.415,0.085,0.325,0.38,0.431,0.462,0.453,0.507,0.459,0.505,0.519,0.523,0.481,0.525,0.528,0.537,0.561,0.528,0.514,0.452,0.265,0.477,0.514,0.501,0.484,0.518,0.528,0.526,0.536,0.537,0.372,-0.004,0.456,0.398,0.317,0.411,0.407,0.093,0.456,0.422,0.409,0.429,0.435,0.426,0.433,0.419,0.438,0.433,0.488,0.415,Activity,Q59976_STRSQ,Medium,Prokaryote +0.175,0.194,0.137,0.14,0.18,0.18,-0.009,0.096,0.2,0.196,0.14,0.027,0.018,0.008,0.024,0.006,0.007,0.013,-0.011,0.161,0.026,0.024,0.002,0.071,0.021,0.0,0.02,0.0,-0.001,0.233,0.167,0.171,0.026,0.015,0.025,0.02,0.13,0.132,0.137,0.173,0.173,0.172,0.006,0.005,-0.002,0.012,0.138,0.15,0.215,0.129,0.153,0.164,0.151,0.175,0.168,0.164,0.16,0.167,0.163,0.164,0.068,0.047,Activity,Q6WV12_9MAXI,Low,Eukaryote +0.257,0.303,0.28,0.252,0.28,0.326,0.315,0.24,0.269,0.297,0.355,0.32,0.338,0.274,0.309,0.309,0.286,0.355,0.315,-0.024,0.355,0.303,0.32,0.361,0.349,0.332,0.366,0.252,0.372,0.303,0.343,0.333,0.079,0.286,0.378,0.338,0.274,0.378,0.384,0.332,0.32,0.32,0.257,0.229,0.372,0.297,0.229,0.326,0.246,0.068,0.315,0.274,0.309,0.326,0.303,0.338,0.338,0.309,0.326,0.338,0.349,0.332,Activity,Q837P4_ENTFA,Medium,Prokaryote +0.082,0.264,0.291,0.291,0.232,0.232,0.055,0.125,0.173,0.2,0.168,0.2,0.2,0.076,0.125,0.2,0.216,0.291,0.259,0.205,0.248,0.27,0.275,0.253,0.216,0.253,0.264,0.345,0.302,0.2,0.248,0.227,0.06,0.248,0.302,0.334,0.184,0.248,0.312,0.259,0.27,0.28,0.146,0.135,0.227,0.119,0.253,0.221,0.275,0.098,0.189,0.205,0.237,0.216,0.237,0.237,0.189,0.232,0.243,0.232,0.211,0.189,Activity,Q837P5_ENTFA,Medium,Prokaryote +0.162,0.21,0.127,0.125,0.189,0.185,-0.004,0.187,0.169,0.173,0.111,-0.038,-0.048,-0.042,-0.059,-0.054,-0.048,-0.049,-0.023,0.129,-0.036,-0.005,0.022,-0.019,-0.024,-0.039,-0.02,0.138,0.144,0.203,0.187,0.194,-0.029,-0.064,-0.015,0.18,0.123,0.13,0.18,0.173,0.182,0.202,-0.035,-0.035,-0.053,-0.054,0.007,0.054,0.161,0.087,0.126,0.119,0.126,0.133,0.12,0.129,0.13,0.12,0.116,0.127,0.038,-0.07,Activity,Q8WTC7_9CNID,Low,Eukaryote +0.477,0.427,0.185,0.221,0.517,0.514,-0.011,0.227,0.013,0.013,0.094,0.01,0.002,0.038,0.015,0.067,0.096,0.395,0.456,0.228,0.186,0.239,0.258,0.233,0.211,0.225,0.173,0.177,0.215,0.46,0.414,0.331,-0.026,0.152,0.19,0.174,0.308,0.339,0.33,0.463,0.484,0.48,0.006,-0.005,0.063,-0.003,0.337,0.31,0.367,0.162,0.197,0.156,0.216,0.216,0.201,0.201,0.204,0.2,0.173,0.205,0.17,0.097,OrganismalFitness,R1AB_SARS2,Medium,Virus +0.204,0.112,0.226,0.213,0.23,0.226,0.362,0.283,0.542,0.485,0.415,0.38,0.397,0.327,0.52,0.56,0.353,0.353,0.432,0.279,0.432,0.476,0.441,0.362,0.459,0.305,0.472,0.345,0.318,0.424,0.279,0.191,0.248,0.388,0.525,0.345,0.331,0.459,0.371,0.349,0.388,0.309,0.472,0.195,0.2,0.323,0.38,0.261,0.586,0.38,0.38,0.406,0.415,0.415,0.419,0.41,0.384,0.432,0.393,0.428,0.283,0.463,Stability,RAD_ANTMA,High,Eukaryote +0.394,0.367,0.367,0.367,0.354,0.367,0.044,0.286,0.354,0.367,0.394,0.354,0.421,0.017,0.192,0.354,0.394,0.381,0.34,0.246,0.205,0.313,0.34,0.3,0.286,0.313,0.286,0.259,0.259,0.34,0.394,0.3,0.111,0.246,0.232,0.286,0.34,0.3,0.313,0.354,0.367,0.394,0.165,0.071,0.421,0.327,0.232,0.354,0.192,0.286,0.34,0.354,0.3,0.34,0.3,0.407,0.34,0.354,0.367,0.327,0.354,0.111,OrganismalFitness,RAF1_HUMAN,Low,Human +0.397,0.442,0.439,0.467,0.454,0.465,0.288,0.376,0.464,0.479,0.368,0.41,0.442,0.417,0.464,0.443,0.489,0.453,0.317,0.497,0.451,0.403,0.403,0.388,0.414,0.4,0.407,0.347,0.277,0.422,0.399,0.378,0.199,0.375,0.407,0.338,0.449,0.45,0.425,0.455,0.461,0.475,0.48,0.186,0.278,0.454,0.289,0.177,0.414,0.198,0.419,0.415,0.43,0.429,0.422,0.422,0.436,0.421,0.432,0.449,0.349,0.444,Activity,RASH_HUMAN,High,Human +0.311,0.294,0.366,0.361,0.347,0.354,0.213,0.247,0.33,0.348,0.344,0.29,0.321,0.326,0.369,0.368,0.344,0.288,0.246,0.386,0.316,0.338,0.359,0.333,0.304,0.346,0.358,0.275,0.317,0.376,0.278,0.235,0.198,0.264,0.333,0.349,0.31,0.364,0.377,0.35,0.368,0.372,0.305,0.215,0.191,0.328,0.207,0.226,0.362,0.221,0.297,0.288,0.313,0.309,0.334,0.322,0.308,0.31,0.271,0.316,0.36,0.354,Expression,RASK_HUMAN,High,Human +0.311,0.294,0.366,0.361,0.347,0.354,0.213,0.247,0.33,0.348,0.344,0.29,0.321,0.326,0.369,0.368,0.344,0.288,0.246,0.386,0.316,0.338,0.359,0.333,0.304,0.346,0.358,0.275,0.317,0.376,0.278,0.235,0.198,0.264,0.333,0.349,0.31,0.364,0.377,0.35,0.368,0.372,0.305,0.215,0.191,0.328,0.207,0.226,0.362,0.221,0.297,0.288,0.313,0.309,0.334,0.322,0.308,0.31,0.271,0.316,0.36,0.354,Binding,RASK_HUMAN,High,Human +0.165,0.072,0.264,0.264,0.267,0.267,0.231,0.243,0.21,0.21,0.363,0.318,0.318,0.255,0.321,0.378,0.426,0.3,0.273,0.18,0.282,0.06,0.228,0.213,0.207,0.111,0.117,0.279,0.192,0.339,0.318,0.288,0.264,0.204,0.309,0.312,0.255,0.27,0.279,0.264,0.264,0.264,0.255,0.186,0.327,0.333,0.333,0.378,0.462,0.345,0.378,0.378,0.411,0.384,0.393,0.399,0.387,0.393,0.381,0.39,0.432,0.417,Stability,RBP1_HUMAN,High,Human +0.277,0.274,0.331,0.34,0.331,0.331,0.213,0.217,0.328,0.34,0.372,0.197,0.236,0.258,0.226,0.461,0.464,0.451,0.429,0.315,0.178,0.182,0.242,0.305,0.239,0.41,0.416,0.359,0.423,0.385,0.467,0.391,0.042,0.185,0.239,0.232,0.299,0.305,0.283,0.362,0.369,0.337,0.229,0.194,0.356,0.229,0.388,0.391,0.464,0.439,0.461,0.461,0.47,0.505,0.467,0.496,0.47,0.48,0.499,0.483,0.464,0.381,Stability,RCD1_ARATH,Medium,Eukaryote +0.286,0.414,0.49,0.471,0.45,0.474,0.074,0.23,0.406,0.414,0.4,0.256,0.334,0.146,0.228,0.205,0.42,0.432,0.432,0.488,0.018,0.065,0.095,0.091,-0.079,0.049,-0.06,-0.002,0.479,0.46,0.427,0.416,0.018,0.054,0.042,0.425,0.36,0.332,0.465,0.45,0.457,0.511,-0.109,0.053,0.234,0.042,0.367,0.442,0.543,0.432,0.427,0.411,0.414,0.404,0.406,0.409,0.411,0.409,0.418,0.418,0.453,0.32,Stability,RCRO_LAMBD,High,Virus +0.235,0.168,0.266,0.282,0.305,0.309,0.121,0.344,0.419,0.419,0.384,0.45,0.462,0.156,0.521,0.458,0.423,0.399,0.286,0.344,0.301,0.321,0.313,0.356,0.403,0.372,0.372,0.38,0.388,0.364,0.356,0.339,0.305,0.231,0.38,0.38,0.29,0.321,0.368,0.305,0.317,0.329,0.384,0.023,0.415,0.466,0.372,0.392,0.447,0.376,0.36,0.341,0.36,0.344,0.38,0.384,0.368,0.368,0.38,0.38,0.368,0.423,Stability,RD23A_HUMAN,High,Human +0.221,0.252,0.281,0.286,0.362,0.367,0.042,0.289,0.381,0.385,0.146,0.072,0.079,0.064,0.058,0.111,0.278,0.292,0.368,0.33,0.286,0.331,0.341,0.355,0.125,0.267,0.263,0.259,0.307,0.357,0.336,0.292,0.086,0.278,0.321,0.347,0.317,0.349,0.358,0.368,0.38,0.392,0.052,0.035,0.15,0.054,0.17,0.191,0.181,0.041,0.233,0.215,0.227,0.229,0.24,0.242,0.229,0.247,0.253,0.248,0.13,0.096,OrganismalFitness,RDRP_I33A0,Low,Virus +0.132,0.128,0.141,0.152,0.156,0.154,0.033,0.219,0.132,0.149,0.104,0.178,0.195,0.029,0.018,0.109,0.152,0.206,0.184,0.115,0.141,0.18,0.171,0.141,0.206,0.19,0.106,0.175,0.19,0.191,0.245,0.266,0.024,0.143,0.182,0.173,0.178,0.182,0.162,0.182,0.19,0.167,0.031,0.037,0.145,0.044,0.177,0.16,0.201,0.139,0.141,0.143,0.203,0.188,0.177,0.204,0.206,0.134,0.219,0.193,0.208,0.136,OrganismalFitness,REV_HV1H2,Medium,Virus +-0.026,0.176,0.24,0.264,0.222,0.231,-0.014,0.192,0.195,0.201,0.207,0.125,0.119,-0.053,0.023,0.005,0.24,0.261,0.204,0.222,-0.068,0.032,0.137,0.101,-0.053,0.116,0.152,0.125,0.149,0.164,0.27,0.247,0.014,0.002,0.113,0.152,0.053,0.14,0.131,0.192,0.179,0.195,0.053,-0.104,0.053,-0.014,0.173,0.137,0.252,0.234,0.279,0.261,0.252,0.267,0.234,0.255,0.255,0.276,0.255,0.264,0.198,0.104,Stability,RFAH_ECOLI,High,Prokaryote +0.353,0.499,0.536,0.536,0.514,0.525,0.19,0.536,0.308,0.255,0.567,0.564,0.539,0.128,0.308,0.351,0.601,0.581,0.612,0.556,0.157,0.488,0.446,0.44,0.415,0.438,0.438,0.471,0.547,0.525,0.502,0.487,0.044,0.415,0.429,0.415,0.466,0.485,0.466,0.514,0.522,0.514,-0.043,-0.113,0.381,0.207,0.589,0.544,0.665,0.595,0.637,0.623,0.615,0.657,0.643,0.626,0.646,0.632,0.626,0.646,0.663,0.544,Stability,RL20_AQUAE,High,Prokaryote +0.199,0.251,0.279,0.324,0.281,0.307,0.088,0.295,0.332,0.335,0.148,0.207,0.238,0.062,0.357,0.377,0.427,0.319,0.428,0.327,0.262,0.427,0.378,0.336,0.386,0.324,0.344,0.311,0.291,0.261,0.308,0.313,0.056,0.302,0.367,0.299,0.328,0.366,0.31,0.351,0.373,0.313,0.221,0.031,0.171,0.218,0.074,0.128,0.163,0.052,0.413,0.378,0.408,0.415,0.407,0.412,0.382,0.416,0.412,0.419,0.25,0.28,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.199,0.251,0.279,0.324,0.281,0.307,0.088,0.295,0.332,0.335,0.148,0.207,0.238,0.062,0.357,0.377,0.427,0.319,0.428,0.327,0.262,0.427,0.378,0.336,0.386,0.324,0.344,0.311,0.291,0.261,0.308,0.313,0.056,0.302,0.367,0.299,0.328,0.366,0.31,0.351,0.373,0.313,0.221,0.031,0.171,0.218,0.074,0.128,0.163,0.052,0.413,0.378,0.408,0.415,0.407,0.412,0.382,0.416,0.412,0.419,0.25,0.28,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.199,0.251,0.279,0.324,0.281,0.307,0.088,0.295,0.332,0.335,0.148,0.207,0.238,0.062,0.357,0.377,0.427,0.319,0.428,0.327,0.262,0.427,0.378,0.336,0.386,0.324,0.344,0.311,0.291,0.261,0.308,0.313,0.056,0.302,0.367,0.299,0.328,0.366,0.31,0.351,0.373,0.313,0.221,0.031,0.171,0.218,0.074,0.128,0.163,0.052,0.413,0.378,0.408,0.415,0.407,0.412,0.382,0.416,0.412,0.419,0.25,0.28,Activity,RL40A_YEAST,Medium,Eukaryote +0.425,0.49,0.484,0.5,0.502,0.494,0.032,0.319,0.478,0.488,0.478,0.478,0.489,0.046,0.419,0.452,0.486,0.492,0.499,0.483,0.425,0.461,0.406,0.376,0.45,0.472,0.462,0.473,0.459,0.496,0.48,0.457,0.138,0.426,0.421,0.331,0.459,0.471,0.435,0.506,0.517,0.502,0.397,0.049,0.467,0.447,0.227,0.425,0.2,0.138,0.462,0.453,0.419,0.453,0.453,0.454,0.46,0.46,0.462,0.464,0.492,0.408,Activity,RNC_ECOLI,Medium,Prokaryote +0.557,0.576,0.549,0.571,0.491,0.533,0.538,0.524,0.557,0.576,0.623,0.667,0.67,0.629,0.661,0.678,0.623,0.598,0.505,0.596,0.609,0.62,0.637,0.579,0.629,0.626,0.626,0.626,0.587,0.664,0.615,0.555,0.502,0.516,0.623,0.631,0.596,0.642,0.642,0.596,0.585,0.593,0.626,0.59,0.664,0.642,0.615,0.557,0.711,0.607,0.607,0.579,0.601,0.612,0.62,0.604,0.612,0.607,0.626,0.612,0.694,0.683,Stability,RPC1_BP434,High,Virus +0.248,0.362,0.426,0.438,0.414,0.374,0.136,0.366,0.355,0.392,0.414,0.422,0.446,0.208,0.208,0.32,0.469,0.496,0.514,0.355,0.176,0.324,0.386,0.378,0.159,0.29,0.296,0.248,0.474,0.326,0.552,0.468,0.063,0.099,0.308,0.428,0.219,0.296,0.404,0.368,0.391,0.446,0.242,0.224,0.362,0.237,0.181,0.312,0.282,0.196,0.504,0.444,0.474,0.498,0.492,0.522,0.474,0.462,0.486,0.51,0.444,0.278,Activity,RPC1_LAMBD,High,Virus +0.248,0.362,0.426,0.438,0.414,0.374,0.136,0.366,0.355,0.392,0.414,0.422,0.446,0.208,0.208,0.32,0.469,0.496,0.514,0.355,0.176,0.324,0.386,0.378,0.159,0.29,0.296,0.248,0.474,0.326,0.552,0.468,0.063,0.099,0.308,0.428,0.219,0.296,0.404,0.368,0.391,0.446,0.242,0.224,0.362,0.237,0.181,0.312,0.282,0.196,0.504,0.444,0.474,0.498,0.492,0.522,0.474,0.462,0.486,0.51,0.444,0.278,Activity,RPC1_LAMBD,High,Virus +0.295,0.289,0.279,0.282,0.272,0.272,0.162,0.168,0.322,0.322,0.356,0.332,0.305,0.225,0.245,0.339,0.326,0.329,0.232,0.242,0.108,0.185,0.252,0.212,0.235,0.252,0.245,0.262,0.245,0.315,0.339,0.324,0.085,0.279,0.222,0.242,0.299,0.259,0.265,0.265,0.255,0.255,0.262,0.175,0.315,0.259,0.423,0.322,0.469,0.389,0.322,0.322,0.312,0.312,0.302,0.326,0.329,0.336,0.312,0.329,0.322,0.463,Stability,RS15_GEOSE,Medium,Prokaryote +0.364,0.426,0.445,0.45,0.452,0.463,0.27,0.416,0.481,0.482,0.467,0.477,0.512,0.354,0.396,0.4,0.48,0.464,0.44,0.026,0.375,0.471,0.478,0.471,0.388,0.487,0.496,0.474,0.448,0.447,0.464,0.357,0.267,0.398,0.468,0.469,0.415,0.484,0.491,0.475,0.488,0.491,0.372,0.174,0.46,0.41,0.313,0.422,0.406,0.121,0.445,0.434,0.431,0.446,0.464,0.448,0.458,0.463,0.456,0.468,0.46,0.422,Expression,S22A1_HUMAN,Medium,Human +0.364,0.426,0.445,0.45,0.452,0.463,0.27,0.416,0.481,0.482,0.467,0.477,0.512,0.354,0.396,0.4,0.48,0.464,0.44,0.026,0.375,0.471,0.478,0.471,0.388,0.487,0.496,0.474,0.448,0.447,0.464,0.357,0.267,0.398,0.468,0.469,0.415,0.484,0.491,0.475,0.488,0.491,0.372,0.174,0.46,0.41,0.313,0.422,0.406,0.121,0.445,0.434,0.431,0.446,0.464,0.448,0.458,0.463,0.456,0.468,0.46,0.422,Activity,S22A1_HUMAN,Medium,Human +0.082,0.074,0.297,0.248,0.285,0.281,0.297,0.438,0.268,0.285,0.418,0.409,0.422,0.136,0.339,0.463,0.405,0.463,0.368,0.38,0.443,0.372,0.355,0.376,0.422,0.451,0.389,0.401,0.405,0.467,0.447,0.418,0.281,0.405,0.434,0.389,0.281,0.397,0.351,0.26,0.285,0.302,0.463,-0.072,0.463,0.451,0.19,0.206,0.289,0.397,0.438,0.281,0.181,0.285,0.264,0.322,0.339,0.434,0.372,0.347,0.455,0.48,Stability,SAV1_MOUSE,High,Eukaryote +0.218,0.179,0.311,0.272,0.315,0.315,0.182,0.128,0.378,0.381,0.253,0.233,0.241,0.186,0.206,0.311,0.463,0.502,0.233,0.276,0.175,0.175,0.214,0.233,0.194,0.206,0.167,0.218,0.311,0.42,0.409,0.358,0.194,0.14,0.155,0.19,0.186,0.194,0.19,0.276,0.292,0.3,0.221,0.182,0.264,0.233,0.526,0.475,0.538,0.479,0.389,0.417,0.424,0.428,0.432,0.436,0.448,0.44,0.405,0.436,0.538,0.444,Stability,SBI_STAAM,Medium,Prokaryote +0.359,0.4,0.352,0.372,0.438,0.452,0.273,0.405,0.483,0.49,0.454,0.467,0.467,0.135,0.255,0.44,0.477,0.478,0.449,0.47,0.43,0.418,0.399,0.394,0.435,0.419,0.43,0.428,0.421,0.456,0.384,0.317,0.283,0.436,0.416,0.386,0.459,0.455,0.432,0.475,0.475,0.461,0.419,0.1,0.461,0.453,0.383,0.43,0.406,0.132,0.455,0.454,0.468,0.47,0.478,0.469,0.475,0.477,0.466,0.482,0.491,0.452,Activity,SC6A4_HUMAN,Medium,Human +0.051,0.084,0.167,0.18,0.236,0.213,0.097,0.012,0.183,0.2,0.18,0.173,0.17,0.127,0.186,0.167,0.19,0.262,0.262,0.17,0.054,0.087,0.084,0.091,0.107,0.111,0.101,0.13,0.134,0.18,0.177,0.109,-0.025,0.035,0.051,0.107,0.114,0.104,0.12,0.19,0.173,0.18,0.13,0.111,0.2,0.163,0.375,0.391,0.381,0.325,0.256,0.276,0.269,0.292,0.259,0.295,0.272,0.249,0.206,0.276,0.447,0.355,Stability,SCIN_STAAR,High,Prokaryote +0.036,0.054,0.089,0.089,0.125,0.143,0.071,0.0,0.071,0.125,0.089,0.143,0.071,0.196,0.143,0.071,0.089,0.054,0.071,0.054,0.018,0.054,0.071,0.089,-0.018,0.018,0.018,0.036,0.107,0.027,0.161,0.143,0.036,0.0,0.0,0.089,-0.018,-0.018,-0.018,0.054,0.036,0.036,0.018,0.125,0.107,0.125,0.036,0.054,0.018,-0.018,0.089,0.054,0.071,0.036,0.054,0.089,0.143,0.107,0.036,0.089,0.125,0.018,OrganismalFitness,SCN5A_HUMAN,Medium,Human +0.477,0.527,0.53,0.53,0.532,0.532,0.118,0.443,0.524,0.525,0.529,0.512,0.522,0.284,0.282,0.518,0.508,0.5,0.505,0.529,0.156,0.19,0.192,0.318,0.163,0.323,0.084,0.323,0.385,0.532,0.527,0.527,-0.24,0.103,0.287,0.342,0.488,0.496,0.46,0.512,0.512,0.508,0.096,-0.009,0.342,0.068,0.376,0.349,0.529,0.522,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.529,0.518,0.525,Stability,SDA_BACSU,Medium,Prokaryote +0.273,0.354,0.394,0.396,0.392,0.394,0.018,0.333,0.417,0.421,0.371,0.379,0.369,0.116,0.281,0.388,0.396,0.436,0.398,0.379,0.346,0.371,0.381,0.369,0.352,0.386,0.369,0.396,0.371,0.354,0.365,0.315,0.152,0.361,0.373,0.379,0.365,0.375,0.379,0.4,0.413,0.415,0.202,0.047,0.39,0.335,0.277,0.375,0.292,0.112,0.419,0.396,0.411,0.427,0.417,0.421,0.398,0.4,0.404,0.423,0.396,0.35,OrganismalFitness,SERC_HUMAN,High,Human +0.133,0.265,0.294,0.29,0.285,0.296,0.129,0.26,0.314,0.319,0.298,0.278,0.304,0.144,0.152,0.166,0.323,0.295,0.213,0.294,0.161,0.282,0.289,0.262,0.166,0.296,0.3,0.304,0.27,0.308,0.303,0.254,0.099,0.15,0.234,0.277,0.152,0.201,0.258,0.26,0.275,0.303,0.156,0.149,0.317,0.191,0.216,0.277,0.202,0.064,0.295,0.296,0.303,0.308,0.306,0.306,0.29,0.307,0.296,0.307,0.217,0.187,OrganismalFitness,SHOC2_HUMAN,Medium,Human +0.17,0.21,0.25,0.253,0.242,0.238,0.289,0.158,0.166,0.202,0.174,0.234,0.218,0.21,0.25,0.226,0.222,0.17,0.186,0.158,0.103,0.115,0.111,0.131,0.19,0.182,0.103,0.194,0.103,0.17,0.04,0.026,0.158,0.079,0.162,0.166,0.147,0.158,0.154,0.234,0.23,0.214,0.186,0.127,0.147,0.246,0.38,0.253,0.412,0.356,0.293,0.277,0.242,0.277,0.273,0.281,0.257,0.269,0.246,0.285,0.25,0.364,Stability,SOX30_HUMAN,High,Human +0.373,0.44,0.464,0.445,0.493,0.498,-0.114,0.46,0.407,0.409,0.367,-0.068,-0.028,-0.055,-0.058,-0.076,-0.057,-0.068,-0.022,0.491,-0.155,-0.022,0.111,0.054,-0.085,-0.055,0.042,0.23,0.312,0.436,0.424,0.412,-0.02,-0.009,-0.024,0.008,0.335,0.327,0.318,0.489,0.487,0.481,-0.159,-0.117,-0.123,-0.074,0.362,0.306,0.563,0.434,0.472,0.455,0.445,0.47,0.451,0.455,0.472,0.46,0.46,0.47,0.409,0.286,Stability,SPA_STAAU,Medium,Prokaryote +0.09,0.132,0.041,0.052,0.121,0.126,0.035,0.036,0.047,0.115,0.18,0.146,0.125,0.148,0.145,0.167,0.188,0.182,0.218,0.15,0.126,0.104,0.101,0.109,0.106,0.129,0.114,0.115,0.156,0.132,0.226,0.205,0.022,0.126,0.086,0.13,0.138,0.113,0.145,0.138,0.115,0.148,0.01,-0.038,0.118,0.048,0.149,0.122,0.211,0.088,0.21,0.188,0.195,0.225,0.214,0.2,0.228,0.235,0.232,0.222,0.217,0.194,Binding,SPG1_STRSG,Low,Prokaryote +0.09,0.132,0.041,0.052,0.121,0.126,0.035,0.036,0.047,0.115,0.18,0.146,0.125,0.148,0.145,0.167,0.188,0.182,0.218,0.15,0.126,0.104,0.101,0.109,0.106,0.129,0.114,0.115,0.156,0.132,0.226,0.205,0.022,0.126,0.086,0.13,0.138,0.113,0.145,0.138,0.115,0.148,0.01,-0.038,0.118,0.048,0.149,0.122,0.211,0.088,0.21,0.188,0.195,0.225,0.214,0.2,0.228,0.235,0.232,0.222,0.217,0.194,Binding,SPG1_STRSG,Low,Prokaryote +0.09,0.132,0.041,0.052,0.121,0.126,0.035,0.036,0.047,0.115,0.18,0.146,0.125,0.148,0.145,0.167,0.188,0.182,0.218,0.15,0.126,0.104,0.101,0.109,0.106,0.129,0.114,0.115,0.156,0.132,0.226,0.205,0.022,0.126,0.086,0.13,0.138,0.113,0.145,0.138,0.115,0.148,0.01,-0.038,0.118,0.048,0.149,0.122,0.211,0.088,0.21,0.188,0.195,0.225,0.214,0.2,0.228,0.235,0.232,0.222,0.217,0.194,Binding,SPG1_STRSG,Medium,Prokaryote +0.09,0.132,0.041,0.052,0.121,0.126,0.035,0.036,0.047,0.115,0.18,0.146,0.125,0.148,0.145,0.167,0.188,0.182,0.218,0.15,0.126,0.104,0.101,0.109,0.106,0.129,0.114,0.115,0.156,0.132,0.226,0.205,0.022,0.126,0.086,0.13,0.138,0.113,0.145,0.138,0.115,0.148,0.01,-0.038,0.118,0.048,0.149,0.122,0.211,0.088,0.21,0.188,0.195,0.225,0.214,0.2,0.228,0.235,0.232,0.222,0.217,0.194,Binding,SPG1_STRSG,Medium,Prokaryote +0.442,0.458,0.461,0.483,0.555,0.519,0.312,0.301,0.489,0.414,0.417,0.384,0.354,0.326,0.376,0.403,0.436,0.425,0.489,0.425,0.343,0.376,0.378,0.354,0.298,0.381,0.301,0.403,0.45,0.571,0.525,0.507,-0.126,0.332,0.409,0.389,0.304,0.309,0.37,0.486,0.513,0.5,0.235,0.13,0.235,0.216,0.395,0.315,0.662,0.494,0.53,0.505,0.527,0.513,0.494,0.522,0.527,0.569,0.516,0.53,0.519,0.519,Stability,SPG2_STRSG,Medium,Prokaryote +0.136,0.179,0.086,0.166,0.264,0.259,-0.046,0.262,0.296,0.3,0.014,-0.054,-0.026,-0.035,-0.036,-0.02,-0.012,-0.002,0.036,0.263,0.231,0.276,0.274,0.286,0.286,0.256,0.222,0.287,0.238,0.215,0.196,0.254,0.152,0.238,0.228,0.268,0.234,0.24,0.244,0.294,0.297,0.302,-0.032,-0.002,-0.006,0.002,0.424,0.399,0.349,0.127,0.181,0.196,0.184,0.233,0.2,0.24,0.201,0.228,0.202,0.224,0.277,0.138,Binding,SPIKE_SARS2,Medium,Virus +0.136,0.179,0.086,0.166,0.264,0.259,-0.046,0.262,0.296,0.3,0.014,-0.054,-0.026,-0.035,-0.036,-0.02,-0.012,-0.002,0.036,0.263,0.231,0.276,0.274,0.286,0.286,0.256,0.222,0.287,0.238,0.215,0.196,0.254,0.152,0.238,0.228,0.268,0.234,0.24,0.244,0.294,0.297,0.302,-0.032,-0.002,-0.006,0.002,0.424,0.399,0.349,0.127,0.181,0.196,0.184,0.233,0.2,0.24,0.201,0.228,0.202,0.224,0.277,0.138,Expression,SPIKE_SARS2,Medium,Virus +0.499,0.446,0.433,0.402,0.426,0.412,0.14,0.378,0.4,0.422,0.402,0.485,0.445,-0.069,0.443,0.437,0.442,0.544,0.489,0.392,0.425,0.417,0.435,0.4,0.458,0.412,0.46,0.396,0.382,0.385,0.405,0.431,0.33,0.373,0.418,0.391,0.463,0.465,0.471,0.406,0.42,0.415,0.367,-0.148,0.335,0.352,0.303,0.248,0.472,0.44,0.433,0.432,0.463,0.455,0.451,0.448,0.433,0.423,0.435,0.433,0.5,0.415,Stability,SPTN1_CHICK,High,Eukaryote +0.324,0.409,0.42,0.437,0.426,0.437,0.064,0.233,0.369,0.414,0.318,0.233,0.324,0.143,0.318,0.386,0.437,0.494,0.369,0.431,0.143,0.296,0.369,0.296,0.182,0.392,0.42,0.347,0.358,0.426,0.414,0.378,0.075,0.149,0.284,0.397,0.358,0.324,0.42,0.471,0.386,0.437,0.16,0.115,0.454,0.403,0.528,0.494,0.528,0.528,0.477,0.431,0.516,0.494,0.482,0.477,0.488,0.471,0.482,0.494,0.482,0.341,Stability,SQSTM_MOUSE,Medium,Eukaryote +0.506,0.529,0.536,0.539,0.567,0.554,-0.141,0.37,0.546,0.544,0.572,0.564,0.559,-0.169,0.539,0.589,0.579,0.584,0.569,0.506,-0.111,0.319,0.231,0.375,0.347,0.42,0.458,0.483,0.435,0.564,0.577,0.574,0.263,0.049,0.15,0.319,0.534,0.519,0.511,0.562,0.559,0.526,0.466,-0.245,0.572,0.476,0.059,0.461,0.587,0.572,0.572,0.567,0.574,0.582,0.587,0.595,0.567,0.567,0.584,0.577,0.655,0.612,Stability,SR43C_ARATH,High,Eukaryote +0.413,0.418,0.559,0.549,0.598,0.585,0.444,0.336,0.618,0.621,0.474,0.536,0.567,0.379,0.575,0.613,0.593,0.544,0.541,0.541,0.549,0.513,0.51,0.495,0.495,0.515,0.523,0.503,0.503,0.539,0.59,0.559,0.485,0.48,0.487,0.539,0.513,0.554,0.559,0.577,0.587,0.585,0.248,0.117,0.456,0.433,0.438,0.446,0.497,0.51,0.621,0.593,0.6,0.6,0.613,0.608,0.598,0.59,0.613,0.608,0.582,0.521,Stability,SRBS1_HUMAN,High,Human +0.394,0.391,0.385,0.387,0.398,0.397,0.399,0.346,0.398,0.421,0.382,0.442,0.461,0.288,0.35,0.348,0.434,0.38,0.391,0.384,0.33,0.316,0.329,0.288,0.338,0.372,0.332,0.317,0.234,0.427,0.453,0.44,0.324,0.297,0.305,0.243,0.375,0.377,0.364,0.396,0.392,0.394,0.431,0.254,0.382,0.423,0.229,0.224,0.3,0.056,0.405,0.385,0.387,0.409,0.421,0.418,0.409,0.422,0.411,0.427,0.408,0.355,Activity,SRC_HUMAN,Medium,Human +0.394,0.391,0.385,0.387,0.398,0.397,0.399,0.346,0.398,0.421,0.382,0.442,0.461,0.288,0.35,0.348,0.434,0.38,0.391,0.384,0.33,0.316,0.329,0.288,0.338,0.372,0.332,0.317,0.234,0.427,0.453,0.44,0.324,0.297,0.305,0.243,0.375,0.377,0.364,0.396,0.392,0.394,0.431,0.254,0.382,0.423,0.229,0.224,0.3,0.056,0.405,0.385,0.387,0.409,0.421,0.418,0.409,0.422,0.411,0.427,0.408,0.355,Activity,SRC_HUMAN,Medium,Human +0.394,0.391,0.385,0.387,0.398,0.397,0.399,0.346,0.398,0.421,0.382,0.442,0.461,0.288,0.35,0.348,0.434,0.38,0.391,0.384,0.33,0.316,0.329,0.288,0.338,0.372,0.332,0.317,0.234,0.427,0.453,0.44,0.324,0.297,0.305,0.243,0.375,0.377,0.364,0.396,0.392,0.394,0.431,0.254,0.382,0.423,0.229,0.224,0.3,0.056,0.405,0.385,0.387,0.409,0.421,0.418,0.409,0.422,0.411,0.427,0.408,0.355,OrganismalFitness,SRC_HUMAN,Medium,Human +0.339,0.293,0.339,0.363,0.392,0.378,0.123,0.37,0.378,0.303,0.341,0.402,0.431,0.213,0.412,0.467,0.433,0.307,0.281,0.448,0.196,0.322,0.378,0.337,0.402,0.295,0.402,0.349,0.269,0.375,0.399,0.426,0.201,0.223,0.416,0.252,0.339,0.46,0.322,0.351,0.465,0.344,0.431,0.056,0.293,0.45,0.438,0.358,0.445,0.37,0.431,0.37,0.429,0.429,0.419,0.453,0.402,0.436,0.412,0.443,0.397,0.453,OrganismalFitness,SUMO1_HUMAN,High,Human +0.152,0.192,0.198,0.234,0.196,0.211,0.202,0.267,0.242,0.248,0.338,0.332,0.324,0.206,0.231,0.229,0.236,0.278,0.309,0.332,0.248,0.326,0.255,0.238,0.194,0.273,0.286,0.259,0.238,0.307,0.311,0.252,0.129,0.257,0.359,0.284,0.189,0.278,0.229,0.225,0.276,0.231,0.211,0.173,0.313,0.24,-0.006,0.206,0.051,-0.041,0.19,0.19,0.189,0.198,0.229,0.196,0.227,0.215,0.229,0.221,0.074,0.059,OrganismalFitness,SYUA_HUMAN,Medium,Human +0.09,0.04,0.077,0.08,0.074,0.07,0.154,0.023,0.06,0.054,0.01,0.03,0.023,0.054,0.02,0.023,-0.07,0.013,0.054,-0.047,0.077,0.037,-0.003,-0.01,0.104,0.07,0.043,-0.017,-0.03,0.03,0.107,0.142,-0.043,0.12,0.147,0.084,0.09,0.11,0.087,0.1,0.117,0.084,0.054,0.107,-0.007,0.03,0.197,0.023,0.12,0.047,0.09,-0.033,-0.013,0.003,0.007,0.027,0.033,0.003,-0.06,0.023,0.01,0.057,OrganismalFitness,TADBP_HUMAN,Low,Human +0.265,0.183,0.238,0.243,0.259,0.243,-0.076,0.396,0.227,0.257,0.159,0.338,0.322,-0.032,0.006,0.009,0.028,-0.057,0.044,0.186,0.393,0.415,0.417,0.423,0.36,0.257,0.12,0.229,0.199,0.314,0.401,0.403,0.319,0.371,0.175,0.227,0.374,0.254,0.229,0.341,0.27,0.257,0.02,-0.027,0.284,0.156,0.202,0.281,0.3,0.178,0.082,0.126,0.139,0.099,0.107,0.107,0.074,0.099,0.09,0.093,0.161,0.145,OrganismalFitness,TAT_HV1BR,High,Virus +0.59,0.537,0.606,0.61,0.655,0.655,0.682,0.312,0.648,0.697,0.552,0.728,0.747,0.67,0.69,0.762,0.731,0.69,0.724,0.514,0.423,0.571,0.556,0.602,0.583,0.579,0.636,0.644,0.575,0.693,0.636,0.587,0.541,0.446,0.602,0.686,0.64,0.705,0.697,0.636,0.655,0.648,0.701,0.103,0.53,0.667,0.541,0.461,0.739,0.712,0.705,0.663,0.644,0.739,0.712,0.735,0.709,0.724,0.697,0.72,0.758,0.754,Stability,TCRG1_MOUSE,Medium,Eukaryote +0.25,0.26,0.437,0.411,0.407,0.42,-0.002,0.122,0.535,0.525,0.512,0.42,0.483,0.099,0.525,0.532,0.496,0.515,0.545,0.496,-0.097,0.266,0.312,0.296,0.266,0.286,0.185,0.368,0.443,0.499,0.551,0.548,0.414,-0.042,0.339,0.306,0.397,0.463,0.473,0.411,0.489,0.483,0.188,0.076,0.424,0.306,0.414,0.401,0.443,0.568,0.542,0.529,0.568,0.587,0.571,0.555,0.555,0.558,0.548,0.561,0.479,0.404,Stability,THO1_YEAST,High,Eukaryote +0.346,0.335,0.443,0.438,0.424,0.421,-0.109,0.318,0.511,0.492,0.405,0.416,0.419,-0.014,0.394,0.456,0.473,0.432,0.47,0.405,0.227,0.316,0.34,0.321,0.351,0.381,0.289,0.332,0.346,0.486,0.454,0.435,0.167,0.067,0.278,0.302,0.397,0.394,0.397,0.462,0.421,0.427,0.153,-0.076,0.4,0.337,0.335,0.473,0.559,0.508,0.459,0.459,0.467,0.47,0.451,0.443,0.44,0.432,0.446,0.454,0.586,0.467,Stability,TNKS2_HUMAN,High,Human +0.19,0.194,0.185,0.198,0.203,0.209,0.059,0.193,0.236,0.221,0.23,0.215,0.257,0.121,0.162,0.216,0.266,0.258,0.306,0.191,0.096,0.107,0.18,0.222,0.119,0.217,0.22,0.209,0.231,0.191,0.199,0.161,0.105,0.117,0.171,0.234,0.19,0.197,0.242,0.208,0.209,0.239,0.131,0.104,0.269,0.152,0.148,0.209,0.184,0.076,0.2,0.207,0.211,0.217,0.226,0.226,0.215,0.229,0.233,0.231,0.217,0.185,OrganismalFitness,TPK1_HUMAN,Medium,Human +0.263,0.306,0.313,0.323,0.322,0.331,0.173,0.302,0.322,0.329,0.349,0.33,0.344,0.225,0.265,0.344,0.349,0.295,0.291,0.341,0.186,0.263,0.301,0.299,0.265,0.304,0.301,0.268,0.295,0.351,0.359,0.342,0.223,0.195,0.302,0.29,0.302,0.337,0.331,0.349,0.334,0.344,0.244,0.148,0.345,0.316,0.322,0.347,0.36,0.162,0.345,0.342,0.349,0.342,0.348,0.356,0.359,0.344,0.355,0.356,0.36,0.324,Expression,TPMT_HUMAN,Medium,Human +0.273,0.222,0.23,0.188,0.162,0.213,0.299,0.316,0.265,0.29,0.29,0.265,0.282,0.247,0.307,0.265,0.179,0.282,0.205,0.213,0.213,0.299,0.239,0.265,0.307,0.128,0.35,0.41,0.239,0.358,0.282,0.133,0.324,0.307,0.29,0.333,0.299,0.299,0.367,0.316,0.307,0.35,0.247,0.247,0.213,0.265,0.119,0.23,0.213,0.0,0.205,0.196,0.273,0.23,0.205,0.273,0.29,0.111,0.188,0.239,0.324,0.299,OrganismalFitness,TPOR_HUMAN,Low,Human +0.516,0.527,0.462,0.479,0.487,0.508,0.188,0.465,0.557,0.562,0.546,0.543,0.557,0.258,0.506,0.532,0.541,0.543,0.524,0.462,0.368,0.519,0.465,0.516,0.436,0.471,0.444,0.46,0.441,0.441,0.527,0.462,0.196,0.487,0.492,0.508,0.511,0.527,0.527,0.506,0.516,0.516,0.395,0.045,0.543,0.465,0.25,0.384,0.387,0.099,0.541,0.495,0.503,0.557,0.53,0.524,0.543,0.546,0.524,0.551,0.535,0.511,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +0.365,0.371,0.348,0.371,0.365,0.363,0.237,0.333,0.383,0.383,0.371,0.395,0.406,0.19,0.377,0.386,0.392,0.383,0.389,0.354,0.283,0.324,0.33,0.339,0.354,0.354,0.313,0.333,0.36,0.348,0.339,0.304,0.146,0.333,0.392,0.365,0.363,0.38,0.363,0.368,0.365,0.357,0.33,0.093,0.377,0.333,0.169,0.263,0.275,0.003,0.383,0.316,0.357,0.377,0.377,0.33,0.36,0.36,0.351,0.377,0.409,0.383,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +0.304,0.407,0.457,0.474,0.438,0.457,-0.027,0.327,0.43,0.44,0.34,0.396,0.432,0.018,0.025,0.326,0.379,0.426,0.49,0.477,0.168,0.337,0.379,0.346,0.348,0.396,0.388,0.359,0.354,0.399,0.371,0.372,0.103,0.168,0.346,0.355,0.243,0.366,0.394,0.371,0.437,0.455,0.209,0.001,0.316,0.423,0.273,0.277,0.385,0.218,0.327,0.346,0.316,0.352,0.338,0.346,0.327,0.343,0.351,0.351,0.293,0.357,OrganismalFitness,UBC9_HUMAN,Medium,Human +0.247,0.336,0.369,0.379,0.37,0.378,0.152,0.349,0.363,0.371,0.341,0.414,0.4,0.334,0.358,0.394,0.374,0.402,0.428,0.383,0.217,0.358,0.35,0.371,0.377,0.383,0.342,0.341,0.369,0.387,0.422,0.418,0.387,0.153,0.167,0.299,0.333,0.324,0.332,0.382,0.375,0.386,0.326,-0.02,0.252,0.259,0.369,0.292,0.394,0.305,0.35,0.385,0.369,0.37,0.374,0.366,0.374,0.375,0.385,0.374,0.411,0.39,Stability,UBE4B_HUMAN,High,Human +0.306,0.312,0.348,0.342,0.348,0.354,0.04,0.046,0.318,0.342,0.276,0.33,0.348,0.318,0.336,0.36,0.33,0.318,0.288,0.318,0.082,0.276,0.27,0.252,0.348,0.3,0.288,0.288,0.252,0.33,0.36,0.3,0.058,-0.009,0.118,0.203,0.312,0.288,0.306,0.342,0.36,0.379,0.306,-0.003,0.33,0.348,0.191,0.27,-0.057,0.034,0.318,0.33,0.354,0.354,0.342,0.36,0.33,0.36,0.342,0.354,0.33,0.288,Activity,UBE4B_MOUSE,Low,Eukaryote +0.374,0.495,0.528,0.544,0.542,0.566,0.142,0.294,0.432,0.478,0.492,0.374,0.454,0.192,0.187,0.129,0.101,0.561,0.522,0.421,0.404,0.393,0.429,0.487,0.443,0.454,0.462,0.443,0.451,0.506,0.528,0.382,0.264,0.283,0.376,0.365,0.374,0.41,0.418,0.553,0.553,0.547,0.093,0.098,0.291,0.101,0.445,0.244,0.588,0.484,0.533,0.536,0.547,0.553,0.561,0.572,0.536,0.555,0.547,0.55,0.586,0.514,Stability,UBR5_HUMAN,Medium,Human +0.12,0.192,0.12,0.148,0.192,0.198,0.187,-0.057,0.264,0.242,0.519,0.441,0.463,0.286,0.419,0.491,0.519,0.491,0.546,0.109,0.297,0.402,0.358,0.485,0.32,0.342,0.331,0.369,0.447,0.441,0.43,0.295,0.048,0.237,0.358,0.397,0.198,0.281,0.342,0.22,0.248,0.281,0.347,0.209,0.441,0.38,0.386,0.425,0.43,0.375,0.53,0.48,0.508,0.53,0.508,0.568,0.474,0.535,0.563,0.535,0.497,0.48,Stability,VG08_BPP22,High,Virus +0.271,0.323,0.432,0.476,0.495,0.499,0.065,0.416,0.441,0.478,0.55,0.485,0.49,0.26,0.158,0.513,0.517,0.474,0.42,0.425,0.234,0.35,0.429,0.385,0.379,0.425,0.411,0.378,0.441,0.474,0.499,0.489,0.283,0.264,0.343,0.357,0.409,0.415,0.409,0.501,0.494,0.48,0.163,0.2,0.36,0.351,0.387,0.346,0.539,0.464,0.545,0.553,0.555,0.539,0.546,0.552,0.553,0.546,0.553,0.552,0.499,0.508,Stability,VILI_CHICK,High,Eukaryote +0.32,0.326,0.343,0.358,0.376,0.376,0.08,0.314,0.408,0.416,0.374,0.379,0.397,0.088,0.285,0.331,0.407,0.389,0.394,0.382,0.136,0.122,0.3,0.326,0.19,0.364,0.354,0.351,0.344,0.364,0.343,0.275,0.126,0.086,0.176,0.366,0.324,0.326,0.418,0.37,0.384,0.404,0.088,0.054,0.391,0.232,0.284,0.35,0.372,0.12,0.404,0.366,0.408,0.405,0.412,0.396,0.406,0.395,0.396,0.414,0.416,0.329,Expression,VKOR1_HUMAN,Medium,Human +0.32,0.326,0.343,0.358,0.376,0.376,0.08,0.314,0.408,0.416,0.374,0.379,0.397,0.088,0.285,0.331,0.407,0.389,0.394,0.382,0.136,0.122,0.3,0.326,0.19,0.364,0.354,0.351,0.344,0.364,0.343,0.275,0.126,0.086,0.176,0.366,0.324,0.326,0.418,0.37,0.384,0.404,0.088,0.054,0.391,0.232,0.284,0.35,0.372,0.12,0.404,0.366,0.408,0.405,0.412,0.396,0.406,0.395,0.396,0.414,0.416,0.329,Activity,VKOR1_HUMAN,Medium,Human +-0.076,0.011,0.102,0.144,0.081,0.11,0.094,0.11,0.342,0.351,0.363,0.264,0.28,0.214,0.342,0.384,0.463,0.496,0.421,0.135,0.007,0.139,0.115,0.222,0.197,0.185,0.139,0.189,0.247,0.069,0.355,0.338,0.231,0.04,0.011,0.073,-0.084,-0.055,-0.08,0.028,0.028,-0.043,0.218,0.036,0.425,0.293,0.463,0.392,0.442,0.442,0.467,0.446,0.446,0.446,0.454,0.454,0.438,0.458,0.434,0.467,0.492,0.417,Stability,VRPI_BPT7,Medium,Virus +0.243,0.511,0.432,0.455,0.434,0.432,-0.144,0.461,0.497,0.504,0.483,0.148,0.309,-0.091,0.106,0.432,0.521,0.544,0.546,0.523,-0.091,-0.167,-0.091,0.188,-0.099,-0.142,-0.186,0.017,0.394,0.449,0.55,0.524,0.004,-0.197,-0.051,0.396,0.273,0.267,0.417,0.404,0.396,0.485,-0.059,-0.212,0.068,-0.04,0.326,0.271,0.432,0.387,0.523,0.531,0.529,0.527,0.529,0.533,0.525,0.54,0.525,0.527,0.459,0.372,Stability,YAIA_ECOLI,Medium,Prokaryote +0.338,0.26,0.356,0.359,0.347,0.358,0.24,0.25,0.055,0.067,0.263,0.208,0.218,0.342,0.361,0.369,0.381,0.3,0.246,0.149,0.129,0.134,0.117,0.114,0.23,0.171,0.201,0.157,0.107,0.245,0.353,0.378,0.093,0.23,0.146,0.151,0.309,0.259,0.273,0.342,0.312,0.337,0.38,-0.07,0.254,0.391,0.285,0.272,0.327,0.145,0.37,0.303,0.343,0.341,0.351,0.327,0.339,0.362,0.367,0.361,0.29,0.348,Binding,YAP1_HUMAN,Low,Human +0.471,0.55,0.553,0.553,0.55,0.553,0.393,0.448,0.526,0.522,0.559,0.553,0.553,0.436,0.438,0.55,0.548,0.55,0.546,0.542,0.421,0.486,0.506,0.506,0.5,0.5,0.464,0.5,0.54,0.497,0.516,0.493,0.502,0.434,0.476,0.446,0.512,0.51,0.506,0.52,0.516,0.514,0.24,0.186,0.359,0.264,0.315,0.381,0.563,0.534,0.559,0.55,0.555,0.557,0.555,0.561,0.553,0.557,0.553,0.555,0.544,0.555,Stability,YNZC_BACSU,Medium,Prokaryote +0.293,0.327,0.335,0.344,0.356,0.361,0.134,0.28,0.346,0.357,0.332,0.315,0.338,0.155,0.247,0.313,0.347,0.341,0.337,0.309,0.24,0.284,0.297,0.302,0.269,0.309,0.305,0.308,0.32,0.37,0.373,0.341,0.157,0.232,0.274,0.304,0.331,0.34,0.353,0.367,0.369,0.372,0.205,0.064,0.301,0.249,0.292,0.318,0.339,0.223,0.364,0.353,0.362,0.368,0.367,0.372,0.365,0.367,0.367,0.375,0.371,0.317,,,, diff --git a/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.csv b/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.csv new file mode 100644 index 0000000..ea1e7ee --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.csv @@ -0,0 +1,63 @@ +Model_rank,Model_name,Model type,Average_MCC,Bootstrap_standard_error_MCC,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,SaProt (650M),Hybrid - Structure & PLM,0.358,0.0,0.354,0.29,0.388,0.283,0.476,0.312,0.353,0.435,0.388,0.412,0.395,0.243,0.334,0.207,0.164,0.187,0.263,SaProt (650M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +2,TranceptEVE L,Hybrid - Alignment & PLM,0.356,0.007,0.373,0.284,0.358,0.359,0.406,0.351,0.37,0.391,0.377,0.4,0.362,0.353,0.31,0.188,0.226,0.221,0.257,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +3,ProtSSN (ensemble),Hybrid - Structure & PLM,0.354,0.005,0.366,0.274,0.355,0.309,0.464,0.312,0.368,0.416,0.382,0.419,0.387,0.284,0.3,0.134,0.065,0.059,0.104,ProtSSN (ensemble of 9 models),"Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +4,TranceptEVE M,Hybrid - Alignment & PLM,0.353,0.008,0.368,0.289,0.347,0.357,0.403,0.34,0.37,0.386,0.377,0.399,0.355,0.344,0.313,0.188,0.2,0.199,0.252,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +5,TranceptEVE S,Hybrid - Alignment & PLM,0.352,0.008,0.361,0.3,0.346,0.353,0.402,0.352,0.361,0.387,0.377,0.392,0.355,0.337,0.307,0.191,0.207,0.202,0.25,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,GEMME,Alignment-based model,0.352,0.01,0.362,0.284,0.336,0.354,0.423,0.348,0.37,0.396,0.373,0.4,0.366,0.366,0.324,0.154,0.214,0.198,0.241,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +7,ProtSSN (k=20 h=1280),Hybrid - Structure & PLM,0.351,0.006,0.361,0.281,0.345,0.303,0.466,0.312,0.362,0.417,0.379,0.416,0.383,0.284,0.298,0.135,0.07,0.059,0.107,"ProtSSN (k=20, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +8,ProtSSN (k=30 h=1280),Hybrid - Structure & PLM,0.347,0.006,0.358,0.276,0.342,0.297,0.459,0.303,0.358,0.413,0.375,0.416,0.379,0.271,0.295,0.118,0.049,0.063,0.111,"ProtSSN (k=30, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +9,VESPA,Protein language model,0.346,0.007,0.369,0.269,0.325,0.348,0.422,0.331,0.367,0.401,0.365,0.399,0.381,0.346,0.308,0.125,0.113,0.067,0.107,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +10,ProtSSN (k=20 h=768),Hybrid - Structure & PLM,0.346,0.005,0.355,0.269,0.347,0.3,0.458,0.301,0.359,0.41,0.374,0.414,0.376,0.274,0.293,0.136,0.072,0.058,0.107,"ProtSSN (k=20, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +11,ProtSSN (k=20 h=512),Hybrid - Structure & PLM,0.345,0.005,0.357,0.266,0.339,0.305,0.459,0.302,0.363,0.408,0.369,0.416,0.383,0.287,0.294,0.132,0.083,0.072,0.11,"ProtSSN (k=20, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +12,ProtSSN (k=30 h=768),Hybrid - Structure & PLM,0.344,0.005,0.356,0.264,0.34,0.3,0.46,0.296,0.362,0.409,0.37,0.418,0.381,0.277,0.293,0.131,0.058,0.058,0.111,"ProtSSN (k=30, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +13,ProtSSN (k=10 h=1280),Hybrid - Structure & PLM,0.343,0.006,0.351,0.267,0.345,0.298,0.452,0.307,0.355,0.403,0.371,0.406,0.37,0.28,0.289,0.115,0.067,0.047,0.098,"ProtSSN (k=10, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +14,EVE (ensemble),Alignment-based model,0.342,0.009,0.357,0.283,0.319,0.35,0.401,0.325,0.362,0.384,0.365,0.381,0.365,0.334,0.291,0.184,0.189,0.203,0.237,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +15,ProtSSN (k=30 h=512),Hybrid - Structure & PLM,0.342,0.006,0.353,0.258,0.341,0.301,0.455,0.307,0.359,0.403,0.372,0.41,0.377,0.272,0.292,0.129,0.063,0.065,0.103,"ProtSSN (k=30, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +16,ProtSSN (k=10 h=512),Hybrid - Structure & PLM,0.341,0.006,0.356,0.262,0.332,0.296,0.46,0.305,0.353,0.412,0.37,0.416,0.38,0.266,0.291,0.137,0.062,0.067,0.114,"ProtSSN (k=10, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +17,Tranception L,Hybrid - Alignment & PLM,0.34,0.007,0.36,0.266,0.358,0.34,0.378,0.34,0.347,0.373,0.363,0.389,0.325,0.336,0.312,0.179,0.221,0.225,0.272,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +18,MSA Transformer (ensemble),Hybrid - Alignment & PLM,0.339,0.007,0.367,0.248,0.352,0.327,0.403,0.304,0.361,0.385,0.357,0.405,0.353,0.317,0.295,0.155,0.203,0.222,0.244,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +19,EVE (single),Alignment-based model,0.335,0.008,0.351,0.263,0.318,0.345,0.397,0.314,0.357,0.379,0.357,0.377,0.361,0.331,0.286,0.179,0.198,0.197,0.235,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +20,ProtSSN (k=10 h=768),Hybrid - Structure & PLM,0.332,0.005,0.344,0.257,0.326,0.287,0.445,0.288,0.348,0.394,0.36,0.396,0.365,0.268,0.278,0.109,0.059,0.054,0.093,"ProtSSN (k=10, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +21,ESM-IF1,Inverse folding model,0.331,0.009,0.283,0.305,0.319,0.246,0.5,0.23,0.343,0.428,0.334,0.391,0.381,0.305,0.334,0.22,0.167,0.211,0.281,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +22,Tranception M,Hybrid - Alignment & PLM,0.33,0.007,0.343,0.267,0.344,0.33,0.366,0.324,0.338,0.358,0.354,0.379,0.309,0.318,0.307,0.169,0.166,0.182,0.241,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +23,DeepSequence (ensemble),Alignment-based model,0.328,0.01,0.345,0.272,0.311,0.327,0.384,0.296,0.34,0.377,0.355,0.376,0.351,0.273,0.272,0.163,0.203,0.216,0.271,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +24,MSA Transformer (single),Hybrid - Alignment & PLM,0.328,0.007,0.351,0.233,0.338,0.321,0.395,0.292,0.351,0.375,0.349,0.392,0.34,0.312,0.287,0.146,0.208,0.214,0.229,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +25,ESM2 (650M),Protein language model,0.327,0.009,0.331,0.262,0.332,0.29,0.42,0.269,0.322,0.41,0.369,0.383,0.355,0.204,0.309,0.173,0.11,0.114,0.153,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +26,Tranception S,Hybrid - Alignment & PLM,0.322,0.008,0.33,0.279,0.326,0.32,0.354,0.332,0.321,0.348,0.345,0.364,0.3,0.305,0.293,0.171,0.168,0.175,0.248,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +27,ESM-1v (ensemble),Protein language model,0.32,0.008,0.323,0.241,0.345,0.305,0.387,0.255,0.32,0.404,0.368,0.367,0.321,0.236,0.292,0.145,0.104,0.103,0.159,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +28,ESM2 (3B),Protein language model,0.32,0.008,0.322,0.247,0.323,0.293,0.415,0.277,0.329,0.391,0.355,0.379,0.362,0.226,0.299,0.144,0.066,0.085,0.149,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +29,DeepSequence (single),Alignment-based model,0.318,0.01,0.338,0.26,0.292,0.314,0.383,0.294,0.33,0.37,0.348,0.372,0.344,0.258,0.265,0.161,0.177,0.218,0.279,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +30,SaProt (35M),Hybrid - Structure & PLM,0.316,0.006,0.283,0.285,0.337,0.219,0.453,0.253,0.306,0.393,0.356,0.378,0.32,0.182,0.307,0.225,0.111,0.13,0.186,SaProt (35M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +31,ESM2 (15B),Protein language model,0.314,0.008,0.311,0.229,0.333,0.301,0.398,0.276,0.328,0.38,0.346,0.377,0.341,0.252,0.285,0.12,0.076,0.092,0.166,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +32,VESPAl,Protein language model,0.312,0.009,0.331,0.255,0.26,0.321,0.395,0.292,0.334,0.373,0.327,0.375,0.353,0.316,0.279,0.107,0.096,0.074,0.105,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +33,ESM-1b,Protein language model,0.311,0.008,0.328,0.221,0.32,0.274,0.41,0.275,0.317,0.387,0.352,0.388,0.343,0.203,0.256,0.127,0.021,0.046,0.124,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +34,MIF-ST,Hybrid - Structure & PLM,0.31,0.008,0.299,0.248,0.348,0.285,0.368,0.293,0.311,0.349,0.314,0.315,0.344,0.31,0.332,0.164,0.233,0.223,0.267,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +35,Progen2 XL,Protein language model,0.306,0.007,0.311,0.225,0.329,0.302,0.365,0.267,0.325,0.357,0.311,0.354,0.334,0.314,0.283,0.141,0.181,0.163,0.223,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +36,EVmutation,Alignment-based model,0.305,0.008,0.342,0.218,0.297,0.322,0.342,0.295,0.339,0.32,0.324,0.343,0.331,0.3,0.242,0.17,0.172,0.181,0.242,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +37,ESM2 (150M),Protein language model,0.302,0.009,0.296,0.253,0.315,0.237,0.41,0.245,0.282,0.392,0.36,0.38,0.297,0.124,0.287,0.167,0.078,0.109,0.148,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +38,Progen2 M,Protein language model,0.299,0.007,0.3,0.228,0.342,0.3,0.324,0.245,0.307,0.341,0.329,0.325,0.285,0.262,0.281,0.099,0.078,0.09,0.127,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +39,Progen2 Base,Protein language model,0.299,0.008,0.306,0.231,0.353,0.295,0.308,0.263,0.292,0.339,0.339,0.332,0.255,0.242,0.283,0.098,0.072,0.1,0.133,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +40,Progen2 L,Protein language model,0.298,0.007,0.307,0.22,0.341,0.296,0.327,0.264,0.306,0.335,0.326,0.342,0.291,0.249,0.279,0.115,0.144,0.146,0.195,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +41,ESM-1v (single),Protein language model,0.296,0.009,0.304,0.21,0.322,0.287,0.357,0.225,0.295,0.385,0.344,0.339,0.303,0.21,0.274,0.126,0.114,0.105,0.152,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +42,Tranception L no retrieval,Protein language model,0.296,0.008,0.309,0.229,0.325,0.301,0.315,0.278,0.297,0.335,0.313,0.315,0.283,0.31,0.27,0.126,0.182,0.191,0.247,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +43,Wavenet,Alignment-based model,0.294,0.011,0.298,0.243,0.279,0.282,0.369,0.225,0.314,0.36,0.317,0.335,0.324,0.262,0.257,0.154,0.148,0.149,0.139,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +44,MIF,Inverse folding model,0.294,0.01,0.243,0.264,0.335,0.23,0.395,0.27,0.292,0.331,0.308,0.286,0.313,0.284,0.315,0.177,0.198,0.191,0.231,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +45,RITA XL,Protein language model,0.293,0.008,0.284,0.225,0.33,0.299,0.328,0.238,0.307,0.334,0.316,0.315,0.275,0.307,0.272,0.115,0.094,0.106,0.166,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +46,RITA L,Protein language model,0.291,0.008,0.283,0.222,0.338,0.293,0.317,0.245,0.299,0.325,0.322,0.317,0.247,0.299,0.268,0.116,0.071,0.087,0.15,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +47,Site-Independent,Alignment-based model,0.286,0.011,0.291,0.264,0.274,0.299,0.301,0.333,0.3,0.263,0.31,0.311,0.256,0.29,0.248,0.177,0.148,0.172,0.24,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +48,CARP (640M),Protein language model,0.286,0.008,0.304,0.202,0.322,0.288,0.313,0.248,0.291,0.33,0.326,0.304,0.286,0.225,0.303,0.127,0.103,0.106,0.129,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +49,RITA M,Protein language model,0.274,0.009,0.273,0.204,0.314,0.292,0.286,0.232,0.278,0.315,0.301,0.294,0.235,0.297,0.26,0.091,0.057,0.09,0.155,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +50,Unirep evotuned,Hybrid - Alignment & PLM,0.274,0.009,0.274,0.226,0.288,0.268,0.311,0.254,0.276,0.307,0.289,0.302,0.27,0.266,0.244,0.141,0.185,0.184,0.228,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +51,Tranception M no retrieval,Protein language model,0.269,0.008,0.266,0.213,0.311,0.28,0.274,0.221,0.277,0.296,0.297,0.278,0.238,0.259,0.254,0.103,0.083,0.101,0.127,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +52,Progen2 S,Protein language model,0.261,0.009,0.257,0.204,0.295,0.261,0.285,0.219,0.257,0.309,0.306,0.28,0.226,0.214,0.251,0.091,0.071,0.087,0.103,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +53,CARP (76M),Protein language model,0.251,0.01,0.256,0.212,0.29,0.215,0.282,0.195,0.231,0.311,0.304,0.275,0.217,0.125,0.257,0.135,0.049,0.068,0.09,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +54,ESM2 (35M),Protein language model,0.249,0.011,0.238,0.234,0.262,0.169,0.344,0.191,0.213,0.349,0.296,0.316,0.23,0.099,0.23,0.155,0.074,0.095,0.151,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +55,RITA S,Protein language model,0.236,0.01,0.231,0.204,0.262,0.249,0.232,0.204,0.237,0.261,0.264,0.228,0.181,0.265,0.217,0.093,0.073,0.083,0.12,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +56,Tranception S no retrieval,Protein language model,0.231,0.01,0.216,0.21,0.264,0.247,0.218,0.189,0.233,0.251,0.251,0.216,0.2,0.232,0.212,0.093,0.074,0.089,0.12,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +57,CARP (38M),Protein language model,0.213,0.011,0.213,0.21,0.237,0.169,0.235,0.156,0.186,0.266,0.249,0.233,0.18,0.101,0.212,0.116,0.05,0.074,0.107,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +58,ProteinMPNN,Inverse folding model,0.195,0.009,0.143,0.126,0.141,0.121,0.445,0.137,0.216,0.333,0.223,0.307,0.262,0.196,0.206,0.176,0.119,0.127,0.224,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +59,ESM2 (8M),Protein language model,0.171,0.012,0.147,0.201,0.192,0.104,0.209,0.149,0.141,0.199,0.19,0.193,0.139,0.075,0.148,0.099,0.063,0.097,0.13,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +60,ProtGPT2,Protein language model,0.151,0.009,0.135,0.118,0.155,0.136,0.21,0.144,0.143,0.203,0.198,0.193,0.103,0.118,0.143,0.102,0.027,0.014,0.046,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +61,Unirep,Protein language model,0.146,0.012,0.134,0.156,0.166,0.103,0.171,0.139,0.128,0.157,0.174,0.173,0.11,0.051,0.132,0.064,0.074,0.093,0.119,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +62,CARP (600K),Protein language model,0.076,0.013,0.076,0.057,0.126,0.046,0.074,0.069,0.067,0.067,0.09,0.054,0.047,0.051,0.077,0.016,0.019,0.062,0.069,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.html b/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.html new file mode 100644 index 0000000..96f9f7f --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/MCC/Summary_performance_DMS_substitutions_MCC.html @@ -0,0 +1,1670 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_MCCBootstrap_standard_error_MCCFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusDepth_1Depth_2Depth_3Depth_4Depth_5+Model detailsReferences
Model_rank
1SaProt (650M)Hybrid - Structure & PLM0.3580.0000.3540.2900.3880.2830.4760.3120.3530.4350.3880.4120.3950.2430.3340.2070.1640.1870.263SaProt (650M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
2TranceptEVE LHybrid - Alignment & PLM0.3560.0070.3730.2840.3580.3590.4060.3510.3700.3910.3770.4000.3620.3530.3100.1880.2260.2210.257TranceptEVE Large model (Tranception Large & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
3ProtSSN (ensemble)Hybrid - Structure & PLM0.3540.0050.3660.2740.3550.3090.4640.3120.3680.4160.3820.4190.3870.2840.3000.1340.0650.0590.104ProtSSN (ensemble of 9 models)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
4TranceptEVE MHybrid - Alignment & PLM0.3530.0080.3680.2890.3470.3570.4030.3400.3700.3860.3770.3990.3550.3440.3130.1880.2000.1990.252TranceptEVE Medium model (Tranception Medium & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
5TranceptEVE SHybrid - Alignment & PLM0.3520.0080.3610.3000.3460.3530.4020.3520.3610.3870.3770.3920.3550.3370.3070.1910.2070.2020.250TranceptEVE Small model (Tranception Small & retrieved EVE model)<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
6GEMMEAlignment-based model0.3520.0100.3620.2840.3360.3540.4230.3480.3700.3960.3730.4000.3660.3660.3240.1540.2140.1980.241GEMME model<a href='https://pubmed.ncbi.nlm.nih.gov/31406981/'>Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.</a>
7ProtSSN (k=20 h=1280)Hybrid - Structure & PLM0.3510.0060.3610.2810.3450.3030.4660.3120.3620.4170.3790.4160.3830.2840.2980.1350.0700.0590.107ProtSSN (k=20, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
8ProtSSN (k=30 h=1280)Hybrid - Structure & PLM0.3470.0060.3580.2760.3420.2970.4590.3030.3580.4130.3750.4160.3790.2710.2950.1180.0490.0630.111ProtSSN (k=30, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
9VESPAProtein language model0.3460.0070.3690.2690.3250.3480.4220.3310.3670.4010.3650.3990.3810.3460.3080.1250.1130.0670.107VESPA model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
10ProtSSN (k=20 h=768)Hybrid - Structure & PLM0.3460.0050.3550.2690.3470.3000.4580.3010.3590.4100.3740.4140.3760.2740.2930.1360.0720.0580.107ProtSSN (k=20, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
11ProtSSN (k=20 h=512)Hybrid - Structure & PLM0.3450.0050.3570.2660.3390.3050.4590.3020.3630.4080.3690.4160.3830.2870.2940.1320.0830.0720.110ProtSSN (k=20, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
12ProtSSN (k=30 h=768)Hybrid - Structure & PLM0.3440.0050.3560.2640.3400.3000.4600.2960.3620.4090.3700.4180.3810.2770.2930.1310.0580.0580.111ProtSSN (k=30, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
13ProtSSN (k=10 h=1280)Hybrid - Structure & PLM0.3430.0060.3510.2670.3450.2980.4520.3070.3550.4030.3710.4060.3700.2800.2890.1150.0670.0470.098ProtSSN (k=10, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
14EVE (ensemble)Alignment-based model0.3420.0090.3570.2830.3190.3500.4010.3250.3620.3840.3650.3810.3650.3340.2910.1840.1890.2030.237EVE model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
15ProtSSN (k=30 h=512)Hybrid - Structure & PLM0.3420.0060.3530.2580.3410.3010.4550.3070.3590.4030.3720.4100.3770.2720.2920.1290.0630.0650.103ProtSSN (k=30, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
16ProtSSN (k=10 h=512)Hybrid - Structure & PLM0.3410.0060.3560.2620.3320.2960.4600.3050.3530.4120.3700.4160.3800.2660.2910.1370.0620.0670.114ProtSSN (k=10, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
17Tranception LHybrid - Alignment & PLM0.3400.0070.3600.2660.3580.3400.3780.3400.3470.3730.3630.3890.3250.3360.3120.1790.2210.2250.272Tranception Large model (700M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
18MSA Transformer (ensemble)Hybrid - Alignment & PLM0.3390.0070.3670.2480.3520.3270.4030.3040.3610.3850.3570.4050.3530.3170.2950.1550.2030.2220.244MSA Transformer (ensemble of 5 MSA samples)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
19EVE (single)Alignment-based model0.3350.0080.3510.2630.3180.3450.3970.3140.3570.3790.3570.3770.3610.3310.2860.1790.1980.1970.235EVE model (single seed)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
20ProtSSN (k=10 h=768)Hybrid - Structure & PLM0.3320.0050.3440.2570.3260.2870.4450.2880.3480.3940.3600.3960.3650.2680.2780.1090.0590.0540.093ProtSSN (k=10, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
21ESM-IF1Inverse folding model0.3310.0090.2830.3050.3190.2460.5000.2300.3430.4280.3340.3910.3810.3050.3340.2200.1670.2110.281ESM-IF1 model<a href='https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html'>Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.</a>
22Tranception MHybrid - Alignment & PLM0.3300.0070.3430.2670.3440.3300.3660.3240.3380.3580.3540.3790.3090.3180.3070.1690.1660.1820.241Tranception Medium model (300M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
23DeepSequence (ensemble)Alignment-based model0.3280.0100.3450.2720.3110.3270.3840.2960.3400.3770.3550.3760.3510.2730.2720.1630.2030.2160.271DeepSequence model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
24MSA Transformer (single)Hybrid - Alignment & PLM0.3280.0070.3510.2330.3380.3210.3950.2920.3510.3750.3490.3920.3400.3120.2870.1460.2080.2140.229MSA Transformer (single MSA sample)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
25ESM2 (650M)Protein language model0.3270.0090.3310.2620.3320.2900.4200.2690.3220.4100.3690.3830.3550.2040.3090.1730.1100.1140.153ESM2 model (650M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
26Tranception SHybrid - Alignment & PLM0.3220.0080.3300.2790.3260.3200.3540.3320.3210.3480.3450.3640.3000.3050.2930.1710.1680.1750.248Tranception Small model (85M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
27ESM-1v (ensemble)Protein language model0.3200.0080.3230.2410.3450.3050.3870.2550.3200.4040.3680.3670.3210.2360.2920.1450.1040.1030.159ESM-1v (ensemble of 5 independently-trained models)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
28ESM2 (3B)Protein language model0.3200.0080.3220.2470.3230.2930.4150.2770.3290.3910.3550.3790.3620.2260.2990.1440.0660.0850.149ESM2 model (3B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
29DeepSequence (single)Alignment-based model0.3180.0100.3380.2600.2920.3140.3830.2940.3300.3700.3480.3720.3440.2580.2650.1610.1770.2180.279DeepSequence model (single seed)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
30SaProt (35M)Hybrid - Structure & PLM0.3160.0060.2830.2850.3370.2190.4530.2530.3060.3930.3560.3780.3200.1820.3070.2250.1110.1300.186SaProt (35M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
31ESM2 (15B)Protein language model0.3140.0080.3110.2290.3330.3010.3980.2760.3280.3800.3460.3770.3410.2520.2850.1200.0760.0920.166ESM2 model (15B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
32VESPAlProtein language model0.3120.0090.3310.2550.2600.3210.3950.2920.3340.3730.3270.3750.3530.3160.2790.1070.0960.0740.105VESPAl model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
33ESM-1bProtein language model0.3110.0080.3280.2210.3200.2740.4100.2750.3170.3870.3520.3880.3430.2030.2560.1270.0210.0460.124ESM-1b (w/ Brandes et al. extensions)[1] Original model: <a href='https://www.biorxiv.org/content/10.1101/622803v4'>Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118.</a> [2] Extensions: <a href='https://www.biorxiv.org/content/10.1101/2022.08.25.505311v1'>Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.</a>
34MIF-STHybrid - Structure & PLM0.3100.0080.2990.2480.3480.2850.3680.2930.3110.3490.3140.3150.3440.3100.3320.1640.2330.2230.267MIF-ST model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
35Progen2 XLProtein language model0.3060.0070.3110.2250.3290.3020.3650.2670.3250.3570.3110.3540.3340.3140.2830.1410.1810.1630.223Progen2 xlarge model (6.4B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
36EVmutationAlignment-based model0.3050.0080.3420.2180.2970.3220.3420.2950.3390.3200.3240.3430.3310.3000.2420.1700.1720.1810.242EVmutation model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
37ESM2 (150M)Protein language model0.3020.0090.2960.2530.3150.2370.4100.2450.2820.3920.3600.3800.2970.1240.2870.1670.0780.1090.148ESM2 model (150M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
38Progen2 MProtein language model0.2990.0070.3000.2280.3420.3000.3240.2450.3070.3410.3290.3250.2850.2620.2810.0990.0780.0900.127Progen2 medium model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
39Progen2 BaseProtein language model0.2990.0080.3060.2310.3530.2950.3080.2630.2920.3390.3390.3320.2550.2420.2830.0980.0720.1000.133Progen2 base model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
40Progen2 LProtein language model0.2980.0070.3070.2200.3410.2960.3270.2640.3060.3350.3260.3420.2910.2490.2790.1150.1440.1460.195Progen2 large model (2.7B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
41ESM-1v (single)Protein language model0.2960.0090.3040.2100.3220.2870.3570.2250.2950.3850.3440.3390.3030.2100.2740.1260.1140.1050.152ESM-1v (single seed)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
42Tranception L no retrievalProtein language model0.2960.0080.3090.2290.3250.3010.3150.2780.2970.3350.3130.3150.2830.3100.2700.1260.1820.1910.247Tranception Large model (700M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
43WavenetAlignment-based model0.2940.0110.2980.2430.2790.2820.3690.2250.3140.3600.3170.3350.3240.2620.2570.1540.1480.1490.139Wavenet model<a href='https://www.nature.com/articles/s41467-021-22732-w'>Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.</a>
44MIFInverse folding model0.2940.0100.2430.2640.3350.2300.3950.2700.2920.3310.3080.2860.3130.2840.3150.1770.1980.1910.231MIF model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
45RITA XLProtein language model0.2930.0080.2840.2250.3300.2990.3280.2380.3070.3340.3160.3150.2750.3070.2720.1150.0940.1060.166RITA xlarge model (1.2B params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
46RITA LProtein language model0.2910.0080.2830.2220.3380.2930.3170.2450.2990.3250.3220.3170.2470.2990.2680.1160.0710.0870.150RITA large model (680M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
47Site-IndependentAlignment-based model0.2860.0110.2910.2640.2740.2990.3010.3330.3000.2630.3100.3110.2560.2900.2480.1770.1480.1720.240Site-Independent model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
48CARP (640M)Protein language model0.2860.0080.3040.2020.3220.2880.3130.2480.2910.3300.3260.3040.2860.2250.3030.1270.1030.1060.129CARP model (640M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
49RITA MProtein language model0.2740.0090.2730.2040.3140.2920.2860.2320.2780.3150.3010.2940.2350.2970.2600.0910.0570.0900.155RITA medium model (300M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
50Unirep evotunedHybrid - Alignment & PLM0.2740.0090.2740.2260.2880.2680.3110.2540.2760.3070.2890.3020.2700.2660.2440.1410.1850.1840.228Unirep model w/ evotuning<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
51Tranception M no retrievalProtein language model0.2690.0080.2660.2130.3110.2800.2740.2210.2770.2960.2970.2780.2380.2590.2540.1030.0830.1010.127Tranception Medium model (300M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
52Progen2 SProtein language model0.2610.0090.2570.2040.2950.2610.2850.2190.2570.3090.3060.2800.2260.2140.2510.0910.0710.0870.103Progen2 small model (150M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
53CARP (76M)Protein language model0.2510.0100.2560.2120.2900.2150.2820.1950.2310.3110.3040.2750.2170.1250.2570.1350.0490.0680.090CARP model (76M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
54ESM2 (35M)Protein language model0.2490.0110.2380.2340.2620.1690.3440.1910.2130.3490.2960.3160.2300.0990.2300.1550.0740.0950.151ESM2 model (35M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
55RITA SProtein language model0.2360.0100.2310.2040.2620.2490.2320.2040.2370.2610.2640.2280.1810.2650.2170.0930.0730.0830.120RITA small model (85M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
56Tranception S no retrievalProtein language model0.2310.0100.2160.2100.2640.2470.2180.1890.2330.2510.2510.2160.2000.2320.2120.0930.0740.0890.120Tranception Small model (85M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
57CARP (38M)Protein language model0.2130.0110.2130.2100.2370.1690.2350.1560.1860.2660.2490.2330.1800.1010.2120.1160.0500.0740.107CARP model (38M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
58ProteinMPNNInverse folding model0.1950.0090.1430.1260.1410.1210.4450.1370.2160.3330.2230.3070.2620.1960.2060.1760.1190.1270.224ProteinMPNN model<a href='https://www.science.org/doi/10.1126/science.add2187'>J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.</a>
59ESM2 (8M)Protein language model0.1710.0120.1470.2010.1920.1040.2090.1490.1410.1990.1900.1930.1390.0750.1480.0990.0630.0970.130ESM2 model (8M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
60ProtGPT2Protein language model0.1510.0090.1350.1180.1550.1360.2100.1440.1430.2030.1980.1930.1030.1180.1430.1020.0270.0140.046ProtGPT2 model<a href='https://www.nature.com/articles/s41467-022-32007-7'>Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.</a>
61UnirepProtein language model0.1460.0120.1340.1560.1660.1030.1710.1390.1280.1570.1740.1730.1100.0510.1320.0640.0740.0930.119Unirep model<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
62CARP (600K)Protein language model0.0760.0130.0760.0570.1260.0460.0740.0690.0670.0670.0900.0540.0470.0510.0770.0160.0190.0620.069CARP model (600K params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.csv b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.csv new file mode 100644 index 0000000..a0a7479 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.csv @@ -0,0 +1,217 @@ +DMS ID,Site-Independent,EVmutation,DeepSequence (single),DeepSequence (ensemble),EVE (single),EVE (ensemble),Unirep,Unirep evotuned,MSA Transformer (single),MSA Transformer (ensemble),ESM-1b,ESM-1v (single),ESM-1v (ensemble),ESM2 (8M),ESM2 (35M),ESM2 (150M),ESM2 (650M),ESM2 (3B),ESM2 (15B),Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,GEMME,VESPA,VESPAl,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,CARP (38M),CARP (600K),CARP (640M),CARP (76M),MIF,MIF-ST,ESM-IF1,ProteinMPNN,ProtSSN (k=10 h=512),ProtSSN (k=10 h=768),ProtSSN (k=10 h=1280),ProtSSN (k=20 h=512),ProtSSN (k=20 h=768),ProtSSN (k=20 h=1280),ProtSSN (k=30 h=512),ProtSSN (k=30 h=768),ProtSSN (k=30 h=1280),ProtSSN (ensemble),SaProt (650M),SaProt (35M),Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A0A140D2T1_ZIKV_Sourisseau_2019,0.278,0.29,0.231,0.219,0.283,0.286,0.1,0.099,0.327,0.333,0.149,0.128,0.164,0.097,0.122,0.107,0.144,0.257,0.271,0.189,0.274,0.277,0.279,0.298,0.254,0.267,0.283,0.248,0.273,0.299,0.262,0.231,0.125,0.259,0.252,0.239,0.28,0.283,0.279,0.281,0.293,0.285,0.123,0.121,0.184,0.131,0.186,0.202,0.163,0.166,0.198,0.207,0.221,0.218,0.22,0.199,0.178,0.199,0.184,0.209,0.155,0.154,9576,OrganismalFitness,A0A140D2T1_ZIKV,Medium,Virus 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+BLAT_ECOLX_Firnberg_2014,0.598,0.787,0.793,0.775,0.808,0.814,0.387,0.514,0.667,0.755,0.706,0.716,0.729,0.56,0.636,0.702,0.722,0.736,0.695,0.684,0.697,0.695,0.706,0.674,0.669,0.734,0.732,0.747,0.657,0.666,0.799,0.789,0.445,0.668,0.736,0.683,0.744,0.774,0.758,0.78,0.804,0.824,0.611,0.375,0.726,0.645,0.723,0.787,0.792,0.606,0.754,0.759,0.768,0.765,0.777,0.788,0.772,0.769,0.768,0.779,0.797,0.676,4783,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Jacquier_2013,0.815,0.937,0.918,0.9,0.921,0.924,0.621,0.76,0.858,0.894,0.869,0.894,0.898,0.802,0.825,0.837,0.863,0.899,0.897,0.85,0.895,0.887,0.912,0.906,0.838,0.882,0.912,0.922,0.903,0.771,0.916,0.916,0.728,0.871,0.918,0.906,0.904,0.921,0.918,0.898,0.908,0.922,0.819,0.571,0.883,0.853,0.823,0.908,0.902,0.84,0.892,0.894,0.901,0.909,0.892,0.901,0.909,0.895,0.903,0.897,0.919,0.872,989,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Stiffler_2015,0.83,0.95,0.94,0.931,0.964,0.962,0.633,0.769,0.86,0.922,0.891,0.907,0.909,0.806,0.856,0.897,0.901,0.933,0.912,0.873,0.897,0.904,0.922,0.905,0.863,0.916,0.929,0.943,0.895,0.877,0.967,0.963,0.683,0.882,0.939,0.906,0.932,0.957,0.946,0.942,0.957,0.973,0.84,0.615,0.913,0.863,0.91,0.953,0.951,0.815,0.933,0.93,0.941,0.936,0.94,0.952,0.941,0.935,0.945,0.944,0.956,0.885,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BRCA1_HUMAN_Findlay_2018,0.881,0.871,0.877,0.878,0.866,0.867,0.738,0.812,0.867,0.869,0.865,0.822,0.822,0.77,0.812,0.834,0.866,0.879,0.85,0.872,0.76,0.862,0.877,0.861,0.823,0.89,0.887,0.873,0.873,0.878,0.879,0.855,0.717,0.781,0.772,0.857,0.877,0.881,0.883,0.878,0.877,0.878,0.793,0.756,0.881,0.835,0.838,0.877,0.751,0.781,0.853,0.86,0.858,0.855,0.85,0.858,0.848,0.859,0.852,0.857,0.818,0.833,1837,OrganismalFitness,BRCA1_HUMAN,Low,Human +BRCA2_HUMAN_Erwood_2022_HEK293T,0.941,0.933,0.936,0.935,0.935,0.932,0.915,0.937,0.856,0.891,0.94,0.923,0.918,0.928,0.925,0.929,0.944,0.935,0.94,0.903,0.93,0.944,0.949,0.943,0.942,0.922,0.921,0.933,0.914,0.932,0.926,0.946,0.927,0.926,0.93,0.931,0.938,0.937,0.939,0.933,0.934,0.933,0.918,0.922,0.939,0.898,0.89,0.892,0.769,0.917,0.943,0.942,0.941,0.938,0.945,0.933,0.948,0.943,0.943,0.944,0.896,0.91,265,OrganismalFitness,BRCA2_HUMAN,,Human +C6KNH7_9INFA_Lee_2018,0.771,0.811,0.803,0.8,0.812,0.812,0.609,0.792,0.809,0.812,0.627,0.77,0.822,0.601,0.604,0.616,0.784,0.81,0.82,0.792,0.798,0.788,0.796,0.786,0.66,0.793,0.796,0.808,0.778,0.828,0.8,0.767,0.667,0.786,0.796,0.787,0.801,0.805,0.801,0.815,0.818,0.817,0.603,0.59,0.698,0.608,0.75,0.795,0.785,0.719,0.789,0.796,0.795,0.793,0.793,0.783,0.794,0.794,0.783,0.795,0.688,0.658,10754,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +CALM1_HUMAN_Weile_2017,0.799,0.817,0.808,0.804,0.805,0.8,0.801,0.79,0.815,0.816,0.827,0.794,0.821,0.797,0.793,0.791,0.773,0.786,0.812,0.813,0.801,0.806,0.821,0.81,0.812,0.827,0.816,0.841,0.832,0.823,0.819,0.794,0.75,0.809,0.832,0.823,0.815,0.827,0.828,0.813,0.807,0.813,0.807,0.794,0.819,0.824,0.781,0.775,0.809,0.758,0.774,0.78,0.781,0.758,0.766,0.783,0.781,0.779,0.763,0.778,0.826,0.822,1813,OrganismalFitness,CALM1_HUMAN,High,Human +CAPSD_AAV2S_Sinai_2021,0.791,0.789,0.786,0.811,0.781,0.784,0.758,0.841,0.75,0.777,0.641,0.666,0.682,0.701,0.729,0.69,0.733,0.665,0.606,0.703,0.683,0.722,0.696,0.712,0.68,0.691,0.729,0.698,0.794,0.798,0.629,0.619,0.616,0.699,0.721,0.83,0.771,0.772,0.821,0.785,0.787,0.817,0.663,0.718,0.776,0.688,0.768,0.753,0.686,0.723,0.673,0.651,0.654,0.656,0.637,0.645,0.659,0.652,0.688,0.663,0.723,0.665,42328,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAR11_HUMAN_Meitlis_2020_gof,0.635,0.638,0.639,0.639,0.641,0.639,0.653,0.607,0.648,0.645,0.656,0.621,0.631,0.61,0.596,0.642,0.642,0.635,0.643,0.634,0.616,0.651,0.652,0.649,0.605,0.655,0.654,0.65,0.634,0.639,0.644,0.629,0.623,0.64,0.622,0.621,0.634,0.641,0.638,0.639,0.646,0.646,0.617,0.594,0.642,0.633,0.614,0.632,0.634,0.628,0.65,0.653,0.637,0.651,0.654,0.641,0.652,0.648,0.642,0.646,0.661,0.649,2374,OrganismalFitness,CAR11_HUMAN,Low,Human +CAR11_HUMAN_Meitlis_2020_lof,0.789,0.79,0.794,0.791,0.785,0.784,0.769,0.736,0.804,0.804,0.818,0.765,0.784,0.738,0.712,0.757,0.786,0.805,0.809,0.789,0.7,0.811,0.808,0.8,0.737,0.823,0.813,0.831,0.765,0.802,0.818,0.784,0.741,0.755,0.777,0.766,0.784,0.804,0.794,0.786,0.8,0.795,0.728,0.72,0.818,0.764,0.733,0.806,0.782,0.753,0.806,0.798,0.787,0.8,0.803,0.793,0.799,0.795,0.796,0.798,0.807,0.765,2395,OrganismalFitness,CAR11_HUMAN,Low,Human +CAS9_STRP1_Spencer_2017_positive,0.836,0.838,0.839,0.839,0.839,0.839,0.831,0.822,0.84,0.84,0.836,0.829,0.829,0.829,0.829,0.837,0.839,0.842,0.841,0.828,0.832,0.831,0.839,0.845,0.831,0.84,0.838,0.837,0.839,0.844,0.842,0.842,0.834,0.825,0.828,0.84,0.838,0.839,0.842,0.839,0.839,0.841,0.829,0.826,0.838,0.829,0.829,0.838,0.829,0.828,0.84,0.837,0.841,0.838,0.837,0.837,0.837,0.84,0.839,0.839,0.842,0.837,8117,Activity,CAS9_STRP1,Medium,Prokaryote +CASP3_HUMAN_Roychowdhury_2020,0.779,0.82,0.82,0.812,0.812,0.818,0.7,0.759,0.818,0.824,0.813,0.796,0.811,0.712,0.809,0.826,0.812,0.823,0.824,0.798,0.711,0.778,0.756,0.829,0.809,0.817,0.824,0.812,0.831,0.802,0.838,0.821,0.754,0.712,0.803,0.819,0.809,0.822,0.822,0.823,0.829,0.824,0.777,0.681,0.817,0.801,0.768,0.807,0.785,0.74,0.812,0.801,0.804,0.807,0.81,0.807,0.819,0.818,0.806,0.805,0.819,0.796,1567,Activity,CASP3_HUMAN,High,Human +CASP7_HUMAN_Roychowdhury_2020,0.747,0.808,0.815,0.816,0.817,0.814,0.607,0.786,0.81,0.813,0.818,0.813,0.816,0.682,0.804,0.813,0.804,0.787,0.786,0.81,0.622,0.794,0.764,0.788,0.782,0.792,0.796,0.785,0.804,0.82,0.804,0.786,0.723,0.664,0.784,0.785,0.799,0.822,0.812,0.812,0.813,0.818,0.746,0.62,0.821,0.806,0.761,0.823,0.788,0.74,0.805,0.811,0.812,0.803,0.804,0.802,0.806,0.805,0.809,0.809,0.819,0.775,1680,Activity,CASP7_HUMAN,Medium,Human +CATR_CHLRE_Tsuboyama_2023_2AMI,0.853,0.808,0.813,0.816,0.825,0.817,0.844,0.774,0.777,0.793,0.805,0.82,0.815,0.819,0.859,0.819,0.82,0.814,0.801,0.62,0.821,0.792,0.8,0.791,0.798,0.798,0.788,0.796,0.8,0.813,0.79,0.799,0.755,0.811,0.803,0.807,0.831,0.821,0.813,0.812,0.81,0.813,0.819,0.82,0.795,0.8,0.847,0.76,0.818,0.842,0.815,0.808,0.815,0.817,0.813,0.811,0.813,0.81,0.809,0.816,0.81,0.814,1903,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X,0.91,0.895,0.916,0.91,0.91,0.912,0.848,0.896,0.909,0.916,0.912,0.9,0.903,0.854,0.901,0.902,0.908,0.915,0.913,0.917,0.861,0.845,0.884,0.891,0.889,0.837,0.835,0.823,0.913,0.906,0.914,0.899,0.733,0.868,0.886,0.905,0.912,0.915,0.916,0.913,0.912,0.918,0.898,0.888,0.901,0.902,0.928,0.922,0.925,0.928,0.908,0.916,0.922,0.915,0.917,0.909,0.911,0.911,0.91,0.917,0.918,0.928,2068,Stability,CBPA2_HUMAN,Medium,Human +CBS_HUMAN_Sun_2020,0.472,0.468,0.477,0.478,0.477,0.48,0.323,0.435,0.478,0.455,0.451,0.463,0.45,0.331,0.394,0.427,0.421,0.447,0.435,0.455,0.465,0.419,0.446,0.452,0.427,0.433,0.446,0.443,0.454,0.47,0.494,0.478,0.386,0.465,0.43,0.431,0.478,0.479,0.49,0.49,0.486,0.474,0.431,0.326,0.472,0.461,0.42,0.453,0.442,0.372,0.432,0.444,0.447,0.439,0.427,0.439,0.448,0.444,0.434,0.445,0.468,0.413,7217,OrganismalFitness,CBS_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28,0.881,0.875,0.889,0.895,0.897,0.904,0.318,0.816,0.89,0.891,0.873,0.89,0.89,0.355,0.912,0.911,0.907,0.899,0.87,0.889,0.815,0.829,0.837,0.838,0.859,0.865,0.858,0.847,0.849,0.887,0.876,0.846,0.641,0.775,0.844,0.857,0.884,0.876,0.893,0.901,0.892,0.892,0.88,0.448,0.833,0.883,0.748,0.757,0.914,0.859,0.906,0.898,0.903,0.899,0.907,0.9,0.905,0.901,0.906,0.909,0.896,0.905,2282,Stability,CBX4_HUMAN,High,Human +CCDB_ECOLI_Adkar_2012,0.782,0.824,0.845,0.844,0.829,0.835,0.563,0.702,0.686,0.776,0.753,0.623,0.641,0.56,0.577,0.655,0.808,0.849,0.813,0.733,0.532,0.533,0.483,0.689,0.493,0.655,0.533,0.668,0.846,0.789,0.877,0.873,0.735,0.572,0.56,0.774,0.724,0.701,0.817,0.828,0.814,0.845,0.59,0.52,0.691,0.587,0.655,0.789,0.646,0.692,0.788,0.737,0.724,0.766,0.826,0.806,0.757,0.822,0.815,0.799,0.801,0.707,1176,Activity,CCDB_ECOLI,High,Prokaryote +CCDB_ECOLI_Tripathi_2016,0.946,0.994,0.991,0.993,0.989,0.988,0.783,0.954,0.89,0.955,0.946,0.866,0.89,0.773,0.78,0.893,0.967,0.991,1.0,0.939,0.763,0.765,0.71,0.914,0.714,0.874,0.746,0.882,0.996,0.953,0.996,0.996,0.917,0.765,0.782,0.988,0.913,0.892,0.99,0.98,0.981,0.993,0.796,0.741,0.905,0.78,0.89,0.983,0.899,0.89,0.974,0.953,0.935,0.957,0.99,0.978,0.967,0.991,0.983,0.977,0.964,0.955,1663,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +CCR5_HUMAN_Gill_2023,0.819,0.818,0.818,0.819,0.821,0.821,0.8,0.841,0.822,0.822,0.823,0.82,0.819,0.81,0.813,0.82,0.807,0.816,0.812,0.823,0.814,0.836,0.832,0.83,0.824,0.832,0.825,0.828,0.832,0.829,0.822,0.815,0.775,0.822,0.828,0.829,0.82,0.83,0.834,0.822,0.823,0.825,0.813,0.772,0.826,0.815,0.809,0.824,0.815,0.805,0.818,0.814,0.813,0.809,0.813,0.82,0.814,0.814,0.814,0.817,0.823,0.827,6137,Binding,CCR5_HUMAN,High,Human +CD19_HUMAN_Klesmith_2019_FMC_singles,0.566,0.555,0.578,0.578,0.566,0.565,0.519,0.523,0.526,0.529,0.523,0.51,0.543,0.54,0.566,0.522,0.517,0.558,0.577,0.491,0.575,0.569,0.584,0.577,0.511,0.554,0.536,0.586,0.565,0.576,0.572,0.548,0.527,0.608,0.586,0.531,0.598,0.587,0.578,0.578,0.574,0.573,0.552,0.529,0.573,0.562,0.693,0.67,0.683,0.553,0.574,0.564,0.591,0.6,0.58,0.595,0.614,0.573,0.61,0.6,0.687,0.621,3761,Binding,CD19_HUMAN,Low,Human +CP2C9_HUMAN_Amorosi_2021_abundance,0.825,0.857,0.852,0.859,0.867,0.864,0.842,0.859,0.856,0.866,0.857,0.867,0.874,0.819,0.854,0.859,0.873,0.87,0.868,0.867,0.853,0.83,0.865,0.863,0.867,0.866,0.868,0.868,0.868,0.861,0.859,0.846,0.634,0.861,0.862,0.862,0.869,0.868,0.87,0.868,0.868,0.866,0.859,0.727,0.862,0.867,0.853,0.862,0.875,0.77,0.868,0.869,0.874,0.87,0.869,0.872,0.874,0.874,0.871,0.873,0.871,0.856,6370,Expression,CP2C9_HUMAN,High,Human +CP2C9_HUMAN_Amorosi_2021_activity,0.753,0.814,0.815,0.827,0.832,0.83,0.796,0.834,0.801,0.833,0.814,0.839,0.857,0.753,0.827,0.84,0.867,0.852,0.829,0.847,0.81,0.773,0.835,0.814,0.841,0.827,0.834,0.821,0.825,0.81,0.8,0.753,0.501,0.834,0.833,0.836,0.842,0.845,0.853,0.845,0.841,0.837,0.829,0.589,0.825,0.856,0.81,0.817,0.842,0.653,0.857,0.849,0.851,0.852,0.857,0.861,0.854,0.854,0.859,0.863,0.852,0.827,6142,Binding,CP2C9_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM,0.824,0.841,0.847,0.852,0.851,0.849,0.672,0.807,0.858,0.848,0.838,0.87,0.874,0.751,0.895,0.86,0.789,0.835,0.822,0.845,0.766,0.745,0.815,0.841,0.726,0.866,0.871,0.847,0.842,0.842,0.857,0.843,0.678,0.729,0.833,0.824,0.853,0.867,0.87,0.852,0.864,0.865,0.756,0.745,0.844,0.862,0.872,0.847,0.902,0.887,0.84,0.839,0.843,0.847,0.842,0.849,0.845,0.851,0.841,0.847,0.855,0.873,3295,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX,0.746,0.847,0.798,0.8,0.821,0.815,0.646,0.781,0.863,0.867,0.777,0.671,0.673,0.617,0.637,0.679,0.728,0.797,0.793,0.768,0.626,0.618,0.643,0.563,0.544,0.564,0.623,0.622,0.796,0.779,0.802,0.768,0.676,0.617,0.597,0.629,0.742,0.739,0.769,0.808,0.806,0.812,0.626,0.633,0.731,0.644,0.781,0.787,0.87,0.835,0.711,0.782,0.764,0.76,0.721,0.76,0.747,0.739,0.747,0.747,0.824,0.75,1580,Stability,CUE1_YEAST,Medium,Eukaryote +D7PM05_CLYGR_Somermeyer_2022,0.63,0.771,0.736,0.695,0.76,0.76,0.32,0.622,0.792,0.789,0.595,0.325,0.328,0.327,0.309,0.302,0.312,0.341,0.357,0.621,0.291,0.27,0.362,0.331,0.261,0.305,0.313,0.393,0.478,0.773,0.752,0.756,0.308,0.325,0.36,0.363,0.691,0.69,0.686,0.764,0.763,0.753,0.443,0.445,0.45,0.45,0.573,0.593,0.611,0.594,0.626,0.62,0.632,0.628,0.614,0.629,0.608,0.627,0.615,0.625,0.513,0.386,24515,Activity,D7PM05_CLYGR,Low,Eukaryote +DLG4_HUMAN_Faure_2021,0.87,0.859,0.871,0.863,0.876,0.876,0.872,0.863,0.834,0.841,0.816,0.865,0.867,0.888,0.914,0.901,0.865,0.853,0.826,0.878,0.851,0.849,0.836,0.823,0.863,0.851,0.843,0.848,0.821,0.874,0.853,0.852,0.817,0.849,0.869,0.836,0.886,0.894,0.88,0.883,0.888,0.885,0.877,0.727,0.832,0.867,0.869,0.817,0.895,0.772,0.837,0.813,0.835,0.833,0.845,0.846,0.829,0.827,0.829,0.835,0.857,0.914,6976,OrganismalFitness,DLG4_HUMAN,Low,Human +DLG4_RAT_McLaughlin_2012,0.921,0.93,0.925,0.925,0.931,0.932,0.926,0.923,0.929,0.929,0.928,0.927,0.927,0.912,0.914,0.936,0.928,0.927,0.928,0.921,0.922,0.921,0.924,0.924,0.923,0.926,0.922,0.921,0.927,0.921,0.935,0.936,0.879,0.917,0.925,0.911,0.922,0.924,0.92,0.927,0.928,0.932,0.929,0.8,0.927,0.922,0.907,0.91,0.906,0.879,0.927,0.882,0.919,0.921,0.923,0.92,0.923,0.922,0.925,0.923,0.926,0.92,1576,Binding,DLG4_RAT,Low,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC,0.841,0.877,0.86,0.848,0.831,0.855,0.751,0.876,0.898,0.906,0.799,0.808,0.789,0.77,0.802,0.801,0.823,0.819,0.902,0.894,0.738,0.784,0.759,0.765,0.763,0.74,0.746,0.782,0.854,0.891,0.906,0.883,0.778,0.77,0.748,0.764,0.839,0.829,0.83,0.858,0.858,0.845,0.787,0.774,0.818,0.785,0.917,0.896,0.926,0.918,0.893,0.899,0.906,0.892,0.899,0.885,0.872,0.879,0.892,0.889,0.854,0.85,1008,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1,0.9,0.89,0.901,0.898,0.904,0.899,0.888,0.835,0.891,0.899,0.91,0.907,0.924,0.903,0.936,0.929,0.933,0.923,0.923,0.91,0.9,0.898,0.908,0.903,0.904,0.885,0.902,0.892,0.889,0.883,0.88,0.879,0.768,0.896,0.887,0.907,0.899,0.899,0.906,0.897,0.898,0.902,0.918,0.729,0.887,0.92,0.92,0.885,0.923,0.925,0.928,0.935,0.938,0.938,0.939,0.937,0.936,0.94,0.933,0.942,0.898,0.926,2264,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,0.774,0.795,0.789,0.776,0.761,0.785,0.546,0.742,0.671,0.644,0.791,0.674,0.707,0.521,0.654,0.679,0.787,0.795,0.78,0.828,0.73,0.796,0.793,0.774,0.648,0.766,0.65,0.743,0.788,0.688,0.808,0.782,0.696,0.571,0.586,0.561,0.779,0.775,0.795,0.798,0.783,0.801,0.61,0.533,0.821,0.699,0.797,0.828,0.822,0.847,0.811,0.814,0.806,0.82,0.819,0.817,0.817,0.819,0.812,0.821,0.838,0.804,2915,Stability,DOCK1_MOUSE,High,Eukaryote +DYR_ECOLI_Nguyen_2023,0.961,0.962,0.961,0.962,0.962,0.96,0.893,0.967,0.962,0.958,0.96,0.967,0.964,0.921,0.963,0.961,0.966,0.963,0.963,0.963,0.934,0.961,0.964,0.964,0.965,0.959,0.965,0.961,0.965,0.96,0.964,0.962,0.901,0.961,0.962,0.964,0.959,0.963,0.961,0.96,0.96,0.96,0.962,0.903,0.962,0.964,0.936,0.963,0.966,0.937,0.968,0.963,0.964,0.963,0.966,0.967,0.966,0.964,0.966,0.966,0.965,0.962,2916,OrganismalFitness,DYR_ECOLI,High,Prokaryote +DYR_ECOLI_Thompson_2019,0.865,0.885,0.884,0.887,0.891,0.888,0.797,0.865,0.888,0.883,0.862,0.882,0.866,0.829,0.87,0.87,0.877,0.874,0.869,0.876,0.815,0.882,0.865,0.867,0.867,0.889,0.9,0.87,0.898,0.9,0.88,0.878,0.816,0.882,0.895,0.903,0.884,0.892,0.891,0.89,0.893,0.892,0.868,0.791,0.873,0.875,0.845,0.869,0.873,0.852,0.875,0.868,0.864,0.871,0.873,0.885,0.87,0.872,0.872,0.871,0.863,0.876,2363,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +ENV_HV1B9_DuenasDecamp_2016,0.643,0.63,0.538,0.601,0.635,0.631,0.249,0.662,0.628,0.634,0.538,0.623,0.624,0.299,0.278,0.305,0.301,0.298,0.469,0.663,0.59,0.645,0.662,0.66,0.564,0.677,0.587,0.655,0.669,0.577,0.709,0.647,0.564,0.618,0.637,0.668,0.642,0.631,0.653,0.639,0.643,0.658,0.566,0.244,0.629,0.623,0.549,0.578,0.497,0.41,0.431,0.527,0.47,0.431,0.528,0.488,0.495,0.51,0.378,0.487,0.353,0.319,375,OrganismalFitness,ENV_HV1B9,Medium,Virus +ENV_HV1BR_Haddox_2016,0.304,0.315,0.33,0.343,0.337,0.336,0.145,0.317,0.302,0.307,0.258,0.287,0.289,0.165,0.174,0.17,0.18,0.182,0.209,0.342,0.303,0.313,0.331,0.339,0.294,0.325,0.337,0.318,0.342,0.267,0.302,0.271,0.216,0.3,0.314,0.324,0.312,0.314,0.321,0.335,0.337,0.341,0.268,0.143,0.294,0.275,0.225,0.293,0.177,0.199,0.211,0.207,0.225,0.225,0.223,0.22,0.216,0.209,0.212,0.222,0.206,0.183,12863,OrganismalFitness,ENV_HV1BR,Medium,Virus +ENVZ_ECOLI_Ghose_2023,0.758,0.754,0.774,0.791,0.808,0.81,0.701,0.671,0.783,0.797,0.803,0.79,0.805,0.721,0.781,0.754,0.756,0.739,0.722,0.772,0.769,0.732,0.769,0.776,0.777,0.753,0.692,0.802,0.724,0.848,0.798,0.808,0.8,0.756,0.789,0.762,0.766,0.763,0.767,0.814,0.813,0.817,0.714,0.753,0.735,0.711,0.681,0.788,0.728,0.692,0.762,0.775,0.767,0.805,0.793,0.78,0.775,0.767,0.817,0.773,0.728,0.776,1121,Activity,ENVZ_ECOLI,High,Prokaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M,0.917,0.935,0.937,0.939,0.935,0.939,0.363,0.912,0.938,0.939,0.912,0.917,0.922,0.391,0.915,0.919,0.937,0.94,0.936,0.938,0.921,0.924,0.917,0.92,0.926,0.925,0.93,0.932,0.922,0.912,0.929,0.91,0.883,0.93,0.913,0.931,0.946,0.934,0.942,0.943,0.937,0.941,0.886,0.551,0.937,0.906,0.947,0.931,0.949,0.942,0.938,0.939,0.938,0.945,0.944,0.941,0.937,0.941,0.942,0.941,0.948,0.946,1960,Stability,EPHB2_HUMAN,High,Human +ERBB2_HUMAN_Elazar_2016,0.782,0.884,0.794,0.766,0.805,0.821,0.798,0.892,0.836,0.899,0.848,0.744,0.79,0.866,0.789,0.788,0.881,0.899,0.896,0.853,0.82,0.866,0.897,0.907,0.667,0.898,0.914,0.919,0.899,0.772,0.884,0.847,0.828,0.878,0.652,0.908,0.858,0.74,0.893,0.823,0.797,0.836,0.793,0.758,0.89,0.767,0.917,0.928,0.741,0.825,0.827,0.844,0.786,0.872,0.857,0.775,0.804,0.866,0.826,0.829,0.779,0.772,326,Expression,ERBB2_HUMAN,Low,Human +ESTA_BACSU_Nutschel_2020,0.708,0.774,0.774,0.777,0.773,0.779,0.657,0.69,0.737,0.796,0.691,0.729,0.75,0.662,0.713,0.69,0.693,0.69,0.717,0.731,0.663,0.663,0.711,0.667,0.718,0.704,0.735,0.739,0.775,0.753,0.732,0.737,0.595,0.667,0.731,0.686,0.725,0.741,0.7,0.784,0.783,0.764,0.719,0.605,0.75,0.728,0.838,0.804,0.792,0.757,0.755,0.701,0.74,0.729,0.732,0.724,0.72,0.714,0.72,0.735,0.744,0.719,2172,Stability,ESTA_BACSU,High,Prokaryote +F7YBW8_MESOW_Aakre_2015,0.143,0.824,0.791,0.884,0.845,0.859,0.159,0.66,0.675,0.712,0.837,0.703,0.726,0.123,0.122,0.192,0.616,0.59,0.818,0.776,0.12,0.112,0.121,0.154,0.17,0.455,0.121,0.671,0.852,0.797,0.914,0.892,0.161,0.119,0.117,0.921,0.144,0.127,0.879,0.727,0.7,0.893,0.175,0.16,0.466,0.196,0.187,0.52,0.2,0.138,0.846,0.87,0.825,0.891,0.872,0.882,0.834,0.845,0.857,0.868,0.471,0.111,9192,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +FECA_ECOLI_Tsuboyama_2023_2D1U,0.666,0.702,0.687,0.686,0.691,0.684,0.529,0.638,0.685,0.728,0.736,0.638,0.69,0.521,0.742,0.759,0.715,0.709,0.718,0.693,0.601,0.675,0.668,0.654,0.669,0.67,0.694,0.679,0.701,0.698,0.714,0.677,0.578,0.522,0.578,0.656,0.639,0.634,0.692,0.668,0.657,0.699,0.703,0.597,0.745,0.719,0.788,0.773,0.767,0.745,0.679,0.71,0.726,0.729,0.718,0.703,0.699,0.698,0.716,0.697,0.746,0.745,1886,Stability,FECA_ECOLI,High,Prokaryote +FKBP3_HUMAN_Tsuboyama_2023_2KFV,0.87,0.869,0.875,0.876,0.878,0.878,0.737,0.829,0.82,0.822,0.735,0.728,0.741,0.729,0.718,0.712,0.761,0.774,0.817,0.818,0.758,0.754,0.716,0.837,0.732,0.831,0.815,0.768,0.799,0.844,0.816,0.807,0.65,0.696,0.724,0.8,0.869,0.87,0.863,0.88,0.876,0.881,0.75,0.738,0.744,0.748,0.881,0.86,0.892,0.884,0.787,0.845,0.859,0.836,0.858,0.827,0.842,0.831,0.8,0.844,0.888,0.82,1237,Stability,FKBP3_HUMAN,Medium,Human +GAL4_YEAST_Kitzman_2015,0.714,0.774,0.82,0.798,0.793,0.802,0.705,0.587,0.842,0.818,0.825,0.775,0.775,0.729,0.759,0.771,0.832,0.847,0.859,0.783,0.776,0.729,0.751,0.738,0.754,0.776,0.794,0.778,0.863,0.809,0.863,0.851,0.67,0.706,0.751,0.714,0.813,0.807,0.828,0.794,0.802,0.811,0.743,0.777,0.824,0.75,0.638,0.804,0.603,0.652,0.851,0.816,0.843,0.833,0.823,0.818,0.858,0.817,0.832,0.835,0.778,0.764,1195,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +GCN4_YEAST_Staller_2018,0.759,0.754,0.748,0.754,0.752,0.749,0.664,0.683,0.759,0.756,0.742,0.739,0.747,0.727,0.719,0.748,0.76,0.757,0.752,0.756,0.647,0.641,0.652,0.647,0.637,0.634,0.632,0.645,0.681,0.758,0.748,0.742,0.661,0.644,0.638,0.754,0.755,0.757,0.764,0.753,0.753,0.762,0.723,0.735,0.723,0.735,0.743,0.759,0.735,0.708,0.744,0.74,0.741,0.741,0.746,0.747,0.741,0.74,0.745,0.742,0.709,0.703,2638,Binding,GCN4_YEAST,Low,Eukaryote +GDIA_HUMAN_Silverstein_2021,0.879,0.859,0.86,0.859,0.849,0.857,0.769,0.871,0.872,0.869,0.864,0.86,0.868,0.737,0.795,0.878,0.864,0.867,0.877,0.864,0.779,0.874,0.865,0.872,0.854,0.876,0.87,0.87,0.871,0.868,0.842,0.844,0.804,0.832,0.86,0.843,0.877,0.861,0.858,0.863,0.861,0.858,0.778,0.706,0.886,0.79,0.88,0.879,0.862,0.742,0.852,0.851,0.861,0.861,0.858,0.872,0.858,0.86,0.862,0.856,0.866,0.844,1154,OrganismalFitness,GDIA_HUMAN,Low,Human +GFP_AEQVI_Sarkisyan_2016,0.917,0.907,0.913,0.913,0.913,0.913,0.416,0.912,0.916,0.915,0.812,0.47,0.472,0.464,0.493,0.449,0.456,0.5,0.562,0.878,0.443,0.457,0.514,0.471,0.403,0.524,0.63,0.912,0.918,0.923,0.899,0.899,0.47,0.435,0.543,0.902,0.924,0.925,0.922,0.919,0.92,0.931,0.65,0.598,0.653,0.658,0.906,0.898,0.923,0.883,0.875,0.882,0.89,0.895,0.889,0.889,0.887,0.889,0.878,0.89,0.871,0.748,51714,Activity,GFP_AEQVI,Low,Eukaryote +GLPA_HUMAN_Elazar_2016,0.765,0.716,0.777,0.776,0.764,0.77,0.624,0.836,0.744,0.729,0.649,0.718,0.682,0.621,0.616,0.696,0.699,0.673,0.724,0.699,0.692,0.652,0.664,0.719,0.672,0.689,0.648,0.692,0.734,0.743,0.739,0.762,0.765,0.655,0.688,0.687,0.794,0.793,0.796,0.765,0.772,0.769,0.643,0.653,0.71,0.674,0.737,0.655,0.755,0.652,0.832,0.761,0.749,0.713,0.752,0.687,0.75,0.797,0.781,0.752,0.776,0.808,245,Expression,GLPA_HUMAN,Low,Human +GRB2_HUMAN_Faure_2021,0.744,0.81,0.812,0.82,0.828,0.832,0.76,0.754,0.83,0.801,0.827,0.777,0.818,0.752,0.831,0.864,0.867,0.812,0.843,0.818,0.824,0.813,0.789,0.774,0.801,0.804,0.767,0.816,0.763,0.807,0.78,0.767,0.781,0.817,0.816,0.721,0.825,0.825,0.766,0.845,0.848,0.831,0.839,0.633,0.803,0.817,0.875,0.8,0.894,0.787,0.842,0.849,0.84,0.852,0.854,0.849,0.841,0.85,0.838,0.855,0.819,0.845,63366,OrganismalFitness,GRB2_HUMAN,Medium,Human +HCP_LAMBD_Tsuboyama_2023_2L6Q,0.892,0.907,0.883,0.885,0.9,0.901,0.778,0.871,0.857,0.91,0.917,0.836,0.871,0.795,0.851,0.905,0.924,0.914,0.925,0.823,0.791,0.819,0.847,0.858,0.846,0.815,0.756,0.825,0.916,0.905,0.905,0.878,0.746,0.749,0.803,0.889,0.886,0.899,0.898,0.89,0.913,0.907,0.832,0.813,0.913,0.83,0.898,0.924,0.914,0.891,0.918,0.921,0.917,0.919,0.932,0.921,0.926,0.928,0.923,0.927,0.927,0.88,1040,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM,0.86,0.85,0.882,0.89,0.898,0.895,0.59,0.776,0.883,0.892,0.872,0.654,0.669,0.59,0.585,0.853,0.888,0.871,0.876,0.868,0.5,0.813,0.85,0.842,0.821,0.832,0.85,0.855,0.836,0.873,0.887,0.852,0.511,0.599,0.438,0.666,0.859,0.841,0.86,0.883,0.879,0.875,0.642,0.623,0.876,0.648,0.767,0.845,0.747,0.681,0.892,0.884,0.891,0.887,0.903,0.901,0.898,0.903,0.898,0.902,0.889,0.563,5586,Stability,HECD1_HUMAN,Medium,Human +HEM3_HUMAN_Loggerenberg_2023,0.57,0.585,0.585,0.577,0.579,0.581,0.468,0.375,0.581,0.59,0.549,0.549,0.55,0.45,0.549,0.554,0.554,0.585,0.575,0.442,0.565,0.565,0.587,0.61,0.549,0.56,0.56,0.55,0.589,0.595,0.601,0.614,0.45,0.562,0.594,0.616,0.582,0.603,0.611,0.587,0.591,0.598,0.535,0.473,0.558,0.553,0.558,0.585,0.526,0.543,0.561,0.545,0.564,0.566,0.565,0.562,0.568,0.556,0.56,0.561,0.574,0.565,5689,Activity,HEM3_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019,0.826,0.882,0.873,0.874,0.864,0.861,0.667,0.385,0.852,0.863,0.86,0.812,0.839,0.549,0.645,0.744,0.867,0.88,0.882,0.8,0.811,0.857,0.871,0.875,0.852,0.867,0.858,0.862,0.859,0.865,0.841,0.837,0.575,0.844,0.859,0.881,0.85,0.842,0.875,0.88,0.872,0.882,0.699,0.676,0.811,0.596,0.854,0.884,0.869,0.825,0.848,0.812,0.837,0.838,0.842,0.838,0.835,0.842,0.843,0.84,0.874,0.574,496137,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +HMDH_HUMAN_Jiang_2019,0.635,0.631,0.633,0.629,0.633,0.632,0.594,0.69,0.626,0.627,0.644,0.644,0.645,0.535,0.536,0.581,0.652,0.638,0.644,0.63,0.627,0.626,0.635,0.637,0.647,0.635,0.632,0.638,0.636,0.638,0.635,0.629,0.561,0.608,0.646,0.644,0.64,0.646,0.644,0.632,0.634,0.633,0.544,0.522,0.668,0.634,0.624,0.655,0.505,0.571,0.647,0.642,0.652,0.654,0.644,0.652,0.646,0.651,0.64,0.643,0.663,0.626,16853,OrganismalFitness,HMDH_HUMAN,Low,Human +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,0.945,0.962,0.959,0.961,0.962,0.963,0.836,0.958,0.962,0.961,0.958,0.947,0.962,0.766,0.8,0.876,0.932,0.942,0.948,0.873,0.963,0.962,0.96,0.962,0.96,0.96,0.963,0.963,0.963,0.963,0.964,0.962,0.943,0.96,0.96,0.961,0.958,0.958,0.958,0.963,0.962,0.963,0.76,0.77,0.962,0.953,0.778,0.956,0.783,0.884,0.937,0.94,0.93,0.93,0.935,0.931,0.935,0.927,0.935,0.933,0.958,0.938,2252,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Flynn_2019,0.793,0.812,0.816,0.816,0.818,0.816,0.728,0.802,0.822,0.813,0.805,0.813,0.816,0.7,0.722,0.732,0.777,0.794,0.795,0.812,0.794,0.815,0.817,0.82,0.803,0.823,0.825,0.823,0.824,0.821,0.82,0.815,0.763,0.811,0.819,0.819,0.808,0.814,0.815,0.815,0.817,0.818,0.73,0.681,0.801,0.771,0.745,0.812,0.701,0.708,0.767,0.771,0.777,0.783,0.771,0.782,0.772,0.771,0.766,0.776,0.801,0.789,13294,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Mishra_2016,0.933,0.943,0.942,0.943,0.944,0.935,0.882,0.896,0.941,0.945,0.942,0.943,0.942,0.858,0.89,0.903,0.928,0.929,0.926,0.934,0.941,0.939,0.942,0.941,0.944,0.942,0.944,0.943,0.944,0.944,0.942,0.938,0.892,0.94,0.941,0.941,0.939,0.94,0.94,0.942,0.943,0.94,0.939,0.794,0.938,0.942,0.862,0.938,0.855,0.84,0.92,0.923,0.917,0.919,0.926,0.923,0.923,0.926,0.917,0.924,0.931,0.928,4323,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HXK4_HUMAN_Gersing_2022_activity,0.616,0.619,0.603,0.602,0.609,0.612,0.515,0.584,0.612,0.613,0.6,0.595,0.603,0.527,0.548,0.62,0.632,0.612,0.604,0.512,0.596,0.602,0.607,0.6,0.612,0.614,0.616,0.61,0.589,0.593,0.587,0.567,0.503,0.609,0.607,0.602,0.617,0.603,0.605,0.615,0.606,0.611,0.568,0.502,0.616,0.609,0.594,0.621,0.593,0.556,0.608,0.615,0.62,0.619,0.618,0.631,0.632,0.622,0.616,0.621,0.632,0.599,8570,OrganismalFitness,HXK4_HUMAN,Medium,Human +HXK4_HUMAN_Gersing_2023_abundance,0.853,0.854,0.851,0.854,0.851,0.85,0.761,0.848,0.857,0.861,0.851,0.854,0.857,0.79,0.813,0.853,0.859,0.855,0.859,0.863,0.855,0.85,0.86,0.854,0.856,0.854,0.859,0.857,0.851,0.86,0.855,0.851,0.805,0.853,0.852,0.857,0.861,0.855,0.859,0.852,0.849,0.849,0.821,0.788,0.848,0.86,0.86,0.849,0.855,0.826,0.854,0.858,0.858,0.852,0.859,0.859,0.862,0.855,0.857,0.858,0.861,0.849,8396,Expression,HXK4_HUMAN,Medium,Human +I6TAH8_I68A0_Doud_2015,0.772,0.763,0.754,0.753,0.776,0.776,0.619,0.744,0.756,0.771,0.617,0.612,0.613,0.611,0.608,0.613,0.625,0.617,0.645,0.731,0.727,0.754,0.761,0.771,0.615,0.626,0.635,0.615,0.755,0.785,0.718,0.713,0.666,0.759,0.755,0.754,0.774,0.769,0.768,0.779,0.776,0.772,0.608,0.613,0.618,0.616,0.677,0.679,0.674,0.661,0.609,0.643,0.646,0.635,0.627,0.615,0.618,0.615,0.606,0.624,0.645,0.625,9462,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +IF1_ECOLI_Kelsic_2016,0.907,0.943,0.934,0.932,0.934,0.935,0.862,0.907,0.892,0.888,0.944,0.936,0.937,0.875,0.936,0.942,0.94,0.932,0.939,0.938,0.932,0.942,0.941,0.942,0.942,0.947,0.947,0.943,0.941,0.886,0.931,0.929,0.857,0.935,0.939,0.949,0.934,0.937,0.945,0.936,0.937,0.937,0.919,0.868,0.945,0.939,0.873,0.931,0.939,0.911,0.919,0.938,0.946,0.939,0.945,0.943,0.933,0.944,0.946,0.946,0.938,0.925,1367,OrganismalFitness,IF1_ECOLI,High,Prokaryote +ILF3_HUMAN_Tsuboyama_2023_2L33,0.788,0.811,0.827,0.821,0.82,0.819,0.751,0.815,0.805,0.809,0.831,0.809,0.804,0.781,0.782,0.796,0.803,0.832,0.825,0.812,0.836,0.819,0.83,0.835,0.868,0.845,0.859,0.843,0.841,0.825,0.822,0.808,0.812,0.742,0.787,0.857,0.79,0.834,0.85,0.8,0.803,0.82,0.788,0.767,0.829,0.799,0.811,0.833,0.812,0.806,0.838,0.809,0.824,0.838,0.84,0.823,0.832,0.827,0.838,0.836,0.843,0.795,1329,Stability,ILF3_HUMAN,High,Human +ISDH_STAAW_Tsuboyama_2023_2LHR,0.765,0.722,0.784,0.781,0.785,0.786,0.773,0.758,0.831,0.799,0.8,0.811,0.821,0.828,0.814,0.876,0.838,0.842,0.854,0.77,0.786,0.771,0.797,0.811,0.819,0.823,0.845,0.781,0.825,0.793,0.818,0.797,0.7,0.765,0.751,0.793,0.765,0.758,0.779,0.781,0.771,0.785,0.811,0.783,0.821,0.809,0.908,0.894,0.887,0.865,0.84,0.837,0.858,0.841,0.831,0.845,0.839,0.844,0.821,0.847,0.882,0.833,1944,Stability,ISDH_STAAW,High,Prokaryote +KCNE1_HUMAN_Muhammad_2023_expression,0.59,0.575,0.569,0.568,0.588,0.587,0.587,0.584,0.566,0.57,0.64,0.645,0.643,0.6,0.595,0.588,0.606,0.572,0.57,0.572,0.641,0.628,0.579,0.595,0.634,0.638,0.603,0.569,0.571,0.571,0.604,0.587,0.38,0.584,0.632,0.604,0.595,0.605,0.596,0.588,0.586,0.584,0.61,0.59,0.577,0.614,0.604,0.582,0.602,0.604,0.609,0.595,0.602,0.614,0.597,0.601,0.595,0.611,0.601,0.608,0.623,0.637,2339,Expression,KCNE1_HUMAN,Medium,Human +KCNE1_HUMAN_Muhammad_2023_function,0.836,0.841,0.841,0.847,0.844,0.847,0.709,0.821,0.837,0.847,0.659,0.723,0.797,0.665,0.655,0.842,0.854,0.812,0.808,0.804,0.625,0.607,0.802,0.822,0.702,0.844,0.836,0.829,0.84,0.849,0.841,0.817,0.367,0.708,0.784,0.857,0.835,0.838,0.854,0.851,0.851,0.853,0.67,0.657,0.843,0.665,0.743,0.831,0.712,0.67,0.831,0.856,0.842,0.843,0.854,0.84,0.84,0.844,0.848,0.851,0.849,0.646,2315,Activity,KCNE1_HUMAN,Medium,Human +KCNH2_HUMAN_Kozek_2020,0.639,0.641,0.639,0.639,0.64,0.64,0.625,0.521,0.614,0.633,0.678,0.549,0.537,0.556,0.525,0.582,0.52,0.558,0.606,0.697,0.652,0.648,0.698,0.679,0.683,0.693,0.63,0.661,0.643,0.678,0.601,0.641,0.593,0.633,0.644,0.661,0.628,0.647,0.68,0.63,0.648,0.646,0.709,0.513,0.68,0.646,0.645,0.461,0.27,0.52,0.702,0.682,0.686,0.717,0.697,0.718,0.676,0.678,0.703,0.702,0.698,0.66,200,Activity,KCNH2_HUMAN,Medium,Human +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.781,0.797,0.795,0.797,0.799,0.799,0.722,0.782,0.792,0.792,0.797,0.796,0.797,0.724,0.773,0.799,0.799,0.797,0.795,0.787,0.802,0.795,0.792,0.79,0.799,0.796,0.795,0.795,0.794,0.797,0.791,0.784,0.731,0.791,0.793,0.792,0.797,0.795,0.793,0.797,0.796,0.795,0.759,0.723,0.792,0.788,0.763,0.79,0.77,0.737,0.795,0.795,0.797,0.798,0.795,0.798,0.796,0.8,0.799,0.8,0.797,0.777,6963,Activity,KCNJ2_MOUSE,Medium,Eukaryote +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.83,0.82,0.823,0.822,0.823,0.824,0.715,0.803,0.821,0.821,0.829,0.822,0.824,0.746,0.834,0.842,0.84,0.838,0.841,0.812,0.826,0.804,0.806,0.818,0.824,0.815,0.815,0.821,0.812,0.821,0.824,0.817,0.763,0.821,0.792,0.812,0.823,0.812,0.813,0.823,0.818,0.819,0.815,0.714,0.821,0.831,0.817,0.813,0.81,0.796,0.832,0.823,0.828,0.827,0.832,0.826,0.831,0.827,0.828,0.829,0.832,0.831,6917,Expression,KCNJ2_MOUSE,Medium,Eukaryote +KKA2_KLEPN_Melnikov_2014,0.471,0.591,0.585,0.612,0.6,0.61,0.366,0.523,0.597,0.627,0.548,0.554,0.587,0.389,0.426,0.506,0.604,0.637,0.641,0.57,0.476,0.52,0.598,0.578,0.484,0.586,0.614,0.557,0.634,0.604,0.57,0.548,0.3,0.406,0.515,0.566,0.549,0.572,0.6,0.621,0.623,0.63,0.423,0.334,0.553,0.466,0.561,0.606,0.606,0.509,0.598,0.597,0.614,0.603,0.608,0.607,0.602,0.583,0.597,0.613,0.623,0.506,4960,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +LGK_LIPST_Klesmith_2015,0.853,0.937,0.935,0.936,0.937,0.937,0.794,0.896,0.881,0.928,0.859,0.916,0.926,0.802,0.826,0.844,0.903,0.929,0.931,0.901,0.826,0.868,0.887,0.908,0.847,0.908,0.933,0.894,0.937,0.945,0.935,0.921,0.802,0.82,0.865,0.948,0.847,0.864,0.938,0.933,0.936,0.944,0.794,0.783,0.876,0.832,0.853,0.907,0.901,0.832,0.907,0.917,0.909,0.918,0.911,0.913,0.912,0.91,0.916,0.918,0.876,0.841,7890,Activity,LGK_LIPST,Medium,Eukaryote +LYAM1_HUMAN_Elazar_2016,0.679,0.693,0.65,0.608,0.666,0.678,0.669,0.682,0.76,0.754,0.732,0.604,0.65,0.687,0.675,0.712,0.636,0.649,0.755,0.747,0.712,0.721,0.706,0.643,0.685,0.63,0.663,0.755,0.666,0.709,0.663,0.616,0.614,0.677,0.712,0.633,0.668,0.711,0.639,0.679,0.704,0.67,0.658,0.724,0.632,0.673,0.75,0.673,0.684,0.652,0.617,0.702,0.775,0.627,0.657,0.676,0.695,0.716,0.535,0.659,0.72,0.758,359,Expression,LYAM1_HUMAN,Medium,Human +MAFG_MOUSE_Tsuboyama_2023_1K1V,0.881,0.88,0.876,0.877,0.868,0.867,0.848,0.846,0.86,0.859,0.857,0.796,0.88,0.858,0.853,0.851,0.867,0.896,0.774,0.844,0.775,0.778,0.836,0.856,0.84,0.846,0.849,0.849,0.843,0.856,0.85,0.858,0.749,0.831,0.857,0.843,0.879,0.889,0.888,0.875,0.886,0.887,0.848,0.705,0.871,0.814,0.9,0.825,0.908,0.908,0.904,0.897,0.912,0.914,0.895,0.917,0.904,0.908,0.903,0.915,0.916,0.899,1429,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV,0.826,0.874,0.876,0.875,0.879,0.878,0.369,0.795,0.829,0.871,0.853,0.739,0.751,0.306,0.273,0.817,0.827,0.798,0.891,0.85,0.289,0.436,0.792,0.768,0.471,0.86,0.819,0.8,0.858,0.846,0.863,0.825,0.79,0.397,0.288,0.34,0.808,0.842,0.846,0.874,0.875,0.875,0.477,0.365,0.868,0.786,0.796,0.879,0.857,0.803,0.847,0.865,0.851,0.834,0.827,0.83,0.835,0.83,0.81,0.837,0.905,0.799,2116,Stability,MBD11_ARATH,Medium,Eukaryote +MET_HUMAN_Estevam_2023,0.859,0.914,0.897,0.912,0.917,0.918,0.867,0.894,0.855,0.874,0.923,0.879,0.879,0.829,0.869,0.888,0.906,0.91,0.92,0.899,0.91,0.926,0.913,0.914,0.927,0.923,0.929,0.926,0.927,0.922,0.917,0.906,0.821,0.856,0.908,0.926,0.875,0.904,0.917,0.91,0.912,0.903,0.879,0.802,0.918,0.903,0.835,0.912,0.876,0.812,0.881,0.896,0.897,0.901,0.9,0.898,0.9,0.899,0.897,0.896,0.884,0.875,5393,Activity,MET_HUMAN,Medium,Human +MK01_HUMAN_Brenan_2016,0.759,0.776,0.777,0.78,0.776,0.773,0.755,0.741,0.762,0.759,0.741,0.759,0.754,0.759,0.752,0.758,0.753,0.759,0.756,0.758,0.761,0.745,0.741,0.736,0.751,0.744,0.747,0.751,0.739,0.762,0.732,0.741,0.765,0.754,0.745,0.733,0.75,0.742,0.733,0.773,0.773,0.767,0.762,0.746,0.758,0.769,0.731,0.747,0.738,0.745,0.766,0.767,0.767,0.763,0.764,0.761,0.758,0.756,0.761,0.764,0.764,0.754,6809,OrganismalFitness,MK01_HUMAN,Medium,Human +MLAC_ECOLI_MacRae_2023,0.847,0.9,0.902,0.902,0.904,0.902,0.752,0.887,0.891,0.899,0.883,0.894,0.902,0.799,0.833,0.835,0.864,0.884,0.891,0.906,0.804,0.901,0.903,0.903,0.848,0.887,0.902,0.885,0.906,0.911,0.903,0.904,0.748,0.779,0.889,0.894,0.852,0.887,0.89,0.898,0.907,0.905,0.81,0.793,0.863,0.837,0.704,0.839,0.797,0.765,0.862,0.856,0.846,0.855,0.868,0.858,0.852,0.854,0.862,0.863,0.84,0.815,4007,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +MSH2_HUMAN_Jia_2020,0.817,0.841,0.831,0.831,0.831,0.833,0.784,0.814,0.837,0.836,0.832,0.834,0.837,0.78,0.818,0.833,0.831,0.838,0.825,0.829,0.804,0.829,0.831,0.832,0.814,0.835,0.833,0.819,0.837,0.83,0.828,0.821,0.797,0.808,0.84,0.831,0.823,0.839,0.832,0.836,0.841,0.838,0.799,0.747,0.839,0.815,0.809,0.835,0.736,0.786,0.828,0.826,0.829,0.826,0.829,0.829,0.832,0.83,0.829,0.83,0.834,0.82,16749,OrganismalFitness,MSH2_HUMAN,Medium,Human +MTH3_HAEAE_RockahShmuel_2015,0.529,0.78,0.784,0.792,0.789,0.784,0.455,0.766,0.73,0.736,0.621,0.73,0.756,0.389,0.484,0.519,0.588,0.612,0.658,0.718,0.505,0.537,0.656,0.73,0.558,0.74,0.776,0.715,0.764,0.771,0.785,0.765,0.547,0.529,0.566,0.747,0.523,0.566,0.725,0.778,0.776,0.782,0.464,0.288,0.523,0.461,0.564,0.688,0.704,0.55,0.621,0.645,0.632,0.601,0.594,0.644,0.633,0.637,0.608,0.63,0.664,0.538,1777,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +MTHR_HUMAN_Weile_2021,0.79,0.798,0.79,0.798,0.787,0.788,0.677,0.78,0.806,0.805,0.807,0.789,0.788,0.711,0.751,0.795,0.8,0.797,0.808,0.769,0.781,0.786,0.789,0.809,0.785,0.783,0.788,0.777,0.817,0.794,0.791,0.782,0.622,0.791,0.792,0.795,0.792,0.793,0.798,0.798,0.798,0.801,0.735,0.699,0.805,0.756,0.746,0.793,0.801,0.725,0.796,0.8,0.795,0.801,0.796,0.796,0.8,0.796,0.801,0.801,0.796,0.766,12464,OrganismalFitness,MTHR_HUMAN,Low,Human +MYO3_YEAST_Tsuboyama_2023_2BTT,0.595,0.686,0.682,0.669,0.69,0.695,0.482,0.482,0.703,0.689,0.75,0.724,0.723,0.569,0.699,0.786,0.836,0.75,0.648,0.685,0.558,0.515,0.576,0.598,0.629,0.686,0.729,0.704,0.751,0.631,0.674,0.63,0.6,0.642,0.587,0.552,0.695,0.61,0.574,0.703,0.691,0.685,0.627,0.517,0.707,0.669,0.695,0.731,0.779,0.706,0.752,0.738,0.741,0.746,0.746,0.752,0.745,0.764,0.757,0.756,0.732,0.762,3297,Stability,MYO3_YEAST,High,Eukaryote +NCAP_I34A1_Doud_2015,0.745,0.737,0.741,0.742,0.739,0.742,0.562,0.726,0.741,0.738,0.559,0.551,0.55,0.552,0.551,0.543,0.556,0.562,0.587,0.708,0.718,0.738,0.748,0.753,0.563,0.566,0.563,0.557,0.731,0.758,0.69,0.69,0.609,0.733,0.74,0.763,0.752,0.755,0.76,0.753,0.757,0.756,0.554,0.558,0.561,0.553,0.634,0.64,0.64,0.614,0.577,0.598,0.597,0.584,0.584,0.575,0.579,0.571,0.555,0.581,0.599,0.58,9462,OrganismalFitness,NCAP_I34A1,Medium,Virus +NKX31_HUMAN_Tsuboyama_2023_2L9R,0.899,0.909,0.913,0.914,0.906,0.913,0.886,0.894,0.897,0.9,0.86,0.885,0.874,0.901,0.886,0.914,0.925,0.921,0.9,0.885,0.903,0.897,0.881,0.878,0.896,0.87,0.898,0.857,0.874,0.902,0.887,0.881,0.874,0.903,0.895,0.872,0.914,0.906,0.903,0.91,0.91,0.912,0.895,0.898,0.87,0.885,0.9,0.874,0.905,0.905,0.922,0.922,0.922,0.922,0.925,0.927,0.921,0.927,0.92,0.926,0.882,0.909,2482,Stability,NKX31_HUMAN,High,Human +NPC1_HUMAN_Erwood_2022_HEK293T,0.932,0.942,0.927,0.939,0.94,0.937,0.635,0.889,0.956,0.955,0.925,0.763,0.794,0.685,0.721,0.858,0.929,0.932,0.915,0.612,0.687,0.838,0.771,0.809,0.789,0.798,0.946,0.893,0.904,0.947,0.94,0.906,0.669,0.716,0.909,0.923,0.908,0.946,0.939,0.927,0.953,0.947,0.717,0.607,0.935,0.806,0.783,0.945,0.576,0.716,0.927,0.925,0.92,0.905,0.902,0.906,0.918,0.912,0.939,0.921,0.916,0.719,637,Activity,NPC1_HUMAN,Low,Human +NPC1_HUMAN_Erwood_2022_RPE1,0.966,0.943,0.944,0.969,0.943,0.969,0.951,0.96,0.982,0.985,0.961,0.691,0.678,0.589,0.717,0.724,0.741,0.847,0.978,0.92,0.901,0.976,0.957,0.955,0.86,0.857,0.974,0.958,0.896,0.968,0.955,0.937,0.887,0.919,0.907,0.904,0.969,0.971,0.96,0.971,0.97,0.944,0.806,0.92,0.936,0.565,0.945,0.926,0.776,0.837,0.701,0.846,0.671,0.798,0.72,0.714,0.732,0.943,0.95,0.741,0.939,0.778,63,Activity,NPC1_HUMAN,Low,Human +NRAM_I33A0_Jiang_2016,0.879,0.947,0.933,0.933,0.952,0.953,0.606,0.919,0.957,0.963,0.575,0.684,0.922,0.568,0.539,0.564,0.7,0.914,0.917,0.872,0.95,0.942,0.924,0.912,0.537,0.898,0.895,0.889,0.945,0.944,0.831,0.841,0.482,0.941,0.93,0.946,0.914,0.923,0.934,0.955,0.953,0.953,0.535,0.568,0.549,0.568,0.729,0.766,0.75,0.644,0.658,0.688,0.713,0.717,0.748,0.773,0.703,0.764,0.641,0.757,0.649,0.561,298,OrganismalFitness,NRAM_I33A0,Low,Virus +NUD15_HUMAN_Suiter_2020,0.693,0.82,0.817,0.8,0.82,0.822,0.522,0.699,0.808,0.841,0.821,0.806,0.836,0.615,0.637,0.658,0.77,0.804,0.806,0.747,0.616,0.697,0.8,0.823,0.674,0.851,0.819,0.812,0.836,0.829,0.848,0.818,0.573,0.642,0.713,0.846,0.716,0.745,0.841,0.818,0.824,0.841,0.666,0.545,0.811,0.685,0.685,0.823,0.71,0.728,0.753,0.759,0.738,0.764,0.754,0.774,0.779,0.78,0.772,0.777,0.826,0.742,2844,Expression,NUD15_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL,0.656,0.85,0.817,0.811,0.813,0.826,0.644,0.75,0.848,0.831,0.753,0.477,0.515,0.636,0.592,0.59,0.651,0.674,0.683,0.81,0.796,0.832,0.844,0.859,0.569,0.79,0.599,0.866,0.862,0.824,0.837,0.838,0.534,0.728,0.621,0.685,0.704,0.634,0.689,0.833,0.808,0.836,0.671,0.723,0.666,0.672,0.899,0.836,0.859,0.877,0.832,0.869,0.851,0.87,0.865,0.863,0.858,0.843,0.856,0.86,0.842,0.672,2028,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6,0.869,0.869,0.88,0.886,0.884,0.885,0.763,0.843,0.901,0.889,0.84,0.86,0.892,0.747,0.862,0.885,0.877,0.871,0.864,0.858,0.841,0.862,0.849,0.848,0.874,0.86,0.862,0.85,0.847,0.88,0.864,0.832,0.816,0.842,0.808,0.858,0.876,0.871,0.881,0.876,0.878,0.885,0.861,0.708,0.868,0.897,0.899,0.883,0.917,0.895,0.874,0.894,0.888,0.877,0.88,0.882,0.891,0.897,0.888,0.89,0.872,0.899,1380,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C,0.815,0.901,0.922,0.927,0.924,0.922,0.619,0.81,0.896,0.896,0.887,0.875,0.882,0.627,0.863,0.893,0.913,0.903,0.894,0.902,0.878,0.867,0.867,0.86,0.873,0.867,0.88,0.86,0.85,0.897,0.903,0.895,0.788,0.624,0.674,0.811,0.875,0.876,0.891,0.922,0.918,0.921,0.832,0.619,0.843,0.853,0.906,0.847,0.934,0.928,0.926,0.929,0.933,0.931,0.932,0.927,0.92,0.922,0.925,0.935,0.934,0.882,3197,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G,0.833,0.832,0.851,0.852,0.815,0.832,0.831,0.824,0.843,0.845,0.857,0.808,0.79,0.825,0.817,0.862,0.881,0.833,0.857,0.864,0.847,0.845,0.864,0.835,0.821,0.843,0.865,0.852,0.852,0.865,0.838,0.83,0.824,0.817,0.816,0.823,0.853,0.82,0.826,0.851,0.825,0.83,0.855,0.822,0.867,0.869,0.881,0.891,0.883,0.911,0.866,0.863,0.866,0.876,0.872,0.867,0.873,0.871,0.862,0.871,0.88,0.836,1134,Stability,ODP2_GEOSE,High,Prokaryote +OPSD_HUMAN_Wan_2019,0.585,0.704,0.757,0.773,0.781,0.78,0.439,0.77,0.627,0.8,0.527,0.67,0.703,0.551,0.646,0.533,0.602,0.648,0.574,0.686,0.749,0.72,0.643,0.647,0.729,0.66,0.674,0.719,0.659,0.763,0.639,0.468,0.166,0.682,0.651,0.538,0.758,0.686,0.686,0.779,0.757,0.77,0.506,0.317,0.665,0.622,0.615,0.697,0.541,0.383,0.536,0.472,0.449,0.472,0.471,0.512,0.527,0.559,0.522,0.516,0.764,0.677,165,Expression,OPSD_HUMAN,High,Human +OTC_HUMAN_Lo_2023,0.669,0.803,0.772,0.774,0.78,0.789,0.4,0.551,0.775,0.797,0.729,0.768,0.778,0.423,0.573,0.626,0.695,0.676,0.7,0.721,0.649,0.687,0.746,0.778,0.659,0.764,0.757,0.756,0.779,0.792,0.763,0.707,0.474,0.63,0.736,0.771,0.685,0.739,0.778,0.788,0.783,0.796,0.509,0.384,0.675,0.581,0.751,0.736,0.809,0.637,0.702,0.717,0.739,0.713,0.71,0.707,0.706,0.739,0.693,0.715,0.807,0.677,1570,Activity,OTC_HUMAN,Medium,Human +OTU7A_HUMAN_Tsuboyama_2023_2L2D,0.61,0.755,0.632,0.652,0.663,0.665,0.583,0.707,0.595,0.599,0.838,0.796,0.797,0.581,0.806,0.806,0.727,0.753,0.804,0.64,0.577,0.715,0.746,0.756,0.729,0.734,0.766,0.742,0.692,0.748,0.764,0.721,0.734,0.607,0.674,0.75,0.628,0.638,0.75,0.666,0.655,0.707,0.651,0.603,0.78,0.827,0.821,0.792,0.84,0.807,0.759,0.769,0.771,0.766,0.772,0.759,0.767,0.73,0.738,0.762,0.869,0.844,635,Stability,OTU7A_HUMAN,High,Human +OXDA_RHOTO_Vanella_2023_activity,0.453,0.585,0.59,0.595,0.597,0.606,0.445,0.451,0.481,0.494,0.547,0.518,0.528,0.435,0.45,0.52,0.542,0.585,0.59,0.494,0.438,0.475,0.504,0.488,0.458,0.502,0.534,0.508,0.577,0.607,0.594,0.572,0.391,0.46,0.467,0.477,0.492,0.49,0.489,0.597,0.6,0.595,0.447,0.421,0.536,0.478,0.541,0.585,0.585,0.499,0.549,0.569,0.562,0.563,0.554,0.577,0.563,0.569,0.546,0.574,0.575,0.49,6396,Activity,OXDA_RHOTO,High,Eukaryote +OXDA_RHOTO_Vanella_2023_expression,0.664,0.737,0.73,0.732,0.738,0.746,0.68,0.673,0.694,0.693,0.727,0.708,0.718,0.666,0.686,0.716,0.728,0.744,0.75,0.65,0.642,0.696,0.711,0.693,0.676,0.716,0.715,0.7,0.73,0.746,0.727,0.718,0.636,0.675,0.674,0.687,0.691,0.683,0.688,0.739,0.741,0.743,0.667,0.657,0.722,0.688,0.748,0.755,0.726,0.63,0.739,0.73,0.729,0.726,0.728,0.736,0.728,0.731,0.727,0.735,0.746,0.71,6769,Expression,OXDA_RHOTO,High,Eukaryote +P53_HUMAN_Giacomelli_2018_Null_Etoposide,0.828,0.833,0.83,0.829,0.828,0.827,0.746,0.829,0.829,0.826,0.841,0.833,0.847,0.741,0.738,0.796,0.825,0.83,0.841,0.822,0.797,0.813,0.836,0.814,0.812,0.846,0.861,0.84,0.838,0.832,0.849,0.837,0.73,0.77,0.815,0.821,0.827,0.836,0.826,0.827,0.832,0.83,0.746,0.727,0.83,0.752,0.815,0.839,0.812,0.811,0.836,0.835,0.83,0.827,0.833,0.838,0.827,0.833,0.829,0.832,0.831,0.774,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_Null_Nutlin,0.592,0.597,0.589,0.59,0.589,0.59,0.539,0.602,0.596,0.597,0.647,0.617,0.646,0.529,0.519,0.573,0.598,0.602,0.619,0.609,0.575,0.612,0.657,0.614,0.594,0.654,0.701,0.683,0.621,0.615,0.654,0.623,0.548,0.552,0.616,0.597,0.598,0.614,0.592,0.59,0.597,0.592,0.54,0.522,0.622,0.532,0.62,0.639,0.624,0.6,0.628,0.619,0.615,0.604,0.611,0.612,0.606,0.615,0.613,0.611,0.618,0.565,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_WT_Nutlin,0.688,0.697,0.698,0.699,0.691,0.689,0.607,0.691,0.691,0.691,0.728,0.698,0.723,0.626,0.634,0.675,0.677,0.692,0.695,0.651,0.663,0.708,0.729,0.711,0.69,0.726,0.744,0.745,0.719,0.699,0.715,0.689,0.572,0.623,0.719,0.721,0.684,0.709,0.708,0.69,0.7,0.703,0.621,0.601,0.705,0.623,0.692,0.727,0.694,0.687,0.698,0.706,0.703,0.704,0.698,0.703,0.689,0.688,0.681,0.698,0.7,0.656,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Kotler_2018,0.859,0.841,0.843,0.839,0.817,0.821,0.587,0.829,0.835,0.839,0.838,0.825,0.835,0.623,0.61,0.773,0.817,0.829,0.831,0.804,0.776,0.798,0.803,0.81,0.793,0.811,0.804,0.804,0.787,0.851,0.821,0.813,0.566,0.727,0.802,0.788,0.863,0.855,0.855,0.833,0.844,0.84,0.622,0.588,0.837,0.637,0.713,0.832,0.764,0.744,0.795,0.827,0.812,0.811,0.808,0.814,0.801,0.808,0.806,0.807,0.851,0.728,1048,OrganismalFitness,P53_HUMAN,Low,Human +P84126_THETH_Chan_2017,0.749,0.861,0.905,0.905,0.874,0.876,0.642,0.855,0.906,0.879,0.891,0.856,0.857,0.721,0.86,0.864,0.901,0.889,0.905,0.897,0.773,0.823,0.827,0.86,0.796,0.858,0.908,0.855,0.945,0.803,0.905,0.877,0.782,0.801,0.755,0.837,0.775,0.744,0.779,0.847,0.844,0.86,0.757,0.582,0.824,0.828,0.748,0.902,0.893,0.698,0.861,0.878,0.856,0.863,0.888,0.892,0.886,0.886,0.899,0.891,0.896,0.895,1519,OrganismalFitness,P84126_THETH,Medium,Prokaryote +PA_I34A1_Wu_2015,0.463,0.46,0.464,0.453,0.457,0.453,0.228,0.429,0.245,0.258,0.231,0.236,0.245,0.22,0.218,0.22,0.215,0.215,0.401,0.391,0.417,0.422,0.41,0.455,0.241,0.387,0.407,0.381,0.409,0.431,0.396,0.417,0.266,0.413,0.435,0.439,0.46,0.454,0.449,0.455,0.459,0.451,0.226,0.223,0.23,0.232,0.265,0.257,0.205,0.273,0.209,0.209,0.222,0.212,0.215,0.225,0.211,0.216,0.215,0.229,0.254,0.213,1820,OrganismalFitness,PA_I34A1,Medium,Virus +PABP_YEAST_Melamed_2013,0.755,0.745,0.735,0.738,0.764,0.76,0.603,0.734,0.753,0.764,0.759,0.765,0.764,0.609,0.634,0.671,0.722,0.717,0.739,0.727,0.731,0.753,0.754,0.761,0.741,0.756,0.764,0.759,0.745,0.776,0.767,0.744,0.524,0.72,0.74,0.741,0.745,0.757,0.751,0.76,0.766,0.763,0.7,0.447,0.759,0.744,0.474,0.75,0.681,0.532,0.709,0.701,0.711,0.709,0.709,0.709,0.712,0.716,0.718,0.713,0.75,0.684,37708,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +PAI1_HUMAN_Huttinger_2021,0.797,0.83,0.823,0.826,0.823,0.823,0.694,0.775,0.807,0.823,0.81,0.797,0.809,0.696,0.8,0.806,0.812,0.816,0.803,0.811,0.701,0.796,0.798,0.806,0.793,0.813,0.813,0.814,0.812,0.816,0.805,0.792,0.721,0.735,0.804,0.81,0.795,0.808,0.811,0.828,0.827,0.829,0.784,0.688,0.805,0.802,0.781,0.831,0.814,0.751,0.815,0.808,0.813,0.812,0.813,0.816,0.812,0.812,0.814,0.817,0.825,0.798,5345,Activity,PAI1_HUMAN,,Human +PHOT_CHLRE_Chen_2023,0.707,0.809,0.89,0.882,0.781,0.78,0.843,0.835,0.886,0.883,0.851,0.877,0.889,0.89,0.903,0.885,0.897,0.882,0.889,0.827,0.845,0.875,0.848,0.872,0.836,0.859,0.871,0.868,0.833,0.827,0.841,0.81,0.756,0.798,0.782,0.867,0.839,0.824,0.866,0.788,0.798,0.791,0.875,0.783,0.872,0.884,0.703,0.848,0.835,0.735,0.838,0.815,0.816,0.819,0.819,0.827,0.826,0.828,0.83,0.826,0.882,0.883,167529,Activity,PHOT_CHLRE,High,Eukaryote +PIN1_HUMAN_Tsuboyama_2023_1I6C,0.873,0.868,0.9,0.898,0.889,0.896,0.847,0.821,0.875,0.896,0.888,0.848,0.89,0.862,0.878,0.892,0.895,0.819,0.806,0.895,0.752,0.885,0.89,0.871,0.826,0.859,0.883,0.878,0.864,0.883,0.889,0.863,0.869,0.812,0.87,0.891,0.869,0.894,0.899,0.89,0.9,0.905,0.851,0.644,0.907,0.839,0.889,0.897,0.889,0.868,0.884,0.895,0.88,0.869,0.865,0.89,0.879,0.888,0.887,0.888,0.903,0.901,802,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M,0.888,0.864,0.868,0.867,0.874,0.87,0.863,0.804,0.873,0.899,0.812,0.85,0.85,0.86,0.887,0.897,0.9,0.885,0.863,0.867,0.826,0.812,0.826,0.822,0.851,0.838,0.841,0.82,0.82,0.858,0.828,0.822,0.806,0.841,0.859,0.797,0.86,0.874,0.836,0.866,0.877,0.861,0.858,0.838,0.791,0.852,0.832,0.773,0.898,0.857,0.892,0.894,0.904,0.9,0.906,0.891,0.898,0.879,0.893,0.897,0.807,0.888,1824,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF,0.866,0.888,0.844,0.835,0.846,0.866,0.864,0.821,0.803,0.807,0.867,0.86,0.871,0.857,0.859,0.894,0.863,0.866,0.865,0.862,0.881,0.89,0.875,0.875,0.887,0.872,0.881,0.902,0.864,0.829,0.87,0.865,0.783,0.874,0.898,0.904,0.872,0.882,0.873,0.877,0.87,0.859,0.848,0.853,0.871,0.843,0.887,0.872,0.876,0.894,0.859,0.861,0.872,0.868,0.863,0.861,0.862,0.868,0.865,0.866,0.89,0.877,1301,Stability,PKN1_HUMAN,High,Human +POLG_CXB3N_Mattenberger_2021,0.768,0.817,0.798,0.816,0.821,0.824,0.574,0.733,0.815,0.821,0.658,0.581,0.596,0.583,0.577,0.624,0.705,0.727,0.754,0.757,0.738,0.788,0.795,0.79,0.632,0.764,0.772,0.765,0.801,0.82,0.759,0.75,0.598,0.599,0.718,0.773,0.756,0.775,0.792,0.793,0.809,0.816,0.58,0.576,0.682,0.585,0.627,0.692,0.596,0.618,0.683,0.686,0.688,0.687,0.684,0.678,0.691,0.69,0.69,0.69,0.597,0.588,15711,OrganismalFitness,POLG_CXB3N,Medium,Virus +POLG_DEN26_Suphatrakul_2023,0.773,0.829,0.767,0.781,0.813,0.816,0.472,0.761,0.896,0.898,0.568,0.46,0.474,0.45,0.459,0.487,0.504,0.547,0.595,0.784,0.761,0.761,0.763,0.715,0.77,0.783,0.8,0.798,0.81,0.87,0.854,0.794,0.512,0.455,0.571,0.795,0.75,0.776,0.834,0.826,0.83,0.856,0.467,0.448,0.629,0.464,0.603,0.727,0.453,0.555,0.523,0.526,0.56,0.534,0.534,0.539,0.529,0.536,0.528,0.548,0.498,0.495,16897,OrganismalFitness,POLG_DEN26,Low,Virus +POLG_HCVJF_Qi_2014,0.679,0.708,0.686,0.692,0.705,0.712,0.186,0.284,0.703,0.7,0.283,0.645,0.643,0.186,0.202,0.196,0.229,0.212,0.237,0.484,0.4,0.556,0.631,0.596,0.542,0.588,0.467,0.5,0.615,0.732,0.601,0.498,0.286,0.646,0.654,0.619,0.667,0.667,0.669,0.682,0.668,0.672,0.197,0.177,0.596,0.228,0.195,0.435,0.517,0.406,0.335,0.4,0.284,0.408,0.33,0.299,0.413,0.298,0.299,0.362,0.276,0.248,1630,OrganismalFitness,POLG_HCVJF,Medium,Virus +POLG_PESV_Tsuboyama_2023_2MXD,0.582,0.867,0.806,0.816,0.825,0.828,0.544,0.815,0.703,0.845,0.818,0.581,0.589,0.584,0.59,0.567,0.613,0.606,0.589,0.891,0.518,0.5,0.496,0.547,0.542,0.512,0.526,0.462,0.577,0.876,0.891,0.895,0.517,0.529,0.506,0.553,0.704,0.706,0.698,0.81,0.813,0.805,0.648,0.595,0.643,0.623,0.845,0.827,0.902,0.884,0.896,0.898,0.887,0.905,0.904,0.917,0.903,0.922,0.904,0.913,0.883,0.784,5130,Stability,POLG_PESV,Medium,Virus +PPARG_HUMAN_Majithia_2016,0.785,0.863,0.872,0.866,0.872,0.867,0.773,0.875,0.865,0.877,0.861,0.879,0.888,0.741,0.766,0.78,0.807,0.855,0.889,0.882,0.868,0.902,0.863,0.866,0.842,0.916,0.917,0.917,0.861,0.897,0.849,0.8,0.783,0.884,0.904,0.905,0.871,0.905,0.9,0.874,0.889,0.885,0.757,0.709,0.873,0.778,0.83,0.888,0.848,0.793,0.825,0.832,0.822,0.816,0.831,0.821,0.825,0.819,0.81,0.821,0.841,0.789,9576,Activity,PPARG_HUMAN,Medium,Human +PPM1D_HUMAN_Miller_2022,0.805,0.807,0.807,0.809,0.807,0.807,0.728,0.774,0.81,0.807,0.808,0.817,0.815,0.744,0.754,0.784,0.804,0.814,0.813,0.801,0.776,0.783,0.818,0.812,0.801,0.818,0.815,0.818,0.813,0.81,0.807,0.787,0.735,0.778,0.803,0.82,0.807,0.812,0.815,0.808,0.809,0.809,0.766,0.717,0.807,0.79,0.792,0.817,0.804,0.772,0.801,0.8,0.798,0.799,0.798,0.798,0.796,0.794,0.8,0.8,0.819,0.783,7889,OrganismalFitness,PPM1D_HUMAN,Low,Human +PR40A_HUMAN_Tsuboyama_2023_1UZC,0.857,0.872,0.916,0.913,0.907,0.91,0.76,0.854,0.917,0.922,0.924,0.805,0.847,0.765,0.771,0.894,0.917,0.915,0.898,0.901,0.864,0.911,0.918,0.902,0.895,0.911,0.917,0.917,0.905,0.901,0.903,0.878,0.811,0.814,0.818,0.858,0.884,0.888,0.895,0.908,0.914,0.909,0.784,0.766,0.884,0.78,0.899,0.862,0.917,0.901,0.919,0.924,0.924,0.922,0.92,0.923,0.923,0.922,0.919,0.926,0.92,0.906,2033,Stability,PR40A_HUMAN,Medium,Human +PRKN_HUMAN_Clausen_2023,0.863,0.87,0.876,0.878,0.874,0.874,0.652,0.823,0.867,0.87,0.874,0.861,0.878,0.683,0.697,0.72,0.764,0.86,0.877,0.852,0.708,0.855,0.877,0.871,0.767,0.876,0.871,0.881,0.862,0.874,0.858,0.828,0.627,0.696,0.85,0.873,0.857,0.871,0.873,0.873,0.875,0.874,0.711,0.584,0.881,0.717,0.855,0.873,0.868,0.773,0.774,0.803,0.815,0.821,0.796,0.791,0.8,0.799,0.799,0.804,0.868,0.787,8756,Expression,PRKN_HUMAN,Low,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE,0.767,0.759,0.726,0.731,0.753,0.754,0.666,0.709,0.722,0.72,0.863,0.796,0.801,0.681,0.71,0.823,0.839,0.806,0.803,0.774,0.661,0.627,0.722,0.735,0.734,0.754,0.511,0.741,0.762,0.737,0.776,0.727,0.658,0.657,0.715,0.742,0.773,0.764,0.759,0.755,0.752,0.765,0.691,0.659,0.813,0.673,0.838,0.811,0.844,0.821,0.841,0.825,0.822,0.823,0.829,0.821,0.83,0.835,0.825,0.835,0.806,0.815,1579,Stability,PSAE_PICP2,Medium,Prokaryote +PTEN_HUMAN_Matreyek_2021,0.766,0.79,0.786,0.78,0.779,0.783,0.686,0.772,0.791,0.799,0.789,0.791,0.805,0.671,0.732,0.775,0.796,0.764,0.767,0.797,0.692,0.807,0.799,0.801,0.727,0.783,0.785,0.799,0.779,0.812,0.783,0.779,0.701,0.73,0.806,0.791,0.765,0.806,0.801,0.788,0.798,0.797,0.701,0.672,0.799,0.751,0.769,0.797,0.78,0.754,0.795,0.79,0.795,0.798,0.791,0.79,0.794,0.801,0.799,0.798,0.785,0.769,5083,Expression,PTEN_HUMAN,Medium,Human +PTEN_HUMAN_Mighell_2018,0.858,0.856,0.858,0.864,0.865,0.863,0.755,0.842,0.855,0.854,0.856,0.857,0.864,0.781,0.831,0.855,0.851,0.847,0.85,0.859,0.806,0.841,0.855,0.841,0.83,0.845,0.845,0.836,0.848,0.853,0.85,0.843,0.718,0.826,0.853,0.843,0.854,0.855,0.856,0.861,0.861,0.86,0.822,0.749,0.85,0.852,0.833,0.851,0.851,0.824,0.855,0.853,0.852,0.853,0.855,0.855,0.852,0.853,0.848,0.851,0.853,0.842,7260,Activity,PTEN_HUMAN,Medium,Human +Q2N0S5_9HIV1_Haddox_2018,0.869,0.849,0.83,0.84,0.856,0.858,0.597,0.822,0.87,0.87,0.847,0.867,0.872,0.623,0.618,0.608,0.635,0.652,0.705,0.843,0.869,0.829,0.818,0.803,0.87,0.828,0.822,0.831,0.824,0.872,0.833,0.83,0.737,0.865,0.837,0.84,0.869,0.858,0.863,0.865,0.857,0.858,0.783,0.58,0.868,0.858,0.723,0.866,0.641,0.712,0.676,0.69,0.716,0.697,0.693,0.698,0.695,0.692,0.68,0.698,0.682,0.649,12729,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,0.791,0.806,0.807,0.807,0.807,0.806,0.707,0.764,0.808,0.809,0.803,0.806,0.81,0.705,0.717,0.784,0.792,0.808,0.803,0.804,0.784,0.768,0.77,0.768,0.811,0.798,0.796,0.798,0.779,0.775,0.783,0.768,0.737,0.773,0.766,0.763,0.789,0.781,0.775,0.804,0.802,0.796,0.787,0.693,0.806,0.794,0.786,0.806,0.77,0.777,0.784,0.791,0.807,0.793,0.804,0.8,0.804,0.805,0.789,0.799,0.818,0.814,3344,Binding,Q53Z42_HUMAN,Medium,Human +Q53Z42_HUMAN_McShan_2019_expression,0.774,0.778,0.785,0.784,0.789,0.786,0.483,0.735,0.783,0.792,0.775,0.759,0.776,0.532,0.559,0.724,0.762,0.803,0.802,0.798,0.736,0.749,0.745,0.741,0.783,0.789,0.784,0.786,0.791,0.76,0.769,0.754,0.619,0.763,0.742,0.74,0.796,0.783,0.777,0.791,0.793,0.791,0.673,0.517,0.783,0.745,0.721,0.786,0.719,0.644,0.749,0.767,0.79,0.777,0.785,0.794,0.795,0.796,0.775,0.782,0.781,0.827,3344,Expression,Q53Z42_HUMAN,Medium,Human +Q59976_STRSQ_Romero_2015,0.845,0.877,0.873,0.872,0.88,0.875,0.757,0.813,0.88,0.88,0.857,0.814,0.829,0.728,0.798,0.826,0.842,0.85,0.854,0.879,0.862,0.883,0.886,0.887,0.868,0.888,0.892,0.885,0.883,0.879,0.868,0.851,0.768,0.86,0.885,0.881,0.861,0.877,0.879,0.876,0.877,0.879,0.813,0.714,0.861,0.833,0.829,0.862,0.86,0.772,0.85,0.844,0.857,0.848,0.852,0.841,0.854,0.847,0.845,0.855,0.861,0.834,2999,Activity,Q59976_STRSQ,Medium,Prokaryote +Q6WV13_9MAXI_Somermeyer_2022,0.658,0.724,0.619,0.623,0.66,0.658,0.429,0.526,0.731,0.728,0.622,0.487,0.473,0.465,0.484,0.465,0.458,0.471,0.441,0.67,0.443,0.496,0.464,0.506,0.445,0.441,0.464,0.448,0.448,0.728,0.698,0.701,0.482,0.484,0.462,0.499,0.63,0.624,0.631,0.658,0.657,0.659,0.545,0.515,0.534,0.533,0.66,0.605,0.643,0.595,0.585,0.594,0.604,0.659,0.645,0.648,0.641,0.638,0.638,0.635,0.524,0.513,31401,Activity,Q6WV12_9MAXI,Low,Eukaryote +Q837P4_ENTFA_Meier_2023,0.898,0.921,0.918,0.93,0.928,0.942,0.838,0.891,0.923,0.927,0.938,0.92,0.94,0.873,0.885,0.901,0.926,0.92,0.926,0.807,0.93,0.934,0.931,0.936,0.922,0.932,0.933,0.939,0.937,0.927,0.923,0.906,0.859,0.928,0.937,0.937,0.926,0.938,0.931,0.94,0.94,0.939,0.896,0.882,0.914,0.897,0.862,0.925,0.886,0.847,0.934,0.931,0.928,0.936,0.933,0.93,0.935,0.927,0.925,0.94,0.948,0.888,697,Activity,Q837P4_ENTFA,Medium,Prokaryote +Q837P5_ENTFA_Meier_2023,0.813,0.919,0.915,0.912,0.918,0.927,0.867,0.883,0.792,0.813,0.861,0.867,0.862,0.767,0.842,0.839,0.844,0.882,0.873,0.85,0.858,0.892,0.924,0.918,0.836,0.897,0.911,0.917,0.935,0.897,0.888,0.908,0.803,0.856,0.895,0.916,0.861,0.886,0.911,0.917,0.919,0.918,0.852,0.777,0.877,0.848,0.84,0.894,0.903,0.796,0.864,0.859,0.85,0.842,0.854,0.831,0.86,0.862,0.842,0.857,0.836,0.846,747,Activity,Q837P5_ENTFA,Medium,Prokaryote +Q8WTC7_9CNID_Somermeyer_2022,0.592,0.684,0.598,0.598,0.646,0.645,0.481,0.666,0.665,0.67,0.594,0.487,0.484,0.478,0.47,0.479,0.484,0.492,0.508,0.612,0.5,0.499,0.511,0.499,0.479,0.478,0.495,0.633,0.64,0.685,0.656,0.661,0.477,0.464,0.478,0.675,0.584,0.592,0.673,0.641,0.649,0.685,0.535,0.538,0.537,0.535,0.604,0.606,0.679,0.605,0.594,0.579,0.598,0.599,0.591,0.609,0.598,0.598,0.595,0.597,0.539,0.45,33510,Activity,Q8WTC7_9CNID,Low,Eukaryote +R1AB_SARS2_Flynn_2022,0.928,0.931,0.916,0.914,0.943,0.946,0.596,0.889,0.621,0.621,0.669,0.626,0.609,0.625,0.647,0.648,0.637,0.889,0.937,0.89,0.838,0.839,0.859,0.854,0.845,0.808,0.828,0.828,0.818,0.935,0.897,0.876,0.602,0.769,0.828,0.792,0.912,0.922,0.913,0.936,0.945,0.939,0.623,0.583,0.68,0.623,0.824,0.843,0.871,0.792,0.736,0.75,0.771,0.778,0.787,0.758,0.763,0.749,0.75,0.778,0.721,0.718,5725,OrganismalFitness,R1AB_SARS2,Medium,Virus +RAD_ANTMA_Tsuboyama_2023_2CJJ,0.797,0.754,0.752,0.754,0.76,0.769,0.739,0.762,0.795,0.807,0.797,0.716,0.761,0.777,0.825,0.835,0.806,0.822,0.75,0.806,0.856,0.8,0.771,0.786,0.808,0.779,0.758,0.773,0.756,0.809,0.748,0.711,0.649,0.772,0.8,0.772,0.819,0.813,0.794,0.784,0.788,0.795,0.802,0.717,0.731,0.786,0.805,0.697,0.831,0.821,0.789,0.82,0.804,0.808,0.821,0.802,0.8,0.807,0.812,0.81,0.77,0.833,912,Stability,RAD_ANTMA,High,Eukaryote +RAF1_HUMAN_Zinkus-Boltz_2019,0.749,0.785,0.775,0.783,0.788,0.789,0.704,0.82,0.782,0.789,0.769,0.805,0.806,0.688,0.713,0.795,0.763,0.786,0.801,0.788,0.806,0.792,0.793,0.796,0.794,0.815,0.814,0.801,0.799,0.802,0.773,0.792,0.738,0.805,0.804,0.791,0.791,0.788,0.787,0.797,0.784,0.788,0.726,0.712,0.747,0.804,0.715,0.749,0.704,0.735,0.764,0.763,0.773,0.751,0.745,0.759,0.763,0.742,0.73,0.753,0.76,0.658,297,OrganismalFitness,RAF1_HUMAN,Low,Human +RASH_HUMAN_Bandaru_2017,0.73,0.717,0.729,0.723,0.718,0.72,0.534,0.654,0.709,0.716,0.714,0.707,0.704,0.723,0.746,0.741,0.726,0.723,0.71,0.74,0.731,0.72,0.71,0.707,0.723,0.704,0.701,0.701,0.686,0.711,0.707,0.705,0.625,0.73,0.722,0.705,0.743,0.733,0.726,0.722,0.721,0.722,0.733,0.685,0.712,0.731,0.674,0.652,0.723,0.658,0.713,0.721,0.712,0.717,0.712,0.718,0.716,0.714,0.708,0.716,0.715,0.729,3134,Activity,RASH_HUMAN,High,Human +RASK_HUMAN_Weng_2022_abundance,0.743,0.724,0.738,0.736,0.735,0.74,0.696,0.696,0.731,0.745,0.724,0.718,0.722,0.763,0.752,0.756,0.736,0.731,0.726,0.733,0.691,0.737,0.736,0.745,0.721,0.748,0.751,0.741,0.75,0.712,0.725,0.711,0.666,0.684,0.732,0.744,0.696,0.737,0.757,0.735,0.743,0.746,0.731,0.741,0.687,0.718,0.727,0.72,0.763,0.726,0.738,0.733,0.735,0.736,0.74,0.733,0.741,0.735,0.736,0.739,0.737,0.746,26012,Expression,RASK_HUMAN,High,Human +RASK_HUMAN_Weng_2022_binding-DARPin_K55,0.802,0.909,0.917,0.918,0.916,0.915,0.619,0.809,0.923,0.931,0.897,0.903,0.908,0.893,0.918,0.919,0.925,0.92,0.883,0.927,0.918,0.911,0.907,0.893,0.926,0.915,0.898,0.898,0.873,0.885,0.903,0.898,0.805,0.917,0.915,0.89,0.914,0.909,0.888,0.919,0.918,0.916,0.905,0.772,0.891,0.908,0.782,0.808,0.916,0.782,0.9,0.888,0.888,0.893,0.896,0.893,0.894,0.897,0.896,0.897,0.895,0.901,24873,Binding,RASK_HUMAN,High,Human +RBP1_HUMAN_Tsuboyama_2023_2KWH,0.824,0.791,0.819,0.828,0.822,0.828,0.829,0.752,0.787,0.813,0.86,0.838,0.848,0.841,0.843,0.858,0.87,0.809,0.819,0.826,0.838,0.779,0.786,0.783,0.839,0.735,0.753,0.789,0.798,0.807,0.819,0.795,0.716,0.825,0.829,0.833,0.83,0.826,0.828,0.826,0.827,0.824,0.854,0.845,0.853,0.848,0.852,0.862,0.863,0.835,0.869,0.858,0.859,0.857,0.866,0.864,0.86,0.856,0.861,0.864,0.849,0.842,1332,Stability,RBP1_HUMAN,High,Human +RCD1_ARATH_Tsuboyama_2023_5OAO,0.787,0.765,0.784,0.784,0.782,0.782,0.704,0.714,0.759,0.767,0.815,0.775,0.793,0.76,0.799,0.809,0.822,0.782,0.815,0.779,0.728,0.617,0.727,0.759,0.715,0.776,0.771,0.746,0.794,0.757,0.765,0.725,0.482,0.745,0.75,0.769,0.799,0.805,0.794,0.797,0.791,0.784,0.775,0.779,0.805,0.777,0.814,0.836,0.821,0.814,0.814,0.812,0.808,0.806,0.806,0.807,0.813,0.805,0.803,0.813,0.806,0.842,1261,Stability,RCD1_ARATH,Medium,Eukaryote +RCRO_LAMBD_Tsuboyama_2023_1ORC,0.777,0.868,0.899,0.903,0.9,0.909,0.667,0.676,0.9,0.91,0.874,0.814,0.878,0.75,0.812,0.846,0.874,0.857,0.866,0.866,0.709,0.752,0.75,0.735,0.614,0.781,0.591,0.718,0.877,0.871,0.902,0.889,0.635,0.665,0.611,0.84,0.789,0.784,0.871,0.905,0.901,0.901,0.786,0.783,0.856,0.776,0.954,0.933,0.941,0.929,0.913,0.913,0.925,0.93,0.924,0.92,0.92,0.921,0.922,0.924,0.928,0.724,2278,Stability,RCRO_LAMBD,High,Virus +RD23A_HUMAN_Tsuboyama_2023_1IFY,0.897,0.871,0.897,0.894,0.898,0.895,0.783,0.855,0.839,0.888,0.861,0.855,0.872,0.792,0.895,0.857,0.862,0.872,0.855,0.883,0.845,0.879,0.859,0.874,0.883,0.885,0.881,0.886,0.873,0.873,0.849,0.828,0.865,0.773,0.857,0.851,0.884,0.896,0.886,0.904,0.905,0.897,0.834,0.709,0.878,0.854,0.886,0.9,0.908,0.911,0.874,0.876,0.879,0.879,0.885,0.88,0.879,0.881,0.873,0.881,0.892,0.888,1019,Stability,RD23A_HUMAN,High,Human +RDRP_I33A0_Li_2023,0.616,0.687,0.779,0.782,0.798,0.797,0.494,0.759,0.754,0.755,0.535,0.516,0.521,0.513,0.513,0.534,0.63,0.726,0.763,0.81,0.744,0.763,0.78,0.787,0.561,0.757,0.746,0.74,0.781,0.82,0.764,0.747,0.539,0.76,0.775,0.793,0.768,0.781,0.787,0.808,0.811,0.811,0.518,0.518,0.562,0.526,0.564,0.596,0.518,0.55,0.646,0.638,0.647,0.638,0.641,0.639,0.641,0.644,0.638,0.643,0.539,0.523,12003,OrganismalFitness,RDRP_I33A0,Low,Virus +REV_HV1H2_Fernandes_2016,0.703,0.694,0.71,0.706,0.709,0.705,0.59,0.703,0.7,0.702,0.602,0.695,0.695,0.61,0.606,0.627,0.704,0.713,0.703,0.688,0.687,0.695,0.686,0.71,0.69,0.715,0.635,0.693,0.703,0.705,0.701,0.706,0.581,0.699,0.703,0.696,0.701,0.705,0.692,0.705,0.702,0.704,0.604,0.594,0.678,0.607,0.67,0.657,0.687,0.693,0.695,0.696,0.697,0.703,0.706,0.699,0.7,0.694,0.705,0.701,0.668,0.682,2147,OrganismalFitness,REV_HV1H2,Medium,Virus +RFAH_ECOLI_Tsuboyama_2023_2LCL,0.631,0.641,0.633,0.625,0.62,0.626,0.588,0.518,0.583,0.66,0.719,0.651,0.687,0.588,0.62,0.636,0.711,0.621,0.576,0.575,0.549,0.622,0.58,0.578,0.658,0.629,0.639,0.598,0.554,0.565,0.598,0.537,0.421,0.595,0.58,0.57,0.618,0.555,0.618,0.641,0.602,0.629,0.633,0.588,0.693,0.636,0.681,0.693,0.765,0.697,0.693,0.698,0.707,0.699,0.682,0.689,0.722,0.698,0.721,0.712,0.729,0.635,1326,Stability,RFAH_ECOLI,High,Prokaryote +RL20_AQUAE_Tsuboyama_2023_1GYZ,0.892,0.944,0.939,0.934,0.935,0.938,0.801,0.928,0.892,0.879,0.941,0.948,0.947,0.818,0.865,0.922,0.939,0.935,0.931,0.935,0.891,0.924,0.913,0.901,0.928,0.902,0.917,0.935,0.916,0.903,0.936,0.935,0.756,0.913,0.936,0.898,0.92,0.935,0.913,0.937,0.941,0.932,0.896,0.823,0.936,0.928,0.956,0.941,0.953,0.948,0.944,0.946,0.947,0.947,0.942,0.944,0.94,0.944,0.943,0.944,0.945,0.945,1461,Stability,RL20_AQUAE,High,Prokaryote +RL40A_YEAST_Mavor_2016,0.808,0.845,0.848,0.863,0.855,0.855,0.526,0.829,0.875,0.876,0.788,0.83,0.859,0.674,0.775,0.809,0.853,0.863,0.861,0.855,0.85,0.852,0.849,0.858,0.869,0.855,0.858,0.856,0.864,0.854,0.855,0.861,0.64,0.844,0.834,0.84,0.85,0.857,0.854,0.874,0.877,0.868,0.832,0.627,0.834,0.826,0.527,0.703,0.67,0.542,0.856,0.87,0.858,0.845,0.867,0.854,0.868,0.868,0.865,0.864,0.868,0.773,1253,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2013,0.926,0.939,0.94,0.951,0.941,0.944,0.718,0.927,0.955,0.955,0.912,0.929,0.953,0.821,0.905,0.891,0.952,0.944,0.952,0.952,0.94,0.946,0.95,0.949,0.959,0.947,0.952,0.948,0.961,0.954,0.959,0.96,0.832,0.933,0.947,0.937,0.942,0.955,0.947,0.958,0.961,0.954,0.944,0.792,0.94,0.934,0.715,0.857,0.843,0.728,0.945,0.96,0.951,0.947,0.955,0.947,0.953,0.953,0.951,0.949,0.956,0.912,1195,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2014,0.952,0.942,0.95,0.954,0.95,0.95,0.87,0.947,0.941,0.955,0.927,0.941,0.948,0.902,0.953,0.894,0.961,0.948,0.957,0.953,0.956,0.958,0.957,0.956,0.96,0.951,0.951,0.955,0.959,0.959,0.952,0.955,0.937,0.956,0.954,0.941,0.952,0.954,0.949,0.952,0.957,0.954,0.946,0.899,0.949,0.94,0.858,0.932,0.893,0.891,0.964,0.964,0.965,0.967,0.966,0.966,0.965,0.965,0.965,0.966,0.954,0.919,1380,Activity,RL40A_YEAST,Medium,Eukaryote +RNC_ECOLI_Weeks_2023,0.968,0.969,0.938,0.943,0.938,0.956,0.865,0.925,0.967,0.962,0.969,0.967,0.967,0.89,0.964,0.967,0.967,0.969,0.972,0.939,0.97,0.969,0.966,0.969,0.97,0.966,0.97,0.966,0.969,0.938,0.969,0.969,0.853,0.965,0.966,0.965,0.965,0.966,0.967,0.961,0.962,0.962,0.966,0.882,0.967,0.965,0.913,0.969,0.882,0.893,0.968,0.966,0.965,0.968,0.966,0.969,0.966,0.965,0.966,0.967,0.969,0.962,4277,Activity,RNC_ECOLI,Medium,Prokaryote +RPC1_BP434_Tsuboyama_2023_1R69,0.848,0.884,0.874,0.879,0.873,0.884,0.834,0.835,0.823,0.817,0.885,0.876,0.879,0.853,0.86,0.868,0.879,0.894,0.888,0.891,0.823,0.888,0.882,0.886,0.849,0.891,0.865,0.887,0.893,0.872,0.883,0.839,0.856,0.777,0.865,0.863,0.822,0.866,0.853,0.886,0.888,0.882,0.848,0.796,0.827,0.825,0.901,0.801,0.903,0.887,0.857,0.863,0.869,0.877,0.874,0.867,0.877,0.866,0.866,0.872,0.885,0.85,1459,Stability,RPC1_BP434,High,Virus +RPC1_LAMBD_Li_2019_high-expression,0.85,0.911,0.91,0.913,0.915,0.913,0.851,0.897,0.904,0.921,0.91,0.922,0.924,0.855,0.879,0.91,0.926,0.912,0.911,0.928,0.794,0.872,0.9,0.898,0.814,0.892,0.889,0.82,0.914,0.889,0.889,0.885,0.746,0.807,0.908,0.905,0.866,0.923,0.925,0.915,0.914,0.917,0.847,0.901,0.886,0.885,0.665,0.905,0.864,0.806,0.927,0.927,0.931,0.926,0.926,0.928,0.929,0.927,0.929,0.933,0.926,0.872,351,Activity,RPC1_LAMBD,High,Virus +RPC1_LAMBD_Li_2019_low-expression,0.69,0.873,0.848,0.855,0.843,0.856,0.739,0.874,0.808,0.827,0.839,0.758,0.829,0.704,0.756,0.754,0.794,0.844,0.858,0.817,0.598,0.733,0.714,0.796,0.637,0.74,0.736,0.68,0.902,0.854,0.889,0.883,0.641,0.668,0.741,0.86,0.712,0.751,0.827,0.84,0.843,0.864,0.68,0.726,0.753,0.742,0.628,0.719,0.79,0.623,0.791,0.797,0.779,0.795,0.805,0.81,0.809,0.77,0.801,0.791,0.806,0.699,351,Activity,RPC1_LAMBD,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32,0.879,0.865,0.867,0.867,0.867,0.869,0.799,0.837,0.867,0.866,0.86,0.859,0.857,0.813,0.831,0.884,0.864,0.868,0.861,0.856,0.86,0.867,0.849,0.849,0.86,0.852,0.884,0.862,0.86,0.86,0.858,0.849,0.823,0.875,0.862,0.852,0.881,0.87,0.865,0.876,0.868,0.864,0.831,0.82,0.878,0.858,0.9,0.887,0.888,0.893,0.866,0.863,0.866,0.87,0.866,0.856,0.865,0.865,0.872,0.868,0.866,0.915,1195,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance,0.862,0.9,0.905,0.91,0.906,0.906,0.823,0.858,0.901,0.904,0.896,0.909,0.911,0.846,0.851,0.88,0.904,0.907,0.902,0.768,0.88,0.907,0.901,0.896,0.891,0.908,0.907,0.905,0.896,0.901,0.909,0.878,0.807,0.88,0.905,0.903,0.896,0.91,0.909,0.91,0.908,0.909,0.853,0.789,0.896,0.863,0.875,0.911,0.878,0.823,0.903,0.9,0.907,0.91,0.907,0.908,0.906,0.908,0.907,0.91,0.901,0.876,9803,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity,0.739,0.803,0.811,0.814,0.812,0.815,0.682,0.755,0.807,0.807,0.8,0.818,0.816,0.718,0.71,0.747,0.803,0.811,0.804,0.635,0.781,0.81,0.802,0.798,0.796,0.81,0.811,0.81,0.796,0.807,0.815,0.79,0.701,0.777,0.809,0.808,0.786,0.814,0.813,0.818,0.817,0.818,0.711,0.636,0.801,0.726,0.773,0.824,0.768,0.687,0.808,0.801,0.809,0.812,0.806,0.811,0.807,0.812,0.812,0.812,0.789,0.751,10094,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB,0.874,0.833,0.842,0.863,0.854,0.869,0.766,0.808,0.844,0.857,0.816,0.829,0.826,0.659,0.847,0.866,0.805,0.842,0.77,0.765,0.784,0.819,0.817,0.822,0.781,0.829,0.816,0.818,0.816,0.804,0.813,0.756,0.76,0.749,0.798,0.801,0.836,0.842,0.849,0.853,0.873,0.863,0.824,0.671,0.854,0.829,0.827,0.869,0.842,0.869,0.834,0.772,0.83,0.807,0.837,0.831,0.809,0.824,0.824,0.827,0.796,0.893,965,Stability,SAV1_MOUSE,High,Eukaryote +SBI_STAAM_Tsuboyama_2023_2JVG,0.761,0.773,0.795,0.794,0.788,0.779,0.648,0.686,0.8,0.823,0.722,0.676,0.679,0.624,0.655,0.711,0.826,0.842,0.79,0.754,0.647,0.745,0.77,0.727,0.671,0.645,0.688,0.656,0.754,0.777,0.831,0.784,0.622,0.596,0.618,0.62,0.728,0.744,0.747,0.751,0.777,0.778,0.659,0.657,0.644,0.702,0.837,0.803,0.843,0.846,0.685,0.705,0.709,0.72,0.726,0.691,0.746,0.77,0.813,0.743,0.851,0.801,1025,Stability,SBI_STAAM,Medium,Prokaryote +SC6A4_HUMAN_Young_2021,0.868,0.888,0.882,0.883,0.887,0.886,0.829,0.901,0.885,0.889,0.89,0.872,0.881,0.783,0.817,0.856,0.868,0.871,0.87,0.891,0.888,0.905,0.904,0.9,0.887,0.904,0.903,0.902,0.902,0.89,0.881,0.85,0.854,0.892,0.902,0.901,0.884,0.892,0.893,0.888,0.89,0.888,0.864,0.781,0.891,0.871,0.868,0.893,0.868,0.823,0.867,0.875,0.875,0.876,0.873,0.875,0.871,0.873,0.867,0.874,0.889,0.87,11576,Activity,SC6A4_HUMAN,Medium,Human +SCIN_STAAR_Tsuboyama_2023_2QFF,0.74,0.766,0.776,0.779,0.781,0.788,0.763,0.722,0.793,0.784,0.783,0.782,0.776,0.738,0.774,0.8,0.803,0.793,0.817,0.759,0.714,0.749,0.755,0.755,0.764,0.767,0.784,0.782,0.76,0.758,0.768,0.746,0.617,0.712,0.686,0.746,0.757,0.758,0.766,0.78,0.789,0.793,0.787,0.769,0.771,0.783,0.793,0.851,0.835,0.803,0.801,0.8,0.808,0.818,0.813,0.836,0.816,0.835,0.81,0.823,0.887,0.851,1212,Stability,SCIN_STAAR,High,Prokaryote +SCN5A_HUMAN_Glazer_2019,0.753,0.728,0.754,0.754,0.754,0.754,0.794,0.603,0.791,0.798,0.753,0.731,0.765,0.77,0.75,0.763,0.778,0.77,0.748,0.76,0.743,0.724,0.739,0.723,0.732,0.748,0.743,0.714,0.795,0.739,0.741,0.738,0.639,0.675,0.697,0.758,0.756,0.784,0.816,0.77,0.787,0.772,0.752,0.714,0.8,0.693,0.637,0.657,0.71,0.606,0.703,0.734,0.727,0.741,0.728,0.736,0.72,0.705,0.72,0.726,0.713,0.737,224,OrganismalFitness,SCN5A_HUMAN,Medium,Human +SDA_BACSU_Tsuboyama_2023_1PV0,0.931,0.927,0.931,0.931,0.936,0.936,0.599,0.877,0.925,0.934,0.922,0.899,0.906,0.688,0.743,0.914,0.922,0.935,0.933,0.917,0.591,0.637,0.618,0.76,0.67,0.886,0.568,0.806,0.92,0.94,0.92,0.9,0.522,0.593,0.743,0.886,0.932,0.93,0.924,0.935,0.935,0.933,0.703,0.654,0.913,0.728,0.901,0.898,0.94,0.935,0.932,0.932,0.934,0.939,0.935,0.937,0.938,0.936,0.94,0.94,0.944,0.945,2770,Stability,SDA_BACSU,Medium,Prokaryote +SERC_HUMAN_Xie_2023,0.882,0.903,0.893,0.898,0.897,0.903,0.661,0.892,0.896,0.898,0.899,0.898,0.9,0.764,0.867,0.89,0.897,0.892,0.889,0.899,0.903,0.9,0.903,0.904,0.903,0.901,0.906,0.905,0.899,0.899,0.903,0.901,0.805,0.898,0.906,0.904,0.894,0.904,0.903,0.903,0.903,0.902,0.857,0.737,0.896,0.879,0.871,0.903,0.787,0.85,0.897,0.901,0.896,0.901,0.905,0.897,0.899,0.897,0.894,0.902,0.901,0.88,1914,OrganismalFitness,SERC_HUMAN,High,Human +SHOC2_HUMAN_Kwon_2022,0.82,0.841,0.836,0.835,0.839,0.838,0.802,0.839,0.85,0.844,0.83,0.827,0.83,0.805,0.806,0.807,0.826,0.838,0.834,0.831,0.81,0.815,0.845,0.838,0.807,0.82,0.821,0.823,0.844,0.844,0.845,0.838,0.806,0.811,0.822,0.842,0.818,0.828,0.84,0.838,0.836,0.843,0.809,0.806,0.84,0.806,0.82,0.84,0.814,0.81,0.832,0.832,0.836,0.834,0.837,0.838,0.832,0.831,0.826,0.833,0.805,0.809,10972,OrganismalFitness,SHOC2_HUMAN,Medium,Human +SOX30_HUMAN_Tsuboyama_2023_7JJK,0.83,0.855,0.835,0.834,0.841,0.838,0.821,0.809,0.818,0.838,0.845,0.84,0.847,0.832,0.84,0.86,0.855,0.835,0.839,0.85,0.835,0.812,0.829,0.823,0.842,0.846,0.839,0.839,0.838,0.835,0.816,0.804,0.829,0.754,0.823,0.839,0.829,0.827,0.842,0.835,0.839,0.838,0.846,0.76,0.837,0.855,0.845,0.836,0.859,0.845,0.849,0.867,0.866,0.859,0.855,0.862,0.848,0.848,0.846,0.86,0.853,0.851,1010,Stability,SOX30_HUMAN,High,Human +SPA_STAAU_Tsuboyama_2023_1LP1,0.777,0.8,0.792,0.794,0.805,0.802,0.545,0.768,0.725,0.727,0.739,0.543,0.54,0.516,0.547,0.513,0.59,0.602,0.588,0.793,0.482,0.583,0.743,0.637,0.548,0.509,0.567,0.746,0.786,0.752,0.738,0.729,0.493,0.548,0.518,0.602,0.779,0.788,0.779,0.806,0.816,0.803,0.566,0.588,0.638,0.63,0.771,0.762,0.818,0.803,0.793,0.785,0.787,0.827,0.763,0.794,0.778,0.801,0.785,0.801,0.755,0.761,2105,Stability,SPA_STAAU,Medium,Prokaryote +SPG1_STRSG_Olson_2014,0.652,0.658,0.475,0.483,0.658,0.668,0.439,0.486,0.455,0.621,0.708,0.675,0.663,0.716,0.683,0.681,0.696,0.677,0.697,0.651,0.711,0.681,0.679,0.683,0.691,0.684,0.687,0.685,0.73,0.682,0.735,0.724,0.489,0.674,0.655,0.661,0.658,0.65,0.661,0.684,0.672,0.688,0.433,0.418,0.555,0.485,0.659,0.613,0.679,0.567,0.726,0.685,0.704,0.718,0.715,0.695,0.724,0.726,0.715,0.721,0.704,0.715,536962,Binding,SPG1_STRSG,Low,Prokaryote +SPG1_STRSG_Wu_2016,0.137,0.172,0.168,0.19,0.184,0.186,0.157,0.151,0.252,0.239,0.249,0.261,0.238,0.232,0.246,0.26,0.282,0.297,0.315,0.236,0.197,0.186,0.196,0.199,0.17,0.214,0.213,0.198,0.198,0.152,0.303,0.285,0.111,0.203,0.168,0.218,0.169,0.159,0.188,0.196,0.191,0.204,0.183,0.137,0.231,0.141,0.238,0.243,0.301,0.203,0.301,0.302,0.306,0.316,0.299,0.3,0.33,0.309,0.317,0.309,0.324,0.276,149360,Binding,SPG1_STRSG,Medium,Prokaryote +SPG2_STRSG_Tsuboyama_2023_5UBS,0.784,0.792,0.779,0.771,0.775,0.774,0.708,0.778,0.81,0.824,0.784,0.788,0.776,0.72,0.765,0.753,0.794,0.796,0.766,0.804,0.724,0.778,0.693,0.702,0.668,0.731,0.71,0.778,0.769,0.826,0.803,0.787,0.47,0.652,0.762,0.799,0.734,0.761,0.743,0.804,0.807,0.81,0.755,0.656,0.778,0.727,0.828,0.787,0.833,0.817,0.789,0.8,0.815,0.818,0.812,0.812,0.81,0.804,0.791,0.813,0.835,0.755,1451,Stability,SPG2_STRSG,Medium,Prokaryote +SPIKE_SARS2_Starr_2020_binding,0.831,0.909,0.816,0.865,0.898,0.896,0.739,0.938,0.949,0.948,0.747,0.741,0.739,0.748,0.774,0.758,0.772,0.775,0.793,0.925,0.928,0.929,0.926,0.931,0.931,0.925,0.913,0.937,0.917,0.939,0.935,0.932,0.864,0.929,0.928,0.915,0.918,0.913,0.921,0.927,0.921,0.919,0.752,0.751,0.748,0.776,0.919,0.863,0.745,0.861,0.845,0.841,0.828,0.849,0.849,0.867,0.847,0.823,0.789,0.855,0.838,0.793,3802,Binding,SPIKE_SARS2,Medium,Virus +SPIKE_SARS2_Starr_2020_expression,0.759,0.882,0.797,0.855,0.882,0.88,0.643,0.847,0.848,0.887,0.633,0.62,0.636,0.634,0.662,0.643,0.635,0.683,0.688,0.864,0.824,0.845,0.836,0.843,0.844,0.846,0.821,0.844,0.829,0.889,0.849,0.849,0.747,0.841,0.842,0.841,0.833,0.834,0.847,0.89,0.891,0.89,0.622,0.622,0.637,0.652,0.899,0.856,0.749,0.756,0.811,0.819,0.824,0.822,0.832,0.835,0.82,0.805,0.812,0.834,0.828,0.716,3798,Expression,SPIKE_SARS2,Medium,Virus +SPTN1_CHICK_Tsuboyama_2023_1TUD,0.832,0.869,0.807,0.774,0.806,0.797,0.613,0.706,0.79,0.799,0.865,0.815,0.838,0.358,0.811,0.819,0.804,0.887,0.809,0.836,0.766,0.813,0.76,0.788,0.826,0.841,0.81,0.834,0.833,0.827,0.877,0.88,0.581,0.78,0.755,0.776,0.834,0.846,0.83,0.803,0.803,0.801,0.751,0.421,0.785,0.737,0.765,0.775,0.902,0.893,0.892,0.887,0.898,0.886,0.887,0.889,0.886,0.894,0.88,0.895,0.883,0.767,3201,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU,0.799,0.851,0.859,0.873,0.878,0.872,0.493,0.81,0.866,0.864,0.84,0.764,0.832,0.567,0.701,0.795,0.865,0.846,0.837,0.84,0.615,0.837,0.755,0.855,0.823,0.834,0.84,0.817,0.831,0.823,0.823,0.804,0.762,0.616,0.841,0.847,0.801,0.873,0.865,0.885,0.879,0.867,0.603,0.547,0.824,0.853,0.879,0.835,0.824,0.83,0.851,0.861,0.863,0.849,0.846,0.863,0.854,0.841,0.84,0.859,0.854,0.772,707,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88,0.862,0.858,0.853,0.851,0.856,0.855,0.416,0.76,0.843,0.843,0.889,0.851,0.883,0.362,0.844,0.903,0.893,0.904,0.913,0.836,0.482,0.703,0.757,0.722,0.734,0.783,0.799,0.819,0.866,0.817,0.883,0.835,0.627,0.676,0.629,0.748,0.887,0.875,0.875,0.875,0.882,0.87,0.798,0.459,0.884,0.854,0.753,0.893,0.916,0.907,0.872,0.883,0.877,0.896,0.874,0.886,0.881,0.889,0.883,0.882,0.914,0.891,1583,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W,0.869,0.885,0.906,0.903,0.886,0.883,0.804,0.782,0.885,0.891,0.92,0.912,0.923,0.811,0.9,0.911,0.909,0.898,0.924,0.893,0.905,0.895,0.913,0.89,0.907,0.909,0.905,0.897,0.9,0.913,0.904,0.881,0.822,0.833,0.878,0.867,0.888,0.902,0.906,0.886,0.898,0.892,0.889,0.723,0.922,0.903,0.891,0.891,0.92,0.916,0.911,0.919,0.916,0.919,0.912,0.921,0.91,0.915,0.914,0.918,0.905,0.911,1556,Stability,SRBS1_HUMAN,High,Human +SRC_HUMAN_Ahler_2019,0.561,0.57,0.554,0.563,0.553,0.557,0.557,0.523,0.575,0.57,0.571,0.568,0.572,0.538,0.545,0.516,0.538,0.549,0.542,0.555,0.554,0.545,0.552,0.541,0.542,0.552,0.556,0.544,0.543,0.571,0.55,0.561,0.548,0.547,0.539,0.532,0.546,0.553,0.552,0.556,0.561,0.556,0.573,0.517,0.558,0.57,0.499,0.532,0.499,0.436,0.54,0.542,0.529,0.564,0.55,0.544,0.532,0.549,0.538,0.538,0.556,0.542,3372,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,0.626,0.633,0.63,0.637,0.633,0.627,0.626,0.616,0.62,0.627,0.643,0.632,0.639,0.614,0.618,0.602,0.605,0.609,0.614,0.623,0.617,0.61,0.617,0.608,0.614,0.621,0.62,0.609,0.616,0.636,0.617,0.625,0.641,0.615,0.605,0.604,0.617,0.615,0.615,0.626,0.625,0.625,0.63,0.597,0.621,0.629,0.592,0.616,0.595,0.577,0.61,0.608,0.602,0.626,0.617,0.607,0.607,0.618,0.609,0.611,0.624,0.614,3637,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Nguyen_2022,0.669,0.685,0.686,0.683,0.674,0.674,0.667,0.67,0.668,0.682,0.69,0.682,0.685,0.661,0.662,0.647,0.664,0.675,0.676,0.681,0.69,0.681,0.683,0.677,0.675,0.684,0.685,0.675,0.677,0.694,0.677,0.68,0.67,0.678,0.671,0.671,0.67,0.672,0.672,0.68,0.675,0.675,0.678,0.65,0.686,0.68,0.636,0.667,0.638,0.629,0.661,0.663,0.661,0.673,0.664,0.664,0.66,0.665,0.658,0.66,0.681,0.661,3366,OrganismalFitness,SRC_HUMAN,Medium,Human +SUMO1_HUMAN_Weile_2017,0.707,0.76,0.779,0.766,0.769,0.771,0.637,0.763,0.758,0.749,0.773,0.775,0.788,0.658,0.723,0.75,0.785,0.73,0.763,0.774,0.67,0.759,0.77,0.778,0.768,0.775,0.763,0.79,0.765,0.717,0.777,0.779,0.621,0.672,0.759,0.771,0.739,0.754,0.772,0.764,0.764,0.772,0.737,0.639,0.772,0.749,0.735,0.774,0.729,0.723,0.768,0.763,0.778,0.77,0.767,0.769,0.78,0.78,0.77,0.777,0.776,0.732,1700,OrganismalFitness,SUMO1_HUMAN,High,Human +SYUA_HUMAN_Newberry_2020,0.826,0.836,0.818,0.814,0.827,0.829,0.809,0.878,0.838,0.845,0.836,0.868,0.861,0.809,0.811,0.824,0.812,0.849,0.86,0.86,0.807,0.867,0.872,0.87,0.811,0.862,0.868,0.86,0.867,0.874,0.877,0.877,0.696,0.824,0.865,0.868,0.836,0.855,0.856,0.835,0.851,0.85,0.812,0.8,0.857,0.808,0.761,0.834,0.809,0.77,0.813,0.795,0.793,0.805,0.806,0.807,0.816,0.824,0.832,0.804,0.834,0.802,2497,OrganismalFitness,SYUA_HUMAN,Medium,Human +TADBP_HUMAN_Bolognesi_2019,0.594,0.591,0.589,0.587,0.59,0.587,0.716,0.592,0.577,0.58,0.595,0.607,0.593,0.643,0.66,0.637,0.557,0.579,0.612,0.59,0.677,0.576,0.595,0.596,0.693,0.608,0.584,0.592,0.593,0.601,0.599,0.622,0.576,0.698,0.732,0.592,0.609,0.616,0.6,0.594,0.594,0.59,0.652,0.697,0.607,0.653,0.696,0.598,0.699,0.61,0.66,0.621,0.613,0.643,0.621,0.634,0.608,0.612,0.59,0.62,0.62,0.657,1196,OrganismalFitness,TADBP_HUMAN,Low,Human +TAT_HV1BR_Fernandes_2016,0.686,0.699,0.694,0.695,0.697,0.698,0.457,0.707,0.701,0.698,0.609,0.707,0.704,0.599,0.615,0.583,0.598,0.578,0.578,0.698,0.701,0.714,0.704,0.707,0.695,0.729,0.609,0.698,0.718,0.695,0.707,0.718,0.618,0.702,0.681,0.712,0.706,0.689,0.694,0.702,0.701,0.702,0.539,0.495,0.686,0.557,0.601,0.697,0.644,0.585,0.54,0.615,0.594,0.623,0.593,0.617,0.565,0.582,0.581,0.58,0.53,0.537,1577,OrganismalFitness,TAT_HV1BR,High,Virus +TCRG1_MOUSE_Tsuboyama_2023_1E0L,0.896,0.882,0.888,0.891,0.881,0.882,0.825,0.799,0.887,0.871,0.85,0.877,0.879,0.885,0.863,0.889,0.904,0.87,0.868,0.824,0.848,0.845,0.826,0.835,0.857,0.848,0.844,0.85,0.841,0.848,0.828,0.798,0.861,0.846,0.863,0.889,0.88,0.881,0.894,0.879,0.883,0.879,0.88,0.691,0.852,0.876,0.849,0.846,0.877,0.879,0.869,0.863,0.864,0.882,0.864,0.88,0.875,0.869,0.874,0.871,0.899,0.88,1058,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG,0.771,0.767,0.79,0.808,0.808,0.823,0.583,0.59,0.799,0.818,0.766,0.816,0.8,0.488,0.791,0.794,0.788,0.816,0.799,0.802,0.538,0.661,0.58,0.519,0.642,0.653,0.515,0.669,0.778,0.836,0.812,0.776,0.747,0.53,0.722,0.605,0.777,0.824,0.777,0.79,0.822,0.808,0.71,0.624,0.81,0.745,0.83,0.835,0.814,0.874,0.85,0.805,0.842,0.84,0.854,0.827,0.834,0.834,0.827,0.841,0.794,0.767,1279,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT,0.848,0.885,0.878,0.88,0.871,0.877,0.577,0.79,0.878,0.891,0.851,0.839,0.852,0.715,0.827,0.848,0.871,0.885,0.886,0.868,0.811,0.887,0.888,0.887,0.829,0.88,0.868,0.889,0.876,0.864,0.898,0.883,0.729,0.62,0.821,0.829,0.844,0.863,0.867,0.879,0.88,0.878,0.832,0.724,0.87,0.829,0.82,0.891,0.913,0.916,0.863,0.889,0.885,0.864,0.879,0.879,0.886,0.878,0.868,0.887,0.905,0.819,1479,Stability,TNKS2_HUMAN,High,Human +TPK1_HUMAN_Weile_2017,0.619,0.642,0.624,0.621,0.62,0.626,0.554,0.644,0.626,0.621,0.63,0.645,0.651,0.551,0.607,0.625,0.654,0.652,0.665,0.631,0.558,0.599,0.605,0.619,0.558,0.649,0.665,0.619,0.667,0.672,0.667,0.643,0.557,0.564,0.623,0.672,0.625,0.631,0.676,0.627,0.638,0.648,0.569,0.55,0.637,0.581,0.632,0.644,0.649,0.598,0.648,0.656,0.648,0.659,0.649,0.657,0.665,0.651,0.645,0.649,0.628,0.6,3181,OrganismalFitness,TPK1_HUMAN,Medium,Human +TPMT_HUMAN_Matreyek_2018,0.805,0.817,0.818,0.816,0.819,0.816,0.735,0.815,0.817,0.819,0.82,0.818,0.826,0.764,0.773,0.804,0.813,0.814,0.807,0.82,0.771,0.761,0.803,0.828,0.801,0.798,0.809,0.799,0.81,0.826,0.829,0.819,0.771,0.787,0.8,0.807,0.81,0.811,0.812,0.82,0.819,0.817,0.783,0.733,0.822,0.803,0.8,0.823,0.824,0.789,0.803,0.804,0.809,0.802,0.809,0.811,0.812,0.815,0.812,0.81,0.817,0.8,3648,Expression,TPMT_HUMAN,Medium,Human +TPOR_HUMAN_Bridgford_2020,0.668,0.597,0.695,0.635,0.627,0.625,0.69,0.641,0.663,0.656,0.678,0.643,0.671,0.589,0.664,0.575,0.571,0.61,0.649,0.589,0.619,0.589,0.659,0.652,0.638,0.546,0.742,0.7,0.676,0.618,0.611,0.618,0.708,0.64,0.633,0.617,0.67,0.682,0.63,0.651,0.667,0.607,0.58,0.627,0.624,0.643,0.486,0.635,0.653,0.56,0.703,0.606,0.625,0.629,0.621,0.634,0.613,0.668,0.664,0.624,0.637,0.691,562,OrganismalFitness,TPOR_HUMAN,Low,Human +TRPC_SACS2_Chan_2017,0.879,0.917,0.91,0.915,0.911,0.913,0.744,0.899,0.91,0.918,0.9,0.888,0.892,0.788,0.826,0.877,0.919,0.921,0.909,0.922,0.781,0.849,0.859,0.867,0.879,0.861,0.909,0.883,0.93,0.912,0.912,0.887,0.735,0.822,0.877,0.846,0.845,0.888,0.874,0.887,0.912,0.909,0.769,0.75,0.884,0.825,0.798,0.896,0.914,0.746,0.898,0.903,0.898,0.911,0.906,0.9,0.904,0.911,0.902,0.912,0.924,0.866,1519,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +TRPC_THEMA_Chan_2017,0.692,0.732,0.652,0.688,0.719,0.712,0.607,0.639,0.705,0.737,0.726,0.732,0.747,0.622,0.719,0.755,0.725,0.743,0.751,0.742,0.67,0.716,0.733,0.756,0.698,0.736,0.689,0.751,0.762,0.657,0.734,0.684,0.554,0.672,0.747,0.738,0.716,0.723,0.723,0.733,0.73,0.723,0.689,0.501,0.765,0.718,0.651,0.758,0.726,0.581,0.718,0.72,0.691,0.73,0.725,0.707,0.705,0.701,0.69,0.713,0.754,0.753,1519,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +UBC9_HUMAN_Weile_2017,0.534,0.615,0.61,0.61,0.607,0.618,0.369,0.531,0.575,0.591,0.582,0.589,0.587,0.405,0.403,0.47,0.522,0.58,0.596,0.606,0.49,0.54,0.538,0.535,0.547,0.579,0.553,0.568,0.574,0.555,0.556,0.536,0.353,0.49,0.558,0.576,0.522,0.597,0.583,0.597,0.62,0.626,0.463,0.398,0.577,0.579,0.489,0.553,0.512,0.486,0.504,0.505,0.523,0.532,0.536,0.534,0.531,0.521,0.519,0.519,0.579,0.484,2563,OrganismalFitness,UBC9_HUMAN,Medium,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X,0.66,0.786,0.785,0.782,0.795,0.798,0.528,0.728,0.773,0.802,0.838,0.799,0.804,0.609,0.784,0.848,0.84,0.829,0.821,0.798,0.542,0.74,0.754,0.777,0.723,0.756,0.752,0.763,0.784,0.765,0.831,0.792,0.734,0.516,0.541,0.792,0.684,0.697,0.797,0.797,0.792,0.818,0.657,0.52,0.683,0.715,0.768,0.749,0.836,0.731,0.848,0.856,0.855,0.854,0.856,0.846,0.842,0.855,0.849,0.854,0.833,0.744,3622,Stability,UBE4B_HUMAN,High,Human +UBE4B_MOUSE_Starita_2013,0.672,0.659,0.658,0.658,0.648,0.656,0.536,0.626,0.647,0.644,0.656,0.687,0.666,0.575,0.656,0.66,0.641,0.635,0.617,0.63,0.547,0.628,0.646,0.633,0.66,0.649,0.649,0.673,0.648,0.636,0.644,0.632,0.549,0.544,0.566,0.629,0.674,0.691,0.682,0.658,0.663,0.664,0.641,0.528,0.626,0.664,0.644,0.625,0.491,0.6,0.667,0.647,0.658,0.648,0.645,0.657,0.639,0.636,0.632,0.651,0.653,0.644,899,Activity,UBE4B_MOUSE,Low,Eukaryote 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+VILI_CHICK_Tsuboyama_2023_1YU5,0.791,0.92,0.936,0.935,0.934,0.93,0.684,0.84,0.924,0.926,0.93,0.914,0.931,0.782,0.753,0.937,0.953,0.932,0.883,0.93,0.732,0.821,0.853,0.849,0.846,0.877,0.864,0.86,0.92,0.933,0.944,0.924,0.766,0.653,0.888,0.887,0.858,0.906,0.901,0.928,0.931,0.931,0.809,0.806,0.934,0.904,0.912,0.922,0.947,0.895,0.953,0.947,0.954,0.952,0.948,0.957,0.951,0.954,0.95,0.955,0.952,0.917,2568,Stability,VILI_CHICK,High,Eukaryote +VKOR1_HUMAN_Chiasson_2020_abundance,0.841,0.829,0.839,0.843,0.835,0.841,0.717,0.816,0.839,0.854,0.822,0.843,0.836,0.7,0.801,0.834,0.84,0.841,0.825,0.852,0.755,0.749,0.771,0.825,0.768,0.777,0.806,0.771,0.814,0.845,0.847,0.82,0.668,0.729,0.768,0.81,0.844,0.844,0.847,0.84,0.843,0.854,0.718,0.661,0.83,0.789,0.805,0.819,0.828,0.784,0.832,0.822,0.84,0.847,0.854,0.855,0.848,0.839,0.84,0.847,0.839,0.833,2695,Expression,VKOR1_HUMAN,Medium,Human +VKOR1_HUMAN_Chiasson_2020_activity,0.817,0.834,0.812,0.817,0.797,0.81,0.693,0.838,0.847,0.843,0.836,0.824,0.815,0.716,0.792,0.829,0.816,0.819,0.841,0.835,0.778,0.768,0.836,0.822,0.736,0.848,0.832,0.825,0.834,0.857,0.828,0.823,0.819,0.725,0.75,0.836,0.788,0.79,0.825,0.795,0.801,0.81,0.737,0.73,0.829,0.749,0.774,0.827,0.832,0.791,0.824,0.81,0.817,0.832,0.832,0.817,0.828,0.811,0.814,0.823,0.811,0.82,697,Activity,VKOR1_HUMAN,Medium,Human +VRPI_BPT7_Tsuboyama_2023_2WNM,0.584,0.739,0.781,0.778,0.767,0.783,0.655,0.728,0.799,0.765,0.785,0.75,0.737,0.697,0.749,0.79,0.803,0.822,0.797,0.752,0.598,0.694,0.676,0.704,0.703,0.647,0.675,0.692,0.763,0.753,0.802,0.788,0.693,0.671,0.74,0.687,0.638,0.649,0.645,0.754,0.757,0.739,0.729,0.672,0.756,0.734,0.803,0.798,0.801,0.851,0.85,0.836,0.863,0.856,0.847,0.861,0.857,0.861,0.853,0.869,0.869,0.808,1047,Stability,VRPI_BPT7,Medium,Virus +YAIA_ECOLI_Tsuboyama_2023_2KVT,0.667,0.852,0.846,0.86,0.856,0.853,0.453,0.803,0.832,0.859,0.789,0.626,0.795,0.577,0.644,0.85,0.873,0.919,0.904,0.875,0.503,0.53,0.57,0.735,0.562,0.573,0.521,0.553,0.841,0.875,0.877,0.867,0.521,0.452,0.564,0.831,0.702,0.727,0.816,0.839,0.842,0.855,0.685,0.564,0.717,0.694,0.876,0.837,0.912,0.862,0.889,0.903,0.88,0.891,0.901,0.894,0.89,0.894,0.897,0.897,0.877,0.749,1890,Stability,YAIA_ECOLI,Medium,Prokaryote +YAP1_HUMAN_Araya_2012,0.464,0.386,0.471,0.468,0.466,0.463,0.374,0.312,0.295,0.288,0.409,0.362,0.357,0.409,0.419,0.441,0.453,0.412,0.39,0.327,0.308,0.301,0.293,0.322,0.332,0.3,0.311,0.322,0.296,0.37,0.382,0.397,0.34,0.358,0.302,0.323,0.427,0.368,0.396,0.45,0.42,0.439,0.484,0.181,0.422,0.48,0.359,0.418,0.332,0.372,0.355,0.348,0.365,0.37,0.347,0.358,0.369,0.373,0.386,0.361,0.478,0.41,10075,Binding,YAP1_HUMAN,Low,Human +YNZC_BACSU_Tsuboyama_2023_2JVD,0.855,0.869,0.855,0.855,0.857,0.856,0.767,0.793,0.876,0.877,0.881,0.872,0.887,0.865,0.883,0.887,0.891,0.887,0.881,0.85,0.845,0.893,0.896,0.896,0.883,0.882,0.882,0.875,0.878,0.863,0.875,0.854,0.845,0.842,0.872,0.868,0.876,0.863,0.865,0.867,0.859,0.865,0.848,0.802,0.867,0.845,0.829,0.872,0.911,0.894,0.894,0.874,0.88,0.877,0.878,0.886,0.878,0.879,0.879,0.881,0.884,0.923,2300,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.html b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.html new file mode 100644 index 0000000..582d970 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_DMS_level.html @@ -0,0 +1,15196 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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A0A247D711_LISMN_Stadelmann_20210.8930.8970.8050.8000.8910.8860.6950.7080.9010.8950.7720.7660.7680.7440.7410.7580.7510.7660.7360.8730.6870.6880.6970.6880.6670.7200.7410.6860.7250.9010.8560.8570.8330.7090.6890.6960.8810.8800.8820.8700.8750.8730.7260.6850.7290.7390.8480.8390.8900.8690.8260.8470.8400.8090.7920.8190.8090.8210.7960.8090.8400.8051653ActivityA0A247D711_LISMNHighProkaryote
A0A2Z5U3Z0_9INFA_Doud_20160.7550.7980.7830.7880.7920.7900.4970.7650.7910.7950.5520.7790.8000.5200.5250.5340.7670.7730.7780.7780.7690.7960.7940.8030.7000.7830.7890.7760.7790.7990.7540.7180.5540.7700.7890.7900.7880.7940.7940.7960.7990.7990.5260.5130.6410.5240.6590.7220.6740.6320.7400.7390.7480.7460.7390.7460.7460.7420.7480.7430.6140.59510715OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A0A2Z5U3Z0_9INFA_Wu_20140.2150.2280.2330.2350.2320.2320.1120.2770.2440.2300.1510.2120.2450.1140.1170.1270.2370.2070.2420.2900.2060.2380.2500.2510.2010.2150.2570.2320.2200.2740.2610.2380.0820.2020.2240.2290.2220.2410.2370.2250.2300.2230.1110.1060.1780.1230.1780.2030.1750.1460.2280.2150.2150.2310.2050.2320.2030.2230.2230.2200.1510.1592350OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A4_HUMAN_Seuma_20220.6620.7350.7610.7610.7250.7330.6390.6320.7430.7450.6840.7080.7410.6110.6090.6330.6570.7530.7250.7200.6850.6700.6910.7110.7240.6710.6870.6860.6920.7620.7000.6950.7580.7080.6930.7490.7110.7040.7380.7360.7330.7460.6140.6140.7470.6210.6770.7140.4350.6120.7090.7360.7090.6830.6880.6870.6950.6480.6840.6960.6300.60814811StabilityA4_HUMANLowHuman
A4D664_9INFA_Soh_20190.4300.4300.4420.4360.4440.4450.2390.4210.3750.3720.2490.2370.2450.2460.2520.2460.2880.2880.3830.4070.3840.4150.4160.3960.2450.3560.3570.3550.3890.4320.3830.3560.2330.3970.4200.4240.4340.4440.4430.4550.4560.4530.2480.2450.2750.2580.2730.2850.2000.2880.2750.2920.2740.3050.2830.2990.2880.3010.2930.2880.2810.24914421OrganismalFitnessA4D664_9INFAMediumVirus
A4GRB6_PSEAI_Chen_20200.7240.8290.8300.8410.8270.8340.6560.7370.8210.8310.8380.8200.8370.7310.7760.8390.8650.8800.8740.8320.7010.7800.8020.8220.7640.8260.8230.8140.8700.8210.8870.8580.6460.7230.8150.8440.8170.8230.8570.8560.8540.8620.7410.6300.8500.8080.8610.8810.7990.8020.8580.8640.8550.8770.8730.8730.8600.8730.8670.8730.8640.7765004OrganismalFitnessA4GRB6_PSEAIHighProkaryote
AACC1_PSEAI_Dandage_20180.7760.8190.7790.7920.7880.8000.7350.7050.8180.8150.7660.7860.7770.7290.7330.7470.7790.7990.7980.7950.7280.7370.7630.7060.7470.7860.7780.7850.7660.7850.7790.7670.6760.7540.7470.7890.8040.8030.7990.7960.7940.7990.7440.7260.7540.7440.7390.7860.7700.7340.7880.7970.7920.7910.7990.8060.7930.8040.8080.8010.7710.7561801OrganismalFitnessAACC1_PSEAIHighProkaryote
ACE2_HUMAN_Chan_20200.7050.6860.7100.7000.6870.6850.6220.6750.6980.7000.6740.6980.6990.6160.6520.6760.6720.6680.6900.6970.6410.6980.7210.6970.6340.7050.7040.7030.7070.6890.6730.6770.6810.6600.6880.6570.7070.6960.6930.6930.6870.6850.6340.6150.6640.6590.6830.6760.6950.6640.6770.6810.6720.6620.6790.6740.6760.6690.6780.6690.6790.6792223BindingACE2_HUMANMediumHuman
ADRB2_HUMAN_Jones_20200.6040.6340.6370.6400.6540.6470.6040.6470.6340.6460.6470.6320.6410.5860.6050.6060.6030.6190.6270.6310.6250.6330.6270.6350.6360.6390.6410.6440.6350.6290.6310.6130.5450.6410.6340.6360.6390.6320.6360.6450.6450.6450.6100.5460.6320.6270.6040.6350.6020.5820.6180.6110.6070.6200.6150.6160.6070.6190.6060.6160.6400.6197800ActivityADRB2_HUMANMediumHuman
AICDA_HUMAN_Gajula_2014_3cycles0.1040.1260.1850.1060.1480.1630.0360.1490.2010.1470.2290.1730.1680.0590.0160.2260.2790.2400.1710.3200.0470.0660.2850.2900.0370.2480.3010.2820.1330.1040.1850.1590.1190.0840.0760.1400.3610.3270.3320.1640.1640.1470.0430.0470.1460.0220.2370.1590.1080.3870.2300.2370.1970.2310.1570.2030.1920.1870.1860.2070.0530.048209ActivityAICDA_HUMANMediumHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O0.8130.8560.8590.8580.8510.8620.4180.8310.8460.8550.8790.5670.8150.4400.6030.8790.8670.8720.8650.8460.5870.4600.8090.8040.4300.8080.7840.6730.8440.8460.8620.8370.8230.4540.5460.4680.8320.8370.8410.8640.8670.8650.6210.5930.8540.7880.7560.8010.8370.8340.8700.8780.8690.8630.8760.8670.8660.8590.8730.8710.8880.6742972StabilityAMFR_HUMANMediumHuman
AMIE_PSEAE_Wrenbeck_20170.7960.8820.8750.8770.8740.8750.7330.8570.8280.8830.8430.8820.8860.7810.8030.8060.8360.8910.8820.8450.8760.8860.8870.8850.8660.8790.8880.8830.8820.8730.8820.8740.7760.8860.8830.8810.8780.8780.8760.8850.8830.8820.7900.7590.8160.8020.8180.8480.8280.8160.8500.8600.8580.8540.8520.8550.8540.8500.8570.8590.8640.8136227ActivityAMIE_PSEAEHighProkaryote
ANCSZ_Hobbs_20220.8200.8170.8110.8100.8180.8180.7870.8080.8140.8190.8060.8200.8220.8180.8340.8290.8290.8290.8260.6760.8340.8070.8110.8080.8400.8150.8170.8000.7970.8190.8040.7940.7540.8040.8000.8110.8190.8160.8210.8220.8200.8220.8200.7950.8100.8120.8080.8250.7850.7450.8270.8310.8190.8140.8250.8220.8280.8290.8270.8280.8250.8234670ActivityANCSZMediumEukaryote
ARGR_ECOLI_Tsuboyama_2023_1AOY0.8390.9080.9100.9100.9090.9070.7930.8680.8250.8820.8720.8260.8950.8270.8510.8890.9010.9120.8960.9010.8500.8830.8980.8950.8580.8800.9010.9010.8910.8870.8780.8480.7700.8660.8700.8700.8710.8810.8880.9130.9010.9080.8270.7870.8740.8560.9180.9130.9140.9190.8990.9050.9080.9080.9070.9040.9040.9120.9020.9080.9270.8841287StabilityARGR_ECOLIMediumProkaryote
B2L11_HUMAN_Dutta_2010_binding-Mcl-10.5940.5180.6050.5980.5950.6040.4250.5670.4920.5240.1800.1580.5520.4240.3520.3800.4740.4940.5630.4790.3630.2720.4790.5290.1950.4930.5360.3860.5360.5640.4580.3890.3710.4900.2240.4570.5970.6050.6370.6170.6240.6280.3550.4020.5180.4450.3860.5020.4490.1580.4030.4980.4890.5300.4280.4600.4910.5040.5110.5220.4240.223170BindingB2L11_HUMANLowHuman
BBC1_YEAST_Tsuboyama_2023_1TG00.6810.7490.7130.7160.7180.6900.6120.6540.7540.7590.7710.7580.7660.5140.7350.7590.8140.8120.8150.7550.6610.7320.7420.7460.5920.7650.7090.7340.7160.6680.7110.6810.6160.7000.6580.6990.7190.6940.7270.7090.6780.6930.6760.5290.7620.7320.7890.7720.8110.7890.7880.8000.7980.8220.8030.8230.8070.8090.8000.8090.8110.7972069StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU0.8770.8930.8880.8870.8780.8870.7270.8440.8610.9010.8170.8580.8730.7730.7910.8250.8900.8880.8940.9000.7370.8380.8310.8510.8110.8740.8130.8750.9050.8930.8790.8600.6790.8130.8230.8660.8720.8720.8890.8840.8820.8880.8080.8010.8380.8340.8840.9070.9200.8960.8850.9060.8950.8970.8990.8930.8960.8920.8910.8980.9130.7881572StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Deng_20120.6810.7980.7750.7800.8060.8020.5320.6110.7180.7700.7470.7420.7460.6110.6830.7180.7320.7490.7380.7310.7080.7060.7460.7140.6940.7570.7500.7720.7240.7260.7890.7710.5460.6970.7800.7370.7640.7990.7810.7850.8070.8150.6510.4930.7540.6910.7310.7600.7460.6810.7700.7860.7750.7850.7850.7850.7970.7820.8050.7930.7540.7154996OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Firnberg_20140.5980.7870.7930.7750.8080.8140.3870.5140.6670.7550.7060.7160.7290.5600.6360.7020.7220.7360.6950.6840.6970.6950.7060.6740.6690.7340.7320.7470.6570.6660.7990.7890.4450.6680.7360.6830.7440.7740.7580.7800.8040.8240.6110.3750.7260.6450.7230.7870.7920.6060.7540.7590.7680.7650.7770.7880.7720.7690.7680.7790.7970.6764783OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Jacquier_20130.8150.9370.9180.9000.9210.9240.6210.7600.8580.8940.8690.8940.8980.8020.8250.8370.8630.8990.8970.8500.8950.8870.9120.9060.8380.8820.9120.9220.9030.7710.9160.9160.7280.8710.9180.9060.9040.9210.9180.8980.9080.9220.8190.5710.8830.8530.8230.9080.9020.8400.8920.8940.9010.9090.8920.9010.9090.8950.9030.8970.9190.872989OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Stiffler_20150.8300.9500.9400.9310.9640.9620.6330.7690.8600.9220.8910.9070.9090.8060.8560.8970.9010.9330.9120.8730.8970.9040.9220.9050.8630.9160.9290.9430.8950.8770.9670.9630.6830.8820.9390.9060.9320.9570.9460.9420.9570.9730.8400.6150.9130.8630.9100.9530.9510.8150.9330.9300.9410.9360.9400.9520.9410.9350.9450.9440.9560.8854996OrganismalFitnessBLAT_ECOLXHighProkaryote
BRCA1_HUMAN_Findlay_20180.8810.8710.8770.8780.8660.8670.7380.8120.8670.8690.8650.8220.8220.7700.8120.8340.8660.8790.8500.8720.7600.8620.8770.8610.8230.8900.8870.8730.8730.8780.8790.8550.7170.7810.7720.8570.8770.8810.8830.8780.8770.8780.7930.7560.8810.8350.8380.8770.7510.7810.8530.8600.8580.8550.8500.8580.8480.8590.8520.8570.8180.8331837OrganismalFitnessBRCA1_HUMANLowHuman
BRCA2_HUMAN_Erwood_2022_HEK293T0.9410.9330.9360.9350.9350.9320.9150.9370.8560.8910.9400.9230.9180.9280.9250.9290.9440.9350.9400.9030.9300.9440.9490.9430.9420.9220.9210.9330.9140.9320.9260.9460.9270.9260.9300.9310.9380.9370.9390.9330.9340.9330.9180.9220.9390.8980.8900.8920.7690.9170.9430.9420.9410.9380.9450.9330.9480.9430.9430.9440.8960.910265OrganismalFitnessBRCA2_HUMANNaNHuman
C6KNH7_9INFA_Lee_20180.7710.8110.8030.8000.8120.8120.6090.7920.8090.8120.6270.7700.8220.6010.6040.6160.7840.8100.8200.7920.7980.7880.7960.7860.6600.7930.7960.8080.7780.8280.8000.7670.6670.7860.7960.7870.8010.8050.8010.8150.8180.8170.6030.5900.6980.6080.7500.7950.7850.7190.7890.7960.7950.7930.7930.7830.7940.7940.7830.7950.6880.65810754OrganismalFitnessC6KNH7_9INFAMediumVirus
CALM1_HUMAN_Weile_20170.7990.8170.8080.8040.8050.8000.8010.7900.8150.8160.8270.7940.8210.7970.7930.7910.7730.7860.8120.8130.8010.8060.8210.8100.8120.8270.8160.8410.8320.8230.8190.7940.7500.8090.8320.8230.8150.8270.8280.8130.8070.8130.8070.7940.8190.8240.7810.7750.8090.7580.7740.7800.7810.7580.7660.7830.7810.7790.7630.7780.8260.8221813OrganismalFitnessCALM1_HUMANHighHuman
CAPSD_AAV2S_Sinai_20210.7910.7890.7860.8110.7810.7840.7580.8410.7500.7770.6410.6660.6820.7010.7290.6900.7330.6650.6060.7030.6830.7220.6960.7120.6800.6910.7290.6980.7940.7980.6290.6190.6160.6990.7210.8300.7710.7720.8210.7850.7870.8170.6630.7180.7760.6880.7680.7530.6860.7230.6730.6510.6540.6560.6370.6450.6590.6520.6880.6630.7230.66542328OrganismalFitnessCAPSD_AAV2SLowVirus
CAR11_HUMAN_Meitlis_2020_gof0.6350.6380.6390.6390.6410.6390.6530.6070.6480.6450.6560.6210.6310.6100.5960.6420.6420.6350.6430.6340.6160.6510.6520.6490.6050.6550.6540.6500.6340.6390.6440.6290.6230.6400.6220.6210.6340.6410.6380.6390.6460.6460.6170.5940.6420.6330.6140.6320.6340.6280.6500.6530.6370.6510.6540.6410.6520.6480.6420.6460.6610.6492374OrganismalFitnessCAR11_HUMANLowHuman
CAR11_HUMAN_Meitlis_2020_lof0.7890.7900.7940.7910.7850.7840.7690.7360.8040.8040.8180.7650.7840.7380.7120.7570.7860.8050.8090.7890.7000.8110.8080.8000.7370.8230.8130.8310.7650.8020.8180.7840.7410.7550.7770.7660.7840.8040.7940.7860.8000.7950.7280.7200.8180.7640.7330.8060.7820.7530.8060.7980.7870.8000.8030.7930.7990.7950.7960.7980.8070.7652395OrganismalFitnessCAR11_HUMANLowHuman
CAS9_STRP1_Spencer_2017_positive0.8360.8380.8390.8390.8390.8390.8310.8220.8400.8400.8360.8290.8290.8290.8290.8370.8390.8420.8410.8280.8320.8310.8390.8450.8310.8400.8380.8370.8390.8440.8420.8420.8340.8250.8280.8400.8380.8390.8420.8390.8390.8410.8290.8260.8380.8290.8290.8380.8290.8280.8400.8370.8410.8380.8370.8370.8370.8400.8390.8390.8420.8378117ActivityCAS9_STRP1MediumProkaryote
CASP3_HUMAN_Roychowdhury_20200.7790.8200.8200.8120.8120.8180.7000.7590.8180.8240.8130.7960.8110.7120.8090.8260.8120.8230.8240.7980.7110.7780.7560.8290.8090.8170.8240.8120.8310.8020.8380.8210.7540.7120.8030.8190.8090.8220.8220.8230.8290.8240.7770.6810.8170.8010.7680.8070.7850.7400.8120.8010.8040.8070.8100.8070.8190.8180.8060.8050.8190.7961567ActivityCASP3_HUMANHighHuman
CASP7_HUMAN_Roychowdhury_20200.7470.8080.8150.8160.8170.8140.6070.7860.8100.8130.8180.8130.8160.6820.8040.8130.8040.7870.7860.8100.6220.7940.7640.7880.7820.7920.7960.7850.8040.8200.8040.7860.7230.6640.7840.7850.7990.8220.8120.8120.8130.8180.7460.6200.8210.8060.7610.8230.7880.7400.8050.8110.8120.8030.8040.8020.8060.8050.8090.8090.8190.7751680ActivityCASP7_HUMANMediumHuman
CATR_CHLRE_Tsuboyama_2023_2AMI0.8530.8080.8130.8160.8250.8170.8440.7740.7770.7930.8050.8200.8150.8190.8590.8190.8200.8140.8010.6200.8210.7920.8000.7910.7980.7980.7880.7960.8000.8130.7900.7990.7550.8110.8030.8070.8310.8210.8130.8120.8100.8130.8190.8200.7950.8000.8470.7600.8180.8420.8150.8080.8150.8170.8130.8110.8130.8100.8090.8160.8100.8141903StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X0.9100.8950.9160.9100.9100.9120.8480.8960.9090.9160.9120.9000.9030.8540.9010.9020.9080.9150.9130.9170.8610.8450.8840.8910.8890.8370.8350.8230.9130.9060.9140.8990.7330.8680.8860.9050.9120.9150.9160.9130.9120.9180.8980.8880.9010.9020.9280.9220.9250.9280.9080.9160.9220.9150.9170.9090.9110.9110.9100.9170.9180.9282068StabilityCBPA2_HUMANMediumHuman
CBS_HUMAN_Sun_20200.4720.4680.4770.4780.4770.4800.3230.4350.4780.4550.4510.4630.4500.3310.3940.4270.4210.4470.4350.4550.4650.4190.4460.4520.4270.4330.4460.4430.4540.4700.4940.4780.3860.4650.4300.4310.4780.4790.4900.4900.4860.4740.4310.3260.4720.4610.4200.4530.4420.3720.4320.4440.4470.4390.4270.4390.4480.4440.4340.4450.4680.4137217OrganismalFitnessCBS_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K280.8810.8750.8890.8950.8970.9040.3180.8160.8900.8910.8730.8900.8900.3550.9120.9110.9070.8990.8700.8890.8150.8290.8370.8380.8590.8650.8580.8470.8490.8870.8760.8460.6410.7750.8440.8570.8840.8760.8930.9010.8920.8920.8800.4480.8330.8830.7480.7570.9140.8590.9060.8980.9030.8990.9070.9000.9050.9010.9060.9090.8960.9052282StabilityCBX4_HUMANHighHuman
CCDB_ECOLI_Adkar_20120.7820.8240.8450.8440.8290.8350.5630.7020.6860.7760.7530.6230.6410.5600.5770.6550.8080.8490.8130.7330.5320.5330.4830.6890.4930.6550.5330.6680.8460.7890.8770.8730.7350.5720.5600.7740.7240.7010.8170.8280.8140.8450.5900.5200.6910.5870.6550.7890.6460.6920.7880.7370.7240.7660.8260.8060.7570.8220.8150.7990.8010.7071176ActivityCCDB_ECOLIHighProkaryote
CCDB_ECOLI_Tripathi_20160.9460.9940.9910.9930.9890.9880.7830.9540.8900.9550.9460.8660.8900.7730.7800.8930.9670.9911.0000.9390.7630.7650.7100.9140.7140.8740.7460.8820.9960.9530.9960.9960.9170.7650.7820.9880.9130.8920.9900.9800.9810.9930.7960.7410.9050.7800.8900.9830.8990.8900.9740.9530.9350.9570.9900.9780.9670.9910.9830.9770.9640.9551663OrganismalFitnessCCDB_ECOLIHighProkaryote
CCR5_HUMAN_Gill_20230.8190.8180.8180.8190.8210.8210.8000.8410.8220.8220.8230.8200.8190.8100.8130.8200.8070.8160.8120.8230.8140.8360.8320.8300.8240.8320.8250.8280.8320.8290.8220.8150.7750.8220.8280.8290.8200.8300.8340.8220.8230.8250.8130.7720.8260.8150.8090.8240.8150.8050.8180.8140.8130.8090.8130.8200.8140.8140.8140.8170.8230.8276137BindingCCR5_HUMANHighHuman
CD19_HUMAN_Klesmith_2019_FMC_singles0.5660.5550.5780.5780.5660.5650.5190.5230.5260.5290.5230.5100.5430.5400.5660.5220.5170.5580.5770.4910.5750.5690.5840.5770.5110.5540.5360.5860.5650.5760.5720.5480.5270.6080.5860.5310.5980.5870.5780.5780.5740.5730.5520.5290.5730.5620.6930.6700.6830.5530.5740.5640.5910.6000.5800.5950.6140.5730.6100.6000.6870.6213761BindingCD19_HUMANLowHuman
CP2C9_HUMAN_Amorosi_2021_abundance0.8250.8570.8520.8590.8670.8640.8420.8590.8560.8660.8570.8670.8740.8190.8540.8590.8730.8700.8680.8670.8530.8300.8650.8630.8670.8660.8680.8680.8680.8610.8590.8460.6340.8610.8620.8620.8690.8680.8700.8680.8680.8660.8590.7270.8620.8670.8530.8620.8750.7700.8680.8690.8740.8700.8690.8720.8740.8740.8710.8730.8710.8566370ExpressionCP2C9_HUMANHighHuman
CP2C9_HUMAN_Amorosi_2021_activity0.7530.8140.8150.8270.8320.8300.7960.8340.8010.8330.8140.8390.8570.7530.8270.8400.8670.8520.8290.8470.8100.7730.8350.8140.8410.8270.8340.8210.8250.8100.8000.7530.5010.8340.8330.8360.8420.8450.8530.8450.8410.8370.8290.5890.8250.8560.8100.8170.8420.6530.8570.8490.8510.8520.8570.8610.8540.8540.8590.8630.8520.8276142BindingCP2C9_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM0.8240.8410.8470.8520.8510.8490.6720.8070.8580.8480.8380.8700.8740.7510.8950.8600.7890.8350.8220.8450.7660.7450.8150.8410.7260.8660.8710.8470.8420.8420.8570.8430.6780.7290.8330.8240.8530.8670.8700.8520.8640.8650.7560.7450.8440.8620.8720.8470.9020.8870.8400.8390.8430.8470.8420.8490.8450.8510.8410.8470.8550.8733295StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX0.7460.8470.7980.8000.8210.8150.6460.7810.8630.8670.7770.6710.6730.6170.6370.6790.7280.7970.7930.7680.6260.6180.6430.5630.5440.5640.6230.6220.7960.7790.8020.7680.6760.6170.5970.6290.7420.7390.7690.8080.8060.8120.6260.6330.7310.6440.7810.7870.8700.8350.7110.7820.7640.7600.7210.7600.7470.7390.7470.7470.8240.7501580StabilityCUE1_YEASTMediumEukaryote
D7PM05_CLYGR_Somermeyer_20220.6300.7710.7360.6950.7600.7600.3200.6220.7920.7890.5950.3250.3280.3270.3090.3020.3120.3410.3570.6210.2910.2700.3620.3310.2610.3050.3130.3930.4780.7730.7520.7560.3080.3250.3600.3630.6910.6900.6860.7640.7630.7530.4430.4450.4500.4500.5730.5930.6110.5940.6260.6200.6320.6280.6140.6290.6080.6270.6150.6250.5130.38624515ActivityD7PM05_CLYGRLowEukaryote
DLG4_HUMAN_Faure_20210.8700.8590.8710.8630.8760.8760.8720.8630.8340.8410.8160.8650.8670.8880.9140.9010.8650.8530.8260.8780.8510.8490.8360.8230.8630.8510.8430.8480.8210.8740.8530.8520.8170.8490.8690.8360.8860.8940.8800.8830.8880.8850.8770.7270.8320.8670.8690.8170.8950.7720.8370.8130.8350.8330.8450.8460.8290.8270.8290.8350.8570.9146976OrganismalFitnessDLG4_HUMANLowHuman
DLG4_RAT_McLaughlin_20120.9210.9300.9250.9250.9310.9320.9260.9230.9290.9290.9280.9270.9270.9120.9140.9360.9280.9270.9280.9210.9220.9210.9240.9240.9230.9260.9220.9210.9270.9210.9350.9360.8790.9170.9250.9110.9220.9240.9200.9270.9280.9320.9290.8000.9270.9220.9070.9100.9060.8790.9270.8820.9190.9210.9230.9200.9230.9220.9250.9230.9260.9201576BindingDLG4_RATLowEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC0.8410.8770.8600.8480.8310.8550.7510.8760.8980.9060.7990.8080.7890.7700.8020.8010.8230.8190.9020.8940.7380.7840.7590.7650.7630.7400.7460.7820.8540.8910.9060.8830.7780.7700.7480.7640.8390.8290.8300.8580.8580.8450.7870.7740.8180.7850.9170.8960.9260.9180.8930.8990.9060.8920.8990.8850.8720.8790.8920.8890.8540.8501008StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO10.9000.8900.9010.8980.9040.8990.8880.8350.8910.8990.9100.9070.9240.9030.9360.9290.9330.9230.9230.9100.9000.8980.9080.9030.9040.8850.9020.8920.8890.8830.8800.8790.7680.8960.8870.9070.8990.8990.9060.8970.8980.9020.9180.7290.8870.9200.9200.8850.9230.9250.9280.9350.9380.9380.9390.9370.9360.9400.9330.9420.8980.9262264StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y0.7740.7950.7890.7760.7610.7850.5460.7420.6710.6440.7910.6740.7070.5210.6540.6790.7870.7950.7800.8280.7300.7960.7930.7740.6480.7660.6500.7430.7880.6880.8080.7820.6960.5710.5860.5610.7790.7750.7950.7980.7830.8010.6100.5330.8210.6990.7970.8280.8220.8470.8110.8140.8060.8200.8190.8170.8170.8190.8120.8210.8380.8042915StabilityDOCK1_MOUSEHighEukaryote
DYR_ECOLI_Nguyen_20230.9610.9620.9610.9620.9620.9600.8930.9670.9620.9580.9600.9670.9640.9210.9630.9610.9660.9630.9630.9630.9340.9610.9640.9640.9650.9590.9650.9610.9650.9600.9640.9620.9010.9610.9620.9640.9590.9630.9610.9600.9600.9600.9620.9030.9620.9640.9360.9630.9660.9370.9680.9630.9640.9630.9660.9670.9660.9640.9660.9660.9650.9622916OrganismalFitnessDYR_ECOLIHighProkaryote
DYR_ECOLI_Thompson_20190.8650.8850.8840.8870.8910.8880.7970.8650.8880.8830.8620.8820.8660.8290.8700.8700.8770.8740.8690.8760.8150.8820.8650.8670.8670.8890.9000.8700.8980.9000.8800.8780.8160.8820.8950.9030.8840.8920.8910.8900.8930.8920.8680.7910.8730.8750.8450.8690.8730.8520.8750.8680.8640.8710.8730.8850.8700.8720.8720.8710.8630.8762363OrganismalFitnessDYR_ECOLIMediumProkaryote
ENV_HV1B9_DuenasDecamp_20160.6430.6300.5380.6010.6350.6310.2490.6620.6280.6340.5380.6230.6240.2990.2780.3050.3010.2980.4690.6630.5900.6450.6620.6600.5640.6770.5870.6550.6690.5770.7090.6470.5640.6180.6370.6680.6420.6310.6530.6390.6430.6580.5660.2440.6290.6230.5490.5780.4970.4100.4310.5270.4700.4310.5280.4880.4950.5100.3780.4870.3530.319375OrganismalFitnessENV_HV1B9MediumVirus
ENV_HV1BR_Haddox_20160.3040.3150.3300.3430.3370.3360.1450.3170.3020.3070.2580.2870.2890.1650.1740.1700.1800.1820.2090.3420.3030.3130.3310.3390.2940.3250.3370.3180.3420.2670.3020.2710.2160.3000.3140.3240.3120.3140.3210.3350.3370.3410.2680.1430.2940.2750.2250.2930.1770.1990.2110.2070.2250.2250.2230.2200.2160.2090.2120.2220.2060.18312863OrganismalFitnessENV_HV1BRMediumVirus
ENVZ_ECOLI_Ghose_20230.7580.7540.7740.7910.8080.8100.7010.6710.7830.7970.8030.7900.8050.7210.7810.7540.7560.7390.7220.7720.7690.7320.7690.7760.7770.7530.6920.8020.7240.8480.7980.8080.8000.7560.7890.7620.7660.7630.7670.8140.8130.8170.7140.7530.7350.7110.6810.7880.7280.6920.7620.7750.7670.8050.7930.7800.7750.7670.8170.7730.7280.7761121ActivityENVZ_ECOLIHighProkaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M0.9170.9350.9370.9390.9350.9390.3630.9120.9380.9390.9120.9170.9220.3910.9150.9190.9370.9400.9360.9380.9210.9240.9170.9200.9260.9250.9300.9320.9220.9120.9290.9100.8830.9300.9130.9310.9460.9340.9420.9430.9370.9410.8860.5510.9370.9060.9470.9310.9490.9420.9380.9390.9380.9450.9440.9410.9370.9410.9420.9410.9480.9461960StabilityEPHB2_HUMANHighHuman
ERBB2_HUMAN_Elazar_20160.7820.8840.7940.7660.8050.8210.7980.8920.8360.8990.8480.7440.7900.8660.7890.7880.8810.8990.8960.8530.8200.8660.8970.9070.6670.8980.9140.9190.8990.7720.8840.8470.8280.8780.6520.9080.8580.7400.8930.8230.7970.8360.7930.7580.8900.7670.9170.9280.7410.8250.8270.8440.7860.8720.8570.7750.8040.8660.8260.8290.7790.772326ExpressionERBB2_HUMANLowHuman
ESTA_BACSU_Nutschel_20200.7080.7740.7740.7770.7730.7790.6570.6900.7370.7960.6910.7290.7500.6620.7130.6900.6930.6900.7170.7310.6630.6630.7110.6670.7180.7040.7350.7390.7750.7530.7320.7370.5950.6670.7310.6860.7250.7410.7000.7840.7830.7640.7190.6050.7500.7280.8380.8040.7920.7570.7550.7010.7400.7290.7320.7240.7200.7140.7200.7350.7440.7192172StabilityESTA_BACSUHighProkaryote
F7YBW8_MESOW_Aakre_20150.1430.8240.7910.8840.8450.8590.1590.6600.6750.7120.8370.7030.7260.1230.1220.1920.6160.5900.8180.7760.1200.1120.1210.1540.1700.4550.1210.6710.8520.7970.9140.8920.1610.1190.1170.9210.1440.1270.8790.7270.7000.8930.1750.1600.4660.1960.1870.5200.2000.1380.8460.8700.8250.8910.8720.8820.8340.8450.8570.8680.4710.1119192OrganismalFitnessF7YBW8_MESOWHighProkaryote
FECA_ECOLI_Tsuboyama_2023_2D1U0.6660.7020.6870.6860.6910.6840.5290.6380.6850.7280.7360.6380.6900.5210.7420.7590.7150.7090.7180.6930.6010.6750.6680.6540.6690.6700.6940.6790.7010.6980.7140.6770.5780.5220.5780.6560.6390.6340.6920.6680.6570.6990.7030.5970.7450.7190.7880.7730.7670.7450.6790.7100.7260.7290.7180.7030.6990.6980.7160.6970.7460.7451886StabilityFECA_ECOLIHighProkaryote
FKBP3_HUMAN_Tsuboyama_2023_2KFV0.8700.8690.8750.8760.8780.8780.7370.8290.8200.8220.7350.7280.7410.7290.7180.7120.7610.7740.8170.8180.7580.7540.7160.8370.7320.8310.8150.7680.7990.8440.8160.8070.6500.6960.7240.8000.8690.8700.8630.8800.8760.8810.7500.7380.7440.7480.8810.8600.8920.8840.7870.8450.8590.8360.8580.8270.8420.8310.8000.8440.8880.8201237StabilityFKBP3_HUMANMediumHuman
GAL4_YEAST_Kitzman_20150.7140.7740.8200.7980.7930.8020.7050.5870.8420.8180.8250.7750.7750.7290.7590.7710.8320.8470.8590.7830.7760.7290.7510.7380.7540.7760.7940.7780.8630.8090.8630.8510.6700.7060.7510.7140.8130.8070.8280.7940.8020.8110.7430.7770.8240.7500.6380.8040.6030.6520.8510.8160.8430.8330.8230.8180.8580.8170.8320.8350.7780.7641195OrganismalFitnessGAL4_YEASTMediumEukaryote
GCN4_YEAST_Staller_20180.7590.7540.7480.7540.7520.7490.6640.6830.7590.7560.7420.7390.7470.7270.7190.7480.7600.7570.7520.7560.6470.6410.6520.6470.6370.6340.6320.6450.6810.7580.7480.7420.6610.6440.6380.7540.7550.7570.7640.7530.7530.7620.7230.7350.7230.7350.7430.7590.7350.7080.7440.7400.7410.7410.7460.7470.7410.7400.7450.7420.7090.7032638BindingGCN4_YEASTLowEukaryote
GDIA_HUMAN_Silverstein_20210.8790.8590.8600.8590.8490.8570.7690.8710.8720.8690.8640.8600.8680.7370.7950.8780.8640.8670.8770.8640.7790.8740.8650.8720.8540.8760.8700.8700.8710.8680.8420.8440.8040.8320.8600.8430.8770.8610.8580.8630.8610.8580.7780.7060.8860.7900.8800.8790.8620.7420.8520.8510.8610.8610.8580.8720.8580.8600.8620.8560.8660.8441154OrganismalFitnessGDIA_HUMANLowHuman
GFP_AEQVI_Sarkisyan_20160.9170.9070.9130.9130.9130.9130.4160.9120.9160.9150.8120.4700.4720.4640.4930.4490.4560.5000.5620.8780.4430.4570.5140.4710.4030.5240.6300.9120.9180.9230.8990.8990.4700.4350.5430.9020.9240.9250.9220.9190.9200.9310.6500.5980.6530.6580.9060.8980.9230.8830.8750.8820.8900.8950.8890.8890.8870.8890.8780.8900.8710.74851714ActivityGFP_AEQVILowEukaryote
GLPA_HUMAN_Elazar_20160.7650.7160.7770.7760.7640.7700.6240.8360.7440.7290.6490.7180.6820.6210.6160.6960.6990.6730.7240.6990.6920.6520.6640.7190.6720.6890.6480.6920.7340.7430.7390.7620.7650.6550.6880.6870.7940.7930.7960.7650.7720.7690.6430.6530.7100.6740.7370.6550.7550.6520.8320.7610.7490.7130.7520.6870.7500.7970.7810.7520.7760.808245ExpressionGLPA_HUMANLowHuman
GRB2_HUMAN_Faure_20210.7440.8100.8120.8200.8280.8320.7600.7540.8300.8010.8270.7770.8180.7520.8310.8640.8670.8120.8430.8180.8240.8130.7890.7740.8010.8040.7670.8160.7630.8070.7800.7670.7810.8170.8160.7210.8250.8250.7660.8450.8480.8310.8390.6330.8030.8170.8750.8000.8940.7870.8420.8490.8400.8520.8540.8490.8410.8500.8380.8550.8190.84563366OrganismalFitnessGRB2_HUMANMediumHuman
HCP_LAMBD_Tsuboyama_2023_2L6Q0.8920.9070.8830.8850.9000.9010.7780.8710.8570.9100.9170.8360.8710.7950.8510.9050.9240.9140.9250.8230.7910.8190.8470.8580.8460.8150.7560.8250.9160.9050.9050.8780.7460.7490.8030.8890.8860.8990.8980.8900.9130.9070.8320.8130.9130.8300.8980.9240.9140.8910.9180.9210.9170.9190.9320.9210.9260.9280.9230.9270.9270.8801040StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM0.8600.8500.8820.8900.8980.8950.5900.7760.8830.8920.8720.6540.6690.5900.5850.8530.8880.8710.8760.8680.5000.8130.8500.8420.8210.8320.8500.8550.8360.8730.8870.8520.5110.5990.4380.6660.8590.8410.8600.8830.8790.8750.6420.6230.8760.6480.7670.8450.7470.6810.8920.8840.8910.8870.9030.9010.8980.9030.8980.9020.8890.5635586StabilityHECD1_HUMANMediumHuman
HEM3_HUMAN_Loggerenberg_20230.5700.5850.5850.5770.5790.5810.4680.3750.5810.5900.5490.5490.5500.4500.5490.5540.5540.5850.5750.4420.5650.5650.5870.6100.5490.5600.5600.5500.5890.5950.6010.6140.4500.5620.5940.6160.5820.6030.6110.5870.5910.5980.5350.4730.5580.5530.5580.5850.5260.5430.5610.5450.5640.5660.5650.5620.5680.5560.5600.5610.5740.5655689ActivityHEM3_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_20190.8260.8820.8730.8740.8640.8610.6670.3850.8520.8630.8600.8120.8390.5490.6450.7440.8670.8800.8820.8000.8110.8570.8710.8750.8520.8670.8580.8620.8590.8650.8410.8370.5750.8440.8590.8810.8500.8420.8750.8800.8720.8820.6990.6760.8110.5960.8540.8840.8690.8250.8480.8120.8370.8380.8420.8380.8350.8420.8430.8400.8740.574496137OrganismalFitnessHIS7_YEASTMediumEukaryote
HMDH_HUMAN_Jiang_20190.6350.6310.6330.6290.6330.6320.5940.6900.6260.6270.6440.6440.6450.5350.5360.5810.6520.6380.6440.6300.6270.6260.6350.6370.6470.6350.6320.6380.6360.6380.6350.6290.5610.6080.6460.6440.6400.6460.6440.6320.6340.6330.5440.5220.6680.6340.6240.6550.5050.5710.6470.6420.6520.6540.6440.6520.6460.6510.6400.6430.6630.62616853OrganismalFitnessHMDH_HUMANLowHuman
HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O20.9450.9620.9590.9610.9620.9630.8360.9580.9620.9610.9580.9470.9620.7660.8000.8760.9320.9420.9480.8730.9630.9620.9600.9620.9600.9600.9630.9630.9630.9630.9640.9620.9430.9600.9600.9610.9580.9580.9580.9630.9620.9630.7600.7700.9620.9530.7780.9560.7830.8840.9370.9400.9300.9300.9350.9310.9350.9270.9350.9330.9580.9382252OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Flynn_20190.7930.8120.8160.8160.8180.8160.7280.8020.8220.8130.8050.8130.8160.7000.7220.7320.7770.7940.7950.8120.7940.8150.8170.8200.8030.8230.8250.8230.8240.8210.8200.8150.7630.8110.8190.8190.8080.8140.8150.8150.8170.8180.7300.6810.8010.7710.7450.8120.7010.7080.7670.7710.7770.7830.7710.7820.7720.7710.7660.7760.8010.78913294OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Mishra_20160.9330.9430.9420.9430.9440.9350.8820.8960.9410.9450.9420.9430.9420.8580.8900.9030.9280.9290.9260.9340.9410.9390.9420.9410.9440.9420.9440.9430.9440.9440.9420.9380.8920.9400.9410.9410.9390.9400.9400.9420.9430.9400.9390.7940.9380.9420.8620.9380.8550.8400.9200.9230.9170.9190.9260.9230.9230.9260.9170.9240.9310.9284323OrganismalFitnessHSP82_YEASTMediumEukaryote
HXK4_HUMAN_Gersing_2022_activity0.6160.6190.6030.6020.6090.6120.5150.5840.6120.6130.6000.5950.6030.5270.5480.6200.6320.6120.6040.5120.5960.6020.6070.6000.6120.6140.6160.6100.5890.5930.5870.5670.5030.6090.6070.6020.6170.6030.6050.6150.6060.6110.5680.5020.6160.6090.5940.6210.5930.5560.6080.6150.6200.6190.6180.6310.6320.6220.6160.6210.6320.5998570OrganismalFitnessHXK4_HUMANMediumHuman
HXK4_HUMAN_Gersing_2023_abundance0.8530.8540.8510.8540.8510.8500.7610.8480.8570.8610.8510.8540.8570.7900.8130.8530.8590.8550.8590.8630.8550.8500.8600.8540.8560.8540.8590.8570.8510.8600.8550.8510.8050.8530.8520.8570.8610.8550.8590.8520.8490.8490.8210.7880.8480.8600.8600.8490.8550.8260.8540.8580.8580.8520.8590.8590.8620.8550.8570.8580.8610.8498396ExpressionHXK4_HUMANMediumHuman
I6TAH8_I68A0_Doud_20150.7720.7630.7540.7530.7760.7760.6190.7440.7560.7710.6170.6120.6130.6110.6080.6130.6250.6170.6450.7310.7270.7540.7610.7710.6150.6260.6350.6150.7550.7850.7180.7130.6660.7590.7550.7540.7740.7690.7680.7790.7760.7720.6080.6130.6180.6160.6770.6790.6740.6610.6090.6430.6460.6350.6270.6150.6180.6150.6060.6240.6450.6259462OrganismalFitnessI6TAH8_I68A0MediumVirus
IF1_ECOLI_Kelsic_20160.9070.9430.9340.9320.9340.9350.8620.9070.8920.8880.9440.9360.9370.8750.9360.9420.9400.9320.9390.9380.9320.9420.9410.9420.9420.9470.9470.9430.9410.8860.9310.9290.8570.9350.9390.9490.9340.9370.9450.9360.9370.9370.9190.8680.9450.9390.8730.9310.9390.9110.9190.9380.9460.9390.9450.9430.9330.9440.9460.9460.9380.9251367OrganismalFitnessIF1_ECOLIHighProkaryote
ILF3_HUMAN_Tsuboyama_2023_2L330.7880.8110.8270.8210.8200.8190.7510.8150.8050.8090.8310.8090.8040.7810.7820.7960.8030.8320.8250.8120.8360.8190.8300.8350.8680.8450.8590.8430.8410.8250.8220.8080.8120.7420.7870.8570.7900.8340.8500.8000.8030.8200.7880.7670.8290.7990.8110.8330.8120.8060.8380.8090.8240.8380.8400.8230.8320.8270.8380.8360.8430.7951329StabilityILF3_HUMANHighHuman
ISDH_STAAW_Tsuboyama_2023_2LHR0.7650.7220.7840.7810.7850.7860.7730.7580.8310.7990.8000.8110.8210.8280.8140.8760.8380.8420.8540.7700.7860.7710.7970.8110.8190.8230.8450.7810.8250.7930.8180.7970.7000.7650.7510.7930.7650.7580.7790.7810.7710.7850.8110.7830.8210.8090.9080.8940.8870.8650.8400.8370.8580.8410.8310.8450.8390.8440.8210.8470.8820.8331944StabilityISDH_STAAWHighProkaryote
KCNE1_HUMAN_Muhammad_2023_expression0.5900.5750.5690.5680.5880.5870.5870.5840.5660.5700.6400.6450.6430.6000.5950.5880.6060.5720.5700.5720.6410.6280.5790.5950.6340.6380.6030.5690.5710.5710.6040.5870.3800.5840.6320.6040.5950.6050.5960.5880.5860.5840.6100.5900.5770.6140.6040.5820.6020.6040.6090.5950.6020.6140.5970.6010.5950.6110.6010.6080.6230.6372339ExpressionKCNE1_HUMANMediumHuman
KCNE1_HUMAN_Muhammad_2023_function0.8360.8410.8410.8470.8440.8470.7090.8210.8370.8470.6590.7230.7970.6650.6550.8420.8540.8120.8080.8040.6250.6070.8020.8220.7020.8440.8360.8290.8400.8490.8410.8170.3670.7080.7840.8570.8350.8380.8540.8510.8510.8530.6700.6570.8430.6650.7430.8310.7120.6700.8310.8560.8420.8430.8540.8400.8400.8440.8480.8510.8490.6462315ActivityKCNE1_HUMANMediumHuman
KCNH2_HUMAN_Kozek_20200.6390.6410.6390.6390.6400.6400.6250.5210.6140.6330.6780.5490.5370.5560.5250.5820.5200.5580.6060.6970.6520.6480.6980.6790.6830.6930.6300.6610.6430.6780.6010.6410.5930.6330.6440.6610.6280.6470.6800.6300.6480.6460.7090.5130.6800.6460.6450.4610.2700.5200.7020.6820.6860.7170.6970.7180.6760.6780.7030.7020.6980.660200ActivityKCNH2_HUMANMediumHuman
KCNJ2_MOUSE_Coyote-Maestas_2022_function0.7810.7970.7950.7970.7990.7990.7220.7820.7920.7920.7970.7960.7970.7240.7730.7990.7990.7970.7950.7870.8020.7950.7920.7900.7990.7960.7950.7950.7940.7970.7910.7840.7310.7910.7930.7920.7970.7950.7930.7970.7960.7950.7590.7230.7920.7880.7630.7900.7700.7370.7950.7950.7970.7980.7950.7980.7960.8000.7990.8000.7970.7776963ActivityKCNJ2_MOUSEMediumEukaryote
KCNJ2_MOUSE_Coyote-Maestas_2022_surface0.8300.8200.8230.8220.8230.8240.7150.8030.8210.8210.8290.8220.8240.7460.8340.8420.8400.8380.8410.8120.8260.8040.8060.8180.8240.8150.8150.8210.8120.8210.8240.8170.7630.8210.7920.8120.8230.8120.8130.8230.8180.8190.8150.7140.8210.8310.8170.8130.8100.7960.8320.8230.8280.8270.8320.8260.8310.8270.8280.8290.8320.8316917ExpressionKCNJ2_MOUSEMediumEukaryote
KKA2_KLEPN_Melnikov_20140.4710.5910.5850.6120.6000.6100.3660.5230.5970.6270.5480.5540.5870.3890.4260.5060.6040.6370.6410.5700.4760.5200.5980.5780.4840.5860.6140.5570.6340.6040.5700.5480.3000.4060.5150.5660.5490.5720.6000.6210.6230.6300.4230.3340.5530.4660.5610.6060.6060.5090.5980.5970.6140.6030.6080.6070.6020.5830.5970.6130.6230.5064960OrganismalFitnessKKA2_KLEPNHighProkaryote
LGK_LIPST_Klesmith_20150.8530.9370.9350.9360.9370.9370.7940.8960.8810.9280.8590.9160.9260.8020.8260.8440.9030.9290.9310.9010.8260.8680.8870.9080.8470.9080.9330.8940.9370.9450.9350.9210.8020.8200.8650.9480.8470.8640.9380.9330.9360.9440.7940.7830.8760.8320.8530.9070.9010.8320.9070.9170.9090.9180.9110.9130.9120.9100.9160.9180.8760.8417890ActivityLGK_LIPSTMediumEukaryote
LYAM1_HUMAN_Elazar_20160.6790.6930.6500.6080.6660.6780.6690.6820.7600.7540.7320.6040.6500.6870.6750.7120.6360.6490.7550.7470.7120.7210.7060.6430.6850.6300.6630.7550.6660.7090.6630.6160.6140.6770.7120.6330.6680.7110.6390.6790.7040.6700.6580.7240.6320.6730.7500.6730.6840.6520.6170.7020.7750.6270.6570.6760.6950.7160.5350.6590.7200.758359ExpressionLYAM1_HUMANMediumHuman
MAFG_MOUSE_Tsuboyama_2023_1K1V0.8810.8800.8760.8770.8680.8670.8480.8460.8600.8590.8570.7960.8800.8580.8530.8510.8670.8960.7740.8440.7750.7780.8360.8560.8400.8460.8490.8490.8430.8560.8500.8580.7490.8310.8570.8430.8790.8890.8880.8750.8860.8870.8480.7050.8710.8140.9000.8250.9080.9080.9040.8970.9120.9140.8950.9170.9040.9080.9030.9150.9160.8991429StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV0.8260.8740.8760.8750.8790.8780.3690.7950.8290.8710.8530.7390.7510.3060.2730.8170.8270.7980.8910.8500.2890.4360.7920.7680.4710.8600.8190.8000.8580.8460.8630.8250.7900.3970.2880.3400.8080.8420.8460.8740.8750.8750.4770.3650.8680.7860.7960.8790.8570.8030.8470.8650.8510.8340.8270.8300.8350.8300.8100.8370.9050.7992116StabilityMBD11_ARATHMediumEukaryote
MET_HUMAN_Estevam_20230.8590.9140.8970.9120.9170.9180.8670.8940.8550.8740.9230.8790.8790.8290.8690.8880.9060.9100.9200.8990.9100.9260.9130.9140.9270.9230.9290.9260.9270.9220.9170.9060.8210.8560.9080.9260.8750.9040.9170.9100.9120.9030.8790.8020.9180.9030.8350.9120.8760.8120.8810.8960.8970.9010.9000.8980.9000.8990.8970.8960.8840.8755393ActivityMET_HUMANMediumHuman
MK01_HUMAN_Brenan_20160.7590.7760.7770.7800.7760.7730.7550.7410.7620.7590.7410.7590.7540.7590.7520.7580.7530.7590.7560.7580.7610.7450.7410.7360.7510.7440.7470.7510.7390.7620.7320.7410.7650.7540.7450.7330.7500.7420.7330.7730.7730.7670.7620.7460.7580.7690.7310.7470.7380.7450.7660.7670.7670.7630.7640.7610.7580.7560.7610.7640.7640.7546809OrganismalFitnessMK01_HUMANMediumHuman
MLAC_ECOLI_MacRae_20230.8470.9000.9020.9020.9040.9020.7520.8870.8910.8990.8830.8940.9020.7990.8330.8350.8640.8840.8910.9060.8040.9010.9030.9030.8480.8870.9020.8850.9060.9110.9030.9040.7480.7790.8890.8940.8520.8870.8900.8980.9070.9050.8100.7930.8630.8370.7040.8390.7970.7650.8620.8560.8460.8550.8680.8580.8520.8540.8620.8630.8400.8154007OrganismalFitnessMLAC_ECOLIMediumProkaryote
MSH2_HUMAN_Jia_20200.8170.8410.8310.8310.8310.8330.7840.8140.8370.8360.8320.8340.8370.7800.8180.8330.8310.8380.8250.8290.8040.8290.8310.8320.8140.8350.8330.8190.8370.8300.8280.8210.7970.8080.8400.8310.8230.8390.8320.8360.8410.8380.7990.7470.8390.8150.8090.8350.7360.7860.8280.8260.8290.8260.8290.8290.8320.8300.8290.8300.8340.82016749OrganismalFitnessMSH2_HUMANMediumHuman
MTH3_HAEAE_RockahShmuel_20150.5290.7800.7840.7920.7890.7840.4550.7660.7300.7360.6210.7300.7560.3890.4840.5190.5880.6120.6580.7180.5050.5370.6560.7300.5580.7400.7760.7150.7640.7710.7850.7650.5470.5290.5660.7470.5230.5660.7250.7780.7760.7820.4640.2880.5230.4610.5640.6880.7040.5500.6210.6450.6320.6010.5940.6440.6330.6370.6080.6300.6640.5381777OrganismalFitnessMTH3_HAEAEMediumProkaryote
MTHR_HUMAN_Weile_20210.7900.7980.7900.7980.7870.7880.6770.7800.8060.8050.8070.7890.7880.7110.7510.7950.8000.7970.8080.7690.7810.7860.7890.8090.7850.7830.7880.7770.8170.7940.7910.7820.6220.7910.7920.7950.7920.7930.7980.7980.7980.8010.7350.6990.8050.7560.7460.7930.8010.7250.7960.8000.7950.8010.7960.7960.8000.7960.8010.8010.7960.76612464OrganismalFitnessMTHR_HUMANLowHuman
MYO3_YEAST_Tsuboyama_2023_2BTT0.5950.6860.6820.6690.6900.6950.4820.4820.7030.6890.7500.7240.7230.5690.6990.7860.8360.7500.6480.6850.5580.5150.5760.5980.6290.6860.7290.7040.7510.6310.6740.6300.6000.6420.5870.5520.6950.6100.5740.7030.6910.6850.6270.5170.7070.6690.6950.7310.7790.7060.7520.7380.7410.7460.7460.7520.7450.7640.7570.7560.7320.7623297StabilityMYO3_YEASTHighEukaryote
NCAP_I34A1_Doud_20150.7450.7370.7410.7420.7390.7420.5620.7260.7410.7380.5590.5510.5500.5520.5510.5430.5560.5620.5870.7080.7180.7380.7480.7530.5630.5660.5630.5570.7310.7580.6900.6900.6090.7330.7400.7630.7520.7550.7600.7530.7570.7560.5540.5580.5610.5530.6340.6400.6400.6140.5770.5980.5970.5840.5840.5750.5790.5710.5550.5810.5990.5809462OrganismalFitnessNCAP_I34A1MediumVirus
NKX31_HUMAN_Tsuboyama_2023_2L9R0.8990.9090.9130.9140.9060.9130.8860.8940.8970.9000.8600.8850.8740.9010.8860.9140.9250.9210.9000.8850.9030.8970.8810.8780.8960.8700.8980.8570.8740.9020.8870.8810.8740.9030.8950.8720.9140.9060.9030.9100.9100.9120.8950.8980.8700.8850.9000.8740.9050.9050.9220.9220.9220.9220.9250.9270.9210.9270.9200.9260.8820.9092482StabilityNKX31_HUMANHighHuman
NPC1_HUMAN_Erwood_2022_HEK293T0.9320.9420.9270.9390.9400.9370.6350.8890.9560.9550.9250.7630.7940.6850.7210.8580.9290.9320.9150.6120.6870.8380.7710.8090.7890.7980.9460.8930.9040.9470.9400.9060.6690.7160.9090.9230.9080.9460.9390.9270.9530.9470.7170.6070.9350.8060.7830.9450.5760.7160.9270.9250.9200.9050.9020.9060.9180.9120.9390.9210.9160.719637ActivityNPC1_HUMANLowHuman
NPC1_HUMAN_Erwood_2022_RPE10.9660.9430.9440.9690.9430.9690.9510.9600.9820.9850.9610.6910.6780.5890.7170.7240.7410.8470.9780.9200.9010.9760.9570.9550.8600.8570.9740.9580.8960.9680.9550.9370.8870.9190.9070.9040.9690.9710.9600.9710.9700.9440.8060.9200.9360.5650.9450.9260.7760.8370.7010.8460.6710.7980.7200.7140.7320.9430.9500.7410.9390.77863ActivityNPC1_HUMANLowHuman
NRAM_I33A0_Jiang_20160.8790.9470.9330.9330.9520.9530.6060.9190.9570.9630.5750.6840.9220.5680.5390.5640.7000.9140.9170.8720.9500.9420.9240.9120.5370.8980.8950.8890.9450.9440.8310.8410.4820.9410.9300.9460.9140.9230.9340.9550.9530.9530.5350.5680.5490.5680.7290.7660.7500.6440.6580.6880.7130.7170.7480.7730.7030.7640.6410.7570.6490.561298OrganismalFitnessNRAM_I33A0LowVirus
NUD15_HUMAN_Suiter_20200.6930.8200.8170.8000.8200.8220.5220.6990.8080.8410.8210.8060.8360.6150.6370.6580.7700.8040.8060.7470.6160.6970.8000.8230.6740.8510.8190.8120.8360.8290.8480.8180.5730.6420.7130.8460.7160.7450.8410.8180.8240.8410.6660.5450.8110.6850.6850.8230.7100.7280.7530.7590.7380.7640.7540.7740.7790.7800.7720.7770.8260.7422844ExpressionNUD15_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL0.6560.8500.8170.8110.8130.8260.6440.7500.8480.8310.7530.4770.5150.6360.5920.5900.6510.6740.6830.8100.7960.8320.8440.8590.5690.7900.5990.8660.8620.8240.8370.8380.5340.7280.6210.6850.7040.6340.6890.8330.8080.8360.6710.7230.6660.6720.8990.8360.8590.8770.8320.8690.8510.8700.8650.8630.8580.8430.8560.8600.8420.6722028StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI60.8690.8690.8800.8860.8840.8850.7630.8430.9010.8890.8400.8600.8920.7470.8620.8850.8770.8710.8640.8580.8410.8620.8490.8480.8740.8600.8620.8500.8470.8800.8640.8320.8160.8420.8080.8580.8760.8710.8810.8760.8780.8850.8610.7080.8680.8970.8990.8830.9170.8950.8740.8940.8880.8770.8800.8820.8910.8970.8880.8900.8720.8991380StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C0.8150.9010.9220.9270.9240.9220.6190.8100.8960.8960.8870.8750.8820.6270.8630.8930.9130.9030.8940.9020.8780.8670.8670.8600.8730.8670.8800.8600.8500.8970.9030.8950.7880.6240.6740.8110.8750.8760.8910.9220.9180.9210.8320.6190.8430.8530.9060.8470.9340.9280.9260.9290.9330.9310.9320.9270.9200.9220.9250.9350.9340.8823197StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G0.8330.8320.8510.8520.8150.8320.8310.8240.8430.8450.8570.8080.7900.8250.8170.8620.8810.8330.8570.8640.8470.8450.8640.8350.8210.8430.8650.8520.8520.8650.8380.8300.8240.8170.8160.8230.8530.8200.8260.8510.8250.8300.8550.8220.8670.8690.8810.8910.8830.9110.8660.8630.8660.8760.8720.8670.8730.8710.8620.8710.8800.8361134StabilityODP2_GEOSEHighProkaryote
OPSD_HUMAN_Wan_20190.5850.7040.7570.7730.7810.7800.4390.7700.6270.8000.5270.6700.7030.5510.6460.5330.6020.6480.5740.6860.7490.7200.6430.6470.7290.6600.6740.7190.6590.7630.6390.4680.1660.6820.6510.5380.7580.6860.6860.7790.7570.7700.5060.3170.6650.6220.6150.6970.5410.3830.5360.4720.4490.4720.4710.5120.5270.5590.5220.5160.7640.677165ExpressionOPSD_HUMANHighHuman
OTC_HUMAN_Lo_20230.6690.8030.7720.7740.7800.7890.4000.5510.7750.7970.7290.7680.7780.4230.5730.6260.6950.6760.7000.7210.6490.6870.7460.7780.6590.7640.7570.7560.7790.7920.7630.7070.4740.6300.7360.7710.6850.7390.7780.7880.7830.7960.5090.3840.6750.5810.7510.7360.8090.6370.7020.7170.7390.7130.7100.7070.7060.7390.6930.7150.8070.6771570ActivityOTC_HUMANMediumHuman
OTU7A_HUMAN_Tsuboyama_2023_2L2D0.6100.7550.6320.6520.6630.6650.5830.7070.5950.5990.8380.7960.7970.5810.8060.8060.7270.7530.8040.6400.5770.7150.7460.7560.7290.7340.7660.7420.6920.7480.7640.7210.7340.6070.6740.7500.6280.6380.7500.6660.6550.7070.6510.6030.7800.8270.8210.7920.8400.8070.7590.7690.7710.7660.7720.7590.7670.7300.7380.7620.8690.844635StabilityOTU7A_HUMANHighHuman
OXDA_RHOTO_Vanella_2023_activity0.4530.5850.5900.5950.5970.6060.4450.4510.4810.4940.5470.5180.5280.4350.4500.5200.5420.5850.5900.4940.4380.4750.5040.4880.4580.5020.5340.5080.5770.6070.5940.5720.3910.4600.4670.4770.4920.4900.4890.5970.6000.5950.4470.4210.5360.4780.5410.5850.5850.4990.5490.5690.5620.5630.5540.5770.5630.5690.5460.5740.5750.4906396ActivityOXDA_RHOTOHighEukaryote
OXDA_RHOTO_Vanella_2023_expression0.6640.7370.7300.7320.7380.7460.6800.6730.6940.6930.7270.7080.7180.6660.6860.7160.7280.7440.7500.6500.6420.6960.7110.6930.6760.7160.7150.7000.7300.7460.7270.7180.6360.6750.6740.6870.6910.6830.6880.7390.7410.7430.6670.6570.7220.6880.7480.7550.7260.6300.7390.7300.7290.7260.7280.7360.7280.7310.7270.7350.7460.7106769ExpressionOXDA_RHOTOHighEukaryote
P53_HUMAN_Giacomelli_2018_Null_Etoposide0.8280.8330.8300.8290.8280.8270.7460.8290.8290.8260.8410.8330.8470.7410.7380.7960.8250.8300.8410.8220.7970.8130.8360.8140.8120.8460.8610.8400.8380.8320.8490.8370.7300.7700.8150.8210.8270.8360.8260.8270.8320.8300.7460.7270.8300.7520.8150.8390.8120.8110.8360.8350.8300.8270.8330.8380.8270.8330.8290.8320.8310.7747467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_Null_Nutlin0.5920.5970.5890.5900.5890.5900.5390.6020.5960.5970.6470.6170.6460.5290.5190.5730.5980.6020.6190.6090.5750.6120.6570.6140.5940.6540.7010.6830.6210.6150.6540.6230.5480.5520.6160.5970.5980.6140.5920.5900.5970.5920.5400.5220.6220.5320.6200.6390.6240.6000.6280.6190.6150.6040.6110.6120.6060.6150.6130.6110.6180.5657467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_WT_Nutlin0.6880.6970.6980.6990.6910.6890.6070.6910.6910.6910.7280.6980.7230.6260.6340.6750.6770.6920.6950.6510.6630.7080.7290.7110.6900.7260.7440.7450.7190.6990.7150.6890.5720.6230.7190.7210.6840.7090.7080.6900.7000.7030.6210.6010.7050.6230.6920.7270.6940.6870.6980.7060.7030.7040.6980.7030.6890.6880.6810.6980.7000.6567467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Kotler_20180.8590.8410.8430.8390.8170.8210.5870.8290.8350.8390.8380.8250.8350.6230.6100.7730.8170.8290.8310.8040.7760.7980.8030.8100.7930.8110.8040.8040.7870.8510.8210.8130.5660.7270.8020.7880.8630.8550.8550.8330.8440.8400.6220.5880.8370.6370.7130.8320.7640.7440.7950.8270.8120.8110.8080.8140.8010.8080.8060.8070.8510.7281048OrganismalFitnessP53_HUMANLowHuman
P84126_THETH_Chan_20170.7490.8610.9050.9050.8740.8760.6420.8550.9060.8790.8910.8560.8570.7210.8600.8640.9010.8890.9050.8970.7730.8230.8270.8600.7960.8580.9080.8550.9450.8030.9050.8770.7820.8010.7550.8370.7750.7440.7790.8470.8440.8600.7570.5820.8240.8280.7480.9020.8930.6980.8610.8780.8560.8630.8880.8920.8860.8860.8990.8910.8960.8951519OrganismalFitnessP84126_THETHMediumProkaryote
PA_I34A1_Wu_20150.4630.4600.4640.4530.4570.4530.2280.4290.2450.2580.2310.2360.2450.2200.2180.2200.2150.2150.4010.3910.4170.4220.4100.4550.2410.3870.4070.3810.4090.4310.3960.4170.2660.4130.4350.4390.4600.4540.4490.4550.4590.4510.2260.2230.2300.2320.2650.2570.2050.2730.2090.2090.2220.2120.2150.2250.2110.2160.2150.2290.2540.2131820OrganismalFitnessPA_I34A1MediumVirus
PABP_YEAST_Melamed_20130.7550.7450.7350.7380.7640.7600.6030.7340.7530.7640.7590.7650.7640.6090.6340.6710.7220.7170.7390.7270.7310.7530.7540.7610.7410.7560.7640.7590.7450.7760.7670.7440.5240.7200.7400.7410.7450.7570.7510.7600.7660.7630.7000.4470.7590.7440.4740.7500.6810.5320.7090.7010.7110.7090.7090.7090.7120.7160.7180.7130.7500.68437708OrganismalFitnessPABP_YEASTMediumEukaryote
PAI1_HUMAN_Huttinger_20210.7970.8300.8230.8260.8230.8230.6940.7750.8070.8230.8100.7970.8090.6960.8000.8060.8120.8160.8030.8110.7010.7960.7980.8060.7930.8130.8130.8140.8120.8160.8050.7920.7210.7350.8040.8100.7950.8080.8110.8280.8270.8290.7840.6880.8050.8020.7810.8310.8140.7510.8150.8080.8130.8120.8130.8160.8120.8120.8140.8170.8250.7985345ActivityPAI1_HUMANNaNHuman
PHOT_CHLRE_Chen_20230.7070.8090.8900.8820.7810.7800.8430.8350.8860.8830.8510.8770.8890.8900.9030.8850.8970.8820.8890.8270.8450.8750.8480.8720.8360.8590.8710.8680.8330.8270.8410.8100.7560.7980.7820.8670.8390.8240.8660.7880.7980.7910.8750.7830.8720.8840.7030.8480.8350.7350.8380.8150.8160.8190.8190.8270.8260.8280.8300.8260.8820.883167529ActivityPHOT_CHLREHighEukaryote
PIN1_HUMAN_Tsuboyama_2023_1I6C0.8730.8680.9000.8980.8890.8960.8470.8210.8750.8960.8880.8480.8900.8620.8780.8920.8950.8190.8060.8950.7520.8850.8900.8710.8260.8590.8830.8780.8640.8830.8890.8630.8690.8120.8700.8910.8690.8940.8990.8900.9000.9050.8510.6440.9070.8390.8890.8970.8890.8680.8840.8950.8800.8690.8650.8900.8790.8880.8870.8880.9030.901802StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M0.8880.8640.8680.8670.8740.8700.8630.8040.8730.8990.8120.8500.8500.8600.8870.8970.9000.8850.8630.8670.8260.8120.8260.8220.8510.8380.8410.8200.8200.8580.8280.8220.8060.8410.8590.7970.8600.8740.8360.8660.8770.8610.8580.8380.7910.8520.8320.7730.8980.8570.8920.8940.9040.9000.9060.8910.8980.8790.8930.8970.8070.8881824StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF0.8660.8880.8440.8350.8460.8660.8640.8210.8030.8070.8670.8600.8710.8570.8590.8940.8630.8660.8650.8620.8810.8900.8750.8750.8870.8720.8810.9020.8640.8290.8700.8650.7830.8740.8980.9040.8720.8820.8730.8770.8700.8590.8480.8530.8710.8430.8870.8720.8760.8940.8590.8610.8720.8680.8630.8610.8620.8680.8650.8660.8900.8771301StabilityPKN1_HUMANHighHuman
POLG_CXB3N_Mattenberger_20210.7680.8170.7980.8160.8210.8240.5740.7330.8150.8210.6580.5810.5960.5830.5770.6240.7050.7270.7540.7570.7380.7880.7950.7900.6320.7640.7720.7650.8010.8200.7590.7500.5980.5990.7180.7730.7560.7750.7920.7930.8090.8160.5800.5760.6820.5850.6270.6920.5960.6180.6830.6860.6880.6870.6840.6780.6910.6900.6900.6900.5970.58815711OrganismalFitnessPOLG_CXB3NMediumVirus
POLG_DEN26_Suphatrakul_20230.7730.8290.7670.7810.8130.8160.4720.7610.8960.8980.5680.4600.4740.4500.4590.4870.5040.5470.5950.7840.7610.7610.7630.7150.7700.7830.8000.7980.8100.8700.8540.7940.5120.4550.5710.7950.7500.7760.8340.8260.8300.8560.4670.4480.6290.4640.6030.7270.4530.5550.5230.5260.5600.5340.5340.5390.5290.5360.5280.5480.4980.49516897OrganismalFitnessPOLG_DEN26LowVirus
POLG_HCVJF_Qi_20140.6790.7080.6860.6920.7050.7120.1860.2840.7030.7000.2830.6450.6430.1860.2020.1960.2290.2120.2370.4840.4000.5560.6310.5960.5420.5880.4670.5000.6150.7320.6010.4980.2860.6460.6540.6190.6670.6670.6690.6820.6680.6720.1970.1770.5960.2280.1950.4350.5170.4060.3350.4000.2840.4080.3300.2990.4130.2980.2990.3620.2760.2481630OrganismalFitnessPOLG_HCVJFMediumVirus
POLG_PESV_Tsuboyama_2023_2MXD0.5820.8670.8060.8160.8250.8280.5440.8150.7030.8450.8180.5810.5890.5840.5900.5670.6130.6060.5890.8910.5180.5000.4960.5470.5420.5120.5260.4620.5770.8760.8910.8950.5170.5290.5060.5530.7040.7060.6980.8100.8130.8050.6480.5950.6430.6230.8450.8270.9020.8840.8960.8980.8870.9050.9040.9170.9030.9220.9040.9130.8830.7845130StabilityPOLG_PESVMediumVirus
PPARG_HUMAN_Majithia_20160.7850.8630.8720.8660.8720.8670.7730.8750.8650.8770.8610.8790.8880.7410.7660.7800.8070.8550.8890.8820.8680.9020.8630.8660.8420.9160.9170.9170.8610.8970.8490.8000.7830.8840.9040.9050.8710.9050.9000.8740.8890.8850.7570.7090.8730.7780.8300.8880.8480.7930.8250.8320.8220.8160.8310.8210.8250.8190.8100.8210.8410.7899576ActivityPPARG_HUMANMediumHuman
PPM1D_HUMAN_Miller_20220.8050.8070.8070.8090.8070.8070.7280.7740.8100.8070.8080.8170.8150.7440.7540.7840.8040.8140.8130.8010.7760.7830.8180.8120.8010.8180.8150.8180.8130.8100.8070.7870.7350.7780.8030.8200.8070.8120.8150.8080.8090.8090.7660.7170.8070.7900.7920.8170.8040.7720.8010.8000.7980.7990.7980.7980.7960.7940.8000.8000.8190.7837889OrganismalFitnessPPM1D_HUMANLowHuman
PR40A_HUMAN_Tsuboyama_2023_1UZC0.8570.8720.9160.9130.9070.9100.7600.8540.9170.9220.9240.8050.8470.7650.7710.8940.9170.9150.8980.9010.8640.9110.9180.9020.8950.9110.9170.9170.9050.9010.9030.8780.8110.8140.8180.8580.8840.8880.8950.9080.9140.9090.7840.7660.8840.7800.8990.8620.9170.9010.9190.9240.9240.9220.9200.9230.9230.9220.9190.9260.9200.9062033StabilityPR40A_HUMANMediumHuman
PRKN_HUMAN_Clausen_20230.8630.8700.8760.8780.8740.8740.6520.8230.8670.8700.8740.8610.8780.6830.6970.7200.7640.8600.8770.8520.7080.8550.8770.8710.7670.8760.8710.8810.8620.8740.8580.8280.6270.6960.8500.8730.8570.8710.8730.8730.8750.8740.7110.5840.8810.7170.8550.8730.8680.7730.7740.8030.8150.8210.7960.7910.8000.7990.7990.8040.8680.7878756ExpressionPRKN_HUMANLowHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE0.7670.7590.7260.7310.7530.7540.6660.7090.7220.7200.8630.7960.8010.6810.7100.8230.8390.8060.8030.7740.6610.6270.7220.7350.7340.7540.5110.7410.7620.7370.7760.7270.6580.6570.7150.7420.7730.7640.7590.7550.7520.7650.6910.6590.8130.6730.8380.8110.8440.8210.8410.8250.8220.8230.8290.8210.8300.8350.8250.8350.8060.8151579StabilityPSAE_PICP2MediumProkaryote
PTEN_HUMAN_Matreyek_20210.7660.7900.7860.7800.7790.7830.6860.7720.7910.7990.7890.7910.8050.6710.7320.7750.7960.7640.7670.7970.6920.8070.7990.8010.7270.7830.7850.7990.7790.8120.7830.7790.7010.7300.8060.7910.7650.8060.8010.7880.7980.7970.7010.6720.7990.7510.7690.7970.7800.7540.7950.7900.7950.7980.7910.7900.7940.8010.7990.7980.7850.7695083ExpressionPTEN_HUMANMediumHuman
PTEN_HUMAN_Mighell_20180.8580.8560.8580.8640.8650.8630.7550.8420.8550.8540.8560.8570.8640.7810.8310.8550.8510.8470.8500.8590.8060.8410.8550.8410.8300.8450.8450.8360.8480.8530.8500.8430.7180.8260.8530.8430.8540.8550.8560.8610.8610.8600.8220.7490.8500.8520.8330.8510.8510.8240.8550.8530.8520.8530.8550.8550.8520.8530.8480.8510.8530.8427260ActivityPTEN_HUMANMediumHuman
Q2N0S5_9HIV1_Haddox_20180.8690.8490.8300.8400.8560.8580.5970.8220.8700.8700.8470.8670.8720.6230.6180.6080.6350.6520.7050.8430.8690.8290.8180.8030.8700.8280.8220.8310.8240.8720.8330.8300.7370.8650.8370.8400.8690.8580.8630.8650.8570.8580.7830.5800.8680.8580.7230.8660.6410.7120.6760.6900.7160.6970.6930.6980.6950.6920.6800.6980.6820.64912729OrganismalFitnessQ2N0S5_9HIV1MediumVirus
Q53Z42_HUMAN_McShan_2019_binding-TAPBPR0.7910.8060.8070.8070.8070.8060.7070.7640.8080.8090.8030.8060.8100.7050.7170.7840.7920.8080.8030.8040.7840.7680.7700.7680.8110.7980.7960.7980.7790.7750.7830.7680.7370.7730.7660.7630.7890.7810.7750.8040.8020.7960.7870.6930.8060.7940.7860.8060.7700.7770.7840.7910.8070.7930.8040.8000.8040.8050.7890.7990.8180.8143344BindingQ53Z42_HUMANMediumHuman
Q53Z42_HUMAN_McShan_2019_expression0.7740.7780.7850.7840.7890.7860.4830.7350.7830.7920.7750.7590.7760.5320.5590.7240.7620.8030.8020.7980.7360.7490.7450.7410.7830.7890.7840.7860.7910.7600.7690.7540.6190.7630.7420.7400.7960.7830.7770.7910.7930.7910.6730.5170.7830.7450.7210.7860.7190.6440.7490.7670.7900.7770.7850.7940.7950.7960.7750.7820.7810.8273344ExpressionQ53Z42_HUMANMediumHuman
Q59976_STRSQ_Romero_20150.8450.8770.8730.8720.8800.8750.7570.8130.8800.8800.8570.8140.8290.7280.7980.8260.8420.8500.8540.8790.8620.8830.8860.8870.8680.8880.8920.8850.8830.8790.8680.8510.7680.8600.8850.8810.8610.8770.8790.8760.8770.8790.8130.7140.8610.8330.8290.8620.8600.7720.8500.8440.8570.8480.8520.8410.8540.8470.8450.8550.8610.8342999ActivityQ59976_STRSQMediumProkaryote
Q6WV13_9MAXI_Somermeyer_20220.6580.7240.6190.6230.6600.6580.4290.5260.7310.7280.6220.4870.4730.4650.4840.4650.4580.4710.4410.6700.4430.4960.4640.5060.4450.4410.4640.4480.4480.7280.6980.7010.4820.4840.4620.4990.6300.6240.6310.6580.6570.6590.5450.5150.5340.5330.6600.6050.6430.5950.5850.5940.6040.6590.6450.6480.6410.6380.6380.6350.5240.51331401ActivityQ6WV12_9MAXILowEukaryote
Q837P4_ENTFA_Meier_20230.8980.9210.9180.9300.9280.9420.8380.8910.9230.9270.9380.9200.9400.8730.8850.9010.9260.9200.9260.8070.9300.9340.9310.9360.9220.9320.9330.9390.9370.9270.9230.9060.8590.9280.9370.9370.9260.9380.9310.9400.9400.9390.8960.8820.9140.8970.8620.9250.8860.8470.9340.9310.9280.9360.9330.9300.9350.9270.9250.9400.9480.888697ActivityQ837P4_ENTFAMediumProkaryote
Q837P5_ENTFA_Meier_20230.8130.9190.9150.9120.9180.9270.8670.8830.7920.8130.8610.8670.8620.7670.8420.8390.8440.8820.8730.8500.8580.8920.9240.9180.8360.8970.9110.9170.9350.8970.8880.9080.8030.8560.8950.9160.8610.8860.9110.9170.9190.9180.8520.7770.8770.8480.8400.8940.9030.7960.8640.8590.8500.8420.8540.8310.8600.8620.8420.8570.8360.846747ActivityQ837P5_ENTFAMediumProkaryote
Q8WTC7_9CNID_Somermeyer_20220.5920.6840.5980.5980.6460.6450.4810.6660.6650.6700.5940.4870.4840.4780.4700.4790.4840.4920.5080.6120.5000.4990.5110.4990.4790.4780.4950.6330.6400.6850.6560.6610.4770.4640.4780.6750.5840.5920.6730.6410.6490.6850.5350.5380.5370.5350.6040.6060.6790.6050.5940.5790.5980.5990.5910.6090.5980.5980.5950.5970.5390.45033510ActivityQ8WTC7_9CNIDLowEukaryote
R1AB_SARS2_Flynn_20220.9280.9310.9160.9140.9430.9460.5960.8890.6210.6210.6690.6260.6090.6250.6470.6480.6370.8890.9370.8900.8380.8390.8590.8540.8450.8080.8280.8280.8180.9350.8970.8760.6020.7690.8280.7920.9120.9220.9130.9360.9450.9390.6230.5830.6800.6230.8240.8430.8710.7920.7360.7500.7710.7780.7870.7580.7630.7490.7500.7780.7210.7185725OrganismalFitnessR1AB_SARS2MediumVirus
RAD_ANTMA_Tsuboyama_2023_2CJJ0.7970.7540.7520.7540.7600.7690.7390.7620.7950.8070.7970.7160.7610.7770.8250.8350.8060.8220.7500.8060.8560.8000.7710.7860.8080.7790.7580.7730.7560.8090.7480.7110.6490.7720.8000.7720.8190.8130.7940.7840.7880.7950.8020.7170.7310.7860.8050.6970.8310.8210.7890.8200.8040.8080.8210.8020.8000.8070.8120.8100.7700.833912StabilityRAD_ANTMAHighEukaryote
RAF1_HUMAN_Zinkus-Boltz_20190.7490.7850.7750.7830.7880.7890.7040.8200.7820.7890.7690.8050.8060.6880.7130.7950.7630.7860.8010.7880.8060.7920.7930.7960.7940.8150.8140.8010.7990.8020.7730.7920.7380.8050.8040.7910.7910.7880.7870.7970.7840.7880.7260.7120.7470.8040.7150.7490.7040.7350.7640.7630.7730.7510.7450.7590.7630.7420.7300.7530.7600.658297OrganismalFitnessRAF1_HUMANLowHuman
RASH_HUMAN_Bandaru_20170.7300.7170.7290.7230.7180.7200.5340.6540.7090.7160.7140.7070.7040.7230.7460.7410.7260.7230.7100.7400.7310.7200.7100.7070.7230.7040.7010.7010.6860.7110.7070.7050.6250.7300.7220.7050.7430.7330.7260.7220.7210.7220.7330.6850.7120.7310.6740.6520.7230.6580.7130.7210.7120.7170.7120.7180.7160.7140.7080.7160.7150.7293134ActivityRASH_HUMANHighHuman
RASK_HUMAN_Weng_2022_abundance0.7430.7240.7380.7360.7350.7400.6960.6960.7310.7450.7240.7180.7220.7630.7520.7560.7360.7310.7260.7330.6910.7370.7360.7450.7210.7480.7510.7410.7500.7120.7250.7110.6660.6840.7320.7440.6960.7370.7570.7350.7430.7460.7310.7410.6870.7180.7270.7200.7630.7260.7380.7330.7350.7360.7400.7330.7410.7350.7360.7390.7370.74626012ExpressionRASK_HUMANHighHuman
RASK_HUMAN_Weng_2022_binding-DARPin_K550.8020.9090.9170.9180.9160.9150.6190.8090.9230.9310.8970.9030.9080.8930.9180.9190.9250.9200.8830.9270.9180.9110.9070.8930.9260.9150.8980.8980.8730.8850.9030.8980.8050.9170.9150.8900.9140.9090.8880.9190.9180.9160.9050.7720.8910.9080.7820.8080.9160.7820.9000.8880.8880.8930.8960.8930.8940.8970.8960.8970.8950.90124873BindingRASK_HUMANHighHuman
RBP1_HUMAN_Tsuboyama_2023_2KWH0.8240.7910.8190.8280.8220.8280.8290.7520.7870.8130.8600.8380.8480.8410.8430.8580.8700.8090.8190.8260.8380.7790.7860.7830.8390.7350.7530.7890.7980.8070.8190.7950.7160.8250.8290.8330.8300.8260.8280.8260.8270.8240.8540.8450.8530.8480.8520.8620.8630.8350.8690.8580.8590.8570.8660.8640.8600.8560.8610.8640.8490.8421332StabilityRBP1_HUMANHighHuman
RCD1_ARATH_Tsuboyama_2023_5OAO0.7870.7650.7840.7840.7820.7820.7040.7140.7590.7670.8150.7750.7930.7600.7990.8090.8220.7820.8150.7790.7280.6170.7270.7590.7150.7760.7710.7460.7940.7570.7650.7250.4820.7450.7500.7690.7990.8050.7940.7970.7910.7840.7750.7790.8050.7770.8140.8360.8210.8140.8140.8120.8080.8060.8060.8070.8130.8050.8030.8130.8060.8421261StabilityRCD1_ARATHMediumEukaryote
RCRO_LAMBD_Tsuboyama_2023_1ORC0.7770.8680.8990.9030.9000.9090.6670.6760.9000.9100.8740.8140.8780.7500.8120.8460.8740.8570.8660.8660.7090.7520.7500.7350.6140.7810.5910.7180.8770.8710.9020.8890.6350.6650.6110.8400.7890.7840.8710.9050.9010.9010.7860.7830.8560.7760.9540.9330.9410.9290.9130.9130.9250.9300.9240.9200.9200.9210.9220.9240.9280.7242278StabilityRCRO_LAMBDHighVirus
RD23A_HUMAN_Tsuboyama_2023_1IFY0.8970.8710.8970.8940.8980.8950.7830.8550.8390.8880.8610.8550.8720.7920.8950.8570.8620.8720.8550.8830.8450.8790.8590.8740.8830.8850.8810.8860.8730.8730.8490.8280.8650.7730.8570.8510.8840.8960.8860.9040.9050.8970.8340.7090.8780.8540.8860.9000.9080.9110.8740.8760.8790.8790.8850.8800.8790.8810.8730.8810.8920.8881019StabilityRD23A_HUMANHighHuman
RDRP_I33A0_Li_20230.6160.6870.7790.7820.7980.7970.4940.7590.7540.7550.5350.5160.5210.5130.5130.5340.6300.7260.7630.8100.7440.7630.7800.7870.5610.7570.7460.7400.7810.8200.7640.7470.5390.7600.7750.7930.7680.7810.7870.8080.8110.8110.5180.5180.5620.5260.5640.5960.5180.5500.6460.6380.6470.6380.6410.6390.6410.6440.6380.6430.5390.52312003OrganismalFitnessRDRP_I33A0LowVirus
REV_HV1H2_Fernandes_20160.7030.6940.7100.7060.7090.7050.5900.7030.7000.7020.6020.6950.6950.6100.6060.6270.7040.7130.7030.6880.6870.6950.6860.7100.6900.7150.6350.6930.7030.7050.7010.7060.5810.6990.7030.6960.7010.7050.6920.7050.7020.7040.6040.5940.6780.6070.6700.6570.6870.6930.6950.6960.6970.7030.7060.6990.7000.6940.7050.7010.6680.6822147OrganismalFitnessREV_HV1H2MediumVirus
RFAH_ECOLI_Tsuboyama_2023_2LCL0.6310.6410.6330.6250.6200.6260.5880.5180.5830.6600.7190.6510.6870.5880.6200.6360.7110.6210.5760.5750.5490.6220.5800.5780.6580.6290.6390.5980.5540.5650.5980.5370.4210.5950.5800.5700.6180.5550.6180.6410.6020.6290.6330.5880.6930.6360.6810.6930.7650.6970.6930.6980.7070.6990.6820.6890.7220.6980.7210.7120.7290.6351326StabilityRFAH_ECOLIHighProkaryote
RL20_AQUAE_Tsuboyama_2023_1GYZ0.8920.9440.9390.9340.9350.9380.8010.9280.8920.8790.9410.9480.9470.8180.8650.9220.9390.9350.9310.9350.8910.9240.9130.9010.9280.9020.9170.9350.9160.9030.9360.9350.7560.9130.9360.8980.9200.9350.9130.9370.9410.9320.8960.8230.9360.9280.9560.9410.9530.9480.9440.9460.9470.9470.9420.9440.9400.9440.9430.9440.9450.9451461StabilityRL20_AQUAEHighProkaryote
RL40A_YEAST_Mavor_20160.8080.8450.8480.8630.8550.8550.5260.8290.8750.8760.7880.8300.8590.6740.7750.8090.8530.8630.8610.8550.8500.8520.8490.8580.8690.8550.8580.8560.8640.8540.8550.8610.6400.8440.8340.8400.8500.8570.8540.8740.8770.8680.8320.6270.8340.8260.5270.7030.6700.5420.8560.8700.8580.8450.8670.8540.8680.8680.8650.8640.8680.7731253OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20130.9260.9390.9400.9510.9410.9440.7180.9270.9550.9550.9120.9290.9530.8210.9050.8910.9520.9440.9520.9520.9400.9460.9500.9490.9590.9470.9520.9480.9610.9540.9590.9600.8320.9330.9470.9370.9420.9550.9470.9580.9610.9540.9440.7920.9400.9340.7150.8570.8430.7280.9450.9600.9510.9470.9550.9470.9530.9530.9510.9490.9560.9121195OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20140.9520.9420.9500.9540.9500.9500.8700.9470.9410.9550.9270.9410.9480.9020.9530.8940.9610.9480.9570.9530.9560.9580.9570.9560.9600.9510.9510.9550.9590.9590.9520.9550.9370.9560.9540.9410.9520.9540.9490.9520.9570.9540.9460.8990.9490.9400.8580.9320.8930.8910.9640.9640.9650.9670.9660.9660.9650.9650.9650.9660.9540.9191380ActivityRL40A_YEASTMediumEukaryote
RNC_ECOLI_Weeks_20230.9680.9690.9380.9430.9380.9560.8650.9250.9670.9620.9690.9670.9670.8900.9640.9670.9670.9690.9720.9390.9700.9690.9660.9690.9700.9660.9700.9660.9690.9380.9690.9690.8530.9650.9660.9650.9650.9660.9670.9610.9620.9620.9660.8820.9670.9650.9130.9690.8820.8930.9680.9660.9650.9680.9660.9690.9660.9650.9660.9670.9690.9624277ActivityRNC_ECOLIMediumProkaryote
RPC1_BP434_Tsuboyama_2023_1R690.8480.8840.8740.8790.8730.8840.8340.8350.8230.8170.8850.8760.8790.8530.8600.8680.8790.8940.8880.8910.8230.8880.8820.8860.8490.8910.8650.8870.8930.8720.8830.8390.8560.7770.8650.8630.8220.8660.8530.8860.8880.8820.8480.7960.8270.8250.9010.8010.9030.8870.8570.8630.8690.8770.8740.8670.8770.8660.8660.8720.8850.8501459StabilityRPC1_BP434HighVirus
RPC1_LAMBD_Li_2019_high-expression0.8500.9110.9100.9130.9150.9130.8510.8970.9040.9210.9100.9220.9240.8550.8790.9100.9260.9120.9110.9280.7940.8720.9000.8980.8140.8920.8890.8200.9140.8890.8890.8850.7460.8070.9080.9050.8660.9230.9250.9150.9140.9170.8470.9010.8860.8850.6650.9050.8640.8060.9270.9270.9310.9260.9260.9280.9290.9270.9290.9330.9260.872351ActivityRPC1_LAMBDHighVirus
RPC1_LAMBD_Li_2019_low-expression0.6900.8730.8480.8550.8430.8560.7390.8740.8080.8270.8390.7580.8290.7040.7560.7540.7940.8440.8580.8170.5980.7330.7140.7960.6370.7400.7360.6800.9020.8540.8890.8830.6410.6680.7410.8600.7120.7510.8270.8400.8430.8640.6800.7260.7530.7420.6280.7190.7900.6230.7910.7970.7790.7950.8050.8100.8090.7700.8010.7910.8060.699351ActivityRPC1_LAMBDHighVirus
RS15_GEOSE_Tsuboyama_2023_1A320.8790.8650.8670.8670.8670.8690.7990.8370.8670.8660.8600.8590.8570.8130.8310.8840.8640.8680.8610.8560.8600.8670.8490.8490.8600.8520.8840.8620.8600.8600.8580.8490.8230.8750.8620.8520.8810.8700.8650.8760.8680.8640.8310.8200.8780.8580.9000.8870.8880.8930.8660.8630.8660.8700.8660.8560.8650.8650.8720.8680.8660.9151195StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance0.8620.9000.9050.9100.9060.9060.8230.8580.9010.9040.8960.9090.9110.8460.8510.8800.9040.9070.9020.7680.8800.9070.9010.8960.8910.9080.9070.9050.8960.9010.9090.8780.8070.8800.9050.9030.8960.9100.9090.9100.9080.9090.8530.7890.8960.8630.8750.9110.8780.8230.9030.9000.9070.9100.9070.9080.9060.9080.9070.9100.9010.8769803ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity0.7390.8030.8110.8140.8120.8150.6820.7550.8070.8070.8000.8180.8160.7180.7100.7470.8030.8110.8040.6350.7810.8100.8020.7980.7960.8100.8110.8100.7960.8070.8150.7900.7010.7770.8090.8080.7860.8140.8130.8180.8170.8180.7110.6360.8010.7260.7730.8240.7680.6870.8080.8010.8090.8120.8060.8110.8070.8120.8120.8120.7890.75110094ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB0.8740.8330.8420.8630.8540.8690.7660.8080.8440.8570.8160.8290.8260.6590.8470.8660.8050.8420.7700.7650.7840.8190.8170.8220.7810.8290.8160.8180.8160.8040.8130.7560.7600.7490.7980.8010.8360.8420.8490.8530.8730.8630.8240.6710.8540.8290.8270.8690.8420.8690.8340.7720.8300.8070.8370.8310.8090.8240.8240.8270.7960.893965StabilitySAV1_MOUSEHighEukaryote
SBI_STAAM_Tsuboyama_2023_2JVG0.7610.7730.7950.7940.7880.7790.6480.6860.8000.8230.7220.6760.6790.6240.6550.7110.8260.8420.7900.7540.6470.7450.7700.7270.6710.6450.6880.6560.7540.7770.8310.7840.6220.5960.6180.6200.7280.7440.7470.7510.7770.7780.6590.6570.6440.7020.8370.8030.8430.8460.6850.7050.7090.7200.7260.6910.7460.7700.8130.7430.8510.8011025StabilitySBI_STAAMMediumProkaryote
SC6A4_HUMAN_Young_20210.8680.8880.8820.8830.8870.8860.8290.9010.8850.8890.8900.8720.8810.7830.8170.8560.8680.8710.8700.8910.8880.9050.9040.9000.8870.9040.9030.9020.9020.8900.8810.8500.8540.8920.9020.9010.8840.8920.8930.8880.8900.8880.8640.7810.8910.8710.8680.8930.8680.8230.8670.8750.8750.8760.8730.8750.8710.8730.8670.8740.8890.87011576ActivitySC6A4_HUMANMediumHuman
SCIN_STAAR_Tsuboyama_2023_2QFF0.7400.7660.7760.7790.7810.7880.7630.7220.7930.7840.7830.7820.7760.7380.7740.8000.8030.7930.8170.7590.7140.7490.7550.7550.7640.7670.7840.7820.7600.7580.7680.7460.6170.7120.6860.7460.7570.7580.7660.7800.7890.7930.7870.7690.7710.7830.7930.8510.8350.8030.8010.8000.8080.8180.8130.8360.8160.8350.8100.8230.8870.8511212StabilitySCIN_STAARHighProkaryote
SCN5A_HUMAN_Glazer_20190.7530.7280.7540.7540.7540.7540.7940.6030.7910.7980.7530.7310.7650.7700.7500.7630.7780.7700.7480.7600.7430.7240.7390.7230.7320.7480.7430.7140.7950.7390.7410.7380.6390.6750.6970.7580.7560.7840.8160.7700.7870.7720.7520.7140.8000.6930.6370.6570.7100.6060.7030.7340.7270.7410.7280.7360.7200.7050.7200.7260.7130.737224OrganismalFitnessSCN5A_HUMANMediumHuman
SDA_BACSU_Tsuboyama_2023_1PV00.9310.9270.9310.9310.9360.9360.5990.8770.9250.9340.9220.8990.9060.6880.7430.9140.9220.9350.9330.9170.5910.6370.6180.7600.6700.8860.5680.8060.9200.9400.9200.9000.5220.5930.7430.8860.9320.9300.9240.9350.9350.9330.7030.6540.9130.7280.9010.8980.9400.9350.9320.9320.9340.9390.9350.9370.9380.9360.9400.9400.9440.9452770StabilitySDA_BACSUMediumProkaryote
SERC_HUMAN_Xie_20230.8820.9030.8930.8980.8970.9030.6610.8920.8960.8980.8990.8980.9000.7640.8670.8900.8970.8920.8890.8990.9030.9000.9030.9040.9030.9010.9060.9050.8990.8990.9030.9010.8050.8980.9060.9040.8940.9040.9030.9030.9030.9020.8570.7370.8960.8790.8710.9030.7870.8500.8970.9010.8960.9010.9050.8970.8990.8970.8940.9020.9010.8801914OrganismalFitnessSERC_HUMANHighHuman
SHOC2_HUMAN_Kwon_20220.8200.8410.8360.8350.8390.8380.8020.8390.8500.8440.8300.8270.8300.8050.8060.8070.8260.8380.8340.8310.8100.8150.8450.8380.8070.8200.8210.8230.8440.8440.8450.8380.8060.8110.8220.8420.8180.8280.8400.8380.8360.8430.8090.8060.8400.8060.8200.8400.8140.8100.8320.8320.8360.8340.8370.8380.8320.8310.8260.8330.8050.80910972OrganismalFitnessSHOC2_HUMANMediumHuman
SOX30_HUMAN_Tsuboyama_2023_7JJK0.8300.8550.8350.8340.8410.8380.8210.8090.8180.8380.8450.8400.8470.8320.8400.8600.8550.8350.8390.8500.8350.8120.8290.8230.8420.8460.8390.8390.8380.8350.8160.8040.8290.7540.8230.8390.8290.8270.8420.8350.8390.8380.8460.7600.8370.8550.8450.8360.8590.8450.8490.8670.8660.8590.8550.8620.8480.8480.8460.8600.8530.8511010StabilitySOX30_HUMANHighHuman
SPA_STAAU_Tsuboyama_2023_1LP10.7770.8000.7920.7940.8050.8020.5450.7680.7250.7270.7390.5430.5400.5160.5470.5130.5900.6020.5880.7930.4820.5830.7430.6370.5480.5090.5670.7460.7860.7520.7380.7290.4930.5480.5180.6020.7790.7880.7790.8060.8160.8030.5660.5880.6380.6300.7710.7620.8180.8030.7930.7850.7870.8270.7630.7940.7780.8010.7850.8010.7550.7612105StabilitySPA_STAAUMediumProkaryote
SPG1_STRSG_Olson_20140.6520.6580.4750.4830.6580.6680.4390.4860.4550.6210.7080.6750.6630.7160.6830.6810.6960.6770.6970.6510.7110.6810.6790.6830.6910.6840.6870.6850.7300.6820.7350.7240.4890.6740.6550.6610.6580.6500.6610.6840.6720.6880.4330.4180.5550.4850.6590.6130.6790.5670.7260.6850.7040.7180.7150.6950.7240.7260.7150.7210.7040.715536962BindingSPG1_STRSGLowProkaryote
SPG1_STRSG_Wu_20160.1370.1720.1680.1900.1840.1860.1570.1510.2520.2390.2490.2610.2380.2320.2460.2600.2820.2970.3150.2360.1970.1860.1960.1990.1700.2140.2130.1980.1980.1520.3030.2850.1110.2030.1680.2180.1690.1590.1880.1960.1910.2040.1830.1370.2310.1410.2380.2430.3010.2030.3010.3020.3060.3160.2990.3000.3300.3090.3170.3090.3240.276149360BindingSPG1_STRSGMediumProkaryote
SPG2_STRSG_Tsuboyama_2023_5UBS0.7840.7920.7790.7710.7750.7740.7080.7780.8100.8240.7840.7880.7760.7200.7650.7530.7940.7960.7660.8040.7240.7780.6930.7020.6680.7310.7100.7780.7690.8260.8030.7870.4700.6520.7620.7990.7340.7610.7430.8040.8070.8100.7550.6560.7780.7270.8280.7870.8330.8170.7890.8000.8150.8180.8120.8120.8100.8040.7910.8130.8350.7551451StabilitySPG2_STRSGMediumProkaryote
SPIKE_SARS2_Starr_2020_binding0.8310.9090.8160.8650.8980.8960.7390.9380.9490.9480.7470.7410.7390.7480.7740.7580.7720.7750.7930.9250.9280.9290.9260.9310.9310.9250.9130.9370.9170.9390.9350.9320.8640.9290.9280.9150.9180.9130.9210.9270.9210.9190.7520.7510.7480.7760.9190.8630.7450.8610.8450.8410.8280.8490.8490.8670.8470.8230.7890.8550.8380.7933802BindingSPIKE_SARS2MediumVirus
SPIKE_SARS2_Starr_2020_expression0.7590.8820.7970.8550.8820.8800.6430.8470.8480.8870.6330.6200.6360.6340.6620.6430.6350.6830.6880.8640.8240.8450.8360.8430.8440.8460.8210.8440.8290.8890.8490.8490.7470.8410.8420.8410.8330.8340.8470.8900.8910.8900.6220.6220.6370.6520.8990.8560.7490.7560.8110.8190.8240.8220.8320.8350.8200.8050.8120.8340.8280.7163798ExpressionSPIKE_SARS2MediumVirus
SPTN1_CHICK_Tsuboyama_2023_1TUD0.8320.8690.8070.7740.8060.7970.6130.7060.7900.7990.8650.8150.8380.3580.8110.8190.8040.8870.8090.8360.7660.8130.7600.7880.8260.8410.8100.8340.8330.8270.8770.8800.5810.7800.7550.7760.8340.8460.8300.8030.8030.8010.7510.4210.7850.7370.7650.7750.9020.8930.8920.8870.8980.8860.8870.8890.8860.8940.8800.8950.8830.7673201StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU0.7990.8510.8590.8730.8780.8720.4930.8100.8660.8640.8400.7640.8320.5670.7010.7950.8650.8460.8370.8400.6150.8370.7550.8550.8230.8340.8400.8170.8310.8230.8230.8040.7620.6160.8410.8470.8010.8730.8650.8850.8790.8670.6030.5470.8240.8530.8790.8350.8240.8300.8510.8610.8630.8490.8460.8630.8540.8410.8400.8590.8540.772707StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N880.8620.8580.8530.8510.8560.8550.4160.7600.8430.8430.8890.8510.8830.3620.8440.9030.8930.9040.9130.8360.4820.7030.7570.7220.7340.7830.7990.8190.8660.8170.8830.8350.6270.6760.6290.7480.8870.8750.8750.8750.8820.8700.7980.4590.8840.8540.7530.8930.9160.9070.8720.8830.8770.8960.8740.8860.8810.8890.8830.8820.9140.8911583StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W0.8690.8850.9060.9030.8860.8830.8040.7820.8850.8910.9200.9120.9230.8110.9000.9110.9090.8980.9240.8930.9050.8950.9130.8900.9070.9090.9050.8970.9000.9130.9040.8810.8220.8330.8780.8670.8880.9020.9060.8860.8980.8920.8890.7230.9220.9030.8910.8910.9200.9160.9110.9190.9160.9190.9120.9210.9100.9150.9140.9180.9050.9111556StabilitySRBS1_HUMANHighHuman
SRC_HUMAN_Ahler_20190.5610.5700.5540.5630.5530.5570.5570.5230.5750.5700.5710.5680.5720.5380.5450.5160.5380.5490.5420.5550.5540.5450.5520.5410.5420.5520.5560.5440.5430.5710.5500.5610.5480.5470.5390.5320.5460.5530.5520.5560.5610.5560.5730.5170.5580.5700.4990.5320.4990.4360.5400.5420.5290.5640.5500.5440.5320.5490.5380.5380.5560.5423372ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM0.6260.6330.6300.6370.6330.6270.6260.6160.6200.6270.6430.6320.6390.6140.6180.6020.6050.6090.6140.6230.6170.6100.6170.6080.6140.6210.6200.6090.6160.6360.6170.6250.6410.6150.6050.6040.6170.6150.6150.6260.6250.6250.6300.5970.6210.6290.5920.6160.5950.5770.6100.6080.6020.6260.6170.6070.6070.6180.6090.6110.6240.6143637ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Nguyen_20220.6690.6850.6860.6830.6740.6740.6670.6700.6680.6820.6900.6820.6850.6610.6620.6470.6640.6750.6760.6810.6900.6810.6830.6770.6750.6840.6850.6750.6770.6940.6770.6800.6700.6780.6710.6710.6700.6720.6720.6800.6750.6750.6780.6500.6860.6800.6360.6670.6380.6290.6610.6630.6610.6730.6640.6640.6600.6650.6580.6600.6810.6613366OrganismalFitnessSRC_HUMANMediumHuman
SUMO1_HUMAN_Weile_20170.7070.7600.7790.7660.7690.7710.6370.7630.7580.7490.7730.7750.7880.6580.7230.7500.7850.7300.7630.7740.6700.7590.7700.7780.7680.7750.7630.7900.7650.7170.7770.7790.6210.6720.7590.7710.7390.7540.7720.7640.7640.7720.7370.6390.7720.7490.7350.7740.7290.7230.7680.7630.7780.7700.7670.7690.7800.7800.7700.7770.7760.7321700OrganismalFitnessSUMO1_HUMANHighHuman
SYUA_HUMAN_Newberry_20200.8260.8360.8180.8140.8270.8290.8090.8780.8380.8450.8360.8680.8610.8090.8110.8240.8120.8490.8600.8600.8070.8670.8720.8700.8110.8620.8680.8600.8670.8740.8770.8770.6960.8240.8650.8680.8360.8550.8560.8350.8510.8500.8120.8000.8570.8080.7610.8340.8090.7700.8130.7950.7930.8050.8060.8070.8160.8240.8320.8040.8340.8022497OrganismalFitnessSYUA_HUMANMediumHuman
TADBP_HUMAN_Bolognesi_20190.5940.5910.5890.5870.5900.5870.7160.5920.5770.5800.5950.6070.5930.6430.6600.6370.5570.5790.6120.5900.6770.5760.5950.5960.6930.6080.5840.5920.5930.6010.5990.6220.5760.6980.7320.5920.6090.6160.6000.5940.5940.5900.6520.6970.6070.6530.6960.5980.6990.6100.6600.6210.6130.6430.6210.6340.6080.6120.5900.6200.6200.6571196OrganismalFitnessTADBP_HUMANLowHuman
TAT_HV1BR_Fernandes_20160.6860.6990.6940.6950.6970.6980.4570.7070.7010.6980.6090.7070.7040.5990.6150.5830.5980.5780.5780.6980.7010.7140.7040.7070.6950.7290.6090.6980.7180.6950.7070.7180.6180.7020.6810.7120.7060.6890.6940.7020.7010.7020.5390.4950.6860.5570.6010.6970.6440.5850.5400.6150.5940.6230.5930.6170.5650.5820.5810.5800.5300.5371577OrganismalFitnessTAT_HV1BRHighVirus
TCRG1_MOUSE_Tsuboyama_2023_1E0L0.8960.8820.8880.8910.8810.8820.8250.7990.8870.8710.8500.8770.8790.8850.8630.8890.9040.8700.8680.8240.8480.8450.8260.8350.8570.8480.8440.8500.8410.8480.8280.7980.8610.8460.8630.8890.8800.8810.8940.8790.8830.8790.8800.6910.8520.8760.8490.8460.8770.8790.8690.8630.8640.8820.8640.8800.8750.8690.8740.8710.8990.8801058StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG0.7710.7670.7900.8080.8080.8230.5830.5900.7990.8180.7660.8160.8000.4880.7910.7940.7880.8160.7990.8020.5380.6610.5800.5190.6420.6530.5150.6690.7780.8360.8120.7760.7470.5300.7220.6050.7770.8240.7770.7900.8220.8080.7100.6240.8100.7450.8300.8350.8140.8740.8500.8050.8420.8400.8540.8270.8340.8340.8270.8410.7940.7671279StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT0.8480.8850.8780.8800.8710.8770.5770.7900.8780.8910.8510.8390.8520.7150.8270.8480.8710.8850.8860.8680.8110.8870.8880.8870.8290.8800.8680.8890.8760.8640.8980.8830.7290.6200.8210.8290.8440.8630.8670.8790.8800.8780.8320.7240.8700.8290.8200.8910.9130.9160.8630.8890.8850.8640.8790.8790.8860.8780.8680.8870.9050.8191479StabilityTNKS2_HUMANHighHuman
TPK1_HUMAN_Weile_20170.6190.6420.6240.6210.6200.6260.5540.6440.6260.6210.6300.6450.6510.5510.6070.6250.6540.6520.6650.6310.5580.5990.6050.6190.5580.6490.6650.6190.6670.6720.6670.6430.5570.5640.6230.6720.6250.6310.6760.6270.6380.6480.5690.5500.6370.5810.6320.6440.6490.5980.6480.6560.6480.6590.6490.6570.6650.6510.6450.6490.6280.6003181OrganismalFitnessTPK1_HUMANMediumHuman
TPMT_HUMAN_Matreyek_20180.8050.8170.8180.8160.8190.8160.7350.8150.8170.8190.8200.8180.8260.7640.7730.8040.8130.8140.8070.8200.7710.7610.8030.8280.8010.7980.8090.7990.8100.8260.8290.8190.7710.7870.8000.8070.8100.8110.8120.8200.8190.8170.7830.7330.8220.8030.8000.8230.8240.7890.8030.8040.8090.8020.8090.8110.8120.8150.8120.8100.8170.8003648ExpressionTPMT_HUMANMediumHuman
TPOR_HUMAN_Bridgford_20200.6680.5970.6950.6350.6270.6250.6900.6410.6630.6560.6780.6430.6710.5890.6640.5750.5710.6100.6490.5890.6190.5890.6590.6520.6380.5460.7420.7000.6760.6180.6110.6180.7080.6400.6330.6170.6700.6820.6300.6510.6670.6070.5800.6270.6240.6430.4860.6350.6530.5600.7030.6060.6250.6290.6210.6340.6130.6680.6640.6240.6370.691562OrganismalFitnessTPOR_HUMANLowHuman
TRPC_SACS2_Chan_20170.8790.9170.9100.9150.9110.9130.7440.8990.9100.9180.9000.8880.8920.7880.8260.8770.9190.9210.9090.9220.7810.8490.8590.8670.8790.8610.9090.8830.9300.9120.9120.8870.7350.8220.8770.8460.8450.8880.8740.8870.9120.9090.7690.7500.8840.8250.7980.8960.9140.7460.8980.9030.8980.9110.9060.9000.9040.9110.9020.9120.9240.8661519OrganismalFitnessTRPC_SACS2MediumProkaryote
TRPC_THEMA_Chan_20170.6920.7320.6520.6880.7190.7120.6070.6390.7050.7370.7260.7320.7470.6220.7190.7550.7250.7430.7510.7420.6700.7160.7330.7560.6980.7360.6890.7510.7620.6570.7340.6840.5540.6720.7470.7380.7160.7230.7230.7330.7300.7230.6890.5010.7650.7180.6510.7580.7260.5810.7180.7200.6910.7300.7250.7070.7050.7010.6900.7130.7540.7531519OrganismalFitnessTRPC_THEMAMediumProkaryote
UBC9_HUMAN_Weile_20170.5340.6150.6100.6100.6070.6180.3690.5310.5750.5910.5820.5890.5870.4050.4030.4700.5220.5800.5960.6060.4900.5400.5380.5350.5470.5790.5530.5680.5740.5550.5560.5360.3530.4900.5580.5760.5220.5970.5830.5970.6200.6260.4630.3980.5770.5790.4890.5530.5120.4860.5040.5050.5230.5320.5360.5340.5310.5210.5190.5190.5790.4842563OrganismalFitnessUBC9_HUMANMediumHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X0.6600.7860.7850.7820.7950.7980.5280.7280.7730.8020.8380.7990.8040.6090.7840.8480.8400.8290.8210.7980.5420.7400.7540.7770.7230.7560.7520.7630.7840.7650.8310.7920.7340.5160.5410.7920.6840.6970.7970.7970.7920.8180.6570.5200.6830.7150.7680.7490.8360.7310.8480.8560.8550.8540.8560.8460.8420.8550.8490.8540.8330.7443622StabilityUBE4B_HUMANHighHuman
UBE4B_MOUSE_Starita_20130.6720.6590.6580.6580.6480.6560.5360.6260.6470.6440.6560.6870.6660.5750.6560.6600.6410.6350.6170.6300.5470.6280.6460.6330.6600.6490.6490.6730.6480.6360.6440.6320.5490.5440.5660.6290.6740.6910.6820.6580.6630.6640.6410.5280.6260.6640.6440.6250.4910.6000.6670.6470.6580.6480.6450.6570.6390.6360.6320.6510.6530.644899ActivityUBE4B_MOUSELowEukaryote
UBR5_HUMAN_Tsuboyama_2023_1I2T0.8670.8770.8450.8560.8720.8670.7750.8000.8620.8690.8590.8180.8420.7920.7950.7810.7730.8630.8590.8310.8520.8690.8840.8840.8780.8830.8890.8910.8850.8800.8730.8480.8130.8300.8630.8520.8640.8720.8780.8710.8690.8790.8040.7830.8850.8200.8810.8850.8850.8850.8590.8770.8870.8820.8860.8780.8740.8810.8740.8810.8860.8611453StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP80.7400.7180.7290.7290.7370.7350.6710.6790.7380.7670.7530.7650.7590.7200.7460.7660.7690.7720.7500.7080.7060.7320.7050.7460.7210.7190.7310.7160.7570.7430.7400.7190.5720.7160.7460.7450.7300.7660.7480.7400.7460.7310.7270.7230.7430.6960.7070.7380.7530.7520.7760.7760.7780.7830.7950.7880.7840.7750.7910.7830.7850.790723StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU50.7910.9200.9360.9350.9340.9300.6840.8400.9240.9260.9300.9140.9310.7820.7530.9370.9530.9320.8830.9300.7320.8210.8530.8490.8460.8770.8640.8600.9200.9330.9440.9240.7660.6530.8880.8870.8580.9060.9010.9280.9310.9310.8090.8060.9340.9040.9120.9220.9470.8950.9530.9470.9540.9520.9480.9570.9510.9540.9500.9550.9520.9172568StabilityVILI_CHICKHighEukaryote
VKOR1_HUMAN_Chiasson_2020_abundance0.8410.8290.8390.8430.8350.8410.7170.8160.8390.8540.8220.8430.8360.7000.8010.8340.8400.8410.8250.8520.7550.7490.7710.8250.7680.7770.8060.7710.8140.8450.8470.8200.6680.7290.7680.8100.8440.8440.8470.8400.8430.8540.7180.6610.8300.7890.8050.8190.8280.7840.8320.8220.8400.8470.8540.8550.8480.8390.8400.8470.8390.8332695ExpressionVKOR1_HUMANMediumHuman
VKOR1_HUMAN_Chiasson_2020_activity0.8170.8340.8120.8170.7970.8100.6930.8380.8470.8430.8360.8240.8150.7160.7920.8290.8160.8190.8410.8350.7780.7680.8360.8220.7360.8480.8320.8250.8340.8570.8280.8230.8190.7250.7500.8360.7880.7900.8250.7950.8010.8100.7370.7300.8290.7490.7740.8270.8320.7910.8240.8100.8170.8320.8320.8170.8280.8110.8140.8230.8110.820697ActivityVKOR1_HUMANMediumHuman
VRPI_BPT7_Tsuboyama_2023_2WNM0.5840.7390.7810.7780.7670.7830.6550.7280.7990.7650.7850.7500.7370.6970.7490.7900.8030.8220.7970.7520.5980.6940.6760.7040.7030.6470.6750.6920.7630.7530.8020.7880.6930.6710.7400.6870.6380.6490.6450.7540.7570.7390.7290.6720.7560.7340.8030.7980.8010.8510.8500.8360.8630.8560.8470.8610.8570.8610.8530.8690.8690.8081047StabilityVRPI_BPT7MediumVirus
YAIA_ECOLI_Tsuboyama_2023_2KVT0.6670.8520.8460.8600.8560.8530.4530.8030.8320.8590.7890.6260.7950.5770.6440.8500.8730.9190.9040.8750.5030.5300.5700.7350.5620.5730.5210.5530.8410.8750.8770.8670.5210.4520.5640.8310.7020.7270.8160.8390.8420.8550.6850.5640.7170.6940.8760.8370.9120.8620.8890.9030.8800.8910.9010.8940.8900.8940.8970.8970.8770.7491890StabilityYAIA_ECOLIMediumProkaryote
YAP1_HUMAN_Araya_20120.4640.3860.4710.4680.4660.4630.3740.3120.2950.2880.4090.3620.3570.4090.4190.4410.4530.4120.3900.3270.3080.3010.2930.3220.3320.3000.3110.3220.2960.3700.3820.3970.3400.3580.3020.3230.4270.3680.3960.4500.4200.4390.4840.1810.4220.4800.3590.4180.3320.3720.3550.3480.3650.3700.3470.3580.3690.3730.3860.3610.4780.41010075BindingYAP1_HUMANLowHuman
YNZC_BACSU_Tsuboyama_2023_2JVD0.8550.8690.8550.8550.8570.8560.7670.7930.8760.8770.8810.8720.8870.8650.8830.8870.8910.8870.8810.8500.8450.8930.8960.8960.8830.8820.8820.8750.8780.8630.8750.8540.8450.8420.8720.8680.8760.8630.8650.8670.8590.8650.8480.8020.8670.8450.8290.8720.9110.8940.8940.8740.8800.8770.8780.8860.8780.8790.8790.8810.8840.9232300StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_Uniprot_Selection_Type_level.csv b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_Uniprot_Selection_Type_level.csv new file mode 100644 index 0000000..1db609d --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/NDCG/DMS_substitutions_NDCG_Uniprot_Selection_Type_level.csv @@ -0,0 +1,7 @@ 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+0.796,0.882,0.875,0.877,0.874,0.875,0.733,0.857,0.828,0.883,0.843,0.882,0.886,0.781,0.803,0.806,0.836,0.891,0.882,0.845,0.876,0.886,0.887,0.885,0.866,0.879,0.888,0.883,0.882,0.873,0.882,0.874,0.776,0.886,0.883,0.881,0.878,0.878,0.876,0.885,0.883,0.882,0.79,0.759,0.816,0.802,0.818,0.848,0.828,0.816,0.85,0.86,0.858,0.854,0.852,0.855,0.854,0.85,0.857,0.859,0.864,0.813,Activity,AMIE_PSEAE,High,Prokaryote +0.82,0.817,0.811,0.81,0.818,0.818,0.787,0.808,0.814,0.819,0.806,0.82,0.822,0.818,0.834,0.829,0.829,0.829,0.826,0.676,0.834,0.807,0.811,0.808,0.84,0.815,0.817,0.8,0.797,0.819,0.804,0.794,0.754,0.804,0.8,0.811,0.819,0.816,0.821,0.822,0.82,0.822,0.82,0.795,0.81,0.812,0.808,0.825,0.785,0.745,0.827,0.831,0.819,0.814,0.825,0.822,0.828,0.829,0.827,0.828,0.825,0.823,Activity,ANCSZ,Medium,Eukaryote +0.839,0.908,0.91,0.91,0.909,0.907,0.793,0.868,0.825,0.882,0.872,0.826,0.895,0.827,0.851,0.889,0.901,0.912,0.896,0.901,0.85,0.883,0.898,0.895,0.858,0.88,0.901,0.901,0.891,0.887,0.878,0.848,0.77,0.866,0.87,0.87,0.871,0.881,0.888,0.913,0.901,0.908,0.827,0.787,0.874,0.856,0.918,0.913,0.914,0.919,0.899,0.905,0.908,0.908,0.907,0.904,0.904,0.912,0.902,0.908,0.927,0.884,Stability,ARGR_ECOLI,Medium,Prokaryote +0.594,0.518,0.605,0.598,0.595,0.604,0.425,0.567,0.492,0.524,0.18,0.158,0.552,0.424,0.352,0.38,0.474,0.494,0.563,0.479,0.363,0.272,0.479,0.529,0.195,0.493,0.536,0.386,0.536,0.564,0.458,0.389,0.371,0.49,0.224,0.457,0.597,0.605,0.637,0.617,0.624,0.628,0.355,0.402,0.518,0.445,0.386,0.502,0.449,0.158,0.403,0.498,0.489,0.53,0.428,0.46,0.491,0.504,0.511,0.522,0.424,0.223,Binding,B2L11_HUMAN,Low,Human +0.681,0.749,0.713,0.716,0.718,0.69,0.612,0.654,0.754,0.759,0.771,0.758,0.766,0.514,0.735,0.759,0.814,0.812,0.815,0.755,0.661,0.732,0.742,0.746,0.592,0.765,0.709,0.734,0.716,0.668,0.711,0.681,0.616,0.7,0.658,0.699,0.719,0.694,0.727,0.709,0.678,0.693,0.676,0.529,0.762,0.732,0.789,0.772,0.811,0.789,0.788,0.8,0.798,0.822,0.803,0.823,0.807,0.809,0.8,0.809,0.811,0.797,Stability,BBC1_YEAST,High,Eukaryote +0.877,0.893,0.888,0.887,0.878,0.887,0.727,0.844,0.861,0.901,0.817,0.858,0.873,0.773,0.791,0.825,0.89,0.888,0.894,0.9,0.737,0.838,0.831,0.851,0.811,0.874,0.813,0.875,0.905,0.893,0.879,0.86,0.679,0.813,0.823,0.866,0.872,0.872,0.889,0.884,0.882,0.888,0.808,0.801,0.838,0.834,0.884,0.907,0.92,0.896,0.885,0.906,0.895,0.897,0.899,0.893,0.896,0.892,0.891,0.898,0.913,0.788,Stability,BCHB_CHLTE,Medium,Prokaryote +0.731,0.868,0.856,0.846,0.875,0.876,0.543,0.664,0.776,0.835,0.803,0.815,0.82,0.695,0.75,0.788,0.804,0.829,0.81,0.784,0.799,0.798,0.822,0.8,0.766,0.822,0.831,0.846,0.795,0.76,0.868,0.86,0.6,0.78,0.843,0.808,0.836,0.863,0.851,0.851,0.869,0.884,0.73,0.514,0.819,0.763,0.797,0.852,0.848,0.736,0.837,0.842,0.846,0.849,0.848,0.856,0.855,0.845,0.855,0.853,0.856,0.787,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.731,0.868,0.856,0.846,0.875,0.876,0.543,0.664,0.776,0.835,0.803,0.815,0.82,0.695,0.75,0.788,0.804,0.829,0.81,0.784,0.799,0.798,0.822,0.8,0.766,0.822,0.831,0.846,0.795,0.76,0.868,0.86,0.6,0.78,0.843,0.808,0.836,0.863,0.851,0.851,0.869,0.884,0.73,0.514,0.819,0.763,0.797,0.852,0.848,0.736,0.837,0.842,0.846,0.849,0.848,0.856,0.855,0.845,0.855,0.853,0.856,0.787,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.731,0.868,0.856,0.846,0.875,0.876,0.543,0.664,0.776,0.835,0.803,0.815,0.82,0.695,0.75,0.788,0.804,0.829,0.81,0.784,0.799,0.798,0.822,0.8,0.766,0.822,0.831,0.846,0.795,0.76,0.868,0.86,0.6,0.78,0.843,0.808,0.836,0.863,0.851,0.851,0.869,0.884,0.73,0.514,0.819,0.763,0.797,0.852,0.848,0.736,0.837,0.842,0.846,0.849,0.848,0.856,0.855,0.845,0.855,0.853,0.856,0.787,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.731,0.868,0.856,0.846,0.875,0.876,0.543,0.664,0.776,0.835,0.803,0.815,0.82,0.695,0.75,0.788,0.804,0.829,0.81,0.784,0.799,0.798,0.822,0.8,0.766,0.822,0.831,0.846,0.795,0.76,0.868,0.86,0.6,0.78,0.843,0.808,0.836,0.863,0.851,0.851,0.869,0.884,0.73,0.514,0.819,0.763,0.797,0.852,0.848,0.736,0.837,0.842,0.846,0.849,0.848,0.856,0.855,0.845,0.855,0.853,0.856,0.787,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.881,0.871,0.877,0.878,0.866,0.867,0.738,0.812,0.867,0.869,0.865,0.822,0.822,0.77,0.812,0.834,0.866,0.879,0.85,0.872,0.76,0.862,0.877,0.861,0.823,0.89,0.887,0.873,0.873,0.878,0.879,0.855,0.717,0.781,0.772,0.857,0.877,0.881,0.883,0.878,0.877,0.878,0.793,0.756,0.881,0.835,0.838,0.877,0.751,0.781,0.853,0.86,0.858,0.855,0.85,0.858,0.848,0.859,0.852,0.857,0.818,0.833,OrganismalFitness,BRCA1_HUMAN,Low,Human +0.941,0.933,0.936,0.935,0.935,0.932,0.915,0.937,0.856,0.891,0.94,0.923,0.918,0.928,0.925,0.929,0.944,0.935,0.94,0.903,0.93,0.944,0.949,0.943,0.942,0.922,0.921,0.933,0.914,0.932,0.926,0.946,0.927,0.926,0.93,0.931,0.938,0.937,0.939,0.933,0.934,0.933,0.918,0.922,0.939,0.898,0.89,0.892,0.769,0.917,0.943,0.942,0.941,0.938,0.945,0.933,0.948,0.943,0.943,0.944,0.896,0.91,OrganismalFitness,BRCA2_HUMAN,,Human +0.771,0.811,0.803,0.8,0.812,0.812,0.609,0.792,0.809,0.812,0.627,0.77,0.822,0.601,0.604,0.616,0.784,0.81,0.82,0.792,0.798,0.788,0.796,0.786,0.66,0.793,0.796,0.808,0.778,0.828,0.8,0.767,0.667,0.786,0.796,0.787,0.801,0.805,0.801,0.815,0.818,0.817,0.603,0.59,0.698,0.608,0.75,0.795,0.785,0.719,0.789,0.796,0.795,0.793,0.793,0.783,0.794,0.794,0.783,0.795,0.688,0.658,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +0.799,0.817,0.808,0.804,0.805,0.8,0.801,0.79,0.815,0.816,0.827,0.794,0.821,0.797,0.793,0.791,0.773,0.786,0.812,0.813,0.801,0.806,0.821,0.81,0.812,0.827,0.816,0.841,0.832,0.823,0.819,0.794,0.75,0.809,0.832,0.823,0.815,0.827,0.828,0.813,0.807,0.813,0.807,0.794,0.819,0.824,0.781,0.775,0.809,0.758,0.774,0.78,0.781,0.758,0.766,0.783,0.781,0.779,0.763,0.778,0.826,0.822,OrganismalFitness,CALM1_HUMAN,High,Human +0.791,0.789,0.786,0.811,0.781,0.784,0.758,0.841,0.75,0.777,0.641,0.666,0.682,0.701,0.729,0.69,0.733,0.665,0.606,0.703,0.683,0.722,0.696,0.712,0.68,0.691,0.729,0.698,0.794,0.798,0.629,0.619,0.616,0.699,0.721,0.83,0.771,0.772,0.821,0.785,0.787,0.817,0.663,0.718,0.776,0.688,0.768,0.753,0.686,0.723,0.673,0.651,0.654,0.656,0.637,0.645,0.659,0.652,0.688,0.663,0.723,0.665,OrganismalFitness,CAPSD_AAV2S,Low,Virus +0.712,0.714,0.716,0.715,0.713,0.712,0.711,0.672,0.726,0.724,0.737,0.693,0.708,0.674,0.654,0.7,0.714,0.72,0.726,0.712,0.658,0.731,0.73,0.724,0.671,0.739,0.734,0.74,0.7,0.72,0.731,0.706,0.682,0.698,0.7,0.694,0.709,0.722,0.716,0.712,0.723,0.72,0.672,0.657,0.73,0.698,0.674,0.719,0.708,0.69,0.728,0.726,0.712,0.726,0.728,0.717,0.726,0.722,0.719,0.722,0.734,0.707,OrganismalFitness,CAR11_HUMAN,Low,Human +0.712,0.714,0.716,0.715,0.713,0.712,0.711,0.672,0.726,0.724,0.737,0.693,0.708,0.674,0.654,0.7,0.714,0.72,0.726,0.712,0.658,0.731,0.73,0.724,0.671,0.739,0.734,0.74,0.7,0.72,0.731,0.706,0.682,0.698,0.7,0.694,0.709,0.722,0.716,0.712,0.723,0.72,0.672,0.657,0.73,0.698,0.674,0.719,0.708,0.69,0.728,0.726,0.712,0.726,0.728,0.717,0.726,0.722,0.719,0.722,0.734,0.707,OrganismalFitness,CAR11_HUMAN,Low,Human +0.836,0.838,0.839,0.839,0.839,0.839,0.831,0.822,0.84,0.84,0.836,0.829,0.829,0.829,0.829,0.837,0.839,0.842,0.841,0.828,0.832,0.831,0.839,0.845,0.831,0.84,0.838,0.837,0.839,0.844,0.842,0.842,0.834,0.825,0.828,0.84,0.838,0.839,0.842,0.839,0.839,0.841,0.829,0.826,0.838,0.829,0.829,0.838,0.829,0.828,0.84,0.837,0.841,0.838,0.837,0.837,0.837,0.84,0.839,0.839,0.842,0.837,Activity,CAS9_STRP1,Medium,Prokaryote +0.779,0.82,0.82,0.812,0.812,0.818,0.7,0.759,0.818,0.824,0.813,0.796,0.811,0.712,0.809,0.826,0.812,0.823,0.824,0.798,0.711,0.778,0.756,0.829,0.809,0.817,0.824,0.812,0.831,0.802,0.838,0.821,0.754,0.712,0.803,0.819,0.809,0.822,0.822,0.823,0.829,0.824,0.777,0.681,0.817,0.801,0.768,0.807,0.785,0.74,0.812,0.801,0.804,0.807,0.81,0.807,0.819,0.818,0.806,0.805,0.819,0.796,Activity,CASP3_HUMAN,High,Human +0.747,0.808,0.815,0.816,0.817,0.814,0.607,0.786,0.81,0.813,0.818,0.813,0.816,0.682,0.804,0.813,0.804,0.787,0.786,0.81,0.622,0.794,0.764,0.788,0.782,0.792,0.796,0.785,0.804,0.82,0.804,0.786,0.723,0.664,0.784,0.785,0.799,0.822,0.812,0.812,0.813,0.818,0.746,0.62,0.821,0.806,0.761,0.823,0.788,0.74,0.805,0.811,0.812,0.803,0.804,0.802,0.806,0.805,0.809,0.809,0.819,0.775,Activity,CASP7_HUMAN,Medium,Human +0.853,0.808,0.813,0.816,0.825,0.817,0.844,0.774,0.777,0.793,0.805,0.82,0.815,0.819,0.859,0.819,0.82,0.814,0.801,0.62,0.821,0.792,0.8,0.791,0.798,0.798,0.788,0.796,0.8,0.813,0.79,0.799,0.755,0.811,0.803,0.807,0.831,0.821,0.813,0.812,0.81,0.813,0.819,0.82,0.795,0.8,0.847,0.76,0.818,0.842,0.815,0.808,0.815,0.817,0.813,0.811,0.813,0.81,0.809,0.816,0.81,0.814,Stability,CATR_CHLRE,High,Eukaryote +0.91,0.895,0.916,0.91,0.91,0.912,0.848,0.896,0.909,0.916,0.912,0.9,0.903,0.854,0.901,0.902,0.908,0.915,0.913,0.917,0.861,0.845,0.884,0.891,0.889,0.837,0.835,0.823,0.913,0.906,0.914,0.899,0.733,0.868,0.886,0.905,0.912,0.915,0.916,0.913,0.912,0.918,0.898,0.888,0.901,0.902,0.928,0.922,0.925,0.928,0.908,0.916,0.922,0.915,0.917,0.909,0.911,0.911,0.91,0.917,0.918,0.928,Stability,CBPA2_HUMAN,Medium,Human +0.472,0.468,0.477,0.478,0.477,0.48,0.323,0.435,0.478,0.455,0.451,0.463,0.45,0.331,0.394,0.427,0.421,0.447,0.435,0.455,0.465,0.419,0.446,0.452,0.427,0.433,0.446,0.443,0.454,0.47,0.494,0.478,0.386,0.465,0.43,0.431,0.478,0.479,0.49,0.49,0.486,0.474,0.431,0.326,0.472,0.461,0.42,0.453,0.442,0.372,0.432,0.444,0.447,0.439,0.427,0.439,0.448,0.444,0.434,0.445,0.468,0.413,OrganismalFitness,CBS_HUMAN,Medium,Human +0.881,0.875,0.889,0.895,0.897,0.904,0.318,0.816,0.89,0.891,0.873,0.89,0.89,0.355,0.912,0.911,0.907,0.899,0.87,0.889,0.815,0.829,0.837,0.838,0.859,0.865,0.858,0.847,0.849,0.887,0.876,0.846,0.641,0.775,0.844,0.857,0.884,0.876,0.893,0.901,0.892,0.892,0.88,0.448,0.833,0.883,0.748,0.757,0.914,0.859,0.906,0.898,0.903,0.899,0.907,0.9,0.905,0.901,0.906,0.909,0.896,0.905,Stability,CBX4_HUMAN,High,Human +0.864,0.909,0.918,0.918,0.909,0.912,0.673,0.828,0.788,0.865,0.849,0.744,0.766,0.667,0.678,0.774,0.888,0.92,0.906,0.836,0.648,0.649,0.596,0.802,0.603,0.764,0.64,0.775,0.921,0.871,0.936,0.934,0.826,0.668,0.671,0.881,0.818,0.796,0.904,0.904,0.898,0.919,0.693,0.631,0.798,0.684,0.772,0.886,0.772,0.791,0.881,0.845,0.83,0.861,0.908,0.892,0.862,0.906,0.899,0.888,0.883,0.831,Activity,CCDB_ECOLI,High,Prokaryote +0.864,0.909,0.918,0.918,0.909,0.912,0.673,0.828,0.788,0.865,0.849,0.744,0.766,0.667,0.678,0.774,0.888,0.92,0.906,0.836,0.648,0.649,0.596,0.802,0.603,0.764,0.64,0.775,0.921,0.871,0.936,0.934,0.826,0.668,0.671,0.881,0.818,0.796,0.904,0.904,0.898,0.919,0.693,0.631,0.798,0.684,0.772,0.886,0.772,0.791,0.881,0.845,0.83,0.861,0.908,0.892,0.862,0.906,0.899,0.888,0.883,0.831,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +0.819,0.818,0.818,0.819,0.821,0.821,0.8,0.841,0.822,0.822,0.823,0.82,0.819,0.81,0.813,0.82,0.807,0.816,0.812,0.823,0.814,0.836,0.832,0.83,0.824,0.832,0.825,0.828,0.832,0.829,0.822,0.815,0.775,0.822,0.828,0.829,0.82,0.83,0.834,0.822,0.823,0.825,0.813,0.772,0.826,0.815,0.809,0.824,0.815,0.805,0.818,0.814,0.813,0.809,0.813,0.82,0.814,0.814,0.814,0.817,0.823,0.827,Binding,CCR5_HUMAN,High,Human +0.566,0.555,0.578,0.578,0.566,0.565,0.519,0.523,0.526,0.529,0.523,0.51,0.543,0.54,0.566,0.522,0.517,0.558,0.577,0.491,0.575,0.569,0.584,0.577,0.511,0.554,0.536,0.586,0.565,0.576,0.572,0.548,0.527,0.608,0.586,0.531,0.598,0.587,0.578,0.578,0.574,0.573,0.552,0.529,0.573,0.562,0.693,0.67,0.683,0.553,0.574,0.564,0.591,0.6,0.58,0.595,0.614,0.573,0.61,0.6,0.687,0.621,Binding,CD19_HUMAN,Low,Human +0.789,0.835,0.833,0.843,0.849,0.847,0.819,0.846,0.828,0.849,0.835,0.853,0.865,0.786,0.84,0.849,0.87,0.861,0.848,0.857,0.832,0.802,0.85,0.838,0.854,0.846,0.851,0.844,0.846,0.836,0.83,0.8,0.568,0.847,0.847,0.849,0.855,0.856,0.861,0.856,0.854,0.851,0.844,0.658,0.843,0.861,0.832,0.839,0.858,0.712,0.862,0.859,0.862,0.861,0.863,0.866,0.864,0.864,0.865,0.868,0.861,0.841,Expression,CP2C9_HUMAN,High,Human +0.789,0.835,0.833,0.843,0.849,0.847,0.819,0.846,0.828,0.849,0.835,0.853,0.865,0.786,0.84,0.849,0.87,0.861,0.848,0.857,0.832,0.802,0.85,0.838,0.854,0.846,0.851,0.844,0.846,0.836,0.83,0.8,0.568,0.847,0.847,0.849,0.855,0.856,0.861,0.856,0.854,0.851,0.844,0.658,0.843,0.861,0.832,0.839,0.858,0.712,0.862,0.859,0.862,0.861,0.863,0.866,0.864,0.864,0.865,0.868,0.861,0.841,Binding,CP2C9_HUMAN,High,Human +0.824,0.841,0.847,0.852,0.851,0.849,0.672,0.807,0.858,0.848,0.838,0.87,0.874,0.751,0.895,0.86,0.789,0.835,0.822,0.845,0.766,0.745,0.815,0.841,0.726,0.866,0.871,0.847,0.842,0.842,0.857,0.843,0.678,0.729,0.833,0.824,0.853,0.867,0.87,0.852,0.864,0.865,0.756,0.745,0.844,0.862,0.872,0.847,0.902,0.887,0.84,0.839,0.843,0.847,0.842,0.849,0.845,0.851,0.841,0.847,0.855,0.873,Stability,CSN4_MOUSE,Medium,Eukaryote +0.746,0.847,0.798,0.8,0.821,0.815,0.646,0.781,0.863,0.867,0.777,0.671,0.673,0.617,0.637,0.679,0.728,0.797,0.793,0.768,0.626,0.618,0.643,0.563,0.544,0.564,0.623,0.622,0.796,0.779,0.802,0.768,0.676,0.617,0.597,0.629,0.742,0.739,0.769,0.808,0.806,0.812,0.626,0.633,0.731,0.644,0.781,0.787,0.87,0.835,0.711,0.782,0.764,0.76,0.721,0.76,0.747,0.739,0.747,0.747,0.824,0.75,Stability,CUE1_YEAST,Medium,Eukaryote +0.63,0.771,0.736,0.695,0.76,0.76,0.32,0.622,0.792,0.789,0.595,0.325,0.328,0.327,0.309,0.302,0.312,0.341,0.357,0.621,0.291,0.27,0.362,0.331,0.261,0.305,0.313,0.393,0.478,0.773,0.752,0.756,0.308,0.325,0.36,0.363,0.691,0.69,0.686,0.764,0.763,0.753,0.443,0.445,0.45,0.45,0.573,0.593,0.611,0.594,0.626,0.62,0.632,0.628,0.614,0.629,0.608,0.627,0.615,0.625,0.513,0.386,Activity,D7PM05_CLYGR,Low,Eukaryote +0.87,0.859,0.871,0.863,0.876,0.876,0.872,0.863,0.834,0.841,0.816,0.865,0.867,0.888,0.914,0.901,0.865,0.853,0.826,0.878,0.851,0.849,0.836,0.823,0.863,0.851,0.843,0.848,0.821,0.874,0.853,0.852,0.817,0.849,0.869,0.836,0.886,0.894,0.88,0.883,0.888,0.885,0.877,0.727,0.832,0.867,0.869,0.817,0.895,0.772,0.837,0.813,0.835,0.833,0.845,0.846,0.829,0.827,0.829,0.835,0.857,0.914,OrganismalFitness,DLG4_HUMAN,Low,Human +0.921,0.93,0.925,0.925,0.931,0.932,0.926,0.923,0.929,0.929,0.928,0.927,0.927,0.912,0.914,0.936,0.928,0.927,0.928,0.921,0.922,0.921,0.924,0.924,0.923,0.926,0.922,0.921,0.927,0.921,0.935,0.936,0.879,0.917,0.925,0.911,0.922,0.924,0.92,0.927,0.928,0.932,0.929,0.8,0.927,0.922,0.907,0.91,0.906,0.879,0.927,0.882,0.919,0.921,0.923,0.92,0.923,0.922,0.925,0.923,0.926,0.92,Binding,DLG4_RAT,Low,Eukaryote +0.841,0.877,0.86,0.848,0.831,0.855,0.751,0.876,0.898,0.906,0.799,0.808,0.789,0.77,0.802,0.801,0.823,0.819,0.902,0.894,0.738,0.784,0.759,0.765,0.763,0.74,0.746,0.782,0.854,0.891,0.906,0.883,0.778,0.77,0.748,0.764,0.839,0.829,0.83,0.858,0.858,0.845,0.787,0.774,0.818,0.785,0.917,0.896,0.926,0.918,0.893,0.899,0.906,0.892,0.899,0.885,0.872,0.879,0.892,0.889,0.854,0.85,Stability,DN7A_SACS2,Medium,Prokaryote +0.9,0.89,0.901,0.898,0.904,0.899,0.888,0.835,0.891,0.899,0.91,0.907,0.924,0.903,0.936,0.929,0.933,0.923,0.923,0.91,0.9,0.898,0.908,0.903,0.904,0.885,0.902,0.892,0.889,0.883,0.88,0.879,0.768,0.896,0.887,0.907,0.899,0.899,0.906,0.897,0.898,0.902,0.918,0.729,0.887,0.92,0.92,0.885,0.923,0.925,0.928,0.935,0.938,0.938,0.939,0.937,0.936,0.94,0.933,0.942,0.898,0.926,Stability,DNJA1_HUMAN,High,Human +0.774,0.795,0.789,0.776,0.761,0.785,0.546,0.742,0.671,0.644,0.791,0.674,0.707,0.521,0.654,0.679,0.787,0.795,0.78,0.828,0.73,0.796,0.793,0.774,0.648,0.766,0.65,0.743,0.788,0.688,0.808,0.782,0.696,0.571,0.586,0.561,0.779,0.775,0.795,0.798,0.783,0.801,0.61,0.533,0.821,0.699,0.797,0.828,0.822,0.847,0.811,0.814,0.806,0.82,0.819,0.817,0.817,0.819,0.812,0.821,0.838,0.804,Stability,DOCK1_MOUSE,High,Eukaryote +0.913,0.924,0.922,0.924,0.926,0.924,0.845,0.916,0.925,0.92,0.911,0.924,0.915,0.875,0.916,0.916,0.922,0.918,0.916,0.92,0.874,0.922,0.914,0.916,0.916,0.924,0.932,0.916,0.932,0.93,0.922,0.92,0.858,0.922,0.928,0.934,0.922,0.928,0.926,0.925,0.926,0.926,0.915,0.847,0.918,0.92,0.891,0.916,0.92,0.895,0.922,0.916,0.914,0.917,0.92,0.926,0.918,0.918,0.919,0.918,0.914,0.919,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.913,0.924,0.922,0.924,0.926,0.924,0.845,0.916,0.925,0.92,0.911,0.924,0.915,0.875,0.916,0.916,0.922,0.918,0.916,0.92,0.874,0.922,0.914,0.916,0.916,0.924,0.932,0.916,0.932,0.93,0.922,0.92,0.858,0.922,0.928,0.934,0.922,0.928,0.926,0.925,0.926,0.926,0.915,0.847,0.918,0.92,0.891,0.916,0.92,0.895,0.922,0.916,0.914,0.917,0.92,0.926,0.918,0.918,0.919,0.918,0.914,0.919,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.913,0.924,0.922,0.924,0.926,0.924,0.845,0.916,0.925,0.92,0.911,0.924,0.915,0.875,0.916,0.916,0.922,0.918,0.916,0.92,0.874,0.922,0.914,0.916,0.916,0.924,0.932,0.916,0.932,0.93,0.922,0.92,0.858,0.922,0.928,0.934,0.922,0.928,0.926,0.925,0.926,0.926,0.915,0.847,0.918,0.92,0.891,0.916,0.92,0.895,0.922,0.916,0.914,0.917,0.92,0.926,0.918,0.918,0.919,0.918,0.914,0.919,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.913,0.924,0.922,0.924,0.926,0.924,0.845,0.916,0.925,0.92,0.911,0.924,0.915,0.875,0.916,0.916,0.922,0.918,0.916,0.92,0.874,0.922,0.914,0.916,0.916,0.924,0.932,0.916,0.932,0.93,0.922,0.92,0.858,0.922,0.928,0.934,0.922,0.928,0.926,0.925,0.926,0.926,0.915,0.847,0.918,0.92,0.891,0.916,0.92,0.895,0.922,0.916,0.914,0.917,0.92,0.926,0.918,0.918,0.919,0.918,0.914,0.919,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.758,0.754,0.774,0.791,0.808,0.81,0.701,0.671,0.783,0.797,0.803,0.79,0.805,0.721,0.781,0.754,0.756,0.739,0.722,0.772,0.769,0.732,0.769,0.776,0.777,0.753,0.692,0.802,0.724,0.848,0.798,0.808,0.8,0.756,0.789,0.762,0.766,0.763,0.767,0.814,0.813,0.817,0.714,0.753,0.735,0.711,0.681,0.788,0.728,0.692,0.762,0.775,0.767,0.805,0.793,0.78,0.775,0.767,0.817,0.773,0.728,0.776,Activity,ENVZ_ECOLI,High,Prokaryote +0.643,0.63,0.538,0.601,0.635,0.631,0.249,0.662,0.628,0.634,0.538,0.623,0.624,0.299,0.278,0.305,0.301,0.298,0.469,0.663,0.59,0.645,0.662,0.66,0.564,0.677,0.587,0.655,0.669,0.577,0.709,0.647,0.564,0.618,0.637,0.668,0.642,0.631,0.653,0.639,0.643,0.658,0.566,0.244,0.629,0.623,0.549,0.578,0.497,0.41,0.431,0.527,0.47,0.431,0.528,0.488,0.495,0.51,0.378,0.487,0.353,0.319,OrganismalFitness,ENV_HV1B9,Medium,Virus +0.304,0.315,0.33,0.343,0.337,0.336,0.145,0.317,0.302,0.307,0.258,0.287,0.289,0.165,0.174,0.17,0.18,0.182,0.209,0.342,0.303,0.313,0.331,0.339,0.294,0.325,0.337,0.318,0.342,0.267,0.302,0.271,0.216,0.3,0.314,0.324,0.312,0.314,0.321,0.335,0.337,0.341,0.268,0.143,0.294,0.275,0.225,0.293,0.177,0.199,0.211,0.207,0.225,0.225,0.223,0.22,0.216,0.209,0.212,0.222,0.206,0.183,OrganismalFitness,ENV_HV1BR,Medium,Virus +0.917,0.935,0.937,0.939,0.935,0.939,0.363,0.912,0.938,0.939,0.912,0.917,0.922,0.391,0.915,0.919,0.937,0.94,0.936,0.938,0.921,0.924,0.917,0.92,0.926,0.925,0.93,0.932,0.922,0.912,0.929,0.91,0.883,0.93,0.913,0.931,0.946,0.934,0.942,0.943,0.937,0.941,0.886,0.551,0.937,0.906,0.947,0.931,0.949,0.942,0.938,0.939,0.938,0.945,0.944,0.941,0.937,0.941,0.942,0.941,0.948,0.946,Stability,EPHB2_HUMAN,High,Human +0.782,0.884,0.794,0.766,0.805,0.821,0.798,0.892,0.836,0.899,0.848,0.744,0.79,0.866,0.789,0.788,0.881,0.899,0.896,0.853,0.82,0.866,0.897,0.907,0.667,0.898,0.914,0.919,0.899,0.772,0.884,0.847,0.828,0.878,0.652,0.908,0.858,0.74,0.893,0.823,0.797,0.836,0.793,0.758,0.89,0.767,0.917,0.928,0.741,0.825,0.827,0.844,0.786,0.872,0.857,0.775,0.804,0.866,0.826,0.829,0.779,0.772,Expression,ERBB2_HUMAN,Low,Human +0.708,0.774,0.774,0.777,0.773,0.779,0.657,0.69,0.737,0.796,0.691,0.729,0.75,0.662,0.713,0.69,0.693,0.69,0.717,0.731,0.663,0.663,0.711,0.667,0.718,0.704,0.735,0.739,0.775,0.753,0.732,0.737,0.595,0.667,0.731,0.686,0.725,0.741,0.7,0.784,0.783,0.764,0.719,0.605,0.75,0.728,0.838,0.804,0.792,0.757,0.755,0.701,0.74,0.729,0.732,0.724,0.72,0.714,0.72,0.735,0.744,0.719,Stability,ESTA_BACSU,High,Prokaryote +0.143,0.824,0.791,0.884,0.845,0.859,0.159,0.66,0.675,0.712,0.837,0.703,0.726,0.123,0.122,0.192,0.616,0.59,0.818,0.776,0.12,0.112,0.121,0.154,0.17,0.455,0.121,0.671,0.852,0.797,0.914,0.892,0.161,0.119,0.117,0.921,0.144,0.127,0.879,0.727,0.7,0.893,0.175,0.16,0.466,0.196,0.187,0.52,0.2,0.138,0.846,0.87,0.825,0.891,0.872,0.882,0.834,0.845,0.857,0.868,0.471,0.111,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.143,0.824,0.791,0.884,0.845,0.859,0.159,0.66,0.675,0.712,0.837,0.703,0.726,0.123,0.122,0.192,0.616,0.59,0.818,0.776,0.12,0.112,0.121,0.154,0.17,0.455,0.121,0.671,0.852,0.797,0.914,0.892,0.161,0.119,0.117,0.921,0.144,0.127,0.879,0.727,0.7,0.893,0.175,0.16,0.466,0.196,0.187,0.52,0.2,0.138,0.846,0.87,0.825,0.891,0.872,0.882,0.834,0.845,0.857,0.868,0.471,0.111,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.666,0.702,0.687,0.686,0.691,0.684,0.529,0.638,0.685,0.728,0.736,0.638,0.69,0.521,0.742,0.759,0.715,0.709,0.718,0.693,0.601,0.675,0.668,0.654,0.669,0.67,0.694,0.679,0.701,0.698,0.714,0.677,0.578,0.522,0.578,0.656,0.639,0.634,0.692,0.668,0.657,0.699,0.703,0.597,0.745,0.719,0.788,0.773,0.767,0.745,0.679,0.71,0.726,0.729,0.718,0.703,0.699,0.698,0.716,0.697,0.746,0.745,Stability,FECA_ECOLI,High,Prokaryote +0.87,0.869,0.875,0.876,0.878,0.878,0.737,0.829,0.82,0.822,0.735,0.728,0.741,0.729,0.718,0.712,0.761,0.774,0.817,0.818,0.758,0.754,0.716,0.837,0.732,0.831,0.815,0.768,0.799,0.844,0.816,0.807,0.65,0.696,0.724,0.8,0.869,0.87,0.863,0.88,0.876,0.881,0.75,0.738,0.744,0.748,0.881,0.86,0.892,0.884,0.787,0.845,0.859,0.836,0.858,0.827,0.842,0.831,0.8,0.844,0.888,0.82,Stability,FKBP3_HUMAN,Medium,Human +0.714,0.774,0.82,0.798,0.793,0.802,0.705,0.587,0.842,0.818,0.825,0.775,0.775,0.729,0.759,0.771,0.832,0.847,0.859,0.783,0.776,0.729,0.751,0.738,0.754,0.776,0.794,0.778,0.863,0.809,0.863,0.851,0.67,0.706,0.751,0.714,0.813,0.807,0.828,0.794,0.802,0.811,0.743,0.777,0.824,0.75,0.638,0.804,0.603,0.652,0.851,0.816,0.843,0.833,0.823,0.818,0.858,0.817,0.832,0.835,0.778,0.764,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +0.759,0.754,0.748,0.754,0.752,0.749,0.664,0.683,0.759,0.756,0.742,0.739,0.747,0.727,0.719,0.748,0.76,0.757,0.752,0.756,0.647,0.641,0.652,0.647,0.637,0.634,0.632,0.645,0.681,0.758,0.748,0.742,0.661,0.644,0.638,0.754,0.755,0.757,0.764,0.753,0.753,0.762,0.723,0.735,0.723,0.735,0.743,0.759,0.735,0.708,0.744,0.74,0.741,0.741,0.746,0.747,0.741,0.74,0.745,0.742,0.709,0.703,Binding,GCN4_YEAST,Low,Eukaryote +0.879,0.859,0.86,0.859,0.849,0.857,0.769,0.871,0.872,0.869,0.864,0.86,0.868,0.737,0.795,0.878,0.864,0.867,0.877,0.864,0.779,0.874,0.865,0.872,0.854,0.876,0.87,0.87,0.871,0.868,0.842,0.844,0.804,0.832,0.86,0.843,0.877,0.861,0.858,0.863,0.861,0.858,0.778,0.706,0.886,0.79,0.88,0.879,0.862,0.742,0.852,0.851,0.861,0.861,0.858,0.872,0.858,0.86,0.862,0.856,0.866,0.844,OrganismalFitness,GDIA_HUMAN,Low,Human +0.917,0.907,0.913,0.913,0.913,0.913,0.416,0.912,0.916,0.915,0.812,0.47,0.472,0.464,0.493,0.449,0.456,0.5,0.562,0.878,0.443,0.457,0.514,0.471,0.403,0.524,0.63,0.912,0.918,0.923,0.899,0.899,0.47,0.435,0.543,0.902,0.924,0.925,0.922,0.919,0.92,0.931,0.65,0.598,0.653,0.658,0.906,0.898,0.923,0.883,0.875,0.882,0.89,0.895,0.889,0.889,0.887,0.889,0.878,0.89,0.871,0.748,Activity,GFP_AEQVI,Low,Eukaryote +0.765,0.716,0.777,0.776,0.764,0.77,0.624,0.836,0.744,0.729,0.649,0.718,0.682,0.621,0.616,0.696,0.699,0.673,0.724,0.699,0.692,0.652,0.664,0.719,0.672,0.689,0.648,0.692,0.734,0.743,0.739,0.762,0.765,0.655,0.688,0.687,0.794,0.793,0.796,0.765,0.772,0.769,0.643,0.653,0.71,0.674,0.737,0.655,0.755,0.652,0.832,0.761,0.749,0.713,0.752,0.687,0.75,0.797,0.781,0.752,0.776,0.808,Expression,GLPA_HUMAN,Low,Human +0.744,0.81,0.812,0.82,0.828,0.832,0.76,0.754,0.83,0.801,0.827,0.777,0.818,0.752,0.831,0.864,0.867,0.812,0.843,0.818,0.824,0.813,0.789,0.774,0.801,0.804,0.767,0.816,0.763,0.807,0.78,0.767,0.781,0.817,0.816,0.721,0.825,0.825,0.766,0.845,0.848,0.831,0.839,0.633,0.803,0.817,0.875,0.8,0.894,0.787,0.842,0.849,0.84,0.852,0.854,0.849,0.841,0.85,0.838,0.855,0.819,0.845,OrganismalFitness,GRB2_HUMAN,Medium,Human +0.892,0.907,0.883,0.885,0.9,0.901,0.778,0.871,0.857,0.91,0.917,0.836,0.871,0.795,0.851,0.905,0.924,0.914,0.925,0.823,0.791,0.819,0.847,0.858,0.846,0.815,0.756,0.825,0.916,0.905,0.905,0.878,0.746,0.749,0.803,0.889,0.886,0.899,0.898,0.89,0.913,0.907,0.832,0.813,0.913,0.83,0.898,0.924,0.914,0.891,0.918,0.921,0.917,0.919,0.932,0.921,0.926,0.928,0.923,0.927,0.927,0.88,Stability,HCP_LAMBD,Medium,Virus +0.86,0.85,0.882,0.89,0.898,0.895,0.59,0.776,0.883,0.892,0.872,0.654,0.669,0.59,0.585,0.853,0.888,0.871,0.876,0.868,0.5,0.813,0.85,0.842,0.821,0.832,0.85,0.855,0.836,0.873,0.887,0.852,0.511,0.599,0.438,0.666,0.859,0.841,0.86,0.883,0.879,0.875,0.642,0.623,0.876,0.648,0.767,0.845,0.747,0.681,0.892,0.884,0.891,0.887,0.903,0.901,0.898,0.903,0.898,0.902,0.889,0.563,Stability,HECD1_HUMAN,Medium,Human +0.57,0.585,0.585,0.577,0.579,0.581,0.468,0.375,0.581,0.59,0.549,0.549,0.55,0.45,0.549,0.554,0.554,0.585,0.575,0.442,0.565,0.565,0.587,0.61,0.549,0.56,0.56,0.55,0.589,0.595,0.601,0.614,0.45,0.562,0.594,0.616,0.582,0.603,0.611,0.587,0.591,0.598,0.535,0.473,0.558,0.553,0.558,0.585,0.526,0.543,0.561,0.545,0.564,0.566,0.565,0.562,0.568,0.556,0.56,0.561,0.574,0.565,Activity,HEM3_HUMAN,Medium,Human +0.826,0.882,0.873,0.874,0.864,0.861,0.667,0.385,0.852,0.863,0.86,0.812,0.839,0.549,0.645,0.744,0.867,0.88,0.882,0.8,0.811,0.857,0.871,0.875,0.852,0.867,0.858,0.862,0.859,0.865,0.841,0.837,0.575,0.844,0.859,0.881,0.85,0.842,0.875,0.88,0.872,0.882,0.699,0.676,0.811,0.596,0.854,0.884,0.869,0.825,0.848,0.812,0.837,0.838,0.842,0.838,0.835,0.842,0.843,0.84,0.874,0.574,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +0.635,0.631,0.633,0.629,0.633,0.632,0.594,0.69,0.626,0.627,0.644,0.644,0.645,0.535,0.536,0.581,0.652,0.638,0.644,0.63,0.627,0.626,0.635,0.637,0.647,0.635,0.632,0.638,0.636,0.638,0.635,0.629,0.561,0.608,0.646,0.644,0.64,0.646,0.644,0.632,0.634,0.633,0.544,0.522,0.668,0.634,0.624,0.655,0.505,0.571,0.647,0.642,0.652,0.654,0.644,0.652,0.646,0.651,0.64,0.643,0.663,0.626,OrganismalFitness,HMDH_HUMAN,Low,Human +0.89,0.906,0.906,0.907,0.908,0.905,0.815,0.885,0.908,0.906,0.902,0.901,0.907,0.775,0.804,0.837,0.879,0.888,0.89,0.873,0.899,0.905,0.906,0.908,0.902,0.908,0.911,0.91,0.91,0.909,0.909,0.905,0.866,0.904,0.907,0.907,0.902,0.904,0.904,0.907,0.907,0.907,0.81,0.748,0.9,0.889,0.795,0.902,0.78,0.811,0.875,0.878,0.875,0.877,0.877,0.879,0.877,0.875,0.873,0.878,0.897,0.885,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.89,0.906,0.906,0.907,0.908,0.905,0.815,0.885,0.908,0.906,0.902,0.901,0.907,0.775,0.804,0.837,0.879,0.888,0.89,0.873,0.899,0.905,0.906,0.908,0.902,0.908,0.911,0.91,0.91,0.909,0.909,0.905,0.866,0.904,0.907,0.907,0.902,0.904,0.904,0.907,0.907,0.907,0.81,0.748,0.9,0.889,0.795,0.902,0.78,0.811,0.875,0.878,0.875,0.877,0.877,0.879,0.877,0.875,0.873,0.878,0.897,0.885,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.89,0.906,0.906,0.907,0.908,0.905,0.815,0.885,0.908,0.906,0.902,0.901,0.907,0.775,0.804,0.837,0.879,0.888,0.89,0.873,0.899,0.905,0.906,0.908,0.902,0.908,0.911,0.91,0.91,0.909,0.909,0.905,0.866,0.904,0.907,0.907,0.902,0.904,0.904,0.907,0.907,0.907,0.81,0.748,0.9,0.889,0.795,0.902,0.78,0.811,0.875,0.878,0.875,0.877,0.877,0.879,0.877,0.875,0.873,0.878,0.897,0.885,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.734,0.736,0.727,0.728,0.73,0.731,0.638,0.716,0.734,0.737,0.726,0.724,0.73,0.659,0.68,0.736,0.746,0.734,0.732,0.688,0.726,0.726,0.734,0.727,0.734,0.734,0.738,0.734,0.72,0.726,0.721,0.709,0.654,0.731,0.73,0.73,0.739,0.729,0.732,0.734,0.728,0.73,0.694,0.645,0.732,0.734,0.727,0.735,0.724,0.691,0.731,0.736,0.739,0.736,0.738,0.745,0.747,0.738,0.736,0.74,0.746,0.724,OrganismalFitness,HXK4_HUMAN,Medium,Human +0.734,0.736,0.727,0.728,0.73,0.731,0.638,0.716,0.734,0.737,0.726,0.724,0.73,0.659,0.68,0.736,0.746,0.734,0.732,0.688,0.726,0.726,0.734,0.727,0.734,0.734,0.738,0.734,0.72,0.726,0.721,0.709,0.654,0.731,0.73,0.73,0.739,0.729,0.732,0.734,0.728,0.73,0.694,0.645,0.732,0.734,0.727,0.735,0.724,0.691,0.731,0.736,0.739,0.736,0.738,0.745,0.747,0.738,0.736,0.74,0.746,0.724,Expression,HXK4_HUMAN,Medium,Human +0.772,0.763,0.754,0.753,0.776,0.776,0.619,0.744,0.756,0.771,0.617,0.612,0.613,0.611,0.608,0.613,0.625,0.617,0.645,0.731,0.727,0.754,0.761,0.771,0.615,0.626,0.635,0.615,0.755,0.785,0.718,0.713,0.666,0.759,0.755,0.754,0.774,0.769,0.768,0.779,0.776,0.772,0.608,0.613,0.618,0.616,0.677,0.679,0.674,0.661,0.609,0.643,0.646,0.635,0.627,0.615,0.618,0.615,0.606,0.624,0.645,0.625,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +0.907,0.943,0.934,0.932,0.934,0.935,0.862,0.907,0.892,0.888,0.944,0.936,0.937,0.875,0.936,0.942,0.94,0.932,0.939,0.938,0.932,0.942,0.941,0.942,0.942,0.947,0.947,0.943,0.941,0.886,0.931,0.929,0.857,0.935,0.939,0.949,0.934,0.937,0.945,0.936,0.937,0.937,0.919,0.868,0.945,0.939,0.873,0.931,0.939,0.911,0.919,0.938,0.946,0.939,0.945,0.943,0.933,0.944,0.946,0.946,0.938,0.925,OrganismalFitness,IF1_ECOLI,High,Prokaryote +0.788,0.811,0.827,0.821,0.82,0.819,0.751,0.815,0.805,0.809,0.831,0.809,0.804,0.781,0.782,0.796,0.803,0.832,0.825,0.812,0.836,0.819,0.83,0.835,0.868,0.845,0.859,0.843,0.841,0.825,0.822,0.808,0.812,0.742,0.787,0.857,0.79,0.834,0.85,0.8,0.803,0.82,0.788,0.767,0.829,0.799,0.811,0.833,0.812,0.806,0.838,0.809,0.824,0.838,0.84,0.823,0.832,0.827,0.838,0.836,0.843,0.795,Stability,ILF3_HUMAN,High,Human +0.765,0.722,0.784,0.781,0.785,0.786,0.773,0.758,0.831,0.799,0.8,0.811,0.821,0.828,0.814,0.876,0.838,0.842,0.854,0.77,0.786,0.771,0.797,0.811,0.819,0.823,0.845,0.781,0.825,0.793,0.818,0.797,0.7,0.765,0.751,0.793,0.765,0.758,0.779,0.781,0.771,0.785,0.811,0.783,0.821,0.809,0.908,0.894,0.887,0.865,0.84,0.837,0.858,0.841,0.831,0.845,0.839,0.844,0.821,0.847,0.882,0.833,Stability,ISDH_STAAW,High,Prokaryote +0.713,0.708,0.705,0.708,0.716,0.717,0.648,0.702,0.702,0.708,0.65,0.684,0.72,0.633,0.625,0.715,0.73,0.692,0.689,0.688,0.633,0.617,0.69,0.708,0.668,0.741,0.72,0.699,0.706,0.71,0.722,0.702,0.374,0.646,0.708,0.73,0.715,0.722,0.725,0.72,0.718,0.718,0.64,0.624,0.71,0.64,0.674,0.706,0.657,0.637,0.72,0.726,0.722,0.728,0.726,0.72,0.718,0.728,0.724,0.73,0.736,0.642,Expression,KCNE1_HUMAN,Medium,Human +0.713,0.708,0.705,0.708,0.716,0.717,0.648,0.702,0.702,0.708,0.65,0.684,0.72,0.633,0.625,0.715,0.73,0.692,0.689,0.688,0.633,0.617,0.69,0.708,0.668,0.741,0.72,0.699,0.706,0.71,0.722,0.702,0.374,0.646,0.708,0.73,0.715,0.722,0.725,0.72,0.718,0.718,0.64,0.624,0.71,0.64,0.674,0.706,0.657,0.637,0.72,0.726,0.722,0.728,0.726,0.72,0.718,0.728,0.724,0.73,0.736,0.642,Activity,KCNE1_HUMAN,Medium,Human +0.639,0.641,0.639,0.639,0.64,0.64,0.625,0.521,0.614,0.633,0.678,0.549,0.537,0.556,0.525,0.582,0.52,0.558,0.606,0.697,0.652,0.648,0.698,0.679,0.683,0.693,0.63,0.661,0.643,0.678,0.601,0.641,0.593,0.633,0.644,0.661,0.628,0.647,0.68,0.63,0.648,0.646,0.709,0.513,0.68,0.646,0.645,0.461,0.27,0.52,0.702,0.682,0.686,0.717,0.697,0.718,0.676,0.678,0.703,0.702,0.698,0.66,Activity,KCNH2_HUMAN,Medium,Human +0.806,0.808,0.809,0.81,0.811,0.812,0.718,0.792,0.806,0.806,0.813,0.809,0.81,0.735,0.804,0.82,0.82,0.818,0.818,0.8,0.814,0.8,0.799,0.804,0.812,0.806,0.805,0.808,0.803,0.809,0.808,0.8,0.747,0.806,0.792,0.802,0.81,0.804,0.803,0.81,0.807,0.807,0.787,0.718,0.806,0.81,0.79,0.802,0.79,0.766,0.814,0.809,0.812,0.812,0.814,0.812,0.814,0.814,0.814,0.814,0.814,0.804,Activity,KCNJ2_MOUSE,Medium,Eukaryote +0.806,0.808,0.809,0.81,0.811,0.812,0.718,0.792,0.806,0.806,0.813,0.809,0.81,0.735,0.804,0.82,0.82,0.818,0.818,0.8,0.814,0.8,0.799,0.804,0.812,0.806,0.805,0.808,0.803,0.809,0.808,0.8,0.747,0.806,0.792,0.802,0.81,0.804,0.803,0.81,0.807,0.807,0.787,0.718,0.806,0.81,0.79,0.802,0.79,0.766,0.814,0.809,0.812,0.812,0.814,0.812,0.814,0.814,0.814,0.814,0.814,0.804,Expression,KCNJ2_MOUSE,Medium,Eukaryote +0.471,0.591,0.585,0.612,0.6,0.61,0.366,0.523,0.597,0.627,0.548,0.554,0.587,0.389,0.426,0.506,0.604,0.637,0.641,0.57,0.476,0.52,0.598,0.578,0.484,0.586,0.614,0.557,0.634,0.604,0.57,0.548,0.3,0.406,0.515,0.566,0.549,0.572,0.6,0.621,0.623,0.63,0.423,0.334,0.553,0.466,0.561,0.606,0.606,0.509,0.598,0.597,0.614,0.603,0.608,0.607,0.602,0.583,0.597,0.613,0.623,0.506,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +0.853,0.937,0.935,0.936,0.937,0.937,0.794,0.896,0.881,0.928,0.859,0.916,0.926,0.802,0.826,0.844,0.903,0.929,0.931,0.901,0.826,0.868,0.887,0.908,0.847,0.908,0.933,0.894,0.937,0.945,0.935,0.921,0.802,0.82,0.865,0.948,0.847,0.864,0.938,0.933,0.936,0.944,0.794,0.783,0.876,0.832,0.853,0.907,0.901,0.832,0.907,0.917,0.909,0.918,0.911,0.913,0.912,0.91,0.916,0.918,0.876,0.841,Activity,LGK_LIPST,Medium,Eukaryote +0.679,0.693,0.65,0.608,0.666,0.678,0.669,0.682,0.76,0.754,0.732,0.604,0.65,0.687,0.675,0.712,0.636,0.649,0.755,0.747,0.712,0.721,0.706,0.643,0.685,0.63,0.663,0.755,0.666,0.709,0.663,0.616,0.614,0.677,0.712,0.633,0.668,0.711,0.639,0.679,0.704,0.67,0.658,0.724,0.632,0.673,0.75,0.673,0.684,0.652,0.617,0.702,0.775,0.627,0.657,0.676,0.695,0.716,0.535,0.659,0.72,0.758,Expression,LYAM1_HUMAN,Medium,Human +0.881,0.88,0.876,0.877,0.868,0.867,0.848,0.846,0.86,0.859,0.857,0.796,0.88,0.858,0.853,0.851,0.867,0.896,0.774,0.844,0.775,0.778,0.836,0.856,0.84,0.846,0.849,0.849,0.843,0.856,0.85,0.858,0.749,0.831,0.857,0.843,0.879,0.889,0.888,0.875,0.886,0.887,0.848,0.705,0.871,0.814,0.9,0.825,0.908,0.908,0.904,0.897,0.912,0.914,0.895,0.917,0.904,0.908,0.903,0.915,0.916,0.899,Stability,MAFG_MOUSE,Medium,Eukaryote +0.826,0.874,0.876,0.875,0.879,0.878,0.369,0.795,0.829,0.871,0.853,0.739,0.751,0.306,0.273,0.817,0.827,0.798,0.891,0.85,0.289,0.436,0.792,0.768,0.471,0.86,0.819,0.8,0.858,0.846,0.863,0.825,0.79,0.397,0.288,0.34,0.808,0.842,0.846,0.874,0.875,0.875,0.477,0.365,0.868,0.786,0.796,0.879,0.857,0.803,0.847,0.865,0.851,0.834,0.827,0.83,0.835,0.83,0.81,0.837,0.905,0.799,Stability,MBD11_ARATH,Medium,Eukaryote +0.859,0.914,0.897,0.912,0.917,0.918,0.867,0.894,0.855,0.874,0.923,0.879,0.879,0.829,0.869,0.888,0.906,0.91,0.92,0.899,0.91,0.926,0.913,0.914,0.927,0.923,0.929,0.926,0.927,0.922,0.917,0.906,0.821,0.856,0.908,0.926,0.875,0.904,0.917,0.91,0.912,0.903,0.879,0.802,0.918,0.903,0.835,0.912,0.876,0.812,0.881,0.896,0.897,0.901,0.9,0.898,0.9,0.899,0.897,0.896,0.884,0.875,Activity,MET_HUMAN,Medium,Human +0.759,0.776,0.777,0.78,0.776,0.773,0.755,0.741,0.762,0.759,0.741,0.759,0.754,0.759,0.752,0.758,0.753,0.759,0.756,0.758,0.761,0.745,0.741,0.736,0.751,0.744,0.747,0.751,0.739,0.762,0.732,0.741,0.765,0.754,0.745,0.733,0.75,0.742,0.733,0.773,0.773,0.767,0.762,0.746,0.758,0.769,0.731,0.747,0.738,0.745,0.766,0.767,0.767,0.763,0.764,0.761,0.758,0.756,0.761,0.764,0.764,0.754,OrganismalFitness,MK01_HUMAN,Medium,Human +0.847,0.9,0.902,0.902,0.904,0.902,0.752,0.887,0.891,0.899,0.883,0.894,0.902,0.799,0.833,0.835,0.864,0.884,0.891,0.906,0.804,0.901,0.903,0.903,0.848,0.887,0.902,0.885,0.906,0.911,0.903,0.904,0.748,0.779,0.889,0.894,0.852,0.887,0.89,0.898,0.907,0.905,0.81,0.793,0.863,0.837,0.704,0.839,0.797,0.765,0.862,0.856,0.846,0.855,0.868,0.858,0.852,0.854,0.862,0.863,0.84,0.815,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +0.817,0.841,0.831,0.831,0.831,0.833,0.784,0.814,0.837,0.836,0.832,0.834,0.837,0.78,0.818,0.833,0.831,0.838,0.825,0.829,0.804,0.829,0.831,0.832,0.814,0.835,0.833,0.819,0.837,0.83,0.828,0.821,0.797,0.808,0.84,0.831,0.823,0.839,0.832,0.836,0.841,0.838,0.799,0.747,0.839,0.815,0.809,0.835,0.736,0.786,0.828,0.826,0.829,0.826,0.829,0.829,0.832,0.83,0.829,0.83,0.834,0.82,OrganismalFitness,MSH2_HUMAN,Medium,Human +0.529,0.78,0.784,0.792,0.789,0.784,0.455,0.766,0.73,0.736,0.621,0.73,0.756,0.389,0.484,0.519,0.588,0.612,0.658,0.718,0.505,0.537,0.656,0.73,0.558,0.74,0.776,0.715,0.764,0.771,0.785,0.765,0.547,0.529,0.566,0.747,0.523,0.566,0.725,0.778,0.776,0.782,0.464,0.288,0.523,0.461,0.564,0.688,0.704,0.55,0.621,0.645,0.632,0.601,0.594,0.644,0.633,0.637,0.608,0.63,0.664,0.538,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +0.79,0.798,0.79,0.798,0.787,0.788,0.677,0.78,0.806,0.805,0.807,0.789,0.788,0.711,0.751,0.795,0.8,0.797,0.808,0.769,0.781,0.786,0.789,0.809,0.785,0.783,0.788,0.777,0.817,0.794,0.791,0.782,0.622,0.791,0.792,0.795,0.792,0.793,0.798,0.798,0.798,0.801,0.735,0.699,0.805,0.756,0.746,0.793,0.801,0.725,0.796,0.8,0.795,0.801,0.796,0.796,0.8,0.796,0.801,0.801,0.796,0.766,OrganismalFitness,MTHR_HUMAN,Low,Human +0.595,0.686,0.682,0.669,0.69,0.695,0.482,0.482,0.703,0.689,0.75,0.724,0.723,0.569,0.699,0.786,0.836,0.75,0.648,0.685,0.558,0.515,0.576,0.598,0.629,0.686,0.729,0.704,0.751,0.631,0.674,0.63,0.6,0.642,0.587,0.552,0.695,0.61,0.574,0.703,0.691,0.685,0.627,0.517,0.707,0.669,0.695,0.731,0.779,0.706,0.752,0.738,0.741,0.746,0.746,0.752,0.745,0.764,0.757,0.756,0.732,0.762,Stability,MYO3_YEAST,High,Eukaryote +0.745,0.737,0.741,0.742,0.739,0.742,0.562,0.726,0.741,0.738,0.559,0.551,0.55,0.552,0.551,0.543,0.556,0.562,0.587,0.708,0.718,0.738,0.748,0.753,0.563,0.566,0.563,0.557,0.731,0.758,0.69,0.69,0.609,0.733,0.74,0.763,0.752,0.755,0.76,0.753,0.757,0.756,0.554,0.558,0.561,0.553,0.634,0.64,0.64,0.614,0.577,0.598,0.597,0.584,0.584,0.575,0.579,0.571,0.555,0.581,0.599,0.58,OrganismalFitness,NCAP_I34A1,Medium,Virus +0.899,0.909,0.913,0.914,0.906,0.913,0.886,0.894,0.897,0.9,0.86,0.885,0.874,0.901,0.886,0.914,0.925,0.921,0.9,0.885,0.903,0.897,0.881,0.878,0.896,0.87,0.898,0.857,0.874,0.902,0.887,0.881,0.874,0.903,0.895,0.872,0.914,0.906,0.903,0.91,0.91,0.912,0.895,0.898,0.87,0.885,0.9,0.874,0.905,0.905,0.922,0.922,0.922,0.922,0.925,0.927,0.921,0.927,0.92,0.926,0.882,0.909,Stability,NKX31_HUMAN,High,Human +0.949,0.942,0.936,0.954,0.942,0.953,0.793,0.924,0.969,0.97,0.943,0.727,0.736,0.637,0.719,0.791,0.835,0.89,0.946,0.766,0.794,0.907,0.864,0.882,0.824,0.828,0.96,0.926,0.9,0.958,0.948,0.922,0.778,0.818,0.908,0.914,0.938,0.958,0.95,0.949,0.962,0.946,0.762,0.764,0.936,0.686,0.864,0.936,0.676,0.776,0.814,0.886,0.796,0.852,0.811,0.81,0.825,0.928,0.944,0.831,0.928,0.748,Activity,NPC1_HUMAN,Low,Human +0.949,0.942,0.936,0.954,0.942,0.953,0.793,0.924,0.969,0.97,0.943,0.727,0.736,0.637,0.719,0.791,0.835,0.89,0.946,0.766,0.794,0.907,0.864,0.882,0.824,0.828,0.96,0.926,0.9,0.958,0.948,0.922,0.778,0.818,0.908,0.914,0.938,0.958,0.95,0.949,0.962,0.946,0.762,0.764,0.936,0.686,0.864,0.936,0.676,0.776,0.814,0.886,0.796,0.852,0.811,0.81,0.825,0.928,0.944,0.831,0.928,0.748,Activity,NPC1_HUMAN,Low,Human +0.879,0.947,0.933,0.933,0.952,0.953,0.606,0.919,0.957,0.963,0.575,0.684,0.922,0.568,0.539,0.564,0.7,0.914,0.917,0.872,0.95,0.942,0.924,0.912,0.537,0.898,0.895,0.889,0.945,0.944,0.831,0.841,0.482,0.941,0.93,0.946,0.914,0.923,0.934,0.955,0.953,0.953,0.535,0.568,0.549,0.568,0.729,0.766,0.75,0.644,0.658,0.688,0.713,0.717,0.748,0.773,0.703,0.764,0.641,0.757,0.649,0.561,OrganismalFitness,NRAM_I33A0,Low,Virus +0.693,0.82,0.817,0.8,0.82,0.822,0.522,0.699,0.808,0.841,0.821,0.806,0.836,0.615,0.637,0.658,0.77,0.804,0.806,0.747,0.616,0.697,0.8,0.823,0.674,0.851,0.819,0.812,0.836,0.829,0.848,0.818,0.573,0.642,0.713,0.846,0.716,0.745,0.841,0.818,0.824,0.841,0.666,0.545,0.811,0.685,0.685,0.823,0.71,0.728,0.753,0.759,0.738,0.764,0.754,0.774,0.779,0.78,0.772,0.777,0.826,0.742,Expression,NUD15_HUMAN,High,Human +0.656,0.85,0.817,0.811,0.813,0.826,0.644,0.75,0.848,0.831,0.753,0.477,0.515,0.636,0.592,0.59,0.651,0.674,0.683,0.81,0.796,0.832,0.844,0.859,0.569,0.79,0.599,0.866,0.862,0.824,0.837,0.838,0.534,0.728,0.621,0.685,0.704,0.634,0.689,0.833,0.808,0.836,0.671,0.723,0.666,0.672,0.899,0.836,0.859,0.877,0.832,0.869,0.851,0.87,0.865,0.863,0.858,0.843,0.856,0.86,0.842,0.672,Stability,NUSA_ECOLI,High,Prokaryote +0.869,0.869,0.88,0.886,0.884,0.885,0.763,0.843,0.901,0.889,0.84,0.86,0.892,0.747,0.862,0.885,0.877,0.871,0.864,0.858,0.841,0.862,0.849,0.848,0.874,0.86,0.862,0.85,0.847,0.88,0.864,0.832,0.816,0.842,0.808,0.858,0.876,0.871,0.881,0.876,0.878,0.885,0.861,0.708,0.868,0.897,0.899,0.883,0.917,0.895,0.874,0.894,0.888,0.877,0.88,0.882,0.891,0.897,0.888,0.89,0.872,0.899,Stability,NUSG_MYCTU,High,Prokaryote +0.815,0.901,0.922,0.927,0.924,0.922,0.619,0.81,0.896,0.896,0.887,0.875,0.882,0.627,0.863,0.893,0.913,0.903,0.894,0.902,0.878,0.867,0.867,0.86,0.873,0.867,0.88,0.86,0.85,0.897,0.903,0.895,0.788,0.624,0.674,0.811,0.875,0.876,0.891,0.922,0.918,0.921,0.832,0.619,0.843,0.853,0.906,0.847,0.934,0.928,0.926,0.929,0.933,0.931,0.932,0.927,0.92,0.922,0.925,0.935,0.934,0.882,Stability,OBSCN_HUMAN,High,Human +0.833,0.832,0.851,0.852,0.815,0.832,0.831,0.824,0.843,0.845,0.857,0.808,0.79,0.825,0.817,0.862,0.881,0.833,0.857,0.864,0.847,0.845,0.864,0.835,0.821,0.843,0.865,0.852,0.852,0.865,0.838,0.83,0.824,0.817,0.816,0.823,0.853,0.82,0.826,0.851,0.825,0.83,0.855,0.822,0.867,0.869,0.881,0.891,0.883,0.911,0.866,0.863,0.866,0.876,0.872,0.867,0.873,0.871,0.862,0.871,0.88,0.836,Stability,ODP2_GEOSE,High,Prokaryote +0.585,0.704,0.757,0.773,0.781,0.78,0.439,0.77,0.627,0.8,0.527,0.67,0.703,0.551,0.646,0.533,0.602,0.648,0.574,0.686,0.749,0.72,0.643,0.647,0.729,0.66,0.674,0.719,0.659,0.763,0.639,0.468,0.166,0.682,0.651,0.538,0.758,0.686,0.686,0.779,0.757,0.77,0.506,0.317,0.665,0.622,0.615,0.697,0.541,0.383,0.536,0.472,0.449,0.472,0.471,0.512,0.527,0.559,0.522,0.516,0.764,0.677,Expression,OPSD_HUMAN,High,Human +0.669,0.803,0.772,0.774,0.78,0.789,0.4,0.551,0.775,0.797,0.729,0.768,0.778,0.423,0.573,0.626,0.695,0.676,0.7,0.721,0.649,0.687,0.746,0.778,0.659,0.764,0.757,0.756,0.779,0.792,0.763,0.707,0.474,0.63,0.736,0.771,0.685,0.739,0.778,0.788,0.783,0.796,0.509,0.384,0.675,0.581,0.751,0.736,0.809,0.637,0.702,0.717,0.739,0.713,0.71,0.707,0.706,0.739,0.693,0.715,0.807,0.677,Activity,OTC_HUMAN,Medium,Human +0.61,0.755,0.632,0.652,0.663,0.665,0.583,0.707,0.595,0.599,0.838,0.796,0.797,0.581,0.806,0.806,0.727,0.753,0.804,0.64,0.577,0.715,0.746,0.756,0.729,0.734,0.766,0.742,0.692,0.748,0.764,0.721,0.734,0.607,0.674,0.75,0.628,0.638,0.75,0.666,0.655,0.707,0.651,0.603,0.78,0.827,0.821,0.792,0.84,0.807,0.759,0.769,0.771,0.766,0.772,0.759,0.767,0.73,0.738,0.762,0.869,0.844,Stability,OTU7A_HUMAN,High,Human +0.558,0.661,0.66,0.664,0.668,0.676,0.562,0.562,0.587,0.593,0.637,0.613,0.623,0.55,0.568,0.618,0.635,0.664,0.67,0.572,0.54,0.585,0.607,0.59,0.567,0.609,0.624,0.604,0.654,0.676,0.66,0.645,0.514,0.568,0.57,0.582,0.591,0.586,0.588,0.668,0.67,0.669,0.557,0.539,0.629,0.583,0.645,0.67,0.656,0.564,0.644,0.65,0.646,0.644,0.641,0.656,0.646,0.65,0.637,0.654,0.66,0.6,Activity,OXDA_RHOTO,High,Eukaryote +0.558,0.661,0.66,0.664,0.668,0.676,0.562,0.562,0.587,0.593,0.637,0.613,0.623,0.55,0.568,0.618,0.635,0.664,0.67,0.572,0.54,0.585,0.607,0.59,0.567,0.609,0.624,0.604,0.654,0.676,0.66,0.645,0.514,0.568,0.57,0.582,0.591,0.586,0.588,0.668,0.67,0.669,0.557,0.539,0.629,0.583,0.645,0.67,0.656,0.564,0.644,0.65,0.646,0.644,0.641,0.656,0.646,0.65,0.637,0.654,0.66,0.6,Expression,OXDA_RHOTO,High,Eukaryote +0.742,0.742,0.74,0.739,0.731,0.732,0.62,0.738,0.738,0.738,0.764,0.743,0.763,0.63,0.625,0.704,0.729,0.738,0.746,0.722,0.703,0.733,0.756,0.737,0.722,0.759,0.778,0.768,0.741,0.749,0.76,0.74,0.604,0.668,0.738,0.732,0.743,0.754,0.745,0.735,0.743,0.741,0.632,0.609,0.748,0.636,0.71,0.759,0.724,0.71,0.739,0.747,0.74,0.736,0.738,0.742,0.731,0.736,0.732,0.737,0.75,0.681,OrganismalFitness,P53_HUMAN,Low,Human +0.742,0.742,0.74,0.739,0.731,0.732,0.62,0.738,0.738,0.738,0.764,0.743,0.763,0.63,0.625,0.704,0.729,0.738,0.746,0.722,0.703,0.733,0.756,0.737,0.722,0.759,0.778,0.768,0.741,0.749,0.76,0.74,0.604,0.668,0.738,0.732,0.743,0.754,0.745,0.735,0.743,0.741,0.632,0.609,0.748,0.636,0.71,0.759,0.724,0.71,0.739,0.747,0.74,0.736,0.738,0.742,0.731,0.736,0.732,0.737,0.75,0.681,OrganismalFitness,P53_HUMAN,Low,Human +0.742,0.742,0.74,0.739,0.731,0.732,0.62,0.738,0.738,0.738,0.764,0.743,0.763,0.63,0.625,0.704,0.729,0.738,0.746,0.722,0.703,0.733,0.756,0.737,0.722,0.759,0.778,0.768,0.741,0.749,0.76,0.74,0.604,0.668,0.738,0.732,0.743,0.754,0.745,0.735,0.743,0.741,0.632,0.609,0.748,0.636,0.71,0.759,0.724,0.71,0.739,0.747,0.74,0.736,0.738,0.742,0.731,0.736,0.732,0.737,0.75,0.681,OrganismalFitness,P53_HUMAN,Low,Human +0.742,0.742,0.74,0.739,0.731,0.732,0.62,0.738,0.738,0.738,0.764,0.743,0.763,0.63,0.625,0.704,0.729,0.738,0.746,0.722,0.703,0.733,0.756,0.737,0.722,0.759,0.778,0.768,0.741,0.749,0.76,0.74,0.604,0.668,0.738,0.732,0.743,0.754,0.745,0.735,0.743,0.741,0.632,0.609,0.748,0.636,0.71,0.759,0.724,0.71,0.739,0.747,0.74,0.736,0.738,0.742,0.731,0.736,0.732,0.737,0.75,0.681,OrganismalFitness,P53_HUMAN,Low,Human +0.749,0.861,0.905,0.905,0.874,0.876,0.642,0.855,0.906,0.879,0.891,0.856,0.857,0.721,0.86,0.864,0.901,0.889,0.905,0.897,0.773,0.823,0.827,0.86,0.796,0.858,0.908,0.855,0.945,0.803,0.905,0.877,0.782,0.801,0.755,0.837,0.775,0.744,0.779,0.847,0.844,0.86,0.757,0.582,0.824,0.828,0.748,0.902,0.893,0.698,0.861,0.878,0.856,0.863,0.888,0.892,0.886,0.886,0.899,0.891,0.896,0.895,OrganismalFitness,P84126_THETH,Medium,Prokaryote +0.755,0.745,0.735,0.738,0.764,0.76,0.603,0.734,0.753,0.764,0.759,0.765,0.764,0.609,0.634,0.671,0.722,0.717,0.739,0.727,0.731,0.753,0.754,0.761,0.741,0.756,0.764,0.759,0.745,0.776,0.767,0.744,0.524,0.72,0.74,0.741,0.745,0.757,0.751,0.76,0.766,0.763,0.7,0.447,0.759,0.744,0.474,0.75,0.681,0.532,0.709,0.701,0.711,0.709,0.709,0.709,0.712,0.716,0.718,0.713,0.75,0.684,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +0.797,0.83,0.823,0.826,0.823,0.823,0.694,0.775,0.807,0.823,0.81,0.797,0.809,0.696,0.8,0.806,0.812,0.816,0.803,0.811,0.701,0.796,0.798,0.806,0.793,0.813,0.813,0.814,0.812,0.816,0.805,0.792,0.721,0.735,0.804,0.81,0.795,0.808,0.811,0.828,0.827,0.829,0.784,0.688,0.805,0.802,0.781,0.831,0.814,0.751,0.815,0.808,0.813,0.812,0.813,0.816,0.812,0.812,0.814,0.817,0.825,0.798,Activity,PAI1_HUMAN,,Human +0.463,0.46,0.464,0.453,0.457,0.453,0.228,0.429,0.245,0.258,0.231,0.236,0.245,0.22,0.218,0.22,0.215,0.215,0.401,0.391,0.417,0.422,0.41,0.455,0.241,0.387,0.407,0.381,0.409,0.431,0.396,0.417,0.266,0.413,0.435,0.439,0.46,0.454,0.449,0.455,0.459,0.451,0.226,0.223,0.23,0.232,0.265,0.257,0.205,0.273,0.209,0.209,0.222,0.212,0.215,0.225,0.211,0.216,0.215,0.229,0.254,0.213,OrganismalFitness,PA_I34A1,Medium,Virus +0.707,0.809,0.89,0.882,0.781,0.78,0.843,0.835,0.886,0.883,0.851,0.877,0.889,0.89,0.903,0.885,0.897,0.882,0.889,0.827,0.845,0.875,0.848,0.872,0.836,0.859,0.871,0.868,0.833,0.827,0.841,0.81,0.756,0.798,0.782,0.867,0.839,0.824,0.866,0.788,0.798,0.791,0.875,0.783,0.872,0.884,0.703,0.848,0.835,0.735,0.838,0.815,0.816,0.819,0.819,0.827,0.826,0.828,0.83,0.826,0.882,0.883,Activity,PHOT_CHLRE,High,Eukaryote +0.873,0.868,0.9,0.898,0.889,0.896,0.847,0.821,0.875,0.896,0.888,0.848,0.89,0.862,0.878,0.892,0.895,0.819,0.806,0.895,0.752,0.885,0.89,0.871,0.826,0.859,0.883,0.878,0.864,0.883,0.889,0.863,0.869,0.812,0.87,0.891,0.869,0.894,0.899,0.89,0.9,0.905,0.851,0.644,0.907,0.839,0.889,0.897,0.889,0.868,0.884,0.895,0.88,0.869,0.865,0.89,0.879,0.888,0.887,0.888,0.903,0.901,Stability,PIN1_HUMAN,High,Human +0.888,0.864,0.868,0.867,0.874,0.87,0.863,0.804,0.873,0.899,0.812,0.85,0.85,0.86,0.887,0.897,0.9,0.885,0.863,0.867,0.826,0.812,0.826,0.822,0.851,0.838,0.841,0.82,0.82,0.858,0.828,0.822,0.806,0.841,0.859,0.797,0.86,0.874,0.836,0.866,0.877,0.861,0.858,0.838,0.791,0.852,0.832,0.773,0.898,0.857,0.892,0.894,0.904,0.9,0.906,0.891,0.898,0.879,0.893,0.897,0.807,0.888,Stability,PITX2_HUMAN,High,Human +0.866,0.888,0.844,0.835,0.846,0.866,0.864,0.821,0.803,0.807,0.867,0.86,0.871,0.857,0.859,0.894,0.863,0.866,0.865,0.862,0.881,0.89,0.875,0.875,0.887,0.872,0.881,0.902,0.864,0.829,0.87,0.865,0.783,0.874,0.898,0.904,0.872,0.882,0.873,0.877,0.87,0.859,0.848,0.853,0.871,0.843,0.887,0.872,0.876,0.894,0.859,0.861,0.872,0.868,0.863,0.861,0.862,0.868,0.865,0.866,0.89,0.877,Stability,PKN1_HUMAN,High,Human +0.768,0.817,0.798,0.816,0.821,0.824,0.574,0.733,0.815,0.821,0.658,0.581,0.596,0.583,0.577,0.624,0.705,0.727,0.754,0.757,0.738,0.788,0.795,0.79,0.632,0.764,0.772,0.765,0.801,0.82,0.759,0.75,0.598,0.599,0.718,0.773,0.756,0.775,0.792,0.793,0.809,0.816,0.58,0.576,0.682,0.585,0.627,0.692,0.596,0.618,0.683,0.686,0.688,0.687,0.684,0.678,0.691,0.69,0.69,0.69,0.597,0.588,OrganismalFitness,POLG_CXB3N,Medium,Virus +0.773,0.829,0.767,0.781,0.813,0.816,0.472,0.761,0.896,0.898,0.568,0.46,0.474,0.45,0.459,0.487,0.504,0.547,0.595,0.784,0.761,0.761,0.763,0.715,0.77,0.783,0.8,0.798,0.81,0.87,0.854,0.794,0.512,0.455,0.571,0.795,0.75,0.776,0.834,0.826,0.83,0.856,0.467,0.448,0.629,0.464,0.603,0.727,0.453,0.555,0.523,0.526,0.56,0.534,0.534,0.539,0.529,0.536,0.528,0.548,0.498,0.495,OrganismalFitness,POLG_DEN26,Low,Virus +0.679,0.708,0.686,0.692,0.705,0.712,0.186,0.284,0.703,0.7,0.283,0.645,0.643,0.186,0.202,0.196,0.229,0.212,0.237,0.484,0.4,0.556,0.631,0.596,0.542,0.588,0.467,0.5,0.615,0.732,0.601,0.498,0.286,0.646,0.654,0.619,0.667,0.667,0.669,0.682,0.668,0.672,0.197,0.177,0.596,0.228,0.195,0.435,0.517,0.406,0.335,0.4,0.284,0.408,0.33,0.299,0.413,0.298,0.299,0.362,0.276,0.248,OrganismalFitness,POLG_HCVJF,Medium,Virus +0.582,0.867,0.806,0.816,0.825,0.828,0.544,0.815,0.703,0.845,0.818,0.581,0.589,0.584,0.59,0.567,0.613,0.606,0.589,0.891,0.518,0.5,0.496,0.547,0.542,0.512,0.526,0.462,0.577,0.876,0.891,0.895,0.517,0.529,0.506,0.553,0.704,0.706,0.698,0.81,0.813,0.805,0.648,0.595,0.643,0.623,0.845,0.827,0.902,0.884,0.896,0.898,0.887,0.905,0.904,0.917,0.903,0.922,0.904,0.913,0.883,0.784,Stability,POLG_PESV,Medium,Virus +0.785,0.863,0.872,0.866,0.872,0.867,0.773,0.875,0.865,0.877,0.861,0.879,0.888,0.741,0.766,0.78,0.807,0.855,0.889,0.882,0.868,0.902,0.863,0.866,0.842,0.916,0.917,0.917,0.861,0.897,0.849,0.8,0.783,0.884,0.904,0.905,0.871,0.905,0.9,0.874,0.889,0.885,0.757,0.709,0.873,0.778,0.83,0.888,0.848,0.793,0.825,0.832,0.822,0.816,0.831,0.821,0.825,0.819,0.81,0.821,0.841,0.789,Activity,PPARG_HUMAN,Medium,Human +0.805,0.807,0.807,0.809,0.807,0.807,0.728,0.774,0.81,0.807,0.808,0.817,0.815,0.744,0.754,0.784,0.804,0.814,0.813,0.801,0.776,0.783,0.818,0.812,0.801,0.818,0.815,0.818,0.813,0.81,0.807,0.787,0.735,0.778,0.803,0.82,0.807,0.812,0.815,0.808,0.809,0.809,0.766,0.717,0.807,0.79,0.792,0.817,0.804,0.772,0.801,0.8,0.798,0.799,0.798,0.798,0.796,0.794,0.8,0.8,0.819,0.783,OrganismalFitness,PPM1D_HUMAN,Low,Human +0.857,0.872,0.916,0.913,0.907,0.91,0.76,0.854,0.917,0.922,0.924,0.805,0.847,0.765,0.771,0.894,0.917,0.915,0.898,0.901,0.864,0.911,0.918,0.902,0.895,0.911,0.917,0.917,0.905,0.901,0.903,0.878,0.811,0.814,0.818,0.858,0.884,0.888,0.895,0.908,0.914,0.909,0.784,0.766,0.884,0.78,0.899,0.862,0.917,0.901,0.919,0.924,0.924,0.922,0.92,0.923,0.923,0.922,0.919,0.926,0.92,0.906,Stability,PR40A_HUMAN,Medium,Human +0.863,0.87,0.876,0.878,0.874,0.874,0.652,0.823,0.867,0.87,0.874,0.861,0.878,0.683,0.697,0.72,0.764,0.86,0.877,0.852,0.708,0.855,0.877,0.871,0.767,0.876,0.871,0.881,0.862,0.874,0.858,0.828,0.627,0.696,0.85,0.873,0.857,0.871,0.873,0.873,0.875,0.874,0.711,0.584,0.881,0.717,0.855,0.873,0.868,0.773,0.774,0.803,0.815,0.821,0.796,0.791,0.8,0.799,0.799,0.804,0.868,0.787,Expression,PRKN_HUMAN,Low,Human +0.767,0.759,0.726,0.731,0.753,0.754,0.666,0.709,0.722,0.72,0.863,0.796,0.801,0.681,0.71,0.823,0.839,0.806,0.803,0.774,0.661,0.627,0.722,0.735,0.734,0.754,0.511,0.741,0.762,0.737,0.776,0.727,0.658,0.657,0.715,0.742,0.773,0.764,0.759,0.755,0.752,0.765,0.691,0.659,0.813,0.673,0.838,0.811,0.844,0.821,0.841,0.825,0.822,0.823,0.829,0.821,0.83,0.835,0.825,0.835,0.806,0.815,Stability,PSAE_PICP2,Medium,Prokaryote +0.812,0.823,0.822,0.822,0.822,0.823,0.72,0.807,0.823,0.826,0.822,0.824,0.834,0.726,0.782,0.815,0.824,0.806,0.808,0.828,0.749,0.824,0.827,0.821,0.778,0.814,0.815,0.818,0.814,0.832,0.816,0.811,0.71,0.778,0.83,0.817,0.81,0.83,0.828,0.824,0.83,0.828,0.762,0.71,0.824,0.802,0.801,0.824,0.816,0.789,0.825,0.822,0.824,0.826,0.823,0.822,0.823,0.827,0.824,0.824,0.819,0.806,Expression,PTEN_HUMAN,Medium,Human +0.812,0.823,0.822,0.822,0.822,0.823,0.72,0.807,0.823,0.826,0.822,0.824,0.834,0.726,0.782,0.815,0.824,0.806,0.808,0.828,0.749,0.824,0.827,0.821,0.778,0.814,0.815,0.818,0.814,0.832,0.816,0.811,0.71,0.778,0.83,0.817,0.81,0.83,0.828,0.824,0.83,0.828,0.762,0.71,0.824,0.802,0.801,0.824,0.816,0.789,0.825,0.822,0.824,0.826,0.823,0.822,0.823,0.827,0.824,0.824,0.819,0.806,Activity,PTEN_HUMAN,Medium,Human +0.869,0.849,0.83,0.84,0.856,0.858,0.597,0.822,0.87,0.87,0.847,0.867,0.872,0.623,0.618,0.608,0.635,0.652,0.705,0.843,0.869,0.829,0.818,0.803,0.87,0.828,0.822,0.831,0.824,0.872,0.833,0.83,0.737,0.865,0.837,0.84,0.869,0.858,0.863,0.865,0.857,0.858,0.783,0.58,0.868,0.858,0.723,0.866,0.641,0.712,0.676,0.69,0.716,0.697,0.693,0.698,0.695,0.692,0.68,0.698,0.682,0.649,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +0.782,0.792,0.796,0.796,0.798,0.796,0.595,0.75,0.796,0.8,0.789,0.782,0.793,0.618,0.638,0.754,0.777,0.806,0.802,0.801,0.76,0.758,0.758,0.754,0.797,0.794,0.79,0.792,0.785,0.768,0.776,0.761,0.678,0.768,0.754,0.752,0.792,0.782,0.776,0.798,0.798,0.794,0.73,0.605,0.794,0.77,0.754,0.796,0.744,0.71,0.766,0.779,0.798,0.785,0.794,0.797,0.8,0.8,0.782,0.79,0.8,0.82,Binding,Q53Z42_HUMAN,Medium,Human +0.782,0.792,0.796,0.796,0.798,0.796,0.595,0.75,0.796,0.8,0.789,0.782,0.793,0.618,0.638,0.754,0.777,0.806,0.802,0.801,0.76,0.758,0.758,0.754,0.797,0.794,0.79,0.792,0.785,0.768,0.776,0.761,0.678,0.768,0.754,0.752,0.792,0.782,0.776,0.798,0.798,0.794,0.73,0.605,0.794,0.77,0.754,0.796,0.744,0.71,0.766,0.779,0.798,0.785,0.794,0.797,0.8,0.8,0.782,0.79,0.8,0.82,Expression,Q53Z42_HUMAN,Medium,Human +0.845,0.877,0.873,0.872,0.88,0.875,0.757,0.813,0.88,0.88,0.857,0.814,0.829,0.728,0.798,0.826,0.842,0.85,0.854,0.879,0.862,0.883,0.886,0.887,0.868,0.888,0.892,0.885,0.883,0.879,0.868,0.851,0.768,0.86,0.885,0.881,0.861,0.877,0.879,0.876,0.877,0.879,0.813,0.714,0.861,0.833,0.829,0.862,0.86,0.772,0.85,0.844,0.857,0.848,0.852,0.841,0.854,0.847,0.845,0.855,0.861,0.834,Activity,Q59976_STRSQ,Medium,Prokaryote +0.658,0.724,0.619,0.623,0.66,0.658,0.429,0.526,0.731,0.728,0.622,0.487,0.473,0.465,0.484,0.465,0.458,0.471,0.441,0.67,0.443,0.496,0.464,0.506,0.445,0.441,0.464,0.448,0.448,0.728,0.698,0.701,0.482,0.484,0.462,0.499,0.63,0.624,0.631,0.658,0.657,0.659,0.545,0.515,0.534,0.533,0.66,0.605,0.643,0.595,0.585,0.594,0.604,0.659,0.645,0.648,0.641,0.638,0.638,0.635,0.524,0.513,Activity,Q6WV12_9MAXI,Low,Eukaryote +0.898,0.921,0.918,0.93,0.928,0.942,0.838,0.891,0.923,0.927,0.938,0.92,0.94,0.873,0.885,0.901,0.926,0.92,0.926,0.807,0.93,0.934,0.931,0.936,0.922,0.932,0.933,0.939,0.937,0.927,0.923,0.906,0.859,0.928,0.937,0.937,0.926,0.938,0.931,0.94,0.94,0.939,0.896,0.882,0.914,0.897,0.862,0.925,0.886,0.847,0.934,0.931,0.928,0.936,0.933,0.93,0.935,0.927,0.925,0.94,0.948,0.888,Activity,Q837P4_ENTFA,Medium,Prokaryote +0.813,0.919,0.915,0.912,0.918,0.927,0.867,0.883,0.792,0.813,0.861,0.867,0.862,0.767,0.842,0.839,0.844,0.882,0.873,0.85,0.858,0.892,0.924,0.918,0.836,0.897,0.911,0.917,0.935,0.897,0.888,0.908,0.803,0.856,0.895,0.916,0.861,0.886,0.911,0.917,0.919,0.918,0.852,0.777,0.877,0.848,0.84,0.894,0.903,0.796,0.864,0.859,0.85,0.842,0.854,0.831,0.86,0.862,0.842,0.857,0.836,0.846,Activity,Q837P5_ENTFA,Medium,Prokaryote +0.592,0.684,0.598,0.598,0.646,0.645,0.481,0.666,0.665,0.67,0.594,0.487,0.484,0.478,0.47,0.479,0.484,0.492,0.508,0.612,0.5,0.499,0.511,0.499,0.479,0.478,0.495,0.633,0.64,0.685,0.656,0.661,0.477,0.464,0.478,0.675,0.584,0.592,0.673,0.641,0.649,0.685,0.535,0.538,0.537,0.535,0.604,0.606,0.679,0.605,0.594,0.579,0.598,0.599,0.591,0.609,0.598,0.598,0.595,0.597,0.539,0.45,Activity,Q8WTC7_9CNID,Low,Eukaryote +0.928,0.931,0.916,0.914,0.943,0.946,0.596,0.889,0.621,0.621,0.669,0.626,0.609,0.625,0.647,0.648,0.637,0.889,0.937,0.89,0.838,0.839,0.859,0.854,0.845,0.808,0.828,0.828,0.818,0.935,0.897,0.876,0.602,0.769,0.828,0.792,0.912,0.922,0.913,0.936,0.945,0.939,0.623,0.583,0.68,0.623,0.824,0.843,0.871,0.792,0.736,0.75,0.771,0.778,0.787,0.758,0.763,0.749,0.75,0.778,0.721,0.718,OrganismalFitness,R1AB_SARS2,Medium,Virus +0.797,0.754,0.752,0.754,0.76,0.769,0.739,0.762,0.795,0.807,0.797,0.716,0.761,0.777,0.825,0.835,0.806,0.822,0.75,0.806,0.856,0.8,0.771,0.786,0.808,0.779,0.758,0.773,0.756,0.809,0.748,0.711,0.649,0.772,0.8,0.772,0.819,0.813,0.794,0.784,0.788,0.795,0.802,0.717,0.731,0.786,0.805,0.697,0.831,0.821,0.789,0.82,0.804,0.808,0.821,0.802,0.8,0.807,0.812,0.81,0.77,0.833,Stability,RAD_ANTMA,High,Eukaryote +0.749,0.785,0.775,0.783,0.788,0.789,0.704,0.82,0.782,0.789,0.769,0.805,0.806,0.688,0.713,0.795,0.763,0.786,0.801,0.788,0.806,0.792,0.793,0.796,0.794,0.815,0.814,0.801,0.799,0.802,0.773,0.792,0.738,0.805,0.804,0.791,0.791,0.788,0.787,0.797,0.784,0.788,0.726,0.712,0.747,0.804,0.715,0.749,0.704,0.735,0.764,0.763,0.773,0.751,0.745,0.759,0.763,0.742,0.73,0.753,0.76,0.658,OrganismalFitness,RAF1_HUMAN,Low,Human +0.73,0.717,0.729,0.723,0.718,0.72,0.534,0.654,0.709,0.716,0.714,0.707,0.704,0.723,0.746,0.741,0.726,0.723,0.71,0.74,0.731,0.72,0.71,0.707,0.723,0.704,0.701,0.701,0.686,0.711,0.707,0.705,0.625,0.73,0.722,0.705,0.743,0.733,0.726,0.722,0.721,0.722,0.733,0.685,0.712,0.731,0.674,0.652,0.723,0.658,0.713,0.721,0.712,0.717,0.712,0.718,0.716,0.714,0.708,0.716,0.715,0.729,Activity,RASH_HUMAN,High,Human +0.772,0.816,0.828,0.827,0.826,0.828,0.658,0.752,0.827,0.838,0.81,0.81,0.815,0.828,0.835,0.838,0.83,0.826,0.804,0.83,0.804,0.824,0.822,0.819,0.824,0.832,0.824,0.82,0.812,0.798,0.814,0.804,0.736,0.8,0.824,0.817,0.805,0.823,0.822,0.827,0.83,0.831,0.818,0.756,0.789,0.813,0.754,0.764,0.84,0.754,0.819,0.81,0.812,0.814,0.818,0.813,0.818,0.816,0.816,0.818,0.816,0.824,Expression,RASK_HUMAN,High,Human +0.772,0.816,0.828,0.827,0.826,0.828,0.658,0.752,0.827,0.838,0.81,0.81,0.815,0.828,0.835,0.838,0.83,0.826,0.804,0.83,0.804,0.824,0.822,0.819,0.824,0.832,0.824,0.82,0.812,0.798,0.814,0.804,0.736,0.8,0.824,0.817,0.805,0.823,0.822,0.827,0.83,0.831,0.818,0.756,0.789,0.813,0.754,0.764,0.84,0.754,0.819,0.81,0.812,0.814,0.818,0.813,0.818,0.816,0.816,0.818,0.816,0.824,Binding,RASK_HUMAN,High,Human +0.824,0.791,0.819,0.828,0.822,0.828,0.829,0.752,0.787,0.813,0.86,0.838,0.848,0.841,0.843,0.858,0.87,0.809,0.819,0.826,0.838,0.779,0.786,0.783,0.839,0.735,0.753,0.789,0.798,0.807,0.819,0.795,0.716,0.825,0.829,0.833,0.83,0.826,0.828,0.826,0.827,0.824,0.854,0.845,0.853,0.848,0.852,0.862,0.863,0.835,0.869,0.858,0.859,0.857,0.866,0.864,0.86,0.856,0.861,0.864,0.849,0.842,Stability,RBP1_HUMAN,High,Human +0.787,0.765,0.784,0.784,0.782,0.782,0.704,0.714,0.759,0.767,0.815,0.775,0.793,0.76,0.799,0.809,0.822,0.782,0.815,0.779,0.728,0.617,0.727,0.759,0.715,0.776,0.771,0.746,0.794,0.757,0.765,0.725,0.482,0.745,0.75,0.769,0.799,0.805,0.794,0.797,0.791,0.784,0.775,0.779,0.805,0.777,0.814,0.836,0.821,0.814,0.814,0.812,0.808,0.806,0.806,0.807,0.813,0.805,0.803,0.813,0.806,0.842,Stability,RCD1_ARATH,Medium,Eukaryote +0.777,0.868,0.899,0.903,0.9,0.909,0.667,0.676,0.9,0.91,0.874,0.814,0.878,0.75,0.812,0.846,0.874,0.857,0.866,0.866,0.709,0.752,0.75,0.735,0.614,0.781,0.591,0.718,0.877,0.871,0.902,0.889,0.635,0.665,0.611,0.84,0.789,0.784,0.871,0.905,0.901,0.901,0.786,0.783,0.856,0.776,0.954,0.933,0.941,0.929,0.913,0.913,0.925,0.93,0.924,0.92,0.92,0.921,0.922,0.924,0.928,0.724,Stability,RCRO_LAMBD,High,Virus +0.897,0.871,0.897,0.894,0.898,0.895,0.783,0.855,0.839,0.888,0.861,0.855,0.872,0.792,0.895,0.857,0.862,0.872,0.855,0.883,0.845,0.879,0.859,0.874,0.883,0.885,0.881,0.886,0.873,0.873,0.849,0.828,0.865,0.773,0.857,0.851,0.884,0.896,0.886,0.904,0.905,0.897,0.834,0.709,0.878,0.854,0.886,0.9,0.908,0.911,0.874,0.876,0.879,0.879,0.885,0.88,0.879,0.881,0.873,0.881,0.892,0.888,Stability,RD23A_HUMAN,High,Human +0.616,0.687,0.779,0.782,0.798,0.797,0.494,0.759,0.754,0.755,0.535,0.516,0.521,0.513,0.513,0.534,0.63,0.726,0.763,0.81,0.744,0.763,0.78,0.787,0.561,0.757,0.746,0.74,0.781,0.82,0.764,0.747,0.539,0.76,0.775,0.793,0.768,0.781,0.787,0.808,0.811,0.811,0.518,0.518,0.562,0.526,0.564,0.596,0.518,0.55,0.646,0.638,0.647,0.638,0.641,0.639,0.641,0.644,0.638,0.643,0.539,0.523,OrganismalFitness,RDRP_I33A0,Low,Virus +0.703,0.694,0.71,0.706,0.709,0.705,0.59,0.703,0.7,0.702,0.602,0.695,0.695,0.61,0.606,0.627,0.704,0.713,0.703,0.688,0.687,0.695,0.686,0.71,0.69,0.715,0.635,0.693,0.703,0.705,0.701,0.706,0.581,0.699,0.703,0.696,0.701,0.705,0.692,0.705,0.702,0.704,0.604,0.594,0.678,0.607,0.67,0.657,0.687,0.693,0.695,0.696,0.697,0.703,0.706,0.699,0.7,0.694,0.705,0.701,0.668,0.682,OrganismalFitness,REV_HV1H2,Medium,Virus +0.631,0.641,0.633,0.625,0.62,0.626,0.588,0.518,0.583,0.66,0.719,0.651,0.687,0.588,0.62,0.636,0.711,0.621,0.576,0.575,0.549,0.622,0.58,0.578,0.658,0.629,0.639,0.598,0.554,0.565,0.598,0.537,0.421,0.595,0.58,0.57,0.618,0.555,0.618,0.641,0.602,0.629,0.633,0.588,0.693,0.636,0.681,0.693,0.765,0.697,0.693,0.698,0.707,0.699,0.682,0.689,0.722,0.698,0.721,0.712,0.729,0.635,Stability,RFAH_ECOLI,High,Prokaryote +0.892,0.944,0.939,0.934,0.935,0.938,0.801,0.928,0.892,0.879,0.941,0.948,0.947,0.818,0.865,0.922,0.939,0.935,0.931,0.935,0.891,0.924,0.913,0.901,0.928,0.902,0.917,0.935,0.916,0.903,0.936,0.935,0.756,0.913,0.936,0.898,0.92,0.935,0.913,0.937,0.941,0.932,0.896,0.823,0.936,0.928,0.956,0.941,0.953,0.948,0.944,0.946,0.947,0.947,0.942,0.944,0.94,0.944,0.943,0.944,0.945,0.945,Stability,RL20_AQUAE,High,Prokaryote +0.895,0.909,0.913,0.923,0.915,0.916,0.705,0.901,0.924,0.929,0.876,0.9,0.92,0.799,0.878,0.865,0.922,0.918,0.923,0.92,0.915,0.919,0.919,0.921,0.929,0.918,0.92,0.92,0.928,0.922,0.922,0.925,0.803,0.911,0.912,0.906,0.915,0.922,0.917,0.928,0.932,0.925,0.907,0.773,0.908,0.9,0.7,0.831,0.802,0.72,0.922,0.931,0.925,0.92,0.929,0.922,0.929,0.929,0.927,0.926,0.926,0.868,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.895,0.909,0.913,0.923,0.915,0.916,0.705,0.901,0.924,0.929,0.876,0.9,0.92,0.799,0.878,0.865,0.922,0.918,0.923,0.92,0.915,0.919,0.919,0.921,0.929,0.918,0.92,0.92,0.928,0.922,0.922,0.925,0.803,0.911,0.912,0.906,0.915,0.922,0.917,0.928,0.932,0.925,0.907,0.773,0.908,0.9,0.7,0.831,0.802,0.72,0.922,0.931,0.925,0.92,0.929,0.922,0.929,0.929,0.927,0.926,0.926,0.868,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.895,0.909,0.913,0.923,0.915,0.916,0.705,0.901,0.924,0.929,0.876,0.9,0.92,0.799,0.878,0.865,0.922,0.918,0.923,0.92,0.915,0.919,0.919,0.921,0.929,0.918,0.92,0.92,0.928,0.922,0.922,0.925,0.803,0.911,0.912,0.906,0.915,0.922,0.917,0.928,0.932,0.925,0.907,0.773,0.908,0.9,0.7,0.831,0.802,0.72,0.922,0.931,0.925,0.92,0.929,0.922,0.929,0.929,0.927,0.926,0.926,0.868,Activity,RL40A_YEAST,Medium,Eukaryote +0.968,0.969,0.938,0.943,0.938,0.956,0.865,0.925,0.967,0.962,0.969,0.967,0.967,0.89,0.964,0.967,0.967,0.969,0.972,0.939,0.97,0.969,0.966,0.969,0.97,0.966,0.97,0.966,0.969,0.938,0.969,0.969,0.853,0.965,0.966,0.965,0.965,0.966,0.967,0.961,0.962,0.962,0.966,0.882,0.967,0.965,0.913,0.969,0.882,0.893,0.968,0.966,0.965,0.968,0.966,0.969,0.966,0.965,0.966,0.967,0.969,0.962,Activity,RNC_ECOLI,Medium,Prokaryote +0.848,0.884,0.874,0.879,0.873,0.884,0.834,0.835,0.823,0.817,0.885,0.876,0.879,0.853,0.86,0.868,0.879,0.894,0.888,0.891,0.823,0.888,0.882,0.886,0.849,0.891,0.865,0.887,0.893,0.872,0.883,0.839,0.856,0.777,0.865,0.863,0.822,0.866,0.853,0.886,0.888,0.882,0.848,0.796,0.827,0.825,0.901,0.801,0.903,0.887,0.857,0.863,0.869,0.877,0.874,0.867,0.877,0.866,0.866,0.872,0.885,0.85,Stability,RPC1_BP434,High,Virus +0.77,0.892,0.879,0.884,0.879,0.885,0.795,0.886,0.856,0.874,0.874,0.84,0.876,0.78,0.818,0.832,0.86,0.878,0.885,0.872,0.696,0.802,0.807,0.847,0.726,0.816,0.812,0.75,0.908,0.871,0.889,0.884,0.694,0.738,0.824,0.883,0.789,0.837,0.876,0.878,0.879,0.891,0.764,0.814,0.82,0.814,0.647,0.812,0.827,0.714,0.859,0.862,0.855,0.86,0.866,0.869,0.869,0.848,0.865,0.862,0.866,0.786,Activity,RPC1_LAMBD,High,Virus +0.77,0.892,0.879,0.884,0.879,0.885,0.795,0.886,0.856,0.874,0.874,0.84,0.876,0.78,0.818,0.832,0.86,0.878,0.885,0.872,0.696,0.802,0.807,0.847,0.726,0.816,0.812,0.75,0.908,0.871,0.889,0.884,0.694,0.738,0.824,0.883,0.789,0.837,0.876,0.878,0.879,0.891,0.764,0.814,0.82,0.814,0.647,0.812,0.827,0.714,0.859,0.862,0.855,0.86,0.866,0.869,0.869,0.848,0.865,0.862,0.866,0.786,Activity,RPC1_LAMBD,High,Virus +0.879,0.865,0.867,0.867,0.867,0.869,0.799,0.837,0.867,0.866,0.86,0.859,0.857,0.813,0.831,0.884,0.864,0.868,0.861,0.856,0.86,0.867,0.849,0.849,0.86,0.852,0.884,0.862,0.86,0.86,0.858,0.849,0.823,0.875,0.862,0.852,0.881,0.87,0.865,0.876,0.868,0.864,0.831,0.82,0.878,0.858,0.9,0.887,0.888,0.893,0.866,0.863,0.866,0.87,0.866,0.856,0.865,0.865,0.872,0.868,0.866,0.915,Stability,RS15_GEOSE,Medium,Prokaryote +0.8,0.852,0.858,0.862,0.859,0.86,0.752,0.806,0.854,0.856,0.848,0.863,0.863,0.782,0.78,0.814,0.854,0.859,0.853,0.702,0.83,0.858,0.852,0.847,0.844,0.859,0.859,0.858,0.846,0.854,0.862,0.834,0.754,0.828,0.857,0.856,0.841,0.862,0.861,0.864,0.862,0.863,0.782,0.712,0.848,0.794,0.824,0.867,0.823,0.755,0.856,0.85,0.858,0.861,0.856,0.86,0.856,0.86,0.86,0.861,0.845,0.814,Expression,S22A1_HUMAN,Medium,Human +0.8,0.852,0.858,0.862,0.859,0.86,0.752,0.806,0.854,0.856,0.848,0.863,0.863,0.782,0.78,0.814,0.854,0.859,0.853,0.702,0.83,0.858,0.852,0.847,0.844,0.859,0.859,0.858,0.846,0.854,0.862,0.834,0.754,0.828,0.857,0.856,0.841,0.862,0.861,0.864,0.862,0.863,0.782,0.712,0.848,0.794,0.824,0.867,0.823,0.755,0.856,0.85,0.858,0.861,0.856,0.86,0.856,0.86,0.86,0.861,0.845,0.814,Activity,S22A1_HUMAN,Medium,Human +0.874,0.833,0.842,0.863,0.854,0.869,0.766,0.808,0.844,0.857,0.816,0.829,0.826,0.659,0.847,0.866,0.805,0.842,0.77,0.765,0.784,0.819,0.817,0.822,0.781,0.829,0.816,0.818,0.816,0.804,0.813,0.756,0.76,0.749,0.798,0.801,0.836,0.842,0.849,0.853,0.873,0.863,0.824,0.671,0.854,0.829,0.827,0.869,0.842,0.869,0.834,0.772,0.83,0.807,0.837,0.831,0.809,0.824,0.824,0.827,0.796,0.893,Stability,SAV1_MOUSE,High,Eukaryote +0.761,0.773,0.795,0.794,0.788,0.779,0.648,0.686,0.8,0.823,0.722,0.676,0.679,0.624,0.655,0.711,0.826,0.842,0.79,0.754,0.647,0.745,0.77,0.727,0.671,0.645,0.688,0.656,0.754,0.777,0.831,0.784,0.622,0.596,0.618,0.62,0.728,0.744,0.747,0.751,0.777,0.778,0.659,0.657,0.644,0.702,0.837,0.803,0.843,0.846,0.685,0.705,0.709,0.72,0.726,0.691,0.746,0.77,0.813,0.743,0.851,0.801,Stability,SBI_STAAM,Medium,Prokaryote +0.868,0.888,0.882,0.883,0.887,0.886,0.829,0.901,0.885,0.889,0.89,0.872,0.881,0.783,0.817,0.856,0.868,0.871,0.87,0.891,0.888,0.905,0.904,0.9,0.887,0.904,0.903,0.902,0.902,0.89,0.881,0.85,0.854,0.892,0.902,0.901,0.884,0.892,0.893,0.888,0.89,0.888,0.864,0.781,0.891,0.871,0.868,0.893,0.868,0.823,0.867,0.875,0.875,0.876,0.873,0.875,0.871,0.873,0.867,0.874,0.889,0.87,Activity,SC6A4_HUMAN,Medium,Human +0.74,0.766,0.776,0.779,0.781,0.788,0.763,0.722,0.793,0.784,0.783,0.782,0.776,0.738,0.774,0.8,0.803,0.793,0.817,0.759,0.714,0.749,0.755,0.755,0.764,0.767,0.784,0.782,0.76,0.758,0.768,0.746,0.617,0.712,0.686,0.746,0.757,0.758,0.766,0.78,0.789,0.793,0.787,0.769,0.771,0.783,0.793,0.851,0.835,0.803,0.801,0.8,0.808,0.818,0.813,0.836,0.816,0.835,0.81,0.823,0.887,0.851,Stability,SCIN_STAAR,High,Prokaryote +0.753,0.728,0.754,0.754,0.754,0.754,0.794,0.603,0.791,0.798,0.753,0.731,0.765,0.77,0.75,0.763,0.778,0.77,0.748,0.76,0.743,0.724,0.739,0.723,0.732,0.748,0.743,0.714,0.795,0.739,0.741,0.738,0.639,0.675,0.697,0.758,0.756,0.784,0.816,0.77,0.787,0.772,0.752,0.714,0.8,0.693,0.637,0.657,0.71,0.606,0.703,0.734,0.727,0.741,0.728,0.736,0.72,0.705,0.72,0.726,0.713,0.737,OrganismalFitness,SCN5A_HUMAN,Medium,Human +0.931,0.927,0.931,0.931,0.936,0.936,0.599,0.877,0.925,0.934,0.922,0.899,0.906,0.688,0.743,0.914,0.922,0.935,0.933,0.917,0.591,0.637,0.618,0.76,0.67,0.886,0.568,0.806,0.92,0.94,0.92,0.9,0.522,0.593,0.743,0.886,0.932,0.93,0.924,0.935,0.935,0.933,0.703,0.654,0.913,0.728,0.901,0.898,0.94,0.935,0.932,0.932,0.934,0.939,0.935,0.937,0.938,0.936,0.94,0.94,0.944,0.945,Stability,SDA_BACSU,Medium,Prokaryote +0.882,0.903,0.893,0.898,0.897,0.903,0.661,0.892,0.896,0.898,0.899,0.898,0.9,0.764,0.867,0.89,0.897,0.892,0.889,0.899,0.903,0.9,0.903,0.904,0.903,0.901,0.906,0.905,0.899,0.899,0.903,0.901,0.805,0.898,0.906,0.904,0.894,0.904,0.903,0.903,0.903,0.902,0.857,0.737,0.896,0.879,0.871,0.903,0.787,0.85,0.897,0.901,0.896,0.901,0.905,0.897,0.899,0.897,0.894,0.902,0.901,0.88,OrganismalFitness,SERC_HUMAN,High,Human +0.82,0.841,0.836,0.835,0.839,0.838,0.802,0.839,0.85,0.844,0.83,0.827,0.83,0.805,0.806,0.807,0.826,0.838,0.834,0.831,0.81,0.815,0.845,0.838,0.807,0.82,0.821,0.823,0.844,0.844,0.845,0.838,0.806,0.811,0.822,0.842,0.818,0.828,0.84,0.838,0.836,0.843,0.809,0.806,0.84,0.806,0.82,0.84,0.814,0.81,0.832,0.832,0.836,0.834,0.837,0.838,0.832,0.831,0.826,0.833,0.805,0.809,OrganismalFitness,SHOC2_HUMAN,Medium,Human +0.83,0.855,0.835,0.834,0.841,0.838,0.821,0.809,0.818,0.838,0.845,0.84,0.847,0.832,0.84,0.86,0.855,0.835,0.839,0.85,0.835,0.812,0.829,0.823,0.842,0.846,0.839,0.839,0.838,0.835,0.816,0.804,0.829,0.754,0.823,0.839,0.829,0.827,0.842,0.835,0.839,0.838,0.846,0.76,0.837,0.855,0.845,0.836,0.859,0.845,0.849,0.867,0.866,0.859,0.855,0.862,0.848,0.848,0.846,0.86,0.853,0.851,Stability,SOX30_HUMAN,High,Human +0.777,0.8,0.792,0.794,0.805,0.802,0.545,0.768,0.725,0.727,0.739,0.543,0.54,0.516,0.547,0.513,0.59,0.602,0.588,0.793,0.482,0.583,0.743,0.637,0.548,0.509,0.567,0.746,0.786,0.752,0.738,0.729,0.493,0.548,0.518,0.602,0.779,0.788,0.779,0.806,0.816,0.803,0.566,0.588,0.638,0.63,0.771,0.762,0.818,0.803,0.793,0.785,0.787,0.827,0.763,0.794,0.778,0.801,0.785,0.801,0.755,0.761,Stability,SPA_STAAU,Medium,Prokaryote +0.394,0.415,0.322,0.336,0.421,0.427,0.298,0.318,0.354,0.43,0.478,0.468,0.45,0.474,0.464,0.471,0.489,0.487,0.506,0.444,0.454,0.434,0.438,0.441,0.43,0.449,0.45,0.442,0.464,0.417,0.519,0.504,0.3,0.438,0.412,0.44,0.414,0.404,0.424,0.44,0.432,0.446,0.308,0.277,0.393,0.313,0.448,0.428,0.49,0.385,0.514,0.494,0.505,0.517,0.507,0.497,0.527,0.518,0.516,0.515,0.514,0.496,Binding,SPG1_STRSG,Low,Prokaryote +0.394,0.415,0.322,0.336,0.421,0.427,0.298,0.318,0.354,0.43,0.478,0.468,0.45,0.474,0.464,0.471,0.489,0.487,0.506,0.444,0.454,0.434,0.438,0.441,0.43,0.449,0.45,0.442,0.464,0.417,0.519,0.504,0.3,0.438,0.412,0.44,0.414,0.404,0.424,0.44,0.432,0.446,0.308,0.277,0.393,0.313,0.448,0.428,0.49,0.385,0.514,0.494,0.505,0.517,0.507,0.497,0.527,0.518,0.516,0.515,0.514,0.496,Binding,SPG1_STRSG,Low,Prokaryote +0.394,0.415,0.322,0.336,0.421,0.427,0.298,0.318,0.354,0.43,0.478,0.468,0.45,0.474,0.464,0.471,0.489,0.487,0.506,0.444,0.454,0.434,0.438,0.441,0.43,0.449,0.45,0.442,0.464,0.417,0.519,0.504,0.3,0.438,0.412,0.44,0.414,0.404,0.424,0.44,0.432,0.446,0.308,0.277,0.393,0.313,0.448,0.428,0.49,0.385,0.514,0.494,0.505,0.517,0.507,0.497,0.527,0.518,0.516,0.515,0.514,0.496,Binding,SPG1_STRSG,Medium,Prokaryote +0.394,0.415,0.322,0.336,0.421,0.427,0.298,0.318,0.354,0.43,0.478,0.468,0.45,0.474,0.464,0.471,0.489,0.487,0.506,0.444,0.454,0.434,0.438,0.441,0.43,0.449,0.45,0.442,0.464,0.417,0.519,0.504,0.3,0.438,0.412,0.44,0.414,0.404,0.424,0.44,0.432,0.446,0.308,0.277,0.393,0.313,0.448,0.428,0.49,0.385,0.514,0.494,0.505,0.517,0.507,0.497,0.527,0.518,0.516,0.515,0.514,0.496,Binding,SPG1_STRSG,Medium,Prokaryote +0.784,0.792,0.779,0.771,0.775,0.774,0.708,0.778,0.81,0.824,0.784,0.788,0.776,0.72,0.765,0.753,0.794,0.796,0.766,0.804,0.724,0.778,0.693,0.702,0.668,0.731,0.71,0.778,0.769,0.826,0.803,0.787,0.47,0.652,0.762,0.799,0.734,0.761,0.743,0.804,0.807,0.81,0.755,0.656,0.778,0.727,0.828,0.787,0.833,0.817,0.789,0.8,0.815,0.818,0.812,0.812,0.81,0.804,0.791,0.813,0.835,0.755,Stability,SPG2_STRSG,Medium,Prokaryote +0.795,0.896,0.806,0.86,0.89,0.888,0.691,0.892,0.898,0.918,0.69,0.68,0.688,0.691,0.718,0.7,0.704,0.729,0.74,0.895,0.876,0.887,0.881,0.887,0.888,0.886,0.867,0.891,0.873,0.914,0.892,0.891,0.806,0.885,0.885,0.878,0.876,0.874,0.884,0.909,0.906,0.905,0.687,0.686,0.692,0.714,0.909,0.859,0.747,0.808,0.828,0.83,0.826,0.835,0.84,0.851,0.833,0.814,0.8,0.844,0.833,0.754,Binding,SPIKE_SARS2,Medium,Virus +0.795,0.896,0.806,0.86,0.89,0.888,0.691,0.892,0.898,0.918,0.69,0.68,0.688,0.691,0.718,0.7,0.704,0.729,0.74,0.895,0.876,0.887,0.881,0.887,0.888,0.886,0.867,0.891,0.873,0.914,0.892,0.891,0.806,0.885,0.885,0.878,0.876,0.874,0.884,0.909,0.906,0.905,0.687,0.686,0.692,0.714,0.909,0.859,0.747,0.808,0.828,0.83,0.826,0.835,0.84,0.851,0.833,0.814,0.8,0.844,0.833,0.754,Expression,SPIKE_SARS2,Medium,Virus +0.832,0.869,0.807,0.774,0.806,0.797,0.613,0.706,0.79,0.799,0.865,0.815,0.838,0.358,0.811,0.819,0.804,0.887,0.809,0.836,0.766,0.813,0.76,0.788,0.826,0.841,0.81,0.834,0.833,0.827,0.877,0.88,0.581,0.78,0.755,0.776,0.834,0.846,0.83,0.803,0.803,0.801,0.751,0.421,0.785,0.737,0.765,0.775,0.902,0.893,0.892,0.887,0.898,0.886,0.887,0.889,0.886,0.894,0.88,0.895,0.883,0.767,Stability,SPTN1_CHICK,High,Eukaryote +0.799,0.851,0.859,0.873,0.878,0.872,0.493,0.81,0.866,0.864,0.84,0.764,0.832,0.567,0.701,0.795,0.865,0.846,0.837,0.84,0.615,0.837,0.755,0.855,0.823,0.834,0.84,0.817,0.831,0.823,0.823,0.804,0.762,0.616,0.841,0.847,0.801,0.873,0.865,0.885,0.879,0.867,0.603,0.547,0.824,0.853,0.879,0.835,0.824,0.83,0.851,0.861,0.863,0.849,0.846,0.863,0.854,0.841,0.84,0.859,0.854,0.772,Stability,SQSTM_MOUSE,Medium,Eukaryote +0.862,0.858,0.853,0.851,0.856,0.855,0.416,0.76,0.843,0.843,0.889,0.851,0.883,0.362,0.844,0.903,0.893,0.904,0.913,0.836,0.482,0.703,0.757,0.722,0.734,0.783,0.799,0.819,0.866,0.817,0.883,0.835,0.627,0.676,0.629,0.748,0.887,0.875,0.875,0.875,0.882,0.87,0.798,0.459,0.884,0.854,0.753,0.893,0.916,0.907,0.872,0.883,0.877,0.896,0.874,0.886,0.881,0.889,0.883,0.882,0.914,0.891,Stability,SR43C_ARATH,High,Eukaryote +0.869,0.885,0.906,0.903,0.886,0.883,0.804,0.782,0.885,0.891,0.92,0.912,0.923,0.811,0.9,0.911,0.909,0.898,0.924,0.893,0.905,0.895,0.913,0.89,0.907,0.909,0.905,0.897,0.9,0.913,0.904,0.881,0.822,0.833,0.878,0.867,0.888,0.902,0.906,0.886,0.898,0.892,0.889,0.723,0.922,0.903,0.891,0.891,0.92,0.916,0.911,0.919,0.916,0.919,0.912,0.921,0.91,0.915,0.914,0.918,0.905,0.911,Stability,SRBS1_HUMAN,High,Human +0.619,0.629,0.623,0.628,0.62,0.619,0.617,0.603,0.621,0.626,0.635,0.627,0.632,0.604,0.608,0.588,0.602,0.611,0.611,0.62,0.62,0.612,0.617,0.609,0.61,0.619,0.62,0.609,0.612,0.634,0.615,0.622,0.62,0.613,0.605,0.602,0.611,0.613,0.613,0.621,0.62,0.619,0.627,0.588,0.622,0.626,0.576,0.605,0.577,0.547,0.604,0.604,0.597,0.621,0.61,0.605,0.6,0.611,0.602,0.603,0.62,0.606,Activity,SRC_HUMAN,Medium,Human +0.619,0.629,0.623,0.628,0.62,0.619,0.617,0.603,0.621,0.626,0.635,0.627,0.632,0.604,0.608,0.588,0.602,0.611,0.611,0.62,0.62,0.612,0.617,0.609,0.61,0.619,0.62,0.609,0.612,0.634,0.615,0.622,0.62,0.613,0.605,0.602,0.611,0.613,0.613,0.621,0.62,0.619,0.627,0.588,0.622,0.626,0.576,0.605,0.577,0.547,0.604,0.604,0.597,0.621,0.61,0.605,0.6,0.611,0.602,0.603,0.62,0.606,Activity,SRC_HUMAN,Medium,Human +0.619,0.629,0.623,0.628,0.62,0.619,0.617,0.603,0.621,0.626,0.635,0.627,0.632,0.604,0.608,0.588,0.602,0.611,0.611,0.62,0.62,0.612,0.617,0.609,0.61,0.619,0.62,0.609,0.612,0.634,0.615,0.622,0.62,0.613,0.605,0.602,0.611,0.613,0.613,0.621,0.62,0.619,0.627,0.588,0.622,0.626,0.576,0.605,0.577,0.547,0.604,0.604,0.597,0.621,0.61,0.605,0.6,0.611,0.602,0.603,0.62,0.606,OrganismalFitness,SRC_HUMAN,Medium,Human +0.707,0.76,0.779,0.766,0.769,0.771,0.637,0.763,0.758,0.749,0.773,0.775,0.788,0.658,0.723,0.75,0.785,0.73,0.763,0.774,0.67,0.759,0.77,0.778,0.768,0.775,0.763,0.79,0.765,0.717,0.777,0.779,0.621,0.672,0.759,0.771,0.739,0.754,0.772,0.764,0.764,0.772,0.737,0.639,0.772,0.749,0.735,0.774,0.729,0.723,0.768,0.763,0.778,0.77,0.767,0.769,0.78,0.78,0.77,0.777,0.776,0.732,OrganismalFitness,SUMO1_HUMAN,High,Human +0.826,0.836,0.818,0.814,0.827,0.829,0.809,0.878,0.838,0.845,0.836,0.868,0.861,0.809,0.811,0.824,0.812,0.849,0.86,0.86,0.807,0.867,0.872,0.87,0.811,0.862,0.868,0.86,0.867,0.874,0.877,0.877,0.696,0.824,0.865,0.868,0.836,0.855,0.856,0.835,0.851,0.85,0.812,0.8,0.857,0.808,0.761,0.834,0.809,0.77,0.813,0.795,0.793,0.805,0.806,0.807,0.816,0.824,0.832,0.804,0.834,0.802,OrganismalFitness,SYUA_HUMAN,Medium,Human +0.594,0.591,0.589,0.587,0.59,0.587,0.716,0.592,0.577,0.58,0.595,0.607,0.593,0.643,0.66,0.637,0.557,0.579,0.612,0.59,0.677,0.576,0.595,0.596,0.693,0.608,0.584,0.592,0.593,0.601,0.599,0.622,0.576,0.698,0.732,0.592,0.609,0.616,0.6,0.594,0.594,0.59,0.652,0.697,0.607,0.653,0.696,0.598,0.699,0.61,0.66,0.621,0.613,0.643,0.621,0.634,0.608,0.612,0.59,0.62,0.62,0.657,OrganismalFitness,TADBP_HUMAN,Low,Human +0.686,0.699,0.694,0.695,0.697,0.698,0.457,0.707,0.701,0.698,0.609,0.707,0.704,0.599,0.615,0.583,0.598,0.578,0.578,0.698,0.701,0.714,0.704,0.707,0.695,0.729,0.609,0.698,0.718,0.695,0.707,0.718,0.618,0.702,0.681,0.712,0.706,0.689,0.694,0.702,0.701,0.702,0.539,0.495,0.686,0.557,0.601,0.697,0.644,0.585,0.54,0.615,0.594,0.623,0.593,0.617,0.565,0.582,0.581,0.58,0.53,0.537,OrganismalFitness,TAT_HV1BR,High,Virus +0.896,0.882,0.888,0.891,0.881,0.882,0.825,0.799,0.887,0.871,0.85,0.877,0.879,0.885,0.863,0.889,0.904,0.87,0.868,0.824,0.848,0.845,0.826,0.835,0.857,0.848,0.844,0.85,0.841,0.848,0.828,0.798,0.861,0.846,0.863,0.889,0.88,0.881,0.894,0.879,0.883,0.879,0.88,0.691,0.852,0.876,0.849,0.846,0.877,0.879,0.869,0.863,0.864,0.882,0.864,0.88,0.875,0.869,0.874,0.871,0.899,0.88,Stability,TCRG1_MOUSE,Medium,Eukaryote +0.771,0.767,0.79,0.808,0.808,0.823,0.583,0.59,0.799,0.818,0.766,0.816,0.8,0.488,0.791,0.794,0.788,0.816,0.799,0.802,0.538,0.661,0.58,0.519,0.642,0.653,0.515,0.669,0.778,0.836,0.812,0.776,0.747,0.53,0.722,0.605,0.777,0.824,0.777,0.79,0.822,0.808,0.71,0.624,0.81,0.745,0.83,0.835,0.814,0.874,0.85,0.805,0.842,0.84,0.854,0.827,0.834,0.834,0.827,0.841,0.794,0.767,Stability,THO1_YEAST,High,Eukaryote +0.848,0.885,0.878,0.88,0.871,0.877,0.577,0.79,0.878,0.891,0.851,0.839,0.852,0.715,0.827,0.848,0.871,0.885,0.886,0.868,0.811,0.887,0.888,0.887,0.829,0.88,0.868,0.889,0.876,0.864,0.898,0.883,0.729,0.62,0.821,0.829,0.844,0.863,0.867,0.879,0.88,0.878,0.832,0.724,0.87,0.829,0.82,0.891,0.913,0.916,0.863,0.889,0.885,0.864,0.879,0.879,0.886,0.878,0.868,0.887,0.905,0.819,Stability,TNKS2_HUMAN,High,Human +0.619,0.642,0.624,0.621,0.62,0.626,0.554,0.644,0.626,0.621,0.63,0.645,0.651,0.551,0.607,0.625,0.654,0.652,0.665,0.631,0.558,0.599,0.605,0.619,0.558,0.649,0.665,0.619,0.667,0.672,0.667,0.643,0.557,0.564,0.623,0.672,0.625,0.631,0.676,0.627,0.638,0.648,0.569,0.55,0.637,0.581,0.632,0.644,0.649,0.598,0.648,0.656,0.648,0.659,0.649,0.657,0.665,0.651,0.645,0.649,0.628,0.6,OrganismalFitness,TPK1_HUMAN,Medium,Human +0.805,0.817,0.818,0.816,0.819,0.816,0.735,0.815,0.817,0.819,0.82,0.818,0.826,0.764,0.773,0.804,0.813,0.814,0.807,0.82,0.771,0.761,0.803,0.828,0.801,0.798,0.809,0.799,0.81,0.826,0.829,0.819,0.771,0.787,0.8,0.807,0.81,0.811,0.812,0.82,0.819,0.817,0.783,0.733,0.822,0.803,0.8,0.823,0.824,0.789,0.803,0.804,0.809,0.802,0.809,0.811,0.812,0.815,0.812,0.81,0.817,0.8,Expression,TPMT_HUMAN,Medium,Human +0.668,0.597,0.695,0.635,0.627,0.625,0.69,0.641,0.663,0.656,0.678,0.643,0.671,0.589,0.664,0.575,0.571,0.61,0.649,0.589,0.619,0.589,0.659,0.652,0.638,0.546,0.742,0.7,0.676,0.618,0.611,0.618,0.708,0.64,0.633,0.617,0.67,0.682,0.63,0.651,0.667,0.607,0.58,0.627,0.624,0.643,0.486,0.635,0.653,0.56,0.703,0.606,0.625,0.629,0.621,0.634,0.613,0.668,0.664,0.624,0.637,0.691,OrganismalFitness,TPOR_HUMAN,Low,Human +0.879,0.917,0.91,0.915,0.911,0.913,0.744,0.899,0.91,0.918,0.9,0.888,0.892,0.788,0.826,0.877,0.919,0.921,0.909,0.922,0.781,0.849,0.859,0.867,0.879,0.861,0.909,0.883,0.93,0.912,0.912,0.887,0.735,0.822,0.877,0.846,0.845,0.888,0.874,0.887,0.912,0.909,0.769,0.75,0.884,0.825,0.798,0.896,0.914,0.746,0.898,0.903,0.898,0.911,0.906,0.9,0.904,0.911,0.902,0.912,0.924,0.866,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +0.692,0.732,0.652,0.688,0.719,0.712,0.607,0.639,0.705,0.737,0.726,0.732,0.747,0.622,0.719,0.755,0.725,0.743,0.751,0.742,0.67,0.716,0.733,0.756,0.698,0.736,0.689,0.751,0.762,0.657,0.734,0.684,0.554,0.672,0.747,0.738,0.716,0.723,0.723,0.733,0.73,0.723,0.689,0.501,0.765,0.718,0.651,0.758,0.726,0.581,0.718,0.72,0.691,0.73,0.725,0.707,0.705,0.701,0.69,0.713,0.754,0.753,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +0.534,0.615,0.61,0.61,0.607,0.618,0.369,0.531,0.575,0.591,0.582,0.589,0.587,0.405,0.403,0.47,0.522,0.58,0.596,0.606,0.49,0.54,0.538,0.535,0.547,0.579,0.553,0.568,0.574,0.555,0.556,0.536,0.353,0.49,0.558,0.576,0.522,0.597,0.583,0.597,0.62,0.626,0.463,0.398,0.577,0.579,0.489,0.553,0.512,0.486,0.504,0.505,0.523,0.532,0.536,0.534,0.531,0.521,0.519,0.519,0.579,0.484,OrganismalFitness,UBC9_HUMAN,Medium,Human +0.66,0.786,0.785,0.782,0.795,0.798,0.528,0.728,0.773,0.802,0.838,0.799,0.804,0.609,0.784,0.848,0.84,0.829,0.821,0.798,0.542,0.74,0.754,0.777,0.723,0.756,0.752,0.763,0.784,0.765,0.831,0.792,0.734,0.516,0.541,0.792,0.684,0.697,0.797,0.797,0.792,0.818,0.657,0.52,0.683,0.715,0.768,0.749,0.836,0.731,0.848,0.856,0.855,0.854,0.856,0.846,0.842,0.855,0.849,0.854,0.833,0.744,Stability,UBE4B_HUMAN,High,Human +0.672,0.659,0.658,0.658,0.648,0.656,0.536,0.626,0.647,0.644,0.656,0.687,0.666,0.575,0.656,0.66,0.641,0.635,0.617,0.63,0.547,0.628,0.646,0.633,0.66,0.649,0.649,0.673,0.648,0.636,0.644,0.632,0.549,0.544,0.566,0.629,0.674,0.691,0.682,0.658,0.663,0.664,0.641,0.528,0.626,0.664,0.644,0.625,0.491,0.6,0.667,0.647,0.658,0.648,0.645,0.657,0.639,0.636,0.632,0.651,0.653,0.644,Activity,UBE4B_MOUSE,Low,Eukaryote +0.867,0.877,0.845,0.856,0.872,0.867,0.775,0.8,0.862,0.869,0.859,0.818,0.842,0.792,0.795,0.781,0.773,0.863,0.859,0.831,0.852,0.869,0.884,0.884,0.878,0.883,0.889,0.891,0.885,0.88,0.873,0.848,0.813,0.83,0.863,0.852,0.864,0.872,0.878,0.871,0.869,0.879,0.804,0.783,0.885,0.82,0.881,0.885,0.885,0.885,0.859,0.877,0.887,0.882,0.886,0.878,0.874,0.881,0.874,0.881,0.886,0.861,Stability,UBR5_HUMAN,Medium,Human +0.74,0.718,0.729,0.729,0.737,0.735,0.671,0.679,0.738,0.767,0.753,0.765,0.759,0.72,0.746,0.766,0.769,0.772,0.75,0.708,0.706,0.732,0.705,0.746,0.721,0.719,0.731,0.716,0.757,0.743,0.74,0.719,0.572,0.716,0.746,0.745,0.73,0.766,0.748,0.74,0.746,0.731,0.727,0.723,0.743,0.696,0.707,0.738,0.753,0.752,0.776,0.776,0.778,0.783,0.795,0.788,0.784,0.775,0.791,0.783,0.785,0.79,Stability,VG08_BPP22,High,Virus +0.791,0.92,0.936,0.935,0.934,0.93,0.684,0.84,0.924,0.926,0.93,0.914,0.931,0.782,0.753,0.937,0.953,0.932,0.883,0.93,0.732,0.821,0.853,0.849,0.846,0.877,0.864,0.86,0.92,0.933,0.944,0.924,0.766,0.653,0.888,0.887,0.858,0.906,0.901,0.928,0.931,0.931,0.809,0.806,0.934,0.904,0.912,0.922,0.947,0.895,0.953,0.947,0.954,0.952,0.948,0.957,0.951,0.954,0.95,0.955,0.952,0.917,Stability,VILI_CHICK,High,Eukaryote +0.829,0.831,0.826,0.83,0.816,0.826,0.705,0.827,0.843,0.848,0.829,0.833,0.825,0.708,0.796,0.831,0.828,0.83,0.833,0.843,0.766,0.758,0.804,0.823,0.752,0.812,0.819,0.798,0.824,0.851,0.837,0.821,0.744,0.727,0.759,0.823,0.816,0.817,0.836,0.818,0.822,0.832,0.728,0.696,0.829,0.769,0.79,0.823,0.83,0.788,0.828,0.816,0.828,0.839,0.843,0.836,0.838,0.825,0.827,0.835,0.825,0.826,Expression,VKOR1_HUMAN,Medium,Human +0.829,0.831,0.826,0.83,0.816,0.826,0.705,0.827,0.843,0.848,0.829,0.833,0.825,0.708,0.796,0.831,0.828,0.83,0.833,0.843,0.766,0.758,0.804,0.823,0.752,0.812,0.819,0.798,0.824,0.851,0.837,0.821,0.744,0.727,0.759,0.823,0.816,0.817,0.836,0.818,0.822,0.832,0.728,0.696,0.829,0.769,0.79,0.823,0.83,0.788,0.828,0.816,0.828,0.839,0.843,0.836,0.838,0.825,0.827,0.835,0.825,0.826,Activity,VKOR1_HUMAN,Medium,Human +0.584,0.739,0.781,0.778,0.767,0.783,0.655,0.728,0.799,0.765,0.785,0.75,0.737,0.697,0.749,0.79,0.803,0.822,0.797,0.752,0.598,0.694,0.676,0.704,0.703,0.647,0.675,0.692,0.763,0.753,0.802,0.788,0.693,0.671,0.74,0.687,0.638,0.649,0.645,0.754,0.757,0.739,0.729,0.672,0.756,0.734,0.803,0.798,0.801,0.851,0.85,0.836,0.863,0.856,0.847,0.861,0.857,0.861,0.853,0.869,0.869,0.808,Stability,VRPI_BPT7,Medium,Virus +0.667,0.852,0.846,0.86,0.856,0.853,0.453,0.803,0.832,0.859,0.789,0.626,0.795,0.577,0.644,0.85,0.873,0.919,0.904,0.875,0.503,0.53,0.57,0.735,0.562,0.573,0.521,0.553,0.841,0.875,0.877,0.867,0.521,0.452,0.564,0.831,0.702,0.727,0.816,0.839,0.842,0.855,0.685,0.564,0.717,0.694,0.876,0.837,0.912,0.862,0.889,0.903,0.88,0.891,0.901,0.894,0.89,0.894,0.897,0.897,0.877,0.749,Stability,YAIA_ECOLI,Medium,Prokaryote +0.464,0.386,0.471,0.468,0.466,0.463,0.374,0.312,0.295,0.288,0.409,0.362,0.357,0.409,0.419,0.441,0.453,0.412,0.39,0.327,0.308,0.301,0.293,0.322,0.332,0.3,0.311,0.322,0.296,0.37,0.382,0.397,0.34,0.358,0.302,0.323,0.427,0.368,0.396,0.45,0.42,0.439,0.484,0.181,0.422,0.48,0.359,0.418,0.332,0.372,0.355,0.348,0.365,0.37,0.347,0.358,0.369,0.373,0.386,0.361,0.478,0.41,Binding,YAP1_HUMAN,Low,Human +0.855,0.869,0.855,0.855,0.857,0.856,0.767,0.793,0.876,0.877,0.881,0.872,0.887,0.865,0.883,0.887,0.891,0.887,0.881,0.85,0.845,0.893,0.896,0.896,0.883,0.882,0.882,0.875,0.878,0.863,0.875,0.854,0.845,0.842,0.872,0.868,0.876,0.863,0.865,0.867,0.859,0.865,0.848,0.802,0.867,0.845,0.829,0.872,0.911,0.894,0.894,0.874,0.88,0.877,0.878,0.886,0.878,0.879,0.879,0.881,0.884,0.923,Stability,YNZC_BACSU,Medium,Prokaryote +0.75,0.787,0.783,0.785,0.789,0.791,0.642,0.741,0.774,0.785,0.761,0.743,0.757,0.658,0.702,0.731,0.755,0.763,0.769,0.77,0.712,0.736,0.75,0.755,0.721,0.757,0.751,0.762,0.779,0.785,0.788,0.772,0.655,0.709,0.731,0.76,0.767,0.772,0.787,0.791,0.792,0.795,0.699,0.635,0.755,0.718,0.742,0.771,0.754,0.726,0.769,0.772,0.772,0.775,0.773,0.774,0.773,0.775,0.771,0.776,0.772,0.735,,,, diff --git a/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.csv b/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.csv new file mode 100644 index 0000000..23c7d93 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.csv @@ -0,0 +1,63 @@ +Model_rank,Model_name,Model type,Average_NDCG,Bootstrap_standard_error_NDCG,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,TranceptEVE L,Hybrid - Alignment & PLM,0.786,0.0,0.794,0.73,0.801,0.767,0.839,0.762,0.781,0.829,0.787,0.816,0.833,0.743,0.8,0.715,0.694,0.67,0.681,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +2,TranceptEVE S,Hybrid - Alignment & PLM,0.784,0.001,0.791,0.731,0.799,0.761,0.838,0.759,0.779,0.824,0.786,0.813,0.826,0.74,0.797,0.711,0.671,0.662,0.673,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +3,TranceptEVE M,Hybrid - Alignment & PLM,0.784,0.001,0.793,0.727,0.799,0.764,0.837,0.759,0.782,0.822,0.787,0.814,0.824,0.743,0.798,0.707,0.669,0.664,0.674,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +4,EVE (ensemble),Alignment-based model,0.783,0.001,0.791,0.724,0.798,0.762,0.838,0.754,0.779,0.826,0.784,0.81,0.829,0.742,0.796,0.708,0.688,0.674,0.666,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +5,EVE (single),Alignment-based model,0.781,0.001,0.789,0.724,0.796,0.761,0.835,0.752,0.778,0.823,0.782,0.81,0.826,0.74,0.794,0.71,0.685,0.671,0.668,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +6,Tranception L,Hybrid - Alignment & PLM,0.779,0.002,0.794,0.724,0.795,0.76,0.824,0.759,0.773,0.819,0.788,0.807,0.813,0.727,0.793,0.705,0.68,0.653,0.668,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +7,MSA Transformer (ensemble),Hybrid - Alignment & PLM,0.777,0.004,0.789,0.708,0.806,0.749,0.835,0.752,0.773,0.819,0.78,0.812,0.823,0.723,0.791,0.693,0.699,0.696,0.696,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +8,GEMME,Alignment-based model,0.777,0.002,0.795,0.711,0.794,0.756,0.828,0.758,0.775,0.813,0.779,0.805,0.817,0.743,0.789,0.69,0.674,0.674,0.708,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +9,EVmutation,Alignment-based model,0.777,0.002,0.792,0.708,0.792,0.757,0.835,0.748,0.776,0.821,0.778,0.813,0.826,0.734,0.793,0.717,0.684,0.674,0.697,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +10,DeepSequence (ensemble),Alignment-based model,0.776,0.003,0.785,0.716,0.787,0.758,0.835,0.743,0.773,0.822,0.781,0.806,0.823,0.73,0.791,0.705,0.671,0.654,0.668,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +11,VESPA,Protein language model,0.775,0.004,0.789,0.711,0.79,0.752,0.835,0.741,0.773,0.824,0.776,0.81,0.833,0.719,0.789,0.664,0.714,0.659,0.645,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +12,DeepSequence (single),Alignment-based model,0.774,0.003,0.786,0.711,0.787,0.754,0.834,0.745,0.771,0.82,0.783,0.807,0.818,0.723,0.791,0.703,0.659,0.653,0.67,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +13,Tranception M,Hybrid - Alignment & PLM,0.768,0.004,0.781,0.718,0.783,0.741,0.816,0.748,0.766,0.795,0.781,0.801,0.781,0.718,0.781,0.688,0.624,0.635,0.648,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +14,SaProt (650M),Hybrid - Structure & PLM,0.768,0.006,0.767,0.72,0.797,0.693,0.861,0.701,0.752,0.832,0.784,0.805,0.827,0.613,0.779,0.705,0.621,0.608,0.606,SaProt (650M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +15,Progen2 XL,Protein language model,0.768,0.004,0.771,0.7,0.786,0.757,0.823,0.73,0.768,0.815,0.772,0.791,0.824,0.718,0.786,0.669,0.661,0.602,0.584,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +16,Tranception S,Hybrid - Alignment & PLM,0.766,0.004,0.774,0.725,0.785,0.734,0.813,0.749,0.758,0.794,0.776,0.797,0.78,0.71,0.776,0.689,0.626,0.636,0.65,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +17,MSA Transformer (single),Hybrid - Alignment & PLM,0.766,0.004,0.781,0.696,0.786,0.745,0.823,0.743,0.767,0.805,0.771,0.809,0.807,0.712,0.782,0.68,0.695,0.695,0.694,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +18,ProtSSN (ensemble),Hybrid - Structure & PLM,0.766,0.005,0.772,0.714,0.776,0.71,0.858,0.711,0.754,0.833,0.776,0.812,0.832,0.645,0.778,0.698,0.624,0.596,0.594,ProtSSN (ensemble of 9 models),"Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +19,ProtSSN (k=30 h=768),Hybrid - Structure & PLM,0.765,0.005,0.774,0.708,0.784,0.707,0.854,0.714,0.752,0.83,0.776,0.809,0.83,0.638,0.776,0.694,0.616,0.59,0.587,"ProtSSN (k=30, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +20,MIF-ST,Hybrid - Structure & PLM,0.765,0.004,0.769,0.707,0.79,0.72,0.839,0.724,0.755,0.817,0.769,0.799,0.827,0.671,0.781,0.684,0.662,0.623,0.645,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +21,ProtSSN (k=20 h=512),Hybrid - Structure & PLM,0.764,0.005,0.773,0.711,0.769,0.709,0.856,0.712,0.753,0.831,0.775,0.811,0.83,0.644,0.777,0.694,0.638,0.614,0.604,"ProtSSN (k=20, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +22,ProtSSN (k=30 h=512),Hybrid - Structure & PLM,0.763,0.005,0.769,0.713,0.776,0.707,0.854,0.705,0.753,0.829,0.774,0.808,0.827,0.639,0.775,0.689,0.615,0.599,0.59,"ProtSSN (k=30, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +23,ProtSSN (k=20 h=1280),Hybrid - Structure & PLM,0.762,0.005,0.772,0.708,0.769,0.71,0.855,0.705,0.751,0.832,0.773,0.812,0.828,0.641,0.776,0.697,0.63,0.6,0.604,"ProtSSN (k=20, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +24,ProtSSN (k=20 h=768),Hybrid - Structure & PLM,0.761,0.006,0.769,0.702,0.772,0.709,0.855,0.704,0.751,0.831,0.772,0.807,0.828,0.643,0.775,0.697,0.617,0.587,0.594,"ProtSSN (k=20, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +25,ProtSSN (k=10 h=1280),Hybrid - Structure & PLM,0.761,0.006,0.767,0.706,0.772,0.706,0.856,0.706,0.752,0.828,0.774,0.809,0.825,0.639,0.775,0.679,0.599,0.578,0.574,"ProtSSN (k=10, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +26,Wavenet,Alignment-based model,0.761,0.005,0.759,0.703,0.777,0.74,0.825,0.72,0.761,0.809,0.769,0.782,0.813,0.709,0.777,0.692,0.645,0.597,0.58,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +27,ProtSSN (k=30 h=1280),Hybrid - Structure & PLM,0.76,0.005,0.771,0.71,0.767,0.701,0.853,0.71,0.745,0.829,0.772,0.806,0.83,0.627,0.773,0.698,0.622,0.597,0.588,"ProtSSN (k=30, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +28,ProtSSN (k=10 h=768),Hybrid - Structure & PLM,0.76,0.005,0.77,0.699,0.77,0.706,0.853,0.703,0.752,0.827,0.774,0.803,0.826,0.64,0.774,0.68,0.61,0.576,0.566,"ProtSSN (k=10, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +29,ESM2 (15B),Protein language model,0.759,0.006,0.75,0.71,0.78,0.728,0.827,0.703,0.753,0.814,0.778,0.775,0.816,0.652,0.777,0.675,0.599,0.553,0.516,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +30,VESPAl,Protein language model,0.759,0.005,0.78,0.697,0.765,0.739,0.814,0.731,0.756,0.804,0.76,0.793,0.817,0.7,0.771,0.643,0.699,0.645,0.632,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +31,ProtSSN (k=10 h=512),Hybrid - Structure & PLM,0.758,0.006,0.769,0.7,0.771,0.702,0.849,0.707,0.746,0.826,0.771,0.807,0.824,0.627,0.771,0.691,0.603,0.574,0.567,"ProtSSN (k=10, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +32,Progen2 L,Protein language model,0.755,0.006,0.763,0.691,0.791,0.735,0.795,0.718,0.744,0.802,0.773,0.768,0.795,0.668,0.771,0.65,0.628,0.589,0.561,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +33,ESM2 (3B),Protein language model,0.755,0.006,0.748,0.706,0.775,0.709,0.835,0.693,0.743,0.819,0.774,0.785,0.811,0.626,0.772,0.682,0.592,0.566,0.511,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +34,Tranception L no retrieval,Protein language model,0.752,0.005,0.768,0.692,0.775,0.749,0.776,0.723,0.742,0.793,0.761,0.742,0.797,0.712,0.772,0.663,0.665,0.607,0.599,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +35,RITA XL,Protein language model,0.752,0.006,0.75,0.698,0.784,0.734,0.792,0.699,0.753,0.784,0.773,0.737,0.777,0.702,0.768,0.646,0.559,0.555,0.518,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +36,Progen2 M,Protein language model,0.751,0.006,0.747,0.696,0.786,0.734,0.794,0.69,0.746,0.798,0.772,0.751,0.781,0.679,0.77,0.634,0.57,0.539,0.485,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +37,ESM-1v (ensemble),Protein language model,0.75,0.006,0.74,0.701,0.776,0.721,0.811,0.677,0.739,0.814,0.77,0.768,0.797,0.641,0.768,0.67,0.595,0.553,0.498,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +38,CARP (640M),Protein language model,0.748,0.006,0.75,0.693,0.771,0.709,0.818,0.695,0.74,0.803,0.774,0.78,0.785,0.633,0.768,0.676,0.593,0.554,0.522,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +39,ESM-IF1,Inverse folding model,0.748,0.007,0.738,0.698,0.762,0.676,0.864,0.681,0.74,0.824,0.754,0.801,0.822,0.629,0.761,0.716,0.619,0.616,0.648,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +40,Progen2 Base,Protein language model,0.748,0.006,0.752,0.696,0.784,0.727,0.779,0.707,0.74,0.781,0.776,0.749,0.76,0.66,0.766,0.64,0.563,0.55,0.506,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +41,ESM2 (650M),Protein language model,0.747,0.007,0.739,0.705,0.764,0.694,0.834,0.67,0.733,0.819,0.768,0.778,0.807,0.599,0.764,0.686,0.601,0.554,0.514,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +42,ESM-1b,Protein language model,0.747,0.009,0.767,0.668,0.767,0.7,0.832,0.691,0.741,0.817,0.771,0.798,0.809,0.608,0.761,0.679,0.579,0.542,0.544,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +43,Site-Independent,Alignment-based model,0.747,0.005,0.751,0.7,0.76,0.72,0.802,0.736,0.744,0.772,0.759,0.781,0.764,0.695,0.764,0.687,0.623,0.637,0.644,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +44,RITA L,Protein language model,0.747,0.006,0.741,0.697,0.778,0.731,0.786,0.697,0.746,0.781,0.769,0.737,0.767,0.697,0.763,0.643,0.552,0.552,0.496,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +45,MIF,Inverse folding model,0.743,0.007,0.736,0.694,0.78,0.662,0.844,0.706,0.727,0.796,0.755,0.771,0.794,0.632,0.757,0.682,0.623,0.619,0.637,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +46,Unirep evotuned,Hybrid - Alignment & PLM,0.739,0.005,0.741,0.682,0.778,0.714,0.778,0.725,0.73,0.761,0.747,0.736,0.771,0.692,0.753,0.623,0.619,0.588,0.554,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +47,SaProt (35M),Hybrid - Structure & PLM,0.734,0.009,0.726,0.684,0.777,0.658,0.826,0.66,0.716,0.8,0.753,0.769,0.785,0.575,0.753,0.714,0.564,0.554,0.507,SaProt (35M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +48,ESM-1v (single),Protein language model,0.732,0.01,0.734,0.664,0.764,0.708,0.791,0.652,0.726,0.802,0.753,0.756,0.784,0.621,0.755,0.659,0.596,0.552,0.493,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +49,RITA M,Protein language model,0.731,0.008,0.724,0.671,0.771,0.721,0.768,0.68,0.725,0.776,0.751,0.716,0.754,0.694,0.752,0.625,0.542,0.54,0.499,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +50,ESM2 (150M),Protein language model,0.728,0.008,0.724,0.691,0.743,0.658,0.821,0.65,0.71,0.8,0.758,0.759,0.775,0.559,0.746,0.681,0.544,0.535,0.485,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +51,Tranception M no retrieval,Protein language model,0.726,0.009,0.732,0.67,0.76,0.723,0.745,0.673,0.724,0.757,0.743,0.712,0.746,0.683,0.748,0.634,0.553,0.542,0.49,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +52,CARP (76M),Protein language model,0.72,0.007,0.718,0.689,0.74,0.663,0.79,0.654,0.7,0.78,0.743,0.755,0.749,0.579,0.734,0.655,0.535,0.523,0.489,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +53,Progen2 S,Protein language model,0.718,0.009,0.712,0.666,0.755,0.692,0.764,0.646,0.709,0.769,0.747,0.711,0.743,0.624,0.739,0.624,0.535,0.527,0.474,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +54,Tranception S no retrieval,Protein language model,0.713,0.007,0.704,0.699,0.747,0.7,0.717,0.668,0.7,0.731,0.722,0.687,0.723,0.66,0.729,0.62,0.542,0.532,0.475,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +55,ProteinMPNN,Inverse folding model,0.713,0.009,0.716,0.633,0.717,0.644,0.854,0.655,0.715,0.795,0.729,0.778,0.782,0.623,0.74,0.692,0.599,0.593,0.602,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +56,RITA S,Protein language model,0.712,0.008,0.701,0.68,0.748,0.701,0.731,0.665,0.698,0.746,0.729,0.687,0.724,0.666,0.731,0.623,0.531,0.529,0.482,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +57,ESM2 (35M),Protein language model,0.703,0.008,0.702,0.678,0.721,0.636,0.777,0.633,0.671,0.78,0.728,0.722,0.742,0.552,0.722,0.674,0.542,0.532,0.488,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +58,CARP (38M),Protein language model,0.701,0.008,0.709,0.673,0.713,0.643,0.765,0.637,0.679,0.761,0.724,0.722,0.733,0.567,0.716,0.643,0.534,0.527,0.496,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +59,ESM2 (8M),Protein language model,0.667,0.008,0.665,0.668,0.697,0.61,0.693,0.622,0.639,0.694,0.679,0.641,0.701,0.54,0.684,0.626,0.538,0.522,0.467,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +60,ProtGPT2,Protein language model,0.653,0.01,0.67,0.62,0.654,0.616,0.706,0.614,0.644,0.693,0.683,0.666,0.672,0.556,0.68,0.618,0.536,0.516,0.426,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +61,Unirep,Protein language model,0.647,0.009,0.659,0.624,0.671,0.603,0.68,0.619,0.628,0.673,0.668,0.642,0.678,0.524,0.672,0.589,0.521,0.528,0.468,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +62,CARP (600K),Protein language model,0.636,0.009,0.659,0.593,0.655,0.585,0.687,0.605,0.622,0.67,0.654,0.64,0.676,0.53,0.658,0.583,0.526,0.522,0.472,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.html b/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.html new file mode 100644 index 0000000..ce9eaba --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/NDCG/Summary_performance_DMS_substitutions_NDCG.html @@ -0,0 +1,1670 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_NDCGBootstrap_standard_error_NDCGFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusDepth_1Depth_2Depth_3Depth_4Depth_5+Model detailsReferences
Model_rank
1TranceptEVE LHybrid - Alignment & PLM0.7860.0000.7940.7300.8010.7670.8390.7620.7810.8290.7870.8160.8330.7430.8000.7150.6940.6700.681TranceptEVE Large model (Tranception Large & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
2TranceptEVE SHybrid - Alignment & PLM0.7840.0010.7910.7310.7990.7610.8380.7590.7790.8240.7860.8130.8260.7400.7970.7110.6710.6620.673TranceptEVE Small model (Tranception Small & retrieved EVE model)<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
3TranceptEVE MHybrid - Alignment & PLM0.7840.0010.7930.7270.7990.7640.8370.7590.7820.8220.7870.8140.8240.7430.7980.7070.6690.6640.674TranceptEVE Medium model (Tranception Medium & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
4EVE (ensemble)Alignment-based model0.7830.0010.7910.7240.7980.7620.8380.7540.7790.8260.7840.8100.8290.7420.7960.7080.6880.6740.666EVE model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
5EVE (single)Alignment-based model0.7810.0010.7890.7240.7960.7610.8350.7520.7780.8230.7820.8100.8260.7400.7940.7100.6850.6710.668EVE model (single seed)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
6Tranception LHybrid - Alignment & PLM0.7790.0020.7940.7240.7950.7600.8240.7590.7730.8190.7880.8070.8130.7270.7930.7050.6800.6530.668Tranception Large model (700M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
7MSA Transformer (ensemble)Hybrid - Alignment & PLM0.7770.0040.7890.7080.8060.7490.8350.7520.7730.8190.7800.8120.8230.7230.7910.6930.6990.6960.696MSA Transformer (ensemble of 5 MSA samples)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
8GEMMEAlignment-based model0.7770.0020.7950.7110.7940.7560.8280.7580.7750.8130.7790.8050.8170.7430.7890.6900.6740.6740.708GEMME model<a href='https://pubmed.ncbi.nlm.nih.gov/31406981/'>Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.</a>
9EVmutationAlignment-based model0.7770.0020.7920.7080.7920.7570.8350.7480.7760.8210.7780.8130.8260.7340.7930.7170.6840.6740.697EVmutation model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
10DeepSequence (ensemble)Alignment-based model0.7760.0030.7850.7160.7870.7580.8350.7430.7730.8220.7810.8060.8230.7300.7910.7050.6710.6540.668DeepSequence model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
11VESPAProtein language model0.7750.0040.7890.7110.7900.7520.8350.7410.7730.8240.7760.8100.8330.7190.7890.6640.7140.6590.645VESPA model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
12DeepSequence (single)Alignment-based model0.7740.0030.7860.7110.7870.7540.8340.7450.7710.8200.7830.8070.8180.7230.7910.7030.6590.6530.670DeepSequence model (single seed)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
13Tranception MHybrid - Alignment & PLM0.7680.0040.7810.7180.7830.7410.8160.7480.7660.7950.7810.8010.7810.7180.7810.6880.6240.6350.648Tranception Medium model (300M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
14SaProt (650M)Hybrid - Structure & PLM0.7680.0060.7670.7200.7970.6930.8610.7010.7520.8320.7840.8050.8270.6130.7790.7050.6210.6080.606SaProt (650M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
15Progen2 XLProtein language model0.7680.0040.7710.7000.7860.7570.8230.7300.7680.8150.7720.7910.8240.7180.7860.6690.6610.6020.584Progen2 xlarge model (6.4B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
16Tranception SHybrid - Alignment & PLM0.7660.0040.7740.7250.7850.7340.8130.7490.7580.7940.7760.7970.7800.7100.7760.6890.6260.6360.650Tranception Small model (85M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
17MSA Transformer (single)Hybrid - Alignment & PLM0.7660.0040.7810.6960.7860.7450.8230.7430.7670.8050.7710.8090.8070.7120.7820.6800.6950.6950.694MSA Transformer (single MSA sample)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
18ProtSSN (ensemble)Hybrid - Structure & PLM0.7660.0050.7720.7140.7760.7100.8580.7110.7540.8330.7760.8120.8320.6450.7780.6980.6240.5960.594ProtSSN (ensemble of 9 models)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
19ProtSSN (k=30 h=768)Hybrid - Structure & PLM0.7650.0050.7740.7080.7840.7070.8540.7140.7520.8300.7760.8090.8300.6380.7760.6940.6160.5900.587ProtSSN (k=30, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
20MIF-STHybrid - Structure & PLM0.7650.0040.7690.7070.7900.7200.8390.7240.7550.8170.7690.7990.8270.6710.7810.6840.6620.6230.645MIF-ST model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
21ProtSSN (k=20 h=512)Hybrid - Structure & PLM0.7640.0050.7730.7110.7690.7090.8560.7120.7530.8310.7750.8110.8300.6440.7770.6940.6380.6140.604ProtSSN (k=20, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
22ProtSSN (k=30 h=512)Hybrid - Structure & PLM0.7630.0050.7690.7130.7760.7070.8540.7050.7530.8290.7740.8080.8270.6390.7750.6890.6150.5990.590ProtSSN (k=30, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
23ProtSSN (k=20 h=1280)Hybrid - Structure & PLM0.7620.0050.7720.7080.7690.7100.8550.7050.7510.8320.7730.8120.8280.6410.7760.6970.6300.6000.604ProtSSN (k=20, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
24ProtSSN (k=20 h=768)Hybrid - Structure & PLM0.7610.0060.7690.7020.7720.7090.8550.7040.7510.8310.7720.8070.8280.6430.7750.6970.6170.5870.594ProtSSN (k=20, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
25ProtSSN (k=10 h=1280)Hybrid - Structure & PLM0.7610.0060.7670.7060.7720.7060.8560.7060.7520.8280.7740.8090.8250.6390.7750.6790.5990.5780.574ProtSSN (k=10, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
26WavenetAlignment-based model0.7610.0050.7590.7030.7770.7400.8250.7200.7610.8090.7690.7820.8130.7090.7770.6920.6450.5970.580Wavenet model<a href='https://www.nature.com/articles/s41467-021-22732-w'>Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.</a>
27ProtSSN (k=30 h=1280)Hybrid - Structure & PLM0.7600.0050.7710.7100.7670.7010.8530.7100.7450.8290.7720.8060.8300.6270.7730.6980.6220.5970.588ProtSSN (k=30, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
28ProtSSN (k=10 h=768)Hybrid - Structure & PLM0.7600.0050.7700.6990.7700.7060.8530.7030.7520.8270.7740.8030.8260.6400.7740.6800.6100.5760.566ProtSSN (k=10, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
29ESM2 (15B)Protein language model0.7590.0060.7500.7100.7800.7280.8270.7030.7530.8140.7780.7750.8160.6520.7770.6750.5990.5530.516ESM2 model (15B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
30VESPAlProtein language model0.7590.0050.7800.6970.7650.7390.8140.7310.7560.8040.7600.7930.8170.7000.7710.6430.6990.6450.632VESPAl model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
31ProtSSN (k=10 h=512)Hybrid - Structure & PLM0.7580.0060.7690.7000.7710.7020.8490.7070.7460.8260.7710.8070.8240.6270.7710.6910.6030.5740.567ProtSSN (k=10, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
32Progen2 LProtein language model0.7550.0060.7630.6910.7910.7350.7950.7180.7440.8020.7730.7680.7950.6680.7710.6500.6280.5890.561Progen2 large model (2.7B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
33ESM2 (3B)Protein language model0.7550.0060.7480.7060.7750.7090.8350.6930.7430.8190.7740.7850.8110.6260.7720.6820.5920.5660.511ESM2 model (3B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
34Tranception L no retrievalProtein language model0.7520.0050.7680.6920.7750.7490.7760.7230.7420.7930.7610.7420.7970.7120.7720.6630.6650.6070.599Tranception Large model (700M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
35RITA XLProtein language model0.7520.0060.7500.6980.7840.7340.7920.6990.7530.7840.7730.7370.7770.7020.7680.6460.5590.5550.518RITA xlarge model (1.2B params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
36Progen2 MProtein language model0.7510.0060.7470.6960.7860.7340.7940.6900.7460.7980.7720.7510.7810.6790.7700.6340.5700.5390.485Progen2 medium model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
37ESM-1v (ensemble)Protein language model0.7500.0060.7400.7010.7760.7210.8110.6770.7390.8140.7700.7680.7970.6410.7680.6700.5950.5530.498ESM-1v (ensemble of 5 independently-trained models)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
38CARP (640M)Protein language model0.7480.0060.7500.6930.7710.7090.8180.6950.7400.8030.7740.7800.7850.6330.7680.6760.5930.5540.522CARP model (640M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
39ESM-IF1Inverse folding model0.7480.0070.7380.6980.7620.6760.8640.6810.7400.8240.7540.8010.8220.6290.7610.7160.6190.6160.648ESM-IF1 model<a href='https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html'>Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.</a>
40Progen2 BaseProtein language model0.7480.0060.7520.6960.7840.7270.7790.7070.7400.7810.7760.7490.7600.6600.7660.6400.5630.5500.506Progen2 base model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
41ESM2 (650M)Protein language model0.7470.0070.7390.7050.7640.6940.8340.6700.7330.8190.7680.7780.8070.5990.7640.6860.6010.5540.514ESM2 model (650M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
42ESM-1bProtein language model0.7470.0090.7670.6680.7670.7000.8320.6910.7410.8170.7710.7980.8090.6080.7610.6790.5790.5420.544ESM-1b (w/ Brandes et al. extensions)[1] Original model: <a href='https://www.biorxiv.org/content/10.1101/622803v4'>Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118.</a> [2] Extensions: <a href='https://www.biorxiv.org/content/10.1101/2022.08.25.505311v1'>Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.</a>
43Site-IndependentAlignment-based model0.7470.0050.7510.7000.7600.7200.8020.7360.7440.7720.7590.7810.7640.6950.7640.6870.6230.6370.644Site-Independent model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
44RITA LProtein language model0.7470.0060.7410.6970.7780.7310.7860.6970.7460.7810.7690.7370.7670.6970.7630.6430.5520.5520.496RITA large model (680M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
45MIFInverse folding model0.7430.0070.7360.6940.7800.6620.8440.7060.7270.7960.7550.7710.7940.6320.7570.6820.6230.6190.637MIF model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
46Unirep evotunedHybrid - Alignment & PLM0.7390.0050.7410.6820.7780.7140.7780.7250.7300.7610.7470.7360.7710.6920.7530.6230.6190.5880.554Unirep model w/ evotuning<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
47SaProt (35M)Hybrid - Structure & PLM0.7340.0090.7260.6840.7770.6580.8260.6600.7160.8000.7530.7690.7850.5750.7530.7140.5640.5540.507SaProt (35M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
48ESM-1v (single)Protein language model0.7320.0100.7340.6640.7640.7080.7910.6520.7260.8020.7530.7560.7840.6210.7550.6590.5960.5520.493ESM-1v (single seed)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
49RITA MProtein language model0.7310.0080.7240.6710.7710.7210.7680.6800.7250.7760.7510.7160.7540.6940.7520.6250.5420.5400.499RITA medium model (300M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
50ESM2 (150M)Protein language model0.7280.0080.7240.6910.7430.6580.8210.6500.7100.8000.7580.7590.7750.5590.7460.6810.5440.5350.485ESM2 model (150M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
51Tranception M no retrievalProtein language model0.7260.0090.7320.6700.7600.7230.7450.6730.7240.7570.7430.7120.7460.6830.7480.6340.5530.5420.490Tranception Medium model (300M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
52CARP (76M)Protein language model0.7200.0070.7180.6890.7400.6630.7900.6540.7000.7800.7430.7550.7490.5790.7340.6550.5350.5230.489CARP model (76M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
53Progen2 SProtein language model0.7180.0090.7120.6660.7550.6920.7640.6460.7090.7690.7470.7110.7430.6240.7390.6240.5350.5270.474Progen2 small model (150M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
54Tranception S no retrievalProtein language model0.7130.0070.7040.6990.7470.7000.7170.6680.7000.7310.7220.6870.7230.6600.7290.6200.5420.5320.475Tranception Small model (85M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
55ProteinMPNNInverse folding model0.7130.0090.7160.6330.7170.6440.8540.6550.7150.7950.7290.7780.7820.6230.7400.6920.5990.5930.602ProteinMPNN model<a href='https://www.science.org/doi/10.1126/science.add2187'>J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.</a>
56RITA SProtein language model0.7120.0080.7010.6800.7480.7010.7310.6650.6980.7460.7290.6870.7240.6660.7310.6230.5310.5290.482RITA small model (85M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
57ESM2 (35M)Protein language model0.7030.0080.7020.6780.7210.6360.7770.6330.6710.7800.7280.7220.7420.5520.7220.6740.5420.5320.488ESM2 model (35M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
58CARP (38M)Protein language model0.7010.0080.7090.6730.7130.6430.7650.6370.6790.7610.7240.7220.7330.5670.7160.6430.5340.5270.496CARP model (38M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
59ESM2 (8M)Protein language model0.6670.0080.6650.6680.6970.6100.6930.6220.6390.6940.6790.6410.7010.5400.6840.6260.5380.5220.467ESM2 model (8M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
60ProtGPT2Protein language model0.6530.0100.6700.6200.6540.6160.7060.6140.6440.6930.6830.6660.6720.5560.6800.6180.5360.5160.426ProtGPT2 model<a href='https://www.nature.com/articles/s41467-022-32007-7'>Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.</a>
61UnirepProtein language model0.6470.0090.6590.6240.6710.6030.6800.6190.6280.6730.6680.6420.6780.5240.6720.5890.5210.5280.468Unirep model<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
62CARP (600K)Protein language model0.6360.0090.6590.5930.6550.5850.6870.6050.6220.6700.6540.6400.6760.5300.6580.5830.5260.5220.472CARP model (600K params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv b/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv new file mode 100644 index 0000000..db931e3 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_DMS_level.csv @@ -0,0 +1,217 @@ +DMS ID,Site-Independent,EVmutation,DeepSequence (single),DeepSequence (ensemble),EVE (single),EVE (ensemble),Unirep,Unirep evotuned,MSA Transformer (single),MSA Transformer (ensemble),ESM-1b,ESM-1v (single),ESM-1v (ensemble),ESM2 (8M),ESM2 (35M),ESM2 (150M),ESM2 (650M),ESM2 (3B),ESM2 (15B),Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,GEMME,VESPA,VESPAl,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,CARP (38M),CARP (600K),CARP (640M),CARP (76M),MIF,MIF-ST,ESM-IF1,ProteinMPNN,ProtSSN (k=10 h=512),ProtSSN (k=10 h=768),ProtSSN (k=10 h=1280),ProtSSN (k=20 h=512),ProtSSN (k=20 h=768),ProtSSN (k=20 h=1280),ProtSSN (k=30 h=512),ProtSSN (k=30 h=768),ProtSSN (k=30 h=1280),ProtSSN (ensemble),SaProt (650M),SaProt (35M),Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A0A140D2T1_ZIKV_Sourisseau_2019,0.383,0.354,0.131,0.103,0.394,0.405,-0.133,0.062,0.436,0.439,-0.001,-0.048,0.015,-0.073,-0.054,-0.058,0.213,0.363,0.375,0.216,0.361,0.309,0.317,0.305,0.329,0.342,0.328,0.312,0.293,0.43,0.319,0.296,0.005,0.304,0.331,0.272,0.362,0.366,0.351,0.361,0.358,0.373,-0.06,-0.073,0.131,-0.061,0.273,0.29,0.287,0.129,0.253,0.251,0.282,0.296,0.277,0.269,0.263,0.269,0.265,0.278,0.199,0.116,9576,OrganismalFitness,A0A140D2T1_ZIKV,Medium,Virus 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+A4D664_9INFA_Soh_2019,0.411,0.34,0.406,0.403,0.409,0.408,0.029,0.364,0.297,0.287,0.044,0.026,0.033,0.022,0.02,0.027,0.149,0.211,0.27,0.26,0.329,0.386,0.404,0.398,0.064,0.26,0.285,0.244,0.33,0.443,0.318,0.296,0.055,0.348,0.371,0.404,0.366,0.38,0.393,0.434,0.439,0.461,0.019,-0.0,0.08,-0.001,0.262,0.254,0.126,0.105,0.183,0.18,0.191,0.216,0.209,0.207,0.2,0.198,0.193,0.204,0.148,0.092,14421,OrganismalFitness,A4D664_9INFA,Medium,Virus +A4GRB6_PSEAI_Chen_2020,0.317,0.492,0.657,0.664,0.623,0.641,0.344,0.529,0.641,0.681,0.68,0.647,0.668,0.431,0.528,0.655,0.738,0.712,0.627,0.567,0.409,0.533,0.564,0.624,0.526,0.622,0.639,0.638,0.705,0.671,0.742,0.673,0.242,0.423,0.552,0.629,0.557,0.588,0.652,0.653,0.66,0.679,0.481,0.071,0.671,0.601,0.664,0.713,0.629,0.422,0.728,0.718,0.711,0.719,0.729,0.733,0.728,0.733,0.735,0.745,0.722,0.513,5004,OrganismalFitness,A4GRB6_PSEAI,High,Prokaryote +AACC1_PSEAI_Dandage_2018,0.282,0.474,0.331,0.414,0.487,0.491,0.196,0.191,0.501,0.504,0.413,0.483,0.489,0.196,0.248,0.256,0.49,0.511,0.518,0.391,0.206,0.232,0.276,0.306,0.273,0.428,0.407,0.413,0.438,0.465,0.507,0.455,0.021,0.259,0.246,0.417,0.427,0.418,0.466,0.471,0.466,0.495,0.216,0.17,0.354,0.243,0.257,0.374,0.357,0.183,0.481,0.492,0.483,0.488,0.495,0.486,0.486,0.477,0.494,0.5,0.461,0.282,1801,OrganismalFitness,AACC1_PSEAI,High,Prokaryote +ACE2_HUMAN_Chan_2020,0.254,0.237,0.187,0.2,0.249,0.242,-0.049,0.077,0.236,0.251,0.244,0.173,0.213,-0.053,0.062,0.216,0.225,0.175,0.178,0.227,0.033,0.129,0.175,0.207,0.042,0.162,0.207,0.208,0.261,0.217,0.164,0.138,0.08,0.044,0.12,0.134,0.217,0.19,0.196,0.228,0.2,0.205,0.005,0.005,0.244,0.078,0.336,0.292,0.317,0.106,0.217,0.255,0.226,0.222,0.236,0.249,0.223,0.221,0.214,0.234,0.289,0.168,2223,Binding,ACE2_HUMAN,Medium,Human +ADRB2_HUMAN_Jones_2020,0.331,0.423,0.506,0.514,0.497,0.517,0.463,0.5,0.531,0.53,0.534,0.526,0.539,0.408,0.444,0.474,0.493,0.505,0.515,0.521,0.509,0.524,0.517,0.493,0.524,0.531,0.538,0.533,0.502,0.545,0.5,0.414,0.298,0.528,0.53,0.506,0.528,0.541,0.533,0.533,0.538,0.537,0.471,0.181,0.528,0.51,0.419,0.477,0.451,0.223,0.486,0.486,0.5,0.508,0.501,0.498,0.493,0.506,0.494,0.509,0.554,0.516,7800,Activity,ADRB2_HUMAN,Medium,Human +AICDA_HUMAN_Gajula_2014_3cycles,0.138,0.396,0.474,0.464,0.429,0.445,-0.287,0.327,0.32,0.385,0.415,0.376,0.478,-0.245,-0.264,0.196,0.328,0.347,0.262,0.479,-0.219,0.14,0.388,0.395,0.013,0.321,0.417,0.413,0.282,0.376,0.45,0.405,0.057,-0.039,0.057,0.339,0.206,0.218,0.367,0.399,0.402,0.444,-0.228,-0.238,0.379,0.258,0.328,0.401,0.328,0.243,0.337,0.404,0.344,0.447,0.359,0.365,0.341,0.343,0.312,0.359,0.257,-0.036,209,Activity,AICDA_HUMAN,Medium,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O,0.358,0.418,0.419,0.407,0.386,0.398,-0.301,0.13,0.22,0.24,0.374,0.07,0.423,-0.209,0.286,0.321,0.261,0.297,0.26,0.062,-0.052,-0.218,-0.051,-0.038,-0.317,-0.089,-0.05,-0.121,0.056,0.396,0.254,0.228,0.09,-0.287,-0.139,-0.275,0.392,0.402,0.356,0.424,0.422,0.374,-0.185,-0.194,0.165,0.114,0.251,0.112,0.433,0.353,0.446,0.326,0.204,0.349,0.342,0.349,0.343,0.339,0.361,0.343,0.347,0.397,2972,Stability,AMFR_HUMAN,Medium,Human +AMIE_PSEAE_Wrenbeck_2017,0.246,0.443,0.496,0.506,0.477,0.464,0.093,0.436,0.401,0.605,0.57,0.613,0.662,0.326,0.408,0.428,0.557,0.674,0.613,0.506,0.555,0.525,0.532,0.542,0.566,0.561,0.534,0.559,0.508,0.558,0.583,0.528,0.265,0.601,0.47,0.405,0.585,0.479,0.438,0.58,0.506,0.481,0.356,0.096,0.483,0.412,0.368,0.469,0.407,0.213,0.555,0.519,0.53,0.537,0.529,0.541,0.523,0.54,0.564,0.556,0.607,0.41,6227,Activity,AMIE_PSEAE,High,Prokaryote +ANCSZ_Hobbs_2022,0.562,0.521,0.445,0.472,0.524,0.528,0.525,0.452,0.503,0.524,0.527,0.522,0.543,0.535,0.592,0.598,0.609,0.598,0.57,0.021,0.462,0.493,0.509,0.5,0.497,0.519,0.513,0.469,0.494,0.557,0.542,0.525,0.318,0.483,0.503,0.475,0.537,0.549,0.538,0.545,0.556,0.543,0.549,0.42,0.496,0.523,0.471,0.458,0.422,0.165,0.566,0.555,0.572,0.578,0.572,0.584,0.569,0.583,0.577,0.589,0.585,0.601,4670,Activity,ANCSZ,Medium,Eukaryote +ARGR_ECOLI_Tsuboyama_2023_1AOY,0.277,0.413,0.411,0.43,0.431,0.423,0.306,0.386,0.43,0.463,0.409,0.296,0.405,0.332,0.297,0.511,0.497,0.499,0.435,0.434,0.372,0.439,0.448,0.403,0.362,0.437,0.473,0.461,0.418,0.46,0.409,0.31,0.073,0.394,0.396,0.43,0.405,0.415,0.439,0.465,0.46,0.471,0.285,0.267,0.45,0.408,0.725,0.651,0.738,0.636,0.513,0.532,0.527,0.529,0.517,0.508,0.503,0.526,0.494,0.533,0.604,0.511,1287,Stability,ARGR_ECOLI,Medium,Prokaryote +B2L11_HUMAN_Dutta_2010_binding-Mcl-1,0.688,0.27,0.668,0.661,0.53,0.646,0.216,0.529,0.188,0.164,0.206,-0.019,0.396,0.174,0.123,0.151,0.27,0.367,0.315,0.336,0.239,0.076,0.304,0.568,0.067,0.251,0.362,0.372,0.366,0.734,0.358,0.338,0.295,0.266,0.213,0.341,0.544,0.469,0.388,0.615,0.57,0.402,0.146,0.138,0.207,0.17,0.287,0.307,0.432,-0.005,0.352,0.367,0.419,0.283,0.255,0.47,0.295,0.288,0.421,0.374,0.242,0.204,170,Binding,B2L11_HUMAN,Low,Human +BBC1_YEAST_Tsuboyama_2023_1TG0,0.216,0.332,0.395,0.399,0.394,0.403,0.212,0.256,0.438,0.454,0.425,0.425,0.451,0.331,0.421,0.423,0.479,0.458,0.499,0.332,0.335,0.346,0.376,0.343,0.144,0.391,0.403,0.382,0.307,0.338,0.345,0.314,0.273,0.354,0.261,0.299,0.392,0.338,0.365,0.421,0.386,0.405,0.21,0.113,0.314,0.242,0.558,0.451,0.644,0.566,0.434,0.444,0.457,0.472,0.463,0.463,0.46,0.458,0.471,0.461,0.54,0.579,2069,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU,0.364,0.418,0.357,0.364,0.365,0.382,0.1,0.248,0.383,0.501,0.394,0.329,0.406,0.166,0.182,0.301,0.51,0.521,0.421,0.407,0.122,0.362,0.22,0.376,0.207,0.421,0.281,0.402,0.435,0.607,0.549,0.496,0.07,0.245,0.294,0.431,0.44,0.44,0.483,0.392,0.396,0.436,0.116,0.109,0.205,0.175,0.579,0.537,0.683,0.564,0.549,0.554,0.563,0.579,0.561,0.581,0.529,0.556,0.543,0.562,0.516,0.296,1572,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Deng_2012,0.322,0.504,0.508,0.521,0.507,0.508,0.075,0.312,0.513,0.55,0.517,0.501,0.518,0.243,0.354,0.461,0.528,0.461,0.352,0.455,0.375,0.378,0.4,0.383,0.391,0.445,0.453,0.406,0.343,0.464,0.519,0.487,0.107,0.392,0.402,0.373,0.457,0.471,0.473,0.513,0.526,0.522,0.33,0.011,0.48,0.406,0.423,0.496,0.46,0.252,0.53,0.535,0.538,0.552,0.548,0.542,0.544,0.53,0.541,0.555,0.539,0.406,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Firnberg_2014,0.471,0.708,0.732,0.747,0.709,0.729,0.134,0.459,0.701,0.741,0.703,0.665,0.707,0.411,0.555,0.658,0.737,0.573,0.424,0.662,0.566,0.563,0.537,0.531,0.595,0.646,0.654,0.577,0.452,0.683,0.758,0.752,0.186,0.551,0.529,0.48,0.646,0.638,0.637,0.731,0.734,0.736,0.54,0.046,0.7,0.595,0.616,0.703,0.696,0.321,0.718,0.699,0.703,0.729,0.739,0.73,0.736,0.72,0.729,0.745,0.754,0.605,4783,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Jacquier_2013,0.456,0.689,0.698,0.72,0.703,0.723,0.113,0.441,0.665,0.712,0.668,0.656,0.684,0.362,0.52,0.626,0.704,0.588,0.503,0.635,0.564,0.537,0.542,0.528,0.573,0.632,0.625,0.578,0.462,0.567,0.719,0.691,0.143,0.54,0.549,0.508,0.628,0.642,0.647,0.716,0.728,0.734,0.482,0.04,0.663,0.562,0.527,0.64,0.603,0.29,0.677,0.671,0.672,0.691,0.686,0.686,0.702,0.677,0.684,0.703,0.709,0.573,989,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Stiffler_2015,0.466,0.707,0.732,0.743,0.711,0.727,0.115,0.459,0.688,0.731,0.698,0.668,0.707,0.399,0.557,0.661,0.731,0.589,0.43,0.655,0.568,0.57,0.528,0.525,0.597,0.651,0.661,0.577,0.46,0.679,0.767,0.761,0.158,0.558,0.522,0.475,0.645,0.63,0.629,0.73,0.731,0.733,0.532,0.03,0.703,0.598,0.604,0.693,0.682,0.324,0.715,0.692,0.698,0.72,0.725,0.724,0.729,0.709,0.724,0.737,0.748,0.593,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BRCA1_HUMAN_Findlay_2018,0.501,0.385,0.441,0.432,0.424,0.466,0.125,0.189,0.387,0.407,0.494,0.404,0.447,0.134,0.392,0.455,0.515,0.497,0.394,0.296,0.145,0.379,0.405,0.361,0.305,0.473,0.457,0.442,0.423,0.438,0.539,0.482,0.22,0.151,0.179,0.409,0.456,0.456,0.514,0.475,0.476,0.533,0.229,0.132,0.534,0.457,0.44,0.469,0.116,0.12,0.515,0.501,0.506,0.502,0.512,0.504,0.513,0.512,0.504,0.519,0.539,0.481,1837,OrganismalFitness,BRCA1_HUMAN,Low,Human +BRCA2_HUMAN_Erwood_2022_HEK293T,0.481,0.346,0.44,0.445,0.447,0.481,0.095,0.477,0.095,-0.03,0.48,0.183,0.103,0.088,0.103,0.416,0.51,0.464,0.473,0.033,0.09,0.494,0.534,0.482,0.533,0.483,0.496,0.499,0.014,0.4,0.297,0.328,0.14,0.119,0.185,0.138,0.42,0.425,0.426,0.419,0.415,0.414,0.163,0.093,0.497,0.047,0.052,0.053,-0.091,0.285,0.436,0.477,0.473,0.424,0.455,0.436,0.456,0.438,0.453,0.465,0.0,0.016,265,OrganismalFitness,BRCA2_HUMAN,,Human +C6KNH7_9INFA_Lee_2018,0.393,0.371,0.351,0.357,0.433,0.436,-0.025,0.451,0.374,0.378,0.06,0.428,0.492,-0.014,-0.022,-0.007,0.483,0.405,0.426,0.343,0.396,0.363,0.378,0.349,0.254,0.454,0.428,0.465,0.37,0.475,0.457,0.36,0.11,0.354,0.374,0.392,0.412,0.419,0.433,0.439,0.441,0.452,-0.024,-0.033,0.294,-0.002,0.513,0.539,0.544,0.227,0.498,0.497,0.519,0.52,0.531,0.51,0.528,0.519,0.521,0.529,0.313,0.185,10754,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +CALM1_HUMAN_Weile_2017,0.175,0.233,0.238,0.233,0.238,0.236,0.174,0.187,0.236,0.253,0.25,0.235,0.264,0.161,0.199,0.195,0.212,0.218,0.225,0.229,0.173,0.227,0.242,0.254,0.232,0.276,0.279,0.3,0.313,0.239,0.21,0.146,0.087,0.216,0.253,0.291,0.218,0.242,0.266,0.238,0.244,0.246,0.213,0.156,0.268,0.251,0.106,0.143,0.164,0.088,0.177,0.182,0.188,0.171,0.189,0.176,0.18,0.192,0.178,0.188,0.287,0.238,1813,OrganismalFitness,CALM1_HUMAN,High,Human +CAPSD_AAV2S_Sinai_2021,0.407,0.345,0.317,0.372,0.346,0.33,0.374,0.424,0.329,0.363,0.183,0.196,0.199,0.254,0.286,0.203,0.277,0.183,0.124,0.258,0.191,0.25,0.269,0.279,0.203,0.198,0.266,0.204,0.399,0.445,0.183,0.177,0.132,0.202,0.262,0.492,0.38,0.386,0.473,0.338,0.343,0.426,0.092,0.16,0.26,0.112,0.402,0.393,0.347,0.319,0.235,0.225,0.216,0.235,0.214,0.215,0.22,0.208,0.228,0.223,0.301,0.222,42328,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAR11_HUMAN_Meitlis_2020_gof,0.265,0.174,0.164,0.15,0.169,0.184,0.1,0.009,0.218,0.245,0.304,0.324,0.302,0.068,0.095,0.337,0.326,0.345,0.357,0.165,0.151,0.198,0.219,0.216,0.037,0.215,0.229,0.226,0.167,0.179,0.326,0.296,0.189,0.005,0.148,0.124,0.203,0.208,0.182,0.204,0.201,0.182,0.089,0.043,0.261,0.215,0.318,0.294,0.335,0.142,0.343,0.337,0.335,0.323,0.33,0.331,0.317,0.344,0.346,0.342,0.408,0.333,2374,OrganismalFitness,CAR11_HUMAN,Low,Human +CAR11_HUMAN_Meitlis_2020_lof,0.421,0.313,0.277,0.258,0.31,0.327,0.108,-0.014,0.299,0.293,0.446,0.49,0.461,0.072,0.115,0.462,0.501,0.533,0.54,0.239,0.141,0.312,0.3,0.277,0.107,0.303,0.306,0.358,0.191,0.338,0.433,0.378,0.334,-0.003,0.264,0.195,0.29,0.325,0.271,0.32,0.326,0.281,0.087,0.041,0.417,0.311,0.417,0.445,0.451,0.175,0.518,0.521,0.515,0.508,0.5,0.518,0.503,0.512,0.526,0.525,0.579,0.425,2395,OrganismalFitness,CAR11_HUMAN,Low,Human +CAS9_STRP1_Spencer_2017_positive,0.159,0.179,0.162,0.166,0.169,0.177,0.04,0.1,0.18,0.183,0.175,0.069,0.073,0.063,0.09,0.154,0.182,0.188,0.194,0.006,0.043,0.081,0.173,0.12,0.043,0.194,0.182,0.179,0.068,0.187,0.199,0.179,0.027,0.035,0.047,0.166,0.17,0.172,0.189,0.184,0.184,0.194,0.061,0.028,0.181,0.106,0.106,0.181,0.036,0.037,0.176,0.177,0.178,0.185,0.18,0.181,0.177,0.178,0.177,0.183,0.177,0.114,8117,Activity,CAS9_STRP1,Medium,Prokaryote +CASP3_HUMAN_Roychowdhury_2020,0.382,0.543,0.61,0.622,0.626,0.632,0.054,0.478,0.644,0.653,0.59,0.595,0.624,0.264,0.574,0.639,0.638,0.554,0.514,0.603,0.233,0.512,0.517,0.532,0.505,0.556,0.554,0.526,0.567,0.608,0.599,0.542,0.217,0.092,0.482,0.52,0.533,0.562,0.582,0.628,0.619,0.628,0.422,-0.006,0.616,0.563,0.414,0.558,0.499,0.263,0.553,0.571,0.571,0.57,0.575,0.567,0.585,0.571,0.576,0.59,0.652,0.513,1567,Activity,CASP3_HUMAN,High,Human +CASP7_HUMAN_Roychowdhury_2020,0.372,0.516,0.619,0.617,0.609,0.613,0.057,0.47,0.6,0.627,0.585,0.611,0.632,0.306,0.598,0.632,0.622,0.572,0.562,0.611,0.206,0.532,0.529,0.558,0.529,0.579,0.589,0.563,0.572,0.642,0.596,0.538,0.332,0.103,0.543,0.521,0.517,0.594,0.586,0.599,0.628,0.628,0.461,0.019,0.646,0.596,0.479,0.612,0.573,0.329,0.585,0.57,0.562,0.587,0.608,0.602,0.591,0.578,0.596,0.605,0.643,0.533,1680,Activity,CASP7_HUMAN,Medium,Human +CATR_CHLRE_Tsuboyama_2023_2AMI,0.533,0.559,0.599,0.606,0.627,0.63,0.68,0.558,0.565,0.528,0.614,0.638,0.657,0.564,0.689,0.688,0.658,0.615,0.644,0.041,0.621,0.603,0.619,0.602,0.641,0.627,0.593,0.561,0.587,0.649,0.611,0.619,0.439,0.626,0.611,0.633,0.649,0.642,0.665,0.622,0.62,0.638,0.44,0.427,0.315,0.46,0.475,0.4,0.679,0.572,0.663,0.652,0.676,0.668,0.687,0.662,0.67,0.653,0.663,0.674,0.62,0.686,1903,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X,0.644,0.718,0.744,0.745,0.716,0.729,0.482,0.577,0.745,0.754,0.77,0.752,0.736,0.529,0.688,0.736,0.702,0.727,0.718,0.739,0.486,0.522,0.699,0.692,0.547,0.665,0.673,0.674,0.71,0.732,0.71,0.654,0.104,0.509,0.642,0.703,0.699,0.706,0.732,0.746,0.736,0.748,0.51,0.302,0.631,0.646,0.701,0.689,0.82,0.773,0.772,0.768,0.778,0.768,0.782,0.78,0.77,0.772,0.769,0.779,0.716,0.835,2068,Stability,CBPA2_HUMAN,Medium,Human +CBS_HUMAN_Sun_2020,0.334,0.358,0.364,0.383,0.371,0.379,0.202,0.251,0.374,0.377,0.347,0.346,0.372,0.085,0.226,0.335,0.342,0.329,0.339,0.35,0.356,0.26,0.285,0.288,0.349,0.273,0.29,0.273,0.31,0.381,0.37,0.344,0.197,0.361,0.283,0.266,0.384,0.332,0.327,0.394,0.363,0.363,0.304,0.068,0.376,0.373,0.237,0.268,0.309,0.079,0.324,0.324,0.331,0.337,0.337,0.335,0.332,0.332,0.335,0.34,0.386,0.289,7217,OrganismalFitness,CBS_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28,0.498,0.548,0.674,0.706,0.633,0.685,-0.266,0.611,0.623,0.613,0.615,0.65,0.648,-0.271,0.735,0.699,0.682,0.609,0.606,0.648,0.486,0.589,0.581,0.587,0.553,0.577,0.589,0.531,0.569,0.633,0.667,0.653,0.423,0.425,0.505,0.54,0.601,0.6,0.623,0.676,0.698,0.701,0.621,-0.32,0.369,0.561,0.167,0.155,0.614,0.588,0.695,0.626,0.69,0.676,0.709,0.668,0.677,0.718,0.664,0.695,0.642,0.751,2282,Stability,CBX4_HUMAN,High,Human +CCDB_ECOLI_Adkar_2012,0.357,0.486,0.52,0.547,0.528,0.531,-0.031,0.185,0.341,0.361,0.424,0.351,0.432,-0.006,-0.018,0.406,0.466,0.463,0.264,0.457,0.027,-0.015,-0.115,0.142,-0.139,0.053,-0.029,0.034,0.425,0.453,0.554,0.555,0.124,0.006,0.056,0.31,0.442,0.426,0.458,0.507,0.494,0.522,0.018,-0.05,0.444,0.02,0.285,0.426,0.343,0.242,0.436,0.401,0.414,0.443,0.437,0.454,0.436,0.464,0.486,0.453,0.438,0.194,1176,Activity,CCDB_ECOLI,High,Prokaryote +CCDB_ECOLI_Tripathi_2016,0.434,0.506,0.524,0.541,0.517,0.528,-0.008,0.294,0.403,0.431,0.49,0.435,0.483,-0.007,-0.001,0.462,0.511,0.522,0.362,0.518,0.034,0.014,-0.074,0.171,-0.107,0.094,-0.029,0.074,0.476,0.505,0.542,0.51,0.112,0.009,0.129,0.424,0.48,0.48,0.532,0.518,0.517,0.548,0.005,-0.055,0.495,0.017,0.317,0.433,0.335,0.23,0.486,0.449,0.449,0.482,0.483,0.48,0.473,0.49,0.517,0.492,0.49,0.326,1663,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +CCR5_HUMAN_Gill_2023,0.271,0.279,0.282,0.289,0.274,0.282,0.316,0.309,0.333,0.343,0.358,0.344,0.356,0.325,0.358,0.362,0.347,0.339,0.341,0.352,0.367,0.366,0.323,0.326,0.369,0.356,0.346,0.36,0.322,0.364,0.3,0.251,0.13,0.367,0.365,0.363,0.367,0.375,0.376,0.324,0.324,0.323,0.354,0.175,0.362,0.361,0.278,0.306,0.291,0.173,0.314,0.305,0.316,0.318,0.313,0.317,0.328,0.323,0.327,0.327,0.348,0.356,6137,Binding,CCR5_HUMAN,High,Human +CD19_HUMAN_Klesmith_2019_FMC_singles,0.189,0.247,0.199,0.211,0.234,0.232,0.051,0.233,0.257,0.261,0.046,0.05,0.072,0.09,0.093,0.054,0.05,0.123,0.217,-0.013,0.096,0.124,0.118,0.119,0.042,0.12,0.112,0.093,0.244,0.283,0.194,0.159,0.077,0.075,0.12,0.093,0.203,0.21,0.202,0.233,0.235,0.231,0.083,0.05,0.104,0.086,0.316,0.258,0.301,0.174,0.158,0.18,0.173,0.196,0.186,0.195,0.204,0.17,0.185,0.195,0.312,0.244,3761,Binding,CD19_HUMAN,Low,Human +CP2C9_HUMAN_Amorosi_2021_abundance,0.505,0.572,0.594,0.611,0.596,0.61,0.529,0.555,0.601,0.615,0.513,0.588,0.621,0.531,0.605,0.625,0.635,0.626,0.591,0.611,0.553,0.548,0.587,0.558,0.571,0.585,0.59,0.572,0.572,0.605,0.573,0.511,0.208,0.581,0.563,0.569,0.618,0.618,0.624,0.634,0.63,0.632,0.579,0.116,0.534,0.607,0.555,0.538,0.607,0.218,0.6,0.598,0.606,0.629,0.631,0.623,0.622,0.611,0.617,0.635,0.637,0.618,6370,Expression,CP2C9_HUMAN,High,Human +CP2C9_HUMAN_Amorosi_2021_activity,0.526,0.59,0.626,0.645,0.616,0.635,0.583,0.615,0.606,0.635,0.519,0.642,0.666,0.57,0.655,0.679,0.679,0.678,0.62,0.652,0.587,0.592,0.619,0.573,0.618,0.587,0.593,0.592,0.582,0.637,0.603,0.544,0.225,0.638,0.605,0.572,0.666,0.658,0.638,0.668,0.659,0.65,0.638,0.094,0.557,0.666,0.603,0.577,0.656,0.263,0.645,0.649,0.652,0.663,0.674,0.672,0.668,0.656,0.662,0.681,0.673,0.667,6142,Binding,CP2C9_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM,0.389,0.504,0.432,0.445,0.47,0.477,0.26,0.4,0.461,0.461,0.481,0.546,0.605,0.449,0.686,0.518,0.484,0.423,0.415,0.406,0.449,0.455,0.502,0.514,0.443,0.587,0.53,0.504,0.501,0.532,0.401,0.389,0.244,0.341,0.474,0.546,0.497,0.536,0.576,0.468,0.513,0.53,0.416,0.326,0.494,0.538,0.562,0.541,0.653,0.62,0.531,0.52,0.532,0.557,0.58,0.556,0.539,0.58,0.548,0.556,0.575,0.72,3295,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX,0.357,0.451,0.435,0.438,0.474,0.445,0.154,0.453,0.516,0.547,0.483,0.427,0.398,0.153,0.428,0.478,0.547,0.498,0.467,0.304,0.174,0.206,0.225,0.102,0.117,0.224,0.255,0.248,0.434,0.42,0.469,0.414,0.231,0.16,0.146,0.213,0.38,0.387,0.393,0.438,0.441,0.448,0.154,0.064,0.462,0.256,0.515,0.501,0.654,0.482,0.549,0.508,0.494,0.552,0.517,0.551,0.538,0.525,0.517,0.538,0.6,0.571,1580,Stability,CUE1_YEAST,Medium,Eukaryote +D7PM05_CLYGR_Somermeyer_2022,0.53,0.625,0.613,0.582,0.629,0.631,0.073,0.475,0.649,0.657,0.439,0.066,0.064,0.068,0.053,0.036,0.05,0.072,0.152,0.438,0.039,0.042,0.114,0.082,0.009,0.047,0.068,0.194,0.361,0.623,0.563,0.574,0.016,0.096,0.125,0.156,0.536,0.536,0.532,0.63,0.629,0.618,0.004,0.024,0.009,0.028,0.292,0.316,0.505,0.396,0.495,0.491,0.496,0.505,0.494,0.502,0.494,0.49,0.487,0.498,0.345,0.208,24515,Activity,D7PM05_CLYGR,Low,Eukaryote +DLG4_HUMAN_Faure_2021,0.679,0.584,0.607,0.577,0.609,0.616,0.723,0.629,0.524,0.535,0.52,0.55,0.608,0.743,0.764,0.728,0.584,0.487,0.443,0.65,0.574,0.583,0.569,0.538,0.605,0.607,0.564,0.562,0.495,0.614,0.634,0.621,0.511,0.576,0.638,0.58,0.653,0.7,0.667,0.643,0.661,0.636,0.581,0.233,0.372,0.528,0.637,0.453,0.672,0.315,0.483,0.37,0.539,0.499,0.52,0.492,0.508,0.486,0.504,0.504,0.521,0.745,6976,OrganismalFitness,DLG4_HUMAN,Low,Human +DLG4_RAT_McLaughlin_2012,0.486,0.442,0.487,0.491,0.526,0.539,0.49,0.437,0.483,0.522,0.469,0.557,0.588,0.411,0.543,0.581,0.543,0.478,0.471,0.444,0.378,0.389,0.373,0.371,0.407,0.42,0.407,0.367,0.395,0.49,0.556,0.537,0.246,0.367,0.392,0.307,0.47,0.478,0.443,0.538,0.544,0.54,0.527,0.076,0.443,0.5,0.439,0.308,0.459,0.135,0.442,0.375,0.444,0.433,0.444,0.43,0.43,0.464,0.46,0.469,0.504,0.535,1576,Binding,DLG4_RAT,Low,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC,0.171,0.198,0.218,0.23,0.2,0.259,-0.071,0.317,0.368,0.386,0.085,0.126,0.131,0.074,0.209,0.255,0.337,0.337,0.37,0.235,-0.068,0.045,0.033,0.066,0.041,0.107,0.057,0.071,0.235,0.298,0.431,0.351,0.054,-0.013,-0.023,0.031,0.212,0.2,0.211,0.243,0.227,0.231,0.106,-0.048,0.299,0.129,0.568,0.56,0.66,0.556,0.424,0.436,0.516,0.507,0.491,0.476,0.472,0.444,0.436,0.49,0.556,0.481,1008,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1,0.738,0.75,0.759,0.757,0.767,0.764,0.756,0.732,0.776,0.79,0.773,0.756,0.785,0.827,0.816,0.817,0.803,0.753,0.798,0.781,0.669,0.736,0.776,0.75,0.782,0.765,0.774,0.721,0.764,0.775,0.729,0.714,0.66,0.78,0.776,0.805,0.808,0.816,0.823,0.774,0.787,0.787,0.67,0.164,0.443,0.684,0.643,0.528,0.779,0.785,0.757,0.761,0.76,0.793,0.789,0.803,0.789,0.786,0.794,0.786,0.726,0.801,2264,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,0.418,0.453,0.421,0.439,0.494,0.501,0.057,0.432,0.406,0.403,0.525,0.472,0.501,0.224,0.458,0.486,0.531,0.508,0.531,0.518,0.34,0.418,0.402,0.408,0.315,0.402,0.365,0.348,0.395,0.448,0.471,0.469,0.216,0.202,0.321,0.252,0.473,0.49,0.494,0.489,0.491,0.507,0.359,-0.177,0.513,0.439,0.38,0.394,0.419,0.439,0.492,0.481,0.497,0.507,0.491,0.512,0.477,0.493,0.504,0.497,0.567,0.531,2915,Stability,DOCK1_MOUSE,High,Eukaryote +DYR_ECOLI_Nguyen_2023,0.34,0.424,0.448,0.453,0.451,0.454,0.004,0.425,0.505,0.481,0.523,0.535,0.545,0.133,0.5,0.525,0.544,0.529,0.511,0.502,0.373,0.421,0.281,0.342,0.435,0.388,0.387,0.454,0.333,0.48,0.502,0.477,0.148,0.405,0.235,0.225,0.46,0.33,0.356,0.459,0.406,0.418,0.469,0.013,0.519,0.499,0.264,0.428,0.374,0.157,0.501,0.459,0.47,0.483,0.492,0.49,0.503,0.495,0.511,0.505,0.511,0.471,2916,OrganismalFitness,DYR_ECOLI,High,Prokaryote +DYR_ECOLI_Thompson_2019,0.384,0.484,0.469,0.472,0.476,0.474,-0.018,0.321,0.47,0.501,0.469,0.416,0.436,0.055,0.349,0.455,0.48,0.506,0.512,0.485,0.199,0.36,0.266,0.31,0.342,0.436,0.476,0.441,0.418,0.451,0.451,0.408,0.1,0.355,0.348,0.347,0.407,0.415,0.423,0.459,0.476,0.481,0.318,-0.019,0.442,0.39,0.248,0.403,0.321,0.111,0.44,0.411,0.415,0.418,0.435,0.422,0.441,0.442,0.455,0.445,0.491,0.387,2363,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +ENV_HV1B9_DuenasDecamp_2016,0.369,0.397,0.238,0.367,0.388,0.377,0.056,0.353,0.341,0.366,0.359,0.415,0.389,0.11,0.055,0.035,0.012,0.048,0.095,0.388,0.38,0.358,0.408,0.419,0.263,0.394,0.401,0.391,0.374,0.389,0.355,0.322,0.253,0.368,0.373,0.404,0.392,0.396,0.407,0.39,0.392,0.392,0.343,-0.04,0.334,0.395,0.355,0.325,0.364,0.231,0.215,0.261,0.274,0.24,0.214,0.233,0.241,0.258,0.24,0.263,0.15,0.102,375,OrganismalFitness,ENV_HV1B9,Medium,Virus +ENV_HV1BR_Haddox_2016,0.338,0.303,0.322,0.323,0.339,0.345,-0.001,0.322,0.345,0.344,0.298,0.32,0.336,-0.009,-0.006,0.004,0.046,0.069,0.16,0.337,0.35,0.358,0.371,0.364,0.345,0.358,0.358,0.354,0.362,0.35,0.325,0.29,0.189,0.344,0.36,0.358,0.359,0.366,0.362,0.367,0.369,0.365,0.233,-0.013,0.321,0.293,0.212,0.245,0.119,0.072,0.18,0.179,0.203,0.226,0.188,0.202,0.2,0.192,0.193,0.209,0.167,0.098,12863,OrganismalFitness,ENV_HV1BR,Medium,Virus +ENVZ_ECOLI_Ghose_2023,0.115,0.095,0.18,0.178,0.187,0.188,0.04,0.19,0.19,0.198,0.154,0.204,0.209,0.194,0.207,0.195,0.172,0.154,0.113,0.183,0.164,0.152,0.201,0.183,0.176,0.164,0.199,0.183,0.163,0.178,0.133,0.135,0.142,0.18,0.184,0.193,0.207,0.213,0.215,0.195,0.201,0.199,0.194,0.134,0.187,0.185,0.055,0.149,0.114,0.036,0.189,0.186,0.168,0.188,0.22,0.197,0.183,0.19,0.2,0.199,0.148,0.224,1121,Activity,ENVZ_ECOLI,High,Prokaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M,0.615,0.706,0.705,0.708,0.732,0.733,-0.32,0.684,0.781,0.789,0.762,0.787,0.789,-0.378,0.768,0.803,0.81,0.769,0.744,0.741,0.555,0.599,0.685,0.656,0.641,0.664,0.684,0.705,0.735,0.795,0.752,0.738,0.677,0.612,0.603,0.644,0.716,0.711,0.728,0.741,0.737,0.735,0.625,-0.253,0.618,0.63,0.665,0.604,0.828,0.76,0.805,0.801,0.806,0.811,0.82,0.809,0.802,0.806,0.8,0.812,0.799,0.805,1960,Stability,EPHB2_HUMAN,High,Human +ERBB2_HUMAN_Elazar_2016,0.386,0.368,0.254,0.25,0.244,0.268,0.449,0.415,0.405,0.416,0.449,0.48,0.49,0.481,0.463,0.469,0.423,0.373,0.295,0.047,0.457,0.499,0.55,0.471,0.505,0.536,0.596,0.598,0.506,0.381,0.424,0.066,0.489,0.553,0.471,0.521,0.494,0.465,0.492,0.447,0.441,0.439,0.468,0.459,0.537,0.463,0.461,0.491,-0.139,0.022,0.41,0.406,0.451,0.44,0.415,0.406,0.432,0.452,0.438,0.446,0.525,0.488,326,Expression,ERBB2_HUMAN,Low,Human +ESTA_BACSU_Nutschel_2020,0.258,0.399,0.389,0.415,0.387,0.387,0.187,0.315,0.345,0.413,0.336,0.305,0.335,0.169,0.27,0.267,0.3,0.281,0.323,0.32,0.121,0.196,0.268,0.286,0.27,0.254,0.311,0.272,0.379,0.379,0.434,0.406,0.075,0.184,0.272,0.261,0.31,0.329,0.326,0.403,0.407,0.397,0.193,0.121,0.289,0.262,0.557,0.489,0.545,0.368,0.366,0.334,0.347,0.343,0.378,0.347,0.347,0.365,0.369,0.37,0.391,0.212,2172,Stability,ESTA_BACSU,High,Prokaryote +F7YBW8_MESOW_Aakre_2015,0.06,0.395,0.395,0.44,0.428,0.43,0.016,0.32,0.382,0.395,0.437,0.382,0.393,-0.091,-0.001,0.049,0.383,0.366,0.434,0.391,-0.075,-0.122,-0.104,-0.005,0.098,0.125,-0.021,0.298,0.403,0.375,0.461,0.441,0.037,-0.036,-0.124,0.433,0.062,0.011,0.425,0.38,0.367,0.444,-0.064,-0.081,0.199,-0.048,-0.008,0.246,0.069,0.035,0.431,0.447,0.423,0.46,0.453,0.457,0.43,0.443,0.447,0.448,0.27,-0.126,9192,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +FECA_ECOLI_Tsuboyama_2023_2D1U,0.38,0.431,0.449,0.458,0.449,0.441,0.023,0.297,0.372,0.456,0.509,0.269,0.433,0.059,0.449,0.536,0.524,0.48,0.386,0.464,0.169,0.272,0.343,0.288,0.156,0.477,0.451,0.292,0.394,0.52,0.409,0.336,0.276,0.065,0.121,0.148,0.355,0.348,0.302,0.415,0.415,0.389,0.395,0.008,0.501,0.447,0.582,0.551,0.588,0.525,0.527,0.483,0.514,0.512,0.5,0.528,0.499,0.489,0.506,0.515,0.595,0.585,1886,Stability,FECA_ECOLI,High,Prokaryote +FKBP3_HUMAN_Tsuboyama_2023_2KFV,0.423,0.394,0.479,0.482,0.487,0.498,0.18,0.342,0.237,0.237,0.171,0.17,0.164,0.172,0.163,0.147,0.188,0.265,0.378,0.287,0.221,0.213,0.218,0.279,0.169,0.229,0.193,0.25,0.22,0.478,0.315,0.319,-0.077,0.095,0.168,0.23,0.398,0.41,0.342,0.484,0.49,0.409,0.18,0.184,0.177,0.21,0.685,0.603,0.687,0.589,0.306,0.397,0.443,0.402,0.397,0.403,0.369,0.368,0.296,0.399,0.581,0.368,1237,Stability,FKBP3_HUMAN,Medium,Human +GAL4_YEAST_Kitzman_2015,0.237,0.403,0.507,0.567,0.486,0.532,0.337,-0.004,0.513,0.575,0.623,0.458,0.462,0.34,0.392,0.528,0.668,0.67,0.655,0.507,0.319,0.355,0.383,0.372,0.364,0.424,0.493,0.451,0.579,0.628,0.651,0.563,0.303,0.279,0.331,0.325,0.56,0.555,0.557,0.508,0.509,0.503,0.395,0.301,0.604,0.446,0.287,0.568,0.308,0.114,0.627,0.634,0.613,0.625,0.632,0.634,0.62,0.617,0.619,0.638,0.558,0.408,1195,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +GCN4_YEAST_Staller_2018,0.253,0.25,0.245,0.248,0.242,0.241,0.182,0.221,0.24,0.24,0.238,0.272,0.284,0.318,0.278,0.266,0.281,0.261,0.248,0.186,0.175,0.189,0.168,0.167,0.158,0.118,0.081,0.134,0.164,0.225,0.255,0.247,0.025,0.175,0.169,0.265,0.258,0.257,0.278,0.258,0.257,0.274,0.107,0.192,0.157,0.126,0.145,0.183,0.201,0.167,0.227,0.22,0.217,0.214,0.226,0.224,0.219,0.221,0.224,0.222,0.221,0.245,2638,Binding,GCN4_YEAST,Low,Eukaryote +GDIA_HUMAN_Silverstein_2021,0.442,0.429,0.439,0.446,0.451,0.452,0.205,0.398,0.466,0.462,0.391,0.423,0.46,0.159,0.247,0.448,0.397,0.356,0.411,0.401,0.253,0.385,0.38,0.409,0.326,0.439,0.425,0.384,0.397,0.415,0.423,0.393,0.276,0.321,0.394,0.356,0.432,0.448,0.437,0.451,0.47,0.461,0.233,0.121,0.432,0.26,0.357,0.421,0.387,0.101,0.389,0.335,0.402,0.378,0.391,0.399,0.377,0.391,0.375,0.405,0.461,0.338,1154,OrganismalFitness,GDIA_HUMAN,Low,Human +GFP_AEQVI_Sarkisyan_2016,0.649,0.644,0.672,0.673,0.679,0.679,0.049,0.635,0.667,0.661,0.524,0.098,0.103,0.078,0.131,0.103,0.108,0.149,0.29,0.598,0.078,0.105,0.181,0.098,0.046,0.188,0.298,0.642,0.647,0.678,0.607,0.615,0.074,0.066,0.182,0.629,0.649,0.651,0.672,0.683,0.685,0.706,0.024,-0.014,0.041,0.036,0.512,0.508,0.713,0.602,0.591,0.6,0.61,0.617,0.609,0.611,0.605,0.606,0.587,0.607,0.623,0.449,51714,Activity,GFP_AEQVI,Low,Eukaryote +GLPA_HUMAN_Elazar_2016,0.227,0.134,0.207,0.21,0.18,0.162,0.307,0.524,0.385,0.361,0.388,0.417,0.412,0.339,0.392,0.453,0.425,0.38,0.47,0.163,0.37,0.388,0.364,0.407,0.363,0.39,0.414,0.42,0.49,0.349,0.435,0.276,0.281,0.423,0.372,0.414,0.423,0.392,0.417,0.391,0.362,0.405,0.405,0.384,0.428,0.341,0.33,0.413,0.31,0.177,0.529,0.513,0.507,0.479,0.5,0.455,0.5,0.434,0.464,0.507,0.443,0.411,245,Expression,GLPA_HUMAN,Low,Human +GRB2_HUMAN_Faure_2021,0.405,0.521,0.507,0.538,0.54,0.546,0.486,0.45,0.539,0.491,0.533,0.455,0.515,0.526,0.598,0.626,0.647,0.535,0.579,0.534,0.544,0.516,0.514,0.453,0.532,0.509,0.446,0.524,0.448,0.504,0.428,0.422,0.464,0.536,0.506,0.404,0.517,0.506,0.436,0.562,0.561,0.532,0.584,0.323,0.53,0.572,0.672,0.527,0.69,0.516,0.621,0.649,0.638,0.638,0.647,0.628,0.629,0.645,0.625,0.65,0.556,0.607,63366,OrganismalFitness,GRB2_HUMAN,Medium,Human +HCP_LAMBD_Tsuboyama_2023_2L6Q,0.315,0.41,0.113,0.147,0.351,0.36,0.241,0.399,0.43,0.485,0.666,0.45,0.496,0.285,0.416,0.574,0.686,0.588,0.577,0.245,0.292,0.306,0.362,0.368,0.324,0.325,0.21,0.345,0.515,0.579,0.636,0.504,0.191,0.262,0.287,0.486,0.43,0.443,0.531,0.374,0.43,0.507,0.31,0.285,0.614,0.376,0.667,0.724,0.73,0.601,0.692,0.698,0.71,0.687,0.714,0.711,0.696,0.714,0.719,0.723,0.768,0.609,1040,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM,0.291,0.37,0.324,0.324,0.332,0.336,0.055,0.234,0.359,0.37,0.303,0.303,0.293,0.134,0.206,0.474,0.387,0.34,0.385,0.337,-0.024,0.232,0.302,0.264,0.16,0.294,0.308,0.247,0.214,0.312,0.332,0.307,0.103,0.135,0.101,0.229,0.327,0.298,0.353,0.327,0.341,0.359,0.089,0.038,0.433,0.138,0.252,0.26,0.38,0.321,0.368,0.408,0.345,0.392,0.411,0.436,0.442,0.428,0.451,0.417,0.414,0.219,5586,Stability,HECD1_HUMAN,Medium,Human +HEM3_HUMAN_Loggerenberg_2023,0.418,0.409,0.403,0.407,0.42,0.418,0.126,0.103,0.432,0.442,0.388,0.383,0.396,0.142,0.387,0.408,0.393,0.418,0.429,0.108,0.349,0.373,0.37,0.375,0.358,0.387,0.376,0.385,0.402,0.435,0.42,0.41,0.093,0.377,0.381,0.377,0.42,0.43,0.438,0.427,0.434,0.44,0.323,0.135,0.384,0.378,0.314,0.358,0.301,0.146,0.357,0.337,0.353,0.363,0.377,0.374,0.371,0.373,0.368,0.377,0.431,0.415,5689,Activity,HEM3_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019,0.472,0.542,0.558,0.557,0.533,0.531,0.146,0.273,0.468,0.508,0.472,0.411,0.477,0.057,0.113,0.44,0.411,0.475,0.48,0.454,0.325,0.402,0.433,0.478,0.404,0.485,0.464,0.516,0.539,0.524,0.4,0.374,0.143,0.397,0.453,0.585,0.488,0.496,0.616,0.573,0.548,0.582,0.14,0.04,0.299,0.02,0.378,0.424,0.529,0.392,0.444,0.35,0.404,0.427,0.44,0.407,0.418,0.45,0.431,0.424,0.521,0.222,496137,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +HMDH_HUMAN_Jiang_2019,0.491,0.421,0.382,0.394,0.456,0.451,0.158,0.282,0.282,0.274,0.275,0.341,0.315,0.046,0.01,0.317,0.509,0.374,0.378,0.302,0.451,0.162,0.175,0.221,0.435,0.197,0.168,0.209,0.213,0.476,0.413,0.401,0.228,0.308,0.247,0.219,0.44,0.34,0.33,0.437,0.361,0.36,0.172,0.069,0.477,0.496,0.323,0.444,-0.184,0.105,0.493,0.492,0.486,0.498,0.489,0.492,0.48,0.5,0.485,0.499,0.529,0.438,16853,OrganismalFitness,HMDH_HUMAN,Low,Human +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,0.316,0.36,0.371,0.376,0.364,0.365,0.194,0.394,0.427,0.437,0.362,0.416,0.453,-0.073,0.071,0.234,0.257,0.309,0.293,0.02,0.374,0.398,0.399,0.386,0.338,0.366,0.411,0.415,0.42,0.397,0.436,0.395,0.225,0.34,0.34,0.34,0.363,0.362,0.361,0.382,0.381,0.381,0.01,-0.056,0.383,0.4,0.068,0.267,0.097,-0.015,0.241,0.257,0.243,0.258,0.255,0.243,0.269,0.25,0.236,0.256,0.367,0.301,2252,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Flynn_2019,0.349,0.367,0.398,0.409,0.401,0.401,0.159,0.347,0.417,0.406,0.367,0.392,0.417,0.018,0.094,0.174,0.283,0.359,0.37,0.391,0.384,0.397,0.389,0.407,0.398,0.419,0.423,0.397,0.434,0.425,0.454,0.441,0.194,0.38,0.394,0.392,0.403,0.415,0.411,0.418,0.427,0.424,0.133,-0.065,0.371,0.313,0.188,0.308,0.11,0.062,0.291,0.283,0.295,0.305,0.293,0.296,0.301,0.296,0.282,0.302,0.386,0.329,13294,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Mishra_2016,0.487,0.461,0.541,0.553,0.543,0.543,0.385,0.399,0.472,0.49,0.491,0.547,0.556,0.178,0.291,0.372,0.461,0.473,0.429,0.485,0.501,0.504,0.494,0.528,0.532,0.51,0.54,0.479,0.515,0.557,0.558,0.55,0.338,0.496,0.51,0.505,0.525,0.534,0.528,0.552,0.558,0.552,0.495,-0.104,0.485,0.533,0.105,0.352,0.096,0.04,0.433,0.416,0.417,0.446,0.442,0.44,0.451,0.46,0.436,0.45,0.458,0.445,4323,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HXK4_HUMAN_Gersing_2022_activity,0.489,0.516,0.477,0.49,0.484,0.492,0.229,0.387,0.511,0.527,0.446,0.464,0.491,0.183,0.292,0.51,0.517,0.508,0.476,0.051,0.453,0.449,0.442,0.421,0.449,0.465,0.452,0.448,0.402,0.503,0.492,0.45,0.179,0.472,0.426,0.37,0.503,0.482,0.446,0.513,0.508,0.493,0.316,0.032,0.501,0.478,0.356,0.439,0.394,0.168,0.507,0.499,0.488,0.497,0.498,0.509,0.502,0.511,0.508,0.513,0.52,0.481,8570,OrganismalFitness,HXK4_HUMAN,Medium,Human +HXK4_HUMAN_Gersing_2023_abundance,0.322,0.362,0.325,0.351,0.341,0.348,0.057,0.329,0.386,0.407,0.338,0.331,0.354,0.136,0.185,0.328,0.367,0.376,0.358,0.401,0.282,0.29,0.314,0.316,0.285,0.307,0.316,0.315,0.32,0.374,0.295,0.253,0.17,0.305,0.304,0.317,0.34,0.349,0.359,0.348,0.362,0.369,0.168,0.071,0.351,0.336,0.399,0.382,0.391,0.16,0.359,0.374,0.371,0.368,0.379,0.376,0.375,0.368,0.371,0.378,0.424,0.329,8396,Expression,HXK4_HUMAN,Medium,Human +I6TAH8_I68A0_Doud_2015,0.347,0.317,0.268,0.264,0.364,0.361,-0.005,0.297,0.299,0.336,0.013,0.018,0.014,0.018,0.025,0.01,0.02,0.013,0.094,0.212,0.308,0.328,0.374,0.377,0.004,0.011,0.097,0.002,0.302,0.368,0.25,0.253,0.171,0.304,0.332,0.337,0.329,0.354,0.348,0.383,0.399,0.401,0.007,0.02,0.016,0.019,0.209,0.209,0.231,0.1,0.075,0.114,0.126,0.135,0.111,0.106,0.089,0.098,0.067,0.109,0.056,0.018,9462,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +IF1_ECOLI_Kelsic_2016,0.328,0.499,0.541,0.539,0.527,0.537,0.177,0.387,0.261,0.255,0.534,0.54,0.565,0.193,0.477,0.55,0.599,0.556,0.515,0.49,0.361,0.438,0.366,0.411,0.451,0.458,0.482,0.443,0.459,0.401,0.52,0.463,0.238,0.458,0.477,0.527,0.463,0.47,0.495,0.529,0.532,0.539,0.371,0.15,0.562,0.516,0.321,0.518,0.475,0.244,0.545,0.534,0.555,0.575,0.577,0.559,0.551,0.571,0.56,0.583,0.616,0.506,1367,OrganismalFitness,IF1_ECOLI,High,Prokaryote +ILF3_HUMAN_Tsuboyama_2023_2L33,0.233,0.336,0.444,0.438,0.437,0.436,0.022,0.315,0.371,0.416,0.353,0.352,0.35,0.192,0.324,0.435,0.318,0.282,0.167,0.48,0.206,0.214,0.257,0.312,0.347,0.233,0.372,0.255,0.259,0.457,0.349,0.313,0.097,0.127,0.282,0.362,0.282,0.349,0.401,0.382,0.389,0.445,0.279,0.045,0.339,0.395,0.418,0.368,0.475,0.342,0.304,0.274,0.343,0.33,0.37,0.395,0.33,0.312,0.414,0.37,0.319,0.349,1329,Stability,ILF3_HUMAN,High,Human +ISDH_STAAW_Tsuboyama_2023_2LHR,0.094,0.101,0.206,0.197,0.231,0.231,0.326,0.128,0.326,0.356,0.453,0.413,0.434,0.377,0.394,0.449,0.429,0.479,0.45,0.185,0.326,0.199,0.218,0.228,0.318,0.335,0.329,0.298,0.36,0.304,0.396,0.334,0.236,0.291,0.249,0.298,0.259,0.219,0.245,0.263,0.221,0.244,0.3,0.248,0.349,0.329,0.592,0.526,0.519,0.49,0.42,0.407,0.429,0.428,0.427,0.429,0.409,0.414,0.419,0.426,0.608,0.522,1944,Stability,ISDH_STAAW,High,Prokaryote +KCNE1_HUMAN_Muhammad_2023_expression,0.13,0.116,0.093,0.128,0.124,0.103,0.209,0.024,0.041,0.053,0.187,0.159,0.134,0.163,0.177,0.129,0.106,0.062,0.073,0.116,0.181,0.183,0.092,0.039,0.191,0.111,0.108,0.055,0.046,0.096,0.089,0.057,-0.17,0.147,0.225,0.097,0.138,0.152,0.115,0.138,0.141,0.121,0.199,0.166,0.093,0.194,0.179,0.116,0.139,0.086,0.088,0.087,0.091,0.071,0.067,0.065,0.105,0.1,0.082,0.086,0.11,0.193,2339,Expression,KCNE1_HUMAN,Medium,Human +KCNE1_HUMAN_Muhammad_2023_function,0.417,0.498,0.479,0.496,0.479,0.514,0.215,0.52,0.514,0.52,0.368,0.474,0.584,0.209,0.162,0.599,0.525,0.465,0.38,0.241,0.272,0.293,0.448,0.539,0.285,0.614,0.58,0.603,0.575,0.488,0.568,0.475,-0.034,0.189,0.376,0.616,0.464,0.5,0.618,0.524,0.551,0.634,0.212,0.208,0.528,0.198,0.202,0.425,0.182,0.093,0.54,0.54,0.523,0.528,0.537,0.513,0.548,0.545,0.548,0.554,0.642,0.238,2315,Activity,KCNE1_HUMAN,Medium,Human +KCNH2_HUMAN_Kozek_2020,0.449,0.421,0.298,0.292,0.211,0.21,0.454,0.151,0.303,0.306,0.307,0.216,0.234,0.253,0.197,0.229,0.28,0.258,0.248,0.382,0.468,0.513,0.495,0.463,0.502,0.491,0.455,0.477,0.481,0.46,0.433,0.318,0.344,0.471,0.525,0.511,0.499,0.554,0.538,0.472,0.517,0.509,0.394,0.223,0.193,0.291,0.219,-0.079,-0.202,0.086,0.53,0.39,0.481,0.435,0.444,0.442,0.476,0.441,0.435,0.475,0.271,0.411,200,Activity,KCNH2_HUMAN,Medium,Human +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.256,0.329,0.334,0.344,0.347,0.353,0.059,0.238,0.315,0.309,0.37,0.348,0.361,0.058,0.289,0.379,0.382,0.385,0.388,0.161,0.345,0.305,0.252,0.241,0.351,0.335,0.316,0.345,0.224,0.372,0.366,0.352,0.148,0.327,0.294,0.235,0.35,0.329,0.289,0.364,0.354,0.335,0.213,-0.022,0.328,0.343,0.189,0.286,0.227,0.084,0.374,0.355,0.365,0.365,0.369,0.361,0.371,0.365,0.365,0.373,0.371,0.266,6963,Activity,KCNJ2_MOUSE,Medium,Eukaryote +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.381,0.341,0.254,0.265,0.338,0.34,0.119,0.224,0.28,0.277,0.323,0.287,0.297,0.176,0.36,0.37,0.352,0.343,0.354,0.218,0.239,0.228,0.217,0.245,0.265,0.265,0.26,0.273,0.251,0.368,0.284,0.255,0.174,0.243,0.252,0.235,0.324,0.331,0.33,0.328,0.338,0.34,0.266,0.065,0.263,0.309,0.305,0.262,0.306,0.167,0.322,0.311,0.33,0.32,0.339,0.321,0.343,0.321,0.317,0.334,0.351,0.349,6917,Expression,KCNJ2_MOUSE,Medium,Eukaryote +KKA2_KLEPN_Melnikov_2014,0.25,0.53,0.437,0.622,0.599,0.603,0.199,0.423,0.538,0.606,0.566,0.597,0.621,0.212,0.261,0.487,0.601,0.645,0.662,0.498,0.285,0.42,0.518,0.54,0.353,0.578,0.577,0.568,0.638,0.627,0.637,0.586,0.148,0.243,0.485,0.586,0.445,0.524,0.588,0.586,0.609,0.63,0.253,0.076,0.559,0.386,0.456,0.582,0.551,0.267,0.587,0.572,0.588,0.578,0.579,0.59,0.574,0.583,0.591,0.599,0.641,0.329,4960,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +LGK_LIPST_Klesmith_2015,0.28,0.426,0.485,0.494,0.481,0.498,0.104,0.465,0.467,0.559,0.444,0.537,0.564,0.179,0.327,0.382,0.515,0.58,0.578,0.499,0.276,0.396,0.462,0.495,0.365,0.492,0.525,0.479,0.552,0.542,0.537,0.46,0.139,0.307,0.398,0.555,0.37,0.404,0.527,0.482,0.495,0.54,0.176,0.051,0.483,0.367,0.398,0.507,0.492,0.145,0.51,0.505,0.511,0.51,0.513,0.52,0.518,0.517,0.52,0.527,0.411,0.334,7890,Activity,LGK_LIPST,Medium,Eukaryote +LYAM1_HUMAN_Elazar_2016,0.314,0.303,0.189,0.158,0.258,0.266,0.361,0.172,0.413,0.416,0.436,0.37,0.371,0.307,0.363,0.336,0.31,0.426,0.454,0.336,0.382,0.391,0.386,0.385,0.362,0.369,0.373,0.298,0.339,0.349,0.313,0.179,0.203,0.353,0.437,0.343,0.362,0.41,0.348,0.343,0.372,0.313,0.307,0.346,0.318,0.35,0.17,0.259,0.163,0.103,0.235,0.317,0.398,0.33,0.279,0.295,0.352,0.348,0.272,0.332,0.419,0.39,359,Expression,LYAM1_HUMAN,Medium,Human +MAFG_MOUSE_Tsuboyama_2023_1K1V,0.613,0.636,0.633,0.635,0.621,0.623,0.405,0.643,0.633,0.626,0.619,0.355,0.6,0.453,0.429,0.471,0.48,0.551,0.413,0.627,0.448,0.494,0.522,0.566,0.46,0.515,0.447,0.536,0.49,0.602,0.621,0.609,0.392,0.329,0.565,0.509,0.589,0.647,0.657,0.629,0.661,0.683,0.312,0.079,0.425,0.246,0.596,0.327,0.638,0.611,0.646,0.615,0.617,0.638,0.623,0.638,0.636,0.642,0.651,0.639,0.719,0.727,1429,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV,0.551,0.723,0.708,0.727,0.758,0.763,-0.058,0.572,0.658,0.744,0.727,0.422,0.512,-0.146,0.025,0.742,0.699,0.685,0.74,0.715,-0.032,0.058,0.576,0.615,0.193,0.561,0.537,0.568,0.714,0.748,0.729,0.67,0.649,-0.282,0.063,0.033,0.586,0.658,0.634,0.729,0.751,0.742,-0.064,-0.203,0.629,0.433,0.521,0.66,0.773,0.576,0.793,0.76,0.792,0.745,0.758,0.741,0.763,0.776,0.774,0.78,0.813,0.72,2116,Stability,MBD11_ARATH,Medium,Eukaryote +MET_HUMAN_Estevam_2023,0.454,0.544,0.538,0.563,0.582,0.584,0.517,0.52,0.55,0.562,0.585,0.55,0.562,0.452,0.515,0.542,0.59,0.596,0.599,0.583,0.526,0.506,0.466,0.492,0.53,0.519,0.519,0.488,0.454,0.548,0.57,0.53,0.286,0.471,0.501,0.525,0.512,0.539,0.563,0.558,0.565,0.573,0.542,0.347,0.571,0.575,0.35,0.42,0.498,0.192,0.54,0.548,0.548,0.55,0.562,0.554,0.557,0.561,0.556,0.566,0.575,0.529,5393,Activity,MET_HUMAN,Medium,Human +MK01_HUMAN_Brenan_2016,0.176,0.198,0.237,0.241,0.223,0.227,0.209,0.163,0.198,0.184,0.033,0.167,0.182,0.17,0.199,0.195,0.178,0.189,0.141,0.184,0.209,0.117,0.069,0.016,0.183,0.089,0.057,0.076,-0.067,0.218,0.176,0.222,0.106,0.169,0.058,0.004,0.193,0.119,0.093,0.227,0.205,0.202,0.196,0.125,0.135,0.184,0.059,-0.0,0.133,0.006,0.161,0.162,0.148,0.166,0.17,0.163,0.163,0.169,0.177,0.171,0.15,0.186,6809,OrganismalFitness,MK01_HUMAN,Medium,Human +MLAC_ECOLI_MacRae_2023,0.225,0.352,0.419,0.424,0.409,0.411,-0.014,0.36,0.412,0.419,0.399,0.416,0.432,-0.011,0.236,0.273,0.382,0.382,0.412,0.387,0.078,0.366,0.38,0.402,0.313,0.383,0.392,0.382,0.424,0.4,0.44,0.41,0.039,0.131,0.315,0.296,0.265,0.327,0.315,0.374,0.396,0.395,0.177,-0.036,0.329,0.235,0.006,0.193,0.123,-0.026,0.319,0.28,0.288,0.293,0.315,0.308,0.295,0.305,0.332,0.313,0.23,0.203,4007,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +MSH2_HUMAN_Jia_2020,0.352,0.399,0.367,0.376,0.383,0.39,0.204,0.339,0.395,0.402,0.349,0.38,0.4,0.213,0.349,0.397,0.339,0.296,0.204,0.364,0.296,0.31,0.264,0.257,0.326,0.317,0.32,0.326,0.301,0.394,0.351,0.324,0.208,0.277,0.351,0.281,0.35,0.388,0.346,0.387,0.405,0.383,0.284,0.103,0.4,0.355,0.293,0.363,0.062,0.099,0.305,0.279,0.291,0.309,0.317,0.3,0.295,0.308,0.339,0.321,0.394,0.366,16749,OrganismalFitness,MSH2_HUMAN,Medium,Human +MTH3_HAEAE_RockahShmuel_2015,0.371,0.612,0.709,0.718,0.696,0.704,0.314,0.644,0.673,0.688,0.581,0.692,0.708,0.19,0.352,0.405,0.527,0.608,0.644,0.658,0.311,0.454,0.6,0.662,0.488,0.663,0.711,0.684,0.727,0.679,0.697,0.652,0.291,0.353,0.495,0.665,0.422,0.496,0.638,0.657,0.679,0.714,0.324,-0.031,0.477,0.341,0.449,0.574,0.583,0.225,0.544,0.531,0.559,0.569,0.561,0.573,0.551,0.571,0.557,0.572,0.58,0.378,1777,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +MTHR_HUMAN_Weile_2021,0.25,0.271,0.249,0.257,0.258,0.257,0.205,0.194,0.292,0.293,0.387,0.299,0.32,0.169,0.414,0.482,0.348,0.276,0.285,0.253,0.443,0.162,0.217,0.265,0.437,0.239,0.264,0.245,0.295,0.296,0.22,0.189,0.143,0.451,0.307,0.212,0.378,0.315,0.254,0.363,0.309,0.269,0.278,0.111,0.379,0.411,0.239,0.338,0.377,0.092,0.324,0.289,0.299,0.297,0.305,0.316,0.335,0.319,0.347,0.325,0.332,0.376,12464,OrganismalFitness,MTHR_HUMAN,Low,Human +MYO3_YEAST_Tsuboyama_2023_2BTT,0.143,0.248,0.334,0.362,0.381,0.412,0.155,0.251,0.428,0.419,0.519,0.482,0.478,0.171,0.411,0.583,0.588,0.507,0.247,0.321,0.351,0.247,0.336,0.311,0.342,0.287,0.364,0.313,0.336,0.283,0.279,0.178,0.126,0.419,0.332,0.282,0.382,0.329,0.292,0.411,0.401,0.382,0.112,0.045,0.431,0.294,0.392,0.427,0.479,0.359,0.517,0.495,0.545,0.512,0.525,0.529,0.513,0.519,0.506,0.525,0.571,0.5,3297,Stability,MYO3_YEAST,High,Eukaryote +NCAP_I34A1_Doud_2015,0.364,0.328,0.334,0.333,0.363,0.364,0.002,0.335,0.373,0.348,0.015,0.019,0.02,0.023,0.026,0.02,0.028,0.031,0.107,0.269,0.352,0.382,0.408,0.413,0.018,0.042,0.108,0.03,0.352,0.377,0.27,0.279,0.126,0.356,0.373,0.415,0.39,0.402,0.424,0.425,0.426,0.441,0.019,0.015,0.027,0.025,0.259,0.27,0.278,0.123,0.129,0.171,0.166,0.184,0.172,0.172,0.155,0.163,0.129,0.171,0.135,0.081,9462,OrganismalFitness,NCAP_I34A1,Medium,Virus +NKX31_HUMAN_Tsuboyama_2023_2L9R,0.428,0.548,0.586,0.584,0.607,0.619,0.582,0.553,0.584,0.608,0.56,0.568,0.572,0.627,0.638,0.674,0.651,0.582,0.602,0.558,0.564,0.557,0.556,0.564,0.576,0.579,0.606,0.54,0.577,0.64,0.525,0.506,0.551,0.494,0.565,0.577,0.578,0.624,0.628,0.601,0.618,0.621,0.454,0.395,0.342,0.408,0.589,0.401,0.643,0.642,0.634,0.626,0.639,0.648,0.669,0.641,0.635,0.629,0.642,0.645,0.566,0.596,2482,Stability,NKX31_HUMAN,High,Human +NPC1_HUMAN_Erwood_2022_HEK293T,0.683,0.7,0.627,0.632,0.7,0.7,0.22,0.529,0.739,0.734,0.701,0.325,0.458,0.221,0.394,0.614,0.694,0.721,0.719,0.147,0.197,0.46,0.412,0.419,0.421,0.491,0.631,0.447,0.495,0.71,0.659,0.548,0.095,0.231,0.547,0.579,0.639,0.675,0.696,0.691,0.708,0.722,0.3,0.134,0.702,0.568,0.499,0.683,0.06,0.211,0.681,0.664,0.687,0.679,0.678,0.664,0.693,0.701,0.685,0.697,0.7,0.392,637,Activity,NPC1_HUMAN,Low,Human +NPC1_HUMAN_Erwood_2022_RPE1,0.779,0.688,0.534,0.554,0.716,0.731,0.417,0.535,0.584,0.707,0.704,0.298,0.357,0.314,0.394,0.629,0.661,0.64,0.709,0.405,0.631,0.49,0.543,0.417,0.408,0.468,0.536,0.373,0.531,0.633,0.74,0.568,0.475,0.466,0.377,0.612,0.787,0.672,0.774,0.783,0.7,0.751,0.506,0.442,0.786,0.391,0.472,0.557,-0.107,0.157,0.574,0.537,0.559,0.482,0.569,0.56,0.571,0.646,0.64,0.595,0.692,0.482,63,Activity,NPC1_HUMAN,Low,Human +NRAM_I33A0_Jiang_2016,0.569,0.565,0.501,0.49,0.584,0.584,0.035,0.39,0.625,0.638,-0.076,0.162,0.448,-0.075,-0.098,0.005,0.161,0.541,0.575,0.343,0.583,0.633,0.584,0.571,0.047,0.53,0.627,0.462,0.654,0.635,0.404,0.441,-0.169,0.512,0.532,0.551,0.592,0.615,0.621,0.628,0.638,0.632,-0.105,-0.127,-0.111,-0.111,0.422,0.414,0.448,0.178,0.19,0.228,0.235,0.297,0.253,0.29,0.263,0.247,0.199,0.254,0.292,0.145,298,OrganismalFitness,NRAM_I33A0,Low,Virus +NUD15_HUMAN_Suiter_2020,0.271,0.453,0.564,0.596,0.591,0.594,-0.005,0.389,0.602,0.665,0.603,0.6,0.645,0.299,0.419,0.46,0.526,0.583,0.599,0.501,0.301,0.454,0.546,0.518,0.412,0.579,0.577,0.549,0.542,0.587,0.623,0.553,0.147,0.36,0.423,0.575,0.433,0.456,0.604,0.587,0.586,0.635,0.386,0.015,0.603,0.429,0.483,0.581,0.534,0.317,0.55,0.521,0.544,0.568,0.556,0.571,0.567,0.559,0.556,0.573,0.679,0.564,2844,Expression,NUD15_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL,0.392,0.589,0.58,0.571,0.602,0.613,0.285,0.465,0.638,0.599,0.56,0.323,0.346,0.322,0.39,0.405,0.494,0.532,0.533,0.56,0.394,0.549,0.572,0.589,0.325,0.497,0.44,0.525,0.572,0.638,0.67,0.706,0.183,0.365,0.419,0.425,0.505,0.493,0.508,0.628,0.601,0.622,0.375,0.246,0.381,0.357,0.682,0.641,0.73,0.658,0.709,0.695,0.699,0.721,0.726,0.741,0.716,0.716,0.719,0.723,0.685,0.575,2028,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6,0.405,0.404,0.417,0.431,0.451,0.434,0.23,0.423,0.485,0.461,0.398,0.361,0.421,0.389,0.561,0.51,0.518,0.525,0.477,0.521,0.264,0.339,0.315,0.347,0.38,0.385,0.461,0.334,0.378,0.502,0.394,0.387,0.321,0.269,0.275,0.372,0.395,0.399,0.426,0.413,0.417,0.424,0.445,0.144,0.395,0.531,0.676,0.56,0.759,0.64,0.487,0.457,0.454,0.507,0.487,0.485,0.485,0.486,0.483,0.488,0.41,0.588,1380,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C,0.54,0.697,0.759,0.777,0.745,0.757,0.384,0.455,0.751,0.763,0.691,0.735,0.759,0.392,0.779,0.819,0.797,0.786,0.755,0.723,0.688,0.689,0.663,0.687,0.589,0.667,0.694,0.726,0.604,0.735,0.694,0.679,0.549,0.262,0.461,0.575,0.687,0.695,0.704,0.755,0.76,0.764,0.543,0.115,0.507,0.633,0.63,0.54,0.782,0.747,0.762,0.76,0.761,0.755,0.772,0.76,0.773,0.767,0.776,0.771,0.825,0.772,3197,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G,-0.211,-0.17,0.339,0.188,0.332,0.332,-0.149,0.093,0.175,0.19,0.064,-0.1,-0.115,-0.069,-0.151,0.14,0.221,0.089,0.21,0.233,0.189,0.174,0.159,0.177,0.171,0.195,0.166,0.192,0.233,0.327,0.062,0.023,0.245,-0.05,0.025,0.012,0.19,0.181,0.178,0.271,0.26,0.29,0.268,-0.07,0.339,0.327,0.452,0.424,0.437,0.322,0.118,0.075,0.085,0.129,0.073,0.127,0.114,0.116,0.094,0.107,0.286,0.171,1134,Stability,ODP2_GEOSE,High,Prokaryote +OPSD_HUMAN_Wan_2019,0.17,0.433,0.445,0.517,0.463,0.466,0.347,0.569,0.563,0.6,0.397,0.499,0.56,0.29,0.508,0.518,0.552,0.449,0.482,0.555,0.528,0.549,0.538,0.578,0.552,0.594,0.598,0.577,0.57,0.511,0.422,0.326,0.17,0.503,0.53,0.485,0.537,0.532,0.503,0.485,0.482,0.488,0.386,0.08,0.493,0.472,0.632,0.518,0.632,0.224,0.402,0.353,0.412,0.421,0.482,0.487,0.418,0.421,0.49,0.472,0.582,0.522,165,Expression,OPSD_HUMAN,High,Human +OTC_HUMAN_Lo_2023,0.524,0.564,0.484,0.522,0.55,0.552,0.127,0.404,0.59,0.592,0.546,0.573,0.581,0.142,0.411,0.512,0.531,0.51,0.534,0.565,0.453,0.461,0.522,0.545,0.476,0.555,0.526,0.54,0.574,0.599,0.475,0.409,0.085,0.429,0.511,0.569,0.522,0.57,0.614,0.567,0.584,0.599,0.279,0.092,0.524,0.463,0.649,0.606,0.674,0.364,0.526,0.547,0.544,0.574,0.556,0.567,0.56,0.548,0.541,0.564,0.616,0.49,1570,Activity,OTC_HUMAN,Medium,Human +OTU7A_HUMAN_Tsuboyama_2023_2L2D,0.116,0.243,0.184,0.201,0.224,0.227,0.192,0.134,0.141,0.146,0.434,0.604,0.581,0.205,0.563,0.588,0.319,0.362,0.408,0.155,0.184,0.178,0.221,0.275,0.345,0.26,0.38,0.307,0.148,0.259,0.406,0.388,0.132,0.168,0.225,0.349,0.201,0.198,0.308,0.242,0.22,0.262,0.365,0.184,0.543,0.548,0.532,0.533,0.61,0.537,0.395,0.342,0.348,0.401,0.422,0.421,0.374,0.409,0.414,0.404,0.642,0.632,635,Stability,OTU7A_HUMAN,High,Human +OXDA_RHOTO_Vanella_2023_activity,0.104,0.292,0.364,0.373,0.356,0.356,0.195,0.251,0.278,0.285,0.383,0.348,0.359,0.122,0.219,0.326,0.37,0.39,0.401,0.259,0.125,0.214,0.303,0.305,0.219,0.343,0.356,0.329,0.407,0.378,0.429,0.408,0.102,0.164,0.213,0.293,0.267,0.275,0.305,0.342,0.344,0.355,0.153,0.014,0.369,0.247,0.295,0.386,0.361,0.139,0.367,0.36,0.355,0.361,0.365,0.374,0.367,0.376,0.358,0.377,0.396,0.242,6396,Activity,OXDA_RHOTO,High,Eukaryote +OXDA_RHOTO_Vanella_2023_expression,0.18,0.253,0.29,0.287,0.276,0.272,0.19,0.193,0.296,0.305,0.327,0.31,0.311,0.221,0.257,0.298,0.315,0.351,0.34,0.175,0.169,0.243,0.252,0.233,0.217,0.264,0.295,0.275,0.31,0.308,0.289,0.241,0.082,0.227,0.211,0.246,0.268,0.259,0.267,0.284,0.278,0.278,0.243,0.144,0.304,0.234,0.385,0.356,0.333,0.142,0.329,0.318,0.308,0.317,0.323,0.308,0.321,0.316,0.311,0.326,0.408,0.321,6769,Expression,OXDA_RHOTO,High,Eukaryote +P53_HUMAN_Giacomelli_2018_Null_Etoposide,0.415,0.424,0.329,0.344,0.432,0.427,-0.088,0.319,0.283,0.291,0.467,0.457,0.509,-0.154,-0.146,0.314,0.432,0.469,0.497,0.116,0.312,0.438,0.453,0.409,0.39,0.472,0.484,0.493,0.368,0.423,0.465,0.41,0.208,0.258,0.449,0.355,0.403,0.462,0.4,0.427,0.469,0.413,-0.151,-0.155,0.475,-0.051,0.389,0.471,0.414,0.186,0.449,0.45,0.451,0.45,0.452,0.47,0.448,0.454,0.452,0.464,0.477,0.186,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_Null_Nutlin,0.35,0.395,0.294,0.304,0.383,0.38,-0.087,0.269,0.287,0.289,0.482,0.447,0.498,-0.158,-0.145,0.29,0.404,0.431,0.459,0.126,0.294,0.447,0.463,0.43,0.375,0.487,0.5,0.523,0.329,0.42,0.45,0.375,0.213,0.239,0.453,0.36,0.354,0.431,0.379,0.372,0.43,0.383,-0.144,-0.153,0.471,-0.063,0.403,0.48,0.426,0.188,0.423,0.427,0.427,0.433,0.431,0.44,0.426,0.43,0.434,0.44,0.469,0.182,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_WT_Nutlin,0.445,0.462,0.342,0.366,0.479,0.481,-0.143,0.3,0.258,0.259,0.551,0.496,0.554,-0.194,-0.174,0.362,0.4,0.446,0.467,0.028,0.41,0.563,0.521,0.48,0.482,0.535,0.55,0.616,0.285,0.507,0.493,0.449,0.225,0.326,0.612,0.408,0.448,0.568,0.433,0.475,0.567,0.438,-0.185,-0.202,0.544,-0.08,0.452,0.553,0.461,0.223,0.438,0.45,0.449,0.452,0.461,0.471,0.437,0.461,0.438,0.463,0.482,0.166,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Kotler_2018,0.629,0.588,0.496,0.556,0.555,0.576,0.109,0.498,0.573,0.585,0.6,0.538,0.622,0.095,0.108,0.628,0.678,0.698,0.702,0.432,0.429,0.461,0.478,0.463,0.47,0.48,0.465,0.47,0.493,0.593,0.631,0.585,0.039,0.398,0.468,0.42,0.611,0.584,0.564,0.604,0.588,0.581,0.09,0.04,0.638,0.221,0.467,0.583,0.479,0.252,0.638,0.659,0.658,0.642,0.645,0.655,0.673,0.658,0.663,0.672,0.705,0.396,1048,OrganismalFitness,P53_HUMAN,Low,Human +P84126_THETH_Chan_2017,0.508,0.58,0.603,0.617,0.564,0.578,0.364,0.526,0.644,0.623,0.569,0.552,0.588,0.319,0.551,0.559,0.605,0.596,0.571,0.618,0.419,0.507,0.478,0.558,0.521,0.584,0.589,0.571,0.653,0.522,0.553,0.469,0.359,0.474,0.499,0.536,0.515,0.526,0.547,0.56,0.568,0.581,0.465,0.032,0.543,0.51,0.337,0.452,0.512,0.125,0.543,0.527,0.487,0.549,0.551,0.519,0.526,0.553,0.561,0.562,0.622,0.61,1519,OrganismalFitness,P84126_THETH,Medium,Prokaryote +PA_I34A1_Wu_2015,0.518,0.519,0.499,0.508,0.539,0.543,0.041,0.358,0.152,0.165,0.037,0.054,0.101,0.024,0.029,0.025,0.038,0.041,0.356,0.325,0.456,0.493,0.533,0.538,0.219,0.408,0.444,0.429,0.438,0.584,0.384,0.374,0.107,0.436,0.475,0.541,0.546,0.561,0.572,0.588,0.592,0.584,0.028,0.019,0.031,0.02,0.244,0.232,0.172,0.095,0.154,0.173,0.184,0.187,0.168,0.178,0.158,0.173,0.125,0.181,0.202,0.132,1820,OrganismalFitness,PA_I34A1,Medium,Virus +PABP_YEAST_Melamed_2013,0.663,0.617,0.541,0.55,0.654,0.648,0.474,0.569,0.637,0.663,0.688,0.662,0.678,0.476,0.566,0.648,0.716,0.684,0.695,0.541,0.638,0.665,0.666,0.692,0.638,0.698,0.7,0.676,0.666,0.674,0.703,0.635,0.261,0.638,0.648,0.64,0.689,0.692,0.688,0.683,0.687,0.684,0.605,-0.027,0.67,0.654,0.361,0.612,0.514,0.197,0.664,0.66,0.672,0.693,0.685,0.689,0.691,0.69,0.718,0.705,0.715,0.622,37708,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +PAI1_HUMAN_Huttinger_2021,0.403,0.395,0.377,0.392,0.402,0.413,0.054,0.314,0.428,0.438,0.435,0.408,0.43,0.083,0.391,0.434,0.448,0.335,0.27,0.422,0.132,0.381,0.364,0.345,0.351,0.395,0.399,0.372,0.33,0.423,0.393,0.356,0.141,0.171,0.37,0.379,0.384,0.404,0.412,0.414,0.424,0.426,0.35,0.049,0.43,0.407,0.379,0.45,0.404,0.152,0.441,0.437,0.439,0.448,0.448,0.445,0.449,0.449,0.446,0.457,0.468,0.391,5345,Activity,PAI1_HUMAN,,Human +PHOT_CHLRE_Chen_2023,0.211,0.44,0.731,0.706,0.352,0.335,0.654,0.552,0.711,0.721,0.647,0.757,0.783,0.768,0.823,0.714,0.749,0.714,0.758,0.652,0.685,0.678,0.596,0.699,0.533,0.616,0.596,0.656,0.602,0.587,0.572,0.49,0.303,0.565,0.511,0.602,0.597,0.564,0.606,0.416,0.445,0.422,0.623,0.395,0.599,0.621,0.2,0.502,0.669,0.41,0.603,0.543,0.553,0.551,0.555,0.57,0.568,0.574,0.583,0.569,0.737,0.775,167529,Activity,PHOT_CHLRE,High,Eukaryote +PIN1_HUMAN_Tsuboyama_2023_1I6C,0.222,0.327,0.68,0.645,0.631,0.661,0.585,0.5,0.67,0.707,0.654,0.576,0.674,0.66,0.646,0.665,0.67,0.52,0.548,0.656,0.456,0.671,0.63,0.61,0.559,0.672,0.681,0.647,0.658,0.689,0.692,0.644,0.531,0.539,0.626,0.667,0.607,0.671,0.72,0.677,0.705,0.73,0.556,-0.189,0.623,0.51,0.547,0.574,0.691,0.669,0.659,0.627,0.498,0.563,0.575,0.604,0.608,0.649,0.626,0.62,0.74,0.742,802,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M,0.579,0.52,0.548,0.555,0.549,0.553,0.506,0.47,0.538,0.561,0.467,0.446,0.436,0.557,0.652,0.675,0.626,0.586,0.507,0.542,0.513,0.454,0.477,0.45,0.547,0.493,0.465,0.428,0.468,0.536,0.403,0.386,0.45,0.558,0.508,0.481,0.596,0.548,0.538,0.573,0.537,0.538,0.468,0.412,0.282,0.333,0.453,0.295,0.693,0.63,0.572,0.579,0.572,0.59,0.602,0.609,0.593,0.594,0.598,0.598,0.47,0.593,1824,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF,0.179,0.188,0.218,0.234,0.249,0.254,0.198,0.187,0.165,0.206,0.253,0.278,0.31,0.314,0.347,0.464,0.298,0.236,0.238,0.277,0.31,0.289,0.278,0.325,0.3,0.291,0.31,0.292,0.295,0.29,0.326,0.317,0.067,0.335,0.278,0.311,0.262,0.285,0.308,0.288,0.28,0.293,0.266,0.279,0.26,0.3,0.512,0.379,0.493,0.469,0.258,0.303,0.317,0.26,0.284,0.287,0.286,0.299,0.314,0.299,0.324,0.464,1301,Stability,PKN1_HUMAN,High,Human +POLG_CXB3N_Mattenberger_2021,0.423,0.39,0.377,0.411,0.46,0.473,-0.036,0.336,0.5,0.499,0.292,-0.059,0.042,-0.079,-0.056,0.177,0.395,0.404,0.426,0.356,0.339,0.39,0.381,0.377,0.138,0.388,0.383,0.369,0.393,0.495,0.386,0.319,0.007,0.049,0.274,0.355,0.342,0.385,0.413,0.387,0.43,0.458,-0.065,-0.066,0.342,-0.054,0.193,0.339,0.113,0.046,0.342,0.361,0.364,0.369,0.364,0.365,0.365,0.365,0.374,0.374,0.11,0.068,15711,OrganismalFitness,POLG_CXB3N,Medium,Virus +POLG_DEN26_Suphatrakul_2023,0.5,0.578,0.289,0.286,0.532,0.533,-0.044,0.434,0.67,0.675,0.318,-0.006,0.0,-0.044,0.007,0.077,0.153,0.275,0.366,0.409,0.453,0.473,0.449,0.456,0.47,0.501,0.493,0.488,0.461,0.622,0.603,0.522,0.035,-0.064,0.115,0.471,0.432,0.383,0.542,0.469,0.397,0.563,-0.055,-0.059,0.391,-0.005,0.36,0.51,0.101,0.116,0.241,0.229,0.244,0.26,0.227,0.261,0.24,0.246,0.223,0.254,0.161,0.032,16897,OrganismalFitness,POLG_DEN26,Low,Virus +POLG_HCVJF_Qi_2014,0.605,0.547,0.41,0.413,0.605,0.614,-0.039,0.196,0.565,0.577,0.178,0.637,0.635,0.101,0.128,0.114,0.116,0.09,0.078,0.26,0.4,0.443,0.452,0.492,0.422,0.475,0.32,0.41,0.517,0.63,0.587,0.485,0.182,0.474,0.505,0.522,0.515,0.547,0.578,0.48,0.528,0.56,0.114,0.053,0.506,0.109,-0.037,0.349,0.643,0.35,0.308,0.378,0.344,0.379,0.342,0.283,0.323,0.318,0.314,0.372,0.173,0.159,1630,OrganismalFitness,POLG_HCVJF,Medium,Virus +POLG_PESV_Tsuboyama_2023_2MXD,0.251,0.455,0.373,0.407,0.471,0.463,0.07,0.42,0.39,0.514,0.375,0.062,0.157,0.132,0.14,0.031,0.16,0.114,0.084,0.592,0.079,0.058,0.016,0.157,0.106,-0.059,0.008,-0.084,0.17,0.507,0.575,0.58,0.131,0.003,-0.033,0.053,0.382,0.383,0.384,0.453,0.463,0.452,0.006,-0.037,0.033,0.026,0.448,0.443,0.65,0.576,0.658,0.664,0.615,0.736,0.705,0.718,0.692,0.731,0.718,0.718,0.716,0.686,5130,Stability,POLG_PESV,Medium,Virus +PPARG_HUMAN_Majithia_2016,0.353,0.539,0.629,0.655,0.612,0.632,0.187,0.339,0.557,0.571,0.623,0.696,0.695,0.028,0.126,0.456,0.594,0.736,0.768,0.717,0.675,0.692,0.25,0.288,0.592,0.722,0.692,0.719,0.299,0.712,0.583,0.509,0.384,0.709,0.683,0.587,0.673,0.7,0.637,0.698,0.706,0.68,0.266,0.03,0.585,0.45,0.594,0.607,0.631,0.302,0.58,0.588,0.593,0.575,0.599,0.583,0.602,0.598,0.585,0.603,0.657,0.499,9576,Activity,PPARG_HUMAN,Medium,Human +PPM1D_HUMAN_Miller_2022,0.55,0.523,0.531,0.536,0.609,0.61,0.035,0.378,0.46,0.517,0.578,0.599,0.618,0.281,0.386,0.47,0.602,0.628,0.618,0.387,0.45,0.526,0.537,0.43,0.516,0.577,0.567,0.557,0.419,0.601,0.594,0.546,0.234,0.428,0.528,0.537,0.582,0.593,0.591,0.615,0.614,0.616,0.361,-0.05,0.575,0.515,0.503,0.579,0.563,0.236,0.574,0.575,0.57,0.586,0.593,0.58,0.584,0.585,0.59,0.595,0.633,0.509,7889,OrganismalFitness,PPM1D_HUMAN,Low,Human +PR40A_HUMAN_Tsuboyama_2023_1UZC,0.562,0.655,0.745,0.736,0.772,0.774,0.518,0.493,0.774,0.783,0.727,0.592,0.681,0.54,0.535,0.821,0.805,0.74,0.727,0.715,0.552,0.694,0.7,0.725,0.634,0.716,0.733,0.723,0.744,0.795,0.777,0.756,0.637,0.593,0.556,0.611,0.735,0.746,0.738,0.764,0.784,0.775,0.438,0.281,0.326,0.431,0.639,0.423,0.794,0.783,0.792,0.809,0.804,0.812,0.828,0.823,0.819,0.816,0.816,0.821,0.801,0.835,2033,Stability,PR40A_HUMAN,Medium,Human +PRKN_HUMAN_Clausen_2023,0.605,0.619,0.6,0.586,0.622,0.614,0.182,0.459,0.525,0.54,0.582,0.608,0.638,0.233,0.292,0.363,0.501,0.658,0.663,0.447,0.281,0.557,0.609,0.576,0.447,0.632,0.623,0.623,0.52,0.622,0.568,0.526,0.266,0.241,0.55,0.593,0.569,0.621,0.64,0.611,0.638,0.654,0.309,0.093,0.591,0.352,0.649,0.618,0.696,0.298,0.542,0.555,0.561,0.583,0.567,0.571,0.579,0.573,0.561,0.583,0.672,0.519,8756,Expression,PRKN_HUMAN,Low,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE,0.572,0.576,0.566,0.566,0.576,0.572,0.3,0.442,0.491,0.497,0.692,0.601,0.615,0.498,0.57,0.678,0.708,0.627,0.583,0.477,0.304,0.242,0.467,0.391,0.462,0.492,-0.202,0.471,0.529,0.553,0.558,0.505,0.098,0.328,0.409,0.43,0.546,0.543,0.498,0.586,0.585,0.541,0.422,0.237,0.635,0.482,0.586,0.626,0.699,0.643,0.7,0.684,0.687,0.685,0.686,0.692,0.681,0.687,0.689,0.699,0.682,0.674,1579,Stability,PSAE_PICP2,Medium,Prokaryote +PTEN_HUMAN_Matreyek_2021,0.372,0.393,0.368,0.39,0.397,0.408,0.153,0.358,0.426,0.471,0.449,0.409,0.454,0.165,0.274,0.462,0.465,0.265,0.29,0.477,0.191,0.46,0.408,0.391,0.27,0.338,0.323,0.377,0.294,0.479,0.39,0.388,0.127,0.275,0.427,0.343,0.383,0.457,0.412,0.419,0.463,0.447,0.242,0.027,0.442,0.347,0.482,0.473,0.482,0.229,0.446,0.428,0.461,0.441,0.44,0.451,0.456,0.455,0.447,0.46,0.499,0.454,5083,Expression,PTEN_HUMAN,Medium,Human +PTEN_HUMAN_Mighell_2018,0.48,0.504,0.511,0.516,0.536,0.54,0.177,0.347,0.504,0.511,0.475,0.469,0.499,0.206,0.407,0.546,0.519,0.308,0.291,0.514,0.335,0.413,0.328,0.279,0.41,0.291,0.278,0.317,0.228,0.525,0.485,0.475,0.058,0.374,0.404,0.299,0.499,0.474,0.418,0.532,0.527,0.531,0.371,-0.009,0.51,0.484,0.421,0.416,0.48,0.202,0.488,0.459,0.488,0.495,0.511,0.505,0.495,0.495,0.509,0.51,0.539,0.51,7260,Activity,PTEN_HUMAN,Medium,Human +Q2N0S5_9HIV1_Haddox_2018,0.493,0.379,0.352,0.393,0.495,0.502,0.003,0.437,0.51,0.515,0.47,0.509,0.537,-0.005,-0.003,-0.002,0.044,0.093,0.168,0.403,0.518,0.403,0.39,0.337,0.517,0.401,0.394,0.436,0.354,0.507,0.478,0.483,0.291,0.494,0.419,0.406,0.52,0.502,0.501,0.526,0.515,0.513,0.413,-0.016,0.502,0.456,0.396,0.463,0.263,0.145,0.226,0.266,0.307,0.301,0.25,0.257,0.26,0.245,0.223,0.28,0.232,0.116,12729,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,0.356,0.341,0.348,0.353,0.358,0.355,0.081,0.245,0.314,0.324,0.235,0.301,0.325,0.192,0.186,0.364,0.33,0.287,0.347,0.329,0.249,0.267,0.257,0.215,0.292,0.297,0.268,0.306,0.299,0.303,0.371,0.341,0.098,0.238,0.268,0.182,0.311,0.334,0.294,0.352,0.365,0.348,0.402,0.202,0.321,0.328,0.311,0.341,0.338,0.192,0.291,0.302,0.333,0.334,0.321,0.321,0.316,0.312,0.312,0.325,0.33,0.465,3344,Binding,Q53Z42_HUMAN,Medium,Human +Q53Z42_HUMAN_McShan_2019_expression,0.524,0.485,0.526,0.538,0.55,0.548,-0.062,0.415,0.541,0.556,0.413,0.467,0.507,0.088,0.106,0.489,0.554,0.55,0.558,0.546,0.415,0.47,0.459,0.448,0.476,0.514,0.492,0.511,0.534,0.533,0.553,0.505,0.183,0.417,0.44,0.405,0.52,0.538,0.533,0.555,0.564,0.563,0.308,0.093,0.526,0.483,0.454,0.49,0.458,0.207,0.533,0.546,0.555,0.551,0.565,0.556,0.557,0.56,0.564,0.57,0.513,0.622,3344,Expression,Q53Z42_HUMAN,Medium,Human +Q59976_STRSQ_Romero_2015,0.475,0.593,0.634,0.643,0.654,0.662,0.363,0.535,0.672,0.679,0.588,0.519,0.543,0.144,0.448,0.516,0.572,0.56,0.57,0.649,0.598,0.651,0.652,0.663,0.622,0.662,0.677,0.673,0.68,0.684,0.633,0.575,0.304,0.606,0.652,0.634,0.616,0.653,0.657,0.666,0.672,0.675,0.49,0.008,0.587,0.524,0.408,0.533,0.533,0.155,0.56,0.537,0.533,0.556,0.557,0.542,0.562,0.545,0.553,0.563,0.634,0.54,2999,Activity,Q59976_STRSQ,Medium,Prokaryote +Q6WV13_9MAXI_Somermeyer_2022,0.327,0.393,0.26,0.264,0.333,0.334,-0.031,0.115,0.413,0.407,0.242,0.036,0.022,0.014,0.046,0.011,0.01,0.01,-0.022,0.294,0.013,0.045,0.003,0.102,0.004,-0.002,0.03,-0.005,-0.013,0.426,0.344,0.354,0.043,0.031,0.022,0.014,0.248,0.246,0.245,0.33,0.327,0.324,0.014,-0.015,-0.004,0.019,0.161,0.156,0.299,0.169,0.228,0.248,0.237,0.287,0.267,0.272,0.261,0.263,0.264,0.261,0.072,0.031,31401,Activity,Q6WV12_9MAXI,Low,Eukaryote +Q837P4_ENTFA_Meier_2023,0.451,0.465,0.489,0.507,0.503,0.517,0.454,0.404,0.479,0.495,0.563,0.545,0.566,0.42,0.475,0.492,0.515,0.545,0.523,-0.019,0.521,0.445,0.441,0.462,0.552,0.535,0.541,0.449,0.521,0.524,0.527,0.477,0.19,0.491,0.516,0.454,0.491,0.538,0.512,0.54,0.549,0.544,0.458,0.37,0.545,0.49,0.231,0.454,0.373,0.114,0.511,0.488,0.499,0.511,0.522,0.529,0.536,0.491,0.524,0.531,0.589,0.483,697,Activity,Q837P4_ENTFA,Medium,Prokaryote +Q837P5_ENTFA_Meier_2023,0.196,0.416,0.421,0.459,0.39,0.395,0.222,0.263,0.282,0.294,0.326,0.353,0.342,0.129,0.24,0.334,0.374,0.436,0.392,0.321,0.356,0.399,0.435,0.41,0.319,0.408,0.428,0.511,0.455,0.336,0.378,0.352,0.186,0.399,0.427,0.51,0.342,0.391,0.477,0.417,0.428,0.457,0.274,0.186,0.356,0.271,0.353,0.296,0.391,0.098,0.326,0.344,0.359,0.313,0.383,0.348,0.318,0.353,0.32,0.355,0.341,0.319,747,Activity,Q837P5_ENTFA,Medium,Prokaryote +Q8WTC7_9CNID_Somermeyer_2022,0.286,0.368,0.217,0.215,0.31,0.305,0.025,0.303,0.316,0.322,0.223,-0.009,-0.018,-0.027,-0.035,-0.023,-0.025,-0.007,0.034,0.23,0.013,0.037,0.057,0.013,-0.005,-0.001,0.037,0.252,0.267,0.373,0.297,0.307,-0.018,-0.026,0.009,0.303,0.231,0.237,0.318,0.304,0.309,0.345,-0.013,-0.008,-0.024,-0.027,0.097,0.143,0.292,0.189,0.242,0.234,0.246,0.26,0.24,0.254,0.249,0.243,0.238,0.247,0.137,-0.001,33510,Activity,Q8WTC7_9CNID,Low,Eukaryote +R1AB_SARS2_Flynn_2022,0.577,0.561,0.212,0.227,0.6,0.605,-0.049,0.292,-0.037,-0.037,0.103,-0.03,-0.04,-0.009,-0.026,0.079,0.105,0.498,0.577,0.266,0.214,0.259,0.274,0.289,0.242,0.236,0.203,0.21,0.224,0.558,0.507,0.408,-0.056,0.157,0.22,0.216,0.35,0.399,0.401,0.546,0.567,0.565,-0.031,-0.051,0.073,-0.038,0.453,0.415,0.496,0.232,0.251,0.228,0.265,0.298,0.275,0.274,0.273,0.268,0.237,0.277,0.241,0.137,5725,OrganismalFitness,R1AB_SARS2,Medium,Virus +RAD_ANTMA_Tsuboyama_2023_2CJJ,0.28,0.183,0.286,0.299,0.324,0.356,0.508,0.373,0.654,0.624,0.507,0.403,0.45,0.458,0.687,0.704,0.493,0.467,0.542,0.412,0.541,0.575,0.536,0.454,0.552,0.419,0.546,0.444,0.417,0.598,0.422,0.365,0.258,0.477,0.601,0.439,0.497,0.588,0.467,0.436,0.486,0.41,0.573,0.222,0.147,0.37,0.46,0.233,0.656,0.548,0.544,0.578,0.577,0.577,0.606,0.581,0.571,0.575,0.573,0.585,0.373,0.582,912,Stability,RAD_ANTMA,High,Eukaryote +RAF1_HUMAN_Zinkus-Boltz_2019,0.405,0.425,0.382,0.389,0.408,0.408,0.045,0.339,0.445,0.439,0.471,0.423,0.482,0.034,0.242,0.442,0.473,0.433,0.399,0.311,0.285,0.378,0.378,0.339,0.377,0.395,0.378,0.355,0.359,0.439,0.473,0.421,0.12,0.287,0.339,0.363,0.372,0.38,0.396,0.403,0.43,0.441,0.221,0.039,0.48,0.387,0.271,0.383,0.264,0.234,0.456,0.43,0.437,0.483,0.451,0.475,0.439,0.453,0.46,0.472,0.432,0.16,297,OrganismalFitness,RAF1_HUMAN,Low,Human +RASH_HUMAN_Bandaru_2017,0.447,0.436,0.444,0.476,0.466,0.48,0.313,0.353,0.426,0.446,0.318,0.36,0.405,0.457,0.514,0.487,0.498,0.44,0.313,0.514,0.437,0.414,0.42,0.396,0.433,0.403,0.401,0.374,0.305,0.434,0.338,0.319,0.18,0.399,0.439,0.377,0.454,0.478,0.45,0.472,0.485,0.487,0.478,0.326,0.322,0.453,0.386,0.252,0.458,0.255,0.431,0.423,0.443,0.428,0.456,0.43,0.452,0.43,0.439,0.455,0.373,0.474,3134,Activity,RASH_HUMAN,High,Human +RASK_HUMAN_Weng_2022_abundance,0.299,0.244,0.356,0.346,0.332,0.353,0.211,0.244,0.244,0.271,0.276,0.21,0.245,0.336,0.367,0.335,0.271,0.231,0.21,0.331,0.266,0.333,0.372,0.384,0.279,0.347,0.402,0.345,0.47,0.357,0.21,0.167,0.208,0.198,0.325,0.398,0.275,0.368,0.436,0.334,0.37,0.388,0.237,0.335,0.058,0.243,0.334,0.327,0.377,0.326,0.264,0.245,0.294,0.279,0.31,0.281,0.298,0.28,0.26,0.287,0.339,0.331,26012,Expression,RASK_HUMAN,High,Human +RASK_HUMAN_Weng_2022_binding-DARPin_K55,0.419,0.47,0.602,0.617,0.598,0.598,0.268,0.35,0.641,0.639,0.585,0.524,0.566,0.573,0.605,0.621,0.652,0.563,0.446,0.677,0.568,0.511,0.549,0.458,0.537,0.534,0.479,0.396,0.358,0.569,0.529,0.49,0.289,0.515,0.589,0.499,0.555,0.601,0.543,0.605,0.616,0.597,0.56,0.298,0.464,0.597,0.239,0.269,0.585,0.281,0.545,0.549,0.542,0.552,0.556,0.554,0.544,0.543,0.51,0.56,0.601,0.573,24873,Binding,RASK_HUMAN,High,Human +RBP1_HUMAN_Tsuboyama_2023_2KWH,0.19,0.108,0.302,0.307,0.305,0.303,0.271,0.277,0.218,0.233,0.43,0.409,0.404,0.321,0.394,0.491,0.552,0.328,0.335,0.259,0.366,0.108,0.267,0.263,0.297,0.078,0.16,0.293,0.202,0.365,0.373,0.318,0.239,0.291,0.364,0.384,0.318,0.34,0.339,0.317,0.334,0.318,0.319,0.255,0.415,0.406,0.487,0.545,0.578,0.459,0.494,0.479,0.491,0.485,0.492,0.486,0.476,0.493,0.48,0.493,0.539,0.526,1332,Stability,RBP1_HUMAN,High,Human +RCD1_ARATH_Tsuboyama_2023_5OAO,0.304,0.284,0.362,0.37,0.367,0.355,0.303,0.255,0.412,0.421,0.486,0.342,0.393,0.368,0.392,0.498,0.505,0.482,0.478,0.391,0.267,0.243,0.33,0.395,0.32,0.481,0.454,0.412,0.465,0.398,0.478,0.406,0.046,0.293,0.308,0.324,0.36,0.361,0.352,0.403,0.402,0.386,0.304,0.285,0.371,0.327,0.442,0.432,0.56,0.532,0.541,0.555,0.559,0.559,0.545,0.555,0.551,0.549,0.556,0.56,0.531,0.501,1261,Stability,RCD1_ARATH,Medium,Eukaryote +RCRO_LAMBD_Tsuboyama_2023_1ORC,0.293,0.518,0.584,0.577,0.557,0.588,0.167,0.263,0.552,0.556,0.558,0.38,0.492,0.226,0.422,0.451,0.596,0.573,0.639,0.573,0.129,0.17,0.225,0.175,-0.041,0.132,-0.003,0.087,0.577,0.59,0.569,0.537,0.096,0.178,0.127,0.567,0.44,0.45,0.58,0.568,0.598,0.621,0.028,0.139,0.471,0.187,0.635,0.646,0.782,0.69,0.592,0.58,0.56,0.585,0.574,0.583,0.568,0.569,0.6,0.584,0.646,0.541,2278,Stability,RCRO_LAMBD,High,Virus +RD23A_HUMAN_Tsuboyama_2023_1IFY,0.33,0.333,0.466,0.466,0.479,0.479,0.196,0.434,0.512,0.517,0.457,0.55,0.592,0.254,0.675,0.533,0.513,0.464,0.39,0.462,0.422,0.422,0.395,0.453,0.545,0.513,0.51,0.477,0.494,0.51,0.414,0.399,0.429,0.288,0.531,0.475,0.484,0.568,0.54,0.482,0.537,0.536,0.512,0.074,0.556,0.548,0.502,0.548,0.582,0.505,0.47,0.454,0.467,0.449,0.478,0.475,0.475,0.474,0.478,0.481,0.482,0.631,1019,Stability,RD23A_HUMAN,High,Human +RDRP_I33A0_Li_2023,0.313,0.367,0.376,0.387,0.482,0.484,0.029,0.355,0.52,0.525,0.166,0.045,0.057,0.033,0.041,0.128,0.322,0.389,0.491,0.427,0.385,0.436,0.454,0.475,0.125,0.358,0.342,0.345,0.413,0.52,0.45,0.395,0.075,0.377,0.423,0.459,0.425,0.452,0.474,0.494,0.508,0.526,0.03,0.02,0.171,0.033,0.215,0.23,0.188,0.055,0.3,0.271,0.279,0.282,0.303,0.296,0.283,0.304,0.31,0.307,0.149,0.11,12003,OrganismalFitness,RDRP_I33A0,Low,Virus +REV_HV1H2_Fernandes_2016,0.206,0.159,0.221,0.227,0.216,0.216,0.038,0.316,0.222,0.232,0.128,0.245,0.267,0.046,0.046,0.173,0.24,0.281,0.274,0.17,0.216,0.259,0.238,0.24,0.29,0.294,0.16,0.253,0.255,0.282,0.35,0.353,0.06,0.231,0.273,0.24,0.245,0.269,0.236,0.246,0.261,0.235,0.053,0.06,0.208,0.071,0.258,0.234,0.304,0.224,0.193,0.221,0.276,0.246,0.267,0.255,0.281,0.241,0.31,0.279,0.274,0.229,2147,OrganismalFitness,REV_HV1H2,Medium,Virus +RFAH_ECOLI_Tsuboyama_2023_2LCL,0.064,0.21,0.212,0.233,0.23,0.227,-0.015,0.238,0.232,0.258,0.248,0.175,0.2,-0.032,0.078,0.055,0.299,0.261,0.246,0.249,-0.067,0.059,0.121,0.115,-0.043,0.13,0.151,0.114,0.158,0.214,0.268,0.213,-0.009,0.009,0.102,0.133,0.107,0.144,0.156,0.22,0.216,0.208,0.115,-0.075,0.193,0.107,0.304,0.248,0.384,0.328,0.331,0.31,0.291,0.333,0.314,0.3,0.314,0.309,0.327,0.319,0.318,0.22,1326,Stability,RFAH_ECOLI,High,Prokaryote +RL20_AQUAE_Tsuboyama_2023_1GYZ,0.341,0.588,0.607,0.601,0.603,0.61,0.224,0.577,0.405,0.352,0.675,0.67,0.657,0.235,0.421,0.487,0.699,0.68,0.676,0.613,0.151,0.493,0.47,0.475,0.473,0.481,0.534,0.545,0.622,0.636,0.563,0.544,0.078,0.481,0.522,0.506,0.54,0.579,0.574,0.588,0.624,0.625,0.041,-0.048,0.467,0.283,0.684,0.64,0.816,0.769,0.716,0.715,0.72,0.726,0.713,0.718,0.723,0.724,0.732,0.728,0.738,0.71,1461,Stability,RL20_AQUAE,High,Prokaryote +RL40A_YEAST_Mavor_2016,0.283,0.37,0.372,0.417,0.38,0.399,0.122,0.372,0.441,0.448,0.192,0.285,0.315,0.104,0.397,0.482,0.518,0.446,0.527,0.43,0.382,0.512,0.48,0.422,0.493,0.433,0.433,0.43,0.4,0.319,0.406,0.408,0.044,0.387,0.451,0.368,0.407,0.456,0.398,0.439,0.469,0.411,0.286,0.071,0.282,0.294,0.068,0.091,0.177,-0.026,0.487,0.457,0.481,0.47,0.499,0.476,0.474,0.498,0.498,0.503,0.338,0.345,1253,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2013,0.313,0.402,0.418,0.47,0.43,0.454,0.086,0.367,0.528,0.528,0.218,0.307,0.367,0.122,0.423,0.534,0.599,0.479,0.556,0.488,0.424,0.553,0.549,0.467,0.537,0.49,0.503,0.487,0.46,0.391,0.523,0.538,0.139,0.437,0.522,0.427,0.459,0.526,0.449,0.494,0.533,0.467,0.357,0.076,0.317,0.319,0.106,0.116,0.253,0.026,0.569,0.529,0.554,0.532,0.566,0.558,0.542,0.582,0.58,0.583,0.381,0.391,1195,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2014,0.371,0.285,0.351,0.387,0.331,0.368,0.161,0.381,0.419,0.442,0.163,0.24,0.267,0.227,0.521,0.529,0.53,0.427,0.438,0.394,0.37,0.466,0.433,0.405,0.439,0.38,0.373,0.336,0.325,0.359,0.35,0.361,0.236,0.39,0.431,0.351,0.425,0.455,0.408,0.42,0.428,0.383,0.292,0.154,0.268,0.295,0.317,0.247,0.38,0.207,0.527,0.511,0.488,0.544,0.547,0.543,0.491,0.517,0.516,0.544,0.302,0.401,1380,Activity,RL40A_YEAST,Medium,Eukaryote +RNC_ECOLI_Weeks_2023,0.544,0.592,0.57,0.584,0.594,0.595,0.058,0.423,0.591,0.591,0.59,0.579,0.593,0.064,0.557,0.588,0.596,0.595,0.592,0.586,0.554,0.57,0.509,0.499,0.557,0.584,0.583,0.586,0.571,0.594,0.578,0.539,0.187,0.538,0.54,0.431,0.581,0.587,0.545,0.617,0.616,0.606,0.529,0.06,0.588,0.567,0.299,0.524,0.281,0.177,0.541,0.53,0.52,0.553,0.544,0.533,0.548,0.543,0.544,0.554,0.608,0.533,4277,Activity,RNC_ECOLI,Medium,Prokaryote +RPC1_BP434_Tsuboyama_2023_1R69,0.627,0.681,0.655,0.673,0.606,0.644,0.67,0.593,0.617,0.659,0.711,0.744,0.75,0.722,0.741,0.752,0.708,0.687,0.63,0.697,0.699,0.75,0.735,0.682,0.742,0.731,0.718,0.723,0.66,0.743,0.666,0.609,0.621,0.633,0.738,0.716,0.72,0.747,0.737,0.699,0.693,0.69,0.749,0.674,0.735,0.728,0.696,0.64,0.793,0.723,0.702,0.672,0.687,0.7,0.699,0.69,0.711,0.702,0.711,0.708,0.756,0.764,1459,Stability,RPC1_BP434,High,Virus +RPC1_LAMBD_Li_2019_high-expression,0.288,0.398,0.484,0.493,0.45,0.447,0.24,0.361,0.457,0.47,0.442,0.461,0.514,0.29,0.289,0.401,0.549,0.52,0.509,0.434,0.176,0.29,0.351,0.386,0.223,0.349,0.323,0.305,0.467,0.383,0.535,0.472,0.11,0.149,0.338,0.457,0.278,0.362,0.464,0.416,0.449,0.491,0.297,0.314,0.418,0.328,0.25,0.407,0.376,0.176,0.544,0.52,0.514,0.532,0.547,0.555,0.533,0.531,0.538,0.553,0.523,0.398,351,Activity,RPC1_LAMBD,High,Virus +RPC1_LAMBD_Li_2019_low-expression,0.246,0.412,0.475,0.498,0.462,0.455,0.165,0.466,0.45,0.468,0.488,0.466,0.481,0.253,0.263,0.363,0.523,0.579,0.591,0.408,0.195,0.338,0.38,0.415,0.182,0.366,0.321,0.299,0.562,0.447,0.635,0.611,0.119,0.135,0.322,0.463,0.235,0.323,0.454,0.42,0.447,0.498,0.269,0.271,0.357,0.28,0.262,0.375,0.393,0.266,0.52,0.488,0.51,0.519,0.503,0.53,0.522,0.511,0.494,0.528,0.518,0.344,351,Activity,RPC1_LAMBD,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32,0.393,0.31,0.336,0.328,0.321,0.326,0.178,0.188,0.387,0.389,0.468,0.394,0.39,0.289,0.326,0.51,0.409,0.372,0.33,0.291,0.199,0.33,0.317,0.323,0.297,0.34,0.416,0.362,0.367,0.375,0.413,0.396,0.117,0.423,0.325,0.306,0.453,0.377,0.365,0.395,0.348,0.32,0.333,0.262,0.443,0.381,0.582,0.44,0.607,0.55,0.396,0.403,0.403,0.399,0.407,0.377,0.415,0.42,0.395,0.413,0.433,0.646,1195,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance,0.507,0.55,0.564,0.573,0.584,0.597,0.38,0.511,0.608,0.607,0.597,0.621,0.655,0.484,0.537,0.558,0.626,0.599,0.566,0.038,0.492,0.596,0.606,0.59,0.526,0.611,0.629,0.6,0.557,0.582,0.571,0.444,0.33,0.522,0.596,0.59,0.566,0.627,0.631,0.618,0.633,0.633,0.504,0.274,0.584,0.555,0.44,0.547,0.534,0.176,0.582,0.571,0.574,0.594,0.607,0.597,0.594,0.599,0.592,0.609,0.63,0.58,9803,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity,0.45,0.517,0.557,0.57,0.553,0.566,0.347,0.508,0.562,0.557,0.553,0.572,0.614,0.448,0.48,0.477,0.575,0.547,0.525,0.03,0.488,0.579,0.57,0.561,0.526,0.584,0.597,0.567,0.533,0.578,0.596,0.508,0.299,0.504,0.577,0.568,0.527,0.59,0.591,0.586,0.604,0.601,0.458,0.238,0.561,0.501,0.408,0.527,0.503,0.169,0.542,0.52,0.514,0.539,0.56,0.542,0.542,0.559,0.552,0.56,0.542,0.5,10094,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB,0.137,0.201,0.394,0.373,0.407,0.399,0.426,0.503,0.41,0.43,0.481,0.472,0.508,0.238,0.496,0.496,0.512,0.52,0.406,0.43,0.491,0.458,0.449,0.479,0.524,0.519,0.476,0.491,0.482,0.518,0.469,0.419,0.353,0.424,0.497,0.499,0.431,0.498,0.489,0.411,0.443,0.444,0.503,-0.042,0.545,0.531,0.336,0.332,0.443,0.502,0.454,0.348,0.234,0.367,0.362,0.395,0.392,0.445,0.426,0.387,0.575,0.607,965,Stability,SAV1_MOUSE,High,Eukaryote +SBI_STAAM_Tsuboyama_2023_2JVG,0.232,0.225,0.38,0.345,0.44,0.403,0.205,0.178,0.481,0.533,0.323,0.268,0.297,0.23,0.266,0.376,0.573,0.658,0.318,0.359,0.228,0.263,0.259,0.235,0.21,0.228,0.221,0.245,0.339,0.549,0.582,0.487,0.169,0.195,0.2,0.225,0.297,0.305,0.314,0.355,0.368,0.382,0.262,0.228,0.316,0.305,0.664,0.627,0.687,0.616,0.529,0.526,0.561,0.55,0.545,0.547,0.569,0.565,0.558,0.561,0.683,0.571,1025,Stability,SBI_STAAM,Medium,Prokaryote +SC6A4_HUMAN_Young_2021,0.387,0.456,0.423,0.433,0.504,0.522,0.352,0.507,0.562,0.574,0.545,0.531,0.542,0.159,0.327,0.502,0.543,0.548,0.524,0.555,0.498,0.511,0.5,0.496,0.512,0.52,0.518,0.525,0.511,0.541,0.463,0.36,0.342,0.513,0.512,0.491,0.53,0.542,0.53,0.549,0.555,0.545,0.489,0.112,0.549,0.531,0.467,0.527,0.501,0.182,0.516,0.526,0.535,0.543,0.545,0.536,0.536,0.538,0.526,0.546,0.578,0.528,11576,Activity,SC6A4_HUMAN,Medium,Human +SCIN_STAAR_Tsuboyama_2023_2QFF,0.083,0.14,0.261,0.264,0.3,0.272,0.154,0.065,0.271,0.258,0.299,0.298,0.269,0.208,0.305,0.279,0.346,0.406,0.414,0.25,0.106,0.162,0.134,0.144,0.155,0.194,0.152,0.2,0.215,0.229,0.296,0.208,-0.012,0.072,0.104,0.187,0.132,0.132,0.156,0.245,0.235,0.255,0.231,0.177,0.3,0.269,0.525,0.527,0.517,0.477,0.438,0.442,0.437,0.48,0.422,0.478,0.43,0.43,0.391,0.454,0.62,0.513,1212,Stability,SCIN_STAAR,High,Prokaryote +SCN5A_HUMAN_Glazer_2019,0.13,0.131,0.162,0.162,0.153,0.158,0.13,-0.014,0.163,0.18,0.141,0.217,0.135,0.216,0.177,0.156,0.183,0.152,0.125,0.126,0.106,0.143,0.151,0.127,0.124,0.106,0.093,0.107,0.167,0.144,0.186,0.181,0.095,0.075,0.068,0.069,0.095,0.086,0.086,0.14,0.14,0.152,0.086,0.137,0.165,0.154,0.03,0.092,0.053,0.014,0.127,0.16,0.157,0.127,0.105,0.135,0.143,0.136,0.169,0.146,0.232,0.168,224,OrganismalFitness,SCN5A_HUMAN,Medium,Human +SDA_BACSU_Tsuboyama_2023_1PV0,0.631,0.646,0.615,0.619,0.647,0.65,0.001,0.303,0.456,0.472,0.606,0.418,0.466,0.249,0.274,0.608,0.594,0.598,0.596,0.53,0.026,0.067,0.003,0.238,0.081,0.123,0.045,0.119,0.214,0.64,0.604,0.581,-0.165,0.01,0.26,0.107,0.607,0.613,0.368,0.652,0.65,0.486,0.037,-0.079,0.336,0.011,0.434,0.41,0.608,0.597,0.65,0.641,0.588,0.647,0.63,0.613,0.622,0.623,0.653,0.633,0.639,0.636,2770,Stability,SDA_BACSU,Medium,Prokaryote +SERC_HUMAN_Xie_2023,0.375,0.483,0.544,0.544,0.52,0.53,0.013,0.456,0.57,0.576,0.518,0.541,0.547,0.183,0.407,0.529,0.558,0.577,0.543,0.535,0.492,0.511,0.513,0.484,0.5,0.53,0.518,0.534,0.504,0.506,0.516,0.469,0.192,0.513,0.528,0.519,0.528,0.54,0.544,0.548,0.551,0.552,0.319,0.031,0.54,0.46,0.382,0.461,0.352,0.189,0.539,0.524,0.54,0.551,0.558,0.548,0.553,0.55,0.543,0.561,0.562,0.478,1914,OrganismalFitness,SERC_HUMAN,High,Human +SHOC2_HUMAN_Kwon_2022,0.208,0.328,0.378,0.383,0.37,0.379,0.226,0.36,0.424,0.422,0.395,0.384,0.41,0.242,0.249,0.267,0.426,0.388,0.29,0.388,0.262,0.363,0.385,0.355,0.267,0.394,0.39,0.4,0.358,0.407,0.416,0.366,0.153,0.254,0.308,0.376,0.264,0.302,0.367,0.367,0.367,0.396,0.262,0.236,0.421,0.291,0.303,0.358,0.286,0.095,0.373,0.367,0.387,0.39,0.396,0.398,0.381,0.392,0.384,0.398,0.322,0.286,10972,OrganismalFitness,SHOC2_HUMAN,Medium,Human +SOX30_HUMAN_Tsuboyama_2023_7JJK,0.225,0.241,0.235,0.242,0.282,0.273,0.369,0.17,0.21,0.246,0.254,0.335,0.336,0.298,0.396,0.333,0.322,0.262,0.261,0.223,0.166,0.167,0.162,0.19,0.264,0.247,0.202,0.253,0.189,0.231,0.087,0.06,0.222,0.133,0.267,0.229,0.217,0.241,0.234,0.275,0.274,0.262,0.271,0.15,0.208,0.356,0.526,0.346,0.53,0.464,0.333,0.38,0.348,0.333,0.326,0.385,0.333,0.334,0.334,0.356,0.333,0.466,1010,Stability,SOX30_HUMAN,High,Human +SPA_STAAU_Tsuboyama_2023_1LP1,0.432,0.509,0.518,0.501,0.552,0.549,-0.093,0.495,0.409,0.408,0.393,-0.041,0.001,-0.032,-0.033,-0.064,0.005,-0.01,0.02,0.514,-0.154,0.032,0.205,0.105,-0.081,-0.041,0.057,0.338,0.428,0.564,0.432,0.442,-0.034,-0.011,0.001,0.066,0.437,0.43,0.427,0.548,0.547,0.536,-0.137,-0.087,-0.071,-0.009,0.512,0.453,0.705,0.56,0.52,0.511,0.493,0.534,0.498,0.515,0.51,0.501,0.504,0.513,0.546,0.414,2105,Stability,SPA_STAAU,Medium,Prokaryote +SPG1_STRSG_Olson_2014,0.239,0.282,-0.004,0.006,0.247,0.272,-0.041,0.017,-0.038,0.171,0.305,0.237,0.192,0.28,0.232,0.264,0.301,0.251,0.337,0.278,0.252,0.216,0.214,0.208,0.232,0.222,0.218,0.239,0.354,0.283,0.478,0.494,0.111,0.223,0.175,0.279,0.243,0.204,0.289,0.229,0.188,0.29,-0.092,-0.112,0.158,0.004,0.362,0.306,0.423,0.147,0.38,0.315,0.364,0.414,0.414,0.36,0.418,0.417,0.41,0.407,0.387,0.374,536962,Binding,SPG1_STRSG,Low,Prokaryote +SPG1_STRSG_Wu_2016,-0.051,0.025,0.048,0.048,0.036,0.042,0.036,-0.048,0.17,0.171,0.121,0.086,0.059,0.03,0.097,0.118,0.128,0.165,0.199,0.083,-0.012,-0.033,-0.051,-0.023,-0.061,0.016,-0.014,-0.031,-0.031,0.102,0.146,0.125,-0.083,0.001,-0.085,-0.024,0.039,0.013,0.021,0.044,0.027,0.027,-0.013,-0.099,0.04,-0.026,0.091,0.023,0.18,0.089,0.144,0.136,0.132,0.176,0.156,0.131,0.194,0.167,0.177,0.159,0.171,0.114,149360,Binding,SPG1_STRSG,Medium,Prokaryote +SPG2_STRSG_Tsuboyama_2023_5UBS,0.439,0.517,0.496,0.539,0.569,0.565,0.325,0.309,0.543,0.478,0.501,0.437,0.409,0.366,0.432,0.451,0.5,0.504,0.528,0.507,0.388,0.433,0.436,0.392,0.343,0.465,0.356,0.485,0.501,0.614,0.561,0.536,-0.192,0.356,0.44,0.462,0.294,0.335,0.382,0.561,0.562,0.569,0.253,0.089,0.286,0.24,0.447,0.369,0.704,0.57,0.564,0.566,0.584,0.592,0.569,0.595,0.588,0.61,0.584,0.589,0.598,0.549,1451,Stability,SPG2_STRSG,Medium,Prokaryote +SPIKE_SARS2_Starr_2020_binding,0.158,0.205,0.062,0.151,0.249,0.252,-0.046,0.369,0.356,0.36,-0.002,-0.064,-0.039,-0.049,-0.036,-0.022,-0.018,-0.008,0.038,0.337,0.305,0.368,0.357,0.364,0.384,0.347,0.315,0.378,0.319,0.247,0.324,0.357,0.171,0.338,0.328,0.355,0.334,0.325,0.317,0.356,0.346,0.341,-0.036,-0.013,-0.022,-0.008,0.517,0.483,0.405,0.16,0.224,0.241,0.226,0.297,0.251,0.301,0.258,0.279,0.228,0.279,0.357,0.159,3802,Binding,SPIKE_SARS2,Medium,Virus +SPIKE_SARS2_Starr_2020_expression,0.2,0.314,0.165,0.279,0.445,0.45,-0.016,0.352,0.413,0.446,0.05,-0.023,0.004,-0.026,0.001,0.01,0.012,0.018,0.072,0.355,0.317,0.382,0.374,0.387,0.379,0.367,0.322,0.397,0.328,0.359,0.313,0.346,0.178,0.344,0.346,0.383,0.361,0.361,0.367,0.451,0.455,0.475,0.005,0.022,0.024,0.041,0.561,0.533,0.465,0.187,0.321,0.354,0.33,0.422,0.379,0.413,0.384,0.392,0.363,0.405,0.498,0.266,3798,Expression,SPIKE_SARS2,Medium,Virus +SPTN1_CHICK_Tsuboyama_2023_1TUD,0.572,0.613,0.647,0.599,0.632,0.615,0.23,0.524,0.552,0.593,0.637,0.649,0.641,-0.133,0.619,0.627,0.637,0.726,0.657,0.624,0.578,0.643,0.604,0.589,0.589,0.613,0.652,0.563,0.601,0.621,0.631,0.632,0.457,0.508,0.57,0.548,0.629,0.641,0.621,0.623,0.628,0.618,0.488,-0.221,0.465,0.498,0.421,0.401,0.62,0.623,0.693,0.679,0.683,0.685,0.696,0.685,0.676,0.674,0.696,0.691,0.667,0.526,3201,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU,0.399,0.522,0.597,0.61,0.604,0.604,0.086,0.449,0.565,0.592,0.514,0.373,0.503,0.186,0.442,0.529,0.618,0.64,0.527,0.607,0.218,0.474,0.49,0.505,0.345,0.544,0.569,0.506,0.538,0.623,0.566,0.526,0.155,0.194,0.444,0.518,0.502,0.54,0.589,0.614,0.604,0.625,0.209,0.119,0.582,0.583,0.651,0.626,0.629,0.619,0.626,0.582,0.635,0.62,0.634,0.628,0.634,0.61,0.603,0.639,0.682,0.514,707,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88,0.596,0.658,0.684,0.681,0.693,0.684,-0.19,0.508,0.638,0.61,0.702,0.639,0.654,-0.267,0.662,0.702,0.675,0.698,0.681,0.634,-0.109,0.377,0.382,0.504,0.493,0.575,0.607,0.608,0.61,0.665,0.674,0.635,0.359,0.039,0.189,0.368,0.667,0.669,0.673,0.685,0.689,0.68,0.575,-0.296,0.703,0.618,0.214,0.637,0.731,0.716,0.677,0.673,0.67,0.692,0.696,0.68,0.668,0.669,0.685,0.684,0.788,0.744,1583,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W,0.36,0.511,0.696,0.694,0.723,0.718,0.549,0.362,0.743,0.742,0.628,0.664,0.7,0.498,0.713,0.731,0.734,0.673,0.694,0.653,0.679,0.649,0.653,0.62,0.642,0.637,0.659,0.63,0.607,0.694,0.696,0.658,0.577,0.597,0.637,0.645,0.66,0.697,0.705,0.701,0.727,0.728,0.388,0.084,0.538,0.532,0.579,0.538,0.64,0.661,0.751,0.731,0.734,0.736,0.746,0.741,0.735,0.735,0.74,0.75,0.707,0.689,1556,Stability,SRBS1_HUMAN,High,Human +SRC_HUMAN_Ahler_2019,0.526,0.508,0.465,0.465,0.496,0.507,0.532,0.44,0.514,0.532,0.469,0.561,0.585,0.447,0.486,0.49,0.555,0.515,0.467,0.484,0.439,0.413,0.421,0.373,0.437,0.46,0.438,0.422,0.33,0.542,0.578,0.574,0.431,0.41,0.411,0.348,0.502,0.502,0.492,0.521,0.52,0.516,0.567,0.397,0.487,0.546,0.309,0.296,0.409,0.098,0.517,0.495,0.492,0.517,0.524,0.521,0.517,0.527,0.524,0.532,0.506,0.479,3372,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,0.435,0.427,0.41,0.433,0.441,0.437,0.454,0.401,0.458,0.454,0.403,0.486,0.506,0.358,0.39,0.393,0.465,0.426,0.409,0.397,0.372,0.35,0.354,0.309,0.37,0.395,0.364,0.35,0.257,0.456,0.498,0.495,0.36,0.339,0.335,0.28,0.414,0.411,0.401,0.443,0.441,0.437,0.475,0.316,0.423,0.458,0.238,0.246,0.324,0.046,0.434,0.408,0.412,0.437,0.441,0.438,0.432,0.442,0.447,0.447,0.431,0.391,3637,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Nguyen_2022,0.428,0.426,0.413,0.428,0.429,0.425,0.442,0.405,0.357,0.411,0.401,0.483,0.506,0.342,0.378,0.387,0.471,0.443,0.415,0.396,0.373,0.344,0.35,0.306,0.381,0.397,0.372,0.358,0.269,0.453,0.493,0.484,0.355,0.348,0.343,0.282,0.416,0.412,0.399,0.44,0.437,0.43,0.477,0.304,0.427,0.466,0.221,0.227,0.31,0.063,0.439,0.405,0.409,0.432,0.441,0.433,0.434,0.444,0.441,0.445,0.43,0.382,3366,OrganismalFitness,SRC_HUMAN,Medium,Human +SUMO1_HUMAN_Weile_2017,0.369,0.373,0.419,0.438,0.48,0.478,0.13,0.425,0.449,0.386,0.433,0.467,0.51,0.25,0.496,0.533,0.509,0.38,0.34,0.517,0.217,0.414,0.443,0.43,0.468,0.386,0.46,0.431,0.333,0.438,0.46,0.445,0.212,0.245,0.473,0.329,0.405,0.511,0.41,0.452,0.522,0.463,0.49,0.086,0.395,0.527,0.497,0.428,0.509,0.412,0.492,0.453,0.5,0.5,0.493,0.517,0.479,0.498,0.491,0.511,0.479,0.524,1700,OrganismalFitness,SUMO1_HUMAN,High,Human +SYUA_HUMAN_Newberry_2020,0.103,0.111,0.119,0.132,0.131,0.139,0.131,0.212,0.171,0.162,0.234,0.242,0.233,0.105,0.142,0.15,0.137,0.206,0.205,0.22,0.138,0.203,0.155,0.141,0.086,0.151,0.167,0.105,0.136,0.227,0.205,0.189,0.005,0.137,0.197,0.181,0.124,0.176,0.159,0.134,0.166,0.156,0.115,0.068,0.16,0.155,-0.052,0.046,-0.06,-0.074,0.069,0.056,0.053,0.088,0.129,0.098,0.127,0.102,0.144,0.098,-0.011,-0.045,2497,OrganismalFitness,SYUA_HUMAN,Medium,Human +TADBP_HUMAN_Bolognesi_2019,0.097,0.061,0.1,0.096,0.08,0.08,0.291,-0.018,0.077,0.072,0.013,0.051,0.048,0.121,0.047,0.021,-0.12,-0.028,0.053,-0.075,0.158,0.063,-0.011,-0.006,0.206,0.082,0.006,0.023,-0.014,0.036,0.088,0.112,-0.051,0.226,0.273,0.121,0.142,0.185,0.121,0.105,0.129,0.109,0.121,0.212,-0.002,0.078,0.271,0.033,0.223,0.059,0.105,0.013,0.004,0.04,0.002,0.034,0.045,-0.011,-0.079,0.021,0.054,0.121,1196,OrganismalFitness,TADBP_HUMAN,Low,Human +TAT_HV1BR_Fernandes_2016,0.293,0.201,0.255,0.268,0.319,0.3,-0.09,0.397,0.274,0.288,0.185,0.343,0.342,-0.034,-0.008,-0.005,0.017,-0.057,0.044,0.203,0.379,0.364,0.396,0.398,0.394,0.269,0.136,0.246,0.223,0.368,0.405,0.387,0.283,0.385,0.211,0.203,0.379,0.263,0.237,0.366,0.295,0.268,0.005,-0.054,0.292,0.181,0.23,0.284,0.344,0.207,0.114,0.134,0.142,0.137,0.116,0.133,0.12,0.117,0.117,0.134,0.157,0.116,1577,OrganismalFitness,TAT_HV1BR,High,Virus +TCRG1_MOUSE_Tsuboyama_2023_1E0L,0.576,0.615,0.644,0.655,0.66,0.662,0.696,0.292,0.677,0.708,0.591,0.739,0.75,0.724,0.707,0.784,0.769,0.707,0.745,0.577,0.49,0.552,0.57,0.609,0.604,0.613,0.66,0.648,0.597,0.68,0.64,0.595,0.642,0.485,0.619,0.669,0.688,0.715,0.71,0.68,0.712,0.695,0.753,0.184,0.629,0.752,0.68,0.596,0.774,0.759,0.721,0.697,0.657,0.757,0.733,0.763,0.723,0.738,0.71,0.735,0.79,0.774,1058,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG,0.309,0.397,0.482,0.473,0.483,0.481,0.072,0.061,0.523,0.52,0.511,0.502,0.532,0.186,0.557,0.548,0.513,0.532,0.546,0.515,-0.047,0.332,0.349,0.305,0.351,0.383,0.279,0.423,0.534,0.543,0.526,0.512,0.48,0.003,0.403,0.337,0.446,0.505,0.495,0.472,0.51,0.511,0.301,0.114,0.463,0.389,0.504,0.484,0.561,0.597,0.555,0.546,0.566,0.573,0.584,0.548,0.561,0.551,0.545,0.561,0.502,0.483,1279,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT,0.464,0.473,0.506,0.495,0.507,0.502,-0.099,0.392,0.573,0.576,0.472,0.538,0.526,0.044,0.547,0.589,0.525,0.529,0.533,0.466,0.262,0.361,0.429,0.394,0.395,0.455,0.385,0.395,0.436,0.528,0.484,0.451,0.25,0.061,0.312,0.333,0.535,0.523,0.517,0.541,0.518,0.518,0.343,-0.03,0.572,0.492,0.526,0.649,0.675,0.624,0.51,0.53,0.516,0.512,0.517,0.511,0.521,0.512,0.513,0.524,0.597,0.604,1479,Stability,TNKS2_HUMAN,High,Human +TPK1_HUMAN_Weile_2017,0.217,0.236,0.219,0.228,0.23,0.229,0.067,0.253,0.286,0.27,0.285,0.27,0.318,0.108,0.199,0.253,0.339,0.328,0.371,0.249,0.088,0.142,0.228,0.272,0.115,0.267,0.273,0.253,0.306,0.255,0.253,0.188,0.117,0.115,0.214,0.301,0.232,0.252,0.305,0.243,0.252,0.277,0.12,0.089,0.309,0.17,0.206,0.267,0.245,0.116,0.253,0.277,0.275,0.278,0.293,0.291,0.281,0.291,0.292,0.298,0.268,0.203,3181,OrganismalFitness,TPK1_HUMAN,Medium,Human +TPMT_HUMAN_Matreyek_2018,0.372,0.456,0.489,0.509,0.499,0.513,0.241,0.445,0.497,0.5,0.546,0.517,0.547,0.308,0.433,0.531,0.539,0.476,0.448,0.511,0.333,0.42,0.481,0.496,0.447,0.506,0.481,0.466,0.424,0.547,0.542,0.489,0.334,0.33,0.471,0.411,0.478,0.513,0.478,0.517,0.537,0.515,0.371,0.204,0.539,0.495,0.476,0.534,0.54,0.238,0.522,0.519,0.53,0.514,0.541,0.535,0.541,0.529,0.534,0.543,0.568,0.469,3648,Expression,TPMT_HUMAN,Medium,Human +TPOR_HUMAN_Bridgford_2020,0.376,0.365,0.283,0.263,0.288,0.296,0.37,0.455,0.454,0.456,0.407,0.368,0.37,0.279,0.378,0.294,0.29,0.422,0.362,0.362,0.25,0.338,0.313,0.377,0.367,0.2,0.471,0.451,0.34,0.45,0.436,0.274,0.395,0.372,0.321,0.419,0.429,0.408,0.451,0.408,0.382,0.437,0.295,0.303,0.353,0.348,0.126,0.357,0.309,0.035,0.344,0.308,0.344,0.354,0.322,0.345,0.396,0.257,0.293,0.341,0.389,0.372,562,OrganismalFitness,TPOR_HUMAN,Low,Human +TRPC_SACS2_Chan_2017,0.575,0.606,0.558,0.574,0.575,0.585,0.173,0.499,0.655,0.671,0.621,0.611,0.643,0.325,0.539,0.618,0.653,0.632,0.601,0.592,0.421,0.564,0.508,0.575,0.498,0.499,0.558,0.521,0.524,0.529,0.59,0.52,0.221,0.52,0.555,0.558,0.575,0.598,0.591,0.59,0.612,0.601,0.434,0.072,0.612,0.498,0.321,0.512,0.507,0.122,0.593,0.594,0.582,0.646,0.614,0.615,0.621,0.63,0.614,0.636,0.626,0.578,1519,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +TRPC_THEMA_Chan_2017,0.405,0.45,0.394,0.411,0.416,0.417,0.395,0.434,0.471,0.47,0.439,0.478,0.506,0.327,0.47,0.503,0.464,0.47,0.499,0.43,0.336,0.399,0.395,0.455,0.437,0.421,0.369,0.387,0.478,0.39,0.427,0.365,0.14,0.329,0.46,0.448,0.39,0.442,0.438,0.418,0.445,0.442,0.397,0.14,0.461,0.395,0.253,0.386,0.393,0.075,0.459,0.41,0.431,0.462,0.463,0.409,0.439,0.441,0.418,0.454,0.504,0.491,1519,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +UBC9_HUMAN_Weile_2017,0.371,0.496,0.521,0.531,0.508,0.522,-0.042,0.403,0.512,0.519,0.42,0.477,0.509,0.001,0.004,0.403,0.473,0.518,0.538,0.557,0.22,0.405,0.445,0.419,0.42,0.479,0.471,0.449,0.442,0.484,0.454,0.432,0.071,0.233,0.403,0.428,0.327,0.452,0.475,0.443,0.517,0.54,0.277,-0.008,0.427,0.492,0.322,0.35,0.446,0.243,0.44,0.417,0.408,0.433,0.425,0.427,0.433,0.44,0.442,0.439,0.37,0.426,2563,OrganismalFitness,UBC9_HUMAN,Medium,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X,0.325,0.443,0.464,0.467,0.466,0.466,0.051,0.346,0.379,0.415,0.391,0.453,0.449,0.347,0.42,0.49,0.46,0.467,0.477,0.421,0.142,0.353,0.357,0.364,0.345,0.374,0.343,0.322,0.355,0.486,0.475,0.453,0.441,0.079,0.11,0.255,0.383,0.385,0.329,0.472,0.477,0.424,0.359,-0.095,0.348,0.344,0.488,0.409,0.52,0.351,0.411,0.436,0.424,0.447,0.448,0.428,0.407,0.429,0.42,0.432,0.5,0.407,3622,Stability,UBE4B_HUMAN,High,Human +UBE4B_MOUSE_Starita_2013,0.412,0.417,0.454,0.468,0.463,0.47,0.078,0.089,0.392,0.394,0.361,0.447,0.471,0.399,0.463,0.459,0.459,0.351,0.349,0.383,0.122,0.324,0.337,0.3,0.444,0.376,0.395,0.376,0.289,0.433,0.437,0.396,0.091,0.026,0.096,0.262,0.419,0.351,0.39,0.467,0.454,0.472,0.409,0.041,0.42,0.458,0.307,0.393,-0.017,0.118,0.426,0.409,0.456,0.458,0.438,0.458,0.432,0.449,0.443,0.454,0.44,0.412,899,Activity,UBE4B_MOUSE,Low,Eukaryote 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+VILI_CHICK_Tsuboyama_2023_1YU5,0.284,0.422,0.594,0.63,0.634,0.637,0.162,0.54,0.553,0.59,0.673,0.63,0.673,0.371,0.316,0.652,0.662,0.589,0.478,0.592,0.293,0.431,0.601,0.524,0.424,0.575,0.494,0.482,0.579,0.599,0.701,0.694,0.322,0.346,0.515,0.521,0.487,0.564,0.563,0.634,0.659,0.642,0.272,0.283,0.426,0.421,0.542,0.433,0.775,0.702,0.672,0.658,0.666,0.658,0.653,0.665,0.649,0.656,0.652,0.662,0.685,0.798,2568,Stability,VILI_CHICK,High,Eukaryote +VKOR1_HUMAN_Chiasson_2020_abundance,0.409,0.416,0.402,0.422,0.437,0.437,0.245,0.396,0.501,0.52,0.438,0.447,0.474,0.251,0.443,0.5,0.493,0.484,0.472,0.464,0.286,0.298,0.396,0.43,0.368,0.471,0.463,0.437,0.442,0.481,0.383,0.288,0.09,0.256,0.364,0.505,0.462,0.483,0.548,0.476,0.492,0.538,0.236,0.18,0.464,0.384,0.446,0.441,0.49,0.279,0.52,0.49,0.53,0.543,0.53,0.513,0.526,0.508,0.525,0.533,0.494,0.479,2695,Expression,VKOR1_HUMAN,Medium,Human +VKOR1_HUMAN_Chiasson_2020_activity,0.329,0.368,0.399,0.41,0.413,0.428,0.0,0.386,0.423,0.441,0.419,0.427,0.448,0.016,0.298,0.369,0.43,0.446,0.445,0.415,0.051,0.073,0.272,0.31,0.132,0.384,0.384,0.36,0.385,0.421,0.431,0.388,0.179,0.022,0.135,0.37,0.339,0.35,0.401,0.409,0.42,0.436,0.084,0.021,0.416,0.226,0.225,0.334,0.366,0.1,0.404,0.358,0.369,0.389,0.406,0.389,0.391,0.394,0.394,0.401,0.427,0.322,697,Activity,VKOR1_HUMAN,Medium,Human +VRPI_BPT7_Tsuboyama_2023_2WNM,-0.119,0.043,0.164,0.194,0.078,0.119,0.082,0.147,0.366,0.356,0.498,0.368,0.374,0.252,0.41,0.507,0.587,0.612,0.468,0.128,-0.02,0.135,0.133,0.222,0.215,0.202,0.131,0.199,0.291,0.084,0.499,0.429,0.242,0.065,0.059,0.122,-0.039,-0.028,-0.057,0.064,0.083,0.006,0.254,0.056,0.501,0.357,0.589,0.557,0.593,0.606,0.612,0.592,0.633,0.639,0.622,0.641,0.615,0.63,0.604,0.64,0.662,0.582,1047,Stability,VRPI_BPT7,Medium,Virus 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scoreSite-IndependentEVmutationDeepSequence (single)DeepSequence (ensemble)EVE (single)EVE (ensemble)UnirepUnirep evotunedMSA Transformer (single)MSA Transformer (ensemble)ESM-1bESM-1v (single)ESM-1v (ensemble)ESM2 (8M)ESM2 (35M)ESM2 (150M)ESM2 (650M)ESM2 (3B)ESM2 (15B)WavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLGEMMEVESPAVESPAlProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LCARP (38M)CARP (600K)CARP (640M)CARP (76M)MIFMIF-STESM-IF1ProteinMPNNProtSSN (k=10 h=512)ProtSSN (k=10 h=768)ProtSSN (k=10 h=1280)ProtSSN (k=20 h=512)ProtSSN (k=20 h=768)ProtSSN (k=20 h=1280)ProtSSN (k=30 h=512)ProtSSN (k=30 h=768)ProtSSN (k=30 h=1280)ProtSSN (ensemble)SaProt (650M)SaProt (35M)Number of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A0A140D2T1_ZIKV_Sourisseau_20190.3830.3540.1310.1030.3940.405-0.1330.0620.4360.439-0.001-0.0480.015-0.073-0.054-0.0580.2130.3630.3750.2160.3610.3090.3170.3050.3290.3420.3280.3120.2930.4300.3190.2960.0050.3040.3310.2720.3620.3660.3510.3610.3580.373-0.060-0.0730.131-0.0610.2730.2900.2870.1290.2530.2510.2820.2960.2770.2690.2630.2690.2650.2780.1990.1169576OrganismalFitnessA0A140D2T1_ZIKVMediumVirus
A0A192B1T2_9HIV1_Haddox_20180.4810.4070.4130.4320.5090.5160.0000.5130.5110.5150.4560.4920.516-0.0030.0150.0350.0800.1320.1640.4650.4960.5070.5090.5050.4970.5010.4630.4900.4840.4960.5410.5070.3270.4920.4830.5140.5090.5030.5130.5240.5180.5280.422-0.0210.4970.4290.3290.4510.2060.1360.2020.2300.2580.2630.2100.2260.2280.2000.1830.2390.1730.08412577OrganismalFitnessA0A192B1T2_9HIV1MediumVirus
A0A1I9GEU1_NEIME_Kennouche_2019-0.0110.0440.1070.0980.0530.054-0.0240.0840.0820.0770.0400.0680.068-0.037-0.047-0.0160.0300.0270.0250.067-0.0100.0470.0710.0880.0500.0880.0800.0890.0950.0450.0460.0360.0300.0360.0550.0990.0310.0410.0570.0580.0610.075-0.055-0.0560.039-0.0430.0510.0550.0390.0400.0540.0420.0390.0350.0370.0520.0390.0580.0410.0470.040-0.005922ActivityA0A1I9GEU1_NEIMEMediumProkaryote
A0A247D711_LISMN_Stadelmann_20210.4360.4590.1090.0410.4280.4280.0030.0260.2990.3200.0870.0880.0930.0640.0840.0970.0660.0840.0840.0590.0210.0150.0400.078-0.0290.0670.0100.0370.1060.4730.3370.3510.0930.0410.0240.0570.3010.2920.3110.2530.2420.2630.0470.0020.0740.0840.4180.4410.4760.3640.2740.3260.3140.3440.3130.3600.3120.3310.2650.3350.4270.2801653ActivityA0A247D711_LISMNHighProkaryote
A0A2Z5U3Z0_9INFA_Doud_20160.4780.4730.4840.5170.5450.5480.0090.4920.4950.4960.1240.5160.550-0.0040.0130.0640.5070.4980.5010.4130.4670.5340.5200.5420.4330.5200.5130.4940.5040.5380.4900.4000.0760.4700.5250.5230.5180.5480.5370.5570.5760.5730.0060.0000.3390.0170.4300.4870.4620.1740.4890.4740.5290.5220.5260.5240.5350.5160.5450.5370.1990.15210715OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A0A2Z5U3Z0_9INFA_Wu_20140.4800.5060.4540.4780.5060.5210.0210.4360.4950.5090.1550.4610.5090.0480.0730.0730.4640.4810.4850.4580.4500.5030.4840.5110.3450.4600.4820.4470.4750.5130.4420.3750.0850.4420.4930.5190.5060.5340.5410.5410.5560.5580.0520.0510.2530.0690.3550.3800.3620.1470.4320.4290.4550.4510.4540.4610.4680.4490.4620.4680.2290.1972350OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A4_HUMAN_Seuma_20220.3940.3850.4490.4250.3120.3160.3450.1670.3370.3510.3020.3160.4040.3820.3660.3720.4310.4800.3520.2610.3270.2810.3180.3060.4060.2970.3200.3130.3140.4780.2510.1860.4870.3930.2730.3700.4410.3720.4580.4360.4000.4160.3660.3620.4210.3560.2790.358-0.1910.0600.4490.4400.4170.4100.4060.3800.4230.3460.3720.4170.4110.38814811StabilityA4_HUMANLowHuman
A4D664_9INFA_Soh_20190.4110.3400.4060.4030.4090.4080.0290.3640.2970.2870.0440.0260.0330.0220.0200.0270.1490.2110.2700.2600.3290.3860.4040.3980.0640.2600.2850.2440.3300.4430.3180.2960.0550.3480.3710.4040.3660.3800.3930.4340.4390.4610.019-0.0000.080-0.0010.2620.2540.1260.1050.1830.1800.1910.2160.2090.2070.2000.1980.1930.2040.1480.09214421OrganismalFitnessA4D664_9INFAMediumVirus
A4GRB6_PSEAI_Chen_20200.3170.4920.6570.6640.6230.6410.3440.5290.6410.6810.6800.6470.6680.4310.5280.6550.7380.7120.6270.5670.4090.5330.5640.6240.5260.6220.6390.6380.7050.6710.7420.6730.2420.4230.5520.6290.5570.5880.6520.6530.6600.6790.4810.0710.6710.6010.6640.7130.6290.4220.7280.7180.7110.7190.7290.7330.7280.7330.7350.7450.7220.5135004OrganismalFitnessA4GRB6_PSEAIHighProkaryote
AACC1_PSEAI_Dandage_20180.2820.4740.3310.4140.4870.4910.1960.1910.5010.5040.4130.4830.4890.1960.2480.2560.4900.5110.5180.3910.2060.2320.2760.3060.2730.4280.4070.4130.4380.4650.5070.4550.0210.2590.2460.4170.4270.4180.4660.4710.4660.4950.2160.1700.3540.2430.2570.3740.3570.1830.4810.4920.4830.4880.4950.4860.4860.4770.4940.5000.4610.2821801OrganismalFitnessAACC1_PSEAIHighProkaryote
ACE2_HUMAN_Chan_20200.2540.2370.1870.2000.2490.242-0.0490.0770.2360.2510.2440.1730.213-0.0530.0620.2160.2250.1750.1780.2270.0330.1290.1750.2070.0420.1620.2070.2080.2610.2170.1640.1380.0800.0440.1200.1340.2170.1900.1960.2280.2000.2050.0050.0050.2440.0780.3360.2920.3170.1060.2170.2550.2260.2220.2360.2490.2230.2210.2140.2340.2890.1682223BindingACE2_HUMANMediumHuman
ADRB2_HUMAN_Jones_20200.3310.4230.5060.5140.4970.5170.4630.5000.5310.5300.5340.5260.5390.4080.4440.4740.4930.5050.5150.5210.5090.5240.5170.4930.5240.5310.5380.5330.5020.5450.5000.4140.2980.5280.5300.5060.5280.5410.5330.5330.5380.5370.4710.1810.5280.5100.4190.4770.4510.2230.4860.4860.5000.5080.5010.4980.4930.5060.4940.5090.5540.5167800ActivityADRB2_HUMANMediumHuman
AICDA_HUMAN_Gajula_2014_3cycles0.1380.3960.4740.4640.4290.445-0.2870.3270.3200.3850.4150.3760.478-0.245-0.2640.1960.3280.3470.2620.479-0.2190.1400.3880.3950.0130.3210.4170.4130.2820.3760.4500.4050.057-0.0390.0570.3390.2060.2180.3670.3990.4020.444-0.228-0.2380.3790.2580.3280.4010.3280.2430.3370.4040.3440.4470.3590.3650.3410.3430.3120.3590.257-0.036209ActivityAICDA_HUMANMediumHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O0.3580.4180.4190.4070.3860.398-0.3010.1300.2200.2400.3740.0700.423-0.2090.2860.3210.2610.2970.2600.062-0.052-0.218-0.051-0.038-0.317-0.089-0.050-0.1210.0560.3960.2540.2280.090-0.287-0.139-0.2750.3920.4020.3560.4240.4220.374-0.185-0.1940.1650.1140.2510.1120.4330.3530.4460.3260.2040.3490.3420.3490.3430.3390.3610.3430.3470.3972972StabilityAMFR_HUMANMediumHuman
AMIE_PSEAE_Wrenbeck_20170.2460.4430.4960.5060.4770.4640.0930.4360.4010.6050.5700.6130.6620.3260.4080.4280.5570.6740.6130.5060.5550.5250.5320.5420.5660.5610.5340.5590.5080.5580.5830.5280.2650.6010.4700.4050.5850.4790.4380.5800.5060.4810.3560.0960.4830.4120.3680.4690.4070.2130.5550.5190.5300.5370.5290.5410.5230.5400.5640.5560.6070.4106227ActivityAMIE_PSEAEHighProkaryote
ANCSZ_Hobbs_20220.5620.5210.4450.4720.5240.5280.5250.4520.5030.5240.5270.5220.5430.5350.5920.5980.6090.5980.5700.0210.4620.4930.5090.5000.4970.5190.5130.4690.4940.5570.5420.5250.3180.4830.5030.4750.5370.5490.5380.5450.5560.5430.5490.4200.4960.5230.4710.4580.4220.1650.5660.5550.5720.5780.5720.5840.5690.5830.5770.5890.5850.6014670ActivityANCSZMediumEukaryote
ARGR_ECOLI_Tsuboyama_2023_1AOY0.2770.4130.4110.4300.4310.4230.3060.3860.4300.4630.4090.2960.4050.3320.2970.5110.4970.4990.4350.4340.3720.4390.4480.4030.3620.4370.4730.4610.4180.4600.4090.3100.0730.3940.3960.4300.4050.4150.4390.4650.4600.4710.2850.2670.4500.4080.7250.6510.7380.6360.5130.5320.5270.5290.5170.5080.5030.5260.4940.5330.6040.5111287StabilityARGR_ECOLIMediumProkaryote
B2L11_HUMAN_Dutta_2010_binding-Mcl-10.6880.2700.6680.6610.5300.6460.2160.5290.1880.1640.206-0.0190.3960.1740.1230.1510.2700.3670.3150.3360.2390.0760.3040.5680.0670.2510.3620.3720.3660.7340.3580.3380.2950.2660.2130.3410.5440.4690.3880.6150.5700.4020.1460.1380.2070.1700.2870.3070.432-0.0050.3520.3670.4190.2830.2550.4700.2950.2880.4210.3740.2420.204170BindingB2L11_HUMANLowHuman
BBC1_YEAST_Tsuboyama_2023_1TG00.2160.3320.3950.3990.3940.4030.2120.2560.4380.4540.4250.4250.4510.3310.4210.4230.4790.4580.4990.3320.3350.3460.3760.3430.1440.3910.4030.3820.3070.3380.3450.3140.2730.3540.2610.2990.3920.3380.3650.4210.3860.4050.2100.1130.3140.2420.5580.4510.6440.5660.4340.4440.4570.4720.4630.4630.4600.4580.4710.4610.5400.5792069StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU0.3640.4180.3570.3640.3650.3820.1000.2480.3830.5010.3940.3290.4060.1660.1820.3010.5100.5210.4210.4070.1220.3620.2200.3760.2070.4210.2810.4020.4350.6070.5490.4960.0700.2450.2940.4310.4400.4400.4830.3920.3960.4360.1160.1090.2050.1750.5790.5370.6830.5640.5490.5540.5630.5790.5610.5810.5290.5560.5430.5620.5160.2961572StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Deng_20120.3220.5040.5080.5210.5070.5080.0750.3120.5130.5500.5170.5010.5180.2430.3540.4610.5280.4610.3520.4550.3750.3780.4000.3830.3910.4450.4530.4060.3430.4640.5190.4870.1070.3920.4020.3730.4570.4710.4730.5130.5260.5220.3300.0110.4800.4060.4230.4960.4600.2520.5300.5350.5380.5520.5480.5420.5440.5300.5410.5550.5390.4064996OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Firnberg_20140.4710.7080.7320.7470.7090.7290.1340.4590.7010.7410.7030.6650.7070.4110.5550.6580.7370.5730.4240.6620.5660.5630.5370.5310.5950.6460.6540.5770.4520.6830.7580.7520.1860.5510.5290.4800.6460.6380.6370.7310.7340.7360.5400.0460.7000.5950.6160.7030.6960.3210.7180.6990.7030.7290.7390.7300.7360.7200.7290.7450.7540.6054783OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Jacquier_20130.4560.6890.6980.7200.7030.7230.1130.4410.6650.7120.6680.6560.6840.3620.5200.6260.7040.5880.5030.6350.5640.5370.5420.5280.5730.6320.6250.5780.4620.5670.7190.6910.1430.5400.5490.5080.6280.6420.6470.7160.7280.7340.4820.0400.6630.5620.5270.6400.6030.2900.6770.6710.6720.6910.6860.6860.7020.6770.6840.7030.7090.573989OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Stiffler_20150.4660.7070.7320.7430.7110.7270.1150.4590.6880.7310.6980.6680.7070.3990.5570.6610.7310.5890.4300.6550.5680.5700.5280.5250.5970.6510.6610.5770.4600.6790.7670.7610.1580.5580.5220.4750.6450.6300.6290.7300.7310.7330.5320.0300.7030.5980.6040.6930.6820.3240.7150.6920.6980.7200.7250.7240.7290.7090.7240.7370.7480.5934996OrganismalFitnessBLAT_ECOLXHighProkaryote
BRCA1_HUMAN_Findlay_20180.5010.3850.4410.4320.4240.4660.1250.1890.3870.4070.4940.4040.4470.1340.3920.4550.5150.4970.3940.2960.1450.3790.4050.3610.3050.4730.4570.4420.4230.4380.5390.4820.2200.1510.1790.4090.4560.4560.5140.4750.4760.5330.2290.1320.5340.4570.4400.4690.1160.1200.5150.5010.5060.5020.5120.5040.5130.5120.5040.5190.5390.4811837OrganismalFitnessBRCA1_HUMANLowHuman
BRCA2_HUMAN_Erwood_2022_HEK293T0.4810.3460.4400.4450.4470.4810.0950.4770.095-0.0300.4800.1830.1030.0880.1030.4160.5100.4640.4730.0330.0900.4940.5340.4820.5330.4830.4960.4990.0140.4000.2970.3280.1400.1190.1850.1380.4200.4250.4260.4190.4150.4140.1630.0930.4970.0470.0520.053-0.0910.2850.4360.4770.4730.4240.4550.4360.4560.4380.4530.4650.0000.016265OrganismalFitnessBRCA2_HUMANNaNHuman
C6KNH7_9INFA_Lee_20180.3930.3710.3510.3570.4330.436-0.0250.4510.3740.3780.0600.4280.492-0.014-0.022-0.0070.4830.4050.4260.3430.3960.3630.3780.3490.2540.4540.4280.4650.3700.4750.4570.3600.1100.3540.3740.3920.4120.4190.4330.4390.4410.452-0.024-0.0330.294-0.0020.5130.5390.5440.2270.4980.4970.5190.5200.5310.5100.5280.5190.5210.5290.3130.18510754OrganismalFitnessC6KNH7_9INFAMediumVirus
CALM1_HUMAN_Weile_20170.1750.2330.2380.2330.2380.2360.1740.1870.2360.2530.2500.2350.2640.1610.1990.1950.2120.2180.2250.2290.1730.2270.2420.2540.2320.2760.2790.3000.3130.2390.2100.1460.0870.2160.2530.2910.2180.2420.2660.2380.2440.2460.2130.1560.2680.2510.1060.1430.1640.0880.1770.1820.1880.1710.1890.1760.1800.1920.1780.1880.2870.2381813OrganismalFitnessCALM1_HUMANHighHuman
CAPSD_AAV2S_Sinai_20210.4070.3450.3170.3720.3460.3300.3740.4240.3290.3630.1830.1960.1990.2540.2860.2030.2770.1830.1240.2580.1910.2500.2690.2790.2030.1980.2660.2040.3990.4450.1830.1770.1320.2020.2620.4920.3800.3860.4730.3380.3430.4260.0920.1600.2600.1120.4020.3930.3470.3190.2350.2250.2160.2350.2140.2150.2200.2080.2280.2230.3010.22242328OrganismalFitnessCAPSD_AAV2SLowVirus
CAR11_HUMAN_Meitlis_2020_gof0.2650.1740.1640.1500.1690.1840.1000.0090.2180.2450.3040.3240.3020.0680.0950.3370.3260.3450.3570.1650.1510.1980.2190.2160.0370.2150.2290.2260.1670.1790.3260.2960.1890.0050.1480.1240.2030.2080.1820.2040.2010.1820.0890.0430.2610.2150.3180.2940.3350.1420.3430.3370.3350.3230.3300.3310.3170.3440.3460.3420.4080.3332374OrganismalFitnessCAR11_HUMANLowHuman
CAR11_HUMAN_Meitlis_2020_lof0.4210.3130.2770.2580.3100.3270.108-0.0140.2990.2930.4460.4900.4610.0720.1150.4620.5010.5330.5400.2390.1410.3120.3000.2770.1070.3030.3060.3580.1910.3380.4330.3780.334-0.0030.2640.1950.2900.3250.2710.3200.3260.2810.0870.0410.4170.3110.4170.4450.4510.1750.5180.5210.5150.5080.5000.5180.5030.5120.5260.5250.5790.4252395OrganismalFitnessCAR11_HUMANLowHuman
CAS9_STRP1_Spencer_2017_positive0.1590.1790.1620.1660.1690.1770.0400.1000.1800.1830.1750.0690.0730.0630.0900.1540.1820.1880.1940.0060.0430.0810.1730.1200.0430.1940.1820.1790.0680.1870.1990.1790.0270.0350.0470.1660.1700.1720.1890.1840.1840.1940.0610.0280.1810.1060.1060.1810.0360.0370.1760.1770.1780.1850.1800.1810.1770.1780.1770.1830.1770.1148117ActivityCAS9_STRP1MediumProkaryote
CASP3_HUMAN_Roychowdhury_20200.3820.5430.6100.6220.6260.6320.0540.4780.6440.6530.5900.5950.6240.2640.5740.6390.6380.5540.5140.6030.2330.5120.5170.5320.5050.5560.5540.5260.5670.6080.5990.5420.2170.0920.4820.5200.5330.5620.5820.6280.6190.6280.422-0.0060.6160.5630.4140.5580.4990.2630.5530.5710.5710.5700.5750.5670.5850.5710.5760.5900.6520.5131567ActivityCASP3_HUMANHighHuman
CASP7_HUMAN_Roychowdhury_20200.3720.5160.6190.6170.6090.6130.0570.4700.6000.6270.5850.6110.6320.3060.5980.6320.6220.5720.5620.6110.2060.5320.5290.5580.5290.5790.5890.5630.5720.6420.5960.5380.3320.1030.5430.5210.5170.5940.5860.5990.6280.6280.4610.0190.6460.5960.4790.6120.5730.3290.5850.5700.5620.5870.6080.6020.5910.5780.5960.6050.6430.5331680ActivityCASP7_HUMANMediumHuman
CATR_CHLRE_Tsuboyama_2023_2AMI0.5330.5590.5990.6060.6270.6300.6800.5580.5650.5280.6140.6380.6570.5640.6890.6880.6580.6150.6440.0410.6210.6030.6190.6020.6410.6270.5930.5610.5870.6490.6110.6190.4390.6260.6110.6330.6490.6420.6650.6220.6200.6380.4400.4270.3150.4600.4750.4000.6790.5720.6630.6520.6760.6680.6870.6620.6700.6530.6630.6740.6200.6861903StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X0.6440.7180.7440.7450.7160.7290.4820.5770.7450.7540.7700.7520.7360.5290.6880.7360.7020.7270.7180.7390.4860.5220.6990.6920.5470.6650.6730.6740.7100.7320.7100.6540.1040.5090.6420.7030.6990.7060.7320.7460.7360.7480.5100.3020.6310.6460.7010.6890.8200.7730.7720.7680.7780.7680.7820.7800.7700.7720.7690.7790.7160.8352068StabilityCBPA2_HUMANMediumHuman
CBS_HUMAN_Sun_20200.3340.3580.3640.3830.3710.3790.2020.2510.3740.3770.3470.3460.3720.0850.2260.3350.3420.3290.3390.3500.3560.2600.2850.2880.3490.2730.2900.2730.3100.3810.3700.3440.1970.3610.2830.2660.3840.3320.3270.3940.3630.3630.3040.0680.3760.3730.2370.2680.3090.0790.3240.3240.3310.3370.3370.3350.3320.3320.3350.3400.3860.2897217OrganismalFitnessCBS_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K280.4980.5480.6740.7060.6330.685-0.2660.6110.6230.6130.6150.6500.648-0.2710.7350.6990.6820.6090.6060.6480.4860.5890.5810.5870.5530.5770.5890.5310.5690.6330.6670.6530.4230.4250.5050.5400.6010.6000.6230.6760.6980.7010.621-0.3200.3690.5610.1670.1550.6140.5880.6950.6260.6900.6760.7090.6680.6770.7180.6640.6950.6420.7512282StabilityCBX4_HUMANHighHuman
CCDB_ECOLI_Adkar_20120.3570.4860.5200.5470.5280.531-0.0310.1850.3410.3610.4240.3510.432-0.006-0.0180.4060.4660.4630.2640.4570.027-0.015-0.1150.142-0.1390.053-0.0290.0340.4250.4530.5540.5550.1240.0060.0560.3100.4420.4260.4580.5070.4940.5220.018-0.0500.4440.0200.2850.4260.3430.2420.4360.4010.4140.4430.4370.4540.4360.4640.4860.4530.4380.1941176ActivityCCDB_ECOLIHighProkaryote
CCDB_ECOLI_Tripathi_20160.4340.5060.5240.5410.5170.528-0.0080.2940.4030.4310.4900.4350.483-0.007-0.0010.4620.5110.5220.3620.5180.0340.014-0.0740.171-0.1070.094-0.0290.0740.4760.5050.5420.5100.1120.0090.1290.4240.4800.4800.5320.5180.5170.5480.005-0.0550.4950.0170.3170.4330.3350.2300.4860.4490.4490.4820.4830.4800.4730.4900.5170.4920.4900.3261663OrganismalFitnessCCDB_ECOLIHighProkaryote
CCR5_HUMAN_Gill_20230.2710.2790.2820.2890.2740.2820.3160.3090.3330.3430.3580.3440.3560.3250.3580.3620.3470.3390.3410.3520.3670.3660.3230.3260.3690.3560.3460.3600.3220.3640.3000.2510.1300.3670.3650.3630.3670.3750.3760.3240.3240.3230.3540.1750.3620.3610.2780.3060.2910.1730.3140.3050.3160.3180.3130.3170.3280.3230.3270.3270.3480.3566137BindingCCR5_HUMANHighHuman
CD19_HUMAN_Klesmith_2019_FMC_singles0.1890.2470.1990.2110.2340.2320.0510.2330.2570.2610.0460.0500.0720.0900.0930.0540.0500.1230.217-0.0130.0960.1240.1180.1190.0420.1200.1120.0930.2440.2830.1940.1590.0770.0750.1200.0930.2030.2100.2020.2330.2350.2310.0830.0500.1040.0860.3160.2580.3010.1740.1580.1800.1730.1960.1860.1950.2040.1700.1850.1950.3120.2443761BindingCD19_HUMANLowHuman
CP2C9_HUMAN_Amorosi_2021_abundance0.5050.5720.5940.6110.5960.6100.5290.5550.6010.6150.5130.5880.6210.5310.6050.6250.6350.6260.5910.6110.5530.5480.5870.5580.5710.5850.5900.5720.5720.6050.5730.5110.2080.5810.5630.5690.6180.6180.6240.6340.6300.6320.5790.1160.5340.6070.5550.5380.6070.2180.6000.5980.6060.6290.6310.6230.6220.6110.6170.6350.6370.6186370ExpressionCP2C9_HUMANHighHuman
CP2C9_HUMAN_Amorosi_2021_activity0.5260.5900.6260.6450.6160.6350.5830.6150.6060.6350.5190.6420.6660.5700.6550.6790.6790.6780.6200.6520.5870.5920.6190.5730.6180.5870.5930.5920.5820.6370.6030.5440.2250.6380.6050.5720.6660.6580.6380.6680.6590.6500.6380.0940.5570.6660.6030.5770.6560.2630.6450.6490.6520.6630.6740.6720.6680.6560.6620.6810.6730.6676142BindingCP2C9_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM0.3890.5040.4320.4450.4700.4770.2600.4000.4610.4610.4810.5460.6050.4490.6860.5180.4840.4230.4150.4060.4490.4550.5020.5140.4430.5870.5300.5040.5010.5320.4010.3890.2440.3410.4740.5460.4970.5360.5760.4680.5130.5300.4160.3260.4940.5380.5620.5410.6530.6200.5310.5200.5320.5570.5800.5560.5390.5800.5480.5560.5750.7203295StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX0.3570.4510.4350.4380.4740.4450.1540.4530.5160.5470.4830.4270.3980.1530.4280.4780.5470.4980.4670.3040.1740.2060.2250.1020.1170.2240.2550.2480.4340.4200.4690.4140.2310.1600.1460.2130.3800.3870.3930.4380.4410.4480.1540.0640.4620.2560.5150.5010.6540.4820.5490.5080.4940.5520.5170.5510.5380.5250.5170.5380.6000.5711580StabilityCUE1_YEASTMediumEukaryote
D7PM05_CLYGR_Somermeyer_20220.5300.6250.6130.5820.6290.6310.0730.4750.6490.6570.4390.0660.0640.0680.0530.0360.0500.0720.1520.4380.0390.0420.1140.0820.0090.0470.0680.1940.3610.6230.5630.5740.0160.0960.1250.1560.5360.5360.5320.6300.6290.6180.0040.0240.0090.0280.2920.3160.5050.3960.4950.4910.4960.5050.4940.5020.4940.4900.4870.4980.3450.20824515ActivityD7PM05_CLYGRLowEukaryote
DLG4_HUMAN_Faure_20210.6790.5840.6070.5770.6090.6160.7230.6290.5240.5350.5200.5500.6080.7430.7640.7280.5840.4870.4430.6500.5740.5830.5690.5380.6050.6070.5640.5620.4950.6140.6340.6210.5110.5760.6380.5800.6530.7000.6670.6430.6610.6360.5810.2330.3720.5280.6370.4530.6720.3150.4830.3700.5390.4990.5200.4920.5080.4860.5040.5040.5210.7456976OrganismalFitnessDLG4_HUMANLowHuman
DLG4_RAT_McLaughlin_20120.4860.4420.4870.4910.5260.5390.4900.4370.4830.5220.4690.5570.5880.4110.5430.5810.5430.4780.4710.4440.3780.3890.3730.3710.4070.4200.4070.3670.3950.4900.5560.5370.2460.3670.3920.3070.4700.4780.4430.5380.5440.5400.5270.0760.4430.5000.4390.3080.4590.1350.4420.3750.4440.4330.4440.4300.4300.4640.4600.4690.5040.5351576BindingDLG4_RATLowEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC0.1710.1980.2180.2300.2000.259-0.0710.3170.3680.3860.0850.1260.1310.0740.2090.2550.3370.3370.3700.235-0.0680.0450.0330.0660.0410.1070.0570.0710.2350.2980.4310.3510.054-0.013-0.0230.0310.2120.2000.2110.2430.2270.2310.106-0.0480.2990.1290.5680.5600.6600.5560.4240.4360.5160.5070.4910.4760.4720.4440.4360.4900.5560.4811008StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO10.7380.7500.7590.7570.7670.7640.7560.7320.7760.7900.7730.7560.7850.8270.8160.8170.8030.7530.7980.7810.6690.7360.7760.7500.7820.7650.7740.7210.7640.7750.7290.7140.6600.7800.7760.8050.8080.8160.8230.7740.7870.7870.6700.1640.4430.6840.6430.5280.7790.7850.7570.7610.7600.7930.7890.8030.7890.7860.7940.7860.7260.8012264StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y0.4180.4530.4210.4390.4940.5010.0570.4320.4060.4030.5250.4720.5010.2240.4580.4860.5310.5080.5310.5180.3400.4180.4020.4080.3150.4020.3650.3480.3950.4480.4710.4690.2160.2020.3210.2520.4730.4900.4940.4890.4910.5070.359-0.1770.5130.4390.3800.3940.4190.4390.4920.4810.4970.5070.4910.5120.4770.4930.5040.4970.5670.5312915StabilityDOCK1_MOUSEHighEukaryote
DYR_ECOLI_Nguyen_20230.3400.4240.4480.4530.4510.4540.0040.4250.5050.4810.5230.5350.5450.1330.5000.5250.5440.5290.5110.5020.3730.4210.2810.3420.4350.3880.3870.4540.3330.4800.5020.4770.1480.4050.2350.2250.4600.3300.3560.4590.4060.4180.4690.0130.5190.4990.2640.4280.3740.1570.5010.4590.4700.4830.4920.4900.5030.4950.5110.5050.5110.4712916OrganismalFitnessDYR_ECOLIHighProkaryote
DYR_ECOLI_Thompson_20190.3840.4840.4690.4720.4760.474-0.0180.3210.4700.5010.4690.4160.4360.0550.3490.4550.4800.5060.5120.4850.1990.3600.2660.3100.3420.4360.4760.4410.4180.4510.4510.4080.1000.3550.3480.3470.4070.4150.4230.4590.4760.4810.318-0.0190.4420.3900.2480.4030.3210.1110.4400.4110.4150.4180.4350.4220.4410.4420.4550.4450.4910.3872363OrganismalFitnessDYR_ECOLIMediumProkaryote
ENV_HV1B9_DuenasDecamp_20160.3690.3970.2380.3670.3880.3770.0560.3530.3410.3660.3590.4150.3890.1100.0550.0350.0120.0480.0950.3880.3800.3580.4080.4190.2630.3940.4010.3910.3740.3890.3550.3220.2530.3680.3730.4040.3920.3960.4070.3900.3920.3920.343-0.0400.3340.3950.3550.3250.3640.2310.2150.2610.2740.2400.2140.2330.2410.2580.2400.2630.1500.102375OrganismalFitnessENV_HV1B9MediumVirus
ENV_HV1BR_Haddox_20160.3380.3030.3220.3230.3390.345-0.0010.3220.3450.3440.2980.3200.336-0.009-0.0060.0040.0460.0690.1600.3370.3500.3580.3710.3640.3450.3580.3580.3540.3620.3500.3250.2900.1890.3440.3600.3580.3590.3660.3620.3670.3690.3650.233-0.0130.3210.2930.2120.2450.1190.0720.1800.1790.2030.2260.1880.2020.2000.1920.1930.2090.1670.09812863OrganismalFitnessENV_HV1BRMediumVirus
ENVZ_ECOLI_Ghose_20230.1150.0950.1800.1780.1870.1880.0400.1900.1900.1980.1540.2040.2090.1940.2070.1950.1720.1540.1130.1830.1640.1520.2010.1830.1760.1640.1990.1830.1630.1780.1330.1350.1420.1800.1840.1930.2070.2130.2150.1950.2010.1990.1940.1340.1870.1850.0550.1490.1140.0360.1890.1860.1680.1880.2200.1970.1830.1900.2000.1990.1480.2241121ActivityENVZ_ECOLIHighProkaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M0.6150.7060.7050.7080.7320.733-0.3200.6840.7810.7890.7620.7870.789-0.3780.7680.8030.8100.7690.7440.7410.5550.5990.6850.6560.6410.6640.6840.7050.7350.7950.7520.7380.6770.6120.6030.6440.7160.7110.7280.7410.7370.7350.625-0.2530.6180.6300.6650.6040.8280.7600.8050.8010.8060.8110.8200.8090.8020.8060.8000.8120.7990.8051960StabilityEPHB2_HUMANHighHuman
ERBB2_HUMAN_Elazar_20160.3860.3680.2540.2500.2440.2680.4490.4150.4050.4160.4490.4800.4900.4810.4630.4690.4230.3730.2950.0470.4570.4990.5500.4710.5050.5360.5960.5980.5060.3810.4240.0660.4890.5530.4710.5210.4940.4650.4920.4470.4410.4390.4680.4590.5370.4630.4610.491-0.1390.0220.4100.4060.4510.4400.4150.4060.4320.4520.4380.4460.5250.488326ExpressionERBB2_HUMANLowHuman
ESTA_BACSU_Nutschel_20200.2580.3990.3890.4150.3870.3870.1870.3150.3450.4130.3360.3050.3350.1690.2700.2670.3000.2810.3230.3200.1210.1960.2680.2860.2700.2540.3110.2720.3790.3790.4340.4060.0750.1840.2720.2610.3100.3290.3260.4030.4070.3970.1930.1210.2890.2620.5570.4890.5450.3680.3660.3340.3470.3430.3780.3470.3470.3650.3690.3700.3910.2122172StabilityESTA_BACSUHighProkaryote
F7YBW8_MESOW_Aakre_20150.0600.3950.3950.4400.4280.4300.0160.3200.3820.3950.4370.3820.393-0.091-0.0010.0490.3830.3660.4340.391-0.075-0.122-0.104-0.0050.0980.125-0.0210.2980.4030.3750.4610.4410.037-0.036-0.1240.4330.0620.0110.4250.3800.3670.444-0.064-0.0810.199-0.048-0.0080.2460.0690.0350.4310.4470.4230.4600.4530.4570.4300.4430.4470.4480.270-0.1269192OrganismalFitnessF7YBW8_MESOWHighProkaryote
FECA_ECOLI_Tsuboyama_2023_2D1U0.3800.4310.4490.4580.4490.4410.0230.2970.3720.4560.5090.2690.4330.0590.4490.5360.5240.4800.3860.4640.1690.2720.3430.2880.1560.4770.4510.2920.3940.5200.4090.3360.2760.0650.1210.1480.3550.3480.3020.4150.4150.3890.3950.0080.5010.4470.5820.5510.5880.5250.5270.4830.5140.5120.5000.5280.4990.4890.5060.5150.5950.5851886StabilityFECA_ECOLIHighProkaryote
FKBP3_HUMAN_Tsuboyama_2023_2KFV0.4230.3940.4790.4820.4870.4980.1800.3420.2370.2370.1710.1700.1640.1720.1630.1470.1880.2650.3780.2870.2210.2130.2180.2790.1690.2290.1930.2500.2200.4780.3150.319-0.0770.0950.1680.2300.3980.4100.3420.4840.4900.4090.1800.1840.1770.2100.6850.6030.6870.5890.3060.3970.4430.4020.3970.4030.3690.3680.2960.3990.5810.3681237StabilityFKBP3_HUMANMediumHuman
GAL4_YEAST_Kitzman_20150.2370.4030.5070.5670.4860.5320.337-0.0040.5130.5750.6230.4580.4620.3400.3920.5280.6680.6700.6550.5070.3190.3550.3830.3720.3640.4240.4930.4510.5790.6280.6510.5630.3030.2790.3310.3250.5600.5550.5570.5080.5090.5030.3950.3010.6040.4460.2870.5680.3080.1140.6270.6340.6130.6250.6320.6340.6200.6170.6190.6380.5580.4081195OrganismalFitnessGAL4_YEASTMediumEukaryote
GCN4_YEAST_Staller_20180.2530.2500.2450.2480.2420.2410.1820.2210.2400.2400.2380.2720.2840.3180.2780.2660.2810.2610.2480.1860.1750.1890.1680.1670.1580.1180.0810.1340.1640.2250.2550.2470.0250.1750.1690.2650.2580.2570.2780.2580.2570.2740.1070.1920.1570.1260.1450.1830.2010.1670.2270.2200.2170.2140.2260.2240.2190.2210.2240.2220.2210.2452638BindingGCN4_YEASTLowEukaryote
GDIA_HUMAN_Silverstein_20210.4420.4290.4390.4460.4510.4520.2050.3980.4660.4620.3910.4230.4600.1590.2470.4480.3970.3560.4110.4010.2530.3850.3800.4090.3260.4390.4250.3840.3970.4150.4230.3930.2760.3210.3940.3560.4320.4480.4370.4510.4700.4610.2330.1210.4320.2600.3570.4210.3870.1010.3890.3350.4020.3780.3910.3990.3770.3910.3750.4050.4610.3381154OrganismalFitnessGDIA_HUMANLowHuman
GFP_AEQVI_Sarkisyan_20160.6490.6440.6720.6730.6790.6790.0490.6350.6670.6610.5240.0980.1030.0780.1310.1030.1080.1490.2900.5980.0780.1050.1810.0980.0460.1880.2980.6420.6470.6780.6070.6150.0740.0660.1820.6290.6490.6510.6720.6830.6850.7060.024-0.0140.0410.0360.5120.5080.7130.6020.5910.6000.6100.6170.6090.6110.6050.6060.5870.6070.6230.44951714ActivityGFP_AEQVILowEukaryote
GLPA_HUMAN_Elazar_20160.2270.1340.2070.2100.1800.1620.3070.5240.3850.3610.3880.4170.4120.3390.3920.4530.4250.3800.4700.1630.3700.3880.3640.4070.3630.3900.4140.4200.4900.3490.4350.2760.2810.4230.3720.4140.4230.3920.4170.3910.3620.4050.4050.3840.4280.3410.3300.4130.3100.1770.5290.5130.5070.4790.5000.4550.5000.4340.4640.5070.4430.411245ExpressionGLPA_HUMANLowHuman
GRB2_HUMAN_Faure_20210.4050.5210.5070.5380.5400.5460.4860.4500.5390.4910.5330.4550.5150.5260.5980.6260.6470.5350.5790.5340.5440.5160.5140.4530.5320.5090.4460.5240.4480.5040.4280.4220.4640.5360.5060.4040.5170.5060.4360.5620.5610.5320.5840.3230.5300.5720.6720.5270.6900.5160.6210.6490.6380.6380.6470.6280.6290.6450.6250.6500.5560.60763366OrganismalFitnessGRB2_HUMANMediumHuman
HCP_LAMBD_Tsuboyama_2023_2L6Q0.3150.4100.1130.1470.3510.3600.2410.3990.4300.4850.6660.4500.4960.2850.4160.5740.6860.5880.5770.2450.2920.3060.3620.3680.3240.3250.2100.3450.5150.5790.6360.5040.1910.2620.2870.4860.4300.4430.5310.3740.4300.5070.3100.2850.6140.3760.6670.7240.7300.6010.6920.6980.7100.6870.7140.7110.6960.7140.7190.7230.7680.6091040StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM0.2910.3700.3240.3240.3320.3360.0550.2340.3590.3700.3030.3030.2930.1340.2060.4740.3870.3400.3850.337-0.0240.2320.3020.2640.1600.2940.3080.2470.2140.3120.3320.3070.1030.1350.1010.2290.3270.2980.3530.3270.3410.3590.0890.0380.4330.1380.2520.2600.3800.3210.3680.4080.3450.3920.4110.4360.4420.4280.4510.4170.4140.2195586StabilityHECD1_HUMANMediumHuman
HEM3_HUMAN_Loggerenberg_20230.4180.4090.4030.4070.4200.4180.1260.1030.4320.4420.3880.3830.3960.1420.3870.4080.3930.4180.4290.1080.3490.3730.3700.3750.3580.3870.3760.3850.4020.4350.4200.4100.0930.3770.3810.3770.4200.4300.4380.4270.4340.4400.3230.1350.3840.3780.3140.3580.3010.1460.3570.3370.3530.3630.3770.3740.3710.3730.3680.3770.4310.4155689ActivityHEM3_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_20190.4720.5420.5580.5570.5330.5310.1460.2730.4680.5080.4720.4110.4770.0570.1130.4400.4110.4750.4800.4540.3250.4020.4330.4780.4040.4850.4640.5160.5390.5240.4000.3740.1430.3970.4530.5850.4880.4960.6160.5730.5480.5820.1400.0400.2990.0200.3780.4240.5290.3920.4440.3500.4040.4270.4400.4070.4180.4500.4310.4240.5210.222496137OrganismalFitnessHIS7_YEASTMediumEukaryote
HMDH_HUMAN_Jiang_20190.4910.4210.3820.3940.4560.4510.1580.2820.2820.2740.2750.3410.3150.0460.0100.3170.5090.3740.3780.3020.4510.1620.1750.2210.4350.1970.1680.2090.2130.4760.4130.4010.2280.3080.2470.2190.4400.3400.3300.4370.3610.3600.1720.0690.4770.4960.3230.444-0.1840.1050.4930.4920.4860.4980.4890.4920.4800.5000.4850.4990.5290.43816853OrganismalFitnessHMDH_HUMANLowHuman
HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O20.3160.3600.3710.3760.3640.3650.1940.3940.4270.4370.3620.4160.453-0.0730.0710.2340.2570.3090.2930.0200.3740.3980.3990.3860.3380.3660.4110.4150.4200.3970.4360.3950.2250.3400.3400.3400.3630.3620.3610.3820.3810.3810.010-0.0560.3830.4000.0680.2670.097-0.0150.2410.2570.2430.2580.2550.2430.2690.2500.2360.2560.3670.3012252OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Flynn_20190.3490.3670.3980.4090.4010.4010.1590.3470.4170.4060.3670.3920.4170.0180.0940.1740.2830.3590.3700.3910.3840.3970.3890.4070.3980.4190.4230.3970.4340.4250.4540.4410.1940.3800.3940.3920.4030.4150.4110.4180.4270.4240.133-0.0650.3710.3130.1880.3080.1100.0620.2910.2830.2950.3050.2930.2960.3010.2960.2820.3020.3860.32913294OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Mishra_20160.4870.4610.5410.5530.5430.5430.3850.3990.4720.4900.4910.5470.5560.1780.2910.3720.4610.4730.4290.4850.5010.5040.4940.5280.5320.5100.5400.4790.5150.5570.5580.5500.3380.4960.5100.5050.5250.5340.5280.5520.5580.5520.495-0.1040.4850.5330.1050.3520.0960.0400.4330.4160.4170.4460.4420.4400.4510.4600.4360.4500.4580.4454323OrganismalFitnessHSP82_YEASTMediumEukaryote
HXK4_HUMAN_Gersing_2022_activity0.4890.5160.4770.4900.4840.4920.2290.3870.5110.5270.4460.4640.4910.1830.2920.5100.5170.5080.4760.0510.4530.4490.4420.4210.4490.4650.4520.4480.4020.5030.4920.4500.1790.4720.4260.3700.5030.4820.4460.5130.5080.4930.3160.0320.5010.4780.3560.4390.3940.1680.5070.4990.4880.4970.4980.5090.5020.5110.5080.5130.5200.4818570OrganismalFitnessHXK4_HUMANMediumHuman
HXK4_HUMAN_Gersing_2023_abundance0.3220.3620.3250.3510.3410.3480.0570.3290.3860.4070.3380.3310.3540.1360.1850.3280.3670.3760.3580.4010.2820.2900.3140.3160.2850.3070.3160.3150.3200.3740.2950.2530.1700.3050.3040.3170.3400.3490.3590.3480.3620.3690.1680.0710.3510.3360.3990.3820.3910.1600.3590.3740.3710.3680.3790.3760.3750.3680.3710.3780.4240.3298396ExpressionHXK4_HUMANMediumHuman
I6TAH8_I68A0_Doud_20150.3470.3170.2680.2640.3640.361-0.0050.2970.2990.3360.0130.0180.0140.0180.0250.0100.0200.0130.0940.2120.3080.3280.3740.3770.0040.0110.0970.0020.3020.3680.2500.2530.1710.3040.3320.3370.3290.3540.3480.3830.3990.4010.0070.0200.0160.0190.2090.2090.2310.1000.0750.1140.1260.1350.1110.1060.0890.0980.0670.1090.0560.0189462OrganismalFitnessI6TAH8_I68A0MediumVirus
IF1_ECOLI_Kelsic_20160.3280.4990.5410.5390.5270.5370.1770.3870.2610.2550.5340.5400.5650.1930.4770.5500.5990.5560.5150.4900.3610.4380.3660.4110.4510.4580.4820.4430.4590.4010.5200.4630.2380.4580.4770.5270.4630.4700.4950.5290.5320.5390.3710.1500.5620.5160.3210.5180.4750.2440.5450.5340.5550.5750.5770.5590.5510.5710.5600.5830.6160.5061367OrganismalFitnessIF1_ECOLIHighProkaryote
ILF3_HUMAN_Tsuboyama_2023_2L330.2330.3360.4440.4380.4370.4360.0220.3150.3710.4160.3530.3520.3500.1920.3240.4350.3180.2820.1670.4800.2060.2140.2570.3120.3470.2330.3720.2550.2590.4570.3490.3130.0970.1270.2820.3620.2820.3490.4010.3820.3890.4450.2790.0450.3390.3950.4180.3680.4750.3420.3040.2740.3430.3300.3700.3950.3300.3120.4140.3700.3190.3491329StabilityILF3_HUMANHighHuman
ISDH_STAAW_Tsuboyama_2023_2LHR0.0940.1010.2060.1970.2310.2310.3260.1280.3260.3560.4530.4130.4340.3770.3940.4490.4290.4790.4500.1850.3260.1990.2180.2280.3180.3350.3290.2980.3600.3040.3960.3340.2360.2910.2490.2980.2590.2190.2450.2630.2210.2440.3000.2480.3490.3290.5920.5260.5190.4900.4200.4070.4290.4280.4270.4290.4090.4140.4190.4260.6080.5221944StabilityISDH_STAAWHighProkaryote
KCNE1_HUMAN_Muhammad_2023_expression0.1300.1160.0930.1280.1240.1030.2090.0240.0410.0530.1870.1590.1340.1630.1770.1290.1060.0620.0730.1160.1810.1830.0920.0390.1910.1110.1080.0550.0460.0960.0890.057-0.1700.1470.2250.0970.1380.1520.1150.1380.1410.1210.1990.1660.0930.1940.1790.1160.1390.0860.0880.0870.0910.0710.0670.0650.1050.1000.0820.0860.1100.1932339ExpressionKCNE1_HUMANMediumHuman
KCNE1_HUMAN_Muhammad_2023_function0.4170.4980.4790.4960.4790.5140.2150.5200.5140.5200.3680.4740.5840.2090.1620.5990.5250.4650.3800.2410.2720.2930.4480.5390.2850.6140.5800.6030.5750.4880.5680.475-0.0340.1890.3760.6160.4640.5000.6180.5240.5510.6340.2120.2080.5280.1980.2020.4250.1820.0930.5400.5400.5230.5280.5370.5130.5480.5450.5480.5540.6420.2382315ActivityKCNE1_HUMANMediumHuman
KCNH2_HUMAN_Kozek_20200.4490.4210.2980.2920.2110.2100.4540.1510.3030.3060.3070.2160.2340.2530.1970.2290.2800.2580.2480.3820.4680.5130.4950.4630.5020.4910.4550.4770.4810.4600.4330.3180.3440.4710.5250.5110.4990.5540.5380.4720.5170.5090.3940.2230.1930.2910.219-0.079-0.2020.0860.5300.3900.4810.4350.4440.4420.4760.4410.4350.4750.2710.411200ActivityKCNH2_HUMANMediumHuman
KCNJ2_MOUSE_Coyote-Maestas_2022_function0.2560.3290.3340.3440.3470.3530.0590.2380.3150.3090.3700.3480.3610.0580.2890.3790.3820.3850.3880.1610.3450.3050.2520.2410.3510.3350.3160.3450.2240.3720.3660.3520.1480.3270.2940.2350.3500.3290.2890.3640.3540.3350.213-0.0220.3280.3430.1890.2860.2270.0840.3740.3550.3650.3650.3690.3610.3710.3650.3650.3730.3710.2666963ActivityKCNJ2_MOUSEMediumEukaryote
KCNJ2_MOUSE_Coyote-Maestas_2022_surface0.3810.3410.2540.2650.3380.3400.1190.2240.2800.2770.3230.2870.2970.1760.3600.3700.3520.3430.3540.2180.2390.2280.2170.2450.2650.2650.2600.2730.2510.3680.2840.2550.1740.2430.2520.2350.3240.3310.3300.3280.3380.3400.2660.0650.2630.3090.3050.2620.3060.1670.3220.3110.3300.3200.3390.3210.3430.3210.3170.3340.3510.3496917ExpressionKCNJ2_MOUSEMediumEukaryote
KKA2_KLEPN_Melnikov_20140.2500.5300.4370.6220.5990.6030.1990.4230.5380.6060.5660.5970.6210.2120.2610.4870.6010.6450.6620.4980.2850.4200.5180.5400.3530.5780.5770.5680.6380.6270.6370.5860.1480.2430.4850.5860.4450.5240.5880.5860.6090.6300.2530.0760.5590.3860.4560.5820.5510.2670.5870.5720.5880.5780.5790.5900.5740.5830.5910.5990.6410.3294960OrganismalFitnessKKA2_KLEPNHighProkaryote
LGK_LIPST_Klesmith_20150.2800.4260.4850.4940.4810.4980.1040.4650.4670.5590.4440.5370.5640.1790.3270.3820.5150.5800.5780.4990.2760.3960.4620.4950.3650.4920.5250.4790.5520.5420.5370.4600.1390.3070.3980.5550.3700.4040.5270.4820.4950.5400.1760.0510.4830.3670.3980.5070.4920.1450.5100.5050.5110.5100.5130.5200.5180.5170.5200.5270.4110.3347890ActivityLGK_LIPSTMediumEukaryote
LYAM1_HUMAN_Elazar_20160.3140.3030.1890.1580.2580.2660.3610.1720.4130.4160.4360.3700.3710.3070.3630.3360.3100.4260.4540.3360.3820.3910.3860.3850.3620.3690.3730.2980.3390.3490.3130.1790.2030.3530.4370.3430.3620.4100.3480.3430.3720.3130.3070.3460.3180.3500.1700.2590.1630.1030.2350.3170.3980.3300.2790.2950.3520.3480.2720.3320.4190.390359ExpressionLYAM1_HUMANMediumHuman
MAFG_MOUSE_Tsuboyama_2023_1K1V0.6130.6360.6330.6350.6210.6230.4050.6430.6330.6260.6190.3550.6000.4530.4290.4710.4800.5510.4130.6270.4480.4940.5220.5660.4600.5150.4470.5360.4900.6020.6210.6090.3920.3290.5650.5090.5890.6470.6570.6290.6610.6830.3120.0790.4250.2460.5960.3270.6380.6110.6460.6150.6170.6380.6230.6380.6360.6420.6510.6390.7190.7271429StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV0.5510.7230.7080.7270.7580.763-0.0580.5720.6580.7440.7270.4220.512-0.1460.0250.7420.6990.6850.7400.715-0.0320.0580.5760.6150.1930.5610.5370.5680.7140.7480.7290.6700.649-0.2820.0630.0330.5860.6580.6340.7290.7510.742-0.064-0.2030.6290.4330.5210.6600.7730.5760.7930.7600.7920.7450.7580.7410.7630.7760.7740.7800.8130.7202116StabilityMBD11_ARATHMediumEukaryote
MET_HUMAN_Estevam_20230.4540.5440.5380.5630.5820.5840.5170.5200.5500.5620.5850.5500.5620.4520.5150.5420.5900.5960.5990.5830.5260.5060.4660.4920.5300.5190.5190.4880.4540.5480.5700.5300.2860.4710.5010.5250.5120.5390.5630.5580.5650.5730.5420.3470.5710.5750.3500.4200.4980.1920.5400.5480.5480.5500.5620.5540.5570.5610.5560.5660.5750.5295393ActivityMET_HUMANMediumHuman
MK01_HUMAN_Brenan_20160.1760.1980.2370.2410.2230.2270.2090.1630.1980.1840.0330.1670.1820.1700.1990.1950.1780.1890.1410.1840.2090.1170.0690.0160.1830.0890.0570.076-0.0670.2180.1760.2220.1060.1690.0580.0040.1930.1190.0930.2270.2050.2020.1960.1250.1350.1840.059-0.0000.1330.0060.1610.1620.1480.1660.1700.1630.1630.1690.1770.1710.1500.1866809OrganismalFitnessMK01_HUMANMediumHuman
MLAC_ECOLI_MacRae_20230.2250.3520.4190.4240.4090.411-0.0140.3600.4120.4190.3990.4160.432-0.0110.2360.2730.3820.3820.4120.3870.0780.3660.3800.4020.3130.3830.3920.3820.4240.4000.4400.4100.0390.1310.3150.2960.2650.3270.3150.3740.3960.3950.177-0.0360.3290.2350.0060.1930.123-0.0260.3190.2800.2880.2930.3150.3080.2950.3050.3320.3130.2300.2034007OrganismalFitnessMLAC_ECOLIMediumProkaryote
MSH2_HUMAN_Jia_20200.3520.3990.3670.3760.3830.3900.2040.3390.3950.4020.3490.3800.4000.2130.3490.3970.3390.2960.2040.3640.2960.3100.2640.2570.3260.3170.3200.3260.3010.3940.3510.3240.2080.2770.3510.2810.3500.3880.3460.3870.4050.3830.2840.1030.4000.3550.2930.3630.0620.0990.3050.2790.2910.3090.3170.3000.2950.3080.3390.3210.3940.36616749OrganismalFitnessMSH2_HUMANMediumHuman
MTH3_HAEAE_RockahShmuel_20150.3710.6120.7090.7180.6960.7040.3140.6440.6730.6880.5810.6920.7080.1900.3520.4050.5270.6080.6440.6580.3110.4540.6000.6620.4880.6630.7110.6840.7270.6790.6970.6520.2910.3530.4950.6650.4220.4960.6380.6570.6790.7140.324-0.0310.4770.3410.4490.5740.5830.2250.5440.5310.5590.5690.5610.5730.5510.5710.5570.5720.5800.3781777OrganismalFitnessMTH3_HAEAEMediumProkaryote
MTHR_HUMAN_Weile_20210.2500.2710.2490.2570.2580.2570.2050.1940.2920.2930.3870.2990.3200.1690.4140.4820.3480.2760.2850.2530.4430.1620.2170.2650.4370.2390.2640.2450.2950.2960.2200.1890.1430.4510.3070.2120.3780.3150.2540.3630.3090.2690.2780.1110.3790.4110.2390.3380.3770.0920.3240.2890.2990.2970.3050.3160.3350.3190.3470.3250.3320.37612464OrganismalFitnessMTHR_HUMANLowHuman
MYO3_YEAST_Tsuboyama_2023_2BTT0.1430.2480.3340.3620.3810.4120.1550.2510.4280.4190.5190.4820.4780.1710.4110.5830.5880.5070.2470.3210.3510.2470.3360.3110.3420.2870.3640.3130.3360.2830.2790.1780.1260.4190.3320.2820.3820.3290.2920.4110.4010.3820.1120.0450.4310.2940.3920.4270.4790.3590.5170.4950.5450.5120.5250.5290.5130.5190.5060.5250.5710.5003297StabilityMYO3_YEASTHighEukaryote
NCAP_I34A1_Doud_20150.3640.3280.3340.3330.3630.3640.0020.3350.3730.3480.0150.0190.0200.0230.0260.0200.0280.0310.1070.2690.3520.3820.4080.4130.0180.0420.1080.0300.3520.3770.2700.2790.1260.3560.3730.4150.3900.4020.4240.4250.4260.4410.0190.0150.0270.0250.2590.2700.2780.1230.1290.1710.1660.1840.1720.1720.1550.1630.1290.1710.1350.0819462OrganismalFitnessNCAP_I34A1MediumVirus
NKX31_HUMAN_Tsuboyama_2023_2L9R0.4280.5480.5860.5840.6070.6190.5820.5530.5840.6080.5600.5680.5720.6270.6380.6740.6510.5820.6020.5580.5640.5570.5560.5640.5760.5790.6060.5400.5770.6400.5250.5060.5510.4940.5650.5770.5780.6240.6280.6010.6180.6210.4540.3950.3420.4080.5890.4010.6430.6420.6340.6260.6390.6480.6690.6410.6350.6290.6420.6450.5660.5962482StabilityNKX31_HUMANHighHuman
NPC1_HUMAN_Erwood_2022_HEK293T0.6830.7000.6270.6320.7000.7000.2200.5290.7390.7340.7010.3250.4580.2210.3940.6140.6940.7210.7190.1470.1970.4600.4120.4190.4210.4910.6310.4470.4950.7100.6590.5480.0950.2310.5470.5790.6390.6750.6960.6910.7080.7220.3000.1340.7020.5680.4990.6830.0600.2110.6810.6640.6870.6790.6780.6640.6930.7010.6850.6970.7000.392637ActivityNPC1_HUMANLowHuman
NPC1_HUMAN_Erwood_2022_RPE10.7790.6880.5340.5540.7160.7310.4170.5350.5840.7070.7040.2980.3570.3140.3940.6290.6610.6400.7090.4050.6310.4900.5430.4170.4080.4680.5360.3730.5310.6330.7400.5680.4750.4660.3770.6120.7870.6720.7740.7830.7000.7510.5060.4420.7860.3910.4720.557-0.1070.1570.5740.5370.5590.4820.5690.5600.5710.6460.6400.5950.6920.48263ActivityNPC1_HUMANLowHuman
NRAM_I33A0_Jiang_20160.5690.5650.5010.4900.5840.5840.0350.3900.6250.638-0.0760.1620.448-0.075-0.0980.0050.1610.5410.5750.3430.5830.6330.5840.5710.0470.5300.6270.4620.6540.6350.4040.441-0.1690.5120.5320.5510.5920.6150.6210.6280.6380.632-0.105-0.127-0.111-0.1110.4220.4140.4480.1780.1900.2280.2350.2970.2530.2900.2630.2470.1990.2540.2920.145298OrganismalFitnessNRAM_I33A0LowVirus
NUD15_HUMAN_Suiter_20200.2710.4530.5640.5960.5910.594-0.0050.3890.6020.6650.6030.6000.6450.2990.4190.4600.5260.5830.5990.5010.3010.4540.5460.5180.4120.5790.5770.5490.5420.5870.6230.5530.1470.3600.4230.5750.4330.4560.6040.5870.5860.6350.3860.0150.6030.4290.4830.5810.5340.3170.5500.5210.5440.5680.5560.5710.5670.5590.5560.5730.6790.5642844ExpressionNUD15_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL0.3920.5890.5800.5710.6020.6130.2850.4650.6380.5990.5600.3230.3460.3220.3900.4050.4940.5320.5330.5600.3940.5490.5720.5890.3250.4970.4400.5250.5720.6380.6700.7060.1830.3650.4190.4250.5050.4930.5080.6280.6010.6220.3750.2460.3810.3570.6820.6410.7300.6580.7090.6950.6990.7210.7260.7410.7160.7160.7190.7230.6850.5752028StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI60.4050.4040.4170.4310.4510.4340.2300.4230.4850.4610.3980.3610.4210.3890.5610.5100.5180.5250.4770.5210.2640.3390.3150.3470.3800.3850.4610.3340.3780.5020.3940.3870.3210.2690.2750.3720.3950.3990.4260.4130.4170.4240.4450.1440.3950.5310.6760.5600.7590.6400.4870.4570.4540.5070.4870.4850.4850.4860.4830.4880.4100.5881380StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C0.5400.6970.7590.7770.7450.7570.3840.4550.7510.7630.6910.7350.7590.3920.7790.8190.7970.7860.7550.7230.6880.6890.6630.6870.5890.6670.6940.7260.6040.7350.6940.6790.5490.2620.4610.5750.6870.6950.7040.7550.7600.7640.5430.1150.5070.6330.6300.5400.7820.7470.7620.7600.7610.7550.7720.7600.7730.7670.7760.7710.8250.7723197StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G-0.211-0.1700.3390.1880.3320.332-0.1490.0930.1750.1900.064-0.100-0.115-0.069-0.1510.1400.2210.0890.2100.2330.1890.1740.1590.1770.1710.1950.1660.1920.2330.3270.0620.0230.245-0.0500.0250.0120.1900.1810.1780.2710.2600.2900.268-0.0700.3390.3270.4520.4240.4370.3220.1180.0750.0850.1290.0730.1270.1140.1160.0940.1070.2860.1711134StabilityODP2_GEOSEHighProkaryote
OPSD_HUMAN_Wan_20190.1700.4330.4450.5170.4630.4660.3470.5690.5630.6000.3970.4990.5600.2900.5080.5180.5520.4490.4820.5550.5280.5490.5380.5780.5520.5940.5980.5770.5700.5110.4220.3260.1700.5030.5300.4850.5370.5320.5030.4850.4820.4880.3860.0800.4930.4720.6320.5180.6320.2240.4020.3530.4120.4210.4820.4870.4180.4210.4900.4720.5820.522165ExpressionOPSD_HUMANHighHuman
OTC_HUMAN_Lo_20230.5240.5640.4840.5220.5500.5520.1270.4040.5900.5920.5460.5730.5810.1420.4110.5120.5310.5100.5340.5650.4530.4610.5220.5450.4760.5550.5260.5400.5740.5990.4750.4090.0850.4290.5110.5690.5220.5700.6140.5670.5840.5990.2790.0920.5240.4630.6490.6060.6740.3640.5260.5470.5440.5740.5560.5670.5600.5480.5410.5640.6160.4901570ActivityOTC_HUMANMediumHuman
OTU7A_HUMAN_Tsuboyama_2023_2L2D0.1160.2430.1840.2010.2240.2270.1920.1340.1410.1460.4340.6040.5810.2050.5630.5880.3190.3620.4080.1550.1840.1780.2210.2750.3450.2600.3800.3070.1480.2590.4060.3880.1320.1680.2250.3490.2010.1980.3080.2420.2200.2620.3650.1840.5430.5480.5320.5330.6100.5370.3950.3420.3480.4010.4220.4210.3740.4090.4140.4040.6420.632635StabilityOTU7A_HUMANHighHuman
OXDA_RHOTO_Vanella_2023_activity0.1040.2920.3640.3730.3560.3560.1950.2510.2780.2850.3830.3480.3590.1220.2190.3260.3700.3900.4010.2590.1250.2140.3030.3050.2190.3430.3560.3290.4070.3780.4290.4080.1020.1640.2130.2930.2670.2750.3050.3420.3440.3550.1530.0140.3690.2470.2950.3860.3610.1390.3670.3600.3550.3610.3650.3740.3670.3760.3580.3770.3960.2426396ActivityOXDA_RHOTOHighEukaryote
OXDA_RHOTO_Vanella_2023_expression0.1800.2530.2900.2870.2760.2720.1900.1930.2960.3050.3270.3100.3110.2210.2570.2980.3150.3510.3400.1750.1690.2430.2520.2330.2170.2640.2950.2750.3100.3080.2890.2410.0820.2270.2110.2460.2680.2590.2670.2840.2780.2780.2430.1440.3040.2340.3850.3560.3330.1420.3290.3180.3080.3170.3230.3080.3210.3160.3110.3260.4080.3216769ExpressionOXDA_RHOTOHighEukaryote
P53_HUMAN_Giacomelli_2018_Null_Etoposide0.4150.4240.3290.3440.4320.427-0.0880.3190.2830.2910.4670.4570.509-0.154-0.1460.3140.4320.4690.4970.1160.3120.4380.4530.4090.3900.4720.4840.4930.3680.4230.4650.4100.2080.2580.4490.3550.4030.4620.4000.4270.4690.413-0.151-0.1550.475-0.0510.3890.4710.4140.1860.4490.4500.4510.4500.4520.4700.4480.4540.4520.4640.4770.1867467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_Null_Nutlin0.3500.3950.2940.3040.3830.380-0.0870.2690.2870.2890.4820.4470.498-0.158-0.1450.2900.4040.4310.4590.1260.2940.4470.4630.4300.3750.4870.5000.5230.3290.4200.4500.3750.2130.2390.4530.3600.3540.4310.3790.3720.4300.383-0.144-0.1530.471-0.0630.4030.4800.4260.1880.4230.4270.4270.4330.4310.4400.4260.4300.4340.4400.4690.1827467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_WT_Nutlin0.4450.4620.3420.3660.4790.481-0.1430.3000.2580.2590.5510.4960.554-0.194-0.1740.3620.4000.4460.4670.0280.4100.5630.5210.4800.4820.5350.5500.6160.2850.5070.4930.4490.2250.3260.6120.4080.4480.5680.4330.4750.5670.438-0.185-0.2020.544-0.0800.4520.5530.4610.2230.4380.4500.4490.4520.4610.4710.4370.4610.4380.4630.4820.1667467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Kotler_20180.6290.5880.4960.5560.5550.5760.1090.4980.5730.5850.6000.5380.6220.0950.1080.6280.6780.6980.7020.4320.4290.4610.4780.4630.4700.4800.4650.4700.4930.5930.6310.5850.0390.3980.4680.4200.6110.5840.5640.6040.5880.5810.0900.0400.6380.2210.4670.5830.4790.2520.6380.6590.6580.6420.6450.6550.6730.6580.6630.6720.7050.3961048OrganismalFitnessP53_HUMANLowHuman
P84126_THETH_Chan_20170.5080.5800.6030.6170.5640.5780.3640.5260.6440.6230.5690.5520.5880.3190.5510.5590.6050.5960.5710.6180.4190.5070.4780.5580.5210.5840.5890.5710.6530.5220.5530.4690.3590.4740.4990.5360.5150.5260.5470.5600.5680.5810.4650.0320.5430.5100.3370.4520.5120.1250.5430.5270.4870.5490.5510.5190.5260.5530.5610.5620.6220.6101519OrganismalFitnessP84126_THETHMediumProkaryote
PA_I34A1_Wu_20150.5180.5190.4990.5080.5390.5430.0410.3580.1520.1650.0370.0540.1010.0240.0290.0250.0380.0410.3560.3250.4560.4930.5330.5380.2190.4080.4440.4290.4380.5840.3840.3740.1070.4360.4750.5410.5460.5610.5720.5880.5920.5840.0280.0190.0310.0200.2440.2320.1720.0950.1540.1730.1840.1870.1680.1780.1580.1730.1250.1810.2020.1321820OrganismalFitnessPA_I34A1MediumVirus
PABP_YEAST_Melamed_20130.6630.6170.5410.5500.6540.6480.4740.5690.6370.6630.6880.6620.6780.4760.5660.6480.7160.6840.6950.5410.6380.6650.6660.6920.6380.6980.7000.6760.6660.6740.7030.6350.2610.6380.6480.6400.6890.6920.6880.6830.6870.6840.605-0.0270.6700.6540.3610.6120.5140.1970.6640.6600.6720.6930.6850.6890.6910.6900.7180.7050.7150.62237708OrganismalFitnessPABP_YEASTMediumEukaryote
PAI1_HUMAN_Huttinger_20210.4030.3950.3770.3920.4020.4130.0540.3140.4280.4380.4350.4080.4300.0830.3910.4340.4480.3350.2700.4220.1320.3810.3640.3450.3510.3950.3990.3720.3300.4230.3930.3560.1410.1710.3700.3790.3840.4040.4120.4140.4240.4260.3500.0490.4300.4070.3790.4500.4040.1520.4410.4370.4390.4480.4480.4450.4490.4490.4460.4570.4680.3915345ActivityPAI1_HUMANNaNHuman
PHOT_CHLRE_Chen_20230.2110.4400.7310.7060.3520.3350.6540.5520.7110.7210.6470.7570.7830.7680.8230.7140.7490.7140.7580.6520.6850.6780.5960.6990.5330.6160.5960.6560.6020.5870.5720.4900.3030.5650.5110.6020.5970.5640.6060.4160.4450.4220.6230.3950.5990.6210.2000.5020.6690.4100.6030.5430.5530.5510.5550.5700.5680.5740.5830.5690.7370.775167529ActivityPHOT_CHLREHighEukaryote
PIN1_HUMAN_Tsuboyama_2023_1I6C0.2220.3270.6800.6450.6310.6610.5850.5000.6700.7070.6540.5760.6740.6600.6460.6650.6700.5200.5480.6560.4560.6710.6300.6100.5590.6720.6810.6470.6580.6890.6920.6440.5310.5390.6260.6670.6070.6710.7200.6770.7050.7300.556-0.1890.6230.5100.5470.5740.6910.6690.6590.6270.4980.5630.5750.6040.6080.6490.6260.6200.7400.742802StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M0.5790.5200.5480.5550.5490.5530.5060.4700.5380.5610.4670.4460.4360.5570.6520.6750.6260.5860.5070.5420.5130.4540.4770.4500.5470.4930.4650.4280.4680.5360.4030.3860.4500.5580.5080.4810.5960.5480.5380.5730.5370.5380.4680.4120.2820.3330.4530.2950.6930.6300.5720.5790.5720.5900.6020.6090.5930.5940.5980.5980.4700.5931824StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF0.1790.1880.2180.2340.2490.2540.1980.1870.1650.2060.2530.2780.3100.3140.3470.4640.2980.2360.2380.2770.3100.2890.2780.3250.3000.2910.3100.2920.2950.2900.3260.3170.0670.3350.2780.3110.2620.2850.3080.2880.2800.2930.2660.2790.2600.3000.5120.3790.4930.4690.2580.3030.3170.2600.2840.2870.2860.2990.3140.2990.3240.4641301StabilityPKN1_HUMANHighHuman
POLG_CXB3N_Mattenberger_20210.4230.3900.3770.4110.4600.473-0.0360.3360.5000.4990.292-0.0590.042-0.079-0.0560.1770.3950.4040.4260.3560.3390.3900.3810.3770.1380.3880.3830.3690.3930.4950.3860.3190.0070.0490.2740.3550.3420.3850.4130.3870.4300.458-0.065-0.0660.342-0.0540.1930.3390.1130.0460.3420.3610.3640.3690.3640.3650.3650.3650.3740.3740.1100.06815711OrganismalFitnessPOLG_CXB3NMediumVirus
POLG_DEN26_Suphatrakul_20230.5000.5780.2890.2860.5320.533-0.0440.4340.6700.6750.318-0.0060.000-0.0440.0070.0770.1530.2750.3660.4090.4530.4730.4490.4560.4700.5010.4930.4880.4610.6220.6030.5220.035-0.0640.1150.4710.4320.3830.5420.4690.3970.563-0.055-0.0590.391-0.0050.3600.5100.1010.1160.2410.2290.2440.2600.2270.2610.2400.2460.2230.2540.1610.03216897OrganismalFitnessPOLG_DEN26LowVirus
POLG_HCVJF_Qi_20140.6050.5470.4100.4130.6050.614-0.0390.1960.5650.5770.1780.6370.6350.1010.1280.1140.1160.0900.0780.2600.4000.4430.4520.4920.4220.4750.3200.4100.5170.6300.5870.4850.1820.4740.5050.5220.5150.5470.5780.4800.5280.5600.1140.0530.5060.109-0.0370.3490.6430.3500.3080.3780.3440.3790.3420.2830.3230.3180.3140.3720.1730.1591630OrganismalFitnessPOLG_HCVJFMediumVirus
POLG_PESV_Tsuboyama_2023_2MXD0.2510.4550.3730.4070.4710.4630.0700.4200.3900.5140.3750.0620.1570.1320.1400.0310.1600.1140.0840.5920.0790.0580.0160.1570.106-0.0590.008-0.0840.1700.5070.5750.5800.1310.003-0.0330.0530.3820.3830.3840.4530.4630.4520.006-0.0370.0330.0260.4480.4430.6500.5760.6580.6640.6150.7360.7050.7180.6920.7310.7180.7180.7160.6865130StabilityPOLG_PESVMediumVirus
PPARG_HUMAN_Majithia_20160.3530.5390.6290.6550.6120.6320.1870.3390.5570.5710.6230.6960.6950.0280.1260.4560.5940.7360.7680.7170.6750.6920.2500.2880.5920.7220.6920.7190.2990.7120.5830.5090.3840.7090.6830.5870.6730.7000.6370.6980.7060.6800.2660.0300.5850.4500.5940.6070.6310.3020.5800.5880.5930.5750.5990.5830.6020.5980.5850.6030.6570.4999576ActivityPPARG_HUMANMediumHuman
PPM1D_HUMAN_Miller_20220.5500.5230.5310.5360.6090.6100.0350.3780.4600.5170.5780.5990.6180.2810.3860.4700.6020.6280.6180.3870.4500.5260.5370.4300.5160.5770.5670.5570.4190.6010.5940.5460.2340.4280.5280.5370.5820.5930.5910.6150.6140.6160.361-0.0500.5750.5150.5030.5790.5630.2360.5740.5750.5700.5860.5930.5800.5840.5850.5900.5950.6330.5097889OrganismalFitnessPPM1D_HUMANLowHuman
PR40A_HUMAN_Tsuboyama_2023_1UZC0.5620.6550.7450.7360.7720.7740.5180.4930.7740.7830.7270.5920.6810.5400.5350.8210.8050.7400.7270.7150.5520.6940.7000.7250.6340.7160.7330.7230.7440.7950.7770.7560.6370.5930.5560.6110.7350.7460.7380.7640.7840.7750.4380.2810.3260.4310.6390.4230.7940.7830.7920.8090.8040.8120.8280.8230.8190.8160.8160.8210.8010.8352033StabilityPR40A_HUMANMediumHuman
PRKN_HUMAN_Clausen_20230.6050.6190.6000.5860.6220.6140.1820.4590.5250.5400.5820.6080.6380.2330.2920.3630.5010.6580.6630.4470.2810.5570.6090.5760.4470.6320.6230.6230.5200.6220.5680.5260.2660.2410.5500.5930.5690.6210.6400.6110.6380.6540.3090.0930.5910.3520.6490.6180.6960.2980.5420.5550.5610.5830.5670.5710.5790.5730.5610.5830.6720.5198756ExpressionPRKN_HUMANLowHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE0.5720.5760.5660.5660.5760.5720.3000.4420.4910.4970.6920.6010.6150.4980.5700.6780.7080.6270.5830.4770.3040.2420.4670.3910.4620.492-0.2020.4710.5290.5530.5580.5050.0980.3280.4090.4300.5460.5430.4980.5860.5850.5410.4220.2370.6350.4820.5860.6260.6990.6430.7000.6840.6870.6850.6860.6920.6810.6870.6890.6990.6820.6741579StabilityPSAE_PICP2MediumProkaryote
PTEN_HUMAN_Matreyek_20210.3720.3930.3680.3900.3970.4080.1530.3580.4260.4710.4490.4090.4540.1650.2740.4620.4650.2650.2900.4770.1910.4600.4080.3910.2700.3380.3230.3770.2940.4790.3900.3880.1270.2750.4270.3430.3830.4570.4120.4190.4630.4470.2420.0270.4420.3470.4820.4730.4820.2290.4460.4280.4610.4410.4400.4510.4560.4550.4470.4600.4990.4545083ExpressionPTEN_HUMANMediumHuman
PTEN_HUMAN_Mighell_20180.4800.5040.5110.5160.5360.5400.1770.3470.5040.5110.4750.4690.4990.2060.4070.5460.5190.3080.2910.5140.3350.4130.3280.2790.4100.2910.2780.3170.2280.5250.4850.4750.0580.3740.4040.2990.4990.4740.4180.5320.5270.5310.371-0.0090.5100.4840.4210.4160.4800.2020.4880.4590.4880.4950.5110.5050.4950.4950.5090.5100.5390.5107260ActivityPTEN_HUMANMediumHuman
Q2N0S5_9HIV1_Haddox_20180.4930.3790.3520.3930.4950.5020.0030.4370.5100.5150.4700.5090.537-0.005-0.003-0.0020.0440.0930.1680.4030.5180.4030.3900.3370.5170.4010.3940.4360.3540.5070.4780.4830.2910.4940.4190.4060.5200.5020.5010.5260.5150.5130.413-0.0160.5020.4560.3960.4630.2630.1450.2260.2660.3070.3010.2500.2570.2600.2450.2230.2800.2320.11612729OrganismalFitnessQ2N0S5_9HIV1MediumVirus
Q53Z42_HUMAN_McShan_2019_binding-TAPBPR0.3560.3410.3480.3530.3580.3550.0810.2450.3140.3240.2350.3010.3250.1920.1860.3640.3300.2870.3470.3290.2490.2670.2570.2150.2920.2970.2680.3060.2990.3030.3710.3410.0980.2380.2680.1820.3110.3340.2940.3520.3650.3480.4020.2020.3210.3280.3110.3410.3380.1920.2910.3020.3330.3340.3210.3210.3160.3120.3120.3250.3300.4653344BindingQ53Z42_HUMANMediumHuman
Q53Z42_HUMAN_McShan_2019_expression0.5240.4850.5260.5380.5500.548-0.0620.4150.5410.5560.4130.4670.5070.0880.1060.4890.5540.5500.5580.5460.4150.4700.4590.4480.4760.5140.4920.5110.5340.5330.5530.5050.1830.4170.4400.4050.5200.5380.5330.5550.5640.5630.3080.0930.5260.4830.4540.4900.4580.2070.5330.5460.5550.5510.5650.5560.5570.5600.5640.5700.5130.6223344ExpressionQ53Z42_HUMANMediumHuman
Q59976_STRSQ_Romero_20150.4750.5930.6340.6430.6540.6620.3630.5350.6720.6790.5880.5190.5430.1440.4480.5160.5720.5600.5700.6490.5980.6510.6520.6630.6220.6620.6770.6730.6800.6840.6330.5750.3040.6060.6520.6340.6160.6530.6570.6660.6720.6750.4900.0080.5870.5240.4080.5330.5330.1550.5600.5370.5330.5560.5570.5420.5620.5450.5530.5630.6340.5402999ActivityQ59976_STRSQMediumProkaryote
Q6WV13_9MAXI_Somermeyer_20220.3270.3930.2600.2640.3330.334-0.0310.1150.4130.4070.2420.0360.0220.0140.0460.0110.0100.010-0.0220.2940.0130.0450.0030.1020.004-0.0020.030-0.005-0.0130.4260.3440.3540.0430.0310.0220.0140.2480.2460.2450.3300.3270.3240.014-0.015-0.0040.0190.1610.1560.2990.1690.2280.2480.2370.2870.2670.2720.2610.2630.2640.2610.0720.03131401ActivityQ6WV12_9MAXILowEukaryote
Q837P4_ENTFA_Meier_20230.4510.4650.4890.5070.5030.5170.4540.4040.4790.4950.5630.5450.5660.4200.4750.4920.5150.5450.523-0.0190.5210.4450.4410.4620.5520.5350.5410.4490.5210.5240.5270.4770.1900.4910.5160.4540.4910.5380.5120.5400.5490.5440.4580.3700.5450.4900.2310.4540.3730.1140.5110.4880.4990.5110.5220.5290.5360.4910.5240.5310.5890.483697ActivityQ837P4_ENTFAMediumProkaryote
Q837P5_ENTFA_Meier_20230.1960.4160.4210.4590.3900.3950.2220.2630.2820.2940.3260.3530.3420.1290.2400.3340.3740.4360.3920.3210.3560.3990.4350.4100.3190.4080.4280.5110.4550.3360.3780.3520.1860.3990.4270.5100.3420.3910.4770.4170.4280.4570.2740.1860.3560.2710.3530.2960.3910.0980.3260.3440.3590.3130.3830.3480.3180.3530.3200.3550.3410.319747ActivityQ837P5_ENTFAMediumProkaryote
Q8WTC7_9CNID_Somermeyer_20220.2860.3680.2170.2150.3100.3050.0250.3030.3160.3220.223-0.009-0.018-0.027-0.035-0.023-0.025-0.0070.0340.2300.0130.0370.0570.013-0.005-0.0010.0370.2520.2670.3730.2970.307-0.018-0.0260.0090.3030.2310.2370.3180.3040.3090.345-0.013-0.008-0.024-0.0270.0970.1430.2920.1890.2420.2340.2460.2600.2400.2540.2490.2430.2380.2470.137-0.00133510ActivityQ8WTC7_9CNIDLowEukaryote
R1AB_SARS2_Flynn_20220.5770.5610.2120.2270.6000.605-0.0490.292-0.037-0.0370.103-0.030-0.040-0.009-0.0260.0790.1050.4980.5770.2660.2140.2590.2740.2890.2420.2360.2030.2100.2240.5580.5070.408-0.0560.1570.2200.2160.3500.3990.4010.5460.5670.565-0.031-0.0510.073-0.0380.4530.4150.4960.2320.2510.2280.2650.2980.2750.2740.2730.2680.2370.2770.2410.1375725OrganismalFitnessR1AB_SARS2MediumVirus
RAD_ANTMA_Tsuboyama_2023_2CJJ0.2800.1830.2860.2990.3240.3560.5080.3730.6540.6240.5070.4030.4500.4580.6870.7040.4930.4670.5420.4120.5410.5750.5360.4540.5520.4190.5460.4440.4170.5980.4220.3650.2580.4770.6010.4390.4970.5880.4670.4360.4860.4100.5730.2220.1470.3700.4600.2330.6560.5480.5440.5780.5770.5770.6060.5810.5710.5750.5730.5850.3730.582912StabilityRAD_ANTMAHighEukaryote
RAF1_HUMAN_Zinkus-Boltz_20190.4050.4250.3820.3890.4080.4080.0450.3390.4450.4390.4710.4230.4820.0340.2420.4420.4730.4330.3990.3110.2850.3780.3780.3390.3770.3950.3780.3550.3590.4390.4730.4210.1200.2870.3390.3630.3720.3800.3960.4030.4300.4410.2210.0390.4800.3870.2710.3830.2640.2340.4560.4300.4370.4830.4510.4750.4390.4530.4600.4720.4320.160297OrganismalFitnessRAF1_HUMANLowHuman
RASH_HUMAN_Bandaru_20170.4470.4360.4440.4760.4660.4800.3130.3530.4260.4460.3180.3600.4050.4570.5140.4870.4980.4400.3130.5140.4370.4140.4200.3960.4330.4030.4010.3740.3050.4340.3380.3190.1800.3990.4390.3770.4540.4780.4500.4720.4850.4870.4780.3260.3220.4530.3860.2520.4580.2550.4310.4230.4430.4280.4560.4300.4520.4300.4390.4550.3730.4743134ActivityRASH_HUMANHighHuman
RASK_HUMAN_Weng_2022_abundance0.2990.2440.3560.3460.3320.3530.2110.2440.2440.2710.2760.2100.2450.3360.3670.3350.2710.2310.2100.3310.2660.3330.3720.3840.2790.3470.4020.3450.4700.3570.2100.1670.2080.1980.3250.3980.2750.3680.4360.3340.3700.3880.2370.3350.0580.2430.3340.3270.3770.3260.2640.2450.2940.2790.3100.2810.2980.2800.2600.2870.3390.33126012ExpressionRASK_HUMANHighHuman
RASK_HUMAN_Weng_2022_binding-DARPin_K550.4190.4700.6020.6170.5980.5980.2680.3500.6410.6390.5850.5240.5660.5730.6050.6210.6520.5630.4460.6770.5680.5110.5490.4580.5370.5340.4790.3960.3580.5690.5290.4900.2890.5150.5890.4990.5550.6010.5430.6050.6160.5970.5600.2980.4640.5970.2390.2690.5850.2810.5450.5490.5420.5520.5560.5540.5440.5430.5100.5600.6010.57324873BindingRASK_HUMANHighHuman
RBP1_HUMAN_Tsuboyama_2023_2KWH0.1900.1080.3020.3070.3050.3030.2710.2770.2180.2330.4300.4090.4040.3210.3940.4910.5520.3280.3350.2590.3660.1080.2670.2630.2970.0780.1600.2930.2020.3650.3730.3180.2390.2910.3640.3840.3180.3400.3390.3170.3340.3180.3190.2550.4150.4060.4870.5450.5780.4590.4940.4790.4910.4850.4920.4860.4760.4930.4800.4930.5390.5261332StabilityRBP1_HUMANHighHuman
RCD1_ARATH_Tsuboyama_2023_5OAO0.3040.2840.3620.3700.3670.3550.3030.2550.4120.4210.4860.3420.3930.3680.3920.4980.5050.4820.4780.3910.2670.2430.3300.3950.3200.4810.4540.4120.4650.3980.4780.4060.0460.2930.3080.3240.3600.3610.3520.4030.4020.3860.3040.2850.3710.3270.4420.4320.5600.5320.5410.5550.5590.5590.5450.5550.5510.5490.5560.5600.5310.5011261StabilityRCD1_ARATHMediumEukaryote
RCRO_LAMBD_Tsuboyama_2023_1ORC0.2930.5180.5840.5770.5570.5880.1670.2630.5520.5560.5580.3800.4920.2260.4220.4510.5960.5730.6390.5730.1290.1700.2250.175-0.0410.132-0.0030.0870.5770.5900.5690.5370.0960.1780.1270.5670.4400.4500.5800.5680.5980.6210.0280.1390.4710.1870.6350.6460.7820.6900.5920.5800.5600.5850.5740.5830.5680.5690.6000.5840.6460.5412278StabilityRCRO_LAMBDHighVirus
RD23A_HUMAN_Tsuboyama_2023_1IFY0.3300.3330.4660.4660.4790.4790.1960.4340.5120.5170.4570.5500.5920.2540.6750.5330.5130.4640.3900.4620.4220.4220.3950.4530.5450.5130.5100.4770.4940.5100.4140.3990.4290.2880.5310.4750.4840.5680.5400.4820.5370.5360.5120.0740.5560.5480.5020.5480.5820.5050.4700.4540.4670.4490.4780.4750.4750.4740.4780.4810.4820.6311019StabilityRD23A_HUMANHighHuman
RDRP_I33A0_Li_20230.3130.3670.3760.3870.4820.4840.0290.3550.5200.5250.1660.0450.0570.0330.0410.1280.3220.3890.4910.4270.3850.4360.4540.4750.1250.3580.3420.3450.4130.5200.4500.3950.0750.3770.4230.4590.4250.4520.4740.4940.5080.5260.0300.0200.1710.0330.2150.2300.1880.0550.3000.2710.2790.2820.3030.2960.2830.3040.3100.3070.1490.11012003OrganismalFitnessRDRP_I33A0LowVirus
REV_HV1H2_Fernandes_20160.2060.1590.2210.2270.2160.2160.0380.3160.2220.2320.1280.2450.2670.0460.0460.1730.2400.2810.2740.1700.2160.2590.2380.2400.2900.2940.1600.2530.2550.2820.3500.3530.0600.2310.2730.2400.2450.2690.2360.2460.2610.2350.0530.0600.2080.0710.2580.2340.3040.2240.1930.2210.2760.2460.2670.2550.2810.2410.3100.2790.2740.2292147OrganismalFitnessREV_HV1H2MediumVirus
RFAH_ECOLI_Tsuboyama_2023_2LCL0.0640.2100.2120.2330.2300.227-0.0150.2380.2320.2580.2480.1750.200-0.0320.0780.0550.2990.2610.2460.249-0.0670.0590.1210.115-0.0430.1300.1510.1140.1580.2140.2680.213-0.0090.0090.1020.1330.1070.1440.1560.2200.2160.2080.115-0.0750.1930.1070.3040.2480.3840.3280.3310.3100.2910.3330.3140.3000.3140.3090.3270.3190.3180.2201326StabilityRFAH_ECOLIHighProkaryote
RL20_AQUAE_Tsuboyama_2023_1GYZ0.3410.5880.6070.6010.6030.6100.2240.5770.4050.3520.6750.6700.6570.2350.4210.4870.6990.6800.6760.6130.1510.4930.4700.4750.4730.4810.5340.5450.6220.6360.5630.5440.0780.4810.5220.5060.5400.5790.5740.5880.6240.6250.041-0.0480.4670.2830.6840.6400.8160.7690.7160.7150.7200.7260.7130.7180.7230.7240.7320.7280.7380.7101461StabilityRL20_AQUAEHighProkaryote
RL40A_YEAST_Mavor_20160.2830.3700.3720.4170.3800.3990.1220.3720.4410.4480.1920.2850.3150.1040.3970.4820.5180.4460.5270.4300.3820.5120.4800.4220.4930.4330.4330.4300.4000.3190.4060.4080.0440.3870.4510.3680.4070.4560.3980.4390.4690.4110.2860.0710.2820.2940.0680.0910.177-0.0260.4870.4570.4810.4700.4990.4760.4740.4980.4980.5030.3380.3451253OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20130.3130.4020.4180.4700.4300.4540.0860.3670.5280.5280.2180.3070.3670.1220.4230.5340.5990.4790.5560.4880.4240.5530.5490.4670.5370.4900.5030.4870.4600.3910.5230.5380.1390.4370.5220.4270.4590.5260.4490.4940.5330.4670.3570.0760.3170.3190.1060.1160.2530.0260.5690.5290.5540.5320.5660.5580.5420.5820.5800.5830.3810.3911195OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20140.3710.2850.3510.3870.3310.3680.1610.3810.4190.4420.1630.2400.2670.2270.5210.5290.5300.4270.4380.3940.3700.4660.4330.4050.4390.3800.3730.3360.3250.3590.3500.3610.2360.3900.4310.3510.4250.4550.4080.4200.4280.3830.2920.1540.2680.2950.3170.2470.3800.2070.5270.5110.4880.5440.5470.5430.4910.5170.5160.5440.3020.4011380ActivityRL40A_YEASTMediumEukaryote
RNC_ECOLI_Weeks_20230.5440.5920.5700.5840.5940.5950.0580.4230.5910.5910.5900.5790.5930.0640.5570.5880.5960.5950.5920.5860.5540.5700.5090.4990.5570.5840.5830.5860.5710.5940.5780.5390.1870.5380.5400.4310.5810.5870.5450.6170.6160.6060.5290.0600.5880.5670.2990.5240.2810.1770.5410.5300.5200.5530.5440.5330.5480.5430.5440.5540.6080.5334277ActivityRNC_ECOLIMediumProkaryote
RPC1_BP434_Tsuboyama_2023_1R690.6270.6810.6550.6730.6060.6440.6700.5930.6170.6590.7110.7440.7500.7220.7410.7520.7080.6870.6300.6970.6990.7500.7350.6820.7420.7310.7180.7230.6600.7430.6660.6090.6210.6330.7380.7160.7200.7470.7370.6990.6930.6900.7490.6740.7350.7280.6960.6400.7930.7230.7020.6720.6870.7000.6990.6900.7110.7020.7110.7080.7560.7641459StabilityRPC1_BP434HighVirus
RPC1_LAMBD_Li_2019_high-expression0.2880.3980.4840.4930.4500.4470.2400.3610.4570.4700.4420.4610.5140.2900.2890.4010.5490.5200.5090.4340.1760.2900.3510.3860.2230.3490.3230.3050.4670.3830.5350.4720.1100.1490.3380.4570.2780.3620.4640.4160.4490.4910.2970.3140.4180.3280.2500.4070.3760.1760.5440.5200.5140.5320.5470.5550.5330.5310.5380.5530.5230.398351ActivityRPC1_LAMBDHighVirus
RPC1_LAMBD_Li_2019_low-expression0.2460.4120.4750.4980.4620.4550.1650.4660.4500.4680.4880.4660.4810.2530.2630.3630.5230.5790.5910.4080.1950.3380.3800.4150.1820.3660.3210.2990.5620.4470.6350.6110.1190.1350.3220.4630.2350.3230.4540.4200.4470.4980.2690.2710.3570.2800.2620.3750.3930.2660.5200.4880.5100.5190.5030.5300.5220.5110.4940.5280.5180.344351ActivityRPC1_LAMBDHighVirus
RS15_GEOSE_Tsuboyama_2023_1A320.3930.3100.3360.3280.3210.3260.1780.1880.3870.3890.4680.3940.3900.2890.3260.5100.4090.3720.3300.2910.1990.3300.3170.3230.2970.3400.4160.3620.3670.3750.4130.3960.1170.4230.3250.3060.4530.3770.3650.3950.3480.3200.3330.2620.4430.3810.5820.4400.6070.5500.3960.4030.4030.3990.4070.3770.4150.4200.3950.4130.4330.6461195StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance0.5070.5500.5640.5730.5840.5970.3800.5110.6080.6070.5970.6210.6550.4840.5370.5580.6260.5990.5660.0380.4920.5960.6060.5900.5260.6110.6290.6000.5570.5820.5710.4440.3300.5220.5960.5900.5660.6270.6310.6180.6330.6330.5040.2740.5840.5550.4400.5470.5340.1760.5820.5710.5740.5940.6070.5970.5940.5990.5920.6090.6300.5809803ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity0.4500.5170.5570.5700.5530.5660.3470.5080.5620.5570.5530.5720.6140.4480.4800.4770.5750.5470.5250.0300.4880.5790.5700.5610.5260.5840.5970.5670.5330.5780.5960.5080.2990.5040.5770.5680.5270.5900.5910.5860.6040.6010.4580.2380.5610.5010.4080.5270.5030.1690.5420.5200.5140.5390.5600.5420.5420.5590.5520.5600.5420.50010094ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB0.1370.2010.3940.3730.4070.3990.4260.5030.4100.4300.4810.4720.5080.2380.4960.4960.5120.5200.4060.4300.4910.4580.4490.4790.5240.5190.4760.4910.4820.5180.4690.4190.3530.4240.4970.4990.4310.4980.4890.4110.4430.4440.503-0.0420.5450.5310.3360.3320.4430.5020.4540.3480.2340.3670.3620.3950.3920.4450.4260.3870.5750.607965StabilitySAV1_MOUSEHighEukaryote
SBI_STAAM_Tsuboyama_2023_2JVG0.2320.2250.3800.3450.4400.4030.2050.1780.4810.5330.3230.2680.2970.2300.2660.3760.5730.6580.3180.3590.2280.2630.2590.2350.2100.2280.2210.2450.3390.5490.5820.4870.1690.1950.2000.2250.2970.3050.3140.3550.3680.3820.2620.2280.3160.3050.6640.6270.6870.6160.5290.5260.5610.5500.5450.5470.5690.5650.5580.5610.6830.5711025StabilitySBI_STAAMMediumProkaryote
SC6A4_HUMAN_Young_20210.3870.4560.4230.4330.5040.5220.3520.5070.5620.5740.5450.5310.5420.1590.3270.5020.5430.5480.5240.5550.4980.5110.5000.4960.5120.5200.5180.5250.5110.5410.4630.3600.3420.5130.5120.4910.5300.5420.5300.5490.5550.5450.4890.1120.5490.5310.4670.5270.5010.1820.5160.5260.5350.5430.5450.5360.5360.5380.5260.5460.5780.52811576ActivitySC6A4_HUMANMediumHuman
SCIN_STAAR_Tsuboyama_2023_2QFF0.0830.1400.2610.2640.3000.2720.1540.0650.2710.2580.2990.2980.2690.2080.3050.2790.3460.4060.4140.2500.1060.1620.1340.1440.1550.1940.1520.2000.2150.2290.2960.208-0.0120.0720.1040.1870.1320.1320.1560.2450.2350.2550.2310.1770.3000.2690.5250.5270.5170.4770.4380.4420.4370.4800.4220.4780.4300.4300.3910.4540.6200.5131212StabilitySCIN_STAARHighProkaryote
SCN5A_HUMAN_Glazer_20190.1300.1310.1620.1620.1530.1580.130-0.0140.1630.1800.1410.2170.1350.2160.1770.1560.1830.1520.1250.1260.1060.1430.1510.1270.1240.1060.0930.1070.1670.1440.1860.1810.0950.0750.0680.0690.0950.0860.0860.1400.1400.1520.0860.1370.1650.1540.0300.0920.0530.0140.1270.1600.1570.1270.1050.1350.1430.1360.1690.1460.2320.168224OrganismalFitnessSCN5A_HUMANMediumHuman
SDA_BACSU_Tsuboyama_2023_1PV00.6310.6460.6150.6190.6470.6500.0010.3030.4560.4720.6060.4180.4660.2490.2740.6080.5940.5980.5960.5300.0260.0670.0030.2380.0810.1230.0450.1190.2140.6400.6040.581-0.1650.0100.2600.1070.6070.6130.3680.6520.6500.4860.037-0.0790.3360.0110.4340.4100.6080.5970.6500.6410.5880.6470.6300.6130.6220.6230.6530.6330.6390.6362770StabilitySDA_BACSUMediumProkaryote
SERC_HUMAN_Xie_20230.3750.4830.5440.5440.5200.5300.0130.4560.5700.5760.5180.5410.5470.1830.4070.5290.5580.5770.5430.5350.4920.5110.5130.4840.5000.5300.5180.5340.5040.5060.5160.4690.1920.5130.5280.5190.5280.5400.5440.5480.5510.5520.3190.0310.5400.4600.3820.4610.3520.1890.5390.5240.5400.5510.5580.5480.5530.5500.5430.5610.5620.4781914OrganismalFitnessSERC_HUMANHighHuman
SHOC2_HUMAN_Kwon_20220.2080.3280.3780.3830.3700.3790.2260.3600.4240.4220.3950.3840.4100.2420.2490.2670.4260.3880.2900.3880.2620.3630.3850.3550.2670.3940.3900.4000.3580.4070.4160.3660.1530.2540.3080.3760.2640.3020.3670.3670.3670.3960.2620.2360.4210.2910.3030.3580.2860.0950.3730.3670.3870.3900.3960.3980.3810.3920.3840.3980.3220.28610972OrganismalFitnessSHOC2_HUMANMediumHuman
SOX30_HUMAN_Tsuboyama_2023_7JJK0.2250.2410.2350.2420.2820.2730.3690.1700.2100.2460.2540.3350.3360.2980.3960.3330.3220.2620.2610.2230.1660.1670.1620.1900.2640.2470.2020.2530.1890.2310.0870.0600.2220.1330.2670.2290.2170.2410.2340.2750.2740.2620.2710.1500.2080.3560.5260.3460.5300.4640.3330.3800.3480.3330.3260.3850.3330.3340.3340.3560.3330.4661010StabilitySOX30_HUMANHighHuman
SPA_STAAU_Tsuboyama_2023_1LP10.4320.5090.5180.5010.5520.549-0.0930.4950.4090.4080.393-0.0410.001-0.032-0.033-0.0640.005-0.0100.0200.514-0.1540.0320.2050.105-0.081-0.0410.0570.3380.4280.5640.4320.442-0.034-0.0110.0010.0660.4370.4300.4270.5480.5470.536-0.137-0.087-0.071-0.0090.5120.4530.7050.5600.5200.5110.4930.5340.4980.5150.5100.5010.5040.5130.5460.4142105StabilitySPA_STAAUMediumProkaryote
SPG1_STRSG_Olson_20140.2390.282-0.0040.0060.2470.272-0.0410.017-0.0380.1710.3050.2370.1920.2800.2320.2640.3010.2510.3370.2780.2520.2160.2140.2080.2320.2220.2180.2390.3540.2830.4780.4940.1110.2230.1750.2790.2430.2040.2890.2290.1880.290-0.092-0.1120.1580.0040.3620.3060.4230.1470.3800.3150.3640.4140.4140.3600.4180.4170.4100.4070.3870.374536962BindingSPG1_STRSGLowProkaryote
SPG1_STRSG_Wu_2016-0.0510.0250.0480.0480.0360.0420.036-0.0480.1700.1710.1210.0860.0590.0300.0970.1180.1280.1650.1990.083-0.012-0.033-0.051-0.023-0.0610.016-0.014-0.031-0.0310.1020.1460.125-0.0830.001-0.085-0.0240.0390.0130.0210.0440.0270.027-0.013-0.0990.040-0.0260.0910.0230.1800.0890.1440.1360.1320.1760.1560.1310.1940.1670.1770.1590.1710.114149360BindingSPG1_STRSGMediumProkaryote
SPG2_STRSG_Tsuboyama_2023_5UBS0.4390.5170.4960.5390.5690.5650.3250.3090.5430.4780.5010.4370.4090.3660.4320.4510.5000.5040.5280.5070.3880.4330.4360.3920.3430.4650.3560.4850.5010.6140.5610.536-0.1920.3560.4400.4620.2940.3350.3820.5610.5620.5690.2530.0890.2860.2400.4470.3690.7040.5700.5640.5660.5840.5920.5690.5950.5880.6100.5840.5890.5980.5491451StabilitySPG2_STRSGMediumProkaryote
SPIKE_SARS2_Starr_2020_binding0.1580.2050.0620.1510.2490.252-0.0460.3690.3560.360-0.002-0.064-0.039-0.049-0.036-0.022-0.018-0.0080.0380.3370.3050.3680.3570.3640.3840.3470.3150.3780.3190.2470.3240.3570.1710.3380.3280.3550.3340.3250.3170.3560.3460.341-0.036-0.013-0.022-0.0080.5170.4830.4050.1600.2240.2410.2260.2970.2510.3010.2580.2790.2280.2790.3570.1593802BindingSPIKE_SARS2MediumVirus
SPIKE_SARS2_Starr_2020_expression0.2000.3140.1650.2790.4450.450-0.0160.3520.4130.4460.050-0.0230.004-0.0260.0010.0100.0120.0180.0720.3550.3170.3820.3740.3870.3790.3670.3220.3970.3280.3590.3130.3460.1780.3440.3460.3830.3610.3610.3670.4510.4550.4750.0050.0220.0240.0410.5610.5330.4650.1870.3210.3540.3300.4220.3790.4130.3840.3920.3630.4050.4980.2663798ExpressionSPIKE_SARS2MediumVirus
SPTN1_CHICK_Tsuboyama_2023_1TUD0.5720.6130.6470.5990.6320.6150.2300.5240.5520.5930.6370.6490.641-0.1330.6190.6270.6370.7260.6570.6240.5780.6430.6040.5890.5890.6130.6520.5630.6010.6210.6310.6320.4570.5080.5700.5480.6290.6410.6210.6230.6280.6180.488-0.2210.4650.4980.4210.4010.6200.6230.6930.6790.6830.6850.6960.6850.6760.6740.6960.6910.6670.5263201StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU0.3990.5220.5970.6100.6040.6040.0860.4490.5650.5920.5140.3730.5030.1860.4420.5290.6180.6400.5270.6070.2180.4740.4900.5050.3450.5440.5690.5060.5380.6230.5660.5260.1550.1940.4440.5180.5020.5400.5890.6140.6040.6250.2090.1190.5820.5830.6510.6260.6290.6190.6260.5820.6350.6200.6340.6280.6340.6100.6030.6390.6820.514707StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N880.5960.6580.6840.6810.6930.684-0.1900.5080.6380.6100.7020.6390.654-0.2670.6620.7020.6750.6980.6810.634-0.1090.3770.3820.5040.4930.5750.6070.6080.6100.6650.6740.6350.3590.0390.1890.3680.6670.6690.6730.6850.6890.6800.575-0.2960.7030.6180.2140.6370.7310.7160.6770.6730.6700.6920.6960.6800.6680.6690.6850.6840.7880.7441583StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W0.3600.5110.6960.6940.7230.7180.5490.3620.7430.7420.6280.6640.7000.4980.7130.7310.7340.6730.6940.6530.6790.6490.6530.6200.6420.6370.6590.6300.6070.6940.6960.6580.5770.5970.6370.6450.6600.6970.7050.7010.7270.7280.3880.0840.5380.5320.5790.5380.6400.6610.7510.7310.7340.7360.7460.7410.7350.7350.7400.7500.7070.6891556StabilitySRBS1_HUMANHighHuman
SRC_HUMAN_Ahler_20190.5260.5080.4650.4650.4960.5070.5320.4400.5140.5320.4690.5610.5850.4470.4860.4900.5550.5150.4670.4840.4390.4130.4210.3730.4370.4600.4380.4220.3300.5420.5780.5740.4310.4100.4110.3480.5020.5020.4920.5210.5200.5160.5670.3970.4870.5460.3090.2960.4090.0980.5170.4950.4920.5170.5240.5210.5170.5270.5240.5320.5060.4793372ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM0.4350.4270.4100.4330.4410.4370.4540.4010.4580.4540.4030.4860.5060.3580.3900.3930.4650.4260.4090.3970.3720.3500.3540.3090.3700.3950.3640.3500.2570.4560.4980.4950.3600.3390.3350.2800.4140.4110.4010.4430.4410.4370.4750.3160.4230.4580.2380.2460.3240.0460.4340.4080.4120.4370.4410.4380.4320.4420.4470.4470.4310.3913637ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Nguyen_20220.4280.4260.4130.4280.4290.4250.4420.4050.3570.4110.4010.4830.5060.3420.3780.3870.4710.4430.4150.3960.3730.3440.3500.3060.3810.3970.3720.3580.2690.4530.4930.4840.3550.3480.3430.2820.4160.4120.3990.4400.4370.4300.4770.3040.4270.4660.2210.2270.3100.0630.4390.4050.4090.4320.4410.4330.4340.4440.4410.4450.4300.3823366OrganismalFitnessSRC_HUMANMediumHuman
SUMO1_HUMAN_Weile_20170.3690.3730.4190.4380.4800.4780.1300.4250.4490.3860.4330.4670.5100.2500.4960.5330.5090.3800.3400.5170.2170.4140.4430.4300.4680.3860.4600.4310.3330.4380.4600.4450.2120.2450.4730.3290.4050.5110.4100.4520.5220.4630.4900.0860.3950.5270.4970.4280.5090.4120.4920.4530.5000.5000.4930.5170.4790.4980.4910.5110.4790.5241700OrganismalFitnessSUMO1_HUMANHighHuman
SYUA_HUMAN_Newberry_20200.1030.1110.1190.1320.1310.1390.1310.2120.1710.1620.2340.2420.2330.1050.1420.1500.1370.2060.2050.2200.1380.2030.1550.1410.0860.1510.1670.1050.1360.2270.2050.1890.0050.1370.1970.1810.1240.1760.1590.1340.1660.1560.1150.0680.1600.155-0.0520.046-0.060-0.0740.0690.0560.0530.0880.1290.0980.1270.1020.1440.098-0.011-0.0452497OrganismalFitnessSYUA_HUMANMediumHuman
TADBP_HUMAN_Bolognesi_20190.0970.0610.1000.0960.0800.0800.291-0.0180.0770.0720.0130.0510.0480.1210.0470.021-0.120-0.0280.053-0.0750.1580.063-0.011-0.0060.2060.0820.0060.023-0.0140.0360.0880.112-0.0510.2260.2730.1210.1420.1850.1210.1050.1290.1090.1210.212-0.0020.0780.2710.0330.2230.0590.1050.0130.0040.0400.0020.0340.045-0.011-0.0790.0210.0540.1211196OrganismalFitnessTADBP_HUMANLowHuman
TAT_HV1BR_Fernandes_20160.2930.2010.2550.2680.3190.300-0.0900.3970.2740.2880.1850.3430.342-0.034-0.008-0.0050.017-0.0570.0440.2030.3790.3640.3960.3980.3940.2690.1360.2460.2230.3680.4050.3870.2830.3850.2110.2030.3790.2630.2370.3660.2950.2680.005-0.0540.2920.1810.2300.2840.3440.2070.1140.1340.1420.1370.1160.1330.1200.1170.1170.1340.1570.1161577OrganismalFitnessTAT_HV1BRHighVirus
TCRG1_MOUSE_Tsuboyama_2023_1E0L0.5760.6150.6440.6550.6600.6620.6960.2920.6770.7080.5910.7390.7500.7240.7070.7840.7690.7070.7450.5770.4900.5520.5700.6090.6040.6130.6600.6480.5970.6800.6400.5950.6420.4850.6190.6690.6880.7150.7100.6800.7120.6950.7530.1840.6290.7520.6800.5960.7740.7590.7210.6970.6570.7570.7330.7630.7230.7380.7100.7350.7900.7741058StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG0.3090.3970.4820.4730.4830.4810.0720.0610.5230.5200.5110.5020.5320.1860.5570.5480.5130.5320.5460.515-0.0470.3320.3490.3050.3510.3830.2790.4230.5340.5430.5260.5120.4800.0030.4030.3370.4460.5050.4950.4720.5100.5110.3010.1140.4630.3890.5040.4840.5610.5970.5550.5460.5660.5730.5840.5480.5610.5510.5450.5610.5020.4831279StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT0.4640.4730.5060.4950.5070.502-0.0990.3920.5730.5760.4720.5380.5260.0440.5470.5890.5250.5290.5330.4660.2620.3610.4290.3940.3950.4550.3850.3950.4360.5280.4840.4510.2500.0610.3120.3330.5350.5230.5170.5410.5180.5180.343-0.0300.5720.4920.5260.6490.6750.6240.5100.5300.5160.5120.5170.5110.5210.5120.5130.5240.5970.6041479StabilityTNKS2_HUMANHighHuman
TPK1_HUMAN_Weile_20170.2170.2360.2190.2280.2300.2290.0670.2530.2860.2700.2850.2700.3180.1080.1990.2530.3390.3280.3710.2490.0880.1420.2280.2720.1150.2670.2730.2530.3060.2550.2530.1880.1170.1150.2140.3010.2320.2520.3050.2430.2520.2770.1200.0890.3090.1700.2060.2670.2450.1160.2530.2770.2750.2780.2930.2910.2810.2910.2920.2980.2680.2033181OrganismalFitnessTPK1_HUMANMediumHuman
TPMT_HUMAN_Matreyek_20180.3720.4560.4890.5090.4990.5130.2410.4450.4970.5000.5460.5170.5470.3080.4330.5310.5390.4760.4480.5110.3330.4200.4810.4960.4470.5060.4810.4660.4240.5470.5420.4890.3340.3300.4710.4110.4780.5130.4780.5170.5370.5150.3710.2040.5390.4950.4760.5340.5400.2380.5220.5190.5300.5140.5410.5350.5410.5290.5340.5430.5680.4693648ExpressionTPMT_HUMANMediumHuman
TPOR_HUMAN_Bridgford_20200.3760.3650.2830.2630.2880.2960.3700.4550.4540.4560.4070.3680.3700.2790.3780.2940.2900.4220.3620.3620.2500.3380.3130.3770.3670.2000.4710.4510.3400.4500.4360.2740.3950.3720.3210.4190.4290.4080.4510.4080.3820.4370.2950.3030.3530.3480.1260.3570.3090.0350.3440.3080.3440.3540.3220.3450.3960.2570.2930.3410.3890.372562OrganismalFitnessTPOR_HUMANLowHuman
TRPC_SACS2_Chan_20170.5750.6060.5580.5740.5750.5850.1730.4990.6550.6710.6210.6110.6430.3250.5390.6180.6530.6320.6010.5920.4210.5640.5080.5750.4980.4990.5580.5210.5240.5290.5900.5200.2210.5200.5550.5580.5750.5980.5910.5900.6120.6010.4340.0720.6120.4980.3210.5120.5070.1220.5930.5940.5820.6460.6140.6150.6210.6300.6140.6360.6260.5781519OrganismalFitnessTRPC_SACS2MediumProkaryote
TRPC_THEMA_Chan_20170.4050.4500.3940.4110.4160.4170.3950.4340.4710.4700.4390.4780.5060.3270.4700.5030.4640.4700.4990.4300.3360.3990.3950.4550.4370.4210.3690.3870.4780.3900.4270.3650.1400.3290.4600.4480.3900.4420.4380.4180.4450.4420.3970.1400.4610.3950.2530.3860.3930.0750.4590.4100.4310.4620.4630.4090.4390.4410.4180.4540.5040.4911519OrganismalFitnessTRPC_THEMAMediumProkaryote
UBC9_HUMAN_Weile_20170.3710.4960.5210.5310.5080.522-0.0420.4030.5120.5190.4200.4770.5090.0010.0040.4030.4730.5180.5380.5570.2200.4050.4450.4190.4200.4790.4710.4490.4420.4840.4540.4320.0710.2330.4030.4280.3270.4520.4750.4430.5170.5400.277-0.0080.4270.4920.3220.3500.4460.2430.4400.4170.4080.4330.4250.4270.4330.4400.4420.4390.3700.4262563OrganismalFitnessUBC9_HUMANMediumHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X0.3250.4430.4640.4670.4660.4660.0510.3460.3790.4150.3910.4530.4490.3470.4200.4900.4600.4670.4770.4210.1420.3530.3570.3640.3450.3740.3430.3220.3550.4860.4750.4530.4410.0790.1100.2550.3830.3850.3290.4720.4770.4240.359-0.0950.3480.3440.4880.4090.5200.3510.4110.4360.4240.4470.4480.4280.4070.4290.4200.4320.5000.4073622StabilityUBE4B_HUMANHighHuman
UBE4B_MOUSE_Starita_20130.4120.4170.4540.4680.4630.4700.0780.0890.3920.3940.3610.4470.4710.3990.4630.4590.4590.3510.3490.3830.1220.3240.3370.3000.4440.3760.3950.3760.2890.4330.4370.3960.0910.0260.0960.2620.4190.3510.3900.4670.4540.4720.4090.0410.4200.4580.3070.393-0.0170.1180.4260.4090.4560.4580.4380.4580.4320.4490.4430.4540.4400.412899ActivityUBE4B_MOUSELowEukaryote
UBR5_HUMAN_Tsuboyama_2023_1I2T0.4420.5500.5400.5380.5660.5770.1520.2510.4980.5280.5280.4250.5110.2260.2260.1580.1970.5910.6010.4760.4220.4140.4670.5140.4730.4620.4950.4880.4920.5570.4970.3880.2880.3040.4550.4010.4840.5190.5020.5920.5940.5830.1330.1210.4220.2090.5570.4390.6370.5740.5600.5880.5970.6090.5990.6120.5890.6050.5850.6000.6610.5971453StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP80.2280.3140.2280.2460.2660.2550.2560.0640.3710.3920.6230.5440.5620.3600.5140.6010.6550.6110.6530.1670.3950.4740.4580.5310.4190.4250.4100.4670.5160.4870.5100.3860.0380.3070.4730.4640.3430.4150.4640.3360.4030.4090.4690.2950.5520.4830.4580.5610.5430.5050.6520.6260.6610.6610.6640.6910.6340.6580.6840.6740.6190.600723StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU50.2840.4220.5940.6300.6340.6370.1620.5400.5530.5900.6730.6300.6730.3710.3160.6520.6620.5890.4780.5920.2930.4310.6010.5240.4240.5750.4940.4820.5790.5990.7010.6940.3220.3460.5150.5210.4870.5640.5630.6340.6590.6420.2720.2830.4260.4210.5420.4330.7750.7020.6720.6580.6660.6580.6530.6650.6490.6560.6520.6620.6850.7982568StabilityVILI_CHICKHighEukaryote
VKOR1_HUMAN_Chiasson_2020_abundance0.4090.4160.4020.4220.4370.4370.2450.3960.5010.5200.4380.4470.4740.2510.4430.5000.4930.4840.4720.4640.2860.2980.3960.4300.3680.4710.4630.4370.4420.4810.3830.2880.0900.2560.3640.5050.4620.4830.5480.4760.4920.5380.2360.1800.4640.3840.4460.4410.4900.2790.5200.4900.5300.5430.5300.5130.5260.5080.5250.5330.4940.4792695ExpressionVKOR1_HUMANMediumHuman
VKOR1_HUMAN_Chiasson_2020_activity0.3290.3680.3990.4100.4130.4280.0000.3860.4230.4410.4190.4270.4480.0160.2980.3690.4300.4460.4450.4150.0510.0730.2720.3100.1320.3840.3840.3600.3850.4210.4310.3880.1790.0220.1350.3700.3390.3500.4010.4090.4200.4360.0840.0210.4160.2260.2250.3340.3660.1000.4040.3580.3690.3890.4060.3890.3910.3940.3940.4010.4270.322697ActivityVKOR1_HUMANMediumHuman
VRPI_BPT7_Tsuboyama_2023_2WNM-0.1190.0430.1640.1940.0780.1190.0820.1470.3660.3560.4980.3680.3740.2520.4100.5070.5870.6120.4680.128-0.0200.1350.1330.2220.2150.2020.1310.1990.2910.0840.4990.4290.2420.0650.0590.122-0.039-0.028-0.0570.0640.0830.0060.2540.0560.5010.3570.5890.5570.5930.6060.6120.5920.6330.6390.6220.6410.6150.6300.6040.6400.6620.5821047StabilityVRPI_BPT7MediumVirus
YAIA_ECOLI_Tsuboyama_2023_2KVT0.2910.6020.5720.5960.5990.591-0.2210.5330.5590.5980.5930.2700.481-0.1070.1730.5720.6250.6840.6840.591-0.091-0.182-0.1300.264-0.125-0.166-0.231-0.0230.5200.6050.6810.643-0.060-0.234-0.0640.5020.3490.3590.5280.5720.5690.604-0.046-0.2610.208-0.0130.4490.4250.6510.5440.5870.6270.5850.6190.6120.6270.6250.6570.6770.6320.6160.4981890StabilityYAIA_ECOLIMediumProkaryote
YAP1_HUMAN_Araya_20120.4280.3210.4640.4600.4490.4550.3290.2890.0180.0360.3330.2800.2850.4110.4570.4510.4660.3820.3130.1890.1800.1760.1580.1670.2990.2260.2570.2070.1500.3320.4250.4510.1390.3020.1960.2170.4020.3260.3590.4350.4050.4390.485-0.1040.3350.4860.3400.3620.3830.1970.4460.3790.4320.4260.4170.4120.4150.4410.4440.4380.3860.42210075BindingYAP1_HUMANLowHuman
YNZC_BACSU_Tsuboyama_2023_2JVD0.5960.6100.5950.6180.6040.6090.4920.3940.5910.5830.6730.6490.6670.5840.6210.6500.6580.6310.6300.5250.4880.5340.6120.6040.5940.6110.5870.5900.6430.5940.5860.5470.5680.5730.5670.5440.6650.6340.6210.6690.6240.6300.4460.3530.5360.4480.4830.5620.6800.6500.7010.6490.6650.6870.7000.6910.6870.6760.6790.6900.6380.7602300StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_Uniprot_Selection_Type_level.csv b/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_Uniprot_Selection_Type_level.csv new file mode 100644 index 0000000..82517d7 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_Uniprot_Selection_Type_level.csv @@ -0,0 +1,7 @@ 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+++ b/benchmarks/DMS_zero_shot/substitutions/Spearman/DMS_substitutions_Spearman_Uniprot_level.csv @@ -0,0 +1,223 @@ +Site_Independent,EVmutation,DeepSequence_single,DeepSequence_ensemble,EVE_single,EVE_ensemble,Unirep,Unirep_evotune,MSA_Transformer_single,MSA_Transformer_ensemble,ESM1b,ESM1v_single,ESM1v_ensemble,ESM2_8M,ESM2_35M,ESM2_150M,ESM2_650M,ESM2_3B,ESM2_15B,Wavenet,RITA_s,RITA_m,RITA_l,RITA_xl,Progen2_small,Progen2_medium,Progen2_base,Progen2_large,Progen2_xlarge,GEMME,VESPA,VESPAl,ProtGPT2,Tranception_S_no_retrieval,Tranception_M_no_retrieval,Tranception_L_no_retrieval,Tranception_S,Tranception_M,Tranception_L,TranceptEVE_S,TranceptEVE_M,TranceptEVE_L,CARP_38M,CARP_600K,CARP_640M,CARP_76M,MIF,MIFST,ESM-IF1,ProteinMPNN,ProtSSN_k10_h512,ProtSSN_k10_h768,ProtSSN_k10_h1280,ProtSSN_k20_h512,ProtSSN_k20_h768,ProtSSN_k20_h1280,ProtSSN_k30_h512,ProtSSN_k30_h768,ProtSSN_k30_h1280,ProtSSN_ensemble,SaProt_650M_AF2,SaProt_35M_AF2,Selection Type,UniProt_ID,MSA_Neff_L_category,Taxon +0.383,0.354,0.131,0.103,0.394,0.405,-0.133,0.062,0.436,0.439,-0.001,-0.048,0.015,-0.073,-0.054,-0.058,0.213,0.363,0.375,0.216,0.361,0.309,0.317,0.305,0.329,0.342,0.328,0.312,0.293,0.43,0.319,0.296,0.005,0.304,0.331,0.272,0.362,0.366,0.351,0.361,0.358,0.373,-0.06,-0.073,0.131,-0.061,0.273,0.29,0.287,0.129,0.253,0.251,0.282,0.296,0.277,0.269,0.263,0.269,0.265,0.278,0.199,0.116,OrganismalFitness,A0A140D2T1_ZIKV,Medium,Virus +0.481,0.407,0.413,0.432,0.509,0.516,0.0,0.513,0.511,0.515,0.456,0.492,0.516,-0.003,0.015,0.035,0.08,0.132,0.164,0.465,0.496,0.507,0.509,0.505,0.497,0.501,0.463,0.49,0.484,0.496,0.541,0.507,0.327,0.492,0.483,0.514,0.509,0.503,0.513,0.524,0.518,0.528,0.422,-0.021,0.497,0.429,0.329,0.451,0.206,0.136,0.202,0.23,0.258,0.263,0.21,0.226,0.228,0.2,0.183,0.239,0.173,0.084,OrganismalFitness,A0A192B1T2_9HIV1,Medium,Virus +-0.011,0.044,0.107,0.098,0.053,0.054,-0.024,0.084,0.082,0.077,0.04,0.068,0.068,-0.037,-0.047,-0.016,0.03,0.027,0.025,0.067,-0.01,0.047,0.071,0.088,0.05,0.088,0.08,0.089,0.095,0.045,0.046,0.036,0.03,0.036,0.055,0.099,0.031,0.041,0.057,0.058,0.061,0.075,-0.055,-0.056,0.039,-0.043,0.051,0.055,0.039,0.04,0.054,0.042,0.039,0.035,0.037,0.052,0.039,0.058,0.041,0.047,0.04,-0.005,Activity,A0A1I9GEU1_NEIME,Medium,Prokaryote +0.436,0.459,0.109,0.041,0.428,0.428,0.003,0.026,0.299,0.32,0.087,0.088,0.093,0.064,0.084,0.097,0.066,0.084,0.084,0.059,0.021,0.015,0.04,0.078,-0.029,0.067,0.01,0.037,0.106,0.473,0.337,0.351,0.093,0.041,0.024,0.057,0.301,0.292,0.311,0.253,0.242,0.263,0.047,0.002,0.074,0.084,0.418,0.441,0.476,0.364,0.274,0.326,0.314,0.344,0.313,0.36,0.312,0.331,0.265,0.335,0.427,0.28,Activity,A0A247D711_LISMN,High,Prokaryote +0.479,0.49,0.469,0.498,0.526,0.534,0.015,0.464,0.495,0.502,0.14,0.489,0.53,0.022,0.043,0.068,0.486,0.49,0.493,0.436,0.458,0.518,0.502,0.526,0.389,0.49,0.498,0.471,0.49,0.526,0.466,0.388,0.08,0.456,0.509,0.521,0.512,0.541,0.539,0.549,0.566,0.566,0.029,0.026,0.296,0.043,0.392,0.434,0.412,0.16,0.46,0.452,0.492,0.487,0.49,0.493,0.502,0.483,0.504,0.503,0.214,0.174,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +0.479,0.49,0.469,0.498,0.526,0.534,0.015,0.464,0.495,0.502,0.14,0.489,0.53,0.022,0.043,0.068,0.486,0.49,0.493,0.436,0.458,0.518,0.502,0.526,0.389,0.49,0.498,0.471,0.49,0.526,0.466,0.388,0.08,0.456,0.509,0.521,0.512,0.541,0.539,0.549,0.566,0.566,0.029,0.026,0.296,0.043,0.392,0.434,0.412,0.16,0.46,0.452,0.492,0.487,0.49,0.493,0.502,0.483,0.504,0.503,0.214,0.174,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +0.411,0.34,0.406,0.403,0.409,0.408,0.029,0.364,0.297,0.287,0.044,0.026,0.033,0.022,0.02,0.027,0.149,0.211,0.27,0.26,0.329,0.386,0.404,0.398,0.064,0.26,0.285,0.244,0.33,0.443,0.318,0.296,0.055,0.348,0.371,0.404,0.366,0.38,0.393,0.434,0.439,0.461,0.019,0.0,0.08,-0.001,0.262,0.254,0.126,0.105,0.183,0.18,0.191,0.216,0.209,0.207,0.2,0.198,0.193,0.204,0.148,0.092,OrganismalFitness,A4D664_9INFA,Medium,Virus +0.317,0.492,0.657,0.664,0.623,0.641,0.344,0.529,0.641,0.681,0.68,0.647,0.668,0.431,0.528,0.655,0.738,0.712,0.627,0.567,0.409,0.533,0.564,0.624,0.526,0.622,0.639,0.638,0.705,0.671,0.742,0.673,0.242,0.423,0.552,0.629,0.557,0.588,0.652,0.653,0.66,0.679,0.481,0.071,0.671,0.601,0.664,0.713,0.629,0.422,0.728,0.718,0.711,0.719,0.729,0.733,0.728,0.733,0.735,0.745,0.722,0.513,OrganismalFitness,A4GRB6_PSEAI,High,Prokaryote +0.394,0.385,0.449,0.425,0.312,0.316,0.345,0.167,0.337,0.351,0.302,0.316,0.404,0.382,0.366,0.372,0.431,0.48,0.352,0.261,0.327,0.281,0.318,0.306,0.406,0.297,0.32,0.313,0.314,0.478,0.251,0.186,0.487,0.393,0.273,0.37,0.441,0.372,0.458,0.436,0.4,0.416,0.366,0.362,0.421,0.356,0.279,0.358,-0.191,0.06,0.449,0.44,0.417,0.41,0.406,0.38,0.423,0.346,0.372,0.417,0.411,0.388,Stability,A4_HUMAN,Low,Human +0.282,0.474,0.331,0.414,0.487,0.491,0.196,0.191,0.501,0.504,0.413,0.483,0.489,0.196,0.248,0.256,0.49,0.511,0.518,0.391,0.206,0.232,0.276,0.306,0.273,0.428,0.407,0.413,0.438,0.465,0.507,0.455,0.021,0.259,0.246,0.417,0.427,0.418,0.466,0.471,0.466,0.495,0.216,0.17,0.354,0.243,0.257,0.374,0.357,0.183,0.481,0.492,0.483,0.488,0.495,0.486,0.486,0.477,0.494,0.5,0.461,0.282,OrganismalFitness,AACC1_PSEAI,High,Prokaryote +0.254,0.237,0.187,0.2,0.249,0.242,-0.049,0.077,0.236,0.251,0.244,0.173,0.213,-0.053,0.062,0.216,0.225,0.175,0.178,0.227,0.033,0.129,0.175,0.207,0.042,0.162,0.207,0.208,0.261,0.217,0.164,0.138,0.08,0.044,0.12,0.134,0.217,0.19,0.196,0.228,0.2,0.205,0.005,0.005,0.244,0.078,0.336,0.292,0.317,0.106,0.217,0.255,0.226,0.222,0.236,0.249,0.223,0.221,0.214,0.234,0.289,0.168,Binding,ACE2_HUMAN,Medium,Human +0.331,0.423,0.506,0.514,0.497,0.517,0.463,0.5,0.531,0.53,0.534,0.526,0.539,0.408,0.444,0.474,0.493,0.505,0.515,0.521,0.509,0.524,0.517,0.493,0.524,0.531,0.538,0.533,0.502,0.545,0.5,0.414,0.298,0.528,0.53,0.506,0.528,0.541,0.533,0.533,0.538,0.537,0.471,0.181,0.528,0.51,0.419,0.477,0.451,0.223,0.486,0.486,0.5,0.508,0.501,0.498,0.493,0.506,0.494,0.509,0.554,0.516,Activity,ADRB2_HUMAN,Medium,Human +0.138,0.396,0.474,0.464,0.429,0.445,-0.287,0.327,0.32,0.385,0.415,0.376,0.478,-0.245,-0.264,0.196,0.328,0.347,0.262,0.479,-0.219,0.14,0.388,0.395,0.013,0.321,0.417,0.413,0.282,0.376,0.45,0.405,0.057,-0.039,0.057,0.339,0.206,0.218,0.367,0.399,0.402,0.444,-0.228,-0.238,0.379,0.258,0.328,0.401,0.328,0.243,0.337,0.404,0.344,0.447,0.359,0.365,0.341,0.343,0.312,0.359,0.257,-0.036,Activity,AICDA_HUMAN,Medium,Human +0.358,0.418,0.419,0.407,0.386,0.398,-0.301,0.13,0.22,0.24,0.374,0.07,0.423,-0.209,0.286,0.321,0.261,0.297,0.26,0.062,-0.052,-0.218,-0.051,-0.038,-0.317,-0.089,-0.05,-0.121,0.056,0.396,0.254,0.228,0.09,-0.287,-0.139,-0.275,0.392,0.402,0.356,0.424,0.422,0.374,-0.185,-0.194,0.165,0.114,0.251,0.112,0.433,0.353,0.446,0.326,0.204,0.349,0.342,0.349,0.343,0.339,0.361,0.343,0.347,0.397,Stability,AMFR_HUMAN,Medium,Human +0.246,0.443,0.496,0.506,0.477,0.464,0.093,0.436,0.401,0.605,0.57,0.613,0.662,0.326,0.408,0.428,0.557,0.674,0.613,0.506,0.555,0.525,0.532,0.542,0.566,0.561,0.534,0.559,0.508,0.558,0.583,0.528,0.265,0.601,0.47,0.405,0.585,0.479,0.438,0.58,0.506,0.481,0.356,0.096,0.483,0.412,0.368,0.469,0.407,0.213,0.555,0.519,0.53,0.537,0.529,0.541,0.523,0.54,0.564,0.556,0.607,0.41,Activity,AMIE_PSEAE,High,Prokaryote +0.562,0.521,0.445,0.472,0.524,0.528,0.525,0.452,0.503,0.524,0.527,0.522,0.543,0.535,0.592,0.598,0.609,0.598,0.57,0.021,0.462,0.493,0.509,0.5,0.497,0.519,0.513,0.469,0.494,0.557,0.542,0.525,0.318,0.483,0.503,0.475,0.537,0.549,0.538,0.545,0.556,0.543,0.549,0.42,0.496,0.523,0.471,0.458,0.422,0.165,0.566,0.555,0.572,0.578,0.572,0.584,0.569,0.583,0.577,0.589,0.585,0.601,Activity,ANCSZ,Medium,Eukaryote +0.277,0.413,0.411,0.43,0.431,0.423,0.306,0.386,0.43,0.463,0.409,0.296,0.405,0.332,0.297,0.511,0.497,0.499,0.435,0.434,0.372,0.439,0.448,0.403,0.362,0.437,0.473,0.461,0.418,0.46,0.409,0.31,0.073,0.394,0.396,0.43,0.405,0.415,0.439,0.465,0.46,0.471,0.285,0.267,0.45,0.408,0.725,0.651,0.738,0.636,0.513,0.532,0.527,0.529,0.517,0.508,0.503,0.526,0.494,0.533,0.604,0.511,Stability,ARGR_ECOLI,Medium,Prokaryote +0.688,0.27,0.668,0.661,0.53,0.646,0.216,0.529,0.188,0.164,0.206,-0.019,0.396,0.174,0.123,0.151,0.27,0.367,0.315,0.336,0.239,0.076,0.304,0.568,0.067,0.251,0.362,0.372,0.366,0.734,0.358,0.338,0.295,0.266,0.213,0.341,0.544,0.469,0.388,0.615,0.57,0.402,0.146,0.138,0.207,0.17,0.287,0.307,0.432,-0.005,0.352,0.367,0.419,0.283,0.255,0.47,0.295,0.288,0.421,0.374,0.242,0.204,Binding,B2L11_HUMAN,Low,Human +0.216,0.332,0.395,0.399,0.394,0.403,0.212,0.256,0.438,0.454,0.425,0.425,0.451,0.331,0.421,0.423,0.479,0.458,0.499,0.332,0.335,0.346,0.376,0.343,0.144,0.391,0.403,0.382,0.307,0.338,0.345,0.314,0.273,0.354,0.261,0.299,0.392,0.338,0.365,0.421,0.386,0.405,0.21,0.113,0.314,0.242,0.558,0.451,0.644,0.566,0.434,0.444,0.457,0.472,0.463,0.463,0.46,0.458,0.471,0.461,0.54,0.579,Stability,BBC1_YEAST,High,Eukaryote +0.364,0.418,0.357,0.364,0.365,0.382,0.1,0.248,0.383,0.501,0.394,0.329,0.406,0.166,0.182,0.301,0.51,0.521,0.421,0.407,0.122,0.362,0.22,0.376,0.207,0.421,0.281,0.402,0.435,0.607,0.549,0.496,0.07,0.245,0.294,0.431,0.44,0.44,0.483,0.392,0.396,0.436,0.116,0.109,0.205,0.175,0.579,0.537,0.683,0.564,0.549,0.554,0.563,0.579,0.561,0.581,0.529,0.556,0.543,0.562,0.516,0.296,Stability,BCHB_CHLTE,Medium,Prokaryote +0.429,0.652,0.668,0.683,0.658,0.672,0.109,0.418,0.642,0.684,0.646,0.622,0.654,0.354,0.497,0.602,0.675,0.553,0.427,0.602,0.518,0.512,0.502,0.492,0.539,0.594,0.598,0.534,0.429,0.598,0.691,0.673,0.148,0.51,0.501,0.459,0.594,0.595,0.596,0.672,0.68,0.681,0.471,0.032,0.636,0.54,0.542,0.633,0.61,0.297,0.66,0.649,0.653,0.673,0.674,0.67,0.678,0.659,0.67,0.685,0.688,0.544,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.429,0.652,0.668,0.683,0.658,0.672,0.109,0.418,0.642,0.684,0.646,0.622,0.654,0.354,0.497,0.602,0.675,0.553,0.427,0.602,0.518,0.512,0.502,0.492,0.539,0.594,0.598,0.534,0.429,0.598,0.691,0.673,0.148,0.51,0.501,0.459,0.594,0.595,0.596,0.672,0.68,0.681,0.471,0.032,0.636,0.54,0.542,0.633,0.61,0.297,0.66,0.649,0.653,0.673,0.674,0.67,0.678,0.659,0.67,0.685,0.688,0.544,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.429,0.652,0.668,0.683,0.658,0.672,0.109,0.418,0.642,0.684,0.646,0.622,0.654,0.354,0.497,0.602,0.675,0.553,0.427,0.602,0.518,0.512,0.502,0.492,0.539,0.594,0.598,0.534,0.429,0.598,0.691,0.673,0.148,0.51,0.501,0.459,0.594,0.595,0.596,0.672,0.68,0.681,0.471,0.032,0.636,0.54,0.542,0.633,0.61,0.297,0.66,0.649,0.653,0.673,0.674,0.67,0.678,0.659,0.67,0.685,0.688,0.544,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.429,0.652,0.668,0.683,0.658,0.672,0.109,0.418,0.642,0.684,0.646,0.622,0.654,0.354,0.497,0.602,0.675,0.553,0.427,0.602,0.518,0.512,0.502,0.492,0.539,0.594,0.598,0.534,0.429,0.598,0.691,0.673,0.148,0.51,0.501,0.459,0.594,0.595,0.596,0.672,0.68,0.681,0.471,0.032,0.636,0.54,0.542,0.633,0.61,0.297,0.66,0.649,0.653,0.673,0.674,0.67,0.678,0.659,0.67,0.685,0.688,0.544,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.501,0.385,0.441,0.432,0.424,0.466,0.125,0.189,0.387,0.407,0.494,0.404,0.447,0.134,0.392,0.455,0.515,0.497,0.394,0.296,0.145,0.379,0.405,0.361,0.305,0.473,0.457,0.442,0.423,0.438,0.539,0.482,0.22,0.151,0.179,0.409,0.456,0.456,0.514,0.475,0.476,0.533,0.229,0.132,0.534,0.457,0.44,0.469,0.116,0.12,0.515,0.501,0.506,0.502,0.512,0.504,0.513,0.512,0.504,0.519,0.539,0.481,OrganismalFitness,BRCA1_HUMAN,Low,Human +0.481,0.346,0.44,0.445,0.447,0.481,0.095,0.477,0.095,-0.03,0.48,0.183,0.103,0.088,0.103,0.416,0.51,0.464,0.473,0.033,0.09,0.494,0.534,0.482,0.533,0.483,0.496,0.499,0.014,0.4,0.297,0.328,0.14,0.119,0.185,0.138,0.42,0.425,0.426,0.419,0.415,0.414,0.163,0.093,0.497,0.047,0.052,0.053,-0.091,0.285,0.436,0.477,0.473,0.424,0.455,0.436,0.456,0.438,0.453,0.465,0.0,0.016,OrganismalFitness,BRCA2_HUMAN,,Human +0.393,0.371,0.351,0.357,0.433,0.436,-0.025,0.451,0.374,0.378,0.06,0.428,0.492,-0.014,-0.022,-0.007,0.483,0.405,0.426,0.343,0.396,0.363,0.378,0.349,0.254,0.454,0.428,0.465,0.37,0.475,0.457,0.36,0.11,0.354,0.374,0.392,0.412,0.419,0.433,0.439,0.441,0.452,-0.024,-0.033,0.294,-0.002,0.513,0.539,0.544,0.227,0.498,0.497,0.519,0.52,0.531,0.51,0.528,0.519,0.521,0.529,0.313,0.185,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +0.175,0.233,0.238,0.233,0.238,0.236,0.174,0.187,0.236,0.253,0.25,0.235,0.264,0.161,0.199,0.195,0.212,0.218,0.225,0.229,0.173,0.227,0.242,0.254,0.232,0.276,0.279,0.3,0.313,0.239,0.21,0.146,0.087,0.216,0.253,0.291,0.218,0.242,0.266,0.238,0.244,0.246,0.213,0.156,0.268,0.251,0.106,0.143,0.164,0.088,0.177,0.182,0.188,0.171,0.189,0.176,0.18,0.192,0.178,0.188,0.287,0.238,OrganismalFitness,CALM1_HUMAN,High,Human +0.407,0.345,0.317,0.372,0.346,0.33,0.374,0.424,0.329,0.363,0.183,0.196,0.199,0.254,0.286,0.203,0.277,0.183,0.124,0.258,0.191,0.25,0.269,0.279,0.203,0.198,0.266,0.204,0.399,0.445,0.183,0.177,0.132,0.202,0.262,0.492,0.38,0.386,0.473,0.338,0.343,0.426,0.092,0.16,0.26,0.112,0.402,0.393,0.347,0.319,0.235,0.225,0.216,0.235,0.214,0.215,0.22,0.208,0.228,0.223,0.301,0.222,OrganismalFitness,CAPSD_AAV2S,Low,Virus +0.343,0.244,0.221,0.204,0.24,0.256,0.104,-0.003,0.258,0.269,0.375,0.407,0.382,0.07,0.105,0.4,0.414,0.439,0.448,0.202,0.146,0.255,0.26,0.246,0.072,0.259,0.268,0.292,0.179,0.258,0.38,0.337,0.262,0.001,0.206,0.16,0.246,0.266,0.226,0.262,0.264,0.232,0.088,0.042,0.339,0.263,0.368,0.37,0.393,0.158,0.43,0.429,0.425,0.416,0.415,0.424,0.41,0.428,0.436,0.434,0.493,0.379,OrganismalFitness,CAR11_HUMAN,Low,Human +0.343,0.244,0.221,0.204,0.24,0.256,0.104,-0.003,0.258,0.269,0.375,0.407,0.382,0.07,0.105,0.4,0.414,0.439,0.448,0.202,0.146,0.255,0.26,0.246,0.072,0.259,0.268,0.292,0.179,0.258,0.38,0.337,0.262,0.001,0.206,0.16,0.246,0.266,0.226,0.262,0.264,0.232,0.088,0.042,0.339,0.263,0.368,0.37,0.393,0.158,0.43,0.429,0.425,0.416,0.415,0.424,0.41,0.428,0.436,0.434,0.493,0.379,OrganismalFitness,CAR11_HUMAN,Low,Human +0.159,0.179,0.162,0.166,0.169,0.177,0.04,0.1,0.18,0.183,0.175,0.069,0.073,0.063,0.09,0.154,0.182,0.188,0.194,0.006,0.043,0.081,0.173,0.12,0.043,0.194,0.182,0.179,0.068,0.187,0.199,0.179,0.027,0.035,0.047,0.166,0.17,0.172,0.189,0.184,0.184,0.194,0.061,0.028,0.181,0.106,0.106,0.181,0.036,0.037,0.176,0.177,0.178,0.185,0.18,0.181,0.177,0.178,0.177,0.183,0.177,0.114,Activity,CAS9_STRP1,Medium,Prokaryote +0.382,0.543,0.61,0.622,0.626,0.632,0.054,0.478,0.644,0.653,0.59,0.595,0.624,0.264,0.574,0.639,0.638,0.554,0.514,0.603,0.233,0.512,0.517,0.532,0.505,0.556,0.554,0.526,0.567,0.608,0.599,0.542,0.217,0.092,0.482,0.52,0.533,0.562,0.582,0.628,0.619,0.628,0.422,-0.006,0.616,0.563,0.414,0.558,0.499,0.263,0.553,0.571,0.571,0.57,0.575,0.567,0.585,0.571,0.576,0.59,0.652,0.513,Activity,CASP3_HUMAN,High,Human +0.372,0.516,0.619,0.617,0.609,0.613,0.057,0.47,0.6,0.627,0.585,0.611,0.632,0.306,0.598,0.632,0.622,0.572,0.562,0.611,0.206,0.532,0.529,0.558,0.529,0.579,0.589,0.563,0.572,0.642,0.596,0.538,0.332,0.103,0.543,0.521,0.517,0.594,0.586,0.599,0.628,0.628,0.461,0.019,0.646,0.596,0.479,0.612,0.573,0.329,0.585,0.57,0.562,0.587,0.608,0.602,0.591,0.578,0.596,0.605,0.643,0.533,Activity,CASP7_HUMAN,Medium,Human +0.533,0.559,0.599,0.606,0.627,0.63,0.68,0.558,0.565,0.528,0.614,0.638,0.657,0.564,0.689,0.688,0.658,0.615,0.644,0.041,0.621,0.603,0.619,0.602,0.641,0.627,0.593,0.561,0.587,0.649,0.611,0.619,0.439,0.626,0.611,0.633,0.649,0.642,0.665,0.622,0.62,0.638,0.44,0.427,0.315,0.46,0.475,0.4,0.679,0.572,0.663,0.652,0.676,0.668,0.687,0.662,0.67,0.653,0.663,0.674,0.62,0.686,Stability,CATR_CHLRE,High,Eukaryote +0.644,0.718,0.744,0.745,0.716,0.729,0.482,0.577,0.745,0.754,0.77,0.752,0.736,0.529,0.688,0.736,0.702,0.727,0.718,0.739,0.486,0.522,0.699,0.692,0.547,0.665,0.673,0.674,0.71,0.732,0.71,0.654,0.104,0.509,0.642,0.703,0.699,0.706,0.732,0.746,0.736,0.748,0.51,0.302,0.631,0.646,0.701,0.689,0.82,0.773,0.772,0.768,0.778,0.768,0.782,0.78,0.77,0.772,0.769,0.779,0.716,0.835,Stability,CBPA2_HUMAN,Medium,Human +0.334,0.358,0.364,0.383,0.371,0.379,0.202,0.251,0.374,0.377,0.347,0.346,0.372,0.085,0.226,0.335,0.342,0.329,0.339,0.35,0.356,0.26,0.285,0.288,0.349,0.273,0.29,0.273,0.31,0.381,0.37,0.344,0.197,0.361,0.283,0.266,0.384,0.332,0.327,0.394,0.363,0.363,0.304,0.068,0.376,0.373,0.237,0.268,0.309,0.079,0.324,0.324,0.331,0.337,0.337,0.335,0.332,0.332,0.335,0.34,0.386,0.289,OrganismalFitness,CBS_HUMAN,Medium,Human +0.498,0.548,0.674,0.706,0.633,0.685,-0.266,0.611,0.623,0.613,0.615,0.65,0.648,-0.271,0.735,0.699,0.682,0.609,0.606,0.648,0.486,0.589,0.581,0.587,0.553,0.577,0.589,0.531,0.569,0.633,0.667,0.653,0.423,0.425,0.505,0.54,0.601,0.6,0.623,0.676,0.698,0.701,0.621,-0.32,0.369,0.561,0.167,0.155,0.614,0.588,0.695,0.626,0.69,0.676,0.709,0.668,0.677,0.718,0.664,0.695,0.642,0.751,Stability,CBX4_HUMAN,High,Human +0.395,0.496,0.522,0.544,0.522,0.53,-0.02,0.24,0.372,0.396,0.457,0.393,0.458,-0.007,-0.01,0.434,0.489,0.493,0.313,0.488,0.03,-0.0,-0.094,0.156,-0.123,0.074,-0.029,0.054,0.45,0.479,0.548,0.532,0.118,0.008,0.092,0.367,0.461,0.453,0.495,0.512,0.506,0.535,0.012,-0.053,0.47,0.019,0.301,0.43,0.339,0.236,0.461,0.425,0.432,0.462,0.46,0.467,0.454,0.477,0.502,0.473,0.464,0.26,Activity,CCDB_ECOLI,High,Prokaryote +0.395,0.496,0.522,0.544,0.522,0.53,-0.02,0.24,0.372,0.396,0.457,0.393,0.458,-0.007,-0.01,0.434,0.489,0.493,0.313,0.488,0.03,-0.0,-0.094,0.156,-0.123,0.074,-0.029,0.054,0.45,0.479,0.548,0.532,0.118,0.008,0.092,0.367,0.461,0.453,0.495,0.512,0.506,0.535,0.012,-0.053,0.47,0.019,0.301,0.43,0.339,0.236,0.461,0.425,0.432,0.462,0.46,0.467,0.454,0.477,0.502,0.473,0.464,0.26,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +0.271,0.279,0.282,0.289,0.274,0.282,0.316,0.309,0.333,0.343,0.358,0.344,0.356,0.325,0.358,0.362,0.347,0.339,0.341,0.352,0.367,0.366,0.323,0.326,0.369,0.356,0.346,0.36,0.322,0.364,0.3,0.251,0.13,0.367,0.365,0.363,0.367,0.375,0.376,0.324,0.324,0.323,0.354,0.175,0.362,0.361,0.278,0.306,0.291,0.173,0.314,0.305,0.316,0.318,0.313,0.317,0.328,0.323,0.327,0.327,0.348,0.356,Binding,CCR5_HUMAN,High,Human +0.189,0.247,0.199,0.211,0.234,0.232,0.051,0.233,0.257,0.261,0.046,0.05,0.072,0.09,0.093,0.054,0.05,0.123,0.217,-0.013,0.096,0.124,0.118,0.119,0.042,0.12,0.112,0.093,0.244,0.283,0.194,0.159,0.077,0.075,0.12,0.093,0.203,0.21,0.202,0.233,0.235,0.231,0.083,0.05,0.104,0.086,0.316,0.258,0.301,0.174,0.158,0.18,0.173,0.196,0.186,0.195,0.204,0.17,0.185,0.195,0.312,0.244,Binding,CD19_HUMAN,Low,Human +0.516,0.581,0.61,0.628,0.606,0.622,0.556,0.585,0.603,0.625,0.516,0.615,0.644,0.55,0.63,0.652,0.657,0.652,0.605,0.632,0.57,0.57,0.603,0.566,0.594,0.586,0.591,0.582,0.577,0.621,0.588,0.528,0.216,0.609,0.584,0.57,0.642,0.638,0.631,0.651,0.645,0.641,0.608,0.105,0.546,0.637,0.579,0.558,0.632,0.24,0.622,0.624,0.629,0.646,0.653,0.648,0.645,0.634,0.64,0.658,0.655,0.643,Expression,CP2C9_HUMAN,High,Human +0.516,0.581,0.61,0.628,0.606,0.622,0.556,0.585,0.603,0.625,0.516,0.615,0.644,0.55,0.63,0.652,0.657,0.652,0.605,0.632,0.57,0.57,0.603,0.566,0.594,0.586,0.591,0.582,0.577,0.621,0.588,0.528,0.216,0.609,0.584,0.57,0.642,0.638,0.631,0.651,0.645,0.641,0.608,0.105,0.546,0.637,0.579,0.558,0.632,0.24,0.622,0.624,0.629,0.646,0.653,0.648,0.645,0.634,0.64,0.658,0.655,0.643,Binding,CP2C9_HUMAN,High,Human +0.389,0.504,0.432,0.445,0.47,0.477,0.26,0.4,0.461,0.461,0.481,0.546,0.605,0.449,0.686,0.518,0.484,0.423,0.415,0.406,0.449,0.455,0.502,0.514,0.443,0.587,0.53,0.504,0.501,0.532,0.401,0.389,0.244,0.341,0.474,0.546,0.497,0.536,0.576,0.468,0.513,0.53,0.416,0.326,0.494,0.538,0.562,0.541,0.653,0.62,0.531,0.52,0.532,0.557,0.58,0.556,0.539,0.58,0.548,0.556,0.575,0.72,Stability,CSN4_MOUSE,Medium,Eukaryote +0.357,0.451,0.435,0.438,0.474,0.445,0.154,0.453,0.516,0.547,0.483,0.427,0.398,0.153,0.428,0.478,0.547,0.498,0.467,0.304,0.174,0.206,0.225,0.102,0.117,0.224,0.255,0.248,0.434,0.42,0.469,0.414,0.231,0.16,0.146,0.213,0.38,0.387,0.393,0.438,0.441,0.448,0.154,0.064,0.462,0.256,0.515,0.501,0.654,0.482,0.549,0.508,0.494,0.552,0.517,0.551,0.538,0.525,0.517,0.538,0.6,0.571,Stability,CUE1_YEAST,Medium,Eukaryote +0.53,0.625,0.613,0.582,0.629,0.631,0.073,0.475,0.649,0.657,0.439,0.066,0.064,0.068,0.053,0.036,0.05,0.072,0.152,0.438,0.039,0.042,0.114,0.082,0.009,0.047,0.068,0.194,0.361,0.623,0.563,0.574,0.016,0.096,0.125,0.156,0.536,0.536,0.532,0.63,0.629,0.618,0.004,0.024,0.009,0.028,0.292,0.316,0.505,0.396,0.495,0.491,0.496,0.505,0.494,0.502,0.494,0.49,0.487,0.498,0.345,0.208,Activity,D7PM05_CLYGR,Low,Eukaryote +0.679,0.584,0.607,0.577,0.609,0.616,0.723,0.629,0.524,0.535,0.52,0.55,0.608,0.743,0.764,0.728,0.584,0.487,0.443,0.65,0.574,0.583,0.569,0.538,0.605,0.607,0.564,0.562,0.495,0.614,0.634,0.621,0.511,0.576,0.638,0.58,0.653,0.7,0.667,0.643,0.661,0.636,0.581,0.233,0.372,0.528,0.637,0.453,0.672,0.315,0.483,0.37,0.539,0.499,0.52,0.492,0.508,0.486,0.504,0.504,0.521,0.745,OrganismalFitness,DLG4_HUMAN,Low,Human +0.486,0.442,0.487,0.491,0.526,0.539,0.49,0.437,0.483,0.522,0.469,0.557,0.588,0.411,0.543,0.581,0.543,0.478,0.471,0.444,0.378,0.389,0.373,0.371,0.407,0.42,0.407,0.367,0.395,0.49,0.556,0.537,0.246,0.367,0.392,0.307,0.47,0.478,0.443,0.538,0.544,0.54,0.527,0.076,0.443,0.5,0.439,0.308,0.459,0.135,0.442,0.375,0.444,0.433,0.444,0.43,0.43,0.464,0.46,0.469,0.504,0.535,Binding,DLG4_RAT,Low,Eukaryote +0.171,0.198,0.218,0.23,0.2,0.259,-0.071,0.317,0.368,0.386,0.085,0.126,0.131,0.074,0.209,0.255,0.337,0.337,0.37,0.235,-0.068,0.045,0.033,0.066,0.041,0.107,0.057,0.071,0.235,0.298,0.431,0.351,0.054,-0.013,-0.023,0.031,0.212,0.2,0.211,0.243,0.227,0.231,0.106,-0.048,0.299,0.129,0.568,0.56,0.66,0.556,0.424,0.436,0.516,0.507,0.491,0.476,0.472,0.444,0.436,0.49,0.556,0.481,Stability,DN7A_SACS2,Medium,Prokaryote +0.738,0.75,0.759,0.757,0.767,0.764,0.756,0.732,0.776,0.79,0.773,0.756,0.785,0.827,0.816,0.817,0.803,0.753,0.798,0.781,0.669,0.736,0.776,0.75,0.782,0.765,0.774,0.721,0.764,0.775,0.729,0.714,0.66,0.78,0.776,0.805,0.808,0.816,0.823,0.774,0.787,0.787,0.67,0.164,0.443,0.684,0.643,0.528,0.779,0.785,0.757,0.761,0.76,0.793,0.789,0.803,0.789,0.786,0.794,0.786,0.726,0.801,Stability,DNJA1_HUMAN,High,Human +0.418,0.453,0.421,0.439,0.494,0.501,0.057,0.432,0.406,0.403,0.525,0.472,0.501,0.224,0.458,0.486,0.531,0.508,0.531,0.518,0.34,0.418,0.402,0.408,0.315,0.402,0.365,0.348,0.395,0.448,0.471,0.469,0.216,0.202,0.321,0.252,0.473,0.49,0.494,0.489,0.491,0.507,0.359,-0.177,0.513,0.439,0.38,0.394,0.419,0.439,0.492,0.481,0.497,0.507,0.491,0.512,0.477,0.493,0.504,0.497,0.567,0.531,Stability,DOCK1_MOUSE,High,Eukaryote +0.362,0.454,0.458,0.462,0.464,0.464,-0.007,0.373,0.488,0.491,0.496,0.476,0.491,0.094,0.424,0.49,0.512,0.518,0.512,0.494,0.286,0.39,0.274,0.326,0.388,0.412,0.432,0.448,0.376,0.466,0.477,0.442,0.124,0.38,0.292,0.286,0.434,0.372,0.389,0.459,0.441,0.45,0.393,-0.003,0.481,0.444,0.256,0.416,0.348,0.134,0.471,0.435,0.442,0.45,0.464,0.456,0.472,0.468,0.483,0.475,0.501,0.429,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.362,0.454,0.458,0.462,0.464,0.464,-0.007,0.373,0.488,0.491,0.496,0.476,0.491,0.094,0.424,0.49,0.512,0.518,0.512,0.494,0.286,0.39,0.274,0.326,0.388,0.412,0.432,0.448,0.376,0.466,0.477,0.442,0.124,0.38,0.292,0.286,0.434,0.372,0.389,0.459,0.441,0.45,0.393,-0.003,0.481,0.444,0.256,0.416,0.348,0.134,0.471,0.435,0.442,0.45,0.464,0.456,0.472,0.468,0.483,0.475,0.501,0.429,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.362,0.454,0.458,0.462,0.464,0.464,-0.007,0.373,0.488,0.491,0.496,0.476,0.491,0.094,0.424,0.49,0.512,0.518,0.512,0.494,0.286,0.39,0.274,0.326,0.388,0.412,0.432,0.448,0.376,0.466,0.477,0.442,0.124,0.38,0.292,0.286,0.434,0.372,0.389,0.459,0.441,0.45,0.393,-0.003,0.481,0.444,0.256,0.416,0.348,0.134,0.471,0.435,0.442,0.45,0.464,0.456,0.472,0.468,0.483,0.475,0.501,0.429,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.362,0.454,0.458,0.462,0.464,0.464,-0.007,0.373,0.488,0.491,0.496,0.476,0.491,0.094,0.424,0.49,0.512,0.518,0.512,0.494,0.286,0.39,0.274,0.326,0.388,0.412,0.432,0.448,0.376,0.466,0.477,0.442,0.124,0.38,0.292,0.286,0.434,0.372,0.389,0.459,0.441,0.45,0.393,-0.003,0.481,0.444,0.256,0.416,0.348,0.134,0.471,0.435,0.442,0.45,0.464,0.456,0.472,0.468,0.483,0.475,0.501,0.429,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.115,0.095,0.18,0.178,0.187,0.188,0.04,0.19,0.19,0.198,0.154,0.204,0.209,0.194,0.207,0.195,0.172,0.154,0.113,0.183,0.164,0.152,0.201,0.183,0.176,0.164,0.199,0.183,0.163,0.178,0.133,0.135,0.142,0.18,0.184,0.193,0.207,0.213,0.215,0.195,0.201,0.199,0.194,0.134,0.187,0.185,0.055,0.149,0.114,0.036,0.189,0.186,0.168,0.188,0.22,0.197,0.183,0.19,0.2,0.199,0.148,0.224,Activity,ENVZ_ECOLI,High,Prokaryote +0.369,0.397,0.238,0.367,0.388,0.377,0.056,0.353,0.341,0.366,0.359,0.415,0.389,0.11,0.055,0.035,0.012,0.048,0.095,0.388,0.38,0.358,0.408,0.419,0.263,0.394,0.401,0.391,0.374,0.389,0.355,0.322,0.253,0.368,0.373,0.404,0.392,0.396,0.407,0.39,0.392,0.392,0.343,-0.04,0.334,0.395,0.355,0.325,0.364,0.231,0.215,0.261,0.274,0.24,0.214,0.233,0.241,0.258,0.24,0.263,0.15,0.102,OrganismalFitness,ENV_HV1B9,Medium,Virus +0.338,0.303,0.322,0.323,0.339,0.345,-0.001,0.322,0.345,0.344,0.298,0.32,0.336,-0.009,-0.006,0.004,0.046,0.069,0.16,0.337,0.35,0.358,0.371,0.364,0.345,0.358,0.358,0.354,0.362,0.35,0.325,0.29,0.189,0.344,0.36,0.358,0.359,0.366,0.362,0.367,0.369,0.365,0.233,-0.013,0.321,0.293,0.212,0.245,0.119,0.072,0.18,0.179,0.203,0.226,0.188,0.202,0.2,0.192,0.193,0.209,0.167,0.098,OrganismalFitness,ENV_HV1BR,Medium,Virus +0.615,0.706,0.705,0.708,0.732,0.733,-0.32,0.684,0.781,0.789,0.762,0.787,0.789,-0.378,0.768,0.803,0.81,0.769,0.744,0.741,0.555,0.599,0.685,0.656,0.641,0.664,0.684,0.705,0.735,0.795,0.752,0.738,0.677,0.612,0.603,0.644,0.716,0.711,0.728,0.741,0.737,0.735,0.625,-0.253,0.618,0.63,0.665,0.604,0.828,0.76,0.805,0.801,0.806,0.811,0.82,0.809,0.802,0.806,0.8,0.812,0.799,0.805,Stability,EPHB2_HUMAN,High,Human +0.386,0.368,0.254,0.25,0.244,0.268,0.449,0.415,0.405,0.416,0.449,0.48,0.49,0.481,0.463,0.469,0.423,0.373,0.295,0.047,0.457,0.499,0.55,0.471,0.505,0.536,0.596,0.598,0.506,0.381,0.424,0.066,0.489,0.553,0.471,0.521,0.494,0.465,0.492,0.447,0.441,0.439,0.468,0.459,0.537,0.463,0.461,0.491,-0.139,0.022,0.41,0.406,0.451,0.44,0.415,0.406,0.432,0.452,0.438,0.446,0.525,0.488,Expression,ERBB2_HUMAN,Low,Human +0.258,0.399,0.389,0.415,0.387,0.387,0.187,0.315,0.345,0.413,0.336,0.305,0.335,0.169,0.27,0.267,0.3,0.281,0.323,0.32,0.121,0.196,0.268,0.286,0.27,0.254,0.311,0.272,0.379,0.379,0.434,0.406,0.075,0.184,0.272,0.261,0.31,0.329,0.326,0.403,0.407,0.397,0.193,0.121,0.289,0.262,0.557,0.489,0.545,0.368,0.366,0.334,0.347,0.343,0.378,0.347,0.347,0.365,0.369,0.37,0.391,0.212,Stability,ESTA_BACSU,High,Prokaryote +0.06,0.395,0.395,0.44,0.428,0.43,0.016,0.32,0.382,0.395,0.437,0.382,0.393,-0.091,-0.001,0.049,0.383,0.366,0.434,0.391,-0.075,-0.122,-0.104,-0.005,0.098,0.125,-0.021,0.298,0.403,0.375,0.461,0.441,0.037,-0.036,-0.124,0.433,0.062,0.011,0.425,0.38,0.367,0.444,-0.064,-0.081,0.199,-0.048,-0.008,0.246,0.069,0.035,0.431,0.447,0.423,0.46,0.453,0.457,0.43,0.443,0.447,0.448,0.27,-0.126,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.06,0.395,0.395,0.44,0.428,0.43,0.016,0.32,0.382,0.395,0.437,0.382,0.393,-0.091,-0.001,0.049,0.383,0.366,0.434,0.391,-0.075,-0.122,-0.104,-0.005,0.098,0.125,-0.021,0.298,0.403,0.375,0.461,0.441,0.037,-0.036,-0.124,0.433,0.062,0.011,0.425,0.38,0.367,0.444,-0.064,-0.081,0.199,-0.048,-0.008,0.246,0.069,0.035,0.431,0.447,0.423,0.46,0.453,0.457,0.43,0.443,0.447,0.448,0.27,-0.126,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.38,0.431,0.449,0.458,0.449,0.441,0.023,0.297,0.372,0.456,0.509,0.269,0.433,0.059,0.449,0.536,0.524,0.48,0.386,0.464,0.169,0.272,0.343,0.288,0.156,0.477,0.451,0.292,0.394,0.52,0.409,0.336,0.276,0.065,0.121,0.148,0.355,0.348,0.302,0.415,0.415,0.389,0.395,0.008,0.501,0.447,0.582,0.551,0.588,0.525,0.527,0.483,0.514,0.512,0.5,0.528,0.499,0.489,0.506,0.515,0.595,0.585,Stability,FECA_ECOLI,High,Prokaryote +0.423,0.394,0.479,0.482,0.487,0.498,0.18,0.342,0.237,0.237,0.171,0.17,0.164,0.172,0.163,0.147,0.188,0.265,0.378,0.287,0.221,0.213,0.218,0.279,0.169,0.229,0.193,0.25,0.22,0.478,0.315,0.319,-0.077,0.095,0.168,0.23,0.398,0.41,0.342,0.484,0.49,0.409,0.18,0.184,0.177,0.21,0.685,0.603,0.687,0.589,0.306,0.397,0.443,0.402,0.397,0.403,0.369,0.368,0.296,0.399,0.581,0.368,Stability,FKBP3_HUMAN,Medium,Human +0.237,0.403,0.507,0.567,0.486,0.532,0.337,-0.004,0.513,0.575,0.623,0.458,0.462,0.34,0.392,0.528,0.668,0.67,0.655,0.507,0.319,0.355,0.383,0.372,0.364,0.424,0.493,0.451,0.579,0.628,0.651,0.563,0.303,0.279,0.331,0.325,0.56,0.555,0.557,0.508,0.509,0.503,0.395,0.301,0.604,0.446,0.287,0.568,0.308,0.114,0.627,0.634,0.613,0.625,0.632,0.634,0.62,0.617,0.619,0.638,0.558,0.408,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +0.253,0.25,0.245,0.248,0.242,0.241,0.182,0.221,0.24,0.24,0.238,0.272,0.284,0.318,0.278,0.266,0.281,0.261,0.248,0.186,0.175,0.189,0.168,0.167,0.158,0.118,0.081,0.134,0.164,0.225,0.255,0.247,0.025,0.175,0.169,0.265,0.258,0.257,0.278,0.258,0.257,0.274,0.107,0.192,0.157,0.126,0.145,0.183,0.201,0.167,0.227,0.22,0.217,0.214,0.226,0.224,0.219,0.221,0.224,0.222,0.221,0.245,Binding,GCN4_YEAST,Low,Eukaryote +0.442,0.429,0.439,0.446,0.451,0.452,0.205,0.398,0.466,0.462,0.391,0.423,0.46,0.159,0.247,0.448,0.397,0.356,0.411,0.401,0.253,0.385,0.38,0.409,0.326,0.439,0.425,0.384,0.397,0.415,0.423,0.393,0.276,0.321,0.394,0.356,0.432,0.448,0.437,0.451,0.47,0.461,0.233,0.121,0.432,0.26,0.357,0.421,0.387,0.101,0.389,0.335,0.402,0.378,0.391,0.399,0.377,0.391,0.375,0.405,0.461,0.338,OrganismalFitness,GDIA_HUMAN,Low,Human +0.649,0.644,0.672,0.673,0.679,0.679,0.049,0.635,0.667,0.661,0.524,0.098,0.103,0.078,0.131,0.103,0.108,0.149,0.29,0.598,0.078,0.105,0.181,0.098,0.046,0.188,0.298,0.642,0.647,0.678,0.607,0.615,0.074,0.066,0.182,0.629,0.649,0.651,0.672,0.683,0.685,0.706,0.024,-0.014,0.041,0.036,0.512,0.508,0.713,0.602,0.591,0.6,0.61,0.617,0.609,0.611,0.605,0.606,0.587,0.607,0.623,0.449,Activity,GFP_AEQVI,Low,Eukaryote +0.227,0.134,0.207,0.21,0.18,0.162,0.307,0.524,0.385,0.361,0.388,0.417,0.412,0.339,0.392,0.453,0.425,0.38,0.47,0.163,0.37,0.388,0.364,0.407,0.363,0.39,0.414,0.42,0.49,0.349,0.435,0.276,0.281,0.423,0.372,0.414,0.423,0.392,0.417,0.391,0.362,0.405,0.405,0.384,0.428,0.341,0.33,0.413,0.31,0.177,0.529,0.513,0.507,0.479,0.5,0.455,0.5,0.434,0.464,0.507,0.443,0.411,Expression,GLPA_HUMAN,Low,Human +0.405,0.521,0.507,0.538,0.54,0.546,0.486,0.45,0.539,0.491,0.533,0.455,0.515,0.526,0.598,0.626,0.647,0.535,0.579,0.534,0.544,0.516,0.514,0.453,0.532,0.509,0.446,0.524,0.448,0.504,0.428,0.422,0.464,0.536,0.506,0.404,0.517,0.506,0.436,0.562,0.561,0.532,0.584,0.323,0.53,0.572,0.672,0.527,0.69,0.516,0.621,0.649,0.638,0.638,0.647,0.628,0.629,0.645,0.625,0.65,0.556,0.607,OrganismalFitness,GRB2_HUMAN,Medium,Human +0.315,0.41,0.113,0.147,0.351,0.36,0.241,0.399,0.43,0.485,0.666,0.45,0.496,0.285,0.416,0.574,0.686,0.588,0.577,0.245,0.292,0.306,0.362,0.368,0.324,0.325,0.21,0.345,0.515,0.579,0.636,0.504,0.191,0.262,0.287,0.486,0.43,0.443,0.531,0.374,0.43,0.507,0.31,0.285,0.614,0.376,0.667,0.724,0.73,0.601,0.692,0.698,0.71,0.687,0.714,0.711,0.696,0.714,0.719,0.723,0.768,0.609,Stability,HCP_LAMBD,Medium,Virus +0.291,0.37,0.324,0.324,0.332,0.336,0.055,0.234,0.359,0.37,0.303,0.303,0.293,0.134,0.206,0.474,0.387,0.34,0.385,0.337,-0.024,0.232,0.302,0.264,0.16,0.294,0.308,0.247,0.214,0.312,0.332,0.307,0.103,0.135,0.101,0.229,0.327,0.298,0.353,0.327,0.341,0.359,0.089,0.038,0.433,0.138,0.252,0.26,0.38,0.321,0.368,0.408,0.345,0.392,0.411,0.436,0.442,0.428,0.451,0.417,0.414,0.219,Stability,HECD1_HUMAN,Medium,Human +0.418,0.409,0.403,0.407,0.42,0.418,0.126,0.103,0.432,0.442,0.388,0.383,0.396,0.142,0.387,0.408,0.393,0.418,0.429,0.108,0.349,0.373,0.37,0.375,0.358,0.387,0.376,0.385,0.402,0.435,0.42,0.41,0.093,0.377,0.381,0.377,0.42,0.43,0.438,0.427,0.434,0.44,0.323,0.135,0.384,0.378,0.314,0.358,0.301,0.146,0.357,0.337,0.353,0.363,0.377,0.374,0.371,0.373,0.368,0.377,0.431,0.415,Activity,HEM3_HUMAN,Medium,Human +0.472,0.542,0.558,0.557,0.533,0.531,0.146,0.273,0.468,0.508,0.472,0.411,0.477,0.057,0.113,0.44,0.411,0.475,0.48,0.454,0.325,0.402,0.433,0.478,0.404,0.485,0.464,0.516,0.539,0.524,0.4,0.374,0.143,0.397,0.453,0.585,0.488,0.496,0.616,0.573,0.548,0.582,0.14,0.04,0.299,0.02,0.378,0.424,0.529,0.392,0.444,0.35,0.404,0.427,0.44,0.407,0.418,0.45,0.431,0.424,0.521,0.222,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +0.491,0.421,0.382,0.394,0.456,0.451,0.158,0.282,0.282,0.274,0.275,0.341,0.315,0.046,0.01,0.317,0.509,0.374,0.378,0.302,0.451,0.162,0.175,0.221,0.435,0.197,0.168,0.209,0.213,0.476,0.413,0.401,0.228,0.308,0.247,0.219,0.44,0.34,0.33,0.437,0.361,0.36,0.172,0.069,0.477,0.496,0.323,0.444,-0.184,0.105,0.493,0.492,0.486,0.498,0.489,0.492,0.48,0.5,0.485,0.499,0.529,0.438,OrganismalFitness,HMDH_HUMAN,Low,Human +0.384,0.396,0.437,0.446,0.436,0.436,0.246,0.38,0.439,0.444,0.407,0.452,0.475,0.041,0.152,0.26,0.334,0.38,0.364,0.299,0.42,0.433,0.427,0.44,0.423,0.432,0.458,0.43,0.456,0.46,0.483,0.462,0.252,0.405,0.415,0.412,0.43,0.437,0.433,0.451,0.455,0.452,0.213,-0.075,0.413,0.415,0.12,0.309,0.101,0.029,0.322,0.319,0.318,0.336,0.33,0.326,0.34,0.335,0.318,0.336,0.404,0.358,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.384,0.396,0.437,0.446,0.436,0.436,0.246,0.38,0.439,0.444,0.407,0.452,0.475,0.041,0.152,0.26,0.334,0.38,0.364,0.299,0.42,0.433,0.427,0.44,0.423,0.432,0.458,0.43,0.456,0.46,0.483,0.462,0.252,0.405,0.415,0.412,0.43,0.437,0.433,0.451,0.455,0.452,0.213,-0.075,0.413,0.415,0.12,0.309,0.101,0.029,0.322,0.319,0.318,0.336,0.33,0.326,0.34,0.335,0.318,0.336,0.404,0.358,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.384,0.396,0.437,0.446,0.436,0.436,0.246,0.38,0.439,0.444,0.407,0.452,0.475,0.041,0.152,0.26,0.334,0.38,0.364,0.299,0.42,0.433,0.427,0.44,0.423,0.432,0.458,0.43,0.456,0.46,0.483,0.462,0.252,0.405,0.415,0.412,0.43,0.437,0.433,0.451,0.455,0.452,0.213,-0.075,0.413,0.415,0.12,0.309,0.101,0.029,0.322,0.319,0.318,0.336,0.33,0.326,0.34,0.335,0.318,0.336,0.404,0.358,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.406,0.439,0.401,0.42,0.412,0.42,0.143,0.358,0.448,0.467,0.392,0.398,0.422,0.16,0.238,0.419,0.442,0.442,0.417,0.226,0.368,0.37,0.378,0.368,0.367,0.386,0.384,0.382,0.361,0.438,0.393,0.352,0.174,0.388,0.365,0.344,0.422,0.416,0.402,0.43,0.435,0.431,0.242,0.052,0.426,0.407,0.378,0.41,0.392,0.164,0.433,0.436,0.43,0.432,0.438,0.442,0.438,0.44,0.44,0.446,0.472,0.405,OrganismalFitness,HXK4_HUMAN,Medium,Human +0.406,0.439,0.401,0.42,0.412,0.42,0.143,0.358,0.448,0.467,0.392,0.398,0.422,0.16,0.238,0.419,0.442,0.442,0.417,0.226,0.368,0.37,0.378,0.368,0.367,0.386,0.384,0.382,0.361,0.438,0.393,0.352,0.174,0.388,0.365,0.344,0.422,0.416,0.402,0.43,0.435,0.431,0.242,0.052,0.426,0.407,0.378,0.41,0.392,0.164,0.433,0.436,0.43,0.432,0.438,0.442,0.438,0.44,0.44,0.446,0.472,0.405,Expression,HXK4_HUMAN,Medium,Human +0.347,0.317,0.268,0.264,0.364,0.361,-0.005,0.297,0.299,0.336,0.013,0.018,0.014,0.018,0.025,0.01,0.02,0.013,0.094,0.212,0.308,0.328,0.374,0.377,0.004,0.011,0.097,0.002,0.302,0.368,0.25,0.253,0.171,0.304,0.332,0.337,0.329,0.354,0.348,0.383,0.399,0.401,0.007,0.02,0.016,0.019,0.209,0.209,0.231,0.1,0.075,0.114,0.126,0.135,0.111,0.106,0.089,0.098,0.067,0.109,0.056,0.018,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +0.328,0.499,0.541,0.539,0.527,0.537,0.177,0.387,0.261,0.255,0.534,0.54,0.565,0.193,0.477,0.55,0.599,0.556,0.515,0.49,0.361,0.438,0.366,0.411,0.451,0.458,0.482,0.443,0.459,0.401,0.52,0.463,0.238,0.458,0.477,0.527,0.463,0.47,0.495,0.529,0.532,0.539,0.371,0.15,0.562,0.516,0.321,0.518,0.475,0.244,0.545,0.534,0.555,0.575,0.577,0.559,0.551,0.571,0.56,0.583,0.616,0.506,OrganismalFitness,IF1_ECOLI,High,Prokaryote +0.233,0.336,0.444,0.438,0.437,0.436,0.022,0.315,0.371,0.416,0.353,0.352,0.35,0.192,0.324,0.435,0.318,0.282,0.167,0.48,0.206,0.214,0.257,0.312,0.347,0.233,0.372,0.255,0.259,0.457,0.349,0.313,0.097,0.127,0.282,0.362,0.282,0.349,0.401,0.382,0.389,0.445,0.279,0.045,0.339,0.395,0.418,0.368,0.475,0.342,0.304,0.274,0.343,0.33,0.37,0.395,0.33,0.312,0.414,0.37,0.319,0.349,Stability,ILF3_HUMAN,High,Human +0.094,0.101,0.206,0.197,0.231,0.231,0.326,0.128,0.326,0.356,0.453,0.413,0.434,0.377,0.394,0.449,0.429,0.479,0.45,0.185,0.326,0.199,0.218,0.228,0.318,0.335,0.329,0.298,0.36,0.304,0.396,0.334,0.236,0.291,0.249,0.298,0.259,0.219,0.245,0.263,0.221,0.244,0.3,0.248,0.349,0.329,0.592,0.526,0.519,0.49,0.42,0.407,0.429,0.428,0.427,0.429,0.409,0.414,0.419,0.426,0.608,0.522,Stability,ISDH_STAAW,High,Prokaryote +0.273,0.307,0.286,0.312,0.302,0.308,0.212,0.272,0.278,0.287,0.277,0.316,0.359,0.186,0.169,0.364,0.316,0.264,0.226,0.178,0.226,0.238,0.27,0.289,0.238,0.362,0.344,0.329,0.31,0.292,0.328,0.266,-0.102,0.168,0.3,0.356,0.301,0.326,0.366,0.331,0.346,0.378,0.206,0.187,0.31,0.196,0.19,0.27,0.16,0.09,0.314,0.314,0.307,0.3,0.302,0.289,0.326,0.322,0.315,0.32,0.376,0.216,Expression,KCNE1_HUMAN,Medium,Human +0.273,0.307,0.286,0.312,0.302,0.308,0.212,0.272,0.278,0.287,0.277,0.316,0.359,0.186,0.169,0.364,0.316,0.264,0.226,0.178,0.226,0.238,0.27,0.289,0.238,0.362,0.344,0.329,0.31,0.292,0.328,0.266,-0.102,0.168,0.3,0.356,0.301,0.326,0.366,0.331,0.346,0.378,0.206,0.187,0.31,0.196,0.19,0.27,0.16,0.09,0.314,0.314,0.307,0.3,0.302,0.289,0.326,0.322,0.315,0.32,0.376,0.216,Activity,KCNE1_HUMAN,Medium,Human +0.449,0.421,0.298,0.292,0.211,0.21,0.454,0.151,0.303,0.306,0.307,0.216,0.234,0.253,0.197,0.229,0.28,0.258,0.248,0.382,0.468,0.513,0.495,0.463,0.502,0.491,0.455,0.477,0.481,0.46,0.433,0.318,0.344,0.471,0.525,0.511,0.499,0.554,0.538,0.472,0.517,0.509,0.394,0.223,0.193,0.291,0.219,-0.079,-0.202,0.086,0.53,0.39,0.481,0.435,0.444,0.442,0.476,0.441,0.435,0.475,0.271,0.411,Activity,KCNH2_HUMAN,Medium,Human +0.318,0.335,0.294,0.304,0.342,0.347,0.089,0.231,0.298,0.293,0.347,0.318,0.329,0.117,0.324,0.374,0.367,0.364,0.371,0.19,0.292,0.266,0.234,0.243,0.308,0.3,0.288,0.309,0.238,0.37,0.325,0.304,0.161,0.285,0.273,0.235,0.337,0.33,0.31,0.346,0.346,0.338,0.24,0.022,0.296,0.326,0.247,0.274,0.266,0.126,0.348,0.333,0.348,0.342,0.354,0.341,0.357,0.343,0.341,0.354,0.361,0.308,Activity,KCNJ2_MOUSE,Medium,Eukaryote +0.318,0.335,0.294,0.304,0.342,0.347,0.089,0.231,0.298,0.293,0.347,0.318,0.329,0.117,0.324,0.374,0.367,0.364,0.371,0.19,0.292,0.266,0.234,0.243,0.308,0.3,0.288,0.309,0.238,0.37,0.325,0.304,0.161,0.285,0.273,0.235,0.337,0.33,0.31,0.346,0.346,0.338,0.24,0.022,0.296,0.326,0.247,0.274,0.266,0.126,0.348,0.333,0.348,0.342,0.354,0.341,0.357,0.343,0.341,0.354,0.361,0.308,Expression,KCNJ2_MOUSE,Medium,Eukaryote +0.25,0.53,0.437,0.622,0.599,0.603,0.199,0.423,0.538,0.606,0.566,0.597,0.621,0.212,0.261,0.487,0.601,0.645,0.662,0.498,0.285,0.42,0.518,0.54,0.353,0.578,0.577,0.568,0.638,0.627,0.637,0.586,0.148,0.243,0.485,0.586,0.445,0.524,0.588,0.586,0.609,0.63,0.253,0.076,0.559,0.386,0.456,0.582,0.551,0.267,0.587,0.572,0.588,0.578,0.579,0.59,0.574,0.583,0.591,0.599,0.641,0.329,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +0.28,0.426,0.485,0.494,0.481,0.498,0.104,0.465,0.467,0.559,0.444,0.537,0.564,0.179,0.327,0.382,0.515,0.58,0.578,0.499,0.276,0.396,0.462,0.495,0.365,0.492,0.525,0.479,0.552,0.542,0.537,0.46,0.139,0.307,0.398,0.555,0.37,0.404,0.527,0.482,0.495,0.54,0.176,0.051,0.483,0.367,0.398,0.507,0.492,0.145,0.51,0.505,0.511,0.51,0.513,0.52,0.518,0.517,0.52,0.527,0.411,0.334,Activity,LGK_LIPST,Medium,Eukaryote +0.314,0.303,0.189,0.158,0.258,0.266,0.361,0.172,0.413,0.416,0.436,0.37,0.371,0.307,0.363,0.336,0.31,0.426,0.454,0.336,0.382,0.391,0.386,0.385,0.362,0.369,0.373,0.298,0.339,0.349,0.313,0.179,0.203,0.353,0.437,0.343,0.362,0.41,0.348,0.343,0.372,0.313,0.307,0.346,0.318,0.35,0.17,0.259,0.163,0.103,0.235,0.317,0.398,0.33,0.279,0.295,0.352,0.348,0.272,0.332,0.419,0.39,Expression,LYAM1_HUMAN,Medium,Human +0.613,0.636,0.633,0.635,0.621,0.623,0.405,0.643,0.633,0.626,0.619,0.355,0.6,0.453,0.429,0.471,0.48,0.551,0.413,0.627,0.448,0.494,0.522,0.566,0.46,0.515,0.447,0.536,0.49,0.602,0.621,0.609,0.392,0.329,0.565,0.509,0.589,0.647,0.657,0.629,0.661,0.683,0.312,0.079,0.425,0.246,0.596,0.327,0.638,0.611,0.646,0.615,0.617,0.638,0.623,0.638,0.636,0.642,0.651,0.639,0.719,0.727,Stability,MAFG_MOUSE,Medium,Eukaryote +0.551,0.723,0.708,0.727,0.758,0.763,-0.058,0.572,0.658,0.744,0.727,0.422,0.512,-0.146,0.025,0.742,0.699,0.685,0.74,0.715,-0.032,0.058,0.576,0.615,0.193,0.561,0.537,0.568,0.714,0.748,0.729,0.67,0.649,-0.282,0.063,0.033,0.586,0.658,0.634,0.729,0.751,0.742,-0.064,-0.203,0.629,0.433,0.521,0.66,0.773,0.576,0.793,0.76,0.792,0.745,0.758,0.741,0.763,0.776,0.774,0.78,0.813,0.72,Stability,MBD11_ARATH,Medium,Eukaryote +0.454,0.544,0.538,0.563,0.582,0.584,0.517,0.52,0.55,0.562,0.585,0.55,0.562,0.452,0.515,0.542,0.59,0.596,0.599,0.583,0.526,0.506,0.466,0.492,0.53,0.519,0.519,0.488,0.454,0.548,0.57,0.53,0.286,0.471,0.501,0.525,0.512,0.539,0.563,0.558,0.565,0.573,0.542,0.347,0.571,0.575,0.35,0.42,0.498,0.192,0.54,0.548,0.548,0.55,0.562,0.554,0.557,0.561,0.556,0.566,0.575,0.529,Activity,MET_HUMAN,Medium,Human +0.176,0.198,0.237,0.241,0.223,0.227,0.209,0.163,0.198,0.184,0.033,0.167,0.182,0.17,0.199,0.195,0.178,0.189,0.141,0.184,0.209,0.117,0.069,0.016,0.183,0.089,0.057,0.076,-0.067,0.218,0.176,0.222,0.106,0.169,0.058,0.004,0.193,0.119,0.093,0.227,0.205,0.202,0.196,0.125,0.135,0.184,0.059,0.0,0.133,0.006,0.161,0.162,0.148,0.166,0.17,0.163,0.163,0.169,0.177,0.171,0.15,0.186,OrganismalFitness,MK01_HUMAN,Medium,Human +0.225,0.352,0.419,0.424,0.409,0.411,-0.014,0.36,0.412,0.419,0.399,0.416,0.432,-0.011,0.236,0.273,0.382,0.382,0.412,0.387,0.078,0.366,0.38,0.402,0.313,0.383,0.392,0.382,0.424,0.4,0.44,0.41,0.039,0.131,0.315,0.296,0.265,0.327,0.315,0.374,0.396,0.395,0.177,-0.036,0.329,0.235,0.006,0.193,0.123,-0.026,0.319,0.28,0.288,0.293,0.315,0.308,0.295,0.305,0.332,0.313,0.23,0.203,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +0.352,0.399,0.367,0.376,0.383,0.39,0.204,0.339,0.395,0.402,0.349,0.38,0.4,0.213,0.349,0.397,0.339,0.296,0.204,0.364,0.296,0.31,0.264,0.257,0.326,0.317,0.32,0.326,0.301,0.394,0.351,0.324,0.208,0.277,0.351,0.281,0.35,0.388,0.346,0.387,0.405,0.383,0.284,0.103,0.4,0.355,0.293,0.363,0.062,0.099,0.305,0.279,0.291,0.309,0.317,0.3,0.295,0.308,0.339,0.321,0.394,0.366,OrganismalFitness,MSH2_HUMAN,Medium,Human +0.371,0.612,0.709,0.718,0.696,0.704,0.314,0.644,0.673,0.688,0.581,0.692,0.708,0.19,0.352,0.405,0.527,0.608,0.644,0.658,0.311,0.454,0.6,0.662,0.488,0.663,0.711,0.684,0.727,0.679,0.697,0.652,0.291,0.353,0.495,0.665,0.422,0.496,0.638,0.657,0.679,0.714,0.324,-0.031,0.477,0.341,0.449,0.574,0.583,0.225,0.544,0.531,0.559,0.569,0.561,0.573,0.551,0.571,0.557,0.572,0.58,0.378,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +0.25,0.271,0.249,0.257,0.258,0.257,0.205,0.194,0.292,0.293,0.387,0.299,0.32,0.169,0.414,0.482,0.348,0.276,0.285,0.253,0.443,0.162,0.217,0.265,0.437,0.239,0.264,0.245,0.295,0.296,0.22,0.189,0.143,0.451,0.307,0.212,0.378,0.315,0.254,0.363,0.309,0.269,0.278,0.111,0.379,0.411,0.239,0.338,0.377,0.092,0.324,0.289,0.299,0.297,0.305,0.316,0.335,0.319,0.347,0.325,0.332,0.376,OrganismalFitness,MTHR_HUMAN,Low,Human +0.143,0.248,0.334,0.362,0.381,0.412,0.155,0.251,0.428,0.419,0.519,0.482,0.478,0.171,0.411,0.583,0.588,0.507,0.247,0.321,0.351,0.247,0.336,0.311,0.342,0.287,0.364,0.313,0.336,0.283,0.279,0.178,0.126,0.419,0.332,0.282,0.382,0.329,0.292,0.411,0.401,0.382,0.112,0.045,0.431,0.294,0.392,0.427,0.479,0.359,0.517,0.495,0.545,0.512,0.525,0.529,0.513,0.519,0.506,0.525,0.571,0.5,Stability,MYO3_YEAST,High,Eukaryote +0.364,0.328,0.334,0.333,0.363,0.364,0.002,0.335,0.373,0.348,0.015,0.019,0.02,0.023,0.026,0.02,0.028,0.031,0.107,0.269,0.352,0.382,0.408,0.413,0.018,0.042,0.108,0.03,0.352,0.377,0.27,0.279,0.126,0.356,0.373,0.415,0.39,0.402,0.424,0.425,0.426,0.441,0.019,0.015,0.027,0.025,0.259,0.27,0.278,0.123,0.129,0.171,0.166,0.184,0.172,0.172,0.155,0.163,0.129,0.171,0.135,0.081,OrganismalFitness,NCAP_I34A1,Medium,Virus +0.428,0.548,0.586,0.584,0.607,0.619,0.582,0.553,0.584,0.608,0.56,0.568,0.572,0.627,0.638,0.674,0.651,0.582,0.602,0.558,0.564,0.557,0.556,0.564,0.576,0.579,0.606,0.54,0.577,0.64,0.525,0.506,0.551,0.494,0.565,0.577,0.578,0.624,0.628,0.601,0.618,0.621,0.454,0.395,0.342,0.408,0.589,0.401,0.643,0.642,0.634,0.626,0.639,0.648,0.669,0.641,0.635,0.629,0.642,0.645,0.566,0.596,Stability,NKX31_HUMAN,High,Human +0.731,0.694,0.58,0.593,0.708,0.716,0.318,0.532,0.662,0.72,0.702,0.312,0.408,0.268,0.394,0.621,0.678,0.68,0.714,0.276,0.414,0.475,0.478,0.418,0.414,0.48,0.584,0.41,0.513,0.672,0.7,0.558,0.285,0.349,0.462,0.595,0.713,0.674,0.735,0.737,0.704,0.736,0.403,0.288,0.744,0.48,0.486,0.62,-0.024,0.184,0.628,0.6,0.623,0.58,0.624,0.612,0.632,0.674,0.663,0.646,0.696,0.437,Activity,NPC1_HUMAN,Low,Human +0.731,0.694,0.58,0.593,0.708,0.716,0.318,0.532,0.662,0.72,0.702,0.312,0.408,0.268,0.394,0.621,0.678,0.68,0.714,0.276,0.414,0.475,0.478,0.418,0.414,0.48,0.584,0.41,0.513,0.672,0.7,0.558,0.285,0.349,0.462,0.595,0.713,0.674,0.735,0.737,0.704,0.736,0.403,0.288,0.744,0.48,0.486,0.62,-0.024,0.184,0.628,0.6,0.623,0.58,0.624,0.612,0.632,0.674,0.663,0.646,0.696,0.437,Activity,NPC1_HUMAN,Low,Human +0.569,0.565,0.501,0.49,0.584,0.584,0.035,0.39,0.625,0.638,-0.076,0.162,0.448,-0.075,-0.098,0.005,0.161,0.541,0.575,0.343,0.583,0.633,0.584,0.571,0.047,0.53,0.627,0.462,0.654,0.635,0.404,0.441,-0.169,0.512,0.532,0.551,0.592,0.615,0.621,0.628,0.638,0.632,-0.105,-0.127,-0.111,-0.111,0.422,0.414,0.448,0.178,0.19,0.228,0.235,0.297,0.253,0.29,0.263,0.247,0.199,0.254,0.292,0.145,OrganismalFitness,NRAM_I33A0,Low,Virus +0.271,0.453,0.564,0.596,0.591,0.594,-0.005,0.389,0.602,0.665,0.603,0.6,0.645,0.299,0.419,0.46,0.526,0.583,0.599,0.501,0.301,0.454,0.546,0.518,0.412,0.579,0.577,0.549,0.542,0.587,0.623,0.553,0.147,0.36,0.423,0.575,0.433,0.456,0.604,0.587,0.586,0.635,0.386,0.015,0.603,0.429,0.483,0.581,0.534,0.317,0.55,0.521,0.544,0.568,0.556,0.571,0.567,0.559,0.556,0.573,0.679,0.564,Expression,NUD15_HUMAN,High,Human +0.392,0.589,0.58,0.571,0.602,0.613,0.285,0.465,0.638,0.599,0.56,0.323,0.346,0.322,0.39,0.405,0.494,0.532,0.533,0.56,0.394,0.549,0.572,0.589,0.325,0.497,0.44,0.525,0.572,0.638,0.67,0.706,0.183,0.365,0.419,0.425,0.505,0.493,0.508,0.628,0.601,0.622,0.375,0.246,0.381,0.357,0.682,0.641,0.73,0.658,0.709,0.695,0.699,0.721,0.726,0.741,0.716,0.716,0.719,0.723,0.685,0.575,Stability,NUSA_ECOLI,High,Prokaryote +0.405,0.404,0.417,0.431,0.451,0.434,0.23,0.423,0.485,0.461,0.398,0.361,0.421,0.389,0.561,0.51,0.518,0.525,0.477,0.521,0.264,0.339,0.315,0.347,0.38,0.385,0.461,0.334,0.378,0.502,0.394,0.387,0.321,0.269,0.275,0.372,0.395,0.399,0.426,0.413,0.417,0.424,0.445,0.144,0.395,0.531,0.676,0.56,0.759,0.64,0.487,0.457,0.454,0.507,0.487,0.485,0.485,0.486,0.483,0.488,0.41,0.588,Stability,NUSG_MYCTU,High,Prokaryote +0.54,0.697,0.759,0.777,0.745,0.757,0.384,0.455,0.751,0.763,0.691,0.735,0.759,0.392,0.779,0.819,0.797,0.786,0.755,0.723,0.688,0.689,0.663,0.687,0.589,0.667,0.694,0.726,0.604,0.735,0.694,0.679,0.549,0.262,0.461,0.575,0.687,0.695,0.704,0.755,0.76,0.764,0.543,0.115,0.507,0.633,0.63,0.54,0.782,0.747,0.762,0.76,0.761,0.755,0.772,0.76,0.773,0.767,0.776,0.771,0.825,0.772,Stability,OBSCN_HUMAN,High,Human +-0.211,-0.17,0.339,0.188,0.332,0.332,-0.149,0.093,0.175,0.19,0.064,-0.1,-0.115,-0.069,-0.151,0.14,0.221,0.089,0.21,0.233,0.189,0.174,0.159,0.177,0.171,0.195,0.166,0.192,0.233,0.327,0.062,0.023,0.245,-0.05,0.025,0.012,0.19,0.181,0.178,0.271,0.26,0.29,0.268,-0.07,0.339,0.327,0.452,0.424,0.437,0.322,0.118,0.075,0.085,0.129,0.073,0.127,0.114,0.116,0.094,0.107,0.286,0.171,Stability,ODP2_GEOSE,High,Prokaryote +0.17,0.433,0.445,0.517,0.463,0.466,0.347,0.569,0.563,0.6,0.397,0.499,0.56,0.29,0.508,0.518,0.552,0.449,0.482,0.555,0.528,0.549,0.538,0.578,0.552,0.594,0.598,0.577,0.57,0.511,0.422,0.326,0.17,0.503,0.53,0.485,0.537,0.532,0.503,0.485,0.482,0.488,0.386,0.08,0.493,0.472,0.632,0.518,0.632,0.224,0.402,0.353,0.412,0.421,0.482,0.487,0.418,0.421,0.49,0.472,0.582,0.522,Expression,OPSD_HUMAN,High,Human +0.524,0.564,0.484,0.522,0.55,0.552,0.127,0.404,0.59,0.592,0.546,0.573,0.581,0.142,0.411,0.512,0.531,0.51,0.534,0.565,0.453,0.461,0.522,0.545,0.476,0.555,0.526,0.54,0.574,0.599,0.475,0.409,0.085,0.429,0.511,0.569,0.522,0.57,0.614,0.567,0.584,0.599,0.279,0.092,0.524,0.463,0.649,0.606,0.674,0.364,0.526,0.547,0.544,0.574,0.556,0.567,0.56,0.548,0.541,0.564,0.616,0.49,Activity,OTC_HUMAN,Medium,Human +0.116,0.243,0.184,0.201,0.224,0.227,0.192,0.134,0.141,0.146,0.434,0.604,0.581,0.205,0.563,0.588,0.319,0.362,0.408,0.155,0.184,0.178,0.221,0.275,0.345,0.26,0.38,0.307,0.148,0.259,0.406,0.388,0.132,0.168,0.225,0.349,0.201,0.198,0.308,0.242,0.22,0.262,0.365,0.184,0.543,0.548,0.532,0.533,0.61,0.537,0.395,0.342,0.348,0.401,0.422,0.421,0.374,0.409,0.414,0.404,0.642,0.632,Stability,OTU7A_HUMAN,High,Human +0.142,0.272,0.327,0.33,0.316,0.314,0.192,0.222,0.287,0.295,0.355,0.329,0.335,0.172,0.238,0.312,0.342,0.37,0.371,0.217,0.147,0.228,0.277,0.269,0.218,0.304,0.326,0.302,0.358,0.343,0.359,0.324,0.092,0.196,0.212,0.269,0.268,0.267,0.286,0.313,0.311,0.316,0.198,0.079,0.336,0.24,0.34,0.371,0.347,0.14,0.348,0.339,0.332,0.339,0.344,0.341,0.344,0.346,0.334,0.352,0.402,0.282,Activity,OXDA_RHOTO,High,Eukaryote +0.142,0.272,0.327,0.33,0.316,0.314,0.192,0.222,0.287,0.295,0.355,0.329,0.335,0.172,0.238,0.312,0.342,0.37,0.371,0.217,0.147,0.228,0.277,0.269,0.218,0.304,0.326,0.302,0.358,0.343,0.359,0.324,0.092,0.196,0.212,0.269,0.268,0.267,0.286,0.313,0.311,0.316,0.198,0.079,0.336,0.24,0.34,0.371,0.347,0.14,0.348,0.339,0.332,0.339,0.344,0.341,0.344,0.346,0.334,0.352,0.402,0.282,Expression,OXDA_RHOTO,High,Eukaryote +0.46,0.467,0.365,0.392,0.462,0.466,-0.052,0.347,0.35,0.356,0.525,0.485,0.546,-0.103,-0.089,0.398,0.479,0.511,0.531,0.176,0.361,0.477,0.479,0.446,0.429,0.494,0.5,0.526,0.369,0.486,0.51,0.455,0.171,0.305,0.496,0.386,0.454,0.511,0.444,0.47,0.514,0.454,-0.098,-0.118,0.532,0.007,0.428,0.522,0.445,0.212,0.487,0.496,0.496,0.494,0.497,0.509,0.496,0.501,0.497,0.51,0.533,0.232,OrganismalFitness,P53_HUMAN,Low,Human +0.46,0.467,0.365,0.392,0.462,0.466,-0.052,0.347,0.35,0.356,0.525,0.485,0.546,-0.103,-0.089,0.398,0.479,0.511,0.531,0.176,0.361,0.477,0.479,0.446,0.429,0.494,0.5,0.526,0.369,0.486,0.51,0.455,0.171,0.305,0.496,0.386,0.454,0.511,0.444,0.47,0.514,0.454,-0.098,-0.118,0.532,0.007,0.428,0.522,0.445,0.212,0.487,0.496,0.496,0.494,0.497,0.509,0.496,0.501,0.497,0.51,0.533,0.232,OrganismalFitness,P53_HUMAN,Low,Human +0.46,0.467,0.365,0.392,0.462,0.466,-0.052,0.347,0.35,0.356,0.525,0.485,0.546,-0.103,-0.089,0.398,0.479,0.511,0.531,0.176,0.361,0.477,0.479,0.446,0.429,0.494,0.5,0.526,0.369,0.486,0.51,0.455,0.171,0.305,0.496,0.386,0.454,0.511,0.444,0.47,0.514,0.454,-0.098,-0.118,0.532,0.007,0.428,0.522,0.445,0.212,0.487,0.496,0.496,0.494,0.497,0.509,0.496,0.501,0.497,0.51,0.533,0.232,OrganismalFitness,P53_HUMAN,Low,Human +0.46,0.467,0.365,0.392,0.462,0.466,-0.052,0.347,0.35,0.356,0.525,0.485,0.546,-0.103,-0.089,0.398,0.479,0.511,0.531,0.176,0.361,0.477,0.479,0.446,0.429,0.494,0.5,0.526,0.369,0.486,0.51,0.455,0.171,0.305,0.496,0.386,0.454,0.511,0.444,0.47,0.514,0.454,-0.098,-0.118,0.532,0.007,0.428,0.522,0.445,0.212,0.487,0.496,0.496,0.494,0.497,0.509,0.496,0.501,0.497,0.51,0.533,0.232,OrganismalFitness,P53_HUMAN,Low,Human +0.508,0.58,0.603,0.617,0.564,0.578,0.364,0.526,0.644,0.623,0.569,0.552,0.588,0.319,0.551,0.559,0.605,0.596,0.571,0.618,0.419,0.507,0.478,0.558,0.521,0.584,0.589,0.571,0.653,0.522,0.553,0.469,0.359,0.474,0.499,0.536,0.515,0.526,0.547,0.56,0.568,0.581,0.465,0.032,0.543,0.51,0.337,0.452,0.512,0.125,0.543,0.527,0.487,0.549,0.551,0.519,0.526,0.553,0.561,0.562,0.622,0.61,OrganismalFitness,P84126_THETH,Medium,Prokaryote +0.663,0.617,0.541,0.55,0.654,0.648,0.474,0.569,0.637,0.663,0.688,0.662,0.678,0.476,0.566,0.648,0.716,0.684,0.695,0.541,0.638,0.665,0.666,0.692,0.638,0.698,0.7,0.676,0.666,0.674,0.703,0.635,0.261,0.638,0.648,0.64,0.689,0.692,0.688,0.683,0.687,0.684,0.605,-0.027,0.67,0.654,0.361,0.612,0.514,0.197,0.664,0.66,0.672,0.693,0.685,0.689,0.691,0.69,0.718,0.705,0.715,0.622,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +0.403,0.395,0.377,0.392,0.402,0.413,0.054,0.314,0.428,0.438,0.435,0.408,0.43,0.083,0.391,0.434,0.448,0.335,0.27,0.422,0.132,0.381,0.364,0.345,0.351,0.395,0.399,0.372,0.33,0.423,0.393,0.356,0.141,0.171,0.37,0.379,0.384,0.404,0.412,0.414,0.424,0.426,0.35,0.049,0.43,0.407,0.379,0.45,0.404,0.152,0.441,0.437,0.439,0.448,0.448,0.445,0.449,0.449,0.446,0.457,0.468,0.391,Activity,PAI1_HUMAN,,Human +0.518,0.519,0.499,0.508,0.539,0.543,0.041,0.358,0.152,0.165,0.037,0.054,0.101,0.024,0.029,0.025,0.038,0.041,0.356,0.325,0.456,0.493,0.533,0.538,0.219,0.408,0.444,0.429,0.438,0.584,0.384,0.374,0.107,0.436,0.475,0.541,0.546,0.561,0.572,0.588,0.592,0.584,0.028,0.019,0.031,0.02,0.244,0.232,0.172,0.095,0.154,0.173,0.184,0.187,0.168,0.178,0.158,0.173,0.125,0.181,0.202,0.132,OrganismalFitness,PA_I34A1,Medium,Virus +0.211,0.44,0.731,0.706,0.352,0.335,0.654,0.552,0.711,0.721,0.647,0.757,0.783,0.768,0.823,0.714,0.749,0.714,0.758,0.652,0.685,0.678,0.596,0.699,0.533,0.616,0.596,0.656,0.602,0.587,0.572,0.49,0.303,0.565,0.511,0.602,0.597,0.564,0.606,0.416,0.445,0.422,0.623,0.395,0.599,0.621,0.2,0.502,0.669,0.41,0.603,0.543,0.553,0.551,0.555,0.57,0.568,0.574,0.583,0.569,0.737,0.775,Activity,PHOT_CHLRE,High,Eukaryote +0.222,0.327,0.68,0.645,0.631,0.661,0.585,0.5,0.67,0.707,0.654,0.576,0.674,0.66,0.646,0.665,0.67,0.52,0.548,0.656,0.456,0.671,0.63,0.61,0.559,0.672,0.681,0.647,0.658,0.689,0.692,0.644,0.531,0.539,0.626,0.667,0.607,0.671,0.72,0.677,0.705,0.73,0.556,-0.189,0.623,0.51,0.547,0.574,0.691,0.669,0.659,0.627,0.498,0.563,0.575,0.604,0.608,0.649,0.626,0.62,0.74,0.742,Stability,PIN1_HUMAN,High,Human +0.579,0.52,0.548,0.555,0.549,0.553,0.506,0.47,0.538,0.561,0.467,0.446,0.436,0.557,0.652,0.675,0.626,0.586,0.507,0.542,0.513,0.454,0.477,0.45,0.547,0.493,0.465,0.428,0.468,0.536,0.403,0.386,0.45,0.558,0.508,0.481,0.596,0.548,0.538,0.573,0.537,0.538,0.468,0.412,0.282,0.333,0.453,0.295,0.693,0.63,0.572,0.579,0.572,0.59,0.602,0.609,0.593,0.594,0.598,0.598,0.47,0.593,Stability,PITX2_HUMAN,High,Human +0.179,0.188,0.218,0.234,0.249,0.254,0.198,0.187,0.165,0.206,0.253,0.278,0.31,0.314,0.347,0.464,0.298,0.236,0.238,0.277,0.31,0.289,0.278,0.325,0.3,0.291,0.31,0.292,0.295,0.29,0.326,0.317,0.067,0.335,0.278,0.311,0.262,0.285,0.308,0.288,0.28,0.293,0.266,0.279,0.26,0.3,0.512,0.379,0.493,0.469,0.258,0.303,0.317,0.26,0.284,0.287,0.286,0.299,0.314,0.299,0.324,0.464,Stability,PKN1_HUMAN,High,Human +0.423,0.39,0.377,0.411,0.46,0.473,-0.036,0.336,0.5,0.499,0.292,-0.059,0.042,-0.079,-0.056,0.177,0.395,0.404,0.426,0.356,0.339,0.39,0.381,0.377,0.138,0.388,0.383,0.369,0.393,0.495,0.386,0.319,0.007,0.049,0.274,0.355,0.342,0.385,0.413,0.387,0.43,0.458,-0.065,-0.066,0.342,-0.054,0.193,0.339,0.113,0.046,0.342,0.361,0.364,0.369,0.364,0.365,0.365,0.365,0.374,0.374,0.11,0.068,OrganismalFitness,POLG_CXB3N,Medium,Virus +0.5,0.578,0.289,0.286,0.532,0.533,-0.044,0.434,0.67,0.675,0.318,-0.006,0.0,-0.044,0.007,0.077,0.153,0.275,0.366,0.409,0.453,0.473,0.449,0.456,0.47,0.501,0.493,0.488,0.461,0.622,0.603,0.522,0.035,-0.064,0.115,0.471,0.432,0.383,0.542,0.469,0.397,0.563,-0.055,-0.059,0.391,-0.005,0.36,0.51,0.101,0.116,0.241,0.229,0.244,0.26,0.227,0.261,0.24,0.246,0.223,0.254,0.161,0.032,OrganismalFitness,POLG_DEN26,Low,Virus +0.605,0.547,0.41,0.413,0.605,0.614,-0.039,0.196,0.565,0.577,0.178,0.637,0.635,0.101,0.128,0.114,0.116,0.09,0.078,0.26,0.4,0.443,0.452,0.492,0.422,0.475,0.32,0.41,0.517,0.63,0.587,0.485,0.182,0.474,0.505,0.522,0.515,0.547,0.578,0.48,0.528,0.56,0.114,0.053,0.506,0.109,-0.037,0.349,0.643,0.35,0.308,0.378,0.344,0.379,0.342,0.283,0.323,0.318,0.314,0.372,0.173,0.159,OrganismalFitness,POLG_HCVJF,Medium,Virus +0.251,0.455,0.373,0.407,0.471,0.463,0.07,0.42,0.39,0.514,0.375,0.062,0.157,0.132,0.14,0.031,0.16,0.114,0.084,0.592,0.079,0.058,0.016,0.157,0.106,-0.059,0.008,-0.084,0.17,0.507,0.575,0.58,0.131,0.003,-0.033,0.053,0.382,0.383,0.384,0.453,0.463,0.452,0.006,-0.037,0.033,0.026,0.448,0.443,0.65,0.576,0.658,0.664,0.615,0.736,0.705,0.718,0.692,0.731,0.718,0.718,0.716,0.686,Stability,POLG_PESV,Medium,Virus +0.353,0.539,0.629,0.655,0.612,0.632,0.187,0.339,0.557,0.571,0.623,0.696,0.695,0.028,0.126,0.456,0.594,0.736,0.768,0.717,0.675,0.692,0.25,0.288,0.592,0.722,0.692,0.719,0.299,0.712,0.583,0.509,0.384,0.709,0.683,0.587,0.673,0.7,0.637,0.698,0.706,0.68,0.266,0.03,0.585,0.45,0.594,0.607,0.631,0.302,0.58,0.588,0.593,0.575,0.599,0.583,0.602,0.598,0.585,0.603,0.657,0.499,Activity,PPARG_HUMAN,Medium,Human +0.55,0.523,0.531,0.536,0.609,0.61,0.035,0.378,0.46,0.517,0.578,0.599,0.618,0.281,0.386,0.47,0.602,0.628,0.618,0.387,0.45,0.526,0.537,0.43,0.516,0.577,0.567,0.557,0.419,0.601,0.594,0.546,0.234,0.428,0.528,0.537,0.582,0.593,0.591,0.615,0.614,0.616,0.361,-0.05,0.575,0.515,0.503,0.579,0.563,0.236,0.574,0.575,0.57,0.586,0.593,0.58,0.584,0.585,0.59,0.595,0.633,0.509,OrganismalFitness,PPM1D_HUMAN,Low,Human +0.562,0.655,0.745,0.736,0.772,0.774,0.518,0.493,0.774,0.783,0.727,0.592,0.681,0.54,0.535,0.821,0.805,0.74,0.727,0.715,0.552,0.694,0.7,0.725,0.634,0.716,0.733,0.723,0.744,0.795,0.777,0.756,0.637,0.593,0.556,0.611,0.735,0.746,0.738,0.764,0.784,0.775,0.438,0.281,0.326,0.431,0.639,0.423,0.794,0.783,0.792,0.809,0.804,0.812,0.828,0.823,0.819,0.816,0.816,0.821,0.801,0.835,Stability,PR40A_HUMAN,Medium,Human +0.605,0.619,0.6,0.586,0.622,0.614,0.182,0.459,0.525,0.54,0.582,0.608,0.638,0.233,0.292,0.363,0.501,0.658,0.663,0.447,0.281,0.557,0.609,0.576,0.447,0.632,0.623,0.623,0.52,0.622,0.568,0.526,0.266,0.241,0.55,0.593,0.569,0.621,0.64,0.611,0.638,0.654,0.309,0.093,0.591,0.352,0.649,0.618,0.696,0.298,0.542,0.555,0.561,0.583,0.567,0.571,0.579,0.573,0.561,0.583,0.672,0.519,Expression,PRKN_HUMAN,Low,Human +0.572,0.576,0.566,0.566,0.576,0.572,0.3,0.442,0.491,0.497,0.692,0.601,0.615,0.498,0.57,0.678,0.708,0.627,0.583,0.477,0.304,0.242,0.467,0.391,0.462,0.492,-0.202,0.471,0.529,0.553,0.558,0.505,0.098,0.328,0.409,0.43,0.546,0.543,0.498,0.586,0.585,0.541,0.422,0.237,0.635,0.482,0.586,0.626,0.699,0.643,0.7,0.684,0.687,0.685,0.686,0.692,0.681,0.687,0.689,0.699,0.682,0.674,Stability,PSAE_PICP2,Medium,Prokaryote +0.426,0.448,0.44,0.453,0.466,0.474,0.165,0.352,0.465,0.491,0.462,0.439,0.477,0.186,0.34,0.504,0.492,0.286,0.29,0.496,0.263,0.436,0.368,0.335,0.34,0.314,0.3,0.347,0.261,0.502,0.438,0.432,0.092,0.324,0.416,0.321,0.441,0.466,0.415,0.476,0.495,0.489,0.306,0.009,0.476,0.416,0.452,0.444,0.481,0.216,0.467,0.444,0.475,0.468,0.476,0.478,0.476,0.475,0.478,0.485,0.519,0.482,Expression,PTEN_HUMAN,Medium,Human +0.426,0.448,0.44,0.453,0.466,0.474,0.165,0.352,0.465,0.491,0.462,0.439,0.477,0.186,0.34,0.504,0.492,0.286,0.29,0.496,0.263,0.436,0.368,0.335,0.34,0.314,0.3,0.347,0.261,0.502,0.438,0.432,0.092,0.324,0.416,0.321,0.441,0.466,0.415,0.476,0.495,0.489,0.306,0.009,0.476,0.416,0.452,0.444,0.481,0.216,0.467,0.444,0.475,0.468,0.476,0.478,0.476,0.475,0.478,0.485,0.519,0.482,Activity,PTEN_HUMAN,Medium,Human +0.493,0.379,0.352,0.393,0.495,0.502,0.003,0.437,0.51,0.515,0.47,0.509,0.537,-0.005,-0.003,-0.002,0.044,0.093,0.168,0.403,0.518,0.403,0.39,0.337,0.517,0.401,0.394,0.436,0.354,0.507,0.478,0.483,0.291,0.494,0.419,0.406,0.52,0.502,0.501,0.526,0.515,0.513,0.413,-0.016,0.502,0.456,0.396,0.463,0.263,0.145,0.226,0.266,0.307,0.301,0.25,0.257,0.26,0.245,0.223,0.28,0.232,0.116,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +0.44,0.413,0.437,0.446,0.454,0.452,0.01,0.33,0.428,0.44,0.324,0.384,0.416,0.14,0.146,0.426,0.442,0.418,0.452,0.438,0.332,0.368,0.358,0.332,0.384,0.406,0.38,0.408,0.416,0.418,0.462,0.423,0.14,0.328,0.354,0.294,0.416,0.436,0.414,0.454,0.464,0.455,0.355,0.148,0.424,0.406,0.382,0.416,0.398,0.2,0.412,0.424,0.444,0.442,0.443,0.438,0.436,0.436,0.438,0.448,0.422,0.544,Binding,Q53Z42_HUMAN,Medium,Human +0.44,0.413,0.437,0.446,0.454,0.452,0.01,0.33,0.428,0.44,0.324,0.384,0.416,0.14,0.146,0.426,0.442,0.418,0.452,0.438,0.332,0.368,0.358,0.332,0.384,0.406,0.38,0.408,0.416,0.418,0.462,0.423,0.14,0.328,0.354,0.294,0.416,0.436,0.414,0.454,0.464,0.455,0.355,0.148,0.424,0.406,0.382,0.416,0.398,0.2,0.412,0.424,0.444,0.442,0.443,0.438,0.436,0.436,0.438,0.448,0.422,0.544,Expression,Q53Z42_HUMAN,Medium,Human +0.475,0.593,0.634,0.643,0.654,0.662,0.363,0.535,0.672,0.679,0.588,0.519,0.543,0.144,0.448,0.516,0.572,0.56,0.57,0.649,0.598,0.651,0.652,0.663,0.622,0.662,0.677,0.673,0.68,0.684,0.633,0.575,0.304,0.606,0.652,0.634,0.616,0.653,0.657,0.666,0.672,0.675,0.49,0.008,0.587,0.524,0.408,0.533,0.533,0.155,0.56,0.537,0.533,0.556,0.557,0.542,0.562,0.545,0.553,0.563,0.634,0.54,Activity,Q59976_STRSQ,Medium,Prokaryote +0.327,0.393,0.26,0.264,0.333,0.334,-0.031,0.115,0.413,0.407,0.242,0.036,0.022,0.014,0.046,0.011,0.01,0.01,-0.022,0.294,0.013,0.045,0.003,0.102,0.004,-0.002,0.03,-0.005,-0.013,0.426,0.344,0.354,0.043,0.031,0.022,0.014,0.248,0.246,0.245,0.33,0.327,0.324,0.014,-0.015,-0.004,0.019,0.161,0.156,0.299,0.169,0.228,0.248,0.237,0.287,0.267,0.272,0.261,0.263,0.264,0.261,0.072,0.031,Activity,Q6WV12_9MAXI,Low,Eukaryote +0.451,0.465,0.489,0.507,0.503,0.517,0.454,0.404,0.479,0.495,0.563,0.545,0.566,0.42,0.475,0.492,0.515,0.545,0.523,-0.019,0.521,0.445,0.441,0.462,0.552,0.535,0.541,0.449,0.521,0.524,0.527,0.477,0.19,0.491,0.516,0.454,0.491,0.538,0.512,0.54,0.549,0.544,0.458,0.37,0.545,0.49,0.231,0.454,0.373,0.114,0.511,0.488,0.499,0.511,0.522,0.529,0.536,0.491,0.524,0.531,0.589,0.483,Activity,Q837P4_ENTFA,Medium,Prokaryote +0.196,0.416,0.421,0.459,0.39,0.395,0.222,0.263,0.282,0.294,0.326,0.353,0.342,0.129,0.24,0.334,0.374,0.436,0.392,0.321,0.356,0.399,0.435,0.41,0.319,0.408,0.428,0.511,0.455,0.336,0.378,0.352,0.186,0.399,0.427,0.51,0.342,0.391,0.477,0.417,0.428,0.457,0.274,0.186,0.356,0.271,0.353,0.296,0.391,0.098,0.326,0.344,0.359,0.313,0.383,0.348,0.318,0.353,0.32,0.355,0.341,0.319,Activity,Q837P5_ENTFA,Medium,Prokaryote +0.286,0.368,0.217,0.215,0.31,0.305,0.025,0.303,0.316,0.322,0.223,-0.009,-0.018,-0.027,-0.035,-0.023,-0.025,-0.007,0.034,0.23,0.013,0.037,0.057,0.013,-0.005,-0.001,0.037,0.252,0.267,0.373,0.297,0.307,-0.018,-0.026,0.009,0.303,0.231,0.237,0.318,0.304,0.309,0.345,-0.013,-0.008,-0.024,-0.027,0.097,0.143,0.292,0.189,0.242,0.234,0.246,0.26,0.24,0.254,0.249,0.243,0.238,0.247,0.137,-0.001,Activity,Q8WTC7_9CNID,Low,Eukaryote +0.577,0.561,0.212,0.227,0.6,0.605,-0.049,0.292,-0.037,-0.037,0.103,-0.03,-0.04,-0.009,-0.026,0.079,0.105,0.498,0.577,0.266,0.214,0.259,0.274,0.289,0.242,0.236,0.203,0.21,0.224,0.558,0.507,0.408,-0.056,0.157,0.22,0.216,0.35,0.399,0.401,0.546,0.567,0.565,-0.031,-0.051,0.073,-0.038,0.453,0.415,0.496,0.232,0.251,0.228,0.265,0.298,0.275,0.274,0.273,0.268,0.237,0.277,0.241,0.137,OrganismalFitness,R1AB_SARS2,Medium,Virus +0.28,0.183,0.286,0.299,0.324,0.356,0.508,0.373,0.654,0.624,0.507,0.403,0.45,0.458,0.687,0.704,0.493,0.467,0.542,0.412,0.541,0.575,0.536,0.454,0.552,0.419,0.546,0.444,0.417,0.598,0.422,0.365,0.258,0.477,0.601,0.439,0.497,0.588,0.467,0.436,0.486,0.41,0.573,0.222,0.147,0.37,0.46,0.233,0.656,0.548,0.544,0.578,0.577,0.577,0.606,0.581,0.571,0.575,0.573,0.585,0.373,0.582,Stability,RAD_ANTMA,High,Eukaryote +0.405,0.425,0.382,0.389,0.408,0.408,0.045,0.339,0.445,0.439,0.471,0.423,0.482,0.034,0.242,0.442,0.473,0.433,0.399,0.311,0.285,0.378,0.378,0.339,0.377,0.395,0.378,0.355,0.359,0.439,0.473,0.421,0.12,0.287,0.339,0.363,0.372,0.38,0.396,0.403,0.43,0.441,0.221,0.039,0.48,0.387,0.271,0.383,0.264,0.234,0.456,0.43,0.437,0.483,0.451,0.475,0.439,0.453,0.46,0.472,0.432,0.16,OrganismalFitness,RAF1_HUMAN,Low,Human +0.447,0.436,0.444,0.476,0.466,0.48,0.313,0.353,0.426,0.446,0.318,0.36,0.405,0.457,0.514,0.487,0.498,0.44,0.313,0.514,0.437,0.414,0.42,0.396,0.433,0.403,0.401,0.374,0.305,0.434,0.338,0.319,0.18,0.399,0.439,0.377,0.454,0.478,0.45,0.472,0.485,0.487,0.478,0.326,0.322,0.453,0.386,0.252,0.458,0.255,0.431,0.423,0.443,0.428,0.456,0.43,0.452,0.43,0.439,0.455,0.373,0.474,Activity,RASH_HUMAN,High,Human +0.359,0.357,0.479,0.482,0.465,0.476,0.24,0.297,0.442,0.455,0.43,0.367,0.406,0.454,0.486,0.478,0.462,0.397,0.328,0.504,0.417,0.422,0.46,0.421,0.408,0.44,0.44,0.37,0.414,0.463,0.37,0.328,0.248,0.357,0.457,0.448,0.415,0.484,0.49,0.47,0.493,0.492,0.398,0.316,0.261,0.42,0.286,0.298,0.481,0.304,0.404,0.397,0.418,0.416,0.433,0.418,0.421,0.412,0.385,0.424,0.47,0.452,Expression,RASK_HUMAN,High,Human +0.359,0.357,0.479,0.482,0.465,0.476,0.24,0.297,0.442,0.455,0.43,0.367,0.406,0.454,0.486,0.478,0.462,0.397,0.328,0.504,0.417,0.422,0.46,0.421,0.408,0.44,0.44,0.37,0.414,0.463,0.37,0.328,0.248,0.357,0.457,0.448,0.415,0.484,0.49,0.47,0.493,0.492,0.398,0.316,0.261,0.42,0.286,0.298,0.481,0.304,0.404,0.397,0.418,0.416,0.433,0.418,0.421,0.412,0.385,0.424,0.47,0.452,Binding,RASK_HUMAN,High,Human +0.19,0.108,0.302,0.307,0.305,0.303,0.271,0.277,0.218,0.233,0.43,0.409,0.404,0.321,0.394,0.491,0.552,0.328,0.335,0.259,0.366,0.108,0.267,0.263,0.297,0.078,0.16,0.293,0.202,0.365,0.373,0.318,0.239,0.291,0.364,0.384,0.318,0.34,0.339,0.317,0.334,0.318,0.319,0.255,0.415,0.406,0.487,0.545,0.578,0.459,0.494,0.479,0.491,0.485,0.492,0.486,0.476,0.493,0.48,0.493,0.539,0.526,Stability,RBP1_HUMAN,High,Human +0.304,0.284,0.362,0.37,0.367,0.355,0.303,0.255,0.412,0.421,0.486,0.342,0.393,0.368,0.392,0.498,0.505,0.482,0.478,0.391,0.267,0.243,0.33,0.395,0.32,0.481,0.454,0.412,0.465,0.398,0.478,0.406,0.046,0.293,0.308,0.324,0.36,0.361,0.352,0.403,0.402,0.386,0.304,0.285,0.371,0.327,0.442,0.432,0.56,0.532,0.541,0.555,0.559,0.559,0.545,0.555,0.551,0.549,0.556,0.56,0.531,0.501,Stability,RCD1_ARATH,Medium,Eukaryote +0.293,0.518,0.584,0.577,0.557,0.588,0.167,0.263,0.552,0.556,0.558,0.38,0.492,0.226,0.422,0.451,0.596,0.573,0.639,0.573,0.129,0.17,0.225,0.175,-0.041,0.132,-0.003,0.087,0.577,0.59,0.569,0.537,0.096,0.178,0.127,0.567,0.44,0.45,0.58,0.568,0.598,0.621,0.028,0.139,0.471,0.187,0.635,0.646,0.782,0.69,0.592,0.58,0.56,0.585,0.574,0.583,0.568,0.569,0.6,0.584,0.646,0.541,Stability,RCRO_LAMBD,High,Virus +0.33,0.333,0.466,0.466,0.479,0.479,0.196,0.434,0.512,0.517,0.457,0.55,0.592,0.254,0.675,0.533,0.513,0.464,0.39,0.462,0.422,0.422,0.395,0.453,0.545,0.513,0.51,0.477,0.494,0.51,0.414,0.399,0.429,0.288,0.531,0.475,0.484,0.568,0.54,0.482,0.537,0.536,0.512,0.074,0.556,0.548,0.502,0.548,0.582,0.505,0.47,0.454,0.467,0.449,0.478,0.475,0.475,0.474,0.478,0.481,0.482,0.631,Stability,RD23A_HUMAN,High,Human +0.313,0.367,0.376,0.387,0.482,0.484,0.029,0.355,0.52,0.525,0.166,0.045,0.057,0.033,0.041,0.128,0.322,0.389,0.491,0.427,0.385,0.436,0.454,0.475,0.125,0.358,0.342,0.345,0.413,0.52,0.45,0.395,0.075,0.377,0.423,0.459,0.425,0.452,0.474,0.494,0.508,0.526,0.03,0.02,0.171,0.033,0.215,0.23,0.188,0.055,0.3,0.271,0.279,0.282,0.303,0.296,0.283,0.304,0.31,0.307,0.149,0.11,OrganismalFitness,RDRP_I33A0,Low,Virus +0.206,0.159,0.221,0.227,0.216,0.216,0.038,0.316,0.222,0.232,0.128,0.245,0.267,0.046,0.046,0.173,0.24,0.281,0.274,0.17,0.216,0.259,0.238,0.24,0.29,0.294,0.16,0.253,0.255,0.282,0.35,0.353,0.06,0.231,0.273,0.24,0.245,0.269,0.236,0.246,0.261,0.235,0.053,0.06,0.208,0.071,0.258,0.234,0.304,0.224,0.193,0.221,0.276,0.246,0.267,0.255,0.281,0.241,0.31,0.279,0.274,0.229,OrganismalFitness,REV_HV1H2,Medium,Virus +0.064,0.21,0.212,0.233,0.23,0.227,-0.015,0.238,0.232,0.258,0.248,0.175,0.2,-0.032,0.078,0.055,0.299,0.261,0.246,0.249,-0.067,0.059,0.121,0.115,-0.043,0.13,0.151,0.114,0.158,0.214,0.268,0.213,-0.009,0.009,0.102,0.133,0.107,0.144,0.156,0.22,0.216,0.208,0.115,-0.075,0.193,0.107,0.304,0.248,0.384,0.328,0.331,0.31,0.291,0.333,0.314,0.3,0.314,0.309,0.327,0.319,0.318,0.22,Stability,RFAH_ECOLI,High,Prokaryote +0.341,0.588,0.607,0.601,0.603,0.61,0.224,0.577,0.405,0.352,0.675,0.67,0.657,0.235,0.421,0.487,0.699,0.68,0.676,0.613,0.151,0.493,0.47,0.475,0.473,0.481,0.534,0.545,0.622,0.636,0.563,0.544,0.078,0.481,0.522,0.506,0.54,0.579,0.574,0.588,0.624,0.625,0.041,-0.048,0.467,0.283,0.684,0.64,0.816,0.769,0.716,0.715,0.72,0.726,0.713,0.718,0.723,0.724,0.732,0.728,0.738,0.71,Stability,RL20_AQUAE,High,Prokaryote +0.322,0.352,0.38,0.425,0.38,0.407,0.123,0.373,0.463,0.473,0.191,0.277,0.316,0.151,0.447,0.515,0.549,0.451,0.507,0.437,0.392,0.51,0.487,0.431,0.49,0.434,0.436,0.418,0.395,0.356,0.426,0.436,0.14,0.405,0.468,0.382,0.43,0.479,0.418,0.451,0.477,0.42,0.312,0.1,0.289,0.303,0.164,0.151,0.27,0.069,0.528,0.499,0.508,0.515,0.537,0.526,0.502,0.532,0.531,0.543,0.34,0.379,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.322,0.352,0.38,0.425,0.38,0.407,0.123,0.373,0.463,0.473,0.191,0.277,0.316,0.151,0.447,0.515,0.549,0.451,0.507,0.437,0.392,0.51,0.487,0.431,0.49,0.434,0.436,0.418,0.395,0.356,0.426,0.436,0.14,0.405,0.468,0.382,0.43,0.479,0.418,0.451,0.477,0.42,0.312,0.1,0.289,0.303,0.164,0.151,0.27,0.069,0.528,0.499,0.508,0.515,0.537,0.526,0.502,0.532,0.531,0.543,0.34,0.379,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.322,0.352,0.38,0.425,0.38,0.407,0.123,0.373,0.463,0.473,0.191,0.277,0.316,0.151,0.447,0.515,0.549,0.451,0.507,0.437,0.392,0.51,0.487,0.431,0.49,0.434,0.436,0.418,0.395,0.356,0.426,0.436,0.14,0.405,0.468,0.382,0.43,0.479,0.418,0.451,0.477,0.42,0.312,0.1,0.289,0.303,0.164,0.151,0.27,0.069,0.528,0.499,0.508,0.515,0.537,0.526,0.502,0.532,0.531,0.543,0.34,0.379,Activity,RL40A_YEAST,Medium,Eukaryote +0.544,0.592,0.57,0.584,0.594,0.595,0.058,0.423,0.591,0.591,0.59,0.579,0.593,0.064,0.557,0.588,0.596,0.595,0.592,0.586,0.554,0.57,0.509,0.499,0.557,0.584,0.583,0.586,0.571,0.594,0.578,0.539,0.187,0.538,0.54,0.431,0.581,0.587,0.545,0.617,0.616,0.606,0.529,0.06,0.588,0.567,0.299,0.524,0.281,0.177,0.541,0.53,0.52,0.553,0.544,0.533,0.548,0.543,0.544,0.554,0.608,0.533,Activity,RNC_ECOLI,Medium,Prokaryote +0.627,0.681,0.655,0.673,0.606,0.644,0.67,0.593,0.617,0.659,0.711,0.744,0.75,0.722,0.741,0.752,0.708,0.687,0.63,0.697,0.699,0.75,0.735,0.682,0.742,0.731,0.718,0.723,0.66,0.743,0.666,0.609,0.621,0.633,0.738,0.716,0.72,0.747,0.737,0.699,0.693,0.69,0.749,0.674,0.735,0.728,0.696,0.64,0.793,0.723,0.702,0.672,0.687,0.7,0.699,0.69,0.711,0.702,0.711,0.708,0.756,0.764,Stability,RPC1_BP434,High,Virus +0.267,0.405,0.48,0.496,0.456,0.451,0.202,0.414,0.454,0.469,0.465,0.464,0.498,0.271,0.276,0.382,0.536,0.55,0.55,0.421,0.186,0.314,0.366,0.4,0.202,0.358,0.322,0.302,0.515,0.415,0.585,0.542,0.114,0.142,0.33,0.46,0.256,0.342,0.459,0.418,0.448,0.494,0.283,0.292,0.387,0.304,0.256,0.391,0.384,0.221,0.532,0.504,0.512,0.526,0.525,0.542,0.528,0.521,0.516,0.54,0.52,0.371,Activity,RPC1_LAMBD,High,Virus +0.267,0.405,0.48,0.496,0.456,0.451,0.202,0.414,0.454,0.469,0.465,0.464,0.498,0.271,0.276,0.382,0.536,0.55,0.55,0.421,0.186,0.314,0.366,0.4,0.202,0.358,0.322,0.302,0.515,0.415,0.585,0.542,0.114,0.142,0.33,0.46,0.256,0.342,0.459,0.418,0.448,0.494,0.283,0.292,0.387,0.304,0.256,0.391,0.384,0.221,0.532,0.504,0.512,0.526,0.525,0.542,0.528,0.521,0.516,0.54,0.52,0.371,Activity,RPC1_LAMBD,High,Virus +0.393,0.31,0.336,0.328,0.321,0.326,0.178,0.188,0.387,0.389,0.468,0.394,0.39,0.289,0.326,0.51,0.409,0.372,0.33,0.291,0.199,0.33,0.317,0.323,0.297,0.34,0.416,0.362,0.367,0.375,0.413,0.396,0.117,0.423,0.325,0.306,0.453,0.377,0.365,0.395,0.348,0.32,0.333,0.262,0.443,0.381,0.582,0.44,0.607,0.55,0.396,0.403,0.403,0.399,0.407,0.377,0.415,0.42,0.395,0.413,0.433,0.646,Stability,RS15_GEOSE,Medium,Prokaryote +0.479,0.534,0.56,0.571,0.568,0.581,0.364,0.51,0.585,0.582,0.575,0.596,0.635,0.466,0.508,0.518,0.6,0.573,0.546,0.034,0.49,0.587,0.588,0.576,0.526,0.597,0.613,0.583,0.545,0.58,0.583,0.476,0.314,0.513,0.586,0.579,0.546,0.608,0.611,0.602,0.618,0.617,0.481,0.256,0.572,0.528,0.424,0.537,0.518,0.172,0.562,0.546,0.544,0.566,0.584,0.57,0.568,0.579,0.572,0.584,0.586,0.54,Expression,S22A1_HUMAN,Medium,Human +0.479,0.534,0.56,0.571,0.568,0.581,0.364,0.51,0.585,0.582,0.575,0.596,0.635,0.466,0.508,0.518,0.6,0.573,0.546,0.034,0.49,0.587,0.588,0.576,0.526,0.597,0.613,0.583,0.545,0.58,0.583,0.476,0.314,0.513,0.586,0.579,0.546,0.608,0.611,0.602,0.618,0.617,0.481,0.256,0.572,0.528,0.424,0.537,0.518,0.172,0.562,0.546,0.544,0.566,0.584,0.57,0.568,0.579,0.572,0.584,0.586,0.54,Activity,S22A1_HUMAN,Medium,Human +0.137,0.201,0.394,0.373,0.407,0.399,0.426,0.503,0.41,0.43,0.481,0.472,0.508,0.238,0.496,0.496,0.512,0.52,0.406,0.43,0.491,0.458,0.449,0.479,0.524,0.519,0.476,0.491,0.482,0.518,0.469,0.419,0.353,0.424,0.497,0.499,0.431,0.498,0.489,0.411,0.443,0.444,0.503,-0.042,0.545,0.531,0.336,0.332,0.443,0.502,0.454,0.348,0.234,0.367,0.362,0.395,0.392,0.445,0.426,0.387,0.575,0.607,Stability,SAV1_MOUSE,High,Eukaryote +0.232,0.225,0.38,0.345,0.44,0.403,0.205,0.178,0.481,0.533,0.323,0.268,0.297,0.23,0.266,0.376,0.573,0.658,0.318,0.359,0.228,0.263,0.259,0.235,0.21,0.228,0.221,0.245,0.339,0.549,0.582,0.487,0.169,0.195,0.2,0.225,0.297,0.305,0.314,0.355,0.368,0.382,0.262,0.228,0.316,0.305,0.664,0.627,0.687,0.616,0.529,0.526,0.561,0.55,0.545,0.547,0.569,0.565,0.558,0.561,0.683,0.571,Stability,SBI_STAAM,Medium,Prokaryote +0.387,0.456,0.423,0.433,0.504,0.522,0.352,0.507,0.562,0.574,0.545,0.531,0.542,0.159,0.327,0.502,0.543,0.548,0.524,0.555,0.498,0.511,0.5,0.496,0.512,0.52,0.518,0.525,0.511,0.541,0.463,0.36,0.342,0.513,0.512,0.491,0.53,0.542,0.53,0.549,0.555,0.545,0.489,0.112,0.549,0.531,0.467,0.527,0.501,0.182,0.516,0.526,0.535,0.543,0.545,0.536,0.536,0.538,0.526,0.546,0.578,0.528,Activity,SC6A4_HUMAN,Medium,Human +0.083,0.14,0.261,0.264,0.3,0.272,0.154,0.065,0.271,0.258,0.299,0.298,0.269,0.208,0.305,0.279,0.346,0.406,0.414,0.25,0.106,0.162,0.134,0.144,0.155,0.194,0.152,0.2,0.215,0.229,0.296,0.208,-0.012,0.072,0.104,0.187,0.132,0.132,0.156,0.245,0.235,0.255,0.231,0.177,0.3,0.269,0.525,0.527,0.517,0.477,0.438,0.442,0.437,0.48,0.422,0.478,0.43,0.43,0.391,0.454,0.62,0.513,Stability,SCIN_STAAR,High,Prokaryote +0.13,0.131,0.162,0.162,0.153,0.158,0.13,-0.014,0.163,0.18,0.141,0.217,0.135,0.216,0.177,0.156,0.183,0.152,0.125,0.126,0.106,0.143,0.151,0.127,0.124,0.106,0.093,0.107,0.167,0.144,0.186,0.181,0.095,0.075,0.068,0.069,0.095,0.086,0.086,0.14,0.14,0.152,0.086,0.137,0.165,0.154,0.03,0.092,0.053,0.014,0.127,0.16,0.157,0.127,0.105,0.135,0.143,0.136,0.169,0.146,0.232,0.168,OrganismalFitness,SCN5A_HUMAN,Medium,Human +0.631,0.646,0.615,0.619,0.647,0.65,0.001,0.303,0.456,0.472,0.606,0.418,0.466,0.249,0.274,0.608,0.594,0.598,0.596,0.53,0.026,0.067,0.003,0.238,0.081,0.123,0.045,0.119,0.214,0.64,0.604,0.581,-0.165,0.01,0.26,0.107,0.607,0.613,0.368,0.652,0.65,0.486,0.037,-0.079,0.336,0.011,0.434,0.41,0.608,0.597,0.65,0.641,0.588,0.647,0.63,0.613,0.622,0.623,0.653,0.633,0.639,0.636,Stability,SDA_BACSU,Medium,Prokaryote +0.375,0.483,0.544,0.544,0.52,0.53,0.013,0.456,0.57,0.576,0.518,0.541,0.547,0.183,0.407,0.529,0.558,0.577,0.543,0.535,0.492,0.511,0.513,0.484,0.5,0.53,0.518,0.534,0.504,0.506,0.516,0.469,0.192,0.513,0.528,0.519,0.528,0.54,0.544,0.548,0.551,0.552,0.319,0.031,0.54,0.46,0.382,0.461,0.352,0.189,0.539,0.524,0.54,0.551,0.558,0.548,0.553,0.55,0.543,0.561,0.562,0.478,OrganismalFitness,SERC_HUMAN,High,Human +0.208,0.328,0.378,0.383,0.37,0.379,0.226,0.36,0.424,0.422,0.395,0.384,0.41,0.242,0.249,0.267,0.426,0.388,0.29,0.388,0.262,0.363,0.385,0.355,0.267,0.394,0.39,0.4,0.358,0.407,0.416,0.366,0.153,0.254,0.308,0.376,0.264,0.302,0.367,0.367,0.367,0.396,0.262,0.236,0.421,0.291,0.303,0.358,0.286,0.095,0.373,0.367,0.387,0.39,0.396,0.398,0.381,0.392,0.384,0.398,0.322,0.286,OrganismalFitness,SHOC2_HUMAN,Medium,Human +0.225,0.241,0.235,0.242,0.282,0.273,0.369,0.17,0.21,0.246,0.254,0.335,0.336,0.298,0.396,0.333,0.322,0.262,0.261,0.223,0.166,0.167,0.162,0.19,0.264,0.247,0.202,0.253,0.189,0.231,0.087,0.06,0.222,0.133,0.267,0.229,0.217,0.241,0.234,0.275,0.274,0.262,0.271,0.15,0.208,0.356,0.526,0.346,0.53,0.464,0.333,0.38,0.348,0.333,0.326,0.385,0.333,0.334,0.334,0.356,0.333,0.466,Stability,SOX30_HUMAN,High,Human +0.432,0.509,0.518,0.501,0.552,0.549,-0.093,0.495,0.409,0.408,0.393,-0.041,0.001,-0.032,-0.033,-0.064,0.005,-0.01,0.02,0.514,-0.154,0.032,0.205,0.105,-0.081,-0.041,0.057,0.338,0.428,0.564,0.432,0.442,-0.034,-0.011,0.001,0.066,0.437,0.43,0.427,0.548,0.547,0.536,-0.137,-0.087,-0.071,-0.009,0.512,0.453,0.705,0.56,0.52,0.511,0.493,0.534,0.498,0.515,0.51,0.501,0.504,0.513,0.546,0.414,Stability,SPA_STAAU,Medium,Prokaryote +0.094,0.154,0.022,0.027,0.142,0.157,-0.003,-0.016,0.066,0.171,0.213,0.161,0.126,0.155,0.164,0.191,0.214,0.208,0.268,0.181,0.12,0.092,0.082,0.092,0.086,0.119,0.102,0.104,0.161,0.192,0.312,0.31,0.014,0.112,0.045,0.128,0.141,0.108,0.155,0.136,0.108,0.158,-0.052,-0.106,0.099,-0.011,0.226,0.164,0.302,0.118,0.262,0.226,0.248,0.295,0.285,0.246,0.306,0.292,0.294,0.283,0.279,0.244,Binding,SPG1_STRSG,Low,Prokaryote +0.094,0.154,0.022,0.027,0.142,0.157,-0.003,-0.016,0.066,0.171,0.213,0.161,0.126,0.155,0.164,0.191,0.214,0.208,0.268,0.181,0.12,0.092,0.082,0.092,0.086,0.119,0.102,0.104,0.161,0.192,0.312,0.31,0.014,0.112,0.045,0.128,0.141,0.108,0.155,0.136,0.108,0.158,-0.052,-0.106,0.099,-0.011,0.226,0.164,0.302,0.118,0.262,0.226,0.248,0.295,0.285,0.246,0.306,0.292,0.294,0.283,0.279,0.244,Binding,SPG1_STRSG,Low,Prokaryote +0.094,0.154,0.022,0.027,0.142,0.157,-0.003,-0.016,0.066,0.171,0.213,0.161,0.126,0.155,0.164,0.191,0.214,0.208,0.268,0.181,0.12,0.092,0.082,0.092,0.086,0.119,0.102,0.104,0.161,0.192,0.312,0.31,0.014,0.112,0.045,0.128,0.141,0.108,0.155,0.136,0.108,0.158,-0.052,-0.106,0.099,-0.011,0.226,0.164,0.302,0.118,0.262,0.226,0.248,0.295,0.285,0.246,0.306,0.292,0.294,0.283,0.279,0.244,Binding,SPG1_STRSG,Medium,Prokaryote +0.094,0.154,0.022,0.027,0.142,0.157,-0.003,-0.016,0.066,0.171,0.213,0.161,0.126,0.155,0.164,0.191,0.214,0.208,0.268,0.181,0.12,0.092,0.082,0.092,0.086,0.119,0.102,0.104,0.161,0.192,0.312,0.31,0.014,0.112,0.045,0.128,0.141,0.108,0.155,0.136,0.108,0.158,-0.052,-0.106,0.099,-0.011,0.226,0.164,0.302,0.118,0.262,0.226,0.248,0.295,0.285,0.246,0.306,0.292,0.294,0.283,0.279,0.244,Binding,SPG1_STRSG,Medium,Prokaryote +0.439,0.517,0.496,0.539,0.569,0.565,0.325,0.309,0.543,0.478,0.501,0.437,0.409,0.366,0.432,0.451,0.5,0.504,0.528,0.507,0.388,0.433,0.436,0.392,0.343,0.465,0.356,0.485,0.501,0.614,0.561,0.536,-0.192,0.356,0.44,0.462,0.294,0.335,0.382,0.561,0.562,0.569,0.253,0.089,0.286,0.24,0.447,0.369,0.704,0.57,0.564,0.566,0.584,0.592,0.569,0.595,0.588,0.61,0.584,0.589,0.598,0.549,Stability,SPG2_STRSG,Medium,Prokaryote +0.179,0.26,0.114,0.215,0.347,0.351,-0.031,0.36,0.384,0.403,0.024,-0.044,-0.018,-0.038,-0.017,-0.006,-0.003,0.005,0.055,0.346,0.311,0.375,0.366,0.376,0.382,0.357,0.318,0.388,0.324,0.303,0.318,0.352,0.174,0.341,0.337,0.369,0.348,0.343,0.342,0.403,0.4,0.408,-0.015,0.004,0.001,0.016,0.539,0.508,0.435,0.174,0.272,0.298,0.278,0.36,0.315,0.357,0.321,0.336,0.296,0.342,0.428,0.213,Binding,SPIKE_SARS2,Medium,Virus +0.179,0.26,0.114,0.215,0.347,0.351,-0.031,0.36,0.384,0.403,0.024,-0.044,-0.018,-0.038,-0.017,-0.006,-0.003,0.005,0.055,0.346,0.311,0.375,0.366,0.376,0.382,0.357,0.318,0.388,0.324,0.303,0.318,0.352,0.174,0.341,0.337,0.369,0.348,0.343,0.342,0.403,0.4,0.408,-0.015,0.004,0.001,0.016,0.539,0.508,0.435,0.174,0.272,0.298,0.278,0.36,0.315,0.357,0.321,0.336,0.296,0.342,0.428,0.213,Expression,SPIKE_SARS2,Medium,Virus +0.572,0.613,0.647,0.599,0.632,0.615,0.23,0.524,0.552,0.593,0.637,0.649,0.641,-0.133,0.619,0.627,0.637,0.726,0.657,0.624,0.578,0.643,0.604,0.589,0.589,0.613,0.652,0.563,0.601,0.621,0.631,0.632,0.457,0.508,0.57,0.548,0.629,0.641,0.621,0.623,0.628,0.618,0.488,-0.221,0.465,0.498,0.421,0.401,0.62,0.623,0.693,0.679,0.683,0.685,0.696,0.685,0.676,0.674,0.696,0.691,0.667,0.526,Stability,SPTN1_CHICK,High,Eukaryote +0.399,0.522,0.597,0.61,0.604,0.604,0.086,0.449,0.565,0.592,0.514,0.373,0.503,0.186,0.442,0.529,0.618,0.64,0.527,0.607,0.218,0.474,0.49,0.505,0.345,0.544,0.569,0.506,0.538,0.623,0.566,0.526,0.155,0.194,0.444,0.518,0.502,0.54,0.589,0.614,0.604,0.625,0.209,0.119,0.582,0.583,0.651,0.626,0.629,0.619,0.626,0.582,0.635,0.62,0.634,0.628,0.634,0.61,0.603,0.639,0.682,0.514,Stability,SQSTM_MOUSE,Medium,Eukaryote +0.596,0.658,0.684,0.681,0.693,0.684,-0.19,0.508,0.638,0.61,0.702,0.639,0.654,-0.267,0.662,0.702,0.675,0.698,0.681,0.634,-0.109,0.377,0.382,0.504,0.493,0.575,0.607,0.608,0.61,0.665,0.674,0.635,0.359,0.039,0.189,0.368,0.667,0.669,0.673,0.685,0.689,0.68,0.575,-0.296,0.703,0.618,0.214,0.637,0.731,0.716,0.677,0.673,0.67,0.692,0.696,0.68,0.668,0.669,0.685,0.684,0.788,0.744,Stability,SR43C_ARATH,High,Eukaryote +0.36,0.511,0.696,0.694,0.723,0.718,0.549,0.362,0.743,0.742,0.628,0.664,0.7,0.498,0.713,0.731,0.734,0.673,0.694,0.653,0.679,0.649,0.653,0.62,0.642,0.637,0.659,0.63,0.607,0.694,0.696,0.658,0.577,0.597,0.637,0.645,0.66,0.697,0.705,0.701,0.727,0.728,0.388,0.084,0.538,0.532,0.579,0.538,0.64,0.661,0.751,0.731,0.734,0.736,0.746,0.741,0.735,0.735,0.74,0.75,0.707,0.689,Stability,SRBS1_HUMAN,High,Human +0.463,0.454,0.429,0.442,0.455,0.456,0.476,0.415,0.443,0.466,0.424,0.51,0.532,0.382,0.418,0.423,0.497,0.461,0.43,0.426,0.395,0.369,0.375,0.329,0.396,0.417,0.391,0.377,0.285,0.484,0.523,0.518,0.382,0.366,0.363,0.303,0.444,0.442,0.431,0.468,0.466,0.461,0.506,0.339,0.446,0.49,0.256,0.256,0.348,0.069,0.463,0.436,0.438,0.462,0.469,0.464,0.461,0.471,0.471,0.475,0.456,0.417,Activity,SRC_HUMAN,Medium,Human +0.463,0.454,0.429,0.442,0.455,0.456,0.476,0.415,0.443,0.466,0.424,0.51,0.532,0.382,0.418,0.423,0.497,0.461,0.43,0.426,0.395,0.369,0.375,0.329,0.396,0.417,0.391,0.377,0.285,0.484,0.523,0.518,0.382,0.366,0.363,0.303,0.444,0.442,0.431,0.468,0.466,0.461,0.506,0.339,0.446,0.49,0.256,0.256,0.348,0.069,0.463,0.436,0.438,0.462,0.469,0.464,0.461,0.471,0.471,0.475,0.456,0.417,Activity,SRC_HUMAN,Medium,Human +0.463,0.454,0.429,0.442,0.455,0.456,0.476,0.415,0.443,0.466,0.424,0.51,0.532,0.382,0.418,0.423,0.497,0.461,0.43,0.426,0.395,0.369,0.375,0.329,0.396,0.417,0.391,0.377,0.285,0.484,0.523,0.518,0.382,0.366,0.363,0.303,0.444,0.442,0.431,0.468,0.466,0.461,0.506,0.339,0.446,0.49,0.256,0.256,0.348,0.069,0.463,0.436,0.438,0.462,0.469,0.464,0.461,0.471,0.471,0.475,0.456,0.417,OrganismalFitness,SRC_HUMAN,Medium,Human +0.369,0.373,0.419,0.438,0.48,0.478,0.13,0.425,0.449,0.386,0.433,0.467,0.51,0.25,0.496,0.533,0.509,0.38,0.34,0.517,0.217,0.414,0.443,0.43,0.468,0.386,0.46,0.431,0.333,0.438,0.46,0.445,0.212,0.245,0.473,0.329,0.405,0.511,0.41,0.452,0.522,0.463,0.49,0.086,0.395,0.527,0.497,0.428,0.509,0.412,0.492,0.453,0.5,0.5,0.493,0.517,0.479,0.498,0.491,0.511,0.479,0.524,OrganismalFitness,SUMO1_HUMAN,High,Human +0.103,0.111,0.119,0.132,0.131,0.139,0.131,0.212,0.171,0.162,0.234,0.242,0.233,0.105,0.142,0.15,0.137,0.206,0.205,0.22,0.138,0.203,0.155,0.141,0.086,0.151,0.167,0.105,0.136,0.227,0.205,0.189,0.005,0.137,0.197,0.181,0.124,0.176,0.159,0.134,0.166,0.156,0.115,0.068,0.16,0.155,-0.052,0.046,-0.06,-0.074,0.069,0.056,0.053,0.088,0.129,0.098,0.127,0.102,0.144,0.098,-0.011,-0.045,OrganismalFitness,SYUA_HUMAN,Medium,Human +0.097,0.061,0.1,0.096,0.08,0.08,0.291,-0.018,0.077,0.072,0.013,0.051,0.048,0.121,0.047,0.021,-0.12,-0.028,0.053,-0.075,0.158,0.063,-0.011,-0.006,0.206,0.082,0.006,0.023,-0.014,0.036,0.088,0.112,-0.051,0.226,0.273,0.121,0.142,0.185,0.121,0.105,0.129,0.109,0.121,0.212,-0.002,0.078,0.271,0.033,0.223,0.059,0.105,0.013,0.004,0.04,0.002,0.034,0.045,-0.011,-0.079,0.021,0.054,0.121,OrganismalFitness,TADBP_HUMAN,Low,Human +0.293,0.201,0.255,0.268,0.319,0.3,-0.09,0.397,0.274,0.288,0.185,0.343,0.342,-0.034,-0.008,-0.005,0.017,-0.057,0.044,0.203,0.379,0.364,0.396,0.398,0.394,0.269,0.136,0.246,0.223,0.368,0.405,0.387,0.283,0.385,0.211,0.203,0.379,0.263,0.237,0.366,0.295,0.268,0.005,-0.054,0.292,0.181,0.23,0.284,0.344,0.207,0.114,0.134,0.142,0.137,0.116,0.133,0.12,0.117,0.117,0.134,0.157,0.116,OrganismalFitness,TAT_HV1BR,High,Virus +0.576,0.615,0.644,0.655,0.66,0.662,0.696,0.292,0.677,0.708,0.591,0.739,0.75,0.724,0.707,0.784,0.769,0.707,0.745,0.577,0.49,0.552,0.57,0.609,0.604,0.613,0.66,0.648,0.597,0.68,0.64,0.595,0.642,0.485,0.619,0.669,0.688,0.715,0.71,0.68,0.712,0.695,0.753,0.184,0.629,0.752,0.68,0.596,0.774,0.759,0.721,0.697,0.657,0.757,0.733,0.763,0.723,0.738,0.71,0.735,0.79,0.774,Stability,TCRG1_MOUSE,Medium,Eukaryote +0.309,0.397,0.482,0.473,0.483,0.481,0.072,0.061,0.523,0.52,0.511,0.502,0.532,0.186,0.557,0.548,0.513,0.532,0.546,0.515,-0.047,0.332,0.349,0.305,0.351,0.383,0.279,0.423,0.534,0.543,0.526,0.512,0.48,0.003,0.403,0.337,0.446,0.505,0.495,0.472,0.51,0.511,0.301,0.114,0.463,0.389,0.504,0.484,0.561,0.597,0.555,0.546,0.566,0.573,0.584,0.548,0.561,0.551,0.545,0.561,0.502,0.483,Stability,THO1_YEAST,High,Eukaryote +0.464,0.473,0.506,0.495,0.507,0.502,-0.099,0.392,0.573,0.576,0.472,0.538,0.526,0.044,0.547,0.589,0.525,0.529,0.533,0.466,0.262,0.361,0.429,0.394,0.395,0.455,0.385,0.395,0.436,0.528,0.484,0.451,0.25,0.061,0.312,0.333,0.535,0.523,0.517,0.541,0.518,0.518,0.343,-0.03,0.572,0.492,0.526,0.649,0.675,0.624,0.51,0.53,0.516,0.512,0.517,0.511,0.521,0.512,0.513,0.524,0.597,0.604,Stability,TNKS2_HUMAN,High,Human +0.217,0.236,0.219,0.228,0.23,0.229,0.067,0.253,0.286,0.27,0.285,0.27,0.318,0.108,0.199,0.253,0.339,0.328,0.371,0.249,0.088,0.142,0.228,0.272,0.115,0.267,0.273,0.253,0.306,0.255,0.253,0.188,0.117,0.115,0.214,0.301,0.232,0.252,0.305,0.243,0.252,0.277,0.12,0.089,0.309,0.17,0.206,0.267,0.245,0.116,0.253,0.277,0.275,0.278,0.293,0.291,0.281,0.291,0.292,0.298,0.268,0.203,OrganismalFitness,TPK1_HUMAN,Medium,Human +0.372,0.456,0.489,0.509,0.499,0.513,0.241,0.445,0.497,0.5,0.546,0.517,0.547,0.308,0.433,0.531,0.539,0.476,0.448,0.511,0.333,0.42,0.481,0.496,0.447,0.506,0.481,0.466,0.424,0.547,0.542,0.489,0.334,0.33,0.471,0.411,0.478,0.513,0.478,0.517,0.537,0.515,0.371,0.204,0.539,0.495,0.476,0.534,0.54,0.238,0.522,0.519,0.53,0.514,0.541,0.535,0.541,0.529,0.534,0.543,0.568,0.469,Expression,TPMT_HUMAN,Medium,Human +0.376,0.365,0.283,0.263,0.288,0.296,0.37,0.455,0.454,0.456,0.407,0.368,0.37,0.279,0.378,0.294,0.29,0.422,0.362,0.362,0.25,0.338,0.313,0.377,0.367,0.2,0.471,0.451,0.34,0.45,0.436,0.274,0.395,0.372,0.321,0.419,0.429,0.408,0.451,0.408,0.382,0.437,0.295,0.303,0.353,0.348,0.126,0.357,0.309,0.035,0.344,0.308,0.344,0.354,0.322,0.345,0.396,0.257,0.293,0.341,0.389,0.372,OrganismalFitness,TPOR_HUMAN,Low,Human +0.575,0.606,0.558,0.574,0.575,0.585,0.173,0.499,0.655,0.671,0.621,0.611,0.643,0.325,0.539,0.618,0.653,0.632,0.601,0.592,0.421,0.564,0.508,0.575,0.498,0.499,0.558,0.521,0.524,0.529,0.59,0.52,0.221,0.52,0.555,0.558,0.575,0.598,0.591,0.59,0.612,0.601,0.434,0.072,0.612,0.498,0.321,0.512,0.507,0.122,0.593,0.594,0.582,0.646,0.614,0.615,0.621,0.63,0.614,0.636,0.626,0.578,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +0.405,0.45,0.394,0.411,0.416,0.417,0.395,0.434,0.471,0.47,0.439,0.478,0.506,0.327,0.47,0.503,0.464,0.47,0.499,0.43,0.336,0.399,0.395,0.455,0.437,0.421,0.369,0.387,0.478,0.39,0.427,0.365,0.14,0.329,0.46,0.448,0.39,0.442,0.438,0.418,0.445,0.442,0.397,0.14,0.461,0.395,0.253,0.386,0.393,0.075,0.459,0.41,0.431,0.462,0.463,0.409,0.439,0.441,0.418,0.454,0.504,0.491,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +0.371,0.496,0.521,0.531,0.508,0.522,-0.042,0.403,0.512,0.519,0.42,0.477,0.509,0.001,0.004,0.403,0.473,0.518,0.538,0.557,0.22,0.405,0.445,0.419,0.42,0.479,0.471,0.449,0.442,0.484,0.454,0.432,0.071,0.233,0.403,0.428,0.327,0.452,0.475,0.443,0.517,0.54,0.277,-0.008,0.427,0.492,0.322,0.35,0.446,0.243,0.44,0.417,0.408,0.433,0.425,0.427,0.433,0.44,0.442,0.439,0.37,0.426,OrganismalFitness,UBC9_HUMAN,Medium,Human +0.325,0.443,0.464,0.467,0.466,0.466,0.051,0.346,0.379,0.415,0.391,0.453,0.449,0.347,0.42,0.49,0.46,0.467,0.477,0.421,0.142,0.353,0.357,0.364,0.345,0.374,0.343,0.322,0.355,0.486,0.475,0.453,0.441,0.079,0.11,0.255,0.383,0.385,0.329,0.472,0.477,0.424,0.359,-0.095,0.348,0.344,0.488,0.409,0.52,0.351,0.411,0.436,0.424,0.447,0.448,0.428,0.407,0.429,0.42,0.432,0.5,0.407,Stability,UBE4B_HUMAN,High,Human +0.412,0.417,0.454,0.468,0.463,0.47,0.078,0.089,0.392,0.394,0.361,0.447,0.471,0.399,0.463,0.459,0.459,0.351,0.349,0.383,0.122,0.324,0.337,0.3,0.444,0.376,0.395,0.376,0.289,0.433,0.437,0.396,0.091,0.026,0.096,0.262,0.419,0.351,0.39,0.467,0.454,0.472,0.409,0.041,0.42,0.458,0.307,0.393,-0.017,0.118,0.426,0.409,0.456,0.458,0.438,0.458,0.432,0.449,0.443,0.454,0.44,0.412,Activity,UBE4B_MOUSE,Low,Eukaryote +0.442,0.55,0.54,0.538,0.566,0.577,0.152,0.251,0.498,0.528,0.528,0.425,0.511,0.226,0.226,0.158,0.197,0.591,0.601,0.476,0.422,0.414,0.467,0.514,0.473,0.462,0.495,0.488,0.492,0.557,0.497,0.388,0.288,0.304,0.455,0.401,0.484,0.519,0.502,0.592,0.594,0.583,0.133,0.121,0.422,0.209,0.557,0.439,0.637,0.574,0.56,0.588,0.597,0.609,0.599,0.612,0.589,0.605,0.585,0.6,0.661,0.597,Stability,UBR5_HUMAN,Medium,Human +0.228,0.314,0.228,0.246,0.266,0.255,0.256,0.064,0.371,0.392,0.623,0.544,0.562,0.36,0.514,0.601,0.655,0.611,0.653,0.167,0.395,0.474,0.458,0.531,0.419,0.425,0.41,0.467,0.516,0.487,0.51,0.386,0.038,0.307,0.473,0.464,0.343,0.415,0.464,0.336,0.403,0.409,0.469,0.295,0.552,0.483,0.458,0.561,0.543,0.505,0.652,0.626,0.661,0.661,0.664,0.691,0.634,0.658,0.684,0.674,0.619,0.6,Stability,VG08_BPP22,High,Virus +0.284,0.422,0.594,0.63,0.634,0.637,0.162,0.54,0.553,0.59,0.673,0.63,0.673,0.371,0.316,0.652,0.662,0.589,0.478,0.592,0.293,0.431,0.601,0.524,0.424,0.575,0.494,0.482,0.579,0.599,0.701,0.694,0.322,0.346,0.515,0.521,0.487,0.564,0.563,0.634,0.659,0.642,0.272,0.283,0.426,0.421,0.542,0.433,0.775,0.702,0.672,0.658,0.666,0.658,0.653,0.665,0.649,0.656,0.652,0.662,0.685,0.798,Stability,VILI_CHICK,High,Eukaryote +0.369,0.392,0.4,0.416,0.425,0.432,0.122,0.391,0.462,0.481,0.428,0.437,0.461,0.134,0.37,0.434,0.462,0.465,0.458,0.44,0.168,0.186,0.334,0.37,0.25,0.428,0.424,0.398,0.414,0.451,0.407,0.338,0.134,0.139,0.25,0.438,0.4,0.416,0.475,0.442,0.456,0.487,0.16,0.1,0.44,0.305,0.336,0.388,0.428,0.19,0.462,0.424,0.45,0.466,0.468,0.451,0.458,0.451,0.46,0.467,0.46,0.4,Expression,VKOR1_HUMAN,Medium,Human +0.369,0.392,0.4,0.416,0.425,0.432,0.122,0.391,0.462,0.481,0.428,0.437,0.461,0.134,0.37,0.434,0.462,0.465,0.458,0.44,0.168,0.186,0.334,0.37,0.25,0.428,0.424,0.398,0.414,0.451,0.407,0.338,0.134,0.139,0.25,0.438,0.4,0.416,0.475,0.442,0.456,0.487,0.16,0.1,0.44,0.305,0.336,0.388,0.428,0.19,0.462,0.424,0.45,0.466,0.468,0.451,0.458,0.451,0.46,0.467,0.46,0.4,Activity,VKOR1_HUMAN,Medium,Human +-0.119,0.043,0.164,0.194,0.078,0.119,0.082,0.147,0.366,0.356,0.498,0.368,0.374,0.252,0.41,0.507,0.587,0.612,0.468,0.128,-0.02,0.135,0.133,0.222,0.215,0.202,0.131,0.199,0.291,0.084,0.499,0.429,0.242,0.065,0.059,0.122,-0.039,-0.028,-0.057,0.064,0.083,0.006,0.254,0.056,0.501,0.357,0.589,0.557,0.593,0.606,0.612,0.592,0.633,0.639,0.622,0.641,0.615,0.63,0.604,0.64,0.662,0.582,Stability,VRPI_BPT7,Medium,Virus +0.291,0.602,0.572,0.596,0.599,0.591,-0.221,0.533,0.559,0.598,0.593,0.27,0.481,-0.107,0.173,0.572,0.625,0.684,0.684,0.591,-0.091,-0.182,-0.13,0.264,-0.125,-0.166,-0.231,-0.023,0.52,0.605,0.681,0.643,-0.06,-0.234,-0.064,0.502,0.349,0.359,0.528,0.572,0.569,0.604,-0.046,-0.261,0.208,-0.013,0.449,0.425,0.651,0.544,0.587,0.627,0.585,0.619,0.612,0.627,0.625,0.657,0.677,0.632,0.616,0.498,Stability,YAIA_ECOLI,Medium,Prokaryote +0.428,0.321,0.464,0.46,0.449,0.455,0.329,0.289,0.018,0.036,0.333,0.28,0.285,0.411,0.457,0.451,0.466,0.382,0.313,0.189,0.18,0.176,0.158,0.167,0.299,0.226,0.257,0.207,0.15,0.332,0.425,0.451,0.139,0.302,0.196,0.217,0.402,0.326,0.359,0.435,0.405,0.439,0.485,-0.104,0.335,0.486,0.34,0.362,0.383,0.197,0.446,0.379,0.432,0.426,0.417,0.412,0.415,0.441,0.444,0.438,0.386,0.422,Binding,YAP1_HUMAN,Low,Human +0.596,0.61,0.595,0.618,0.604,0.609,0.492,0.394,0.591,0.583,0.673,0.649,0.667,0.584,0.621,0.65,0.658,0.631,0.63,0.525,0.488,0.534,0.612,0.604,0.594,0.611,0.587,0.59,0.643,0.594,0.586,0.547,0.568,0.573,0.567,0.544,0.665,0.634,0.621,0.669,0.624,0.63,0.446,0.353,0.536,0.448,0.483,0.562,0.68,0.65,0.701,0.649,0.665,0.687,0.7,0.691,0.687,0.676,0.679,0.69,0.638,0.76,Stability,YNZC_BACSU,Medium,Prokaryote +0.364,0.416,0.424,0.434,0.451,0.457,0.172,0.35,0.435,0.45,0.418,0.395,0.426,0.202,0.316,0.397,0.437,0.43,0.424,0.388,0.306,0.357,0.371,0.379,0.34,0.388,0.385,0.39,0.403,0.471,0.461,0.422,0.194,0.298,0.35,0.381,0.425,0.435,0.446,0.464,0.468,0.471,0.266,0.088,0.387,0.323,0.384,0.411,0.432,0.29,0.454,0.445,0.453,0.463,0.463,0.463,0.46,0.461,0.46,0.471,0.47,0.409,,,, diff --git a/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv b/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv new file mode 100644 index 0000000..5d07eb1 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv @@ -0,0 +1,63 @@ +Model_rank,Model_name,Model type,Average_Spearman,Bootstrap_standard_error_Spearman,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,SaProt (650M),Hybrid - Structure & PLM,0.456,0.0,0.458,0.378,0.488,0.363,0.592,0.395,0.45,0.543,0.478,0.529,0.51,0.32,0.458,0.311,0.266,0.25,0.318,SaProt (650M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +2,TranceptEVE L,Hybrid - Alignment & PLM,0.456,0.008,0.487,0.376,0.457,0.458,0.5,0.451,0.467,0.49,0.473,0.513,0.453,0.461,0.447,0.282,0.363,0.322,0.38,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +3,GEMME,Alignment-based model,0.454,0.011,0.482,0.383,0.438,0.45,0.519,0.455,0.47,0.496,0.469,0.516,0.465,0.472,0.448,0.279,0.335,0.334,0.422,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +4,TranceptEVE M,Hybrid - Alignment & PLM,0.454,0.009,0.479,0.386,0.452,0.452,0.502,0.44,0.468,0.486,0.474,0.513,0.448,0.446,0.442,0.283,0.316,0.306,0.368,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +5,TranceptEVE S,Hybrid - Alignment & PLM,0.452,0.009,0.475,0.396,0.443,0.447,0.497,0.449,0.46,0.483,0.47,0.504,0.451,0.438,0.437,0.278,0.318,0.306,0.367,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +6,ProtSSN (ensemble),Hybrid - Structure & PLM,0.449,0.006,0.466,0.366,0.449,0.396,0.568,0.401,0.458,0.521,0.47,0.528,0.491,0.37,0.459,0.293,0.297,0.237,0.293,ProtSSN (ensemble of 9 models),"Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +7,ProtSSN (k=20 h=1280),Hybrid - Structure & PLM,0.442,0.007,0.458,0.366,0.435,0.385,0.566,0.395,0.448,0.519,0.464,0.525,0.482,0.363,0.45,0.283,0.298,0.235,0.294,"ProtSSN (k=20, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +8,ProtSSN (k=20 h=512),Hybrid - Structure & PLM,0.44,0.006,0.457,0.353,0.437,0.39,0.563,0.394,0.453,0.512,0.458,0.524,0.487,0.373,0.45,0.293,0.315,0.254,0.303,"ProtSSN (k=20, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +9,EVE (ensemble),Alignment-based model,0.439,0.01,0.464,0.386,0.408,0.446,0.491,0.425,0.453,0.48,0.454,0.495,0.455,0.434,0.429,0.277,0.326,0.304,0.352,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +10,ProtSSN (k=20 h=768),Hybrid - Structure & PLM,0.439,0.006,0.459,0.347,0.439,0.386,0.563,0.387,0.45,0.516,0.462,0.525,0.483,0.357,0.448,0.296,0.286,0.228,0.294,"ProtSSN (k=20, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +11,ProtSSN (k=30 h=512),Hybrid - Structure & PLM,0.438,0.006,0.455,0.35,0.443,0.384,0.557,0.393,0.447,0.508,0.461,0.519,0.478,0.356,0.445,0.277,0.279,0.233,0.29,"ProtSSN (k=30, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +12,ProtSSN (k=30 h=768),Hybrid - Structure & PLM,0.437,0.006,0.457,0.351,0.435,0.383,0.561,0.386,0.449,0.512,0.458,0.525,0.482,0.356,0.447,0.285,0.282,0.226,0.291,"ProtSSN (k=30, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +13,ProtSSN (k=30 h=1280),Hybrid - Structure & PLM,0.437,0.007,0.454,0.357,0.431,0.383,0.561,0.388,0.445,0.515,0.46,0.522,0.481,0.35,0.444,0.3,0.287,0.221,0.291,"ProtSSN (k=30, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +14,VESPA,Protein language model,0.435,0.007,0.468,0.366,0.404,0.438,0.5,0.427,0.455,0.483,0.44,0.503,0.472,0.443,0.435,0.183,0.359,0.298,0.323,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +15,Tranception L,Hybrid - Alignment & PLM,0.434,0.008,0.465,0.349,0.45,0.433,0.471,0.432,0.438,0.471,0.455,0.497,0.412,0.438,0.423,0.256,0.354,0.31,0.381,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,ProtSSN (k=10 h=1280),Hybrid - Structure & PLM,0.433,0.007,0.446,0.352,0.436,0.38,0.548,0.391,0.44,0.502,0.455,0.512,0.469,0.358,0.441,0.246,0.258,0.209,0.277,"ProtSSN (k=10, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +17,EVE (single),Alignment-based model,0.432,0.009,0.458,0.372,0.404,0.44,0.487,0.417,0.448,0.476,0.446,0.491,0.452,0.43,0.423,0.275,0.321,0.3,0.355,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +18,MSA Transformer (ensemble),Hybrid - Alignment & PLM,0.432,0.009,0.473,0.329,0.446,0.418,0.492,0.403,0.45,0.482,0.439,0.516,0.445,0.421,0.428,0.225,0.371,0.366,0.412,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +19,ProtSSN (k=10 h=512),Hybrid - Structure & PLM,0.429,0.007,0.451,0.344,0.42,0.376,0.557,0.391,0.436,0.509,0.454,0.521,0.477,0.338,0.437,0.283,0.272,0.214,0.289,"ProtSSN (k=10, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +20,Tranception M,Hybrid - Alignment & PLM,0.427,0.009,0.448,0.361,0.441,0.419,0.465,0.417,0.432,0.454,0.452,0.489,0.39,0.414,0.411,0.248,0.241,0.257,0.339,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +21,ProtSSN (k=10 h=768),Hybrid - Structure & PLM,0.422,0.007,0.441,0.337,0.417,0.369,0.547,0.372,0.434,0.496,0.446,0.503,0.466,0.346,0.431,0.271,0.276,0.208,0.272,"ProtSSN (k=10, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +22,ESM-IF1,Inverse folding model,0.422,0.011,0.368,0.389,0.407,0.32,0.624,0.3,0.431,0.544,0.417,0.502,0.492,0.389,0.439,0.346,0.285,0.27,0.353,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +23,DeepSequence (ensemble),Alignment-based model,0.419,0.012,0.455,0.363,0.39,0.411,0.476,0.383,0.428,0.471,0.443,0.486,0.438,0.358,0.405,0.268,0.332,0.316,0.376,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +24,Tranception S,Hybrid - Alignment & PLM,0.418,0.009,0.436,0.372,0.42,0.409,0.452,0.428,0.415,0.443,0.44,0.476,0.385,0.401,0.398,0.242,0.255,0.267,0.342,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +25,MSA Transformer (single),Hybrid - Alignment & PLM,0.416,0.009,0.455,0.312,0.429,0.411,0.475,0.388,0.436,0.465,0.43,0.501,0.425,0.406,0.412,0.211,0.372,0.358,0.403,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +26,ESM2 (650M),Protein language model,0.414,0.011,0.425,0.337,0.415,0.368,0.523,0.335,0.406,0.516,0.457,0.486,0.457,0.261,0.422,0.25,0.209,0.16,0.213,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +27,DeepSequence (single),Alignment-based model,0.407,0.012,0.447,0.349,0.371,0.395,0.473,0.382,0.415,0.463,0.437,0.479,0.428,0.338,0.392,0.256,0.295,0.302,0.368,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +28,ESM-1v (ensemble),Protein language model,0.406,0.012,0.42,0.32,0.429,0.386,0.477,0.326,0.405,0.5,0.458,0.464,0.413,0.294,0.403,0.218,0.178,0.151,0.215,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +29,SaProt (35M),Hybrid - Structure & PLM,0.406,0.008,0.372,0.357,0.439,0.285,0.575,0.323,0.392,0.506,0.443,0.492,0.425,0.242,0.409,0.354,0.178,0.172,0.231,SaProt (35M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +30,ESM2 (3B),Protein language model,0.405,0.01,0.417,0.321,0.403,0.378,0.509,0.348,0.415,0.49,0.442,0.477,0.457,0.294,0.414,0.216,0.194,0.165,0.212,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +31,ESM2 (15B),Protein language model,0.4,0.01,0.405,0.317,0.405,0.387,0.488,0.357,0.414,0.472,0.432,0.467,0.436,0.333,0.405,0.205,0.226,0.169,0.228,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +32,MIF-ST,Hybrid - Structure & PLM,0.4,0.009,0.39,0.321,0.438,0.364,0.485,0.376,0.403,0.455,0.399,0.413,0.455,0.405,0.431,0.263,0.333,0.282,0.307,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +33,EVmutation,Alignment-based model,0.395,0.008,0.44,0.317,0.378,0.409,0.43,0.403,0.423,0.408,0.41,0.448,0.413,0.393,0.376,0.277,0.344,0.31,0.395,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +34,ESM-1b,Protein language model,0.394,0.009,0.428,0.287,0.406,0.349,0.5,0.35,0.398,0.482,0.435,0.491,0.438,0.26,0.384,0.229,0.184,0.148,0.268,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +35,VESPAl,Protein language model,0.393,0.009,0.429,0.347,0.326,0.402,0.461,0.382,0.412,0.447,0.394,0.472,0.433,0.403,0.386,0.143,0.332,0.272,0.31,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +36,Progen2 XL,Protein language model,0.391,0.009,0.402,0.302,0.418,0.387,0.445,0.354,0.405,0.445,0.386,0.458,0.418,0.402,0.385,0.187,0.283,0.235,0.289,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +37,ESM2 (150M),Protein language model,0.386,0.012,0.391,0.326,0.402,0.3,0.51,0.306,0.358,0.495,0.45,0.475,0.392,0.157,0.386,0.242,0.122,0.141,0.2,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +38,MIF,Inverse folding model,0.382,0.01,0.327,0.336,0.43,0.297,0.522,0.349,0.375,0.446,0.398,0.387,0.413,0.365,0.411,0.268,0.261,0.229,0.251,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +39,Progen2 L,Protein language model,0.38,0.009,0.406,0.293,0.427,0.379,0.396,0.348,0.381,0.42,0.411,0.432,0.363,0.323,0.372,0.146,0.23,0.205,0.265,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +40,Progen2 M,Protein language model,0.379,0.009,0.393,0.295,0.433,0.381,0.396,0.318,0.382,0.426,0.413,0.417,0.353,0.335,0.373,0.132,0.144,0.133,0.174,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +41,Progen2 Base,Protein language model,0.377,0.01,0.396,0.294,0.437,0.378,0.383,0.342,0.368,0.423,0.422,0.422,0.328,0.316,0.37,0.128,0.136,0.144,0.196,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +42,Tranception L no retrieval,Protein language model,0.374,0.01,0.401,0.288,0.413,0.386,0.381,0.358,0.371,0.416,0.391,0.394,0.353,0.404,0.363,0.174,0.305,0.247,0.325,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +43,ESM-1v (single),Protein language model,0.374,0.011,0.396,0.268,0.405,0.361,0.437,0.287,0.371,0.476,0.427,0.433,0.385,0.258,0.376,0.192,0.184,0.146,0.207,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +44,Wavenet,Alignment-based model,0.373,0.013,0.379,0.325,0.35,0.363,0.449,0.299,0.389,0.451,0.393,0.425,0.405,0.34,0.358,0.207,0.241,0.209,0.29,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +45,RITA XL,Protein language model,0.372,0.011,0.366,0.302,0.414,0.381,0.398,0.315,0.382,0.413,0.396,0.399,0.342,0.394,0.358,0.141,0.144,0.157,0.23,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +46,CARP (640M),Protein language model,0.368,0.01,0.395,0.273,0.397,0.365,0.412,0.314,0.374,0.428,0.417,0.395,0.38,0.284,0.391,0.213,0.173,0.151,0.171,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +47,RITA L,Protein language model,0.365,0.01,0.359,0.29,0.42,0.374,0.383,0.316,0.369,0.403,0.396,0.399,0.309,0.384,0.348,0.136,0.13,0.138,0.204,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +48,Site-Independent,Alignment-based model,0.359,0.014,0.369,0.344,0.343,0.379,0.358,0.426,0.373,0.316,0.38,0.389,0.312,0.375,0.337,0.238,0.232,0.261,0.338,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +49,RITA M,Protein language model,0.349,0.01,0.352,0.273,0.405,0.369,0.348,0.304,0.349,0.388,0.379,0.367,0.295,0.375,0.337,0.112,0.124,0.143,0.2,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +50,Tranception M no retrieval,Protein language model,0.348,0.01,0.349,0.284,0.406,0.359,0.342,0.293,0.349,0.376,0.38,0.357,0.295,0.34,0.332,0.14,0.094,0.12,0.182,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +51,Unirep evotuned,Hybrid - Alignment & PLM,0.347,0.012,0.355,0.305,0.365,0.344,0.366,0.33,0.344,0.371,0.357,0.378,0.33,0.348,0.319,0.156,0.247,0.228,0.295,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +52,Progen2 S,Protein language model,0.335,0.011,0.333,0.275,0.384,0.334,0.349,0.283,0.321,0.389,0.385,0.358,0.286,0.271,0.327,0.111,0.106,0.111,0.162,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +53,CARP (76M),Protein language model,0.327,0.012,0.342,0.282,0.369,0.268,0.376,0.247,0.301,0.406,0.388,0.366,0.296,0.157,0.336,0.204,0.096,0.109,0.117,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +54,ESM2 (35M),Protein language model,0.32,0.013,0.314,0.291,0.343,0.212,0.439,0.239,0.271,0.448,0.371,0.408,0.308,0.12,0.305,0.24,0.113,0.131,0.198,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +55,RITA S,Protein language model,0.304,0.012,0.294,0.275,0.336,0.325,0.289,0.276,0.297,0.332,0.331,0.299,0.232,0.344,0.287,0.111,0.09,0.112,0.173,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +56,Tranception S no retrieval,Protein language model,0.302,0.012,0.288,0.286,0.349,0.317,0.27,0.258,0.295,0.317,0.318,0.283,0.257,0.304,0.279,0.113,0.096,0.117,0.167,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +57,CARP (38M),Protein language model,0.279,0.014,0.285,0.268,0.312,0.215,0.314,0.196,0.239,0.356,0.322,0.307,0.248,0.127,0.279,0.168,0.083,0.111,0.133,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +58,ProteinMPNN,Inverse folding model,0.257,0.011,0.197,0.163,0.198,0.161,0.565,0.173,0.279,0.433,0.284,0.394,0.343,0.262,0.292,0.259,0.167,0.17,0.273,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +59,ESM2 (8M),Protein language model,0.225,0.014,0.201,0.26,0.266,0.136,0.262,0.194,0.179,0.261,0.241,0.248,0.194,0.09,0.203,0.135,0.087,0.115,0.176,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +60,Unirep,Protein language model,0.189,0.016,0.182,0.202,0.216,0.135,0.21,0.181,0.161,0.205,0.215,0.233,0.146,0.066,0.175,0.07,0.098,0.123,0.171,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +61,ProtGPT2,Protein language model,0.188,0.012,0.176,0.149,0.193,0.164,0.257,0.177,0.173,0.253,0.244,0.242,0.132,0.138,0.181,0.139,0.041,0.036,0.064,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +62,CARP (600K),Protein language model,0.106,0.016,0.112,0.084,0.171,0.056,0.105,0.095,0.087,0.098,0.122,0.086,0.072,0.055,0.108,0.025,0.026,0.068,0.1,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.html b/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.html new file mode 100644 index 0000000..0b62c5e --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.html @@ -0,0 +1,1670 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_SpearmanBootstrap_standard_error_SpearmanFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusDepth_1Depth_2Depth_3Depth_4Depth_5+Model detailsReferences
Model_rank
1SaProt (650M)Hybrid - Structure & PLM0.4560.0000.4580.3780.4880.3630.5920.3950.4500.5430.4780.5290.5100.3200.4580.3110.2660.2500.318SaProt (650M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
2TranceptEVE LHybrid - Alignment & PLM0.4560.0080.4870.3760.4570.4580.5000.4510.4670.4900.4730.5130.4530.4610.4470.2820.3630.3220.380TranceptEVE Large model (Tranception Large & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
3GEMMEAlignment-based model0.4540.0110.4820.3830.4380.4500.5190.4550.4700.4960.4690.5160.4650.4720.4480.2790.3350.3340.422GEMME model<a href='https://pubmed.ncbi.nlm.nih.gov/31406981/'>Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.</a>
4TranceptEVE MHybrid - Alignment & PLM0.4540.0090.4790.3860.4520.4520.5020.4400.4680.4860.4740.5130.4480.4460.4420.2830.3160.3060.368TranceptEVE Medium model (Tranception Medium & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
5TranceptEVE SHybrid - Alignment & PLM0.4520.0090.4750.3960.4430.4470.4970.4490.4600.4830.4700.5040.4510.4380.4370.2780.3180.3060.367TranceptEVE Small model (Tranception Small & retrieved EVE model)<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
6ProtSSN (ensemble)Hybrid - Structure & PLM0.4490.0060.4660.3660.4490.3960.5680.4010.4580.5210.4700.5280.4910.3700.4590.2930.2970.2370.293ProtSSN (ensemble of 9 models)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
7ProtSSN (k=20 h=1280)Hybrid - Structure & PLM0.4420.0070.4580.3660.4350.3850.5660.3950.4480.5190.4640.5250.4820.3630.4500.2830.2980.2350.294ProtSSN (k=20, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
8ProtSSN (k=20 h=512)Hybrid - Structure & PLM0.4400.0060.4570.3530.4370.3900.5630.3940.4530.5120.4580.5240.4870.3730.4500.2930.3150.2540.303ProtSSN (k=20, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
9EVE (ensemble)Alignment-based model0.4390.0100.4640.3860.4080.4460.4910.4250.4530.4800.4540.4950.4550.4340.4290.2770.3260.3040.352EVE model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
10ProtSSN (k=20 h=768)Hybrid - Structure & PLM0.4390.0060.4590.3470.4390.3860.5630.3870.4500.5160.4620.5250.4830.3570.4480.2960.2860.2280.294ProtSSN (k=20, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
11ProtSSN (k=30 h=512)Hybrid - Structure & PLM0.4380.0060.4550.3500.4430.3840.5570.3930.4470.5080.4610.5190.4780.3560.4450.2770.2790.2330.290ProtSSN (k=30, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
12ProtSSN (k=30 h=768)Hybrid - Structure & PLM0.4370.0060.4570.3510.4350.3830.5610.3860.4490.5120.4580.5250.4820.3560.4470.2850.2820.2260.291ProtSSN (k=30, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
13ProtSSN (k=30 h=1280)Hybrid - Structure & PLM0.4370.0070.4540.3570.4310.3830.5610.3880.4450.5150.4600.5220.4810.3500.4440.3000.2870.2210.291ProtSSN (k=30, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
14VESPAProtein language model0.4350.0070.4680.3660.4040.4380.5000.4270.4550.4830.4400.5030.4720.4430.4350.1830.3590.2980.323VESPA model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
15Tranception LHybrid - Alignment & PLM0.4340.0080.4650.3490.4500.4330.4710.4320.4380.4710.4550.4970.4120.4380.4230.2560.3540.3100.381Tranception Large model (700M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
16ProtSSN (k=10 h=1280)Hybrid - Structure & PLM0.4330.0070.4460.3520.4360.3800.5480.3910.4400.5020.4550.5120.4690.3580.4410.2460.2580.2090.277ProtSSN (k=10, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
17EVE (single)Alignment-based model0.4320.0090.4580.3720.4040.4400.4870.4170.4480.4760.4460.4910.4520.4300.4230.2750.3210.3000.355EVE model (single seed)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
18MSA Transformer (ensemble)Hybrid - Alignment & PLM0.4320.0090.4730.3290.4460.4180.4920.4030.4500.4820.4390.5160.4450.4210.4280.2250.3710.3660.412MSA Transformer (ensemble of 5 MSA samples)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
19ProtSSN (k=10 h=512)Hybrid - Structure & PLM0.4290.0070.4510.3440.4200.3760.5570.3910.4360.5090.4540.5210.4770.3380.4370.2830.2720.2140.289ProtSSN (k=10, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
20Tranception MHybrid - Alignment & PLM0.4270.0090.4480.3610.4410.4190.4650.4170.4320.4540.4520.4890.3900.4140.4110.2480.2410.2570.339Tranception Medium model (300M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
21ProtSSN (k=10 h=768)Hybrid - Structure & PLM0.4220.0070.4410.3370.4170.3690.5470.3720.4340.4960.4460.5030.4660.3460.4310.2710.2760.2080.272ProtSSN (k=10, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
22ESM-IF1Inverse folding model0.4220.0110.3680.3890.4070.3200.6240.3000.4310.5440.4170.5020.4920.3890.4390.3460.2850.2700.353ESM-IF1 model<a href='https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html'>Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.</a>
23DeepSequence (ensemble)Alignment-based model0.4190.0120.4550.3630.3900.4110.4760.3830.4280.4710.4430.4860.4380.3580.4050.2680.3320.3160.376DeepSequence model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
24Tranception SHybrid - Alignment & PLM0.4180.0090.4360.3720.4200.4090.4520.4280.4150.4430.4400.4760.3850.4010.3980.2420.2550.2670.342Tranception Small model (85M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
25MSA Transformer (single)Hybrid - Alignment & PLM0.4160.0090.4550.3120.4290.4110.4750.3880.4360.4650.4300.5010.4250.4060.4120.2110.3720.3580.403MSA Transformer (single MSA sample)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
26ESM2 (650M)Protein language model0.4140.0110.4250.3370.4150.3680.5230.3350.4060.5160.4570.4860.4570.2610.4220.2500.2090.1600.213ESM2 model (650M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
27DeepSequence (single)Alignment-based model0.4070.0120.4470.3490.3710.3950.4730.3820.4150.4630.4370.4790.4280.3380.3920.2560.2950.3020.368DeepSequence model (single seed)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
28ESM-1v (ensemble)Protein language model0.4060.0120.4200.3200.4290.3860.4770.3260.4050.5000.4580.4640.4130.2940.4030.2180.1780.1510.215ESM-1v (ensemble of 5 independently-trained models)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
29SaProt (35M)Hybrid - Structure & PLM0.4060.0080.3720.3570.4390.2850.5750.3230.3920.5060.4430.4920.4250.2420.4090.3540.1780.1720.231SaProt (35M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
30ESM2 (3B)Protein language model0.4050.0100.4170.3210.4030.3780.5090.3480.4150.4900.4420.4770.4570.2940.4140.2160.1940.1650.212ESM2 model (3B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
31ESM2 (15B)Protein language model0.4000.0100.4050.3170.4050.3870.4880.3570.4140.4720.4320.4670.4360.3330.4050.2050.2260.1690.228ESM2 model (15B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
32MIF-STHybrid - Structure & PLM0.4000.0090.3900.3210.4380.3640.4850.3760.4030.4550.3990.4130.4550.4050.4310.2630.3330.2820.307MIF-ST model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
33EVmutationAlignment-based model0.3950.0080.4400.3170.3780.4090.4300.4030.4230.4080.4100.4480.4130.3930.3760.2770.3440.3100.395EVmutation model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
34ESM-1bProtein language model0.3940.0090.4280.2870.4060.3490.5000.3500.3980.4820.4350.4910.4380.2600.3840.2290.1840.1480.268ESM-1b (w/ Brandes et al. extensions)[1] Original model: <a href='https://www.biorxiv.org/content/10.1101/622803v4'>Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118.</a> [2] Extensions: <a href='https://www.biorxiv.org/content/10.1101/2022.08.25.505311v1'>Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.</a>
35VESPAlProtein language model0.3930.0090.4290.3470.3260.4020.4610.3820.4120.4470.3940.4720.4330.4030.3860.1430.3320.2720.310VESPAl model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
36Progen2 XLProtein language model0.3910.0090.4020.3020.4180.3870.4450.3540.4050.4450.3860.4580.4180.4020.3850.1870.2830.2350.289Progen2 xlarge model (6.4B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
37ESM2 (150M)Protein language model0.3860.0120.3910.3260.4020.3000.5100.3060.3580.4950.4500.4750.3920.1570.3860.2420.1220.1410.200ESM2 model (150M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
38MIFInverse folding model0.3820.0100.3270.3360.4300.2970.5220.3490.3750.4460.3980.3870.4130.3650.4110.2680.2610.2290.251MIF model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
39Progen2 LProtein language model0.3800.0090.4060.2930.4270.3790.3960.3480.3810.4200.4110.4320.3630.3230.3720.1460.2300.2050.265Progen2 large model (2.7B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
40Progen2 MProtein language model0.3790.0090.3930.2950.4330.3810.3960.3180.3820.4260.4130.4170.3530.3350.3730.1320.1440.1330.174Progen2 medium model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
41Progen2 BaseProtein language model0.3770.0100.3960.2940.4370.3780.3830.3420.3680.4230.4220.4220.3280.3160.3700.1280.1360.1440.196Progen2 base model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
42Tranception L no retrievalProtein language model0.3740.0100.4010.2880.4130.3860.3810.3580.3710.4160.3910.3940.3530.4040.3630.1740.3050.2470.325Tranception Large model (700M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
43ESM-1v (single)Protein language model0.3740.0110.3960.2680.4050.3610.4370.2870.3710.4760.4270.4330.3850.2580.3760.1920.1840.1460.207ESM-1v (single seed)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
44WavenetAlignment-based model0.3730.0130.3790.3250.3500.3630.4490.2990.3890.4510.3930.4250.4050.3400.3580.2070.2410.2090.290Wavenet model<a href='https://www.nature.com/articles/s41467-021-22732-w'>Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.</a>
45RITA XLProtein language model0.3720.0110.3660.3020.4140.3810.3980.3150.3820.4130.3960.3990.3420.3940.3580.1410.1440.1570.230RITA xlarge model (1.2B params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
46CARP (640M)Protein language model0.3680.0100.3950.2730.3970.3650.4120.3140.3740.4280.4170.3950.3800.2840.3910.2130.1730.1510.171CARP model (640M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
47RITA LProtein language model0.3650.0100.3590.2900.4200.3740.3830.3160.3690.4030.3960.3990.3090.3840.3480.1360.1300.1380.204RITA large model (680M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
48Site-IndependentAlignment-based model0.3590.0140.3690.3440.3430.3790.3580.4260.3730.3160.3800.3890.3120.3750.3370.2380.2320.2610.338Site-Independent model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
49RITA MProtein language model0.3490.0100.3520.2730.4050.3690.3480.3040.3490.3880.3790.3670.2950.3750.3370.1120.1240.1430.200RITA medium model (300M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
50Tranception M no retrievalProtein language model0.3480.0100.3490.2840.4060.3590.3420.2930.3490.3760.3800.3570.2950.3400.3320.1400.0940.1200.182Tranception Medium model (300M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
51Unirep evotunedHybrid - Alignment & PLM0.3470.0120.3550.3050.3650.3440.3660.3300.3440.3710.3570.3780.3300.3480.3190.1560.2470.2280.295Unirep model w/ evotuning<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
52Progen2 SProtein language model0.3350.0110.3330.2750.3840.3340.3490.2830.3210.3890.3850.3580.2860.2710.3270.1110.1060.1110.162Progen2 small model (150M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
53CARP (76M)Protein language model0.3270.0120.3420.2820.3690.2680.3760.2470.3010.4060.3880.3660.2960.1570.3360.2040.0960.1090.117CARP model (76M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
54ESM2 (35M)Protein language model0.3200.0130.3140.2910.3430.2120.4390.2390.2710.4480.3710.4080.3080.1200.3050.2400.1130.1310.198ESM2 model (35M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
55RITA SProtein language model0.3040.0120.2940.2750.3360.3250.2890.2760.2970.3320.3310.2990.2320.3440.2870.1110.0900.1120.173RITA small model (85M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
56Tranception S no retrievalProtein language model0.3020.0120.2880.2860.3490.3170.2700.2580.2950.3170.3180.2830.2570.3040.2790.1130.0960.1170.167Tranception Small model (85M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
57CARP (38M)Protein language model0.2790.0140.2850.2680.3120.2150.3140.1960.2390.3560.3220.3070.2480.1270.2790.1680.0830.1110.133CARP model (38M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
58ProteinMPNNInverse folding model0.2570.0110.1970.1630.1980.1610.5650.1730.2790.4330.2840.3940.3430.2620.2920.2590.1670.1700.273ProteinMPNN model<a href='https://www.science.org/doi/10.1126/science.add2187'>J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.</a>
59ESM2 (8M)Protein language model0.2250.0140.2010.2600.2660.1360.2620.1940.1790.2610.2410.2480.1940.0900.2030.1350.0870.1150.176ESM2 model (8M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
60UnirepProtein language model0.1890.0160.1820.2020.2160.1350.2100.1810.1610.2050.2150.2330.1460.0660.1750.0700.0980.1230.171Unirep model<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
61ProtGPT2Protein language model0.1880.0120.1760.1490.1930.1640.2570.1770.1730.2530.2440.2420.1320.1380.1810.1390.0410.0360.064ProtGPT2 model<a href='https://www.nature.com/articles/s41467-022-32007-7'>Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.</a>
62CARP (600K)Protein language model0.1060.0160.1120.0840.1710.0560.1050.0950.0870.0980.1220.0860.0720.0550.1080.0250.0260.0680.100CARP model (600K params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.csv b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.csv new file mode 100644 index 0000000..7b3dcde --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.csv @@ -0,0 +1,217 @@ +DMS ID,Site-Independent,EVmutation,DeepSequence (single),DeepSequence (ensemble),EVE (single),EVE (ensemble),Unirep,Unirep evotuned,MSA Transformer (single),MSA Transformer (ensemble),ESM-1b,ESM-1v (single),ESM-1v (ensemble),ESM2 (8M),ESM2 (35M),ESM2 (150M),ESM2 (650M),ESM2 (3B),ESM2 (15B),Wavenet,RITA S,RITA M,RITA L,RITA XL,Progen2 S,Progen2 M,Progen2 Base,Progen2 L,Progen2 XL,GEMME,VESPA,VESPAl,ProtGPT2,Tranception S no retrieval,Tranception M no retrieval,Tranception L no retrieval,Tranception S,Tranception M,Tranception L,TranceptEVE S,TranceptEVE M,TranceptEVE L,CARP (38M),CARP (600K),CARP (640M),CARP (76M),MIF,MIF-ST,ESM-IF1,ProteinMPNN,ProtSSN (k=10 h=512),ProtSSN (k=10 h=768),ProtSSN (k=10 h=1280),ProtSSN (k=20 h=512),ProtSSN (k=20 h=768),ProtSSN (k=20 h=1280),ProtSSN (k=30 h=512),ProtSSN (k=30 h=768),ProtSSN (k=30 h=1280),ProtSSN (ensemble),SaProt (650M),SaProt (35M),Number of Mutants,Selection Type,UniProt ID,MSA_Neff_L_category,Taxon +A0A140D2T1_ZIKV_Sourisseau_2019,0.278,0.279,0.204,0.205,0.273,0.28,0.093,0.086,0.315,0.318,0.117,0.1,0.124,0.081,0.096,0.086,0.127,0.237,0.262,0.178,0.268,0.28,0.285,0.278,0.251,0.267,0.286,0.254,0.277,0.291,0.257,0.219,0.099,0.223,0.232,0.241,0.265,0.267,0.286,0.268,0.269,0.285,0.104,0.103,0.148,0.101,0.169,0.201,0.151,0.153,0.18,0.168,0.183,0.183,0.181,0.166,0.148,0.172,0.159,0.171,0.16,0.135,9576,OrganismalFitness,A0A140D2T1_ZIKV,Medium,Virus +A0A192B1T2_9HIV1_Haddox_2018,0.276,0.27,0.269,0.273,0.277,0.28,0.095,0.269,0.293,0.293,0.234,0.259,0.266,0.103,0.094,0.095,0.103,0.11,0.117,0.281,0.279,0.28,0.286,0.291,0.273,0.289,0.279,0.282,0.293,0.293,0.263,0.248,0.221,0.274,0.284,0.289,0.281,0.284,0.286,0.286,0.282,0.285,0.192,0.072,0.267,0.261,0.138,0.268,0.121,0.145,0.129,0.148,0.154,0.138,0.143,0.141,0.144,0.126,0.121,0.143,0.137,0.115,12577,OrganismalFitness,A0A192B1T2_9HIV1,Medium,Virus +A0A1I9GEU1_NEIME_Kennouche_2019,0.086,0.097,0.108,0.108,0.086,0.097,0.075,0.129,0.108,0.075,0.118,0.129,0.108,0.086,0.097,0.118,0.075,0.086,0.108,0.065,0.108,0.097,0.086,0.086,0.086,0.097,0.086,0.108,0.075,0.065,0.108,0.097,0.183,0.086,0.108,0.097,0.108,0.086,0.097,0.086,0.097,0.097,0.075,0.032,0.097,0.065,0.129,0.108,0.14,0.118,0.129,0.097,0.118,0.14,0.075,0.118,0.097,0.151,0.097,0.097,0.14,0.054,922,Activity,A0A1I9GEU1_NEIME,Medium,Prokaryote +A0A247D711_LISMN_Stadelmann_2021,0.139,0.151,0.12,0.114,0.163,0.151,0.078,0.096,0.163,0.169,0.12,0.12,0.127,0.09,0.09,0.114,0.133,0.12,0.127,0.157,0.09,0.096,0.114,0.114,0.066,0.127,0.133,0.078,0.114,0.163,0.157,0.187,0.06,0.09,0.108,0.084,0.133,0.139,0.151,0.157,0.157,0.157,0.114,0.102,0.12,0.108,0.139,0.145,0.193,0.175,0.163,0.181,0.12,0.12,0.145,0.12,0.12,0.127,0.139,0.145,0.133,0.102,1653,Activity,A0A247D711_LISMN,High,Prokaryote +A0A2Z5U3Z0_9INFA_Doud_2016,0.257,0.327,0.31,0.316,0.317,0.313,0.089,0.271,0.324,0.326,0.109,0.293,0.329,0.103,0.104,0.104,0.271,0.301,0.297,0.299,0.305,0.322,0.324,0.329,0.203,0.311,0.332,0.315,0.303,0.314,0.244,0.19,0.105,0.294,0.319,0.308,0.31,0.313,0.31,0.329,0.327,0.324,0.091,0.095,0.166,0.1,0.176,0.218,0.194,0.162,0.247,0.261,0.257,0.264,0.255,0.264,0.252,0.265,0.267,0.258,0.178,0.166,10715,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A0A2Z5U3Z0_9INFA_Wu_2014,0.209,0.234,0.234,0.247,0.238,0.234,0.098,0.272,0.247,0.243,0.153,0.217,0.247,0.136,0.136,0.14,0.213,0.2,0.213,0.306,0.221,0.234,0.26,0.255,0.183,0.217,0.26,0.213,0.243,0.243,0.247,0.217,0.085,0.221,0.226,0.23,0.243,0.26,0.26,0.23,0.238,0.23,0.128,0.111,0.179,0.145,0.162,0.196,0.196,0.136,0.209,0.221,0.217,0.209,0.2,0.221,0.196,0.2,0.209,0.204,0.123,0.145,2350,OrganismalFitness,A0A2Z5U3Z0_9INFA,Medium,Virus +A4_HUMAN_Seuma_2022,0.104,0.158,0.167,0.166,0.128,0.134,0.072,0.13,0.164,0.155,0.089,0.123,0.149,0.069,0.068,0.076,0.078,0.126,0.126,0.141,0.096,0.105,0.096,0.121,0.104,0.099,0.095,0.095,0.103,0.144,0.13,0.14,0.159,0.076,0.146,0.173,0.097,0.155,0.164,0.119,0.144,0.152,0.06,0.059,0.134,0.07,0.111,0.092,0.032,0.111,0.142,0.148,0.128,0.122,0.115,0.131,0.125,0.115,0.131,0.124,0.087,0.091,14811,Stability,A4_HUMAN,Low,Human +A4D664_9INFA_Soh_2019,0.242,0.245,0.247,0.247,0.256,0.257,0.105,0.228,0.191,0.188,0.096,0.095,0.096,0.105,0.109,0.104,0.135,0.146,0.198,0.222,0.204,0.23,0.234,0.215,0.107,0.168,0.171,0.173,0.2,0.237,0.199,0.191,0.106,0.202,0.231,0.235,0.234,0.245,0.245,0.258,0.259,0.261,0.105,0.109,0.125,0.112,0.122,0.129,0.072,0.13,0.124,0.123,0.105,0.133,0.123,0.13,0.119,0.127,0.129,0.119,0.118,0.109,14421,OrganismalFitness,A4D664_9INFA,Medium,Virus +A4GRB6_PSEAI_Chen_2020,0.18,0.204,0.198,0.22,0.184,0.184,0.138,0.176,0.244,0.226,0.257,0.234,0.253,0.146,0.216,0.263,0.309,0.261,0.271,0.23,0.136,0.172,0.194,0.232,0.184,0.23,0.238,0.234,0.242,0.192,0.253,0.21,0.054,0.174,0.204,0.216,0.238,0.236,0.214,0.226,0.214,0.212,0.138,0.092,0.305,0.21,0.283,0.309,0.248,0.222,0.329,0.307,0.293,0.337,0.331,0.323,0.317,0.343,0.329,0.331,0.271,0.234,5004,OrganismalFitness,A4GRB6_PSEAI,High,Prokaryote +AACC1_PSEAI_Dandage_2018,0.177,0.26,0.188,0.204,0.193,0.193,0.144,0.094,0.249,0.238,0.204,0.21,0.199,0.133,0.155,0.166,0.215,0.21,0.238,0.227,0.149,0.166,0.199,0.138,0.16,0.204,0.193,0.193,0.21,0.276,0.16,0.122,0.083,0.199,0.177,0.227,0.227,0.204,0.21,0.21,0.21,0.215,0.138,0.127,0.16,0.166,0.227,0.238,0.227,0.188,0.249,0.221,0.221,0.232,0.232,0.265,0.215,0.238,0.265,0.249,0.171,0.166,1801,OrganismalFitness,AACC1_PSEAI,High,Prokaryote +ACE2_HUMAN_Chan_2020,0.157,0.157,0.211,0.193,0.166,0.157,0.054,0.148,0.157,0.148,0.103,0.143,0.139,0.058,0.09,0.108,0.139,0.121,0.139,0.193,0.126,0.202,0.22,0.188,0.157,0.188,0.184,0.197,0.184,0.197,0.157,0.139,0.143,0.108,0.188,0.17,0.17,0.152,0.175,0.184,0.17,0.184,0.085,0.072,0.121,0.067,0.076,0.121,0.143,0.09,0.121,0.13,0.117,0.108,0.117,0.139,0.126,0.112,0.121,0.099,0.112,0.103,2223,Binding,ACE2_HUMAN,Medium,Human +ADRB2_HUMAN_Jones_2020,0.142,0.168,0.168,0.174,0.201,0.206,0.142,0.206,0.171,0.188,0.178,0.156,0.177,0.146,0.15,0.158,0.158,0.167,0.172,0.159,0.168,0.174,0.172,0.171,0.188,0.179,0.183,0.181,0.159,0.162,0.155,0.133,0.121,0.186,0.168,0.182,0.165,0.165,0.174,0.191,0.192,0.204,0.163,0.118,0.172,0.177,0.156,0.181,0.167,0.127,0.155,0.16,0.135,0.165,0.16,0.153,0.15,0.154,0.145,0.154,0.171,0.16,7800,Activity,ADRB2_HUMAN,Medium,Human +AICDA_HUMAN_Gajula_2014_3cycles,0.143,0.238,0.333,0.19,0.238,0.238,0.048,0.143,0.19,0.19,0.238,0.19,0.19,0.095,0.0,0.143,0.19,0.143,0.095,0.19,0.048,0.095,0.286,0.19,0.048,0.19,0.19,0.19,0.19,0.19,0.19,0.238,0.095,0.095,0.095,0.143,0.238,0.19,0.238,0.238,0.238,0.19,0.048,0.048,0.19,0.048,0.333,0.143,0.143,0.333,0.143,0.333,0.143,0.286,0.143,0.143,0.143,0.143,0.143,0.143,0.048,0.048,209,Activity,AICDA_HUMAN,Medium,Human +AMFR_HUMAN_Tsuboyama_2023_4G3O,0.289,0.372,0.372,0.369,0.359,0.379,0.07,0.336,0.352,0.369,0.433,0.101,0.319,0.057,0.124,0.409,0.386,0.409,0.413,0.366,0.057,0.117,0.346,0.342,0.114,0.339,0.342,0.265,0.383,0.336,0.383,0.346,0.326,0.057,0.094,0.114,0.339,0.336,0.346,0.383,0.379,0.383,0.141,0.134,0.396,0.315,0.359,0.356,0.416,0.406,0.406,0.409,0.399,0.386,0.403,0.396,0.379,0.386,0.403,0.396,0.43,0.235,2972,Stability,AMFR_HUMAN,Medium,Human +AMIE_PSEAE_Wrenbeck_2017,0.143,0.114,0.13,0.124,0.127,0.119,0.103,0.159,0.119,0.141,0.181,0.144,0.128,0.152,0.167,0.197,0.193,0.233,0.127,0.165,0.188,0.175,0.157,0.13,0.151,0.151,0.148,0.169,0.103,0.104,0.13,0.133,0.074,0.188,0.136,0.112,0.127,0.12,0.106,0.124,0.122,0.124,0.159,0.146,0.193,0.17,0.185,0.226,0.207,0.143,0.188,0.194,0.18,0.175,0.159,0.154,0.165,0.169,0.165,0.17,0.234,0.161,6227,Activity,AMIE_PSEAE,High,Prokaryote +ANCSZ_Hobbs_2022,0.214,0.208,0.208,0.206,0.206,0.199,0.156,0.176,0.154,0.154,0.156,0.171,0.165,0.195,0.231,0.251,0.244,0.225,0.212,0.086,0.169,0.128,0.146,0.148,0.173,0.15,0.141,0.137,0.113,0.191,0.139,0.126,0.139,0.165,0.126,0.137,0.193,0.167,0.169,0.203,0.186,0.188,0.206,0.169,0.152,0.188,0.173,0.158,0.188,0.133,0.251,0.242,0.214,0.225,0.225,0.236,0.238,0.257,0.233,0.24,0.208,0.236,4670,Activity,ANCSZ,Medium,Eukaryote +ARGR_ECOLI_Tsuboyama_2023_1AOY,0.209,0.194,0.248,0.24,0.24,0.217,0.155,0.132,0.171,0.202,0.233,0.178,0.233,0.194,0.233,0.287,0.264,0.24,0.202,0.178,0.217,0.132,0.14,0.14,0.217,0.116,0.155,0.147,0.147,0.163,0.109,0.062,0.031,0.178,0.132,0.132,0.194,0.217,0.186,0.248,0.209,0.194,0.194,0.155,0.233,0.248,0.372,0.31,0.302,0.279,0.24,0.279,0.264,0.264,0.256,0.256,0.233,0.256,0.271,0.256,0.295,0.31,1287,Stability,ARGR_ECOLI,Medium,Prokaryote +B2L11_HUMAN_Dutta_2010_binding-Mcl-1,0.294,0.235,0.353,0.294,0.294,0.294,0.176,0.294,0.294,0.294,0.059,0.059,0.471,0.235,0.235,0.235,0.353,0.294,0.294,0.294,0.176,0.059,0.176,0.235,0.059,0.235,0.294,0.118,0.235,0.235,0.235,0.235,0.176,0.294,0.118,0.176,0.353,0.353,0.294,0.353,0.353,0.294,0.235,0.235,0.412,0.176,0.176,0.353,0.235,0.059,0.235,0.294,0.235,0.235,0.176,0.235,0.235,0.294,0.235,0.235,0.353,0.176,170,Binding,B2L11_HUMAN,Low,Human +BBC1_YEAST_Tsuboyama_2023_1TG0,0.217,0.28,0.203,0.203,0.213,0.179,0.184,0.174,0.285,0.285,0.271,0.29,0.309,0.126,0.275,0.3,0.372,0.329,0.329,0.251,0.208,0.227,0.251,0.242,0.126,0.261,0.198,0.227,0.217,0.188,0.184,0.14,0.15,0.232,0.188,0.242,0.222,0.232,0.266,0.208,0.174,0.198,0.227,0.077,0.217,0.232,0.285,0.246,0.348,0.343,0.324,0.338,0.329,0.382,0.353,0.396,0.362,0.362,0.362,0.353,0.314,0.353,2069,Stability,BBC1_YEAST,High,Eukaryote +BCHB_CHLTE_Tsuboyama_2023_2KRU,0.253,0.247,0.228,0.241,0.234,0.247,0.127,0.184,0.196,0.278,0.241,0.297,0.304,0.133,0.158,0.228,0.335,0.266,0.31,0.272,0.12,0.215,0.272,0.31,0.241,0.291,0.234,0.297,0.278,0.253,0.215,0.177,0.095,0.177,0.228,0.259,0.253,0.272,0.272,0.241,0.241,0.253,0.19,0.158,0.266,0.234,0.297,0.342,0.386,0.323,0.386,0.361,0.342,0.373,0.354,0.354,0.361,0.361,0.31,0.361,0.361,0.222,1572,Stability,BCHB_CHLTE,Medium,Prokaryote +BLAT_ECOLX_Deng_2012,0.238,0.334,0.34,0.348,0.366,0.366,0.11,0.17,0.276,0.34,0.298,0.29,0.32,0.172,0.25,0.268,0.294,0.268,0.256,0.298,0.258,0.26,0.276,0.238,0.248,0.336,0.304,0.306,0.228,0.3,0.34,0.32,0.098,0.25,0.296,0.246,0.298,0.338,0.314,0.36,0.386,0.378,0.204,0.074,0.318,0.248,0.334,0.326,0.324,0.218,0.338,0.354,0.336,0.38,0.352,0.352,0.368,0.352,0.376,0.362,0.33,0.29,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Firnberg_2014,0.229,0.385,0.404,0.394,0.41,0.41,0.14,0.19,0.3,0.362,0.338,0.317,0.356,0.2,0.262,0.323,0.35,0.321,0.252,0.333,0.292,0.265,0.258,0.235,0.296,0.346,0.306,0.315,0.208,0.296,0.335,0.317,0.14,0.271,0.292,0.238,0.317,0.338,0.321,0.404,0.412,0.423,0.235,0.123,0.367,0.279,0.362,0.381,0.406,0.215,0.365,0.352,0.35,0.369,0.381,0.367,0.369,0.373,0.358,0.377,0.402,0.298,4783,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Jacquier_2013,0.131,0.225,0.214,0.217,0.217,0.219,0.099,0.139,0.174,0.201,0.182,0.187,0.193,0.134,0.144,0.155,0.187,0.179,0.179,0.168,0.179,0.176,0.195,0.171,0.166,0.179,0.195,0.203,0.174,0.142,0.187,0.195,0.115,0.182,0.193,0.195,0.19,0.203,0.201,0.214,0.209,0.219,0.128,0.078,0.179,0.152,0.15,0.193,0.182,0.155,0.19,0.201,0.206,0.195,0.184,0.198,0.198,0.184,0.201,0.195,0.19,0.155,989,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BLAT_ECOLX_Stiffler_2015,0.204,0.35,0.356,0.336,0.374,0.384,0.088,0.222,0.256,0.334,0.314,0.294,0.324,0.182,0.216,0.274,0.306,0.292,0.258,0.292,0.288,0.286,0.26,0.25,0.272,0.336,0.336,0.302,0.226,0.292,0.356,0.364,0.084,0.288,0.29,0.264,0.33,0.328,0.324,0.38,0.396,0.4,0.18,0.09,0.328,0.214,0.318,0.348,0.344,0.19,0.352,0.32,0.334,0.356,0.364,0.356,0.354,0.33,0.342,0.364,0.346,0.244,4996,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +BRCA1_HUMAN_Findlay_2018,0.223,0.196,0.234,0.245,0.217,0.239,0.136,0.136,0.201,0.207,0.168,0.152,0.163,0.152,0.12,0.147,0.168,0.212,0.13,0.158,0.136,0.212,0.223,0.158,0.207,0.228,0.239,0.185,0.179,0.196,0.212,0.19,0.12,0.13,0.147,0.179,0.207,0.217,0.223,0.255,0.25,0.212,0.141,0.168,0.228,0.179,0.185,0.228,0.13,0.141,0.174,0.185,0.168,0.163,0.163,0.152,0.158,0.179,0.163,0.163,0.147,0.168,1837,OrganismalFitness,BRCA1_HUMAN,Low,Human +BRCA2_HUMAN_Erwood_2022_HEK293T,0.259,0.185,0.148,0.185,0.259,0.185,0.148,0.111,0.111,0.148,0.148,0.111,0.037,0.111,0.148,0.185,0.185,0.185,0.111,0.037,0.185,0.259,0.296,0.148,0.222,0.148,0.111,0.259,0.074,0.111,0.148,0.222,0.111,0.185,0.222,0.185,0.148,0.185,0.185,0.185,0.185,0.185,0.111,0.148,0.185,0.185,0.148,0.111,0.037,0.111,0.222,0.185,0.148,0.111,0.222,0.148,0.111,0.148,0.222,0.148,0.074,0.111,265,OrganismalFitness,BRCA2_HUMAN,,Human +C6KNH7_9INFA_Lee_2018,0.255,0.295,0.288,0.283,0.302,0.305,0.088,0.247,0.298,0.301,0.098,0.27,0.331,0.089,0.092,0.097,0.25,0.271,0.305,0.251,0.262,0.254,0.266,0.242,0.143,0.264,0.273,0.294,0.242,0.3,0.271,0.225,0.125,0.243,0.269,0.257,0.28,0.287,0.285,0.302,0.309,0.304,0.097,0.092,0.172,0.099,0.236,0.294,0.276,0.177,0.262,0.261,0.269,0.267,0.262,0.246,0.259,0.265,0.253,0.266,0.181,0.157,10754,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +CALM1_HUMAN_Weile_2017,0.088,0.082,0.066,0.055,0.055,0.055,0.099,0.049,0.071,0.049,0.055,0.077,0.071,0.11,0.093,0.077,0.055,0.038,0.099,0.055,0.082,0.077,0.077,0.06,0.06,0.055,0.049,0.077,0.038,0.038,0.049,0.055,0.088,0.099,0.077,0.038,0.071,0.082,0.049,0.066,0.055,0.06,0.06,0.104,0.066,0.071,0.115,0.066,0.06,0.121,0.077,0.071,0.077,0.071,0.077,0.066,0.066,0.077,0.071,0.077,0.06,0.077,1813,OrganismalFitness,CALM1_HUMAN,High,Human +CAPSD_AAV2S_Sinai_2021,0.263,0.258,0.246,0.279,0.258,0.262,0.174,0.265,0.233,0.254,0.136,0.121,0.129,0.131,0.147,0.134,0.165,0.148,0.112,0.174,0.122,0.129,0.133,0.153,0.119,0.122,0.154,0.126,0.18,0.272,0.13,0.12,0.098,0.133,0.142,0.263,0.23,0.231,0.275,0.253,0.255,0.273,0.128,0.163,0.19,0.147,0.176,0.188,0.145,0.156,0.162,0.145,0.133,0.145,0.125,0.133,0.142,0.14,0.175,0.147,0.152,0.118,42328,OrganismalFitness,CAPSD_AAV2S,Low,Virus +CAR11_HUMAN_Meitlis_2020_gof,0.076,0.071,0.071,0.071,0.08,0.071,0.223,0.055,0.071,0.071,0.071,0.101,0.088,0.122,0.109,0.168,0.092,0.084,0.088,0.092,0.113,0.071,0.097,0.076,0.038,0.076,0.08,0.067,0.097,0.059,0.071,0.059,0.097,0.151,0.08,0.055,0.084,0.084,0.071,0.08,0.084,0.076,0.118,0.092,0.092,0.151,0.088,0.071,0.113,0.118,0.16,0.134,0.13,0.139,0.143,0.126,0.143,0.139,0.13,0.13,0.168,0.172,2374,OrganismalFitness,CAR11_HUMAN,Low,Human +CAR11_HUMAN_Meitlis_2020_lof,0.062,0.075,0.071,0.067,0.062,0.071,0.204,0.042,0.092,0.092,0.067,0.146,0.112,0.121,0.104,0.183,0.112,0.129,0.1,0.125,0.125,0.058,0.096,0.075,0.062,0.071,0.088,0.062,0.121,0.05,0.075,0.062,0.067,0.167,0.079,0.075,0.067,0.067,0.05,0.075,0.075,0.075,0.121,0.092,0.088,0.183,0.108,0.083,0.154,0.129,0.158,0.121,0.133,0.108,0.142,0.133,0.146,0.138,0.129,0.125,0.208,0.188,2395,OrganismalFitness,CAR11_HUMAN,Low,Human +CAS9_STRP1_Spencer_2017_positive,0.096,0.112,0.111,0.105,0.111,0.112,0.096,0.106,0.116,0.117,0.11,0.096,0.106,0.097,0.103,0.126,0.106,0.117,0.11,0.097,0.099,0.096,0.117,0.116,0.1,0.112,0.096,0.111,0.101,0.105,0.117,0.117,0.111,0.091,0.099,0.128,0.111,0.116,0.123,0.101,0.101,0.117,0.095,0.091,0.111,0.099,0.099,0.111,0.103,0.094,0.118,0.107,0.115,0.102,0.111,0.111,0.099,0.112,0.1,0.11,0.127,0.115,8117,Activity,CAS9_STRP1,Medium,Prokaryote +CASP3_HUMAN_Roychowdhury_2020,0.159,0.204,0.153,0.115,0.134,0.153,0.089,0.159,0.21,0.223,0.146,0.115,0.153,0.089,0.146,0.166,0.178,0.146,0.21,0.159,0.146,0.159,0.159,0.153,0.14,0.159,0.178,0.172,0.204,0.146,0.229,0.223,0.115,0.102,0.14,0.172,0.134,0.166,0.166,0.146,0.172,0.159,0.096,0.076,0.159,0.14,0.159,0.166,0.127,0.102,0.185,0.178,0.191,0.14,0.172,0.166,0.185,0.166,0.172,0.166,0.146,0.134,1567,Activity,CASP3_HUMAN,High,Human +CASP7_HUMAN_Roychowdhury_2020,0.149,0.202,0.238,0.214,0.25,0.244,0.06,0.155,0.226,0.25,0.268,0.19,0.232,0.107,0.208,0.274,0.208,0.196,0.19,0.238,0.095,0.214,0.155,0.167,0.202,0.179,0.179,0.167,0.196,0.226,0.167,0.155,0.107,0.083,0.19,0.137,0.208,0.226,0.19,0.22,0.232,0.244,0.185,0.06,0.262,0.274,0.238,0.28,0.244,0.19,0.196,0.214,0.244,0.232,0.238,0.226,0.214,0.22,0.226,0.22,0.226,0.208,1680,Activity,CASP7_HUMAN,Medium,Human +CATR_CHLRE_Tsuboyama_2023_2AMI,0.272,0.147,0.147,0.152,0.183,0.162,0.236,0.094,0.131,0.136,0.115,0.173,0.147,0.225,0.257,0.209,0.152,0.157,0.131,0.089,0.12,0.126,0.11,0.105,0.073,0.089,0.089,0.073,0.105,0.152,0.126,0.173,0.183,0.131,0.11,0.099,0.199,0.173,0.126,0.168,0.152,0.157,0.194,0.183,0.084,0.126,0.314,0.131,0.183,0.293,0.183,0.115,0.141,0.152,0.141,0.147,0.157,0.141,0.141,0.157,0.099,0.257,1903,Stability,CATR_CHLRE,High,Eukaryote +CBPA2_HUMAN_Tsuboyama_2023_1O6X,0.227,0.159,0.227,0.227,0.237,0.237,0.222,0.184,0.174,0.198,0.304,0.242,0.256,0.227,0.246,0.251,0.222,0.227,0.246,0.237,0.174,0.193,0.261,0.251,0.29,0.261,0.285,0.246,0.213,0.184,0.184,0.14,0.155,0.237,0.29,0.285,0.227,0.242,0.246,0.261,0.237,0.261,0.29,0.261,0.275,0.29,0.295,0.309,0.242,0.285,0.179,0.242,0.246,0.217,0.237,0.232,0.184,0.203,0.203,0.232,0.237,0.309,2068,Stability,CBPA2_HUMAN,Medium,Human +CBS_HUMAN_Sun_2020,0.168,0.161,0.169,0.166,0.176,0.175,0.1,0.141,0.173,0.162,0.163,0.15,0.162,0.098,0.127,0.127,0.132,0.145,0.157,0.161,0.143,0.133,0.166,0.173,0.147,0.152,0.154,0.163,0.152,0.163,0.145,0.13,0.109,0.15,0.141,0.151,0.158,0.177,0.194,0.172,0.18,0.179,0.13,0.102,0.158,0.15,0.147,0.147,0.169,0.116,0.139,0.136,0.147,0.141,0.145,0.151,0.15,0.154,0.155,0.151,0.147,0.127,7217,OrganismalFitness,CBS_HUMAN,Medium,Human +CBX4_HUMAN_Tsuboyama_2023_2K28,0.437,0.406,0.441,0.445,0.472,0.48,0.013,0.31,0.445,0.424,0.428,0.397,0.402,0.022,0.533,0.533,0.48,0.445,0.41,0.445,0.306,0.341,0.328,0.332,0.376,0.358,0.371,0.341,0.332,0.445,0.432,0.376,0.192,0.301,0.328,0.341,0.45,0.402,0.441,0.48,0.424,0.441,0.437,0.096,0.319,0.406,0.371,0.262,0.52,0.467,0.507,0.48,0.485,0.48,0.52,0.459,0.472,0.472,0.476,0.507,0.437,0.507,2282,Stability,CBX4_HUMAN,High,Human +CCDB_ECOLI_Adkar_2012,0.124,0.152,0.146,0.146,0.118,0.129,0.096,0.107,0.079,0.118,0.146,0.056,0.079,0.101,0.112,0.096,0.157,0.112,0.14,0.107,0.062,0.045,0.067,0.096,0.017,0.101,0.09,0.146,0.112,0.107,0.197,0.163,0.067,0.056,0.073,0.124,0.118,0.129,0.152,0.135,0.146,0.146,0.096,0.079,0.118,0.124,0.062,0.152,0.135,0.101,0.174,0.118,0.174,0.213,0.185,0.169,0.163,0.169,0.14,0.169,0.157,0.09,1176,Activity,CCDB_ECOLI,High,Prokaryote +CCDB_ECOLI_Tripathi_2016,0.117,0.125,0.124,0.125,0.124,0.124,0.09,0.117,0.108,0.117,0.115,0.112,0.112,0.093,0.097,0.112,0.12,0.124,0.127,0.12,0.093,0.09,0.083,0.11,0.085,0.102,0.086,0.102,0.125,0.118,0.126,0.127,0.108,0.089,0.099,0.124,0.118,0.116,0.124,0.122,0.122,0.124,0.096,0.085,0.114,0.095,0.111,0.123,0.113,0.105,0.121,0.118,0.115,0.12,0.124,0.122,0.121,0.124,0.123,0.122,0.12,0.117,1663,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +CCR5_HUMAN_Gill_2023,0.146,0.143,0.156,0.164,0.156,0.14,0.114,0.231,0.164,0.165,0.169,0.146,0.154,0.124,0.135,0.148,0.141,0.154,0.138,0.17,0.13,0.196,0.167,0.161,0.157,0.196,0.18,0.196,0.178,0.178,0.141,0.125,0.119,0.167,0.175,0.167,0.156,0.159,0.169,0.144,0.148,0.148,0.135,0.079,0.183,0.138,0.133,0.177,0.14,0.132,0.148,0.148,0.143,0.128,0.127,0.138,0.135,0.144,0.125,0.135,0.172,0.151,6137,Binding,CCR5_HUMAN,High,Human +CD19_HUMAN_Klesmith_2019_FMC_singles,0.167,0.127,0.164,0.162,0.146,0.138,0.143,0.122,0.103,0.109,0.18,0.167,0.196,0.186,0.228,0.175,0.167,0.183,0.138,0.09,0.194,0.162,0.207,0.162,0.149,0.225,0.159,0.204,0.149,0.162,0.13,0.101,0.127,0.241,0.252,0.167,0.191,0.18,0.156,0.164,0.154,0.151,0.215,0.183,0.218,0.212,0.34,0.321,0.324,0.138,0.228,0.191,0.202,0.215,0.218,0.218,0.236,0.21,0.228,0.218,0.353,0.302,3761,Binding,CD19_HUMAN,Low,Human +CP2C9_HUMAN_Amorosi_2021_abundance,0.25,0.272,0.267,0.279,0.298,0.294,0.242,0.273,0.278,0.3,0.261,0.284,0.309,0.217,0.284,0.275,0.289,0.284,0.278,0.295,0.287,0.253,0.276,0.267,0.284,0.275,0.279,0.279,0.284,0.262,0.246,0.212,0.11,0.284,0.281,0.276,0.294,0.292,0.289,0.3,0.298,0.295,0.287,0.129,0.272,0.281,0.276,0.279,0.309,0.154,0.275,0.279,0.29,0.287,0.286,0.292,0.3,0.292,0.289,0.284,0.292,0.273,6370,Expression,CP2C9_HUMAN,High,Human +CP2C9_HUMAN_Amorosi_2021_activity,0.285,0.332,0.345,0.359,0.369,0.371,0.309,0.319,0.311,0.359,0.32,0.372,0.38,0.285,0.364,0.377,0.397,0.376,0.354,0.395,0.346,0.307,0.332,0.291,0.359,0.327,0.324,0.302,0.306,0.328,0.247,0.198,0.117,0.367,0.359,0.335,0.379,0.364,0.372,0.379,0.374,0.367,0.371,0.14,0.346,0.385,0.314,0.328,0.353,0.202,0.403,0.363,0.366,0.38,0.379,0.39,0.366,0.379,0.385,0.387,0.387,0.354,6142,Binding,CP2C9_HUMAN,High,Human +CSN4_MOUSE_Tsuboyama_2023_1UFM,0.358,0.33,0.327,0.339,0.339,0.342,0.176,0.309,0.379,0.361,0.355,0.394,0.418,0.27,0.464,0.397,0.248,0.336,0.324,0.327,0.318,0.339,0.348,0.355,0.224,0.376,0.409,0.352,0.333,0.33,0.348,0.321,0.203,0.236,0.358,0.358,0.385,0.385,0.373,0.355,0.367,0.358,0.248,0.224,0.336,0.394,0.421,0.33,0.467,0.455,0.342,0.352,0.339,0.336,0.333,0.339,0.342,0.336,0.318,0.336,0.367,0.452,3295,Stability,CSN4_MOUSE,Medium,Eukaryote +CUE1_YEAST_Tsuboyama_2023_2MYX,0.297,0.323,0.316,0.316,0.342,0.329,0.158,0.278,0.361,0.361,0.304,0.278,0.209,0.146,0.19,0.259,0.291,0.304,0.304,0.234,0.133,0.146,0.171,0.108,0.101,0.114,0.184,0.184,0.31,0.259,0.278,0.209,0.165,0.108,0.12,0.177,0.297,0.291,0.316,0.329,0.335,0.342,0.196,0.127,0.259,0.152,0.329,0.329,0.354,0.348,0.278,0.342,0.31,0.342,0.278,0.297,0.316,0.285,0.31,0.31,0.361,0.304,1580,Stability,CUE1_YEAST,Medium,Eukaryote +D7PM05_CLYGR_Somermeyer_2022,0.25,0.361,0.315,0.293,0.336,0.333,0.098,0.222,0.369,0.381,0.237,0.097,0.1,0.097,0.092,0.088,0.085,0.097,0.103,0.233,0.079,0.061,0.092,0.084,0.056,0.081,0.08,0.108,0.147,0.337,0.32,0.314,0.056,0.079,0.098,0.104,0.293,0.295,0.29,0.334,0.336,0.33,0.135,0.131,0.137,0.138,0.257,0.249,0.305,0.23,0.274,0.281,0.286,0.283,0.277,0.283,0.269,0.286,0.268,0.285,0.198,0.136,24515,Activity,D7PM05_CLYGR,Low,Eukaryote +DLG4_HUMAN_Faure_2021,0.322,0.319,0.328,0.322,0.338,0.34,0.314,0.305,0.282,0.292,0.268,0.321,0.318,0.378,0.423,0.37,0.315,0.318,0.279,0.348,0.284,0.289,0.278,0.277,0.287,0.292,0.291,0.297,0.265,0.321,0.274,0.285,0.272,0.287,0.308,0.287,0.331,0.36,0.351,0.345,0.354,0.35,0.285,0.209,0.249,0.287,0.385,0.285,0.341,0.216,0.284,0.256,0.282,0.291,0.282,0.302,0.262,0.282,0.277,0.282,0.317,0.398,6976,OrganismalFitness,DLG4_HUMAN,Low,Human +DLG4_RAT_McLaughlin_2012,0.158,0.215,0.158,0.139,0.165,0.171,0.184,0.133,0.127,0.165,0.184,0.133,0.127,0.108,0.139,0.177,0.101,0.127,0.133,0.101,0.108,0.108,0.101,0.114,0.089,0.127,0.095,0.108,0.127,0.133,0.203,0.234,0.12,0.114,0.133,0.108,0.133,0.133,0.12,0.133,0.133,0.158,0.158,0.089,0.146,0.133,0.19,0.127,0.108,0.133,0.108,0.076,0.063,0.089,0.089,0.082,0.076,0.095,0.095,0.082,0.095,0.133,1576,Binding,DLG4_RAT,Low,Eukaryote +DN7A_SACS2_Tsuboyama_2023_1JIC,0.168,0.149,0.139,0.119,0.109,0.129,0.089,0.109,0.228,0.287,0.119,0.089,0.119,0.089,0.178,0.198,0.248,0.267,0.218,0.228,0.079,0.089,0.069,0.109,0.119,0.149,0.079,0.119,0.218,0.208,0.129,0.059,0.168,0.099,0.099,0.079,0.168,0.168,0.158,0.149,0.158,0.119,0.139,0.089,0.188,0.168,0.287,0.317,0.267,0.307,0.416,0.366,0.347,0.277,0.307,0.297,0.297,0.228,0.327,0.356,0.218,0.277,1008,Stability,DN7A_SACS2,Medium,Prokaryote +DNJA1_HUMAN_Tsuboyama_2023_2LO1,0.3,0.282,0.295,0.295,0.313,0.313,0.322,0.264,0.3,0.308,0.33,0.322,0.374,0.388,0.485,0.432,0.449,0.392,0.401,0.317,0.308,0.317,0.357,0.37,0.313,0.273,0.3,0.308,0.291,0.264,0.189,0.198,0.242,0.282,0.26,0.361,0.304,0.308,0.317,0.308,0.3,0.313,0.383,0.256,0.295,0.352,0.427,0.348,0.454,0.463,0.467,0.507,0.511,0.485,0.485,0.489,0.48,0.515,0.458,0.502,0.304,0.383,2264,Stability,DNJA1_HUMAN,High,Human +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,0.342,0.325,0.329,0.295,0.288,0.318,0.099,0.25,0.223,0.192,0.305,0.185,0.233,0.027,0.192,0.233,0.305,0.305,0.274,0.332,0.24,0.284,0.291,0.26,0.103,0.223,0.134,0.195,0.312,0.164,0.318,0.291,0.247,0.075,0.075,0.048,0.325,0.356,0.329,0.322,0.315,0.322,0.134,0.058,0.325,0.192,0.384,0.377,0.363,0.445,0.349,0.363,0.366,0.39,0.353,0.38,0.366,0.363,0.339,0.373,0.404,0.37,2915,Stability,DOCK1_MOUSE,High,Eukaryote +DYR_ECOLI_Nguyen_2023,0.165,0.101,0.126,0.128,0.108,0.093,0.099,0.104,0.2,0.15,0.154,0.205,0.174,0.126,0.183,0.165,0.174,0.161,0.17,0.185,0.154,0.152,0.143,0.148,0.187,0.079,0.079,0.123,0.088,0.088,0.141,0.117,0.024,0.099,0.064,0.073,0.134,0.084,0.075,0.088,0.075,0.086,0.161,0.106,0.141,0.167,0.148,0.187,0.198,0.159,0.185,0.174,0.172,0.163,0.183,0.183,0.181,0.172,0.2,0.174,0.203,0.192,2916,OrganismalFitness,DYR_ECOLI,High,Prokaryote +DYR_ECOLI_Thompson_2019,0.131,0.127,0.101,0.101,0.118,0.105,0.127,0.127,0.11,0.131,0.152,0.143,0.143,0.105,0.139,0.16,0.165,0.169,0.143,0.152,0.118,0.068,0.127,0.084,0.11,0.152,0.169,0.127,0.131,0.114,0.122,0.089,0.038,0.084,0.118,0.194,0.127,0.143,0.169,0.114,0.118,0.118,0.131,0.089,0.156,0.152,0.148,0.127,0.135,0.122,0.143,0.173,0.152,0.165,0.165,0.194,0.173,0.173,0.186,0.177,0.143,0.194,2363,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +ENV_HV1B9_DuenasDecamp_2016,0.4,0.35,0.3,0.3,0.35,0.35,0.025,0.375,0.375,0.35,0.3,0.4,0.35,0.025,0.025,0.05,0.075,0.05,0.175,0.4,0.275,0.35,0.375,0.35,0.25,0.4,0.3,0.375,0.4,0.3,0.425,0.375,0.325,0.325,0.375,0.425,0.35,0.375,0.375,0.375,0.4,0.4,0.3,0.0,0.35,0.325,0.325,0.3,0.175,0.15,0.125,0.275,0.225,0.15,0.275,0.2,0.175,0.225,0.175,0.175,0.1,0.125,375,OrganismalFitness,ENV_HV1B9,Medium,Virus +ENV_HV1BR_Haddox_2016,0.206,0.211,0.227,0.234,0.231,0.228,0.084,0.214,0.203,0.206,0.155,0.193,0.191,0.099,0.103,0.099,0.108,0.113,0.13,0.239,0.198,0.212,0.233,0.239,0.189,0.218,0.233,0.211,0.237,0.171,0.198,0.172,0.128,0.2,0.215,0.225,0.211,0.213,0.221,0.23,0.232,0.235,0.165,0.076,0.196,0.175,0.139,0.193,0.104,0.113,0.123,0.134,0.145,0.144,0.15,0.141,0.134,0.133,0.135,0.145,0.129,0.113,12863,OrganismalFitness,ENV_HV1BR,Medium,Virus +ENVZ_ECOLI_Ghose_2023,0.133,0.115,0.124,0.106,0.115,0.097,0.097,0.115,0.124,0.115,0.08,0.088,0.071,0.097,0.124,0.08,0.071,0.088,0.08,0.08,0.097,0.053,0.106,0.062,0.133,0.062,0.088,0.08,0.062,0.088,0.08,0.115,0.133,0.106,0.097,0.062,0.106,0.097,0.088,0.115,0.106,0.106,0.124,0.088,0.115,0.053,0.062,0.097,0.071,0.08,0.088,0.088,0.088,0.08,0.08,0.062,0.097,0.08,0.08,0.08,0.071,0.088,1121,Activity,ENVZ_ECOLI,High,Prokaryote +EPHB2_HUMAN_Tsuboyama_2023_1F0M,0.321,0.327,0.383,0.367,0.388,0.388,0.005,0.296,0.372,0.372,0.311,0.327,0.301,0.015,0.327,0.347,0.372,0.388,0.347,0.357,0.347,0.347,0.306,0.321,0.327,0.316,0.372,0.352,0.337,0.281,0.311,0.235,0.235,0.357,0.301,0.357,0.398,0.352,0.383,0.403,0.362,0.378,0.26,0.112,0.327,0.327,0.444,0.378,0.423,0.408,0.383,0.398,0.388,0.429,0.403,0.403,0.388,0.398,0.408,0.393,0.423,0.429,1960,Stability,EPHB2_HUMAN,High,Human +ERBB2_HUMAN_Elazar_2016,0.121,0.242,0.121,0.152,0.212,0.212,0.152,0.182,0.242,0.303,0.121,0.121,0.182,0.182,0.182,0.152,0.242,0.182,0.212,0.091,0.152,0.182,0.182,0.182,0.152,0.182,0.212,0.273,0.182,0.152,0.091,0.121,0.182,0.152,0.091,0.182,0.182,0.121,0.182,0.212,0.212,0.182,0.152,0.182,0.212,0.121,0.212,0.182,0.03,0.091,0.152,0.152,0.182,0.152,0.121,0.121,0.121,0.182,0.212,0.121,0.121,0.152,326,Expression,ERBB2_HUMAN,Low,Human +ESTA_BACSU_Nutschel_2020,0.234,0.271,0.275,0.266,0.252,0.261,0.22,0.188,0.202,0.284,0.229,0.28,0.28,0.229,0.243,0.22,0.225,0.197,0.225,0.234,0.257,0.261,0.284,0.22,0.307,0.252,0.294,0.312,0.257,0.206,0.179,0.193,0.087,0.243,0.229,0.165,0.261,0.248,0.161,0.289,0.284,0.243,0.294,0.206,0.284,0.275,0.427,0.353,0.298,0.28,0.284,0.234,0.275,0.289,0.312,0.294,0.28,0.248,0.271,0.289,0.225,0.271,2172,Stability,ESTA_BACSU,High,Prokaryote +F7YBW8_MESOW_Aakre_2015,0.087,0.609,0.564,0.672,0.626,0.642,0.095,0.448,0.492,0.523,0.62,0.503,0.528,0.063,0.067,0.112,0.48,0.399,0.618,0.567,0.065,0.028,0.062,0.093,0.103,0.253,0.063,0.426,0.625,0.554,0.699,0.659,0.068,0.062,0.053,0.69,0.092,0.072,0.647,0.517,0.493,0.68,0.1,0.084,0.316,0.109,0.096,0.383,0.136,0.08,0.624,0.657,0.601,0.677,0.649,0.667,0.601,0.614,0.639,0.647,0.323,0.028,9192,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +FECA_ECOLI_Tsuboyama_2023_2D1U,0.212,0.243,0.233,0.217,0.249,0.233,0.111,0.116,0.228,0.275,0.275,0.228,0.243,0.116,0.286,0.328,0.296,0.249,0.254,0.222,0.185,0.175,0.169,0.169,0.159,0.185,0.249,0.238,0.19,0.201,0.185,0.159,0.164,0.122,0.153,0.175,0.217,0.18,0.212,0.212,0.206,0.217,0.291,0.138,0.28,0.286,0.312,0.27,0.265,0.286,0.254,0.265,0.291,0.265,0.291,0.249,0.249,0.275,0.27,0.249,0.296,0.291,1886,Stability,FECA_ECOLI,High,Prokaryote +FKBP3_HUMAN_Tsuboyama_2023_2KFV,0.153,0.177,0.169,0.177,0.21,0.202,0.04,0.097,0.089,0.089,0.073,0.081,0.065,0.056,0.056,0.073,0.105,0.089,0.089,0.073,0.065,0.097,0.024,0.177,0.065,0.185,0.121,0.137,0.089,0.145,0.081,0.081,0.081,0.048,0.097,0.145,0.169,0.153,0.169,0.185,0.177,0.169,0.056,0.073,0.121,0.081,0.218,0.258,0.282,0.234,0.218,0.274,0.234,0.177,0.226,0.25,0.202,0.202,0.194,0.234,0.218,0.177,1237,Stability,FKBP3_HUMAN,Medium,Human +GAL4_YEAST_Kitzman_2015,0.075,0.1,0.25,0.175,0.183,0.192,0.092,0.1,0.25,0.208,0.2,0.158,0.142,0.108,0.167,0.158,0.242,0.258,0.242,0.158,0.133,0.092,0.15,0.15,0.142,0.125,0.15,0.158,0.267,0.183,0.242,0.267,0.158,0.092,0.15,0.1,0.167,0.15,0.167,0.183,0.192,0.217,0.117,0.192,0.208,0.125,0.075,0.2,0.075,0.083,0.267,0.2,0.275,0.242,0.217,0.217,0.275,0.217,0.242,0.25,0.175,0.183,1195,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +GCN4_YEAST_Staller_2018,0.242,0.223,0.201,0.231,0.227,0.22,0.091,0.117,0.227,0.22,0.197,0.178,0.182,0.174,0.14,0.186,0.227,0.22,0.223,0.22,0.064,0.064,0.076,0.064,0.053,0.042,0.053,0.061,0.144,0.22,0.208,0.212,0.095,0.053,0.049,0.223,0.231,0.231,0.239,0.223,0.22,0.246,0.148,0.14,0.152,0.186,0.17,0.216,0.152,0.148,0.193,0.182,0.186,0.186,0.197,0.208,0.201,0.197,0.197,0.197,0.136,0.125,2638,Binding,GCN4_YEAST,Low,Eukaryote +GDIA_HUMAN_Silverstein_2021,0.121,0.112,0.129,0.138,0.138,0.147,0.155,0.129,0.121,0.129,0.138,0.112,0.121,0.069,0.086,0.121,0.138,0.138,0.138,0.138,0.095,0.164,0.155,0.19,0.121,0.164,0.138,0.129,0.138,0.147,0.172,0.181,0.112,0.121,0.103,0.112,0.138,0.121,0.129,0.138,0.121,0.147,0.112,0.052,0.155,0.103,0.172,0.164,0.147,0.078,0.138,0.147,0.147,0.164,0.164,0.181,0.155,0.138,0.164,0.155,0.138,0.121,1154,OrganismalFitness,GDIA_HUMAN,Low,Human +GFP_AEQVI_Sarkisyan_2016,0.277,0.275,0.28,0.279,0.287,0.287,0.061,0.256,0.297,0.298,0.188,0.083,0.086,0.085,0.092,0.079,0.078,0.09,0.112,0.241,0.074,0.073,0.089,0.08,0.066,0.098,0.118,0.259,0.268,0.296,0.238,0.239,0.087,0.069,0.093,0.249,0.285,0.283,0.279,0.288,0.289,0.304,0.128,0.11,0.134,0.134,0.279,0.28,0.308,0.254,0.235,0.239,0.25,0.253,0.243,0.251,0.241,0.245,0.233,0.247,0.262,0.188,51714,Activity,GFP_AEQVI,Low,Eukaryote +GLPA_HUMAN_Elazar_2016,0.32,0.2,0.36,0.36,0.36,0.36,0.28,0.28,0.2,0.2,0.2,0.4,0.32,0.28,0.28,0.32,0.36,0.32,0.2,0.2,0.32,0.28,0.36,0.32,0.24,0.36,0.32,0.4,0.24,0.2,0.28,0.24,0.24,0.28,0.32,0.36,0.4,0.4,0.44,0.36,0.36,0.36,0.28,0.24,0.4,0.32,0.28,0.2,0.32,0.16,0.48,0.44,0.32,0.36,0.4,0.28,0.32,0.48,0.36,0.4,0.32,0.4,245,Expression,GLPA_HUMAN,Low,Human +GRB2_HUMAN_Faure_2021,0.303,0.36,0.354,0.363,0.376,0.384,0.317,0.288,0.38,0.339,0.365,0.305,0.345,0.321,0.384,0.425,0.442,0.346,0.393,0.366,0.371,0.357,0.328,0.31,0.343,0.347,0.287,0.381,0.283,0.345,0.281,0.266,0.343,0.377,0.362,0.245,0.381,0.376,0.291,0.399,0.402,0.375,0.398,0.196,0.35,0.362,0.46,0.337,0.491,0.345,0.416,0.437,0.415,0.427,0.438,0.434,0.417,0.437,0.412,0.439,0.359,0.402,63366,OrganismalFitness,GRB2_HUMAN,Medium,Human +HCP_LAMBD_Tsuboyama_2023_2L6Q,0.269,0.26,0.192,0.212,0.202,0.221,0.192,0.192,0.125,0.192,0.346,0.269,0.317,0.221,0.269,0.298,0.337,0.231,0.221,0.106,0.125,0.183,0.212,0.26,0.231,0.26,0.221,0.212,0.26,0.212,0.231,0.163,0.173,0.106,0.163,0.24,0.269,0.279,0.26,0.231,0.25,0.25,0.26,0.173,0.298,0.212,0.25,0.337,0.269,0.202,0.298,0.317,0.356,0.317,0.356,0.337,0.337,0.327,0.317,0.356,0.423,0.279,1040,Stability,HCP_LAMBD,Medium,Virus +HECD1_HUMAN_Tsuboyama_2023_3DKM,0.581,0.547,0.619,0.633,0.649,0.651,0.222,0.469,0.606,0.623,0.599,0.174,0.229,0.143,0.152,0.608,0.617,0.605,0.614,0.581,0.191,0.503,0.538,0.538,0.501,0.549,0.544,0.565,0.533,0.596,0.642,0.581,0.109,0.199,0.168,0.333,0.606,0.574,0.606,0.63,0.617,0.623,0.243,0.231,0.608,0.343,0.46,0.592,0.474,0.345,0.639,0.606,0.628,0.615,0.644,0.653,0.63,0.639,0.646,0.644,0.673,0.22,5586,Stability,HECD1_HUMAN,Medium,Human +HEM3_HUMAN_Loggerenberg_2023,0.165,0.174,0.172,0.172,0.174,0.174,0.121,0.063,0.181,0.178,0.155,0.167,0.146,0.09,0.172,0.183,0.155,0.169,0.163,0.072,0.174,0.174,0.174,0.167,0.16,0.172,0.163,0.16,0.176,0.183,0.158,0.156,0.109,0.172,0.193,0.165,0.163,0.19,0.158,0.185,0.181,0.172,0.121,0.098,0.165,0.158,0.163,0.188,0.139,0.144,0.167,0.172,0.169,0.167,0.165,0.17,0.172,0.172,0.17,0.17,0.167,0.178,5689,Activity,HEM3_HUMAN,Medium,Human +HIS7_YEAST_Pokusaeva_2019,0.165,0.202,0.209,0.209,0.203,0.199,0.133,0.086,0.188,0.191,0.204,0.174,0.185,0.1,0.13,0.179,0.2,0.202,0.194,0.17,0.175,0.198,0.225,0.211,0.203,0.213,0.191,0.194,0.191,0.177,0.199,0.202,0.088,0.211,0.212,0.197,0.19,0.188,0.186,0.209,0.205,0.202,0.148,0.132,0.197,0.109,0.182,0.209,0.188,0.154,0.196,0.178,0.19,0.193,0.194,0.191,0.193,0.196,0.196,0.193,0.203,0.107,496137,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +HMDH_HUMAN_Jiang_2019,0.135,0.122,0.124,0.12,0.128,0.122,0.133,0.193,0.121,0.122,0.126,0.13,0.123,0.122,0.123,0.136,0.149,0.13,0.133,0.111,0.137,0.108,0.116,0.125,0.143,0.106,0.108,0.108,0.124,0.12,0.127,0.127,0.114,0.124,0.111,0.12,0.125,0.125,0.122,0.116,0.12,0.12,0.124,0.117,0.161,0.16,0.132,0.129,0.105,0.126,0.145,0.129,0.14,0.149,0.144,0.15,0.143,0.151,0.14,0.139,0.155,0.148,16853,OrganismalFitness,HMDH_HUMAN,Low,Human +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,0.102,0.142,0.142,0.155,0.137,0.146,0.119,0.168,0.133,0.137,0.137,0.137,0.146,0.08,0.066,0.115,0.124,0.15,0.111,0.088,0.164,0.168,0.173,0.159,0.15,0.159,0.15,0.173,0.164,0.159,0.19,0.19,0.084,0.159,0.168,0.155,0.119,0.137,0.133,0.155,0.15,0.155,0.106,0.075,0.159,0.142,0.119,0.155,0.115,0.111,0.142,0.137,0.119,0.111,0.133,0.133,0.137,0.155,0.142,0.133,0.142,0.159,2252,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Flynn_2019,0.125,0.092,0.108,0.109,0.109,0.111,0.106,0.129,0.11,0.108,0.115,0.111,0.107,0.094,0.114,0.111,0.156,0.15,0.139,0.126,0.114,0.108,0.116,0.11,0.128,0.128,0.108,0.119,0.105,0.097,0.126,0.115,0.095,0.12,0.129,0.12,0.119,0.132,0.126,0.111,0.117,0.109,0.108,0.095,0.115,0.119,0.117,0.112,0.086,0.12,0.15,0.14,0.146,0.145,0.146,0.149,0.143,0.138,0.135,0.15,0.109,0.138,13294,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HSP82_YEAST_Mishra_2016,0.159,0.182,0.178,0.194,0.185,0.187,0.129,0.127,0.182,0.199,0.18,0.194,0.192,0.074,0.088,0.111,0.162,0.159,0.132,0.155,0.215,0.192,0.206,0.182,0.21,0.196,0.199,0.196,0.187,0.18,0.196,0.206,0.118,0.182,0.176,0.192,0.189,0.178,0.173,0.182,0.189,0.189,0.189,0.051,0.173,0.199,0.104,0.176,0.079,0.127,0.129,0.125,0.125,0.143,0.176,0.15,0.141,0.141,0.148,0.15,0.171,0.164,4323,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +HXK4_HUMAN_Gersing_2022_activity,0.232,0.161,0.149,0.151,0.156,0.156,0.098,0.127,0.149,0.148,0.128,0.116,0.135,0.117,0.127,0.172,0.174,0.151,0.138,0.12,0.14,0.117,0.141,0.134,0.14,0.14,0.153,0.138,0.125,0.109,0.133,0.119,0.085,0.144,0.139,0.148,0.165,0.133,0.155,0.162,0.14,0.158,0.135,0.107,0.14,0.144,0.179,0.16,0.159,0.142,0.162,0.176,0.162,0.173,0.165,0.176,0.182,0.163,0.166,0.17,0.17,0.162,8570,OrganismalFitness,HXK4_HUMAN,Medium,Human +HXK4_HUMAN_Gersing_2023_abundance,0.152,0.16,0.162,0.173,0.154,0.16,0.086,0.16,0.156,0.179,0.162,0.173,0.192,0.09,0.113,0.148,0.173,0.167,0.174,0.185,0.165,0.186,0.186,0.19,0.173,0.185,0.194,0.183,0.17,0.187,0.165,0.165,0.111,0.164,0.171,0.193,0.167,0.179,0.187,0.158,0.162,0.165,0.123,0.106,0.164,0.163,0.158,0.154,0.163,0.129,0.162,0.161,0.162,0.148,0.167,0.167,0.175,0.163,0.165,0.165,0.175,0.155,8396,Expression,HXK4_HUMAN,Medium,Human +I6TAH8_I68A0_Doud_2015,0.277,0.262,0.234,0.233,0.285,0.282,0.083,0.225,0.247,0.275,0.092,0.086,0.087,0.082,0.077,0.088,0.1,0.091,0.124,0.216,0.23,0.253,0.261,0.263,0.077,0.094,0.119,0.083,0.238,0.284,0.185,0.188,0.143,0.261,0.251,0.252,0.27,0.266,0.26,0.283,0.28,0.268,0.08,0.088,0.092,0.086,0.186,0.185,0.186,0.146,0.103,0.136,0.13,0.136,0.121,0.117,0.105,0.109,0.1,0.12,0.124,0.11,9462,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +IF1_ECOLI_Kelsic_2016,0.13,0.217,0.196,0.232,0.21,0.21,0.196,0.159,0.087,0.087,0.254,0.181,0.196,0.101,0.188,0.261,0.239,0.196,0.203,0.196,0.188,0.232,0.203,0.203,0.225,0.217,0.261,0.225,0.152,0.304,0.203,0.159,0.145,0.21,0.188,0.21,0.181,0.152,0.203,0.203,0.196,0.203,0.145,0.13,0.261,0.232,0.109,0.232,0.217,0.174,0.217,0.268,0.239,0.239,0.232,0.246,0.232,0.21,0.254,0.239,0.188,0.188,1367,OrganismalFitness,IF1_ECOLI,High,Prokaryote +ILF3_HUMAN_Tsuboyama_2023_2L33,0.075,0.075,0.195,0.203,0.195,0.211,0.06,0.12,0.083,0.12,0.203,0.12,0.113,0.053,0.053,0.075,0.083,0.165,0.188,0.188,0.203,0.173,0.173,0.188,0.271,0.211,0.248,0.18,0.165,0.218,0.12,0.075,0.203,0.083,0.226,0.233,0.06,0.158,0.18,0.105,0.128,0.165,0.09,0.06,0.158,0.128,0.09,0.15,0.083,0.15,0.188,0.105,0.158,0.15,0.165,0.165,0.158,0.143,0.15,0.158,0.218,0.09,1329,Stability,ILF3_HUMAN,High,Human +ISDH_STAAW_Tsuboyama_2023_2LHR,0.226,0.179,0.236,0.236,0.221,0.236,0.313,0.128,0.226,0.282,0.246,0.323,0.344,0.344,0.369,0.451,0.374,0.421,0.395,0.19,0.318,0.2,0.195,0.215,0.349,0.354,0.369,0.333,0.251,0.205,0.179,0.123,0.159,0.231,0.241,0.287,0.236,0.215,0.267,0.251,0.231,0.251,0.313,0.272,0.349,0.338,0.451,0.467,0.333,0.313,0.251,0.292,0.297,0.308,0.292,0.287,0.287,0.297,0.272,0.292,0.415,0.308,1944,Stability,ISDH_STAAW,High,Prokaryote +KCNE1_HUMAN_Muhammad_2023_expression,0.043,0.03,0.021,0.017,0.047,0.047,0.077,0.141,0.03,0.034,0.085,0.09,0.073,0.073,0.077,0.047,0.073,0.043,0.017,0.026,0.073,0.09,0.081,0.12,0.085,0.162,0.068,0.034,0.034,0.034,0.038,0.038,0.111,0.064,0.107,0.094,0.064,0.073,0.073,0.051,0.047,0.056,0.081,0.098,0.013,0.081,0.068,0.021,0.085,0.132,0.068,0.051,0.064,0.068,0.056,0.064,0.073,0.06,0.06,0.06,0.051,0.085,2339,Expression,KCNE1_HUMAN,Medium,Human +KCNE1_HUMAN_Muhammad_2023_function,0.194,0.224,0.203,0.207,0.22,0.22,0.19,0.155,0.22,0.237,0.194,0.233,0.25,0.19,0.194,0.246,0.207,0.22,0.172,0.168,0.198,0.177,0.185,0.177,0.207,0.19,0.25,0.228,0.19,0.194,0.216,0.185,0.034,0.203,0.241,0.216,0.19,0.203,0.198,0.224,0.22,0.211,0.185,0.159,0.203,0.185,0.224,0.181,0.172,0.134,0.198,0.22,0.216,0.177,0.228,0.228,0.233,0.198,0.211,0.207,0.297,0.177,2315,Activity,KCNE1_HUMAN,Medium,Human +KCNH2_HUMAN_Kozek_2020,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.05,0.1,0.1,0.1,0.1,0.05,0.05,0.05,0.05,0.05,0.2,0.35,0.05,0.05,0.05,0.1,0.0,0.0,0.05,0.0,0.0,0.0,0.1,0.0,0.05,0.05,0.0,0.0,0.05,0.05,0.15,0.1,0.1,0.15,0.15,0.1,0.15,0.1,0.15,0.15,0.0,0.1,200,Activity,KCNH2_HUMAN,Medium,Human +KCNJ2_MOUSE_Coyote-Maestas_2022_function,0.146,0.156,0.136,0.155,0.162,0.158,0.108,0.125,0.139,0.136,0.166,0.154,0.156,0.106,0.149,0.166,0.168,0.164,0.161,0.129,0.162,0.161,0.149,0.128,0.161,0.158,0.154,0.155,0.148,0.149,0.141,0.131,0.102,0.159,0.165,0.138,0.156,0.158,0.145,0.154,0.158,0.146,0.129,0.113,0.136,0.155,0.142,0.143,0.149,0.113,0.158,0.162,0.166,0.158,0.162,0.152,0.148,0.171,0.161,0.164,0.136,0.159,6963,Activity,KCNJ2_MOUSE,Medium,Eukaryote +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,0.149,0.108,0.136,0.127,0.12,0.132,0.104,0.072,0.104,0.107,0.152,0.146,0.117,0.152,0.188,0.208,0.185,0.159,0.189,0.077,0.175,0.092,0.075,0.082,0.185,0.094,0.085,0.139,0.078,0.1,0.085,0.091,0.077,0.162,0.072,0.085,0.132,0.095,0.078,0.13,0.105,0.1,0.175,0.127,0.098,0.189,0.153,0.087,0.129,0.126,0.175,0.156,0.182,0.156,0.184,0.162,0.171,0.169,0.175,0.169,0.169,0.192,6917,Expression,KCNJ2_MOUSE,Medium,Eukaryote +KKA2_KLEPN_Melnikov_2014,0.159,0.22,0.216,0.24,0.222,0.224,0.107,0.169,0.208,0.238,0.212,0.19,0.226,0.113,0.123,0.169,0.226,0.24,0.238,0.2,0.151,0.188,0.22,0.216,0.165,0.204,0.228,0.188,0.242,0.218,0.169,0.149,0.077,0.125,0.167,0.194,0.185,0.19,0.21,0.232,0.23,0.236,0.123,0.101,0.202,0.143,0.22,0.23,0.228,0.171,0.224,0.214,0.228,0.216,0.216,0.222,0.22,0.208,0.216,0.226,0.236,0.181,4960,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +LGK_LIPST_Klesmith_2015,0.217,0.432,0.426,0.427,0.446,0.447,0.133,0.347,0.272,0.406,0.251,0.351,0.388,0.119,0.174,0.227,0.324,0.404,0.435,0.326,0.161,0.241,0.298,0.319,0.185,0.328,0.406,0.305,0.445,0.493,0.371,0.319,0.139,0.171,0.245,0.47,0.214,0.246,0.444,0.436,0.447,0.47,0.105,0.085,0.283,0.175,0.231,0.361,0.376,0.158,0.305,0.347,0.337,0.354,0.337,0.347,0.323,0.335,0.323,0.352,0.269,0.19,7890,Activity,LGK_LIPST,Medium,Eukaryote +LYAM1_HUMAN_Elazar_2016,0.139,0.25,0.25,0.25,0.222,0.25,0.278,0.111,0.25,0.25,0.25,0.222,0.222,0.25,0.278,0.222,0.194,0.167,0.278,0.25,0.333,0.333,0.278,0.25,0.222,0.25,0.194,0.25,0.083,0.222,0.194,0.056,0.194,0.278,0.333,0.222,0.194,0.333,0.222,0.278,0.278,0.25,0.25,0.278,0.194,0.194,0.278,0.222,0.194,0.194,0.194,0.306,0.333,0.25,0.25,0.25,0.278,0.278,0.139,0.278,0.306,0.306,359,Expression,LYAM1_HUMAN,Medium,Human +MAFG_MOUSE_Tsuboyama_2023_1K1V,0.315,0.315,0.35,0.35,0.343,0.336,0.343,0.245,0.287,0.28,0.294,0.322,0.406,0.385,0.378,0.378,0.413,0.455,0.273,0.231,0.224,0.217,0.259,0.301,0.28,0.273,0.294,0.273,0.273,0.252,0.21,0.217,0.161,0.231,0.301,0.231,0.301,0.315,0.294,0.343,0.315,0.322,0.385,0.259,0.35,0.357,0.413,0.315,0.364,0.385,0.413,0.441,0.448,0.469,0.427,0.476,0.462,0.462,0.434,0.462,0.462,0.483,1429,Stability,MAFG_MOUSE,Medium,Eukaryote +MBD11_ARATH_Tsuboyama_2023_6ACV,0.288,0.382,0.33,0.33,0.335,0.335,0.057,0.151,0.274,0.302,0.264,0.255,0.226,0.028,0.028,0.264,0.259,0.264,0.377,0.241,0.024,0.061,0.193,0.179,0.094,0.264,0.274,0.236,0.288,0.274,0.325,0.297,0.316,0.028,0.042,0.047,0.269,0.264,0.283,0.316,0.33,0.316,0.09,0.057,0.307,0.25,0.288,0.335,0.288,0.288,0.34,0.387,0.344,0.344,0.316,0.335,0.33,0.311,0.274,0.33,0.382,0.264,2116,Stability,MBD11_ARATH,Medium,Eukaryote +MET_HUMAN_Estevam_2023,0.148,0.185,0.163,0.174,0.163,0.178,0.163,0.174,0.156,0.167,0.191,0.178,0.174,0.143,0.148,0.172,0.183,0.172,0.187,0.163,0.185,0.187,0.159,0.193,0.193,0.181,0.183,0.191,0.2,0.193,0.17,0.172,0.12,0.161,0.178,0.196,0.167,0.174,0.198,0.176,0.17,0.176,0.17,0.139,0.187,0.178,0.15,0.183,0.169,0.119,0.161,0.163,0.165,0.167,0.156,0.176,0.172,0.169,0.187,0.159,0.185,0.167,5393,Activity,MET_HUMAN,Medium,Human +MK01_HUMAN_Brenan_2016,0.091,0.134,0.134,0.144,0.138,0.122,0.084,0.075,0.116,0.095,0.076,0.119,0.107,0.104,0.103,0.09,0.072,0.085,0.098,0.097,0.101,0.094,0.082,0.09,0.095,0.109,0.103,0.109,0.101,0.122,0.082,0.095,0.109,0.087,0.101,0.09,0.078,0.098,0.088,0.125,0.125,0.106,0.097,0.095,0.113,0.12,0.06,0.109,0.07,0.097,0.084,0.104,0.095,0.088,0.085,0.079,0.09,0.081,0.081,0.088,0.098,0.104,6809,OrganismalFitness,MK01_HUMAN,Medium,Human +MLAC_ECOLI_MacRae_2023,0.095,0.145,0.147,0.137,0.132,0.142,0.115,0.14,0.162,0.15,0.152,0.155,0.177,0.12,0.112,0.112,0.137,0.152,0.18,0.167,0.142,0.14,0.145,0.15,0.147,0.15,0.177,0.16,0.145,0.157,0.12,0.14,0.15,0.112,0.117,0.135,0.09,0.122,0.115,0.127,0.142,0.137,0.1,0.087,0.14,0.11,0.057,0.115,0.115,0.067,0.12,0.112,0.117,0.117,0.13,0.112,0.112,0.112,0.115,0.11,0.125,0.097,4007,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +MSH2_HUMAN_Jia_2020,0.133,0.162,0.143,0.147,0.147,0.149,0.1,0.1,0.156,0.153,0.146,0.164,0.164,0.105,0.131,0.153,0.152,0.159,0.146,0.152,0.114,0.127,0.137,0.146,0.138,0.144,0.146,0.134,0.162,0.143,0.147,0.135,0.132,0.128,0.153,0.155,0.134,0.153,0.152,0.159,0.167,0.157,0.122,0.093,0.156,0.134,0.13,0.152,0.101,0.118,0.149,0.144,0.15,0.142,0.148,0.159,0.154,0.153,0.154,0.158,0.154,0.134,16749,OrganismalFitness,MSH2_HUMAN,Medium,Human +MTH3_HAEAE_RockahShmuel_2015,0.144,0.316,0.337,0.326,0.321,0.342,0.134,0.337,0.294,0.299,0.203,0.289,0.316,0.102,0.128,0.155,0.176,0.182,0.23,0.283,0.139,0.16,0.235,0.299,0.203,0.321,0.326,0.283,0.305,0.321,0.332,0.305,0.193,0.171,0.182,0.299,0.16,0.16,0.278,0.342,0.342,0.337,0.128,0.053,0.155,0.123,0.166,0.235,0.262,0.134,0.214,0.225,0.225,0.198,0.203,0.209,0.219,0.219,0.209,0.214,0.209,0.107,1777,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +MTHR_HUMAN_Weile_2021,0.122,0.123,0.113,0.123,0.113,0.108,0.096,0.121,0.133,0.138,0.123,0.126,0.126,0.118,0.124,0.135,0.116,0.132,0.138,0.122,0.121,0.118,0.126,0.149,0.13,0.12,0.131,0.119,0.162,0.137,0.139,0.148,0.075,0.127,0.126,0.127,0.11,0.118,0.125,0.109,0.116,0.123,0.122,0.11,0.123,0.126,0.112,0.122,0.127,0.118,0.12,0.115,0.121,0.118,0.117,0.118,0.12,0.118,0.126,0.117,0.132,0.116,12464,OrganismalFitness,MTHR_HUMAN,Low,Human +MYO3_YEAST_Tsuboyama_2023_2BTT,0.227,0.206,0.276,0.212,0.236,0.245,0.073,0.061,0.297,0.285,0.294,0.261,0.264,0.109,0.23,0.355,0.421,0.318,0.179,0.233,0.139,0.091,0.164,0.155,0.188,0.261,0.255,0.227,0.291,0.209,0.209,0.167,0.17,0.212,0.139,0.127,0.291,0.221,0.2,0.276,0.255,0.261,0.194,0.073,0.27,0.2,0.248,0.315,0.339,0.27,0.303,0.306,0.297,0.312,0.303,0.306,0.291,0.303,0.3,0.303,0.297,0.348,3297,Stability,MYO3_YEAST,High,Eukaryote +NCAP_I34A1_Doud_2015,0.258,0.246,0.246,0.247,0.259,0.263,0.111,0.22,0.266,0.261,0.106,0.099,0.094,0.101,0.093,0.096,0.099,0.102,0.129,0.182,0.235,0.264,0.264,0.263,0.098,0.111,0.109,0.107,0.242,0.268,0.199,0.194,0.148,0.249,0.252,0.285,0.26,0.271,0.27,0.28,0.282,0.278,0.094,0.099,0.106,0.093,0.161,0.166,0.178,0.144,0.103,0.118,0.127,0.122,0.114,0.114,0.115,0.124,0.102,0.115,0.139,0.13,9462,OrganismalFitness,NCAP_I34A1,Medium,Virus +NKX31_HUMAN_Tsuboyama_2023_2L9R,0.434,0.426,0.434,0.454,0.442,0.474,0.353,0.361,0.378,0.406,0.305,0.398,0.369,0.402,0.394,0.478,0.51,0.486,0.458,0.406,0.386,0.365,0.329,0.329,0.378,0.317,0.357,0.301,0.321,0.373,0.349,0.369,0.305,0.39,0.386,0.337,0.43,0.402,0.402,0.442,0.446,0.454,0.382,0.406,0.321,0.345,0.386,0.341,0.418,0.418,0.482,0.426,0.474,0.45,0.482,0.478,0.454,0.494,0.438,0.466,0.349,0.41,2482,Stability,NKX31_HUMAN,High,Human +NPC1_HUMAN_Erwood_2022_HEK293T,0.391,0.391,0.406,0.422,0.453,0.453,0.141,0.281,0.391,0.453,0.344,0.172,0.219,0.156,0.219,0.297,0.438,0.391,0.422,0.125,0.203,0.234,0.141,0.297,0.266,0.188,0.406,0.266,0.219,0.359,0.281,0.219,0.078,0.156,0.281,0.375,0.359,0.422,0.422,0.406,0.422,0.453,0.156,0.125,0.375,0.281,0.344,0.375,0.062,0.125,0.375,0.422,0.391,0.375,0.344,0.359,0.344,0.359,0.359,0.391,0.391,0.266,637,Activity,NPC1_HUMAN,Low,Human +NPC1_HUMAN_Erwood_2022_RPE1,0.429,0.429,0.429,0.429,0.429,0.429,0.286,0.286,0.429,0.714,0.286,0.143,0.143,0.143,0.286,0.286,0.429,0.0,0.429,0.143,0.286,0.571,0.571,0.286,0.143,0.286,0.429,0.571,0.571,0.429,0.429,0.286,0.0,0.286,0.429,0.286,0.429,0.429,0.429,0.429,0.429,0.429,0.286,0.286,0.286,0.0,0.286,0.0,0.143,0.143,0.143,0.143,0.143,0.286,0.0,0.143,0.143,0.143,0.286,0.143,0.143,0.143,63,Activity,NPC1_HUMAN,Low,Human +NRAM_I33A0_Jiang_2016,0.433,0.567,0.467,0.433,0.567,0.567,0.067,0.6,0.667,0.7,0.067,0.133,0.6,0.0,0.0,0.067,0.233,0.567,0.5,0.4,0.7,0.667,0.533,0.533,0.0,0.433,0.567,0.4,0.633,0.567,0.4,0.4,0.067,0.6,0.533,0.6,0.533,0.567,0.6,0.633,0.6,0.567,0.0,0.033,0.0,0.033,0.267,0.2,0.4,0.233,0.167,0.2,0.3,0.233,0.3,0.4,0.267,0.267,0.333,0.333,0.233,0.033,298,OrganismalFitness,NRAM_I33A0,Low,Virus +NUD15_HUMAN_Suiter_2020,0.193,0.26,0.281,0.274,0.274,0.284,0.053,0.179,0.253,0.267,0.256,0.207,0.284,0.105,0.126,0.14,0.193,0.239,0.263,0.204,0.102,0.13,0.249,0.253,0.133,0.298,0.267,0.239,0.312,0.309,0.267,0.211,0.098,0.123,0.119,0.305,0.196,0.204,0.302,0.274,0.288,0.309,0.116,0.07,0.284,0.14,0.196,0.33,0.211,0.168,0.214,0.249,0.211,0.228,0.239,0.263,0.239,0.256,0.246,0.26,0.298,0.2,2844,Expression,NUD15_HUMAN,High,Human +NUSA_ECOLI_Tsuboyama_2023_1WCL,0.123,0.256,0.163,0.167,0.172,0.172,0.133,0.128,0.202,0.138,0.177,0.03,0.059,0.103,0.103,0.084,0.148,0.167,0.217,0.163,0.172,0.202,0.197,0.217,0.094,0.143,0.113,0.207,0.251,0.138,0.217,0.217,0.074,0.163,0.143,0.148,0.158,0.118,0.143,0.172,0.163,0.192,0.172,0.138,0.103,0.133,0.374,0.305,0.276,0.345,0.246,0.291,0.266,0.261,0.286,0.246,0.246,0.246,0.241,0.261,0.271,0.158,2028,Stability,NUSA_ECOLI,High,Prokaryote +NUSG_MYCTU_Tsuboyama_2023_2MI6,0.283,0.217,0.225,0.246,0.239,0.246,0.159,0.181,0.283,0.225,0.232,0.254,0.246,0.123,0.261,0.254,0.232,0.217,0.217,0.138,0.116,0.152,0.152,0.152,0.254,0.181,0.167,0.167,0.174,0.246,0.232,0.196,0.217,0.167,0.145,0.181,0.21,0.203,0.21,0.196,0.188,0.21,0.217,0.08,0.188,0.297,0.254,0.21,0.37,0.319,0.283,0.341,0.312,0.275,0.29,0.29,0.304,0.319,0.268,0.297,0.188,0.275,1380,Stability,NUSG_MYCTU,High,Prokaryote +OBSCN_HUMAN_Tsuboyama_2023_1V1C,0.372,0.484,0.547,0.559,0.575,0.541,0.119,0.378,0.5,0.494,0.509,0.469,0.478,0.131,0.453,0.497,0.569,0.519,0.512,0.55,0.447,0.434,0.431,0.428,0.438,0.416,0.45,0.412,0.403,0.494,0.497,0.509,0.328,0.169,0.122,0.319,0.466,0.475,0.491,0.538,0.534,0.531,0.381,0.144,0.406,0.431,0.562,0.438,0.669,0.578,0.578,0.591,0.588,0.6,0.606,0.603,0.553,0.578,0.6,0.609,0.631,0.5,3197,Stability,OBSCN_HUMAN,High,Human +ODP2_GEOSE_Tsuboyama_2023_1W4G,0.184,0.132,0.237,0.167,0.246,0.132,0.193,0.149,0.114,0.114,0.193,0.184,0.184,0.175,0.158,0.211,0.175,0.184,0.167,0.193,0.105,0.114,0.184,0.096,0.088,0.114,0.167,0.105,0.158,0.202,0.123,0.096,0.123,0.167,0.044,0.167,0.158,0.044,0.079,0.158,0.114,0.132,0.175,0.175,0.184,0.184,0.237,0.193,0.246,0.307,0.228,0.211,0.246,0.254,0.263,0.237,0.263,0.263,0.228,0.246,0.202,0.193,1134,Stability,ODP2_GEOSE,High,Prokaryote +OPSD_HUMAN_Wan_2019,0.176,0.412,0.412,0.412,0.471,0.412,0.176,0.412,0.294,0.412,0.353,0.294,0.294,0.294,0.235,0.235,0.294,0.412,0.176,0.412,0.412,0.353,0.235,0.294,0.471,0.353,0.353,0.412,0.235,0.294,0.235,0.235,0.0,0.353,0.235,0.118,0.412,0.294,0.294,0.412,0.412,0.412,0.353,0.059,0.353,0.412,0.176,0.353,0.176,0.176,0.235,0.235,0.176,0.176,0.176,0.294,0.235,0.235,0.235,0.176,0.412,0.294,165,Expression,OPSD_HUMAN,High,Human +OTC_HUMAN_Lo_2023,0.316,0.342,0.297,0.291,0.31,0.304,0.139,0.152,0.316,0.297,0.291,0.323,0.335,0.158,0.291,0.342,0.335,0.297,0.297,0.304,0.323,0.272,0.272,0.278,0.278,0.348,0.304,0.297,0.234,0.304,0.241,0.171,0.133,0.266,0.278,0.278,0.329,0.304,0.323,0.31,0.297,0.316,0.209,0.127,0.316,0.259,0.367,0.304,0.291,0.259,0.304,0.348,0.342,0.361,0.329,0.348,0.348,0.342,0.342,0.342,0.38,0.342,1570,Activity,OTC_HUMAN,Medium,Human +OTU7A_HUMAN_Tsuboyama_2023_2L2D,0.188,0.266,0.203,0.203,0.219,0.203,0.078,0.125,0.109,0.141,0.25,0.297,0.234,0.109,0.281,0.328,0.156,0.172,0.188,0.156,0.109,0.062,0.078,0.078,0.109,0.062,0.078,0.094,0.047,0.031,0.109,0.109,0.062,0.141,0.125,0.141,0.188,0.156,0.125,0.203,0.172,0.188,0.172,0.141,0.25,0.344,0.25,0.203,0.328,0.281,0.219,0.219,0.219,0.188,0.219,0.203,0.234,0.172,0.172,0.219,0.359,0.328,635,Stability,OTU7A_HUMAN,High,Human +OXDA_RHOTO_Vanella_2023_activity,0.127,0.192,0.231,0.223,0.238,0.233,0.114,0.131,0.141,0.145,0.181,0.162,0.172,0.112,0.114,0.156,0.18,0.209,0.217,0.15,0.125,0.142,0.156,0.155,0.119,0.161,0.181,0.162,0.225,0.238,0.191,0.177,0.072,0.133,0.131,0.134,0.138,0.128,0.136,0.223,0.233,0.234,0.122,0.095,0.177,0.136,0.195,0.242,0.248,0.162,0.184,0.216,0.202,0.205,0.197,0.223,0.208,0.223,0.194,0.216,0.222,0.148,6396,Activity,OXDA_RHOTO,High,Eukaryote +OXDA_RHOTO_Vanella_2023_expression,0.096,0.14,0.168,0.161,0.155,0.164,0.149,0.123,0.133,0.12,0.149,0.133,0.134,0.096,0.12,0.151,0.161,0.173,0.18,0.081,0.092,0.145,0.136,0.134,0.111,0.162,0.152,0.14,0.152,0.177,0.127,0.121,0.09,0.108,0.114,0.134,0.109,0.09,0.114,0.155,0.149,0.158,0.111,0.096,0.145,0.114,0.176,0.189,0.186,0.146,0.164,0.146,0.142,0.139,0.16,0.158,0.161,0.16,0.168,0.158,0.183,0.157,6769,Expression,OXDA_RHOTO,High,Eukaryote +P53_HUMAN_Giacomelli_2018_Null_Etoposide,0.099,0.103,0.108,0.1,0.107,0.118,0.098,0.096,0.079,0.078,0.167,0.131,0.158,0.1,0.083,0.107,0.111,0.135,0.143,0.07,0.115,0.151,0.181,0.123,0.116,0.165,0.194,0.201,0.096,0.078,0.142,0.102,0.118,0.107,0.138,0.072,0.103,0.126,0.079,0.099,0.104,0.099,0.111,0.091,0.154,0.087,0.139,0.171,0.13,0.129,0.131,0.12,0.118,0.106,0.103,0.118,0.114,0.123,0.11,0.108,0.124,0.09,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_Null_Nutlin,0.084,0.078,0.074,0.075,0.08,0.082,0.116,0.107,0.06,0.059,0.193,0.126,0.174,0.108,0.091,0.112,0.114,0.127,0.149,0.094,0.119,0.166,0.218,0.114,0.112,0.194,0.25,0.244,0.099,0.084,0.171,0.118,0.131,0.119,0.154,0.063,0.106,0.129,0.066,0.078,0.087,0.071,0.126,0.11,0.167,0.099,0.151,0.182,0.163,0.114,0.146,0.155,0.126,0.123,0.112,0.119,0.127,0.122,0.12,0.12,0.146,0.111,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Giacomelli_2018_WT_Nutlin,0.074,0.058,0.07,0.062,0.059,0.059,0.074,0.054,0.048,0.046,0.11,0.092,0.107,0.112,0.126,0.111,0.08,0.099,0.106,0.035,0.126,0.099,0.099,0.07,0.09,0.108,0.11,0.112,0.059,0.052,0.09,0.075,0.067,0.071,0.099,0.058,0.079,0.09,0.064,0.058,0.063,0.06,0.091,0.096,0.099,0.096,0.106,0.1,0.115,0.107,0.099,0.098,0.09,0.082,0.099,0.091,0.086,0.076,0.08,0.083,0.09,0.099,7467,OrganismalFitness,P53_HUMAN,Low,Human +P53_HUMAN_Kotler_2018,0.324,0.257,0.276,0.286,0.238,0.229,0.124,0.267,0.238,0.257,0.286,0.219,0.248,0.143,0.133,0.238,0.267,0.276,0.267,0.219,0.2,0.267,0.219,0.229,0.21,0.2,0.21,0.2,0.162,0.286,0.267,0.219,0.038,0.162,0.229,0.229,0.324,0.305,0.314,0.276,0.286,0.257,0.143,0.114,0.2,0.114,0.19,0.229,0.21,0.181,0.21,0.248,0.248,0.229,0.238,0.238,0.21,0.229,0.238,0.248,0.286,0.219,1048,OrganismalFitness,P53_HUMAN,Low,Human +P84126_THETH_Chan_2017,0.276,0.414,0.388,0.401,0.382,0.388,0.184,0.289,0.401,0.375,0.408,0.368,0.382,0.204,0.401,0.355,0.401,0.368,0.414,0.375,0.263,0.329,0.342,0.382,0.322,0.395,0.395,0.355,0.447,0.316,0.362,0.322,0.118,0.309,0.316,0.355,0.289,0.27,0.309,0.355,0.362,0.355,0.257,0.112,0.382,0.395,0.204,0.388,0.362,0.178,0.329,0.362,0.322,0.349,0.362,0.395,0.375,0.362,0.401,0.388,0.395,0.388,1519,OrganismalFitness,P84126_THETH,Medium,Prokaryote +PA_I34A1_Wu_2015,0.335,0.335,0.335,0.324,0.335,0.324,0.115,0.269,0.088,0.132,0.11,0.11,0.093,0.104,0.093,0.104,0.104,0.099,0.247,0.236,0.28,0.275,0.264,0.33,0.121,0.258,0.258,0.225,0.264,0.291,0.264,0.242,0.126,0.275,0.28,0.302,0.335,0.319,0.319,0.33,0.341,0.319,0.11,0.11,0.104,0.11,0.115,0.099,0.071,0.137,0.088,0.093,0.099,0.088,0.088,0.11,0.071,0.082,0.082,0.115,0.104,0.071,1820,OrganismalFitness,PA_I34A1,Medium,Virus +PABP_YEAST_Melamed_2013,0.279,0.269,0.275,0.275,0.29,0.285,0.182,0.234,0.269,0.276,0.277,0.287,0.289,0.172,0.191,0.21,0.242,0.245,0.259,0.249,0.255,0.285,0.275,0.282,0.27,0.281,0.292,0.281,0.25,0.291,0.273,0.249,0.121,0.244,0.27,0.261,0.269,0.285,0.272,0.286,0.291,0.286,0.242,0.115,0.288,0.264,0.126,0.288,0.2,0.148,0.233,0.216,0.229,0.221,0.223,0.217,0.221,0.227,0.228,0.225,0.269,0.213,37708,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +PAI1_HUMAN_Huttinger_2021,0.207,0.215,0.207,0.211,0.211,0.211,0.086,0.172,0.2,0.222,0.196,0.198,0.217,0.121,0.196,0.209,0.2,0.193,0.179,0.202,0.146,0.17,0.151,0.2,0.178,0.179,0.181,0.183,0.168,0.168,0.138,0.121,0.129,0.142,0.196,0.183,0.194,0.215,0.198,0.224,0.217,0.224,0.185,0.133,0.196,0.183,0.217,0.247,0.219,0.155,0.213,0.202,0.219,0.213,0.211,0.226,0.224,0.221,0.221,0.224,0.232,0.206,5345,Activity,PAI1_HUMAN,,Human +PHOT_CHLRE_Chen_2023,0.273,0.396,0.515,0.502,0.368,0.354,0.401,0.393,0.501,0.49,0.434,0.47,0.502,0.505,0.529,0.49,0.526,0.497,0.507,0.362,0.397,0.458,0.413,0.463,0.406,0.438,0.459,0.459,0.423,0.389,0.426,0.381,0.342,0.325,0.317,0.442,0.412,0.388,0.453,0.376,0.39,0.379,0.463,0.288,0.464,0.49,0.23,0.415,0.396,0.269,0.412,0.377,0.378,0.39,0.386,0.401,0.397,0.402,0.401,0.395,0.491,0.484,167529,Activity,PHOT_CHLRE,High,Eukaryote +PIN1_HUMAN_Tsuboyama_2023_1I6C,0.333,0.383,0.42,0.444,0.407,0.395,0.346,0.185,0.333,0.432,0.321,0.296,0.358,0.37,0.358,0.395,0.42,0.296,0.346,0.346,0.296,0.222,0.16,0.111,0.21,0.185,0.123,0.099,0.086,0.198,0.247,0.185,0.198,0.321,0.247,0.21,0.395,0.37,0.296,0.383,0.346,0.309,0.321,0.148,0.358,0.358,0.272,0.321,0.284,0.259,0.296,0.383,0.383,0.321,0.309,0.358,0.346,0.333,0.346,0.37,0.407,0.395,802,Stability,PIN1_HUMAN,High,Human +PITX2_HUMAN_Tsuboyama_2023_2L7M,0.366,0.339,0.333,0.322,0.333,0.339,0.295,0.213,0.273,0.35,0.219,0.29,0.273,0.415,0.383,0.415,0.393,0.361,0.328,0.322,0.213,0.186,0.186,0.186,0.273,0.219,0.235,0.18,0.191,0.311,0.24,0.273,0.23,0.246,0.301,0.164,0.295,0.35,0.251,0.328,0.344,0.322,0.295,0.311,0.18,0.257,0.388,0.202,0.443,0.377,0.377,0.41,0.421,0.41,0.41,0.404,0.388,0.388,0.41,0.404,0.18,0.339,1824,Stability,PITX2_HUMAN,High,Human +PKN1_HUMAN_Tsuboyama_2023_1URF,0.145,0.282,0.145,0.137,0.122,0.153,0.229,0.046,0.107,0.099,0.069,0.13,0.099,0.183,0.16,0.191,0.092,0.046,0.061,0.176,0.176,0.16,0.198,0.153,0.153,0.137,0.16,0.183,0.069,0.046,0.092,0.099,0.099,0.221,0.191,0.191,0.176,0.176,0.168,0.183,0.168,0.153,0.153,0.214,0.13,0.183,0.252,0.153,0.16,0.214,0.115,0.115,0.122,0.115,0.107,0.107,0.099,0.122,0.115,0.115,0.198,0.153,1301,Stability,PKN1_HUMAN,High,Human +POLG_CXB3N_Mattenberger_2021,0.309,0.38,0.361,0.387,0.392,0.399,0.088,0.261,0.394,0.389,0.176,0.09,0.113,0.097,0.091,0.136,0.231,0.252,0.288,0.279,0.288,0.348,0.352,0.328,0.145,0.316,0.319,0.31,0.372,0.391,0.289,0.284,0.096,0.099,0.251,0.316,0.286,0.321,0.342,0.349,0.373,0.375,0.098,0.092,0.196,0.1,0.151,0.215,0.101,0.121,0.197,0.211,0.211,0.212,0.199,0.202,0.214,0.223,0.212,0.218,0.109,0.102,15711,OrganismalFitness,POLG_CXB3N,Medium,Virus +POLG_DEN26_Suphatrakul_2023,0.327,0.395,0.341,0.358,0.384,0.386,0.086,0.299,0.473,0.477,0.143,0.089,0.102,0.08,0.086,0.109,0.134,0.14,0.164,0.331,0.33,0.325,0.331,0.292,0.331,0.344,0.34,0.351,0.349,0.454,0.411,0.314,0.104,0.089,0.16,0.366,0.288,0.332,0.405,0.383,0.393,0.433,0.089,0.086,0.198,0.088,0.188,0.278,0.07,0.136,0.138,0.138,0.166,0.146,0.152,0.154,0.144,0.15,0.14,0.157,0.12,0.114,16897,OrganismalFitness,POLG_DEN26,Low,Virus +POLG_HCVJF_Qi_2014,0.362,0.411,0.368,0.38,0.393,0.405,0.074,0.153,0.387,0.387,0.123,0.387,0.35,0.086,0.092,0.08,0.11,0.092,0.104,0.239,0.196,0.276,0.325,0.288,0.282,0.27,0.239,0.239,0.294,0.387,0.313,0.221,0.067,0.368,0.356,0.288,0.362,0.374,0.362,0.393,0.368,0.344,0.086,0.074,0.331,0.117,0.08,0.245,0.233,0.19,0.172,0.153,0.123,0.153,0.141,0.129,0.221,0.153,0.166,0.178,0.092,0.098,1630,OrganismalFitness,POLG_HCVJF,Medium,Virus +POLG_PESV_Tsuboyama_2023_2MXD,0.164,0.519,0.435,0.454,0.464,0.464,0.088,0.48,0.333,0.497,0.489,0.113,0.129,0.099,0.123,0.117,0.156,0.15,0.127,0.595,0.097,0.078,0.086,0.09,0.109,0.049,0.08,0.051,0.131,0.558,0.608,0.62,0.113,0.105,0.094,0.088,0.296,0.294,0.281,0.437,0.437,0.429,0.185,0.127,0.189,0.154,0.55,0.507,0.561,0.581,0.622,0.63,0.63,0.651,0.653,0.663,0.634,0.69,0.637,0.663,0.569,0.326,5130,Stability,POLG_PESV,Medium,Virus +PPARG_HUMAN_Majithia_2016,0.16,0.237,0.259,0.253,0.255,0.241,0.146,0.193,0.252,0.242,0.245,0.274,0.29,0.124,0.135,0.138,0.158,0.22,0.277,0.288,0.278,0.293,0.175,0.175,0.234,0.308,0.332,0.342,0.179,0.269,0.162,0.076,0.117,0.294,0.268,0.274,0.275,0.33,0.308,0.255,0.275,0.266,0.124,0.121,0.25,0.142,0.244,0.274,0.248,0.162,0.184,0.167,0.146,0.137,0.193,0.147,0.164,0.148,0.127,0.153,0.198,0.149,9576,Activity,PPARG_HUMAN,Medium,Human +PPM1D_HUMAN_Miller_2022,0.18,0.176,0.18,0.187,0.18,0.177,0.128,0.154,0.176,0.173,0.183,0.21,0.2,0.131,0.14,0.161,0.174,0.188,0.201,0.163,0.172,0.174,0.205,0.181,0.177,0.202,0.192,0.214,0.174,0.181,0.172,0.135,0.113,0.163,0.188,0.206,0.191,0.192,0.205,0.187,0.185,0.186,0.142,0.115,0.191,0.171,0.169,0.201,0.188,0.135,0.177,0.18,0.176,0.167,0.181,0.168,0.163,0.168,0.173,0.181,0.215,0.158,7889,OrganismalFitness,PPM1D_HUMAN,Low,Human +PR40A_HUMAN_Tsuboyama_2023_1UZC,0.191,0.23,0.343,0.343,0.299,0.284,0.216,0.216,0.392,0.392,0.397,0.191,0.265,0.221,0.225,0.358,0.387,0.402,0.265,0.299,0.363,0.358,0.387,0.333,0.353,0.392,0.363,0.353,0.363,0.382,0.299,0.25,0.221,0.206,0.245,0.235,0.294,0.275,0.245,0.319,0.314,0.324,0.216,0.221,0.299,0.24,0.338,0.338,0.426,0.319,0.382,0.412,0.382,0.387,0.397,0.407,0.402,0.392,0.407,0.402,0.402,0.431,2033,Stability,PR40A_HUMAN,Medium,Human +PRKN_HUMAN_Clausen_2023,0.205,0.189,0.201,0.207,0.21,0.217,0.12,0.187,0.17,0.187,0.197,0.236,0.227,0.17,0.185,0.225,0.256,0.293,0.292,0.197,0.183,0.213,0.194,0.168,0.187,0.249,0.178,0.241,0.179,0.186,0.151,0.128,0.11,0.155,0.202,0.167,0.209,0.202,0.183,0.216,0.211,0.187,0.185,0.11,0.264,0.185,0.289,0.243,0.308,0.161,0.249,0.271,0.284,0.269,0.236,0.245,0.273,0.273,0.275,0.268,0.276,0.271,8756,Expression,PRKN_HUMAN,Low,Human +PSAE_SYNP2_Tsuboyama_2023_1PSE,0.177,0.165,0.127,0.133,0.152,0.152,0.127,0.07,0.108,0.095,0.373,0.158,0.165,0.12,0.19,0.266,0.278,0.209,0.196,0.133,0.12,0.057,0.12,0.139,0.146,0.152,0.044,0.089,0.101,0.133,0.146,0.133,0.108,0.12,0.089,0.127,0.19,0.177,0.139,0.152,0.165,0.146,0.165,0.139,0.203,0.139,0.278,0.259,0.316,0.291,0.31,0.241,0.259,0.222,0.259,0.234,0.266,0.259,0.222,0.266,0.209,0.272,1579,Stability,PSAE_PICP2,Medium,Prokaryote +PTEN_HUMAN_Matreyek_2021,0.157,0.183,0.171,0.165,0.153,0.163,0.102,0.159,0.181,0.2,0.185,0.191,0.212,0.104,0.136,0.157,0.183,0.147,0.169,0.181,0.11,0.204,0.191,0.204,0.151,0.169,0.173,0.181,0.179,0.208,0.167,0.165,0.118,0.12,0.208,0.183,0.145,0.2,0.194,0.165,0.193,0.185,0.118,0.094,0.194,0.149,0.173,0.204,0.159,0.13,0.2,0.198,0.193,0.179,0.189,0.181,0.179,0.181,0.193,0.2,0.194,0.169,5083,Expression,PTEN_HUMAN,Medium,Human +PTEN_HUMAN_Mighell_2018,0.208,0.16,0.179,0.185,0.175,0.175,0.116,0.139,0.153,0.15,0.132,0.138,0.139,0.123,0.169,0.187,0.16,0.161,0.169,0.156,0.125,0.132,0.172,0.153,0.15,0.171,0.157,0.161,0.16,0.153,0.161,0.138,0.107,0.146,0.135,0.152,0.191,0.147,0.175,0.175,0.153,0.174,0.16,0.11,0.136,0.198,0.149,0.135,0.179,0.157,0.15,0.156,0.161,0.157,0.147,0.16,0.16,0.15,0.16,0.15,0.196,0.207,7260,Activity,PTEN_HUMAN,Medium,Human +Q2N0S5_9HIV1_Haddox_2018,0.24,0.211,0.189,0.19,0.211,0.214,0.079,0.189,0.225,0.229,0.218,0.233,0.236,0.088,0.082,0.086,0.11,0.104,0.123,0.208,0.238,0.191,0.184,0.163,0.236,0.192,0.185,0.185,0.178,0.232,0.195,0.196,0.162,0.232,0.204,0.207,0.234,0.229,0.23,0.219,0.215,0.215,0.202,0.07,0.222,0.22,0.165,0.237,0.109,0.149,0.125,0.14,0.152,0.148,0.139,0.151,0.134,0.141,0.122,0.147,0.123,0.108,12729,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,0.15,0.168,0.171,0.165,0.168,0.168,0.112,0.1,0.179,0.174,0.153,0.165,0.179,0.147,0.176,0.174,0.168,0.188,0.159,0.165,0.135,0.097,0.097,0.094,0.179,0.171,0.15,0.144,0.121,0.115,0.126,0.115,0.144,0.121,0.088,0.079,0.135,0.126,0.1,0.168,0.162,0.147,0.168,0.124,0.176,0.15,0.2,0.191,0.153,0.147,0.141,0.135,0.165,0.15,0.162,0.156,0.156,0.153,0.144,0.147,0.197,0.185,3344,Binding,Q53Z42_HUMAN,Medium,Human +Q53Z42_HUMAN_McShan_2019_expression,0.289,0.283,0.301,0.295,0.31,0.301,0.095,0.17,0.28,0.28,0.286,0.262,0.292,0.14,0.158,0.259,0.283,0.298,0.292,0.289,0.229,0.208,0.19,0.185,0.312,0.307,0.265,0.301,0.265,0.182,0.235,0.199,0.196,0.226,0.176,0.176,0.277,0.253,0.205,0.301,0.298,0.28,0.188,0.14,0.298,0.256,0.298,0.31,0.25,0.161,0.262,0.262,0.292,0.295,0.301,0.286,0.28,0.31,0.289,0.28,0.304,0.357,3344,Expression,Q53Z42_HUMAN,Medium,Human +Q59976_STRSQ_Romero_2015,0.193,0.25,0.243,0.253,0.27,0.267,0.16,0.243,0.27,0.27,0.23,0.177,0.207,0.143,0.18,0.2,0.227,0.233,0.233,0.267,0.25,0.287,0.287,0.297,0.27,0.31,0.313,0.287,0.283,0.277,0.227,0.183,0.15,0.243,0.283,0.297,0.217,0.25,0.273,0.273,0.273,0.277,0.213,0.13,0.243,0.243,0.207,0.23,0.223,0.147,0.2,0.203,0.227,0.2,0.22,0.2,0.223,0.22,0.217,0.213,0.257,0.22,2999,Activity,Q59976_STRSQ,Medium,Prokaryote +Q6WV13_9MAXI_Somermeyer_2022,0.136,0.145,0.111,0.11,0.127,0.124,0.08,0.097,0.132,0.132,0.1,0.092,0.079,0.074,0.078,0.072,0.07,0.072,0.072,0.119,0.088,0.109,0.082,0.116,0.093,0.07,0.074,0.062,0.077,0.149,0.117,0.117,0.087,0.095,0.089,0.115,0.112,0.117,0.112,0.123,0.128,0.125,0.099,0.079,0.084,0.092,0.186,0.152,0.18,0.148,0.131,0.138,0.114,0.149,0.144,0.145,0.141,0.133,0.135,0.141,0.11,0.102,31401,Activity,Q6WV12_9MAXI,Low,Eukaryote +Q837P4_ENTFA_Meier_2023,0.2,0.229,0.214,0.2,0.257,0.257,0.086,0.129,0.243,0.271,0.257,0.229,0.3,0.129,0.186,0.186,0.214,0.286,0.271,0.1,0.271,0.257,0.257,0.357,0.286,0.271,0.286,0.3,0.3,0.271,0.286,0.257,0.1,0.329,0.271,0.3,0.314,0.271,0.286,0.257,0.229,0.243,0.186,0.071,0.243,0.2,0.129,0.229,0.2,0.086,0.3,0.243,0.243,0.271,0.229,0.2,0.271,0.271,0.271,0.286,0.3,0.214,697,Activity,Q837P4_ENTFA,Medium,Prokaryote +Q837P5_ENTFA_Meier_2023,0.067,0.293,0.267,0.28,0.307,0.307,0.12,0.2,0.133,0.133,0.253,0.267,0.24,0.067,0.12,0.12,0.187,0.267,0.227,0.147,0.2,0.2,0.293,0.227,0.187,0.227,0.293,0.267,0.333,0.2,0.24,0.28,0.093,0.24,0.2,0.28,0.24,0.2,0.267,0.24,0.24,0.24,0.093,0.093,0.213,0.16,0.12,0.2,0.227,0.133,0.2,0.227,0.227,0.2,0.213,0.173,0.227,0.213,0.24,0.2,0.213,0.227,747,Activity,Q837P5_ENTFA,Medium,Prokaryote +Q8WTC7_9CNID_Somermeyer_2022,0.183,0.207,0.173,0.179,0.189,0.189,0.097,0.15,0.195,0.199,0.124,0.107,0.107,0.094,0.101,0.104,0.095,0.115,0.11,0.141,0.108,0.11,0.093,0.11,0.097,0.108,0.116,0.185,0.189,0.198,0.12,0.123,0.09,0.104,0.101,0.191,0.179,0.18,0.195,0.192,0.191,0.206,0.108,0.113,0.117,0.112,0.184,0.179,0.201,0.163,0.141,0.138,0.144,0.148,0.144,0.152,0.14,0.158,0.141,0.15,0.132,0.119,33510,Activity,Q8WTC7_9CNID,Low,Eukaryote +R1AB_SARS2_Flynn_2022,0.169,0.171,0.148,0.161,0.166,0.161,0.077,0.12,0.059,0.059,0.086,0.058,0.056,0.068,0.07,0.073,0.084,0.147,0.147,0.117,0.138,0.15,0.145,0.15,0.134,0.131,0.129,0.119,0.143,0.147,0.119,0.119,0.113,0.112,0.113,0.15,0.161,0.155,0.164,0.164,0.164,0.168,0.058,0.056,0.079,0.07,0.154,0.122,0.169,0.147,0.098,0.122,0.119,0.112,0.113,0.103,0.092,0.092,0.105,0.096,0.106,0.077,5725,OrganismalFitness,R1AB_SARS2,Medium,Virus +RAD_ANTMA_Tsuboyama_2023_2CJJ,0.315,0.163,0.13,0.185,0.13,0.185,0.228,0.163,0.196,0.217,0.13,0.076,0.109,0.283,0.315,0.261,0.217,0.239,0.141,0.174,0.337,0.13,0.12,0.174,0.152,0.163,0.152,0.098,0.098,0.239,0.163,0.13,0.207,0.13,0.174,0.207,0.293,0.272,0.25,0.185,0.196,0.196,0.228,0.283,0.13,0.207,0.217,0.141,0.25,0.25,0.185,0.283,0.228,0.207,0.25,0.239,0.239,0.261,0.261,0.25,0.098,0.261,912,Stability,RAD_ANTMA,High,Eukaryote +RAF1_HUMAN_Zinkus-Boltz_2019,0.133,0.167,0.133,0.167,0.2,0.2,0.033,0.2,0.167,0.167,0.2,0.133,0.2,0.1,0.167,0.133,0.133,0.167,0.167,0.167,0.133,0.2,0.167,0.133,0.2,0.2,0.2,0.2,0.133,0.167,0.167,0.167,0.067,0.167,0.2,0.133,0.133,0.167,0.133,0.167,0.133,0.133,0.133,0.067,0.167,0.133,0.067,0.2,0.067,0.067,0.167,0.133,0.2,0.133,0.1,0.133,0.133,0.1,0.133,0.133,0.1,0.067,297,OrganismalFitness,RAF1_HUMAN,Low,Human +RASH_HUMAN_Bandaru_2017,0.166,0.092,0.086,0.092,0.08,0.089,0.073,0.057,0.092,0.086,0.086,0.076,0.061,0.162,0.182,0.156,0.111,0.099,0.105,0.127,0.096,0.099,0.07,0.08,0.086,0.08,0.08,0.067,0.067,0.108,0.054,0.073,0.064,0.115,0.086,0.08,0.124,0.105,0.111,0.092,0.089,0.089,0.134,0.229,0.115,0.115,0.191,0.111,0.121,0.194,0.118,0.115,0.127,0.108,0.111,0.118,0.115,0.115,0.089,0.118,0.083,0.153,3134,Activity,RASH_HUMAN,High,Human +RASK_HUMAN_Weng_2022_abundance,0.174,0.153,0.167,0.174,0.167,0.175,0.107,0.156,0.171,0.186,0.157,0.154,0.157,0.215,0.187,0.197,0.174,0.169,0.176,0.171,0.135,0.154,0.16,0.169,0.154,0.173,0.168,0.163,0.177,0.132,0.153,0.133,0.109,0.132,0.159,0.169,0.143,0.171,0.187,0.173,0.176,0.179,0.183,0.203,0.124,0.172,0.165,0.146,0.2,0.162,0.165,0.16,0.161,0.162,0.169,0.16,0.171,0.164,0.162,0.168,0.177,0.188,26012,Expression,RASK_HUMAN,High,Human +RASK_HUMAN_Weng_2022_binding-DARPin_K55,0.235,0.258,0.274,0.271,0.267,0.27,0.07,0.201,0.289,0.305,0.246,0.253,0.26,0.279,0.282,0.286,0.293,0.267,0.251,0.272,0.238,0.261,0.258,0.249,0.279,0.274,0.252,0.246,0.226,0.242,0.23,0.209,0.176,0.24,0.267,0.256,0.229,0.262,0.258,0.272,0.275,0.273,0.256,0.165,0.271,0.267,0.148,0.219,0.291,0.19,0.238,0.225,0.23,0.23,0.237,0.228,0.237,0.233,0.232,0.234,0.268,0.242,24873,Binding,RASK_HUMAN,High,Human +RBP1_HUMAN_Tsuboyama_2023_2KWH,0.239,0.157,0.239,0.239,0.239,0.239,0.284,0.112,0.209,0.209,0.351,0.343,0.351,0.328,0.343,0.351,0.366,0.157,0.187,0.254,0.328,0.097,0.164,0.082,0.343,0.201,0.194,0.104,0.104,0.157,0.142,0.104,0.104,0.299,0.351,0.291,0.269,0.246,0.239,0.261,0.246,0.224,0.358,0.351,0.343,0.343,0.321,0.321,0.328,0.284,0.336,0.291,0.306,0.299,0.328,0.321,0.291,0.306,0.313,0.328,0.321,0.321,1332,Stability,RBP1_HUMAN,High,Human +RCD1_ARATH_Tsuboyama_2023_5OAO,0.181,0.142,0.165,0.165,0.15,0.157,0.165,0.087,0.142,0.142,0.283,0.26,0.276,0.252,0.307,0.252,0.205,0.157,0.197,0.094,0.228,0.157,0.181,0.205,0.157,0.205,0.213,0.181,0.197,0.134,0.11,0.047,0.071,0.228,0.165,0.244,0.213,0.228,0.22,0.189,0.173,0.173,0.236,0.26,0.22,0.22,0.228,0.26,0.228,0.276,0.189,0.213,0.181,0.189,0.189,0.189,0.213,0.173,0.181,0.189,0.173,0.276,1261,Stability,RCD1_ARATH,Medium,Eukaryote +RCRO_LAMBD_Tsuboyama_2023_1ORC,0.197,0.272,0.298,0.351,0.346,0.311,0.237,0.175,0.281,0.311,0.303,0.272,0.364,0.25,0.307,0.311,0.289,0.281,0.276,0.263,0.18,0.25,0.228,0.25,0.145,0.303,0.175,0.25,0.294,0.289,0.281,0.254,0.184,0.219,0.14,0.254,0.263,0.246,0.272,0.298,0.303,0.281,0.254,0.311,0.316,0.263,0.539,0.404,0.469,0.469,0.382,0.399,0.377,0.408,0.399,0.377,0.39,0.346,0.351,0.386,0.465,0.158,2278,Stability,RCRO_LAMBD,High,Virus +RD23A_HUMAN_Tsuboyama_2023_1IFY,0.441,0.304,0.304,0.324,0.304,0.294,0.304,0.196,0.245,0.235,0.216,0.245,0.255,0.275,0.382,0.275,0.225,0.196,0.186,0.284,0.147,0.255,0.196,0.216,0.265,0.235,0.216,0.245,0.176,0.196,0.235,0.206,0.294,0.137,0.245,0.216,0.353,0.353,0.294,0.333,0.324,0.294,0.255,0.245,0.245,0.284,0.382,0.314,0.441,0.422,0.216,0.245,0.255,0.255,0.284,0.265,0.275,0.245,0.255,0.255,0.235,0.392,1019,Stability,RD23A_HUMAN,High,Human +RDRP_I33A0_Li_2023,0.184,0.259,0.386,0.395,0.396,0.392,0.071,0.346,0.356,0.353,0.117,0.096,0.107,0.102,0.096,0.108,0.214,0.301,0.338,0.405,0.327,0.366,0.377,0.397,0.131,0.332,0.314,0.306,0.356,0.416,0.345,0.335,0.122,0.339,0.36,0.398,0.351,0.376,0.386,0.412,0.415,0.413,0.102,0.092,0.132,0.106,0.138,0.182,0.098,0.136,0.224,0.218,0.232,0.221,0.221,0.219,0.22,0.238,0.229,0.226,0.119,0.111,12003,OrganismalFitness,RDRP_I33A0,Low,Virus +REV_HV1H2_Fernandes_2016,0.13,0.135,0.144,0.135,0.149,0.144,0.112,0.14,0.135,0.126,0.084,0.144,0.126,0.107,0.093,0.065,0.13,0.107,0.107,0.112,0.116,0.098,0.093,0.13,0.102,0.149,0.098,0.126,0.13,0.135,0.135,0.126,0.116,0.13,0.14,0.112,0.144,0.153,0.121,0.153,0.144,0.14,0.102,0.088,0.112,0.102,0.07,0.098,0.116,0.126,0.135,0.13,0.126,0.098,0.116,0.093,0.112,0.116,0.102,0.112,0.079,0.121,2147,OrganismalFitness,REV_HV1H2,Medium,Virus +RFAH_ECOLI_Tsuboyama_2023_2LCL,0.233,0.248,0.18,0.173,0.195,0.165,0.165,0.075,0.105,0.173,0.248,0.173,0.226,0.18,0.158,0.211,0.241,0.12,0.068,0.09,0.143,0.165,0.18,0.158,0.256,0.158,0.195,0.15,0.083,0.083,0.135,0.105,0.053,0.203,0.083,0.068,0.211,0.09,0.158,0.195,0.135,0.173,0.226,0.18,0.233,0.241,0.241,0.173,0.301,0.286,0.188,0.188,0.195,0.18,0.188,0.188,0.241,0.188,0.195,0.195,0.278,0.226,1326,Stability,RFAH_ECOLI,High,Prokaryote +RL20_AQUAE_Tsuboyama_2023_1GYZ,0.095,0.184,0.129,0.136,0.129,0.129,0.163,0.122,0.184,0.163,0.252,0.204,0.238,0.19,0.184,0.19,0.19,0.136,0.129,0.122,0.116,0.156,0.122,0.15,0.102,0.122,0.129,0.15,0.143,0.048,0.129,0.109,0.088,0.136,0.156,0.109,0.143,0.163,0.143,0.143,0.143,0.136,0.224,0.15,0.15,0.224,0.333,0.184,0.361,0.313,0.204,0.204,0.211,0.184,0.177,0.197,0.177,0.184,0.184,0.19,0.177,0.272,1461,Stability,RL20_AQUAE,High,Prokaryote +RL40A_YEAST_Mavor_2016,0.159,0.167,0.167,0.19,0.19,0.183,0.095,0.143,0.175,0.19,0.135,0.19,0.159,0.095,0.143,0.19,0.151,0.151,0.206,0.19,0.175,0.135,0.127,0.111,0.183,0.167,0.151,0.19,0.135,0.159,0.143,0.167,0.087,0.119,0.119,0.119,0.159,0.167,0.167,0.183,0.198,0.167,0.143,0.143,0.135,0.111,0.103,0.056,0.127,0.04,0.198,0.167,0.175,0.167,0.175,0.151,0.151,0.183,0.183,0.183,0.111,0.135,1253,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2013,0.117,0.192,0.175,0.217,0.208,0.217,0.017,0.175,0.217,0.233,0.15,0.15,0.192,0.058,0.142,0.208,0.183,0.183,0.233,0.167,0.2,0.175,0.208,0.15,0.125,0.175,0.167,0.167,0.192,0.242,0.242,0.233,0.175,0.192,0.15,0.15,0.183,0.158,0.158,0.192,0.217,0.2,0.183,0.1,0.192,0.158,0.042,0.125,0.108,0.05,0.125,0.142,0.117,0.142,0.133,0.142,0.125,0.142,0.175,0.125,0.192,0.175,1195,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +RL40A_YEAST_Roscoe_2014,0.123,0.094,0.116,0.13,0.116,0.109,0.072,0.138,0.101,0.109,0.159,0.138,0.123,0.123,0.217,0.116,0.152,0.138,0.138,0.145,0.174,0.167,0.138,0.167,0.188,0.13,0.101,0.123,0.123,0.138,0.109,0.13,0.123,0.174,0.13,0.109,0.138,0.116,0.116,0.123,0.13,0.123,0.13,0.145,0.145,0.109,0.087,0.101,0.101,0.094,0.145,0.167,0.152,0.188,0.174,0.159,0.138,0.138,0.145,0.159,0.138,0.123,1380,Activity,RL40A_YEAST,Medium,Eukaryote +RNC_ECOLI_Weeks_2023,0.147,0.196,0.117,0.121,0.129,0.131,0.077,0.143,0.138,0.133,0.147,0.136,0.154,0.103,0.147,0.136,0.152,0.171,0.173,0.131,0.189,0.199,0.161,0.182,0.182,0.152,0.168,0.152,0.182,0.143,0.173,0.173,0.119,0.161,0.173,0.15,0.15,0.166,0.168,0.14,0.136,0.15,0.173,0.093,0.138,0.131,0.152,0.168,0.131,0.121,0.143,0.161,0.152,0.152,0.154,0.164,0.147,0.147,0.164,0.147,0.166,0.129,4277,Activity,RNC_ECOLI,Medium,Prokaryote +RPC1_BP434_Tsuboyama_2023_1R69,0.274,0.295,0.233,0.26,0.247,0.26,0.281,0.171,0.185,0.199,0.295,0.288,0.322,0.301,0.315,0.322,0.267,0.26,0.295,0.281,0.219,0.356,0.336,0.26,0.274,0.329,0.247,0.295,0.247,0.253,0.247,0.24,0.178,0.185,0.295,0.301,0.199,0.281,0.288,0.301,0.281,0.281,0.329,0.274,0.26,0.267,0.356,0.308,0.301,0.37,0.274,0.24,0.274,0.281,0.267,0.253,0.247,0.26,0.247,0.26,0.322,0.308,1459,Stability,RPC1_BP434,High,Virus +RPC1_LAMBD_Li_2019_high-expression,0.139,0.083,0.111,0.111,0.111,0.111,0.167,0.028,0.139,0.139,0.083,0.139,0.139,0.194,0.167,0.194,0.167,0.083,0.139,0.194,0.139,0.139,0.222,0.139,0.194,0.139,0.139,0.167,0.111,0.194,0.028,0.0,0.083,0.139,0.139,0.111,0.139,0.139,0.167,0.139,0.111,0.111,0.167,0.222,0.139,0.139,0.194,0.25,0.139,0.167,0.139,0.111,0.167,0.139,0.139,0.167,0.139,0.167,0.139,0.167,0.278,0.25,351,Activity,RPC1_LAMBD,High,Virus +RPC1_LAMBD_Li_2019_low-expression,0.194,0.25,0.222,0.25,0.222,0.222,0.139,0.111,0.306,0.25,0.167,0.194,0.25,0.222,0.194,0.194,0.278,0.222,0.167,0.222,0.111,0.25,0.25,0.167,0.083,0.194,0.222,0.194,0.222,0.222,0.194,0.167,0.139,0.222,0.25,0.194,0.194,0.194,0.167,0.222,0.222,0.222,0.167,0.194,0.194,0.222,0.111,0.25,0.222,0.056,0.25,0.25,0.194,0.25,0.278,0.25,0.278,0.25,0.25,0.278,0.194,0.111,351,Activity,RPC1_LAMBD,High,Virus +RS15_GEOSE_Tsuboyama_2023_1A32,0.142,0.083,0.108,0.117,0.108,0.117,0.092,0.083,0.108,0.092,0.108,0.1,0.083,0.15,0.208,0.192,0.092,0.125,0.142,0.067,0.108,0.108,0.075,0.083,0.133,0.083,0.125,0.1,0.117,0.05,0.075,0.042,0.183,0.117,0.083,0.1,0.133,0.092,0.1,0.117,0.083,0.083,0.192,0.15,0.125,0.217,0.275,0.15,0.158,0.292,0.117,0.117,0.108,0.142,0.117,0.092,0.108,0.108,0.125,0.125,0.15,0.325,1195,Stability,RS15_GEOSE,Medium,Prokaryote +S22A1_HUMAN_Yee_2023_abundance,0.191,0.244,0.262,0.276,0.256,0.255,0.177,0.193,0.262,0.261,0.257,0.262,0.272,0.193,0.204,0.238,0.288,0.269,0.255,0.109,0.242,0.267,0.238,0.229,0.234,0.25,0.248,0.266,0.227,0.254,0.266,0.206,0.13,0.223,0.262,0.254,0.253,0.288,0.272,0.269,0.265,0.269,0.216,0.151,0.272,0.221,0.214,0.288,0.22,0.149,0.276,0.268,0.288,0.291,0.286,0.28,0.284,0.276,0.279,0.29,0.269,0.231,9803,Expression,S22A1_HUMAN,Medium,Human +S22A1_HUMAN_Yee_2023_activity,0.167,0.19,0.214,0.213,0.21,0.216,0.133,0.17,0.203,0.211,0.197,0.215,0.198,0.168,0.152,0.171,0.204,0.208,0.18,0.094,0.192,0.199,0.191,0.176,0.187,0.193,0.185,0.201,0.166,0.182,0.191,0.159,0.14,0.168,0.205,0.205,0.171,0.212,0.215,0.217,0.212,0.218,0.164,0.127,0.207,0.157,0.196,0.232,0.193,0.146,0.206,0.195,0.214,0.216,0.212,0.208,0.207,0.217,0.201,0.211,0.206,0.184,10094,Activity,S22A1_HUMAN,Medium,Human +SAV1_MOUSE_Tsuboyama_2023_2YSB,0.454,0.381,0.423,0.433,0.464,0.474,0.309,0.103,0.454,0.474,0.258,0.268,0.258,0.196,0.423,0.412,0.258,0.278,0.175,0.206,0.103,0.103,0.072,0.093,0.268,0.134,0.103,0.093,0.103,0.062,0.216,0.206,0.144,0.216,0.196,0.165,0.402,0.412,0.392,0.433,0.443,0.433,0.361,0.216,0.34,0.32,0.381,0.371,0.392,0.412,0.351,0.289,0.361,0.268,0.371,0.361,0.309,0.351,0.33,0.351,0.227,0.412,965,Stability,SAV1_MOUSE,High,Eukaryote +SBI_STAAM_Tsuboyama_2023_2JVG,0.204,0.194,0.223,0.214,0.233,0.214,0.194,0.107,0.214,0.282,0.262,0.184,0.204,0.155,0.175,0.223,0.32,0.272,0.223,0.126,0.194,0.204,0.184,0.165,0.175,0.184,0.204,0.165,0.184,0.165,0.291,0.184,0.117,0.146,0.146,0.126,0.175,0.194,0.204,0.214,0.223,0.223,0.184,0.223,0.214,0.184,0.311,0.33,0.311,0.34,0.291,0.311,0.34,0.32,0.311,0.282,0.272,0.359,0.359,0.34,0.301,0.272,1025,Stability,SBI_STAAM,Medium,Prokaryote +SC6A4_HUMAN_Young_2021,0.164,0.22,0.201,0.202,0.218,0.204,0.159,0.298,0.217,0.238,0.242,0.212,0.222,0.106,0.135,0.177,0.196,0.213,0.217,0.262,0.244,0.306,0.304,0.286,0.251,0.294,0.285,0.273,0.269,0.226,0.195,0.139,0.187,0.257,0.294,0.289,0.215,0.241,0.251,0.209,0.221,0.217,0.205,0.096,0.247,0.213,0.232,0.266,0.219,0.142,0.193,0.206,0.221,0.219,0.211,0.215,0.21,0.199,0.187,0.201,0.221,0.205,11576,Activity,SC6A4_HUMAN,Medium,Human +SCIN_STAAR_Tsuboyama_2023_2QFF,0.156,0.139,0.123,0.139,0.131,0.139,0.238,0.074,0.23,0.189,0.246,0.246,0.254,0.172,0.246,0.279,0.328,0.262,0.303,0.098,0.164,0.189,0.197,0.23,0.27,0.254,0.279,0.254,0.238,0.131,0.098,0.082,0.148,0.115,0.123,0.213,0.164,0.172,0.172,0.123,0.148,0.156,0.254,0.238,0.262,0.27,0.287,0.303,0.221,0.197,0.328,0.336,0.336,0.352,0.32,0.402,0.311,0.385,0.27,0.352,0.369,0.393,1212,Stability,SCIN_STAAR,High,Prokaryote +SCN5A_HUMAN_Glazer_2019,0.13,0.087,0.13,0.13,0.13,0.13,0.217,0.174,0.174,0.174,0.13,0.13,0.174,0.174,0.13,0.087,0.13,0.13,0.174,0.174,0.13,0.174,0.13,0.13,0.13,0.13,0.13,0.087,0.13,0.13,0.087,0.174,0.174,0.087,0.13,0.174,0.043,0.13,0.174,0.087,0.13,0.13,0.174,0.174,0.174,0.087,0.043,0.043,0.13,0.043,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.043,0.13,224,OrganismalFitness,SCN5A_HUMAN,Medium,Human +SDA_BACSU_Tsuboyama_2023_1PV0,0.531,0.52,0.574,0.563,0.588,0.596,0.13,0.458,0.552,0.578,0.538,0.495,0.516,0.209,0.264,0.523,0.538,0.567,0.545,0.531,0.141,0.181,0.191,0.274,0.191,0.466,0.101,0.347,0.52,0.592,0.509,0.469,0.014,0.116,0.264,0.462,0.542,0.542,0.523,0.578,0.585,0.574,0.224,0.184,0.52,0.264,0.534,0.552,0.592,0.563,0.563,0.567,0.57,0.585,0.57,0.57,0.592,0.578,0.596,0.592,0.592,0.585,2770,Stability,SDA_BACSU,Medium,Prokaryote +SERC_HUMAN_Xie_2023,0.13,0.156,0.135,0.146,0.141,0.156,0.089,0.146,0.135,0.13,0.135,0.156,0.12,0.073,0.12,0.141,0.12,0.146,0.125,0.135,0.13,0.13,0.151,0.141,0.109,0.109,0.13,0.13,0.156,0.151,0.13,0.161,0.115,0.135,0.167,0.156,0.146,0.135,0.151,0.146,0.141,0.141,0.125,0.073,0.125,0.12,0.135,0.125,0.141,0.141,0.141,0.125,0.13,0.135,0.125,0.109,0.12,0.141,0.12,0.125,0.151,0.135,1914,OrganismalFitness,SERC_HUMAN,High,Human +SHOC2_HUMAN_Kwon_2022,0.14,0.148,0.156,0.156,0.156,0.154,0.123,0.143,0.16,0.15,0.139,0.145,0.147,0.124,0.12,0.117,0.138,0.127,0.117,0.144,0.124,0.125,0.138,0.123,0.109,0.127,0.134,0.141,0.137,0.143,0.15,0.153,0.111,0.12,0.142,0.144,0.132,0.148,0.136,0.149,0.153,0.157,0.127,0.126,0.138,0.119,0.139,0.139,0.119,0.119,0.143,0.151,0.166,0.146,0.152,0.158,0.154,0.142,0.138,0.148,0.124,0.123,10972,OrganismalFitness,SHOC2_HUMAN,Medium,Human +SOX30_HUMAN_Tsuboyama_2023_7JJK,0.168,0.149,0.089,0.109,0.119,0.129,0.139,0.089,0.089,0.139,0.139,0.158,0.129,0.178,0.149,0.208,0.188,0.178,0.109,0.099,0.089,0.05,0.099,0.069,0.099,0.089,0.079,0.099,0.119,0.099,0.099,0.089,0.099,0.099,0.05,0.079,0.149,0.099,0.099,0.149,0.119,0.109,0.149,0.099,0.109,0.178,0.139,0.139,0.139,0.178,0.178,0.218,0.228,0.208,0.198,0.208,0.178,0.188,0.178,0.218,0.178,0.158,1010,Stability,SOX30_HUMAN,High,Human +SPA_STAAU_Tsuboyama_2023_1LP1,0.327,0.384,0.346,0.346,0.36,0.351,0.085,0.275,0.251,0.246,0.289,0.095,0.1,0.076,0.104,0.071,0.152,0.171,0.142,0.327,0.028,0.118,0.308,0.156,0.081,0.057,0.109,0.303,0.322,0.218,0.237,0.204,0.043,0.09,0.085,0.123,0.332,0.336,0.332,0.37,0.379,0.379,0.085,0.1,0.185,0.166,0.28,0.265,0.37,0.355,0.341,0.384,0.389,0.417,0.351,0.379,0.36,0.398,0.351,0.393,0.37,0.318,2105,Stability,SPA_STAAU,Medium,Prokaryote +SPG1_STRSG_Olson_2014,0.208,0.214,0.094,0.098,0.204,0.206,0.089,0.081,0.108,0.191,0.221,0.2,0.199,0.237,0.212,0.204,0.205,0.203,0.217,0.17,0.221,0.206,0.204,0.213,0.212,0.205,0.215,0.214,0.22,0.219,0.239,0.239,0.141,0.193,0.187,0.186,0.203,0.198,0.195,0.204,0.199,0.201,0.073,0.087,0.101,0.089,0.201,0.189,0.216,0.155,0.247,0.199,0.218,0.215,0.213,0.209,0.226,0.225,0.224,0.227,0.202,0.209,536962,Binding,SPG1_STRSG,Low,Prokaryote +SPG1_STRSG_Wu_2016,0.135,0.197,0.187,0.206,0.199,0.202,0.174,0.168,0.261,0.246,0.279,0.283,0.267,0.259,0.272,0.283,0.306,0.311,0.32,0.274,0.222,0.212,0.222,0.231,0.202,0.241,0.24,0.229,0.229,0.195,0.333,0.323,0.137,0.232,0.2,0.243,0.185,0.178,0.208,0.216,0.214,0.228,0.186,0.158,0.221,0.165,0.248,0.239,0.334,0.227,0.338,0.34,0.344,0.348,0.34,0.338,0.361,0.349,0.354,0.347,0.34,0.305,149360,Binding,SPG1_STRSG,Medium,Prokaryote +SPG2_STRSG_Tsuboyama_2023_5UBS,0.151,0.096,0.13,0.11,0.144,0.13,0.219,0.151,0.137,0.205,0.233,0.24,0.205,0.171,0.219,0.199,0.226,0.274,0.233,0.192,0.185,0.253,0.199,0.144,0.123,0.205,0.178,0.26,0.082,0.212,0.144,0.103,0.021,0.164,0.301,0.137,0.212,0.233,0.171,0.164,0.158,0.158,0.205,0.158,0.199,0.178,0.205,0.212,0.212,0.185,0.185,0.192,0.26,0.212,0.219,0.233,0.219,0.192,0.185,0.212,0.288,0.205,1451,Stability,SPG2_STRSG,Medium,Prokaryote +SPIKE_SARS2_Starr_2020_binding,0.155,0.227,0.181,0.196,0.208,0.213,0.099,0.227,0.229,0.232,0.104,0.111,0.114,0.109,0.126,0.116,0.111,0.111,0.097,0.229,0.205,0.22,0.222,0.21,0.21,0.215,0.2,0.215,0.191,0.227,0.213,0.213,0.159,0.208,0.22,0.227,0.22,0.217,0.222,0.232,0.234,0.232,0.101,0.106,0.109,0.116,0.29,0.227,0.196,0.159,0.155,0.2,0.217,0.203,0.198,0.208,0.184,0.176,0.167,0.2,0.215,0.15,3802,Binding,SPIKE_SARS2,Medium,Virus +SPIKE_SARS2_Starr_2020_expression,0.141,0.234,0.139,0.193,0.219,0.224,0.098,0.195,0.216,0.244,0.095,0.108,0.108,0.103,0.134,0.108,0.111,0.08,0.072,0.229,0.167,0.18,0.162,0.162,0.198,0.172,0.159,0.18,0.167,0.237,0.201,0.201,0.129,0.172,0.175,0.211,0.165,0.185,0.203,0.211,0.226,0.226,0.103,0.09,0.129,0.118,0.357,0.365,0.326,0.165,0.237,0.278,0.283,0.27,0.303,0.275,0.283,0.254,0.283,0.293,0.332,0.208,3798,Expression,SPIKE_SARS2,Medium,Virus +SPTN1_CHICK_Tsuboyama_2023_1TUD,0.399,0.486,0.396,0.374,0.433,0.396,0.231,0.318,0.371,0.368,0.495,0.386,0.449,0.065,0.417,0.436,0.386,0.517,0.399,0.427,0.38,0.452,0.38,0.393,0.399,0.467,0.421,0.449,0.47,0.427,0.47,0.486,0.1,0.374,0.33,0.346,0.411,0.433,0.396,0.396,0.396,0.399,0.283,0.125,0.346,0.312,0.374,0.383,0.567,0.558,0.558,0.548,0.558,0.539,0.558,0.548,0.551,0.567,0.536,0.567,0.533,0.349,3201,Stability,SPTN1_CHICK,High,Eukaryote +SQSTM_MOUSE_Tsuboyama_2023_2RRU,0.296,0.324,0.423,0.437,0.437,0.423,0.042,0.239,0.324,0.38,0.31,0.155,0.296,0.155,0.254,0.282,0.352,0.324,0.352,0.338,0.141,0.296,0.211,0.296,0.324,0.31,0.268,0.268,0.31,0.268,0.239,0.254,0.169,0.183,0.254,0.324,0.338,0.423,0.394,0.451,0.423,0.408,0.183,0.169,0.282,0.38,0.408,0.282,0.366,0.408,0.366,0.366,0.352,0.338,0.296,0.38,0.324,0.296,0.296,0.352,0.296,0.324,707,Stability,SQSTM_MOUSE,Medium,Eukaryote +SR43C_ARATH_Tsuboyama_2023_2N88,0.308,0.308,0.302,0.283,0.296,0.302,0.057,0.157,0.252,0.264,0.377,0.327,0.34,0.038,0.346,0.371,0.384,0.409,0.409,0.308,0.044,0.189,0.22,0.17,0.245,0.233,0.277,0.296,0.302,0.189,0.327,0.264,0.126,0.189,0.132,0.208,0.352,0.289,0.296,0.321,0.314,0.296,0.277,0.057,0.358,0.333,0.233,0.384,0.428,0.453,0.346,0.34,0.358,0.39,0.352,0.327,0.365,0.39,0.365,0.358,0.44,0.365,1583,Stability,SR43C_ARATH,High,Eukaryote +SRBS1_HUMAN_Tsuboyama_2023_2O2W,0.231,0.179,0.378,0.333,0.256,0.301,0.218,0.237,0.276,0.314,0.308,0.288,0.301,0.179,0.333,0.301,0.263,0.288,0.333,0.199,0.269,0.244,0.269,0.205,0.231,0.237,0.263,0.192,0.212,0.301,0.186,0.167,0.212,0.237,0.231,0.269,0.256,0.256,0.276,0.308,0.321,0.295,0.231,0.083,0.269,0.288,0.301,0.218,0.378,0.353,0.327,0.327,0.314,0.308,0.314,0.333,0.321,0.327,0.295,0.333,0.237,0.327,1556,Stability,SRBS1_HUMAN,High,Human +SRC_HUMAN_Ahler_2019,0.121,0.112,0.086,0.101,0.089,0.089,0.13,0.074,0.101,0.098,0.083,0.101,0.092,0.151,0.183,0.13,0.115,0.124,0.089,0.071,0.083,0.056,0.074,0.062,0.077,0.08,0.077,0.071,0.065,0.115,0.074,0.092,0.121,0.08,0.071,0.083,0.086,0.083,0.109,0.086,0.089,0.089,0.148,0.157,0.068,0.109,0.098,0.083,0.08,0.095,0.133,0.148,0.112,0.133,0.139,0.104,0.107,0.133,0.13,0.124,0.086,0.163,3372,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,0.11,0.091,0.096,0.102,0.102,0.082,0.118,0.069,0.055,0.077,0.08,0.091,0.091,0.137,0.132,0.104,0.088,0.099,0.091,0.06,0.052,0.038,0.074,0.049,0.058,0.06,0.077,0.049,0.063,0.124,0.066,0.085,0.121,0.066,0.058,0.06,0.074,0.074,0.077,0.082,0.069,0.077,0.107,0.118,0.052,0.093,0.096,0.074,0.066,0.088,0.107,0.115,0.093,0.113,0.121,0.091,0.099,0.124,0.104,0.107,0.077,0.137,3637,Activity,SRC_HUMAN,Medium,Human +SRC_HUMAN_Nguyen_2022,0.107,0.083,0.095,0.08,0.065,0.065,0.092,0.08,0.089,0.077,0.074,0.086,0.086,0.131,0.131,0.104,0.101,0.107,0.086,0.08,0.104,0.083,0.101,0.074,0.092,0.086,0.089,0.077,0.065,0.107,0.071,0.098,0.101,0.089,0.077,0.083,0.083,0.086,0.089,0.083,0.071,0.077,0.122,0.119,0.08,0.089,0.11,0.083,0.077,0.092,0.101,0.116,0.095,0.095,0.104,0.086,0.08,0.101,0.095,0.098,0.092,0.131,3366,OrganismalFitness,SRC_HUMAN,Medium,Human +SUMO1_HUMAN_Weile_2017,0.118,0.141,0.124,0.141,0.112,0.118,0.082,0.165,0.165,0.147,0.229,0.188,0.206,0.071,0.141,0.153,0.182,0.153,0.159,0.176,0.118,0.159,0.153,0.176,0.182,0.176,0.147,0.2,0.188,0.329,0.194,0.2,0.088,0.088,0.176,0.176,0.124,0.135,0.188,0.129,0.129,0.141,0.124,0.082,0.171,0.129,0.129,0.182,0.188,0.141,0.153,0.153,0.159,0.165,0.171,0.165,0.165,0.176,0.176,0.176,0.194,0.106,1700,OrganismalFitness,SUMO1_HUMAN,High,Human +SYUA_HUMAN_Newberry_2020,0.064,0.032,0.036,0.024,0.036,0.024,0.08,0.044,0.052,0.036,0.028,0.04,0.024,0.036,0.06,0.044,0.032,0.032,0.032,0.04,0.056,0.044,0.02,0.04,0.048,0.028,0.032,0.012,0.036,0.06,0.044,0.06,0.032,0.028,0.036,0.036,0.02,0.032,0.024,0.028,0.028,0.02,0.052,0.064,0.024,0.044,0.064,0.024,0.04,0.076,0.036,0.024,0.032,0.016,0.036,0.04,0.048,0.036,0.044,0.028,0.052,0.048,2497,OrganismalFitness,SYUA_HUMAN,Medium,Human +TADBP_HUMAN_Bolognesi_2019,0.008,0.017,0.008,0.008,0.008,0.008,0.267,0.0,0.008,0.008,0.067,0.092,0.042,0.125,0.133,0.1,0.05,0.017,0.008,0.008,0.158,0.017,0.008,0.008,0.217,0.033,0.0,0.008,0.008,0.008,0.0,0.0,0.017,0.2,0.267,0.0,0.05,0.092,0.008,0.008,0.017,0.0,0.183,0.25,0.067,0.083,0.2,0.067,0.208,0.117,0.142,0.1,0.1,0.108,0.092,0.117,0.058,0.083,0.1,0.108,0.142,0.158,1196,OrganismalFitness,TADBP_HUMAN,Low,Human +TAT_HV1BR_Fernandes_2016,0.12,0.139,0.139,0.139,0.139,0.139,0.076,0.133,0.158,0.171,0.127,0.152,0.139,0.114,0.127,0.082,0.089,0.108,0.12,0.158,0.12,0.152,0.152,0.158,0.133,0.146,0.101,0.133,0.133,0.158,0.095,0.095,0.12,0.127,0.139,0.146,0.139,0.127,0.127,0.146,0.139,0.146,0.082,0.063,0.108,0.114,0.101,0.108,0.101,0.089,0.07,0.101,0.133,0.133,0.108,0.101,0.082,0.082,0.133,0.095,0.089,0.114,1577,OrganismalFitness,TAT_HV1BR,High,Virus +TCRG1_MOUSE_Tsuboyama_2023_1E0L,0.396,0.34,0.377,0.387,0.349,0.349,0.443,0.085,0.368,0.302,0.104,0.217,0.226,0.368,0.17,0.283,0.358,0.151,0.226,0.132,0.094,0.075,0.104,0.142,0.123,0.085,0.104,0.142,0.057,0.085,0.075,0.047,0.349,0.132,0.16,0.274,0.302,0.34,0.34,0.311,0.34,0.311,0.33,0.16,0.17,0.264,0.189,0.142,0.274,0.358,0.311,0.245,0.208,0.349,0.283,0.34,0.292,0.302,0.245,0.292,0.358,0.236,1058,Stability,TCRG1_MOUSE,Medium,Eukaryote +THO1_YEAST_Tsuboyama_2023_2WQG,0.297,0.289,0.352,0.375,0.391,0.391,0.211,0.227,0.312,0.359,0.312,0.375,0.352,0.102,0.352,0.344,0.336,0.328,0.359,0.391,0.164,0.289,0.227,0.141,0.273,0.305,0.086,0.289,0.422,0.336,0.281,0.219,0.352,0.117,0.336,0.211,0.367,0.43,0.352,0.367,0.422,0.391,0.281,0.258,0.383,0.297,0.422,0.445,0.445,0.508,0.375,0.359,0.398,0.359,0.383,0.359,0.367,0.359,0.344,0.352,0.406,0.391,1279,Stability,THO1_YEAST,High,Eukaryote +TNKS2_HUMAN_Tsuboyama_2023_5JRT,0.297,0.277,0.243,0.243,0.216,0.216,0.034,0.196,0.297,0.338,0.25,0.23,0.25,0.061,0.27,0.291,0.257,0.25,0.243,0.196,0.209,0.291,0.257,0.277,0.216,0.284,0.203,0.264,0.23,0.23,0.297,0.284,0.142,0.135,0.209,0.216,0.277,0.291,0.27,0.236,0.23,0.236,0.25,0.162,0.351,0.236,0.345,0.345,0.372,0.419,0.223,0.297,0.27,0.264,0.27,0.291,0.297,0.277,0.284,0.264,0.338,0.311,1479,Stability,TNKS2_HUMAN,High,Human +TPK1_HUMAN_Weile_2017,0.135,0.129,0.132,0.11,0.122,0.122,0.097,0.132,0.135,0.116,0.141,0.15,0.119,0.091,0.129,0.132,0.125,0.129,0.144,0.122,0.094,0.132,0.107,0.141,0.113,0.144,0.125,0.11,0.135,0.15,0.129,0.119,0.088,0.094,0.144,0.138,0.129,0.125,0.138,0.122,0.119,0.135,0.103,0.088,0.116,0.094,0.147,0.138,0.141,0.138,0.132,0.147,0.129,0.15,0.144,0.141,0.141,0.135,0.135,0.122,0.141,0.113,3181,OrganismalFitness,TPK1_HUMAN,Medium,Human +TPMT_HUMAN_Matreyek_2018,0.181,0.219,0.216,0.197,0.195,0.197,0.107,0.189,0.189,0.192,0.205,0.197,0.2,0.132,0.156,0.205,0.203,0.2,0.184,0.208,0.137,0.164,0.205,0.205,0.167,0.186,0.208,0.192,0.205,0.216,0.244,0.2,0.132,0.129,0.2,0.175,0.175,0.205,0.175,0.205,0.214,0.208,0.132,0.09,0.205,0.173,0.178,0.238,0.222,0.173,0.175,0.173,0.178,0.175,0.181,0.178,0.173,0.197,0.178,0.178,0.197,0.181,3648,Expression,TPMT_HUMAN,Medium,Human +TPOR_HUMAN_Bridgford_2020,0.368,0.211,0.404,0.281,0.228,0.228,0.351,0.228,0.281,0.228,0.298,0.281,0.298,0.211,0.263,0.263,0.193,0.228,0.246,0.175,0.246,0.281,0.228,0.263,0.298,0.193,0.351,0.351,0.298,0.175,0.175,0.123,0.386,0.246,0.263,0.281,0.368,0.351,0.263,0.281,0.298,0.228,0.228,0.246,0.211,0.298,0.105,0.281,0.298,0.193,0.263,0.246,0.263,0.263,0.281,0.281,0.281,0.263,0.281,0.281,0.281,0.368,562,OrganismalFitness,TPOR_HUMAN,Low,Human +TRPC_SACS2_Chan_2017,0.382,0.513,0.461,0.474,0.48,0.474,0.125,0.414,0.48,0.48,0.454,0.428,0.434,0.191,0.329,0.401,0.48,0.5,0.447,0.48,0.178,0.355,0.388,0.414,0.395,0.322,0.401,0.421,0.487,0.513,0.474,0.395,0.158,0.322,0.355,0.342,0.375,0.414,0.408,0.461,0.454,0.454,0.237,0.145,0.388,0.296,0.283,0.434,0.454,0.151,0.428,0.441,0.428,0.461,0.421,0.447,0.421,0.447,0.441,0.467,0.513,0.329,1519,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +TRPC_THEMA_Chan_2017,0.303,0.355,0.276,0.316,0.342,0.329,0.197,0.243,0.289,0.336,0.316,0.309,0.336,0.211,0.316,0.336,0.368,0.349,0.336,0.355,0.23,0.283,0.316,0.382,0.329,0.329,0.316,0.362,0.368,0.224,0.336,0.257,0.092,0.27,0.349,0.362,0.309,0.329,0.322,0.336,0.342,0.336,0.316,0.086,0.349,0.303,0.309,0.368,0.316,0.191,0.336,0.349,0.329,0.362,0.342,0.322,0.342,0.322,0.289,0.336,0.382,0.375,1519,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +UBC9_HUMAN_Weile_2017,0.152,0.187,0.171,0.171,0.187,0.187,0.07,0.125,0.14,0.156,0.16,0.144,0.144,0.074,0.093,0.144,0.152,0.16,0.148,0.16,0.109,0.121,0.128,0.109,0.128,0.144,0.128,0.136,0.125,0.144,0.097,0.101,0.054,0.109,0.113,0.128,0.136,0.148,0.16,0.167,0.183,0.175,0.144,0.086,0.128,0.171,0.156,0.16,0.132,0.144,0.144,0.163,0.191,0.16,0.152,0.175,0.167,0.148,0.148,0.16,0.144,0.132,2563,OrganismalFitness,UBC9_HUMAN,Medium,Human +UBE4B_HUMAN_Tsuboyama_2023_3L1X,0.251,0.386,0.408,0.399,0.427,0.424,0.193,0.333,0.383,0.43,0.49,0.433,0.455,0.229,0.427,0.51,0.499,0.477,0.46,0.402,0.143,0.38,0.397,0.43,0.322,0.408,0.413,0.405,0.419,0.386,0.466,0.405,0.328,0.132,0.16,0.446,0.311,0.311,0.402,0.435,0.438,0.427,0.264,0.171,0.27,0.311,0.372,0.325,0.521,0.342,0.512,0.545,0.534,0.534,0.543,0.51,0.512,0.529,0.526,0.526,0.499,0.388,3622,Stability,UBE4B_HUMAN,High,Human +UBE4B_MOUSE_Starita_2013,0.156,0.122,0.122,0.122,0.1,0.122,0.122,0.122,0.133,0.111,0.111,0.133,0.111,0.078,0.122,0.122,0.122,0.122,0.089,0.056,0.089,0.056,0.089,0.1,0.078,0.089,0.089,0.1,0.1,0.111,0.056,0.067,0.1,0.111,0.078,0.089,0.156,0.156,0.156,0.122,0.122,0.122,0.1,0.144,0.067,0.122,0.133,0.089,0.044,0.156,0.156,0.122,0.133,0.122,0.133,0.167,0.144,0.133,0.133,0.156,0.167,0.156,899,Activity,UBE4B_MOUSE,Low,Eukaryote 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+VILI_CHICK_Tsuboyama_2023_1YU5,0.249,0.436,0.502,0.525,0.486,0.502,0.125,0.28,0.444,0.459,0.432,0.412,0.444,0.171,0.117,0.463,0.553,0.494,0.401,0.467,0.109,0.265,0.284,0.296,0.346,0.346,0.331,0.292,0.428,0.447,0.463,0.424,0.187,0.148,0.346,0.319,0.346,0.42,0.385,0.467,0.486,0.463,0.187,0.237,0.479,0.385,0.428,0.475,0.541,0.397,0.541,0.51,0.545,0.545,0.514,0.556,0.553,0.533,0.525,0.556,0.556,0.424,2568,Stability,VILI_CHICK,High,Eukaryote +VKOR1_HUMAN_Chiasson_2020_abundance,0.252,0.174,0.215,0.219,0.196,0.196,0.137,0.163,0.211,0.204,0.167,0.193,0.181,0.126,0.215,0.241,0.204,0.222,0.178,0.207,0.181,0.152,0.248,0.215,0.211,0.144,0.167,0.148,0.137,0.204,0.167,0.133,0.126,0.159,0.196,0.141,0.23,0.219,0.185,0.196,0.189,0.215,0.137,0.104,0.196,0.215,0.204,0.174,0.2,0.163,0.211,0.181,0.219,0.215,0.222,0.219,0.207,0.181,0.215,0.207,0.156,0.23,2695,Expression,VKOR1_HUMAN,Medium,Human +VKOR1_HUMAN_Chiasson_2020_activity,0.171,0.171,0.186,0.171,0.171,0.186,0.071,0.157,0.157,0.171,0.129,0.143,0.157,0.071,0.114,0.157,0.171,0.143,0.157,0.2,0.1,0.1,0.143,0.114,0.1,0.114,0.143,0.129,0.086,0.171,0.114,0.143,0.186,0.057,0.057,0.1,0.143,0.157,0.143,0.157,0.143,0.157,0.071,0.057,0.143,0.086,0.171,0.186,0.186,0.143,0.129,0.157,0.129,0.171,0.229,0.157,0.143,0.143,0.129,0.157,0.157,0.114,697,Activity,VKOR1_HUMAN,Medium,Human +VRPI_BPT7_Tsuboyama_2023_2WNM,0.048,0.152,0.143,0.162,0.133,0.124,0.076,0.048,0.095,0.057,0.181,0.133,0.133,0.114,0.133,0.19,0.229,0.257,0.162,0.076,0.086,0.143,0.095,0.133,0.152,0.105,0.086,0.124,0.248,0.124,0.2,0.19,0.114,0.105,0.162,0.133,0.076,0.095,0.095,0.143,0.133,0.124,0.152,0.105,0.181,0.124,0.276,0.238,0.21,0.267,0.333,0.267,0.333,0.333,0.295,0.362,0.352,0.324,0.343,0.352,0.381,0.267,1047,Stability,VRPI_BPT7,Medium,Virus +YAIA_ECOLI_Tsuboyama_2023_2KVT,0.18,0.275,0.302,0.302,0.296,0.296,0.069,0.249,0.36,0.349,0.265,0.122,0.312,0.079,0.095,0.344,0.392,0.487,0.439,0.317,0.048,0.058,0.048,0.212,0.063,0.074,0.053,0.074,0.36,0.328,0.307,0.265,0.053,0.048,0.116,0.392,0.18,0.243,0.333,0.296,0.312,0.339,0.164,0.079,0.18,0.175,0.439,0.392,0.434,0.365,0.429,0.444,0.381,0.423,0.429,0.45,0.439,0.429,0.402,0.444,0.439,0.222,1890,Stability,YAIA_ECOLI,Medium,Prokaryote +YAP1_HUMAN_Araya_2012,0.22,0.155,0.232,0.231,0.233,0.228,0.176,0.073,0.065,0.057,0.175,0.135,0.116,0.181,0.202,0.225,0.209,0.191,0.152,0.093,0.08,0.068,0.065,0.086,0.096,0.06,0.066,0.089,0.062,0.136,0.125,0.136,0.138,0.133,0.067,0.092,0.188,0.127,0.15,0.209,0.184,0.204,0.259,0.034,0.178,0.263,0.148,0.182,0.125,0.162,0.115,0.123,0.138,0.151,0.107,0.13,0.149,0.147,0.166,0.137,0.234,0.195,10075,Binding,YAP1_HUMAN,Low,Human +YNZC_BACSU_Tsuboyama_2023_2JVD,0.374,0.374,0.378,0.374,0.378,0.378,0.383,0.33,0.391,0.409,0.457,0.448,0.457,0.461,0.474,0.439,0.483,0.47,0.43,0.378,0.435,0.491,0.509,0.517,0.478,0.443,0.478,0.43,0.422,0.435,0.422,0.391,0.439,0.435,0.452,0.426,0.426,0.391,0.383,0.4,0.387,0.391,0.422,0.361,0.422,0.439,0.439,0.496,0.548,0.53,0.53,0.474,0.504,0.504,0.513,0.517,0.496,0.5,0.491,0.496,0.457,0.591,2300,Stability,YNZC_BACSU,Medium,Prokaryote diff --git a/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.html b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.html new file mode 100644 index 0000000..d385cf4 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_DMS_level.html @@ -0,0 +1,15196 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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scoreSite-IndependentEVmutationDeepSequence (single)DeepSequence (ensemble)EVE (single)EVE (ensemble)UnirepUnirep evotunedMSA Transformer (single)MSA Transformer (ensemble)ESM-1bESM-1v (single)ESM-1v (ensemble)ESM2 (8M)ESM2 (35M)ESM2 (150M)ESM2 (650M)ESM2 (3B)ESM2 (15B)WavenetRITA SRITA MRITA LRITA XLProgen2 SProgen2 MProgen2 BaseProgen2 LProgen2 XLGEMMEVESPAVESPAlProtGPT2Tranception S no retrievalTranception M no retrievalTranception L no retrievalTranception STranception MTranception LTranceptEVE STranceptEVE MTranceptEVE LCARP (38M)CARP (600K)CARP (640M)CARP (76M)MIFMIF-STESM-IF1ProteinMPNNProtSSN (k=10 h=512)ProtSSN (k=10 h=768)ProtSSN (k=10 h=1280)ProtSSN (k=20 h=512)ProtSSN (k=20 h=768)ProtSSN (k=20 h=1280)ProtSSN (k=30 h=512)ProtSSN (k=30 h=768)ProtSSN (k=30 h=1280)ProtSSN (ensemble)SaProt (650M)SaProt (35M)Number of MutantsSelection TypeUniProt IDMSA_Neff_L_categoryTaxon
A0A140D2T1_ZIKV_Sourisseau_20190.2780.2790.2040.2050.2730.2800.0930.0860.3150.3180.1170.1000.1240.0810.0960.0860.1270.2370.2620.1780.2680.2800.2850.2780.2510.2670.2860.2540.2770.2910.2570.2190.0990.2230.2320.2410.2650.2670.2860.2680.2690.2850.1040.1030.1480.1010.1690.2010.1510.1530.1800.1680.1830.1830.1810.1660.1480.1720.1590.1710.1600.1359576OrganismalFitnessA0A140D2T1_ZIKVMediumVirus
A0A192B1T2_9HIV1_Haddox_20180.2760.2700.2690.2730.2770.2800.0950.2690.2930.2930.2340.2590.2660.1030.0940.0950.1030.1100.1170.2810.2790.2800.2860.2910.2730.2890.2790.2820.2930.2930.2630.2480.2210.2740.2840.2890.2810.2840.2860.2860.2820.2850.1920.0720.2670.2610.1380.2680.1210.1450.1290.1480.1540.1380.1430.1410.1440.1260.1210.1430.1370.11512577OrganismalFitnessA0A192B1T2_9HIV1MediumVirus
A0A1I9GEU1_NEIME_Kennouche_20190.0860.0970.1080.1080.0860.0970.0750.1290.1080.0750.1180.1290.1080.0860.0970.1180.0750.0860.1080.0650.1080.0970.0860.0860.0860.0970.0860.1080.0750.0650.1080.0970.1830.0860.1080.0970.1080.0860.0970.0860.0970.0970.0750.0320.0970.0650.1290.1080.1400.1180.1290.0970.1180.1400.0750.1180.0970.1510.0970.0970.1400.054922ActivityA0A1I9GEU1_NEIMEMediumProkaryote
A0A247D711_LISMN_Stadelmann_20210.1390.1510.1200.1140.1630.1510.0780.0960.1630.1690.1200.1200.1270.0900.0900.1140.1330.1200.1270.1570.0900.0960.1140.1140.0660.1270.1330.0780.1140.1630.1570.1870.0600.0900.1080.0840.1330.1390.1510.1570.1570.1570.1140.1020.1200.1080.1390.1450.1930.1750.1630.1810.1200.1200.1450.1200.1200.1270.1390.1450.1330.1021653ActivityA0A247D711_LISMNHighProkaryote
A0A2Z5U3Z0_9INFA_Doud_20160.2570.3270.3100.3160.3170.3130.0890.2710.3240.3260.1090.2930.3290.1030.1040.1040.2710.3010.2970.2990.3050.3220.3240.3290.2030.3110.3320.3150.3030.3140.2440.1900.1050.2940.3190.3080.3100.3130.3100.3290.3270.3240.0910.0950.1660.1000.1760.2180.1940.1620.2470.2610.2570.2640.2550.2640.2520.2650.2670.2580.1780.16610715OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A0A2Z5U3Z0_9INFA_Wu_20140.2090.2340.2340.2470.2380.2340.0980.2720.2470.2430.1530.2170.2470.1360.1360.1400.2130.2000.2130.3060.2210.2340.2600.2550.1830.2170.2600.2130.2430.2430.2470.2170.0850.2210.2260.2300.2430.2600.2600.2300.2380.2300.1280.1110.1790.1450.1620.1960.1960.1360.2090.2210.2170.2090.2000.2210.1960.2000.2090.2040.1230.1452350OrganismalFitnessA0A2Z5U3Z0_9INFAMediumVirus
A4_HUMAN_Seuma_20220.1040.1580.1670.1660.1280.1340.0720.1300.1640.1550.0890.1230.1490.0690.0680.0760.0780.1260.1260.1410.0960.1050.0960.1210.1040.0990.0950.0950.1030.1440.1300.1400.1590.0760.1460.1730.0970.1550.1640.1190.1440.1520.0600.0590.1340.0700.1110.0920.0320.1110.1420.1480.1280.1220.1150.1310.1250.1150.1310.1240.0870.09114811StabilityA4_HUMANLowHuman
A4D664_9INFA_Soh_20190.2420.2450.2470.2470.2560.2570.1050.2280.1910.1880.0960.0950.0960.1050.1090.1040.1350.1460.1980.2220.2040.2300.2340.2150.1070.1680.1710.1730.2000.2370.1990.1910.1060.2020.2310.2350.2340.2450.2450.2580.2590.2610.1050.1090.1250.1120.1220.1290.0720.1300.1240.1230.1050.1330.1230.1300.1190.1270.1290.1190.1180.10914421OrganismalFitnessA4D664_9INFAMediumVirus
A4GRB6_PSEAI_Chen_20200.1800.2040.1980.2200.1840.1840.1380.1760.2440.2260.2570.2340.2530.1460.2160.2630.3090.2610.2710.2300.1360.1720.1940.2320.1840.2300.2380.2340.2420.1920.2530.2100.0540.1740.2040.2160.2380.2360.2140.2260.2140.2120.1380.0920.3050.2100.2830.3090.2480.2220.3290.3070.2930.3370.3310.3230.3170.3430.3290.3310.2710.2345004OrganismalFitnessA4GRB6_PSEAIHighProkaryote
AACC1_PSEAI_Dandage_20180.1770.2600.1880.2040.1930.1930.1440.0940.2490.2380.2040.2100.1990.1330.1550.1660.2150.2100.2380.2270.1490.1660.1990.1380.1600.2040.1930.1930.2100.2760.1600.1220.0830.1990.1770.2270.2270.2040.2100.2100.2100.2150.1380.1270.1600.1660.2270.2380.2270.1880.2490.2210.2210.2320.2320.2650.2150.2380.2650.2490.1710.1661801OrganismalFitnessAACC1_PSEAIHighProkaryote
ACE2_HUMAN_Chan_20200.1570.1570.2110.1930.1660.1570.0540.1480.1570.1480.1030.1430.1390.0580.0900.1080.1390.1210.1390.1930.1260.2020.2200.1880.1570.1880.1840.1970.1840.1970.1570.1390.1430.1080.1880.1700.1700.1520.1750.1840.1700.1840.0850.0720.1210.0670.0760.1210.1430.0900.1210.1300.1170.1080.1170.1390.1260.1120.1210.0990.1120.1032223BindingACE2_HUMANMediumHuman
ADRB2_HUMAN_Jones_20200.1420.1680.1680.1740.2010.2060.1420.2060.1710.1880.1780.1560.1770.1460.1500.1580.1580.1670.1720.1590.1680.1740.1720.1710.1880.1790.1830.1810.1590.1620.1550.1330.1210.1860.1680.1820.1650.1650.1740.1910.1920.2040.1630.1180.1720.1770.1560.1810.1670.1270.1550.1600.1350.1650.1600.1530.1500.1540.1450.1540.1710.1607800ActivityADRB2_HUMANMediumHuman
AICDA_HUMAN_Gajula_2014_3cycles0.1430.2380.3330.1900.2380.2380.0480.1430.1900.1900.2380.1900.1900.0950.0000.1430.1900.1430.0950.1900.0480.0950.2860.1900.0480.1900.1900.1900.1900.1900.1900.2380.0950.0950.0950.1430.2380.1900.2380.2380.2380.1900.0480.0480.1900.0480.3330.1430.1430.3330.1430.3330.1430.2860.1430.1430.1430.1430.1430.1430.0480.048209ActivityAICDA_HUMANMediumHuman
AMFR_HUMAN_Tsuboyama_2023_4G3O0.2890.3720.3720.3690.3590.3790.0700.3360.3520.3690.4330.1010.3190.0570.1240.4090.3860.4090.4130.3660.0570.1170.3460.3420.1140.3390.3420.2650.3830.3360.3830.3460.3260.0570.0940.1140.3390.3360.3460.3830.3790.3830.1410.1340.3960.3150.3590.3560.4160.4060.4060.4090.3990.3860.4030.3960.3790.3860.4030.3960.4300.2352972StabilityAMFR_HUMANMediumHuman
AMIE_PSEAE_Wrenbeck_20170.1430.1140.1300.1240.1270.1190.1030.1590.1190.1410.1810.1440.1280.1520.1670.1970.1930.2330.1270.1650.1880.1750.1570.1300.1510.1510.1480.1690.1030.1040.1300.1330.0740.1880.1360.1120.1270.1200.1060.1240.1220.1240.1590.1460.1930.1700.1850.2260.2070.1430.1880.1940.1800.1750.1590.1540.1650.1690.1650.1700.2340.1616227ActivityAMIE_PSEAEHighProkaryote
ANCSZ_Hobbs_20220.2140.2080.2080.2060.2060.1990.1560.1760.1540.1540.1560.1710.1650.1950.2310.2510.2440.2250.2120.0860.1690.1280.1460.1480.1730.1500.1410.1370.1130.1910.1390.1260.1390.1650.1260.1370.1930.1670.1690.2030.1860.1880.2060.1690.1520.1880.1730.1580.1880.1330.2510.2420.2140.2250.2250.2360.2380.2570.2330.2400.2080.2364670ActivityANCSZMediumEukaryote
ARGR_ECOLI_Tsuboyama_2023_1AOY0.2090.1940.2480.2400.2400.2170.1550.1320.1710.2020.2330.1780.2330.1940.2330.2870.2640.2400.2020.1780.2170.1320.1400.1400.2170.1160.1550.1470.1470.1630.1090.0620.0310.1780.1320.1320.1940.2170.1860.2480.2090.1940.1940.1550.2330.2480.3720.3100.3020.2790.2400.2790.2640.2640.2560.2560.2330.2560.2710.2560.2950.3101287StabilityARGR_ECOLIMediumProkaryote
B2L11_HUMAN_Dutta_2010_binding-Mcl-10.2940.2350.3530.2940.2940.2940.1760.2940.2940.2940.0590.0590.4710.2350.2350.2350.3530.2940.2940.2940.1760.0590.1760.2350.0590.2350.2940.1180.2350.2350.2350.2350.1760.2940.1180.1760.3530.3530.2940.3530.3530.2940.2350.2350.4120.1760.1760.3530.2350.0590.2350.2940.2350.2350.1760.2350.2350.2940.2350.2350.3530.176170BindingB2L11_HUMANLowHuman
BBC1_YEAST_Tsuboyama_2023_1TG00.2170.2800.2030.2030.2130.1790.1840.1740.2850.2850.2710.2900.3090.1260.2750.3000.3720.3290.3290.2510.2080.2270.2510.2420.1260.2610.1980.2270.2170.1880.1840.1400.1500.2320.1880.2420.2220.2320.2660.2080.1740.1980.2270.0770.2170.2320.2850.2460.3480.3430.3240.3380.3290.3820.3530.3960.3620.3620.3620.3530.3140.3532069StabilityBBC1_YEASTHighEukaryote
BCHB_CHLTE_Tsuboyama_2023_2KRU0.2530.2470.2280.2410.2340.2470.1270.1840.1960.2780.2410.2970.3040.1330.1580.2280.3350.2660.3100.2720.1200.2150.2720.3100.2410.2910.2340.2970.2780.2530.2150.1770.0950.1770.2280.2590.2530.2720.2720.2410.2410.2530.1900.1580.2660.2340.2970.3420.3860.3230.3860.3610.3420.3730.3540.3540.3610.3610.3100.3610.3610.2221572StabilityBCHB_CHLTEMediumProkaryote
BLAT_ECOLX_Deng_20120.2380.3340.3400.3480.3660.3660.1100.1700.2760.3400.2980.2900.3200.1720.2500.2680.2940.2680.2560.2980.2580.2600.2760.2380.2480.3360.3040.3060.2280.3000.3400.3200.0980.2500.2960.2460.2980.3380.3140.3600.3860.3780.2040.0740.3180.2480.3340.3260.3240.2180.3380.3540.3360.3800.3520.3520.3680.3520.3760.3620.3300.2904996OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Firnberg_20140.2290.3850.4040.3940.4100.4100.1400.1900.3000.3620.3380.3170.3560.2000.2620.3230.3500.3210.2520.3330.2920.2650.2580.2350.2960.3460.3060.3150.2080.2960.3350.3170.1400.2710.2920.2380.3170.3380.3210.4040.4120.4230.2350.1230.3670.2790.3620.3810.4060.2150.3650.3520.3500.3690.3810.3670.3690.3730.3580.3770.4020.2984783OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Jacquier_20130.1310.2250.2140.2170.2170.2190.0990.1390.1740.2010.1820.1870.1930.1340.1440.1550.1870.1790.1790.1680.1790.1760.1950.1710.1660.1790.1950.2030.1740.1420.1870.1950.1150.1820.1930.1950.1900.2030.2010.2140.2090.2190.1280.0780.1790.1520.1500.1930.1820.1550.1900.2010.2060.1950.1840.1980.1980.1840.2010.1950.1900.155989OrganismalFitnessBLAT_ECOLXHighProkaryote
BLAT_ECOLX_Stiffler_20150.2040.3500.3560.3360.3740.3840.0880.2220.2560.3340.3140.2940.3240.1820.2160.2740.3060.2920.2580.2920.2880.2860.2600.2500.2720.3360.3360.3020.2260.2920.3560.3640.0840.2880.2900.2640.3300.3280.3240.3800.3960.4000.1800.0900.3280.2140.3180.3480.3440.1900.3520.3200.3340.3560.3640.3560.3540.3300.3420.3640.3460.2444996OrganismalFitnessBLAT_ECOLXHighProkaryote
BRCA1_HUMAN_Findlay_20180.2230.1960.2340.2450.2170.2390.1360.1360.2010.2070.1680.1520.1630.1520.1200.1470.1680.2120.1300.1580.1360.2120.2230.1580.2070.2280.2390.1850.1790.1960.2120.1900.1200.1300.1470.1790.2070.2170.2230.2550.2500.2120.1410.1680.2280.1790.1850.2280.1300.1410.1740.1850.1680.1630.1630.1520.1580.1790.1630.1630.1470.1681837OrganismalFitnessBRCA1_HUMANLowHuman
BRCA2_HUMAN_Erwood_2022_HEK293T0.2590.1850.1480.1850.2590.1850.1480.1110.1110.1480.1480.1110.0370.1110.1480.1850.1850.1850.1110.0370.1850.2590.2960.1480.2220.1480.1110.2590.0740.1110.1480.2220.1110.1850.2220.1850.1480.1850.1850.1850.1850.1850.1110.1480.1850.1850.1480.1110.0370.1110.2220.1850.1480.1110.2220.1480.1110.1480.2220.1480.0740.111265OrganismalFitnessBRCA2_HUMANNaNHuman
C6KNH7_9INFA_Lee_20180.2550.2950.2880.2830.3020.3050.0880.2470.2980.3010.0980.2700.3310.0890.0920.0970.2500.2710.3050.2510.2620.2540.2660.2420.1430.2640.2730.2940.2420.3000.2710.2250.1250.2430.2690.2570.2800.2870.2850.3020.3090.3040.0970.0920.1720.0990.2360.2940.2760.1770.2620.2610.2690.2670.2620.2460.2590.2650.2530.2660.1810.15710754OrganismalFitnessC6KNH7_9INFAMediumVirus
CALM1_HUMAN_Weile_20170.0880.0820.0660.0550.0550.0550.0990.0490.0710.0490.0550.0770.0710.1100.0930.0770.0550.0380.0990.0550.0820.0770.0770.0600.0600.0550.0490.0770.0380.0380.0490.0550.0880.0990.0770.0380.0710.0820.0490.0660.0550.0600.0600.1040.0660.0710.1150.0660.0600.1210.0770.0710.0770.0710.0770.0660.0660.0770.0710.0770.0600.0771813OrganismalFitnessCALM1_HUMANHighHuman
CAPSD_AAV2S_Sinai_20210.2630.2580.2460.2790.2580.2620.1740.2650.2330.2540.1360.1210.1290.1310.1470.1340.1650.1480.1120.1740.1220.1290.1330.1530.1190.1220.1540.1260.1800.2720.1300.1200.0980.1330.1420.2630.2300.2310.2750.2530.2550.2730.1280.1630.1900.1470.1760.1880.1450.1560.1620.1450.1330.1450.1250.1330.1420.1400.1750.1470.1520.11842328OrganismalFitnessCAPSD_AAV2SLowVirus
CAR11_HUMAN_Meitlis_2020_gof0.0760.0710.0710.0710.0800.0710.2230.0550.0710.0710.0710.1010.0880.1220.1090.1680.0920.0840.0880.0920.1130.0710.0970.0760.0380.0760.0800.0670.0970.0590.0710.0590.0970.1510.0800.0550.0840.0840.0710.0800.0840.0760.1180.0920.0920.1510.0880.0710.1130.1180.1600.1340.1300.1390.1430.1260.1430.1390.1300.1300.1680.1722374OrganismalFitnessCAR11_HUMANLowHuman
CAR11_HUMAN_Meitlis_2020_lof0.0620.0750.0710.0670.0620.0710.2040.0420.0920.0920.0670.1460.1120.1210.1040.1830.1120.1290.1000.1250.1250.0580.0960.0750.0620.0710.0880.0620.1210.0500.0750.0620.0670.1670.0790.0750.0670.0670.0500.0750.0750.0750.1210.0920.0880.1830.1080.0830.1540.1290.1580.1210.1330.1080.1420.1330.1460.1380.1290.1250.2080.1882395OrganismalFitnessCAR11_HUMANLowHuman
CAS9_STRP1_Spencer_2017_positive0.0960.1120.1110.1050.1110.1120.0960.1060.1160.1170.1100.0960.1060.0970.1030.1260.1060.1170.1100.0970.0990.0960.1170.1160.1000.1120.0960.1110.1010.1050.1170.1170.1110.0910.0990.1280.1110.1160.1230.1010.1010.1170.0950.0910.1110.0990.0990.1110.1030.0940.1180.1070.1150.1020.1110.1110.0990.1120.1000.1100.1270.1158117ActivityCAS9_STRP1MediumProkaryote
CASP3_HUMAN_Roychowdhury_20200.1590.2040.1530.1150.1340.1530.0890.1590.2100.2230.1460.1150.1530.0890.1460.1660.1780.1460.2100.1590.1460.1590.1590.1530.1400.1590.1780.1720.2040.1460.2290.2230.1150.1020.1400.1720.1340.1660.1660.1460.1720.1590.0960.0760.1590.1400.1590.1660.1270.1020.1850.1780.1910.1400.1720.1660.1850.1660.1720.1660.1460.1341567ActivityCASP3_HUMANHighHuman
CASP7_HUMAN_Roychowdhury_20200.1490.2020.2380.2140.2500.2440.0600.1550.2260.2500.2680.1900.2320.1070.2080.2740.2080.1960.1900.2380.0950.2140.1550.1670.2020.1790.1790.1670.1960.2260.1670.1550.1070.0830.1900.1370.2080.2260.1900.2200.2320.2440.1850.0600.2620.2740.2380.2800.2440.1900.1960.2140.2440.2320.2380.2260.2140.2200.2260.2200.2260.2081680ActivityCASP7_HUMANMediumHuman
CATR_CHLRE_Tsuboyama_2023_2AMI0.2720.1470.1470.1520.1830.1620.2360.0940.1310.1360.1150.1730.1470.2250.2570.2090.1520.1570.1310.0890.1200.1260.1100.1050.0730.0890.0890.0730.1050.1520.1260.1730.1830.1310.1100.0990.1990.1730.1260.1680.1520.1570.1940.1830.0840.1260.3140.1310.1830.2930.1830.1150.1410.1520.1410.1470.1570.1410.1410.1570.0990.2571903StabilityCATR_CHLREHighEukaryote
CBPA2_HUMAN_Tsuboyama_2023_1O6X0.2270.1590.2270.2270.2370.2370.2220.1840.1740.1980.3040.2420.2560.2270.2460.2510.2220.2270.2460.2370.1740.1930.2610.2510.2900.2610.2850.2460.2130.1840.1840.1400.1550.2370.2900.2850.2270.2420.2460.2610.2370.2610.2900.2610.2750.2900.2950.3090.2420.2850.1790.2420.2460.2170.2370.2320.1840.2030.2030.2320.2370.3092068StabilityCBPA2_HUMANMediumHuman
CBS_HUMAN_Sun_20200.1680.1610.1690.1660.1760.1750.1000.1410.1730.1620.1630.1500.1620.0980.1270.1270.1320.1450.1570.1610.1430.1330.1660.1730.1470.1520.1540.1630.1520.1630.1450.1300.1090.1500.1410.1510.1580.1770.1940.1720.1800.1790.1300.1020.1580.1500.1470.1470.1690.1160.1390.1360.1470.1410.1450.1510.1500.1540.1550.1510.1470.1277217OrganismalFitnessCBS_HUMANMediumHuman
CBX4_HUMAN_Tsuboyama_2023_2K280.4370.4060.4410.4450.4720.4800.0130.3100.4450.4240.4280.3970.4020.0220.5330.5330.4800.4450.4100.4450.3060.3410.3280.3320.3760.3580.3710.3410.3320.4450.4320.3760.1920.3010.3280.3410.4500.4020.4410.4800.4240.4410.4370.0960.3190.4060.3710.2620.5200.4670.5070.4800.4850.4800.5200.4590.4720.4720.4760.5070.4370.5072282StabilityCBX4_HUMANHighHuman
CCDB_ECOLI_Adkar_20120.1240.1520.1460.1460.1180.1290.0960.1070.0790.1180.1460.0560.0790.1010.1120.0960.1570.1120.1400.1070.0620.0450.0670.0960.0170.1010.0900.1460.1120.1070.1970.1630.0670.0560.0730.1240.1180.1290.1520.1350.1460.1460.0960.0790.1180.1240.0620.1520.1350.1010.1740.1180.1740.2130.1850.1690.1630.1690.1400.1690.1570.0901176ActivityCCDB_ECOLIHighProkaryote
CCDB_ECOLI_Tripathi_20160.1170.1250.1240.1250.1240.1240.0900.1170.1080.1170.1150.1120.1120.0930.0970.1120.1200.1240.1270.1200.0930.0900.0830.1100.0850.1020.0860.1020.1250.1180.1260.1270.1080.0890.0990.1240.1180.1160.1240.1220.1220.1240.0960.0850.1140.0950.1110.1230.1130.1050.1210.1180.1150.1200.1240.1220.1210.1240.1230.1220.1200.1171663OrganismalFitnessCCDB_ECOLIHighProkaryote
CCR5_HUMAN_Gill_20230.1460.1430.1560.1640.1560.1400.1140.2310.1640.1650.1690.1460.1540.1240.1350.1480.1410.1540.1380.1700.1300.1960.1670.1610.1570.1960.1800.1960.1780.1780.1410.1250.1190.1670.1750.1670.1560.1590.1690.1440.1480.1480.1350.0790.1830.1380.1330.1770.1400.1320.1480.1480.1430.1280.1270.1380.1350.1440.1250.1350.1720.1516137BindingCCR5_HUMANHighHuman
CD19_HUMAN_Klesmith_2019_FMC_singles0.1670.1270.1640.1620.1460.1380.1430.1220.1030.1090.1800.1670.1960.1860.2280.1750.1670.1830.1380.0900.1940.1620.2070.1620.1490.2250.1590.2040.1490.1620.1300.1010.1270.2410.2520.1670.1910.1800.1560.1640.1540.1510.2150.1830.2180.2120.3400.3210.3240.1380.2280.1910.2020.2150.2180.2180.2360.2100.2280.2180.3530.3023761BindingCD19_HUMANLowHuman
CP2C9_HUMAN_Amorosi_2021_abundance0.2500.2720.2670.2790.2980.2940.2420.2730.2780.3000.2610.2840.3090.2170.2840.2750.2890.2840.2780.2950.2870.2530.2760.2670.2840.2750.2790.2790.2840.2620.2460.2120.1100.2840.2810.2760.2940.2920.2890.3000.2980.2950.2870.1290.2720.2810.2760.2790.3090.1540.2750.2790.2900.2870.2860.2920.3000.2920.2890.2840.2920.2736370ExpressionCP2C9_HUMANHighHuman
CP2C9_HUMAN_Amorosi_2021_activity0.2850.3320.3450.3590.3690.3710.3090.3190.3110.3590.3200.3720.3800.2850.3640.3770.3970.3760.3540.3950.3460.3070.3320.2910.3590.3270.3240.3020.3060.3280.2470.1980.1170.3670.3590.3350.3790.3640.3720.3790.3740.3670.3710.1400.3460.3850.3140.3280.3530.2020.4030.3630.3660.3800.3790.3900.3660.3790.3850.3870.3870.3546142BindingCP2C9_HUMANHighHuman
CSN4_MOUSE_Tsuboyama_2023_1UFM0.3580.3300.3270.3390.3390.3420.1760.3090.3790.3610.3550.3940.4180.2700.4640.3970.2480.3360.3240.3270.3180.3390.3480.3550.2240.3760.4090.3520.3330.3300.3480.3210.2030.2360.3580.3580.3850.3850.3730.3550.3670.3580.2480.2240.3360.3940.4210.3300.4670.4550.3420.3520.3390.3360.3330.3390.3420.3360.3180.3360.3670.4523295StabilityCSN4_MOUSEMediumEukaryote
CUE1_YEAST_Tsuboyama_2023_2MYX0.2970.3230.3160.3160.3420.3290.1580.2780.3610.3610.3040.2780.2090.1460.1900.2590.2910.3040.3040.2340.1330.1460.1710.1080.1010.1140.1840.1840.3100.2590.2780.2090.1650.1080.1200.1770.2970.2910.3160.3290.3350.3420.1960.1270.2590.1520.3290.3290.3540.3480.2780.3420.3100.3420.2780.2970.3160.2850.3100.3100.3610.3041580StabilityCUE1_YEASTMediumEukaryote
D7PM05_CLYGR_Somermeyer_20220.2500.3610.3150.2930.3360.3330.0980.2220.3690.3810.2370.0970.1000.0970.0920.0880.0850.0970.1030.2330.0790.0610.0920.0840.0560.0810.0800.1080.1470.3370.3200.3140.0560.0790.0980.1040.2930.2950.2900.3340.3360.3300.1350.1310.1370.1380.2570.2490.3050.2300.2740.2810.2860.2830.2770.2830.2690.2860.2680.2850.1980.13624515ActivityD7PM05_CLYGRLowEukaryote
DLG4_HUMAN_Faure_20210.3220.3190.3280.3220.3380.3400.3140.3050.2820.2920.2680.3210.3180.3780.4230.3700.3150.3180.2790.3480.2840.2890.2780.2770.2870.2920.2910.2970.2650.3210.2740.2850.2720.2870.3080.2870.3310.3600.3510.3450.3540.3500.2850.2090.2490.2870.3850.2850.3410.2160.2840.2560.2820.2910.2820.3020.2620.2820.2770.2820.3170.3986976OrganismalFitnessDLG4_HUMANLowHuman
DLG4_RAT_McLaughlin_20120.1580.2150.1580.1390.1650.1710.1840.1330.1270.1650.1840.1330.1270.1080.1390.1770.1010.1270.1330.1010.1080.1080.1010.1140.0890.1270.0950.1080.1270.1330.2030.2340.1200.1140.1330.1080.1330.1330.1200.1330.1330.1580.1580.0890.1460.1330.1900.1270.1080.1330.1080.0760.0630.0890.0890.0820.0760.0950.0950.0820.0950.1331576BindingDLG4_RATLowEukaryote
DN7A_SACS2_Tsuboyama_2023_1JIC0.1680.1490.1390.1190.1090.1290.0890.1090.2280.2870.1190.0890.1190.0890.1780.1980.2480.2670.2180.2280.0790.0890.0690.1090.1190.1490.0790.1190.2180.2080.1290.0590.1680.0990.0990.0790.1680.1680.1580.1490.1580.1190.1390.0890.1880.1680.2870.3170.2670.3070.4160.3660.3470.2770.3070.2970.2970.2280.3270.3560.2180.2771008StabilityDN7A_SACS2MediumProkaryote
DNJA1_HUMAN_Tsuboyama_2023_2LO10.3000.2820.2950.2950.3130.3130.3220.2640.3000.3080.3300.3220.3740.3880.4850.4320.4490.3920.4010.3170.3080.3170.3570.3700.3130.2730.3000.3080.2910.2640.1890.1980.2420.2820.2600.3610.3040.3080.3170.3080.3000.3130.3830.2560.2950.3520.4270.3480.4540.4630.4670.5070.5110.4850.4850.4890.4800.5150.4580.5020.3040.3832264StabilityDNJA1_HUMANHighHuman
DOCK1_MOUSE_Tsuboyama_2023_2M0Y0.3420.3250.3290.2950.2880.3180.0990.2500.2230.1920.3050.1850.2330.0270.1920.2330.3050.3050.2740.3320.2400.2840.2910.2600.1030.2230.1340.1950.3120.1640.3180.2910.2470.0750.0750.0480.3250.3560.3290.3220.3150.3220.1340.0580.3250.1920.3840.3770.3630.4450.3490.3630.3660.3900.3530.3800.3660.3630.3390.3730.4040.3702915StabilityDOCK1_MOUSEHighEukaryote
DYR_ECOLI_Nguyen_20230.1650.1010.1260.1280.1080.0930.0990.1040.2000.1500.1540.2050.1740.1260.1830.1650.1740.1610.1700.1850.1540.1520.1430.1480.1870.0790.0790.1230.0880.0880.1410.1170.0240.0990.0640.0730.1340.0840.0750.0880.0750.0860.1610.1060.1410.1670.1480.1870.1980.1590.1850.1740.1720.1630.1830.1830.1810.1720.2000.1740.2030.1922916OrganismalFitnessDYR_ECOLIHighProkaryote
DYR_ECOLI_Thompson_20190.1310.1270.1010.1010.1180.1050.1270.1270.1100.1310.1520.1430.1430.1050.1390.1600.1650.1690.1430.1520.1180.0680.1270.0840.1100.1520.1690.1270.1310.1140.1220.0890.0380.0840.1180.1940.1270.1430.1690.1140.1180.1180.1310.0890.1560.1520.1480.1270.1350.1220.1430.1730.1520.1650.1650.1940.1730.1730.1860.1770.1430.1942363OrganismalFitnessDYR_ECOLIMediumProkaryote
ENV_HV1B9_DuenasDecamp_20160.4000.3500.3000.3000.3500.3500.0250.3750.3750.3500.3000.4000.3500.0250.0250.0500.0750.0500.1750.4000.2750.3500.3750.3500.2500.4000.3000.3750.4000.3000.4250.3750.3250.3250.3750.4250.3500.3750.3750.3750.4000.4000.3000.0000.3500.3250.3250.3000.1750.1500.1250.2750.2250.1500.2750.2000.1750.2250.1750.1750.1000.125375OrganismalFitnessENV_HV1B9MediumVirus
ENV_HV1BR_Haddox_20160.2060.2110.2270.2340.2310.2280.0840.2140.2030.2060.1550.1930.1910.0990.1030.0990.1080.1130.1300.2390.1980.2120.2330.2390.1890.2180.2330.2110.2370.1710.1980.1720.1280.2000.2150.2250.2110.2130.2210.2300.2320.2350.1650.0760.1960.1750.1390.1930.1040.1130.1230.1340.1450.1440.1500.1410.1340.1330.1350.1450.1290.11312863OrganismalFitnessENV_HV1BRMediumVirus
ENVZ_ECOLI_Ghose_20230.1330.1150.1240.1060.1150.0970.0970.1150.1240.1150.0800.0880.0710.0970.1240.0800.0710.0880.0800.0800.0970.0530.1060.0620.1330.0620.0880.0800.0620.0880.0800.1150.1330.1060.0970.0620.1060.0970.0880.1150.1060.1060.1240.0880.1150.0530.0620.0970.0710.0800.0880.0880.0880.0800.0800.0620.0970.0800.0800.0800.0710.0881121ActivityENVZ_ECOLIHighProkaryote
EPHB2_HUMAN_Tsuboyama_2023_1F0M0.3210.3270.3830.3670.3880.3880.0050.2960.3720.3720.3110.3270.3010.0150.3270.3470.3720.3880.3470.3570.3470.3470.3060.3210.3270.3160.3720.3520.3370.2810.3110.2350.2350.3570.3010.3570.3980.3520.3830.4030.3620.3780.2600.1120.3270.3270.4440.3780.4230.4080.3830.3980.3880.4290.4030.4030.3880.3980.4080.3930.4230.4291960StabilityEPHB2_HUMANHighHuman
ERBB2_HUMAN_Elazar_20160.1210.2420.1210.1520.2120.2120.1520.1820.2420.3030.1210.1210.1820.1820.1820.1520.2420.1820.2120.0910.1520.1820.1820.1820.1520.1820.2120.2730.1820.1520.0910.1210.1820.1520.0910.1820.1820.1210.1820.2120.2120.1820.1520.1820.2120.1210.2120.1820.0300.0910.1520.1520.1820.1520.1210.1210.1210.1820.2120.1210.1210.152326ExpressionERBB2_HUMANLowHuman
ESTA_BACSU_Nutschel_20200.2340.2710.2750.2660.2520.2610.2200.1880.2020.2840.2290.2800.2800.2290.2430.2200.2250.1970.2250.2340.2570.2610.2840.2200.3070.2520.2940.3120.2570.2060.1790.1930.0870.2430.2290.1650.2610.2480.1610.2890.2840.2430.2940.2060.2840.2750.4270.3530.2980.2800.2840.2340.2750.2890.3120.2940.2800.2480.2710.2890.2250.2712172StabilityESTA_BACSUHighProkaryote
F7YBW8_MESOW_Aakre_20150.0870.6090.5640.6720.6260.6420.0950.4480.4920.5230.6200.5030.5280.0630.0670.1120.4800.3990.6180.5670.0650.0280.0620.0930.1030.2530.0630.4260.6250.5540.6990.6590.0680.0620.0530.6900.0920.0720.6470.5170.4930.6800.1000.0840.3160.1090.0960.3830.1360.0800.6240.6570.6010.6770.6490.6670.6010.6140.6390.6470.3230.0289192OrganismalFitnessF7YBW8_MESOWHighProkaryote
FECA_ECOLI_Tsuboyama_2023_2D1U0.2120.2430.2330.2170.2490.2330.1110.1160.2280.2750.2750.2280.2430.1160.2860.3280.2960.2490.2540.2220.1850.1750.1690.1690.1590.1850.2490.2380.1900.2010.1850.1590.1640.1220.1530.1750.2170.1800.2120.2120.2060.2170.2910.1380.2800.2860.3120.2700.2650.2860.2540.2650.2910.2650.2910.2490.2490.2750.2700.2490.2960.2911886StabilityFECA_ECOLIHighProkaryote
FKBP3_HUMAN_Tsuboyama_2023_2KFV0.1530.1770.1690.1770.2100.2020.0400.0970.0890.0890.0730.0810.0650.0560.0560.0730.1050.0890.0890.0730.0650.0970.0240.1770.0650.1850.1210.1370.0890.1450.0810.0810.0810.0480.0970.1450.1690.1530.1690.1850.1770.1690.0560.0730.1210.0810.2180.2580.2820.2340.2180.2740.2340.1770.2260.2500.2020.2020.1940.2340.2180.1771237StabilityFKBP3_HUMANMediumHuman
GAL4_YEAST_Kitzman_20150.0750.1000.2500.1750.1830.1920.0920.1000.2500.2080.2000.1580.1420.1080.1670.1580.2420.2580.2420.1580.1330.0920.1500.1500.1420.1250.1500.1580.2670.1830.2420.2670.1580.0920.1500.1000.1670.1500.1670.1830.1920.2170.1170.1920.2080.1250.0750.2000.0750.0830.2670.2000.2750.2420.2170.2170.2750.2170.2420.2500.1750.1831195OrganismalFitnessGAL4_YEASTMediumEukaryote
GCN4_YEAST_Staller_20180.2420.2230.2010.2310.2270.2200.0910.1170.2270.2200.1970.1780.1820.1740.1400.1860.2270.2200.2230.2200.0640.0640.0760.0640.0530.0420.0530.0610.1440.2200.2080.2120.0950.0530.0490.2230.2310.2310.2390.2230.2200.2460.1480.1400.1520.1860.1700.2160.1520.1480.1930.1820.1860.1860.1970.2080.2010.1970.1970.1970.1360.1252638BindingGCN4_YEASTLowEukaryote
GDIA_HUMAN_Silverstein_20210.1210.1120.1290.1380.1380.1470.1550.1290.1210.1290.1380.1120.1210.0690.0860.1210.1380.1380.1380.1380.0950.1640.1550.1900.1210.1640.1380.1290.1380.1470.1720.1810.1120.1210.1030.1120.1380.1210.1290.1380.1210.1470.1120.0520.1550.1030.1720.1640.1470.0780.1380.1470.1470.1640.1640.1810.1550.1380.1640.1550.1380.1211154OrganismalFitnessGDIA_HUMANLowHuman
GFP_AEQVI_Sarkisyan_20160.2770.2750.2800.2790.2870.2870.0610.2560.2970.2980.1880.0830.0860.0850.0920.0790.0780.0900.1120.2410.0740.0730.0890.0800.0660.0980.1180.2590.2680.2960.2380.2390.0870.0690.0930.2490.2850.2830.2790.2880.2890.3040.1280.1100.1340.1340.2790.2800.3080.2540.2350.2390.2500.2530.2430.2510.2410.2450.2330.2470.2620.18851714ActivityGFP_AEQVILowEukaryote
GLPA_HUMAN_Elazar_20160.3200.2000.3600.3600.3600.3600.2800.2800.2000.2000.2000.4000.3200.2800.2800.3200.3600.3200.2000.2000.3200.2800.3600.3200.2400.3600.3200.4000.2400.2000.2800.2400.2400.2800.3200.3600.4000.4000.4400.3600.3600.3600.2800.2400.4000.3200.2800.2000.3200.1600.4800.4400.3200.3600.4000.2800.3200.4800.3600.4000.3200.400245ExpressionGLPA_HUMANLowHuman
GRB2_HUMAN_Faure_20210.3030.3600.3540.3630.3760.3840.3170.2880.3800.3390.3650.3050.3450.3210.3840.4250.4420.3460.3930.3660.3710.3570.3280.3100.3430.3470.2870.3810.2830.3450.2810.2660.3430.3770.3620.2450.3810.3760.2910.3990.4020.3750.3980.1960.3500.3620.4600.3370.4910.3450.4160.4370.4150.4270.4380.4340.4170.4370.4120.4390.3590.40263366OrganismalFitnessGRB2_HUMANMediumHuman
HCP_LAMBD_Tsuboyama_2023_2L6Q0.2690.2600.1920.2120.2020.2210.1920.1920.1250.1920.3460.2690.3170.2210.2690.2980.3370.2310.2210.1060.1250.1830.2120.2600.2310.2600.2210.2120.2600.2120.2310.1630.1730.1060.1630.2400.2690.2790.2600.2310.2500.2500.2600.1730.2980.2120.2500.3370.2690.2020.2980.3170.3560.3170.3560.3370.3370.3270.3170.3560.4230.2791040StabilityHCP_LAMBDMediumVirus
HECD1_HUMAN_Tsuboyama_2023_3DKM0.5810.5470.6190.6330.6490.6510.2220.4690.6060.6230.5990.1740.2290.1430.1520.6080.6170.6050.6140.5810.1910.5030.5380.5380.5010.5490.5440.5650.5330.5960.6420.5810.1090.1990.1680.3330.6060.5740.6060.6300.6170.6230.2430.2310.6080.3430.4600.5920.4740.3450.6390.6060.6280.6150.6440.6530.6300.6390.6460.6440.6730.2205586StabilityHECD1_HUMANMediumHuman
HEM3_HUMAN_Loggerenberg_20230.1650.1740.1720.1720.1740.1740.1210.0630.1810.1780.1550.1670.1460.0900.1720.1830.1550.1690.1630.0720.1740.1740.1740.1670.1600.1720.1630.1600.1760.1830.1580.1560.1090.1720.1930.1650.1630.1900.1580.1850.1810.1720.1210.0980.1650.1580.1630.1880.1390.1440.1670.1720.1690.1670.1650.1700.1720.1720.1700.1700.1670.1785689ActivityHEM3_HUMANMediumHuman
HIS7_YEAST_Pokusaeva_20190.1650.2020.2090.2090.2030.1990.1330.0860.1880.1910.2040.1740.1850.1000.1300.1790.2000.2020.1940.1700.1750.1980.2250.2110.2030.2130.1910.1940.1910.1770.1990.2020.0880.2110.2120.1970.1900.1880.1860.2090.2050.2020.1480.1320.1970.1090.1820.2090.1880.1540.1960.1780.1900.1930.1940.1910.1930.1960.1960.1930.2030.107496137OrganismalFitnessHIS7_YEASTMediumEukaryote
HMDH_HUMAN_Jiang_20190.1350.1220.1240.1200.1280.1220.1330.1930.1210.1220.1260.1300.1230.1220.1230.1360.1490.1300.1330.1110.1370.1080.1160.1250.1430.1060.1080.1080.1240.1200.1270.1270.1140.1240.1110.1200.1250.1250.1220.1160.1200.1200.1240.1170.1610.1600.1320.1290.1050.1260.1450.1290.1400.1490.1440.1500.1430.1510.1400.1390.1550.14816853OrganismalFitnessHMDH_HUMANLowHuman
HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O20.1020.1420.1420.1550.1370.1460.1190.1680.1330.1370.1370.1370.1460.0800.0660.1150.1240.1500.1110.0880.1640.1680.1730.1590.1500.1590.1500.1730.1640.1590.1900.1900.0840.1590.1680.1550.1190.1370.1330.1550.1500.1550.1060.0750.1590.1420.1190.1550.1150.1110.1420.1370.1190.1110.1330.1330.1370.1550.1420.1330.1420.1592252OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Flynn_20190.1250.0920.1080.1090.1090.1110.1060.1290.1100.1080.1150.1110.1070.0940.1140.1110.1560.1500.1390.1260.1140.1080.1160.1100.1280.1280.1080.1190.1050.0970.1260.1150.0950.1200.1290.1200.1190.1320.1260.1110.1170.1090.1080.0950.1150.1190.1170.1120.0860.1200.1500.1400.1460.1450.1460.1490.1430.1380.1350.1500.1090.13813294OrganismalFitnessHSP82_YEASTMediumEukaryote
HSP82_YEAST_Mishra_20160.1590.1820.1780.1940.1850.1870.1290.1270.1820.1990.1800.1940.1920.0740.0880.1110.1620.1590.1320.1550.2150.1920.2060.1820.2100.1960.1990.1960.1870.1800.1960.2060.1180.1820.1760.1920.1890.1780.1730.1820.1890.1890.1890.0510.1730.1990.1040.1760.0790.1270.1290.1250.1250.1430.1760.1500.1410.1410.1480.1500.1710.1644323OrganismalFitnessHSP82_YEASTMediumEukaryote
HXK4_HUMAN_Gersing_2022_activity0.2320.1610.1490.1510.1560.1560.0980.1270.1490.1480.1280.1160.1350.1170.1270.1720.1740.1510.1380.1200.1400.1170.1410.1340.1400.1400.1530.1380.1250.1090.1330.1190.0850.1440.1390.1480.1650.1330.1550.1620.1400.1580.1350.1070.1400.1440.1790.1600.1590.1420.1620.1760.1620.1730.1650.1760.1820.1630.1660.1700.1700.1628570OrganismalFitnessHXK4_HUMANMediumHuman
HXK4_HUMAN_Gersing_2023_abundance0.1520.1600.1620.1730.1540.1600.0860.1600.1560.1790.1620.1730.1920.0900.1130.1480.1730.1670.1740.1850.1650.1860.1860.1900.1730.1850.1940.1830.1700.1870.1650.1650.1110.1640.1710.1930.1670.1790.1870.1580.1620.1650.1230.1060.1640.1630.1580.1540.1630.1290.1620.1610.1620.1480.1670.1670.1750.1630.1650.1650.1750.1558396ExpressionHXK4_HUMANMediumHuman
I6TAH8_I68A0_Doud_20150.2770.2620.2340.2330.2850.2820.0830.2250.2470.2750.0920.0860.0870.0820.0770.0880.1000.0910.1240.2160.2300.2530.2610.2630.0770.0940.1190.0830.2380.2840.1850.1880.1430.2610.2510.2520.2700.2660.2600.2830.2800.2680.0800.0880.0920.0860.1860.1850.1860.1460.1030.1360.1300.1360.1210.1170.1050.1090.1000.1200.1240.1109462OrganismalFitnessI6TAH8_I68A0MediumVirus
IF1_ECOLI_Kelsic_20160.1300.2170.1960.2320.2100.2100.1960.1590.0870.0870.2540.1810.1960.1010.1880.2610.2390.1960.2030.1960.1880.2320.2030.2030.2250.2170.2610.2250.1520.3040.2030.1590.1450.2100.1880.2100.1810.1520.2030.2030.1960.2030.1450.1300.2610.2320.1090.2320.2170.1740.2170.2680.2390.2390.2320.2460.2320.2100.2540.2390.1880.1881367OrganismalFitnessIF1_ECOLIHighProkaryote
ILF3_HUMAN_Tsuboyama_2023_2L330.0750.0750.1950.2030.1950.2110.0600.1200.0830.1200.2030.1200.1130.0530.0530.0750.0830.1650.1880.1880.2030.1730.1730.1880.2710.2110.2480.1800.1650.2180.1200.0750.2030.0830.2260.2330.0600.1580.1800.1050.1280.1650.0900.0600.1580.1280.0900.1500.0830.1500.1880.1050.1580.1500.1650.1650.1580.1430.1500.1580.2180.0901329StabilityILF3_HUMANHighHuman
ISDH_STAAW_Tsuboyama_2023_2LHR0.2260.1790.2360.2360.2210.2360.3130.1280.2260.2820.2460.3230.3440.3440.3690.4510.3740.4210.3950.1900.3180.2000.1950.2150.3490.3540.3690.3330.2510.2050.1790.1230.1590.2310.2410.2870.2360.2150.2670.2510.2310.2510.3130.2720.3490.3380.4510.4670.3330.3130.2510.2920.2970.3080.2920.2870.2870.2970.2720.2920.4150.3081944StabilityISDH_STAAWHighProkaryote
KCNE1_HUMAN_Muhammad_2023_expression0.0430.0300.0210.0170.0470.0470.0770.1410.0300.0340.0850.0900.0730.0730.0770.0470.0730.0430.0170.0260.0730.0900.0810.1200.0850.1620.0680.0340.0340.0340.0380.0380.1110.0640.1070.0940.0640.0730.0730.0510.0470.0560.0810.0980.0130.0810.0680.0210.0850.1320.0680.0510.0640.0680.0560.0640.0730.0600.0600.0600.0510.0852339ExpressionKCNE1_HUMANMediumHuman
KCNE1_HUMAN_Muhammad_2023_function0.1940.2240.2030.2070.2200.2200.1900.1550.2200.2370.1940.2330.2500.1900.1940.2460.2070.2200.1720.1680.1980.1770.1850.1770.2070.1900.2500.2280.1900.1940.2160.1850.0340.2030.2410.2160.1900.2030.1980.2240.2200.2110.1850.1590.2030.1850.2240.1810.1720.1340.1980.2200.2160.1770.2280.2280.2330.1980.2110.2070.2970.1772315ActivityKCNE1_HUMANMediumHuman
KCNH2_HUMAN_Kozek_20200.0000.0500.0000.0000.0000.0000.0000.1000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.2000.0500.1000.1000.1000.1000.0500.0500.0500.0500.0500.2000.3500.0500.0500.0500.1000.0000.0000.0500.0000.0000.0000.1000.0000.0500.0500.0000.0000.0500.0500.1500.1000.1000.1500.1500.1000.1500.1000.1500.1500.0000.100200ActivityKCNH2_HUMANMediumHuman
KCNJ2_MOUSE_Coyote-Maestas_2022_function0.1460.1560.1360.1550.1620.1580.1080.1250.1390.1360.1660.1540.1560.1060.1490.1660.1680.1640.1610.1290.1620.1610.1490.1280.1610.1580.1540.1550.1480.1490.1410.1310.1020.1590.1650.1380.1560.1580.1450.1540.1580.1460.1290.1130.1360.1550.1420.1430.1490.1130.1580.1620.1660.1580.1620.1520.1480.1710.1610.1640.1360.1596963ActivityKCNJ2_MOUSEMediumEukaryote
KCNJ2_MOUSE_Coyote-Maestas_2022_surface0.1490.1080.1360.1270.1200.1320.1040.0720.1040.1070.1520.1460.1170.1520.1880.2080.1850.1590.1890.0770.1750.0920.0750.0820.1850.0940.0850.1390.0780.1000.0850.0910.0770.1620.0720.0850.1320.0950.0780.1300.1050.1000.1750.1270.0980.1890.1530.0870.1290.1260.1750.1560.1820.1560.1840.1620.1710.1690.1750.1690.1690.1926917ExpressionKCNJ2_MOUSEMediumEukaryote
KKA2_KLEPN_Melnikov_20140.1590.2200.2160.2400.2220.2240.1070.1690.2080.2380.2120.1900.2260.1130.1230.1690.2260.2400.2380.2000.1510.1880.2200.2160.1650.2040.2280.1880.2420.2180.1690.1490.0770.1250.1670.1940.1850.1900.2100.2320.2300.2360.1230.1010.2020.1430.2200.2300.2280.1710.2240.2140.2280.2160.2160.2220.2200.2080.2160.2260.2360.1814960OrganismalFitnessKKA2_KLEPNHighProkaryote
LGK_LIPST_Klesmith_20150.2170.4320.4260.4270.4460.4470.1330.3470.2720.4060.2510.3510.3880.1190.1740.2270.3240.4040.4350.3260.1610.2410.2980.3190.1850.3280.4060.3050.4450.4930.3710.3190.1390.1710.2450.4700.2140.2460.4440.4360.4470.4700.1050.0850.2830.1750.2310.3610.3760.1580.3050.3470.3370.3540.3370.3470.3230.3350.3230.3520.2690.1907890ActivityLGK_LIPSTMediumEukaryote
LYAM1_HUMAN_Elazar_20160.1390.2500.2500.2500.2220.2500.2780.1110.2500.2500.2500.2220.2220.2500.2780.2220.1940.1670.2780.2500.3330.3330.2780.2500.2220.2500.1940.2500.0830.2220.1940.0560.1940.2780.3330.2220.1940.3330.2220.2780.2780.2500.2500.2780.1940.1940.2780.2220.1940.1940.1940.3060.3330.2500.2500.2500.2780.2780.1390.2780.3060.306359ExpressionLYAM1_HUMANMediumHuman
MAFG_MOUSE_Tsuboyama_2023_1K1V0.3150.3150.3500.3500.3430.3360.3430.2450.2870.2800.2940.3220.4060.3850.3780.3780.4130.4550.2730.2310.2240.2170.2590.3010.2800.2730.2940.2730.2730.2520.2100.2170.1610.2310.3010.2310.3010.3150.2940.3430.3150.3220.3850.2590.3500.3570.4130.3150.3640.3850.4130.4410.4480.4690.4270.4760.4620.4620.4340.4620.4620.4831429StabilityMAFG_MOUSEMediumEukaryote
MBD11_ARATH_Tsuboyama_2023_6ACV0.2880.3820.3300.3300.3350.3350.0570.1510.2740.3020.2640.2550.2260.0280.0280.2640.2590.2640.3770.2410.0240.0610.1930.1790.0940.2640.2740.2360.2880.2740.3250.2970.3160.0280.0420.0470.2690.2640.2830.3160.3300.3160.0900.0570.3070.2500.2880.3350.2880.2880.3400.3870.3440.3440.3160.3350.3300.3110.2740.3300.3820.2642116StabilityMBD11_ARATHMediumEukaryote
MET_HUMAN_Estevam_20230.1480.1850.1630.1740.1630.1780.1630.1740.1560.1670.1910.1780.1740.1430.1480.1720.1830.1720.1870.1630.1850.1870.1590.1930.1930.1810.1830.1910.2000.1930.1700.1720.1200.1610.1780.1960.1670.1740.1980.1760.1700.1760.1700.1390.1870.1780.1500.1830.1690.1190.1610.1630.1650.1670.1560.1760.1720.1690.1870.1590.1850.1675393ActivityMET_HUMANMediumHuman
MK01_HUMAN_Brenan_20160.0910.1340.1340.1440.1380.1220.0840.0750.1160.0950.0760.1190.1070.1040.1030.0900.0720.0850.0980.0970.1010.0940.0820.0900.0950.1090.1030.1090.1010.1220.0820.0950.1090.0870.1010.0900.0780.0980.0880.1250.1250.1060.0970.0950.1130.1200.0600.1090.0700.0970.0840.1040.0950.0880.0850.0790.0900.0810.0810.0880.0980.1046809OrganismalFitnessMK01_HUMANMediumHuman
MLAC_ECOLI_MacRae_20230.0950.1450.1470.1370.1320.1420.1150.1400.1620.1500.1520.1550.1770.1200.1120.1120.1370.1520.1800.1670.1420.1400.1450.1500.1470.1500.1770.1600.1450.1570.1200.1400.1500.1120.1170.1350.0900.1220.1150.1270.1420.1370.1000.0870.1400.1100.0570.1150.1150.0670.1200.1120.1170.1170.1300.1120.1120.1120.1150.1100.1250.0974007OrganismalFitnessMLAC_ECOLIMediumProkaryote
MSH2_HUMAN_Jia_20200.1330.1620.1430.1470.1470.1490.1000.1000.1560.1530.1460.1640.1640.1050.1310.1530.1520.1590.1460.1520.1140.1270.1370.1460.1380.1440.1460.1340.1620.1430.1470.1350.1320.1280.1530.1550.1340.1530.1520.1590.1670.1570.1220.0930.1560.1340.1300.1520.1010.1180.1490.1440.1500.1420.1480.1590.1540.1530.1540.1580.1540.13416749OrganismalFitnessMSH2_HUMANMediumHuman
MTH3_HAEAE_RockahShmuel_20150.1440.3160.3370.3260.3210.3420.1340.3370.2940.2990.2030.2890.3160.1020.1280.1550.1760.1820.2300.2830.1390.1600.2350.2990.2030.3210.3260.2830.3050.3210.3320.3050.1930.1710.1820.2990.1600.1600.2780.3420.3420.3370.1280.0530.1550.1230.1660.2350.2620.1340.2140.2250.2250.1980.2030.2090.2190.2190.2090.2140.2090.1071777OrganismalFitnessMTH3_HAEAEMediumProkaryote
MTHR_HUMAN_Weile_20210.1220.1230.1130.1230.1130.1080.0960.1210.1330.1380.1230.1260.1260.1180.1240.1350.1160.1320.1380.1220.1210.1180.1260.1490.1300.1200.1310.1190.1620.1370.1390.1480.0750.1270.1260.1270.1100.1180.1250.1090.1160.1230.1220.1100.1230.1260.1120.1220.1270.1180.1200.1150.1210.1180.1170.1180.1200.1180.1260.1170.1320.11612464OrganismalFitnessMTHR_HUMANLowHuman
MYO3_YEAST_Tsuboyama_2023_2BTT0.2270.2060.2760.2120.2360.2450.0730.0610.2970.2850.2940.2610.2640.1090.2300.3550.4210.3180.1790.2330.1390.0910.1640.1550.1880.2610.2550.2270.2910.2090.2090.1670.1700.2120.1390.1270.2910.2210.2000.2760.2550.2610.1940.0730.2700.2000.2480.3150.3390.2700.3030.3060.2970.3120.3030.3060.2910.3030.3000.3030.2970.3483297StabilityMYO3_YEASTHighEukaryote
NCAP_I34A1_Doud_20150.2580.2460.2460.2470.2590.2630.1110.2200.2660.2610.1060.0990.0940.1010.0930.0960.0990.1020.1290.1820.2350.2640.2640.2630.0980.1110.1090.1070.2420.2680.1990.1940.1480.2490.2520.2850.2600.2710.2700.2800.2820.2780.0940.0990.1060.0930.1610.1660.1780.1440.1030.1180.1270.1220.1140.1140.1150.1240.1020.1150.1390.1309462OrganismalFitnessNCAP_I34A1MediumVirus
NKX31_HUMAN_Tsuboyama_2023_2L9R0.4340.4260.4340.4540.4420.4740.3530.3610.3780.4060.3050.3980.3690.4020.3940.4780.5100.4860.4580.4060.3860.3650.3290.3290.3780.3170.3570.3010.3210.3730.3490.3690.3050.3900.3860.3370.4300.4020.4020.4420.4460.4540.3820.4060.3210.3450.3860.3410.4180.4180.4820.4260.4740.4500.4820.4780.4540.4940.4380.4660.3490.4102482StabilityNKX31_HUMANHighHuman
NPC1_HUMAN_Erwood_2022_HEK293T0.3910.3910.4060.4220.4530.4530.1410.2810.3910.4530.3440.1720.2190.1560.2190.2970.4380.3910.4220.1250.2030.2340.1410.2970.2660.1880.4060.2660.2190.3590.2810.2190.0780.1560.2810.3750.3590.4220.4220.4060.4220.4530.1560.1250.3750.2810.3440.3750.0620.1250.3750.4220.3910.3750.3440.3590.3440.3590.3590.3910.3910.266637ActivityNPC1_HUMANLowHuman
NPC1_HUMAN_Erwood_2022_RPE10.4290.4290.4290.4290.4290.4290.2860.2860.4290.7140.2860.1430.1430.1430.2860.2860.4290.0000.4290.1430.2860.5710.5710.2860.1430.2860.4290.5710.5710.4290.4290.2860.0000.2860.4290.2860.4290.4290.4290.4290.4290.4290.2860.2860.2860.0000.2860.0000.1430.1430.1430.1430.1430.2860.0000.1430.1430.1430.2860.1430.1430.14363ActivityNPC1_HUMANLowHuman
NRAM_I33A0_Jiang_20160.4330.5670.4670.4330.5670.5670.0670.6000.6670.7000.0670.1330.6000.0000.0000.0670.2330.5670.5000.4000.7000.6670.5330.5330.0000.4330.5670.4000.6330.5670.4000.4000.0670.6000.5330.6000.5330.5670.6000.6330.6000.5670.0000.0330.0000.0330.2670.2000.4000.2330.1670.2000.3000.2330.3000.4000.2670.2670.3330.3330.2330.033298OrganismalFitnessNRAM_I33A0LowVirus
NUD15_HUMAN_Suiter_20200.1930.2600.2810.2740.2740.2840.0530.1790.2530.2670.2560.2070.2840.1050.1260.1400.1930.2390.2630.2040.1020.1300.2490.2530.1330.2980.2670.2390.3120.3090.2670.2110.0980.1230.1190.3050.1960.2040.3020.2740.2880.3090.1160.0700.2840.1400.1960.3300.2110.1680.2140.2490.2110.2280.2390.2630.2390.2560.2460.2600.2980.2002844ExpressionNUD15_HUMANHighHuman
NUSA_ECOLI_Tsuboyama_2023_1WCL0.1230.2560.1630.1670.1720.1720.1330.1280.2020.1380.1770.0300.0590.1030.1030.0840.1480.1670.2170.1630.1720.2020.1970.2170.0940.1430.1130.2070.2510.1380.2170.2170.0740.1630.1430.1480.1580.1180.1430.1720.1630.1920.1720.1380.1030.1330.3740.3050.2760.3450.2460.2910.2660.2610.2860.2460.2460.2460.2410.2610.2710.1582028StabilityNUSA_ECOLIHighProkaryote
NUSG_MYCTU_Tsuboyama_2023_2MI60.2830.2170.2250.2460.2390.2460.1590.1810.2830.2250.2320.2540.2460.1230.2610.2540.2320.2170.2170.1380.1160.1520.1520.1520.2540.1810.1670.1670.1740.2460.2320.1960.2170.1670.1450.1810.2100.2030.2100.1960.1880.2100.2170.0800.1880.2970.2540.2100.3700.3190.2830.3410.3120.2750.2900.2900.3040.3190.2680.2970.1880.2751380StabilityNUSG_MYCTUHighProkaryote
OBSCN_HUMAN_Tsuboyama_2023_1V1C0.3720.4840.5470.5590.5750.5410.1190.3780.5000.4940.5090.4690.4780.1310.4530.4970.5690.5190.5120.5500.4470.4340.4310.4280.4380.4160.4500.4120.4030.4940.4970.5090.3280.1690.1220.3190.4660.4750.4910.5380.5340.5310.3810.1440.4060.4310.5620.4380.6690.5780.5780.5910.5880.6000.6060.6030.5530.5780.6000.6090.6310.5003197StabilityOBSCN_HUMANHighHuman
ODP2_GEOSE_Tsuboyama_2023_1W4G0.1840.1320.2370.1670.2460.1320.1930.1490.1140.1140.1930.1840.1840.1750.1580.2110.1750.1840.1670.1930.1050.1140.1840.0960.0880.1140.1670.1050.1580.2020.1230.0960.1230.1670.0440.1670.1580.0440.0790.1580.1140.1320.1750.1750.1840.1840.2370.1930.2460.3070.2280.2110.2460.2540.2630.2370.2630.2630.2280.2460.2020.1931134StabilityODP2_GEOSEHighProkaryote
OPSD_HUMAN_Wan_20190.1760.4120.4120.4120.4710.4120.1760.4120.2940.4120.3530.2940.2940.2940.2350.2350.2940.4120.1760.4120.4120.3530.2350.2940.4710.3530.3530.4120.2350.2940.2350.2350.0000.3530.2350.1180.4120.2940.2940.4120.4120.4120.3530.0590.3530.4120.1760.3530.1760.1760.2350.2350.1760.1760.1760.2940.2350.2350.2350.1760.4120.294165ExpressionOPSD_HUMANHighHuman
OTC_HUMAN_Lo_20230.3160.3420.2970.2910.3100.3040.1390.1520.3160.2970.2910.3230.3350.1580.2910.3420.3350.2970.2970.3040.3230.2720.2720.2780.2780.3480.3040.2970.2340.3040.2410.1710.1330.2660.2780.2780.3290.3040.3230.3100.2970.3160.2090.1270.3160.2590.3670.3040.2910.2590.3040.3480.3420.3610.3290.3480.3480.3420.3420.3420.3800.3421570ActivityOTC_HUMANMediumHuman
OTU7A_HUMAN_Tsuboyama_2023_2L2D0.1880.2660.2030.2030.2190.2030.0780.1250.1090.1410.2500.2970.2340.1090.2810.3280.1560.1720.1880.1560.1090.0620.0780.0780.1090.0620.0780.0940.0470.0310.1090.1090.0620.1410.1250.1410.1880.1560.1250.2030.1720.1880.1720.1410.2500.3440.2500.2030.3280.2810.2190.2190.2190.1880.2190.2030.2340.1720.1720.2190.3590.328635StabilityOTU7A_HUMANHighHuman
OXDA_RHOTO_Vanella_2023_activity0.1270.1920.2310.2230.2380.2330.1140.1310.1410.1450.1810.1620.1720.1120.1140.1560.1800.2090.2170.1500.1250.1420.1560.1550.1190.1610.1810.1620.2250.2380.1910.1770.0720.1330.1310.1340.1380.1280.1360.2230.2330.2340.1220.0950.1770.1360.1950.2420.2480.1620.1840.2160.2020.2050.1970.2230.2080.2230.1940.2160.2220.1486396ActivityOXDA_RHOTOHighEukaryote
OXDA_RHOTO_Vanella_2023_expression0.0960.1400.1680.1610.1550.1640.1490.1230.1330.1200.1490.1330.1340.0960.1200.1510.1610.1730.1800.0810.0920.1450.1360.1340.1110.1620.1520.1400.1520.1770.1270.1210.0900.1080.1140.1340.1090.0900.1140.1550.1490.1580.1110.0960.1450.1140.1760.1890.1860.1460.1640.1460.1420.1390.1600.1580.1610.1600.1680.1580.1830.1576769ExpressionOXDA_RHOTOHighEukaryote
P53_HUMAN_Giacomelli_2018_Null_Etoposide0.0990.1030.1080.1000.1070.1180.0980.0960.0790.0780.1670.1310.1580.1000.0830.1070.1110.1350.1430.0700.1150.1510.1810.1230.1160.1650.1940.2010.0960.0780.1420.1020.1180.1070.1380.0720.1030.1260.0790.0990.1040.0990.1110.0910.1540.0870.1390.1710.1300.1290.1310.1200.1180.1060.1030.1180.1140.1230.1100.1080.1240.0907467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_Null_Nutlin0.0840.0780.0740.0750.0800.0820.1160.1070.0600.0590.1930.1260.1740.1080.0910.1120.1140.1270.1490.0940.1190.1660.2180.1140.1120.1940.2500.2440.0990.0840.1710.1180.1310.1190.1540.0630.1060.1290.0660.0780.0870.0710.1260.1100.1670.0990.1510.1820.1630.1140.1460.1550.1260.1230.1120.1190.1270.1220.1200.1200.1460.1117467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Giacomelli_2018_WT_Nutlin0.0740.0580.0700.0620.0590.0590.0740.0540.0480.0460.1100.0920.1070.1120.1260.1110.0800.0990.1060.0350.1260.0990.0990.0700.0900.1080.1100.1120.0590.0520.0900.0750.0670.0710.0990.0580.0790.0900.0640.0580.0630.0600.0910.0960.0990.0960.1060.1000.1150.1070.0990.0980.0900.0820.0990.0910.0860.0760.0800.0830.0900.0997467OrganismalFitnessP53_HUMANLowHuman
P53_HUMAN_Kotler_20180.3240.2570.2760.2860.2380.2290.1240.2670.2380.2570.2860.2190.2480.1430.1330.2380.2670.2760.2670.2190.2000.2670.2190.2290.2100.2000.2100.2000.1620.2860.2670.2190.0380.1620.2290.2290.3240.3050.3140.2760.2860.2570.1430.1140.2000.1140.1900.2290.2100.1810.2100.2480.2480.2290.2380.2380.2100.2290.2380.2480.2860.2191048OrganismalFitnessP53_HUMANLowHuman
P84126_THETH_Chan_20170.2760.4140.3880.4010.3820.3880.1840.2890.4010.3750.4080.3680.3820.2040.4010.3550.4010.3680.4140.3750.2630.3290.3420.3820.3220.3950.3950.3550.4470.3160.3620.3220.1180.3090.3160.3550.2890.2700.3090.3550.3620.3550.2570.1120.3820.3950.2040.3880.3620.1780.3290.3620.3220.3490.3620.3950.3750.3620.4010.3880.3950.3881519OrganismalFitnessP84126_THETHMediumProkaryote
PA_I34A1_Wu_20150.3350.3350.3350.3240.3350.3240.1150.2690.0880.1320.1100.1100.0930.1040.0930.1040.1040.0990.2470.2360.2800.2750.2640.3300.1210.2580.2580.2250.2640.2910.2640.2420.1260.2750.2800.3020.3350.3190.3190.3300.3410.3190.1100.1100.1040.1100.1150.0990.0710.1370.0880.0930.0990.0880.0880.1100.0710.0820.0820.1150.1040.0711820OrganismalFitnessPA_I34A1MediumVirus
PABP_YEAST_Melamed_20130.2790.2690.2750.2750.2900.2850.1820.2340.2690.2760.2770.2870.2890.1720.1910.2100.2420.2450.2590.2490.2550.2850.2750.2820.2700.2810.2920.2810.2500.2910.2730.2490.1210.2440.2700.2610.2690.2850.2720.2860.2910.2860.2420.1150.2880.2640.1260.2880.2000.1480.2330.2160.2290.2210.2230.2170.2210.2270.2280.2250.2690.21337708OrganismalFitnessPABP_YEASTMediumEukaryote
PAI1_HUMAN_Huttinger_20210.2070.2150.2070.2110.2110.2110.0860.1720.2000.2220.1960.1980.2170.1210.1960.2090.2000.1930.1790.2020.1460.1700.1510.2000.1780.1790.1810.1830.1680.1680.1380.1210.1290.1420.1960.1830.1940.2150.1980.2240.2170.2240.1850.1330.1960.1830.2170.2470.2190.1550.2130.2020.2190.2130.2110.2260.2240.2210.2210.2240.2320.2065345ActivityPAI1_HUMANNaNHuman
PHOT_CHLRE_Chen_20230.2730.3960.5150.5020.3680.3540.4010.3930.5010.4900.4340.4700.5020.5050.5290.4900.5260.4970.5070.3620.3970.4580.4130.4630.4060.4380.4590.4590.4230.3890.4260.3810.3420.3250.3170.4420.4120.3880.4530.3760.3900.3790.4630.2880.4640.4900.2300.4150.3960.2690.4120.3770.3780.3900.3860.4010.3970.4020.4010.3950.4910.484167529ActivityPHOT_CHLREHighEukaryote
PIN1_HUMAN_Tsuboyama_2023_1I6C0.3330.3830.4200.4440.4070.3950.3460.1850.3330.4320.3210.2960.3580.3700.3580.3950.4200.2960.3460.3460.2960.2220.1600.1110.2100.1850.1230.0990.0860.1980.2470.1850.1980.3210.2470.2100.3950.3700.2960.3830.3460.3090.3210.1480.3580.3580.2720.3210.2840.2590.2960.3830.3830.3210.3090.3580.3460.3330.3460.3700.4070.395802StabilityPIN1_HUMANHighHuman
PITX2_HUMAN_Tsuboyama_2023_2L7M0.3660.3390.3330.3220.3330.3390.2950.2130.2730.3500.2190.2900.2730.4150.3830.4150.3930.3610.3280.3220.2130.1860.1860.1860.2730.2190.2350.1800.1910.3110.2400.2730.2300.2460.3010.1640.2950.3500.2510.3280.3440.3220.2950.3110.1800.2570.3880.2020.4430.3770.3770.4100.4210.4100.4100.4040.3880.3880.4100.4040.1800.3391824StabilityPITX2_HUMANHighHuman
PKN1_HUMAN_Tsuboyama_2023_1URF0.1450.2820.1450.1370.1220.1530.2290.0460.1070.0990.0690.1300.0990.1830.1600.1910.0920.0460.0610.1760.1760.1600.1980.1530.1530.1370.1600.1830.0690.0460.0920.0990.0990.2210.1910.1910.1760.1760.1680.1830.1680.1530.1530.2140.1300.1830.2520.1530.1600.2140.1150.1150.1220.1150.1070.1070.0990.1220.1150.1150.1980.1531301StabilityPKN1_HUMANHighHuman
POLG_CXB3N_Mattenberger_20210.3090.3800.3610.3870.3920.3990.0880.2610.3940.3890.1760.0900.1130.0970.0910.1360.2310.2520.2880.2790.2880.3480.3520.3280.1450.3160.3190.3100.3720.3910.2890.2840.0960.0990.2510.3160.2860.3210.3420.3490.3730.3750.0980.0920.1960.1000.1510.2150.1010.1210.1970.2110.2110.2120.1990.2020.2140.2230.2120.2180.1090.10215711OrganismalFitnessPOLG_CXB3NMediumVirus
POLG_DEN26_Suphatrakul_20230.3270.3950.3410.3580.3840.3860.0860.2990.4730.4770.1430.0890.1020.0800.0860.1090.1340.1400.1640.3310.3300.3250.3310.2920.3310.3440.3400.3510.3490.4540.4110.3140.1040.0890.1600.3660.2880.3320.4050.3830.3930.4330.0890.0860.1980.0880.1880.2780.0700.1360.1380.1380.1660.1460.1520.1540.1440.1500.1400.1570.1200.11416897OrganismalFitnessPOLG_DEN26LowVirus
POLG_HCVJF_Qi_20140.3620.4110.3680.3800.3930.4050.0740.1530.3870.3870.1230.3870.3500.0860.0920.0800.1100.0920.1040.2390.1960.2760.3250.2880.2820.2700.2390.2390.2940.3870.3130.2210.0670.3680.3560.2880.3620.3740.3620.3930.3680.3440.0860.0740.3310.1170.0800.2450.2330.1900.1720.1530.1230.1530.1410.1290.2210.1530.1660.1780.0920.0981630OrganismalFitnessPOLG_HCVJFMediumVirus
POLG_PESV_Tsuboyama_2023_2MXD0.1640.5190.4350.4540.4640.4640.0880.4800.3330.4970.4890.1130.1290.0990.1230.1170.1560.1500.1270.5950.0970.0780.0860.0900.1090.0490.0800.0510.1310.5580.6080.6200.1130.1050.0940.0880.2960.2940.2810.4370.4370.4290.1850.1270.1890.1540.5500.5070.5610.5810.6220.6300.6300.6510.6530.6630.6340.6900.6370.6630.5690.3265130StabilityPOLG_PESVMediumVirus
PPARG_HUMAN_Majithia_20160.1600.2370.2590.2530.2550.2410.1460.1930.2520.2420.2450.2740.2900.1240.1350.1380.1580.2200.2770.2880.2780.2930.1750.1750.2340.3080.3320.3420.1790.2690.1620.0760.1170.2940.2680.2740.2750.3300.3080.2550.2750.2660.1240.1210.2500.1420.2440.2740.2480.1620.1840.1670.1460.1370.1930.1470.1640.1480.1270.1530.1980.1499576ActivityPPARG_HUMANMediumHuman
PPM1D_HUMAN_Miller_20220.1800.1760.1800.1870.1800.1770.1280.1540.1760.1730.1830.2100.2000.1310.1400.1610.1740.1880.2010.1630.1720.1740.2050.1810.1770.2020.1920.2140.1740.1810.1720.1350.1130.1630.1880.2060.1910.1920.2050.1870.1850.1860.1420.1150.1910.1710.1690.2010.1880.1350.1770.1800.1760.1670.1810.1680.1630.1680.1730.1810.2150.1587889OrganismalFitnessPPM1D_HUMANLowHuman
PR40A_HUMAN_Tsuboyama_2023_1UZC0.1910.2300.3430.3430.2990.2840.2160.2160.3920.3920.3970.1910.2650.2210.2250.3580.3870.4020.2650.2990.3630.3580.3870.3330.3530.3920.3630.3530.3630.3820.2990.2500.2210.2060.2450.2350.2940.2750.2450.3190.3140.3240.2160.2210.2990.2400.3380.3380.4260.3190.3820.4120.3820.3870.3970.4070.4020.3920.4070.4020.4020.4312033StabilityPR40A_HUMANMediumHuman
PRKN_HUMAN_Clausen_20230.2050.1890.2010.2070.2100.2170.1200.1870.1700.1870.1970.2360.2270.1700.1850.2250.2560.2930.2920.1970.1830.2130.1940.1680.1870.2490.1780.2410.1790.1860.1510.1280.1100.1550.2020.1670.2090.2020.1830.2160.2110.1870.1850.1100.2640.1850.2890.2430.3080.1610.2490.2710.2840.2690.2360.2450.2730.2730.2750.2680.2760.2718756ExpressionPRKN_HUMANLowHuman
PSAE_SYNP2_Tsuboyama_2023_1PSE0.1770.1650.1270.1330.1520.1520.1270.0700.1080.0950.3730.1580.1650.1200.1900.2660.2780.2090.1960.1330.1200.0570.1200.1390.1460.1520.0440.0890.1010.1330.1460.1330.1080.1200.0890.1270.1900.1770.1390.1520.1650.1460.1650.1390.2030.1390.2780.2590.3160.2910.3100.2410.2590.2220.2590.2340.2660.2590.2220.2660.2090.2721579StabilityPSAE_PICP2MediumProkaryote
PTEN_HUMAN_Matreyek_20210.1570.1830.1710.1650.1530.1630.1020.1590.1810.2000.1850.1910.2120.1040.1360.1570.1830.1470.1690.1810.1100.2040.1910.2040.1510.1690.1730.1810.1790.2080.1670.1650.1180.1200.2080.1830.1450.2000.1940.1650.1930.1850.1180.0940.1940.1490.1730.2040.1590.1300.2000.1980.1930.1790.1890.1810.1790.1810.1930.2000.1940.1695083ExpressionPTEN_HUMANMediumHuman
PTEN_HUMAN_Mighell_20180.2080.1600.1790.1850.1750.1750.1160.1390.1530.1500.1320.1380.1390.1230.1690.1870.1600.1610.1690.1560.1250.1320.1720.1530.1500.1710.1570.1610.1600.1530.1610.1380.1070.1460.1350.1520.1910.1470.1750.1750.1530.1740.1600.1100.1360.1980.1490.1350.1790.1570.1500.1560.1610.1570.1470.1600.1600.1500.1600.1500.1960.2077260ActivityPTEN_HUMANMediumHuman
Q2N0S5_9HIV1_Haddox_20180.2400.2110.1890.1900.2110.2140.0790.1890.2250.2290.2180.2330.2360.0880.0820.0860.1100.1040.1230.2080.2380.1910.1840.1630.2360.1920.1850.1850.1780.2320.1950.1960.1620.2320.2040.2070.2340.2290.2300.2190.2150.2150.2020.0700.2220.2200.1650.2370.1090.1490.1250.1400.1520.1480.1390.1510.1340.1410.1220.1470.1230.10812729OrganismalFitnessQ2N0S5_9HIV1MediumVirus
Q53Z42_HUMAN_McShan_2019_binding-TAPBPR0.1500.1680.1710.1650.1680.1680.1120.1000.1790.1740.1530.1650.1790.1470.1760.1740.1680.1880.1590.1650.1350.0970.0970.0940.1790.1710.1500.1440.1210.1150.1260.1150.1440.1210.0880.0790.1350.1260.1000.1680.1620.1470.1680.1240.1760.1500.2000.1910.1530.1470.1410.1350.1650.1500.1620.1560.1560.1530.1440.1470.1970.1853344BindingQ53Z42_HUMANMediumHuman
Q53Z42_HUMAN_McShan_2019_expression0.2890.2830.3010.2950.3100.3010.0950.1700.2800.2800.2860.2620.2920.1400.1580.2590.2830.2980.2920.2890.2290.2080.1900.1850.3120.3070.2650.3010.2650.1820.2350.1990.1960.2260.1760.1760.2770.2530.2050.3010.2980.2800.1880.1400.2980.2560.2980.3100.2500.1610.2620.2620.2920.2950.3010.2860.2800.3100.2890.2800.3040.3573344ExpressionQ53Z42_HUMANMediumHuman
Q59976_STRSQ_Romero_20150.1930.2500.2430.2530.2700.2670.1600.2430.2700.2700.2300.1770.2070.1430.1800.2000.2270.2330.2330.2670.2500.2870.2870.2970.2700.3100.3130.2870.2830.2770.2270.1830.1500.2430.2830.2970.2170.2500.2730.2730.2730.2770.2130.1300.2430.2430.2070.2300.2230.1470.2000.2030.2270.2000.2200.2000.2230.2200.2170.2130.2570.2202999ActivityQ59976_STRSQMediumProkaryote
Q6WV13_9MAXI_Somermeyer_20220.1360.1450.1110.1100.1270.1240.0800.0970.1320.1320.1000.0920.0790.0740.0780.0720.0700.0720.0720.1190.0880.1090.0820.1160.0930.0700.0740.0620.0770.1490.1170.1170.0870.0950.0890.1150.1120.1170.1120.1230.1280.1250.0990.0790.0840.0920.1860.1520.1800.1480.1310.1380.1140.1490.1440.1450.1410.1330.1350.1410.1100.10231401ActivityQ6WV12_9MAXILowEukaryote
Q837P4_ENTFA_Meier_20230.2000.2290.2140.2000.2570.2570.0860.1290.2430.2710.2570.2290.3000.1290.1860.1860.2140.2860.2710.1000.2710.2570.2570.3570.2860.2710.2860.3000.3000.2710.2860.2570.1000.3290.2710.3000.3140.2710.2860.2570.2290.2430.1860.0710.2430.2000.1290.2290.2000.0860.3000.2430.2430.2710.2290.2000.2710.2710.2710.2860.3000.214697ActivityQ837P4_ENTFAMediumProkaryote
Q837P5_ENTFA_Meier_20230.0670.2930.2670.2800.3070.3070.1200.2000.1330.1330.2530.2670.2400.0670.1200.1200.1870.2670.2270.1470.2000.2000.2930.2270.1870.2270.2930.2670.3330.2000.2400.2800.0930.2400.2000.2800.2400.2000.2670.2400.2400.2400.0930.0930.2130.1600.1200.2000.2270.1330.2000.2270.2270.2000.2130.1730.2270.2130.2400.2000.2130.227747ActivityQ837P5_ENTFAMediumProkaryote
Q8WTC7_9CNID_Somermeyer_20220.1830.2070.1730.1790.1890.1890.0970.1500.1950.1990.1240.1070.1070.0940.1010.1040.0950.1150.1100.1410.1080.1100.0930.1100.0970.1080.1160.1850.1890.1980.1200.1230.0900.1040.1010.1910.1790.1800.1950.1920.1910.2060.1080.1130.1170.1120.1840.1790.2010.1630.1410.1380.1440.1480.1440.1520.1400.1580.1410.1500.1320.11933510ActivityQ8WTC7_9CNIDLowEukaryote
R1AB_SARS2_Flynn_20220.1690.1710.1480.1610.1660.1610.0770.1200.0590.0590.0860.0580.0560.0680.0700.0730.0840.1470.1470.1170.1380.1500.1450.1500.1340.1310.1290.1190.1430.1470.1190.1190.1130.1120.1130.1500.1610.1550.1640.1640.1640.1680.0580.0560.0790.0700.1540.1220.1690.1470.0980.1220.1190.1120.1130.1030.0920.0920.1050.0960.1060.0775725OrganismalFitnessR1AB_SARS2MediumVirus
RAD_ANTMA_Tsuboyama_2023_2CJJ0.3150.1630.1300.1850.1300.1850.2280.1630.1960.2170.1300.0760.1090.2830.3150.2610.2170.2390.1410.1740.3370.1300.1200.1740.1520.1630.1520.0980.0980.2390.1630.1300.2070.1300.1740.2070.2930.2720.2500.1850.1960.1960.2280.2830.1300.2070.2170.1410.2500.2500.1850.2830.2280.2070.2500.2390.2390.2610.2610.2500.0980.261912StabilityRAD_ANTMAHighEukaryote
RAF1_HUMAN_Zinkus-Boltz_20190.1330.1670.1330.1670.2000.2000.0330.2000.1670.1670.2000.1330.2000.1000.1670.1330.1330.1670.1670.1670.1330.2000.1670.1330.2000.2000.2000.2000.1330.1670.1670.1670.0670.1670.2000.1330.1330.1670.1330.1670.1330.1330.1330.0670.1670.1330.0670.2000.0670.0670.1670.1330.2000.1330.1000.1330.1330.1000.1330.1330.1000.067297OrganismalFitnessRAF1_HUMANLowHuman
RASH_HUMAN_Bandaru_20170.1660.0920.0860.0920.0800.0890.0730.0570.0920.0860.0860.0760.0610.1620.1820.1560.1110.0990.1050.1270.0960.0990.0700.0800.0860.0800.0800.0670.0670.1080.0540.0730.0640.1150.0860.0800.1240.1050.1110.0920.0890.0890.1340.2290.1150.1150.1910.1110.1210.1940.1180.1150.1270.1080.1110.1180.1150.1150.0890.1180.0830.1533134ActivityRASH_HUMANHighHuman
RASK_HUMAN_Weng_2022_abundance0.1740.1530.1670.1740.1670.1750.1070.1560.1710.1860.1570.1540.1570.2150.1870.1970.1740.1690.1760.1710.1350.1540.1600.1690.1540.1730.1680.1630.1770.1320.1530.1330.1090.1320.1590.1690.1430.1710.1870.1730.1760.1790.1830.2030.1240.1720.1650.1460.2000.1620.1650.1600.1610.1620.1690.1600.1710.1640.1620.1680.1770.18826012ExpressionRASK_HUMANHighHuman
RASK_HUMAN_Weng_2022_binding-DARPin_K550.2350.2580.2740.2710.2670.2700.0700.2010.2890.3050.2460.2530.2600.2790.2820.2860.2930.2670.2510.2720.2380.2610.2580.2490.2790.2740.2520.2460.2260.2420.2300.2090.1760.2400.2670.2560.2290.2620.2580.2720.2750.2730.2560.1650.2710.2670.1480.2190.2910.1900.2380.2250.2300.2300.2370.2280.2370.2330.2320.2340.2680.24224873BindingRASK_HUMANHighHuman
RBP1_HUMAN_Tsuboyama_2023_2KWH0.2390.1570.2390.2390.2390.2390.2840.1120.2090.2090.3510.3430.3510.3280.3430.3510.3660.1570.1870.2540.3280.0970.1640.0820.3430.2010.1940.1040.1040.1570.1420.1040.1040.2990.3510.2910.2690.2460.2390.2610.2460.2240.3580.3510.3430.3430.3210.3210.3280.2840.3360.2910.3060.2990.3280.3210.2910.3060.3130.3280.3210.3211332StabilityRBP1_HUMANHighHuman
RCD1_ARATH_Tsuboyama_2023_5OAO0.1810.1420.1650.1650.1500.1570.1650.0870.1420.1420.2830.2600.2760.2520.3070.2520.2050.1570.1970.0940.2280.1570.1810.2050.1570.2050.2130.1810.1970.1340.1100.0470.0710.2280.1650.2440.2130.2280.2200.1890.1730.1730.2360.2600.2200.2200.2280.2600.2280.2760.1890.2130.1810.1890.1890.1890.2130.1730.1810.1890.1730.2761261StabilityRCD1_ARATHMediumEukaryote
RCRO_LAMBD_Tsuboyama_2023_1ORC0.1970.2720.2980.3510.3460.3110.2370.1750.2810.3110.3030.2720.3640.2500.3070.3110.2890.2810.2760.2630.1800.2500.2280.2500.1450.3030.1750.2500.2940.2890.2810.2540.1840.2190.1400.2540.2630.2460.2720.2980.3030.2810.2540.3110.3160.2630.5390.4040.4690.4690.3820.3990.3770.4080.3990.3770.3900.3460.3510.3860.4650.1582278StabilityRCRO_LAMBDHighVirus
RD23A_HUMAN_Tsuboyama_2023_1IFY0.4410.3040.3040.3240.3040.2940.3040.1960.2450.2350.2160.2450.2550.2750.3820.2750.2250.1960.1860.2840.1470.2550.1960.2160.2650.2350.2160.2450.1760.1960.2350.2060.2940.1370.2450.2160.3530.3530.2940.3330.3240.2940.2550.2450.2450.2840.3820.3140.4410.4220.2160.2450.2550.2550.2840.2650.2750.2450.2550.2550.2350.3921019StabilityRD23A_HUMANHighHuman
RDRP_I33A0_Li_20230.1840.2590.3860.3950.3960.3920.0710.3460.3560.3530.1170.0960.1070.1020.0960.1080.2140.3010.3380.4050.3270.3660.3770.3970.1310.3320.3140.3060.3560.4160.3450.3350.1220.3390.3600.3980.3510.3760.3860.4120.4150.4130.1020.0920.1320.1060.1380.1820.0980.1360.2240.2180.2320.2210.2210.2190.2200.2380.2290.2260.1190.11112003OrganismalFitnessRDRP_I33A0LowVirus
REV_HV1H2_Fernandes_20160.1300.1350.1440.1350.1490.1440.1120.1400.1350.1260.0840.1440.1260.1070.0930.0650.1300.1070.1070.1120.1160.0980.0930.1300.1020.1490.0980.1260.1300.1350.1350.1260.1160.1300.1400.1120.1440.1530.1210.1530.1440.1400.1020.0880.1120.1020.0700.0980.1160.1260.1350.1300.1260.0980.1160.0930.1120.1160.1020.1120.0790.1212147OrganismalFitnessREV_HV1H2MediumVirus
RFAH_ECOLI_Tsuboyama_2023_2LCL0.2330.2480.1800.1730.1950.1650.1650.0750.1050.1730.2480.1730.2260.1800.1580.2110.2410.1200.0680.0900.1430.1650.1800.1580.2560.1580.1950.1500.0830.0830.1350.1050.0530.2030.0830.0680.2110.0900.1580.1950.1350.1730.2260.1800.2330.2410.2410.1730.3010.2860.1880.1880.1950.1800.1880.1880.2410.1880.1950.1950.2780.2261326StabilityRFAH_ECOLIHighProkaryote
RL20_AQUAE_Tsuboyama_2023_1GYZ0.0950.1840.1290.1360.1290.1290.1630.1220.1840.1630.2520.2040.2380.1900.1840.1900.1900.1360.1290.1220.1160.1560.1220.1500.1020.1220.1290.1500.1430.0480.1290.1090.0880.1360.1560.1090.1430.1630.1430.1430.1430.1360.2240.1500.1500.2240.3330.1840.3610.3130.2040.2040.2110.1840.1770.1970.1770.1840.1840.1900.1770.2721461StabilityRL20_AQUAEHighProkaryote
RL40A_YEAST_Mavor_20160.1590.1670.1670.1900.1900.1830.0950.1430.1750.1900.1350.1900.1590.0950.1430.1900.1510.1510.2060.1900.1750.1350.1270.1110.1830.1670.1510.1900.1350.1590.1430.1670.0870.1190.1190.1190.1590.1670.1670.1830.1980.1670.1430.1430.1350.1110.1030.0560.1270.0400.1980.1670.1750.1670.1750.1510.1510.1830.1830.1830.1110.1351253OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20130.1170.1920.1750.2170.2080.2170.0170.1750.2170.2330.1500.1500.1920.0580.1420.2080.1830.1830.2330.1670.2000.1750.2080.1500.1250.1750.1670.1670.1920.2420.2420.2330.1750.1920.1500.1500.1830.1580.1580.1920.2170.2000.1830.1000.1920.1580.0420.1250.1080.0500.1250.1420.1170.1420.1330.1420.1250.1420.1750.1250.1920.1751195OrganismalFitnessRL40A_YEASTMediumEukaryote
RL40A_YEAST_Roscoe_20140.1230.0940.1160.1300.1160.1090.0720.1380.1010.1090.1590.1380.1230.1230.2170.1160.1520.1380.1380.1450.1740.1670.1380.1670.1880.1300.1010.1230.1230.1380.1090.1300.1230.1740.1300.1090.1380.1160.1160.1230.1300.1230.1300.1450.1450.1090.0870.1010.1010.0940.1450.1670.1520.1880.1740.1590.1380.1380.1450.1590.1380.1231380ActivityRL40A_YEASTMediumEukaryote
RNC_ECOLI_Weeks_20230.1470.1960.1170.1210.1290.1310.0770.1430.1380.1330.1470.1360.1540.1030.1470.1360.1520.1710.1730.1310.1890.1990.1610.1820.1820.1520.1680.1520.1820.1430.1730.1730.1190.1610.1730.1500.1500.1660.1680.1400.1360.1500.1730.0930.1380.1310.1520.1680.1310.1210.1430.1610.1520.1520.1540.1640.1470.1470.1640.1470.1660.1294277ActivityRNC_ECOLIMediumProkaryote
RPC1_BP434_Tsuboyama_2023_1R690.2740.2950.2330.2600.2470.2600.2810.1710.1850.1990.2950.2880.3220.3010.3150.3220.2670.2600.2950.2810.2190.3560.3360.2600.2740.3290.2470.2950.2470.2530.2470.2400.1780.1850.2950.3010.1990.2810.2880.3010.2810.2810.3290.2740.2600.2670.3560.3080.3010.3700.2740.2400.2740.2810.2670.2530.2470.2600.2470.2600.3220.3081459StabilityRPC1_BP434HighVirus
RPC1_LAMBD_Li_2019_high-expression0.1390.0830.1110.1110.1110.1110.1670.0280.1390.1390.0830.1390.1390.1940.1670.1940.1670.0830.1390.1940.1390.1390.2220.1390.1940.1390.1390.1670.1110.1940.0280.0000.0830.1390.1390.1110.1390.1390.1670.1390.1110.1110.1670.2220.1390.1390.1940.2500.1390.1670.1390.1110.1670.1390.1390.1670.1390.1670.1390.1670.2780.250351ActivityRPC1_LAMBDHighVirus
RPC1_LAMBD_Li_2019_low-expression0.1940.2500.2220.2500.2220.2220.1390.1110.3060.2500.1670.1940.2500.2220.1940.1940.2780.2220.1670.2220.1110.2500.2500.1670.0830.1940.2220.1940.2220.2220.1940.1670.1390.2220.2500.1940.1940.1940.1670.2220.2220.2220.1670.1940.1940.2220.1110.2500.2220.0560.2500.2500.1940.2500.2780.2500.2780.2500.2500.2780.1940.111351ActivityRPC1_LAMBDHighVirus
RS15_GEOSE_Tsuboyama_2023_1A320.1420.0830.1080.1170.1080.1170.0920.0830.1080.0920.1080.1000.0830.1500.2080.1920.0920.1250.1420.0670.1080.1080.0750.0830.1330.0830.1250.1000.1170.0500.0750.0420.1830.1170.0830.1000.1330.0920.1000.1170.0830.0830.1920.1500.1250.2170.2750.1500.1580.2920.1170.1170.1080.1420.1170.0920.1080.1080.1250.1250.1500.3251195StabilityRS15_GEOSEMediumProkaryote
S22A1_HUMAN_Yee_2023_abundance0.1910.2440.2620.2760.2560.2550.1770.1930.2620.2610.2570.2620.2720.1930.2040.2380.2880.2690.2550.1090.2420.2670.2380.2290.2340.2500.2480.2660.2270.2540.2660.2060.1300.2230.2620.2540.2530.2880.2720.2690.2650.2690.2160.1510.2720.2210.2140.2880.2200.1490.2760.2680.2880.2910.2860.2800.2840.2760.2790.2900.2690.2319803ExpressionS22A1_HUMANMediumHuman
S22A1_HUMAN_Yee_2023_activity0.1670.1900.2140.2130.2100.2160.1330.1700.2030.2110.1970.2150.1980.1680.1520.1710.2040.2080.1800.0940.1920.1990.1910.1760.1870.1930.1850.2010.1660.1820.1910.1590.1400.1680.2050.2050.1710.2120.2150.2170.2120.2180.1640.1270.2070.1570.1960.2320.1930.1460.2060.1950.2140.2160.2120.2080.2070.2170.2010.2110.2060.18410094ActivityS22A1_HUMANMediumHuman
SAV1_MOUSE_Tsuboyama_2023_2YSB0.4540.3810.4230.4330.4640.4740.3090.1030.4540.4740.2580.2680.2580.1960.4230.4120.2580.2780.1750.2060.1030.1030.0720.0930.2680.1340.1030.0930.1030.0620.2160.2060.1440.2160.1960.1650.4020.4120.3920.4330.4430.4330.3610.2160.3400.3200.3810.3710.3920.4120.3510.2890.3610.2680.3710.3610.3090.3510.3300.3510.2270.412965StabilitySAV1_MOUSEHighEukaryote
SBI_STAAM_Tsuboyama_2023_2JVG0.2040.1940.2230.2140.2330.2140.1940.1070.2140.2820.2620.1840.2040.1550.1750.2230.3200.2720.2230.1260.1940.2040.1840.1650.1750.1840.2040.1650.1840.1650.2910.1840.1170.1460.1460.1260.1750.1940.2040.2140.2230.2230.1840.2230.2140.1840.3110.3300.3110.3400.2910.3110.3400.3200.3110.2820.2720.3590.3590.3400.3010.2721025StabilitySBI_STAAMMediumProkaryote
SC6A4_HUMAN_Young_20210.1640.2200.2010.2020.2180.2040.1590.2980.2170.2380.2420.2120.2220.1060.1350.1770.1960.2130.2170.2620.2440.3060.3040.2860.2510.2940.2850.2730.2690.2260.1950.1390.1870.2570.2940.2890.2150.2410.2510.2090.2210.2170.2050.0960.2470.2130.2320.2660.2190.1420.1930.2060.2210.2190.2110.2150.2100.1990.1870.2010.2210.20511576ActivitySC6A4_HUMANMediumHuman
SCIN_STAAR_Tsuboyama_2023_2QFF0.1560.1390.1230.1390.1310.1390.2380.0740.2300.1890.2460.2460.2540.1720.2460.2790.3280.2620.3030.0980.1640.1890.1970.2300.2700.2540.2790.2540.2380.1310.0980.0820.1480.1150.1230.2130.1640.1720.1720.1230.1480.1560.2540.2380.2620.2700.2870.3030.2210.1970.3280.3360.3360.3520.3200.4020.3110.3850.2700.3520.3690.3931212StabilitySCIN_STAARHighProkaryote
SCN5A_HUMAN_Glazer_20190.1300.0870.1300.1300.1300.1300.2170.1740.1740.1740.1300.1300.1740.1740.1300.0870.1300.1300.1740.1740.1300.1740.1300.1300.1300.1300.1300.0870.1300.1300.0870.1740.1740.0870.1300.1740.0430.1300.1740.0870.1300.1300.1740.1740.1740.0870.0430.0430.1300.0430.1740.1740.1740.1740.1740.1740.1740.1740.1740.1740.0430.130224OrganismalFitnessSCN5A_HUMANMediumHuman
SDA_BACSU_Tsuboyama_2023_1PV00.5310.5200.5740.5630.5880.5960.1300.4580.5520.5780.5380.4950.5160.2090.2640.5230.5380.5670.5450.5310.1410.1810.1910.2740.1910.4660.1010.3470.5200.5920.5090.4690.0140.1160.2640.4620.5420.5420.5230.5780.5850.5740.2240.1840.5200.2640.5340.5520.5920.5630.5630.5670.5700.5850.5700.5700.5920.5780.5960.5920.5920.5852770StabilitySDA_BACSUMediumProkaryote
SERC_HUMAN_Xie_20230.1300.1560.1350.1460.1410.1560.0890.1460.1350.1300.1350.1560.1200.0730.1200.1410.1200.1460.1250.1350.1300.1300.1510.1410.1090.1090.1300.1300.1560.1510.1300.1610.1150.1350.1670.1560.1460.1350.1510.1460.1410.1410.1250.0730.1250.1200.1350.1250.1410.1410.1410.1250.1300.1350.1250.1090.1200.1410.1200.1250.1510.1351914OrganismalFitnessSERC_HUMANHighHuman
SHOC2_HUMAN_Kwon_20220.1400.1480.1560.1560.1560.1540.1230.1430.1600.1500.1390.1450.1470.1240.1200.1170.1380.1270.1170.1440.1240.1250.1380.1230.1090.1270.1340.1410.1370.1430.1500.1530.1110.1200.1420.1440.1320.1480.1360.1490.1530.1570.1270.1260.1380.1190.1390.1390.1190.1190.1430.1510.1660.1460.1520.1580.1540.1420.1380.1480.1240.12310972OrganismalFitnessSHOC2_HUMANMediumHuman
SOX30_HUMAN_Tsuboyama_2023_7JJK0.1680.1490.0890.1090.1190.1290.1390.0890.0890.1390.1390.1580.1290.1780.1490.2080.1880.1780.1090.0990.0890.0500.0990.0690.0990.0890.0790.0990.1190.0990.0990.0890.0990.0990.0500.0790.1490.0990.0990.1490.1190.1090.1490.0990.1090.1780.1390.1390.1390.1780.1780.2180.2280.2080.1980.2080.1780.1880.1780.2180.1780.1581010StabilitySOX30_HUMANHighHuman
SPA_STAAU_Tsuboyama_2023_1LP10.3270.3840.3460.3460.3600.3510.0850.2750.2510.2460.2890.0950.1000.0760.1040.0710.1520.1710.1420.3270.0280.1180.3080.1560.0810.0570.1090.3030.3220.2180.2370.2040.0430.0900.0850.1230.3320.3360.3320.3700.3790.3790.0850.1000.1850.1660.2800.2650.3700.3550.3410.3840.3890.4170.3510.3790.3600.3980.3510.3930.3700.3182105StabilitySPA_STAAUMediumProkaryote
SPG1_STRSG_Olson_20140.2080.2140.0940.0980.2040.2060.0890.0810.1080.1910.2210.2000.1990.2370.2120.2040.2050.2030.2170.1700.2210.2060.2040.2130.2120.2050.2150.2140.2200.2190.2390.2390.1410.1930.1870.1860.2030.1980.1950.2040.1990.2010.0730.0870.1010.0890.2010.1890.2160.1550.2470.1990.2180.2150.2130.2090.2260.2250.2240.2270.2020.209536962BindingSPG1_STRSGLowProkaryote
SPG1_STRSG_Wu_20160.1350.1970.1870.2060.1990.2020.1740.1680.2610.2460.2790.2830.2670.2590.2720.2830.3060.3110.3200.2740.2220.2120.2220.2310.2020.2410.2400.2290.2290.1950.3330.3230.1370.2320.2000.2430.1850.1780.2080.2160.2140.2280.1860.1580.2210.1650.2480.2390.3340.2270.3380.3400.3440.3480.3400.3380.3610.3490.3540.3470.3400.305149360BindingSPG1_STRSGMediumProkaryote
SPG2_STRSG_Tsuboyama_2023_5UBS0.1510.0960.1300.1100.1440.1300.2190.1510.1370.2050.2330.2400.2050.1710.2190.1990.2260.2740.2330.1920.1850.2530.1990.1440.1230.2050.1780.2600.0820.2120.1440.1030.0210.1640.3010.1370.2120.2330.1710.1640.1580.1580.2050.1580.1990.1780.2050.2120.2120.1850.1850.1920.2600.2120.2190.2330.2190.1920.1850.2120.2880.2051451StabilitySPG2_STRSGMediumProkaryote
SPIKE_SARS2_Starr_2020_binding0.1550.2270.1810.1960.2080.2130.0990.2270.2290.2320.1040.1110.1140.1090.1260.1160.1110.1110.0970.2290.2050.2200.2220.2100.2100.2150.2000.2150.1910.2270.2130.2130.1590.2080.2200.2270.2200.2170.2220.2320.2340.2320.1010.1060.1090.1160.2900.2270.1960.1590.1550.2000.2170.2030.1980.2080.1840.1760.1670.2000.2150.1503802BindingSPIKE_SARS2MediumVirus
SPIKE_SARS2_Starr_2020_expression0.1410.2340.1390.1930.2190.2240.0980.1950.2160.2440.0950.1080.1080.1030.1340.1080.1110.0800.0720.2290.1670.1800.1620.1620.1980.1720.1590.1800.1670.2370.2010.2010.1290.1720.1750.2110.1650.1850.2030.2110.2260.2260.1030.0900.1290.1180.3570.3650.3260.1650.2370.2780.2830.2700.3030.2750.2830.2540.2830.2930.3320.2083798ExpressionSPIKE_SARS2MediumVirus
SPTN1_CHICK_Tsuboyama_2023_1TUD0.3990.4860.3960.3740.4330.3960.2310.3180.3710.3680.4950.3860.4490.0650.4170.4360.3860.5170.3990.4270.3800.4520.3800.3930.3990.4670.4210.4490.4700.4270.4700.4860.1000.3740.3300.3460.4110.4330.3960.3960.3960.3990.2830.1250.3460.3120.3740.3830.5670.5580.5580.5480.5580.5390.5580.5480.5510.5670.5360.5670.5330.3493201StabilitySPTN1_CHICKHighEukaryote
SQSTM_MOUSE_Tsuboyama_2023_2RRU0.2960.3240.4230.4370.4370.4230.0420.2390.3240.3800.3100.1550.2960.1550.2540.2820.3520.3240.3520.3380.1410.2960.2110.2960.3240.3100.2680.2680.3100.2680.2390.2540.1690.1830.2540.3240.3380.4230.3940.4510.4230.4080.1830.1690.2820.3800.4080.2820.3660.4080.3660.3660.3520.3380.2960.3800.3240.2960.2960.3520.2960.324707StabilitySQSTM_MOUSEMediumEukaryote
SR43C_ARATH_Tsuboyama_2023_2N880.3080.3080.3020.2830.2960.3020.0570.1570.2520.2640.3770.3270.3400.0380.3460.3710.3840.4090.4090.3080.0440.1890.2200.1700.2450.2330.2770.2960.3020.1890.3270.2640.1260.1890.1320.2080.3520.2890.2960.3210.3140.2960.2770.0570.3580.3330.2330.3840.4280.4530.3460.3400.3580.3900.3520.3270.3650.3900.3650.3580.4400.3651583StabilitySR43C_ARATHHighEukaryote
SRBS1_HUMAN_Tsuboyama_2023_2O2W0.2310.1790.3780.3330.2560.3010.2180.2370.2760.3140.3080.2880.3010.1790.3330.3010.2630.2880.3330.1990.2690.2440.2690.2050.2310.2370.2630.1920.2120.3010.1860.1670.2120.2370.2310.2690.2560.2560.2760.3080.3210.2950.2310.0830.2690.2880.3010.2180.3780.3530.3270.3270.3140.3080.3140.3330.3210.3270.2950.3330.2370.3271556StabilitySRBS1_HUMANHighHuman
SRC_HUMAN_Ahler_20190.1210.1120.0860.1010.0890.0890.1300.0740.1010.0980.0830.1010.0920.1510.1830.1300.1150.1240.0890.0710.0830.0560.0740.0620.0770.0800.0770.0710.0650.1150.0740.0920.1210.0800.0710.0830.0860.0830.1090.0860.0890.0890.1480.1570.0680.1090.0980.0830.0800.0950.1330.1480.1120.1330.1390.1040.1070.1330.1300.1240.0860.1633372ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM0.1100.0910.0960.1020.1020.0820.1180.0690.0550.0770.0800.0910.0910.1370.1320.1040.0880.0990.0910.0600.0520.0380.0740.0490.0580.0600.0770.0490.0630.1240.0660.0850.1210.0660.0580.0600.0740.0740.0770.0820.0690.0770.1070.1180.0520.0930.0960.0740.0660.0880.1070.1150.0930.1130.1210.0910.0990.1240.1040.1070.0770.1373637ActivitySRC_HUMANMediumHuman
SRC_HUMAN_Nguyen_20220.1070.0830.0950.0800.0650.0650.0920.0800.0890.0770.0740.0860.0860.1310.1310.1040.1010.1070.0860.0800.1040.0830.1010.0740.0920.0860.0890.0770.0650.1070.0710.0980.1010.0890.0770.0830.0830.0860.0890.0830.0710.0770.1220.1190.0800.0890.1100.0830.0770.0920.1010.1160.0950.0950.1040.0860.0800.1010.0950.0980.0920.1313366OrganismalFitnessSRC_HUMANMediumHuman
SUMO1_HUMAN_Weile_20170.1180.1410.1240.1410.1120.1180.0820.1650.1650.1470.2290.1880.2060.0710.1410.1530.1820.1530.1590.1760.1180.1590.1530.1760.1820.1760.1470.2000.1880.3290.1940.2000.0880.0880.1760.1760.1240.1350.1880.1290.1290.1410.1240.0820.1710.1290.1290.1820.1880.1410.1530.1530.1590.1650.1710.1650.1650.1760.1760.1760.1940.1061700OrganismalFitnessSUMO1_HUMANHighHuman
SYUA_HUMAN_Newberry_20200.0640.0320.0360.0240.0360.0240.0800.0440.0520.0360.0280.0400.0240.0360.0600.0440.0320.0320.0320.0400.0560.0440.0200.0400.0480.0280.0320.0120.0360.0600.0440.0600.0320.0280.0360.0360.0200.0320.0240.0280.0280.0200.0520.0640.0240.0440.0640.0240.0400.0760.0360.0240.0320.0160.0360.0400.0480.0360.0440.0280.0520.0482497OrganismalFitnessSYUA_HUMANMediumHuman
TADBP_HUMAN_Bolognesi_20190.0080.0170.0080.0080.0080.0080.2670.0000.0080.0080.0670.0920.0420.1250.1330.1000.0500.0170.0080.0080.1580.0170.0080.0080.2170.0330.0000.0080.0080.0080.0000.0000.0170.2000.2670.0000.0500.0920.0080.0080.0170.0000.1830.2500.0670.0830.2000.0670.2080.1170.1420.1000.1000.1080.0920.1170.0580.0830.1000.1080.1420.1581196OrganismalFitnessTADBP_HUMANLowHuman
TAT_HV1BR_Fernandes_20160.1200.1390.1390.1390.1390.1390.0760.1330.1580.1710.1270.1520.1390.1140.1270.0820.0890.1080.1200.1580.1200.1520.1520.1580.1330.1460.1010.1330.1330.1580.0950.0950.1200.1270.1390.1460.1390.1270.1270.1460.1390.1460.0820.0630.1080.1140.1010.1080.1010.0890.0700.1010.1330.1330.1080.1010.0820.0820.1330.0950.0890.1141577OrganismalFitnessTAT_HV1BRHighVirus
TCRG1_MOUSE_Tsuboyama_2023_1E0L0.3960.3400.3770.3870.3490.3490.4430.0850.3680.3020.1040.2170.2260.3680.1700.2830.3580.1510.2260.1320.0940.0750.1040.1420.1230.0850.1040.1420.0570.0850.0750.0470.3490.1320.1600.2740.3020.3400.3400.3110.3400.3110.3300.1600.1700.2640.1890.1420.2740.3580.3110.2450.2080.3490.2830.3400.2920.3020.2450.2920.3580.2361058StabilityTCRG1_MOUSEMediumEukaryote
THO1_YEAST_Tsuboyama_2023_2WQG0.2970.2890.3520.3750.3910.3910.2110.2270.3120.3590.3120.3750.3520.1020.3520.3440.3360.3280.3590.3910.1640.2890.2270.1410.2730.3050.0860.2890.4220.3360.2810.2190.3520.1170.3360.2110.3670.4300.3520.3670.4220.3910.2810.2580.3830.2970.4220.4450.4450.5080.3750.3590.3980.3590.3830.3590.3670.3590.3440.3520.4060.3911279StabilityTHO1_YEASTHighEukaryote
TNKS2_HUMAN_Tsuboyama_2023_5JRT0.2970.2770.2430.2430.2160.2160.0340.1960.2970.3380.2500.2300.2500.0610.2700.2910.2570.2500.2430.1960.2090.2910.2570.2770.2160.2840.2030.2640.2300.2300.2970.2840.1420.1350.2090.2160.2770.2910.2700.2360.2300.2360.2500.1620.3510.2360.3450.3450.3720.4190.2230.2970.2700.2640.2700.2910.2970.2770.2840.2640.3380.3111479StabilityTNKS2_HUMANHighHuman
TPK1_HUMAN_Weile_20170.1350.1290.1320.1100.1220.1220.0970.1320.1350.1160.1410.1500.1190.0910.1290.1320.1250.1290.1440.1220.0940.1320.1070.1410.1130.1440.1250.1100.1350.1500.1290.1190.0880.0940.1440.1380.1290.1250.1380.1220.1190.1350.1030.0880.1160.0940.1470.1380.1410.1380.1320.1470.1290.1500.1440.1410.1410.1350.1350.1220.1410.1133181OrganismalFitnessTPK1_HUMANMediumHuman
TPMT_HUMAN_Matreyek_20180.1810.2190.2160.1970.1950.1970.1070.1890.1890.1920.2050.1970.2000.1320.1560.2050.2030.2000.1840.2080.1370.1640.2050.2050.1670.1860.2080.1920.2050.2160.2440.2000.1320.1290.2000.1750.1750.2050.1750.2050.2140.2080.1320.0900.2050.1730.1780.2380.2220.1730.1750.1730.1780.1750.1810.1780.1730.1970.1780.1780.1970.1813648ExpressionTPMT_HUMANMediumHuman
TPOR_HUMAN_Bridgford_20200.3680.2110.4040.2810.2280.2280.3510.2280.2810.2280.2980.2810.2980.2110.2630.2630.1930.2280.2460.1750.2460.2810.2280.2630.2980.1930.3510.3510.2980.1750.1750.1230.3860.2460.2630.2810.3680.3510.2630.2810.2980.2280.2280.2460.2110.2980.1050.2810.2980.1930.2630.2460.2630.2630.2810.2810.2810.2630.2810.2810.2810.368562OrganismalFitnessTPOR_HUMANLowHuman
TRPC_SACS2_Chan_20170.3820.5130.4610.4740.4800.4740.1250.4140.4800.4800.4540.4280.4340.1910.3290.4010.4800.5000.4470.4800.1780.3550.3880.4140.3950.3220.4010.4210.4870.5130.4740.3950.1580.3220.3550.3420.3750.4140.4080.4610.4540.4540.2370.1450.3880.2960.2830.4340.4540.1510.4280.4410.4280.4610.4210.4470.4210.4470.4410.4670.5130.3291519OrganismalFitnessTRPC_SACS2MediumProkaryote
TRPC_THEMA_Chan_20170.3030.3550.2760.3160.3420.3290.1970.2430.2890.3360.3160.3090.3360.2110.3160.3360.3680.3490.3360.3550.2300.2830.3160.3820.3290.3290.3160.3620.3680.2240.3360.2570.0920.2700.3490.3620.3090.3290.3220.3360.3420.3360.3160.0860.3490.3030.3090.3680.3160.1910.3360.3490.3290.3620.3420.3220.3420.3220.2890.3360.3820.3751519OrganismalFitnessTRPC_THEMAMediumProkaryote
UBC9_HUMAN_Weile_20170.1520.1870.1710.1710.1870.1870.0700.1250.1400.1560.1600.1440.1440.0740.0930.1440.1520.1600.1480.1600.1090.1210.1280.1090.1280.1440.1280.1360.1250.1440.0970.1010.0540.1090.1130.1280.1360.1480.1600.1670.1830.1750.1440.0860.1280.1710.1560.1600.1320.1440.1440.1630.1910.1600.1520.1750.1670.1480.1480.1600.1440.1322563OrganismalFitnessUBC9_HUMANMediumHuman
UBE4B_HUMAN_Tsuboyama_2023_3L1X0.2510.3860.4080.3990.4270.4240.1930.3330.3830.4300.4900.4330.4550.2290.4270.5100.4990.4770.4600.4020.1430.3800.3970.4300.3220.4080.4130.4050.4190.3860.4660.4050.3280.1320.1600.4460.3110.3110.4020.4350.4380.4270.2640.1710.2700.3110.3720.3250.5210.3420.5120.5450.5340.5340.5430.5100.5120.5290.5260.5260.4990.3883622StabilityUBE4B_HUMANHighHuman
UBE4B_MOUSE_Starita_20130.1560.1220.1220.1220.1000.1220.1220.1220.1330.1110.1110.1330.1110.0780.1220.1220.1220.1220.0890.0560.0890.0560.0890.1000.0780.0890.0890.1000.1000.1110.0560.0670.1000.1110.0780.0890.1560.1560.1560.1220.1220.1220.1000.1440.0670.1220.1330.0890.0440.1560.1560.1220.1330.1220.1330.1670.1440.1330.1330.1560.1670.156899ActivityUBE4B_MOUSELowEukaryote
UBR5_HUMAN_Tsuboyama_2023_1I2T0.1710.1920.1370.1510.1640.1640.1850.0680.1580.1440.1300.1850.2400.1920.1920.2050.0960.2050.1850.1300.1370.1580.1640.1780.1990.1300.1780.1580.1920.1780.1640.0890.1850.1850.1780.1920.1440.1640.1640.1640.1580.1920.2120.2120.2120.1640.2260.1850.1920.2530.1640.2190.2470.2400.2400.2120.2190.2260.2120.2330.2120.2401453StabilityUBR5_HUMANMediumHuman
VG08_BPP22_Tsuboyama_2023_2GP80.2880.1780.1640.1640.1920.2050.2330.1100.1780.2600.1780.2740.2470.2740.2190.1920.2470.1780.1230.1230.2330.2190.1100.1100.2050.0960.1510.1230.0820.2050.0960.0680.1230.1920.2600.0820.2330.3290.1640.2050.2050.1640.2050.2600.2190.1640.0820.0680.0960.2050.2050.1510.2330.2050.2330.2330.2050.2470.2740.2190.2600.192723StabilityVG08_BPP22HighVirus
VILI_CHICK_Tsuboyama_2023_1YU50.2490.4360.5020.5250.4860.5020.1250.2800.4440.4590.4320.4120.4440.1710.1170.4630.5530.4940.4010.4670.1090.2650.2840.2960.3460.3460.3310.2920.4280.4470.4630.4240.1870.1480.3460.3190.3460.4200.3850.4670.4860.4630.1870.2370.4790.3850.4280.4750.5410.3970.5410.5100.5450.5450.5140.5560.5530.5330.5250.5560.5560.4242568StabilityVILI_CHICKHighEukaryote
VKOR1_HUMAN_Chiasson_2020_abundance0.2520.1740.2150.2190.1960.1960.1370.1630.2110.2040.1670.1930.1810.1260.2150.2410.2040.2220.1780.2070.1810.1520.2480.2150.2110.1440.1670.1480.1370.2040.1670.1330.1260.1590.1960.1410.2300.2190.1850.1960.1890.2150.1370.1040.1960.2150.2040.1740.2000.1630.2110.1810.2190.2150.2220.2190.2070.1810.2150.2070.1560.2302695ExpressionVKOR1_HUMANMediumHuman
VKOR1_HUMAN_Chiasson_2020_activity0.1710.1710.1860.1710.1710.1860.0710.1570.1570.1710.1290.1430.1570.0710.1140.1570.1710.1430.1570.2000.1000.1000.1430.1140.1000.1140.1430.1290.0860.1710.1140.1430.1860.0570.0570.1000.1430.1570.1430.1570.1430.1570.0710.0570.1430.0860.1710.1860.1860.1430.1290.1570.1290.1710.2290.1570.1430.1430.1290.1570.1570.114697ActivityVKOR1_HUMANMediumHuman
VRPI_BPT7_Tsuboyama_2023_2WNM0.0480.1520.1430.1620.1330.1240.0760.0480.0950.0570.1810.1330.1330.1140.1330.1900.2290.2570.1620.0760.0860.1430.0950.1330.1520.1050.0860.1240.2480.1240.2000.1900.1140.1050.1620.1330.0760.0950.0950.1430.1330.1240.1520.1050.1810.1240.2760.2380.2100.2670.3330.2670.3330.3330.2950.3620.3520.3240.3430.3520.3810.2671047StabilityVRPI_BPT7MediumVirus
YAIA_ECOLI_Tsuboyama_2023_2KVT0.1800.2750.3020.3020.2960.2960.0690.2490.3600.3490.2650.1220.3120.0790.0950.3440.3920.4870.4390.3170.0480.0580.0480.2120.0630.0740.0530.0740.3600.3280.3070.2650.0530.0480.1160.3920.1800.2430.3330.2960.3120.3390.1640.0790.1800.1750.4390.3920.4340.3650.4290.4440.3810.4230.4290.4500.4390.4290.4020.4440.4390.2221890StabilityYAIA_ECOLIMediumProkaryote
YAP1_HUMAN_Araya_20120.2200.1550.2320.2310.2330.2280.1760.0730.0650.0570.1750.1350.1160.1810.2020.2250.2090.1910.1520.0930.0800.0680.0650.0860.0960.0600.0660.0890.0620.1360.1250.1360.1380.1330.0670.0920.1880.1270.1500.2090.1840.2040.2590.0340.1780.2630.1480.1820.1250.1620.1150.1230.1380.1510.1070.1300.1490.1470.1660.1370.2340.19510075BindingYAP1_HUMANLowHuman
YNZC_BACSU_Tsuboyama_2023_2JVD0.3740.3740.3780.3740.3780.3780.3830.3300.3910.4090.4570.4480.4570.4610.4740.4390.4830.4700.4300.3780.4350.4910.5090.5170.4780.4430.4780.4300.4220.4350.4220.3910.4390.4350.4520.4260.4260.3910.3830.4000.3870.3910.4220.3610.4220.4390.4390.4960.5480.5300.5300.4740.5040.5040.5130.5170.4960.5000.4910.4960.4570.5912300StabilityYNZC_BACSUMediumProkaryote
\ No newline at end of file diff --git a/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_Uniprot_Selection_Type_level.csv b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_Uniprot_Selection_Type_level.csv new file mode 100644 index 0000000..d32dcbd --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Top_recall/DMS_substitutions_Top_recall_Uniprot_Selection_Type_level.csv @@ -0,0 +1,7 @@ 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+0.143,0.238,0.333,0.19,0.238,0.238,0.048,0.143,0.19,0.19,0.238,0.19,0.19,0.095,0.0,0.143,0.19,0.143,0.095,0.19,0.048,0.095,0.286,0.19,0.048,0.19,0.19,0.19,0.19,0.19,0.19,0.238,0.095,0.095,0.095,0.143,0.238,0.19,0.238,0.238,0.238,0.19,0.048,0.048,0.19,0.048,0.333,0.143,0.143,0.333,0.143,0.333,0.143,0.286,0.143,0.143,0.143,0.143,0.143,0.143,0.048,0.048,Activity,AICDA_HUMAN,Medium,Human +0.289,0.372,0.372,0.369,0.359,0.379,0.07,0.336,0.352,0.369,0.433,0.101,0.319,0.057,0.124,0.409,0.386,0.409,0.413,0.366,0.057,0.117,0.346,0.342,0.114,0.339,0.342,0.265,0.383,0.336,0.383,0.346,0.326,0.057,0.094,0.114,0.339,0.336,0.346,0.383,0.379,0.383,0.141,0.134,0.396,0.315,0.359,0.356,0.416,0.406,0.406,0.409,0.399,0.386,0.403,0.396,0.379,0.386,0.403,0.396,0.43,0.235,Stability,AMFR_HUMAN,Medium,Human +0.143,0.114,0.13,0.124,0.127,0.119,0.103,0.159,0.119,0.141,0.181,0.144,0.128,0.152,0.167,0.197,0.193,0.233,0.127,0.165,0.188,0.175,0.157,0.13,0.151,0.151,0.148,0.169,0.103,0.104,0.13,0.133,0.074,0.188,0.136,0.112,0.127,0.12,0.106,0.124,0.122,0.124,0.159,0.146,0.193,0.17,0.185,0.226,0.207,0.143,0.188,0.194,0.18,0.175,0.159,0.154,0.165,0.169,0.165,0.17,0.234,0.161,Activity,AMIE_PSEAE,High,Prokaryote +0.214,0.208,0.208,0.206,0.206,0.199,0.156,0.176,0.154,0.154,0.156,0.171,0.165,0.195,0.231,0.251,0.244,0.225,0.212,0.086,0.169,0.128,0.146,0.148,0.173,0.15,0.141,0.137,0.113,0.191,0.139,0.126,0.139,0.165,0.126,0.137,0.193,0.167,0.169,0.203,0.186,0.188,0.206,0.169,0.152,0.188,0.173,0.158,0.188,0.133,0.251,0.242,0.214,0.225,0.225,0.236,0.238,0.257,0.233,0.24,0.208,0.236,Activity,ANCSZ,Medium,Eukaryote +0.209,0.194,0.248,0.24,0.24,0.217,0.155,0.132,0.171,0.202,0.233,0.178,0.233,0.194,0.233,0.287,0.264,0.24,0.202,0.178,0.217,0.132,0.14,0.14,0.217,0.116,0.155,0.147,0.147,0.163,0.109,0.062,0.031,0.178,0.132,0.132,0.194,0.217,0.186,0.248,0.209,0.194,0.194,0.155,0.233,0.248,0.372,0.31,0.302,0.279,0.24,0.279,0.264,0.264,0.256,0.256,0.233,0.256,0.271,0.256,0.295,0.31,Stability,ARGR_ECOLI,Medium,Prokaryote +0.294,0.235,0.353,0.294,0.294,0.294,0.176,0.294,0.294,0.294,0.059,0.059,0.471,0.235,0.235,0.235,0.353,0.294,0.294,0.294,0.176,0.059,0.176,0.235,0.059,0.235,0.294,0.118,0.235,0.235,0.235,0.235,0.176,0.294,0.118,0.176,0.353,0.353,0.294,0.353,0.353,0.294,0.235,0.235,0.412,0.176,0.176,0.353,0.235,0.059,0.235,0.294,0.235,0.235,0.176,0.235,0.235,0.294,0.235,0.235,0.353,0.176,Binding,B2L11_HUMAN,Low,Human +0.217,0.28,0.203,0.203,0.213,0.179,0.184,0.174,0.285,0.285,0.271,0.29,0.309,0.126,0.275,0.3,0.372,0.329,0.329,0.251,0.208,0.227,0.251,0.242,0.126,0.261,0.198,0.227,0.217,0.188,0.184,0.14,0.15,0.232,0.188,0.242,0.222,0.232,0.266,0.208,0.174,0.198,0.227,0.077,0.217,0.232,0.285,0.246,0.348,0.343,0.324,0.338,0.329,0.382,0.353,0.396,0.362,0.362,0.362,0.353,0.314,0.353,Stability,BBC1_YEAST,High,Eukaryote +0.253,0.247,0.228,0.241,0.234,0.247,0.127,0.184,0.196,0.278,0.241,0.297,0.304,0.133,0.158,0.228,0.335,0.266,0.31,0.272,0.12,0.215,0.272,0.31,0.241,0.291,0.234,0.297,0.278,0.253,0.215,0.177,0.095,0.177,0.228,0.259,0.253,0.272,0.272,0.241,0.241,0.253,0.19,0.158,0.266,0.234,0.297,0.342,0.386,0.323,0.386,0.361,0.342,0.373,0.354,0.354,0.361,0.361,0.31,0.361,0.361,0.222,Stability,BCHB_CHLTE,Medium,Prokaryote +0.2,0.324,0.328,0.324,0.342,0.345,0.109,0.18,0.252,0.309,0.283,0.272,0.298,0.172,0.218,0.255,0.284,0.265,0.236,0.273,0.254,0.247,0.247,0.224,0.246,0.299,0.285,0.282,0.209,0.257,0.304,0.299,0.109,0.248,0.268,0.236,0.284,0.302,0.29,0.34,0.351,0.355,0.187,0.091,0.298,0.223,0.291,0.312,0.314,0.194,0.311,0.307,0.306,0.325,0.32,0.318,0.322,0.31,0.319,0.324,0.317,0.247,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.2,0.324,0.328,0.324,0.342,0.345,0.109,0.18,0.252,0.309,0.283,0.272,0.298,0.172,0.218,0.255,0.284,0.265,0.236,0.273,0.254,0.247,0.247,0.224,0.246,0.299,0.285,0.282,0.209,0.257,0.304,0.299,0.109,0.248,0.268,0.236,0.284,0.302,0.29,0.34,0.351,0.355,0.187,0.091,0.298,0.223,0.291,0.312,0.314,0.194,0.311,0.307,0.306,0.325,0.32,0.318,0.322,0.31,0.319,0.324,0.317,0.247,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.2,0.324,0.328,0.324,0.342,0.345,0.109,0.18,0.252,0.309,0.283,0.272,0.298,0.172,0.218,0.255,0.284,0.265,0.236,0.273,0.254,0.247,0.247,0.224,0.246,0.299,0.285,0.282,0.209,0.257,0.304,0.299,0.109,0.248,0.268,0.236,0.284,0.302,0.29,0.34,0.351,0.355,0.187,0.091,0.298,0.223,0.291,0.312,0.314,0.194,0.311,0.307,0.306,0.325,0.32,0.318,0.322,0.31,0.319,0.324,0.317,0.247,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.2,0.324,0.328,0.324,0.342,0.345,0.109,0.18,0.252,0.309,0.283,0.272,0.298,0.172,0.218,0.255,0.284,0.265,0.236,0.273,0.254,0.247,0.247,0.224,0.246,0.299,0.285,0.282,0.209,0.257,0.304,0.299,0.109,0.248,0.268,0.236,0.284,0.302,0.29,0.34,0.351,0.355,0.187,0.091,0.298,0.223,0.291,0.312,0.314,0.194,0.311,0.307,0.306,0.325,0.32,0.318,0.322,0.31,0.319,0.324,0.317,0.247,OrganismalFitness,BLAT_ECOLX,High,Prokaryote +0.223,0.196,0.234,0.245,0.217,0.239,0.136,0.136,0.201,0.207,0.168,0.152,0.163,0.152,0.12,0.147,0.168,0.212,0.13,0.158,0.136,0.212,0.223,0.158,0.207,0.228,0.239,0.185,0.179,0.196,0.212,0.19,0.12,0.13,0.147,0.179,0.207,0.217,0.223,0.255,0.25,0.212,0.141,0.168,0.228,0.179,0.185,0.228,0.13,0.141,0.174,0.185,0.168,0.163,0.163,0.152,0.158,0.179,0.163,0.163,0.147,0.168,OrganismalFitness,BRCA1_HUMAN,Low,Human +0.259,0.185,0.148,0.185,0.259,0.185,0.148,0.111,0.111,0.148,0.148,0.111,0.037,0.111,0.148,0.185,0.185,0.185,0.111,0.037,0.185,0.259,0.296,0.148,0.222,0.148,0.111,0.259,0.074,0.111,0.148,0.222,0.111,0.185,0.222,0.185,0.148,0.185,0.185,0.185,0.185,0.185,0.111,0.148,0.185,0.185,0.148,0.111,0.037,0.111,0.222,0.185,0.148,0.111,0.222,0.148,0.111,0.148,0.222,0.148,0.074,0.111,OrganismalFitness,BRCA2_HUMAN,,Human +0.255,0.295,0.288,0.283,0.302,0.305,0.088,0.247,0.298,0.301,0.098,0.27,0.331,0.089,0.092,0.097,0.25,0.271,0.305,0.251,0.262,0.254,0.266,0.242,0.143,0.264,0.273,0.294,0.242,0.3,0.271,0.225,0.125,0.243,0.269,0.257,0.28,0.287,0.285,0.302,0.309,0.304,0.097,0.092,0.172,0.099,0.236,0.294,0.276,0.177,0.262,0.261,0.269,0.267,0.262,0.246,0.259,0.265,0.253,0.266,0.181,0.157,OrganismalFitness,C6KNH7_9INFA,Medium,Virus +0.088,0.082,0.066,0.055,0.055,0.055,0.099,0.049,0.071,0.049,0.055,0.077,0.071,0.11,0.093,0.077,0.055,0.038,0.099,0.055,0.082,0.077,0.077,0.06,0.06,0.055,0.049,0.077,0.038,0.038,0.049,0.055,0.088,0.099,0.077,0.038,0.071,0.082,0.049,0.066,0.055,0.06,0.06,0.104,0.066,0.071,0.115,0.066,0.06,0.121,0.077,0.071,0.077,0.071,0.077,0.066,0.066,0.077,0.071,0.077,0.06,0.077,OrganismalFitness,CALM1_HUMAN,High,Human +0.263,0.258,0.246,0.279,0.258,0.262,0.174,0.265,0.233,0.254,0.136,0.121,0.129,0.131,0.147,0.134,0.165,0.148,0.112,0.174,0.122,0.129,0.133,0.153,0.119,0.122,0.154,0.126,0.18,0.272,0.13,0.12,0.098,0.133,0.142,0.263,0.23,0.231,0.275,0.253,0.255,0.273,0.128,0.163,0.19,0.147,0.176,0.188,0.145,0.156,0.162,0.145,0.133,0.145,0.125,0.133,0.142,0.14,0.175,0.147,0.152,0.118,OrganismalFitness,CAPSD_AAV2S,Low,Virus +0.069,0.073,0.071,0.069,0.071,0.071,0.214,0.048,0.081,0.081,0.069,0.124,0.1,0.122,0.106,0.176,0.102,0.107,0.094,0.108,0.119,0.064,0.096,0.076,0.05,0.074,0.084,0.064,0.109,0.054,0.073,0.06,0.082,0.159,0.08,0.065,0.076,0.076,0.06,0.078,0.08,0.076,0.12,0.092,0.09,0.167,0.098,0.077,0.134,0.124,0.159,0.128,0.132,0.124,0.142,0.13,0.144,0.138,0.13,0.128,0.188,0.18,OrganismalFitness,CAR11_HUMAN,Low,Human +0.069,0.073,0.071,0.069,0.071,0.071,0.214,0.048,0.081,0.081,0.069,0.124,0.1,0.122,0.106,0.176,0.102,0.107,0.094,0.108,0.119,0.064,0.096,0.076,0.05,0.074,0.084,0.064,0.109,0.054,0.073,0.06,0.082,0.159,0.08,0.065,0.076,0.076,0.06,0.078,0.08,0.076,0.12,0.092,0.09,0.167,0.098,0.077,0.134,0.124,0.159,0.128,0.132,0.124,0.142,0.13,0.144,0.138,0.13,0.128,0.188,0.18,OrganismalFitness,CAR11_HUMAN,Low,Human +0.096,0.112,0.111,0.105,0.111,0.112,0.096,0.106,0.116,0.117,0.11,0.096,0.106,0.097,0.103,0.126,0.106,0.117,0.11,0.097,0.099,0.096,0.117,0.116,0.1,0.112,0.096,0.111,0.101,0.105,0.117,0.117,0.111,0.091,0.099,0.128,0.111,0.116,0.123,0.101,0.101,0.117,0.095,0.091,0.111,0.099,0.099,0.111,0.103,0.094,0.118,0.107,0.115,0.102,0.111,0.111,0.099,0.112,0.1,0.11,0.127,0.115,Activity,CAS9_STRP1,Medium,Prokaryote +0.159,0.204,0.153,0.115,0.134,0.153,0.089,0.159,0.21,0.223,0.146,0.115,0.153,0.089,0.146,0.166,0.178,0.146,0.21,0.159,0.146,0.159,0.159,0.153,0.14,0.159,0.178,0.172,0.204,0.146,0.229,0.223,0.115,0.102,0.14,0.172,0.134,0.166,0.166,0.146,0.172,0.159,0.096,0.076,0.159,0.14,0.159,0.166,0.127,0.102,0.185,0.178,0.191,0.14,0.172,0.166,0.185,0.166,0.172,0.166,0.146,0.134,Activity,CASP3_HUMAN,High,Human +0.149,0.202,0.238,0.214,0.25,0.244,0.06,0.155,0.226,0.25,0.268,0.19,0.232,0.107,0.208,0.274,0.208,0.196,0.19,0.238,0.095,0.214,0.155,0.167,0.202,0.179,0.179,0.167,0.196,0.226,0.167,0.155,0.107,0.083,0.19,0.137,0.208,0.226,0.19,0.22,0.232,0.244,0.185,0.06,0.262,0.274,0.238,0.28,0.244,0.19,0.196,0.214,0.244,0.232,0.238,0.226,0.214,0.22,0.226,0.22,0.226,0.208,Activity,CASP7_HUMAN,Medium,Human +0.272,0.147,0.147,0.152,0.183,0.162,0.236,0.094,0.131,0.136,0.115,0.173,0.147,0.225,0.257,0.209,0.152,0.157,0.131,0.089,0.12,0.126,0.11,0.105,0.073,0.089,0.089,0.073,0.105,0.152,0.126,0.173,0.183,0.131,0.11,0.099,0.199,0.173,0.126,0.168,0.152,0.157,0.194,0.183,0.084,0.126,0.314,0.131,0.183,0.293,0.183,0.115,0.141,0.152,0.141,0.147,0.157,0.141,0.141,0.157,0.099,0.257,Stability,CATR_CHLRE,High,Eukaryote +0.227,0.159,0.227,0.227,0.237,0.237,0.222,0.184,0.174,0.198,0.304,0.242,0.256,0.227,0.246,0.251,0.222,0.227,0.246,0.237,0.174,0.193,0.261,0.251,0.29,0.261,0.285,0.246,0.213,0.184,0.184,0.14,0.155,0.237,0.29,0.285,0.227,0.242,0.246,0.261,0.237,0.261,0.29,0.261,0.275,0.29,0.295,0.309,0.242,0.285,0.179,0.242,0.246,0.217,0.237,0.232,0.184,0.203,0.203,0.232,0.237,0.309,Stability,CBPA2_HUMAN,Medium,Human +0.168,0.161,0.169,0.166,0.176,0.175,0.1,0.141,0.173,0.162,0.163,0.15,0.162,0.098,0.127,0.127,0.132,0.145,0.157,0.161,0.143,0.133,0.166,0.173,0.147,0.152,0.154,0.163,0.152,0.163,0.145,0.13,0.109,0.15,0.141,0.151,0.158,0.177,0.194,0.172,0.18,0.179,0.13,0.102,0.158,0.15,0.147,0.147,0.169,0.116,0.139,0.136,0.147,0.141,0.145,0.151,0.15,0.154,0.155,0.151,0.147,0.127,OrganismalFitness,CBS_HUMAN,Medium,Human +0.437,0.406,0.441,0.445,0.472,0.48,0.013,0.31,0.445,0.424,0.428,0.397,0.402,0.022,0.533,0.533,0.48,0.445,0.41,0.445,0.306,0.341,0.328,0.332,0.376,0.358,0.371,0.341,0.332,0.445,0.432,0.376,0.192,0.301,0.328,0.341,0.45,0.402,0.441,0.48,0.424,0.441,0.437,0.096,0.319,0.406,0.371,0.262,0.52,0.467,0.507,0.48,0.485,0.48,0.52,0.459,0.472,0.472,0.476,0.507,0.437,0.507,Stability,CBX4_HUMAN,High,Human +0.12,0.138,0.135,0.136,0.121,0.126,0.093,0.112,0.094,0.118,0.13,0.084,0.096,0.097,0.105,0.104,0.138,0.118,0.134,0.113,0.078,0.068,0.075,0.103,0.051,0.102,0.088,0.124,0.118,0.112,0.162,0.145,0.088,0.072,0.086,0.124,0.118,0.122,0.138,0.128,0.134,0.135,0.096,0.082,0.116,0.11,0.086,0.138,0.124,0.103,0.148,0.118,0.144,0.166,0.154,0.146,0.142,0.147,0.132,0.146,0.138,0.104,Activity,CCDB_ECOLI,High,Prokaryote +0.12,0.138,0.135,0.136,0.121,0.126,0.093,0.112,0.094,0.118,0.13,0.084,0.096,0.097,0.105,0.104,0.138,0.118,0.134,0.113,0.078,0.068,0.075,0.103,0.051,0.102,0.088,0.124,0.118,0.112,0.162,0.145,0.088,0.072,0.086,0.124,0.118,0.122,0.138,0.128,0.134,0.135,0.096,0.082,0.116,0.11,0.086,0.138,0.124,0.103,0.148,0.118,0.144,0.166,0.154,0.146,0.142,0.147,0.132,0.146,0.138,0.104,OrganismalFitness,CCDB_ECOLI,High,Prokaryote +0.146,0.143,0.156,0.164,0.156,0.14,0.114,0.231,0.164,0.165,0.169,0.146,0.154,0.124,0.135,0.148,0.141,0.154,0.138,0.17,0.13,0.196,0.167,0.161,0.157,0.196,0.18,0.196,0.178,0.178,0.141,0.125,0.119,0.167,0.175,0.167,0.156,0.159,0.169,0.144,0.148,0.148,0.135,0.079,0.183,0.138,0.133,0.177,0.14,0.132,0.148,0.148,0.143,0.128,0.127,0.138,0.135,0.144,0.125,0.135,0.172,0.151,Binding,CCR5_HUMAN,High,Human +0.167,0.127,0.164,0.162,0.146,0.138,0.143,0.122,0.103,0.109,0.18,0.167,0.196,0.186,0.228,0.175,0.167,0.183,0.138,0.09,0.194,0.162,0.207,0.162,0.149,0.225,0.159,0.204,0.149,0.162,0.13,0.101,0.127,0.241,0.252,0.167,0.191,0.18,0.156,0.164,0.154,0.151,0.215,0.183,0.218,0.212,0.34,0.321,0.324,0.138,0.228,0.191,0.202,0.215,0.218,0.218,0.236,0.21,0.228,0.218,0.353,0.302,Binding,CD19_HUMAN,Low,Human +0.267,0.302,0.306,0.319,0.334,0.332,0.275,0.296,0.294,0.33,0.29,0.328,0.344,0.251,0.324,0.326,0.343,0.33,0.316,0.345,0.316,0.28,0.304,0.279,0.322,0.301,0.302,0.29,0.295,0.295,0.246,0.205,0.114,0.326,0.32,0.306,0.336,0.328,0.33,0.34,0.336,0.331,0.329,0.134,0.309,0.333,0.295,0.304,0.331,0.178,0.339,0.321,0.328,0.334,0.332,0.341,0.333,0.336,0.337,0.336,0.34,0.314,Expression,CP2C9_HUMAN,High,Human +0.267,0.302,0.306,0.319,0.334,0.332,0.275,0.296,0.294,0.33,0.29,0.328,0.344,0.251,0.324,0.326,0.343,0.33,0.316,0.345,0.316,0.28,0.304,0.279,0.322,0.301,0.302,0.29,0.295,0.295,0.246,0.205,0.114,0.326,0.32,0.306,0.336,0.328,0.33,0.34,0.336,0.331,0.329,0.134,0.309,0.333,0.295,0.304,0.331,0.178,0.339,0.321,0.328,0.334,0.332,0.341,0.333,0.336,0.337,0.336,0.34,0.314,Binding,CP2C9_HUMAN,High,Human +0.358,0.33,0.327,0.339,0.339,0.342,0.176,0.309,0.379,0.361,0.355,0.394,0.418,0.27,0.464,0.397,0.248,0.336,0.324,0.327,0.318,0.339,0.348,0.355,0.224,0.376,0.409,0.352,0.333,0.33,0.348,0.321,0.203,0.236,0.358,0.358,0.385,0.385,0.373,0.355,0.367,0.358,0.248,0.224,0.336,0.394,0.421,0.33,0.467,0.455,0.342,0.352,0.339,0.336,0.333,0.339,0.342,0.336,0.318,0.336,0.367,0.452,Stability,CSN4_MOUSE,Medium,Eukaryote +0.297,0.323,0.316,0.316,0.342,0.329,0.158,0.278,0.361,0.361,0.304,0.278,0.209,0.146,0.19,0.259,0.291,0.304,0.304,0.234,0.133,0.146,0.171,0.108,0.101,0.114,0.184,0.184,0.31,0.259,0.278,0.209,0.165,0.108,0.12,0.177,0.297,0.291,0.316,0.329,0.335,0.342,0.196,0.127,0.259,0.152,0.329,0.329,0.354,0.348,0.278,0.342,0.31,0.342,0.278,0.297,0.316,0.285,0.31,0.31,0.361,0.304,Stability,CUE1_YEAST,Medium,Eukaryote +0.25,0.361,0.315,0.293,0.336,0.333,0.098,0.222,0.369,0.381,0.237,0.097,0.1,0.097,0.092,0.088,0.085,0.097,0.103,0.233,0.079,0.061,0.092,0.084,0.056,0.081,0.08,0.108,0.147,0.337,0.32,0.314,0.056,0.079,0.098,0.104,0.293,0.295,0.29,0.334,0.336,0.33,0.135,0.131,0.137,0.138,0.257,0.249,0.305,0.23,0.274,0.281,0.286,0.283,0.277,0.283,0.269,0.286,0.268,0.285,0.198,0.136,Activity,D7PM05_CLYGR,Low,Eukaryote +0.322,0.319,0.328,0.322,0.338,0.34,0.314,0.305,0.282,0.292,0.268,0.321,0.318,0.378,0.423,0.37,0.315,0.318,0.279,0.348,0.284,0.289,0.278,0.277,0.287,0.292,0.291,0.297,0.265,0.321,0.274,0.285,0.272,0.287,0.308,0.287,0.331,0.36,0.351,0.345,0.354,0.35,0.285,0.209,0.249,0.287,0.385,0.285,0.341,0.216,0.284,0.256,0.282,0.291,0.282,0.302,0.262,0.282,0.277,0.282,0.317,0.398,OrganismalFitness,DLG4_HUMAN,Low,Human +0.158,0.215,0.158,0.139,0.165,0.171,0.184,0.133,0.127,0.165,0.184,0.133,0.127,0.108,0.139,0.177,0.101,0.127,0.133,0.101,0.108,0.108,0.101,0.114,0.089,0.127,0.095,0.108,0.127,0.133,0.203,0.234,0.12,0.114,0.133,0.108,0.133,0.133,0.12,0.133,0.133,0.158,0.158,0.089,0.146,0.133,0.19,0.127,0.108,0.133,0.108,0.076,0.063,0.089,0.089,0.082,0.076,0.095,0.095,0.082,0.095,0.133,Binding,DLG4_RAT,Low,Eukaryote +0.168,0.149,0.139,0.119,0.109,0.129,0.089,0.109,0.228,0.287,0.119,0.089,0.119,0.089,0.178,0.198,0.248,0.267,0.218,0.228,0.079,0.089,0.069,0.109,0.119,0.149,0.079,0.119,0.218,0.208,0.129,0.059,0.168,0.099,0.099,0.079,0.168,0.168,0.158,0.149,0.158,0.119,0.139,0.089,0.188,0.168,0.287,0.317,0.267,0.307,0.416,0.366,0.347,0.277,0.307,0.297,0.297,0.228,0.327,0.356,0.218,0.277,Stability,DN7A_SACS2,Medium,Prokaryote +0.3,0.282,0.295,0.295,0.313,0.313,0.322,0.264,0.3,0.308,0.33,0.322,0.374,0.388,0.485,0.432,0.449,0.392,0.401,0.317,0.308,0.317,0.357,0.37,0.313,0.273,0.3,0.308,0.291,0.264,0.189,0.198,0.242,0.282,0.26,0.361,0.304,0.308,0.317,0.308,0.3,0.313,0.383,0.256,0.295,0.352,0.427,0.348,0.454,0.463,0.467,0.507,0.511,0.485,0.485,0.489,0.48,0.515,0.458,0.502,0.304,0.383,Stability,DNJA1_HUMAN,High,Human +0.342,0.325,0.329,0.295,0.288,0.318,0.099,0.25,0.223,0.192,0.305,0.185,0.233,0.027,0.192,0.233,0.305,0.305,0.274,0.332,0.24,0.284,0.291,0.26,0.103,0.223,0.134,0.195,0.312,0.164,0.318,0.291,0.247,0.075,0.075,0.048,0.325,0.356,0.329,0.322,0.315,0.322,0.134,0.058,0.325,0.192,0.384,0.377,0.363,0.445,0.349,0.363,0.366,0.39,0.353,0.38,0.366,0.363,0.339,0.373,0.404,0.37,Stability,DOCK1_MOUSE,High,Eukaryote +0.148,0.114,0.114,0.114,0.113,0.099,0.113,0.115,0.155,0.14,0.153,0.174,0.158,0.115,0.161,0.162,0.169,0.165,0.156,0.168,0.136,0.11,0.135,0.116,0.148,0.115,0.124,0.125,0.11,0.101,0.132,0.103,0.031,0.092,0.091,0.134,0.13,0.113,0.122,0.101,0.096,0.102,0.146,0.098,0.148,0.16,0.148,0.157,0.166,0.14,0.164,0.174,0.162,0.164,0.174,0.188,0.177,0.172,0.193,0.176,0.173,0.193,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.148,0.114,0.114,0.114,0.113,0.099,0.113,0.115,0.155,0.14,0.153,0.174,0.158,0.115,0.161,0.162,0.169,0.165,0.156,0.168,0.136,0.11,0.135,0.116,0.148,0.115,0.124,0.125,0.11,0.101,0.132,0.103,0.031,0.092,0.091,0.134,0.13,0.113,0.122,0.101,0.096,0.102,0.146,0.098,0.148,0.16,0.148,0.157,0.166,0.14,0.164,0.174,0.162,0.164,0.174,0.188,0.177,0.172,0.193,0.176,0.173,0.193,OrganismalFitness,DYR_ECOLI,High,Prokaryote +0.148,0.114,0.114,0.114,0.113,0.099,0.113,0.115,0.155,0.14,0.153,0.174,0.158,0.115,0.161,0.162,0.169,0.165,0.156,0.168,0.136,0.11,0.135,0.116,0.148,0.115,0.124,0.125,0.11,0.101,0.132,0.103,0.031,0.092,0.091,0.134,0.13,0.113,0.122,0.101,0.096,0.102,0.146,0.098,0.148,0.16,0.148,0.157,0.166,0.14,0.164,0.174,0.162,0.164,0.174,0.188,0.177,0.172,0.193,0.176,0.173,0.193,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.148,0.114,0.114,0.114,0.113,0.099,0.113,0.115,0.155,0.14,0.153,0.174,0.158,0.115,0.161,0.162,0.169,0.165,0.156,0.168,0.136,0.11,0.135,0.116,0.148,0.115,0.124,0.125,0.11,0.101,0.132,0.103,0.031,0.092,0.091,0.134,0.13,0.113,0.122,0.101,0.096,0.102,0.146,0.098,0.148,0.16,0.148,0.157,0.166,0.14,0.164,0.174,0.162,0.164,0.174,0.188,0.177,0.172,0.193,0.176,0.173,0.193,OrganismalFitness,DYR_ECOLI,Medium,Prokaryote +0.133,0.115,0.124,0.106,0.115,0.097,0.097,0.115,0.124,0.115,0.08,0.088,0.071,0.097,0.124,0.08,0.071,0.088,0.08,0.08,0.097,0.053,0.106,0.062,0.133,0.062,0.088,0.08,0.062,0.088,0.08,0.115,0.133,0.106,0.097,0.062,0.106,0.097,0.088,0.115,0.106,0.106,0.124,0.088,0.115,0.053,0.062,0.097,0.071,0.08,0.088,0.088,0.088,0.08,0.08,0.062,0.097,0.08,0.08,0.08,0.071,0.088,Activity,ENVZ_ECOLI,High,Prokaryote +0.4,0.35,0.3,0.3,0.35,0.35,0.025,0.375,0.375,0.35,0.3,0.4,0.35,0.025,0.025,0.05,0.075,0.05,0.175,0.4,0.275,0.35,0.375,0.35,0.25,0.4,0.3,0.375,0.4,0.3,0.425,0.375,0.325,0.325,0.375,0.425,0.35,0.375,0.375,0.375,0.4,0.4,0.3,0.0,0.35,0.325,0.325,0.3,0.175,0.15,0.125,0.275,0.225,0.15,0.275,0.2,0.175,0.225,0.175,0.175,0.1,0.125,OrganismalFitness,ENV_HV1B9,Medium,Virus +0.206,0.211,0.227,0.234,0.231,0.228,0.084,0.214,0.203,0.206,0.155,0.193,0.191,0.099,0.103,0.099,0.108,0.113,0.13,0.239,0.198,0.212,0.233,0.239,0.189,0.218,0.233,0.211,0.237,0.171,0.198,0.172,0.128,0.2,0.215,0.225,0.211,0.213,0.221,0.23,0.232,0.235,0.165,0.076,0.196,0.175,0.139,0.193,0.104,0.113,0.123,0.134,0.145,0.144,0.15,0.141,0.134,0.133,0.135,0.145,0.129,0.113,OrganismalFitness,ENV_HV1BR,Medium,Virus +0.321,0.327,0.383,0.367,0.388,0.388,0.005,0.296,0.372,0.372,0.311,0.327,0.301,0.015,0.327,0.347,0.372,0.388,0.347,0.357,0.347,0.347,0.306,0.321,0.327,0.316,0.372,0.352,0.337,0.281,0.311,0.235,0.235,0.357,0.301,0.357,0.398,0.352,0.383,0.403,0.362,0.378,0.26,0.112,0.327,0.327,0.444,0.378,0.423,0.408,0.383,0.398,0.388,0.429,0.403,0.403,0.388,0.398,0.408,0.393,0.423,0.429,Stability,EPHB2_HUMAN,High,Human +0.121,0.242,0.121,0.152,0.212,0.212,0.152,0.182,0.242,0.303,0.121,0.121,0.182,0.182,0.182,0.152,0.242,0.182,0.212,0.091,0.152,0.182,0.182,0.182,0.152,0.182,0.212,0.273,0.182,0.152,0.091,0.121,0.182,0.152,0.091,0.182,0.182,0.121,0.182,0.212,0.212,0.182,0.152,0.182,0.212,0.121,0.212,0.182,0.03,0.091,0.152,0.152,0.182,0.152,0.121,0.121,0.121,0.182,0.212,0.121,0.121,0.152,Expression,ERBB2_HUMAN,Low,Human +0.234,0.271,0.275,0.266,0.252,0.261,0.22,0.188,0.202,0.284,0.229,0.28,0.28,0.229,0.243,0.22,0.225,0.197,0.225,0.234,0.257,0.261,0.284,0.22,0.307,0.252,0.294,0.312,0.257,0.206,0.179,0.193,0.087,0.243,0.229,0.165,0.261,0.248,0.161,0.289,0.284,0.243,0.294,0.206,0.284,0.275,0.427,0.353,0.298,0.28,0.284,0.234,0.275,0.289,0.312,0.294,0.28,0.248,0.271,0.289,0.225,0.271,Stability,ESTA_BACSU,High,Prokaryote +0.087,0.609,0.564,0.672,0.626,0.642,0.095,0.448,0.492,0.523,0.62,0.503,0.528,0.063,0.067,0.112,0.48,0.399,0.618,0.567,0.065,0.028,0.062,0.093,0.103,0.253,0.063,0.426,0.625,0.554,0.699,0.659,0.068,0.062,0.053,0.69,0.092,0.072,0.647,0.517,0.493,0.68,0.1,0.084,0.316,0.109,0.096,0.383,0.136,0.08,0.624,0.657,0.601,0.677,0.649,0.667,0.601,0.614,0.639,0.647,0.323,0.028,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.087,0.609,0.564,0.672,0.626,0.642,0.095,0.448,0.492,0.523,0.62,0.503,0.528,0.063,0.067,0.112,0.48,0.399,0.618,0.567,0.065,0.028,0.062,0.093,0.103,0.253,0.063,0.426,0.625,0.554,0.699,0.659,0.068,0.062,0.053,0.69,0.092,0.072,0.647,0.517,0.493,0.68,0.1,0.084,0.316,0.109,0.096,0.383,0.136,0.08,0.624,0.657,0.601,0.677,0.649,0.667,0.601,0.614,0.639,0.647,0.323,0.028,OrganismalFitness,F7YBW8_MESOW,High,Prokaryote +0.212,0.243,0.233,0.217,0.249,0.233,0.111,0.116,0.228,0.275,0.275,0.228,0.243,0.116,0.286,0.328,0.296,0.249,0.254,0.222,0.185,0.175,0.169,0.169,0.159,0.185,0.249,0.238,0.19,0.201,0.185,0.159,0.164,0.122,0.153,0.175,0.217,0.18,0.212,0.212,0.206,0.217,0.291,0.138,0.28,0.286,0.312,0.27,0.265,0.286,0.254,0.265,0.291,0.265,0.291,0.249,0.249,0.275,0.27,0.249,0.296,0.291,Stability,FECA_ECOLI,High,Prokaryote +0.153,0.177,0.169,0.177,0.21,0.202,0.04,0.097,0.089,0.089,0.073,0.081,0.065,0.056,0.056,0.073,0.105,0.089,0.089,0.073,0.065,0.097,0.024,0.177,0.065,0.185,0.121,0.137,0.089,0.145,0.081,0.081,0.081,0.048,0.097,0.145,0.169,0.153,0.169,0.185,0.177,0.169,0.056,0.073,0.121,0.081,0.218,0.258,0.282,0.234,0.218,0.274,0.234,0.177,0.226,0.25,0.202,0.202,0.194,0.234,0.218,0.177,Stability,FKBP3_HUMAN,Medium,Human +0.075,0.1,0.25,0.175,0.183,0.192,0.092,0.1,0.25,0.208,0.2,0.158,0.142,0.108,0.167,0.158,0.242,0.258,0.242,0.158,0.133,0.092,0.15,0.15,0.142,0.125,0.15,0.158,0.267,0.183,0.242,0.267,0.158,0.092,0.15,0.1,0.167,0.15,0.167,0.183,0.192,0.217,0.117,0.192,0.208,0.125,0.075,0.2,0.075,0.083,0.267,0.2,0.275,0.242,0.217,0.217,0.275,0.217,0.242,0.25,0.175,0.183,OrganismalFitness,GAL4_YEAST,Medium,Eukaryote +0.242,0.223,0.201,0.231,0.227,0.22,0.091,0.117,0.227,0.22,0.197,0.178,0.182,0.174,0.14,0.186,0.227,0.22,0.223,0.22,0.064,0.064,0.076,0.064,0.053,0.042,0.053,0.061,0.144,0.22,0.208,0.212,0.095,0.053,0.049,0.223,0.231,0.231,0.239,0.223,0.22,0.246,0.148,0.14,0.152,0.186,0.17,0.216,0.152,0.148,0.193,0.182,0.186,0.186,0.197,0.208,0.201,0.197,0.197,0.197,0.136,0.125,Binding,GCN4_YEAST,Low,Eukaryote +0.121,0.112,0.129,0.138,0.138,0.147,0.155,0.129,0.121,0.129,0.138,0.112,0.121,0.069,0.086,0.121,0.138,0.138,0.138,0.138,0.095,0.164,0.155,0.19,0.121,0.164,0.138,0.129,0.138,0.147,0.172,0.181,0.112,0.121,0.103,0.112,0.138,0.121,0.129,0.138,0.121,0.147,0.112,0.052,0.155,0.103,0.172,0.164,0.147,0.078,0.138,0.147,0.147,0.164,0.164,0.181,0.155,0.138,0.164,0.155,0.138,0.121,OrganismalFitness,GDIA_HUMAN,Low,Human +0.277,0.275,0.28,0.279,0.287,0.287,0.061,0.256,0.297,0.298,0.188,0.083,0.086,0.085,0.092,0.079,0.078,0.09,0.112,0.241,0.074,0.073,0.089,0.08,0.066,0.098,0.118,0.259,0.268,0.296,0.238,0.239,0.087,0.069,0.093,0.249,0.285,0.283,0.279,0.288,0.289,0.304,0.128,0.11,0.134,0.134,0.279,0.28,0.308,0.254,0.235,0.239,0.25,0.253,0.243,0.251,0.241,0.245,0.233,0.247,0.262,0.188,Activity,GFP_AEQVI,Low,Eukaryote +0.32,0.2,0.36,0.36,0.36,0.36,0.28,0.28,0.2,0.2,0.2,0.4,0.32,0.28,0.28,0.32,0.36,0.32,0.2,0.2,0.32,0.28,0.36,0.32,0.24,0.36,0.32,0.4,0.24,0.2,0.28,0.24,0.24,0.28,0.32,0.36,0.4,0.4,0.44,0.36,0.36,0.36,0.28,0.24,0.4,0.32,0.28,0.2,0.32,0.16,0.48,0.44,0.32,0.36,0.4,0.28,0.32,0.48,0.36,0.4,0.32,0.4,Expression,GLPA_HUMAN,Low,Human +0.303,0.36,0.354,0.363,0.376,0.384,0.317,0.288,0.38,0.339,0.365,0.305,0.345,0.321,0.384,0.425,0.442,0.346,0.393,0.366,0.371,0.357,0.328,0.31,0.343,0.347,0.287,0.381,0.283,0.345,0.281,0.266,0.343,0.377,0.362,0.245,0.381,0.376,0.291,0.399,0.402,0.375,0.398,0.196,0.35,0.362,0.46,0.337,0.491,0.345,0.416,0.437,0.415,0.427,0.438,0.434,0.417,0.437,0.412,0.439,0.359,0.402,OrganismalFitness,GRB2_HUMAN,Medium,Human +0.269,0.26,0.192,0.212,0.202,0.221,0.192,0.192,0.125,0.192,0.346,0.269,0.317,0.221,0.269,0.298,0.337,0.231,0.221,0.106,0.125,0.183,0.212,0.26,0.231,0.26,0.221,0.212,0.26,0.212,0.231,0.163,0.173,0.106,0.163,0.24,0.269,0.279,0.26,0.231,0.25,0.25,0.26,0.173,0.298,0.212,0.25,0.337,0.269,0.202,0.298,0.317,0.356,0.317,0.356,0.337,0.337,0.327,0.317,0.356,0.423,0.279,Stability,HCP_LAMBD,Medium,Virus +0.581,0.547,0.619,0.633,0.649,0.651,0.222,0.469,0.606,0.623,0.599,0.174,0.229,0.143,0.152,0.608,0.617,0.605,0.614,0.581,0.191,0.503,0.538,0.538,0.501,0.549,0.544,0.565,0.533,0.596,0.642,0.581,0.109,0.199,0.168,0.333,0.606,0.574,0.606,0.63,0.617,0.623,0.243,0.231,0.608,0.343,0.46,0.592,0.474,0.345,0.639,0.606,0.628,0.615,0.644,0.653,0.63,0.639,0.646,0.644,0.673,0.22,Stability,HECD1_HUMAN,Medium,Human +0.165,0.174,0.172,0.172,0.174,0.174,0.121,0.063,0.181,0.178,0.155,0.167,0.146,0.09,0.172,0.183,0.155,0.169,0.163,0.072,0.174,0.174,0.174,0.167,0.16,0.172,0.163,0.16,0.176,0.183,0.158,0.156,0.109,0.172,0.193,0.165,0.163,0.19,0.158,0.185,0.181,0.172,0.121,0.098,0.165,0.158,0.163,0.188,0.139,0.144,0.167,0.172,0.169,0.167,0.165,0.17,0.172,0.172,0.17,0.17,0.167,0.178,Activity,HEM3_HUMAN,Medium,Human +0.165,0.202,0.209,0.209,0.203,0.199,0.133,0.086,0.188,0.191,0.204,0.174,0.185,0.1,0.13,0.179,0.2,0.202,0.194,0.17,0.175,0.198,0.225,0.211,0.203,0.213,0.191,0.194,0.191,0.177,0.199,0.202,0.088,0.211,0.212,0.197,0.19,0.188,0.186,0.209,0.205,0.202,0.148,0.132,0.197,0.109,0.182,0.209,0.188,0.154,0.196,0.178,0.19,0.193,0.194,0.191,0.193,0.196,0.196,0.193,0.203,0.107,OrganismalFitness,HIS7_YEAST,Medium,Eukaryote +0.135,0.122,0.124,0.12,0.128,0.122,0.133,0.193,0.121,0.122,0.126,0.13,0.123,0.122,0.123,0.136,0.149,0.13,0.133,0.111,0.137,0.108,0.116,0.125,0.143,0.106,0.108,0.108,0.124,0.12,0.127,0.127,0.114,0.124,0.111,0.12,0.125,0.125,0.122,0.116,0.12,0.12,0.124,0.117,0.161,0.16,0.132,0.129,0.105,0.126,0.145,0.129,0.14,0.149,0.144,0.15,0.143,0.151,0.14,0.139,0.155,0.148,OrganismalFitness,HMDH_HUMAN,Low,Human +0.129,0.139,0.143,0.153,0.144,0.148,0.118,0.141,0.142,0.148,0.144,0.147,0.148,0.083,0.089,0.112,0.147,0.153,0.127,0.123,0.164,0.156,0.165,0.15,0.163,0.161,0.152,0.163,0.152,0.145,0.171,0.17,0.099,0.154,0.158,0.156,0.142,0.149,0.144,0.149,0.152,0.151,0.134,0.074,0.149,0.153,0.113,0.148,0.093,0.119,0.14,0.134,0.13,0.133,0.152,0.144,0.14,0.145,0.142,0.144,0.141,0.154,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.129,0.139,0.143,0.153,0.144,0.148,0.118,0.141,0.142,0.148,0.144,0.147,0.148,0.083,0.089,0.112,0.147,0.153,0.127,0.123,0.164,0.156,0.165,0.15,0.163,0.161,0.152,0.163,0.152,0.145,0.171,0.17,0.099,0.154,0.158,0.156,0.142,0.149,0.144,0.149,0.152,0.151,0.134,0.074,0.149,0.153,0.113,0.148,0.093,0.119,0.14,0.134,0.13,0.133,0.152,0.144,0.14,0.145,0.142,0.144,0.141,0.154,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.129,0.139,0.143,0.153,0.144,0.148,0.118,0.141,0.142,0.148,0.144,0.147,0.148,0.083,0.089,0.112,0.147,0.153,0.127,0.123,0.164,0.156,0.165,0.15,0.163,0.161,0.152,0.163,0.152,0.145,0.171,0.17,0.099,0.154,0.158,0.156,0.142,0.149,0.144,0.149,0.152,0.151,0.134,0.074,0.149,0.153,0.113,0.148,0.093,0.119,0.14,0.134,0.13,0.133,0.152,0.144,0.14,0.145,0.142,0.144,0.141,0.154,OrganismalFitness,HSP82_YEAST,Medium,Eukaryote +0.192,0.16,0.156,0.162,0.155,0.158,0.092,0.144,0.152,0.163,0.145,0.144,0.164,0.104,0.12,0.16,0.174,0.159,0.156,0.152,0.153,0.152,0.163,0.162,0.156,0.162,0.174,0.16,0.148,0.148,0.149,0.142,0.098,0.154,0.155,0.17,0.166,0.156,0.171,0.16,0.151,0.162,0.129,0.106,0.152,0.154,0.168,0.157,0.161,0.136,0.162,0.168,0.162,0.16,0.166,0.172,0.178,0.163,0.166,0.168,0.172,0.158,OrganismalFitness,HXK4_HUMAN,Medium,Human +0.192,0.16,0.156,0.162,0.155,0.158,0.092,0.144,0.152,0.163,0.145,0.144,0.164,0.104,0.12,0.16,0.174,0.159,0.156,0.152,0.153,0.152,0.163,0.162,0.156,0.162,0.174,0.16,0.148,0.148,0.149,0.142,0.098,0.154,0.155,0.17,0.166,0.156,0.171,0.16,0.151,0.162,0.129,0.106,0.152,0.154,0.168,0.157,0.161,0.136,0.162,0.168,0.162,0.16,0.166,0.172,0.178,0.163,0.166,0.168,0.172,0.158,Expression,HXK4_HUMAN,Medium,Human +0.277,0.262,0.234,0.233,0.285,0.282,0.083,0.225,0.247,0.275,0.092,0.086,0.087,0.082,0.077,0.088,0.1,0.091,0.124,0.216,0.23,0.253,0.261,0.263,0.077,0.094,0.119,0.083,0.238,0.284,0.185,0.188,0.143,0.261,0.251,0.252,0.27,0.266,0.26,0.283,0.28,0.268,0.08,0.088,0.092,0.086,0.186,0.185,0.186,0.146,0.103,0.136,0.13,0.136,0.121,0.117,0.105,0.109,0.1,0.12,0.124,0.11,OrganismalFitness,I6TAH8_I68A0,Medium,Virus +0.13,0.217,0.196,0.232,0.21,0.21,0.196,0.159,0.087,0.087,0.254,0.181,0.196,0.101,0.188,0.261,0.239,0.196,0.203,0.196,0.188,0.232,0.203,0.203,0.225,0.217,0.261,0.225,0.152,0.304,0.203,0.159,0.145,0.21,0.188,0.21,0.181,0.152,0.203,0.203,0.196,0.203,0.145,0.13,0.261,0.232,0.109,0.232,0.217,0.174,0.217,0.268,0.239,0.239,0.232,0.246,0.232,0.21,0.254,0.239,0.188,0.188,OrganismalFitness,IF1_ECOLI,High,Prokaryote +0.075,0.075,0.195,0.203,0.195,0.211,0.06,0.12,0.083,0.12,0.203,0.12,0.113,0.053,0.053,0.075,0.083,0.165,0.188,0.188,0.203,0.173,0.173,0.188,0.271,0.211,0.248,0.18,0.165,0.218,0.12,0.075,0.203,0.083,0.226,0.233,0.06,0.158,0.18,0.105,0.128,0.165,0.09,0.06,0.158,0.128,0.09,0.15,0.083,0.15,0.188,0.105,0.158,0.15,0.165,0.165,0.158,0.143,0.15,0.158,0.218,0.09,Stability,ILF3_HUMAN,High,Human +0.226,0.179,0.236,0.236,0.221,0.236,0.313,0.128,0.226,0.282,0.246,0.323,0.344,0.344,0.369,0.451,0.374,0.421,0.395,0.19,0.318,0.2,0.195,0.215,0.349,0.354,0.369,0.333,0.251,0.205,0.179,0.123,0.159,0.231,0.241,0.287,0.236,0.215,0.267,0.251,0.231,0.251,0.313,0.272,0.349,0.338,0.451,0.467,0.333,0.313,0.251,0.292,0.297,0.308,0.292,0.287,0.287,0.297,0.272,0.292,0.415,0.308,Stability,ISDH_STAAW,High,Prokaryote +0.118,0.127,0.112,0.112,0.134,0.134,0.134,0.148,0.125,0.136,0.14,0.162,0.162,0.132,0.136,0.146,0.14,0.132,0.094,0.097,0.136,0.134,0.133,0.148,0.146,0.176,0.159,0.131,0.112,0.114,0.127,0.112,0.073,0.134,0.174,0.155,0.127,0.138,0.136,0.138,0.134,0.134,0.133,0.128,0.108,0.133,0.146,0.101,0.128,0.133,0.133,0.136,0.14,0.122,0.142,0.146,0.153,0.129,0.136,0.134,0.174,0.131,Expression,KCNE1_HUMAN,Medium,Human +0.118,0.127,0.112,0.112,0.134,0.134,0.134,0.148,0.125,0.136,0.14,0.162,0.162,0.132,0.136,0.146,0.14,0.132,0.094,0.097,0.136,0.134,0.133,0.148,0.146,0.176,0.159,0.131,0.112,0.114,0.127,0.112,0.073,0.134,0.174,0.155,0.127,0.138,0.136,0.138,0.134,0.134,0.133,0.128,0.108,0.133,0.146,0.101,0.128,0.133,0.133,0.136,0.14,0.122,0.142,0.146,0.153,0.129,0.136,0.134,0.174,0.131,Activity,KCNE1_HUMAN,Medium,Human +0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.05,0.1,0.1,0.1,0.1,0.05,0.05,0.05,0.05,0.05,0.2,0.35,0.05,0.05,0.05,0.1,0.0,0.0,0.05,0.0,0.0,0.0,0.1,0.0,0.05,0.05,0.0,0.0,0.05,0.05,0.15,0.1,0.1,0.15,0.15,0.1,0.15,0.1,0.15,0.15,0.0,0.1,Activity,KCNH2_HUMAN,Medium,Human +0.148,0.132,0.136,0.141,0.141,0.145,0.106,0.098,0.122,0.122,0.159,0.15,0.136,0.129,0.168,0.187,0.176,0.162,0.175,0.103,0.168,0.126,0.112,0.105,0.173,0.126,0.12,0.147,0.113,0.124,0.113,0.111,0.09,0.16,0.118,0.112,0.144,0.126,0.111,0.142,0.132,0.123,0.152,0.12,0.117,0.172,0.148,0.115,0.139,0.12,0.166,0.159,0.174,0.157,0.173,0.157,0.16,0.17,0.168,0.166,0.153,0.176,Activity,KCNJ2_MOUSE,Medium,Eukaryote +0.148,0.132,0.136,0.141,0.141,0.145,0.106,0.098,0.122,0.122,0.159,0.15,0.136,0.129,0.168,0.187,0.176,0.162,0.175,0.103,0.168,0.126,0.112,0.105,0.173,0.126,0.12,0.147,0.113,0.124,0.113,0.111,0.09,0.16,0.118,0.112,0.144,0.126,0.111,0.142,0.132,0.123,0.152,0.12,0.117,0.172,0.148,0.115,0.139,0.12,0.166,0.159,0.174,0.157,0.173,0.157,0.16,0.17,0.168,0.166,0.153,0.176,Expression,KCNJ2_MOUSE,Medium,Eukaryote +0.159,0.22,0.216,0.24,0.222,0.224,0.107,0.169,0.208,0.238,0.212,0.19,0.226,0.113,0.123,0.169,0.226,0.24,0.238,0.2,0.151,0.188,0.22,0.216,0.165,0.204,0.228,0.188,0.242,0.218,0.169,0.149,0.077,0.125,0.167,0.194,0.185,0.19,0.21,0.232,0.23,0.236,0.123,0.101,0.202,0.143,0.22,0.23,0.228,0.171,0.224,0.214,0.228,0.216,0.216,0.222,0.22,0.208,0.216,0.226,0.236,0.181,OrganismalFitness,KKA2_KLEPN,High,Prokaryote +0.217,0.432,0.426,0.427,0.446,0.447,0.133,0.347,0.272,0.406,0.251,0.351,0.388,0.119,0.174,0.227,0.324,0.404,0.435,0.326,0.161,0.241,0.298,0.319,0.185,0.328,0.406,0.305,0.445,0.493,0.371,0.319,0.139,0.171,0.245,0.47,0.214,0.246,0.444,0.436,0.447,0.47,0.105,0.085,0.283,0.175,0.231,0.361,0.376,0.158,0.305,0.347,0.337,0.354,0.337,0.347,0.323,0.335,0.323,0.352,0.269,0.19,Activity,LGK_LIPST,Medium,Eukaryote +0.139,0.25,0.25,0.25,0.222,0.25,0.278,0.111,0.25,0.25,0.25,0.222,0.222,0.25,0.278,0.222,0.194,0.167,0.278,0.25,0.333,0.333,0.278,0.25,0.222,0.25,0.194,0.25,0.083,0.222,0.194,0.056,0.194,0.278,0.333,0.222,0.194,0.333,0.222,0.278,0.278,0.25,0.25,0.278,0.194,0.194,0.278,0.222,0.194,0.194,0.194,0.306,0.333,0.25,0.25,0.25,0.278,0.278,0.139,0.278,0.306,0.306,Expression,LYAM1_HUMAN,Medium,Human +0.315,0.315,0.35,0.35,0.343,0.336,0.343,0.245,0.287,0.28,0.294,0.322,0.406,0.385,0.378,0.378,0.413,0.455,0.273,0.231,0.224,0.217,0.259,0.301,0.28,0.273,0.294,0.273,0.273,0.252,0.21,0.217,0.161,0.231,0.301,0.231,0.301,0.315,0.294,0.343,0.315,0.322,0.385,0.259,0.35,0.357,0.413,0.315,0.364,0.385,0.413,0.441,0.448,0.469,0.427,0.476,0.462,0.462,0.434,0.462,0.462,0.483,Stability,MAFG_MOUSE,Medium,Eukaryote +0.288,0.382,0.33,0.33,0.335,0.335,0.057,0.151,0.274,0.302,0.264,0.255,0.226,0.028,0.028,0.264,0.259,0.264,0.377,0.241,0.024,0.061,0.193,0.179,0.094,0.264,0.274,0.236,0.288,0.274,0.325,0.297,0.316,0.028,0.042,0.047,0.269,0.264,0.283,0.316,0.33,0.316,0.09,0.057,0.307,0.25,0.288,0.335,0.288,0.288,0.34,0.387,0.344,0.344,0.316,0.335,0.33,0.311,0.274,0.33,0.382,0.264,Stability,MBD11_ARATH,Medium,Eukaryote +0.148,0.185,0.163,0.174,0.163,0.178,0.163,0.174,0.156,0.167,0.191,0.178,0.174,0.143,0.148,0.172,0.183,0.172,0.187,0.163,0.185,0.187,0.159,0.193,0.193,0.181,0.183,0.191,0.2,0.193,0.17,0.172,0.12,0.161,0.178,0.196,0.167,0.174,0.198,0.176,0.17,0.176,0.17,0.139,0.187,0.178,0.15,0.183,0.169,0.119,0.161,0.163,0.165,0.167,0.156,0.176,0.172,0.169,0.187,0.159,0.185,0.167,Activity,MET_HUMAN,Medium,Human +0.091,0.134,0.134,0.144,0.138,0.122,0.084,0.075,0.116,0.095,0.076,0.119,0.107,0.104,0.103,0.09,0.072,0.085,0.098,0.097,0.101,0.094,0.082,0.09,0.095,0.109,0.103,0.109,0.101,0.122,0.082,0.095,0.109,0.087,0.101,0.09,0.078,0.098,0.088,0.125,0.125,0.106,0.097,0.095,0.113,0.12,0.06,0.109,0.07,0.097,0.084,0.104,0.095,0.088,0.085,0.079,0.09,0.081,0.081,0.088,0.098,0.104,OrganismalFitness,MK01_HUMAN,Medium,Human +0.095,0.145,0.147,0.137,0.132,0.142,0.115,0.14,0.162,0.15,0.152,0.155,0.177,0.12,0.112,0.112,0.137,0.152,0.18,0.167,0.142,0.14,0.145,0.15,0.147,0.15,0.177,0.16,0.145,0.157,0.12,0.14,0.15,0.112,0.117,0.135,0.09,0.122,0.115,0.127,0.142,0.137,0.1,0.087,0.14,0.11,0.057,0.115,0.115,0.067,0.12,0.112,0.117,0.117,0.13,0.112,0.112,0.112,0.115,0.11,0.125,0.097,OrganismalFitness,MLAC_ECOLI,Medium,Prokaryote +0.133,0.162,0.143,0.147,0.147,0.149,0.1,0.1,0.156,0.153,0.146,0.164,0.164,0.105,0.131,0.153,0.152,0.159,0.146,0.152,0.114,0.127,0.137,0.146,0.138,0.144,0.146,0.134,0.162,0.143,0.147,0.135,0.132,0.128,0.153,0.155,0.134,0.153,0.152,0.159,0.167,0.157,0.122,0.093,0.156,0.134,0.13,0.152,0.101,0.118,0.149,0.144,0.15,0.142,0.148,0.159,0.154,0.153,0.154,0.158,0.154,0.134,OrganismalFitness,MSH2_HUMAN,Medium,Human +0.144,0.316,0.337,0.326,0.321,0.342,0.134,0.337,0.294,0.299,0.203,0.289,0.316,0.102,0.128,0.155,0.176,0.182,0.23,0.283,0.139,0.16,0.235,0.299,0.203,0.321,0.326,0.283,0.305,0.321,0.332,0.305,0.193,0.171,0.182,0.299,0.16,0.16,0.278,0.342,0.342,0.337,0.128,0.053,0.155,0.123,0.166,0.235,0.262,0.134,0.214,0.225,0.225,0.198,0.203,0.209,0.219,0.219,0.209,0.214,0.209,0.107,OrganismalFitness,MTH3_HAEAE,Medium,Prokaryote +0.122,0.123,0.113,0.123,0.113,0.108,0.096,0.121,0.133,0.138,0.123,0.126,0.126,0.118,0.124,0.135,0.116,0.132,0.138,0.122,0.121,0.118,0.126,0.149,0.13,0.12,0.131,0.119,0.162,0.137,0.139,0.148,0.075,0.127,0.126,0.127,0.11,0.118,0.125,0.109,0.116,0.123,0.122,0.11,0.123,0.126,0.112,0.122,0.127,0.118,0.12,0.115,0.121,0.118,0.117,0.118,0.12,0.118,0.126,0.117,0.132,0.116,OrganismalFitness,MTHR_HUMAN,Low,Human +0.227,0.206,0.276,0.212,0.236,0.245,0.073,0.061,0.297,0.285,0.294,0.261,0.264,0.109,0.23,0.355,0.421,0.318,0.179,0.233,0.139,0.091,0.164,0.155,0.188,0.261,0.255,0.227,0.291,0.209,0.209,0.167,0.17,0.212,0.139,0.127,0.291,0.221,0.2,0.276,0.255,0.261,0.194,0.073,0.27,0.2,0.248,0.315,0.339,0.27,0.303,0.306,0.297,0.312,0.303,0.306,0.291,0.303,0.3,0.303,0.297,0.348,Stability,MYO3_YEAST,High,Eukaryote +0.258,0.246,0.246,0.247,0.259,0.263,0.111,0.22,0.266,0.261,0.106,0.099,0.094,0.101,0.093,0.096,0.099,0.102,0.129,0.182,0.235,0.264,0.264,0.263,0.098,0.111,0.109,0.107,0.242,0.268,0.199,0.194,0.148,0.249,0.252,0.285,0.26,0.271,0.27,0.28,0.282,0.278,0.094,0.099,0.106,0.093,0.161,0.166,0.178,0.144,0.103,0.118,0.127,0.122,0.114,0.114,0.115,0.124,0.102,0.115,0.139,0.13,OrganismalFitness,NCAP_I34A1,Medium,Virus +0.434,0.426,0.434,0.454,0.442,0.474,0.353,0.361,0.378,0.406,0.305,0.398,0.369,0.402,0.394,0.478,0.51,0.486,0.458,0.406,0.386,0.365,0.329,0.329,0.378,0.317,0.357,0.301,0.321,0.373,0.349,0.369,0.305,0.39,0.386,0.337,0.43,0.402,0.402,0.442,0.446,0.454,0.382,0.406,0.321,0.345,0.386,0.341,0.418,0.418,0.482,0.426,0.474,0.45,0.482,0.478,0.454,0.494,0.438,0.466,0.349,0.41,Stability,NKX31_HUMAN,High,Human +0.41,0.41,0.418,0.426,0.441,0.441,0.213,0.284,0.41,0.584,0.315,0.157,0.181,0.15,0.252,0.292,0.434,0.196,0.426,0.134,0.244,0.402,0.356,0.292,0.205,0.237,0.418,0.418,0.395,0.394,0.355,0.252,0.039,0.221,0.355,0.33,0.394,0.426,0.426,0.418,0.426,0.441,0.221,0.206,0.33,0.14,0.315,0.188,0.102,0.134,0.259,0.282,0.267,0.33,0.172,0.251,0.244,0.251,0.322,0.267,0.267,0.205,Activity,NPC1_HUMAN,Low,Human +0.41,0.41,0.418,0.426,0.441,0.441,0.213,0.284,0.41,0.584,0.315,0.157,0.181,0.15,0.252,0.292,0.434,0.196,0.426,0.134,0.244,0.402,0.356,0.292,0.205,0.237,0.418,0.418,0.395,0.394,0.355,0.252,0.039,0.221,0.355,0.33,0.394,0.426,0.426,0.418,0.426,0.441,0.221,0.206,0.33,0.14,0.315,0.188,0.102,0.134,0.259,0.282,0.267,0.33,0.172,0.251,0.244,0.251,0.322,0.267,0.267,0.205,Activity,NPC1_HUMAN,Low,Human +0.433,0.567,0.467,0.433,0.567,0.567,0.067,0.6,0.667,0.7,0.067,0.133,0.6,0.0,0.0,0.067,0.233,0.567,0.5,0.4,0.7,0.667,0.533,0.533,0.0,0.433,0.567,0.4,0.633,0.567,0.4,0.4,0.067,0.6,0.533,0.6,0.533,0.567,0.6,0.633,0.6,0.567,0.0,0.033,0.0,0.033,0.267,0.2,0.4,0.233,0.167,0.2,0.3,0.233,0.3,0.4,0.267,0.267,0.333,0.333,0.233,0.033,OrganismalFitness,NRAM_I33A0,Low,Virus +0.193,0.26,0.281,0.274,0.274,0.284,0.053,0.179,0.253,0.267,0.256,0.207,0.284,0.105,0.126,0.14,0.193,0.239,0.263,0.204,0.102,0.13,0.249,0.253,0.133,0.298,0.267,0.239,0.312,0.309,0.267,0.211,0.098,0.123,0.119,0.305,0.196,0.204,0.302,0.274,0.288,0.309,0.116,0.07,0.284,0.14,0.196,0.33,0.211,0.168,0.214,0.249,0.211,0.228,0.239,0.263,0.239,0.256,0.246,0.26,0.298,0.2,Expression,NUD15_HUMAN,High,Human +0.123,0.256,0.163,0.167,0.172,0.172,0.133,0.128,0.202,0.138,0.177,0.03,0.059,0.103,0.103,0.084,0.148,0.167,0.217,0.163,0.172,0.202,0.197,0.217,0.094,0.143,0.113,0.207,0.251,0.138,0.217,0.217,0.074,0.163,0.143,0.148,0.158,0.118,0.143,0.172,0.163,0.192,0.172,0.138,0.103,0.133,0.374,0.305,0.276,0.345,0.246,0.291,0.266,0.261,0.286,0.246,0.246,0.246,0.241,0.261,0.271,0.158,Stability,NUSA_ECOLI,High,Prokaryote +0.283,0.217,0.225,0.246,0.239,0.246,0.159,0.181,0.283,0.225,0.232,0.254,0.246,0.123,0.261,0.254,0.232,0.217,0.217,0.138,0.116,0.152,0.152,0.152,0.254,0.181,0.167,0.167,0.174,0.246,0.232,0.196,0.217,0.167,0.145,0.181,0.21,0.203,0.21,0.196,0.188,0.21,0.217,0.08,0.188,0.297,0.254,0.21,0.37,0.319,0.283,0.341,0.312,0.275,0.29,0.29,0.304,0.319,0.268,0.297,0.188,0.275,Stability,NUSG_MYCTU,High,Prokaryote +0.372,0.484,0.547,0.559,0.575,0.541,0.119,0.378,0.5,0.494,0.509,0.469,0.478,0.131,0.453,0.497,0.569,0.519,0.512,0.55,0.447,0.434,0.431,0.428,0.438,0.416,0.45,0.412,0.403,0.494,0.497,0.509,0.328,0.169,0.122,0.319,0.466,0.475,0.491,0.538,0.534,0.531,0.381,0.144,0.406,0.431,0.562,0.438,0.669,0.578,0.578,0.591,0.588,0.6,0.606,0.603,0.553,0.578,0.6,0.609,0.631,0.5,Stability,OBSCN_HUMAN,High,Human +0.184,0.132,0.237,0.167,0.246,0.132,0.193,0.149,0.114,0.114,0.193,0.184,0.184,0.175,0.158,0.211,0.175,0.184,0.167,0.193,0.105,0.114,0.184,0.096,0.088,0.114,0.167,0.105,0.158,0.202,0.123,0.096,0.123,0.167,0.044,0.167,0.158,0.044,0.079,0.158,0.114,0.132,0.175,0.175,0.184,0.184,0.237,0.193,0.246,0.307,0.228,0.211,0.246,0.254,0.263,0.237,0.263,0.263,0.228,0.246,0.202,0.193,Stability,ODP2_GEOSE,High,Prokaryote +0.176,0.412,0.412,0.412,0.471,0.412,0.176,0.412,0.294,0.412,0.353,0.294,0.294,0.294,0.235,0.235,0.294,0.412,0.176,0.412,0.412,0.353,0.235,0.294,0.471,0.353,0.353,0.412,0.235,0.294,0.235,0.235,0.0,0.353,0.235,0.118,0.412,0.294,0.294,0.412,0.412,0.412,0.353,0.059,0.353,0.412,0.176,0.353,0.176,0.176,0.235,0.235,0.176,0.176,0.176,0.294,0.235,0.235,0.235,0.176,0.412,0.294,Expression,OPSD_HUMAN,High,Human +0.316,0.342,0.297,0.291,0.31,0.304,0.139,0.152,0.316,0.297,0.291,0.323,0.335,0.158,0.291,0.342,0.335,0.297,0.297,0.304,0.323,0.272,0.272,0.278,0.278,0.348,0.304,0.297,0.234,0.304,0.241,0.171,0.133,0.266,0.278,0.278,0.329,0.304,0.323,0.31,0.297,0.316,0.209,0.127,0.316,0.259,0.367,0.304,0.291,0.259,0.304,0.348,0.342,0.361,0.329,0.348,0.348,0.342,0.342,0.342,0.38,0.342,Activity,OTC_HUMAN,Medium,Human +0.188,0.266,0.203,0.203,0.219,0.203,0.078,0.125,0.109,0.141,0.25,0.297,0.234,0.109,0.281,0.328,0.156,0.172,0.188,0.156,0.109,0.062,0.078,0.078,0.109,0.062,0.078,0.094,0.047,0.031,0.109,0.109,0.062,0.141,0.125,0.141,0.188,0.156,0.125,0.203,0.172,0.188,0.172,0.141,0.25,0.344,0.25,0.203,0.328,0.281,0.219,0.219,0.219,0.188,0.219,0.203,0.234,0.172,0.172,0.219,0.359,0.328,Stability,OTU7A_HUMAN,High,Human +0.112,0.166,0.2,0.192,0.196,0.198,0.132,0.127,0.137,0.132,0.165,0.148,0.153,0.104,0.117,0.154,0.17,0.191,0.198,0.115,0.108,0.144,0.146,0.145,0.115,0.162,0.166,0.151,0.188,0.208,0.159,0.149,0.081,0.12,0.122,0.134,0.124,0.109,0.125,0.189,0.191,0.196,0.116,0.096,0.161,0.125,0.186,0.216,0.217,0.154,0.174,0.181,0.172,0.172,0.178,0.19,0.184,0.192,0.181,0.187,0.202,0.152,Activity,OXDA_RHOTO,High,Eukaryote +0.112,0.166,0.2,0.192,0.196,0.198,0.132,0.127,0.137,0.132,0.165,0.148,0.153,0.104,0.117,0.154,0.17,0.191,0.198,0.115,0.108,0.144,0.146,0.145,0.115,0.162,0.166,0.151,0.188,0.208,0.159,0.149,0.081,0.12,0.122,0.134,0.124,0.109,0.125,0.189,0.191,0.196,0.116,0.096,0.161,0.125,0.186,0.216,0.217,0.154,0.174,0.181,0.172,0.172,0.178,0.19,0.184,0.192,0.181,0.187,0.202,0.152,Expression,OXDA_RHOTO,High,Eukaryote +0.145,0.124,0.132,0.131,0.121,0.122,0.103,0.131,0.106,0.11,0.189,0.142,0.172,0.116,0.108,0.142,0.143,0.159,0.166,0.105,0.14,0.171,0.179,0.134,0.132,0.167,0.191,0.189,0.104,0.125,0.168,0.128,0.088,0.115,0.155,0.106,0.153,0.162,0.131,0.128,0.135,0.122,0.118,0.103,0.155,0.099,0.147,0.17,0.154,0.133,0.146,0.155,0.146,0.135,0.138,0.142,0.134,0.138,0.137,0.14,0.161,0.13,OrganismalFitness,P53_HUMAN,Low,Human +0.145,0.124,0.132,0.131,0.121,0.122,0.103,0.131,0.106,0.11,0.189,0.142,0.172,0.116,0.108,0.142,0.143,0.159,0.166,0.105,0.14,0.171,0.179,0.134,0.132,0.167,0.191,0.189,0.104,0.125,0.168,0.128,0.088,0.115,0.155,0.106,0.153,0.162,0.131,0.128,0.135,0.122,0.118,0.103,0.155,0.099,0.147,0.17,0.154,0.133,0.146,0.155,0.146,0.135,0.138,0.142,0.134,0.138,0.137,0.14,0.161,0.13,OrganismalFitness,P53_HUMAN,Low,Human +0.145,0.124,0.132,0.131,0.121,0.122,0.103,0.131,0.106,0.11,0.189,0.142,0.172,0.116,0.108,0.142,0.143,0.159,0.166,0.105,0.14,0.171,0.179,0.134,0.132,0.167,0.191,0.189,0.104,0.125,0.168,0.128,0.088,0.115,0.155,0.106,0.153,0.162,0.131,0.128,0.135,0.122,0.118,0.103,0.155,0.099,0.147,0.17,0.154,0.133,0.146,0.155,0.146,0.135,0.138,0.142,0.134,0.138,0.137,0.14,0.161,0.13,OrganismalFitness,P53_HUMAN,Low,Human +0.145,0.124,0.132,0.131,0.121,0.122,0.103,0.131,0.106,0.11,0.189,0.142,0.172,0.116,0.108,0.142,0.143,0.159,0.166,0.105,0.14,0.171,0.179,0.134,0.132,0.167,0.191,0.189,0.104,0.125,0.168,0.128,0.088,0.115,0.155,0.106,0.153,0.162,0.131,0.128,0.135,0.122,0.118,0.103,0.155,0.099,0.147,0.17,0.154,0.133,0.146,0.155,0.146,0.135,0.138,0.142,0.134,0.138,0.137,0.14,0.161,0.13,OrganismalFitness,P53_HUMAN,Low,Human +0.276,0.414,0.388,0.401,0.382,0.388,0.184,0.289,0.401,0.375,0.408,0.368,0.382,0.204,0.401,0.355,0.401,0.368,0.414,0.375,0.263,0.329,0.342,0.382,0.322,0.395,0.395,0.355,0.447,0.316,0.362,0.322,0.118,0.309,0.316,0.355,0.289,0.27,0.309,0.355,0.362,0.355,0.257,0.112,0.382,0.395,0.204,0.388,0.362,0.178,0.329,0.362,0.322,0.349,0.362,0.395,0.375,0.362,0.401,0.388,0.395,0.388,OrganismalFitness,P84126_THETH,Medium,Prokaryote +0.279,0.269,0.275,0.275,0.29,0.285,0.182,0.234,0.269,0.276,0.277,0.287,0.289,0.172,0.191,0.21,0.242,0.245,0.259,0.249,0.255,0.285,0.275,0.282,0.27,0.281,0.292,0.281,0.25,0.291,0.273,0.249,0.121,0.244,0.27,0.261,0.269,0.285,0.272,0.286,0.291,0.286,0.242,0.115,0.288,0.264,0.126,0.288,0.2,0.148,0.233,0.216,0.229,0.221,0.223,0.217,0.221,0.227,0.228,0.225,0.269,0.213,OrganismalFitness,PABP_YEAST,Medium,Eukaryote +0.207,0.215,0.207,0.211,0.211,0.211,0.086,0.172,0.2,0.222,0.196,0.198,0.217,0.121,0.196,0.209,0.2,0.193,0.179,0.202,0.146,0.17,0.151,0.2,0.178,0.179,0.181,0.183,0.168,0.168,0.138,0.121,0.129,0.142,0.196,0.183,0.194,0.215,0.198,0.224,0.217,0.224,0.185,0.133,0.196,0.183,0.217,0.247,0.219,0.155,0.213,0.202,0.219,0.213,0.211,0.226,0.224,0.221,0.221,0.224,0.232,0.206,Activity,PAI1_HUMAN,,Human +0.335,0.335,0.335,0.324,0.335,0.324,0.115,0.269,0.088,0.132,0.11,0.11,0.093,0.104,0.093,0.104,0.104,0.099,0.247,0.236,0.28,0.275,0.264,0.33,0.121,0.258,0.258,0.225,0.264,0.291,0.264,0.242,0.126,0.275,0.28,0.302,0.335,0.319,0.319,0.33,0.341,0.319,0.11,0.11,0.104,0.11,0.115,0.099,0.071,0.137,0.088,0.093,0.099,0.088,0.088,0.11,0.071,0.082,0.082,0.115,0.104,0.071,OrganismalFitness,PA_I34A1,Medium,Virus +0.273,0.396,0.515,0.502,0.368,0.354,0.401,0.393,0.501,0.49,0.434,0.47,0.502,0.505,0.529,0.49,0.526,0.497,0.507,0.362,0.397,0.458,0.413,0.463,0.406,0.438,0.459,0.459,0.423,0.389,0.426,0.381,0.342,0.325,0.317,0.442,0.412,0.388,0.453,0.376,0.39,0.379,0.463,0.288,0.464,0.49,0.23,0.415,0.396,0.269,0.412,0.377,0.378,0.39,0.386,0.401,0.397,0.402,0.401,0.395,0.491,0.484,Activity,PHOT_CHLRE,High,Eukaryote +0.333,0.383,0.42,0.444,0.407,0.395,0.346,0.185,0.333,0.432,0.321,0.296,0.358,0.37,0.358,0.395,0.42,0.296,0.346,0.346,0.296,0.222,0.16,0.111,0.21,0.185,0.123,0.099,0.086,0.198,0.247,0.185,0.198,0.321,0.247,0.21,0.395,0.37,0.296,0.383,0.346,0.309,0.321,0.148,0.358,0.358,0.272,0.321,0.284,0.259,0.296,0.383,0.383,0.321,0.309,0.358,0.346,0.333,0.346,0.37,0.407,0.395,Stability,PIN1_HUMAN,High,Human +0.366,0.339,0.333,0.322,0.333,0.339,0.295,0.213,0.273,0.35,0.219,0.29,0.273,0.415,0.383,0.415,0.393,0.361,0.328,0.322,0.213,0.186,0.186,0.186,0.273,0.219,0.235,0.18,0.191,0.311,0.24,0.273,0.23,0.246,0.301,0.164,0.295,0.35,0.251,0.328,0.344,0.322,0.295,0.311,0.18,0.257,0.388,0.202,0.443,0.377,0.377,0.41,0.421,0.41,0.41,0.404,0.388,0.388,0.41,0.404,0.18,0.339,Stability,PITX2_HUMAN,High,Human +0.145,0.282,0.145,0.137,0.122,0.153,0.229,0.046,0.107,0.099,0.069,0.13,0.099,0.183,0.16,0.191,0.092,0.046,0.061,0.176,0.176,0.16,0.198,0.153,0.153,0.137,0.16,0.183,0.069,0.046,0.092,0.099,0.099,0.221,0.191,0.191,0.176,0.176,0.168,0.183,0.168,0.153,0.153,0.214,0.13,0.183,0.252,0.153,0.16,0.214,0.115,0.115,0.122,0.115,0.107,0.107,0.099,0.122,0.115,0.115,0.198,0.153,Stability,PKN1_HUMAN,High,Human +0.309,0.38,0.361,0.387,0.392,0.399,0.088,0.261,0.394,0.389,0.176,0.09,0.113,0.097,0.091,0.136,0.231,0.252,0.288,0.279,0.288,0.348,0.352,0.328,0.145,0.316,0.319,0.31,0.372,0.391,0.289,0.284,0.096,0.099,0.251,0.316,0.286,0.321,0.342,0.349,0.373,0.375,0.098,0.092,0.196,0.1,0.151,0.215,0.101,0.121,0.197,0.211,0.211,0.212,0.199,0.202,0.214,0.223,0.212,0.218,0.109,0.102,OrganismalFitness,POLG_CXB3N,Medium,Virus +0.327,0.395,0.341,0.358,0.384,0.386,0.086,0.299,0.473,0.477,0.143,0.089,0.102,0.08,0.086,0.109,0.134,0.14,0.164,0.331,0.33,0.325,0.331,0.292,0.331,0.344,0.34,0.351,0.349,0.454,0.411,0.314,0.104,0.089,0.16,0.366,0.288,0.332,0.405,0.383,0.393,0.433,0.089,0.086,0.198,0.088,0.188,0.278,0.07,0.136,0.138,0.138,0.166,0.146,0.152,0.154,0.144,0.15,0.14,0.157,0.12,0.114,OrganismalFitness,POLG_DEN26,Low,Virus +0.362,0.411,0.368,0.38,0.393,0.405,0.074,0.153,0.387,0.387,0.123,0.387,0.35,0.086,0.092,0.08,0.11,0.092,0.104,0.239,0.196,0.276,0.325,0.288,0.282,0.27,0.239,0.239,0.294,0.387,0.313,0.221,0.067,0.368,0.356,0.288,0.362,0.374,0.362,0.393,0.368,0.344,0.086,0.074,0.331,0.117,0.08,0.245,0.233,0.19,0.172,0.153,0.123,0.153,0.141,0.129,0.221,0.153,0.166,0.178,0.092,0.098,OrganismalFitness,POLG_HCVJF,Medium,Virus +0.164,0.519,0.435,0.454,0.464,0.464,0.088,0.48,0.333,0.497,0.489,0.113,0.129,0.099,0.123,0.117,0.156,0.15,0.127,0.595,0.097,0.078,0.086,0.09,0.109,0.049,0.08,0.051,0.131,0.558,0.608,0.62,0.113,0.105,0.094,0.088,0.296,0.294,0.281,0.437,0.437,0.429,0.185,0.127,0.189,0.154,0.55,0.507,0.561,0.581,0.622,0.63,0.63,0.651,0.653,0.663,0.634,0.69,0.637,0.663,0.569,0.326,Stability,POLG_PESV,Medium,Virus +0.16,0.237,0.259,0.253,0.255,0.241,0.146,0.193,0.252,0.242,0.245,0.274,0.29,0.124,0.135,0.138,0.158,0.22,0.277,0.288,0.278,0.293,0.175,0.175,0.234,0.308,0.332,0.342,0.179,0.269,0.162,0.076,0.117,0.294,0.268,0.274,0.275,0.33,0.308,0.255,0.275,0.266,0.124,0.121,0.25,0.142,0.244,0.274,0.248,0.162,0.184,0.167,0.146,0.137,0.193,0.147,0.164,0.148,0.127,0.153,0.198,0.149,Activity,PPARG_HUMAN,Medium,Human +0.18,0.176,0.18,0.187,0.18,0.177,0.128,0.154,0.176,0.173,0.183,0.21,0.2,0.131,0.14,0.161,0.174,0.188,0.201,0.163,0.172,0.174,0.205,0.181,0.177,0.202,0.192,0.214,0.174,0.181,0.172,0.135,0.113,0.163,0.188,0.206,0.191,0.192,0.205,0.187,0.185,0.186,0.142,0.115,0.191,0.171,0.169,0.201,0.188,0.135,0.177,0.18,0.176,0.167,0.181,0.168,0.163,0.168,0.173,0.181,0.215,0.158,OrganismalFitness,PPM1D_HUMAN,Low,Human +0.191,0.23,0.343,0.343,0.299,0.284,0.216,0.216,0.392,0.392,0.397,0.191,0.265,0.221,0.225,0.358,0.387,0.402,0.265,0.299,0.363,0.358,0.387,0.333,0.353,0.392,0.363,0.353,0.363,0.382,0.299,0.25,0.221,0.206,0.245,0.235,0.294,0.275,0.245,0.319,0.314,0.324,0.216,0.221,0.299,0.24,0.338,0.338,0.426,0.319,0.382,0.412,0.382,0.387,0.397,0.407,0.402,0.392,0.407,0.402,0.402,0.431,Stability,PR40A_HUMAN,Medium,Human +0.205,0.189,0.201,0.207,0.21,0.217,0.12,0.187,0.17,0.187,0.197,0.236,0.227,0.17,0.185,0.225,0.256,0.293,0.292,0.197,0.183,0.213,0.194,0.168,0.187,0.249,0.178,0.241,0.179,0.186,0.151,0.128,0.11,0.155,0.202,0.167,0.209,0.202,0.183,0.216,0.211,0.187,0.185,0.11,0.264,0.185,0.289,0.243,0.308,0.161,0.249,0.271,0.284,0.269,0.236,0.245,0.273,0.273,0.275,0.268,0.276,0.271,Expression,PRKN_HUMAN,Low,Human +0.177,0.165,0.127,0.133,0.152,0.152,0.127,0.07,0.108,0.095,0.373,0.158,0.165,0.12,0.19,0.266,0.278,0.209,0.196,0.133,0.12,0.057,0.12,0.139,0.146,0.152,0.044,0.089,0.101,0.133,0.146,0.133,0.108,0.12,0.089,0.127,0.19,0.177,0.139,0.152,0.165,0.146,0.165,0.139,0.203,0.139,0.278,0.259,0.316,0.291,0.31,0.241,0.259,0.222,0.259,0.234,0.266,0.259,0.222,0.266,0.209,0.272,Stability,PSAE_PICP2,Medium,Prokaryote +0.182,0.172,0.175,0.175,0.164,0.169,0.109,0.149,0.167,0.175,0.158,0.164,0.176,0.113,0.153,0.172,0.172,0.154,0.169,0.168,0.118,0.168,0.182,0.178,0.15,0.17,0.165,0.171,0.169,0.18,0.164,0.152,0.112,0.133,0.172,0.167,0.168,0.174,0.184,0.17,0.173,0.18,0.139,0.102,0.165,0.174,0.161,0.169,0.169,0.144,0.175,0.177,0.177,0.168,0.168,0.17,0.169,0.165,0.176,0.175,0.195,0.188,Expression,PTEN_HUMAN,Medium,Human +0.182,0.172,0.175,0.175,0.164,0.169,0.109,0.149,0.167,0.175,0.158,0.164,0.176,0.113,0.153,0.172,0.172,0.154,0.169,0.168,0.118,0.168,0.182,0.178,0.15,0.17,0.165,0.171,0.169,0.18,0.164,0.152,0.112,0.133,0.172,0.167,0.168,0.174,0.184,0.17,0.173,0.18,0.139,0.102,0.165,0.174,0.161,0.169,0.169,0.144,0.175,0.177,0.177,0.168,0.168,0.17,0.169,0.165,0.176,0.175,0.195,0.188,Activity,PTEN_HUMAN,Medium,Human +0.24,0.211,0.189,0.19,0.211,0.214,0.079,0.189,0.225,0.229,0.218,0.233,0.236,0.088,0.082,0.086,0.11,0.104,0.123,0.208,0.238,0.191,0.184,0.163,0.236,0.192,0.185,0.185,0.178,0.232,0.195,0.196,0.162,0.232,0.204,0.207,0.234,0.229,0.23,0.219,0.215,0.215,0.202,0.07,0.222,0.22,0.165,0.237,0.109,0.149,0.125,0.14,0.152,0.148,0.139,0.151,0.134,0.141,0.122,0.147,0.123,0.108,OrganismalFitness,Q2N0S5_9HIV1,Medium,Virus +0.219,0.225,0.236,0.23,0.239,0.234,0.104,0.135,0.23,0.227,0.219,0.214,0.236,0.144,0.167,0.216,0.225,0.243,0.225,0.227,0.182,0.152,0.144,0.14,0.246,0.239,0.208,0.222,0.193,0.148,0.18,0.157,0.17,0.174,0.132,0.128,0.206,0.19,0.152,0.234,0.23,0.214,0.178,0.132,0.237,0.203,0.249,0.25,0.202,0.154,0.202,0.198,0.228,0.222,0.231,0.221,0.218,0.231,0.216,0.214,0.25,0.271,Binding,Q53Z42_HUMAN,Medium,Human +0.219,0.225,0.236,0.23,0.239,0.234,0.104,0.135,0.23,0.227,0.219,0.214,0.236,0.144,0.167,0.216,0.225,0.243,0.225,0.227,0.182,0.152,0.144,0.14,0.246,0.239,0.208,0.222,0.193,0.148,0.18,0.157,0.17,0.174,0.132,0.128,0.206,0.19,0.152,0.234,0.23,0.214,0.178,0.132,0.237,0.203,0.249,0.25,0.202,0.154,0.202,0.198,0.228,0.222,0.231,0.221,0.218,0.231,0.216,0.214,0.25,0.271,Expression,Q53Z42_HUMAN,Medium,Human +0.193,0.25,0.243,0.253,0.27,0.267,0.16,0.243,0.27,0.27,0.23,0.177,0.207,0.143,0.18,0.2,0.227,0.233,0.233,0.267,0.25,0.287,0.287,0.297,0.27,0.31,0.313,0.287,0.283,0.277,0.227,0.183,0.15,0.243,0.283,0.297,0.217,0.25,0.273,0.273,0.273,0.277,0.213,0.13,0.243,0.243,0.207,0.23,0.223,0.147,0.2,0.203,0.227,0.2,0.22,0.2,0.223,0.22,0.217,0.213,0.257,0.22,Activity,Q59976_STRSQ,Medium,Prokaryote +0.136,0.145,0.111,0.11,0.127,0.124,0.08,0.097,0.132,0.132,0.1,0.092,0.079,0.074,0.078,0.072,0.07,0.072,0.072,0.119,0.088,0.109,0.082,0.116,0.093,0.07,0.074,0.062,0.077,0.149,0.117,0.117,0.087,0.095,0.089,0.115,0.112,0.117,0.112,0.123,0.128,0.125,0.099,0.079,0.084,0.092,0.186,0.152,0.18,0.148,0.131,0.138,0.114,0.149,0.144,0.145,0.141,0.133,0.135,0.141,0.11,0.102,Activity,Q6WV12_9MAXI,Low,Eukaryote +0.2,0.229,0.214,0.2,0.257,0.257,0.086,0.129,0.243,0.271,0.257,0.229,0.3,0.129,0.186,0.186,0.214,0.286,0.271,0.1,0.271,0.257,0.257,0.357,0.286,0.271,0.286,0.3,0.3,0.271,0.286,0.257,0.1,0.329,0.271,0.3,0.314,0.271,0.286,0.257,0.229,0.243,0.186,0.071,0.243,0.2,0.129,0.229,0.2,0.086,0.3,0.243,0.243,0.271,0.229,0.2,0.271,0.271,0.271,0.286,0.3,0.214,Activity,Q837P4_ENTFA,Medium,Prokaryote +0.067,0.293,0.267,0.28,0.307,0.307,0.12,0.2,0.133,0.133,0.253,0.267,0.24,0.067,0.12,0.12,0.187,0.267,0.227,0.147,0.2,0.2,0.293,0.227,0.187,0.227,0.293,0.267,0.333,0.2,0.24,0.28,0.093,0.24,0.2,0.28,0.24,0.2,0.267,0.24,0.24,0.24,0.093,0.093,0.213,0.16,0.12,0.2,0.227,0.133,0.2,0.227,0.227,0.2,0.213,0.173,0.227,0.213,0.24,0.2,0.213,0.227,Activity,Q837P5_ENTFA,Medium,Prokaryote +0.183,0.207,0.173,0.179,0.189,0.189,0.097,0.15,0.195,0.199,0.124,0.107,0.107,0.094,0.101,0.104,0.095,0.115,0.11,0.141,0.108,0.11,0.093,0.11,0.097,0.108,0.116,0.185,0.189,0.198,0.12,0.123,0.09,0.104,0.101,0.191,0.179,0.18,0.195,0.192,0.191,0.206,0.108,0.113,0.117,0.112,0.184,0.179,0.201,0.163,0.141,0.138,0.144,0.148,0.144,0.152,0.14,0.158,0.141,0.15,0.132,0.119,Activity,Q8WTC7_9CNID,Low,Eukaryote +0.169,0.171,0.148,0.161,0.166,0.161,0.077,0.12,0.059,0.059,0.086,0.058,0.056,0.068,0.07,0.073,0.084,0.147,0.147,0.117,0.138,0.15,0.145,0.15,0.134,0.131,0.129,0.119,0.143,0.147,0.119,0.119,0.113,0.112,0.113,0.15,0.161,0.155,0.164,0.164,0.164,0.168,0.058,0.056,0.079,0.07,0.154,0.122,0.169,0.147,0.098,0.122,0.119,0.112,0.113,0.103,0.092,0.092,0.105,0.096,0.106,0.077,OrganismalFitness,R1AB_SARS2,Medium,Virus +0.315,0.163,0.13,0.185,0.13,0.185,0.228,0.163,0.196,0.217,0.13,0.076,0.109,0.283,0.315,0.261,0.217,0.239,0.141,0.174,0.337,0.13,0.12,0.174,0.152,0.163,0.152,0.098,0.098,0.239,0.163,0.13,0.207,0.13,0.174,0.207,0.293,0.272,0.25,0.185,0.196,0.196,0.228,0.283,0.13,0.207,0.217,0.141,0.25,0.25,0.185,0.283,0.228,0.207,0.25,0.239,0.239,0.261,0.261,0.25,0.098,0.261,Stability,RAD_ANTMA,High,Eukaryote +0.133,0.167,0.133,0.167,0.2,0.2,0.033,0.2,0.167,0.167,0.2,0.133,0.2,0.1,0.167,0.133,0.133,0.167,0.167,0.167,0.133,0.2,0.167,0.133,0.2,0.2,0.2,0.2,0.133,0.167,0.167,0.167,0.067,0.167,0.2,0.133,0.133,0.167,0.133,0.167,0.133,0.133,0.133,0.067,0.167,0.133,0.067,0.2,0.067,0.067,0.167,0.133,0.2,0.133,0.1,0.133,0.133,0.1,0.133,0.133,0.1,0.067,OrganismalFitness,RAF1_HUMAN,Low,Human +0.166,0.092,0.086,0.092,0.08,0.089,0.073,0.057,0.092,0.086,0.086,0.076,0.061,0.162,0.182,0.156,0.111,0.099,0.105,0.127,0.096,0.099,0.07,0.08,0.086,0.08,0.08,0.067,0.067,0.108,0.054,0.073,0.064,0.115,0.086,0.08,0.124,0.105,0.111,0.092,0.089,0.089,0.134,0.229,0.115,0.115,0.191,0.111,0.121,0.194,0.118,0.115,0.127,0.108,0.111,0.118,0.115,0.115,0.089,0.118,0.083,0.153,Activity,RASH_HUMAN,High,Human +0.204,0.206,0.221,0.222,0.217,0.222,0.088,0.178,0.23,0.246,0.202,0.204,0.209,0.247,0.234,0.242,0.233,0.218,0.214,0.222,0.186,0.208,0.209,0.209,0.217,0.224,0.21,0.205,0.202,0.187,0.192,0.171,0.142,0.186,0.213,0.213,0.186,0.217,0.222,0.222,0.226,0.226,0.22,0.184,0.198,0.22,0.156,0.182,0.246,0.176,0.202,0.192,0.196,0.196,0.203,0.194,0.204,0.198,0.197,0.201,0.222,0.215,Expression,RASK_HUMAN,High,Human +0.204,0.206,0.221,0.222,0.217,0.222,0.088,0.178,0.23,0.246,0.202,0.204,0.209,0.247,0.234,0.242,0.233,0.218,0.214,0.222,0.186,0.208,0.209,0.209,0.217,0.224,0.21,0.205,0.202,0.187,0.192,0.171,0.142,0.186,0.213,0.213,0.186,0.217,0.222,0.222,0.226,0.226,0.22,0.184,0.198,0.22,0.156,0.182,0.246,0.176,0.202,0.192,0.196,0.196,0.203,0.194,0.204,0.198,0.197,0.201,0.222,0.215,Binding,RASK_HUMAN,High,Human +0.239,0.157,0.239,0.239,0.239,0.239,0.284,0.112,0.209,0.209,0.351,0.343,0.351,0.328,0.343,0.351,0.366,0.157,0.187,0.254,0.328,0.097,0.164,0.082,0.343,0.201,0.194,0.104,0.104,0.157,0.142,0.104,0.104,0.299,0.351,0.291,0.269,0.246,0.239,0.261,0.246,0.224,0.358,0.351,0.343,0.343,0.321,0.321,0.328,0.284,0.336,0.291,0.306,0.299,0.328,0.321,0.291,0.306,0.313,0.328,0.321,0.321,Stability,RBP1_HUMAN,High,Human +0.181,0.142,0.165,0.165,0.15,0.157,0.165,0.087,0.142,0.142,0.283,0.26,0.276,0.252,0.307,0.252,0.205,0.157,0.197,0.094,0.228,0.157,0.181,0.205,0.157,0.205,0.213,0.181,0.197,0.134,0.11,0.047,0.071,0.228,0.165,0.244,0.213,0.228,0.22,0.189,0.173,0.173,0.236,0.26,0.22,0.22,0.228,0.26,0.228,0.276,0.189,0.213,0.181,0.189,0.189,0.189,0.213,0.173,0.181,0.189,0.173,0.276,Stability,RCD1_ARATH,Medium,Eukaryote +0.197,0.272,0.298,0.351,0.346,0.311,0.237,0.175,0.281,0.311,0.303,0.272,0.364,0.25,0.307,0.311,0.289,0.281,0.276,0.263,0.18,0.25,0.228,0.25,0.145,0.303,0.175,0.25,0.294,0.289,0.281,0.254,0.184,0.219,0.14,0.254,0.263,0.246,0.272,0.298,0.303,0.281,0.254,0.311,0.316,0.263,0.539,0.404,0.469,0.469,0.382,0.399,0.377,0.408,0.399,0.377,0.39,0.346,0.351,0.386,0.465,0.158,Stability,RCRO_LAMBD,High,Virus +0.441,0.304,0.304,0.324,0.304,0.294,0.304,0.196,0.245,0.235,0.216,0.245,0.255,0.275,0.382,0.275,0.225,0.196,0.186,0.284,0.147,0.255,0.196,0.216,0.265,0.235,0.216,0.245,0.176,0.196,0.235,0.206,0.294,0.137,0.245,0.216,0.353,0.353,0.294,0.333,0.324,0.294,0.255,0.245,0.245,0.284,0.382,0.314,0.441,0.422,0.216,0.245,0.255,0.255,0.284,0.265,0.275,0.245,0.255,0.255,0.235,0.392,Stability,RD23A_HUMAN,High,Human +0.184,0.259,0.386,0.395,0.396,0.392,0.071,0.346,0.356,0.353,0.117,0.096,0.107,0.102,0.096,0.108,0.214,0.301,0.338,0.405,0.327,0.366,0.377,0.397,0.131,0.332,0.314,0.306,0.356,0.416,0.345,0.335,0.122,0.339,0.36,0.398,0.351,0.376,0.386,0.412,0.415,0.413,0.102,0.092,0.132,0.106,0.138,0.182,0.098,0.136,0.224,0.218,0.232,0.221,0.221,0.219,0.22,0.238,0.229,0.226,0.119,0.111,OrganismalFitness,RDRP_I33A0,Low,Virus +0.13,0.135,0.144,0.135,0.149,0.144,0.112,0.14,0.135,0.126,0.084,0.144,0.126,0.107,0.093,0.065,0.13,0.107,0.107,0.112,0.116,0.098,0.093,0.13,0.102,0.149,0.098,0.126,0.13,0.135,0.135,0.126,0.116,0.13,0.14,0.112,0.144,0.153,0.121,0.153,0.144,0.14,0.102,0.088,0.112,0.102,0.07,0.098,0.116,0.126,0.135,0.13,0.126,0.098,0.116,0.093,0.112,0.116,0.102,0.112,0.079,0.121,OrganismalFitness,REV_HV1H2,Medium,Virus +0.233,0.248,0.18,0.173,0.195,0.165,0.165,0.075,0.105,0.173,0.248,0.173,0.226,0.18,0.158,0.211,0.241,0.12,0.068,0.09,0.143,0.165,0.18,0.158,0.256,0.158,0.195,0.15,0.083,0.083,0.135,0.105,0.053,0.203,0.083,0.068,0.211,0.09,0.158,0.195,0.135,0.173,0.226,0.18,0.233,0.241,0.241,0.173,0.301,0.286,0.188,0.188,0.195,0.18,0.188,0.188,0.241,0.188,0.195,0.195,0.278,0.226,Stability,RFAH_ECOLI,High,Prokaryote +0.095,0.184,0.129,0.136,0.129,0.129,0.163,0.122,0.184,0.163,0.252,0.204,0.238,0.19,0.184,0.19,0.19,0.136,0.129,0.122,0.116,0.156,0.122,0.15,0.102,0.122,0.129,0.15,0.143,0.048,0.129,0.109,0.088,0.136,0.156,0.109,0.143,0.163,0.143,0.143,0.143,0.136,0.224,0.15,0.15,0.224,0.333,0.184,0.361,0.313,0.204,0.204,0.211,0.184,0.177,0.197,0.177,0.184,0.184,0.19,0.177,0.272,Stability,RL20_AQUAE,High,Prokaryote +0.133,0.151,0.153,0.179,0.171,0.17,0.061,0.152,0.164,0.177,0.148,0.159,0.158,0.092,0.167,0.171,0.162,0.157,0.192,0.167,0.183,0.159,0.158,0.143,0.165,0.157,0.14,0.16,0.15,0.18,0.165,0.177,0.128,0.162,0.133,0.126,0.16,0.147,0.147,0.166,0.182,0.163,0.152,0.129,0.157,0.126,0.077,0.094,0.112,0.061,0.156,0.159,0.148,0.166,0.161,0.151,0.138,0.154,0.168,0.156,0.147,0.144,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.133,0.151,0.153,0.179,0.171,0.17,0.061,0.152,0.164,0.177,0.148,0.159,0.158,0.092,0.167,0.171,0.162,0.157,0.192,0.167,0.183,0.159,0.158,0.143,0.165,0.157,0.14,0.16,0.15,0.18,0.165,0.177,0.128,0.162,0.133,0.126,0.16,0.147,0.147,0.166,0.182,0.163,0.152,0.129,0.157,0.126,0.077,0.094,0.112,0.061,0.156,0.159,0.148,0.166,0.161,0.151,0.138,0.154,0.168,0.156,0.147,0.144,OrganismalFitness,RL40A_YEAST,Medium,Eukaryote +0.133,0.151,0.153,0.179,0.171,0.17,0.061,0.152,0.164,0.177,0.148,0.159,0.158,0.092,0.167,0.171,0.162,0.157,0.192,0.167,0.183,0.159,0.158,0.143,0.165,0.157,0.14,0.16,0.15,0.18,0.165,0.177,0.128,0.162,0.133,0.126,0.16,0.147,0.147,0.166,0.182,0.163,0.152,0.129,0.157,0.126,0.077,0.094,0.112,0.061,0.156,0.159,0.148,0.166,0.161,0.151,0.138,0.154,0.168,0.156,0.147,0.144,Activity,RL40A_YEAST,Medium,Eukaryote +0.147,0.196,0.117,0.121,0.129,0.131,0.077,0.143,0.138,0.133,0.147,0.136,0.154,0.103,0.147,0.136,0.152,0.171,0.173,0.131,0.189,0.199,0.161,0.182,0.182,0.152,0.168,0.152,0.182,0.143,0.173,0.173,0.119,0.161,0.173,0.15,0.15,0.166,0.168,0.14,0.136,0.15,0.173,0.093,0.138,0.131,0.152,0.168,0.131,0.121,0.143,0.161,0.152,0.152,0.154,0.164,0.147,0.147,0.164,0.147,0.166,0.129,Activity,RNC_ECOLI,Medium,Prokaryote +0.274,0.295,0.233,0.26,0.247,0.26,0.281,0.171,0.185,0.199,0.295,0.288,0.322,0.301,0.315,0.322,0.267,0.26,0.295,0.281,0.219,0.356,0.336,0.26,0.274,0.329,0.247,0.295,0.247,0.253,0.247,0.24,0.178,0.185,0.295,0.301,0.199,0.281,0.288,0.301,0.281,0.281,0.329,0.274,0.26,0.267,0.356,0.308,0.301,0.37,0.274,0.24,0.274,0.281,0.267,0.253,0.247,0.26,0.247,0.26,0.322,0.308,Stability,RPC1_BP434,High,Virus +0.166,0.166,0.166,0.18,0.166,0.166,0.153,0.07,0.222,0.194,0.125,0.166,0.194,0.208,0.18,0.194,0.223,0.152,0.153,0.208,0.125,0.194,0.236,0.153,0.138,0.166,0.18,0.18,0.166,0.208,0.111,0.084,0.111,0.18,0.194,0.152,0.166,0.166,0.167,0.18,0.166,0.166,0.167,0.208,0.166,0.18,0.152,0.25,0.18,0.112,0.194,0.18,0.18,0.194,0.209,0.209,0.209,0.209,0.194,0.223,0.236,0.18,Activity,RPC1_LAMBD,High,Virus +0.166,0.166,0.166,0.18,0.166,0.166,0.153,0.07,0.222,0.194,0.125,0.166,0.194,0.208,0.18,0.194,0.223,0.152,0.153,0.208,0.125,0.194,0.236,0.153,0.138,0.166,0.18,0.18,0.166,0.208,0.111,0.084,0.111,0.18,0.194,0.152,0.166,0.166,0.167,0.18,0.166,0.166,0.167,0.208,0.166,0.18,0.152,0.25,0.18,0.112,0.194,0.18,0.18,0.194,0.209,0.209,0.209,0.209,0.194,0.223,0.236,0.18,Activity,RPC1_LAMBD,High,Virus +0.142,0.083,0.108,0.117,0.108,0.117,0.092,0.083,0.108,0.092,0.108,0.1,0.083,0.15,0.208,0.192,0.092,0.125,0.142,0.067,0.108,0.108,0.075,0.083,0.133,0.083,0.125,0.1,0.117,0.05,0.075,0.042,0.183,0.117,0.083,0.1,0.133,0.092,0.1,0.117,0.083,0.083,0.192,0.15,0.125,0.217,0.275,0.15,0.158,0.292,0.117,0.117,0.108,0.142,0.117,0.092,0.108,0.108,0.125,0.125,0.15,0.325,Stability,RS15_GEOSE,Medium,Prokaryote +0.179,0.217,0.238,0.244,0.233,0.236,0.155,0.182,0.232,0.236,0.227,0.238,0.235,0.18,0.178,0.205,0.246,0.238,0.218,0.102,0.217,0.233,0.214,0.202,0.211,0.222,0.216,0.234,0.196,0.218,0.228,0.182,0.135,0.196,0.233,0.229,0.212,0.25,0.244,0.243,0.238,0.244,0.19,0.139,0.24,0.189,0.205,0.26,0.207,0.148,0.241,0.232,0.251,0.254,0.249,0.244,0.246,0.246,0.24,0.25,0.238,0.208,Expression,S22A1_HUMAN,Medium,Human +0.179,0.217,0.238,0.244,0.233,0.236,0.155,0.182,0.232,0.236,0.227,0.238,0.235,0.18,0.178,0.205,0.246,0.238,0.218,0.102,0.217,0.233,0.214,0.202,0.211,0.222,0.216,0.234,0.196,0.218,0.228,0.182,0.135,0.196,0.233,0.229,0.212,0.25,0.244,0.243,0.238,0.244,0.19,0.139,0.24,0.189,0.205,0.26,0.207,0.148,0.241,0.232,0.251,0.254,0.249,0.244,0.246,0.246,0.24,0.25,0.238,0.208,Activity,S22A1_HUMAN,Medium,Human +0.454,0.381,0.423,0.433,0.464,0.474,0.309,0.103,0.454,0.474,0.258,0.268,0.258,0.196,0.423,0.412,0.258,0.278,0.175,0.206,0.103,0.103,0.072,0.093,0.268,0.134,0.103,0.093,0.103,0.062,0.216,0.206,0.144,0.216,0.196,0.165,0.402,0.412,0.392,0.433,0.443,0.433,0.361,0.216,0.34,0.32,0.381,0.371,0.392,0.412,0.351,0.289,0.361,0.268,0.371,0.361,0.309,0.351,0.33,0.351,0.227,0.412,Stability,SAV1_MOUSE,High,Eukaryote +0.204,0.194,0.223,0.214,0.233,0.214,0.194,0.107,0.214,0.282,0.262,0.184,0.204,0.155,0.175,0.223,0.32,0.272,0.223,0.126,0.194,0.204,0.184,0.165,0.175,0.184,0.204,0.165,0.184,0.165,0.291,0.184,0.117,0.146,0.146,0.126,0.175,0.194,0.204,0.214,0.223,0.223,0.184,0.223,0.214,0.184,0.311,0.33,0.311,0.34,0.291,0.311,0.34,0.32,0.311,0.282,0.272,0.359,0.359,0.34,0.301,0.272,Stability,SBI_STAAM,Medium,Prokaryote +0.164,0.22,0.201,0.202,0.218,0.204,0.159,0.298,0.217,0.238,0.242,0.212,0.222,0.106,0.135,0.177,0.196,0.213,0.217,0.262,0.244,0.306,0.304,0.286,0.251,0.294,0.285,0.273,0.269,0.226,0.195,0.139,0.187,0.257,0.294,0.289,0.215,0.241,0.251,0.209,0.221,0.217,0.205,0.096,0.247,0.213,0.232,0.266,0.219,0.142,0.193,0.206,0.221,0.219,0.211,0.215,0.21,0.199,0.187,0.201,0.221,0.205,Activity,SC6A4_HUMAN,Medium,Human +0.156,0.139,0.123,0.139,0.131,0.139,0.238,0.074,0.23,0.189,0.246,0.246,0.254,0.172,0.246,0.279,0.328,0.262,0.303,0.098,0.164,0.189,0.197,0.23,0.27,0.254,0.279,0.254,0.238,0.131,0.098,0.082,0.148,0.115,0.123,0.213,0.164,0.172,0.172,0.123,0.148,0.156,0.254,0.238,0.262,0.27,0.287,0.303,0.221,0.197,0.328,0.336,0.336,0.352,0.32,0.402,0.311,0.385,0.27,0.352,0.369,0.393,Stability,SCIN_STAAR,High,Prokaryote +0.13,0.087,0.13,0.13,0.13,0.13,0.217,0.174,0.174,0.174,0.13,0.13,0.174,0.174,0.13,0.087,0.13,0.13,0.174,0.174,0.13,0.174,0.13,0.13,0.13,0.13,0.13,0.087,0.13,0.13,0.087,0.174,0.174,0.087,0.13,0.174,0.043,0.13,0.174,0.087,0.13,0.13,0.174,0.174,0.174,0.087,0.043,0.043,0.13,0.043,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.174,0.043,0.13,OrganismalFitness,SCN5A_HUMAN,Medium,Human +0.531,0.52,0.574,0.563,0.588,0.596,0.13,0.458,0.552,0.578,0.538,0.495,0.516,0.209,0.264,0.523,0.538,0.567,0.545,0.531,0.141,0.181,0.191,0.274,0.191,0.466,0.101,0.347,0.52,0.592,0.509,0.469,0.014,0.116,0.264,0.462,0.542,0.542,0.523,0.578,0.585,0.574,0.224,0.184,0.52,0.264,0.534,0.552,0.592,0.563,0.563,0.567,0.57,0.585,0.57,0.57,0.592,0.578,0.596,0.592,0.592,0.585,Stability,SDA_BACSU,Medium,Prokaryote +0.13,0.156,0.135,0.146,0.141,0.156,0.089,0.146,0.135,0.13,0.135,0.156,0.12,0.073,0.12,0.141,0.12,0.146,0.125,0.135,0.13,0.13,0.151,0.141,0.109,0.109,0.13,0.13,0.156,0.151,0.13,0.161,0.115,0.135,0.167,0.156,0.146,0.135,0.151,0.146,0.141,0.141,0.125,0.073,0.125,0.12,0.135,0.125,0.141,0.141,0.141,0.125,0.13,0.135,0.125,0.109,0.12,0.141,0.12,0.125,0.151,0.135,OrganismalFitness,SERC_HUMAN,High,Human +0.14,0.148,0.156,0.156,0.156,0.154,0.123,0.143,0.16,0.15,0.139,0.145,0.147,0.124,0.12,0.117,0.138,0.127,0.117,0.144,0.124,0.125,0.138,0.123,0.109,0.127,0.134,0.141,0.137,0.143,0.15,0.153,0.111,0.12,0.142,0.144,0.132,0.148,0.136,0.149,0.153,0.157,0.127,0.126,0.138,0.119,0.139,0.139,0.119,0.119,0.143,0.151,0.166,0.146,0.152,0.158,0.154,0.142,0.138,0.148,0.124,0.123,OrganismalFitness,SHOC2_HUMAN,Medium,Human +0.168,0.149,0.089,0.109,0.119,0.129,0.139,0.089,0.089,0.139,0.139,0.158,0.129,0.178,0.149,0.208,0.188,0.178,0.109,0.099,0.089,0.05,0.099,0.069,0.099,0.089,0.079,0.099,0.119,0.099,0.099,0.089,0.099,0.099,0.05,0.079,0.149,0.099,0.099,0.149,0.119,0.109,0.149,0.099,0.109,0.178,0.139,0.139,0.139,0.178,0.178,0.218,0.228,0.208,0.198,0.208,0.178,0.188,0.178,0.218,0.178,0.158,Stability,SOX30_HUMAN,High,Human +0.327,0.384,0.346,0.346,0.36,0.351,0.085,0.275,0.251,0.246,0.289,0.095,0.1,0.076,0.104,0.071,0.152,0.171,0.142,0.327,0.028,0.118,0.308,0.156,0.081,0.057,0.109,0.303,0.322,0.218,0.237,0.204,0.043,0.09,0.085,0.123,0.332,0.336,0.332,0.37,0.379,0.379,0.085,0.1,0.185,0.166,0.28,0.265,0.37,0.355,0.341,0.384,0.389,0.417,0.351,0.379,0.36,0.398,0.351,0.393,0.37,0.318,Stability,SPA_STAAU,Medium,Prokaryote +0.172,0.206,0.14,0.152,0.202,0.204,0.132,0.124,0.184,0.218,0.25,0.242,0.233,0.248,0.242,0.244,0.256,0.257,0.268,0.222,0.222,0.209,0.213,0.222,0.207,0.223,0.227,0.222,0.224,0.207,0.286,0.281,0.139,0.213,0.194,0.214,0.194,0.188,0.202,0.21,0.207,0.215,0.13,0.122,0.161,0.127,0.224,0.214,0.275,0.191,0.292,0.27,0.281,0.282,0.276,0.274,0.294,0.287,0.289,0.287,0.271,0.257,Binding,SPG1_STRSG,Low,Prokaryote +0.172,0.206,0.14,0.152,0.202,0.204,0.132,0.124,0.184,0.218,0.25,0.242,0.233,0.248,0.242,0.244,0.256,0.257,0.268,0.222,0.222,0.209,0.213,0.222,0.207,0.223,0.227,0.222,0.224,0.207,0.286,0.281,0.139,0.213,0.194,0.214,0.194,0.188,0.202,0.21,0.207,0.215,0.13,0.122,0.161,0.127,0.224,0.214,0.275,0.191,0.292,0.27,0.281,0.282,0.276,0.274,0.294,0.287,0.289,0.287,0.271,0.257,Binding,SPG1_STRSG,Low,Prokaryote +0.172,0.206,0.14,0.152,0.202,0.204,0.132,0.124,0.184,0.218,0.25,0.242,0.233,0.248,0.242,0.244,0.256,0.257,0.268,0.222,0.222,0.209,0.213,0.222,0.207,0.223,0.227,0.222,0.224,0.207,0.286,0.281,0.139,0.213,0.194,0.214,0.194,0.188,0.202,0.21,0.207,0.215,0.13,0.122,0.161,0.127,0.224,0.214,0.275,0.191,0.292,0.27,0.281,0.282,0.276,0.274,0.294,0.287,0.289,0.287,0.271,0.257,Binding,SPG1_STRSG,Medium,Prokaryote +0.172,0.206,0.14,0.152,0.202,0.204,0.132,0.124,0.184,0.218,0.25,0.242,0.233,0.248,0.242,0.244,0.256,0.257,0.268,0.222,0.222,0.209,0.213,0.222,0.207,0.223,0.227,0.222,0.224,0.207,0.286,0.281,0.139,0.213,0.194,0.214,0.194,0.188,0.202,0.21,0.207,0.215,0.13,0.122,0.161,0.127,0.224,0.214,0.275,0.191,0.292,0.27,0.281,0.282,0.276,0.274,0.294,0.287,0.289,0.287,0.271,0.257,Binding,SPG1_STRSG,Medium,Prokaryote +0.151,0.096,0.13,0.11,0.144,0.13,0.219,0.151,0.137,0.205,0.233,0.24,0.205,0.171,0.219,0.199,0.226,0.274,0.233,0.192,0.185,0.253,0.199,0.144,0.123,0.205,0.178,0.26,0.082,0.212,0.144,0.103,0.021,0.164,0.301,0.137,0.212,0.233,0.171,0.164,0.158,0.158,0.205,0.158,0.199,0.178,0.205,0.212,0.212,0.185,0.185,0.192,0.26,0.212,0.219,0.233,0.219,0.192,0.185,0.212,0.288,0.205,Stability,SPG2_STRSG,Medium,Prokaryote +0.148,0.23,0.16,0.194,0.214,0.218,0.098,0.211,0.222,0.238,0.1,0.11,0.111,0.106,0.13,0.112,0.111,0.096,0.084,0.229,0.186,0.2,0.192,0.186,0.204,0.194,0.18,0.198,0.179,0.232,0.207,0.207,0.144,0.19,0.198,0.219,0.192,0.201,0.213,0.222,0.23,0.229,0.102,0.098,0.119,0.117,0.324,0.296,0.261,0.162,0.196,0.239,0.25,0.237,0.25,0.242,0.233,0.215,0.225,0.246,0.274,0.179,Binding,SPIKE_SARS2,Medium,Virus +0.148,0.23,0.16,0.194,0.214,0.218,0.098,0.211,0.222,0.238,0.1,0.11,0.111,0.106,0.13,0.112,0.111,0.096,0.084,0.229,0.186,0.2,0.192,0.186,0.204,0.194,0.18,0.198,0.179,0.232,0.207,0.207,0.144,0.19,0.198,0.219,0.192,0.201,0.213,0.222,0.23,0.229,0.102,0.098,0.119,0.117,0.324,0.296,0.261,0.162,0.196,0.239,0.25,0.237,0.25,0.242,0.233,0.215,0.225,0.246,0.274,0.179,Expression,SPIKE_SARS2,Medium,Virus +0.399,0.486,0.396,0.374,0.433,0.396,0.231,0.318,0.371,0.368,0.495,0.386,0.449,0.065,0.417,0.436,0.386,0.517,0.399,0.427,0.38,0.452,0.38,0.393,0.399,0.467,0.421,0.449,0.47,0.427,0.47,0.486,0.1,0.374,0.33,0.346,0.411,0.433,0.396,0.396,0.396,0.399,0.283,0.125,0.346,0.312,0.374,0.383,0.567,0.558,0.558,0.548,0.558,0.539,0.558,0.548,0.551,0.567,0.536,0.567,0.533,0.349,Stability,SPTN1_CHICK,High,Eukaryote +0.296,0.324,0.423,0.437,0.437,0.423,0.042,0.239,0.324,0.38,0.31,0.155,0.296,0.155,0.254,0.282,0.352,0.324,0.352,0.338,0.141,0.296,0.211,0.296,0.324,0.31,0.268,0.268,0.31,0.268,0.239,0.254,0.169,0.183,0.254,0.324,0.338,0.423,0.394,0.451,0.423,0.408,0.183,0.169,0.282,0.38,0.408,0.282,0.366,0.408,0.366,0.366,0.352,0.338,0.296,0.38,0.324,0.296,0.296,0.352,0.296,0.324,Stability,SQSTM_MOUSE,Medium,Eukaryote +0.308,0.308,0.302,0.283,0.296,0.302,0.057,0.157,0.252,0.264,0.377,0.327,0.34,0.038,0.346,0.371,0.384,0.409,0.409,0.308,0.044,0.189,0.22,0.17,0.245,0.233,0.277,0.296,0.302,0.189,0.327,0.264,0.126,0.189,0.132,0.208,0.352,0.289,0.296,0.321,0.314,0.296,0.277,0.057,0.358,0.333,0.233,0.384,0.428,0.453,0.346,0.34,0.358,0.39,0.352,0.327,0.365,0.39,0.365,0.358,0.44,0.365,Stability,SR43C_ARATH,High,Eukaryote +0.231,0.179,0.378,0.333,0.256,0.301,0.218,0.237,0.276,0.314,0.308,0.288,0.301,0.179,0.333,0.301,0.263,0.288,0.333,0.199,0.269,0.244,0.269,0.205,0.231,0.237,0.263,0.192,0.212,0.301,0.186,0.167,0.212,0.237,0.231,0.269,0.256,0.256,0.276,0.308,0.321,0.295,0.231,0.083,0.269,0.288,0.301,0.218,0.378,0.353,0.327,0.327,0.314,0.308,0.314,0.333,0.321,0.327,0.295,0.333,0.237,0.327,Stability,SRBS1_HUMAN,High,Human +0.113,0.095,0.092,0.094,0.085,0.079,0.113,0.074,0.082,0.084,0.079,0.093,0.09,0.14,0.149,0.113,0.101,0.11,0.089,0.07,0.08,0.059,0.083,0.062,0.076,0.075,0.081,0.066,0.064,0.115,0.07,0.092,0.114,0.078,0.069,0.075,0.081,0.081,0.092,0.084,0.076,0.081,0.126,0.131,0.067,0.097,0.101,0.08,0.074,0.092,0.114,0.126,0.1,0.114,0.121,0.094,0.095,0.119,0.11,0.11,0.085,0.144,Activity,SRC_HUMAN,Medium,Human +0.113,0.095,0.092,0.094,0.085,0.079,0.113,0.074,0.082,0.084,0.079,0.093,0.09,0.14,0.149,0.113,0.101,0.11,0.089,0.07,0.08,0.059,0.083,0.062,0.076,0.075,0.081,0.066,0.064,0.115,0.07,0.092,0.114,0.078,0.069,0.075,0.081,0.081,0.092,0.084,0.076,0.081,0.126,0.131,0.067,0.097,0.101,0.08,0.074,0.092,0.114,0.126,0.1,0.114,0.121,0.094,0.095,0.119,0.11,0.11,0.085,0.144,Activity,SRC_HUMAN,Medium,Human +0.113,0.095,0.092,0.094,0.085,0.079,0.113,0.074,0.082,0.084,0.079,0.093,0.09,0.14,0.149,0.113,0.101,0.11,0.089,0.07,0.08,0.059,0.083,0.062,0.076,0.075,0.081,0.066,0.064,0.115,0.07,0.092,0.114,0.078,0.069,0.075,0.081,0.081,0.092,0.084,0.076,0.081,0.126,0.131,0.067,0.097,0.101,0.08,0.074,0.092,0.114,0.126,0.1,0.114,0.121,0.094,0.095,0.119,0.11,0.11,0.085,0.144,OrganismalFitness,SRC_HUMAN,Medium,Human +0.118,0.141,0.124,0.141,0.112,0.118,0.082,0.165,0.165,0.147,0.229,0.188,0.206,0.071,0.141,0.153,0.182,0.153,0.159,0.176,0.118,0.159,0.153,0.176,0.182,0.176,0.147,0.2,0.188,0.329,0.194,0.2,0.088,0.088,0.176,0.176,0.124,0.135,0.188,0.129,0.129,0.141,0.124,0.082,0.171,0.129,0.129,0.182,0.188,0.141,0.153,0.153,0.159,0.165,0.171,0.165,0.165,0.176,0.176,0.176,0.194,0.106,OrganismalFitness,SUMO1_HUMAN,High,Human +0.064,0.032,0.036,0.024,0.036,0.024,0.08,0.044,0.052,0.036,0.028,0.04,0.024,0.036,0.06,0.044,0.032,0.032,0.032,0.04,0.056,0.044,0.02,0.04,0.048,0.028,0.032,0.012,0.036,0.06,0.044,0.06,0.032,0.028,0.036,0.036,0.02,0.032,0.024,0.028,0.028,0.02,0.052,0.064,0.024,0.044,0.064,0.024,0.04,0.076,0.036,0.024,0.032,0.016,0.036,0.04,0.048,0.036,0.044,0.028,0.052,0.048,OrganismalFitness,SYUA_HUMAN,Medium,Human +0.008,0.017,0.008,0.008,0.008,0.008,0.267,0.0,0.008,0.008,0.067,0.092,0.042,0.125,0.133,0.1,0.05,0.017,0.008,0.008,0.158,0.017,0.008,0.008,0.217,0.033,0.0,0.008,0.008,0.008,0.0,0.0,0.017,0.2,0.267,0.0,0.05,0.092,0.008,0.008,0.017,0.0,0.183,0.25,0.067,0.083,0.2,0.067,0.208,0.117,0.142,0.1,0.1,0.108,0.092,0.117,0.058,0.083,0.1,0.108,0.142,0.158,OrganismalFitness,TADBP_HUMAN,Low,Human +0.12,0.139,0.139,0.139,0.139,0.139,0.076,0.133,0.158,0.171,0.127,0.152,0.139,0.114,0.127,0.082,0.089,0.108,0.12,0.158,0.12,0.152,0.152,0.158,0.133,0.146,0.101,0.133,0.133,0.158,0.095,0.095,0.12,0.127,0.139,0.146,0.139,0.127,0.127,0.146,0.139,0.146,0.082,0.063,0.108,0.114,0.101,0.108,0.101,0.089,0.07,0.101,0.133,0.133,0.108,0.101,0.082,0.082,0.133,0.095,0.089,0.114,OrganismalFitness,TAT_HV1BR,High,Virus +0.396,0.34,0.377,0.387,0.349,0.349,0.443,0.085,0.368,0.302,0.104,0.217,0.226,0.368,0.17,0.283,0.358,0.151,0.226,0.132,0.094,0.075,0.104,0.142,0.123,0.085,0.104,0.142,0.057,0.085,0.075,0.047,0.349,0.132,0.16,0.274,0.302,0.34,0.34,0.311,0.34,0.311,0.33,0.16,0.17,0.264,0.189,0.142,0.274,0.358,0.311,0.245,0.208,0.349,0.283,0.34,0.292,0.302,0.245,0.292,0.358,0.236,Stability,TCRG1_MOUSE,Medium,Eukaryote +0.297,0.289,0.352,0.375,0.391,0.391,0.211,0.227,0.312,0.359,0.312,0.375,0.352,0.102,0.352,0.344,0.336,0.328,0.359,0.391,0.164,0.289,0.227,0.141,0.273,0.305,0.086,0.289,0.422,0.336,0.281,0.219,0.352,0.117,0.336,0.211,0.367,0.43,0.352,0.367,0.422,0.391,0.281,0.258,0.383,0.297,0.422,0.445,0.445,0.508,0.375,0.359,0.398,0.359,0.383,0.359,0.367,0.359,0.344,0.352,0.406,0.391,Stability,THO1_YEAST,High,Eukaryote +0.297,0.277,0.243,0.243,0.216,0.216,0.034,0.196,0.297,0.338,0.25,0.23,0.25,0.061,0.27,0.291,0.257,0.25,0.243,0.196,0.209,0.291,0.257,0.277,0.216,0.284,0.203,0.264,0.23,0.23,0.297,0.284,0.142,0.135,0.209,0.216,0.277,0.291,0.27,0.236,0.23,0.236,0.25,0.162,0.351,0.236,0.345,0.345,0.372,0.419,0.223,0.297,0.27,0.264,0.27,0.291,0.297,0.277,0.284,0.264,0.338,0.311,Stability,TNKS2_HUMAN,High,Human +0.135,0.129,0.132,0.11,0.122,0.122,0.097,0.132,0.135,0.116,0.141,0.15,0.119,0.091,0.129,0.132,0.125,0.129,0.144,0.122,0.094,0.132,0.107,0.141,0.113,0.144,0.125,0.11,0.135,0.15,0.129,0.119,0.088,0.094,0.144,0.138,0.129,0.125,0.138,0.122,0.119,0.135,0.103,0.088,0.116,0.094,0.147,0.138,0.141,0.138,0.132,0.147,0.129,0.15,0.144,0.141,0.141,0.135,0.135,0.122,0.141,0.113,OrganismalFitness,TPK1_HUMAN,Medium,Human +0.181,0.219,0.216,0.197,0.195,0.197,0.107,0.189,0.189,0.192,0.205,0.197,0.2,0.132,0.156,0.205,0.203,0.2,0.184,0.208,0.137,0.164,0.205,0.205,0.167,0.186,0.208,0.192,0.205,0.216,0.244,0.2,0.132,0.129,0.2,0.175,0.175,0.205,0.175,0.205,0.214,0.208,0.132,0.09,0.205,0.173,0.178,0.238,0.222,0.173,0.175,0.173,0.178,0.175,0.181,0.178,0.173,0.197,0.178,0.178,0.197,0.181,Expression,TPMT_HUMAN,Medium,Human +0.368,0.211,0.404,0.281,0.228,0.228,0.351,0.228,0.281,0.228,0.298,0.281,0.298,0.211,0.263,0.263,0.193,0.228,0.246,0.175,0.246,0.281,0.228,0.263,0.298,0.193,0.351,0.351,0.298,0.175,0.175,0.123,0.386,0.246,0.263,0.281,0.368,0.351,0.263,0.281,0.298,0.228,0.228,0.246,0.211,0.298,0.105,0.281,0.298,0.193,0.263,0.246,0.263,0.263,0.281,0.281,0.281,0.263,0.281,0.281,0.281,0.368,OrganismalFitness,TPOR_HUMAN,Low,Human +0.382,0.513,0.461,0.474,0.48,0.474,0.125,0.414,0.48,0.48,0.454,0.428,0.434,0.191,0.329,0.401,0.48,0.5,0.447,0.48,0.178,0.355,0.388,0.414,0.395,0.322,0.401,0.421,0.487,0.513,0.474,0.395,0.158,0.322,0.355,0.342,0.375,0.414,0.408,0.461,0.454,0.454,0.237,0.145,0.388,0.296,0.283,0.434,0.454,0.151,0.428,0.441,0.428,0.461,0.421,0.447,0.421,0.447,0.441,0.467,0.513,0.329,OrganismalFitness,TRPC_SACS2,Medium,Prokaryote +0.303,0.355,0.276,0.316,0.342,0.329,0.197,0.243,0.289,0.336,0.316,0.309,0.336,0.211,0.316,0.336,0.368,0.349,0.336,0.355,0.23,0.283,0.316,0.382,0.329,0.329,0.316,0.362,0.368,0.224,0.336,0.257,0.092,0.27,0.349,0.362,0.309,0.329,0.322,0.336,0.342,0.336,0.316,0.086,0.349,0.303,0.309,0.368,0.316,0.191,0.336,0.349,0.329,0.362,0.342,0.322,0.342,0.322,0.289,0.336,0.382,0.375,OrganismalFitness,TRPC_THEMA,Medium,Prokaryote +0.152,0.187,0.171,0.171,0.187,0.187,0.07,0.125,0.14,0.156,0.16,0.144,0.144,0.074,0.093,0.144,0.152,0.16,0.148,0.16,0.109,0.121,0.128,0.109,0.128,0.144,0.128,0.136,0.125,0.144,0.097,0.101,0.054,0.109,0.113,0.128,0.136,0.148,0.16,0.167,0.183,0.175,0.144,0.086,0.128,0.171,0.156,0.16,0.132,0.144,0.144,0.163,0.191,0.16,0.152,0.175,0.167,0.148,0.148,0.16,0.144,0.132,OrganismalFitness,UBC9_HUMAN,Medium,Human +0.251,0.386,0.408,0.399,0.427,0.424,0.193,0.333,0.383,0.43,0.49,0.433,0.455,0.229,0.427,0.51,0.499,0.477,0.46,0.402,0.143,0.38,0.397,0.43,0.322,0.408,0.413,0.405,0.419,0.386,0.466,0.405,0.328,0.132,0.16,0.446,0.311,0.311,0.402,0.435,0.438,0.427,0.264,0.171,0.27,0.311,0.372,0.325,0.521,0.342,0.512,0.545,0.534,0.534,0.543,0.51,0.512,0.529,0.526,0.526,0.499,0.388,Stability,UBE4B_HUMAN,High,Human +0.156,0.122,0.122,0.122,0.1,0.122,0.122,0.122,0.133,0.111,0.111,0.133,0.111,0.078,0.122,0.122,0.122,0.122,0.089,0.056,0.089,0.056,0.089,0.1,0.078,0.089,0.089,0.1,0.1,0.111,0.056,0.067,0.1,0.111,0.078,0.089,0.156,0.156,0.156,0.122,0.122,0.122,0.1,0.144,0.067,0.122,0.133,0.089,0.044,0.156,0.156,0.122,0.133,0.122,0.133,0.167,0.144,0.133,0.133,0.156,0.167,0.156,Activity,UBE4B_MOUSE,Low,Eukaryote +0.171,0.192,0.137,0.151,0.164,0.164,0.185,0.068,0.158,0.144,0.13,0.185,0.24,0.192,0.192,0.205,0.096,0.205,0.185,0.13,0.137,0.158,0.164,0.178,0.199,0.13,0.178,0.158,0.192,0.178,0.164,0.089,0.185,0.185,0.178,0.192,0.144,0.164,0.164,0.164,0.158,0.192,0.212,0.212,0.212,0.164,0.226,0.185,0.192,0.253,0.164,0.219,0.247,0.24,0.24,0.212,0.219,0.226,0.212,0.233,0.212,0.24,Stability,UBR5_HUMAN,Medium,Human +0.288,0.178,0.164,0.164,0.192,0.205,0.233,0.11,0.178,0.26,0.178,0.274,0.247,0.274,0.219,0.192,0.247,0.178,0.123,0.123,0.233,0.219,0.11,0.11,0.205,0.096,0.151,0.123,0.082,0.205,0.096,0.068,0.123,0.192,0.26,0.082,0.233,0.329,0.164,0.205,0.205,0.164,0.205,0.26,0.219,0.164,0.082,0.068,0.096,0.205,0.205,0.151,0.233,0.205,0.233,0.233,0.205,0.247,0.274,0.219,0.26,0.192,Stability,VG08_BPP22,High,Virus +0.249,0.436,0.502,0.525,0.486,0.502,0.125,0.28,0.444,0.459,0.432,0.412,0.444,0.171,0.117,0.463,0.553,0.494,0.401,0.467,0.109,0.265,0.284,0.296,0.346,0.346,0.331,0.292,0.428,0.447,0.463,0.424,0.187,0.148,0.346,0.319,0.346,0.42,0.385,0.467,0.486,0.463,0.187,0.237,0.479,0.385,0.428,0.475,0.541,0.397,0.541,0.51,0.545,0.545,0.514,0.556,0.553,0.533,0.525,0.556,0.556,0.424,Stability,VILI_CHICK,High,Eukaryote +0.212,0.172,0.2,0.195,0.184,0.191,0.104,0.16,0.184,0.188,0.148,0.168,0.169,0.098,0.164,0.199,0.188,0.182,0.167,0.204,0.14,0.126,0.196,0.164,0.156,0.129,0.155,0.138,0.112,0.188,0.14,0.138,0.156,0.108,0.126,0.12,0.186,0.188,0.164,0.176,0.166,0.186,0.104,0.08,0.169,0.15,0.188,0.18,0.193,0.153,0.17,0.169,0.174,0.193,0.226,0.188,0.175,0.162,0.172,0.182,0.156,0.172,Expression,VKOR1_HUMAN,Medium,Human +0.212,0.172,0.2,0.195,0.184,0.191,0.104,0.16,0.184,0.188,0.148,0.168,0.169,0.098,0.164,0.199,0.188,0.182,0.167,0.204,0.14,0.126,0.196,0.164,0.156,0.129,0.155,0.138,0.112,0.188,0.14,0.138,0.156,0.108,0.126,0.12,0.186,0.188,0.164,0.176,0.166,0.186,0.104,0.08,0.169,0.15,0.188,0.18,0.193,0.153,0.17,0.169,0.174,0.193,0.226,0.188,0.175,0.162,0.172,0.182,0.156,0.172,Activity,VKOR1_HUMAN,Medium,Human +0.048,0.152,0.143,0.162,0.133,0.124,0.076,0.048,0.095,0.057,0.181,0.133,0.133,0.114,0.133,0.19,0.229,0.257,0.162,0.076,0.086,0.143,0.095,0.133,0.152,0.105,0.086,0.124,0.248,0.124,0.2,0.19,0.114,0.105,0.162,0.133,0.076,0.095,0.095,0.143,0.133,0.124,0.152,0.105,0.181,0.124,0.276,0.238,0.21,0.267,0.333,0.267,0.333,0.333,0.295,0.362,0.352,0.324,0.343,0.352,0.381,0.267,Stability,VRPI_BPT7,Medium,Virus +0.18,0.275,0.302,0.302,0.296,0.296,0.069,0.249,0.36,0.349,0.265,0.122,0.312,0.079,0.095,0.344,0.392,0.487,0.439,0.317,0.048,0.058,0.048,0.212,0.063,0.074,0.053,0.074,0.36,0.328,0.307,0.265,0.053,0.048,0.116,0.392,0.18,0.243,0.333,0.296,0.312,0.339,0.164,0.079,0.18,0.175,0.439,0.392,0.434,0.365,0.429,0.444,0.381,0.423,0.429,0.45,0.439,0.429,0.402,0.444,0.439,0.222,Stability,YAIA_ECOLI,Medium,Prokaryote +0.22,0.155,0.232,0.231,0.233,0.228,0.176,0.073,0.065,0.057,0.175,0.135,0.116,0.181,0.202,0.225,0.209,0.191,0.152,0.093,0.08,0.068,0.065,0.086,0.096,0.06,0.066,0.089,0.062,0.136,0.125,0.136,0.138,0.133,0.067,0.092,0.188,0.127,0.15,0.209,0.184,0.204,0.259,0.034,0.178,0.263,0.148,0.182,0.125,0.162,0.115,0.123,0.138,0.151,0.107,0.13,0.149,0.147,0.166,0.137,0.234,0.195,Binding,YAP1_HUMAN,Low,Human +0.374,0.374,0.378,0.374,0.378,0.378,0.383,0.33,0.391,0.409,0.457,0.448,0.457,0.461,0.474,0.439,0.483,0.47,0.43,0.378,0.435,0.491,0.509,0.517,0.478,0.443,0.478,0.43,0.422,0.435,0.422,0.391,0.439,0.435,0.452,0.426,0.426,0.391,0.383,0.4,0.387,0.391,0.422,0.361,0.422,0.439,0.439,0.496,0.548,0.53,0.53,0.474,0.504,0.504,0.513,0.517,0.496,0.5,0.491,0.496,0.457,0.591,Stability,YNZC_BACSU,Medium,Prokaryote +0.207,0.23,0.231,0.233,0.237,0.237,0.14,0.181,0.221,0.232,0.213,0.201,0.215,0.147,0.185,0.208,0.223,0.218,0.217,0.21,0.179,0.191,0.199,0.196,0.184,0.203,0.201,0.206,0.209,0.218,0.212,0.194,0.132,0.174,0.186,0.203,0.219,0.222,0.227,0.237,0.236,0.237,0.175,0.132,0.209,0.188,0.221,0.228,0.23,0.203,0.235,0.238,0.238,0.238,0.238,0.239,0.235,0.237,0.235,0.241,0.235,0.214,,,, diff --git a/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.csv b/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.csv new file mode 100644 index 0000000..00ea548 --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.csv @@ -0,0 +1,63 @@ +Model_rank,Model_name,Model type,Average_Top_recall,Bootstrap_standard_error_Top_recall,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,SaProt (650M),Hybrid - Structure & PLM,0.232,0.0,0.191,0.233,0.235,0.17,0.331,0.191,0.232,0.278,0.23,0.269,0.266,0.196,0.207,0.261,0.189,0.185,0.244,SaProt (650M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +2,TranceptEVE S,Hybrid - Alignment & PLM,0.232,0.006,0.198,0.223,0.226,0.228,0.286,0.231,0.242,0.254,0.226,0.27,0.231,0.286,0.21,0.258,0.213,0.208,0.278,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +3,EVE (single),Alignment-based model,0.23,0.006,0.199,0.217,0.223,0.228,0.284,0.226,0.241,0.254,0.225,0.27,0.235,0.281,0.208,0.258,0.239,0.217,0.279,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +4,TranceptEVE M,Hybrid - Alignment & PLM,0.23,0.006,0.198,0.218,0.227,0.228,0.28,0.23,0.242,0.248,0.224,0.271,0.227,0.286,0.209,0.252,0.213,0.212,0.278,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +5,TranceptEVE L,Hybrid - Alignment & PLM,0.23,0.006,0.2,0.218,0.224,0.229,0.279,0.227,0.241,0.251,0.222,0.271,0.234,0.283,0.208,0.257,0.23,0.204,0.277,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +6,EVE (ensemble),Alignment-based model,0.23,0.006,0.199,0.214,0.225,0.228,0.283,0.227,0.241,0.253,0.225,0.271,0.232,0.281,0.209,0.259,0.244,0.222,0.272,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +7,ProtSSN (ensemble),Hybrid - Structure & PLM,0.227,0.005,0.191,0.196,0.22,0.191,0.337,0.192,0.239,0.284,0.228,0.278,0.274,0.215,0.214,0.248,0.21,0.186,0.232,ProtSSN (ensemble of 9 models),"Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +8,ProtSSN (k=30 h=768),Hybrid - Structure & PLM,0.227,0.005,0.19,0.202,0.228,0.185,0.329,0.193,0.233,0.281,0.227,0.275,0.269,0.208,0.209,0.241,0.195,0.181,0.227,"ProtSSN (k=30, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +9,DeepSequence (ensemble),Alignment-based model,0.226,0.006,0.194,0.213,0.218,0.223,0.282,0.216,0.235,0.255,0.223,0.27,0.229,0.267,0.203,0.257,0.222,0.2,0.276,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +10,ProtSSN (k=20 h=1280),Hybrid - Structure & PLM,0.226,0.005,0.187,0.2,0.215,0.19,0.335,0.19,0.236,0.285,0.228,0.28,0.267,0.213,0.21,0.252,0.208,0.191,0.242,"ProtSSN (k=20, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +11,DeepSequence (single),Alignment-based model,0.225,0.006,0.199,0.216,0.214,0.219,0.28,0.218,0.235,0.252,0.225,0.271,0.226,0.257,0.204,0.256,0.196,0.198,0.272,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +12,MIF-ST,Hybrid - Structure & PLM,0.225,0.004,0.191,0.223,0.221,0.192,0.3,0.194,0.232,0.261,0.215,0.258,0.264,0.226,0.206,0.23,0.22,0.181,0.267,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +13,ProtSSN (k=10 h=1280),Hybrid - Structure & PLM,0.225,0.005,0.187,0.195,0.22,0.189,0.335,0.189,0.237,0.282,0.229,0.27,0.269,0.214,0.212,0.232,0.183,0.164,0.216,"ProtSSN (k=10, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +14,ProtSSN (k=10 h=768),Hybrid - Structure & PLM,0.225,0.005,0.193,0.195,0.22,0.187,0.33,0.186,0.239,0.278,0.231,0.27,0.27,0.203,0.208,0.233,0.205,0.167,0.215,"ProtSSN (k=10, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +15,ProtSSN (k=20 h=512),Hybrid - Structure & PLM,0.224,0.005,0.196,0.196,0.212,0.187,0.331,0.188,0.237,0.279,0.226,0.276,0.271,0.208,0.208,0.247,0.215,0.192,0.238,"ProtSSN (k=20, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +16,MSA Transformer (ensemble),Hybrid - Alignment & PLM,0.224,0.007,0.2,0.204,0.218,0.219,0.278,0.227,0.232,0.251,0.216,0.264,0.233,0.275,0.204,0.232,0.223,0.235,0.289,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +17,ProtSSN (k=20 h=768),Hybrid - Structure & PLM,0.224,0.006,0.189,0.19,0.218,0.189,0.332,0.18,0.235,0.283,0.227,0.27,0.268,0.213,0.209,0.254,0.19,0.177,0.24,"ProtSSN (k=20, h=768)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +18,ProtSSN (k=30 h=512),Hybrid - Structure & PLM,0.223,0.004,0.189,0.2,0.218,0.183,0.327,0.183,0.234,0.278,0.223,0.274,0.266,0.205,0.205,0.243,0.191,0.176,0.222,"ProtSSN (k=30, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +19,ProtSSN (k=30 h=1280),Hybrid - Structure & PLM,0.223,0.005,0.187,0.199,0.218,0.189,0.321,0.195,0.229,0.276,0.225,0.266,0.265,0.207,0.208,0.248,0.194,0.178,0.226,"ProtSSN (k=30, h=1280)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +20,ProtSSN (k=10 h=512),Hybrid - Structure & PLM,0.222,0.005,0.188,0.198,0.216,0.184,0.325,0.192,0.23,0.277,0.224,0.273,0.269,0.195,0.205,0.244,0.192,0.166,0.214,"ProtSSN (k=10, h=512)","Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv." +21,ESM-IF1,Inverse folding model,0.222,0.005,0.184,0.208,0.205,0.17,0.344,0.181,0.228,0.288,0.224,0.279,0.268,0.194,0.209,0.27,0.176,0.172,0.257,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +22,EVmutation,Alignment-based model,0.222,0.007,0.201,0.204,0.209,0.223,0.272,0.213,0.235,0.249,0.212,0.262,0.236,0.278,0.2,0.257,0.228,0.219,0.296,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +23,Tranception L,Hybrid - Alignment & PLM,0.22,0.007,0.2,0.205,0.21,0.221,0.265,0.226,0.232,0.239,0.216,0.259,0.223,0.271,0.202,0.251,0.208,0.197,0.268,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +24,Tranception M,Hybrid - Alignment & PLM,0.218,0.006,0.188,0.208,0.211,0.21,0.271,0.227,0.228,0.231,0.218,0.26,0.2,0.27,0.199,0.246,0.164,0.19,0.261,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +25,ESM2 (650M),Protein language model,0.217,0.006,0.178,0.213,0.215,0.177,0.3,0.178,0.213,0.27,0.221,0.257,0.251,0.168,0.196,0.233,0.179,0.152,0.179,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +26,Tranception S,Hybrid - Alignment & PLM,0.216,0.006,0.186,0.215,0.208,0.202,0.271,0.222,0.22,0.238,0.214,0.256,0.208,0.257,0.197,0.243,0.168,0.19,0.262,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +27,MIF,Inverse folding model,0.216,0.007,0.178,0.201,0.214,0.16,0.327,0.194,0.218,0.265,0.221,0.25,0.246,0.206,0.21,0.244,0.189,0.16,0.241,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +28,ESM2 (3B),Protein language model,0.213,0.006,0.176,0.207,0.212,0.184,0.286,0.187,0.215,0.255,0.213,0.256,0.246,0.18,0.192,0.231,0.17,0.168,0.177,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +29,MSA Transformer (single),Hybrid - Alignment & PLM,0.213,0.007,0.19,0.194,0.201,0.219,0.259,0.214,0.225,0.237,0.206,0.258,0.221,0.26,0.192,0.216,0.23,0.234,0.29,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +30,GEMME,Alignment-based model,0.211,0.007,0.196,0.198,0.198,0.223,0.24,0.213,0.228,0.222,0.202,0.231,0.218,0.284,0.186,0.228,0.188,0.211,0.296,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +31,ESM-1v (ensemble),Protein language model,0.211,0.006,0.173,0.213,0.21,0.193,0.265,0.182,0.21,0.251,0.209,0.236,0.234,0.203,0.193,0.213,0.17,0.151,0.17,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +32,SaProt (35M),Hybrid - Structure & PLM,0.211,0.005,0.168,0.198,0.225,0.152,0.311,0.172,0.209,0.26,0.218,0.261,0.237,0.147,0.203,0.274,0.15,0.166,0.186,SaProt (35M),"Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." +33,ESM2 (15B),Protein language model,0.208,0.007,0.18,0.196,0.199,0.195,0.27,0.184,0.217,0.245,0.209,0.242,0.244,0.192,0.189,0.222,0.172,0.156,0.179,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +34,CARP (640M),Protein language model,0.208,0.006,0.176,0.206,0.212,0.179,0.265,0.174,0.209,0.241,0.215,0.231,0.224,0.18,0.188,0.215,0.164,0.151,0.182,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +35,ESM2 (150M),Protein language model,0.204,0.006,0.168,0.204,0.196,0.151,0.299,0.162,0.195,0.264,0.222,0.249,0.223,0.129,0.186,0.234,0.127,0.14,0.165,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +36,Wavenet,Alignment-based model,0.203,0.007,0.167,0.204,0.19,0.204,0.252,0.175,0.215,0.237,0.203,0.219,0.215,0.243,0.182,0.239,0.18,0.158,0.202,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +37,ESM-1b,Protein language model,0.203,0.007,0.18,0.178,0.197,0.176,0.282,0.161,0.214,0.251,0.209,0.241,0.247,0.169,0.182,0.215,0.165,0.146,0.196,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +38,Progen2 M,Protein language model,0.202,0.006,0.172,0.19,0.221,0.198,0.229,0.177,0.21,0.218,0.204,0.202,0.206,0.224,0.185,0.192,0.151,0.134,0.143,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +39,Progen2 L,Protein language model,0.202,0.007,0.185,0.175,0.223,0.202,0.224,0.193,0.208,0.216,0.204,0.203,0.219,0.214,0.183,0.195,0.183,0.158,0.191,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +40,VESPA,Protein language model,0.201,0.007,0.18,0.192,0.184,0.207,0.242,0.192,0.218,0.22,0.19,0.231,0.223,0.247,0.176,0.207,0.216,0.204,0.248,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +41,Site-Independent,Alignment-based model,0.201,0.007,0.17,0.198,0.179,0.196,0.261,0.209,0.207,0.224,0.202,0.246,0.192,0.245,0.191,0.248,0.173,0.192,0.261,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +42,Progen2 XL,Protein language model,0.199,0.007,0.181,0.179,0.184,0.214,0.237,0.194,0.221,0.215,0.185,0.231,0.231,0.251,0.18,0.219,0.209,0.166,0.215,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +43,Progen2 Base,Protein language model,0.197,0.006,0.183,0.182,0.205,0.198,0.219,0.19,0.206,0.21,0.205,0.195,0.201,0.218,0.183,0.189,0.146,0.14,0.15,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +44,Tranception L no retrieval,Protein language model,0.197,0.008,0.181,0.185,0.191,0.213,0.216,0.199,0.209,0.208,0.191,0.201,0.212,0.253,0.181,0.21,0.204,0.165,0.223,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +45,RITA L,Protein language model,0.195,0.007,0.173,0.178,0.203,0.204,0.215,0.181,0.208,0.204,0.199,0.185,0.194,0.246,0.177,0.195,0.13,0.141,0.149,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +46,ESM-1v (single),Protein language model,0.194,0.007,0.166,0.175,0.204,0.182,0.245,0.151,0.196,0.241,0.195,0.226,0.217,0.178,0.182,0.202,0.162,0.148,0.162,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +47,RITA XL,Protein language model,0.193,0.007,0.17,0.173,0.202,0.202,0.216,0.178,0.212,0.197,0.194,0.188,0.2,0.243,0.175,0.202,0.139,0.147,0.179,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +48,CARP (76M),Protein language model,0.188,0.006,0.155,0.185,0.195,0.152,0.254,0.151,0.181,0.234,0.197,0.22,0.2,0.145,0.176,0.2,0.133,0.142,0.176,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +49,RITA M,Protein language model,0.187,0.008,0.168,0.163,0.199,0.198,0.207,0.179,0.194,0.204,0.194,0.177,0.178,0.25,0.175,0.182,0.126,0.133,0.154,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +50,ProteinMPNN,Inverse folding model,0.186,0.008,0.149,0.146,0.152,0.138,0.342,0.146,0.201,0.271,0.196,0.267,0.227,0.189,0.186,0.242,0.173,0.142,0.22,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +51,ESM2 (35M),Protein language model,0.185,0.006,0.155,0.197,0.181,0.138,0.254,0.153,0.163,0.245,0.197,0.219,0.193,0.124,0.174,0.223,0.127,0.139,0.17,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +52,VESPAl,Protein language model,0.184,0.008,0.172,0.183,0.159,0.194,0.214,0.181,0.197,0.201,0.176,0.216,0.197,0.225,0.155,0.189,0.195,0.188,0.238,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +53,Tranception M no retrieval,Protein language model,0.184,0.008,0.163,0.176,0.19,0.198,0.194,0.178,0.194,0.187,0.188,0.174,0.175,0.234,0.173,0.179,0.124,0.133,0.141,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +54,Progen2 S,Protein language model,0.183,0.007,0.155,0.166,0.204,0.172,0.218,0.144,0.184,0.212,0.196,0.177,0.191,0.17,0.169,0.178,0.129,0.141,0.134,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +55,Unirep evotuned,Hybrid - Alignment & PLM,0.181,0.007,0.163,0.174,0.186,0.188,0.192,0.19,0.188,0.18,0.177,0.182,0.177,0.227,0.158,0.176,0.157,0.152,0.208,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +56,RITA S,Protein language model,0.178,0.007,0.155,0.169,0.194,0.185,0.186,0.177,0.174,0.191,0.181,0.163,0.164,0.226,0.166,0.168,0.114,0.13,0.146,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +57,CARP (38M),Protein language model,0.177,0.006,0.147,0.188,0.177,0.14,0.233,0.148,0.165,0.218,0.185,0.198,0.181,0.141,0.165,0.188,0.131,0.147,0.169,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +58,Tranception S no retrieval,Protein language model,0.177,0.006,0.154,0.188,0.182,0.183,0.177,0.173,0.174,0.183,0.174,0.159,0.171,0.215,0.166,0.166,0.119,0.133,0.134,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +59,ESM2 (8M),Protein language model,0.153,0.007,0.126,0.178,0.162,0.117,0.181,0.141,0.136,0.173,0.155,0.155,0.15,0.12,0.144,0.178,0.123,0.132,0.159,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +60,Unirep,Protein language model,0.139,0.008,0.116,0.138,0.141,0.123,0.176,0.145,0.129,0.164,0.148,0.158,0.143,0.113,0.138,0.158,0.108,0.13,0.142,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +61,ProtGPT2,Protein language model,0.133,0.009,0.113,0.138,0.126,0.121,0.168,0.122,0.136,0.147,0.144,0.156,0.114,0.133,0.125,0.173,0.111,0.112,0.114,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +62,CARP (600K),Protein language model,0.131,0.008,0.116,0.124,0.132,0.106,0.176,0.13,0.122,0.157,0.14,0.148,0.131,0.115,0.131,0.143,0.102,0.125,0.137,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.html b/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.html new file mode 100644 index 0000000..2b227ad --- /dev/null +++ b/benchmarks/DMS_zero_shot/substitutions/Top_recall/Summary_performance_DMS_substitutions_Top_recall.html @@ -0,0 +1,1670 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model_nameModel typeAverage_Top_recallBootstrap_standard_error_Top_recallFunction_ActivityFunction_BindingFunction_ExpressionFunction_OrganismalFitnessFunction_StabilityLow_MSA_depthMedium_MSA_depthHigh_MSA_depthTaxa_HumanTaxa_Other_EukaryoteTaxa_ProkaryoteTaxa_VirusDepth_1Depth_2Depth_3Depth_4Depth_5+Model detailsReferences
Model_rank
1SaProt (650M)Hybrid - Structure & PLM0.2320.0000.1910.2330.2350.1700.3310.1910.2320.2780.2300.2690.2660.1960.2070.2610.1890.1850.244SaProt (650M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
2TranceptEVE SHybrid - Alignment & PLM0.2320.0060.1980.2230.2260.2280.2860.2310.2420.2540.2260.2700.2310.2860.2100.2580.2130.2080.278TranceptEVE Small model (Tranception Small & retrieved EVE model)<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
3EVE (single)Alignment-based model0.2300.0060.1990.2170.2230.2280.2840.2260.2410.2540.2250.2700.2350.2810.2080.2580.2390.2170.279EVE model (single seed)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
4TranceptEVE MHybrid - Alignment & PLM0.2300.0060.1980.2180.2270.2280.2800.2300.2420.2480.2240.2710.2270.2860.2090.2520.2130.2120.278TranceptEVE Medium model (Tranception Medium & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
5TranceptEVE LHybrid - Alignment & PLM0.2300.0060.2000.2180.2240.2290.2790.2270.2410.2510.2220.2710.2340.2830.2080.2570.2300.2040.277TranceptEVE Large model (Tranception Large & retrieved EVE model)<a href='https://www.biorxiv.org/content/10.1101/2022.12.07.519495v1?rss=1'>Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.</a>
6EVE (ensemble)Alignment-based model0.2300.0060.1990.2140.2250.2280.2830.2270.2410.2530.2250.2710.2320.2810.2090.2590.2440.2220.272EVE model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41586-021-04043-8'>Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.</a>
7ProtSSN (ensemble)Hybrid - Structure & PLM0.2270.0050.1910.1960.2200.1910.3370.1920.2390.2840.2280.2780.2740.2150.2140.2480.2100.1860.232ProtSSN (ensemble of 9 models)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
8ProtSSN (k=30 h=768)Hybrid - Structure & PLM0.2270.0050.1900.2020.2280.1850.3290.1930.2330.2810.2270.2750.2690.2080.2090.2410.1950.1810.227ProtSSN (k=30, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
9DeepSequence (ensemble)Alignment-based model0.2260.0060.1940.2130.2180.2230.2820.2160.2350.2550.2230.2700.2290.2670.2030.2570.2220.2000.276DeepSequence model (ensemble of 5 independently-trained models)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
10ProtSSN (k=20 h=1280)Hybrid - Structure & PLM0.2260.0050.1870.2000.2150.1900.3350.1900.2360.2850.2280.2800.2670.2130.2100.2520.2080.1910.242ProtSSN (k=20, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
11DeepSequence (single)Alignment-based model0.2250.0060.1990.2160.2140.2190.2800.2180.2350.2520.2250.2710.2260.2570.2040.2560.1960.1980.272DeepSequence model (single seed)<a href='https://www.nature.com/articles/s41592-018-0138-4'>Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.</a>
12MIF-STHybrid - Structure & PLM0.2250.0040.1910.2230.2210.1920.3000.1940.2320.2610.2150.2580.2640.2260.2060.2300.2200.1810.267MIF-ST model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
13ProtSSN (k=10 h=1280)Hybrid - Structure & PLM0.2250.0050.1870.1950.2200.1890.3350.1890.2370.2820.2290.2700.2690.2140.2120.2320.1830.1640.216ProtSSN (k=10, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
14ProtSSN (k=10 h=768)Hybrid - Structure & PLM0.2250.0050.1930.1950.2200.1870.3300.1860.2390.2780.2310.2700.2700.2030.2080.2330.2050.1670.215ProtSSN (k=10, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
15ProtSSN (k=20 h=512)Hybrid - Structure & PLM0.2240.0050.1960.1960.2120.1870.3310.1880.2370.2790.2260.2760.2710.2080.2080.2470.2150.1920.238ProtSSN (k=20, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
16MSA Transformer (ensemble)Hybrid - Alignment & PLM0.2240.0070.2000.2040.2180.2190.2780.2270.2320.2510.2160.2640.2330.2750.2040.2320.2230.2350.289MSA Transformer (ensemble of 5 MSA samples)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
17ProtSSN (k=20 h=768)Hybrid - Structure & PLM0.2240.0060.1890.1900.2180.1890.3320.1800.2350.2830.2270.2700.2680.2130.2090.2540.1900.1770.240ProtSSN (k=20, h=768)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
18ProtSSN (k=30 h=512)Hybrid - Structure & PLM0.2230.0040.1890.2000.2180.1830.3270.1830.2340.2780.2230.2740.2660.2050.2050.2430.1910.1760.222ProtSSN (k=30, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
19ProtSSN (k=30 h=1280)Hybrid - Structure & PLM0.2230.0050.1870.1990.2180.1890.3210.1950.2290.2760.2250.2660.2650.2070.2080.2480.1940.1780.226ProtSSN (k=30, h=1280)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
20ProtSSN (k=10 h=512)Hybrid - Structure & PLM0.2220.0050.1880.1980.2160.1840.3250.1920.2300.2770.2240.2730.2690.1950.2050.2440.1920.1660.214ProtSSN (k=10, h=512)<a href='https://www.biorxiv.org/content/10.1101/2023.12.01.569522v1'>Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.</a>
21ESM-IF1Inverse folding model0.2220.0050.1840.2080.2050.1700.3440.1810.2280.2880.2240.2790.2680.1940.2090.2700.1760.1720.257ESM-IF1 model<a href='https://www.biorxiv.org/content/10.1101/2022.04.10.487779v2.full.pdf+html'>Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.</a>
22EVmutationAlignment-based model0.2220.0070.2010.2040.2090.2230.2720.2130.2350.2490.2120.2620.2360.2780.2000.2570.2280.2190.296EVmutation model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
23Tranception LHybrid - Alignment & PLM0.2200.0070.2000.2050.2100.2210.2650.2260.2320.2390.2160.2590.2230.2710.2020.2510.2080.1970.268Tranception Large model (700M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
24Tranception MHybrid - Alignment & PLM0.2180.0060.1880.2080.2110.2100.2710.2270.2280.2310.2180.2600.2000.2700.1990.2460.1640.1900.261Tranception Medium model (300M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
25ESM2 (650M)Protein language model0.2170.0060.1780.2130.2150.1770.3000.1780.2130.2700.2210.2570.2510.1680.1960.2330.1790.1520.179ESM2 model (650M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
26Tranception SHybrid - Alignment & PLM0.2160.0060.1860.2150.2080.2020.2710.2220.2200.2380.2140.2560.2080.2570.1970.2430.1680.1900.262Tranception Small model (85M params) with retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
27MIFInverse folding model0.2160.0070.1780.2010.2140.1600.3270.1940.2180.2650.2210.2500.2460.2060.2100.2440.1890.1600.241MIF model<a href='https://www.biorxiv.org/content/10.1101/2022.05.25.493516v3'>Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.</a>
28ESM2 (3B)Protein language model0.2130.0060.1760.2070.2120.1840.2860.1870.2150.2550.2130.2560.2460.1800.1920.2310.1700.1680.177ESM2 model (3B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
29MSA Transformer (single)Hybrid - Alignment & PLM0.2130.0070.1900.1940.2010.2190.2590.2140.2250.2370.2060.2580.2210.2600.1920.2160.2300.2340.290MSA Transformer (single MSA sample)<a href='http://proceedings.mlr.press/v139/rao21a.html'>Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.</a>
30GEMMEAlignment-based model0.2110.0070.1960.1980.1980.2230.2400.2130.2280.2220.2020.2310.2180.2840.1860.2280.1880.2110.296GEMME model<a href='https://pubmed.ncbi.nlm.nih.gov/31406981/'>Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.</a>
31ESM-1v (ensemble)Protein language model0.2110.0060.1730.2130.2100.1930.2650.1820.2100.2510.2090.2360.2340.2030.1930.2130.1700.1510.170ESM-1v (ensemble of 5 independently-trained models)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
32SaProt (35M)Hybrid - Structure & PLM0.2110.0050.1680.1980.2250.1520.3110.1720.2090.2600.2180.2610.2370.1470.2030.2740.1500.1660.186SaProt (35M)<a href='https://www.biorxiv.org/content/10.1101/2023.10.01.560349v5'>Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.</a>
33ESM2 (15B)Protein language model0.2080.0070.1800.1960.1990.1950.2700.1840.2170.2450.2090.2420.2440.1920.1890.2220.1720.1560.179ESM2 model (15B params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
34CARP (640M)Protein language model0.2080.0060.1760.2060.2120.1790.2650.1740.2090.2410.2150.2310.2240.1800.1880.2150.1640.1510.182CARP model (640M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
35ESM2 (150M)Protein language model0.2040.0060.1680.2040.1960.1510.2990.1620.1950.2640.2220.2490.2230.1290.1860.2340.1270.1400.165ESM2 model (150M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
36WavenetAlignment-based model0.2030.0070.1670.2040.1900.2040.2520.1750.2150.2370.2030.2190.2150.2430.1820.2390.1800.1580.202Wavenet model<a href='https://www.nature.com/articles/s41467-021-22732-w'>Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.</a>
37ESM-1bProtein language model0.2030.0070.1800.1780.1970.1760.2820.1610.2140.2510.2090.2410.2470.1690.1820.2150.1650.1460.196ESM-1b (w/ Brandes et al. extensions)[1] Original model: <a href='https://www.biorxiv.org/content/10.1101/622803v4'>Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118.</a> [2] Extensions: <a href='https://www.biorxiv.org/content/10.1101/2022.08.25.505311v1'>Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.</a>
38Progen2 MProtein language model0.2020.0060.1720.1900.2210.1980.2290.1770.2100.2180.2040.2020.2060.2240.1850.1920.1510.1340.143Progen2 medium model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
39Progen2 LProtein language model0.2020.0070.1850.1750.2230.2020.2240.1930.2080.2160.2040.2030.2190.2140.1830.1950.1830.1580.191Progen2 large model (2.7B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
40VESPAProtein language model0.2010.0070.1800.1920.1840.2070.2420.1920.2180.2200.1900.2310.2230.2470.1760.2070.2160.2040.248VESPA model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
41Site-IndependentAlignment-based model0.2010.0070.1700.1980.1790.1960.2610.2090.2070.2240.2020.2460.1920.2450.1910.2480.1730.1920.261Site-Independent model<a href='https://www.nature.com/articles/nbt.3769'>Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.</a>
42Progen2 XLProtein language model0.1990.0070.1810.1790.1840.2140.2370.1940.2210.2150.1850.2310.2310.2510.1800.2190.2090.1660.215Progen2 xlarge model (6.4B params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
43Progen2 BaseProtein language model0.1970.0060.1830.1820.2050.1980.2190.1900.2060.2100.2050.1950.2010.2180.1830.1890.1460.1400.150Progen2 base model (760M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
44Tranception L no retrievalProtein language model0.1970.0080.1810.1850.1910.2130.2160.1990.2090.2080.1910.2010.2120.2530.1810.2100.2040.1650.223Tranception Large model (700M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
45RITA LProtein language model0.1950.0070.1730.1780.2030.2040.2150.1810.2080.2040.1990.1850.1940.2460.1770.1950.1300.1410.149RITA large model (680M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
46ESM-1v (single)Protein language model0.1940.0070.1660.1750.2040.1820.2450.1510.1960.2410.1950.2260.2170.1780.1820.2020.1620.1480.162ESM-1v (single seed)<a href='https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html'>Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.</a>
47RITA XLProtein language model0.1930.0070.1700.1730.2020.2020.2160.1780.2120.1970.1940.1880.2000.2430.1750.2020.1390.1470.179RITA xlarge model (1.2B params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
48CARP (76M)Protein language model0.1880.0060.1550.1850.1950.1520.2540.1510.1810.2340.1970.2200.2000.1450.1760.2000.1330.1420.176CARP model (76M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
49RITA MProtein language model0.1870.0080.1680.1630.1990.1980.2070.1790.1940.2040.1940.1770.1780.2500.1750.1820.1260.1330.154RITA medium model (300M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
50ProteinMPNNInverse folding model0.1860.0080.1490.1460.1520.1380.3420.1460.2010.2710.1960.2670.2270.1890.1860.2420.1730.1420.220ProteinMPNN model<a href='https://www.science.org/doi/10.1126/science.add2187'>J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.</a>
51ESM2 (35M)Protein language model0.1850.0060.1550.1970.1810.1380.2540.1530.1630.2450.1970.2190.1930.1240.1740.2230.1270.1390.170ESM2 model (35M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
52VESPAlProtein language model0.1840.0080.1720.1830.1590.1940.2140.1810.1970.2010.1760.2160.1970.2250.1550.1890.1950.1880.238VESPAl model<a href='https://link.springer.com/article/10.1007/s00439-021-02411-y'>Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.</a>
53Tranception M no retrievalProtein language model0.1840.0080.1630.1760.1900.1980.1940.1780.1940.1870.1880.1740.1750.2340.1730.1790.1240.1330.141Tranception Medium model (300M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
54Progen2 SProtein language model0.1830.0070.1550.1660.2040.1720.2180.1440.1840.2120.1960.1770.1910.1700.1690.1780.1290.1410.134Progen2 small model (150M params)<a href='https://arxiv.org/abs/2206.13517'> Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. </a>
55Unirep evotunedHybrid - Alignment & PLM0.1810.0070.1630.1740.1860.1880.1920.1900.1880.1800.1770.1820.1770.2270.1580.1760.1570.1520.208Unirep model w/ evotuning<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
56RITA SProtein language model0.1780.0070.1550.1690.1940.1850.1860.1770.1740.1910.1810.1630.1640.2260.1660.1680.1140.1300.146RITA small model (85M params)<a href='https://arxiv.org/abs/2205.05789'>Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.</a>
57CARP (38M)Protein language model0.1770.0060.1470.1880.1770.1400.2330.1480.1650.2180.1850.1980.1810.1410.1650.1880.1310.1470.169CARP model (38M params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
58Tranception S no retrievalProtein language model0.1770.0060.1540.1880.1820.1830.1770.1730.1740.1830.1740.1590.1710.2150.1660.1660.1190.1330.134Tranception Small model (85M params) without retrieval<a href='https://proceedings.mlr.press/v162/notin22a.html'>Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.</a>
59ESM2 (8M)Protein language model0.1530.0070.1260.1780.1620.1170.1810.1410.1360.1730.1550.1550.1500.1200.1440.1780.1230.1320.159ESM2 model (8M params)<a href='https://www.science.org/doi/abs/10.1126/science.ade2574'>Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.</a>
60UnirepProtein language model0.1390.0080.1160.1380.1410.1230.1760.1450.1290.1640.1480.1580.1430.1130.1380.1580.1080.1300.142Unirep model<a href='https://www.nature.com/articles/s41592-019-0598-1'>Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.</a>
61ProtGPT2Protein language model0.1330.0090.1130.1380.1260.1210.1680.1220.1360.1470.1440.1560.1140.1330.1250.1730.1110.1120.114ProtGPT2 model<a href='https://www.nature.com/articles/s41467-022-32007-7'>Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.</a>
62CARP (600K)Protein language model0.1310.0080.1160.1240.1320.1060.1760.1300.1220.1570.1400.1480.1310.1150.1310.1430.1020.1250.137CARP model (600K params)<a href='https://www.biorxiv.org/content/10.1101/2022.05.19.492714v4'>Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.</a>
\ No newline at end of file diff --git a/benchmarks/clinical_supervised/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv b/benchmarks/clinical_supervised/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv new file mode 100644 index 0000000..a818eaa --- /dev/null +++ b/benchmarks/clinical_supervised/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv @@ -0,0 +1,18 @@ +Model_rank,Model_name,Model type,Average_AUC,Bootstrap_standard_error_AUC +1,ClinPred,Supervised,0.981,0.0 +2,MetaRNN,Supervised,0.977,0.001 +3,BayesDel (addAF),Supervised,0.972,0.001 +4,VEST4,Supervised,0.929,0.003 +5,REVEL,Supervised,0.928,0.003 +6,BayesDel (noAF),Supervised,0.925,0.003 +7,VARITY (R),Supervised,0.921,0.003 +8,VARITY (ER),Supervised,0.918,0.003 +9,gMVP,Supervised,0.914,0.004 +10,CADD,Supervised,0.905,0.003 +11,PolyPhen2 (HVAR),Supervised,0.896,0.003 +12,DEOGEN2,Supervised,0.894,0.004 +13,MPC,Supervised,0.881,0.004 +14,PolyPhen2 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/dev/null +++ b/benchmarks/clinical_zero_shot/indels/AUC/Summary_performance_clinical_indels_AUC.csv @@ -0,0 +1,18 @@ +Model_rank,Model_name,Model type,Average_AUC +1,Provean,Unsupervised,0.927 +2,RITA L,Unsupervised,0.921 +3,RITA XL,Unsupervised,0.915 +4,RITA M,Unsupervised,0.891 +5,Tranception L no retrieval,Unsupervised,0.863 +6,ProGen2 L,Unsupervised,0.847 +7,Tranception M no retrieval,Unsupervised,0.845 +8,ProGen2 M,Unsupervised,0.843 +9,ProGen2 Base,Unsupervised,0.842 +10,Tranception L,Unsupervised,0.84 +11,ProGen2 XL,Unsupervised,0.84 +12,Tranception M,Unsupervised,0.828 +13,RITA S,Unsupervised,0.785 +14,ProGen2 S,Unsupervised,0.763 +15,Hidden Markov Model,Unsupervised,0.675 +16,ProtGPT2,Unsupervised,0.648 +17,UniRep,Unsupervised,0.401 diff --git a/benchmarks/clinical_zero_shot/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv b/benchmarks/clinical_zero_shot/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv new file mode 100644 index 0000000..a7bc81f --- /dev/null +++ b/benchmarks/clinical_zero_shot/substitutions/AUC/Summary_performance_clinical_substitutions_AUC.csv @@ -0,0 +1,13 @@ +Model_rank,Model_name,Model type,Average_AUC,Bootstrap_standard_error_AUC +1,TranceptEVE,Unsupervised,0.92,0.0 +2,GEMME,Unsupervised,0.919,0.002 +3,EVE,Unsupervised,0.917,0.002 +4,ESM-1b,Unsupervised,0.892,0.003 +5,PROVEAN,Unsupervised,0.886,0.003 +6,SIFT,Unsupervised,0.878,0.003 +7,SIFT4G,Unsupervised,0.877,0.003 +8,MutationAssessor,Unsupervised,0.877,0.003 +9,MutPred,Unsupervised,0.875,0.005 +10,PrimateAI,Unsupervised,0.855,0.004 +11,LIST-S2,Unsupervised,0.842,0.004 +12,LRT,Unsupervised,0.805,0.004 diff --git a/benchmarks/clinical_zero_shot/substitutions/AUC/clinical_substitutions_AUC_DMS_level.csv b/benchmarks/clinical_zero_shot/substitutions/AUC/clinical_substitutions_AUC_DMS_level.csv new file mode 100644 index 0000000..d25e76a --- /dev/null +++ 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file:///home/conda/feedstock_root/build_artifacts/terminado_1600709672075/work +testpath==0.4.4 +Theano @ file:///home/conda/feedstock_root/build_artifacts/theano_1604228519858/work +threadpoolctl @ file:///tmp/tmp79xdzxkt/threadpoolctl-2.1.0-py3-none-any.whl +toml @ file:///home/conda/feedstock_root/build_artifacts/toml_1589469402899/work +torch==1.4.0 +tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1602488893411/work +tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1602171507552/work +traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1600970644261/work +typeguard==2.9.1 +typing-extensions==3.7.4.3 +uritemplate==3.0.1 +urllib3==1.25.9 +voluptuous==0.11.7 +wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1600965781394/work +webencodings==0.5.1 +Werkzeug==1.0.1 +widgetsnbextension @ file:///home/conda/feedstock_root/build_artifacts/widgetsnbextension_1594164347302/work +wrapt==1.12.1 +xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1600638299066/work +xlrd @ file:///home/conda/feedstock_root/build_artifacts/xlrd_1595712200082/work +yarl==1.5.1 +zc.lockfile==2.0 +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1601765966131/work diff --git a/environments/proteingym_env.txt b/environments/proteingym_env.txt new file mode 100644 index 0000000..f1f365b --- /dev/null +++ b/environments/proteingym_env.txt @@ -0,0 +1,127 @@ +absl-py==1.4.0 +aiohttp==3.8.5 +aiosignal==1.3.1 +appdirs==1.4.4 +async-timeout==4.0.3 +attrs==23.1.0 +billiard==4.2.0 +biopython==1.82 +biotite==0.38.0 +bokeh==3.3.0 +Brotli==1.1.0 +brotlipy==0.7.0 +certifi==2023.7.22 +cffi==1.16.0 +charset-normalizer==3.3.2 +chex==0.1.7 +click==8.1.7 +contourpy==1.2.0 +cryptography==41.0.7 +cycler==0.12.1 +datasets==2.14.4 +dill==0.3.7 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +dpcpp-cpp-rt==2024.0.2 +etils==1.4.1 +filelock==3.12.3 +flax==0.7.2 +fonttools==4.44.0 +frozenlist==1.4.0 +fsspec==2023.6.0 +gitdb==4.0.10 +GitPython==3.1.32 +h5py==3.9.0 +huggingface-hub==0.16.4 +idna==3.6 +importlib-metadata==6.8.0 +importlib-resources==6.0.1 +intel-cmplr-lib-rt==2024.0.2 +intel-cmplr-lic-rt==2024.0.2 +intel-opencl-rt==2024.0.2 +intel-openmp==2024.0.2 +jax==0.4.14 +jaxlib==0.4.14 +Jinja2==3.1.2 +joblib==1.2.0 +kiwisolver==1.4.5 +llvmlite==0.41.1 +markdown-it-py==3.0.0 +MarkupSafe==2.1.3 +matplotlib==3.8.1 +mdurl==0.1.2 +mkl==2024.0.0 +mkl-fft==1.3.6 +mkl-random==1.2.2 +mkl-service==2.4.0 +ml-dtypes==0.2.0 +mmtf-python==1.1.3 +msgpack==1.0.5 +multidict==6.0.4 +multiprocess==0.70.15 +nest-asyncio==1.5.7 +networkx==3.2.1 +numba==0.58.1 +numba-progress==1.1.0 +numpy==1.24.4 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cudnn-cu11==8.5.0.96 +opt-einsum==3.3.0 +optax==0.1.7 +orbax-checkpoint==0.3.5 +packaging==23.2 +pandas==1.5.1 +pathtools==0.1.2 +pillow==10.2.0 +platformdirs==4.1.0 +pooch==1.8.0 +protobuf==4.24.2 +psutil==5.9.5 +pyarrow==13.0.0 +pycparser==2.21 +pyfaidx==0.6.4 +Pygments==2.16.1 +pyOpenSSL==23.3.0 +pyparsing==3.1.1 +PySocks==1.7.1 +python-dateutil==2.8.2 +pytz==2022.6 +PyYAML==6.0.1 +regex==2023.8.8 +requests==2.31.0 +rich==13.5.2 +ruamel.yaml==0.18.5 +ruamel.yaml.clib==0.2.8 +safetensors==0.3.3 +scikit-learn==1.3.2 +scipy==1.10.1 +seaborn==0.13.0 +sentencepiece==0.1.99 +sentry-sdk==1.30.0 +sequence-models==1.7.0 +setproctitle==1.3.2 +six==1.16.0 +smmap==5.0.0 +tbb==2021.11.0 +tensorstore==0.1.41 +threadpoolctl==3.1.0 +tokenizers==0.13.3 +toolz==0.12.0 +torch==1.13.1 +torch_geometric==2.4.0 +torchaudio==0.13.1 +torchvision==0.14.1 +tornado==6.3.3 +tqdm==4.66.1 +transformers==4.32.1 +typing_extensions==4.9.0 +tzdata==2023.4 +urllib3==2.1.0 +vespa-effect==1.0.2 +wandb==0.15.9 +xxhash==3.3.0 +xyzservices==2023.10.1 +yarl==1.9.2 +zipp==3.16.2 diff --git a/notebooks/TranceptEVE_example.ipynb b/notebooks/TranceptEVE_example.ipynb new file mode 100644 index 0000000..36b5759 --- /dev/null +++ b/notebooks/TranceptEVE_example.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "410842ba-dace-4df2-8fff-7ae4809eac27", + "metadata": {}, + "source": [ + "This notebook walks through all the steps required to run TranceptEVE on a deep mutational scan (DMS)\n", + "To run this notebook you need: \n", + "* a csv with all the mutant sequences in the same format as the other proteingym datasets (i.e. they need a mutated_sequence column with all the mutated sequences and a DMS_score column with the experimental values)\n", + "* A multiple sequence alignment for the target protein of the DMS\n", + "* A copy of the Tranception checkpoint (small, medium or large) that you want to use\n", + "* To update the reference file with an additional row describing the DMS (required columns to fill in are \"DMS_id\",\"DMS_filename\",\"MSA_filename\",\"MSA_theta\",\"MSA_start\",\"MSA_end\",\"weight_file_name\", and \"target_seq\")" + ] + }, + { + "cell_type": "markdown", + "id": "67c24600-47dc-49f8-9782-cb7cb1db0b30", + "metadata": {}, + "source": [ + "# Step 0: Alignment Generation\n", + "If you don't have an alignment for your target protein, there are several methods for generating one:\n", + "* [EVCouplings](evcouplings.org): You can use the online webserver at [evcouplings.org](evcouplings.org) or download the software and run it locally from [https://github.com/debbiemarkslab/EVcouplings](https://github.com/debbiemarkslab/EVcouplings)\n", + "* [ColabFold](https://github.com/sokrypton/ColabFold) also includes an MSA generation pipeline.\n", + "* [BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi), the Basic Local Alignment Search Tool, also has a web server for generating alignments\n", + "* [MUSCLE](https://www.drive5.com/muscle5/) is another commonly used library for alignment generation\n", + "\n", + "All the DMS alignments in the original ProteinGym paper were generated using EVCouplings" + ] + }, + { + "cell_type": "markdown", + "id": "ff7855b8-1c61-46c8-8a4d-dcc002199a53", + "metadata": {}, + "source": [ + "# Step 1: Training EVE models on wild type MSA \n", + "The script at proteingym/baselines/EVE/train_VAE.py can be used to train EVE models on an alignment. Below is essentially the same code to run train_VAE.py that is present in scripts/scoring_DMS_zero_shot/training_EVE_models.sh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8847c7c2-540c-46a1-8946-cb343707a449", + "metadata": {}, + "outputs": [], + "source": [ + "# This is the index of the DMS you want to run in the reference file csv.\n", + "DMS_index=0 # change this to whatever row your new DMS is on \n", + "# You can train multiple EVE model with different seeds for initialization and then pass them all to TranceptEVE. \n", + "# The log prior in TranceptEVE will then be the ensemble of all those models \n", + "random_seeds = [0,1,2,3,4]\n", + "\n", + "model_parameters_location='../proteingym/baselines/EVE/EVE/default_model_params.json'\n", + "training_logs_location='../proteingym/baselines/EVE/logs/'\n", + "DMS_reference_file_path=\"../reference_files/DMS_substitutions.csv\"\n", + "\n", + "# replace the below with the locations of the MSAs and assay csvs on your machine \n", + "\n", + "DMS_MSA_data_folder=\"Folder containing multiple sequence alignments in a2m format\" \n", + "# This is where the EVE models will be written out. The filenames are in the format input-msa-name_seed\n", + "DMS_EVE_model_folder=\"Folder where EVE models will be saved\"\n", + "# if you don't already have weights here for the MSA, the EVE training script will generate them \n", + "DMS_MSA_weights_folder=\"Folder where MSA weights will be saved\" \n", + "\n", + "# Note that these models generally take a few hours to train, so it is likely easier to run the training_EVE_models.sh script mentioned above and \n", + "# train several in parallel than to do them sequentially in a notebook here. \n", + "for seed in random_seeds:\n", + " command = f\"../proteingym/baselines/EVE/train_VAE.py \\\n", + " --MSA_data_folder {DMS_MSA_data_folder} \\\n", + " --DMS_reference_file_path {DMS_reference_file_path} \\\n", + " --protein_index {DMS_index} \\\n", + " --MSA_weights_location {DMS_MSA_weights_folder} \\\n", + " --VAE_checkpoint_location {DMS_EVE_model_folder} \\\n", + " --model_parameters_location {model_parameters_location} \\\n", + " --training_logs_location {training_logs_location} \\\n", + " --threshold_focus_cols_frac_gaps 1 \\\n", + " --seed {seed} \\\n", + " --skip_existing \\\n", + " --experimental_stream_data\"\n", + " !python $command\n" + ] + }, + { + "cell_type": "markdown", + "id": "d755a049-3d0e-4449-a993-0c06191e151c", + "metadata": {}, + "source": [ + "# Step 2: Scoring with TranceptEVE\n", + "Now that the EVE models are trained, we can use them in conjunction with the downloaded Tranception checkpoint to run TranceptEVE. The below code is essentially the same as the script at scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_substitutions.sh. The code for scoring indels is essentially the same, with two extra parameters (A flag for setting Tranception to indel mode, \n", + "and a path to an installation of Clustal Omega which is an alignment tool used as part of the retrieval process with indels). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a90a60ea-0cd9-4574-b30a-17313af499c6", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# These values need to match those in the prior steps, so that the script finds the correct EVE models \n", + "DMS_index=0 \n", + "random_seeds = [0,1,2,3,4]\n", + "DMS_MSA_data_folder=\"Folder containing MSA files in a2m format\"\n", + "model_parameters_location='../proteingym/baselines/EVE/EVE/default_model_params.json'\n", + "DMS_EVE_model_folder=\"Folder containing EVE models\"\n", + "DMS_MSA_weights_folder=\"Folder containing MSA weights\"\n", + "DMS_reference_file_path=\"../reference_files/DMS_substitutions.csv\"\n", + "\n", + "# These are new for trancepteve \n", + "\n", + "inference_time_retrieval_type=\"TranceptEVE\"\n", + "# This is the number of samples taken from each EVE model to generate the log prior. This is done at the start of the script and then cached\n", + "# so that later runs with the same EVE models don't have to recompute it. \n", + "EVE_num_samples_log_proba=200000\n", + "# For long proteins, \"sliding\" rather than \"optimal\" may be ideal for this parameter. \n", + "scoring_window=\"optimal\" \n", + "\n", + "# These can be changed based on where the Tranception checkpoint and DMS data files are stored and where you want the output scores to write to \n", + "DMS_data_folder=\"Folder containing DMS assay csvs\"\n", + "checkpoint = \"Tranception model checkpoint path\"\n", + "output_scores_folder=\"Path to folder where scores will be saved\" \n", + "\n", + "command = f\"../proteingym/baselines/trancepteve/score_trancepteve.py \\\n", + " --checkpoint {checkpoint} \\\n", + " --DMS_reference_file_path {DMS_reference_file_path} \\\n", + " --DMS_data_folder {DMS_data_folder} \\\n", + " --DMS_index {DMS_index} \\\n", + " --output_scores_folder {output_scores_folder} \\\n", + " --inference_time_retrieval_type {inference_time_retrieval_type} \\\n", + " --MSA_folder {DMS_MSA_data_folder} \\\n", + " --MSA_weights_folder {DMS_MSA_weights_folder} \\\n", + " --EVE_num_samples_log_proba {EVE_num_samples_log_proba} \\\n", + " --EVE_model_parameters_location {model_parameters_location} \\\n", + " --EVE_model_folder {DMS_EVE_model_folder} \\\n", + " --scoring_window {scoring_window} \\\n", + " --EVE_seeds {\" \".join(random_seeds)} \\\n", + " --EVE_recalibrate_probas\"\n", + "!python $command" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/proteingym/__init__.py b/proteingym/__init__.py new file mode 100644 index 0000000..9b41f58 --- /dev/null +++ b/proteingym/__init__.py @@ -0,0 +1 @@ +from proteingym import utils \ No newline at end of file diff --git a/proteingym/baselines/EVE/EVE/VAE_decoder.py b/proteingym/baselines/EVE/EVE/VAE_decoder.py new file mode 100644 index 0000000..7bfa57a --- /dev/null +++ b/proteingym/baselines/EVE/EVE/VAE_decoder.py @@ -0,0 +1,295 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class VAE_Bayesian_MLP_decoder(nn.Module): + """ + Bayesian MLP decoder class for the VAE model. + """ + def __init__(self, params): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of the sizes of the hidden layers (all DNNs) + - z_dim: (Int) Dimension of latent space + - first_hidden_nonlinearity: (Str) Type of non-linear activation applied on the first (set of) hidden layer(s) + - last_hidden_nonlinearity: (Str) Type of non-linear activation applied on the very last hidden layer (pre-sparsity) + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + - convolve_output: (Bool) Whether to perform 1d convolution on output (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on output + - include_temperature_scaler: (Bool) Whether we apply the global temperature scaler + - include_sparsity: (Bool) Whether we use the sparsity inducing scheme on the output from the last hidden layer + - num_tiles_sparsity: (Int) Number of tiles to use in the sparsity inducing scheme (the more the tiles, the stronger the sparsity) + - bayesian_decoder: (Bool) Whether the decoder is bayesian or not + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.bayesian_decoder = True + self.dropout_proba = params['dropout_proba'] + self.convolve_output = params['convolve_output'] + self.convolution_depth = params['convolution_output_depth'] + self.include_temperature_scaler = params['include_temperature_scaler'] + self.include_sparsity = params['include_sparsity'] + self.num_tiles_sparsity = params['num_tiles_sparsity'] + + self.mu_bias_init = 0.1 + self.logvar_init = -10.0 + self.logit_scale_p = 0.001 + + self.hidden_layers_mean=nn.ModuleDict() + self.hidden_layers_log_var=nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers_mean[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + self.hidden_layers_log_var[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers_mean[str(layer_index)].bias, self.mu_bias_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].weight, self.logvar_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].bias, self.logvar_init) + else: + self.hidden_layers_mean[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + self.hidden_layers_log_var[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers_mean[str(layer_index)].bias, self.mu_bias_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].weight, self.logvar_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].bias, self.logvar_init) + + if params['first_hidden_nonlinearity'] == 'relu': + self.first_hidden_nonlinearity = nn.ReLU() + elif params['first_hidden_nonlinearity'] == 'tanh': + self.first_hidden_nonlinearity = nn.Tanh() + elif params['first_hidden_nonlinearity'] == 'sigmoid': + self.first_hidden_nonlinearity = nn.Sigmoid() + elif params['first_hidden_nonlinearity'] == 'elu': + self.first_hidden_nonlinearity = nn.ELU() + elif params['first_hidden_nonlinearity'] == 'linear': + self.first_hidden_nonlinearity = nn.Identity() + + if params['last_hidden_nonlinearity'] == 'relu': + self.last_hidden_nonlinearity = nn.ReLU() + elif params['last_hidden_nonlinearity'] == 'tanh': + self.last_hidden_nonlinearity = nn.Tanh() + elif params['last_hidden_nonlinearity'] == 'sigmoid': + self.last_hidden_nonlinearity = nn.Sigmoid() + elif params['last_hidden_nonlinearity'] == 'elu': + self.last_hidden_nonlinearity = nn.ELU() + elif params['last_hidden_nonlinearity'] == 'linear': + self.last_hidden_nonlinearity = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + if self.convolve_output: + self.output_convolution_mean = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + self.output_convolution_log_var = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + nn.init.constant_(self.output_convolution_log_var.weight, self.logvar_init) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + if self.include_sparsity: + self.sparsity_weight_mean = nn.Parameter(torch.zeros(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + self.sparsity_weight_log_var = nn.Parameter(torch.ones(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + nn.init.constant_(self.sparsity_weight_log_var, self.logvar_init) + + self.last_hidden_layer_weight_mean = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + self.last_hidden_layer_weight_log_var = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + nn.init.xavier_normal_(self.last_hidden_layer_weight_mean) #Glorot initialization + nn.init.constant_(self.last_hidden_layer_weight_log_var, self.logvar_init) + + self.last_hidden_layer_bias_mean = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + self.last_hidden_layer_bias_log_var = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + nn.init.constant_(self.last_hidden_layer_bias_mean, self.mu_bias_init) + nn.init.constant_(self.last_hidden_layer_bias_log_var, self.logvar_init) + + if self.include_temperature_scaler: + self.temperature_scaler_mean = nn.Parameter(torch.ones(1)) + self.temperature_scaler_log_var = nn.Parameter(torch.ones(1) * self.logvar_init) + + def sampler(self, mean, log_var): + """ + Samples a latent vector via reparametrization trick + """ + eps = torch.randn_like(mean).to(self.device) + z = torch.exp(0.5*log_var) * eps + mean + return z + + def forward(self, z): + batch_size = z.shape[0] + if self.dropout_proba > 0.0: + x = self.dropout_layer(z) + else: + x = z + + for layer_index in range(len(self.hidden_layers_sizes)-1): + layer_i_weight = self.sampler(self.hidden_layers_mean[str(layer_index)].weight, self.hidden_layers_log_var[str(layer_index)].weight) + layer_i_bias = self.sampler(self.hidden_layers_mean[str(layer_index)].bias, self.hidden_layers_log_var[str(layer_index)].bias) + x = self.first_hidden_nonlinearity(F.linear(x, weight=layer_i_weight, bias=layer_i_bias)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + last_index = len(self.hidden_layers_sizes)-1 + last_layer_weight = self.sampler(self.hidden_layers_mean[str(last_index)].weight, self.hidden_layers_log_var[str(last_index)].weight) + last_layer_bias = self.sampler(self.hidden_layers_mean[str(last_index)].bias, self.hidden_layers_log_var[str(last_index)].bias) + x = self.last_hidden_nonlinearity(F.linear(x, weight=last_layer_weight, bias=last_layer_bias)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + W_out = self.sampler(self.last_hidden_layer_weight_mean, self.last_hidden_layer_weight_log_var) + b_out = self.sampler(self.last_hidden_layer_bias_mean, self.last_hidden_layer_bias_log_var) + + if self.convolve_output: + output_convolution_weight = self.sampler(self.output_convolution_mean.weight, self.output_convolution_log_var.weight) + W_out = torch.mm(W_out.view(self.seq_len * self.hidden_layers_sizes[-1], self.channel_size), + output_convolution_weight.view(self.channel_size,self.alphabet_size)) #product of size (H * seq_len, alphabet) + + if self.include_sparsity: + sparsity_weights = self.sampler(self.sparsity_weight_mean,self.sparsity_weight_log_var) + sparsity_tiled = sparsity_weights.repeat(self.num_tiles_sparsity,1) + sparsity_tiled = nn.Sigmoid()(sparsity_tiled).unsqueeze(2) + + W_out = W_out.view(self.hidden_layers_sizes[-1], self.seq_len, self.alphabet_size) * sparsity_tiled + + W_out = W_out.view(self.seq_len * self.alphabet_size, self.hidden_layers_sizes[-1]) + + x = F.linear(x, weight=W_out, bias=b_out) + + if self.include_temperature_scaler: + temperature_scaler = self.sampler(self.temperature_scaler_mean,self.temperature_scaler_log_var) + x = torch.log(1.0+torch.exp(temperature_scaler)) * x + + x = x.view(batch_size, self.seq_len, self.alphabet_size) + x_recon_log = F.log_softmax(x, dim=-1) #of shape (batch_size, seq_len, alphabet) + + return x_recon_log + +class VAE_Standard_MLP_decoder(nn.Module): + """ + Standard MLP decoder class for the VAE model. + """ + def __init__(self, seq_len, alphabet_size, hidden_layers_sizes, z_dim, first_hidden_nonlinearity, last_hidden_nonlinearity, dropout_proba, + convolve_output, convolution_depth, include_temperature_scaler, include_sparsity, num_tiles_sparsity): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of the sizes of the hidden layers (all DNNs) + - z_dim: (Int) Dimension of latent space + - first_hidden_nonlinearity: (Str) Type of non-linear activation applied on the first (set of) hidden layer(s) + - last_hidden_nonlinearity: (Str) Type of non-linear activation applied on the very last hidden layer (pre-sparsity) + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + - convolve_output: (Bool) Whether to perform 1d convolution on output (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on output + - include_temperature_scaler: (Bool) Whether we apply the global temperature scaler + - include_sparsity: (Bool) Whether we use the sparsity inducing scheme on the output from the last hidden layer + - num_tiles_sparsity: (Int) Number of tiles to use in the sparsity inducing scheme (the more the tiles, the stronger the sparsity) + - bayesian_decoder: (Bool) Whether the decoder is bayesian or not + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.bayesian_decoder = False + self.dropout_proba = params['dropout_proba'] + self.convolve_output = params['convolve_output'] + self.convolution_depth = params['convolution_depth'] + self.include_temperature_scaler = params['include_temperature_scaler'] + self.include_sparsity = params['include_sparsity'] + self.num_tiles_sparsity = params['num_tiles_sparsity'] + + self.mu_bias_init = 0.1 + + self.hidden_layers=nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + else: + self.hidden_layers[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + + if params['first_hidden_nonlinearity'] == 'relu': + self.first_hidden_nonlinearity = nn.ReLU() + elif params['first_hidden_nonlinearity'] == 'tanh': + self.first_hidden_nonlinearity = nn.Tanh() + elif params['first_hidden_nonlinearity'] == 'sigmoid': + self.first_hidden_nonlinearity = nn.Sigmoid() + elif params['first_hidden_nonlinearity'] == 'elu': + self.first_hidden_nonlinearity = nn.ELU() + elif params['first_hidden_nonlinearity'] == 'linear': + self.first_hidden_nonlinearity = nn.Identity() + + if params['last_hidden_nonlinearity'] == 'relu': + self.last_hidden_nonlinearity = nn.ReLU() + elif params['last_hidden_nonlinearity'] == 'tanh': + self.last_hidden_nonlinearity = nn.Tanh() + elif params['last_hidden_nonlinearity'] == 'sigmoid': + self.last_hidden_nonlinearity = nn.Sigmoid() + elif params['last_hidden_nonlinearity'] == 'elu': + self.last_hidden_nonlinearity = nn.ELU() + elif params['last_hidden_nonlinearity'] == 'linear': + self.last_hidden_nonlinearity = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + if self.convolve_output: + self.output_convolution = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + if self.include_sparsity: + self.sparsity_weight = nn.Parameter(torch.randn(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + + self.W_out = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + nn.init.xavier_normal_(self.W_out) #Initialize weights with Glorot initialization + self.b_out = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + nn.init.constant_(self.b_out, self.mu_bias_init) + + if self.include_temperature_scaler: + self.temperature_scaler = nn.Parameter(torch.ones(1)) + + def forward(self, z): + batch_size = z.shape[0] + if self.dropout_proba > 0.0: + x = self.dropout_layer(z) + else: + x=z + + for layer_index in range(len(self.hidden_layers_sizes)-1): + x = self.first_hidden_nonlinearity(self.hidden_layers[str(layer_index)](x)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + x = self.last_hidden_nonlinearity(self.hidden_layers[str(len(self.hidden_layers_sizes)-1)](x)) #of size (batch_size,H) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + W_out = self.W_out.data + + if self.convolve_output: + W_out = torch.mm(W_out.view(self.seq_len * self.hidden_layers_sizes[-1], self.channel_size), + self.output_convolution.weight.view(self.channel_size,self.alphabet_size)) + + if self.include_sparsity: + sparsity_tiled = self.sparsity_weight.repeat(self.num_tiles_sparsity,1) #of size (H,seq_len) + sparsity_tiled = nn.Sigmoid()(sparsity_tiled).unsqueeze(2) #of size (H,seq_len,1) + W_out = W_out.view(self.hidden_layers_sizes[-1], self.seq_len, self.alphabet_size) * sparsity_tiled + + W_out = W_out.view(self.seq_len * self.alphabet_size, self.hidden_layers_sizes[-1]) + + x = F.linear(x, weight=W_out, bias=self.b_out) + + if self.include_temperature_scaler: + x = torch.log(1.0+torch.exp(self.temperature_scaler)) * x + + x = x.view(batch_size, self.seq_len, self.alphabet_size) + x_recon_log = F.log_softmax(x, dim=-1) #of shape (batch_size, seq_len, alphabet) + + return x_recon_log \ No newline at end of file diff --git a/proteingym/baselines/EVE/EVE/VAE_encoder.py b/proteingym/baselines/EVE/EVE/VAE_encoder.py new file mode 100644 index 0000000..3fd40fe --- /dev/null +++ b/proteingym/baselines/EVE/EVE/VAE_encoder.py @@ -0,0 +1,88 @@ +import torch +import torch.nn as nn + +class VAE_MLP_encoder(nn.Module): + """ + MLP encoder class for the VAE model. + """ + def __init__(self,params): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of sizes of DNN linear layers + - z_dim: (Int) Size of latent space + - convolve_input: (Bool) Whether to perform 1d convolution on input (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on input + - nonlinear_activation: (Str) Type of non-linear activation to apply on each hidden layer + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.convolve_input = params['convolve_input'] + self.convolution_depth = params['convolution_input_depth'] + self.dropout_proba = params['dropout_proba'] + + self.mu_bias_init = 0.1 + self.log_var_bias_init = -10.0 + + #Convolving input with kernels of size 1 to capture potential similarities across amino acids when encoding sequences + if self.convolve_input: + self.input_convolution = nn.Conv1d(in_channels=self.alphabet_size,out_channels=self.convolution_depth,kernel_size=1,stride=1,bias=False) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + self.hidden_layers=torch.nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers[str(layer_index)] = nn.Linear((self.channel_size*self.seq_len),self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + else: + self.hidden_layers[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + + self.fc_mean = nn.Linear(self.hidden_layers_sizes[-1],self.z_dim) + nn.init.constant_(self.fc_mean.bias, self.mu_bias_init) + self.fc_log_var = nn.Linear(self.hidden_layers_sizes[-1],self.z_dim) + nn.init.constant_(self.fc_log_var.bias, self.log_var_bias_init) + + # set up non-linearity + if params['nonlinear_activation'] == 'relu': + self.nonlinear_activation = nn.ReLU() + elif params['nonlinear_activation'] == 'tanh': + self.nonlinear_activation = nn.Tanh() + elif params['nonlinear_activation'] == 'sigmoid': + self.nonlinear_activation = nn.Sigmoid() + elif params['nonlinear_activation'] == 'elu': + self.nonlinear_activation = nn.ELU() + elif params['nonlinear_activation'] == 'linear': + self.nonlinear_activation = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + def forward(self, x): + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + if self.convolve_input: + x = x.permute(0,2,1) + x = self.input_convolution(x) + x = x.view(-1,self.seq_len*self.channel_size) + else: + x = x.view(-1,self.seq_len*self.channel_size) + + for layer_index in range(len(self.hidden_layers_sizes)): + x = self.nonlinear_activation(self.hidden_layers[str(layer_index)](x)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + z_mean = self.fc_mean(x) + z_log_var = self.fc_log_var(x) + + return z_mean, z_log_var \ No newline at end of file diff --git a/proteingym/baselines/EVE/EVE/VAE_model.py b/proteingym/baselines/EVE/EVE/VAE_model.py new file mode 100644 index 0000000..d1103bd --- /dev/null +++ b/proteingym/baselines/EVE/EVE/VAE_model.py @@ -0,0 +1,551 @@ +import datetime +import os +import sys +from resource import getrusage, RUSAGE_SELF + +import numpy as np +import pandas as pd +import time +from tqdm import tqdm +from scipy.special import erfinv +from sklearn.model_selection import train_test_split + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.backends.cudnn as cudnn + +from utils.data_utils import one_hot_3D, get_dataloader +from . import VAE_encoder, VAE_decoder + + +class VAE_model(nn.Module): + """ + Class for the VAE model with estimation of weights distribution parameters via Mean-Field VI. + """ + + def __init__(self, + model_name, + data, + encoder_parameters, + decoder_parameters, + random_seed, + seq_len=None, + alphabet_size=None, + Neff=None, + ): + + super().__init__() + + self.model_name = model_name + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dtype = torch.float32 + self.random_seed = random_seed + torch.manual_seed(random_seed) + + self.seq_len = seq_len if seq_len is not None else data.seq_len + self.alphabet_size = alphabet_size if alphabet_size is not None else data.alphabet_size + self.Neff = Neff if Neff is not None else data.Neff + + self.encoder_parameters = encoder_parameters + self.decoder_parameters = decoder_parameters + + encoder_parameters['seq_len'] = self.seq_len + encoder_parameters['alphabet_size'] = self.alphabet_size + decoder_parameters['seq_len'] = self.seq_len + decoder_parameters['alphabet_size'] = self.alphabet_size + + self.encoder = VAE_encoder.VAE_MLP_encoder(params=encoder_parameters) + if decoder_parameters['bayesian_decoder']: + self.decoder = VAE_decoder.VAE_Bayesian_MLP_decoder(params=decoder_parameters) + else: + self.decoder = VAE_decoder.VAE_Standard_MLP_decoder(params=decoder_parameters) + self.logit_sparsity_p = decoder_parameters['logit_sparsity_p'] + + def sample_latent(self, mu, log_var): + """ + Samples a latent vector via reparametrization trick + """ + eps = torch.randn_like(mu).to(self.device) + z = torch.exp(0.5 * log_var) * eps + mu + return z + + def KLD_diag_gaussians(self, mu, logvar, p_mu, p_logvar): + """ + KL divergence between diagonal gaussian with prior diagonal gaussian. + """ + KLD = 0.5 * (p_logvar - logvar) + 0.5 * (torch.exp(logvar) + torch.pow(mu - p_mu, 2)) / ( + torch.exp(p_logvar) + 1e-20) - 0.5 + + return torch.sum(KLD) + + def annealing_factor(self, annealing_warm_up, training_step): + """ + Annealing schedule of KL to focus on reconstruction error in early stages of training + """ + if training_step < annealing_warm_up: + return training_step / annealing_warm_up + else: + return 1 + + def KLD_global_parameters(self): + """ + KL divergence between the variational distributions and the priors (for the decoder weights). + """ + KLD_decoder_params = 0.0 + zero_tensor = torch.tensor(0.0).to(self.device) + + for layer_index in range(len(self.decoder.hidden_layers_sizes)): + for param_type in ['weight', 'bias']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)[ + 'hidden_layers_mean.' + str(layer_index) + '.' + param_type].flatten(), + self.decoder.state_dict(keep_vars=True)[ + 'hidden_layers_log_var.' + str(layer_index) + '.' + param_type].flatten(), + zero_tensor, + zero_tensor + ) + + for param_type in ['weight', 'bias']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['last_hidden_layer_' + param_type + '_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['last_hidden_layer_' + param_type + '_log_var'].flatten(), + zero_tensor, + zero_tensor + ) + + if self.decoder.include_sparsity: + self.logit_scale_sigma = 4.0 + self.logit_scale_mu = 2.0 ** 0.5 * self.logit_scale_sigma * erfinv(2.0 * self.logit_sparsity_p - 1.0) + + sparsity_mu = torch.tensor(self.logit_scale_mu).to(self.device) + sparsity_log_var = torch.log(torch.tensor(self.logit_scale_sigma ** 2)).to(self.device) + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['sparsity_weight_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['sparsity_weight_log_var'].flatten(), + sparsity_mu, + sparsity_log_var + ) + + if self.decoder.convolve_output: + for param_type in ['weight']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['output_convolution_mean.' + param_type].flatten(), + self.decoder.state_dict(keep_vars=True)['output_convolution_log_var.' + param_type].flatten(), + zero_tensor, + zero_tensor + ) + + if self.decoder.include_temperature_scaler: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['temperature_scaler_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['temperature_scaler_log_var'].flatten(), + zero_tensor, + zero_tensor + ) + return KLD_decoder_params + + def loss_function(self, x_recon_log, x, mu, log_var, kl_latent_scale, kl_global_params_scale, annealing_warm_up, + training_step, Neff): + """ + Returns mean of negative ELBO, reconstruction loss and KL divergence across batch x. + """ + BCE = F.binary_cross_entropy_with_logits(x_recon_log, x, reduction='sum') / x.shape[0] + KLD_latent = (-0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())) / x.shape[0] + if self.decoder.bayesian_decoder: + KLD_decoder_params_normalized = self.KLD_global_parameters() / Neff + else: + KLD_decoder_params_normalized = 0.0 + warm_up_scale = self.annealing_factor(annealing_warm_up, training_step) + neg_ELBO = BCE + warm_up_scale * ( + kl_latent_scale * KLD_latent + kl_global_params_scale * KLD_decoder_params_normalized) + return neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized + + def all_likelihood_components(self, x): + """ + Returns tensors of ELBO, reconstruction loss and KL divergence for each point in batch x. + """ + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + recon_x_log = recon_x_log.view(-1, self.alphabet_size * self.seq_len) + x = x.view(-1, self.alphabet_size * self.seq_len) + + BCE_batch_tensor = torch.sum(F.binary_cross_entropy_with_logits(recon_x_log, x, reduction='none'), dim=1) + KLD_batch_tensor = (-0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=1)) + + ELBO_batch_tensor = -(BCE_batch_tensor + KLD_batch_tensor) + + return ELBO_batch_tensor, BCE_batch_tensor, KLD_batch_tensor + + def all_likelihood_components_z(self, x, mu, log_var): + """Skip the encoder part and directly sample z""" + # Need to run mu, log_var = self.encoder(x) first + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + recon_x_log = recon_x_log.view(-1, self.alphabet_size * self.seq_len) + x = x.view(-1, self.alphabet_size * self.seq_len) + + BCE_batch_tensor = torch.sum(F.binary_cross_entropy_with_logits(recon_x_log, x, reduction='none'), dim=1) + KLD_batch_tensor = (-0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=1)) + + ELBO_batch_tensor = -(BCE_batch_tensor + KLD_batch_tensor) + + return ELBO_batch_tensor, BCE_batch_tensor, KLD_batch_tensor + + def train_model(self, data, training_parameters, use_dataloader=False): + """ + Training procedure for the VAE model. + If use_validation_set is True then: + - we split the alignment data in train/val sets. + - we train up to num_training_steps steps but store the version of the model with lowest loss on validation set across training + If not, then we train the model for num_training_steps and save the model at the end of training + """ + if torch.cuda.is_available(): + cudnn.benchmark = True + self.train() + + if training_parameters['log_training_info']: + filename = training_parameters['training_logs_location'] + os.sep + self.model_name + "_losses.csv" + with open(filename, "a") as logs: + logs.write("Number of sequences in alignment file:\t" + str(data.num_sequences) + "\n") + logs.write("Neff:\t" + str(self.Neff) + "\n") + logs.write("Alignment sequence length:\t" + str(data.seq_len) + "\n") + + optimizer = optim.Adam(self.parameters(), lr=training_parameters['learning_rate'], + weight_decay=training_parameters['l2_regularization']) + + if training_parameters['use_lr_scheduler']: + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=training_parameters['lr_scheduler_step_size'], + gamma=training_parameters['lr_scheduler_gamma']) + + if training_parameters['use_validation_set']: + # TODO fix this for use with a dataloader + x_train, x_val, weights_train, weights_val = train_test_split(data.one_hot_encoding, data.weights, + test_size=training_parameters[ + 'validation_set_pct'], + random_state=self.random_seed) + best_val_loss = float('inf') + best_model_step_index = 0 + else: + x_train = data.one_hot_encoding + weights_train = data.weights + best_val_loss = None + best_model_step_index = training_parameters['num_training_steps'] + + seq_sample_probs = weights_train / np.sum(weights_train) + assert len(data.seq_name_to_sequence) == weights_train.shape[0] # One weight per sequence + + # TMP TODO: Keep old behaviour for comparison + if use_dataloader: + # Stream one-hot encodings + dataloader = get_dataloader(msa_data=data, batch_size=training_parameters['batch_size'], num_training_steps=training_parameters['num_training_steps']) + else: + batch_order = np.arange(x_train.shape[0]) + assert batch_order.shape == seq_sample_probs.shape, f"batch_order and seq_sample_probs must have the same shape. batch_order.shape={batch_order.shape}, seq_sample_probs.shape={seq_sample_probs.shape}" + def get_mock_dataloader(): + while True: + # Sample a batch according to sequence weight + batch_index = np.random.choice(batch_order, training_parameters['batch_size'], p=seq_sample_probs).tolist() + batch = x_train[batch_index] + yield batch + dataloader = get_mock_dataloader() + + self.Neff_training = np.sum(weights_train) + + start = time.time() + train_loss = 0 + print("debug starting training here:") + for training_step, batch in enumerate(tqdm(dataloader, desc="Training model", total=training_parameters['num_training_steps'], mininterval=2)):#mininterval=10)): + + if training_step >= training_parameters['num_training_steps']: + print("debug Breaking at step", training_step) + break + x = batch.to(self.device, dtype=self.dtype) + if training_step == 0: + print("Got batch 1") + + optimizer.zero_grad() + + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized = self.loss_function( + recon_x_log, x, mu, log_var, + training_parameters['kl_latent_scale'], + training_parameters['kl_global_params_scale'], + training_parameters['annealing_warm_up'], + training_step, + self.Neff_training) + + neg_ELBO.backward() + optimizer.step() + + if training_parameters['use_lr_scheduler']: + scheduler.step() + + if training_step % training_parameters['log_training_freq'] == 0: + progress = "|Train : Update {0}. Negative ELBO : {1:.3f}, BCE: {2:.3f}, KLD_latent: {3:.3f}, KLD_decoder_params_norm: {4:.3f}, Time: {5:.2f} |".format( + training_step, neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized, time.time() - start) + print(progress) + + if training_parameters['log_training_info']: + with open(filename, "a+") as logs: + logs.write(progress + "\n") + + if training_step % training_parameters['save_model_params_freq'] == 0: + self.save(model_checkpoint=training_parameters[ + 'model_checkpoint_location'] + os.sep + self.model_name + "_step_" + str( + training_step), + encoder_parameters=self.encoder_parameters, + decoder_parameters=self.decoder_parameters, + training_parameters=training_parameters) + + if training_parameters['use_validation_set'] and training_step % training_parameters[ + 'validation_freq'] == 0: + x_val = torch.tensor(x_val, dtype=self.dtype).to(self.device) + val_neg_ELBO, val_BCE, val_KLD_latent, val_KLD_global_parameters = self.test_model(x_val, weights_val, + training_parameters[ + 'batch_size']) + + progress_val = "\t\t\t|Val : Update {0}. Negative ELBO : {1:.3f}, BCE: {2:.3f}, KLD_latent: {3:.3f}, KLD_decoder_params_norm: {4:.3f}, Time: {5:.2f} |".format( + training_step, val_neg_ELBO, val_BCE, val_KLD_latent, val_KLD_global_parameters, + time.time() - start) + print(progress_val) + if training_parameters['log_training_info']: + with open(filename, "a+") as logs: + logs.write(progress_val + "\n") + + if val_neg_ELBO < best_val_loss: + best_val_loss = val_neg_ELBO + best_model_step_index = training_step + self.save(model_checkpoint=training_parameters[ + 'model_checkpoint_location'] + os.sep + self.model_name + "_best", + encoder_parameters=self.encoder_parameters, + decoder_parameters=self.decoder_parameters, + training_parameters=training_parameters) + self.train() + print("TMP: Finished training, last training_step=", training_step) + + + def test_model(self, x_val, weights_val, batch_size): + self.eval() + + with torch.no_grad(): + val_batch_order = np.arange(x_val.shape[0]) + val_seq_sample_probs = weights_val / np.sum(weights_val) + + val_batch_index = np.random.choice(val_batch_order, batch_size, p=val_seq_sample_probs).tolist() + x = torch.tensor(x_val[val_batch_index], dtype=self.dtype).to(self.device) + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + neg_ELBO, BCE, KLD_latent, KLD_global_parameters = self.loss_function(recon_x_log, x, mu, log_var, + kl_latent_scale=1.0, + kl_global_params_scale=1.0, + annealing_warm_up=0, training_step=1, + Neff=self.Neff_training) # set annealing factor to 1 + + return neg_ELBO.item(), BCE.item(), KLD_latent.item(), KLD_global_parameters.item() + + def save(self, model_checkpoint, encoder_parameters, decoder_parameters, training_parameters, batch_size=256): + # Create intermediate dirs above this + os.makedirs(os.path.dirname(model_checkpoint), exist_ok=True) + torch.save({ + 'model_state_dict': self.state_dict(), + 'encoder_parameters': encoder_parameters, + 'decoder_parameters': decoder_parameters, + 'training_parameters': training_parameters, + }, model_checkpoint) + + def compute_evol_indices(self, msa_data, list_mutations_location, num_samples, batch_size=256, + mutant_column="mutations", num_chunks=1, aggregation_method="full"): + list_valid_mutations = [] + evol_indices = [] + + full_data = pd.read_csv(list_mutations_location, header=0) + print("Full length: ", len(full_data)) + size_per_chunk = int(len(full_data) / num_chunks) + print("size_per_chunk: " + str(size_per_chunk)) + + for chunk in range(num_chunks): + print("chunk #: " + str(chunk)) + data_chunk = full_data[chunk * size_per_chunk:(chunk + 1) * size_per_chunk] + list_valid_mutations_chunk, evol_indices_chunk, _, _ = self.compute_evol_indices_chunk(msa_data=msa_data, + list_mutations_location=data_chunk, + num_samples=num_samples, + batch_size=batch_size, + mutant_column=mutant_column, + aggregation_method=aggregation_method) + list_valid_mutations.extend(list(list_valid_mutations_chunk)) + evol_indices.extend(list(evol_indices_chunk)) + return list_valid_mutations, evol_indices, '', '' + + def compute_evol_indices_chunk(self, msa_data, list_mutations_location, num_samples, batch_size=256, + mutant_column="mutations", aggregation_method="full"): + """ + The column in the list_mutations dataframe that contains the mutant(s) for a given variant should be called "mutations" + """ + + # Note: wt is added inside this function, so no need to add a row in csv/dataframe input with wt + + # Multiple mutations are to be passed colon-separated + list_mutations = list_mutations_location # pd.read_csv(list_mutations_location, header=0) + + # Remove (multiple) mutations that are invalid + list_valid_mutations = ['wt'] + list_valid_mutated_sequences = {} + list_valid_mutated_sequences['wt'] = msa_data.focus_seq_trimmed # first sequence in the list is the wild_type + + if aggregation_method not in ["full", "batch", "online"]: + raise ValueError("Invalid aggregation method: {}".format(aggregation_method)) + + for mutation in list_mutations[mutant_column]: + try: + individual_substitutions = str(mutation).split(':') + except Exception as e: + print("Error with mutant {}".format(str(mutation))) + print("Specific error: " + str(e)) + continue + mutated_sequence = list(msa_data.focus_seq_trimmed)[:] + fully_valid_mutation = True + for mut in individual_substitutions: + try: + wt_aa, pos, mut_aa = mut[0], int(mut[1:-1]), mut[-1] + if wt_aa == mut_aa: + continue + # Log specific invalid mutants + if pos not in msa_data.uniprot_focus_col_to_wt_aa_dict: + print("pos {} not in uniprot_focus_col_to_wt_aa_dict".format(pos)) + fully_valid_mutation = False + # Given it's in the dict, check if it's a valid mutation + elif msa_data.uniprot_focus_col_to_wt_aa_dict[pos] != wt_aa: + print("wt_aa {} != uniprot_focus_col_to_wt_aa_dict[{}] {}".format( + wt_aa, pos, msa_data.uniprot_focus_col_to_wt_aa_dict[pos])) + fully_valid_mutation = False + if mut not in msa_data.mutant_to_letter_pos_idx_focus_list: + print("mut {} not in mutant_to_letter_pos_idx_focus_list".format(mut)) + fully_valid_mutation = False + + if fully_valid_mutation: + wt_aa, pos, idx_focus = msa_data.mutant_to_letter_pos_idx_focus_list[mut] + mutated_sequence[idx_focus] = mut_aa # perform the corresponding AA substitution + else: + print("Not a valid mutant: " + mutation) + break + + except Exception as e: + print("Error processing mutation {} in mutant {}".format(str(mut), str(mutation))) + print("Specific error: " + str(e)) + fully_valid_mutation = False + break + + if fully_valid_mutation: + list_valid_mutations.append(mutation) + list_valid_mutated_sequences[mutation] = ''.join(mutated_sequence) + + # One-hot encoding of mutated sequences + mutated_sequences_one_hot = one_hot_3D(list_valid_mutations, list_valid_mutated_sequences, + alphabet=msa_data.alphabet, seq_length=len(msa_data.focus_cols)) + + # TODO for low memory might need to calculate one-hot on the fly, or fix chunked calculation with elbo - elbo_wt + mutated_sequences_one_hot = torch.tensor(mutated_sequences_one_hot, dtype=torch.bool) + print("One-hot encoding of mutated sequences complete") + print(f"{datetime.datetime.now()} Peak memory in GB: {getrusage(RUSAGE_SELF).ru_maxrss / 1024 ** 2:.3f}") + # https://stackoverflow.com/questions/54361763/pytorch-why-is-the-memory-occupied-by-the-tensor-variable-so-small/54365012#54365012 + print( + f"tmp debug: storage size of mutated_sequences_one_hot: {sys.getsizeof(mutated_sequences_one_hot.storage()) / 1e9:.4f} GB") + dataloader = torch.utils.data.DataLoader(mutated_sequences_one_hot, batch_size=batch_size, shuffle=False, + num_workers=4, pin_memory=True) + + if aggregation_method == "full": + prediction_matrix = torch.zeros((len(list_valid_mutations), num_samples), dtype=self.dtype) + print( + f"tmp debug: storage size of mutated_sequences_one_hot: {sys.getsizeof(prediction_matrix.storage()) / 1e9:.4f} GB") + with torch.no_grad(): + for i, batch in enumerate(tqdm(dataloader, 'Looping through mutation batches')): + x = batch.type(self.dtype).to(self.device) + for j in tqdm(range(num_samples), + 'Looping through number of samples for batch #: ' + str(i + 1), mininterval=5): + seq_predictions, _, _ = self.all_likelihood_components(x) + prediction_matrix[i * batch_size:i * batch_size + len(x), j] = seq_predictions + tqdm.write('\n') + mean_predictions = prediction_matrix.mean(dim=1, keepdim=False) + std_predictions = prediction_matrix.std(dim=1, keepdim=False) + delta_elbos = mean_predictions - mean_predictions[0] + evol_indices = - delta_elbos.detach().cpu().numpy() + + elif aggregation_method == "batch": + # Reduce memory by factor of num_batches (num_valid_mutations / batch_size) + # Note: This will mean that higher memory GPU needs higher RAM because we store larger sample batches before aggregating + mean_predictions = torch.zeros(len(list_valid_mutations)) + std_predictions = torch.zeros(len(list_valid_mutations)) + + with torch.no_grad(): + for i, batch in enumerate(tqdm(dataloader, 'Looping through mutation batches')): + x = batch.type(self.dtype).to(self.device) + + # Simplest: Aggregate mean and std each batch (instead of online per sample) + # Reduce memory by factor of num_batches (num_valid_mutations / batch_size) + batch_samples = torch.zeros(size=(len(x), 20_000), + dtype=self.dtype) # Store these on CPU to save GPU memory + for j in tqdm(range(num_samples), + 'Looping through number of samples for batch #: ' + str(i + 1), mininterval=1): + seq_predictions, _, _ = self.all_likelihood_components(x) + batch_samples[:, j] = seq_predictions.detach().cpu() + + # Aggregate mean and std for this batch, this should be negligibly quick + mean_predictions[i * batch_size:i * batch_size + len(x)] = batch_samples.mean(dim=1, keepdim=False) + std_predictions[i * batch_size:i * batch_size + len(x)] = batch_samples.std(dim=1, keepdim=False) + tqdm.tqdm.write('\n') + + delta_elbos = mean_predictions - mean_predictions[0] + evol_indices = - delta_elbos.detach().cpu().numpy() + elif aggregation_method == "online": + # Extension: Completely online, reduce memory by factor of num_samples (20k) with hopefully small overhead + mean_predictions = torch.zeros(len(list_valid_mutations)) + std_predictions = torch.zeros(len(list_valid_mutations)) + with torch.no_grad(): + for i, batch in enumerate(tqdm(dataloader, 'Looping through mutation batches')): + x = batch.type(self.dtype).to(self.device) + print( + f"{datetime.datetime.now()} tmp Peak memory in GB: {getrusage(RUSAGE_SELF).ru_maxrss / 1024 ** 2:.3f}") + # Simplest: Aggregate mean and std online per sample + online_mean = torch.zeros(len(x), dtype=self.dtype, device=self.device) + online_s = torch.zeros(len(x), dtype=self.dtype, device=self.device) + + # Run this once per batch to speed up remaining loop + mu, log_var = self.encoder(x) + + for j in tqdm(range(num_samples), + 'Looping through number of samples for batch #: ' + str(i + 1), mininterval=5): + seq_predictions, _, _ = self.all_likelihood_components_z(x, mu, log_var) + # Using Welford's method https://stackoverflow.com/a/15638726/10447904 + # All still on GPU + if j == 0: + online_mean = seq_predictions.detach() + # online_s stays 0 + else: + delta = seq_predictions - online_mean + online_mean = online_mean + delta / (j + 1) # / n in original formula + online_s = online_s + delta * (seq_predictions - online_mean) + + variance = online_s / (num_samples - 1) # j will end as n-1 + std = variance.sqrt() + # Fill in mean and std arrays for this batch + mean_predictions[i * batch_size:i * batch_size + len(x)] = online_mean.detach().cpu() + std_predictions[i * batch_size:i * batch_size + len(x)] = std.detach().cpu() + tqdm.tqdm.write('\n') + + delta_elbos = mean_predictions - mean_predictions[0] + evol_indices = - delta_elbos.detach().cpu().numpy() + else: + raise ValueError("Invalid aggregation method. Must be one of 'full', 'batch' or 'online'.") + + return list_valid_mutations, evol_indices, mean_predictions[ + 0].detach().cpu().numpy(), std_predictions.detach().cpu().numpy() diff --git a/proteingym/baselines/EVE/EVE/deepseq_model_params.json b/proteingym/baselines/EVE/EVE/deepseq_model_params.json new file mode 100644 index 0000000..1dddaef --- /dev/null +++ b/proteingym/baselines/EVE/EVE/deepseq_model_params.json @@ -0,0 +1,39 @@ +{ "encoder_parameters": { + "hidden_layers_sizes" : [1500,1500], + "z_dim" : 30, + "convolve_input" : false, + "convolution_input_depth" : 40, + "nonlinear_activation" : "relu", + "dropout_proba" : 0.0 +}, +"decoder_parameters": { + "hidden_layers_sizes" : [100,2000], + "z_dim" : 30, + "bayesian_decoder" : true, + "first_hidden_nonlinearity" : "relu", + "last_hidden_nonlinearity" : "relu", + "dropout_proba" : 0.1, + "convolve_output" : true, + "convolution_output_depth" : 40, + "include_temperature_scaler" : true, + "include_sparsity" : true, + "num_tiles_sparsity" : 4, + "logit_sparsity_p" : 0.001 +}, +"training_parameters": { + "num_training_steps" : 300000, + "learning_rate" : 1e-3, + "batch_size" : 256, + "annealing_warm_up" : 0, + "kl_latent_scale" : 1.0, + "kl_global_params_scale" : 1.0, + "l2_regularization" : 0.0, + "use_lr_scheduler" : false, + "use_validation_set" : false, + "validation_set_pct" : 0.2, + "validation_freq" : 1000, + "log_training_info" : true, + "log_training_freq" : 1000, + "save_model_params_freq" : 500000 +} +} \ No newline at end of file diff --git a/proteingym/baselines/EVE/EVE/default_model_params.json b/proteingym/baselines/EVE/EVE/default_model_params.json new file mode 100644 index 0000000..5d3b93c --- /dev/null +++ b/proteingym/baselines/EVE/EVE/default_model_params.json @@ -0,0 +1,41 @@ +{ "encoder_parameters": { + "hidden_layers_sizes" : [2000,1000,300], + "z_dim" : 50, + "convolve_input" : false, + "convolution_input_depth" : 40, + "nonlinear_activation" : "relu", + "dropout_proba" : 0.0 + }, + "decoder_parameters": { + "hidden_layers_sizes" : [300,1000,2000], + "z_dim" : 50, + "bayesian_decoder" : true, + "first_hidden_nonlinearity" : "relu", + "last_hidden_nonlinearity" : "relu", + "dropout_proba" : 0.1, + "convolve_output" : true, + "convolution_output_depth" : 40, + "include_temperature_scaler" : true, + "include_sparsity" : false, + "num_tiles_sparsity" : 0, + "logit_sparsity_p" : 0 + }, + "training_parameters": { + "num_training_steps" : 400000, + "learning_rate" : 1e-4, + "batch_size" : 256, + "annealing_warm_up" : 0, + "kl_latent_scale" : 1.0, + "kl_global_params_scale" : 1.0, + "l2_regularization" : 0.0, + "use_lr_scheduler" : false, + "use_validation_set" : false, + "validation_set_pct" : 0.2, + "validation_freq" : 1000, + "log_training_info" : true, + "log_training_freq" : 1000, + "save_model_params_freq" : 500000 + } +} + + diff --git a/proteingym/baselines/EVE/calc_weights.py b/proteingym/baselines/EVE/calc_weights.py new file mode 100644 index 0000000..4e1b429 --- /dev/null +++ b/proteingym/baselines/EVE/calc_weights.py @@ -0,0 +1,120 @@ +# Basically train_VAE.py but just calculating the weights +import argparse +import os +import time + +import numpy as np +import pandas as pd + +from utils import data_utils + + +def create_argparser(): + parser = argparse.ArgumentParser(description='VAE') + + # If we don't have a mapping file, just use a single MSA path + parser.add_argument("--MSA_filepath", type=str, help="Full path to MSA") + + # If we have a mapping file with one MSA path per line + parser.add_argument('--MSA_data_folder', type=str, help='Folder where MSAs are stored', required=True) + parser.add_argument('--DMS_reference_file_path', type=str, help='List of proteins and corresponding MSA file name', required=True) + parser.add_argument('--DMS_index', type=int, help='Row index of protein in input mapping file', required=True) + parser.add_argument('--MSA_weights_location', type=str, + help='Location where weights for each sequence in the MSA will be stored', required=True) + parser.add_argument('--theta_reweighting', type=float, help='Parameters for MSA sequence re-weighting') + parser.add_argument("--num_cpus", type=int, help="Number of CPUs to use", default=1) + parser.add_argument("--skip_existing", help="Will quit gracefully if weights file already exists", action="store_true", default=False) + parser.add_argument("--overwrite", help="Will overwrite existing weights file", action="store_true", default=False) + parser.add_argument("--calc_method", choices=["evcouplings", "eve", "both", "identity"], help="Method to use for calculating weights. Note: Both produce the same results as we modified the evcouplings numba code to mirror the eve calculation", default="evcouplings") + parser.add_argument("--threshold_focus_cols_frac_gaps", type=float, + help="Maximum fraction of gaps allowed in focus columns - see data_utils.MSA_processing") + return parser + + +def main(args): + print("Arguments:", args) + + weights_file = None + + if args.MSA_filepath is not None: + assert os.path.isfile(args.MSA_filepath), f"MSA filepath {args.MSA_filepath} doesn't exist" + msa_location = args.MSA_filepath + else: + # Use mapping file + assert os.path.isfile(args.DMS_reference_file_path), f"DMS reference file {args.DMS_reference_file_path} doesn't seem to exist" + mapping_file = pd.read_csv(args.DMS_reference_file_path) + protein_name = mapping_file['MSA_filename'][args.DMS_index].split(".a2m")[0] + msa_location = args.MSA_data_folder + os.sep + mapping_file['MSA_filename'][args.DMS_index] + print("Protein name: " + str(protein_name)) + # If weights_file is in the df_mapping, use that instead + if "weight_file_name" in mapping_file.columns: + weights_file = args.MSA_weights_location + os.sep + mapping_file["weight_file_name"][args.DMS_index] + print("Using weights filename from mapping file:", weights_file) + + print("MSA file: " + str(msa_location)) + + if args.theta_reweighting is not None: + theta = args.theta_reweighting + print(f"Using custom theta value {theta} instead of loading from mapping file.") + else: + try: + theta = float(mapping_file['MSA_theta'][args.DMS_index]) + except KeyError as e: + # Overriding previous errors is bad, but we're being nice to the user + raise KeyError("Couldn't load theta from mapping file. " + "NOT using default value of theta=0.2; please specify theta manually. Specific line:", + mapping_file[args.DMS_index], + "Previous error:", e) + assert not np.isnan(theta), "Theta is NaN, please provide a custom theta value" + + print("Theta MSA re-weighting: " + str(theta)) + + # Using data_kwargs so that if options aren't set, they'll be set to default values + data_kwargs = {} + if args.threshold_focus_cols_frac_gaps is not None: + print("Using custom threshold_focus_cols_frac_gaps: ", args.threshold_focus_cols_frac_gaps) + data_kwargs['threshold_focus_cols_frac_gaps'] = args.threshold_focus_cols_frac_gaps + + if not os.path.isdir(args.MSA_weights_location): + os.makedirs(args.MSA_weights_location, exist_ok=True) + raise NotADirectoryError(f"{args.MSA_weights_location} is not a directory." + f"Could create it automatically, but at the moment raising an error.") + else: + print(f"MSA weights directory: {args.MSA_weights_location}") + + if weights_file is None: + print("Weights filename not found - writing to new file") + weights_file = args.MSA_weights_location + os.sep + protein_name + '_theta_' + str(theta) + '.npy' + + print(f"Writing to {weights_file}") + # First check that the weights file doesn't exist + if os.path.isfile(weights_file) and not args.overwrite: + if args.skip_existing: + print("Weights file already exists, skipping, since --skip_existing was specified") + exit(0) + else: + raise FileExistsError(f"File {weights_file} already exists. " + f"Please delete it if you want to re-calculate it. " + f"If you want to skip existing files, use --skip_existing.") + + # The msa_data processing has a side effect of saving a weights file + _ = data_utils.MSA_processing( + MSA_location=msa_location, + theta=theta, + use_weights=True, + weights_location=weights_file, + num_cpus=args.num_cpus, + weights_calc_method=args.calc_method, + overwrite_weights=args.overwrite, + debug_only_weights=True, + **data_kwargs, + ) + + +if __name__ == '__main__': + start = time.perf_counter() + parser = create_argparser() + args = parser.parse_args() + main(args) + end = time.perf_counter() + print(f"calc_weights.py took {end-start:.2f} seconds in total.") diff --git a/proteingym/baselines/EVE/compute_evol_indices_DMS.py b/proteingym/baselines/EVE/compute_evol_indices_DMS.py new file mode 100644 index 0000000..d34c575 --- /dev/null +++ b/proteingym/baselines/EVE/compute_evol_indices_DMS.py @@ -0,0 +1,138 @@ +import datetime +import os,sys +import json +import argparse +from resource import getrusage, RUSAGE_SELF + +import pandas as pd +import torch + +from EVE import VAE_model +from utils import data_utils + +if __name__=='__main__': + + parser = argparse.ArgumentParser(description='Evol indices') + parser.add_argument('--MSA_data_folder', type=str, help='Folder where MSAs are stored') + parser.add_argument('--DMS_reference_file_path', type=str, help='List of proteins and corresponding MSA file name') + parser.add_argument('--protein_index', type=int, help='Row index of protein in input mapping file') + parser.add_argument('--MSA_weights_location', type=str, help='Location where weights for each sequence in the MSA will be stored') + parser.add_argument('--theta_reweighting', type=float, help='Parameters for MSA sequence re-weighting') + parser.add_argument('--random_seeds',type=int,nargs="+", help='Random seed for VAE model initialization') + parser.add_argument('--VAE_checkpoint_location', type=str, help='Location where VAE model checkpoints will be stored') + parser.add_argument('--model_parameters_location', type=str, help='Location of VAE model parameters') + parser.add_argument('--DMS_data_folder', type=str, help='Location of all mutations to compute the evol indices for') + parser.add_argument('--output_scores_folder', type=str, help='Output location of computed evol indices') + parser.add_argument('--num_samples_compute_evol_indices', type=int, help='Num of samples to approximate delta elbo when computing evol indices') + parser.add_argument('--batch_size', default=256, type=int, help='Batch size when computing evol indices') + parser.add_argument("--skip_existing", action="store_true", help="Skip scoring if output file already exists") + parser.add_argument("--aggregation_method", choices=["full", "batch", "online"], default="full", help="Method to aggregate evol indices") + parser.add_argument("--threshold_focus_cols_frac_gaps", type=float, + help="Maximum fraction of gaps allowed in focus columns - see data_utils.MSA_processing") + args = parser.parse_args() + + print("Arguments:", args) + + assert os.path.isfile(args.DMS_reference_file_path), 'MSA list file does not exist: {}'.format(args.DMS_reference_file_path) + mapping_file = pd.read_csv(args.DMS_reference_file_path) + DMS_id = mapping_file['DMS_id'][args.protein_index] + protein_name = mapping_file['MSA_filename'][args.protein_index].split(".a2m")[0] + DMS_filename = mapping_file['DMS_filename'][args.protein_index] + mutant = mapping_file['DMS_filename'][args.protein_index] + msa_location = args.MSA_data_folder + os.sep + mapping_file['MSA_filename'][args.protein_index] + DMS_mutant_column = "mutant" + if not DMS_filename.startswith(DMS_id): + print(f"Warning: DMS id does not match DMS filename: {DMS_id} vs {DMS_filename}. Continuing for now.") + + # Check filepaths are valid + evol_indices_output_filename = os.path.join(args.output_scores_folder, f'{DMS_id}.csv') + + + if os.path.isfile(evol_indices_output_filename): + print("Output file already exists: " + str(evol_indices_output_filename)) + + if args.skip_existing: + print("Skipping scoring since args.skip_existing is True") + sys.exit(0) + else: + print("Overwriting existing file: " + str(evol_indices_output_filename)) + print("To skip scoring for existing files, use --skip_existing") + # Check if surrounding directory exists + else: + print("Output file: " + str(evol_indices_output_filename)) + assert os.path.isdir(os.path.dirname(evol_indices_output_filename)), \ + 'Output directory does not exist: {}. Please create directory before running script.\nOutput filename given: {}.\nDebugging curdir={}'\ + .format(os.path.dirname(evol_indices_output_filename), evol_indices_output_filename, os.getcwd()) + + if args.theta_reweighting is not None: + theta = args.theta_reweighting + else: + try: + theta = float(mapping_file['MSA_theta'][args.protein_index]) + except: + theta = 0.2 + print("Theta MSA re-weighting: "+str(theta)) + + # Using data_kwargs so that if options aren't set, they'll be set to default values + data_kwargs = {} + if args.threshold_focus_cols_frac_gaps is not None: + print("Using custom threshold_focus_cols_frac_gaps: ", args.threshold_focus_cols_frac_gaps) + data_kwargs['threshold_focus_cols_frac_gaps'] = args.threshold_focus_cols_frac_gaps + + data = data_utils.MSA_processing( + MSA_location=msa_location, + theta=theta, + use_weights=False, # Don't need weights for evol indices + **data_kwargs, + ) + + + args.mutations_location = args.DMS_data_folder + os.sep + DMS_filename + for seed in args.random_seeds: + model_name = protein_name + f"_seed_{seed}" + print("Model name: "+str(model_name)) + + model_params = json.load(open(args.model_parameters_location)) + + model = VAE_model.VAE_model( + model_name=model_name, + data=data, + encoder_parameters=model_params["encoder_parameters"], + decoder_parameters=model_params["decoder_parameters"], + random_seed=42 + ) + model = model.to(model.device) + checkpoint_name = str(args.VAE_checkpoint_location) + os.sep + model_name + assert os.path.isfile(checkpoint_name), 'Checkpoint file does not exist: {}'.format(checkpoint_name) + + try: + checkpoint = torch.load(checkpoint_name, map_location=model.device) # Added map_location so that this works with CPU too + model.load_state_dict(checkpoint['model_state_dict']) + print("Initialized VAE with checkpoint '{}' ".format(checkpoint_name)) + except Exception as e: + print("Unable to load VAE model checkpoint {}".format(checkpoint_name)) + raise e + + list_valid_mutations, evol_indices, _, _ = model.compute_evol_indices( + msa_data=data, + list_mutations_location=args.mutations_location, + mutant_column=DMS_mutant_column, + num_samples=args.num_samples_compute_evol_indices, + batch_size=args.batch_size, + aggregation_method=args.aggregation_method + ) + + df = {} + df['mutant'] = list_valid_mutations + df[f'evol_indices_seed_{seed}'] = evol_indices + df = pd.DataFrame(df) + + if os.path.exists(evol_indices_output_filename) and seed != args.random_seeds[0]: + prev_df = pd.read_csv(evol_indices_output_filename) + prev_len = len(prev_df) + df = pd.merge(prev_df, df, on='mutant', how='inner') + # checking that the mutants match after the first seed (first seed will overwrite original score file) + assert len(df) == len(prev_df), "Length of merged dataframe doesn't match previous length, mutants must not match across seeds" + df.to_csv(evol_indices_output_filename, index=False) + else: + df.to_csv(evol_indices_output_filename, index=False) diff --git a/proteingym/baselines/EVE/train_VAE.py b/proteingym/baselines/EVE/train_VAE.py new file mode 100644 index 0000000..32a9371 --- /dev/null +++ b/proteingym/baselines/EVE/train_VAE.py @@ -0,0 +1,167 @@ +import argparse +import json +import time +import os + + +import pandas as pd + +from EVE import VAE_model +from utils import data_utils + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='VAE') + parser.add_argument('--MSA_data_folder', type=str, + help='Folder where MSAs are stored', required=True) + parser.add_argument('--DMS_reference_file_path', type=str, + help='List of proteins and corresponding MSA file name', required=True) + parser.add_argument('--protein_index', type=int, + help='Row index of protein in input mapping file', required=True) + parser.add_argument('--MSA_weights_location', type=str, + help='Location where weights for each sequence in the MSA will be stored', required=True) + parser.add_argument('--theta_reweighting', type=float, + help='Parameters for MSA sequence re-weighting') + parser.add_argument('--VAE_checkpoint_location', type=str, + help='Location where VAE model checkpoints will be stored', required=True) + parser.add_argument('--model_parameters_location', type=str, + help='Location of VAE model parameters', required=True) + parser.add_argument('--training_logs_location', type=str, + help='Location of VAE model parameters') + parser.add_argument("--seed", type=int, help="Random seed", default=42) + parser.add_argument( + '--z_dim', type=int, help='Specify a different latent dim than in the params file') + parser.add_argument("--threshold_focus_cols_frac_gaps", type=float, + help="Maximum fraction of gaps allowed in focus columns - see data_utils.MSA_processing") + parser.add_argument('--force_load_weights', action='store_true', + help="Force loading of weights from MSA_weights_location (useful if you want to make sure you're using precalculated weights). Will fail if weight file doesn't exist.", + default=False) + parser.add_argument("--overwrite_weights", + help="Will overwrite weights file if it already exists", action="store_true", default=False) + parser.add_argument("--skip_existing", help="Will quit gracefully if model checkpoint file already exists", + action="store_true", default=False) + parser.add_argument("--batch_size", type=int, + help="Batch size for training", default=None) + parser.add_argument("--experimental_stream_data", + help="Load one-hot-encodings on the fly", action="store_true", default=False) + + args = parser.parse_args() + + print("Arguments:", args) + + assert os.path.isfile( + args.DMS_reference_file_path), f"MSA file list {args.DMS_reference_file_path} doesn't seem to exist" + mapping_file = pd.read_csv(args.DMS_reference_file_path) + + if mapping_file["MSA_filename"].duplicated().any(): + print(f"Note: Duplicate MSA_filename detected in the mapping file. Deduplicating to only have one EVE model per alignment.") + mapping_file = mapping_file.drop_duplicates(subset=["MSA_filename"]) + protein_name = mapping_file['MSA_filename'][args.protein_index].split(".a2m")[ + 0] + msa_location = args.MSA_data_folder + os.sep + \ + mapping_file['MSA_filename'][args.protein_index] + print("Protein name: " + str(protein_name)) + print("MSA file: " + str(msa_location)) + + if args.theta_reweighting is not None: + theta = args.theta_reweighting + else: + try: + theta = float(mapping_file['MSA_theta'][args.protein_index]) + except: + print("Couldn't load theta from mapping file. Using default value of 0.2") + theta = 0.2 + + model_name = protein_name + f"_seed_{args.seed}" + print("Model name: " + str(model_name)) + model_checkpoint_final_path = args.VAE_checkpoint_location + os.sep + model_name + if os.path.isfile(model_checkpoint_final_path): + if args.skip_existing: + print( + "Model checkpoint already exists, skipping, since --skip_existing was specified") + exit(0) + else: + raise FileExistsError(f"Model checkpoint {model_checkpoint_final_path} already exists. \ + Use --skip_existing to skip without raising an error, or delete the destination file if you want to rerun.") + + # Using data_kwargs so that if options aren't set, they'll be set to default values + data_kwargs = {} + if args.threshold_focus_cols_frac_gaps is not None: + print("Using custom threshold_focus_cols_frac_gaps: ", + args.threshold_focus_cols_frac_gaps) + data_kwargs['threshold_focus_cols_frac_gaps'] = args.threshold_focus_cols_frac_gaps + + if args.overwrite_weights: + print("Overwriting weights file") + data_kwargs['overwrite_weights'] = True + + print("Theta MSA re-weighting: " + str(theta)) + + # Load weights file if it's in the mapping file + if "weight_file_name" in mapping_file.columns: + weights_file = args.MSA_weights_location + os.sep + \ + mapping_file["weight_file_name"][args.protein_index] + print("Using weights filename from mapping file") + else: + print( + f"weight_file_name not provided in mapping file. Using default weights filename of {protein_name}_theta_{theta}.npy") + weights_file = args.MSA_weights_location + os.sep + \ + protein_name + '_theta_' + str(theta) + '.npy' + + print(f"Weights location: {weights_file}") + + if args.force_load_weights: + print("Flag force_load_weights enabled - Forcing that we use weights from file:", weights_file) + if not os.path.isfile(weights_file): + raise FileNotFoundError(f"Weights file {weights_file} doesn't exist." + f"To recompute weights, remove the flag --force_load_weights.") + + data = data_utils.MSA_processing( + MSA_location=msa_location, + theta=theta, + use_weights=True, + weights_location=weights_file, + debug_only_weights=args.experimental_stream_data, + **data_kwargs, + ) + + assert os.path.isfile( + args.model_parameters_location), args.model_parameters_location + model_params = json.load(open(args.model_parameters_location)) + + # Overwrite params if necessary + if args.z_dim: + model_params["encoder_parameters"]["z_dim"] = args.z_dim + model_params["decoder_parameters"]["z_dim"] = args.z_dim + if args.batch_size is not None: + print("Using batch_size from command line: ", args.batch_size) + model_params["training_parameters"]["batch_size"] = args.batch_size + + model = VAE_model.VAE_model( + model_name=model_name, + data=data, + encoder_parameters=model_params["encoder_parameters"], + decoder_parameters=model_params["decoder_parameters"], + random_seed=args.seed + ) + model = model.to(model.device) + + model_params["training_parameters"]['training_logs_location'] = args.training_logs_location + model_params["training_parameters"]['model_checkpoint_location'] = args.VAE_checkpoint_location + + print("debug batch size=", + model_params["training_parameters"]["batch_size"]) + print("Starting to train model: " + model_name) + start = time.perf_counter() + model.train_model( + data=data, training_parameters=model_params["training_parameters"], use_dataloader=args.experimental_stream_data) + end = time.perf_counter() + # Show time in hours,minutes,seconds + print( + f"Finished in {(end - start)//60//60}hours {(end - start)//60%60} minutes and {(end - start)%60} seconds") + + print("Saving model: " + model_name) + model.save(model_checkpoint=model_checkpoint_final_path, + encoder_parameters=model_params["encoder_parameters"], + decoder_parameters=model_params["decoder_parameters"], + training_parameters=model_params["training_parameters"] + ) diff --git a/proteingym/baselines/EVE/utils/__init__.py b/proteingym/baselines/EVE/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/proteingym/baselines/EVE/utils/constants.py b/proteingym/baselines/EVE/utils/constants.py new file mode 100644 index 0000000..b6de0a9 --- /dev/null +++ b/proteingym/baselines/EVE/utils/constants.py @@ -0,0 +1,6 @@ +# Copied from EVCouplings +GAP = "-" +MATCH_GAP = GAP +INSERT_GAP = "." +ALPHABET_PROTEIN_NOGAP = "ACDEFGHIKLMNPQRSTVWY" +ALPHABET_PROTEIN_GAP = GAP + ALPHABET_PROTEIN_NOGAP diff --git a/proteingym/baselines/EVE/utils/data_utils.py b/proteingym/baselines/EVE/utils/data_utils.py new file mode 100644 index 0000000..a889a01 --- /dev/null +++ b/proteingym/baselines/EVE/utils/data_utils.py @@ -0,0 +1,471 @@ +import multiprocessing +import os +import time +from collections import defaultdict + +import numpy as np +import pandas as pd +from tqdm import tqdm +import torch +from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler + +from utils.weights import map_from_alphabet, map_matrix, compute_sequence_weights, calc_weights_evcouplings + +# TODOs: Can get rid of one_hot_encodings when only calculating weights right? + +# constants +GAP = "-" +MATCH_GAP = GAP +INSERT_GAP = "." + +ALPHABET_PROTEIN_NOGAP = "ACDEFGHIKLMNPQRSTVWY" +ALPHABET_PROTEIN_GAP = GAP + ALPHABET_PROTEIN_NOGAP + + +class MSA_processing: + def __init__(self, + MSA_location="", + theta=0.2, + use_weights=True, + weights_location="./data/weights", + preprocess_MSA=True, + threshold_sequence_frac_gaps=0.5, + threshold_focus_cols_frac_gaps=0.3, + remove_sequences_with_indeterminate_AA_in_focus_cols=True, + num_cpus=1, + weights_calc_method="evcouplings", + overwrite_weights=False, + debug_only_weights=False, + ): + + """ + Parameters: + - msa_location: (path) Location of the MSA data. Constraints on input MSA format: + - focus_sequence is the first one in the MSA data + - first line is structured as follows: ">focus_seq_name/start_pos-end_pos" (e.g., >SPIKE_SARS2/310-550) + - corresponding sequence data located on following line(s) + - then all other sequences follow with ">name" on first line, corresponding data on subsequent lines + - theta: (float) Sequence weighting hyperparameter. Generally: Prokaryotic and eukaryotic families = 0.2; Viruses = 0.01 + - use_weights: (bool) If False, sets all sequence weights to 1. If True, checks weights_location -- if non empty uses that; + otherwise compute weights from scratch and store them at weights_location + - weights_location: (path) File to load from/save to the sequence weights + - preprocess_MSA: (bool) performs pre-processing of MSA to remove short fragments and positions that are not well covered. + - threshold_sequence_frac_gaps: (float, between 0 and 1) Threshold value to define fragments + - sequences with a fraction of gap characters above threshold_sequence_frac_gaps are removed + - default is set to 0.5 (i.e., fragments with 50% or more gaps are removed) + - threshold_focus_cols_frac_gaps: (float, between 0 and 1) Threshold value to define focus columns + - positions with a fraction of gap characters above threshold_focus_cols_pct_gaps will be set to lower case (and not included in the focus_cols) + - default is set to 0.3 (i.e., focus positions are the ones with 30% of gaps or less, i.e., 70% or more residue occupancy) + - remove_sequences_with_indeterminate_AA_in_focus_cols: (bool) Remove all sequences that have indeterminate AA (e.g., B, J, X, Z) at focus positions of the wild type + - num_cpus: (int) Number of CPUs to use for parallel weights calculation processing. If set to -1, all available CPUs are used. If set to 1, weights are computed in serial. + - weights_calc_method: (str) Method to use for calculating sequence weights. Options: "evcouplings","eve" or "identity". (default "evcouplings") + - Note: For now the "evcouplings" method is modified to be equivalent to the "eve" method, + but the "evcouplings" method is faster as it uses numba. + - overwrite_weights: (bool) If True, calculate weights and overwrite weights file. If False, load weights from weights_location if it exists. + TODO these weights options should be more like calc_weights=[True/False], and the weights_location should be a list of locations to load from/save to. + """ + np.random.seed(2021) + self.MSA_location = MSA_location + self.weights_location = weights_location + self.theta = theta + self.alphabet = ALPHABET_PROTEIN_NOGAP + self.use_weights = use_weights + self.overwrite_weights = overwrite_weights + self.preprocess_MSA = preprocess_MSA + self.threshold_sequence_frac_gaps = threshold_sequence_frac_gaps + self.threshold_focus_cols_frac_gaps = threshold_focus_cols_frac_gaps + self.remove_sequences_with_indeterminate_AA_in_focus_cols = remove_sequences_with_indeterminate_AA_in_focus_cols + self.debug_only_weights = debug_only_weights + self.weights_calc_method = weights_calc_method + + # Defined by gen_alignment + self.aa_dict = {} + self.focus_seq_name = "" + self.seq_name_to_sequence = defaultdict(str) + self.focus_seq, self.focus_cols, self.focus_seq_trimmed, self.seq_len, self.alphabet_size = [None] * 5 + self.focus_start_loc, self.focus_stop_loc = None, None + self.uniprot_focus_col_to_wt_aa_dict, self.uniprot_focus_col_to_focus_idx = None, None + self.one_hot_encoding, self.weights, self.Neff, self.num_sequences = [None] * 4 + + # Defined by create_all_singles + self.mutant_to_letter_pos_idx_focus_list = None + self.all_single_mutations = None + + # Fill in the instance variables + self.gen_alignment() + self.calc_weights(num_cpus=num_cpus, method=weights_calc_method) + + if not self.debug_only_weights: + print("Creating all single mutations") + self.create_all_singles() + + def gen_alignment(self): + """ Read training alignment and store basics in class instance """ + self.aa_dict = {} + for i, aa in enumerate(self.alphabet): + self.aa_dict[aa] = i + + self.seq_name_to_sequence = defaultdict(str) + name = "" + with open(self.MSA_location, "r") as msa_data: + for i, line in enumerate(msa_data): + line = line.rstrip() + if line.startswith(">"): + name = line + if i == 0: + self.focus_seq_name = name + else: + self.seq_name_to_sequence[name] += line + print("Number of sequences in MSA (before preprocessing):", len(self.seq_name_to_sequence)) + + ## MSA pre-processing to remove inadequate columns and sequences + if self.preprocess_MSA: + # Overwrite self.seq_name_to_sequence + self.seq_name_to_sequence = self.preprocess_msa( + seq_name_to_sequence=self.seq_name_to_sequence, + focus_seq_name=self.focus_seq_name, + threshold_sequence_frac_gaps=self.threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=self.threshold_focus_cols_frac_gaps + ) + + self.focus_seq = self.seq_name_to_sequence[self.focus_seq_name] + self.focus_cols = [ix for ix, s in enumerate(self.focus_seq) if s == s.upper() and s != '-'] + self.focus_seq_trimmed = "".join([self.focus_seq[ix] for ix in self.focus_cols]) + self.seq_len = len(self.focus_cols) + self.alphabet_size = len(self.alphabet) + + # Connect local sequence index with uniprot index (index shift inferred from 1st row of MSA) + focus_loc = self.focus_seq_name.split("/")[-1] + start, stop = focus_loc.split("-") + self.focus_start_loc = int(start) + self.focus_stop_loc = int(stop) + self.uniprot_focus_col_to_wt_aa_dict \ + = {idx_col + int(start): self.focus_seq[idx_col] for idx_col in self.focus_cols} + self.uniprot_focus_col_to_focus_idx \ + = {idx_col + int(start): idx_col for idx_col in self.focus_cols} + + # Move all letters to CAPS; keeps focus columns only + for seq_name, sequence in self.seq_name_to_sequence.items(): + sequence = sequence.replace(".", "-") + self.seq_name_to_sequence[seq_name] = "".join( + [sequence[ix].upper() for ix in self.focus_cols]) # Makes a List[str] instead of str + + # Remove sequences that have indeterminate AA (e.g., B, J, X, Z) in the focus columns + if self.remove_sequences_with_indeterminate_AA_in_focus_cols: + alphabet_set = set(list(self.alphabet)) + seq_names_to_remove = [] + for seq_name, sequence in self.seq_name_to_sequence.items(): + for letter in sequence: + if letter not in alphabet_set and letter != "-": + seq_names_to_remove.append(seq_name) + continue + seq_names_to_remove = list(set(seq_names_to_remove)) + for seq_name in seq_names_to_remove: + del self.seq_name_to_sequence[seq_name] + + print("Number of sequences after preprocessing:", len(self.seq_name_to_sequence)) + + if self.debug_only_weights and self.weights_calc_method == "evcouplings": + print("Weights-only mode with evcouplings: Skipping one-hot encodings") + else: + # Encode the sequences + print("One-hot encoding sequences") + self.one_hot_encoding = one_hot_3D( + seq_keys=self.seq_name_to_sequence.keys(), # Note: Dicts are unordered for python < 3.6 + seq_name_to_sequence=self.seq_name_to_sequence, + alphabet=self.alphabet, + seq_length=self.seq_len, + ) + + # Using staticmethod to keep this under the MSAProcessing namespace, but this is apparently not best practice + @staticmethod + def preprocess_msa(seq_name_to_sequence, focus_seq_name, threshold_sequence_frac_gaps, threshold_focus_cols_frac_gaps): + """Remove inadequate columns and sequences from MSA, overwrite self.seq_name_to_sequence.""" + print("Pre-processing MSA to remove inadequate columns and sequences...") + msa_df = pd.DataFrame.from_dict(seq_name_to_sequence, orient='index', columns=['sequence']) + # Data clean up + msa_df.sequence = msa_df.sequence.apply(lambda x: x.replace(".", "-")).apply( + lambda x: ''.join([aa.upper() for aa in x])) + # Remove columns that would be gaps in the wild type + non_gap_wt_cols = [aa != '-' for aa in msa_df.sequence[focus_seq_name]] + msa_df['sequence'] = msa_df['sequence'].apply( + lambda x: ''.join([aa for aa, non_gap_ind in zip(x, non_gap_wt_cols) if non_gap_ind])) + assert 0.0 <= threshold_sequence_frac_gaps <= 1.0, "Invalid fragment filtering parameter" + assert 0.0 <= threshold_focus_cols_frac_gaps <= 1.0, "Invalid focus position filtering parameter" + print("Calculating proportion of gaps") + msa_array = np.array([list(seq) for seq in msa_df.sequence]) + gaps_array = np.array(list(map(lambda seq: [aa == '-' for aa in seq], msa_array))) + # Identify fragments with too many gaps + seq_gaps_frac = gaps_array.mean(axis=1) + seq_below_threshold = seq_gaps_frac <= threshold_sequence_frac_gaps + print("Proportion of sequences dropped due to fraction of gaps: " + str( + round(float(1 - seq_below_threshold.sum() / seq_below_threshold.shape) * 100, 2)) + "%") + # Identify focus columns + columns_gaps_frac = gaps_array[seq_below_threshold].mean(axis=0) + index_cols_below_threshold = columns_gaps_frac <= threshold_focus_cols_frac_gaps + print("Proportion of non-focus columns removed: " + str( + round(float(1 - index_cols_below_threshold.sum() / index_cols_below_threshold.shape) * 100, 2)) + "%") + # Lower case non focus cols and filter fragment sequences + def _lower_case_and_filter_fragments(seq): + return ''.join([aa.lower() if aa_ix in index_cols_below_threshold else aa for aa_ix, aa in enumerate(seq)]) + msa_df['sequence'] = msa_df['sequence'].apply( + lambda seq: ''.join([aa.upper() if upper_case_ind else aa.lower() for aa, upper_case_ind in + zip(seq, index_cols_below_threshold)])) + msa_df = msa_df[seq_below_threshold] + # Overwrite seq_name_to_sequence with clean version + seq_name_to_sequence = defaultdict(str) + for seq_idx in range(len(msa_df['sequence'])): + seq_name_to_sequence[msa_df.index[seq_idx]] = msa_df.sequence[seq_idx] + + return seq_name_to_sequence + + def calc_weights(self, num_cpus=1, method="evcouplings"): + """ + If num_cpus == 1, weights are computed in serial. + If num_cpus == -1, weights are computed in parallel using all available cores. + Note: This will use multiprocessing.cpu_count() to get the number of available cores, which on clusters may + return all cores, not just the number of cores available to the user. + """ + # Refactored into its own function so that we can call it separately + if self.use_weights: + if os.path.isfile(self.weights_location) and not self.overwrite_weights: + print("Loading sequence weights from disk") + self.weights = np.load(file=self.weights_location) + else: + print("Computing sequence weights") + if num_cpus == -1: + #multiprocessing.cpu_count() + num_cpus = get_num_cpus() + + if method == "evcouplings": + alphabet_mapper = map_from_alphabet(ALPHABET_PROTEIN_GAP, default=GAP) + arrays = [] + for seq in self.seq_name_to_sequence.values(): + arrays.append(np.array(list(seq))) + sequences = np.vstack(arrays) + sequences_mapped = map_matrix(sequences, alphabet_mapper) + print("Starting EVCouplings calculation") + start = time.perf_counter() + self.weights = calc_weights_evcouplings(sequences_mapped, identity_threshold=1 - self.theta, + empty_value=0, num_cpus=num_cpus) # GAP = 0 + end = time.perf_counter() + print(f"EVCouplings weights took {end - start:.2f} seconds") + elif method == "eve": + list_seq = self.one_hot_encoding.numpy() + start = time.perf_counter() + self.weights = compute_sequence_weights(list_seq, self.theta, num_cpus=num_cpus) + end = time.perf_counter() + print(f"EVE weights took {end - start:.2f} seconds") + elif method == "identity": + self.weights = np.ones(self.one_hot_encoding.shape[0]) + else: + raise ValueError(f"Unknown method: {method}. Must be either 'evcouplings', 'eve' or 'identity'.") + print("Saving sequence weights to disk") + np.save(file=self.weights_location, arr=self.weights) + else: + # If not using weights, use an isotropic weight matrix + print("Not weighting sequence data") + self.weights = np.ones(self.one_hot_encoding.shape[0]) + + self.Neff = np.sum(self.weights) + self.num_sequences = self.weights.shape[0] + + print("Neff =", str(self.Neff)) + + if self.debug_only_weights and self.weights_calc_method == "evcouplings": + print("Num sequences: ", self.num_sequences) + else: + print("Data Shape =", self.one_hot_encoding.shape) + + return self.weights + + def create_all_singles(self): + start_idx = self.focus_start_loc + focus_seq_index = 0 + self.mutant_to_letter_pos_idx_focus_list = {} + list_valid_mutations = [] + # find all possible valid mutations that can be run with this alignment + alphabet_set = set(list(self.alphabet)) + for i, letter in enumerate(self.focus_seq): + if letter in alphabet_set and letter != "-": + for mut in self.alphabet: + pos = start_idx + i + if mut != letter: + mutant = letter + str(pos) + mut + self.mutant_to_letter_pos_idx_focus_list[mutant] = [letter, pos, focus_seq_index] + list_valid_mutations.append(mutant) + focus_seq_index += 1 + self.all_single_mutations = list_valid_mutations + + def save_all_singles(self, output_filename): + with open(output_filename, "w") as output: + output.write('mutations') + for mutation in self.all_single_mutations: + output.write('\n') + output.write(mutation) + + +def generate_mutated_sequences(msa_data, list_mutations): + """ + Copied from VAE_model.compute_evol_indices. + + Generate mutated sequences using a MSAProcessing data object and list of mutations of the form "A42T" where position + 42 on the wild type is changed from A to T. + Multiple mutations are separated by colons e.g. "A42T:C9A" + + Returns a tuple (list_valid_mutations, valid_mutated_sequences), + e.g. (['wt', 'A3T'], {'wt': 'AGAKLI', 'A3T': 'AGTKLI'}) + """ + list_valid_mutations = ['wt'] + valid_mutated_sequences = {} + valid_mutated_sequences['wt'] = msa_data.focus_seq_trimmed # first sequence in the list is the wild_type + + # Remove (multiple) mutations that are invalid + for mutation in list_mutations: + individual_substitutions = mutation.split(':') + mutated_sequence = list(msa_data.focus_seq_trimmed)[:] + fully_valid_mutation = True + for mut in individual_substitutions: + wt_aa, pos, mut_aa = mut[0], int(mut[1:-1]), mut[-1] + if pos not in msa_data.uniprot_focus_col_to_wt_aa_dict \ + or msa_data.uniprot_focus_col_to_wt_aa_dict[pos] != wt_aa \ + or mut not in msa_data.mutant_to_letter_pos_idx_focus_list: + print("Not a valid mutant: " + mutation) + fully_valid_mutation = False + break + else: + wt_aa, pos, idx_focus = msa_data.mutant_to_letter_pos_idx_focus_list[mut] + mutated_sequence[idx_focus] = mut_aa # perform the corresponding AA substitution + + if fully_valid_mutation: + list_valid_mutations.append(mutation) + valid_mutated_sequences[mutation] = ''.join(mutated_sequence) + + return list_valid_mutations, valid_mutated_sequences + + +# Copied from VAE_model.compute_evol_indices +# One-hot encoding of sequences +def one_hot_3D(seq_keys, seq_name_to_sequence, alphabet, seq_length): + """ + Take in a list of sequence names/keys and corresponding sequences, and generate a one-hot array according to an alphabet. + """ + aa_dict = {letter: i for (i, letter) in enumerate(alphabet)} + + one_hot_out = np.zeros((len(seq_keys), seq_length, len(alphabet))) + for i, seq_key in enumerate(tqdm(seq_keys, desc="One-hot encoding sequences", mininterval=1)): + sequence = seq_name_to_sequence[seq_key] + for j, letter in enumerate(sequence): + if letter in aa_dict: + k = aa_dict[letter] + one_hot_out[i, j, k] = 1.0 + one_hot_out = torch.tensor(one_hot_out) + return one_hot_out + + +def gen_one_hot_to_sequence(one_hot_tensor, alphabet): + """Reverse of one_hot_3D. Need the msa_data again. Returns a list of sequences.""" + for seq_tensor in one_hot_tensor: # iterate through outer dimension + seq = "" + letters_idx = seq_tensor.argmax(-1) + + for idx in letters_idx.tolist(): # Could also do map(di.get, letters_idx) + letter = alphabet[idx] + seq += letter + yield seq + + +def one_hot_to_sequence_list(one_hot_tensor, alphabet): + return list(gen_one_hot_to_sequence(one_hot_tensor, alphabet)) + +def get_one_hot_3D_fn(msa_data): + aa_dict = {letter: i for (i, letter) in enumerate(msa_data.alphabet)} + + def fn(batch_seqs): + one_hot_out = np.zeros((len(batch_seqs), msa_data.seq_len, len(msa_data.alphabet))) + for i, sequence in enumerate(batch_seqs): + for j, letter in enumerate(sequence): + if letter in aa_dict: + k = aa_dict[letter] + one_hot_out[i, j, k] = 1.0 + one_hot_out = torch.tensor(one_hot_out) + return one_hot_out + return fn + +def get_num_cpus(): + if 'SLURM_CPUS_PER_TASK' in os.environ: + num_cpus = int(os.environ['SLURM_CPUS_PER_TASK']) + print("SLURM_CPUS_PER_TASK:", os.environ['SLURM_CPUS_PER_TASK']) + print("Using all available cores (calculated using SLURM_CPUS_PER_TASK):", num_cpus) + else: + num_cpus = len(os.sched_getaffinity(0)) + print("Using all available cores (calculated using len(os.sched_getaffinity(0))):", num_cpus) + return num_cpus + +class OneHotDataset(Dataset): + def __init__(self, seq_keys, seq_name_to_sequence, alphabet, seq_length, total_length=None): + self.seq_keys = list(seq_keys) + self.seq_name_to_sequence = seq_name_to_sequence + self.alphabet = alphabet + self.seq_length = seq_length + self.aa_dict = {letter: i for (i, letter) in enumerate(alphabet)} + if total_length is None: + self.total_length = len(self.seq_keys) + else: + self.total_length = int(total_length) + + def __len__(self): + return self.total_length + + def __getitem__(self, idx): + seq_key = self.seq_keys[idx] + sequence = self.seq_name_to_sequence[seq_key] + return sequence + +class InfiniteDataLoader(DataLoader): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.iter_loader = super().__iter__() + + def __iter__(self): + return self + + def __next__(self): + try: + batch = next(self.iter_loader) + except StopIteration: + # If the inner DataLoader has exhausted the dataset, reset it + self.iter_loader = super().__iter__() + batch = next(self.iter_loader) + return batch + +def get_dataloader(msa_data: MSA_processing, batch_size, num_training_steps): + print("Going to hackily set the length of the dataset to the number of training steps, not the actual number of sequences.") + dataset = OneHotDataset( + seq_keys=msa_data.seq_name_to_sequence.keys(), + seq_name_to_sequence=msa_data.seq_name_to_sequence, + alphabet=msa_data.alphabet, + seq_length=msa_data.seq_len) #, total_length=num_training_steps + # This can take a ton of memory if the weights or num_training_steps*batch_size are large + sampler = WeightedRandomSampler(weights=msa_data.weights, num_samples=num_training_steps*batch_size, replacement=True) + num_cpus = 1 # get_num_cpus() # TODO test with only 1 CPU + + one_hot_fn = get_one_hot_3D_fn(msa_data) + + def collate_fn(batch_seqs): + # Construct a batch of one-hot-encodings + batch_seq_tensor = one_hot_fn(batch_seqs) + return batch_seq_tensor + + + # dataloader = DataLoader(dataset, + + # Other option for avoiding the problem of the dataset running out: Wrap it with an iterable that refreshes it every time + dataloader = InfiniteDataLoader( + dataset=dataset, + batch_size=batch_size, + num_workers=num_cpus, # collate_fn is not parallelized, so no speedup with multiple CPUs + sampler=sampler, + collate_fn=collate_fn,) #pin_memory=True + + return dataloader \ No newline at end of file diff --git a/proteingym/baselines/EVE/utils/default_uncertainty_threshold.json b/proteingym/baselines/EVE/utils/default_uncertainty_threshold.json new file mode 100644 index 0000000..bbc14e9 --- /dev/null +++ b/proteingym/baselines/EVE/utils/default_uncertainty_threshold.json @@ -0,0 +1,20 @@ +{ + "deciles": { + "1":0.2197550519787074, + "2":0.3073271315004903, + "3":0.380894592583295, + "4":0.452448591753785, + "5":0.5199203860577154, + "6":0.5610673789803645, + "7":0.6028917246467301, + "8":0.6483370062758973, + "9":0.678595588420331, + "10":0.6931471766074847 + }, + "quartiles": { + "1":0.3443212858392093, + "2":0.5199203860577154, + "3":0.6272364669515783, + "4":0.6931471766074847 + } +} \ No newline at end of file diff --git a/proteingym/baselines/EVE/utils/performance_helpers.py b/proteingym/baselines/EVE/utils/performance_helpers.py new file mode 100644 index 0000000..248dde8 --- /dev/null +++ b/proteingym/baselines/EVE/utils/performance_helpers.py @@ -0,0 +1,143 @@ +from sklearn.metrics import roc_auc_score +import numpy as np +import pandas as pd + +def compute_stats(input_array): + return { + 'mean':input_array.mean(), + 'std':input_array.std(), + 'min':input_array.min(), + 'max':input_array.max(), + 'P1':np.percentile(input_array,1), + 'P5':np.percentile(input_array,5), + 'P10':np.percentile(input_array,10), + 'P25':np.percentile(input_array,25), + 'P33':np.percentile(input_array,33), + 'P40':np.percentile(input_array,40), + 'P45':np.percentile(input_array,45), + 'P50':np.median(input_array), + 'P55':np.percentile(input_array,55), + 'P60':np.percentile(input_array,60), + 'P66':np.percentile(input_array,66), + 'P75':np.percentile(input_array,75), + 'P90':np.percentile(input_array,90), + 'P95':np.percentile(input_array,95), + 'P99':np.percentile(input_array,99) +} + +def compute_accuracy_with_uncertain(class_pred, labels): + temp_df = pd.DataFrame({'class_pred': class_pred.copy(),'labels': labels.copy()}) + initial_num_obs = len(temp_df['labels']) + temp_df=temp_df[temp_df['class_pred'] != 'Uncertain'] + filtered_num_obs = len(temp_df['labels']) + temp_df['class_pred_bin'] = temp_df['class_pred'].map(lambda x: 1 if x == 'Pathogenic' else 0) + correct_classification = (temp_df['class_pred_bin'] == temp_df['labels']).astype(int) + accuracy = round(correct_classification.mean()*100,1) + pct_mutations_kept = round(filtered_num_obs/float(initial_num_obs)*100,1) + return accuracy, pct_mutations_kept + +def compute_AUC_overall_with_uncertain(scores, class_pred, labels): + temp_df = pd.DataFrame({'class_pred': class_pred.copy(),'labels': labels.copy(), 'scores': scores.copy()}) + temp_df=temp_df[temp_df['class_pred'] != 'Uncertain'] + AUC = roc_auc_score(y_true=temp_df['labels'], y_score=temp_df['scores']) + return round(AUC*100,1) + +def compute_avg_protein_level_AUC_with_uncertain(scores, class_pred, labels, protein_ID): + temp_df = pd.DataFrame({'class_pred': class_pred.copy(),'labels': labels.copy(), 'scores': scores.copy(), 'protein_ID': protein_ID.copy()}) + temp_df=temp_df[temp_df['class_pred'] != 'Uncertain'] + def compute_auc_group(group): + protein_scores = group['scores'] + protein_labels = group['labels'] + try: + result = roc_auc_score(y_true=protein_labels, y_score=protein_scores) + except: + result = np.nan + return result + protein_level_AUC = temp_df.groupby('protein_ID').apply(compute_auc_group) + avg_AUC = protein_level_AUC.mean(skipna=True) + return round(avg_AUC*100,1) + +def compute_pathogenic_rate_with_uncertain(class_pred, labels): + temp_df = pd.DataFrame({'class_pred': class_pred.copy(),'labels': labels.copy()}) + temp_df=temp_df[temp_df['class_pred'] != 'Uncertain'] + rate = len(temp_df[temp_df['class_pred'] == 'Pathogenic']) / float(len(temp_df)) + return round(rate*100,1) + +def compute_uncertainty_deciles(score_dataframe, score_name="EVE_scores", uncertainty_name='uncertainty', suffix=''): + uncertainty_deciles_name='uncertainty_deciles'+suffix + score_dataframe[uncertainty_deciles_name] = pd.qcut(score_dataframe[uncertainty_name], q=10, labels=range(1,11)).astype(int) + uncertainty_cutoffs_deciles={} + scores_at_uncertainty_deciles_cuttoffs_UB_lower_part={} + scores_at_uncertainty_deciles_cuttoffs_LB_upper_part={} + for decile in range(1,11): + uncertainty_cutoffs_deciles[str(decile)]= np.max(score_dataframe[uncertainty_name][score_dataframe[uncertainty_deciles_name] == decile]) + scores_at_uncertainty_deciles_cuttoffs_UB_lower_part[str(decile)]= np.max(score_dataframe[score_name][(score_dataframe[uncertainty_deciles_name] == decile) & (score_dataframe[score_name] < 0.5)]) + scores_at_uncertainty_deciles_cuttoffs_LB_upper_part[str(decile)]= np.min(score_dataframe[score_name][(score_dataframe[uncertainty_deciles_name] == decile) & (score_dataframe[score_name] > 0.5)]) + return uncertainty_cutoffs_deciles, scores_at_uncertainty_deciles_cuttoffs_UB_lower_part, scores_at_uncertainty_deciles_cuttoffs_LB_upper_part + +def compute_uncertainty_quartiles(score_dataframe, score_name="EVE_scores", uncertainty_name='uncertainty', suffix=''): + uncertainty_deciles_name='uncertainty_quartiles'+suffix + score_dataframe[uncertainty_deciles_name] = pd.qcut(score_dataframe[uncertainty_name], q=4, labels=range(1,5)).astype(int) + uncertainty_cutoffs_quartiles={} + scores_at_uncertainty_quartiles_cuttoffs_UB_lower_part={} + scores_at_uncertainty_quartiles_cuttoffs_LB_upper_part={} + for quartile in range(1,5): + uncertainty_cutoffs_quartiles[str(quartile)]= np.max(score_dataframe[uncertainty_name][score_dataframe[uncertainty_deciles_name] == quartile]) + scores_at_uncertainty_quartiles_cuttoffs_UB_lower_part[str(quartile)]= np.max(score_dataframe[score_name][(score_dataframe[uncertainty_deciles_name] == quartile) & (score_dataframe[score_name] < 0.5)]) + scores_at_uncertainty_quartiles_cuttoffs_LB_upper_part[str(quartile)]= np.min(score_dataframe[score_name][(score_dataframe[uncertainty_deciles_name] == quartile) & (score_dataframe[score_name] > 0.5)]) + return uncertainty_cutoffs_quartiles, scores_at_uncertainty_quartiles_cuttoffs_UB_lower_part, scores_at_uncertainty_quartiles_cuttoffs_LB_upper_part + +def compute_performance_by_uncertainty_decile(score_dataframe, metric="Accuracy", verbose=False, score_name="EVE_scores", uncertainty_name="uncertainty", label_name='ClinVar_labels', protein_name='protein_name', class_100pct_retained_name='EVE_classes_100_pct_retained', suffix=''): + uncertainty_cutoffs_deciles, scores_at_uncertainty_deciles_cuttoffs_UB_lower_part, scores_at_uncertainty_deciles_cuttoffs_LB_upper_part = compute_uncertainty_deciles(score_dataframe, score_name, uncertainty_name, suffix) + performance_by_uncertainty_deciles = {} + pathogenic_rate_by_uncertainty_deciles = {} + for decile in range(1,11): + classification_name = 'class_pred_removing_'+str((10-decile)*10)+"_pct_most_uncertain"+suffix + score_dataframe[classification_name] = score_dataframe[class_100pct_retained_name] + score_dataframe.loc[score_dataframe['uncertainty_deciles'+suffix] > decile, classification_name] = 'Uncertain' + if metric=="Accuracy": + performance_decile = compute_accuracy_with_uncertain(score_dataframe[classification_name], score_dataframe[label_name])[0] + elif metric =="Avg_AUC": + performance_decile = compute_avg_protein_level_AUC_with_uncertain(scores=score_dataframe[score_name], class_pred=score_dataframe[classification_name], labels=score_dataframe[label_name], protein_ID=score_dataframe[protein_name]) + performance_by_uncertainty_deciles[decile] = performance_decile + pathogenic_rate_by_uncertainty_deciles[decile] = compute_pathogenic_rate_with_uncertain(class_pred=score_dataframe[classification_name], labels=score_dataframe[label_name]) + if verbose: + print(str(metric)+" when dropping the "+str((10-decile)*10)+"% of cases with highest uncertainty:\t"+str(performance_by_uncertainty_deciles[decile])+"% \t with pathogenic rate of "+str(pathogenic_rate_by_uncertainty_deciles[decile])+"%\n") + print("Uncertainty decile #"+str(decile)+" cutoff: "+str(uncertainty_cutoffs_deciles[str(decile)])+"\n") + print("Score upper bound for lower part in uncertainty decile: "+str(scores_at_uncertainty_deciles_cuttoffs_UB_lower_part[str(decile)])+"\n") + print("Score lower bound for higher part in uncertainty decile: "+str(scores_at_uncertainty_deciles_cuttoffs_LB_upper_part[str(decile)])+"\n") + return performance_by_uncertainty_deciles, pathogenic_rate_by_uncertainty_deciles + +def compute_performance_by_uncertainty_quartile(score_dataframe, metric="Accuracy", verbose=False, score_name="EVE_scores", uncertainty_name="uncertainty", label_name='ClinVar_labels', protein_name='protein_name', class_100pct_retained_name='EVE_classes_100_pct_retained', suffix=''): + uncertainty_cutoffs_quartiles, scores_at_uncertainty_quartiles_cuttoffs_UB_lower_part, scores_at_uncertainty_quartiles_cuttoffs_LB_upper_part = compute_uncertainty_quartiles(score_dataframe, score_name, uncertainty_name, suffix) + performance_by_uncertainty_quartiles = {} + pathogenic_rate_by_uncertainty_quartiles = {} + for quartile in range(1,5): + classification_name = 'class_pred_removing_'+str((4-quartile)*25)+"_pct_most_uncertain"+suffix + score_dataframe[classification_name] = score_dataframe[class_100pct_retained_name] + score_dataframe.loc[score_dataframe['uncertainty_quartiles'+suffix] > quartile, classification_name] = 'Uncertain' + if metric=="Accuracy": + performance_quartile = compute_accuracy_with_uncertain(score_dataframe[classification_name], score_dataframe[label_name])[0] + elif metric =="Avg_AUC": + performance_quartile = compute_avg_protein_level_AUC_with_uncertain(scores=score_dataframe[score_name], class_pred=score_dataframe[classification_name], labels=score_dataframe[label_name], protein_ID=score_dataframe[protein_name]) + performance_by_uncertainty_quartiles[quartile] = performance_quartile + pathogenic_rate_by_uncertainty_quartiles[quartile] = compute_pathogenic_rate_with_uncertain(class_pred=score_dataframe[classification_name], labels=score_dataframe[label_name]) + if verbose: + print(str(metric)+" when dropping the "+str((4-quartile)*25)+"% of cases with highest uncertainty:\t"+str(performance_by_uncertainty_quartiles[quartile])+"% \t with pathogenic rate of "+str(pathogenic_rate_by_uncertainty_quartiles[quartile])+"%\n") + print("Uncertainty quartile #"+str(quartile)+" cutoff: "+str(uncertainty_cutoffs_quartiles[str(quartile)])+"\n") + print("Score upper bound for lower part in uncertainty quartile: "+str(scores_at_uncertainty_quartiles_cuttoffs_UB_lower_part[str(quartile)])+"\n") + print("Score lower bound for higher part in uncertainty quartile: "+str(scores_at_uncertainty_quartiles_cuttoffs_LB_upper_part[str(quartile)])+"\n") + return performance_by_uncertainty_quartiles, pathogenic_rate_by_uncertainty_quartiles + +def predictive_entropy_binary_classifier(class1_scores, eps=1e-8): + class1_scores = pd.Series(class1_scores).map(lambda x: x - eps if x==1.0 else x + eps if x==0 else x) + class0_scores = 1 - class1_scores + return - np.array((np.log(class1_scores) * class1_scores + np.log(class0_scores) * class0_scores)) + +def compute_weighted_score_two_GMMs(X_pred, main_model, protein_model, cluster_index_main, cluster_index_protein, protein_weight): + return protein_model.predict_proba(X_pred)[:,cluster_index_protein] * protein_weight + (main_model.predict_proba(X_pred)[:,cluster_index_main]) * (1 - protein_weight) + +def compute_weighted_class_two_GMMs(X_pred, main_model, protein_model, cluster_index_main, cluster_index_protein, protein_weight): + """By construct, 1 is always index of pathogenic, 0 always that of benign""" + proba_pathogenic = protein_model.predict_proba(X_pred)[:,cluster_index_protein] * protein_weight + (main_model.predict_proba(X_pred)[:,cluster_index_main]) * (1 - protein_weight) + return (proba_pathogenic > 0.5).astype(int) diff --git a/proteingym/baselines/EVE/utils/plot_helpers.py b/proteingym/baselines/EVE/utils/plot_helpers.py new file mode 100644 index 0000000..a150c16 --- /dev/null +++ b/proteingym/baselines/EVE/utils/plot_helpers.py @@ -0,0 +1,52 @@ +import os +import tqdm +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +def plot_histograms(all_evol_indices, dict_models, dict_pathogenic_cluster_index, protein_GMM_weight, plot_location, output_eve_scores_filename_suffix, protein_list): + x = np.linspace(-10, 20, 2000) + logprob = dict_models['main'].score_samples(x.reshape(-1,1)) + pdf = np.exp(logprob) + component_share = dict_models['main'].predict_proba(x.reshape(-1, 1)) + pdf_pathogenic = component_share[:,dict_pathogenic_cluster_index['main']] * pdf + pdf_benign = component_share[:,1 - dict_pathogenic_cluster_index['main']] * pdf + plt.plot(x,pdf, '--k', color='black') + plt.plot(x,pdf_pathogenic, '--k', color = 'xkcd:red',linewidth=4) + plt.plot(x,pdf_benign, '--k', color = 'xkcd:sky blue',linewidth=4) + plt.hist(all_evol_indices['evol_indices'], color = 'xkcd:grey', bins = 80, histtype='stepfilled', alpha=0.4, density=True) + plt.xlabel("Evolutionary index", fontsize=13) + plt.ylabel("% of variants", fontsize=13) + plt.xticks(fontsize=10) + plt.yticks(fontsize=10) + plt.savefig(plot_location+os.sep+'histogram_random_samples_'+str(output_eve_scores_filename_suffix)+"_all.png", dpi=800, bbox_inches='tight') + plt.clf() + if protein_GMM_weight > 0.0: + for protein in tqdm.tqdm(protein_list,"Plot protein histograms"): + x = np.linspace(-10, 20, 2000) + logprob = dict_models[protein].score_samples(x.reshape(-1,1)) + pdf = np.exp(logprob) + component_share = dict_models[protein].predict_proba(x.reshape(-1, 1)) + pdf_pathogenic = component_share[:,dict_pathogenic_cluster_index[protein]] * pdf + pdf_benign = component_share[:, 1 - dict_pathogenic_cluster_index[protein]] * pdf + plt.plot(x,pdf, '--k', color='black') + plt.plot(x,pdf_pathogenic, '--k', color = 'xkcd:red',linewidth=4) + plt.plot(x,pdf_benign, '--k', color = 'xkcd:sky blue',linewidth=4) + plt.hist(all_evol_indices['evol_indices'][all_evol_indices['protein_name']==protein], color = 'xkcd:grey', bins = 80, histtype='stepfilled', alpha=0.4, density=True) + plt.xlabel("Evolutionary index", fontsize=13) + plt.ylabel("% of variants", fontsize=13) + plt.xticks(fontsize=10) + plt.yticks(fontsize=10) + plt.savefig(plot_location+os.sep+'histogram_random_samples_'+str(output_eve_scores_filename_suffix)+"_"+str(protein)+".png", dpi=800, bbox_inches='tight') + plt.clf() + +def plot_scores_vs_labels(score_df, plot_location, output_eve_scores_filename_suffix, mutation_name='mutations', score_name="EVE_scores", label_name='labels'): + score_df_local = score_df.copy() + score_df_local = score_df_local[score_df_local[mutation_name] !='w-1t'] #Remove wild type sequence + score_df_local['mutation_position'] = score_df[mutation_name].map(lambda x: int(x[1:-1])) + labels = score_df_local[label_name] + pathogenic = plt.scatter(x=score_df_local['mutation_position'][labels==1], y=score_df_local[score_name][labels==1], color='xkcd:red') + benign = plt.scatter(x=score_df_local['mutation_position'][labels==0], y=score_df_local[score_name][labels==0], color='xkcd:sky blue') + plt.legend([pathogenic,benign],['pathogenic','benign']) + plt.savefig(plot_location+os.sep+'scores_vs_labels_plots_'+str(output_eve_scores_filename_suffix)+".png", dpi=400, bbox_inches='tight') + plt.clf() \ No newline at end of file diff --git a/proteingym/baselines/EVE/utils/weights.py b/proteingym/baselines/EVE/utils/weights.py new file mode 100644 index 0000000..81bf247 --- /dev/null +++ b/proteingym/baselines/EVE/utils/weights.py @@ -0,0 +1,505 @@ +import multiprocessing +import time +from collections import defaultdict + +import numba +from numba import prange +from numba_progress import ProgressBar + +import numpy as np +from tqdm import tqdm + + +def compute_weight_eve(seq, list_seq, theta): + # seq shape: (L * alphabet_size,) + number_non_empty_positions = np.sum(seq) # = np.dot(seq,seq), assuming it is a flattened one-hot matrix + if number_non_empty_positions > 0: + # Dot product of one-hot vectors x and y = (x == y).sum() + matches = np.dot(list_seq, seq) + denom = matches / number_non_empty_positions # number_non_empty_positions = np.dot(seq,seq) + denom = np.sum(denom > 1 - theta) # Lood: Keeping >, and changing EVCouplings code to > + return 1 / denom + else: + return 0.0 # return 0 weight if sequence is fully empty + + +def _compute_weight_global(i): + seq = list_seq_global[i] + # seq shape: (L * alphabet_size,) + number_non_empty_positions = np.sum(seq) # = np.dot(seq,seq), assuming it is a flattened one-hot matrix + if number_non_empty_positions > 0: + matches = np.dot(list_seq_global, seq) + denom = matches / number_non_empty_positions # number_non_empty_positions = np.dot(seq,seq) + denom = np.sum(denom > 1 - theta_global) # Lood: Keeping >, and changing EVCouplings code to > + return 1 / denom + else: + return 0.0 # return 0 weight if sequence is fully empty + + +def _init_worker_calc_eve(list_seq, theta): + # Initialize the worker process + # Note: Using global is not ideal, but not sure how else + # It should be safe since processes have private global variables + global list_seq_global + global theta_global + list_seq_global = list_seq + theta_global = theta + + +def compute_sequence_weights(list_seq, theta, num_cpus=1): + _N, _seq_len, _alphabet_size = list_seq.shape # = len(self.seq_name_to_sequence.keys()), len(self.focus_cols), len(self.alphabet) + list_seq = list_seq.reshape((_N, _seq_len * _alphabet_size)) + print(f"Using {num_cpus} cpus for EVE weights computation") + + if num_cpus > 1: + # Compute weights in parallel + with multiprocessing.Pool(processes=num_cpus, initializer=_init_worker_calc_eve, initargs=(list_seq, theta)) as pool: + # func = functools.partial(compute_weight, list_seq=list_seq, theta=theta) + chunksize = max(min(8, int(_N / num_cpus / 4)), 1) + print("chunksize: " + str(chunksize)) + # imap: Lazy version of map + # Parallel progress bars are complicated, so just used a single one + weights_map = tqdm(pool.imap(_compute_weight_global, range(_N), chunksize=chunksize), + total=_N, desc="Computing weights parallel EVE") + weights = np.array(list(weights_map)) + else: + weights_map = map(lambda seq: compute_weight_eve(seq, list_seq=list_seq, theta=theta), list_seq) + weights = np.array(list(tqdm(weights_map, total=_N, desc="Computing weights serial EVE"))) + + return weights + + +def is_empty_sequence_matrix(matrix, empty_value): + assert len(matrix.shape) == 2, f"Matrix must be 2D; shape={matrix.shape}" + assert isinstance(empty_value, (int, float)), f"empty_value must be a number; type={type(empty_value)}" + # Check for each sequence if all positions are equal to empty_value + empty_idx = np.all((matrix == empty_value), axis=1) + return empty_idx + + +# See calc_num_cluster_members_nogaps +@numba.jit(nopython=True) # , fastmath=True, parallel=True) +def calc_num_clusters_i(matrix, identity_threshold, invalid_value, i: int, L_non_gaps: float): + N, L = matrix.shape + L_non_gaps = 1.0 * L_non_gaps # Show numba it's a float + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_clusters_i = 1 # Self + # compare all pairs of sequences + for j in range(N): + if i == j: + continue + pair_matches = 0 + for k in range(L): + # Edit(Lood): Don't count gaps as matches + if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: + pair_matches += 1 + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps > identity_threshold: + num_clusters_i += 1 + + return num_clusters_i + + +# Below are util functions copied from EVCouplings: https://github.com/debbiemarkslab/EVcouplings +# This code looks slow but it's because it's written as a numba kernel +# Fastmath should be safe here, as we can assume that there are no NaNs in the input etc. +@numba.jit(nopython=True) # , fastmath=True, parallel=True +def calc_num_cluster_members_nogaps(matrix, identity_threshold, invalid_value): + """ + From EVCouplings: https://github.com/debbiemarkslab/EVcouplings/blob/develop/evcouplings/align/alignment.py#L1172 + Calculate number of sequences in alignment + within given identity_threshold of each other + Parameters + ---------- + matrix : np.array + N x L matrix containing N sequences of length L. + Matrix must be mapped to range(0, num_symbols) using + map_matrix function + identity_threshold : float + Sequences with at least this pairwise identity will be + grouped in the same cluster. + Returns + ------- + np.array + Vector of length N containing number of cluster + members for each sequence (inverse of sequence + weight) + """ + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + for i in range(N - 1): + for j in range(i + 1, N): + pair_matches = 0 + for k in range(L): + # Edit(Lood): Don't count gaps as matches + if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: + pair_matches += 1 + + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors[i] += 1 + if pair_matches / L_non_gaps[j] > identity_threshold: + num_neighbors[j] += 1 + + return num_neighbors + + +def calc_weights_evcouplings(matrix_mapped, identity_threshold, empty_value, num_cpus=1): + """ + From EVCouplings: https://github.com/debbiemarkslab/EVcouplings + Calculate weights for sequences in alignment by + clustering all sequences with sequence identity + greater or equal to the given threshold. + Parameters + ---------- + identity_threshold : float + Sequence identity threshold + """ + empty_idx = is_empty_sequence_matrix(matrix_mapped, + empty_value=empty_value) # e.g. sequences with just gaps or lowercase, no valid AAs + N = matrix_mapped.shape[0] + + # Original EVCouplings code structure, plus gap handling + if num_cpus != 1: + # Numba native parallel: + # print("Calculating weights using Numba parallel (experimental) since num_cpus > 1. " + # "If you want to disable multiprocessing set num_cpus=1.") + # print("Default number of threads for Numba:", numba.config.NUMBA_NUM_THREADS) + # # num_cpus > numba.config.NUMBA_NUM_THREADS will give an error. + # # But we'll leave it so that the user has to be explicit. + # numba.set_num_threads(num_cpus) + # print("Set number of threads to:", numba.get_num_threads()) + # num_cluster_members = calc_num_cluster_members_nogaps_parallel(matrix_mapped[~empty_idx], identity_threshold, + # invalid_value=empty_value) + + + update_frequency=1000 + with ProgressBar(total=N, update_interval=30, miniters=update_frequency) as progress: # can also use tqdm mininterval, maxinterval etc + num_cluster_members = calc_num_cluster_members_nogaps_parallel_print(matrix_mapped[~empty_idx], identity_threshold, + invalid_value=empty_value, progress_proxy=progress, update_frequency=update_frequency) + # print("Num CPUs for EVCouplings code:", num_cpus) + # print( + # f"Calculating weights using Numba JIT and multiprocessing (experimental) since num_cpus ({num_cpus}) > 1. " + # "If you want to disable multiprocessing set num_cpus=1.") + # with multiprocessing.Pool(processes=num_cpus, initializer=_init_worker_ev, + # initargs=(matrix_mapped[~empty_idx], empty_value, identity_threshold)) as pool: + # # Simply: Chunksize is between 1 and 64, preferably N / num_cpus / 16, + # # so every CPU gets a 16th of their expected total every time they ask for more work. + # # Too small values: Too much overhead sending simple indexes to workers, and them sending back results. + # # Too large: May wait a while for the last worker's task to finish. + # chunksize = max(1, min(64, int(N / num_cpus / 16))) + # print("chunksize: " + str(chunksize)) + + # # imap: Lazy version of map + # # Parallel progress bars are complicated and pollute logs + # cluster_map = tqdm(pool.imap(_worker_func, range(N), chunksize=chunksize), total=N, mininterval=1) + # num_cluster_members = np.array(list(cluster_map)) + else: + num_cluster_members = calc_num_cluster_members_nogaps(matrix_mapped[~empty_idx], identity_threshold, + invalid_value=empty_value) + + # Empty sequences: weight 0 + weights = np.zeros((N)) + weights[~empty_idx] = 1.0 / num_cluster_members + return weights + + +# Multiprocessing with numba jit: +def _init_worker_ev(matrix, empty_value, identity_threshold): + global matrix_mapped_global + matrix_mapped_global = matrix + L = matrix.shape[1] + global empty_value_global + empty_value_global = empty_value + global identity_threshold_global + identity_threshold_global = identity_threshold + global L_i_global + L_i_global = L - np.sum(matrix == empty_value, axis=1) + print("Initialising worker") + global global_func_num_clusters_i + global_func_num_clusters_i = _global_calc_cluster_factory() + try: + start = time.perf_counter() + _ = global_func_num_clusters_i(0) # Timeout, and numba verbosity? + end = time.perf_counter() + print(f"Initialising worker took: {end - start:.2f}") + except Exception as e: + print("Worker initialisation failed:", e) + raise e + print("Function compiled") + + +def _worker_func(i): + return global_func_num_clusters_i(i) + + +def _global_calc_cluster_factory(): + # @numba.jit(nopython=True) + def func(i): + return calc_num_clusters_i(matrix_mapped_global, identity_threshold_global, empty_value_global, i, + L_non_gaps=L_i_global[i]) + return func + + +# Copied from EVCouplings +def map_from_alphabet(alphabet, default): + """ + Creates a mapping dictionary from a given alphabet. + Parameters + ---------- + alphabet : str + Alphabet for remapping. Elements will + be remapped according to alphabet starting + from 0 + default : Elements in matrix that are not + contained in alphabet will be treated as + this character + Raises + ------ + ValueError + For invalid default character + """ + map_ = { + c: i for i, c in enumerate(alphabet) + } + + try: + default = map_[default] + except KeyError: + raise ValueError( + "Default {} is not in alphabet {}".format(default, alphabet) + ) + + return defaultdict(lambda: default, map_) + + +# Copied from EVCouplings +def map_matrix(matrix, map_): + """ + Map elements in a numpy array using alphabet + Parameters + ---------- + matrix : np.array + Matrix that should be remapped + map_ : defaultdict + Map that will be applied to matrix elements + Returns + ------- + np.array + Remapped matrix + """ + return np.vectorize(map_.__getitem__)(matrix) + +# The main function + +@numba.jit(nopython=True, fastmath=True, parallel=True) +def calc_num_cluster_members_nogaps_parallel(matrix, identity_threshold, invalid_value): + """ + From EVCouplings: https://github.com/debbiemarkslab/EVcouplings + Calculate number of sequences in alignment + within given identity_threshold of each other + Parameters + ---------- + matrix : np.array + N x L matrix containing N sequences of length L. + Matrix must be mapped to range(0, num_symbols) using + map_matrix function + identity_threshold : float + Sequences with at least this pairwise identity will be + grouped in the same cluster. + invalid_value : int + Value in matrix that is considered invalid, e.g. gap or lowercase character. + Returns + ------- + np.array + Vector of length N containing number of cluster + members for each sequence (inverse of sequence + weight) + """ + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + # Edit: Rewrote loop without any dependencies between inner and outer loops, so that it can be parallelized + for i in prange(N): + num_neighbors_i = 1 # num_neighbors_i = 0 # TODO why did I make this 0 again? Probably because I thought I'd have to count i == j + for j in range(N): + if i == j: + continue + pair_matches = 0 + for k in range(L): # This should hopefully be vectorised by numba + if matrix[i, k] == matrix[j, k] and matrix[ + i, k] != invalid_value: # Edit(Lood): Don't count gaps as matches + pair_matches += 1 + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so this similarity is asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors_i += 1 + + num_neighbors[i] = num_neighbors_i + + return num_neighbors + +@numba.jit(nopython=True, fastmath=True, parallel=True) +def calc_num_cluster_members_nogaps_parallel_print(matrix, identity_threshold, invalid_value, progress_proxy=None, update_frequency=1000): + """ + From EVCouplings: https://github.com/debbiemarkslab/EVcouplings + Calculate number of sequences in alignment + within given identity_threshold of each other + Parameters + ---------- + matrix : np.array + N x L matrix containing N sequences of length L. + Matrix must be mapped to range(0, num_symbols) using + map_matrix function + identity_threshold : float + Sequences with at least this pairwise identity will be + grouped in the same cluster. + invalid_value : int + Value in matrix that is considered invalid, e.g. gap or lowercase character. + Returns + ------- + np.array + Vector of length N containing number of cluster + members for each sequence (inverse of sequence + weight) + """ + + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + # Edit: Rewrote loop without any dependencies between inner and outer loops, so that it can be parallelized + for i in prange(N): + num_neighbors_i = 1 # num_neighbors_i = 0 # TODO why did I make this 0 again? Probably because I thought I'd have to count i == j + for j in range(N): + if i == j: + continue + pair_matches = 0 + for k in range(L): # This should hopefully be vectorised by numba + if matrix[i, k] == matrix[j, k] and matrix[ + i, k] != invalid_value: # Edit(Lood): Don't count gaps as matches + pair_matches += 1 + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so this similarity is asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors_i += 1 + + num_neighbors[i] = num_neighbors_i + if progress_proxy is not None and i % update_frequency == 0: + progress_proxy.update(update_frequency) + + return num_neighbors + +######################## +# Failed JIT parallel ideas: +# N, L = matrix_mapped[~empty_idx].shape +# L_i = L - np.sum(matrix_mapped[~empty_idx] == empty_value, axis=1) + +# Idea 1: Calculate the full pair matrix (i,j) = number of matches between sequence i and sequence j +# neighbour_matrix = calc_num_pairs(matrix_mapped[~empty_idx], identity_threshold, invalid_value=empty_value) +# num_cluster_members = (pairs_matrix / L_i[:, None] >= identity_threshold).sum(axis=1) + +# Idea 2: Calculate the pair matrix but applying thresholding inside the loop +# num_cluster_members = neighbour_matrix.sum(axis=1) +# num_cluster_members = calc_num_cluster_members_nogaps_all_vs_all( +# matrix_mapped[~empty_idx], identity_threshold, invalid_value=empty_value # matrix_mapped[~empty_idx] +# ) + +# Idea 3) + +# Inside calling function calc_weights_evcouplings_parallel +# if num_cpus > 1: +# Compute weights in parallel +# print("Num CPUs for EVCouplings code:", num_cpus) +# with multiprocessing.Pool(processes=num_cpus, initializer=init_worker_ev, initargs=(matrix_mapped[~empty_idx], empty_value, identity_threshold)) as pool: +# # func = functools.partial(compute_weight, list_seq=list_seq, theta=theta) +# chunksize = max(min(32, int(N / num_cpus / 4)), 1) +# print("chunksize: " + str(chunksize)) +# # imap: Lazy version of map +# # Parallel progress bars are complicated +# cluster_map = tqdm(pool.imap(_worker_func, range(N), chunksize=chunksize), total=N) + +# @numba.jit(nopython=True, fastmath=True, parallel=True) +# def calc_num_pairs(matrix, identity_threshold, invalid_value): +# """ +# From EVCouplings: https://github.com/debbiemarkslab/EVcouplings +# Calculate number of sequences in alignment +# within given identity_threshold of each other +# Parameters +# ---------- +# matrix : np.array +# N x L matrix containing N sequences of length L. +# Matrix must be mapped to range(0, num_symbols) using +# map_matrix function +# identity_threshold : float +# Sequences with at least this pairwise identity will be +# grouped in the same cluster. +# Returns +# ------- +# np.array +# Vector of length N containing number of cluster +# members for each sequence (inverse of sequence +# weight) +# """ +# N, L = matrix.shape +# # L = 1.0 * L # need to tell numba that L is a float +# +# # Empty sequences are filtered out before this function and are ignored +# # minimal cluster size is 1 (self) +# # L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length +# neighbour_matrix = np.eye(N) # dtype=np.bool +# # Crucial: We assume none of the sequences are empty +# # Construct a loop that counts a neighbour if the pairwise identity is above the threshold +# pairs_j = np.zeros(N, dtype=np.int32) +# for i in range(N): +# # Calculate the non-gapped length of sequence i +# # L_i = np.sum(matrix[i] != invalid_value) # Can either use L_i or L_j to calculate the neighbor matrix, the output will simply be transposed +# pairs_j[:] = 0 +# for j in range(N): +# num_pairs = 0 +# for k in range(L): +# if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: +# num_pairs += 1 +# pairs_j[j] = num_pairs # Could also just add this as an array at the end of j loop +# neighbour_matrix[i] = pairs_j # Could also calc identity threshold here +# +# return neighbour_matrix + +# @numba.jit(nopython=True, parallel=True) +# def num_cluster_members_from_pair(matrix, identity_threshold, invalid_value, L_i): +# N = matrix.shape[0] +# pairs_matrix = np.zeros((N)) +# for i in prange(N): +# pairs_matrix[i] = calc_num_clusters_i(matrix, identity_threshold=identity_threshold, +# invalid_value=invalid_value, i=i, +# L_non_gaps=L_i[i]) +# return pairs_matrix + +# Slower than numpy.prange +# @numba.jit(nopython=True, parallel=True) +# def func_all_i(matrix, identity_threshold, invalid_value, L_i): +# N = matrix.shape[0] +# num_clusters = np.zeros(N) +# for i in prange(N): +# num_clusters[i] = calc_num_clusters_i(matrix, identity_threshold=identity_threshold, +# invalid_value=invalid_value, i=i, L_non_gaps=L_i[i]) +# return num_clusters \ No newline at end of file diff --git a/proteingym/baselines/EVmutation/calculations.py b/proteingym/baselines/EVmutation/calculations.py new file mode 100644 index 0000000..b8c07a9 --- /dev/null +++ b/proteingym/baselines/EVmutation/calculations.py @@ -0,0 +1,299 @@ +""" +High-level mutation calculation functions for EVmutation + +.. todo:: + + implement segment handling + +Authors: + Thomas A. Hopf + Anna G. Green (generalization for multiple segments) +""" + +import numpy as np +import pandas as pd +from evcouplings.utils.calculations import entropy_map + + +COMPONENT_TO_INDEX = { + "full": 0, + "couplings": 1, + "fields": 2, +} + + +def extract_mutations(mutation_string, offset=0, sep=":"): + """ + Turns a string containing mutations of the format I100V into a list of tuples with + format (100, 'I', 'V') (index, from, to) + + Parameters + ---------- + mutation_string : str + Comma-separated list of one or more mutations (e.g. "K50R,I100V") + offset : int, default: 0 + Offset to be added to the index/position of each mutation + sep : str, default "," + String used to separate multiple mutations + + Returns + ------- + list of tuples + List of tuples of the form (index+offset, from, to) + """ + try: + if mutation_string.lower() not in ["wild", "wt", ""]: + mutations = mutation_string.split(sep) + return list(map( + lambda x: (int(x[1:-1]) + offset, x[0], x[-1]), + mutations + )) + except AttributeError: + return [] + + +def predict_mutation_table(model, table, output_column="prediction_epistatic", + mutant_column="mutant", hamiltonian="full", offset=0, + segment=None, sep=":"): + """ + Predicts all mutants in a dataframe and adds predictions + as a new column. + + If mutant_column is None, the dataframe index is used, + otherwise the given column. + + Mutations which cannot be calculated (e.g. not covered + by alignment, or invalid substitution) using object are + set to NaN. + + Parameters + ---------- + model : CouplingsModel + CouplingsModel instance used to compute mutation + effects + table : pandas.DataFrame + DataFrame with mutants to which delta of + statistical energy will be added + mutant_column: str + Name of column in table that contains mutants + output_column : str + Name of column in returned dataframe that will + contain computed effects + hamiltonian: {"full", "couplings", "fields"}, + default: "full" + Use full Hamiltonian of exponential model (default), + or only couplings / fields for statistical energy + calculation. + segment: str, default: None + Specificy a segment identifier to use for the positions in the mutation + table. This will only be used if the mutation table doesn't already have + a segments column. + + Returns + ------- + pandas.DataFrame + Dataframe with added column (mutant_column) that contains computed + mutation effects + """ + def _predict_mutant(m): + # try: + delta_E = model.delta_hamiltonian(m) + return delta_E[_component] + + # except ValueError: + # return np.nan + + # select Hamiltonian component for prediction + if hamiltonian in COMPONENT_TO_INDEX: + _component = COMPONENT_TO_INDEX[hamiltonian] + else: + raise ValueError( + "Invalid selection for hamiltonian. " + "Valid values are: " + ", ".join(COMPONENT_TO_INDEX) + ) + + # make sure there is a target sequence for which we + # can compute statistical energy difference + if not model.has_target_seq: + raise ValueError( + "CouplingsModel object does not have a target " + "sequence (non-focus mode). " + "Set target sequence, or rerun inference in focus mode." + ) + pred = table.copy() + + # get column which contains mutations + if mutant_column is None: + mutations = pred.index + else: + mutations = pred.loc[:, mutant_column] + + # if there is a segment column, use that to apply + # segment information to every mutation + if "segment" in pred.columns and pred.loc[:, "segment"].notnull().all(): + segments = pred.loc[:, "segment"] + + # split each comma-delimited string of mutations into a list + mutations_separated = [extract_mutations(x,offset=offset) for x in mutations] #map(extract_mutations, mutations) + + # split each comma-delimited string of segments into a list + segments_separated = [x.split(",") for x in segments] + mutation_list = [] + + # create a list of mutation in the format + # [[((segment, pos), aa_from, aa_to), ((segment, pos) aa_from, aa_to)], [((segment, pos) aa_from, aa_to)]] + if len([segments_separated]) != len([mutations_separated]): + raise( + ValueError, + "Number of mutations provided does not match number of segments of origin provided." + ) + + for segment_subset, mutation_subset in zip(segments_separated, mutations_separated): + _mutation_list = [ + ((seg, pos), aa_from, aa_to) for + (seg, (pos, aa_from, aa_to)) in zip( + segment_subset, mutation_subset + ) + ] + mutation_list.append(_mutation_list) + + # else if the segment argument was provided + # designate that as the segment for every mutation + elif segment is not None: + mutations_separated = [extract_mutations(x,offset=offset) for x in mutations] #map(extract_mutations, mutations) + mutation_list = [] + for mutation_subset in mutations_separated: + _mutation_list = [ + ((segment, pos), aa_from, aa_to) for + (pos, aa_from, aa_to) in mutation_subset + ] + mutation_list.append(_mutation_list) + + else: + mutation_list = [extract_mutations(x,offset=offset) for x in mutations] #map(extract_mutations, mutations) + # predict mutations and add to table + pred.loc[:, output_column] = [ + _predict_mutant(m) for m in mutation_list + ] + + return pred + + +def single_mutant_matrix(model, output_column="prediction_epistatic", + exclude_self_subs=True): + """ + Create table with all possible single substitutions of + target sequence in CouplingsModel object. + + Parameters + ---------- + model : CouplingsModel + Model that will be used to predict single mutants + output_column : str, default: "prediction_epistatic" + Name of column in Dataframe that will contain predictions + exclude_self_subs : bool, default: True + Exclude self-substitutions (e.g. A100A) from results + + Returns + ------- + pandas.DataFrame + DataFrame with predictions for all single mutants + """ + res = [] + cons = entropy_map(model) + + # iterate all positions and substitutions per position + for pos in model.index_list: + for subs in model.alphabet: + # do not predict gaps + if subs in ["-", "."]: + continue + + # exclude self-substitutions? + if exclude_self_subs and subs == model.seq(pos): + continue + + # if position is a tuple, it is in format + # (segment_id, position). Else, there is + # no segment information + if isinstance(pos, tuple): + position_str = pos[1] + segment = pos[0] + + else: + position_str = pos + segment = np.nan + + wt = model.seq(pos) + mutant = "{}{}{}".format(wt, position_str, subs) + + res.append( + { + "segment": segment, + "mutant": mutant, + "pos": position_str, + "wt": wt, + "subs": subs, + "frequency": model.fi(pos, subs), + "column_conservation": cons[pos], + output_column: model.smm(pos, subs), + } + ) + + pred = pd.DataFrame(res) + return pred.loc[ + :, ["segment", "mutant", "pos", "wt", "subs", "frequency", + "column_conservation", output_column] + ] + + +def split_mutants(x, mutant_column="mutant"): + """ + Splits mutation strings into individual columns in DataFrame + (wild-type symbol(s), position(s), substitution(s), number of mutations). + This function is e.g. helpful when computing average + effects per position using pandas groupby() operations + + Parameters + ---------- + x : pandas.DataFrame + Table with mutants + mutant_column : str, default: "mutant" + Column which contains mutants, set to None + to use index of DataFrame + + Returns + ------- + pandas.DataFrame + DataFrame with added columns "num_subs", "pos", "wt" + and "subs" that contain the number of mutations, + and split mutation strings (if higher-order mutations, + symbols/numbers are comma-separated) + """ + def _split(mut_str): + try: + return sorted(extract_mutations(mut_str)) + except ValueError: + return np.nan + + def _join(index): + return [ + ",".join([str(subs[index]) for subs in mutant]) + for mutant in spl + ] + + # get column which contains mutations + if mutant_column is None: + mutations = x.index + else: + mutations = x.loc[:, mutant_column] + + # extract wt/pos/subs where possible + spl = mutations.map(_split) + + # then store in individual columns + x.loc[:, "num_mutations"] = [len(mutant) for mutant in spl] + for i, column in enumerate(["pos", "wt", "subs"]): + x.loc[:, column] = _join(i) + + return x diff --git a/proteingym/baselines/EVmutation/score_mutants.py b/proteingym/baselines/EVmutation/score_mutants.py new file mode 100644 index 0000000..624ae25 --- /dev/null +++ b/proteingym/baselines/EVmutation/score_mutants.py @@ -0,0 +1,62 @@ +import os +import pandas as pd +import argparse +from evcouplings.couplings import CouplingsModel +from evcouplings.mutate import predict_mutation_table, single_mutant_matrix +import calculations + +def score_multi_aa(DMS_filename,DMS_id, DMS_folder, output_score_folder, couplings_model, offset=0): + """ + Makes EVcouplings predictions (epistatic and independent) for multi-AA mutants. Calculates Spearman between + predictions and DMS dataset. + """ + c = CouplingsModel(couplings_model) + c0 = c.to_independent_model() + data = pd.read_csv(DMS_folder + os.sep + DMS_filename) + data = calculations.predict_mutation_table(c, data, "prediction_epistatic", sep=":", offset=offset) + data = calculations.predict_mutation_table(c0, data, "prediction_independent", sep=":", offset=offset) + data.to_csv(output_score_folder + os.sep + DMS_id + ".csv", index=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Score EVcouplings models on DMS data') + parser.add_argument('--DMS_reference_file_path', type=str, help="Path to DMS reference file") + parser.add_argument('--DMS_data_folder',type=str, help="Path to DMS data folder") + parser.add_argument('--model_folder',type=str, help="Path to EVcouplings model folder") + parser.add_argument('--output_scores_folder',type=str, help="Path to output score folder") + parser.add_argument('--DMS_index',type=int,help="Index of DMS assay to score") + args = parser.parse_args() + mapping = pd.read_csv(args.DMS_reference_file_path) + + list_DMS = mapping["DMS_id"] + DMS_id = list_DMS[args.DMS_index] + + # Hardcoding RASK_HUMAN_Ursu case (has a special alignment id since it differs from the other RASK_HUMAN assays) + if DMS_id == "RASK_HUMAN_Ursu_2020": + UniProt_id = "RASK_HUMAN_Ursu_2020" + else: + list_UniProt = mapping["UniProt_ID"] + + UniProt_id = list_UniProt[args.DMS_index] + if UniProt_id == "F7YBW7_MESOW": + UniProt_id = "F7YBW8_MESOW" + + DMS_filename = mapping["DMS_filename"][mapping["DMS_id"]==DMS_id].values[0] + + sequence = mapping["target_seq"][mapping["DMS_id"]==DMS_id].values[0] + + job_name_prefix = UniProt_id + + + offset = mapping["MSA_start"][mapping["DMS_id"]==DMS_id].values[0] - 1 + print("Offset: {}".format(offset)) + print(f"{args.model_folder}/{job_name_prefix}/{job_name_prefix}/couplings/{job_name_prefix}.model") + if os.path.exists(f"{args.model_folder}/{job_name_prefix}/{job_name_prefix}/couplings/{job_name_prefix}.model"): + score_multi_aa(DMS_filename=DMS_filename, + DMS_id=DMS_id, + DMS_folder=args.DMS_data_folder, + output_score_folder=args.output_scores_folder, + couplings_model=f"{args.model_folder}/{job_name_prefix}/{job_name_prefix}/couplings/{job_name_prefix}.model", + offset=-offset) + else: + print(f"No model file for: {job_name_prefix}") \ No newline at end of file diff --git a/proteingym/baselines/HMM/score_hmm.py b/proteingym/baselines/HMM/score_hmm.py new file mode 100644 index 0000000..3d58dc7 --- /dev/null +++ b/proteingym/baselines/HMM/score_hmm.py @@ -0,0 +1,111 @@ +import argparse +import pandas as pd +import os +import numpy as np + +""" +This script scores a folder of variants using the HMM model. +""" +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Score a folder of variants using the HMM model') + #We may pass in all required information about the dataset via the provided reference files, or specify all relevant fields manually + parser.add_argument('--DMS_reference_file', type=str, help='Path to reference file with list of target sequences (and filepaths to their associated variants) to score') + parser.add_argument('--DMS_index', type=int, help='Index of sequence and variants to score in reference file') + parser.add_argument("--hmmer_path", type=str, help="Path to hmmer installation") + parser.add_argument('--DMS_folder', type=str, help='Path to folder that contains the protein variants for each target sequence') + parser.add_argument('--output_scores_folder', default='./', type=str, help='Name of folder to write model scores to') + parser.add_argument("--intermediate_outputs_folder", type=str, default="./intermediate_outputs", help="Path to folder to write intermediate outputs to") + parser.add_argument('--MSA_folder', default='.', type=str, help='Path to MSA for neighborhood scoring') + + #Fields to be passed manually if reference file is not used + parser.add_argument('--target_seq', default=None, type=str, help='Full wild type sequence that is mutated in the experiment') + parser.add_argument('--DMS_file_name', default=None, type=str, help='Name of experiment file') + parser.add_argument('--MSA_filename', default=None, type=str, help='Name of MSA (eg., a2m) file constructed on the wild type sequence') + parser.add_argument('--MSA_start', default=None, type=int, help='Sequence position that the MSA starts at (1-indexing)') + parser.add_argument('--MSA_end', default=None, type=int, help='Sequence position that the MSA ends at (1-indexing)') + parser.add_argument("--mutant_column",default="mutant",type=str) + parser.add_argument("--mutated_sequence_column",default="mutated_sequence",type=str) + args = parser.parse_args() + + if not os.path.exists(args.output_scores_folder): + os.makedirs(args.output_scores_folder) + if not os.path.exists(args.intermediate_outputs_folder): + os.makedirs(args.intermediate_outputs_folder) + if not os.path.exists(args.intermediate_outputs_folder + os.sep + "hmm"): + os.mkdir(args.intermediate_outputs_folder + os.sep + "hmm") + if not os.path.exists(args.intermediate_outputs_folder + os.sep + "fa"): + os.mkdir(args.intermediate_outputs_folder + os.sep + "fa") + if not os.path.exists(args.intermediate_outputs_folder + os.sep + "a2m"): + os.mkdir(args.intermediate_outputs_folder + os.sep + "a2m") + if not os.path.exists(args.intermediate_outputs_folder + os.sep + "raw_scores"): + os.mkdir(args.intermediate_outputs_folder + os.sep + "raw_scores") + hmm_path = args.intermediate_outputs_folder + os.sep + "hmm" + fa_path = args.intermediate_outputs_folder + os.sep + "fa" + raw_score_path = args.intermediate_outputs_folder + os.sep + "raw_scores" + + if args.DMS_reference_file: + DMS_mapfile = pd.read_csv(args.DMS_reference_file) + list_target_sequences = DMS_mapfile["DMS_id"].tolist() + DMS_id=list_target_sequences[args.DMS_index] + print(f"Computing HMM scores for {DMS_id})") + target_seq = DMS_mapfile["target_seq"][DMS_mapfile["DMS_id"]==DMS_id].values[0].upper() + DMS_file_name = DMS_mapfile["DMS_filename"][DMS_mapfile["DMS_id"]==DMS_id].values[0] + MSA_data_file = DMS_mapfile["MSA_filename"].tolist()[args.DMS_index] + MSA_data_file = args.MSA_folder + os.sep + MSA_data_file if type(MSA_data_file) != float else None + MSA_start = DMS_mapfile["MSA_start"].tolist()[args.DMS_index] + MSA_start = int(MSA_start) - 1 if not np.isnan(MSA_start) else None # MSA_start typically based on 1-indexing + MSA_end = DMS_mapfile["MSA_end"].tolist()[args.DMS_index] + MSA_end = int(MSA_end) if not np.isnan(MSA_end) else None + else: + target_seqs=args.target_seq + DMS_file_names=args.DMS_file_name + DMS_id = DMS_file_names.split(".")[0] + MSA_data_file = args.MSA_folder + os.sep + args.MSA_filename if args.MSA_folder is not None else None + MSA_start = args.MSA_start - 1 # MSA_start based on 1-indexing + MSA_end = args.MSA_end + # mutated_sequence_column = "mutant" + mutated_sequence_column = "mutated_sequence" + # checking all a3m files in alignment and calling reformat if an a2m or hmm file are not already generated + print("Checking for alignment files and reformatting a3m files if needed") + if MSA_data_file is None: + print(f"Alignment required for HMM scoring") + exit() + basename = os.path.splitext(MSA_data_file)[0].split(os.sep)[-1] + # Building HMMs from a2ms if HMMs do not already exist + print("Building HMMs if they don't already exist") + if not os.path.exists(hmm_path + os.sep + basename + ".hmm"): + os.system(f"{args.hmmer_path}" + os.sep + "bin" + os.sep + 'hmmbuild --amino ' + f"{hmm_path + os.sep + basename + '.hmm'} {MSA_data_file}") + + print("Writing out variants to fasta file for hmm scoring") + if not os.path.exists(args.DMS_folder + os.sep + DMS_file_name): + print(f"Warning: {DMS_file_name} not found in {args.DMS_folder}. Skipping.") + variant_df = pd.read_csv(args.DMS_folder + os.sep + DMS_file_name) + with open(fa_path + os.sep + basename + ".fa", "w+") as f: + f.write(f">WT\n{target_seq}\n") + for j, row in variant_df.iterrows(): + f.write(f">Mutant {j}\n{row[mutated_sequence_column]}\n") + + print("Scoring variants with HMM forward-backward algorithm") + hmm_file = hmm_path + os.sep + basename + ".hmm" + fa_file = fa_path + os.sep + basename + ".fa" + output_file = raw_score_path + os.sep + basename + ".csv" + os.system(f"{args.hmmer_path}" + os.sep + "src" + os.sep + f"generic_fwdback_example {hmm_file} {fa_file} > {output_file}") + + # Postprocessing scores to have label columns, wt ratio scores + print("Postprocessing scores to match output format") + if not os.path.exists(os.path.join(args.DMS_folder,DMS_file_name)): + print(f"Warning: {DMS_file_name} not found in {args.DMS_folder}. Skipping.") + df = pd.read_csv(os.path.join(raw_score_path, basename + ".csv")) + # removing extra whitespace from seq_name column + df["seq_name"] = df["seq_name"].apply(lambda x: x.replace(" ","")) + wt_logprob = df[df["seq_name"] == "WT"]["logprob"].values[0] + df["wt_ratio"] = df["logprob"].astype(float) - float(wt_logprob) + # Occasionaly there are characters in sequences outside the alphabet of the hmm model, result in logprobs of -inf for both sequences and nan model scores. + # We set those NaNs equal to 0 here, assuming no difference between the mutants as it's outside the hmm alphabet. + df["wt_ratio"] = df["wt_ratio"].fillna(0) + # Dropping WT score here to match other output formats + df = df[df["seq_name"] != "WT"] + df = df.rename(columns={"seq":"mutant"}) + variant_df = pd.read_csv(os.path.join(args.DMS_folder, DMS_file_name)) + df = df.merge(variant_df[["mutant","DMS_score"]], on="mutant", how="left") + df.to_csv(os.path.join(args.output_scores_folder,DMS_id + ".csv"), index=False) \ No newline at end of file diff --git a/proteingym/baselines/__init__.py b/proteingym/baselines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/proteingym/baselines/carp_mif/__init__.py b/proteingym/baselines/carp_mif/__init__.py new file mode 100644 index 0000000..e4bfb0a --- /dev/null +++ b/proteingym/baselines/carp_mif/__init__.py @@ -0,0 +1 @@ +from . import carp_mif_utils \ No newline at end of file diff --git a/proteingym/baselines/carp_mif/carp_mif_utils.py b/proteingym/baselines/carp_mif/carp_mif_utils.py new file mode 100644 index 0000000..3997336 --- /dev/null +++ b/proteingym/baselines/carp_mif/carp_mif_utils.py @@ -0,0 +1,38 @@ +import torch +from sequence_models.collaters import SimpleCollater, StructureCollater, BGCCollater +from sequence_models.pretrained import load_carp,load_gnn,MIF +from sequence_models.constants import PROTEIN_ALPHABET + +CARP_URL = 'https://zenodo.org/record/6564798/files/' +MIF_URL = 'https://zenodo.org/record/6573779/files/' +BIG_URL = 'https://zenodo.org/record/6857704/files/' + +def load_model_and_alphabet(model_name, model_dir=None): + if not model_name.endswith(".pt"): + if 'big' in model_name: + url = BIG_URL + '%s.pt?download=1' %model_name + elif 'carp' in model_name: + url = CARP_URL + '%s.pt?download=1' %model_name + elif 'mif' in model_name: + url = MIF_URL + '%s.pt?download=1' %model_name + model_data = torch.hub.load_state_dict_from_url(url, progress=False, map_location="cpu", model_dir=model_dir) + else: + model_data = torch.load(model_name, map_location="cpu") + if 'big' in model_data['model']: + pfam_to_domain = model_data['pfam_to_domain'] + tokens = model_data['tokens'] + collater = BGCCollater(tokens, pfam_to_domain) + else: + collater = SimpleCollater(PROTEIN_ALPHABET, pad=True) + if 'carp' in model_data['model']: + model = load_carp(model_data) + elif model_data['model'] in ['mif', 'mif-st']: + gnn = load_gnn(model_data) + cnn = None + if model_data['model'] == 'mif-st': + url = CARP_URL + '%s.pt?download=1' % 'carp_640M' + cnn_data = torch.hub.load_state_dict_from_url(url, progress=False, map_location="cpu") + cnn = load_carp(cnn_data) + collater = StructureCollater(collater, n_connections=30) + model = MIF(gnn, cnn=cnn) + return model, collater \ No newline at end of file diff --git a/proteingym/baselines/carp_mif/compute_fitness.py b/proteingym/baselines/carp_mif/compute_fitness.py new file mode 100644 index 0000000..7750605 --- /dev/null +++ b/proteingym/baselines/carp_mif/compute_fitness.py @@ -0,0 +1,174 @@ +import os +import argparse +import tqdm + +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer +from scipy.stats import spearmanr +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss + +from sequence_models.constants import PROTEIN_ALPHABET, PAD, MASK +from sequence_models.pdb_utils import parse_PDB, process_coords + +from proteingym.baselines.carp_mif.carp_mif_utils import load_model_and_alphabet + +def label_row(rows, sequence, token_probs, alphabet, offset_idx=1): + rows = rows.split(":") + score = 0 + for row in rows: + wt, idx, mt = row[0], int(row[1:-1]) - offset_idx, row[-1] + + assert sequence[idx] == wt, "The listed wildtype does not match the provided sequence" + + wt_encoded, mt_encoded = alphabet.index(wt), alphabet.index(mt) + + score_obj = token_probs[0, idx, mt_encoded] - token_probs[0, idx, wt_encoded] + score += score_obj.item() + return score / len(rows) + +def process_batch_mif(prot,pdb_file,tokenizer,device='cuda:0'): + coords, wt, _ = parse_PDB(pdb_file) + coords = { + 'N': coords[:, 0], + 'CA': coords[:, 1], + 'C': coords[:, 2] + } + dist, omega, theta, phi = process_coords(coords) + batch = [[prot, torch.tensor(dist, dtype=torch.float), + torch.tensor(omega, dtype=torch.float), + torch.tensor(theta, dtype=torch.float), torch.tensor(phi, dtype=torch.float)]] + input_ids, nodes, edges, connections, edge_mask = tokenizer(batch) + input_ids = input_ids.to(device) + nodes = nodes.to(device) + edges = edges.to(device) + connections = connections.to(device) + edge_mask = edge_mask.to(device) + return input_ids,nodes,edges,connections,edge_mask + +def calc_fitness(model, DMS_data, tokenizer, device='cuda:0', model_context_len=1024, mode="masked_marginals", alphabet=PROTEIN_ALPHABET, mutation_col='mutant', target_seq=None, pdb_file=None, model_name=None, offset_idx=1): + if mode=="pseudo_likelihood": + prots=np.array(DMS_data['mutated_sequence']) + loss_fn = CrossEntropyLoss() + log_proba_list = [] + with torch.no_grad(): + for prot in tqdm.tqdm(prots): + loss_fn = CrossEntropyLoss() + if 'carp' in model_name: + input_ids = tokenizer([[prot]])[0].to(device) + logits = model(input_ids, logits=True)['logits'] + elif 'mif' in model_name: + input_ids,nodes,edges,connections,edge_mask = process_batch_mif(prot,pdb_file,tokenizer,device) + logits = model(input_ids, nodes, edges, connections, edge_mask, result='logits') + log_proba = - loss_fn(target=input_ids.view(-1), input=logits.view(-1,logits.size(-1))).detach().cpu().numpy() + log_proba_list += [log_proba] + elif mode=="masked_marginals": + all_token_probs = [] + if 'carp' in model_name: + input_ids = tokenizer([[target_seq]])[0].to(device) + elif 'mif' in model_name: + input_ids,nodes,edges,connections,edge_mask = process_batch_mif(target_seq,pdb_file,tokenizer,device) + for i in tqdm.tqdm(range(input_ids.size(1))): + input_ids_masked = input_ids.clone() + input_ids_masked[0, i] = PROTEIN_ALPHABET.index(MASK) + with torch.no_grad(): + if 'carp' in model_name: + logits = model(input_ids_masked.cuda(), logits=True)["logits"] + elif 'mif' in model_name: + logits = model(input_ids, nodes, edges, connections, edge_mask, result='logits') + token_probs = torch.log_softmax(logits, dim=-1) + all_token_probs.append(token_probs[:, i]) + token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0) + log_proba_list = DMS_data.apply( + lambda row: label_row( + row[mutation_col], + target_seq, + token_probs, + PROTEIN_ALPHABET, + offset_idx + ), + axis=1, + ) + return np.array(log_proba_list) + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab="ACDEFGHIKLMNPQRSTVWY"): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + parser = argparse.ArgumentParser(description='Tranception scoring') + parser.add_argument('--model_name', default=None, type=str, help='Name of or path to [CARP,MIF,Evodiff] model') + parser.add_argument('--model_path', default=None, type=str, help='Location where model parameters should be stored') + parser.add_argument('--DMS_reference_file_path', default='/home/pn73/Tranception/proteingym/ProteinGym_reference_file_substitutions.csv', type=str, help='Path to reference file') + parser.add_argument('--DMS_data_folder', default='/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/Tranception_open_source/DMS_files/ProteinGym_substitutions', type=str, help='Path of DMS folder') + parser.add_argument('--structure_data_folder', default='', type=str, help='Path of structure folder') + parser.add_argument('--DMS_index', type=int, help='Index of proteins to compute scores for') + parser.add_argument('--output_scores_folder', default=None, type=str, help='Name of folder to write model scores to') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + parser.add_argument('--fitness_computation_mode', default="masked_marginals", type=str, help='Fitness computtation mode [masked_marginals|pseudo_likelihood]') + parser.add_argument('--performance_file', default='CARP_performance.csv', type=str, help='Name of folder to write model scores to') + args = parser.parse_args() + + model, tokenizer = load_model_and_alphabet(args.model_name, args.model_path) + model.cuda() + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + if not os.path.exists(args.output_scores_folder): os.mkdir(args.output_scores_folder) + args.output_scores_folder = args.output_scores_folder + os.sep + args.model_name + if not os.path.exists(args.output_scores_folder): os.mkdir(args.output_scores_folder) + scoring_filename = args.output_scores_folder+os.sep+DMS_id+'.csv' + print("Computing scores for: {} with model: {}".format(DMS_id, args.model_name)) + + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: get_mutated_sequence(target_seq, x)) if not args.indel_mode else DMS_data['mutant'] + + if 'mif' in args.model_name: + pdb_filenames = mapping_protein_seq_DMS["pdb_file"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].split('|') #if sequence is large (eg., BRCA2_HUMAN) the structure is split in several chunks + pdb_ranges = mapping_protein_seq_DMS["pdb_range"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].split('|') + model_scores=[] + for pdb_index, pdb_filename in enumerate(pdb_filenames): + pdb_file = args.structure_data_folder + os.sep + pdb_filename + pdb_range = [int(x) for x in pdb_ranges[pdb_index].split("-")] + target_seq_split = target_seq[pdb_range[0]-1:pdb_range[1]] #pdb_range is 1-indexed + DMS_data["mutated_position"] = DMS_data['mutant'].apply(lambda x: int(x.split(':')[0][1:-1])) #if multiple mutant, will extract position of first mutant + filtered_DMS_data = DMS_data[(DMS_data["mutated_position"] >= pdb_range[0]) & (DMS_data["mutated_position"] <= pdb_range[1])] + model_scores.append(calc_fitness(model=model, DMS_data=filtered_DMS_data, tokenizer=tokenizer, mode=args.fitness_computation_mode, target_seq=target_seq_split, pdb_file=pdb_file, model_name=args.model_name, offset_idx=pdb_range[0])) + model_scores = np.concatenate(model_scores) + else: + model_scores = calc_fitness(model=model, DMS_data=DMS_data, tokenizer=tokenizer, mode=args.fitness_computation_mode, target_seq=target_seq, pdb_file=None, model_name=args.model_name) + + DMS_data[args.model_name+'_score']=model_scores + DMS_data[['mutant',args.model_name+'_score','DMS_score']].to_csv(scoring_filename, index=False) + spearman, _ = spearmanr(DMS_data[args.model_name+'_score'], DMS_data['DMS_score']) + + if not os.path.exists(args.performance_file) or os.stat(args.performance_file).st_size==0: + with open(args.performance_file,"w") as performance_file: + performance_file.write("DMS_id,spearman\n") + with open(args.performance_file, "a") as performance_file: + performance_file.write(",".join([DMS_id,str(spearman)])+"\n") + +if __name__ == '__main__': + main() diff --git a/proteingym/baselines/esm/__init__.py b/proteingym/baselines/esm/__init__.py new file mode 100644 index 0000000..2aed3a7 --- /dev/null +++ b/proteingym/baselines/esm/__init__.py @@ -0,0 +1 @@ +import esm \ No newline at end of file diff --git a/proteingym/baselines/esm/compute_fitness.py b/proteingym/baselines/esm/compute_fitness.py new file mode 100644 index 0000000..bdcd51c --- /dev/null +++ b/proteingym/baselines/esm/compute_fitness.py @@ -0,0 +1,562 @@ +import argparse +import pathlib +import os,sys +import math +import random +import numpy as np +import pandas as pd +from tqdm import tqdm +from Bio import SeqIO +import itertools +from typing import List, Tuple +import torch + +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.esm import esm +from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretrained, MSATransformer + +from utils.scoring_utils import get_optimal_window, set_mutant_offset, undo_mutant_offset +from utils.data_utils import DMS_file_cleanup +from utils.msa_utils import MSA_processing + +def standardization(x): + """Assumes input is numpy array or pandas series""" + return (x - x.mean()) / x.std() + +def sample_msa(filename: str, nseq: int, sampling_strategy: str, random_seed: int, weight_filename=None, processed_msa=None): + """Reads the first nseq sequences from an MSA file, automatically removes insertions.""" + print("Sampling sequences from MSA with strategy: "+str(sampling_strategy)) + random.seed(random_seed) + if sampling_strategy=='first_x_rows': + msa = [ + (record.description, str(record.seq)) + for record in itertools.islice(SeqIO.parse(filename, "fasta"), nseq) + ] + elif sampling_strategy=='random': + msa = [ + (record.description, str(record.seq)) for record in SeqIO.parse(filename, "fasta") + ] + nseq = min(len(msa),nseq) + msa = random.sample(msa, nseq) + elif sampling_strategy=='sequence-reweighting': + # If MSA has already been processed, just use it here + if processed_msa is None: + if weight_filename is None: + print("Need weight filename if using sequence-reweighting sample strategy") + MSA = MSA_processing( + MSA_location=filename, + use_weights=True, + weights_location=weight_filename + ) + print("Name of focus_seq: "+str(MSA.focus_seq_name)) + else: + MSA = processed_msa + + # Make sure we always keep the WT in the subsampled MSA + msa = [(MSA.focus_seq_name,MSA.raw_seq_name_to_sequence[MSA.focus_seq_name])] + + non_wt_weights = np.array([w for k, w in MSA.seq_name_to_weight.items() if k != MSA.focus_seq_name]) + non_wt_sequences = [(k, s) for k, s in MSA.seq_name_to_sequence.items() if k != MSA.focus_seq_name] + non_wt_weights = non_wt_weights / non_wt_weights.sum() # Renormalize weights + + # Sample the rest of the MSA according to their weights + if len(non_wt_sequences) > 0: + msa.extend(random.choices(non_wt_sequences, weights=non_wt_weights, k=nseq-1)) + + # This is intended to filter out weights, but after hhfiltering our msa is already the correct set + # for seq_name in MSA.raw_seq_name_to_sequence.keys(): + # if seq_name == MSA.focus_seq_name: + # msa.append((seq_name,MSA.raw_seq_name_to_sequence[seq_name])) + # del MSA.seq_name_to_weight[seq_name] + # else: + # if seq_name in MSA.seq_name_to_weight: + # all_sequences_msa.append((seq_name,MSA.raw_seq_name_to_sequence[seq_name])) + # weights.append(MSA.seq_name_to_weight[seq_name]) + # if len(all_sequences_msa)>0: + # weights = np.array(weights) / np.array(list(MSA.seq_name_to_weight.values())).sum() + # print("Check sum weights MSA: "+str(weights.sum())) + # msa.extend(random.choices(all_sequences_msa, weights=weights, k=nseq-1)) + + print("Check sum weights MSA: "+str(non_wt_weights.sum())) + + msa = [(desc, seq.upper()) for desc, seq in msa] + print("First 10 elements of sampled MSA: ") + print(msa[:10]) + #[('CCDB_ECOLI/1-101', 'MQFKVYTYKRESRYRLFVDVQSDIIDTPGRRMVIPLASARLLSDKVSRELYPVVHIGDESWRMMTTDMASVPVSVIGEEVADLSHRENDIKNAINLMFWGI'), ('UniRef100_A0A6M6E7X5/22-113', '.........--GKNIPCVILQNNKGNASNTTIIVPIIAESNYIKSSPTYVHIRKYNLDEDSIAVCDQIRVIDKKRITKAAKALSEEKKQIEDGI--.....'), ('UniRef100_A0A6M6E7X5/144-229', '.........--EKAFPALVIGRDNK-EKQTLLIAPLLQAKKR.YLLPTQAKIPNGCLSDGKILSLEQMRVIDKSRIVSQNIKFSDTLLEIE-----.....'), ('UniRef100_A0A6M6E7X5/256-332', '........SEQRGLRPCLIIQNDTGNKLSTTIVLPLTSSVPKVDLVVNVIVKQEEFVGRSSIVLCNQIQTIDSSRIKQV-----------------.....'), ('UniRef100_A0A6M6E7X5/406-489', '.........------PAVCIQNSYGNHYSVLIVAPLISSKRT.RLLPTQVKIDFNDSGELMVAALEQVRVIDKRRVVDVLDELPEEKKEILNAYCVS....'), ('UniRef100_UPI000B4B97B8/22-115', '........SEQKGERPAVVVQNDFGNRASTTLIVPLTSNFKT.-EIPTHVNISSKELGVDSIALCEQVRVISKERIIQVRTILSKEVKEIDDALLISF...'), ('UniRef100_UPI000B4B97B8/135-229', '........NEQKGNRPAIVIQNDVGNKYSTLIVAPLQLKKKK.-RLPTHVEIPGNLISKDSIALLEQVRVVDKERVTGVVQNLTEEFKNIEEALLVSF...'), ('UniRef100_UPI0009C11867/73-161', '.......GSEQNGLRPVVIIQNNLGNKYGTLIVAPITSQDKK.-DLPVHSEIYNNSLEKDSTILLEQVTTIDKNKVKEFVGHLTRNEKKLNIALAR.....'), ('UniRef100_F4A1A6/19-111', '........SEQGGVRPVLVVQNDIGNKYSTVIVAAITSQINK.AKLPTHVEISASDYGKDSVILLEQIRTIDKKRLREKIGYLSAETKKVDEALQISF...'), ('UniRef100_A0A150FSC1/22-114', '........SEQGGVRPVLVIQNDIGNKYSTVIVAAITSQINK.AKLPIHIEIKANGLNKDSVVLLEQIRTIDKKRLREKIGHFDEEKEKVDQAIQISL...')] + return msa + + +def process_msa(filename: str, weight_filename: str, filter_msa: bool, path_to_hhfilter: str, hhfilter_min_cov=75, hhfilter_max_seq_id=100, hhfilter_min_seq_id=0) -> List[Tuple[str, str]]: + if filter_msa: + input_folder = '/'.join(filename.split('/')[:-1]) + msa_name = filename.split('/')[-1].split('.')[0] + if not os.path.isdir(input_folder+os.sep+'preprocessed'): + os.mkdir(input_folder+os.sep+'preprocessed') + if not os.path.isdir(input_folder+os.sep+'hhfiltered'): + os.mkdir(input_folder+os.sep+'hhfiltered') + preprocessed_filename = input_folder+os.sep+'preprocessed'+os.sep+msa_name + os.system('cat '+filename+' | tr "." "-" >> '+preprocessed_filename+'.a2m') + os.system('dd if='+preprocessed_filename+'.a2m of='+preprocessed_filename+'_UC.a2m conv=ucase') + output_filename = input_folder+os.sep+'hhfiltered'+os.sep+msa_name+'_hhfiltered_cov_'+str(hhfilter_min_cov)+'_maxid_'+str(hhfilter_max_seq_id)+'_minid_'+str(hhfilter_min_seq_id)+'.a2m' + os.system(path_to_hhfilter+os.sep+'bin/hhfilter -cov '+str(hhfilter_min_cov)+' -id '+str(hhfilter_max_seq_id)+' -qid '+str(hhfilter_min_seq_id)+' -i '+preprocessed_filename+'_UC.a2m -o '+output_filename) + filename = output_filename + + MSA = MSA_processing( + MSA_location=filename, + use_weights=True, + weights_location=weight_filename + ) + print("Name of focus_seq: "+str(MSA.focus_seq_name)) + return MSA + + +def create_parser(): + parser = argparse.ArgumentParser( + description="Label a deep mutational scan with predictions from an ensemble of ESM-1v models." # noqa + ) + parser.add_argument( + "--model_type", + type=str, + help="MSA_transformer Vs ESM1v Vs ESM1b", + default="MSA_transformer", + nargs="+", + ) + parser.add_argument( + "--model-location", + type=str, + help="PyTorch model file OR name of pretrained model to download (see README for models)", + nargs="+", + ) + parser.add_argument( + "--sequence", + type=str, + help="Base sequence to which mutations were applied", + ) + parser.add_argument( + "--dms-input", + type=pathlib.Path, + help="CSV file containing the deep mutational scan", + ) + parser.add_argument( + "--dms_index", + type=int, + help="Index of DMS in mapping file", + ) + parser.add_argument( + "--dms_mapping", + type=str, + help="Location of DMS_mapping", + ) + parser.add_argument( + "--mutation-col", + type=str, + default="mutant", + help="column in the deep mutational scan labeling the mutation as 'AiB'" + ) + parser.add_argument( + "--dms-output", + type=pathlib.Path, + help="Output file containing the deep mutational scan along with predictions", + ) + parser.add_argument( + "--offset-idx", + type=int, + default=1, + help="Offset of the mutation positions in `--mutation-col`" + ) + parser.add_argument( + "--scoring-strategy", + type=str, + default="wt-marginals", + choices=["wt-marginals", "pseudo-ppl", "masked-marginals"], + help="" + ) + parser.add_argument( + "--msa-path", + type=pathlib.Path, + help="path to MSA (required for MSA Transformer)" + ) + parser.add_argument( + "--msa-sampling-strategy", + type=str, + default='sequence-reweighting', + help="Strategy to sample sequences from MSA [sequence-reweighting|random|first_x_rows]" + ) + parser.add_argument( + "--msa-samples", + type=int, + default=400, + help="number of sequences to randomly sample from the MSA" + ) + parser.add_argument( + "--msa-weights-folder", + type=str, + default=None, + help="Folder with weights to sample MSA sequences in 'sequence-reweighting' scheme" + ) + parser.add_argument( + '--seeds', + type=int, + default=1, + help='Random seed used during training', + nargs="+" + ) + parser.add_argument( + '--filter-msa', + action='store_true', + help='Whether to use hhfilter to filter input MSA before sampling' + ) + parser.add_argument( + '--hhfilter-min-cov', + type=int, + default=75, + help='minimum coverage with query (%)' + ) + parser.add_argument( + '--hhfilter-max-seq-id', + type=int, + default=90, + help='maximum pairwise identity (%)' + ) + parser.add_argument( + '--hhfilter-min-seq-id', + type=int, + default=0, + help='minimum sequence identity with query (%)' + ) + parser.add_argument( + '--path-to-hhfilter', + type=str, + default='/n/groups/marks/software/hhsuite/hhsuite-3.3.0', + help='Path to hhfilter binaries' + ) + parser.add_argument( + '--scoring-window', + type=str, + default='optimal', + help='Approach to handle long sequences [optimal|overlapping]' + ) + parser.add_argument( + '--overwrite-prior-scores', + action='store_true', + help='Whether to overwrite prior scores in the dataframe' + ) + #No ref file provided + parser.add_argument('--target_seq', default=None, type=str, help='WT sequence mutated in the assay') + parser.add_argument('--weight_file_name', default=None, type=str, help='Wild type sequence mutated in the assay (to be provided if not using a reference file)') + parser.add_argument('--MSA_start', default=None, type=int, help='Index of first AA covered by the MSA relative to target_seq coordinates (1-indexing)') + parser.add_argument('--MSA_end', default=None, type=int, help='Index of last AA covered by the MSA relative to target_seq coordinates (1-indexing)') + + parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available") + return parser + +def label_row(row, sequence, token_probs, alphabet, offset_idx): + score=0 + for mutation in row.split(":"): + wt, idx, mt = mutation[0], int(mutation[1:-1]) - offset_idx, mutation[-1] + assert sequence[idx] == wt, "The listed wildtype does not match the provided sequence" + + wt_encoded, mt_encoded = alphabet.get_idx(wt), alphabet.get_idx(mt) + + # add 1 for BOS + score += (token_probs[0, 1 + idx, mt_encoded] - token_probs[0, 1 + idx, wt_encoded]).item() + return score + +def get_mutated_sequence(row, wt_sequence, offset_idx): + wt, idx, mt = row[0], int(row[1:-1]) - offset_idx, row[-1] + assert wt_sequence[idx] == wt, "The listed wildtype does not match the provided sequence" + # modify the sequence + sequence = wt_sequence[:idx] + mt + wt_sequence[(idx + 1) :] + return sequence +def compute_pppl(sequence, model, alphabet, MSA_data = None, mode = "ESM1v"): + # encode the sequence + data = [ + ("protein1", sequence), + ] + if mode == "MSA_Transformer": + data = [data + MSA_data[0]] + batch_converter = alphabet.get_batch_converter() + + _, _, batch_tokens = batch_converter(data) + # compute probabilities at each position + log_probs = [] + for i in range(1, len(sequence) - 1): + batch_tokens_masked = batch_tokens.clone() + batch_tokens_masked[0, i] = alphabet.mask_idx + with torch.no_grad(): + token_probs = torch.log_softmax(model(batch_tokens_masked.cuda())["logits"], dim=-1) # This might OOM because the MSA batch is large (400 by default) + if mode == "ESM1v": + log_probs.append(token_probs[0, i, alphabet.get_idx(sequence[i])].item()) # vocab size + elif mode == "MSA_Transformer": + log_probs.append(token_probs[0, 0, i, alphabet.get_idx(sequence[i])].item()) # vocab size + return sum(log_probs) + + +def main(args): + if not os.path.exists(args.dms_output): os.mkdir(args.dms_output) + print("Arguments:", args) + + # Load the deep mutational scan + mutant_col = args.mutation_col # Default "mutant" + if args.dms_index is not None: + mapping_protein_seq_DMS = pd.read_csv(args.dms_mapping) + DMS_id = mapping_protein_seq_DMS["DMS_id"][args.dms_index] + print("Compute scores for DMS: "+str(DMS_id)) + row = mapping_protein_seq_DMS[mapping_protein_seq_DMS["DMS_id"]==DMS_id] + if len(row) == 0: + raise ValueError("No mappings found for DMS: "+str(DMS_id)) + elif len(row) > 1: + raise ValueError("Multiple mappings found for DMS: "+str(DMS_id)) + + row = row.iloc[0] + row = row.replace(np.nan, "") # Makes it more manageable to use in strings + + args.sequence = row["target_seq"].upper() + args.dms_input = str(args.dms_input)+os.sep+row["DMS_filename"] + + mutant_col = row["DMS_mutant_column"] if "DMS_mutant_column" in mapping_protein_seq_DMS.columns else mutant_col + args.dms_output=str(args.dms_output)+os.sep+DMS_id+'.csv' + + target_seq_start_index = row["start_idx"] if "start_idx" in mapping_protein_seq_DMS.columns and row["start_idx"]!="" else 1 + target_seq_end_index = target_seq_start_index + len(args.sequence) + + if "MSA_transformer" in args.model_type: # model_type is a list + # Check MSA_filename exists (might be NaN / empty) + msa_filename = row["MSA_filename"] + if msa_filename == "": + raise ValueError("No MSA found for DMS: "+str(DMS_id)) + + args.msa_path= str(args.msa_path)+os.sep+msa_filename # msa_path is expected to be the path to the directory where MSAs are located. + + msa_start_index = int(row["MSA_start"]) if "MSA_start" in mapping_protein_seq_DMS.columns else 1 + msa_end_index = int(row["MSA_end"]) if "MSA_end" in mapping_protein_seq_DMS.columns else len(args.sequence) + + MSA_weight_file_name = args.msa_weights_folder + os.sep + row["weight_file_name"] if ("weight_file_name" in mapping_protein_seq_DMS.columns and args.msa_weights_folder is not None) else None + if ((target_seq_start_index!=msa_start_index) or (target_seq_end_index!=msa_end_index)): + args.sequence = args.sequence[msa_start_index-1:msa_end_index] + target_seq_start_index = msa_start_index + target_seq_end_index = msa_end_index + df = pd.read_csv(args.dms_input) + else: + + DMS_id = str(args.dms_input).split(os.sep)[-1].split('.csv')[0] + args.dms_output=str(args.dms_output)+os.sep+DMS_id+'.csv' + target_seq_start_index = args.offset_idx + args.sequence = args.target_seq.upper() + if (args.MSA_start is None) or (args.MSA_end is None): + if args.msa_path: print("MSA start and end not provided -- Assuming the MSA is covering the full WT sequence") + args.MSA_start = 1 + args.MSA_end = len(args.target_seq) + msa_start_index = args.MSA_start + msa_end_index = args.MSA_end + MSA_weight_file_name = args.msa_weights_folder + os.sep + args.weight_file_name if args.msa_weights_folder is not None else None + df = pd.read_csv(args.dms_input) + + if len(df) == 0: + raise ValueError("No rows found in the dataframe") + print(f"df shape: {df.shape}", flush=True) + + # inference for each model + print("Starting model scoring") + for model_location in args.model_location: + model, alphabet = pretrained.load_model_and_alphabet(model_location) + model_location = model_location.split("/")[-1].split(".")[0] + model.eval() + if torch.cuda.is_available() and not args.nogpu: + model = model.cuda() + print("Transferred model to GPU") + else: + print(f"Not using GPU. torch.cuda.is_available(): {torch.cuda.is_available()}, args.nogpu: {args.nogpu}") + + batch_converter = alphabet.get_batch_converter() + + if isinstance(model, MSATransformer): + args.offset_idx = msa_start_index + # Process MSA once, then sample from it + processed_msa = process_msa(filename=args.msa_path, weight_filename=MSA_weight_file_name, filter_msa=args.filter_msa, hhfilter_min_cov=args.hhfilter_min_cov, hhfilter_max_seq_id=args.hhfilter_max_seq_id, hhfilter_min_seq_id=args.hhfilter_min_seq_id, path_to_hhfilter=args.path_to_hhfilter) + for seed in args.seeds: + if os.path.exists(args.dms_output): + prior_score_df = pd.read_csv(args.dms_output) + if f"{model_location}_seed{seed}" in prior_score_df.columns and not args.overwrite_prior_scores: + print(f"Skipping seed {seed} as it is already in the dataframe") + df = prior_score_df + continue + else: + prior_score_df = None + data = [sample_msa(sampling_strategy=args.msa_sampling_strategy, filename=args.msa_path, nseq=args.msa_samples, weight_filename=MSA_weight_file_name, processed_msa=processed_msa, random_seed=seed)] + assert (args.scoring_strategy in ["masked-marginals","pseudo-ppl"]), "Zero-shot scoring strategy not supported with MSA Transformer" + + batch_labels, batch_strs, batch_tokens = batch_converter(data) + print(f"Batch sizes: {batch_tokens.size()}") + + if args.scoring_strategy == "masked-marginals": + all_token_probs = [] + for i in tqdm(range(batch_tokens.size(2)), desc="Scoring masked-marginals"): + batch_tokens_masked = batch_tokens.clone() + batch_tokens_masked[0, 0, i] = alphabet.mask_idx # mask out first sequence + if batch_tokens.size(-1) > 1024: + large_batch_tokens_masked=batch_tokens_masked.clone() + start, end = get_optimal_window(mutation_position_relative=i, seq_len_wo_special=len(args.sequence)+2, model_window=1024) + print("Start index {} - end index {}".format(start,end)) + batch_tokens_masked = large_batch_tokens_masked[:,:,start:end] + else: + start=0 + with torch.no_grad(): + token_probs = torch.log_softmax( + model(batch_tokens_masked.cuda())["logits"], dim=-1 + ) + all_token_probs.append(token_probs[:, 0, i-start].detach().cpu()) # vocab size + token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0) + df[f"{model_location}_seed{seed}"] = df.apply( + lambda row: label_row( + row[mutant_col], args.sequence, token_probs.detach().cpu(), alphabet, args.offset_idx + ), + axis=1, + ) + elif args.scoring_strategy == "pseudo-ppl": + tqdm.pandas() + if 'mutated_sequence' not in df: + df['mutated_sequence'] = df.progress_apply( + lambda row: get_mutated_sequence( + row[mutant_col], args.sequence, args.offset_idx + ), + axis=1, + ) + df[f"{model_location}_seed{seed}"] = df.progress_apply( + lambda row: compute_pppl( + row['mutated_sequence'], model, alphabet, MSA_data = data, mode = "MSA_Transformer" + ), + axis=1, + ) + if os.path.exists(args.dms_output) and not args.overwrite_prior_scores: + prior_score_df = pd.read_csv(args.dms_output) + assert f"{model_location}_seed{seed}" not in prior_score_df.columns, f"Column {model_location}_seed{seed} already exists in {args.dms_output}" + prior_score_df = prior_score_df.merge(df[[f"{model_location}_seed{seed}","mutant"]],on="mutant") + prior_score_df.to_csv(args.dms_output, index=False) + df = prior_score_df + else: + df.to_csv(args.dms_output, index=False) + else: + args.offset_idx = target_seq_start_index + data = [ + ("protein1", args.sequence), + ] + batch_labels, batch_strs, batch_tokens = batch_converter(data) + + if args.scoring_strategy == "wt-marginals": + with torch.no_grad(): + if batch_tokens.size(1) > 1024 and args.scoring_window=="overlapping": + batch_size, seq_len = batch_tokens.shape #seq_len includes BOS and EOS + token_probs = torch.zeros((batch_size,seq_len,len(alphabet))).cuda() # Note: batch_size = 1 (need to keep batch dimension to score with model though) + token_weights = torch.zeros((batch_size,seq_len)).cuda() + weights = torch.ones(1024).cuda() # 1 for 256≤i<1022-256 + for i in range(1,257): + weights[i] = 1 / (1 + math.exp(-(i-128)/16)) + for i in range(1022-256,1023): + weights[i] = 1 / (1 + math.exp((i-1022+128)/16)) + start_left_window = 0 + end_left_window = 1023 #First window is indexed [0-1023] + start_right_window = (batch_tokens.size(1) - 1) - 1024 + 1 #Last index is len-1 + end_right_window = batch_tokens.size(1) - 1 + while True: + # Left window update + left_window_probs = torch.log_softmax(model(batch_tokens[:,start_left_window:end_left_window+1].cuda())["logits"], dim=-1) + token_probs[:,start_left_window:end_left_window+1] += left_window_probs * weights.view(-1,1) + token_weights[:,start_left_window:end_left_window+1] += weights + # Right window update + right_window_probs = torch.log_softmax(model(batch_tokens[:,start_right_window:end_right_window+1].cuda())["logits"], dim=-1) + token_probs[:,start_right_window:end_right_window+1] += right_window_probs * weights.view(-1,1) + token_weights[:,start_right_window:end_right_window+1] += weights + if end_left_window > start_right_window: + #overlap between windows in that last scoring so we break from the loop + break + start_left_window+=511 + end_left_window+=511 + start_right_window-=511 + end_right_window-=511 + #If central overlap not wide engouh, we add one more window at the center + final_overlap = end_left_window - start_right_window + 1 + if final_overlap < 511: + start_central_window = int(seq_len / 2) - 512 + end_central_window = start_central_window + 1023 + central_window_probs = torch.log_softmax(model(batch_tokens[:,start_central_window:end_central_window+1].cuda())["logits"], dim=-1) + token_probs[:,start_central_window:end_central_window+1] += central_window_probs * weights.view(-1,1) + token_weights[:,start_central_window:end_central_window+1] += weights + #Weight normalization + token_probs = token_probs / token_weights.view(-1,1) #Add 1 to broadcast + else: + token_probs = torch.log_softmax(model(batch_tokens.cuda())["logits"], dim=-1) + df[model_location] = df.apply( + lambda row: label_row( + row[mutant_col], + args.sequence, + token_probs, + alphabet, + args.offset_idx, + ), + axis=1, + ) + elif args.scoring_strategy == "masked-marginals": + print("Scoring with masked-marginals and model {}".format(model_location)) + all_token_probs = [] + for i in tqdm(range(batch_tokens.size(1)), desc="Scoring masked-marginals"): + batch_tokens_masked = batch_tokens.clone() + batch_tokens_masked[0, i] = alphabet.mask_idx + if batch_tokens.size(1) > 1024 and args.scoring_window=="optimal": + large_batch_tokens_masked=batch_tokens_masked.clone() + start, end = get_optimal_window(mutation_position_relative=i, seq_len_wo_special=len(args.sequence)+2, model_window=1024) + batch_tokens_masked = large_batch_tokens_masked[:,start:end] + elif batch_tokens.size(1) > 1024 and args.scoring_window=="overlapping": + print("Overlapping not yet implemented for masked-marginals") + sys.exit(0) + else: + start=0 + with torch.no_grad(): + token_probs = torch.log_softmax(model(batch_tokens_masked.cuda())["logits"], dim=-1) + all_token_probs.append(token_probs[:, i-start]) # vocab size + token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0) + df[model_location] = df.apply( + lambda row: label_row( + row[mutant_col], + args.sequence, + token_probs, + alphabet, + args.offset_idx, + ), + axis=1, + ) + elif args.scoring_strategy == "pseudo-ppl": + tqdm.pandas() + if 'mutated_sequence' not in df: + df['mutated_sequence'] = df.progress_apply( + lambda row: get_mutated_sequence( + row[mutant_col], args.sequence, args.offset_idx + ), + axis=1, + ) + df[model_location] = df.progress_apply( + lambda row: compute_pppl( + row['mutated_sequence'], model, alphabet + ), + axis=1, + ) + # Compute ensemble score Ensemble_ESM1v, standardizes each model score and then averages them + # note this assumes that all the input model checkpoints are ESM1v + if "ESM1v" in args.model_type: + df["Ensemble_ESM1v"] = 0.0 + for model_location in args.model_location: + model_location = model_location.split("/")[-1].split(".")[0] + df["Ensemble_ESM1v"] += df[model_location] + df["Ensemble_ESM1v"] /= len(args.model_location) + elif "MSA_transformer" in args.model_type: + df[f"{model_location}_ensemble"] = 0.0 + for seed in args.seeds: + df[f"{model_location}_ensemble"] += df[f"{model_location}_seed{seed}"] + df[f"{model_location}_ensemble"] /= len(args.seeds) + df.to_csv(args.dms_output,index=False) + + +if __name__ == "__main__": + parser = create_parser() + args = parser.parse_args() + main(args) diff --git a/proteingym/baselines/esm/compute_fitness_esm_if1.py b/proteingym/baselines/esm/compute_fitness_esm_if1.py new file mode 100644 index 0000000..39f9193 --- /dev/null +++ b/proteingym/baselines/esm/compute_fitness_esm_if1.py @@ -0,0 +1,108 @@ +import argparse +import numpy as np +from pathlib import Path +import torch +import torch.nn.functional as F +from tqdm import tqdm +import time +import pandas as pd +import os +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.esm import esm +import esm.inverse_folding +from esm.inverse_folding.util import CoordBatchConverter + +SCORE_NATIVE = False + + +def get_sequence_loss_batch(model, alphabet, coords_list, seq_list): + device = next(model.parameters()).device + batch_converter = CoordBatchConverter(alphabet) + assert len(coords_list) == len(seq_list) + batch = [(coords, None, seq) for coords, seq in zip(coords_list, seq_list)] + coords, confidence, strs, tokens, padding_mask = batch_converter(batch, device=device) + prev_output_tokens = tokens[:, :-1].to(device) + target = tokens[:, 1:] + target_padding_mask = (target == alphabet.padding_idx) + logits, _ = model.forward(coords, padding_mask, confidence, prev_output_tokens) + loss = F.cross_entropy(logits, target, reduction='none') + losses = loss.cpu().detach().numpy() + target_padding_masks = target_padding_mask.cpu().numpy() + return losses, target_padding_masks + +def score_sequence_batch(model, alphabet, coords_list, seq_list): + losses, target_padding_masks = get_sequence_loss_batch(model, alphabet, coords_list, seq_list) + print("debug: losses and target_padding_mask shapes: ", losses.shape, target_padding_masks.shape) + ll_fullseqs_batch = -np.sum(losses * ~target_padding_masks, axis=1) / np.sum(~target_padding_masks, axis=1) + return ll_fullseqs_batch + + +def score_singlechain_backbone_batch(model, alphabet, pdb_file, chain, mutation_file, output_filepath, batch_size=1,nogpu=False): + if not nogpu: + assert torch.cuda.is_available(), "Expected GPU. If you want to use CPU, you have to specify --nogpu every time." + model = model.cuda() + print("Transferred model to GPU") + else: + print(f"Running model on CPU: torch cuda is available={torch.cuda.is_available()} nogpu={nogpu}") + + start_time = time.perf_counter() + coords, native_seq = esm.inverse_folding.util.load_coords(pdb_file, chain) + print(f"Coords loaded in {time.perf_counter() - start_time} seconds") + mut_df = pd.read_csv(mutation_file) + seq_list = mut_df["mutated_sequence"].tolist() + header_list = mut_df["mutant"].tolist() + coords_list = [coords] * len(seq_list) + + print(f"Sequences loaded in {time.perf_counter() - start_time} seconds") + + start_scoring = time.perf_counter() + + with open(output_filepath, 'w') as fout: + fout.write('mutant,esmif1_ll\n') + for i in tqdm(range(0, len(seq_list), batch_size)): + batch = seq_list[i:i+batch_size] + coords_batch = coords_list[i:i+batch_size] + ll_fullseq = score_sequence_batch(model, alphabet, coords_batch, batch) + with open(output_filepath, 'a') as fout: + for header, ll in zip(header_list[i:i+batch_size], ll_fullseq): + fout.write(header + ',' + str(ll) + '\n') + + print(f"Scoring in {time.perf_counter() - start_scoring} seconds") + + print(f'Results saved to {output_filepath}') + print(f"Total time: {time.perf_counter() - start_time}") + +def main(): + parser = argparse.ArgumentParser(description='Score sequences based on a given structure.') + parser.add_argument('--DMS_reference_file_path',type=str,help='path to DMS reference file') + parser.add_argument('--DMS_data_folder',type=str,help="path to folder containing DMS data") + parser.add_argument('--structure_folder',type=str,help='folder containing pdb files for each DMS') + parser.add_argument('--DMS_index',type=int,help='index of DMS in DMS reference file') + parser.add_argument('--model_location',type=str,help='path to model') + parser.add_argument('--batch_size',type=int,default=1) + parser.add_argument('--output_scores_folder',type=str,help='path to folder where scores will be saved') + parser.add_argument('--chain', type=str,help='chain id for the chain of interest', default='A') + parser.set_defaults(multichain_backbone=False) + parser.add_argument('--multichain-backbone', action='store_true',help='use the backbones of all chains in the input for conditioning') + parser.add_argument('--singlechain-backbone', dest='multichain_backbone',action='store_false',help='use the backbone of only target chain in the input for conditioning') + parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available") + + args = parser.parse_args() + mapping_df = pd.read_csv(args.DMS_reference_file_path) + DMS_id = mapping_df.iloc[args.DMS_index]['DMS_id'] + DMS_filename = mapping_df.iloc[args.DMS_index]['DMS_filename'] + output_filename = args.output_scores_folder + os.sep + DMS_id + ".csv" + if not os.path.exists(args.output_scores_folder): + os.makedirs(args.output_scores_folder) + + pdb_file = args.structure_folder + os.sep + mapping_df.iloc[args.DMS_index]['pdb_file'] + model, alphabet = esm.pretrained.load_model_and_alphabet(args.model_location) + model = model.eval() + mutation_filename = args.DMS_data_folder + os.sep + DMS_filename + score_singlechain_backbone_batch(model, alphabet, pdb_file=pdb_file, batch_size=args.batch_size, chain=args.chain,mutation_file=mutation_filename,output_filepath=output_filename,nogpu=args.nogpu) + + + +if __name__ == '__main__': + main() diff --git a/proteingym/baselines/esm/esm/__init__.py b/proteingym/baselines/esm/esm/__init__.py new file mode 100644 index 0000000..907081d --- /dev/null +++ b/proteingym/baselines/esm/esm/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .version import version as __version__ # noqa + +from .data import Alphabet, BatchConverter, FastaBatchedDataset # noqa +from .model.esm1 import ProteinBertModel # noqa +from .model.esm2 import ESM2 # noqa +from .model.msa_transformer import MSATransformer #noqa +from . import pretrained # noqa diff --git a/proteingym/baselines/esm/esm/axial_attention.py b/proteingym/baselines/esm/esm/axial_attention.py new file mode 100644 index 0000000..b508d9d --- /dev/null +++ b/proteingym/baselines/esm/esm/axial_attention.py @@ -0,0 +1,297 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import torch +import torch.nn as nn + +# Adapted from the Tranception codebase (https://github.com/OATML-Markslab/Tranception/blob/main/tranception/model_pytorch.py#L73) with the following 3 main changes: + # Works on the 2D inputs of ProteinNPT + # Adapted to MLM (instead of autoregressive transformer) + # Handles separately protein embeddings and targets + +class SpatialDepthWiseConvolution(nn.Module): + def __init__(self, head_dim: int, kernel_size: int = 3, num_targets: int = 1): + super().__init__() + self.kernel_size = kernel_size + self.conv = nn.Conv1d(in_channels=head_dim, out_channels=head_dim, kernel_size=(kernel_size,), padding='same', groups=head_dim) + self.num_targets = num_targets + + def forward(self, x: torch.Tensor): + # Need to separate the targets from protein embeddings (convolutions only apply to protein embeddings) + x , y = x[:,:-self.num_targets], x[:,-self.num_targets:] + # Apply conv. Input x is of dim (num_rows, seq_len, batch_size, self.num_heads, self.head_dim) + num_rows, seq_len, batch_size, num_heads, head_dim = x.shape + x = x.permute(0, 2, 3, 4, 1).contiguous() + x = x.view(num_rows * batch_size * num_heads, head_dim, seq_len) + x = self.conv(x) + x = x.view(num_rows, batch_size, num_heads, head_dim, seq_len) + x = x.permute(0, 4, 1, 2, 3) + + # Concatenate back with targets + x = torch.cat([x,y], dim=1) + return x + +class RowSelfAttention(nn.Module): + """Compute self-attention over rows of a 2D input.""" + + def __init__( + self, + embed_dim, + num_heads, + dropout=0.0, + max_tokens_per_msa: int = 2 ** 16, + tranception_attention: bool = False, + num_targets: int = 1, + ): + super().__init__() + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim ** -0.5 + self.max_tokens_per_msa = max_tokens_per_msa + self.attn_shape = "hnij" + + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + + self.out_proj = nn.Linear(embed_dim, embed_dim) + self.dropout_module = nn.Dropout(dropout) + + self.tranception_attention = tranception_attention + self.num_targets = num_targets + if self.tranception_attention: + assert self.num_heads%4==0, "Invalid number of heads. Tranception requires the number of heads to be a multiple of 4." + self.num_heads_per_kernel_size = self.num_heads // 4 + self.query_depthwiseconv = nn.ModuleDict() + self.key_depthwiseconv = nn.ModuleDict() + self.value_depthwiseconv = nn.ModuleDict() + for kernel_idx, kernel in enumerate([3,5,7]): + self.query_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel,self.num_targets) + self.key_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel,self.num_targets) + self.value_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel,self.num_targets) + + def align_scaling(self, q): + num_rows = q.size(0) + return self.scaling / math.sqrt(num_rows) + + def _batched_forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + max_rows = max(1, self.max_tokens_per_msa // num_cols) + attns = 0 + scaling = self.align_scaling(x) + for start in range(0, num_rows, max_rows): + attn_weights = self.compute_attention_weights( + x[start : start + max_rows], + scaling, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask[:, start : start + max_rows] + if self_attn_padding_mask is not None + else None, + ) + attns += attn_weights + attn_probs = attns.softmax(-1) + attn_probs = self.dropout_module(attn_probs) + + outputs = [] + for start in range(0, num_rows, max_rows): + output = self.compute_attention_update(x[start : start + max_rows], attn_probs) + outputs.append(output) + + output = torch.cat(outputs, 0) + return output, attn_probs + + def compute_attention_weights( + self, + x, + scaling: float, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + q = self.q_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + k = self.k_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + q *= scaling + if self_attn_padding_mask is not None: + # Zero out any padded aligned positions - this is important since + # we take a sum across the alignment axis. + q *= 1 - self_attn_padding_mask.permute(1, 2, 0).unsqueeze(3).unsqueeze(4).to(q) + + if self.tranception_attention: + # We do not do anything on the first self.num_heads_per_kernel_size heads (kernel =1) + query_list=[q[:,:,:,:self.num_heads_per_kernel_size,:]] + key_list=[k[:,:,:,:self.num_heads_per_kernel_size,:]] + for kernel_idx in range(3): + query_list.append(self.query_depthwiseconv[str(kernel_idx)](q[:,:,:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:])) + key_list.append(self.key_depthwiseconv[str(kernel_idx)](k[:,:,:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:])) + q=torch.cat(query_list, dim=1) + k=torch.cat(key_list, dim=1) + + attn_weights = torch.einsum(f"rinhd,rjnhd->{self.attn_shape}", q, k) + + if self_attn_mask is not None: + raise NotImplementedError + # Mask Size: [B x R x C], Weights Size: [H x B x C x C] + + if self_attn_padding_mask is not None: + attn_weights = attn_weights.masked_fill( + self_attn_padding_mask[:, 0].unsqueeze(0).unsqueeze(2), + -10000, + ) + + return attn_weights + + def compute_attention_update( + self, + x, + attn_probs, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + v = self.v_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + + if self.tranception_attention: + value_list=[v[:,:,:,:self.num_heads_per_kernel_size,:]] + for kernel_idx in range(3): + value_list.append(self.value_depthwiseconv[str(kernel_idx)](v[:,:,:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:])) + v=torch.cat(value_list, dim=1) + + context = torch.einsum(f"{self.attn_shape},rjnhd->rinhd", attn_probs, v) + context = context.contiguous().view(num_rows, num_cols, batch_size, embed_dim) + output = self.out_proj(context) + return output + + def forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + if (num_rows * num_cols > self.max_tokens_per_msa) and not torch.is_grad_enabled(): + return self._batched_forward(x, self_attn_mask, self_attn_padding_mask) + else: + scaling = self.align_scaling(x) + attn_weights = self.compute_attention_weights( + x, scaling, self_attn_mask, self_attn_padding_mask + ) + attn_probs = attn_weights.softmax(-1) + attn_probs = self.dropout_module(attn_probs) + output = self.compute_attention_update(x, attn_probs) + return output, attn_probs + + +class ColumnSelfAttention(nn.Module): + """Compute self-attention over columns of a 2D input.""" + + def __init__( + self, + embed_dim, + num_heads, + dropout=0.0, + max_tokens_per_msa: int = 2 ** 16, + ): + super().__init__() + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim ** -0.5 + self.max_tokens_per_msa = max_tokens_per_msa + + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + + self.out_proj = nn.Linear(embed_dim, embed_dim) + self.dropout_module = nn.Dropout(dropout) + + def _batched_forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + max_cols = max(1, self.max_tokens_per_msa // num_rows) + outputs = [] + attns = [] + for start in range(0, num_cols, max_cols): + output, attn = self( + x[:, start : start + max_cols], + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask[:, :, start : start + max_cols] + if self_attn_padding_mask is not None + else None, + ) + outputs.append(output) + attns.append(attn) + output = torch.cat(outputs, 1) + attns = torch.cat(attns, 1) + return output, attns + + def compute_attention_update( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + if num_rows == 1: + # if there is only 1 position, this is equivalent and doesn't break with padding + attn_probs = torch.ones( + self.num_heads, + num_cols, + batch_size, + num_rows, + num_rows, + device=x.device, + dtype=x.dtype, + ) + output = self.out_proj(self.v_proj(x)) + else: + q = self.q_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + k = self.k_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + v = self.v_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim) + q *= self.scaling + + attn_weights = torch.einsum("icnhd,jcnhd->hcnij", q, k) + + if self_attn_mask is not None: + raise NotImplementedError + if self_attn_padding_mask is not None: + attn_weights = attn_weights.masked_fill( + self_attn_padding_mask.permute(2, 0, 1).unsqueeze(0).unsqueeze(3), + -10000, + ) + + attn_probs = attn_weights.softmax(-1) + attn_probs = self.dropout_module(attn_probs) + context = torch.einsum("hcnij,jcnhd->icnhd", attn_probs, v) + context = context.contiguous().view(num_rows, num_cols, batch_size, embed_dim) + output = self.out_proj(context) + return output, attn_probs + + def forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + ): + num_rows, num_cols, batch_size, embed_dim = x.size() + # if False and num_rows * num_cols > 2 ** 14 and not torch.is_grad_enabled(): + if (num_rows * num_cols) > self.max_tokens_per_msa and not torch.is_grad_enabled(): + return self._batched_forward( + x, + self_attn_mask, + self_attn_padding_mask, + ) + else: + return self.compute_attention_update(x, self_attn_mask, self_attn_padding_mask) diff --git a/proteingym/baselines/esm/esm/constants.py b/proteingym/baselines/esm/esm/constants.py new file mode 100644 index 0000000..fc9abb1 --- /dev/null +++ b/proteingym/baselines/esm/esm/constants.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# fmt: off +proteinseq_toks = { + 'toks': ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', 'X', 'B', 'U', 'Z', 'O', '.', '-'] +} +# fmt: on diff --git a/proteingym/baselines/esm/esm/data.py b/proteingym/baselines/esm/esm/data.py new file mode 100644 index 0000000..8bff147 --- /dev/null +++ b/proteingym/baselines/esm/esm/data.py @@ -0,0 +1,493 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import os +from typing import Sequence, Tuple, List, Union +import pickle +import re +import shutil +import torch +from pathlib import Path +from .constants import proteinseq_toks + +RawMSA = Sequence[Tuple[str, str]] + + +class FastaBatchedDataset(object): + def __init__(self, sequence_labels, sequence_strs): + self.sequence_labels = list(sequence_labels) + self.sequence_strs = list(sequence_strs) + + @classmethod + def from_file(cls, fasta_file): + sequence_labels, sequence_strs = [], [] + cur_seq_label = None + buf = [] + + def _flush_current_seq(): + nonlocal cur_seq_label, buf + if cur_seq_label is None: + return + sequence_labels.append(cur_seq_label) + sequence_strs.append("".join(buf)) + cur_seq_label = None + buf = [] + + with open(fasta_file, "r") as infile: + for line_idx, line in enumerate(infile): + if line.startswith(">"): # label line + _flush_current_seq() + line = line[1:].strip() + if len(line) > 0: + cur_seq_label = line + else: + cur_seq_label = f"seqnum{line_idx:09d}" + else: # sequence line + buf.append(line.strip()) + + _flush_current_seq() + + assert len(set(sequence_labels)) == len( + sequence_labels + ), "Found duplicate sequence labels" + + return cls(sequence_labels, sequence_strs) + + def __len__(self): + return len(self.sequence_labels) + + def __getitem__(self, idx): + return self.sequence_labels[idx], self.sequence_strs[idx] + + def get_batch_indices(self, toks_per_batch, extra_toks_per_seq=0): + sizes = [(len(s), i) for i, s in enumerate(self.sequence_strs)] + sizes.sort() + batches = [] + buf = [] + max_len = 0 + + def _flush_current_buf(): + nonlocal max_len, buf + if len(buf) == 0: + return + batches.append(buf) + buf = [] + max_len = 0 + + for sz, i in sizes: + sz += extra_toks_per_seq + if max(sz, max_len) * (len(buf) + 1) > toks_per_batch: + _flush_current_buf() + max_len = max(max_len, sz) + buf.append(i) + + _flush_current_buf() + return batches + + +class Alphabet(object): + def __init__( + self, + standard_toks: Sequence[str], + prepend_toks: Sequence[str] = ("", "", "", ""), + append_toks: Sequence[str] = ("", "", ""), + prepend_bos: bool = True, + append_eos: bool = False, + use_msa: bool = False, + ): + self.standard_toks = list(standard_toks) + self.prepend_toks = list(prepend_toks) + self.append_toks = list(append_toks) + self.prepend_bos = prepend_bos + self.append_eos = append_eos + self.use_msa = use_msa + + self.all_toks = list(self.prepend_toks) + self.all_toks.extend(self.standard_toks) + for i in range((8 - (len(self.all_toks) % 8)) % 8): + self.all_toks.append(f"") + self.all_toks.extend(self.append_toks) + + self.tok_to_idx = {tok: i for i, tok in enumerate(self.all_toks)} + + self.unk_idx = self.tok_to_idx[""] + self.padding_idx = self.get_idx("") + self.cls_idx = self.get_idx("") + self.mask_idx = self.get_idx("") + self.eos_idx = self.get_idx("") + self.all_special_tokens = ['', '', '', '', ''] + self.unique_no_split_tokens = self.all_toks + + def __len__(self): + return len(self.all_toks) + + def get_idx(self, tok): + return self.tok_to_idx.get(tok, self.unk_idx) + + def get_tok(self, ind): + return self.all_toks[ind] + + def to_dict(self): + return self.tok_to_idx.copy() + + def get_batch_converter(self, truncation_seq_length: int = None): + if self.use_msa: + return MSABatchConverter(self, truncation_seq_length) + else: + return BatchConverter(self, truncation_seq_length) + + @classmethod + def from_architecture(cls, name: str) -> "Alphabet": + if name in ("ESM-1", "protein_bert_base"): + standard_toks = proteinseq_toks["toks"] + prepend_toks: Tuple[str, ...] = ("", "", "", "") + append_toks: Tuple[str, ...] = ("", "", "") + prepend_bos = True + append_eos = False + use_msa = False + elif name in ("ESM-1b", "roberta_large"): + standard_toks = proteinseq_toks["toks"] + prepend_toks = ("", "", "", "") + append_toks = ("",) + prepend_bos = True + append_eos = True + use_msa = False + elif name in ("MSA Transformer", "msa_transformer"): + standard_toks = proteinseq_toks["toks"] + prepend_toks = ("", "", "", "") + append_toks = ("",) + prepend_bos = True + append_eos = False + use_msa = True + elif "invariant_gvp" in name.lower(): + standard_toks = proteinseq_toks["toks"] + prepend_toks = ("", "", "", "") + append_toks = ("", "", "") + prepend_bos = True + append_eos = False + use_msa = False + else: + raise ValueError("Unknown architecture selected") + return cls(standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa) + + def _tokenize(self, text) -> str: + return text.split() + + def tokenize(self, text, **kwargs) -> List[str]: + """ + Inspired by https://github.com/huggingface/transformers/blob/master/src/transformers/tokenization_utils.py + Converts a string in a sequence of tokens, using the tokenizer. + + Args: + text (:obj:`str`): + The sequence to be encoded. + + Returns: + :obj:`List[str]`: The list of tokens. + """ + + def split_on_token(tok, text): + result = [] + split_text = text.split(tok) + for i, sub_text in enumerate(split_text): + # AddedToken can control whitespace stripping around them. + # We use them for GPT2 and Roberta to have different behavior depending on the special token + # Cf. https://github.com/huggingface/transformers/pull/2778 + # and https://github.com/huggingface/transformers/issues/3788 + # We strip left and right by default + if i < len(split_text) - 1: + sub_text = sub_text.rstrip() + if i > 0: + sub_text = sub_text.lstrip() + + if i == 0 and not sub_text: + result.append(tok) + elif i == len(split_text) - 1: + if sub_text: + result.append(sub_text) + else: + pass + else: + if sub_text: + result.append(sub_text) + result.append(tok) + return result + + def split_on_tokens(tok_list, text): + if not text.strip(): + return [] + + tokenized_text = [] + text_list = [text] + for tok in tok_list: + tokenized_text = [] + for sub_text in text_list: + if sub_text not in self.unique_no_split_tokens: + tokenized_text.extend(split_on_token(tok, sub_text)) + else: + tokenized_text.append(sub_text) + text_list = tokenized_text + + return list( + itertools.chain.from_iterable( + ( + self._tokenize(token) + if token not in self.unique_no_split_tokens + else [token] + for token in tokenized_text + ) + ) + ) + + no_split_token = self.unique_no_split_tokens + tokenized_text = split_on_tokens(no_split_token, text) + return tokenized_text + + def encode(self, text): + return [self.tok_to_idx[tok] for tok in self.tokenize(text)] + + +class BatchConverter(object): + """Callable to convert an unprocessed (labels + strings) batch to a + processed (labels + tensor) batch. + """ + + def __init__(self, alphabet, truncation_seq_length: int = None): + self.alphabet = alphabet + self.truncation_seq_length = truncation_seq_length + + def __call__(self, raw_batch: Sequence[Tuple[str, str]]): + # RoBERTa uses an eos token, while ESM-1 does not. + batch_size = len(raw_batch) + batch_labels, seq_str_list = zip(*raw_batch) + seq_encoded_list = [self.alphabet.encode(seq_str) for seq_str in seq_str_list] + if self.truncation_seq_length: + seq_encoded_list = [seq_str[:self.truncation_seq_length] for seq_str in seq_encoded_list] + max_len = max(len(seq_encoded) for seq_encoded in seq_encoded_list) + tokens = torch.empty( + ( + batch_size, + max_len + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos), + ), + dtype=torch.int64, + ) + tokens.fill_(self.alphabet.padding_idx) + labels = [] + strs = [] + + for i, (label, seq_str, seq_encoded) in enumerate( + zip(batch_labels, seq_str_list, seq_encoded_list) + ): + labels.append(label) + strs.append(seq_str) + if self.alphabet.prepend_bos: + tokens[i, 0] = self.alphabet.cls_idx + seq = torch.tensor(seq_encoded, dtype=torch.int64) + tokens[ + i, + int(self.alphabet.prepend_bos) : len(seq_encoded) + + int(self.alphabet.prepend_bos), + ] = seq + if self.alphabet.append_eos: + tokens[i, len(seq_encoded) + int(self.alphabet.prepend_bos)] = self.alphabet.eos_idx + + return labels, strs, tokens + + +class MSABatchConverter(BatchConverter): + def __call__(self, inputs: Union[Sequence[RawMSA], RawMSA]): + if isinstance(inputs[0][0], str): + # Input is a single MSA + raw_batch: Sequence[RawMSA] = [inputs] # type: ignore + else: + raw_batch = inputs # type: ignore + + batch_size = len(raw_batch) + max_alignments = max(len(msa) for msa in raw_batch) + max_seqlen = max(len(msa[0][1]) for msa in raw_batch) + + tokens = torch.empty( + ( + batch_size, + max_alignments, + max_seqlen + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos), + ), + dtype=torch.int64, + ) + tokens.fill_(self.alphabet.padding_idx) + labels = [] + strs = [] + + for i, msa in enumerate(raw_batch): + msa_seqlens = set(len(seq) for _, seq in msa) + if not len(msa_seqlens) == 1: + raise RuntimeError( + "Received unaligned sequences for input to MSA, all sequence " + "lengths must be equal." + ) + msa_labels, msa_strs, msa_tokens = super().__call__(msa) + labels.append(msa_labels) + strs.append(msa_strs) + tokens[i, : msa_tokens.size(0), : msa_tokens.size(1)] = msa_tokens + + return labels, strs, tokens + + +def read_fasta( + path, + keep_gaps=True, + keep_insertions=True, + to_upper=False, +): + with open(path, "r") as f: + for result in read_alignment_lines( + f, keep_gaps=keep_gaps, keep_insertions=keep_insertions, to_upper=to_upper + ): + yield result + + +def read_alignment_lines( + lines, + keep_gaps=True, + keep_insertions=True, + to_upper=False, +): + seq = desc = None + + def parse(s): + if not keep_gaps: + s = re.sub("-", "", s) + if not keep_insertions: + s = re.sub("[a-z]", "", s) + return s.upper() if to_upper else s + + for line in lines: + # Line may be empty if seq % file_line_width == 0 + if len(line) > 0 and line[0] == ">": + if seq is not None: + yield desc, parse(seq) + desc = line.strip().lstrip(">") + seq = "" + else: + assert isinstance(seq, str) + seq += line.strip() + assert isinstance(seq, str) and isinstance(desc, str) + yield desc, parse(seq) + + +class ESMStructuralSplitDataset(torch.utils.data.Dataset): + """ + Structural Split Dataset as described in section A.10 of the supplement of our paper. + https://doi.org/10.1101/622803 + + We use the full version of SCOPe 2.07, clustered at 90% sequence identity, + generated on January 23, 2020. + + For each SCOPe domain: + - We extract the sequence from the corresponding PDB file + - We extract the 3D coordinates of the Carbon beta atoms, aligning them + to the sequence. We put NaN where Cb atoms are missing. + - From the 3D coordinates, we calculate a pairwise distance map, based + on L2 distance + - We use DSSP to generate secondary structure labels for the corresponding + PDB file. This is also aligned to the sequence. We put - where SSP + labels are missing. + + For each SCOPe classification level of family/superfamily/fold (in order of difficulty), + we have split the data into 5 partitions for cross validation. These are provided + in a downloaded splits folder, in the format: + splits/{split_level}/{cv_partition}/{train|valid}.txt + where train is the partition and valid is the concatentation of the remaining 4. + + For each SCOPe domain, we provide a pkl dump that contains: + - seq : The domain sequence, stored as an L-length string + - ssp : The secondary structure labels, stored as an L-length string + - dist : The distance map, stored as an LxL numpy array + - coords : The 3D coordinates, stored as an Lx3 numpy array + + """ + + base_folder = "structural-data" + file_list = [ + # url tar filename filename MD5 Hash + ( + "https://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gz", + "splits.tar.gz", + "splits", + "456fe1c7f22c9d3d8dfe9735da52411d", + ), + ( + "https://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gz", + "pkl.tar.gz", + "pkl", + "644ea91e56066c750cd50101d390f5db", + ), + ] + + def __init__( + self, + split_level, + cv_partition, + split, + root_path=os.path.expanduser("~/.cache/torch/data/esm"), + download=False, + ): + super().__init__() + assert split in [ + "train", + "valid", + ], "train_valid must be 'train' or 'valid'" + self.root_path = root_path + self.base_path = os.path.join(self.root_path, self.base_folder) + + # check if root path has what you need or else download it + if download: + self.download() + + self.split_file = os.path.join( + self.base_path, "splits", split_level, cv_partition, f"{split}.txt" + ) + self.pkl_dir = os.path.join(self.base_path, "pkl") + self.names = [] + with open(self.split_file) as f: + self.names = f.read().splitlines() + + def __len__(self): + return len(self.names) + + def _check_exists(self) -> bool: + for (_, _, filename, _) in self.file_list: + fpath = os.path.join(self.base_path, filename) + if not os.path.exists(fpath) or not os.path.isdir(fpath): + return False + return True + + def download(self): + + if self._check_exists(): + print("Files already downloaded and verified") + return + + from torchvision.datasets.utils import download_url + + for url, tar_filename, filename, md5_hash in self.file_list: + download_path = os.path.join(self.base_path, tar_filename) + download_url(url=url, root=self.base_path, filename=tar_filename, md5=md5_hash) + shutil.unpack_archive(download_path, self.base_path) + + def __getitem__(self, idx): + """ + Returns a dict with the following entires + - seq : Str (domain sequence) + - ssp : Str (SSP labels) + - dist : np.array (distance map) + - coords : np.array (3D coordinates) + """ + name = self.names[idx] + pkl_fname = os.path.join(self.pkl_dir, name[1:3], f"{name}.pkl") + with open(pkl_fname, "rb") as f: + obj = pickle.load(f) + return obj diff --git a/proteingym/baselines/esm/esm/inverse_folding/__init__.py b/proteingym/baselines/esm/esm/inverse_folding/__init__.py new file mode 100644 index 0000000..2906fc5 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import gvp_transformer +from . import util +from . import multichain_util diff --git a/proteingym/baselines/esm/esm/inverse_folding/features.py b/proteingym/baselines/esm/esm/inverse_folding/features.py new file mode 100644 index 0000000..f67f3fe --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/features.py @@ -0,0 +1,352 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# +# Portions of this file were adapted from the open source code for the following +# two papers: +# +# Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019). Generative +# models for graph-based protein design. Advances in Neural Information +# Processing Systems, 32. +# +# Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., & Dror, R. (2020). +# Learning from Protein Structure with Geometric Vector Perceptrons. In +# International Conference on Learning Representations. +# +# MIT License +# +# Copyright (c) 2020 Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend, Ron Dror +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# ================================================================ +# The below license applies to the portions of the code (parts of +# src/datasets.py and src/models.py) adapted from Ingraham, et al. +# ================================================================ +# +# MIT License +# +# Copyright (c) 2019 John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .gvp_utils import flatten_graph +from .gvp_modules import GVP, LayerNorm +from .util import normalize, norm, nan_to_num, rbf + + +class GVPInputFeaturizer(nn.Module): + + @staticmethod + def get_node_features(coords, coord_mask, with_coord_mask=True): + # scalar features + node_scalar_features = GVPInputFeaturizer._dihedrals(coords) + if with_coord_mask: + node_scalar_features = torch.cat([ + node_scalar_features, + coord_mask.float().unsqueeze(-1) + ], dim=-1) + # vector features + X_ca = coords[:, :, 1] + orientations = GVPInputFeaturizer._orientations(X_ca) + sidechains = GVPInputFeaturizer._sidechains(coords) + node_vector_features = torch.cat([orientations, sidechains.unsqueeze(-2)], dim=-2) + return node_scalar_features, node_vector_features + + @staticmethod + def _orientations(X): + forward = normalize(X[:, 1:] - X[:, :-1]) + backward = normalize(X[:, :-1] - X[:, 1:]) + forward = F.pad(forward, [0, 0, 0, 1]) + backward = F.pad(backward, [0, 0, 1, 0]) + return torch.cat([forward.unsqueeze(-2), backward.unsqueeze(-2)], -2) + + @staticmethod + def _sidechains(X): + n, origin, c = X[:, :, 0], X[:, :, 1], X[:, :, 2] + c, n = normalize(c - origin), normalize(n - origin) + bisector = normalize(c + n) + perp = normalize(torch.cross(c, n, dim=-1)) + vec = -bisector * math.sqrt(1 / 3) - perp * math.sqrt(2 / 3) + return vec + + @staticmethod + def _dihedrals(X, eps=1e-7): + X = torch.flatten(X[:, :, :3], 1, 2) + bsz = X.shape[0] + dX = X[:, 1:] - X[:, :-1] + U = normalize(dX, dim=-1) + u_2 = U[:, :-2] + u_1 = U[:, 1:-1] + u_0 = U[:, 2:] + + # Backbone normals + n_2 = normalize(torch.cross(u_2, u_1, dim=-1), dim=-1) + n_1 = normalize(torch.cross(u_1, u_0, dim=-1), dim=-1) + + # Angle between normals + cosD = torch.sum(n_2 * n_1, -1) + cosD = torch.clamp(cosD, -1 + eps, 1 - eps) + D = torch.sign(torch.sum(u_2 * n_1, -1)) * torch.acos(cosD) + + # This scheme will remove phi[0], psi[-1], omega[-1] + D = F.pad(D, [1, 2]) + D = torch.reshape(D, [bsz, -1, 3]) + # Lift angle representations to the circle + D_features = torch.cat([torch.cos(D), torch.sin(D)], -1) + return D_features + + @staticmethod + def _positional_embeddings(edge_index, + num_embeddings=None, + num_positional_embeddings=16, + period_range=[2, 1000]): + # From https://github.com/jingraham/neurips19-graph-protein-design + num_embeddings = num_embeddings or num_positional_embeddings + d = edge_index[0] - edge_index[1] + + frequency = torch.exp( + torch.arange(0, num_embeddings, 2, dtype=torch.float32, + device=edge_index.device) + * -(np.log(10000.0) / num_embeddings) + ) + angles = d.unsqueeze(-1) * frequency + E = torch.cat((torch.cos(angles), torch.sin(angles)), -1) + return E + + @staticmethod + def _dist(X, coord_mask, padding_mask, top_k_neighbors, eps=1e-8): + """ Pairwise euclidean distances """ + bsz, maxlen = X.size(0), X.size(1) + coord_mask_2D = torch.unsqueeze(coord_mask,1) * torch.unsqueeze(coord_mask,2) + residue_mask = ~padding_mask + residue_mask_2D = torch.unsqueeze(residue_mask,1) * torch.unsqueeze(residue_mask,2) + dX = torch.unsqueeze(X,1) - torch.unsqueeze(X,2) + D = coord_mask_2D * norm(dX, dim=-1) + + # sorting preference: first those with coords, then among the residues that + # exist but are masked use distance in sequence as tie breaker, and then the + # residues that came from padding are last + seqpos = torch.arange(maxlen, device=X.device) + Dseq = torch.abs(seqpos.unsqueeze(1) - seqpos.unsqueeze(0)).repeat(bsz, 1, 1) + D_adjust = nan_to_num(D) + (~coord_mask_2D) * (1e8 + Dseq*1e6) + ( + ~residue_mask_2D) * (1e10) + + if top_k_neighbors == -1: + D_neighbors = D_adjust + E_idx = seqpos.repeat( + *D_neighbors.shape[:-1], 1) + else: + # Identify k nearest neighbors (including self) + k = min(top_k_neighbors, X.size(1)) + D_neighbors, E_idx = torch.topk(D_adjust, k, dim=-1, largest=False) + + coord_mask_neighbors = (D_neighbors < 5e7) + residue_mask_neighbors = (D_neighbors < 5e9) + return D_neighbors, E_idx, coord_mask_neighbors, residue_mask_neighbors + + +class Normalize(nn.Module): + def __init__(self, features, epsilon=1e-6): + super(Normalize, self).__init__() + self.gain = nn.Parameter(torch.ones(features)) + self.bias = nn.Parameter(torch.zeros(features)) + self.epsilon = epsilon + + def forward(self, x, dim=-1): + mu = x.mean(dim, keepdim=True) + sigma = torch.sqrt(x.var(dim, keepdim=True) + self.epsilon) + gain = self.gain + bias = self.bias + # Reshape + if dim != -1: + shape = [1] * len(mu.size()) + shape[dim] = self.gain.size()[0] + gain = gain.view(shape) + bias = bias.view(shape) + return gain * (x - mu) / (sigma + self.epsilon) + bias + + +class DihedralFeatures(nn.Module): + def __init__(self, node_embed_dim): + """ Embed dihedral angle features. """ + super(DihedralFeatures, self).__init__() + # 3 dihedral angles; sin and cos of each angle + node_in = 6 + # Normalization and embedding + self.node_embedding = nn.Linear(node_in, node_embed_dim, bias=True) + self.norm_nodes = Normalize(node_embed_dim) + + def forward(self, X): + """ Featurize coordinates as an attributed graph """ + V = self._dihedrals(X) + V = self.node_embedding(V) + V = self.norm_nodes(V) + return V + + @staticmethod + def _dihedrals(X, eps=1e-7, return_angles=False): + # First 3 coordinates are N, CA, C + X = X[:,:,:3,:].reshape(X.shape[0], 3*X.shape[1], 3) + + # Shifted slices of unit vectors + dX = X[:,1:,:] - X[:,:-1,:] + U = F.normalize(dX, dim=-1) + u_2 = U[:,:-2,:] + u_1 = U[:,1:-1,:] + u_0 = U[:,2:,:] + # Backbone normals + n_2 = F.normalize(torch.cross(u_2, u_1, dim=-1), dim=-1) + n_1 = F.normalize(torch.cross(u_1, u_0, dim=-1), dim=-1) + + # Angle between normals + cosD = (n_2 * n_1).sum(-1) + cosD = torch.clamp(cosD, -1+eps, 1-eps) + D = torch.sign((u_2 * n_1).sum(-1)) * torch.acos(cosD) + + # This scheme will remove phi[0], psi[-1], omega[-1] + D = F.pad(D, (1,2), 'constant', 0) + D = D.view((D.size(0), int(D.size(1)/3), 3)) + phi, psi, omega = torch.unbind(D,-1) + + if return_angles: + return phi, psi, omega + + # Lift angle representations to the circle + D_features = torch.cat((torch.cos(D), torch.sin(D)), 2) + return D_features + + +class GVPGraphEmbedding(GVPInputFeaturizer): + + def __init__(self, args): + super().__init__() + self.top_k_neighbors = args.top_k_neighbors + self.num_positional_embeddings = 16 + self.remove_edges_without_coords = True + node_input_dim = (7, 3) + edge_input_dim = (34, 1) + node_hidden_dim = (args.node_hidden_dim_scalar, + args.node_hidden_dim_vector) + edge_hidden_dim = (args.edge_hidden_dim_scalar, + args.edge_hidden_dim_vector) + self.embed_node = nn.Sequential( + GVP(node_input_dim, node_hidden_dim, activations=(None, None)), + LayerNorm(node_hidden_dim, eps=1e-4) + ) + self.embed_edge = nn.Sequential( + GVP(edge_input_dim, edge_hidden_dim, activations=(None, None)), + LayerNorm(edge_hidden_dim, eps=1e-4) + ) + self.embed_confidence = nn.Linear(16, args.node_hidden_dim_scalar) + + def forward(self, coords, coord_mask, padding_mask, confidence): + with torch.no_grad(): + node_features = self.get_node_features(coords, coord_mask) + edge_features, edge_index = self.get_edge_features( + coords, coord_mask, padding_mask) + node_embeddings_scalar, node_embeddings_vector = self.embed_node(node_features) + edge_embeddings = self.embed_edge(edge_features) + + rbf_rep = rbf(confidence, 0., 1.) + node_embeddings = ( + node_embeddings_scalar + self.embed_confidence(rbf_rep), + node_embeddings_vector + ) + + node_embeddings, edge_embeddings, edge_index = flatten_graph( + node_embeddings, edge_embeddings, edge_index) + return node_embeddings, edge_embeddings, edge_index + + def get_edge_features(self, coords, coord_mask, padding_mask): + X_ca = coords[:, :, 1] + # Get distances to the top k neighbors + E_dist, E_idx, E_coord_mask, E_residue_mask = GVPInputFeaturizer._dist( + X_ca, coord_mask, padding_mask, self.top_k_neighbors) + # Flatten the graph to be batch size 1 for torch_geometric package + dest = E_idx + B, L, k = E_idx.shape[:3] + src = torch.arange(L, device=E_idx.device).view([1, L, 1]).expand(B, L, k) + # After flattening, [2, B, E] + edge_index = torch.stack([src, dest], dim=0).flatten(2, 3) + # After flattening, [B, E] + E_dist = E_dist.flatten(1, 2) + E_coord_mask = E_coord_mask.flatten(1, 2).unsqueeze(-1) + E_residue_mask = E_residue_mask.flatten(1, 2) + # Calculate relative positional embeddings and distance RBF + pos_embeddings = GVPInputFeaturizer._positional_embeddings( + edge_index, + num_positional_embeddings=self.num_positional_embeddings, + ) + D_rbf = rbf(E_dist, 0., 20.) + # Calculate relative orientation + X_src = X_ca.unsqueeze(2).expand(-1, -1, k, -1).flatten(1, 2) + X_dest = torch.gather( + X_ca, + 1, + edge_index[1, :, :].unsqueeze(-1).expand([B, L*k, 3]) + ) + coord_mask_src = coord_mask.unsqueeze(2).expand(-1, -1, k).flatten(1, 2) + coord_mask_dest = torch.gather( + coord_mask, + 1, + edge_index[1, :, :].expand([B, L*k]) + ) + E_vectors = X_src - X_dest + # For the ones without coordinates, substitute in the average vector + E_vector_mean = torch.sum(E_vectors * E_coord_mask, dim=1, + keepdims=True) / torch.sum(E_coord_mask, dim=1, keepdims=True) + E_vectors = E_vectors * E_coord_mask + E_vector_mean * ~(E_coord_mask) + # Normalize and remove nans + edge_s = torch.cat([D_rbf, pos_embeddings], dim=-1) + edge_v = normalize(E_vectors).unsqueeze(-2) + edge_s, edge_v = map(nan_to_num, (edge_s, edge_v)) + # Also add indications of whether the coordinates are present + edge_s = torch.cat([ + edge_s, + (~coord_mask_src).float().unsqueeze(-1), + (~coord_mask_dest).float().unsqueeze(-1), + ], dim=-1) + edge_index[:, ~E_residue_mask] = -1 + if self.remove_edges_without_coords: + edge_index[:, ~E_coord_mask.squeeze(-1)] = -1 + return (edge_s, edge_v), edge_index.transpose(0, 1) diff --git a/proteingym/baselines/esm/esm/inverse_folding/gvp_encoder.py b/proteingym/baselines/esm/esm/inverse_folding/gvp_encoder.py new file mode 100644 index 0000000..a565201 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/gvp_encoder.py @@ -0,0 +1,56 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .features import GVPGraphEmbedding +from .gvp_modules import GVPConvLayer, LayerNorm +from .gvp_utils import unflatten_graph + + + +class GVPEncoder(nn.Module): + + def __init__(self, args): + super().__init__() + self.args = args + self.embed_graph = GVPGraphEmbedding(args) + + node_hidden_dim = (args.node_hidden_dim_scalar, + args.node_hidden_dim_vector) + edge_hidden_dim = (args.edge_hidden_dim_scalar, + args.edge_hidden_dim_vector) + + conv_activations = (F.relu, torch.sigmoid) + self.encoder_layers = nn.ModuleList( + GVPConvLayer( + node_hidden_dim, + edge_hidden_dim, + drop_rate=args.dropout, + vector_gate=True, + attention_heads=0, + n_message=3, + conv_activations=conv_activations, + n_edge_gvps=0, + eps=1e-4, + layernorm=True, + ) + for i in range(args.num_encoder_layers) + ) + + def forward(self, coords, coord_mask, padding_mask, confidence): + node_embeddings, edge_embeddings, edge_index = self.embed_graph( + coords, coord_mask, padding_mask, confidence) + + for i, layer in enumerate(self.encoder_layers): + node_embeddings, edge_embeddings = layer(node_embeddings, + edge_index, edge_embeddings) + + node_embeddings = unflatten_graph(node_embeddings, coords.shape[0]) + return node_embeddings diff --git a/proteingym/baselines/esm/esm/inverse_folding/gvp_modules.py b/proteingym/baselines/esm/esm/inverse_folding/gvp_modules.py new file mode 100644 index 0000000..c60cbd3 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/gvp_modules.py @@ -0,0 +1,475 @@ +# Contents of this file are from the open source code for +# +# Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., & Dror, R. (2020). +# Learning from Protein Structure with Geometric Vector Perceptrons. In +# International Conference on Learning Representations. +# +# MIT License +# +# Copyright (c) 2020 Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend, Ron Dror +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import typing as T +import torch +from torch import nn +import torch.nn.functional as F +from torch_geometric.nn import MessagePassing + +def tuple_size(tp): + return tuple([0 if a is None else a.size() for a in tp]) + +def tuple_sum(tp1, tp2): + s1, v1 = tp1 + s2, v2 = tp2 + if v2 is None and v2 is None: + return (s1 + s2, None) + return (s1 + s2, v1 + v2) + +def tuple_cat(*args, dim=-1): + ''' + Concatenates any number of tuples (s, V) elementwise. + + :param dim: dimension along which to concatenate when viewed + as the `dim` index for the scalar-channel tensors. + This means that `dim=-1` will be applied as + `dim=-2` for the vector-channel tensors. + ''' + dim %= len(args[0][0].shape) + s_args, v_args = list(zip(*args)) + return torch.cat(s_args, dim=dim), torch.cat(v_args, dim=dim) + +def tuple_index(x, idx): + ''' + Indexes into a tuple (s, V) along the first dimension. + + :param idx: any object which can be used to index into a `torch.Tensor` + ''' + return x[0][idx], x[1][idx] + +def randn(n, dims, device="cpu"): + ''' + Returns random tuples (s, V) drawn elementwise from a normal distribution. + + :param n: number of data points + :param dims: tuple of dimensions (n_scalar, n_vector) + + :return: (s, V) with s.shape = (n, n_scalar) and + V.shape = (n, n_vector, 3) + ''' + return torch.randn(n, dims[0], device=device), \ + torch.randn(n, dims[1], 3, device=device) + +def _norm_no_nan(x, axis=-1, keepdims=False, eps=1e-8, sqrt=True): + ''' + L2 norm of tensor clamped above a minimum value `eps`. + + :param sqrt: if `False`, returns the square of the L2 norm + ''' + # clamp is slow + # out = torch.clamp(torch.sum(torch.square(x), axis, keepdims), min=eps) + out = torch.sum(torch.square(x), axis, keepdims) + eps + return torch.sqrt(out) if sqrt else out + +def _split(x, nv): + ''' + Splits a merged representation of (s, V) back into a tuple. + Should be used only with `_merge(s, V)` and only if the tuple + representation cannot be used. + + :param x: the `torch.Tensor` returned from `_merge` + :param nv: the number of vector channels in the input to `_merge` + ''' + v = torch.reshape(x[..., -3*nv:], x.shape[:-1] + (nv, 3)) + s = x[..., :-3*nv] + return s, v + +def _merge(s, v): + ''' + Merges a tuple (s, V) into a single `torch.Tensor`, where the + vector channels are flattened and appended to the scalar channels. + Should be used only if the tuple representation cannot be used. + Use `_split(x, nv)` to reverse. + ''' + v = torch.reshape(v, v.shape[:-2] + (3*v.shape[-2],)) + return torch.cat([s, v], -1) + +class GVP(nn.Module): + ''' + Geometric Vector Perceptron. See manuscript and README.md + for more details. + + :param in_dims: tuple (n_scalar, n_vector) + :param out_dims: tuple (n_scalar, n_vector) + :param h_dim: intermediate number of vector channels, optional + :param activations: tuple of functions (scalar_act, vector_act) + :param tuple_io: whether to keep accepting tuple inputs and outputs when vi + or vo = 0 + ''' + def __init__(self, in_dims, out_dims, h_dim=None, vector_gate=False, + activations=(F.relu, torch.sigmoid), tuple_io=True, + eps=1e-8): + super(GVP, self).__init__() + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.tuple_io = tuple_io + if self.vi: + self.h_dim = h_dim or max(self.vi, self.vo) + self.wh = nn.Linear(self.vi, self.h_dim, bias=False) + self.ws = nn.Linear(self.h_dim + self.si, self.so) + if self.vo: + self.wv = nn.Linear(self.h_dim, self.vo, bias=False) + if vector_gate: + self.wg = nn.Linear(self.so, self.vo) + else: + self.ws = nn.Linear(self.si, self.so) + + self.vector_gate = vector_gate + self.scalar_act, self.vector_act = activations + self.eps = eps + + def forward(self, x): + ''' + :param x: tuple (s, V) of `torch.Tensor`, + or (if vectors_in is 0), a single `torch.Tensor` + :return: tuple (s, V) of `torch.Tensor`, + or (if vectors_out is 0), a single `torch.Tensor` + ''' + if self.vi: + s, v = x + v = torch.transpose(v, -1, -2) + vh = self.wh(v) + vn = _norm_no_nan(vh, axis=-2, eps=self.eps) + s = self.ws(torch.cat([s, vn], -1)) + if self.scalar_act: + s = self.scalar_act(s) + if self.vo: + v = self.wv(vh) + v = torch.transpose(v, -1, -2) + if self.vector_gate: + g = self.wg(s).unsqueeze(-1) + else: + g = _norm_no_nan(v, axis=-1, keepdims=True, eps=self.eps) + if self.vector_act: + g = self.vector_act(g) + v = v * g + else: + if self.tuple_io: + assert x[1] is None + x = x[0] + s = self.ws(x) + if self.scalar_act: + s = self.scalar_act(s) + if self.vo: + v = torch.zeros(list(s.shape)[:-1] + [self.vo, 3], + device=s.device) + + if self.vo: + return (s, v) + elif self.tuple_io: + return (s, None) + else: + return s + + +class _VDropout(nn.Module): + ''' + Vector channel dropout where the elements of each + vector channel are dropped together. + ''' + def __init__(self, drop_rate): + super(_VDropout, self).__init__() + self.drop_rate = drop_rate + + def forward(self, x): + ''' + :param x: `torch.Tensor` corresponding to vector channels + ''' + if x is None: + return None + device = x.device + if not self.training: + return x + mask = torch.bernoulli( + (1 - self.drop_rate) * torch.ones(x.shape[:-1], device=device) + ).unsqueeze(-1) + x = mask * x / (1 - self.drop_rate) + return x + +class Dropout(nn.Module): + ''' + Combined dropout for tuples (s, V). + Takes tuples (s, V) as input and as output. + ''' + def __init__(self, drop_rate): + super(Dropout, self).__init__() + self.sdropout = nn.Dropout(drop_rate) + self.vdropout = _VDropout(drop_rate) + + def forward(self, x): + ''' + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + ''' + if type(x) is torch.Tensor: + return self.sdropout(x) + s, v = x + return self.sdropout(s), self.vdropout(v) + +class LayerNorm(nn.Module): + ''' + Combined LayerNorm for tuples (s, V). + Takes tuples (s, V) as input and as output. + ''' + def __init__(self, dims, tuple_io=True, eps=1e-8): + super(LayerNorm, self).__init__() + self.tuple_io = tuple_io + self.s, self.v = dims + self.scalar_norm = nn.LayerNorm(self.s) + self.eps = eps + + def forward(self, x): + ''' + :param x: tuple (s, V) of `torch.Tensor`, + or single `torch.Tensor` + (will be assumed to be scalar channels) + ''' + if not self.v: + if self.tuple_io: + return self.scalar_norm(x[0]), None + return self.scalar_norm(x) + s, v = x + vn = _norm_no_nan(v, axis=-1, keepdims=True, sqrt=False, eps=self.eps) + nonzero_mask = (vn > 2 * self.eps) + vn = torch.sum(vn * nonzero_mask, dim=-2, keepdim=True + ) / (self.eps + torch.sum(nonzero_mask, dim=-2, keepdim=True)) + vn = torch.sqrt(vn + self.eps) + v = nonzero_mask * (v / vn) + return self.scalar_norm(s), v + +class GVPConv(MessagePassing): + ''' + Graph convolution / message passing with Geometric Vector Perceptrons. + Takes in a graph with node and edge embeddings, + and returns new node embeddings. + + This does NOT do residual updates and pointwise feedforward layers + ---see `GVPConvLayer`. + + :param in_dims: input node embedding dimensions (n_scalar, n_vector) + :param out_dims: output node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_layers: number of GVPs in the message function + :param module_list: preconstructed message function, overrides n_layers + :param aggr: should be "add" if some incoming edges are masked, as in + a masked autoregressive decoder architecture + ''' + def __init__(self, in_dims, out_dims, edge_dims, n_layers=3, + vector_gate=False, module_list=None, aggr="mean", eps=1e-8, + activations=(F.relu, torch.sigmoid)): + super(GVPConv, self).__init__(aggr=aggr) + self.eps = eps + self.si, self.vi = in_dims + self.so, self.vo = out_dims + self.se, self.ve = edge_dims + + module_list = module_list or [] + if not module_list: + if n_layers == 1: + module_list.append( + GVP((2*self.si + self.se, 2*self.vi + self.ve), + (self.so, self.vo), activations=(None, None))) + else: + module_list.append( + GVP((2*self.si + self.se, 2*self.vi + self.ve), out_dims, + vector_gate=vector_gate, activations=activations) + ) + for i in range(n_layers - 2): + module_list.append(GVP(out_dims, out_dims, + vector_gate=vector_gate)) + module_list.append(GVP(out_dims, out_dims, + activations=(None, None))) + self.message_func = nn.Sequential(*module_list) + + def forward(self, x, edge_index, edge_attr): + ''' + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + ''' + x_s, x_v = x + message = self.propagate(edge_index, + s=x_s, v=x_v.reshape(x_v.shape[0], 3*x_v.shape[1]), + edge_attr=edge_attr) + return _split(message, self.vo) + + def message(self, s_i, v_i, s_j, v_j, edge_attr): + v_j = v_j.view(v_j.shape[0], v_j.shape[1]//3, 3) + v_i = v_i.view(v_i.shape[0], v_i.shape[1]//3, 3) + message = tuple_cat((s_j, v_j), edge_attr, (s_i, v_i)) + message = self.message_func(message) + return _merge(*message) + + +class GVPConvLayer(nn.Module): + ''' + Full graph convolution / message passing layer with + Geometric Vector Perceptrons. Residually updates node embeddings with + aggregated incoming messages, applies a pointwise feedforward + network to node embeddings, and returns updated node embeddings. + + To only compute the aggregated messages, see `GVPConv`. + + :param node_dims: node embedding dimensions (n_scalar, n_vector) + :param edge_dims: input edge embedding dimensions (n_scalar, n_vector) + :param n_message: number of GVPs to use in message function + :param n_feedforward: number of GVPs to use in feedforward function + :param drop_rate: drop probability in all dropout layers + :param autoregressive: if `True`, this `GVPConvLayer` will be used + with a different set of input node embeddings for messages + where src >= dst + ''' + def __init__(self, node_dims, edge_dims, vector_gate=False, + n_message=3, n_feedforward=2, drop_rate=.1, + autoregressive=False, attention_heads=0, + conv_activations=(F.relu, torch.sigmoid), + n_edge_gvps=0, layernorm=True, eps=1e-8): + + super(GVPConvLayer, self).__init__() + if attention_heads == 0: + self.conv = GVPConv( + node_dims, node_dims, edge_dims, n_layers=n_message, + vector_gate=vector_gate, + aggr="add" if autoregressive else "mean", + activations=conv_activations, + eps=eps, + ) + else: + raise NotImplementedError + if layernorm: + self.norm = nn.ModuleList([LayerNorm(node_dims, eps=eps) for _ in range(2)]) + else: + self.norm = nn.ModuleList([nn.Identity() for _ in range(2)]) + self.dropout = nn.ModuleList([Dropout(drop_rate) for _ in range(2)]) + + ff_func = [] + if n_feedforward == 1: + ff_func.append(GVP(node_dims, node_dims, activations=(None, None))) + else: + hid_dims = 4*node_dims[0], 2*node_dims[1] + ff_func.append(GVP(node_dims, hid_dims, vector_gate=vector_gate)) + for i in range(n_feedforward-2): + ff_func.append(GVP(hid_dims, hid_dims, vector_gate=vector_gate)) + ff_func.append(GVP(hid_dims, node_dims, activations=(None, None))) + self.ff_func = nn.Sequential(*ff_func) + + self.edge_message_func = None + if n_edge_gvps > 0: + si, vi = node_dims + se, ve = edge_dims + module_list = [ + GVP((2*si + se, 2*vi + ve), edge_dims, vector_gate=vector_gate) + ] + for i in range(n_edge_gvps - 2): + module_list.append(GVP(edge_dims, edge_dims, + vector_gate=vector_gate)) + if n_edge_gvps > 1: + module_list.append(GVP(edge_dims, edge_dims, + activations=(None, None))) + self.edge_message_func = nn.Sequential(*module_list) + if layernorm: + self.edge_norm = LayerNorm(edge_dims, eps=eps) + else: + self.edge_norm = nn.Identity() + self.edge_dropout = Dropout(drop_rate) + + def forward(self, x, edge_index, edge_attr, + autoregressive_x=None, node_mask=None): + ''' + :param x: tuple (s, V) of `torch.Tensor` + :param edge_index: array of shape [2, n_edges] + :param edge_attr: tuple (s, V) of `torch.Tensor` + :param autoregressive_x: tuple (s, V) of `torch.Tensor`. + If not `None`, will be used as srcqq node embeddings + for forming messages where src >= dst. The corrent node + embeddings `x` will still be the base of the update and the + pointwise feedforward. + :param node_mask: array of type `bool` to index into the first + dim of node embeddings (s, V). If not `None`, only + these nodes will be updated. + ''' + if self.edge_message_func: + src, dst = edge_index + if autoregressive_x is None: + x_src = x[0][src], x[1][src] + else: + mask = (src < dst).unsqueeze(-1) + x_src = ( + torch.where(mask, x[0][src], autoregressive_x[0][src]), + torch.where(mask.unsqueeze(-1), x[1][src], + autoregressive_x[1][src]) + ) + x_dst = x[0][dst], x[1][dst] + x_edge = ( + torch.cat([x_src[0], edge_attr[0], x_dst[0]], dim=-1), + torch.cat([x_src[1], edge_attr[1], x_dst[1]], dim=-2) + ) + edge_attr_dh = self.edge_message_func(x_edge) + edge_attr = self.edge_norm(tuple_sum(edge_attr, + self.edge_dropout(edge_attr_dh))) + + if autoregressive_x is not None: + # Guarding this import here to remove the dependency on torch_scatter, since this isn't used + # in ESM-IF1 + from torch_scatter import scatter_add + src, dst = edge_index + mask = src < dst + edge_index_forward = edge_index[:, mask] + edge_index_backward = edge_index[:, ~mask] + edge_attr_forward = tuple_index(edge_attr, mask) + edge_attr_backward = tuple_index(edge_attr, ~mask) + + dh = tuple_sum( + self.conv(x, edge_index_forward, edge_attr_forward), + self.conv(autoregressive_x, edge_index_backward, edge_attr_backward) + ) + + count = scatter_add(torch.ones_like(dst), dst, + dim_size=dh[0].size(0)).clamp(min=1).unsqueeze(-1) + + dh = dh[0] / count, dh[1] / count.unsqueeze(-1) + + else: + dh = self.conv(x, edge_index, edge_attr) + + if node_mask is not None: + x_ = x + x, dh = tuple_index(x, node_mask), tuple_index(dh, node_mask) + + x = self.norm[0](tuple_sum(x, self.dropout[0](dh))) + + dh = self.ff_func(x) + x = self.norm[1](tuple_sum(x, self.dropout[1](dh))) + + if node_mask is not None: + x_[0][node_mask], x_[1][node_mask] = x[0], x[1] + x = x_ + + return x, edge_attr diff --git a/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer.py b/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer.py new file mode 100644 index 0000000..fabd22f --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer.py @@ -0,0 +1,140 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from typing import Any, Dict, List, Optional, Tuple, NamedTuple +import torch +from torch import nn +from torch import Tensor +import torch.nn.functional as F +from scipy.spatial import transform + +from esm.data import Alphabet + +from .features import DihedralFeatures +from .gvp_encoder import GVPEncoder +from .gvp_utils import unflatten_graph +from .gvp_transformer_encoder import GVPTransformerEncoder +from .transformer_decoder import TransformerDecoder +from .util import rotate, CoordBatchConverter + + +class GVPTransformerModel(nn.Module): + """ + GVP-Transformer inverse folding model. + + Architecture: Geometric GVP-GNN as initial layers, followed by + sequence-to-sequence Transformer encoder and decoder. + """ + + def __init__(self, args, alphabet): + super().__init__() + encoder_embed_tokens = self.build_embedding( + args, alphabet, args.encoder_embed_dim, + ) + decoder_embed_tokens = self.build_embedding( + args, alphabet, args.decoder_embed_dim, + ) + encoder = self.build_encoder(args, alphabet, encoder_embed_tokens) + decoder = self.build_decoder(args, alphabet, decoder_embed_tokens) + self.args = args + self.encoder = encoder + self.decoder = decoder + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + encoder = GVPTransformerEncoder(args, src_dict, embed_tokens) + return encoder + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + decoder = TransformerDecoder( + args, + tgt_dict, + embed_tokens, + ) + return decoder + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.padding_idx + emb = nn.Embedding(num_embeddings, embed_dim, padding_idx) + nn.init.normal_(emb.weight, mean=0, std=embed_dim ** -0.5) + nn.init.constant_(emb.weight[padding_idx], 0) + return emb + + def forward( + self, + coords, + padding_mask, + confidence, + prev_output_tokens, + return_all_hiddens: bool = False, + features_only: bool = False, + ): + encoder_out = self.encoder(coords, padding_mask, confidence, + return_all_hiddens=return_all_hiddens) + logits, extra = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + features_only=features_only, + return_all_hiddens=return_all_hiddens, + ) + return logits, extra + + def sample(self, coords, partial_seq=None, temperature=1.0, confidence=None, device=None): + """ + Samples sequences based on multinomial sampling (no beam search). + + Args: + coords: L x 3 x 3 list representing one backbone + partial_seq: Optional, partial sequence with mask tokens if part of + the sequence is known + temperature: sampling temperature, use low temperature for higher + sequence recovery and high temperature for higher diversity + confidence: optional length L list of confidence scores for coordinates + """ + L = len(coords) + # Convert to batch format + batch_converter = CoordBatchConverter(self.decoder.dictionary) + batch_coords, confidence, _, _, padding_mask = ( + batch_converter([(coords, confidence, None)], device=device) + ) + + # Start with prepend token + mask_idx = self.decoder.dictionary.get_idx('') + sampled_tokens = torch.full((1, 1+L), mask_idx, dtype=int) + sampled_tokens[0, 0] = self.decoder.dictionary.get_idx('') + if partial_seq is not None: + for i, c in enumerate(partial_seq): + sampled_tokens[0, i+1] = self.decoder.dictionary.get_idx(c) + + # Save incremental states for faster sampling + incremental_state = dict() + + # Run encoder only once + encoder_out = self.encoder(batch_coords, padding_mask, confidence) + + # Make sure all tensors are on the same device if a GPU is present + if device: + sampled_tokens = sampled_tokens.to(device) + + # Decode one token at a time + for i in range(1, L+1): + logits, _ = self.decoder( + sampled_tokens[:, :i], + encoder_out, + incremental_state=incremental_state, + ) + logits = logits[0].transpose(0, 1) + logits /= temperature + probs = F.softmax(logits, dim=-1) + if sampled_tokens[0, i] == mask_idx: + sampled_tokens[:, i] = torch.multinomial(probs, 1).squeeze(-1) + sampled_seq = sampled_tokens[0, 1:] + + # Convert back to string via lookup + return ''.join([self.decoder.dictionary.get_tok(a) for a in sampled_seq]) diff --git a/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer_encoder.py b/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer_encoder.py new file mode 100644 index 0000000..b613347 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/gvp_transformer_encoder.py @@ -0,0 +1,184 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# Contents of this file were adapted from the open source fairseq repository. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import math +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from esm.modules import SinusoidalPositionalEmbedding +from .features import GVPInputFeaturizer, DihedralFeatures +from .gvp_encoder import GVPEncoder +from .transformer_layer import TransformerEncoderLayer +from .util import nan_to_num, get_rotation_frames, rotate, rbf + + +class GVPTransformerEncoder(nn.Module): + """ + Transformer encoder consisting of *args.encoder.layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, args, dictionary, embed_tokens): + super().__init__() + self.args = args + self.dictionary = dictionary + + self.dropout_module = nn.Dropout(args.dropout) + + embed_dim = embed_tokens.embedding_dim + self.padding_idx = embed_tokens.padding_idx + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) + self.embed_positions = SinusoidalPositionalEmbedding( + embed_dim, + self.padding_idx, + ) + self.embed_gvp_input_features = nn.Linear(15, embed_dim) + self.embed_confidence = nn.Linear(16, embed_dim) + self.embed_dihedrals = DihedralFeatures(embed_dim) + + gvp_args = argparse.Namespace() + for k, v in vars(args).items(): + if k.startswith("gvp_"): + setattr(gvp_args, k[4:], v) + self.gvp_encoder = GVPEncoder(gvp_args) + gvp_out_dim = gvp_args.node_hidden_dim_scalar + (3 * + gvp_args.node_hidden_dim_vector) + self.embed_gvp_output = nn.Linear(gvp_out_dim, embed_dim) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [self.build_encoder_layer(args) for i in range(args.encoder_layers)] + ) + self.num_layers = len(self.layers) + self.layer_norm = nn.LayerNorm(embed_dim) + + def build_encoder_layer(self, args): + return TransformerEncoderLayer(args) + + def forward_embedding(self, coords, padding_mask, confidence): + """ + Args: + coords: N, CA, C backbone coordinates in shape length x 3 (atoms) x 3 + padding_mask: boolean Tensor (true for padding) of shape length + confidence: confidence scores between 0 and 1 of shape length + """ + components = dict() + coord_mask = torch.all(torch.all(torch.isfinite(coords), dim=-1), dim=-1) + coords = nan_to_num(coords) + mask_tokens = ( + padding_mask * self.dictionary.padding_idx + + ~padding_mask * self.dictionary.get_idx("") + ) + components["tokens"] = self.embed_tokens(mask_tokens) * self.embed_scale + components["diherals"] = self.embed_dihedrals(coords) + + # GVP encoder + gvp_out_scalars, gvp_out_vectors = self.gvp_encoder(coords, + coord_mask, padding_mask, confidence) + R = get_rotation_frames(coords) + # Rotate to local rotation frame for rotation-invariance + gvp_out_features = torch.cat([ + gvp_out_scalars, + rotate(gvp_out_vectors, R.transpose(-2, -1)).flatten(-2, -1), + ], dim=-1) + components["gvp_out"] = self.embed_gvp_output(gvp_out_features) + + components["confidence"] = self.embed_confidence( + rbf(confidence, 0., 1.)) + + # In addition to GVP encoder outputs, also directly embed GVP input node + # features to the Transformer + scalar_features, vector_features = GVPInputFeaturizer.get_node_features( + coords, coord_mask, with_coord_mask=False) + features = torch.cat([ + scalar_features, + rotate(vector_features, R.transpose(-2, -1)).flatten(-2, -1), + ], dim=-1) + components["gvp_input_features"] = self.embed_gvp_input_features(features) + + embed = sum(components.values()) + # for k, v in components.items(): + # print(k, torch.mean(v, dim=(0,1)), torch.std(v, dim=(0,1))) + + x = embed + x = x + self.embed_positions(mask_tokens) + x = self.dropout_module(x) + return x, components + + def forward( + self, + coords, + encoder_padding_mask, + confidence, + return_all_hiddens: bool = False, + ): + """ + Args: + coords (Tensor): backbone coordinates + shape batch_size x num_residues x num_atoms (3 for N, CA, C) x 3 + encoder_padding_mask (ByteTensor): the positions of + padding elements of shape `(batch_size x num_residues)` + confidence (Tensor): the confidence score of shape (batch_size x + num_residues). The value is between 0. and 1. for each residue + coordinate, or -1. if no coordinate is given + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(num_residues, batch_size, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch_size, num_residues)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch_size, num_residues, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(num_residues, batch_size, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + x, encoder_embedding = self.forward_embedding(coords, + encoder_padding_mask, confidence) + # account for padding while computing the representation + x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x)) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + encoder_states = [] + + if return_all_hiddens: + encoder_states.append(x) + + # encoder layers + for layer in self.layers: + x = layer( + x, encoder_padding_mask=encoder_padding_mask + ) + if return_all_hiddens: + assert encoder_states is not None + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask], # B x T + "encoder_embedding": [encoder_embedding], # dictionary + "encoder_states": encoder_states, # List[T x B x C] + } diff --git a/proteingym/baselines/esm/esm/inverse_folding/gvp_utils.py b/proteingym/baselines/esm/esm/inverse_folding/gvp_utils.py new file mode 100644 index 0000000..fc0617f --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/gvp_utils.py @@ -0,0 +1,68 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +def flatten_graph(node_embeddings, edge_embeddings, edge_index): + """ + Flattens the graph into a batch size one (with disconnected subgraphs for + each example) to be compatible with pytorch-geometric package. + Args: + node_embeddings: node embeddings in tuple form (scalar, vector) + - scalar: shape batch size x nodes x node_embed_dim + - vector: shape batch size x nodes x node_embed_dim x 3 + edge_embeddings: edge embeddings of in tuple form (scalar, vector) + - scalar: shape batch size x edges x edge_embed_dim + - vector: shape batch size x edges x edge_embed_dim x 3 + edge_index: shape batch_size x 2 (source node and target node) x edges + Returns: + node_embeddings: node embeddings in tuple form (scalar, vector) + - scalar: shape batch total_nodes x node_embed_dim + - vector: shape batch total_nodes x node_embed_dim x 3 + edge_embeddings: edge embeddings of in tuple form (scalar, vector) + - scalar: shape batch total_edges x edge_embed_dim + - vector: shape batch total_edges x edge_embed_dim x 3 + edge_index: shape 2 x total_edges + """ + x_s, x_v = node_embeddings + e_s, e_v = edge_embeddings + batch_size, N = x_s.shape[0], x_s.shape[1] + node_embeddings = (torch.flatten(x_s, 0, 1), torch.flatten(x_v, 0, 1)) + edge_embeddings = (torch.flatten(e_s, 0, 1), torch.flatten(e_v, 0, 1)) + + edge_mask = torch.any(edge_index != -1, dim=1) + # Re-number the nodes by adding batch_idx * N to each batch + edge_index = edge_index + (torch.arange(batch_size, device=edge_index.device) * + N).unsqueeze(-1).unsqueeze(-1) + edge_index = edge_index.permute(1, 0, 2).flatten(1, 2) + edge_mask = edge_mask.flatten() + edge_index = edge_index[:, edge_mask] + edge_embeddings = ( + edge_embeddings[0][edge_mask, :], + edge_embeddings[1][edge_mask, :] + ) + return node_embeddings, edge_embeddings, edge_index + + +def unflatten_graph(node_embeddings, batch_size): + """ + Unflattens node embeddings. + Args: + node_embeddings: node embeddings in tuple form (scalar, vector) + - scalar: shape batch total_nodes x node_embed_dim + - vector: shape batch total_nodes x node_embed_dim x 3 + batch_size: int + Returns: + node_embeddings: node embeddings in tuple form (scalar, vector) + - scalar: shape batch size x nodes x node_embed_dim + - vector: shape batch size x nodes x node_embed_dim x 3 + """ + x_s, x_v = node_embeddings + x_s = x_s.reshape(batch_size, -1, x_s.shape[1]) + x_v = x_v.reshape(batch_size, -1, x_v.shape[1], x_v.shape[2]) + return (x_s, x_v) + + diff --git a/proteingym/baselines/esm/esm/inverse_folding/multichain_util.py b/proteingym/baselines/esm/esm/inverse_folding/multichain_util.py new file mode 100644 index 0000000..40c4b7b --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/multichain_util.py @@ -0,0 +1,152 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import biotite.structure +import numpy as np +import torch +from typing import Sequence, Tuple, List + +from esm.inverse_folding.util import ( + load_structure, + extract_coords_from_structure, + load_coords, + get_sequence_loss, + get_encoder_output, +) + + +def extract_coords_from_complex(structure: biotite.structure.AtomArray): + """ + Args: + structure: biotite AtomArray + Returns: + Tuple (coords_list, seq_list) + - coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + - seqs: Dictionary mapping chain ids to native sequences of each chain + """ + coords = {} + seqs = {} + all_chains = biotite.structure.get_chains(structure) + for chain_id in all_chains: + chain = structure[structure.chain_id == chain_id] + coords[chain_id], seqs[chain_id] = extract_coords_from_structure(chain) + return coords, seqs + + +def load_complex_coords(fpath, chains): + """ + Args: + fpath: filepath to either pdb or cif file + chains: the chain ids (the order matters for autoregressive model) + Returns: + Tuple (coords_list, seq_list) + - coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + - seqs: Dictionary mapping chain ids to native sequences of each chain + """ + structure = load_structure(fpath, chains) + return extract_coords_from_complex(structure) + + +def _concatenate_coords(coords, target_chain_id, padding_length=10): + """ + Args: + coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + target_chain_id: The chain id to sample sequences for + padding_length: Length of padding between concatenated chains + Returns: + Tuple (coords, seq) + - coords is an L x 3 x 3 array for N, CA, C coordinates, a + concatenation of the chains with padding in between + - seq is the extracted sequence, with padding tokens inserted + between the concatenated chains + """ + pad_coords = np.full((padding_length, 3, 3), np.nan, dtype=np.float32) + # For best performance, put the target chain first in concatenation. + coords_list = [coords[target_chain_id]] + for chain_id in coords: + if chain_id == target_chain_id: + continue + coords_list.append(pad_coords) + coords_list.append(coords[chain_id]) + coords_concatenated = np.concatenate(coords_list, axis=0) + return coords_concatenated + + +def sample_sequence_in_complex(model, coords, target_chain_id, temperature=1., + padding_length=10): + """ + Samples sequence for one chain in a complex. + Args: + model: An instance of the GVPTransformer model + coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + target_chain_id: The chain id to sample sequences for + padding_length: padding length in between chains + Returns: + Sampled sequence for the target chain + """ + target_chain_len = coords[target_chain_id].shape[0] + all_coords = _concatenate_coords(coords, target_chain_id) + device = next(model.parameters()).device + + # Supply padding tokens for other chains to avoid unused sampling for speed + padding_pattern = [''] * all_coords.shape[0] + for i in range(target_chain_len): + padding_pattern[i] = '' + sampled = model.sample(all_coords, partial_seq=padding_pattern, + temperature=temperature, device=device) + sampled = sampled[:target_chain_len] + return sampled + + +def score_sequence_in_complex(model, alphabet, coords, target_chain_id, + target_seq, padding_length=10): + """ + Scores sequence for one chain in a complex. + Args: + model: An instance of the GVPTransformer model + alphabet: Alphabet for the model + coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + target_chain_id: The chain id to sample sequences for + target_seq: Target sequence for the target chain for scoring. + padding_length: padding length in between chains + Returns: + Tuple (ll_fullseq, ll_withcoord) + - ll_fullseq: Average log-likelihood over the full target chain + - ll_withcoord: Average log-likelihood in target chain excluding those + residues without coordinates + """ + all_coords = _concatenate_coords(coords, target_chain_id) + + loss, target_padding_mask = get_sequence_loss(model, alphabet, all_coords, + target_seq) + ll_fullseq = -np.sum(loss * ~target_padding_mask) / np.sum( + ~target_padding_mask) + + # Also calculate average when excluding masked portions + coord_mask = np.all(np.isfinite(coords[target_chain_id]), axis=(-1, -2)) + ll_withcoord = -np.sum(loss * coord_mask) / np.sum(coord_mask) + return ll_fullseq, ll_withcoord + + +def get_encoder_output_for_complex(model, alphabet, coords, target_chain_id): + """ + Args: + model: An instance of the GVPTransformer model + alphabet: Alphabet for the model + coords: Dictionary mapping chain ids to L x 3 x 3 array for N, CA, C + coordinates representing the backbone of each chain + target_chain_id: The chain id to sample sequences for + Returns: + Dictionary mapping chain id to encoder output for each chain + """ + all_coords = _concatenate_coords(coords, target_chain_id) + all_rep = get_encoder_output(model, alphabet, all_coords) + target_chain_len = coords[target_chain_id].shape[0] + return all_rep[:target_chain_len] diff --git a/proteingym/baselines/esm/esm/inverse_folding/transformer_decoder.py b/proteingym/baselines/esm/esm/inverse_folding/transformer_decoder.py new file mode 100644 index 0000000..59ec6f3 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/transformer_decoder.py @@ -0,0 +1,228 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# Contents of this file were adapted from the open source fairseq repository. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from esm.modules import SinusoidalPositionalEmbedding +from .transformer_layer import TransformerDecoderLayer + + +def fill_with_neg_inf(t): + """FP16-compatible function that fills a tensor with -inf.""" + return t.float().fill_(float("-inf")).type_as(t) + + +class TransformerDecoder(nn.Module): + """ + Transformer decoder consisting of *args.decoder.layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + args, + dictionary, + embed_tokens, + ): + super().__init__() + self.args = args + self.dictionary = dictionary + self._future_mask = torch.empty(0) + + self.dropout_module = nn.Dropout(args.dropout) + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = args.decoder_embed_dim + self.embed_dim = embed_dim + + self.padding_idx = embed_tokens.padding_idx + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) + + self.project_in_dim = ( + nn.Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + self.embed_positions = SinusoidalPositionalEmbedding( + embed_dim, + self.padding_idx, + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_decoder_layer(args) + for _ in range(args.decoder_layers) + ] + ) + self.num_layers = len(self.layers) + self.layer_norm = nn.LayerNorm(embed_dim) + + self.build_output_projection(args, dictionary) + + def build_output_projection(self, args, dictionary): + self.output_projection = nn.Linear( + args.decoder_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, mean=0, std=args.decoder_embed_dim ** -0.5 + ) + + def build_decoder_layer(self, args): + return TransformerDecoderLayer(args) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + ) + + if not features_only: + x = self.output_layer(x) + x = x.transpose(1, 2) # B x T x C -> B x C x T + return x, extra + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + bs, slen = prev_output_tokens.size() + + enc: Optional[Tensor] = None + padding_mask: Optional[Tensor] = None + if encoder_out is not None and len(encoder_out["encoder_out"]) > 0: + enc = encoder_out["encoder_out"][0] + assert ( + enc.size()[1] == bs + ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}" + if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0: + padding_mask = encoder_out["encoder_padding_mask"][0] + + # embed positions + positions = self.embed_positions( + prev_output_tokens + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + x += positions + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + self_attn_padding_mask: Optional[Tensor] = None + if prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + enc, + padding_mask, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=False, + need_head_weights=False, + ) + inner_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x C x T + x = x.transpose(0, 1) + + return x, {"inner_states": inner_states} + + def output_layer(self, features): + """Project features to the vocabulary size.""" + return self.output_projection(features) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] diff --git a/proteingym/baselines/esm/esm/inverse_folding/transformer_layer.py b/proteingym/baselines/esm/esm/inverse_folding/transformer_layer.py new file mode 100644 index 0000000..87830a3 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/transformer_layer.py @@ -0,0 +1,304 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# Contents of this file were adapted from the open source fairseq repository. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from esm.multihead_attention import MultiheadAttention +from torch import Tensor + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer block. + `layernorm -> dropout -> add residual` + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + + def __init__(self, args): + super().__init__() + self.args = args + self.embed_dim = args.encoder_embed_dim + self.self_attn = self.build_self_attention(self.embed_dim, args) + self.self_attn_layer_norm = torch.nn.LayerNorm(self.embed_dim) + self.dropout_module = nn.Dropout(args.dropout) + self.activation_fn = F.relu + self.fc1 = self.build_fc1( + self.embed_dim, + args.encoder_ffn_embed_dim, + ) + self.fc2 = self.build_fc2( + args.encoder_ffn_embed_dim, + self.embed_dim, + ) + + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def build_fc1(self, input_dim, output_dim): + return nn.Linear(input_dim, output_dim) + + def build_fc2(self, input_dim, output_dim): + return nn.Linear(input_dim, output_dim) + + def build_self_attention(self, embed_dim, args): + return MultiheadAttention( + embed_dim, + args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + ) + + def residual_connection(self, x, residual): + return residual + x + + def forward( + self, + x, + encoder_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor] = None, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor): binary ByteTensor of shape + `(batch, seq_len)` where padding elements are indicated by ``1``. + attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, + where `tgt_len` is the length of output and `src_len` is the + length of input, though here both are equal to `seq_len`. + `attn_mask[tgt_i, src_j] = 1` means that when calculating the + embedding for `tgt_i`, we exclude (mask out) `src_j`. This is + useful for strided self-attention. + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + # anything in original attn_mask = 1, becomes -1e8 + # anything in original attn_mask = 0, becomes 0 + # Note that we cannot use -inf here, because at some edge cases, + # the attention weight (before softmax) for some padded element in query + # will become -inf, which results in NaN in model parameters + if attn_mask is not None: + attn_mask = attn_mask.masked_fill( + attn_mask.to(torch.bool), -1e8 if x.dtype == torch.float32 else -1e4 + ) + + residual = x + x = self.self_attn_layer_norm(x) + x, _ = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=encoder_padding_mask, + need_weights=False, + attn_mask=attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + + residual = x + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + return x + + +class TransformerDecoderLayer(nn.Module): + """Decoder layer block. + `layernorm -> dropout -> add residual` + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.dropout_module = nn.Dropout(args.dropout) + + self.self_attn = self.build_self_attention( + self.embed_dim, + args, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + self.nh = self.self_attn.num_heads + self.head_dim = self.self_attn.head_dim + + self.activation_fn = F.relu + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = self.build_encoder_attention(self.embed_dim, args) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.ffn_layernorm = ( + LayerNorm(args.decoder_ffn_embed_dim) + if getattr(args, "scale_fc", False) + else None + ) + self.w_resid = ( + nn.Parameter( + torch.ones( + self.embed_dim, + ), + requires_grad=True, + ) + if getattr(args, "scale_resids", False) + else None + ) + + self.fc1 = self.build_fc1( + self.embed_dim, + args.decoder_ffn_embed_dim, + ) + self.fc2 = self.build_fc2( + args.decoder_ffn_embed_dim, + self.embed_dim, + ) + + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + self.need_attn = True + + def build_fc1(self, input_dim, output_dim): + return nn.Linear(input_dim, output_dim) + + def build_fc2(self, input_dim, output_dim): + return nn.Linear(input_dim, output_dim) + + def build_self_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=True, + ) + + def build_encoder_attention(self, embed_dim, args): + return MultiheadAttention( + embed_dim, + args.decoder_attention_heads, + kdim=args.encoder_embed_dim, + vdim=args.encoder_embed_dim, + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) + + def residual_connection(self, x, residual): + return residual + x + + def forward( + self, + x, + encoder_out: Optional[torch.Tensor] = None, + encoder_padding_mask: Optional[torch.Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + prev_self_attn_state: Optional[List[torch.Tensor]] = None, + prev_attn_state: Optional[List[torch.Tensor]] = None, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + if need_head_weights: + need_attn = True + + residual = x + x = self.self_attn_layer_norm(x) + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + + if self.encoder_attn is not None and encoder_out is not None: + residual = x + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, + ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + + residual = x + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + if self.ffn_layernorm is not None: + x = self.ffn_layernorm(x) + x = self.fc2(x) + x = self.dropout_module(x) + if self.w_resid is not None: + residual = torch.mul(self.w_resid, residual) + x = self.residual_connection(x, residual) + return x, attn, None diff --git a/proteingym/baselines/esm/esm/inverse_folding/util.py b/proteingym/baselines/esm/esm/inverse_folding/util.py new file mode 100644 index 0000000..e38b700 --- /dev/null +++ b/proteingym/baselines/esm/esm/inverse_folding/util.py @@ -0,0 +1,323 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import math + +import biotite.structure +from biotite.structure.io import pdbx, pdb +from biotite.structure.residues import get_residues +from biotite.structure import filter_backbone +from biotite.structure import get_chains +from biotite.sequence import ProteinSequence +import numpy as np +from scipy.spatial import transform +from scipy.stats import special_ortho_group +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data as data +from typing import Sequence, Tuple, List + +from esm.data import BatchConverter + + +def load_structure(fpath, chain=None): + """ + Args: + fpath: filepath to either pdb or cif file + chain: the chain id or list of chain ids to load + Returns: + biotite.structure.AtomArray + """ + if fpath.endswith('cif'): + with open(fpath) as fin: + pdbxf = pdbx.PDBxFile.read(fin) + structure = pdbx.get_structure(pdbxf, model=1) + elif fpath.endswith('pdb'): + with open(fpath) as fin: + pdbf = pdb.PDBFile.read(fin) + structure = pdb.get_structure(pdbf, model=1) + bbmask = filter_backbone(structure) + structure = structure[bbmask] + all_chains = get_chains(structure) + if len(all_chains) == 0: + raise ValueError('No chains found in the input file.') + if chain is None: + chain_ids = all_chains + elif isinstance(chain, list): + chain_ids = chain + else: + chain_ids = [chain] + for chain in chain_ids: + if chain not in all_chains: + raise ValueError(f'Chain {chain} not found in input file') + chain_filter = [a.chain_id in chain_ids for a in structure] + structure = structure[chain_filter] + return structure + + +def extract_coords_from_structure(structure: biotite.structure.AtomArray): + """ + Args: + structure: An instance of biotite AtomArray + Returns: + Tuple (coords, seq) + - coords is an L x 3 x 3 array for N, CA, C coordinates + - seq is the extracted sequence + """ + coords = get_atom_coords_residuewise(["N", "CA", "C"], structure) + residue_identities = get_residues(structure)[1] + seq = ''.join([ProteinSequence.convert_letter_3to1(r) for r in residue_identities]) + return coords, seq + + +def load_coords(fpath, chain): + """ + Args: + fpath: filepath to either pdb or cif file + chain: the chain id + Returns: + Tuple (coords, seq) + - coords is an L x 3 x 3 array for N, CA, C coordinates + - seq is the extracted sequence + """ + structure = load_structure(fpath, chain) + return extract_coords_from_structure(structure) + + +def get_atom_coords_residuewise(atoms: List[str], struct: biotite.structure.AtomArray): + """ + Example for atoms argument: ["N", "CA", "C"] + """ + def filterfn(s, axis=None): + filters = np.stack([s.atom_name == name for name in atoms], axis=1) + sum = filters.sum(0) + if not np.all(sum <= np.ones(filters.shape[1])): + raise RuntimeError("structure has multiple atoms with same name") + index = filters.argmax(0) + coords = s[index].coord + coords[sum == 0] = float("nan") + return coords + + return biotite.structure.apply_residue_wise(struct, struct, filterfn) + + +def get_sequence_loss(model, alphabet, coords, seq): + device = next(model.parameters()).device + batch_converter = CoordBatchConverter(alphabet) + batch = [(coords, None, seq)] + coords, confidence, strs, tokens, padding_mask = batch_converter( + batch, device=device) + + prev_output_tokens = tokens[:, :-1].to(device) + target = tokens[:, 1:] + target_padding_mask = (target == alphabet.padding_idx) + logits, _ = model.forward(coords, padding_mask, confidence, prev_output_tokens) + loss = F.cross_entropy(logits, target, reduction='none') + loss = loss[0].cpu().detach().numpy() + target_padding_mask = target_padding_mask[0].cpu().numpy() + return loss, target_padding_mask + + +def score_sequence(model, alphabet, coords, seq): + loss, target_padding_mask = get_sequence_loss(model, alphabet, coords, seq) + ll_fullseq = -np.sum(loss * ~target_padding_mask) / np.sum(~target_padding_mask) + # Also calculate average when excluding masked portions + coord_mask = np.all(np.isfinite(coords), axis=(-1, -2)) + ll_withcoord = -np.sum(loss * coord_mask) / np.sum(coord_mask) + return ll_fullseq, ll_withcoord + + +def get_encoder_output(model, alphabet, coords): + device = next(model.parameters()).device + batch_converter = CoordBatchConverter(alphabet) + batch = [(coords, None, None)] + coords, confidence, strs, tokens, padding_mask = batch_converter( + batch, device=device) + encoder_out = model.encoder.forward(coords, padding_mask, confidence, + return_all_hiddens=False) + # remove beginning and end (bos and eos tokens) + return encoder_out['encoder_out'][0][1:-1, 0] + + +def rotate(v, R): + """ + Rotates a vector by a rotation matrix. + + Args: + v: 3D vector, tensor of shape (length x batch_size x channels x 3) + R: rotation matrix, tensor of shape (length x batch_size x 3 x 3) + + Returns: + Rotated version of v by rotation matrix R. + """ + R = R.unsqueeze(-3) + v = v.unsqueeze(-1) + return torch.sum(v * R, dim=-2) + + +def get_rotation_frames(coords): + """ + Returns a local rotation frame defined by N, CA, C positions. + + Args: + coords: coordinates, tensor of shape (batch_size x length x 3 x 3) + where the third dimension is in order of N, CA, C + + Returns: + Local relative rotation frames in shape (batch_size x length x 3 x 3) + """ + v1 = coords[:, :, 2] - coords[:, :, 1] + v2 = coords[:, :, 0] - coords[:, :, 1] + e1 = normalize(v1, dim=-1) + u2 = v2 - e1 * torch.sum(e1 * v2, dim=-1, keepdim=True) + e2 = normalize(u2, dim=-1) + e3 = torch.cross(e1, e2, dim=-1) + R = torch.stack([e1, e2, e3], dim=-2) + return R + + +def nan_to_num(ts, val=0.0): + """ + Replaces nans in tensor with a fixed value. + """ + val = torch.tensor(val, dtype=ts.dtype, device=ts.device) + return torch.where(~torch.isfinite(ts), val, ts) + + +def rbf(values, v_min, v_max, n_bins=16): + """ + Returns RBF encodings in a new dimension at the end. + """ + rbf_centers = torch.linspace(v_min, v_max, n_bins, device=values.device) + rbf_centers = rbf_centers.view([1] * len(values.shape) + [-1]) + rbf_std = (v_max - v_min) / n_bins + v_expand = torch.unsqueeze(values, -1) + z = (values.unsqueeze(-1) - rbf_centers) / rbf_std + return torch.exp(-z ** 2) + + +def norm(tensor, dim, eps=1e-8, keepdim=False): + """ + Returns L2 norm along a dimension. + """ + return torch.sqrt( + torch.sum(torch.square(tensor), dim=dim, keepdim=keepdim) + eps) + + +def normalize(tensor, dim=-1): + """ + Normalizes a tensor along a dimension after removing nans. + """ + return nan_to_num( + torch.div(tensor, norm(tensor, dim=dim, keepdim=True)) + ) + + +class CoordBatchConverter(BatchConverter): + def __call__(self, raw_batch: Sequence[Tuple[Sequence, str]], device=None): + """ + Args: + raw_batch: List of tuples (coords, confidence, seq) + In each tuple, + coords: list of floats, shape L x 3 x 3 + confidence: list of floats, shape L; or scalar float; or None + seq: string of length L + Returns: + coords: Tensor of shape batch_size x L x 3 x 3 + confidence: Tensor of shape batch_size x L + strs: list of strings + tokens: LongTensor of shape batch_size x L + padding_mask: ByteTensor of shape batch_size x L + """ + self.alphabet.cls_idx = self.alphabet.get_idx("") + batch = [] + for coords, confidence, seq in raw_batch: + if confidence is None: + confidence = 1. + if isinstance(confidence, float) or isinstance(confidence, int): + confidence = [float(confidence)] * len(coords) + if seq is None: + seq = 'X' * len(coords) + batch.append(((coords, confidence), seq)) + + coords_and_confidence, strs, tokens = super().__call__(batch) + + # pad beginning and end of each protein due to legacy reasons + coords = [ + F.pad(torch.tensor(cd), (0, 0, 0, 0, 1, 1), value=np.inf) + for cd, _ in coords_and_confidence + ] + confidence = [ + F.pad(torch.tensor(cf), (1, 1), value=-1.) + for _, cf in coords_and_confidence + ] + coords = self.collate_dense_tensors(coords, pad_v=np.nan) + confidence = self.collate_dense_tensors(confidence, pad_v=-1.) + if device is not None: + coords = coords.to(device) + confidence = confidence.to(device) + tokens = tokens.to(device) + padding_mask = torch.isnan(coords[:,:,0,0]) + coord_mask = torch.isfinite(coords.sum(-2).sum(-1)) + confidence = confidence * coord_mask + (-1.) * padding_mask + return coords, confidence, strs, tokens, padding_mask + + def from_lists(self, coords_list, confidence_list=None, seq_list=None, device=None): + """ + Args: + coords_list: list of length batch_size, each item is a list of + floats in shape L x 3 x 3 to describe a backbone + confidence_list: one of + - None, default to highest confidence + - list of length batch_size, each item is a scalar + - list of length batch_size, each item is a list of floats of + length L to describe the confidence scores for the backbone + with values between 0. and 1. + seq_list: either None or a list of strings + Returns: + coords: Tensor of shape batch_size x L x 3 x 3 + confidence: Tensor of shape batch_size x L + strs: list of strings + tokens: LongTensor of shape batch_size x L + padding_mask: ByteTensor of shape batch_size x L + """ + batch_size = len(coords_list) + if confidence_list is None: + confidence_list = [None] * batch_size + if seq_list is None: + seq_list = [None] * batch_size + raw_batch = zip(coords_list, confidence_list, seq_list) + return self.__call__(raw_batch, device) + + @staticmethod + def collate_dense_tensors(samples, pad_v): + """ + Takes a list of tensors with the following dimensions: + [(d_11, ..., d_1K), + (d_21, ..., d_2K), + ..., + (d_N1, ..., d_NK)] + and stack + pads them into a single tensor of: + (N, max_i=1,N { d_i1 }, ..., max_i=1,N {diK}) + """ + if len(samples) == 0: + return torch.Tensor() + if len(set(x.dim() for x in samples)) != 1: + raise RuntimeError( + f"Samples has varying dimensions: {[x.dim() for x in samples]}" + ) + (device,) = tuple(set(x.device for x in samples)) # assumes all on same device + max_shape = [max(lst) for lst in zip(*[x.shape for x in samples])] + result = torch.empty( + len(samples), *max_shape, dtype=samples[0].dtype, device=device + ) + result.fill_(pad_v) + for i in range(len(samples)): + result_i = result[i] + t = samples[i] + result_i[tuple(slice(0, k) for k in t.shape)] = t + return result diff --git a/proteingym/baselines/esm/esm/model/esm1.py b/proteingym/baselines/esm/esm/model/esm1.py new file mode 100644 index 0000000..b199662 --- /dev/null +++ b/proteingym/baselines/esm/esm/model/esm1.py @@ -0,0 +1,200 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..modules import ( + TransformerLayer, + LearnedPositionalEmbedding, + SinusoidalPositionalEmbedding, + RobertaLMHead, + ESM1bLayerNorm, + ContactPredictionHead, +) + + +class ProteinBertModel(nn.Module): + @classmethod + def add_args(cls, parser): + parser.add_argument( + "--num_layers", default=36, type=int, metavar="N", help="number of layers" + ) + parser.add_argument( + "--embed_dim", default=1280, type=int, metavar="N", help="embedding dimension" + ) + parser.add_argument( + "--logit_bias", action="store_true", help="whether to apply bias to logits" + ) + parser.add_argument( + "--ffn_embed_dim", + default=5120, + type=int, + metavar="N", + help="embedding dimension for FFN", + ) + parser.add_argument( + "--attention_heads", + default=20, + type=int, + metavar="N", + help="number of attention heads", + ) + + def __init__(self, args, alphabet): + super().__init__() + self.args = args + self.alphabet_size = len(alphabet) + self.padding_idx = alphabet.padding_idx + self.mask_idx = alphabet.mask_idx + self.cls_idx = alphabet.cls_idx + self.eos_idx = alphabet.eos_idx + self.prepend_bos = alphabet.prepend_bos + self.append_eos = alphabet.append_eos + self.emb_layer_norm_before = getattr(self.args, "emb_layer_norm_before", False) + if self.args.arch == "roberta_large": + self.model_version = "ESM-1b" + self._init_submodules_esm1b() + else: + self.model_version = "ESM-1" + self._init_submodules_esm1() + + def _init_submodules_common(self): + self.embed_tokens = nn.Embedding( + self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx + ) + self.layers = nn.ModuleList( + [ + TransformerLayer( + self.args.embed_dim, + self.args.ffn_embed_dim, + self.args.attention_heads, + add_bias_kv=(self.model_version != "ESM-1b"), + use_esm1b_layer_norm=(self.model_version == "ESM-1b"), + ) + for _ in range(self.args.layers) + ] + ) + + self.contact_head = ContactPredictionHead( + self.args.layers * self.args.attention_heads, + self.prepend_bos, + self.append_eos, + eos_idx=self.eos_idx, + ) + + def _init_submodules_esm1b(self): + self._init_submodules_common() + self.embed_scale = 1 + self.embed_positions = LearnedPositionalEmbedding( + self.args.max_positions, self.args.embed_dim, self.padding_idx + ) + self.emb_layer_norm_before = ( + ESM1bLayerNorm(self.args.embed_dim) if self.emb_layer_norm_before else None + ) + self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim) + self.lm_head = RobertaLMHead( + embed_dim=self.args.embed_dim, + output_dim=self.alphabet_size, + weight=self.embed_tokens.weight, + ) + + def _init_submodules_esm1(self): + self._init_submodules_common() + self.embed_scale = math.sqrt(self.args.embed_dim) + self.embed_positions = SinusoidalPositionalEmbedding(self.args.embed_dim, self.padding_idx) + self.embed_out = nn.Parameter(torch.zeros((self.alphabet_size, self.args.embed_dim))) + self.embed_out_bias = None + if self.args.final_bias: + self.embed_out_bias = nn.Parameter(torch.zeros(self.alphabet_size)) + + def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False): + if return_contacts: + need_head_weights = True + + assert tokens.ndim == 2 + padding_mask = tokens.eq(self.padding_idx) # B, T + + x = self.embed_scale * self.embed_tokens(tokens) + + if getattr(self.args, "token_dropout", False): + x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0) + # x: B x T x C + mask_ratio_train = 0.15 * 0.8 + src_lengths = (~padding_mask).sum(-1) + mask_ratio_observed = (tokens == self.mask_idx).sum(-1).float() / src_lengths + x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None] + + x = x + self.embed_positions(tokens) + + if self.model_version == "ESM-1b": + if self.emb_layer_norm_before: + x = self.emb_layer_norm_before(x) + if padding_mask is not None: + x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) + + repr_layers = set(repr_layers) + hidden_representations = {} + if 0 in repr_layers: + hidden_representations[0] = x + + if need_head_weights: + attn_weights = [] + + # (B, T, E) => (T, B, E) + x = x.transpose(0, 1) + + if not padding_mask.any(): + padding_mask = None + + for layer_idx, layer in enumerate(self.layers): + x, attn = layer( + x, self_attn_padding_mask=padding_mask, need_head_weights=need_head_weights + ) + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x.transpose(0, 1) + if need_head_weights: + # (H, B, T, T) => (B, H, T, T) + attn_weights.append(attn.transpose(1, 0)) + + if self.model_version == "ESM-1b": + x = self.emb_layer_norm_after(x) + x = x.transpose(0, 1) # (T, B, E) => (B, T, E) + + # last hidden representation should have layer norm applied + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x + x = self.lm_head(x) + else: + x = F.linear(x, self.embed_out, bias=self.embed_out_bias) + x = x.transpose(0, 1) # (T, B, E) => (B, T, E) + + result = {"logits": x, "representations": hidden_representations} + if need_head_weights: + # attentions: B x L x H x T x T + attentions = torch.stack(attn_weights, 1) + if self.model_version == "ESM-1": + # ESM-1 models have an additional null-token for attention, which we remove + attentions = attentions[..., :-1] + if padding_mask is not None: + attention_mask = 1 - padding_mask.type_as(attentions) + attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2) + attentions = attentions * attention_mask[:, None, None, :, :] + result["attentions"] = attentions + if return_contacts: + contacts = self.contact_head(tokens, attentions) + result["contacts"] = contacts + + return result + + def predict_contacts(self, tokens): + return self(tokens, return_contacts=True)["contacts"] + + @property + def num_layers(self): + return self.args.layers diff --git a/proteingym/baselines/esm/esm/model/esm2.py b/proteingym/baselines/esm/esm/model/esm2.py new file mode 100644 index 0000000..e5613e5 --- /dev/null +++ b/proteingym/baselines/esm/esm/model/esm2.py @@ -0,0 +1,146 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Union +import torch +import torch.nn as nn +import esm +from ..modules import ContactPredictionHead, ESM1bLayerNorm, RobertaLMHead, TransformerLayer + + +class ESM2(nn.Module): + def __init__( + self, + num_layers: int = 33, + embed_dim: int = 1280, + attention_heads: int = 20, + alphabet: Union[esm.data.Alphabet, str] = "ESM-1b", + token_dropout: bool = True, + ): + super().__init__() + self.num_layers = num_layers + self.embed_dim = embed_dim + self.attention_heads = attention_heads + if not (isinstance(alphabet, esm.data.Alphabet) or alphabet.__class__.__name__ == "Alphabet"): + alphabet = esm.data.Alphabet.from_architecture(alphabet) + self.alphabet = alphabet + self.alphabet_size = len(alphabet) + self.padding_idx = alphabet.padding_idx + self.mask_idx = alphabet.mask_idx + self.cls_idx = alphabet.cls_idx + self.eos_idx = alphabet.eos_idx + self.prepend_bos = alphabet.prepend_bos + self.append_eos = alphabet.append_eos + self.token_dropout = token_dropout + + self._init_submodules() + + def _init_submodules(self): + self.embed_scale = 1 + self.embed_tokens = nn.Embedding( + self.alphabet_size, + self.embed_dim, + padding_idx=self.padding_idx, + ) + + self.layers = nn.ModuleList( + [ + TransformerLayer( + self.embed_dim, + 4 * self.embed_dim, + self.attention_heads, + add_bias_kv=False, + use_esm1b_layer_norm=True, + use_rotary_embeddings=True, + ) + for _ in range(self.num_layers) + ] + ) + + self.contact_head = ContactPredictionHead( + self.num_layers * self.attention_heads, + self.prepend_bos, + self.append_eos, + eos_idx=self.eos_idx, + ) + self.emb_layer_norm_after = ESM1bLayerNorm(self.embed_dim) + + self.lm_head = RobertaLMHead( + embed_dim=self.embed_dim, + output_dim=self.alphabet_size, + weight=self.embed_tokens.weight, + ) + + def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False): + if return_contacts: + need_head_weights = True + + assert tokens.ndim == 2 + padding_mask = tokens.eq(self.padding_idx) # B, T + + x = self.embed_scale * self.embed_tokens(tokens) + + if self.token_dropout: + x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0) + # x: B x T x C + mask_ratio_train = 0.15 * 0.8 + src_lengths = (~padding_mask).sum(-1) + mask_ratio_observed = (tokens == self.mask_idx).sum(-1).to(x.dtype) / src_lengths + x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None] + + if padding_mask is not None: + x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) + + repr_layers = set(repr_layers) + hidden_representations = {} + if 0 in repr_layers: + hidden_representations[0] = x + + if need_head_weights: + attn_weights = [] + + # (B, T, E) => (T, B, E) + x = x.transpose(0, 1) + + if not padding_mask.any(): + padding_mask = None + + for layer_idx, layer in enumerate(self.layers): + x, attn = layer( + x, + self_attn_padding_mask=padding_mask, + need_head_weights=need_head_weights, + ) + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x.transpose(0, 1) + if need_head_weights: + # (H, B, T, T) => (B, H, T, T) + attn_weights.append(attn.transpose(1, 0)) + + x = self.emb_layer_norm_after(x) + x = x.transpose(0, 1) # (T, B, E) => (B, T, E) + + # last hidden representation should have layer norm applied + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x + x = self.lm_head(x) + + result = {"logits": x, "representations": hidden_representations} + if need_head_weights: + # attentions: B x L x H x T x T + attentions = torch.stack(attn_weights, 1) + if padding_mask is not None: + attention_mask = 1 - padding_mask.type_as(attentions) + attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2) + attentions = attentions * attention_mask[:, None, None, :, :] + result["attentions"] = attentions + if return_contacts: + contacts = self.contact_head(tokens, attentions) + result["contacts"] = contacts + + return result + + def predict_contacts(self, tokens): + return self(tokens, return_contacts=True)["contacts"] diff --git a/proteingym/baselines/esm/esm/model/msa_transformer.py b/proteingym/baselines/esm/esm/model/msa_transformer.py new file mode 100644 index 0000000..ef21cf5 --- /dev/null +++ b/proteingym/baselines/esm/esm/model/msa_transformer.py @@ -0,0 +1,238 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from ..modules import ( + AxialTransformerLayer, + LearnedPositionalEmbedding, + RobertaLMHead, + ESM1bLayerNorm, + ContactPredictionHead, +) + +from ..axial_attention import RowSelfAttention, ColumnSelfAttention + + + +class MSATransformer(nn.Module): + @classmethod + def add_args(cls, parser): + # fmt: off + parser.add_argument( + "--num_layers", + default=12, + type=int, + metavar="N", + help="number of layers" + ) + parser.add_argument( + "--embed_dim", + default=768, + type=int, + metavar="N", + help="embedding dimension" + ) + parser.add_argument( + "--logit_bias", + action="store_true", + help="whether to apply bias to logits" + ) + parser.add_argument( + "--ffn_embed_dim", + default=3072, + type=int, + metavar="N", + help="embedding dimension for FFN", + ) + parser.add_argument( + "--attention_heads", + default=12, + type=int, + metavar="N", + help="number of attention heads", + ) + parser.add_argument( + "--dropout", + default=0.1, + type=float, + help="Dropout to apply." + ) + parser.add_argument( + "--attention_dropout", + default=0.1, + type=float, + help="Dropout to apply." + ) + parser.add_argument( + "--activation_dropout", + default=0.1, + type=float, + help="Dropout to apply." + ) + parser.add_argument( + "--max_tokens_per_msa", + default=2 ** 14, + type=int, + help=( + "Used during inference to batch attention computations in a single " + "forward pass. This allows increased input sizes with less memory." + ), + ) + # fmt: on + + def __init__(self, args, alphabet): + super().__init__() + self.args = args + self.alphabet_size = len(alphabet) + self.padding_idx = alphabet.padding_idx + self.mask_idx = alphabet.mask_idx + self.cls_idx = alphabet.cls_idx + self.eos_idx = alphabet.eos_idx + self.prepend_bos = alphabet.prepend_bos + self.append_eos = alphabet.append_eos + + self.embed_tokens = nn.Embedding( + self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx + ) + + if getattr(self.args, "embed_positions_msa", False): + emb_dim = getattr(self.args, "embed_positions_msa_dim", self.args.embed_dim) + self.msa_position_embedding = nn.Parameter( + 0.01 * torch.randn(1, 1024, 1, emb_dim), + requires_grad=True, + ) + else: + self.register_parameter("msa_position_embedding", None) + + self.dropout_module = nn.Dropout(self.args.dropout) + self.layers = nn.ModuleList( + [ + AxialTransformerLayer( + self.args.embed_dim, + self.args.ffn_embed_dim, + self.args.attention_heads, + self.args.dropout, + self.args.attention_dropout, + self.args.activation_dropout, + getattr(self.args, "max_tokens_per_msa", self.args.max_tokens), + ) + for _ in range(self.args.layers) + ] + ) + + self.contact_head = ContactPredictionHead( + self.args.layers * self.args.attention_heads, + self.prepend_bos, + self.append_eos, + eos_idx=self.eos_idx, + ) + self.embed_positions = LearnedPositionalEmbedding( + self.args.max_positions, + self.args.embed_dim, + self.padding_idx, + ) + self.emb_layer_norm_before = ESM1bLayerNorm(self.args.embed_dim) + self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim) + self.lm_head = RobertaLMHead( + embed_dim=self.args.embed_dim, + output_dim=self.alphabet_size, + weight=self.embed_tokens.weight, + ) + + def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False): + if return_contacts: + need_head_weights = True + + assert tokens.ndim == 3 + batch_size, num_alignments, seqlen = tokens.size() + padding_mask = tokens.eq(self.padding_idx) # B, R, C + if not padding_mask.any(): + padding_mask = None + + x = self.embed_tokens(tokens) + x += self.embed_positions(tokens.view(batch_size * num_alignments, seqlen)).view(x.size()) + if self.msa_position_embedding is not None: + if x.size(1) > 1024: + raise RuntimeError( + "Using model with MSA position embedding trained on maximum MSA " + f"depth of 1024, but received {x.size(1)} alignments." + ) + x += self.msa_position_embedding[:, :num_alignments] + + x = self.emb_layer_norm_before(x) + + x = self.dropout_module(x) + + if padding_mask is not None: + x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) + + repr_layers = set(repr_layers) + hidden_representations = {} + if 0 in repr_layers: + hidden_representations[0] = x + + if need_head_weights: + row_attn_weights = [] + col_attn_weights = [] + + # B x R x C x D -> R x C x B x D + x = x.permute(1, 2, 0, 3) + + for layer_idx, layer in enumerate(self.layers): + x = layer( + x, + self_attn_padding_mask=padding_mask, + need_head_weights=need_head_weights, + ) + if need_head_weights: + x, col_attn, row_attn = x + # H x C x B x R x R -> B x H x C x R x R + col_attn_weights.append(col_attn.permute(2, 0, 1, 3, 4)) + # H x B x C x C -> B x H x C x C + row_attn_weights.append(row_attn.permute(1, 0, 2, 3)) + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x.permute(2, 0, 1, 3) + + x = self.emb_layer_norm_after(x) + x = x.permute(2, 0, 1, 3) # R x C x B x D -> B x R x C x D + + # last hidden representation should have layer norm applied + if (layer_idx + 1) in repr_layers: + hidden_representations[layer_idx + 1] = x + x = self.lm_head(x) + + result = {"logits": x, "representations": hidden_representations} + if need_head_weights: + # col_attentions: B x L x H x C x R x R + col_attentions = torch.stack(col_attn_weights, 1) + # row_attentions: B x L x H x C x C + row_attentions = torch.stack(row_attn_weights, 1) + result["col_attentions"] = col_attentions + result["row_attentions"] = row_attentions + if return_contacts: + contacts = self.contact_head(tokens, row_attentions) + result["contacts"] = contacts + + return result + + def predict_contacts(self, tokens): + return self(tokens, return_contacts=True)["contacts"] + + @property + def num_layers(self): + return self.args.layers + + def max_tokens_per_msa_(self, value: int) -> None: + """The MSA Transformer automatically batches attention computations when + gradients are disabled to allow you to pass in larger MSAs at test time than + you can fit in GPU memory. By default this occurs when more than 2^14 tokens + are passed in the input MSA. You can set this value to infinity to disable + this behavior. + """ + for module in self.modules(): + if isinstance(module, (RowSelfAttention, ColumnSelfAttention)): + module.max_tokens_per_msa = value diff --git a/proteingym/baselines/esm/esm/modules.py b/proteingym/baselines/esm/esm/modules.py new file mode 100644 index 0000000..437f764 --- /dev/null +++ b/proteingym/baselines/esm/esm/modules.py @@ -0,0 +1,432 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .multihead_attention import MultiheadAttention # noqa +from .axial_attention import ColumnSelfAttention, RowSelfAttention + + +def gelu(x): + """Implementation of the gelu activation function. + + For information: OpenAI GPT's gelu is slightly different + (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + + +def symmetrize(x): + "Make layer symmetric in final two dimensions, used for contact prediction." + return x + x.transpose(-1, -2) + + +def apc(x): + "Perform average product correct, used for contact prediction." + a1 = x.sum(-1, keepdims=True) + a2 = x.sum(-2, keepdims=True) + a12 = x.sum((-1, -2), keepdims=True) + + avg = a1 * a2 + avg.div_(a12) # in-place to reduce memory + normalized = x - avg + return normalized + + +class ESM1LayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12, affine=True): + """Construct a layernorm layer in the TF style (eps inside the sqrt).""" + super().__init__() + self.hidden_size = (hidden_size,) if isinstance(hidden_size, int) else tuple(hidden_size) + self.eps = eps + self.affine = bool(affine) + if self.affine: + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + else: + self.weight, self.bias = None, None + + def forward(self, x): + dims = tuple(-(i + 1) for i in range(len(self.hidden_size))) + means = x.mean(dims, keepdim=True) + x_zeromean = x - means + variances = x_zeromean.pow(2).mean(dims, keepdim=True) + x = x_zeromean / torch.sqrt(variances + self.eps) + if self.affine: + x = (self.weight * x) + self.bias + return x + + +try: + from apex.normalization import FusedLayerNorm as _FusedLayerNorm + + class ESM1bLayerNorm(_FusedLayerNorm): + @torch.jit.unused + def forward(self, x): + if not x.is_cuda: + return super().forward(x) + else: + with torch.cuda.device(x.device): + return super().forward(x) + +except ImportError: + from torch.nn import LayerNorm as ESM1bLayerNorm + + +class TransformerLayer(nn.Module): + """Transformer layer block.""" + + def __init__( + self, + embed_dim, + ffn_embed_dim, + attention_heads, + add_bias_kv=True, + use_esm1b_layer_norm=False, + use_rotary_embeddings: bool = False, + ): + super().__init__() + self.embed_dim = embed_dim + self.ffn_embed_dim = ffn_embed_dim + self.attention_heads = attention_heads + self.use_rotary_embeddings = use_rotary_embeddings + self._init_submodules(add_bias_kv, use_esm1b_layer_norm) + + def _init_submodules(self, add_bias_kv, use_esm1b_layer_norm): + BertLayerNorm = ESM1bLayerNorm if use_esm1b_layer_norm else ESM1LayerNorm + + self.self_attn = MultiheadAttention( + self.embed_dim, + self.attention_heads, + add_bias_kv=add_bias_kv, + add_zero_attn=False, + use_rotary_embeddings=self.use_rotary_embeddings, + ) + self.self_attn_layer_norm = BertLayerNorm(self.embed_dim) + + self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim) + self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim) + + self.final_layer_norm = BertLayerNorm(self.embed_dim) + + def forward( + self, x, self_attn_mask=None, self_attn_padding_mask=None, need_head_weights=False + ): + residual = x + x = self.self_attn_layer_norm(x) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=True, + need_head_weights=need_head_weights, + attn_mask=self_attn_mask, + ) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = gelu(self.fc1(x)) + x = self.fc2(x) + x = residual + x + + return x, attn + + +class AxialTransformerLayer(nn.Module): + """Implements an Axial MSA Transformer block.""" + + def __init__( + self, + embedding_dim: int = 768, + ffn_embedding_dim: int = 3072, + num_attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + max_tokens_per_msa: int = 2**14, + deactivate_col_attention: bool = False, + tranception_attention: bool = False, + num_targets: int = 1, + ) -> None: + super().__init__() + + # Initialize parameters + self.embedding_dim = embedding_dim + self.dropout_prob = dropout + self.deactivate_col_attention = deactivate_col_attention + + row_self_attention = RowSelfAttention( + embedding_dim, + num_attention_heads, + dropout=dropout, + max_tokens_per_msa=max_tokens_per_msa, + tranception_attention=tranception_attention, + num_targets=num_targets, + ) + + if not self.deactivate_col_attention: + column_self_attention = ColumnSelfAttention( + embedding_dim, + num_attention_heads, + dropout=dropout, + max_tokens_per_msa=max_tokens_per_msa, + ) + else: + print("No column attention in the underlying axial transformer module") + + feed_forward_layer = FeedForwardNetwork( + embedding_dim, + ffn_embedding_dim, + activation_dropout=activation_dropout, + max_tokens_per_msa=max_tokens_per_msa, + ) + + self.row_self_attention = self.build_residual(row_self_attention) + if not self.deactivate_col_attention: self.column_self_attention = self.build_residual(column_self_attention) + self.feed_forward_layer = self.build_residual(feed_forward_layer) + + def build_residual(self, layer: nn.Module): + return NormalizedResidualBlock( + layer, + self.embedding_dim, + self.dropout_prob, + ) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: Optional[torch.Tensor] = None, + self_attn_padding_mask: Optional[torch.Tensor] = None, + need_head_weights: bool = False, + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer implementation. + """ + x, row_attn = self.row_self_attention( + x, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + ) + + if not self.deactivate_col_attention: + x, column_attn = self.column_self_attention( + x, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + ) + else: + column_attn = None + + x = self.feed_forward_layer(x) + if need_head_weights: + return x, column_attn, row_attn + else: + return x + + +class LearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + Padding ids are ignored by either offsetting based on padding_idx + or by setting padding_idx to None and ensuring that the appropriate + position ids are passed to the forward function. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int): + if padding_idx is not None: + num_embeddings_ = num_embeddings + padding_idx + 1 + else: + num_embeddings_ = num_embeddings + super().__init__(num_embeddings_, embedding_dim, padding_idx) + self.max_positions = num_embeddings + + def forward(self, input: torch.Tensor): + """Input is expected to be of size [bsz x seqlen].""" + if input.size(1) > self.max_positions: + raise ValueError( + f"Sequence length {input.size(1)} above maximum " + f" sequence length of {self.max_positions}" + ) + mask = input.ne(self.padding_idx).int() + positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + self.padding_idx + return F.embedding( + positions, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) + + +class SinusoidalPositionalEmbedding(nn.Module): + def __init__(self, embed_dim, padding_idx, learned=False): + super().__init__() + self.embed_dim = embed_dim + self.padding_idx = padding_idx + self.register_buffer("_float_tensor", torch.FloatTensor(1)) + self.weights = None + + def forward(self, x): + bsz, seq_len = x.shape + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + self.weights = self.get_embedding(max_pos) + self.weights = self.weights.type_as(self._float_tensor) + + positions = self.make_positions(x) + return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() + + def make_positions(self, x): + mask = x.ne(self.padding_idx) + range_buf = torch.arange(x.size(1), device=x.device).expand_as(x) + self.padding_idx + 1 + positions = range_buf.expand_as(x) + return positions * mask.long() + self.padding_idx * (1 - mask.long()) + + def get_embedding(self, num_embeddings): + half_dim = self.embed_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if self.embed_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if self.padding_idx is not None: + emb[self.padding_idx, :] = 0 + return emb + + +class RobertaLMHead(nn.Module): + """Head for masked language modeling.""" + + def __init__(self, embed_dim, output_dim, weight): + super().__init__() + self.dense = nn.Linear(embed_dim, embed_dim) + self.layer_norm = ESM1bLayerNorm(embed_dim) + self.weight = weight + self.bias = nn.Parameter(torch.zeros(output_dim)) + + def forward(self, features): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + # project back to size of vocabulary with bias + x = F.linear(x, self.weight) + self.bias + return x + + +class ContactPredictionHead(nn.Module): + """Performs symmetrization, apc, and computes a logistic regression on the output features""" + + def __init__( + self, + in_features: int, + prepend_bos: bool, + append_eos: bool, + bias=True, + eos_idx: Optional[int] = None, + ): + super().__init__() + self.in_features = in_features + self.prepend_bos = prepend_bos + self.append_eos = append_eos + if append_eos and eos_idx is None: + raise ValueError("Using an alphabet with eos token, but no eos token was passed in.") + self.eos_idx = eos_idx + self.regression = nn.Linear(in_features, 1, bias) + self.activation = nn.Sigmoid() + + def forward(self, tokens, attentions): + # remove eos token attentions + if self.append_eos: + eos_mask = tokens.ne(self.eos_idx).to(attentions) + eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2) + attentions = attentions * eos_mask[:, None, None, :, :] + attentions = attentions[..., :-1, :-1] + # remove cls token attentions + if self.prepend_bos: + attentions = attentions[..., 1:, 1:] + batch_size, layers, heads, seqlen, _ = attentions.size() + attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen) + + # features: B x C x T x T + attentions = attentions.to( + self.regression.weight.device + ) # attentions always float32, may need to convert to float16 + attentions = apc(symmetrize(attentions)) + attentions = attentions.permute(0, 2, 3, 1) + return self.activation(self.regression(attentions).squeeze(3)) + + +class NormalizedResidualBlock(nn.Module): + def __init__( + self, + layer: nn.Module, + embedding_dim: int, + dropout: float = 0.1, + ): + super().__init__() + self.embedding_dim = embedding_dim + + self.layer = layer + self.dropout_module = nn.Dropout( + dropout, + ) + self.layer_norm = ESM1bLayerNorm(self.embedding_dim) + + def forward(self, x, *args, **kwargs): + residual = x + x = self.layer_norm(x) + outputs = self.layer(x, *args, **kwargs) + if isinstance(outputs, tuple): + x, *out = outputs + else: + x = outputs + out = None + + x = self.dropout_module(x) + x = residual + x + + if out is not None: + return (x,) + tuple(out) + else: + return x + + +class FeedForwardNetwork(nn.Module): + def __init__( + self, + embedding_dim: int, + ffn_embedding_dim: int, + activation_dropout: float = 0.1, + max_tokens_per_msa: int = 2**14, + ): + super().__init__() + self.embedding_dim = embedding_dim + self.ffn_embedding_dim = ffn_embedding_dim + self.max_tokens_per_msa = max_tokens_per_msa + self.activation_fn = nn.GELU() + self.activation_dropout_module = nn.Dropout( + activation_dropout, + ) + self.fc1 = nn.Linear(embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, embedding_dim) + + def forward(self, x): + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + return x diff --git a/proteingym/baselines/esm/esm/multihead_attention.py b/proteingym/baselines/esm/esm/multihead_attention.py new file mode 100644 index 0000000..6092903 --- /dev/null +++ b/proteingym/baselines/esm/esm/multihead_attention.py @@ -0,0 +1,508 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn +from torch.nn import Parameter +from .rotary_embedding import RotaryEmbedding + +import uuid + + +def utils_softmax(x, dim: int, onnx_trace: bool = False): + if onnx_trace: + return F.softmax(x.float(), dim=dim) + else: + return F.softmax(x, dim=dim, dtype=torch.float32) + + +class FairseqIncrementalState(object): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.init_incremental_state() + + def init_incremental_state(self): + self._incremental_state_id = str(uuid.uuid4()) + + def _get_full_incremental_state_key(self, key: str) -> str: + return "{}.{}".format(self._incremental_state_id, key) + + def get_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, + ) -> Optional[Dict[str, Optional[Tensor]]]: + """Helper for getting incremental state for an nn.Module.""" + full_key = self._get_full_incremental_state_key(key) + if incremental_state is None or full_key not in incremental_state: + return None + return incremental_state[full_key] + + def set_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + key: str, + value: Dict[str, Optional[Tensor]], + ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]: + """Helper for setting incremental state for an nn.Module.""" + if incremental_state is not None: + full_key = self._get_full_incremental_state_key(key) + incremental_state[full_key] = value + return incremental_state + + +def with_incremental_state(cls): + cls.__bases__ = (FairseqIncrementalState,) + tuple( + b for b in cls.__bases__ if b != FairseqIncrementalState + ) + return cls + + +@with_incremental_state +class MultiheadAttention(nn.Module): + """Multi-headed attention. + + See "Attention Is All You Need" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv: bool = False, + add_zero_attn: bool = False, + self_attention: bool = False, + encoder_decoder_attention: bool = False, + use_rotary_embeddings: bool = False, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim**-0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias) + self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + self.onnx_trace = False + self.rot_emb = None + if use_rotary_embeddings: + self.rot_emb = RotaryEmbedding(dim=self.head_dim) + + self.enable_torch_version = False + if hasattr(F, "multi_head_attention_forward"): + self.enable_torch_version = True + else: + self.enable_torch_version = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + + if ( + not self.rot_emb + and self.enable_torch_version + and not self.onnx_trace + and incremental_state is None + and not static_kv + # A workaround for quantization to work. Otherwise JIT compilation + # treats bias in linear module as method. + and not torch.jit.is_scripting() + and not need_head_weights + ): + assert key is not None and value is not None + return F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + torch.empty([0]), + torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout, + self.out_proj.weight, + self.out_proj.bias, + self.training, + key_padding_mask, + need_weights, + attn_mask, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + ) + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if k is not None: + k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + if v is not None: + v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + src_len = k.size(1) + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask), + ], + dim=1, + ) + + if self.rot_emb: + q, k = self.rot_emb(q, k) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf") + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils_softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = F.dropout( + attn_weights_float.type_as(attn_weights), + p=self.dropout, + training=self.training, + ) + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).type_as(attn).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + elif key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1) + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + @torch.jit.export + def reorder_incremental_state( + self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + input_buffer_k = input_buffer[k] + if input_buffer_k is not None: + if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size( + 0 + ): + break + input_buffer[k] = input_buffer_k.index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + items_to_add = {} + keys_to_remove = [] + for k in state_dict.keys(): + if k.endswith(prefix + "in_proj_weight"): + # in_proj_weight used to be q + k + v with same dimensions + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] + items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] + items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] + + keys_to_remove.append(k) + + k_bias = prefix + "in_proj_bias" + if k_bias in state_dict.keys(): + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] + items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][dim : 2 * dim] + items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] + + keys_to_remove.append(prefix + "in_proj_bias") + + for k in keys_to_remove: + del state_dict[k] + + for key, value in items_to_add.items(): + state_dict[key] = value diff --git a/proteingym/baselines/esm/esm/pretrained.py b/proteingym/baselines/esm/esm/pretrained.py new file mode 100644 index 0000000..c39c0ad --- /dev/null +++ b/proteingym/baselines/esm/esm/pretrained.py @@ -0,0 +1,417 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +import urllib +import warnings +from argparse import Namespace +from pathlib import Path + +import torch +# sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.esm import esm +from .model.esm2 import ESM2 + + +def _has_regression_weights(model_name): + """Return whether we expect / require regression weights; + Right now that is all models except ESM-1v and ESM-IF""" + return not ("esm1v" in model_name or "esm_if" in model_name) + + +def load_model_and_alphabet(model_name): + if model_name.endswith(".pt"): # treat as filepath + return load_model_and_alphabet_local(model_name) + else: + return load_model_and_alphabet_hub(model_name) + + +def load_hub_workaround(url): + try: + data = torch.hub.load_state_dict_from_url(url, progress=False, map_location="cpu") + except RuntimeError: + # Pytorch version issue - see https://github.com/pytorch/pytorch/issues/43106 + fn = Path(url).name + data = torch.load( + f"{torch.hub.get_dir()}/checkpoints/{fn}", + map_location="cpu", + ) + except urllib.error.HTTPError as e: + raise Exception(f"Could not load {url}, check if you specified a correct model name?") + return data + + +def load_regression_hub(model_name): + url = f"https://dl.fbaipublicfiles.com/fair-esm/regression/{model_name}-contact-regression.pt" + regression_data = load_hub_workaround(url) + return regression_data + + +def _download_model_and_regression_data(model_name): + url = f"https://dl.fbaipublicfiles.com/fair-esm/models/{model_name}.pt" + model_data = load_hub_workaround(url) + if _has_regression_weights(model_name): + regression_data = load_regression_hub(model_name) + else: + regression_data = None + return model_data, regression_data + + +def load_model_and_alphabet_hub(model_name): + model_data, regression_data = _download_model_and_regression_data(model_name) + return load_model_and_alphabet_core(model_name, model_data, regression_data) + + +def load_model_and_alphabet_local(model_location): + """Load from local path. The regression weights need to be co-located""" + model_location = Path(model_location) + model_data = torch.load(str(model_location), map_location="cpu") + model_name = model_location.stem + #if _has_regression_weights(model_name): + # regression_location = str(model_location.with_suffix("")) + "-contact-regression.pt" + # regression_data = torch.load(regression_location, map_location="cpu") + #else: + regression_data = None + return load_model_and_alphabet_core(model_name, model_data, regression_data) + + +def has_emb_layer_norm_before(model_state): + """Determine whether layer norm needs to be applied before the encoder""" + return any(k.startswith("emb_layer_norm_before") for k, param in model_state.items()) + + +def _load_model_and_alphabet_core_v1(model_data): + from proteingym.baselines.esm import esm # since esm.inverse_folding is imported below, you actually have to re-import esm here + alphabet = esm.Alphabet.from_architecture(model_data["args"].arch) + if model_data["args"].arch == "roberta_large": + # upgrade state dict + pra = lambda s: "".join(s.split("encoder_")[1:] if "encoder" in s else s) + prs1 = lambda s: "".join(s.split("encoder.")[1:] if "encoder" in s else s) + prs2 = lambda s: "".join( + s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s + ) + model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} + model_state = {prs1(prs2(arg[0])): arg[1] for arg in model_data["model"].items()} + model_state["embed_tokens.weight"][alphabet.mask_idx].zero_() # For token drop + model_args["emb_layer_norm_before"] = has_emb_layer_norm_before(model_state) + model_type = esm.ProteinBertModel + + elif model_data["args"].arch == "protein_bert_base": + + # upgrade state dict + pra = lambda s: "".join(s.split("decoder_")[1:] if "decoder" in s else s) + prs = lambda s: "".join(s.split("decoder.")[1:] if "decoder" in s else s) + model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} + model_state = {prs(arg[0]): arg[1] for arg in model_data["model"].items()} + model_type = esm.ProteinBertModel + elif model_data["args"].arch == "msa_transformer": + + # upgrade state dict + pra = lambda s: "".join(s.split("encoder_")[1:] if "encoder" in s else s) + prs1 = lambda s: "".join(s.split("encoder.")[1:] if "encoder" in s else s) + prs2 = lambda s: "".join( + s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s + ) + prs3 = lambda s: s.replace("row", "column") if "row" in s else s.replace("column", "row") + model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()} + model_state = {prs1(prs2(prs3(arg[0]))): arg[1] for arg in model_data["model"].items()} + if model_args.get("embed_positions_msa", False): + emb_dim = model_state["msa_position_embedding"].size(-1) + model_args["embed_positions_msa_dim"] = emb_dim # initial release, bug: emb_dim==1 + + model_type = esm.MSATransformer + + elif "invariant_gvp" in model_data["args"].arch: + import esm.inverse_folding + + model_type = esm.inverse_folding.gvp_transformer.GVPTransformerModel + model_args = vars(model_data["args"]) # convert Namespace -> dict + + def update_name(s): + # Map the module names in checkpoints trained with internal code to + # the updated module names in open source code + s = s.replace("W_v", "embed_graph.embed_node") + s = s.replace("W_e", "embed_graph.embed_edge") + s = s.replace("embed_scores.0", "embed_confidence") + s = s.replace("embed_score.", "embed_graph.embed_confidence.") + s = s.replace("seq_logits_projection.", "") + s = s.replace("embed_ingraham_features", "embed_dihedrals") + s = s.replace("embed_gvp_in_local_frame.0", "embed_gvp_output") + s = s.replace("embed_features_in_local_frame.0", "embed_gvp_input_features") + return s + + model_state = { + update_name(sname): svalue + for sname, svalue in model_data["model"].items() + if "version" not in sname + } + + else: + raise ValueError("Unknown architecture selected") + + model = model_type( + Namespace(**model_args), + alphabet, + ) + + return model, alphabet, model_state + + +def _load_model_and_alphabet_core_v2(model_data): + def upgrade_state_dict(state_dict): + """Removes prefixes 'model.encoder.sentence_encoder.' and 'model.encoder.'.""" + prefixes = ["encoder.sentence_encoder.", "encoder."] + pattern = re.compile("^" + "|".join(prefixes)) + state_dict = {pattern.sub("", name): param for name, param in state_dict.items()} + return state_dict + + cfg = model_data["cfg"]["model"] + state_dict = model_data["model"] + state_dict = upgrade_state_dict(state_dict) + alphabet = esm.data.Alphabet.from_architecture("ESM-1b") + model = ESM2( + num_layers=cfg.encoder_layers, + embed_dim=cfg.encoder_embed_dim, + attention_heads=cfg.encoder_attention_heads, + alphabet=alphabet, + token_dropout=cfg.token_dropout, + ) + return model, alphabet, state_dict + + +def load_model_and_alphabet_core(model_name, model_data, regression_data=None): + if regression_data is not None: + model_data["model"].update(regression_data["model"]) + if model_name.startswith("esm2"): + model, alphabet, model_state = _load_model_and_alphabet_core_v2(model_data) + else: + model, alphabet, model_state = _load_model_and_alphabet_core_v1(model_data) + + expected_keys = set(model.state_dict().keys()) + found_keys = set(model_state.keys()) + + if regression_data is None: + expected_missing = {"contact_head.regression.weight", "contact_head.regression.bias"} + error_msgs = [] + missing = (expected_keys - found_keys) - expected_missing + if missing: + error_msgs.append(f"Missing key(s) in state_dict: {missing}.") + unexpected = found_keys - expected_keys + if unexpected: + error_msgs.append(f"Unexpected key(s) in state_dict: {unexpected}.") + + if error_msgs: + raise RuntimeError( + "Error(s) in loading state_dict for {}:\n\t{}".format( + model.__class__.__name__, "\n\t".join(error_msgs) + ) + ) + if expected_missing - found_keys: + warnings.warn( + "Regression weights not found, predicting contacts will not produce correct results." + ) + + model.load_state_dict(model_state, strict=regression_data is not None) + + return model, alphabet + + +def esm1_t34_670M_UR50S(): + """34 layer transformer model with 670M params, trained on Uniref50 Sparse. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1_t34_670M_UR50S") + + +def esm1_t34_670M_UR50D(): + """34 layer transformer model with 670M params, trained on Uniref50 Dense. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1_t34_670M_UR50D") + + +def esm1_t34_670M_UR100(): + """34 layer transformer model with 670M params, trained on Uniref100. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1_t34_670M_UR100") + + +def esm1_t12_85M_UR50S(): + """12 layer transformer model with 85M params, trained on Uniref50 Sparse. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1_t12_85M_UR50S") + + +def esm1_t6_43M_UR50S(): + """6 layer transformer model with 43M params, trained on Uniref50 Sparse. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1_t6_43M_UR50S") + + +def esm1b_t33_650M_UR50S(): + """33 layer transformer model with 650M params, trained on Uniref50 Sparse. + This is our best performing model, which will be described in a future publication. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1b_t33_650M_UR50S") + + +def esm_msa1_t12_100M_UR50S(): + warnings.warn( + "This model had a minor bug in the positional embeddings, " + "please use ESM-MSA-1b: esm.pretrained.esm_msa1b_t12_100M_UR50S()", + ) + return load_model_and_alphabet_hub("esm_msa1_t12_100M_UR50S") + + +def esm_msa1b_t12_100M_UR50S(): + return load_model_and_alphabet_hub("esm_msa1b_t12_100M_UR50S") + + +def esm1v_t33_650M_UR90S(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 1 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_1") + + +def esm1v_t33_650M_UR90S_1(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 1 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_1") + + +def esm1v_t33_650M_UR90S_2(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 2 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_2") + + +def esm1v_t33_650M_UR90S_3(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 3 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_3") + + +def esm1v_t33_650M_UR90S_4(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 4 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_4") + + +def esm1v_t33_650M_UR90S_5(): + """33 layer transformer model with 650M params, trained on Uniref90. + This is model 5 of a 5 model ensemble. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm1v_t33_650M_UR90S_5") + + +def esm_if1_gvp4_t16_142M_UR50(): + """Inverse folding model with 142M params, with 4 GVP-GNN layers, 8 + Transformer encoder layers, and 8 Transformer decoder layers, trained on + CATH structures and 12 million alphafold2 predicted structures from UniRef50 + sequences. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm_if1_gvp4_t16_142M_UR50") + + +def esm2_t6_8M_UR50D(): + """6 layer ESM-2 model with 8M params, trained on UniRef50. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t6_8M_UR50D") + + +def esm2_t12_35M_UR50D(): + """12 layer ESM-2 model with 35M params, trained on UniRef50. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t12_35M_UR50D") + + +def esm2_t30_150M_UR50D(): + """30 layer ESM-2 model with 150M params, trained on UniRef50. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t30_150M_UR50D") + + +def esm2_t33_650M_UR50D(): + """33 layer ESM-2 model with 650M params, trained on UniRef50. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t33_650M_UR50D") + + +def esm2_t36_3B_UR50D(): + """36 layer ESM-2 model with 3B params, trained on UniRef50. + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t36_3B_UR50D") + + +def esm2_t48_15B_UR50D(): + """48 layer ESM-2 model with 15B params, trained on UniRef50. + If you have OOM while loading this model, please refer to README + on how to employ FSDP and ZeRO CPU offloading + + Returns a tuple of (Model, Alphabet). + """ + return load_model_and_alphabet_hub("esm2_t48_15B_UR50D") + + +def esmfold_v0(): + """ + ESMFold v0 model with 3B ESM-2, 48 folding blocks. + This version was used for the paper (Lin et al, 2022). It was trained + on all PDB chains until 2020-05, to ensure temporal holdout with CASP14 + and the CAMEO validation and test set reported there. + """ + import esm.esmfold.v1.pretrained + return esm.esmfold.v1.pretrained.esmfold_v0() + + +def esmfold_v1(): + """ + ESMFold v1 model using 3B ESM-2, 48 folding blocks. + ESMFold provides fast high accuracy atomic level structure prediction + directly from the individual sequence of a protein. ESMFold uses the ESM2 + protein language model to extract meaningful representations from the + protein sequence. + """ + import esm.esmfold.v1.pretrained + return esm.esmfold.v1.pretrained.esmfold_v1() diff --git a/proteingym/baselines/esm/esm/rotary_embedding.py b/proteingym/baselines/esm/esm/rotary_embedding.py new file mode 100644 index 0000000..e862196 --- /dev/null +++ b/proteingym/baselines/esm/esm/rotary_embedding.py @@ -0,0 +1,69 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch + + +def rotate_half(x): + x1, x2 = x.chunk(2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(x, cos, sin): + cos = cos[:, : x.shape[-2], :] + sin = sin[:, : x.shape[-2], :] + + return (x * cos) + (rotate_half(x) * sin) + + +class RotaryEmbedding(torch.nn.Module): + """ + The rotary position embeddings from RoFormer_ (Su et. al). + A crucial insight from the method is that the query and keys are + transformed by rotation matrices which depend on the relative positions. + Other implementations are available in the Rotary Transformer repo_ and in + GPT-NeoX_, GPT-NeoX was an inspiration + .. _RoFormer: https://arxiv.org/abs/2104.09864 + .. _repo: https://github.com/ZhuiyiTechnology/roformer + .. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox + .. warning: Please note that this embedding is not registered on purpose, as it is transformative + (it does not create the embedding dimension) and will likely be picked up (imported) on a ad-hoc basis + """ + + def __init__(self, dim: int, *_, **__): + super().__init__() + # Generate and save the inverse frequency buffer (non trainable) + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + + self._seq_len_cached = None + self._cos_cached = None + self._sin_cached = None + + def _update_cos_sin_tables(self, x, seq_dimension=1): + seq_len = x.shape[seq_dimension] + + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if seq_len != self._seq_len_cached or self._cos_cached.device != x.device: + self._seq_len_cached = seq_len + t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + + self._cos_cached = emb.cos()[None, :, :] + self._sin_cached = emb.sin()[None, :, :] + + return self._cos_cached, self._sin_cached + + def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2) + + return ( + apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached), + apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached), + ) diff --git a/proteingym/baselines/esm/esm/version.py b/proteingym/baselines/esm/esm/version.py new file mode 100644 index 0000000..0b33889 --- /dev/null +++ b/proteingym/baselines/esm/esm/version.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +version = "2.0.1" diff --git a/proteingym/baselines/gemme/compute_fitness.py b/proteingym/baselines/gemme/compute_fitness.py new file mode 100644 index 0000000..41e7e9d --- /dev/null +++ b/proteingym/baselines/gemme/compute_fitness.py @@ -0,0 +1,122 @@ +import pandas as pd +import os +import argparse +import subprocess +import sys + +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from utils.scoring_utils import set_mutant_offset, undo_mutant_offset +""" +Run GEMME on selected DMS and saves fitness scores +Note that GEMME has JET2 as a dependency (and psiblast if used for sequence search) +and requires installations of java, python2, and R +GEMME also assumes that the first sequence of the alignment is the query sequence +""" +if __name__ == "__main__": + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + parser = argparse.ArgumentParser(description='Tranception scoring') + #We may pass in all required information about the DMS via the provided reference files, or specify all relevant fields manually + parser.add_argument('--DMS_reference_file_path', default=None, type=str, help='Path to reference file with list of DMS to score') + parser.add_argument('--DMS_index', default=0, type=int, help='Index of DMS assay in reference file') + #Fields to be passed manually if reference file is not used + parser.add_argument('--DMS_file_name', default=None, type=str, help='Name of DMS assay file') + parser.add_argument('--MSA_filename', default=None, type=str, help='Name of MSA (eg., a2m) file constructed on the wild type sequence') + parser.add_argument('--MSA_start', default=None, type=int, help='Sequence position that the MSA starts at (1-indexing)') + parser.add_argument('--MSA_end', default=None, type=int, help='Sequence position that the MSA ends at (1-indexing)') + parser.add_argument('--DMS_data_folder', type=str, help='Path to folder that contains all DMS assay datasets') + parser.add_argument('--output_scores_folder', default='./', type=str, help='Name of folder to write model scores to') + parser.add_argument('--MSA_folder', default='.', type=str, help='Path to MSA for neighborhood scoring') + + # GEMME parameters + parser.add_argument("--temp_folder", default="./gemme_tmp", type=str, help="Path to temporary folder to store intermediate files") + parser.add_argument("--GEMME_path", default="/n/groups/marks/software/GEMME/GEMME", type=str, help="Path to GEMME installation") + parser.add_argument("--JET_path", default="/n/groups/marks/software/JET2/JET2", type=str, help="Path to JET2 installation") + parser.add_argument("--nseqs", type=int, default=20000) + args = parser.parse_args() + + if not os.path.isdir(args.temp_folder): + os.mkdir(args.temp_folder) + + if args.DMS_reference_file_path: + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Compute scores for DMS: "+str(DMS_id)) + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + MSA_data_file = args.MSA_folder + os.sep + mapping_protein_seq_DMS["MSA_filename"][args.DMS_index] if args.MSA_folder is not None else None + MSA_start = mapping_protein_seq_DMS["MSA_start"][args.DMS_index] + MSA_end = mapping_protein_seq_DMS["MSA_end"][args.DMS_index] + else: + target_seq=args.target_seq + DMS_file_name=args.DMS_file_name + DMS_id = DMS_file_name.split(".")[0] + MSA_data_file = args.MSA_folder + os.sep + args.MSA_filename if args.MSA_folder is not None else None + MSA_start = args.MSA_start + MSA_end = args.MSA_end + # This is necessary because GEMME splits filenames and things on periods and underscores and dashes and things, so we need to remove those to avoid + # any filepath/name issues + condensed_DMS_id = DMS_id.replace("_","").replace(".","") + if not os.path.isdir(args.temp_folder + os.sep + condensed_DMS_id): + os.mkdir(args.temp_folder + os.sep + condensed_DMS_id) + full_temp_folder = args.temp_folder + os.sep + condensed_DMS_id + # get mutant files in right format for GEMME + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name) + # mutant_temp_file = DMS_id.replace(".","-").replace("_","@") + "_mutants.txt" + mutant_temp_file = condensed_DMS_id + "_mutants.txt" + if "mutant" not in DMS_data.columns: + raise ValueError("DMS data file must contain a column named 'mutant' with the mutant sequences to score") + env = os.environ.copy() + env["GEMME_PATH"] = args.GEMME_path + env["JET_PATH"] = args.JET_path + with open(full_temp_folder + os.sep + mutant_temp_file, "w+") as mutant_file: + for mutant in DMS_data["mutant"]: + offset_mutant = set_mutant_offset(mutant, MSA_start) + mutant_csv = offset_mutant.replace(":", ",") + mutant_file.write(mutant_csv + "\n") + + # Converting alignment to uppercase if necessary, only uppercases lines that are not headers + MSA_upper_file = condensed_DMS_id + "_MSA_upper.txt" + if MSA_data_file is not None: + with open(MSA_data_file, "r") as f: + lines = f.readlines() + with open(full_temp_folder + os.sep + MSA_upper_file, "w") as f: + for i,line in enumerate(lines): + if line[0] == ">": + # if i == 0: + # f.write(">" + condensed_DMS_id + "\n") + # else: + if not "/" in line: + newline = line.split("\t")[0].replace("_","").replace(".","").rstrip() + "/" + str(MSA_start) + "-" + str(MSA_end) + "\n" + f.write(newline) + else: + f.write(line.replace("_","").replace(".","")) + else: + f.write(line.upper()) + else: + raise ValueError("MSA data file must be provided to run GEMME") + + # run GEMME using subprocess + command = f"python2 {args.GEMME_path}/gemme.py {MSA_upper_file} -r input -f {MSA_upper_file} -m {mutant_temp_file} -N {args.nseqs}" + print(command) + proc_obj = subprocess.run(command, shell=True, env=env, cwd=full_temp_folder, check=True) + # parse output files + # find file with suffix _evolCombi.txt + for file in os.listdir(full_temp_folder): + if file.endswith("_evolCombi.txt"): + evol_combi_file = file + break + else: + raise ValueError("GEMME output file not found") + score_df = pd.read_csv(full_temp_folder + os.sep + evol_combi_file, sep=" ") + score_df = score_df.reset_index().rename(columns={"index":"mutant","x":"GEMME_score"}) + if not type(score_df["mutant"][0]) == str: + print("Weird R dataframe conversion error inside GEMME, remapping mutants to dataframe") + score_df["mutant"] = DMS_data["mutant"] + else: + score_df["mutant"] = score_df["mutant"].apply(undo_mutant_offset, MSA_start=MSA_start) + score_df["mutant"] = score_df["mutant"].apply(lambda x: x.replace(",",":")) + DMS_data_merged = pd.merge(DMS_data, score_df, on="mutant", how="left") + DMS_data_merged.to_csv(args.output_scores_folder + os.sep + DMS_id + ".csv", index=False) + os.system(f"rm -rf {full_temp_folder}") \ No newline at end of file diff --git a/proteingym/baselines/progen2/compute_fitness.py b/proteingym/baselines/progen2/compute_fitness.py new file mode 100644 index 0000000..0da3de4 --- /dev/null +++ b/proteingym/baselines/progen2/compute_fitness.py @@ -0,0 +1,170 @@ +import os +import argparse +import tqdm +import json + +from scipy.stats import spearmanr +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss + +from tokenizers import Tokenizer +from models.progen.modeling_progen import ProGenForCausalLM + + +######################################################################## +# model + +def create_model(ckpt, fp16): + if fp16: + return ProGenForCausalLM.from_pretrained(ckpt, revision='float16', torch_dtype=torch.float16, low_cpu_mem_usage=True) + else: + return ProGenForCausalLM.from_pretrained(ckpt) + + +def create_tokenizer_custom(file): + with open(file, 'r') as f: + return Tokenizer.from_str(f.read()) + +######################################################################## +# fitness + +def calc_fitness(model, prots, tokenizer, device='cuda:0', model_context_len=1024, fp16=False, reduction='mean'): + loss_list = [] + loss_fn = CrossEntropyLoss() + with torch.no_grad(): + with torch.cuda.amp.autocast(enabled=fp16): + for prot in tqdm.tqdm(prots): + loss_val = 0 + + sequence_chunks=[] + if len(prot) < model_context_len: + sequence_chunks = [prot] + else: + len_target_seq = len(prot) + num_windows = 1 + int( len_target_seq / model_context_len) + start=0 + for window_index in range(1, num_windows+1): + sequence_chunks.append(prot[start:start+model_context_len]) + start += model_context_len + + for chunk in sequence_chunks: + for p in [chunk, chunk[::-1]]: + ids = torch.tensor(tokenizer.encode(p).ids).to(device) + + input_ids = ids[:-1] + targets = ids[1:] + + logits=model(input_ids).logits + + # remove terminals + bos_token, eos_token = 3, 4 + if targets[-1] in [bos_token, eos_token]: + logits = logits[:-1, ...] + targets = targets[:-1] + assert (targets == bos_token).sum() == 0 + assert (targets == eos_token).sum() == 0 + + # remove unused logits + first_token, last_token = 5, 29 + logits = logits[:, first_token:(last_token+1)] + targets = targets - first_token + + assert logits.shape[1] == (last_token - first_token + 1) + + loss = loss_fn(target=targets.view(-1), input=logits.view(-1,logits.size(-1))) + loss_val += - loss.item() + + loss_val /= 2.0 #normalizing for mirroring + + if reduction=='mean': + loss_val /= len(prot) #average by seq length + + loss_list += [loss_val] + return np.array(loss_list) + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab="ACDEFGHIKLMNPQRSTVWY"): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "1"+"".join(mutated_seq)+"2" + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + + models_151M = [ 'progen2-small' ] + models_754M = [ 'progen2-medium', 'progen2-oas', 'progen2-base' ] + models_2B = [ 'progen2-large', 'progen2-BFD90' ] + models_6B = [ 'progen2-xlarge' ] + models = models_151M + models_754M + models_2B + models_6B + + parser = argparse.ArgumentParser(description='Tranception scoring') + parser.add_argument('--Progen2_model_name_or_path', default="/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/progen2/progen2-small", type=str, help='Name of or path to Progen2 model') + parser.add_argument('--DMS_reference_file_path', default='/home/pn73/Tranception/proteingym/ProteinGym_reference_file_substitutions.csv', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_data_folder', default='/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/Tranception_open_source/DMS_files/ProteinGym_substitutions', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_index', type=int, help='Path of DMS folder') + parser.add_argument('--output_scores_folder', default=None, type=str, help='Name of folder to write model scores to') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + parser.add_argument('--fp16', action='store_true', help='Whether to score sequences with half precision') + parser.add_argument('--test', action='store_true', help='Test mode of fitness computation') + args = parser.parse_args() + + model = create_model(ckpt=args.Progen2_model_name_or_path, fp16=args.fp16).cuda() + config = json.load(open(args.Progen2_model_name_or_path+os.sep+'config.json',"r")) + print("Maximum context length: {}".format(config['n_positions'])) + + dir_path = os.path.dirname(os.path.realpath(__file__)) + tokenizer_path = os.path.join(dir_path, 'tokenizer.json') + tokenizer = create_tokenizer_custom(file=tokenizer_path) + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Computing scores for: {} with Progen2: {}".format(DMS_id, args.Progen2_model_name_or_path)) + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + if not args.indel_mode and "mutated_sequence" not in DMS_data.columns: + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: get_mutated_sequence(target_seq, x)) # if not args.indel_mode else DMS_data['mutant'].map(lambda x: "1"+x+"2") + + if args.test: + x_uniref90bfd30 = '2GFLPFRGADEGLAAREAATLAARGTAARAYREDSWAVPVPRGLLGDLTARVAALGAASPPPADPLAVTLDLHHVTAEVALTTVLDAATLVHGQTRVLSAEDAAEAATAAAAATEAYLERLQDFVLFMSASVRVWRRGNAAGATGPEWDQWYTVADRDALGSAPTHLAVLGRQADALCHFVLDRVAWGTCGTPLWSGDEDLGNVVATFAGYADRLATAPRDLIM1' + x_oas = '1EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMHWVRQAPWKGLEYVSAISSNGGSTYYANSVKGRFTISRDNSKNTLYLQMGSLRAEDMAVYYCARDESGYSYGWGYYFDYWGQGTLVTVSS2' + x_bfd90 = '1TAPRSTRASGSEGSRPPGIPAKGRRCLPSRAGSVTPRFRHARQGTATVAKEQGRKLIASNRKARHDYHIEDTFEAGLVLTGTEVKSLRMGRASLIDGYAVFYGEELWLEGVHIPEYLNGNWTNHTPRRRRKLLLNRSELTKLAHKTSESGHTIVPLALYFKDGRAKVEIAVAKGKKAYDKRHALRERQDQREV2' + model_size = args.Progen2_model_name_or_path.split('/')[-1] + print("Model: {}".format(model_size)) + checkpoint_x_ll = { + 'progen2-small': (x_uniref90bfd30, -2.4), + 'progen2-medium': (x_uniref90bfd30, -1.9), + 'progen2-base': (x_uniref90bfd30, -1.9), + 'progen2-large': (x_uniref90bfd30, -1.8), + 'progen2-xlarge': (x_uniref90bfd30, -1.0), + } + model_scores = calc_fitness(model=model, prots=np.array([checkpoint_x_ll[model_size][0]]), tokenizer=tokenizer, fp16=args.fp16, reduction='sum') + print(model_scores, checkpoint_x_ll[model_size][1], abs(model_scores - checkpoint_x_ll[model_size][1])) + assert abs(model_scores - checkpoint_x_ll[model_size][1]) < 0.1 + + model_scores = calc_fitness(model=model, prots=np.array(DMS_data['mutated_sequence']), model_context_len=int(config['n_positions']), tokenizer=tokenizer, fp16=args.fp16) + + DMS_data['Progen2_score']=model_scores + scoring_filename = args.output_scores_folder+os.sep+DMS_id+'.csv' + DMS_data[['mutant','Progen2_score','DMS_score']].to_csv(scoring_filename, index=False) + +if __name__ == '__main__': + main() diff --git a/proteingym/baselines/progen2/models/progen/configuration_progen.py b/proteingym/baselines/progen2/models/progen/configuration_progen.py new file mode 100644 index 0000000..d32f5a6 --- /dev/null +++ b/proteingym/baselines/progen2/models/progen/configuration_progen.py @@ -0,0 +1,87 @@ +# coding=utf-8 +# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Modified configuration implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/configuration_gptj.py + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class ProGenConfig(PretrainedConfig): + model_type = "progen" + + def __init__( + self, + vocab_size=50400, + n_positions=2048, + n_ctx=2048, + n_embd=4096, + n_layer=28, + n_head=16, + rotary_dim=64, + n_inner=None, + activation_function="gelu_new", + resid_pdrop=0.0, + embd_pdrop=0.0, + attn_pdrop=0.0, + layer_norm_epsilon=1e-5, + initializer_range=0.02, + scale_attn_weights=True, + gradient_checkpointing=False, + use_cache=True, + bos_token_id=50256, + eos_token_id=50256, + **kwargs + ): + super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) + + self.vocab_size = vocab_size + self.n_ctx = n_ctx + self.n_positions = n_positions + self.n_embd = n_embd + self.n_layer = n_layer + self.n_head = n_head + self.n_inner = n_inner + self.rotary_dim = rotary_dim + self.activation_function = activation_function + self.resid_pdrop = resid_pdrop + self.embd_pdrop = embd_pdrop + self.attn_pdrop = attn_pdrop + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_range = initializer_range + self.gradient_checkpointing = gradient_checkpointing + self.scale_attn_weights = scale_attn_weights + self.use_cache = use_cache + + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + + @property + def max_position_embeddings(self): + return self.n_positions + + @property + def hidden_size(self): + return self.n_embd + + @property + def num_attention_heads(self): + return self.n_head + + @property + def num_hidden_layers(self): + return self.n_layer \ No newline at end of file diff --git a/proteingym/baselines/progen2/models/progen/modeling_progen.py b/proteingym/baselines/progen2/models/progen/modeling_progen.py new file mode 100644 index 0000000..48873c4 --- /dev/null +++ b/proteingym/baselines/progen2/models/progen/modeling_progen.py @@ -0,0 +1,687 @@ +# coding=utf-8 +# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Modified forward-pass implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py + +from typing import Tuple + +import numpy as np + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.utils.model_parallel_utils import assert_device_map, get_device_map +from .configuration_progen import ProGenConfig + + +logger = logging.get_logger(__name__) + + +def fixed_pos_embedding(x, seq_dim=1, seq_len=None): + dim = x.shape[-1] + if seq_len is None: + seq_len = x.shape[seq_dim] + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim)) + sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(seq_len), inv_freq).to(x.device).float() + return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp) + + +def rotate_every_two(x): + x1 = x[:, :, :, ::2] + x2 = x[:, :, :, 1::2] + x = torch.stack((-x2, x1), axis=-1) + return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)') + + +def apply_rotary_pos_emb(x, sincos, offset=0): + sin, cos = map(lambda t: t[None, offset : x.shape[1] + offset, None, :].repeat_interleave(2, 3), sincos) + # einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2) + return (x * cos) + (rotate_every_two(x) * sin) + + +class ProGenAttention(nn.Module): + def __init__(self, config): + super().__init__() + + max_positions = config.max_position_embeddings + self.register_buffer( + "bias", + torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( + 1, 1, max_positions, max_positions + ), + ) + self.register_buffer("masked_bias", torch.tensor(-1e9)) + + self.attn_dropout = nn.Dropout(config.attn_pdrop) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + self.embed_dim = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_attention_heads + if self.head_dim * self.num_attention_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and `num_attention_heads`: {self.num_attention_heads})." + ) + self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()) + self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False) + + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.rotary_dim = None + if config.rotary_dim is not None: + self.rotary_dim = config.rotary_dim + + def _split_heads(self, x, n_head, dim_head, mp_num): + reshaped = x.reshape(x.shape[:-1] + (n_head//mp_num, dim_head)) + reshaped = reshaped.reshape(x.shape[:-2] + (-1, ) + reshaped.shape[-1:]) + return reshaped + + def _merge_heads(self, tensor, num_attention_heads, attn_head_size): + """ + Merges attn_head_size dim and num_attn_heads dim into n_ctx + """ + if len(tensor.shape) == 5: + tensor = tensor.permute(0, 1, 3, 2, 4).contiguous() + elif len(tensor.shape) == 4: + tensor = tensor.permute(0, 2, 1, 3).contiguous() + else: + raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}") + new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,) + return tensor.view(new_shape) + + def _attn( + self, + query, + key, + value, + attention_mask=None, + head_mask=None, + ): + + # compute causal mask from causal mask buffer + query_length, key_length = query.size(-2), key.size(-2) + causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] + + # Keep the attention weights computation in fp32 to avoid overflow issues + query = query.to(torch.float32) + key = key.to(torch.float32) + + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + attn_weights = attn_weights / self.scale_attn + attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)) + + if attention_mask is not None: + # Apply the attention mask + attn_weights = attn_weights + attention_mask + + attn_weights = nn.Softmax(dim=-1)(attn_weights) + attn_weights = attn_weights.to(value.dtype) + attn_weights = self.attn_dropout(attn_weights) + + # Mask heads if we want to + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + + return attn_output, attn_weights + + def forward( + self, + hidden_states, + attention_mask=None, + layer_past=None, + head_mask=None, + use_cache=False, + output_attentions=False, + ): + + qkv = self.qkv_proj(hidden_states) + # TODO(enijkamp): factor out number of logical TPU-v3/v4 cores or make forward pass agnostic + # mp_num = 4 + mp_num = 8 + qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1)) + + local_dim = self.head_dim * self.num_attention_heads // mp_num + query, value, key = torch.split(qkv_split, local_dim, dim=-1) + query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num) + key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num) + + value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num) + value = value.permute(0, 2, 1, 3) + + seq_len = key.shape[1] + offset = 0 + + if layer_past is not None: + offset = layer_past[0].shape[-2] + seq_len += offset + + if self.rotary_dim is not None: + k_rot = key[:, :, :, : self.rotary_dim] + k_pass = key[:, :, :, self.rotary_dim :] + + q_rot = query[:, :, :, : self.rotary_dim] + q_pass = query[:, :, :, self.rotary_dim :] + + sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len) + k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset) + q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=offset) + + key = torch.cat([k_rot, k_pass], dim=-1) + query = torch.cat([q_rot, q_pass], dim=-1) + else: + sincos = fixed_pos_embedding(key, 1, seq_len=seq_len) + key = apply_rotary_pos_emb(key, sincos, offset=offset) + query = apply_rotary_pos_emb(query, sincos, offset=offset) + + key = key.permute(0, 2, 1, 3) + query = query.permute(0, 2, 1, 3) + + if layer_past is not None: + past_key = layer_past[0] + past_value = layer_past[1] + key = torch.cat((past_key, key), dim=-2) + value = torch.cat((past_value, value), dim=-2) + + if use_cache is True: + present = (key, value) + else: + present = None + + # compute self-attention: V x Softmax(QK^T) + attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) + + attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim) + + attn_output = self.out_proj(attn_output) + attn_output = self.resid_dropout(attn_output) + + outputs = (attn_output, present) + if output_attentions: + outputs += (attn_weights,) + + return outputs # a, present, (attentions) + + +class ProGenMLP(nn.Module): + def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim + super().__init__() + embed_dim = config.n_embd + + self.fc_in = nn.Linear(embed_dim, intermediate_size) + self.fc_out = nn.Linear(intermediate_size, embed_dim) + + self.act = ACT2FN[config.activation_function] + self.dropout = nn.Dropout(config.resid_pdrop) + + def forward(self, hidden_states): + hidden_states = self.fc_in(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.fc_out(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class ProGenBlock(nn.Module): + def __init__(self, config): + super().__init__() + inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd + self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) + self.attn = ProGenAttention(config) + self.mlp = ProGenMLP(inner_dim, config) + + def forward( + self, + hidden_states, + layer_past=None, + attention_mask=None, + head_mask=None, + use_cache=False, + output_attentions=False, + ): + residual = hidden_states + hidden_states = self.ln_1(hidden_states) + attn_outputs = self.attn( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + attn_output = attn_outputs[0] # output_attn: a, present, (attentions) + outputs = attn_outputs[1:] + + feed_forward_hidden_states = self.mlp(hidden_states) + hidden_states = attn_output + feed_forward_hidden_states + residual + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs # hidden_states, present, (attentions) + + +class ProGenPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ProGenConfig + base_model_prefix = "transformer" + is_parallelizable = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, (nn.Linear,)): + # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +class ProGenModel(ProGenPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embed_dim = config.n_embd + self.vocab_size = config.vocab_size + self.wte = nn.Embedding(config.vocab_size, self.embed_dim) + self.drop = nn.Dropout(config.embd_pdrop) + self.h = nn.ModuleList([ProGenBlock(config) for _ in range(config.n_layer)]) + self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) + self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads) + self.init_weights() + + # Model parallel + self.model_parallel = False + self.device_map = None + + + def parallelize(self, device_map=None): + # Check validity of device_map + self.device_map = ( + get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map + ) + assert_device_map(self.device_map, len(self.h)) + self.model_parallel = True + self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) + self.last_device = "cuda:" + str(max(self.device_map.keys())) + self.wte = self.wte.to(self.first_device) + # Load onto devices + for k, v in self.device_map.items(): + for block in v: + cuda_device = "cuda:" + str(k) + self.h[block] = self.h[block].to(cuda_device) + # ln_f to last + self.ln_f = self.ln_f.to(self.last_device) + + + def deparallelize(self): + self.model_parallel = False + self.device_map = None + self.first_device = "cpu" + self.last_device = "cpu" + self.wte = self.wte.to("cpu") + for index in range(len(self.h)): + self.h[index] = self.h[index].to("cpu") + self.ln_f = self.ln_f.to("cpu") + torch.cuda.empty_cache() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + past_length = past_key_values[0][0].size(-2) + + if position_ids is None: + position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + # Attention mask. + if attention_mask is not None: + assert batch_size > 0, "batch_size has to be defined and > 0" + attention_mask = attention_mask.view(batch_size, -1) + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask[:, None, None, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * -10000.0 + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x num_attention_heads x N x N + # head_mask has shape n_layer x batch x num_attention_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + + hidden_states = inputs_embeds + + if token_type_ids is not None: + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + + # Model parallel + if self.model_parallel: + torch.cuda.set_device(hidden_states.device) + # Ensure layer_past is on same device as hidden_states (might not be correct) + if layer_past is not None: + layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) + # Ensure that attention_mask is always on the same device as hidden_states + if attention_mask is not None: + attention_mask = attention_mask.to(hidden_states.device) + if isinstance(head_mask, torch.Tensor): + head_mask = head_mask.to(hidden_states.device) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " + "`use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask[i], + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + # Model Parallel: If it's the last layer for that device, put things on the next device + if self.model_parallel: + for k, v in self.device_map.items(): + if i == v[-1] and "cuda:" + str(k) != self.last_device: + hidden_states = hidden_states.to("cuda:" + str(k + 1)) + + hidden_states = self.ln_f(hidden_states) + + hidden_states = hidden_states.view(*output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ProGenForCausalLM(ProGenPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head\.weight"] + + def __init__(self, config): + super().__init__(config) + self.transformer = ProGenModel(config) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size) + self.init_weights() + + # Model parallel + self.model_parallel = False + self.device_map = None + + def parallelize(self, device_map=None): + self.device_map = ( + get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) + if device_map is None + else device_map + ) + assert_device_map(self.device_map, len(self.transformer.h)) + self.transformer.parallelize(self.device_map) + self.lm_head = self.lm_head.to(self.transformer.first_device) + self.model_parallel = True + + def deparallelize(self): + self.transformer.deparallelize() + self.transformer = self.transformer.to("cpu") + self.lm_head = self.lm_head.to("cpu") + self.model_parallel = False + torch.cuda.empty_cache() + + def get_output_embeddings(self): + return None + + def set_output_embeddings(self, new_embeddings): + return + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to + ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]`` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + # Set device for model parallelism + if self.model_parallel: + torch.cuda.set_device(self.transformer.first_device) + hidden_states = hidden_states.to(self.lm_head.weight.device) + + # make sure sampling in fp16 works correctly and + # compute loss in fp32 to match with mesh-tf version + # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179 + lm_logits = self.lm_head(hidden_states).to(torch.float32) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) \ No newline at end of file diff --git a/proteingym/baselines/progen2/tokenizer.json b/proteingym/baselines/progen2/tokenizer.json new file mode 100644 index 0000000..1230f29 --- /dev/null +++ b/proteingym/baselines/progen2/tokenizer.json @@ -0,0 +1,91 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "special": true, + "content": "<|pad|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 1, + "special": true, + "content": "<|bos|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 2, + "special": true, + "content": "<|eos|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + } + ], + "normalizer": null, + "pre_tokenizer": { + "type": "ByteLevel", + "add_prefix_space": false, + "trim_offsets": true + }, + "post_processor": { + "type": "ByteLevel", + "add_prefix_space": true, + "trim_offsets": true + }, + "decoder": { + "type": "ByteLevel", + "add_prefix_space": true, + "trim_offsets": true + }, + "model": { + "type": "BPE", + "dropout": null, + "unk_token": null, + "continuing_subword_prefix": null, + "end_of_word_suffix": null, + "fuse_unk": false, + "vocab": { + "<|pad|>": 0, + "<|bos|>": 1, + "<|eos|>": 2, + "1": 3, + "2": 4, + "A": 5, + "B": 6, + "C": 7, + "D": 8, + "E": 9, + "F": 10, + "G": 11, + "H": 12, + "I": 13, + "K": 14, + "L": 15, + "M": 16, + "N": 17, + "O": 18, + "P": 19, + "Q": 20, + "R": 21, + "S": 22, + "T": 23, + "U": 24, + "V": 25, + "W": 26, + "X": 27, + "Y": 28, + "Z": 29 + }, + "merges": [] + } + } \ No newline at end of file diff --git a/proteingym/baselines/protein_mpnn/compute_fitness.py b/proteingym/baselines/protein_mpnn/compute_fitness.py new file mode 100755 index 0000000..4d8c0b5 --- /dev/null +++ b/proteingym/baselines/protein_mpnn/compute_fitness.py @@ -0,0 +1,275 @@ +import argparse +import os.path +import pandas as pd + + +def main(args): + + import json, time, os, sys, glob + import shutil + import warnings + import numpy as np + import torch + from torch import optim + from torch.utils.data import DataLoader + from torch.utils.data.dataset import random_split, Subset + import copy + import torch.nn as nn + import torch.nn.functional as F + import random + import os.path + import subprocess + + from protein_mpnn_utils import loss_nll, loss_smoothed, gather_edges, gather_nodes, gather_nodes_t, cat_neighbors_nodes, _scores, _S_to_seq, tied_featurize, parse_PDB, parse_fasta + from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN + + if args.seed: + seed=args.seed + else: + seed=int(np.random.randint(0, high=999, size=1, dtype=int)[0]) + + torch.manual_seed(seed) + random.seed(seed) + np.random.seed(seed) + + hidden_dim = 128 + num_layers = 3 + + mapping_df = pd.read_csv(args.DMS_reference_file_path) + DMS_id = mapping_df.iloc[args.DMS_index]['DMS_id'] + DMS_filename = mapping_df.iloc[args.DMS_index]['DMS_filename'] + output_filename = args.output_scores_folder + os.sep + DMS_id + ".csv" + pdb_file = args.structure_folder + os.sep + mapping_df.iloc[args.DMS_index]['pdb_file'] + if not os.path.exists(args.output_scores_folder): + os.makedirs(args.output_scores_folder) + + checkpoint_path = args.checkpoint + + NUM_BATCHES = args.num_seq_per_target//args.batch_size + BATCH_COPIES = args.batch_size + temperatures = [float(item) for item in args.sampling_temp.split()] + omit_AAs_list = args.omit_AAs + alphabet = 'ACDEFGHIKLMNPQRSTVWYX' + alphabet_dict = dict(zip(alphabet, range(21))) + print_all = args.suppress_print == 0 + omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32) + device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") + if os.path.isfile(args.chain_id_jsonl): + with open(args.chain_id_jsonl, 'r') as json_file: + json_list = list(json_file) + for json_str in json_list: + chain_id_dict = json.loads(json_str) + else: + chain_id_dict = None + if print_all: + print(40*'-') + print('chain_id_jsonl is NOT loaded') + + if os.path.isfile(args.fixed_positions_jsonl): + with open(args.fixed_positions_jsonl, 'r') as json_file: + json_list = list(json_file) + for json_str in json_list: + fixed_positions_dict = json.loads(json_str) + else: + if print_all: + print(40*'-') + print('fixed_positions_jsonl is NOT loaded') + fixed_positions_dict = None + + + if os.path.isfile(args.pssm_jsonl): + with open(args.pssm_jsonl, 'r') as json_file: + json_list = list(json_file) + pssm_dict = {} + for json_str in json_list: + pssm_dict.update(json.loads(json_str)) + else: + if print_all: + print(40*'-') + print('pssm_jsonl is NOT loaded') + pssm_dict = None + + + if os.path.isfile(args.omit_AA_jsonl): + with open(args.omit_AA_jsonl, 'r') as json_file: + json_list = list(json_file) + for json_str in json_list: + omit_AA_dict = json.loads(json_str) + else: + if print_all: + print(40*'-') + print('omit_AA_jsonl is NOT loaded') + omit_AA_dict = None + + + if os.path.isfile(args.bias_AA_jsonl): + with open(args.bias_AA_jsonl, 'r') as json_file: + json_list = list(json_file) + for json_str in json_list: + bias_AA_dict = json.loads(json_str) + else: + if print_all: + print(40*'-') + print('bias_AA_jsonl is NOT loaded') + bias_AA_dict = None + + + if os.path.isfile(args.tied_positions_jsonl): + with open(args.tied_positions_jsonl, 'r') as json_file: + json_list = list(json_file) + for json_str in json_list: + tied_positions_dict = json.loads(json_str) + else: + if print_all: + print(40*'-') + print('tied_positions_jsonl is NOT loaded') + tied_positions_dict = None + + + if os.path.isfile(args.bias_by_res_jsonl): + with open(args.bias_by_res_jsonl, 'r') as json_file: + json_list = list(json_file) + + for json_str in json_list: + bias_by_res_dict = json.loads(json_str) + if print_all: + print('bias by residue dictionary is loaded') + else: + if print_all: + print(40*'-') + print('bias by residue dictionary is not loaded, or not provided') + bias_by_res_dict = None + + + if print_all: + print(40*'-') + bias_AAs_np = np.zeros(len(alphabet)) + if bias_AA_dict: + for n, AA in enumerate(alphabet): + if AA in list(bias_AA_dict.keys()): + bias_AAs_np[n] = bias_AA_dict[AA] + + pdb_dict_list = parse_PDB(pdb_file, ca_only=False) + dataset_valid = StructureDatasetPDB(pdb_dict_list, truncate=None, max_length=args.max_length) + all_chain_list = [item[-1:] for item in list(pdb_dict_list[0]) if item[:9]=='seq_chain'] #['A','B', 'C',...] + if args.pdb_path_chains: + designed_chain_list = [str(item) for item in args.pdb_path_chains.split()] + else: + designed_chain_list = all_chain_list + fixed_chain_list = [letter for letter in all_chain_list if letter not in designed_chain_list] + chain_id_dict = {} + chain_id_dict[pdb_dict_list[0]['name']]= (designed_chain_list, fixed_chain_list) + + checkpoint = torch.load(checkpoint_path, map_location=device) + noise_level_print = checkpoint['noise_level'] + model = ProteinMPNN(ca_only=False, num_letters=21, node_features=hidden_dim, edge_features=hidden_dim, hidden_dim=hidden_dim, num_encoder_layers=num_layers, num_decoder_layers=num_layers, augment_eps=args.backbone_noise, k_neighbors=checkpoint['num_edges']) + model.to(device) + model.load_state_dict(checkpoint['model_state_dict']) + model.eval() + + if print_all: + print(40*'-') + print('Number of edges:', checkpoint['num_edges']) + print(f'Training noise level: {noise_level_print}A') + + # Timing + start_time = time.time() + total_residues = 0 + protein_list = [] + total_step = 0 + with torch.no_grad(): + test_sum, test_weights = 0., 0. + dms_df = pd.read_csv(args.DMS_data_folder + os.sep + DMS_filename) + # For loop should iterate once for our case (not using multiple structures/proteins) + if len(dataset_valid) > 1: + raise NotImplementedError("This script is meant for a single protein in dataset_valid. Has not yet been extended to multiple proteins") + for ix, protein in enumerate(dataset_valid): + score_list = [] + global_score_list = [] + all_probs_list = [] + all_log_probs_list = [] + S_sample_list = [] + batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)] + X, S, mask, lengths, chain_M, chain_encoding_all, chain_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef, pssm_bias, pssm_log_odds_all, bias_by_res_all, tied_beta = tied_featurize(batch_clones, device, chain_id_dict, fixed_positions_dict, omit_AA_dict, tied_positions_dict, pssm_dict, bias_by_res_dict, ca_only=False) + pssm_log_odds_mask = (pssm_log_odds_all > args.pssm_threshold).float() + name_ = batch_clones[0]['name'] + loop_c = 0 + fasta_names, fasta_seqs = dms_df["mutant"].tolist(), dms_df["mutated_sequence"].tolist() + loop_c = len(fasta_seqs) + global_score_list = [] + global_seq_list = [] + for fc in range(loop_c): + native_score_list = [] + global_native_score_list = [] + input_seq_length = len(fasta_seqs[fc]) + S_input = torch.tensor([alphabet_dict[AA] for AA in fasta_seqs[fc]], device=device)[None,:].repeat(X.shape[0], 1) + S[:,:input_seq_length] = S_input #assumes that S and S_input are alphabetically sorted for masked_chains + for j in range(NUM_BATCHES): + randn_1 = torch.randn(chain_M.shape, device=X.device) + log_probs = model(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1) + mask_for_loss = mask*chain_M*chain_M_pos + scores = _scores(S, log_probs, mask_for_loss) + native_score = scores.cpu().data.numpy() + native_score_list.append(native_score) + global_scores = _scores(S, log_probs, mask) + global_native_score = global_scores.cpu().data.numpy() + global_native_score_list.append(global_native_score) + native_score = np.concatenate(native_score_list, 0) + global_native_score = np.concatenate(global_native_score_list, 0) + ns_mean = native_score.mean() + ns_mean_print = np.format_float_positional(np.float32(ns_mean), unique=False, precision=4) + ns_std = native_score.std() + ns_std_print = np.format_float_positional(np.float32(ns_std), unique=False, precision=4) + + global_ns_mean = global_native_score.mean() + global_ns_mean_print = np.format_float_positional(np.float32(global_ns_mean), unique=False, precision=4) + global_ns_std = global_native_score.std() + global_ns_std_print = np.format_float_positional(np.float32(global_ns_std), unique=False, precision=4) + + ns_sample_size = native_score.shape[0] + seq_str = _S_to_seq(S[0,], chain_M[0,]) + global_score_list.append(-1*global_native_score[0]) + global_seq_list.append(seq_str) + if print_all: + if fc == 0: + print(f'Score for {name_} from PDB, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size}, global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}') + else: + print(f'Score for {name_}_{fc} from FASTA, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size}, global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}') + # sanity check + assert all([global_seq_list[i] == fasta_seqs[i] for i in range(len(global_seq_list))]) + score_df = pd.DataFrame({'mutant': fasta_names, 'mutated_sequence': fasta_seqs, 'pmpnn_ll': global_score_list}) + score_df.to_csv(output_filename, index=False) + +if __name__ == "__main__": + argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + argparser.add_argument('--DMS_reference_file_path',type=str,help='path to DMS reference file') + argparser.add_argument('--DMS_data_folder',type=str,help="path to folder containing DMS data") + argparser.add_argument('--structure_folder',type=str,help='folder containing pdb files for each DMS') + argparser.add_argument('--DMS_index',type=int,help='index of DMS in DMS reference file') + argparser.add_argument('--checkpoint',type=str,help='path to model') + argparser.add_argument("--suppress_print", type=int, default=0, help="0 for False, 1 for True") + argparser.add_argument("--seed", type=int, default=0, help="If set to 0 then a random seed will be picked;") + argparser.add_argument("--backbone_noise", type=float, default=0.00, help="Standard deviation of Gaussian noise to add to backbone atoms") + argparser.add_argument("--num_seq_per_target", type=int, default=1, help="Number of sequences to generate per target") + argparser.add_argument("--batch_size", type=int, default=1, help="Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory") + argparser.add_argument("--max_length", type=int, default=200000, help="Max sequence length") + argparser.add_argument("--sampling_temp", type=str, default="0.1", help="A string of temperatures, 0.2 0.25 0.5. Sampling temperature for amino acids. Suggested values 0.1, 0.15, 0.2, 0.25, 0.3. Higher values will lead to more diversity.") + argparser.add_argument("--output_scores_folder", type=str, help="Path to a folder to output sequences, e.g. /home/out/") + argparser.add_argument("--pdb_path", type=str, default='', help="Path to a single PDB to be designed") + argparser.add_argument("--pdb_path_chains", type=str, default='', help="Define which chains need to be designed for a single PDB ") + argparser.add_argument("--jsonl_path", type=str, help="Path to a folder with parsed pdb into jsonl") + argparser.add_argument("--chain_id_jsonl",type=str, default='', help="Path to a dictionary specifying which chains need to be designed and which ones are fixed, if not specied all chains will be designed.") + argparser.add_argument("--fixed_positions_jsonl", type=str, default='', help="Path to a dictionary with fixed positions") + argparser.add_argument("--omit_AAs", type=list, default='X', help="Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.") + argparser.add_argument("--bias_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies AA composion bias if neededi, e.g. {A: -1.1, F: 0.7} would make A less likely and F more likely.") + argparser.add_argument("--bias_by_res_jsonl", default='', help="Path to dictionary with per position bias.") + argparser.add_argument("--omit_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies which amino acids need to be omited from design at specific chain indices") + argparser.add_argument("--pssm_jsonl", type=str, default='', help="Path to a dictionary with pssm") + argparser.add_argument("--pssm_multi", type=float, default=0.0, help="A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions") + argparser.add_argument("--pssm_threshold", type=float, default=0.0, help="A value between -inf + inf to restric per position AAs") + argparser.add_argument("--pssm_log_odds_flag", type=int, default=0, help="0 for False, 1 for True") + argparser.add_argument("--pssm_bias_flag", type=int, default=0, help="0 for False, 1 for True") + argparser.add_argument("--tied_positions_jsonl", type=str, default='', help="Path to a dictionary with tied positions") + + args = argparser.parse_args() + main(args) diff --git a/proteingym/baselines/protein_mpnn/protein_mpnn_utils.py b/proteingym/baselines/protein_mpnn/protein_mpnn_utils.py new file mode 100755 index 0000000..a8fd008 --- /dev/null +++ b/proteingym/baselines/protein_mpnn/protein_mpnn_utils.py @@ -0,0 +1,1383 @@ +from __future__ import print_function +import json, time, os, sys, glob +import shutil +import numpy as np +import torch +from torch import optim +from torch.utils.data import DataLoader +from torch.utils.data.dataset import random_split, Subset + +import copy +import torch.nn as nn +import torch.nn.functional as F +import random +import itertools + +#A number of functions/classes are adopted from: https://github.com/jingraham/neurips19-graph-protein-design + +def parse_fasta(filename,limit=-1, omit=[]): + header = [] + sequence = [] + lines = open(filename, "r") + for line in lines: + line = line.rstrip() + if line[0] == ">": + if len(header) == limit: + break + header.append(line[1:]) + sequence.append([]) + else: + if omit: + line = [item for item in line if item not in omit] + line = ''.join(line) + line = ''.join(line) + sequence[-1].append(line) + lines.close() + sequence = [''.join(seq) for seq in sequence] + return np.array(header), np.array(sequence) + +def _scores(S, log_probs, mask): + """ Negative log probabilities """ + criterion = torch.nn.NLLLoss(reduction='none') + loss = criterion( + log_probs.contiguous().view(-1,log_probs.size(-1)), + S.contiguous().view(-1) + ).view(S.size()) + scores = torch.sum(loss * mask, dim=-1) / torch.sum(mask, dim=-1) + return scores + +def _S_to_seq(S, mask): + alphabet = 'ACDEFGHIKLMNPQRSTVWYX' + seq = ''.join([alphabet[c] for c, m in zip(S.tolist(), mask.tolist()) if m > 0]) + return seq + +def parse_PDB_biounits(x, atoms=['N','CA','C'], chain=None): + ''' + input: x = PDB filename + atoms = atoms to extract (optional) + output: (length, atoms, coords=(x,y,z)), sequence + ''' + + alpha_1 = list("ARNDCQEGHILKMFPSTWYV-") + states = len(alpha_1) + alpha_3 = ['ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE', + 'LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL','GAP'] + + aa_1_N = {a:n for n,a in enumerate(alpha_1)} + aa_3_N = {a:n for n,a in enumerate(alpha_3)} + aa_N_1 = {n:a for n,a in enumerate(alpha_1)} + aa_1_3 = {a:b for a,b in zip(alpha_1,alpha_3)} + aa_3_1 = {b:a for a,b in zip(alpha_1,alpha_3)} + + def AA_to_N(x): + # ["ARND"] -> [[0,1,2,3]] + x = np.array(x); + if x.ndim == 0: x = x[None] + return [[aa_1_N.get(a, states-1) for a in y] for y in x] + + def N_to_AA(x): + # [[0,1,2,3]] -> ["ARND"] + x = np.array(x); + if x.ndim == 1: x = x[None] + return ["".join([aa_N_1.get(a,"-") for a in y]) for y in x] + + xyz,seq,min_resn,max_resn = {},{},1e6,-1e6 + for line in open(x,"rb"): + line = line.decode("utf-8","ignore").rstrip() + + if line[:6] == "HETATM" and line[17:17+3] == "MSE": + line = line.replace("HETATM","ATOM ") + line = line.replace("MSE","MET") + + if line[:4] == "ATOM": + ch = line[21:22] + if ch == chain or chain is None: + atom = line[12:12+4].strip() + resi = line[17:17+3] + resn = line[22:22+5].strip() + x,y,z = [float(line[i:(i+8)]) for i in [30,38,46]] + + if resn[-1].isalpha(): + resa,resn = resn[-1],int(resn[:-1])-1 + else: + resa,resn = "",int(resn)-1 +# resn = int(resn) + if resn < min_resn: + min_resn = resn + if resn > max_resn: + max_resn = resn + if resn not in xyz: + xyz[resn] = {} + if resa not in xyz[resn]: + xyz[resn][resa] = {} + if resn not in seq: + seq[resn] = {} + if resa not in seq[resn]: + seq[resn][resa] = resi + + if atom not in xyz[resn][resa]: + xyz[resn][resa][atom] = np.array([x,y,z]) + + # convert to numpy arrays, fill in missing values + seq_,xyz_ = [],[] + try: + for resn in range(min_resn,max_resn+1): + if resn in seq: + for k in sorted(seq[resn]): seq_.append(aa_3_N.get(seq[resn][k],20)) + else: seq_.append(20) + if resn in xyz: + for k in sorted(xyz[resn]): + for atom in atoms: + if atom in xyz[resn][k]: xyz_.append(xyz[resn][k][atom]) + else: xyz_.append(np.full(3,np.nan)) + else: + for atom in atoms: xyz_.append(np.full(3,np.nan)) + return np.array(xyz_).reshape(-1,len(atoms),3), N_to_AA(np.array(seq_)) + except TypeError: + return 'no_chain', 'no_chain' + +def parse_PDB(path_to_pdb, input_chain_list=None, ca_only=False): + c=0 + pdb_dict_list = [] + init_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G','H', 'I', 'J','K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T','U', 'V','W','X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j','k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't','u', 'v','w','x', 'y', 'z'] + extra_alphabet = [str(item) for item in list(np.arange(300))] + chain_alphabet = init_alphabet + extra_alphabet + + if input_chain_list: + chain_alphabet = input_chain_list + + + biounit_names = [path_to_pdb] + for biounit in biounit_names: + my_dict = {} + s = 0 + concat_seq = '' + concat_N = [] + concat_CA = [] + concat_C = [] + concat_O = [] + concat_mask = [] + coords_dict = {} + for letter in chain_alphabet: + if ca_only: + sidechain_atoms = ['CA'] + else: + sidechain_atoms = ['N', 'CA', 'C', 'O'] + xyz, seq = parse_PDB_biounits(biounit, atoms=sidechain_atoms, chain=letter) + if type(xyz) != str: + concat_seq += seq[0] + my_dict['seq_chain_'+letter]=seq[0] + coords_dict_chain = {} + if ca_only: + coords_dict_chain['CA_chain_'+letter]=xyz.tolist() + else: + coords_dict_chain['N_chain_' + letter] = xyz[:, 0, :].tolist() + coords_dict_chain['CA_chain_' + letter] = xyz[:, 1, :].tolist() + coords_dict_chain['C_chain_' + letter] = xyz[:, 2, :].tolist() + coords_dict_chain['O_chain_' + letter] = xyz[:, 3, :].tolist() + my_dict['coords_chain_'+letter]=coords_dict_chain + s += 1 + fi = biounit.rfind("/") + my_dict['name']=biounit[(fi+1):-4] + my_dict['num_of_chains'] = s + my_dict['seq'] = concat_seq + if s <= len(chain_alphabet): + pdb_dict_list.append(my_dict) + c+=1 + return pdb_dict_list + + + +def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None, ca_only=False): + """ Pack and pad batch into torch tensors """ + alphabet = 'ACDEFGHIKLMNPQRSTVWYX' + B = len(batch) + lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths + L_max = max([len(b['seq']) for b in batch]) + if ca_only: + X = np.zeros([B, L_max, 1, 3]) + else: + X = np.zeros([B, L_max, 4, 3]) + residue_idx = -100*np.ones([B, L_max], dtype=np.int32) + chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted + pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted + pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted + pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted + chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted + bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32) + chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted + S = np.zeros([B, L_max], dtype=np.int32) + omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32) + # Build the batch + letter_list_list = [] + visible_list_list = [] + masked_list_list = [] + masked_chain_length_list_list = [] + tied_pos_list_of_lists_list = [] + for i, b in enumerate(batch): + if chain_dict != None: + masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F] + else: + masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_'] + visible_chains = [] + masked_chains.sort() #sort masked_chains + visible_chains.sort() #sort visible_chains + all_chains = masked_chains + visible_chains + for i, b in enumerate(batch): + mask_dict = {} + a = 0 + x_chain_list = [] + chain_mask_list = [] + chain_seq_list = [] + chain_encoding_list = [] + c = 1 + letter_list = [] + global_idx_start_list = [0] + visible_list = [] + masked_list = [] + masked_chain_length_list = [] + fixed_position_mask_list = [] + omit_AA_mask_list = [] + pssm_coef_list = [] + pssm_bias_list = [] + pssm_log_odds_list = [] + bias_by_res_list = [] + l0 = 0 + l1 = 0 + for step, letter in enumerate(all_chains): + if letter in visible_chains: + letter_list.append(letter) + visible_list.append(letter) + chain_seq = b[f'seq_chain_{letter}'] + chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq]) + chain_length = len(chain_seq) + global_idx_start_list.append(global_idx_start_list[-1]+chain_length) + chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary + chain_mask = np.zeros(chain_length) #0.0 for visible chains + if ca_only: + x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff + if len(x_chain.shape) == 2: + x_chain = x_chain[:,None,:] + else: + x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] + x_chain_list.append(x_chain) + chain_mask_list.append(chain_mask) + chain_seq_list.append(chain_seq) + chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0])) + l1 += chain_length + residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1) + l0 += chain_length + c+=1 + fixed_position_mask = np.ones(chain_length) + fixed_position_mask_list.append(fixed_position_mask) + omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32) + omit_AA_mask_list.append(omit_AA_mask_temp) + pssm_coef = np.zeros(chain_length) + pssm_bias = np.zeros([chain_length, 21]) + pssm_log_odds = 10000.0*np.ones([chain_length, 21]) + pssm_coef_list.append(pssm_coef) + pssm_bias_list.append(pssm_bias) + pssm_log_odds_list.append(pssm_log_odds) + bias_by_res_list.append(np.zeros([chain_length, 21])) + if letter in masked_chains: + masked_list.append(letter) + letter_list.append(letter) + chain_seq = b[f'seq_chain_{letter}'] + chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq]) + chain_length = len(chain_seq) + global_idx_start_list.append(global_idx_start_list[-1]+chain_length) + masked_chain_length_list.append(chain_length) + chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary + chain_mask = np.ones(chain_length) #1.0 for masked + if ca_only: + x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff + if len(x_chain.shape) == 2: + x_chain = x_chain[:,None,:] + else: + x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] + x_chain_list.append(x_chain) + chain_mask_list.append(chain_mask) + chain_seq_list.append(chain_seq) + chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0])) + l1 += chain_length + residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1) + l0 += chain_length + c+=1 + fixed_position_mask = np.ones(chain_length) + if fixed_position_dict!=None: + fixed_pos_list = fixed_position_dict[b['name']][letter] + if fixed_pos_list: + fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0 + fixed_position_mask_list.append(fixed_position_mask) + omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32) + if omit_AA_dict!=None: + for item in omit_AA_dict[b['name']][letter]: + idx_AA = np.array(item[0])-1 + AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0]) + idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx]) + omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1 + omit_AA_mask_list.append(omit_AA_mask_temp) + pssm_coef = np.zeros(chain_length) + pssm_bias = np.zeros([chain_length, 21]) + pssm_log_odds = 10000.0*np.ones([chain_length, 21]) + if pssm_dict: + if pssm_dict[b['name']][letter]: + pssm_coef = pssm_dict[b['name']][letter]['pssm_coef'] + pssm_bias = pssm_dict[b['name']][letter]['pssm_bias'] + pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds'] + pssm_coef_list.append(pssm_coef) + pssm_bias_list.append(pssm_bias) + pssm_log_odds_list.append(pssm_log_odds) + if bias_by_res_dict: + bias_by_res_list.append(bias_by_res_dict[b['name']][letter]) + else: + bias_by_res_list.append(np.zeros([chain_length, 21])) + + + letter_list_np = np.array(letter_list) + tied_pos_list_of_lists = [] + tied_beta = np.ones(L_max) + if tied_positions_dict!=None: + tied_pos_list = tied_positions_dict[b['name']] + if tied_pos_list: + set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list]))) + for tied_item in tied_pos_list: + one_list = [] + for k, v in tied_item.items(): + start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]] + if isinstance(v[0], list): + for v_count in range(len(v[0])): + one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first + tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count] + else: + for v_ in v: + one_list.append(start_idx+v_-1)#make 0 to be the first + tied_pos_list_of_lists.append(one_list) + tied_pos_list_of_lists_list.append(tied_pos_list_of_lists) + + + + x = np.concatenate(x_chain_list,0) #[L, 4, 3] + all_sequence = "".join(chain_seq_list) + m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted + chain_encoding = np.concatenate(chain_encoding_list,0) + m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted + + pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted + pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted + pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted + + bias_by_res_ = np.concatenate(bias_by_res_list, 0) #[L,21], 0.0 for places where AA frequencies don't need to be tweaked + + l = len(all_sequence) + x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, )) + X[i,:,:,:] = x_pad + + m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, )) + m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, )) + omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, )) + chain_M[i,:] = m_pad + chain_M_pos[i,:] = m_pos_pad + omit_AA_mask[i,] = omit_AA_mask_pad + + chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, )) + chain_encoding_all[i,:] = chain_encoding_pad + + pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, )) + pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) + pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) + + pssm_coef_all[i,:] = pssm_coef_pad + pssm_bias_all[i,:] = pssm_bias_pad + pssm_log_odds_all[i,:] = pssm_log_odds_pad + + bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) + bias_by_res_all[i,:] = bias_by_res_pad + + # Convert to labels + indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32) + S[i, :l] = indices + letter_list_list.append(letter_list) + visible_list_list.append(visible_list) + masked_list_list.append(masked_list) + masked_chain_length_list_list.append(masked_chain_length_list) + + + isnan = np.isnan(X) + mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32) + X[isnan] = 0. + + # Conversion + pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device) + pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device) + pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device) + + tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device) + + jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32) + bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device) + phi_mask = np.pad(jumps, [[0,0],[1,0]]) + psi_mask = np.pad(jumps, [[0,0],[0,1]]) + omega_mask = np.pad(jumps, [[0,0],[0,1]]) + dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3] + dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device) + residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device) + S = torch.from_numpy(S).to(dtype=torch.long,device=device) + X = torch.from_numpy(X).to(dtype=torch.float32, device=device) + mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device) + chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device) + chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device) + omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device) + chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device) + if ca_only: + X_out = X[:,:,0] + else: + X_out = X + return X_out, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta + + + +def loss_nll(S, log_probs, mask): + """ Negative log probabilities """ + criterion = torch.nn.NLLLoss(reduction='none') + loss = criterion( + log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1) + ).view(S.size()) + loss_av = torch.sum(loss * mask) / torch.sum(mask) + return loss, loss_av + + +def loss_smoothed(S, log_probs, mask, weight=0.1): + """ Negative log probabilities """ + S_onehot = torch.nn.functional.one_hot(S, 21).float() + + # Label smoothing + S_onehot = S_onehot + weight / float(S_onehot.size(-1)) + S_onehot = S_onehot / S_onehot.sum(-1, keepdim=True) + + loss = -(S_onehot * log_probs).sum(-1) + loss_av = torch.sum(loss * mask) / torch.sum(mask) + return loss, loss_av + +class StructureDataset(): + def __init__(self, jsonl_file, verbose=True, truncate=None, max_length=100, + alphabet='ACDEFGHIKLMNPQRSTVWYX-'): + alphabet_set = set([a for a in alphabet]) + discard_count = { + 'bad_chars': 0, + 'too_long': 0, + 'bad_seq_length': 0 + } + + with open(jsonl_file) as f: + self.data = [] + + lines = f.readlines() + start = time.time() + for i, line in enumerate(lines): + entry = json.loads(line) + seq = entry['seq'] + name = entry['name'] + + # Convert raw coords to np arrays + #for key, val in entry['coords'].items(): + # entry['coords'][key] = np.asarray(val) + + # Check if in alphabet + bad_chars = set([s for s in seq]).difference(alphabet_set) + if len(bad_chars) == 0: + if len(entry['seq']) <= max_length: + if True: + self.data.append(entry) + else: + discard_count['bad_seq_length'] += 1 + else: + discard_count['too_long'] += 1 + else: + if verbose: + print(name, bad_chars, entry['seq']) + discard_count['bad_chars'] += 1 + + # Truncate early + if truncate is not None and len(self.data) == truncate: + return + + if verbose and (i + 1) % 1000 == 0: + elapsed = time.time() - start + print('{} entries ({} loaded) in {:.1f} s'.format(len(self.data), i+1, elapsed)) + if verbose: + print('discarded', discard_count) + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +class StructureDatasetPDB(): + def __init__(self, pdb_dict_list, verbose=True, truncate=None, max_length=100, + alphabet='ACDEFGHIKLMNPQRSTVWYX-'): + alphabet_set = set([a for a in alphabet]) + discard_count = { + 'bad_chars': 0, + 'too_long': 0, + 'bad_seq_length': 0 + } + + self.data = [] + + start = time.time() + for i, entry in enumerate(pdb_dict_list): + seq = entry['seq'] + name = entry['name'] + + bad_chars = set([s for s in seq]).difference(alphabet_set) + if len(bad_chars) == 0: + if len(entry['seq']) <= max_length: + self.data.append(entry) + else: + discard_count['too_long'] += 1 + else: + discard_count['bad_chars'] += 1 + + # Truncate early + if truncate is not None and len(self.data) == truncate: + return + + if verbose and (i + 1) % 1000 == 0: + elapsed = time.time() - start + + #print('Discarded', discard_count) + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + + +class StructureLoader(): + def __init__(self, dataset, batch_size=100, shuffle=True, + collate_fn=lambda x:x, drop_last=False): + self.dataset = dataset + self.size = len(dataset) + self.lengths = [len(dataset[i]['seq']) for i in range(self.size)] + self.batch_size = batch_size + sorted_ix = np.argsort(self.lengths) + + # Cluster into batches of similar sizes + clusters, batch = [], [] + batch_max = 0 + for ix in sorted_ix: + size = self.lengths[ix] + if size * (len(batch) + 1) <= self.batch_size: + batch.append(ix) + batch_max = size + else: + clusters.append(batch) + batch, batch_max = [], 0 + if len(batch) > 0: + clusters.append(batch) + self.clusters = clusters + + def __len__(self): + return len(self.clusters) + + def __iter__(self): + np.random.shuffle(self.clusters) + for b_idx in self.clusters: + batch = [self.dataset[i] for i in b_idx] + yield batch + + + +# The following gather functions +def gather_edges(edges, neighbor_idx): + # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C] + neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1)) + edge_features = torch.gather(edges, 2, neighbors) + return edge_features + +def gather_nodes(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C] + # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C] + neighbors_flat = neighbor_idx.view((neighbor_idx.shape[0], -1)) + neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + # Gather and re-pack + neighbor_features = torch.gather(nodes, 1, neighbors_flat) + neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1]) + return neighbor_features + +def gather_nodes_t(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C] + idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + neighbor_features = torch.gather(nodes, 1, idx_flat) + return neighbor_features + +def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx): + h_nodes = gather_nodes(h_nodes, E_idx) + h_nn = torch.cat([h_neighbors, h_nodes], -1) + return h_nn + + +class EncLayer(nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(EncLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(num_hidden) + self.norm2 = nn.LayerNorm(num_hidden) + self.norm3 = nn.LayerNorm(num_hidden) + + self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = nn.Linear(num_hidden, num_hidden, bias=True) + self.W11 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W12 = nn.Linear(num_hidden, num_hidden, bias=True) + self.W13 = nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None): + """ Parallel computation of full transformer layer """ + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + h_V = self.norm1(h_V + self.dropout1(dh)) + + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV))))) + h_E = self.norm3(h_E + self.dropout3(h_message)) + return h_V, h_E + + +class DecLayer(nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(DecLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(num_hidden) + self.norm2 = nn.LayerNorm(num_hidden) + + self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, mask_V=None, mask_attend=None): + """ Parallel computation of full transformer layer """ + + # Concatenate h_V_i to h_E_ij + h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_E.size(-2),-1) + h_EV = torch.cat([h_V_expand, h_E], -1) + + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + + h_V = self.norm1(h_V + self.dropout1(dh)) + + # Position-wise feedforward + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + return h_V + + + +class PositionWiseFeedForward(nn.Module): + def __init__(self, num_hidden, num_ff): + super(PositionWiseFeedForward, self).__init__() + self.W_in = nn.Linear(num_hidden, num_ff, bias=True) + self.W_out = nn.Linear(num_ff, num_hidden, bias=True) + self.act = torch.nn.GELU() + def forward(self, h_V): + h = self.act(self.W_in(h_V)) + h = self.W_out(h) + return h + +class PositionalEncodings(nn.Module): + def __init__(self, num_embeddings, max_relative_feature=32): + super(PositionalEncodings, self).__init__() + self.num_embeddings = num_embeddings + self.max_relative_feature = max_relative_feature + self.linear = nn.Linear(2*max_relative_feature+1+1, num_embeddings) + + def forward(self, offset, mask): + d = torch.clip(offset + self.max_relative_feature, 0, 2*self.max_relative_feature)*mask + (1-mask)*(2*self.max_relative_feature+1) + d_onehot = torch.nn.functional.one_hot(d, 2*self.max_relative_feature+1+1) + E = self.linear(d_onehot.float()) + return E + + + +class CA_ProteinFeatures(nn.Module): + def __init__(self, edge_features, node_features, num_positional_embeddings=16, + num_rbf=16, top_k=30, augment_eps=0., num_chain_embeddings=16): + """ Extract protein features """ + super(CA_ProteinFeatures, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + # Positional encoding + self.embeddings = PositionalEncodings(num_positional_embeddings) + # Normalization and embedding + node_in, edge_in = 3, num_positional_embeddings + num_rbf*9 + 7 + self.node_embedding = nn.Linear(node_in, node_features, bias=False) #NOT USED + self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False) + self.norm_nodes = nn.LayerNorm(node_features) + self.norm_edges = nn.LayerNorm(edge_features) + + + def _quaternions(self, R): + """ Convert a batch of 3D rotations [R] to quaternions [Q] + R [...,3,3] + Q [...,4] + """ + # Simple Wikipedia version + # en.wikipedia.org/wiki/Rotation_matrix#Quaternion + # For other options see math.stackexchange.com/questions/2074316/calculating-rotation-axis-from-rotation-matrix + diag = torch.diagonal(R, dim1=-2, dim2=-1) + Rxx, Ryy, Rzz = diag.unbind(-1) + magnitudes = 0.5 * torch.sqrt(torch.abs(1 + torch.stack([ + Rxx - Ryy - Rzz, + - Rxx + Ryy - Rzz, + - Rxx - Ryy + Rzz + ], -1))) + _R = lambda i,j: R[:,:,:,i,j] + signs = torch.sign(torch.stack([ + _R(2,1) - _R(1,2), + _R(0,2) - _R(2,0), + _R(1,0) - _R(0,1) + ], -1)) + xyz = signs * magnitudes + # The relu enforces a non-negative trace + w = torch.sqrt(F.relu(1 + diag.sum(-1, keepdim=True))) / 2. + Q = torch.cat((xyz, w), -1) + Q = F.normalize(Q, dim=-1) + return Q + + def _orientations_coarse(self, X, E_idx, eps=1e-6): + dX = X[:,1:,:] - X[:,:-1,:] + dX_norm = torch.norm(dX,dim=-1) + dX_mask = (3.6 0: + Ca = Ca + self.augment_eps * torch.randn_like(Ca) + + D_neighbors, E_idx, mask_neighbors = self._dist(Ca, mask) + + Ca_0 = torch.zeros(Ca.shape, device=Ca.device) + Ca_2 = torch.zeros(Ca.shape, device=Ca.device) + Ca_0[:,1:,:] = Ca[:,:-1,:] + Ca_1 = Ca + Ca_2[:,:-1,:] = Ca[:,1:,:] + + V, O_features = self._orientations_coarse(Ca, E_idx) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) #Ca_1-Ca_1 + RBF_all.append(self._get_rbf(Ca_0, Ca_0, E_idx)) + RBF_all.append(self._get_rbf(Ca_2, Ca_2, E_idx)) + + RBF_all.append(self._get_rbf(Ca_0, Ca_1, E_idx)) + RBF_all.append(self._get_rbf(Ca_0, Ca_2, E_idx)) + + RBF_all.append(self._get_rbf(Ca_1, Ca_0, E_idx)) + RBF_all.append(self._get_rbf(Ca_1, Ca_2, E_idx)) + + RBF_all.append(self._get_rbf(Ca_2, Ca_0, E_idx)) + RBF_all.append(self._get_rbf(Ca_2, Ca_1, E_idx)) + + + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + + offset = residue_idx[:,:,None]-residue_idx[:,None,:] + offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K] + + d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long() + E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all, O_features), -1) + + + E = self.edge_embedding(E) + E = self.norm_edges(E) + + return E, E_idx + + + + +class ProteinFeatures(nn.Module): + def __init__(self, edge_features, node_features, num_positional_embeddings=16, + num_rbf=16, top_k=30, augment_eps=0., num_chain_embeddings=16): + """ Extract protein features """ + super(ProteinFeatures, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + self.embeddings = PositionalEncodings(num_positional_embeddings) + node_in, edge_in = 6, num_positional_embeddings + num_rbf*25 + self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = nn.LayerNorm(edge_features) + + def _dist(self, X, mask, eps=1E-6): + mask_2D = torch.unsqueeze(mask,1) * torch.unsqueeze(mask,2) + dX = torch.unsqueeze(X,1) - torch.unsqueeze(X,2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1. - mask_2D) * D_max + sampled_top_k = self.top_k + D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2., 22., self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1,1,1,-1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-((D_expand - D_mu) / D_sigma)**2) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt(torch.sum((A[:,:,None,:] - B[:,None,:,:])**2,-1) + 1e-6) #[B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:,:,:,None], E_idx)[:,:,:,0] #[B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, X, mask, residue_idx, chain_labels): + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + + b = X[:,:,1,:] - X[:,:,0,:] + c = X[:,:,2,:] - X[:,:,1,:] + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + X[:,:,1,:] + Ca = X[:,:,1,:] + N = X[:,:,0,:] + C = X[:,:,2,:] + O = X[:,:,3,:] + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) #Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) #N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) #C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) #O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) #Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) #Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) #Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) #Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) #Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) #N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) #N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) #N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) #Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) #Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) #O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) #N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) #C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) #O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) #Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) #C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) #O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) #Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) #C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) #O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) #C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = residue_idx[:,:,None]-residue_idx[:,None,:] + offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K] + + d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long() #find self vs non-self interaction + E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + return E, E_idx + + + +class ProteinMPNN(nn.Module): + def __init__(self, num_letters, node_features, edge_features, + hidden_dim, num_encoder_layers=3, num_decoder_layers=3, + vocab=21, k_neighbors=64, augment_eps=0.05, dropout=0.1, ca_only=False): + super(ProteinMPNN, self).__init__() + + # Hyperparameters + self.node_features = node_features + self.edge_features = edge_features + self.hidden_dim = hidden_dim + + # Featurization layers + if ca_only: + self.features = CA_ProteinFeatures(node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps) + self.W_v = nn.Linear(node_features, hidden_dim, bias=True) + else: + self.features = ProteinFeatures(node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps) + + self.W_e = nn.Linear(edge_features, hidden_dim, bias=True) + self.W_s = nn.Embedding(vocab, hidden_dim) + + # Encoder layers + self.encoder_layers = nn.ModuleList([ + EncLayer(hidden_dim, hidden_dim*2, dropout=dropout) + for _ in range(num_encoder_layers) + ]) + + # Decoder layers + self.decoder_layers = nn.ModuleList([ + DecLayer(hidden_dim, hidden_dim*3, dropout=dropout) + for _ in range(num_decoder_layers) + ]) + self.W_out = nn.Linear(hidden_dim, num_letters, bias=True) + + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, use_input_decoding_order=False, decoding_order=None): + """ Graph-conditioned sequence model """ + device=X.device + # Prepare node and edge embeddings + E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) + h_E = self.W_e(E) + + # Encoder is unmasked self-attention + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + # Concatenate sequence embeddings for autoregressive decoder + h_S = self.W_s(S) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + + chain_M = chain_M*mask #update chain_M to include missing regions + if not use_input_decoding_order: + decoding_order = torch.argsort((chain_M+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + mask_size = E_idx.shape[1] + permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() + order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1. - mask_attend) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = F.log_softmax(logits, dim=-1) + return log_probs + + + + def sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, bias_by_res=None): + device = X.device + # Prepare node and edge embeddings + E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + + # Encoder is unmasked self-attention + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + # Decoder uses masked self-attention + chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions + decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + mask_size = E_idx.shape[1] + permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() + order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1. - mask_attend) + + N_batch, N_nodes = X.size(0), X.size(1) + log_probs = torch.zeros((N_batch, N_nodes, 21), device=device) + all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32) + h_S = torch.zeros_like(h_V, device=device) + S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))] + constant = torch.tensor(omit_AAs_np, device=device) + constant_bias = torch.tensor(bias_AAs_np, device=device) + #chain_mask_combined = chain_mask*chain_M_pos + omit_AA_mask_flag = omit_AA_mask != None + + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for t_ in range(N_nodes): + t = decoding_order[:,t_] #[B] + chain_mask_gathered = torch.gather(chain_mask, 1, t[:,None]) #[B] + mask_gathered = torch.gather(mask, 1, t[:,None]) #[B] + bias_by_res_gathered = torch.gather(bias_by_res, 1, t[:,None,None].repeat(1,1,21))[:,0,:] #[B, 21] + if (mask_gathered==0).all(): #for padded or missing regions only + S_t = torch.gather(S_true, 1, t[:,None]) + else: + # Hidden layers + E_idx_t = torch.gather(E_idx, 1, t[:,None,None].repeat(1,1,E_idx.shape[-1])) + h_E_t = torch.gather(h_E, 1, t[:,None,None,None].repeat(1,1,h_E.shape[-2], h_E.shape[-1])) + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = torch.gather(h_EXV_encoder_fw, 1, t[:,None,None,None].repeat(1,1,h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1])) + mask_t = torch.gather(mask, 1, t[:,None]) + for l, layer in enumerate(self.decoder_layers): + # Updated relational features for future states + h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) + h_V_t = torch.gather(h_V_stack[l], 1, t[:,None,None].repeat(1,1,h_V_stack[l].shape[-1])) + h_ESV_t = torch.gather(mask_bw, 1, t[:,None,None,None].repeat(1,1,mask_bw.shape[-2], mask_bw.shape[-1])) * h_ESV_decoder_t + h_EXV_encoder_t + h_V_stack[l+1].scatter_(1, t[:,None,None].repeat(1,1,h_V.shape[-1]), layer(h_V_t, h_ESV_t, mask_V=mask_t)) + # Sampling step + h_V_t = torch.gather(h_V_stack[-1], 1, t[:,None,None].repeat(1,1,h_V_stack[-1].shape[-1]))[:,0] + logits = self.W_out(h_V_t) / temperature + probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1) + if pssm_bias_flag: + pssm_coef_gathered = torch.gather(pssm_coef, 1, t[:,None])[:,0] + pssm_bias_gathered = torch.gather(pssm_bias, 1, t[:,None,None].repeat(1,1,pssm_bias.shape[-1]))[:,0] + probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered + if pssm_log_odds_flag: + pssm_log_odds_mask_gathered = torch.gather(pssm_log_odds_mask, 1, t[:,None, None].repeat(1,1,pssm_log_odds_mask.shape[-1]))[:,0] #[B, 21] + probs_masked = probs*pssm_log_odds_mask_gathered + probs_masked += probs * 0.001 + probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] + if omit_AA_mask_flag: + omit_AA_mask_gathered = torch.gather(omit_AA_mask, 1, t[:,None, None].repeat(1,1,omit_AA_mask.shape[-1]))[:,0] #[B, 21] + probs_masked = probs*(1.0-omit_AA_mask_gathered) + probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] + S_t = torch.multinomial(probs, 1) + all_probs.scatter_(1, t[:,None,None].repeat(1,1,21), (chain_mask_gathered[:,:,None,]*probs[:,None,:]).float()) + S_true_gathered = torch.gather(S_true, 1, t[:,None]) + S_t = (S_t*chain_mask_gathered+S_true_gathered*(1.0-chain_mask_gathered)).long() + temp1 = self.W_s(S_t) + h_S.scatter_(1, t[:,None,None].repeat(1,1,temp1.shape[-1]), temp1) + S.scatter_(1, t[:,None], S_t) + output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order} + return output_dict + + + def tied_sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, tied_pos=None, tied_beta=None, bias_by_res=None): + device = X.device + # Prepare node and edge embeddings + E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + # Encoder is unmasked self-attention + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + # Decoder uses masked self-attention + chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions + decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + + new_decoding_order = [] + for t_dec in list(decoding_order[0,].cpu().data.numpy()): + if t_dec not in list(itertools.chain(*new_decoding_order)): + list_a = [item for item in tied_pos if t_dec in item] + if list_a: + new_decoding_order.append(list_a[0]) + else: + new_decoding_order.append([t_dec]) + decoding_order = torch.tensor(list(itertools.chain(*new_decoding_order)), device=device)[None,].repeat(X.shape[0],1) + + mask_size = E_idx.shape[1] + permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() + order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1. - mask_attend) + + N_batch, N_nodes = X.size(0), X.size(1) + log_probs = torch.zeros((N_batch, N_nodes, 21), device=device) + all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32) + h_S = torch.zeros_like(h_V, device=device) + S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))] + constant = torch.tensor(omit_AAs_np, device=device) + constant_bias = torch.tensor(bias_AAs_np, device=device) + omit_AA_mask_flag = omit_AA_mask != None + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for t_list in new_decoding_order: + logits = 0.0 + logit_list = [] + done_flag = False + for t in t_list: + if (mask[:,t]==0).all(): + S_t = S_true[:,t] + for t in t_list: + h_S[:,t,:] = self.W_s(S_t) + S[:,t] = S_t + done_flag = True + break + else: + E_idx_t = E_idx[:,t:t+1,:] + h_E_t = h_E[:,t:t+1,:,:] + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = h_EXV_encoder_fw[:,t:t+1,:,:] + mask_t = mask[:,t:t+1] + for l, layer in enumerate(self.decoder_layers): + h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) + h_V_t = h_V_stack[l][:,t:t+1,:] + h_ESV_t = mask_bw[:,t:t+1,:,:] * h_ESV_decoder_t + h_EXV_encoder_t + h_V_stack[l+1][:,t,:] = layer(h_V_t, h_ESV_t, mask_V=mask_t).squeeze(1) + h_V_t = h_V_stack[-1][:,t,:] + logit_list.append((self.W_out(h_V_t) / temperature)/len(t_list)) + logits += tied_beta[t]*(self.W_out(h_V_t) / temperature)/len(t_list) + if done_flag: + pass + else: + bias_by_res_gathered = bias_by_res[:,t,:] #[B, 21] + probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1) + if pssm_bias_flag: + pssm_coef_gathered = pssm_coef[:,t] + pssm_bias_gathered = pssm_bias[:,t] + probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered + if pssm_log_odds_flag: + pssm_log_odds_mask_gathered = pssm_log_odds_mask[:,t] + probs_masked = probs*pssm_log_odds_mask_gathered + probs_masked += probs * 0.001 + probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] + if omit_AA_mask_flag: + omit_AA_mask_gathered = omit_AA_mask[:,t] + probs_masked = probs*(1.0-omit_AA_mask_gathered) + probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] + S_t_repeat = torch.multinomial(probs, 1).squeeze(-1) + S_t_repeat = (chain_mask[:,t]*S_t_repeat + (1-chain_mask[:,t])*S_true[:,t]).long() #hard pick fixed positions + for t in t_list: + h_S[:,t,:] = self.W_s(S_t_repeat) + S[:,t] = S_t_repeat + all_probs[:,t,:] = probs.float() + output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order} + return output_dict + + + def conditional_probs(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, backbone_only=False): + """ Graph-conditioned sequence model """ + device=X.device + # Prepare node and edge embeddings + E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) + h_V_enc = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) + h_E = self.W_e(E) + + # Encoder is unmasked self-attention + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V_enc, h_E = layer(h_V_enc, h_E, E_idx, mask, mask_attend) + + # Concatenate sequence embeddings for autoregressive decoder + h_S = self.W_s(S) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V_enc, h_EX_encoder, E_idx) + + + chain_M = chain_M*mask #update chain_M to include missing regions + + chain_M_np = chain_M.cpu().numpy() + idx_to_loop = np.argwhere(chain_M_np[0,:]==1)[:,0] + log_conditional_probs = torch.zeros([X.shape[0], chain_M.shape[1], 21], device=device).float() + + for idx in idx_to_loop: + h_V = torch.clone(h_V_enc) + order_mask = torch.zeros(chain_M.shape[1], device=device).float() + if backbone_only: + order_mask = torch.ones(chain_M.shape[1], device=device).float() + order_mask[idx] = 0. + else: + order_mask = torch.zeros(chain_M.shape[1], device=device).float() + order_mask[idx] = 1. + decoding_order = torch.argsort((order_mask[None,]+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + mask_size = E_idx.shape[1] + permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() + order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1. - mask_attend) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = F.log_softmax(logits, dim=-1) + log_conditional_probs[:,idx,:] = log_probs[:,idx,:] + return log_conditional_probs + + + def unconditional_probs(self, X, mask, residue_idx, chain_encoding_all): + """ Graph-conditioned sequence model """ + device=X.device + # Prepare node and edge embeddings + E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) + h_E = self.W_e(E) + + # Encoder is unmasked self-attention + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_V), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + order_mask_backward = torch.zeros([X.shape[0], X.shape[1], X.shape[1]], device=device) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1. - mask_attend) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for layer in self.decoder_layers: + h_V = layer(h_V, h_EXV_encoder_fw, mask) + + logits = self.W_out(h_V) + log_probs = F.log_softmax(logits, dim=-1) + return log_probs + diff --git a/proteingym/baselines/protgpt2/compute_fitness.py b/proteingym/baselines/protgpt2/compute_fitness.py new file mode 100644 index 0000000..8b9904d --- /dev/null +++ b/proteingym/baselines/protgpt2/compute_fitness.py @@ -0,0 +1,98 @@ +import os +import argparse +import tqdm + +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer +from scipy.stats import spearmanr +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss + +def calc_fitness(model, prots, tokenizer, device='cuda:0', model_context_len=1023, mirror=True): + loss_list = [] + loss_fn = CrossEntropyLoss(reduction='mean') # We compute the CrossEnt per token (to correct for variable length post-mutation) + with torch.no_grad(): + for prot in tqdm.tqdm(prots): + loss_val = 0 + + sequence_chunks=[] + if len(prot) < model_context_len: + sequence_chunks = [prot] + else: + len_target_seq = len(prot) + num_windows = 1 + int( len_target_seq / model_context_len) + start=0 + for window_index in range(1, num_windows+1): + sequence_chunks.append(prot[start:start+model_context_len]) + start += model_context_len + + for chunk in sequence_chunks: + list_proteins_directions = [chunk] if not mirror else [chunk, chunk[::-1]] + for p in list_proteins_directions: + ids = torch.tensor([tokenizer.encode(p)]).to(device) + input_ids = ids[:, :-1] + targets = ids[:, 1:] + + logits=model(input_ids).logits + loss = loss_fn(target=targets.view(-1), input=logits.view(-1,logits.size(-1))) + loss_val += -loss.item() + num_chunks = len(sequence_chunks) if not mirror else 2 * len(sequence_chunks) + loss_list += [loss_val / num_chunks] #Average the CrossEnt per token across all chunks + return np.array(loss_list) + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab="ACDEFGHIKLMNPQRSTVWY"): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + parser = argparse.ArgumentParser(description='Tranception scoring') + parser.add_argument('--ProtGPT2_model_name_or_path', default="./", type=str, help='Name of or path to ProtGPT2 model') + parser.add_argument('--DMS_reference_file_path', default='./proteingym/ProteinGym_reference_file_substitutions.csv', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_data_folder', default='./DMS_files/ProteinGym_substitutions', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_index', type=int, help='Index of DMS to score') + parser.add_argument('--output_scores_folder', default=None, type=str, help='Name of folder to write model scores to') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + args = parser.parse_args() + + model = AutoModelForCausalLM.from_pretrained(args.ProtGPT2_model_name_or_path,trust_remote_code=True) + model.cuda() + tokenizer = AutoTokenizer.from_pretrained("/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/ProtGPT2") + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Computing scores for: {} with ProtGP2: {}".format(DMS_id, args.ProtGPT2_model_name_or_path)) + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + if not args.indel_mode and 'mutated_sequence' not in DMS_data.columns: + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: get_mutated_sequence(target_seq, x)) + # if args.indel_mode: + # DMS_data['mutated_sequence'] = DMS_data['mutant'] + model_scores = calc_fitness(model=model, prots=np.array(DMS_data['mutated_sequence']), tokenizer=tokenizer) + + DMS_data['ProtGPT2_score']=model_scores + scoring_filename = args.output_scores_folder+os.sep+DMS_id+'.csv' + DMS_data[['mutated_sequence','ProtGPT2_score','DMS_score']].to_csv(scoring_filename, index=False) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/baselines/protssn/README.md b/proteingym/baselines/protssn/README.md new file mode 100644 index 0000000..9983e0f --- /dev/null +++ b/proteingym/baselines/protssn/README.md @@ -0,0 +1,81 @@ +# ProtSSN + +## Environment + +Please make sure you have installed **[Anaconda3](https://www.anaconda.com/download)** or **[Miniconda3](https://docs.conda.io/projects/miniconda/en/latest/)**. +The `torch_geometric` package should be updated to 2.3 or higher. + +```shell +conda env create -f protssn_environment.yaml +conda activate protssn +``` + +## Model Checkpoints + +There are 9 distinct models that can be used, separately or via ensembling. +If you only want to only use **one model**, authors recommend using **k20_h512**. + +| # Version | # Param | # Link | +| --------- | ------- | ------------------------------------------------------------ | +| k10_h512 | 148 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k10_h512.pt | +| k10_h768 | 160 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k10_h768.pt | +| k10_h1280 | 184 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k10_h1280.pt | +| k20_h512 | 148 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k20_h512.pt | +| k20_h768 | 160 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k20_h768.pt | +| k20_h1280 | 184 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k20_h1280.pt | +| k30_h512 | 148 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k30_h512.pt | +| k30_h768 | 160 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k30_h768.pt | +| k30_h1280 | 184 | https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k30_h1280.pt | + +```shell +mkdir model +cd model +wget https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/model/protssn_k20_h512.pt +``` + +`ProtSSN.tar` contains all the model checkpoints. The **training records** and **configs** can be found in `model/history` and `model/config`. + +```shell +wget https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/ProtSSN.model.tar +tar -xvf ProtSSN.model.tar +rm ProtSSN.model.tar +``` +## Data + +The pdb and csv files can be downloaded from https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/ProteinGym_substitutions_pdb-csv_checked.zip. +The files should be arranged as follows: + +``` +data/proteingym-benchmark +|——DATASET +|——|——Protein1 +|——|——|——Protein1.pdb +|——|——|——Protein1.tsv +|——|——Protein2 +|——|——... +``` + +- Because the model requires **structure (PDB)** as input, authors recommend using [**Alphafold**](https://github.com/google-deepmind/alphafold) for folding, or **[ESMFold](https://github.com/facebookresearch/esm)**. +- You may also search the structure for a protein of interest via its Uniprot ID in the **AlphaFold database** (https://alphafold.ebi.ac.uk/). + +## Usage + +Please refer to the scoring script under `scripts/scoring_DMS_zero_shot/scoring_ProtSSN_substitutions.sh` + +## Acknowledgements + +For more details about ProtSSN, please refer to the official [ProtSSN GitHub repo](https://github.com/tyang816/ProtSSN). + +Please cite the following paper if you use ProtSSN in your work: + +``` +@article{tan2023protssn, + title={Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability}, + author={Tan, Yang and Zhou, Bingxin and Zheng, Lirong and Fan, Guisheng and Hong, Liang}, + journal={bioRxiv}, + pages={2023--12}, + year={2023}, + publisher={Cold Spring Harbor Laboratory} +} +``` + diff --git a/proteingym/baselines/protssn/compute_fitness.py b/proteingym/baselines/protssn/compute_fitness.py new file mode 100644 index 0000000..8e914c2 --- /dev/null +++ b/proteingym/baselines/protssn/compute_fitness.py @@ -0,0 +1,190 @@ +import argparse +import json +import warnings +import torch +import os +import sys +import yaml +import numpy as np +import pandas as pd +from torch import nn +from torch_geometric.loader import DataLoader +from numpy import nan +from typing import * +from tqdm import tqdm +from scipy.stats import spearmanr +from transformers import logging +from src.models import PLM_model, GNN_model +from src.data import build_mutant_dataset +from src.utils.utils import param_num + +# set path +current_dir = os.getcwd() +sys.path.append(current_dir) +# ignore warning information +logging.set_verbosity_error() +warnings.filterwarnings("ignore") + +amino_acids_type = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', + 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'] + +def label_row(rows, sequence, token_probs, offset_idx=1): + s = [] + sep = ";" + if ":" in rows: + sep = ":" + for row in rows.split(sep): + if row.lower() == "wt": + s.append(0) + continue + try: + wt, idx, mt = row[0], int(row[1:-1]) - offset_idx, row[-1] + except: + print(f"row: {row}, sequence: {sequence}") + raise ValueError + assert sequence[idx] == wt, f"The {row}, {sequence[idx]}" + wt_encoded, mt_encoded = amino_acids_type.index(wt), amino_acids_type.index(mt) + score = token_probs[idx, mt_encoded] - token_probs[idx, wt_encoded] + score = score.item() + s.append(score) + + return sum(s) + + +def predict(args, plm_model, gnn_model, loader): + gnn_model.eval() + softmax = nn.Softmax() + result_dict = {"name": [], "count": [], args.score_name: []} + + with torch.no_grad(): + for data in loader: + protein_name = data.protein_name[0] + graph_data = plm_model(data) + out, _ = gnn_model(graph_data) + seq = "".join([amino_acids_type[i] for i in data.y]) + out = torch.log(softmax(out[:, :20]) + 1e-9) + + # check the mutant file + mutant_file_tsv = os.path.join(args.mutant_dataset_dir, "DATASET", protein_name, f"{protein_name}.tsv") + mutant_file_csv = os.path.join(args.mutant_dataset_dir, "DATASET", protein_name, f"{protein_name}.csv") + if os.path.exists(mutant_file_tsv): + mutant_df = pd.read_table(mutant_file_tsv) + elif os.path.exists(mutant_file_csv): + mutant_df = pd.read_csv(mutant_file_csv) + else: + raise ValueError(f"Invalid file: {mutant_file_tsv} or {mutant_file_csv}") + + # check the offset + if protein_name == "A0A140D2T1_ZIKV_Sourisseau_2019": + offset = 291 + else: + offset = 1 + + # label the mutant + mutant_df[args.score_name] = mutant_df[args.mutant_pos_col].apply( + lambda x: label_row(x, seq, out.cpu().numpy(), offset) + ) + result_file = os.path.join(args.output_scores_folder, protein_name + ".csv") + if not os.path.exists(result_file): + mutant_df.to_csv(result_file, index=False) + + result = pd.read_csv(result_file) + result[args.score_name] = mutant_df[args.score_name] + result.to_csv(result_file, index=False) + + # save the spearmanr score + result_dict['count'].append(len(result)) + result_dict['name'].append(protein_name) + spearmanr_score = spearmanr(result[args.mutant_score_col], result[args.score_name]).correlation + if spearmanr_score is nan: + spearmanr_score = 0 + result_dict[args.score_name].append(spearmanr_score) + + print(f">>> {protein_name}: {spearmanr_score}; mutant_num: {len(result)}") + + if args.score_info is not None: + if os.path.exists(args.score_info): + total_result = pd.read_csv(args.score_info) + total_result[args.score_name] = result_dict[args.score_name] + total_result.to_csv(args.score_info, index=False) + else: + pd.DataFrame(result_dict).to_csv(args.score_info, index=False) + + print(f">>> {args.score_name} average spearmanr: {np.mean(result_dict[args.score_name])}\n") + +def ensemble(args): + print("----------------- Ensemble -----------------") + result_files = os.listdir(args.output_scores_folder) + sp_scores = [] + for file in tqdm(result_files): + result_file = os.path.join(args.output_scores_folder, file) + result_df = pd.read_csv(result_file) + models_pred = [result_df[col].to_list() for col in result_df.columns if col.startswith("ProtSSN")] + ensemble_pred = np.mean(models_pred, axis=0) + result_df["ProtSSN_ensemble"] = ensemble_pred + result_df.to_csv(result_file, index=False) + sp_score = spearmanr(result_df[args.mutant_score_col], result_df["ProtSSN_ensemble"]).correlation + sp_scores.append(sp_score) + print(">>> Ensemble spearmanr: ", np.mean(sp_scores)) + +def prepare(args, dataset_name, k, h): + # for build dataset + args.mutant_name = f"{dataset_name}_k{k}" + mutant_dataset = build_mutant_dataset(args) + protein_names = mutant_dataset.protein_names + print(f">>> Protein names: {protein_names}") + mutant_loader = DataLoader(mutant_dataset, batch_size=1, shuffle=False) + print(f">>> Number of proteins: {len(mutant_dataset)}") + gnn_model = GNN_model(args) + print(f">>> k{k}_h{h} {param_num(gnn_model)}") + gnn_model_path = os.path.join(args.gnn_model_dir, f"protssn_k{k}_h{h}.pt") + gnn_model.load_state_dict(torch.load(gnn_model_path)) + return args, mutant_loader, gnn_model + +def create_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--gnn", type=str, default="egnn", help="gat, gcn, or egnn") + parser.add_argument("--gnn_config", type=str, default="src/config/egnn.yaml", help="gnn config") + parser.add_argument("--gnn_model_dir", type=str, default="model/", help="test model name") + parser.add_argument("--gnn_model_name", type=str, default=None, nargs="+", help="test model name") + + parser.add_argument("--plm", type=str, default="facebook/esm2_t33_650M_UR50D", help="esm param number") + parser.add_argument("--use_ensemble", action="store_true", help="use ensemble model") + + # dataset + parser.add_argument("--mutant_dataset_dir", type=str, default="data/evaluation", help="mutation dataset") + parser.add_argument("--mutant_name", type=str, default=None, help="name of mutation dataset") + parser.add_argument("--mutant_pos_col", type=str, default="mutant", help="mutation column name") + parser.add_argument("--mutant_score_col", type=str, default="DMS_score", help="the model output score column name") + + parser.add_argument("--score_info", type=str, default=None, help="the model output spearmanr score file") + parser.add_argument("--output_scores_folder", type=str, default="result/", help="the result output path") + parser.add_argument("--repo_path", type=str, default=None, help="Path to ProteinGym repo") + + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = create_parser() + args.gnn_config = yaml.load(open(args.gnn_config), Loader=yaml.FullLoader)[args.gnn] + if args.repo_path is None: args.repo_path = os.path.dirname(os.path.dirname(os.getcwd())) + plm_model = PLM_model(args) + args.plm_hidden_size = plm_model.model.config.hidden_size + dataset_name = args.mutant_dataset_dir.split("/")[-1] + os.makedirs(args.output_scores_folder, exist_ok=True) + + for gnn in args.gnn_model_name: + k, h = gnn.split("_") + k, h = int(k[1:]), int(h[1:]) + print(f"--------------- ProtSSN k{k}_h{h} ---------------") + assert k in [10, 20, 30], f"Invalid k: {k}" + assert h in [512, 768, 1280], f"Invalid h: {h}" + args.gnn_config["hidden_channels"] = h + args.c_alpha_max_neighbors = k + args.score_name = f"ProtSSN_k{k}_h{h}" + args, mutant_loader, gnn_model = prepare(args, dataset_name, k, h) + predict(args=args, plm_model=plm_model, gnn_model=gnn_model, loader=mutant_loader) + + if args.use_ensemble: + ensemble(args) diff --git a/proteingym/baselines/protssn/norm/cath_k10_mean_attr.pt b/proteingym/baselines/protssn/norm/cath_k10_mean_attr.pt new file mode 100644 index 0000000..8c3fb69 Binary files /dev/null and b/proteingym/baselines/protssn/norm/cath_k10_mean_attr.pt differ diff --git a/proteingym/baselines/protssn/norm/cath_k20_mean_attr.pt b/proteingym/baselines/protssn/norm/cath_k20_mean_attr.pt new file mode 100644 index 0000000..126555d Binary files /dev/null and b/proteingym/baselines/protssn/norm/cath_k20_mean_attr.pt differ diff --git a/proteingym/baselines/protssn/norm/cath_k30_mean_attr.pt b/proteingym/baselines/protssn/norm/cath_k30_mean_attr.pt new file mode 100644 index 0000000..47a7399 Binary files /dev/null and b/proteingym/baselines/protssn/norm/cath_k30_mean_attr.pt differ diff --git a/proteingym/baselines/protssn/protssn_environment.yaml b/proteingym/baselines/protssn/protssn_environment.yaml new file mode 100644 index 0000000..0a373cb --- /dev/null +++ b/proteingym/baselines/protssn/protssn_environment.yaml @@ -0,0 +1,298 @@ +name: protssn +channels: + - schrodinger + - bioconda + - ostrokach + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - pytorch + - nvidia + - pyg +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_kmp_llvm + - appdirs=1.4.4=pyhd3eb1b0_0 + - blas=1.0=mkl + - blessings=1.7=py310h06a4308_1002 + - brotlipy=0.7.0=py310h7f8727e_1002 + - bzip2=1.0.8=h7b6447c_0 + - c-ares=1.19.1=h5eee18b_0 + - ca-certificates=2023.08.22=h06a4308_0 + - certifi=2023.11.17=py310h06a4308_0 + - cffi=1.15.1=py310h74dc2b5_0 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cryptography=39.0.1=py310h9ce1e76_0 + - cuda-cudart=11.7.99=0 + - cuda-cupti=11.7.101=0 + - cuda-libraries=11.7.1=0 + - cuda-nvrtc=11.7.99=0 + - cuda-nvtx=11.7.91=0 + - cuda-runtime=11.7.1=0 + - curl=8.2.1=h37d81fd_0 + - deepspeed=0.9.2=cpu_py310h11dbdba_0 + - dssp=2.2.1=1 + - ffmpeg=4.3=hf484d3e_0 + - freetype=2.12.1=h4a9f257_0 + - giflib=5.2.1=h5eee18b_3 + - gmp=6.2.1=h295c915_3 + - gnutls=3.6.15=he1e5248_0 + - gpustat=0.6.0=pyhd3eb1b0_1 + - hjson-py=3.1.0=pyhd8ed1ab_0 + - idna=3.4=py310h06a4308_0 + - intel-openmp=2021.4.0=h06a4308_3561 + - jinja2=3.1.2=py310h06a4308_0 + - joblib=1.2.0=py310h06a4308_0 + - jpeg=9e=h5eee18b_1 + - krb5=1.20.1=h568e23c_1 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=3.0=h295c915_0 + - libaio=0.3.113=h5eee18b_0 + - libblas=3.9.0=12_linux64_mkl + - libcblas=3.9.0=12_linux64_mkl + - libcublas=11.10.3.66=0 + - libcufft=10.7.2.124=h4fbf590_0 + - libcufile=1.6.1.9=0 + - libcurand=10.3.2.106=0 + - libcurl=8.2.1=h91b91d3_0 + - libcusolver=11.4.0.1=0 + - libcusparse=11.7.4.91=0 + - libdeflate=1.17=h5eee18b_0 + - libedit=3.1.20221030=h5eee18b_0 + - libev=4.33=h7f8727e_1 + - libffi=3.3=he6710b0_2 + - libgcc-ng=13.1.0=he5830b7_0 + - libgfortran-ng=7.5.0=ha8ba4b0_17 + - libgfortran4=7.5.0=ha8ba4b0_17 + - libiconv=1.16=h7f8727e_2 + - libidn2=2.3.4=h5eee18b_0 + - liblapack=3.9.0=12_linux64_mkl + - libnghttp2=1.52.0=ha637b67_1 + - libnpp=11.7.4.75=0 + - libnvjpeg=11.8.0.2=0 + - libpng=1.6.39=h5eee18b_0 + - libssh2=1.10.0=h37d81fd_2 + - libstdcxx-ng=13.1.0=hfd8a6a1_0 + - libtasn1=4.19.0=h5eee18b_0 + - libtiff=4.5.0=h6a678d5_2 + - libunistring=0.9.10=h27cfd23_0 + - libuuid=1.41.5=h5eee18b_0 + - libwebp=1.2.4=h11a3e52_1 + - libwebp-base=1.2.4=h5eee18b_1 + - llvm-openmp=14.0.6=h9e868ea_0 + - lz4-c=1.9.4=h6a678d5_0 + - markupsafe=2.1.1=py310h7f8727e_0 + - mkl=2021.4.0=h06a4308_640 + - mkl-service=2.4.0=py310h7f8727e_0 + - mkl_fft=1.3.1=py310hd6ae3a3_0 + - mkl_random=1.2.2=py310h00e6091_0 + - ncurses=6.4=h6a678d5_0 + - nettle=3.7.3=hbbd107a_1 + - nvidia-ml=7.352.0=pyhd3eb1b0_0 + - openh264=2.1.1=h4ff587b_0 + - openssl=1.1.1w=h7f8727e_0 + - packaging=23.1=py310h06a4308_0 + - pillow=9.4.0=py310h6a678d5_0 + - pip=23.0.1=py310h06a4308_0 + - pooch=1.4.0=pyhd3eb1b0_0 + - psutil=5.9.0=py310h5eee18b_0 + - py-cpuinfo=8.0.0=pyhd3eb1b0_1 + - pycparser=2.21=pyhd3eb1b0_0 + - pyg=2.3.0=py310_torch_1.13.0_cu117 + - pyopenssl=23.0.0=py310h06a4308_0 + - pyparsing=3.0.9=py310h06a4308_0 + - pysocks=1.7.1=py310h06a4308_0 + - python=3.10.0=h12debd9_5 + - python_abi=3.10=2_cp310 + - pytorch=1.13.1=py3.10_cuda11.7_cudnn8.5.0_0 + - pytorch-cuda=11.7=h778d358_5 + - pytorch-mutex=1.0=cuda + - readline=8.2=h5eee18b_0 + - scikit-learn=1.2.2=py310h6a678d5_1 + - seqkit=2.5.0=h9ee0642_0 + - setuptools=66.0.0=py310h06a4308_0 + - six=1.16.0=pyhd3eb1b0_1 + - sqlite=3.41.2=h5eee18b_0 + - threadpoolctl=2.2.0=pyh0d69192_0 + - tk=8.6.12=h1ccaba5_0 + - tmalign=20170708=h0e1e685_2 + - torchaudio=0.13.1=py310_cu117 + - torchvision=0.14.1=py310_cu117 + - tqdm=4.65.0=py310h2f386ee_0 + - urllib3=1.26.15=py310h06a4308_0 + - wheel=0.38.4=py310h06a4308_0 + - xz=5.4.2=h5eee18b_0 + - zlib=1.2.13=h5eee18b_0 + - zstd=1.5.5=hc292b87_0 + - pip: + - accelerate==0.25.0 + - aiofiles==23.2.1 + - aiohttp==3.9.1 + - aiosignal==1.3.1 + - altair==5.2.0 + - annotated-types==0.6.0 + - antlr4-python3-runtime==4.9.3 + - anyio==3.7.1 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - asttokens==2.4.1 + - async-lru==2.0.4 + - async-timeout==4.0.3 + - attrs==23.1.0 + - babel==2.14.0 + - beartype==0.16.4 + - beautifulsoup4==4.12.2 + - bio==1.6.0 + - biopython==1.81 + - biothings-client==0.3.1 + - biotite==0.38.0 + - bleach==6.1.0 + - click==8.1.7 + - colorama==0.4.6 + - comm==0.2.0 + - conda-pack==0.7.1 + - contourpy==1.2.0 + - cycler==0.12.1 + - debugpy==1.8.0 + - decorator==5.1.1 + - defusedxml==0.7.1 + - dm-tree==0.1.8 + - einops==0.7.0 + - ema-pytorch==0.3.1 + - exceptiongroup==1.2.0 + - executing==2.0.1 + - fair-esm==2.0.0 + - fastapi==0.105.0 + - fastjsonschema==2.19.0 + - ffmpy==0.3.1 + - filelock==3.13.1 + - fonttools==4.45.1 + - fqdn==1.5.1 + - frozenlist==1.4.0 + - fsspec==2023.10.0 + - gemmi==0.6.4 + - gprofiler-official==1.0.0 + - gradio==4.11.0 + - gradio-client==0.7.3 + - h11==0.14.0 + - httpcore==1.0.2 + - httpx==0.25.2 + - huggingface-hub==0.19.4 + - icetk==0.0.7 + - importlib-resources==6.1.1 + - iniconfig==2.0.0 + - ipykernel==6.27.1 + - ipython==8.18.1 + - isoduration==20.11.0 + - jedi==0.19.1 + - json5==0.9.14 + - jsonpointer==2.4 + - jsonschema==4.20.0 + - jsonschema-specifications==2023.11.2 + - jupyter-client==8.6.0 + - jupyter-core==5.5.0 + - jupyter-events==0.9.0 + - jupyter-lsp==2.2.1 + - jupyter-server==2.12.1 + - jupyter-server-terminals==0.5.0 + - jupyterlab==4.0.9 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.25.2 + - kiwisolver==1.4.5 + - latex2mathml==3.77.0 + - lightning==2.1.2 + - lightning-utilities==0.10.0 + - markdown==3.5.1 + - markdown-it-py==3.0.0 + - matplotlib==3.8.2 + - matplotlib-inline==0.1.6 + - mdtex2html==1.2.0 + - mdurl==0.1.2 + - mistune==3.0.2 + - msgpack==1.0.7 + - multidict==6.0.4 + - mygene==3.2.2 + - nbclient==0.9.0 + - nbconvert==7.12.0 + - nbformat==5.9.2 + - nest-asyncio==1.5.8 + - networkx==3.2.1 + - notebook-shim==0.2.3 + - numpy==1.26.2 + - omegaconf==2.3.0 + - orjson==3.9.10 + - overrides==7.4.0 + - pandas==2.1.3 + - pandocfilters==1.5.0 + - parso==0.8.3 + - peft==0.7.1 + - pexpect==4.9.0 + - platformdirs==4.1.0 + - pluggy==1.3.0 + - prometheus-client==0.19.0 + - prompt-toolkit==3.0.43 + - protobuf==3.18.3 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - pydantic==2.5.2 + - pydantic-core==2.14.5 + - pydub==0.25.1 + - pygments==2.17.2 + - pytest==7.4.3 + - python-dateutil==2.8.2 + - python-json-logger==2.0.7 + - python-multipart==0.0.6 + - pytorch-lightning==2.1.2 + - pytz==2023.3.post1 + - pyyaml==6.0.1 + - pyzmq==25.1.2 + - rdkit==2023.9.2 + - referencing==0.32.0 + - regex==2023.10.3 + - requests==2.31.0 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rich==13.7.0 + - rpds-py==0.13.2 + - safetensors==0.4.0 + - scipy==1.11.4 + - seaborn==0.13.0 + - semantic-version==2.10.0 + - send2trash==1.8.2 + - sentencepiece==0.1.99 + - shellingham==1.5.4 + - sniffio==1.3.0 + - soupsieve==2.5 + - stack-data==0.6.3 + - starlette==0.27.0 + - svgwrite==1.4.3 + - terminado==0.18.0 + - tinycss2==1.2.1 + - tokenizers==0.15.0 + - tomli==2.0.1 + - tomlkit==0.12.0 + - toolz==0.12.0 + - torch-cluster==1.6.3 + - torch-scatter==2.1.2 + - torch-sparse==0.6.18 + - torchmetrics==1.2.1 + - tornado==6.4 + - traitlets==5.14.0 + - transformers==4.35.2 + - typer==0.9.0 + - types-python-dateutil==2.8.19.14 + - typing-extensions==4.9.0 + - tzdata==2023.3 + - uri-template==1.3.0 + - uvicorn==0.24.0.post1 + - wcwidth==0.2.12 + - webcolors==1.13 + - webencodings==0.5.1 + - websocket-client==1.7.0 + - websockets==11.0.3 + - yarl==1.9.4 \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/build_sav.py b/proteingym/baselines/protssn/src/build_sav.py new file mode 100644 index 0000000..82a9095 --- /dev/null +++ b/proteingym/baselines/protssn/src/build_sav.py @@ -0,0 +1,40 @@ +import argparse +import os +import pandas as pd +from tqdm import tqdm +from utils.utils import load_coords + +parser = argparse.ArgumentParser(description='make single mutant tsv') +parser.add_argument("-d", "--dataset", type=str, default=None) +args = parser.parse_args() + +one_letter = { + 'VAL':'V', 'ILE':'I', 'LEU':'L', 'GLU':'E', 'GLN':'Q', + 'ASP':'D', 'ASN':'N', 'HIS':'H', 'TRP':'W', 'PHE':'F', 'TYR':'Y', + 'ARG':'R', 'LYS':'K', 'SER':'S', 'THR':'T', 'MET':'M', 'ALA':'A', + 'GLY':'G', 'PRO':'P', 'CYS':'C' + } +AA = list(one_letter.values()) + +base_dir = os.path.join(args.dataset, "DATASET") +proteins = os.listdir(base_dir) +for p in proteins: + fasta_file = os.path.join(base_dir, p, f"{p}.fasta") + pdb_file = os.path.join(base_dir, p, f"{p}.pdb") + if os.path.exists(fasta_file): + seq = open(fasta_file, "r").readlines()[1].strip() + elif os.path.exists(pdb_file): + _, seq = load_coords(pdb_file, "A") + else: + raise ValueError(f"Invalid file: {fasta_file} or {pdb_file}") + data = {"mutant":[], "score":[]} + for idx,s in enumerate(seq): + for a in AA: + if a == s: + continue + data["mutant"].append(f"{s}{idx+1}{a}") + data["score"].append(0) + + print(f"{p} contains { len(data['mutant'])}") + out_file = os.path.join(base_dir, p, f"{p}.csv") + pd.DataFrame(data).to_csv(out_file, index=False) diff --git a/proteingym/baselines/protssn/src/config/egnn.yaml b/proteingym/baselines/protssn/src/config/egnn.yaml new file mode 100644 index 0000000..e2c3de2 --- /dev/null +++ b/proteingym/baselines/protssn/src/config/egnn.yaml @@ -0,0 +1,9 @@ +egnn: + edge_attr_dim: 93 + output_dim: 20 + dropout: 0 + n_layers: 6 + residual: False + embedding: False + embedding_dim: 64 + mlp_num: 2 \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/config/fsdp_config.yaml b/proteingym/baselines/protssn/src/config/fsdp_config.yaml new file mode 100644 index 0000000..e313ce3 --- /dev/null +++ b/proteingym/baselines/protssn/src/config/fsdp_config.yaml @@ -0,0 +1,25 @@ +compute_environment: LOCAL_MACHINE +deepspeed_config: {} +distributed_type: FSDP +downcast_bf16: 'no' +dynamo_config: {} +dynamo_backend: 'no' +fsdp_config: + fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP + fsdp_backward_prefetch_policy: BACKWARD_PRE + fsdp_offload_params: false + fsdp_sharding_strategy: 1 + fsdp_state_dict_type: FULL_STATE_DICT + fsdp_transformer_layer_cls_to_wrap: EsmLayer +machine_rank: 0 +main_training_function: main +megatron_lm_config: {} +mixed_precision: 'no' +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/proteingym/baselines/protssn/src/config/gat.yaml b/proteingym/baselines/protssn/src/config/gat.yaml new file mode 100644 index 0000000..f2f026b --- /dev/null +++ b/proteingym/baselines/protssn/src/config/gat.yaml @@ -0,0 +1,5 @@ +gat: + hidden_channels: 512 + edge_attr_dim: 93 + dropout: 0.5 + n_layers: 4 \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/config/gcn.yaml b/proteingym/baselines/protssn/src/config/gcn.yaml new file mode 100644 index 0000000..193309c --- /dev/null +++ b/proteingym/baselines/protssn/src/config/gcn.yaml @@ -0,0 +1,5 @@ +gcn: + hidden_channels: 512 + edge_attr_dim: 93 + dropout: 0.5 + n_layers: 4 \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/data.py b/proteingym/baselines/protssn/src/data.py new file mode 100644 index 0000000..e4baef5 --- /dev/null +++ b/proteingym/baselines/protssn/src/data.py @@ -0,0 +1,64 @@ +import os, sys +# set path +current_dir = os.getcwd() +sys.path.append(current_dir) +import argparse +from src.dataset.cath_dataset import CathDataset +from src.dataset.mutant_dataset import MutantDataset +from src.utils.dataset_utils import NormalizeProtein + + +def build_cath_dataset(args, split): + dataset = CathDataset( + root=args.cath_dataset, + split=split, + divide_num=1, + divide_idx=0, + c_alpha_max_neighbors=args.c_alpha_max_neighbors, + set_length=None, + p=args.noise_ratio, + normalize_file=args.repo_path+os.sep+f'/proteingym/baselines/protssn/norm/cath_k{args.c_alpha_max_neighbors}_mean_attr.pt', + ) + return dataset + + +def build_mutant_dataset(args): + mm_dataset = MutantDataset( + root=args.mutant_dataset_dir, + name=args.mutant_name, + raw_dir=args.mutant_dataset_dir+"/DATASET", + c_alpha_max_neighbors=args.c_alpha_max_neighbors, + pre_transform=NormalizeProtein( + filename=args.repo_path+os.sep+f'/proteingym/baselines/protssn/norm/cath_k{args.c_alpha_max_neighbors}_mean_attr.pt' + ), + ) + return mm_dataset + +def prepare_train_val_dataset(args): + # load protein dataset like CATHs40 + train_dataset = build_cath_dataset(args, "train") + val_dataset = build_cath_dataset(args, "val") + + return train_dataset, val_dataset + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--c_alpha_max_neighbors", type=int, default=30, help="number of nearest nodes to build graph") + + parser.add_argument("--data_type", type=str, choices=["cath", "mutant"], default="cath", help="type of dataset") + # build cath train dataset + parser.add_argument("--cath_dataset", type=str, default="data/cath40_k30", help="name of cath dataset") + parser.add_argument("--noise_ratio", type=float, default=0.05) + + # build zero-shot mutant prediction dataset + parser.add_argument("--mutant_dataset_dir",type=str,default="data/proteingym-benchmark",help="dir of mutation dataset") + parser.add_argument("--mutant_name",type=str,default="proteingym_k30",help="name of mutation dataset") + + args = parser.parse_args() + + if args.data_type == "cath": + cath_dataset = build_cath_dataset(args, "train") + elif args.data_type == "mutant": + mutant_dataset = build_mutant_dataset(args) \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/dataset/cath_dataset.py b/proteingym/baselines/protssn/src/dataset/cath_dataset.py new file mode 100644 index 0000000..298987d --- /dev/null +++ b/proteingym/baselines/protssn/src/dataset/cath_dataset.py @@ -0,0 +1,753 @@ + +import os +import torch +import sys +import math +import random +import warnings +import torch +import os +import sys +import torch.nn.functional as F +import scipy.spatial as spa +import numpy as np +from tqdm import tqdm +from scipy.special import softmax +from Bio.PDB import PDBParser, ShrakeRupley +from Bio.PDB.PDBExceptions import PDBConstructionWarning +from rdkit.Chem import GetPeriodicTable +from typing import Callable, List, Optional +from torch.utils.data import DataLoader +from torch_geometric.data import InMemoryDataset, Data +current_dir = os.getcwd() +sys.path.append(current_dir) +from src.utils.dataset_utils import safe_index, one_hot_res, log, dihedral, NormalizeProtein, dataset_argument_, get_stat + +cwd = os.getcwd() + +sys.path.append(cwd + '/src/dataset_utils') +warnings.filterwarnings("ignore") + +one_letter = { + 'VAL':'V', 'ILE':'I', 'LEU':'L', 'GLU':'E', 'GLN':'Q', + 'ASP':'D', 'ASN':'N', 'HIS':'H', 'TRP':'W', 'PHE':'F', 'TYR':'Y', + 'ARG':'R', 'LYS':'K', 'SER':'S', 'THR':'T', 'MET':'M', 'ALA':'A', + 'GLY':'G', 'PRO':'P', 'CYS':'C' + } + +class CathDataset(InMemoryDataset): + r""" + Args: + root (string): Root directory where the dataset should be saved. + name (string): The name of the dataset. + raw_dir (string, optional): Root directory where the + original dataset stored(default: :obj:`None`) + + num_residue_type (int, optional): The number of amino acid types. + (default: obj:'20') + micro_radius (int, optional): The radius of micro-environment + centered on the mask node. (default: obj:'20') + c_alpha_max_neighbors (int, optional): The number of maximum + connected nodes. (default: obj:'10') + cutoff (int, optional): The maximum connected nodes distance + (default: obj:'30') + seq_dist_cut (int, optional): one-hot encoding the sequence distance + edge attribute + (default: obj:) + [0.25,0.5,0.75,0.9,0.95,0.98,0.99] + [ 2. 3. 13. 63. 127. 247. 347.] + num_val (int, optional): The number of validation samples in case of "random" split. (default: 500) + num_test (int, optional): The number of test samples in case of "random" split. (default: 1000) + + # use_localdatastet (bool) (bool,optional): If :obj:'True', online dataset + # will be downloaded. If not, local pdb files will be used + # (default: obj:'True') + + transform (callable, optional): A function/transform that takes in an + :obj:`torch_geometric.data.Data` object and returns a transformed + version. The data object will be transformed before every access. + (default: :obj:`None`) + pre_transform (callable, optional): A function/transform that takes in + an :obj:`torch_geometric.data.Data` object and returns a + transformed version. The data object will be transformed before + being saved to disk. (default: :obj:`None`) + pre_filter (callable, optional): A function that takes in an + :obj:`torch_geometric.data.Data` object and returns a boolean + value, indicating whether the data object should be included in the + final dataset. (default: :obj:`None`) + """ + + splits = ['train', 'val', 'test'] + allowable_features = { + 'possible_atomic_num_list': list(range(1, 119)) + ['misc'], + 'possible_chirality_list': [ + 'CHI_UNSPECIFIED', + 'CHI_TETRAHEDRAL_CW', + 'CHI_TETRAHEDRAL_CCW', + 'CHI_OTHER' + ], + 'possible_degree_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'], + 'possible_numring_list': [0, 1, 2, 3, 4, 5, 6, 'misc'], + 'possible_implicit_valence_list': [0, 1, 2, 3, 4, 5, 6, 'misc'], + 'possible_formal_charge_list': [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 'misc'], + 'possible_numH_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'], + 'possible_number_radical_e_list': [0, 1, 2, 3, 4, 'misc'], + 'possible_hybridization_list': [ + 'SP', 'SP2', 'SP3', 'SP3D', 'SP3D2', 'misc' + ], + 'possible_is_aromatic_list': [False, True], + 'possible_is_in_ring3_list': [False, True], + 'possible_is_in_ring4_list': [False, True], + 'possible_is_in_ring5_list': [False, True], + 'possible_is_in_ring6_list': [False, True], + 'possible_is_in_ring7_list': [False, True], + 'possible_is_in_ring8_list': [False, True], + 'possible_amino_acids': ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', + 'MET', + 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL', 'HIP', 'HIE', 'TPO', 'HID', 'LEV', + 'MEU', + 'PTR', 'GLV', 'CYT', 'SEP', 'HIZ', 'CYM', 'GLM', 'ASQ', 'TYS', 'CYX', 'GLZ', 'misc'], + 'possible_atom_type_2': ['C*', 'CA', 'CB', 'CD', 'CE', 'CG', 'CH', 'CZ', 'N*', 'ND', 'NE', 'NH', 'NZ', 'O*', + 'OD', + 'OE', 'OG', 'OH', 'OX', 'S*', 'SD', 'SG', 'misc'], + 'possible_atom_type_3': ['C', 'CA', 'CB', 'CD', 'CD1', 'CD2', 'CE', 'CE1', 'CE2', 'CE3', 'CG', 'CG1', 'CG2', + 'CH2', + 'CZ', 'CZ2', 'CZ3', 'N', 'ND1', 'ND2', 'NE', 'NE1', 'NE2', 'NH1', 'NH2', 'NZ', 'O', + 'OD1', + 'OD2', 'OE1', 'OE2', 'OG', 'OG1', 'OH', 'OXT', 'SD', 'SG', 'misc'], + } + + def __init__(self, root: str, + split: str = 'train', + num_residue_type: int = 20, + micro_radius: int = 20, + c_alpha_max_neighbors: int = 10, + cutoff: int = 30, + seq_dist_cut: int = 64, + use_micro: bool = False, + use_angle: bool = False, + use_omega: bool = False, + transform: Optional[Callable] = None, + pre_transform: Optional[Callable] = None, + pre_filter: Optional[Callable] = None, + divide_num: int = 1, + divide_idx: int = 0, + set_length: int = 500, + num_val: int = 10, + is_normalize: bool = True, + normalize_file: str = None, + p: float = 0.5, + use_sasa: bool =False, + use_bfactor: bool = False, + use_dihedral: bool = False, + use_coordinate: bool = False, + use_denoise: bool = False, + noise_type: str = 'wild', + temperature = 1.0 + ): + self.p=p + self.use_sasa=use_sasa + self.use_bfactor=use_bfactor + self.use_dihedral=use_dihedral + self.use_coordinate=use_coordinate + self.use_denoise=use_denoise + self.noise_type = noise_type + self.temperature = temperature + + self.split = split + assert self.split in self.splits + + self.num_residue_type = num_residue_type + self.micro_radius = micro_radius + self.c_alpha_max_neighbors = c_alpha_max_neighbors + self.seq_dist_cut = seq_dist_cut + self.use_micro = use_micro + self.use_angle = use_angle + self.use_omega = use_omega + self.cutoff = cutoff + + self.num_val = num_val + self.divide_num = divide_num + self.divide_idx = divide_idx + self.set_length = set_length + + self.is_normalize = is_normalize + self.normalize_file = normalize_file + + self.wrong_proteins = ['1kp0A01', '2atcA02'] + + self.sr = ShrakeRupley(probe_radius=1.4, # in A. Default is 1.40 roughly the radius of a water molecule. + n_points=100) # resolution of the surface of each atom. Default is 100. A higher number of points results in more precise measurements, but slows down the calculation. + self.periodic_table = GetPeriodicTable() + self.biopython_parser = PDBParser() + + super().__init__(root, transform, pre_transform, pre_filter) + self.dataset = torch.load(self.processed_paths[self.splits.index(self.split)]) + # self.data, self.slices = torch.load( + # self.processed_paths[self.splits.index(self.split)]) + # self.nums_amino_cum = self.slices['x'] + + @property + def raw_file_names(self) -> str: + raw_file_names = os.path.join('data', 'cath', "dompdb") + if not os.path.exists(raw_file_names): + os.mkdir(raw_file_names) + return raw_file_names + + @property + def raw_dir(self) -> str: + if not os.path.exists(self.root): + os.mkdir(self.root) + raw_dir = os.path.join(self.root, 'raw') + if not os.path.exists(raw_dir): + os.mkdir(raw_dir) + return raw_dir + + @property + def saved_graph_dir(self) -> str: + dir_root = os.path.join(self.root) + if not os.path.exists(dir_root): + os.mkdir(dir_root) + dir_name = os.path.join(dir_root, 'graph_seq') + if not os.path.exists(dir_name): + os.mkdir(dir_name) + if not self.set_length: + self.set_length = len(os.listdir(dir_name)) + return dir_name + + @property + def saved_amino_cum(self) -> str: + amino_cum_name = os.path.join( + self.root, 'amino_cum.pt') + return amino_cum_name + + @property + def processed_dir(self) -> str: + return os.path.join(self.root, 'processed_seq') + + @property + def processed_file_names(self) -> str: + return ['train.pt', 'val.pt'] + + + def write_info(self): + written_filename = os.path.join(self.root, 'wrong_protein_names.txt') + file = open(written_filename, "w+") + for protein_name in self.wrong_proteins: + file.writelines(protein_name + '\n') + file.close() + + def process(self): + #generate graph data and save in graph dir + self.generate_protein_graph() + # self.write_info() + + filenames = os.listdir(self.saved_graph_dir) + protein_length = len(filenames) + if self.set_length: + protein_length = min(protein_length, self.set_length) + + if not self.normalize_file: + self.normalize_file = get_stat(self.saved_graph_dir) + + random.shuffle(filenames) + train_list = [f for f in filenames if "_" in f or "-" in f] + filenames = [f for f in filenames if "_" not in f or "-" not in f] + train_list.extend(filenames[:-self.num_val]) + filenames_list = [train_list, filenames[-self.num_val:]] + + for k in range(2):####split train,val,test + data_list = [] + + ###move special name to test set + special_name_list = ["p53-dimer.pdb.pt"] + for special_name in special_name_list: + if special_name in filenames_list[0]: + filenames_list[0].remove(special_name) + filenames_list[1].append(special_name) + for i in tqdm(range(len(filenames_list[k]))): + file = filenames_list[k][i] + try: + graph1 = torch.load(os.path.join(self.saved_graph_dir, file))##load processed graph data torch pt file + except: + print(file) + continue + del graph1['distances'] + del graph1['edge_dist'] + del graph1['mu_r_norm'] + del graph1['seq'] + data_list.append(graph1) + if self.is_normalize: + normalize_transform = NormalizeProtein(filename=self.normalize_file) + data_list = [d for d in data_list if normalize_transform(d)] + if self.pre_filter is not None: + data_list = [d for d in data_list if self.pre_filter(d)] + if self.pre_transform is not None: + data_list = [self.pre_transform(d) for d in data_list] + + torch.save(data_list, self.processed_paths[k]) + + def generate_protein_graph(self): + names = os.listdir(self.raw_file_names) + print(names) + names.sort() + n = int(np.ceil(len(names) / self.divide_num)) + names = names[n * self.divide_idx:min(len(names), n * (self.divide_idx + 1))] + for idx, name in enumerate(tqdm(names)): + saved_graph_filename = os.path.join(self.saved_graph_dir, name + '.pt') + if os.path.exists(saved_graph_filename): + continue + protein_filename = os.path.join(self.raw_file_names, name) + if (name in self.wrong_proteins) or (not protein_filename): + continue + try: + rec, rec_coords, c_alpha_coords, n_coords, c_coords,seq = self.get_receptor_inference(protein_filename) + except: + continue + if rec !=False: + if len(seq)>len(c_alpha_coords): + del seq[-(len(seq)-len(c_alpha_coords)):] + #meet "dna" data will remove the file and rec will be false + # print(self.c_alpha_max_neighbors) + rec_graph = self.get_calpha_graph(rec, c_alpha_coords, n_coords, c_coords, rec_coords,seq) + if not rec_graph: + self.wrong_proteins.append(name) + continue + torch.save(rec_graph, saved_graph_filename) + + def rec_residue_featurizer(self, rec, chain_id, one_hot=True, add_feature=None): + count = 0 + flag_sasa=1 + try: + self.sr.compute(rec, level="R") + except: + flag_sasa=0 + for i, chain in enumerate(rec.get_chains()): + if i != chain_id: + continue + num_res = len(list(chain.get_residues()))#len([_ for _ in rec.get_residues()]) + num_feature = 2 + if add_feature.any(): + num_feature += add_feature.shape[1] + res_feature = torch.zeros(num_res, self.num_residue_type + num_feature) + for i, residue in enumerate(chain.get_residues()): + if flag_sasa==0: + residue.sasa=0 + sasa = residue.sasa + for atom in residue: + if atom.name == 'CA': + bfactor = atom.bfactor + assert not np.isinf(bfactor) + assert not np.isnan(bfactor) + assert not np.isinf(sasa) + assert not np.isnan(sasa) + + residx = safe_index( + self.allowable_features['possible_amino_acids'], residue.get_resname()) + res_feat_1 = one_hot_res( + residx, num_residue_type=self.num_residue_type) if one_hot else [residx] + if not res_feat_1: + return False + res_feat_1.append(sasa) + res_feat_1.append(bfactor) + if num_feature > 2: + res_feat_1.extend(list(add_feature[count, :])) + res_feature[count, :] = torch.tensor(res_feat_1, dtype=torch.float32) + count += 1 + # print("numnodes:", num_res, count,len(list(chain.get_residues()))) + for k in range(self.num_residue_type, self.num_residue_type + 2): + mean = res_feature[:, k].mean() + std = res_feature[:, k].std() + res_feature[:, k] = (res_feature[:, k] -mean) / (std + 0.000000001) + return res_feature + + def get_node_features(self, n_coords, c_coords, c_alpha_coords, coord_mask, with_coord_mask=True, use_angle=False, + use_omega=False): + num_res = n_coords.shape[0] + if use_omega: + num_angle_type = 3 + angles = np.zeros((num_res, num_angle_type)) + for i in range(num_res - 1): + # These angles are called φ (phi) which involves the backbone atoms C-N-Cα-C + angles[i, 0] = dihedral( + c_coords[i], n_coords[i], c_alpha_coords[i], n_coords[i + 1]) + # psi involves the backbone atoms N-Cα-C-N. + angles[i, 1] = dihedral( + n_coords[i], c_alpha_coords[i], c_coords[i], n_coords[i + 1]) + angles[i, 2] = dihedral( + c_alpha_coords[i], c_coords[i], n_coords[i + 1], c_alpha_coords[i + 1]) + else: + num_angle_type = 2 + angles = np.zeros((num_res, num_angle_type)) + for i in range(num_res - 1): + # These angles are called φ (phi) which involves the backbone atoms C-N-Cα-C + angles[i, 0] = dihedral( + c_coords[i], n_coords[i], c_alpha_coords[i], n_coords[i + 1]) + # psi involves the backbone atoms N-Cα-C-N. + angles[i, 1] = dihedral( + n_coords[i], c_alpha_coords[i], c_coords[i], n_coords[i + 1]) + if use_angle: + node_scalar_features = angles + else: + node_scalar_features = np.zeros((num_res, num_angle_type * 2)) + for i in range(num_angle_type): + node_scalar_features[:, 2 * i] = np.sin(angles[:, i]) + node_scalar_features[:, 2 * i + 1] = np.cos(angles[:, i]) + + if with_coord_mask: + node_scalar_features = torch.cat([ + node_scalar_features, + coord_mask.float().unsqueeze(-1) + ], dim=-1) + node_vector_features = None + return node_scalar_features, node_vector_features + + def get_calpha_graph(self, rec, c_alpha_coords, n_coords, c_coords, coords, seq): + chain_id = 0 + scalar_feature, vec_feature = self.get_node_features(n_coords, c_coords, c_alpha_coords, coord_mask=None, with_coord_mask=False, use_angle=self.use_angle, use_omega=self.use_omega) + # Extract 3D coordinates and n_i,u_i,v_i + # vectors of representative residues ################ + residue_representatives_loc_list = [] + n_i_list = [] + u_i_list = [] + v_i_list = [] + for i, chain in enumerate(rec.get_chains()): + if i != chain_id: + continue + for i, residue in enumerate(chain.get_residues()): + n_coord = n_coords[i] + c_alpha_coord = c_alpha_coords[i] + c_coord = c_coords[i] + u_i = (n_coord - c_alpha_coord) / \ + np.linalg.norm(n_coord - c_alpha_coord) + t_i = (c_coord - c_alpha_coord) / \ + np.linalg.norm(c_coord - c_alpha_coord) + n_i = np.cross(u_i, t_i) / \ + np.linalg.norm(np.cross(u_i, t_i)) # main chain + v_i = np.cross(n_i, u_i) + assert (math.fabs( + np.linalg.norm(v_i) - 1.) < 1e-5), "protein utils protein_to_graph_dips, v_i norm larger than 1" + n_i_list.append(n_i) + u_i_list.append(u_i) + v_i_list.append(v_i) + residue_representatives_loc_list.append(c_alpha_coord) + + residue_representatives_loc_feat = np.stack(residue_representatives_loc_list, axis=0) # (N_res, 3) + n_i_feat = np.stack(n_i_list, axis=0) + u_i_feat = np.stack(u_i_list, axis=0) + v_i_feat = np.stack(v_i_list, axis=0) + num_residues = len(c_alpha_coords) + if num_residues <= 1: + raise ValueError(f"rec contains only 1 residue!") + ################### Build the k-NN graph ############################## + assert num_residues == residue_representatives_loc_feat.shape[0] + assert residue_representatives_loc_feat.shape[1] == 3 + distances = spa.distance.cdist(c_alpha_coords, c_alpha_coords) + + src_list = [] + dst_list = [] + dist_list = [] + mean_norm_list = [] + for i in range(num_residues): + dst = list(np.where(distances[i, :] < self.cutoff)[0]) + dst.remove(i) + if self.c_alpha_max_neighbors != None and len(dst) > self.c_alpha_max_neighbors: + dst = list(np.argsort(distances[i, :]))[ + 1: self.c_alpha_max_neighbors + 1] + if len(dst) == 0: + # choose second because first is i itself + dst = list(np.argsort(distances[i, :]))[1:2] + log( + f'The c_alpha_cutoff {self.cutoff} was too small for one c_alpha such that it had no neighbors. So we connected it to the closest other c_alpha') + assert i not in dst + src = [i] * len(dst) + src_list.extend(src) + dst_list.extend(dst) + valid_dist = list(distances[i, dst]) + dist_list.extend(valid_dist) + valid_dist_np = distances[i, dst] + sigma = np.array([1., 2., 5., 10., 30.]).reshape((-1, 1)) + weights = softmax(- valid_dist_np.reshape((1, -1))** 2 / sigma, axis=1) # (sigma_num, neigh_num) + # print(weights) why weight?? + assert weights[0].sum() > 1 - 1e-2 and weights[0].sum() < 1.01 + diff_vecs = residue_representatives_loc_feat[src, :] - residue_representatives_loc_feat[dst, :] # (neigh_num, 3) + mean_vec = weights.dot(diff_vecs) # (sigma_num, 3) + denominator = weights.dot(np.linalg.norm(diff_vecs, axis=1)) # (sigma_num,) + mean_vec_ratio_norm = np.linalg.norm(mean_vec, axis=1) / denominator # (sigma_num,) + mean_norm_list.append(mean_vec_ratio_norm) + assert len(src_list) == len(dst_list) + assert len(dist_list) == len(dst_list) + residue_representatives_loc_feat = torch.from_numpy(residue_representatives_loc_feat.astype(np.float32)) + x = self.rec_residue_featurizer(rec, chain_id, one_hot=True, add_feature=scalar_feature) + if isinstance(x, bool) and (not x): + return False + ######key part to generate graph!!!!!main + graph = Data( + x=x,## 26 feature 20+sasa+b factor+ two face angle + pos=residue_representatives_loc_feat, + edge_attr=self.get_edge_features(src_list, dst_list, dist_list, divisor=4), ##edge features + edge_index=torch.tensor([src_list, dst_list]), + edge_dist=torch.tensor(dist_list), + distances=torch.tensor(distances), + mu_r_norm=torch.from_numpy(np.array(mean_norm_list).astype(np.float32)), + seq = seq) ##about density capture + # Loop over all edges of the graph and build the various p_ij, q_ij, k_ij, t_ij pairs + edge_feat_ori_list = [] + for i in range(len(dist_list)): + src = src_list[i] + dst = dst_list[i] + # place n_i, u_i, v_i as lines in a 3x3 basis matrix + basis_matrix = np.stack( + (n_i_feat[dst, :], u_i_feat[dst, :], v_i_feat[dst, :]), axis=0) + p_ij = np.matmul(basis_matrix,residue_representatives_loc_feat[src, :] - residue_representatives_loc_feat[dst, :]) + q_ij = np.matmul(basis_matrix, n_i_feat[src, :]) # shape (3,) + k_ij = np.matmul(basis_matrix, u_i_feat[src, :]) + t_ij = np.matmul(basis_matrix, v_i_feat[src, :]) + s_ij = np.concatenate((p_ij, q_ij, k_ij, t_ij), axis=0) # shape (12,) + edge_feat_ori_list.append(s_ij) + + edge_feat_ori_feat = np.stack(edge_feat_ori_list, axis=0) # shape (num_edges, 4, 3) + edge_feat_ori_feat = torch.from_numpy(edge_feat_ori_feat.astype(np.float32)) + graph.edge_attr = torch.cat([graph.edge_attr, edge_feat_ori_feat], axis=1) # (num_edges, 17) + # graph = self.remove_node(graph, graph.x.shape[0]-1)###remove the last node, can not calculate the two face angle + # self.get_calpha_graph_single(graph, 6) + return graph + + def remove_node(self, graph, node_idx): + new_graph = Data.clone(graph) + # delete node + new_graph.x = torch.cat( + [new_graph.x[:node_idx, :], new_graph.x[node_idx + 1:, :]]) + new_graph.pos = torch.cat( + [new_graph.pos[:node_idx, :], new_graph.pos[node_idx + 1:, :]]) + new_graph.mu_r_norm = torch.cat( + [new_graph.mu_r_norm[:node_idx, :], new_graph.mu_r_norm[node_idx + 1:, :]]) + + # delete edge + keep_edge = (torch.sum(new_graph.edge_index == node_idx, dim=0) == 0) + new_graph.edge_index = new_graph.edge_index[:, keep_edge] + new_graph.edge_attr = new_graph.edge_attr[keep_edge, :] + return new_graph + + def get_edge_features(self, src_list, dst_list, dist_list, divisor=4): + seq_edge = torch.absolute(torch.tensor( + src_list) - torch.tensor(dst_list)).reshape(-1, 1) + seq_edge = torch.where(seq_edge > self.seq_dist_cut, + self.seq_dist_cut, seq_edge) + seq_edge = F.one_hot( + seq_edge, num_classes=self.seq_dist_cut + 1).reshape((-1, self.seq_dist_cut + 1)) + contact_sig = torch.where(torch.tensor( + dist_list) <= 8, 1, 0).reshape(-1, 1) + # avg distance = 7. So divisor = (4/7)*7 = 4 + dist_fea = self.distance_featurizer(dist_list, divisor=divisor) + return torch.concat([seq_edge, dist_fea, contact_sig], dim=-1) + + def get_receptor_inference(self, rec_path): + chain_id=0 + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=PDBConstructionWarning) + structure = self.biopython_parser.get_structure('random_id', rec_path) + rec = structure[0]##len(structure)=1 + head = self.biopython_parser.get_header()['head'] + if head.find('dna') > -1: + return False, False, False, False, False,False + coords = [] + c_alpha_coords = [] + n_coords = [] + c_coords = [] + valid_chain_ids = [] + lengths = [] + seq = [] + for i, chain in enumerate(rec): + print("chain num",i,chain_id,chain) + if i != chain_id:##select chain A:i=0 or B:i=1 + continue + chain_coords = [] # num_residues, num_atoms, 3 + chain_c_alpha_coords = [] + chain_n_coords = [] + chain_c_coords = [] + count = 0 + invalid_res_ids = [] + for res_idx, residue in enumerate(chain): + if residue.get_resname() == 'HOH': + invalid_res_ids.append(residue.get_id()) + continue + residue_coords = [] + c_alpha, n, c = None, None, None + for atom in residue: + if atom.name == 'CA': + c_alpha = list(atom.get_vector()) + seq.append(str(residue).split(" ")[1]) + if atom.name == 'N': + n = list(atom.get_vector()) + if atom.name == 'C': + c = list(atom.get_vector()) + residue_coords.append(list(atom.get_vector())) + # only append residue if it is an amino acid and not some weired molecule that is part of the complex + if c_alpha != None and n != None and c != None: + chain_c_alpha_coords.append(c_alpha) + chain_n_coords.append(n) + chain_c_coords.append(c) + chain_coords.append(np.array(residue_coords)) + count += 1 + else: + invalid_res_ids.append(residue.get_id()) + for res_id in invalid_res_ids: + chain.detach_child(res_id) + lengths.append(count) + coords.append(chain_coords) + c_alpha_coords.append(np.array(chain_c_alpha_coords)) + n_coords.append(np.array(chain_n_coords)) + c_coords.append(np.array(chain_c_coords)) + if len(chain_coords) > 0: + valid_chain_ids.append(chain.get_id()) + valid_coords = [] + valid_c_alpha_coords = [] + valid_n_coords = [] + valid_c_coords = [] + valid_lengths = [] + invalid_chain_ids = [] + for i, chain in enumerate(rec): + # print("chain:",i,chain, len(valid_coords), len(valid_chain_ids), len(coords), coords[0][0].shape, len(coords[0])) + if i != chain_id: + continue + if chain.get_id() in valid_chain_ids: + valid_coords.append(coords[0]) + valid_c_alpha_coords.append(c_alpha_coords[0]) + valid_n_coords.append(n_coords[0]) + valid_c_coords.append(c_coords[0]) + valid_lengths.append(lengths[0]) + else: + invalid_chain_ids.append(chain.get_id()) + # list with n_residues arrays: [n_atoms, 3] + coords = [item for sublist in valid_coords for item in sublist] + if len(valid_c_alpha_coords) == 0: + return False, False, False, False, False,False + c_alpha_coords = np.concatenate(valid_c_alpha_coords, axis=0) # [n_residues, 3] + n_coords = np.concatenate(valid_n_coords, axis=0) # [n_residues, 3] + c_coords = np.concatenate(valid_c_coords, axis=0) # [n_residues, 3] + + for invalid_id in invalid_chain_ids: + rec.detach_child(invalid_id) + + assert len(c_alpha_coords) == len(n_coords) + assert len(c_alpha_coords) == len(c_coords) + assert sum(valid_lengths) == len(c_alpha_coords) + return rec, coords, c_alpha_coords, n_coords, c_coords,seq + + def len(self): + return len(self.dataset) + + def get_statistic_info(self): + node_num = torch.zeros(self.length_total) + edge_num = torch.zeros(self.length_total) + for i in tqdm(range(self.length_total)): + graph = self.get(i) + node_num[i] = graph.x.shape[0] + edge_num[i] = graph.edge_index.shape[1] + num_node_min = torch.min(node_num) + num_node_max = torch.max(node_num) + num_node_avg = torch.mean(node_num) + num_edge_min = torch.min(edge_num) + num_edge_max = torch.max(edge_num) + num_edge_avg = torch.mean(edge_num) + print(f'Graph Num: {self.length_total}') + print( + f'Min Nodes: {num_node_min:.2f} Max Nodes: {num_node_max:.2f}. Avg Nodes: {num_node_avg:.2f}') + print( + f'Min Edges: {num_edge_min:.2f} Max Edges: {num_edge_max:.2f}. Avg Edges: {num_edge_avg:.2f}') + + def _get_noise(self, token_len: int, prob: List=[]): + prob = prob if prob else [0.08, 0.05, 0.04, 0.06, 0.01, 0.04, 0.07, 0.07, 0.02, 0.06, 0.1, 0.06, + 0.02, 0.04, 0.04, 0.06, 0.05, 0.01, 0.03, 0.07] + multant_pos = ((torch.rand(token_len) <= self.p)).nonzero().flatten() + if len(multant_pos) == 0: + return None, None + multant_trg = torch.multinomial(torch.tensor(prob), len(multant_pos), replacement=True) + return multant_pos, multant_trg + + + def _token_rep_noise(self, data, multant_pos, multant_trg, rep_noise_type='window_3'): + num_classes = 20 + multant_rep = data.token_rep.clone() + for mut_pos, mut_trg in zip(multant_pos, multant_trg): + mut_trg_ = F.one_hot(mut_trg, num_classes=num_classes) + if rep_noise_type == 'mean': + trg_rep = data.token_rep[(data.x[:,:20] == mut_trg_).sum(1) == num_classes].mean(0) + if torch.isnan(trg_rep).sum() > 0: + continue + multant_rep[mut_pos] = trg_rep + elif "window" in rep_noise_type: + window_size = int(rep_noise_type.split("_")[-1]) + start_pos = mut_pos - math.ceil(window_size/2) + end_pos = start_pos + window_size + if end_pos > len(data.token_rep): + start_pos = mut_pos - window_size + trg_rep = data.token_rep[start_pos:].mean(0) + elif start_pos < 0: + end_pos = window_size + trg_rep = data.token_rep[:end_pos].mean(0) + else: + trg_rep = data.token_rep[start_pos:end_pos].mean(0) + multant_rep[mut_pos] = trg_rep + return multant_rep + + def get(self, idx): + # idx_protein = idx + # idx_x0, idx_x1 = self.slices['x'][idx_protein], self.slices['x'][idx_protein + 1] + # idx_edge0, idx_edge1 = self.slices['edge_index'][idx_protein], self.slices['edge_index'][idx_protein + 1] + + # data = Data( + # x=self.data.x[idx_x0:idx_x1, :], + # pos=self.data.pos[idx_x0:idx_x1, :], + # edge_index=self.data.edge_index[:, idx_edge0:idx_edge1], + # edge_attr=self.data.edge_attr[idx_edge0:idx_edge1, :], + # lenth=idx_x1-idx_x0 + # ) + data = self.dataset[idx] + + token_len = data.x.shape[0] + data.y = data.x[:token_len, :self.num_residue_type].argmax(1) + multant_pos, multant_trg = self._get_noise(token_len=token_len) + if multant_pos is not None: + noisey = data.x[:, :20].argmax(dim=1) + noisey[multant_pos] = multant_trg + data.x[:,:20] = F.one_hot(noisey, num_classes=20) + + return data + + + def find_idx(self, idx_protein, amino_idx): + idx = (self.distances[idx_protein][:-1, amino_idx]< self.micro_radius).nonzero(as_tuple=True)[0] + return idx + + def get_calpha_graph_single(self, graph, idx_protein, amino_idx): + choosen_amino_idx = self.find_idx(idx_protein, amino_idx) + keep_edge_index = [] + for edge_idx in range(graph.num_edges): + edge = graph.edge_index.t()[edge_idx] + if (edge[0] in choosen_amino_idx) and (edge[1] in choosen_amino_idx): + keep_edge_index.append(edge_idx) + graph1 = Data(x=graph.x[choosen_amino_idx, :], + pos=graph.pos[choosen_amino_idx, :], + edge_index=graph.edge_index[:, keep_edge_index], + edge_attr=graph.edge_attr[keep_edge_index, :], + mu_r_norm=graph.mu_r_norm[choosen_amino_idx, :]) + return graph1 + + def __repr__(self) -> str: + return f'{self.__class__.__name__}()' + + def distance_featurizer(self, dist_list, divisor) -> torch.Tensor: + # you want to use a divisor that is close to 4/7 times the average distance that you want to encode + length_scale_list = [1.5 ** x for x in range(15)] + center_list = [0. for _ in range(15)] + num_edge = len(dist_list) + dist_list = np.array(dist_list) + transformed_dist = [np.exp(- ((dist_list / divisor) ** 2) / float(length_scale)) + for length_scale, center in zip(length_scale_list, center_list)] + transformed_dist = np.array(transformed_dist).T + transformed_dist = transformed_dist.reshape((num_edge, -1)) + return torch.from_numpy(transformed_dist.astype(np.float32)) + + \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/dataset/mutant_dataset.py b/proteingym/baselines/protssn/src/dataset/mutant_dataset.py new file mode 100644 index 0000000..3ffa2b1 --- /dev/null +++ b/proteingym/baselines/protssn/src/dataset/mutant_dataset.py @@ -0,0 +1,637 @@ + + +import os +import torch +import math +import warnings +import torch.nn.functional as F +import pandas as pd +import numpy as np +import scipy.spatial as spa +from torch_geometric.data import Data, Dataset, InMemoryDataset +from tqdm import tqdm +from scipy.special import softmax +from Bio.PDB import PDBParser, ShrakeRupley +from Bio.PDB.PDBExceptions import PDBConstructionWarning +from rdkit.Chem import GetPeriodicTable +from typing import Callable, List, Optional +from src.utils.dataset_utils import safe_index, one_hot_res, log, dihedral +warnings.filterwarnings("ignore") + +one_letter ={'VAL':'V', 'ILE':'I', 'LEU':'L', 'GLU':'E', 'GLN':'Q', + 'ASP':'D', 'ASN':'N', 'HIS':'H', 'TRP':'W', 'PHE':'F', 'TYR':'Y', + 'ARG':'R', 'LYS':'K', 'SER':'S', 'THR':'T', 'MET':'M', 'ALA':'A', + 'GLY':'G', 'PRO':'P', 'CYS':'C'} +class MutantDataset(Dataset): + r""" + Args: + root (string): Root directory where the dataset should be saved. + name (string): The name of the dataset. + raw_dir (string, optional): Root directory where the + original dataset stored(default: :obj:`None`) + + num_residue_type (int, optional): The number of amino acid types. + (default: obj:'20') + micro_radius (int, optional): The radius of micro-environment + centered on the mask node. (default: obj:'20') + c_alpha_max_neighbors (int, optional): The number of maximum + connected nodes. (default: obj:'10') + cutoff (int, optional): The maximum connected nodes distance + (default: obj:'30') + seq_dist_cut (int, optional): one-hot encoding the sequence distance + edge attribute + (default: obj:) + [0.25,0.5,0.75,0.9,0.95,0.98,0.99] + [ 2. 3. 13. 63. 127. 247. 347.] + + # use_localdatastet (bool) (bool,optional): If :obj:'True', online dataset + # will be downloaded. If not, local pdb files will be used + # (default: obj:'True') + + transform (callable, optional): A function/transform that takes in an + :obj:`torch_geometric.data.Data` object and returns a transformed + version. The data object will be transformed before every access. + (default: :obj:`None`) + pre_transform (callable, optional): A function/transform that takes in + an :obj:`torch_geometric.data.Data` object and returns a + transformed version. The data object will be transformed before + being saved to disk. (default: :obj:`None`) + pre_filter (callable, optional): A function that takes in an + :obj:`torch_geometric.data.Data` object and returns a boolean + value, indicating whether the data object should be included in the + final dataset. (default: :obj:`None`) + """ + allowable_features = { + 'possible_atomic_num_list': list(range(1, 119)) + ['misc'], + 'possible_chirality_list': [ + 'CHI_UNSPECIFIED', + 'CHI_TETRAHEDRAL_CW', + 'CHI_TETRAHEDRAL_CCW', + 'CHI_OTHER' + ], + 'possible_degree_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'], + 'possible_numring_list': [0, 1, 2, 3, 4, 5, 6, 'misc'], + 'possible_implicit_valence_list': [0, 1, 2, 3, 4, 5, 6, 'misc'], + 'possible_formal_charge_list': [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 'misc'], + 'possible_numH_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'], + 'possible_number_radical_e_list': [0, 1, 2, 3, 4, 'misc'], + 'possible_hybridization_list': [ + 'SP', 'SP2', 'SP3', 'SP3D', 'SP3D2', 'misc' + ], + 'possible_is_aromatic_list': [False, True], + 'possible_is_in_ring3_list': [False, True], + 'possible_is_in_ring4_list': [False, True], + 'possible_is_in_ring5_list': [False, True], + 'possible_is_in_ring6_list': [False, True], + 'possible_is_in_ring7_list': [False, True], + 'possible_is_in_ring8_list': [False, True], + 'possible_amino_acids': ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', + 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL', 'HIP', 'HIE', 'TPO', 'HID', 'LEV', 'MEU', + 'PTR', 'GLV', 'CYT', 'SEP', 'HIZ', 'CYM', 'GLM', 'ASQ', 'TYS', 'CYX', 'GLZ', 'misc'], + 'possible_atom_type_2': ['C*', 'CA', 'CB', 'CD', 'CE', 'CG', 'CH', 'CZ', 'N*', 'ND', 'NE', 'NH', 'NZ', 'O*', 'OD', + 'OE', 'OG', 'OH', 'OX', 'S*', 'SD', 'SG', 'misc'], + 'possible_atom_type_3': ['C', 'CA', 'CB', 'CD', 'CD1', 'CD2', 'CE', 'CE1', 'CE2', 'CE3', 'CG', 'CG1', 'CG2', 'CH2', + 'CZ', 'CZ2', 'CZ3', 'N', 'ND1', 'ND2', 'NE', 'NE1', 'NE2', 'NH1', 'NH2', 'NZ', 'O', 'OD1', + 'OD2', 'OE1', 'OE2', 'OG', 'OG1', 'OH', 'OXT', 'SD', 'SG', 'misc'], + } + amino_acids_type = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', + 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'] + + def __init__(self, root: str, name: str, raw_dir: str, + num_residue_type: int = 20, + micro_radius: int = 20, + c_alpha_max_neighbors: int = 10, + cutoff: int = 30, + seq_dist_cut: int = 64, + use_micro: bool = False, + use_angle: bool = False, + use_omega: bool = False, + transform: Optional[Callable] = None, + pre_transform: Optional[Callable] = None, + pre_filter: Optional[Callable] = None, + divide_num: int = 1, + divide_idx: int = 0, + replace_graph: bool = False, + replace_process: bool = False + ): + self.divide_num = divide_num + self.divide_idx = divide_idx + self.replace_graph = replace_graph + self.replace_process = replace_process + + self.root = root + self.name = name + self.raw_root = raw_dir + self.num_residue_type = num_residue_type + self.micro_radius = micro_radius + self.c_alpha_max_neighbors = c_alpha_max_neighbors + self.cutoff = cutoff + self.seq_dist_cut = seq_dist_cut + self.use_micro = use_micro + self.use_angle = use_angle + self.use_omega = use_omega + + self.protein_names = [] + self.wrong_protein_names = [] + self.total_protein_names = [] + if os.path.exists(self.total_protein_name_file): + self.protein_names = open(self.saved_protein_name_file, 'r').read().splitlines() + if os.path.exists(self.wrong_protein_name_file): + self.wrong_protein_names = open(self.wrong_protein_name_file, 'r').read().splitlines() + if os.path.exists(self.total_protein_name_file): + self.total_protein_names = open(self.total_protein_name_file, 'r').read().splitlines() + + # in A. Default is 1.40 roughly the radius of a water molecule. + # resolution of the surface of each atom. Default is 100. A higher number of points results in more precise measurements, but slows down the calculation. + self.sr = ShrakeRupley(probe_radius=1.4, n_points=100) + self.biopython_parser = PDBParser() + + self.saved_graph_path = self.mk_saved_graph_path() + super().__init__(root, transform, pre_transform, pre_filter) + # After processing protein from pdb --> Data + + self.length_total = len(self.protein_names) + + @property + def raw_file_names(self) -> str: + return self.raw_root + + @property + def raw_dir(self) -> str: + return self.raw_root + + def mk_saved_graph_path(self) -> str: + os.makedirs(os.path.join(self.root, self.name.capitalize()), exist_ok=True) + graph_dir = os.path.join(self.root, self.name.capitalize(), 'graph') + os.makedirs(graph_dir, exist_ok=True) + return graph_dir + + @property + def total_protein_name_file(self) -> str: + return os.path.join(self.root, self.name.capitalize(), 'total_proteins.txt') + + @property + def saved_protein_name_file(self) -> str: + return os.path.join(self.root, self.name.capitalize(), 'saved_proteins.txt') + + @property + def wrong_protein_name_file(self) -> str: + return os.path.join(self.root, self.name.capitalize(), 'wrong_proteins.txt') + + @property + def processed_dir(self) -> str: + return os.path.join(self.root, self.name.capitalize(), 'processed') + + @property + def processed_file_names(self) -> str: + return [p+".pt" for p in self.protein_names] + + def download(self): + pass + + def process(self): + # if self.replace_graph: + self.generate_protein_graph_evaluation() + + exist_proteins = [] + proteins = open(self.saved_protein_name_file, 'r').read().splitlines() + for p in proteins: + file = p + '.pt' + if os.path.exists(os.path.join(self.saved_graph_path, file)): + exist_proteins.append(file) + + protein_num = len(exist_proteins) + if (not self.replace_process) and (len(os.listdir(self.processed_dir)) >= protein_num): + return 0 + + process_bar = tqdm(exist_proteins) + for protein in process_bar: + process_bar.set_description(f"Processing {protein}") + + graph_data = torch.load(os.path.join(self.saved_graph_path, protein)) + tmpseq = [one_letter[amino] for amino in graph_data.seq] + graph_data.seq = "".join(tmpseq) + + if self.pre_filter is not None: + graph_data = self.pre_filter(graph_data) + + if self.pre_transform is not None: + graph_data = self.pre_transform(graph_data) + + saved_prcessed_name = os.path.join(self.processed_dir, protein) + torch.save(graph_data, saved_prcessed_name) + + def generate_protein_graph_evaluation(self): + self.total_protein_names = sorted(os.listdir(self.raw_dir)) + process_bar = tqdm(self.total_protein_names) + for name in process_bar: + process_bar.set_description(f"Processing {name}") + protein_dir = os.path.join(self.raw_dir, name) + + if os.path.exists(os.path.join(self.saved_graph_path, name + '.pt')) or not os.path.isdir(protein_dir): + continue + + pdb_suffix = ".pdb" + pdb_file = os.path.join(protein_dir, name + pdb_suffix) + assert os.path.exists(pdb_file), f"{pdb_file} does not exist" + + rec, rec_coords, c_alpha_coords, n_coords, c_coords,seq = self.get_receptor_inference( + pdb_file) + + rec_graph = self.get_calpha_graph(rec, c_alpha_coords, n_coords, c_coords,seq) + if not rec_graph: + self.wrong_protein_names.append(name) + continue + torch.save(rec_graph, os.path.join(self.saved_graph_path, name + '.pt')) + + with open(self.total_protein_name_file, 'w') as fp: + for item in self.total_protein_names: + fp.writelines("%s\n" % item) + print(f"Total proteins: {self.total_protein_names}") + + self.protein_names = sorted([name.split(".")[0] for name in os.listdir(self.saved_graph_path)]) + with open(self.saved_protein_name_file, 'w') as fp: + for item in self.protein_names: + fp.writelines("%s\n" % item) + + with open(self.wrong_protein_name_file, 'w') as fp: + for item in self.wrong_protein_names: + fp.writelines("%s\n" % item) + print(f"Wrong proteins: {self.wrong_protein_names}") + + def rec_residue_featurizer(self, rec, one_hot=True, add_feature=None): + num_res = len([_ for _ in rec.get_residues()]) + num_feature = 2 + if add_feature.any(): + num_feature += add_feature.shape[1] + res_feature = torch.zeros(num_res, self.num_residue_type + num_feature) + count = 0 + self.sr.compute(rec, level="R") + for residue in rec.get_residues(): + sasa = residue.sasa + for atom in residue: + if atom.name == 'CA': + bfactor = atom.bfactor + assert not np.isinf(bfactor) + assert not np.isnan(bfactor) + assert not np.isinf(sasa) + assert not np.isnan(sasa) + + residx = safe_index( + self.allowable_features['possible_amino_acids'], residue.get_resname()) + res_feat_1 = one_hot_res( + residx, num_residue_type=self.num_residue_type) if one_hot else [residx] + if not res_feat_1: + return False + res_feat_1.append(sasa) + res_feat_1.append(bfactor) + if num_feature > 2: + res_feat_1.extend(list(add_feature[count, :])) + res_feature[count, :] = torch.tensor( + res_feat_1, dtype=torch.float32) + count += 1 + + for k in range(self.num_residue_type, self.num_residue_type + 2): + mean = res_feature[:, k].mean() + std = res_feature[:, k].std() + res_feature[:, k] = (res_feature[:, k] - mean) / (std + 0.000000001) + return res_feature + + def get_node_features(self, n_coords, c_coords, c_alpha_coords, coord_mask, with_coord_mask=True, use_angle=False, use_omega=False): + num_res = n_coords.shape[0] + if use_omega: + num_angle_type = 3 + angles = np.zeros((num_res, num_angle_type)) + for i in range(num_res-1): + # These angles are called φ (phi) which involves the backbone atoms C-N-Cα-C + angles[i, 0] = dihedral(c_coords[i], n_coords[i], c_alpha_coords[i], n_coords[i+1]) + # psi involves the backbone atoms N-Cα-C-N. + angles[i, 1] = dihedral(n_coords[i], c_alpha_coords[i], c_coords[i], n_coords[i+1]) + angles[i, 2] = dihedral(c_alpha_coords[i], c_coords[i], n_coords[i+1], c_alpha_coords[i+1]) + else: + num_angle_type = 2 + angles = np.zeros((num_res, num_angle_type)) + for i in range(num_res-1): + # These angles are called φ (phi) which involves the backbone atoms C-N-Cα-C + angles[i, 0] = dihedral(c_coords[i], n_coords[i], c_alpha_coords[i], n_coords[i+1]) + # psi involves the backbone atoms N-Cα-C-N. + angles[i, 1] = dihedral(n_coords[i], c_alpha_coords[i], c_coords[i], n_coords[i+1]) + if use_angle: + node_scalar_features = angles + else: + node_scalar_features = np.zeros((num_res, num_angle_type*2)) + for i in range(num_angle_type): + node_scalar_features[:, 2*i] = np.sin(angles[:, i]) + node_scalar_features[:, 2*i + 1] = np.cos(angles[:, i]) + + if with_coord_mask: + node_scalar_features = torch.cat([ + node_scalar_features, + coord_mask.float().unsqueeze(-1) + ], dim=-1) + node_vector_features = None + return node_scalar_features, node_vector_features + + def get_calpha_graph(self, rec, c_alpha_coords, n_coords, c_coords,seq): + scalar_feature, vec_feature = self.get_node_features( + n_coords, c_coords, c_alpha_coords, coord_mask=None, + with_coord_mask=False, use_angle=self.use_angle, use_omega=self.use_omega + ) + # Extract 3D coordinates and n_i,u_i,v_i + # vectors of representative residues ################ + residue_representatives_loc_list = [] + n_i_list = [] + u_i_list = [] + v_i_list = [] + for i, residue in enumerate(rec.get_residues()): + n_coord = n_coords[i] + c_alpha_coord = c_alpha_coords[i] + c_coord = c_coords[i] + u_i = (n_coord - c_alpha_coord) / np.linalg.norm(n_coord - c_alpha_coord) + t_i = (c_coord - c_alpha_coord) / np.linalg.norm(c_coord - c_alpha_coord) + n_i = np.cross(u_i, t_i) / np.linalg.norm(np.cross(u_i, t_i)) # main chain + v_i = np.cross(n_i, u_i) + assert (math.fabs(np.linalg.norm(v_i) - 1.) < 1e-5), "protein utils protein_to_graph_dips, v_i norm larger than 1" + n_i_list.append(n_i) + u_i_list.append(u_i) + v_i_list.append(v_i) + residue_representatives_loc_list.append(c_alpha_coord) + + # (N_res, 3) + residue_representatives_loc_feat = np.stack(residue_representatives_loc_list, axis=0) + + n_i_feat = np.stack(n_i_list, axis=0) + u_i_feat = np.stack(u_i_list, axis=0) + v_i_feat = np.stack(v_i_list, axis=0) + num_residues = len(c_alpha_coords) + if num_residues <= 1: + raise ValueError(f"rec contains only 1 residue!") + + ################### Build the k-NN graph ############################## + assert num_residues == residue_representatives_loc_feat.shape[0] + assert residue_representatives_loc_feat.shape[1] == 3 + distances = spa.distance.cdist(c_alpha_coords, c_alpha_coords) + + src_list = [] + dst_list = [] + dist_list = [] + mean_norm_list = [] + for i in range(num_residues): + dst = list(np.where(distances[i, :] < self.cutoff)[0]) + dst.remove(i) + if self.c_alpha_max_neighbors != None and len(dst) > self.c_alpha_max_neighbors: + dst = list(np.argsort(distances[i, :]))[1: self.c_alpha_max_neighbors + 1] + if len(dst) == 0: + # choose second because first is i itself + dst = list(np.argsort(distances[i, :]))[1:2] + log(f'The c_alpha_cutoff {self.cutoff} was too small for one c_alpha such that it had no neighbors. So we connected it to the closest other c_alpha') + assert i not in dst + + src = [i] * len(dst) + src_list.extend(src) + dst_list.extend(dst) + valid_dist = list(distances[i, dst]) + dist_list.extend(valid_dist) + valid_dist_np = distances[i, dst] + + sigma = np.array([1., 2., 5., 10., 30.]).reshape((-1, 1)) + # (sigma_num, neigh_num) + weights = softmax(-valid_dist_np.reshape((1, -1)) ** 2 / sigma, axis=1) + # print(weights) + assert weights[0].sum() > 1 - 1e-2 and weights[0].sum() < 1.01 + # (neigh_num, 3) + diff_vecs = residue_representatives_loc_feat[src, :] - residue_representatives_loc_feat[dst, :] + # (sigma_num, 3) + mean_vec = weights.dot(diff_vecs) + # (sigma_num,) + denominator = weights.dot(np.linalg.norm(diff_vecs, axis=1)) + # (sigma_num,) + mean_vec_ratio_norm = np.linalg.norm(mean_vec, axis=1) / denominator + mean_norm_list.append(mean_vec_ratio_norm) + + assert len(src_list) == len(dst_list) + assert len(dist_list) == len(dst_list) + + residue_representatives_loc_feat = torch.from_numpy(residue_representatives_loc_feat.astype(np.float32)) + x = self.rec_residue_featurizer(rec, one_hot=True, add_feature=scalar_feature) + + if isinstance(x, bool) and (not x): + return False + + graph = Data( + x=x, + pos=residue_representatives_loc_feat, + edge_attr=self.get_edge_features(src_list, dst_list, dist_list, divisor=4), + edge_index=torch.tensor([src_list, dst_list]), + edge_dist=torch.tensor(dist_list), + distances=torch.tensor(distances), + mu_r_norm=torch.from_numpy(np.array(mean_norm_list).astype(np.float32)), + seq=seq + ) + + # Loop over all edges of the graph and build the various p_ij, q_ij, k_ij, t_ij pairs + edge_feat_ori_list = [] + for i in range(len(dist_list)): + src = src_list[i] + dst = dst_list[i] + # place n_i, u_i, v_i as lines in a 3x3 basis matrix + basis_matrix = np.stack((n_i_feat[dst, :], u_i_feat[dst, :], v_i_feat[dst, :]), axis=0) + p_ij = np.matmul( + basis_matrix, + residue_representatives_loc_feat[src, :] - residue_representatives_loc_feat[dst, :] + ) + q_ij = np.matmul(basis_matrix, n_i_feat[src, :]) # shape (3,) + k_ij = np.matmul(basis_matrix, u_i_feat[src, :]) + t_ij = np.matmul(basis_matrix, v_i_feat[src, :]) + s_ij = np.concatenate((p_ij, q_ij, k_ij, t_ij), axis=0) # shape (12,) + edge_feat_ori_list.append(s_ij) + + edge_feat_ori_feat = np.stack(edge_feat_ori_list, axis=0) # shape (num_edges, 4, 3) + edge_feat_ori_feat = torch.from_numpy(edge_feat_ori_feat.astype(np.float32)) + + graph.edge_attr = torch.cat([graph.edge_attr, edge_feat_ori_feat], axis=1) # (num_edges, 17) + #graph = self.remove_node(graph, graph.x.shape[0]-1) + # self.get_calpha_graph_single(graph, 6) + return graph + + def remove_node(self, graph, node_idx): + new_graph = Data.clone(graph) + # delete node + new_graph.x = torch.cat( + [new_graph.x[:node_idx, :], new_graph.x[node_idx+1:, :]]) + new_graph.pos = torch.cat( + [new_graph.pos[:node_idx, :], new_graph.pos[node_idx+1:, :]]) + new_graph.mu_r_norm = torch.cat( + [new_graph.mu_r_norm[:node_idx, :], new_graph.mu_r_norm[node_idx+1:, :]]) + + # delete edge + keep_edge = (torch.sum(new_graph.edge_index == node_idx, dim=0) == 0) + new_graph.edge_index = new_graph.edge_index[:, keep_edge] + new_graph.edge_attr = new_graph.edge_attr[keep_edge, :] + return new_graph + + def get_edge_features(self, src_list, dst_list, dist_list, divisor=4): + seq_edge = torch.absolute(torch.tensor(src_list) - torch.tensor(dst_list)).reshape(-1, 1) + seq_edge = torch.where(seq_edge > self.seq_dist_cut, self.seq_dist_cut, seq_edge) + seq_edge = F.one_hot(seq_edge, num_classes=self.seq_dist_cut + 1).reshape((-1, self.seq_dist_cut + 1)) + + contact_sig = torch.where(torch.tensor(dist_list) <= 8, 1, 0).reshape(-1, 1) + # avg distance = 7. So divisor = (4/7)*7 = 4 + dist_fea = self.distance_featurizer(dist_list, divisor=divisor) + + return torch.concat([seq_edge, dist_fea, contact_sig], dim=-1) + + def get_receptor_inference(self, rec_path): + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=PDBConstructionWarning) + structure = self.biopython_parser.get_structure('random_id', rec_path) + rec = structure[0] + coords = [] + c_alpha_coords = [] + n_coords = [] + c_coords = [] + valid_chain_ids = [] + lengths = [] + seq = [] + for i, chain in enumerate(rec): + chain_coords = [] # num_residues, num_atoms, 3 + chain_c_alpha_coords = [] + chain_n_coords = [] + chain_c_coords = [] + count = 0 + invalid_res_ids = [] + for res_idx, residue in enumerate(chain): + if residue.get_resname() == 'HOH': + invalid_res_ids.append(residue.get_id()) + continue + residue_coords = [] + c_alpha, n, c = None, None, None + for atom in residue: + if atom.name == 'CA': + c_alpha = list(atom.get_vector()) + seq.append(str(residue).split(" ")[1]) + if atom.name == 'N': + n = list(atom.get_vector()) + if atom.name == 'C': + c = list(atom.get_vector()) + residue_coords.append(list(atom.get_vector())) + # only append residue if it is an amino acid and not some weired molecule that is part of the complex + if c_alpha != None and n != None and c != None: + chain_c_alpha_coords.append(c_alpha) + chain_n_coords.append(n) + chain_c_coords.append(c) + chain_coords.append(np.array(residue_coords)) + count += 1 + else: + invalid_res_ids.append(residue.get_id()) + for res_id in invalid_res_ids: + chain.detach_child(res_id) + lengths.append(count) + coords.append(chain_coords) + c_alpha_coords.append(np.array(chain_c_alpha_coords)) + n_coords.append(np.array(chain_n_coords)) + c_coords.append(np.array(chain_c_coords)) + if len(chain_coords) > 0: + valid_chain_ids.append(chain.get_id()) + valid_coords = [] + valid_c_alpha_coords = [] + valid_n_coords = [] + valid_c_coords = [] + valid_lengths = [] + invalid_chain_ids = [] + for i, chain in enumerate(rec): + if chain.get_id() in valid_chain_ids: + valid_coords.append(coords[i]) + valid_c_alpha_coords.append(c_alpha_coords[i]) + valid_n_coords.append(n_coords[i]) + valid_c_coords.append(c_coords[i]) + valid_lengths.append(lengths[i]) + else: + invalid_chain_ids.append(chain.get_id()) + # list with n_residues arrays: [n_atoms, 3] + coords = [item for sublist in valid_coords for item in sublist] + + c_alpha_coords = np.concatenate(valid_c_alpha_coords, axis=0) # [n_residues, 3] + n_coords = np.concatenate(valid_n_coords, axis=0) # [n_residues, 3] + c_coords = np.concatenate(valid_c_coords, axis=0) # [n_residues, 3] + + for invalid_id in invalid_chain_ids: + rec.detach_child(invalid_id) + + assert len(c_alpha_coords) == len(n_coords) + assert len(c_alpha_coords) == len(c_coords) + assert sum(valid_lengths) == len(c_alpha_coords) + return rec, coords, c_alpha_coords, n_coords, c_coords,seq + + def len(self): + return len(os.listdir(self.saved_graph_path)) + + def get_statistic_info(self): + node_num = torch.zeros(self.length_total) + edge_num = torch.zeros(self.length_total) + for i in tqdm(range(self.length_total)): + graph = self.get(i) + node_num[i] = graph.x.shape[0] + edge_num[i] = graph.edge_index.shape[1] + # if i == 1000: + # break + num_node_min = torch.min(node_num) + num_node_max = torch.max(node_num) + num_node_avg = torch.mean(node_num) + num_edge_min = torch.min(edge_num) + num_edge_max = torch.max(edge_num) + num_edge_avg = torch.mean(edge_num) + print(f'Graph Num: {self.length_total}') + print( + f'Min Nodes: {num_node_min:.2f} Max Nodes: {num_node_max:.2f}. Avg Nodes: {num_node_avg:.2f}') + print( + f'Min Edges: {num_edge_min:.2f} Max Edges: {num_edge_max:.2f}. Avg Edges: {num_edge_avg:.2f}') + + def get(self, idx): + protein_name = self.protein_names[idx] + data = torch.load(os.path.join(self.processed_dir, f'{protein_name}.pt')) + notes_number = list((data.x[:, :20].argmax(dim=1)).size())[0] + data.y = torch.argmax(data.x[torch.tensor(range(notes_number)), :self.num_residue_type], dim=1) + data.protein_name = protein_name + return data + + def find_idx(self, idx_protein, amino_idx): + idx = (self.distances[idx_protein][:-1, amino_idx] + < self.micro_radius).nonzero(as_tuple=True)[0] + return idx + + def get_calpha_graph_single(self, graph, idx_protein, amino_idx): + choosen_amino_idx = self.find_idx(idx_protein, amino_idx) + keep_edge_index = [] + + for edge_idx in range(graph.num_edges): + edge = graph.edge_index.t()[edge_idx] + if (edge[0] in choosen_amino_idx) and (edge[1] in choosen_amino_idx): + keep_edge_index.append(edge_idx) + + graph1 = Data( + x=graph.x[choosen_amino_idx, :], + pos=graph.pos[choosen_amino_idx, :], + edge_index=graph.edge_index[:, keep_edge_index], + edge_attr=graph.edge_attr[keep_edge_index, :], + mu_r_norm=graph.mu_r_norm[choosen_amino_idx, :] + ) + return graph1 + + def __repr__(self) -> str: + return f'{self.__class__.__name__}{self.name.capitalize()}()' + + def distance_featurizer(self, dist_list, divisor) -> torch.Tensor: + # you want to use a divisor that is close to 4/7 times the average distance that you want to encode + length_scale_list = [1.5 ** x for x in range(15)] + center_list = [0. for _ in range(15)] + + num_edge = len(dist_list) + dist_list = np.array(dist_list) + + transformed_dist = [np.exp(- ((dist_list / divisor) ** 2) / float(length_scale)) + for length_scale, center in zip(length_scale_list, center_list)] + + transformed_dist = np.array(transformed_dist).T + transformed_dist = transformed_dist.reshape((num_edge, -1)) + return torch.from_numpy(transformed_dist.astype(np.float32)) diff --git a/proteingym/baselines/protssn/src/models.py b/proteingym/baselines/protssn/src/models.py new file mode 100644 index 0000000..a60ee52 --- /dev/null +++ b/proteingym/baselines/protssn/src/models.py @@ -0,0 +1,127 @@ +import torch +import random +import gc +import torch.nn as nn +from torch_geometric.data import Batch, Dataset +from transformers import AutoTokenizer, EsmModel +from typing import * +from src.module.egnn.network import EGNN +from src.module.gcn.network import GCN +from src.module.gat.network import GAT + +class PLM_model(nn.Module): + possible_amino_acids = [ + 'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', + 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL' + ] + one_letter = { + 'VAL':'V', 'ILE':'I', 'LEU':'L', 'GLU':'E', 'GLN':'Q', + 'ASP':'D', 'ASN':'N', 'HIS':'H', 'TRP':'W', 'PHE':'F', 'TYR':'Y', + 'ARG':'R', 'LYS':'K', 'SER':'S', 'THR':'T', 'MET':'M', 'ALA':'A', + 'GLY':'G', 'PRO':'P', 'CYS':'C' + } + + def __init__(self, args): + super().__init__() + # load global config + self.args = args + + # esm on the first cuda + self.tokenizer = AutoTokenizer.from_pretrained(self.args.plm) + self.model = EsmModel.from_pretrained(self.args.plm).cuda() + + + def forward(self, batch): + with torch.no_grad(): + if not isinstance(batch, List): + batch = [batch] + # get the target sequence + one_hot_seqs = [list(elem.x[:,:20].argmax(1)) for elem in batch] + muted_res_seqs = ["".join([self.one_letter[self.possible_amino_acids[idx]] for idx in seq_idx]) for seq_idx in one_hot_seqs] + one_hot_truth_seqs = [elem.y for elem in batch] + truth_res_seqs = ["".join([self.one_letter[self.possible_amino_acids[idx]] for idx in seq_idx]) for seq_idx in one_hot_truth_seqs] + + if not hasattr(self.args, "noise_type"): + input_seqs = truth_res_seqs + elif self.args.noise_type == 'mask': + input_seqs = self._mask_input_sequence(truth_res_seqs) + elif self.args.noise_type == 'mut': + input_seqs = muted_res_seqs + else: + raise ValueError(f"No implement of {self.args.noise_type}") + + batch_graph = self._nlp_inference(input_seqs, batch) + return batch_graph + + @torch.no_grad() + def _mask_input_sequence(self, truth_res_seqs): + input_seqs = [] + self.mask_ratio = self.args.noise_ratio + for truth_seq in truth_res_seqs: + masked_seq = "" + for truth_token in truth_seq: + pattern = torch.multinomial(torch.tensor([1 - self.args.noise_ratio, + self.mask_ratio*0.8, + self.mask_ratio*0.1, + self.mask_ratio*0.1]), + num_samples=1, + replacement=True) + # 80% of the time, we replace masked input tokens with mask_token ([MASK]) + if pattern == 1: + masked_seq += '' + # 10% of the time, we replace masked input tokens with random word + elif pattern == 2: + masked_seq += random.sample(list(self.one_letter.values()), 1)[0] + # The rest of the time (10% of the time) we keep the masked input tokens unchanged + else: + masked_seq += truth_token + input_seqs.append(masked_seq) + return input_seqs + + + @torch.no_grad() + def _nlp_inference(self, input_seqs, batch): + inputs = self.tokenizer(input_seqs, return_tensors="pt", padding=True).to("cuda:0") + batch_lens = (inputs["attention_mask"] == 1).sum(1) - 2 + outputs = self.model(**inputs) + last_hidden_states = outputs.last_hidden_state + for idx, (hidden_state, seq_len) in enumerate(zip(last_hidden_states, batch_lens)): + batch[idx].esm_rep = hidden_state[1: 1+seq_len] + del batch[idx].seq + + # move to the GNN devices + batch = [elem.cuda() for elem in batch] + batch_graph = Batch.from_data_list(batch) + gc.collect() + torch.cuda.empty_cache() + return batch_graph + + + +class GNN_model(nn.Module): + def __init__(self, args): + super().__init__() + # load graph network config which usually not change + self.gnn_config = args.gnn_config + # load global config + self.args = args + + # calculate input dim according to the input feature + self.out_dim = 20 + self.input_dim = self.args.plm_hidden_size + + # gnn on the rest cudas + if "egnn" == self.args.gnn: + self.GNN_model = EGNN(self.gnn_config, self.args, self.input_dim, self.out_dim) + elif "gcn" == self.args.gnn: + self.GNN_model = GCN(self.gnn_config, self.input_dim, self.out_dim) + elif "gat" == self.args.gnn: + self.GNN_model = GAT(self.gnn_config,self.input_dim, self.out_dim) + else: + raise KeyError(f"No implement of {self.opt['gnn']}") + self.GNN_model = self.GNN_model.cuda() + + def forward(self, batch_graph): + gnn_out = self.GNN_model(batch_graph) + return gnn_out + diff --git a/proteingym/baselines/protssn/src/module/egnn/__init__.py b/proteingym/baselines/protssn/src/module/egnn/__init__.py new file mode 100644 index 0000000..af6f786 --- /dev/null +++ b/proteingym/baselines/protssn/src/module/egnn/__init__.py @@ -0,0 +1,2 @@ +from .egnn_pytorch import EGNN, EGNN_Network +from .egnn_pytorch_geometric import EGNN_Sparse, EGNN_Sparse_Network diff --git a/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch.py b/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch.py new file mode 100644 index 0000000..5376a12 --- /dev/null +++ b/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch.py @@ -0,0 +1,454 @@ +import torch +from torch import nn, einsum, broadcast_tensors +import torch.nn.functional as F + +from einops import rearrange, repeat +from einops.layers.torch import Rearrange + +# helper functions + +def exists(val): + return val is not None + +def safe_div(num, den, eps = 1e-8): + res = num.div(den.clamp(min = eps)) + res.masked_fill_(den == 0, 0.) + return res + +def batched_index_select(values, indices, dim = 1): + value_dims = values.shape[(dim + 1):] + values_shape, indices_shape = map(lambda t: list(t.shape), (values, indices)) + indices = indices[(..., *((None,) * len(value_dims)))] + indices = indices.expand(*((-1,) * len(indices_shape)), *value_dims) + value_expand_len = len(indices_shape) - (dim + 1) + values = values[(*((slice(None),) * dim), *((None,) * value_expand_len), ...)] + + value_expand_shape = [-1] * len(values.shape) + expand_slice = slice(dim, (dim + value_expand_len)) + value_expand_shape[expand_slice] = indices.shape[expand_slice] + values = values.expand(*value_expand_shape) + + dim += value_expand_len + return values.gather(dim, indices) + +def fourier_encode_dist(x, num_encodings = 4, include_self = True): + x = x.unsqueeze(-1) + device, dtype, orig_x = x.device, x.dtype, x + scales = 2 ** torch.arange(num_encodings, device = device, dtype = dtype) + x = x / scales + x = torch.cat([x.sin(), x.cos()], dim=-1) + x = torch.cat((x, orig_x), dim = -1) if include_self else x + return x + +def embedd_token(x, dims, layers): + stop_concat = -len(dims) + to_embedd = x[:, stop_concat:].long() + for i,emb_layer in enumerate(layers): + # the portion corresponding to `to_embedd` part gets dropped + x = torch.cat([ x[:, :stop_concat], + emb_layer( to_embedd[:, i] ) + ], dim=-1) + stop_concat = x.shape[-1] + return x + +# swish activation fallback + +class Swish_(nn.Module): + def forward(self, x): + return x * x.sigmoid() + +SiLU = nn.SiLU if hasattr(nn, 'SiLU') else Swish_ + +# helper classes + +# this follows the same strategy for normalization as done in SE3 Transformers +# https://github.com/lucidrains/se3-transformer-pytorch/blob/main/se3_transformer_pytorch/se3_transformer_pytorch.py#L95 + +class CoorsNorm(nn.Module): + def __init__(self, eps = 1e-8, scale_init = 1.): + super().__init__() + self.eps = eps + scale = torch.zeros(1).fill_(scale_init) + self.scale = nn.Parameter(scale) + + def forward(self, coors): + norm = coors.norm(dim = -1, keepdim = True) + normed_coors = coors / norm.clamp(min = self.eps) + return normed_coors * self.scale + +# global linear attention + +class Attention(nn.Module): + def __init__(self, dim, heads = 8, dim_head = 64): + super().__init__() + inner_dim = heads * dim_head + self.heads = heads + self.scale = dim_head ** -0.5 + + self.to_q = nn.Linear(dim, inner_dim, bias = False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False) + self.to_out = nn.Linear(inner_dim, dim) + + def forward(self, x, context, mask = None): + h = self.heads + + q = self.to_q(x) + kv = self.to_kv(context).chunk(2, dim = -1) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, *kv)) + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + + if exists(mask): + mask_value = -torch.finfo(dots.dtype).max + mask = rearrange(mask, 'b n -> b () () n') + dots.masked_fill_(~mask, mask_value) + + attn = dots.softmax(dim = -1) + out = einsum('b h i j, b h j d -> b h i d', attn, v) + + out = rearrange(out, 'b h n d -> b n (h d)', h = h) + return self.to_out(out) + +class GlobalLinearAttention(nn.Module): + def __init__( + self, + *, + dim, + heads = 8, + dim_head = 64 + ): + super().__init__() + self.norm_seq = nn.LayerNorm(dim) + self.norm_queries = nn.LayerNorm(dim) + self.attn1 = Attention(dim, heads, dim_head) + self.attn2 = Attention(dim, heads, dim_head) + + self.ff = nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Linear(dim * 4, dim) + ) + + def forward(self, x, queries, mask = None): + res_x, res_queries = x, queries + x, queries = self.norm_seq(x), self.norm_queries(queries) + + induced = self.attn1(queries, x, mask = mask) + out = self.attn2(x, induced) + + x = out + res_x + queries = induced + res_queries + + x = self.ff(x) + x + return x, queries + +# classes + +class EGNN(nn.Module): + def __init__( + self, + dim, + edge_dim = 0, + m_dim = 16, + fourier_features = 0, + num_nearest_neighbors = 0, + dropout = 0.0, + init_eps = 1e-3, + norm_feats = False, + norm_coors = False, + norm_coors_scale_init = 1e-2, + update_feats = True, + update_coors = True, + only_sparse_neighbors = False, + valid_radius = float('inf'), + m_pool_method = 'sum', + soft_edges = False, + coor_weights_clamp_value = None + ): + super().__init__() + assert m_pool_method in {'sum', 'mean'}, 'pool method must be either sum or mean' + assert update_feats or update_coors, 'you must update either features, coordinates, or both' + + self.fourier_features = fourier_features + + edge_input_dim = (fourier_features * 2) + (dim * 2) + edge_dim + 1 + dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + + self.edge_mlp = nn.Sequential( + nn.Linear(edge_input_dim, edge_input_dim * 2), + dropout, + SiLU(), + nn.Linear(edge_input_dim * 2, m_dim), + SiLU() + ) + + self.edge_gate = nn.Sequential( + nn.Linear(m_dim, 1), + nn.Sigmoid() + ) if soft_edges else None + + self.node_norm = nn.LayerNorm(dim) if norm_feats else nn.Identity() + self.coors_norm = CoorsNorm(scale_init = norm_coors_scale_init) if norm_coors else nn.Identity() + + self.m_pool_method = m_pool_method + + self.node_mlp = nn.Sequential( + nn.Linear(dim + m_dim, dim * 2), + dropout, + SiLU(), + nn.Linear(dim * 2, dim), + ) if update_feats else None + + self.coors_mlp = nn.Sequential( + nn.Linear(m_dim, m_dim * 4), + dropout, + SiLU(), + nn.Linear(m_dim * 4, 1) + ) if update_coors else None + + self.num_nearest_neighbors = num_nearest_neighbors + self.only_sparse_neighbors = only_sparse_neighbors + self.valid_radius = valid_radius + + self.coor_weights_clamp_value = coor_weights_clamp_value + + self.init_eps = init_eps + self.apply(self.init_) + + def init_(self, module): + if type(module) in {nn.Linear}: + # seems to be needed to keep the network from exploding to NaN with greater depths + nn.init.normal_(module.weight, std = self.init_eps) + + def forward(self, feats, coors, edges = None, mask = None, adj_mat = None): + b, n, d, device, fourier_features, num_nearest, valid_radius, only_sparse_neighbors = *feats.shape, feats.device, self.fourier_features, self.num_nearest_neighbors, self.valid_radius, self.only_sparse_neighbors + + if exists(mask): + num_nodes = mask.sum(dim = -1) + + use_nearest = num_nearest > 0 or only_sparse_neighbors + + rel_coors = rearrange(coors, 'b i d -> b i () d') - rearrange(coors, 'b j d -> b () j d') + rel_dist = (rel_coors ** 2).sum(dim = -1, keepdim = True) + + i = j = n + + if use_nearest: + ranking = rel_dist[..., 0].clone() + + if exists(mask): + rank_mask = mask[:, :, None] * mask[:, None, :] + ranking.masked_fill_(~rank_mask, 1e5) + + if exists(adj_mat): + if len(adj_mat.shape) == 2: + adj_mat = repeat(adj_mat.clone(), 'i j -> b i j', b = b) + + if only_sparse_neighbors: + num_nearest = int(adj_mat.float().sum(dim = -1).max().item()) + valid_radius = 0 + + self_mask = rearrange(torch.eye(n, device = device, dtype = torch.bool), 'i j -> () i j') + + adj_mat = adj_mat.masked_fill(self_mask, False) + ranking.masked_fill_(self_mask, -1.) + ranking.masked_fill_(adj_mat, 0.) + + nbhd_ranking, nbhd_indices = ranking.topk(num_nearest, dim = -1, largest = False) + + nbhd_mask = nbhd_ranking <= valid_radius + + rel_coors = batched_index_select(rel_coors, nbhd_indices, dim = 2) + rel_dist = batched_index_select(rel_dist, nbhd_indices, dim = 2) + + if exists(edges): + edges = batched_index_select(edges, nbhd_indices, dim = 2) + + j = num_nearest + + if fourier_features > 0: + rel_dist = fourier_encode_dist(rel_dist, num_encodings = fourier_features) + rel_dist = rearrange(rel_dist, 'b i j () d -> b i j d') + + if use_nearest: + feats_j = batched_index_select(feats, nbhd_indices, dim = 1) + else: + feats_j = rearrange(feats, 'b j d -> b () j d') + + feats_i = rearrange(feats, 'b i d -> b i () d') + feats_i, feats_j = broadcast_tensors(feats_i, feats_j) + + edge_input = torch.cat((feats_i, feats_j, rel_dist), dim = -1) + + if exists(edges): + edge_input = torch.cat((edge_input, edges), dim = -1) + + m_ij = self.edge_mlp(edge_input) + + if exists(self.edge_gate): + m_ij = m_ij * self.edge_gate(m_ij) + + if exists(mask): + mask_i = rearrange(mask, 'b i -> b i ()') + + if use_nearest: + mask_j = batched_index_select(mask, nbhd_indices, dim = 1) + mask = (mask_i * mask_j) & nbhd_mask + else: + mask_j = rearrange(mask, 'b j -> b () j') + mask = mask_i * mask_j + + if exists(self.coors_mlp): + coor_weights = self.coors_mlp(m_ij) + coor_weights = rearrange(coor_weights, 'b i j () -> b i j') + + rel_coors = self.coors_norm(rel_coors) + + if exists(mask): + coor_weights.masked_fill_(~mask, 0.) + + if exists(self.coor_weights_clamp_value): + clamp_value = self.coor_weights_clamp_value + coor_weights.clamp_(min = -clamp_value, max = clamp_value) + + coors_out = einsum('b i j, b i j c -> b i c', coor_weights, rel_coors) + coors + else: + coors_out = coors + + if exists(self.node_mlp): + if exists(mask): + m_ij_mask = rearrange(mask, '... -> ... ()') + m_ij = m_ij.masked_fill(~m_ij_mask, 0.) + + if self.m_pool_method == 'mean': + if exists(mask): + # masked mean + mask_sum = m_ij_mask.sum(dim = -2) + m_i = safe_div(m_ij.sum(dim = -2), mask_sum) + else: + m_i = m_ij.mean(dim = -2) + + elif self.m_pool_method == 'sum': + m_i = m_ij.sum(dim = -2) + + normed_feats = self.node_norm(feats) + node_mlp_input = torch.cat((normed_feats, m_i), dim = -1) + node_out = self.node_mlp(node_mlp_input) + feats + else: + node_out = feats + + return node_out, coors_out + +class EGNN_Network(nn.Module): + def __init__( + self, + *, + depth, + dim, + num_tokens = None, + num_edge_tokens = None, + num_positions = None, + edge_dim = 0, + num_adj_degrees = None, + adj_dim = 0, + global_linear_attn_every = 0, + global_linear_attn_heads = 8, + global_linear_attn_dim_head = 64, + num_global_tokens = 4, + **kwargs + ): + super().__init__() + assert not (exists(num_adj_degrees) and num_adj_degrees < 1), 'make sure adjacent degrees is greater than 1' + self.num_positions = num_positions + + self.token_emb = nn.Embedding(num_tokens, dim) if exists(num_tokens) else None + self.pos_emb = nn.Embedding(num_positions, dim) if exists(num_positions) else None + self.edge_emb = nn.Embedding(num_edge_tokens, edge_dim) if exists(num_edge_tokens) else None + self.has_edges = edge_dim > 0 + + self.num_adj_degrees = num_adj_degrees + self.adj_emb = nn.Embedding(num_adj_degrees + 1, adj_dim) if exists(num_adj_degrees) and adj_dim > 0 else None + + edge_dim = edge_dim if self.has_edges else 0 + adj_dim = adj_dim if exists(num_adj_degrees) else 0 + + has_global_attn = global_linear_attn_every > 0 + self.global_tokens = None + if has_global_attn: + self.global_tokens = nn.Parameter(torch.randn(num_global_tokens, dim)) + + self.layers = nn.ModuleList([]) + for ind in range(depth): + is_global_layer = has_global_attn and (ind % global_linear_attn_every) == 0 + + self.layers.append(nn.ModuleList([ + GlobalLinearAttention(dim = dim, heads = global_linear_attn_heads, dim_head = global_linear_attn_dim_head) if is_global_layer else None, + EGNN(dim = dim, edge_dim = (edge_dim + adj_dim), norm_feats = True, **kwargs), + ])) + + def forward( + self, + feats, + coors, + adj_mat = None, + edges = None, + mask = None, + return_coor_changes = False + ): + b, device = feats.shape[0], feats.device + + if exists(self.token_emb): + feats = self.token_emb(feats) + + if exists(self.pos_emb): + n = feats.shape[1] + assert n <= self.num_positions, f'given sequence length {n} must be less than the number of positions {self.num_positions} set at init' + pos_emb = self.pos_emb(torch.arange(n, device = device)) + feats += rearrange(pos_emb, 'n d -> () n d') + + if exists(edges) and exists(self.edge_emb): + edges = self.edge_emb(edges) + + # create N-degrees adjacent matrix from 1st degree connections + if exists(self.num_adj_degrees): + assert exists(adj_mat), 'adjacency matrix must be passed in (keyword argument adj_mat)' + + if len(adj_mat.shape) == 2: + adj_mat = repeat(adj_mat.clone(), 'i j -> b i j', b = b) + + adj_indices = adj_mat.clone().long() + + for ind in range(self.num_adj_degrees - 1): + degree = ind + 2 + + next_degree_adj_mat = (adj_mat.float() @ adj_mat.float()) > 0 + next_degree_mask = (next_degree_adj_mat.float() - adj_mat.float()).bool() + adj_indices.masked_fill_(next_degree_mask, degree) + adj_mat = next_degree_adj_mat.clone() + + if exists(self.adj_emb): + adj_emb = self.adj_emb(adj_indices) + edges = torch.cat((edges, adj_emb), dim = -1) if exists(edges) else adj_emb + + # setup global attention + + global_tokens = None + if exists(self.global_tokens): + global_tokens = repeat(self.global_tokens, 'n d -> b n d', b = b) + + # go through layers + + coor_changes = [coors] + + for global_attn, egnn in self.layers: + if exists(global_attn): + feats, global_tokens = global_attn(feats, global_tokens, mask = mask) + + feats, coors = egnn(feats, coors, adj_mat = adj_mat, edges = edges, mask = mask) + coor_changes.append(coors) + + if return_coor_changes: + return feats, coors, coor_changes + + return feats, coors diff --git a/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch_geometric.py b/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch_geometric.py new file mode 100644 index 0000000..ebb0598 --- /dev/null +++ b/proteingym/baselines/protssn/src/module/egnn/egnn_pytorch_geometric.py @@ -0,0 +1,467 @@ +import torch +from torch import nn, einsum, broadcast_tensors +import torch.nn.functional as F + +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +# types + +from typing import Optional, List, Union + +# pytorch geometric + +try: + import torch_geometric + from torch_geometric.nn import MessagePassing + from torch_geometric.typing import Adj, Size, OptTensor, Tensor +except: + Tensor = OptTensor = Adj = MessagePassing = Size = object + PYG_AVAILABLE = False + + # to stop throwing errors from type suggestions + Adj = object + Size = object + OptTensor = object + Tensor = object + +from .egnn_pytorch import * + +# global linear attention + +class Attention_Sparse(Attention): + def __init__(self, dim, heads = 8, dim_head = 64): + """ Wraps the attention class to operate with pytorch-geometric inputs. """ + super(Attention_Sparse, self).__init__(dim, heads = 8, dim_head = 64) + + def sparse_forward(self, x, context, batch=None, batch_uniques=None, mask=None): + assert batch is not None or batch_uniques is not None, "Batch/(uniques) must be passed for block_sparse_attn" + if batch_uniques is None: + batch_uniques = torch.unique(batch, return_counts=True) + # only one example in batch - do dense - faster + if batch_uniques[0].shape[0] == 1: + x, context = map(lambda t: rearrange(t, 'h d -> () h d'), (x, context)) + return self.forward(x, context, mask=None).squeeze() # get rid of batch dim + # multiple examples in batch - do block-sparse by dense loop + else: + x_list = [] + aux_count = 0 + for bi,n_idxs in zip(*batch_uniques): + x_list.append( + self.sparse_forward( + x[aux_count:aux_count+n_idxs], + context[aux_count:aux_count+n_idxs], + batch_uniques = (bi.unsqueeze(-1), n_idxs.unsqueeze(-1)) + ) + ) + return torch.cat(x_list, dim=0) + + +class GlobalLinearAttention_Sparse(nn.Module): + def __init__( + self, + *, + dim, + heads = 8, + dim_head = 64 + ): + super().__init__() + self.norm_seq = torch_geometric.nn.norm.LayerNorm(dim) + self.norm_queries = torch_geometric.nn.norm.LayerNorm(dim) + self.attn1 = Attention_Sparse(dim, heads, dim_head) + self.attn2 = Attention_Sparse(dim, heads, dim_head) + + # can't concat pyg norms with torch sequentials + self.ff_norm = torch_geometric.nn.norm.LayerNorm(dim) + self.ff = nn.Sequential( + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Linear(dim * 4, dim) + ) + + def forward(self, x, queries, batch=None, batch_uniques=None, mask = None): + res_x, res_queries = x, queries + x, queries = self.norm_seq(x, batch=batch), self.norm_queries(queries, batch=batch) + + induced = self.attn1.sparse_forward(queries, x, batch=batch, batch_uniques=batch_uniques, mask = mask) + out = self.attn2.sparse_forward(x, induced, batch=batch, batch_uniques=batch_uniques) + + x = out + res_x + queries = induced + res_queries + + x_norm = self.ff_norm(x, batch=batch) + x = self.ff(x_norm) + x_norm + return x, queries + + +# define pytorch-geometric equivalents + +class EGNN_Sparse(MessagePassing): + """ Different from the above since it separates the edge assignment + from the computation (this allows for great reduction in time and + computations when the graph is locally or sparse connected). + * aggr: one of ["add", "mean", "max"] + """ + def __init__( + self, + feats_dim, + pos_dim=3, + edge_attr_dim = 0, + m_dim = 16, + fourier_features = 0, + soft_edge = 0, + norm_feats = False, + norm_coors = False, + norm_coors_scale_init = 1e-2, + update_feats = True, + update_coors = False, + dropout = 0., + coor_weights_clamp_value = None, + aggr = "add", + mlp_num = 2, + **kwargs + ): + assert aggr in {'add', 'sum', 'max', 'mean'}, 'pool method must be a valid option' + assert update_feats or update_coors, 'you must update either features, coordinates, or both' + kwargs.setdefault('aggr', aggr) + super(EGNN_Sparse, self).__init__(**kwargs) + # model params + self.fourier_features = fourier_features + self.feats_dim = feats_dim + self.pos_dim = pos_dim + self.m_dim = m_dim + self.soft_edge = soft_edge + self.norm_feats = norm_feats + self.norm_coors = norm_coors + self.update_coors = update_coors + self.update_feats = update_feats + self.coor_weights_clamp_value = None + self.mlp_num = mlp_num + self.edge_input_dim = (fourier_features * 2) + edge_attr_dim + 1 + (feats_dim * 2) + self.dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + + # EDGES + if self.mlp_num >2: + self.edge_mlp = nn.Sequential( + nn.Linear(self.edge_input_dim, self.edge_input_dim * 8), + self.dropout, + SiLU(), + nn.Linear(self.edge_input_dim * 8, self.edge_input_dim * 4), + self.dropout, + SiLU(), + nn.Linear(self.edge_input_dim * 4, self.edge_input_dim * 2), + self.dropout, + SiLU(), + nn.Linear(self.edge_input_dim * 2, m_dim), + SiLU(), + ) if update_feats else None + else: + self.edge_mlp = nn.Sequential( + nn.Linear(self.edge_input_dim, self.edge_input_dim * 2), + self.dropout, + SiLU(), + nn.Linear(self.edge_input_dim * 2, m_dim), + SiLU() + ) + + self.edge_weight = nn.Sequential(nn.Linear(m_dim, 1), + nn.Sigmoid() + ) if soft_edge else None + + # NODES - can't do identity in node_norm bc pyg expects 2 inputs, but identity expects 1. + self.node_norm = torch_geometric.nn.norm.LayerNorm(feats_dim) if norm_feats else None + self.coors_norm = CoorsNorm(scale_init = norm_coors_scale_init) if norm_coors else nn.Identity() + if self.mlp_num >2: + self.node_mlp = nn.Sequential( + nn.Linear(feats_dim + m_dim, feats_dim * 8), + self.dropout, + SiLU(), + nn.Linear(feats_dim * 8, feats_dim * 4), + self.dropout, + SiLU(), + nn.Linear(feats_dim * 4, feats_dim * 2), + self.dropout, + SiLU(), + nn.Linear(feats_dim * 2, feats_dim), + ) if update_feats else None + else: + self.node_mlp = nn.Sequential( + nn.Linear(feats_dim + m_dim, feats_dim * 2), + self.dropout, + SiLU(), + nn.Linear(feats_dim * 2, feats_dim), + ) if update_feats else None + + # COORS + self.coors_mlp = nn.Sequential( + nn.Linear(m_dim, m_dim * 4), + self.dropout, + SiLU(), + nn.Linear(self.m_dim * 4, 1) + ) if update_coors else None + + self.apply(self.init_) + + def init_(self, module): + if type(module) in {nn.Linear}: + # seems to be needed to keep the network from exploding to NaN with greater depths + nn.init.xavier_normal_(module.weight) + nn.init.zeros_(module.bias) + + def forward(self, x: Tensor, edge_index: Adj, + edge_attr: OptTensor = None, batch: Adj = None, + angle_data: List = None, size: Size = None) -> Tensor: + """ Inputs: + * x: (n_points, d) where d is pos_dims + feat_dims + * edge_index: (2, n_edges) + * edge_attr: tensor (n_edges, n_feats) excluding basic distance feats. + * batch: (n_points,) long tensor. specifies xloud belonging for each point + * angle_data: list of tensors (levels, n_edges_i, n_length_path) long tensor. + * size: None + """ + coors, feats = x[:, :self.pos_dim], x[:, self.pos_dim:] + + rel_coors = coors[edge_index[0]] - coors[edge_index[1]] + rel_dist = (rel_coors ** 2).sum(dim=-1, keepdim=True) + + if self.fourier_features > 0: + rel_dist = fourier_encode_dist(rel_dist, num_encodings = self.fourier_features) + rel_dist = rearrange(rel_dist, 'n () d -> n d') + + if exists(edge_attr): + edge_attr_feats = torch.cat([edge_attr, rel_dist], dim=-1) + else: + edge_attr_feats = rel_dist + + hidden_out, coors_out = self.propagate(edge_index, x=feats, edge_attr=edge_attr_feats, + coors=coors, rel_coors=rel_coors, + batch=batch) + return torch.cat([coors_out, hidden_out], dim=-1) + + + def message(self, x_i, x_j, edge_attr) -> Tensor: + m_ij = self.edge_mlp( torch.cat([x_i, x_j, edge_attr], dim=-1) ) + return m_ij + + def propagate(self, edge_index: Adj, size: Size = None, **kwargs): + """The initial call to start propagating messages. + Args: + `edge_index` holds the indices of a general (sparse) + assignment matrix of shape :obj:`[N, M]`. + size (tuple, optional) if none, the size will be inferred + and assumed to be quadratic. + **kwargs: Any additional data which is needed to construct and + aggregate messages, and to update node embeddings. + """ + size = self._check_input(edge_index, size) + coll_dict = self._collect(self._user_args, edge_index, size, kwargs) + msg_kwargs = self.inspector.distribute('message', coll_dict) + aggr_kwargs = self.inspector.distribute('aggregate', coll_dict) + update_kwargs = self.inspector.distribute('update', coll_dict) + + # get messages + m_ij = self.message(**msg_kwargs) + + # update coors if specified + if self.update_coors: + coor_wij = self.coors_mlp(m_ij) + # clamp if arg is set + if self.coor_weights_clamp_value: + coor_weights_clamp_value = self.coor_weights_clamp_value + # coor_weights.clamp_(min = -clamp_value, max = clamp_value) + + # normalize if needed + kwargs["rel_coors"] = self.coors_norm(kwargs["rel_coors"]) + + mhat_i = self.aggregate(coor_wij * kwargs["rel_coors"], **aggr_kwargs) + coors_out = kwargs["coors"] + mhat_i + else: + coors_out = kwargs["coors"] + + # update feats if specified + if self.update_feats: + # weight the edges if arg is passed + if self.soft_edge: + m_ij = m_ij * self.edge_weight(m_ij) + m_i = self.aggregate(m_ij, **aggr_kwargs) + + hidden_feats = self.node_norm(kwargs["x"], kwargs["batch"]) if self.node_norm else kwargs["x"] + hidden_out = self.node_mlp( torch.cat([hidden_feats, m_i], dim = -1) ) + hidden_out = kwargs["x"] + hidden_out + else: + hidden_out = kwargs["x"] + + # return tuple + return self.update((hidden_out, coors_out), **update_kwargs) + + def __repr__(self): + dict_print = {} + return "E(n)-GNN Layer for Graphs " + str(self.__dict__) + + +class EGNN_Sparse_Network(nn.Module): + r"""Sample GNN model architecture that uses the EGNN-Sparse + message passing layer to learn over point clouds. + Main MPNN layer introduced in https://arxiv.org/abs/2102.09844v1 + + Inputs will be standard GNN: x, edge_index, edge_attr, batch, ... + + Args: + * n_layers: int. number of MPNN layers + * ... : same interpretation as the base layer. + * embedding_nums: list. number of unique keys to embedd. for points + 1 entry per embedding needed. + * embedding_dims: list. point - number of dimensions of + the resulting embedding. 1 entry per embedding needed. + * edge_embedding_nums: list. number of unique keys to embedd. for edges. + 1 entry per embedding needed. + * edge_embedding_dims: list. point - number of dimensions of + the resulting embedding. 1 entry per embedding needed. + * recalc: int. Recalculate edge feats every `recalc` MPNN layers. 0 for no recalc + * verbose: bool. verbosity level. + ----- + Diff with normal layer: one has to do preprocessing before (radius, global token, ...) + """ + def __init__(self, n_layers, feats_dim, + pos_dim = 3, + edge_attr_dim = 0, + m_dim = 16, + fourier_features = 0, + soft_edge = 0, + embedding_nums=[], + embedding_dims=[], + edge_embedding_nums=[], + edge_embedding_dims=[], + update_coors=True, + update_feats=True, + norm_feats=True, + norm_coors=False, + norm_coors_scale_init = 1e-2, + dropout=0., + coor_weights_clamp_value=None, + aggr="add", + global_linear_attn_every = 0, + global_linear_attn_heads = 8, + global_linear_attn_dim_head = 64, + num_global_tokens = 4, + recalc=0 ,): + super().__init__() + + self.n_layers = n_layers + + # Embeddings? solve here + self.embedding_nums = embedding_nums + self.embedding_dims = embedding_dims + self.emb_layers = nn.ModuleList() + self.edge_embedding_nums = edge_embedding_nums + self.edge_embedding_dims = edge_embedding_dims + self.edge_emb_layers = nn.ModuleList() + + # instantiate point and edge embedding layers + + for i in range( len(self.embedding_dims) ): + self.emb_layers.append(nn.Embedding(num_embeddings = embedding_nums[i], + embedding_dim = embedding_dims[i])) + feats_dim += embedding_dims[i] - 1 + + for i in range( len(self.edge_embedding_dims) ): + self.edge_emb_layers.append(nn.Embedding(num_embeddings = edge_embedding_nums[i], + embedding_dim = edge_embedding_dims[i])) + edge_attr_dim += edge_embedding_dims[i] - 1 + # rest + self.mpnn_layers = nn.ModuleList() + self.feats_dim = feats_dim + self.pos_dim = pos_dim + self.edge_attr_dim = edge_attr_dim + self.m_dim = m_dim + self.fourier_features = fourier_features + self.soft_edge = soft_edge + self.norm_feats = norm_feats + self.norm_coors = norm_coors + self.norm_coors_scale_init = norm_coors_scale_init + self.update_feats = update_feats + self.update_coors = update_coors + self.dropout = dropout + self.coor_weights_clamp_value = coor_weights_clamp_value + self.recalc = recalc + + self.has_global_attn = global_linear_attn_every > 0 + self.global_tokens = None + self.global_linear_attn_every = global_linear_attn_every + if self.has_global_attn: + self.global_tokens = nn.Parameter(torch.randn(num_global_tokens, self.feats_dim)) + + # instantiate layers + for i in range(n_layers): + layer = EGNN_Sparse(feats_dim = feats_dim, + pos_dim = pos_dim, + edge_attr_dim = edge_attr_dim, + m_dim = m_dim, + fourier_features = fourier_features, + soft_edge = soft_edge, + norm_feats = norm_feats, + norm_coors = norm_coors, + norm_coors_scale_init = norm_coors_scale_init, + update_feats = update_feats, + update_coors = update_coors, + dropout = dropout, + coor_weights_clamp_value = coor_weights_clamp_value) + + # global attention case + is_global_layer = self.has_global_attn and (i % self.global_linear_attn_every) == 0 + if is_global_layer: + attn_layer = GlobalLinearAttention_Sparse(dim = self.feats_dim, + heads = global_linear_attn_heads, + dim_head = global_linear_attn_dim_head) + self.mpnn_layers.append(nn.ModuleList([attn_layer,layer])) + # normal case + else: + self.mpnn_layers.append(layer) + + + def forward(self, x, edge_index, batch, edge_attr, + bsize=None, recalc_edge=None, verbose=0): + """ Recalculate edge features every `self.recalc_edge` with the + `recalc_edge` function if self.recalc_edge is set. + + * x: (N, pos_dim+feats_dim) will be unpacked into coors, feats. + """ + # NODES - Embedd each dim to its target dimensions: + x = embedd_token(x, self.embedding_dims, self.emb_layers) + + # regulates wether to embedd edges each layer + edges_need_embedding = False + for i,layer in enumerate(self.mpnn_layers): + + # EDGES - Embedd each dim to its target dimensions: + if edges_need_embedding: + edge_attr = embedd_token(edge_attr, self.edge_embedding_dims, self.edge_emb_layers) + edges_need_embedding = False + + # attn tokens + self.global_tokens = None + if exists(self.global_tokens): + unique, amounts = torch.unique(batch, return_counts=True) + num_idxs = torch.cat([torch.arange(num_idxs_i,device=self.global_tokens.device) for num_idxs_i in amounts], dim=-1) + global_tokens = self.global_tokens[num_idxs] + + # pass layers + is_global_layer = self.has_global_attn and (i % self.global_linear_attn_every) == 0 + if not is_global_layer: + x = layer(x, edge_index, edge_attr, batch=batch, size=bsize) + else: + # only pass feats to the attn layer + # unique, amounts = torch.unique(batch, return_counts=True) + x_attn = layer[0](x[:, self.pos_dim:], x[:, self.pos_dim:],batch)[0]#global_tokens + # merge attn-ed feats and coords + x = torch.cat( (x[:, :self.pos_dim], x_attn), dim=-1) + x = layer[-1](x, edge_index, edge_attr, batch=batch, size=bsize) + + # recalculate edge info - not needed if last layer + if self.recalc and ((i%self.recalc == 0) and not (i == len(self.mpnn_layers)-1)) : + edge_index, edge_attr, _ = recalc_edge(x) # returns attr, idx, any_other_info + edges_need_embedding = True + + return x + + def __repr__(self): + return 'EGNN_Sparse_Network of: {0} layers'.format(len(self.mpnn_layers)) \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/module/egnn/network.py b/proteingym/baselines/protssn/src/module/egnn/network.py new file mode 100644 index 0000000..8aa6e0e --- /dev/null +++ b/proteingym/baselines/protssn/src/module/egnn/network.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +from src.module.egnn import EGNN_Sparse +from src.module.egnn.utils import get_edge_feature_dims, get_node_feature_dims + +class nodeEncoder(torch.nn.Module): + + def __init__(self, emb_dim): + super(nodeEncoder, self).__init__() + + self.atom_embedding_list = torch.nn.ModuleList() + self.node_feature_dim = get_node_feature_dims() + for i, dim in enumerate(self.node_feature_dim): + emb = torch.nn.Linear(dim, emb_dim) + torch.nn.init.xavier_uniform_(emb.weight.data) + self.atom_embedding_list.append(emb) + + def forward(self, x): + x_embedding = 0 + feature_dim_count = 0 + for i in range(len(self.node_feature_dim)): + x_embedding += self.atom_embedding_list[i]( + x[:, feature_dim_count:feature_dim_count + self.node_feature_dim[i]]) + feature_dim_count += self.node_feature_dim[i] + return x_embedding + + +class edgeEncoder(torch.nn.Module): + def __init__(self, emb_dim): + super(edgeEncoder, self).__init__() + self.atom_embedding_list = torch.nn.ModuleList() + self.edge_feature_dims = get_edge_feature_dims() + for i, dim in enumerate(self.edge_feature_dims): + emb = torch.nn.Linear(dim, emb_dim) + torch.nn.init.xavier_uniform_(emb.weight.data) + self.atom_embedding_list.append(emb) + + def forward(self, x): + x_embedding = 0 + feature_dim_count = 0 + for i in range(len(self.edge_feature_dims)): + x_embedding += self.atom_embedding_list[i]( + x[:, feature_dim_count:feature_dim_count + self.edge_feature_dims[i]]) + feature_dim_count += self.edge_feature_dims[i] + return x_embedding + +class GNNClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, hidden_size, hidden_dropout_prob): + super().__init__() + self.dense = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(hidden_dropout_prob) + self.out_proj = nn.Linear(hidden_size, 1) + + def forward(self, features, batch): + features = features.reshape(max(batch)+1, -1, features.shape[-1]) + x = torch.mean(features, dim=1) # average pool over the tokens + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class EGNN(nn.Module): + def __init__(self, gnn_config, config, input_dim, out_dim): + super(EGNN, self).__init__() + self.gnn_config = gnn_config + self.config = config + self.mpnn_layes = nn.ModuleList([ + EGNN_Sparse( + input_dim, + m_dim=int(self.gnn_config["hidden_channels"]), + edge_attr_dim=int(self.gnn_config["edge_attr_dim"]), + dropout=int(self.gnn_config["dropout"]), + mlp_num=int(self.gnn_config["mlp_num"])) + for _ in range(int(self.gnn_config["n_layers"]))]) + + if gnn_config["embedding"]: + self.node_embedding = nodeEncoder(input_dim) + self.edge_embedding = edgeEncoder(input_dim) + + self.lin = nn.Linear(input_dim, out_dim) + self.droplayer = nn.Dropout(int(self.gnn_config["dropout"])) + + + def forward(self, data): + x, pos, edge_index, edge_attr, batch = ( + data.x, data.pos, + data.edge_index, + data.edge_attr, data.batch + ) + + input_x = data.esm_rep + input_x = torch.cat([pos, input_x], dim=1) + + if self.gnn_config['embedding']: + input_x = self.node_embedding(input_x) + edge_attr = self.edge_embedding(edge_attr) + + for i, layer in enumerate(self.mpnn_layes): + h = layer(input_x, edge_index, edge_attr, batch) + if self.gnn_config['residual']: + input_x = input_x + h + else: + input_x = h + + x = input_x[:, 3:] + x = self.droplayer(x) + x = self.lin(x) + + return x, input_x[:, 3:] \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/module/egnn/utils.py b/proteingym/baselines/protssn/src/module/egnn/utils.py new file mode 100644 index 0000000..c31020a --- /dev/null +++ b/proteingym/baselines/protssn/src/module/egnn/utils.py @@ -0,0 +1,32 @@ +import torch +from torch import sin, cos, atan2, acos + +def rot_z(gamma): + return torch.tensor([ + [cos(gamma), -sin(gamma), 0], + [sin(gamma), cos(gamma), 0], + [0, 0, 1] + ], dtype=gamma.dtype) + +def rot_y(beta): + return torch.tensor([ + [cos(beta), 0, sin(beta)], + [0, 1, 0], + [-sin(beta), 0, cos(beta)] + ], dtype=beta.dtype) + +def rot(alpha, beta, gamma): + return rot_z(alpha) @ rot_y(beta) @ rot_z(gamma) + +def get_node_feature_dims(): + ''' + each node has 25 dim feature corrsponding to residual type, sasa, dihedral, mu_r_norm + ''' + return [20, 1,1, 4, 5,640] + + +def get_edge_feature_dims(): + ''' + each node has 93 dim feature corrsponding to one hot sequence distance, interatomic distance, local frame orientation + ''' + return [65, 1, 15, 12] \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/module/gat/network.py b/proteingym/baselines/protssn/src/module/gat/network.py new file mode 100644 index 0000000..86e3c6e --- /dev/null +++ b/proteingym/baselines/protssn/src/module/gat/network.py @@ -0,0 +1,59 @@ +import torch +from torch_geometric.nn import GATConv +import torch.nn.functional as F + + +class GAT(torch.nn.Module): + def __init__(self, config, feat_type, input_dim, out_dim): + super(GAT, self).__init__() + self.config = config + self.feat_type = feat_type + self.hidden_dim = config["hidden_channels"] + self.input_dim, self.out_dim = input_dim, out_dim + + self.convs = torch.nn.ModuleList() + self.convs.append(GATConv(input_dim, self.hidden_dim)) + + self.bns = torch.nn.ModuleList() + self.bns.append(torch.nn.BatchNorm1d(self.hidden_dim)) + for _ in range(config["n_layers"] - 2): + self.convs.append( + GATConv(self.hidden_dim, self.hidden_dim)) + self.bns.append(torch.nn.BatchNorm1d(self.hidden_dim)) + self.convs.append(GATConv(self.hidden_dim, self.out_dim)) + + self.dropout_prob = config["dropout"] + + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, data): + x, pos, mu_r_norm, edge_index, edge_attr, batch = ( + data.x.float(), + data.pos.float(), + data.mu_r_norm.float(), + data.edge_index, + data.edge_attr.float(), + data.batch + ) + + input_x = torch.empty([pos.shape[0], 0]).to(x.device) + if "manual" in self.feat_type: + input_x = torch.cat([input_x, x], dim=1) + if "esm" in self.feat_type: + esm_rep = data.esm_rep.float() + input_x = torch.cat([input_x, esm_rep], dim=1) + x = input_x + edge_index = edge_index + + for i, conv in enumerate(self.convs[:-1]): + x = conv(x, edge_index) + x = self.bns[i](x) + x = F.relu(x) + x = F.dropout(x, p=self.dropout_prob, training=self.training) + x = self.convs[-1](x,edge_index) + return x \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/module/gcn/network.py b/proteingym/baselines/protssn/src/module/gcn/network.py new file mode 100644 index 0000000..55d582d --- /dev/null +++ b/proteingym/baselines/protssn/src/module/gcn/network.py @@ -0,0 +1,58 @@ +import torch +from torch_geometric.nn import GCNConv +import torch.nn.functional as F + + +class GCN(torch.nn.Module): + def __init__(self, config, feat_type, input_dim, out_dim): + super(GCN, self).__init__() + self.config = config + self.feat_type = feat_type + self.hidden_dim = config["hidden_channels"] + self.input_dim, self.out_dim = input_dim, out_dim + + self.convs = torch.nn.ModuleList() + self.convs.append(GCNConv(input_dim, self.hidden_dim)) + + self.bns = torch.nn.ModuleList() + self.bns.append(torch.nn.BatchNorm1d(self.hidden_dim)) + for _ in range(config["n_layers"] - 2): + self.convs.append( + GCNConv(self.hidden_dim, self.hidden_dim)) + self.bns.append(torch.nn.BatchNorm1d(self.hidden_dim)) + self.convs.append(GCNConv(self.hidden_dim, self.out_dim)) + + self.dropout_prob = config["dropout"] + + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, data): + x, pos, mu_r_norm, edge_index, edge_attr, batch, = ( + data.x.float(), + data.pos.float(), + data.mu_r_norm.float(), + data.edge_index, + data.edge_attr.float(), + data.batch) + + input_x = torch.empty([pos.shape[0], 0]).to(x.device) + if "manual" in self.feat_type: + input_x = torch.cat([input_x, x], dim=1) + if "esm" in self.feat_type: + esm_rep = data.esm_rep.float() + input_x = torch.cat([input_x, esm_rep], dim=1) + x = input_x + edge_index = edge_index + + for i, conv in enumerate(self.convs[:-1]): + x = conv(x, edge_index) + x = self.bns[i](x) + x = F.relu(x) + x = F.dropout(x, p=self.dropout_prob, training=self.training) + x = self.convs[-1](x,edge_index) + return x \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/utils/data_utils.py b/proteingym/baselines/protssn/src/utils/data_utils.py new file mode 100644 index 0000000..74e2434 --- /dev/null +++ b/proteingym/baselines/protssn/src/utils/data_utils.py @@ -0,0 +1,33 @@ +import random +import torch.utils.data as data + + +class BatchSampler(data.Sampler): + def __init__(self, dataset, max_len=5000, batch_token_num=3096, shuffle=True): + self.node_num = [d.x.shape[0] for d in dataset] + self.idx = [i for i in range(len(self.node_num)) + if self.node_num[i] <= max_len] + self.shuffle = shuffle + self.batches = [] + self.max_len = max_len + self.batch_token_num = batch_token_num + + def _form_batches(self): + if self.shuffle: random.shuffle(self.idx) + idx = self.idx + while idx: + batch = [] + n_nodes = 0 + while idx and n_nodes + self.node_num[idx[0]] <= self.batch_token_num: + next_idx, idx = idx[0], idx[1:] + n_nodes += self.node_num[next_idx] + batch.append(next_idx) + self.batches.append(batch) + + def __len__(self): + if not self.batches: self._form_batches() + return len(self.batches) + + def __iter__(self): + if not self.batches: self._form_batches() + for batch in self.batches: yield batch \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/utils/dataset_utils.py b/proteingym/baselines/protssn/src/utils/dataset_utils.py new file mode 100644 index 0000000..11b87d0 --- /dev/null +++ b/proteingym/baselines/protssn/src/utils/dataset_utils.py @@ -0,0 +1,610 @@ +import torch +import sys +import os +import random +import csv +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from datetime import datetime +from numpy import array, cross, pi, arccos, sqrt +from tqdm import tqdm +from time import time +from torch_geometric.transforms import BaseTransform +from scipy.stats import spearmanr +from numpy import nan + +def dataset_argument_(root): + dataset_arg = {} + if root == "cath40_k10_dyn_imem": + dataset_arg['root'] = f"data/{root}" + dataset_arg['name'] = '40' + dataset_arg['set_length'] = None + dataset_arg['normal_file'] = None + dataset_arg['divide_num'] = 1 + dataset_arg['divide_idx'] = 0 + dataset_arg['c_alpha_max_neighbors'] = 10 + return dataset_arg + +def get_stat(graph_root, limited_num=None, num_subgroup=1000, max_limits=100000): + # obtain mean and std of graphs in graph_root + # graph_root: string, calculate mean and std of all attributes of graphs in graph_root + # limited_num: int, optional, just calculated limited number of graphs in graph_root + # num_Subgroup: int, group all graphs in graph_root, the number of each subgroup is num_subgroup + # max_limits: int, set the initial minimum value as max_limits + + wrong_proteins = [] + filenames = os.listdir(graph_root) + random.shuffle(filenames) + # set sample length + n = len(filenames) + if limited_num: + n = min(n, limited_num) + count = 0 + if n < num_subgroup * 10: + num_subgroup = 1 + + # initialize scalar value + num_node_min, num_edge_min = torch.tensor( + [max_limits]), torch.tensor([max_limits]) + num_node_max, num_node_avg, num_edge_max, num_edge_avg = torch.tensor( + [0]), torch.tensor([0]), torch.tensor([0]), torch.tensor([0]) + + # initialize mean, std + graph = torch.load(os.path.join(graph_root, filenames[0])) + x, pos, edge_attr = graph.x, graph.pos, graph.edge_attr + x_mean = torch.zeros(x.shape[1]) + x_max = torch.zeros(x.shape[1]) + x_min = torch.tensor([max_limits for i in range(x.shape[1])]) + x_std = torch.zeros(x.shape[1]) + pos_mean = torch.zeros(pos.shape[1]) + pos_std = torch.zeros(pos.shape[1]) + edge_attr_mean = torch.zeros(edge_attr.shape[1]) + edge_attr_std = torch.zeros(edge_attr.shape[1]) + + # initialize sub mean, std + x_mean_1 = torch.zeros(x.shape[1]) + x_std_1 = torch.zeros(x.shape[1]) + pos_mean_1 = torch.zeros(pos.shape[1]) + pos_std_1 = torch.zeros(pos.shape[1]) + + edge_attr_mean_1 = torch.zeros(edge_attr.shape[1]) + edge_attr_std_1 = torch.zeros(edge_attr.shape[1]) + + for i in tqdm(range(n)): + file = filenames[i] + graph = torch.load(os.path.join(graph_root, file)) + x, pos, mu_r_norm, edge_attr = graph.x, graph.pos, graph.mu_r_norm, graph.edge_attr + if torch.isnan(x).any(): + wrong_proteins.append(file) + continue + count += 1 + node_num = graph.x.shape[0] + edge_num = graph.edge_attr.shape[0] + num_node_min = min(num_node_min, node_num) + num_edge_min = min(num_edge_min, edge_num) + num_node_max = max(num_node_max, node_num) + num_edge_max = max(num_edge_max, edge_num) + num_node_avg += node_num + num_edge_avg += edge_num + + x_max = torch.max(x_max, x.max(axis=0).values) + x_min = torch.min(x_min, x.min(axis=0).values) + x_mean_1 += x.nanmean(axis=0) + x_std_1 += x.std(axis=0) + pos_mean_1 += pos.mean(axis=0) + pos_std_1 += pos.std(axis=0) + edge_attr_mean_1 += edge_attr.mean(axis=0) + edge_attr_std_1 += edge_attr.std(axis=0) + + if count == num_subgroup: + x_mean += x_mean_1.div_(num_subgroup) + x_std += x_std_1.div_(num_subgroup) + pos_mean += pos_mean_1.div_(num_subgroup) + pos_std += pos_std_1.div_(num_subgroup) + edge_attr_mean += edge_attr_mean_1.div_(num_subgroup) + edge_attr_std += edge_attr_std_1.div_(num_subgroup) + + x_mean_1 = torch.zeros(x.shape[1]) + x_std_1 = torch.zeros(x.shape[1]) + pos_mean_1 = torch.zeros(pos.shape[1]) + pos_std_1 = torch.zeros(pos.shape[1]) + edge_attr_mean_1 = torch.zeros(edge_attr.shape[1]) + edge_attr_std_1 = torch.zeros(edge_attr.shape[1]) + count = 0 + + num_node_avg = num_node_avg/n + num_edge_avg = num_edge_avg/n + n_2 = n // num_subgroup + x_mean = x_mean.div_(n_2) + x_std = x_std.div_(n_2) + pos_mean = pos_mean.div_(n_2) + pos_std = pos_std.div_(n_2) + edge_attr_mean = edge_attr_mean.div_(n_2) + edge_attr_std = edge_attr_std.div_(n_2) + + dic = {'x_max': x_max, 'x_min': x_min, 'x_mean': x_mean, 'x_std': x_std, + 'pos_mean': pos_mean, 'pos_std': pos_std, + 'edge_attr_mean': edge_attr_mean, 'edge_attr_std': edge_attr_std, + 'num_graph': n - len(wrong_proteins), + 'num_node_min': num_node_min, 'num_edge_min': num_edge_min, + 'num_node_max': num_node_max, 'num_edge_max': num_edge_max, + 'num_node_avg': num_node_avg, 'num_edge_avg': num_edge_avg} + + filename = 'mean_attr' + saved_filename_pt = os.path.join( + '/'.join(graph_root.split('/')[:-1]), filename + '.pt') + torch.save(dic, saved_filename_pt) + saved_filename = os.path.join( + '/'.join(graph_root.split('/')[:-1]), filename + '.csv') + w = csv.writer(open(saved_filename, 'w')) + for key, val in dic.items(): + w.writerow([key, val]) + + saved_filename = os.path.join( + '/'.join(graph_root.split('/')[:-1]), filename + '_proteins.txt') + with open(saved_filename, 'w') as f: + for i in range(n): + f.write(str(filenames[i]) + '\n') + + saved_filename = os.path.join( + '/'.join(graph_root.split('/')[:-1]), filename + '_wrong_proteins.txt') + with open(saved_filename, 'w') as f: + for file in wrong_proteins: + f.write(file + '\n') + + return saved_filename_pt + +# @functional_transform('normalize_protein') + + +class NormalizeProtein(BaseTransform): + r"""Centers and normalizes node positions to the interval :math:`(-1, 1)` + (functional name: :obj:`normalize_scale`). + """ + + def __init__(self, filename, skip_x=20, skip_edge_attr=64, safe_domi=1e-10): + + dic = torch.load(filename) + self.skip_x = skip_x + self.skip_edge_attr = skip_edge_attr + self.safe_domi = safe_domi + self.x_mean = dic['x_mean'] + self.x_std = dic['x_std'] + self.pos_mean = dic['pos_mean'] + self.pos_std = torch.mean(dic['pos_std']) + self.edge_attr_mean = dic['edge_attr_mean'] + self.edge_attr_std = dic['edge_attr_std'] + + def __call__(self, data): + data.x[:, self.skip_x:] = (data.x[:, self.skip_x:] - self.x_mean[self.skip_x:] + ).div_(self.x_std[self.skip_x:] + self.safe_domi) + data.pos = data.pos - data.pos.mean(dim=-2, keepdim=False) + data.pos = data.pos.div_(self.pos_std + self.safe_domi) + data.edge_attr[:, self.skip_edge_attr:] = (data.edge_attr[:, self.skip_edge_attr:] + - self.edge_attr_mean[self.skip_edge_attr:]).div_(self.edge_attr_std[self.skip_edge_attr:] + self.safe_domi) + + return data + + +class DihedralGeometryError(Exception): + pass + + +class AngleGeometryError(Exception): + pass + + +ROUND_ERROR = 1e-14 + + +class Logger(object): + def __init__(self, logpath, syspart=sys.stdout): + self.terminal = syspart + self.log = open(logpath, "a") + + def write(self, message): + + self.terminal.write(message) + self.log.write(message) + self.log.flush() + + def flush(self): + # this flush method is needed for python 3 compatibility. + # this handles the flush command by doing nothing. + # you might want to specify some extra behavior here. + pass + + +def log(*args): + print(f'[{datetime.now()}]', *args) + + +def safe_index(l, e): + """ + Return index of element e in list l. If e is not present, return the last index + """ + try: + return l.index(e) + except: + return len(l) - 1 + + +def one_hot_res(type_idx, num_residue_type=20): + rec_feat = [0 for _ in range(num_residue_type)] + if type_idx < num_residue_type: + rec_feat[type_idx] = 1 + return rec_feat + else: + # print("Warning: residue type index exceeds "+num_residue_type+" !") + return False + + +def nan_to_num(ts, val=0.0): + """ + Replaces nans in tensor with a fixed value. + """ + val = torch.tensor(val, dtype=ts.dtype, device=ts.device) + return torch.where(~torch.isfinite(ts), val, ts) + + +def normalize(tensor, dim=-1): + """ + Normalizes a tensor along a dimension after removing nans. + """ + return nan_to_num( + torch.div(tensor, norm(tensor, dim=dim, keepdim=True)) + ) + +# def norm(tensor, dim, eps=1e-8, keepdim=False): +# """ +# Returns L2 norm along a dimension. +# """ +# return torch.sqrt( +# torch.sum(torch.square(tensor), dim=dim, keepdim=keepdim) + eps) + + +def norm(a): + """Returns the norm of a matrix or vector + Calculates the Euclidean norm of a vector. + Applies the Frobenius norm function to a matrix + (a.k.a. Euclidian matrix norm) + a = numpy array + """ + return sqrt(sum((a*a).flat)) + + +def create_vector(vec): + """Returns a vector as a numpy array.""" + return array([vec[0], vec[1], vec[2]]) + + +def create_vectors(vec1, vec2, vec3, vec4): + """Returns dihedral angle, takes four + Scientific.Geometry.Vector objects + (dihedral does not work for them because + the Win and Linux libraries are not identical. + """ + return map(create_vector, [vec1, vec2, vec3, vec4]) + + +def fix_rounding_error(x): + """If x is almost in the range 0-1, fixes it. + Specifically, if x is between -ROUND_ERROR and 0, returns 0. + If x is between 1 and 1+ROUND_ERROR, returns 1. + """ + if -ROUND_ERROR < x < 0: + return 0 + elif 1 < x < 1+ROUND_ERROR: + return 1 + else: + return + + +def angle(v1, v2): + """ + calculates the angle between two vectors. + v1 and v2 are numpy.array objects. + returns a float containing the angle in radians. + """ + length_product = norm(v1)*norm(v2) + if length_product == 0: + raise AngleGeometryError( + "Cannot calculate angle for vectors with length zero") + cosine = scalar(v1, v2)/length_product + # angle = arccos(fix_rounding_error(cosine)) + angle = arccos(cosine) + + return angle + + +def scalar(v1, v2): + """ + calculates the scalar product of two vectors + v1 and v2 are numpy.array objects. + returns a float for a one-dimensional array. + """ + return sum(v1*v2) + + +def dihedral(vec1, vec2, vec3, vec4): + """ + Returns a float value for the dihedral angle between + the four vectors. They define the bond for which the + torsion is calculated (~) as: + V1 - V2 ~ V3 - V4 + The vectors vec1 .. vec4 can be array objects, lists or tuples of length + three containing floats. + For Scientific.geometry.Vector objects the behavior is different + on Windows and Linux. Therefore, the latter is not a featured input type + even though it may work. + If the dihedral angle cant be calculated (because vectors are collinear), + the function raises a DihedralGeometryError + """ + # create array instances. + v1, v2, v3, v4 = create_vectors(vec1, vec2, vec3, vec4) + all_vecs = [v1, v2, v3, v4] + + # rule out that two of the atoms are identical + # except the first and last, which may be. + for i in range(len(all_vecs)-1): + for j in range(i+1, len(all_vecs)): + if i > 0 or j < 3: # exclude the (1,4) pair + equals = all_vecs[i] == all_vecs[j] + if equals.all(): + raise DihedralGeometryError( + "Vectors #%i and #%i may not be identical!" % (i, j)) + + # calculate vectors representing bonds + v12 = v2-v1 + v23 = v3-v2 + v34 = v4-v3 + + # calculate vectors perpendicular to the bonds + normal1 = cross(v12, v23) + normal2 = cross(v23, v34) + + # check for linearity + if norm(normal1) == 0 or norm(normal2) == 0: + raise DihedralGeometryError( + "Vectors are in one line; cannot calculate normals!") + + # normalize them to length 1.0 + normal1 = normal1/norm(normal1) + normal2 = normal2/norm(normal2) + + # calculate torsion and convert to degrees + torsion = angle(normal1, normal2) * 180.0/pi + + # take into account the determinant + # (the determinant is a scalar value distinguishing + # between clockwise and counter-clockwise torsion. + if scalar(normal1, v34) >= 0: + return torsion + else: + torsion = 360-torsion + if torsion == 360: + torsion = 0.0 + return torsion + + +def seq_dist_distrib(loader): + before = time() + q = np.array([0.25, 0.5, 0.75, 0.9, 0.95, 0.98, 0.99]) + seq_dist = torch.Tensor(0) + for _, data in tqdm(enumerate(loader)): + edge_attr = data.edge_attr + seq_dist = torch.cat([seq_dist, edge_attr[:, 0]]) + print('time elapsed: ', time() - before) + print(np.quantile(np.array(seq_dist), q)) + bins = 200 + sns.distplot(seq_dist.numpy(), hist=True, kde=False, bins=bins) + plt.hist(seq_dist.numpy(), bins=bins, range=(0, 200)) + plt.title('Histogram of Sequence Distance') + plt.xlabel('Sequence Distance') + plt.ylabel('Times') + plt.savefig('protein/dataset_alpha_Fold/Val_seq_dist' + str(bins) + '.png') + return seq_dist + + +def mutat_test4(loader, device, model, dataset): + # printed cor arranged along the protein names in dataset + model.eval() + m = torch.nn.Softmax() + correct = 0 + protein_names = dataset.protein_names + n = len(protein_names) + + true_score = [torch.tensor([]).to(device) for _ in range(n)] + pred_score = [torch.tensor([]).to(device) for _ in range(n)] + with torch.no_grad(): + # Iterate in batches over the training/test dataset. + for data in loader: + + ### calculate in model + data = data.to(device) + protein_idx = data.protein_idx + print(protein_idx) + #data = pre_transform(data) + x = torch.cat([data.pos, data.mu_r_norm, data.x], + dim=1) # data.mu_r_norm, + out = model(x, edge_index=data.edge_index, + edge_attr=data.edge_attr, batch=data.batch) + out = torch.log(m(out[:, :20])) + # out = out.cpu() + + # obtain score info + score_info = data.score_info[0] + num_mutat = len(score_info) + true_score[protein_idx] = torch.zeros(num_mutat) + pred_score[protein_idx] = torch.zeros(num_mutat) + for mutat_idx in range(num_mutat): + mutat_info, true_score[protein_idx][mutat_idx] = score_info[mutat_idx] + for item in mutat_info: + # (log(prob_out) - log(prob_wlid)) + # print(protein_idx,mutat_idx) + if int(item[1]) >= out.shape[0]: + continue + pred_score[protein_idx][mutat_idx] += ( + out[int(item[1]), int(item[2])] - out[int(item[1]), int(item[0])]).cpu() + spearman_coeef = np.zeros(n) + for i in range(n): + if len(true_score[i].cpu().numpy()) == 0: + continue + spearvalue = spearmanr(true_score[i].cpu().numpy( + ), pred_score[i].cpu().numpy()).correlation + if spearvalue is nan: + pass + else: + spearman_coeef[i] = spearvalue + for i in range(len(spearman_coeef)): + print(protein_names[i]) + print(spearman_coeef[i]) + # Derive ratio of correct predictions. + return correct / len(loader.dataset), spearman_coeef.mean() + + +def normalize_prob(coeff): + sum_ = coeff.sum(dim = 1) + score = coeff/(sum_.view(20,-1)) + return score + +def substitute_label(y,temperature=1.0): + original_score = torch.tensor([ + [ 8., 3., 2., 2., 4., 3., 3., 4., 2., 3., 3., 3., 3., 2., + 3., 5., 4., 1., 2., 4.], + [ 3., 9., 4., 2., 1., 5., 4., 2., 4., 1., 2., 6., 3., 1., + 2., 3., 3., 1., 2., 1.], + [ 2., 4., 10., 5., 1., 4., 4., 4., 5., 1., 1., 4., 2., 1., + 2., 5., 4., 0., 2., 1.], + [ 2., 2., 5., 10., 1., 4., 6., 3., 3., 1., 0., 3., 1., 1., + 3., 4., 3., 0., 1., 1.], + [ 4., 1., 1., 1., 13., 1., 0., 1., 1., 3., 3., 1., 3., 2., + 1., 3., 3., 2., 2., 3.], + [ 3., 5., 4., 4., 1., 9., 6., 2., 4., 1., 2., 5., 4., 1., + 3., 4., 3., 2., 3., 2.], + [ 3., 4., 4., 6., 0., 6., 9., 2., 4., 1., 1., 5., 2., 1., + 3., 4., 3., 1., 2., 2.], + [ 4., 2., 4., 3., 1., 2., 2., 10., 2., 0., 0., 2., 1., 1., + 2., 4., 2., 2., 1., 1.], + [ 2., 4., 5., 3., 1., 4., 4., 2., 12., 1., 1., 3., 2., 3., + 2., 3., 2., 2., 6., 1.], + [ 3., 1., 1., 1., 3., 1., 1., 0., 1., 8., 6., 1., 5., 4., + 1., 2., 3., 1., 3., 7.], + [ 3., 2., 1., 0., 3., 2., 1., 0., 1., 6., 8., 2., 6., 4., + 1., 2., 3., 2., 3., 5.], + [ 3., 6., 4., 3., 1., 5., 5., 2., 3., 1., 2., 9., 3., 1., + 3., 4., 3., 1., 2., 2.], + [ 3., 3., 2., 1., 3., 4., 2., 1., 2., 5., 6., 3., 9., 4., + 2., 3., 3., 3., 3., 5.], + [ 2., 1., 1., 1., 2., 1., 1., 1., 3., 4., 4., 1., 4., 10., + 0., 2., 2., 5., 7., 3.], + [ 3., 2., 2., 3., 1., 3., 3., 2., 2., 1., 1., 3., 2., 0., + 11., 3., 3., 0., 1., 2.], + [ 5., 3., 5., 4., 3., 4., 4., 4., 3., 2., 2., 4., 3., 2., + 3., 8., 5., 1., 2., 2.], + [ 4., 3., 4., 3., 3., 3., 3., 2., 2., 3., 3., 3., 3., 2., + 3., 5., 9., 2., 2., 4.], + [ 1., 1., 0., 0., 2., 2., 1., 2., 2., 1., 2., 1., 3., 5., + 0., 1., 2., 15., 6., 1.], + [ 2., 2., 2., 1., 2., 3., 2., 1., 6., 3., 3., 2., 3., 7., + 1., 2., 2., 6., 11., 3.], + [ 4., 1., 1., 1., 3., 2., 2., 1., 1., 7., 5., 2., 5., 3., + 2., 2., 4., 1., 3., 8.]], dtype=torch.float64,device=y.device) + + score = normalize_prob(original_score) + tempering_score = score**temperature + normalized_score = normalize_prob(tempering_score) + out_prob = normalized_score[y] + + return out_prob + +def substitution_label_smooth(y): # [1,4,5,6,7] + ''' + mapping tensor convert dataset label ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'] + to substitution tabel label ['C', 'S', 'T','A','G','P' ,'D','E', 'Q' ,'N', 'H','R','K','M' ,'I','L','V','W','Y','F'] + ''' + + score = torch.tensor([[8.4015e-01, 5.6609e-03, 2.0825e-03, 2.0825e-03, 1.5388e-02, 5.6609e-03, + 5.6609e-03, 1.5388e-02, 2.0825e-03, 5.6609e-03, 5.6609e-03, 5.6609e-03, + 5.6609e-03, 2.0825e-03, 5.6609e-03, 4.1829e-02, 1.5388e-02, 7.6612e-04, + 2.0825e-03, 1.5388e-02], + [2.2443e-03, 9.0541e-01, 6.1006e-03, 8.2563e-04, 3.0373e-04, 1.6583e-02, + 6.1006e-03, 8.2563e-04, 6.1006e-03, 3.0373e-04, 8.2563e-04, 4.5078e-02, + 2.2443e-03, 3.0373e-04, 8.2563e-04, 2.2443e-03, 2.2443e-03, 3.0373e-04, + 8.2563e-04, 3.0373e-04], + [3.2347e-04, 2.3901e-03, 9.6424e-01, 6.4970e-03, 1.1900e-04, 2.3901e-03, + 2.3901e-03, 2.3901e-03, 6.4970e-03, 1.1900e-04, 1.1900e-04, 2.3901e-03, + 3.2347e-04, 1.1900e-04, 3.2347e-04, 6.4970e-03, 2.3901e-03, 4.3776e-05, + 3.2347e-04, 1.1900e-04], + [3.2378e-04, 3.2378e-04, 6.5034e-03, 9.6518e-01, 1.1911e-04, 2.3925e-03, + 1.7678e-02, 8.8013e-04, 8.8013e-04, 1.1911e-04, 4.3819e-05, 8.8013e-04, + 1.1911e-04, 1.1911e-04, 8.8013e-04, 2.3925e-03, 8.8013e-04, 4.3819e-05, + 1.1911e-04, 1.1911e-04], + [1.2335e-04, 6.1412e-06, 6.1412e-06, 6.1412e-06, 9.9950e-01, 6.1412e-06, + 2.2592e-06, 6.1412e-06, 6.1412e-06, 4.5377e-05, 4.5377e-05, 6.1412e-06, + 4.5377e-05, 1.6693e-05, 6.1412e-06, 4.5377e-05, 4.5377e-05, 1.6693e-05, + 1.6693e-05, 4.5377e-05], + [2.1845e-03, 1.6142e-02, 5.9382e-03, 5.9382e-03, 2.9565e-04, 8.8131e-01, + 4.3878e-02, 8.0365e-04, 5.9382e-03, 2.9565e-04, 8.0365e-04, 1.6142e-02, + 5.9382e-03, 2.9565e-04, 2.1845e-03, 5.9382e-03, 2.1845e-03, 8.0365e-04, + 2.1845e-03, 8.0365e-04], + [2.1417e-03, 5.8217e-03, 5.8217e-03, 4.3017e-02, 1.0663e-04, 4.3017e-02, + 8.6401e-01, 7.8788e-04, 5.8217e-03, 2.8984e-04, 2.8984e-04, 1.5825e-02, + 7.8788e-04, 2.8984e-04, 2.1417e-03, 5.8217e-03, 2.1417e-03, 2.8984e-04, + 7.8788e-04, 7.8788e-04], + [2.4500e-03, 3.3157e-04, 2.4500e-03, 9.0130e-04, 1.2198e-04, 3.3157e-04, + 3.3157e-04, 9.8840e-01, 3.3157e-04, 4.4873e-05, 4.4873e-05, 3.3157e-04, + 1.2198e-04, 1.2198e-04, 3.3157e-04, 2.4500e-03, 3.3157e-04, 3.3157e-04, + 1.2198e-04, 1.2198e-04], + [4.5164e-05, 3.3372e-04, 9.0714e-04, 1.2277e-04, 1.6615e-05, 3.3372e-04, + 3.3372e-04, 4.5164e-05, 9.9480e-01, 1.6615e-05, 1.6615e-05, 1.2277e-04, + 4.5164e-05, 1.2277e-04, 4.5164e-05, 1.2277e-04, 4.5164e-05, 4.5164e-05, + 2.4659e-03, 1.6615e-05], + [4.1869e-03, 5.6664e-04, 5.6664e-04, 5.6664e-04, 4.1869e-03, 5.6664e-04, + 5.6664e-04, 2.0845e-04, 5.6664e-04, 6.2139e-01, 8.4096e-02, 5.6664e-04, + 3.0937e-02, 1.1381e-02, 5.6664e-04, 1.5403e-03, 4.1869e-03, 5.6664e-04, + 4.1869e-03, 2.2860e-01], + [4.8740e-03, 1.7930e-03, 6.5962e-04, 2.4266e-04, 4.8740e-03, 1.7930e-03, + 6.5962e-04, 2.4266e-04, 6.5962e-04, 9.7896e-02, 7.2336e-01, 1.7930e-03, + 9.7896e-02, 1.3249e-02, 6.5962e-04, 1.7930e-03, 4.8740e-03, 1.7930e-03, + 4.8740e-03, 3.6014e-02], + [2.2137e-03, 4.4462e-02, 6.0173e-03, 2.2137e-03, 2.9959e-04, 1.6357e-02, + 1.6357e-02, 8.1436e-04, 2.2137e-03, 2.9959e-04, 8.1436e-04, 8.9305e-01, + 2.2137e-03, 2.9959e-04, 2.2137e-03, 6.0173e-03, 2.2137e-03, 2.9959e-04, + 8.1436e-04, 8.1436e-04], + [2.2052e-03, 2.2052e-03, 8.1125e-04, 2.9844e-04, 2.2052e-03, 5.9944e-03, + 8.1125e-04, 2.9844e-04, 8.1125e-04, 1.6294e-02, 4.4293e-02, 2.2052e-03, + 8.8965e-01, 5.9944e-03, 8.1125e-04, 2.2052e-03, 2.2052e-03, 2.2052e-03, + 2.2052e-03, 1.6294e-02], + [3.1409e-04, 1.1555e-04, 1.1555e-04, 1.1555e-04, 3.1409e-04, 1.1555e-04, + 1.1555e-04, 1.1555e-04, 8.5379e-04, 2.3209e-03, 2.3209e-03, 1.1555e-04, + 2.3209e-03, 9.3630e-01, 4.2508e-05, 3.1409e-04, 3.1409e-04, 6.3087e-03, + 4.6616e-02, 8.5379e-04], + [3.3436e-04, 1.2300e-04, 1.2300e-04, 3.3436e-04, 4.5250e-05, 3.3436e-04, + 3.3436e-04, 1.2300e-04, 1.2300e-04, 4.5250e-05, 4.5250e-05, 3.3436e-04, + 1.2300e-04, 1.6647e-05, 9.9671e-01, 3.3436e-04, 3.3436e-04, 1.6647e-05, + 4.5250e-05, 1.2300e-04], + [3.8657e-02, 5.2316e-03, 3.8657e-02, 1.4221e-02, 5.2316e-03, 1.4221e-02, + 1.4221e-02, 1.4221e-02, 5.2316e-03, 1.9246e-03, 1.9246e-03, 1.4221e-02, + 5.2316e-03, 1.9246e-03, 5.2316e-03, 7.7644e-01, 3.8657e-02, 7.0802e-04, + 1.9246e-03, 1.9246e-03], + [6.3097e-03, 2.3212e-03, 6.3097e-03, 2.3212e-03, 2.3212e-03, 2.3212e-03, + 2.3212e-03, 8.5392e-04, 8.5392e-04, 2.3212e-03, 2.3212e-03, 2.3212e-03, + 2.3212e-03, 8.5392e-04, 2.3212e-03, 1.7151e-02, 9.3644e-01, 8.5392e-04, + 8.5392e-04, 6.3097e-03], + [8.3137e-07, 8.3137e-07, 3.0584e-07, 3.0584e-07, 2.2599e-06, 2.2599e-06, + 8.3137e-07, 2.2599e-06, 2.2599e-06, 8.3137e-07, 2.2599e-06, 8.3137e-07, + 6.1430e-06, 4.5391e-05, 3.0584e-07, 8.3137e-07, 2.2599e-06, 9.9980e-01, + 1.2339e-04, 8.3137e-07], + [1.1928e-04, 1.1928e-04, 1.1928e-04, 4.3882e-05, 1.1928e-04, 3.2425e-04, + 1.1928e-04, 4.3882e-05, 6.5127e-03, 3.2425e-04, 3.2425e-04, 1.1928e-04, + 3.2425e-04, 1.7703e-02, 4.3882e-05, 1.1928e-04, 1.1928e-04, 6.5127e-03, + 9.6656e-01, 3.2425e-04], + [1.1877e-02, 5.9130e-04, 5.9130e-04, 5.9130e-04, 4.3692e-03, 1.6073e-03, + 1.6073e-03, 5.9130e-04, 5.9130e-04, 2.3855e-01, 3.2284e-02, 1.6073e-03, + 3.2284e-02, 4.3692e-03, 1.6073e-03, 1.6073e-03, 1.1877e-02, 5.9130e-04, + 4.3692e-03, 6.4844e-01]], dtype=torch.float64, device=y.device) + out_prob = score[y] + + return out_prob # [] \ No newline at end of file diff --git a/proteingym/baselines/protssn/src/utils/utils.py b/proteingym/baselines/protssn/src/utils/utils.py new file mode 100644 index 0000000..532a8ef --- /dev/null +++ b/proteingym/baselines/protssn/src/utils/utils.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import biotite.structure +import numpy as np +import pandas as pd +from biotite.structure.io import pdbx, pdb +from biotite.structure.residues import get_residues +from biotite.structure import filter_backbone +from biotite.structure import get_chains +from biotite.sequence import ProteinSequence +from typing import * +from Bio import SeqIO + +def param_num(model): + total = sum([param.numel() for param in model.parameters() if param.requires_grad]) + num_M = total/1e6 + if num_M >= 1000: + return "Number of parameter: %.2fB" % (num_M/1e3) + else: + return "Number of parameter: %.2fM" % (num_M) + +def create_mutant_file(root): + root = "data/evaluation/ProteinGym_substitutions/" + proteins = os.listdir(root) + for protein in proteins: + files = os.listdir(os.path.join(root, protein)) + files_ = sorted([file for file in files if file.split(".")[1].isdigit()]) + df = pd.read_table(os.path.join(root,protein, files_[0])) + if len(files_) > 1: + for i in range(1, len(files_)): + file_ = pd.read_table(os.path.join(root,protein, files_[i])) + df = pd.concat([df, file_]) + df.to_csv(os.path.join(root, protein, f"{protein}.tsv"), sep="\t", index=False) + else: + df.to_csv(os.path.join(root, protein, f"{protein}.tsv"), sep="\t", index=False) + + +def read_fasta(file_path, key): + return str(getattr(SeqIO.read(file_path, 'fasta'), key)) + +def load_structure(fpath, chain=None): + """ + Args: + fpath: filepath to either pdb or cif file + chain: the chain id or list of chain ids to load + Returns: + biotite.structure.AtomArray + """ + if fpath.endswith('cif'): + with open(fpath) as fin: + pdbxf = pdbx.PDBxFile.read(fin) + structure = pdbx.get_structure(pdbxf, model=1) + # elif fpath.endswith('pdb'): + else: + with open(fpath) as fin: + pdbf = pdb.PDBFile.read(fin) + structure = pdb.get_structure(pdbf, model=1) + bbmask = filter_backbone(structure) + structure = structure[bbmask] + all_chains = get_chains(structure) + if len(all_chains) == 0: + raise ValueError('No chains found in the input file.') + if chain is None: + chain_ids = all_chains + elif isinstance(chain, list): + chain_ids = chain + else: + chain_ids = [chain] + for chain in chain_ids: + if chain not in all_chains: + raise ValueError(f'Chain {chain} not found in input file') + chain_filter = [a.chain_id in chain_ids for a in structure] + structure = structure[chain_filter] + return structure + + +def extract_coords_from_structure(structure: biotite.structure.AtomArray): + """ + Args: + structure: An instance of biotite AtomArray + Returns: + Tuple (coords, seq) + - coords is an L x 3 x 3 array for N, CA, C coordinates + - seq is the extracted sequence + """ + coords = get_atom_coords_residuewise(["N", "CA", "C"], structure) + residue_identities = get_residues(structure)[1] + seq = ''.join([ProteinSequence.convert_letter_3to1(r) for r in residue_identities]) + return coords, seq + + +def load_coords(fpath, chain): + """ + Args: + fpath: filepath to either pdb or cif file + chain: the chain id + Returns: + Tuple (coords, seq) + - coords is an L x 3 x 3 array for N, CA, C coordinates + - seq is the extracted sequence + """ + structure = load_structure(fpath, chain) + return extract_coords_from_structure(structure) + + +def get_atom_coords_residuewise(atoms: List[str], struct: biotite.structure.AtomArray): + """ + Example for atoms argument: ["N", "CA", "C"] + """ + + def filterfn(s, axis=None): + filters = np.stack([s.atom_name == name for name in atoms], axis=1) + sum = filters.sum(0) + if not np.all(sum <= np.ones(filters.shape[1])): + raise RuntimeError("structure has multiple atoms with same name") + index = filters.argmax(0) + coords = s[index].coord + coords[sum == 0] = float("nan") + return coords + + return biotite.structure.apply_residue_wise(struct, struct, filterfn) + +def rotate(v, R): + """ + Rotates a vector by a rotation matrix. + + Args: + v: 3D vector, tensor of shape (length x batch_size x channels x 3) + R: rotation matrix, tensor of shape (length x batch_size x 3 x 3) + + Returns: + Rotated version of v by rotation matrix R. + """ + R = R.unsqueeze(-3) + v = v.unsqueeze(-1) + return torch.sum(v * R, dim=-2) + + +def get_rotation_frames(coords): + """ + Returns a local rotation frame defined by N, CA, C positions. + + Args: + coords: coordinates, tensor of shape (batch_size x length x 3 x 3) + where the third dimension is in order of N, CA, C + + Returns: + Local relative rotation frames in shape (batch_size x length x 3 x 3) + """ + v1 = coords[:, :, 2] - coords[:, :, 1] + v2 = coords[:, :, 0] - coords[:, :, 1] + e1 = normalize(v1, dim=-1) + u2 = v2 - e1 * torch.sum(e1 * v2, dim=-1, keepdim=True) + e2 = normalize(u2, dim=-1) + e3 = torch.cross(e1, e2, dim=-1) + R = torch.stack([e1, e2, e3], dim=-2) + return R + + +def nan_to_num(ts, val=0.0): + """ + Replaces nans in tensor with a fixed value. + """ + val = torch.tensor(val, dtype=ts.dtype, device=ts.device) + return torch.where(~torch.isfinite(ts), val, ts) + + +def rbf(values, v_min, v_max, n_bins=16): + """ + Returns RBF encodings in a new dimension at the end. + """ + rbf_centers = torch.linspace(v_min, v_max, n_bins, device=values.device) + rbf_centers = rbf_centers.view([1] * len(values.shape) + [-1]) + rbf_std = (v_max - v_min) / n_bins + v_expand = torch.unsqueeze(values, -1) + z = (values.unsqueeze(-1) - rbf_centers) / rbf_std + return torch.exp(-z ** 2) + + +def norm(tensor, dim, eps=1e-8, keepdim=False): + """ + Returns L2 norm along a dimension. + """ + return torch.sqrt( + torch.sum(torch.square(tensor), dim=dim, keepdim=keepdim) + eps) + + +def normalize(tensor, dim=-1): + """ + Normalizes a tensor along a dimension after removing nans. + """ + return nan_to_num( + torch.div(tensor, norm(tensor, dim=dim, keepdim=True)) + ) diff --git a/proteingym/baselines/rita/compute_fitness.py b/proteingym/baselines/rita/compute_fitness.py new file mode 100644 index 0000000..56c5288 --- /dev/null +++ b/proteingym/baselines/rita/compute_fitness.py @@ -0,0 +1,96 @@ +import os +import argparse +import tqdm + +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer +from scipy.stats import spearmanr +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss + +def calc_fitness(model, prots, tokenizer, device='cuda:0', model_context_len=1023): + loss_list = [] + loss_fn = CrossEntropyLoss() + with torch.no_grad(): + for prot in tqdm.tqdm(prots): + loss_val = 0 + + sequence_chunks=[] + if len(prot) < model_context_len: + sequence_chunks = [prot] + else: + len_target_seq = len(prot) + num_windows = 1 + int( len_target_seq / model_context_len) + start=0 + for window_index in range(1, num_windows+1): + sequence_chunks.append(prot[start:start+model_context_len]) + start += model_context_len + + for chunk in sequence_chunks: + for p in [chunk, chunk[::-1]]: + ids = torch.tensor([tokenizer.encode(p)]).to(device) + input_ids = ids[:, :-1] + targets = ids[:, 1:] + + logits=model(input_ids).logits + loss = loss_fn(target=targets.view(-1), input=logits.view(-1,logits.size(-1))) + loss_val += -loss.item() + + loss_list += [loss_val] + return np.array(loss_list) + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab="ACDEFGHIKLMNPQRSTVWY"): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + parser = argparse.ArgumentParser(description='Tranception scoring') + parser.add_argument('--RITA_model_name_or_path', default="/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/RITA/small", type=str, help='Name of or path to RITA model') + parser.add_argument('--DMS_reference_file_path', default='/home/pn73/Tranception/proteingym/ProteinGym_reference_file_substitutions.csv', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_data_folder', default='/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/Tranception_open_source/DMS_files/ProteinGym_substitutions', type=str, help='Path of DMS folder') + parser.add_argument('--DMS_index', type=int, help='Path of DMS folder') + parser.add_argument('--output_scores_folder', default=None, type=str, help='Name of folder to write model scores to') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + args = parser.parse_args() + model = AutoModelForCausalLM.from_pretrained(args.RITA_model_name_or_path,trust_remote_code=True) + model.cuda() + tokenizer = AutoTokenizer.from_pretrained("/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/RITA/tokenizer") + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Computing scores for: {} with RITA: {}".format(DMS_id, args.RITA_model_name_or_path)) + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + # Daniel R: shouldn't need this now that all DMS files have both "mutated_sequence" and "mutant" columns + if not args.indel_mode and "mutated_sequence" not in DMS_data.columns: + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: get_mutated_sequence(target_seq, x)) # if not args.indel_mode else DMS_data['mutant'] + + model_scores = calc_fitness(model=model, prots=np.array(DMS_data['mutated_sequence']), tokenizer=tokenizer) + + DMS_data['RITA_score']=model_scores + scoring_filename = args.output_scores_folder+os.sep+DMS_id+'.csv' + DMS_data[['mutant','RITA_score','DMS_score']].to_csv(scoring_filename, index=False) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/baselines/rita/rita_configuration.py b/proteingym/baselines/rita/rita_configuration.py new file mode 100644 index 0000000..063043c --- /dev/null +++ b/proteingym/baselines/rita/rita_configuration.py @@ -0,0 +1,31 @@ +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +class RITAConfig(PretrainedConfig): + model_type = "rita" + + def __init__( + self, + vocab_size=26, + d_model=768, + num_layers=12, + max_seq_len=1024, + num_heads=12, + dropout=0., + ff_ratio=4, + eos_token_id=2, + initializer_range=0.02, + **kwargs, + ): + super().__init__(eos_token_id=eos_token_id, **kwargs) + self.vocab_size = vocab_size + self.d_model = d_model + self.num_heads = num_heads + self.d_feedforward = d_model*ff_ratio + self.num_layers = num_layers + self.max_seq_len=max_seq_len + self.dropout = dropout + self.eos_token_id=eos_token_id + self.initializer_range=0.02 diff --git a/proteingym/baselines/rita/rita_modeling.py b/proteingym/baselines/rita/rita_modeling.py new file mode 100644 index 0000000..a209135 --- /dev/null +++ b/proteingym/baselines/rita/rita_modeling.py @@ -0,0 +1,488 @@ +import math +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss, BCEWithLogitsLoss, MSELoss + +from transformers.modeling_outputs import ( + BaseModelOutput, + CausalLMOutput, + SequenceClassifierOutput +) + +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +from .rita_configuration import RITAConfig +import torch.nn.functional as F +logger = logging.get_logger(__name__) + +@torch.jit.script +def RITA_gelu(hidden_states): + return hidden_states * 0.5 * (1.0 + torch.tanh(0.79788456 * hidden_states * (1 + 0.044715 * hidden_states * hidden_states))) + +class RITAGELU(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, hidden_states): + return RITA_gelu(hidden_states) + +def rotate_half(x): + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=x1.ndim - 1) + +class RotaryEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + assert config.d_model % config.num_heads == 0 + + self.d_model = config.d_model + self.num_heads = config.num_heads + self.max_seq_len = config.max_seq_len + + head_dim = self.d_model // self.num_heads + inv_freq = 1.0 / (10000 ** (torch.arange(0, head_dim, 2).float() / head_dim)) + self.register_buffer('inv_freq', inv_freq) + self.seq_len_cached = None + self.cos_cached = None + self.sin_cached = None + + def forward(self, x: torch.FloatTensor, seq_dim=1) -> torch.FloatTensor: + seq_len = x.shape[seq_dim] + if seq_len != self.seq_len_cached: + self.seq_len_cached = seq_len + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + self.cos_cached = emb.cos()[None, None, :, :] + self.sin_cached = emb.sin()[None, None, :, :] + return self.cos_cached, self.sin_cached + + def apply_rotary_pos_emb(self, q, k, cos, sin): + return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) + + +class SelfAttention(nn.Module): + """Implementation of MultiHeadAttention following `Karpathy's MinGPT `_. + modified to use rotary embeddings. + + Parameters + ---------- + d_model: int, + total dimension of the model. + num_heads: int, + number of parallel attention heads. + num_layers: int, + number of layers in the model, used for the Megatron-like init. + rotaty_embedding: Optional[Block], default None, + a RotaryEmbedding Block to add positionnal information in Queries and Keys + dropout: float, default 0.1, + amount of dropout on the attention weights. + sigma: float, default 0.02, + standard deviation used for the init. + trainable: bool, default True, + if False, the Module parameters will be hidden from the optimizer. + """ + + def __init__( + self, + d_model: int, + num_heads: int, + num_layers: int, + rotary_embedding= None, + dropout: float = 0.1, + sigma=0.02, + use_cache: bool = False, + bias=True, + ): + super().__init__() + assert d_model % num_heads == 0 + self.d_model = d_model + self.num_heads = num_heads + self.head_dim = self.d_model // self.num_heads + self.num_layers = num_layers + self.dropout = dropout + self.sigma = sigma + self.bias = bias + + # key, query, value projections for all heads + self.key = nn.Linear(d_model, d_model, bias=bias) + self.query = nn.Linear(d_model, d_model, bias=bias) + self.value = nn.Linear(d_model, d_model, bias=bias) + # regularization + self.attn_drop = nn.Dropout(dropout) + self.resid_drop = nn.Dropout(dropout) + # output projection + self.proj = nn.Linear(d_model, d_model, bias=bias) + + self.rotary_embedding = rotary_embedding + self.layer_id = None # will be set by the Transformer itself + self.use_cache = use_cache + self.qkv = None + self.bias = bias + + def forward( + self, + x, + causal_mask: Optional[torch.BoolTensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + + N, L, D = x.size() # Batch_size, Context_size, d_model + + # calculate query, key, values for all heads in batch and move head forward to be the batch dim + k = ( + self.key(x).view(N, L, self.num_heads, D // self.num_heads).transpose(1, 2) + ) # (N, nh, L, hs) + q = ( + self.query(x).view(N, L, self.num_heads, D // self.num_heads).transpose(1, 2) + ) # (N, nh, L, hs) + v = ( + self.value(x).view(N, L, self.num_heads, D // self.num_heads).transpose(1, 2) + ) # (N, nh, L, hs) + + if self.rotary_embedding is not None: + cos, sin = self.rotary_embedding(x) + q, k = self.rotary_embedding.apply_rotary_pos_emb(q, k, cos, sin) + + # causal self-attention; Self-attend: (N, nh, L, hs) x (N, nh, hs, L) -> (N, nh, L, L) + att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + + if causal_mask is not None: + att[:,:,-L:, -L: ].masked_fill_(causal_mask.view(1, 1, L, L), float("-inf")) + + att = ( + att.transpose(0, 2) + .masked_fill(attention_mask.view(1, 1, N, L)==0, float("-inf")) + .transpose(0, 2) + if attention_mask is not None + else att + ) + + att = F.softmax(att, dim=-1) + att = self.attn_drop(att) + y = att @ v # (N, nh, L, L) x (N, nh, L, hs) -> (N, nh, L, hs) + y = ( + y.transpose(1, 2).contiguous().view(N, L, D) + ) # re-assemble all head outputs side by side + + # output projection + y = self.resid_drop(self.proj(y)) + return y + +class DecoderLayer(nn.Module): + """Transformer block containing the self-attention module and the feedfoward module.""" + + def __init__( + self, config + ): + super().__init__() + self.self_attention = SelfAttention(config.d_model, config.num_heads, config.dropout, rotary_embedding=RotaryEmbedding(config)) + self.attn_norm = nn.LayerNorm(config.d_model) + self.attn_dropout = nn.Dropout(config.dropout) + + self.mlp = nn.Sequential( + nn.Linear(config.d_model, config.d_feedforward, bias=True), + RITAGELU(), + nn.Linear(config.d_feedforward, config.d_model, bias=True), + ) + self.mlp_norm = nn.LayerNorm(config.d_model) + self.mlp_dropout = nn.Dropout(config.dropout) + + def forward( + self, + x: torch.FloatTensor, + causal_mask: torch.BoolTensor, + attention_mask: Optional[torch.BoolTensor] = None, + ) -> torch.FloatTensor: + y = self.attn_norm(x) + y = self.self_attention(y, causal_mask=causal_mask, attention_mask=attention_mask) + x = x + self.attn_dropout(y) + + y = self.mlp_norm(x) + y = self.mlp(y) + x = x + self.mlp_dropout(y) + return x + +class RITAModel(PreTrainedModel): + config_class = RITAConfig + base_model_prefix = "transformer" + is_parallelizable = False + + def __init__( + self, + config + ): + super().__init__(config) + self.embedding = nn.Embedding(config.vocab_size, config.d_model) + self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_layers)]) + self.final_norm = nn.LayerNorm(config.d_model) + + def forward( + self, + input_ids=None, + past_key_values=None, # NOT USED + attention_mask=None, + causal_mask=None, + token_type_ids=None, # NOT USED + position_ids=None, # NOT USED + head_mask=None, # NOT USED + inputs_embeds=None, + encoder_hidden_states=None, # NOT USED + encoder_causal_mask=None, # NOT USED + labels=None, + use_cache=None, # NOT USED + output_attentions=None, # NOT USED + output_hidden_states=None, # NOT USED + return_dict=None # NOT USED + ) -> torch.FloatTensor: + if inputs_embeds == None: + x = self.embedding(input_ids) # N x L x D + else: + x = inputs_embeds + if causal_mask == None: + causal_mask = (torch.triu(torch.ones(input_ids.size(1), input_ids.size(1))) == 0).transpose(0, 1).contiguous().to(input_ids.device) + for layer in self.layers: + x = layer(x, causal_mask=causal_mask, attention_mask=attention_mask) + x = self.final_norm(x) # N x L x D + + return BaseModelOutput( + hidden_states=x, + ) + + #Some common HF functions. + def get_input_embeddings(self): + return self.embedding + + def set_input_embeddings(self, new_embeddings): + self.embedding = new_embeddings + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +class RITAModelForCausalLM(PreTrainedModel): + config_class = RITAConfig + base_model_prefix = "transformer" + is_parallelizable = False + + def __init__( + self, + config + ): + super().__init__(config) + self.transformer = RITAModel(config) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + + def forward( + self, + input_ids=None, + past_key_values=None, # NOT USED + attention_mask=None, + causal_mask=None, + token_type_ids=None, # NOT USED + position_ids=None, # NOT USED + head_mask=None, # NOT USED + inputs_embeds=None, + encoder_hidden_states=None, # NOT USED + encoder_causal_mask=None, # NOT USED + labels=None, + use_cache=None, # NOT USED + output_attentions=None, # NOT USED + output_hidden_states=None, # NOT USED + return_dict=None # NOT USED + ) -> torch.FloatTensor: + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + causal_mask=causal_mask, + attention_mask = attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = self.lm_head(transformer_outputs.hidden_states) + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + return CausalLMOutput( + loss=loss, + logits=logits, + hidden_states=transformer_outputs.hidden_states, + ) + + #Some common HF functions. + def get_input_embeddings(self): + return self.transformer.embedding + + def set_input_embeddings(self, new_embeddings): + self.transformer.embedding = new_embeddings + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, lm_head): + self.lm_head = lm_head + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +class RITAModelForSequenceClassification(PreTrainedModel): + config_class = RITAConfig + base_model_prefix = "transformer" + is_parallelizable = False + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.transformer = RITAModel(config) + self.score = nn.Linear(config.d_model, self.num_labels, bias=False) + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + causal_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + causal_mask=causal_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size, sequence_length = input_ids.shape[:2] + else: + batch_size, sequence_length = inputs_embeds.shape[:2] + + assert ( + self.config.pad_token_id is not None or batch_size == 1 + ), "Cannot handle batch sizes > 1 if no padding token is defined." + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1 + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + f"unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=self.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=pooled_logits, + ) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) diff --git a/proteingym/baselines/saprot/compute_fitness.py b/proteingym/baselines/saprot/compute_fitness.py new file mode 100644 index 0000000..0cde6a8 --- /dev/null +++ b/proteingym/baselines/saprot/compute_fitness.py @@ -0,0 +1,166 @@ +import os +import argparse + +from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer +from scipy.stats import spearmanr +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss +from foldseek_util import get_struc_seq +from tqdm import tqdm + +foldseek_struc_vocab = "pynwrqhgdlvtmfsaeikc#" + + +def predict_mut(model, tokenizer, seq: str, mut_info: str) -> float: + """ + Predict the mutational effect of a given mutation + Args: + seq: The wild type sequence + + mut_info: The mutation information in the format of "A123B", where A is the original amino acid, 123 is the + position and B is the mutated amino acid. If multiple mutations are provided, they should be + separated by colon, e.g. "A123B:C124D". + + Returns: + The predicted mutational effect + """ + tokens = tokenizer.tokenize(seq) + for single in mut_info.split(":"): + pos = int(single[1:-1]) + tokens[pos - 1] = "#" + tokens[pos - 1][-1] + + mask_seq = " ".join(tokens) + inputs = tokenizer(mask_seq, return_tensors="pt") + inputs = {k: v.to(model.device) for k, v in inputs.items()} + + with torch.no_grad(): + outputs = model(**inputs) + logits = outputs.logits + probs = logits.softmax(dim=-1) + + score = 0 + for single in mut_info.split(":"): + ori_aa, pos, mut_aa = single[0], int(single[1:-1]), single[-1] + ori_st = tokenizer.get_vocab()[ori_aa + foldseek_struc_vocab[0]] + mut_st = tokenizer.get_vocab()[mut_aa + foldseek_struc_vocab[0]] + + ori_prob = probs[0, pos, ori_st: ori_st + len(foldseek_struc_vocab)].sum() + mut_prob = probs[0, pos, mut_st: mut_st + len(foldseek_struc_vocab)].sum() + + score += torch.log(mut_prob / ori_prob) + + return score + + +def calc_fitness(foldseek_bin, model, DMS_data, tokenizer, mutation_col='mutant', target_seq=None, pdb_file=None, offset_idx=1): + # Get 3Di sequence + struc_seq = get_struc_seq(foldseek_bin, pdb_file, ["A"], plddt_mask=True, plddt_threshold=70)["A"][1].lower() + + seq = "".join([a + b for a, b in zip(target_seq, struc_seq)]) + log_proba_list = [] + for mut_info in tqdm(DMS_data[mutation_col]): + mutations = [] + for row in mut_info.split(":"): + ori, pos, mut = row[0], row[1: -1], row[-1] + pos = int(pos) - offset_idx + 1 + mutations.append(f"{ori}{pos}{mut}") + + all_mut = ":".join(mutations) + score = predict_mut(model, tokenizer, seq, all_mut).item() + log_proba_list.append(score) + + return np.array(log_proba_list) + + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab="ACDEFGHIKLMNPQRSTVWY"): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: " + str(mutation)) + relative_position = position - start_idx + assert (from_AA == focus_seq[relative_position]), "Invalid from_AA or mutant position: " + str( + mutation) + " from_AA: " + str(from_AA) + " relative pos: " + str(relative_position) + " focus_seq: " + str( + focus_seq) + assert (to_AA in AA_vocab), "Mutant to_AA is invalid: " + str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with SaProt. + """ + parser = argparse.ArgumentParser(description='SaProt scoring') + parser.add_argument('--foldseek_bin', + default="", + type=str, help='Path to foldseek binary file') + parser.add_argument('--SaProt_model_name_or_path', + default="", + type=str, help='Name of or path to SaProt model') + parser.add_argument('--DMS_reference_file_path', + default='/sujin/Datasets/ProteinGym/v1.0/DMS_substitutions.csv', + type=str, help='Path of DMS folder') + parser.add_argument('--DMS_data_folder', + default='/sujin/Datasets/ProteinGym/v1.0/DMS_ProteinGym_substitutions', + type=str, help='Path of DMS folder') + parser.add_argument('--structure_data_folder', default='', type=str, help='Path of structure folder') + parser.add_argument('--DMS_index', type=int, help='Path of DMS folder') + parser.add_argument('--output_scores_folder', default=None, type=str, + help='Name of folder to write model scores to') + parser.add_argument('--indel_mode', action='store_true', + help='Whether to score sequences with insertions and deletions') + args = parser.parse_args() + model = AutoModelForMaskedLM.from_pretrained(args.SaProt_model_name_or_path, trust_remote_code=True) + model.cuda() + tokenizer = AutoTokenizer.from_pretrained(args.SaProt_model_name_or_path) + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id = list_DMS[args.DMS_index] + + scoring_filename = args.output_scores_folder + os.sep + DMS_id + '.csv' + if os.path.exists(scoring_filename): + print("Scores already computed for: {}".format(DMS_id)) + + print("Computing scores for: {} with SaProt: {}".format(DMS_id, args.SaProt_model_name_or_path)) + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"] == DMS_id].values[0] + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"] == DMS_id].values[0].upper() + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply( + lambda x: get_mutated_sequence(target_seq, x)) if not args.indel_mode else DMS_data['mutant'] + + pdb_filenames = mapping_protein_seq_DMS["pdb_file"][mapping_protein_seq_DMS["DMS_id"] == DMS_id].values[ + 0].split('|') # if sequence is large (eg., BRCA2_HUMAN) the structure is split in several chunks + pdb_ranges = mapping_protein_seq_DMS["pdb_range"][mapping_protein_seq_DMS["DMS_id"] == DMS_id].values[0].split( + '|') + model_scores = [] + for pdb_index, pdb_filename in enumerate(pdb_filenames): + pdb_file = args.structure_data_folder + os.sep + pdb_filename + pdb_range = [int(x) for x in pdb_ranges[pdb_index].split("-")] + target_seq_split = target_seq[pdb_range[0] - 1:pdb_range[1]] # pdb_range is 1-indexed + DMS_data["mutated_position"] = DMS_data['mutant'].apply(lambda x: int(x.split(':')[0][1:-1])) #if multiple mutant, will extract position of first mutant + filtered_DMS_data = DMS_data[ + (DMS_data["mutated_position"] >= pdb_range[0]) & (DMS_data["mutated_position"] <= pdb_range[1])] + model_scores.append(calc_fitness(foldseek_bin=args.foldseek_bin, model=model, DMS_data=filtered_DMS_data, + tokenizer=tokenizer, target_seq=target_seq_split, + pdb_file=pdb_file, offset_idx=pdb_range[0])) + + model_scores = np.concatenate(model_scores) + + DMS_data['SaProt_score'] = model_scores + scoring_filename = args.output_scores_folder + os.sep + DMS_id + '.csv' + DMS_data[['mutant', 'SaProt_score', 'DMS_score']].to_csv(scoring_filename, index=False) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/baselines/saprot/foldseek_util.py b/proteingym/baselines/saprot/foldseek_util.py new file mode 100644 index 0000000..cfbaa6a --- /dev/null +++ b/proteingym/baselines/saprot/foldseek_util.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import re +import sys + +sys.path.append(".") + + +# Get structural seqs from pdb file +def get_struc_seq(foldseek, + path, + chains: list = None, + process_id: int = 0, + plddt_mask: bool = False, + plddt_threshold: float = 70.) -> dict: + """ + + Args: + foldseek: Binary executable file of foldseek + path: Path to pdb file + chains: Chains to be extracted from pdb file. If None, all chains will be extracted. + process_id: Process ID for temporary files. This is used for parallel processing. + plddt_mask: If True, mask regions with plddt < plddt_threshold. plddt scores are from the pdb file. + plddt_threshold: Threshold for plddt. If plddt is lower than this value, the structure will be masked. + + Returns: + seq_dict: A dict of structural seqs. The keys are chain IDs. The values are tuples of + (seq, struc_seq, combined_seq). + """ + assert os.path.exists(foldseek), f"Foldseek not found: {foldseek}" + assert os.path.exists(path), f"Pdb file not found: {path}" + + tmp_save_path = f"get_struc_seq_{process_id}.tsv" + cmd = f"{foldseek} structureto3didescriptor -v 0 --threads 1 --chain-name-mode 1 {path} {tmp_save_path}" + os.system(cmd) + + seq_dict = {} + name = os.path.basename(path) + with open(tmp_save_path, "r") as r: + for i, line in enumerate(r): + desc, seq, struc_seq = line.split("\t")[:3] + + # Mask low plddt + if plddt_mask: + plddts = extract_plddt(path) + assert len(plddts) == len(struc_seq), f"Length mismatch: {len(plddts)} != {len(struc_seq)}" + + # Mask regions with plddt < threshold + indices = np.where(plddts < plddt_threshold)[0] + np_seq = np.array(list(struc_seq)) + np_seq[indices] = "#" + struc_seq = "".join(np_seq) + + name_chain = desc.split(" ")[0] + chain = name_chain.replace(name, "").split("_")[-1] + + if chains is None or chain in chains: + if chain not in seq_dict: + combined_seq = "".join([a + b.lower() for a, b in zip(seq, struc_seq)]) + seq_dict[chain] = (seq, struc_seq, combined_seq) + + os.remove(tmp_save_path) + os.remove(tmp_save_path + ".dbtype") + return seq_dict + + +def extract_plddt(pdb_path: str) -> np.ndarray: + """ + Extract plddt scores from pdb file. + Args: + pdb_path: Path to pdb file. + + Returns: + plddts: plddt scores. + """ + with open(pdb_path, "r") as r: + plddt_dict = {} + for line in r: + line = re.sub(' +', ' ', line).strip() + splits = line.split(" ") + + if splits[0] == "ATOM": + # If position < 1000 + if len(splits[4]) == 1: + pos = int(splits[5]) + + # If position >= 1000, the blank will be removed, e.g. "A 999" -> "A1000" + # So the length of splits[4] is not 1 + else: + pos = int(splits[4][1:]) + + plddt = float(splits[-2]) + + if pos not in plddt_dict: + plddt_dict[pos] = [plddt] + else: + plddt_dict[pos].append(plddt) + + plddts = np.array([np.mean(v) for v in plddt_dict.values()]) + return plddts \ No newline at end of file diff --git a/proteingym/baselines/saprot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv b/proteingym/baselines/saprot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv new file mode 100644 index 0000000..199dacc --- /dev/null +++ b/proteingym/baselines/saprot/substitutions/Spearman/Summary_performance_DMS_substitutions_Spearman.csv @@ -0,0 +1,53 @@ +Model_rank,Model_name,Model type,Average_Spearman,Bootstrap_standard_error_Spearman,Function_Activity,Function_Binding,Function_Expression,Function_OrganismalFitness,Function_Stability,Low_MSA_depth,Medium_MSA_depth,High_MSA_depth,Taxa_Human,Taxa_Other_Eukaryote,Taxa_Prokaryote,Taxa_Virus,Depth_1,Depth_2,Depth_3,Depth_4,Depth_5+,Model details,References +1,TranceptEVE L,Hybrid model,0.456,0.0,0.487,0.376,0.457,0.46,0.5,0.451,0.467,0.492,0.471,0.498,0.473,0.453,0.446,0.28,0.35,0.32,0.382,TranceptEVE Large model (Tranception Large & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +2,TranceptEVE M,Hybrid model,0.455,0.004,0.479,0.386,0.452,0.454,0.502,0.44,0.468,0.488,0.473,0.498,0.466,0.441,0.441,0.281,0.304,0.309,0.375,TranceptEVE Medium model (Tranception Medium & retrieved EVE model),"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop." +3,GEMME,Alignment-based model,0.455,0.007,0.482,0.383,0.438,0.452,0.519,0.455,0.47,0.497,0.468,0.51,0.473,0.469,0.446,0.274,0.321,0.324,0.414,GEMME model,"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619." +4,TranceptEVE S,Hybrid model,0.452,0.004,0.475,0.396,0.443,0.449,0.497,0.449,0.46,0.484,0.468,0.49,0.467,0.433,0.435,0.275,0.304,0.304,0.372,TranceptEVE Small model (Tranception Small & retrieved EVE model),"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +5,SaProt_650M_AF2,Protein language model,0.449,0.008,0.45,0.373,0.471,0.362,0.587,0.379,0.446,0.542,0.467,0.518,0.523,0.295,0.45,0.309,0.27,0.271,0.321,, +6,EVE (ensemble),Alignment-based model,0.439,0.006,0.464,0.386,0.408,0.447,0.491,0.425,0.453,0.481,0.453,0.487,0.468,0.428,0.427,0.273,0.308,0.298,0.355,EVE model (ensemble of 5 independently-trained models),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +7,VESPA,Protein language model,0.436,0.006,0.468,0.366,0.404,0.44,0.5,0.427,0.455,0.484,0.438,0.492,0.49,0.432,0.434,0.183,0.357,0.302,0.328,VESPA model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +8,Tranception L,Hybrid model,0.434,0.004,0.465,0.349,0.45,0.436,0.471,0.432,0.438,0.473,0.453,0.483,0.431,0.432,0.423,0.258,0.352,0.318,0.387,Tranception Large model (700M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +9,MSA Transformer (ensemble),Hybrid model,0.434,0.009,0.469,0.337,0.446,0.421,0.495,0.404,0.45,0.488,0.437,0.505,0.463,0.414,0.426,0.238,0.384,0.366,0.408,MSA Transformer (ensemble of 5 MSA samples),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +10,EVE (single),Alignment-based model,0.433,0.005,0.458,0.372,0.404,0.442,0.487,0.417,0.448,0.477,0.445,0.484,0.464,0.424,0.422,0.271,0.304,0.296,0.359,EVE model (single seed),"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature." +11,Tranception M,Hybrid model,0.427,0.005,0.448,0.361,0.441,0.422,0.465,0.417,0.432,0.456,0.451,0.476,0.404,0.415,0.41,0.247,0.24,0.27,0.353,Tranception Medium model (300M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +12,ESM-IF1,Inverse folding model,0.422,0.011,0.368,0.389,0.407,0.324,0.624,0.3,0.431,0.544,0.415,0.497,0.507,0.374,0.438,0.345,0.29,0.289,0.358,ESM-IF1 model,"Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv." +13,MSA Transformer (single),Hybrid model,0.421,0.009,0.457,0.33,0.435,0.409,0.476,0.393,0.435,0.473,0.427,0.491,0.451,0.39,0.411,0.239,0.39,0.368,0.413,MSA Transformer (single MSA sample),"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML." +14,DeepSequence (ensemble),Alignment-based model,0.419,0.008,0.455,0.363,0.39,0.413,0.476,0.383,0.428,0.473,0.442,0.469,0.46,0.344,0.404,0.264,0.313,0.309,0.378,DeepSequence model (ensemble of 5 independently-trained models),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +15,Tranception S,Hybrid model,0.418,0.006,0.436,0.372,0.42,0.411,0.452,0.428,0.415,0.444,0.438,0.463,0.396,0.405,0.397,0.24,0.249,0.272,0.352,Tranception Small model (85M params) with retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +16,ESM2 (650M),Protein language model,0.414,0.012,0.425,0.337,0.415,0.369,0.523,0.335,0.406,0.515,0.456,0.471,0.476,0.238,0.421,0.247,0.22,0.162,0.218,ESM2 model (650M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +17,ESM-1v (ensemble),Protein language model,0.41,0.011,0.414,0.318,0.431,0.387,0.5,0.326,0.418,0.502,0.458,0.446,0.454,0.289,0.4,0.221,0.186,0.151,0.203,ESM-1v (ensemble of 5 independently-trained models),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +18,DeepSequence (single),Alignment-based model,0.408,0.009,0.447,0.349,0.372,0.397,0.473,0.382,0.415,0.465,0.436,0.465,0.448,0.323,0.391,0.252,0.277,0.293,0.37,DeepSequence model (single seed),"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822." +19,ESM2 (3B),Protein language model,0.406,0.011,0.417,0.321,0.403,0.379,0.509,0.348,0.415,0.49,0.441,0.461,0.477,0.274,0.414,0.213,0.202,0.166,0.217,ESM2 model (3B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +20,MIF-ST,Inverse folding model,0.402,0.01,0.39,0.321,0.438,0.373,0.486,0.376,0.403,0.456,0.404,0.415,0.463,0.396,0.431,0.265,0.334,0.298,0.298,MIF-ST model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +21,ESM2 (15B),Protein language model,0.401,0.01,0.405,0.317,0.405,0.388,0.488,0.357,0.414,0.473,0.431,0.449,0.459,0.313,0.405,0.204,0.239,0.172,0.234,ESM2 model (15B params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +22,SaProt_35M_AF2,Protein language model,0.399,0.01,0.371,0.349,0.422,0.285,0.57,0.31,0.387,0.504,0.434,0.481,0.432,0.225,0.403,0.343,0.179,0.187,0.229,, +23,EVmutation,Alignment-based model,0.395,0.006,0.44,0.317,0.378,0.411,0.43,0.403,0.423,0.41,0.409,0.444,0.422,0.388,0.375,0.274,0.324,0.301,0.394,EVmutation model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +24,ESM-1b,Protein language model,0.394,0.01,0.428,0.287,0.406,0.351,0.5,0.35,0.398,0.482,0.434,0.475,0.455,0.241,0.383,0.227,0.187,0.149,0.27,ESM-1b (w/ Brandes et al. extensions),"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv." +25,VESPAl,Protein language model,0.394,0.007,0.429,0.347,0.326,0.404,0.461,0.382,0.412,0.449,0.392,0.461,0.451,0.392,0.385,0.144,0.324,0.276,0.32,VESPAl model,"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647." +26,Progen2 XL,Protein language model,0.391,0.008,0.402,0.302,0.418,0.387,0.445,0.354,0.405,0.444,0.384,0.442,0.439,0.391,0.385,0.184,0.28,0.219,0.28,Progen2 xlarge model (6.4B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +27,ESM2 (150M),Protein language model,0.387,0.013,0.391,0.326,0.402,0.305,0.51,0.306,0.358,0.497,0.449,0.46,0.407,0.137,0.386,0.243,0.146,0.173,0.231,ESM2 model (150M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +28,MIF,Inverse folding model,0.383,0.011,0.327,0.336,0.43,0.295,0.524,0.349,0.374,0.446,0.397,0.39,0.417,0.352,0.409,0.269,0.257,0.24,0.24,MIF model,"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv." +29,Progen2 L,Protein language model,0.38,0.008,0.406,0.293,0.427,0.379,0.396,0.348,0.381,0.42,0.41,0.414,0.369,0.333,0.371,0.144,0.232,0.199,0.258,Progen2 large model (2.7B params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +30,ESM-1v (single),Protein language model,0.38,0.013,0.39,0.266,0.406,0.362,0.476,0.286,0.393,0.481,0.432,0.422,0.43,0.256,0.373,0.192,0.187,0.142,0.197,ESM-1v (single seed),"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS." +31,Progen2 M,Protein language model,0.379,0.009,0.393,0.295,0.433,0.381,0.396,0.318,0.382,0.425,0.411,0.406,0.356,0.342,0.372,0.13,0.158,0.135,0.177,Progen2 medium model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +32,Progen2 Base,Protein language model,0.378,0.009,0.396,0.294,0.437,0.379,0.383,0.342,0.368,0.423,0.421,0.408,0.331,0.328,0.369,0.13,0.145,0.157,0.208,Progen2 base model (760M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +33,Tranception L no retrieval,Protein language model,0.374,0.008,0.401,0.288,0.413,0.389,0.381,0.358,0.371,0.419,0.389,0.376,0.381,0.395,0.363,0.178,0.308,0.257,0.334,Tranception Large model (700M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +34,RITA XL,Protein language model,0.372,0.009,0.366,0.302,0.414,0.381,0.398,0.315,0.382,0.412,0.394,0.384,0.353,0.402,0.356,0.139,0.136,0.154,0.233,RITA xlarge model (1.2B params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +35,CARP (640M),Protein language model,0.369,0.012,0.395,0.273,0.397,0.364,0.414,0.314,0.375,0.428,0.416,0.386,0.39,0.273,0.389,0.213,0.187,0.164,0.162,CARP model (640M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +36,RITA L,Protein language model,0.365,0.009,0.359,0.29,0.42,0.374,0.383,0.316,0.369,0.403,0.394,0.386,0.319,0.391,0.347,0.137,0.135,0.147,0.21,RITA large model (680M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +37,Site-Independent,Alignment-based model,0.359,0.01,0.369,0.344,0.343,0.382,0.358,0.426,0.373,0.32,0.379,0.385,0.316,0.383,0.336,0.235,0.226,0.267,0.35,Site-Independent model,"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135." +38,RITA M,Protein language model,0.35,0.01,0.352,0.273,0.405,0.371,0.348,0.304,0.349,0.39,0.378,0.349,0.31,0.385,0.336,0.114,0.134,0.168,0.222,RITA medium model (300M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +39,Tranception M no retrieval,Protein language model,0.348,0.009,0.349,0.284,0.406,0.362,0.342,0.293,0.349,0.379,0.379,0.335,0.314,0.349,0.331,0.142,0.107,0.15,0.21,Tranception Medium model (300M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +40,Unirep evotuned,Hybrid model,0.347,0.009,0.355,0.305,0.365,0.346,0.366,0.33,0.344,0.372,0.355,0.363,0.346,0.349,0.319,0.154,0.25,0.226,0.294,Unirep model w/ evotuning,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +41,Progen2 S,Protein language model,0.336,0.011,0.333,0.275,0.384,0.337,0.349,0.283,0.321,0.391,0.384,0.334,0.298,0.285,0.327,0.113,0.12,0.133,0.187,Progen2 small model (150M params)," Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. " +42,CARP (76M),Protein language model,0.328,0.013,0.342,0.282,0.369,0.269,0.377,0.247,0.301,0.406,0.387,0.355,0.301,0.15,0.334,0.203,0.11,0.134,0.127,CARP model (76M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +43,ESM2 (35M),Protein language model,0.321,0.014,0.314,0.291,0.343,0.218,0.439,0.239,0.271,0.451,0.37,0.394,0.324,0.102,0.305,0.242,0.134,0.164,0.229,ESM2 model (35M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +44,RITA S,Protein language model,0.304,0.011,0.294,0.275,0.336,0.327,0.289,0.276,0.297,0.334,0.33,0.281,0.245,0.358,0.285,0.11,0.096,0.125,0.19,RITA small model (85M params),"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789." +45,Tranception S no retrieval,Protein language model,0.303,0.012,0.288,0.286,0.349,0.319,0.27,0.258,0.295,0.32,0.317,0.263,0.272,0.315,0.277,0.114,0.1,0.132,0.189,Tranception Small model (85M params) without retrieval,"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML." +46,CARP (38M),Protein language model,0.279,0.014,0.285,0.268,0.312,0.217,0.315,0.196,0.24,0.357,0.321,0.298,0.252,0.125,0.277,0.169,0.103,0.137,0.143,CARP model (38M params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." +47,ProteinMPNN,Inverse folding model,0.258,0.011,0.197,0.163,0.198,0.165,0.566,0.173,0.28,0.434,0.282,0.395,0.354,0.248,0.292,0.257,0.171,0.186,0.278,ProteinMPNN model,"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378." +48,ESM2 (8M),Protein language model,0.226,0.015,0.201,0.26,0.266,0.141,0.262,0.194,0.179,0.264,0.239,0.236,0.213,0.078,0.202,0.136,0.099,0.132,0.195,ESM2 model (8M params),"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379." +49,Wavenet,Alignment-based model,0.217,0.017,0.219,0.186,0.195,0.303,0.182,0.207,0.255,0.207,0.145,0.305,0.293,0.283,0.177,0.059,0.218,0.181,0.258,Wavenet model,"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12." +50,Unirep,Protein language model,0.19,0.016,0.182,0.202,0.216,0.141,0.21,0.181,0.161,0.209,0.213,0.219,0.165,0.057,0.174,0.071,0.111,0.141,0.191,Unirep model,"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8." +51,ProtGPT2,Protein language model,0.188,0.011,0.176,0.149,0.193,0.166,0.256,0.175,0.173,0.253,0.242,0.235,0.133,0.141,0.18,0.138,0.041,0.034,0.078,ProtGPT2 model,"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13." +52,CARP (600K),Protein language model,0.106,0.017,0.112,0.084,0.171,0.059,0.105,0.095,0.087,0.101,0.121,0.082,0.087,0.042,0.107,0.026,0.043,0.083,0.096,CARP model (600K params),"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv." diff --git a/proteingym/baselines/trancepteve/score_trancepteve.py b/proteingym/baselines/trancepteve/score_trancepteve.py new file mode 100644 index 0000000..e9db2d6 --- /dev/null +++ b/proteingym/baselines/trancepteve/score_trancepteve.py @@ -0,0 +1,209 @@ +import os +import argparse +import json +import pandas as pd + +import torch + +from transformers import PreTrainedTokenizerFast +import trancepteve +from trancepteve import config, model_pytorch +from trancepteve.utils import dms_utils + +dir_path = os.path.dirname(os.path.abspath(__file__)) + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with TranceptEVE. + """ + parser = argparse.ArgumentParser(description='TranceptEVE scoring') + parser.add_argument('--checkpoint', type=str, help='Path of Tranception model checkpoint') + parser.add_argument('--model_framework', default='pytorch', type=str, help='Underlying framework [pytorch|JAX]') + parser.add_argument('--batch_size_inference', default=20, type=int, help='Batch size for inference') + + #We may pass in all required information about the DMS via the provided reference files, or specify all relevant fields manually + parser.add_argument('--DMS_reference_file_path', default=None, type=str, help='Path to reference file with list of DMS to score') + parser.add_argument('--DMS_index', default=0, type=int, help='Index of DMS assay in reference file') + #Fields to be passed manually if reference file is not used + parser.add_argument('--target_seq', default=None, type=str, help='Full wild type sequence that is mutated in the DMS asssay') + parser.add_argument('--DMS_file_name', default=None, type=str, help='Name of DMS assay file') + parser.add_argument('--MSA_filename', default=None, type=str, help='Name of MSA (eg., a2m) file constructed on the wild type sequence') + parser.add_argument('--MSA_weight_file_name', default=None, type=str, help='Weight of sequences in the MSA (optional)') + parser.add_argument('--MSA_start', default=None, type=int, help='Sequence position that the MSA starts at (1-indexing)') + parser.add_argument('--MSA_end', default=None, type=int, help='Sequence position that the MSA ends at (1-indexing)') + parser.add_argument('--UniprotID', default=None, type=str, help='Uniprot ID of protein (EVE retrieval only)') + parser.add_argument('--MSA_threshold_sequence_frac_gaps', default=None, type=float, help='MSA processing: pct fragments threshold') + parser.add_argument('--MSA_threshold_focus_cols_frac_gaps', default=None, type=float, help='MSA processing: pct col filled threshold') + + parser.add_argument('--DMS_data_folder', type=str, help='Path to folder that contains all DMS assay datasets') + parser.add_argument('--output_scores_folder', default='./', type=str, help='Name of folder to write model scores to') + parser.add_argument('--deactivate_scoring_mirror', action='store_true', help='Whether to deactivate sequence scoring from both directions (Left->Right and Right->Left)') + parser.add_argument('--indel_mode', action='store_true', help='Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions') + parser.add_argument('--scoring_window', default="optimal", type=str, help='Sequence window selection mode (when sequence length longer than model context size)') + + parser.add_argument('--num_workers', default=8, type=int, help='Number of workers for model scoring data loader') + parser.add_argument('--inference_time_retrieval_type', default=None, type=str, help='Type of inference time retrieval [None,Tranception,TranceptEVE]') + parser.add_argument('--retrieval_weights_manual', action='store_true', help='Whether to manually select the MSA/EVE aggregation weights') + parser.add_argument('--retrieval_inference_MSA_weight', default=0.5, type=float, help='Coefficient (alpha) used when aggregating autoregressive transformer and MSA retrieval') + parser.add_argument('--retrieval_inference_EVE_weight', default=0.5, type=float, help='Coefficient (beta) used when aggregating autoregressive transformer and EVE retrieval') + + parser.add_argument('--MSA_folder', default='.', type=str, help='Path to MSA for neighborhood scoring') + parser.add_argument('--MSA_weights_folder', default=None, type=str, help='Path to MSA weights for neighborhood scoring') + parser.add_argument('--clustal_omega_location', default=None, type=str, help='Path to Clustal Omega (only needed with scoring indels with retrieval)') + + parser.add_argument('--EVE_model_folder', type=str, help='Path to folder containing the EVE model(s)') + parser.add_argument('--EVE_seeds', nargs='*', help='Seeds of the EVE model(s) to be leveraged') + parser.add_argument('--EVE_num_samples_log_proba', default=10, type=int, help='Number of samples to compute the EVE log proba') + parser.add_argument('--EVE_model_parameters_location', default=None, type=str, help='Path to EVE model parameters') + parser.add_argument('--MSA_recalibrate_probas', action='store_true', help='Whether to normalize EVE & MSA log probas (matching temp. of Transformer)') + parser.add_argument('--EVE_recalibrate_probas', action='store_true', help='Whether to normalize EVE & MSA log probas (matching temp. of Transformer)') + parser.add_argument('--clinvar_scoring', action='store_true', help='Tweaks when scoring ClinVar input file') + args = parser.parse_args() + print(args) + model_name = args.checkpoint.split("/")[-1] + + tokenizer = PreTrainedTokenizerFast(tokenizer_file=dir_path+os.sep+"trancepteve/utils/tokenizers/Basic_tokenizer", + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]" + ) + + if args.DMS_reference_file_path: + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Compute scores for DMS: "+str(DMS_id)) + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + UniProt_ID = mapping_protein_seq_DMS["UniProt_ID"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] if "UniProt_ID" in mapping_protein_seq_DMS else "No ID" + if args.inference_time_retrieval_type is not None: + MSA_data_file = args.MSA_folder + os.sep + mapping_protein_seq_DMS["MSA_filename"][args.DMS_index] if args.MSA_folder is not None else None + MSA_weight_file_name = args.MSA_weights_folder + os.sep + mapping_protein_seq_DMS["weight_file_name"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] if args.MSA_weights_folder else None + MSA_start = int(mapping_protein_seq_DMS["MSA_start"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) - 1 # MSA_start typically based on 1-indexing + MSA_end = int(mapping_protein_seq_DMS["MSA_end"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) + MSA_threshold_sequence_frac_gaps = float(mapping_protein_seq_DMS["MSA_threshold_sequence_frac_gaps"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) if "MSA_threshold_sequence_frac_gaps" in mapping_protein_seq_DMS else 0.5 + if args.clinvar_scoring: + MSA_threshold_focus_cols_frac_gaps = float(mapping_protein_seq_DMS["MSA_threshold_focus_cols_frac_gaps"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) if "MSA_threshold_focus_cols_frac_gaps" in mapping_protein_seq_DMS else 1.0 + else: + MSA_threshold_focus_cols_frac_gaps = float(mapping_protein_seq_DMS["MSA_threshold_focus_cols_frac_gaps"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) if "MSA_threshold_focus_cols_frac_gaps" in mapping_protein_seq_DMS else 1.0 + print("Sequence (fragment) gap threshold: "+str(MSA_threshold_sequence_frac_gaps)) + print("Focus column gap threshold: "+str(MSA_threshold_focus_cols_frac_gaps)) + else: + target_seq=args.target_seq + DMS_file_name=args.DMS_file_name + DMS_id = DMS_file_name.split(".")[0] + UniprotID = args.UniprotID + if args.inference_time_retrieval_type is not None: + MSA_data_file = args.MSA_folder + os.sep + args.MSA_filename if args.MSA_folder is not None else None + MSA_weight_file_name = args.MSA_weights_folder + os.sep + args.MSA_weight_file_name if args.MSA_weights_folder is not None else None + MSA_start = args.MSA_start - 1 # MSA_start based on 1-indexing + MSA_end = args.MSA_end + MSA_threshold_sequence_frac_gaps=args.MSA_threshold_sequence_frac_gaps + MSA_threshold_focus_cols_frac_gaps=args.MSA_threshold_focus_cols_frac_gaps + + config = json.load(open(args.checkpoint+os.sep+'config.json')) + config = trancepteve.config.TranceptEVEConfig(**config) + config.attention_mode="tranception" + config.position_embedding="grouped_alibi" + config.tokenizer = tokenizer + config.full_target_seq = target_seq + config.scoring_window = args.scoring_window + + if args.inference_time_retrieval_type is not None: + config.inference_time_retrieval_type = args.inference_time_retrieval_type + config.retrieval_aggregation_mode = "aggregate_indel" if args.indel_mode else "aggregate_substitution" + config.MSA_filename = MSA_data_file + config.MSA_weight_file_name = MSA_weight_file_name + config.MSA_start = MSA_start + config.MSA_end = MSA_end + config.MSA_threshold_sequence_frac_gaps = MSA_threshold_sequence_frac_gaps + config.MSA_threshold_focus_cols_frac_gaps = MSA_threshold_focus_cols_frac_gaps + config.retrieval_weights_manual = args.retrieval_weights_manual + config.retrieval_inference_MSA_weight = args.retrieval_inference_MSA_weight + config.retrieval_inference_EVE_weight = args.retrieval_inference_EVE_weight + + if "TranceptEVE" in args.inference_time_retrieval_type: + EVE_model_paths = [] + EVE_seeds = args.EVE_seeds + num_seeds = len(EVE_seeds) + print("Number of distinct EVE models to be leveraged: {}".format(num_seeds)) + for seed in EVE_seeds: + print(f"{args.EVE_model_folder}/{os.path.basename(MSA_data_file.split('.a2m')[0])}_seed_{seed}") + if os.path.exists(f"{args.EVE_model_folder}/{os.path.basename(MSA_data_file.split('.a2m')[0])}_seed_{seed}"): + EVE_model_name = f"{os.path.basename(MSA_data_file.split('.a2m')[0])}_seed_{seed}" + elif os.path.exists(f"{args.EVE_model_folder}/{UniProt_ID}_seed_{seed}"): + EVE_model_name = f"{UniProt_ID}_seed_{seed}" + else: + print(f"No EVE Model available for {MSA_data_file} with random seed {seed} in {args.EVE_model_folder}. Exiting") + exit(1) + + EVE_model_paths.append(args.EVE_model_folder + os.sep + EVE_model_name) + config.EVE_model_paths = EVE_model_paths + config.EVE_num_samples_log_proba = args.EVE_num_samples_log_proba + config.EVE_model_parameters_location = args.EVE_model_parameters_location + config.MSA_recalibrate_probas = args.MSA_recalibrate_probas + config.EVE_recalibrate_probas = args.EVE_recalibrate_probas + else: + num_seeds=0 + if args.indel_mode: + config.clustal_omega_location = args.clustal_omega_location + else: + config.inference_time_retrieval_type = None + config.retrieval_aggregation_mode = None + + if args.model_framework=="pytorch": + model = trancepteve.model_pytorch.TrancepteveLMHeadModel.from_pretrained(pretrained_model_name_or_path=args.checkpoint,config=config) + if torch.cuda.is_available(): + model.cuda() + model.eval() + + if not os.path.isdir(args.output_scores_folder): + os.mkdir(args.output_scores_folder) + mutation_type = '_indels' if args.indel_mode else '_substitutions' + mirror_type = '_no_mirror' if args.deactivate_scoring_mirror else '' + normalization = '_norm-EVE' if args.EVE_recalibrate_probas else '' + normalization = normalization + '_norm-MSA' if args.MSA_recalibrate_probas else normalization + retrieval_weights = '_MSA-' + str(args.retrieval_inference_MSA_weight) +'_EVE-'+ str(args.retrieval_inference_EVE_weight) if args.retrieval_weights_manual else '' + retrieval_type = ('_retrieval_' + args.inference_time_retrieval_type + retrieval_weights + '_' + str(num_seeds) + '-EVE-models' + normalization) if args.inference_time_retrieval_type is not None else '_no_retrieval' + scoring_filename = args.output_scores_folder + if not os.path.isdir(scoring_filename): + os.mkdir(scoring_filename) + scoring_filename += os.sep + DMS_id + '.csv' + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + with torch.no_grad(): + all_scores = model.score_mutants( + DMS_data=DMS_data, + target_seq=target_seq, + scoring_mirror=not args.deactivate_scoring_mirror, + batch_size_inference=args.batch_size_inference, + num_workers=args.num_workers, + indel_mode=args.indel_mode + ) + if len(all_scores) > 0 and args.clinvar_scoring: all_scores = pd.merge(all_scores,DMS_data,how="left",on="mutant") + all_scores.to_csv(scoring_filename, index=False) + + if args.clinvar_scoring: + experiment_name = args.output_scores_folder.split('/')[-1] + print(experiment_name) + + with open("ClinVar_scoring_Tranception_20221130",'a+') as filew: + if os.stat("ClinVar_scoring_Tranception_20221130").st_size == 0: + header = "DMS_id,num_mutants_scored,num_mutants_scored_no_na,processed_MSA_depth,retrieval_inference_MSA_weight,retrieval_inference_EVE_weight\n" + filew.write(header) + all_scores_no_na = all_scores.dropna() + stat = ",".join([str(x) for x in [DMS_id,len(all_scores),len(all_scores_no_na),model.MSA_processed_depth,model.EVE_processed_depth,model.retrieval_inference_MSA_weight,model.retrieval_inference_EVE_weight]]) + filew.write(stat+"\n") + else: + with open("TranceptEVE_aggregation_coefficients_log",'a+') as filew: + if os.stat("TranceptEVE_aggregation_coefficients_log").st_size == 0: + header = "DMS_id,num_mutants_scored,num_mutants_scored_no_na,processed_MSA_depth,retrieval_inference_MSA_weight,retrieval_inference_EVE_weight\n" + filew.write(header) + all_scores_no_na = all_scores.dropna() + stat = ",".join([str(x) for x in [DMS_id,len(all_scores),len(all_scores_no_na),model.MSA_processed_depth,model.EVE_processed_depth,model.retrieval_inference_MSA_weight,model.retrieval_inference_EVE_weight]]) + filew.write(stat+"\n") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_decoder.py b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_decoder.py new file mode 100644 index 0000000..7bfa57a --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_decoder.py @@ -0,0 +1,295 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class VAE_Bayesian_MLP_decoder(nn.Module): + """ + Bayesian MLP decoder class for the VAE model. + """ + def __init__(self, params): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of the sizes of the hidden layers (all DNNs) + - z_dim: (Int) Dimension of latent space + - first_hidden_nonlinearity: (Str) Type of non-linear activation applied on the first (set of) hidden layer(s) + - last_hidden_nonlinearity: (Str) Type of non-linear activation applied on the very last hidden layer (pre-sparsity) + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + - convolve_output: (Bool) Whether to perform 1d convolution on output (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on output + - include_temperature_scaler: (Bool) Whether we apply the global temperature scaler + - include_sparsity: (Bool) Whether we use the sparsity inducing scheme on the output from the last hidden layer + - num_tiles_sparsity: (Int) Number of tiles to use in the sparsity inducing scheme (the more the tiles, the stronger the sparsity) + - bayesian_decoder: (Bool) Whether the decoder is bayesian or not + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.bayesian_decoder = True + self.dropout_proba = params['dropout_proba'] + self.convolve_output = params['convolve_output'] + self.convolution_depth = params['convolution_output_depth'] + self.include_temperature_scaler = params['include_temperature_scaler'] + self.include_sparsity = params['include_sparsity'] + self.num_tiles_sparsity = params['num_tiles_sparsity'] + + self.mu_bias_init = 0.1 + self.logvar_init = -10.0 + self.logit_scale_p = 0.001 + + self.hidden_layers_mean=nn.ModuleDict() + self.hidden_layers_log_var=nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers_mean[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + self.hidden_layers_log_var[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers_mean[str(layer_index)].bias, self.mu_bias_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].weight, self.logvar_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].bias, self.logvar_init) + else: + self.hidden_layers_mean[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + self.hidden_layers_log_var[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers_mean[str(layer_index)].bias, self.mu_bias_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].weight, self.logvar_init) + nn.init.constant_(self.hidden_layers_log_var[str(layer_index)].bias, self.logvar_init) + + if params['first_hidden_nonlinearity'] == 'relu': + self.first_hidden_nonlinearity = nn.ReLU() + elif params['first_hidden_nonlinearity'] == 'tanh': + self.first_hidden_nonlinearity = nn.Tanh() + elif params['first_hidden_nonlinearity'] == 'sigmoid': + self.first_hidden_nonlinearity = nn.Sigmoid() + elif params['first_hidden_nonlinearity'] == 'elu': + self.first_hidden_nonlinearity = nn.ELU() + elif params['first_hidden_nonlinearity'] == 'linear': + self.first_hidden_nonlinearity = nn.Identity() + + if params['last_hidden_nonlinearity'] == 'relu': + self.last_hidden_nonlinearity = nn.ReLU() + elif params['last_hidden_nonlinearity'] == 'tanh': + self.last_hidden_nonlinearity = nn.Tanh() + elif params['last_hidden_nonlinearity'] == 'sigmoid': + self.last_hidden_nonlinearity = nn.Sigmoid() + elif params['last_hidden_nonlinearity'] == 'elu': + self.last_hidden_nonlinearity = nn.ELU() + elif params['last_hidden_nonlinearity'] == 'linear': + self.last_hidden_nonlinearity = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + if self.convolve_output: + self.output_convolution_mean = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + self.output_convolution_log_var = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + nn.init.constant_(self.output_convolution_log_var.weight, self.logvar_init) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + if self.include_sparsity: + self.sparsity_weight_mean = nn.Parameter(torch.zeros(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + self.sparsity_weight_log_var = nn.Parameter(torch.ones(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + nn.init.constant_(self.sparsity_weight_log_var, self.logvar_init) + + self.last_hidden_layer_weight_mean = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + self.last_hidden_layer_weight_log_var = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + nn.init.xavier_normal_(self.last_hidden_layer_weight_mean) #Glorot initialization + nn.init.constant_(self.last_hidden_layer_weight_log_var, self.logvar_init) + + self.last_hidden_layer_bias_mean = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + self.last_hidden_layer_bias_log_var = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + nn.init.constant_(self.last_hidden_layer_bias_mean, self.mu_bias_init) + nn.init.constant_(self.last_hidden_layer_bias_log_var, self.logvar_init) + + if self.include_temperature_scaler: + self.temperature_scaler_mean = nn.Parameter(torch.ones(1)) + self.temperature_scaler_log_var = nn.Parameter(torch.ones(1) * self.logvar_init) + + def sampler(self, mean, log_var): + """ + Samples a latent vector via reparametrization trick + """ + eps = torch.randn_like(mean).to(self.device) + z = torch.exp(0.5*log_var) * eps + mean + return z + + def forward(self, z): + batch_size = z.shape[0] + if self.dropout_proba > 0.0: + x = self.dropout_layer(z) + else: + x = z + + for layer_index in range(len(self.hidden_layers_sizes)-1): + layer_i_weight = self.sampler(self.hidden_layers_mean[str(layer_index)].weight, self.hidden_layers_log_var[str(layer_index)].weight) + layer_i_bias = self.sampler(self.hidden_layers_mean[str(layer_index)].bias, self.hidden_layers_log_var[str(layer_index)].bias) + x = self.first_hidden_nonlinearity(F.linear(x, weight=layer_i_weight, bias=layer_i_bias)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + last_index = len(self.hidden_layers_sizes)-1 + last_layer_weight = self.sampler(self.hidden_layers_mean[str(last_index)].weight, self.hidden_layers_log_var[str(last_index)].weight) + last_layer_bias = self.sampler(self.hidden_layers_mean[str(last_index)].bias, self.hidden_layers_log_var[str(last_index)].bias) + x = self.last_hidden_nonlinearity(F.linear(x, weight=last_layer_weight, bias=last_layer_bias)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + W_out = self.sampler(self.last_hidden_layer_weight_mean, self.last_hidden_layer_weight_log_var) + b_out = self.sampler(self.last_hidden_layer_bias_mean, self.last_hidden_layer_bias_log_var) + + if self.convolve_output: + output_convolution_weight = self.sampler(self.output_convolution_mean.weight, self.output_convolution_log_var.weight) + W_out = torch.mm(W_out.view(self.seq_len * self.hidden_layers_sizes[-1], self.channel_size), + output_convolution_weight.view(self.channel_size,self.alphabet_size)) #product of size (H * seq_len, alphabet) + + if self.include_sparsity: + sparsity_weights = self.sampler(self.sparsity_weight_mean,self.sparsity_weight_log_var) + sparsity_tiled = sparsity_weights.repeat(self.num_tiles_sparsity,1) + sparsity_tiled = nn.Sigmoid()(sparsity_tiled).unsqueeze(2) + + W_out = W_out.view(self.hidden_layers_sizes[-1], self.seq_len, self.alphabet_size) * sparsity_tiled + + W_out = W_out.view(self.seq_len * self.alphabet_size, self.hidden_layers_sizes[-1]) + + x = F.linear(x, weight=W_out, bias=b_out) + + if self.include_temperature_scaler: + temperature_scaler = self.sampler(self.temperature_scaler_mean,self.temperature_scaler_log_var) + x = torch.log(1.0+torch.exp(temperature_scaler)) * x + + x = x.view(batch_size, self.seq_len, self.alphabet_size) + x_recon_log = F.log_softmax(x, dim=-1) #of shape (batch_size, seq_len, alphabet) + + return x_recon_log + +class VAE_Standard_MLP_decoder(nn.Module): + """ + Standard MLP decoder class for the VAE model. + """ + def __init__(self, seq_len, alphabet_size, hidden_layers_sizes, z_dim, first_hidden_nonlinearity, last_hidden_nonlinearity, dropout_proba, + convolve_output, convolution_depth, include_temperature_scaler, include_sparsity, num_tiles_sparsity): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of the sizes of the hidden layers (all DNNs) + - z_dim: (Int) Dimension of latent space + - first_hidden_nonlinearity: (Str) Type of non-linear activation applied on the first (set of) hidden layer(s) + - last_hidden_nonlinearity: (Str) Type of non-linear activation applied on the very last hidden layer (pre-sparsity) + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + - convolve_output: (Bool) Whether to perform 1d convolution on output (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on output + - include_temperature_scaler: (Bool) Whether we apply the global temperature scaler + - include_sparsity: (Bool) Whether we use the sparsity inducing scheme on the output from the last hidden layer + - num_tiles_sparsity: (Int) Number of tiles to use in the sparsity inducing scheme (the more the tiles, the stronger the sparsity) + - bayesian_decoder: (Bool) Whether the decoder is bayesian or not + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.bayesian_decoder = False + self.dropout_proba = params['dropout_proba'] + self.convolve_output = params['convolve_output'] + self.convolution_depth = params['convolution_depth'] + self.include_temperature_scaler = params['include_temperature_scaler'] + self.include_sparsity = params['include_sparsity'] + self.num_tiles_sparsity = params['num_tiles_sparsity'] + + self.mu_bias_init = 0.1 + + self.hidden_layers=nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + else: + self.hidden_layers[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + + if params['first_hidden_nonlinearity'] == 'relu': + self.first_hidden_nonlinearity = nn.ReLU() + elif params['first_hidden_nonlinearity'] == 'tanh': + self.first_hidden_nonlinearity = nn.Tanh() + elif params['first_hidden_nonlinearity'] == 'sigmoid': + self.first_hidden_nonlinearity = nn.Sigmoid() + elif params['first_hidden_nonlinearity'] == 'elu': + self.first_hidden_nonlinearity = nn.ELU() + elif params['first_hidden_nonlinearity'] == 'linear': + self.first_hidden_nonlinearity = nn.Identity() + + if params['last_hidden_nonlinearity'] == 'relu': + self.last_hidden_nonlinearity = nn.ReLU() + elif params['last_hidden_nonlinearity'] == 'tanh': + self.last_hidden_nonlinearity = nn.Tanh() + elif params['last_hidden_nonlinearity'] == 'sigmoid': + self.last_hidden_nonlinearity = nn.Sigmoid() + elif params['last_hidden_nonlinearity'] == 'elu': + self.last_hidden_nonlinearity = nn.ELU() + elif params['last_hidden_nonlinearity'] == 'linear': + self.last_hidden_nonlinearity = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + if self.convolve_output: + self.output_convolution = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + if self.include_sparsity: + self.sparsity_weight = nn.Parameter(torch.randn(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len)) + + self.W_out = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1])) + nn.init.xavier_normal_(self.W_out) #Initialize weights with Glorot initialization + self.b_out = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len)) + nn.init.constant_(self.b_out, self.mu_bias_init) + + if self.include_temperature_scaler: + self.temperature_scaler = nn.Parameter(torch.ones(1)) + + def forward(self, z): + batch_size = z.shape[0] + if self.dropout_proba > 0.0: + x = self.dropout_layer(z) + else: + x=z + + for layer_index in range(len(self.hidden_layers_sizes)-1): + x = self.first_hidden_nonlinearity(self.hidden_layers[str(layer_index)](x)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + x = self.last_hidden_nonlinearity(self.hidden_layers[str(len(self.hidden_layers_sizes)-1)](x)) #of size (batch_size,H) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + W_out = self.W_out.data + + if self.convolve_output: + W_out = torch.mm(W_out.view(self.seq_len * self.hidden_layers_sizes[-1], self.channel_size), + self.output_convolution.weight.view(self.channel_size,self.alphabet_size)) + + if self.include_sparsity: + sparsity_tiled = self.sparsity_weight.repeat(self.num_tiles_sparsity,1) #of size (H,seq_len) + sparsity_tiled = nn.Sigmoid()(sparsity_tiled).unsqueeze(2) #of size (H,seq_len,1) + W_out = W_out.view(self.hidden_layers_sizes[-1], self.seq_len, self.alphabet_size) * sparsity_tiled + + W_out = W_out.view(self.seq_len * self.alphabet_size, self.hidden_layers_sizes[-1]) + + x = F.linear(x, weight=W_out, bias=self.b_out) + + if self.include_temperature_scaler: + x = torch.log(1.0+torch.exp(self.temperature_scaler)) * x + + x = x.view(batch_size, self.seq_len, self.alphabet_size) + x_recon_log = F.log_softmax(x, dim=-1) #of shape (batch_size, seq_len, alphabet) + + return x_recon_log \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_encoder.py b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_encoder.py new file mode 100644 index 0000000..3fd40fe --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_encoder.py @@ -0,0 +1,88 @@ +import torch +import torch.nn as nn + +class VAE_MLP_encoder(nn.Module): + """ + MLP encoder class for the VAE model. + """ + def __init__(self,params): + """ + Required input parameters: + - seq_len: (Int) Sequence length of sequence alignment + - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object) + - hidden_layers_sizes: (List) List of sizes of DNN linear layers + - z_dim: (Int) Size of latent space + - convolve_input: (Bool) Whether to perform 1d convolution on input (kernel size 1, stide 1) + - convolution_depth: (Int) Size of the 1D-convolution on input + - nonlinear_activation: (Str) Type of non-linear activation to apply on each hidden layer + - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied + """ + super().__init__() + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.seq_len = params['seq_len'] + self.alphabet_size = params['alphabet_size'] + self.hidden_layers_sizes = params['hidden_layers_sizes'] + self.z_dim = params['z_dim'] + self.convolve_input = params['convolve_input'] + self.convolution_depth = params['convolution_input_depth'] + self.dropout_proba = params['dropout_proba'] + + self.mu_bias_init = 0.1 + self.log_var_bias_init = -10.0 + + #Convolving input with kernels of size 1 to capture potential similarities across amino acids when encoding sequences + if self.convolve_input: + self.input_convolution = nn.Conv1d(in_channels=self.alphabet_size,out_channels=self.convolution_depth,kernel_size=1,stride=1,bias=False) + self.channel_size = self.convolution_depth + else: + self.channel_size = self.alphabet_size + + self.hidden_layers=torch.nn.ModuleDict() + for layer_index in range(len(self.hidden_layers_sizes)): + if layer_index==0: + self.hidden_layers[str(layer_index)] = nn.Linear((self.channel_size*self.seq_len),self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + else: + self.hidden_layers[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index]) + nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init) + + self.fc_mean = nn.Linear(self.hidden_layers_sizes[-1],self.z_dim) + nn.init.constant_(self.fc_mean.bias, self.mu_bias_init) + self.fc_log_var = nn.Linear(self.hidden_layers_sizes[-1],self.z_dim) + nn.init.constant_(self.fc_log_var.bias, self.log_var_bias_init) + + # set up non-linearity + if params['nonlinear_activation'] == 'relu': + self.nonlinear_activation = nn.ReLU() + elif params['nonlinear_activation'] == 'tanh': + self.nonlinear_activation = nn.Tanh() + elif params['nonlinear_activation'] == 'sigmoid': + self.nonlinear_activation = nn.Sigmoid() + elif params['nonlinear_activation'] == 'elu': + self.nonlinear_activation = nn.ELU() + elif params['nonlinear_activation'] == 'linear': + self.nonlinear_activation = nn.Identity() + + if self.dropout_proba > 0.0: + self.dropout_layer = nn.Dropout(p=self.dropout_proba) + + def forward(self, x): + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + if self.convolve_input: + x = x.permute(0,2,1) + x = self.input_convolution(x) + x = x.view(-1,self.seq_len*self.channel_size) + else: + x = x.view(-1,self.seq_len*self.channel_size) + + for layer_index in range(len(self.hidden_layers_sizes)): + x = self.nonlinear_activation(self.hidden_layers[str(layer_index)](x)) + if self.dropout_proba > 0.0: + x = self.dropout_layer(x) + + z_mean = self.fc_mean(x) + z_log_var = self.fc_log_var(x) + + return z_mean, z_log_var \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_model.py b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_model.py new file mode 100644 index 0000000..1410592 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/VAE_model.py @@ -0,0 +1,347 @@ +import os +import numpy as np +import pandas as pd +import time +import tqdm +from scipy.special import erfinv +from sklearn.model_selection import train_test_split + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.backends.cudnn as cudnn + +from . import VAE_encoder, VAE_decoder + +class VAE_model(nn.Module): + """ + Class for the VAE model with estimation of weights distribution parameters via Mean-Field VI. + """ + def __init__(self, + model_name, + data, + encoder_parameters, + decoder_parameters, + random_seed + ): + + super().__init__() + + self.model_name = model_name + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dtype = torch.float32 + self.random_seed = random_seed + torch.manual_seed(random_seed) + + self.seq_len = data.seq_len + self.alphabet_size = data.alphabet_size + self.Neff = data.Neff + + self.encoder_parameters=encoder_parameters + self.decoder_parameters=decoder_parameters + + encoder_parameters['seq_len'] = self.seq_len + encoder_parameters['alphabet_size'] = self.alphabet_size + decoder_parameters['seq_len'] = self.seq_len + decoder_parameters['alphabet_size'] = self.alphabet_size + + self.encoder = VAE_encoder.VAE_MLP_encoder(params=encoder_parameters) + if decoder_parameters['bayesian_decoder']: + self.decoder = VAE_decoder.VAE_Bayesian_MLP_decoder(params=decoder_parameters) + else: + self.decoder = VAE_decoder.VAE_Standard_MLP_decoder(params=decoder_parameters) + self.logit_sparsity_p = decoder_parameters['logit_sparsity_p'] + + def sample_latent(self, mu, log_var): + """ + Samples a latent vector via reparametrization trick + """ + eps = torch.randn_like(mu).to(self.device) + z = torch.exp(0.5*log_var) * eps + mu + return z + + def KLD_diag_gaussians(self, mu, logvar, p_mu, p_logvar): + """ + KL divergence between diagonal gaussian with prior diagonal gaussian. + """ + KLD = 0.5 * (p_logvar - logvar) + 0.5 * (torch.exp(logvar) + torch.pow(mu-p_mu,2)) / (torch.exp(p_logvar)+1e-20) - 0.5 + + return torch.sum(KLD) + + def annealing_factor(self, annealing_warm_up, training_step): + """ + Annealing schedule of KL to focus on reconstruction error in early stages of training + """ + if training_step < annealing_warm_up: + return training_step/annealing_warm_up + else: + return 1 + + def KLD_global_parameters(self): + """ + KL divergence between the variational distributions and the priors (for the decoder weights). + """ + KLD_decoder_params = 0.0 + zero_tensor = torch.tensor(0.0).to(self.device) + + for layer_index in range(len(self.decoder.hidden_layers_sizes)): + for param_type in ['weight','bias']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['hidden_layers_mean.'+str(layer_index)+'.'+param_type].flatten(), + self.decoder.state_dict(keep_vars=True)['hidden_layers_log_var.'+str(layer_index)+'.'+param_type].flatten(), + zero_tensor, + zero_tensor + ) + + for param_type in ['weight','bias']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['last_hidden_layer_'+param_type+'_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['last_hidden_layer_'+param_type+'_log_var'].flatten(), + zero_tensor, + zero_tensor + ) + + if self.decoder.include_sparsity: + self.logit_scale_sigma = 4.0 + self.logit_scale_mu = 2.0**0.5 * self.logit_scale_sigma * erfinv(2.0 * self.logit_sparsity_p - 1.0) + + sparsity_mu = torch.tensor(self.logit_scale_mu).to(self.device) + sparsity_log_var = torch.log(torch.tensor(self.logit_scale_sigma**2)).to(self.device) + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['sparsity_weight_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['sparsity_weight_log_var'].flatten(), + sparsity_mu, + sparsity_log_var + ) + + if self.decoder.convolve_output: + for param_type in ['weight']: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['output_convolution_mean.'+param_type].flatten(), + self.decoder.state_dict(keep_vars=True)['output_convolution_log_var.'+param_type].flatten(), + zero_tensor, + zero_tensor + ) + + if self.decoder.include_temperature_scaler: + KLD_decoder_params += self.KLD_diag_gaussians( + self.decoder.state_dict(keep_vars=True)['temperature_scaler_mean'].flatten(), + self.decoder.state_dict(keep_vars=True)['temperature_scaler_log_var'].flatten(), + zero_tensor, + zero_tensor + ) + return KLD_decoder_params + + def loss_function(self, x_recon_log, x, mu, log_var, kl_latent_scale, kl_global_params_scale, annealing_warm_up, training_step, Neff): + """ + Returns mean of negative ELBO, reconstruction loss and KL divergence across batch x. + """ + BCE = F.binary_cross_entropy_with_logits(x_recon_log, x, reduction='sum') / x.shape[0] + KLD_latent = (-0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())) / x.shape[0] + if self.decoder.bayesian_decoder: + KLD_decoder_params_normalized = self.KLD_global_parameters() / Neff + else: + KLD_decoder_params_normalized = 0.0 + warm_up_scale = self.annealing_factor(annealing_warm_up,training_step) + neg_ELBO = BCE + warm_up_scale * (kl_latent_scale * KLD_latent + kl_global_params_scale * KLD_decoder_params_normalized) + return neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized + + def all_likelihood_components(self, x): + """ + Returns tensors of ELBO, reconstruction loss and KL divergence for each point in batch x. + """ + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + recon_x_log = recon_x_log.view(-1,self.alphabet_size*self.seq_len) + x = x.view(-1,self.alphabet_size*self.seq_len) + + BCE_batch_tensor = torch.sum(F.binary_cross_entropy_with_logits(recon_x_log, x, reduction='none'),dim=1) + KLD_batch_tensor = (-0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(),dim=1)) + + ELBO_batch_tensor = -(BCE_batch_tensor + KLD_batch_tensor) + + return ELBO_batch_tensor, BCE_batch_tensor, KLD_batch_tensor + + def train_model(self, data, training_parameters): + """ + Training procedure for the VAE model. + If use_validation_set is True then: + - we split the alignment data in train/val sets. + - we train up to num_training_steps steps but store the version of the model with lowest loss on validation set across training + If not, then we train the model for num_training_steps and save the model at the end of training + """ + if torch.cuda.is_available(): + cudnn.benchmark = True + self.train() + + if training_parameters['log_training_info']: + filename = training_parameters['training_logs_location']+os.sep+self.model_name+"_losses.csv" + with open(filename, "a") as logs: + logs.write("Number of sequences in alignment file:\t"+str(data.num_sequences)+"\n") + logs.write("Neff:\t"+str(self.Neff)+"\n") + logs.write("Alignment sequence length:\t"+str(data.seq_len)+"\n") + + optimizer = optim.Adam(self.parameters(), lr=training_parameters['learning_rate'], weight_decay = training_parameters['l2_regularization']) + + if training_parameters['use_lr_scheduler']: + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=training_parameters['lr_scheduler_step_size'], gamma=training_parameters['lr_scheduler_gamma']) + + if training_parameters['use_validation_set']: + x_train, x_val, weights_train, weights_val = train_test_split(data.one_hot_encoding, data.weights, test_size=training_parameters['validation_set_pct'], random_state=self.random_seed) + best_val_loss = float('inf') + best_model_step_index=0 + else: + x_train = data.one_hot_encoding + weights_train = data.weights + best_val_loss = None + best_model_step_index = training_parameters['num_training_steps'] + + batch_order = np.arange(x_train.shape[0]) + seq_sample_probs = weights_train / np.sum(weights_train) + + self.Neff_training = np.sum(weights_train) + N_training = x_train.shape[0] + + start = time.time() + train_loss = 0 + + for training_step in tqdm.tqdm(range(1,training_parameters['num_training_steps']+1), desc="Training model"): + + batch_index = np.random.choice(batch_order, training_parameters['batch_size'], p=seq_sample_probs).tolist() + x = torch.tensor(x_train[batch_index], dtype=self.dtype).to(self.device) + optimizer.zero_grad() + + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized = self.loss_function(recon_x_log, x, mu, log_var, training_parameters['kl_latent_scale'], training_parameters['kl_global_params_scale'], training_parameters['annealing_warm_up'], training_step, self.Neff_training) + + neg_ELBO.backward() + optimizer.step() + + if training_parameters['use_lr_scheduler']: + scheduler.step() + + if training_step % training_parameters['log_training_freq'] == 0: + progress = "|Train : Update {0}. Negative ELBO : {1:.3f}, BCE: {2:.3f}, KLD_latent: {3:.3f}, KLD_decoder_params_norm: {4:.3f}, Time: {5:.2f} |".format(training_step, neg_ELBO, BCE, KLD_latent, KLD_decoder_params_normalized, time.time() - start) + print(progress) + + if training_parameters['log_training_info']: + with open(filename, "a") as logs: + logs.write(progress+"\n") + + if training_step % training_parameters['save_model_params_freq']==0: + self.save(model_checkpoint=training_parameters['model_checkpoint_location']+os.sep+self.model_name+"_step_"+str(training_step), + encoder_parameters=self.encoder_parameters, + decoder_parameters=self.decoder_parameters, + training_parameters=training_parameters) + + if training_parameters['use_validation_set'] and training_step % training_parameters['validation_freq'] == 0: + x_val = torch.tensor(x_val, dtype=self.dtype).to(self.device) + val_neg_ELBO, val_BCE, val_KLD_latent, val_KLD_global_parameters = self.test_model(x_val, weights_val, training_parameters['batch_size']) + + progress_val = "\t\t\t|Val : Update {0}. Negative ELBO : {1:.3f}, BCE: {2:.3f}, KLD_latent: {3:.3f}, KLD_decoder_params_norm: {4:.3f}, Time: {5:.2f} |".format(training_step, val_neg_ELBO, val_BCE, val_KLD_latent, val_KLD_global_parameters, time.time() - start) + print(progress_val) + if training_parameters['log_training_info']: + with open(filename, "a") as logs: + logs.write(progress_val+"\n") + + if val_neg_ELBO < best_val_loss: + best_val_loss = val_neg_ELBO + best_model_step_index = training_step + self.save(model_checkpoint=training_parameters['model_checkpoint_location']+os.sep+self.model_name+"_best", + encoder_parameters=self.encoder_parameters, + decoder_parameters=self.decoder_parameters, + training_parameters=training_parameters) + self.train() + + def test_model(self, x_val, weights_val, batch_size): + self.eval() + + with torch.no_grad(): + val_batch_order = np.arange(x_val.shape[0]) + val_seq_sample_probs = weights_val / np.sum(weights_val) + + val_batch_index = np.random.choice(val_batch_order, batch_size, p=val_seq_sample_probs).tolist() + x = torch.tensor(x_val[val_batch_index], dtype=self.dtype).to(self.device) + mu, log_var = self.encoder(x) + z = self.sample_latent(mu, log_var) + recon_x_log = self.decoder(z) + + neg_ELBO, BCE, KLD_latent, KLD_global_parameters = self.loss_function(recon_x_log, x, mu, log_var, kl_latent_scale=1.0, kl_global_params_scale=1.0, annealing_warm_up=0, training_step=1, Neff = self.Neff_training) #set annealing factor to 1 + + return neg_ELBO.item(), BCE.item(), KLD_latent.item(), KLD_global_parameters.item() + + + def save(self, model_checkpoint, encoder_parameters, decoder_parameters, training_parameters, batch_size=256): + torch.save({ + 'model_state_dict':self.state_dict(), + 'encoder_parameters':encoder_parameters, + 'decoder_parameters':decoder_parameters, + 'training_parameters':training_parameters, + }, model_checkpoint) + + def compute_evol_indices(self, msa_data, list_mutations_location, num_samples, batch_size=256): + """ + The column in the list_mutations dataframe that contains the mutant(s) for a given variant should be called "mutations" + """ + #Multiple mutations are to be passed colon-separated + list_mutations=pd.read_csv(list_mutations_location, header=0) + if 'var' in list_mutations.columns: + list['mutations']=list['var'] + elif 'mutant_id' in list_mutations.columns: + list['mutations']=list['mutant_id'] + else: + list['mutations']=list['mutant'] + #Remove (multiple) mutations that are invalid + list_valid_mutations = ['wt'] + list_valid_mutated_sequences = {} + list_valid_mutated_sequences['wt'] = msa_data.focus_seq_trimmed # first sequence in the list is the wild_type + for mutation in list_mutations['mutations']: + individual_substitutions = mutation.split(':') + mutated_sequence = list(msa_data.focus_seq_trimmed)[:] + fully_valid_mutation = True + for mut in individual_substitutions: + wt_aa, pos, mut_aa = mut[0], int(mut[1:-1]), mut[-1] + if pos not in msa_data.uniprot_focus_col_to_wt_aa_dict or msa_data.uniprot_focus_col_to_wt_aa_dict[pos] != wt_aa or mut not in msa_data.mutant_to_letter_pos_idx_focus_list: + print ("Not a valid mutant: "+mutation) + fully_valid_mutation = False + break + else: + wt_aa,pos,idx_focus = msa_data.mutant_to_letter_pos_idx_focus_list[mut] + mutated_sequence[idx_focus] = mut_aa #perform the corresponding AA substitution + + if fully_valid_mutation: + list_valid_mutations.append(mutation) + list_valid_mutated_sequences[mutation] = ''.join(mutated_sequence) + + #One-hot encoding of mutated sequences + mutated_sequences_one_hot = np.zeros((len(list_valid_mutations),len(msa_data.focus_cols),len(msa_data.alphabet))) + for i,mutation in enumerate(list_valid_mutations): + sequence = list_valid_mutated_sequences[mutation] + for j,letter in enumerate(sequence): + if letter in msa_data.aa_dict: + k = msa_data.aa_dict[letter] + mutated_sequences_one_hot[i,j,k] = 1.0 + + mutated_sequences_one_hot = torch.tensor(mutated_sequences_one_hot) + dataloader = torch.utils.data.DataLoader(mutated_sequences_one_hot, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) + prediction_matrix = torch.zeros((len(list_valid_mutations),num_samples)) + + with torch.no_grad(): + for i, batch in enumerate(tqdm.tqdm(dataloader, 'Looping through mutation batches')): + x = batch.type(self.dtype).to(self.device) + for j in tqdm.tqdm(range(num_samples), 'Looping through number of samples for batch #: '+str(i+1)): + seq_predictions, _, _ = self.all_likelihood_components(x) + prediction_matrix[i*batch_size:i*batch_size+len(x),j] = seq_predictions + tqdm.tqdm.write('\n') + mean_predictions = prediction_matrix.mean(dim=1, keepdim=False) + std_predictions = prediction_matrix.std(dim=1, keepdim=False) + delta_elbos = mean_predictions - mean_predictions[0] + evol_indices = - delta_elbos.detach().cpu().numpy() + + return list_valid_mutations, evol_indices, mean_predictions[0].detach().cpu().numpy(), std_predictions.detach().cpu().numpy() \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/default_model_params.json b/proteingym/baselines/trancepteve/trancepteve/EVE/default_model_params.json new file mode 100644 index 0000000..5d3b93c --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/default_model_params.json @@ -0,0 +1,41 @@ +{ "encoder_parameters": { + "hidden_layers_sizes" : [2000,1000,300], + "z_dim" : 50, + "convolve_input" : false, + "convolution_input_depth" : 40, + "nonlinear_activation" : "relu", + "dropout_proba" : 0.0 + }, + "decoder_parameters": { + "hidden_layers_sizes" : [300,1000,2000], + "z_dim" : 50, + "bayesian_decoder" : true, + "first_hidden_nonlinearity" : "relu", + "last_hidden_nonlinearity" : "relu", + "dropout_proba" : 0.1, + "convolve_output" : true, + "convolution_output_depth" : 40, + "include_temperature_scaler" : true, + "include_sparsity" : false, + "num_tiles_sparsity" : 0, + "logit_sparsity_p" : 0 + }, + "training_parameters": { + "num_training_steps" : 400000, + "learning_rate" : 1e-4, + "batch_size" : 256, + "annealing_warm_up" : 0, + "kl_latent_scale" : 1.0, + "kl_global_params_scale" : 1.0, + "l2_regularization" : 0.0, + "use_lr_scheduler" : false, + "use_validation_set" : false, + "validation_set_pct" : 0.2, + "validation_freq" : 1000, + "log_training_info" : true, + "log_training_freq" : 1000, + "save_model_params_freq" : 500000 + } +} + + diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/merge_DeepSequence.py b/proteingym/baselines/trancepteve/trancepteve/EVE/merge_DeepSequence.py new file mode 100644 index 0000000..92f5b5d --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/merge_DeepSequence.py @@ -0,0 +1,35 @@ +import pandas as pd +import os + +individual_files = '/n/groups/marks/projects/marks_lab_and_oatml/ProteinGym/model_scores/zero_shot_substitutions/DeepSequence' +folder_name = '/n/groups/marks/projects/marks_lab_and_oatml/ProteinGym/model_scores/zero_shot_substitutions/DeepSequence/merged' + +mapping = "/home/pn73/protein_transformer/utils/mapping_files/ProteinGym_reference_file_substitutions_20220227.csv" +#mapping='/home/pn73/ProteinGym/ProteinGym_reference_file_substitutions.csv' +mapping = pd.read_csv(mapping) + +for DMS_id in mapping["DMS_id"][:87]: + print("DMS id : {}".format(DMS_id)) + #if DMS_id in exclude: + # print("Exclude : {}".format(DMS_id)) + # continue + + evol_seed = {} + for i in [1000,2000,3000,4000,5000]: + #evol_seed[i] = pd.read_csv(individual_files+os.sep+"seed_"+str(i)+os.sep+DMS_id+'_20000_samples_Aug7_seed_'+str(i)+'.csv') + evol_seed[i] = pd.read_csv(individual_files+os.sep+DMS_id+'_20000_samples_Aug7_seed_'+str(i)+'.csv') + evol_seed[i] = evol_seed[i].groupby(['mutant']).mean().reset_index() + + evol_ensemble = evol_seed[1000] + for i in [2000,3000,4000,5000]: + evol_ensemble = pd.merge(evol_ensemble,evol_seed[i], on='mutant', how='left', suffixes=("_seed_"+str(i-1000),"_seed_"+str(i))) + evol_ensemble['evol_indices_seed_5000'] = evol_ensemble['evol_indices'] + + evol_ensemble['evol_indices_ensemble']=0 + for i in [1000,2000,3000,4000,5000]: + evol_ensemble['evol_indices_ensemble'] += evol_ensemble["evol_indices_seed_"+str(i)] / 5.0 + + final_list = ['mutant']+['evol_indices_seed_'+str(i) for i in [1000,2000,3000,4000,5000]]+['evol_indices_ensemble'] + evol_ensemble = evol_ensemble[final_list] + + evol_ensemble.to_csv(folder_name+os.sep+DMS_id+'.csv', index=False) \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/EVE/merge_EVE.py b/proteingym/baselines/trancepteve/trancepteve/EVE/merge_EVE.py new file mode 100644 index 0000000..d8a9e14 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/EVE/merge_EVE.py @@ -0,0 +1,50 @@ +import pandas as pd +import os + +individual_files = '/n/groups/marks/projects/marks_lab_and_oatml/ProteinGym/model_scores/zero_shot_substitutions/EVE' #'/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/model_scores/EVE_all_mutants/20220807_EVE_models_all_mutants' +folder_name = '/n/groups/marks/projects/marks_lab_and_oatml/ProteinGym/model_scores/zero_shot_substitutions/EVE/merged' + +#mapping = "/home/pn73/protein_transformer/utils/mapping_files/ProteinGym_reference_file_substitutions_20220227.csv" +mapping='/home/pn73/ProteinGym/ProteinGym_reference_file_substitutions.csv' +mapping = pd.read_csv(mapping) + +#exclude=[ +#'A4D664_9INFA_Soh_CCL141_2019', +#'CAPSD_AAV2S_Sinai_substitutions_2021', +#'ENV_HV1B9_DuenasDecamp_2016', +#'I6TAH8_I68A0_Doud_2015', +#'NCAP_I34A1_Doud_2015', +#'NRAM_I33A0_Jiang_standard_2016', +#'P53_HUMAN_Kotler_2018', +#'PA_I34A1_Wu_2015', +#'R1AB_SARS2_Flynn_growth_2022', +#'SPG1_STRSG_Olson_2014', +#'SPIKE_SARS2_Starr_bind_2020', +#'SPIKE_SARS2_Starr_expr_2020' +#] + +for DMS_id in mapping["DMS_id"][87:100]: + print("DMS id : {}".format(DMS_id)) + #if DMS_id in exclude: + # print("Exclude : {}".format(DMS_id)) + # continue + + evol_seed = {} + for i in [1000,2000,3000,4000,5000]: + #evol_seed[i] = pd.read_csv(individual_files+os.sep+"seed_"+str(i)+os.sep+DMS_id+'_20000_samples_Aug7_seed_'+str(i)+'.csv') + evol_seed[i] = pd.read_csv(individual_files+os.sep+DMS_id+'_20000_samples_Aug7_seed_'+str(i)+'.csv') + evol_seed[i] = evol_seed[i].groupby(['mutant']).mean().reset_index() + + evol_ensemble = evol_seed[1000] + for i in [2000,3000,4000,5000]: + evol_ensemble = pd.merge(evol_ensemble,evol_seed[i], on='mutant', how='left', suffixes=("_seed_"+str(i-1000),"_seed_"+str(i))) + evol_ensemble['evol_indices_seed_5000'] = evol_ensemble['evol_indices'] + + evol_ensemble['evol_indices_ensemble']=0 + for i in [1000,2000,3000,4000,5000]: + evol_ensemble['evol_indices_ensemble'] += evol_ensemble["evol_indices_seed_"+str(i)] / 5.0 + + final_list = ['mutant']+['evol_indices_seed_'+str(i) for i in [1000,2000,3000,4000,5000]]+['evol_indices_ensemble'] + evol_ensemble = evol_ensemble[final_list] + + evol_ensemble.to_csv(folder_name+os.sep+DMS_id+'.csv', index=False) \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/__init__.py b/proteingym/baselines/trancepteve/trancepteve/__init__.py new file mode 100644 index 0000000..230e531 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/__init__.py @@ -0,0 +1 @@ +from . import config, utils \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/activations.py b/proteingym/baselines/trancepteve/trancepteve/activations.py new file mode 100644 index 0000000..25702ef --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/activations.py @@ -0,0 +1,114 @@ +import math + +import torch +from packaging import version +from torch import nn + +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +def _gelu_python(x): + """ + Original Implementation of the GELU activation function in Google BERT repo when initially created. For + information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional + Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + + +def gelu_new(x): + """ + Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see + the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) + + +if version.parse(torch.__version__) < version.parse("1.4"): + gelu = _gelu_python +else: + gelu = nn.functional.gelu + + +def gelu_fast(x): + return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))) + + +def quick_gelu(x): + return x * torch.sigmoid(1.702 * x) + + +def _silu_python(x): + """ + See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear + Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function + Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated + Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with + later. + """ + return x * torch.sigmoid(x) + + +if version.parse(torch.__version__) < version.parse("1.7"): + silu = _silu_python +else: + silu = nn.functional.silu + + +def _mish_python(x): + """ + See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also + visit the official repository for the paper: https://github.com/digantamisra98/Mish + """ + return x * torch.tanh(nn.functional.softplus(x)) + + +if version.parse(torch.__version__) < version.parse("1.9"): + mish = _mish_python +else: + mish = nn.functional.mish + + +def linear_act(x): + return x + +def squared_relu(x): + """ + Squared ReLU variant that is fastest with Pytorch. + """ + x = nn.functional.relu(x) + return x*x + +def squared_relu_xla(x): + """ + Squared ReLU variant that is fastest with JAX. + """ + x = nn.functional.relu(x) + return x**2 + +tranception_ACT2FN = { + "relu": nn.functional.relu, + "silu": silu, + "swish": silu, + "gelu": gelu, + "tanh": torch.tanh, + "gelu_new": gelu_new, + "gelu_fast": gelu_fast, + "quick_gelu": quick_gelu, + "mish": mish, + "linear": linear_act, + "sigmoid": torch.sigmoid, + "squared_relu": squared_relu, + "squared_relu_xla": squared_relu_xla, +} + + +def get_activation(activation_string): + if activation_string in tranception_ACT2FN: + return tranception_ACT2FN[activation_string] + else: + raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(tranception_ACT2FN.keys())}") \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/config.py b/proteingym/baselines/trancepteve/trancepteve/config.py new file mode 100644 index 0000000..749346c --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/config.py @@ -0,0 +1,56 @@ +from transformers import GPT2Config + +class TranceptEVEConfig(GPT2Config): + """ + Config subclass for Tranception model architecture. + """ + def __init__( + self, + attention_mode="tranception", + position_embedding="grouped_alibi", + tokenizer=None, + full_target_seq=None, + scoring_window=None, + inference_time_retrieval_type="TranceptEVE", + retrieval_aggregation_mode=None, #[substitutions Vs indels] + retrieval_weights_manual=False, + retrieval_inference_MSA_weight=0.3, + retrieval_inference_EVE_weight=0.7, + MSA_filename=None, + MSA_weight_file_name=None, + MSA_start=None, + MSA_end=None, + MSA_threshold_sequence_frac_gaps=None, + MSA_threshold_focus_cols_frac_gaps=None, + clustal_omega_location=None, + EVE_model_paths=None, + EVE_num_samples_log_proba=None, + EVE_model_parameters_location=None, + MSA_recalibrate_probas=False, + EVE_recalibrate_probas=True, + **kwargs + ): + super().__init__(**kwargs) + self.model_type = "tranception" + self.attention_mode = attention_mode + self.position_embedding = position_embedding + self.tokenizer = tokenizer + self.full_target_seq = full_target_seq + self.scoring_window = scoring_window + self.inference_time_retrieval_type = inference_time_retrieval_type + self.retrieval_aggregation_mode = retrieval_aggregation_mode + self.retrieval_weights_manual = retrieval_weights_manual + self.retrieval_inference_MSA_weight = retrieval_inference_MSA_weight + self.retrieval_inference_EVE_weight = retrieval_inference_EVE_weight + self.MSA_filename = MSA_filename + self.MSA_weight_file_name = MSA_weight_file_name + self.MSA_start = MSA_start + self.MSA_end = MSA_end + self.MSA_threshold_sequence_frac_gaps = MSA_threshold_sequence_frac_gaps + self.MSA_threshold_focus_cols_frac_gaps = MSA_threshold_focus_cols_frac_gaps + self.clustal_omega_location = clustal_omega_location + self.EVE_model_paths = EVE_model_paths + self.EVE_num_samples_log_proba = EVE_num_samples_log_proba + self.EVE_model_parameters_location = EVE_model_parameters_location + self.MSA_recalibrate_probas = MSA_recalibrate_probas + self.EVE_recalibrate_probas = EVE_recalibrate_probas \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/model_pytorch.py b/proteingym/baselines/trancepteve/trancepteve/model_pytorch.py new file mode 100644 index 0000000..d23286a --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/model_pytorch.py @@ -0,0 +1,1232 @@ +from dataclasses import dataclass +from typing import Optional, Tuple +import math +import os,sys +import numpy as np +import pandas as pd +import json +import tqdm +import pickle +import uuid +import torch +from torch import nn +from torch.nn import CrossEntropyLoss, NLLLoss +from torch.utils.data.sampler import SequentialSampler +import torch.nn.functional as F +from transformers import GPT2PreTrainedModel + +from transformers.modeling_utils import ( + Conv1D, + PreTrainedModel, + SequenceSummary, + find_pruneable_heads_and_indices, + prune_conv1d_layer, +) +from transformers.file_utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + SequenceClassifierOutputWithPast, + TokenClassifierOutput +) +from transformers.utils.model_parallel_utils import assert_device_map, get_device_map +from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizerFast +from datasets import Dataset + +from trancepteve.activations import tranception_ACT2FN +from trancepteve.config import TranceptEVEConfig +from trancepteve.outputs import ( + TranceptionCausalLMOutputWithCrossAttentions, +) +from trancepteve.utils import msa_utils +from trancepteve.utils import scoring_utils +from trancepteve.EVE import VAE_model + +def nanmean(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs) + +def logistic(x): + return 1 / (1 + math.exp(-x)) + +def normalize(x): + return (x - x.mean()) / x.std() + +def entropy(x, ignore_tokenizer_characters=True): + """ + Compute entropy over the last dimension of tensor x (assumes it is a log softmax input) + """ + exp_x = torch.exp(x.float()) + if ignore_tokenizer_characters: + entropy = (- exp_x[:,5:]*x[:,5:]).mean(dim=-1) + else: + entropy = (- exp_x*x).mean(dim=-1) + return entropy + +def get_slopes(n, mode="standard_alibi", verbose=False): + """ + Function to compute the m constant for each attention head. Code has been adapted from the official ALiBi codebase at: + https://github.com/ofirpress/attention_with_linear_biases/blob/master/fairseq/models/transformer.py + """ + def get_slopes_power_of_2(n): + start = (2**(-2**-(math.log2(n)-3))) + ratio = start + return [start*ratio**i for i in range(n)] + if mode=="grouped_alibi": + n = n // 4 + if math.log2(n).is_integer(): + result = get_slopes_power_of_2(n) + else: + #Workaround when the number of heads is not a power of 2 + closest_power_of_2 = 2**math.floor(math.log2(n)) + result = get_slopes_power_of_2(closest_power_of_2) + get_slopes(2*closest_power_of_2)[0::2][:n-closest_power_of_2] + if mode=="grouped_alibi": + result = result * 4 + if verbose: + print("ALiBi slopes: {}".format(result)) + return result + +class SpatialDepthWiseConvolution(nn.Module): + def __init__(self, head_dim: int, kernel_size: int = 3): + super().__init__() + self.kernel_size = kernel_size + self.conv = nn.Conv1d(in_channels=head_dim, out_channels=head_dim, kernel_size=(kernel_size,), padding=(kernel_size - 1,), groups=head_dim) + + def forward(self, x: torch.Tensor): + batch_size, heads, seq_len, head_dim = x.shape + x = x.permute(0, 1, 3, 2).contiguous() + x = x.view(batch_size * heads, head_dim, seq_len) + x = self.conv(x) + if self.kernel_size>1: + x = x[:, :, :-(self.kernel_size - 1)] + x = x.view(batch_size, heads, head_dim, seq_len) + x = x.permute(0, 1, 3, 2) + return x + +class TranceptionBlockAttention(nn.Module): + def __init__(self, config, is_cross_attention=False, SDWC_kernel_size=None): + super().__init__() + + max_positions = config.max_position_embeddings + self.register_buffer( + "bias", + torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view( + 1, 1, max_positions, max_positions + ), + ) + self.register_buffer("masked_bias", torch.tensor(-1e4)) + + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.split_size = self.embed_dim + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + ) + + self.scale_attn_weights = config.scale_attn_weights + self.is_cross_attention = is_cross_attention + + if self.is_cross_attention: + self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) + self.q_attn = Conv1D(self.embed_dim, self.embed_dim) + else: + self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) + self.c_proj = Conv1D(self.embed_dim, self.embed_dim) + + self.attn_dropout = nn.Dropout(config.attn_pdrop) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + self.pruned_heads = set() + + self.attention_mode=config.attention_mode + + if self.attention_mode=="tranception": + assert self.num_heads%4==0, "Invalid number of heads. Tranception requires the number of heads to be a multiple of 4." + self.num_heads_per_kernel_size = self.num_heads // 4 + self.query_depthwiseconv = nn.ModuleDict() + self.key_depthwiseconv = nn.ModuleDict() + self.value_depthwiseconv = nn.ModuleDict() + for kernel_idx, kernel in enumerate([3,5,7]): + self.query_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + self.key_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + self.value_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) + index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) + + # Prune conv1d layers + self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) + self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) + + # Update hyper params + self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) + self.num_heads = self.num_heads - len(heads) + self.pruned_heads = self.pruned_heads.union(heads) + + def _attn(self, query, key, value, attention_mask=None, head_mask=None, alibi_bias=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / (float(value.size(-1)) ** 0.5) + + if not self.is_cross_attention: + # if only "normal" attention layer implements causal mask + query_length, key_length = query.size(-2), key.size(-2) + causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool() + attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)) + + if alibi_bias is not None: + attn_weights = attn_weights + alibi_bias[:,:,:attn_weights.size(-1)] + + if attention_mask is not None: + # Apply the attention mask + attn_weights = attn_weights + attention_mask + + attn_weights = nn.Softmax(dim=-1)(attn_weights) + attn_weights = self.attn_dropout(attn_weights) + + # Mask heads if we want to + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + """ + Splits hidden_size dim into attn_head_size and num_heads + """ + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(*new_shape) + return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) + + def _merge_heads(self, tensor, num_heads, attn_head_size): + """ + Merges attn_head_size dim and num_attn_heads dim into hidden_size + """ + tensor = tensor.permute(0, 2, 1, 3).contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states, + layer_past=None, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=False, + output_attentions=False, + alibi_bias=None, + ): + if encoder_hidden_states is not None: + if not hasattr(self, "q_attn"): + raise ValueError( + "If class is used as cross attention, the weights `q_attn` have to be defined. " + "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." + ) + + query = self.q_attn(hidden_states) + key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) + attention_mask = encoder_attention_mask + else: + query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + if layer_past is not None: + past_key, past_value = layer_past + key = torch.cat((past_key, key), dim=-2) + value = torch.cat((past_value, value), dim=-2) + + if use_cache is True: + present = (key, value) + else: + present = None + + if self.attention_mode=="tranception": + # We do not do anything on the first self.num_heads_per_kernel_size heads (kernel =1) + query_list=[query[:,:self.num_heads_per_kernel_size,:,:]] + key_list=[key[:,:self.num_heads_per_kernel_size,:,:]] + value_list=[value[:,:self.num_heads_per_kernel_size,:,:]] + for kernel_idx in range(3): + query_list.append(self.query_depthwiseconv[str(kernel_idx)](query[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + key_list.append(self.key_depthwiseconv[str(kernel_idx)](key[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + value_list.append(self.value_depthwiseconv[str(kernel_idx)](value[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + query=torch.cat(query_list, dim=1) + key=torch.cat(key_list, dim=1) + value=torch.cat(value_list, dim=1) + + attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask, alibi_bias=alibi_bias) + + attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) + attn_output = self.c_proj(attn_output) + attn_output = self.resid_dropout(attn_output) + + outputs = (attn_output, present) + if output_attentions: + outputs += (attn_weights,) + + return outputs # a, present, (attentions) + +class TranceptionBlockMLP(nn.Module): + def __init__(self, intermediate_size, config): + super().__init__() + embed_dim = config.hidden_size + self.c_fc = Conv1D(intermediate_size, embed_dim) + self.c_proj = Conv1D(embed_dim, intermediate_size) + self.act = tranception_ACT2FN[config.activation_function] + self.dropout = nn.Dropout(config.resid_pdrop) + + def forward(self, hidden_states): + hidden_states = self.c_fc(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.c_proj(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + +class TranceptionBlock(nn.Module): + def __init__(self, config, SDWC_kernel_size=None): + super().__init__() + hidden_size = config.hidden_size + inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size + + self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + self.attn = TranceptionBlockAttention(config, SDWC_kernel_size=SDWC_kernel_size) + self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + + if config.add_cross_attention: + self.crossattention = TranceptionBlockAttention(config, is_cross_attention=True, SDWC_kernel_size=SDWC_kernel_size) + self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + + self.mlp = TranceptionBlockMLP(inner_dim, config) + + def forward( + self, + hidden_states, + layer_past=None, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=False, + output_attentions=False, + alibi_bias=None, + ): + residual = hidden_states + hidden_states = self.ln_1(hidden_states) + attn_outputs = self.attn( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + alibi_bias=alibi_bias, + ) + attn_output = attn_outputs[0] # output_attn: a, present, (attentions) + outputs = attn_outputs[1:] + # residual connection + hidden_states = attn_output + residual + + if encoder_hidden_states is not None: + # add one self-attention block for cross-attention + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " + "cross-attention layers by setting `config.add_cross_attention=True`" + ) + residual = hidden_states + hidden_states = self.ln_cross_attn(hidden_states) + cross_attn_outputs = self.crossattention( + hidden_states, + attention_mask=attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + ) + attn_output = cross_attn_outputs[0] + # residual connection + hidden_states = residual + attn_output + outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights + + residual = hidden_states + hidden_states = self.ln_2(hidden_states) + + feed_forward_hidden_states = self.mlp(hidden_states) + + # residual connection + hidden_states = residual + feed_forward_hidden_states + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs # hidden_states, present, (attentions, cross_attentions) + +class TranceptionModel(GPT2PreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + def __init__(self, config): + super().__init__(config) + + self.embed_dim = config.hidden_size + self.wte = nn.Embedding(config.vocab_size, self.embed_dim) + self.position_embedding = config.position_embedding if hasattr(config, "position_embedding") else "learned" + if self.position_embedding=="learned": + self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) + self.alibi = None + elif self.position_embedding=="grouped_alibi": + maxpos = config.n_positions + attn_heads = config.n_head + self.slopes = torch.Tensor(get_slopes(attn_heads, mode=self.position_embedding)) + #The softmax operation is invariant to translation, and bias functions used are always linear. + alibi = self.slopes.unsqueeze(1).unsqueeze(1) * torch.arange(maxpos).unsqueeze(0).unsqueeze(0).expand(attn_heads, -1, -1) + alibi = alibi.view(attn_heads, 1, maxpos) + self.register_buffer('alibi',alibi) + + self.drop = nn.Dropout(config.embd_pdrop) + self.h = nn.ModuleList([TranceptionBlock(config) for _ in range(config.num_hidden_layers)]) + self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) + + self.init_weights() + + # Model parallel + self.model_parallel = False + self.device_map = None + self.gradient_checkpointing = False + + def parallelize(self, device_map=None, num_cores=None): + self.device_map = ( + get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map + ) + device_prefix="cuda:" + assert_device_map(self.device_map, len(self.h)) + self.model_parallel = True + self.first_device = "cpu" if "cpu" in self.device_map.keys() else device_prefix + str(min(self.device_map.keys())) + self.last_device = device_prefix + str(max(self.device_map.keys())) + self.wte = self.wte.to(self.first_device) + if self.position_embedding=="learned": + self.wpe = self.wpe.to(self.first_device) + for k, v in self.device_map.items(): + print("k,v :"+str(k)+","+str(v)) + for block in v: + cuda_device = device_prefix + str(k) + self.h[block] = self.h[block].to(cuda_device) + self.ln_f = self.ln_f.to(self.last_device) + + def deparallelize(self): + self.model_parallel = False + self.device_map = None + self.first_device = "cpu" + self.last_device = "cpu" + self.wte = self.wte.to("cpu") + if self.position_embedding=="learned": + self.wpe = self.wpe.to("cpu") + for index in range(len(self.h)): + self.h[index] = self.h[index].to("cpu") + self.ln_f = self.ln_f.to("cpu") + torch.cuda.empty_cache() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} + """ + for layer, heads in heads_to_prune.items(): + self.h[layer].attn.prune_heads(heads) + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + past_length = past_key_values[0][0].size(-2) + if position_ids is None: + position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + # GPT2Attention mask. + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask[:, None, None, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * -10000.0 + + # If a 2D ou 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.add_cross_attention and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # head_mask has shape n_layer x batch x n_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + if self.position_embedding=="learned": + position_embeds = self.wpe(position_ids) + hidden_states = inputs_embeds + position_embeds + else: + hidden_states = inputs_embeds + + if token_type_ids is not None: + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + all_hidden_states = () if output_hidden_states else None + + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + # Model parallel + if self.model_parallel: + torch.cuda.set_device(hidden_states.device) + # Ensure layer_past is on same device as hidden_states (might not be correct) + if layer_past is not None: + layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) + # Ensure that attention_mask is always on the same device as hidden_states + if attention_mask is not None: + attention_mask = attention_mask.to(hidden_states.device) + if isinstance(head_mask, torch.Tensor): + head_mask = head_mask.to(hidden_states.device) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + if use_cache: + print("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + alibi_bias=self.alibi if hasattr(self, "alibi") else None + ) + + hidden_states = outputs[0] + + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) + + if self.model_parallel: + device_prefix="cuda:" + for k, v in self.device_map.items(): + if i == v[-1] and device_prefix + str(k) != self.last_device: + hidden_states = hidden_states.to(device_prefix + str(k + 1)) + + hidden_states = self.ln_f(hidden_states) + + hidden_states = hidden_states.view(*output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] + if v is not None + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + +class TrancepteveLMHeadModel(GPT2PreTrainedModel): + _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"] + def __init__(self, config): + super().__init__(config) + self.transformer = TranceptionModel(config) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.config = config + self.clustal_hash = str(uuid.uuid4()) + self.clustal_hash_eve = str(uuid.uuid4()) + self.init_weights() + + self.default_model_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # Model parallel + self.model_parallel = False + self.device_map = None + + self.inference_time_retrieval_type = config.inference_time_retrieval_type if hasattr(config, "inference_time_retrieval_type") else None + self.retrieval_aggregation_mode = config.retrieval_aggregation_mode if hasattr(config, "retrieval_aggregation_mode") else None + if self.inference_time_retrieval_type is not None: + print("Model leverages both autoregressive and retrieval inference (Type: {})".format(self.inference_time_retrieval_type)) + self.retrieval_weights_manual = config.retrieval_weights_manual + self.MSA_filename = config.MSA_filename if hasattr(config, "MSA_filename") else None + self.MSA_folder = '/'.join(self.MSA_filename.split(os.sep)[:-1]) + self.MSA_name = self.MSA_filename.split(os.sep)[-1] + self.MSA_start=config.MSA_start + self.MSA_end=config.MSA_end + self.full_target_seq = config.full_target_seq if hasattr(config, "full_target_seq") else '' + self.full_protein_length = len(self.full_target_seq) + self.EVE_model_paths = config.EVE_model_paths + self.EVE_recalibrate_probas = config.EVE_recalibrate_probas + if self.inference_time_retrieval_type.startswith("Trancept"): + self.MSA_log_prior, self.MSA_processed_depth = msa_utils.get_msa_prior( + MSA_data_file=self.MSA_filename, + MSA_weight_file_name=config.MSA_weight_file_name, + retrieval_aggregation_mode=self.retrieval_aggregation_mode, + MSA_start=self.MSA_start, + MSA_end=self.MSA_end, + len_target_seq=self.full_protein_length, + vocab=config.tokenizer.get_vocab(), + filter_MSA=True, + threshold_sequence_frac_gaps=config.MSA_threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=config.MSA_threshold_focus_cols_frac_gaps, + verbose=True + ) + self.MSA_log_prior = torch.log(torch.tensor(self.MSA_log_prior).float().to(self.default_model_device)) + else: + self.MSA_processed_depth = 0 + if self.inference_time_retrieval_type=="TranceptEVE": + assert (self.EVE_model_paths is not None) and len(self.EVE_model_paths) >=1 , "Could not find a reference for EVE model" + self.EVE_models, self.EVE_MSA, self.EVE_log_prior = self.get_EVE_models_and_log_prior(EVE_model_paths=self.EVE_model_paths, + EVE_model_parameters_location=config.EVE_model_parameters_location, + full_sequence_len=self.full_protein_length, + MSA_start=self.MSA_start, + MSA_end=self.MSA_end, + EVE_num_samples_log_proba=config.EVE_num_samples_log_proba, + threshold_sequence_frac_gaps=config.MSA_threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=config.MSA_threshold_focus_cols_frac_gaps, + verbose=True) + self.EVE_log_prior = self.EVE_log_prior.to(self.default_model_device) + self.EVE_processed_depth = len(self.EVE_MSA.seq_name_to_sequence.keys()) + else: + self.EVE_processed_depth = 0 + + if self.retrieval_weights_manual: + self.retrieval_inference_MSA_weight = config.retrieval_inference_MSA_weight if hasattr(config, "retrieval_inference_MSA_weight") else 0.5 + self.retrieval_inference_EVE_weight = config.retrieval_inference_EVE_weight if hasattr(config, "retrieval_inference_EVE_weight") else 0.5 + elif self.inference_time_retrieval_type=="Tranception": + # Using weights from original Tranception paper + self.retrieval_inference_MSA_weight = 0.6 + self.retrieval_inference_EVE_weight = 0.0 + elif self.inference_time_retrieval_type=="TranceptEVE": + if self.retrieval_aggregation_mode=="aggregate_indel": + if self.MSA_processed_depth < 10: + self.retrieval_inference_MSA_weight = 0.0 + self.retrieval_inference_EVE_weight = 0.0 + else: + self.retrieval_inference_MSA_weight = 0.5 + self.retrieval_inference_EVE_weight = 0.1 + else: + if self.MSA_processed_depth < 10: + self.retrieval_inference_MSA_weight = 0.0 + elif self.MSA_processed_depth < 10**2: + self.retrieval_inference_MSA_weight = 0.1 + elif self.MSA_processed_depth < 10**3: + self.retrieval_inference_MSA_weight = 0.3 + elif self.MSA_processed_depth < 10**4: + self.retrieval_inference_MSA_weight = 0.4 + elif self.MSA_processed_depth < 10**5: + self.retrieval_inference_MSA_weight = 0.4 + else: + self.retrieval_inference_MSA_weight = 0.5 + + if self.EVE_processed_depth < 10: + self.retrieval_inference_EVE_weight = 0.0 + elif self.EVE_processed_depth < 10**2: + self.retrieval_inference_EVE_weight = 0.3 + elif self.EVE_processed_depth < 10**3: + self.retrieval_inference_EVE_weight = 0.6 + elif self.EVE_processed_depth < 10**4: + self.retrieval_inference_EVE_weight = 0.7 + elif self.EVE_processed_depth < 10**5: + self.retrieval_inference_EVE_weight = 0.7 + else: + self.retrieval_inference_EVE_weight = 0.8 + print("Aggregation weights of retrieved MSA & EVE model are based on processed MSA depth: MSA({}) and EVE({})".format(self.retrieval_inference_MSA_weight,self.retrieval_inference_EVE_weight)) + else: + print("Model only uses autoregressive inference") + + def parallelize(self, device_map=None, num_cores=None, num_pipelines=1): + self.num_pipelines=num_pipelines + self.device_map = ( + get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) + if device_map is None + else device_map + ) + assert_device_map(self.device_map, len(self.transformer.h)) + self.transformer.parallelize(self.device_map, num_cores=num_cores) + self.lm_head = self.lm_head.to(self.transformer.first_device) + self.model_parallel = True + + def deparallelize(self): + self.transformer.deparallelize() + self.transformer = self.transformer.to("cpu") + self.lm_head = self.lm_head.to("cpu") + self.model_parallel = False + torch.cuda.empty_cache() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + "flip": kwargs.get("flip", None), + } + + def get_transformer_log_softmax(self, sequence, batch_size=20, inference_time_retrieval_type="Tranception"): + model_context_len = self.config.n_ctx - 2 + sequence_df = pd.DataFrame({'mutated_sequence':[sequence]}) + num_windows = 1 + int( len(sequence) / model_context_len) + df_list=[] + start=0 + for window_index in range(1, num_windows+1): + df_sliced = sequence_df.copy() + df_sliced['sliced_mutated_sequence'] = df_sliced['mutated_sequence'].map(lambda x: x[start:start+model_context_len]) + df_sliced['start_slice'] = [start] + df_sliced['end_slice'] = df_sliced['mutated_sequence'].map(lambda x: min(len(x), start+model_context_len)) + df_list.append(df_sliced) + start += model_context_len + df_final = pd.concat(df_list,axis=0) + df = df_final.drop_duplicates() + self.eval() + with torch.no_grad(): + ds = Dataset.from_pandas(df) + ds.set_transform(self.encode_batch) + data_collator = DataCollatorForLanguageModeling( + tokenizer=self.config.tokenizer, + mlm=False) + sampler = SequentialSampler(ds) + ds_loader = torch.utils.data.DataLoader(ds, batch_size=num_windows, sampler=sampler, collate_fn=data_collator, num_workers=5, pin_memory=True, drop_last=False) + for encoded_batch in tqdm.tqdm(ds_loader): + encoded_batch['start_slice'] = df['start_slice'].values + encoded_batch['end_slice'] = df['end_slice'].values + for k, v in encoded_batch.items(): + if isinstance(v, torch.Tensor): + encoded_batch[k] = v.to(self.device) + shift_labels = encoded_batch['labels'][..., 1:].contiguous() + shift_log_probas=self.forward(**encoded_batch,return_dict=True, retrieval_aggregation_mode="aggregate_substitution",inference_time_retrieval_type=inference_time_retrieval_type).fused_shift_log_probas + vocab_size = shift_log_probas.shape[-1] + if num_windows>1: #Trim last positions + shift_log_probas_trimmed = torch.zeros((len(sequence)+1,vocab_size)).to(shift_log_probas.device) + shift_labels_trimmed = torch.zeros((len(sequence)+1,)).to(shift_log_probas.device) + start_index = 0 + for window in range(num_windows): + if window < num_windows - 1: + shift_log_probas_trimmed[start_index:start_index+model_context_len] = shift_log_probas[window,:model_context_len] + shift_labels_trimmed[start_index:start_index+model_context_len] = shift_labels[window,:model_context_len] + else: + remaining_residues = len(sequence) + 1 - start_index + shift_log_probas_trimmed[start_index:] = shift_log_probas[window,:remaining_residues] + shift_labels_trimmed[start_index:] = shift_labels[window,:remaining_residues] + start_index+=model_context_len + shift_log_probas = shift_log_probas_trimmed + shift_labels = shift_labels_trimmed.long() + #remove dummy tokens at the end -- adding 1 for the EOS token + shift_log_probas=shift_log_probas.view(-1,vocab_size)[:len(sequence)+1] + shift_labels=shift_labels.view(-1)[:len(sequence)+1] + assert shift_log_probas.shape[0]==len(shift_labels), "Length of log probas vector does not match length of labels" + return shift_log_probas, shift_labels + + def iterative_recalibrations(self, log_proba_to_calibrate, avg_log_proba_target, distance_stop_criterion=0.001, max_steps = 1000): + loss = abs(log_proba_to_calibrate.mean() - avg_log_proba_target) + step = 0 + while (loss > distance_stop_criterion): + T = log_proba_to_calibrate.mean() / avg_log_proba_target + log_proba_to_calibrate = torch.log_softmax(log_proba_to_calibrate / T, dim=-1) + loss = abs(log_proba_to_calibrate.mean() - avg_log_proba_target) + step += 1 + if step > max_steps: + break + return log_proba_to_calibrate + + def recalibrate_MSA_probas(self): + log_softmax_wt_LR, shift_labels_LR = self.get_transformer_log_softmax(sequence=self.full_target_seq, inference_time_retrieval_type=None) + log_softmax_wt_RL, shift_labels_RL = self.get_transformer_log_softmax(sequence=self.full_target_seq[::-1], inference_time_retrieval_type=None) + log_probas_transformer_mean = (log_softmax_wt_LR[self.MSA_start:self.MSA_end,5:].mean() + log_softmax_wt_RL[self.MSA_start:self.MSA_end,5:].mean())/2.0 + log_probas_MSA_mean = self.MSA_log_prior[self.MSA_start:self.MSA_end,5:].mean() + T_optimal = log_probas_MSA_mean / log_probas_transformer_mean + print("Optimal temperature for MSA proba recalibration: {}".format(T_optimal)) + self.MSA_log_prior[self.MSA_start:self.MSA_end,5:] = self.iterative_recalibrations(self.MSA_log_prior[self.MSA_start:self.MSA_end,5:], avg_log_proba_target=log_probas_transformer_mean) + + def recalibrate_EVE_probas(self): + log_softmax_wt_LR, shift_labels_LR = self.get_transformer_log_softmax(sequence=self.full_target_seq) + log_softmax_wt_RL, shift_labels_RL = self.get_transformer_log_softmax(sequence=self.full_target_seq[::-1]) + reindexed_focus_cols = [self.MSA_start+position for position in self.EVE_MSA.focus_cols] + log_probas_transformer_mean = (log_softmax_wt_LR[reindexed_focus_cols,5:].mean() + log_softmax_wt_RL[reindexed_focus_cols,5:].mean())/2.0 + log_probas_EVE_mean = self.EVE_log_prior[reindexed_focus_cols,5:].mean() + T_optimal = log_probas_EVE_mean / log_probas_transformer_mean + print("Optimal temperature for EVE proba recalibration: {}".format(T_optimal)) + self.EVE_log_prior[reindexed_focus_cols,5:] = self.iterative_recalibrations(self.EVE_log_prior[reindexed_focus_cols,5:], avg_log_proba_target=log_probas_transformer_mean) + + def get_EVE_model(self, EVE_model_path, EVE_model_parameters_location, threshold_sequence_frac_gaps=None, threshold_focus_cols_frac_gaps=None): + assert self.MSA_filename is not None, "MSA_filename not specified" + assert self.MSA_folder is not None, "MSA_folder not specified" + assert os.path.exists(self.config.MSA_weight_file_name), "MSA weights file not found" + if threshold_focus_cols_frac_gaps!=1.0: print("threshold_focus_cols_frac_gaps not 1.0. Only well-covered positions are factored in the EVE retrieval aggregation.") + MSA = msa_utils.MSA_processing( + MSA_location=self.MSA_filename, + #theta=theta, #Dont need to specify weights since EVE model should be trained separately beforehand / we are using weights directly as is + use_weights=True, + threshold_sequence_frac_gaps=threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=threshold_focus_cols_frac_gaps, + weights_location=self.config.MSA_weight_file_name + ) + model_params = json.load(open(EVE_model_parameters_location)) + model = VAE_model.VAE_model( + model_name='EVE_model', + data=MSA, + encoder_parameters=model_params["encoder_parameters"], + decoder_parameters=model_params["decoder_parameters"], + random_seed=42 + ) + model = model.to(model.device) + #try: + if True: + checkpoint = torch.load(EVE_model_path) + model.load_state_dict(checkpoint['model_state_dict']) + print("Initialized EVE model with checkpoint '{}' ".format(EVE_model_path)) + #except: + else: + print("Unable to locate EVE model checkpoint {}".format(EVE_model_path)) + sys.exit(0) + return model, MSA + + def get_EVE_models_and_log_prior(self, EVE_model_paths, EVE_model_parameters_location, full_sequence_len, MSA_start, MSA_end, sequences_to_score=None, EVE_num_samples_log_proba=10, alphabet="ACDEFGHIKLMNPQRSTVWY", verbose=False, threshold_sequence_frac_gaps=None, threshold_focus_cols_frac_gaps=None): + """ + Create ensemble if multiple models passed through EVE_model_paths. + """ + num_EVE_models = len(EVE_model_paths) + EVE_ensemble_retrieved_MSA = {} + EVE_log_prior = 0 + for model_index, EVE_model_path in enumerate(EVE_model_paths): + EVE_ensemble_retrieved_MSA[model_index], EVE_MSA = self.get_EVE_model(EVE_model_path, EVE_model_parameters_location, threshold_sequence_frac_gaps=threshold_sequence_frac_gaps, threshold_focus_cols_frac_gaps=threshold_focus_cols_frac_gaps) + log_prior_list = EVE_model_path.split('/') + log_prior_folder = '/'.join(log_prior_list[:-1])+os.sep+'log_prior' + if not os.path.exists(log_prior_folder): + os.mkdir(log_prior_folder) + log_prior_name = '_'.join([log_prior_list[-1],str(EVE_num_samples_log_proba),'log_space']) + log_prior_location = log_prior_folder+os.sep+log_prior_name + sequences_to_score = [EVE_MSA.focus_seq_trimmed] if sequences_to_score is None else sequences_to_score + if not os.path.exists(log_prior_location): + print("Computing EVE log prior") + EVE_log_prior_single = self.get_EVE_log_prior_single(EVE_model=EVE_ensemble_retrieved_MSA[model_index], + sequences_to_score=sequences_to_score, + full_sequence_len=full_sequence_len, + MSA_start=MSA_start, + MSA_end=MSA_end, + EVE_MSA=EVE_MSA, + EVE_num_samples_log_proba=EVE_num_samples_log_proba, + alphabet=alphabet, + verbose=verbose) + with open(log_prior_location,'wb') as f: pickle.dump(EVE_log_prior_single, f) + else: + print("Loading EVE log prior from disk") + with open(log_prior_location,'rb') as f: EVE_log_prior_single = pickle.load(f) + EVE_log_prior += EVE_log_prior_single + EVE_log_prior = EVE_log_prior / len(EVE_model_paths) + return EVE_ensemble_retrieved_MSA, EVE_MSA, EVE_log_prior + + def get_EVE_log_prior_single(self, EVE_model, sequences_to_score, full_sequence_len, MSA_start, MSA_end, EVE_MSA, EVE_num_samples_log_proba=10, alphabet="ACDEFGHIKLMNPQRSTVWY", verbose=False, average_mode="log_space"): + reference_seq = sequences_to_score[0] + self.focus_seq_one_hot_encoding = np.zeros((len(sequences_to_score),len(reference_seq),len(alphabet))) + aa_dict = {} + for i,aa in enumerate(alphabet): + aa_dict[aa] = i + for i,sequence in enumerate(sequences_to_score): + for j,letter in enumerate(sequence): + if letter in aa_dict: + k = aa_dict[letter] + self.focus_seq_one_hot_encoding[i,j,k] = 1.0 + EVE_model.eval() + recon_x_log = 0 + with torch.no_grad(): + x = torch.tensor(self.focus_seq_one_hot_encoding, dtype=EVE_model.dtype).to(EVE_model.device) + mu, log_var = EVE_model.encoder(x) + for _ in tqdm.tqdm(range(EVE_num_samples_log_proba), desc="Sampling EVE log probabilities", mininterval=10): + z = EVE_model.sample_latent(mu, log_var) + recon_x_log += EVE_model.decoder(z) + recon_x_log = recon_x_log / EVE_num_samples_log_proba #Average over iterations + recon_x_log = recon_x_log.view(len(sequences_to_score),len(reference_seq),len(alphabet)) + EVE_log_prior = torch.ones(len(sequences_to_score),full_sequence_len,len(alphabet)+5, dtype=EVE_model.dtype, device=EVE_model.device) * (- np.inf) + reindexed_focus_cols = [MSA_start+position for position in EVE_MSA.focus_cols] + EVE_log_prior[:,reindexed_focus_cols,5:] = recon_x_log + if verbose: print("Target seq len is {}, MSA length is {}, start position is {}, end position is {} and log_prior shape is: {}".format(len(reference_seq),MSA_end-MSA_start,MSA_start,MSA_end,EVE_log_prior.shape)) + EVE_log_prior = EVE_log_prior.squeeze() #if only 1 sequence to score, drops the first dimension + return EVE_log_prior + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + flip=None, + start_slice=None, + end_slice=None, + mutated_sequence=None, + sliced_mutated_sequence=None, + retrieval_aggregation_mode=None, + inference_time_retrieval_type=None + ): + r""" + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to + ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]`` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + retrieval_aggregation_mode = retrieval_aggregation_mode if retrieval_aggregation_mode is not None else self.retrieval_aggregation_mode + inference_time_retrieval_type = inference_time_retrieval_type if inference_time_retrieval_type is not None else self.inference_time_retrieval_type + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict + ) + hidden_states = transformer_outputs[0] + + # Set device for model parallelism + if self.model_parallel: + torch.cuda.set_device(self.transformer.first_device) + hidden_states = hidden_states.to(self.lm_head.weight.device) + self.MSA_log_prior = self.MSA_log_prior.to(self.lm_head.weight.device) + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous().to(shift_logits.device) + + if retrieval_aggregation_mode is not None: + batch_size = input_ids.size(0) + if retrieval_aggregation_mode=="aggregate_indel": + assert batch_size==1, "Aggregate indel is only supported for batch size of 1" + truncated_sequence_text = mutated_sequence[0][start_slice[0]:end_slice[0]] + if len(truncated_sequence_text)!=shift_logits.shape[1]-1: # shift_logits only has one extra token compared to truncated_sequence_text (the BOS token) + print("Tokenization error -- seq length: {} and shift_logits length - 1 : {}".format(len(truncated_sequence_text),shift_logits.shape[1]-1)) + try: + MSA_log_prior, MSA_start, MSA_end, keep_column, new_column = msa_utils.update_retrieved_MSA_log_prior_indel(self, self.MSA_log_prior, self.MSA_start, self.MSA_end, mutated_sequence[0], self.clustal_hash) + except: + print("Issue processing the following sequence: {}".format(mutated_sequence[0])) + if inference_time_retrieval_type == "TranceptEVE": EVE_log_prior = msa_utils.update_retrieved_MSA_log_prior_indel(self, self.EVE_log_prior, self.MSA_start, self.MSA_end, mutated_sequence[0], self.clustal_hash_eve)[0] + elif retrieval_aggregation_mode=="aggregate_substitution": + MSA_log_prior=self.MSA_log_prior + MSA_start=self.MSA_start + MSA_end=self.MSA_end + if inference_time_retrieval_type == "TranceptEVE": EVE_log_prior=self.EVE_log_prior + + shift_log_probas = torch.log_softmax(shift_logits,dim=-1) + fused_shift_log_probas = shift_log_probas.clone() + if flip is None: + flip = torch.zeros(batch_size).to(fused_shift_log_probas.device) + flip = flip > 0 + + for seq_index in range(batch_size): + if MSA_start < end_slice[seq_index] and MSA_end > start_slice[seq_index]: #first check whether the MSA region is even in the sliced interval + min_prior_slice = max(start_slice[seq_index], MSA_start) + max_prior_slice = min(end_slice[seq_index], MSA_end) + else: #If there is no overlap, there is no averaging with the MSA / retrieval + continue + if max_prior_slice <= min_prior_slice: + print("Non overlapping region detected: min_prior_slice {} and max_prior_slice {}".format(min_prior_slice,max_prior_slice)) + continue + slice_MSA_prior = MSA_log_prior[min_prior_slice:max_prior_slice,:].to(fused_shift_log_probas.device) + if inference_time_retrieval_type == "TranceptEVE": slice_EVE_prior = EVE_log_prior[min_prior_slice:max_prior_slice,:].to(fused_shift_log_probas.device) + + if flip[seq_index]: + slice_MSA_prior = torch.flip(slice_MSA_prior,dims=(0,)) + if inference_time_retrieval_type == "TranceptEVE": slice_EVE_prior = torch.flip(slice_EVE_prior,dims=(0,)) + min_logits_slice = max(0,end_slice[seq_index]-MSA_end) + max_logits_slice = min_logits_slice + (max_prior_slice-min_prior_slice) + else: + min_logits_slice = max(0, MSA_start-start_slice[seq_index]) + max_logits_slice = min_logits_slice + (max_prior_slice-min_prior_slice) + + if inference_time_retrieval_type=="Tranception": + fused_shift_log_probas[seq_index,min_logits_slice:max_logits_slice,5:] = (1-self.retrieval_inference_MSA_weight) * shift_log_probas[seq_index,min_logits_slice:max_logits_slice,5:] + self.retrieval_inference_MSA_weight * slice_MSA_prior[...,5:] + elif inference_time_retrieval_type=="TranceptEVE": + fused_shift_log_probas[seq_index,min_logits_slice:max_logits_slice,5:] = (1-self.retrieval_inference_EVE_weight) * ((1-self.retrieval_inference_MSA_weight) * shift_log_probas[seq_index,min_logits_slice:max_logits_slice,5:] + self.retrieval_inference_MSA_weight * slice_MSA_prior[...,5:]) + self.retrieval_inference_EVE_weight * slice_EVE_prior[...,5:] + else: + print("inference_time_retrieval_type not recognized") + sys.exit(0) + + if inference_time_retrieval_type=="TranceptEVE" and self.config.MSA_threshold_focus_cols_frac_gaps<1.0 and retrieval_aggregation_mode!="aggregate_indel": + reindexed_non_focus_cols_in_shift_log_probas_coordinates = [ix for ix in range(fused_shift_log_probas.shape[1]) if fused_shift_log_probas[seq_index,ix,5:].min() == (- np.inf)] + reindexed_non_focus_cols_in_full_seq_coordinates = [ix + start_slice[seq_index] for ix in reindexed_non_focus_cols_in_shift_log_probas_coordinates] + reindexed_non_focus_cols_in_full_seq_coordinates_in_MSA_overlap = [ix for ix in reindexed_non_focus_cols_in_full_seq_coordinates if ix >= MSA_start and ix < MSA_end] + + reindexed_non_focus_cols_in_slice_MSA_coordinates_in_MSA_overlap = [ix - min_prior_slice for ix in reindexed_non_focus_cols_in_full_seq_coordinates_in_MSA_overlap] + reindexed_non_focus_cols_in_shift_log_probas_coordinates_in_MSA_overlap = [ix - start_slice[seq_index] for ix in reindexed_non_focus_cols_in_full_seq_coordinates_in_MSA_overlap] + reindexed_non_focus_cols_in_shift_log_probas_coordinates_not_in_MSA_overlap = [ix - start_slice[seq_index] for ix in reindexed_non_focus_cols_in_full_seq_coordinates if ix not in reindexed_non_focus_cols_in_full_seq_coordinates_in_MSA_overlap] + + #If positions to remove are in the MSA range, we leverage the MSA prior + fused_shift_log_probas[seq_index,reindexed_non_focus_cols_in_shift_log_probas_coordinates_in_MSA_overlap,5:]=(1-self.retrieval_inference_MSA_weight) * shift_log_probas[seq_index,reindexed_non_focus_cols_in_shift_log_probas_coordinates_in_MSA_overlap,5:] + self.retrieval_inference_MSA_weight * slice_MSA_prior[reindexed_non_focus_cols_in_slice_MSA_coordinates_in_MSA_overlap,5:] + #Otherwise we fully rely on the autoregressive transformer predictions + fused_shift_log_probas[seq_index,reindexed_non_focus_cols_in_shift_log_probas_coordinates_not_in_MSA_overlap,5:]=(1-self.retrieval_inference_MSA_weight) * shift_log_probas[seq_index,reindexed_non_focus_cols_in_shift_log_probas_coordinates_not_in_MSA_overlap,5:] + + if retrieval_aggregation_mode=="aggregate_indel": + try: + # If a given residue column is an added zero-column, then we overwrite prior fusion and only predict based on the autoregressive transformer inference mode. + inserted_retrieval_positions = [True if slice_MSA_prior[i].sum()==0 else False for i in range(len(slice_MSA_prior))]+[True] #Last True is for the end of sentence token + fused_shift_log_probas[:,inserted_retrieval_positions,:]=shift_log_probas[:,inserted_retrieval_positions,:] + except: + print("Error when adding zero column(s) to account for insertion mutations.") + loss_fct = NLLLoss(reduction='none') + loss = loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1]) + mask = attention_mask[..., 1:].float() + mask[mask==0]=float('nan') + loss *= mask + loss = nanmean(loss, dim=1).mean() + else: + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + fused_shift_log_probas = None + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return TranceptionCausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + cross_attentions=transformer_outputs.cross_attentions, + fused_shift_log_probas=fused_shift_log_probas + ) + + @staticmethod + def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) + + def score_mutants(self, DMS_data, target_seq=None, scoring_mirror=True, batch_size_inference=10, num_workers=10, indel_mode=False): + """ + Method to score mutants in an input DMS file. + DMS_data: (dataframe) Dataframe containing the list of mutated sequences for scoring. + target_seq: (string) Full reference sequence (wild type) that is mutated in the DMS assay. If not None, returned scores are delta log likelihood wrt that sequence. + scoring_mirror: (bool) Whether to score mutated sequences from both directions (Left->Right and Right->Left). + batch_size_inference: (int) Batch size for scoring. + num_workers: (int) Number of workers to be used in the data loader. + indel_mode: (bool) Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions. + """ + df = DMS_data.copy() + if self.config.MSA_recalibrate_probas: self.recalibrate_MSA_probas() + if self.config.EVE_recalibrate_probas: self.recalibrate_EVE_probas() + if ('mutated_sequence' not in df) and (not indel_mode): df['mutated_sequence'] = df['mutant'].apply(lambda x: scoring_utils.get_mutated_sequence(target_seq, x)) + # if indel_mode: + # df["mutated_sequence"] = df["mutant"] + assert ('mutated_sequence' in df), "DMS file to score does not have mutated_sequence column" + if 'mutant' not in df: df['mutant'] = df['mutated_sequence'] #if mutant not in DMS file we default to mutated_sequence + df = df[['mutated_sequence','mutant']] + if target_seq is not None: + df_left_to_right_slices = scoring_utils.get_sequence_slices(df, target_seq=target_seq, model_context_len = self.config.n_ctx - 2, indel_mode=indel_mode, scoring_window=self.config.scoring_window) + else: + df_left_to_right_slices = scoring_utils.get_sequence_slices(df, target_seq=list(df['mutated_sequence'])[0], model_context_len = self.config.n_ctx - 2, indel_mode=indel_mode, scoring_window='sliding') + print("Scoring sequences from left to right") + scores_L_to_R = scoring_utils.get_tranception_scores_mutated_sequences(model=self, mutated_sequence_df=df_left_to_right_slices, batch_size_inference=batch_size_inference, score_var_name='avg_score_L_to_R', target_seq=target_seq, num_workers=num_workers, indel_mode=indel_mode) + if scoring_mirror: + print("Scoring sequences from right to left") + df_right_to_left_slices = df_left_to_right_slices.copy() + df_right_to_left_slices['sliced_mutated_sequence'] = df_right_to_left_slices['sliced_mutated_sequence'].apply(lambda x: x[::-1]) + scores_R_to_L = scoring_utils.get_tranception_scores_mutated_sequences(model=self, mutated_sequence_df=df_right_to_left_slices, batch_size_inference=batch_size_inference, score_var_name='avg_score_R_to_L', target_seq=target_seq, num_workers=num_workers, reverse=True, indel_mode=indel_mode) + all_scores = pd.merge(scores_L_to_R, scores_R_to_L, on='mutated_sequence', how='left', suffixes=('','_R_to_L')) + all_scores['avg_score'] = (all_scores['avg_score_L_to_R'] + all_scores['avg_score_R_to_L']) / 2.0 + else: + all_scores = scores_L_to_R + all_scores['avg_score'] = all_scores['avg_score_L_to_R'] + #By design "get_tranception_scores_mutated_sequences" drops the WT from the output. We add it back if that was one of the sequences to score in the DMS (score=0 by definition) + if target_seq in df.mutated_sequence.values: + if scoring_mirror: + wt_row = pd.DataFrame([[target_seq,0,0,0]], columns=['mutated_sequence','avg_score_L_to_R','avg_score_R_to_L','avg_score']) + else: + wt_row = pd.DataFrame([[target_seq,0,0]], columns=['mutated_sequence','avg_score_L_to_R','avg_score']) + all_scores = pd.concat([all_scores,wt_row], ignore_index=True) + if len(all_scores) > 0 and indel_mode==False : all_scores = pd.merge(all_scores,df,how="left",on='mutated_sequence') #Add back mutation triplet to scoring file (not needed for indels) + return all_scores + + def encode_batch(self, protein_sequence, sequence_name="sliced_mutated_sequence"): + """ + Method to process an input AA sequence batch (protein_sequence) and return a tokenized sequence (via the tokenizer associated to the model). + """ + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='X', char_replacements='ACDEFGHIKLMNPQRSTVWY') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='B', char_replacements='DN') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='J', char_replacements='IL') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='Z', char_replacements='EQ') + return self.config.tokenizer(list(protein_sequence[sequence_name]), add_special_tokens=True, truncation=True, padding=True, max_length=self.config.n_ctx) \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/outputs.py b/proteingym/baselines/trancepteve/trancepteve/outputs.py new file mode 100644 index 0000000..889b7bb --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/outputs.py @@ -0,0 +1,47 @@ +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch + +from transformers.file_utils import ModelOutput + +@dataclass +class TranceptionCausalLMOutputWithCrossAttentions(ModelOutput): + """ + Class for Tranception causal language model (or autoregressive) outputs. + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Cross attentions weights after the attention softmax, used to compute the weighted average in the + cross-attention heads. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key, + value states of the self-attention and the cross-attention layers if model is used in encoder-decoder + setting. Only relevant if `config.is_decoder = True`. + Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + fused_shift_log_probas (`torch.FloatTensor` of shape (batch_size, sequence_length, config.vocab_size), *optional*, returned when config.retrieval_aggregation_mode is not None. + log_probas for each residue position after aggregating autoregressive logits and retrieval logits. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor]] = None + fused_shift_log_probas: Optional[torch.FloatTensor] = None \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/__init__.py b/proteingym/baselines/trancepteve/trancepteve/utils/__init__.py new file mode 100644 index 0000000..90ad887 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/__init__.py @@ -0,0 +1 @@ +from . import scoring_utils, msa_utils \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/dms_utils.py b/proteingym/baselines/trancepteve/trancepteve/utils/dms_utils.py new file mode 100644 index 0000000..fd2ddee --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/dms_utils.py @@ -0,0 +1,30 @@ +import pandas as pd +import numpy as np +from trancepteve.utils import scoring_utils + +def DMS_file_cleanup(DMS_filename, target_seq, start_idx=1, end_idx=None, DMS_mutant_column='mutant', DMS_phenotype_name='score', DMS_directionality=1, AA_vocab = "ACDEFGHIKLMNPQRSTVWY"): + """ + Function to process the raw substitution DMS assay data (eg., removing invalid mutants, aggregate silent mutations). + """ + DMS_data = pd.read_csv(DMS_filename, low_memory=False) + end_idx = start_idx + len(target_seq) - 1 if end_idx is None else end_idx + DMS_data['mutant'] = DMS_data[DMS_mutant_column] + + DMS_data=DMS_data[DMS_data['mutant'].notnull()].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([len(y)>=3 for y in x.split(":")]))].copy() #Mutant triplets should have at least 3 or more characters + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([(y[0] in AA_vocab) and (y[1:-1].isnumeric()) and (y[-1] in AA_vocab) for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([int(y[1:-1])-start_idx >=0 and int(y[1:-1]) <= end_idx for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([y[0]==target_seq[int(y[1:-1])-start_idx] for y in x.split(":")]))].copy() + + DMS_data[DMS_phenotype_name]=pd.to_numeric(DMS_data[DMS_phenotype_name],errors='coerce') + DMS_data=DMS_data[np.isfinite(DMS_data[DMS_phenotype_name])] + DMS_data.dropna(subset = [DMS_phenotype_name], inplace=True) + DMS_data['DMS_score'] = DMS_data[DMS_phenotype_name] * DMS_directionality + DMS_data=DMS_data[['mutant','DMS_score']] + DMS_data=DMS_data.groupby('mutant').mean().reset_index() + + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: scoring_utils.get_mutated_sequence(target_seq, x)) + DMS_data=DMS_data[['mutant','mutated_sequence','DMS_score']] + + return DMS_data + diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json b/proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json new file mode 100644 index 0000000..4b8e910 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json @@ -0,0 +1,41 @@ +{ "encoder_parameters": { + "hidden_layers_sizes" : [2000,1000,300], + "z_dim" : 50, + "convolve_input" : false, + "convolution_input_depth" : 40, + "nonlinear_activation" : "relu", + "dropout_proba" : 0.0 +}, +"decoder_parameters": { + "hidden_layers_sizes" : [300,1000,2000], + "z_dim" : 50, + "bayesian_decoder" : true, + "first_hidden_nonlinearity" : "relu", + "last_hidden_nonlinearity" : "relu", + "dropout_proba" : 0.1, + "convolve_output" : true, + "convolution_output_depth" : 40, + "include_temperature_scaler" : true, + "include_sparsity" : false, + "num_tiles_sparsity" : 0, + "logit_sparsity_p" : 0 +}, +"training_parameters": { + "num_training_steps" : 400000, + "learning_rate" : 1e-4, + "batch_size" : 256, + "annealing_warm_up" : 0, + "kl_latent_scale" : 1.0, + "kl_global_params_scale" : 1.0, + "l2_regularization" : 0.0, + "use_lr_scheduler" : false, + "use_validation_set" : false, + "validation_set_pct" : 0.2, + "validation_freq" : 1000, + "log_training_info" : true, + "log_training_freq" : 1000, + "save_model_params_freq" : 500000 +} +} + + diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/msa_utils.py b/proteingym/baselines/trancepteve/trancepteve/utils/msa_utils.py new file mode 100644 index 0000000..2c4d3f3 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/msa_utils.py @@ -0,0 +1,397 @@ +import numpy as np +import pandas as pd +from collections import defaultdict +import random +import os +import torch +from Bio.Align.Applications import ClustalOmegaCommandline + +def filter_msa(msa_data, num_sequences_kept=3): + """ + Helper function to filter an input MSA msa_data (obtained via process_msa_data) and keep only num_sequences_kept aligned sequences. + If the MSA already has fewer sequences than num_sequences_kept, we keep the MSA as is. + If filtering, we always keep the first sequence of the MSA (ie. the wild type) by default. + Sampling is done without replacement. + """ + if len(list(msa_data.keys())) <= num_sequences_kept: + return msa_data + filtered_msa = {} + wt_name = next(iter(msa_data)) + filtered_msa[wt_name] = msa_data[wt_name] + del msa_data[wt_name] + sequence_names = list(msa_data.keys()) + sequence_names_sampled = random.sample(sequence_names,k=num_sequences_kept-1) + for seq in sequence_names_sampled: + filtered_msa[seq] = msa_data[seq] + return filtered_msa + +def process_msa_data(MSA_data_file): + """ + Helper function that takes as input a path to a MSA file (expects a2m format) and returns a dict mapping sequence ID to the corresponding AA sequence. + """ + msa_data = defaultdict(str) + sequence_name = "" + with open(MSA_data_file, "r") as msa_file: + for i, line in enumerate(msa_file): + line = line.rstrip() + if line.startswith(">"): + sequence_name = line + else: + msa_data[sequence_name] += line.upper() + return msa_data + +def get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab): + vocab_size = len(vocab.keys()) + num_sequences_msa = len(msa_data.keys()) + one_hots = np.zeros((num_sequences_msa,MSA_end-MSA_start,vocab_size)) + for i,seq_name in enumerate(msa_data.keys()): + sequence = msa_data[seq_name] + for j,letter in enumerate(sequence): + if letter in vocab: + k = vocab[letter] + one_hots[i,j,k] = 1.0 + return one_hots + +def one_hot(sequence_string,vocab): + one_hots = np.zeros((len(sequence_string),len(vocab.keys()))) + for j,letter in enumerate(sequence_string): + if letter in vocab: + k = vocab[letter] + one_hots[j,k] = 1.0 + return one_hots.flatten() + +def get_msa_prior(MSA_data_file, MSA_weight_file_name, MSA_start, MSA_end, len_target_seq, vocab, retrieval_aggregation_mode="aggregate_substitution", filter_MSA=True, verbose=False, threshold_sequence_frac_gaps=None, threshold_focus_cols_frac_gaps=None): + """ + Function to enable retrieval inference mode, via computation of (weighted) pseudocounts of AAs at each position of the retrieved MSA. + MSA_data_file: (string) path to MSA file (expects a2m format). + MSA_weight_file_name: (string) path to sequence weights in MSA. + MSA_start: (int) Sequence position that the MSA starts at (1-indexing). + MSA_end: (int) Sequence position that the MSA ends at (1-indexing). + len_target_seq: (int) Full length of sequence to be scored. + vocab: (dict) Vocabulary of the tokenizer. + retrieval_aggregation_mode: (string) Mode for retrieval inference (aggregate_substitution Vs aggregate_indel). If None, places a uniform prior over each token. + filter_MSA: (bool) Whether to filter out sequences with very low hamming similarity (< 0.2) to the reference sequence in the MSA (first sequence). + verbose: (bool) Whether to print to the console processing details along the way. + """ + msa_data = process_msa_data(MSA_data_file) + vocab_size = len(vocab.keys()) + if verbose: print("Target seq len is {}, MSA length is {}, start position is {}, end position is {} and vocab size is {}".format(len_target_seq,MSA_end-MSA_start,MSA_start,MSA_end,vocab_size)) + + if filter_MSA: + if verbose: print("Num sequences in MSA pre filtering: {}".format(len(msa_data.keys()))) + list_sequence_names = list(msa_data.keys()) + focus_sequence_name = list(msa_data.keys())[0] + ref_sequence_hot = one_hot(msa_data[focus_sequence_name],vocab) + for sequence_name in list_sequence_names: + seq_hot = one_hot(msa_data[sequence_name],vocab) + hamming_similarity_seq_ref = np.dot(ref_sequence_hot,seq_hot) / np.dot(ref_sequence_hot,ref_sequence_hot) + if hamming_similarity_seq_ref < 0.2: + del msa_data[sequence_name] + if verbose: print("Num sequences in MSA post filtering: {}".format(len(msa_data.keys()))) + + if MSA_weight_file_name is not None: + if verbose: print("Using weights in {} for sequences in MSA.".format(MSA_weight_file_name)) + #assert os.path.exists(MSA_weight_file_name), "Weights file not located on disk." + #if threshold_focus_cols_frac_gaps!=1.0: print("threshold_focus_cols_frac_gaps not 1.0. Only well-covered positions are factored in the MSA retrieval aggregation.") + MSA_EVE = MSA_processing( + MSA_location=MSA_data_file, + use_weights=True, + threshold_sequence_frac_gaps=threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=1.0, #threshold_focus_cols_frac_gaps, # By default we always keep all columns in the rettrieved MSA + weights_location=MSA_weight_file_name + ) + #We scan through all sequences to see if we have a weight for them as per EVE pre-processing. We drop them otherwise. + dropped_sequences=0 + list_sequence_names = list(msa_data.keys()) + MSA_weight=[] + for sequence_name in list_sequence_names: + if sequence_name not in MSA_EVE.seq_name_to_sequence: + dropped_sequences +=1 + del msa_data[sequence_name] + else: + MSA_weight.append(MSA_EVE.seq_name_to_weight[sequence_name]) + if verbose: print("Dropped {} sequences from MSA due to absent sequence weights".format(dropped_sequences)) + else: + MSA_weight = [1] * len(list(msa_data.keys())) + + processed_MSA_depth = len(list(msa_data.keys())) + + if retrieval_aggregation_mode=="aggregate_substitution" or retrieval_aggregation_mode=="aggregate_indel": + one_hots = get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab) + MSA_weight = np.expand_dims(np.array(MSA_weight),axis=(1,2)) + base_rate = 1e-5 + base_rates = np.ones_like(one_hots) * base_rate + weighted_one_hots = (one_hots + base_rates) * MSA_weight + MSA_weight_norm_counts = weighted_one_hots.sum(axis=-1).sum(axis=0) + MSA_weight_norm_counts = np.tile(MSA_weight_norm_counts.reshape(-1,1), (1,vocab_size)) + one_hots_avg = weighted_one_hots.sum(axis=0) / MSA_weight_norm_counts + msa_prior = np.zeros((len_target_seq,vocab_size)) + msa_prior[MSA_start:MSA_end,:]=one_hots_avg + else: + msa_prior = np.ones((len_target_seq,vocab_size)) / vocab_size + + if verbose: + for idx, position in enumerate(msa_prior): + if len(position)!=25: + print("Size error") + if not round(position.sum(),2)==1.0: + print("Position at index {} does not add up to 1: {}".format(idx, position.sum())) + return msa_prior, processed_MSA_depth + + +def update_retrieved_MSA_log_prior_indel(model, MSA_log_prior, MSA_start, MSA_end, mutated_sequence, clustal_hash): + """ + Function to process MSA when scoring indels. + To identify positions to add / remove in the retrieved MSA, we append and align the sequence to be scored to the original MSA for that protein family with Clustal Omega. + If the original MSA is relatively deep (over 100k sequences), we sample (by default) 100k rows at random from that MSA to speed computations. + MSA sampling is performed only once (for the first sequence to be scored). Subsequent scoring use the same MSA sample. + """ + random_string = str(int(random.random()*10**10)) #Avoids conflicts if running several iterations concurrently and shared memory + model_id = '_'.join([str(x) for x in [model.inference_time_retrieval_type, random_string]]) + if not os.path.isdir(model.MSA_folder + os.sep + "Sampled"): + os.mkdir(model.MSA_folder + os.sep + "Sampled") + sampled_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Sampled_" + clustal_hash + "_" + model.MSA_filename.split(os.sep)[-1] + + if not os.path.exists(sampled_MSA_location): + msa_data = process_msa_data(model.MSA_filename) + msa_data_sampled = filter_msa(msa_data, num_sequences_kept=100000) #If MSA has less than 100k sequences, the sample is identical to original MSA + with open(sampled_MSA_location, 'w') as sampled_write_location: + for index, key in enumerate(msa_data_sampled): + key_name = ">REFERENCE_SEQUENCE" if index==0 else key + msa_data_sampled[key] = msa_data_sampled[key].upper() + msa_data_sampled[key] = msa_data_sampled[key].replace(".","-") + sampled_write_location.write(key_name+"\n"+"\n".join([msa_data_sampled[key][i:i+80] for i in range(0, len(msa_data_sampled[key]), 80)])+"\n") + + seq_to_align_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Seq_to_align_" + clustal_hash + "_" + model_id + '_' + model.MSA_filename.split(os.sep)[-1] + sequence_text_split = [mutated_sequence[i:i+80] for i in range(0, len(mutated_sequence), 80)] + sequence_text_split_split_join = "\n".join([">SEQ_TO_SCORE"]+sequence_text_split) + os.system("echo '"+sequence_text_split_split_join+"' > "+seq_to_align_location) + + expanded_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Expanded_" + clustal_hash + "_" + model_id + '_' + model.MSA_filename.split(os.sep)[-1] + clustalw_cline = ClustalOmegaCommandline(cmd=model.config.clustal_omega_location, + profile1=sampled_MSA_location, + profile2=seq_to_align_location, + outfile=expanded_MSA_location, + force=True) + stdout, stderr = clustalw_cline() + msa_data = process_msa_data(expanded_MSA_location) + aligned_seqA, aligned_seqB = msa_data[">SEQ_TO_SCORE"], msa_data[">REFERENCE_SEQUENCE"] + try: + keep_column=[] # Whether to keep the column (True) or not (False, ie. deletions) + new_column=[] # Whether to add one column (ie. insertions) + for column_index_pairwise_alignment in range(len(aligned_seqA)): + if aligned_seqA[column_index_pairwise_alignment]=="-" and aligned_seqB[column_index_pairwise_alignment]=="-": + continue + elif aligned_seqA[column_index_pairwise_alignment]=="-": + keep_column.append(False) + new_column.append(False) + elif aligned_seqB[column_index_pairwise_alignment]=="-": + MSA_log_prior=torch.cat((MSA_log_prior[:column_index_pairwise_alignment], torch.zeros(MSA_log_prior.shape[1]).view(1,-1).cuda(), MSA_log_prior[column_index_pairwise_alignment:]),dim=0) + keep_column.append(True) #keep the zero column we just added + new_column.append(True) + else: + keep_column.append(True) + new_column.append(False) + MSA_log_prior = MSA_log_prior[keep_column] + MSA_end = MSA_start + len(MSA_log_prior) + except: + print("Error when processing the following alignment: {}".format(expanded_MSA_location)) + #cleanups + os.system("rm -f "+seq_to_align_location) + os.system("rm -f "+expanded_MSA_location) + return MSA_log_prior, MSA_start, MSA_end, keep_column, new_column + +def update_weight_vector_indel(weight, keep_column, new_column): + new_weight = [] + num_insertions = 0 + for position_index in range(len(keep_column)): + if new_column[position_index]: + # if the position is newly added (insertions) we add a zero weight --> Predictions at inserted positions rely fully on the autoregressive transformer + new_weight.append(0) + num_insertions+=1 + elif keep_column[position_index]: + # otherwise, if the position is kept (not a deletion) we append the corresponding value from the weight tensor + weight_relative_index = position_index - num_insertions + new_weight.append(weight[weight_relative_index].item()) + return torch.tensor(new_weight).view(-1,1).cuda() + +class MSA_processing: + def __init__(self, + MSA_location="", + theta=0.2, + use_weights=True, + weights_location=None, + preprocess_MSA=True, + threshold_sequence_frac_gaps=0.5, + threshold_focus_cols_frac_gaps=0.3, + remove_sequences_with_indeterminate_AA_in_focus_cols=True + ): + + """ + This MSA_processing class is directly borrowed from the EVE codebase: https://github.com/OATML-Markslab/EVE + + Parameters: + - msa_location: (path) Location of the MSA data. Constraints on input MSA format: + - focus_sequence is the first one in the MSA data + - first line is structured as follows: ">focus_seq_name/start_pos-end_pos" (e.g., >SPIKE_SARS2/310-550) + - corespondding sequence data located on following line(s) + - then all other sequences follow with ">name" on first line, corresponding data on subsequent lines + - theta: (float) Sequence weighting hyperparameter. Generally: Prokaryotic and eukaryotic families = 0.2; Viruses = 0.01 + - use_weights: (bool) If False, sets all sequence weights to 1. If True, checks weights_location -- if non empty uses that; + otherwise compute weights from scratch and store them at weights_location + - weights_location: (path) Location to load from/save to the sequence weights + - preprocess_MSA: (bool) performs pre-processing of MSA to remove short fragments and positions that are not well covered. + - threshold_sequence_frac_gaps: (float, between 0 and 1) Threshold value to define fragments + - sequences with a fraction of gap characters above threshold_sequence_frac_gaps are removed + - default is set to 0.5 (i.e., fragments with 50% or more gaps are removed) + - threshold_focus_cols_frac_gaps: (float, between 0 and 1) Threshold value to define focus columns + - positions with a fraction of gap characters above threshold_focus_cols_pct_gaps will be set to lower case (and not included in the focus_cols) + - default is set to 0.3 (i.e., focus positions are the ones with 30% of gaps or less, i.e., 70% or more residue occupancy) + - remove_sequences_with_indeterminate_AA_in_focus_cols: (bool) Remove all sequences that have indeterminate AA (e.g., B, J, X, Z) at focus positions of the wild type + """ + np.random.seed(2021) + self.MSA_location = MSA_location + self.weights_location = weights_location + self.theta = theta + self.alphabet = "ACDEFGHIKLMNPQRSTVWY" + self.use_weights = use_weights + self.preprocess_MSA = preprocess_MSA + self.threshold_sequence_frac_gaps = threshold_sequence_frac_gaps + self.threshold_focus_cols_frac_gaps = threshold_focus_cols_frac_gaps + self.remove_sequences_with_indeterminate_AA_in_focus_cols = remove_sequences_with_indeterminate_AA_in_focus_cols + + self.gen_alignment() + + def gen_alignment(self, verbose=False): + """ Read training alignment and store basics in class instance """ + self.aa_dict = {} + for i,aa in enumerate(self.alphabet): + self.aa_dict[aa] = i + + self.seq_name_to_sequence = defaultdict(str) + name = "" + with open(self.MSA_location, "r") as msa_data: + for i, line in enumerate(msa_data): + line = line.rstrip() + if line.startswith(">"): + name = line + if i==0: + self.focus_seq_name = name + else: + self.seq_name_to_sequence[name] += line + + + ## MSA pre-processing to remove inadequate columns and sequences + if self.preprocess_MSA: + msa_df = pd.DataFrame.from_dict(self.seq_name_to_sequence, orient='index', columns=['sequence']) + # Data clean up + msa_df.sequence = msa_df.sequence.apply(lambda x: x.replace(".","-")).apply(lambda x: ''.join([aa.upper() for aa in x])) + # Remove columns that would be gaps in the wild type + non_gap_wt_cols = [aa!='-' for aa in msa_df.sequence[self.focus_seq_name]] + msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa for aa,non_gap_ind in zip(x, non_gap_wt_cols) if non_gap_ind])) + assert 0.0 <= self.threshold_sequence_frac_gaps <= 1.0,"Invalid fragment filtering parameter" + assert 0.0 <= self.threshold_focus_cols_frac_gaps <= 1.0,"Invalid focus position filtering parameter" + msa_array = np.array([list(seq) for seq in msa_df.sequence]) + gaps_array = np.array(list(map(lambda seq: [aa=='-' for aa in seq], msa_array))) + # Identify fragments with too many gaps + seq_gaps_frac = gaps_array.mean(axis=1) + seq_below_threshold = seq_gaps_frac <= self.threshold_sequence_frac_gaps + if verbose: print("Proportion of sequences dropped due to fraction of gaps: "+str(round(float(1 - seq_below_threshold.sum()/seq_below_threshold.shape)*100,2))+"%") + # Identify focus columns + columns_gaps_frac = gaps_array[seq_below_threshold].mean(axis=0) + index_cols_below_threshold = columns_gaps_frac <= self.threshold_focus_cols_frac_gaps + if verbose: print("Proportion of non-focus columns removed: "+str(round(float(1 - index_cols_below_threshold.sum()/index_cols_below_threshold.shape)*100,2))+"%") + # Lower case non focus cols and filter fragment sequences + msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa.upper() if upper_case_ind else aa.lower() for aa, upper_case_ind in zip(x, index_cols_below_threshold)])) + msa_df = msa_df[seq_below_threshold] + # Overwrite seq_name_to_sequence with clean version + self.seq_name_to_sequence = defaultdict(str) + for seq_idx in range(len(msa_df['sequence'])): + self.seq_name_to_sequence[msa_df.index[seq_idx]] = msa_df.sequence[seq_idx] + + self.focus_seq = self.seq_name_to_sequence[self.focus_seq_name] + self.focus_cols = [ix for ix, s in enumerate(self.focus_seq) if s == s.upper() and s!='-'] + self.non_focus_cols = [ix for ix in range(len(self.focus_seq)) if ix not in self.focus_cols] + self.focus_seq_trimmed = [self.focus_seq[ix] for ix in self.focus_cols] + self.seq_len = len(self.focus_cols) + self.alphabet_size = len(self.alphabet) + + # Connect local sequence index with uniprot index (index shift inferred from 1st row of MSA) + try: + focus_loc = self.focus_seq_name.split("/")[-1] + start,stop = focus_loc.split("-") + self.focus_start_loc = int(start) + self.focus_stop_loc = int(stop) + except: + start,stop = 1,len(self.focus_seq) + self.focus_start_loc = int(start) + self.focus_stop_loc = int(stop) + self.uniprot_focus_col_to_wt_aa_dict \ + = {idx_col+int(start):self.focus_seq[idx_col] for idx_col in self.focus_cols} + self.uniprot_focus_col_to_focus_idx \ + = {idx_col+int(start):idx_col for idx_col in self.focus_cols} + + # Move all letters to CAPS; keeps focus columns only + self.raw_seq_name_to_sequence = self.seq_name_to_sequence.copy() + for seq_name,sequence in self.seq_name_to_sequence.items(): + sequence = sequence.replace(".","-") + self.seq_name_to_sequence[seq_name] = [sequence[ix].upper() for ix in self.focus_cols] + + # Remove sequences that have indeterminate AA (e.g., B, J, X, Z) in the focus columns + if self.remove_sequences_with_indeterminate_AA_in_focus_cols: + alphabet_set = set(list(self.alphabet)) + seq_names_to_remove = [] + for seq_name,sequence in self.seq_name_to_sequence.items(): + for letter in sequence: + if letter not in alphabet_set and letter != "-": + seq_names_to_remove.append(seq_name) + continue + seq_names_to_remove = list(set(seq_names_to_remove)) + for seq_name in seq_names_to_remove: + del self.seq_name_to_sequence[seq_name] + + # Encode the sequences + self.one_hot_encoding = np.zeros((len(self.seq_name_to_sequence.keys()),len(self.focus_cols),len(self.alphabet))) + if verbose: print("One-hot encoded sequences shape:" + str(self.one_hot_encoding.shape)) + for i,seq_name in enumerate(self.seq_name_to_sequence.keys()): + sequence = self.seq_name_to_sequence[seq_name] + for j,letter in enumerate(sequence): + if letter in self.aa_dict: + k = self.aa_dict[letter] + self.one_hot_encoding[i,j,k] = 1.0 + + if self.use_weights: + if (self.weights_location is not None) and (not os.path.isfile(self.weights_location)): + print("Provided weights location is invalid") + sys.exit(0) + try: + self.weights = np.load(file=self.weights_location) + if verbose: print("Loaded sequence weights from disk") + except: + if verbose: print ("Computing sequence weights") + list_seq = self.one_hot_encoding + list_seq = list_seq.reshape((list_seq.shape[0], list_seq.shape[1] * list_seq.shape[2])) + def compute_weight(seq): + number_non_empty_positions = np.dot(seq,seq) + if number_non_empty_positions>0: + denom = np.dot(list_seq,seq) / np.dot(seq,seq) + denom = np.sum(denom > 1 - self.theta) + return 1/denom + else: + return 0.0 #return 0 weight if sequence is fully empty + self.weights = np.array(list(map(compute_weight,list_seq))) + np.save(file=self.weights_location, arr=self.weights) + else: + # If not using weights, use an isotropic weight matrix + if verbose: print("Not weighting sequence data") + self.weights = np.ones(self.one_hot_encoding.shape[0]) + + self.Neff = np.sum(self.weights) + self.num_sequences = self.one_hot_encoding.shape[0] + self.seq_name_to_weight={} + for i,seq_name in enumerate(self.seq_name_to_sequence.keys()): + self.seq_name_to_weight[seq_name]=self.weights[i] + + if verbose: + print ("Neff =",str(self.Neff)) + print ("Data Shape =",self.one_hot_encoding.shape) \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/scoring_utils.py b/proteingym/baselines/trancepteve/trancepteve/utils/scoring_utils.py new file mode 100644 index 0000000..6d084da --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/scoring_utils.py @@ -0,0 +1,224 @@ +import os +import tqdm +import re +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss, NLLLoss +from torch.utils.data.sampler import Sampler, SequentialSampler +import torch.nn.functional as F + +from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizerFast +from datasets import Dataset,logging +logging.set_verbosity_error() + +AA_vocab = "ACDEFGHIKLMNPQRSTVWY" + +def entropy(x, ignore_tokenizer_characters=True): + """ + Compute entropy over the last dimension of tensor x (assumes it is a log softmax input) + """ + exp_x = torch.exp(x.float()) + if ignore_tokenizer_characters: + entropy = (- exp_x[...,5:]*x[...,5:]).mean(dim=-1) + else: + entropy = (- exp_x*x).mean(dim=-1) + return entropy + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab=AA_vocab): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def nanmean(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs) + +def nansum(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) + +def get_optimal_window(mutation_position_relative, seq_len_wo_special, model_window): + """ + Helper function that selects an optimal sequence window that fits the maximum model context size. + If the sequence length is less than the maximum context size, the full sequence is returned. + """ + half_model_window = model_window // 2 + if seq_len_wo_special <= model_window: + return [0,seq_len_wo_special] + elif mutation_position_relative < half_model_window: + return [0,model_window] + elif mutation_position_relative >= seq_len_wo_special - half_model_window: + return [seq_len_wo_special - model_window, seq_len_wo_special] + else: + return [max(0,mutation_position_relative-half_model_window), min(seq_len_wo_special,mutation_position_relative+half_model_window)] + +def sequence_replace_single(sequence, char_to_replace, char_replacements): + char_replacements = list(char_replacements) + positions = [m.start() for m in re.finditer(char_to_replace, sequence)] + replacements = np.random.choice(a=char_replacements, size=len(positions), replace=True) + sequence=list(sequence) + for idx, position in enumerate(positions): + sequence[position]=replacements[idx] + return ''.join(sequence) + +def sequence_replace(sequences, char_to_replace, char_replacements): + """ + Helper function that replaces all Amino Acids passsed in via char_to_replace (as a string of AAs) with Amino Acids sampled from char_replacements (also a string of eligible AAs). + """ + return [sequence_replace_single(sequence, char_to_replace, char_replacements) for sequence in sequences] + +def get_tranception_scores_mutated_sequences(model, mutated_sequence_df, batch_size_inference, score_var_name, target_seq, num_workers=10, reverse=False, indel_mode=False, scoring_window=None): + """ + Helper function that takes as input a set of mutated sequences (in a pandas dataframe) and returns scores for each mutation. + If target_seq is not None, returns the delta log likelihood wrt that target sequence -- otherwise returns the log likelihood of the protein sequences. + """ + scoring_window = scoring_window if scoring_window is not None else model.config.scoring_window + model.eval() #Enforce eval mode just in case + scores = {} + scores['mutated_sequence']=[] + scores['sliced_mutated_sequence']=[] + scores['window_start']=[] + scores['window_end']=[] + scores['score']=[] + with torch.no_grad(): + ds = Dataset.from_pandas(mutated_sequence_df) + ds.set_transform(model.encode_batch) + data_collator = DataCollatorForLanguageModeling( + tokenizer=model.config.tokenizer, + mlm=False) + sampler = SequentialSampler(ds) + ds_loader = torch.utils.data.DataLoader(ds, batch_size=batch_size_inference, sampler=sampler, collate_fn=data_collator, num_workers=num_workers, pin_memory=True, drop_last=False) + mutant_index=0 + for encoded_batch in tqdm.tqdm(ds_loader): + full_batch_length = len(encoded_batch['input_ids']) + mutated_sequence = np.array(mutated_sequence_df['mutated_sequence'][mutant_index:mutant_index+full_batch_length]) + scores['mutated_sequence'] += list(mutated_sequence) + sliced_mutated_sequence = np.array(mutated_sequence_df['sliced_mutated_sequence'][mutant_index:mutant_index+full_batch_length]) + scores['sliced_mutated_sequence'] += list(sliced_mutated_sequence) + window_start = np.array(mutated_sequence_df['window_start'][mutant_index:mutant_index+full_batch_length]) + scores['window_start'] += list(window_start) + window_end = np.array(mutated_sequence_df['window_end'][mutant_index:mutant_index+full_batch_length]) + scores['window_end'] += list(window_end) + for k, v in encoded_batch.items(): + if isinstance(v, torch.Tensor): + encoded_batch[k] = v.to(model.device) + shift_labels = encoded_batch['labels'][..., 1:].contiguous() + if (hasattr(model.config,"retrieval_aggregation_mode")) and (model.config.retrieval_aggregation_mode is not None): + if reverse: + encoded_batch['flip']=torch.tensor([1]*full_batch_length) + encoded_batch['start_slice']=window_start + encoded_batch['end_slice']=window_end + encoded_batch['mutated_sequence'] = mutated_sequence #only mutated_sequence is flipped if the scoring_mirror branch of score_mutants. No need to flip mutated_sequence for MSA re-aligning + encoded_batch['sliced_mutated_sequence'] = sliced_mutated_sequence + fused_shift_log_probas=model(**encoded_batch,return_dict=True).fused_shift_log_probas + loss_fct = NLLLoss(reduction='none') + loss = - loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1]) + else: + lm_logits=model(**encoded_batch,return_dict=True).logits + shift_logits = lm_logits[..., :-1, :].contiguous() + loss_fct = NLLLoss(reduction='none') + shift_log_probas = torch.log_softmax(shift_logits, dim=-1) + loss = - loss_fct(input=shift_log_probas.view(-1, shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(shift_log_probas.shape[0],shift_log_probas.shape[1]) + mask = encoded_batch['attention_mask'][..., 1:].float() + mask[mask==0]=float('nan') + loss *= mask + loss = nansum(loss, dim=1) + scores_batch = list(loss.cpu().numpy()) + full_batch_length = len(encoded_batch['input_ids']) + scores['score'] += scores_batch + mutant_index+=full_batch_length + scores = pd.DataFrame(scores) + if scoring_window=="sliding": + scores = scores[['mutated_sequence','score']].groupby('mutated_sequence').sum().reset_index() #We need to aggregate scores when using sliding mode + #Normalization by sequence length + scores['score'] = scores['score'] / scores['mutated_sequence'].map(lambda x: len(x)) + if target_seq is not None: + scores_mutated_seq = scores[scores.mutated_sequence != target_seq] + scores_wt = scores[scores.mutated_sequence == target_seq] + merge_delta = 'mutated_sequence' if scoring_window=="sliding" else 'window_start' + if scoring_window=="optimal": + delta_scores = pd.merge(scores_mutated_seq,scores_wt,how='left',on=[merge_delta],suffixes=('','_wt')) + delta_scores[score_var_name] = delta_scores['score'] - delta_scores['score_wt'] + elif scoring_window=="sliding": + delta_scores = scores_mutated_seq.copy() + delta_scores[score_var_name] = delta_scores['score'] - list(scores_wt['score'])[0] # In sliding mode there is a single reference window for the WT + elif scoring_window=="raw_score": + delta_scores = scores_mutated_seq + delta_scores[score_var_name] = delta_scores['score'] + return delta_scores[['mutated_sequence',score_var_name]] + else: + scores[score_var_name] = scores['score'] + return scores[['mutated_sequence',score_var_name]] + +def get_sequence_slices(df, target_seq, model_context_len, start_idx=1, scoring_window="optimal", indel_mode=False): + """ + Helper function that takes as input a (pandas) dataframe df that contains a list of mutant triplets (substitutions) or full mutated sequences (indels) for scoring. + It returns a processed DMS in which sequences have been sliced to satisfy the maximum context window of the model. + df: (dataframe) Input dataframe to be processed + target_seq: (string) Full reference sequence (wild type) that is mutated in the DMS assay. + model_context_len: (int) Maximum context size for the model. + start_idx: (int) Integer to move to 0-indexing of positions (mutation triplet are typically based on 1-indexing). + scoring_window: (string) Method to slice sequences longer than maximum context size: + - optimal selects a single window as large as possible via the get_optimal_window function (this is the default) + - sliding splits the full sequence in contiguous (non-overlapping) chunks that are of size equal to the max context (except the last chunk which may be shorter) + indel_mode: (bool) Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions. + Note: when scoring indels for sequences that would be longer than the model max context length, it is preferable to use the "sliding" scoring_window. Use "optimal" otherwise. + """ + len_target_seq = len(target_seq) + num_mutants = len(df['mutated_sequence']) + df=df.reset_index(drop=True) + if scoring_window=="optimal" or scoring_window=="raw_score": + df['mutation_barycenter'] = df['mutant'].apply(lambda x: int(np.array([int(mutation[1:-1]) - start_idx for mutation in x.split(':')]).mean())) if not indel_mode else df['mutated_sequence'].apply(lambda x: len(x)//2) + df['scoring_optimal_window'] = df['mutation_barycenter'].apply(lambda x: get_optimal_window(x, len_target_seq, model_context_len)) if not indel_mode else df['mutated_sequence'].apply(lambda x: (0,len(x))) + df['sliced_mutated_sequence'] = [df['mutated_sequence'][index][df['scoring_optimal_window'][index][0]:df['scoring_optimal_window'][index][1]] for index in range(num_mutants)] + df['window_start'] = df['scoring_optimal_window'].map(lambda x: x[0]) + df['window_end'] = df['scoring_optimal_window'].map(lambda x: x[1]) + del df['scoring_optimal_window'], df['mutation_barycenter'] + if 'mutant' in df: del df['mutant'] + df_wt=df.copy() + df_wt['mutated_sequence'] = [target_seq] * num_mutants + if indel_mode: # For indels, we set the wild type reference to be always the same (full length) sequence. We assume here that the length is lower than model context size (otherwise "Sliding" mode should be used) + df_wt['window_end'] = df_wt['mutated_sequence'].map(lambda x:len(x)) + df_wt['sliced_mutated_sequence'] = [target_seq[df_wt['window_start'][index]:df_wt['window_end'][index]] for index in range(num_mutants)] + df = pd.concat([df,df_wt], axis=0) + df = df.drop_duplicates() + elif scoring_window=="sliding": + num_windows = 1 + int( len_target_seq / model_context_len) + df_list=[] + start=0 + for window_index in range(1, num_windows+1): + df_sliced = df.copy() + df_sliced['sliced_mutated_sequence'] = df_sliced['mutated_sequence'].map(lambda x: x[start:start+model_context_len]) + df_sliced['window_start'] = [start] * num_mutants + df_sliced['window_end'] = df_sliced['mutated_sequence'].map(lambda x: min(len(x), start+model_context_len)) + df_sliced_wt = df_sliced.copy() + df_sliced_wt['mutated_sequence'] = [target_seq] * num_mutants + df_sliced_wt['sliced_mutated_sequence'] = df_sliced_wt['mutated_sequence'].map(lambda x: x[start:start+model_context_len]) + df_sliced_wt['window_end'] = df_sliced_wt['mutated_sequence'].map(lambda x: min(len(x), start+model_context_len)) #Need to adjust end index if WT and sequence are not same full length + df_list.append(df_sliced) + df_list.append(df_sliced_wt) + start += model_context_len + df_final = pd.concat(df_list,axis=0) + if 'mutant' in df_final: del df_final['mutant'] + df = df_final.drop_duplicates() + return df.reset_index(drop=True) \ No newline at end of file diff --git a/proteingym/baselines/trancepteve/trancepteve/utils/tokenizers/Basic_tokenizer b/proteingym/baselines/trancepteve/trancepteve/utils/tokenizers/Basic_tokenizer new file mode 100644 index 0000000..b6af745 --- /dev/null +++ b/proteingym/baselines/trancepteve/trancepteve/utils/tokenizers/Basic_tokenizer @@ -0,0 +1 @@ +{"version":"1.0","truncation":null,"padding":null,"added_tokens":[{"id":0,"special":true,"content":"[UNK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":1,"special":true,"content":"[CLS]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":2,"special":true,"content":"[SEP]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":3,"special":true,"content":"[PAD]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":4,"special":true,"content":"[MASK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false}],"normalizer":null,"pre_tokenizer":{"type":"Whitespace"},"post_processor":{"type":"TemplateProcessing","single":[{"SpecialToken":{"id":"[CLS]","type_id":0}},{"Sequence":{"id":"A","type_id":0}},{"SpecialToken":{"id":"[SEP]","type_id":0}}],"pair":[{"SpecialToken":{"id":"[CLS]","type_id":0}},{"Sequence":{"id":"A","type_id":0}},{"SpecialToken":{"id":"[SEP]","type_id":0}},{"Sequence":{"id":"B","type_id":1}},{"SpecialToken":{"id":"[SEP]","type_id":1}}],"special_tokens":{"[CLS]":{"id":"[CLS]","ids":[1],"tokens":["[CLS]"]},"[SEP]":{"id":"[SEP]","ids":[2],"tokens":["[SEP]"]}}},"decoder":null,"model":{"type":"BPE","dropout":null,"unk_token":"[UNK]","continuing_subword_prefix":null,"end_of_word_suffix":null,"fuse_unk":false,"vocab":{"[UNK]":0,"[CLS]":1,"[SEP]":2,"[PAD]":3,"[MASK]":4,"A":5,"C":6,"D":7,"E":8,"F":9,"G":10,"H":11,"I":12,"K":13,"L":14,"M":15,"N":16,"P":17,"Q":18,"R":19,"S":20,"T":21,"V":22,"W":23,"Y":24},"merges":[]}} \ No newline at end of file diff --git a/proteingym/baselines/tranception/__init__.py b/proteingym/baselines/tranception/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/proteingym/baselines/tranception/score_tranception_proteingym.py b/proteingym/baselines/tranception/score_tranception_proteingym.py new file mode 100644 index 0000000..6a7a8e5 --- /dev/null +++ b/proteingym/baselines/tranception/score_tranception_proteingym.py @@ -0,0 +1,125 @@ +import os +import argparse +import json +import pandas as pd + +import torch + +from transformers import PreTrainedTokenizerFast +import tranception +from tranception import config, model_pytorch + +dir_path = os.path.dirname(os.path.abspath(__file__)) + +def main(): + """ + Main script to score sets of mutated protein sequences (substitutions or indels) with Tranception. + """ + parser = argparse.ArgumentParser(description='Tranception scoring') + parser.add_argument('--checkpoint', type=str, help='Path of Tranception model checkpoint') + parser.add_argument('--model_framework', default='pytorch', type=str, help='Underlying framework [pytorch|JAX]') + parser.add_argument('--batch_size_inference', default=20, type=int, help='Batch size for inference') + + #We may pass in all required information about the DMS via the provided reference files, or specify all relevant fields manually + parser.add_argument('--DMS_reference_file_path', default=None, type=str, help='Path to reference file with list of DMS to score') + parser.add_argument('--DMS_index', default=0, type=int, help='Index of DMS assay in reference file') + #Fields to be passed manually if reference file is not used + parser.add_argument('--target_seq', default=None, type=str, help='Full wild type sequence that is mutated in the DMS asssay') + parser.add_argument('--DMS_file_name', default=None, type=str, help='Name of DMS assay file') + parser.add_argument('--MSA_filename', default=None, type=str, help='Name of MSA (eg., a2m) file constructed on the wild type sequence') + parser.add_argument('--MSA_weight_file_name', default=None, type=str, help='Weight of sequences in the MSA (optional)') + parser.add_argument('--MSA_start', default=None, type=int, help='Sequence position that the MSA starts at (1-indexing)') + parser.add_argument('--MSA_end', default=None, type=int, help='Sequence position that the MSA ends at (1-indexing)') + + parser.add_argument('--DMS_data_folder', type=str, help='Path to folder that contains all DMS assay datasets') + parser.add_argument('--output_scores_folder', default='./', type=str, help='Name of folder to write model scores to') + parser.add_argument('--deactivate_scoring_mirror', action='store_true', help='Whether to deactivate sequence scoring from both directions (Left->Right and Right->Left)') + parser.add_argument('--indel_mode', action='store_true', help='Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions') + parser.add_argument('--scoring_window', default="optimal", type=str, help='Sequence window selection mode (when sequence length longer than model context size)') + parser.add_argument('--num_workers', default=10, type=int, help='Number of workers for model scoring data loader') + parser.add_argument('--inference_time_retrieval', action='store_true', help='Whether to perform inference-time retrieval') + parser.add_argument('--retrieval_inference_weight', default=0.6, type=float, help='Coefficient (alpha) used when aggregating autoregressive transformer and retrieval') + parser.add_argument('--MSA_folder', default='.', type=str, help='Path to MSA for neighborhood scoring') + parser.add_argument('--MSA_weights_folder', default=None, type=str, help='Path to MSA weights for neighborhood scoring') + parser.add_argument('--clustal_omega_location', default=None, type=str, help='Path to Clustal Omega (only needed with scoring indels with retrieval)') + args = parser.parse_args() + + model_name = args.checkpoint.split("/")[-1] + + tokenizer = PreTrainedTokenizerFast(tokenizer_file=dir_path+os.sep+"tranception/utils/tokenizers/Basic_tokenizer", + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]" + ) + + if args.DMS_reference_file_path: + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + list_DMS = mapping_protein_seq_DMS["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Compute scores for DMS: "+str(DMS_id)) + target_seq = mapping_protein_seq_DMS["target_seq"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0].upper() + DMS_file_name = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + if args.inference_time_retrieval: + MSA_data_file = args.MSA_folder + os.sep + mapping_protein_seq_DMS["MSA_filename"][args.DMS_index] if args.MSA_folder is not None else None + MSA_weight_file_name = args.MSA_weights_folder + os.sep + mapping_protein_seq_DMS["weight_file_name"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] if args.MSA_weights_folder else None + MSA_start = int(mapping_protein_seq_DMS["MSA_start"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) - 1 # MSA_start typically based on 1-indexing + MSA_end = int(mapping_protein_seq_DMS["MSA_end"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0]) + else: + target_seq=args.target_seq + DMS_file_name=args.DMS_file_name + DMS_id = DMS_file_name.split(".")[0] + if args.inference_time_retrieval: + MSA_data_file = args.MSA_folder + os.sep + args.MSA_filename if args.MSA_folder is not None else None + MSA_weight_file_name = args.MSA_weights_folder + os.sep + args.MSA_weight_file_name if args.MSA_weights_folder is not None else None + MSA_start = args.MSA_start - 1 # MSA_start based on 1-indexing + MSA_end = args.MSA_end + + config = json.load(open(args.checkpoint+os.sep+'config.json')) + config = tranception.config.TranceptionConfig(**config) + config.attention_mode="tranception" + config.position_embedding="grouped_alibi" + config.tokenizer = tokenizer + config.scoring_window = args.scoring_window + + if args.inference_time_retrieval: + config.retrieval_aggregation_mode = "aggregate_indel" if args.indel_mode else "aggregate_substitution" + config.MSA_filename=MSA_data_file + config.full_protein_length=len(target_seq) + config.MSA_weight_file_name=MSA_weight_file_name + config.retrieval_inference_weight=args.retrieval_inference_weight + config.MSA_start = MSA_start + config.MSA_end = MSA_end + if args.indel_mode: + config.clustal_omega_location = args.clustal_omega_location + else: + config.retrieval_aggregation_mode = None + + if args.model_framework=="pytorch": + model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path=args.checkpoint,config=config) + if torch.cuda.is_available(): + model.cuda() + model.eval() + + if not os.path.isdir(args.output_scores_folder): + os.mkdir(args.output_scores_folder) + retrieval_type = '_retrieval_' + str(args.retrieval_inference_weight) if args.inference_time_retrieval else '_no_retrieval' + mutation_type = '_indels' if args.indel_mode else '_substitutions' + mirror_type = '_no_mirror' if args.deactivate_scoring_mirror else '' + scoring_filename = args.output_scores_folder + os.sep + DMS_id + ".csv" + + + DMS_data = pd.read_csv(args.DMS_data_folder + os.sep + DMS_file_name, low_memory=False) + all_scores = model.score_mutants( + DMS_data=DMS_data, + target_seq=target_seq, + scoring_mirror=not args.deactivate_scoring_mirror, + batch_size_inference=args.batch_size_inference, + num_workers=args.num_workers, + indel_mode=args.indel_mode + ) + all_scores.to_csv(scoring_filename, index=False) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/__init__.py b/proteingym/baselines/tranception/tranception/__init__.py new file mode 100644 index 0000000..d782e9b --- /dev/null +++ b/proteingym/baselines/tranception/tranception/__init__.py @@ -0,0 +1 @@ +from . import config \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/activations.py b/proteingym/baselines/tranception/tranception/activations.py new file mode 100644 index 0000000..25702ef --- /dev/null +++ b/proteingym/baselines/tranception/tranception/activations.py @@ -0,0 +1,114 @@ +import math + +import torch +from packaging import version +from torch import nn + +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +def _gelu_python(x): + """ + Original Implementation of the GELU activation function in Google BERT repo when initially created. For + information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional + Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) + + +def gelu_new(x): + """ + Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see + the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) + + +if version.parse(torch.__version__) < version.parse("1.4"): + gelu = _gelu_python +else: + gelu = nn.functional.gelu + + +def gelu_fast(x): + return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))) + + +def quick_gelu(x): + return x * torch.sigmoid(1.702 * x) + + +def _silu_python(x): + """ + See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear + Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function + Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated + Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with + later. + """ + return x * torch.sigmoid(x) + + +if version.parse(torch.__version__) < version.parse("1.7"): + silu = _silu_python +else: + silu = nn.functional.silu + + +def _mish_python(x): + """ + See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also + visit the official repository for the paper: https://github.com/digantamisra98/Mish + """ + return x * torch.tanh(nn.functional.softplus(x)) + + +if version.parse(torch.__version__) < version.parse("1.9"): + mish = _mish_python +else: + mish = nn.functional.mish + + +def linear_act(x): + return x + +def squared_relu(x): + """ + Squared ReLU variant that is fastest with Pytorch. + """ + x = nn.functional.relu(x) + return x*x + +def squared_relu_xla(x): + """ + Squared ReLU variant that is fastest with JAX. + """ + x = nn.functional.relu(x) + return x**2 + +tranception_ACT2FN = { + "relu": nn.functional.relu, + "silu": silu, + "swish": silu, + "gelu": gelu, + "tanh": torch.tanh, + "gelu_new": gelu_new, + "gelu_fast": gelu_fast, + "quick_gelu": quick_gelu, + "mish": mish, + "linear": linear_act, + "sigmoid": torch.sigmoid, + "squared_relu": squared_relu, + "squared_relu_xla": squared_relu_xla, +} + + +def get_activation(activation_string): + if activation_string in tranception_ACT2FN: + return tranception_ACT2FN[activation_string] + else: + raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(tranception_ACT2FN.keys())}") \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/config.py b/proteingym/baselines/tranception/tranception/config.py new file mode 100644 index 0000000..1b35cb0 --- /dev/null +++ b/proteingym/baselines/tranception/tranception/config.py @@ -0,0 +1,36 @@ +from transformers import GPT2Config + +class TranceptionConfig(GPT2Config): + """ + Config subclass for Tranception model architecture. + """ + def __init__( + self, + attention_mode="tranception", + position_embedding="grouped_alibi", + tokenizer=None, + retrieval_aggregation_mode=None, + retrieval_inference_weight=0.6, + MSA_filename=None, + MSA_weight_file_name=None, + MSA_start=None, + MSA_end=None, + full_protein_length=None, + clustal_omega_location=None, + scoring_window=None, + **kwargs + ): + super().__init__(**kwargs) + self.model_type="tranception" + self.attention_mode=attention_mode + self.position_embedding=position_embedding + self.tokenizer = tokenizer + self.retrieval_aggregation_mode = retrieval_aggregation_mode + self.retrieval_inference_weight = retrieval_inference_weight + self.MSA_filename = MSA_filename + self.MSA_weight_file_name = MSA_weight_file_name + self.MSA_start=MSA_start + self.MSA_end=MSA_end + self.full_protein_length = full_protein_length + self.clustal_omega_location = clustal_omega_location + self.scoring_window=scoring_window \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/model_pytorch.py b/proteingym/baselines/tranception/tranception/model_pytorch.py new file mode 100644 index 0000000..e9f8ff2 --- /dev/null +++ b/proteingym/baselines/tranception/tranception/model_pytorch.py @@ -0,0 +1,939 @@ +from dataclasses import dataclass +from typing import Optional, Tuple +import math +import os +import pandas as pd +import uuid +import torch +from torch import nn +from torch.nn import CrossEntropyLoss, NLLLoss +import torch.nn.functional as F +from transformers import GPT2PreTrainedModel + +from transformers.modeling_utils import ( + Conv1D, + PreTrainedModel, + SequenceSummary, + find_pruneable_heads_and_indices, + prune_conv1d_layer, +) +from transformers.file_utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + SequenceClassifierOutputWithPast, + TokenClassifierOutput +) +from transformers.utils.model_parallel_utils import assert_device_map, get_device_map + +from tranception.activations import tranception_ACT2FN +from tranception.config import TranceptionConfig +from tranception.outputs import ( + TranceptionCausalLMOutputWithCrossAttentions, +) +from tranception.utils import msa_utils +from tranception.utils import scoring_utils + +def nanmean(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs) + +def get_slopes(n, mode="standard_alibi", verbose=False): + """ + Function to compute the m constant for each attention head. Code has been adapted from the official ALiBi codebase at: + https://github.com/ofirpress/attention_with_linear_biases/blob/master/fairseq/models/transformer.py + """ + def get_slopes_power_of_2(n): + start = (2**(-2**-(math.log2(n)-3))) + ratio = start + return [start*ratio**i for i in range(n)] + if mode=="grouped_alibi": + n = n // 4 + if math.log2(n).is_integer(): + result = get_slopes_power_of_2(n) + else: + #Workaround when the number of heads is not a power of 2 + closest_power_of_2 = 2**math.floor(math.log2(n)) + result = get_slopes_power_of_2(closest_power_of_2) + get_slopes(2*closest_power_of_2)[0::2][:n-closest_power_of_2] + if mode=="grouped_alibi": + result = result * 4 + if verbose: + print("ALiBi slopes: {}".format(result)) + return result + +class SpatialDepthWiseConvolution(nn.Module): + def __init__(self, head_dim: int, kernel_size: int = 3): + super().__init__() + self.kernel_size = kernel_size + self.conv = nn.Conv1d(in_channels=head_dim, out_channels=head_dim, kernel_size=(kernel_size,), padding=(kernel_size - 1,), groups=head_dim) + + def forward(self, x: torch.Tensor): + batch_size, heads, seq_len, head_dim = x.shape + x = x.permute(0, 1, 3, 2).contiguous() + x = x.view(batch_size * heads, head_dim, seq_len) + x = self.conv(x) + if self.kernel_size>1: + x = x[:, :, :-(self.kernel_size - 1)] + x = x.view(batch_size, heads, head_dim, seq_len) + x = x.permute(0, 1, 3, 2) + return x + +class TranceptionBlockAttention(nn.Module): + def __init__(self, config, is_cross_attention=False, SDWC_kernel_size=None): + super().__init__() + + max_positions = config.max_position_embeddings + self.register_buffer( + "bias", + torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view( + 1, 1, max_positions, max_positions + ), + ) + self.register_buffer("masked_bias", torch.tensor(-1e4)) + + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.split_size = self.embed_dim + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + ) + + self.scale_attn_weights = config.scale_attn_weights + self.is_cross_attention = is_cross_attention + + if self.is_cross_attention: + self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) + self.q_attn = Conv1D(self.embed_dim, self.embed_dim) + else: + self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim) + self.c_proj = Conv1D(self.embed_dim, self.embed_dim) + + self.attn_dropout = nn.Dropout(config.attn_pdrop) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + self.pruned_heads = set() + + self.attention_mode=config.attention_mode + + if self.attention_mode=="tranception": + assert self.num_heads%4==0, "Invalid number of heads. Tranception requires the number of heads to be a multiple of 4." + self.num_heads_per_kernel_size = self.num_heads // 4 + self.query_depthwiseconv = nn.ModuleDict() + self.key_depthwiseconv = nn.ModuleDict() + self.value_depthwiseconv = nn.ModuleDict() + for kernel_idx, kernel in enumerate([3,5,7]): + self.query_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + self.key_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + self.value_depthwiseconv[str(kernel_idx)] = SpatialDepthWiseConvolution(self.head_dim,kernel) + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) + index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) + + # Prune conv1d layers + self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) + self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) + + # Update hyper params + self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) + self.num_heads = self.num_heads - len(heads) + self.pruned_heads = self.pruned_heads.union(heads) + + def _attn(self, query, key, value, attention_mask=None, head_mask=None, alibi_bias=None): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) + + if self.scale_attn_weights: + attn_weights = attn_weights / (float(value.size(-1)) ** 0.5) + + if not self.is_cross_attention: + # if only "normal" attention layer implements causal mask + query_length, key_length = query.size(-2), key.size(-2) + causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool() + attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)) + + if alibi_bias is not None: + attn_weights = attn_weights + alibi_bias[:,:,:attn_weights.size(-1)] + + if attention_mask is not None: + # Apply the attention mask + attn_weights = attn_weights + attention_mask + + attn_weights = nn.Softmax(dim=-1)(attn_weights) + attn_weights = self.attn_dropout(attn_weights) + + # Mask heads if we want to + if head_mask is not None: + attn_weights = attn_weights * head_mask + + attn_output = torch.matmul(attn_weights, value) + + return attn_output, attn_weights + + def _split_heads(self, tensor, num_heads, attn_head_size): + """ + Splits hidden_size dim into attn_head_size and num_heads + """ + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(*new_shape) + return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) + + def _merge_heads(self, tensor, num_heads, attn_head_size): + """ + Merges attn_head_size dim and num_attn_heads dim into hidden_size + """ + tensor = tensor.permute(0, 2, 1, 3).contiguous() + new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) + return tensor.view(new_shape) + + def forward( + self, + hidden_states, + layer_past=None, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=False, + output_attentions=False, + alibi_bias=None, + ): + if encoder_hidden_states is not None: + if not hasattr(self, "q_attn"): + raise ValueError( + "If class is used as cross attention, the weights `q_attn` have to be defined. " + "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." + ) + + query = self.q_attn(hidden_states) + key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) + attention_mask = encoder_attention_mask + else: + query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) + + query = self._split_heads(query, self.num_heads, self.head_dim) + key = self._split_heads(key, self.num_heads, self.head_dim) + value = self._split_heads(value, self.num_heads, self.head_dim) + + if layer_past is not None: + past_key, past_value = layer_past + key = torch.cat((past_key, key), dim=-2) + value = torch.cat((past_value, value), dim=-2) + + if use_cache is True: + present = (key, value) + else: + present = None + + if self.attention_mode=="tranception": + # We do not do anything on the first self.num_heads_per_kernel_size heads (kernel =1) + query_list=[query[:,:self.num_heads_per_kernel_size,:,:]] + key_list=[key[:,:self.num_heads_per_kernel_size,:,:]] + value_list=[value[:,:self.num_heads_per_kernel_size,:,:]] + for kernel_idx in range(3): + query_list.append(self.query_depthwiseconv[str(kernel_idx)](query[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + key_list.append(self.key_depthwiseconv[str(kernel_idx)](key[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + value_list.append(self.value_depthwiseconv[str(kernel_idx)](value[:,(kernel_idx+1)*self.num_heads_per_kernel_size:(kernel_idx+2)*self.num_heads_per_kernel_size,:,:])) + query=torch.cat(query_list, dim=1) + key=torch.cat(key_list, dim=1) + value=torch.cat(value_list, dim=1) + + attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask, alibi_bias=alibi_bias) + + attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) + attn_output = self.c_proj(attn_output) + attn_output = self.resid_dropout(attn_output) + + outputs = (attn_output, present) + if output_attentions: + outputs += (attn_weights,) + + return outputs # a, present, (attentions) + +class TranceptionBlockMLP(nn.Module): + def __init__(self, intermediate_size, config): + super().__init__() + embed_dim = config.hidden_size + self.c_fc = Conv1D(intermediate_size, embed_dim) + self.c_proj = Conv1D(embed_dim, intermediate_size) + self.act = tranception_ACT2FN[config.activation_function] + self.dropout = nn.Dropout(config.resid_pdrop) + + def forward(self, hidden_states): + hidden_states = self.c_fc(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.c_proj(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + +class TranceptionBlock(nn.Module): + def __init__(self, config, SDWC_kernel_size=None): + super().__init__() + hidden_size = config.hidden_size + inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size + + self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + self.attn = TranceptionBlockAttention(config, SDWC_kernel_size=SDWC_kernel_size) + self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + + if config.add_cross_attention: + self.crossattention = TranceptionBlockAttention(config, is_cross_attention=True, SDWC_kernel_size=SDWC_kernel_size) + self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + + self.mlp = TranceptionBlockMLP(inner_dim, config) + + def forward( + self, + hidden_states, + layer_past=None, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=False, + output_attentions=False, + alibi_bias=None, + ): + residual = hidden_states + hidden_states = self.ln_1(hidden_states) + attn_outputs = self.attn( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + alibi_bias=alibi_bias, + ) + attn_output = attn_outputs[0] # output_attn: a, present, (attentions) + outputs = attn_outputs[1:] + # residual connection + hidden_states = attn_output + residual + + if encoder_hidden_states is not None: + # add one self-attention block for cross-attention + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with " + "cross-attention layers by setting `config.add_cross_attention=True`" + ) + residual = hidden_states + hidden_states = self.ln_cross_attn(hidden_states) + cross_attn_outputs = self.crossattention( + hidden_states, + attention_mask=attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + ) + attn_output = cross_attn_outputs[0] + # residual connection + hidden_states = residual + attn_output + outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights + + residual = hidden_states + hidden_states = self.ln_2(hidden_states) + + feed_forward_hidden_states = self.mlp(hidden_states) + + # residual connection + hidden_states = residual + feed_forward_hidden_states + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs # hidden_states, present, (attentions, cross_attentions) + +class TranceptionModel(GPT2PreTrainedModel): + _keys_to_ignore_on_load_missing = ["attn.masked_bias"] + def __init__(self, config): + super().__init__(config) + + self.embed_dim = config.hidden_size + self.wte = nn.Embedding(config.vocab_size, self.embed_dim) + self.position_embedding = config.position_embedding if hasattr(config, "position_embedding") else "learned" + if self.position_embedding=="learned": + self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) + self.alibi = None + elif self.position_embedding=="grouped_alibi": + maxpos = config.n_positions + attn_heads = config.n_head + self.slopes = torch.Tensor(get_slopes(attn_heads, mode=self.position_embedding)) + #The softmax operation is invariant to translation, and bias functions used are always linear. + alibi = self.slopes.unsqueeze(1).unsqueeze(1) * torch.arange(maxpos).unsqueeze(0).unsqueeze(0).expand(attn_heads, -1, -1) + alibi = alibi.view(attn_heads, 1, maxpos) + self.register_buffer('alibi',alibi) + + self.drop = nn.Dropout(config.embd_pdrop) + self.h = nn.ModuleList([TranceptionBlock(config) for _ in range(config.num_hidden_layers)]) + self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) + + self.init_weights() + + # Model parallel + self.model_parallel = False + self.device_map = None + self.gradient_checkpointing = False + + def parallelize(self, device_map=None, num_cores=None): + self.device_map = ( + get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map + ) + device_prefix="cuda:" + assert_device_map(self.device_map, len(self.h)) + self.model_parallel = True + self.first_device = "cpu" if "cpu" in self.device_map.keys() else device_prefix + str(min(self.device_map.keys())) + self.last_device = device_prefix + str(max(self.device_map.keys())) + self.wte = self.wte.to(self.first_device) + if self.position_embedding=="learned": + self.wpe = self.wpe.to(self.first_device) + for k, v in self.device_map.items(): + print("k,v :"+str(k)+","+str(v)) + for block in v: + cuda_device = device_prefix + str(k) + self.h[block] = self.h[block].to(cuda_device) + self.ln_f = self.ln_f.to(self.last_device) + + def deparallelize(self): + self.model_parallel = False + self.device_map = None + self.first_device = "cpu" + self.last_device = "cpu" + self.wte = self.wte.to("cpu") + if self.position_embedding=="learned": + self.wpe = self.wpe.to("cpu") + for index in range(len(self.h)): + self.h[index] = self.h[index].to("cpu") + self.ln_f = self.ln_f.to("cpu") + torch.cuda.empty_cache() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} + """ + for layer, heads in heads_to_prune.items(): + self.h[layer].attn.prune_heads(heads) + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, input_shape[-1]) + if position_ids is not None: + position_ids = position_ids.view(-1, input_shape[-1]) + + if past_key_values is None: + past_length = 0 + past_key_values = tuple([None] * len(self.h)) + else: + past_length = past_key_values[0][0].size(-2) + if position_ids is None: + position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + # GPT2Attention mask. + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask[:, None, None, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * -10000.0 + + # If a 2D ou 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.add_cross_attention and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # head_mask has shape n_layer x batch x n_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + if self.position_embedding=="learned": + position_embeds = self.wpe(position_ids) + hidden_states = inputs_embeds + position_embeds + else: + hidden_states = inputs_embeds + + if token_type_ids is not None: + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + all_hidden_states = () if output_hidden_states else None + + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + # Model parallel + if self.model_parallel: + torch.cuda.set_device(hidden_states.device) + # Ensure layer_past is on same device as hidden_states (might not be correct) + if layer_past is not None: + layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) + # Ensure that attention_mask is always on the same device as hidden_states + if attention_mask is not None: + attention_mask = attention_mask.to(hidden_states.device) + if isinstance(head_mask, torch.Tensor): + head_mask = head_mask.to(hidden_states.device) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + if use_cache: + print("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + alibi_bias=self.alibi if hasattr(self, "alibi") else None + ) + + hidden_states = outputs[0] + + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) + + if self.model_parallel: + device_prefix="cuda:" + for k, v in self.device_map.items(): + if i == v[-1] and device_prefix + str(k) != self.last_device: + hidden_states = hidden_states.to(device_prefix + str(k + 1)) + + hidden_states = self.ln_f(hidden_states) + + hidden_states = hidden_states.view(*output_shape) + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] + if v is not None + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + +class TranceptionLMHeadModel(GPT2PreTrainedModel): + _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"] + def __init__(self, config): + super().__init__(config) + self.transformer = TranceptionModel(config) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.config = config + + self.init_weights() + + self.default_model_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # Model parallel + self.model_parallel = False + self.device_map = None + self.clustal_hash = str(uuid.uuid4()) + self.retrieval_aggregation_mode = config.retrieval_aggregation_mode if hasattr(config, "retrieval_aggregation_mode") else None + if self.retrieval_aggregation_mode is not None: + print("Model leverages both autoregressive and retrieval inference") + self.MSA_filename = config.MSA_filename if hasattr(config, "MSA_filename") else False + self.MSA_folder = '/'.join(self.MSA_filename.split(os.sep)[:-1]) + self.MSA_name = self.MSA_filename.split(os.sep)[-1] + self.retrieval_inference_weight_LR = config.retrieval_inference_weight if hasattr(config, "retrieval_inference_weight") else 0.6 + self.retrieval_inference_weight_RL = config.retrieval_inference_weight if hasattr(config, "retrieval_inference_weight") else 0.6 + self.MSA_start=config.MSA_start + self.MSA_end=config.MSA_end + self.full_protein_length = config.full_protein_length if hasattr(config, "full_protein_length") else -1 + + self.MSA_log_prior = torch.log(torch.tensor( + msa_utils.get_msa_prior( + MSA_data_file=self.MSA_filename, + MSA_weight_file_name=config.MSA_weight_file_name, + retrieval_aggregation_mode=self.retrieval_aggregation_mode, + MSA_start=self.MSA_start, + MSA_end=self.MSA_end, + len_target_seq=self.full_protein_length, + vocab=config.tokenizer.get_vocab(), + verbose=False + ) + ).float().to(self.default_model_device)) + else: + print("Model only uses autoregressive inference") + + def parallelize(self, device_map=None, num_cores=None, num_pipelines=1): + self.num_pipelines=num_pipelines + self.device_map = ( + get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) + if device_map is None + else device_map + ) + assert_device_map(self.device_map, len(self.transformer.h)) + self.transformer.parallelize(self.device_map, num_cores=num_cores) + self.lm_head = self.lm_head.to(self.transformer.first_device) + self.model_parallel = True + + def deparallelize(self): + self.transformer.deparallelize() + self.transformer = self.transformer.to("cpu") + self.lm_head = self.lm_head.to("cpu") + self.model_parallel = False + torch.cuda.empty_cache() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + "flip": kwargs.get("flip", None), + } + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + flip=None, + start_slice=None, + end_slice=None, + mutated_sequence=None, + ): + r""" + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to + ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]`` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict + ) + hidden_states = transformer_outputs[0] + + # Set device for model parallelism + if self.model_parallel: + torch.cuda.set_device(self.transformer.first_device) + hidden_states = hidden_states.to(self.lm_head.weight.device) + self.MSA_log_prior = self.MSA_log_prior.to(self.lm_head.weight.device) + + lm_logits = self.lm_head(hidden_states) + + loss = None + fused_shift_log_probas = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + + if self.retrieval_aggregation_mode is not None: + batch_size = input_ids.size(0) + if self.retrieval_aggregation_mode=="aggregate_indel": + assert batch_size==1, "Aggregate indel is only supported for batch size of 1" + truncated_sequence_text = mutated_sequence[0][start_slice[0]:end_slice[0]] + if len(truncated_sequence_text)!=shift_logits.shape[1]-1: # shift_logits only has one extra token compared to truncated_sequence_text (the BOS token) + print("Tokenization error -- seq length: {} and shift_logits length - 1 : {}".format(len(mutated_sequence),shift_logits.shape[1]-1)) + MSA_log_prior, MSA_start, MSA_end = msa_utils.update_retrieved_MSA_log_prior_indel(self, self.MSA_log_prior, self.MSA_start, self.MSA_end, mutated_sequence[0], self.clustal_hash) + + elif self.retrieval_aggregation_mode=="aggregate_substitution": + MSA_log_prior=self.MSA_log_prior + MSA_start=self.MSA_start + MSA_end=self.MSA_end + + shift_log_probas = torch.log_softmax(shift_logits,dim=-1) + fused_shift_log_probas = shift_log_probas.clone() + if flip is None: + flip = torch.zeros(batch_size).to(fused_shift_log_probas.device) + flip = flip > 0 + + for seq_index in range(batch_size): + min_prior_slice = max(start_slice[seq_index], MSA_start) + max_prior_slice = min(end_slice[seq_index], MSA_end) + + # This will cascade into an error + if max_prior_slice <= min_prior_slice: + print("Non overlapping region detected: min_prior_slice {} and max_prior_slice {}".format(min_prior_slice,max_prior_slice)) + continue + + slice_prior = MSA_log_prior[min_prior_slice:max_prior_slice,:].to(fused_shift_log_probas.device) + if flip[seq_index]: + slice_prior = torch.flip(slice_prior,dims=(0,)) + min_logits_slice = max(0,end_slice[seq_index]-MSA_end) + max_logits_slice = min_logits_slice + (max_prior_slice-min_prior_slice) + fused_shift_log_probas[seq_index,min_logits_slice:max_logits_slice,:] = (1-self.retrieval_inference_weight_RL)*shift_log_probas[seq_index,min_logits_slice:max_logits_slice,:] + self.retrieval_inference_weight_RL*slice_prior + else: + min_logits_slice = max(0, MSA_start-start_slice[seq_index]) + max_logits_slice = min_logits_slice + (max_prior_slice-min_prior_slice) + fused_shift_log_probas[seq_index,min_logits_slice:max_logits_slice,:] = (1-self.retrieval_inference_weight_LR)*shift_log_probas[seq_index,min_logits_slice:max_logits_slice,:] + self.retrieval_inference_weight_LR*slice_prior + + if self.retrieval_aggregation_mode=="aggregate_indel": + try: + # If a given residue colume is an added zero-column, then we overwrite prior fusion and only predict based on the autoregressive transformer inference mode. + inserted_retrieval_positions = [True if slice_prior[i].sum()==0 else False for i in range(len(slice_prior))]+[True] #Last True is for the end of sentence token + fused_shift_log_probas[:,inserted_retrieval_positions,:]=shift_log_probas[:,inserted_retrieval_positions,:] + except Exception as e: + print("Error when adding zero column(s) to account for insertion mutations.") + raise e + + loss_fct = NLLLoss(reduction='none') + loss = loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1]) + mask = attention_mask[..., 1:].float() + mask[mask==0]=float('nan') + loss *= mask + loss = nanmean(loss, dim=1).mean() + else: + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return TranceptionCausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + cross_attentions=transformer_outputs.cross_attentions, + fused_shift_log_probas=fused_shift_log_probas + ) + + + @staticmethod + def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) + + def score_mutants(self, DMS_data, target_seq=None, scoring_mirror=True, batch_size_inference=10, num_workers=10, indel_mode=False): + """ + Method to score mutants in an input DMS file. + DMS_data: (dataframe) Dataframe containing the list of mutated sequences for scoring. + target_seq: (string) Full reference sequence (wild type) that is mutated in the DMS assay. If not None, returned scores are delta log likelihood wrt that sequence. + scoring_mirror: (bool) Whether to score mutated sequences from both directions (Left->Right and Right->Left). + batch_size_inference: (int) Batch size for scoring. + num_workers: (int) Number of workers to be used in the data loader. + indel_mode: (bool) Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions. + """ + df = DMS_data.copy() + if ('mutated_sequence' not in df) and (not indel_mode): df['mutated_sequence'] = df['mutant'].apply(lambda x: scoring_utils.get_mutated_sequence(target_seq, x)) + + # Daniel R: Temporary since indel files all have a 'mutant' column containing the mutated sequence. Should probably + # make all of those mutated sequence columns and take this out entirely + # if indel_mode: + # df["mutated_sequence"] = df["mutant"] + + assert ('mutated_sequence' in df), "DMS file to score does not have mutated_sequence column" + if 'mutant' not in df: df['mutant'] = df['mutated_sequence'] #if mutant not in DMS file we default to mutated_sequence + #if 'DMS_score' in df: del df['DMS_score'] + #if 'DMS_score_bin' in df: del df['DMS_score_bin'] + df = df[['mutated_sequence','mutant']] + if target_seq is not None: + df_left_to_right_slices = scoring_utils.get_sequence_slices(df, target_seq=target_seq, model_context_len = self.config.n_ctx - 2, indel_mode=indel_mode, scoring_window=self.config.scoring_window) + else: + df_left_to_right_slices = scoring_utils.get_sequence_slices(df, target_seq=list(df['mutated_sequence'])[0], model_context_len = self.config.n_ctx - 2, indel_mode=indel_mode, scoring_window='sliding') + print("Scoring sequences from left to right") + scores_L_to_R = scoring_utils.get_tranception_scores_mutated_sequences(model=self, mutated_sequence_df=df_left_to_right_slices, batch_size_inference=batch_size_inference, score_var_name='avg_score_L_to_R', target_seq=target_seq, num_workers=num_workers, indel_mode=indel_mode) + if scoring_mirror: + print("Scoring sequences from right to left") + df_right_to_left_slices = df_left_to_right_slices.copy() + df_right_to_left_slices['sliced_mutated_sequence'] = df_right_to_left_slices['sliced_mutated_sequence'].apply(lambda x: x[::-1]) + scores_R_to_L = scoring_utils.get_tranception_scores_mutated_sequences(model=self, mutated_sequence_df=df_right_to_left_slices, batch_size_inference=batch_size_inference, score_var_name='avg_score_R_to_L', target_seq=target_seq, num_workers=num_workers, reverse=True, indel_mode=indel_mode) + all_scores = pd.merge(scores_L_to_R, scores_R_to_L, on='mutated_sequence', how='left', suffixes=('','_R_to_L')) + all_scores['avg_score'] = (all_scores['avg_score_L_to_R'] + all_scores['avg_score_R_to_L']) / 2.0 + else: + all_scores = scores_L_to_R + all_scores['avg_score'] = all_scores['avg_score_L_to_R'] + #By design "get_tranception_scores_mutated_sequences" drops the WT from the output. We add it back if that was one of the sequences to score in the DMS (score=0 by definition) + if indel_mode: + mutant_column = "mutant" + else: + mutant_column = "mutated_sequence" + if target_seq in DMS_data[mutant_column].values: + if scoring_mirror: + wt_row = pd.DataFrame([[target_seq,0,0,0]], columns=[mutant_column,'avg_score_L_to_R','avg_score_R_to_L','avg_score']) + else: + wt_row = pd.DataFrame([[target_seq,0,0]], columns=[mutant_column,'avg_score_L_to_R','avg_score']) + all_scores = pd.concat([all_scores,wt_row], ignore_index=True) + return all_scores + + def encode_batch(self, protein_sequence, sequence_name="sliced_mutated_sequence"): + """ + Method to process an input AA sequence batch (protein_sequence) and return a tokenized sequence (via the tokenizer associated to the model). + """ + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='X', char_replacements='ACDEFGHIKLMNPQRSTVWY') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='B', char_replacements='DN') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='J', char_replacements='IL') + protein_sequence[sequence_name] = scoring_utils.sequence_replace(sequences=protein_sequence[sequence_name], char_to_replace='Z', char_replacements='EQ') + return self.config.tokenizer(list(protein_sequence[sequence_name]), add_special_tokens=True, truncation=True, padding=True, max_length=self.config.n_ctx) + diff --git a/proteingym/baselines/tranception/tranception/outputs.py b/proteingym/baselines/tranception/tranception/outputs.py new file mode 100644 index 0000000..8b119fc --- /dev/null +++ b/proteingym/baselines/tranception/tranception/outputs.py @@ -0,0 +1,48 @@ +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch + +from transformers.file_utils import ModelOutput + +@dataclass +class TranceptionCausalLMOutputWithCrossAttentions(ModelOutput): + """ + Class for Tranception causal language model (or autoregressive) outputs. + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Cross attentions weights after the attention softmax, used to compute the weighted average in the + cross-attention heads. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key, + value states of the self-attention and the cross-attention layers if model is used in encoder-decoder + setting. Only relevant if `config.is_decoder = True`. + Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + fused_shift_log_probas (`torch.FloatTensor` of shape (batch_size, sequence_length, config.vocab_size), *optional*, returned when config.retrieval_aggregation_mode is not None. + log_probas for each residue position after aggregating autoregressive logits and retrieval logits. + + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor]] = None + fused_shift_log_probas: Optional[torch.FloatTensor] = None \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/utils/__init__.py b/proteingym/baselines/tranception/tranception/utils/__init__.py new file mode 100644 index 0000000..90ad887 --- /dev/null +++ b/proteingym/baselines/tranception/tranception/utils/__init__.py @@ -0,0 +1 @@ +from . import scoring_utils, msa_utils \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/utils/dms_utils.py b/proteingym/baselines/tranception/tranception/utils/dms_utils.py new file mode 100644 index 0000000..3c46e1c --- /dev/null +++ b/proteingym/baselines/tranception/tranception/utils/dms_utils.py @@ -0,0 +1,31 @@ +import pandas as pd +import numpy as np +from tranception.utils import scoring_utils + +def DMS_file_cleanup(DMS_filename, target_seq, start_idx=1, end_idx=None, DMS_mutant_column='mutant', DMS_phenotype_name='score', DMS_directionality=1, AA_vocab = "ACDEFGHIKLMNPQRSTVWY"): + """ + Function to process the raw substitution DMS assay data (eg., removing invalid mutants, aggregate silent mutations). + """ + DMS_data = pd.read_csv(DMS_filename, low_memory=False) + end_idx = start_idx + len(target_seq) - 1 if end_idx is None else end_idx + DMS_data['mutant'] = DMS_data[DMS_mutant_column] + + DMS_data=DMS_data[DMS_data['mutant'].notnull()].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([len(y)>=3 for y in x.split(":")]))].copy() #Mutant triplets should have at least 3 or more characters + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([(y[0] in AA_vocab) and (y[1:-1].isnumeric()) and (y[-1] in AA_vocab) for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([int(y[1:-1])-start_idx >=0 and int(y[1:-1]) <= end_idx for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([y[0]==target_seq[int(y[1:-1])-start_idx] for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([y[0]!=y[-1] for y in x.split(":")]))].copy() + + DMS_data[DMS_phenotype_name]=pd.to_numeric(DMS_data[DMS_phenotype_name],errors='coerce') + DMS_data=DMS_data[np.isfinite(DMS_data[DMS_phenotype_name])] + DMS_data.dropna(subset = [DMS_phenotype_name], inplace=True) + DMS_data['DMS_score'] = DMS_data[DMS_phenotype_name] * DMS_directionality + DMS_data=DMS_data[['mutant','DMS_score']] + DMS_data=DMS_data.groupby('mutant').mean().reset_index() + + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: scoring_utils.get_mutated_sequence(target_seq, x)) + DMS_data=DMS_data[['mutant','mutated_sequence','DMS_score']] + + return DMS_data + diff --git a/proteingym/baselines/tranception/tranception/utils/msa_utils.py b/proteingym/baselines/tranception/tranception/utils/msa_utils.py new file mode 100644 index 0000000..e457082 --- /dev/null +++ b/proteingym/baselines/tranception/tranception/utils/msa_utils.py @@ -0,0 +1,369 @@ +import numpy as np +import pandas as pd +from collections import defaultdict +import random +import os +import torch +from Bio.Align.Applications import ClustalOmegaCommandline + +def filter_msa(msa_data, num_sequences_kept=3): + """ + Helper function to filter an input MSA msa_data (obtained via process_msa_data) and keep only num_sequences_kept aligned sequences. + If the MSA already has fewer sequences than num_sequences_kept, we keep the MSA as is. + If filtering, we always keep the first sequence of the MSA (ie. the wild type) by default. + Sampling is done without replacement. + """ + if len(list(msa_data.keys())) <= num_sequences_kept: + return msa_data + filtered_msa = {} + wt_name = next(iter(msa_data)) + filtered_msa[wt_name] = msa_data[wt_name] + del msa_data[wt_name] + sequence_names = list(msa_data.keys()) + sequence_names_sampled = random.sample(sequence_names,k=num_sequences_kept-1) + for seq in sequence_names_sampled: + filtered_msa[seq] = msa_data[seq] + return filtered_msa + +def process_msa_data(MSA_data_file): + """ + Helper function that takes as input a path to a MSA file (expects a2m format) and returns a dict mapping sequence ID to the corresponding AA sequence. + """ + msa_data = defaultdict(str) + sequence_name = "" + with open(MSA_data_file, "r") as msa_file: + for i, line in enumerate(msa_file): + line = line.rstrip() + if line.startswith(">"): + sequence_name = line + else: + msa_data[sequence_name] += line.upper() + return msa_data + +def get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab): + vocab_size = len(vocab.keys()) + num_sequences_msa = len(msa_data.keys()) + one_hots = np.zeros((num_sequences_msa,MSA_end-MSA_start,vocab_size)) + for i,seq_name in enumerate(msa_data.keys()): + sequence = msa_data[seq_name] + for j,letter in enumerate(sequence): + if letter in vocab: + k = vocab[letter] + one_hots[i,j,k] = 1.0 + return one_hots + +def one_hot(sequence_string,vocab): + one_hots = np.zeros((len(sequence_string),len(vocab.keys()))) + for j,letter in enumerate(sequence_string): + if letter in vocab: + k = vocab[letter] + one_hots[j,k] = 1.0 + return one_hots.flatten() + +def get_msa_prior(MSA_data_file, MSA_weight_file_name, MSA_start, MSA_end, len_target_seq, vocab, retrieval_aggregation_mode="aggregate_substitution", filter_MSA=True, verbose=False): + """ + Function to enable retrieval inference mode, via computation of (weighted) pseudocounts of AAs at each position of the retrieved MSA. + MSA_data_file: (string) path to MSA file (expects a2m format). + MSA_weight_file_name: (string) path to sequence weights in MSA. + MSA_start: (int) Sequence position that the MSA starts at (1-indexing). + MSA_end: (int) Sequence position that the MSA ends at (1-indexing). + len_target_seq: (int) Full length of sequence to be scored. + vocab: (dict) Vocabulary of the tokenizer. + retrieval_aggregation_mode: (string) Mode for retrieval inference (aggregate_substitution Vs aggregate_indel). If None, places a uniform prior over each token. + filter_MSA: (bool) Whether to filter out sequences with very low hamming similarity (< 0.2) to the reference sequence in the MSA (first sequence). + verbose: (bool) Whether to print to the console processing details along the way. + """ + msa_data = process_msa_data(MSA_data_file) + vocab_size = len(vocab.keys()) + if verbose: print("Target seq len is {}, MSA length is {}, start position is {}, end position is {} and vocab size is {}".format(len_target_seq,MSA_end-MSA_start,MSA_start,MSA_end,vocab_size)) + + if filter_MSA: + if verbose: print("Num sequences in MSA pre filtering: {}".format(len(msa_data.keys()))) + list_sequence_names = list(msa_data.keys()) + focus_sequence_name = list(msa_data.keys())[0] + ref_sequence_hot = one_hot(msa_data[focus_sequence_name],vocab) + for sequence_name in list_sequence_names: + seq_hot = one_hot(msa_data[sequence_name],vocab) + hamming_similarity_seq_ref = np.dot(ref_sequence_hot,seq_hot) / np.dot(ref_sequence_hot,ref_sequence_hot) + if hamming_similarity_seq_ref < 0.2: + del msa_data[sequence_name] + if verbose: print("Num sequences in MSA post filtering: {}".format(len(msa_data.keys()))) + + if MSA_weight_file_name is not None: + if verbose: print("Using weights in {} for sequences in MSA.".format(MSA_weight_file_name)) + assert os.path.exists(MSA_weight_file_name), "Weights file not located on disk." + MSA_EVE = MSA_processing( + MSA_location=MSA_data_file, + use_weights=True, + weights_location=MSA_weight_file_name + ) + #We scan through all sequences to see if we have a weight for them as per EVE pre-processing. We drop them otherwise. + dropped_sequences=0 + list_sequence_names = list(msa_data.keys()) + MSA_weight=[] + for sequence_name in list_sequence_names: + if sequence_name not in MSA_EVE.seq_name_to_sequence: + dropped_sequences +=1 + del msa_data[sequence_name] + else: + MSA_weight.append(MSA_EVE.seq_name_to_weight[sequence_name]) + if verbose: print("Dropped {} sequences from MSA due to absent sequence weights".format(dropped_sequences)) + else: + MSA_weight = [1] * len(list(msa_data.keys())) + + if retrieval_aggregation_mode=="aggregate_substitution" or retrieval_aggregation_mode=="aggregate_indel": + one_hots = get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab) + MSA_weight = np.expand_dims(np.array(MSA_weight),axis=(1,2)) + base_rate = 1e-5 + base_rates = np.ones_like(one_hots) * base_rate + weighted_one_hots = (one_hots + base_rates) * MSA_weight + MSA_weight_norm_counts = weighted_one_hots.sum(axis=-1).sum(axis=0) + MSA_weight_norm_counts = np.tile(MSA_weight_norm_counts.reshape(-1,1), (1,vocab_size)) + one_hots_avg = weighted_one_hots.sum(axis=0) / MSA_weight_norm_counts + msa_prior = np.zeros((len_target_seq,vocab_size)) + print(MSA_start) + print(MSA_end) + print(one_hots_avg.shape) + msa_prior[MSA_start:MSA_end,:]=one_hots_avg + else: + msa_prior = np.ones((len_target_seq,vocab_size)) / vocab_size + + if verbose: + for idx, position in enumerate(msa_prior): + if len(position)!=25: + print("Size error") + if not round(position.sum(),2)==1.0: + print("Position at index {} does not add up to 1: {}".format(idx, position.sum())) + + return msa_prior + + +def update_retrieved_MSA_log_prior_indel(model, MSA_log_prior, MSA_start, MSA_end, mutated_sequence, clustal_hash): + """ + Function to process MSA when scoring indels. + To identify positions to add / remove in the retrieved MSA, we append and align the sequence to be scored to the original MSA for that protein family with Clustal Omega. + If the original MSA is relatively deep (over 100k sequences), we sample (by default) 100k rows at random from that MSA to speed computations. + MSA sampling is performed only once (for the first sequence to be scored). Subsequent scoring use the same MSA sample. + """ + if not os.path.isdir(model.MSA_folder + os.sep + "Sampled"): + os.mkdir(model.MSA_folder + os.sep + "Sampled") + sampled_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Sampled_" + clustal_hash + "_" + model.MSA_filename.split(os.sep)[-1] + + if not os.path.exists(sampled_MSA_location): + msa_data = process_msa_data(model.MSA_filename) + msa_data_sampled = filter_msa(msa_data, num_sequences_kept=100000) #If MSA has less than 100k sequences, the sample is identical to original MSA + with open(sampled_MSA_location, 'w') as sampled_write_location: + for index, key in enumerate(msa_data_sampled): + key_name = ">REFERENCE_SEQUENCE" if index==0 else key + msa_data_sampled[key] = msa_data_sampled[key].upper() + msa_data_sampled[key] = msa_data_sampled[key].replace(".","-") + sampled_write_location.write(key_name+"\n"+"\n".join([msa_data_sampled[key][i:i+80] for i in range(0, len(msa_data_sampled[key]), 80)])+"\n") + + seq_to_align_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Seq_to_align_" + clustal_hash + "_" + model.MSA_filename.split(os.sep)[-1] + sequence_text_split = [mutated_sequence[i:i+80] for i in range(0, len(mutated_sequence), 80)] + sequence_text_split_split_join = "\n".join([">SEQ_TO_SCORE"]+sequence_text_split) + os.system("echo '"+sequence_text_split_split_join+"' > "+seq_to_align_location) + + expanded_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Expanded_" + clustal_hash + "_" + model.MSA_filename.split(os.sep)[-1] + clustalw_cline = ClustalOmegaCommandline(cmd=model.config.clustal_omega_location, + profile1=sampled_MSA_location, + profile2=seq_to_align_location, + outfile=expanded_MSA_location, + force=True) + stdout, stderr = clustalw_cline() + msa_data = process_msa_data(expanded_MSA_location) + aligned_seqA, aligned_seqB = msa_data[">SEQ_TO_SCORE"], msa_data[">REFERENCE_SEQUENCE"] + try: + keep_column=[] + for column_index_pairwise_alignment in range(len(aligned_seqA)): + if aligned_seqA[column_index_pairwise_alignment]=="-" and aligned_seqB[column_index_pairwise_alignment]=="-": + continue # Skips if both are gaps + elif aligned_seqA[column_index_pairwise_alignment]=="-": + keep_column.append(False) # Skips if the query SEQ_TO_SCORE is a gap + elif aligned_seqB[column_index_pairwise_alignment]=="-": + MSA_log_prior=torch.cat((MSA_log_prior[:column_index_pairwise_alignment], torch.zeros(MSA_log_prior.shape[1]).view(1,-1).cuda(), MSA_log_prior[column_index_pairwise_alignment:]),dim=0) + keep_column.append(True) #keep the zero column we just added + else: + keep_column.append(True) + MSA_log_prior = MSA_log_prior[keep_column] + MSA_end = MSA_start + len(MSA_log_prior) + except: + print("Error when processing the following alignment: {}".format(expanded_MSA_location)) + return MSA_log_prior, MSA_start, MSA_end + +class MSA_processing: + def __init__(self, + MSA_location="", + theta=0.2, + use_weights=True, + weights_location="./data/weights", + preprocess_MSA=True, + threshold_sequence_frac_gaps=0.5, + threshold_focus_cols_frac_gaps=1.0, + remove_sequences_with_indeterminate_AA_in_focus_cols=True + ): + + """ + This MSA_processing class is directly borrowed from the EVE codebase: https://github.com/OATML-Markslab/EVE + + Parameters: + - msa_location: (path) Location of the MSA data. Constraints on input MSA format: + - focus_sequence is the first one in the MSA data + - first line is structured as follows: ">focus_seq_name/start_pos-end_pos" (e.g., >SPIKE_SARS2/310-550) + - corespondding sequence data located on following line(s) + - then all other sequences follow with ">name" on first line, corresponding data on subsequent lines + - theta: (float) Sequence weighting hyperparameter. Generally: Prokaryotic and eukaryotic families = 0.2; Viruses = 0.01 + - use_weights: (bool) If False, sets all sequence weights to 1. If True, checks weights_location -- if non empty uses that; + otherwise compute weights from scratch and store them at weights_location + - weights_location: (path) Location to load from/save to the sequence weights + - preprocess_MSA: (bool) performs pre-processing of MSA to remove short fragments and positions that are not well covered. + - threshold_sequence_frac_gaps: (float, between 0 and 1) Threshold value to define fragments + - sequences with a fraction of gap characters above threshold_sequence_frac_gaps are removed + - default is set to 0.5 (i.e., fragments with 50% or more gaps are removed) + - threshold_focus_cols_frac_gaps: (float, between 0 and 1) Threshold value to define focus columns + - positions with a fraction of gap characters above threshold_focus_cols_pct_gaps will be set to lower case (and not included in the focus_cols) + - default is set to 0.3 (i.e., focus positions are the ones with 30% of gaps or less, i.e., 70% or more residue occupancy) + - remove_sequences_with_indeterminate_AA_in_focus_cols: (bool) Remove all sequences that have indeterminate AA (e.g., B, J, X, Z) at focus positions of the wild type + """ + np.random.seed(2021) + self.MSA_location = MSA_location + self.weights_location = weights_location + self.theta = theta + self.alphabet = "ACDEFGHIKLMNPQRSTVWY" + self.use_weights = use_weights + self.preprocess_MSA = preprocess_MSA + self.threshold_sequence_frac_gaps = threshold_sequence_frac_gaps + self.threshold_focus_cols_frac_gaps = threshold_focus_cols_frac_gaps + self.remove_sequences_with_indeterminate_AA_in_focus_cols = remove_sequences_with_indeterminate_AA_in_focus_cols + + self.gen_alignment() + + def gen_alignment(self, verbose=False): + """ Read training alignment and store basics in class instance """ + self.aa_dict = {} + for i,aa in enumerate(self.alphabet): + self.aa_dict[aa] = i + + self.seq_name_to_sequence = defaultdict(str) + name = "" + with open(self.MSA_location, "r") as msa_data: + for i, line in enumerate(msa_data): + line = line.rstrip() + if line.startswith(">"): + name = line + if i==0: + self.focus_seq_name = name + else: + self.seq_name_to_sequence[name] += line + + + ## MSA pre-processing to remove inadequate columns and sequences + if self.preprocess_MSA: + msa_df = pd.DataFrame.from_dict(self.seq_name_to_sequence, orient='index', columns=['sequence']) + # Data clean up + msa_df.sequence = msa_df.sequence.apply(lambda x: x.replace(".","-")).apply(lambda x: ''.join([aa.upper() for aa in x])) + # Remove columns that would be gaps in the wild type + non_gap_wt_cols = [aa!='-' for aa in msa_df.sequence[self.focus_seq_name]] + msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa for aa,non_gap_ind in zip(x, non_gap_wt_cols) if non_gap_ind])) + assert 0.0 <= self.threshold_sequence_frac_gaps <= 1.0,"Invalid fragment filtering parameter" + assert 0.0 <= self.threshold_focus_cols_frac_gaps <= 1.0,"Invalid focus position filtering parameter" + msa_array = np.array([list(seq) for seq in msa_df.sequence]) + gaps_array = np.array(list(map(lambda seq: [aa=='-' for aa in seq], msa_array))) + # Identify fragments with too many gaps + seq_gaps_frac = gaps_array.mean(axis=1) + seq_below_threshold = seq_gaps_frac <= self.threshold_sequence_frac_gaps + if verbose: print("Proportion of sequences dropped due to fraction of gaps: "+str(round(float(1 - seq_below_threshold.sum()/seq_below_threshold.shape)*100,2))+"%") + # Identify focus columns + columns_gaps_frac = gaps_array[seq_below_threshold].mean(axis=0) + index_cols_below_threshold = columns_gaps_frac <= self.threshold_focus_cols_frac_gaps + if verbose: print("Proportion of non-focus columns removed: "+str(round(float(1 - index_cols_below_threshold.sum()/index_cols_below_threshold.shape)*100,2))+"%") + # Lower case non focus cols and filter fragment sequences + msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa.upper() if upper_case_ind else aa.lower() for aa, upper_case_ind in zip(x, index_cols_below_threshold)])) + msa_df = msa_df[seq_below_threshold] + # Overwrite seq_name_to_sequence with clean version + self.seq_name_to_sequence = defaultdict(str) + for seq_idx in range(len(msa_df['sequence'])): + self.seq_name_to_sequence[msa_df.index[seq_idx]] = msa_df.sequence[seq_idx] + + self.focus_seq = self.seq_name_to_sequence[self.focus_seq_name] + self.focus_cols = [ix for ix, s in enumerate(self.focus_seq) if s == s.upper() and s!='-'] + self.focus_seq_trimmed = [self.focus_seq[ix] for ix in self.focus_cols] + self.seq_len = len(self.focus_cols) + self.alphabet_size = len(self.alphabet) + + # Connect local sequence index with uniprot index (index shift inferred from 1st row of MSA) + try: + focus_loc = self.focus_seq_name.split("/")[-1] + start,stop = focus_loc.split("-") + self.focus_start_loc = int(start) + self.focus_stop_loc = int(stop) + except: + start,stop = 1,len(self.focus_seq) + self.focus_start_loc = int(start) + self.focus_stop_loc = int(stop) + self.uniprot_focus_col_to_wt_aa_dict \ + = {idx_col+int(start):self.focus_seq[idx_col] for idx_col in self.focus_cols} + self.uniprot_focus_col_to_focus_idx \ + = {idx_col+int(start):idx_col for idx_col in self.focus_cols} + + # Move all letters to CAPS; keeps focus columns only + self.raw_seq_name_to_sequence = self.seq_name_to_sequence.copy() + for seq_name,sequence in self.seq_name_to_sequence.items(): + sequence = sequence.replace(".","-") + self.seq_name_to_sequence[seq_name] = [sequence[ix].upper() for ix in self.focus_cols] + + # Remove sequences that have indeterminate AA (e.g., B, J, X, Z) in the focus columns + if self.remove_sequences_with_indeterminate_AA_in_focus_cols: + alphabet_set = set(list(self.alphabet)) + seq_names_to_remove = [] + for seq_name,sequence in self.seq_name_to_sequence.items(): + for letter in sequence: + if letter not in alphabet_set and letter != "-": + seq_names_to_remove.append(seq_name) + continue + seq_names_to_remove = list(set(seq_names_to_remove)) + for seq_name in seq_names_to_remove: + del self.seq_name_to_sequence[seq_name] + + # Encode the sequences + self.one_hot_encoding = np.zeros((len(self.seq_name_to_sequence.keys()),len(self.focus_cols),len(self.alphabet))) + if verbose: print("One-hot encoded sequences shape:" + str(self.one_hot_encoding.shape)) + for i,seq_name in enumerate(self.seq_name_to_sequence.keys()): + sequence = self.seq_name_to_sequence[seq_name] + for j,letter in enumerate(sequence): + if letter in self.aa_dict: + k = self.aa_dict[letter] + self.one_hot_encoding[i,j,k] = 1.0 + + if self.use_weights: + try: + self.weights = np.load(file=self.weights_location) + if verbose: print("Loaded sequence weights from disk") + except: + if verbose: print ("Computing sequence weights") + list_seq = self.one_hot_encoding + list_seq = list_seq.reshape((list_seq.shape[0], list_seq.shape[1] * list_seq.shape[2])) + def compute_weight(seq): + number_non_empty_positions = np.dot(seq,seq) + if number_non_empty_positions>0: + denom = np.dot(list_seq,seq) / np.dot(seq,seq) + denom = np.sum(denom > 1 - self.theta) + return 1/denom + else: + return 0.0 #return 0 weight if sequence is fully empty + self.weights = np.array(list(map(compute_weight,list_seq))) + np.save(file=self.weights_location, arr=self.weights) + else: + # If not using weights, use an isotropic weight matrix + if verbose: print("Not weighting sequence data") + self.weights = np.ones(self.one_hot_encoding.shape[0]) + + self.Neff = np.sum(self.weights) + self.num_sequences = self.one_hot_encoding.shape[0] + self.seq_name_to_weight={} + for i,seq_name in enumerate(self.seq_name_to_sequence.keys()): + self.seq_name_to_weight[seq_name]=self.weights[i] + + if verbose: + print ("Neff =",str(self.Neff)) + print ("Data Shape =",self.one_hot_encoding.shape) \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/utils/scoring_utils.py b/proteingym/baselines/tranception/tranception/utils/scoring_utils.py new file mode 100644 index 0000000..36cba5c --- /dev/null +++ b/proteingym/baselines/tranception/tranception/utils/scoring_utils.py @@ -0,0 +1,203 @@ +import os +import tqdm +import re +import numpy as np +import pandas as pd + +import torch +from torch.nn import CrossEntropyLoss, NLLLoss +from torch.utils.data.sampler import Sampler, SequentialSampler + +from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizerFast +from datasets import Dataset + +AA_vocab = "ACDEFGHIKLMNPQRSTVWY" + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab=AA_vocab): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def nanmean(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs) + +def nansum(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) + +def get_optimal_window(mutation_position_relative, seq_len_wo_special, model_window): + """ + Helper function that selects an optimal sequence window that fits the maximum model context size. + If the sequence length is less than the maximum context size, the full sequence is returned. + """ + half_model_window = model_window // 2 + if seq_len_wo_special <= model_window: + return [0,seq_len_wo_special] + elif mutation_position_relative < half_model_window: + return [0,model_window] + elif mutation_position_relative >= seq_len_wo_special - half_model_window: + return [seq_len_wo_special - model_window, seq_len_wo_special] + else: + return [max(0,mutation_position_relative-half_model_window), min(seq_len_wo_special,mutation_position_relative+half_model_window)] + +def sequence_replace_single(sequence, char_to_replace, char_replacements): + char_replacements = list(char_replacements) + positions = [m.start() for m in re.finditer(char_to_replace, sequence)] + replacements = np.random.choice(a=char_replacements, size=len(positions), replace=True) + sequence=list(sequence) + for idx, position in enumerate(positions): + sequence[position]=replacements[idx] + return ''.join(sequence) + +def sequence_replace(sequences, char_to_replace, char_replacements): + """ + Helper function that replaces all Amino Acids passsed in via char_to_replace (as a string of AAs) with Amino Acids sampled from char_replacements (also a string of eligible AAs). + """ + return [sequence_replace_single(sequence, char_to_replace, char_replacements) for sequence in sequences] + +def get_tranception_scores_mutated_sequences(model, mutated_sequence_df, batch_size_inference, score_var_name, target_seq, num_workers=10, reverse=False, indel_mode=False): + """ + Helper function that takes as input a set of mutated sequences (in a pandas dataframe) and returns scores for each mutation. + If target_seq is not None, returns the delta log likelihood wrt that target sequence -- otherwise returns the log likelihood of the protein sequences. + """ + scores = {} + scores['mutated_sequence']=[] + scores['sliced_mutated_sequence']=[] + scores['window_start']=[] + scores['window_end']=[] + scores['score']=[] + with torch.no_grad(): + ds = Dataset.from_pandas(mutated_sequence_df) + ds.set_transform(model.encode_batch) + data_collator = DataCollatorForLanguageModeling( + tokenizer=model.config.tokenizer, + mlm=False) + sampler = SequentialSampler(ds) + ds_loader = torch.utils.data.DataLoader(ds, batch_size=batch_size_inference, sampler=sampler, collate_fn=data_collator, num_workers=num_workers, pin_memory=True, drop_last=False) + mutant_index=0 + for encoded_batch in tqdm.tqdm(ds_loader, desc="Scoring batches"): + full_batch_length = len(encoded_batch['input_ids']) + mutated_sequence = np.array(mutated_sequence_df['mutated_sequence'][mutant_index:mutant_index+full_batch_length]) + scores['mutated_sequence'] += list(mutated_sequence) + sliced_mutated_sequence = np.array(mutated_sequence_df['sliced_mutated_sequence'][mutant_index:mutant_index+full_batch_length]) + scores['sliced_mutated_sequence'] += list(sliced_mutated_sequence) + window_start = np.array(mutated_sequence_df['window_start'][mutant_index:mutant_index+full_batch_length]) + scores['window_start'] += list(window_start) + window_end = np.array(mutated_sequence_df['window_end'][mutant_index:mutant_index+full_batch_length]) + scores['window_end'] += list(window_end) + for k, v in encoded_batch.items(): + if isinstance(v, torch.Tensor): + encoded_batch[k] = v.to(model.device) + shift_labels = encoded_batch['labels'][..., 1:].contiguous() + if (hasattr(model.config,"retrieval_aggregation_mode")) and (model.config.retrieval_aggregation_mode is not None): + if reverse: + encoded_batch['flip']=torch.tensor([1]*full_batch_length) + encoded_batch['start_slice']=window_start + encoded_batch['end_slice']=window_end + encoded_batch['mutated_sequence'] = mutated_sequence #only mutated_sequence is flipped if the scoring_mirror branch of score_mutants. No need to flip mutated_sequence for MSA re-aligning + fused_shift_log_probas=model(**encoded_batch,return_dict=True).fused_shift_log_probas + loss_fct = NLLLoss(reduction='none') + loss = - loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1]) + else: + lm_logits=model(**encoded_batch,return_dict=True).logits + shift_logits = lm_logits[..., :-1, :].contiguous() + loss_fct = CrossEntropyLoss(reduction='none') + loss = - loss_fct(input=shift_logits.view(-1, shift_logits.size(-1)), target=shift_labels.view(-1)).view(shift_logits.shape[0],shift_logits.shape[1]) + mask = encoded_batch['attention_mask'][..., 1:].float() + mask[mask==0]=float('nan') + loss *= mask + loss = nansum(loss, dim=1) + scores_batch = list(loss.cpu().numpy()) + full_batch_length = len(encoded_batch['input_ids']) + scores['score'] += scores_batch + mutant_index+=full_batch_length + scores = pd.DataFrame(scores) + if model.config.scoring_window=="sliding": + scores = scores[['mutated_sequence','score']].groupby('mutated_sequence').sum().reset_index() #We need to aggregate scores when using sliding mode + scores['score'] = scores['score'] / scores['mutated_sequence'].map(lambda x: len(x)) + if target_seq is not None: + scores_mutated_seq = scores[scores.mutated_sequence != target_seq] + scores_wt = scores[scores.mutated_sequence == target_seq] + merge_delta = 'mutated_sequence' if model.config.scoring_window=="sliding" else 'window_start' + if model.config.scoring_window=="optimal": + delta_scores = pd.merge(scores_mutated_seq,scores_wt,how='left',on=[merge_delta],suffixes=('','_wt')) + delta_scores[score_var_name] = delta_scores['score'] - delta_scores['score_wt'] + elif model.config.scoring_window=="sliding": + delta_scores = scores_mutated_seq.copy() + delta_scores[score_var_name] = delta_scores['score'] - list(scores_wt['score'])[0] # In sliding mode there is a single reference window for the WT + return delta_scores[['mutated_sequence',score_var_name]] + else: + scores[score_var_name] = scores['score'] + return scores[['mutated_sequence',score_var_name]] + +def get_sequence_slices(df, target_seq, model_context_len, start_idx=1, scoring_window="optimal", indel_mode=False): + """ + Helper function that takes as input a (pandas) dataframe df that contains a list of mutant triplets (substitutions) or full mutated sequences (indels) for scoring. + It returns a processed DMS in which sequences have been sliced to satisfy the maximum context window of the model. + df: (dataframe) Input dataframe to be processed + target_seq: (string) Full reference sequence (wild type) that is mutated in the DMS assay. + model_context_len: (int) Maximum context size for the model. + start_idx: (int) Integer to move to 0-indexing of positions (mutation triplet are typically based on 1-indexing). + scoring_window: (string) Method to slice sequences longer than maximum context size: + - optimal selects a single window as large as possible via the get_optimal_window function (this is the default) + - sliding splits the full sequence in contiguous (non-overlapping) chunks that are of size equal to the max context (except the last chunk which may be shorter) + indel_mode: (bool) Flag to be used when scoring insertions and deletions. Otherwise assumes substitutions. + Note: when scoring indels for sequences that would be longer than the model max context length, it is preferable to use the "sliding" scoring_window. Use "optimal" otherwise. + """ + len_target_seq = len(target_seq) + num_mutants = len(df['mutated_sequence']) + df=df.reset_index(drop=True) + if scoring_window=="optimal": + df['mutation_barycenter'] = df['mutant'].apply(lambda x: int(np.array([int(mutation[1:-1]) - start_idx for mutation in x.split(':')]).mean())) if not indel_mode else df['mutated_sequence'].apply(lambda x: len(x)//2) + df['scoring_optimal_window'] = df['mutation_barycenter'].apply(lambda x: get_optimal_window(x, len_target_seq, model_context_len)) if not indel_mode else df['mutated_sequence'].apply(lambda x: (0,len(x))) + df['sliced_mutated_sequence'] = [df['mutated_sequence'][index][df['scoring_optimal_window'][index][0]:df['scoring_optimal_window'][index][1]] for index in range(num_mutants)] + df['window_start'] = df['scoring_optimal_window'].map(lambda x: x[0]) + df['window_end'] = df['scoring_optimal_window'].map(lambda x: x[1]) + del df['scoring_optimal_window'], df['mutation_barycenter'] + if 'mutant' in df: del df['mutant'] + df_wt=df.copy() + df_wt['mutated_sequence'] = [target_seq] * num_mutants + if indel_mode: # For indels, we set the wild type reference to be always the same (full length) sequence. We assume here that the length is lower than model context size (otherwise "Sliding" mode should be used) + df_wt['window_end'] = df_wt['mutated_sequence'].map(lambda x:len(x)) + df_wt['sliced_mutated_sequence'] = [target_seq[df_wt['window_start'][index]:df_wt['window_end'][index]] for index in range(num_mutants)] + df = pd.concat([df,df_wt], axis=0) + df = df.drop_duplicates() + elif scoring_window=="sliding": + num_windows = 1 + int( len_target_seq / model_context_len) + df_list=[] + start=0 + for window_index in range(1, num_windows+1): + df_sliced = df.copy() + df_sliced['sliced_mutated_sequence'] = df_sliced['mutated_sequence'].map(lambda x: x[start:start+model_context_len]) + df_sliced['window_start'] = [start] * num_mutants + df_sliced['window_end'] = df_sliced['mutated_sequence'].map(lambda x: min(len(x), start+model_context_len)) + df_sliced_wt = df_sliced.copy() + df_sliced_wt['mutated_sequence'] = [target_seq] * num_mutants + df_sliced_wt['sliced_mutated_sequence'] = df_sliced_wt['mutated_sequence'].map(lambda x: x[start:start+model_context_len]) + df_sliced_wt['window_end'] = df_sliced_wt['mutated_sequence'].map(lambda x: min(len(x), start+model_context_len)) #Need to adjust end index if WT and sequence are not same full length + df_list.append(df_sliced) + df_list.append(df_sliced_wt) + start += model_context_len + df_final = pd.concat(df_list,axis=0) + if 'mutant' in df_final: del df_final['mutant'] + df = df_final.drop_duplicates() + return df.reset_index(drop=True) \ No newline at end of file diff --git a/proteingym/baselines/tranception/tranception/utils/tokenizers/Basic_tokenizer b/proteingym/baselines/tranception/tranception/utils/tokenizers/Basic_tokenizer new file mode 100644 index 0000000..b6af745 --- /dev/null +++ b/proteingym/baselines/tranception/tranception/utils/tokenizers/Basic_tokenizer @@ -0,0 +1 @@ +{"version":"1.0","truncation":null,"padding":null,"added_tokens":[{"id":0,"special":true,"content":"[UNK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":1,"special":true,"content":"[CLS]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":2,"special":true,"content":"[SEP]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":3,"special":true,"content":"[PAD]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":4,"special":true,"content":"[MASK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false}],"normalizer":null,"pre_tokenizer":{"type":"Whitespace"},"post_processor":{"type":"TemplateProcessing","single":[{"SpecialToken":{"id":"[CLS]","type_id":0}},{"Sequence":{"id":"A","type_id":0}},{"SpecialToken":{"id":"[SEP]","type_id":0}}],"pair":[{"SpecialToken":{"id":"[CLS]","type_id":0}},{"Sequence":{"id":"A","type_id":0}},{"SpecialToken":{"id":"[SEP]","type_id":0}},{"Sequence":{"id":"B","type_id":1}},{"SpecialToken":{"id":"[SEP]","type_id":1}}],"special_tokens":{"[CLS]":{"id":"[CLS]","ids":[1],"tokens":["[CLS]"]},"[SEP]":{"id":"[SEP]","ids":[2],"tokens":["[SEP]"]}}},"decoder":null,"model":{"type":"BPE","dropout":null,"unk_token":"[UNK]","continuing_subword_prefix":null,"end_of_word_suffix":null,"fuse_unk":false,"vocab":{"[UNK]":0,"[CLS]":1,"[SEP]":2,"[PAD]":3,"[MASK]":4,"A":5,"C":6,"D":7,"E":8,"F":9,"G":10,"H":11,"I":12,"K":13,"L":14,"M":15,"N":16,"P":17,"Q":18,"R":19,"S":20,"T":21,"V":22,"W":23,"Y":24},"merges":[]}} \ No newline at end of file diff --git a/proteingym/baselines/unirep/unirep.py b/proteingym/baselines/unirep/unirep.py new file mode 100644 index 0000000..4f9b67d --- /dev/null +++ b/proteingym/baselines/unirep/unirep.py @@ -0,0 +1,591 @@ +""" +The trained 1900-dimensional mLSTM babbler. +Source: https://github.com/churchlab/UniRep/blob/master/unirep.py +""" + +import os +import tensorflow.compat.v1 as tf +tf.disable_v2_behavior() +import tensorflow_addons as tfa +import tensorflow_probability as tfp + +import numpy as np +import pandas as pd +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.unirep.utils import aa_seq_to_int, int_to_aa +from baselines.unirep.utils.unirep_utils import bucketbatchpad + +# Helpers +def tf_get_shape(tensor): + static_shape = tensor.shape.as_list() + dynamic_shape = tf.unstack(tf.shape(tensor)) + dims = [s[1] if s[0] is None else s[0] + for s in zip(static_shape, dynamic_shape)] + return dims + +def sample_with_temp(logits, t): + """ + Takes temperature between 0 and 1 -> zero most conservative, 1 most liberal. Samples. + """ + t_adjusted = logits / t # broadcast temperature normalization + softed = tf.nn.softmax(t_adjusted) + + # Make a categorical distribution from the softmax and sample + return tfp.distributions.Categorical(probs=softed).sample() + +def initialize_uninitialized(sess): + """ + from https://stackoverflow.com/questions/35164529/in-tensorflow-is-there-any-way-to-just-initialize-uninitialised-variables + """ + global_vars = tf.global_variables() + is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars]) + not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f] + if len(not_initialized_vars): + sess.run(tf.variables_initializer(not_initialized_vars)) + + +# Setup to initialize from the correctly named model files. +class mLSTMCell1900(tf.nn.rnn_cell.RNNCell): + + def __init__(self, + num_units, + model_path="./", + wn=True, + scope='mlstm', + var_device='cpu:0', + ): + # Really not sure if I should reuse here + super(mLSTMCell1900, self).__init__() + self._num_units = num_units + self._model_path = model_path + self._wn = wn + self._scope = scope + self._var_device = var_device + + @property + def state_size(self): + # The state is a tuple of c and h + return (self._num_units, self._num_units) + + @property + def output_size(self): + # The output is h + return (self._num_units) + + def zero_state(self, batch_size, dtype): + c = tf.zeros([batch_size, self._num_units], dtype=dtype) + h = tf.zeros([batch_size, self._num_units], dtype=dtype) + return (c, h) + + def call(self, inputs, state): + # Inputs will be a [batch_size, input_dim] tensor. + # Eg, input_dim for a 10-D embedding is 10 + nin = inputs.get_shape()[1].value + + # Unpack the state tuple + c_prev, h_prev = state + with tf.variable_scope(self._scope): + wx_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_wx:0.npy")) + wh_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_wh:0.npy")) + wmx_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_wmx:0.npy")) + wmh_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_wmh:0.npy")) + b_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_b:0.npy")) + gx_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_gx:0.npy")) + gh_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_gh:0.npy")) + gmx_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_gmx:0.npy")) + gmh_init = np.load(os.path.join(self._model_path, "rnn_mlstm_mlstm_gmh:0.npy")) + wx = tf.get_variable( + "wx", initializer=wx_init) + wh = tf.get_variable( + "wh", initializer=wh_init) + wmx = tf.get_variable( + "wmx", initializer=wmx_init) + wmh = tf.get_variable( + "wmh", initializer=wmh_init) + b = tf.get_variable( + "b", initializer=b_init) + if self._wn: + gx = tf.get_variable( + "gx", initializer=gx_init) + gh = tf.get_variable( + "gh", initializer=gh_init) + gmx = tf.get_variable( + "gmx", initializer=gmx_init) + gmh = tf.get_variable( + "gmh", initializer=gmh_init) + + if self._wn: + wx = tf.nn.l2_normalize(wx, axis=0) * gx + wh = tf.nn.l2_normalize(wh, axis=0) * gh + wmx = tf.nn.l2_normalize(wmx, axis=0) * gmx + wmh = tf.nn.l2_normalize(wmh, axis=0) * gmh + m = tf.matmul(inputs, wmx) * tf.matmul(h_prev, wmh) + z = tf.matmul(inputs, wx) + tf.matmul(m, wh) + b + i, f, o, u = tf.split(z, 4, 1) + i = tf.nn.sigmoid(i) + f = tf.nn.sigmoid(f) + o = tf.nn.sigmoid(o) + u = tf.tanh(u) + c = f * c_prev + i * u + h = o * tf.tanh(c) + return h, (c, h) + +class mLSTMCell(tf.nn.rnn_cell.RNNCell): + + def __init__(self, + num_units, + wx_init=tf.orthogonal_initializer(), + wh_init=tf.orthogonal_initializer(), + wmx_init=tf.orthogonal_initializer(), + wmh_init=tf.orthogonal_initializer(), + b_init=tf.orthogonal_initializer(), + gx_init=tf.ones_initializer(), + gh_init=tf.ones_initializer(), + gmx_init=tf.ones_initializer(), + gmh_init=tf.ones_initializer(), + wn=True, + scope='mlstm', + var_device='cpu:0', + ): + # Really not sure if I should reuse here + super(mLSTMCell, self).__init__() + self._num_units = num_units + self._wn = wn + self._scope = scope + self._var_device = var_device + self._wx_init = wx_init + self._wh_init = wh_init + self._wmx_init = wmx_init + self._wmh_init = wmh_init + self._b_init = b_init + self._gx_init = gx_init + self._gh_init = gh_init + self._gmx_init = gmx_init + self._gmh_init = gmh_init + + @property + def state_size(self): + # The state is a tuple of c and h + return (self._num_units, self._num_units) + + @property + def output_size(self): + # The output is h + return (self._num_units) + + def zero_state(self, batch_size, dtype): + c = tf.zeros([batch_size, self._num_units], dtype=dtype) + h = tf.zeros([batch_size, self._num_units], dtype=dtype) + return (c, h) + + def call(self, inputs, state): + # Inputs will be a [batch_size, input_dim] tensor. + # Eg, input_dim for a 10-D embedding is 10 + nin = inputs.get_shape()[1].value + + # Unpack the state tuple + c_prev, h_prev = state + with tf.variable_scope(self._scope): + wx = tf.get_variable( + "wx", initializer=self._wx_init) + wh = tf.get_variable( + "wh", initializer=self._wh_init) + wmx = tf.get_variable( + "wmx", initializer=self._wmx_init) + wmh = tf.get_variable( + "wmh", initializer=self._wmh_init) + b = tf.get_variable( + "b", initializer=self._b_init) + if self._wn: + gx = tf.get_variable( + "gx", initializer=self._gx_init) + gh = tf.get_variable( + "gh", initializer=self._gh_init) + gmx = tf.get_variable( + "gmx", initializer=self._gmx_init) + gmh = tf.get_variable( + "gmh", initializer=self._gmh_init) + + if self._wn: + wx = tf.nn.l2_normalize(wx, dim=0) * gx + wh = tf.nn.l2_normalize(wh, dim=0) * gh + wmx = tf.nn.l2_normalize(wmx, dim=0) * gmx + wmh = tf.nn.l2_normalize(wmh, dim=0) * gmh + m = tf.matmul(inputs, wmx) * tf.matmul(h_prev, wmh) + z = tf.matmul(inputs, wx) + tf.matmul(m, wh) + b + i, f, o, u = tf.split(z, 4, 1) + i = tf.nn.sigmoid(i) + f = tf.nn.sigmoid(f) + o = tf.nn.sigmoid(o) + u = tf.tanh(u) + c = f * c_prev + i * u + h = o * tf.tanh(c) + return h, (c, h) + +class mLSTMCellStackNPY(tf.nn.rnn_cell.RNNCell): + + def __init__(self, + num_units=256, + num_layers=4, + dropout=None, + res_connect=False, + wn=True, + scope='mlstm_stack', + var_device='cpu:0', + model_path="./" + ): + # Really not sure if I should reuse here + super(mLSTMCellStackNPY, self).__init__() + self._model_path=model_path + self._num_units = num_units + self._num_layers = num_layers + self._dropout = dropout + self._res_connect = res_connect + self._wn = wn + self._scope = scope + self._var_device = var_device + bs = "rnn_mlstm_stack_mlstm_stack" # base scope see weight file names + join = lambda x: os.path.join(self._model_path, x) + layers = [mLSTMCell( + num_units=self._num_units, + wn=self._wn, + scope=self._scope + str(i), + var_device=self._var_device, + wx_init=np.load(join(bs + "{0}_mlstm_stack{1}_wx:0.npy".format(i,i))), + wh_init=np.load(join(bs + "{0}_mlstm_stack{1}_wh:0.npy".format(i,i))), + wmx_init=np.load(join(bs + "{0}_mlstm_stack{1}_wmx:0.npy".format(i,i))), + wmh_init=np.load(join(bs + "{0}_mlstm_stack{1}_wmh:0.npy".format(i,i))), + b_init=np.load(join(bs + "{0}_mlstm_stack{1}_b:0.npy".format(i,i))), + gx_init=np.load(join(bs + "{0}_mlstm_stack{1}_gx:0.npy".format(i,i))), + gh_init=np.load(join(bs + "{0}_mlstm_stack{1}_gh:0.npy".format(i,i))), + gmx_init=np.load(join(bs + "{0}_mlstm_stack{1}_gmx:0.npy".format(i,i))), + gmh_init=np.load(join(bs + "{0}_mlstm_stack{1}_gmh:0.npy".format(i,i))) + ) for i in range(self._num_layers)] + if self._dropout: + layers = [ + tf.nn.rnn_cell.DropoutWrapper( + layer, output_keep_prob=1-self._dropout) for layer in layers[:-1]] + layers[-1:] + self._layers = layers + + @property + def state_size(self): + # The state is a tuple of c and h + return ( + tuple(self._num_units for _ in range(self._num_layers)), + tuple(self._num_units for _ in range(self._num_layers)) + ) + + @property + def output_size(self): + # The output is h + return (self._num_units) + + def zero_state(self, batch_size, dtype): + c_stack = tuple(tf.zeros([batch_size, self._num_units], dtype=dtype) for _ in range(self._num_layers)) + h_stack = tuple(tf.zeros([batch_size, self._num_units], dtype=dtype) for _ in range(self._num_layers)) + return (c_stack, h_stack) + + def call(self, inputs, state): + # Inputs will be a [batch_size, input_dim] tensor. + # Eg, input_dim for a 10-D embedding is 10 + + # Unpack the state tuple + c_prev, h_prev = state + + new_outputs = [] + new_cs = [] + new_hs = [] + for i, layer in enumerate(self._layers): + if i == 0: + h, (c,h_state) = layer(inputs, (c_prev[i],h_prev[i])) + else: + h, (c,h_state) = layer(new_outputs[-1], (c_prev[i],h_prev[i])) + new_outputs.append(h) + new_cs.append(c) + new_hs.append(h_state) + + if self._res_connect: + # Make sure number of layers does not affect the scale of the output + scale_factor = tf.constant(1 / float(self._num_layers)) + final_output = tf.scalar_mul(scale_factor,tf.add_n(new_outputs)) + else: + final_output = new_outputs[-1] + + return final_output, (tuple(new_cs), tuple(new_hs)) + + +class babbler1900(): + + def __init__(self, + model_path="./pbab_weights", + batch_size=256 + ): + self._rnn_size = 1900 + self._vocab_size = 26 + self._embed_dim = 10 + self._wn = True + self._shuffle_buffer = 10000 + self._model_path = model_path + self._batch_size = batch_size + self._batch_size_placeholder = tf.placeholder(tf.int32, shape=[], name="batch_size") + self._minibatch_x_placeholder = tf.placeholder( + tf.int32, shape=[None, None], name="minibatch_x") + self._initial_state_placeholder = ( + tf.placeholder(tf.float32, shape=[None, self._rnn_size]), + tf.placeholder(tf.float32, shape=[None, self._rnn_size]) + ) + self._minibatch_y_placeholder = tf.placeholder( + tf.int32, shape=[None, None], name="minibatch_y") + # Batch size dimensional placeholder which gives the + # Lengths of the input sequence batch. Used to index into + # The final_hidden output and select the stop codon -1 + # final hidden for the graph operation. + self._seq_length_placeholder = tf.placeholder( + tf.int32, shape=[None], name="seq_len") + self._temp_placeholder = tf.placeholder(tf.float32, shape=[], name="temp") + rnn = mLSTMCell1900(self._rnn_size, model_path=model_path, wn=self._wn) + zero_state = rnn.zero_state(self._batch_size, tf.float32) + single_zero = rnn.zero_state(1, tf.float32) + mask = tf.sign(self._minibatch_y_placeholder) # 1 for nonpad, zero for pad + inverse_mask = 1 - mask # 0 for nonpad, 1 for pad + + total_padded = tf.reduce_sum(inverse_mask) + + pad_adjusted_targets = (self._minibatch_y_placeholder - 1) + inverse_mask + + embed_matrix = tf.get_variable( + "embed_matrix", dtype=tf.float32, + initializer=np.load(os.path.join(self._model_path, "embed_matrix:0.npy")) + ) + embed_cell = tf.nn.embedding_lookup(embed_matrix, self._minibatch_x_placeholder) + self._output, self._final_state = tf.nn.dynamic_rnn( + rnn, + embed_cell, + initial_state=self._initial_state_placeholder, + swap_memory=True, + parallel_iterations=1 + ) + + # If we are training a model on top of the rep model, we need to access + # the final_hidden rep from output. Recall we are padding these sequences + # to max length, so the -1 position will not necessarily be the right rep. + # to get the right rep, I will use the provided sequence length to index. + # Subtract one for the last place + indices = self._seq_length_placeholder - 1 + self._top_final_hidden = tf.gather_nd(self._output, + tf.stack([tf.range(tf_get_shape(self._output)[0], + dtype=tf.int32), indices], axis=1)) + fmask = tf.cast(mask, tf.float32)[:, :, None] + self._avg_hidden = tf.reduce_sum(fmask * self._output, + axis=1) / tf.reduce_sum(fmask, axis=1) + # LEFTOFF self._output is a batch size, seq_len, num_hidden. + # I want to average along num_hidden, but I'll have to figure out how to mask out + # the dimensions along sequence_length which are longer than the given sequence. + flat = tf.reshape(self._output, [-1, self._rnn_size]) + if os.path.exists(os.path.join(self._model_path, "fully_connected_weights:0.npy")): + weights_name="fully_connected_weights" + bias_name="fully_connected_biases" + else: + weights_name="dense_kernel" + bias_name="dense_bias" + weights_init = tf.constant_initializer( + np.load(os.path.join(self._model_path, f"{weights_name}:0.npy"))) + bias_init = tf.constant_initializer( + np.load(os.path.join(self._model_path, f"{bias_name}:0.npy"))) + self.dense_layer = tf.keras.layers.Dense(self._vocab_size-1, + activation=None, kernel_initializer=weights_init, + bias_initializer=bias_init) + logits_flat = self.dense_layer(flat) + seqlen = tf_get_shape(self._minibatch_x_placeholder)[1] + self._logits = tf.reshape( + logits_flat, [batch_size, seqlen, self._vocab_size-1]) + self.batch_losses = tfa.seq2seq.sequence_loss( + self._logits, + tf.cast(pad_adjusted_targets, tf.int32), + tf.cast(mask, tf.float32), + average_across_batch=False + ) + self._loss = tf.reduce_mean(self.batch_losses) + self._sample = sample_with_temp(self._logits, self._temp_placeholder) + with tf.Session() as sess: + self._zero_state = sess.run(zero_state) + self._single_zero = sess.run(single_zero) + + + def get_rep(self,seq): + """ + Input a valid amino acid sequence, + outputs a tuple of average hidden, final hidden, final cell representation arrays. + Unfortunately, this method accepts one sequence at a time and is as such quite + slow. + """ + with tf.Session() as sess: + initialize_uninitialized(sess) + # Strip any whitespace and convert to integers with the correct coding + int_seq = aa_seq_to_int(seq.strip())[:-1] + # Final state is a cell_state, hidden_state tuple. Output is + # all hidden states + final_state_, hs = sess.run( + [self._final_state, self._output], feed_dict={ + self._batch_size_placeholder: 1, + self._minibatch_x_placeholder: [int_seq], + self._initial_state_placeholder: self._zero_state} + ) + + final_cell, final_hidden = final_state_ + # Drop the batch dimension so it is just seq len by + # representation size + final_cell = final_cell[0] + final_hidden = final_hidden[0] + hs = hs[0] + avg_hidden = np.mean(hs, axis=0) + return avg_hidden, final_hidden, final_cell + + def get_babble(self, seed, length=250, temp=1): + """ + Return a babble at temperature temp (on (0,1] with 1 being the noisiest) + starting with seed and continuing to length length. + Unfortunately, this method accepts one sequence at a time and is as such quite + slow. + """ + with tf.Session() as sess: + initialize_uninitialized(sess) + int_seed = aa_seq_to_int(seed.strip())[:-1] + + # No need for padding because this is a single element + seed_samples, final_state_ = sess.run( + [self._sample, self._final_state], + feed_dict={ + self._minibatch_x_placeholder: [int_seed], + self._initial_state_placeholder: self._zero_state, + self._batch_size_placeholder: 1, + self._temp_placeholder: temp + } + ) + # Just the actual character prediction + pred_int = seed_samples[0, -1] + 1 + seed = seed + int_to_aa[pred_int] + + for i in range(length - len(seed)): + pred_int, final_state_ = sess.run( + [self._sample, self._final_state], + feed_dict={ + self._minibatch_x_placeholder: [[pred_int]], + self._initial_state_placeholder: final_state_, + self._batch_size_placeholder: 1, + self._temp_placeholder: temp + } + ) + pred_int = pred_int[0, 0] + 1 + seed = seed + int_to_aa[pred_int] + return seed + + def get_rep_ops(self): + """ + Return tensorflow operations for the final_hidden state and placeholder. + """ + return self._top_final_hidden, self._avg_hidden, self._minibatch_x_placeholder, self._batch_size_placeholder, self._seq_length_placeholder, self._initial_state_placeholder + + def get_babbler_ops(self): + """ + Return tensorflow operations for + the logits, masked loss, minibatch_x placeholder, minibatch y placeholder, batch_size placeholder, initial_state placeholder + Use if you plan on using babbler1900 as an initialization for another babbler, + eg for fine tuning the babbler to babble a differenct distribution. + """ + return self._logits, self._loss, self._minibatch_x_placeholder, self._minibatch_y_placeholder, self._batch_size_placeholder, self._initial_state_placeholder + + def dump_weights(self,sess,dir_name="./1900_weights"): + """ + Saves the weights of the model in dir_name in the format required + for loading in this module. Must be called within a tf.Session + For which the weights are already initialized. + """ + vs = tf.trainable_variables() + for v in vs: + name = v.name + value = sess.run(v) + np.save(os.path.join(dir_name,name.replace('/', '_') + ".npy"), np.array(value)) + + + + def format_seq(self,seq,stop=False): + """ + Takes an amino acid sequence, returns a list of integers in the codex of the babbler. + Here, the default is to strip the stop symbol (stop=False) which would have + otherwise been added to the end of the sequence. If you are trying to generate + a rep, do not include the stop. It is probably best to ignore the stop if you are + co-tuning the babbler and a top model as well. + """ + if stop: + int_seq = aa_seq_to_int(seq.strip()) + else: + int_seq = aa_seq_to_int(seq.strip())[:-1] + return int_seq + + + def bucket_batch_pad(self,filepath, upper=2000, lower=50, interval=10): + """ + Read sequences from a filepath, batch them into buckets of similar lengths, and + pad out to the longest sequence. + Upper, lower and interval define how the buckets are created. + Any sequence shorter than lower will be grouped together, as with any greater + than upper. Interval defines the "walls" of all the other buckets. + WARNING: Define large intervals for small datasets because the default behavior + is to repeat the same sequence to fill a batch. If there is only one sequence + within a bucket, it will be repeated batch_size -1 times to fill the batch. + """ + self._bucket_upper = upper + self._bucket_lower = lower + self._bucket_interval = interval + self._bucket = [self._bucket_lower + (i * self._bucket_interval) for i in range(int(self._bucket_upper / self._bucket_interval))] + self._bucket_batch = bucketbatchpad( + batch_size=self._batch_size, + pad_shape=([None]), + window_size=self._batch_size, + bounds=self._bucket, + path_to_data=filepath, + shuffle_buffer=self._shuffle_buffer, + repeat=None + ).make_one_shot_iterator().get_next() + return self._bucket_batch + + def split_to_tuple(self, seq_batch): + """ + NOTICE THAT BY DEFAULT THIS STRIPS THE LAST CHARACTER. + IF USING IN COMBINATION WITH format_seq then set stop=True there. + Return a list of batch, target tuples. + The input (array-like) should + look like + 1. . . . . . . . sequence_length + . + . + . + batch_size + """ + q = None + num_steps = seq_batch.shape[1] + # Minibatches should start at zero index and go to -1 + # Don't even try to get what is happenning here its a brainfuck and + # probably inefficient + xypairs = [ + (seq_batch[:, :-1][:, idx:idx + num_steps], seq_batch[:, idx + 1:idx + num_steps + 1]) for idx in np.arange(len(seq_batch[0]))[0:-1:num_steps] + ] + if q: + for e in xypairs: + q.put(e) + else: + return xypairs[0] + + def is_valid_seq(self, seq, max_len=4000): + """ + True if seq is valid for the babbler, False otherwise. + """ + l = len(seq) + ## important: added X here, not in original unirep code + valid_aas = "MRHKDESTNQCUGPAVIFYWLOX" + if (l <= max_len) and set(seq) <= set(valid_aas): + return True + else: + return False \ No newline at end of file diff --git a/proteingym/baselines/unirep/unirep_evotune.py b/proteingym/baselines/unirep/unirep_evotune.py new file mode 100644 index 0000000..5812f1c --- /dev/null +++ b/proteingym/baselines/unirep/unirep_evotune.py @@ -0,0 +1,156 @@ +''' +Unsupervised fine-tuning of UniRep on evolutionary data, "evo-tuning" +Source: https://github.com/chloechsu/combining-evolutionary-and-assay-labelled-data/blob/main/src/unirep_evotune.py +''' + +import argparse +import os +import sys +import pathlib +import random + +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +import tensorflow.compat.v1 as tf +tf.disable_v2_behavior() +# append to path the parent directory two levels up +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.unirep.unirep import babbler1900 as babbler +from baselines.unirep import utils + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--seqs_fasta_path', type=pathlib.Path) + parser.add_argument('--save_weights_dir', type=pathlib.Path) + parser.add_argument('--mapping_path', type=str) + parser.add_argument('--DMS_index', type=int) + parser.add_argument('--initial_weights_dir', type=pathlib.Path, default="weights/unirep/global") + parser.add_argument('--batch_size', type=int, default=128) + parser.add_argument('--max_seq_len', type=int) + parser.add_argument('--num_steps', type=int, default=10000) + parser.add_argument('--learning_rate', type=float, default=0.00001) + args = parser.parse_args() + + # Set seeds + tf.set_random_seed(0) + np.random.seed(0) + + print("Num GPUs Available: ",len(tf.config.experimental.list_physical_devices('GPU'))) + + mapping = pd.read_csv(args.mapping_path) + list_DMS = mapping["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + print("Fine tuning Unirep params for: {}".format(DMS_id)) + DMS_file_name = mapping["DMS_filename"][mapping["DMS_id"]==DMS_id].values[0] + MSA_file_name = mapping["MSA_filename"][mapping["DMS_id"]==DMS_id].values[0] + if args.max_seq_len is None: + args.max_seq_len = mapping["seq_len"][mapping["DMS_id"]==DMS_id].values[0] + print("Max seq len: {}".format(args.max_seq_len)) + + #Adjust number of training steps to match teh 65 epochs in the paper + MSA_num_seqs = mapping["MSA_num_seqs"][mapping["DMS_id"]==DMS_id].values[0] + args.num_steps = min(args.num_steps, int(65*MSA_num_seqs/args.batch_size)) + print("Training for {} steps".format(args.num_steps)) + + # args.save_weights_dir = pathlib.Path(str(args.save_weights_dir) + os.sep + DMS_file_name.split('.csv')[0]) + # Now saving under MSA filename, as several assays share alignments + args.save_weights_dir = pathlib.Path(str(args.save_weights_dir) + os.sep + MSA_file_name.split(".a2m")[0]) + + + # Load pre-trained models + # Sync relevant weight files + # !aws s3 sync --no-sign-request --quiet s3://unirep-public/1900_weights/ 1900_weights/ + # Import the mLSTM babbler model + # Where model weights are stored. + b = babbler(batch_size=args.batch_size, model_path=args.initial_weights_dir) + + # Load seqs from fasta. + seqs_all = utils.read_fasta(str(args.seqs_fasta_path)+os.sep+MSA_file_name) + seqs = dict() + seqs['train'], seqs['val'] = train_test_split(seqs_all, test_size=0.2) + + #print(seqs['train']) + bucket_ops = { + 'train': None, + 'val': None, + } + for mode in ['train', 'val']: + prefix = (str(args.seqs_fasta_path)+os.sep+MSA_file_name).replace('.a2m', '') + formatted_seqs_path = prefix + f'_{mode}_formatted.txt' + with open(formatted_seqs_path, "w") as destination: + for i,seq in enumerate(seqs[mode]): + seq = seq.upper().replace('-', 'X') + seq = seq.replace('.', 'X') + if b.is_valid_seq(seq, max_len=10 * args.max_seq_len): + if len(seq) > args.max_seq_len: + sample_start = random.randint(0,len(seq)-args.max_seq_len) + seq = seq[sample_start:sample_start+args.max_seq_len] + formatted = ",".join(map(str,b.format_seq(seq))) + destination.write(formatted) + destination.write('\n') + bucket_ops[mode] = b.bucket_batch_pad(formatted_seqs_path, + lower=100, upper=args.max_seq_len, interval=50) + + logits, seqloss, x_ph, y_ph, batch_size_ph, initial_state_ph = ( + b.get_babbler_ops()) + optimizer = tf.train.AdamOptimizer(args.learning_rate) + tuning_op = optimizer.minimize(seqloss) + + args.save_weights_dir.mkdir(parents=True, exist_ok=True) + + train_loss = np.zeros(args.num_steps) + val_loss = np.zeros(args.num_steps) + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + sess.graph.finalize() + for i in range(args.num_steps): + print(f"Step {i}") + batch_train = sess.run(bucket_ops['train']) + train_loss[i], __, = sess.run([seqloss, tuning_op], + feed_dict={ + x_ph: batch_train[:, :-1], + y_ph: batch_train[:, 1:], + batch_size_ph: args.batch_size, + initial_state_ph:b._zero_state + }, + ) + batch_val = sess.run(bucket_ops['val']) + val_loss[i] = sess.run(seqloss, + feed_dict={ + x_ph: batch_val[:, :-1], + y_ph: batch_val[:, 1:], + batch_size_ph: args.batch_size, + initial_state_ph:b._zero_state + }, + ) + print("Step {0}: {1} (train), {2} (val)".format( + i, train_loss[i], val_loss[i])) + # Save periodically + if i % 1000 == 0 and i > 0: + suffix = f'_{int(i / 1000)}k' + savedir = os.path.join(args.save_weights_dir, suffix) + pathlib.Path(savedir).mkdir(exist_ok=True) + # Save weights + b.dump_weights(sess, dir_name=savedir) + # Save loss trajectories + np.savetxt( + os.path.join(args.save_weights_dir, 'loss_trajectory_train.npy'), + train_loss) + np.savetxt( + os.path.join(args.save_weights_dir, 'loss_trajectory_val.npy'), + val_loss) + # Save final weights + b.dump_weights(sess, dir_name=args.save_weights_dir) + # Save loss trajectories + np.savetxt( + os.path.join(args.save_weights_dir, 'loss_trajectory_train.npy'), + train_loss) + np.savetxt( + os.path.join(args.save_weights_dir, 'loss_trajectory_val.npy'), + val_loss) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/proteingym/baselines/unirep/unirep_inference.py b/proteingym/baselines/unirep/unirep_inference.py new file mode 100644 index 0000000..37371d7 --- /dev/null +++ b/proteingym/baselines/unirep/unirep_inference.py @@ -0,0 +1,116 @@ +''' +Infers log-likelihoods from UniRep models. +Source: https://github.com/chloechsu/combining-evolutionary-and-assay-labelled-data/blob/main/src/unirep_inference.py +''' + +import argparse +import os +import sys +import pathlib + +import numpy as np +import pandas as pd +import tensorflow as tf +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +from baselines.unirep.unirep import babbler1900 +from baselines.unirep.utils import load_and_filter_seqs, save, format_batch_seqs, nonpad_len + +def run_inference(seqs, model_weight_path, output_dir, output_filename=None, + batch_size=64, save_hidden=False): + if len(seqs) < batch_size: + batch_size = len(seqs) + babbler_class = babbler1900 + # Load model weights + b = babbler_class(batch_size=batch_size, model_path=model_weight_path) + # Load ops + final_hidden_op, avg_hidden_op, x_ph, batch_size_ph, seq_len_ph, init_state_ph = b.get_rep_ops() + logits_op, loss_op, x_ph, y_ph, batch_size_ph, init_state_ph = b.get_babbler_ops() + batch_loss_op = b.batch_losses + + final_hidden_vals = [] + avg_hidden_vals = [] + loss_vals = [] + with tf.compat.v1.Session() as sess: + sess.run(tf.compat.v1.global_variables_initializer()) + n_batches = int(len(seqs) / batch_size) + leftover = len(seqs) % batch_size + n_batches += int(bool(leftover)) + for i in range(n_batches): + print('----Running inference for batch # %d------' % i) + if i == n_batches - 1: + batch_seqs = seqs[-batch_size:] + else: + batch_seqs = seqs[i*batch_size:(i+1)*batch_size] + batch_seqs = [seq.replace('-', 'X') for seq in batch_seqs] + batch = format_batch_seqs(batch_seqs) + length = nonpad_len(batch) + # Run final hidden op + avg_hidden_, loss_ = sess.run( + [avg_hidden_op, batch_loss_op], + feed_dict={ + # Important! Shift input and expected target by 1. + x_ph: batch[:, :-1], + y_ph: batch[:, 1:], + batch_size_ph: batch.shape[0], + seq_len_ph: length, + init_state_ph:b._zero_state + }) + if i == n_batches - 1: + loss_vals.append(loss_[-leftover:]) + if save_hidden: + avg_hidden_vals.append(avg_hidden_[-leftover:]) + else: + loss_vals.append(loss_) + if save_hidden: + avg_hidden_vals.append(avg_hidden_) + + loss_vals = np.concatenate(loss_vals, axis=0) + if output_filename is not None: + output_df=pd.DataFrame({'mutated_sequence':seqs, + 'Unirep_score':loss_vals}) + output_df.to_csv(output_dir+os.sep+output_filename+'.csv',index=False) + #else: + # loss_filename = os.path.join(output_dir, f'loss.npy') + # save(loss_filename, loss_vals) + + #if save_hidden: + # avg_hidden_vals = np.concatenate(avg_hidden_vals, axis=0) + # avg_hidden_filename = os.path.join(output_dir, f'avg_hidden.npy') + # save(avg_hidden_filename, avg_hidden_vals) + + print('Ran inference on %d sequences. Saved results to %s.' % + (len(seqs), output_dir)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model_path', type=str) + parser.add_argument('--data_path', type=str) + parser.add_argument('--mapping_path', type=str) + parser.add_argument('--output_dir', type=str) + parser.add_argument('--DMS_index', type=int) + parser.add_argument('--batch_size', type=int, default=64) + parser.add_argument('--evotune', action='store_true') + parser.add_argument('--save_hidden', dest='save_hidden', action='store_true') + args = parser.parse_args() + + pathlib.Path(args.output_dir).mkdir(parents=True, exist_ok=True) + + mapping = pd.read_csv(args.mapping_path) + list_DMS = mapping["DMS_id"] + DMS_id=list_DMS[args.DMS_index] + DMS_file_name = mapping["DMS_filename"][mapping["DMS_id"]==DMS_id].values[0] + MSA_file_name = mapping["MSA_filename"][mapping["DMS_id"]==DMS_id].values[0] + if args.evotune: + args.model_path = args.model_path + os.sep + MSA_file_name.split('.a2m')[0] + print("Computing scores for: {} with Unirep {}".format(DMS_id, args.model_path)) + + + seqs = load_and_filter_seqs(args.data_path + os.sep + DMS_file_name) + #np.savetxt(os.path.join(args.output_dir, 'seqs.npy'), seqs, '%s') + + run_inference(seqs, args.model_path, + args.output_dir, + output_filename=DMS_id, + batch_size=args.batch_size, + save_hidden=args.save_hidden) \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/__init__.py b/proteingym/baselines/unirep/utils/__init__.py new file mode 100644 index 0000000..d60f95e --- /dev/null +++ b/proteingym/baselines/unirep/utils/__init__.py @@ -0,0 +1,8 @@ +from utils.data_utils import * +from utils.experiment_utils import * +from utils.io_utils import * +from utils.metric_utils import * +from utils.plot_utils import * +#from utils.notebook_utils import * +#from utils.esm_utils import * +#from utils.georgiev_utils import * \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/data_utils.py b/proteingym/baselines/unirep/utils/data_utils.py new file mode 100644 index 0000000..85406c0 --- /dev/null +++ b/proteingym/baselines/unirep/utils/data_utils.py @@ -0,0 +1,238 @@ +""" +Utilities for data processing. +""" +import numpy as np +import os +from sklearn.model_selection import train_test_split + + +""" +File formatting note. +Data should be preprocessed as a sequence of comma-seperated ints with +sequences /n seperated +""" + +# Lookup tables +aa_to_int = { + 'M':1, + 'R':2, + 'H':3, + 'K':4, + 'D':5, + 'E':6, + 'S':7, + 'T':8, + 'N':9, + 'Q':10, 'C':11, + 'U':12, + 'G':13, + 'P':14, + 'A':15, + 'V':16, + 'I':17, + 'F':18, + 'Y':19, + 'W':20, + 'L':21, + 'O':22, #Pyrrolysine + 'X':23, # Unknown + 'Z':23, # Glutamic acid or GLutamine + 'B':23, # Asparagine or aspartic acid + 'J':23, # Leucine or isoleucine + 'start':24, + 'stop':25, + '-':26, +} + +int_to_aa = {value:key for key, value in aa_to_int.items()} + +def get_aa_to_int(): + """ + Get the lookup table (for easy import) + """ + return aa_to_int + +def get_int_to_aa(): + """ + Get the lookup table (for easy import) + """ + return int_to_aa + +# Helper functions + +def aa_seq_to_int(s): + """ + Return the int sequence as a list for a given string of amino acids + """ + return [24] + [aa_to_int[a] for a in s] + [25] + +def int_seq_to_aa(s): + """ + Return the int sequence as a list for a given string of amino acids + """ + return "".join([int_to_aa[i] for i in s]) + + +def nonpad_len(batch): + nonzero = batch > 0 + lengths = np.sum(nonzero, axis=1) + return lengths + + +def format_seq(seq,stop=False): + """ + Takes an amino acid sequence, returns a list of integers in the codex of the babbler. + Here, the default is to strip the stop symbol (stop=False) which would have + otherwise been added to the end of the sequence. If you are trying to generate + a rep, do not include the stop. It is probably best to ignore the stop if you are + co-tuning the babbler and a top model as well. + """ + if stop: + int_seq = aa_seq_to_int(seq.strip()) + else: + int_seq = aa_seq_to_int(seq.strip())[:-1] + return int_seq + + +def format_batch_seqs(seqs): + maxlen = -1 + for s in seqs: + if len(s) > maxlen: + maxlen = len(s) + formatted = [] + for seq in seqs: + pad_len = maxlen - len(seq) + padded = np.pad(format_seq(seq), (0, pad_len), 'constant', constant_values=0) + formatted.append(padded) + return np.stack(formatted) + + +def is_valid_seq(seq, max_len=4000): + """ + True if seq is valid for the babbler, False otherwise. + """ + l = len(seq) + valid_aas = "MRHKDESTNQCUGPAVIFYWLO" + if (l <= max_len) and set(seq) <= set(valid_aas): + return True + else: + return False + + +def seqs_to_onehot(seqs): + seqs = format_batch_seqs(seqs) + X = np.zeros((seqs.shape[0], seqs.shape[1]*24), dtype=int) + for i in range(seqs.shape[1]): + for j in range(24): + X[:, i*24+j] = (seqs[:, i] == j) + return X + + +def seqs_to_binary_onehot(seqs, wt): + seqs = np.array([list(s) for s in seqs]) + X = np.zeros((seqs.shape[0], seqs.shape[1]), dtype=int) + for i in range(seqs.shape[1]): + X[:, i] = (seqs[:, i] != wt[i]) + return X + + +def dict2str(d): + return ';'.join([f'{k}={v}' for k, v in d.items()]) + + +def seq2mutation(seq, model, return_str=False, ignore_gaps=False, + sep=":", offset=1): + mutations = [] + for pf, pm in model.index_map.items(): + if seq[pf-offset] != model.target_seq[pm]: + if ignore_gaps and ( + seq[pf-offset] == '-' or seq[pf-offset] not in model.alphabet): + continue + mutations.append((pf, model.target_seq[pm], seq[pf-offset])) + if return_str: + return sep.join([m[1] + str(m[0]) + m[2] for m in mutations]) + return mutations + + +def seq2mutation_fromwt(seq, wt, ignore_gaps=False, sep=':', offset=1, + focus_only=True): + mutations = [] + for i in range(offset, offset+len(seq)): + if ignore_gaps and ( seq[i-offset] == '-'): + continue + if wt[i-offset].islower() and focus_only: + continue + if seq[i-offset].upper() != wt[i-offset].upper(): + mutations.append((i, wt[i-offset].upper(), seq[i-offset].upper())) + return mutations + + +def seqs2subs(seqs, wt, ignore_gaps=False): + pos = [] + subs = [] + for s in seqs: + p = [] + su = [] + for j in range(len(wt)): + if s[j] != wt[j]: + if ignore_gaps and (s[j] == '-' or s[j] == 'X'): + continue + p.append(j) + su.append(s[j]) + pos.append(np.array(p)) + subs.append(np.array(su)) + return pos, subs + + +def seq2effect(seqs, model, offset=1, ignore_gaps=False): + effects = np.zeros(len(seqs)) + for i in range(len(seqs)): + mutations = seq2mutation(seqs[i], model, + ignore_gaps=ignore_gaps, offset=offset) + dE, _, _ = model.delta_hamiltonian(mutations) + effects[i] = dE + return effects + + +def mutant2seq(mut, wt, offset): + if mut.upper() == 'WT': + return wt + chars = list(wt) + mut = mut.replace(':', ',') + mut = mut.replace(';', ',') + for m in mut.split(','): + idx = int(m[1:-1])-offset + assert wt[idx] == m[0] + chars[idx] = m[-1] + return ''.join(chars) + +def get_blosum_scores(seqs, wt, matrix): + scores = np.zeros(len(seqs)) + wt_score = 0 + for j in range(len(wt)): + wt_score += matrix[wt[j], wt[j]] + for i, s in enumerate(seqs): + for j in range(len(wt)): + if s[j] not in matrix.alphabet: + print(f'unexpected AA {s[j]} (seq {i}, pos {j})') + scores[i] += matrix[wt[j], s[j]] + return scores - wt_score + + +def get_wt_seq(mutation_descriptions): + wt_len = 0 + for m in mutation_descriptions: + if m == 'WT': + continue + if int(m[1:-1]) > wt_len: + wt_len = int(m[1:-1]) + wt = ['?' for _ in range(wt_len)] + for m in mutation_descriptions: + if m == 'WT': + continue + idx, wt_char = int(m[1:-1])-1, m[0] # 1-index to 0-index + if wt[idx] == '?': + wt[idx] = wt_char + else: + assert wt[idx] == wt_char + return ''.join(wt), wt_len \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/experiment_utils.py b/proteingym/baselines/unirep/utils/experiment_utils.py new file mode 100644 index 0000000..c5498e4 --- /dev/null +++ b/proteingym/baselines/unirep/utils/experiment_utils.py @@ -0,0 +1,132 @@ +import numpy as np +import pandas as pd +from sklearn.linear_model import Ridge +from sklearn import svm + +from utils.metric_utils import get_spearman_fractions, wt_improvement_metric, topk_median + + +def test_regression_multiseeds(X, y, n_train, n_seeds, y_wt, y_cutoff=None, + mutation_counts=None, mutation_count_cutoff=None): + spm = np.zeros((n_seeds, len(SPEARMAN_FRACTIONS))) + r2 = np.zeros(n_seeds) + wt_imprv = np.zeros(n_seeds) + topk_med = np.zeros(n_seeds) + best_alpha = np.zeros(n_seeds) + for i in range(n_seeds): + spm[i], r2[i], wt_imprv[i], topk_med[i], best_alpha[i] = test_regression( + X, y, n_train, y_wt, y_cutoff, i, mutation_counts, + mutation_count_cutoff) + df = pd.DataFrame({ + 'R2 score': r2, + 'Improvement over WT': wt_imprv, + 'Top K median': topk_med, + 'N train': n_train, + 'Best alpha': best_alpha, + }) + for i, f in enumerate(SPEARMAN_FRACTIONS): + df[f'Spearman correlation at {f:.1f}'] = spm[:, i] + df['Spearman correlation'] = spm[:, -1] + return df + + +def test_regression(X, y, n_train, y_wt, y_cutoff=None, seed=0, + mutation_counts=None, mutation_count_cutoff=None): + if y_cutoff is not None: + is_valid = (y >= y_cutoff) + X, y = X[is_valid], y[is_valid] + if mutation_counts is not None: + mutation_counts = mutation_counts[is_valid] + X_tr, X_eval, X_test, y_tr, y_eval, y_test = train_eval_test_split( + X, y, seed, n_train, mutation_counts, mutation_count_cutoff) + # Model selection. + best_alpha = None + best_spm = -999.9 + for alpha in [0.001, 0.01, 0.1, 0.2, 0.3, 0.5, 1.0, 2.0]: + model = Ridge(alpha=alpha) + model.fit(X_tr, y_tr) + y_pred = model.predict(X_eval) + spm = spearmanr(y_pred, y_eval).correlation + if spm > best_spm: + best_alpha = alpha + best_spm = spm + model = Ridge(alpha=best_alpha) + model.fit(X_tr, y_tr) + y_pred = model.predict(X_test) + spm = get_spearman_fractions(y_pred, y_test) + r2 = model.score(X_test, y_test) + wt_imprv = wt_improvement_metric(y_pred, y_test, y_wt) + topk_med = topk_median(y_pred, y_test) + return spm, r2, wt_imprv, topk_med, best_alpha + + +def test_classification_multiseeds(X, y, n_train, n_seeds, y_cutoff=None, + mutation_counts=None, mutation_count_cutoff=None): + acc = np.zeros(n_seeds) + best_C = np.zeros(n_seeds) + for i in range(n_seeds): + acc[i], best_C[i] = test_classification( + X, y, n_train, y_cutoff, i, mutation_counts, + mutation_count_cutoff) + return acc, best_C + + +def test_classification(X, y, n_train, y_cutoff, seed=0, + mutation_counts=None, mutation_count_cutoff=None): + y = (y > y_cutoff).astype(int) + X_tr, X_eval, X_test, y_tr, y_eval, y_test = train_eval_test_split( + X, y, seed, n_train, mutation_counts, mutation_count_cutoff) + while len(np.unique(y_tr)) < 2: + X_tr, X_eval, X_test, y_tr, y_eval, y_test = train_eval_test_split( + X, y, seed+np.random.randint(10000), n_train, mutation_counts, + mutation_count_cutoff) + best_C = None + best_acc = -999.9 + for C in [0.01, 0.1, 0.5, 1.0, 2.0]: + model = svm.LinearSVC(C=C) + model.fit(X_tr, y_tr) + acc = model.score(X_eval, y_eval) + if acc > best_acc: + best_C = C + best_acc = acc + model = svm.LinearSVC(C=best_C) + model.fit(X_tr, y_tr) + return model.score(X_test, y_test), best_C + + +def run_regression(feature_reps, y, y_wt, y_cutoff, n_seeds, + mutation_counts=None, mutation_count_cutoff=None): + results = pd.DataFrame() + for feature_rep, X in feature_reps.items(): + print('Staring runs for', feature_rep) + for n_train in [8, 24, 96, 192, 480, 960, 9600]: + if n_train >= 0.8 * X.shape[0]: + continue + print('n_train:', n_train) + df = test_regression_multiseeds(X, y, n_train, n_seeds, y_wt, + y_cutoff, mutation_counts, mutation_count_cutoff) + df['Feature rep'] = feature_rep + results = pd.concat([results, df], axis=0) + return results + + +def run_classification(feature_reps, y, y_cutoff, n_seeds, + mutation_counts=None, mutation_count_cutoff=None): + results = pd.DataFrame() + for feature_rep, X in feature_reps.items(): + print('Staring runs for', feature_rep) + for n_train in [8, 24, 96, 192, 480, 960]: + if n_train >= 0.8 * X.shape[0]: + continue + print('n_train:', n_train) + acc, best_C = test_classification_multiseeds( + X, y, n_train, n_seeds, y_cutoff, mutation_counts, + mutation_count_cutoff) + df = pd.DataFrame({ + 'Accuracy': acc, + 'Best reg coeff': best_C, + 'N train': n_train, + 'Feature rep': feature_rep, + }) + results = pd.concat([results, df], axis=0) + return results \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/io_utils.py b/proteingym/baselines/unirep/utils/io_utils.py new file mode 100644 index 0000000..56354ee --- /dev/null +++ b/proteingym/baselines/unirep/utils/io_utils.py @@ -0,0 +1,193 @@ +import fileinput +import glob +import os + +from Bio import SeqIO +import filelock +import numpy as np +import pandas as pd + +from utils.data_utils import is_valid_seq, seqs_to_onehot + + +def merge_dfs(in_rgx, out_path, index_cols, groupby_cols, ignore_cols): + """ + Merge multiple pandas DataFrames into one and provides a summary file. + Args: + - in_rgx: regex for input filepath + - out_path: output path + - index_cols: index column names for DataFrame + - groupby_cols: groupby column names in the summary step + - ignore_cols: columns to be ignored in the summary step + """ + lock = filelock.FileLock(out_path + '.lock') + with lock: + frames = [] + for f in glob.glob(in_rgx): + try: + frames.append(pd.read_csv(f)) + os.remove(f) + except pd.errors.EmptyDataError: + continue + df = pd.concat(frames, axis=0, sort=True).sort_values(index_cols) + df.set_index(index_cols).to_csv(out_path, float_format='%.4f') + + #df = df.drop(columns=ignore_cols) + #means = df.groupby(groupby_cols).mean() + #stds = df.groupby(groupby_cols).std() + #summary = pd.merge(means, stds, on=groupby_cols, suffixes=('_mean', '_std')) + #summary = summary.sort_index(axis=1) + #save_path = out_path.replace(".csv", "_summary.csv") + #summary.to_csv(save_path, float_format='%.4f') + #return summary + + +def parse_var(s): + """ + Parse a key, value pair, separated by '=' + That's the reverse of ShellArgs. + On the command line (argparse) a declaration will typically look like: + foo=hello + or + foo="hello world" + """ + items = s.split('=') + key = items[0].strip() # we remove blanks around keys, as is logical + if len(items) > 1: + # rejoin the rest: + value = '='.join(items[1:]) + return (key, value) + + +def parse_vars(items): + """ + Parse a series of key-value pairs and return a dictionary + """ + d = {} + + if items: + for item in items: + key, value = parse_var(item) + try: + d[key] = float(value) + except: + d[key] = value + return d + + +def load_data_split(dataset_name, split_id, seed=0, ignore_gaps=False): + data_path = os.path.join('data', dataset_name, 'data.csv') + # Sample shuffles the DataFrame. + data_pre_split = pd.read_csv(data_path).sample(frac=1.0, random_state=seed) + if not ignore_gaps: + is_valid = data_pre_split['seq'].apply(is_valid_seq) + data_pre_split = data_pre_split[is_valid] + if split_id == -1: + return data_pre_split + return np.array_split(data_pre_split, 3)[split_id] + + +def get_wt_log_fitness(dataset_name): + data_path = os.path.join('data', dataset_name, 'data.csv') + data = pd.read_csv(data_path) + try: + return data[data.n_mut == 0].log_fitness.mean() + except: + return data.log_fitness.mean() + + +def get_log_fitness_cutoff(dataset_name): + data_path = os.path.join('data', dataset_name, 'log_fitness_cutoff.npy') + return np.loadtxt(data_path).item() + + +def count_rows(filename_glob_pattern): + cnt = 0 + for f in sorted(glob.glob(filename_glob_pattern)): + with open(f) as fp: + for line in fp: + cnt += 1 + return cnt + + +def load_rows_by_numbers(filename_glob_pattern, line_numbers): + lns_sorted = sorted(line_numbers) + lns_idx = np.argsort(line_numbers) + n_rows = len(line_numbers) + current_ln = 0 # current (accumulated) line number in opened file + j = 0 # index in lns + rows = None + for f in sorted(glob.glob(filename_glob_pattern)): + with open(f) as fp: + for line in fp: + while j < n_rows and lns_sorted[j] == current_ln: + thisrow = np.array([float(x) for x in line.split(' ')]) + if rows is None: + rows = np.full((n_rows, len(thisrow)), np.nan) + rows[lns_idx[j], :] = thisrow + j += 1 + current_ln += 1 + assert j == n_rows, (f"Expected {n_rows} rows, found {j}. " + f"Scanned {current_ln} lines from {filename_glob_pattern}.") + return rows + + +def load(filename_glob_pattern): + files = sorted(glob.glob(filename_glob_pattern)) + if len(files) == 0: + print("No files found for", filename_glob_pattern) + return np.loadtxt(fileinput.input(files)) + + +def save(filename_pattern, data, entries_per_file=2000): + n_files = int(data.shape[0] / entries_per_file) + if data.shape[0] % entries_per_file > 0: + n_files += 1 + for i in range(n_files): + filename = filename_pattern + f'-{i:03d}-of-{n_files:03d}' + l_idx = i * entries_per_file + r_idx = min(l_idx + entries_per_file, data.shape[0]) + np.savetxt(filename, data[l_idx:r_idx]) + + +def load_and_filter_seqs(data_filename, mode="ProteinGym"): + """ + seqs_filename: file to write out filtered sequences + """ + df = pd.read_csv(data_filename, low_memory=False) + if mode=="ProteinGym": + if "mutated_sequence" in df.columns.values: + all_sequences = np.unique(df.mutated_sequence.values) + # assumes "mutant" column contains full mutated sequence instead + else: + all_sequences = np.unique(df.mutant.values) + elif 'Sequence' in df.columns.values: + all_sequences = np.unique(df.Sequence.values) + else: + all_sequences = np.unique(df.seq.values) + seqs = [] + stop_codon_cnt = 0 + for seq in all_sequences: + seq = seq.strip('*') + if is_valid_seq(seq): + seqs.append(seq) + else: + if '*' in seq: + stop_codon_cnt += 1 + else: + print('Invalid seq', seq) + print('Formatted %d sequences. Discarded %d with stop codon.' % (len(seqs), stop_codon_cnt)) + return seqs + + +def read_fasta(filename, return_ids=False): + records = SeqIO.parse(filename, 'fasta') + seqs = list() + ids = list() + for record in records: + seqs.append(str(record.seq)) + ids.append(str(record.id)) + if return_ids: + return seqs, ids + else: + return seqs \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/metric_utils.py b/proteingym/baselines/unirep/utils/metric_utils.py new file mode 100644 index 0000000..9b714a1 --- /dev/null +++ b/proteingym/baselines/unirep/utils/metric_utils.py @@ -0,0 +1,57 @@ +import numpy as np +from scipy.stats import spearmanr +from sklearn.metrics import r2_score, roc_auc_score, ndcg_score + +SPEARMAN_FRACTIONS = np.linspace(0.1, 1.0, 10) + + +def spearman(y_pred, y_true): + if np.var(y_pred) < 1e-6 or np.var(y_true) < 1e-6: + return 0.0 + return spearmanr(y_pred, y_true).correlation + + +def spearman_scoring_fn(sklearn_estimator, X, y): + return spearman(sklearn_estimator.predict(X), y) + + +def ndcg(y_pred, y_true): + y_true_normalized = (y_true - y_true.mean()) / y_true.std() + return ndcg_score(y_true_normalized.reshape(1, -1), y_pred.reshape(1, -1)) + + +def topk_mean(y_pred, y_true, topk=96): + return np.mean(y_true[np.argsort(y_pred)[-topk:]]) + + +def r2(y_pred, y_true): + return r2_score(y_true, y_pred) + + +def hit_rate(y_pred, y_true, y_ref=0.0, topk=96): + n_above = np.sum(y_true[np.argsort(y_pred)[-topk:]] > y_ref) + return float(n_above) / float(topk) + + +def aucroc(y_pred, y_true, y_cutoff): + y_true_bin = (y_true >= y_cutoff) + return roc_auc_score(y_true_bin, y_pred, average='micro') + + +def get_spearman_fractions(y_pred, y_true): + results = np.zeros(len(SPEARMAN_FRACTIONS)) + for i, f in enumerate(SPEARMAN_FRACTIONS): + k = int(f * len(y_true)) + idx = np.argsort(y_true)[-k:] + results[i] = spearmanr(y_pred[idx], y_true[idx]).correlation + return results + + +def wt_improvement_metric(y_pred, y_true, y_wt, topk=96): + hr = hit_rate(y_pred, y_true, y_wt, topk) + baseline = float(np.sum(y_true > y_wt)) / len(y_true) + return hr / baseline + + +def topk_median(y_pred, y_true, topk=96): + return np.median(y_true[np.argsort(y_pred)[-topk:]]) \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/notebook_utils.py b/proteingym/baselines/unirep/utils/notebook_utils.py new file mode 100644 index 0000000..a2c3c0c --- /dev/null +++ b/proteingym/baselines/unirep/utils/notebook_utils.py @@ -0,0 +1,140 @@ +import os +from Bio import SeqIO +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib +from matplotlib.ticker import MultipleLocator, FixedLocator +import seaborn as sns + +from utils.io_utils import load, read_fasta +from utils.data_utils import seq2effect + +# EVMutation imports +from couplings_model import CouplingsModel + + +def add_unirep_model(df, model_names, path, name): + # The inference for UniRep has sorted sequences. + df = df.sort_values('seq') + seqlen = len(df.seq.values[0]) + df[name] = -seqlen * load(path) + model_names.append(name) + return df, model_names + + +def add_ev_model(df, model_names, path, name, dataset, include_indep=False): + wt = read_fasta(os.path.join('../data', dataset, 'wt.fasta'))[0] + + couplings_model = CouplingsModel(path) + df[f'{name}'] = seq2effect(df.seq.values, couplings_model) + model_names.append(name) + + if include_indep: + indep_model = couplings_model.to_independent_model() + df[f'{name}_indep'] = seq2effect(df.seq.values, wt, indep_model) + model_names.append(f'{name}_indep') + return df, model_names + + +def add_hmm_model(df, model_names, path, name, dataset): + df = df.sort_values('seq') + records = SeqIO.parse(os.path.join('../data', dataset, 'seqs.fasta'), + 'fasta') + ids = [] + seqs = [] + for rec in records: + seqs.append(str(rec.seq)) + ids.append(str(rec.id)) + id2seq = pd.Series(index=ids, data=seqs, name='seq') + hmm_ll = pd.read_csv(path)[['target', 'score_full']] + hmm_ll = hmm_ll.join(id2seq, on='target', how='left') + hmm_ll = hmm_ll.drop_duplicates(subset='seq') + df[name] = hmm_ll.sort_values('seq')['score_full'].values + model_names.append(name) + return df, model_names + + +metric_display_name = { + 'ndcg': 'NDCG', + 'topk_mean': 'Top 96 mean', + 'spearman': 'Spearman correlation', +} + + +def retrieve_metric(df, metric_name, n_mut=None, predictor=None): + tmp = df + if predictor is not None: + if isinstance(predictor, str): + predictor = [predictor] + tmp = tmp.loc[tmp.predictor.apply(lambda x: x in predictor)] + if n_mut is not None: + metric_name = f'{metric_name}_{n_mut}mut' + tmp = tmp[['predictor', 'n_train', metric_name]] + return tmp.rename(columns={metric_name:'val'}) + + +def metric_lineplot(df, predictors, metric, predictor_names, dataset_name, + max_n_mut, savename='figure', legend=None, mutcounts=None, **kwargs): + fig, axes = plt.subplots(1, max_n_mut+1, + figsize=((max_n_mut+1)*3, 4), + sharex=True, sharey=True) + ax = axes[0] + nmut_to_title = { + 1: 'Single mutants', + 2: 'Double mutants', + 3: 'Triple mutants', + 4: 'Quadruple mutants', + } + nmut_to_title.update({i: f'{i}th-order Mutants' for i in range(5, 11)}) + tmp = retrieve_metric(df, metric, n_mut=None, predictor=predictors) + sns.lineplot(data=tmp, x='n_train', y='val', + hue='predictor', style='predictor', ax=ax, + hue_order=predictors, style_order=predictors, **kwargs) + #ax.hlines(df[df.predictor == 'mutation'].mean().spearman, 48, 240, color='dimgrey') + ax.set_title(f'mutants of all orders') + ax.set_ylabel(metric_display_name[metric]) + ax.set_xlabel('Training data size') + for n_mut in range(1, max_n_mut+1): + ax = axes[n_mut] + tmp = retrieve_metric(df, metric, n_mut=n_mut, predictor=predictors) + sns.lineplot(data=tmp, x='n_train', y='val', + hue='predictor', style='predictor', ax=ax, + hue_order=predictors, style_order=predictors, **kwargs) + ax.set_title(nmut_to_title[n_mut]) + ax.set_ylabel(metric_display_name[metric]) + ax.set_xlabel('Training data size') + if mutcounts is not None: + for i in range(max_n_mut+1): + axes[i].annotate(f'Data size: {int(mutcounts[i])}', + xy=(0.29, 0.03), xycoords='axes fraction', + fontsize=9) + + if legend is not None: + handles, labels = legend['handles'], legend['labels'] + lgd = fig.legend(handles, labels, bbox_to_anchor=legend['loc'], + loc='upper left', ncol=1, fontsize=11, frameon=False) + + ax.xaxis.set_minor_locator(MultipleLocator(24)) + ax.xaxis.set_major_locator(MultipleLocator(48)) + ax.xaxis.set_major_formatter('{x:.0f}') + + for ax in axes: + ax.get_legend().remove() + + pad = 8 + ax = axes[0] + annot = ax.annotate(dataset_name, xy=(0, 0.5), xytext=(-ax.yaxis.labelpad - pad, 0), + xycoords=ax.yaxis.label, textcoords='offset points', + size='large', ha='right', va='center', rotation=90, fontsize=14) + ax.annotate('Test on:', xy=(-0.05, 0.492), xycoords=ax.title, textcoords='offset points', + size='large', ha='right', va='center', rotation=0, fontsize=13) + + plt.subplots_adjust(top=0.80, wspace=0.1) + if legend is not None: + plt.savefig('../figs/' + savename + '.png', format='png', dpi=600, + bbox_extra_artists=(annot,lgd,), bbox_inches='tight', pad_inches=0) + else: + plt.savefig('../figs/' + savename + '.png', format='png', dpi=600, + bbox_inches='tight', pad_inches=0) + plt.show() \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/plot_utils.py b/proteingym/baselines/unirep/utils/plot_utils.py new file mode 100644 index 0000000..908026a --- /dev/null +++ b/proteingym/baselines/unirep/utils/plot_utils.py @@ -0,0 +1,53 @@ +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from functools import partial +from utils.metric_utils import spearman, topk_mean, hit_rate, aucroc + + +def get_stratified_metrics(df, model_name, max_n_mut, metric_fn): + strat_metrics = np.zeros(max_n_mut+1) + for i in range(0, max_n_mut+1): + # 0th position is overall aggregated metric + tmp = df + if i > 0: + tmp = df[df.n_mut == i] + strat_metrics[i] = metric_fn(tmp[model_name], tmp.log_fitness) + return strat_metrics + + +def plot_stratified_metrics(ax, df, models, max_n_mut, metric_fn, vmin, vmax): + strat_matrix = np.zeros((len(models), 1+max_n_mut)) + xticklabels=['All'] + list(range(1, max_n_mut+1)) + for i, m in enumerate(models): + strat_matrix[i] = get_stratified_metrics(df, m, max_n_mut, metric_fn) + sns.heatmap(strat_matrix, yticklabels=models, xticklabels=xticklabels, + vmin=vmin, vmax=vmax, ax=ax, cmap='viridis') + ax.set_xlabel('# Mutations') + ax.vlines([1], *ax.get_ylim(), colors='black') + + +def plot_auc_and_corr(df, models, functional_threshold, wt_log_fitness, + max_n_mut=5, vmin=[None, None, None], vmax=[None, None, None], topk=96): + fig, axes = plt.subplots(1, 3, figsize=(14, 4), sharex=True, sharey=True) + + ax = axes[0] + fn = partial(aucroc, y_cutoff=functional_threshold) + plot_stratified_metrics(ax, df, models, max_n_mut, fn, vmin[0], vmax[0]) + ax.set_title(f'Functional vs Non-Functional AUC-ROC') + + ax = axes[1] + fn = partial(aucroc, y_cutoff=wt_log_fitness) + plot_stratified_metrics(ax, df[df.log_fitness >= functional_threshold], + models, max_n_mut, fn, vmin[1], vmax[1]) + ax.set_title(f'Functional, =WT AUC-ROC') + + ax = axes[2] + plot_stratified_metrics(ax, df[df.log_fitness >= functional_threshold], + models, max_n_mut, spearman, vmin[2], vmax[2]) + ax.set_title('Rank Correlation (Functional)') + + fig.suptitle('Model performance, stratified by # mutations') + plt.subplots_adjust(wspace=0.1, top=0.85) + plt.show() \ No newline at end of file diff --git a/proteingym/baselines/unirep/utils/unirep_utils.py b/proteingym/baselines/unirep/utils/unirep_utils.py new file mode 100644 index 0000000..b54096b --- /dev/null +++ b/proteingym/baselines/unirep/utils/unirep_utils.py @@ -0,0 +1,143 @@ +import os +import tensorflow.compat.v1 as tf + +def tf_str_len(s): + """ + Returns length of tf.string s + """ + return tf.size(tf.string_split([s],"")) + +def tf_rank1_tensor_len(t): + """ + Returns the length of a rank 1 tensor t as rank 0 int32 + """ + l = tf.reduce_sum(tf.sign(tf.abs(t)), 0) + return tf.cast(l, tf.int32) + + +def tf_seq_to_tensor(s): + """ + Input a tf.string of comma seperated integers. + Returns Rank 1 tensor the length of the input sequence of type int32 + """ + return tf.string_to_number( + tf.sparse_tensor_to_dense(tf.string_split([s],","), default_value='0'), out_type=tf.int32 + )[0] + +def smart_length(length, bucket_bounds=tf.constant([128, 256])): + """ + Hash the given length into the windows given by bucket bounds. + """ + # num_buckets = tf_len(bucket_bounds) + tf.constant(1) + # Subtract length so that smaller bins are negative, then take sign + # Eg: len is 129, sign = [-1,1] + signed = tf.sign(bucket_bounds - length) + + # Now make 1 everywhere that length is greater than bound, else 0 + greater = tf.sign(tf.abs(signed - tf.constant(1))) + + # Now simply sum to count the number of bounds smaller than length + key = tf.cast(tf.reduce_sum(greater), tf.int64) + + # This will be between 0 and len(bucket_bounds) + return key + +def pad_batch(ds, batch_size, padding=None, padded_shapes=([None])): + """ + Helper for bucket batch pad- pads with zeros + """ + return ds.padded_batch(batch_size, + padded_shapes=padded_shapes, + padding_values=padding + ) + +def aas_to_int_seq(aa_seq): + int_seq = "" + for aa in aa_seq: + int_seq += str(aa_to_int[aa]) + "," + return str(aa_to_int['start']) + "," + int_seq + str(aa_to_int['stop']) + +# Preprocessing in python +def fasta_to_input_format(source, destination): + # I don't know exactly how to do this in tf, so resorting to python. + # Should go line by line so everything is not loaded into memory + + sourcefile = os.path.join(source) + destination = os.path.join(destiation) + with open(sourcefile, 'r') as f: + with open(destination, 'w') as dest: + seq = "" + for line in f: + if line[0] == '>' and not seq == "": + dest.write(aas_to_int_seq(seq) + '\n') + seq = "" + elif not line[0] == '>': + seq += line.replace("\n","") + +# Real data pipelines + +def bucketbatchpad( + batch_size=256, + path_to_data=os.path.join("./data/SwissProt/sprot_ints.fasta"), # Preprocessed- see note + compressed="", # See tf.contrib.data.TextLineDataset init args + bounds=[128,256], # Default buckets of < 128, 128><256, >256 + # Unclear exactly what this does, should proly equal batchsize + window_size=256, # NOT a tensor + padding=None, # Use default padding of zero, otherwise see Dataset docs + shuffle_buffer=None, # None or the size of the buffer to shuffle with + pad_shape=([None]), + repeat=1, + filt=None +): + """ + Streams data from path_to_data that is correctly preprocessed. + Divides into buckets given by bounds and pads to full length. + Returns a dataset which will return a padded batch of batchsize + with iteration. + """ + batch_size=tf.constant(batch_size, tf.int64) + bounds=tf.constant(bounds) + window_size=tf.constant(window_size, tf.int64) + + path_to_data = os.path.join(path_to_data) + # Parse strings to tensors + dataset = tf.data.TextLineDataset(path_to_data).map(tf_seq_to_tensor) + if filt is not None: + dataset = dataset.filter(filt) + + if shuffle_buffer: + # Stream elements uniformly randomly from a buffer + dataset = dataset.shuffle(buffer_size=shuffle_buffer) + # Apply a repeat. Because this is after the shuffle, all elements of the dataset should be seen before repeat. + # See https://stackoverflow.com/questions/44132307/tf-contrib-data-dataset-repeat-with-shuffle-notice-epoch-end-mixed-epochs + dataset = dataset.repeat(count=repeat) + # Apply grouping to bucket and pad + group_fn = tf.data.experimental.group_by_window( + key_func=lambda seq: smart_length(tf_rank1_tensor_len(seq), bucket_bounds=bounds), # choose a bucket + reduce_func=lambda key, ds: pad_batch(ds, batch_size, padding=padding, padded_shapes=pad_shape), # apply reduce funtion to pad + window_size=window_size) + grouped_dataset = dataset.apply(group_fn) + return grouped_dataset + +def shufflebatch( + batch_size=256, + shuffle_buffer=None, + repeat=1, + path_to_data="./data/SwissProt/sprot_ints.fasta" +): + """ + Draws from an (optionally shuffled) dataset, repeats dataset repeat times, + and serves batches of the specified size. + """ + + path_to_data = os.path.join(path_to_data) + # Parse strings to tensors + dataset = tf.contrib.data.TextLineDataset(path_to_data).map(tf_seq_to_tensor) + if shuffle_buffer: + # Stream elements uniformly randomly from a buffer + dataset = dataset.shuffle(buffer_size=shuffle_buffer) + # Apply a repeat. Because this is after the shuffle, all elements of the dataset should be seen before repeat. + # See https://stackoverflow.com/questions/44132307/tf-contrib-data-dataset-repeat-with-shuffle-notice-epoch-end-mixed-epochs + dataset = dataset.repeat(count=repeat) + dataset = dataset.batch(batch_size) + return dataset \ No newline at end of file diff --git a/proteingym/baselines/vespa/compute_fitness.py b/proteingym/baselines/vespa/compute_fitness.py new file mode 100644 index 0000000..b364c39 --- /dev/null +++ b/proteingym/baselines/vespa/compute_fitness.py @@ -0,0 +1,115 @@ +import pandas as pd +import os +import argparse +import subprocess +import json +import numpy as np +import tqdm + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser = argparse.ArgumentParser(description='Tranception scoring') + + parser.add_argument("--cache_location", type=str, help="Location of T5 weight cache", default=None) + parser.add_argument("--skip_VESPA_computation",type=str,help="Skip running VESPA if it's been prerun and we just want to compute fitness.") + parser.add_argument("--wt_fasta_file",type=str,default="./WT_sequences.fasta",help="Location of fasta file containing all wild type sequences to compute VESPA scores for.") + parser.add_argument("--vespa_tmp_dir",type=str,default="./vespa_tmp",help="Location of temporary files for VESPA") + parser.add_argument('--DMS_reference_file_path', default=None, type=str, help='Path to reference file with list of DMS to score') + parser.add_argument("--MSA_data_folder", type=str, help="Folder where MSAs are stored", default=None) + parser.add_argument('--DMS_data_folder', type=str, help='Path to folder that contains all DMS assay datasets') + parser.add_argument('--DMS_index', default=0, type=int, help='Index of DMS assay in reference file') + parser.add_argument("--DMS_index_range_start",type=int, help="Start of range of DMS assays", default=None) + parser.add_argument("--DMS_index_range_end",type=int, help="Index of DMS assay in reference file", default=None) + + args = parser.parse_args() + if not os.path.exists(args.vespa_tmp_dir): + os.makedirs(args.vespa_tmp_dir) + + if args.DMS_reference_file_path is not None: + if args.DMS_index_range_start is not None and args.DMS_index_range_end is not None: + DMS_reference_file = pd.read_csv(args.DMS_reference_file_path) + DMS_filenames = DMS_reference_file['DMS_filename'][args.DMS_index_range_start:args.DMS_index_range_end+1].tolist() + MSA_filenames = DMS_reference_file['MSA_filename'][args.DMS_index_range_start:args.DMS_index_range_end+1].unique().tolist() + target_seqs = DMS_reference_file['target_seq'][args.DMS_index_range_start:args.DMS_index_range_end+1].tolist() + else: + DMS_reference_file = pd.read_csv(args.DMS_reference_file_path) + DMS_filenames = [DMS_reference_file['DMS_filename'][args.DMS_index]] + MSA_filenames = [DMS_reference_file['MSA_filename'][args.DMS_index]] + target_seqs = [DMS_reference_file['target_seq'][args.DMS_index]] + else: + DMS_filenames = [args.DMS_file_name] + MSA_filenames = [args.MSA_file_name] + target_seqs = [args.target_seq] + if not args.skip_VESPA_computation: + # Build fasta file of wild types sequences based on first sequence of each alignment in MSA_filenames + all_WT_sequences_fasta = args.vespa_tmp_dir + os.sep + args.wt_fasta_file + index_map = {"indices":[],"MSA_filename":[]} + for i,filename in enumerate(MSA_filenames): + index_map["indices"].append(i) + index_map["MSA_filename"].append(filename) + f = os.path.join(args.MSA_data_folder, filename) + target_seq="" + with open(f, 'r') as msa_data: + for i, line in enumerate(msa_data): + line = line.rstrip() + if line.startswith(">") and i==0: + with open(all_WT_sequences_fasta,'a+') as seq_wt_file: + seq_wt_file.write(line+"\n") + elif line.startswith(">"): + break + else: + with open(all_WT_sequences_fasta,'a+') as seq_wt_file: + seq_wt_file.write(line+"\n") + subprocess.run(["vespa",args.wt_fasta_file,"--prott5_weights_cache", args.cache_location, "--vespa"], cwd=args.vespa_tmp_dir, check=True) + else: + index_map = {"indices":[],"MSA_filename":[]} + for i,filename in enumerate(MSA_filenames): + index_map["indices"].append(i) + index_map["MSA_filename"].append(filename) + print(f"Skipping VESPA computation, scoring DMS assays {DMS_filenames} with precomputed VESPA scores. Note that this assumes that the WT sequence file contains the wild type sequences in the same order as the DMS reference file.") + # Score each DMS assay + assert os.path.exists(f"{args.vespa_tmp_dir}/vespa_run_directory/output/map.json") + map_dict = json.load(open(f"{args.vespa_tmp_dir}/vespa_run_directory/output/map.json","r")) + index_df = pd.DataFrame(index_map) + index_df["VESPA_scoring_file_name"] = index_df["indices"].apply(lambda x: map_dict[str(x)]) + index_df = index_df.rename(columns={"indices":"VESPA_scoring_file_index"}) + DMS_reference_file_subset = DMS_reference_file.iloc[args.DMS_index_range_start:args.DMS_index_range_end+1] + DMS_reference_file_subset = DMS_reference_file_subset.merge(index_df, how="left",on="MSA_filename") + list_DMS = DMS_reference_file_subset["DMS_id"] + VESPA_scores_folder = f"{args.vespa_tmp_dir}/vespa_run_directory/output" + for DMS_id in list_DMS: + print(DMS_id) + DMS_filename = DMS_reference_file_subset["DMS_filename"][DMS_reference_file_subset["DMS_id"]==DMS_id].values[0] + VESPA_scores_filename = int(DMS_reference_file_subset["VESPA_scoring_file_index"][DMS_reference_file_subset["DMS_id"]==DMS_id].values[0]) + print(VESPA_scores_filename) + VESPA_scores_filename = str(VESPA_scores_filename) + '.csv' + DMS_file = pd.read_csv(args.DMS_data_folder+os.sep+DMS_filename) #mutant,mutated_sequence,DMS_score,DMS_score_bin + VESPA_scores = pd.read_csv(VESPA_scores_folder+os.sep+VESPA_scores_filename, sep=";") + MSA_start = DMS_reference_file_subset["MSA_start"][DMS_reference_file_subset["DMS_id"]==DMS_id].values[0] + VESPA_scores['mutant'] = VESPA_scores['Mutant'].apply(lambda x: x[0] + str(int(int(x[1:-1])+MSA_start)) + x[-1]) + VESPA_scores['VESPA'] = np.log(1 - VESPA_scores['VESPA']) #log proba of being functional (raw score is proba that mutation has an "effect") + VESPA_scores['VESPAl'] = np.log(1 - VESPA_scores['VESPAl']) + mapping_mutant_VESPA={} + mapping_mutant_VESPAl={} + for mutant in tqdm.tqdm(DMS_file['mutant']): + VESPA_score_singles_sum = 0 + VESPAl_score_singles_sum = 0 + #Proba of multiple mutants to be benign is the product that each mutant is benign + num_synonomous = 0 + for single in mutant.split(":"): + # skipping synomymous mutations + if single[0] == single[-1]: + num_synonomous += 1 + continue + VESPA_score_singles_sum += VESPA_scores['VESPA'][VESPA_scores['mutant']==single].values[0] + VESPAl_score_singles_sum += VESPA_scores['VESPAl'][VESPA_scores['mutant']==single].values[0] + assert VESPA_score_singles_sum!=0 or num_synonomous == len(mutant.split(":")), "Missing VESPA scores" + mapping_mutant_VESPA[mutant] = VESPA_score_singles_sum + mapping_mutant_VESPAl[mutant] = VESPAl_score_singles_sum + mapping_mutant_VESPA = pd.DataFrame.from_dict(mapping_mutant_VESPA, orient='index').reset_index() + mapping_mutant_VESPA.columns = ['mutant','VESPA'] + mapping_mutant_VESPAl = pd.DataFrame.from_dict(mapping_mutant_VESPAl, orient='index').reset_index() + mapping_mutant_VESPAl.columns = ['mutant','VESPAl'] + final_scores_VESPA = pd.merge(DMS_file,mapping_mutant_VESPA, how='left',on='mutant') + final_scores_VESPA = pd.merge(final_scores_VESPA,mapping_mutant_VESPAl, how='left',on='mutant') + print(final_scores_VESPA) \ No newline at end of file diff --git a/proteingym/constants.json b/proteingym/constants.json new file mode 100644 index 0000000..75bfbba --- /dev/null +++ b/proteingym/constants.json @@ -0,0 +1,262 @@ +{ + "model_details":{ + "Tranception_L_no_retrieval":"Tranception Large model (700M params) without retrieval", + "Tranception_M_no_retrieval":"Tranception Medium model (300M params) without retrieval", + "Tranception_S_no_retrieval":"Tranception Small model (85M params) without retrieval", + "Tranception_S":"Tranception Small model (85M params) with retrieval", + "Tranception_M":"Tranception Medium model (300M params) with retrieval", + "Tranception_L":"Tranception Large model (700M params) with retrieval", + "EVE_single":"EVE model (single seed)", + "EVE_ensemble":"EVE model (ensemble of 5 independently-trained models)", + "MSA_Transformer_single":"MSA Transformer (single MSA sample)", + "MSA_Transformer_ensemble":"MSA Transformer (ensemble of 5 MSA samples)", + "ESM1v_single":"ESM-1v (single seed)", + "ESM1v_ensemble":"ESM-1v (ensemble of 5 independently-trained models)", + "Wavenet":"Wavenet model", + "DeepSequence_single":"DeepSequence model (single seed)", + "DeepSequence_ensemble":"DeepSequence model (ensemble of 5 independently-trained models)", + "Site_Independent":"Site-Independent model", + "EVmutation":"EVmutation model", + "RITA_s":"RITA small model (85M params)", + "RITA_m":"RITA medium model (300M params)", + "RITA_l":"RITA large model (680M params)", + "RITA_xl":"RITA xlarge model (1.2B params)", + "RITA_ensemble":"Ensemble of the 4 RITA models", + "Progen2_small":"Progen2 small model (150M params)", + "Progen2_medium":"Progen2 medium model (760M params)", + "Progen2_base":"Progen2 base model (760M params)", + "Progen2_large":"Progen2 large model (2.7B params)", + "Progen2_xlarge":"Progen2 xlarge model (6.4B params)", + "Progen2_ensemble":"Ensemble of the 5 Progen2 models", + "Unirep":"Unirep model", + "Unirep_evotune":"Unirep model w/ evotuning", + "GEMME":"GEMME model", + "VESPA":"VESPA model", + "VESPAl":"VESPAl model", + "ProtGPT2":"ProtGPT2 model", + "ESM1b":"ESM-1b (w/ Brandes et al. extensions)", + "TranceptEVE_S":"TranceptEVE Small model (Tranception Small & retrieved EVE model)", + "TranceptEVE_M":"TranceptEVE Medium model (Tranception Medium & retrieved EVE model)", + "TranceptEVE_L":"TranceptEVE Large model (Tranception Large & retrieved EVE model)", + "HMM": "Profile Hidden Markov model", + "Provean": "Provean model", + "ESM-IF1": "ESM-IF1 model", + "ESM2_650M": "ESM2 model (650M params)", + "ESM2_3B": "ESM2 model (3B params)", + "ESM2_15B": "ESM2 model (15B params)", + "ESM2_150M": "ESM2 model (150M params)", + "ESM2_35M":"ESM2 model (35M params)", + "ESM2_8M":"ESM2 model (8M params)", + "MIFST":"MIF-ST model", + "MIF":"MIF model", + "CARP_640M": "CARP model (640M params)", + "CARP_76M": "CARP model (76M params)", + "CARP_38M": "CARP model (38M params)", + "CARP_600K": "CARP model (600K params)", + "ProteinMPNN": "ProteinMPNN model", + "ProtSSN_k10_h512": "ProtSSN (k=10, h=512)", + "ProtSSN_k10_h768": "ProtSSN (k=10, h=768)", + "ProtSSN_k10_h1280": "ProtSSN (k=10, h=1280)", + "ProtSSN_k20_h512": "ProtSSN (k=20, h=512)", + "ProtSSN_k20_h768": "ProtSSN (k=20, h=768)", + "ProtSSN_k20_h1280": "ProtSSN (k=20, h=1280)", + "ProtSSN_k30_h512": "ProtSSN (k=30, h=512)", + "ProtSSN_k30_h768": "ProtSSN (k=30, h=768)", + "ProtSSN_k30_h1280": "ProtSSN (k=30, h=1280)", + "ProtSSN_ensemble": "ProtSSN (ensemble of 9 models)", + "SaProt_650M_AF2": "SaProt (650M)", + "SaProt_35M_AF2": "SaProt (35M)" + }, + "model_references":{ + "Tranception_L_no_retrieval":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "Tranception_M_no_retrieval":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "Tranception_S_no_retrieval":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "Tranception_S":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "Tranception_M":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "Tranception_L":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "EVE_single":"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.", + "EVE_ensemble":"Frazer, J., Notin, P., Dias, M., Gomez, A.N., Min, J.K., Brock, K.P., Gal, Y., & Marks, D.S. (2021). Disease variant prediction with deep generative models of evolutionary data. Nature.", + "MSA_Transformer_single":"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.", + "MSA_Transformer_ensemble":"Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML.", + "ESM1v_single":"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.", + "ESM1v_ensemble":"Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS.", + "Wavenet":"Shin, J., Riesselman, A.J., Kollasch, A.W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A.C., & Marks, D.S. (2021). Protein design and variant prediction using autoregressive generative models. Nature Communications, 12.", + "DeepSequence_single":"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.", + "DeepSequence_ensemble":"Riesselman, A.J., Ingraham, J., & Marks, D.S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15, 816-822.", + "Site_Independent":"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.", + "EVmutation":"Hopf, T.A., Ingraham, J., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., & Marks, D.S. (2017). Mutation effects predicted from sequence co-variation. Nature Biotechnology, 35, 128-135.", + "RITA_s":"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.", + "RITA_m":"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.", + "RITA_l":"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.", + "RITA_xl":"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.", + "RITA_ensemble":"Hesslow, D., Zanichelli, N., Notin, P., Poli, I., & Marks, D.S. (2022). RITA: a Study on Scaling Up Generative Protein Sequence Models. ArXiv, abs/2205.05789.", + "Progen2_small":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Progen2_medium":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Progen2_base":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Progen2_large":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Progen2_xlarge":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Progen2_ensemble":" Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., & Madani, A. (2022). ProGen2: Exploring the Boundaries of Protein Language Models. ArXiv, abs/2206.13517. ", + "Unirep":"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.", + "Unirep_evotune":"Alley, E.C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G.M. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods, 1-8.", + "GEMME":"Laine, É., Karami, Y., & Carbone, A. (2019). GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects. Molecular Biology and Evolution, 36, 2604 - 2619.", + "ProtGPT2":"Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13.", + "VESPA":"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.", + "VESPAl":"Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Bernhofer, M., Erckert, K., & Rost, B. (2021). Embeddings from protein language models predict conservation and variant effects. Human Genetics, 141, 1629 - 1647.", + "ESM1b":"[1] Original model: Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118. [2] Extensions: Brandes, N., Goldman, G., Wang, C.H., Ye, C.J., & Ntranos, V. (2022). Genome-wide prediction of disease variants with a deep protein language model. bioRxiv.", + "TranceptEVE_S":"Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.", + "TranceptEVE_M":"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.", + "TranceptEVE_L":"Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop.", + "HMM":"HMMER: biosequence analysis using profile hidden Markov models", + "Provean": "Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012). Predicting the functional effect of amino acid substitutions and indels. PloS one.", + "ESM-IF1": "Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives (2022). Learning Inverse Folding from Millions of Predicted Structures. BioRxiv.", + "ESM2_650M":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "ESM2_3B":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "ESM2_15B":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "ESM2_150M":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "ESM2_35M":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "ESM2_8M":"Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan Dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, Vol. 379.", + "MIFST":"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.", + "MIF":"Kevin K. Yang, Hugh Yeh, Niccolo Zanichelli (2023). Masked Inverse folding with Sequence Transfer for Protein Representation Learning. BioRxiv.", + "CARP_640M":"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.", + "CARP_76M":"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.", + "CARP_38M":"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.", + "CARP_600K":"Kevin K. Yang, Nicolo Fusi, Alex X. Lu (2023). Convolutions are competitive with transformers for protein sequence pretraining. BioRxiv.", + "ProteinMPNN":"J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan,B. Koepnick, H. Nguyen, A. Kang, B. Sankaran,A. K. Bera, N. P. King,D. Baker (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science, Vol 378.", + "ProtSSN_k10_h512": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k10_h768": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k10_h1280": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k20_h512": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k20_h768": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k20_h1280": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k30_h512": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k30_h768": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_k30_h1280": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "ProtSSN_ensemble": "Yang Tan, Bingxin Zhou, Lirong Zheng, Guisheng Fan, Liang Hong. (2023). Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability. bioRxiv.", + "SaProt_650M_AF2": "Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv.", + "SaProt_35M_AF2": "Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan. (2024). SaProt: Protein Language Modeling with Structure-aware Vocabulary. bioRxiv." + }, + "clean_names":{ + "Tranception_L_no_retrieval":"Tranception L no retrieval", + "Tranception_M_no_retrieval": "Tranception M no retrieval", + "Tranception_S_no_retrieval": "Tranception S no retrieval", + "Tranception_S":"Tranception S", + "Tranception_M":"Tranception M", + "Tranception_L":"Tranception L", + "EVE_single":"EVE (single)", + "EVE_ensemble":"EVE (ensemble)", + "MSA_Transformer_single":"MSA Transformer (single)", + "MSA_Transformer_ensemble":"MSA Transformer (ensemble)", + "ESM1v_single":"ESM-1v (single)", + "ESM1v_ensemble":"ESM-1v (ensemble)", + "Wavenet":"Wavenet", + "DeepSequence_single":"DeepSequence (single)", + "DeepSequence_ensemble":"DeepSequence (ensemble)", + "Site_Independent":"Site-Independent", + "EVmutation":"EVmutation", + "RITA_s":"RITA S", + "RITA_m":"RITA M", + "RITA_l":"RITA L", + "RITA_xl":"RITA XL", + "RITA_ensemble":"RITA (ensemble)", + "Progen2_small":"Progen2 S", + "Progen2_medium":"Progen2 M", + "Progen2_base":"Progen2 Base", + "Progen2_large":"Progen2 L", + "Progen2_xlarge":"Progen2 XL", + "Progen2_ensemble":"Progen2 (ensemble)", + "Unirep":"Unirep", + "Unirep_evotune":"Unirep evotuned", + "GEMME":"GEMME", + "ProtGPT2":"ProtGPT2", + "VESPA":"VESPA", + "VESPAl":"VESPAl", + "ESM1b": "ESM-1b", + "TranceptEVE_S": "TranceptEVE S", + "TranceptEVE_M":"TranceptEVE M", + "TranceptEVE_L":"TranceptEVE L", + "HMM":"Hidden Markov Model", + "Provean": "Provean", + "ESM-IF1": "ESM-IF1", + "ESM2_650M": "ESM2 (650M)", + "ESM2_3B": "ESM2 (3B)", + "ESM2_15B": "ESM2 (15B)", + "ESM2_150M": "ESM2 (150M)", + "ESM2_35M": "ESM2 (35M)", + "ESM2_8M": "ESM2 (8M)", + "MIFST":"MIF-ST", + "MIF":"MIF", + "CARP_640M":"CARP (640M)", + "CARP_76M":"CARP (76M)", + "CARP_38M":"CARP (38M)", + "CARP_600K":"CARP (600K)", + "ProteinMPNN":"ProteinMPNN", + "DMS_id":"DMS ID", + "number_mutants":"Number of Mutants", + "UniProt_ID":"UniProt ID", + "Neff_L_category": "Neff/L Category", + "Model_rank":"Rank", + "Model_name":"Model name", + "ProtSSN_k10_h512": "ProtSSN (k=10 h=512)", + "ProtSSN_k10_h768": "ProtSSN (k=10 h=768)", + "ProtSSN_k10_h1280": "ProtSSN (k=10 h=1280)", + "ProtSSN_k20_h512": "ProtSSN (k=20 h=512)", + "ProtSSN_k20_h768": "ProtSSN (k=20 h=768)", + "ProtSSN_k20_h1280": "ProtSSN (k=20 h=1280)", + "ProtSSN_k30_h512": "ProtSSN (k=30 h=512)", + "ProtSSN_k30_h768": "ProtSSN (k=30 h=768)", + "ProtSSN_k30_h1280": "ProtSSN (k=30 h=1280)", + "ProtSSN_ensemble": "ProtSSN (ensemble)", + "SaProt_650M_AF2": "SaProt (650M)", + "SaProt_35M_AF2": "SaProt (35M)" + }, + "supervised_model_details": { + "ProteinNPT":"ProteinNPT Model", + "MSA Transformer Embeddings": "MSA Transformer Embeddings", + "ESM-1v Embeddings": "ESM-1v Embeddings", + "Tranception Embeddings": "Tranception Embeddings", + "TranceptEVE + One-Hot Encodings": "TranceptEVE + One-Hot Encodings", + "ESM-1v + One-Hot Encodings": "ESM-1v + One-Hot Encodings", + "Tranception + One-Hot Encodings": "Tranception + One-Hot Encodings", + "DeepSequence + One-Hot Encodings": "DeepSequence + One-Hot Encodings", + "MSA_Transformer + One-Hot Encodings": "MSA Transformer + One-Hot Encodings", + "One-Hot Encodings": "One-Hot Encodings" + }, + "supervised_model_references":{ + "ProteinNPT": "Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "MSA Transformer Embeddings":"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "ESM-1v Embeddings":"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "Tranception Embeddings":"[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "TranceptEVE + One-Hot Encodings": "[1] Original model: Notin, P., Van Niekerk, L., Kollasch, A., Ritter, D., Gal, Y. & Marks, D.S. & (2022). TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction. NeurIPS, LMRL workshop. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "ESM-1v + One-Hot Encodings":"[1] Original model: Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. NeurIPS. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "Tranception + One-Hot Encodings": "[1] Original model: Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A.N., Marks, D.S., & Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "DeepSequence + One-Hot Encodings":"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5", + "MSA_Transformer + One-Hot Encodings":"[1] Original model: Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J.F., Abbeel, P., Sercu, T., & Rives, A. (2021). MSA Transformer. ICML. [2] Extension: Notin, P., Weitzman, R., Marks, D. S., & Gal, Y. (2023). ProteinNPT: Improving protein property prediction and design with non-parametric transformers. Thirty-Seventh Conference on Neural Information Processing Systems", + "One-Hot Encodings":"Hsu, C., Nisonoff, H., Fannjiang, C. et al. Learning protein fitness models from evolutionary and assay-labeled data. Nat Biotechnol 40, 1114–1122 (2022). https://doi.org/10.1038/s41587-021-01146-5" + }, + "supervised_clean_names":{ + "ProteinNPT":"ProteinNPT", + "Embeddings - Augmented - MSA Transformer":"MSA Transformer Embeddings", + "Embeddings - MSA Transformer":"MSA Transformer Embeddings", + "Embeddings - Augmented - EMS1v":"ESM-1v Embeddings", + "Embeddings - Augmented - Tranception":"Tranception Embeddings", + "Embeddings - Tranception":"Tranception Embeddings", + "OHE - Augmented - TransceptEVE":"TranceptEVE + One-Hot Encodings", + "OHE - Augmented - Tranception":"Tranception + One-Hot Encodings", + "OHE - Augmented - MSA Transformer":"MSA_Transformer + One-Hot Encodings", + "OHE - Augmented - DeepSequence":"DeepSequence + One-Hot Encodings", + "OHE - Augmented - ESM1v":"ESM-1v + One-Hot Encodings", + "OHE - Not augmented":"One-Hot Encodings" + }, + "supervised_model_types":{ + "ProteinNPT":"Embedding", + "MSA Transformer Embeddings":"Embedding", + "Tranception Embeddings":"Embedding", + "ESM-1v Embeddings":"Embedding", + "TranceptEVE + One-Hot Encodings":"One-hot Encoding", + "Tranception + One-Hot Encodings":"One-hot Encoding", + "MSA_Transformer + One-Hot Encodings":"One-hot Encoding", + "DeepSequence + One-Hot Encodings":"One-hot Encoding", + "ESM-1v + One-Hot Encodings":"One-hot Encoding", + "One-Hot Encodings":"One-hot Encoding" + } +} \ No newline at end of file diff --git a/proteingym/merge.py b/proteingym/merge.py new file mode 100644 index 0000000..2881ad5 --- /dev/null +++ b/proteingym/merge.py @@ -0,0 +1,119 @@ +import pandas as pd +import numpy as np +import os +import json +import argparse +from tqdm import tqdm + + +def standardization(x): + """Assumes input is numpy array or pandas series""" + return (x - x.mean()) / x.std() + + +proteingym_folder_path = os.path.dirname(os.path.realpath(__file__)) + + +def main(): + parser = argparse.ArgumentParser(description='ProteinGym score merging') + parser.add_argument('--DMS_assays_location', type=str, default='~/.cache/ProteinGym/ProteinGym/DMS_assays/substitutions', + help='Path to folder containing all model scores') + parser.add_argument('--model_scores_location', type=str, default='~/.cache/ProteinGym/model_scores/zero_shot_substitutions', + help='Path to folder containing all model scores') + parser.add_argument('--merged_scores_dir', type=str, default="merged_scores", + help='Name of folder where all merged scores should be stored (in model_scores_location)') + parser.add_argument('--mutation_type', default='substitutions', + type=str, help='Type of mutations (substitutions | indels)') + parser.add_argument('--dataset', default='DMS', type=str, + help='Dataset to merge (DMS | clinical)') + parser.add_argument('--DMS_reference_file', default=f'{os.path.dirname(proteingym_folder_path)}/reference_files/DMS_substitutions.csv', + type=str, help='Path to reference file containing DMS assay information') + parser.add_argument('--config_file', default=f'{os.path.dirname(proteingym_folder_path)}/config.json', + type=str, help='Path to config file containing model information') + args = parser.parse_args() + + reference_file = pd.read_csv(args.DMS_reference_file) + list_DMS = reference_file["DMS_id"] + + with open(args.config_file) as f: + config = json.load(f) + if args.dataset == "DMS": + reference_field = "model_list_zero_shot_substitutions_DMS" if args.mutation_type == "substitutions" else "model_list_zero_shot_indels_DMS" + elif args.dataset == "clinical": + reference_field = "model_list_zero_shot_substitutions_clinical" if args.mutation_type == "substitutions" else "model_list_zero_shot_indels_clinical" + + list_models = config[reference_field].keys() + pbar = tqdm(enumerate(list_DMS), total=len(list_DMS)) + for DMS_index, DMS_id in pbar: + target_seq = reference_file["target_seq"][reference_file["DMS_id"] + == DMS_id].values[0] + pbar.set_description("Processing DMS assay {}".format(DMS_id)) + DMS_filename = reference_file["DMS_filename"][reference_file["DMS_id"] + == DMS_id].values[0] + full_DMS_path = os.path.join(args.DMS_assays_location, DMS_filename) + if os.path.exists(full_DMS_path): + DMS_file = pd.read_csv(os.path.join( + args.DMS_assays_location, DMS_filename)) + else: + print("Could not find DMS file {}. Skipping.".format(full_DMS_path)) + continue + + if "mutated_sequence" not in DMS_file: + DMS_file["mutated_sequence"] = DMS_file["mutant"] + + score_files = {} + all_model_scores = DMS_file + orig_DMS_length = len(all_model_scores) + for model in list_models: + mutant_merge_key = config[reference_field][model]["key"] + input_score_name = config[reference_field][model]["input_score_name"] + # Mutant merge key depends on the model for subs + DMS_mutant_column = mutant_merge_key if args.mutation_type == "substitutions" else "mutated_sequence" + + score_files[model] = pd.read_csv(os.path.join( + args.model_scores_location, config[reference_field][model]["location"], DMS_id+".csv")) + if "sequence" in score_files[model]: + score_files[model]["mutated_sequence"] = score_files[model]["sequence"] + score_files[model][model] = config[reference_field][model]["directionality"] * \ + score_files[model][input_score_name] + score_files[model] = score_files[model][[mutant_merge_key, model]] + score_files[model].drop_duplicates(inplace=True) + score_files[model] = score_files[model].groupby( + mutant_merge_key).mean().reset_index() + # check that score_files[model][mutant_merge_key] and all_model_scores[DMS_mutant_column] are the same + if set(score_files[model][mutant_merge_key]) & set(all_model_scores[DMS_mutant_column]) == set(): + print("Warning: No overlap on mutants for {} with model {}. Skipping".format(DMS_id, model)) + continue + elif set(score_files[model][mutant_merge_key]) < set(all_model_scores[DMS_mutant_column]): + # print difference between two key sets + print("WARNING: {} and {} do not have the same mutants. Skipping." \ + .format(model, DMS_id)) + continue + + score_files[model] = score_files[model].rename(columns={mutant_merge_key: DMS_mutant_column}) + all_model_scores = pd.merge(all_model_scores, score_files[model], on=DMS_mutant_column, how='left') + + if len(all_model_scores) != orig_DMS_length: + print("WARNING: Merge on {} for {} changed length. mutant_merge_keys are likely different between them.".format( + model, DMS_id)) + print("Length DMS: {}".format(orig_DMS_length)) + print("Length {}: {}".format(model, len(score_files[model]))) + print("Length all_model_score unique keys: {}".format( + len(all_model_scores[mutant_merge_key].unique()))) + print("Length DMS unique keys: {}".format( + len(score_files[model][mutant_merge_key].unique()))) + print("Length merged file: {}".format(len(all_model_scores))) + continue + num_mutants_expected = reference_file[reference_file["DMS_id"] == DMS_id]["DMS_total_number_mutants"].values[0] + if len(all_model_scores) != num_mutants_expected: + print(f"Warning: Insufficient mutants for {DMS_id}: {len(all_model_scores)}, expected {num_mutants_expected}. Original DMS file length: {orig_DMS_length}") + if not os.path.isdir(os.path.join(args.model_scores_location, args.merged_scores_dir)): + os.mkdir(os.path.join( + args.model_scores_location, args.merged_scores_dir)) + all_model_scores.to_csv(os.path.join( + args.model_scores_location, args.merged_scores_dir, f"{DMS_id}.csv"), index=False) + print("Length merged file: {}".format(len(all_model_scores))) + + +if __name__ == '__main__': + main() diff --git a/proteingym/performance_DMS_benchmarks.py b/proteingym/performance_DMS_benchmarks.py new file mode 100644 index 0000000..8c9ed71 --- /dev/null +++ b/proteingym/performance_DMS_benchmarks.py @@ -0,0 +1,406 @@ +import pandas as pd +import numpy as np +import os +import argparse +from scipy.stats import spearmanr +from sklearn.metrics import roc_auc_score, matthews_corrcoef +import warnings +import json +warnings.simplefilter(action='ignore', category=FutureWarning) + +def minmax(x): + return ( (x - np.min(x)) / (np.max(x) - np.min(x)) ) + +def calc_ndcg(y_true, y_score, **kwargs): + ''' + Inputs: + y_true: an array of the true scores where higher score is better + y_score: an array of the predicted scores where higher score is better + Options: + quantile: If True, uses the top k quantile of the distribution + top: under the quantile setting this is the top quantile to + keep in the gains calc. This is a PERCENTAGE (i.e input 10 for top 10%) + Notes: + Currently we're calculating NDCG on the continuous value of the DMS + I tried it on the binary value as well and the metrics seemed mostly + the same. + ''' + if 'quantile' not in kwargs: + kwargs['quantile'] = True + if 'top' not in kwargs: + kwargs['top'] = 10 + if kwargs['quantile']: + k = np.floor(y_true.shape[0]*(kwargs['top']/100)).astype(int) + else: + k = kwargs['top'] + if isinstance(y_true, pd.Series): + y_true = y_true.values + if isinstance(y_score, pd.Series): + y_score = y_score.values + gains = minmax(y_true) + ranks = np.argsort(np.argsort(-y_score)) + 1 + + if k == 'all': + k = len(ranks) + #sub to top k + ranks_k = ranks[ranks <= k] + gains_k = gains[ranks <= k] + #all terms with a gain of 0 go to 0 + ranks_fil = ranks_k[gains_k != 0] + gains_fil = gains_k[gains_k != 0] + + #if none of the ranks made it return 0 + if len(ranks_fil) == 0: + return (0) + + #discounted cumulative gains + dcg = np.sum([g/np.log2(r+1) for r,g in zip(ranks_fil, gains_fil)]) + + #ideal dcg - calculated based on the top k actual gains + ideal_ranks = np.argsort(np.argsort(-gains)) + 1 + ideal_ranks_k = ideal_ranks[ideal_ranks <= k] + ideal_gains_k = gains[ideal_ranks <= k] + ideal_ranks_fil = ideal_ranks_k[ideal_gains_k != 0] + ideal_gains_fil = ideal_gains_k[ideal_gains_k != 0] + idcg = np.sum([g/np.log2(r+1) for r,g in zip(ideal_ranks_fil, ideal_gains_fil)]) + + #normalize + ndcg = dcg/idcg + + return (ndcg) +def calc_toprecall(true_scores, model_scores, top_true=10, top_model=10): + top_true = (true_scores >= np.percentile(true_scores, 100-top_true)) + top_model = (model_scores >= np.percentile(model_scores, 100-top_model)) + + TP = (top_true) & (top_model) + recall = TP.sum() / (top_true.sum()) if top_true.sum() > 0 else 0 + + return (recall) + +def standardization(x): + """Assumes input is numpy array or pandas series""" + return (x - x.mean()) / x.std() + +def compute_bootstrap_standard_error(df, number_assay_reshuffle=10000): + """ + Computes the non-parametric bootstrap standard error for the mean estimate of a given performance metric (eg., Spearman, AUC) across DMS assays (ie., the sample standard deviation of the mean across bootstrap samples) + """ + model_names = df.columns + mean_performance_across_samples = [] + for sample in range(number_assay_reshuffle): + mean_performance_across_samples.append(df.sample(frac=1.0, replace=True).mean(axis=0)) #Resample a dataset of the same size (with replacement) then take the sample mean + mean_performance_across_samples=pd.DataFrame(data=mean_performance_across_samples,columns=model_names) + return mean_performance_across_samples.std(ddof=1) + +def compute_bootstrap_standard_error_functional_categories(df, number_assay_reshuffle=10000): + """ + Computes the non-parametric bootstrap standard error for the mean estimate of a given performance metric (eg., Spearman, AUC) across DMS assays (ie., the sample standard deviation of the mean across bootstrap samples) + """ + model_names = df.columns + mean_performance_across_samples = {} + for category, group in df.groupby("Selection Type"): + mean_performance_across_samples[category] = [] + for sample in range(number_assay_reshuffle): + mean_performance_across_samples[category].append(group.sample(frac=1.0, replace=True).mean(axis=0)) #Resample a dataset of the same size (with replacement) then take the sample mean + mean_performance_across_samples[category]=pd.DataFrame(data=mean_performance_across_samples[category]) + categories = list(mean_performance_across_samples.keys()) + combined_averages = mean_performance_across_samples[categories[0]].copy() + for category in categories[1:]: + combined_averages += mean_performance_across_samples[category] + combined_averages /= len(categories) + return combined_averages.std(ddof=1) + + +proteingym_folder_path = os.path.dirname(os.path.realpath(__file__)) + +def main(): + parser = argparse.ArgumentParser(description='ProteinGym performance analysis') + parser.add_argument('--input_scoring_files_folder', type=str, help='Name of folder where all input scores are present (expects one scoring file per DMS)') + parser.add_argument('--output_performance_file_folder', default='./outputs/tranception_performance', type=str, help='Name of folder where to save performance analysis files') + parser.add_argument('--DMS_reference_file_path', type=str, help='Reference file with list of DMSs to consider') + parser.add_argument('--DMS_data_folder', type=str, help='Path to folder that contains all DMS datasets') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + parser.add_argument('--performance_by_depth', action='store_true', help='Whether to compute performance by mutation depth') + parser.add_argument('--config_file', default=f'{os.path.dirname(proteingym_folder_path)}/config.json', type=str, help='Path to config file containing model information') + args = parser.parse_args() + + mapping_protein_seq_DMS = pd.read_csv(args.DMS_reference_file_path) + mapping_protein_seq_DMS["MSA_Neff_L_category"] = mapping_protein_seq_DMS["MSA_Neff_L_category"].apply(lambda x: x[0].upper() + x[1:] if type(x) == str else x) + num_DMS=len(mapping_protein_seq_DMS) + print("There are {} DMSs in mapping file".format(num_DMS)) + + with open(args.config_file) as f: + config = json.load(f) + with open(f"{os.path.dirname(os.path.realpath(__file__))}/constants.json") as f: + constants = json.load(f) + uniprot_function_lookup = mapping_protein_seq_DMS[["UniProt_ID","coarse_selection_type"]] + uniprot_function_lookup.columns = ["UniProt_ID", "Selection Type"] + uniprot_Neff_lookup = mapping_protein_seq_DMS[['UniProt_ID','MSA_Neff_L_category']].drop_duplicates() + uniprot_Neff_lookup.columns=['UniProt_ID','MSA_Neff_L_category'] + uniprot_taxon_lookup = mapping_protein_seq_DMS[['UniProt_ID','taxon']].drop_duplicates() + uniprot_taxon_lookup.columns=['UniProt_ID','Taxon'] + if args.indel_mode: + args.performance_by_depth = False + + score_variables = list(config["model_list_zero_shot_substitutions_DMS"].keys()) if not args.indel_mode else list(config["model_list_zero_shot_indels_DMS"].keys()) + if not os.path.isdir(args.output_performance_file_folder): + os.mkdir(args.output_performance_file_folder) + for metric in ['Spearman','AUC','MCC',"NDCG","Top_recall"]: + if not os.path.isdir(args.output_performance_file_folder+os.sep+metric): + os.mkdir(args.output_performance_file_folder+os.sep+metric) + + model_types={} + for model in score_variables: + model_types[model]=config["model_list_zero_shot_substitutions_DMS"][model]["model_type"] if not args.indel_mode else config["model_list_zero_shot_indels_DMS"][model]["model_type"] + model_types=pd.DataFrame.from_dict(model_types,columns=['Model type'],orient='index') + model_details=pd.DataFrame.from_dict(constants["model_details"],columns=['Model details'],orient='index') + model_references=pd.DataFrame.from_dict(constants["model_references"],columns=['References'],orient='index') + clean_names = constants["clean_names"] + performance_all_DMS={} + output_filename={} + for metric in ['Spearman','AUC','MCC', "NDCG", "Top_recall"]: + performance_all_DMS[metric]={} + mutation_type = "substitutions" if not args.indel_mode else "indels" + output_filename[metric]="DMS_" + mutation_type + "_" + metric + for i, score in enumerate(score_variables): + performance_all_DMS[metric][score]=i + if not args.indel_mode and args.performance_by_depth: + for depth in ['1','2','3','4','5+']: + performance_all_DMS[metric][score+'_'+depth] = i + performance_all_DMS[metric]['number_mutants']=-1 + performance_all_DMS[metric]["Selection Type"] = -1 + performance_all_DMS[metric]["UniProt_ID"] = -1 + performance_all_DMS[metric]['MSA_Neff_L_category']=-1 + performance_all_DMS[metric]['Taxon']=-1 + performance_all_DMS[metric]=pd.DataFrame.from_dict(performance_all_DMS[metric],orient='index').reset_index() + performance_all_DMS[metric].columns=['score','score_index'] + + list_DMS = mapping_protein_seq_DMS["DMS_id"] + i = 0 + for DMS_id in list_DMS: + try: + print(DMS_id) + UniProt_ID = mapping_protein_seq_DMS["UniProt_ID"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + DMS_filename = mapping_protein_seq_DMS["DMS_filename"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + selection_type = mapping_protein_seq_DMS["coarse_selection_type"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + MSA_Neff_L_category = mapping_protein_seq_DMS["MSA_Neff_L_category"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + Taxon = mapping_protein_seq_DMS["taxon"][mapping_protein_seq_DMS["DMS_id"]==DMS_id].values[0] + + DMS_file = pd.read_csv(args.DMS_data_folder+os.sep+DMS_filename) + print("Length DMS: {}".format(len(DMS_file))) + merged_scores = pd.read_csv(args.input_scoring_files_folder + os.sep + DMS_id + ".csv") #We assume no missing value (all models were enforced to score all mutants) + if 'mutant' not in merged_scores: merged_scores['mutant'] = merged_scores['mutated_sequence'] #if mutant not in DMS file we default to mutated_sequence (eg., for indels) + except: + print(f"Scoring file for {DMS_id} missing") + continue + + if not args.indel_mode and args.performance_by_depth: + merged_scores['mutation_depth']=merged_scores['mutant'].apply(lambda x: len(x.split(":"))) + merged_scores['mutation_depth_grouped']=merged_scores['mutation_depth'].apply(lambda x: '5+' if x >=5 else str(x)) + performance_DMS = {} + for metric in ['Spearman','AUC','MCC','NDCG','Top_recall']: + performance_DMS[metric]={} + for score in score_variables: + if score not in merged_scores: + print("Model scores for {} not in merged scores for DMS {}".format(score,DMS_id)) + performance_DMS["Spearman"][score] = np.nan + performance_DMS["AUC"][score] = np.nan + performance_DMS["MCC"][score] = np.nan + performance_DMS["NDCG"][score] = np.nan + performance_DMS["Top_recall"][score] = np.nan + continue + performance_DMS['Spearman'][score] = spearmanr(merged_scores['DMS_score'], merged_scores[score])[0] + performance_DMS["NDCG"][score] = calc_ndcg(merged_scores['DMS_score'], merged_scores[score]) + performance_DMS["Top_recall"][score] = calc_toprecall(merged_scores['DMS_score'], merged_scores[score]) + try: + performance_DMS['AUC'][score] = roc_auc_score(y_true=merged_scores['DMS_score_bin'], y_score=merged_scores[score]) + except: + print("AUC issue with: {} for model: {}".format(DMS_id,score)) + performance_DMS['AUC'][score] = np.nan + try: + median_cutoff=merged_scores[score].median() + merged_scores[score+"_bin"]=merged_scores[score].map(lambda x: 1 if x >= median_cutoff else 0) + performance_DMS['MCC'][score] = matthews_corrcoef(y_true=merged_scores['DMS_score_bin'], y_pred=merged_scores[score+"_bin"]) + except: + print("MCC issue with: {} for model: {}".format(DMS_id,score)) + performance_DMS['MCC'][score] = np.nan + + if not args.indel_mode and args.performance_by_depth: + for score in score_variables: + if score not in merged_scores: + print("Model scores for {} not in merged scores for DMS {}".format(score,DMS_id)) + for depth in ['1','2','3','4','5+']: + performance_DMS["Spearman"][score+'_'+depth] = np.nan + performance_DMS["AUC"][score+'_'+depth] = np.nan + performance_DMS["MCC"][score+'_'+depth] = np.nan + performance_DMS["NDCG"][score+'_'+depth] = np.nan + performance_DMS["Top_recall"][score+'_'+depth] = np.nan + continue + for depth in ['1','2','3','4','5+']: + merged_scores_depth = merged_scores[merged_scores.mutation_depth_grouped==depth] + if len(merged_scores_depth) > 0: + performance_DMS['Spearman'][score+'_'+depth] = spearmanr(merged_scores_depth['DMS_score'], merged_scores_depth[score])[0] + performance_DMS["NDCG"][score+'_'+depth] = calc_ndcg(merged_scores_depth['DMS_score'], merged_scores_depth[score]) + performance_DMS["Top_recall"][score+'_'+depth] = calc_toprecall(merged_scores_depth['DMS_score'], merged_scores_depth[score]) + try: + performance_DMS['AUC'][score+'_'+depth] = roc_auc_score(y_true=merged_scores_depth['DMS_score_bin'], y_score=merged_scores_depth[score]) + except: + performance_DMS['AUC'][score+'_'+depth] = np.nan + try: + performance_DMS['MCC'][score+'_'+depth] = matthews_corrcoef(y_true=merged_scores_depth['DMS_score_bin'], y_pred=merged_scores_depth[score+"_bin"]) + except: + performance_DMS['MCC'][score+'_'+depth] = np.nan + else: + performance_DMS['Spearman'][score+'_'+depth] = np.nan + performance_DMS['AUC'][score+'_'+depth] = np.nan + performance_DMS['MCC'][score+'_'+depth] = np.nan + performance_DMS["NDCG"][score+'_'+depth] = np.nan + performance_DMS["Top_recall"][score+'_'+depth] = np.nan + print("Number of mutants: {}".format(len(merged_scores['DMS_score'].values))) + for metric in ['Spearman','AUC','MCC','NDCG','Top_recall']: + performance_DMS[metric]['number_mutants']=len(merged_scores['DMS_score'].values) + performance_DMS[metric]['UniProt_ID'] = UniProt_ID + performance_DMS[metric]["Selection Type"] = selection_type + performance_DMS[metric]['MSA_Neff_L_category'] = MSA_Neff_L_category + performance_DMS[metric]['Taxon'] = Taxon + performance_DMS[metric] = pd.DataFrame.from_dict(performance_DMS[metric],orient='index').reset_index() + performance_DMS[metric].columns=['score',DMS_id] + performance_all_DMS[metric]=pd.merge(performance_all_DMS[metric],performance_DMS[metric],on='score',how='left') + for metric in ['Spearman','AUC','MCC','NDCG','Top_recall']: + performance_all_DMS[metric]=performance_all_DMS[metric].set_index('score') + del performance_all_DMS[metric]['score_index'] + performance_all_DMS[metric]=performance_all_DMS[metric].transpose() + for var in performance_all_DMS[metric]: + if var not in ['UniProt_ID','MSA_Neff_L_category','Taxon',"Selection Type"]: + performance_all_DMS[metric][var]=performance_all_DMS[metric][var].astype(float).round(3) + if var in ['number_mutants']: + performance_all_DMS[metric][var]=performance_all_DMS[metric][var].astype(int) + if not args.indel_mode and args.performance_by_depth: + all_columns = performance_all_DMS[metric].columns + performance_all_DMS_html=performance_all_DMS[metric].copy() + performance_all_DMS_html.columns=performance_all_DMS_html.columns.map(lambda x: clean_names[x] if x in clean_names else x) + all_not_depth_columns = all_columns[[all_columns[x].split("_")[-1] not in ['1','2','3','4','5+'] for x in range(len(all_columns))]] + all_not_depth_columns_clean = all_not_depth_columns.map(lambda x: clean_names[x] if x in clean_names else x) + performance_all_DMS_html[all_not_depth_columns_clean].to_html(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_DMS_level.html') + DMS_perf_to_save = performance_all_DMS[metric].copy()[all_not_depth_columns] + DMS_perf_to_save.columns = DMS_perf_to_save.columns.map(lambda x: clean_names[x] if x in clean_names else x) + DMS_perf_to_save.to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_DMS_level.csv', index_label="DMS ID") + else: + performance_all_DMS_html=performance_all_DMS[metric].copy() + performance_all_DMS_html.columns = performance_all_DMS_html.columns.map(lambda x: clean_names[x] if x in clean_names else x) + performance_all_DMS_html.to_html(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_DMS_level.html') + DMS_perf_to_save = performance_all_DMS[metric].copy() + DMS_perf_to_save.columns = DMS_perf_to_save.columns.map(lambda x: clean_names[x] if x in clean_names else x) + DMS_perf_to_save.to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_DMS_level.csv', index_label="DMS ID") + + if not args.indel_mode: + uniprot_metric_performance = performance_all_DMS[metric].groupby(['UniProt_ID']).mean(numeric_only=True) + uniprot_function_metric_performance = performance_all_DMS[metric].groupby(['UniProt_ID',"Selection Type"]).mean(numeric_only=True) + uniprot_metric_performance = uniprot_metric_performance.reset_index() + uniprot_metric_performance = pd.merge(uniprot_metric_performance,uniprot_Neff_lookup,on='UniProt_ID', how='left') + uniprot_metric_performance = pd.merge(uniprot_metric_performance,uniprot_taxon_lookup,on='UniProt_ID', how='left') + uniprot_metric_performance = pd.merge(uniprot_metric_performance,uniprot_function_lookup,on="UniProt_ID",how="left") + del uniprot_metric_performance['number_mutants'] + del uniprot_function_metric_performance["number_mutants"] + uniprot_level_average = uniprot_metric_performance.mean(numeric_only=True) + uniprot_function_level_average = uniprot_function_metric_performance.groupby("Selection Type").mean(numeric_only=True) + # bootstrap_standard_error = pd.DataFrame(compute_bootstrap_standard_error_functional_categories(uniprot_function_metric_performance.subtract(uniprot_function_metric_performance['TranceptEVE_L'],axis=0)),columns=["Bootstrap_standard_error_"+metric]) + uniprot_function_level_average = uniprot_function_level_average.reset_index() + final_average = uniprot_function_level_average.mean(numeric_only=True) + if args.performance_by_depth: + cols = [column for column in all_not_depth_columns if column not in ["number_mutants","Taxon","MSA_Neff_L_category","Selection Type","UniProt_ID"]] + top_model = final_average.loc[cols].idxmax() + else: + top_model = final_average.idxmax() + bootstrap_standard_error = pd.DataFrame(compute_bootstrap_standard_error_functional_categories(uniprot_function_metric_performance.subtract(uniprot_function_metric_performance[top_model],axis=0)),columns=["Bootstrap_standard_error_"+metric]) + uniprot_metric_performance.loc['Average'] = uniprot_level_average + uniprot_function_level_average.loc['Average'] = final_average + uniprot_metric_performance=uniprot_metric_performance.round(3) + uniprot_function_level_average=uniprot_function_level_average.round(3) + if args.performance_by_depth: + uniprot_metric_performance[[column for column in all_not_depth_columns if column != "number_mutants"]].to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_Uniprot_level.csv', index=False) + performance_by_depth = {} + all_not_depth_columns = [x for x in all_not_depth_columns if x not in ['number_mutants',"UniProt_ID","MSA_Neff_L_category","Taxon"]] + for depth in ['1','2','3','4','5+']: + depth_columns = all_columns[[all_columns[x].split("_")[-1]==depth for x in range(len(all_columns))]] + performance_by_depth[depth] = uniprot_function_metric_performance[depth_columns].mean(numeric_only=True).reset_index() + performance_by_depth[depth]['model_name'] = performance_by_depth[depth]['score'].map(lambda x: '_'.join(x.split('_')[:-1])) + performance_by_depth[depth]=performance_by_depth[depth][['model_name',0]] + performance_by_depth[depth].columns = ['model_name','Depth_'+depth] + performance_by_depth[depth].set_index('model_name', inplace=True) + uniprot_function_level_average = uniprot_function_level_average[all_not_depth_columns] + else: + uniprot_metric_performance.to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + '_Uniprot_level.csv', index=False) + uniprot_function_level_average.to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + output_filename[metric] + "_Uniprot_Selection_Type_level.csv",index=False) + if args.performance_by_depth: + performance_by_MSA_depth = performance_all_DMS[metric].groupby(["UniProt_ID","MSA_Neff_L_category"]).mean(numeric_only=True).groupby(["MSA_Neff_L_category"]).mean(numeric_only=True)[[col for col in all_not_depth_columns if col != "Selection Type"]].transpose() + else: + performance_by_MSA_depth = performance_all_DMS[metric].groupby(["UniProt_ID","MSA_Neff_L_category"]).mean(numeric_only=True).groupby(["MSA_Neff_L_category"]).mean(numeric_only=True).transpose() + performance_by_MSA_depth = performance_by_MSA_depth[['Low','Medium','High']] + performance_by_MSA_depth.columns = ['Low_MSA_depth','Medium_MSA_depth','High_MSA_depth'] + if args.performance_by_depth: + performance_by_taxon = performance_all_DMS[metric].groupby(["UniProt_ID","Taxon"]).mean(numeric_only=True).groupby(["Taxon"]).mean(numeric_only=True)[[col for col in all_not_depth_columns if col != "Selection Type"]].transpose() + else: + performance_by_taxon = performance_all_DMS[metric].groupby(["UniProt_ID","Taxon"]).mean(numeric_only=True).groupby(["Taxon"]).mean(numeric_only=True).transpose() + performance_by_taxon = performance_by_taxon[['Human','Eukaryote','Prokaryote','Virus']] + performance_by_taxon.columns = ['Taxa_Human','Taxa_Other_Eukaryote','Taxa_Prokaryote','Taxa_Virus'] + performance_by_function = uniprot_function_level_average.drop(labels="Average",axis=0).set_index("Selection Type").transpose() + performance_by_function.columns = ["Function_"+x for x in performance_by_function.columns] + + summary_performance = pd.merge(pd.DataFrame(final_average,columns=['Average_'+metric]), performance_by_MSA_depth,left_index=True, right_index=True,how='inner') + summary_performance = pd.merge(summary_performance, performance_by_taxon,left_index=True, right_index=True,how='inner') + summary_performance = pd.merge(summary_performance, performance_by_function,left_index=True, right_index=True, how='inner') + if args.performance_by_depth: + for depth in ['1','2','3','4','5+']: + summary_performance = pd.merge(summary_performance, performance_by_depth[depth],left_index=True, right_index=True,how='inner') + final_column_order = ['Model_name','Model type','Average_'+metric,'Bootstrap_standard_error_'+metric,'Function_Activity','Function_Binding','Function_Expression','Function_OrganismalFitness','Function_Stability','Low_MSA_depth','Medium_MSA_depth','High_MSA_depth','Taxa_Human','Taxa_Other_Eukaryote','Taxa_Prokaryote','Taxa_Virus','Depth_1','Depth_2','Depth_3','Depth_4','Depth_5+','Model details','References'] + + else: + performance_all_DMS[metric].loc["Average"] = performance_all_DMS[metric].mean(numeric_only=True) + uniprot_metric_performance = performance_all_DMS[metric].groupby(['UniProt_ID']).mean(numeric_only=True) + uniprot_function_metric_performance = performance_all_DMS[metric].groupby(['UniProt_ID',"Selection Type"]).mean(numeric_only=True) + uniprot_metric_performance = pd.merge(uniprot_metric_performance,uniprot_function_lookup,on="UniProt_ID",how="left") + del uniprot_metric_performance['number_mutants'] + uniprot_level_average = uniprot_metric_performance.mean(numeric_only=True) + del uniprot_function_metric_performance["number_mutants"] + uniprot_function_level_average = uniprot_function_metric_performance.groupby("Selection Type").mean(numeric_only=True) + # bootstrap_standard_error = pd.DataFrame(compute_bootstrap_standard_error_functional_categories(uniprot_function_metric_performance.subtract(uniprot_function_metric_performance['TranceptEVE_M'],axis=0)),columns=["Bootstrap_standard_error_"+metric]) + uniprot_function_level_average = uniprot_function_level_average.reset_index() + final_average = uniprot_function_level_average.mean(numeric_only=True) + top_model = final_average.idxmax() + bootstrap_standard_error = pd.DataFrame(compute_bootstrap_standard_error_functional_categories(uniprot_function_metric_performance.subtract(uniprot_function_metric_performance[top_model],axis=0)),columns=["Bootstrap_standard_error_"+metric]) + uniprot_metric_performance.loc['Average'] = uniprot_level_average + uniprot_function_level_average.loc['Average'] = final_average + uniprot_metric_performance=uniprot_metric_performance.round(3) + uniprot_function_level_average=uniprot_function_level_average.round(3) + + performance_by_MSA_depth = performance_all_DMS[metric].groupby(["UniProt_ID","MSA_Neff_L_category"]).mean(numeric_only=True).groupby(["MSA_Neff_L_category"]).mean(numeric_only=True).transpose() + performance_by_MSA_depth = performance_by_MSA_depth[['Low','Medium','High']] + performance_by_MSA_depth.columns = ['Low_MSA_depth','Medium_MSA_depth','High_MSA_depth'] + performance_by_taxon = performance_all_DMS[metric].groupby(["UniProt_ID","Taxon"]).mean(numeric_only=True).groupby(["Taxon"]).mean(numeric_only=True).transpose() + performance_by_taxon = performance_by_taxon[['Human','Eukaryote','Prokaryote','Virus']] + performance_by_taxon.columns = ['Taxa_Human','Taxa_Other_Eukaryote','Taxa_Prokaryote','Taxa_Virus'] + performance_by_function = uniprot_function_level_average.drop(labels="Average",axis=0).set_index("Selection Type").transpose() + performance_by_function.columns = ["Function_"+x for x in performance_by_function.columns] + + summary_performance = pd.merge(pd.DataFrame(final_average,columns=['Average_'+metric]), performance_by_MSA_depth,left_index=True,right_index=True,how='inner') + summary_performance = pd.merge(summary_performance, performance_by_taxon,left_index=True, right_index=True,how='inner') + summary_performance = pd.merge(summary_performance, performance_by_function,left_index=True, right_index=True, how='inner') + final_column_order = ['Model_name','Model type','Average_'+metric,'Bootstrap_standard_error_'+metric,'Function_Activity','Function_Binding','Function_Expression','Function_OrganismalFitness','Function_Stability','Low_MSA_depth','Medium_MSA_depth','High_MSA_depth','Taxa_Human','Taxa_Other_Eukaryote','Taxa_Prokaryote','Taxa_Virus','Model details','References'] + summary_performance.sort_values(by='Average_'+metric,ascending=False,inplace=True) + summary_performance.index.name = 'Model_name' + summary_performance.reset_index(inplace=True) + summary_performance.index = range(1,len(summary_performance)+1) + summary_performance.index.name = 'Model_rank' + summary_performance = pd.merge(summary_performance, bootstrap_standard_error, left_on='Model_name', right_index=True, how='left') + summary_performance = pd.merge(summary_performance, model_types, left_on='Model_name', right_index=True, how='left') + summary_performance = pd.merge(summary_performance, model_details, left_on='Model_name', right_index=True, how='left') + summary_performance = pd.merge(summary_performance, model_references, left_on='Model_name', right_index=True, how='left') + summary_performance=summary_performance.round(3) + summary_performance['Model_name']=summary_performance['Model_name'].map(lambda x: clean_names[x] if x in clean_names else x) + summary_performance=summary_performance.reindex(columns=final_column_order) + summary_performance.to_csv(args.output_performance_file_folder + os.sep + metric + os.sep + 'Summary_performance_'+output_filename[metric]+'.csv') + summary_performance.to_html(args.output_performance_file_folder + os.sep + metric + os.sep + 'Summary_performance_'+output_filename[metric]+'.html') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/proteingym/performance_DMS_supervised_benchmarks.py b/proteingym/performance_DMS_supervised_benchmarks.py new file mode 100644 index 0000000..c4621eb --- /dev/null +++ b/proteingym/performance_DMS_supervised_benchmarks.py @@ -0,0 +1,171 @@ +import pandas as pd +import os +import argparse +from tqdm import tqdm +import json + +""" +This is the script used to compute statistics for the supervised scoring models. +It uses the score files output from the runs done for the ProteinNPT paper, and the +code to run those supervised models is available in the ProteinNPT repo +""" + + +def compute_bootstrap_standard_error_functional_categories(df, number_assay_reshuffle=10000, top_model="ProteinNPT"): + """ + Computes the non-parametric bootstrap standard error for the mean estimate of a given performance metric (eg., Spearman, AUC) across DMS assays (ie., the sample standard deviation of the mean across bootstrap samples) + """ + model_errors = {} + for model_name, group in tqdm(df.groupby("model_name")): + group_centered = group.subtract(df.loc[top_model],axis=0) + mean_performance_across_samples = {} + for category, group2 in group_centered.groupby("coarse_selection_type"): + mean_performance_across_samples[category] = [] + for sample in range(number_assay_reshuffle): + mean_performance_across_samples[category].append(group2.sample(frac=1.0, replace=True).mean(axis=0)) #Resample a dataset of the same size (with replacement) then take the sample mean + mean_performance_across_samples[category]=pd.DataFrame(data=mean_performance_across_samples[category]) + categories = list(mean_performance_across_samples.keys()) + combined_averages = mean_performance_across_samples[categories[0]].copy() + for category in categories[1:]: + combined_averages += mean_performance_across_samples[category] + combined_averages /= len(categories) + model_errors[model_name] = combined_averages.std(ddof=1) + return pd.DataFrame(model_errors).transpose() + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='ProteinGym supervised stats script') + parser.add_argument('--input_scoring_file', type=str, help='Name of the file where all input scores are present (expects one scoring file per DMS)') + parser.add_argument('--output_performance_file_folder', default='./outputs/tranception_performance', type=str, help='Name of folder where to save performance analysis files') + parser.add_argument('--DMS_reference_file_path', type=str, help='Reference file with list of DMSs to consider') + parser.add_argument('--indel_mode', action='store_true', help='Whether to score sequences with insertions and deletions') + + args = parser.parse_args() + metrics = ["Spearman","MSE"] + score_column = {"Spearman":"Spearman_fitness","MSE":"loss_fitness"} + with open(f"{os.path.dirname(os.path.realpath(__file__))}/constants.json") as f: + constants = json.load(f) + if not os.path.exists(args.output_performance_file_folder): + os.makedirs(args.output_performance_file_folder) + + ref_df = pd.read_csv(args.DMS_reference_file_path) + ref_df["MSA_Neff_L_category"] = ref_df["MSA_Neff_L_category"].apply(lambda x: x[0].upper() + x[1:] if type(x) == str else x) + score_df = pd.read_csv(args.input_scoring_file) + old_ids = ref_df["Old_DMS_ID"].unique() + score_df["assay_id"] = score_df["assay_id"].apply(lambda x: ref_df["DMS_id"][ref_df["Old_DMS_ID"]==x].values[0] if x in old_ids else x) + score_df = score_df.merge(ref_df[["DMS_id","MSA_Neff_L_category","coarse_selection_type","taxon"]],left_on="assay_id",right_on="DMS_id",how="left") + score_df = score_df[["assay_id","model_name", "DMS_id","UniProt_id","MSA_Neff_L_category","coarse_selection_type","taxon","fold_variable_name","Spearman_fitness","loss_fitness"]] + if args.indel_mode: + cv_schemes = ["fold_random_5"] + else: + cv_schemes = ["fold_random_5","fold_modulo_5","fold_contiguous_5"] + for metric in metrics: + if not os.path.exists(os.path.join(args.output_performance_file_folder,f"{metric}")): + os.makedirs(os.path.join(args.output_performance_file_folder,f"{metric}")) + output_folder = os.path.join(args.output_performance_file_folder,f"{metric}") + all_DMS_perf = None + all_DMS_cv_schemes_perf = {cv_scheme:None for cv_scheme in cv_schemes} + for DMS_id in tqdm(ref_df["DMS_id"].unique()): + performance_all_DMS = {} + performance_all_DMS_cv_scheme = {cv_scheme:{} for cv_scheme in cv_schemes} + score_subset = score_df[score_df['DMS_id']==DMS_id] + models = score_subset["model_name"].unique() + for model in models: + performance_all_DMS[model] = 0.0 + for cv_scheme in cv_schemes: + cv_subset = score_subset[score_subset["fold_variable_name"]==cv_scheme] + for model in models: + performance_all_DMS[model] += cv_subset[score_column[metric]][cv_subset["model_name"]==model].values[0]/len(cv_schemes) + performance_all_DMS_cv_scheme[cv_scheme][model] = cv_subset[score_column[metric]][cv_subset["model_name"]==model].values[0] + performance_all_DMS = pd.DataFrame.from_dict(performance_all_DMS,orient="index").reset_index(names="model_names") + performance_all_DMS.columns = ["model_names",DMS_id] + performance_all_DMS_cv_scheme = {cv_scheme:pd.DataFrame.from_dict(performance_all_DMS_cv_scheme[cv_scheme],orient="index").reset_index(names="model_names") for cv_scheme in cv_schemes} + for cv_scheme in cv_schemes: + performance_all_DMS_cv_scheme[cv_scheme].columns = ["model_names",DMS_id] + if all_DMS_perf is None: + all_DMS_perf = performance_all_DMS + all_DMS_cv_schemes_perf = {cv_scheme:performance_all_DMS_cv_scheme[cv_scheme] for cv_scheme in cv_schemes} + else: + all_DMS_perf = all_DMS_perf.merge(performance_all_DMS,on="model_names",how="inner") + all_DMS_cv_schemes_perf = {cv_scheme:all_DMS_cv_schemes_perf[cv_scheme].merge(performance_all_DMS_cv_scheme[cv_scheme],on="model_names",how="inner") for cv_scheme in cv_schemes} + all_DMS_perf = all_DMS_perf.set_index("model_names").transpose().reset_index(names="DMS_id") + all_DMS_perf.columns = [constants["supervised_clean_names"][x] if x in constants["supervised_clean_names"] else x for x in all_DMS_perf.columns] + if args.indel_mode: + all_DMS_perf.round(3).to_csv(os.path.join(output_folder,f"DMS_indels_{metric}_DMS_level.csv"),index=False) + else: + all_DMS_perf.round(3).to_csv(os.path.join(output_folder,f"DMS_substitutions_{metric}_DMS_level.csv"),index=False) + for cv_scheme in cv_schemes: + all_DMS_cv_schemes_perf[cv_scheme] = all_DMS_cv_schemes_perf[cv_scheme].set_index("model_names").transpose().reset_index(names="DMS_id") + all_DMS_cv_schemes_perf[cv_scheme].columns = [constants["supervised_clean_names"][x] if x in constants["supervised_clean_names"] else x for x in all_DMS_cv_schemes_perf[cv_scheme].columns] + if args.indel_mode: + all_DMS_cv_schemes_perf[cv_scheme].round(3).to_csv(os.path.join(output_folder,f"DMS_indels_{metric}_DMS_level_{cv_scheme}.csv"),index=False) + else: + all_DMS_cv_schemes_perf[cv_scheme].round(3).to_csv(os.path.join(output_folder,f"DMS_substitutions_{metric}_DMS_level_{cv_scheme}.csv"),index=False) + + def pivot_model_df(df, value_column, score_column): + df = df[["model_name",value_column,score_column]] + df = df.pivot(index="model_name",columns=value_column,values=score_column) + return df + + # computing function groupings within CV schemes, then averaging them + all_summary_performance = None + for cv_scheme in cv_schemes: + cv_subset = score_df[score_df["fold_variable_name"] == cv_scheme] + if len(cv_subset) == 0: + raise ValueError("No scores found for cross-validation scheme {}".format(cv_scheme)) + cv_uniprot_function = cv_subset.groupby(["model_name","UniProt_id","coarse_selection_type"]).mean() + if args.indel_mode: + top_model = "Embeddings - Augmented - EMS1v" + else: + top_model = "ProteinNPT" + bootstrap_standard_error = compute_bootstrap_standard_error_functional_categories(cv_uniprot_function,top_model=top_model,number_assay_reshuffle=10000) + bootstrap_standard_error = bootstrap_standard_error[score_column[metric]].reset_index() + bootstrap_standard_error.columns = ["model_name",f"Bootstrap_standard_error_{metric}"] + cv_function_average = cv_uniprot_function.groupby(["model_name","coarse_selection_type"]).mean() + cv_final_average = cv_function_average.groupby("model_name").mean() + performance_by_MSA_depth = cv_subset.groupby(["model_name","UniProt_id","MSA_Neff_L_category"]).mean().groupby(["model_name","MSA_Neff_L_category"]).mean() + performance_by_taxon = cv_subset.groupby(["model_name","UniProt_id","taxon"]).mean().groupby(["model_name","taxon"]).mean() + performance_by_MSA_depth = pivot_model_df(performance_by_MSA_depth.reset_index(),"MSA_Neff_L_category",score_column[metric]) + performance_by_MSA_depth.columns = ['Low_MSA_depth','Medium_MSA_depth','High_MSA_depth'] + performance_by_taxon = pivot_model_df(performance_by_taxon.reset_index(),"taxon",score_column[metric]) + performance_by_taxon.columns = ['Taxa_Human','Taxa_Other_Eukaryote','Taxa_Prokaryote','Taxa_Virus'] + cv_function_average = pivot_model_df(cv_function_average.reset_index(),"coarse_selection_type",score_column[metric]) + cv_function_average.columns = ["Function_"+x for x in cv_function_average.columns] + cv_final_average = cv_final_average.reset_index()[["model_name",score_column[metric]]].copy() + cv_final_average.columns = ["model_name",f"Average_{metric}"] + summary_performance = pd.merge(cv_final_average,performance_by_MSA_depth,on="model_name",how="inner") + summary_performance = pd.merge(summary_performance,performance_by_taxon,on="model_name",how="inner") + summary_performance = pd.merge(summary_performance,cv_function_average,on="model_name",how="inner") + summary_performance = pd.merge(summary_performance,bootstrap_standard_error,on="model_name",how="inner") + if all_summary_performance is None: + all_summary_performance = summary_performance.set_index("model_name")/len(cv_schemes) + all_summary_performance[f"Average_{metric}_{cv_scheme}"] = all_summary_performance[f"Average_{metric}"]*len(cv_schemes) + else: + ignore_columns = [f"Average_{metric}_{cv_approach}" for cv_approach in cv_schemes] + all_summary_performance[[column for column in all_summary_performance.columns if column not in ignore_columns]] += summary_performance.set_index("model_name")/len(cv_schemes) + all_summary_performance[f"Average_{metric}_{cv_scheme}"] = summary_performance[f"Average_{metric}"].values + all_summary_performance = all_summary_performance.reset_index(names="Model_name") + if metric == "MSE": + ascending = True + else: + ascending = False + all_summary_performance.sort_values(by=f"Average_{metric}",ascending=ascending,inplace=True) + all_summary_performance.index = range(1,len(all_summary_performance)+1) + all_summary_performance.index.name = 'Model_rank' + all_summary_performance = all_summary_performance.round(3) + all_summary_performance["Model_name"] = all_summary_performance["Model_name"].apply(lambda x: constants["supervised_clean_names"][x] if x in constants["supervised_clean_names"] else x) + all_summary_performance["References"] = all_summary_performance["Model_name"].apply(lambda x: constants["supervised_model_references"][x] if x in constants["supervised_model_references"] else "") + all_summary_performance["Model details"] = all_summary_performance["Model_name"].apply(lambda x: constants["supervised_model_details"][x] if x in constants["supervised_model_details"] else "") + all_summary_performance["Model type"] = all_summary_performance["Model_name"].apply(lambda x: constants["supervised_model_types"][x] if x in constants["supervised_model_types"] else "") + if args.indel_mode: + all_summary_performance["Function_Binding"] = "N/A" + column_order = ["Model_name","Model type",f"Average_{metric}",f"Bootstrap_standard_error_{metric}",f"Average_{metric}_fold_random_5","Function_Activity","Function_Binding","Function_Expression","Function_OrganismalFitness","Function_Stability","Low_MSA_depth","Medium_MSA_depth","High_MSA_depth","Taxa_Human","Taxa_Other_Eukaryote","Taxa_Prokaryote","Taxa_Virus","References","Model details"] + else: + column_order = ["Model_name","Model type",f"Average_{metric}",f"Bootstrap_standard_error_{metric}",f"Average_{metric}_fold_random_5",f"Average_{metric}_fold_modulo_5",f"Average_{metric}_fold_contiguous_5","Function_Activity","Function_Binding","Function_Expression","Function_OrganismalFitness","Function_Stability","Low_MSA_depth","Medium_MSA_depth","High_MSA_depth","Taxa_Human","Taxa_Other_Eukaryote","Taxa_Prokaryote","Taxa_Virus","References","Model details"] + all_summary_performance = all_summary_performance[column_order] + if args.indel_mode: + all_summary_performance.to_csv(os.path.join(output_folder,f"Summary_performance_DMS_indels_{metric}.csv")) + else: + all_summary_performance.to_csv(os.path.join(output_folder,f"Summary_performance_DMS_substitutions_{metric}.csv")) + + + \ No newline at end of file diff --git a/proteingym/utils/__init__.py b/proteingym/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/proteingym/utils/data_utils.py b/proteingym/utils/data_utils.py new file mode 100644 index 0000000..fdfd0c0 --- /dev/null +++ b/proteingym/utils/data_utils.py @@ -0,0 +1,30 @@ +import pandas as pd +import numpy as np +from utils.scoring_utils import get_mutated_sequence + +def DMS_file_cleanup(DMS_filename, target_seq, start_idx=1, end_idx=None, DMS_mutant_column='mutant', DMS_phenotype_name='score', DMS_directionality=1, AA_vocab = "ACDEFGHIKLMNPQRSTVWY"): + """ + Borrowed from the Tranception codebase: https://github.com/OATML-Markslab/Tranception/blob/main/tranception/utils/dms_utils.py + Function to process the raw substitution DMS assay data (eg., removing invalid mutants, aggregate silent mutations). + """ + DMS_data = pd.read_csv(DMS_filename, low_memory=False) + end_idx = start_idx + len(target_seq) - 1 if end_idx is None else end_idx + DMS_data['mutant'] = DMS_data[DMS_mutant_column] + + DMS_data=DMS_data[DMS_data['mutant'].notnull()].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([len(y)>=3 for y in x.split(":")]))].copy() #Mutant triplets should have at least 3 or more characters + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([(y[0] in AA_vocab) and (y[1:-1].isnumeric()) and (y[-1] in AA_vocab) for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([int(y[1:-1])-start_idx >=0 and int(y[1:-1]) <= end_idx for y in x.split(":")]))].copy() + DMS_data=DMS_data[DMS_data['mutant'].apply(lambda x: all([y[0]==target_seq[int(y[1:-1])-start_idx] for y in x.split(":")]))].copy() + + DMS_data[DMS_phenotype_name]=pd.to_numeric(DMS_data[DMS_phenotype_name],errors='coerce') + DMS_data=DMS_data[np.isfinite(DMS_data[DMS_phenotype_name])] + DMS_data.dropna(subset = [DMS_phenotype_name], inplace=True) + DMS_data['DMS_score'] = DMS_data[DMS_phenotype_name] * DMS_directionality + DMS_data=DMS_data[['mutant','DMS_score']] + DMS_data=DMS_data.groupby('mutant').mean().reset_index() + + DMS_data['mutated_sequence'] = DMS_data['mutant'].apply(lambda x: get_mutated_sequence(target_seq, x)) + DMS_data=DMS_data[['mutant','mutated_sequence','DMS_score']] + + return DMS_data diff --git a/proteingym/utils/msa_utils.py b/proteingym/utils/msa_utils.py new file mode 100644 index 0000000..4116f2b --- /dev/null +++ b/proteingym/utils/msa_utils.py @@ -0,0 +1,282 @@ +import os +import random +from collections import defaultdict +import numpy as np +import pandas as pd +import tempfile +from tqdm import tqdm +from numba import njit, prange +from Bio import SeqIO +from Bio.SeqRecord import SeqRecord +from Bio.Seq import Seq +import subprocess + +from utils.weights import map_from_alphabet, map_matrix, calc_weights_fast + +# constants +GAP = "-" +MATCH_GAP = GAP +INSERT_GAP = "." + +ALPHABET_PROTEIN_NOGAP = "ACDEFGHIKLMNPQRSTVWY" +ALPHABET_PROTEIN_GAP = GAP + ALPHABET_PROTEIN_NOGAP + +class MSA_processing: + def __init__(self, + MSA_location="", + theta=0.2, + use_weights=True, + weights_location="./data/weights", + preprocess_MSA=True, + threshold_sequence_frac_gaps=0.5, + threshold_focus_cols_frac_gaps=1.0, + remove_sequences_with_indeterminate_AA_in_focus_cols=True, + weights_calc_method="eve", + num_cpus=1, + skip_one_hot_encodings=False, + ): + + """ + This class was borrowed from our EVE codebase: https://github.com/OATML-Markslab/EVE + Parameters: + - msa_location: (path) Location of the MSA data. Constraints on input MSA format: + - focus_sequence is the first one in the MSA data + - first line is structured as follows: ">focus_seq_name/start_pos-end_pos" (e.g., >SPIKE_SARS2/310-550) + - corespondding sequence data located on following line(s) + - then all other sequences follow with ">name" on first line, corresponding data on subsequent lines + - theta: (float) Sequence weighting hyperparameter. Generally: Prokaryotic and eukaryotic families = 0.2; Viruses = 0.01 + - use_weights: (bool) If False, sets all sequence weights to 1. If True, checks weights_location -- if non empty uses that; + otherwise compute weights from scratch and store them at weights_location + - weights_location: (path) Location to load from/save to the sequence weights + - preprocess_MSA: (bool) performs pre-processing of MSA to remove short fragments and positions that are not well covered. + - threshold_sequence_frac_gaps: (float, between 0 and 1) Threshold value to define fragments + - sequences with a fraction of gap characters above threshold_sequence_frac_gaps are removed + - default is set to 0.5 (i.e., fragments with 50% or more gaps are removed) + - threshold_focus_cols_frac_gaps: (float, between 0 and 1) Threshold value to define focus columns + - positions with a fraction of gap characters above threshold_focus_cols_pct_gaps will be set to lower case (and not included in the focus_cols) + - default is set to 0.3 (i.e., focus positions are the ones with 30% of gaps or less, i.e., 70% or more residue occupancy) + - remove_sequences_with_indeterminate_AA_in_focus_cols: (bool) Remove all sequences that have indeterminate AA (e.g., B, J, X, Z) at focus positions of the wild type + - weights_calc_method: (str) Method to use for calculating sequence weights. Options: "eve" or "identity". (default "eve") + - num_cpus: (int) Number of CPUs to use for parallel weights calculation processing. If set to -1, all available CPUs are used. If set to 1, weights are computed in serial. + - skip_one_hot_encodings: (bool) If True, only use this class to calculate weights. Skip the one-hot encodings (which can be very memory/compute intensive) + and don't calculate all singles. + """ + np.random.seed(2021) + self.MSA_location = MSA_location + self.weights_location = weights_location + self.theta = theta + self.alphabet = ALPHABET_PROTEIN_NOGAP + self.use_weights = use_weights + self.preprocess_MSA = preprocess_MSA + self.threshold_sequence_frac_gaps = threshold_sequence_frac_gaps + self.threshold_focus_cols_frac_gaps = threshold_focus_cols_frac_gaps + self.remove_sequences_with_indeterminate_AA_in_focus_cols = remove_sequences_with_indeterminate_AA_in_focus_cols + self.weights_calc_method = weights_calc_method + self.skip_one_hot_encodings = skip_one_hot_encodings + + # Defined by gen_alignment + self.aa_dict = {} + self.focus_seq_name = "" + self.seq_name_to_sequence = defaultdict(str) + self.focus_seq, self.focus_cols, self.focus_seq_trimmed, self.seq_len, self.alphabet_size = [None] * 5 + self.focus_start_loc, self.focus_stop_loc = None, None + self.uniprot_focus_col_to_wt_aa_dict, self.uniprot_focus_col_to_focus_idx = None, None + self.one_hot_encoding, self.weights, self.Neff, self.num_sequences = [None] * 4 + + # Fill in the instance variables + self.gen_alignment() + + # Note: One-hot encodings might take up huge amounts of memory, and this could be skipped in many use cases + if not self.skip_one_hot_encodings: + #print("One-hot encoding sequences") + self.one_hot_encoding = one_hot_3D( + seq_keys=self.seq_name_to_sequence.keys(), # Note: Dicts are unordered for python < 3.6 + seq_name_to_sequence=self.seq_name_to_sequence, + alphabet=self.alphabet, + seq_length=self.seq_len, + ) + print ("Data Shape =", self.one_hot_encoding.shape) + + self.calc_weights(num_cpus=num_cpus, method=weights_calc_method) + + def gen_alignment(self): + """ Read training alignment and store basics in class instance """ + self.aa_dict = {} + for i, aa in enumerate(self.alphabet): + self.aa_dict[aa] = i + + self.seq_name_to_sequence = defaultdict(str) + name = "" + with open(self.MSA_location, "r") as msa_data: + for i, line in enumerate(msa_data): + line = line.rstrip() + if line.startswith(">"): + name = line + if i == 0: + self.focus_seq_name = name + else: + self.seq_name_to_sequence[name] += line + print("Number of sequences in MSA (before preprocessing):", len(self.seq_name_to_sequence)) + + ## MSA pre-processing to remove inadequate columns and sequences + if self.preprocess_MSA: + # Overwrite self.seq_name_to_sequence + self.seq_name_to_sequence = self.preprocess_msa( + seq_name_to_sequence=self.seq_name_to_sequence, + focus_seq_name=self.focus_seq_name, + threshold_sequence_frac_gaps=self.threshold_sequence_frac_gaps, + threshold_focus_cols_frac_gaps=self.threshold_focus_cols_frac_gaps + ) + + self.focus_seq = self.seq_name_to_sequence[self.focus_seq_name] + self.focus_cols = [ix for ix, s in enumerate(self.focus_seq) if s == s.upper() and s != '-'] + self.focus_seq_trimmed = "".join([self.focus_seq[ix] for ix in self.focus_cols]) + self.seq_len = len(self.focus_cols) + self.alphabet_size = len(self.alphabet) + + # Move all letters to CAPS; keeps focus columns only + self.raw_seq_name_to_sequence = self.seq_name_to_sequence.copy() + for seq_name, sequence in self.seq_name_to_sequence.items(): + sequence = sequence.replace(".", "-") + self.seq_name_to_sequence[seq_name] = "".join( + [sequence[ix].upper() for ix in self.focus_cols]) # Makes a List[str] instead of str + + # Remove sequences that have indeterminate AA (e.g., B, J, X, Z) in the focus columns + if self.remove_sequences_with_indeterminate_AA_in_focus_cols: + num_sequences_removed_due_to_indeterminate_AAs = 0 + num_sequences_before_indeterminate_AA_drop = len(self.seq_name_to_sequence) + alphabet_set = set(list(self.alphabet)) + seq_names_to_remove = [] + for seq_name, sequence in self.seq_name_to_sequence.items(): + for letter in sequence: + if letter not in alphabet_set and letter != "-": + seq_names_to_remove.append(seq_name) + continue + seq_names_to_remove = list(set(seq_names_to_remove)) + for seq_name in seq_names_to_remove: + num_sequences_removed_due_to_indeterminate_AAs+=1 + del self.seq_name_to_sequence[seq_name] + print("Proportion of sequences dropped due to indeterminate AAs: {}%".format(round(float(num_sequences_removed_due_to_indeterminate_AAs/num_sequences_before_indeterminate_AA_drop*100),2))) + + print("Number of sequences after preprocessing:", len(self.seq_name_to_sequence)) + self.num_sequences = len(self.seq_name_to_sequence.keys()) + + # Using staticmethod to keep this under the MSAProcessing namespace, but this is apparently not best practice + @staticmethod + def preprocess_msa(seq_name_to_sequence, focus_seq_name, threshold_sequence_frac_gaps, threshold_focus_cols_frac_gaps): + """Remove inadequate columns and sequences from MSA, overwrite self.seq_name_to_sequence.""" + msa_df = pd.DataFrame.from_dict(seq_name_to_sequence, orient='index', columns=['sequence']) + # Data clean up + msa_df.sequence = msa_df.sequence.apply(lambda x: x.replace(".", "-")).apply( + lambda x: ''.join([aa.upper() for aa in x])) + # Remove columns that would be gaps in the wild type + non_gap_wt_cols = [aa != '-' for aa in msa_df.sequence[focus_seq_name]] + msa_df['sequence'] = msa_df['sequence'].apply( + lambda x: ''.join([aa for aa, non_gap_ind in zip(x, non_gap_wt_cols) if non_gap_ind])) + assert 0.0 <= threshold_sequence_frac_gaps <= 1.0, "Invalid fragment filtering parameter" + assert 0.0 <= threshold_focus_cols_frac_gaps <= 1.0, "Invalid focus position filtering parameter" + print("Calculating proportion of gaps") + msa_array = np.array([list(seq) for seq in msa_df.sequence]) + gaps_array = np.array(list(map(lambda seq: [aa == '-' for aa in seq], msa_array))) + # Identify fragments with too many gaps + seq_gaps_frac = gaps_array.mean(axis=1) + seq_below_threshold = seq_gaps_frac <= threshold_sequence_frac_gaps + print("Proportion of sequences dropped due to fraction of gaps: " + str( + round(float(1 - seq_below_threshold.sum() / seq_below_threshold.shape) * 100, 2)) + "%") + # Identify focus columns + columns_gaps_frac = gaps_array[seq_below_threshold].mean(axis=0) + index_cols_below_threshold = columns_gaps_frac <= threshold_focus_cols_frac_gaps + print("Proportion of non-focus columns removed: " + str( + round(float(1 - index_cols_below_threshold.sum() / index_cols_below_threshold.shape) * 100, 2)) + "%") + # Lower case non focus cols and filter fragment sequences + def _lower_case_and_filter_fragments(seq): + return ''.join([aa.lower() if aa_ix in index_cols_below_threshold else aa for aa_ix, aa in enumerate(seq)]) + msa_df['sequence'] = msa_df['sequence'].apply( + lambda seq: ''.join([aa.upper() if upper_case_ind else aa.lower() for aa, upper_case_ind in + zip(seq, index_cols_below_threshold)])) + msa_df = msa_df[seq_below_threshold] + # Overwrite seq_name_to_sequence with clean version + seq_name_to_sequence = defaultdict(str) + # Create a dictionary from msa_df.index to msa_df.sequence + seq_name_to_sequence = dict(zip(msa_df.index, msa_df.sequence)) + # for seq_idx in range(len(msa_df['sequence'])): + # seq_name_to_sequence[msa_df.index[seq_idx]] = msa_df.sequence[seq_idx] + + return seq_name_to_sequence + + def calc_weights(self, num_cpus=1, method="eve"): + """ + From the EVE repo, but modified to skip printing out progress bar / time taken + (because for ProteinNPT embeddings, weights will usually be computed on the fly for small subsamples of MSA). + + If num_cpus == 1, weights are computed in serial. + If num_cpus == -1, weights are computed in parallel using all available cores. + Note: This will use multiprocessing.cpu_count() to get the number of available cores, which on clusters may + return all cores, not just the number of cores available to the user. + """ + # Refactored into its own function so that we can call it separately + if self.use_weights: + if os.path.isfile(self.weights_location): + print("Loading sequence weights from disk: {}".format(self.weights_location)) + self.weights = np.load(file=self.weights_location) + else: + print("Computing sequence weights") + if num_cpus == -1: + num_cpus = get_num_cpus() + + if method == "eve": + alphabet_mapper = map_from_alphabet(ALPHABET_PROTEIN_GAP, default=GAP) + arrays = [] + for seq in self.seq_name_to_sequence.values(): + arrays.append(np.array(list(seq))) + sequences = np.vstack(arrays) + sequences_mapped = map_matrix(sequences, alphabet_mapper) + self.weights = calc_weights_fast(sequences_mapped, identity_threshold=1 - self.theta, + empty_value=0, num_cpus=num_cpus) # GAP = 0 + elif method == "identity": + self.weights = np.ones(self.one_hot_encoding.shape[0]) + else: + raise ValueError(f"Unknown method: {method}. Must be either 'eve' or 'identity'.") + print("Saving sequence weights to disk") + np.save(file=self.weights_location, arr=self.weights) + else: + # If not using weights, use an isotropic weight matrix + print("Not weighting sequence data") + self.weights = np.ones(self.one_hot_encoding.shape[0]) + + self.Neff = np.sum(self.weights) + print("Neff =", str(self.Neff)) + print("Number of sequences: ", self.num_sequences) + assert self.weights.shape[0] == self.num_sequences, f"Expected {self.num_sequences} sequences, loaded weights have {self.weights.shape[0]}" + self.seq_name_to_weight={} # For later, if we want to remove certain sequences and associated weights + for i,seq_name in enumerate(self.seq_name_to_sequence.keys()): + self.seq_name_to_weight[seq_name]=self.weights[i] + + return self.weights + +# One-hot encoding of sequences +def one_hot_3D(seq_keys, seq_name_to_sequence, alphabet, seq_length): + """ + Take in a list of sequence names/keys and corresponding sequences, and generate a one-hot array according to an alphabet. + """ + aa_dict = {letter: i for (i, letter) in enumerate(alphabet)} + + one_hot_out = np.zeros((len(seq_keys), seq_length, len(alphabet))) + for i, seq_key in enumerate(seq_keys): + sequence = seq_name_to_sequence[seq_key] + for j, letter in enumerate(sequence): + if letter in aa_dict: + k = aa_dict[letter] + one_hot_out[i, j, k] = 1.0 + # one_hot_out = torch.tensor(one_hot_out) + return one_hot_out + +def get_num_cpus(): + if 'SLURM_CPUS_PER_TASK' in os.environ: + num_cpus = int(os.environ['SLURM_CPUS_PER_TASK']) + print("SLURM_CPUS_PER_TASK:", os.environ['SLURM_CPUS_PER_TASK']) + print("Using all available cores (calculated using SLURM_CPUS_PER_TASK):", num_cpus) + else: + num_cpus = len(os.sched_getaffinity(0)) + print("Using all available cores (calculated using len(os.sched_getaffinity(0))):", num_cpus) + return num_cpus \ No newline at end of file diff --git a/proteingym/utils/scoring_utils.py b/proteingym/utils/scoring_utils.py new file mode 100644 index 0000000..3286b05 --- /dev/null +++ b/proteingym/utils/scoring_utils.py @@ -0,0 +1,76 @@ +import numpy as np +import pandas as pd +import torch + +AA_vocab = "ACDEFGHIKLMNPQRSTVWY" +unusual_AA ="OU" #Pyrrolysine O and selenocysteine U +indeterminate_AA = "BJXZ" #B = Asparagine or Aspartic acid; J = leucine or isoleucine; X = Any/Unknown ; Z = Glutamine or glutamic acid + +def standardize(x, epsilon = 1e-10): + return (x - x.mean()) / (x.std() + epsilon) + +def nanmean(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs) + +def nansum(v, *args, inplace=False, **kwargs): + if not inplace: + v = v.clone() + is_nan = torch.isnan(v) + v[is_nan] = 0 + return v.sum(*args, **kwargs) + +def get_mutated_sequence(focus_seq, mutant, start_idx=1, AA_vocab=AA_vocab): + """ + Helper function that mutates an input sequence (focus_seq) via an input mutation triplet (substitutions only). + Mutation triplet are typically based on 1-indexing: start_idx is used for switching to 0-indexing. + """ + mutated_seq = list(focus_seq) + for mutation in mutant.split(":"): + try: + from_AA, position, to_AA = mutation[0], int(mutation[1:-1]), mutation[-1] + except: + print("Issue with mutant: "+str(mutation)) + relative_position = position - start_idx + assert (from_AA==focus_seq[relative_position]), "Invalid from_AA or mutant position: "+str(mutation)+" from_AA: "+str(from_AA) + " relative pos: "+str(relative_position) + " focus_seq: "+str(focus_seq) + assert (to_AA in AA_vocab) , "Mutant to_AA is invalid: "+str(mutation) + mutated_seq[relative_position] = to_AA + return "".join(mutated_seq) + +def get_optimal_window(mutation_position_relative, seq_len_wo_special, model_window): + half_model_window = model_window // 2 + if seq_len_wo_special <= model_window: + return [0,seq_len_wo_special] + elif mutation_position_relative < half_model_window: + return [0,model_window] + elif mutation_position_relative >= seq_len_wo_special - half_model_window: + return [seq_len_wo_special - model_window, seq_len_wo_special] + else: + return [max(0,mutation_position_relative-half_model_window), min(seq_len_wo_special,mutation_position_relative+half_model_window)] + +def set_mutant_offset(mutant, MSA_start, mutant_delim=":"): + """ + Adjusts the offset of a mutant sequence to match the MSA start and end positions + """ + indiv_mutants = mutant.split(mutant_delim) + new_mutants = [] + for indiv_mutant in indiv_mutants: + wt, pos, sub = indiv_mutant[0], int(indiv_mutant[1:-1]), indiv_mutant[-1] + shift_pos = pos - MSA_start + 1 + new_mutants.append(wt + str(int(shift_pos)) + sub) + return mutant_delim.join(new_mutants) + +def undo_mutant_offset(mutant, MSA_start, mutant_delim=","): + """ + Undoes the offset adjustment of a mutant sequence to match the MSA start and end positions + """ + indiv_mutants = mutant.split(mutant_delim) + new_mutants = [] + for indiv_mutant in indiv_mutants: + wt, pos, sub = indiv_mutant[0], int(indiv_mutant[1:-1]), indiv_mutant[-1] + shift_pos = pos + MSA_start - 1 + new_mutants.append(wt + str(int(shift_pos)) + sub) + return mutant_delim.join(new_mutants) \ No newline at end of file diff --git a/proteingym/utils/weights.py b/proteingym/utils/weights.py new file mode 100644 index 0000000..f0b1d14 --- /dev/null +++ b/proteingym/utils/weights.py @@ -0,0 +1,257 @@ +# This class is copied from the EVE codebase https://github.com/OATML-Markslab/EVE, removing the progress bar (since we're calculating weights on the fly so don't need one) +import multiprocessing +import time +from collections import defaultdict + +import numba +from numba import prange +# from numba_progress import ProgressBar + +import numpy as np +from tqdm import tqdm + +def calc_weights_fast(matrix_mapped, identity_threshold, empty_value, num_cpus=1): + """ + Modified from EVCouplings: https://github.com/debbiemarkslab/EVcouplings + + Note: Numba by default uses `multiprocessing.cpu_count()` threads. + On a cluster where a process might only have access to a subset of CPUs, this may be less than the number of CPUs available. + The caller should ideally use len(os.sched_getaffinity(0)) to get the number of CPUs available to the process. + + Calculate weights for sequences in alignment by + clustering all sequences with sequence identity + greater or equal to the given threshold. + Parameters + ---------- + identity_threshold : float + Sequence identity threshold + """ + empty_idx = is_empty_sequence_matrix(matrix_mapped, empty_value=empty_value) # e.g. sequences with just gaps or lowercase, no valid AAs + N = matrix_mapped.shape[0] + + # Original EVCouplings code structure, plus gap handling + if num_cpus != 1: + # print("Calculating weights using Numba parallel (experimental) since num_cpus > 1. If you want to disable multiprocessing set num_cpus=1.") + # print("Default number of threads for Numba:", numba.config.NUMBA_NUM_THREADS) + + # num_cpus > numba.config.NUMBA_NUM_THREADS will give an error. + # But we'll leave it so that the user has to be explicit. + numba.set_num_threads(num_cpus) + print("Set number of threads to:", numba.get_num_threads()) # Sometimes Numba uses all the CPUs anyway + + num_cluster_members = calc_num_cluster_members_nogaps_parallel(matrix_mapped[~empty_idx], identity_threshold, + invalid_value=empty_value) + + else: + # Use the serial version + num_cluster_members = calc_num_cluster_members_nogaps(matrix_mapped[~empty_idx], identity_threshold, + invalid_value=empty_value) + + # Empty sequences: weight 0 + weights = np.zeros((N)) + weights[~empty_idx] = 1.0 / num_cluster_members + return weights + +# Below are util functions copied from EVCouplings +def is_empty_sequence_matrix(matrix, empty_value): + assert len(matrix.shape) == 2, f"Matrix must be 2D; shape={matrix.shape}" + assert isinstance(empty_value, (int, float)), f"empty_value must be a number; type={type(empty_value)}" + # Check for each sequence if all positions are equal to empty_value + empty_idx = np.all((matrix == empty_value), axis=1) + return empty_idx + + +def map_from_alphabet(alphabet, default): + """ + Creates a mapping dictionary from a given alphabet. + Parameters + ---------- + alphabet : str + Alphabet for remapping. Elements will + be remapped according to alphabet starting + from 0 + default : Elements in matrix that are not + contained in alphabet will be treated as + this character + Raises + ------ + ValueError + For invalid default character + """ + map_ = { + c: i for i, c in enumerate(alphabet) + } + + try: + default = map_[default] + except KeyError: + raise ValueError( + "Default {} is not in alphabet {}".format(default, alphabet) + ) + + return defaultdict(lambda: default, map_) + + + +def map_matrix(matrix, map_): + """ + Map elements in a numpy array using alphabet + Parameters + ---------- + matrix : np.array + Matrix that should be remapped + map_ : defaultdict + Map that will be applied to matrix elements + Returns + ------- + np.array + Remapped matrix + """ + return np.vectorize(map_.__getitem__)(matrix) + + +# Fastmath should be safe here, as we can assume that there are no NaNs in the input etc. +@numba.jit(nopython=True, fastmath=True) #parallel=True +def calc_num_cluster_members_nogaps(matrix, identity_threshold, invalid_value): + """ + From EVCouplings: https://github.com/debbiemarkslab/EVcouplings/blob/develop/evcouplings/align/alignment.py#L1172. + Modified to use non-gapped length and not counting gaps as sequence similarity matches. + + Calculate number of sequences in alignment + within given identity_threshold of each other + Parameters + ---------- + matrix : np.array + N x L matrix containing N sequences of length L. + Matrix must be mapped to range(0, num_symbols) using + map_matrix function + identity_threshold : float + Sequences with at least this pairwise identity will be + grouped in the same cluster. + Returns + ------- + np.array + Vector of length N containing number of cluster + members for each sequence (inverse of sequence + weight) + """ + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + for i in range(N - 1): + for j in range(i + 1, N): + pair_matches = 0 + for k in range(L): + # Edit(Lood): Don't count gaps as matches + if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: + pair_matches += 1 + + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors[i] += 1 + if pair_matches / L_non_gaps[j] > identity_threshold: + num_neighbors[j] += 1 + + return num_neighbors + + +@numba.jit(nopython=True, fastmath=True, parallel=True) +def calc_num_cluster_members_nogaps_parallel(matrix, identity_threshold, invalid_value): + """ + Parallel implementation of calc_num_cluster_members_nogaps above. + + Calculate number of sequences in alignment + within given identity_threshold of each other + Parameters + ---------- + matrix : np.array + N x L matrix containing N sequences of length L. + Matrix must be mapped to range(0, num_symbols) using + map_matrix function + identity_threshold : float + Sequences with at least this pairwise identity will be + grouped in the same cluster. + invalid_value : int + Value in matrix that is considered invalid, e.g. gap or lowercase character. + Returns + ------- + np.array + Vector of length N containing number of cluster + members for each sequence (inverse of sequence + weight) + """ + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + # Edit: Rewrote loop without any dependencies between inner and outer loops, so that it can be parallelized + for i in prange(N): + num_neighbors_i = 1 + for j in range(N): + if i == j: + continue + pair_matches = 0 + for k in range(L): # This should hopefully be vectorised by numba + # Edit(Lood): Don't count gaps as matches + if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: + pair_matches += 1 + + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so this similarity is asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors_i += 1 + + num_neighbors[i] = num_neighbors_i + + return num_neighbors + +@numba.jit(nopython=True, fastmath=True, parallel=True) +def calc_num_cluster_members_nogaps_parallel_print(matrix, identity_threshold, invalid_value, progress_proxy=None, update_frequency=1000): + """ + Modified calc_num_cluster_members_nogaps_parallel to add tqdm progress bar - useful for multi-hour weights calc. + + progress_proxy : numba_progress.ProgressBar + A handle on the progress bar to update + update_frequency : int + Similar to miniters in tqdm, how many iterations between updating the progress bar (which then will only print every `update_interval` seconds) + """ + + N, L = matrix.shape + L = 1.0 * L + + # Empty sequences are filtered out before this function and are ignored + # minimal cluster size is 1 (self) + num_neighbors = np.ones((N)) + L_non_gaps = L - np.sum(matrix == invalid_value, axis=1) # Edit: From EVE, use the non-gapped length + # compare all pairs of sequences + # Edit: Rewrote loop without any dependencies between inner and outer loops, so that it can be parallelized + for i in prange(N): + num_neighbors_i = 1 + for j in range(N): + if i == j: + continue + pair_matches = 0 + for k in range(L): # This should hopefully be vectorised by numba + # Edit(Lood): Don't count gaps as matches + if matrix[i, k] == matrix[j, k] and matrix[i, k] != invalid_value: + pair_matches += 1 + # Edit(Lood): Calculate identity as fraction of non-gapped positions (so this similarity is asymmetric) + # Note: Changed >= to > to match EVE / DeepSequence code + if pair_matches / L_non_gaps[i] > identity_threshold: + num_neighbors_i += 1 + + num_neighbors[i] = num_neighbors_i + if progress_proxy is not None and i % update_frequency == 0: + progress_proxy.update(update_frequency) + + return num_neighbors diff --git a/reference_files/DMS_indels.csv b/reference_files/DMS_indels.csv new file mode 100644 index 0000000..d174144 --- /dev/null +++ b/reference_files/DMS_indels.csv @@ -0,0 +1,67 @@ +DMS_id,DMS_filename,UniProt_ID,taxon,source_organism,target_seq,seq_len,DMS_total_number_mutants,DMS_binarization_cutoff,DMS_binarization_method,first_author,title,year,jo,molecule_name,selection_assay,selection_type,MSA_filename,MSA_start,MSA_end,MSA_len,MSA_bitscore,MSA_theta,MSA_num_seqs,MSA_perc_cov,MSA_num_cov,MSA_N_eff,MSA_Neff_L,MSA_Neff_L_category,MSA_num_significant,MSA_num_significant_L,raw_DMS_filename,raw_DMS_phenotype_name,raw_DMS_directionality,raw_DMS_mutant_column,weight_file_name,ProteinGym_version,coarse_selection_type +A4_HUMAN_Seuma_2022_indels,A4_HUMAN_Seuma_2022_indels.csv,A4_HUMAN,Human,Homo 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nucleation",2022,10.1038/s41467-022-34742-3,APP,aggregation,survival assessment assay,A4_HUMAN_2023-08-07_b01.a2m,1,770,770,0.1,0.2,5272,0.987,760,99.3,0.1306578947,Low,0,0,,nscore_c,1,mutated_sequence,A4_HUMAN_theta0.2_2023-08-07_b01.npy,1,Stability +AMFR_HUMAN_Tsuboyama_2023_4G3O_indels,AMFR_HUMAN_Tsuboyama_2023_4G3O_indels.csv,AMFR_HUMAN,Human,Homo sapiens,YFQGQLNAMAHQIQEMFPQVPYHLVLQDLQLTRSVEITTDNILEGRI,47,117,-1.504736022,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,E3 ubiquitin-protein ligase AMFR,Stability,cDNA display proteolysis,AMFR_HUMAN_2023-08-07_b04.a2m,1,47,47,0.4,0.2,17787,0.872,41,1166.9,28.46097561,Medium,12,0.2926829268,,ddG_ML_float,1,aa_seq,AMFR_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +ARGR_ECOLI_Tsuboyama_2023_1AOY_indels,ARGR_ECOLI_Tsuboyama_2023_1AOY_indels.csv,ARGR_ECOLI,Prokaryote,Escherichia coli,QEELVKAFKALLKEEKFSSQGEIVAALQEQGFDNINQSKVSRMLTKFGAVRTRNAKMEMVYCLPAELGV,69,181,-0.4541373765,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Arginine repressor,Stability,cDNA display proteolysis,ARGR_ECOLI_2023-08-07_b04.a2m,1,69,69,0.4,0.2,21443,0.913,63,3719.2,59.03492063,Medium,29,0.4603174603,,ddG_ML_float,1,aa_seq,ARGR_ECOLI_theta0.2_2023-08-07_b04.npy,1,Stability +B1LPA6_ECOSM_Russ_2020_indels,B1LPA6_ECOSM_Russ_2020_indels.csv,B1LPA6_ECOSM,Prokaryote,Escherichia coli,MTSENPLLALREKISALDEKLLALLAERRELAVEVGKAKLLSHRPVRDIDRERDLLERLITLGKAHHLDAHYITRLFQLIIEDSVLTQQALLQQHLNKINPHSARIAFLGPKGSYSHLAARQYAARHFEQFIESGCAKFADIFNQVETGQADYAVVPIENTSSGAINDVYDLLQHTSLSIVGEMTLTIDHCLLVSGTTDLSTINTVYSHPQPFQQCSKFLNRYPHWKIEYTESTSAAMEKVAQAKSPHVAALGSEAGGTLYGLQVLERIEANQRQNFTRFVVLARKAINVSDQVPAKTTLLMATGQQAGALVEALLVLRNHSLIMTRLESRPIHGNPWEEMFYLDIQANLESAEMQKALKELGEITRSMKVLGCYPSENVVPVDPT,386,3074,0.4,manual,Russ,An evolution-based model for designing chorismate mutase enzymes,2020,10.1126/science.aba3304,chorismate mutase,enzyme activity,enzyme activity,B1LPA6_ECOSM_full_04-30-2022_b05.a2m,1,386,386,0.5,0.2,33872,0.699,270,6160,22.81481481,Medium,341,1.262962963,B1LPA6_ECOSM_Russ_2020.csv,activity,1,mutant,B1LPA6_ECOSM_theta_0.2.npy,0.1,Activity +BBC1_YEAST_Tsuboyama_2023_1TG0_indels,BBC1_YEAST_Tsuboyama_2023_1TG0_indels.csv,BBC1_YEAST,Eukaryote,Saccharomyces cerevisiae,EVPFKVVAQFPYKSDYEDDLNFEKDQEIIVTSVEDAEWYFGEYQDSNGDVIEGIFPKSFVAVQG,64,134,-1.271998543,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Myosin tail region-interacting protein MTI1,Stability,cDNA display proteolysis,BBC1_YEAST_2023-08-07_b05.a2m,1,64,64,0.5,0.2,604824,0.844,54,17529.2,324.6148148,High,55,1.018518519,,ddG_ML_float,1,aa_seq,BBC1_YEAST_theta0.2_2023-08-07_b05.npy,1,Stability 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coli,MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW,286,4751,0.015686274,median,Gonzalez,Fitness Effects of Single Amino Acid Insertions and Deletions in TEM-1 β-Lactamase,2019,10.1016/j.jmb.2019.04.030,Beta-lactamase TEM,"antibiotic resistance, MIC",Amp resistance,BLAT_ECOLX_full_11-26-2021_b02.a2m,1,286,286,0.2,0.2,209644,0.752,215,47605,221.4186047,High,446,2.074418605,BLAT_ECOLX_Gonzalez_indels_2019.csv,DMS_score,1,sequence,BLAT_ECOLX_theta_0.2.npy,0.1,OrganismalFitness +CAPSD_AAV2S_Sinai_2021_designed_indels,CAPSD_AAV2S_Sinai_2021_designed_indels.csv,CAPSD_AAV2S,Virus,Adeno-associated virus 2 (isolate Srivastava/1982) (AAV-2),MAADGYLPDWLEDTLSEGIRQWWKLKPGPPPPKPAERHKDDSRGLVLPGYKYLGPFNGLDKGEPVNEADAAALEHDKAYDRQLDSGDNPYLKYNHADAEFQERLKEDTSFGGNLGRAVFQAKKRVLEPLGLVEEPVKTAPGKKRPVEHSPVEPDSSSGTGKAGQQPARKRLNFGQTGDADSVPDPQPLGQPPAAPSGLGTNTMATGSGAPMADNNEGADGVGNSSGNWHCDSTWMGDRVITTSTRTWALPTYNNHLYKQISSQSGASNDNHYFGYSTPWGYFDFNRFHCHFSPRDWQRLINNNWGFRPKRLNFKLFNIQVKEVTQNDGTTTIANNLTSTVQVFTDSEYQLPYVLGSAHQGCLPPFPADVFMVPQYGYLTLNNGSQAVGRSSFYCLEYFPSQMLRTGNNFTFSYTFEDVPFHSSYAHSQSLDRLMNPLIDQYLYYLSRTNTPSGTTTQSRLQFSQAGASDIRDQSRNWLPGPCYRQQRVSKTSADNNNSEYSWTGATKYHLNGRDSLVNPGPAMASHKDDEEKFFPQSGVLIFGKQGSEKTNVDIEKVMITDEEEIRTTNPVATEQYGSVSTNLQRGNRQAATADVNTQGVLPGMVWQDRDVYLQGPIWAKIPHTDGHFHPSPLMGGFGLKHPPPQILIKNTPVPANPSTTFSAAKFASFITQYSTGQVSVEIEWELQKENSKRWNPEIQYTSNYNKSVNVDFTVDTNGVYSEPRPIGTRYLTRNL,735,225998,-2.18477642,median,Sinai,Generative AAV capsid diversification by latent interpolation,2021,10.1101/2021.04.16.440236,AAV,viability for AAV capsid production,,CAPSD_AAV2S_uniprot_t099_msc70_mcc70_b0.8.a2m,1,735,735,0.8,0.01,604,0.782,575,213.8,0.371826087,Low,1943,3.379130435,CAPSD_AAV2S_Sinai_indels_2021.csv,label,1,mutated_sequence,CAPSD_AAV2S_theta_0.01.npy,0.1,OrganismalFitness +CAPSD_AAV2S_Sinai_2021_library_indels,CAPSD_AAV2S_Sinai_2021_library_indels.csv,CAPSD_AAV2S,Virus,Adeno-associated virus 2 (isolate Srivastava/1982) (AAV-2),MAADGYLPDWLEDTLSEGIRQWWKLKPGPPPPKPAERHKDDSRGLVLPGYKYLGPFNGLDKGEPVNEADAAALEHDKAYDRQLDSGDNPYLKYNHADAEFQERLKEDTSFGGNLGRAVFQAKKRVLEPLGLVEEPVKTAPGKKRPVEHSPVEPDSSSGTGKAGQQPARKRLNFGQTGDADSVPDPQPLGQPPAAPSGLGTNTMATGSGAPMADNNEGADGVGNSSGNWHCDSTWMGDRVITTSTRTWALPTYNNHLYKQISSQSGASNDNHYFGYSTPWGYFDFNRFHCHFSPRDWQRLINNNWGFRPKRLNFKLFNIQVKEVTQNDGTTTIANNLTSTVQVFTDSEYQLPYVLGSAHQGCLPPFPADVFMVPQYGYLTLNNGSQAVGRSSFYCLEYFPSQMLRTGNNFTFSYTFEDVPFHSSYAHSQSLDRLMNPLIDQYLYYLSRTNTPSGTTTQSRLQFSQAGASDIRDQSRNWLPGPCYRQQRVSKTSADNNNSEYSWTGATKYHLNGRDSLVNPGPAMASHKDDEEKFFPQSGVLIFGKQGSEKTNVDIEKVMITDEEEIRTTNPVATEQYGSVSTNLQRGNRQAATADVNTQGVLPGMVWQDRDVYLQGPIWAKIPHTDGHFHPSPLMGGFGLKHPPPQILIKNTPVPANPSTTFSAAKFASFITQYSTGQVSVEIEWELQKENSKRWNPEIQYTSNYNKSVNVDFTVDTNGVYSEPRPIGTRYLTRNL,735,24908,-2.18477642,median,Sinai,Generative AAV capsid diversification by latent interpolation,2021,10.1101/2021.04.16.440236,AAV,viability for AAV capsid production,,CAPSD_AAV2S_uniprot_t099_msc70_mcc70_b0.8.a2m,1,735,735,0.8,0.01,604,0.782,575,213.8,0.371826087,Low,1943,3.379130435,CAPSD_AAV2S_Sinai_indels_2021.csv,label,1,mutated_sequence,CAPSD_AAV2S_theta_0.01.npy,0.1,OrganismalFitness +CATR_CHLRE_Tsuboyama_2023_2AMI_indels,CATR_CHLRE_Tsuboyama_2023_2AMI_indels.csv,CATR_CHLRE,Eukaryote,Chlamydomonas reinhardtii,GLTEEQKQEIREAFDLFDTDGSGTIDAKELKVAMRALGFEPKKEEIKKMISEIDKDGSGTIDFEEFLTMMTA,72,197,-0.5681612987,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Caltractin,Stability,cDNA display proteolysis,CATR_CHLRE_2023-08-07_b03.a2m,1,72,72,0.3,0.2,551057,0.903,65,75596.9,1163.029231,High,57,0.8769230769,,ddG_ML_float,1,aa_seq,CATR_CHLRE_theta0.2_2023-08-07_b03.npy,1,Stability +CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels,CBPA2_HUMAN_Tsuboyama_2023_1O6X_indels.csv,CBPA2_HUMAN,Human,Homo sapiens,VGDQVLEIVPSNEEQIKNLLQLEAQEHLQLDFWKSPTTPGETAHVRVPFVNVQAVKVFLESQGIAYSIMIED,72,205,-1.221174658,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Carboxypeptidase A2,Stability,cDNA display proteolysis,CBPA2_HUMAN_2023-08-07_b03.a2m,1,72,72,0.3,0.2,12711,0.986,71,3086.5,43.47183099,Medium,34,0.4788732394,,ddG_ML_float,1,aa_seq,CBPA2_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +CBX4_HUMAN_Tsuboyama_2023_2K28_indels,CBX4_HUMAN_Tsuboyama_2023_2K28_indels.csv,CBX4_HUMAN,Human,Homo sapiens,AVESIEKKRIRKGRVEYLVKWRGWSPKYNTWEPEENILDPRLLIAFQNRE,50,129,-1.635037732,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,E3 SUMO-protein ligase CBX4,Stability,cDNA display proteolysis,CBX4_HUMAN_2023-08-07_b03.a2m,1,50,50,0.3,0.2,108263,0.96,48,13404.4,279.2583333,High,23,0.4791666667,,ddG_ML_float,1,aa_seq,CBX4_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +CSN4_MOUSE_Tsuboyama_2023_1UFM_indels,CSN4_MOUSE_Tsuboyama_2023_1UFM_indels.csv,CSN4_MOUSE,Eukaryote,Mus musculus,SSGGSSILDRAVIEHNLLSASKLYNNITFEELGALLEIPAAKAEKIASQMITEGRMNGFIDQIDGIVHFETR,72,195,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,COP9 signalosome complex subunit 4,Stability,cDNA display proteolysis,CSN4_MOUSE_2023-08-07_b03.a2m,1,72,72,0.3,0.2,39217,0.889,64,3492.9,54.5765625,Medium,9,0.140625,,ddG_ML_float,1,aa_seq,CSN4_MOUSE_theta0.2_2023-08-07_b03.npy,1,Stability +CUE1_YEAST_Tsuboyama_2023_2MYX_indels,CUE1_YEAST_Tsuboyama_2023_2MYX_indels.csv,CUE1_YEAST,Eukaryote,Saccharomyces cerevisiae,GGHPVTTQMVETVQNLAPNLHPEQIRYSLENTGSVEETVERYLRGDEFSFPP,52,140,-1.319713733,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Coupling of ubiquitin conjugation to ER degradation protein 1,Stability,cDNA display proteolysis,CUE1_YEAST_2023-08-07_b08.a2m,1,52,52,0.8,0.2,3213,0.923,48,387.1,8.064583333,Medium,10,0.2083333333,,ddG_ML_float,1,aa_seq,CUE1_YEAST_theta0.2_2023-08-07_b08.npy,1,Stability +DN7A_SACS2_Tsuboyama_2023_1JIC_indels,DN7A_SACS2_Tsuboyama_2023_1JIC_indels.csv,DN7A_SACS2,Prokaryote,Saccharolobus solfataricus,TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQ,55,136,-0.472754253,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,DNA-binding protein 7a,Stability,cDNA display proteolysis,DN7A_SACS2_2023-08-07_b02.a2m,1,55,55,0.2,0.2,42895,0.764,42,1248.1,29.71666667,Medium,13,0.3095238095,,ddG_ML_float,1,aa_seq,DN7A_SACS2_theta0.2_2023-08-07_b02.npy,1,Stability +DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels,DNJA1_HUMAN_Tsuboyama_2023_2LO1_indels.csv,DNJA1_HUMAN,Human,Homo sapiens,TTYYDVLGVKPNATQEELKKAYRKLALKYHPDKNPNEGEKFKQISQAYEVLSDAKKRELYDKGGE,65,174,-2.239788161,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,DnaJ homolog subfamily A member 1,Stability,cDNA display proteolysis,DNJA1_HUMAN_2023-08-07_b07.a2m,1,65,65,0.7,0.2,280284,0.969,63,35361.9,561.3,High,52,0.8253968254,,ddG_ML_float,1,aa_seq,DNJA1_HUMAN_theta0.2_2023-08-07_b07.npy,1,Stability +DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels,DOCK1_MOUSE_Tsuboyama_2023_2M0Y_indels.csv,DOCK1_MOUSE,Eukaryote,Mus musculus,WVPTKREEKYGVAFYNYDARGADELSLQIGDTVHILETYEGWYRGYTLRKKSKKGIFPASYIHLKE,66,154,-1.104437518,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Dedicator of cytokinesis protein 1,Stability,cDNA display proteolysis,DOCK1_MOUSE_2023-08-07_b03.a2m,1,66,66,0.3,0.2,705447,0.848,56,22172.3,395.9339286,High,55,0.9821428571,,ddG_ML_float,1,aa_seq,DOCK1_MOUSE_theta0.2_2023-08-07_b03.npy,1,Stability +EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels,EPHB2_HUMAN_Tsuboyama_2023_1F0M_indels.csv,EPHB2_HUMAN,Human,Homo sapiens,SFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMN,66,185,-1.932053964,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Ephrin type-B receptor 2,Stability,cDNA display proteolysis,EPHB2_HUMAN_2023-08-07_b04.a2m,1,66,66,0.4,0.2,212234,0.894,59,8426.3,142.8186441,High,29,0.4915254237,,ddG_ML_float,1,aa_seq,EPHB2_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +FECA_ECOLI_Tsuboyama_2023_2D1U_indels,FECA_ECOLI_Tsuboyama_2023_2D1U_indels.csv,FECA_ECOLI,Eukaryote,Escherichia coli,QVNIAPGSLDKALNQYAAHSGFTLSVDASLTRGKQSNGLHGDYDVESGLQQLLDGSGLQVKPLGNNSWTLEP,72,193,-0.813576222,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Fe(3+) dicitrate transport protein FecA,Stability,cDNA display proteolysis,FECA_ECOLI_2023-08-07_b06.a2m,1,72,72,0.6,0.2,74248,0.986,71,9949.9,140.1394366,High,63,0.8873239437,,ddG_ML_float,1,aa_seq,FECA_ECOLI_theta0.2_2023-08-07_b06.npy,1,Stability +HCP_LAMBD_Tsuboyama_2023_2L6Q_indels,HCP_LAMBD_Tsuboyama_2023_2L6Q_indels.csv,HCP_LAMBD,Virus,Escherichia phage lambda (Bacteriophage lambda),VRQEELAAARAALHDLMTGKRVATVQKDGRRVEFTATSVSDLKKYIAELEVQTGM,55,148,-0.350614016,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Head completion protein,Stability,cDNA display proteolysis,HCP_LAMBD_2023-08-07_b05.a2m,1,55,55,0.5,0.2,2128,0.945,52,606.5,11.66346154,Medium,15,0.2884615385,,ddG_ML_float,1,aa_seq,HCP_LAMBD_theta0.2_2023-08-07_b05.npy,1,Stability +HECD1_HUMAN_Tsuboyama_2023_3DKM_indels,HECD1_HUMAN_Tsuboyama_2023_3DKM_indels.csv,HECD1_HUMAN,Human,Homo sapiens,NLYFQGLKYMVPGARVTRGLDWKWRDQDGSPQGEGTVTGELHNGWIDVTWDAGGSNSYRMGAEGKFDLKLAP,72,154,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,E3 ubiquitin-protein ligase HECTD1,Stability,cDNA display proteolysis,HECD1_HUMAN_2023-08-07_b03.a2m,1,72,72,0.3,0.2,18660,0.903,65,1192.3,18.34307692,Medium,24,0.3692307692,,ddG_ML_float,1,aa_seq,HECD1_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +HIS7_YEAST_Pokusaeva_2019_indels,HIS7_YEAST_Pokusaeva_2019_indels.csv,HIS7_YEAST,Eukaryote,Saccharomyces cerevisiae,MTEQKALVKRITNETKIQIAISLKGGPLAIEHSIFPEKEAEAVAEQATQSQVINVHTGIGFLDHMIHALAKHSGWSLIVECIGDLHIDDHHTTEDCGIALGQAFKEALGAVRGVKRFGSGFAPLDEALSRAVVDLSNRPYAVVELGLQREKVGDLSCEMIPHFLESFAEASRITLHVDCLRGKNDHHRSESAFKALAVAIREATSPNGTNDVPSTKGVLM,220,6102,0.25,manual,Pokusaeva,An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape,2019,10.1371/journal.pgen.1008079,IGP dehydratase (HIS3),Growth,Growth,HIS7_YEAST_full_11-26-2021_b09.a2m,1,220,220,0.9,0.2,40154,0.873,192,5191.3,27.03802083,Medium,318,1.65625,HIS7_YEAST_Pokusaeva_indels_2019.csv,DMS_score,1,sequence,HIS7_YEAST_theta_0.2.npy,0.1,OrganismalFitness +ILF3_HUMAN_Tsuboyama_2023_2L33_indels,ILF3_HUMAN_Tsuboyama_2023_2L33_indels.csv,ILF3_HUMAN,Human,Homo sapiens,MLTKHGKNPVMELNEKRRGLKYELISETGGSHDKRFVMEVEVDGQKFQGAGSNKKVAKAYAALAALEKLFP,71,193,-0.4,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Interleukin enhancer-binding factor 3,Stability,cDNA display proteolysis,ILF3_HUMAN_2023-08-07_b03.a2m,1,71,71,0.3,0.2,145438,0.915,65,21228,326.5846154,High,57,0.8769230769,,ddG_ML_float,1,aa_seq,ILF3_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +KCNJ2_MOUSE_Macdonald_2022_indels,KCNJ2_MOUSE_Macdonald_2022_indels.csv,KCNJ2_MOUSE,Eukaryote,Mus Musculus,MGSVRTNRYSIVSSEEDGMKLATMAVANGFGNGKSKVHTRQQCRSRFVKKDGHCNVQFINVGEKGQRYLADIFTTCVDIRWRWMLVIFCLAFVLSWLFFGCVFWLIALLHGDLDTSKVSKACVSEVNSFTAAFLFSIETQTTIGYGFRCVTDECPIAVFMVVFQSIVGCIIDAFIIGAVMAKMAKPKKRNETLVFSHNAVIAMRDGKLCLMWRVGNLRKSHLVEAHVRAQLLKSRITSEGEYIPLDQIDINVGFDSGIDRIFLVSPITIVHEIDEDSPLYDLSKQDIDNADFEIVVILEGMVEATAMTTQCRSSYLANEILWGHRYEPVLFEEKHYYKVDYSRFHKTYEVPNTPLCSARDLAEKKYILSNANSFCYENEVALTSKEEEEDSENGVPESTSTDSPPGIDLHNQASVPLEPRPLRRESEI,428,10501,-2,manual,Macdonald,"DIMPLE: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology",2022,10.1186/s13059-023-02880-6,Kir2.1,surface trafficking,FACS,KCNJ2_MOUSE_b01.a2m,1,428,428,0.1,0.2,20953,0.743,318,2154.5,6.77515723,Medium,160,0.50314465,KCNJ2_MOUSE_Macdonald_2022_indels_noflag.csv,score,1,mutated_sequence_no_flag,KCNJ2_MOUSE_b01_theta_0.2.npy,1,Expression +MAFG_MOUSE_Tsuboyama_2023_1K1V_indels,MAFG_MOUSE_Tsuboyama_2023_1K1V_indels.csv,MAFG_MOUSE,Eukaryote,Mus musculus,LTDEELVTMSVRELNQHLRGLSKEEIIQLKQRRRTLKNRGY,41,115,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription factor MafG,Stability,cDNA display proteolysis,MAFG_MOUSE_2023-08-07_b07.a2m,1,41,41,0.7,0.2,6178,1,41,156.7,3.82195122,Medium,4,0.09756097561,,ddG_ML_float,1,aa_seq,MAFG_MOUSE_theta0.2_2023-08-07_b07.npy,1,Stability +MBD11_ARATH_Tsuboyama_2023_6ACV_indels,MBD11_ARATH_Tsuboyama_2023_6ACV_indels.csv,MBD11_ARATH,Eukaryote,Arabidopsis thaliana,VSVELPAPSSWKKLFYPNKVGSVKKTEVVFVAPTGEEISNRKQLEQYLKSHPGNPAIAEFDWTTSG,66,131,-1.578921171,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Methyl-CpG-binding domain-containing protein 11,Stability,cDNA display proteolysis,MBD11_ARATH_2023-08-07_b03.a2m,1,66,66,0.3,0.2,26035,0.909,60,1510.5,25.175,Medium,11,0.1833333333,,ddG_ML_float,1,aa_seq,MBD11_ARATH_theta0.2_2023-08-07_b03.npy,1,Stability +MYO3_YEAST_Tsuboyama_2023_2BTT_indels,MYO3_YEAST_Tsuboyama_2023_2BTT_indels.csv,MYO3_YEAST,Eukaryote,Saccharomyces cerevisiae,KDPKFEAAYDFPGSGSSSELPLKKGDIVFISRDEPSGWSLAKLLDGSKEGWVPTAYMTPYK,61,80,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Myosin-3,Stability,cDNA display proteolysis,MYO3_YEAST_2023-08-07_b07.a2m,1,61,61,0.7,0.2,442941,0.885,54,12893.2,238.762963,High,51,0.9444444444,,ddG_ML_float,1,aa_seq,MYO3_YEAST_theta0.2_2023-08-07_b07.npy,1,Stability +NKX31_HUMAN_Tsuboyama_2023_2L9R_indels,NKX31_HUMAN_Tsuboyama_2023_2L9R_indels.csv,NKX31_HUMAN,Human,Homo sapiens,HSHMSHTQVIELERKFSHQKYLSAPERAHLAKNLKLTETQVKIWFQNRRYKTKRKQLSSEL,61,178,-0.3,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Homeobox protein Nkx-3.1,Stability,cDNA display proteolysis,NKX31_HUMAN_2023-08-07_b04.a2m,1,61,61,0.4,0.2,319273,0.902,55,8440.8,153.4690909,High,27,0.4909090909,,ddG_ML_float,1,aa_seq,NKX31_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +NUSA_ECOLI_Tsuboyama_2023_1WCL_indels,NUSA_ECOLI_Tsuboyama_2023_1WCL_indels.csv,NUSA_ECOLI,Prokaryote,Escherichia coli,EAHAAIDTFTKYLDIDEDFATVLVEEGFSTLEELAYVPMKELLEIEGLDEPTVEALRERAKNALATIAQ,69,191,-1.318069467,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription termination/antitermination protein NusA,Stability,cDNA display proteolysis,NUSA_ECOLI_2023-08-07_b03.a2m,1,69,69,0.3,0.2,205612,0.812,56,39002.5,696.4732143,High,38,0.6785714286,,ddG_ML_float,1,aa_seq,NUSA_ECOLI_theta0.2_2023-08-07_b03.npy,1,Stability +NUSG_MYCTU_Tsuboyama_2023_2MI6_indels,NUSG_MYCTU_Tsuboyama_2023_2MI6_indels.csv,NUSG_MYCTU,Prokaryote,Mycobacterium tuberculosis,DYEVGESVTVMDGPFATLPATISEVNAEQQKLKVLVSIFGRETPVELTFGQVSKI,55,157,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription termination/antitermination protein NusG,Stability,cDNA display proteolysis,NUSG_MYCTU_2023-08-07_b03.a2m,1,55,55,0.3,0.2,102004,0.964,53,16625.7,313.6924528,High,41,0.7735849057,,ddG_ML_float,1,aa_seq,NUSG_MYCTU_theta0.2_2023-08-07_b03.npy,1,Stability +OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels,OBSCN_HUMAN_Tsuboyama_2023_1V1C_indels.csv,OBSCN_HUMAN,Human,Homo sapiens,FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL,65,169,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Obscurin,Stability,cDNA display proteolysis,OBSCN_HUMAN_2023-08-07_b02.a2m,1,65,65,0.2,0.2,718751,0.815,53,23710.7,447.3716981,High,54,1.018867925,,ddG_ML_float,1,aa_seq,OBSCN_HUMAN_theta0.2_2023-08-07_b02.npy,1,Stability +ODP2_GEOSE_Tsuboyama_2023_1W4G_indels,ODP2_GEOSE_Tsuboyama_2023_1W4G_indels.csv,ODP2_GEOSE,Prokaryote,Geobacillus stearothermophilus,NRRVIAMPSVRKWAREKGVDIRLVQGTGKNGRVLKEDIDAFLAG,44,47,-0.4168227551,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex,Stability,cDNA display proteolysis,ODP2_GEOSE_2023-08-07_b07.a2m,1,44,44,0.7,0.2,163835,0.909,40,14834.6,370.865,High,21,0.525,,ddG_ML_float,1,aa_seq,ODP2_GEOSE_theta0.2_2023-08-07_b07.npy,1,Stability +OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels,OTU7A_HUMAN_Tsuboyama_2023_2L2D_indels.csv,OTU7A_HUMAN,Human,Homo sapiens,TLDMDAVLSDFVRSTGAEPGLARDLLEGKNWDLTAALSDYEQ,42,84,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,OTU domain-containing protein 7A,Stability,cDNA display proteolysis,OTU7A_HUMAN_2023-08-07_b02.a2m,1,42,42,0.2,0.2,1359071,0.881,37,514715.2,13911.22162,High,28,0.7567567568,,ddG_ML_float,1,aa_seq,OTU7A_HUMAN_theta0.2_2023-08-07_b02.npy,1,Stability +P53_HUMAN_Kotler_2018_indels,P53_HUMAN_Kotler_2018_indels.csv,P53_HUMAN,Human,Homo sapiens,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,393,341,0.206718584,median,Kotler,A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation,2018,10.1016/j.molcel.2018.06.012,p53,growth,Growth,P53_HUMAN_full_11-26-2021_b09.a2m,1,393,393,0.9,0.2,4129,0.863,339,148,0.4365781711,Low,15,0.04424778761,P53_HUMAN_Kotler_deletions_2018.csv,RFS_H1299,-1,sequence,P53_HUMAN_Kotler_theta_0.2.npy,0.1,OrganismalFitness +PIN1_HUMAN_Tsuboyama_2023_1I6C_indels,PIN1_HUMAN_Tsuboyama_2023_1I6C_indels.csv,PIN1_HUMAN,Human,Homo sapiens,KLPPGWEKRMSRSSGRVYYFNHITNASQWERPSGNSSSG,39,106,-0.6844420472,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1,Stability,cDNA display proteolysis,PIN1_HUMAN_2023-08-07_b02.a2m,1,39,39,0.2,0.2,248269,0.821,32,10833.2,338.5375,High,13,0.40625,,ddG_ML_float,1,aa_seq,PIN1_HUMAN_theta0.2_2023-08-07_b02.npy,1,Stability +PITX2_HUMAN_Tsuboyama_2023_2L7M_indels,PITX2_HUMAN_Tsuboyama_2023_2L7M_indels.csv,PITX2_HUMAN,Human,Homo sapiens,THFTSQQLQELEATFQRNHYPDMSTREEIAVWTNLTEARVRVWFKNRRAKWR,52,117,-1.201366007,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Pituitary homeobox 2,Stability,cDNA display proteolysis,PITX2_HUMAN_2023-08-07_b04.a2m,1,52,52,0.4,0.2,344174,1,52,9819.6,188.8384615,High,25,0.4807692308,,ddG_ML_float,1,aa_seq,PITX2_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +PKN1_HUMAN_Tsuboyama_2023_1URF_indels,PKN1_HUMAN_Tsuboyama_2023_1URF_indels.csv,PKN1_HUMAN,Human,Homo sapiens,GIPATNLSRVAGLEKQLAIELKVKQGAENMIQTYSNGSTKDRKLLLTAQQMLQDSKTKIDIIRMQLRRALQ,71,187,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Serine/threonine-protein kinase N1,Stability,cDNA display proteolysis,PKN1_HUMAN_2023-08-07_b01.a2m,1,71,71,0.1,0.2,187829,0.845,60,53755.8,895.93,High,13,0.2166666667,,ddG_ML_float,1,aa_seq,PKN1_HUMAN_theta0.2_2023-08-07_b01.npy,1,Stability +POLG_PESV_Tsuboyama_2023_2MXD_indels,POLG_PESV_Tsuboyama_2023_2MXD_indels.csv,POLG_PESV,Virus,Porcine enteric sapovirus (isolate Swine/United States/Cowden/1980),ALRDDEYDEWQDIIRDWRKEMTVQQFLDLKERALSGASDPDSQRYNAWLELRA,53,149,-1.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Genome polyprotein,Stability,cDNA display proteolysis,POLG_PESV_2023-08-07_b03.a2m,1,53,53,0.3,0.2,20190,0.887,47,3718.4,79.11489362,Medium,12,0.2553191489,,ddG_ML_float,1,aa_seq,POLG_PESV_theta0.2_2023-08-07_b03.npy,1,Stability +PR40A_HUMAN_Tsuboyama_2023_1UZC_indels,PR40A_HUMAN_Tsuboyama_2023_1UZC_indels.csv,PR40A_HUMAN,Human,Homo sapiens,TYTWNTKEEAKQAFKELLKEKRVPSNASWEQAMKMIINDPRYSALAKLSEKKQAFNAYKVQTE,63,168,-1.362579422,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Pre-mRNA-processing factor 40 homolog A,Stability,cDNA display proteolysis,PR40A_HUMAN_2023-08-07_b03.a2m,1,63,63,0.3,0.2,63560,0.857,54,3663.8,67.84814815,Medium,16,0.2962962963,,ddG_ML_float,1,aa_seq,PR40A_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +PSAE_PICP2_Tsuboyama_2023_1PSE_indels,PSAE_PICP2_Tsuboyama_2023_1PSE_indels.csv,PSAE_PICP2,Prokaryote,Synechococcus sp,AIERGSKVKILRKESYWYGDVGTVASIDKSGIIYPVIVRFNKVNYNGFSGSAGGLNTNNFAEHELEVV,68,175,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Photosystem I reaction center subunit IV,Stability,cDNA display proteolysis,PSAE_PICP2_2023-08-07_b09.a2m,1,68,68,0.9,0.2,1785,0.868,59,130.7,2.215254237,Medium,9,0.1525423729,,ddG_ML_float,1,aa_seq,PSAE_PICP2_theta0.2_2023-08-07_b09.npy,1,Stability +PTEN_HUMAN_Mighell_2018_indels,PTEN_HUMAN_Mighell_2018_indels.csv,PTEN_HUMAN,Human,Homo sapiens,MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV,403,314,-2.020820613,median,Mighell,A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships,2018,10.1016/j.ajhg.2018.03.018,PTEN,"growth (surrogate for enzymatic activity/hydrolysis of lipid phosphates to restore PIP2, which affects proliferation rate)",lipid phosphatase activity,PTEN_HUMAN_full_11-26-2021_b01.a2m,1,403,403,0.1,0.2,19058,0.752,303,1425.3,4.703960396,Medium,52,0.1716171617,PTEN_HUMAN_Mighell_deletions_2018.csv,DMS_score,1,sequence,PTEN_HUMAN_theta_0.2.npy,0.1,Activity +Q8EG35_SHEON_Campbell_2022_indels,Q8EG35_SHEON_Campbell_2022_indels.csv,Q8EG35_SHEON,Prokaryote,Shewanella oneidensis,MKNCLKMKNLLPALTITMAMSAVMALVVTPNAYASKWDEKMTPEQVEATLDKKFAEGNYSPKGADSCLMCHKKSEKVMDLFKGVHGAIDSSKSPMAGLQCEACHGPLGQHNKGGNEPMITFGKQSTLSADKQNSVCMSCHQDDKRMSWNGGHHDNADVACASCHQVHVAKDPVLSKNTEMEVCTSCHTKQKADMNKRSSHPLKWAQMTCSDCHNPHGSMTDSDLNKPSVNDTCYSCHAEKRGPKLWEHAPVTENCVTCHNPHGSVNDGMLKTRAPQLCQQCHASDGHASNAYLGNTGLGSNVGDNAFTGGRSCLNCHSQVHGSNHPSGKLLQR,333,331,0.003035782214,median,Campbell,Determinants of Multiheme Cytochrome Extracellular Electron Transfer Uncovered by Systematic Peptide Insertion,2022,10.1021/acs.biochem.2c00148,MtrA,extracellular electron transfer,survival assessment assay,Q8EG35_SHEON_b03.a2m,1,333,333,0.3,0.2,2866,0.778,259,1289.1,4.97722008,Medium,89,0.34362934,Q8EG35_SHEON_Campbell_2023.csv,selected_avg,1,mutated_sequence,Q8EG35_SHEON_b03_theta_0.2.npy,1,OrganismalFitness +RAD_ANTMA_Tsuboyama_2023_2CJJ_indels,RAD_ANTMA_Tsuboyama_2023_2CJJ_indels.csv,RAD_ANTMA,Eukaryote,Antirrhinum majus,PWSAKENKAFERALAVYDKDTPDRWANVARAVEGRTPEEVKKHYEILVEDIKYI,54,97,-0.3943851731,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription factor RADIALIS,Stability,cDNA display proteolysis,RAD_ANTMA_2023-08-07_b01.a2m,1,54,54,0.1,0.2,423275,0.833,45,38133.9,847.42,High,27,0.6,,ddG_ML_float,1,aa_seq,RAD_ANTMA_theta0.2_2023-08-07_b01.npy,1,Stability +RCD1_ARATH_Tsuboyama_2023_5OAO_indels,RCD1_ARATH_Tsuboyama_2023_5OAO_indels.csv,RCD1_ARATH,Eukaryote,Arabidopsis thaliana,PTLFAAISHKVAENDMLLINADYQQLRDKKMTRAEFVRKLRVIVGDDLLRSTITTLQ,57,124,-0.3828831078,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Inactive poly [ADP-ribose] polymerase RCD1,Stability,cDNA display proteolysis,RCD1_ARATH_2023-08-07_b02.a2m,1,57,57,0.2,0.2,6525,0.93,53,1578.5,29.78301887,Medium,2,0.03773584906,,ddG_ML_float,1,aa_seq,RCD1_ARATH_theta0.2_2023-08-07_b02.npy,1,Stability +RD23A_HUMAN_Tsuboyama_2023_1IFY_indels,RD23A_HUMAN_Tsuboyama_2023_1IFY_indels.csv,RD23A_HUMAN,Human,Homo sapiens,SEYETMLTEIMSMGYERERVVAALRASYNNPHRAVEYLLTGIPG,44,120,-0.7285205281,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,UV excision repair protein RAD23 homolog A,Stability,cDNA display proteolysis,RD23A_HUMAN_2023-08-07_b04.a2m,1,44,44,0.4,0.2,100991,0.864,38,7912.9,208.2342105,High,21,0.5526315789,,ddG_ML_float,1,aa_seq,RD23A_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +RPC1_BP434_Tsuboyama_2023_1R69_indels,RPC1_BP434_Tsuboyama_2023_1R69_indels.csv,RPC1_BP434,Virus,Enterobacteria phage 434 (Bacteriophage 434),SISSRVKSKRIQLGLNQAELAQKVGTTQQSIEQLENGKTKRPRFLPELASALGVSVDWLLN,61,164,-1.349855239,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Repressor protein CI,Stability,cDNA display proteolysis,RPC1_BP434_2023-08-07_b05.a2m,1,61,61,0.5,0.2,820224,0.951,58,192520.2,3319.313793,High,73,1.25862069,,ddG_ML_float,1,aa_seq,RPC1_BP434_theta0.2_2023-08-07_b05.npy,1,Stability +RS15_GEOSE_Tsuboyama_2023_1A32_indels,RS15_GEOSE_Tsuboyama_2023_1A32_indels.csv,RS15_GEOSE,Prokaryote,Geobacillus stearothermophilus,SPEVQIAILTEQINNLNEHLRVHKKDHHSRRGLLKMVGKRRRLLAYLRNKDVARYREIVEKLG,63,176,-0.1292928041,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Small ribosomal subunit protein uS15,Stability,cDNA display proteolysis,RS15_GEOSE_2023-08-07_b06.a2m,1,63,63,0.6,0.2,44428,1,63,4519.5,71.73809524,Medium,35,0.5555555556,,ddG_ML_float,1,aa_seq,RS15_GEOSE_theta0.2_2023-08-07_b06.npy,1,Stability +S22A1_HUMAN_Yee_2023_abundance_indels,S22A1_HUMAN_Yee_2023_abundance_indels.csv,S22A1_HUMAN,Human,Homo sapiens,PTVDDILEQVGESGWFQKQAFLILCLLSAAFAPICVGIVFLGFTPDHHCQSPGVAELSQRCGWSPAEELNYTVPGLGPAGEAFLGQCRRYEVDWNQSALSCVDPLASLATNRSHLPLGPCQDGWVYDTPGSSIVTEFNLVCADSWKLDLFQSCLNAGFLFGSLGVGYFADRFGRKLCLLGTVLVNAVSGVLMAFSPNYMSMLLFRLLQGLVSKGNWMAGYTLITEFVGSGSRRTVAIMYQMAFTVGLVALTGLAYALPHWRWLQLAVSLPTFLFLLYYWCVPESPRWLLSQKRNTEAIKIMDHIAQKNGKLPPADLKMLSLEEDVTEKLSPSFADLFRTPRLRKRTFILMYLWFTDSVLYQGLILHMGATSGNLYLDFLYSALVEIPGAFIALITIDRVGRIYPMAMSNLLAGAACLVMIFISPDLHWLNIIIMCVGRMGITIAIQMICLVNAELYPTFVRNLGVMVCSSLCDIGGIITPFIVFRLREVWQALPLILFAVLGLLAAGVTLLLPETKGVALPETMKDAENLGRKAKPKENTIYLKVQTSEPSGT,553,430,-0.75,manual,Yee,The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics,2023,10.1101/2023.06.06.543963,Oct1,abundance,FACS,S22A1_HUMAN_2023-08-07_b02.a2m,1,553,553,0.2,0.2,198790,0.807,446,32557.5,72.99887892,Medium,485,1.087443946,543963_file04.xlsx,GFP_score,1,mutated_sequence,S22A1_HUMAN_theta0.2_2023-08-07_b02.npy,1,Expression +S22A1_HUMAN_Yee_2023_activity_indels,S22A1_HUMAN_Yee_2023_activity_indels.csv,S22A1_HUMAN,Human,Homo sapiens,PTVDDILEQVGESGWFQKQAFLILCLLSAAFAPICVGIVFLGFTPDHHCQSPGVAELSQRCGWSPAEELNYTVPGLGPAGEAFLGQCRRYEVDWNQSALSCVDPLASLATNRSHLPLGPCQDGWVYDTPGSSIVTEFNLVCADSWKLDLFQSCLNAGFLFGSLGVGYFADRFGRKLCLLGTVLVNAVSGVLMAFSPNYMSMLLFRLLQGLVSKGNWMAGYTLITEFVGSGSRRTVAIMYQMAFTVGLVALTGLAYALPHWRWLQLAVSLPTFLFLLYYWCVPESPRWLLSQKRNTEAIKIMDHIAQKNGKLPPADLKMLSLEEDVTEKLSPSFADLFRTPRLRKRTFILMYLWFTDSVLYQGLILHMGATSGNLYLDFLYSALVEIPGAFIALITIDRVGRIYPMAMSNLLAGAACLVMIFISPDLHWLNIIIMCVGRMGITIAIQMICLVNAELYPTFVRNLGVMVCSSLCDIGGIITPFIVFRLREVWQALPLILFAVLGLLAAGVTLLLPETKGVALPETMKDAENLGRKAKPKENTIYLKVQTSEPSGT,553,490,1,manual,Yee,The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics,2023,10.1101/2023.06.06.543963,Oct1,uptake of cytotoxic substrate,Growth,S22A1_HUMAN_2023-08-07_b02.a2m,1,553,553,0.2,0.2,198790,0.807,446,32557.5,72.99887892,Medium,485,1.087443946,543963_file04.xlsx,SM73_1_score,-1,mutated_sequence,S22A1_HUMAN_theta0.2_2023-08-07_b02.npy,1,Activity +SAV1_MOUSE_Tsuboyama_2023_2YSB_indels,SAV1_MOUSE_Tsuboyama_2023_2YSB_indels.csv,SAV1_MOUSE,Eukaryote,Mus musculus,GEDLPLPPGWSVDWTMRGRKYYIDHNTNTTHWSHPLESGPSSG,43,86,-0.6280556038,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Protein salvador homolog 1,Stability,cDNA display proteolysis,SAV1_MOUSE_2023-08-07_b06.a2m,1,43,43,0.6,0.2,177542,0.791,34,4627.6,136.1058824,High,14,0.4117647059,,ddG_ML_float,1,aa_seq,SAV1_MOUSE_theta0.2_2023-08-07_b06.npy,1,Stability +SDA_BACSU_Tsuboyama_2023_1PV0_indels,SDA_BACSU_Tsuboyama_2023_1PV0_indels.csv,SDA_BACSU,Prokaryote,Bacillus subtilis,MRKLSDELLIESYFKATEMNLNRDFIELIENEIKRRSLGHIISV,44,127,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Sporulation inhibitor sda,Stability,cDNA display proteolysis,SDA_BACSU_2023-08-07_b05.a2m,1,44,44,0.5,0.2,1953,0.886,39,876.8,22.48205128,Medium,4,0.1025641026,,ddG_ML_float,1,aa_seq,SDA_BACSU_theta0.2_2023-08-07_b05.npy,1,Stability +SOX30_HUMAN_Tsuboyama_2023_7JJK_indels,SOX30_HUMAN_Tsuboyama_2023_7JJK_indels.csv,SOX30_HUMAN,Human,Homo sapiens,RPMNAFMVWARIHRPALAKANPAANNAEISVQLGLEWNKLSEEQKKPYYDEAQKIKE,57,109,-0.3216404755,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription factor SOX-30,Stability,cDNA display proteolysis,SOX30_HUMAN_2023-08-07_b03.a2m,1,57,57,0.3,0.2,158104,0.982,56,14909.6,266.2428571,High,36,0.6428571429,,ddG_ML_float,1,aa_seq,SOX30_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +SPG2_STRSG_Tsuboyama_2023_5UBS_indels,SPG2_STRSG_Tsuboyama_2023_5UBS_indels.csv,SPG2_STRSG,Prokaryote,Streptococcus sp. group G,MTFKLIINGKTLKGETTTEAVDAATAEKVFKQYFNDNGIDGEWTYDDATKTFTITE,56,148,-1.000627629,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Immunoglobulin G-binding protein G,Stability,cDNA display proteolysis,SPG2_STRSG_2023-08-07_b03.a2m,1,56,56,0.3,0.2,39899,0.75,42,2567.6,61.13333333,Medium,6,0.1428571429,,ddG_ML_float,1,aa_seq,SPG2_STRSG_theta0.2_2023-08-07_b03.npy,1,Stability +SPTN1_CHICK_Tsuboyama_2023_1TUD_indels,SPTN1_CHICK_Tsuboyama_2023_1TUD_indels.csv,SPTN1_CHICK,Eukaryote,Gallus gallus,RQGFVPAAYVKKLDSGTGKELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVN,60,129,-2.360476078,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,"Spectrin alpha chain, non-erythrocytic 1",Stability,cDNA display proteolysis,SPTN1_CHICK_2023-08-07_b03.a2m,1,60,60,0.3,0.2,420793,0.933,56,15051.5,268.7767857,High,47,0.8392857143,,ddG_ML_float,1,aa_seq,SPTN1_CHICK_theta0.2_2023-08-07_b03.npy,1,Stability +SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels,SQSTM_MOUSE_Tsuboyama_2023_2RRU_indels.csv,SQSTM_MOUSE,Eukaryote,Mus musculus,RLIESLSQMLSMGFSDEGGWLTRLLQTKNYDIGAALDTIQ,40,111,-0.8554856463,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Sequestosome-1,Stability,cDNA display proteolysis,SQSTM_MOUSE_2023-08-07_b05.a2m,1,40,40,0.5,0.2,34660,0.925,37,3244.5,87.68918919,Medium,13,0.3513513514,,ddG_ML_float,1,aa_seq,SQSTM_MOUSE_theta0.2_2023-08-07_b05.npy,1,Stability +SR43C_ARATH_Tsuboyama_2023_2N88_indels,SR43C_ARATH_Tsuboyama_2023_2N88_indels.csv,SR43C_ARATH,Eukaryote,Arabidopsis thaliana,AVAESVIGKRVGDDGKTIEYLVKWTDMSDATWEPQDNVDSTLVLLYQQ,48,135,-1.591761235,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,"Signal recognition particle 43 kDa protein, chloroplastic",Stability,cDNA display proteolysis,SR43C_ARATH_2023-08-07_b02.a2m,1,48,48,0.2,0.2,101118,0.917,44,12180.6,276.8318182,High,26,0.5909090909,,ddG_ML_float,1,aa_seq,SR43C_ARATH_theta0.2_2023-08-07_b02.npy,1,Stability +SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels,SRBS1_HUMAN_Tsuboyama_2023_2O2W_indels.csv,SRBS1_HUMAN,Human,Homo sapiens,GIDPFTGEAIAKFNFNGDTQVEMSFRKGERITLLRQVDENWYEGRIPGTSRQGIFPITYVDVIKRPL,67,154,-1.169019411,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Sorbin and SH3 domain-containing protein 1,Stability,cDNA display proteolysis,SRBS1_HUMAN_2023-08-07_b03.a2m,1,67,67,0.3,0.2,708655,0.836,56,22689,405.1607143,High,60,1.071428571,,ddG_ML_float,1,aa_seq,SRBS1_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels,TCRG1_MOUSE_Tsuboyama_2023_1E0L_indels.csv,TCRG1_MOUSE,Eukaryote,Mus musculus,GATAVSEWTEYKTADGKTYYYNNRTLESTWEKPQELK,37,99,-1.2,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Transcription elongation regulator 1,Stability,cDNA display proteolysis,TCRG1_MOUSE_2023-08-07_b08.a2m,1,37,37,0.8,0.2,43363,0.865,32,2819.7,88.115625,Medium,14,0.4375,,ddG_ML_float,1,aa_seq,TCRG1_MOUSE_theta0.2_2023-08-07_b08.npy,1,Stability +THO1_YEAST_Tsuboyama_2023_2WQG_indels,THO1_YEAST_Tsuboyama_2023_2WQG_indels.csv,THO1_YEAST,Eukaryote,Saccharomyces cerevisiae,SADYSSLTVVQLKDLLTKRNLSVGGLKNEWVQRLIKDDEES,41,82,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Protein THO1,Stability,cDNA display proteolysis,THO1_YEAST_2023-08-07_b05.a2m,1,41,41,0.5,0.2,54877,0.805,33,8516.7,258.0818182,High,15,0.4545454545,,ddG_ML_float,1,aa_seq,THO1_YEAST_theta0.2_2023-08-07_b05.npy,1,Stability +TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels,TNKS2_HUMAN_Tsuboyama_2023_5JRT_indels.csv,TNKS2_HUMAN,Human,Homo sapiens,FSITQFVRNLGLEHLMDIFEREQITLRVLVEMGHKELKEIGINAYGHREKLIKGVERLI,59,171,-0.9451205822,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Poly [ADP-ribose] polymerase tankyrase-2,Stability,cDNA display proteolysis,TNKS2_HUMAN_2023-08-07_b03.a2m,1,59,59,0.3,0.2,270654,0.949,56,11206,200.1071429,High,26,0.4642857143,,ddG_ML_float,1,aa_seq,TNKS2_HUMAN_theta0.2_2023-08-07_b03.npy,1,Stability +UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels,UBE4B_HUMAN_Tsuboyama_2023_3L1X_indels.csv,UBE4B_HUMAN,Human,Homo sapiens,DAPDEFRDPLMDTLMTDPVRLPSGTIMDRSIILRHLLNSPTDPFNRQTLTESMLEPVPELKEQIQAWMR,69,147,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Ubiquitin conjugation factor E4 B,Stability,cDNA display proteolysis,UBE4B_HUMAN_2023-08-07_b04.a2m,1,69,69,0.4,0.2,310943,0.928,64,34185.4,534.146875,High,52,0.8125,,ddG_ML_float,1,aa_seq,UBE4B_HUMAN_theta0.2_2023-08-07_b04.npy,1,Stability +UBR5_HUMAN_Tsuboyama_2023_1I2T_indels,UBR5_HUMAN_Tsuboyama_2023_1I2T_indels.csv,UBR5_HUMAN,Human,Homo sapiens,HRQALGERLYPRVQAMQPAFASKITGMLLELSPAQLLLLLASEDSLRARVDEAMELII,58,156,-0.4460165437,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,E3 ubiquitin-protein ligase UBR5,Stability,cDNA display proteolysis,UBR5_HUMAN_2023-08-07_b05.a2m,1,58,58,0.5,0.2,17888,0.966,56,1031.7,18.42321429,Medium,14,0.25,,ddG_ML_float,1,aa_seq,UBR5_HUMAN_theta0.2_2023-08-07_b05.npy,1,Stability +VG08_BPP22_Tsuboyama_2023_2GP8_indels,VG08_BPP22_Tsuboyama_2023_2GP8_indels.csv,VG08_BPP22,Virus,Salmonella phage P22 (Bacteriophage P22),ITGDVSAANKDAIRKQMDAAASKGDVETYRKLKAKLKGIR,40,101,-0.2013306011,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Scaffolding protein,Stability,cDNA display proteolysis,VG08_BPP22_2023-08-07_b05.a2m,1,40,40,0.5,0.2,102464,0.875,35,12963.6,370.3885714,High,13,0.3714285714,,ddG_ML_float,1,aa_seq,VG08_BPP22_theta0.2_2023-08-07_b05.npy,1,Stability +VILI_CHICK_Tsuboyama_2023_1YU5_indels,VILI_CHICK_Tsuboyama_2023_1YU5_indels.csv,VILI_CHICK,Eukaryote,Gallus gallus,KLETFPLDVLVNTAAEDLPRGVDPSRKENHLSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF,65,156,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Villin-1,Stability,cDNA display proteolysis,VILI_CHICK_2023-08-07_b01.a2m,1,65,65,0.1,0.2,254210,0.769,50,46507.8,930.156,High,19,0.38,,ddG_ML_float,1,aa_seq,VILI_CHICK_theta0.2_2023-08-07_b01.npy,1,Stability +VRPI_BPT7_Tsuboyama_2023_2WNM_indels,VRPI_BPT7_Tsuboyama_2023_2WNM_indels.csv,VRPI_BPT7,Virus,Escherichia phage T7 (Bacteriophage T7),SLSVDNKKFWATVESSEHSFEVPIYAETLDEALELAEWQYVPAGFEVTRVRPCVAP,56,154,-1.1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,Bacterial RNA polymerase inhibitor,Stability,cDNA display proteolysis,VRPI_BPT7_2023-08-07_b02.a2m,1,56,56,0.2,0.2,6266,0.875,49,1555.8,31.75102041,Medium,3,0.0612244898,,ddG_ML_float,1,aa_seq,VRPI_BPT7_theta0.2_2023-08-07_b02.npy,1,Stability +YNZC_BACSU_Tsuboyama_2023_2JVD_indels,YNZC_BACSU_Tsuboyama_2023_2JVD_indels.csv,YNZC_BACSU,Prokaryote,Bacillus subtilis,MISNAKIARINELAAKAKAGVITEEEKAEQQKLRQEYLK,39,104,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-69,UPF0291 protein YnzC,Stability,cDNA display proteolysis,YNZC_BACSU_2023-08-07_b07.a2m,1,39,39,0.7,0.2,7116,0.974,38,1588.3,41.79736842,Medium,13,0.3421052632,,ddG_ML_float,1,aa_seq,YNZC_BACSU_theta0.2_2023-08-07_b07.npy,1,Stability \ No newline at end of file diff --git a/reference_files/DMS_substitutions.csv b/reference_files/DMS_substitutions.csv new file mode 100644 index 0000000..cc89480 --- /dev/null +++ b/reference_files/DMS_substitutions.csv @@ -0,0 +1,218 @@ +DMS_id,DMS_filename,UniProt_ID,taxon,source_organism,target_seq,seq_len,includes_multiple_mutants,DMS_total_number_mutants,DMS_number_single_mutants,DMS_number_multiple_mutants,DMS_binarization_cutoff,DMS_binarization_method,first_author,title,year,jo,region_mutated,molecule_name,selection_assay,selection_type,MSA_filename,MSA_start,MSA_end,MSA_len,MSA_bitscore,MSA_theta,MSA_num_seqs,MSA_perc_cov,MSA_num_cov,MSA_N_eff,MSA_Neff_L,MSA_Neff_L_category,MSA_num_significant,MSA_num_significant_L,raw_DMS_filename,raw_DMS_phenotype_name,raw_DMS_directionality,raw_DMS_mutant_column,weight_file_name,pdb_file,pdb_range,ProteinGym_version,raw_mut_offset,coarse_selection_type +A0A140D2T1_ZIKV_Sourisseau_2019,A0A140D2T1_ZIKV_Sourisseau_2019.csv,A0A140D2T1_ZIKV,Virus,Zika virus (ZIKV),MKNPKKKSGGFRIVNMLKRGVARVNPLGGLKRLPAGLLLGHGPIRMVLAILAFLRFTAIKPSLGLINRWGSVGKKEAMEIIKKFKKDLAAMLRIINARKERKRRGADTSIGIIGLLLTTAMAAEITRRGSAYYMYLDRSDAGKAISFATTLGVNKCHVQIMDLGHMCDATMSYECPMLDEGVEPDDVDCWCNTTSTWVVYGTCHHKKGEARRSRRAVTLPSHSTRKLQTRSQTWLESREYTKHLIKVENWIFRNPGFALVAVAIAWLLGSSTSQKVIYLVMILLIAPAYSIRCIGVSNRDFVEGMSGGTWVDVVLEHGGCVTVMAQDKPTVDIELVTTTVSNMAEVRSYCYEASISDMASDSRCPTQGEAYLDKQSDTQYVCKRTLVDRGWGNGCGLFGKGSLVTCAKFTCSKKMTGKSIQPENLEYRIMLSVHGSQHSGMIVNDTGYETDENRAKVEVTPNSPRAEATLGGFGSLGLDCEPRTGLDFSDLYYLTMNNKHWLVHKEWFHDIPLPWHAGADTGTPHWNNKEALVEFKDAHAKRQTVVVLGSQEGAVHTALAGALEAEMDGAKGKLFSGHLKCRLKMDKLRLKGVSYSLCTAAFTFTKVPAETLHGTVTVEVQYAGTDGPCKIPVQMAVDMQTLTPVGRLITANPVITESTENSKMMLELDPPFGDSYIVIGVGDKKITHHWHRSGSTIGKAFEATVRGAKRMAVLGDTAWDFGSVGGVFNSLGKGIHQIFGAAFKSLFGGMSWFSQILIGTLLVWLGLNTKNGSISLTCLALGGVMIFLSTAVSADVGCSVDFSKKETRCGTGVFIYNDVEAWRDRYKYHPDSPRRLAAAVKQAWEEGICGISSVSRMENIMWKSVEGELNAILEENGVQLTVVVGSVKNPMWRGPQRLPVPVNELPHGWKAWGKSYFVRAAKTNNSFVVDGDTLKECPLEHRAWNSFLVEDHGFGVFHTSVWLKVREDYSLECDPAVIGTAVKGREAAHSDLGYWIESEKNDTWRLKRAHLIEMKTCEWPKSHTLWTDGVEESDLIIPKSLAGPLSHHNTREGYRTQVKGPWHSEELEIRFEECPGTKVYVEETCGTRGPSLRSTTASGRVIEEWCCRECTMPPLSFRAKDGCWYGMEIRPRKEPESNLVRSMVTAGSTDHMDHFSLGVLVILLMVQEGLKKRMTTKIIMSTSMAVLVVMILGGFSMSDLAKLVILMGATFAEMNTGGDVAHLALVAAFKVRPALLVSFIFRANWTPRESMLLALASCLLQTAISALEGDLMVLINGFALAWLAIRAMAVPRTDNIALPILAALTPLARGTLLVAWRAGLATCGGIMLLSLKGKGSVKKNLPFVMALGLTAVRVVDPINVVGLLLLTRSGKRSWPPSEVLTAVGLICALAGGFAKADIEMAGPMAAVGLLIVSYVVSGKSVDMYIERAGDITWEKDAEVTGNSPRLDVALDESGDFSLVEEDGPPMREIILKVVLMAICGMNPIAIPFAAGAWYVYVKTGKRSGALWDVPAPKEVKKGETTDGVYRVMTRRLLGSTQVGVGVMQEGVFHTMWHVTKGAALRSGEGRLDPYWGDVKQDLVSYCGPWKLDAAWDGLSEVQLLAVPPGERARNIQTLPGIFKTKDGDIGAVALDYPAGTSGSPILDKCGRVIGLYGNGVVIKNGSYVSAITQGKREEETPVECFEPSMLKKKQLTVLDLHPGAGKTRRVLPEIVREAIKKRLRTVILAPTRVVAAEMEEALRGLPVRYMTTAVNVTHSGTEIVDLMCHATFTSRLLQPIRVPNYNLYIMDEAHFTDPSSIAARGYISTRVEMGEAAAIFMTATPPGTRDAFPDSNSPIMDTEVEVPERAWSSGFDWVTDHSGKTVWFVPSVRNGNEIAACLTKAGKRVIQLSRKTFETEFQKTKNQEWDFVITTDISEMGANFKADRVIDSRRCLKPVILDGERVILAGPMPVTHASAAQRRGRIGRNPNKPGDEYMYGGGCAETDEGHAHWLEARMLLDNIYLQDGLIASLYRPEADKVAAIEGEFKLRTEQRKTFVELMKRGDLPVWLAYQVASAGITYTDRRWCFDGTTNNTIMEDSVPAEVWTKYGEKRVLKPRWMDARVCSDHAALKSFKEFAAGKRGAALGVMEALGTLPGHMTERFQEAIDNLAVLMRAETGSRPYKAAAAQLPETLETIMLLGLLGTVSLGIFFVLMRNKGIGKMGFGMVTLGASAWLMWLSEIEPARIACVLIVVFLLLVVLIPEPEKQRSPQDNQMAIIIMVAVGLLGLITANELGWLERTKNDIAHLMGRREEGATMGFSMDIDLRPASAWAIYAALTTLITPAVQHAVTTSYNNYSLMAMATQAGVLFGMGKGMPFYAWDLGVPLLMMGCYSQLTPLTLIVAIILLVAHYMYLIPGLQAAAARAAQKRTAAGIMKNPVVDGIVVTDIDTMTIDPQVEKKMGQVLLIAVAISSAVLLRTAWGWGEAGALITAATSTLWEGSPNKYWNSSTATSLCNIFRGSYLAGASLIYTVTRNAGLVKRRGGGTGETLGEKWKARLNQMSALEFYSYKKSGITEVCREEARRALKDGVATGGHAVSRGSAKLRWLVERGYLQPYGKVVDLGCGRGGWSYYAATIRKVQEVRGYTKGGPGHEEPMLVQSYGWNIVRLKSGVDVFHMAAEPCDTLLCDIGESSSSPEVEETRTLRVLSMVGDWLEKRPGAFCIKVLCPYTSTMMETMERLQRRHGGGLVRVPLSRNSTHEMYWVSGAKSNIIKSVSTTSQLLLGRMDGPRRPVKYEEDVNLGSGTRAVASCAEAPNMKIIGRRIERIRNEHAETWFLDENHPYRTWAYHGSYEAPTQGSASSLVNGVVRLLSKPWDVVTGVTGIAMTDTTPYGQQRVFKEKVDTRVPDPQEGTRQVMNIVSSWLWKELGKRKRPRVCTKEEFINKVRSNAALGAIFEEEKEWKTAVEAVNDPRFWALVDREREHHLRGECHSCVYNMMGKREKKQGEFGKAKGSRAIWYMWLGARFLEFEALGFLNEDHWMGRENSGGGVEGLGLQRLGYILEEMNRAPGGKMYADDTAGWDTRISKFDLENEALITNQMEEGHRTLALAVIKYTYQNKVVKVLRPAEGGKTVMDIISRQDQRGSGQVVTYALNTFTNLVVQLIRNMEAEEVLEMQDLWLLRKPEKVTRWLQSNGWDRLKRMAVSGDDCVVKPIDDRFAHALRFLNDMGKVRKDTQEWKPSTGWSNWEEVPFCSHHFNKLYLKDGRSIVVPCRHQDELIGRARVSPGAGWSIRETACLAKSYAQMWQLLYFHRRDLRLMANAICSAVPVDWVPTGRTTWSIHGKGEWMTTEDMLMVWNRVWIEENDHMEDKTPVTKWTDIPYLGKREDLWCGSLIGHRPRTTWAENIKDTVNMVRRIIGDEEKYMDYLSTQVRYLGEEGSTPGVL,3423,FALSE,9576,9576,0,0.04324892146,median,Sourisseau,Deep Mutational Scanning Comprehensively Maps How Zika Envelope Protein Mutations Affect Viral Growth and Antibody Escape,2019,10.1128/JVI.01291-19,291-794,Zika virus env,Viral replication,Growth,A0A140D2T1_ZIKV_theta0.99_281-804_11-26-2021_b02.a2m,281,804,524,0.2,0.01,16501,0.948,497,1357.9,2.732193159,medium,329,0.661971831,A0A140D2T1_ZIKV_Sourisseau_growth_2019.csv,effect,1,mutant,A0A140D2T1_ZIKV_theta_0.01.npy,A0A140D2T1_ZIKV.pdb,291-794,0.1,,OrganismalFitness +A0A192B1T2_9HIV1_Haddox_2018,A0A192B1T2_9HIV1_Haddox_2018.csv,A0A192B1T2_9HIV1,Virus,Human immunodeficiency virus 1,MRVKGIQMNSQHLLRWGIMILGMIMICSVAGNLWVTVYYGVPVWKDAETTLFCASDAKAYDAEVHNIWATHACVPTDPNPQEINLENVTEEFNMWKNNMVEQMHTDIISLWDQGLKPCVKLTPLCVTLDCHNVTYNITSDMKEEITNCSYNVTTVIRDKKQKVSSLFYKLDVVQIGGNNRTNSQYRLINCNTSAITQACPKVTFEPIPIHYCAPAGFAILKCKDEKFNGTGLCKNVSTVQCTHGIKPVVSTQLLLNGSLAEGEVRIRSENITNNAKNIIVQLASPVTINCIRPNNNTRKSVHLGPGQAFYATDGIIGEIRQAHCNVSKKEWNSTLQKVANQLRPYFKNNTIIKFANSSGGDLEITTHSFNCGGEFFYCNTSGLFNSTWEFNSTWNNSNSTENITLQCRIKQIINMWQRAGQAIYAPPIPGVIRCKSNITGLILTRDGGSNKNTSETFRPGGGDMRDNWRSELYKYKVVKIEPIGVAPTRAKRRVVEREKRAVGIGAVFIGFLGAAGSTMGAASVTLTVQARQLLSGIVQQQSNLLRAIEAQQHLLKLTVWGIKQLQARVLAVERYLKDQQLLGIWGCSGKLICTTNVPWNSSWSNKSQDEIWGNMTWLQWDKEVSNYTQIIYTLIEESQNQQEKNEQDLLALDKWASLWNWFNISQWLWYIKIFIIIVGGLIGLRIVFAVLSVINRVRQGYSPLSFQTRTPNPGELDRPGRIEEEGGEQDRGRSIRLVSGFLALAWDDLRSLCLFSYHRLRDFILIATRTVELLGHSSLKGLRLGWESLKYLGNLLVYWGRELKISAINLCDTIAIAVAGWTDRVIELGQRLCRAILHIPRRIRQGFERALL,852,FALSE,12577,12577,0,-2.2,manual,Haddox,Mapping mutational effects along the evolutionary landscape of HIV envelope,2018,10.7554/eLife.34420,30-691,HIV env (BF520),Viral replication,Growth,A0A192B1T2_9HIV1_theta0.99_full_11-26-2021_b09.a2m,1,852,852,0.9,0.01,74854,0.986,840,36319.9,43.23797619,medium,2382,2.835714286,A0A192B1T2_9HIV1_Haddox_2018.csv,fitness,1,mutant,A0A192B1T2_9HIV1_theta_0.01.npy,A0A192B1T2_9HIV1.pdb,1-852,0.1,,OrganismalFitness +A0A1I9GEU1_NEIME_Kennouche_2019,A0A1I9GEU1_NEIME_Kennouche_2019.csv,A0A1I9GEU1_NEIME,Prokaryote,Neisseria meningitidis,FTLIELMIVIAIVGILAAVALPAYQDYTARAQVSEAILLAEGQKSAVTEYYLNHGEWPGDNSSAGVATSADIKGKYVQSVTVANGVITAQMASSNVNNEIKSKKLSLWAKRQNGSVKWFCGQPVTRTTATATDVAAANGKTDDKINTKHLPSTCRDDSSAS,161,FALSE,922,922,0,0.141,median,Kennouche,Deep mutational scanning of the Neisseria meningitidis major pilin reveals the importance of pilus tip-mediated adhesion,2019,10.15252/embj.2019102145,1-161,pilin (PilE),"piliation (20D9 anti-pilus monoclonal Ab), aggregation, adhesion (human umbilical vein endothelial cells (HUVECs))",,A0A1I9GEU1_NEIME_full_11-26-2021_b08.a2m,1,161,161,0.8,0.2,5553,0.857,138,2183.6,15.82318841,medium,72,0.5217391304,A0A1I9GEU1_NEIME_Kennouche_2019.csv,piliation_log2_ratio,1,mutants,A0A1I9GEU1_NEIME_theta_0.2.npy,A0A1I9GEU1_NEIME.pdb,1-161,0.1,,Activity +A0A247D711_LISMN_Stadelmann_2021,A0A247D711_LISMN_Stadelmann_2021.csv,A0A247D711_LISMN,Prokaryote,Listeria monocytogenes,MNINDLIREIKNKDYTVKLSGTDSNSITQLIIRVNNDGNEYVISESENESIVEKFISAFKNGWNQEYEDEEEFYNDMQTITLKSELN,87,FALSE,1653,1653,0,-0.0155627327,median,Stadelmann,A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins,2021,10.1101/2021.08.21.457204,1-87,Anti-CRISPR protein AcrIIA4,activity against SpyCas9 inducing an RFP reporter,Flow cytometry,A0A247D711_LISMN_full_b0.3.a2m,1,87,87,0.2,0.2,1316890,1,87,188739.9,2169.424138,High,209,2.402298851,A0A247D711_LISMN_Stadelmann_2021.csv,mean_prediction,1,mutant,A0A247D711_LISMN_b03_theta_0.2.npy,A0A247D711_LISMN.pdb,1-87,1,,Activity +A0A2Z5U3Z0_9INFA_Doud_2016,A0A2Z5U3Z0_9INFA_Doud_2016.csv,A0A2Z5U3Z0_9INFA,Virus,Influenza A virus (A/WSN/1933(H1N1)),MKAKLLVLLYAFVATDADTICIGYHANNSTDTVDTILEKNVAVTHSVNLLEDSHNGKLCKLKGIAPLQLGKCNITGWLLGNPECDSLLPARSWSYIVETPNSENGACYPGDLIDYEELREQLSSVSSLERFEIFPKESSWPNHTFNGVTVSCSHRGKSSFYRNLLWLTKKGDSYPKLTNSYVNNKGKEVLVLWGVHHPSSSDEQQSLYSNGNAYVSVASSNYNRRFTPEIAARPKVRDQHGRMNYYWTLLEPGDTIIFEATGNLIAPWYAFALSRGFESGIITSNASMHECNTKCQTPQGAINSNLPFQNIHPVTIGECPKYVRSTKLRMVTGLRNIPSIQYRGLFGAIAGFIEGGWTGMIDGWYGYHHQNEQGSGYAADQKSTQNAINGITNKVNSVIEKMNTQFTAVGKEFNNLEKRMENLNKKVDDGFLDIWTYNAELLVLLENERTLDFHDLNVKNLYEKVKSQLKNNAKEIGNGCFEFYHKCDNECMESVRNGTYDYPKYSEESKLNREKIDGVKLESMGVYQILAIYSTVASSLVLLVSLGAISFWMCSNGSLQCRICI,565,FALSE,10715,10715,0,-2.239942981,median,Doud,Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin,2016,10.3390/v8060155,2-565,Influenza hemagglutinin,viral replication,Growth,A0A2Z5U3Z0_9INFA_theta0.99_full_11-26-2021_b09.a2m,1,565,565,0.9,0.01,57581,0.968,547,9809.4,17.93308958,medium,925,1.691042048,A0A2Z5U3Z0_9INFA_Doud_2016.csv,transformed_pref,1,mutant,A0A2Z5U3Z0_9INFA_theta_0.01.npy,A0A2Z5U3Z0_9INFA.pdb,1-565,0.1,,OrganismalFitness +A0A2Z5U3Z0_9INFA_Wu_2014,A0A2Z5U3Z0_9INFA_Wu_2014.csv,A0A2Z5U3Z0_9INFA,Virus,Influenza A virus (A/WSN/1933(H1N1)),MKAKLLVLLYAFVATDADTICIGYHANNSTDTVDTILEKNVAVTHSVNLLEDSHNGKLCKLKGIAPLQLGKCNITGWLLGNPECDSLLPARSWSYIVETPNSENGACYPGDLIDYEELREQLSSVSSLERFEIFPKESSWPNHTFNGVTVSCSHRGKSSFYRNLLWLTKKGDSYPKLTNSYVNNKGKEVLVLWGVHHPSSSDEQQSLYSNGNAYVSVASSNYNRRFTPEIAARPKVRDQHGRMNYYWTLLEPGDTIIFEATGNLIAPWYAFALSRGFESGIITSNASMHECNTKCQTPQGAINSNLPFQNIHPVTIGECPKYVRSTKLRMVTGLRNIPSIQYRGLFGAIAGFIEGGWTGMIDGWYGYHHQNEQGSGYAADQKSTQNAINGITNKVNSVIEKMNTQFTAVGKEFNNLEKRMENLNKKVDDGFLDIWTYNAELLVLLENERTLDFHDLNVKNLYEKVKSQLKNNAKEIGNGCFEFYHKCDNECMESVRNGTYDYPKYSEESKLNREKIDGVKLESMGVYQILAIYSTVASSLVLLVSLGAISFWMCSNGSLQCRICI,565,FALSE,2350,2350,0,0.0947955855,median,Wu,High-throughput profiling of influenza A virus hemagglutinin gene at single-nucleotide resolution,2014,10.1038/srep04942,6-560,Influenza hemagglutinin,Viral replication,Growth,A0A2Z5U3Z0_9INFA_theta0.99_full_11-26-2021_b09.a2m,1,565,565,0.9,0.01,57581,0.968,547,9809.4,17.93308958,medium,925,1.691042048,A0A2Z5U3Z0_9INFA_Wu_2014.csv,RF Index,1,mutant,A0A2Z5U3Z0_9INFA_theta_0.01.npy,A0A2Z5U3Z0_9INFA.pdb,1-565,0.1,,OrganismalFitness +A4_HUMAN_Seuma_2022,A4_HUMAN_Seuma_2022.csv,A4_HUMAN,Human,Homo sapiens,MLPGLALLLLAAWTARALEVPTDGNAGLLAEPQIAMFCGRLNMHMNVQNGKWDSDPSGTKTCIDTKEGILQYCQEVYPELQITNVVEANQPVTIQNWCKRGRKQCKTHPHFVIPYRCLVGEFVSDALLVPDKCKFLHQERMDVCETHLHWHTVAKETCSEKSTNLHDYGMLLPCGIDKFRGVEFVCCPLAEESDNVDSADAEEDDSDVWWGGADTDYADGSEDKVVEVAEEEEVAEVEEEEADDDEDDEDGDEVEEEAEEPYEEATERTTSIATTTTTTTESVEEVVREVCSEQAETGPCRAMISRWYFDVTEGKCAPFFYGGCGGNRNNFDTEEYCMAVCGSAMSQSLLKTTQEPLARDPVKLPTTAASTPDAVDKYLETPGDENEHAHFQKAKERLEAKHRERMSQVMREWEEAERQAKNLPKADKKAVIQHFQEKVESLEQEAANERQQLVETHMARVEAMLNDRRRLALENYITALQAVPPRPRHVFNMLKKYVRAEQKDRQHTLKHFEHVRMVDPKKAAQIRSQVMTHLRVIYERMNQSLSLLYNVPAVAEEIQDEVDELLQKEQNYSDDVLANMISEPRISYGNDALMPSLTETKTTVELLPVNGEFSLDDLQPWHSFGADSVPANTENEVEPVDARPAADRGLTTRPGSGLTNIKTEEISEVKMDAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIATVIVITLVMLKKKQYTSIHHGVVEVDAAVTPEERHLSKMQQNGYENPTYKFFEQMQN,770,TRUE,14811,796,14015,-2,manual,Seuma,"An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation",2022,10.1038/s41467-022-34742-3,672-713,APP,aggregation,survival assessment assay,A4_HUMAN_2023-08-07_b01.a2m,1,770,770,0.1,0.2,5272,0.987,760,99.3,0.1306578947,Low,0,0,MS_BL_BB_indels_processed_data.tsv,nscore,1,mutant,A4_HUMAN_theta0.2_2023-08-07_b01.npy,A4_HUMAN.pdb,1-770,1,,Stability +A4D664_9INFA_Soh_2019,A4D664_9INFA_Soh_2019.csv,A4D664_9INFA,Virus,Influenza A virus (A/green-winged teal/Ohio/175/1986(H2N1)),MERIKELRDLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPALRMKWMMAMKYPITADKRIMEMIPERNEQGQTLWSKTNDAGSDRVMVSPLAVTWWNRNGPTTSTVHYPKVYKTYFEKVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSESQLTITREKKEELQDCKIAPLMVAYMLERELVRKTRFLPVAGGTSSVYIEVLHLTQGTCWEQMYTPGGEVRNDDVDQSLIIAARNIVRRATVSADPLASLLEMCHSTQIGGIRMVDILRQNPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTSGSSVKREEEVLTGNLQTLKIRVHEGYEEFTMVGRRATAILRKATRRLIQLIVSGRDEQSIAEAIIVALVFSQEDCMIKAVRGDLNFVNRANQRLNPMHQLLRHFQKDAKVLFQNWGIEPIDNVMGMIGILPDMTPSTEMSLRGIRVSKMGVDEYSSTERVVVSIDRFLRVRDQRGNVLLSPEEVSETQGTEKLTITYSSSMMWEINGPESVLVNTYQWIIRNWETVKIQWSQDPTMLYNKMEFEPFQSLVPKAARGQYSGFVRTLFQQMRDVLGTFDTVQIIKLLPFAAAPPEQSRMQFSSLTVNVRGSGMRILVRGNSPVFNYNKATKRLTVLGKDAGALTEDPDEGTAGVESAVLRGFLILGKEDKRYGPALSINELSNLAKGEKANVLIGQGDVVLVMKRKRDSSILTDSQTATKRIRMAIN,759,FALSE,14421,14421,0,0.2170105627,median,Soh,Comprehensive mapping of adaptation of the avian influenza polymerase protein PB2 to humans,2019,10.7554/eLife.45079,1-759,Influenza polymerase basic protein 2,Viral replication (avian cells: CCL141 (duck)),Growth,A4D664_9INFA_theta0.99_full_11-26-2021_b09.a2m,1,759,759,0.9,0.01,26683,1,759,1730.2,2.279578393,medium,3736,4.92226614,A4D664_9INFA_Soh_2019.csv,effectCCL141,1,mutant,A4D664_9INFA_theta_0.01.npy,A4D664_9INFA.pdb,1-759,0.1,,OrganismalFitness +A4GRB6_PSEAI_Chen_2020,A4GRB6_PSEAI_Chen_2020.csv,A4GRB6_PSEAI,Prokaryote,Pseudomonas aeruginosa,MFKLLSKLLVYLTASIMAIASPLAFSVDSSGEYPTVSEIPVGEVRLYQIADGVWSHIATQSFDGAVYPSNGLIVRDGDELLLIDTAWGAKNTAALLAEIEKQIGLPVTRAVSTHFHDDRVGGVDVLRAAGVATYASPSTRRLAEVEGNEIPTHSLEGLSSSGDAVRFGPVELFYPGAAHSTDNLIVYVPSASVLYGGCAIYELSRTSAGNVADADLAEWPTSIERIQQHYPEAQFVIPGHGLPGGLDLLKHTTNVVKAHTNRSVVE,266,FALSE,5004,5004,0,-2.1,manual,Chen,"Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase",2020,10.7554/eLife.56707,1-266,Beta-lactamase VIM-2,"drug resistance (128/16/2.0 ug/mL ampicillin, 4.0/0.5 ug/mL cefotaxime, 0.031 ug/mL meropenem @ 25C, 37C)",Antibiotics resistance,A4GRB6_PSEAI_full_11-26-2021_b03.a2m,1,266,266,0.3,0.2,108496,0.726,193,31234.2,161.8352332,high,317,1.642487047,A4GRB6_PSEAI_Chen_2020.csv,0.031ug_mL_MEM_37C,1,mutant,A4GRB6_PSEAI_theta_0.2.npy,A4GRB6_PSEAI.pdb,1-266,0.1,,OrganismalFitness +AACC1_PSEAI_Dandage_2018,AACC1_PSEAI_Dandage_2018.csv,AACC1_PSEAI,Prokaryote,Pseudomonas aeruginosa,MLRSSNDVTQQGSRPKTKLGGSSMGIIRTCRLGPDQVKSMRAALDLFGREFGDVATYSQHQPDSDYLGNLLRSKTFIALAAFDQEAVVGALAAYVLPKFEQPRSEIYIYDLAVSGEHRRQGIATALINLLKHEANALGAYVIYVQADYGDDPAVALYTKLGIREEVMHFDIDPSTAT,177,FALSE,1801,1801,0,0.7172234411,median,Dandage,Differential strengths of molecular determinants guide environment specific mutational fates,2018,10.1371/journal.pgen.1007419,12-172,GMR (aacC1),"Antibiotic resistance under: heat/cold resistance (32C, 37C (ref), 42C), chemical stability (chemical chaperones TMAO, glycerol), antibiotic resistance (gentamicin), or combo",Antibiotics resistance,AACC1_PSEAI_full_04-29-2022_b03.a2m,1,177,177,0.3,0.2,539868,0.746,132,170256.3,1289.820455,high,235,1.78030303,AACC1_PSEAI_Dandage_2018.csv,30C,1,Mutation,AACC1_PSEAI_theta_0.2.npy,AACC1_PSEAI.pdb,1-177,0.1,,OrganismalFitness +ACE2_HUMAN_Chan_2020,ACE2_HUMAN_Chan_2020.csv,ACE2_HUMAN,Human,Homo sapiens,MSSSSWLLLSLVAVTAAQSTIEEQAKTFLDKFNHEAEDLFYQSSLASWNYNTNITEENVQNMNNAGDKWSAFLKEQSTLAQMYPLQEIQNLTVKLQLQALQQNGSSVLSEDKSKRLNTILNTMSTIYSTGKVCNPDNPQECLLLEPGLNEIMANSLDYNERLWAWESWRSEVGKQLRPLYEEYVVLKNEMARANHYEDYGDYWRGDYEVNGVDGYDYSRGQLIEDVEHTFEEIKPLYEHLHAYVRAKLMNAYPSYISPIGCLPAHLLGDMWGRFWTNLYSLTVPFGQKPNIDVTDAMVDQAWDAQRIFKEAEKFFVSVGLPNMTQGFWENSMLTDPGNVQKAVCHPTAWDLGKGDFRILMCTKVTMDDFLTAHHEMGHIQYDMAYAAQPFLLRNGANEGFHEAVGEIMSLSAATPKHLKSIGLLSPDFQEDNETEINFLLKQALTIVGTLPFTYMLEKWRWMVFKGEIPKDQWMKKWWEMKREIVGVVEPVPHDETYCDPASLFHVSNDYSFIRYYTRTLYQFQFQEALCQAAKHEGPLHKCDISNSTEAGQKLFNMLRLGKSEPWTLALENVVGAKNMNVRPLLNYFEPLFTWLKDQNKNSFVGWSTDWSPYADQSIKVRISLKSALGDKAYEWNDNEMYLFRSSVAYAMRQYFLKVKNQMILFGEEDVRVANLKPRISFNFFVTAPKNVSDIIPRTEVEKAIRMSRSRINDAFRLNDNSLEFLGIQPTLGPPNQPPVSIWLIVFGVVMGVIVVGIVILIFTGIRDRKKKNKARSGENPYASIDISKGENNPGFQNTDDVQTSF,805,FALSE,2223,2223,0,-0.266564268,median,Chan,Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2,2020,10.1126/science.abc0870,19-518,ACE2,Binding affinity,Flow Cytometry Assay,ACE2_HUMAN_2023-10-12_b05.a2m,1,805,805,0.5,0.2,11106,0.743,598,1506.7,2.519565217,Medium,349,0.5836120401,,score,1,mutant,ACE2_HUMAN_theta0.2_2023-10-12_b05.npy,ACE2_HUMAN.pdb,1-805,1,,Binding +ADRB2_HUMAN_Jones_2020,ADRB2_HUMAN_Jones_2020.csv,ADRB2_HUMAN,Human,Homo sapiens,MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL,413,FALSE,7800,7800,0,1.859961867,median,Jones,Structural and Functional Characterization of G Protein-Coupled Receptors with Deep Mutational Scanning,2020,10.7554/eLife.54895,2-413,ADRB2,"transcription (luciferase reporter, isoproterenol (beta2AR agonist)-induced)",Receptor activity,ADRB2_HUMAN_full_11-26-2021_b03.a2m,1,413,413,0.3,0.2,204722,0.712,294,25459.6,86.59727891,medium,234,0.7959183673,ADRB2_HUMAN_Jones_2020.csv,0.625,1,mutant_id,ADRB2_HUMAN_theta_0.2.npy,ADRB2_HUMAN.pdb,1-413,0.1,,Activity +AICDA_HUMAN_Gajula_2014_3cycles,AICDA_HUMAN_Gajula_2014_3cycles.csv,AICDA_HUMAN,Human,Homo sapiens,MDSLLMNRRKFLYQFKNVRWAKGRRETYLCYVVKRRDSATSFSLDFGYLRNKNGCHVELLFLRYISDWDLDPGRCYRVTWFTSWSPCYDCARHVADFLRGNPNLSLRIFTARLYFCEDRKAEPEGLRRLHRAGVQIAIMTFKDYFYCWNTFVENHERTFKAWEGLHENSVRLSRQLRRILLPLYEVDDLRDAFRTLGL,198,FALSE,209,209,0,1,manual,Gajula,High-throughput mutagenesis reveals functional determinants for DNA targeting by activation-induced deaminase,2014,10.1093/nar/gku689,113-123,AID,Enzymatic activity,bulk RNA-sequencing,AICDA_HUMAN_2023-08-07_b01.a2m,1,198,198,0.1,0.2,18148,0.879,174,3340,19.1954023,Medium,101,0.5804597701,urn_mavedb_00000106-c-1_scores.csv,DMS_score,1,mutant,AICDA_HUMAN_theta0.2_2023-08-07_b01.npy,AICDA_HUMAN.pdb,1-198,1,,Activity +AMFR_HUMAN_Tsuboyama_2023_4G3O,AMFR_HUMAN_Tsuboyama_2023_4G3O.csv,AMFR_HUMAN,Human,Homo sapiens,YFQGQLNAMAHQIQEMFPQVPYHLVLQDLQLTRSVEITTDNILEGRI,47,TRUE,2972,820,2152,-1.504736022,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-47,E3 ubiquitin-protein ligase AMFR,Stability,cDNA display proteolysis,AMFR_HUMAN_2023-08-07_b04.a2m,1,47,47,0.4,0.2,17787,0.872,41,1166.9,28.46097561,Medium,12,0.2926829268,Tsuboyama2023_Dataset2_Dataset3,ddG_ML_float,1,mut_type,AMFR_HUMAN_theta0.2_2023-08-07_b04.npy,AMFR_HUMAN.pdb,1-47,1,,Stability +AMIE_PSEAE_Wrenbeck_2017,AMIE_PSEAE_Wrenbeck_2017.csv,AMIE_PSEAE,Prokaryote,Pseudomonas aeruginosa,MRHGDISSSNDTVGVAVVNYKMPRLHTAAEVLDNARKIAEMIVGMKQGLPGMDLVVFPEYSLQGIMYDPAEMMETAVAIPGEETEIFSRACRKANVWGVFSLTGERHEEHPRKAPYNTLVLIDNNGEIVQKYRKIIPWCPIEGWYPGGQTYVSEGPKGMKISLIICDDGNYPEIWRDCAMKGAELIVRCQGYMYPAKDQQVMMAKAMAWANNCYVAVANAAGFDGVYSYFGHSAIIGFDGRTLGECGEEEMGIQYAQLSLSQIRDARANDQSQNHLFKILHRGYSGLQASGDGDRGLAECPFEFYRTWVTDAEKARENVERLTRSTTGVAQCPVGRLPYEGLEKEA,346,FALSE,6227,6227,0,-0.2222,median,Wrenbeck,Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded,2017,10.1038/ncomms15695,1-341,Aliphatic amidase,Enzyme function,Growth,AMIE_PSEAE_full_11-26-2021_b02.a2m,1,346,346,0.2,0.2,140703,0.725,251,29959.3,119.359761,high,557,2.219123506,AMIE_PSEAE_Wrenbeck_2017.csv,isobutyramide_normalized_fitness,1,mutant,AMIE_PSEAE_theta_0.2.npy,AMIE_PSEAE.pdb,1-346,0.1,,Activity +ANCSZ_Hobbs_2022,ANCSZ_Hobbs_2022.csv,ANCSZ,Eukaryote,Reconstructed ancestor,MADSANHLPYFYGSITREEAEDYLKQGGMSDGLFLLRQSLNSLGGYVLSVVYDRQCHHYTIERQLNGTYAIAGGKPHSGPAELCEYHSQDSDGLVCLLKKPCNRPPGVQPKVGPFEDLKDQLIREYVRQTWNLEGEALEQAIISQRPQLEKLIATTAHEKMPWFHGKISREESERRLLSGAQPNGKFLIRERDENGSYALSLLYEKKVYHYRIDRDKSGKLSIPDGKKFDTLWQLVEHYSHKPDGLLCVLTEPCPNPDSPAGALGAPAPPLPGSHPKLETAGGIISRIKSYSFPKPGFKKKPPSERPKSALNVNGYVPRPKPLGAEGGSRRAMPMDTNVYESPYSDPEELKDKKLYLKREQLMLEEGELGSGNFGTVKKGVYKMRKKEIPVAVKVLKSENDPAVKDELMKEAEFMHQLDNPYIVRMIGICEAESLMLVMELAPLGPLNKFLQKHKDQITVENIVELMHQVSMGMKYLEEKNFVHRDLAARNVLLVNQHYAKISDFGLSKALGADDNYYKAKTAGKWPLKWYAPECINFHKFSSKSDVWSFGVTMWEAFSYGQKPYKGMKGQEVLPFIENGERMECPAECPEEMYELMKDCWTYKADDRPGFVAVELRLRDYYYDISK,627,FALSE,4670,4670,0,-0.0574121626,median,Hobbs,Saturation mutagenesis of a predicted ancestral Syk-family kinase,2022,10.1002/pro.4411,352-627,ancestral spleen tyrosine kinase,successful phosphorylation of bait peptide,enzymatic activity,ANCSZ_b0.4.a2m,1,627,627,0.4,0.2,7424,1,627,1036.7,1.653429027,Medium,109,0.1738437002,ANCSZ_Hobbs_2022.csv,DMS_value,1,mutant,ANCSZ_theta_0.2.npy,ANCSZ.pdb,1-627,1,,Activity +ARGR_ECOLI_Tsuboyama_2023_1AOY,ARGR_ECOLI_Tsuboyama_2023_1AOY.csv,ARGR_ECOLI,Prokaryote,Escherichia coli,QEELVKAFKALLKEEKFSSQGEIVAALQEQGFDNINQSKVSRMLTKFGAVRTRNAKMEMVYCLPAELGV,69,FALSE,1287,1287,0,-0.4541373765,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-69,Arginine repressor,Stability,cDNA display proteolysis,ARGR_ECOLI_2023-08-07_b04.a2m,1,69,69,0.4,0.2,21443,0.913,63,3719.2,59.03492063,Medium,29,0.4603174603,Tsuboyama2023_Dataset2_Dataset4,ddG_ML_float,1,mut_type,ARGR_ECOLI_theta0.2_2023-08-07_b04.npy,ARGR_ECOLI.pdb,1-69,1,,Stability +B2L11_HUMAN_Dutta_2010_binding-Mcl-1,B2L11_HUMAN_Dutta_2010_binding-Mcl-1.csv,B2L11_HUMAN,Human,Homo sapiens,MAKQPSDVSSECDREGRQLQPAERPPQLRPGAPTSLQTEPQGNPEGNHGGEGDSCPHGSPQGPLAPPASPGPFATRSPLFIFMRRSSLLSRSSSGYFSFDTDRSPAPMSCDKSTQTPSPPCQAFNHYLSAMASMRQAEPADMRPEIWIAQELRRIGDEFNAYYARRVFLNNYQAAEDHPRMVILRLLRYIVRLVWRMH,198,FALSE,170,170,0,16002529.37,median,Dutta,Determinants of BH3 Binding Specificity for Mcl-1 versus Bcl-xL,2010,10.1016/j.jmb.2010.03.058,148-159,BCL2L11,Binding to Mcl-1 (FACS; yeast-displayed and antibody stained for binding partner),FACS,B2L11_HUMAN_2023-08-07_b04.a2m,1,198,198,0.4,0.2,660,0.995,197,88.5,0.4492385787,Low,2,0.01015228426,,score,1,mut_proteingym,B2L11_HUMAN_theta0.2_2023-08-07_b04.npy,B2L11_HUMAN.pdb,1-198,1,147,Binding +BBC1_YEAST_Tsuboyama_2023_1TG0,BBC1_YEAST_Tsuboyama_2023_1TG0.csv,BBC1_YEAST,Eukaryote,Saccharomyces cerevisiae,EVPFKVVAQFPYKSDYEDDLNFEKDQEIIVTSVEDAEWYFGEYQDSNGDVIEGIFPKSFVAVQG,64,TRUE,2069,1084,985,-1.271998543,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-64,Myosin tail region-interacting protein MTI1,Stability,cDNA display proteolysis,BBC1_YEAST_2023-08-07_b05.a2m,1,64,64,0.5,0.2,604824,0.844,54,17529.2,324.6148148,High,55,1.018518519,Tsuboyama2023_Dataset2_Dataset5,ddG_ML_float,1,mut_type,BBC1_YEAST_theta0.2_2023-08-07_b05.npy,BBC1_YEAST.pdb,1-64,1,,Stability +BCHB_CHLTE_Tsuboyama_2023_2KRU,BCHB_CHLTE_Tsuboyama_2023_2KRU.csv,BCHB_CHLTE,Prokaryote,Chlorobaculum tepidum,ELSWTAEAEKMLGKVPFFVRKKVRKNTDNYAREIGEPVVTADVFRKAKEHLG,52,TRUE,1572,890,682,-0.9540616602,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-52,Light-independent protochlorophyllide reductase subunit B,Stability,cDNA display proteolysis,BCHB_CHLTE_2023-08-07_b04.a2m,1,52,52,0.4,0.2,12079,0.923,48,2630.8,54.80833333,Medium,18,0.375,Tsuboyama2023_Dataset2_Dataset6,ddG_ML_float,1,mut_type,BCHB_CHLTE_theta0.2_2023-08-07_b04.npy,BCHB_CHLTE.pdb,1-52,1,,Stability +BLAT_ECOLX_Deng_2012,BLAT_ECOLX_Deng_2012.csv,BLAT_ECOLX,Prokaryote,Escherichia coli,MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW,286,FALSE,4996,4996,0,-2.913548,median,Deng,Deep Sequencing of Systematic Combinatorial Libraries Reveals Œ≤-Lactamase Sequence Constraints at High Resolution,2012,10.1016/j.jmb.2012.09.014,24-286,Beta-lactamase TEM,"antibiotic resistance, MIC",Amp resistance,BLAT_ECOLX_full_11-26-2021_b02.a2m,1,286,286,0.2,0.2,209644,0.752,215,47605,221.4186047,high,446,2.074418605,BLAT_ECOLX_Deng_2012.csv,ddG_stat,-1,mutant,BLAT_ECOLX_theta_0.2.npy,BLAT_ECOLX.pdb,1-286,0.1,,OrganismalFitness +BLAT_ECOLX_Firnberg_2014,BLAT_ECOLX_Firnberg_2014.csv,BLAT_ECOLX,Prokaryote,Escherichia coli,MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW,286,FALSE,4783,4783,0,0.4257,median,Firnberg,"A Comprehensive, High-Resolution Map of a Gene's Fitness Landscape",2014,10.1093/molbev/msu081,24-286,Beta-lactamase TEM,Growth (0.25-1024 ug/mL ampicillin) doubling,Growth,BLAT_ECOLX_full_11-26-2021_b02.a2m,1,286,286,0.2,0.2,209644,0.752,215,47605,221.4186047,high,446,2.074418605,BLAT_ECOLX_Firnberg_2014.csv,linear,1,mutant,BLAT_ECOLX_theta_0.2.npy,BLAT_ECOLX.pdb,1-286,0.1,,OrganismalFitness +BLAT_ECOLX_Jacquier_2013,BLAT_ECOLX_Jacquier_2013.csv,BLAT_ECOLX,Prokaryote,Escherichia coli,MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW,286,FALSE,989,989,0,-0.666666667,median,Jacquier,Capturing the mutational landscape of the beta-lactamase TEM-1,2013,10.1073/pnas.1215206110,24-286,Beta-lactamase TEM,MIC,Amoxicillin resistance,BLAT_ECOLX_full_11-26-2021_b02.a2m,1,286,286,0.2,0.2,209644,0.752,215,47605,221.4186047,high,446,2.074418605,BLAT_ECOLX_Jacquier_2013.csv,MIC_score,1,mutant,BLAT_ECOLX_theta_0.2.npy,BLAT_ECOLX.pdb,1-286,0.1,,OrganismalFitness +BLAT_ECOLX_Stiffler_2015,BLAT_ECOLX_Stiffler_2015.csv,BLAT_ECOLX,Prokaryote,Escherichia coli,MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW,286,FALSE,4996,4996,0,-1.159498916,median,Stiffler,Evolvability as a Function of Purifying Selection in TEM-1 β-lactamase,2015,10.1016/j.cell.2015.01.035,24-286,Beta-lactamase TEM,Growth (10-2500 ug/mL ampicillin),Growth,BLAT_ECOLX_full_11-26-2021_b02.a2m,1,286,286,0.2,0.2,209644,0.752,215,47605,221.4186047,high,446,2.074418605,BLAT_ECOLX_Stiffler_2015.csv,2500,1,mutant,BLAT_ECOLX_theta_0.2.npy,BLAT_ECOLX.pdb,1-286,0.1,,OrganismalFitness +BRCA1_HUMAN_Findlay_2018,BRCA1_HUMAN_Findlay_2018.csv,BRCA1_HUMAN,Human,Homo sapiens,MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDSSEDTVNKATYCSVGDQELLQITPQGTRDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTEKRAAERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVEKAEFCNKSKQPGLARSQHNRWAGSKETCNDRRTPSTEKKVDLNADPLCERKEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHDGESESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSKSVESNIEDKIFGKTYRKKASLPNLSHVTENLIIGAFVTEPQIIQERPLTNKLKRKRRPTSGLHPEDFIKKADLAVQKTPEMINQGTNQTEQNGQVMNITNSGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNIHNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEIKKKKYNQMPVRHSRNLQLMEGKEPATGAKKSNKPNEQTSKRHDSDTFPELKLTNAPGSFTKCSNTSELKEFVNPSLPREEKEEKLETVKVSNNAEDPKDLMLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNKCVSQCAAFENPKGLIHGCSKDNRNDTEGFKYPLGHEVNHSRETSIEMEESELDAQYLQNTFKVSKRQSFAPFSNPGNAEEECATFSAHSGSLKKQSPKVTFECEQKEENQGKNESNIKPVQTVNITAGFPVVGQKDKPVDNAKCSIKGGSRFCLSSQFRGNETGLITPNKHGLLQNPYRIPPLFPIKSFVKTKCKKNLLEENFEEHSMSPEREMGNENIPSTVSTISRNNIRENVFKEASSSNINEVGSSTNEVGSSINEIGSSDENIQAELGRNRGPKLNAMLRLGVLQPEVYKQSLPGSNCKHPEIKKQEYEEVVQTVNTDFSPYLISDNLEQPMGSSHASQVCSETPDDLLDDGEIKEDTSFAENDIKESSAVFSKSVQKGELSRSPSPFTHTHLAQGYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVNNIPSQSTRHSTVATECLSKNTEENLLSLKNSLNDCSNQVILAKASQEHHLSEETKCSASLFSSQCSELEDLTANTNTQDPFLIGSSKQMRHQSESQGVGLSDKELVSDDEERGTGLEENNQEEQSMDSNLGEAASGCESETSVSEDCSGLSSQSDILTTQQRDTMQHNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLRNPEQSTSEKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSKCPSLDDRWYMHSCSGSLQNRNYPSQEELIKVVDVEEQQLEESGPHDLTETSYLPRQDLEGTPYLESGISLFSDDPESDPSEDRAPESARVGNIPSSTSALKVPQLKVAESAQSPAAAHTTDTAGYNAMEESVSREKPELTASTERVNKRMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLIPQIPHSHY,1863,FALSE,1837,1837,0,-1,manual,Findlay,Accurate classification of BRCA1 variants with saturation genome editing,2018,10.1038/s41586-018-0461-z,1-1855,BRCA1,Growth,Growth,BRCA1_HUMAN_full_11-26-2021_b02.a2m,1,1863,1863,0.2,0.2,1008,0.769,1432,108.4,0.07569832402,low,0,0,BRCA1_HUMAN_Findlay_2018.csv,function_score,1,mutant,BRCA1_HUMAN_theta_0.2.npy,BRCA1_HUMAN.pdb,1-1863,0.1,,OrganismalFitness +BRCA2_HUMAN_Erwood_2022_HEK293T,BRCA2_HUMAN_Erwood_2022_HEK293T.csv,BRCA2_HUMAN,Human,Homo sapiens,MPIGSKERPTFFEIFKTRCNKADLGPISLNWFEELSSEAPPYNSEPAEESEHKNNNYEPNLFKTPQRKPSYNQLASTPIIFKEQGLTLPLYQSPVKELDKFKLDLGRNVPNSRHKSLRTVKTKMDQADDVSCPLLNSCLSESPVVLQCTHVTPQRDKSVVCGSLFHTPKFVKGRQTPKHISESLGAEVDPDMSWSSSLATPPTLSSTVLIVRNEEASETVFPHDTTANVKSYFSNHDESLKKNDRFIASVTDSENTNQREAASHGFGKTSGNSFKVNSCKDHIGKSMPNVLEDEVYETVVDTSEEDSFSLCFSKCRTKNLQKVRTSKTRKKIFHEANADECEKSKNQVKEKYSFVSEVEPNDTDPLDSNVANQKPFESGSDKISKEVVPSLACEWSQLTLSGLNGAQMEKIPLLHISSCDQNISEKDLLDTENKRKKDFLTSENSLPRISSLPKSEKPLNEETVVNKRDEEQHLESHTDCILAVKQAISGTSPVASSFQGIKKSIFRIRESPKETFNASFSGHMTDPNFKKETEASESGLEIHTVCSQKEDSLCPNLIDNGSWPATTTQNSVALKNAGLISTLKKKTNKFIYAIHDETSYKGKKIPKDQKSELINCSAQFEANAFEAPLTFANADSGLLHSSVKRSCSQNDSEEPTLSLTSSFGTILRKCSRNETCSNNTVISQDLDYKEAKCNKEKLQLFITPEADSLSCLQEGQCENDPKSKKVSDIKEEVLAAACHPVQHSKVEYSDTDFQSQKSLLYDHENASTLILTPTSKDVLSNLVMISRGKESYKMSDKLKGNNYESDVELTKNIPMEKNQDVCALNENYKNVELLPPEKYMRVASPSRKVQFNQNTNLRVIQKNQEETTSISKITVNPDSEELFSDNENNFVFQVANERNNLALGNTKELHETDLTCVNEPIFKNSTMVLYGDTGDKQATQVSIKKDLVYVLAEENKNSVKQHIKMTLGQDLKSDISLNIDKIPEKNNDYMNKWAGLLGPISNHSFGGSFRTASNKEIKLSEHNIKKSKMFFKDIEEQYPTSLACVEIVNTLALDNQKKLSKPQSINTVSAHLQSSVVVSDCKNSHITPQMLFSKQDFNSNHNLTPSQKAEITELSTILEESGSQFEFTQFRKPSYILQKSTFEVPENQMTILKTTSEECRDADLHVIMNAPSIGQVDSSKQFEGTVEIKRKFAGLLKNDCNKSASGYLTDENEVGFRGFYSAHGTKLNVSTEALQKAVKLFSDIENISEETSAEVHPISLSSSKCHDSVVSMFKIENHNDKTVSEKNNKCQLILQNNIEMTTGTFVEEITENYKRNTENEDNKYTAASRNSHNLEFDGSDSSKNDTVCIHKDETDLLFTDQHNICLKLSGQFMKEGNTQIKEDLSDLTFLEVAKAQEACHGNTSNKEQLTATKTEQNIKDFETSDTFFQTASGKNISVAKESFNKIVNFFDQKPEELHNFSLNSELHSDIRKNKMDILSYEETDIVKHKILKESVPVGTGNQLVTFQGQPERDEKIKEPTLLGFHTASGKKVKIAKESLDKVKNLFDEKEQGTSEITSFSHQWAKTLKYREACKDLELACETIEITAAPKCKEMQNSLNNDKNLVSIETVVPPKLLSDNLCRQTENLKTSKSIFLKVKVHENVEKETAKSPATCYTNQSPYSVIENSALAFYTSCSRKTSVSQTSLLEAKKWLREGIFDGQPERINTADYVGNYLYENNSNSTIAENDKNHLSEKQDTYLSNSSMSNSYSYHSDEVYNDSGYLSKNKLDSGIEPVLKNVEDQKNTSFSKVISNVKDANAYPQTVNEDICVEELVTSSSPCKNKNAAIKLSISNSNNFEVGPPAFRIASGKIVCVSHETIKKVKDIFTDSFSKVIKENNENKSKICQTKIMAGCYEALDDSEDILHNSLDNDECSTHSHKVFADIQSEEILQHNQNMSGLEKVSKISPCDVSLETSDICKCSIGKLHKSVSSANTCGIFSTASGKSVQVSDASLQNARQVFSEIEDSTKQVFSKVLFKSNEHSDQLTREENTAIRTPEHLISQKGFSYNVVNSSAFSGFSTASGKQVSILESSLHKVKGVLEEFDLIRTEHSLHYSPTSRQNVSKILPRVDKRNPEHCVNSEMEKTCSKEFKLSNNLNVEGGSSENNHSIKVSPYLSQFQQDKQQLVLGTKVSLVENIHVLGKEQASPKNVKMEIGKTETFSDVPVKTNIEVCSTYSKDSENYFETEAVEIAKAFMEDDELTDSKLPSHATHSLFTCPENEEMVLSNSRIGKRRGEPLILVGEPSIKRNLLNEFDRIIENQEKSLKASKSTPDGTIKDRRLFMHHVSLEPITCVPFRTTKERQEIQNPNFTAPGQEFLSKSHLYEHLTLEKSSSNLAVSGHPFYQVSATRNEKMRHLITTGRPTKVFVPPFKTKSHFHRVEQCVRNINLEENRQKQNIDGHGSDDSKNKINDNEIHQFNKNNSNQAVAVTFTKCEEEPLDLITSLQNARDIQDMRIKKKQRQRVFPQPGSLYLAKTSTLPRISLKAAVGGQVPSACSHKQLYTYGVSKHCIKINSKNAESFQFHTEDYFGKESLWTGKGIQLADGGWLIPSNDGKAGKEEFYRALCDTPGVDPKLISRIWVYNHYRWIIWKLAAMECAFPKEFANRCLSPERVLLQLKYRYDTEIDRSRRSAIKKIMERDDTAAKTLVLCVSDIISLSANISETSSNKTSSADTQKVAIIELTDGWYAVKAQLDPPLLAVLKNGRLTVGQKIILHGAELVGSPDACTPLEAPESLMLKISANSTRPARWYTKLGFFPDPRPFPLPLSSLFSDGGNVGCVDVIIQRAYPIQWMEKTSSGLYIFRNEREEEKEAAKYVEAQQKRLEALFTKIQEEFEEHEENTTKPYLPSRALTRQQVRALQDGAELYEAVKNAADPAYLEGYFSEEQLRALNNHRQMLNDKKQAQIQLEIRKAMESAEQKEQGLSRDVTTVWKLRIVSYSKKEKDSVILSIWRPSSDLYSLLTEGKRYRIYHLATSKSKSKSERANIQLAATKKTQYQQLPVSDEILFQIYQPREPLHFSKFLDPDFQPSCSEVDLIGFVVSVVKKTGLAPFVYLSDECYNLLAIKFWIDLNEDIIKPHMLIAASNLQWRPESKSGLLTLFAGDFSVFSASPKEGHFQETFNKMKNTVENIDILCNEAENKLMHILHANDPKWSTPTKDCTSGPYTAQIIPGTGNKLLMSSPNCEIYYQSPLSLCMAKRKSVSTPVSAQMTSKSCKGEKEIDDQKNCKKRRALDFLSRLPLPPPVSPICTFVSPAAQKAFQPPRSCGTKYETPIKKKELNSPQMTPFKKFNEISLLESNSIADEELALINTQALLSGSTGEKQFISVSESTRTAPTSSEDYLRLKRRCTTSLIKEQESSQASTEECEKNKQDTITTKKYI,3418,FALSE,265,265,0,0.8,manual,Erwood,Saturation variant interpretation using CRISPR prime editing,2022,10.1038/s41587-021-01201-1,388-2654,BRCA2,Fitness,Growth,BRCA2_HUMAN_2023-10-12_b01.a2m,1,3418,3418,0.1,0.2,933,,,,,,,,41587_2021_1201_MOESM3_ESM.xlsx,Function Score,1,Protein Annotation,BRCA2_HUMAN_theta0.2_2023-10-12_b01.npy,BRCA2_HUMAN_1-1000.pdb|BRCA2_HUMAN_1001-2085.pdb|BRCA2_HUMAN_2086-2832.pdb,1-1000|1001-2085|2086-2832,1,,OrganismalFitness +C6KNH7_9INFA_Lee_2018,C6KNH7_9INFA_Lee_2018.csv,C6KNH7_9INFA,Virus,Influenza A virus (A/Perth/16/2009(H3N2)),MKTIIALSYILCLVFAQKLPGNDNSTATLCLGHHAVPNGTIVKTITNDQIEVTNATELVQSSSTGEICDSPHQILDGKNCTLIDALLGDPQCDDFQNKKWDLFVERSKAYSNCYPYDVPDYASLRSLVASSGTLEFNNESFNWTGVTQNGTSSACIRRSKNSFFSRLNWLTHLNFKYPALNVTMPNNEQFDKLYIWGVLHPGTDKDQIFLYAQASGRITVSTKRSQQIVSPNIGSRPRVRNIPSRISIYWTIVKPGDILLINSTGNLIAPRGYFKIRSGKSSIMRSDAPIGKCNSECITPNGSIPNDKPFQNVNRITYGACPRYVKQNTLKLATGMRNVPEKQTRGIFGAIAGFIENGWEGMVDGWYGFRHQNSEGRGQAADLKSTQAAIDQINGKLNRLIGKTNEKFHQIEKEFSEVEGRIQDLEKYVEDTKIDLWSYNAELLVALENQHTIDLTDSEMNKLFEKTKKQLRENAEDMGNGCFKIYHKCDNACIGSIRNGTYDHDVYRDEALNNRFQIKGVELKSGYKDWILWISFAISCFLLCVALLGFIMWACQKGNIRCNICI,566,FALSE,10754,10754,0,-1.720276237,median,Lee,Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants,2018,10.1073/pnas.1806133115,1-566,Influenza hemagglutinin,Viral replication,Growth,C6KNH7_9INFA_theta0.99_full_11-26-2021_b09.a2m,1,566,566,0.9,0.01,57453,0.977,553,10569.8,19.11356239,medium,964,1.743218807,C6KNH7_9INFA_Lee_2018.csv,log_fitness_by_syn_mut_fitness,1,mutant,C6KNH7_9INFA_theta_0.01.npy,C6KNH7_9INFA.pdb,1-566,0.1,,OrganismalFitness +CALM1_HUMAN_Weile_2017,CALM1_HUMAN_Weile_2017.csv,CALM1_HUMAN,Human,Homo sapiens,MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMTAK,149,FALSE,1813,1813,0,0.872790117,median,Weile,A framework for exhaustively mapping functional missense variants,2017,10.15252/msb.20177908,2-149,CALM1,Yeast growth,complementation,CALM1_HUMAN_full_11-26-2021_b03.a2m,1,149,149,0.3,0.2,177633,0.893,133,28985.1,217.9330827,high,96,0.7218045113,CALM1_HUMAN_Weile_2017.csv,screenscore,1,mutant,CALM1_HUMAN_theta_0.2.npy,CALM1_HUMAN.pdb,1-149,0.1,,OrganismalFitness +CAPSD_AAV2S_Sinai_2021,CAPSD_AAV2S_Sinai_2021.csv,CAPSD_AAV2S,Virus,Adeno-associated virus 2 (isolate Srivastava/1982) (AAV-2),MAADGYLPDWLEDTLSEGIRQWWKLKPGPPPPKPAERHKDDSRGLVLPGYKYLGPFNGLDKGEPVNEADAAALEHDKAYDRQLDSGDNPYLKYNHADAEFQERLKEDTSFGGNLGRAVFQAKKRVLEPLGLVEEPVKTAPGKKRPVEHSPVEPDSSSGTGKAGQQPARKRLNFGQTGDADSVPDPQPLGQPPAAPSGLGTNTMATGSGAPMADNNEGADGVGNSSGNWHCDSTWMGDRVITTSTRTWALPTYNNHLYKQISSQSGASNDNHYFGYSTPWGYFDFNRFHCHFSPRDWQRLINNNWGFRPKRLNFKLFNIQVKEVTQNDGTTTIANNLTSTVQVFTDSEYQLPYVLGSAHQGCLPPFPADVFMVPQYGYLTLNNGSQAVGRSSFYCLEYFPSQMLRTGNNFTFSYTFEDVPFHSSYAHSQSLDRLMNPLIDQYLYYLSRTNTPSGTTTQSRLQFSQAGASDIRDQSRNWLPGPCYRQQRVSKTSADNNNSEYSWTGATKYHLNGRDSLVNPGPAMASHKDDEEKFFPQSGVLIFGKQGSEKTNVDIEKVMITDEEEIRTTNPVATEQYGSVSTNLQRGNRQAATADVNTQGVLPGMVWQDRDVYLQGPIWAKIPHTDGHFHPSPLMGGFGLKHPPPQILIKNTPVPANPSTTFSAAKFASFITQYSTGQVSVEIEWELQKENSKRWNPEIQYTSNYNKSVNVDFTVDTNGVYSEPRPIGTRYLTRNL,735,TRUE,42328,532,41796,-1.2,manual,Sinai,Generative AAV capsid diversification by latent interpolation,2021,10.1101/2021.04.16.440236,561-588,AAV,viability for AAV capsid production,,CAPSD_AAV2S_uniprot_t099_msc70_mcc70_b0.8.a2m,1,735,735,0.8,0.01,604,0.782,575,213.8,0.371826087,low,1943,3.379130435,CAPSD_AAV2S_Sinai_substitutions_2021.csv,viral_selection,1,mutant,CAPSD_AAV2S_theta_0.01.npy,CAPSD_AAV2S.pdb,1-735,0.1,,OrganismalFitness +CAR11_HUMAN_Meitlis_2020_gof,CAR11_HUMAN_Meitlis_2020_gof.csv,CAR11_HUMAN,Human,Homo sapiens,MPGGGPEMDDYMETLKDEEDALWENVECNRHMLSRYINPAKLTPYLRQCKVIDEQDEDEVLNAPMLPSKINRAGRLLDILHTKGQRGYVVFLESLEFYYPELYKLVTGKEPTRRFSTIVVEEGHEGLTHFLMNEVIKLQQQMKAKDLQRCELLARLRQLEDEKKQMTLTRVELLTFQERYYKMKEERDSYNDELVKVKDDNYNLAMRYAQLSEEKNMAVMRSRDLQLEIDQLKHRLNKMEEECKLERNQSLKLKNDIENRPKKEQVLELERENEMLKTKNQELQSIIQAGKRSLPDSDKAILDILEHDRKEALEDRQELVNRIYNLQEEARQAEELRDKYLEEKEDLELKCSTLGKDCEMYKHRMNTVMLQLEEVERERDQAFHSRDEAQTQYSQCLIEKDKYRKQIRELEEKNDEMRIEMVRREACIVNLESKLRRLSKDSNNLDQSLPRNLPVTIISQDFGDASPRTNGQEADDSSTSEESPEDSKYFLPYHPPQRRMNLKGIQLQRAKSPISLKRTSDFQAKGHEEEGTDASPSSCGSLPITNSFTKMQPPRSRSSIMSITAEPPGNDSIVRRYKEDAPHRSTVEEDNDSGGFDALDLDDDSHERYSFGPSSIHSSSSSHQSEGLDAYDLEQVNLMFRKFSLERPFRPSVTSVGHVRGPGPSVQHTTLNGDSLTSQLTLLGGNARGSFVHSVKPGSLAEKAGLREGHQLLLLEGCIRGERQSVPLDTCTKEEAHWTIQRCSGPVTLHYKVNHEGYRKLVKDMEDGLITSGDSFYIRLNLNISSQLDACTMSLKCDDVVHVRDTMYQDRHEWLCARVDPFTDHDLDMGTIPSYSRAQQLLLVKLQRLMHRGSREEVDGTHHTLRALRNTLQPEEALSTSDPRVSPRLSRASFLFGQLLQFVSRSENKYKRMNSNERVRIISGSPLGSLARSSLDATKLLTEKQEELDPESELGKNLSLIPYSLVRAFYCERRRPVLFTPTVLAKTLVQRLLNSGGAMEFTICKSDIVTRDEFLRRQKTETIIYSREKNPNAFECIAPANIEAVAAKNKHCLLEAGIGCTRDLIKSNIYPIVLFIRVCEKNIKRFRKLLPRPETEEEFLRVCRLKEKELEALPCLYATVEPDMWGSVEELLRVVKDKIGEEQRKTIWVDEDQL,1154,FALSE,2374,2374,0,0.14475,manual,Meitlis,Multiplexed Functional Assessment of Genetic Variants in CARD11,2020,10.1016/j.ajhg.2020.10.015.,4-146,CARD11,Signaling (in presence of ibrutinib),survival assessment assay,CAR11_HUMAN_2023-10-12_b02.a2m,1,1154,1154,0.2,0.2,1352,0.998,1152,53.7,0.04661458333,Low,0,0,mmc2.xlsx,log2_score,1,mutant,CAR11_HUMAN_theta0.2_2023-10-12_b02.npy,CAR11_HUMAN.pdb,1-1154,1,,OrganismalFitness +CAR11_HUMAN_Meitlis_2020_lof,CAR11_HUMAN_Meitlis_2020_lof.csv,CAR11_HUMAN,Human,Homo sapiens,MPGGGPEMDDYMETLKDEEDALWENVECNRHMLSRYINPAKLTPYLRQCKVIDEQDEDEVLNAPMLPSKINRAGRLLDILHTKGQRGYVVFLESLEFYYPELYKLVTGKEPTRRFSTIVVEEGHEGLTHFLMNEVIKLQQQMKAKDLQRCELLARLRQLEDEKKQMTLTRVELLTFQERYYKMKEERDSYNDELVKVKDDNYNLAMRYAQLSEEKNMAVMRSRDLQLEIDQLKHRLNKMEEECKLERNQSLKLKNDIENRPKKEQVLELERENEMLKTKNQELQSIIQAGKRSLPDSDKAILDILEHDRKEALEDRQELVNRIYNLQEEARQAEELRDKYLEEKEDLELKCSTLGKDCEMYKHRMNTVMLQLEEVERERDQAFHSRDEAQTQYSQCLIEKDKYRKQIRELEEKNDEMRIEMVRREACIVNLESKLRRLSKDSNNLDQSLPRNLPVTIISQDFGDASPRTNGQEADDSSTSEESPEDSKYFLPYHPPQRRMNLKGIQLQRAKSPISLKRTSDFQAKGHEEEGTDASPSSCGSLPITNSFTKMQPPRSRSSIMSITAEPPGNDSIVRRYKEDAPHRSTVEEDNDSGGFDALDLDDDSHERYSFGPSSIHSSSSSHQSEGLDAYDLEQVNLMFRKFSLERPFRPSVTSVGHVRGPGPSVQHTTLNGDSLTSQLTLLGGNARGSFVHSVKPGSLAEKAGLREGHQLLLLEGCIRGERQSVPLDTCTKEEAHWTIQRCSGPVTLHYKVNHEGYRKLVKDMEDGLITSGDSFYIRLNLNISSQLDACTMSLKCDDVVHVRDTMYQDRHEWLCARVDPFTDHDLDMGTIPSYSRAQQLLLVKLQRLMHRGSREEVDGTHHTLRALRNTLQPEEALSTSDPRVSPRLSRASFLFGQLLQFVSRSENKYKRMNSNERVRIISGSPLGSLARSSLDATKLLTEKQEELDPESELGKNLSLIPYSLVRAFYCERRRPVLFTPTVLAKTLVQRLLNSGGAMEFTICKSDIVTRDEFLRRQKTETIIYSREKNPNAFECIAPANIEAVAAKNKHCLLEAGIGCTRDLIKSNIYPIVLFIRVCEKNIKRFRKLLPRPETEEEFLRVCRLKEKELEALPCLYATVEPDMWGSVEELLRVVKDKIGEEQRKTIWVDEDQL,1154,FALSE,2395,2395,0,-0.4635,manual,Meitlis,Multiplexed Functional Assessment of Genetic Variants in CARD11,2020,10.1016/j.ajhg.2020.10.015.,4-146,CARD11,Signaling,survival assessment assay,CAR11_HUMAN_2023-10-12_b02.a2m,1,1154,1154,0.2,0.2,1352,0.998,1152,53.7,0.04661458333,Low,0,0,mmc3.xlsx,log2_score,1,mutant,CAR11_HUMAN_theta0.2_2023-10-12_b02.npy,CAR11_HUMAN.pdb,1-1154,1,,OrganismalFitness +CAS9_STRP1_Spencer_2017_positive,CAS9_STRP1_Spencer_2017_positive.csv,CAS9_STRP1,Prokaryote,Streptococcus pyogenes,MDKKYSIGLDIGTNSVGWAVITDEYKVPSKKFKVLGNTDRHSIKKNLIGALLFDSGETAEATRLKRTARRRYTRRKNRICYLQEIFSNEMAKVDDSFFHRLEESFLVEEDKKHERHPIFGNIVDEVAYHEKYPTIYHLRKKLVDSTDKADLRLIYLALAHMIKFRGHFLIEGDLNPDNSDVDKLFIQLVQTYNQLFEENPINASGVDAKAILSARLSKSRRLENLIAQLPGEKKNGLFGNLIALSLGLTPNFKSNFDLAEDAKLQLSKDTYDDDLDNLLAQIGDQYADLFLAAKNLSDAILLSDILRVNTEITKAPLSASMIKRYDEHHQDLTLLKALVRQQLPEKYKEIFFDQSKNGYAGYIDGGASQEEFYKFIKPILEKMDGTEELLVKLNREDLLRKQRTFDNGSIPHQIHLGELHAILRRQEDFYPFLKDNREKIEKILTFRIPYYVGPLARGNSRFAWMTRKSEETITPWNFEEVVDKGASAQSFIERMTNFDKNLPNEKVLPKHSLLYEYFTVYNELTKVKYVTEGMRKPAFLSGEQKKAIVDLLFKTNRKVTVKQLKEDYFKKIECFDSVEISGVEDRFNASLGTYHDLLKIIKDKDFLDNEENEDILEDIVLTLTLFEDREMIEERLKTYAHLFDDKVMKQLKRRRYTGWGRLSRKLINGIRDKQSGKTILDFLKSDGFANRNFMQLIHDDSLTFKEDIQKAQVSGQGDSLHEHIANLAGSPAIKKGILQTVKVVDELVKVMGRHKPENIVIEMARENQTTQKGQKNSRERMKRIEEGIKELGSQILKEHPVENTQLQNEKLYLYYLQNGRDMYVDQELDINRLSDYDVDHIVPQSFLKDDSIDNKVLTRSDKNRGKSDNVPSEEVVKKMKNYWRQLLNAKLITQRKFDNLTKAERGGLSELDKAGFIKRQLVETRQITKHVAQILDSRMNTKYDENDKLIREVKVITLKSKLVSDFRKDFQFYKVREINNYHHAHDAYLNAVVGTALIKKYPKLESEFVYGDYKVYDVRKMIAKSEQEIGKATAKYFFYSNIMNFFKTEITLANGEIRKRPLIETNGETGEIVWDKGRDFATVRKVLSMPQVNIVKKTEVQTGGFSKESILPKRNSDKLIARKKDWDPKKYGGFDSPTVAYSVLVVAKVEKGKSKKLKSVKELLGITIMERSSFEKNPIDFLEAKGYKEVKKDLIIKLPKYSLFELENGRKRMLASAGELQKGNELALPSKYVNFLYLASHYEKLKGSPEDNEQKQLFVEQHKHYLDEIIEQISEFSKRVILADANLDKVLSAYNKHRDKPIREQAENIIHLFTLTNLGAPAAFKYFDTTIDRKRYTSTKEVLDATLIHQSITGLYETRIDLSQLGGD,1368,FALSE,8117,8117,0,-0.2654328586,median,Spencer,Deep mutational scanning of S. pyogenes Cas9 reveals important functional domains,2017,10.1038/s41598-017-17081-y,1-1368,Streptococcus pyogenes Cas9,count of mutation where survival depends on expression of Cas9 and correct cleavage,Flow cytometry,CAS9_STRP1_2023-08-07_b01.a2m,1,1368,1368,0.1,0.2,5349,0.992,1357,1532.3,1.12918,Medium,241,0.17759764,SPCAS9_Spencer_positive_2022.csv,Log2 Fold Change after Positive Selection,1,mutant,CAS9_STRP1_theta0.2_2023-08-07_b01.npy,CAS9_STRP1.pdb,1-1368,1,,Activity +CASP3_HUMAN_Roychowdhury_2020,CASP3_HUMAN_Roychowdhury_2020.csv,CASP3_HUMAN,Human,Homo sapiens,MSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYHLEHHHHHH,258,FALSE,1567,1567,0,0.03725973017,median,Roychowdhury,Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation,2022,10.1038/s41420-021-00799-0,2-258,CASP3,Fluorescence measurement,,CASP3_HUMAN_2023-08-07_b01.a2m,1,258,258,0.1,0.2,86012,0.884,228,28096.2,123.2289474,High,307,1.346491228,CASP3_HUMAN_Roychowdhury_2020.csv,coef,1,mutant,CASP3_HUMAN_theta0.2_2023-08-07_b01.npy,CASP3_HUMAN.pdb,1-258,1,,Activity +CASP7_HUMAN_Roychowdhury_2020,CASP7_HUMAN_Roychowdhury_2020.csv,CASP7_HUMAN,Human,Homo sapiens,MAKPDRSSFVPSLFSKKKKNVTMRSIKTTRDRVPTYQYNMNFEKLGKCIIINNKNFDKVTGMGVRNGTDKDAEALFKCFRSLGFDVIVYNDCSCAKMQDLLKKASEEDHTNAACFACILLSHGEENVIYGKDGVTPIKDLTAHFRGDRCKTLLEKPKLFFIQACRGTELDDGIQADSGPINDTDANPRYKIPVEADFLFAYSTVPGYYSWRSPGRGSWFVQALCSILEEHGKDLEIMQILTRVNDRVARHFESQSDDPHFHEKKQIPCVVSMLTKELYFSQ,281,FALSE,1680,1680,0,-0.3340768074,median,Roychowdhury,Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation,2022,10.1038/s41420-021-00799-0,2-281,CASP7,Fluorescence measurement,,CASP7_HUMAN_2023-08-07_b01.a2m,1,281,281,0.1,0.2,71075,0.854,240,21588.4,89.95166667,Medium,298,1.241666667,CASP7_HUMAN_Roychowdhury_2020.csv,coef,1,mutant,CASP7_HUMAN_theta0.2_2023-08-07_b01.npy,CASP7_HUMAN.pdb,1-281,1,,Activity +CATR_CHLRE_Tsuboyama_2023_2AMI,CATR_CHLRE_Tsuboyama_2023_2AMI.csv,CATR_CHLRE,Eukaryote,Chlamydomonas reinhardtii,GLTEEQKQEIREAFDLFDTDGSGTIDAKELKVAMRALGFEPKKEEIKKMISEIDKDGSGTIDFEEFLTMMTA,72,TRUE,1903,1340,563,-0.5681612987,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-72,Caltractin,Stability,cDNA display proteolysis,CATR_CHLRE_2023-08-07_b03.a2m,1,72,72,0.3,0.2,551057,0.903,65,75596.9,1163.029231,High,57,0.8769230769,Tsuboyama2023_Dataset2_Dataset7,ddG_ML_float,1,mut_type,CATR_CHLRE_theta0.2_2023-08-07_b03.npy,CATR_CHLRE.pdb,1-72,1,,Stability +CBPA2_HUMAN_Tsuboyama_2023_1O6X,CBPA2_HUMAN_Tsuboyama_2023_1O6X.csv,CBPA2_HUMAN,Human,Homo sapiens,VGDQVLEIVPSNEEQIKNLLQLEAQEHLQLDFWKSPTTPGETAHVRVPFVNVQAVKVFLESQGIAYSIMIED,72,TRUE,2068,1357,711,-1.221174658,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-72,Carboxypeptidase A2,Stability,cDNA display proteolysis,CBPA2_HUMAN_2023-08-07_b03.a2m,1,72,72,0.3,0.2,12711,0.986,71,3086.5,43.47183099,Medium,34,0.4788732394,Tsuboyama2023_Dataset2_Dataset8,ddG_ML_float,1,mut_type,CBPA2_HUMAN_theta0.2_2023-08-07_b03.npy,CBPA2_HUMAN.pdb,1-72,1,,Stability +CBS_HUMAN_Sun_2020,CBS_HUMAN_Sun_2020.csv,CBS_HUMAN,Human,Homo sapiens,MPSETPQAEVGPTGCPHRSGPHSAKGSLEKGSPEDKEAKEPLWIRPDAPSRCTWQLGRPASESPHHHTAPAKSPKILPDILKKIGDTPMVRINKIGKKFGLKCELLAKCEFFNAGGSVKDRISLRMIEDAERDGTLKPGDTIIEPTSGNTGIGLALAAAVRGYRCIIVMPEKMSSEKVDVLRALGAEIVRTPTNARFDSPESHVGVAWRLKNEIPNSHILDQYRNASNPLAHYDTTADEILQQCDGKLDMLVASVGTGGTITGIARKLKEKCPGCRIIGVDPEGSILAEPEELNQTEQTTYEVEGIGYDFIPTVLDRTVVDKWFKSNDEEAFTFARMLIAQEGLLCGGSAGSTVAVAVKAAQELQEGQRCVVILPDSVRNYMTKFLSDRWMLQKGFLKEEDLTEKKPWWWHLRVQELGLSAPLTVLPTITCGHTIEILREKGFDQAPVVDEAGVILGMVTLGNMLSSLLAGKVQPSDQVGKVIYKQFKQIRLTDTLGRLSHILEMDHFALVVHEQIQYHSTGKSSQRQMVFGVVTAIDLLNFVAAQERDQK,551,FALSE,7217,7217,0,0.3753910128,median,Sun,A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase,2020,10.1186/s13073-020-0711-1,2-551,cystathionine beta-synthase,Yeast Growth,Growth,CBS_HUMAN_2023-10-12_b08.a2m,1,551,551,0.8,0.2,19563,0.833,459,1886,4.108932462,Medium,289,0.6296296296,,score,1,mutant,CBS_HUMAN_theta0.2_2023-10-12_b08.npy,CBS_HUMAN.pdb,1-551,1,,OrganismalFitness +CBX4_HUMAN_Tsuboyama_2023_2K28,CBX4_HUMAN_Tsuboyama_2023_2K28.csv,CBX4_HUMAN,Human,Homo sapiens,AVESIEKKRIRKGRVEYLVKWRGWSPKYNTWEPEENILDPRLLIAFQNRE,50,TRUE,2282,917,1365,-1.635037732,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-50,E3 SUMO-protein ligase CBX4,Stability,cDNA display proteolysis,CBX4_HUMAN_2023-08-07_b03.a2m,1,50,50,0.3,0.2,108263,0.96,48,13404.4,279.2583333,High,23,0.4791666667,Tsuboyama2023_Dataset2_Dataset9,ddG_ML_float,1,mut_type,CBX4_HUMAN_theta0.2_2023-08-07_b03.npy,CBX4_HUMAN.pdb,1-50,1,,Stability +CCDB_ECOLI_Adkar_2012,CCDB_ECOLI_Adkar_2012.csv,CCDB_ECOLI,Prokaryote,Escherichia coli,MQFKVYTYKRESRYRLFVDVQSDIIDTPGRRMVIPLASARLLSDKVSRELYPVVHIGDESWRMMTTDMASVPVSVIGEEVADLSHRENDIKNAINLMFWGI,101,FALSE,1176,1176,0,-19,median,Adkar,Protein model discrimination using mutational sensitivity derived from deep sequencing,2012,10.1016/j.str.2011.11.021,2-101,Toxin CcdB,Protein toxicity (negative effect on cell growth),toxin activity,CCDB_ECOLI_full_11-26-2021_b02.a2m,1,101,101,0.2,0.2,43564,0.851,86,16821.5,195.5988372,high,61,0.7093023256,CCDB_ECOLI_Adkar_2012.csv,score,-1,mutant,CCDB_ECOLI_theta_0.2.npy,CCDB_ECOLI.pdb,1-101,0.1,,Activity +CCDB_ECOLI_Tripathi_2016,CCDB_ECOLI_Tripathi_2016.csv,CCDB_ECOLI,Prokaryote,Escherichia coli,MQFKVYTYKRESRYRLFVDVQSDIIDTPGRRMVIPLASARLLSDKVSRELYPVVHIGDESWRMMTTDMASVPVSVIGEEVADLSHRENDIKNAINLMFWGI,101,FALSE,1663,1663,0,-3.5,manual,Tripathi,"Molecular Determinants of Mutant Phenotypes, Inferred from Saturation Mutagenesis Data",2016,10.1093/molbev/msw182,2-101,Toxin CcdB,growth (surrogate for toxicity/activity of CCDB),Growth,CCDB_ECOLI_full_11-26-2021_b02.a2m,1,101,101,0.2,0.2,43564,0.851,86,16821.5,195.5988372,high,61,0.7093023256,CCDB_ECOLI_Tripathi_2016.csv,score,-1,mutant,CCDB_ECOLI_theta_0.2.npy,CCDB_ECOLI.pdb,1-101,0.1,,OrganismalFitness +CCR5_HUMAN_Gill_2023,CCR5_HUMAN_Gill_2023.csv,CCR5_HUMAN,Human,Homo sapiens,MDYQVSSPIYDINYYTSEPCQKINVKQIAARLLPPLYSLVFIFGFVGNMLVILILINCKRLKSMTDIYLLNLAISDLFFLLTVPFWAHYAAAQWDFGNTMCQLLTGLYFIGFFSGIFFIILLTIDRYLAVVHAVFALKARTVTFGVVTSVITWVVAVFASLPGIIFTRSQKEGLHYTCSSHFPYSQYQFWKNFQTLKIVILGLVLPLLVMVICYSGILKTLLRCRNEKKRHRAVRLIFTIMIVYFLFWAPYNIVLLLNTFQEFFGLNNCSSSNRLDQAMQVTETLGMTHCCINPIIYAFVGEKFRNYLLVFFQKHIAKRFCKCCSIFQQEAPERASSVYTRSTGEQEISVGL,352,FALSE,6137,6137,0,-0.06,median,Gill,Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis,2023,10.1101/2023.03.25.534231,2-344,CCR5,"binding affinity, surface expression",FACS,CCR5_HUMAN_2023-08-07_b03.a2m,1,352,352,0.3,0.2,632074,0.83,292,63056,215.9452055,High,309,1.058219178,,avg_score,1,mutant,CCR5_HUMAN_theta0.2_2023-08-07_b03.npy,CCR5_HUMAN.pdb,1-352,1,,Binding +CD19_HUMAN_Klesmith_2019_FMC_singles,CD19_HUMAN_Klesmith_2019_FMC_singles.csv,CD19_HUMAN,Human,Homo sapiens,MPPPRLLFFLLFLTPMEVRPEEPLVVKVEEGDNAVLQCLKGTSDGPTQQLTWSRESPLKPFLKLSLGLPGLGIHMRPLAIWLFIFNVSQQMGGFYLCQPGPPSEKAWQPGWTVNVEGSGELFRWNVSDLGGLGCGLKNRSSEGPSSPSGKLMSPKLYVWAKDRPEIWEGEPPCLPPRDSLNQSLSQDLTMAPGSTLWLSCGVPPDSVSRGPLSWTHVHPKGPKSLLSLELKDDRPARDMWVMETGLLLPRATAQDAGKYYCHRGNLTMSFHLEITARPVLWHWLLRTGGWKVSAVTLAYLIFCLCSLVGILHLQRALVLRRKRKRMTDPTRRFFKVTPPPGSGPQNQYGNVLSLPTPTSGLGRAQRWAAGLGGTAPSYGNPSSDVQADGALGSRSPPGVGPEEEEGEGYEEPDSEEDSEFYENDSNLGQDQLSQDGSGYENPEDEPLGPEDEDSFSNAESYENEDEELTQPVARTMDFLSPHGSAWDPSREATSLGSQSYEDMRGILYAAPQLRSIRGQPGPNHEEDADSYENMDNPDGPDPAWGGGGRMGTWSTR,556,FALSE,3761,3761,0,0,manual,Klesmith,Retargeting CD19 Chimeric Antigen Receptor T Cells via Engineered CD19-Fusion Proteins,2019,10.1021/acs.molpharmaceut.9b00418,20-291,CD19,Binding affinity,FACS,CD19_HUMAN_2023-10-12_b01.a2m,1,556,556,0.1,0.2,1183,1,556,275.2,0.4949640288,Low,11,0.01978417266,single-site/Clinical_FMC_T1_Fitness.tsv,Fitness,1,mutant_offset,CD19_HUMAN_theta0.2_2023-10-12_b01.npy,CD19_HUMAN.pdb,1-556,1,,Binding +CP2C9_HUMAN_Amorosi_2021_abundance,CP2C9_HUMAN_Amorosi_2021_abundance.csv,CP2C9_HUMAN,Human,Homo sapiens,MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV,490,FALSE,6370,6370,0,0.7723244345,median,Amorosi,Massively parallel characterization of CYP2C9 variant enzyme activity and abundance,2021,10.1016/j.ajhg.2021.07.001,2-490,Cytochrome P450 2C9,protein abundance,protein abundance,CP2C9_HUMAN_full_11-26-2021_b04.a2m,1,490,490,0.4,0.2,264279,0.886,434,81212.1,187.1246544,high,1092,2.516129032,CP2C9_HUMAN_Amorosi_2021.csv,abundance_score,1,variant,CP2C9_HUMAN_theta_0.2.npy,CP2C9_HUMAN.pdb,1-490,0.1,,Expression +CP2C9_HUMAN_Amorosi_2021_activity,CP2C9_HUMAN_Amorosi_2021_activity.csv,CP2C9_HUMAN,Human,Homo sapiens,MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV,490,FALSE,6142,6142,0,0.5476104185,median,Amorosi,Massively parallel characterization of CYP2C9 variant enzyme activity and abundance,2021,10.1016/j.ajhg.2021.07.001,1-490,Cytochrome P450 2C9,"activity, binding (to fluorescent CYP probe)","Activity, binding",CP2C9_HUMAN_full_11-26-2021_b04.a2m,1,490,490,0.4,0.2,264279,0.886,434,81212.1,187.1246544,high,1092,2.516129032,CP2C9_HUMAN_Amorosi_2021.csv,activity_score,1,variant,CP2C9_HUMAN_theta_0.2.npy,CP2C9_HUMAN.pdb,1-490,0.1,,Binding +CSN4_MOUSE_Tsuboyama_2023_1UFM,CSN4_MOUSE_Tsuboyama_2023_1UFM.csv,CSN4_MOUSE,Eukaryote,Mus musculus,SSGGSSILDRAVIEHNLLSASKLYNNITFEELGALLEIPAAKAEKIASQMITEGRMNGFIDQIDGIVHFETR,72,TRUE,3295,1353,1942,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-72,COP9 signalosome complex subunit 4,Stability,cDNA display proteolysis,CSN4_MOUSE_2023-08-07_b03.a2m,1,72,72,0.3,0.2,39217,0.889,64,3492.9,54.5765625,Medium,9,0.140625,Tsuboyama2023_Dataset2_Dataset10,ddG_ML_float,1,mut_type,CSN4_MOUSE_theta0.2_2023-08-07_b03.npy,CSN4_MOUSE.pdb,1-72,1,,Stability +CUE1_YEAST_Tsuboyama_2023_2MYX,CUE1_YEAST_Tsuboyama_2023_2MYX.csv,CUE1_YEAST,Eukaryote,Saccharomyces cerevisiae,GGHPVTTQMVETVQNLAPNLHPEQIRYSLENTGSVEETVERYLRGDEFSFPP,52,TRUE,1580,955,625,-1.319713733,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-52,Coupling of ubiquitin conjugation to ER degradation protein 1,Stability,cDNA display proteolysis,CUE1_YEAST_2023-08-07_b08.a2m,1,52,52,0.8,0.2,3213,0.923,48,387.1,8.064583333,Medium,10,0.2083333333,Tsuboyama2023_Dataset2_Dataset11,ddG_ML_float,1,mut_type,CUE1_YEAST_theta0.2_2023-08-07_b08.npy,CUE1_YEAST.pdb,1-52,1,,Stability +D7PM05_CLYGR_Somermeyer_2022,D7PM05_CLYGR_Somermeyer_2022.csv,D7PM05_CLYGR,Eukaryote,Clytia gregaria,MTALTEGAKLFEKEIPYITELEGDVEGMKFIIKGEGTGDATTGTIKAKYICTTGDLPVPWATILSSLSYGVFCFAKYPRHIADFFKSTQPDGYSQDRIISFDNDGQYDVKAKVTYENGTLYNRVTVKGTGFKSNGNILGMRVLYHSPPHAVYILPDRKNGGMKIEYNKAFDVMGGGHQMARHAQFNKPLGAWEEDYPLYHHLTVWTSFGKDPDDDETDHLTIVEVIKAVDLETYR,235,TRUE,24515,1169,23346,12500,manual,Somermeyer,Heterogeneity of the GFP fitness landscape and data-driven protein design,2022,10.7554/eLife.75842,2-235,Green fluorescent protein cgreGFP,Fluorescence,FACS,D7PM05_CLYGR_full_b0.2.a2m,1,235,235,0.2,0.2,694,1,235,137.6,0.5855319149,Low,4,0.0170212766,D7PM05_CLYGR_Somermeyer_2022.csv,replicates_mean_brightness,1,mutant,D7PM05_CLYGR_theta_0.2.npy,D7PM05_CLYGR.pdb,1-235,1,,Activity +DLG4_HUMAN_Faure_2021,DLG4_HUMAN_Faure_2021.csv,DLG4_HUMAN,Human,Homo sapiens,MDCLCIVTTKKYRYQDEDTPPLEHSPAHLPNQANSPPVIVNTDTLEAPGYELQVNGTEGEMEYEEITLERGNSGLGFSIAGGTDNPHIGDDPSIFITKIIPGGAAAQDGRLRVNDSILFVNEVDVREVTHSAAVEALKEAGSIVRLYVMRRKPPAEKVMEIKLIKGPKGLGFSIAGGVGNQHIPGDNSIYVTKIIEGGAAHKDGRLQIGDKILAVNSVGLEDVMHEDAVAALKNTYDVVYLKVAKPSNAYLSDSYAPPDITTSYSQHLDNEISHSSYLGTDYPTAMTPTSPRRYSPVAKDLLGEEDIPREPRRIVIHRGSTGLGFNIVGGEDGEGIFISFILAGGPADLSGELRKGDQILSVNGVDLRNASHEQAAIALKNAGQTVTIIAQYKPEEYSRFEAKIHDLREQLMNSSLGSGTASLRSNPKRGFYIRALFDYDKTKDCGFLSQALSFRFGDVLHVIDASDEEWWQARRVHSDSETDDIGFIPSKRRVERREWSRLKAKDWGSSSGSQGREDSVLSYETVTQMEVHYARPIIILGPTKDRANDDLLSEFPDKFGSCVPHTTRPKREYEIDGRDYHFVSSREKMEKDIQAHKFIEAGQYNSHLYGTSVQSVREVAEQGKHCILDVSANAVRRLQAAHLHPIAIFIRPRSLENVLEINKRITEEQARKAFDRATKLEQEFTECFSAIVEGDSFEEIYHKVKRVIEDLSGPYIWVPARERL,724,TRUE,6976,1280,5696,-0.5602585328,median,Faure,Mapping the energetic and allosteric landscapes of protein binding domains,2022,10.1038/s41586-022-04586-4,311-394,PSD95-PDZ3,Yeast growth,Growth,DLG4_HUMAN_full_11-26-2021_b02.a2m,1,724,724,0.2,0.2,25338,0.825,597,354.3,0.5934673367,low,7,0.01172529313,DLG4_HUMAN_Faure_2021.csv,fitness,1,mutant,DLG4_HUMAN_theta_0.2.npy,DLG4_HUMAN.pdb,1-724,0.1,,OrganismalFitness +DLG4_RAT_McLaughlin_2012,DLG4_RAT_McLaughlin_2012.csv,DLG4_RAT,Eukaryote,Rattus norvegicus,MDCLCIVTTKKYRYQDEDTPPLEHSPAHLPNQANSPPVIVNTDTLEAPGYELQVNGTEGEMEYEEITLERGNSGLGFSIAGGTDNPHIGDDPSIFITKIIPGGAAAQDGRLRVNDSILFVNEVDVREVTHSAAVEALKEAGSIVRLYVMRRKPPAEKVMEIKLIKGPKGLGFSIAGGVGNQHIPGDNSIYVTKIIEGGAAHKDGRLQIGDKILAVNSVGLEDVMHEDAVAALKNTYDVVYLKVAKPSNAYLSDSYAPPDITTSYSQHLDNEISHSSYLGTDYPTAMTPTSPRRYSPVAKDLLGEEDIPREPRRIVIHRGSTGLGFNIVGGEDGEGIFISFILAGGPADLSGELRKGDQILSVNGVDLRNASHEQAAIALKNAGQTVTIIAQYKPEEYSRFEAKIHDLREQLMNSSLGSGTASLRSNPKRGFYIRALFDYDKTKDCGFLSQALSFRFGDVLHVIDAGDEEWWQARRVHSDSETDDIGFIPSKRRVERREWSRLKAKDWGSSSGSQGREDSVLSYETVTQMEVHYARPIIILGPTKDRANDDLLSEFPDKFGSCVPHTTRPKREYEIDGRDYHFVSSREKMEKDIQAHKFIEAGQYNSHLYGTSVQSVREVAEQGKHCILDVSANAVRRLQAAHLHPIAIFIRPRSLENVLEINKRITEEQARKAFDRATKLEQEFTECFSAIVEGDSFEEIYHKVKRVIEDLSGPYIWVPARERL,724,FALSE,1576,1576,0,-0.25,manual,McLaughlin,The spatial architecture of protein function and adaptation,2012,10.1038/nature11500,311-393,"Dlg4, (PSD95_PDZ3)",peptide binding - natural ligand,Binding,DLG4_RAT_full_11-26-2021_b03.a2m,1,724,724,0.3,0.2,24705,0.841,609,283.9,0.4661740558,low,6,0.009852216749,DLG4_RAT_McLaughlin_2012.csv,CRIPT,1,mutant,DLG4_RAT_theta_0.2.npy,DLG4_RAT.pdb,1-724,0.1,,Binding +DN7A_SACS2_Tsuboyama_2023_1JIC,DN7A_SACS2_Tsuboyama_2023_1JIC.csv,DN7A_SACS2,Prokaryote,Saccharolobus solfataricus,TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQ,55,FALSE,1008,1008,0,-0.472754253,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,DNA-binding protein 7a,Stability,cDNA display proteolysis,DN7A_SACS2_2023-08-07_b02.a2m,1,55,55,0.2,0.2,42895,0.764,42,1248.1,29.71666667,Medium,13,0.3095238095,Tsuboyama2023_Dataset2_Dataset12,ddG_ML_float,1,mut_type,DN7A_SACS2_theta0.2_2023-08-07_b02.npy,DN7A_SACS2.pdb,1-55,1,,Stability +DNJA1_HUMAN_Tsuboyama_2023_2LO1,DNJA1_HUMAN_Tsuboyama_2023_2LO1.csv,DNJA1_HUMAN,Human,Homo sapiens,TTYYDVLGVKPNATQEELKKAYRKLALKYHPDKNPNEGEKFKQISQAYEVLSDAKKRELYDKGGE,65,TRUE,2264,1216,1048,-2.239788161,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-65,DnaJ homolog subfamily A member 1,Stability,cDNA display proteolysis,DNJA1_HUMAN_2023-08-07_b07.a2m,1,65,65,0.7,0.2,280284,0.969,63,35361.9,561.3,High,52,0.8253968254,Tsuboyama2023_Dataset2_Dataset13,ddG_ML_float,1,mut_type,DNJA1_HUMAN_theta0.2_2023-08-07_b07.npy,DNJA1_HUMAN.pdb,1-65,1,,Stability +DOCK1_MOUSE_Tsuboyama_2023_2M0Y,DOCK1_MOUSE_Tsuboyama_2023_2M0Y.csv,DOCK1_MOUSE,Eukaryote,Mus musculus,WVPTKREEKYGVAFYNYDARGADELSLQIGDTVHILETYEGWYRGYTLRKKSKKGIFPASYIHLKE,66,TRUE,2915,1213,1702,-1.104437518,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-66,Dedicator of cytokinesis protein 1,Stability,cDNA display proteolysis,DOCK1_MOUSE_2023-08-07_b03.a2m,1,66,66,0.3,0.2,705447,0.848,56,22172.3,395.9339286,High,55,0.9821428571,Tsuboyama2023_Dataset2_Dataset14,ddG_ML_float,1,mut_type,DOCK1_MOUSE_theta0.2_2023-08-07_b03.npy,DOCK1_MOUSE.pdb,1-66,1,,Stability +DYR_ECOLI_Nguyen_2023,DYR_ECOLI_Nguyen_2023.csv,DYR_ECOLI,Prokaryote,Escherichia coli,MISLIAALAVDRVIGMENAMPWNLPADLAWFKRNTLNKPVIMGRHTWESIGRPLPGRKNIILSSQPGTDDRVTWVKSVDEAIAACGDVPEIMVIGGGRVYEQFLPKAQKLYLTHIDAEVEGDTHFPDYEPDDWESVFSEFHDADAQNSHSYCFEILERR,159,FALSE,2916,2916,0,0.8,manual,Nguyen,The Genetic Landscape of a Metabolic Interaction,2023,10.1101/2023.05.28.542639,2-159,DHFR,cell growth in ∆DHFR bacteria,Growth,DYR_ECOLI_2023-08-07_b01.a2m,1,159,159,0.1,0.2,188828,0.969,154,47685.7,309.6474026,High,337,2.188311688,542639_file03.xlsx,Avg Growth - WT TYMS,1,Mutation,DYR_ECOLI_theta0.2_2023-08-07_b01.npy,DYR_ECOLI.pdb,1-159,1,,OrganismalFitness +DYR_ECOLI_Thompson_2019,DYR_ECOLI_Thompson_2019.csv,DYR_ECOLI,Prokaryote,Escherichia coli,MISLIAALAVDRVIGMENAMPWNLPADLAWFKRNTLNKPVIMGRHTWESIGRPLPGRKNIILSSQPGTDDRVTWVKSVDEAIAACGDVPEIMVIGGGRVYEQFLPKAQKLYLTHIDAEVEGDTHFPDYEPDDWESVFSEFHDADAQNSHSYCFEILERR,159,FALSE,2363,2363,0,-0.5,manual,Thompson,Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme,2019,10.7554/eLife.53476,2-159,DHFR reductase,"growth (turbidostat; -Lon for natural absence of Lon protease in E. coli, +Lon for exogenous protease)",Growth,DYR_ECOLI_full_11-26-2021_b08.a2m,1,159,159,0.8,0.2,41921,0.981,156,12203.2,78.22564103,medium,265,1.698717949,DYR_ECOLI_Thompson_plusLon_2019.csv,PlusLon_selection_coefficient,1,mutant,DYR_ECOLI_theta_0.2.npy,DYR_ECOLI.pdb,1-159,0.1,,OrganismalFitness +ENV_HV1B9_DuenasDecamp_2016,ENV_HV1B9_DuenasDecamp_2016.csv,ENV_HV1B9,Virus,Human immunodeficiency virus type 1 group M subtype B (strain 89.6) (HIV-1),MRVKEIRKNWQHLRGGILLLGMLMICSAAKEKTWVTIYYGVPVWREATTTLFCASDAKAYDTEVHNVWATHACVPTDPNPQEVVLGNVTENFNMWKNNMVDQMHEDIISLWDESLKPCVKLTPLCVTLNCTNLNITKNTTNPTSSSWGMMEKGEIKNCSFYITTSIRNKVKKEYALFNRLDVVPIENTNNTKYRLISCNTSVITQACPKVSFQPIPIHYCVPAGFAMLKCNNKTFNGSGPCTNVSTVQCTHGIRPVVSTQLLLNGSLAEEDIVIRSENFTDNAKTIIVQLNESVVINCTRPNNNTRRRLSIGPGRAFYARRNIIGDIRQAHCNISRAKWNNTLQQIVIKLREKFRNKTIAFNQPSGGDPEIVRHSFNCGGEFFYCNTAQLFNSTWNVTGGTNGTEGNDIITLQCRIKQIINMWQKVGKAMYAPPITGQIRCSSNITGLLLTRDGGNSTETETEIFRPGGGDMRDNWRSELYKYKVVRIEPIGVAPTRAKRRTVQREKRAVGIGAVFLGFLGAAGSTMGAASVTLTVQARLLLSGIVQQQNNLLRAIEAQQHMLQLTVWGIKQLQARVLALERYLRDQQLMGIWGCSGKLICTTSVPWNVSWSNKSVDDIWNNMTWMEWEREIDNYTDYIYDLLEKSQTQQEKNEKELLELDKWASLWNWFDITNWLWYIRLFIMIVGGLIGLRIVFAVLSIVNRVRQGYSPLSFQTLLPASRGPDRPEGTEEEGGERDRDRSGPLVNGFLALFWVDLRNLCLFLYHLLRNLLLIVTRIVELLGRRGWEALKYWWNLLQYWSQELKNSAVSLLNATAIAVAEGTDRVIKIVQRACRAIRNIPTRIRQGLERALL,853,FALSE,375,375,0,-0.8,manual,Duenas-Decamp,Saturation Mutagenesis of the HIV-1 Envelope CD4 Binding Loop Reveals Residues Controlling Distinct Trimer Conformations,2016,10.1371/journal.ppat.1005988,361-380,HIV env,Viral replication,Growth,ENV_HV1B9_S364P-M373R_b0.3.a2m,1,853,853,0.3,0.01,87271,0.989,844,11807.8,13.99028436,medium,947,1.122037915,ENV_HV1B9_DuenasDecamp_2016.csv,Fitness_Effect,1,mutant,ENV_HV1B9_theta_0.01.npy,ENV_HV1B9.pdb,1-853,0.1,,OrganismalFitness +ENV_HV1BR_Haddox_2016,ENV_HV1BR_Haddox_2016.csv,ENV_HV1BR,Virus,Human immunodeficiency virus type 1 group M subtype B (isolate BRU/LAI) (HIV-1),MRVKEKYQHLWRWGWKWGTMLLGILMICSATEKLWVTVYYGVPVWKEATTTLFCASDAKAYDTEVHNVWATHACVPTDPNPQEVVLVNVTENFNMWKNDMVEQMHEDIISLWDQSLKPCVKLTPLCVSLKCTDLGNATNTNSSNTNSSSGEMMMEKGEIKNCSFNISTSIRGKVQKEYAFFYKLDIIPIDNDTTSYTLTSCNTSVITQACPKVSFEPIPIHYCAPAGFAILKCNNKTFNGTGPCTNVSTVQCTHGIRPVVSTQLLLNGSLAEEEVVIRSANFTDNAKTIIVQLNQSVEINCTRPNNNTRKSIRIQRGPGRAFVTIGKIGNMRQAHCNISRAKWNATLKQIASKLREQFGNNKTIIFKQSSGGDPEIVTHSFNCGGEFFYCNSTQLFNSTWFNSTWSTEGSNNTEGSDTITLPCRIKQFINMWQEVGKAMYAPPISGQIRCSSNITGLLLTRDGGNNNNGSEIFRPGGGDMRDNWRSELYKYKVVKIEPLGVAPTKAKRRVVQREKRAVGIGALFLGFLGAAGSTMGAASMTLTVQARQLLSGIVQQQNNLLRAIEAQQHLLQLTVWGIKQLQARILAVERYLKDQQLLGIWGCSGKLICTTAVPWNASWSNKSLEQIWNNMTWMEWDREINNYTSLIHSLIEESQNQQEKNEQELLELDKWASLWNWFNITNWLWYIKIFIMIVGGLVGLRIVFAVLSIVNRVRQGYSPLSFQTHLPTPRGPDRPEGIEEEGGERDRDRSIRLVNGSLALIWDDLRSLCLFSYHRLRDLLLIVTRIVELLGRRGWEALKYWWNLLQYWSQELKNSAVSLLNATAIAVAEGTDRVIEVVQGACRAIRHIPRRIRQGLERILL,861,FALSE,12863,12863,0,0.0191127558,median,Haddox,Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV‚Äôs Envelope Protein on Viral Replication in Cell Culture,2016,10.1371/journal.ppat.1006114,31-707,HIV env,Viral replication,Growth,ENV_HV1BR_theta0.99_full_11-26-2021_b09.a2m,1,861,861,0.9,0.01,74844,0.98,844,36809.8,43.61350711,medium,2359,2.795023697,ENV_HV1BR_Haddox_2016.csv,score,1,mutant,ENV_HV1BR_theta_0.01.npy,ENV_HV1BR.pdb,1-861,0.1,,OrganismalFitness +ENVZ_ECOLI_Ghose_2023,ENVZ_ECOLI_Ghose_2023.csv,ENVZ_ECOLI,Prokaryote,Escherichia coli,LADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEECNAIIEQFIDYLR,60,FALSE,1121,1121,0,3.5,manual,Ghose,Marginal specificity in protein interactions constrains evolution of a paralogous family,2023,10.1073/pnas.2221163120,1-60,EnvZ kinase,fluorescent reporter,FACS,ENVZ_ECOLI_2023-08-07_b02.a2m,1,60,60,0.2,0.2,1879223,0.933,56,254652.1,4547.358929,High,55,0.9821428571,ENVZ_ECOLI_Ghose_2023.csv,mean_on,1,mutant,ENVZ_ECOLI_theta0.2_2023-08-07_b02.npy,ENVZ_ECOLI.pdb,1-60,1,,Activity +EPHB2_HUMAN_Tsuboyama_2023_1F0M,EPHB2_HUMAN_Tsuboyama_2023_1F0M.csv,EPHB2_HUMAN,Human,Homo sapiens,SFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMN,66,TRUE,1960,1239,721,-1.932053964,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-66,Ephrin type-B receptor 2,Stability,cDNA display proteolysis,EPHB2_HUMAN_2023-08-07_b04.a2m,1,66,66,0.4,0.2,212234,0.894,59,8426.3,142.8186441,High,29,0.4915254237,Tsuboyama2023_Dataset2_Dataset15,ddG_ML_float,1,mut_type,EPHB2_HUMAN_theta0.2_2023-08-07_b04.npy,EPHB2_HUMAN.pdb,1-66,1,,Stability +ERBB2_HUMAN_Elazar_2016,ERBB2_HUMAN_Elazar_2016.csv,ERBB2_HUMAN,Human,Homo sapiens,MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV,1255,FALSE,326,326,0,0.0678339381,median,Elazar,Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane,2016,10.7554/eLife.12125,651-674,ErbB2 membrane domain,Membrane-protein insertion,TOXCAT-Beta-lactamase (TbL) screen,ERBB2_HUMAN_2023-10-12_b02.a2m,1,1255,1255,0.2,0.2,8311,0.981,1231,447.9,0.363850528,Low,187,0.1519090171,urn_mavedb_00000051-b-1_scores.csv,score,-1,mutant,ERBB2_HUMAN_theta0.2_2023-10-12_b02.npy,ERBB2_HUMAN.pdb,1-1255,1,650,Expression +ESTA_BACSU_Nutschel_2020,ESTA_BACSU_Nutschel_2020.csv,ESTA_BACSU,Prokaryote,Bacillus subtilis,MKFVKRRIIALVTILMLSVTSLFALQPSAKAAEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN,212,FALSE,2172,2172,0,46.34,median,Nutschel,Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for Bacillus subtilis Lipase A,2020,10.1021/acs.jcim.9b00954,32-205,estA,thermostability,thermostability,ESTA_BACSU_full_11-26-2021_b03.a2m,1,212,212,0.3,0.2,234310,0.774,164,64492.5,393.2469512,high,292,1.780487805,ESTA_BACSU_Nutschel_2020.csv,T50,1,Variants of BsLipA,ESTA_BACSU_theta_0.2.npy,ESTA_BACSU.pdb,1-212,0.1,,Stability +F7YBW8_MESOW_Ding_2023,F7YBW8_MESOW_Ding_2023.csv,F7YBW8_MESOW,Prokaryote,Mesorhizobium opportunistum,MANVEKMSVAVTPQQAAVMREAVEAGEYATASEIVREAVRDWLAKRELRHDDIRRLRQLWDEGKASGRPEPVDFDALRKEARQKLTEVPPNGR,93,TRUE,7922,80,7842,0.3,manual,Ding,Protein design using structure-based residue preferences,2023,10.1101/2022.10.31.514613,48-82,Antitoxin ParD3,growth enrichment,,F7YBW8_MESOW_full_01-07-2022_b02.a2m,1,93,93,0.2,0.2,38613,0.774,72,16262.4,225.8666667,high,31,0.4305555556,df_at_10pos.csv,DMS_score,1,mutant,F7YBW8_MESOW_theta_0.2.npy,F7YBW8_MESOW.pdb,1-93,1,,OrganismalFitness +F7YBW8_MESOW_Aakre_2015,F7YBW8_MESOW_Aakre_2015.csv,F7YBW8_MESOW,Prokaryote,Mesorhizobium opportunistum,MANVEKMSVAVTPQQAAVMREAVEAGEYATASEIVREAVRDWLAKRELRHDDIRRLRQLWDEGKASGRPEPVDFDALRKEARQKLTEVPPNGR,93,TRUE,9192,37,9155,-0.001724,median,Aakre,Evolving New Protein-Protein Interaction Specificity through Promiscuous Intermediates,2015,10.1016/j.cell.2015.09.055,59-64,Antitoxin ParD3,fitness,Growth (antitoxin neutralization of ParE3),F7YBW8_MESOW_full_01-07-2022_b02.a2m,1,93,93,0.2,0.2,38613,0.774,72,16262.4,225.8666667,high,31,0.4305555556,F7YBW8_MESOW_Aakre_2015.csv,fitness,1,mutant,F7YBW8_MESOW_theta_0.2.npy,F7YBW8_MESOW.pdb,1-93,0.1,,OrganismalFitness +FECA_ECOLI_Tsuboyama_2023_2D1U,FECA_ECOLI_Tsuboyama_2023_2D1U.csv,FECA_ECOLI,Prokaryote,Escherichia coli,QVNIAPGSLDKALNQYAAHSGFTLSVDASLTRGKQSNGLHGDYDVESGLQQLLDGSGLQVKPLGNNSWTLEP,72,TRUE,1886,1219,667,-0.813576222,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-72,Fe(3+) dicitrate transport protein FecA,Stability,cDNA display proteolysis,FECA_ECOLI_2023-08-07_b06.a2m,1,72,72,0.6,0.2,74248,0.986,71,9949.9,140.1394366,High,63,0.8873239437,Tsuboyama2023_Dataset2_Dataset16,ddG_ML_float,1,mut_type,FECA_ECOLI_theta0.2_2023-08-07_b06.npy,FECA_ECOLI.pdb,1-72,1,,Stability +FKBP3_HUMAN_Tsuboyama_2023_2KFV,FKBP3_HUMAN_Tsuboyama_2023_2KFV.csv,FKBP3_HUMAN,Human,Homo sapiens,VPQRAWTVEQLRSEQLPKKDIIKFLQEHGSDSFLAEHKLLGNIKNVAKTANKDHLVTAYNHLFETKRFK,69,FALSE,1237,1237,0,-0.1631252002,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-69,Peptidyl-prolyl cis-trans isomerase FKBP3,Stability,cDNA display proteolysis,FKBP3_HUMAN_2023-08-07_b03.a2m,1,69,69,0.3,0.2,3216,0.957,66,132,2,Medium,7,0.1060606061,Tsuboyama2023_Dataset2_Dataset17,ddG_ML_float,1,mut_type,FKBP3_HUMAN_theta0.2_2023-08-07_b03.npy,FKBP3_HUMAN.pdb,1-69,1,,Stability +GAL4_YEAST_Kitzman_2015,GAL4_YEAST_Kitzman_2015.csv,GAL4_YEAST,Eukaryote,Saccharomyces cerevisiae,MKLLSSIEQACDICRLKKLKCSKEKPKCAKCLKNNWECRYSPKTKRSPLTRAHLTEVESRLERLEQLFLLIFPREDLDMILKMDSLQDIKALLTGLFVQDNVNKDAVTDRLASVETDMPLTLRQHRISATSSSEESSNKGQRQLTVSIDSAAHHDNSTIPLDFMPRDALHGFDWSEEDDMSDGLPFLKTDPNNNGFFGDGSLLCILRSIGFKPENYTNSNVNRLPTMITDRYTLASRSTTSRLLQSYLNNFHPYCPIVHSPTLMMLYNNQIEIASKDQWQILFNCILAIGAWCIEGESTDIDVFYYQNAKSHLTSKVFESGSIILVTALHLLSRYTQWRQKTNTSYNFHSFSIRMAISLGLNRDLPSSFSDSSILEQRRRIWWSVYSWEIQLSLLYGRSIQLSQNTISFPSSVDDVQRTTTGPTIYHGIIETARLLQVFTKIYELDKTVTAEKSPICAKKCLMICNEIEEVSRQAPKFLQMDISTTALTNLLKEHPWLSFTRFELKWKQLSLIIYVLRDFFTNFTQKKSQLEQDQNDHQSYEVKRCSIMLSDAAQRTVMSVSSYMDNHNVTPYFAWNCSYYLFNAVLVPIKTLLSNSKSNAENNETAQLLQQINTVLMLLKKLATFKIQTCEKYIQVLEEVCAPFLLSQCAIPLPHISYNNSNGSAIKNIVGSATIAQYPTLPEENVNNISVKYVSPGSVGPSPVPLKSGASFSDLVKLLSNRPPSRNSPVTIPRSTPSHRSVTPFLGQQQQLQSLVPLTPSALFGGANFNQSGNIADSSLSFTFTNSSNGPNLITTQTNSQALSQPIASSNVHDNFMNNEITASKIDDGNNSKPLSPGWTDQTAYNAFGITTGMFNTTTMDDVYNYLFDDEDTPPNPKKE,881,FALSE,1195,1195,0,-8,manual,Kitzman,Massively parallel single-amino-acid mutagenesis,2015,10.1038/nmeth.3223,2-65,GAL4,"Growth (no selection, 24h)",Growth,GAL4_YEAST_full_11-26-2021_b02.a2m,1,881,881,0.2,0.2,16159,0.707,623,7942.3,12.74847512,medium,163,0.2616372392,GAL4_YEAST_Kitzman_2015.csv,SEL_C_64h,1,mutant,GAL4_YEAST_theta_0.2.npy,GAL4_YEAST.pdb,1-881,0.1,,OrganismalFitness +GCN4_YEAST_Staller_2018,GCN4_YEAST_Staller_2018.csv,GCN4_YEAST,Eukaryote,Saccharomyces cerevisiae,MSEYQPSLFALNPMGFSPLDGSKSTNENVSASTSTAKPMVGQLIFDKFIKTEEDPIIKQDTPSNLDFDFALPQTATAPDAKTVLPIPELDDAVVESFFSSSTDSTPMFEYENLEDNSKEWTSLFDNDIPVTTDDVSLADKAIESTEEVSLVPSNLEVSTTSFLPTPVLEDAKLTQTRKVKKPNSVVKKSHHVGKDDESRLDHLGVVAYNRKQRSIPLSPIVPESSDPAALKRARNTEAARRSRARKLQRMKQLEDKVEELLSKNYHLENEVARLKKLVGER,281,TRUE,2638,33,2605,1.293757864,median,Staller,A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain,2018,10.1016/j.cels.2018.01.015,101-144,Gcn4,Binding,FACS,GCN4_YEAST_full_24-02-2022_b03.a2m,1,281,281,0.3,0.2,350,0.719,202,177.9,0.8806930693,low,1,0.00495049505,GCN4_YEAST_Staller_2018.csv,Induction,1,mutant,GCN4_YEAST_theta_0.2.npy,GCN4_YEAST.pdb,1-281,0.1,,Binding +GDIA_HUMAN_Silverstein_2021,GDIA_HUMAN_Silverstein_2021.csv,GDIA_HUMAN,Human,Homo sapiens,MDEEYDVIVLGTGLTECILSGIMSVNGKKVLHMDRNPYYGGESSSITPLEELYKRFQLLEGPPESMGRGRDWNVDLIPKFLMANGQLVKMLLYTEVTRYLDFKVVEGSFVYKGGKIYKVPSTETEALASNLMGMFEKRRFRKFLVFVANFDENDPKTFEGVDPQTTSMRDVYRKFDLGQDVIDFTGHALALYRTDDYLDQPCLETVNRIKLYSESLARYGKSPYLYPLYGLGELPQGFARLSAIYGGTYMLNKPVDDIIMENGKVVGVKSEGEVARCKQLICDPSYIPDRVRKAGQVIRIICILSHPIKNTNDANSCQIIIPQNQVNRKSDIYVCMISYAHNVAAQGKYIAIASTTVETTDPEKEVEPALELLEPIDQKFVAISDLYEPIDDGCESQVFCSCSYDATTHFETTCNDIKDIYKRMAGTAFDFENMKRKQNDVFGEAEQ,447,FALSE,1154,1154,0,0.8425936955,median,Silverstein,A systematic genotype-phenotype map for missense variants in the human intellectual disability-associated gene GDI1,2021,10.1101/2021.10.06.463360,2-447,GDI1,Yeast Growth,Growth,GDIA_HUMAN_2023-10-12_b05.a2m,1,447,447,0.5,0.2,5196,0.996,445,398.1,0.8946067416,Low,86,0.193258427,media-1.xlsx,fitness,1,mutant,GDIA_HUMAN_theta0.2_2023-10-12_b05.npy,GDIA_HUMAN.pdb,1-447,1,,OrganismalFitness +GFP_AEQVI_Sarkisyan_2016,GFP_AEQVI_Sarkisyan_2016.csv,GFP_AEQVI,Eukaryote,Aequorea victoria,MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK,238,TRUE,51714,1084,50630,2.5,manual,Sarkisyan,Local fitness landscape of the green fluorescent protein,2016,10.1038/nature17995,3-237,GFP,Fluorescence,FACS,GFP_AEQVI_full_04-29-2022_b08.a2m,1,238,238,0.8,0.2,396,0.975,232,14.9,0.06422413793,low,0,0,GFP_AEQVI_Sarkisyan_2016.csv,mean_medianBrightness_per_aaseq,1,mutant,GFP_AEQVI_theta_0.2.npy,GFP_AEQVI.pdb,1-238,0.1,,Activity +GLPA_HUMAN_Elazar_2016,GLPA_HUMAN_Elazar_2016.csv,GLPA_HUMAN,Human,Homo sapiens,MYGKIIFVLLLSEIVSISASSTTGVAMHTSTSSSVTKSYISSQTNDTHKRDTYAATPRAHEVSEISVRTVYPPEEETGERVQLAHHFSEPEITLIIFGVMAGVIGTILLISYGIRRLIKKSPSDVKPLPSPDTDVPLSSVEIENPETSDQ,150,FALSE,245,245,0,0.9321105779,median,Elazar,Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane,2016,10.7554/eLife.12125,94-108,Glycophorin A membrane domain,Membrane-protein insertion,TOXCAT-Beta-lactamase (TbL) screen,GLPA_HUMAN_2023-10-12_b03.a2m,1,150,150,0.3,0.2,273,1,150,81,0.54,Low,1,0.006666666667,urn_mavedb_00000051-c-1_scores.csv,score,-1,mutant,GLPA_HUMAN_theta0.2_2023-10-12_b03.npy,GLPA_HUMAN.pdb,1-150,1,93,Expression +GRB2_HUMAN_Faure_2021,GRB2_HUMAN_Faure_2021.csv,GRB2_HUMAN,Human,Homo sapiens,MEAIAKYDFKATADDELSFKRGDILKVLNEECDQNWYKAELNGKDGFIPKNYIEMKPHPWFFGKIPRAKAEEMLSKQRHDGAFLIRESESAPGDFSLSVKFGNDVQHFKVLRDGAGKYFLWVVKFNSLNELVDYHRSTSVSRNQQIFLRDIEQVPQQPTYVQALFDFDPQEDGELGFRRGDFIHVMDNSDPNWWKGACHGQTGMFPRNYVTPVNRNV,217,TRUE,63366,1034,62332,-0.7,manual,Faure,Mapping the energetic and allosteric landscapes of protein binding domains,2022,10.1038/s41586-022-04586-4,159-214,GRB2-SH3,Yeast growth,Growth,GRB2_HUMAN_full_11-26-2021_b05.a2m,1,217,217,0.5,0.2,33228,0.816,177,1485.9,8.394915254,medium,42,0.2372881356,GRB2_HUMAN_Faure_2021.csv,fitness,1,mutant,GRB2_HUMAN_theta_0.2.npy,GRB2_HUMAN.pdb,1-217,0.1,,OrganismalFitness +HCP_LAMBD_Tsuboyama_2023_2L6Q,HCP_LAMBD_Tsuboyama_2023_2L6Q.csv,HCP_LAMBD,Virus,Escherichia phage lambda (Bacteriophage lambda),VRQEELAAARAALHDLMTGKRVATVQKDGRRVEFTATSVSDLKKYIAELEVQTGM,55,FALSE,1040,1040,0,-0.350614016,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,Head completion protein,Stability,cDNA display proteolysis,HCP_LAMBD_2023-08-07_b05.a2m,1,55,55,0.5,0.01,2128,0.945,52,606.5,11.66346154,Medium,15,0.2884615385,Tsuboyama2023_Dataset2_Dataset18,ddG_ML_float,1,mut_type,HCP_LAMBD_theta0.01_2023-08-07_b05.npy,HCP_LAMBD.pdb,1-55,1,,Stability +HECD1_HUMAN_Tsuboyama_2023_3DKM,HECD1_HUMAN_Tsuboyama_2023_3DKM.csv,HECD1_HUMAN,Human,Homo sapiens,NLYFQGLKYMVPGARVTRGLDWKWRDQDGSPQGEGTVTGELHNGWIDVTWDAGGSNSYRMGAEGKFDLKLAP,72,TRUE,5586,1244,4342,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-72,E3 ubiquitin-protein ligase HECTD1,Stability,cDNA display proteolysis,HECD1_HUMAN_2023-08-07_b03.a2m,1,72,72,0.3,0.2,18660,0.903,65,1192.3,18.34307692,Medium,24,0.3692307692,Tsuboyama2023_Dataset2_Dataset19,ddG_ML_float,1,mut_type,HECD1_HUMAN_theta0.2_2023-08-07_b03.npy,HECD1_HUMAN.pdb,1-72,1,,Stability +HEM3_HUMAN_Loggerenberg_2023,HEM3_HUMAN_Loggerenberg_2023.csv,HEM3_HUMAN,Human,Homo sapiens,MSGNGNAAATAEENSPKMRVIRVGTRKSQLARIQTDSVVATLKASYPGLQFEIIAMSTTGDKILDTALSKIGEKSLFTKELEHALEKNEVDLVVHSLKDLPTVLPPGFTIGAICKRENPHDAVVFHPKFVGKTLETLPEKSVVGTSSLRRAAQLQRKFPHLEFRSIRGNLNTRLRKLDEQQEFSAIILATAGLQRMGWHNRVGQILHPEECMYAVGQGALGVEVRAKDQDILDLVGVLHDPETLLRCIAERAFLRHLEGGCSVPVAVHTAMKDGQLYLTGGVWSLDGSDSIQETMQATIHVPAQHEDGPEDDPQLVGITARNIPRGPQLAAQNLGISLANLLLSKGAKNILDVARQLNDAH,361,FALSE,5689,5689,0,0.6142990455,median,van Loggerenberg,Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation,2023,10.1101/2023.02.06.527353,19-360,hydroxymethylbilane synthase,activity,Yeast complementation,HEM3_HUMAN_2023-08-07_b02.a2m,1,361,361,0.2,0.2,59544,0.85,307,11510.2,37.49250814,Medium,500,1.628664495,,score,1,mutant,HEM3_HUMAN_theta0.2_2023-08-07_b02.npy,HEM3_HUMAN.pdb,1-361,1,,Activity +HIS7_YEAST_Pokusaeva_2019,HIS7_YEAST_Pokusaeva_2019.csv,HIS7_YEAST,Eukaryote,Saccharomyces cerevisiae,MTEQKALVKRITNETKIQIAISLKGGPLAIEHSIFPEKEAEAVAEQATQSQVINVHTGIGFLDHMIHALAKHSGWSLIVECIGDLHIDDHHTTEDCGIALGQAFKEALGAVRGVKRFGSGFAPLDEALSRAVVDLSNRPYAVVELGLQREKVGDLSCEMIPHFLESFAEASRITLHVDCLRGKNDHHRSESAFKALAVAIREATSPNGTNDVPSTKGVLM,220,TRUE,496137,168,495969,0.3,manual,Pokusaeva,An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape,2019,10.1371/journal.pgen.1008079,6-211,IGP dehydratase (HIS3),Growth,Growth,HIS7_YEAST_full_11-26-2021_b09.a2m,1,220,220,0.9,0.2,40154,0.873,192,5191.3,27.03802083,medium,318,1.65625,HIS7_YEAST_Pokusaeva_2019.csv,selection,1,mutant,HIS7_YEAST_theta_0.2.npy,HIS7_YEAST.pdb,1-220,0.1,,OrganismalFitness +HMDH_HUMAN_Jiang_2019,HMDH_HUMAN_Jiang_2019.csv,HMDH_HUMAN,Human,Homo sapiens,MLSRLFRMHGLFVASHPWEVIVGTVTLTICMMSMNMFTGNNKICGWNYECPKFEEDVLSSDIIILTITRCIAILYIYFQFQNLRQLGSKYILGIAGLFTIFSSFVFSTVVIHFLDKELTGLNEALPFFLLLIDLSRASTLAKFALSSNSQDEVRENIARGMAILGPTFTLDALVECLVIGVGTMSGVRQLEIMCCFGCMSVLANYFVFMTFFPACVSLVLELSRESREGRPIWQLSHFARVLEEEENKPNPVTQRVKMIMSLGLVLVHAHSRWIADPSPQNSTADTSKVSLGLDENVSKRIEPSVSLWQFYLSKMISMDIEQVITLSLALLLAVKYIFFEQTETESTLSLKNPITSPVVTQKKVPDNCCRREPMLVRNNQKCDSVEEETGINRERKVEVIKPLVAETDTPNRATFVVGNSSLLDTSSVLVTQEPEIELPREPRPNEECLQILGNAEKGAKFLSDAEIIQLVNAKHIPAYKLETLMETHERGVSIRRQLLSKKLSEPSSLQYLPYRDYNYSLVMGACCENVIGYMPIPVGVAGPLCLDEKEFQVPMATTEGCLVASTNRGCRAIGLGGGASSRVLADGMTRGPVVRLPRACDSAEVKAWLETSEGFAVIKEAFDSTSRFARLQKLHTSIAGRNLYIRFQSRSGDAMGMNMISKGTEKALSKLHEYFPEMQILAVSGNYCTDKKPAAINWIEGRGKSVVCEAVIPAKVVREVLKTTTEAMIEVNINKNLVGSAMAGSIGGYNAHAANIVTAIYIACGQDAAQNVGSSNCITLMEASGPTNEDLYISCTMPSIEIGTVGGGTNLLPQQACLQMLGVQGACKDNPGENARQLARIVCGTVMAGELSLMAALAAGHLVKSHMIHNRSKINLQDLQGACTKKTA,888,FALSE,16853,16853,0,0.48275,median,Jiang,Exhaustive mapping of missense variation in coronary heart disease-related genes,2019,https://hdl.handle.net/1807/98076,2-888,3-hydroxy-3-methylglutaryl-coenzyme A reductase,Fitness,Resistance to statin inhibition,HMDH_HUMAN_2023-10-12_b05.a2m,1,888,888,0.5,0.2,3153,0.995,884,554.6,0.6273755656,Low,778,0.8800904977,urn_mavedb_00000035-a-1_scores.csv,score,1,mutant,HMDH_HUMAN_theta0.2_2023-10-12_b05.npy,HMDH_HUMAN.pdb,1-888,1,,OrganismalFitness +HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2,HSP82_YEAST_Cote-Hammarlof_2020_growth-H2O2.csv,HSP82_YEAST,Eukaryote,Saccharomyces cerevisiae,MASETFEFQAEITQLMSLIINTVYSNKEIFLRELISNASDALDKIRYKSLSDPKQLETEPDLFIRITPKPEQKVLEIRDSGIGMTKAELINNLGTIAKSGTKAFMEALSAGADVSMIGQFGVGFYSLFLVADRVQVISKSNDDEQYIWESNAGGSFTVTLDEVNERIGRGTILRLFLKDDQLEYLEEKRIKEVIKRHSEFVAYPIQLVVTKEVEKEVPIPEEEKKDEEKKDEEKKDEDDKKPKLEEVDEEEEKKPKTKKVKEEVQEIEELNKTKPLWTRNPSDITQEEYNAFYKSISNDWEDPLYVKHFSVEGQLEFRAILFIPKRAPFDLFESKKKKNNIKLYVRRVFITDEAEDLIPEWLSFVKGVVDSEDLPLNLSREMLQQNKIMKVIRKNIVKKLIEAFNEIAEDSEQFEKFYSAFSKNIKLGVHEDTQNRAALAKLLRYNSTKSVDELTSLTDYVTRMPEHQKNIYYITGESLKAVEKSPFLDALKAKNFEVLFLTDPIDEYAFTQLKEFEGKTLVDITKDFELEETDEEKAEREKEIKEYEPLTKALKEILGDQVEKVVVSYKLLDAPAAIRTGQFGWSANMERIMKAQALRDSSMSSYMSSKKTFEISPKSPIIKELKKRVDEGGAQDKTVKDLTKLLYETALLTSGFSLDEPTSFASRINRLISLGLNIDEDEETETAPEASTAAPVEEVPADTEMEEVD,709,FALSE,2252,2252,0,-0.0020874765,median,Cote-Hammarlof,The Adaptive Potential of the Middle Domain of Yeast Hsp90,2020,10.1093/molbev/msaa211,291-409,HSP82,Growth (H2O2 stress),,HSP82_YEAST_2023-08-07_b01.a2m,1,709,709,0.1,0.2,48695,0.917,650,4395.2,6.761846154,Medium,531,0.8169230769,,score,1,mut_proteingym,HSP82_YEAST_theta0.2_2023-08-07_b01.npy,HSP82_YEAST.pdb,1-709,1,290,OrganismalFitness +HSP82_YEAST_Flynn_2019,HSP82_YEAST_Flynn_2019.csv,HSP82_YEAST,Eukaryote,Saccharomyces cerevisiae,MASETFEFQAEITQLMSLIINTVYSNKEIFLRELISNASDALDKIRYKSLSDPKQLETEPDLFIRITPKPEQKVLEIRDSGIGMTKAELINNLGTIAKSGTKAFMEALSAGADVSMIGQFGVGFYSLFLVADRVQVISKSNDDEQYIWESNAGGSFTVTLDEVNERIGRGTILRLFLKDDQLEYLEEKRIKEVIKRHSEFVAYPIQLVVTKEVEKEVPIPEEEKKDEEKKDEEKKDEDDKKPKLEEVDEEEEKKPKTKKVKEEVQEIEELNKTKPLWTRNPSDITQEEYNAFYKSISNDWEDPLYVKHFSVEGQLEFRAILFIPKRAPFDLFESKKKKNNIKLYVRRVFITDEAEDLIPEWLSFVKGVVDSEDLPLNLSREMLQQNKIMKVIRKNIVKKLIEAFNEIAEDSEQFEKFYSAFSKNIKLGVHEDTQNRAALAKLLRYNSTKSVDELTSLTDYVTRMPEHQKNIYYITGESLKAVEKSPFLDALKAKNFEVLFLTDPIDEYAFTQLKEFEGKTLVDITKDFELEETDEEKAEREKEIKEYEPLTKALKEILGDQVEKVVVSYKLLDAPAAIRTGQFGWSANMERIMKAQALRDSSMSSYMSSKKTFEISPKSPIIKELKKRVDEGGAQDKTVKDLTKLLYETALLTSGFSLDEPTSFASRINRLISLGLNIDEDEETETAPEASTAAPVEEVPADTEMEEVD,709,FALSE,13294,13294,0,-0.3,manual,Flynn,Comprehensive fitness maps of Hsp90 show widespread environmental dependence,2019,10.7554/eLife.53810,2-709,HSP82,"growth, nitrogen depletion (0.0125% ammonium sulfate), hyperosmotic shock (0.8 M NaCl), alcohol stress (7.5% ethanol), sulfhydryl-oxidation (0.85 mM diamide), temperature shock (37C)",,HSP82_YEAST_full_11-26-2021_b01.a2m,1,709,709,0.1,0.2,38923,0.862,611,3684.8,6.030769231,medium,433,0.7086743044,HSP82_YEAST_Flynn_2019.csv,s (37°C),1,mutant,HSP82_YEAST_theta_0.2.npy,HSP82_YEAST.pdb,1-709,1,,OrganismalFitness +HSP82_YEAST_Mishra_2016,HSP82_YEAST_Mishra_2016.csv,HSP82_YEAST,Eukaryote,Saccharomyces cerevisiae,MASETFEFQAEITQLMSLIINTVYSNKEIFLRELISNASDALDKIRYKSLSDPKQLETEPDLFIRITPKPEQKVLEIRDSGIGMTKAELINNLGTIAKSGTKAFMEALSAGADVSMIGQFGVGFYSLFLVADRVQVISKSNDDEQYIWESNAGGSFTVTLDEVNERIGRGTILRLFLKDDQLEYLEEKRIKEVIKRHSEFVAYPIQLVVTKEVEKEVPIPEEEKKDEEKKDEEKKDEDDKKPKLEEVDEEEEKKPKTKKVKEEVQEIEELNKTKPLWTRNPSDITQEEYNAFYKSISNDWEDPLYVKHFSVEGQLEFRAILFIPKRAPFDLFESKKKKNNIKLYVRRVFITDEAEDLIPEWLSFVKGVVDSEDLPLNLSREMLQQNKIMKVIRKNIVKKLIEAFNEIAEDSEQFEKFYSAFSKNIKLGVHEDTQNRAALAKLLRYNSTKSVDELTSLTDYVTRMPEHQKNIYYITGESLKAVEKSPFLDALKAKNFEVLFLTDPIDEYAFTQLKEFEGKTLVDITKDFELEETDEEKAEREKEIKEYEPLTKALKEILGDQVEKVVVSYKLLDAPAAIRTGQFGWSANMERIMKAQALRDSSMSSYMSSKKTFEISPKSPIIKELKKRVDEGGAQDKTVKDLTKLLYETALLTSGFSLDEPTSFASRINRLISLGLNIDEDEETETAPEASTAAPVEEVPADTEMEEVD,709,FALSE,4323,4323,0,-0.4,manual,Mishra,Systematic Mutant Analyses Elucidate General and Client-Specific Aspects of Hsp90 Function,2016,10.1016/j.celrep.2016.03.046,2-231,HSP82,Growth,Growth,HSP82_YEAST_full_11-26-2021_b01.a2m,1,709,709,0.1,0.2,38923,0.862,611,3684.8,6.030769231,medium,433,0.7086743044,HSP82_YEAST_Mishra_2016.csv,selection_coefficient,1,mutant,HSP82_YEAST_theta_0.2.npy,HSP82_YEAST.pdb,1-709,0.1,,OrganismalFitness +HXK4_HUMAN_Gersing_2022_activity,HXK4_HUMAN_Gersing_2022_activity.csv,HXK4_HUMAN,Human,Homo sapiens,MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ,465,FALSE,8570,8570,0,0.5631652235,median,Gersing,A comprehensive map of human glucokinase variant activity,2022,10.1101/2022.05.04.490571,2-465,glucokinase regulatory protein,functional complementation to reduced growth on glucose medium,enzymatic activity,HXK4_HUMAN_b0.1.a2m,1,465,465,0.1,0.2,23354,1,465,2336.1,5.023870968,Medium,181,0.3892473118,HXK4_HUMAN_Gersing_2022.csv,score,1,mutant,HXK4_HUMAN_theta_0.2.npy,HXK4_HUMAN.pdb,1-465,1,,OrganismalFitness +HXK4_HUMAN_Gersing_2023_abundance,HXK4_HUMAN_Gersing_2023_abundance.csv,HXK4_HUMAN,Human,Homo sapiens,MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ,465,FALSE,8396,8396,0,0.6,manual,Gersing,Characterizing glucokinase variant mechanisms using a multiplexed abundance assay,2023,10.1101/2023.05.24.542036,2-465,GCK,abundance,Growth,HXK4_HUMAN_2023-08-07_b01.a2m,1,465,465,0.1,0.2,24177,0.966,449,2626.4,5.849443207,Medium,170,0.3786191537,HXK4_HUMAN_Gersing_2022.csv,score,1,mutant,HXK4_HUMAN_theta0.2_2023-08-07_b01.npy,HXK4_HUMAN.pdb,1-465,1,,Expression +I6TAH8_I68A0_Doud_2015,I6TAH8_I68A0_Doud_2015.csv,I6TAH8_I68A0,Virus,Influenza A virus (strain A/Aichi/2/1968 H3N2),MASQGTKRSYEQMETDGERQNATEIRASVGKMIDGIGRFYIQMCTELKLSDYEGRLIQNSLTIERMVLSAFDERRNKYLEEHPSAGKDPKKTGGPIYKRVDRKWMRELVLYDKEEIRRIWRQANNGDDATAGLTHMMIWHSNLNDTTYQRTRALVRTGMDPRMCSLMQGSTLPRRSGAAGAAVKGVGTMVMELIRMIKRGINDRNFWRGENGRKTRSAYERMCNILKGKFQTAAQRAMMDQVRESRNPGNAEIEDLIFLARSALILRGSVAHKSCLPACVYGPAVASGYDFEKEGYSLVGIDPFKLLQNSQVYSLIRPNENPAHKSQLVWMACNSAAFEDLRVLSFIRGTKVSPRGKLSTRGVQIASNENMDAMESSTLELRSRYWAIRTRSGGNTNQQRASAGQISVQPAFSVQRNLPFDKPTIMAAFTGNTEGRTSDMRAEIIRMMEGAKPEEMSFQGRGVFELSDERAANPIVPSFDMSNEGSYFFGDNAEEYDN,498,FALSE,9462,9462,0,-2.329469119,median,Doud,Site-Specific Amino Acid Preferences Are Mostly Conserved in Two Closely Related Protein Homologs,2015,10.1093/molbev/msv167,1-498,Influenza nucleoprotein,,Growth,I6TAH8_I68A0_theta0.99_full_11-26-2021_b09.a2m,1,498,498,0.9,0.01,15390,1,498,1493.3,2.998594378,medium,2118,4.253012048,I6TAH8_I68A0_Doud_2015.csv,log_fitness_by_syn_mut_fitness,1,mutant,I6TAH8_I68A0_theta_0.01.npy,I6TAH8_I68A0.pdb,1-498,0.1,,OrganismalFitness +IF1_ECOLI_Kelsic_2016,IF1_ECOLI_Kelsic_2016.csv,IF1_ECOLI,Prokaryote,Escherichia coli,MAKEDNIEMQGTVLETLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR,72,FALSE,1367,1367,0,0.8,manual,Kelsic,RNA Structural Determinants of Optimal Codons Revealed by MAGE-Seq,2016,10.1016/j.cels.2016.11.004,1-72,infA,Growth,Growth,IF1_ECOLI_full_11-26-2021_b02.a2m,1,72,72,0.2,0.2,361806,0.806,58,38189,658.4310345,high,46,0.7931034483,IF1_ECOLI_Kelsic_2016.csv,fitness_rich,1,mutant,IF1_ECOLI_theta_0.2.npy,IF1_ECOLI.pdb,1-72,0.1,,OrganismalFitness +ILF3_HUMAN_Tsuboyama_2023_2L33,ILF3_HUMAN_Tsuboyama_2023_2L33.csv,ILF3_HUMAN,Human,Homo sapiens,MLTKHGKNPVMELNEKRRGLKYELISETGGSHDKRFVMEVEVDGQKFQGAGSNKKVAKAYAALAALEKLFP,71,FALSE,1329,1329,0,-0.4,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-71,Interleukin enhancer-binding factor 3,Stability,cDNA display proteolysis,ILF3_HUMAN_2023-08-07_b03.a2m,1,71,71,0.3,0.2,145438,0.915,65,21228,326.5846154,High,57,0.8769230769,Tsuboyama2023_Dataset2_Dataset20,ddG_ML_float,1,mut_type,ILF3_HUMAN_theta0.2_2023-08-07_b03.npy,ILF3_HUMAN.pdb,1-71,1,,Stability +ISDH_STAAW_Tsuboyama_2023_2LHR,ISDH_STAAW_Tsuboyama_2023_2LHR.csv,ISDH_STAAW,Prokaryote,Staphylococcus aureus,YNLQKLLAPYHKAKTLERQVYELEKLQEKLPEKYKAEYKKKLDQTRVELADQVKS,55,TRUE,1944,940,1004,-0.7942702247,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,Iron-regulated surface determinant protein H,Stability,cDNA display proteolysis,ISDH_STAAW_2023-08-07_b01.a2m,1,55,55,0.1,0.2,115488,0.818,45,38123.1,847.18,High,6,0.1333333333,Tsuboyama2023_Dataset2_Dataset21,ddG_ML_float,1,mut_type,ISDH_STAAW_theta0.2_2023-08-07_b01.npy,ISDH_STAAW.pdb,1-55,1,,Stability +KCNE1_HUMAN_Muhammad_2023_expression,KCNE1_HUMAN_Muhammad_2023_expression.csv,KCNE1_HUMAN,Human,Homo sapiens,MILSNTTAVTPFLTKLWQETVQQGGNMSGLARRSPRSGDGKLEALYVLMVLGFFGFFTLGIMLSYIRSKKLEHSNDPFNVYIESDAWQEKDKAYVQARVLESYRSCYVVENHLAIEQPNTHLPETKPSP,129,FALSE,2339,2339,0,0.75,manual,Muhammad,"High-throughput functional mapping of variants in an arrhythmia gene, KCNE1, reveals novel biology",2023,10.1101/2023.04.28.538612,1-128,KCNE1,cell surface expression,FACS,KCNE1_HUMAN_2023-08-07_b02.a2m,1,129,129,0.2,0.2,2118,0.969,125,213.7,1.7096,Medium,5,0.04,KCNE1_HUMAN_Muhammad_2023.csv,TrafScore,1,mutant,KCNE1_HUMAN_theta0.2_2023-08-07_b02.npy,KCNE1_HUMAN.pdb,1-129,1,,Expression +KCNE1_HUMAN_Muhammad_2023_function,KCNE1_HUMAN_Muhammad_2023_function.csv,KCNE1_HUMAN,Human,Homo sapiens,MILSNTTAVTPFLTKLWQETVQQGGNMSGLARRSPRSGDGKLEALYVLMVLGFFGFFTLGIMLSYIRSKKLEHSNDPFNVYIESDAWQEKDKAYVQARVLESYRSCYVVENHLAIEQPNTHLPETKPSP,129,FALSE,2315,2315,0,0.9043514345,median,Muhammad,"High-throughput functional mapping of variants in an arrhythmia gene, KCNE1, reveals novel biology",2023,10.1101/2023.04.28.538612,1-128,KCNE1,potassium channel function,Growth,KCNE1_HUMAN_2023-08-07_b02.a2m,1,129,129,0.2,0.2,2118,0.969,125,213.7,1.7096,Medium,5,0.04,KCNE1_HUMAN_Muhammad_2023.csv,funcScore,1,mutant,KCNE1_HUMAN_theta0.2_2023-08-07_b02.npy,KCNE1_HUMAN.pdb,1-129,1,,Activity +KCNH2_HUMAN_Kozek_2020,KCNH2_HUMAN_Kozek_2020.csv,KCNH2_HUMAN,Human,Homo sapiens,MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS,1159,FALSE,200,200,0,58.87492867,median,Kozek,High-throughput discovery of trafficking-deficient variants in the cardiac potassium channel KCNH2: Deep mutational scan of KCNH2 trafficking,2020,10.1016/j.hrthm.2020.05.041,545-555,KCNH2,Voltage,Voltage,KCNH2_HUMAN_535-565_11-26-2021_b05.a2m,535,565,31,0.5,0.2,13907,1,31,186.6,6.019354839,medium,1,0.03225806452,KCNH2_HUMAN_Kozek_2020.csv,score.ave,1,var,KCNH2_HUMAN_theta_0.2.npy,KCNH2_HUMAN.pdb,1-1159,0.1,,Activity +KCNJ2_MOUSE_Coyote-Maestas_2022_function,KCNJ2_MOUSE_Coyote-Maestas_2022_function.csv,KCNJ2_MOUSE,Eukaryote,Mus musculus,MGSVRTNRYSIVSSEEDGMKLATMAVANGFGNGKSKVHTRQQCRSRFVKKDGHCNVQFINVGEKGQRYLADIFTTCVDIRWRWMLVIFCLAFVLSWLFFGCVFWLIALLHGDLDTSKVSKACVSEVNSFTAAFLFSIETQTTIGYGFRCVTDECPIAVFMVVFQSIVGCIIDAFIIGAVMAKMAKPKKRNETLVFSHNAVIAMRDGKLCLMWRVGNLRKSHLVEAHVRAQLLKSRITSEGEYIPLDQIDINVGFDSGIDRIFLVSPITIVHEIDEDSPLYDLSKQDIDNADFEIVVILEGMVEATAMTTQCRSSYLANEILWGHRYEPVLFEEKHYYKVDYSRFHKTYEVPNTPLCSARDLAEKKYILSNANSFCYENEVALTSKEEEEDSENGVPESTSTDSPPGIDLHNQASVPLEPRPLRRESEI,428,FALSE,6963,6963,0,0.039,median,Coyote-Maestas,"Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning",2022,10.7554/eLife.76903,2-392,Kir2.1,Ion conduction,FACS,KCNJ2_MOUSE_b01.a2m,1,428,428,0.1,0.2,20953,0.86,370,986.7,2.666756757,Medium,94,0.2540540541,,function_score,1,mutant_noflag,KCNJ2_MOUSE_b01_theta_0.2.npy,KCNJ2_MOUSE.pdb,1-428,1,,Activity +KCNJ2_MOUSE_Coyote-Maestas_2022_surface,KCNJ2_MOUSE_Coyote-Maestas_2022_surface.csv,KCNJ2_MOUSE,Eukaryote,Mus musculus,MGSVRTNRYSIVSSEEDGMKLATMAVANGFGNGKSKVHTRQQCRSRFVKKDGHCNVQFINVGEKGQRYLADIFTTCVDIRWRWMLVIFCLAFVLSWLFFGCVFWLIALLHGDLDTSKVSKACVSEVNSFTAAFLFSIETQTTIGYGFRCVTDECPIAVFMVVFQSIVGCIIDAFIIGAVMAKMAKPKKRNETLVFSHNAVIAMRDGKLCLMWRVGNLRKSHLVEAHVRAQLLKSRITSEGEYIPLDQIDINVGFDSGIDRIFLVSPITIVHEIDEDSPLYDLSKQDIDNADFEIVVILEGMVEATAMTTQCRSSYLANEILWGHRYEPVLFEEKHYYKVDYSRFHKTYEVPNTPLCSARDLAEKKYILSNANSFCYENEVALTSKEEEEDSENGVPESTSTDSPPGIDLHNQASVPLEPRPLRRESEI,428,FALSE,6917,6917,0,-0.157352583,median,Coyote-Maestas,"Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning",2022,10.7554/eLife.76903,2-392,Kir2.1,Surface trafficking,FACS,KCNJ2_MOUSE_b01.a2m,1,428,428,0.1,0.2,20953,0.86,370,986.7,2.666756757,Medium,94,0.2540540541,,surface_score,1,mutant_noflag,KCNJ2_MOUSE_b01_theta_0.2.npy,KCNJ2_MOUSE.pdb,1-428,1,,Expression +KKA2_KLEPN_Melnikov_2014,KKA2_KLEPN_Melnikov_2014.csv,KKA2_KLEPN,Prokaryote,Klebsiella pneumoniae,MIEQDGLHAGSPAAWVERLFGYDWAQQTIGCSDAAVFRLSAQGRPVLFVKTDLSGALNELQDEAARLSWLATTGVPCAAVLDVVTEAGRDWLLLGEVPGQDLLSSHLAPAEKVSIMADAMRRLHTLDPATCPFDHQAKHRIERARTRMEAGLVDQDDLDEEHQGLAPAELFARLKARMPDGEDLVVTHGDACLPNIMVENGRFSGFIDCGRLGVADRYQDIALATRDIAEELGGEWADRFLVLYGIAAPDSQRIAFYRLLDEFF,264,FALSE,4960,4960,0,0.5,manual,Melnikov,Comprehensive mutational scanning of a kinasein vivoreveals substrate-dependent fitness landscapes,2014,10.1093/nar/gku511,1-264,"APH(3’)II, neo","Growth (225 ug/mL kanamycin) 1:1, 1:2, 1:4, 1:8 dilutions",Growth,KKA2_KLEPN_full_11-26-2021_b02.a2m,1,264,264,0.2,0.2,234760,0.795,210,76876.7,366.0795238,high,377,1.795238095,KKA2_KLEPN_Melnikov_2014.csv,Kan18_avg,1,mutant,KKA2_KLEPN_theta_0.2.npy,KKA2_KLEPN.pdb,1-264,0.1,,OrganismalFitness +LGK_LIPST_Klesmith_2015,LGK_LIPST_Klesmith_2015.csv,LGK_LIPST,Eukaryote,Lipomyces starkeyi,MPIATSTGDNVLDFTVLGLNSGTSMDGIDCALCHFYQKTPDAPMEFELLEYGEVPLAQPIKQRVMRMILEDTTSPSELSEVNVILGEHFADAVRQFAAERNVDLSTIDAIASHGQTIWLLSMPEEGQVKSALTMAEGAIIAARTGITSITDFRISDQAAGRQGAPLIAFFDALLLHHPTKLRACQNIGGIANVCFIPPDVDGRRTDEYYDFDTGPGNVFIDAVVRHFTNGEQEYDKDGAMGKRGKVDQELVDDFLKMPYFQLDPPKTTGREVFRDTLAHDLIRRAEAKGLSPDDIVATTTRITAQAIVDHYRRYAPSQEIDEIFMCGGGAYNPNIVEFIQQSYPNTKIMMLDEAGVPAGAKEAITFAWQGMECLVGRSIPVPTRVETRQHYVLGKVSPGLNYRSVMKKGMAFGGDAQQLPWVSEMIVKKKGKVITNNWA,439,FALSE,7890,7890,0,-0.6245,median,Klesmith,Comprehensive Sequence-Flux Mapping of a Levoglucosan Utilization Pathway in E. coli,2015,10.1021/acssynbio.5b00131,1-439,LGK (levoglucosan kinase),Growth,Growth,LGK_LIPST_full_11-26-2021_b03.a2m,1,439,439,0.3,0.2,31069,0.813,357,7971,22.32773109,medium,588,1.647058824,B3VI55_LIPST_Klesmith_2015.csv,SelectionTwo,1,mutant,LGK_LIPST_theta_0.2.npy,LGK_LIPST.pdb,1-439,0.1,,Activity +LYAM1_HUMAN_Elazar_2016,LYAM1_HUMAN_Elazar_2016.csv,LYAM1_HUMAN,Human,Homo sapiens,MIFPWKCQSTQRDLWNIFKLWGWTMLCCDFLAHHGTDCWTYHYSEKPMNWQRARRFCRDNYTDLVAIQNKAEIEYLEKTLPFSRSYYWIGIRKIGGIWTWVGTNKSLTEEAENWGDGEPNNKKNKEDCVEIYIKRNKDAGKWNDDACHKLKAALCYTASCQPWSCSGHGECVEIINNYTCNCDVGYYGPQCQFVIQCEPLEAPELGTMDCTHPLGNFSFSSQCAFSCSEGTNLTGIEETTCGPFGNWSSPEPTCQVIQCEPLSAPDLGIMNCSHPLASFSFTSACTFICSEGTELIGKKKTICESSGIWSNPSPICQKLDKSFSMIKEGDYNPLFIPVAVMVTAFSGLAFIIWLARRLKKGKKSKRSMNDPY,372,FALSE,359,359,0,1.306138768,median,Elazar,Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane,2016,10.7554/eLife.12125,333-355,L-selectin,Membrane-protein insertion,TOXCAT-Beta-lactamase (TbL) screen,LYAM1_HUMAN_2023-10-12_b04.a2m,1,372,372,0.4,0.2,3974,0.825,307,412.3,1.342996743,Medium,41,0.1335504886,urn_mavedb_00000051-a-1_scores.csv,score,-1,mutant,LYAM1_HUMAN_theta0.2_2023-10-12_b04.npy,LYAM1_HUMAN.pdb,1-372,1,332,Expression +MAFG_MOUSE_Tsuboyama_2023_1K1V,MAFG_MOUSE_Tsuboyama_2023_1K1V.csv,MAFG_MOUSE,Eukaryote,Mus musculus,LTDEELVTMSVRELNQHLRGLSKEEIIQLKQRRRTLKNRGY,41,TRUE,1429,762,667,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-41,Transcription factor MafG,Stability,cDNA display proteolysis,MAFG_MOUSE_2023-08-07_b07.a2m,1,41,41,0.7,0.2,6178,1,41,156.7,3.82195122,Medium,4,0.09756097561,Tsuboyama2023_Dataset2_Dataset22,ddG_ML_float,1,mut_type,MAFG_MOUSE_theta0.2_2023-08-07_b07.npy,MAFG_MOUSE.pdb,1-41,1,,Stability +MBD11_ARATH_Tsuboyama_2023_6ACV,MBD11_ARATH_Tsuboyama_2023_6ACV.csv,MBD11_ARATH,Eukaryote,Arabidopsis thaliana,VSVELPAPSSWKKLFYPNKVGSVKKTEVVFVAPTGEEISNRKQLEQYLKSHPGNPAIAEFDWTTSG,66,TRUE,2116,1155,961,-1.578921171,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-66,Methyl-CpG-binding domain-containing protein 11,Stability,cDNA display proteolysis,MBD11_ARATH_2023-08-07_b03.a2m,1,66,66,0.3,0.2,26035,0.909,60,1510.5,25.175,Medium,11,0.1833333333,Tsuboyama2023_Dataset2_Dataset23,ddG_ML_float,1,mut_type,MBD11_ARATH_theta0.2_2023-08-07_b03.npy,MBD11_ARATH.pdb,1-66,1,,Stability +MET_HUMAN_Estevam_2023,MET_HUMAN_Estevam_2023.csv,MET_HUMAN,Human,Homo sapiens,NPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFI,287,FALSE,5393,5393,0,-2,manual,Estevam,Conserved regulatory motifs in the juxtamembrane domain and kinase N-lobe revealed through deep mutational scanning of the MET receptor tyrosine kinase domain,2023,10.1101/2023.08.03.551866,1-287,MET RTK,Human cell line with growth linked to kinase activity in the absense of IL-3,Growth,MET_HUMAN_2023-08-07_b09.a2m,1,287,287,0.9,0.2,185885,0.951,273,5338.5,19.5549451,Medium,200,0.73,ex14_scores.csv,IL3_withdrawal_score,1,mutant,MET_HUMAN_theta0.2_2023-08-07_b09.npy,MET_HUMAN.pdb,1-287,1,,Activity +MK01_HUMAN_Brenan_2016,MK01_HUMAN_Brenan_2016.csv,MK01_HUMAN,Human,Homo sapiens,MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS,360,FALSE,6809,6809,0,-8.040790936,median,Brenan,Phenotypic Characterization of a Comprehensive Set of MAPK1 /ERK2 Missense Mutants,2016,10.1016/j.celrep.2016.09.061,2-360,MAPK1,Growth,inhibitor resistance,MK01_HUMAN_full_11-26-2021_b06.a2m,1,360,360,0.6,0.2,124248,0.806,290,8815.9,30.39965517,medium,287,0.9896551724,MK01_HUMAN_Brenan_2016.csv,DOX_Average,-1,mutant,MK01_HUMAN_theta_0.2.npy,MK01_HUMAN.pdb,1-360,0.1,,OrganismalFitness +MLAC_ECOLI_MacRae_2023,MLAC_ECOLI_MacRae_2023.csv,MLAC_ECOLI,Prokaryote,Escherichia coli,MFKRLMMVALLVIAPLSAATAADQTNPYKLMDEAAQKTFDRLKNEQPQIRANPDYLRTIVDQELLPYVQVKYAGALVLGQYYKSATPAQREAYFAAFREYLKQAYGQALAMYHGQTYQIAPEQPLGDKTIVPIRVTIIDPNGRPPVRLDFQWRKNSQTGNWQAYDMIAEGVSMITTKQNEWGTLLRTKGIDGLTAQLKSISQQKITLEEKK,211,FALSE,4007,4007,0,-0.1041157905,median,MacRae,Protein-protein interactions in the Mla lipid transport system probed by computational structure prediction and deep mutational scanning,2023,10.1016/j.jbc.2023.104744,1-211,MlaC lipid transporter,cell growth in ∆mlaC and selective medium,Growth,MLAC_ECOLI_2023-08-07_b02.a2m,1,211,211,0.2,0.2,22874,0.934,197,7904.3,40.12335025,Medium,126,0.6395939086,MLAC_ECOLI_MacRae_2023.csv,score,1,mutant,MLAC_ECOLI_theta0.2_2023-08-07_b02.npy,MLAC_ECOLI.pdb,1-211,1,,OrganismalFitness +MSH2_HUMAN_Jia_2020,MSH2_HUMAN_Jia_2020.csv,MSH2_HUMAN,Human,Homo sapiens,MAVQPKETLQLESAAEVGFVRFFQGMPEKPTTTVRLFDRGDFYTAHGEDALLAAREVFKTQGVIKYMGPAGAKNLQSVVLSKMNFESFVKDLLLVRQYRVEVYKNRAGNKASKENDWYLAYKASPGNLSQFEDILFGNNDMSASIGVVGVKMSAVDGQRQVGVGYVDSIQRKLGLCEFPDNDQFSNLEALLIQIGPKECVLPGGETAGDMGKLRQIIQRGGILITERKKADFSTKDIYQDLNRLLKGKKGEQMNSAVLPEMENQVAVSSLSAVIKFLELLSDDSNFGQFELTTFDFSQYMKLDIAAVRALNLFQGSVEDTTGSQSLAALLNKCKTPQGQRLVNQWIKQPLMDKNRIEERLNLVEAFVEDAELRQTLQEDLLRRFPDLNRLAKKFQRQAANLQDCYRLYQGINQLPNVIQALEKHEGKHQKLLLAVFVTPLTDLRSDFSKFQEMIETTLDMDQVENHEFLVKPSFDPNLSELREIMNDLEKKMQSTLISAARDLGLDPGKQIKLDSSAQFGYYFRVTCKEEKVLRNNKNFSTVDIQKNGVKFTNSKLTSLNEEYTKNKTEYEEAQDAIVKEIVNISSGYVEPMQTLNDVLAQLDAVVSFAHVSNGAPVPYVRPAILEKGQGRIILKASRHACVEVQDEIAFIPNDVYFEKDKQMFHIITGPNMGGKSTYIRQTGVIVLMAQIGCFVPCESAEVSIVDCILARVGAGDSQLKGVSTFMAEMLETASILRSATKDSLIIIDELGRGTSTYDGFGLAWAISEYIATKIGAFCMFATHFHELTALANQIPTVNNLHVTALTTEETLTMLYQVKKGVCDQSFGIHVAELANFPKHVIECAKQKALELEEFQYIGESQGYDIMEPAAKKCYLEREQGEKIIQEFLSKVKQMPFTEMSEENITIKLKQLKAEVIAKNNSFVNEIISRIKVTT,934,FALSE,16749,16749,0,1,manual,Jia,Massively parallel functional testing of MSH2 missense variants conferring Lynch Syndrome risk,2020,10.1016/j.ajhg.2020.12.003,1-934,MSH2,"drug resistance (surrogate for protein activity, 6-thioguanine (6-TG))",,MSH2_HUMAN_full_11-26-2021_b05.a2m,1,934,934,0.5,0.2,61226,0.901,842,10716.4,12.72731591,medium,1035,1.229216152,MSH2_HUMAN_Jia_2020.csv,LOF score,-1,Variant,MSH2_HUMAN_theta_0.2.npy,MSH2_HUMAN.pdb,1-934,0.1,,OrganismalFitness +MTH3_HAEAE_RockahShmuel_2015,MTH3_HAEAE_RockahShmuel_2015.csv,MTH3_HAEAE,Prokaryote,Haemophilus aegyptius,MNLISLFSGAGGLDLGFQKAGFRIIAANEYDKSIWKTYESNHSAKLIKGDISKISSDEFPKCDGIIGGPPCQSWSEGGSLRGIDDPRGKLFYEYIRILKQKKPKFFLAENVKGMLAQRHNKAVQEFIQEFDNAGYDVHIILLNANDYGVAQDRKRVFYIGFRKELNINYLPPIPHLIKPTLKDVIWDLKDNPIPALDKNKTNGNKCIYPNHEYFIGSYSTIFMSRNRVRQWNEPAFTVQASGRQCQLHPQAPVMLKVSKNLNKFVEGKEHLYRRLTVRECARVQGFPDDFIFHYESLNDGYKMIGNAVPVNLAYEIAKTIKSALEIRKGN,330,FALSE,1777,1777,0,0.01,manual,Rockah-Shmuel,Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations,2015,10.1371/journal.pcbi.1004421,2-330,DNA methylase HaeIII,Growth,Activity,MTH3_HAEAE_full_11-26-2021_b02.a2m,1,330,330,0.2,0.2,82734,0.891,294,26962.4,91.70884354,medium,582,1.979591837,MTH3_HAEAE_Rockah-Shmuel_2015.csv,Wrel_G17_filtered,1,mutant,MTH3_HAEAE_theta_0.2.npy,MTH3_HAEAE.pdb,1-330,0.1,,OrganismalFitness +MTHR_HUMAN_Weile_2021,MTHR_HUMAN_Weile_2021.csv,MTHR_HUMAN,Human,Homo sapiens,MVNEARGNSSLNPCLEGSASSGSESSKDSSRCSTPGLDPERHERLREKMRRRLESGDKWFSLEFFPPRTAEGAVNLISRFDRMAAGGPLYIDVTWHPAGDPGSDKETSSMMIASTAVNYCGLETILHMTCCRQRLEEITGHLHKAKQLGLKNIMALRGDPIGDQWEEEEGGFNYAVDLVKHIRSEFGDYFDICVAGYPKGHPEAGSFEADLKHLKEKVSAGADFIITQLFFEADTFFRFVKACTDMGITCPIVPGIFPIQGYHSLRQLVKLSKLEVPQEIKDVIEPIKDNDAAIRNYGIELAVSLCQELLASGLVPGLHFYTLNREMATTEVLKRLGMWTEDPRRPLPWALSAHPKRREEDVRPIFWASRPKSYIYRTQEWDEFPNGRWGNSSSPAFGELKDYYLFYLKSKSPKEELLKMWGEELTSEESVFEVFVLYLSGEPNRNGHKVTCLPWNDEPLAAETSLLKEELLRVNRQGILTINSQPNINGKPSSDPIVGWGPSGGYVFQKAYLEFFTSRETAEALLQVLKKYELRVNYHLVNVKGENITNAPELQPNAVTWGIFPGREIIQPTVVDPVSFMFWKDEAFALWIERWGKLYEEESPSRTIIQYIHDNYFLVNLVDNDFPLDNCLWQVVEDTLELLNRPTQNARETEAP,656,FALSE,12464,12464,0,0.746,median,Weile,Shifting landscapes of human MTHFR missense-variant effects,2021,10.1016/j.ajhg.2021.05.009,1-656,MTHFR reductase,Growth,,MTHR_HUMAN_2023-08-07_b02.a2m,1,656,656,0.2,0.2,4783,0.96,630,614.5,0.9753968254,Low,65,0.1031746032,urn_mavedb_00000049-a-6_scores.csv,score,1,mutant,MTHR_HUMAN_theta0.2_2023-08-07_b02.npy,MTHR_HUMAN.pdb,1-656,1,,OrganismalFitness +MYO3_YEAST_Tsuboyama_2023_2BTT,MYO3_YEAST_Tsuboyama_2023_2BTT.csv,MYO3_YEAST,Eukaryote,Saccharomyces cerevisiae,KDPKFEAAYDFPGSGSSSELPLKKGDIVFISRDEPSGWSLAKLLDGSKEGWVPTAYMTPYK,61,TRUE,3297,947,2350,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-61,Myosin-3,Stability,cDNA display proteolysis,MYO3_YEAST_2023-08-07_b07.a2m,1,61,61,0.7,0.2,442941,0.885,54,12893.2,238.762963,High,51,0.9444444444,Tsuboyama2023_Dataset2_Dataset24,ddG_ML_float,1,mut_type,MYO3_YEAST_theta0.2_2023-08-07_b07.npy,MYO3_YEAST.pdb,1-61,1,,Stability +NCAP_I34A1_Doud_2015,NCAP_I34A1_Doud_2015.csv,NCAP_I34A1,Virus,Influenza A virus (strain A/Puerto Rico/8/1934 H1N1),MASQGTKRSYEQMETDGERQNATEIRASVGKMIGGIGRFYIQMCTELKLSDYEGRLIQNSLTIERMVLSAFDERRNKYLEEHPSAGKDPKKTGGPIYRRVNGKWMRELILYDKEEIRRIWRQANNGDDATAGLTHMMIWHSNLNDATYQRTRALVRTGMDPRMCSLMQGSTLPRRSGAAGAAVKGVGTMVMELVRMIKRGINDRNFWRGENGRKTRIAYERMCNILKGKFQTAAQKAMMDQVRESRNPGNAEFEDLTFLARSALILRGSVAHKSCLPACVYGPAVASGYDFEREGYSLVGIDPFRLLQNSQVYSLIRPNENPAHKSQLVWMACHSAAFEDLRVLSFIKGTKVLPRGKLSTRGVQIASNENMETMESSTLELRSRYWAIRTRSGGNTNQQRASAGQISIQPTFSVQRNLPFDRTTIMAAFNGNTEGRTSDMRTEIIRMMESARPEDVSFQGRGVFELSDEKAASPIVPSFDMSNEGSYFFGDNAEEYDN,498,FALSE,9462,9462,0,-2.872717233,median,Doud,Site-Specific Amino Acid Preferences Are Mostly Conserved in Two Closely Related Protein Homologs,2015,10.1093/molbev/msv167,1-498,Influenza nucleoprotein,,Growth,NCAP_I34A1_theta0.99_full_11-26-2021_b09.a2m,1,498,498,0.9,0.01,15390,1,498,1493.2,2.998393574,medium,2116,4.248995984,NCAP_I34A1_Doud_2015.csv,log_fitness_by_syn_mut_fitness,1,mutant,NCAP_I34A1_theta_0.01.npy,NCAP_I34A1.pdb,1-498,0.1,,OrganismalFitness +NKX31_HUMAN_Tsuboyama_2023_2L9R,NKX31_HUMAN_Tsuboyama_2023_2L9R.csv,NKX31_HUMAN,Human,Homo sapiens,HSHMSHTQVIELERKFSHQKYLSAPERAHLAKNLKLTETQVKIWFQNRRYKTKRKQLSSEL,61,TRUE,2482,1149,1333,-0.3,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-61,Homeobox protein Nkx-3.1,Stability,cDNA display proteolysis,NKX31_HUMAN_2023-08-07_b04.a2m,1,61,61,0.4,0.2,319273,0.902,55,8440.8,153.4690909,High,27,0.4909090909,Tsuboyama2023_Dataset2_Dataset25,ddG_ML_float,1,mut_type,NKX31_HUMAN_theta0.2_2023-08-07_b04.npy,NKX31_HUMAN.pdb,1-61,1,,Stability +NPC1_HUMAN_Erwood_2022_HEK293T,NPC1_HUMAN_Erwood_2022_HEK293T.csv,NPC1_HUMAN,Human,Homo sapiens,MTARGLALGLLLLLLCPAQVFSQSCVWYGECGIAYGDKRYNCEYSGPPKPLPKDGYDLVQELCPGFFFGNVSLCCDVRQLQTLKDNLQLPLQFLSRCPSCFYNLLNLFCELTCSPRQSQFLNVTATEDYVDPVTNQTKTNVKELQYYVGQSFANAMYNACRDVEAPSSNDKALGLLCGKDADACNATNWIEYMFNKDNGQAPFTITPVFSDFPVHGMEPMNNATKGCDESVDEVTAPCSCQDCSIVCGPKPQPPPPPAPWTILGLDAMYVIMWITYMAFLLVFFGAFFAVWCYRKRYFVSEYTPIDSNIAFSVNASDKGEASCCDPVSAAFEGCLRRLFTRWGSFCVRNPGCVIFFSLVFITACSSGLVFVRVTTNPVDLWSAPSSQARLEKEYFDQHFGPFFRTEQLIIRAPLTDKHIYQPYPSGADVPFGPPLDIQILHQVLDLQIAIENITASYDNETVTLQDICLAPLSPYNTNCTILSVLNYFQNSHSVLDHKKGDDFFVYADYHTHFLYCVRAPASLNDTSLLHDPCLGTFGGPVFPWLVLGGYDDQNYNNATALVITFPVNNYYNDTEKLQRAQAWEKEFINFVKNYKNPNLTISFTAERSIEDELNRESDSDVFTVVISYAIMFLYISLALGHMKSCRRLLVDSKVSLGIAGILIVLSSVACSLGVFSYIGLPLTLIVIEVIPFLVLAVGVDNIFILVQAYQRDERLQGETLDQQLGRVLGEVAPSMFLSSFSETVAFFLGALSVMPAVHTFSLFAGLAVFIDFLLQITCFVSLLGLDIKRQEKNRLDIFCCVRGAEDGTSVQASESCLFRFFKNSYSPLLLKDWMRPIVIAIFVGVLSFSIAVLNKVDIGLDQSLSMPDDSYMVDYFKSISQYLHAGPPVYFVLEEGHDYTSSKGQNMVCGGMGCNNDSLVQQIFNAAQLDNYTRIGFAPSSWIDDYFDWVKPQSSCCRVDNITDQFCNASVVDPACVRCRPLTPEGKQRPQGGDFMRFLPMFLSDNPNPKCGKGGHAAYSSAVNILLGHGTRVGATYFMTYHTVLQTSADFIDALKKARLIASNVTETMGINGSAYRVFPYSVFYVFYEQYLTIIDDTIFNLGVSLGAIFLVTMVLLGCELWSAVIMCATIAMVLVNMFGVMWLWGISLNAVSLVNLVMSCGISVEFCSHITRAFTVSMKGSRVERAEEALAHMGSSVFSGITLTKFGGIVVLAFAKSQIFQIFYFRMYLAMVLLGATHGLIFLPVLLSYIGPSVNKAKSCATEERYKGTERERLLNF,1278,FALSE,637,637,0,0.8,manual,Erwood,Saturation variant interpretation using CRISPR prime editing,2022,10.1038/s41587-021-01201-1,347-1190,NPC intracellular cholesterol transporter,Fluorescence measurement,Flow Cytometry Assay,NPC1_HUMAN_2023-10-12_b07.a2m,1,1278,1278,0.7,0.2,6333,0.987,1261,918.9,0.7287073751,Low,137,0.1086439334,41587_2021_1201_MOESM3_ESM.xlsx,Function Score,1,Protein Annotation,NPC1_HUMAN_theta0.2_2023-10-12_b07.npy,NPC1_HUMAN.pdb,1-1278,1,,Activity +NPC1_HUMAN_Erwood_2022_RPE1,NPC1_HUMAN_Erwood_2022_RPE1.csv,NPC1_HUMAN,Human,Homo sapiens,MTARGLALGLLLLLLCPAQVFSQSCVWYGECGIAYGDKRYNCEYSGPPKPLPKDGYDLVQELCPGFFFGNVSLCCDVRQLQTLKDNLQLPLQFLSRCPSCFYNLLNLFCELTCSPRQSQFLNVTATEDYVDPVTNQTKTNVKELQYYVGQSFANAMYNACRDVEAPSSNDKALGLLCGKDADACNATNWIEYMFNKDNGQAPFTITPVFSDFPVHGMEPMNNATKGCDESVDEVTAPCSCQDCSIVCGPKPQPPPPPAPWTILGLDAMYVIMWITYMAFLLVFFGAFFAVWCYRKRYFVSEYTPIDSNIAFSVNASDKGEASCCDPVSAAFEGCLRRLFTRWGSFCVRNPGCVIFFSLVFITACSSGLVFVRVTTNPVDLWSAPSSQARLEKEYFDQHFGPFFRTEQLIIRAPLTDKHIYQPYPSGADVPFGPPLDIQILHQVLDLQIAIENITASYDNETVTLQDICLAPLSPYNTNCTILSVLNYFQNSHSVLDHKKGDDFFVYADYHTHFLYCVRAPASLNDTSLLHDPCLGTFGGPVFPWLVLGGYDDQNYNNATALVITFPVNNYYNDTEKLQRAQAWEKEFINFVKNYKNPNLTISFTAERSIEDELNRESDSDVFTVVISYAIMFLYISLALGHMKSCRRLLVDSKVSLGIAGILIVLSSVACSLGVFSYIGLPLTLIVIEVIPFLVLAVGVDNIFILVQAYQRDERLQGETLDQQLGRVLGEVAPSMFLSSFSETVAFFLGALSVMPAVHTFSLFAGLAVFIDFLLQITCFVSLLGLDIKRQEKNRLDIFCCVRGAEDGTSVQASESCLFRFFKNSYSPLLLKDWMRPIVIAIFVGVLSFSIAVLNKVDIGLDQSLSMPDDSYMVDYFKSISQYLHAGPPVYFVLEEGHDYTSSKGQNMVCGGMGCNNDSLVQQIFNAAQLDNYTRIGFAPSSWIDDYFDWVKPQSSCCRVDNITDQFCNASVVDPACVRCRPLTPEGKQRPQGGDFMRFLPMFLSDNPNPKCGKGGHAAYSSAVNILLGHGTRVGATYFMTYHTVLQTSADFIDALKKARLIASNVTETMGINGSAYRVFPYSVFYVFYEQYLTIIDDTIFNLGVSLGAIFLVTMVLLGCELWSAVIMCATIAMVLVNMFGVMWLWGISLNAVSLVNLVMSCGISVEFCSHITRAFTVSMKGSRVERAEEALAHMGSSVFSGITLTKFGGIVVLAFAKSQIFQIFYFRMYLAMVLLGATHGLIFLPVLLSYIGPSVNKAKSCATEERYKGTERERLLNF,1278,FALSE,63,63,0,0.8,manual,Erwood,Saturation variant interpretation using CRISPR prime editing,2022,10.1038/s41587-021-01201-1,420-920,NPC intracellular cholesterol transporter,Fluorescence measurement,Flow Cytometry Assay,NPC1_HUMAN_2023-10-12_b07.a2m,1,1278,1278,0.7,0.2,6333,0.987,1261,918.9,0.7287073751,Low,137,0.1086439334,41587_2021_1201_MOESM3_ESM.xlsx,Function Score,1,Protein Annotation,NPC1_HUMAN_theta0.2_2023-10-12_b07.npy,NPC1_HUMAN.pdb,1-1278,1,,Activity +NRAM_I33A0_Jiang_2016,NRAM_I33A0_Jiang_2016.csv,NRAM_I33A0,Virus,Influenza A virus (strain A/Wilson-Smith/1933 H1N1),MNPNQKIITIGSICMVVGIISLILQIGNIISIWISHSIQTGNQNHTGICNQGIITYNVVAGQDSTSVILTGNSSLCPIRGWAIHSKDNGIRIGSKGDVFVIREPFISCSHLECRTFFLTQGALLNDKHSNGTVKDRSPYRALMSCPVGEAPSPYNSRFESVAWSASACHDGMGWLTIGISGPDNGAVAVLKYNGIITETIKSWRKKILRTQESECTCVNGSCFTIMTDGPSNGLASYKIFKIEKGKVTKSIELNAPNSHYEECSCYPDTGKVMCVCRDNWHGSNRPWVSFDQNLDYQIGYICSGVFGDNPRPKDGPGSCGPVSADGANGVKGFSYRYGNGVWIGRTKSDSSRHGFEMIWDPNGWTETDSRFSVRQDVVAMTDRSGYSGSFVQHPELTGLDCMRPCFWVELIRGRPEEETIWTSGSIISFCGVNSDTVDWSWPDGAELPFTIDK,453,FALSE,298,298,0,-0.7772013612,median,Jiang,A Balance between Inhibitor Binding and Substrate Processing Confers Influenza Drug Resistance,2016,10.1016/j.jmb.2015.11.027,67-285,Influenza neuraminidase,,Growth,NRAM_I33A0_full_11-26-2021_b01.a2m,1,453,453,0.1,0.01,47174,0.976,442,33.1,0.07488687783,low,0,0,NRAM_I33A0_Jiang_2016.csv,Standard Conditions,1,mutant,NRAM_I33A0_theta_0.01.npy,NRAM_I33A0.pdb,1-453,0.1,,OrganismalFitness +NUD15_HUMAN_Suiter_2020,NUD15_HUMAN_Suiter_2020.csv,NUD15_HUMAN,Human,Homo sapiens,MTASAQPRGRRPGVGVGVVVTSCKHPRCVLLGKRKGSVGAGSFQLPGGHLEFGETWEECAQRETWEEAALHLKNVHFASVVNSFIEKENYHYVTILMKGEVDVTHDSEPKNVEPEKNESWEWVPWEELPPLDQLFWGLRCLKEQGYDPFKEDLNHLVGYKGNHL,164,FALSE,2844,2844,0,0.25,manual,Suiter,Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity,2020,10.1073/pnas.1915680117,2-164,NUDT15,,"VAMP-seq, drug sensitivity",NUD15_HUMAN_full_11-26-2021_b04.a2m,1,164,164,0.4,0.2,153922,0.72,118,43847.8,371.5915254,high,151,1.279661017,NUD15_HUMAN_Suiter_2020.csv,Final NUDT15 activity Score,1,mutant,NUD15_HUMAN_theta_0.2.npy,NUD15_HUMAN.pdb,1-164,0.1,,Expression +NUSA_ECOLI_Tsuboyama_2023_1WCL,NUSA_ECOLI_Tsuboyama_2023_1WCL.csv,NUSA_ECOLI,Prokaryote,Escherichia coli,EAHAAIDTFTKYLDIDEDFATVLVEEGFSTLEELAYVPMKELLEIEGLDEPTVEALRERAKNALATIAQ,69,TRUE,2028,1306,722,-1.318069467,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-69,Transcription termination/antitermination protein NusA,Stability,cDNA display proteolysis,NUSA_ECOLI_2023-08-07_b03.a2m,1,69,69,0.3,0.2,205612,0.812,56,39002.5,696.4732143,High,38,0.6785714286,Tsuboyama2023_Dataset2_Dataset26,ddG_ML_float,1,mut_type,NUSA_ECOLI_theta0.2_2023-08-07_b03.npy,NUSA_ECOLI.pdb,1-69,1,,Stability +NUSG_MYCTU_Tsuboyama_2023_2MI6,NUSG_MYCTU_Tsuboyama_2023_2MI6.csv,NUSG_MYCTU,Prokaryote,Mycobacterium tuberculosis,DYEVGESVTVMDGPFATLPATISEVNAEQQKLKVLVSIFGRETPVELTFGQVSKI,55,TRUE,1380,1019,361,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,Transcription termination/antitermination protein NusG,Stability,cDNA display proteolysis,NUSG_MYCTU_2023-08-07_b03.a2m,1,55,55,0.3,0.2,102004,0.964,53,16625.7,313.6924528,High,41,0.7735849057,Tsuboyama2023_Dataset2_Dataset27,ddG_ML_float,1,mut_type,NUSG_MYCTU_theta0.2_2023-08-07_b03.npy,NUSG_MYCTU.pdb,1-55,1,,Stability +OBSCN_HUMAN_Tsuboyama_2023_1V1C,OBSCN_HUMAN_Tsuboyama_2023_1V1C.csv,OBSCN_HUMAN,Human,Homo sapiens,FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL,65,TRUE,3197,1213,1984,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-65,Obscurin,Stability,cDNA display proteolysis,OBSCN_HUMAN_2023-08-07_b02.a2m,1,65,65,0.2,0.2,718751,0.815,53,23710.7,447.3716981,High,54,1.018867925,Tsuboyama2023_Dataset2_Dataset28,ddG_ML_float,1,mut_type,OBSCN_HUMAN_theta0.2_2023-08-07_b02.npy,OBSCN_HUMAN.pdb,1-65,1,,Stability +ODP2_GEOSE_Tsuboyama_2023_1W4G,ODP2_GEOSE_Tsuboyama_2023_1W4G.csv,ODP2_GEOSE,Prokaryote,Geobacillus stearothermophilus,NRRVIAMPSVRKWAREKGVDIRLVQGTGKNGRVLKEDIDAFLAG,44,TRUE,1134,669,465,-0.4168227551,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-44,Dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex,Stability,cDNA display proteolysis,ODP2_GEOSE_2023-08-07_b07.a2m,1,44,44,0.7,0.2,163835,0.909,40,14834.6,370.865,High,21,0.525,Tsuboyama2023_Dataset2_Dataset29,ddG_ML_float,1,mut_type,ODP2_GEOSE_theta0.2_2023-08-07_b07.npy,ODP2_GEOSE.pdb,1-44,1,,Stability +OPSD_HUMAN_Wan_2019,OPSD_HUMAN_Wan_2019.csv,OPSD_HUMAN,Human,Homo sapiens,MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA,348,FALSE,165,165,0,0.5795144905,median,Wan,Characterizing variants of unknown significance in rhodopsin: A functional genomics approach,2019,10.1002/humu.23762,4-347,Rhodopsin,Expression,Flow Cytometry Assay,OPSD_HUMAN_2023-10-12_b04.a2m,1,348,348,0.4,0.2,342311,0.876,305,36900.5,120.9852459,High,247,0.8098360656,urn_mavedb_00000099-a-1_scores.csv,score,1,mutant,OPSD_HUMAN_theta0.2_2023-10-12_b04.npy,OPSD_HUMAN.pdb,1-348,1,,Expression +OTC_HUMAN_Lo_2023,OTC_HUMAN_Lo_2023.csv,OTC_HUMAN,Human,Homo sapiens,MLFNLRILLNNAAFRNGHNFMVRNFRCGQPLQNKVQLKGRDLLTLKNFTGEEIKYMLWLSADLKFRIKQKGEYLPLLQGKSLGMIFEKRSTRTRLSTETGFALLGGHPCFLTTQDIHLGVNESLTDTARVLSSMADAVLARVYKQSDLDTLAKEASIPIINGLSDLYHPIQILADYLTLQEHYSSLKGLTLSWIGDGNNILHSIMMSAAKFGMHLQAATPKGYEPDASVTKLAEQYAKENGTKLLLTNDPLEAAHGGNVLITDTWISMGQEEEKKKRLQAFQGYQVTMKTAKVAASDWTFLHCLPRKPEEVDDEVFYSPRSLVFPEAENRKWTIMAVMVSLLTDYSPQLQKPKF,354,FALSE,1570,1570,0,0.417,median,Lo,"The functional impact of 1,570 individual amino acid substitutions in human OTC",2023,10.1016/j.ajhg.2023.03.019,33-354,OTC,Enzymatic activity,,OTC_HUMAN_2023-08-07_b02.a2m,1,354,354,0.2,0.2,135607,0.87,308,18646.2,60.53961039,Medium,641,2.081168831,urn_mavedb_00000112-a-1_scores.csv,DMS_score,1,mutant,OTC_HUMAN_theta0.2_2023-08-07_b02.npy,OTC_HUMAN.pdb,1-354,1,,Activity +OTU7A_HUMAN_Tsuboyama_2023_2L2D,OTU7A_HUMAN_Tsuboyama_2023_2L2D.csv,OTU7A_HUMAN,Human,Homo sapiens,TLDMDAVLSDFVRSTGAEPGLARDLLEGKNWDLTAALSDYEQ,42,FALSE,635,635,0,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-42,OTU domain-containing protein 7A,Stability,cDNA display proteolysis,OTU7A_HUMAN_2023-08-07_b02.a2m,1,42,42,0.2,0.2,1359071,0.881,37,514715.2,13911.22162,High,28,0.7567567568,Tsuboyama2023_Dataset2_Dataset30,ddG_ML_float,1,mut_type,OTU7A_HUMAN_theta0.2_2023-08-07_b02.npy,OTU7A_HUMAN.pdb,1-42,1,,Stability +OXDA_RHOTO_Vanella_2023_activity,OXDA_RHOTO_Vanella_2023_activity.csv,OXDA_RHOTO,Eukaryote,Rhodotorula gracilis,HSQKRVVVLGSGVIGLSSALILARKGYSVHILARDLPEDVSSQTFASPWAGANWTPFMTLTDGPRQAKWEESTFKKWVELVPTGHAMWLKGTRRFAQNEDGLLGHWYKDITPNYRPLPSSECPPGAIGVTYDTLSVHAPKYCQYLARELQKLGATFERRTVTSLEQAFDGADLVVNATGLGAKSIAGIDDQAAEPIRGQTVLVKSPCKRCTMDSSDPASPAYIIPRPGGEVICGGTYGVGDWDLSVNPETVQRILKHCLRLDPTISSDGTIEGIEVLRHNVGLRPARRGGPRVEAERIVLPLDRTKSPLSLGRGSARAAKEKEVTLVHAYGFSSAGYQQSWGAAEDVAQLVDEAFQRYHGAARE,364,FALSE,6396,6396,0,-0.2,manual,Vanella,Understanding Activity-Stability Tradeoffs in Biocatalysts by Enzyme Proximity Sequencing,2023,10.1101/2023.02.24.529916,1-364,D-amino acid oxidase (DAOx),fluorescent label of enzyme product,FACS,OXDA_RHOTO_2023-08-07_b02.a2m,1,364,364,0.2,0.2,520184,0.876,319,98000.4,307.2112853,High,892,2.796238245,Figure_2.xlsx,activity fitness,1,mutant,OXDA_RHOTO_theta0.2_2023-08-07_b02.npy,OXDA_RHOTO.pdb,1-364,1,,Activity +OXDA_RHOTO_Vanella_2023_expression,OXDA_RHOTO_Vanella_2023_expression.csv,OXDA_RHOTO,Eukaryote,Rhodotorula gracilis,HSQKRVVVLGSGVIGLSSALILARKGYSVHILARDLPEDVSSQTFASPWAGANWTPFMTLTDGPRQAKWEESTFKKWVELVPTGHAMWLKGTRRFAQNEDGLLGHWYKDITPNYRPLPSSECPPGAIGVTYDTLSVHAPKYCQYLARELQKLGATFERRTVTSLEQAFDGADLVVNATGLGAKSIAGIDDQAAEPIRGQTVLVKSPCKRCTMDSSDPASPAYIIPRPGGEVICGGTYGVGDWDLSVNPETVQRILKHCLRLDPTISSDGTIEGIEVLRHNVGLRPARRGGPRVEAERIVLPLDRTKSPLSLGRGSARAAKEKEVTLVHAYGFSSAGYQQSWGAAEDVAQLVDEAFQRYHGAARE,364,FALSE,6769,6769,0,-0.2,manual,Vanella,Understanding Activity-Stability Tradeoffs in Biocatalysts by Enzyme Proximity Sequencing,2023,10.1101/2023.02.24.529916,1-364,D-amino acid oxidase (DAOx),cell surface expression,FACS,OXDA_RHOTO_2023-08-07_b02.a2m,1,364,364,0.2,0.2,520184,0.876,319,98000.4,307.2112853,High,892,2.796238245,Figure_2.xlsx,expression fitness,1,mutant,OXDA_RHOTO_theta0.2_2023-08-07_b02.npy,OXDA_RHOTO.pdb,1-364,1,,Expression +P53_HUMAN_Giacomelli_2018_Null_Etoposide,P53_HUMAN_Giacomelli_2018_Null_Etoposide.csv,P53_HUMAN,Human,Homo sapiens,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,393,FALSE,7467,7467,0,-0.5,manual,Giacomelli,Mutational processes shape the landscape of TP53 mutations in human cancer,2018,10.1038/s41588-018-0204-y,1-393,p53,"drug resistance (nutlin-3, etoposide)",Growth,P53_HUMAN_full_04-29-2022_b09.a2m,1,393,393,0.9,0.2,5069,0.858,337,153.2,0.4545994065,low,7,0.02077151335,P53_HUMAN_Giacomelli_2018.csv,A549_p53NULL_Etoposide_Z-score,1,Allele,P53_HUMAN_theta_0.2.npy,P53_HUMAN.pdb,1-393,0.1,,OrganismalFitness +P53_HUMAN_Giacomelli_2018_Null_Nutlin,P53_HUMAN_Giacomelli_2018_Null_Nutlin.csv,P53_HUMAN,Human,Homo sapiens,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,393,FALSE,7467,7467,0,0.04438920187,median,Giacomelli,Mutational processes shape the landscape of TP53 mutations in human cancer,2018,10.1038/s41588-018-0204-y,1-393,p53,"drug resistance (nutlin-3, etoposide)",Growth,P53_HUMAN_full_04-29-2022_b09.a2m,1,393,393,0.9,0.2,5069,0.858,337,153.2,0.4545994065,low,7,0.02077151335,P53_HUMAN_Giacomelli_2018.csv,A549_p53NULL_Nutlin-3_Z-score,-1,Allele,P53_HUMAN_theta_0.2.npy,P53_HUMAN.pdb,1-393,0.1,,OrganismalFitness +P53_HUMAN_Giacomelli_2018_WT_Nutlin,P53_HUMAN_Giacomelli_2018_WT_Nutlin.csv,P53_HUMAN,Human,Homo sapiens,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,393,FALSE,7467,7467,0,-1,manual,Giacomelli,Mutational processes shape the landscape of TP53 mutations in human cancer,2018,10.1038/s41588-018-0204-y,1-393,p53,"drug resistance (nutlin-3, etoposide)",Growth,P53_HUMAN_full_04-29-2022_b09.a2m,1,393,393,0.9,0.2,5069,0.858,337,153.2,0.4545994065,low,7,0.02077151335,P53_HUMAN_Giacomelli_2018.csv,A549_p53WT_Nutlin-3_Z-score,-1,Allele,P53_HUMAN_theta_0.2.npy,P53_HUMAN.pdb,1-393,0.1,,OrganismalFitness +P53_HUMAN_Kotler_2018,P53_HUMAN_Kotler_2018.csv,P53_HUMAN,Human,Homo sapiens,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,393,FALSE,1048,1048,0,1,manual,Kotler,A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation,2018,10.1016/j.molcel.2018.06.012,102-292,p53,growth,Growth,P53_HUMAN_full_11-26-2021_b09.a2m,1,393,393,0.9,0.2,4129,0.863,339,148,0.4365781711,low,15,0.04424778761,P53_HUMAN_Kotler_2018.csv,RFS_H1299,-1,mutant,P53_HUMAN_Kotler_theta_0.2.npy,P53_HUMAN.pdb,1-393,0.1,,OrganismalFitness +P84126_THETH_Chan_2017,P84126_THETH_Chan_2017.csv,P84126_THETH,Prokaryote,Thermus thermophilus,MRPDLSRVPGVLGEIARKRASEVAPYPLPEPPSVPSFKEALLRPGLSVIAEVKRQSPSEGLIREVDPVEAALAYARGGARAVSVLTEPHRFGGSLLDLKRVREAVDLPLLRKDFVVDPFMLEEARAFGASAALLIVALLGELTGAYLEEARRLGLEALVEVHTERELEIALEAGAEVLGINNRDLATLHINLETAPRLGRLARKRGFGGVLVAESGYSRKEELKALEGLFDAVLIGTSLMRAPDLEAALRELVG,254,FALSE,1519,1519,0,-0.5,manual,Chan,Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints,2017,10.1038/ncomms14614,44-238,TIM Barrell (T. thermophilus),fitness,Growth,P84126_THETH_full_11-26-2021_b04.a2m,1,254,254,0.4,0.2,53441,0.941,239,10704.6,44.78912134,medium,390,1.631799163,P84126_THETH_Chan_2017.csv,fitness,1,mutant,P84126_THETH_theta_0.2.npy,P84126_THETH.pdb,1-254,0.1,,OrganismalFitness +PA_I34A1_Wu_2015,PA_I34A1_Wu_2015.csv,PA_I34A1,Virus,Influenza A virus (strain A/Puerto Rico/8/1934 H1N1),MEDFVRQCFNPMIVELAEKAMKEYGEDLKIETNKFAAICTHLEVCFMYSDFHFIDEQGESIVVELGDPNALLKHRFEIIEGRDRTIAWTVVNSICNTTGAEKPKFLPDLYDYKKNRFIEIGVTRREVHIYYLEKANKIKSEKTHIHIFSFTGEEMATKADYTLDEESRARIKTRLFTIRQEMASRGLWDSFRQSERGEETIEERFEITGTMRKLADQSLPPNFSSLEKFRAYVDGFEPNGYIEGKLSQMSKEVNARIEPFLKSTPRPLRLPDGPPCSQRSKFLLMDALKLSIEDPSHEGEGIPLYDAIKCMRTFFGWKEPNVVKPHEKGINPNYLLSWKQVLAELQDIENEEKIPRTKNMKKTSQLKWALGENMAPEKVDFDDCKDVGDLKQYDSDEPELRSLASWIQNEFNKACELTDSSWIELDEIGEDAAPIEHIASMRRNYFTAEVSHCRATEYIMKGVYINTALLNASCAAMDDFQLIPMISKCRTKEGRRKTNLYGFIIKGRSHLRNDTDVVNFVSMEFSLTDPRLEPHKWEKYCVLEVGDMLLRSAIGHVSRPMFLYVRTNGTSKIKMKWGMEMRRCLLQSLQQIESMIEAESSVKEKDMTKEFFENKSETWPVGESPKGVEEGSIGKVCRTLLAKSVFNSLYASPQLEGFSAESRKLLLIVQALRDNLEPGTFDLGGLYEAIEECLINDPWVLLNASWFNSFLTHALR,716,FALSE,1820,1820,0,0.290683953,median,Wu,Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality,2015,10.1371/journal.pgen.1005310,8-716,Influenza polymerase acidic protein,Viral replication,Growth,PA_I34A1_full_theta0.99_04-29-2022_b09.a2m,1,716,716,0.9,0.01,26750,1,716,1608,2.245810056,medium,3706,5.175977654,PA_I34A1_Wu_2015.csv,RF_index,1,mutant,PA_I34A1_theta_0.01.npy,PA_I34A1.pdb,1-716,0.1,,OrganismalFitness +PABP_YEAST_Melamed_2013,PABP_YEAST_Melamed_2013.csv,PABP_YEAST,Eukaryote,Saccharomyces cerevisiae,MADITDKTAEQLENLNIQDDQKQAATGSESQSVENSSASLYVGDLEPSVSEAHLYDIFSPIGSVSSIRVCRDAITKTSLGYAYVNFNDHEAGRKAIEQLNYTPIKGRLCRIMWSQRDPSLRKKGSGNIFIKNLHPDIDNKALYDTFSVFGDILSSKIATDENGKSKGFGFVHFEEEGAAKEAIDALNGMLLNGQEIYVAPHLSRKERDSQLEETKAHYTNLYVKNINSETTDEQFQELFAKFGPIVSASLEKDADGKLKGFGFVNYEKHEDAVKAVEALNDSELNGEKLYVGRAQKKNERMHVLKKQYEAYRLEKMAKYQGVNLFVKNLDDSVDDEKLEEEFAPYGTITSAKVMRTENGKSKGFGFVCFSTPEEATKAITEKNQQIVAGKPLYVAIAQRKDVRRSQLAQQIQARNQMRYQQATAAAAAAAAGMPGQFMPPMFYGVMPPRGVPFNGPNPQQMNPMGGMPKNGMPPQFRNGPVYGVPPQGGFPRNANDNNQFYQQKQRQALGEQLYKKVSAKTSNEEAAGKITGMILDLPPQEVFPLLESDELFEQHYKEASAAYESFKKEQEQQTEQA,577,TRUE,37708,1187,36521,0.3,manual,Melamed,Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein,2013,10.1261/rna.040709.113,126-200,PAB1,"Growth (essential function), RNA binding",Growth,PABP_YEAST_full_11-26-2021_b07.a2m,1,577,577,0.7,0.2,7866,0.919,530,855.1,1.613396226,medium,83,0.1566037736,PABP_YEAST_Melamed_2013.csv,linear,1,mutant,PABP_YEAST_theta_0.2.npy,PABP_YEAST.pdb,1-577,0.1,,OrganismalFitness +PAI1_HUMAN_Huttinger_2021,PAI1_HUMAN_Huttinger_2021.csv,PAI1_HUMAN,Human,Homo sapiens,MQMSPALTCLVLGLALVFGEGSAVHHPPSYVAHLASDFGVRVFQQVAQASKDRNVVFSPYGVASVLAMLQLTTGGETQQQIQAAMGFKIDDKGMAPALRHLYKELMGPWNKDEISTTDAIFVQRDLKLVQGFMPHFFRLFRSTVKQVDFSEVERARFIINDWVKTHTKGMISNLLGKGAVDQLTRLVLVNALYFNGQWKTPFPDSSTHRRLFHKSDGSTVSVPMMAQTNKFNYTEFTTPDGHYYDILELPYHGDTLSMFIAAPYEKEVPLSALTNILSAQLISHWKGNMTRLPRLLVLPKFSLETEVDLRKPLENLGMTDMFRQFQADFTSLSDQEPLHVAQALQKVKIEVNESGTVASSSTAVIVSARMAPEEIIMDRPFLFVVRHNPTGTVLFMGQVMEP,402,FALSE,5345,5345,0,0.029313547,median,Huttinger,Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape,2021,10.1038/s41598-021-97871-7,24-402,"PAI-1, SERPINE1",PAI-1 inhibition of uPA,phage fitness,PAI1_HUMAN_2023-10-12_b05.a2m,1,402,402,0.5,0.2,52528,,,,,,,,PAI1_HUMAN_Huttinger_2021,log2FoldChange,1,mutation,PAI1_HUMAN_theta0.2_2023-10-12_b05.npy,PAI1_HUMAN.pdb,1-402,1,,Activity +PHOT_CHLRE_Chen_2023,PHOT_CHLRE_Chen_2023.csv,PHOT_CHLRE,Eukaryote,Chlamydomonas reinhardtii,AGLRHTFVVADATLPDCPLVYASEGFYAMTGYGPDEVLGHNARFLQGEGTDPKEVQKIRDAIKKGEACSVRLLNYRKDGTPFWNLLTVTPIKTPDGRVSKFVGVQVDVTSKTEGKALA,118,TRUE,167529,2122,165407,0.6317018878,median,Chen,Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects,2023,10.1021/acssynbio.2c00662,1-118,Phototropin,Fluorescence,FACS,PHOT_CHLRE_2023-08-07_b02.a2m,1,118,118,0.2,0.2,1627150,0.873,103,610128.5,5923.57767,High,232,2.252427184,sb2c00662_si_001.xlsx,mean,1,mutant,PHOT_CHLRE_theta0.2_2023-08-07_b02.npy,PHOT_CHLRE.pdb,1-118,1,,Activity +PIN1_HUMAN_Tsuboyama_2023_1I6C,PIN1_HUMAN_Tsuboyama_2023_1I6C.csv,PIN1_HUMAN,Human,Homo sapiens,KLPPGWEKRMSRSSGRVYYFNHITNASQWERPSGNSSSG,39,TRUE,802,686,116,-0.6844420472,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-39,Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1,Stability,cDNA display proteolysis,PIN1_HUMAN_2023-08-07_b02.a2m,1,39,39,0.2,0.2,248269,0.821,32,10833.2,338.5375,High,13,0.40625,Tsuboyama2023_Dataset2_Dataset31,ddG_ML_float,1,mut_type,PIN1_HUMAN_theta0.2_2023-08-07_b02.npy,PIN1_HUMAN.pdb,1-39,1,,Stability +PITX2_HUMAN_Tsuboyama_2023_2L7M,PITX2_HUMAN_Tsuboyama_2023_2L7M.csv,PITX2_HUMAN,Human,Homo sapiens,THFTSQQLQELEATFQRNHYPDMSTREEIAVWTNLTEARVRVWFKNRRAKWR,52,TRUE,1824,938,886,-1.201366007,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-52,Pituitary homeobox 2,Stability,cDNA display proteolysis,PITX2_HUMAN_2023-08-07_b04.a2m,1,52,52,0.4,0.2,344174,1,52,9819.6,188.8384615,High,25,0.4807692308,Tsuboyama2023_Dataset2_Dataset32,ddG_ML_float,1,mut_type,PITX2_HUMAN_theta0.2_2023-08-07_b04.npy,PITX2_HUMAN.pdb,1-52,1,,Stability +PKN1_HUMAN_Tsuboyama_2023_1URF,PKN1_HUMAN_Tsuboyama_2023_1URF.csv,PKN1_HUMAN,Human,Homo sapiens,GIPATNLSRVAGLEKQLAIELKVKQGAENMIQTYSNGSTKDRKLLLTAQQMLQDSKTKIDIIRMQLRRALQ,71,FALSE,1301,1301,0,-0.5,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-71,Serine/threonine-protein kinase N1,Stability,cDNA display proteolysis,PKN1_HUMAN_2023-08-07_b01.a2m,1,71,71,0.1,0.2,187829,0.845,60,53755.8,895.93,High,13,0.2166666667,Tsuboyama2023_Dataset2_Dataset33,ddG_ML_float,1,mut_type,PKN1_HUMAN_theta0.2_2023-08-07_b01.npy,PKN1_HUMAN.pdb,1-71,1,,Stability +POLG_CXB3N_Mattenberger_2021,POLG_CXB3N_Mattenberger_2021.csv,POLG_CXB3N,Virus,Coxsackievirus B3 (strain Nancy),MGAQVSTQKTGAHETRLNASGNSIIHYTNINYYKDAASNSANRQDFTQDPGKFTEPVKDIMIKSLPALNSPTVEECGYSDRARSITLGNSTITTQECANVVVGYGVWPDYLKDSEATAEDQPTQPDVATCRFYTLDSVQWQKTSPGWWWKLPDALSNLGLFGQNMQYHYLGRTGYTVHVQCNASKFHQGCLLVVCVPEAEMGCATLDNTPSSAELLGGDSAKEFADKPVASGSNKLVQRVVYNAGMGVGVGNLTIFPHQWINLRTNNSATIVMPYTNSVPMDNMFRHNNVTLMVIPFVPLDYCPGSTTYVPITVTIAPMCAEYNGLRLAGHQGLPTMNTPGSCQFLTSDDFQSPSAMPQYDVTPEMRIPGEVKNLMEIAEVDSVVPVQNVGEKVNSMEAYQIPVRSNEGSGTQVFGFPLQPGYSSVFSRTLLGEILNYYTHWSGSIKLTFMFCGSAMATGKFLLAYSPPGAGAPTKRVDAMLGTHVIWDVGLQSSCVLCIPWISQTHYRFVASDEYTAGGFITCWYQTNIVVPADAQSSCYIMCFVSACNDFSVRLLKDTPFISQQNFFQGPVEDAITAAIGRVADTVGTGPTNSEAIPALTAAETGHTSQVVPGDTMQTRHVKNYHSRSESTIENFLCRSACVYFTEYKNSGAKRYAEWVLTPRQAAQLRRKLEFFTYVRFDLELTFVITSTQQPSTTQNQDAQILTHQIMYVPPGGPVPDKVDSYVWQTSTNPSVFWTEGNAPPRMSIPFLSIGNAYSNFYDGWSEFSRNGVYGINTLNNMGTLYARHVNAGSTGPIKSTIRIYFKPKHVKAWIPRPPRLCQYEKAKNVNFQPSGVTTTRQSITTMTNTGAFGQQSGAVYVGNYRVVNRHLATSADWQNCVWESYNRDLLVSTTTAHGCDIIARCQCTTGVYFCASKNKHYPISFEGPGLVEVQESEYYPRRYQSHVLLAAGFSEPGDCGGILRCEHGVIGIVTMGGEGVVGFADIRDLLWLEDDAMEQGVKDYVEQLGNAFGSGFTNQICEQVNLLKESLVGQDSILEKSLKALVKIISALVIVVRNHDDLITVTATLALIGCTSSPWRWLKQKVSQYYGIPMAERQNNSWLKKFTEMTNACKGMEWIAVKIQKFIEWLKVKILPEVREKHEFLNRLKQLPLLESQIATIEQSAPSQSDQEQLFSNVQYFAHYCRKYAPLYAAEAKRVFSLEKKMSNYIQFKSKCRIEPVCLLLHGSPGAGKSVATNLIGRSLAEKLNSSVYSLPPDPDHFDGYKQQAVVIMDDLCQNPDGKDVSLFCQMVSSVDFVPPMAALEEKGILFTSPFVLASTNAGSINAPTVSDSRALARRFHFDMNIEVISMYSQNGKINMPMSVKTCDDECCPVNFKKCCPLVCGKAIQFIDRRTQVRYSLDMLVTEMFREYNHRHSVGTTLEALFQGPPVYREIKISVAPETPPPPAIADLLKSVDSEAVREYCKEKGWLVPEINSTLQIEKHVSRAFICLQALTTFVSVAGIIYIIYKLFAGFQGAYTGVPNQKPRVPTLRQAKVQGPAFEFAVAMMKRNSSTVKTEYGEFTMLGIYDRWAVLPRHAKPGPTILMNDQEVGVLDAKELVDKDGTNLELTLLKLNRNEKFRDIRGFLAKEEVEVNEAVLAINTSKFPNMYIPVGQVTEYGFLNLGGTPTKRMLMYNFPTRAGQCGGVLMSTGKVLGIHVGGNGHQGFSAALLKHYFNDEQGEIEFIESSKDAGFPVINTPSKTKLEPSVFHQVFEGNKEPAVLRSGDPRLKANFEEAIFSKYIGNVNTHVDEYMLEAVDHYAGQLATLDISTEPMKLEDAVYGTEGLEALDLTTSAGYPYVALGIKKRDILSKKTKDLTKLKECMDKYGLNLPMVTYVKDELRSIEKVAKGKSRLIEASSLNDSVAMRQTFGNLYKTFHLNPGVVTGSAVGCDPDLFWSKIPVMLDGHLIAFDYSGYDASLSPVWFACLKMLLEKLGYTHKETNYIDYLCNSHHLYRDKHYFVRGGMPSGCSGTSIFNSMINNIIIRTLMLKVYKGIDLDQFRMIAYGDDVIASYPWPIDASLLAEAGKGYGLIMTPADKGECFNEVTWTNATFLKRYFRADEQYPFLVHPVMPMKDIHESIRWTKDPKNTQDHVRSLCLLAWHNGEHEYEEFIRKIRSVPVGRCLTLPAFSTLRRKWLDSF,2185,FALSE,15711,15711,0,-2.76355725,median,Mattenberger,Globally defining the effects of mutations in a picornavirus capsid,2021,10.7554/eLife.64256,1-851,Picornavirus capsid,Viral replication,Growth,POLG_CXB3N_1-861_theta0.99_04-29-2022_b07.a2m,1,861,861,0.7,0.01,7909,0.959,826,1515.2,1.834382567,medium,94,0.1138014528,POLG_CXB3N_Mattenberger_2021.csv,log_fitness_by_syn_mut_fitness,1,mutant,POLG_CXB3N_theta_0.01.npy,POLG_CXB3N.pdb,1-2185,0.1,,OrganismalFitness +POLG_DEN26_Suphatrakul_2023,POLG_DEN26_Suphatrakul_2023.csv,POLG_DEN26,Virus,Dengue virus type 2 (strain Thailand/16681/1984) (DENV-2),GTGNIGETLGEKWKSRLNALGKSEFQIYKKSGIQEVDRTLAKEGIKRGETDHHAVSRGSAKLRWFVERNMVTPEGKVVDLGCGRGGWSYYCGGLKNVREVKGLTKGGPGHEEPIPMSTYGWNLVRLQSGVDVFFIPPEKCDTLLCDIGESSPNPTVEAGRTLRVLNLVENWLNNNTQFCIKVLNPYMPSVIEKMEALQRKYGGALVRNPLSRNSTHEMYWVSNASGNIVSSVNMISRMLINRFTMRYKKATYEPDVDLGSGTRNIGIESEIPNLDIIGKRIEKIKQEHETSWHYDQDHPYKTWAYHGSYETKQTGSASSMVNGVVRLLTKPWDVVPMVTQMAMTDTTPFGQQRVFKEKVDTRTQEPKEGTKKLMKITAEWLWKELGKKKTPRMCTREEFTRKVRSNAALGAIFTDENKWKSAREAVEDSRFWELVDKERNLHLEGKCETCVYNMMGKREKKLGEFGKAKGSRAIWYMWLGARFLEFEALGFLNEDHWFSRENSLSGVEGEGLHKLGYILRDVSKKEGGAMYADDTAGWDTRITLEDLKNEEMVTNHMEGEHKKLAEAIFKLTYQNKVVRVQRPTPRGTVMDIISRRDQRGSGQVGTYGLNTFTNMEAQLIRQMEGEGVFKSIQHLTITEEIAVQNWLARVGRERLSRMAISGDDCVVKPLDDRFASALTALNDMGKIRKDIQQWEPSRGWNDWTQVPFCSHHFHELIMKDGRVLVVPCRNQDELIGRARISQGAGWSLRETACLGKSYAQMWSLMYFHRRDLRLAANAICSAVPSHWVPTSRTTWSIHAKHEWMTTEDMLTVWNRVWIQENPWMEDKTPVESWEEIPYLGKREDQWCGSLIGLTSRATWAKNIQAAINQVRSLIGNEEYTDYMPSMKRFRREEEEAGVLW,900,FALSE,16897,16897,0,-5.373371442,median,Suphatrakul,Functional analysis of flavivirus replicase by deep mutational scanning of dengue NS5,2023,10.1101/2023.03.07.531617,1-900,Flavivirus NS5,Viral replication,Growth,POLG_DEN26_2023-08-07_b01.a2m,1,900,900,0.1,0.01,10676,1,900,114.5,0.1272222222,Low,0,0,POLG_DEN26_Suphatrakul_2023.csv,score,1,mutant,POLG_DEN26_theta0.01_2023-08-07_b01.npy,POLG_DEN26.pdb,1-900,1,,OrganismalFitness +POLG_HCVJF_Qi_2014,POLG_HCVJF_Qi_2014.csv,POLG_HCVJF,Virus,Hepatitis C virus genotype 2a (isolate JFH-1) (HCV),MSTNPKPQRKTKRNTNRRPEDVKFPGGGQIVGGVYLLPRRGPRLGVRTTRKTSERSQPRGRRQPIPKDRRSTGKAWGKPGRPWPLYGNEGLGWAGWLLSPRGSRPSWGPTDPRHRSRNVGKVIDTLTCGFADLMGYIPVVGAPLSGAARAVAHGVRVLEDGVNYATGNLPGFPFSIFLLALLSCITVPVSAAQVKNTSSSYMVTNDCSNDSITWQLEAAVLHVPGCVPCERVGNTSRCWVPVSPNMAVRQPGALTQGLRTHIDMVVMSATFCSALYVGDLCGGVMLAAQVFIVSPQYHWFVQECNCSIYPGTITGHRMAWDMMMNWSPTATMILAYVMRVPEVIIDIVSGAHWGVMFGLAYFSMQGAWAKVIVILLLAAGVDAGTTTVGGAVARSTNVIAGVFSHGPQQNIQLINTNGSWHINRTALNCNDSLNTGFLAALFYTNRFNSSGCPGRLSACRNIEAFRIGWGTLQYEDNVTNPEDMRPYCWHYPPKPCGVVPARSVCGPVYCFTPSPVVVGTTDRRGVPTYTWGENETDVFLLNSTRPPQGSWFGCTWMNSTGFTKTCGAPPCRTRADFNASTDLLCPTDCFRKHPDATYIKCGSGPWLTPKCLVHYPYRLWHYPCTVNFTIFKIRMYVGGVEHRLTAACNFTRGDRCDLEDRDRSQLSPLLHSTTEWAILPCTYSDLPALSTGLLHLHQNIVDVQYMYGLSPAITKYVVRWEWVVLLFLLLADARVCACLWMLILLGQAEAALEKLVVLHAASAANCHGLLYFAIFFVAAWHIRGRVVPLTTYCLTGLWPFCLLLMALPRQAYAYDAPVHGQIGVGLLILITLFTLTPGYKTLLGQCLWWLCYLLTLGEAMIQEWVPPMQVRGGRDGIAWAVTIFCPGVVFDITKWLLALLGPAYLLRAALTHVPYFVRAHALIRVCALVKQLAGGRYVQVALLALGRWTGTYIYDHLTPMSDWAASGLRDLAVAVEPIIFSPMEKKVIVWGAETAACGDILHGLPVSARLGQEILLGPADGYTSKGWKLLAPITAYAQQTRGLLGAIVVSMTGRDRTEQAGEVQILSTVSQSFLGTTISGVLWTVYHGAGNKTLAGLRGPVTQMYSSAEGDLVGWPSPPGTKSLEPCKCGAVDLYLVTRNADVIPARRRGDKRGALLSPRPISTLKGSSGGPVLCPRGHVVGLFRAAVCSRGVAKSIDFIPVETLDVVTRSPTFSDNSTPPAVPQTYQVGYLHAPTGSGKSTKVPVAYAAQGYKVLVLNPSVAATLGFGAYLSKAHGINPNIRTGVRTVMTGEAITYSTYGKFLADGGCASGAYDIIICDECHAVDATSILGIGTVLDQAETAGVRLTVLATATPPGSVTTPHPDIEEVGLGREGEIPFYGRAIPLSCIKGGRHLIFCHSKKKCDELAAALRGMGLNAVAYYRGLDVSIIPAQGDVVVVATDALMTGYTGDFDSVIDCNVAVTQAVDFSLDPTFTITTQTVPQDAVSRSQRRGRTGRGRQGTYRYVSTGERASGMFDSVVLCECYDAGAAWYDLTPAETTVRLRAYFNTPGLPVCQDHLEFWEAVFTGLTHIDAHFLSQTKQAGENFAYLVAYQATVCARAKAPPPSWDAMWKCLARLKPTLAGPTPLLYRLGPITNEVTLTHPGTKYIATCMQADLEVMTSTWVLAGGVLAAVAAYCLATGCVSIIGRLHVNQRVVVAPDKEVLYEAFDEMEECASRAALIEEGQRIAEMLKSKIQGLLQQASKQAQDIQPAMQASWPKVEQFWARHMWNFISGIQYLAGLSTLPGNPAVASMMAFSAALTSPLSTSTTILLNIMGGWLASQIAPPAGATGFVVSGLVGAAVGSIGLGKVLVDILAGYGAGISGALVAFKIMSGEKPSMEDVINLLPGILSPGALVVGVICAAILRRHVGPGEGAVQWMNRLIAFASRGNHVAPTHYVTESDASQRVTQLLGSLTITSLLRRLHNWITEDCPIPCSGSWLRDVWDWVCTILTDFKNWLTSKLFPKLPGLPFISCQKGYKGVWAGTGIMTTRCPCGANISGNVRLGSMRITGPKTCMNTWQGTFPINCYTEGQCAPKPPTNYKTAIWRVAASEYAEVTQHGSYSYVTGLTTDNLKIPCQLPSPEFFSWVDGVQIHRFAPTPKPFFRDEVSFCVGLNSYAVGSQLPCEPEPDADVLRSMLTDPPHITAETAARRLARGSPPSEASSSVSQLSAPSLRATCTTHSNTYDVDMVDANLLMEGGVAQTEPESRVPVLDFLEPMAEEESDLEPSIPSECMLPRSGFPRALPAWARPDYNPPLVESWRRPDYQPPTVAGCALPPPKKAPTPPPRRRRTVGLSESTISEALQQLAIKTFGQPPSSGDAGSSTGAGAAESGGPTSPGEPAPSETGSASSMPPLEGEPGDPDLESDQVELQPPPQGGGVAPGSGSGSWSTCSEEDDTTVCCSMSYSWTGALITPCSPEEEKLPINPLSNSLLRYHNKVYCTTSKSASQRAKKVTFDRTQVLDAHYDSVLKDIKLAASKVSARLLTLEEACQLTPPHSARSKYGFGAKEVRSLSGRAVNHIKSVWKDLLEDPQTPIPTTIMAKNEVFCVDPAKGGKKPARLIVYPDLGVRVCEKMALYDITQKLPQAVMGASYGFQYSPAQRVEYLLKAWAEKKDPMGFSYDTRCFDSTVTERDIRTEESIYQACSLPEEARTAIHSLTERLYVGGPMFNSKGQTCGYRRCRASGVLTTSMGNTITCYVKALAACKAAGIVAPTMLVCGDDLVVISESQGTEEDERNLRAFTEAMTRYSAPPGDPPRPEYDLELITSCSSNVSVALGPRGRRRYYLTRDPTTPLARAAWETVRHSPINSWLGNIIQYAPTIWVRMVLMTHFFSILMVQDTLDQNLNFEMYGSVYSVNPLDLPAIIERLHGLDAFSMHTYSHHELTRVASALRKLGAPPLRVWKSRARAVRASLISRGGKAAVCGRYLFNWAVKTKLKLTPLPEARLLDLSSWFTVGAGGGDIFHSVSRARPRSLLFGLLLLFVGVGLFLLPAR,3033,FALSE,1630,1630,0,-0.95,manual,Qi,A Quantitative High-Resolution Genetic Profile Rapidly Identifies Sequence Determinants of Hepatitis C Viral Fitness and Drug Sensitivity,2014,10.1371/journal.ppat.1004064,1994-2079,NS5A,Viral replication,Growth,POLG_HCVJF_theta0.99_1984-2089_11-26-2021_b08.a2m,1984,2089,106,0.8,0.01,16556,1,106,4421.2,41.70943396,medium,93,0.8773584906,POLG_HCVJF_Qi_2014.csv,fitness,1,mutant,POLG_HCVJF_theta_0.01.npy,POLG_HCVJF.pdb,1981-2224,0.1,,OrganismalFitness +POLG_PESV_Tsuboyama_2023_2MXD,POLG_PESV_Tsuboyama_2023_2MXD.csv,POLG_PESV,Virus,Porcine enteric sapovirus (isolate Swine/United States/Cowden/1980),ALRDDEYDEWQDIIRDWRKEMTVQQFLDLKERALSGASDPDSQRYNAWLELRA,53,TRUE,5130,995,4135,-1.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-53,Genome polyprotein,Stability,cDNA display proteolysis,POLG_PESV_2023-08-07_b03.a2m,1,53,53,0.3,0.01,20190,0.887,47,3718.4,79.11489362,Medium,12,0.2553191489,Tsuboyama2023_Dataset2_Dataset34,ddG_ML_float,1,mut_type,POLG_PESV_theta0.01_2023-08-07_b03.npy,POLG_PESV.pdb,1-53,1,,Stability +PPARG_HUMAN_Majithia_2016,PPARG_HUMAN_Majithia_2016.csv,PPARG_HUMAN,Human,Homo sapiens,MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY,505,FALSE,9576,9576,0,-2.5,manual,Majithia,Prospective functional classification of all possible missense variants in PPARG,2016,10.1038/ng.3700,2-505,PPARG,Expression of CD36,FACS,PPARG_HUMAN_2023-10-12_b04.a2m,1,505,505,0.4,0.2,39993,0.8,404,3092.1,7.653712871,Medium,86,0.2128712871,https://miter.broadinstitute.org/mitergrade/?query=p.Y505A&prevalence=1.0e-5,Experimental function score,1,mutant,PPARG_HUMAN_theta0.2_2023-10-12_b04.npy,PPARG_HUMAN.pdb,1-505,1,,Activity +PPM1D_HUMAN_Miller_2022,PPM1D_HUMAN_Miller_2022.csv,PPM1D_HUMAN,Human,Homo sapiens,MAGLYSLGVSVFSDQGGRKYMEDVTQIVVEPEPTAEEKPSPRRSLSQPLPPRPSPAALPGGEVSGKGPAVAAREARDPLPDAGASPAPSRCCRRRSSVAFFAVCDGHGGREAAQFAREHLWGFIKKQKGFTSSEPAKVCAAIRKGFLACHLAMWKKLAEWPKTMTGLPSTSGTTASVVIIRGMKMYVAHVGDSGVVLGIQDDPKDDFVRAVEVTQDHKPELPKERERIEGLGGSVMNKSGVNRVVWKRPRLTHNGPVRRSTVIDQIPFLAVARALGDLWSYDFFSGEFVVSPEPDTSVHTLDPQKHKYIILGSDGLWNMIPPQDAISMCQDQEEKKYLMGEHGQSCAKMLVNRALGRWRQRMLRADNTSAIVICISPEVDNQGNFTNEDELYLNLTDSPSYNSQETCVMTPSPCSTPPVKSLEEDPWPRVNSKDHIPALVRSNAFSENFLEVSAEIARENVQGVVIPSKDPEPLEENCAKALTLRIHDSLNNSLPIGLVPTNSTNTVMDQKNLKMSTPGQMKAQEIERTPPTNFKRTLEESNSGPLMKKHRRNGLSRSSGAQPASLPTTSQRKNSVKLTMRRRLRGQKKIGNPLLHQHRKTVCVC,605,FALSE,7889,7889,0,0.01275,median,Miller,Allosteric inhibition of PPM1D serine/threonine phosphatase via an altered conformational state,2022,10.1038/s41467-022-30463-9,2-421,Protein phosphatase 1D,Fitness with GFP reporter,quantification and selection of GFP-positive cells by flow cytometry after DNA damage induced by daunorubicin,PPM1D_HUMAN_2023-10-12_b01.a2m,1,605,605,0.1,0.2,1844,0.993,601,346.3,0.5762063228,Low,27,0.04492512479,PPM1D_HUMAN_Miller_2022_raw.xlsx,fitness,1,mutant,PPM1D_HUMAN_theta0.2_2023-10-12_b01.npy,PPM1D_HUMAN.pdb,1-605,1,,OrganismalFitness +PR40A_HUMAN_Tsuboyama_2023_1UZC,PR40A_HUMAN_Tsuboyama_2023_1UZC.csv,PR40A_HUMAN,Human,Homo sapiens,TYTWNTKEEAKQAFKELLKEKRVPSNASWEQAMKMIINDPRYSALAKLSEKKQAFNAYKVQTE,63,TRUE,2033,1163,870,-1.362579422,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-63,Pre-mRNA-processing factor 40 homolog A,Stability,cDNA display proteolysis,PR40A_HUMAN_2023-08-07_b03.a2m,1,63,63,0.3,0.2,63560,0.857,54,3663.8,67.84814815,Medium,16,0.2962962963,Tsuboyama2023_Dataset2_Dataset35,ddG_ML_float,1,mut_type,PR40A_HUMAN_theta0.2_2023-08-07_b03.npy,PR40A_HUMAN.pdb,1-63,1,,Stability +PRKN_HUMAN_Clausen_2023,PRKN_HUMAN_Clausen_2023.csv,PRKN_HUMAN,Human,Homo sapiens,MIVFVRFNSSHGFPVEVDSDTSIFQLKEVVAKRQGVPADQLRVIFAGKELRNDWTVQNCDLDQQSIVHIVQRPWRKGQEMNATGGDDPRNAAGGCEREPQSLTRVDLSSSVLPGDSVGLAVILHTDSRKDSPPAGSPAGRSIYNSFYVYCKGPCQRVQPGKLRVQCSTCRQATLTLTQGPSCWDDVLIPNRMSGECQSPHCPGTSAEFFFKCGAHPTSDKETSVALHLIATNSRNITCITCTDVRSPVLVFQCNSRHVICLDCFHLYCVTRLNDRQFVHDPQLGYSLPCVAGCPNSLIKELHHFRILGEEQYNRYQQYGAEECVLQMGGVLCPRPGCGAGLLPEPDQRKVTCEGGNGLGCGFAFCRECKEAYHEGECSAVFEASGTTTQAYRVDERAAEQARWEAASKETIKKTTKPCPRCHVPVEKNGGCMHMKCPQPQCRLEWCWNCGCEWNRVCMGDHWFDV,465,FALSE,8756,8756,0,0.75,manual,Clausen,A mutational atlas for Parkin proteostasis,2023,10.1101/2023.06.08.544160,1-465,Parkin,protein stability,FACS,PRKN_HUMAN_2023-08-07_b05.a2m,1,465,465,0.5,0.2,1457,0.998,464,195.2,0.4206896552,Low,21,0.04525862069,urn_mavedb_00000114-a-1_scores.csv,score,1,mutant,PRKN_HUMAN_theta0.2_2023-08-07_b05.npy,PRKN_HUMAN.pdb,1-465,1,,Expression +PSAE_PICP2_Tsuboyama_2023_1PSE,PSAE_PICP2_Tsuboyama_2023_1PSE.csv,PSAE_PICP2,Prokaryote,Picosynechococcus,AIERGSKVKILRKESYWYGDVGTVASIDKSGIIYPVIVRFNKVNYNGFSGSAGGLNTNNFAEHELEVV,68,TRUE,1579,1219,360,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-68,Photosystem I reaction center subunit IV,Stability,cDNA display proteolysis,PSAE_PICP2_2023-08-07_b09.a2m,1,68,68,0.9,0.2,1785,0.868,59,130.7,2.215254237,Medium,9,0.1525423729,Tsuboyama2023_Dataset2_Dataset36,ddG_ML_float,1,mut_type,PSAE_PICP2_theta0.2_2023-08-07_b09.npy,PSAE_PICP2.pdb,1-68,1,,Stability +PTEN_HUMAN_Matreyek_2021,PTEN_HUMAN_Matreyek_2021.csv,PTEN_HUMAN,Human,Homo sapiens,MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV,403,FALSE,5083,5083,0,0.7708605475,median,Matreyek,Integrating thousands of PTEN variant activity and abundance measurements reveals variant subgroups and new dominant negatives in cancers,2021,10.1186/s13073-021-00984-x,1-403,PTEN,Protein abundance (FACS sorting for abundance of GFP-fused target),Protein stability,PTEN_HUMAN_full_11-26-2021_b01.a2m,1,403,403,0.1,0.2,19058,0.752,303,1425.3,4.703960396,medium,52,0.1716171617,PTEN_HUMAN_Matreyek_2021.csv,score_total,1,variant,PTEN_HUMAN_theta_0.2.npy,PTEN_HUMAN.pdb,1-403,0.1,,Expression +PTEN_HUMAN_Mighell_2018,PTEN_HUMAN_Mighell_2018.csv,PTEN_HUMAN,Human,Homo sapiens,MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV,403,FALSE,7260,7260,0,-1.5,manual,Mighell,A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships,2018,10.1016/j.ajhg.2018.03.018,1-403,PTEN,"growth (surrogate for enzymatic activity/hydrolysis of lipid phosphates to restore PIP2, which affects proliferation rate)",lipid phosphatase activity,PTEN_HUMAN_full_11-26-2021_b01.a2m,1,403,403,0.1,0.2,19058,0.752,303,1425.3,4.703960396,medium,52,0.1716171617,PTEN_HUMAN_Mighell_2018.csv,Fitness_score,1,mutant,PTEN_HUMAN_theta_0.2.npy,PTEN_HUMAN.pdb,1-403,0.1,,Activity +Q2N0S5_9HIV1_Haddox_2018,Q2N0S5_9HIV1_Haddox_2018.csv,Q2N0S5_9HIV1,Virus,Human immunodeficiency virus 1,MRVMGIQRNCQHLFRWGTMILGMIIICSAAENLWVTVYYGVPVWKDAETTLFCASDAKAYETEKHNVWATHACVPTDPNPQEIHLENVTEEFNMWKNNMVEQMHTDIISLWDQSLKPCVKLTPLCVTLQCTNVTNNITDDMRGELKNCSFNMTTELRDKKQKVYSLFYRLDVVQINENQGNRSNNSNKEYRLINCNTSAITQACPKVSFEPIPIHYCAPAGFAILKCKDKKFNGTGPCPSVSTVQCTHGIKPVVSTQLLLNGSLAEEEVMIRSENITNNAKNILVQFNTPVQINCTRPNNNTRKSIRIGPGQAFYATGDIIGDIRQAHCNVSKATWNETLGKVVKQLRKHFGNNTIIRFANSSGGDLEVTTHSFNCGGEFFYCNTSGLFNSTWISNTSVQGSNSTGSNDSITLPCRIKQIINMWQRIGQAMYAPPIQGVIRCVSNITGLILTRDGGSTNSTTETFRPGGGDMRDNWRSELYKYKVVKIEPLGVAPTRAKRRVVGREKRAVGIGAVFLGFLGAAGSTMGAASMTLTVQARNLLSGIVQQQSNLLRAIEAQQHLLKLTVWGIKQLQARVLAVERYLRDQQLLGIWGCSGKLICTTNVPWNSSWSNRNLSEIWDNMTWLQWDKEISNYTQIIYGLLEESQNQQEKNEQDLLALDKWASLWNWFDISNWLWYIKIFIMIVGGLIGLRIVFAVLSVIHRVRQGYSPLSFQTHTPNPRGLDRPERIEEEDGEQDRGRSTRLVSGFLALAWDDLRSLCLFCYHRLRDFILIAARIVELLGHSSLKGLRLGWEGLKYLWNLLAYWGRELKISAINLFDTIAIAVAEWTDRVIEIGQRLCRAFLHIPRRIRQGLERALL,860,FALSE,12729,12729,0,-2,manual,Haddox,Mapping mutational effects along the evolutionary landscape of HIV envelope,2018,10.7554/eLife.34420,30-699,HIV env (BG505),Viral replication,Growth,Q2N0S5_9HIV1_full_theta0.99_04-29-2022_b09.a2m,1,860,860,0.9,0.01,75014,0.976,839,36369.7,43.3488677,medium,2462,2.934445769,Q2N0S5_9HIV1_Haddox_2018.csv,fitness,1,mutant,Q2N0S5_9HIV1_theta_0.01.npy,Q2N0S5_9HIV1.pdb,1-860,0.1,,OrganismalFitness +Q53Z42_HUMAN_McShan_2019_binding-TAPBPR,Q53Z42_HUMAN_McShan_2019_binding-TAPBPR.csv,Q53Z42_HUMAN,Human,Homo sapiens,MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRMEPRAPWIEQEGPEYWDGETRKVKAHSQTHRVDLGTLRGYYNQSEAGSHTVQRMYGCDVGSDWRFLRGYHQYAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQLRAYLEGTCVEWLRRYLENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGAVVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV,365,FALSE,3344,3344,0,0.19,median,McShan,Molecular determinants of chaperone interactions on MHC-I for folding and antigen repertoire selection,2019,10.1073/pnas.1915562116,26-205,HLA-A,binding affinity (TAPBPR),,Q53Z42_HUMAN_2023-08-07_b01.a2m,1,365,365,0.1,0.2,41636,0.986,360,4986.2,13.85055556,Medium,210,0.5833333333,,score,1,mut_proteingym,Q53Z42_HUMAN_theta0.2_2023-08-07_b01.npy,Q53Z42_HUMAN.pdb,1-365,1,25,Binding +Q53Z42_HUMAN_McShan_2019_expression,Q53Z42_HUMAN_McShan_2019_expression.csv,Q53Z42_HUMAN,Human,Homo sapiens,MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRMEPRAPWIEQEGPEYWDGETRKVKAHSQTHRVDLGTLRGYYNQSEAGSHTVQRMYGCDVGSDWRFLRGYHQYAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQLRAYLEGTCVEWLRRYLENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGAVVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV,365,FALSE,3344,3344,0,-0.73,median,McShan,Molecular determinants of chaperone interactions on MHC-I for folding and antigen repertoire selection,2019,10.1073/pnas.1915562116,26-205,HLA-A,surface expression,,Q53Z42_HUMAN_2023-08-07_b01.a2m,1,365,365,0.1,0.2,41636,0.986,360,4986.2,13.85055556,Medium,210,0.5833333333,,score,1,mut_proteingym,Q53Z42_HUMAN_theta0.2_2023-08-07_b01.npy,Q53Z42_HUMAN.pdb,1-365,1,25,Expression +Q59976_STRSQ_Romero_2015,Q59976_STRSQ_Romero_2015.csv,Q59976_STRSQ,Prokaryote,Streptomyces sp,MVPAAQQTAMAPDAALTFPEGFLWGSATASYQIEGAAAEDGRTPSIWDTYARTPGRVRNGDTGDVATDHYHRWREDVALMAELGLGAYRFSLAWPRIQPTGRGPALQKGLDFYRRLADELLAKGIQPVATLYHWDLPQELENAGGWPERATAERFAEYAAIAADALGDRVKTWTTLNEPWCSAFLGYGSGVHAPGRTDPVAALRAAHHLNLGHGLAVQALRDRLPADAQCSVTLNIHHVRPLTDSDADADAVRRIDALANRVFTGPMLQGAYPEDLVKDTAGLTDWSFVRDGDLRLAHQKLDFLGVNYYSPTLVSEADGSGTHNSDGHGRSAHSPWPGADRVAFHQPPGETTAMGWAVDPSGLYELLRRLSSDFPALPLVITENGAAFHDYADPEGNVNDPERIAYVRDHLAAVHRAIKDGSDVRGYFLWSLLDNFEWAHGYSKRFGAVYVDYPTGTRIPKASARWYAEVARTGVLPTAGDPNSSSVDKLAAALEHHHHHH,501,FALSE,2999,2999,0,-1,manual,Romero,Dissecting enzyme function with microfluidic-based deep mutational scanning,2015,10.1073/pnas.1422285112,2-501,β-glucosidase,Enzyme function,Activity,Q59976_STRSQ_full_11-26-2021_b03.a2m,1,501,501,0.3,0.2,105913,0.882,442,13981.2,31.63167421,medium,850,1.923076923,Q59976_STRSQ_Romero_2015.csv,enrichment,1,mutant,Q59976_STRSQ_theta_0.2.npy,Q59976_STRSQ.pdb,1-501,0.1,,Activity +Q6WV12_9MAXI_Somermeyer_2022,Q6WV12_9MAXI_Somermeyer_2022.csv,Q6WV12_9MAXI,Eukaryote,Pontellina plumata,MPAMKIECRITGTLNGVEFELVGGGEGTPEQGRMTNKMKSTKGALTFSPYLLSHVMGYGFYHFGTYPSGYENPFLHAINNGGYTNTRIEKYEDGGVLHVSFSYRYEAGRVIGDFKVVGTGFPEDSVIFTDKIIRSNATVEHLHPMGDNVLVGSFARTFSLRDGGYYSFVVDSHMHFKSAIHPSILQNGGPMFAFRRVEELHSNTELGIVEYQHAFKTPIAFA,222,TRUE,31401,1141,30260,15721.24977,median,Somermeyer,Heterogeneity of the GFP fitness landscape and data-driven protein design,2022,10.7554/eLife.75842,2-222,Green fluorescent protein ppluGFP2,Fluorescence,FACS,Q6WV12_9MAXI_full_b0.6.a2m,1,222,222,0.6,0.2,506,1,222,95.9,0.431981982,Low,4,0.01801801802,Q6WV12_9MAXI_Somermeyer_2022.csv,replicates_mean_brightness,1,mutant,Q6WV12_9MAXI_theta_0.2.npy,Q6WV12_9MAXI.pdb,1-222,1,,Activity +Q837P4_ENTFA_Meier_2023,Q837P4_ENTFA_Meier_2023.csv,Q837P4_ENTFA,Prokaryote,Enterococcus faecalis,MTDLIKASKFFYHYLKRYKVSFLFIFLAIFAATYLQVKAPQFVGEAIQELAKYAVNVMQGKDDKSAFVSVIWKLLIFYVLTSAASFIYSILFTQVVGKSTNRMRIGLFNKLEKLTIRFFDSHQDGEILSRFTSDLDNIQNSLNQALLQVLTNIALLVGVLIMMFRQNVELAWATIASTPIAILIAVFVISKARKYVDLQQDEVGKLNGYMDEKISGQRVIITNGLQEETIDGFLEQNEKVRAATYKGQVYSGLLFPMMQGMSLVNTAIVIFFGGWLAINGSVDRAAALGLVVMFVQYSQQYYQPLMQISSGYSMIQLAVTGARRLNEMFDEPDEIRPENGEKLEEINKAVALNHVVFGYNPETPVLKDVSIHVDKGEMVALVGPTGSGKTTIMNLMNRFYDVNEGAVTFDGVDIREMDLDSLRSHVGIVLQESVLFSGTIRENIAFGKPEATDEEIVQAAKQANIHEFIVNLEQGYDTEITEENNLFSTGQKQLVSIARTIITNPELLILDEATSNVDTVTEAKIQKAMDEAIKGRTSFVIAHRLKTILNADRIIVLRDGEVIEEGNHHELVEQDGFYAELYKNQFVFE,589,FALSE,697,697,0,-0.6270963227,median,Meier,Deep mutational scan of a drug efflux pump reveals its structure–function landscape,2023,10.1038/s41589-022-01205-1,32-543,EfrD ABC transporter,Drug efflux,Growth,Q837P4_ENTFA_2023-08-07_b09.a2m,1,589,589,0.9,0.2,343933,0.975,574,54079.8,94.2151568,Medium,1123,1.95644599,41589_2022_1205_MOESM4_ESM.xlsx,avg_score,1,mutant,Q837P4_ENTFA_theta0.2_2023-08-07_b09.npy,Q837P4_ENTFA.pdb,1-589,1,,Activity +Q837P5_ENTFA_Meier_2023,Q837P5_ENTFA_Meier_2023.csv,Q837P5_ENTFA,Prokaryote,Enterococcus faecalis,MDLIIQHAKKYKGSVVIALLAVIVMVVSALWQPKLLQQVLEAIMNDDSDKMKNLGIQLIAIAGLGLVAGVINTIFSAKVAQGVSADIREATFRKIQTFSFGNIEKFSAGNLVVRLTNDVTQIQNVIMIALQTLFRIPFLFIGSFILAMLTLPQLWWVIVALVIAVILISMLSFSQMGKHFMIIQNLIDKINGIAKENLLGIRVVKSFVQEKNQLSRFTKVSEELTTHNLIVGSLFAVMIPAFMLVANLAVVGSIFFVSNLVKDDPTLIGGVASFMNYLMQIMMAIIIGGMMMMMTSRAAVSIKRIKEVMETEPDVTYKKVPEQELIGSVEFDHVSFRYPGDEEDTLKDISFSIQPGEMIGIVGATGAGKSTLAQLIPRLFDPTEGKIEVGGVDLREVNEHSLRKTVSFVLQKAILFSGTIAQNLRHGKRDASEADMERASGIAQAKEFIEKLAEGYDAPVEERSNNFSGGQKQRLSITRGVIGEPKILILDDSTSALDARSERLVREALDKELKETTTIVIAQKISSVVHADRILVLDNGRLVGEGTHEELAATNPVYQEIYETQKGKEEA,571,FALSE,747,747,0,-0.85731232,median,Meier,Deep mutational scan of a drug efflux pump reveals its structure–function landscape,2023,10.1038/s41589-022-01205-1,25-523,EfrC ABC transporter,Drug efflux,Growth,Q837P5_ENTFA_2023-08-07_b09.a2m,1,571,571,0.9,0.2,346355,0.993,567,54910.5,96.8439153,Medium,1135,2.00176367,,avg_score,1,mutant,Q837P5_ENTFA_theta0.2_2023-08-07_b09.npy,Q837P5_ENTFA.pdb,1-571,1,,Activity +Q8WTC7_9CNID_Somermeyer_2022,Q8WTC7_9CNID_Somermeyer_2022.csv,Q8WTC7_9CNID,Eukaryote,Aequorea macrodactyla,MSKGEELFTGIVPVLIELDGDVHGHKFSVRGEGEGDADYGKLEIKFICTTGKLPVPWPTLVTTLSYGILCFARYPEHMKMNDFFKSAMPEGYIQERTIFFQDDGKYKTRGEVKFEGDTLVNRIELKGMDFKEDGNILGHKLEYNFNSHNVYIMPDKANNGLKVNFKIRHNIEGGGVQLADHYQTNVPLGDGPVLIPINHYLSCQTAISKDRNETRDHMVFLEFFSACGHTHGMDELYK,238,TRUE,33510,1201,32309,5000,manual,Somermeyer,Heterogeneity of the GFP fitness landscape and data-driven protein design,2022,10.7554/eLife.75842,2-238,Green fluorescent protein amacGFP,Fluorescence,FACS,Q8WTC7_9CNID_full_b0.5.a2m,1,238,238,0.5,0.2,655,1,238,118.5,0.4978991597,Low,5,0.02100840336,Q8WTC8_9CNID_Somermeyer_2022.csv,replicates_mean_brightness,1,mutant,Q8WTC7_9CNID_theta_0.2.npy,Q8WTC7_9CNID.pdb,1-238,1,,Activity +R1AB_SARS2_Flynn_2022,R1AB_SARS2_Flynn_2022.csv,R1AB_SARS2,Virus,Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2),SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ,306,FALSE,5725,5725,0,0.5,manual,Flynn,Comprehensive fitness landscape of SARS-CoV-2 Mpro reveals insights into viral resistance mechanisms,2022,10.7554/eLife.77433,1-306,SARS-CoV-2 Mpro,"FRET, Growth",,R1AB_SARS2_02-19-2022_b07.a2m,1,306,306,0.7,0.01,182169,1,306,326.3,1.066339869,medium,79,0.2581699346,R1AB_SARS2_Flynn_2022.csv,average_growth,1,mutant,R1AB_SARS2_theta_0.01.npy,R1AB_SARS2.pdb,1-306,0.1,,OrganismalFitness +RAD_ANTMA_Tsuboyama_2023_2CJJ,RAD_ANTMA_Tsuboyama_2023_2CJJ.csv,RAD_ANTMA,Eukaryote,Antirrhinum majus,PWSAKENKAFERALAVYDKDTPDRWANVARAVEGRTPEEVKKHYEILVEDIKYI,54,TRUE,912,774,138,-0.3943851731,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-54,Transcription factor RADIALIS,Stability,cDNA display proteolysis,RAD_ANTMA_2023-08-07_b01.a2m,1,54,54,0.1,0.2,423275,0.833,45,38133.9,847.42,High,27,0.6,Tsuboyama2023_Dataset2_Dataset37,ddG_ML_float,1,mut_type,RAD_ANTMA_theta0.2_2023-08-07_b01.npy,RAD_ANTMA.pdb,1-54,1,,Stability +RAF1_HUMAN_Zinkus-Boltz_2019,RAF1_HUMAN_Zinkus-Boltz_2019.csv,RAF1_HUMAN,Human,Homo sapiens,MEHIQGAWKTISNGFGFKDAVFDGSSCISPTIVQQFGYQRRASDDGKLTDPSKTSNTIRVFLPNKQRTVVNVRNGMSLHDCLMKALKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAASLIGEELQVDFLDHVPLTTHNFARKTFLKLAFCDICQKFLLNGFRCQTCGYKFHEHCSTKVPTMCVDWSNIRQLLLFPNSTIGDSGVPALPSLTMRRMRESVSRMPVSSQHRYSTPHAFTFNTSSPSSEGSLSQRQRSTSTPNVHMVSTTLPVDSRMIEDAIRSHSESASPSALSSSPNNLSPTGWSQPKTPVPAQRERAPVSGTQEKNKIRPRGQRDSSYYWEIEASEVMLSTRIGSGSFGTVYKGKWHGDVAVKILKVVDPTPEQFQAFRNEVAVLRKTRHVNILLFMGYMTKDNLAIVTQWCEGSSLYKHLHVQETKFQMFQLIDIARQTAQGMDYLHAKNIIHRDMKSNNIFLHEGLTVKIGDFGLATVKSRWSGSQQVEQPTGSVLWMAPEVIRMQDNNPFSFQSDVYSYGIVLYELMTGELPYSHINNRDQIIFMVGRGYASPDLSKLYKNCPKAMKRLVADCVKKVKEERPLFPQILSSIELLQHSLPKINRSASEPSLHRAAHTEDINACTLTTSPRLPVF,648,FALSE,297,297,0,-0.0671,median,Zinkus-Boltz,A Phage-Assisted Continuous Selection Approach for Deep Mutational Scanning of Protein–Protein Interactions,2019,10.1021/acschembio.9b00669,52-90,RAF oncogene,Viral Replication,binding assays,RAF1_HUMAN_2023-10-12_b05.a2m,1,648,648,0.5,0.2,9685,0.972,630,350.5,0.5563492063,Low,30,0.04761904762,urn_mavedb_00000061-a-1_scores.csv,score,1,mutant,RAF1_HUMAN_theta0.2_2023-10-12_b05.npy,RAF1_HUMAN.pdb,1-648,1,,OrganismalFitness +RASH_HUMAN_Bandaru_2017,RASH_HUMAN_Bandaru_2017.csv,RASH_HUMAN,Human,Homo sapiens,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS,189,FALSE,3134,3134,0,-0.25,manual,Bandaru,Deconstruction of the Ras switching cycle through saturation mutagenesis,2017,10.7554/eLife.27810,2-166,HRAS,C-Raf binding and GEF,activity,RASH_HUMAN_full_11-26-2021_b03.a2m,1,189,189,0.3,0.2,204751,0.862,163,23971.6,147.0650307,high,205,1.257668712,RASH_HUMAN_Bandaru_2017.csv,unregulated,1,mutant,RASH_HUMAN_theta_0.2.npy,RASH_HUMAN.pdb,1-189,0.1,,Activity +RASK_HUMAN_Weng_2022_abundance,RASK_HUMAN_Weng_2022_abundance.csv,RASK_HUMAN,Human,Homo sapiens,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM,188,TRUE,26012,3066,22946,-0.504113408,median,Weng,The energetic and allosteric landscape for KRAS inhibition,2022,10.1101/2022.12.06.519122,2-188,KRAS,Yeast growth,,RASK_HUMAN_2023-08-07_b03.a2m,1,188,188,0.3,0.2,260539,0.888,167,27850.5,166.7694611,High,211,1.263473054,kras_fitness.xlsx,fitness,1,mutant,RASK_HUMAN_theta0.2_2023-08-07_b03.npy,RASK_HUMAN.pdb,1-188,1,,Expression +RASK_HUMAN_Weng_2022_binding-DARPin_K55,RASK_HUMAN_Weng_2022_binding-DARPin_K55.csv,RASK_HUMAN,Human,Homo sapiens,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM,188,TRUE,24873,3084,21789,-0.4605455262,median,Weng,The energetic and allosteric landscape for KRAS inhibition,2022,10.1101/2022.12.06.519127,2-188,KRAS,Yeast growth,,RASK_HUMAN_2023-08-07_b03.a2m,1,188,188,0.3,0.2,260539,0.888,167,27850.5,166.7694611,High,211,1.263473054,kras_fitness.xlsx,fitness,1,mutant,RASK_HUMAN_theta0.2_2023-08-07_b03.npy,RASK_HUMAN.pdb,1-188,1,,Binding +RBP1_HUMAN_Tsuboyama_2023_2KWH,RBP1_HUMAN_Tsuboyama_2023_2KWH.csv,RBP1_HUMAN,Human,Homo sapiens,ETQAGIKEEIRRQEFLLNSLHRDLQGGIKDLSKEERLWEVQRILTALKRKLR,52,TRUE,1332,975,357,-0.2693189895,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-52,RalA-binding protein 1,Stability,cDNA display proteolysis,RBP1_HUMAN_2023-08-07_b01.a2m,1,52,52,0.1,0.2,135922,0.827,43,50510,1174.651163,High,6,0.1395348837,Tsuboyama2023_Dataset2_Dataset38,ddG_ML_float,1,mut_type,RBP1_HUMAN_theta0.2_2023-08-07_b01.npy,RBP1_HUMAN.pdb,1-52,1,,Stability +RCD1_ARATH_Tsuboyama_2023_5OAO,RCD1_ARATH_Tsuboyama_2023_5OAO.csv,RCD1_ARATH,Eukaryote,Arabidopsis thaliana,PTLFAAISHKVAENDMLLINADYQQLRDKKMTRAEFVRKLRVIVGDDLLRSTITTLQ,57,TRUE,1261,988,273,-0.3828831078,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-57,Inactive poly [ADP-ribose] polymerase RCD1,Stability,cDNA display proteolysis,RCD1_ARATH_2023-08-07_b02.a2m,1,57,57,0.2,0.2,6525,0.93,53,1578.5,29.78301887,Medium,2,0.03773584906,Tsuboyama2023_Dataset2_Dataset39,ddG_ML_float,1,mut_type,RCD1_ARATH_theta0.2_2023-08-07_b02.npy,RCD1_ARATH.pdb,1-57,1,,Stability +RCRO_LAMBD_Tsuboyama_2023_1ORC,RCRO_LAMBD_Tsuboyama_2023_1ORC.csv,RCRO_LAMBD,Virus,Escherichia phage lambda (Bacteriophage lambda),QRITLKDYAMRFGQTKTAKDLGVYQSAINKAIHAGRKIFLTINADGSVYAEEVKDGEVKPFPS,63,TRUE,2278,1195,1083,-1.255848942,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-63,Regulatory protein cro,Stability,cDNA display proteolysis,RCRO_LAMBD_2023-08-07_b03.a2m,1,63,63,0.3,0.2,392895,0.762,48,51658.6,1076.220833,High,32,0.6666666667,Tsuboyama2023_Dataset2_Dataset40,ddG_ML_float,1,mut_type,RCRO_LAMBD_theta0.2_2023-08-07_b03.npy,RCRO_LAMBD.pdb,1-63,1,,Stability +RD23A_HUMAN_Tsuboyama_2023_1IFY,RD23A_HUMAN_Tsuboyama_2023_1IFY.csv,RD23A_HUMAN,Human,Homo sapiens,SEYETMLTEIMSMGYERERVVAALRASYNNPHRAVEYLLTGIPG,44,TRUE,1019,798,221,-0.7285205281,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-44,UV excision repair protein RAD23 homolog A,Stability,cDNA display proteolysis,RD23A_HUMAN_2023-08-07_b04.a2m,1,44,44,0.4,0.2,100991,0.864,38,7912.9,208.2342105,High,21,0.5526315789,Tsuboyama2023_Dataset2_Dataset41,ddG_ML_float,1,mut_type,RD23A_HUMAN_theta0.2_2023-08-07_b04.npy,RD23A_HUMAN.pdb,1-44,1,,Stability +RDRP_I33A0_Li_2023,RDRP_I33A0_Li_2023.csv,RDRP_I33A0,Virus,Influenza A virus (strain A/Wilson-Smith/1933 H1N1),MDVNPTLLFLKVPAQNAISTTFPYTGDPPYSHGTGTGYTMDTVNRTHQYSERGRWTTNTETGAPQLNPIDGPLPEDNEPSGYAQTDCVLEAMAFLEESHPGIFETSCLETMEVVQQTRVDKLTQGRQTYDWTLNRNQPAATALANTIEVFRSNGLTANESGRLIDFLKDVMESMNKEEMEITTHFQRKRRVRDNMTKKMVTQRTIGKRKQRLNKRSYLIRALTLNTMTKDAERGKLKRRAIATPGMQIRGFVYFVETLARSICEKLEQSGLPVGGNEKKAKLANVVRKMMTNSQDTEISFTITGDNTKWNENQNPRMFLAMITYITRNQPEWFRNVLSIAPIMFSNKMARLGKGYMFESKSMKIRTQIPAEMLASIDLKYFNDSTRKKIEKIRPLLIDGTASLSPGMMMGMFNMLSTVLGVSILNLGQKRHTKTTYWWDGLQSSDDFALIVNAPNHEGIQAGVNRFYRTCKLLGINMSKKKSYINRTGTFEFTSFFYRYGFVANFSMELPSFGVSGINESADMSIGVTVIKNNMINNDLGPATAQMALQLFIKDYRYTYRCHRGDTQIQTRRSFEIKKLWEQTHSKAGLLVSDGGPNLYNIRNLHIPEVCLKWELMDEDYQGRLCNPLNPFVNHKDIESVNNAVIMPAHGPAKNMEYDAVATTHSWIPKRNRSILNTSQRGILEDEQMYQKCCNLFEKFFPSSSYRRPVGISSMVEAMVSRARIDARIDFESGRIKKEEFTEIMKICSTIEELRRQK,757,FALSE,12003,12003,0,-1,manual,Li,Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein,2023,10.1101/2023.08.27.554986,1-757,Influenza RNA polymerase PB1,Viral Replication,Growth,RDRP_I33A0_2023-08-07_b01.a2m,1,757,757,0.1,0.01,26589,1,757,102.8,0.1357992074,Low,0,0,554986_file16.csv,fitness,1,mutant,RDRP_I33A0_theta0.01_2023-08-07_b01.npy,RDRP_I33A0.pdb,1-757,1,,OrganismalFitness +REV_HV1H2_Fernandes_2016,REV_HV1H2_Fernandes_2016.csv,REV_HV1H2,Virus,Human immunodeficiency virus type 1 group M subtype B (isolate HXB2) (HIV-1),MAGRSGDSDEELIRTVRLIKLLYQSNPPPNPEGTRQARRNRRRRWRERQRQIHSISERILSTYLGRSAEPVPLQLPPLERLTLDCNEDCGTSGTQGVGSPQILVESPTVLESGTKE,116,FALSE,2147,2147,0,-0.06744744968,median,Fernandes,Functional Segregation of Overlapping Genes in HIV,2016,10.1016/j.cell.2016.11.031,1-116,HIV rev,Viral replication,Growth,REV_HV1H2_full_theta0.99_04-29-2022_b09.a2m,1,116,116,0.9,0.01,15839,0.948,110,9951.8,90.47090909,medium,54,0.4909090909,REV_HV1H2_Fernandes_2016.csv,sel_coeff_mean,1,mutant,REV_HV1H2_theta_0.01.npy,REV_HV1H2.pdb,1-116,0.1,,OrganismalFitness +RFAH_ECOLI_Tsuboyama_2023_2LCL,RFAH_ECOLI_Tsuboyama_2023_2LCL.csv,RFAH_ECOLI,Prokaryote,Escherichia coli,ATPYPGDKVIITEGAFEGFQAIFTEPDGEARSMLLLNLINKEIKHSVKNTEFRKL,55,TRUE,1326,969,357,-0.4014057355,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,Transcription antitermination protein RfaH,Stability,cDNA display proteolysis,RFAH_ECOLI_2023-08-07_b04.a2m,1,55,55,0.4,0.2,86049,0.927,51,11748.4,230.3607843,High,35,0.6862745098,Tsuboyama2023_Dataset2_Dataset42,ddG_ML_float,1,mut_type,RFAH_ECOLI_theta0.2_2023-08-07_b04.npy,RFAH_ECOLI.pdb,1-55,1,,Stability +RL20_AQUAE_Tsuboyama_2023_1GYZ,RL20_AQUAE_Tsuboyama_2023_1GYZ.csv,RL20_AQUAE,Prokaryote,Aquifex aeolicus,WIARINAAVRAYGLNYSTFINGLKKAGIELDRKILADMAVRDPQAFEQVVNKVKEALQV,59,TRUE,1461,1121,340,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-59,Large ribosomal subunit protein bL20,Stability,cDNA display proteolysis,RL20_AQUAE_2023-08-07_b01.a2m,1,59,59,0.1,0.2,397758,0.814,48,104766.4,2182.633333,High,34,0.7083333333,Tsuboyama2023_Dataset2_Dataset43,ddG_ML_float,1,mut_type,RL20_AQUAE_theta0.2_2023-08-07_b01.npy,RL20_AQUAE.pdb,1-59,1,,Stability +RL40A_YEAST_Mavor_2016,RL40A_YEAST_Mavor_2016.csv,RL40A_YEAST,Eukaryote,Saccharomyces cerevisiae,MQIFVKTLTGKTITLEVESSDTIDNVKSKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGIIEPSLKALASKYNCDKSVCRKCYARLPPRATNCRKRKCGHTNQLRPKKKLK,128,FALSE,1253,1253,0,-0.2,manual,Mavor,Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting,2016,10.7554/eLife.15802,2-76,Ubiquitin,Growth,Growth,RL40A_YEAST_full_11-26-2021_b01.a2m,1,128,128,0.1,0.2,16228,0.695,89,3974.4,44.65617978,medium,12,0.1348314607,RL401_YEAST_Mavor_2016.csv,DMSO,1,mutant,RL40A_YEAST_theta_0.2.npy,RL40A_YEAST.pdb,1-128,0.1,,OrganismalFitness +RL40A_YEAST_Roscoe_2013,RL40A_YEAST_Roscoe_2013.csv,RL40A_YEAST,Eukaryote,Saccharomyces cerevisiae,MQIFVKTLTGKTITLEVESSDTIDNVKSKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGIIEPSLKALASKYNCDKSVCRKCYARLPPRATNCRKRKCGHTNQLRPKKKLK,128,FALSE,1195,1195,0,-0.2,manual,Roscoe,Analyses of the Effects of All Ubiquitin Point Mutants on Yeast Growth Rate,2013,10.1016/j.jmb.2013.01.032,2-76,Ubiquitin,Growth (essential function),Growth,RL40A_YEAST_full_11-26-2021_b01.a2m,1,128,128,0.1,0.2,16228,0.695,89,3974.4,44.65617978,medium,12,0.1348314607,RL401_YEAST_Roscoe_2013.csv,Selection Coefficient,1,mutant,RL40A_YEAST_theta_0.2.npy,RL40A_YEAST.pdb,1-128,0.1,,OrganismalFitness +RL40A_YEAST_Roscoe_2014,RL40A_YEAST_Roscoe_2014.csv,RL40A_YEAST,Eukaryote,Saccharomyces cerevisiae,MQIFVKTLTGKTITLEVESSDTIDNVKSKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGIIEPSLKALASKYNCDKSVCRKCYARLPPRATNCRKRKCGHTNQLRPKKKLK,128,FALSE,1380,1380,0,0.5,manual,Roscoe,"Systematic Exploration of Ubiquitin Sequence, E1 Activation Efficiency, and Experimental Fitness in Yeast",2014,10.1016/j.jmb.2014.05.019,2-76,Ubiquitin,E1 reactivity,Binding,RL40A_YEAST_full_11-26-2021_b01.a2m,1,128,128,0.1,0.2,16228,0.695,89,3974.4,44.65617978,medium,12,0.1348314607,RL401_YEAST_Roscoe_2014.csv,rel_react,1,mutant,RL40A_YEAST_theta_0.2.npy,RL40A_YEAST.pdb,1-128,0.1,,Activity +RNC_ECOLI_Weeks_2023,RNC_ECOLI_Weeks_2023.csv,RNC_ECOLI,Prokaryote,Escherichia coli,MNPIVINRLQRKLGYTFNHQELLQQALTHRSASSKHNERLEFLGDSILSYVIANALYHRFPRVDEGDMSRMRATLVRGNTLAELAREFELGECLRLGPGELKSGGFRRESILADTVEALIGGVFLDSDIQTVEKLILNWYQTRLDEISPGDKQKDPKTRLQEYLQGRHLPLPTYLVVQVRGEAHDQEFTIHCQVSGLSEPVVGTGSSRRKAEQAAAEQALKKLELE,226,FALSE,4277,4277,0,-0.054826707,median,Weeks,Fitness and Functional Landscapes of the E. coli RNase III Gene rnc,2023,10.1093/molbev/msad047,1-226,RNase III,Fluorescence,FACS,RNC_ECOLI_2023-08-07_b06.a2m,1,226,226,0.6,0.2,66507,0.969,219,16221.4,74.07031963,Medium,275,1.255707763,RNC_ECOLI_Weeks_2023.csv,Functional Score Weighted Mean,1,mutant,RNC_ECOLI_theta0.2_2023-08-07_b06.npy,RNC_ECOLI.pdb,1-226,1,,Activity +RPC1_BP434_Tsuboyama_2023_1R69,RPC1_BP434_Tsuboyama_2023_1R69.csv,RPC1_BP434,Virus,Enterobacteria phage 434 (Bacteriophage 434),SISSRVKSKRIQLGLNQAELAQKVGTTQQSIEQLENGKTKRPRFLPELASALGVSVDWLLN,61,TRUE,1459,1124,335,-1.349855239,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-61,Repressor protein CI,Stability,cDNA display proteolysis,RPC1_BP434_2023-08-07_b05.a2m,1,61,61,0.5,0.01,820224,0.951,58,192520.2,3319.313793,High,73,1.25862069,Tsuboyama2023_Dataset2_Dataset44,ddG_ML_float,1,mut_type,RPC1_BP434_theta0.01_2023-08-07_b05.npy,RPC1_BP434.pdb,1-61,1,,Stability +RPC1_LAMBD_Li_2019_high-expression,RPC1_LAMBD_Li_2019_high-expression.csv,RPC1_LAMBD,Virus,Escherichia phage lambda (Bacteriophage lambda),MSTKKKPLTQEQLEDARRLKAIYEKKKNELGLSQESVADKMGMGQSGVGALFNGINALNAYNAALLAKILKVSVEEFSPSIAREIYEMYEAVSMQPSLRSEYEYPVFSHVQAGMFSPELRTFTKGDAERWVSTTKKASDSAFWLEVEGNSMTAPTGSKPSFPDGMLILVDPEQAVEPGDFCIARLGGDEFTFKKLIRDSGQVFLQPLNPQYPMIPCNESCSVVGKVIASQWPEETFG,237,FALSE,351,351,0,7,manual,Li,Changes in gene expression predictably shift and switch genetic interactions,2019,10.1038/s41467-019-11735-3,19-77,CI,Repressor activity (FACS sorting for expression of GFP reporter),FACS,RPC1_LAMBD_2023-08-07_b03.a2m,1,237,237,0.3,0.2,100755,0.886,210,28172.8,134.1561905,High,219,1.042857143,,H_GFP_mean_scaled,-1,mut_proteingym,RPC1_LAMBD_theta0.2_2023-08-07_b03.npy,RPC1_LAMBD.pdb,1-237,1,18,Activity +RPC1_LAMBD_Li_2019_low-expression,RPC1_LAMBD_Li_2019_low-expression.csv,RPC1_LAMBD,Virus,Escherichia phage lambda (Bacteriophage lambda),MSTKKKPLTQEQLEDARRLKAIYEKKKNELGLSQESVADKMGMGQSGVGALFNGINALNAYNAALLAKILKVSVEEFSPSIAREIYEMYEAVSMQPSLRSEYEYPVFSHVQAGMFSPELRTFTKGDAERWVSTTKKASDSAFWLEVEGNSMTAPTGSKPSFPDGMLILVDPEQAVEPGDFCIARLGGDEFTFKKLIRDSGQVFLQPLNPQYPMIPCNESCSVVGKVIASQWPEETFG,237,FALSE,351,351,0,8.481244509,median,Li,Changes in gene expression predictably shift and switch genetic interactions,2019,10.1038/s41467-019-11735-3,19-77,CI,Repressor activity (FACS sorting for expression of GFP reporter),FACS,RPC1_LAMBD_2023-08-07_b03.a2m,1,237,237,0.3,0.2,100755,0.886,210,28172.8,134.1561905,High,219,1.042857143,,L_GFP_mean_scaled,-1,mut_proteingym,RPC1_LAMBD_theta0.2_2023-08-07_b03.npy,RPC1_LAMBD.pdb,1-237,1,18,Activity +RS15_GEOSE_Tsuboyama_2023_1A32,RS15_GEOSE_Tsuboyama_2023_1A32.csv,RS15_GEOSE,Prokaryote,Geobacillus stearothermophilus,SPEVQIAILTEQINNLNEHLRVHKKDHHSRRGLLKMVGKRRRLLAYLRNKDVARYREIVEKLG,63,FALSE,1195,1195,0,-0.1292928041,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-63,Small ribosomal subunit protein uS15,Stability,cDNA display proteolysis,RS15_GEOSE_2023-08-07_b06.a2m,1,63,63,0.6,0.2,44428,1,63,4519.5,71.73809524,Medium,35,0.5555555556,Tsuboyama2023_Dataset2_Dataset45,ddG_ML_float,1,mut_type,RS15_GEOSE_theta0.2_2023-08-07_b06.npy,RS15_GEOSE.pdb,1-63,1,,Stability +S22A1_HUMAN_Yee_2023_abundance,S22A1_HUMAN_Yee_2023_abundance.csv,S22A1_HUMAN,Human,Homo sapiens,PTVDDILEQVGESGWFQKQAFLILCLLSAAFAPICVGIVFLGFTPDHHCQSPGVAELSQRCGWSPAEELNYTVPGLGPAGEAFLGQCRRYEVDWNQSALSCVDPLASLATNRSHLPLGPCQDGWVYDTPGSSIVTEFNLVCADSWKLDLFQSCLNAGFLFGSLGVGYFADRFGRKLCLLGTVLVNAVSGVLMAFSPNYMSMLLFRLLQGLVSKGNWMAGYTLITEFVGSGSRRTVAIMYQMAFTVGLVALTGLAYALPHWRWLQLAVSLPTFLFLLYYWCVPESPRWLLSQKRNTEAIKIMDHIAQKNGKLPPADLKMLSLEEDVTEKLSPSFADLFRTPRLRKRTFILMYLWFTDSVLYQGLILHMGATSGNLYLDFLYSALVEIPGAFIALITIDRVGRIYPMAMSNLLAGAACLVMIFISPDLHWLNIIIMCVGRMGITIAIQMICLVNAELYPTFVRNLGVMVCSSLCDIGGIITPFIVFRLREVWQALPLILFAVLGLLAAGVTLLLPETKGVALPETMKDAENLGRKAKPKENTIYLKVQTSEPSGT,553,FALSE,9803,9803,0,-1,manual,Yee,The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics,2023,10.1101/2023.06.06.543963,1-549,Oct1,abundance,FACS,S22A1_HUMAN_2023-08-07_b02.a2m,1,553,553,0.2,0.2,198790,0.807,446,32557.5,72.99887892,Medium,485,1.087443946,543963_file04.xlsx,GFP_score,1,mutant,S22A1_HUMAN_theta0.2_2023-08-07_b02.npy,S22A1_HUMAN.pdb,1-553,1,,Expression +S22A1_HUMAN_Yee_2023_activity,S22A1_HUMAN_Yee_2023_activity.csv,S22A1_HUMAN,Human,Homo sapiens,PTVDDILEQVGESGWFQKQAFLILCLLSAAFAPICVGIVFLGFTPDHHCQSPGVAELSQRCGWSPAEELNYTVPGLGPAGEAFLGQCRRYEVDWNQSALSCVDPLASLATNRSHLPLGPCQDGWVYDTPGSSIVTEFNLVCADSWKLDLFQSCLNAGFLFGSLGVGYFADRFGRKLCLLGTVLVNAVSGVLMAFSPNYMSMLLFRLLQGLVSKGNWMAGYTLITEFVGSGSRRTVAIMYQMAFTVGLVALTGLAYALPHWRWLQLAVSLPTFLFLLYYWCVPESPRWLLSQKRNTEAIKIMDHIAQKNGKLPPADLKMLSLEEDVTEKLSPSFADLFRTPRLRKRTFILMYLWFTDSVLYQGLILHMGATSGNLYLDFLYSALVEIPGAFIALITIDRVGRIYPMAMSNLLAGAACLVMIFISPDLHWLNIIIMCVGRMGITIAIQMICLVNAELYPTFVRNLGVMVCSSLCDIGGIITPFIVFRLREVWQALPLILFAVLGLLAAGVTLLLPETKGVALPETMKDAENLGRKAKPKENTIYLKVQTSEPSGT,553,FALSE,10094,10094,0,1,manual,Yee,The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics,2023,10.1101/2023.06.06.543963,1-549,Oct1,uptake of cytotoxic substrate,Growth,S22A1_HUMAN_2023-08-07_b02.a2m,1,553,553,0.2,0.2,198790,0.807,446,32557.5,72.99887892,Medium,485,1.087443946,543963_file04.xlsx,SM73_1_score,-1,mutant,S22A1_HUMAN_theta0.2_2023-08-07_b02.npy,S22A1_HUMAN.pdb,1-553,1,,Activity +SAV1_MOUSE_Tsuboyama_2023_2YSB,SAV1_MOUSE_Tsuboyama_2023_2YSB.csv,SAV1_MOUSE,Eukaryote,Mus musculus,GEDLPLPPGWSVDWTMRGRKYYIDHNTNTTHWSHPLESGPSSG,43,TRUE,965,679,286,-0.6280556038,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-43,Protein salvador homolog 1,Stability,cDNA display proteolysis,SAV1_MOUSE_2023-08-07_b06.a2m,1,43,43,0.6,0.2,177542,0.791,34,4627.6,136.1058824,High,14,0.4117647059,Tsuboyama2023_Dataset2_Dataset46,ddG_ML_float,1,mut_type,SAV1_MOUSE_theta0.2_2023-08-07_b06.npy,SAV1_MOUSE.pdb,1-43,1,,Stability +SBI_STAAM_Tsuboyama_2023_2JVG,SBI_STAAM_Tsuboyama_2023_2JVG.csv,SBI_STAAM,Prokaryote,Staphylococcus aureus,VRHDERVKSANDAISKLNEKDSIENRRLAQREVNKAPMDVKEHLQKQLDALVAQKD,56,FALSE,1025,1025,0,-0.5166138978,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-56,Immunoglobulin-binding protein Sbi,Stability,cDNA display proteolysis,SBI_STAAM_2023-08-07_b04.a2m,1,56,56,0.4,0.2,14476,0.875,49,1539.4,31.41632653,Medium,21,0.4285714286,Tsuboyama2023_Dataset2_Dataset47,ddG_ML_float,1,mut_type,SBI_STAAM_theta0.2_2023-08-07_b04.npy,SBI_STAAM.pdb,1-56,1,,Stability +SC6A4_HUMAN_Young_2021,SC6A4_HUMAN_Young_2021.csv,SC6A4_HUMAN,Human,Homo sapiens,METTPLNSQKQLSACEDGEDCQENGVLQKVVPTPGDKVESGQISNGYSAVPSPGAGDDTRHSIPATTTTLVAELHQGERETWGKKVDFLLSVIGYAVDLGNVWRFPYICYQNGGGAFLLPYTIMAIFGGIPLFYMELALGQYHRNGCISIWRKICPIFKGIGYAICIIAFYIASYYNTIMAWALYYLISSFTDQLPWTSCKNSWNTGNCTNYFSEDNITWTLHSTSPAEEFYTRHVLQIHRSKGLQDLGGISWQLALCIMLIFTVIYFSIWKGVKTSGKVVWVTATFPYIILSVLLVRGATLPGAWRGVLFYLKPNWQKLLETGVWIDAAAQIFFSLGPGFGVLLAFASYNKFNNNCYQDALVTSVVNCMTSFVSGFVIFTVLGYMAEMRNEDVSEVAKDAGPSLLFITYAEAIANMPASTFFAIIFFLMLITLGLDSTFAGLEGVITAVLDEFPHVWAKRRERFVLAVVITCFFGSLVTLTFGGAYVVKLLEEYATGPAVLTVALIEAVAVSWFYGITQFCRDVKEMLGFSPGWFWRICWVAISPLFLLFIICSFLMSPPQLRLFQYNYPYWSIILGYCIGTSSFICIPTYIAYRLIITPGTFKERIIKSITPETPTEIPCGDIRLNAV,630,FALSE,11576,11576,0,-0.1560688323,median,Young,Deep Mutagenesis of a Transporter for Uptake of a Non-Native Substrate Identifies Conformationally Dynamic Regions,2021,10.1101/2021.04.19.440442,2-630,Sodium-dependent serotonin transporter,Fluorescence,Fluorescence,SC6A4_HUMAN_full_11-26-2021_b02.a2m,1,630,630,0.2,0.2,40971,0.805,507,5278.9,10.41203156,medium,278,0.5483234714,SC6A4_HUMAN_Young_2021.csv,avg_MYC,1,mutant,SC6A4_HUMAN_theta_0.2.npy,SC6A4_HUMAN.pdb,1-630,0.1,,Activity +SCIN_STAAR_Tsuboyama_2023_2QFF,SCIN_STAAR_Tsuboyama_2023_2QFF.csv,SCIN_STAAR,Prokaryote,Staphylococcus aureus,QNEKLANELKSLLDELNVNELATGSLNTYYKRTIKISGQKAMYALKSKDFKKMSEAKYQLQKIYNEIDEA,70,FALSE,1212,1212,0,-0.4037152866,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-70,Staphylococcal complement inhibitor,Stability,cDNA display proteolysis,SCIN_STAAR_2023-08-07_b02.a2m,1,70,70,0.2,0.2,38043,0.9,63,11146.3,176.9253968,High,4,0.06349206349,Tsuboyama2023_Dataset2_Dataset48,ddG_ML_float,1,mut_type,SCIN_STAAR_theta0.2_2023-08-07_b02.npy,SCIN_STAAR.pdb,1-70,1,,Stability +SCN5A_HUMAN_Glazer_2019,SCN5A_HUMAN_Glazer_2019.csv,SCN5A_HUMAN,Human,Homo sapiens,MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV,2016,FALSE,224,224,0,-88.35,median,Glazer,Deep Mutational Scan of an SCN5A Voltage Sensor,2019,10.1161/CIRCGEN.119.002786,1621-1632,SCN5A,"drug resistance (triple-drug assay: veratridine + brevetoxin + ouabain; surrogate for sodium channel dysfunction, select against function)",,SCN5A_HUMAN_1611-1642_11-26-2021_b03.a2m,1611,1642,32,0.3,0.2,49973,0.812,26,743.1,28.58076923,medium,2,0.07692307692,SCN5A_HUMAN_Glazer_2019.csv,dms,-1,mutation,SCN5A_HUMAN_theta_0.2.npy,SCN5A_HUMAN.pdb,1-2016,0.1,,OrganismalFitness +SDA_BACSU_Tsuboyama_2023_1PV0,SDA_BACSU_Tsuboyama_2023_1PV0.csv,SDA_BACSU,Prokaryote,Bacillus subtilis,MRKLSDELLIESYFKATEMNLNRDFIELIENEIKRRSLGHIISV,44,TRUE,2770,834,1936,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-44,Sporulation inhibitor sda,Stability,cDNA display proteolysis,SDA_BACSU_2023-08-07_b05.a2m,1,44,44,0.5,0.2,1953,0.886,39,876.8,22.48205128,Medium,4,0.1025641026,Tsuboyama2023_Dataset2_Dataset49,ddG_ML_float,1,mut_type,SDA_BACSU_theta0.2_2023-08-07_b05.npy,SDA_BACSU.pdb,1-44,1,,Stability +SERC_HUMAN_Xie_2023,SERC_HUMAN_Xie_2023.csv,SERC_HUMAN,Human,Homo sapiens,MDAPRQVVNFGPGPAKLPHSVLLEIQKELLDYKGVGISVLEMSHRSSDFAKIINNTENLVRELLAVPDNYKVIFLQGGGCGQFSAVPLNLIGLKAGRCADYVVTGAWSAKAAEEAKKFGTINIVHPKLGSYTKIPDPSTWNLNPDASYVYYCANETVHGVEFDFIPDVKGAVLVCDMSSNFLSKPVDVSKFGVIFAGAQKNVGSAGVTVVIVRDDLLGFALRECPSVLEYKVQAGNSSLYNTPPCFSIYVMGLVLEWIKNNGGAAAMEKLSSIKSQTIYEIIDNSQGFYVCPVEPQNRSKMNIPFRIGNAKGDDALEKRFLDKALELNMLSLKGHRSVGGIRASLYNAVTIEDVQKLAAFMKKFLEMHQL,370,FALSE,1914,1914,0,0.9360658319,median,Xie,Predicting the functional effect of compound heterozygous genotypes from large scale variant effect maps,2023,10.1101/2023.01.11.523651,2-370,PSAT1,Yeast growth,,SERC_HUMAN_2023-08-07_b02.a2m,1,370,370,0.2,0.2,232438,0.949,351,42521.5,121.1438746,High,899,2.561253561,urn_mavedb_00000107-b-1_scores-2.csv,score,1,mutant,SERC_HUMAN_theta0.2_2023-08-07_b02.npy,SERC_HUMAN.pdb,1-370,1,,OrganismalFitness +SHOC2_HUMAN_Kwon_2022,SHOC2_HUMAN_Kwon_2022.csv,SHOC2_HUMAN,Human,Homo sapiens,MSSSLGKEKDSKEKDPKVPSAKEREKEAKASGGFGKESKEKEPKTKGKDAKDGKKDSSAAQPGVAFSVDNTIKRPNPAPGTRKKSSNAEVIKELNKCREENSMRLDLSKRSIHILPSSIKELTQLTELYLYSNKLQSLPAEVGCLVNLMTLALSENSLTSLPDSLDNLKKLRMLDLRHNKLREIPSVVYRLDSLTTLYLRFNRITTVEKDIKNLSKLSMLSIRENKIKQLPAEIGELCNLITLDVAHNQLEHLPKEIGNCTQITNLDLQHNELLDLPDTIGNLSSLSRLGLRYNRLSAIPRSLAKCSALEELNLENNNISTLPESLLSSLVKLNSLTLARNCFQLYPVGGPSQFSTIYSLNMEHNRINKIPFGIFSRAKVLSKLNMKDNQLTSLPLDFGTWTSMVELNLATNQLTKIPEDVSGLVSLEVLILSNNLLKKLPHGLGNLRKLRELDLEENKLESLPNEIAYLKDLQKLVLTNNQLTTLPRGIGHLTNLTHLGLGENLLTHLPEEIGTLENLEELYLNDNPNLHSLPFELALCSKLSIMSIENCPLSHLPPQIVAGGPSFIIQFLKMQGPYRAMV,582,FALSE,10972,10972,0,-0.34,median,Kwon,Structure–function analysis of the SHOC2–MRAS–PP1C holophosphatase complex,2022,10.1038/s41586-022-04928-2,2-582,Leucine-rich repeat protein SHOC-2,Drug resistance,Survival (dosed with trametinib),SHOC2_HUMAN_2023-10-12_b04.a2m,1,582,582,0.4,0.2,22163,0.777,452,8806.8,19.4840708,Medium,379,0.8384955752,2022.3.16.Extended Data Table 4.csv,LFC_scaled,1,variant.by.aa,SHOC2_HUMAN_theta0.2_2023-10-12_b04.npy,SHOC2_HUMAN.pdb,1-582,1,,OrganismalFitness +SOX30_HUMAN_Tsuboyama_2023_7JJK,SOX30_HUMAN_Tsuboyama_2023_7JJK.csv,SOX30_HUMAN,Human,Homo sapiens,RPMNAFMVWARIHRPALAKANPAANNAEISVQLGLEWNKLSEEQKKPYYDEAQKIKE,57,FALSE,1010,1010,0,-0.3216404755,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-57,Transcription factor SOX-30,Stability,cDNA display proteolysis,SOX30_HUMAN_2023-08-07_b03.a2m,1,57,57,0.3,0.2,158104,0.982,56,14909.6,266.2428571,High,36,0.6428571429,Tsuboyama2023_Dataset2_Dataset50,ddG_ML_float,1,mut_type,SOX30_HUMAN_theta0.2_2023-08-07_b03.npy,SOX30_HUMAN.pdb,1-57,1,,Stability +SPA_STAAU_Tsuboyama_2023_1LP1,SPA_STAAU_Tsuboyama_2023_1LP1.csv,SPA_STAAU,Prokaryote,Staphylococcus aureus,KFNKELSVAGREIVTLPNLNDPQKKAFIFSLWDDPSQSANLLAEAKKLNDAQAPK,55,TRUE,2105,1035,1070,-0.9794586971,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-55,Immunoglobulin G-binding protein A,Stability,cDNA display proteolysis,SPA_STAAU_2023-08-07_b04.a2m,1,55,55,0.4,0.2,184804,0.927,51,2042.1,40.04117647,Medium,25,0.4901960784,Tsuboyama2023_Dataset2_Dataset51,ddG_ML_float,1,mut_type,SPA_STAAU_theta0.2_2023-08-07_b04.npy,SPA_STAAU.pdb,1-55,1,,Stability +SPG1_STRSG_Olson_2014,SPG1_STRSG_Olson_2014.csv,SPG1_STRSG,Prokaryote,Streptococcus sp. group G,MEKEKKVKYFLRKSAFGLASVSAAFLVGSTVFAVDSPIEDTPIIRNGGELTNLLGNSETTLALRNEESATADLTAAAVADTVAAAAAENAGAAAWEAAAAADALAKAKADALKEFNKYGVSDYYKNLINNAKTVEGIKDLQAQVVESAKKARISEATDGLSDFLKSQTPAEDTVKSIELAEAKVLANRELDKYGVSDYHKNLINNAKTVEGVKELIDEILAALPKTDQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKPEVIDASELTPAVTTYKLVINGKTLKGETTTKAVDAETAEKAFKQYANDNGVDGVWTYDDATKTFTVTEMVTEVPGDAPTEPEKPEASIPLVPLTPATPIAKDDAKKDDTKKEDAKKPEAKKDDAKKAETLPTTGEGSNPFFTAAALAVMAGAGALAVASKRKED,448,TRUE,536962,1045,535917,-4,manual,Olson,A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain,2014,10.1016/j.cub.2014.09.072,228-282,GB1,Binding (IgG),Binding,SPG1_STRSG_full_11-26-2021_b07.a2m,1,448,448,0.7,0.2,44,0.913,409,3.3,0.008068459658,low,0,0,SPG1_STRSG_Olson_2014.csv,lnW,1,mutant,SPG1_STRSG_theta_0.2.npy,SPG1_STRSG.pdb,1-448,0.1,,Binding +SPG1_STRSG_Wu_2016,SPG1_STRSG_Wu_2016.csv,SPG1_STRSG,Prokaryote,Streptococcus sp. group G,MEKEKKVKYFLRKSAFGLASVSAAFLVGSTVFAVDSPIEDTPIIRNGGELTNLLGNSETTLALRNEESATADLTAAAVADTVAAAAAENAGAAAWEAAAAADALAKAKADALKEFNKYGVSDYYKNLINNAKTVEGIKDLQAQVVESAKKARISEATDGLSDFLKSQTPAEDTVKSIELAEAKVLANRELDKYGVSDYHKNLINNAKTVEGVKELIDEILAALPKTDQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKPEVIDASELTPAVTTYKLVINGKTLKGETTTKAVDAETAEKAFKQYANDNGVDGVWTYDDATKTFTVTEMVTEVPGDAPTEPEKPEASIPLVPLTPATPIAKDDAKKDDTKKEDAKKPEAKKDDAKKAETLPTTGEGSNPFFTAAALAVMAGAGALAVASKRKED,448,TRUE,149360,76,149284,0.1224388752,median,Wu,Adaptation in protein fitness landscapes is facilitated by indirect paths,2016,10.7554/eLife.16965,265-280,GB1,Binding (IgG),binding,SPG1_STRSG_full_b0.1.a2m,1,448,448,0.1,0.2,3109,1,448,600.4,1.340178571,Medium,97,0.2165178571,SPG1_STRSG_Wu_2016.csv,Fitness,1,Variants,SPG1_STRSG_b01_theta_0.2.npy,SPG1_STRSG.pdb,1-448,1,,Binding +SPG2_STRSG_Tsuboyama_2023_5UBS,SPG2_STRSG_Tsuboyama_2023_5UBS.csv,SPG2_STRSG,Prokaryote,Streptococcus sp. group G,MTFKLIINGKTLKGETTTEAVDAATAEKVFKQYFNDNGIDGEWTYDDATKTFTITE,56,TRUE,1451,1029,422,-1.000627629,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-56,Immunoglobulin G-binding protein G,Stability,cDNA display proteolysis,SPG2_STRSG_2023-08-07_b03.a2m,1,56,56,0.3,0.2,39899,0.75,42,2567.6,61.13333333,Medium,6,0.1428571429,Tsuboyama2023_Dataset2_Dataset52,ddG_ML_float,1,mut_type,SPG2_STRSG_theta0.2_2023-08-07_b03.npy,SPG2_STRSG.pdb,1-56,1,,Stability +SPIKE_SARS2_Starr_2020_binding,SPIKE_SARS2_Starr_2020_binding.csv,SPIKE_SARS2,Virus,Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2),MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,1273,FALSE,3802,3802,0,-0.5,manual,Starr,Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding,2020,10.1016/j.cell.2020.08.012,331-531,Spike RBD,ACE2 binding,Binding,SPIKE_SARS2_theta0.99_full_11-26-2021_b01.a2m,1,1273,1273,0.1,0.01,36931,0.998,1271,1405.2,1.105586153,medium,2059,1.619984264,SPIKE_SARS2_Starr_2020.csv,bind_avg,1,mutation,SPIKE_SARS2_theta_0.01.npy,SPIKE_SARS2.pdb,1-1273,0.1,,Binding +SPIKE_SARS2_Starr_2020_expression,SPIKE_SARS2_Starr_2020_expression.csv,SPIKE_SARS2,Virus,Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2),MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,1273,FALSE,3798,3798,0,-1,manual,Starr,Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding,2020,10.1016/j.cell.2020.08.012,331-531,Spike RBD,ACE2 binding,Binding,SPIKE_SARS2_theta0.99_full_11-26-2021_b01.a2m,1,1273,1273,0.1,0.01,36931,0.998,1271,1405.2,1.105586153,medium,2059,1.619984264,SPIKE_SARS2_Starr_2020.csv,expr_avg,1,mutation,SPIKE_SARS2_theta_0.01.npy,SPIKE_SARS2.pdb,1-1273,0.1,,Expression +SPTN1_CHICK_Tsuboyama_2023_1TUD,SPTN1_CHICK_Tsuboyama_2023_1TUD.csv,SPTN1_CHICK,Eukaryote,Gallus gallus,RQGFVPAAYVKKLDSGTGKELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVN,60,TRUE,3201,1051,2150,-2.360476078,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-60,"Spectrin alpha chain, non-erythrocytic 1",Stability,cDNA display proteolysis,SPTN1_CHICK_2023-08-07_b03.a2m,1,60,60,0.3,0.2,420793,0.933,56,15051.5,268.7767857,High,47,0.8392857143,Tsuboyama2023_Dataset2_Dataset53,ddG_ML_float,1,mut_type,SPTN1_CHICK_theta0.2_2023-08-07_b03.npy,SPTN1_CHICK.pdb,1-60,1,,Stability +SQSTM_MOUSE_Tsuboyama_2023_2RRU,SQSTM_MOUSE_Tsuboyama_2023_2RRU.csv,SQSTM_MOUSE,Eukaryote,Mus musculus,RLIESLSQMLSMGFSDEGGWLTRLLQTKNYDIGAALDTIQ,40,FALSE,707,707,0,-0.8554856463,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-40,Sequestosome-1,Stability,cDNA display proteolysis,SQSTM_MOUSE_2023-08-07_b05.a2m,1,40,40,0.5,0.2,34660,0.925,37,3244.5,87.68918919,Medium,13,0.3513513514,Tsuboyama2023_Dataset2_Dataset54,ddG_ML_float,1,mut_type,SQSTM_MOUSE_theta0.2_2023-08-07_b05.npy,SQSTM_MOUSE.pdb,1-40,1,,Stability +SR43C_ARATH_Tsuboyama_2023_2N88,SR43C_ARATH_Tsuboyama_2023_2N88.csv,SR43C_ARATH,Eukaryote,Arabidopsis thaliana,AVAESVIGKRVGDDGKTIEYLVKWTDMSDATWEPQDNVDSTLVLLYQQ,48,TRUE,1583,889,694,-1.591761235,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-48,"Signal recognition particle 43 kDa protein, chloroplastic",Stability,cDNA display proteolysis,SR43C_ARATH_2023-08-07_b02.a2m,1,48,48,0.2,0.2,101118,0.917,44,12180.6,276.8318182,High,26,0.5909090909,Tsuboyama2023_Dataset2_Dataset55,ddG_ML_float,1,mut_type,SR43C_ARATH_theta0.2_2023-08-07_b02.npy,SR43C_ARATH.pdb,1-48,1,,Stability +SRBS1_HUMAN_Tsuboyama_2023_2O2W,SRBS1_HUMAN_Tsuboyama_2023_2O2W.csv,SRBS1_HUMAN,Human,Homo sapiens,GIDPFTGEAIAKFNFNGDTQVEMSFRKGERITLLRQVDENWYEGRIPGTSRQGIFPITYVDVIKRPL,67,TRUE,1556,1211,345,-1.169019411,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-67,Sorbin and SH3 domain-containing protein 1,Stability,cDNA display proteolysis,SRBS1_HUMAN_2023-08-07_b03.a2m,1,67,67,0.3,0.2,708655,0.836,56,22689,405.1607143,High,60,1.071428571,Tsuboyama2023_Dataset2_Dataset56,ddG_ML_float,1,mut_type,SRBS1_HUMAN_theta0.2_2023-08-07_b03.npy,SRBS1_HUMAN.pdb,1-67,1,,Stability +SRC_HUMAN_Ahler_2019,SRC_HUMAN_Ahler_2019.csv,SRC_HUMAN,Human,Homo sapiens,MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL,536,FALSE,3372,3372,0,-1,manual,Ahler,"A Combined Approach Reveals a Regulatory Mechanism Coupling Src's Kinase Activity, Localization, and Phosphotransferase-Independent Functions",2019,10.1016/j.molcel.2019.02.003,270-519,SRC,growth (surrogate for phosphorylation activity),Growth,SRC_HUMAN_full_11-26-2021_b06.a2m,1,536,536,0.6,0.2,26974,0.808,433,1405.1,3.245034642,medium,86,0.1986143187,SRC_HUMAN_Ahler_CD_2019.csv,Activity_Score,1,mutant_uniprot_1,SRC_HUMAN_theta_0.2.npy,SRC_HUMAN.pdb,1-536,0.1,,Activity +SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM,SRC_HUMAN_Chakraborty_2023_binding-DAS_25uM.csv,SRC_HUMAN,Human,Homo sapiens,MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL,536,FALSE,3637,3637,0,-0.086077486,median,Chakraborty,Profiling of the drug resistance of thousands of Src tyrosine kinase mutants uncovers a regulatory network that couples autoinhibition to catalytic domain dynamics,2022,10.1101/2021.12.05.471322,270-519,SRC,Fluorescence measurement,,SRC_HUMAN_2023-08-07_b06.a2m,1,536,536,0.6,0.2,37675,0.869,466,1789,3.839055794,Medium,117,0.2510729614,GSE190495_Src_DAS_25_Score.csv,DMS_score,1,mutant,SRC_HUMAN_theta0.2_2023-08-07_b06.npy,SRC_HUMAN.pdb,1-536,1,,Activity +SRC_HUMAN_Nguyen_2022,SRC_HUMAN_Nguyen_2022.csv,SRC_HUMAN,Human,Homo sapiens,MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL,536,FALSE,3366,3366,0,0.535786927,median,Nguyen,Molecular Determinants of Hsp90 Dependence of Src Kinase Revealed by Deep Mutational Scanning,2022,10.1002/pro.4656,270-519,SRC,growth enrichment,,SRC_HUMAN_2023-08-07_b06.a2m,1,536,536,0.6,0.2,37675,0.869,466,1789,3.839055794,Medium,117,0.2510729614,diffsel_calib.csv,diffsel,-1,mutant,SRC_HUMAN_theta0.2_2023-08-07_b06.npy,SRC_HUMAN.pdb,1-536,1,,OrganismalFitness +SUMO1_HUMAN_Weile_2017,SUMO1_HUMAN_Weile_2017.csv,SUMO1_HUMAN,Human,Homo sapiens,MSDQEAKPSTEDLGDKKEGEYIKLKVIGQDSSEIHFKVKMTTHLKKLKESYCQRQGVPMNSLRFLFEGQRIADNHTPKELGMEEEDVIEVYQEQTGGHSTV,101,FALSE,1700,1700,0,0.3,manual,Weile,A framework for exhaustively mapping functional missense variants,2017,10.15252/msb.20177908,2-97,Small ubiquitin-related modifier 1,Yeast growth,complementation,SUMO1_HUMAN_full_11-26-2021_b02.a2m,1,101,101,0.2,0.2,85570,0.703,71,13120.2,184.7915493,high,67,0.9436619718,SUMO1_HUMAN_Weile_2017.csv,screenscore,1,mutant,SUMO1_HUMAN_theta_0.2.npy,SUMO1_HUMAN.pdb,1-101,0.1,,OrganismalFitness +SYUA_HUMAN_Newberry_2020,SYUA_HUMAN_Newberry_2020.csv,SYUA_HUMAN,Human,Homo sapiens,MDVYMKGLSKAKEGVVAAAEKTKQGVAEAAGKTKEGVLFVGSKTKEGVVHGVATVAEKTKEQVTNVGGAVVTGVTAVAQKTVEGAGSIAAATGYVKKDQLGKNEEGAPQEGILEDMPVDPDNEAFEMPSEEGFQDFEPEA,140,FALSE,2497,2497,0,-0.1,manual,Newberry,Robust Sequence Determinants of α-Synuclein Toxicity in Yeast Implicate Membrane Binding,2020,10.1021/acschembio.0c00339,1-140,alpha-synuclein,Growth,Growth,SYUA_HUMAN_full_04-29-2022_b01.a2m,1,140,140,0.1,0.2,15711,0.707,99,6509.6,65.75353535,medium,62,0.6262626263,SYUA_HUMAN_Newberry_2020.csv,Fitness Score,-1,mutant,SYUA_HUMAN_theta_0.2.npy,SYUA_HUMAN.pdb,1-140,0.1,,OrganismalFitness +TADBP_HUMAN_Bolognesi_2019,TADBP_HUMAN_Bolognesi_2019.csv,TADBP_HUMAN,Human,Homo sapiens,MSEYIRVTEDENDEPIEIPSEDDGTVLLSTVTAQFPGACGLRYRNPVSQCMRGVRLVEGILHAPDAGWGNLVYVVNYPKDNKRKMDETDASSAVKVKRAVQKTSDLIVLGLPWKTTEQDLKEYFSTFGEVLMVQVKKDLKTGHSKGFGFVRFTEYETQVKVMSQRHMIDGRWCDCKLPNSKQSQDEPLRSRKVFVGRCTEDMTEDELREFFSQYGDVMDVFIPKPFRAFAFVTFADDQIAQSLCGEDLIIKGISVHISNAEPKHNSNRQLERSGRFGGNPGGFGNQGGFGNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWGMMGMLASQQNQSGPSGNNQNQGNMQREPNQAFGSGNNSYSGSNSGAAIGWGSASNAGSGSGFNGGFGSSMDSKSSGWGM,414,FALSE,1196,1196,0,0.003661517102,median,Bolognesi,The mutational landscape of a prion-like domain,2019,10.1038/s41467-019-12101-z,290-373,TARDBP,growth (surrogate for toxicity),Growth,TADBP_HUMAN_full_11-26-2021_b09.a2m,1,414,414,0.9,0.2,1211,0.911,377,147.3,0.3907161804,low,8,0.02122015915,TADBP_HUMAN_Bolognesi_2019.csv,toxicity,1,mutant_uniprot_1,TADBP_HUMAN_theta_0.2.npy,TADBP_HUMAN.pdb,1-414,0.1,,OrganismalFitness +TAT_HV1BR_Fernandes_2016,TAT_HV1BR_Fernandes_2016.csv,TAT_HV1BR,Virus,Human immunodeficiency virus type 1 group M subtype B (isolate BRU/LAI) (HIV-1),MEPVDPRLEPWKHPGSQPKTACTNCYCKKCCFHCQVCFMTKALGISYGRKKRRQRRRAHQNSQTHQASLSKQPTSQSRGDPTGPKE,86,FALSE,1577,1577,0,-0.2,manual,Fernandes,Functional Segregation of Overlapping Genes in HIV,2016,10.1016/j.cell.2016.11.031,1-86,HIV tat,Viral replication,Growth,TAT_HV1BR_full_theta0.99_04-29-2022_b09.a2m,1,86,86,0.9,0.01,12155,0.988,85,9925,116.7647059,high,49,0.5764705882,TAT_HV1BR_Fernandes_2016.csv,sel_coeff_mean,1,mutant,TAT_HV1BR_theta_0.01.npy,TAT_HV1BR.pdb,1-86,0.1,,OrganismalFitness +TCRG1_MOUSE_Tsuboyama_2023_1E0L,TCRG1_MOUSE_Tsuboyama_2023_1E0L.csv,TCRG1_MOUSE,Eukaryote,Mus musculus,GATAVSEWTEYKTADGKTYYYNNRTLESTWEKPQELK,37,TRUE,1058,621,437,-1.2,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-37,Transcription elongation regulator 1,Stability,cDNA display proteolysis,TCRG1_MOUSE_2023-08-07_b08.a2m,1,37,37,0.8,0.2,43363,0.865,32,2819.7,88.115625,Medium,14,0.4375,Tsuboyama2023_Dataset2_Dataset57,ddG_ML_float,1,mut_type,TCRG1_MOUSE_theta0.2_2023-08-07_b08.npy,TCRG1_MOUSE.pdb,1-37,1,,Stability +THO1_YEAST_Tsuboyama_2023_2WQG,THO1_YEAST_Tsuboyama_2023_2WQG.csv,THO1_YEAST,Eukaryote,Saccharomyces cerevisiae,SADYSSLTVVQLKDLLTKRNLSVGGLKNEWVQRLIKDDEES,41,TRUE,1279,656,623,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-41,Protein THO1,Stability,cDNA display proteolysis,THO1_YEAST_2023-08-07_b05.a2m,1,41,41,0.5,0.2,54877,0.805,33,8516.7,258.0818182,High,15,0.4545454545,Tsuboyama2023_Dataset2_Dataset58,ddG_ML_float,1,mut_type,THO1_YEAST_theta0.2_2023-08-07_b05.npy,THO1_YEAST.pdb,1-41,1,,Stability +TNKS2_HUMAN_Tsuboyama_2023_5JRT,TNKS2_HUMAN_Tsuboyama_2023_5JRT.csv,TNKS2_HUMAN,Human,Homo sapiens,FSITQFVRNLGLEHLMDIFEREQITLRVLVEMGHKELKEIGINAYGHREKLIKGVERLI,59,TRUE,1479,1118,361,-0.9451205822,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-59,Poly [ADP-ribose] polymerase tankyrase-2,Stability,cDNA display proteolysis,TNKS2_HUMAN_2023-08-07_b03.a2m,1,59,59,0.3,0.2,270654,0.949,56,11206,200.1071429,High,26,0.4642857143,Tsuboyama2023_Dataset2_Dataset59,ddG_ML_float,1,mut_type,TNKS2_HUMAN_theta0.2_2023-08-07_b03.npy,TNKS2_HUMAN.pdb,1-59,1,,Stability +TPK1_HUMAN_Weile_2017,TPK1_HUMAN_Weile_2017.csv,TPK1_HUMAN,Human,Homo sapiens,MEHAFTPLEPLLSTGNLKYCLVILNQPLDNYFRHLWNKALLRACADGGANRLYDITEGERESFLPEFINGDFDSIRPEVREYYATKGCELISTPDQDHTDFTKCLKMLQKKIEEKDLKVDVIVTLGGLAGRFDQIMASVNTLFQATHITPFPIIIIQEESLIYLLQPGKHRLHVDTGMEGDWCGLIPVGQPCMQVTTTGLKWNLTNDVLAFGTLVSTSNTYDGSGVVTVETDHPLLWTMAIKS,243,FALSE,3181,3181,0,0.5,manual,Weile,A framework for exhaustively mapping functional missense variants,2017,10.15252/msb.20177908,2-243,Thiamin pyrophosphokinase 1,Yeast growth,complementation,TPK1_HUMAN_full_11-26-2021_b02.a2m,1,243,243,0.2,0.2,21515,0.823,200,7122.6,35.613,medium,234,1.17,TPK1_HUMAN_Weile_2017.csv,screenscore,1,mutant,TPK1_HUMAN_theta_0.2.npy,TPK1_HUMAN.pdb,1-243,0.1,,OrganismalFitness +TPMT_HUMAN_Matreyek_2018,TPMT_HUMAN_Matreyek_2018.csv,TPMT_HUMAN,Human,Homo sapiens,MDGTRTSLDIEEYSDTEVQKNQVLTLEEWQDKWVNGKTAFHQEQGHQLLKKHLDTFLKGKSGLRVFFPLCGKAVEMKWFADRGHSVVGVEISELGIQEFFTEQNLSYSEEPITEIPGTKVFKSSSGNISLYCCSIFDLPRTNIGKFDMIWDRGALVAINPGDRKCYADTMFSLLGKKFQYLLCVLSYDPTKHPGPPFYVPHAEIERLFGKICNIRCLEKVDAFEERHKSWGIDCLFEKLYLLTEK,245,FALSE,3648,3648,0,0.5,manual,Matreyek,Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing,2018,10.1038/s41588-018-0122-z,1-245,Thiopurine S-methyltransferase,Protein abundance (FACS sorting for abundance of GFP-fused target),Protein stability,TPMT_HUMAN_full_11-26-2021_b03.a2m,1,245,245,0.3,0.2,19526,0.731,179,6296.8,35.17765363,medium,109,0.6089385475,TPMT_HUMAN_Matreyek_2018.csv,score,1,mutant,TPMT_HUMAN_theta_0.2.npy,TPMT_HUMAN.pdb,1-245,0.1,,Expression +TPOR_HUMAN_Bridgford_2020,TPOR_HUMAN_Bridgford_2020.csv,TPOR_HUMAN,Human,Homo sapiens,MPSWALFMVTSCLLLAPQNLAQVSSQDVSLLASDSEPLKCFSRTFEDLTCFWDEEEAAPSGTYQLLYAYPREKPRACPLSSQSMPHFGTRYVCQFPDQEEVRLFFPLHLWVKNVFLNQTRTQRVLFVDSVGLPAPPSIIKAMGGSQPGELQISWEEPAPEISDFLRYELRYGPRDPKNSTGPTVIQLIATETCCPALQRPHSASALDQSPCAQPTMPWQDGPKQTSPSREASALTAEGGSCLISGLQPGNSYWLQLRSEPDGISLGGSWGSWSLPVTVDLPGDAVALGLQCFTLDLKNVTCQWQQQDHASSQGFFYHSRARCCPRDRYPIWENCEEEEKTNPGLQTPQFSRCHFKSRNDSIIHILVEVTTAPGTVHSYLGSPFWIHQAVRLPTPNLHWREISSGHLELEWQHPSSWAAQETCYQLRYTGEGHQDWKVLEPPLGARGGTLELRPRSRYRLQLRARLNGPTYQGPWSSWSDPTRVETATETAWISLVTALHLVLGLNAVLGLLLLRWQFPAHYRRLRHALWPSLPDLHRVLGQYLRDTAALSPPKATVSDTCEEVEPSLLEILPKSSERTPLPLCSSQAQMDYRRLQPSCLGTMPLSVCPPMAESGSCCTTHIANHSYLPLSYWQQP,635,FALSE,562,562,0,-0.1,manual,Bridgford,Novel drivers and modifiers of MPL-dependent oncogenic transformation identified by deep mutational scanning,2020,10.1182/blood.2019002561,487-517,MPL,growth/survival (surrogate for TpoR/MPL enhanced constitutive activation),Growth,TPOR_HUMAN_full_11-26-2021_b01.a2m,1,635,635,0.1,0.2,937,0.825,524,128.4,0.2450381679,low,0,0,TPOR_HUMAN_Bridgford_S505N_2020.csv,score,1,mutant_uniprot_1,TPOR_HUMAN_theta_0.2.npy,TPOR_HUMAN.pdb,1-635,0.1,,OrganismalFitness +TRPC_SACS2_Chan_2017,TRPC_SACS2_Chan_2017.csv,TRPC_SACS2,Prokaryote,Thermus thermophilus,MPRYLKGWLKDVVQLSLRRPSFRASRQRPIISLNERILEFNKRNITAIIAEYKRKSPSGLDVERDPIEYSKFMERYAVGLSILTEEKYFNGSYETLRKIASSVSIPILMKDFIVKESQIDDAYNLGADTVLLIVKILTERELESLLEYARSYGMEPLIEINDENDLDIALRIGARFIGINSRDLETLEINKENQRKLISMIPSNVVKVAESGISERNEIEELRKLGVNAFLIGSSLMRNPEKIKEFIL,248,FALSE,1519,1519,0,-0.5,manual,Chan,Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints,2017,10.1038/ncomms14614,44-235,TIM Barrell (S. solfataricus),fitness,Growth,TRPC_SACS2_full_11-26-2021_b07.a2m,1,248,248,0.7,0.2,52935,0.944,234,10651.1,45.51752137,medium,364,1.555555556,TRPC_SACS2_Chan_2017.csv,fitness,1,mutant,TRPC_SACS2_theta_0.2.npy,TRPC_SACS2.pdb,1-248,0.1,,OrganismalFitness +TRPC_THEMA_Chan_2017,TRPC_THEMA_Chan_2017.csv,TRPC_THEMA,Prokaryote,Thermus thermophilus,MRRLWEIVEAKKKDILEIDGENLIVQRRNHRFLEVLSGKERVKIIAEFKKASPSAGDINADASLEDFIRMYDELADAISILTEKHYFKGDPAFVRAARNLTSRPILAKDFYIDTVQVKLASSVGADAILIIARILTAEQIKEIYEAAEELGMDSLVEVHSREDLEKVFSVIRPKIIGINTRDLDTFEIKKNVLWELLPLVPDDTVVVAESGIKDPRELKDLRGKVNAVLVGTSIMKAENPRRFLEEMRAWSE,252,FALSE,1519,1519,0,-0.5,manual,Chan,Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints,2017,10.1038/ncomms14614,40-233,TIM Barrell (T. maritima),fitness,Growth,TRPC_THEMA_full_11-26-2021_b07.a2m,1,252,252,0.7,0.2,52988,0.948,239,10582.5,44.27824268,medium,380,1.589958159,TRPC_THEMA_Chan_2017.csv,fitness,1,mutant,TRPC_THEMA_theta_0.2.npy,TRPC_THEMA.pdb,1-252,0.1,,OrganismalFitness +UBC9_HUMAN_Weile_2017,UBC9_HUMAN_Weile_2017.csv,UBC9_HUMAN,Human,Homo sapiens,MSGIALSRLAQERKAWRKDHPFGFVAVPTKNPDGTMNLMNWECAIPGKKGTPWEGGLFKLRMLFKDDYPSSPPKCKFEPPLFHPNVYPSGTVCLSILEEDKDWRPAITIKQILLGIQELLNEPNIQDPAQAEAYTIYCQNRVEYEKRVRAQAKKFAPSY,159,FALSE,2563,2563,0,0.384407289,median,Weile,A framework for exhaustively mapping functional missense variants,2017,10.15252/msb.20177908,1-158,SUMO-conjugating enzyme UBC9,Yeast growth,complementation,UBC9_HUMAN_full_11-26-2021_b03.a2m,1,159,159,0.3,0.2,69788,0.849,135,8394,62.17777778,medium,89,0.6592592593,UBC9_HUMAN_Weile_2017.csv,screenscore,1,mutant,UBC9_HUMAN_theta_0.2.npy,UBC9_HUMAN.pdb,1-159,0.1,,OrganismalFitness +UBE4B_HUMAN_Tsuboyama_2023_3L1X,UBE4B_HUMAN_Tsuboyama_2023_3L1X.csv,UBE4B_HUMAN,Human,Homo sapiens,DAPDEFRDPLMDTLMTDPVRLPSGTIMDRSIILRHLLNSPTDPFNRQTLTESMLEPVPELKEQIQAWMR,69,TRUE,3622,1118,2504,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-69,Ubiquitin conjugation factor E4 B,Stability,cDNA display proteolysis,UBE4B_HUMAN_2023-08-07_b04.a2m,1,69,69,0.4,0.2,310943,0.928,64,34185.4,534.146875,High,52,0.8125,Tsuboyama2023_Dataset2_Dataset60,ddG_ML_float,1,mut_type,UBE4B_HUMAN_theta0.2_2023-08-07_b04.npy,UBE4B_HUMAN.pdb,1-69,1,,Stability +UBE4B_MOUSE_Starita_2013,UBE4B_MOUSE_Starita_2013.csv,UBE4B_MOUSE,Eukaryote,Mus musculus,MEELSADEIRRRRLARLAGGQTSQPTTPLTSPQRENPPGPPIAASAPGPSQSLGLNVHNMTPATSPIGAAGVAHRSQSSEGVSSLSSSPSNSLETQSQSLSRSQSMDIDGVSCEKSMSQVDVDSGIENMEVDENDRREKRSLSDKEPSSGPEVSEEQALQLVCKIFRVSWKDRDRDVIFLSSLSAQFKQNPKEVFSDFKDLIGQILMEVLMMSTQTRDENPFASLTATSQPIATAARSPDRNLMLNTGSSSGTSPMFCNMGSFSTSSLSSLGASGGASNWDSYSDHFTIETCKETDMLNYLIECFDRVGIEEKKAPKMCSQPAVSQLLSNIRSQCISHTALVLQGSLTQPRSLQQPSFLVPYMLCRNLPYGFIQELVRTTHQDEEVFKQIFIPILQGLALAAKECSLESDYFKYPLMALGELCETKFGKTHPMCNLVASLPLWLPKSLSPGSGRELQRLSYLGAFFSFSVFAEDDAKVVEKYFSGPAITLENTRVVSQSLQHYLELGRQELFKILHSILLNGETREAALSYMAALVNANMKKAQMQADDRLVSTDGFMLNLLWVLQQLSTKIKLETVDPTYIFHPRCRITLPNDETRINATMEDVNERLTELYGDQPPFSEPKFPTECFFLTLHAHHLSILPSCRRYIRRLRAIRELNRTVEDLKNNESQWKDSPLATRHREMLKRCKTQLKKLVRCKACADAGLLDESFLRRCLNFYGLLIQLMLRILDPAYPDVTLPLNSEVPKVFAALPEFYVEDVAEFLFFIVQYSPQVLYEPCTQDIVMFLVVMLCNQNYIRNPYLVAKLVEVMFMTNPSVQPRTQKFFEMIENHPLSTKLLVPSLMKFYTDVEHTGATSEFYDKFTIRYHISTIFKSLWQNIAHHGTFMEEFNSGKQFVRYINMLINDTTFLLDESLESLKRIHEVQEEMKNKEQWDQLPRDQQQARQSQLAQDERVSRSYLALATETVDMFHLLTKQVQKPFLRPELGPRLAAMLNFNLQQLCGPKCRDLKVENPEKYGFEPKKLLDQLTDIYLQLDCARFAKAIADDQRSYSKELFEEVISKMRKAGIKSTIAIEKFKLLAEKVEEIVAKNARAEIDYSDAPDEFRDPLMDTLMTDPVRLPSGTVMDRSIILRHLLNSPTDPFNRQMLTESMLEPVPELKEQIQAWMREKQSSDH,1173,FALSE,899,899,0,-1.8,manual,Starita,Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis,2013,10.1073/pnas.1303309110,1072-1173,Ube4b,Ligase activity (phage display),Auto-ubiquitination,UBE4B_MOUSE_full_11-26-2021_b05.a2m,1,1173,1173,0.5,0.2,4743,0.765,897,679.4,0.7574136009,low,49,0.05462653289,UBE4B_MOUSE_Starita_2013.csv,log2_ratio,1,mutant,UBE4B_MOUSE_theta_0.2.npy,UBE4B_MOUSE.pdb,1-1173,0.1,,Activity +UBR5_HUMAN_Tsuboyama_2023_1I2T,UBR5_HUMAN_Tsuboyama_2023_1I2T.csv,UBR5_HUMAN,Human,Homo sapiens,HRQALGERLYPRVQAMQPAFASKITGMLLELSPAQLLLLLASEDSLRARVDEAMELII,58,TRUE,1453,1094,359,-0.4460165437,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-58,E3 ubiquitin-protein ligase UBR5,Stability,cDNA display proteolysis,UBR5_HUMAN_2023-08-07_b05.a2m,1,58,58,0.5,0.2,17888,0.966,56,1031.7,18.42321429,Medium,14,0.25,Tsuboyama2023_Dataset2_Dataset61,ddG_ML_float,1,mut_type,UBR5_HUMAN_theta0.2_2023-08-07_b05.npy,UBR5_HUMAN.pdb,1-58,1,,Stability +VG08_BPP22_Tsuboyama_2023_2GP8,VG08_BPP22_Tsuboyama_2023_2GP8.csv,VG08_BPP22,Virus,Salmonella phage P22 (Bacteriophage P22),ITGDVSAANKDAIRKQMDAAASKGDVETYRKLKAKLKGIR,40,FALSE,723,723,0,-0.2013306011,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-40,Scaffolding protein,Stability,cDNA display proteolysis,VG08_BPP22_2023-08-07_b05.a2m,1,40,40,0.5,0.01,102464,0.875,35,12963.6,370.3885714,High,13,0.3714285714,Tsuboyama2023_Dataset2_Dataset62,ddG_ML_float,1,mut_type,VG08_BPP22_theta0.01_2023-08-07_b05.npy,VG08_BPP22.pdb,1-40,1,,Stability +VILI_CHICK_Tsuboyama_2023_1YU5,VILI_CHICK_Tsuboyama_2023_1YU5.csv,VILI_CHICK,Eukaryote,Gallus gallus,KLETFPLDVLVNTAAEDLPRGVDPSRKENHLSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF,65,TRUE,2568,1202,1366,-0.7,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-65,Villin-1,Stability,cDNA display proteolysis,VILI_CHICK_2023-08-07_b01.a2m,1,65,65,0.1,0.2,254210,0.769,50,46507.8,930.156,High,19,0.38,Tsuboyama2023_Dataset2_Dataset63,ddG_ML_float,1,mut_type,VILI_CHICK_theta0.2_2023-08-07_b01.npy,VILI_CHICK.pdb,1-65,1,,Stability +VKOR1_HUMAN_Chiasson_2020_abundance,VKOR1_HUMAN_Chiasson_2020_abundance.csv,VKOR1_HUMAN,Human,Homo sapiens,MGSTWGSPGWVRLALCLTGLVLSLYALHVKAARARDRDYRALCDVGTAISCSRVFSSRWGRGFGLVEHVLGQDSILNQSNSIFGCIFYTLQLLLGCLRTRWASVLMLLSSLVSLAGSVYLAWILFFVLYDFCIVCITTYAINVSLMWLSFRKVQEPQGKAKRH,163,FALSE,2695,2695,0,0.7480893367,median,Chiasson,"Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact",2020,10.7554/eLife.58026,2-163,VKORC1,protein abundance (eGFP fusion reporter),Fluorescence,VKOR1_HUMAN_full_11-26-2021_b03.a2m,1,163,163,0.3,0.2,14510,0.779,127,4655,36.65354331,medium,97,0.7637795276,VKOR1_HUMAN_Chiasson_2020.csv,abundance_score,1,variant,VKOR1_HUMAN_theta_0.2.npy,VKOR1_HUMAN.pdb,1-163,0.1,,Expression +VKOR1_HUMAN_Chiasson_2020_activity,VKOR1_HUMAN_Chiasson_2020_activity.csv,VKOR1_HUMAN,Human,Homo sapiens,MGSTWGSPGWVRLALCLTGLVLSLYALHVKAARARDRDYRALCDVGTAISCSRVFSSRWGRGFGLVEHVLGQDSILNQSNSIFGCIFYTLQLLLGCLRTRWASVLMLLSSLVSLAGSVYLAWILFFVLYDFCIVCITTYAINVSLMWLSFRKVQEPQGKAKRH,163,FALSE,697,697,0,0.7,manual,Chiasson,"Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact",2020,10.7554/eLife.58026,3-163,VKORC1,carboxylation activity (carboxylation reporter on cell surface),enzymatic activity,VKOR1_HUMAN_full_11-26-2021_b03.a2m,1,163,163,0.3,0.2,14510,0.779,127,4655,36.65354331,medium,97,0.7637795276,VKOR1_HUMAN_Chiasson_2020.csv,activity_score,1,variant,VKOR1_HUMAN_theta_0.2.npy,VKOR1_HUMAN.pdb,1-163,0.1,,Activity +VRPI_BPT7_Tsuboyama_2023_2WNM,VRPI_BPT7_Tsuboyama_2023_2WNM.csv,VRPI_BPT7,Virus,Escherichia phage T7 (Bacteriophage T7),SLSVDNKKFWATVESSEHSFEVPIYAETLDEALELAEWQYVPAGFEVTRVRPCVAP,56,FALSE,1047,1047,0,-1.1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-56,Bacterial RNA polymerase inhibitor,Stability,cDNA display proteolysis,VRPI_BPT7_2023-08-07_b02.a2m,1,56,56,0.2,0.01,6266,0.875,49,1555.8,31.75102041,Medium,3,0.0612244898,Tsuboyama2023_Dataset2_Dataset64,ddG_ML_float,1,mut_type,VRPI_BPT7_theta0.01_2023-08-07_b02.npy,VRPI_BPT7.pdb,1-56,1,,Stability +YAIA_ECOLI_Tsuboyama_2023_2KVT,YAIA_ECOLI_Tsuboyama_2023_2KVT.csv,YAIA_ECOLI,Prokaryote,Escherichia coli,PREAYIVTIEKGKPGQTVTWYQLRADHPKPDSLISEHPTAQEAMDAKKRYED,52,TRUE,1890,928,962,-1.953132017,median,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-52,Uncharacterized protein YaiA,Stability,cDNA display proteolysis,YAIA_ECOLI_2023-08-07_b03.a2m,1,52,52,0.3,0.2,5877,0.788,41,737.2,17.9804878,Medium,5,0.1219512195,Tsuboyama2023_Dataset2_Dataset65,ddG_ML_float,1,mut_type,YAIA_ECOLI_theta0.2_2023-08-07_b03.npy,YAIA_ECOLI.pdb,1-52,1,,Stability +YAP1_HUMAN_Araya_2012,YAP1_HUMAN_Araya_2012.csv,YAP1_HUMAN,Human,Homo sapiens,MDPGQQPPPQPAPQGQGQPPSQPPQGQGPPSGPGQPAPAATQAAPQAPPAGHQIVHVRGDSETDLEALFNAVMNPKTANVPQTVPMRLRKLPDSFFKPPEPKSHSRQASTDAGTAGALTPQHVRAHSSPASLQLGAVSPGTLTPTGVVSGPAATPTAQHLRQSSFEIPDDVPLPAGWEMAKTSSGQRYFLNHIDQTTTWQDPRKAMLSQMNVTAPTSPPVQQNMMNSASGPLPDGWEQAMTQDGEIYYINHKNKTTSWLDPRLDPRFAMNQRISQSAPVKQPPPLAPQSPQGGVMGGSNSNQQQQMRLQQLQMEKERLRLKQQELLRQAMRNINPSTANSPKCQELALRSQLPTLEQDGGTQNPVSSPGMSQELRTMTTNSSDPFLNSGTYHSRDESTDSGLSMSSYSVPRTPDDFLNSVDEMDTGDTINQSTLPSQQNRFPDYLEAIPGTNVDLGTLEGDGMNIEGEELMPSLQEALSSDILNDMESVLAATKLDKESFLTWL,504,TRUE,10075,362,9713,0.6236402571,median,Araya,"A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function",2012,10.1073/pnas.1209751109,170-203,YAP1,peptide binding,Binding,YAP1_HUMAN_full_11-26-2021_b02.a2m,1,504,504,0.2,0.2,1604,0.859,433,132.6,0.3062355658,low,1,0.002309468822,YAP1_HUMAN_Araya_2012.csv,W,1,mutant,YAP1_HUMAN_theta_0.2.npy,YAP1_HUMAN.pdb,1-504,0.1,,Binding +YNZC_BACSU_Tsuboyama_2023_2JVD,YNZC_BACSU_Tsuboyama_2023_2JVD.csv,YNZC_BACSU,Prokaryote,Bacillus subtilis,MISNAKIARINELAAKAKAGVITEEEKAEQQKLRQEYLK,39,TRUE,2300,714,1586,-1,manual,Tsuboyama,Mega-scale experimental analysis of protein folding stability in biology and design,2023,10.1038/s41586-023-06328-6,1-39,UPF0291 protein YnzC,Stability,cDNA display proteolysis,YNZC_BACSU_2023-08-07_b07.a2m,1,39,39,0.7,0.2,7116,0.974,38,1588.3,41.79736842,Medium,13,0.3421052632,Tsuboyama2023_Dataset2_Dataset66,ddG_ML_float,1,mut_type,YNZC_BACSU_theta0.2_2023-08-07_b07.npy,YNZC_BACSU.pdb,1-39,1,,Stability \ No newline at end of file diff --git a/reference_files/clinical_indels.csv b/reference_files/clinical_indels.csv new file mode 100644 index 0000000..3aba056 --- /dev/null +++ b/reference_files/clinical_indels.csv @@ -0,0 +1,1556 @@ +DMS_id,DMS_filename,target_seq,mutated_sequence_column,MSA_start,MSA_end,MSA_filename,weight_file_name,dataset +NP_000007.1,NP_000007.1.csv,MAAGFGRCCRVLRSISRFHWRSQHTKANRQREPGLGFSFEFTEQQKEFQATARKFAREEIIPVAAEYDKTGEYPVPLIRRAWELGLMNTHIPENCGGLGLGTFDACLISEELAYGCTGVQTAIEGNSLGQMPIIIAGNDQQKKKYLGRMTEEPLMCAYCVTEPGAGSDVAGIKTKAEKKGDEYIINGQKMWITNGGKANWYFLLARSDPDPKAPANKAFTGFIVEADTPGIQIGRKELNMGQRCSDTRGIVFEDVKVPKENVLIGDGAGFKVAMGAFDKTRPVVAAGAVGLAQRALDEATKYALERKTFGKLLVEHQAISFMLAEMAMKVELARMSYQRAAWEVDSGRRNTYYASIAKAFAGDIANQLATDAVQILGGNGFNTEYPVEKLMRDAKIYQIYEGTSQIQRLIVAREHIDKYKN,mutated_sequence,1.0,421.0,NP_000007.1.a2m,NP_000007.1.npy,ClinVar +NP_000008.1,NP_000008.1.csv,MAAALLARASGPARRALCPRAWRQLHTIYQSVELPETHQMLLQTCRDFAEKELFPIAAQVDKEHLFPAAQVKKMGGLGLLAMDVPEELGGAGLDYLAYAIAMEEISRGCASTGVIMSVNNSLYLGPILKFGSKEQKQAWVTPFTSGDKIGCFALSEPGNGSDAGAASTTARAEGDSWVLNGTKAWITNAWEASAAVVFASTDRALQNKGISAFLVPMPTPGLTLGKKEDKLGIRGSSTANLIFEDCRIPKDSILGEPGMGFKIAMQTLDMGRIGIASQALGIAQTALDCAVNYAENRMAFGAPLTKLQVIQFKLADMALALESARLLTWRAAMLKDNKKPFIKEAAMAKLAASEAATAISHQAIQILGGMGYVTEMPAERHYRDARITEIYEGTSEIQRLVIAGHLLRSYRS,mutated_sequence,1.0,412.0,NP_000008.1.a2m,NP_000008.1.npy,ClinVar +NP_000009.1,NP_000009.1.csv,MQAARMAASLGRQLLRLGGGSSRLTALLGQPRPGPARRPYAGGAAQLALDKSDSHPSDALTRKKPAKAESKSFAVGMFKGQLTTDQVFPYPSVLNEEQTQFLKELVEPVSRFFEEVNDPAKNDALEMVEETTWQGLKELGAFGLQVPSELGGVGLCNTQYARLVEIVGMHDLGVGITLGAHQSIGFKGILLFGTKAQKEKYLPKLASGETVAAFCLTEPSSGSDAASIRTSAVPSPCGKYYTLNGSKLWISNGGLADIFTVFAKTPVTDPATGAVKEKITAFVVERGFGGITHGPPEKKMGIKASNTAEVFFDGVRVPSENVLGEVGSGFKVAMHILNNGRFGMAAALAGTMRGIIAKAVDHATNRTQFGEKIHNFGLIQEKLARMVMLQYVTESMAYMVSANMDQGATDFQIEAAISKIFGSEAAWKVTDECIQIMGGMGFMKEPGVERVLRDLRIFRIFEGTNDILRLFVALQGCMDKGKELSGLGSALKNPFGNAGLLLGEAGKQLRRRAGLGSGLSLSGLVHPELSRSGELAVRALEQFATVVEAKLIKHKKGIVNEQFLLQRLADGAIDLYAMVVVLSRASRSLSEGHPTAQHEKMLCDTWCIEAAARIREGMAALQSDPWQQELYRNFKSISKALVERGGVVTSNPLGF,mutated_sequence,1.0,655.0,NP_000009.1.a2m,NP_000009.1.npy,ClinVar +NP_000010.1,NP_000010.1.csv,MAVLAALLRSGARSRSPLLRRLVQEIRYVERSYVSKPTLKEVVIVSATRTPIGSFLGSLSLLPATKLGSIAIQGAIEKAGIPKEEVKEAYMGNVLQGGEGQAPTRQAVLGAGLPISTPCTTINKVCASGMKAIMMASQSLMCGHQDVMVAGGMESMSNVPYVMNRGSTPYGGVKLEDLIVKDGLTDVYNKIHMGSCAENTAKKLNIARNEQDAYAINSYTRSKAAWEAGKFGNEVIPVTVTVKGQPDVVVKEDEEYKRVDFSKVPKLKTVFQKENGTVTAANASTLNDGAAALVLMTADAAKRLNVTPLARIVAFADAAVEPIDFPIAPVYAASMVLKDVGLKKEDIAMWEVNEAFSLVVLANIKMLEIDPQKVNINGGAVSLGHPIGMSGARIVGHLTHALKQGEYGLASICNGGGGASAMLIQKL,mutated_sequence,1.0,427.0,NP_000010.1.a2m,NP_000010.1.npy,ClinVar +NP_000011.2,NP_000011.2.csv,MTLGSPRKGLLMLLMALVTQGDPVKPSRGPLVTCTCESPHCKGPTCRGAWCTVVLVREEGRHPQEHRGCGNLHRELCRGRPTEFVNHYCCDSHLCNHNVSLVLEATQPPSEQPGTDGQLALILGPVLALLALVALGVLGLWHVRRRQEKQRGLHSELGESSLILKASEQGDSMLGDLLDSDCTTGSGSGLPFLVQRTVARQVALVECVGKGRYGEVWRGLWHGESVAVKIFSSRDEQSWFRETEIYNTVLLRHDNILGFIASDMTSRNSSTQLWLITHYHEHGSLYDFLQRQTLEPHLALRLAVSAACGLAHLHVEIFGTQGKPAIAHRDFKSRNVLVKSNLQCCIADLGLAVMHSQGSDYLDIGNNPRVGTKRYMAPEVLDEQIRTDCFESYKWTDIWAFGLVLWEIARRTIVNGIVEDYRPPFYDVVPNDPSFEDMKKVVCVDQQTPTIPNRLAADPVLSGLAQMMRECWYPNPSARLTALRIKKTLQKISNSPEKPKVIQ,mutated_sequence,1.0,503.0,NP_000011.2.a2m,NP_000011.2.npy,ClinVar +NP_000012.1,NP_000012.1.csv,MTELPAPLSYFQNAQMSEDNHLSNTVRSQNDNRERQEHNDRRSLGHPEPLSNGRPQGNSRQVVEQDEEEDEELTLKYGAKHVIMLFVPVTLCMVVVVATIKSVSFYTRKDGQLIYTPFTEDTETVGQRALHSILNAAIMISVIVVMTILLVVLYKYRCYKVIHAWLIISSLLLLFFFSFIYLGEVFKTYNVAVDYITVALLIWNFGVVGMISIHWKGPLRLQQAYLIMISALMALVFIKYLPEWTAWLILAVISVYDLVAVLCPKGPLRMLVETAQERNETLFPALIYSSTMVWLVNMAEGDPEAQRRVSKNSKYNAESTERESQDTVAENDDGGFSEEWEAQRDSHLGPHRSTPESRAAVQELSSSILAGEDPEERGVKLGLGDFIFYSVLVGKASATASGDWNTTIACFVAILIGLCLTLLLLAIFKKALPALPISITFGLVFYFATDYLVQPFMDQLAFHQFYI,mutated_sequence,1.0,467.0,NP_000012.1.a2m,NP_000012.1.npy,ClinVar +NP_000017.1,NP_000017.1.csv,MAAGGDHGSPDSYRSPLASRYASPEMCFVFSDRYKFRTWRQLWLWLAEAEQTLGLPITDEQIQEMKSNLENIDFKMAAEEEKRLRHDVMAHVHTFGHCCPKAAGIIHLGATSCYVGDNTDLIILRNALDLLLPKLARVISRLADFAKERASLPTLGFTHFQPAQLTTVGKRCCLWIQDLCMDLQNLKRVRDDLRFRGVKGTTGTQASFLQLFEGDDHKVEQLDKMVTEKAGFKRAFIITGQTYTRKVDIEVLSVLASLGASVHKICTDIRLLANLKEMEEPFEKQQIGSSAMPYKRNPMRSERCCSLARHLMTLVMDPLQTASVQWFERTLDDSANRRICLAEAFLTADTILNTLQNISEGLVVYPKVIERRIRQELPFMATENIIMAMVKAGGSRQDCHEKIRVLSQQAASVVKQEGGDNDLIERIQVDAYFSPIHSQLDHLLDPSSFTGRASQQVQRFLEEEVYPLLKPYESVMKVKAELCL,mutated_sequence,1.0,484.0,NP_000017.1.a2m,NP_000017.1.npy,ClinVar +NP_000018.2,NP_000018.2.csv,MARKSNLPVLLVPFLLCQALVRCSSPLPLVVNTWPFKNATEAAWRALASGGSALDAVESGCAMCEREQCDGSVGFGGSPDELGETTLDAMIMDGTTMDVGAVGDLRRIKNAIGVARKVLEHTTHTLLVGESATTFAQSMGFINEDLSTTASQALHSDWLARNCQPNYWRNVIPDPSKYCGPYKPPGILKQDIPIHKETEDDRGHDTIGMVVIHKTGHIAAGTSTNGIKFKIHGRVGDSPIPGAGAYADDTAGAAAATGNGDILMRFLPSYQAVEYMRRGEDPTIACQKVISRIQKHFPEFFGAVICANVTGSYGAACNKLSTFTQFSFMVYNSEKNQPTEEKVDCI,mutated_sequence,1.0,346.0,NP_000018.2.a2m,NP_000018.2.npy,ClinVar +NP_000021.1,NP_000021.1.csv,MASHKLLVTPPKALLKPLSIPNQLLLGPGPSNLPPRIMAAGGLQMIGSMSKDMYQIMDEIKEGIQYVFQTRNPLTLVISGSGHCALEAALVNVLEPGDSFLVGANGIWGQRAVDIGERIGARVHPMTKDPGGHYTLQEVEEGLAQHKPVLLFLTHGESSTGVLQPLDGFGELCHRYKCLLLVDSVASLGGTPLYMDRQGIDILYSGSQKALNAPPGTSLISFSDKAKKKMYSRKTKPFSFYLDIKWLANFWGCDDQPRMYHHTIPVISLYSLRESLALIAEQGLENSWRQHREAAAYLHGRLQALGLQLFVKDPALRLPTVTTVAVPAGYDWRDIVSYVIDHFDIEIMGGLGPSTGKVLRIGLLGCNATRENVDRVTEALRAALQHCPKKKL,mutated_sequence,1.0,392.0,NP_000021.1.a2m,NP_000021.1.npy,ClinVar +NP_000024.2,NP_000024.2.csv,MPVLSRPRPWRGNTLKRTAVLLALAAYGAHKVYPLVRQCLAPARGLQAPAGEPTQEASGVAAAKAGMNRVFLQRLLWLLRLLFPRVLCRETGLLALHSAALVSRTFLSVYVARLDGRLARCIVRKDPRAFGWQLLQWLLIALPATFVNSAIRYLEGQLALSFRSRLVAHAYRLYFSQQTYYRVSNMDGRLRNPDQSLTEDVVAFAASVAHLYSNLTKPLLDVAVTSYTLLRAARSRGAGTAWPSAIAGLVVFLTANVLRAFSPKFGELVAEEARRKGELRYMHSRVVANSEEIAFYGGHEVELALLQRSYQDLASQINLILLERLWYVMLEQFLMKYVWSASGLLMVAVPIITATGYSESDAEAVKKAALEKKEEELVSERTEAFTIARNLLTAAADAIERIMSSYKEVTELAGYTARVHEMFQVFEDVQRCHFKRPRELEDAQAGSGTIGRSGVRVEGPLKIRGQVVDVEQGIICENIPIVTPSGEVVVASLNIRVEEGMHLLITGPNGCGKSSLFRILGGLWPTYGGVLYKPPPQRMFYIPQRPYMSVGSLRDQVIYPDSVEDMQRKGYSEQDLEAILDVVHLHHILQREGGWEAMCDWKDVLSGGEKQRIGMARMFYHRPKYALLDECTSAVSIDVEGKIFQAAKDAGIALLSITHRPSLWKYHTHLLQFDGEGGWKFEKLDSAARLSLTEEKQRLEQQLAGIPKMQRRLQELCQILGEAVAPAHVPAPSPQGPGGLQGAST,mutated_sequence,1.0,745.0,NP_000024.2.a2m,NP_000024.2.npy,ClinVar +NP_000026.2,NP_000026.2.csv,MAHRFPALTQEQKKELSEIAQSIVANGKGILAADESVGTMGNRLQRIKVENTEENRRQFREILFSVDSSINQSIGGVILFHETLYQKDSQGKLFRNILKEKGIVVGIKLDQGGAPLAGTNKETTIQGLDGLSERCAQYKKDGVDFGKWRAVLRIADQCPSSLAIQENANALARYASICQQNGLVPIVEPEVIPDGDHDLEHCQYVTEKVLAAVYKALNDHHVYLEGTLLKPNMVTAGHACTKKYTPEQVAMATVTALHRTVPAAVPGICFLSGGMSEEDATLNLNAINLCPLPKPWKLSFSYGRALQASALAAWGGKAANKEATQEAFMKRAMANCQAAKGQYVHTGSSGAASTQSLFTACYTY,mutated_sequence,1.0,364.0,NP_000026.2.a2m,NP_000026.2.npy,ClinVar +NP_000032.1,NP_000032.1.csv,MKVLWAALLVTFLAGCQAKVEQAVETEPEPELRQQTEWQSGQRWELALGRFWDYLRWVQTLSEQVQEELLSSQVTQELRALMDETMKELKAYKSELEEQLTPVAEETRARLSKELQAAQARLGADMEDVCGRLVQYRGEVQAMLGQSTEELRVRLASHLRKLRKRLLRDADDLQKRLAVYQAGAREGAERGLSAIRERLGPLVEQGRVRAATVGSLAGQPLQERAQAWGERLRARMEEMGSRTRDRLDEVKEQVAEVRAKLEEQAQQIRLQAEAFQARLKSWFEPLVEDMQRQWAGLVEKVQAAVGTSAAPVPSDNH,mutated_sequence,1.0,317.0,NP_000032.1.a2m,NP_000032.1.npy,ClinVar +NP_000034.1,NP_000034.1.csv,MLGIWTLLPLVLTSVARLSSKSVNAQVTDINSKGLELRKTVTTVETQNLEGLHHDGQFCHKPCPPGERKARDCTVNGDEPDCVPCQEGKEYTDKAHFSSKCRRCRLCDEGHGLEVEINCTRTQNTKCRCKPNFFCNSTVCEHCDPCTKCEHGIIKECTLTSNTKCKEEGSRSNLGWLCLLLLPIPLIVWVKRKEVQKTCRKHRKENQGSHESPTLNPETVAINLSDVDLSKYITTIAGVMTLSQVKGFVRKNGVNEAKIDEIKNDNVQDTAEQKVQLLRNWHQLHGKKEAYDTLIKDLKKANLCTLAEKIQTIILKDITSDSENSNFRNEIQSLV,mutated_sequence,1.0,335.0,NP_000034.1.a2m,NP_000034.1.npy,ClinVar +NP_000035.2,NP_000035.2.csv,MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAASAAPPGASLLLLQQQQQQQQQQQQQQQQQQQQQQQETSPRQQQQQQGEDGSPQAHRRGPTGYLVLDEEQQPSQPQSALECHPERGCVPEPGAAVAASKGLPQQLPAPPDEDDSAAPSTLSLLGPTFPGLSSCSADLKDILSEASTMQLLQQQQQEAVSEGSSSGRAREASGAPTSSKDNYLGGTSTISDNAKELCKAVSVSMGLGVEALEHLSPGEQLRGDCMYAPLLGVPPAVRPTPCAPLAECKGSLLDDSAGKSTEDTAEYSPFKGGYTKGLEGESLGCSGSAAAGSSGTLELPSTLSLYKSGALDEAAAYQSRDYYNFPLALAGPPPPPPPPHPHARIKLENPLDYGSAWAAAAAQCRYGDLASLHGAGAAGPGSGSPSAAASSSWHTLFTAEEGQLYGPCGGGGGGGGGGGGGGGGGGGGGGGEAGAVAPYGYTRPPQGLAGQESDFTAPDVWYPGGMVSRVPYPSPTCVKSEMGPWMDSYSGPYGDMRLETARDHVLPIDYYFPPQKTCLICGDEASGCHYGALTCGSCKVFFKRAAEGKQKYLCASRNDCTIDKFRRKNCPSCRLRKCYEAGMTLGARKLKKLGNLKLQEEGEASSTTSPTEETTQKLTVSHIEGYECQPIFLNVLEAIEPGVVCAGHDNNQPDSFAALLSSLNELGERQLVHVVKWAKALPGFRNLHVDDQMAVIQYSWMGLMVFAMGWRSFTNVNSRMLYFAPDLVFNEYRMHKSRMYSQCVRMRHLSQEFGWLQITPQEFLCMKALLLFSIIPVDGLKNQKFFDELRMNYIKELDRIIACKRKNPTSCSRRFYQLTKLLDSVQPIARELHQFTFDLLIKSHMVSVDFPEMMAEIISVQVPKILSGKVKPIYFHTQ,mutated_sequence,1.0,920.0,NP_000035.2.a2m,NP_000035.2.npy,ClinVar +NP_000037.2,NP_000037.2.csv,MGPRGAASLPRGPGPRRLLLPVVLPLLLLLLLAPPGSGAGASRPPHLVFLLADDLGWNDVGFHGSRIRTPHLDALAAGGVLLDNYYTQPLCTPSRSQLLTGRYQIRTGLQHQIIWPCQPSCVPLDEKLLPQLLKEAGYTTHMVGKWHLGMYRKECLPTRRGFDTYFGYLLGSEDYYSHERCTLIDALNVTRCALDFRDGEEVATGYKNMYSTNIFTKRAIALITNHPPEKPLFLYLALQSVHEPLQVPEEYLKPYDFIQDKNRHHYAGMVSLMDEAVGNVTAALKSSGLWNNTVFIFSTDNGGQTLAGGNNWPLRGRKWSLWEGGVRGVGFVASPLLKQKGVKNRELIHISDWLPTLVKLARGHTNGTKPLDGFDVWKTISEGSPSPRIELLHNIDPNFVDSSPCPRNSMAPAKDDSSLPEYSAFNTSVHAAIRHGNWKLLTGYPGCGYWFPPPSQYNVSEIPSSDPPTKTLWLFDIDRDPEERHDLSREYPHIVTKLLSRLQFYHKHSVPVYFPAQDPRCDPKATGVWGPWM,mutated_sequence,1.0,533.0,NP_000037.2.a2m,NP_000037.2.npy,ClinVar +NP_000038.2,NP_000038.2.csv,MLHLHHSCLCFRSWLPAMLAVLLSLAPSASSDISASRPNILLLMADDLGIGDIGCYGNNTMRTPNIDRLAEDGVKLTQHISAASLCTPSRAAFLTGRYPVRSGMVSSIGYRVLQWTGASGGLPTNETTFAKILKEKGYATGLIGKWHLGLNCESASDHCHHPLHHGFDHFYGMPFSLMGDCARWELSEKRVNLEQKLNFLFQVLALVALTLVAGKLTHLIPVSWMPVIWSALSAVLLLASSYFVGALIVHADCFLMRNHTITEQPMCFQRTTPLILQEVASFLKRNKHGPFLLFVSFLHVHIPLITMENFLGKSLHGLYGDNVEEMDWMVGRILDTLDVEGLSNSTLIYFTSDHGGSLENQLGNTQYGGWNGIYKGGKGMGGWEGGIRVPGIFRWPGVLPAGRVIGEPTSLMDVFPTVVRLAGGEVPQDRVIDGQDLLPLLLGTAQHSDHEFLMHYCERFLHAARWHQRDRGTMWKVHFVTPVFQPEGAGACYGRKVCPCFGEKVVHHDPPLLFDLSRDPSETHILTPASEPVFYQVMERVQQAVWEHQRTLSPVPLQLDRLGNIWRPWLQPCCGPFPLCWCLREDDPQ,mutated_sequence,1.0,589.0,NP_000038.2.a2m,NP_000038.2.npy,ClinVar +NP_000042.3,NP_000042.3.csv,MSLVLNDLLICCRQLEHDRATERKKEVEKFKRLIRDPETIKHLDRHSDSKQGKYLNWDAVFRFLQKYIQKETECLRIAKPNVSASTQASRQKKMQEISSLVKYFIKCANRRAPRLKCQELLNYIMDTVKDSSNGAIYGADCSNILLKDILSVRKYWCEISQQQWLELFSVYFRLYLKPSQDVHRVLVARIIHAVTKGCCSQTDGLNSKFLDFFSKAIQCARQEKSSSGLNHILAALTIFLKTLAVNFRIRVCELGDEILPTLLYIWTQHRLNDSLKEVIIELFQLQIYIHHPKGAKTQEKGAYESTKWRSILYNLYDLLVNEISHIGSRGKYSSGFRNIAVKENLIELMADICHQVFNEDTRSLEISQSYTTTQRESSDYSVPCKRKKIELGWEVIKDHLQKSQNDFDLVPWLQIATQLISKYPASLPNCELSPLLMILSQLLPQQRHGERTPYVLRCLTEVALCQDKRSNLESSQKSDLLKLWNKIWCITFRGISSEQIQAENFGLLGAIIQGSLVEVDREFWKLFTGSACRPSCPAVCCLTLALTTSIVPGTVKMGIEQNMCEVNRSFSLKESIMKWLLFYQLEGDLENSTEVPPILHSNFPHLVLEKILVSLTMKNCKAAMNFFQSVPECEHHQKDKEELSFSEVEELFLQTTFDKMDFLTIVRECGIEKHQSSIGFSVHQNLKESLDRCLLGLSEQLLNNYSSEITNSETLVRCSRLLVGVLGCYCYMGVIAEEEAYKSELFQKAKSLMQCAGESITLFKNKTNEEFRIGSLRNMMQLCTRCLSNCTKKSPNKIASGFFLRLLTSKLMNDIADICKSLASFIKKPFDRGEVESMEDDTNGNLMEVEDQSSMNLFNDYPDSSVSDANEPGESQSTIGAINPLAEEYLSKQDLLFLDMLKFLCLCVTTAQTNTVSFRAADIRRKLLMLIDSSTLEPTKSLHLHMYLMLLKELPGEEYPLPMEDVLELLKPLSNVCSLYRRDQDVCKTILNHVLHVVKNLGQSNMDSENTRDAQGQFLTVIGAFWHLTKERKYIFSVRMALVNCLKTLLEADPYSKWAILNVMGKDFPVNEVFTQFLADNHHQVRMLAAESINRLFQDTKGDSSRLLKALPLKLQQTAFENAYLKAQEGMREMSHSAENPETLDEIYNRKSVLLTLIAVVLSCSPICEKQALFALCKSVKENGLEPHLVKKVLEKVSETFGYRRLEDFMASHLDYLVLEWLNLQDTEYNLSSFPFILLNYTNIEDFYRSCYKVLIPHLVIRSHFDEVKSIANQIQEDWKSLLTDCFPKILVNILPYFAYEGTRDSGMAQQRETATKVYDMLKSENLLGKQIDHLFISNLPEIVVELLMTLHEPANSSASQSTDLCDFSGDLDPAPNPPHFPSHVIKATFAYISNCHKTKLKSILEILSKSPDSYQKILLAICEQAAETNNVYKKHRILKIYHLFVSLLLKDIKSGLGGAWAFVLRDVIYTLIHYINQRPSCIMDVSLRSFSLCCDLLSQVCQTAVTYCKDALENHLHVIVGTLIPLVYEQVEVQKQVLDLLKYLVIDNKDNENLYITIKLLDPFPDHVVFKDLRITQQKIKYSRGPFSLLEEINHFLSVSVYDALPLTRLEGLKDLRRQLELHKDQMVDIMRASQDNPQDGIMVKLVVNLLQLSKMAINHTGEKEVLEAVGSCLGEVGPIDFSTIAIQHSKDASYTKALKLFEDKELQWTFIMLTYLNNTLVEDCVKVRSAAVTCLKNILATKTGHSFWEIYKMTTDPMLAYLQPFRTSRKKFLEVPRFDKENPFEGLDDINLWIPLSENHDIWIKTLTCAFLDSGGTKCEILQLLKPMCEVKTDFCQTVLPYLIHDILLQDTNESWRNLLSTHVQGFFTSCLRHFSQTSRSTTPANLDSESEHFFRCCLDKKSQRTMLAVVDYMRRQKRPSSGTIFNDAFWLDLNYLEVAKVAQSCAAHFTALLYAEIYADKKSMDDQEKRSLAFEEGSQSTTISSLSEKSKEETGISLQDLLLEIYRSIGEPDSLYGCGGGKMLQPITRLRTYEHEAMWGKALVTYDLETAIPSSTRQAGIIQALQNLGLCHILSVYLKGLDYENKDWCPELEELHYQAAWRNMQWDHCTSVSKEVEGTSYHESLYNALQSLRDREFSTFYESLKYARVKEVEEMCKRSLESVYSLYPTLSRLQAIGELESIGELFSRSVTHRQLSEVYIKWQKHSQLLKDSDFSFQEPIMALRTVILEILMEKEMDNSQRECIKDILTKHLVELSILARTFKNTQLPERAIFQIKQYNSVSCGVSEWQLEEAQVFWAKKEQSLALSILKQMIKKLDASCAANNPSLKLTYTECLRVCGNWLAETCLENPAVIMQTYLEKAVEVAGNYDGESSDELRNGKMKAFLSLARFSDTQYQRIENYMKSSEFENKQALLKRAKEEVGLLREHKIQTNRYTVKVQRELELDELALRALKEDRKRFLCKAVENYINCLLSGEEHDMWVFRLCSLWLENSGVSEVNGMMKRDGMKIPTYKFLPLMYQLAARMGTKMMGGLGFHEVLNNLISRISMDHPHHTLFIILALANANRDEFLTKPEVARRSRITKNVPKQSSQLDEDRTEAANRIICTIRSRRPQMVRSVEALCDAYIILANLDATQWKTQRKGINIPADQPITKLKNLEDVVVPTMEIKVDHTGEYGNLVTIQSFKAEFRLAGGVNLPKIIDCVGSDGKERRQLVKGRDDLRQDAVMQQVFQMCNTLLQRNTETRKRKLTICTYKVVPLSQRSGVLEWCTGTVPIGEFLVNNEDGAHKRYRPNDFSAFQCQKKMMEVQKKSFEEKYEVFMDVCQNFQPVFRYFCMEKFLDPAIWFEKRLAYTRSVATSSIVGYILGLGDRHVQNILINEQSAELVHIDLGVAFEQGKILPTPETVPFRLTRDIVDGMGITGVEGVFRRCCEKTMEVMRNSQETLLTIVEVLLYDPLFDWTMNPLKALYLQQRPEDETELHPTLNADDQECKRNLSDIDQSFNKVAERVLMRLQEKLKGVEEGTVLSVGGQVNLLIQQAIDPKNLSRLFPGWKAWV,mutated_sequence,1.0,3056.0,NP_000042.3.a2m,NP_000042.3.npy,ClinVar +NP_000043.4,NP_000043.4.csv,MDPSMGVNSVTISVEGMTCNSCVWTIEQQIGKVNGVHHIKVSLEEKNATIIYDPKLQTPKTLQEAIDDMGFDAVIHNPDPLPVLTDTLFLTVTASLTLPWDHIQSTLLKTKGVTDIKIYPQKRTVAVTIIPSIVNANQIKELVPELSLDTGTLEKKSGACEDHSMAQAGEVVLKMKVEGMTCHSCTSTIEGKIGKLQGVQRIKVSLDNQEATIVYQPHLISVEEMKKQIEAMGFPAFVKKQPKYLKLGAIDVERLKNTPVKSSEGSQQRSPSYTNDSTATFIIDGMHCKSCVSNIESTLSALQYVSSIVVSLENRSAIVKYNASSVTPESLRKAIEAVSPGLYRVSITSEVESTSNSPSSSSLQKIPLNVVSQPLTQETVINIDGMTCNSCVQSIEGVISKKPGVKSIRVSLANSNGTVEYDPLLTSPETLRGAIEDMGFDATLSDTNEPLVVIAQPSSEMPLLTSTNEFYTKGMTPVQDKEEGKNSSKCYIQVTGMTCASCVANIERNLRREEGIYSILVALMAGKAEVRYNPAVIQPPMIAEFIRELGFGATVIENADEGDGVLELVVRGMTCASCVHKIESSLTKHRGILYCSVALATNKAHIKYDPEIIGPRDIIHTIESLGFEASLVKKDRSASHLDHKREIRQWRRSFLVSLFFCIPVMGLMIYMMVMDHHFATLHHNQNMSKEEMINLHSSMFLERQILPGLSVMNLLSFLLCVPVQFFGGWYFYIQAYKALKHKTANMDVLIVLATTIAFAYSLIILLVAMYERAKVNPITFFDTPPMLFVFIALGRWLEHIAKGKTSEALAKLISLQATEATIVTLDSDNILLSEEQVDVELVQRGDIIKVVPGGKFPVDGRVIEGHSMVDESLITGEAMPVAKKPGSTVIAGSINQNGSLLICATHVGADTTLSQIVKLVEEAQTSKAPIQQFADKLSGYFVPFIVFVSIATLLVWIVIGFLNFEIVETYFPGYNRSISRTETIIRFAFQASITVLCIACPCSLGLATPTAVMVGTGVGAQNGILIKGGEPLEMAHKVKVVVFDKTGTITHGTPVVNQVKVLTESNRISHHKILAIVGTAESNSEHPLGTAITKYCKQELDTETLGTCIDFQVVPGCGISCKVTNIEGLLHKNNWNIEDNNIKNASLVQIDASNEQSSTSSSMIIDAQISNALNAQQYKVLIGNREWMIRNGLVINNDVNDFMTEHERKGRTAVLVAVDDELCGLIAIADTVKPEAELAIHILKSMGLEVVLMTGDNSKTARSIASQVGITKVFAEVLPSHKVAKVKQLQEEGKRVAMVGDGINDSPALAMANVGIAIGTGTDVAIEAADVVLIRNDLLDVVASIDLSRKTVKRIRINFVFALIYNLVGIPIAAGVFMPIGLVLQPWMGSAAMAASSVSVVLSSLFLKLYRKPTYESYELPARSQIGQKSPSEISVHVGIDDTSRNSPKLGLLDRIVNYSRASINSLLSDKRSLNSVVTSEPDKHSLLVGDFREDDDTAL,mutated_sequence,1.0,1500.0,NP_000043.4.a2m,NP_000043.4.npy,ClinVar +NP_000044.2,NP_000044.2.csv,MPEQERQITAREGASRKILSKLSLPTRAWEPAMKKSFAFDNVGYEGGLDGLGPSSQVATSTVRILGMTCQSCVKSIEDRISNLKGIISMKVSLEQGSATVKYVPSVVCLQQVCHQIGDMGFEASIAEGKAASWPSRSLPAQEAVVKLRVEGMTCQSCVSSIEGKVRKLQGVVRVKVSLSNQEAVITYQPYLIQPEDLRDHVNDMGFEAAIKSKVAPLSLGPIDIERLQSTNPKRPLSSANQNFNNSETLGHQGSHVVTLQLRIDGMHCKSCVLNIEENIGQLLGVQSIQVSLENKTAQVKYDPSCTSPVALQRAIEALPPGNFKVSLPDGAEGSGTDHRSSSSHSPGSPPRNQVQGTCSTTLIAIAGMTCASCVHSIEGMISQLEGVQQISVSLAEGTATVLYNPSVISPEELRAAIEDMGFEASVVSESCSTNPLGNHSAGNSMVQTTDGTPTSVQEVAPHTGRLPANHAPDILAKSPQSTRAVAPQKCFLQIKGMTCASCVSNIERNLQKEAGVLSVLVALMAGKAEIKYDPEVIQPLEIAQFIQDLGFEAAVMEDYAGSDGNIELTITGMTCASCVHNIESKLTRTNGITYASVALATSKALVKFDPEIIGPRDIIKIIEEIGFHASLAQRNPNAHHLDHKMEIKQWKKSFLCSLVFGIPVMALMIYMLIPSNEPHQSMVLDHNIIPGLSILNLIFFILCTFVQLLGGWYFYVQAYKSLRHRSANMDVLIVLATSIAYVYSLVILVVAVAEKAERSPVTFFDTPPMLFVFIALGRWLEHLAKSKTSEALAKLMSLQATEATVVTLGEDNLIIREEQVPMELVQRGDIVKVVPGGKFPVDGKVLEGNTMADESLITGEAMPVTKKPGSTVIAGSINAHGSVLIKATHVGNDTTLAQIVKLVEEAQMSKAPIQQLADRFSGYFVPFIIIMSTLTLVVWIVIGFIDFGVVQRYFPNPNKHISQTEVIIRFAFQTSITVLCIACPCSLGLATPTAVMVGTGVAAQNGILIKGGKPLEMAHKIKTVMFDKTGTITHGVPRVMRVLLLGDVATLPLRKVLAVVGTAEASSEHPLGVAVTKYCKEELGTETLGYCTDFQAVPGCGIGCKVSNVEGILAHSERPLSAPASHLNEAGSLPAEKDAVPQTFSVLIGNREWLRRNGLTISSDVSDAMTDHEMKGQTAILVAIDGVLCGMIAIADAVKQEAALAVHTLQSMGVDVVLITGDNRKTARAIATQVGINKVFAEVLPSHKVAKVQELQNKGKKVAMVGDGVNDSPALAQADMGVAIGTGTDVAIEAADVVLIRNDLLDVVASIHLSKRTVRRIRINLVLALIYNLVGIPIAAGVFMPIGIVLQPWMGSAAMAASSVSVVLSSLQLKCYKKPDLERYEAQAHGHMKPLTASQVSVHIGMDDRWRDSPRATPWDQVSYVSQVSLSSLTSDKPSRHSAAADDDGDKWSLLLNGRDEEQYI,mutated_sequence,1.0,1465.0,NP_000044.2.a2m,NP_000044.2.npy,ClinVar +NP_000045.1,NP_000045.1.csv,MLMASTTSAVPGHPSLPSLPSNSSQERPLDTRDPLLARAELALLSIVFVAVALSNGLVLAALARRGRRGHWAPIHVFIGHLCLADLAVALFQVLPQLAWKATDRFRGPDALCRAVKYLQMVGMYASSYMILAMTLDRHRAICRPMLAYRHGSGAHWNRPVLVAWAFSLLLSLPQLFIFAQRNVEGGSGVTDCWACFAEPWGRRTYVTWIALMVFVAPTLGIAACQVLIFREIHASLVPGPSERPGGRRRGRRTGSPGEGAHVSAAVAKTVRMTLVIVVVYVLCWAPFFLVQLWAAWDPEAPLEGAPFVLLMLLASLNSCTNPWIYASFSSSVSSELRSLLCCARGRTPPSLGPQDESCTTASSSLAKDTSS,mutated_sequence,1.0,371.0,NP_000045.1.a2m,NP_000045.1.npy,ClinVar +NP_000048.1,NP_000048.1.csv,MAAVPQNNLQEQLERHSARTLNNKLSLSKPKFSGFTFKKKTSSDNNVSVTNVSVAKTPVLRNKDVNVTEDFSFSEPLPNTTNQQRVKDFFKNAPAGQETQRGGSKSLLPDFLQTPKEVVCTTQNTPTVKKSRDTALKKLEFSSSPDSLSTINDWDDMDDFDTSETSKSFVTPPQSHFVRVSTAQKSKKGKRNFFKAQLYTTNTVKTDLPPPSSESEQIDLTEEQKDDSEWLSSDVICIDDGPIAEVHINEDAQESDSLKTHLEDERDNSEKKKNLEEAELHSTEKVPCIEFDDDDYDTDFVPPSPEEIISASSSSSKCLSTLKDLDTSDRKEDVLSTSKDLLSKPEKMSMQELNPETSTDCDARQISLQQQLIHVMEHICKLIDTIPDDKLKLLDCGNELLQQRNIRRKLLTEVDFNKSDASLLGSLWRYRPDSLDGPMEGDSCPTGNSMKELNFSHLPSNSVSPGDCLLTTTLGKTGFSATRKNLFERPLFNTHLQKSFVSSNWAETPRLGKKNESSYFPGNVLTSTAVKDQNKHTASINDLERETQPSYDIDNFDIDDFDDDDDWEDIMHNLAASKSSTAAYQPIKEGRPIKSVSERLSSAKTDCLPVSSTAQNINFSESIQNYTDKSAQNLASRNLKHERFQSLSFPHTKEMMKIFHKKFGLHNFRTNQLEAINAALLGEDCFILMPTGGGKSLCYQLPACVSPGVTVVISPLRSLIVDQVQKLTSLDIPATYLTGDKTDSEATNIYLQLSKKDPIIKLLYVTPEKICASNRLISTLENLYERKLLARFVIDEAHCVSQWGHDFRQDYKRMNMLRQKFPSVPVMALTATANPRVQKDILTQLKILRPQVFSMSFNRHNLKYYVLPKKPKKVAFDCLEWIRKHHPYDSGIIYCLSRRECDTMADTLQRDGLAALAYHAGLSDSARDEVQQKWINQDGCQVICATIAFGMGIDKPDVRFVIHASLPKSVEGYYQESGRAGRDGEISHCLLFYTYHDVTRLKRLIMMEKDGNHHTRETHFNNLYSMVHYCENITECRRIQLLAYFGENGFNPDFCKKHPDVSCDNCCKTKDYKTRDVTDDVKSIVRFVQEHSSSQGMRNIKHVGPSGRFTMNMLVDIFLGSKSAKIQSGIFGKGSAYSRHNAERLFKKLILDKILDEDLYINANDQAIAYVMLGNKAQTVLNGNLKVDFMETENSSSVKKQKALVAKVSQREEMVKKCLGELTEVCKSLGKVFGVHYFNIFNTVTLKKLAESLSSDPEVLLQIDGVTEDKLEKYGAEVISVLQKYSEWTSPAEDSSPGISLSSSRGPGRSAAEELDEEIPVSSHYFASKTRNERKRKKMPASQRSKRRKTASSGSKAKGGSATCRKISSKTKSSSIIGSSSASHTSQATSGANSKLGIMAPPKPINRPFLKPSYAFS,mutated_sequence,1.0,1417.0,NP_000048.1.a2m,NP_000048.1.npy,ClinVar +NP_000050.3,NP_000050.3.csv,MPIGSKERPTFFEIFKTRCNKADLGPISLNWFEELSSEAPPYNSEPAEESEHKNNNYEPNLFKTPQRKPSYNQLASTPIIFKEQGLTLPLYQSPVKELDKFKLDLGRNVPNSRHKSLRTVKTKMDQADDVSCPLLNSCLSESPVVLQCTHVTPQRDKSVVCGSLFHTPKFVKGRQTPKHISESLGAEVDPDMSWSSSLATPPTLSSTVLIVRNEEASETVFPHDTTANVKSYFSNHDESLKKNDRFIASVTDSENTNQREAASHGFGKTSGNSFKVNSCKDHIGKSMPNVLEDEVYETVVDTSEEDSFSLCFSKCRTKNLQKVRTSKTRKKIFHEANADECEKSKNQVKEKYSFVSEVEPNDTDPLDSNVANQKPFESGSDKISKEVVPSLACEWSQLTLSGLNGAQMEKIPLLHISSCDQNISEKDLLDTENKRKKDFLTSENSLPRISSLPKSEKPLNEETVVNKRDEEQHLESHTDCILAVKQAISGTSPVASSFQGIKKSIFRIRESPKETFNASFSGHMTDPNFKKETEASESGLEIHTVCSQKEDSLCPNLIDNGSWPATTTQNSVALKNAGLISTLKKKTNKFIYAIHDETSYKGKKIPKDQKSELINCSAQFEANAFEAPLTFANADSGLLHSSVKRSCSQNDSEEPTLSLTSSFGTILRKCSRNETCSNNTVISQDLDYKEAKCNKEKLQLFITPEADSLSCLQEGQCENDPKSKKVSDIKEEVLAAACHPVQHSKVEYSDTDFQSQKSLLYDHENASTLILTPTSKDVLSNLVMISRGKESYKMSDKLKGNNYESDVELTKNIPMEKNQDVCALNENYKNVELLPPEKYMRVASPSRKVQFNQNTNLRVIQKNQEETTSISKITVNPDSEELFSDNENNFVFQVANERNNLALGNTKELHETDLTCVNEPIFKNSTMVLYGDTGDKQATQVSIKKDLVYVLAEENKNSVKQHIKMTLGQDLKSDISLNIDKIPEKNNDYMNKWAGLLGPISNHSFGGSFRTASNKEIKLSEHNIKKSKMFFKDIEEQYPTSLACVEIVNTLALDNQKKLSKPQSINTVSAHLQSSVVVSDCKNSHITPQMLFSKQDFNSNHNLTPSQKAEITELSTILEESGSQFEFTQFRKPSYILQKSTFEVPENQMTILKTTSEECRDADLHVIMNAPSIGQVDSSKQFEGTVEIKRKFAGLLKNDCNKSASGYLTDENEVGFRGFYSAHGTKLNVSTEALQKAVKLFSDIENISEETSAEVHPISLSSSKCHDSVVSMFKIENHNDKTVSEKNNKCQLILQNNIEMTTGTFVEEITENYKRNTENEDNKYTAASRNSHNLEFDGSDSSKNDTVCIHKDETDLLFTDQHNICLKLSGQFMKEGNTQIKEDLSDLTFLEVAKAQEACHGNTSNKEQLTATKTEQNIKDFETSDTFFQTASGKNISVAKESFNKIVNFFDQKPEELHNFSLNSELHSDIRKNKMDILSYEETDIVKHKILKESVPVGTGNQLVTFQGQPERDEKIKEPTLLGFHTASGKKVKIAKESLDKVKNLFDEKEQGTSEITSFSHQWAKTLKYREACKDLELACETIEITAAPKCKEMQNSLNNDKNLVSIETVVPPKLLSDNLCRQTENLKTSKSIFLKVKVHENVEKETAKSPATCYTNQSPYSVIENSALAFYTSCSRKTSVSQTSLLEAKKWLREGIFDGQPERINTADYVGNYLYENNSNSTIAENDKNHLSEKQDTYLSNSSMSNSYSYHSDEVYNDSGYLSKNKLDSGIEPVLKNVEDQKNTSFSKVISNVKDANAYPQTVNEDICVEELVTSSSPCKNKNAAIKLSISNSNNFEVGPPAFRIASGKIVCVSHETIKKVKDIFTDSFSKVIKENNENKSKICQTKIMAGCYEALDDSEDILHNSLDNDECSTHSHKVFADIQSEEILQHNQNMSGLEKVSKISPCDVSLETSDICKCSIGKLHKSVSSANTCGIFSTASGKSVQVSDASLQNARQVFSEIEDSTKQVFSKVLFKSNEHSDQLTREENTAIRTPEHLISQKGFSYNVVNSSAFSGFSTASGKQVSILESSLHKVKGVLEEFDLIRTEHSLHYSPTSRQNVSKILPRVDKRNPEHCVNSEMEKTCSKEFKLSNNLNVEGGSSENNHSIKVSPYLSQFQQDKQQLVLGTKVSLVENIHVLGKEQASPKNVKMEIGKTETFSDVPVKTNIEVCSTYSKDSENYFETEAVEIAKAFMEDDELTDSKLPSHATHSLFTCPENEEMVLSNSRIGKRRGEPLILVGEPSIKRNLLNEFDRIIENQEKSLKASKSTPDGTIKDRRLFMHHVSLEPITCVPFRTTKERQEIQNPNFTAPGQEFLSKSHLYEHLTLEKSSSNLAVSGHPFYQVSATRNEKMRHLITTGRPTKVFVPPFKTKSHFHRVEQCVRNINLEENRQKQNIDGHGSDDSKNKINDNEIHQFNKNNSNQAVAVTFTKCEEEPLDLITSLQNARDIQDMRIKKKQRQRVFPQPGSLYLAKTSTLPRISLKAAVGGQVPSACSHKQLYTYGVSKHCIKINSKNAESFQFHTEDYFGKESLWTGKGIQLADGGWLIPSNDGKAGKEEFYRALCDTPGVDPKLISRIWVYNHYRWIIWKLAAMECAFPKEFANRCLSPERVLLQLKYRYDTEIDRSRRSAIKKIMERDDTAAKTLVLCVSDIISLSANISETSSNKTSSADTQKVAIIELTDGWYAVKAQLDPPLLAVLKNGRLTVGQKIILHGAELVGSPDACTPLEAPESLMLKISANSTRPARWYTKLGFFPDPRPFPLPLSSLFSDGGNVGCVDVIIQRAYPIQWMEKTSSGLYIFRNEREEEKEAAKYVEAQQKRLEALFTKIQEEFEEHEENTTKPYLPSRALTRQQVRALQDGAELYEAVKNAADPAYLEGYFSEEQLRALNNHRQMLNDKKQAQIQLEIRKAMESAEQKEQGLSRDVTTVWKLRIVSYSKKEKDSVILSIWRPSSDLYSLLTEGKRYRIYHLATSKSKSKSERANIQLAATKKTQYQQLPVSDEILFQIYQPREPLHFSKFLDPDFQPSCSEVDLIGFVVSVVKKTGLAPFVYLSDECYNLLAIKFWIDLNEDIIKPHMLIAASNLQWRPESKSGLLTLFAGDFSVFSASPKEGHFQETFNKMKNTVENIDILCNEAENKLMHILHANDPKWSTPTKDCTSGPYTAQIIPGTGNKLLMSSPNCEIYYQSPLSLCMAKRKSVSTPVSAQMTSKSCKGEKEIDDQKNCKKRRALDFLSRLPLPPPVSPICTFVSPAAQKAFQPPRSCGTKYETPIKKKELNSPQMTPFKKFNEISLLESNSIADEELALINTQALLSGSTGEKQFISVSESTRTAPTSSEDYLRLKRRCTTSLIKEQESSQASTEECEKNKQDTITTKKYI,mutated_sequence,1.0,3418.0,NP_000050.3.a2m,NP_000050.3.npy,ClinVar +NP_000052.1,NP_000052.1.csv,MAAVILESIFLKRSQQKKKTSPLNFKKRLFLLTVHKLSYYEYDFERGRRGSKKGSIDVEKITCVETVVPEKNPPPERQIPRRGEESSEMEQISIIERFPYPFQVVYDEGPLYVFSPTEELRKRWIHQLKNVIRYNSDLVQKYHPCFWIDGQYLCCSQTAKNAMGCQILENRNGSLKPGSSHRKTKKPLPPTPEEDQILKKPLPPEPAAAPVSTSELKKVVALYDYMPMNANDLQLRKGDEYFILEESNLPWWRARDKNGQEGYIPSNYVTEAEDSIEMYEWYSKHMTRSQAEQLLKQEGKEGGFIVRDSSKAGKYTVSVFAKSTGDPQGVIRHYVVCSTPQSQYYLAEKHLFSTIPELINYHQHNSAGLISRLKYPVSQQNKNAPSTAGLGYGSWEIDPKDLTFLKELGTGQFGVVKYGKWRGQYDVAIKMIKEGSMSEDEFIEEAKVMMNLSHEKLVQLYGVCTKQRPIFIITEYMANGCLLNYLREMRHRFQTQQLLEMCKDVCEAMEYLESKQFLHRDLAARNCLVNDQGVVKVSDFGLSRYVLDDEYTSSVGSKFPVRWSPPEVLMYSKFSSKSDIWAFGVLMWEIYSLGKMPYERFTNSETAEHIAQGLRLYRPHLASEKVYTIMYSCWHEKADERPTFKILLSNILDVMDEES,mutated_sequence,1.0,659.0,NP_000052.1.a2m,NP_000052.1.npy,ClinVar +NP_000053.2,NP_000053.2.csv,MASRLTLLTLLLLLLAGDRASSNPNATSSSSQDPESLQDRGEGKVATTVISKMLFVEPILEVSSLPTTNSTTNSATKITANTTDEPTTQPTTEPTTQPTIQPTQPTTQLPTDSPTQPTTGSFCPGPVTLCSDLESHSTEAVLGDALVDFSLKLYHAFSAMKKVETNMAFSPFSIASLLTQVLLGAGENTKTNLESILSYPKDFTCVHQALKGFTTKGVTSVSQIFHSPDLAIRDTFVNASRTLYSSSPRVLSNNSDANLELINTWVAKNTNNKISRLLDSLPSDTRLVLLNAIYLSAKWKTTFDPKKTRMEPFHFKNSVIKVPMMNSKKYPVAHFIDQTLKAKVGQLQLSHNLSLVILVPQNLKHRLEDMEQALSPSVFKAIMEKLEMSKFQPTLLTLPRIKVTTSQDMLSIMEKLEFFDFSYDLNLCGLTEDPDLQVSAMQHQTVLELTETGVEAAAASAISVARTLLVFEVQQPFLFVLWDQQHKFPVFMGRVYDPRA,mutated_sequence,1.0,500.0,NP_000053.2.a2m,NP_000053.2.npy,ClinVar +NP_000061.1,NP_000061.1.csv,MPTVISASVAPRTAAEPRSPGPVPHPAQSKATEAGGGNPSGIYSAIISRNFPIIGVKEKTFEQLHKKCLEKKVLYVDPEFPPDETSLFYSQKFPIQFVWKRPPEICENPRFIIDGANRTDICQGELGDCWFLAAIACLTLNQHLLFRVIPHDQSFIENYAGIFHFQFWRYGEWVDVVIDDCLPTYNNQLVFTKSNHRNEFWSALLEKAYAKLHGSYEALKGGNTTEAMEDFTGGVAEFFEIRDAPSDMYKIMKKAIERGSLMGCSIDDGTNMTYGTSPSGLNMGELIARMVRNMDNSLLQDSDLDPRGSDERPTRTIIPVQYETRMACGLVRGHAYSVTGLDEVPFKGEKVKLVRLRNPWGQVEWNGSWSDRWKDWSFVDKDEKARLQHQVTEDGEFWMSYEDFIYHFTKLEICNLTADALQSDKLQTWTVSVNEGRWVRGCSAGGCRNFPDTFWTNPQYRLKLLEEDDDPDDSEVICSFLVALMQKNRRKDRKLGASLFTIGFAIYEVPKEMHGNKQHLQKDFFLYNASKARSKTYINMREVSQRFRLPPSEYVIVPSTYEPHQEGEFILRVFSEKRNLSEEVENTISVDRPVKKKKTKPIIFVSDRANSNKELGVDQESEEGKGKTSPDKQKQSPQPQPGSSDQESEEQQQFRNIFKQIAGDDMEICADELKKVLNTVVNKHKDLKTHGFTLESCRSMIALMDTDGSGKLNLQEFHHLWNKIKAWQKIFKHYDTDQSGTINSYEMRNAVNDAGFHLNNQLYDIITMRYADKHMNIDFDSFICCFVRLEGMFRAFHAFDKDGDGIIKLNVLEWLQLTMYA,mutated_sequence,1.0,821.0,NP_000061.1.a2m,NP_000061.1.npy,ClinVar +NP_000068.1,NP_000068.1.csv,MEPAAGSSMEPSADWLATAAARGRVEEVRALLEAGALPNAPNSYGRRPIQVMMMGSARVAELLLLHGAEPNCADPATLTRPVHDAAREGFLDTLVVLHRAGARLDVRDAWGRLPVDLAEELGHRDVARYLRAAAGGTRGSNHARIDAAEGPSDIPD,mutated_sequence,1.0,156.0,NP_000068.1.a2m,NP_000068.1.npy,ClinVar +NP_000071.1,NP_000071.1.csv,MARAPLGVLLLLGLLGRGVGKNEELRLYHHLFNNYDPGSRPVREPEDTVTISLKVTLTNLISLNEKEETLTTSVWIGIDWQDYRLNYSKDDFGGIETLRVPSELVWLPEIVLENNIDGQFGVAYDANVLVYEGGSVTWLPPAIYRSVCAVEVTYFPFDWQNCSLIFRSQTYNAEEVEFTFAVDNDGKTINKIDIDTEAYTENGEWAIDFCPGVIRRHHGGATDGPGETDVIYSLIIRRKPLFYVINIIVPCVLISGLVLLAYFLPAQAGGQKCTVSINVLLAQTVFLFLIAQKIPETSLSVPLLGRFLIFVMVVATLIVMNCVIVLNVSQRTPTTHAMSPRLRHVLLELLPRLLGSPPPPEAPRAASPPRRASSVGLLLRAEELILKKPRSELVFEGQRHRQGTWTAAFCQSLGAAAPEVRCCVDAVNFVAESTRDQEATGEEVSDWVRMGNALDNICFWAALVLFSVGSSLIFLGAYFNRVPDLPYAPCIQP,mutated_sequence,1.0,493.0,NP_000071.1.a2m,NP_000071.1.npy,ClinVar +NP_000073.1,NP_000073.1.csv,MLGFLSARQTGLEDPLRLRRAESTRRVLGLELNKDRDVERIHGGGINTLDIEPVEGRYMLSGGSDGVIVLYDLENSSRQSYYTCKAVCSIGRDHPDVHRYSVETVQWYPHDTGMFTSSSFDKTLKVWDTNTLQTADVFNFEETVYSHHMSPVSTKHCLVAVGTRGPKVQLCDLKSGSCSHILQGHRQEILAVSWSPRYDYILATASADSRVKLWDVRRASGCLITLDQHNGKKSQAVESANTAHNGKVNGLCFTSDGLHLLTVGTDNRMRLWNSSNGENTLVNYGKVCNNSKKGLKFTVSCGCSSEFVFVPYGSTIAVYTVYSGEQITMLKGHYKTVDCCVFQSNFQELYSGSRDCNILAWVPSLYEPVPDDDETTTKSQLNPAFEDAWSSSDEEG,mutated_sequence,1.0,396.0,NP_000073.1.a2m,NP_000073.1.npy,ClinVar +NP_000074.3,NP_000074.3.csv,MEQSRSQQRGGEQSWWGSDPQYQYMPFEHCTSYGLPSENGGLQHRLRKDAGPRHNVHPTQIYGHHKEQFSDREQDIGMPKKTGSSSTVDSKDEDHYSKCQDCIHRLGQVVRRKLGEDGIFLVLLGLLMALVSWSMDYVSAKSLQAYKWSYAQMQPSLPLQFLVWVTFPLVLILFSALFCHLISPQAVGSGIPEMKTILRGVVLKEYLTMKAFVAKVVALTAGLGSGIPVGKEGPFVHIASICAAVLSKFMSVFCGVYEQPYYYSDILTVGCAVGVGCCFGTPLGGVLFSIEVTSTYFAVRNYWRGFFAATFSAFVFRVLAVWNKDAVTITALFRTNFRMDFPFDLKELPAFAAIGICCGLLGAVFVYLHRQVMLGVRKHKALSQFLAKHRLLYPGIVTFVIASFTFPPGMGQFMAGELMPREAISTLFDNNTWVKHAGDPESLGQSAVWIHPRVNVVIIIFLFFVMKFWMSIVATTMPIPCGGFMPVFVLGAAFGRLVGEIMAMLFPDGILFDDIIYKILPGGYAVIGAAALTGAVSHTVSTAVICFELTGQIAHILPMMVAVILANMVAQSLQPSLYDSIIQVKKLPYLPDLGWNQLSKYTIFVEDIMVRDVKFVSASYTYGELRTLLQTTTVKTLPLVDSKDSMILLGSVERSELQALLQRHLCPERRLRAAQEMARKLSELPYDGKARLAGEGLPGAPPGRPESFAFVDEDEDEDLSGKSELPPSLALHPSTTAPLSPEEPNGPLPGHKQQPEAPEPAGQRPSIFQSLLHCLLGRARPTKKKTTQDSTDLVDNMSPEEIEAWEQEQLSQPVCFDSCCIDQSPFQLVEQTTLHKTHTLFSLLGLHLAYVTSMGKLRGVLALEELQKAIEGHTKSGVQLRPPLASFRNTTSTRKSTGAPPSSAENWNLPEDRPGATGTGDVIAASPETPVPSPSPEPPLSLAPGKVEGELEELELVESPGLEEELADILQGPSLRSTDEEDEDELIL,mutated_sequence,1.0,988.0,NP_000074.3.a2m,NP_000074.3.npy,ClinVar +NP_000079.2,NP_000079.2.csv,MFSFVDLRLLLLLAATALLTHGQEEGQVEGQDEDIPPITCVQNGLRYHDRDVWKPEPCRICVCDNGKVLCDDVICDETKNCPGAEVPEGECCPVCPDGSESPTDQETTGVEGPKGDTGPRGPRGPAGPPGRDGIPGQPGLPGPPGPPGPPGPPGLGGNFAPQLSYGYDEKSTGGISVPGPMGPSGPRGLPGPPGAPGPQGFQGPPGEPGEPGASGPMGPRGPPGPPGKNGDDGEAGKPGRPGERGPPGPQGARGLPGTAGLPGMKGHRGFSGLDGAKGDAGPAGPKGEPGSPGENGAPGQMGPRGLPGERGRPGAPGPAGARGNDGATGAAGPPGPTGPAGPPGFPGAVGAKGEAGPQGPRGSEGPQGVRGEPGPPGPAGAAGPAGNPGADGQPGAKGANGAPGIAGAPGFPGARGPSGPQGPGGPPGPKGNSGEPGAPGSKGDTGAKGEPGPVGVQGPPGPAGEEGKRGARGEPGPTGLPGPPGERGGPGSRGFPGADGVAGPKGPAGERGSPGPAGPKGSPGEAGRPGEAGLPGAKGLTGSPGSPGPDGKTGPPGPAGQDGRPGPPGPPGARGQAGVMGFPGPKGAAGEPGKAGERGVPGPPGAVGPAGKDGEAGAQGPPGPAGPAGERGEQGPAGSPGFQGLPGPAGPPGEAGKPGEQGVPGDLGAPGPSGARGERGFPGERGVQGPPGPAGPRGANGAPGNDGAKGDAGAPGAPGSQGAPGLQGMPGERGAAGLPGPKGDRGDAGPKGADGSPGKDGVRGLTGPIGPPGPAGAPGDKGESGPSGPAGPTGARGAPGDRGEPGPPGPAGFAGPPGADGQPGAKGEPGDAGAKGDAGPPGPAGPAGPPGPIGNVGAPGAKGARGSAGPPGATGFPGAAGRVGPPGPSGNAGPPGPPGPAGKEGGKGPRGETGPAGRPGEVGPPGPPGPAGEKGSPGADGPAGAPGTPGPQGIAGQRGVVGLPGQRGERGFPGLPGPSGEPGKQGPSGASGERGPPGPMGPPGLAGPPGESGREGAPGAEGSPGRDGSPGAKGDRGETGPAGPPGAPGAPGAPGPVGPAGKSGDRGETGPAGPAGPVGPVGARGPAGPQGPRGDKGETGEQGDRGIKGHRGFSGLQGPPGPPGSPGEQGPSGASGPAGPRGPPGSAGAPGKDGLNGLPGPIGPPGPRGRTGDAGPVGPPGPPGPPGPPGPPSAGFDFSFLPQPPQEKAHDGGRYYRADDANVVRDRDLEVDTTLKSLSQQIENIRSPEGSRKNPARTCRDLKMCHSDWKSGEYWIDPNQGCNLDAIKVFCNMETGETCVYPTQPSVAQKNWYISKNPKDKRHVWFGESMTDGFQFEYGGQGSDPADVAIQLTFLRLMSTEASQNITYHCKNSVAYMDQQTGNLKKALLLQGSNEIEIRAEGNSRFTYSVTVDGCTSHTGAWGKTVIEYKTTKTSRLPIIDVAPLDVGAPDQEFGFDVGPVCFL,mutated_sequence,1.0,1464.0,NP_000079.2.a2m,NP_000079.2.npy,ClinVar +NP_000080.2,NP_000080.2.csv,MLSFVDTRTLLLLAVTLCLATCQSLQEETVRKGPAGDRGPRGERGPPGPPGRDGEDGPTGPPGPPGPPGPPGLGGNFAAQYDGKGVGLGPGPMGLMGPRGPPGAAGAPGPQGFQGPAGEPGEPGQTGPAGARGPAGPPGKAGEDGHPGKPGRPGERGVVGPQGARGFPGTPGLPGFKGIRGHNGLDGLKGQPGAPGVKGEPGAPGENGTPGQTGARGLPGERGRVGAPGPAGARGSDGSVGPVGPAGPIGSAGPPGFPGAPGPKGEIGAVGNAGPAGPAGPRGEVGLPGLSGPVGPPGNPGANGLTGAKGAAGLPGVAGAPGLPGPRGIPGPVGAAGATGARGLVGEPGPAGSKGESGNKGEPGSAGPQGPPGPSGEEGKRGPNGEAGSAGPPGPPGLRGSPGSRGLPGADGRAGVMGPPGSRGASGPAGVRGPNGDAGRPGEPGLMGPRGLPGSPGNIGPAGKEGPVGLPGIDGRPGPIGPAGARGEPGNIGFPGPKGPTGDPGKNGDKGHAGLAGARGAPGPDGNNGAQGPPGPQGVQGGKGEQGPPGPPGFQGLPGPSGPAGEVGKPGERGLHGEFGLPGPAGPRGERGPPGESGAAGPTGPIGSRGPSGPPGPDGNKGEPGVVGAVGTAGPSGPSGLPGERGAAGIPGGKGEKGEPGLRGEIGNPGRDGARGAPGAVGAPGPAGATGDRGEAGAAGPAGPAGPRGSPGERGEVGPAGPNGFAGPAGAAGQPGAKGERGAKGPKGENGVVGPTGPVGAAGPAGPNGPPGPAGSRGDGGPPGMTGFPGAAGRTGPPGPSGISGPPGPPGPAGKEGLRGPRGDQGPVGRTGEVGAVGPPGFAGEKGPSGEAGTAGPPGTPGPQGLLGAPGILGLPGSRGERGLPGVAGAVGEPGPLGIAGPPGARGPPGAVGSPGVNGAPGEAGRDGNPGNDGPPGRDGQPGHKGERGYPGNIGPVGAAGAPGPHGPVGPAGKHGNRGETGPSGPVGPAGAVGPRGPSGPQGIRGDKGEPGEKGPRGLPGLKGHNGLQGLPGIAGHHGDQGAPGSVGPAGPRGPAGPSGPAGKDGRTGHPGTVGPAGIRGPQGHQGPAGPPGPPGPPGPPGVSGGGYDFGYDGDFYRADQPRSAPSLRPKDYEVDATLKSLNNQIETLLTPEGSRKNPARTCRDLRLSHPEWSSGYYWIDPNQGCTMDAIKVYCDFSTGETCIRAQPENIPAKNWYRSSKDKKHVWLGETINAGSQFEYNVEGVTSKEMATQLAFMRLLANYASQNITYHCKNSIAYMDEETGNLKKAVILQGSNDVELVAEGNSRFTYTVLVDGCSKKTNEWGKTIIEYKTNKPSRLPFLDIAPLDIGGADQEFFVDIGPVCFK,mutated_sequence,1.0,1366.0,NP_000080.2.a2m,NP_000080.2.npy,ClinVar +NP_000081.2,NP_000081.2.csv,MMSFVQKGSWLLLALLHPTIILAQQEAVEGGCSHLGQSYADRDVWKPEPCQICVCDSGSVLCDDIICDDQELDCPNPEIPFGECCAVCPQPPTAPTRPPNGQGPQGPKGDPGPPGIPGRNGDPGIPGQPGSPGSPGPPGICESCPTGPQNYSPQYDSYDVKSGVAVGGLAGYPGPAGPPGPPGPPGTSGHPGSPGSPGYQGPPGEPGQAGPSGPPGPPGAIGPSGPAGKDGESGRPGRPGERGLPGPPGIKGPAGIPGFPGMKGHRGFDGRNGEKGETGAPGLKGENGLPGENGAPGPMGPRGAPGERGRPGLPGAAGARGNDGARGSDGQPGPPGPPGTAGFPGSPGAKGEVGPAGSPGSNGAPGQRGEPGPQGHAGAQGPPGPPGINGSPGGKGEMGPAGIPGAPGLMGARGPPGPAGANGAPGLRGGAGEPGKNGAKGEPGPRGERGEAGIPGVPGAKGEDGKDGSPGEPGANGLPGAAGERGAPGFRGPAGPNGIPGEKGPAGERGAPGPAGPRGAAGEPGRDGVPGGPGMRGMPGSPGGPGSDGKPGPPGSQGESGRPGPPGPSGPRGQPGVMGFPGPKGNDGAPGKNGERGGPGGPGPQGPPGKNGETGPQGPPGPTGPGGDKGDTGPPGPQGLQGLPGTGGPPGENGKPGEPGPKGDAGAPGAPGGKGDAGAPGERGPPGLAGAPGLRGGAGPPGPEGGKGAAGPPGPPGAAGTPGLQGMPGERGGLGSPGPKGDKGEPGGPGADGVPGKDGPRGPTGPIGPPGPAGQPGDKGEGGAPGLPGIAGPRGSPGERGETGPPGPAGFPGAPGQNGEPGGKGERGAPGEKGEGGPPGVAGPPGGSGPAGPPGPQGVKGERGSPGGPGAAGFPGARGLPGPPGSNGNPGPPGPSGSPGKDGPPGPAGNTGAPGSPGVSGPKGDAGQPGEKGSPGAQGPPGAPGPLGIAGITGARGLAGPPGMPGPRGSPGPQGVKGESGKPGANGLSGERGPPGPQGLPGLAGTAGEPGRDGNPGSDGLPGRDGSPGGKGDRGENGSPGAPGAPGHPGPPGPVGPAGKSGDRGESGPAGPAGAPGPAGSRGAPGPQGPRGDKGETGERGAAGIKGHRGFPGNPGAPGSPGPAGQQGAIGSPGPAGPRGPVGPSGPPGKDGTSGHPGPIGPPGPRGNRGERGSEGSPGHPGQPGPPGPPGAPGPCCGGVGAAAIAGIGGEKAGGFAPYYGDEPMDFKINTDEIMTSLKSVNGQIESLISPDGSRKNPARNCRDLKFCHPELKSGEYWVDPNQGCKLDAIKVFCNMETGETCISANPLNVPRKHWWTDSSAEKKHVWFGESMDGGFQFSYGNPELPEDVLDVHLAFLRLLSSRASQNITYHCKNSIAYMDQASGNVKKALKLMGSNEGEFKAEGNSKFTYTVLEDGCTKHTGEWSKTVFEYRTRKAVRLPIVDIAPYDIGGPDQEFGVDVGPVCFL,mutated_sequence,1.0,1466.0,NP_000081.2.a2m,NP_000081.2.npy,ClinVar +NP_000082.2,NP_000082.2.csv,MSARTAPRPQVLLLPLLLVLLAAAPAASKGCVCKDKGQCFCDGAKGEKGEKGFPGPPGSPGQKGFTGPEGLPGPQGPKGFPGLPGLTGSKGVRGISGLPGFSGSPGLPGTPGNTGPYGLVGVPGCSGSKGEQGFPGLPGTLGYPGIPGAAGLKGQKGAPAKEEDIELDAKGDPGLPGAPGPQGLPGPPGFPGPVGPPGPPGFFGFPGAMGPRGPKGHMGERVIGHKGERGVKGLTGPPGPPGTVIVTLTGPDNRTDLKGEKGDKGAMGEPGPPGPSGLPGESYGSEKGAPGDPGLQGKPGKDGVPGFPGSEGVKGNRGFPGLMGEDGIKGQKGDIGPPGFRGPTEYYDTYQEKGDEGTPGPPGPRGARGPQGPSGPPGVPGSPGSSRPGLRGAPGWPGLKGSKGERGRPGKDAMGTPGSPGCAGSPGLPGSPGPPGPPGDIVFRKGPPGDHGLPGYLGSPGIPGVDGPKGEPGLLCTQCPYIPGPPGLPGLPGLHGVKGIPGRQGAAGLKGSPGSPGNTGLPGFPGFPGAQGDPGLKGEKGETLQPEGQVGVPGDPGLRGQPGRKGLDGIPGTPGVKGLPGPKGELALSGEKGDQGPPGDPGSPGSPGPAGPAGPPGYGPQGEPGLQGTQGVPGAPGPPGEAGPRGELSVSTPVPGPPGPPGPPGHPGPQGPPGIPGSLGKCGDPGLPGPDGEPGIPGIGFPGPPGPKGDQGFPGTKGSLGCPGKMGEPGLPGKPGLPGAKGEPAVAMPGGPGTPGFPGERGNSGEHGEIGLPGLPGLPGTPGNEGLDGPRGDPGQPGPPGEQGPPGRCIEGPRGAQGLPGLNGLKGQQGRRGKTGPKGDPGIPGLDRSGFPGETGSPGIPGHQGEMGPLGQRGYPGNPGILGPPGEDGVIGMMGFPGAIGPPGPPGNPGTPGQRGSPGIPGVKGQRGTPGAKGEQGDKGNPGPSEISHVIGDKGEPGLKGFAGNPGEKGNRGVPGMPGLKGLKGLPGPAGPPGPRGDLGSTGNPGEPGLRGIPGSMGNMGMPGSKGKRGTLGFPGRAGRPGLPGIHGLQGDKGEPGYSEGTRPGPPGPTGDPGLPGDMGKKGEMGQPGPPGHLGPAGPEGAPGSPGSPGLPGKPGPHGDLGFKGIKGLLGPPGIRGPPGLPGFPGSPGPMGIRGDQGRDGIPGPAGEKGETGLLRAPPGPRGNPGAQGAKGDRGAPGFPGLPGRKGAMGDAGPRGPTGIEGFPGPPGLPGAIIPGQTGNRGPPGSRGSPGAPGPPGPPGSHVIGIKGDKGSMGHPGPKGPPGTAGDMGPPGRLGAPGTPGLPGPRGDPGFQGFPGVKGEKGNPGFLGSIGPPGPIGPKGPPGVRGDPGTLKIISLPGSPGPPGTPGEPGMQGEPGPPGPPGNLGPCGPRGKPGKDGKPGTPGPAGEKGNKGSKGEPGPAGSDGLPGLKGKRGDSGSPATWTTRGFVFTRHSQTTAIPSCPEGTVPLYSGFSFLFVQGNQRAHGQDLGTLGSCLQRFTTMPFLFCNVNDVCNFASRNDYSYWLSTPALMPMNMAPITGRALEPYISRCTVCEGPAIAIAVHSQTTDIPPCPHGWISLWKGFSFIMFTSAGSEGTGQALASPGSCLEEFRASPFLECHGRGTCNYYSNSYSFWLASLNPERMFRKPIPSTVKAGELEKIISRCQVCMKKRH,mutated_sequence,1.0,1670.0,NP_000082.2.a2m,NP_000082.2.npy,ClinVar +NP_000083.3,NP_000083.3.csv,MWSLHIVLMRCSFRLTKSLATGPWSLILILFSVQYVYGSGKKYIGPCGGRDCSVCHCVPEKGSRGPPGPPGPQGPIGPLGAPGPIGLSGEKGMRGDRGPPGAAGDKGDKGPTGVPGFPGLDGIPGHPGPPGPRGKPGMSGHNGSRGDPGFPGGRGALGPGGPLGHPGEKGEKGNSVFILGAVKGIQGDRGDPGLPGLPGSWGAGGPAGPTGYPGEPGLVGPPGQPGRPGLKGNPGVGVKGQMGDPGEVGQQGSPGPTLLVEPPDFCLYKGEKGIKGIPGMVGLPGPPGRKGESGIGAKGEKGIPGFPGPRGDPGSYGSPGFPGLKGELGLVGDPGLFGLIGPKGDPGNRGHPGPPGVLVTPPLPLKGPPGDPGFPGRYGETGDVGPPGPPGLLGRPGEACAGMIGPPGPQGFPGLPGLPGEAGIPGRPDSAPGKPGKPGSPGLPGAPGLQGLPGSSVIYCSVGNPGPQGIKGKVGPPGGRGPKGEKGNEGLCACEPGPMGPPGPPGLPGRQGSKGDLGLPGWLGTKGDPGPPGAEGPPGLPGKHGASGPPGNKGAKGDMVVSRVKGHKGERGPDGPPGFPGQPGSHGRDGHAGEKGDPGPPGDHEDATPGGKGFPGPLGPPGKAGPVGPPGLGFPGPPGERGHPGVPGHPGVRGPDGLKGQKGDTISCNVTYPGRHGPPGFDGPPGPKGFPGPQGAPGLSGSDGHKGRPGTPGTAEIPGPPGFRGDMGDPGFGGEKGSSPVGPPGPPGSPGVNGQKGIPGDPAFGHLGPPGKRGLSGVPGIKGPRGDPGCPGAEGPAGIPGFLGLKGPKGREGHAGFPGVPGPPGHSCERGAPGIPGQPGLPGYPGSPGAPGGKGQPGDVGPPGPAGMKGLPGLPGRPGAHGPPGLPGIPGPFGDDGLPGPPGPKGPRGLPGFPGFPGERGKPGAEGCPGAKGEPGEKGMSGLPGDRGLRGAKGAIGPPGDEGEMAIISQKGTPGEPGPPGDDGFPGERGDKGTPGMQGRRGEPGRYGPPGFHRGEPGEKGQPGPPGPPGPPGSTGLRGFIGFPGLPGDQGEPGSPGPPGFSGIDGARGPKGNKGDPASHFGPPGPKGEPGSPGCPGHFGASGEQGLPGIQGPRGSPGRPGPPGSSGPPGCPGDHGMPGLRGQPGEMGDPGPRGLQGDPGIPGPPGIKGPSGSPGLNGLHGLKGQKGTKGASGLHDVGPPGPVGIPGLKGERGDPGSPGISPPGPRGKKGPPGPPGSSGPPGPAGATGRAPKDIPDPGPPGDQGPPGPDGPRGAPGPPGLPGSVDLLRGEPGDCGLPGPPGPPGPPGPPGYKGFPGCDGKDGQKGPVGFPGPQGPHGFPGPPGEKGLPGPPGRKGPTGLPGPRGEPGPPADVDDCPRIPGLPGAPGMRGPEGAMGLPGMRGPSGPGCKGEPGLDGRRGVDGVPGSPGPPGRKGDTGEDGYPGGPGPPGPIGDPGPKGFGPGYLGGFLLVLHSQTDQEPTCPLGMPRLWTGYSLLYLEGQEKAHNQDLGLAGSCLPVFSTLPFAYCNIHQVCHYAQRNDRSYWLASAAPLPMMPLSEEAIRPYVSRCAVCEAPAQAVAVHSQDQSIPPCPQTWRSLWIGYSFLMHTGAGDQGGGQALMSPGSCLEDFRAAPFLECQGRQGTCHFFANKYSFWLTTVKADLQFSSAPAPDTLKESQAQRQKISRCQVCVKYS,mutated_sequence,1.0,1690.0,NP_000083.3.a2m,NP_000083.3.npy,ClinVar +NP_000084.3,NP_000084.3.csv,MDVHTRWKARSALRPGAPLLPPLLLLLLWAPPPSRAAQPADLLKVLDFHNLPDGITKTTGFCATRRSSKGPDVAYRVTKDAQLSAPTKQLYPASAFPEDFSILTTVKAKKGSQAFLVSIYNEQGIQQIGLELGRSPVFLYEDHTGKPGPEDYPLFRGINLSDGKWHRIALSVHKKNVTLILDCKKKTTKFLDRSDHPMIDINGIIVFGTRILDEEVFEGDIQQLLFVSDHRAAYDYCEHYSPDCDTAVPDTPQSQDPNPDEYYTEGDGEGETYYYEYPYYEDPEDLGKEPTPSKKPVEAAKETTEVPEELTPTPTEAAPMPETSEGAGKEEDVGIGDYDYVPSEDYYTPSPYDDLTYGEGEENPDQPTDPGAGAEIPTSTADTSNSSNPAPPPGEGADDLEGEFTEETIRNLDENYYDPYYDPTSSPSEIGPGMPANQDTIYEGIGGPRGEKGQKGEPAIIEPGMLIEGPPGPEGPAGLPGPPGTMGPTGQVGDPGERGPPGRPGLPGADGLPGPPGTMLMLPFRFGGGGDAGSKGPMVSAQESQAQAILQQARLALRGPAGPMGLTGRPGPVGPPGSGGLKGEPGDVGPQGPRGVQGPPGPAGKPGRRGRAGSDGARGMPGQTGPKGDRGFDGLAGLPGEKGHRGDPGPSGPPGPPGDDGERGDDGEVGPRGLPGEPGPRGLLGPKGPPGPPGPPGVTGMDGQPGPKGNVGPQGEPGPPGQQGNPGAQGLPGPQGAIGPPGEKGPLGKPGLPGMPGADGPPGHPGKEGPPGEKGGQGPPGPQGPIGYPGPRGVKGADGIRGLKGTKGEKGEDGFPGFKGDMGIKGDRGEIGPPGPRGEDGPEGPKGRGGPNGDPGPLGPPGEKGKLGVPGLPGYPGRQGPKGSIGFPGFPGANGEKGGRGTPGKPGPRGQRGPTGPRGERGPRGITGKPGPKGNSGGDGPAGPPGERGPNGPQGPTGFPGPKGPPGPPGKDGLPGHPGQRGETGFQGKTGPPGPPGVVGPQGPTGETGPMGERGHPGPPGPPGEQGLPGLAGKEGTKGDPGPAGLPGKDGPPGLRGFPGDRGLPGPVGALGLKGNEGPPGPPGPAGSPGERGPAGAAGPIGIPGRPGPQGPPGPAGEKGAPGEKGPQGPAGRDGLQGPVGLPGPAGPVGPPGEDGDKGEIGEPGQKGSKGDKGEQGPPGPTGPQGPIGQPGPSGADGEPGPRGQQGLFGQKGDEGPRGFPGPPGPVGLQGLPGPPGEKGETGDVGQMGPPGPPGPRGPSGAPGADGPQGPPGGIGNPGAVGEKGEPGEAGEPGLPGEGGPPGPKGERGEKGESGPSGAAGPPGPKGPPGDDGPKGSPGPVGFPGDPGPPGEPGPAGQDGPPGDKGDDGEPGQTGSPGPTGEPGPSGPPGKRGPPGPAGPEGRQGEKGAKGEAGLEGPPGKTGPIGPQGAPGKPGPDGLRGIPGPVGEQGLPGSPGPDGPPGPMGPPGLPGLKGDSGPKGEKGHPGLIGLIGPPGEQGEKGDRGLPGPQGSSGPKGEQGITGPSGPIGPPGPPGLPGPPGPKGAKGSSGPTGPKGEAGHPGPPGPPGPPGEVIQPLPIQASRTRRNIDASQLLDDGNGENYVDYADGMEEIFGSLNSLKLEIEQMKRPLGTQQNPARTCKDLQLCHPDFPDGEYWVDPNQGCSRDSFKVYCNFTAGGSTCVFPDKKSEGARITSWPKENPGSWFSEFKRGKLLSYVDAEGNPVGVVQMTFLRLLSASAHQNVTYHCYQSVAWQDAATGSYDKALRFLGSNDEEMSYDNNPYIRALVDGCATKKGYQKTVLEIDTPKVEQVPIVDIMFNDFGEASQKFGFEVGPACFMG,mutated_sequence,1.0,1838.0,NP_000084.3.a2m,NP_000084.3.npy,ClinVar +NP_000085.1,NP_000085.1.csv,MTLRLLVAALCAGILAEAPRVRAQHRERVTCTRLYAADIVFLLDGSSSIGRSNFREVRSFLEGLVLPFSGAASAQGVRFATVQYSDDPRTEFGLDALGSGGDVIRAIRELSYKGGNTRTGAAILHVADHVFLPQLARPGVPKVCILITDGKSQDLVDTAAQRLKGQGVKLFAVGIKNADPEELKRVASQPTSDFFFFVNDFSILRTLLPLVSRRVCTTAGGVPVTRPPDDSTSAPRDLVLSEPSSQSLRVQWTAASGPVTGYKVQYTPLTGLGQPLPSERQEVNVPAGETSVRLRGLRPLTEYQVTVIALYANSIGEAVSGTARTTALEGPELTIQNTTAHSLLVAWRSVPGATGYRVTWRVLSGGPTQQQELGPGQGSVLLRDLEPGTDYEVTVSTLFGRSVGPATSLMARTDASVEQTLRPVILGPTSILLSWNLVPEARGYRLEWRRETGLEPPQKVVLPSDVTRYQLDGLQPGTEYRLTLYTLLEGHEVATPATVVPTGPELPVSPVTDLQATELPGQRVRVSWSPVPGATQYRIIVRSTQGVERTLVLPGSQTAFDLDDVQAGLSYTVRVSARVGPREGSASVLTVRREPETPLAVPGLRVVVSDATRVRVAWGPVPGASGFRISWSTGSGPESSQTLPPDSTATDITGLQPGTTYQVAVSVLRGREEGPAAVIVARTDPLGPVRTVHVTQASSSSVTITWTRVPGATGYRVSWHSAHGPEKSQLVSGEATVAELDGLEPDTEYTVHVRAHVAGVDGPPASVVVRTAPEPVGRVSRLQILNASSDVLRITWVGVTGATAYRLAWGRSEGGPMRHQILPGNTDSAEIRGLEGGVSYSVRVTALVGDREGTPVSIVVTTPPEAPPALGTLHVVQRGEHSLRLRWEPVPRAQGFLLHWQPEGGQEQSRVLGPELSSYHLDGLEPATQYRVRLSVLGPAGEGPSAEVTARTESPRVPSIELRVVDTSIDSVTLAWTPVSRASSYILSWRPLRGPGQEVPGSPQTLPGISSSQRVTGLEPGVSYIFSLTPVLDGVRGPEASVTQTPVCPRGLADVVFLPHATQDNAHRAEATRRVLERLVLALGPLGPQAVQVGLLSYSHRPSPLFPLNGSHDLGIILQRIRDMPYMDPSGNNLGTAVVTAHRYMLAPDAPGRRQHVPGVMVLLVDEPLRGDIFSPIREAQASGLNVVMLGMAGADPEQLRRLAPGMDSVQTFFAVDDGPSLDQAVSGLATALCQASFTTQPRPEPCPVYCPKGQKGEPGEMGLRGQVGPPGDPGLPGRTGAPGPQGPPGSATAKGERGFPGADGRPGSPGRAGNPGTPGAPGLKGSPGLPGPRGDPGERGPRGPKGEPGAPGQVIGGEGPGLPGRKGDPGPSGPPGPRGPLGDPGPRGPPGLPGTAMKGDKGDRGERGPPGPGEGGIAPGEPGLPGLPGSPGPQGPVGPPGKKGEKGDSEDGAPGLPGQPGSPGEQGPRGPPGAIGPKGDRGFPGPLGEAGEKGERGPPGPAGSRGLPGVAGRPGAKGPEGPPGPTGRQGEKGEPGRPGDPAVVGPAVAGPKGEKGDVGPAGPRGATGVQGERGPPGLVLPGDPGPKGDPGDRGPIGLTGRAGPPGDSGPPGEKGDPGRPGPPGPVGPRGRDGEVGEKGDEGPPGDPGLPGKAGERGLRGAPGVRGPVGEKGDQGDPGEDGRNGSPGSSGPKGDRGEPGPPGPPGRLVDTGPGAREKGEPGDRGQEGPRGPKGDPGLPGAPGERGIEGFRGPPGPQGDPGVRGPAGEKGDRGPPGLDGRSGLDGKPGAAGPSGPNGAAGKAGDPGRDGLPGLRGEQGLPGPSGPPGLPGKPGEDGKPGLNGKNGEPGDPGEDGRKGEKGDSGASGREGRDGPKGERGAPGILGPQGPPGLPGPVGPPGQGFPGVPGGTGPKGDRGETGSKGEQGLPGERGLRGEPGSVPNVDRLLETAGIKASALREIVETWDESSGSFLPVPERRRGPKGDSGEQGPPGKEGPIGFPGERGLKGDRGDPGPQGPPGLALGERGPPGPSGLAGEPGKPGIPGLPGRAGGVGEAGRPGERGERGEKGERGEQGRDGPPGLPGTPGPPGPPGPKVSVDEPGPGLSGEQGPPGLKGAKGEPGSNGDQGPKGDRGVPGIKGDRGEPGPRGQDGNPGLPGERGMAGPEGKPGLQGPRGPPGPVGGHGDPGPPGAPGLAGPAGPQGPSGLKGEPGETGPPGRGLTGPTGAVGLPGPPGPSGLVGPQGSPGLPGQVGETGKPGAPGRDGASGKDGDRGSPGVPGSPGLPGPVGPKGEPGPTGAPGQAVVGLPGAKGEKGAPGGLAGDLVGEPGAKGDRGLPGPRGEKGEAGRAGEPGDPGEDGQKGAPGPKGFKGDPGVGVPGSPGPPGPPGVKGDLGLPGLPGAPGVVGFPGQTGPRGEMGQPGPSGERGLAGPPGREGIPGPLGPPGPPGSVGPPGASGLKGDKGDPGVGLPGPRGERGEPGIRGEDGRPGQEGPRGLTGPPGSRGERGEKGDVGSAGLKGDKGDSAVILGPPGPRGAKGDMGERGPRGLDGDKGPRGDNGDPGDKGSKGEPGDKGSAGLPGLRGLLGPQGQPGAAGIPGDPGSPGKDGVPGIRGEKGDVGFMGPRGLKGERGVKGACGLDGEKGDKGEAGPPGRPGLAGHKGEMGEPGVPGQSGAPGKEGLIGPKGDRGFDGQPGPKGDQGEKGERGTPGIGGFPGPSGNDGSAGPPGPPGSVGPRGPEGLQGQKGERGPPGERVVGAPGVPGAPGERGEQGRPGPAGPRGEKGEAALTEDDIRGFVRQEMSQHCACQGQFIASGSRPLPSYAADTAGSQLHAVPVLRVSHAEEEERVPPEDDEYSEYSEYSVEEYQDPEAPWDSDDPCSLPLDEGSCTAYTLRWYHRAVTGSTEACHPFVYGGCGGNANRFGTREACERRCPPRVVQSQGTGTAQD,mutated_sequence,1.0,2944.0,NP_000085.1.a2m,NP_000085.1.npy,ClinVar +NP_000086.2,NP_000086.2.csv,MVPDTACVLLLTLAALGASGQGQSPLGSDLGPQMLRELQETNAALQDVRELLRQQVREITFLKNTVMECDACGMQQSVRTGLPSVRPLLHCAPGFCFPGVACIQTESGARCGPCPAGFTGNGSHCTDVNECNAHPCFPRVRCINTSPGFRCEACPPGYSGPTHQGVGLAFAKANKQVCTDINECETGQHNCVPNSVCINTRGSFQCGPCQPGFVGDQASGCQRRAQRFCPDGSPSECHEHADCVLERDGSRSCVCAVGWAGNGILCGRDTDLDGFPDEKLRCPERQCRKDNCVTVPNSGQEDVDRDGIGDACDPDADGDGVPNEKDNCPLVRNPDQRNTDEDKWGDACDNCRSQKNDDQKDTDQDGRGDACDDDIDGDRIRNQADNCPRVPNSDQKDSDGDGIGDACDNCPQKSNPDQADVDHDFVGDACDSDQDQDGDGHQDSRDNCPTVPNSAQEDSDHDGQGDACDDDDDNDGVPDSRDNCRLVPNPGQEDADRDGVGDVCQDDFDADKVVDKIDVCPENAEVTLTDFRAFQTVVLDPEGDAQIDPNWVVLNQGREIVQTMNSDPGLAVGYTAFNGVDFEGTFHVNTVTDDDYAGFIFGYQDSSSFYVVMWKQMEQTYWQANPFRAVAEPGIQLKAVKSSTGPGEQLRNALWHTGDTESQVRLLWKDPRNVGWKDKKSYRWFLQHRPQVGYIRVRFYEGPELVADSNVVLDTTMRGGRLGVFCFSQENIIWANLRYRCNDTIPEDYETHQLRQA,mutated_sequence,1.0,757.0,NP_000086.2.a2m,NP_000086.2.npy,ClinVar +NP_000089.1,NP_000089.1.csv,MVPRLLLRAWPRGPAVGPGAPSRPLSAGSGPGQYLQRSIVPTMHYQDSLPRLPIPKLEDTIRRYLSAQKPLLNDGQFRKTEQFCKSFENGIGKELHEQLVALDKQNKHTSYISGPWFDMYLSARDSVVLNFNPFMAFNPDPKSEYNDQLTRATNMTVSAIRFLKTLRAGLLEPEVFHLNPAKSDTITFKRLIRFVPSSLSWYGAYLVNAYPLDMSQYFRLFNSTRLPKPSRDELFTDDKARHLLVLRKGNFYIFDVLDQDGNIVSPSEIQAHLKYILSDSSPAPEFPLAYLTSENRDIWAELRQKLMSSGNEESLRKVDSAVFCLCLDDFPIKDLVHLSHNMLHGDGTNRWFDKSFNLIIAKDGSTAVHFEHSWGDGVAVLRFFNEVFKDSTQTPAVTPQSQPATTDSTVTVQKLNFELTDALKTGITAAKEKFDATMKTLTIDCVQFQRGGKEFLKKQKLSPDAVAQLAFQMAFLRQYGQTVATYESCSTAAFKHGRTETIRPASVYTKRCSEAFVREPSRHSAGELQQMMVECSKYHGQLTKEAAMGQGFDRHLFALRHLAAAKGIILPELYLDPAYGQINHNVLSTSTLSSPAVNLGGFAPVVSDGFGVGYAVHDNWIGCNVSSYPGRNAREFLQCVEKALEDMFDALEGKSIKS,mutated_sequence,1.0,658.0,NP_000089.1.a2m,NP_000089.1.npy,ClinVar +NP_000093.1,NP_000093.1.csv,MWELVALLLLTLAYLFWPKRRCPGAKYPKSLLSLPLVGSLPFLPRHGHMHNNFFKLQKKYGPIYSVRMGTKTTVIVGHHQLAKEVLIKKGKDFSGRPQMATLDIASNNRKGIAFADSGAHWQLHRRLAMATFALFKDGDQKLEKIICQEISTLCDMLATHNGQSIDISFPVFVAVTNVISLICFNTSYKNGDPELNVIQNYNEGIIDNLSKDSLVDLVPWLKIFPNKTLEKLKSHVKIRNDLLNKILENYKEKFRSDSITNMLDTLMQAKMNSDNGNAGPDQDSELLSDNHILTTIGDIFGAGVETTTSVVKWTLAFLLHNPQVKKKLYEEIDQNVGFSRTPTISDRNRLLLLEATIREVLRLRPVAPMLIPHKANVDSSIGEFAVDKGTEVIINLWALHHNEKEWHQPDQFMPERFLNPAGTQLISPSVSYLPFGAGPRSCIGEILARQELFLIMAWLLQRFDLEVPDDGQLPSLEGIPKVVFLIDSFKVKIKVRQAWREAQAEGST,mutated_sequence,1.0,508.0,NP_000093.1.a2m,NP_000093.1.npy,ClinVar +NP_000095.2,NP_000095.2.csv,MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ,mutated_sequence,1.0,543.0,NP_000095.2.a2m,NP_000095.2.npy,ClinVar +NP_000102.1,NP_000102.1.csv,MIEPFGNQYIVARPVYSTNAFEENHKKTGRHHKTFLDHLKVCCSCSPQKAKRIVLSLFPIASWLPAYRLKEWLLSDIVSGISTGIVAVLQGLAFALLVDIPPVYGLYASFFPAIIYLFFGTSRHISVGPFPILSMMVGLAVSGAVSKAVPDRNATTLGLPNNSNNSSLLDDERVRVAAAASVTVLSGIIQLAFGILRIGFVVIYLSESLISGFTTAAAVHVLVSQLKFIFQLTVPSHTDPVSIFKVLYSVFSQIEKTNIADLVTALIVLLVVSIVKEINQRFKDKLPVPIPIEFIMTVIAAGVSYGCDFKNRFKVAVVGDMNPGFQPPITPDVETFQNTVGDCFGIAMVAFAVAFSVASVYSLKYDYPLDGNQELIALGLGNIVCGVFRGFAGSTALSRSAVQESTGGKTQIAGLIGAIIVLIVVLAIGFLLAPLQKSVLAALALGNLKGMLMQFAEIGRLWRKDKYDCLIWIMTFIFTIVLGLGLGLAASVAFQLLTIVFRTQFPKCSTLANIGRTNIYKNKKDYYDMYEPEGVKIFRCPSPIYFANIGFFRRKLIDAVGFSPLRILRKRNKALRKIRKLQKQGLLQVTPKGFICTVDTIKDSDEELDNNQIEVLDQPINTTDLPFHIDWNDDLPLNIEVPKISLHSLILDFSAVSFLDVSSVRGLKSILQEFIRIKVDVYIVGTDDDFIEKLNRYEFFDGEVKSSIFFLTIHDAVLHILMKKDYSTSKFNPSQEKDGKIDFTINTNGGLRNRVYEVPVETKF,mutated_sequence,1.0,764.0,NP_000102.1.a2m,NP_000102.1.npy,ClinVar +NP_000103.2,NP_000103.2.csv,MSSESKEQHNVSPRDSAEGNDSYPSGIHLELQRESSTDFKQFETNDQCRPYHRILIERQEKSDTNFKEFVIKKLQKNCQCSPAKAKNMILGFLPVLQWLPKYDLKKNILGDVMSGLIVGILLVPQSIAYSLLAGQEPVYGLYTSFFASIIYFLLGTSRHISVGIFGVLCLMIGETVDRELQKAGYDNAHSAPSLGMVSNGSTLLNHTSDRICDKSCYAIMVGSTVTFIAGVYQVAMGFFQVGFVSVYLSDALLSGFVTGASFTILTSQAKYLLGLNLPRTNGVGSLITTWIHVFRNIHKTNLCDLITSLLCLLVLLPTKELNEHFKSKLKAPIPIELVVVVAATLASHFGKLHENYNSSIAGHIPTGFMPPKVPEWNLIPSVAVDAIAISIIGFAITVSLSEMFAKKHGYTVKANQEMYAIGFCNIIPSFFHCFTTSAALAKTLVKESTGCHTQLSGVVTALVLLLVLLVIAPLFYSLQKSVLGVITIVNLRGALRKFRDLPKMWSISRMDTVIWFVTMLSSALLSTEIGLLVGVCFSIFCVILRTQKPKSSLLGLVEESEVFESVSAYKNLQIKPGIKIFRFVAPLYYINKECFKSALYKQTVNPILIKVAWKKAAKRKIKEKVVTLGGIQDEMSVQLSHDPLELHTIVIDCSAIQFLDTAGIHTLKEVRRDYEAIGIQVLLAQCNPTVRDSLTNGEYCKKEEENLLFYSVYEAMAFAEVSKNQKGVCVPNGLSLSSD,mutated_sequence,1.0,739.0,NP_000103.2.a2m,NP_000103.2.npy,ClinVar +NP_000104.1,NP_000104.1.csv,MKLGRAVLGLLLLAPSVVQAVEPISLGLALAGVLTGYIYPRLYCLFAECCGQKRSLSREALQKDLDDNLFGQHLAKKIILNAVFGFINNPKPKKPLTLSLHGWTGTGKNFVSKIIAENIYEGGLNSDYVHLFVATLHFPHASNITLYKDQLQLWIRGNVSACARSIFIFDEMDKMHAGLIDAIKPFLDYYDLVDGVSYQKAMFIFLSNAGAERITDVALDFWRSGKQREDIKLKDIEHALSVSVFNNKNSGFWHSSLIDRNLIDYFVPFLPLEYKHLKMCIRVEMQSRGYEIDEDIVSRVAEEMTFFPKEERVFSDKGCKTVFTKLDYYYDD,mutated_sequence,1.0,332.0,NP_000104.1.a2m,NP_000104.1.npy,ClinVar +NP_000117.1,NP_000117.1.csv,MFRAAAPGQLRRAASLLRFQSTLVIAEHANDSLAPITLNTITAATRLGGEVSCLVAGTKCDKVAQDLCKVAGIAKVLVAQHDVYKGLLPEELTPLILATQKQFNYTHICAGASAFGKNLLPRVAAKLEVAPISDIIAIKSPDTFVRTIYAGNALCTVKCDEKVKVFSVRGTSFDAAATSGGSASSEKASSTSPVEISEWLDQKLTKSDRPELTGAKVVVSGGRGLKSGENFKLLYDLADQLHAAVGASRAAVDAGFVPNDMQVGQTGKIVAPELYIAVGISGAIQHLAGMKDSKTIVAINKDPEAPIFQVADYGIVADLFKVVPEMTEILKKK,mutated_sequence,1.0,333.0,NP_000117.1.a2m,NP_000117.1.npy,ClinVar +NP_000118.2,NP_000118.2.csv,MQAKKRYFILLSAGSCLALLFYFGGLQFRASRSHSRREEHSGRNGLHHPSPDHFWPRFPDALRPFVPWDQLENEDSSVHISPRQKRDANSSIYKGKKCRMESCFDFTLCKKNGFKVYVYPQQKGEKIAESYQNILAAIEGSRFYTSDPSQACLFVLSLDTLDRDQLSPQYVHNLRSKVQSLHLWNNGRNHLIFNLYSGTWPDYTEDVGFDIGQAMLAKASISTENFRPNFDVSIPLFSKDHPRTGGERGFLKFNTIPPLRKYMLVFKGKRYLTGIGSDTRNALYHVHNGEDVVLLTTCKHGKDWQKHKDSRCDRDNTEYEKYDYREMLHNATFCLVPRGRRLGSFRFLEALQAACVPVMLSNGWELPFSEVINWNQAAVIGDERLLLQIPSTIRSIHQDKILALRQQTQFLWEAYFSSVEKIVLTTLEIIQDRIFKHISRNSLIWNKHPGGLFVLPQYSSYLGDFPYYYANLGLKPPSKFTAVIHAVTPLVSQSQPVLKLLVAAAKSQYCAQIIVLWNCDKPLPAKHRWPATAVPVVVIEGESKVMSSRFLPYDNIITDAVLSLDEDTVLSTTEVDFAFTVWQSFPERIVGYPARSHFWDNSKERWGYTSKWTNDYSMVLTGAAIYHKYYHYLYSHYLPASLKNMVDQLANCEDILMNFLVSAVTKLPPIKVTQKKQYKETMMGQTSRASRWADPDHFAQRQSCMNTFASWFGYMPLIHSQMRLDPVLFKDQVSILRKKYRDIERL,mutated_sequence,1.0,746.0,NP_000118.2.a2m,NP_000118.2.npy,ClinVar +NP_000119.1,NP_000119.1.csv,MIFLYQVVHFILFTSVSGECVTQLLKDTCFEGGDITTVFTPSAKYCQVVCTYHPRCLLFTFTAESPSEDPTRWFTCVLKDSVTETLPRVNRTAAISGYSFKQCSHQISACNKDIYVDLDMKGINYNSSVAKSAQECQERCTDDVHCHFFTYATRQFPSLEHRNICLLKHTQTGTPTRITKLDKVVSGFSLKSCALSNLACIRDIFPNTVFADSNIDSVMAPDAFVCGRICTHHPGCLFFTFFSQEWPKESQRNLCLLKTSESGLPSTRIKKSKALSGFSLQSCRHSIPVFCHSSFYHDTDFLGEELDIVAAKSHEACQKLCTNAVRCQFFTYTPAQASCNEGKGKCYLKLSSNGSPTKILHGRGGISGYTLRLCKMDNECTTKIKPRIVGGTASVRGEWPWQVTLHTTSPTQRHLCGGSIIGNQWILTAAHCFYGVESPKILRVYSGILNQSEIKEDTSFFGVQEIIIHDQYKMAESGYDIALLKLETTVNYTDSQRPICLPSKGDRNVIYTDCWVTGWGYRKLRDKIQNTLQKAKIPLVTNEECQKRYRGHKITHKMICAGYREGGKDACKGDSGGPLSCKHNEVWHLVGITSWGEGCAQRERPGVYTNVVEYVDWILEKTQAV,mutated_sequence,1.0,625.0,NP_000119.1.a2m,NP_000119.1.npy,ClinVar +NP_000123.1,NP_000123.1.csv,MQIELSTCFFLCLLRFCFSATRRYYLGAVELSWDYMQSDLGELPVDARFPPRVPKSFPFNTSVVYKKTLFVEFTDHLFNIAKPRPPWMGLLGPTIQAEVYDTVVITLKNMASHPVSLHAVGVSYWKASEGAEYDDQTSQREKEDDKVFPGGSHTYVWQVLKENGPMASDPLCLTYSYLSHVDLVKDLNSGLIGALLVCREGSLAKEKTQTLHKFILLFAVFDEGKSWHSETKNSLMQDRDAASARAWPKMHTVNGYVNRSLPGLIGCHRKSVYWHVIGMGTTPEVHSIFLEGHTFLVRNHRQASLEISPITFLTAQTLLMDLGQFLLFCHISSHQHDGMEAYVKVDSCPEEPQLRMKNNEEAEDYDDDLTDSEMDVVRFDDDNSPSFIQIRSVAKKHPKTWVHYIAAEEEDWDYAPLVLAPDDRSYKSQYLNNGPQRIGRKYKKVRFMAYTDETFKTREAIQHESGILGPLLYGEVGDTLLIIFKNQASRPYNIYPHGITDVRPLYSRRLPKGVKHLKDFPILPGEIFKYKWTVTVEDGPTKSDPRCLTRYYSSFVNMERDLASGLIGPLLICYKESVDQRGNQIMSDKRNVILFSVFDENRSWYLTENIQRFLPNPAGVQLEDPEFQASNIMHSINGYVFDSLQLSVCLHEVAYWYILSIGAQTDFLSVFFSGYTFKHKMVYEDTLTLFPFSGETVFMSMENPGLWILGCHNSDFRNRGMTALLKVSSCDKNTGDYYEDSYEDISAYLLSKNNAIEPRSFSQNSRHPSTRQKQFNATTIPENDIEKTDPWFAHRTPMPKIQNVSSSDLLMLLRQSPTPHGLSLSDLQEAKYETFSDDPSPGAIDSNNSLSEMTHFRPQLHHSGDMVFTPESGLQLRLNEKLGTTAATELKKLDFKVSSTSNNLISTIPSDNLAAGTDNTSSLGPPSMPVHYDSQLDTTLFGKKSSPLTESGGPLSLSEENNDSKLLESGLMNSQESSWGKNVSSTESGRLFKGKRAHGPALLTKDNALFKVSISLLKTNKTSNNSATNRKTHIDGPSLLIENSPSVWQNILESDTEFKKVTPLIHDRMLMDKNATALRLNHMSNKTTSSKNMEMVQQKKEGPIPPDAQNPDMSFFKMLFLPESARWIQRTHGKNSLNSGQGPSPKQLVSLGPEKSVEGQNFLSEKNKVVVGKGEFTKDVGLKEMVFPSSRNLFLTNLDNLHENNTHNQEKKIQEEIEKKETLIQENVVLPQIHTVTGTKNFMKNLFLLSTRQNVEGSYDGAYAPVLQDFRSLNDSTNRTKKHTAHFSKKGEEENLEGLGNQTKQIVEKYACTTRISPNTSQQNFVTQRSKRALKQFRLPLEETELEKRIIVDDTSTQWSKNMKHLTPSTLTQIDYNEKEKGAITQSPLSDCLTRSHSIPQANRSPLPIAKVSSFPSIRPIYLTRVLFQDNSSHLPAASYRKKDSGVQESSHFLQGAKKNNLSLAILTLEMTGDQREVGSLGTSATNSVTYKKVENTVLPKPDLPKTSGKVELLPKVHIYQKDLFPTETSNGSPGHLDLVEGSLLQGTEGAIKWNEANRPGKVPFLRVATESSAKTPSKLLDPLAWDNHYGTQIPKEEWKSQEKSPEKTAFKKKDTILSLNACESNHAIAAINEGQNKPEIEVTWAKQGRTERLCSQNPPVLKRHQREITRTTLQSDQEEIDYDDTISVEMKKEDFDIYDEDENQSPRSFQKKTRHYFIAAVERLWDYGMSSSPHVLRNRAQSGSVPQFKKVVFQEFTDGSFTQPLYRGELNEHLGLLGPYIRAEVEDNIMVTFRNQASRPYSFYSSLISYEEDQRQGAEPRKNFVKPNETKTYFWKVQHHMAPTKDEFDCKAWAYFSDVDLEKDVHSGLIGPLLVCHTNTLNPAHGRQVTVQEFALFFTIFDETKSWYFTENMERNCRAPCNIQMEDPTFKENYRFHAINGYIMDTLPGLVMAQDQRIRWYLLSMGSNENIHSIHFSGHVFTVRKKEEYKMALYNLYPGVFETVEMLPSKAGIWRVECLIGEHLHAGMSTLFLVYSNKCQTPLGMASGHIRDFQITASGQYGQWAPKLARLHYSGSINAWSTKEPFSWIKVDLLAPMIIHGIKTQGARQKFSSLYISQFIIMYSLDGKKWQTYRGNSTGTLMVFFGNVDSSGIKHNIFNPPIIARYIRLHPTHYSIRSTLRMELMGCDLNSCSMPLGMESKAISDAQITASSYFTNMFATWSPSKARLHLQGRSNAWRPQVNNPKEWLQVDFQKTMKVTGVTTQGVKSLLTSMYVKEFLISSSQDGHQWTLFFQNGKVKVFQGNQDSFTPVVNSLDPPLLTRYLRIHPQSWVHQIALRMEVLGCEAQDLY,mutated_sequence,1.0,2351.0,NP_000123.1.a2m,NP_000123.1.npy,ClinVar +NP_000126.2,NP_000126.2.csv,MSDSWVPNSASGQDPGGRRRAWAELLAGRVKREKYNPERAQKLKESAVRLLRSHQDLNALLLEVEGPLCKKLSLSKVIDCDSSEAYANHSSSFIGSALQDQASRLGVPVGILSAGMVASSVGQICTAPAETSHPVLLTVEQRKKLSSLLEFAQYLLAHSMFSRLSFCQELWKIQSSLLLEAVWHLHVQGIVSLQELLESHPDMHAVGSWLFRNLCCLCEQMEASCQHADVARAMLSDFVQMFVLRGFQKNSDLRRTVEPEKMPQVTVDVLQRMLIFALDALAAGVQEESSTHKIVRCWFGVFSGHTLGSVISTDPLKRFFSHTLTQILTHSPVLKASDAVQMQREWSFARTHPLLTSLYRRLFVMLSAEELVGHLQEVLETQEVHWQRVLSFVSALVVCFPEAQQLLEDWVARLMAQAFESCQLDSMVTAFLVVRQAALEGPSAFLSYADWFKASFGSTRGYHGCSKKALVFLFTFLSELVPFESPRYLQVHILHPPLVPGKYRSLLTDYISLAKTRLADLKVSIENMGLYEDLSSAGDITEPHSQALQDVEKAIMVFEHTGNIPVTVMEASIFRRPYYVSHFLPALLTPRVLPKVPDSRVAFIESLKRADKIPPSLYSTYCQACSAAEEKPEDAALGVRAEPNSAEEPLGQLTAALGELRASMTDPSQRDVISAQVAVISERLRAVLGHNEDDSSVEISKIQLSINTPRLEPREHMAVDLLLTSFCQNLMAASSVAPPERQGPWAALFVRTMCGRVLPAVLTRLCQLLRHQGPSLSAPHVLGLAALAVHLGESRSALPEVDVGPPAPGAGLPVPALFDSLLTCRTRDSLFFCLKFCTAAISYSLCKFSSQSRDTLCSCLSPGLIKKFQFLMFRLFSEARQPLSEEDVASLSWRPLHLPSADWQRAALSLWTHRTFREVLKEEDVHLTYQDWLHLELEIQPEADALSDTERQDFHQWAIHEHFLPESSASGGCDGDLQAACTILVNALMDFHQSSRSYDHSENSDLVFGGRTGNEDIISRLQEMVADLELQQDLIVPLGHTPSQEHFLFEIFRRRLQALTSGWSVAASLQRQRELLMYKRILLRLPSSVLCGSSFQAEQPITARCEQFFHLVNSEMRNFCSHGGALTQDITAHFFRGLLNACLRSRDPSLMVDFILAKCQTKCPLILTSALVWWPSLEPVLLCRWRRHCQSPLPRELQKLQEGRQFASDFLSPEAASPAPNPDWLSAAALHFAIQQVREENIRKQLKKLDCEREELLVFLFFFSLMGLLSSHLTSNSTTDLPKAFHVCAAILECLEKRKISWLALFQLTESDLRLGRLLLRVAPDQHTRLLPFAFYSLLSYFHEDAAIREEAFLHVAVDMYLKLVQLFVAGDTSTVSPPAGRSLELKGQGNPVELITKARLFLLQLIPRCPKKSFSHVAELLADRGDCDPEVSAALQSRQQAAPDADLSQEPHLF,mutated_sequence,1.0,1455.0,NP_000126.2.a2m,NP_000126.2.npy,ClinVar +NP_000129.3,NP_000129.3.csv,MRRGRLLEIALGFTVLLASYTSHGADANLEAGNVKETRASRAKRRGGGGHDALKGPNVCGSRYNAYCCPGWKTLPGGNQCIVPICRHSCGDGFCSRPNMCTCPSGQIAPSCGSRSIQHCNIRCMNGGSCSDDHCLCQKGYIGTHCGQPVCESGCLNGGRCVAPNRCACTYGFTGPQCERDYRTGPCFTVISNQMCQGQLSGIVCTKTLCCATVGRAWGHPCEMCPAQPHPCRRGFIPNIRTGACQDVDECQAIPGLCQGGNCINTVGSFECKCPAGHKLNEVSQKCEDIDECSTIPGICEGGECTNTVSSYFCKCPPGFYTSPDGTRCIDVRPGYCYTALTNGRCSNQLPQSITKMQCCCDAGRCWSPGVTVAPEMCPIRATEDFNKLCSVPMVIPGRPEYPPPPLGPIPPVLPVPPGFPPGPQIPVPRPPVEYLYPSREPPRVLPVNVTDYCQLVRYLCQNGRCIPTPGSYRCECNKGFQLDLRGECIDVDECEKNPCAGGECINNQGSYTCQCRAGYQSTLTRTECRDIDECLQNGRICNNGRCINTDGSFHCVCNAGFHVTRDGKNCEDMDECSIRNMCLNGMCINEDGSFKCICKPGFQLASDGRYCKDINECETPGICMNGRCVNTDGSYRCECFPGLAVGLDGRVCVDTHMRSTCYGGYKRGQCIKPLFGAVTKSECCCASTEYAFGEPCQPCPAQNSAEYQALCSSGPGMTSAGSDINECALDPDICPNGICENLRGTYKCICNSGYEVDSTGKNCVDINECVLNSLLCDNGQCRNTPGSFVCTCPKGFIYKPDLKTCEDIDECESSPCINGVCKNSPGSFICECSSESTLDPTKTICIETIKGTCWQTVIDGRCEININGATLKSQCCSSLGAAWGSPCTLCQVDPICGKGYSRIKGTQCEDIDECEVFPGVCKNGLCVNTRGSFKCQCPSGMTLDATGRICLDIRLETCFLRYEDEECTLPIAGRHRMDACCCSVGAAWGTEECEECPMRNTPEYEELCPRGPGFATKEITNGKPFFKDINECKMIPSLCTHGKCRNTIGSFKCRCDSGFALDSEERNCTDIDECRISPDLCGRGQCVNTPGDFECKCDEGYESGFMMMKNCMDIDECQRDPLLCRGGVCHNTEGSYRCECPPGHQLSPNISACIDINECELSAHLCPNGRCVNLIGKYQCACNPGYHSTPDRLFCVDIDECSIMNGGCETFCTNSEGSYECSCQPGFALMPDQRSCTDIDECEDNPNICDGGQCTNIPGEYRCLCYDGFMASEDMKTCVDVNECDLNPNICLSGTCENTKGSFICHCDMGYSGKKGKTGCTDINECEIGAHNCGKHAVCTNTAGSFKCSCSPGWIGDGIKCTDLDECSNGTHMCSQHADCKNTMGSYRCLCKEGYTGDGFTCTDLDECSENLNLCGNGQCLNAPGGYRCECDMGFVPSADGKACEDIDECSLPNICVFGTCHNLPGLFRCECEIGYELDRSGGNCTDVNECLDPTTCISGNCVNTPGSYICDCPPDFELNPTRVGCVDTRSGNCYLDIRPRGDNGDTACSNEIGVGVSKASCCCSLGKAWGTPCEMCPAVNTSEYKILCPGGEGFRPNPITVILEDIDECQELPGLCQGGKCINTFGSFQCRCPTGYYLNEDTRVCDDVNECETPGICGPGTCYNTVGNYTCICPPDYMQVNGGNNCMDMRRSLCYRNYYADNQTCDGELLFNMTKKMCCCSYNIGRAWNKPCEQCPIPSTDEFATLCGSQRPGFVIDIYTGLPVDIDECREIPGVCENGVCINMVGSFRCECPVGFFYNDKLLVCEDIDECQNGPVCQRNAECINTAGSYRCDCKPGYRFTSTGQCNDRNECQEIPNICSHGQCIDTVGSFYCLCHTGFKTNDDQTMCLDINECERDACGNGTCRNTIGSFNCRCNHGFILSHNNDCIDVDECASGNGNLCRNGQCINTVGSFQCQCNEGYEVAPDGRTCVDINECLLEPRKCAPGTCQNLDGSYRCICPPGYSLQNEKCEDIDECVEEPEICALGTCSNTEGSFKCLCPEGFSLSSSGRRCQDLRMSYCYAKFEGGKCSSPKSRNHSKQECCCALKGEGWGDPCELCPTEPDEAFRQICPYGSGIIVGPDDSAVDMDECKEPDVCKHGQCINTDGSYRCECPFGYILAGNECVDTDECSVGNPCGNGTCKNVIGGFECTCEEGFEPGPMMTCEDINECAQNPLLCAFRCVNTYGSYECKCPVGYVLREDRRMCKDEDECEEGKHDCTEKQMECKNLIGTYMCICGPGYQRRPDGEGCVDENECQTKPGICENGRCLNTRGSYTCECNDGFTASPNQDECLDNREGYCFTEVLQNMCQIGSSNRNPVTKSECCCDGGRGWGPHCEICPFQGTVAFKKLCPHGRGFMTNGADIDECKVIHDVCRNGECVNDRGSYHCICKTGYTPDITGTSCVDLNECNQAPKPCNFICKNTEGSYQCSCPKGYILQEDGRSCKDLDECATKQHNCQFLCVNTIGGFTCKCPPGFTQHHTSCIDNNECTSDINLCGSKGICQNTPGSFTCECQRGFSLDQTGSSCEDVDECEGNHRCQHGCQNIIGGYRCSCPQGYLQHYQWNQCVDENECLSAHICGGASCHNTLGSYKCMCPAGFQYEQFSGGCQDINECGSAQAPCSYGCSNTEGGYLCGCPPGYFRIGQGHCVSGMGMGRGNPEPPVSGEMDDNSLSPEACYECKINGYPKRGRKRRSTNETDASNIEDQSETEANVSLASWDVEKTAIFAFNISHVSNKVRILELLPALTTLTNHNRYLIESGNEDGFFKINQKEGISYLHFTKKKPVAGTYSLQISSTPLYKKKELNQLEDKYDKDYLSGELGDNLKMKIQVLLH,mutated_sequence,1.0,2871.0,NP_000129.3.a2m,NP_000129.3.npy,ClinVar +NP_000132.3,NP_000132.3.csv,MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT,mutated_sequence,1.0,821.0,NP_000132.3.a2m,NP_000132.3.npy,ClinVar +NP_000134.2,NP_000134.2.csv,MYRALRLLARSRPLVRAPAAALASAPGLGGAAVPSFWPPNAARMASQNSFRIEYDTFGELKVPNDKYYGAQTVRSTMNFKIGGVTERMPTPVIKAFGILKRAAAEVNQDYGLDPKIANAIMKAADEVAEGKLNDHFPLVVWQTGSGTQTNMNVNEVISNRAIEMLGGELGSKIPVHPNDHVNKSQSSNDTFPTAMHIAAAIEVHEVLLPGLQKLHDALDAKSKEFAQIIKIGRTHTQDAVPLTLGQEFSGYVQQVKYAMTRIKAAMPRIYELAAGGTAVGTGLNTRIGFAEKVAAKVAALTGLPFVTAPNKFEALAAHDALVELSGAMNTTACSLMKIANDIRFLGSGPRSGLGELILPENEPGSSIMPGKVNPTQCEAMTMVAAQVMGNHVAVTVGGSNGHFELNVFKPMMIKNVLHSARLLGDASVSFTENCVVGIQANTERINKLMNESLMLVTALNPHIGYDKAAKIAKTAHKNGSTLKETAIELGYLTAEQFDEWVKPKDMLGPK,mutated_sequence,1.0,510.0,NP_000134.2.a2m,NP_000134.2.npy,ClinVar +NP_000135.2,NP_000135.2.csv,MWTLGRRAVAGLLASPSPAQAQTLTRVPRPAELAPLCGRRGLRTDIDATCTPRRASSNQRGLNQIWNVKKQSVYLMNLRKSGTLGHPGSLDETTYERLAEETLDSLAEFFEDLADKPYTFEDYDVSFGSGVLTVKLGGDLGTYVINKQTPNKQIWLSSPSSGPKRYDWTGKNWVYSHDGVSLHELLAAELTKALKTKLDLSSLAYSGKDA,mutated_sequence,1.0,210.0,NP_000135.2.a2m,NP_000135.2.npy,ClinVar +NP_000142.2,NP_000142.2.csv,MEEGMNVLHDFGIQSTHYLQVNYQDSQDWFILVSVIADLRNAFYVLFPIWFHLQEAVGIKLLWVAVIGDWLNLVFKWILFGQRPYWWVLDTDYYSNTSVPLIKQFPVTCETGPGSPSGHAMGTAGVYYVMVTSTLSIFQGKIKPTYRFRCLNVILWLGFWAVQLNVCLSRIYLAAHFPHQVVAGVLSGIAVAETFSHIHSIYNASLKKYFLITFFLFSFAIGFYLLLKGLGVDLLWTLEKAQRWCEQPEWVHIDTTPFASLLKNLGTLFGLGLALNSSMYRESCKGKLSKWLPFRLSSIVASLVLLHVFDSLKPPSQVELVFYVLSFCKSAVVPLASVSVIPYCLAQVLGQPHKKSL,mutated_sequence,1.0,357.0,NP_000142.2.a2m,NP_000142.2.npy,ClinVar +NP_000143.2,NP_000143.2.csv,MGVRHPPCSHRLLAVCALVSLATAALLGHILLHDFLLVPRELSGSSPVLEETHPAHQQGASRPGPRDAQAHPGRPRAVPTQCDVPPNSRFDCAPDKAITQEQCEARGCCYIPAKQGLQGAQMGQPWCFFPPSYPSYKLENLSSSEMGYTATLTRTTPTFFPKDILTLRLDVMMETENRLHFTIKDPANRRYEVPLETPHVHSRAPSPLYSVEFSEEPFGVIVRRQLDGRVLLNTTVAPLFFADQFLQLSTSLPSQYITGLAEHLSPLMLSTSWTRITLWNRDLAPTPGANLYGSHPFYLALEDGGSAHGVFLLNSNAMDVVLQPSPALSWRSTGGILDVYIFLGPEPKSVVQQYLDVVGYPFMPPYWGLGFHLCRWGYSSTAITRQVVENMTRAHFPLDVQWNDLDYMDSRRDFTFNKDGFRDFPAMVQELHQGGRRYMMIVDPAISSSGPAGSYRPYDEGLRRGVFITNETGQPLIGKVWPGSTAFPDFTNPTALAWWEDMVAEFHDQVPFDGMWIDMNEPSNFIRGSEDGCPNNELENPPYVPGVVGGTLQAATICASSHQFLSTHYNLHNLYGLTEAIASHRALVKARGTRPFVISRSTFAGHGRYAGHWTGDVWSSWEQLASSVPEILQFNLLGVPLVGADVCGFLGNTSEELCVRWTQLGAFYPFMRNHNSLLSLPQEPYSFSEPAQQAMRKALTLRYALLPHLYTLFHQAHVAGETVARPLFLEFPKDSSTWTVDHQLLWGEALLITPVLQAGKAEVTGYFPLGTWYDLQTVPVEALGSLPPPPAAPREPAIHSEGQWVTLPAPLDTINVHLRAGYIIPLQGPGLTTTESRQQPMALAVALTKGGEARGELFWDDGESLEVLERGAYTQVIFLARNNTIVNELVRVTSEGAGLQLQKVTVLGVATAPQQVLSNGVPVSNFTYSPDTKVLDICVSLLMGEQFLVSWC,mutated_sequence,1.0,952.0,NP_000143.2.a2m,NP_000143.2.npy,ClinVar +NP_000144.2,NP_000144.2.csv,MAEWLLSASWQRRAKAMTAAAGSAGRAAVPLLLCALLAPGGAYVLDDSDGLGREFDGIGAVSGGGATSRLLVNYPEPYRSQILDYLFKPNFGASLHILKVEIGGDGQTTDGTEPSHMHYALDENYFRGYEWWLMKEAKKRNPNITLIGLPWSFPGWLGKGFDWPYVNLQLTAYYVVTWIVGAKRYHDLDIDYIGIWNERSYNANYIKILRKMLNYQGLQRVKIIASDNLWESISASMLLDAELFKVVDVIGAHYPGTHSAKDAKLTGKKLWSSEDFSTLNSDMGAGCWGRILNQNYINGYMTSTIAWNLVASYYEQLPYGRCGLMTAQEPWSGHYVVESPVWVSAHTTQFTQPGWYYLKTVGHLEKGGSYVALTDGLGNLTIIIETMSHKHSKCIRPFLPYFNVSQQFATFVLKGSFSEIPELQVWYTKLGKTSERFLFKQLDSLWLLDSDGSFTLSLHEDELFTLTTLTTGRKGSYPLPPKSQPFPSTYKDDFNVDYPFFSEAPNFADQTGVFEYFTNIEDPGEHHFTLRQVLNQRPITWAADASNTISIIGDYNWTNLTIKCDVYIETPDTGGVFIAGRVNKGGILIRSARGIFFWIFANGSYRVTGDLAGWIIYALGRVEVTAKKWYTLTLTIKGHFTSGMLNDKSLWTDIPVNFPKNGWAAIGTHSFEFAQFDNFLVEATR,mutated_sequence,1.0,685.0,NP_000144.2.a2m,NP_000144.2.npy,ClinVar +NP_000146.2,NP_000146.2.csv,MSRSGTDPQQRQQASEADAAAATFRANDHQHIRYNPLQDEWVLVSAHRMKRPWQGQVEPQLLKTVPRHDPLNPLCPGAIRANGEVNPQYDSTFLFDNDFPALQPDAPSPGPSDHPLFQAKSARGVCKVMCFHPWSDVTLPLMSVPEIRAVVDAWASVTEELGAQYPWVQIFENKGAMMGCSNPHPHCQVWASSFLPDIAQREERSQQAYKSQHGEPLLMEYSRQELLRKERLVLTSEHWLVLVPFWATWPYQTLLLPRRHVRRLPELTPAERDDLASIMKKLLTKYDNLFETSFPYSMGWHGAPTGSEAGANWNHWQLHAHYYPPLLRSATVRKFMVGYEMLAQAQRDLTPEQAAERLRALPEVHYHLGQKDRETATIA,mutated_sequence,1.0,379.0,NP_000146.2.a2m,NP_000146.2.npy,ClinVar +NP_000148.2,NP_000148.2.csv,MEFSSPSREECPKPLSRVSIMAGSLTGLLLLQAVSWASGARPCIPKSFGYSSVVCVCNATYCDSFDPPTFPALGTFSRYESTRSGRRMELSMGPIQANHTGTGLLLTLQPEQKFQKVKGFGGAMTDAAALNILALSPPAQNLLLKSYFSEEGIGYNIIRVPMASCDFSIRTYTYADTPDDFQLHNFSLPEEDTKLKIPLIHRALQLAQRPVSLLASPWTSPTWLKTNGAVNGKGSLKGQPGDIYHQTWARYFVKFLDAYAEHKLQFWAVTAENEPSAGLLSGYPFQCLGFTPEHQRDFIARDLGPTLANSTHHNVRLLMLDDQRLLLPHWAKVVLTDPEAAKYVHGIAVHWYLDFLAPAKATLGETHRLFPNTMLFASEACVGSKFWEQSVRLGSWDRGMQYSHSIITNLLYHVVGWTDWNLALNPEGGPNWVRNFVDSPIIVDITKDTFYKQPMFYHLGHFSKFIPEGSQRVGLVASQKNDLDAVALMHPDGSAVVVVLNRSSKDVPLTIKDPAVGFLETISPGYSIHTYLWRRQ,mutated_sequence,1.0,536.0,NP_000148.2.a2m,NP_000148.2.npy,ClinVar +NP_000152.1,NP_000152.1.csv,MEKGPVRAPAEKPRGARCSNGFPERDPPRPGPSRPAEKPPRPEAKSAQPADGWKGERPRSEEDNELNLPNLAAAYSSILSSLGENPQRQGLLKTPWRAASAMQFFTKGYQETISDVLNDAIFDEDHDEMVIVKDIDMFSMCEHHLVPFVGKVHIGYLPNKQVLGLSKLARIVEIYSRRLQVQERLTKQIAVAITEALRPAGVGVVVEATHMCMVMRGVQKMNSKTVTSTMLGVFREDPKTREEFLTLIRS,mutated_sequence,1.0,250.0,NP_000152.1.a2m,NP_000152.1.npy,ClinVar +NP_000153.1,NP_000153.1.csv,MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ,mutated_sequence,1.0,465.0,NP_000153.1.a2m,NP_000153.1.npy,ClinVar +NP_000156.1,NP_000156.1.csv,MGDWSALGKLLDKVQAYSTAGGKVWLSVLFIFRILLLGTAVESAWGDEQSAFRCNTQQPGCENVCYDKSFPISHVRFWVLQIIFVSVPTLLYLAHVFYVMRKEEKLNKKEEELKVAQTDGVNVDMHLKQIEIKKFKYGIEEHGKVKMRGGLLRTYIISILFKSIFEVAFLLIQWYIYGFSLSAVYTCKRDPCPHQVDCFLSRPTEKTIFIIFMLVVSLVSLALNIIELFYVFFKGVKDRVKGKSDPYHATSGALSPAKDCGSQKYAYFNGCSSPTAPLSPMSPPGYKLVTGDRNNSSCRNYNKQASEQNWANYSAEQNRMGQAGSTISNSHAQPFDFPDDNQNSKKLAAGHELQPLAIVDQRPSSRASSRASSRPRPDDLEI,mutated_sequence,1.0,382.0,NP_000156.1.a2m,NP_000156.1.npy,ClinVar +NP_000157.1,NP_000157.1.csv,MNWTGLYTLLSGVNRHSTAIGRVWLSVIFIFRIMVLVVAAESVWGDEKSSFICNTLQPGCNSVCYDQFFPISHVRLWSLQLILVSTPALLVAMHVAHQQHIEKKMLRLEGHGDPLHLEEVKRHKVHISGTLWWTYVISVVFRLLFEAVFMYVFYLLYPGYAMVRLVKCDVYPCPNTVDCFVSRPTEKTVFTVFMLAASGICIILNVAEVVYLIIRACARRAQRRSNPPSRKGSGFGHRLSPEYKQNEINKLLSEQDGSLKDILRRSPGTGAGLAEKSDRCSAC,mutated_sequence,1.0,283.0,NP_000157.1.a2m,NP_000157.1.npy,ClinVar +NP_000160.1,NP_000160.1.csv,MQLRNPELHLGCALALRFLALVSWDIPGARALDNGLARTPTMGWLHWERFMCNLDCQEEPDSCISEKLFMEMAELMVSEGWKDAGYEYLCIDDCWMAPQRDSEGRLQADPQRFPHGIRQLANYVHSKGLKLGIYADVGNKTCAGFPGSFGYYDIDAQTFADWGVDLLKFDGCYCDSLENLADGYKHMSLALNRTGRSIVYSCEWPLYMWPFQKPNYTEIRQYCNHWRNFADIDDSWKSIKSILDWTSFNQERIVDVAGPGGWNDPDMLVIGNFGLSWNQQVTQMALWAIMAAPLFMSNDLRHISPQAKALLQDKDVIAINQDPLGKQGYQLRQGDNFEVWERPLSGLAWAVAMINRQEIGGPRSYTIAVASLGKGVACNPACFITQLLPVKRKLGFYEWTSRLRSHINPTGTVLLQLENTMQMSLKDLL,mutated_sequence,1.0,429.0,NP_000160.1.a2m,NP_000160.1.npy,ClinVar +NP_000161.2,NP_000161.2.csv,MQSCARAWGLRLGRGVGGGRRLAGGSGPCWAPRSRDSSSGGGDSAAAGASRLLERLLPRHDDFARRHIGPGDKDQREMLQTLGLASIDELIEKTVPANIRLKRPLKMEDPVCENEILATLHAISSKNQIWRSYIGMGYYNCSVPQTILRNLLENSGWITQYTPYQPEVSQGRLESLLNYQTMVCDITGLDMANASLLDEGTAAAEALQLCYRHNKRRKFLVDPRCHPQTIAVVQTRAKYTGVLTELKLPCEMDFSGKDVSGVLFQYPDTEGKVEDFTELVERAHQSGSLACCATDLLALCILRPPGEFGVDIALGSSQRFGVPLGYGGPHAAFFAVRESLVRMMPGRMVGVTRDATGKEVYRLALQTREQHIRRDKATSNICTAQALLANMAAMFAIYHGSHGLEHIARRVHNATLILSEGLKRAGHQLQHDLFFDTLKIQCGCSVKEVLGRAAQRQINFRLFEDGTLGISLDETVNEKDLDDLLWIFGCESSAELVAESMGEECRGIPGSVFKRTSPFLTHQVFNSYHSETNIVRYMKKLENKDISLVHSMIPLGSCTMKLNSSSELAPITWKEFANIHPFVPLDQAQGYQQLFRELEKDLCELTGYDQVCFQPNSGAQGEYAGLATIRAYLNQKGEGHRTVCLIPKSAHGTNPASAHMAGMKIQPVEVDKYGNIDAVHLKAMVDKHKENLAAIMITYPSTNGVFEENISDVCDLIHQHGGQVYLDGANMNAQVGICRPGDFGSDVSHLNLHKTFCIPHGGGGPGMGPIGVKKHLAPFLPNHPVISLKRNEDACPVGTVSAAPWGSSSILPISWAYIKMMGGKGLKQATETAILNANYMAKRLETHYRILFRGARGYVGHEFILDTRPFKKSANIEAVDVAKRLQDYGFHAPTMSWPVAGTLMVEPTESEDKAELDRFCDAMISIRQEIADIEEGRIDPRVNPLKMSPHSLTCVTSSHWDRPYSREVAAFPLPFVKPENKFWPTIARIDDIYGDQHLVCTCPPMEVYESPFSEQKRASS,mutated_sequence,1.0,1020.0,NP_000161.2.a2m,NP_000161.2.npy,ClinVar +NP_000169.1,NP_000169.1.csv,MATNWGSLLQDKQQLEELARQAVDRALAEGVLLRTSQEPTSSEVVSYAPFTLFPSLVPSALLEQAYAVQMDFNLLVDAVSQNAAFLEQTLSSTIKQDDFTARLFDIHKQVLKEGIAQTVFLGLNRSDYMFQRSADGSPALKQIEINTISASFGGLASRTPAVHRHVLSVLSKTKEAGKILSNNPSKGLALGIAKAWELYGSPNALVLLIAQEKERNIFDQRAIENELLARNIHVIRRTFEDISEKGSLDQDRRLFVDGQEIAVVYFRDGYMPRQYSLQNWEARLLLERSHAAKCPDIATQLAGTKKVQQELSRPGMLEMLLPGQPEAVARLRATFAGLYSLDVGEEGDQAIAEALAAPSRFVLKPQREGGGNNLYGEEMVQALKQLKDSEERASYILMEKIEPEPFENCLLRPGSPARVVQCISELGIFGVYVRQEKTLVMNKHVGHLLRTKAIEHADGGVAAGVAVLDNPYPV,mutated_sequence,1.0,474.0,NP_000169.1.a2m,NP_000169.1.npy,ClinVar +NP_000170.1,NP_000170.1.csv,MSRQSTLYSFFPKSPALSDANKASARASREGGRAAAAPGASPSPGGDAAWSEAGPGPRPLARSASPPKAKNLNGGLRRSVAPAAPTSCDFSPGDLVWAKMEGYPWWPCLVYNHPFDGTFIREKGKSVRVHVQFFDDSPTRGWVSKRLLKPYTGSKSKEAQKGGHFYSAKPEILRAMQRADEALNKDKIKRLELAVCDEPSEPEEEEEMEVGTTYVTDKSEEDNEIESEEEVQPKTQGSRRSSRQIKKRRVISDSESDIGGSDVEFKPDTKEEGSSDEISSGVGDSESEGLNSPVKVARKRKRMVTGNGSLKRKSSRKETPSATKQATSISSETKNTLRAFSAPQNSESQAHVSGGGDDSSRPTVWYHETLEWLKEEKRRDEHRRRPDHPDFDASTLYVPEDFLNSCTPGMRKWWQIKSQNFDLVICYKVGKFYELYHMDALIGVSELGLVFMKGNWAHSGFPEIAFGRYSDSLVQKGYKVARVEQTETPEMMEARCRKMAHISKYDRVVRREICRIITKGTQTYSVLEGDPSENYSKYLLSLKEKEEDSSGHTRAYGVCFVDTSLGKFFIGQFSDDRHCSRFRTLVAHYPPVQVLFEKGNLSKETKTILKSSLSCSLQEGLIPGSQFWDASKTLRTLLEEEYFREKLSDGIGVMLPQVLKGMTSESDSIGLTPGEKSELALSALGGCVFYLKKCLIDQELLSMANFEEYIPLDSDTVSTTRSGAIFTKAYQRMVLDAVTLNNLEIFLNGTNGSTEGTLLERVDTCHTPFGKRLLKQWLCAPLCNHYAINDRLDAIEDLMVVPDKISEVVELLKKLPDLERLLSKIHNVGSPLKSQNHPDSRAIMYEETTYSKKKIIDFLSALEGFKVMCKIIGIMEEVADGFKSKILKQVISLQTKNPEGRFPDLTVELNRWDTAFDHEKARKTGLITPKAGFDSDYDQALADIRENEQSLLEYLEKQRNRIGCRTIVYWGIGRNRYQLEIPENFTTRNLPEEYELKSTKKGCKRYWTKTIEKKLANLINAEERRDVSLKDCMRRLFYNFDKNYKDWQSAVECIAVLDVLLCLANYSRGGDGPMCRPVILLPEDTPPFLELKGSRHPCITKTFFGDDFIPNDILIGCEEEEQENGKAYCVLVTGPNMGGKSTLMRQAGLLAVMAQMGCYVPAEVCRLTPIDRVFTRLGASDRIMSGESTFFVELSETASILMHATAHSLVLVDELGRGTATFDGTAIANAVVKELAETIKCRTLFSTHYHSLVEDYSQNVAVRLGHMACMVENECEDPSQETITFLYKFIKGACPKSYGFNAARLANLPEEVIQKGHRKAREFEKMNQSLRLFREVCLASERSTVDAEAVHKLLTLIKEL,mutated_sequence,1.0,1360.0,NP_000170.1.a2m,NP_000170.1.npy,ClinVar +NP_000177.2,NP_000177.2.csv,MRLLAKIICLMLWAICVAEDCNELPPRRNTEILTGSWSDQTYPEGTQAIYKCRPGYRSLGNVIMVCRKGEWVALNPLRKCQKRPCGHPGDTPFGTFTLTGGNVFEYGVKAVYTCNEGYQLLGEINYRECDTDGWTNDIPICEVVKCLPVTAPENGKIVSSAMEPDREYHFGQAVRFVCNSGYKIEGDEEMHCSDDGFWSKEKPKCVEISCKSPDVINGSPISQKIIYKENERFQYKCNMGYEYSERGDAVCTESGWRPLPSCEEKSCDNPYIPNGDYSPLRIKHRTGDEITYQCRNGFYPATRGNTAKCTSTGWIPAPRCTLKPCDYPDIKHGGLYHENMRRPYFPVAVGKYYSYYCDEHFETPSGSYWDHIHCTQDGWSPAVPCLRKCYFPYLENGYNQNHGRKFVQGKSIDVACHPGYALPKAQTTVTCMENGWSPTPRCIRVKTCSKSSIDIENGFISESQYTYALKEKAKYQCKLGYVTADGETSGSITCGKDGWSAQPTCIKSCDIPVFMNARTKNDFTWFKLNDTLDYECHDGYESNTGSTTGSIVCGYNGWSDLPICYERECELPKIDVHLVPDRKKDQYKVGEVLKFSCKPGFTIVGPNSVQCYHFGLSPDLPICKEQVQSCGPPPELLNGNVKEKTKEEYGHSEVVEYYCNPRFLMKGPNKIQCVDGEWTTLPVCIVEESTCGDIPELEHGWAQLSSPPYYYGDSVEFNCSESFTMIGHRSITCIHGVWTQLPQCVAIDKLKKCKSSNLIILEEHLKNKKEFDHNSNIRYRCRGKEGWIHTVCINGRWDPEVNCSMAQIQLCPPPPQIPNSHNMTTTLNYRDGEKVSVLCQENYLIQEGEEITCKDGRWQSIPLCVEKIPCSQPPQIEHGTINSSRSSQESYAHGTKLSYTCEGGFRISEENETTCYMGKWSSPPQCEGLPCKSPPEISHGVVAHMSDSYQYGEEVTYKCFEGFGIDGPAIAKCLGEKWSHPPSCIKTDCLSLPSFENAIPMGEKKDVYKAGEQVTYTCATYYKMDGASNVTCINSRWTGRPTCRDTSCVNPPTVQNAYIVSRQMSKYPSGERVRYQCRSPYEMFGDEEVMCLNGNWTEPPQCKDSTGKCGPPPPIDNGDITSFPLSVYAPASSVEYQCQNLYQLEGNKRITCRNGQWSEPPKCLHPCVISREIMENYNIALRWTAKQKLYSRTGESVEFVCKRGYRLSSRSHTLRTTCWDGKLEYPTCAKR,mutated_sequence,1.0,1231.0,NP_000177.2.a2m,NP_000177.2.npy,ClinVar +NP_000184.1,NP_000184.1.csv,MLLLARCLLLVLVSSLLVCSGLACGPGRGFGKRRHPKKLTPLAYKQFIPNVAEKTLGASGRYEGKISRNSERFKELTPNYNPDIIFKDEENTGADRLMTQRCKDKLNALAISVMNQWPGVKLRVTEGWDEDGHHSEESLHYEGRAVDITTSDRDRSKYGMLARLAVEAGFDWVYYESKAHIHCSVKAENSVAAKSGGCFPGSATVHLEQGGTKLVKDLSPGDRVLAADDQGRLLYSDFLTFLDRDDGAKKVFYVIETREPRERLLLTAAHLLFVAPHNDSATGEPEASSGSGPPSGGALGPRALFASRVRPGQRVYVVAERDGDRRLLPAAVHSVTLSEEAAGAYAPLTAQGTILINRVLASCYAVIEEHSWAHRAFAPFRLAHALLAALAPARTDRGGDSGGGDRGGGGGRVALTAPGAADAPGAGATAGIHWYSQLLYQIGTWLLDSEALHPLGMAVKSS,mutated_sequence,1.0,462.0,NP_000184.1.a2m,NP_000184.1.npy,ClinVar +NP_000187.3,NP_000187.3.csv,MERWPWPSGGAWLLVAARALLQLLRSDLRLGRPLLAALALLAALDWLCQRLLPPPAALAVLAAAGWIALSRLARPQRLPVATRAVLITGCDSGFGKETAKKLDSMGFTVLATVLELNSPGAIELRTCCSPRLRLLQMDLTKPGDISRVLEFTKAHTTSTGLWGLVNNAGHNEVVADAELSPVATFRSCMEVNFFGALELTKGLLPLLRSSRGRIVTVGSPAGDMPYPCLGAYGTSKAAVALLMDTFSCELLPWGVKVSIIQPGCFKTESVRNVGQWEKRKQLLLANLPQELLQAYGKDYIEHLHGQFLHSLRLAMSDLTPVVDAITDALLAARPRRRYYPGQGLGLMYFIHYYLPEGLRRRFLQAFFISHCLPRALQPGQPGTTPPQDAAQDPNLSPGPSPAVAR,mutated_sequence,1.0,405.0,NP_000187.3.a2m,NP_000187.3.npy,ClinVar +NP_000188.1,NP_000188.1.csv,MGDVLEQFFILTGLLVCLACLAKCVRFSRCVLLNYWKVLPKSFLRSMGQWAVITGAGDGIGKAYSFELAKRGLNVVLISRTLEKLEAIATEIERTTGRSVKIIQADFTKDDIYEHIKEKLAGLEIGILVNNVGMLPNLLPSHFLNAPDEIQSLIHCNITSVVKMTQLILKHMESRQKGLILNISSGIALFPWPLYSMYSASKAFVCAFSKALQEEYKAKEVIIQVLTPYAVSTAMTKYLNTNVITKTADEFVKESLNYVTIGGETCGCLAHEILAGFLSLIPAWAFYSGAFQRLLLTHYVAYLKLNTKVR,mutated_sequence,1.0,310.0,NP_000188.1.a2m,NP_000188.1.npy,ClinVar +NP_000193.1,NP_000193.1.csv,MPPPRTGRGLLWLGLVLSSVCVALGSETQANSTTDALNVLLIIVDDLRPSLGCYGDKLVRSPNIDQLASHSLLFQNAFAQQAVCAPSRVSFLTGRRPDTTRLYDFNSYWRVHAGNFSTIPQYFKENGYVTMSVGKVFHPGISSNHTDDSPYSWSFPPYHPSSEKYENTKTCRGPDGELHANLLCPVDVLDVPEGTLPDKQSTEQAIQLLEKMKTSASPFFLAVGYHKPHIPFRYPKEFQKLYPLENITLAPDPEVPDGLPPVAYNPWMDIRQREDVQALNISVPYGPIPVDFQRKIRQSYFASVSYLDTQVGRLLSALDDLQLANSTIIAFTSDHGWALGEHGEWAKYSNFDVATHVPLIFYVPGRTASLPEAGEKLFPYLDPFDSASQLMEPGRQSMDLVELVSLFPTLAGLAGLQVPPRCPVPSFHVELCREGKNLLKHFRFRDLEEDPYLPGNPRELIAYSQYPRPSDIPQWNSDKPSLKDIKIMGYSIRTIDYRYTVWVGFNPDEFLANFSDIHAGELYFVDSDPLQDHNMYNDSQGGDLFQLLMP,mutated_sequence,1.0,550.0,NP_000193.1.a2m,NP_000193.1.npy,ClinVar +NP_000194.2,NP_000194.2.csv,MRPLRPRAALLALLASLLAAPPVAPAEAPHLVHVDAARALWPLRRFWRSTGFCPPLPHSQADQYVLSWDQQLNLAYVGAVPHRGIKQVRTHWLLELVTTRGSTGRGLSYNFTHLDGYLDLLRENQLLPGFELMGSASGHFTDFEDKQQVFEWKDLVSSLARRYIGRYGLAHVSKWNFETWNEPDHHDFDNVSMTMQGFLNYYDACSEGLRAASPALRLGGPGDSFHTPPRSPLSWGLLRHCHDGTNFFTGEAGVRLDYISLHRKGARSSISILEQEKVVAQQIRQLFPKFADTPIYNDEADPLVGWSLPQPWRADVTYAAMVVKVIAQHQNLLLANTTSAFPYALLSNDNAFLSYHPHPFAQRTLTARFQVNNTRPPHVQLLRKPVLTAMGLLALLDEEQLWAEVSQAGTVLDSNHTVGVLASAHRPQGPADAWRAAVLIYASDDTRAHPNRSVAVTLRLRGVPPGPGLVYVTRYLDNGLCSPDGEWRRLGRPVFPTAEQFRRMRAAEDPVAAAPRPLPAGGRLTLRPALRLPSLLLVHVCARPEKPPGQVTRLRALPLTQGQLVLVWSDEHVGSKCLWTYEIQFSQDGKAYTPVSRKPSTFNLFVFSPDTGAVSGSYRVRALDYWARPGPFSDPVPYLEVPVPRGPPSPGNP,mutated_sequence,1.0,653.0,NP_000194.2.a2m,NP_000194.2.npy,ClinVar +NP_000197.1,NP_000197.1.csv,MLKPSLPFTSLLFLQLPLLGVGLNTTILTPNGNEDTTADFFLTTMPTDSLSVSTLPLPEVQCFVFNVEYMNCTWNSSSEPQPTNLTLHYWYKNSDNDKVQKCSHYLFSEEITSGCQLQKKEIHLYQTFVVQLQDPREPRRQATQMLKLQNLVIPWAPENLTLHKLSESQLELNWNNRFLNHCLEHLVQYRTDWDHSWTEQSVDYRHKFSLPSVDGQKRYTFRVRSRFNPLCGSAQHWSEWSHPIHWGSNTSKENPFLFALEAVVISVGSMGLIISLLCVYFWLERTMPRIPTLKNLEDLVTEYHGNFSAWSGVSKGLAESLQPDYSERLCLVSEIPPKGGALGEGPGASPCNQHSPYWAPPCYTLKPET,mutated_sequence,1.0,369.0,NP_000197.1.a2m,NP_000197.1.npy,ClinVar +NP_000199.2,NP_000199.2.csv,MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS,mutated_sequence,1.0,1382.0,NP_000199.2.a2m,NP_000199.2.npy,ClinVar +NP_000203.2,NP_000203.2.csv,MRARPRPRPLWATVLALGALAGVGVGGPNICTTRGVSSCQQCLAVSPMCAWCSDEALPLGSPRCDLKENLLKDNCAPESIEFPVSEARVLEDRPLSDKGSGDSSQVTQVSPQRIALRLRPDDSKNFSIQVRQVEDYPVDIYYLMDLSYSMKDDLWSIQNLGTKLATQMRKLTSNLRIGFGAFVDKPVSPYMYISPPEALENPCYDMKTTCLPMFGYKHVLTLTDQVTRFNEEVKKQSVSRNRDAPEGGFDAIMQATVCDEKIGWRNDASHLLVFTTDAKTHIALDGRLAGIVQPNDGQCHVGSDNHYSASTTMDYPSLGLMTEKLSQKNINLIFAVTENVVNLYQNYSELIPGTTVGVLSMDSSNVLQLIVDAYGKIRSKVELEVRDLPEELSLSFNATCLNNEVIPGLKSCMGLKIGDTVSFSIEAKVRGCPQEKEKSFTIKPVGFKDSLIVQVTFDCDCACQAQAEPNSHRCNNGNGTFECGVCRCGPGWLGSQCECSEEDYRPSQQDECSPREGQPVCSQRGECLCGQCVCHSSDFGKITGKYCECDDFSCVRYKGEMCSGHGQCSCGDCLCDSDWTGYYCNCTTRTDTCMSSNGLLCSGRGKCECGSCVCIQPGSYGDTCEKCPTCPDACTFKKECVECKKFDRGALHDENTCNRYCRDEIESVKELKDTGKDAVNCTYKNEDDCVVRFQYYEDSSGKSILYVVEEPECPKGPDILVVLLSVMGAILLIGLAALLIWKLLITIHDRKEFAKFEEERARAKWDTANNPLYKEATSTFTNITYRGT,mutated_sequence,1.0,788.0,NP_000203.2.a2m,NP_000203.2.npy,ClinVar +NP_000206.2,NP_000206.2.csv,MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS,mutated_sequence,1.0,1124.0,NP_000206.2.a2m,NP_000206.2.npy,ClinVar +NP_000209.2,NP_000209.2.csv,MAAASSPPRAERKRWGWGRLPGARRGSAGLAKKCPFSLELAEGGPAGGALYAPIAPGAPGPAPPASPAAPAAPPVASDLGPRPPVSLDPRVSIYSTRRPVLARTHVQGRVYNFLERPTGWKCFVYHFAVFLIVLVCLIFSVLSTIEQYAALATGTLFWMEIVLVVFFGTEYVVRLWSAGCRSKYVGLWGRLRFARKPISIIDLIVVVASMVVLCVGSKGQVFATSAIRGIRFLQILRMLHVDRQGGTWRLLGSVVFIHRQELITTLYIGFLGLIFSSYFVYLAEKDAVNESGRVEFGSYADALWWGVVTVTTIGYGDKVPQTWVGKTIASCFSVFAISFFALPAGILGSGFALKVQQKQRQKHFNRQIPAAASLIQTAWRCYAAENPDSSTWKIYIRKAPRSHTLLSPSPKPKKSVVVKKKKFKLDKDNGVTPGEKMLTVPHITCDPPEERRLDHFSVDGYDSSVRKSPTLLEVSMPHFMRTNSFAEDLDLEGETLLTPITHISQLREHHRATIKVIRRMQYFVAKKKFQQARKPYDVRDVIEQYSQGHLNLMVRIKELQRRLDQSIGKPSLFISVSEKSKDRGSNTIGARLNRVEDKVTQLDQRLALITDMLHQLLSLHGGSTPGSGGPPREGGAHITQPCGSGGSVDPELFLPSNTLPTYEQLTVPRRGPDEGS,mutated_sequence,1.0,676.0,NP_000209.2.a2m,NP_000209.2.npy,ClinVar +NP_000213.1,NP_000213.1.csv,MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV,mutated_sequence,1.0,976.0,NP_000213.1.a2m,NP_000213.1.npy,ClinVar +NP_000220.1,NP_000220.1.csv,MGPPGSPWQWVTLLLGLLLPPAAPFWLLNVLFPPHTTPKAELSNHTRPVILVPGCLGNQLEAKLDKPDVVNWMCYRKTEDFFTIWLDLNMFLPLGVDCWIDNTRVVYNRSSGLVSNAPGVQIRVPGFGKTYSVEYLDSSKLAGYLHTLVQNLVNNGYVRDETVRAAPYDWRLEPGQQEEYYRKLAGLVEEMHAAYGKPVFLIGHSLGCLHLLYFLLRQPQAWKDRFIDGFISLGAPWGGSIKPMLVLASGDNQGIPIMSSIKLKEEQRITTTSPWMFPSRMAWPEDHVFISTPSFNYTGRDFQRFFADLHFEEGWYMWLQSRDLLAGLPAPGVEVYCLYGVGLPTPRTYIYDHGFPYTDPVGVLYEDGDDTVATRSTELCGLWQGRQPQPVHLLPLHGIQHLNMVFSNLTLEHINAILLGAYRQGPPASPTASPEPPPPE,mutated_sequence,1.0,440.0,NP_000220.1.a2m,NP_000220.1.npy,ClinVar +NP_000223.1,NP_000223.1.csv,MAAAAAAAAEQQSSNGPVKKSMREKAVERRSVNKEHNSNFKAGYIPIDEDRLHKTGLRGRKGNLAICVIILLFILAVINLIITLVIWAVIRIGPNGCDSMEFHESGLLRFKQVSDMGVIHPLYKSTVGGRRNENLVITGNNQPIVFQQGTTKLSVENNKTSITSDIGMQFFDPRTQNILFSTDYETHEFHLPSGVKSLNVQKASTERITSNATSDLNIKVDGRAIVRGNEGVFIMGKTIEFHMGGNMELKAENSIILNGSVMVSTTRLPSSSSGDQLGSGDWVRYKLCMCADGTLFKVQVTSQNMGCQISDNPCGNTH,mutated_sequence,1.0,318.0,NP_000223.1.a2m,NP_000223.1.npy,ClinVar +NP_000224.2,NP_000224.2.csv,MKQRFSALQLLKLLLLLQPPLPRALREALCPEPCNCVPDGALRCPGPTAGLTRLSLAYLPVKVIPSQAFRGLNEVIKIEISQIDSLERIEANAFDNLLNLSEILIQNTKNLRYIEPGAFINLPRLKYLSICNTGIRKFPDVTKVFSSESNFILEICDNLHITTIPGNAFQGMNNESVTLKLYGNGFEEVQSHAFNGTTLTSLELKENVHLEKMHNGAFRGATGPKTLDISSTKLQALPSYGLESIQRLIATSSYSLKKLPSRETFVNLLEATLTYPSHCCAFRNLPTKEQNFSHSISENFSKQCESTVRKVNNKTLYSSMLAESELSGWDYEYGFCLPKTPRCAPEPDAFNPCEDIMGYDFLRVLIWLINILAIMGNMTVLFVLLTSRYKLTVPRFLMCNLSFADFCMGLYLLLIASVDSQTKGQYYNHAIDWQTGSGCSTAGFFTVFASELSVYTLTVITLERWHTITYAIHLDQKLRLRHAILIMLGGWLFSSLIAMLPLVGVSNYMKVSICFPMDVETTLSQVYILTILILNVVAFFIICACYIKIYFAVRNPELMATNKDTKIAKKMAILIFTDFTCMAPISFFAISAAFKVPLITVTNSKVLLVLFYPINSCANPFLYAIFTKTFQRDFFLLLSKFGCCKRRAELYRRKDFSAYTSNCKNGFTGSNKPSQSTLKLSTLHCQGTALLDKTRYTEC,mutated_sequence,1.0,699.0,NP_000224.2.a2m,NP_000224.2.npy,ClinVar +NP_000226.2,NP_000226.2.csv,MKMRFLGLVVCLVLWTLHSEGSGGKLTAVDPETNMNVSEIISYWGFPSEEYLVETEDGYILCLNRIPHGRKNHSDKGPKPVVFLQHGLLADSSNWVTNLANSSLGFILADAGFDVWMGNSRGNTWSRKHKTLSVSQDEFWAFSYDEMAKYDLPASINFILNKTGQEQVYYVGHSQGTTIGFIAFSQIPELAKRIKMFFALGPVASVAFCTSPMAKLGRLPDHLIKDLFGDKEFLPQSAFLKWLGTHVCTHVILKELCGNLCFLLCGFNERNLNMSRVDVYTTHSPAGTSVQNMLHWSQAVKFQKFQAFDWGSSAKNYFHYNQSYPPTYNVKDMLVPTAVWSGGHDWLADVYDVNILLTQITNLVFHESIPEWEHLDFIWGLDAPWRLYNKIINLMRKYQ,mutated_sequence,1.0,399.0,NP_000226.2.a2m,NP_000226.2.npy,ClinVar +NP_000229.1,NP_000229.1.csv,MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS,mutated_sequence,1.0,1159.0,NP_000229.1.a2m,NP_000229.1.npy,ClinVar +NP_000237.2,NP_000237.2.csv,MRCLAPRPAGSYLSEPQGSSQCATMELGPLEGGYLELLNSDADPLCLYHFYDQMDLAGEEEIELYSEPDTDTINCDQFSRLLCDMEGDEETREAYANIAELDQYVFQDSQLEGLSKDIFKHIGPDEVIGESMEMPAEVGQKSQKRPFPEELPADLKHWKPAEPPTVVTGSLLVGPVSDCSTLPCLPLPALFNQEPASGQMRLEKTDQIPMPFSSSSLSCLNLPEGPIQFVPTISTLPHGLWQISEAGTGVSSIFIYHGEVPQASQVPPPSGFTVHGLPTSPDRPGSTSPFAPSATDLPSMPEPALTSRANMTEHKTSPTQCPAAGEVSNKLPKWPEPVEQFYRSLQDTYGAEPAGPDGILVEVDLVQARLERSSSKSLERELATPDWAERQLAQGGLAEVLLAAKEHRRPRETRVIAVLGKAGQGKSYWAGAVSRAWACGRLPQYDFVFSVPCHCLNRPGDAYGLQDLLFSLGPQPLVAADEVFSHILKRPDRVLLILDGFEELEAQDGFLHSTCGPAPAEPCSLRGLLAGLFQKKLLRGCTLLLTARPRGRLVQSLSKADALFELSGFSMEQAQAYVMRYFESSGMTEHQDRALTLLRDRPLLLSHSHSPTLCRAVCQLSEALLELGEDAKLPSTLTGLYVGLLGRAALDSPPGALAELAKLAWELGRRHQSTLQEDQFPSADVRTWAMAKGLVQHPPRAAESELAFPSFLLQCFLGALWLALSGEIKDKELPQYLALTPRKKRPYDNWLEGVPRFLAGLIFQPPARCLGALLGPSAAASVDRKQKVLARYLKRLQPGTLRARQLLELLHCAHEAEEAGIWQHVVQELPGRLSFLGTRLTPPDAHVLGKALEAAGQDFSLDLRSTGICPSGLGSLVGLSCVTRFRAALSDTVALWESLQQHGETKLLQAAEEKFTIEPFKAKSLKDVEDLGKLVQTQRTRSSSEDTAGELPAVRDLKKLEFALGPVSGPQAFPKLVRILTAFSSLQHLDLDALSENKIGDEGVSQLSATFPQLKSLETLNLSQNNITDLGAYKLAEALPSLAASLLRLSLYNNCICDVGAESLARVLPDMVSLRVMDVQYNKFTAAGAQQLAASLRRCPHVETLAMWTPTIPFSVQEHLQQQDSRISLR,mutated_sequence,1.0,1130.0,NP_000237.2.a2m,NP_000237.2.npy,ClinVar +NP_000240.1,NP_000240.1.csv,MSFVAGVIRRLDETVVNRIAAGEVIQRPANAIKEMIENCLDAKSTSIQVIVKEGGLKLIQIQDNGTGIRKEDLDIVCERFTTSKLQSFEDLASISTYGFRGEALASISHVAHVTITTKTADGKCAYRASYSDGKLKAPPKPCAGNQGTQITVEDLFYNIATRRKALKNPSEEYGKILEVVGRYSVHNAGISFSVKKQGETVADVRTLPNASTVDNIRSIFGNAVSRELIEIGCEDKTLAFKMNGYISNANYSVKKCIFLLFINHRLVESTSLRKAIETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILERVQQHIESKLLGSNSSRMYFTQTLLPGLAGPSGEMVKSTTSLTSSSTSGSSDKVYAHQMVRTDSREQKLDAFLQPLSKPLSSQPQAIVTEDKTDISSGRARQQDEEMLELPAPAEVAAKNQSLEGDTTKGTSEMSEKRGPTSSNPRKRHREDSDVEMVEDDSRKEMTAACTPRRRIINLTSVLSLQEEINEQGHEVLREMLHNHSFVGCVNPQWALAQHQTKLYLLNTTKLSEELFYQILIYDFANFGVLRLSEPAPLFDLAMLALDSPESGWTEEDGPKEGLAEYIVEFLKKKAEMLADYFSLEIDEEGNLIGLPLLIDNYVPPLEGLPIFILRLATEVNWDEEKECFESLSKECAMFYSIRKQYISEESTLSGQQSEVPGSIPNSWKWTVEHIVYKALRSHILPPKHFTEDGNILQLANLPDLYKVFERC,mutated_sequence,1.0,756.0,NP_000240.1.a2m,NP_000240.1.npy,ClinVar +NP_000242.1,NP_000242.1.csv,MAVQPKETLQLESAAEVGFVRFFQGMPEKPTTTVRLFDRGDFYTAHGEDALLAAREVFKTQGVIKYMGPAGAKNLQSVVLSKMNFESFVKDLLLVRQYRVEVYKNRAGNKASKENDWYLAYKASPGNLSQFEDILFGNNDMSASIGVVGVKMSAVDGQRQVGVGYVDSIQRKLGLCEFPDNDQFSNLEALLIQIGPKECVLPGGETAGDMGKLRQIIQRGGILITERKKADFSTKDIYQDLNRLLKGKKGEQMNSAVLPEMENQVAVSSLSAVIKFLELLSDDSNFGQFELTTFDFSQYMKLDIAAVRALNLFQGSVEDTTGSQSLAALLNKCKTPQGQRLVNQWIKQPLMDKNRIEERLNLVEAFVEDAELRQTLQEDLLRRFPDLNRLAKKFQRQAANLQDCYRLYQGINQLPNVIQALEKHEGKHQKLLLAVFVTPLTDLRSDFSKFQEMIETTLDMDQVENHEFLVKPSFDPNLSELREIMNDLEKKMQSTLISAARDLGLDPGKQIKLDSSAQFGYYFRVTCKEEKVLRNNKNFSTVDIQKNGVKFTNSKLTSLNEEYTKNKTEYEEAQDAIVKEIVNISSGYVEPMQTLNDVLAQLDAVVSFAHVSNGAPVPYVRPAILEKGQGRIILKASRHACVEVQDEIAFIPNDVYFEKDKQMFHIITGPNMGGKSTYIRQTGVIVLMAQIGCFVPCESAEVSIVDCILARVGAGDSQLKGVSTFMAEMLETASILRSATKDSLIIIDELGRGTSTYDGFGLAWAISEYIATKIGAFCMFATHFHELTALANQIPTVNNLHVTALTTEETLTMLYQVKKGVCDQSFGIHVAELANFPKHVIECAKQKALELEEFQYIGESQGYDIMEPAAKKCYLEREQGEKIIQEFLSKVKQMPFTEMSEENITIKLKQLKAEVIAKNNSFVNEIISRIKVTT,mutated_sequence,1.0,934.0,NP_000242.1.a2m,NP_000242.1.npy,ClinVar +NP_000243.1,NP_000243.1.csv,MASASTSKYNSHSLENESIKRTSRDGVNRDLTEAVPRLPGETLITDKEVIYICPFNGPIKGRVYITNYRLYLRSLETDSSLILDVPLGVISRIEKMGGATSRGENSYGLDITCKDMRNLRFALKQEGHSRRDMFEILTRYAFPLAHSLPLFAFLNEEKFNVDGWTVYNPVEEYRRQGLPNHHWRITFINKCYELCDTYPALLVVPYRASDDDLRRVATFRSRNRIPVLSWIHPENKTVIVRCSQPLVGMSGKRNKDDEKYLDVIRETNKQISKLTIYDARPSVNAVANKATGGGYESDDAYHNAELFFLDIHNIHVMRESLKKVKDIVYPNVEESHWLSSLESTHWLEHIKLVLTGAIQVADKVSSGKSSVLVHCSDGWDRTAQLTSLAMLMLDSFYRSIEGFEILVQKEWISFGHKFASRIGHGDKNHTDADRSPIFLQFIDCVWQMSKQFPTAFEFNEQFLIIILDHLYSCRFGTFLFNCESARERQKVTERTVSLWSLINSNKEKFKNPFYTKEINRVLYPVASMRHLELWVNYYIRWNPRIKQQQPNPVEQRYMELLALRDEYIKRLEELQLANSAKLSDPPTSPSSPSQMMPHVQTHF,mutated_sequence,1.0,603.0,NP_000243.1.a2m,NP_000243.1.npy,ClinVar +NP_000246.2,NP_000246.2.csv,MLRAKNQLFLLSPHYLRQVKESSGSRLIQQRLLHQQQPLHPEWAALAKKQLKGKNPEDLIWHTPEGISIKPLYSKRDTMDLPEELPGVKPFTRGPYPTMYTFRPWTIRQYAGFSTVEESNKFYKDNIKAGQQGLSVAFDLATHRGYDSDNPRVRGDVGMAGVAIDTVEDTKILFDGIPLEKMSVSMTMNGAVIPVLANFIVTGEEQGVPKEKLTGTIQNDILKEFMVRNTYIFPPEPSMKIIADIFEYTAKHMPKFNSISISGYHMQEAGADAILELAYTLADGLEYSRTGLQAGLTIDEFAPRLSFFWGIGMNFYMEIAKMRAGRRLWAHLIEKMFQPKNSKSLLLRAHCQTSGWSLTEQDPYNNIVRTAIEAMAAVFGGTQSLHTNSFDEALGLPTVKSARIARNTQIIIQEESGIPKVADPWGGSYMMECLTNDVYDAALKLINEIEEMGGMAKAVAEGIPKLRIEECAARRQARIDSGSEVIVGVNKYQLEKEDAVEVLAIDNTSVRNRQIEKLKKIKSSRDQALAERCLAALTECAASGDGNILALAVDASRARCTVGEITDALKKVFGEHKANDRMVSGAYRQEFGESKEITSAIKRVHKFMEREGRRPRLLVAKMGQDGHDRGAKVIATGFADLGFDVDIGPLFQTPREVAQQAVDADVHAVGISTLAAGHKTLVPELIKELNSLGRPDILVMCGGVIPPQDYEFLFEVGVSNVFGPGTRIPKAAVQVLDDIEKCLEKKQQSV,mutated_sequence,1.0,750.0,NP_000246.2.a2m,NP_000246.2.npy,ClinVar +NP_000247.2,NP_000247.2.csv,MPEPGKKPVSAFSKKPRSVEVAAGSPAVFEAETERAGVKVRWQRGGSDISASNKYGLATEGTRHTLTVREVGPADQGSYAVIAGSSKVKFDLKVIEAEKAEPMLAPAPAPAEATGAPGEAPAPAAELGESAPSPKGSSSAALNGPTPGAPDDPIGLFVMRPQDGEVTVGGSITFSARVAGASLLKPPVVKWFKGKWVDLSSKVGQHLQLHDSYDRASKVYLFELHITDAQPAFTGSYRCEVSTKDKFDCSNFNLTVHEAMGTGDLDLLSAFRRTSLAGGGRRISDSHEDTGILDFSSLLKKRDSFRTPRDSKLEAPAEEDVWEILRQAPPSEYERIAFQYGVTDLRGMLKRLKGMRRDEKKSTAFQKKLEPAYQVSKGHKIRLTVELADHDAEVKWLKNGQEIQMSGSKYIFESIGAKRTLTISQCSLADDAAYQCVVGGEKCSTELFVKEPPVLITRPLEDQLVMVGQRVEFECEVSEEGAQVKWLKDGVELTREETFKYRFKKDGQRHHLIINEAMLEDAGHYALCTSGGQALAELIVQEKKLEVYQSIADLMVGAKDQAVFKCEVSDENVRGVWLKNGKELVPDSRIKVSHIGRVHKLTIDDVTPADEADYSFVPEGFACNLSAKLHFMEVKIDFVPRQEPPKIHLDCPGRIPDTIVVVAGNKLRLDVPISGDPAPTVIWQKAITQGNKAPARPAPDAPEDTGDSDEWVFDKKLLCETEGRVRVETTKDRSIFTVEGAEKEDEGVYTVTVKNPVGEDQVNLTVKVIDVPDAPAAPKISNVGEDSCTVQWEPPAYDGGQPILGYILERKKKKSYRWMRLNFDLIQELSHEARRMIEGVVYEMRVYAVNAIGMSRPSPASQPFMPIGPPSEPTHLAVEDVSDTTVSLKWRPPERVGAGGLDGYSVEYCPEGCSEWVAALQGLTEHTSILVKDLPTGARLLFRVRAHNMAGPGAPVTTTEPVTVQEILQRPRLQLPRHLRQTIQKKVGEPVNLLIPFQGKPRPQVTWTKEGQPLAGEEVSIRNSPTDTILFIRAARRVHSGTYQVTVRIENMEDKATLVLQVVDKPSPPQDLRVTDAWGLNVALEWKPPQDVGNTELWGYTVQKADKKTMEWFTVLEHYRRTHCVVPELIIGNGYYFRVFSQNMVGFSDRAATTKEPVFIPRPGITYEPPNYKALDFSEAPSFTQPLVNRSVIAGYTAMLCCAVRGSPKPKISWFKNGLDLGEDARFRMFSKQGVLTLEIRKPCPFDGGIYVCRATNLQGEARCECRLEVRVPQ,mutated_sequence,1.0,1274.0,NP_000247.2.a2m,NP_000247.2.npy,ClinVar +NP_000248.2,NP_000248.2.csv,MGDSEMAVFGAAAPYLRKSEKERLEAQTRPFDLKKDVFVPDDKQEFVKAKIVSREGGKVTAETEYGKTVTVKEDQVMQQNPPKFDKIEDMAMLTFLHEPAVLYNLKDRYGSWMIYTYSGLFCVTVNPYKWLPVYTPEVVAAYRGKKRSEAPPHIFSISDNAYQYMLTDRENQSILITGESGAGKTVNTKRVIQYFAVIAAIGDRSKKDQSPGKGTLEDQIIQANPALEAFGNAKTVRNDNSSRFGKFIRIHFGATGKLASADIETYLLEKSRVIFQLKAERDYHIFYQILSNKKPELLDMLLITNNPYDYAFISQGETTVASIDDAEELMATDNAFDVLGFTSEEKNSMYKLTGAIMHFGNMKFKLKQREEQAEPDGTEEADKSAYLMGLNSADLLKGLCHPRVKVGNEYVTKGQNVQQVIYATGALAKAVYERMFNWMVTRINATLETKQPRQYFIGVLDIAGFEIFDFNSFEQLCINFTNEKLQQFFNHHMFVLEQEEYKKEGIEWTFIDFGMDLQACIDLIEKPMGIMSILEEECMFPKATDMTFKAKLFDNHLGKSANFQKPRNIKGKPEAHFSLIHYAGIVDYNIIGWLQKNKDPLNETVVGLYQKSSLKLLSTLFANYAGADAPIEKGKGKAKKGSSFQTVSALHRENLNKLMTNLRSTHPHFVRCIIPNETKSPGVMDNPLVMHQLRCNGVLEGIRICRKGFPNRILYGDFRQRYRILNPAAIPEGQFIDSRKGAEKLLSSLDIDHNQYKFGHTKVFFKAGLLGLLEEMRDERLSRIITRIQAQSRGVLARMEYKKLLERRDSLLVIQWNIRAFMGVKNWPWMKLYFKIKPLLKSAEREKEMASMKEEFTRLKEALEKSEARRKELEEKMVSLLQEKNDLQLQVQAEQDNLADAEERCDQLIKNKIQLEAKVKEMNERLEDEEEMNAELTAKKRKLEDECSELKRDIDDLELTLAKVEKEKHATENKVKNLTEEMAGLDEIIAKLTKEKKALQEAHQQALDDLQAEEDKVNTLTKAKVKLEQQVDDLEGSLEQEKKVRMDLERAKRKLEGDLKLTQESIMDLENDKQQLDERLKKKDFELNALNARIEDEQALGSQLQKKLKELQARIEELEEELEAERTARAKVEKLRSDLSRELEEISERLEEAGGATSVQIEMNKKREAEFQKMRRDLEEATLQHEATAAALRKKHADSVAELGEQIDNLQRVKQKLEKEKSEFKLELDDVTSNMEQIIKAKANLEKMCRTLEDQMNEHRSKAEETQRSVNDLTSQRAKLQTENGELSRQLDEKEALISQLTRGKLTYTQQLEDLKRQLEEEVKAKNALAHALQSARHDCDLLREQYEEETEAKAELQRVLSKANSEVAQWRTKYETDAIQRTEELEEAKKKLAQRLQEAEEAVEAVNAKCSSLEKTKHRLQNEIEDLMVDVERSNAAAAALDKKQRNFDKILAEWKQKYEESQSELESSQKEARSLSTELFKLKNAYEESLEHLETFKRENKNLQEEISDLTEQLGSSGKTIHELEKVRKQLEAEKMELQSALEEAEASLEHEEGKILRAQLEFNQIKAEIERKLAEKDEEMEQAKRNHLRVVDSLQTSLDAETRSRNEALRVKKKMEGDLNEMEIQLSHANRMAAEAQKQVKSLQSLLKDTQIQLDDAVRANDDLKENIAIVERRNNLLQAELEELRAVVEQTERSRKLAEQELIETSERVQLLHSQNTSLINQKKKMDADLSQLQTEVEEAVQECRNAEEKAKKAITDAAMMAEELKKEQDTSAHLERMKKNMEQTIKDLQHRLDEAEQIALKGGKKQLQKLEARVRELENELEAEQKRNAESVKGMRKSERRIKELTYQTEEDRKNLLRLQDLVDKLQLKVKAYKRQAEEAEEQANTNLSKFRKVQHELDEAEERADIAESQVNKLRAKSRDIGTKGLNEE,mutated_sequence,1.0,1935.0,NP_000248.2.a2m,NP_000248.2.npy,ClinVar +NP_000251.3,NP_000251.3.csv,MVILQQGDHVWMDLRLGQEFDVPIGAVVKLCDSGQVQVVDDEDNEHWISPQNATHIKPMHPTSVHGVEDMIRLGDLNEAGILRNLLIRYRDHLIYTYTGSILVAVNPYQLLSIYSPEHIRQYTNKKIGEMPPHIFAIADNCYFNMKRNSRDQCCIISGESGAGKTESTKLILQFLAAISGQHSWIEQQVLEATPILEAFGNAKTIRNDNSSRFGKYIDIHFNKRGAIEGAKIEQYLLEKSRVCRQALDERNYHVFYCMLEGMSEDQKKKLGLGQASDYNYLAMGNCITCEGRVDSQEYANIRSAMKVLMFTDTENWEISKLLAAILHLGNLQYEARTFENLDACEVLFSPSLATAASLLEVNPPDLMSCLTSRTLITRGETVSTPLSREQALDVRDAFVKGIYGRLFVWIVDKINAAIYKPPSQDVKNSRRSIGLLDIFGFENFAVNSFEQLCINFANEHLQQFFVRHVFKLEQEEYDLESIDWLHIEFTDNQDALDMIANKPMNIISLIDEESKFPKGTDTTMLHKLNSQHKLNANYIPPKNNHETQFGINHFAGIVYYETQGFLEKNRDTLHGDIIQLVHSSRNKFIKQIFQADVAMGAETRKRSPTLSSQFKRSLELLMRTLGACQPFFVRCIKPNEFKKPMLFDRHLCVRQLRYSGMMETIRIRRAGYPIRYSFVEFVERYRVLLPGVKPAYKQGDLRGTCQRMAEAVLGTHDDWQIGKTKIFLKDHHDMLLEVERDKAITDRVILLQKVIRGFKDRSNFLKLKNAATLIQRHWRGHNCRKNYGLMRLGFLRLQALHRSRKLHQQYRLARQRIIQFQARCRAYLVRKAFRHRLWAVLTVQAYARGMIARRLHQRLRAEYLWRLEAEKMRLAEEEKLRKEMSAKKAKEEAERKHQERLAQLAREDAERELKEKEAARRKKELLEQMERARHEPVNHSDMVDKMFGFLGTSGGLPGQEGQAPSGFEDLERGRREMVEEDLDAALPLPDEDEEDLSEYKFAKFAATYFQGTTTHSYTRRPLKQPLLYHDDEGDQLAALAVWITILRFMGDLPEPKYHTAMSDGSEKIPVMTKIYETLGKKTYKRELQALQGEGEAQLPEGQKKSSVRHKLVHLTLKKKSKLTEEVTKRLHDGESTVQGNSMLEDRPTSNLEKLHFIIGNGILRPALRDEIYCQISKQLTHNPSKSSYARGWILVSLCVGCFAPSEKFVKYLRNFIHGGPPGYAPYCEERLRRTFVNGTRTQPPSWLELQATKSKKPIMLPVTFMDGTTKTLLTDSATTAKELCNALADKISLKDRFGFSLYIALFDKVSSLGSGSDHVMDAISQCEQYAKEQGAQERNAPWRLFFRKEVFTPWHSPSEDNVATNLIYQQVVRGVKFGEYRCEKEDDLAELASQQYFVDYGSEMILERLLNLVPTYIPDREITPLKTLEKWAQLAIAAHKKGIYAQRRTDAQKVKEDVVSYARFKWPLLFSRFYEAYKFSGPSLPKNDVIVAVNWTGVYFVDEQEQVLLELSFPEIMAVSSSRECRVWLSLGCSDLGCAAPHSGWAGLTPAGPCSPCWSCRGAKTTAPSFTLATIKGDEYTFTSSNAEDIRDLVVTFLEGLRKRSKYVVALQDNPNPAGEESGFLSFAKGDLIILDHDTGEQVMNSGWANGINERTKQRGDFPTDSVYVMPTVTMPPREIVALVTMTPDQRQDVVRLLQLRTAEPEVRAKPYTLEEFSYDYFRPPPKHTLSRVMVSKARGKDRLWSHTREPLKQALLKKLLGSEELSQEACLAFIAVLKYMGDYPSKRTRSVNELTDQIFEGPLKAEPLKDEAYVQILKQLTDNHIRYSEERGWELLWLCTGLFPPSNILLPHVQRFLQSRKHCPLAIDCLQRLQKALRNGSRKYPPHLVEVEAIQHKTTQIFHKVYFPDDTDEAFEVESSTKAKDFCQNIATRLLLKSSEGFSLFVKIADKVLSVPENDFFFDFVRHLTDWIKKARPIKDGIVPSLTYQVFFMKKLWTTTVPGKDPMADSIFHYYQELPKYLRGYHKCTREEVLQLGALIYRVKFEEDKSYFPSIPKLLRELVPQDLIRQVSPDDWKRSIVAYFNKHAGKSKEEAKLAFLKLIFKWPTFGSAFFEVKQTTEPNFPEILLIAINKYGVSLIDPKTKDILTTHPFTKISNWSSGNTYFHITIGNLVRGSKLLCETSLGYKMDDLLTSYISQMLTAMSKQRGSRSGK,mutated_sequence,1.0,2215.0,NP_000251.3.a2m,NP_000251.3.npy,ClinVar +NP_000252.1,NP_000252.1.csv,MRFFCARCCSFGPEMPAVQLLLLACLVWDVGARTAQLRKANDQSGRCQYTFSVASPNESSCPEQSQAMSVIHNLQRDSSTQRLDLEATKARLSSLESLLHQLTLDQAARPQETQEGLQRELGTLRRERDQLETQTRELETAYSNLLRDKSVLEEEKKRLRQENENLARRLESSSQEVARLRRGQCPQTRDTARAVPPGSREVSTWNLDTLAFQELKSELTEVPASRILKESPSGYLRSGEGDTGCGELVWVGEPLTLRTAETITGKYGVWMRDPKPTYPYTQETTWRIDTVGTDVRQVFEYDLISQFMQGYPSKVHILPRPLESTGAVVYSGSLYFQGAESRTVIRYELNTETVKAEKEIPGAGYHGQFPYSWGGYTDIDLAVDEAGLWVIYSTDEAKGAIVLSKLNPENLELEQTWETNIRKQSVANAFIICGTLYTVSSYTSADATVNFAYDTGTGISKTLTIPFKNRYKYSSMIDYNPLEKKLFAWDNLNMVTYDIKLSKM,mutated_sequence,1.0,504.0,NP_000252.1.a2m,NP_000252.1.npy,ClinVar +NP_000255.2,NP_000255.2.csv,MASAGNAAEPQDRGGGGSGCIGAPGRPAGGGRRRRTGGLRRAAAPDRDYLHRPSYCDAAFALEQISKGKATGRKAPLWLRAKFQRLLFKLGCYIQKNCGKFLVVGLLIFGAFAVGLKAANLETNVEELWVEVGGRVSRELNYTRQKIGEEAMFNPQLMIQTPKEEGANVLTTEALLQHLDSALQASRVHVYMYNRQWKLEHLCYKSGELITETGYMDQIIEYLYPCLIITPLDCFWEGAKLQSGTAYLLGKPPLRWTNFDPLEFLEELKKINYQVDSWEEMLNKAEVGHGYMDRPCLNPADPDCPATAPNKNSTKPLDMALVLNGGCHGLSRKYMHWQEELIVGGTVKNSTGKLVSAHALQTMFQLMTPKQMYEHFKGYEYVSHINWNEDKAAAILEAWQRTYVEVVHQSVAQNSTQKVLSFTTTTLDDILKSFSDVSVIRVASGYLLMLAYACLTMLRWDCSKSQGAVGLAGVLLVALSVAAGLGLCSLIGISFNAATTQVLPFLALGVGVDDVFLLAHAFSETGQNKRIPFEDRTGECLKRTGASVALTSISNVTAFFMAALIPIPALRAFSLQAAVVVVFNFAMVLLIFPAILSMDLYRREDRRLDIFCCFTSPCVSRVIQVEPQAYTDTHDNTRYSPPPPYSSHSFAHETQITMQSTVQLRTEYDPHTHVYYTTAEPRSEISVQPVTVTQDTLSCQSPESTSSTRDLLSQFSDSSLHCLEPPCTKWTLSSFAEKHYAPFLLKPKAKVVVIFLFLGLLGVSLYGTTRVRDGLDLTDIVPRETREYDFIAAQFKYFSFYNMYIVTQKADYPNIQHLLYDLHRSFSNVKYVMLEENKQLPKMWLHYFRDWLQGLQDAFDSDWETGKIMPNNYKNGSDDGVLAYKLLVQTGSRDKPIDISQLTKQRLVDADGIINPSAFYIYLTAWVSNDPVAYAASQANIRPHRPEWVHDKADYMPETRLRIPAAEPIEYAQFPFYLNGLRDTSDFVEAIEKVRTICSNYTSLGLSSYPNGYPFLFWEQYIGLRHWLLLFISVVLACTFLVCAVFLLNPWTAGIIVMVLALMTVELFGMMGLIGIKLSAVPVVILIASVGIGVEFTVHVALAFLTAIGDKNRRAVLALEHMFAPVLDGAVSTLLGVLMLAGSEFDFIVRYFFAVLAILTILGVLNGLVLLPVLLSFFGPYPEVSPANGLNRLPTPSPEPPPSVVRFAMPPGHTHSGSDSSDSEYSSQTTVSGLSEELRHYEAQQGAGGPAHQVIVEATENPVFAHSTVVHPESRHHPPSNPRQQPHLDSGSLPPGRQGQQPRRDPPREGLWPPPYRPRRDAFEISTEGHSGPSNRARWGPRGARSHNPRNPASTAMGSSVPGYCQPITTVTASASVTVAVHPPPVPGPGRNPRGGLCPGYPETDHGLFEDPHVPFHVRCERRDSKVEVIELQDVECEERPRGSSSN,mutated_sequence,1.0,1447.0,NP_000255.2.a2m,NP_000255.2.npy,ClinVar +NP_000257.1,NP_000257.1.csv,MRKHVLAASFSMLSLLVIMGDTDSKTDSSFIMDSDPRRCMRHHYVDSISHPLYKCSSKMVLLARCEGHCSQASRSEPLVSFSTVLKQPFRSSCHCCRPQTSKLKALRLRCSGGMRLTATYRYILSCHCEECNS,mutated_sequence,1.0,133.0,NP_000257.1.a2m,NP_000257.1.npy,ClinVar +NP_000259.1,NP_000259.1.csv,MAGAIASRMSFSSLKRKQPKTFTVRIVTMDAEMEFNCEMKWKGKDLFDLVCRTLGLRETWFFGLQYTIKDTVAWLKMDKKVLDHDVSKEEPVTFHFLAKFYPENAEEELVQEITQHLFFLQVKKQILDEKIYCPPEASVLLASYAVQAKYGDYDPSVHKRGFLAQEELLPKRVINLYQMTPEMWEERITAWYAEHRGRARDEAEMEYLKIAQDLEMYGVNYFAIRNKKGTELLLGVDALGLHIYDPENRLTPKISFPWNEIRNISYSDKEFTIKPLDKKIDVFKFNSSKLRVNKLILQLCIGNHDLFMRRRKADSLEVQQMKAQAREEKARKQMERQRLAREKQMREEAERTRDELERRLLQMKEEATMANEALMRSEETADLLAEKAQITEEEAKLLAQKAAEAEQEMQRIKATAIRTEEEKRLMEQKVLEAEVLALKMAEESERRAKEADQLKQDLQEAREAERRAKQKLLEIATKPTYPPMNPIPAPLPPDIPSFNLIGDSLSFDFKDTDMKRLSMEIEKEKVEYMEKSKHLQEQLNELKTEIEALKLKERETALDILHNENSDRGGSSKHNTIKKLTLQSAKSRVAFFEEL,mutated_sequence,1.0,595.0,NP_000259.1.a2m,NP_000259.1.npy,ClinVar +NP_000262.2,NP_000262.2.csv,MTARGLALGLLLLLLCPAQVFSQSCVWYGECGIAYGDKRYNCEYSGPPKPLPKDGYDLVQELCPGFFFGNVSLCCDVRQLQTLKDNLQLPLQFLSRCPSCFYNLLNLFCELTCSPRQSQFLNVTATEDYVDPVTNQTKTNVKELQYYVGQSFANAMYNACRDVEAPSSNDKALGLLCGKDADACNATNWIEYMFNKDNGQAPFTITPVFSDFPVHGMEPMNNATKGCDESVDEVTAPCSCQDCSIVCGPKPQPPPPPAPWTILGLDAMYVIMWITYMAFLLVFFGAFFAVWCYRKRYFVSEYTPIDSNIAFSVNASDKGEASCCDPVSAAFEGCLRRLFTRWGSFCVRNPGCVIFFSLVFITACSSGLVFVRVTTNPVDLWSAPSSQARLEKEYFDQHFGPFFRTEQLIIRAPLTDKHIYQPYPSGADVPFGPPLDIQILHQVLDLQIAIENITASYDNETVTLQDICLAPLSPYNTNCTILSVLNYFQNSHSVLDHKKGDDFFVYADYHTHFLYCVRAPASLNDTSLLHDPCLGTFGGPVFPWLVLGGYDDQNYNNATALVITFPVNNYYNDTEKLQRAQAWEKEFINFVKNYKNPNLTISFTAERSIEDELNRESDSDVFTVVISYAIMFLYISLALGHMKSCRRLLVDSKVSLGIAGILIVLSSVACSLGVFSYIGLPLTLIVIEVIPFLVLAVGVDNIFILVQAYQRDERLQGETLDQQLGRVLGEVAPSMFLSSFSETVAFFLGALSVMPAVHTFSLFAGLAVFIDFLLQITCFVSLLGLDIKRQEKNRLDIFCCVRGAEDGTSVQASESCLFRFFKNSYSPLLLKDWMRPIVIAIFVGVLSFSIAVLNKVDIGLDQSLSMPDDSYMVDYFKSISQYLHAGPPVYFVLEEGHDYTSSKGQNMVCGGMGCNNDSLVQQIFNAAQLDNYTRIGFAPSSWIDDYFDWVKPQSSCCRVDNITDQFCNASVVDPACVRCRPLTPEGKQRPQGGDFMRFLPMFLSDNPNPKCGKGGHAAYSSAVNILLGHGTRVGATYFMTYHTVLQTSADFIDALKKARLIASNVTETMGINGSAYRVFPYSVFYVFYEQYLTIIDDTIFNLGVSLGAIFLVTMVLLGCELWSAVIMCATIAMVLVNMFGVMWLWGISLNAVSLVNLVMSCGISVEFCSHITRAFTVSMKGSRVERAEEALAHMGSSVFSGITLTKFGGIVVLAFAKSQIFQIFYFRMYLAMVLLGATHGLIFLPVLLSYIGPSVNKAKSCATEERYKGTERERLLNF,mutated_sequence,1.0,1278.0,NP_000262.2.a2m,NP_000262.2.npy,ClinVar +NP_000265.1,NP_000265.1.csv,MFSKLAHLQRFAVLSRGVHSSVASATSVATKKTVQGPPTSDDIFEREYKYGAHNYHPLPVALERGKGIYLWDVEGRKYFDFLSSYSAVNQGHCHPKIVNALKSQVDKLTLTSRAFYNNVLGEYEEYITKLFNYHKVLPMNTGVEAGETACKLARKWGYTVKGIQKYKAKIVFAAGNFWGRTLSAISSSTDPTSYDGFGPFMPGFDIIPYNDLPALERALQDPNVAAFMVEPIQGEAGVVVPDPGYLMGVRELCTRHQVLFIADEIQTGLARTGRWLAVDYENVRPDIVLLGKALSGGLYPVSAVLCDDDIMLTIKPGEHGSTYGGNPLGCRVAIAALEVLEEENLAENADKLGIILRNELMKLPSDVVTAVRGKGLLNAIVIKETKDWDAWKVCLRLRDNGLLAKPTHGDIIRFAPPLVIKEDELRESIEIINKTILSF,mutated_sequence,1.0,439.0,NP_000265.1.a2m,NP_000265.1.npy,ClinVar +NP_000266.2,NP_000266.2.csv,MHLEGRDGRRYPGAPAVELLQTSVPSGLAELVAGKRRLPRGAGGADPSHSCPRGAAGQSSWAPAGQEFASFLTKGRSHSSLPQMSSSRSKDSCFTENTPLLRNSLQEKGSRCIPVYHPEFITAEESWEDSSADWERRYLLSREVSGLSASASSEKGDLLDSPHIRLRLSKLRRCVQWLKVMGLFAFVVLCSILFSLYPDQGKLWQLLALSPLENYSVNLSSHVDSTLLQVDLAGALVASGPSRPGREEHIVVELTQADALGSRWRRPQQVTHNWTVYLNPRRSEHSVMSRTFEVLTRETVSISIRASLQQTQAVPLLMAHQYLRGSVETQVTIATAILAGVYALIIFEIVHRTLAAMLGSLAALAALAVIGDRPSLTHVVEWIDFETLALLFGMMILVAIFSETGFFDYCAVKAYRLSRGRVWAMIIMLCLIAAVLSAFLDNVTTMLLFTPVTIRLCEVLNLDPRQVLIAEVIFTNIGGAATAIGDPPNVIIVSNQELRKMGLDFAGFTAHMFIGICLVLLVCFPLLRLLYWNRKLYNKEPSEIVELKHEIHVWRLTAQRISPASREETAVRRLLLGKVLALEHLLARRLHTFHRQISQEDKNWETNIQELQKKHRISDGILLAKCLTVLGFVIFMFFLNSFVPGIHLDLGWIAILGAIWLLILADIHDFEIILHRVEWATLLFFAALFVLMEALAHLHLIEYVGEQTALLIKMVPEEQRLIAAIVLVVWVSALASSLIDNIPFTATMIPVLLNLSHDPEVGLPAPPLMYALAFGACLGGNGTLIGASANVVCAGIAEQHGYGFSFMEFFRLGFPMMVVSCTVGMCYLLVAHVVVGWN,mutated_sequence,1.0,838.0,NP_000266.2.a2m,NP_000266.2.npy,ClinVar +NP_000267.2,NP_000267.2.csv,MEPPLPVGAQPLATVEGMEMKGPLREPCALTLAQRNGQYELIIQLHEKEQHVQDIIPINSHFRCVQEAEETLLIDIASNSGCKIRVQGDWIRERRFEIPDEEHCLKFLSAVLAAQKAQSQLLVPEQKDSSSWYQKLDTKDKPSVFSGLLGFEDNFSSMNLDKKINSQNQPTGIHREPPPPPFSVNKMLPREKEASNKEQPKVTNTMRKLFVPNTQSGQREGLIKHILAKREKEYVNIQTFRFFVGTWNVNGQSPDSGLEPWLNCDPNPPDIYCIGFQELDLSTEAFFYFESVKEQEWSMAVERGLHSKAKYKKVQLVRLVGMMLLIFARKDQCRYIRDIATETVGTGIMGKMGNKGGVAVRFVFHNTTFCIVNSHLAAHVEDFERRNQDYKDICARMSFVVPNQTLPQLNIMKHEVVIWLGDLNYRLCMPDANEVKSLINKKDLQRLLKFDQLNIQRTQKKAFVDFNEGEIKFIPTYKYDSKTDRWDSSGKCRVPAWCDRILWRGTNVNQLNYRSHMELKTSDHKPVSALFHIGVKVVDERRYRKVFEDSVRIMDRMENDFLPSLELSRREFVFENVKFRQLQKEKFQISNNGQVPCHFSFIPKLNDSQYCKPWLRAEPFEGYLEPNETVDISLDVYVSKDSVTILNSGEDKIEDILVLHLDRGKDYFLTISGNYLPSCFGTSLEALCRMKRPIREVPVTKLIDLEEDSFLEKEKSLLQMVPLDEGASERPLQVPKEIWLLVDHLFKYACHQEDLFQTPGMQEELQQIIDCLDTSIPETIPGSNHSVAEALLIFLEALPEPVICYELYQRCLDSAYDPRICRQVISQLPRCHRNVFRYLMAFLRELLKFSEYNSVNANMIATLFTSLLLRPPPNLMARQTPSDRQRAIQFLLGFLLGSEED,mutated_sequence,1.0,901.0,NP_000267.2.a2m,NP_000267.2.npy,ClinVar +NP_000268.1,NP_000268.1.csv,MSTAVLENPGLGRKLSDFGQETSYIEDNCNQNGAISLIFSLKEEVGALAKVLRLFEENDVNLTHIESRPSRLKKDEYEFFTHLDKRSLPALTNIIKILRHDIGATVHELSRDKKKDTVPWFPRTIQELDRFANQILSYGAELDADHPGFKDPVYRARRKQFADIAYNYRHGQPIPRVEYMEEEKKTWGTVFKTLKSLYKTHACYEYNHIFPLLEKYCGFHEDNIPQLEDVSQFLQTCTGFRLRPVAGLLSSRDFLGGLAFRVFHCTQYIRHGSKPMYTPEPDICHELLGHVPLFSDRSFAQFSQEIGLASLGAPDEYIEKLATIYWFTVEFGLCKQGDSIKAYGAGLLSSFGELQYCLSEKPKLLPLELEKTAIQNYTVTEFQPLYYVAESFNDAKEKVRNFAATIPRPFSVRYDPYTQRIEVLDNTQQLKILADSINSEIGILCSALQKIK,mutated_sequence,1.0,452.0,NP_000268.1.a2m,NP_000268.1.npy,ClinVar +NP_000269.3,NP_000269.3.csv,MDMHCKADPFSAMHPGHGGVNQLGGVFVNGRPLPDVVRQRIVELAHQGVRPCDISRQLRVSHGCVSKILGRYYETGSIKPGVIGGSKPKVATPKVVDKIAEYKRQNPTMFAWEIRDRLLAEGICDNDTVPSVSSINRIIRTKVQQPFHPTPDGAGTGVTAPGHTIVPSTASPPVSSASNDPVGSYSINGILGIPRSNGEKRKRDEDVSEGSVPNGDSQSGVDSLRKHLRADTFTQQQLEALDRVFERPSYPDVFQASEHIKSEQGNEYSLPALTPGLDEVKSSLSASTNPELGSNVSGTQTYPVVTGRDMASTTLPGYPPHVPPTGQGSYPTSTLAGMVPGSEFSGNPYSHPQYTAYNEAWRFSNPALLSSPYYYSAAPRGSAPAAAAAAYDRH,mutated_sequence,1.0,394.0,NP_000269.3.a2m,NP_000269.3.npy,ClinVar +NP_000273.2,NP_000273.2.csv,MAGFWVGTAPLVAAGRRGRWPPQQLMLSAALRTLKHVLYYSRQCLMVSRNLGSVGYDPNEKTFDKILVANRGEIACRVIRTCKKMGIKTVAIHSDVDASSVHVKMADEAVCVGPAPTSKSYLNMDAIMEAIKKTRAQAVHPGYGFLSENKEFARCLAAEDVVFIGPDTHAIQAMGDKIESKLLAKKAEVNTIPGFDGVVKDAEEAVRIAREIGYPVMIKASAGGGGKGMRIAWDDEETRDGFRLSSQEAASSFGDDRLLIEKFIDNPRHIEIQVLGDKHGNALWLNERECSIQRRNQKVVEEAPSIFLDAETRRAMGEQAVALARAVKYSSAGTVEFLVDSKKNFYFLEMNTRLQVEHPVTECITGLDLVQEMIRVAKGYPLRHKQADIRINGWAVECRVYAEDPYKSFGLPSIGRLSQYQEPLHLPGVRVDSGIQPGSDISIYYDPMISKLITYGSDRTEALKRMADALDNYVIRGVTHNIALLREVIINSRFVKGDISTKFLSDVYPDGFKGHMLTKSEKNQLLAIASSLFVAFQLRAQHFQENSRMPVIKPDIANWELSVKLHDKVHTVVASNNGSVFSVEVDGSKLNVTSTWNLASPLLSVSVDGTQRTVQCLSREAGGNMSIQFLGTVYKVNILTRLAAELNKFMLEKVTEDTSSVLRSPMPGVVVAVSVKPGDAVAEGQEICVIEAMKMQNSMTAGKTGTVKSVHCQAGDTVGEGDLLVELE,mutated_sequence,1.0,728.0,NP_000273.2.a2m,NP_000273.2.npy,ClinVar +NP_000275.1,NP_000275.1.csv,MRKMLAAVSRVLSGASQKPASRVLVASRNFANDATFEIKKCDLHRLEEGPPVTTVLTREDGLKYYRMMQTVRRMELKADQLYKQKIIRGFCHLCDGQEACCVGLEAGINPTDHLITAYRAHGFTFTRGLSVREILAELTGRKGGCAKGKGGSMHMYAKNFYGGNGIVGAQVPLGAGIALACKYNGKDEVCLTLYGDGAANQGQIFEAYNMAALWKLPCIFICENNRYGMGTSVERAAASTDYYKRGDFIPGLRVDGMDILCVREATRFAAAYCRSGKGPILMELQTYRYHGHSMSDPGVSYRTREEIQEVRSKSDPIMLLKDRMVNSNLASVEELKEIDVEVRKEIEDAAQFATADPEPPLEELGYHIYSSDPPFEVRGANQWIKFKSVS,mutated_sequence,1.0,390.0,NP_000275.1.a2m,NP_000275.1.npy,ClinVar +NP_000276.2,NP_000276.2.csv,MAAATGPSFWLGNETLKVPLALFALNRQRLCERLRKNPAVQAGSIVVLQGGEETQRYCTDTGVLFRQESFFHWAFGVTEPGCYGVIDVDTGKSTLFVPRLPASHATWMGKIHSKEHFKEKYAVDDVQYVDEIASVLTSQKPSVLLTLRGVNTDSGSVCREASFDGISKFEVNNTILHPEIVECRVFKTDMELEVLRYTNKISSEAHREVMKAVKVGMKEYELESLFEHYCYSRGGMRHSSYTCICGSGENSAVLHYGHAGAPNDRTIQNGDMCLFDMGGEYYCFASDITCSFPANGKFTADQKAVYEAVLRSSRAVMGAMKPGVWWPDMHRLADRIHLEELAHMGILSGSVDAMVQAHLGAVFMPHGLGHFLGIDVHDVGGYPEGVERIDEPGLRSLRTARHLQPGMVLTVEPGIYFIDHLLDEALADPARASFLNREVLQRFRGFGGVRIEEDVVVTDSGIELLTCVPRTVEEIEACMAGCDKAFTPFSGPK,mutated_sequence,1.0,493.0,NP_000276.2.a2m,NP_000276.2.npy,ClinVar +NP_000277.1,NP_000277.1.csv,MAEHGAHFTAASVADDQPSIFEVVAQDSLMTAVRPALQHVVKVLAESNPTHYGFLWRWFDEIFTLLDLLLQQHYLSRTSASFSENFYGLKRIVMGDTHKSQRLASAGLPKQQLWKSIMFLVLLPYLKVKLEKLVSSLREEDEYSIHPPSSRWKRFYRAFLAAYPFVNMAWEGWFLVQQLRYILGKAQHHSPLLRLAGVQLGRLTVQDIQALEHKPAKASMMQQPARSVSEKINSALKKAVGGVALSLSTGLSVGVFFLQFLDWWYSSENQETIKSLTALPTPPPPVHLDYNSDSPLLPKMKTVCPLCRKTRVNDTVLATSGYVFCYRCVFHYVRSHQACPITGYPTEVQHLIKLYSPEN,mutated_sequence,1.0,359.0,NP_000277.1.a2m,NP_000277.1.npy,ClinVar +NP_000282.1,NP_000282.1.csv,MSLSNKLTLDKLDVKGKRVVMRVDFNVPMKNNQITNNQRIKAAVPSIKFCLDNGAKSVVLMSHLGRPDGVPMPDKYSLEPVAVELKSLLGKDVLFLKDCVGPEVEKACANPAAGSVILLENLRFHVEEEGKGKDASGNKVKAEPAKIEAFRASLSKLGDVYVNDAFGTAHRAHSSMVGVNLPQKAGGFLMKKELNYFAKALESPERPFLAILGGAKVADKIQLINNMLDKVNEMIIGGGMAFTFLKVLNNMEIGTSLFDEEGAKIVKDLMSKAEKNGVKITLPVDFVTADKFDENAKTGQATVASGIPAGWMGLDCGPESSKKYAEAVTRAKQIVWNGPVGVFEWEAFARGTKALMDEVVKATSRGCITIIGGGDTATCCAKWNTEDKVSHVSTGGGASLELLEGKVLPGVDALSNI,mutated_sequence,1.0,417.0,NP_000282.1.a2m,NP_000282.1.npy,ClinVar +NP_000283.1,NP_000283.1.csv,MRSRSNSGVRLDGYARLVQQTILCYQNPVTGLLSASHEQKDAWVRDNIYSILAVWGLGMAYRKNADRDEDKAKAYELEQNVVKLMRGLLQCMMRQVAKVEKFKHTQSTKDSLHAKYNTATCGTVVGDDQWGHLQVDATSLFLLFLAQMTASGLRIIFTLDEVAFIQNLVFYIEAAYKVADYGMWERGDKTNQGIPELNASSVGMAKAALEAIDELDLFGAHGGRKSVIHVLPDEVEHCQSILFSMLPRASTSKEIDAGLLSIISFPAFAVEDVNLVNVTKNEIISKLQGRYGCCRFLRDGYKTPREDPNRLHYDPAELKLFENIECEWPVFWTYFIIDGVFSGDAVQVQEYREALEGILIRGKNGIRLVPELYAVPPNKVDEEYKNPHTVDRVPMGKVPHLWGQSLYILSSLLAEGFLAAGEIDPLNRRFSTSVKPDVVVQVTVLAENNHIKDLLRKHGVNVQSIADIHPIQVQPGRILSHIYAKLGRNKNMNLSGRPYRHIGVLGTSKLYVIRNQIFTFTPQFTDQHHFYLALDNEMIVEMLRIELAYLCTCWRMTGRPTLTFPISRTMLTNDGSDIHSAVLSTIRKLEDGYFGGARVKLGNLSEFLTTSFYTYLTFLDPDCDEKLFDNASEGTFSPDSDSDLVGYLEDTCNQESQDELDHYINHLLQSTSLRSYLPPLCKNTEDRHVFSAIHSTRDILSVMAKAKGLEVPFVPMTLPTKVLSAHRKSLNLVDSPQPLLEKVPESDFQWPRDDHGDVDCEKLVEQLKDCSNLQDQADILYILYVIKGPSWDTNLSGQHGVTVQNLLGELYGKAGLNQEWGLIRYISGLLRKKVEVLAEACTDLLSHQKQLTVGLPPEPREKIISAPLPPEELTKLIYEASGQDISIAVLTQEIVVYLAMYVRAQPSLFVEMLRLRIGLIIQVMATELARSLNCSGEEASESLMNLSPFDMKNLLHHILSGKEFGVERSVRPIHSSTSSPTISIHEVGHTGVTKTERSGINRLRSEMKQMTRRFSADEQFFSVGQAASSSAHSSKSARSSTPSSPTGTSSSDSGGHHIGWGERQGQWLRRRRLDGAINRVPVGFYQRVWKILQKCHGLSIDGYVLPSSTTREMTPHEIKFAVHVESVLNRVPQPEYRQLLVEAIMVLTLLSDTEMTSIGGIIHVDQIVQMASQLFLQDQVSIGAMDTLEKDQATGICHFFYDSAPSGAYGTMTYLTRAVASYLQELLPNSGCQMQ,mutated_sequence,1.0,1235.0,NP_000283.1.a2m,NP_000283.1.npy,ClinVar +NP_000286.3,NP_000286.3.csv,MPSSVSWGILLLAGLCCLVPVSLAEDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFAFSLYRQLAHQSNSTNIFFSPVSIATAFAMLSLGTKADTHDEILEGLNFNLTEIPEAQIHEGFQELLRTLNQPDSQLQLTTGNGLFLSEGLKLVDKFLEDVKKLYHSEAFTVNFGDTEEAKKQINDYVEKGTQGKIVDLVKELDRDTVFALVNYIFFKGKWERPFEVKDTEEEDFHVDQVTTVKVPMMKRLGMFNIQHCKKLSSWVLLMKYLGNATAIFFLPDEGKLQHLENELTHDIITKFLENEDRRSASLHLPKLSITGTYDLKSVLGQLGITKVFSNGADLSGVTEEAPLKLSKAVHKAVLTIDEKGTEAAGAMFLEAIPMSIPPEVKFNKPFVFLMIEQNTKSPLFMGKVVNPTQK,mutated_sequence,1.0,418.0,NP_000286.3.a2m,NP_000286.3.npy,ClinVar +NP_000288.1,NP_000288.1.csv,MVNSSRVQPQQPGDAKRPPAPRAPDPGRLMAGCAAVGASLAAPGGLCEQRGLEIEMQRIRQAAARDPPAGAAASPSPPLSSCSRQAWSRDNPGFEAEEEEEEVEGEEGGMVVEMDVEWRPGSRRSAASSAVSSVGARSRGLGGYHGAGHPSGRRRRREDQGPPCPSPVGGGDPLHRHLPLEGQPPRVAWAERLVRGLRGLWGTRLMEESSTNREKYLKSVLRELVTYLLFLIVLCILTYGMMSSNVYYYTRMMSQLFLDTPVSKTEKTNFKTLSSMEDFWKFTEGSLLDGLYWKMQPSNQTEADNRSFIFYENLLLGVPRIRQLRVRNGSCSIPQDLRDEIKECYDVYSVSSEDRAPFGPRNGTAWIYTSEKDLNGSSHWGIIATYSGAGYYLDLSRTREETAAQVASLKKNVWLDRGTRATFIDFSVYNANINLFCVVRLLVEFPATGGVIPSWQFQPLKLIRYVTTFDFFLAACEIIFCFFIFYYVVEEILEIRIHKLHYFRSFWNCLDVVIVVLSVVAIGINIYRTSNVEVLLQFLEDQNTFPNFEHLAYWQIQFNNIAAVTVFFVWIKLFKFINFNRTMSQLSTTMSRCAKDLFGFAIMFFIIFLAYAQLAYLVFGTQVDDFSTFQECIFTQFRIILGDINFAEIEEANRVLGPIYFTTFVFFMFFILLNMFLAIINDTYSEVKSDLAQQKAEMELSDLIRKGYHKALVKLKLKKNTVDDISESLRQGGGKLNFDELRQDLKGKGHTDAEIEAIFTKYDQDGDQELTEHEHQQMRDDLEKEREDLDLDHSSLPRPMSSRSFPRSLDDSEEDDDEDSGHSSRRRGSISSGVSYEEFQVLVRRVDRMEHSIGSIVSKIDAVIVKLEIMERAKLKRREVLGRLLDGVAEDERLGRDSEIHREQMERLVREELERWESDDAASQISHGLGTPVGLNGQPRPRSSRPSSSQSTEGMEGAGGNGSSNVHV,mutated_sequence,1.0,968.0,NP_000288.1.a2m,NP_000288.1.npy,ClinVar +NP_000289.1,NP_000289.1.csv,MSIQENISSLQLRSWVSKSQRDLAKSILIGAPGGPAGYLRRASVAQLTQELGTAFFQQQQLPAAMADTFLEHLCLLDIDSEPVAARSTSIIATIGPASRSVERLKEMIKAGMNIARLNFSHGSHEYHAESIANVREAVESFAGSPLSYRPVAIALDTKGPEIRTGILQGGPESEVELVKGSQVLVTVDPAFRTRGNANTVWVDYPNIVRVVPVGGRIYIDDGLISLVVQKIGPEGLVTQVENGGVLGSRKGVNLPGAQVDLPGLSEQDVRDLRFGVEHGVDIVFASFVRKASDVAAVRAALGPEGHGIKIISKIENHEGVKRFDEILEVSDGIMVARGDLGIEIPAEKVFLAQKMMIGRCNLAGKPVVCATQMLESMITKPRPTRAETSDVANAVLDGADCIMLSGETAKGNFPVEAVKMQHAIAREAEAAVYHRQLFEELRRAAPLSRDPTEVTAIGAVEAAFKCCAAAIIVLTTTGRSAQLLSRYRPRAAVIAVTRSAQAARQVHLCRGVFPLLYREPPEAIWADDVDRRVQFGIESGKLRGFLRVGDLVIVVTGWRPGSGYTNIMRVLSIS,mutated_sequence,1.0,574.0,NP_000289.1.a2m,NP_000289.1.npy,ClinVar +NP_000292.1,NP_000292.1.csv,MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFTCRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKNGITCQKWSSTSPHRPRFSPATHPSEGLEENYCRNPDNDPQGPWCYTTDPEKRYDYCDILECEEECMHCSGENYDGKISKTMSGLECQAWDSQSPHAHGYIPSKFPNKNLKKNYCRNPDRELRPWCFTTDPNKRWELCDIPRCTTPPPSSGPTYQCLKGTGENYRGNVAVTVSGHTCQHWSAQTPHTHNRTPENFPCKNLDENYCRNPDGKRAPWCHTTNSQVRWEYCKIPSCDSSPVSTEQLAPTAPPELTPVVQDCYHGDGQSYRGTSSTTTTGKKCQSWSSMTPHRHQKTPENYPNAGLTMNYCRNPDADKGPWCFTTDPSVRWEYCNLKKCSGTEASVVAPPPVVLLPDVETPSEEDCMFGNGKGYRGKRATTVTGTPCQDWAAQEPHRHSIFTPETNPRAGLEKNYCRNPDGDVGGPWCYTTNPRKLYDYCDVPQCAAPSFDCGKPQVEPKKCPGRVVGGCVAHPHSWPWQVSLRTRFGMHFCGGTLISPEWVLTAAHCLEKSPRPSSYKVILGAHQEVNLEPHVQEIEVSRLFLEPTRKDIALLKLSSPAVITDKVIPACLPSPNYVVADRTECFITGWGETQGTFGAGLLKEAQLPVIENKVCNRYEFLNGRVQSTELCAGHLAGGTDSCQGDSGGPLVCFEKDKYILQGVTSWGLGCARPNKPGVYVRVSRFVTWIEGVMRNN,mutated_sequence,1.0,810.0,NP_000292.1.a2m,NP_000292.1.npy,ClinVar +NP_000293.2,NP_000293.2.csv,MRPLLLLALLGWLLLAEAKGDAKPEDNLLVLTVATKETEGFRRFKRSAQFFNYKIQALGLGEDWNVEKGTSAGGGQKVRLLKKALEKHADKEDLVILFADSYDVLFASGPRELLKKFRQARSQVVFSAEELIYPDRRLETKYPVVSDGKRFLGSGGFIGYAPNLSKLVAEWEGQDSDSDQLFYTKIFLDPEKREQINITLDHRCRIFQNLDGALDEVVLKFEMGHVRARNLAYDTLPVLIHGNGPTKLQLNYLGNYIPRFWTFETGCTVCDEGLRSLKGIGDEALPTVLVGVFIEQPTPFVSLFFQRLLRLHYPQKHMRLFIHNHEQHHKAQVEEFLAQHGSEYQSVKLVGPEVRMANADARNMGADLCRQDRSCTYYFSVDADVALTEPNSLRLLIQQNKNVIAPLMTRHGRLWSNFWGALSADGYYARSEDYVDIVQGRRVGVWNVPYISNIYLIKGSALRGELQSSDLFHHSKLDPDMAFCANIRQQDVFMFLTNRHTLGHLLSLDSYRTTHLHNDLWEVFSNPEDWKEKYIHQNYTKALAGKLVETPCPDVYWFPIFTEVACDELVEEMEHFGQWSLGNNKDNRIQGGYENVPTIDIHMNQIGFEREWHKFLLEYIAPMTEKLYPGYYTRAQFDLAFVVRYKPDEQPSLMPHHDASTFTINIALNRVGVDYEGGGCRFLRYNCSIRAPRKGWTLMHPGRLTHYHEGLPTTRGTRYIAVSFVDP,mutated_sequence,1.0,727.0,NP_000293.2.a2m,NP_000293.2.npy,ClinVar +NP_000301.1,NP_000301.1.csv,MASPGCLWLLAVALLPWTCASRALQHLDPPAPLPLVIWHGMGDSCCNPLSMGAIKKMVEKKIPGIYVLSLEIGKTLMEDVENSFFLNVNSQVTTVCQALAKDPKLQQGYNAMGFSQGGQFLRAVAQRCPSPPMINLISVGGQHQGVFGLPRCPGESSHICDFIRKTLNAGAYSKVVQERLVQAEYWHDPIKEDVYRNHSIFLADINQERGINESYKKNLMALKKFVMVKFLNDSIVDPVDSEWFGFYRSGQAKETIPLQETSLYTQDRLGLKEMDNAGQLVFLATEGDHLQLSEEWFYAHIIPFLG,mutated_sequence,1.0,306.0,NP_000301.1.a2m,NP_000301.1.npy,ClinVar +NP_000305.3,NP_000305.3.csv,MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV,mutated_sequence,1.0,403.0,NP_000305.3.a2m,NP_000305.3.npy,ClinVar +NP_000308.1,NP_000308.1.csv,MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE,mutated_sequence,1.0,145.0,NP_000308.1.a2m,NP_000308.1.npy,ClinVar +NP_000311.2,NP_000311.2.csv,MAAAAAAGEARRVLVYGGRGALGSRCVQAFRARNWWVASVDVVENEEASASIIVKMTDSFTEQADQVTAEVGKLLGEEKVDAILCVAGGWAGGNAKSKSLFKNCDLMWKQSIWTSTISSHLATKHLKEGGLLTLAGAKAALDGTPGMIGYGMAKGAVHQLCQSLAGKNSGMPPGAAAIAVLPVTLDTPMNRKSMPEADFSSWTPLEFLVETFHDWITGKNRPSSGSLIQVVTTEGRTELTPAYF,mutated_sequence,1.0,244.0,NP_000311.2.a2m,NP_000311.2.npy,ClinVar +NP_000312.2,NP_000312.2.csv,MPPKTPRKTAATAAAAAAEPPAPPPPPPPEEDPEQDSGPEDLPLVRLEFEETEEPDFTALCQKLKIPDHVRERAWLTWEKVSSVDGVLGGYIQKKKELWGICIFIAAVDLDEMSFTFTELQKNIEISVHKFFNLLKEIDTSTKVDNAMSRLLKKYDVLFALFSKLERTCELIYLTQPSSSISTEINSALVLKVSWITFLLAKGEVLQMEDDLVISFQLMLCVLDYFIKLSPPMLLKEPYKTAVIPINGSPRTPRRGQNRSARIAKQLENDTRIIEVLCKEHECNIDEVKNVYFKNFIPFMNSLGLVTSNGLPEVENLSKRYEEIYLKNKDLDARLFLDHDKTLQTDSIDSFETQRTPRKSNLDEEVNVIPPHTPVRTVMNTIQQLMMILNSASDQPSENLISYFNNCTVNPKESILKRVKDIGYIFKEKFAKAVGQGCVEIGSQRYKLGVRLYYRVMESMLKSEEERLSIQNFSKLLNDNIFHMSLLACALEVVMATYSRSTSQNLDSGTDLSFPWILNVLNLKAFDFYKVIESFIKAEGNLTREMIKHLERCEHRIMESLAWLSDSPLFDLIKQSKDREGPTDHLESACPLNLPLQNNHTAADMYLSPVRSPKKKGSTTRVNSTANAETQATSAFQTQKPLKSTSLSLFYKKVYRLAYLRLNTLCERLLSEHPELEHIIWTLFQHTLQNEYELMRDRHLDQIMMCSMYGICKVKNIDLKFKIIVTAYKDLPHAVQETFKRVLIKEEEYDSIIVFYNSVFMQRLKTNILQYASTRPPTLSPIPHIPRSPYKFPSSPLRIPGGNIYISPLKSPYKISEGLPTPTKMTPRSRILVSIGESFGTSEKFQKINQMVCNSDRVLKRSAEGSNPPKPLKKLRFDIEGSDEADGSKHLPGESKFQQKLAEMTSTRTRMQKQKMNDSMDTSNKEEK,mutated_sequence,1.0,928.0,NP_000312.2.a2m,NP_000312.2.npy,ClinVar +NP_000313.2,NP_000313.2.csv,MALLKVKFDQKKRVKLAQGLWLMNWFSVLAGIIIFSLGLFLKIELRKRSDVMNNSESHFVPNSLIGMGVLSCVFNSLAGKICYDALDPAKYARWKPWLKPYLAICVLFNIILFLVALCCFLLRGSLENTLGQGLKNGMKYYRDTDTPGRCFMKKTIDMLQIEFKCCGNNGFRDWFEIQWISNRYLDFSSKEVKDRIKSNVDGRYLVDGVPFSCCNPSSPRPCIQYQITNNSAHYSYDHQTEELNLWVRGCRAALLSYYSSLMNSMGVVTLLIWLFEVTITIGLRYLQTSLDGVSNPEESESESQGWLLERSVPETWKAFLESVKKLGKGNQVEAEGADAGQAPEAG,mutated_sequence,1.0,346.0,NP_000313.2.a2m,NP_000313.2.npy,ClinVar +NP_000320.1,NP_000320.1.csv,MSIQVEHPAGGYKKLFETVEELSSPLTAHVTGRIPLWLTGSLLRCGPGLFEVGSEPFYHLFDGQALLHKFDFKEGHVTYHRRFIRTDAYVRAMTEKRIVITEFGTCAFPDPCKNIFSRFFSYFRGVEVTDNALVNVYPVGEDYYACTETNFITKINPETLETIKQVDLCNYVSVNGATAHPHIENDGTVYNIGNCFGKNFSIAYNIVKIPPLQADKEDPISKSEIVVQFPCSDRFKPSYVHSFGLTPNYIVFVETPVKINLFKFLSSWSLWGANYMDCFESNETMGVWLHIADKKRKKYLNNKYRTSPFNLFHHINTYEDNGFLIVDLCCWKGFEFVYNYLYLANLRENWEEVKKNARKAPQPEVRRYVLPLNIDKADTGKNLVTLPNTTATAILCSDETIWLEPEVLFSGPRQAFEFPQINYQKYCGKPYTYAYGLGLNHFVPDRLCKLNVKTKETWVWQEPDSYPSEPIFVSHPDALEEDDGVVLSVVVSPGAGQKPAYLLILNAKDLSEVARAEVEINIPVTFHGLFKKS,mutated_sequence,1.0,533.0,NP_000320.1.a2m,NP_000320.1.npy,ClinVar +NP_000325.4,NP_000325.4.csv,MARPSLCTLVPLGPECLRPFTRESLAAIEQRAVEEEARLQRNKQMEIEEPERKPRSDLEAGKNLPMIYGDPPPEVIGIPLEDLDPYYSNKKTFIVLNKGKAIFRFSATPALYLLSPFSVVRRGAIKVLIHALFSMFIMITILTNCVFMTMSDPPPWSKNVEYTFTGIYTFESLIKILARGFCVDDFTFLRDPWNWLDFSVIMMAYLTEFVDLGNISALRTFRVLRALKTITVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALVGLQLFMGNLRQKCVRWPPPFNDTNTTWYSNDTWYGNDTWYGNEMWYGNDSWYANDTWNSHASWATNDTFDWDAYISDEGNFYFLEGSNDALLCGNSSDAGHCPEGYECIKTGRNPNYGYTSYDTFSWAFLALFRLMTQDYWENLFQLTLRAAGKTYMIFFVVIIFLGSFYLINLILAVVAMAYAEQNEATLAEDKEKEEEFQQMLEKFKKHQEELEKAKAAQALEGGEADGDPAHGKDCNGSLDTSQGEKGAPRQSSSGDSGISDAMEELEEAHQKCPPWWYKCAHKVLIWNCCAPWLKFKNIIHLIVMDPFVDLGITICIVLNTLFMAMEHYPMTEHFDNVLTVGNLVFTGIFTAEMVLKLIAMDPYEYFQQGWNIFDSIIVTLSLVELGLANVQGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKIALDCNLPRWHMHDFFHSFLIVFRILCGEWIETMWDCMEVAGQAMCLTVFLMVMVIGNLVVLNLFLALLLSSFSADSLAASDEDGEMNNLQIAIGRIKLGIGFAKAFLLGLLHGKILSPKDIMLSLGEADGAGEAGEAGETAPEDEKKEPPEEDLKKDNHILNHMGLADGPPSSLELDHLNFINNPYLTIQVPIASEESDLEMPTEEETDTFSEPEDSKKPPQPLYDGNSSVCSTADYKPPEEDPEEQAEENPEGEQPEECFTEACVQRWPCLYVDISQGRGKKWWTLRRACFKIVEHNWFETFIVFMILLSSGALAFEDIYIEQRRVIRTILEYADKVFTYIFIMEMLLKWVAYGFKVYFTNAWCWLDFLIVDVSIISLVANWLGYSELGPIKSLRTLRALRPLRALSRFEGMRVVVNALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYYCINTTTSERFDISEVNNKSECESLMHTGQVRWLNVKVNYDNVGLGYLSLLQVATFKGWMDIMYAAVDSREKEEQPQYEVNLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGKDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPQNKIQGMVYDLVTKQAFDITIMILICLNMVTMMVETDNQSQLKVDILYNINMIFIIIFTGECVLKMLALRQYYFTVGWNIFDFVVVILSIVGLALSDLIQKYFVSPTLFRVIRLARIGRVLRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMSNFAYVKKESGIDDMFNFETFGNSIICLFEITTSAGWDGLLNPILNSGPPDCDPNLENPGTSVKGDCGNPSIGICFFCSYIIISFLIVVNMYIAIILENFNVATEESSEPLGEDDFEMFYETWEKFDPDATQFIAYSRLSDFVDTLQEPLRIAKPNKIKLITLDLPMVPGDKIHCLDILFALTKEVLGDSGEMDALKQTMEEKFMAANPSKVSYEPITTTLKRKHEEVCAIKIQRAYRRHLLQRSMKQASYMYRHSHDGSGDDAPEKEGLLANTMSKMYGHENGNSSSPSPEEKGEAGDAGPTMGLMPISPSDTAWPPAPPPGQTVRPGVKESLV,mutated_sequence,1.0,1836.0,NP_000325.4.a2m,NP_000325.4.npy,ClinVar +NP_000326.2,NP_000326.2.csv,MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV,mutated_sequence,1.0,2015.0,NP_000326.2.a2m,NP_000326.2.npy,ClinVar +NP_000333.1,NP_000333.1.csv,MEELQDDYEDMMEENLEQEEYEDPDIPESQMEEPAAHDTEATATDYHTTSHPGTHKVYVELQELVMDEKNQELRWMEAARWVQLEENLGENGAWGRPHLSHLTFWSLLELRRVFTKGTVLLDLQETSLAGVANQLLDRFIFEDQIRPQDREELLRALLLKHSHAGELEALGGVKPAVLTRSGDPSQPLLPQHSSLETQLFCEQGDGGTEGHSPSGILEKIPPDSEATLVLVGRADFLEQPVLGFVRLQEAAELEAVELPVPIRFLFVLLGPEAPHIDYTQLGRAAATLMSERVFRIDAYMAQSRGELLHSLEGFLDCSLVLPPTDAPSEQALLSLVPVQRELLRRRYQSSPAKPDSSFYKGLDLNGGPDDPLQQTGQLFGGLVRDIRRRYPYYLSDITDAFSPQVLAAVIFIYFAALSPAITFGGLLGEKTRNQMGVSELLISTAVQGILFALLGAQPLLVVGFSGPLLVFEEAFFSFCETNGLEYIVGRVWIGFWLILLVVLVVAFEGSFLVRFISRYTQEIFSFLISLIFIYETFSKLIKIFQDHPLQKTYNYNVLMVPKPQGPLPNTALLSLVLMAGTFFFAMMLRKFKNSSYFPGKLRRVIGDFGVPISILIMVLVDFFIQDTYTQKLSVPDGFKVSNSSARGWVIHPLGLRSEFPIWMMFASALPALLVFILIFLESQITTLIVSKPERKMVKGSGFHLDLLLVVGMGGVAALFGMPWLSATTVRSVTHANALTVMGKASTPGAAAQIQEVKEQRISGLLVAVLVGLSILMEPILSRIPLAVLFGIFLYMGVTSLSGIQLFDRILLLFKPPKYHPDVPYVKRVKTWRMHLFTGIQIICLAVLWVVKSTPASLALPFVLILTVPLRRVLLPLIFRNVELQCLDADDAKATFDEEEGRDEYDEVAMPV,mutated_sequence,1.0,911.0,NP_000333.1.a2m,NP_000333.1.npy,ClinVar +NP_000339.2,NP_000339.2.csv,MQVQCQQSPVLAGSATLVALGALALYVAKPSGYGKHTESLKPAATRLPARAAWFLQELPSFAVPAGILARQPLSLFGPPGTVLLGLFCLHYFHRTFVYSLLNRGRPYPAILILRGTAFCTGNGVLQGYYLIYCAEYPDGWYTDIRFSLGVFLFILGMGINIHSDYILRQLRKPGEISYRIPQGGLFTYVSGANFLGEIIEWIGYALATWSLPALAFAFFSLCFLGLRAFHHHRFYLKMFEDYPKSRKALIPFIF,mutated_sequence,1.0,254.0,NP_000339.2.a2m,NP_000339.2.npy,ClinVar +NP_000341.2,NP_000341.2.csv,MGFVRQIQLLLWKNWTLRKRQKIRFVVELVWPLSLFLVLIWLRNANPLYSHHECHFPNKAMPSAGMLPWLQGIFCNVNNPCFQSPTPGESPGIVSNYNNSILARVYRDFQELLMNAPESQHLGRIWTELHILSQFMDTLRTHPERIAGRGIRIRDILKDEETLTLFLIKNIGLSDSVVYLLINSQVRPEQFAHGVPDLALKDIACSEALLERFIIFSQRRGAKTVRYALCSLSQGTLQWIEDTLYANVDFFKLFRVLPTLLDSRSQGINLRSWGGILSDMSPRIQEFIHRPSMQDLLWVTRPLMQNGGPETFTKLMGILSDLLCGYPEGGGSRVLSFNWYEDNNYKAFLGIDSTRKDPIYSYDRRTTSFCNALIQSLESNPLTKIAWRAAKPLLMGKILYTPDSPAARRILKNANSTFEELEHVRKLVKAWEEVGPQIWYFFDNSTQMNMIRDTLGNPTVKDFLNRQLGEEGITAEAILNFLYKGPRESQADDMANFDWRDIFNITDRTLRLVNQYLECLVLDKFESYNDETQLTQRALSLLEENMFWAGVVFPDMYPWTSSLPPHVKYKIRMDIDVVEKTNKIKDRYWDSGPRADPVEDFRYIWGGFAYLQDMVEQGITRSQVQAEAPVGIYLQQMPYPCFVDDSFMIILNRCFPIFMVLAWIYSVSMTVKSIVLEKELRLKETLKNQGVSNAVIWCTWFLDSFSIMSMSIFLLTIFIMHGRILHYSDPFILFLFLLAFSTATIMLCFLLSTFFSKASLAAACSGVIYFTLYLPHILCFAWQDRMTAELKKAVSLLSPVAFGFGTEYLVRFEEQGLGLQWSNIGNSPTEGDEFSFLLSMQMMLLDAAVYGLLAWYLDQVFPGDYGTPLPWYFLLQESYWLGGEGCSTREERALEKTEPLTEETEDPEHPEGIHDSFFEREHPGWVPGVCVKNLVKIFEPCGRPAVDRLNITFYENQITAFLGHNGAGKTTTLSILTGLLPPTSGTVLVGGRDIETSLDAVRQSLGMCPQHNILFHHLTVAEHMLFYAQLKGKSQEEAQLEMEAMLEDTGLHHKRNEEAQDLSGGMQRKLSVAIAFVGDAKVVILDEPTSGVDPYSRRSIWDLLLKYRSGRTIIMSTHHMDEADLLGDRIAIIAQGRLYCSGTPLFLKNCFGTGLYLTLVRKMKNIQSQRKGSEGTCSCSSKGFSTTCPAHVDDLTPEQVLDGDVNELMDVVLHHVPEAKLVECIGQELIFLLPNKNFKHRAYASLFRELEETLADLGLSSFGISDTPLEEIFLKVTEDSDSGPLFAGGAQQKRENVNPRHPCLGPREKAGQTPQDSNVCSPGAPAAHPEGQPPPEPECPGPQLNTGTQLVLQHVQALLVKRFQHTIRSHKDFLAQIVLPATFVFLALMLSIVIPPFGEYPALTLHPWIYGQQYTFFSMDEPGSEQFTVLADVLLNKPGFGNRCLKEGWLPEYPCGNSTPWKTPSVSPNITQLFQKQKWTQVNPSPSCRCSTREKLTMLPECPEGAGGLPPPQRTQRSTEILQDLTDRNISDFLVKTYPALIRSSLKSKFWVNEQRYGGISIGGKLPVVPITGEALVGFLSDLGRIMNVSGGPITREASKEIPDFLKHLETEDNIKVWFNNKGWHALVSFLNVAHNAILRASLPKDRSPEEYGITVISQPLNLTKEQLSEITVLTTSVDAVVAICVIFSMSFVPASFVLYLIQERVNKSKHLQFISGVSPTTYWVTNFLWDIMNYSVSAGLVVGIFIGFQKKAYTSPENLPALVALLLLYGWAVIPMMYPASFLFDVPSTAYVALSCANLFIGINSSAITFILELFENNRTLLRFNAVLRKLLIVFPHFCLGRGLIDLALSQAVTDVYARFGEEHSANPFHWDLIGKNLFAMVVEGVVYFLLTLLVQRHFFLSQWIAEPTKEPIVDEDDDVAEERQRIITGGNKTDILRLHELTKIYPGTSSPAVDRLCVGVRPGECFGLLGVNGAGKTTTFKMLTGDTTVTSGDATVAGKSILTNISEVHQNMGYCPQFDAIDELLTGREHLYLYARLRGVPAEEIEKVANWSIKSLGLTVYADCLAGTYSGGNKRKLSTAIALIGCPPLVLLDEPTTGMDPQARRMLWNVIVSIIREGRAVVLTSHSMEECEALCTRLAIMVKGAFRCMGTIQHLKSKFGDGYIVTMKIKSPKDDLLPDLNPVEQFFQGNFPGSVQRERHYNMLQFQVSSSSLARIFQLLLSHKDSLLIEEYSVTQTTLDQVFVNFAKQQTESHDLPLHPRAAGASRQAQD,mutated_sequence,1.0,2273.0,NP_000341.2.a2m,NP_000341.2.npy,ClinVar +NP_000349.1,NP_000349.1.csv,MALFVRLLALALALALGPAATLAGPAKSPYQLVLQHSRLRGRQHGPNVCAVQKVIGTNRKYFTNCKQWYQRKICGKSTVISYECCPGYEKVPGEKGCPAALPLSNLYETLGVVGSTTTQLYTDRTEKLRPEMEGPGSFTIFAPSNEAWASLPAEVLDSLVSNVNIELLNALRYHMVGRRVLTDELKHGMTLTSMYQNSNIQIHHYPNGIVTVNCARLLKADHHATNGVVHLIDKVISTITNNIQQIIEIEDTFETLRAAVAASGLNTMLEGNGQYTLLAPTNEAFEKIPSETLNRILGDPEALRDLLNNHILKSAMCAEAIVAGLSVETLEGTTLEVGCSGDMLTINGKAIISNKDILATNGVIHYIDELLIPDSAKTLFELAAESDVSTAIDLFRQAGLGNHLSGSERLTLLAPLNSVFKDGTPPIDAHTRNLLRNHIIKDQLASKYLYHGQTLETLGGKKLRVFVYRNSLCIENSCIAAHDKRGRYGTLFTMDRVLTPPMGTVMDVLKGDNRFSMLVAAIQSAGLTETLNREGVYTVFAPTNEAFRALPPRERSRLLGDAKELANILKYHIGDEILVSGGIGALVRLKSLQGDKLEVSLKNNVVSVNKEPVAEPDIMATNGVVHVITNVLQPPANRPQERGDELADSALEIFKQASAFSRASQRSVRLAPVYQKLLERMKH,mutated_sequence,1.0,683.0,NP_000349.1.a2m,NP_000349.1.npy,ClinVar +NP_000350.1,NP_000350.1.csv,MMDGPRSDVGRWGGNPLQPPTTPSPEPEPEPDGRSRRGGGRSFWARCCGCCSCRNAADDDWGPEPSDSRGRGSSSGTRRPGSRGSDSRRPVSRGSGVNAAGDGTIREGMLVVNGVDLLSSRSDQNRREHHTDEYEYDELIVRRGQPFHMLLLLSRTYESSDRITLELLIGNNPEVGKGTHVIIPVGKGGSGGWKAQVVKASGQNLNLRVHTSPNAIIGKFQFTVRTQSDAGEFQLPFDPRNEIYILFNPWCPEDIVYVDHEDWRQEYVLNESGRIYYGTEAQIGERTWNYGQFDHGVLDACLYILDRRGMPYGGRGDPVNVSRVISAMVNSLDDNGVLIGNWSGDYSRGTNPSAWVGSVEILLSYLRTGYSVPYGQCWVFAGVTTTVLRCLGLATRTVTNFNSAHDTDTSLTMDIYFDENMKPLEHLNHDSVWNFHVWNDCWMKRPDLPSGFDGWQVVDATPQETSSGIFCCGPCSVESIKNGLVYMKYDTPFIFAEVNSDKVYWQRQDDGSFKIVYVEEKAIGTLIVTKAISSNMREDITYLYKHPEGSDAERKAVETAAAHGSKPNVYANRGSAEDVAMQVEAQDAVMGQDLMVSVMLINHSSSRRTVKLHLYLSVTFYTGVSGTIFKETKKEVELAPGASDRVTMPVAYKEYRPHLVDQGAMLLNVSGHVKESGQVLAKQHTFRLRTPDLSLTLLGAAVVGQECEVQIVFKNPLPVTLTNVVFRLEGSGLQRPKILNVGDIGGNETVTLRQSFVPVRPGPRQLIASLDSPQLSQVHGVIQVDVAPAPGDGGFFSDAGGDSHLGETIPMASRGGA,mutated_sequence,1.0,817.0,NP_000350.1.a2m,NP_000350.1.npy,ClinVar +NP_000351.2,NP_000351.2.csv,MPTPDATTPQAKGFRRAVSELDAKQAEAIMSPRFIGRRQSLIEDARKEREAAVAAAAAAVPSEPGDPLEAVAFEEKEGKAVLNLLFSPRATKPSALSRAVKVFETFEAKIHHLETRPAQRPRAGGPHLEYFVRLEVRRGDLAALLSGVRQVSEDVRSPAGPKVPWFPRKVSELDKCHHLVTKFDPDLDLDHPGFSDQVYRQRRKLIAEIAFQYRHGDPIPRVEYTAEEIATWKEVYTTLKGLYATHACGEHLEAFALLERFSGYREDNIPQLEDVSRFLKERTGFQLRPVAGLLSARDFLASLAFRVFQCTQYIRHASSPMHSPEPDCCHELLGHVPMLADRTFAQFSQDIGLASLGASDEEIEKLSTLYWFTVEFGLCKQNGEVKAYGAGLLSSYGELLHCLSEEPEIRAFDPEAAAVQPYQDQTYQSVYFVSESFSDAKDKLRSYASRIQRPFSVKFDPYTLAIDVLDSPQAVRRSLEGVQDELDTLAHALSAIG,mutated_sequence,1.0,497.0,NP_000351.2.a2m,NP_000351.2.npy,ClinVar +NP_000354.4,NP_000354.4.csv,MADGSSDAAREPRPAPAPIRRRSSNYRAYATEPHAKKKSKISASRKLQLKTLLLQIAKQELEREAEERRGEKGRALSTRCQPLELAGLGFAELQDLCRQLHARVDKVDEERYDIEAKVTKNITEIADLTQKIFDLRGKFKRPTLRRVRISADAMMQALLGARAKESLDLRAHLKQVKKEDTEKENREVGDWRKNIDALSGMEGRKKKFES,mutated_sequence,1.0,210.0,NP_000354.4.a2m,NP_000354.4.npy,ClinVar +NP_000359.1,NP_000359.1.csv,MAQQANVGELLAMLDSPMLGVRDDVTAVFKENLNSDRGPMLVNTLVDYYLETSSQPALHILTTLQEPHDKHLLDRINEYVGKAATRLSILSLLGHVIRLQPSWKHKLSQAPLLPSLLKCLKMDTDVVVLTTGVLVLITMLPMIPQSGKQHLLDFFDIFGRLSSWCLKKPGHVAEVYLVHLHASVYALFHRLYGMYPCNFVSFLRSHYSMKENLETFEEVVKPMMEHVRIHPELVTGSKDHELDPRRWKRLETHDVVIECAKISLDPTEASYEDGYSVSHQISARFPHRSADVTTSPYADTQNSYGCATSTPYSTSRLMLLNMPGQLPQTLSSPSTRLITEPPQATLWSPSMVCGMTTPPTSPGNVPPDLSHPYSKVFGTTAGGKGTPLGTPATSPPPAPLCHSDDYVHISLPQATVTPPRKEERMDSARPCLHRQHHLLNDRGSEEPPGSKGSVTLSDLPGFLGDLASEEDSIEKDKEEAAISRELSEITTAEAEPVVPRGGFDSPFYRDSLPGSQRKTHSAASSSQGASVNPEPLHSSLDKLGPDTPKQAFTPIDLPCGSADESPAGDRECQTSLETSIFTPSPCKIPPPTRVGFGSGQPPPYDHLFEVALPKTAHHFVIRKTEELLKKAKGNTEEDGVPSTSPMEVLDRLIQQGADAHSKELNKLPLPSKSVDWTHFGGSPPSDEIRTLRDQLLLLHNQLLYERFKRQQHALRNRRLLRKVIKAAALEEHNAAMKDQLKLQEKDIQMWKVSLQKEQARYNQLQEQRDTMVTKLHSQIRQLQHDREEFYNQSQELQTKLEDCRNMIAELRIELKKANNKVCHTELLLSQVSQKLSNSESVQQQMEFLNRQLLVLGEVNELYLEQLQNKHSDTTKEVEMMKAAYRKELEKNRSHVLQQTQRLDTSQKRILELESHLAKKDHLLLEQKKYLEDVKLQARGQLQAAESRYEAQKRITQVFELEILDLYGRLEKDGLLKKLEEEKAEAAEAAEERLDCCNDGCSDSMVGHNEEASGHNGETKTPRPSSARGSSGSRGGGGSSSSSSELSTPEKPPHQRAGPFSSRWETTMGEASASIPTTVGSLPSSKSFLGMKARELFRNKSESQCDEDGMTSSLSESLKTELGKDLGVEAKIPLNLDGPHPSPPTPDSVGQLHIMDYNETHHEHS,mutated_sequence,1.0,1164.0,NP_000359.1.a2m,NP_000359.1.npy,ClinVar +NP_000362.1,NP_000362.1.csv,MASHRLLLLCLAGLVFVSEAGPTGTGESKCPLMVKVLDAVRGSPAINVAVHVFRKAADDTWEPFASGKTSESGELHGLTTEEEFVEGIYKVEIDTKSYWKALGISPFHEHAEVVFTANDSGPRRYTIAALLSPYSYSTTAVVTNPKE,mutated_sequence,1.0,147.0,NP_000362.1.a2m,NP_000362.1.npy,ClinVar +NP_000363.1,NP_000363.1.csv,MLLAVLYCLLWSFQTSAGHFPRACVSSKNLMEKECCPPWSGDRSPCGQLSGRGSCQNILLSNAPLGPQFPFTGVDDRESWPSVFYNRTCQCSGNFMGFNCGNCKFGFWGPNCTERRLLVRRNIFDLSAPEKDKFFAYLTLAKHTISSDYVIPIGTYGQMKNGSTPMFNDINIYDLFVWMHYYVSMDALLGGSEIWRDIDFAHEAPAFLPWHRLFLLRWEQEIQKLTGDENFTIPYWDWRDAEKCDICTDEYMGGQHPTNPNLLSPASFFSSWQIVCSRLEEYNSHQSLCNGTPEGPLRRNPGNHDKSRTPRLPSSADVEFCLSLTQYESGSMDKAANFSFRNTLEGFASPLTGIADASQSSMHNALHIYMNGTMSQVQGSANDPIFLLHHAFVDSIFEQWLRRHRPLQEVYPEANAPIGHNRESYMVPFIPLYRNGDFFISSKDLGYDYSYLQDSDPDSFQDYIKSYLEQASRIWSWLLGAAMVGAVLTALLAGLVSLLCRHKRKQLPEEKQPLLMEKEDYHSLYQSHL,mutated_sequence,1.0,529.0,NP_000363.1.a2m,NP_000363.1.npy,ClinVar +NP_000371.1,NP_000371.1.csv,MAAADGALPEAAALEQPAELPASVRASIERKRQRALMLRQARLAARPYSATAAAATGGMANVKAAPKIIDTGGGFILEEEEEEEQKIGKVVHQPGPVMEFDYVICEECGKEFMDSYLMNHFDLPTCDNCRDADDKHKLITKTEAKQEYLLKDCDLEKREPPLKFIVKKNPHHSQWGDMKLYLKLQIVKRSLEVWGSQEALEEAKEVRQENREKMKQKKFDKKVKELRRAVRSSVWKRETIVHQHEYGPEENLEDDMYRKTCTMCGHELTYEKM,mutated_sequence,1.0,273.0,NP_000371.1.a2m,NP_000371.1.npy,ClinVar +NP_000372.1,NP_000372.1.csv,METLESELTCPICLELFEDPLLLPCAHSLCFNCAHRILVSHCATNESVESITAFQCPTCRHVITLSQRGLDGLKRNVTLQNIIDRFQKASVSGPNSPSETRRERAFDANTMTSAEKVLCQFCDQDPAQDAVKTCVTCEVSYCDECLKATHPNKKPFTGHRLIEPIPDSHIRGLMCLEHEDEKVNMYCVTDDQLICALCKLVGRHRDHQVAALSERYDKLKQNLESNLTNLIKRNTELETLLAKLIQTCQHVEVNASRQEAKLTEECDLLIEIIQQRRQIIGTKIKEGKVMRLRKLAQQIANCKQCIERSASLISQAEHSLKENDHARFLQTAKNITERVSMATASSQVLIPEINLNDTFDTFALDFSREKKLLECLDYLTAPNPPTIREELCTASYDTITVHWTSDDEFSVVSYELQYTIFTGQANVVSLCNSADSWMIVPNIKQNHYTVHGLQSGTKYIFMVKAINQAGSRSSEPGKLKTNSQPFKLDPKSAHRKLKVSHDNLTVERDESSSKKSHTPERFTSQGSYGVAGNVFIDSGRHYWEVVISGSTWYAIGLAYKSAPKHEWIGKNSASWALCRCNNNWVVRHNSKEIPIEPAPHLRRVGILLDYDNGSIAFYDALNSIHLYTFDVAFAQPVCPTFTVWNKCLTIITGLPIPDHLDCTEQLP,mutated_sequence,1.0,667.0,NP_000372.1.a2m,NP_000372.1.npy,ClinVar +NP_000373.1,NP_000373.1.csv,MELEVRRVRQAFLSGRSRPLRFRLQQLEALRRMVQEREKDILTAIAADLCKSEFNVYSQEVITVLGEIDFMLENLPEWVTAKPVKKNVLTMLDEAYIQPQPLGVVLIIGAWNYPFVLTIQPLIGAIAAGNAVIIKPSELSENTAKILAKLLPQYLDQDLYIVINGGVEETTELLKQRFDHIFYTGNTAVGKIVMEAAAKHLTPVTLELGGKSPCYIDKDCDLDIVCRRITWGKYMNCGQTCIAPDYILCEASLQNQIVWKIKETVKEFYGENIKESPDYERIINLRHFKRILSLLEGQKIAFGGETDEATRYIAPTVLTDVDPKTKVMQEEIFGPILPIVPVKNVDEAINFINEREKPLALYVFSHNHKLIKRMIDETSSGGVTGNDVIMHFTLNSFPFGGVGSSGMGAYHGKHSFDTFSHQRPCLLKSLKREGANKLRYPPNSQSKVDWGKFFLLKRFNKEKLGLLLLTFLGIVAAVLVKAEYY,mutated_sequence,1.0,485.0,NP_000373.1.a2m,NP_000373.1.npy,ClinVar +NP_000375.3,NP_000375.3.csv,MDPPRPALLALLALPALLLLLLAGARAEEEMLENVSLVCPKDATRFKHLRKYTYNYEAESSSGVPGTADSRSATRINCKVELEVPQLCSFILKTSQCTLKEVYGFNPEGKALLKKTKNSEEFAAAMSRYELKLAIPEGKQVFLYPEKDEPTYILNIKRGIISALLVPPETEEAKQVLFLDTVYGNCSTHFTVKTRKGNVATEISTERDLGQCDRFKPIRTGISPLALIKGMTRPLSTLISSSQSCQYTLDAKRKHVAEAICKEQHLFLPFSYKNKYGMVAQVTQTLKLEDTPKINSRFFGEGTKKMGLAFESTKSTSPPKQAEAVLKTLQELKKLTISEQNIQRANLFNKLVTELRGLSDEAVTSLLPQLIEVSSPITLQALVQCGQPQCSTHILQWLKRVHANPLLIDVVTYLVALIPEPSAQQLREIFNMARDQRSRATLYALSHAVNNYHKTNPTGTQELLDIANYLMEQIQDDCTGDEDYTYLILRVIGNMGQTMEQLTPELKSSILKCVQSTKPSLMIQKAAIQALRKMEPKDKDQEVLLQTFLDDASPGDKRLAAYLMLMRSPSQADINKIVQILPWEQNEQVKNFVASHIANILNSEELDIQDLKKLVKEALKESQLPTVMDFRKFSRNYQLYKSVSLPSLDPASAKIEGNLIFDPNNYLPKESMLKTTLTAFGFASADLIEIGLEGKGFEPTLEALFGKQGFFPDSVNKALYWVNGQVPDGVSKVLVDHFGYTKDDKHEQDMVNGIMLSVEKLIKDLKSKEVPEARAYLRILGEELGFASLHDLQLLGKLLLMGARTLQGIPQMIGEVIRKGSKNDFFLHYIFMENAFELPTGAGLQLQISSSGVIAPGAKAGVKLEVANMQAELVAKPSVSVEFVTNMGIIIPDFARSGVQMNTNFFHESGLEAHVALKAGKLKFIIPSPKRPVKLLSGGNTLHLVSTTKTEVIPPLIENRQSWSVCKQVFPGLNYCTSGAYSNASSTDSASYYPLTGDTRLELELRPTGEIEQYSVSATYELQREDRALVDTLKFVTQAEGAKQTEATMTFKYNRQSMTLSSEVQIPDFDVDLGTILRVNDESTEGKTSYRLTLDIQNKKITEVALMGHLSCDTKEERKIKGVISIPRLQAEARSEILAHWSPAKLLLQMDSSATAYGSTVSKRVAWHYDEEKIEFEWNTGTNVDTKKMTSNFPVDLSDYPKSLHMYANRLLDHRVPQTDMTFRHVGSKLIVAMSSWLQKASGSLPYTQTLQDHLNSLKEFNLQNMGLPDFHIPENLFLKSDGRVKYTLNKNSLKIEIPLPFGGKSSRDLKMLETVRTPALHFKSVGFHLPSREFQVPTFTIPKLYQLQVPLLGVLDLSTNVYSNLYNWSASYSGGNTSTDHFSLRARYHMKADSVVDLLSYNVQGSGETTYDHKNTFTLSCDGSLRHKFLDSNIKFSHVEKLGNNPVSKGLLIFDASSSWGPQMSASVHLDSKKKQHLFVKEVKIDGQFRVSSFYAKGTYGLSCQRDPNTGRLNGESNLRFNSSYLQGTNQITGRYEDGTLSLTSTSDLQSGIIKNTASLKYENYELTLKSDTNGKYKNFATSNKMDMTFSKQNALLRSEYQADYESLRFFSLLSGSLNSHGLELNADILGTDKINSGAHKATLRIGQDGISTSATTNLKCSLLVLENELNAELGLSGASMKLTTNGRFREHNAKFSLDGKAALTELSLGSAYQAMILGVDSKNIFNFKVSQEGLKLSNDMMGSYAEMKFDHTNSLNIAGLSLDFSSKLDNIYSSDKFYKQTVNLQLQPYSLVTTLNSDLKYNALDLTNNGKLRLEPLKLHVAGNLKGAYQNNEIKHIYAISSAALSASYKADTVAKVQGVEFSHRLNTDIAGLASAIDMSTNYNSDSLHFSNVFRSVMAPFTMTIDAHTNGNGKLALWGEHTGQLYSKFLLKAEPLAFTFSHDYKGSTSHHLVSRKSISAALEHKVSALLTPAEQTGTWKLKTQFNNNEYSQDLDAYNTKDKIGVELTGRTLADLTLLDSPIKVPLLLSEPINIIDALEMRDAVEKPQEFTIVAFVKYDKNQDVHSINLPFFETLQEYFERNRQTIIVVLENVQRNLKHINIDQFVRKYRAALGKLPQQANDYLNSFNWERQVSHAKEKLTALTKKYRITENDIQIALDDAKINFNEKLSQLQTYMIQFDQYIKDSYDLHDLKIAIANIIDEIIEKLKSLDEHYHIRVNLVKTIHDLHLFIENIDFNKSGSSTASWIQNVDTKYQIRIQIQEKLQQLKRHIQNIDIQHLAGKLKQHIEAIDVRVLLDQLGTTISFERINDILEHVKHFVINLIGDFEVAEKINAFRAKVHELIERYEVDQQIQVLMDKLVELAHQYKLKETIQKLSNVLQQVKIKDYFEKLVGFIDDAVKKLNELSFKTFIEDVNKFLDMLIKKLKSFDYHQFVDETNDKIREVTQRLNGEIQALELPQKAEALKLFLEETKATVAVYLESLQDTKITLIINWLQEALSSASLAHMKAKFRETLEDTRDRMYQMDIQQELQRYLSLVGQVYSTLVTYISDWWTLAAKNLTDFAEQYSIQDWAKRMKALVEQGFTVPEIKTILGTMPAFEVSLQALQKATFQTPDFIVPLTDLRIPSVQINFKDLKNIKIPSRFSTPEFTILNTFHIPSFTIDFVEMKVKIIRTIDQMLNSELQWPVPDIYLRDLKVEDIPLARITLPDFRLPEIAIPEFIIPTLNLNDFQVPDLHIPEFQLPHISHTIEVPTFGKLYSILKIQSPLFTLDANADIGNGTTSANEAGIAASITAKGESKLEVLNFDFQANAQLSNPKINPLALKESVKFSSKYLRTEHGSEMLFFGNAIEGKSNTVASLHTEKNTLELSNGVIVKINNQLTLDSNTKYFHKLNIPKLDFSSQADLRNEIKTLLKAGHIAWTSSGKGSWKWACPRFSDEGTHESQISFTIEGPLTSFGLSNKINSKHLRVNQNLVYESGSLNFSKLEIQSQVDSQHVGHSVLTAKGMALFGEGKAEFTGRHDAHLNGKVIGTLKNSLFFSAQPFEITASTNNEGNLKVRFPLRLTGKIDFLNNYALFLSPSAQQASWQVSARFNQYKYNQNFSAGNNENIMEAHVGINGEANLDFLNIPLTIPEMRLPYTIITTPPLKDFSLWEKTGLKEFLKTTKQSFDLSVKAQYKKNKHRHSITNPLAVLCEFISQSIKSFDRHFEKNRNNALDFVTKSYNETKIKFDKYKAEKSHDELPRTFQIPGYTVPVVNVEVSPFTIEMSAFGYVFPKAVSMPSFSILGSDVRVPSYTLILPSLELPVLHVPRNLKLSLPDFKELCTISHIFIPAMGNITYDFSFKSSVITLNTNAELFNQSDIVAHLLSSSSSVIDALQYKLEGTTRLTRKRGLKLATALSLSNKFVEGSHNSTVSLTTKNMEVSVATTTKAQIPILRMNFKQELNGNTKSKPTVSSSMEFKYDFNSSMLYSTAKGAVDHKLSLESLTSYFSIESSTKGDVKGSVLSREYSGTIASEANTYLNSKSTRSSVKLQGTSKIDDIWNLEVKENFAGEATLQRIYSLWEHSTKNHLQLEGLFFTNGEHTSKATLELSPWQMSALVQVHASQPSSFHDFPDLGQEVALNANTKNQKIRWKNEVRIHSGSFQSQVELSNDQEKAHLDIAGSLEGHLRFLKNIILPVYDKSLWDFLKLDVTTSIGRRQHLRVSTAFVYTKNPNGYSFSIPVKVLADKFIIPGLKLNDLNSVLVMPTFHVPFTDLQVPSCKLDFREIQIYKKLRTSSFALNLPTLPEVKFPEVDVLTKYSQPEDSLIPFFEITVPESQLTVSQFTLPKSVSDGIAALDLNAVANKIADFELPTIIVPEQTIEIPSIKFSVPAGIVIPSFQALTARFEVDSPVYNATWSASLKNKADYVETVLDSTCSSTVQFLEYELNVLGTHKIEDGTLASKTKGTFAHRDFSAEYEEDGKYEGLQEWEGKAHLNIKSPAFTDLHLRYQKDKKGISTSAASPAVGTVGMDMDEDDDFSKWNFYYSPQSSPDKKLTIFKTELRVRESDEETQIKVNWEEEAASGLLTSLKDNVPKATGVLYDYVNKYHWEHTGLTLREVSSKLRRNLQNNAEWVYQGAIRQIDDIDVRFQKAASGTTGTYQEWKDKAQNLYQELLTQEGQASFQGLKDNVFDGLVRVTQEFHMKVKHLIDSLIDFLNFPRFQFPGKPGIYTREELCTMFIREVGTVLSQVYSKVHNGSEILFSYFQDLVITLPFELRKHKLIDVISMYRELLKDLSKEAQEVFKAIQSLKTTEVLRNLQDLLQFIFQLIEDNIKQLKEMKFTYLINYIQDEINTIFSDYIPYVFKLLKENLCLNLHKFNEFIQNELQEASQELQQIHQYIMALREEYFDPSIVGWTVKYYELEEKIVSLIKNLLVALKDFHSEYIVSASNFTSQLSSQVEQFLHRNIQEYLSILTDPDGKGKEKIAELSATAQEIIKSQAIATKKIISDYHQQFRYKLQDFSDQLSDYYEKFIAESKRLIDLSIQNYHTFLIYITELLKKLQSTTVMNPYMKLAPGELTIIL,mutated_sequence,1.0,4563.0,NP_000375.3.a2m,NP_000375.3.npy,ClinVar +NP_000381.1,NP_000381.1.csv,MADTLPSEFDVIVIGTGLPESIIAAACSRSGRRVLHVDSRSYYGGNWASFSFSGLLSWLKEYQENSDIVSDSPVWQDQILENEEAIALSRKDKTIQHVEVFCYASQDLHEDVEEAGALQKNHALVTSANSTEAADSAFLPTEDESLSTMSCEMLTEQTPSSDPENALEVNGAEVTGEKENHCDDKTCVPSTSAEDMSENVPIAEDTTEQPKKNRITYSQIIKEGRRFNIDLVSKLLYSRGLLIDLLIKSNVSRYAEFKNITRILAFREGRVEQVPCSRADVFNSKQLTMVEKRMLMKFLTFCMEYEKYPDEYKGYEEITFYEYLKTQKLTPNLQYIVMHSIAMTSETASSTIDGLKATKNFLHCLGRYGNTPFLFPLYGQGELPQCFCRMCAVFGGIYCLRHSVQCLVVDKESRKCKAIIDQFGQRIISEHFLVEDSYFPENMCSRVQYRQISRAVLITDRSVLKTDSDQQISILTVPAEEPGTFAVRVIELCSSTMTCMKGTYLVHLTCTSSKTAREDLESVVQKLFVPYTEMEIENEQVEKPRILWALYFNMRDSSDISRSCYNDLPSNVYVCSGPDCGLGNDNAVKQAETLFQEICPNEDFCPPPPNPEDIILDGDSLQPEASESSAIPEANSETFKESTNLGNLEESSE,mutated_sequence,1.0,653.0,NP_000381.1.a2m,NP_000381.1.npy,ClinVar +NP_000382.3,NP_000382.3.csv,MGLQACLLGLFALILSGKCSYSPEPDQRRTLPPGWVSLGRADPEEELSLTFALRQQNVERLSELVQAVSDPSSPQYGKYLTLENVADLVRPSPLTLHTVQKWLLAAGAQKCHSVITQDFLTCWLSIRQAELLLPGAEFHHYVGGPTETHVVRSPHPYQLPQALAPHVDFVGGLHRFPPTSSLRQRPEPQVTGTVGLHLGVTPSVIRKRYNLTSQDVGSGTSNNSQACAQFLEQYFHDSDLAQFMRLFGGNFAHQASVARVVGQQGRGRAGIEASLDVQYLMSAGANISTWVYSSPGRHEGQEPFLQWLMLLSNESALPHVHTVSYGDDEDSLSSAYIQRVNTELMKAAARGLTLLFASGDSGAGCWSVSGRHQFRPTFPASSPYVTTVGGTSFQEPFLITNEIVDYISGGGFSNVFPRPSYQEEAVTKFLSSSPHLPPSSYFNASGRAYPDVAALSDGYWVVSNRVPIPWVSGTSASTPVFGGILSLINEHRILSGRPPLGFLNPRLYQQHGAGLFDVTRGCHESCLDEEVEGQGFCSGPGWDPVTGWGTPNFPALLKTLLNP,mutated_sequence,1.0,563.0,NP_000382.3.a2m,NP_000382.3.npy,ClinVar +NP_000388.2,NP_000388.2.csv,MGNWAVNEGLSIFVILVWLGLNVFLFVWYYRVYDIPPKFFYTRKLLGSALALARAPAACLNFNCMLILLPVCRNLLSFLRGSSACCSTRVRRQLDRNLTFHKMVAWMIALHSAIHTIAHLFNVEWCVNARVNNSDPYSVALSELGDRQNESYLNFARKRIKNPEGGLYLAVTLLAGITGVVITLCLILIITSSTKTIRRSYFEVFWYTHHLFVIFFIGLAIHGAERIVRGQTAESLAVHNITVCEQKISEWGKIKECPIPQFAGNPPMTWKWIVGPMFLYLCERLVRFWRSQQKVVITKVVTHPFKTIELQMKKKGFKMEVGQYIFVKCPKVSKLEWHPFTLTSAPEEDFFSIHIRIVGDWTEGLFNACGCDKQEFQDAWKLPKIAVDGPFGTASEDVFSYEVVMLVGAGIGVTPFASILKSVWYKYCNNATNLKLKKIYFYWLCRDTHAFEWFADLLQLLESQMQERNNAGFLSYNIYLTGWDESQANHFAVHHDEEKDVITGLKQKTLYGRPNWDNEFKTIASQHPNTRIGVFLCGPEALAETLSKQSISNSESGPRGVHFIFNKENF,mutated_sequence,1.0,570.0,NP_000388.2.a2m,NP_000388.2.npy,ClinVar +NP_000389.1,NP_000389.1.csv,MGAQLSTLGHMVLFPVWFLYSLLMKLFQRSTPAITLESPDIKYPLRLIDREIISHDTRRFRFALPSPQHILGLPVGQHIYLSARIDGNLVVRPYTPISSDDDKGFVDLVIKVYFKDTHPKFPAGGKMSQYLESMQIGDTIEFRGPSGLLVYQGKGKFAIRPDKKSNPIIRTVKSVGMIAGGTGITPMLQVIRAIMKDPDDHTVCHLLFANQTEKDILLRPELEELRNKHSARFKLWYTLDRAPEAWDYGQGFVNEEMIRDHLPPPEEEPLVLMCGPPPMIQYACLPNLDHVGHPTERCFVF,mutated_sequence,1.0,301.0,NP_000389.1.a2m,NP_000389.1.npy,ClinVar +NP_000391.1,NP_000391.1.csv,MKLNVDGLLVYFPYDYIYPEQFSYMRELKRTLDAKGHGVLEMPSGTGKTVSLLALIMAYQRAYPLEVTKLIYCSRTVPEIEKVIEELRKLLNFYEKQEGEKLPFLGLALSSRKNLCIHPEVTPLRFGKDVDGKCHSLTASYVRAQYQHDTSLPHCRFYEEFDAHGREVPLPAGIYNLDDLKALGRRQGWCPYFLARYSILHANVVVYSYHYLLDPKIADLVSKELARKAVVVFDEAHNIDNVCIDSMSVNLTRRTLDRCQGNLETLQKTVLRIKETDEQRLRDEYRRLVEGLREASAARETDAHLANPVLPDEVLQEAVPGSIRTAEHFLGFLRRLLEYVKWRLRVQHVVQESPPAFLSGLAQRVCIQRKPLRFCAERLRSLLHTLEITDLADFSPLTLLANFATLVSTYAKGFTIIIEPFDDRTPTIANPILHFSCMDASLAIKPVFERFQSVIITSGTLSPLDIYPKILDFHPVTMATFTMTLARVCLCPMIIGRGNDQVAISSKFETREDIAVIRNYGNLLLEMSAVVPDGIVAFFTSYQYMESTVASWYEQGILENIQRNKLLFIETQDGAETSVALEKYQEACENGRGAILLSVARGKVSEGIDFVHHYGRAVIMFGVPYVYTQSRILKARLEYLRDQFQIRENDFLTFDAMRHAAQCVGRAIRGKTDYGLMVFADKRFARGDKRGKLPRWIQEHLTDANLNLTVDEGVQVAKYFLRQMAQPFHREDQLGLSLLSLEQLESEETLKRIEQIAQQL,mutated_sequence,1.0,760.0,NP_000391.1.a2m,NP_000391.1.npy,ClinVar +NP_000395.3,NP_000395.3.csv,MPGFLVRILPLLLVLLLLGPTRGLRNATQRMFEIDYSRDSFLKDGQPFRYISGSIHYSRVPRFYWKDRLLKMKMAGLNAIQTYVPWNFHEPWPGQYQFSEDHDVEYFLRLAHELGLLVILRPGPYICAEWEMGGLPAWLLEKESILLRSSDPDYLAAVDKWLGVLLPKMKPLLYQNGGPVITVQVENEYGSYFACDFDYLRFLQKRFRHHLGDDVVLFTTDGAHKTFLKCGALQGLYTTVDFGTGSNITDAFLSQRKCEPKGPLINSEFYTGWLDHWGQPHSTIKTEAVASSLYDILARGASVNLYMFIGGTNFAYWNGANSPYAAQPTSYDYDAPLSEAGDLTEKYFALRNIIQKFEKVPEGPIPPSTPKFAYGKVTLEKLKTVGAALDILCPSGPIKSLYPLTFIQVKQHYGFVLYRTTLPQDCSNPAPLSSPLNGVHDRAYVAVDGIPQGVLERNNVITLNITGKAGATLDLLVENMGRVNYGAYINDFKGLVSNLTLSSNILTDWTIFPLDTEDAVCSHLGGWGHRDSGHHDEAWAHNSSNYTLPAFYMGNFSIPSGIPDLPQDTFIQFPGWTKGQVWINGFNLGRYWPARGPQLTLFVPQHILMTSAPNTITVLELEWAPCSSDDPELCAVTFVDRPVIGSSVTYDHPSKPVEKRLMPPPPQKNKDSWLDHV,mutated_sequence,1.0,677.0,NP_000395.3.a2m,NP_000395.3.npy,ClinVar +NP_000396.2,NP_000396.2.csv,MQSLMQAPLLIALGLLLAAPAQAHLKKPSQLSSFSWDNCDEGKDPAVIRSLTLEPDPIIVPGNVTLSVMGSTSVPLSSPLKVDLVLEKEVAGLWIKIPCTDYIGSCTFEHFCDVLDMLIPTGEPCPEPLRTYGLPCHCPFKEGTYSLPKSEFVVPDLELPSWLTTGNYRIESVLSSSGKRLGCIKIAASLKGI,mutated_sequence,1.0,193.0,NP_000396.2.a2m,NP_000396.2.npy,ClinVar +NP_000401.1,NP_000401.1.csv,MGPRARPALLLLMLLQTAVLQGRLLRSHSLHYLFMGASEQDLGLSLFEALGYVDDQLFVFYDHESRRVEPRTPWVSSRISSQMWLQLSQSLKGWDHMFTVDFWTIMENHNHSKESHTLQVILGCEMQEDNSTEGYWKYGYDGQDHLEFCPDTLDWRAAEPRAWPTKLEWERHKIRARQNRAYLERDCPAQLQQLLELGRGVLDQQVPPLVKVTHHVTSSVTTLRCRALNYYPQNITMKWLKDKQPMDAKEFEPKDVLPNGDGTYQGWITLAVPPGEEQRYTCQVEHPGLDQPLIVIWEPSPSGTLVIGVISGIAVFVVILFIGILFIILRKRQGSRGAMGHYVLAERE,mutated_sequence,1.0,348.0,NP_000401.1.a2m,NP_000401.1.npy,ClinVar +NP_000410.2,NP_000410.2.csv,MARALCPLQALWLLEWVLLLLGPCAAPPAWALNLDPVQLTFYAGPNGSQFGFSLDFHKDSHGRVAIVVGAPRTLGPSQEETGGVFLCPWRAEGGQCPSLLFDLRDETRNVGSQTLQTFKARQGLGASVVSWSDVIVACAPWQHWNVLEKTEEAEKTPVGSCFLAQPESGRRAEYSPCRGNTLSRIYVENDFSWDKRYCEAGFSSVVTQAGELVLGAPGGYYFLGLLAQAPVADIFSSYRPGILLWHVSSQSLSFDSSNPEYFDGYWGYSVAVGEFDGDLNTTEYVVGAPTWSWTLGAVEILDSYYQRLHRLRGEQMASYFGHSVAVTDVNGDGRHDLLVGAPLYMESRADRKLAEVGRVYLFLQPRGPHALGAPSLLLTGTQLYGRFGSAIAPLGDLDRDGYNDIAVAAPYGGPSGRGQVLVFLGQSEGLRSRPSQVLDSPFPTGSAFGFSLRGAVDIDDNGYPDLIVGAYGANQVAVYRAQPVVKASVQLLVQDSLNPAVKSCVLPQTKTPVSCFNIQMCVGATGHNIPQKLSLNAELQLDRQKPRQGRRVLLLGSQQAGTTLNLDLGGKHSPICHTTMAFLRDEADFRDKLSPIVLSLNVSLPPTEAGMAPAVVLHGDTHVQEQTRIVLDCGEDDVCVPQLQLTASVTGSPLLVGADNVLELQMDAANEGEGAYEAELAVHLPQGAHYMRALSNVEGFERLICNQKKENETRVVLCELGNPMKKNAQIGIAMLVSVGNLEEAGESVSFQLQIRSKNSQNPNSKIVLLDVPVRAEAQVELRGNSFPASLVVAAEEGEREQNSLDSWGPKVEHTYELHNNGPGTVNGLHLSIHLPGQSQPSDLLYILDIQPQGGLQCFPQPPVNPLKVDWGLPIPSPSPIHPAHHKRDRRQIFLPEPEQPSRLQDPVLVSCDSAPCTVVQCDLQEMARGQRAMVTVLAFLWLPSLYQRPLDQFVLQSHAWFNVSSLPYAVPPLSLPRGEAQVWTQLLRALEERAIPIWWVLVGVLGGLLLLTILVLAMWKVGFFKRNRPPLEEDDEEGE,mutated_sequence,1.0,1039.0,NP_000410.2.a2m,NP_000410.2.npy,ClinVar +NP_000413.1,NP_000413.1.csv,MTTSIRQFTSSSSIKGSSGLGGGSSRTSCRLSGGLGAGSCRLGSAGGLGSTLGGSSYSSCYSFGSGGGYGSSFGGVDGLLAGGEKATMQNLNDRLASYLDKVRALEEANTELEVKIRDWYQRQAPGPARDYSQYYRTIEELQNKILTATVDNANILLQIDNARLAADDFRTKFETEQALRLSVEADINGLRRVLDELTLARADLEMQIENLKEELAYLKKNHEEEMNALRGQVGGEINVEMDAAPGVDLSRILNEMRDQYEKMAEKNRKDAEDWFFSKTEELNREVATNSELVQSGKSEISELRRTMQALEIELQSQLSMKASLEGNLAETENRYCVQLSQIQGLIGSVEEQLAQLRCEMEQQNQEYKILLDVKTRLEQEIATYRRLLEGEDAHLTQYKKEPVTTRQVRTIVEEVQDGKVISSREQVHQTTR,mutated_sequence,1.0,432.0,NP_000413.1.a2m,NP_000413.1.npy,ClinVar +NP_000417.3,NP_000417.3.csv,MPGAAGVLLLLLLSGGLGGVQAQRPQQQRQSQAHQQRGLFPAVLNLASNALITTNATCGEKGPEMYCKLVEHVPGQPVRNPQCRICNQNSSNPNQRHPITNAIDGKNTWWQSPSIKNGIEYHYVTITLDLQQVFQIAYVIVKAANSPRPGNWILERSLDDVEYKPWQYHAVTDTECLTLYNIYPRTGPPSYAKDDEVICTSFYSKIHPLENGEIHISLINGRPSADDPSPELLEFTSARYIRLRFQRIRTLNADLMMFAHKDPREIDPIVTRRYYYSVKDISVGGMCICYGHARACPLDPATNKSRCECEHNTCGDSCDQCCPGFHQKPWRAGTFLTKTECEACNCHGKAEECYYDENVARRNLSLNIRGKYIGGGVCINCTQNTAGINCETCTDGFFRPKGVSPNYPRPCQPCHCDPIGSLNEVCVKDEKHARRGLAPGSCHCKTGFGGVSCDRCARGYTGYPDCKACNCSGLGSKNEDPCFGPCICKENVEGGDCSRCKSGFFNLQEDNWKGCDECFCSGVSNRCQSSYWTYGKIQDMSGWYLTDLPGRIRVAPQQDDLDSPQQISISNAEARQALPHSYYWSAPAPYLGNKLPAVGGQLTFTISYDLEEEEEDTERVLQLMIILEGNDLSISTAQDEVYLHPSEEHTNVLLLKEESFTIHGTHFPVRRKEFMTVLANLKRVLLQITYSFGMDAIFRLSSVNLESAVSYPTDGSIAAAVEVCQCPPGYTGSSCESCWPRHRRVNGTIFGGICEPCQCFGHAESCDDVTGECLNCKDHTGGPYCDKCLPGFYGEPTKGTSEDCQPCACPLNIPSNNFSPTCHLDRSLGLICDGCPVGYTGPRCERCAEGYFGQPSVPGGSCQPCQCNDNLDFSIPGSCDSLSGSCLICKPGTTGRYCELCADGYFGDAVDAKNCQPCRCNAGGSFSEVCHSQTGQCECRANVQGQRCDKCKAGTFGLQSARGCVPCNCNSFGSKSFDCEESGQCWCQPGVTGKKCDRCAHGYFNFQEGGCTACECSHLGNNCDPKTGRCICPPNTIGEKCSKCAPNTWGHSITTGCKACNCSTVGSLDFQCNVNTGQCNCHPKFSGAKCTECSRGHWNYPRCNLCDCFLPGTDATTCDSETKKCSCSDQTGQCTCKVNVEGIHCDRCRPGKFGLDAKNPLGCSSCYCFGTTTQCSEAKGLIRTWVTLKAEQTILPLVDEALQHTTTKGIVFQHPEIVAHMDLMREDLHLEPFYWKLPEQFEGKKLMAYGGKLKYAIYFEAREETGFSTYNPQVIIRGGTPTHARIIVRHMAAPLIGQLTRHEIEMTEKEWKYYGDDPRVHRTVTREDFLDILYDIHYILIKATYGNFMRQSRISEISMEVAEQGRGTTMTPPADLIEKCDCPLGYSGLSCEACLPGFYRLRSQPGGRTPGPTLGTCVPCQCNGHSSLCDPETSICQNCQHHTAGDFCERCALGYYGIVKGLPNDCQQCACPLISSSNNFSPSCVAEGLDDYRCTACPRGYEGQYCERCAPGYTGSPGNPGGSCQECECDPYGSLPVPCDPVTGFCTCRPGATGRKCDGCKHWHAREGWECVFCGDECTGLLLGDLARLEQMVMSINLTGPLPAPYKMLYGLENMTQELKHLLSPQRAPERLIQLAEGNLNTLVTEMNELLTRATKVTADGEQTGQDAERTNTRAKSLGEFIKELARDAEAVNEKAIKLNETLGTRDEAFERNLEGLQKEIDQMIKELRRKNLETQKEIAEDELVAAEALLKKVKKLFGESRGENEEMEKDLREKLADYKNKVDDAWDLLREATDKIREANRLFAVNQKNMTALEKKKEAVESGKRQIENTLKEGNDILDEANRLADEINSIIDYVEDIQTKLPPMSEELNDKIDDLSQEIKDRKLAEKVSQAESHAAQLNDSSAVLDGILDEAKNISFNATAAFKAYSNIKDYIDEAEKVAKEAKDLAHEATKLATGPRGLLKEDAKGCLQKSFRILNEAKKLANDVKENEDHLNGLKTRIENADARNGDLLRTLNDTLGKLSAIPNDTAAKLQAVKDKARQANDTAKDVLAQITELHQNLDGLKKNYNKLADSVAKTNAVVKDPSKNKIIADADATVKNLEQEADRLIDKLKPIKELEDNLKKNISEIKELINQARKQANSIKVSVSSGGDCIRTYKPEIKKGSYNNIVVNVKTAVADNLLFYLGSAKFIDFLAIEMRKGKVSFLWDVGSGVGRVEYPDLTIDDSYWYRIVASRTGRNGTISVRALDGPKASIVPSTHHSTSPPGYTILDVDANAMLFVGGLTGKLKKADAVRVITFTGCMGETYFDNKPIGLWNFREKEGDCKGCTVSPQVEDSEGTIQFDGEGYALVSRPIRWYPNISTVMFKFRTFSSSALLMYLATRDLRDFMSVELTDGHIKVSYDLGSGMASVVSNQNHNDGKWKSFTLSRIQKQANISIVDIDTNQEENIATSSSGNNFGLDLKADDKIYFGGLPTLRNLSMKARPEVNLKKYSGCLKDIEISRTPYNILSSPDYVGVTKGCSLENVYTVSFPKPGFVELSPVPIDVGTEINLSFSTKNESGIILLGSGGTPAPPRRKRRQTGQAYYAILLNRGRLEVHLSTGARTMRKIVIRPEPNLFHDGREHSVHVERTRGIFTVQVDENRRYMQNLTVEQPIEVKKLFVGGAPPEFQPSPLRNIPPFEGCIWNLVINSVPMDFARPVSFKNADIGRCAHQKLREDEDGAAPAEIVIQPEPVPTPAFPTPTPVLTHGPCAAESEPALLIGSKQFGLSRNSHIAIAFDDTKVKNRLTIELEVRTEAESGLLFYMARINHADFATVQLRNGLPYFSYDLGSGDTHTMIPTKINDGQWHKIKIMRSKQEGILYVDGASNRTISPKKADILDVVGMLYVGGLPINYTTRRIGPVTYSIDGCVRNLHMAEAPADLEQPTSSFHVGTCFANAQRGTYFDGTGFAKAVGGFKVGLDLLVEFEFRTTTTTGVLLGISSQKMDGMGIEMIDEKLMFHVDNGAGRFTAVYDAGVPGHLCDGQWHKVTANKIKHRIELTVDGNQVEAQSPNPASTSADTNDPVFVGGFPDDLKQFGLTTSIPFRGCIRSLKLTKGTGKPLEVNFAKALELRGVQPVSCPAN,mutated_sequence,1.0,3122.0,NP_000417.3.a2m,NP_000417.3.npy,ClinVar +NP_000423.2,NP_000423.2.csv,MAPKKAKKRAGGANSNVFSMFEQTQIQEFKEAFTIMDQNRDGFIDKNDLRDTFAALGRVNVKNEEIDEMIKEAPGPINFTVFLTMFGEKLKGADPEETILNAFKVFDPEGKGVLKADYVREMLTTQAERFSKEEVDQMFAAFPPDVTGNLDYKNLVHIITHGEEKD,mutated_sequence,1.0,166.0,NP_000423.2.a2m,NP_000423.2.npy,ClinVar +NP_000424.2,NP_000424.2.csv,MSLVEAISLWNEGVLAADKKDWKGALDAFSAVQDPHSRICFNIGCMYTILKNMTEAEKAFTRSINRDKHLAVAYFQRGMLYYQTEKYDLAIKDLKEALIQLRGNQLIDYKILGLQFKLFACEVLYNIAFMYAKKEEWKKAEEQLALATSMKSEPRHSKIDKAMECVWKQKLYEPVVIPVGKLFRPNERQVAQLAKKDYLGKATVVASVVDQDSFSGFAPLQPQAAEPPPRPKTPEIFRALEGEAHRVLFGFVPETKEELQVMPGNIVFVLKKGNDNWATVMFNGQKGLVPCNYLEPVELRIHPQQQPQEESSPQSDIPAPPSSKAPGRPQLSPGQKQKEEPKEVKLSVPMPYTLKVHYKYTVVMKTQPGLPYSQVRDMVSKKLELRLEHTKLSYRPRDSNELVPLSEDSMKDAWGQVKNYCLTLWCENTVGDQGFPDEPKESEKADANNQTTEPQLKKGSQVEALFSYEATQPEDLEFQEGDIILVLSKVNEEWLEGECKGKVGIFPKVFVEDCATTDLESTRREV,mutated_sequence,1.0,526.0,NP_000424.2.a2m,NP_000424.2.npy,ClinVar +NP_000430.3,NP_000430.3.csv,MERRAWSLQCTAFVLFCAWCALNSAKAKRQFVNEWAAEIPGGPEAASAIAEELGYDLLGQIGSLENHYLFKHKNHPRRSRRSAFHITKRLSDDDRVIWAEQQYEKERSKRSALRDSALNLFNDPMWNQQWYLQDTRMTAALPKLDLHVIPVWQKGITGKGVVITVLDDGLEWNHTDIYANYDPEASYDFNDNDHDPFPRYDPTNENKHGTRCAGEIAMQANNHKCGVGVAYNSKVGGIRMLDGIVTDAIEASSIGFNPGHVDIYSASWGPNDDGKTVEGPGRLAQKAFEYGVKQGRQGKGSIFVWASGNGGRQGDNCDCDGYTDSIYTISISSASQQGLSPWYAEKCSSTLATSYSSGDYTDQRITSADLHNDCTETHTGTSASAPLAAGIFALALEANPNLTWRDMQHLVVWTSEYDPLANNPGWKKNGAGLMVNSRFGFGLLNAKALVDLADPRTWRSVPEKKECVVKDNDFEPRALKANGEVIIEIPTRACEGQENAIKSLEHVQFEATIEYSRRGDLHVTLTSAAGTSTVLLAERERDTSPNGFKNWDFMSVHTWGENPIGTWTLRITDMSGRIQNEGRIVNWKLILHGTSSQPEHMKQPRVYTSYNTVQNDRRGVEKMVDPGEEQPTQENPKENTLVSKSPSSSSVGGRRDELEEGAPSQAMLRLLQSAFSKNSPPKQSPKKSPSAKLNIPYENFYEALEKLNKPSQLKDSEDSLYNDYVDVFYNTKPYKHRDDRLLQALVDILNEEN,mutated_sequence,1.0,753.0,NP_000430.3.a2m,NP_000430.3.npy,ClinVar +NP_000432.1,NP_000432.1.csv,MAAPGGRSEPPQLPEYSCSYMVSRPVYSELAFQQQHERRLQERKTLRESLAKCCSCSRKRAFGVLKTLVPILEWLPKYRVKEWLLSDVISGVSTGLVATLQGMAYALLAAVPVGYGLYSAFFPILTYFIFGTSRHISVGPFPVVSLMVGSVVLSMAPDEHFLVSSSNGTVLNTTMIDTAARDTARVLIASALTLLVGIIQLIFGGLQIGFIVRYLADPLVGGFTTAAAFQVLVSQLKIVLNVSTKNYNGVLSIIYTLVEIFQNIGDTNLADFTAGLLTIVVCMAVKELNDRFRHKIPVPIPIEVIVTIIATAISYGANLEKNYNAGIVKSIPRGFLPPELPPVSLFSEMLAASFSIAVVAYAIAVSVGKVYATKYDYTIDGNQEFIAFGISNIFSGFFSCFVATTALSRTAVQESTGGKTQVAGIISAAIVMIAILALGKLLEPLQKSVLAAVVIANLKGMFMQLCDIPRLWRQNKIDAVIWVFTCIVSIILGLDLGLLAGLIFGLLTVVLRVQFPSWNGLGSIPSTDIYKSTKNYKNIEEPQGVKILRFSSPIFYGNVDGFKKCIKSTVGFDAIRVYNKRLKALRKIQKLIKSGQLRATKNGIISDAVSTNNAFEPDEDIEDLEELDIPTKEIEIQVDWNSELPVKVNVPKVPIHSLVLDCGAISFLDVVGVRSLRVIVKEFQRIDVNVYFASLQDYVIEKLEQCGFFDDNIRKDTFFLTVHDAILYLQNQVKSQEGQGSILETITLIQDCKDTLELIETELTEEELDVQDEAMRTLAS,mutated_sequence,1.0,780.0,NP_000432.1.a2m,NP_000432.1.npy,ClinVar +NP_000435.3,NP_000435.3.csv,MEAETGSSVETGKKANRGTRIALVVFVGGTLVLGTILFLVSQGLLSLQAKQEYCLKPECIEAAAAILSKVNLSVDPCDNFFRFACDGWISNNPIPEDMPSYGVYPWLRHNVDLKLKELLEKSISRRRDTEAIQKAKILYSSCMNEKAIEKADAKPLLHILRHSPFRWPVLESNIGPEGVWSERKFSLLQTLATFRGQYSNSVFIRLYVSPDDKASNEHILKLDQATLSLAVREDYLDNSTEAKSYRDALYKFMVDTAVLLGANSSRAEHDMKSVLRLEIKIAEIMIPHENRTSEAMYNKMNISELSAMIPQFDWLGYIKKVIDTRLYPHLKDISPSENVVVRVPQYFKDLFRILGSERKKTIANYLVWRMVYSRIPNLSRRFQYRWLEFSRVIQGTTTLLPQWDKCVNFIESALPYVVGKMFVDVYFQEDKKEMMEELVEGVRWAFIDMLEKENEWMDAGTKRKAKEKARAVLAKVGYPEFIMNDTHVNEDLKAIKFSEADYFGNVLQTRKYLAQSDFFWLRKAVPKTEWFTNPTTVNAFYSASTNQIRFPAGELQKPFFWGTEYPRSLSYGAIGVIVGHEFTHGFDNNGRKYDKNGNLDPWWSTESEEKFKEKTKCMINQYSNYYWKKAGLNVKGKRTLGENIADNGGLREAFRAYRKWINDRRQGLEEPLLPGITFTNNQLFFLSYAHVRCNSYRPEAAREQVQIGAHSPPQFRVNGAISNFEEFQKAFNCPPNSTMNRGMDSCRLW,mutated_sequence,1.0,749.0,NP_000435.3.a2m,NP_000435.3.npy,ClinVar +NP_000442.1,NP_000442.1.csv,MEELTAFVSKSFDQKSKDGNGGGGGGGGKKDSITYREVLESGLARSRELGTSDSSLQDITEGGGHCPVHLFKDHVDNDKEKLKEFGTARVAEGIYECKEKREDVKSEDEDGQTKLKQRRSRTNFTLEQLNELERLFDETHYPDAFMREELSQRLGLSEARVQVWFQNRRAKCRKQENQMHKGVILGTANHLDACRVAPYVNMGALRMPFQQVQAQLQLEGVAHAHPHLHPHLAAHAPYLMFPPPPFGLPIASLAESASAAAVVAAAAKSNSKNSSIADLRLKARKHAEALGL,mutated_sequence,1.0,292.0,NP_000442.1.a2m,NP_000442.1.npy,ClinVar +NP_000446.1,NP_000446.1.csv,MEVVDPQQLGMFTEGELMSVGMDTFIHRIDSTEVIYQPRRKRAKLIGKYLMGDLLGEGSYGKVKEVLDSETLCRRAVKILKKKKLRRIPNGEANVKKEIQLLRRLRHKNVIQLVDVLYNEEKQKMYMVMEYCVCGMQEMLDSVPEKRFPVCQAHGYFCQLIDGLEYLHSQGIVHKDIKPGNLLLTTGGTLKISDLGVAEALHPFAADDTCRTSQGSPAFQPPEIANGLDTFSGFKVDIWSAGVTLYNITTGLYPFEGDNIYKLFENIGKGSYAIPGDCGPPLSDLLKGMLEYEPAKRFSIRQIRQHSWFRKKHPPAEAPVPIPPSPDTKDRWRSMTVVPYLEDLHGADEDEDLFDIEDDIIYTQDFTVPGQVPEEEASHNGQRRGLPKAVCMNGTEAAQLSTKSRAEGRAPNPARKACSASSKIRRLSACKQQ,mutated_sequence,1.0,433.0,NP_000446.1.a2m,NP_000446.1.npy,ClinVar +NP_000449.1,NP_000449.1.csv,MVSKLTSLQQELLSALLSSGVTKEVLVQALEELLPSPNFGVKLETLPLSPGSGAEPDTKPVFHTLTNGHAKGRLSGDEGSEDGDDYDTPPILKELQALNTEEAAEQRAEVDRMLSEDPWRAAKMIKGYMQQHNIPQREVVDVTGLNQSHLSQHLNKGTPMKTQKRAALYTWYVRKQREILRQFNQTVQSSGNMTDKSSQDQLLFLFPEFSQQSHGPGQSDDACSEPTNKKMRRNRFKWGPASQQILYQAYDRQKNPSKEEREALVEECNRAECLQRGVSPSKAHGLGSNLVTEVRVYNWFANRRKEEAFRQKLAMDAYSSNQTHSLNPLLSHGSPHHQPSSSPPNKLSGVRYSQQGNNEITSSSTISHHGNSAMVTSQSVLQQVSPASLDPGHNLLSPDGKMISVSGGGLPPVSTLTNIHSLSHHNPQQSQNLIMTPLSGVMAIAQSLNTSQAQSVPVINSVAGSLAALQPVQFSQQLHSPHQQPLMQQSPGSHMAQQPFMAAVTQLQNSHMYAHKQEPPQYSHTSRFPSAMVVTDTSSISTLTNMSSSKQCPLQAW,mutated_sequence,1.0,557.0,NP_000449.1.a2m,NP_000449.1.npy,ClinVar +NP_000454.1,NP_000454.1.csv,MAVESQGGRPLVLGLLLCVLGPVVSHAGKILLIPVDGSHWLSMLGAIQQLQQRGHEIVVLAPDASLYIRDGAFYTLKTYPVPFQREDVKESFVSLGHNVFENDSFLQRVIKTYKKIKKDSAMLLSGCSHLLHNKELMASLAESSFDVMLTDPFLPCSPIVAQYLSLPTVFFLHALPCSLEFEATQCPNPFSYVPRPLSSHSDHMTFLQRVKNMLIAFSQNFLCDVVYSPYATLASEFLQREVTVQDLLSSASVWLFRSDFVKDYPRPIMPNMVFVGGINCLHQNPLSQEFEAYINASGEHGIVVFSLGSMVSEIPEKKAMAIADALGKIPQTVLWRYTGTRPSNLANNTILVKWLPQNDLLGHPMTRAFITHAGSHGVYESICNGVPMVMMPLFGDQMDNAKRMETKGAGVTLNVLEMTSEDLENALKAVINDKSYKENIMRLSSLHKDRPVEPLDLAVFWVEFVMRHKGAPHLRPAAHDLTWYQYHSLDVIGFLLAVVLTVAFITFKCCAYGYRKCLGKKGRVKKAHKSKTH,mutated_sequence,1.0,533.0,NP_000454.1.a2m,NP_000454.1.npy,ClinVar +NP_000457.1,NP_000457.1.csv,MWGSDRLAGAGGGGAAVTVAFTNARDCFLHLPRRLVAQLHLLQNQAIEVVWSHQPAFLSWVEGRHFSDQGENVAEINRQVGQKLGLSNGGQVFLKPCSHVVSCQQVEVEPLSADDWEILELHAVSLEQHLLDQIRIVFPKAIFPVWVDQQTYIFIQIVALIPAASYGRLETDTKLLIQPKTRRAKENTFSKADAEYKKLHSYGRDQKGMMKELQTKQLQSNTVGITESNENESEIPVDSSSVASLWTMIGSIFSFQSEKKQETSWGLTEINAFKNMQSKVVPLDNIFRVCKSQPPSIYNASATSVFHKHCAIHVFPWDQEYFDVEPSFTVTYGKLVKLLSPKQQQSKTKQNVLSPEKEKQMSEPLDQKKIRSDHNEEDEKACVLQVVWNGLEELNNAIKYTKNVEVLHLGKVWIPDDLRKRLNIEMHAVVRITPVEVTPKIPRSLKLQPRENLPKDISEEDIKTVFYSWLQQSTTTMLPLVISEEEFIKLETKDGLKEFSLSIVHSWEKEKDKNIFLLSPNLLQKTTIQVLLDPMVKEENSEEIDFILPFLKLSSLGGVNSLGVSSLEHITHSLLGRPLSRQLMSLVAGLRNGALLLTGGKGSGKSTLAKAICKEAFDKLDAHVERVDCKALRGKRLENIQKTLEVAFSEAVWMQPSVVLLDDLDLIAGLPAVPEHEHSPDAVQSQRLAHALNDMIKEFISMGSLVALIATSQSQQSLHPLLVSAQGVHIFQCVQHIQPPNQEQRCEILCNVIKNKLDCDINKFTDLDLQHVAKETGGFVARDFTVLVDRAIHSRLSRQSISTREKLVLTTLDFQKALRGFLPASLRSVNLHKPRDLGWDKIGGLHEVRQILMDTIQLPAKYPELFANLPIRQRTGILLYGPPGTGKTLLAGVIARESRMNFISVKGPELLSKYIGASEQAVRDIFIRAQAAKPCILFFDEFESIAPRRGHDNTGVTDRVVNQLLTQLDGVEGLQGVYVLAATSRPDLIDPALLRPGRLDKCVYCPPPDQVSRLEILNVLSDSLPLADDVDLQHVASVTDSFTGADLKALLYNAQLEALHGMLLSSGLQDGSSSSDSDLSLSSMVFLNHSSGSDDSAGDGECGLDQSLVSLEMSEILPDESKFNMYRLYFGSSYESELGNGTSSDLSSQCLSAPSSMTQDLPGVPGKDQLFSQPPVLRTASQEGCQELTQEQRDQLRADISIIKGRYRSQSGEDESMNQPGPIKTRLAISQSHLMTALGHTRPSISEDDWKNFAELYESFQNPKRRKNQSGTMFRPGQKVTLA,mutated_sequence,1.0,1283.0,NP_000457.1.a2m,NP_000457.1.npy,ClinVar +NP_000465.1,NP_000465.1.csv,MMQDVSSSPVSPADDSLSNSEEEPDRQQPPSGKRGGRKRRSSRRSAGGGAGPGGAAGGGVGGGDEPGSPAQGKRGKKSAGCGGGGGAGGGGGSSSGGGSPQSYEELQTQRVMANVRERQRTQSLNEAFAALRKIIPTLPSDKLSKIQTLKLAARYIDFLYQVLQSDELDSKMASCSYVAHERLSYAFSVWRMEGAWSMSASH,mutated_sequence,1.0,202.0,NP_000465.1.a2m,NP_000465.1.npy,ClinVar +NP_000467.1,NP_000467.1.csv,MEEKLKKTKIIFVVGGPGSGKGTQCEKIVQKYGYTHLSTGDLLRSEVSSGSARGKKLSEIMEKGQLVPLETVLDMLRDAMVAKVNTSKGFLIDGYPREVQQGEEFERRIGQPTLLLYVDAGPETMTQRLLKRGETSGRVDDNEETIKKRLETYYKATEPVIAFYEKRGIVRKVNAEGSVDSVFSQVCTHLDALK,mutated_sequence,1.0,194.0,NP_000467.1.a2m,NP_000467.1.npy,ClinVar +NP_000468.1,NP_000468.1.csv,MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAENCDKSLHTLFGDKLCTVATLRETYGEMADCCAKQEPERNECFLQHKDDNPNLPRLVRPEVDVMCTAFHDNEETFLKKYLYEIARRHPYFYAPELLFFAKRYKAAFTECCQAADKAACLLPKLDELRDEGKASSAKQRLKCASLQKFGERAFKAWAVARLSQRFPKAEFAEVSKLVTDLTKVHTECCHGDLLECADDRADLAKYICENQDSISSKLKECCEKPLLEKSHCIAEVENDEMPADLPSLAADFVESKDVCKNYAEAKDVFLGMFLYEYARRHPDYSVVLLLRLAKTYETTLEKCCAAADPHECYAKVFDEFKPLVEEPQNLIKQNCELFEQLGEYKFQNALLVRYTKKVPQVSTPTLVEVSRNLGKVGSKCCKHPEAKRMPCAEDYLSVVLNQLCVLHEKTPVSDRVTKCCTESLVNRRPCFSALEVDETYVPKEFNAETFTFHADICTLSEKERQIKKQTALVELVKHKPKATKEQLKAVMDDFAAFVEKCCKADDKETCFAEEGKKLVAASQAALGL,mutated_sequence,1.0,609.0,NP_000468.1.a2m,NP_000468.1.npy,ClinVar +NP_000469.3,NP_000469.3.csv,MISPFLVLAIGTCLTNSLVPEKEKDPKYWRDQAQETLKYALELQKLNTNVAKNVIMFLGDGMGVSTVTAARILKGQLHHNPGEETRLEMDKFPFVALSKTYNTNAQVPDSAGTATAYLCGVKANEGTVGVSAATERSRCNTTQGNEVTSILRWAKDAGKSVGIVTTTRVNHATPSAAYAHSADRDWYSDNEMPPEALSQGCKDIAYQLMHNIRDIDVIMGGGRKYMYPKNKTDVEYESDEKARGTRLDGLDLVDTWKSFKPRYKHSHFIWNRTELLTLDPHNVDYLLGLFEPGDMQYELNRNNVTDPSLSEMVVVAIQILRKNPKGFFLLVEGGRIDHGHHEGKAKQALHEAVEMDRAIGQAGSLTSSEDTLTVVTADHSHVFTFGGYTPRGNSIFGLAPMLSDTDKKPFTAILYGNGPGYKVVGGERENVSMVDYAHNNYQAQSAVPLRHETHGGEDVAVFSKGPMAHLLHGVHEQNYVPHVMAYAACIGANLGHCAPASSAGSLAAGPLLLALALYPLSVLF,mutated_sequence,1.0,524.0,NP_000469.3.a2m,NP_000469.3.npy,ClinVar +NP_000476.1,NP_000476.1.csv,MADSELQLVEQRIRSFPDFPTPGVVFRDISPVLKDPASFRAAIGLLARHLKATHGGRIDYIAGLDSRGFLFGPSLAQELGLGCVLIRKRGKLPGPTLWASYSLEYGKAELEIQKDALEPGQRVVVVDDLLATGGTMNAACELLGRLQAEVLECVSLVELTSLKGREKLAPVPFFSLLQYE,mutated_sequence,1.0,180.0,NP_000476.1.a2m,NP_000476.1.npy,ClinVar +NP_000478.3,NP_000478.3.csv,MSMGAPRSLLLALAAGLAVARPPNIVLIFADDLGYGDLGCYGHPSSTTPNLDQLAAGGLRFTDFYVPVSLCTPSRAALLTGRLPVRMGMYPGVLVPSSRGGLPLEEVTVAEVLAARGYLTGMAGKWHLGVGPEGAFLPPHQGFHRFLGIPYSHDQGPCQNLTCFPPATPCDGGCDQGLVPIPLLANLSVEAQPPWLPGLEARYMAFAHDLMADAQRQDRPFFLYYASHHTHYPQFSGQSFAERSGRGPFGDSLMELDAAVGTLMTAIGDLGLLEETLVIFTADNGPETMRMSRGGCSGLLRCGKGTTYEGGVREPALAFWPGHIAPGVTHELASSLDLLPTLAALAGAPLPNVTLDGFDLSPLLLGTGKSPRQSLFFYPSYPDEVRGVFAVRTGKYKAHFFTQGSAHSDTTADPACHASSSLTAHEPPLLYDLSKDPGENYNLLGGVAGATPEVLQALKQLQLLKAQLDAAVTFGPSQVARGEDPALQICCHPGCTPRPACCHCPDPHA,mutated_sequence,1.0,509.0,NP_000478.3.a2m,NP_000478.3.npy,ClinVar +NP_000479.1,NP_000479.1.csv,MYSNVIGTVTSGKRKVYLLSLLLIGFWDCVTCHGSPVDICTAKPRDIPMNPMCIYRSPEKKATEDEGSEQKIPEATNRRVWELSKANSRFATTFYQHLADSKNDNDNIFLSPLSISTAFAMTKLGACNDTLQQLMEVFKFDTISEKTSDQIHFFFAKLNCRLYRKANKSSKLVSANRLFGDKSLTFNETYQDISELVYGAKLQPLDFKENAEQSRAAINKWVSNKTEGRITDVIPSEAINELTVLVLVNTIYFKGLWKSKFSPENTRKELFYKADGESCSASMMYQEGKFRYRRVAEGTQVLELPFKGDDITMVLILPKPEKSLAKVEKELTPEVLQEWLDELEEMMLVVHMPRFRIEDGFSLKEQLQDMGLVDLFSPEKSKLPGIVAEGRDDLYVSDAFHKAFLEVNEEGSEAAASTAVVIAGRSLNPNRVTFKANRPFLVFIREVPLNTIIFMGRVANPCVK,mutated_sequence,1.0,464.0,NP_000479.1.a2m,NP_000479.1.npy,ClinVar +NP_000480.3,NP_000480.3.csv,MTAEPMSESKLNTLVQKLHDFLAHSSEESEETSSPPRLAMNQNTDKISGSGSNSDMMENSKEEGTSSSEKSKSSGSSRSKRKPSIVTKYVESDDEKPLDDETVNEDASNENSENDITMQSLPKGTVIVQPEPVLNEDKDDFKGPEFRSRSKMKTENLKKRGEDGLHGIVSCTACGQQVNHFQKDSIYRHPSLQVLICKNCFKYYMSDDISRDSDGMDEQCRWCAEGGNLICCDFCHNAFCKKCILRNLGRKELSTIMDENNQWYCYICHPEPLLDLVTACNSVFENLEQLLQQNKKKIKVDSEKSNKVYEHTSRFSPKKTSSNCNGEEKKLDDSCSGSVTYSYSALIVPKEMIKKAKKLIETTANMNSSYVKFLKQATDNSEISSATKLRQLKAFKSVLADIKKAHLALEEDLNSEFRAMDAVNKEKNTKEHKVIDAKFETKARKGEKPCALEKKDISKSEAKLSRKQVDSEHMHQNVPTEEQRTNKSTGGEHKKSDRKEEPQYEPANTSEDLDMDIVSVPSSVPEDIFENLETAMEVQSSVDHQGDGSSGTEQEVESSSVKLNISSKDNRGGIKSKTTAKVTKELYVKLTPVSLSNSPIKGADCQEVPQDKDGYKSCGLNPKLEKCGLGQENSDNEHLVENEVSLLLEESDLRRSPRVKTTPLRRPTETNPVTSNSDEECNETVKEKQKLSVPVRKKDKRNSSDSAIDNPKPNKLPKSKQSETVDQNSDSDEMLAILKEVSRMSHSSSSDTDINEIHTNHKTLYDLKTQAGKDDKGKRKRKSSTSGSDFDTKKGKSAKSSIISKKKRQTQSESSNYDSELEKEIKSMSKIGAARTTKKRIPNTKDFDSSEDEKHSKKGMDNQGHKNLKTSQEGSSDDAERKQERETFSSAEGTVDKDTTIMELRDRLPKKQQASASTDGVDKLSGKEESFTSLEVRKVAETKEKSKHLKTKTCKKVQDGLSDIAEKFLKKDQSDETSEDDKKQSKKGTEEKKKPSDFKKKVIKMEQQYESSSDGTEKLPEREEICHFPKGIKQIKNGTTDGEKKSKKIRDKTSKKKDELSDYAEKSTGKGDSCDSSEDKKSKNGAYGREKKRCKLLGKSSRKRQDCSSSDTEKYSMKEDGCNSSDKRLKRIELRERRNLSSKRNTKEIQSGSSSSDAEESSEDNKKKKQRTSSKKKAVIVKEKKRNSLRTSTKRKQADITSSSSSDIEDDDQNSIGEGSSDEQKIKPVTENLVLSSHTGFCQSSGDEALSKSVPVTVDDDDDDNDPENRIAKKMLLEEIKANLSSDEDGSSDDEPEEGKKRTGKQNEENPGDEEAKNQVNSESDSDSEESKKPRYRHRLLRHKLTVSDGESGEEKKTKPKEHKEVKGRNRRKVSSEDSEDSDFQESGVSEEVSESEDEQRPRTRSAKKAELEENQRSYKQKKKRRRIKVQEDSSSENKSNSEEEEEEKEEEEEEEEEEEEEEEDENDDSKSPGKGRKKIRKILKDDKLRTETQNALKEEEERRKRIAEREREREKLREVIEIEDASPTKCPITTKLVLDEDEETKEPLVQVHRNMVIKLKPHQVDGVQFMWDCCCESVKKTKKSPGSGCILAHCMGLGKTLQVVSFLHTVLLCDKLDFSTALVVCPLNTALNWMNEFEKWQEGLKDDEKLEVSELATVKRPQERSYMLQRWQEDGGVMIIGYEMYRNLAQGRNVKSRKLKEIFNKALVDPGPDFVVCDEGHILKNEASAVSKAMNSIRSRRRIILTGTPLQNNLIEYHCMVNFIKENLLGSIKEFRNRFINPIQNGQCADSTMVDVRVMKKRAHILYEMLAGCVQRKDYTALTKFLPPKHEYVLAVRMTSIQCKLYQYYLDHLTGVGNNSEGGRGKAGAKLFQDFQMLSRIWTHPWCLQLDYISKENKGYFDEDSMDEFIASDSDETSMSLSSDDYTKKKKKGKKGKKDSSSSGSGSDNDVEVIKVWNSRSRGGGEGNVDETGNNPSVSLKLEESKATSSSNPSSPAPDWYKDFVTDADAEVLEHSGKMVLLFEILRMAEEIGDKVLVFSQSLISLDLIEDFLELASREKTEDKDKPLIYKGEGKWLRNIDYYRLDGSTTAQSRKKWAEEFNDETNVRGRLFIISTKAGSLGINLVAANRVIIFDASWNPSYDIQSIFRVYRFGQTKPVYVYRFLAQGTMEDKIYDRQVTKQSLSFRVVDQQQVERHFTMNELTELYTFEPDLLDDPNSEKKKKRDTPMLPKDTILAELLQIHKEHIVGYHEHDSLLDHKEEEELTEEERKAAWAEYEAEKKGLTMRFNIPTGTNLPPVSFNSQTPYIPFNLGALSAMSNQQLEDLINQGREKVVEATNSVTAVRIQPLEDIISAVWKENMNLSEAQVQALALSRQASQELDVKRREAIYNDVLTKQQMLISCVQRILMNRRLQQQYNQQQQQQMTYQQATLGHLMMPKPPNLIMNPSNYQQIDMRGMYQPVAGGMQPPPLQRAPPPMRSKNPGPSQGKSM,mutated_sequence,1.0,2492.0,NP_000480.3.a2m,NP_000480.3.npy,ClinVar +NP_000481.2,NP_000481.2.csv,MPDTMLPACFLGLLAFSSACYFQNCPRGGKRAMSDLELRQCLPCGPGGKGRCFGPSICCADELGCFVGTAEALRCQEENYLPSPCQSGQKACGSGGRCAAFGVCCNDESCVTEPECREGFHRRARASDRSNATQLDGPAGALLLRLVQLAGAPEPFEPAQPDAY,mutated_sequence,1.0,164.0,NP_000481.2.a2m,NP_000481.2.npy,ClinVar +NP_000483.3,NP_000483.3.csv,MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLELSDIYQIPSVDSADNLSEKLEREWDRELASKKNPKLINALRRCFFWRFMFYGIFLYLGEVTKAVQPLLLGRIIASYDPDNKEERSIAIYLGIGLCLLFIVRTLLLHPAIFGLHHIGMQMRIAMFSLIYKKTLKLSSRVLDKISIGQLVSLLSNNLNKFDEGLALAHFVWIAPLQVALLMGLIWELLQASAFCGLGFLIVLALFQAGLGRMMMKYRDQRAGKISERLVITSEMIENIQSVKAYCWEEAMEKMIENLRQTELKLTRKAAYVRYFNSSAFFFSGFFVVFLSVLPYALIKGIILRKIFTTISFCIVLRMAVTRQFPWAVQTWYDSLGAINKIQDFLQKQEYKTLEYNLTTTEVVMENVTAFWEEGFGELFEKAKQNNNNRKTSNGDDSLFFSNFSLLGTPVLKDINFKIERGQLLAVAGSTGAGKTSLLMVIMGELEPSEGKIKHSGRISFCSQFSWIMPGTIKENIIFGVSYDEYRYRSVIKACQLEEDISKFAEKDNIVLGEGGITLSGGQRARISLARAVYKDADLYLLDSPFGYLDVLTEKEIFESCVCKLMANKTRILVTSKMEHLKKADKILILHEGSSYFYGTFSELQNLQPDFSSKLMGCDSFDQFSAERRNSILTETLHRFSLEGDAPVSWTETKKQSFKQTGEFGEKRKNSILNPINSIRKFSIVQKTPLQMNGIEEDSDEPLERRLSLVPDSEQGEAILPRISVISTGPTLQARRRQSVLNLMTHSVNQGQNIHRKTTASTRKVSLAPQANLTELDIYSRRLSQETGLEISEEINEEDLKECFFDDMESIPAVTTWNTYLRYITVHKSLIFVLIWCLVIFLAEVAASLVVLWLLGNTPLQDKGNSTHSRNNSYAVIITSTSSYYVFYIYVGVADTLLAMGFFRGLPLVHTLITVSKILHHKMLHSVLQAPMSTLNTLKAGGILNRFSKDIAILDDLLPLTIFDFIQLLLIVIGAIAVVAVLQPYIFVATVPVIVAFIMLRAYFLQTSQQLKQLESEGRSPIFTHLVTSLKGLWTLRAFGRQPYFETLFHKALNLHTANWFLYLSTLRWFQMRIEMIFVIFFIAVTFISILTTGEGEGRVGIILTLAMNIMSTLQWAVNSSIDVDSLMRSVSRVFKFIDMPTEGKPTKSTKPYKNGQLSKVMIIENSHVKKDDIWPSGGQMTVKDLTAKYTEGGNAILENISFSISPGQRVGLLGRTGSGKSTLLSAFLRLLNTEGEIQIDGVSWDSITLQQWRKAFGVIPQKVFIFSGTFRKNLDPYEQWSDQEIWKVADEVGLRSVIEQFPGKLDFVLVDGGCVLSHGHKQLMCLARSVLSKAKILLLDEPSAHLDPVTYQIIRRTLKQAFADCTVILCEHRIEAMLECQQFLVIEENKVRQYDSIQKLLNERSLFRQAISPSDRVKLFPHRNSSKCKSKPQIAALKEETEEEVQDTRL,mutated_sequence,1.0,1480.0,NP_000483.3.a2m,NP_000483.3.npy,ClinVar +NP_000484.2,NP_000484.2.csv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mutated_sequence,1.0,680.0,NP_000484.2.a2m,NP_000484.2.npy,ClinVar +NP_000485.3,NP_000485.3.csv,MDVTKKNKRDGTEVTERIVTETVTTRLTSLPPKGGTSNGYAKTASLGGGSRLEKQSLTHGSSGYINSTGSTRGHASTSSYRRAHSPASTLPNSPGSTFERKTHVTRHAYEGSSSGNSSPEYPRKEFASSSTRGRSQTRESEIRVRLQSASPSTRWTELDDVKRLLKGSRSASVSPTRNSSNTLPIPKKGTVETKIVTASSQSVSGTYDATILDANLPSHVWSSTLPAGSSMGTYHNNMTTQSSSLLNTNAYSAGSVFGVPNNMASCSPTLHPGLSTSSSVFGMQNNLAPSLTTLSHGTTTTSTAYGVKKNMPQSPAAVNTGVSTSAACTTSVQSDDLLHKDCKFLILEKDNTPAKKEMELLIMTKDSGKVFTASPASIAATSFSEDTLKKEKQAAYNADSGLKAEANGDLKTVSTKGKTTTADIHSYGSSGGGGSGGGGGVGGAGGGPWGPAPAWCPCGSCCSWWKWLLGLLLTWLLLLGLLFGLIALAEEVRKLKARVDELERIRRSILPYGDSMDRIEKDRLQGMAPAAGADLDKIGLHSDSQEELWMFVRKKLMMEQENGNLRGSPGPKGDMGSPGPKGDRGFPGTPGIPGPLGHPGPQGPKGQKGSVGDPGMEGPMGQRGREGPMGPRGEAGPPGSGEKGERGAAGEPGPHGPPGVPGSVGPKGSSGSPGPQGPPGPVGLQGLRGEVGLPGVKGDKGPMGPPGPKGDQGEKGPRGLTGEPGMRGLPGAVGEPGAKGAMGPAGPDGHQGPRGEQGLTGMPGIRGPPGPSGDPGKPGLTGPQGPQGLPGTPGRPGIKGEPGAPGKIVTSEGSSMLTVPGPPGPPGAMGPPGPPGAPGPAGPAGLPGHQEVLNLQGPPGPPGPRGPPGPSIPGPPGPRGPPGEGLPGPPGPPGSFLSNSETFLSGPPGPPGPPGPKGDQGPPGPRGHQGEQGLPGFSTSGSSSFGLNLQGPPGPPGPQGPKGDKGDPGVPGALGIPSGPSEGGSSSTMYVSGPPGPPGPPGPPGSISSSGQEIQQYISEYMQSDSIRSYLSGVQGPPGPPGPPGPVTTITGETFDYSELASHVVSYLRTSGYGVSLFSSSISSEDILAVLQRDDVRQYLRQYLMGPRGPPGPPGASGDGSLLSLDYAELSSRILSYMSSSGISIGLPGPPGPPGLPGTSYEELLSLLRGSEFRGIVGPPGPPGPPGIPGNVWSSISVEDLSSYLHTAGLSFIPGPPGPPGPPGPRGPPGVSGALATYAAENSDSFRSELISYLTSPDVRSFIVGPPGPPGPQGPPGDSRLLSTDASHSRGSSSSSHSSSVRRGSSYSSSMSTGGGGAGSLGAGGAFGEAAGDRGPYGTDIGPGGGYGAAAEGGMYAGNGGLLGADFAGDLDYNELAVRVSESMQRQGLLQGMAYTVQGPPGQPGPQGPPGISKVFSAYSNVTADLMDFFQTYGAIQGPPGQKGEMGTPGPKGDRGPAGPPGHPGPPGPRGHKGEKGDKGDQVYAGRRRRRSIAVKP,mutated_sequence,1.0,1497.0,NP_000485.3.a2m,NP_000485.3.npy,ClinVar +NP_000489.3,NP_000489.3.csv,MALRAKAEVCVAAPWLSLQRARALGTRAARAPRTVLPFEAMPQHPGNRWLRLLQIWREQGYEHLHLEMHQTFQELGPIFRYNLGGPRMVCVMLPEDVEKLQQVDSLHPCRMILEPWVAYRQHRGHKCGVFLLNGPEWRFNRLRLNPDVLSPKAVQRFLPMVDAVARDFSQALKKKVLQNARGSLTLDVQPSIFHYTIEASNLALFGERLGLVGHSPSSASLNFLHALEVMFKSTVQLMFMPRSLSRWISPKVWKEHFEAWDCIFQYGDNCIQKIYQELAFNRPQHYTGIVAELLLKAELSLEAIKANSMELTAGSVDTTAFPLLMTLFELARNPDVQQILRQESLAAAASISEHPQKATTELPLLRAALKETLRLYPVGLFLERVVSSDLVLQNYHIPAGTLVQVFLYSLGRNAALFPRPERYNPQRWLDIRGSGRNFHHVPFGFGMRQCLGRRLAEAEMLLLLHHVLKHFLVETLTQEDIKMVYSFILRPGTSPLLTFRAIN,mutated_sequence,1.0,503.0,NP_000489.3.a2m,NP_000489.3.npy,ClinVar +NP_000497.1,NP_000497.1.csv,MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE,mutated_sequence,1.0,622.0,NP_000497.1.a2m,NP_000497.1.npy,ClinVar +NP_000498.2,NP_000498.2.csv,MADQAPFDTDVNTLTRFVMEEGRKARGTGELTQLLNSLCTAVKAISSAVRKAGIAHLYGIAGSTNVTGDQVKKLDVLSNDLVMNMLKSSFATCVLVSEEDKHAIIVEPEKRGKYVVCFDPLDGSSNIDCLVSVGTIFGIYRKKSTDEPSEKDALQPGRNLVAAGYALYGSATMLVLAMDCGVNCFMLDPAIGEFILVDKDVKIKKKGKIYSLNEGYARDFDPAVTEYIQRKKFPPDNSAPYGARYVGSMVADVHRTLVYGGIFLYPANKKSPNGKLRLLYECNPMAYVMEKAGGMATTGKEAVLDVIPTDIHQRAPVILGSPDDVLEFLKVYEKHSAQ,mutated_sequence,1.0,338.0,NP_000498.2.a2m,NP_000498.2.npy,ClinVar +NP_000499.1,NP_000499.1.csv,MFSMRIVCLVLSVVGTAWTADSGEGDFLAEGGGVRGPRVVERHQSACKDSDWPFCSDEDWNYKCPSGCRMKGLIDEVNQDFTNRINKLKNSLFEYQKNNKDSHSLTTNIMEILRGDFSSANNRDNTYNRVSEDLRSRIEVLKRKVIEKVQHIQLLQKNVRAQLVDMKRLEVDIDIKIRSCRGSCSRALAREVDLKDYEDQQKQLEQVIAKDLLPSRDRQHLPLIKMKPVPDLVPGNFKSQLQKVPPEWKALTDMPQMRMELERPGGNEITRGGSTSYGTGSETESPRNPSSAGSWNSGSSGPGSTGNRNPGSSGTGGTATWKPGSSGPGSTGSWNSGSSGTGSTGNQNPGSPRPGSTGTWNPGSSERGSAGHWTSESSVSGSTGQWHSESGSFRPDSPGSGNARPNNPDWGTFEEVSGNVSPGTRREYHTEKLVTSKGDKELRTGKEKVTSGSTTTTRRSCSKTVTKTVIGPDGHKEVTKEVVTSEDGSDCPEAMDLGTLSGIGTLDGFRHRHPDEAAFFDTASTGKTFPGFFSPMLGEFVSETESRGSESGIFTNTKESSSHHPGIAEFPSRGKSSSYSKQFTSSTSYNRGDSTFESKSYKMADEAGSEADHEGTHSTKRGHAKSRPVRDCDDVLQTHPSGTQSGIFNIKLPGSSKIFSVYCDQETSLGGWLLIQQRMDGSLNFNRTWQDYKRGFGSLNDEGEGEFWLGNDYLHLLTQRGSVLRVELEDWAGNEAYAEYHFRVGSEAEGYALQVSSYEGTAGDALIEGSVEEGAEYTSHNNMQFSTFDRDADQWEENCAEVYGGGWWYNNCQAANLNGIYYPGGSYDPRNNSPYEIENGVVWVSFRGADYSLRAVRMKIRPLVTQ,mutated_sequence,1.0,866.0,NP_000499.1.a2m,NP_000499.1.npy,ClinVar +NP_000503.1,NP_000503.1.csv,MAAVVAATRWWQLLLVLSAAGMGASGAPQPPNILLLLMDDMGWGDLGVYGEPSRETPNLDRMAAEGLLFPNFYSANPLCSPSRAALLTGRLPIRNGFYTTNAHARNAYTPQEIVGGIPDSEQLLPELLKKAGYVSKIVGKWHLGHRPQFHPLKHGFDEWFGSPNCHFGPYDNKARPNIPVYRDWEMVGRYYEEFPINLKTGEANLTQIYLQEALDFIKRQARHHPFFLYWAVDATHAPVYASKPFLGTSQRGRYGDAVREIDDSIGKILELLQDLHVADNTFVFFTSDNGAALISAPEQGGSNGPFLCGKQTTFEGGMREPALAWWPGHVTAGQVSHQLGSIMDLFTTSLALAGLTPPSDRAIDGLNLLPTLLQGRLMDRPIFYYRGDTLMAATLGQHKAHFWTWTNSWENFRQGIDFCPGQNVSGVTTHNLEDHTKLPLIFHLGRDPGERFPLSFASAEYQEALSRITSVVQQHQEALVPAQPQLNVCNWAVMNWAPPGCEKLGKCLTPPESIPKKCLWSH,mutated_sequence,1.0,522.0,NP_000503.1.a2m,NP_000503.1.npy,ClinVar +NP_000507.1,NP_000507.1.csv,MGCLGNSKTEDQRNEEKAQREANKKIEKQLQKDKQVYRATHRLLLLGAGESGKSTIVKQMRILHVNGFNGEGGEEDPQAARSNSDGEKATKVQDIKNNLKEAIETIVAAMSNLVPPVELANPENQFRVDYILSVMNVPDFDFPPEFYEHAKALWEDEGVRACYERSNEYQLIDCAQYFLDKIDVIKQADYVPSDQDLLRCRVLTSGIFETKFQVDKVNFHMFDVGGQRDERRKWIQCFNDVTAIIFVVASSSYNMVIREDNQTNRLQEALNLFKSIWNNRWLRTISVILFLNKQDLLAEKVLAGKSKIEDYFPEFARYTTPEDATPEPGEDPRVTRAKYFIRDEFLRISTASGDGRHYCYPHFTCAVDTENIRRVFNDCRDIIQRMHLRQYELL,mutated_sequence,1.0,394.0,NP_000507.1.a2m,NP_000507.1.npy,ClinVar +NP_000508.1,NP_000508.1.csv,MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR,mutated_sequence,1.0,142.0,NP_000508.1.a2m,NP_000508.1.npy,ClinVar +NP_000509.1,NP_000509.1.csv,MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH,mutated_sequence,1.0,147.0,NP_000509.1.a2m,NP_000509.1.npy,ClinVar +NP_000511.2,NP_000511.2.csv,MTSSRLWFSLLLAAAFAGRATALWPWPQNFQTSDQRYVLYPNNFQFQYDVSSAAQPGCSVLDEAFQRYRDLLFGSGSWPRPYLTGKRHTLEKNVLVVSVVTPGCNQLPTLESVENYTLTINDDQCLLLSETVWGALRGLETFSQLVWKSAEGTFFINKTEIEDFPRFPHRGLLLDTSRHYLPLSSILDTLDVMAYNKLNVFHWHLVDDPSFPYESFTFPELMRKGSYNPVTHIYTAQDVKEVIEYARLRGIRVLAEFDTPGHTLSWGPGIPGLLTPCYSGSEPSGTFGPVNPSLNNTYEFMSTFFLEVSSVFPDFYLHLGGDEVDFTCWKSNPEIQDFMRKKGFGEDFKQLESFYIQTLLDIVSSYGKGYVVWQEVFDNKVKIQPDTIIQVWREDIPVNYMKELELVTKAGFRALLSAPWYLNRISYGPDWKDFYIVEPLAFEGTPEQKALVIGGEACMWGEYVDNTNLVPRLWPRAGAVAERLWSNKLTSDLTFAYERLSHFRCELLRRGVQAQPLNVGFCEQEFEQT,mutated_sequence,1.0,529.0,NP_000511.2.a2m,NP_000511.2.npy,ClinVar +NP_000512.2,NP_000512.2.csv,MELCGLGLPRPPMLLALLLATLLAAMLALLTQVALVVQVAEAARAPSVSAKPGPALWPLPLLVKMTPNLLHLAPENFYISHSPNSTAGPSCTLLEEAFRRYHGYIFGFYKWHHEPAEFQAKTQVQQLLVSITLQSECDAFPNISSDESYTLLVKEPVAVLKANRVWGALRGLETFSQLVYQDSYGTFTINESTIIDSPRFSHRGILIDTSRHYLPVKIILKTLDAMAFNKFNVLHWHIVDDQSFPYQSITFPELSNKGSYSLSHVYTPNDVRMVIEYARLRGIRVLPEFDTPGHTLSWGKGQKDLLTPCYSRQNKLDSFGPINPTLNTTYSFLTTFFKEISEVFPDQFIHLGGDEVEFKCWESNPKIQDFMRQKGFGTDFKKLESFYIQKVLDIIATINKGSIVWQEVFDDKAKLAPGTIVEVWKDSAYPEELSRVTASGFPVILSAPWYLDLISYGQDWRKYYKVEPLDFGGTQKQKQLFIGGEACLWGEYVDATNLTPRLWPRASAVGERLWSSKDVRDMDDAYDRLTRHRCRMVERGIAAQPLYAGYCNHENM,mutated_sequence,1.0,556.0,NP_000512.2.a2m,NP_000512.2.npy,ClinVar +NP_000517.3,NP_000517.3.csv,MTTCSRQFTSSSSMKGSCGIGGGIGGGSSRISSVLAGGSCRAPSTYGGGLSVSSSRFSSGGACGLGGGYGGGFSSSSSSFGSGFGGGYGGGLGAGLGGGFGGGFAGGDGLLVGSEKVTMQNLNDRLASYLDKVRALEEANADLEVKIRDWYQRQRPAEIKDYSPYFKTIEDLRNKILTATVDNANVLLQIDNARLAADDFRTKYETELNLRMSVEADINGLRRVLDELTLARADLEMQIESLKEELAYLKKNHEEEMNALRGQVGGDVNVEMDAAPGVDLSRILNEMRDQYEKMAEKNRKDAEEWFFTKTEELNREVATNSELVQSGKSEISELRRTMQNLEIELQSQLSMKASLENSLEETKGRYCMQLAQIQEMIGSVEEQLAQLRCEMEQQNQEYKILLDVKTRLEQEIATYRRLLEGEDAHLSSSQFSSGSQSSRDVTSSSRQIRTKVMDVHDGKVVSTHEQVLRTKN,mutated_sequence,1.0,472.0,NP_000517.3.a2m,NP_000517.3.npy,ClinVar +NP_000518.1,NP_000518.1.csv,MGPWGWKLRWTVALLLAAAGTAVGDRCERNEFQCQDGKCISYKWVCDGSAECQDGSDESQETCLSVTCKSGDFSCGGRVNRCIPQFWRCDGQVDCDNGSDEQGCPPKTCSQDEFRCHDGKCISRQFVCDSDRDCLDGSDEASCPVLTCGPASFQCNSSTCIPQLWACDNDPDCEDGSDEWPQRCRGLYVFQGDSSPCSAFEFHCLSGECIHSSWRCDGGPDCKDKSDEENCAVATCRPDEFQCSDGNCIHGSRQCDREYDCKDMSDEVGCVNVTLCEGPNKFKCHSGECITLDKVCNMARDCRDWSDEPIKECGTNECLDNNGGCSHVCNDLKIGYECLCPDGFQLVAQRRCEDIDECQDPDTCSQLCVNLEGGYKCQCEEGFQLDPHTKACKAVGSIAYLFFTNRHEVRKMTLDRSEYTSLIPNLRNVVALDTEVASNRIYWSDLSQRMICSTQLDRAHGVSSYDTVISRDIQAPDGLAVDWIHSNIYWTDSVLGTVSVADTKGVKRKTLFRENGSKPRAIVVDPVHGFMYWTDWGTPAKIKKGGLNGVDIYSLVTENIQWPNGITLDLLSGRLYWVDSKLHSISSIDVNGGNRKTILEDEKRLAHPFSLAVFEDKVFWTDIINEAIFSANRLTGSDVNLLAENLLSPEDMVLFHNLTQPRGVNWCERTTLSNGGCQYLCLPAPQINPHSPKFTCACPDGMLLARDMRSCLTEAEAAVATQETSTVRLKVSSTAVRTQHTTTRPVPDTSRLPGATPGLTTVEIVTMSHQALGDVAGRGNEKKPSSVRALSIVLPIVLLVFLCLGVFLLWKNWRLKNINSINFDNPVYQKTTEDEVHICHNQDGYSYPSRQMVSLEDDVA,mutated_sequence,1.0,860.0,NP_000518.1.a2m,NP_000518.1.npy,ClinVar +NP_000521.2,NP_000521.2.csv,MAPGAPSSSPSPILAVLLFSSLVLSPAQAIVVYTDREVHGAVGSRVTLHCSFWSSEWVSDDISFTWRYQPEGGRDAISIFHYAKGQPYIDEVGTFKERIQWVGDPRWKDGSIVIHNLDYSDNGTFTCDVKNPPDIVGKTSQVTLYVFEKVPTRYGVVLGAVIGGVLGVVLLLLLLFYVVRYCWLRRQAALQRRLSAMEKGKLHKPGKDASKRGRQTPVLYAMLDHSRSTKAVSEKKAKGLGESRKDKK,mutated_sequence,1.0,248.0,NP_000521.2.a2m,NP_000521.2.npy,ClinVar +NP_000522.3,NP_000522.3.csv,MLFNLRILLNNAAFRNGHNFMVRNFRCGQPLQNKVQLKGRDLLTLKNFTGEEIKYMLWLSADLKFRIKQKGEYLPLLQGKSLGMIFEKRSTRTRLSTETGFALLGGHPCFLTTQDIHLGVNESLTDTARVLSSMADAVLARVYKQSDLDTLAKEASIPIINGLSDLYHPIQILADYLTLQEHYSSLKGLTLSWIGDGNNILHSIMMSAAKFGMHLQAATPKGYEPDASVTKLAEQYAKENGTKLLLTNDPLEAAHGGNVLITDTWISMGQEEEKKKRLQAFQGYQVTMKTAKVAASDWTFLHCLPRKPEEVDDEVFYSPRSLVFPEAENRKWTIMAVMVSLLTDYSPQLQKPKF,mutated_sequence,1.0,354.0,NP_000522.3.a2m,NP_000522.3.npy,ClinVar +NP_000523.2,NP_000523.2.csv,MAAALRVAAVGARLSVLASGLRAAVRSLCSQATSVNERIENKRRTALLGGGQRRIDAQHKRGKLTARERISLLLDPGSFVESDMFVEHRCADFGMAADKNKFPGDSVVTGRGRINGRLVYVFSQDFTVFGGSLSGAHAQKICKIMDQAITVGAPVIGLNDSGGARIQEGVESLAGYADIFLRNVTASGVIPQISLIMGPCAGGAVYSPALTDFTFMVKDTSYLFITGPDVVKSVTNEDVTQEELGGAKTHTTMSGVAHRAFENDVDALCNLRDFFNYLPLSSQDPAPVRECHDPSDRLVPELDTIVPLESTKAYNMVDIIHSVVDEREFFEIMPNYAKNIIVGFARMNGRTVGIVGNQPKVASGCLDINSSVKGARFVRFCDAFNIPLITFVDVPGFLPGTAQEYGGIIRHGAKLLYAFAEATVPKVTVITRKAYGGAYDVMSSKHLCGDTNYAWPTAEIAVMGAKGAVEIIFKGHENVEAAQAEYIEKFANPFPAAVRGFVDDIIQPSSTRARICCDLDVLASKKVQRPWRKHANIPL,mutated_sequence,1.0,539.0,NP_000523.2.a2m,NP_000523.2.npy,ClinVar +NP_000524.3,NP_000524.3.csv,MGLLECCARCLVGAPFASLVATGLCFFGVALFCGCGHEALTGTEKLIETYFSKNYQDYEYLINVIHAFQYVIYGTASFFFLYGALLLAEGFYTTGAVRQIFGDYKTTICGKGLSATVTGGQKGRGSRGQHQAHSLERVCHCLGKWLGHPDKFVGITYALTVVWLLVFACSAVPVYIYFNTWTTCQSIAFPSKTSASIGSLCADARMYGVLPWNAFPGKVCGSNLLSICKTAEFQMTFHLFIAAFVGAAATLVSLLTFMIAATYNFAVLKLMGRGTKF,mutated_sequence,1.0,277.0,NP_000524.3.a2m,NP_000524.3.npy,ClinVar +NP_000526.2,NP_000526.2.csv,MERAESSSTEPAKAIKPIDRKSVHQICSGQVVLSLSTAVKELVENSLDAGATNIDLKLKDYGVDLIEVSDNGCGVEEENFEGLTLKHHTSKIQEFADLTQVETFGFRGEALSSLCALSDVTISTCHASAKVGTRLMFDHNGKIIQKTPYPRPRGTTVSVQQLFSTLPVRHKEFQRNIKKEYAKMVQVLHAYCIISAGIRVSCTNQLGQGKRQPVVCTGGSPSIKENIGSVFGQKQLQSLIPFVQLPPSDSVCEEYGLSCSDALHNLFYISGFISQCTHGVGRSSTDRQFFFINRRPCDPAKVCRLVNEVYHMYNRHQYPFVVLNISVDSECVDINVTPDKRQILLQEEKLLLAVLKTSLIGMFDSDVNKLNVSQQPLLDVEGNLIKMHAADLEKPMVEKQDQSPSLRTGEEKKDVSISRLREAFSLRHTTENKPHSPKTPEPRRSPLGQKRGMLSSSTSGAISDKGVLRPQKEAVSSSHGPSDPTDRAEVEKDSGHGSTSVDSEGFSIPDTGSHCSSEYAASSPGDRGSQEHVDSQEKAPKTDDSFSDVDCHSNQEDTGCKFRVLPQPTNLATPNTKRFKKEEILSSSDICQKLVNTQDMSASQVDVAVKINKKVVPLDFSMSSLAKRIKQLHHEAQQSEGEQNYRKFRAKICPGENQAAEDELRKEISKTMFAEMEIIGQFNLGFIITKLNEDIFIVDQHATDEKYNFEMLQQHTVLQGQRLIAPQTLNLTAVNEAVLIENLEIFRKNGFDFVIDENAPVTERAKLISLPTSKNWTFGPQDVDELIFMLSDSPGVMCRPSRVKQMFASRACRKSVMIGTALNTSEMKKLITHMGEMDHPWNCPHGRPTMRHIANLGVISQN,mutated_sequence,1.0,862.0,NP_000526.2.a2m,NP_000526.2.npy,ClinVar +NP_000527.2,NP_000527.2.csv,MSLQMVTVSNNIALIQPGFSLMNFDGQVFFFGQKGWPKRSCPTGVFHLDVKHNHVKLKPTIFSKDSCYLPPLRYPATCTFKGSLESEKHQYIIHGGKTPNNEVSDKIYVMSIVCKNNKKVTFRCTEKDLVGDVPEARYGHSINVVYSRGKSMGVLFGGRSYMPSTHRTTEKWNSVADCLPCVFLVDFEFGCATSYILPELQDGLSFHVSIAKNDTIYILGGHSLANNIRPANLYRIRVDLPLGSPAVNCTVLPGGISVSSAILTQTNNDEFVIVGGYQLENQKRMICNIISLEDNKIEIREMETPDWTPDIKHSKIWFGSNMGNGTVFLGIPGDNKQVVSEGFYFYMLKCAEDDTNEEQTTFTNSQTSTEDPGDSTPFEDSEEFCFSAEANSFDGDDEFDTYNEDDEEDESETGYWITCCPTCDVDINTWVPFYSTELNKPAMIYCSHGDGHWVHAQCMDLAERTLIHLSAGSNKYYCNEHVEIARALHTPQRVLPLKKPPMKSLRKKGSGKILTPAKKSFLRRLFD,mutated_sequence,1.0,527.0,NP_000527.2.a2m,NP_000527.2.npy,ClinVar +NP_000528.1,NP_000528.1.csv,MDGWRRMPRWGLLLLLWGSCTFGLPTDTTTFKRIFLKRMPSIRESLKERGVDMARLGPEWSQPMKRLTLGNTTSSVILTNYMDTQYYGEIGIGTPPQTFKVVFDTGSSNVWVPSSKCSRLYTACVYHKLFDASDSSSYKHNGTELTLRYSTGTVSGFLSQDIITVGGITVTQMFGEVTEMPALPFMLAEFDGVVGMGFIEQAIGRVTPIFDNIISQGVLKEDVFSFYYNRDSENSQSLGGQIVLGGSDPQHYEGNFHYINLIKTGVWQIQMKGVSVGSSTLLCEDGCLALVDTGASYISGSTSSIEKLMEALGAKKRLFDYVVKCNEGPTLPDISFHLGGKEYTLTSADYVFQESYSSKKLCTLAIHAMDIPPPTGPTWALGATFIRKFYTEFDRRNNRIGFALAR,mutated_sequence,1.0,406.0,NP_000528.1.a2m,NP_000528.1.npy,ClinVar +NP_000530.1,NP_000530.1.csv,MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA,mutated_sequence,1.0,348.0,NP_000530.1.a2m,NP_000530.1.npy,ClinVar +NP_000531.2,NP_000531.2.csv,MGDAEGEDEVQFLRTDDEVVLQCSATVLKEQLKLCLAAEGFGNRLCFLEPTSNAQNVPPDLAICCFVLEQSLSVRALQEMLANTVEAGVESSQGGGHRTLLYGHAILLRHAHSRMYLSCLTTSRSMTDKLAFDVGLQEDATGEACWWTMHPASKQRSEGEKVRVGDDIILVSVSSERYLHLSTASGELQVDASFMQTLWNMNPICSRCEEGFVTGGHVLRLFHGHMDECLTISPADSDDQRRLVYYEGGAVCTHARSLWRLEPLRISWSGSHLRWGQPLRVRHVTTGQYLALTEDQGLVVVDASKAHTKATSFCFRISKEKLDVAPKRDVEGMGPPEIKYGESLCFVQHVASGLWLTYAAPDPKALRLGVLKKKAMLHQEGHMDDALSLTRCQQEESQAARMIHSTNGLYNQFIKSLDSFSGKPRGSGPPAGTALPIEGVILSLQDLIIYFEPPSEDLQHEEKQSKLRSLRNRQSLFQEEGMLSMVLNCIDRLNVYTTAAHFAEFAGEEAAESWKEIVNLLYELLASLIRGNRSNCALFSTNLDWLVSKLDRLEASSGILEVLYCVLIESPEVLNIIQENHIKSIISLLDKHGRNHKVLDVLCSLCVCNGVAVRSNQDLITENLLPGRELLLQTNLINYVTSIRPNIFVGRAEGTTQYSKWYFEVMVDEVTPFLTAQATHLRVGWALTEGYTPYPGAGEGWGGNGVGDDLYSYGFDGLHLWTGHVARPVTSPGQHLLAPEDVISCCLDLSVPSISFRINGCPVQGVFESFNLDGLFFPVVSFSAGVKVRFLLGGRHGEFKFLPPPGYAPCHEAVLPRERLHLEPIKEYRREGPRGPHLVGPSRCLSHTDFVPCPVDTVQIVLPPHLERIREKLAENIHELWALTRIEQGWTYGPVRDDNKRLHPCLVDFHSLPEPERNYNLQMSGETLKTLLALGCHVGMADEKAEDNLKKTKLPKTYMMSNGYKPAPLDLSHVRLTPAQTTLVDRLAENGHNVWARDRVGQGWSYSAVQDIPARRNPRLVPYRLLDEATKRSNRDSLCQAVRTLLGYGYNIEPPDQEPSQVENQSRCDRVRIFRAEKSYTVQSGRWYFEFEAVTTGEMRVGWARPELRPDVELGADELAYVFNGHRGQRWHLGSEPFGRPWQPGDVVGCMIDLTENTIIFTLNGEVLMSDSGSETAFREIEIGDGFLPVCSLGPGQVGHLNLGQDVSSLRFFAICGLQEGFEPFAINMQRPVTTWFSKGLPQFEPVPLEHPHYEVSRVDGTVDTPPCLRLTHRTWGSQNSLVEMLFLRLSLPVQFHQHFRCTAGATPLAPPGLQPPAEDEARAAEPDPDYENLRRSAGGWSEAENGKEGTAKEGAPGGTPQAGGEAQPARAENEKDATTEKNKKRGFLFKAKKVAMMTQPPATPTLPRLPHDVVPADNRDDPEIILNTTTYYYSVRVFAGQEPSCVWAGWVTPDYHQHDMSFDLSKVRVVTVTMGDEQGNVHSSLKCSNCYMVWGGDFVSPGQQGRISHTDLVIGCLVDLATGLMTFTANGKESNTFFQVEPNTKLFPAVFVLPTHQNVIQFELGKQKNIMPLSAAMFQSERKNPAPQCPPRLEMQMLMPVSWSRMPNHFLQVETRRAGERLGWAVQCQEPLTMMALHIPEENRCMDILELSERLDLQRFHSHTLRLYRAVCALGNNRVAHALCSHVDQAQLLHALEDAHLPGPLRAGYYDLLISIHLESACRSRRSMLSEYIVPLTPETRAITLFPPGRSTENGHPRHGLPGVGVTTSLRPPHHFSPPCFVAALPAAGAAEAPARLSPAIPLEALRDKALRMLGEAVRDGGQHARDPVGGSVEFQFVPVLKLVSTLLVMGIFGDEDVKQILKMIEPEVFTEEEEEEDEEEEGEEEDEEEKEEDEEETAQEKEDEEKEEEEAAEGEKEEGLEEGLLQMKLPESVKLQMCHLLEYFCDQELQHRVESLAAFAERYVDKLQANQRSRYGLLIKAFSMTAAETARRTREFRSPPQEQINMLLQFKDGTDEEDCPLPEEIRQDLLDFHQDLLAHCGIQLDGEEEEPEEETTLGSRLMSLLEKVRLVKKKEEKPEEERSAEESKPRSLQELVSHMVVRWAQEDFVQSPELVRAMFSLLHRQYDGLGELLRALPRAYTISPSSVEDTMSLLECLGQIRSLLIVQMGPQEENLMIQSIGNIMNNKVFYQHPNLMRALGMHETVMEVMVNVLGGGESKEIRFPKMVTSCCRFLCYFCRISRQNQRSMFDHLSYLLENSGIGLGMQGSTPLDVAAASVIDNNELALALQEQDLEKVVSYLAGCGLQSCPMLVAKGYPDIGWNPCGGERYLDFLRFAVFVNGESVEENANVVVRLLIRKPECFGPALRGEGGSGLLAAIEEAIRISEDPARDGPGIRRDRRREHFGEEPPEENRVHLGHAIMSFYAALIDLLGRCAPEMHLIQAGKGEALRIRAILRSLVPLEDLVGIISLPLQIPTLGKDGALVQPKMSASFVPDHKASMVLFLDRVYGIENQDFLLHVLDVGFLPDMRAAASLDTATFSTTEMALALNRYLCLAVLPLITKCAPLFAGTEHRAIMVDSMLHTVYRLSRGRSLTKAQRDVIEDCLMSLCRYIRPSMLQHLLRRLVFDVPILNEFAKMPLKLLTNHYERCWKYYCLPTGWANFGVTSEEELHLTRKLFWGIFDSLAHKKYDPELYRMAMPCLCAIAGALPPDYVDASYSSKAEKKATVDAEGNFDPRPVETLNVIIPEKLDSFINKFAEYTHEKWAFDKIQNNWSYGENIDEELKTHPMLRPYKTFSEKDKEIYRWPIKESLKAMIAWEWTIEKAREGEEEKTEKKKTRKISQSAQTYDPREGYNPQPPDLSAVTLSRELQAMAEQLAENYHNTWGRKKKQELEAKGGGTHPLLVPYDTLTAKEKARDREKAQELLKFLQMNGYAVTRGLKDMELDSSSIEKRFAFGFLQQLLRWMDISQEFIAHLEAVVSSGRVEKSPHEQEIKFFAKILLPLINQYFTNHCLYFLSTPAKVLGSGGHASNKEKEMITSLFCKLAALVRHRVSLFGTDAPAVVNCLHILARSLDARTVMKSGPEIVKAGLRSFFESASEDIEKMVENLRLGKVSQARTQVKGVGQNLTYTTVALLPVLTTLFQHIAQHQFGDDVILDDVQVSCYRTLCSIYSLGTTKNTYVEKLRPALGECLARLAAAMPVAFLEPQLNEYNACSVYTTKSPRERAILGLPNSVEEMCPDIPVLERLMADIGGLAESGARYTEMPHVIEITLPMLCSYLPRWWERGPEAPPSALPAGAPPPCTAVTSDHLNSLLGNILRIIVNNLGIDEASWMKRLAVFAQPIVSRARPELLQSHFIPTIGRLRKRAGKVVSEEEQLRLEAKAEAQEGELLVRDEFSVLCRDLYALYPLLIRYVDNNRAQWLTEPNPSAEELFRMVGEIFIYWSKSHNFKREEQNFVVQNEINNMSFLTADNKSKMAKAGDIQSGGSDQERTKKKRRGDRYSVQTSLIVATLKKMLPIGLNMCAPTDQDLITLAKTRYALKDTDEEVREFLHNNLHLQGKVEGSPSLRWQMALYRGVPGREEDADDPEKIVRRVQEVSAVLYYLDQTEHPYKSKKAVWHKLLSKQRRRAVVACFRMTPLYNLPTHRACNMFLESYKAAWILTEDHSFEDRMIDDLSKAGEQEEEEEEVEEKKPDPLHQLVLHFSRTALTEKSKLDEDYLYMAYADIMAKSCHLEEGGENGEAEEEVEVSFEEKQMEKQRLLYQQARLHTRGAAEMVLQMISACKGETGAMVSSTLKLGISILNGGNAEVQQKMLDYLKDKKEVGFFQSIQALMQTCSVLDLNAFERQNKAEGLGMVNEDGTVINRQNGEKVMADDEFTQDLFRFLQLLCEGHNNDFQNYLRTQTGNTTTINIIICTVDYLLRLQESISDFYWYYSGKDVIEEQGKRNFSKAMSVAKQVFNSLTEYIQGPCTGNQQSLAHSRLWDAVVGFLHVFAHMMMKLAQDSSQIELLKELLDLQKDMVVMLLSLLEGNVVNGMIARQMVDMLVESSSNVEMILKFFDMFLKLKDIVGSEAFQDYVTDPRGLISKKDFQKAMDSQKQFSGPEIQFLLSCSEADENEMINCEEFANRFQEPARDIGFNVAVLLTNLSEHVPHDPRLHNFLELAESILEYFRPYLGRIEIMGASRRIERIYFEISETNRAQWEMPQVKESKRQFIFDVVNEGGEAEKMELFVSFCEDTIFEMQIAAQISEPEGEPETDEDEGAGAAEAGAEGAEEGAAGLEGTAATAAAGATARVVAAAGRALRGLSYRSLRRRVRRLRRLTAREAATAVAALLWAAVTRAGAAGAGAAAGALGLLWGSLFGGGLVEGAKKVTVTELLAGMPDPTSDEVHGEQPAGPGGDADGEGASEGAGDAAEGAGDEEEAVHEAGPGGADGAVAVTDGGPFRPEGAGGLGDMGDTTPAEPPTPEGSPILKRKLGVDGVEEELPPEPEPEPEPELEPEKADAENGEKEEVPEPTPEPPKKQAPPSPPPKKEEAGGEFWGELEVQRVKFLNYLSRNFYTLRFLALFLAFAINFILLFYKVSDSPPGEDDMEGSAAGDVSGAGSGGSSGWGLGAGEEAEGDEDENMVYYFLEESTGYMEPALRCLSLLHTLVAFLCIIGYNCLKVPLVIFKREKELARKLEFDGLYITEQPEDDDVKGQWDRLVLNTPSFPSNYWDKFVKRKVLDKHGDIYGRERIAELLGMDLATLEITAHNERKPNPPPGLLTWLMSIDVKYQIWKFGVIFTDNSFLYLGWYMVMSLLGHYNNFFFAAHLLDIAMGVKTLRTILSSVTHNGKQLVMTVGLLAVVVYLYTVVAFNFFRKFYNKSEDEDEPDMKCDDMMTCYLFHMYVGVRAGGGIGDEIEDPAGDEYELYRVVFDITFFFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETKCFICGIGSDYFDTTPHGFETHTLEEHNLANYMFFLMYLINKDETEHTGQESYVWKMYQERCWDFFPAGDCFRKQYEDQLS,mutated_sequence,1.0,5038.0,NP_000531.2.a2m,NP_000531.2.npy,ClinVar +NP_000534.3,NP_000534.3.csv,MPRYGASLRQSCPRSGREQGQDGTAGAPGLLWMGLVLALALALALALALSDSRVLWAPAEAHPLSPQGHPARLHRIVPRLRDVFGWGNLTCPICKGLFTAINLGLKKEPNVARVGSVAIKLCNLLKIAPPAVCQSIVHLFEDDMVEVWRRSVLSPSEACGLLLGSTCGHWDIFSSWNISLPTVPKPPPKPPSPPAPGAPVSRILFLTDLHWDHDYLEGTDPDCADPLCCRRGSGLPPASRPGAGYWGEYSKCDLPLRTLESLLSGLGPAGPFDMVYWTGDIPAHDVWHQTRQDQLRALTTVTALVRKFLGPVPVYPAVGNHESTPVNSFPPPFIEGNHSSRWLYEAMAKAWEPWLPAEALRTLRIGGFYALSPYPGLRLISLNMNFCSRENFWLLINSTDPAGQLQWLVGELQAAEDRGDKVHIIGHIPPGHCLKSWSWNYYRIVARYENTLAAQFFGHTHVDEFEVFYDEETLSRPLAVAFLAPSATTYIGLNPGYRVYQIDGNYSGSSHVVLDHETYILNLTQANIPGAIPHWQLLYRARETYGLPNTLPTAWHNLVYRMRGDMQLFQTFWFLYHKGHPPSEPCGTPCRLATLCAQLSARADSPALCRHLMPDGSLPEAQSLWPRPLFC,mutated_sequence,1.0,631.0,NP_000534.3.a2m,NP_000534.3.npy,ClinVar +NP_000536.6,NP_000536.6.csv,MVSKLSQLQTELLAALLESGLSKEALIQALGEPGPYLLAGEGPLDKGESCGGGRGELAELPNGLGETRGSEDETDDDGEDFTPPILKELENLSPEEAAHQKAVVETLLQEDPWRVAKMVKSYLQQHNIPQREVVDTTGLNQSHLSQHLNKGTPMKTQKRAALYTWYVRKQREVAQQFTHAGQGGLIEEPTGDELPTKKGRRNRFKWGPASQQILFQAYERQKNPSKEERETLVEECNRAECIQRGVSPSQAQGLGSNLVTEVRVYNWFANRRKEEAFRHKLAMDTYSGPPPGPGPGPALPAHSSPGLPPPALSPSKVHGVRYGQPATSETAEVPSSSGGPLVTVSTPLHQVSPTGLEPSHSLLSTEAKLVSAAGGPLPPVSTLTALHSLEQTSPGLNQQPQNLIMASLPGVMTIGPGEPASLGPTFTNTGASTLVIGLASTQAQSVPVINSMGSSLTTLQPVQFSQPLHPSYQQPLMPPVQSHVTQSPFMATMAQLQSPHALYSHKPEVAQYTHTGLLPQTMLITDTTNLSALASLTPTKQVFTSDTEASSESGLHTPASQATTLHVPSQDPASIQHLQPAHRLSASPTVSSSSLVLYQSSDSSNGQSHLLPSNHSVIETFISTQMASSSQ,mutated_sequence,1.0,631.0,NP_000536.6.a2m,NP_000536.6.npy,ClinVar +NP_000537.3,NP_000537.3.csv,MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD,mutated_sequence,1.0,393.0,NP_000537.3.a2m,NP_000537.3.npy,ClinVar +NP_000539.2,NP_000539.2.csv,MAKPTSKDSGLKEKFKILLGLGTPRPNPRSAEGKQTEFIITAEILRELSMECGLNNRIRMIGQICEVAKTKKFEEHAVEALWKAVADLLQPERPLEARHAVLALLKAIVQGQGERLGVLRALFFKVIKDYPSNEDLHERLEVFKALTDNGRHITYLEEELADFVLQWMDVGLSSEFLLVLVNLVKFNSCYLDEYIARMVQMICLLCVRTASSVDIEVSLQVLDAVVCYNCLPAESLPLFIVTLCRTINVKELCEPCWKLMRNLLGTHLGHSAIYNMCHLMEDRAYMEDAPLLRGAVFFVGMALWGAHRLYSLRNSPTSVLPSFYQAMACPNEVVSYEIVLSITRLIKKYRKELQVVAWDILLNIIERLLQQLQTLDSPELRTIVHDLLTTVEELCDQNEFHGSQERYFELVERCADQRPESSLLNLISYRAQSIHPAKDGWIQNLQALMERFFRSESRGAVRIKVLDVLSFVLLINRQFYEEELINSVVISQLSHIPEDKDHQVRKLATQLLVDLAEGCHTHHFNSLLDIIEKVMARSLSPPPELEERDVAAYSASLEDVKTAVLGLLVILQTKLYTLPASHATRVYEMLVSHIQLHYKHSYTLPIASSIRLQAFDFLLLLRADSLHRLGLPNKDGVVRFSPYCVCDYMEPERGSEKKTSGPLSPPTGPPGPAPAGPAVRLGSVPYSLLFRVLLQCLKQESDWKVLKLVLGRLPESLRYKVLIFTSPCSVDQLCSALCSMLSGPKTLERLRGAPEGFSRTDLHLAVVPVLTALISYHNYLDKTKQREMVYCLEQGLIHRCASQCVVALSICSVEMPDIIIKALPVLVVKLTHISATASMAVPLLEFLSTLARLPHLYRNFAAEQYASVFAISLPYTNPSKFNQYIVCLAHHVIAMWFIRCRLPFRKDFVPFITKGLRSNVLLSFDDTPEKDSFRARSTSLNERPKSLRIARPPKQGLNNSPPVKEFKESSAAEAFRCRSISVSEHVVRSRIQTSLTSASLGSADENSVAQADDSLKNLHLELTETCLDMMARYVFSNFTAVPKRSPVGEFLLAGGRTKTWLVGNKLVTVTTSVGTGTRSLLGLDSGELQSGPESSSSPGVHVRQTKEAPAKLESQAGQQVSRGARDRVRSMSGGHGLRVGALDVPASQFLGSATSPGPRTAPAAKPEKASAGTRVPVQEKTNLAAYVPLLTQGWAEILVRRPTGNTSWLMSLENPLSPFSSDINNMPLQELSNALMAAERFKEHRDTALYKSLSVPAASTAKPPPLPRSNTVASFSSLYQSSCQGQLHRSVSWADSAVVMEEGSPGEVPVLVEPPGLEDVEAALGMDRRTDAYSRSSSVSSQEEKSLHAEELVGRGIPIERVVSSEGGRPSVDLSFQPSQPLSKSSSSPELQTLQDILGDPGDKADVGRLSPEVKARSQSGTLDGESAAWSASGEDSRGQPEGPLPSSSPRSPSGLRPRGYTISDSAPSRRGKRVERDALKSRATASNAEKVPGINPSFVFLQLYHSPFFGDESNKPILLPNESQSFERSVQLLDQIPSYDTHKIAVLYVGEGQSNSELAILSNEHGSYRYTEFLTGLGRLIELKDCQPDKVYLGGLDVCGEDGQFTYCWHDDIMQAVFHIATLMPTKDVDKHRCDKKRHLGNDFVSIVYNDSGEDFKLGTIKGQFNFVHVIVTPLDYECNLVSLQCRKDMEGLVDTSVAKIVSDRNLPFVARQMALHANMASQVHHSRSNPTDIYPSKWIARLRHIKRLRQRICEEAAYSNPSLPLVHPPSHSKAPAQTPAEPTPGYEVGQRKRLISSVEDFTEFV,mutated_sequence,1.0,1807.0,NP_000539.2.a2m,NP_000539.2.npy,ClinVar +NP_000542.1,NP_000542.1.csv,MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPEELGAEEEMEAGRPRPVLRSVNSREPSQVIFCNRSPRVVLPVWLNFDGEPQPYPTLPPGTGRRIHSYRGHLWLFRDAGTHDGLLVNQTELFVPSLNVDGQPIFANITLPVYTLKERCLQVVRSLVKPENYRRLDIVRSLYEDLEDHPNVQKDLERLTQERIAHQRMGD,mutated_sequence,1.0,213.0,NP_000542.1.a2m,NP_000542.1.npy,ClinVar +NP_000543.3,NP_000543.3.csv,MIPARFAGVLLALALILPGTLCAEGTRGRSSTARCSLFGSDFVNTFDGSMYSFAGYCSYLLAGGCQKRSFSIIGDFQNGKRVSLSVYLGEFFDIHLFVNGTVTQGDQRVSMPYASKGLYLETEAGYYKLSGEAYGFVARIDGSGNFQVLLSDRYFNKTCGLCGNFNIFAEDDFMTQEGTLTSDPYDFANSWALSSGEQWCERASPPSSSCNISSGEMQKGLWEQCQLLKSTSVFARCHPLVDPEPFVALCEKTLCECAGGLECACPALLEYARTCAQEGMVLYGWTDHSACSPVCPAGMEYRQCVSPCARTCQSLHINEMCQERCVDGCSCPEGQLLDEGLCVESTECPCVHSGKRYPPGTSLSRDCNTCICRNSQWICSNEECPGECLVTGQSHFKSFDNRYFTFSGICQYLLARDCQDHSFSIVIETVQCADDRDAVCTRSVTVRLPGLHNSLVKLKHGAGVAMDGQDVQLPLLKGDLRIQHTVTASVRLSYGEDLQMDWDGRGRLLVKLSPVYAGKTCGLCGNYNGNQGDDFLTPSGLAEPRVEDFGNAWKLHGDCQDLQKQHSDPCALNPRMTRFSEEACAVLTSPTFEACHRAVSPLPYLRNCRYDVCSCSDGRECLCGALASYAAACAGRGVRVAWREPGRCELNCPKGQVYLQCGTPCNLTCRSLSYPDEECNEACLEGCFCPPGLYMDERGDCVPKAQCPCYYDGEIFQPEDIFSDHHTMCYCEDGFMHCTMSGVPGSLLPDAVLSSPLSHRSKRSLSCRPPMVKLVCPADNLRAEGLECTKTCQNYDLECMSMGCVSGCLCPPGMVRHENRCVALERCPCFHQGKEYAPGETVKIGCNTCVCQDRKWNCTDHVCDATCSTIGMAHYLTFDGLKYLFPGECQYVLVQDYCGSNPGTFRILVGNKGCSHPSVKCKKRVTILVEGGEIELFDGEVNVKRPMKDETHFEVVESGRYIILLLGKALSVVWDRHLSISVVLKQTYQEKVCGLCGNFDGIQNNDLTSSNLQVEEDPVDFGNSWKVSSQCADTRKVPLDSSPATCHNNIMKQTMVDSSCRILTSDVFQDCNKLVDPEPYLDVCIYDTCSCESIGDCACFCDTIAAYAHVCAQHGKVVTWRTATLCPQSCEERNLRENGYECEWRYNSCAPACQVTCQHPEPLACPVQCVEGCHAHCPPGKILDELLQTCVDPEDCPVCEVAGRRFASGKKVTLNPSDPEHCQICHCDVVNLTCEACQEPGGLVVPPTDAPVSPTTLYVEDISEPPLHDFYCSRLLDLVFLLDGSSRLSEAEFEVLKAFVVDMMERLRISQKWVRVAVVEYHDGSHAYIGLKDRKRPSELRRIASQVKYAGSQVASTSEVLKYTLFQIFSKIDRPEASRITLLLMASQEPQRMSRNFVRYVQGLKKKKVIVIPVGIGPHANLKQIRLIEKQAPENKAFVLSSVDELEQQRDEIVSYLCDLAPEAPPPTLPPDMAQVTVGPGLLGVSTLGPKRNSMVLDVAFVLEGSDKIGEADFNRSKEFMEEVIQRMDVGQDSIHVTVLQYSYMVTVEYPFSEAQSKGDILQRVREIRYQGGNRTNTGLALRYLSDHSFLVSQGDREQAPNLVYMVTGNPASDEIKRLPGDIQVVPIGVGPNANVQELERIGWPNAPILIQDFETLPREAPDLVLQRCCSGEGLQIPTLSPAPDCSQPLDVILLLDGSSSFPASYFDEMKSFAKAFISKANIGPRLTQVSVLQYGSITTIDVPWNVVPEKAHLLSLVDVMQREGGPSQIGDALGFAVRYLTSEMHGARPGASKAVVILVTDVSVDSVDAAADAARSNRVTVFPIGIGDRYDAAQLRILAGPAGDSNVVKLQRIEDLPTMVTLGNSFLHKLCSGFVRICMDEDGNEKRPGDVWTLPDQCHTVTCQPDGQTLLKSHRVNCDRGLRPSCPNSQSPVKVEETCGCRWTCPCVCTGSSTRHIVTFDGQNFKLTGSCSYVLFQNKEQDLEVILHNGACSPGARQGCMKSIEVKHSALSVELHSDMEVTVNGRLVSVPYVGGNMEVNVYGAIMHEVRFNHLGHIFTFTPQNNEFQLQLSPKTFASKTYGLCGICDENGANDFMLRDGTVTTDWKTLVQEWTVQRPGQTCQPILEEQCLVPDSSHCQVLLLPLFAECHKVLAPATFYAICQQDSCHQEQVCEVIASYAHLCRTNGVCVDWRTPDFCAMSCPPSLVYNHCEHGCPRHCDGNVSSCGDHPSEGCFCPPDKVMLEGSCVPEEACTQCIGEDGVQHQFLEAWVPDHQPCQICTCLSGRKVNCTTQPCPTAKAPTCGLCEVARLRQNADQCCPEYECVCDPVSCDLPPVPHCERGLQPTLTNPGECRPNFTCACRKEECKRVSPPSCPPHRLPTLRKTQCCDEYECACNCVNSTVSCPLGYLASTATNDCGCTTTTCLPDKVCVHRSTIYPVGQFWEEGCDVCTCTDMEDAVMGLRVAQCSQKPCEDSCRSGFTYVLHEGECCGRCLPSACEVVTGSPRGDSQSSWKSVGSQWASPENPCLINECVRVKEEVFIQQRNVSCPQLEVPVCPSGFQLSCKTSACCPSCRCERMEACMLNGTVIGPGKTVMIDVCTTCRCMVQVGVISGFKLECRKTTCNPCPLGYKEENNTGECCGRCLPTACTIQLRGGQIMTLKRDETLQDGCDTHFCKVNERGEYFWEKRVTGCPPFDEHKCLAEGGKIMKIPGTCCDTCEEPECNDITARLQYVKVGSCKSEVEVDIHYCQGKCASKAMYSIDINDVQDQCSCCSPTRTEPMQVALHCTNGSVVYHEVLNAMECKCSPRKCSK,mutated_sequence,1.0,2813.0,NP_000543.3.a2m,NP_000543.3.npy,ClinVar +NP_000608.1,NP_000608.1.csv,MVLGPEQKMSDDSVSGDHGESASLGNINPAYSNPSLSQSPGDSEEYFATYFNEKISIPEEEYSCFSFRKLWAFTGPGFLMSIAYLDPGNIESDLQSGAVAGFKLLWILLLATLVGLLLQRLAARLGVVTGLHLAEVCHRQYPKVPRVILWLMVELAIIGSDMQEVIGSAIAINLLSVGRIPLWGGVLITIADTFVFLFLDKYGLRKLEAFFGFLITIMALTFGYEYVTVKPSQSQVLKGMFVPSCSGCRTPQIEQAVGIVGAVIMPHNMYLHSALVKSRQVNRNNKQEVREANKYFFIESCIALFVSFIINVFVVSVFAEAFFGKTNEQVVEVCTNTSSPHAGLFPKDNSTLAVDIYKGGVVLGCYFGPAALYIWAVGILAAGQSSTMTGTYSGQFVMEGFLNLKWSRFARVVLTRSIAIIPTLLVAVFQDVEHLTGMNDFLNVLQSLQLPFALIPILTFTSLRPVMSDFANGLGWRIAGGILVLIICSINMYFVVVYVRDLGHVALYVVAAVVSVAYLGFVFYLGWQCLIALGMSFLDCGHTVSISKGLLTEEATRGYVK,mutated_sequence,1.0,561.0,NP_000608.1.a2m,NP_000608.1.npy,ClinVar +NP_000633.2,NP_000633.2.csv,MGHSKQIRILLLNEMEKLEKTLFRLEQGYELQFRLGPTLQGKAVTVYTNYPFPGETFNREKFRSLDWENPTEREDDSDKYCKLNLQQSGSFQYYFLQGNEKSGGGYIVVDPILRVGADNHVLPLDCVTLQTFLAKCLGPFDEWESRLRVAKESGYNMIHFTPLQTLGLSRSCYSLANQLELNPDFSRPNRKYTWNDVGQLVEKLKKEWNVICITDVVYNHTAANSKWIQEHPECAYNLVNSPHLKPAWVLDRALWRFSCDVAEGKYKEKGIPALIENDHHMNSIRKIIWEDIFPKLKLWEFFQVDVNKAVEQFRRLLTQENRRVTKSDPNQHLTIIQDPEYRRFGCTVDMNIALTTFIPHDKGPAAIEECCNWFHKRMEELNSEKHRLINYHQEQAVNCLLGNVFYERLAGHGPKLGPVTRKHPLVTRYFTFPFEEIDFSMEESMIHLPNKACFLMAHNGWVMGDDPLRNFAEPGSEVYLRRELICWGDSVKLRYGNKPEDCPYLWAHMKKYTEITATYFQGVRLDNCHSTPLHVAEYMLDAARNLQPNLYVVAELFTGSEDLDNVFVTRLGISSLIREAMSAYNSHEEGRLVYRYGGEPVGSFVQPCLRPLMPAIAHALFMDITHDNECPIVHRSAYDALPSTTIVSMACCASGSTRGYDELVPHQISVVSEERFYTKWNPEALPSNTGEVNFQSGIIAARCAISKLHQELGAKGFIQVYVDQVDEDIVAVTRHSPSIHQSVVAVSRTAFRNPKTSFYSKEVPQMCIPGKIEEVVLEARTIERNTKPYRKDENSINGTPDITVEIREHIQLNESKIVKQAGVATKGPNEYIQEIEFENLSPGSVIIFRVSLDPHAQVAVGILRNHLTQFSPHFKSGSLAVDNADPILKIPFASLASRLTLAELNQILYRCESEEKEDGGGCYDIPNWSALKYAGLQGLMSVLAEIRPKNDLGHPFCNNLRSGDWMIDYVSNRLISRSGTIAEVGKWLQAMFFYLKQIPRYLIPCYFDAILIGAYTTLLDTAWKQMSSFVQNGSTFVKHLSLGSVQLCGVGKFPSLPILSPALMDVPYRLNEITKEKEQCCVSLAAGLPHFSSGIFRCWGRDTFIALRGILLITGRYVEARNIILAFAGTLRHGLIPNLLGEGIYARYNCRDAVWWWLQCIQDYCKMVPNGLDILKCPVSRMYPTDDSAPLPAGTLDQPLFEVIQEAMQKHMQGIQFRERNAGPQIDRNMKDEGFNITAGVDEETGFVYGGNRFNCGTWMDKMGESDRARNRGIPATPRDGSAVEIVGLSKSAVRWLLELSKKNIFPYHEVTVKRHGKAIKVSYDEWNRKIQDNFEKLFHVSEDPSDLNEKHPNLVHKRGIYKDSYGASSPWCDYQLRPNFTIAMVVAPELFTTEKAWKALEIAEKKLLGPLGMKTLDPDDMVYCGIYDNALDNDNYNLAKGFNYHQGPEWLWPIGYFLRAKLYFSRLMGPETTAKTIVLVKNVLSRHYVHLERSPWKGLPELTNENAQYCPFSCETQAWSIATILETLYDL,mutated_sequence,1.0,1532.0,NP_000633.2.a2m,NP_000633.2.npy,ClinVar +NP_000711.1,NP_000711.1.csv,MMMMMMMKKMQHQRQQQADHANEANYARGTRLPLSGEGPTSQPNSSKQTVLSWQAAIDAARQAKAAQTMSTSAPPPVGSLSQRKRQQYAKSKKQGNSSNSRPARALFCLSLNNPIRRACISIVEWKPFDIFILLAIFANCVALAIYIPFPEDDSNSTNHNLEKVEYAFLIIFTVETFLKIIAYGLLLHPNAYVRNGWNLLDFVIVIVGLFSVILEQLTKETEGGNHSSGKSGGFDVKALRAFRVLRPLRLVSGVPSLQVVLNSIIKAMVPLLHIALLVLFVIIIYAIIGLELFIGKMHKTCFFADSDIVAEEDPAPCAFSGNGRQCTANGTECRSGWVGPNGGITNFDNFAFAMLTVFQCITMEGWTDVLYWVNDAIGWEWPWVYFVSLIILGSFFVLNLVLGVLSGEFSKEREKAKARGDFQKLREKQQLEEDLKGYLDWITQAEDIDPENEEEGGEEGKRNTSMPTSETESVNTENVSGEGENRGCCGSLWCWWRRRGAAKAGPSGCRRWGQAISKSKLSRRWRRWNRFNRRRCRAAVKSVTFYWLVIVLVFLNTLTISSEHYNQPDWLTQIQDIANKVLLALFTCEMLVKMYSLGLQAYFVSLFNRFDCFVVCGGITETILVELEIMSPLGISVFRCVRLLRIFKVTRHWTSLSNLVASLLNSMKSIASLLLLLFLFIIIFSLLGMQLFGGKFNFDETQTKRSTFDNFPQALLTVFQILTGEDWNAVMYDGIMAYGGPSSSGMIVCIYFIILFICGNYILLNVFLAIAVDNLADAESLNTAQKEEAEEKERKKIARKESLENKKNNKPEVNQIANSDNKVTIDDYREEDEDKDPYPPCDVPVGEEEEEEEEDEPEVPAGPRPRRISELNMKEKIAPIPEGSAFFILSKTNPIRVGCHKLINHHIFTNLILVFIMLSSAALAAEDPIRSHSFRNTILGYFDYAFTAIFTVEILLKMTTFGAFLHKGAFCRNYFNLLDMLVVGVSLVSFGIQSSAISVVKILRVLRVLRPLRAINRAKGLKHVVQCVFVAIRTIGNIMIVTTLLQFMFACIGVQLFKGKFYRCTDEAKSNPEECRGLFILYKDGDVDSPVVRERIWQNSDFNFDNVLSAMMALFTVSTFEGWPALLYKAIDSNGENIGPIYNHRVEISIFFIIYIIIVAFFMMNIFVGFVIVTFQEQGEKEYKNCELDKNQRQCVEYALKARPLRRYIPKNPYQYKFWYVVNSSPFEYMMFVLIMLNTLCLAMQHYEQSKMFNDAMDILNMVFTGVFTVEMVLKVIAFKPKGYFSDAWNTFDSLIVIGSIIDVALSEADPTESENVPVPTATPGNSEESNRISITFFRLFRVMRLVKLLSRGEGIRTLLWTFIKSFQALPYVALLIAMLFFIYAVIGMQMFGKVAMRDNNQINRNNNFQTFPQAVLLLFRCATGEAWQEIMLACLPGKLCDPESDYNPGEEYTCGSNFAIVYFISFYMLCAFLIINLFVAVIMDNFDYLTRDWSILGPHHLDEFKRIWSEYDPEAKGRIKHLDVVTLLRRIQPPLGFGKLCPHRVACKRLVAMNMPLNSDGTVMFNATLFALVRTALKIKTEGNLEQANEELRAVIKKIWKKTSMKLLDQVVPPAGDDEVTVGKFYATFLIQDYFRKFKKRKEQGLVGKYPAKNTTIALQAGLRTLHDIGPEIRRAISCDLQDDEPEETKREEEDDVFKRNGALLGNHVNHVNSDRRDSLQQTNTTHRPLHVQRPSIPPASDTEKPLFPPAGNSVCHNHHNHNSIGKQVPTSTNANLNNANMSKAAHGKRPSIGNLEHVSENGHHSSHKHDREPQRRSSVKRTRYYETYIRSDSGDEQLPTICREDPEIHGYFRDPHCLGEQEYFSSEECYEDDSSPTWSRQNYGYYSRYPGRNIDSERPRGYHHPQGFLEDDDSPVCYDSRRSPRRRLLPPTPASHRRSSFNFECLRRQSSQEEVPSSPIFPHRTALPLHLMQQQIMAVAGLDSSKAQKYSPSHSTRSWATPPATPPYRDWTPCYTPLIQVEQSEALDQVNGSLPSLHRSSWYTDEPDISYRTFTPASLTVPSSFRNKNSDKQRSADSLVEAVLISEGLGRYARDPKFVSATKHEIADACDLTIDEMESAASTLLNGNVRPRANGDVGPLSHRQDYELQDFGPGYSDEEPDPGRDEEDLADEMICITTL,mutated_sequence,1.0,2181.0,NP_000711.1.a2m,NP_000711.1.npy,ClinVar +NP_000735.1,NP_000735.1.csv,MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI,mutated_sequence,1.0,627.0,NP_000735.1.a2m,NP_000735.1.npy,ClinVar +NP_000738.2,NP_000738.2.csv,MTPGALLMLLGALGAPLAPGVRGSEAEGRLREKLFSGYDSSVRPAREVGDRVRVSVGLILAQLISLNEKDEEMSTKVYLDLEWTDYRLSWDPAEHDGIDSLRITAESVWLPDVVLLNNNDGNFDVALDISVVVSSDGSVRWQPPGIYRSSCSIQVTYFPFDWQNCTMVFSSYSYDSSEVSLQTGLGPDGQGHQEIHIHEGTFIENGQWEIIHKPSRLIQPPGDPRGGREGQRQEVIFYLIIRRKPLFYLVNVIAPCILITLLAIFVFYLPPDAGEKMGLSIFALLTLTVFLLLLADKVPETSLSVPIIIKYLMFTMVLVTFSVILSVVVLNLHHRSPHTHQMPLWVRQIFIHKLPLYLRLKRPKPERDLMPEPPHCSSPGSGWGRGTDEYFIRKPPSDFLFPKPNRFQPELSAPDLRRFIDGPNRAVALLPELREVVSSISYIARQLQEQEDHDALKEDWQFVAMVVDRLFLWTFIIFTSVGTLVIFLDATYHLPPPDPFP,mutated_sequence,1.0,501.0,NP_000738.2.a2m,NP_000738.2.npy,ClinVar +NP_000772.2,NP_000772.2.csv,MLAKGLPPRSVLVKGCQTFLSAPREGLGRLRVPTGEGAGISTRSPRPFNEIPSPGDNGWLNLYHFWRETGTHKVHLHHVQNFQKYGPIYREKLGNVESVYVIDPEDVALLFKSEGPNPERFLIPPWVAYHQYYQRPIGVLLKKSAAWKKDRVALNQEVMAPEATKNFLPLLDAVSRDFVSVLHRRIKKAGSGNYSGDISDDLFRFAFESITNVIFGERQGMLEEVVNPEAQRFIDAIYQMFHTSVPMLNLPPDLFRLFRTKTWKDHVAAWDVIFSKADIYTQNFYWELRQKGSVHHDYRGILYRLLGDSKMSFEDIKANVTEMLAGGVDTTSMTLQWHLYEMARNLKVQDMLRAEVLAARHQAQGDMATMLQLVPLLKASIKETLRLHPISVTLQRYLVNDLVLRDYMIPAKTLVQVAIYALGREPTFFFDPENFDPTRWLSKDKNITYFRNLGFGWGVRQCLGRRIAELEMTIFLINMLENFRVEIQHLSDVGTTFNLILMPEKPISFTFWPFNQEATQQ,mutated_sequence,1.0,521.0,NP_000772.2.a2m,NP_000772.2.npy,ClinVar +NP_000780.1,NP_000780.1.csv,MGAASGRRGPGLLLPLPLLLLLPPQPALALDPGLQPGNFSADEAGAQLFAQSYNSSAEQVLFQSVAASWAHDTNITAENARRQEEAALLSQEFAEAWGQKAKELYEPIWQNFTDPQLRRIIGAVRTLGSANLPLAKRQQYNALLSNMSRIYSTAKVCLPNKTATCWSLDPDLTNILASSRSYAMLLFAWEGWHNAAGIPLKPLYEDFTALSNEAYKQDGFTDTGAYWRSWYNSPTFEDDLEHLYQQLEPLYLNLHAFVRRALHRRYGDRYINLRGPIPAHLLGDMWAQSWENIYDMVVPFPDKPNLDVTSTMLQQGWNATHMFRVAEEFFTSLELSPMPPEFWEGSMLEKPADGREVVCHASAWDFYNRKDFRIKQCTRVTMDQLSTVHHEMGHIQYYLQYKDLPVSLRRGANPGFHEAIGDVLALSVSTPEHLHKIGLLDRVTNDTESDINYLLKMALEKIAFLPFGYLVDQWRWGVFSGRTPPSRYNFDWWYLRTKYQGICPPVTRNETHFDAGAKFHVPNVTPYIRYFVSFVLQFQFHEALCKEAGYEGPLHQCDIYRSTKAGAKLRKVLQAGSSRPWQEVLKDMVGLDALDAQPLLKYFQPVTQWLQEQNQQNGEVLGWPEYQWHPPLPDNYPEGIDLVTDEAEASKFVEEYDRTSQVVWNEYAEANWNYNTNITTETSKILLQKNMQIANHTLKYGTQARKFDVNQLQNTTIKRIIKKVQDLERAALPAQELEEYNKILLDMETTYSVATVCHPNGSCLQLEPDLTNVMATSRKYEDLLWAWEGWRDKAGRAILQFYPKYVELINQAARLNGYVDAGDSWRSMYETPSLEQDLERLFQELQPLYLNLHAYVRRALHRHYGAQHINLEGPIPAHLLGNMWAQTWSNIYDLVVPFPSAPSMDTTEAMLKQGWTPRRMFKEADDFFTSLGLLPVPPEFWNKSMLEKPTDGREVVCHASAWDFYNGKDFRIKQCTTVNLEDLVVAHHEMGHIQYFMQYKDLPVALREGANPGFHEAIGDVLALSVSTPKHLHSLNLLSSEGGSDEHDINFLMKMALDKIAFIPFSYLVDQWRWRVFDGSITKENYNQEWWSLRLKYQGLCPPVPRTQGDFDPGAKFHIPSSVPYIRYFVSFIIQFQFHEALCQAAGHTGPLHKCDIYQSKEAGQRLATAMKLGFSRPWPEAMQLITGQPNMSASAMLSYFKPLLDWLRTENELHGEKLGWPQYNWTPNSARSEGPLPDSGRVSFLGLDLDAQQARVGQWLLLFLGIALLVATLGLSQRLFSIRHRSLHRHSHGPQFGSEVELRHS,mutated_sequence,1.0,1306.0,NP_000780.1.a2m,NP_000780.1.npy,ClinVar +NP_000808.2,NP_000808.2.csv,MASSTPSSSATSSNAGADPNTTNLRPTTYDTWCGVAHGCTRKLGLKICGFLQRTNSLEEKSRLVSAFKERQSSKNLLSCENSDRDARFRRTETDFSNLFARDLLPAKNGEEQTVQFLLEVVDILLNYVRKTFDRSTKVLDFHHPHQLLEGMEGFNLELSDHPESLEQILVDCRDTLKYGVRTGHPRFFNQLSTGLDIIGLAGEWLTSTANTNMFTYEIAPVFVLMEQITLKKMREIVGWSSKDGDGIFSPGGAISNMYSIMAARYKYFPEVKTKGMAAVPKLVLFTSEQSHYSIKKAGAALGFGTDNVILIKCNERGKIIPADFEAKILEAKQKGYVPFYVNATAGTTVYGAFDPIQEIADICEKYNLWLHVDAAWGGGLLMSRKHRHKLNGIERANSVTWNPHKMMGVLLQCSAILVKEKGILQGCNQMCAGYLFQPDKQYDVSYDTGDKAIQCGRHVDIFKFWLMWKAKGTVGFENQINKCLELAEYLYAKIKNREEFEMVFNGEPEHTNVCFWYIPQSLRGVPDSPQRREKLHKVAPKIKALMMESGTTMVGYQPQGDKANFFRMVISNPAATQSDIDFLIEEIERLGQDL,mutated_sequence,1.0,594.0,NP_000808.2.a2m,NP_000808.2.npy,ClinVar +NP_000866.1,NP_000866.1.csv,MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC,mutated_sequence,1.0,1367.0,NP_000866.1.a2m,NP_000866.1.npy,ClinVar +NP_000882.1,NP_000882.1.csv,MGSVRTNRYSIVSSEEDGMKLATMAVANGFGNGKSKVHTRQQCRSRFVKKDGHCNVQFINVGEKGQRYLADIFTTCVDIRWRWMLVIFCLAFVLSWLFFGCVFWLIALLHGDLDASKEGKACVSEVNSFTAAFLFSIETQTTIGYGFRCVTDECPIAVFMVVFQSIVGCIIDAFIIGAVMAKMAKPKKRNETLVFSHNAVIAMRDGKLCLMWRVGNLRKSHLVEAHVRAQLLKSRITSEGEYIPLDQIDINVGFDSGIDRIFLVSPITIVHEIDEDSPLYDLSKQDIDNADFEIVVILEGMVEATAMTTQCRSSYLANEILWGHRYEPVLFEEKHYYKVDYSRFHKTYEVPNTPLCSARDLAEKKYILSNANSFCYENEVALTSKEEDDSENGVPESTSTDTPPDIDLHNQASVPLEPRPLRRESEI,mutated_sequence,1.0,427.0,NP_000882.1.a2m,NP_000882.1.npy,ClinVar +NP_000885.1,NP_000885.1.csv,MEMLQGLLLLLLLSMGGAWASREPLRPWCHPINAILAVEKEGCPVCITVNTTICAGYCPTMMRVLQAVLPPLPQVVCTYRDVRFESIRLPGCPRGVDPVVSFPVALSCRCGPCRRSTSDCGGPKDHPLTCDHPQLSGLLFL,mutated_sequence,1.0,141.0,NP_000885.1.a2m,NP_000885.1.npy,ClinVar +NP_000890.1,NP_000890.1.csv,MKKTQTWILTCIYLQLLLFNPLVKTEGICRNRVTNNVKDVTKLVANLPKDYMITLKYVPGMDVLPSHCWISEMVVQLSDSLTDLLDKFSNISEGLSNYSIIDKLVNIVDDLVECVKENSSKDLKKSFKSPEPRLFTPEEFFRIFNRSIDAFKDFVVASETSDCVVSSTLSPEKDSRVSVTKPFMLPPVAASSLRNDSSSSNRKAKNPPGDSSLHWAAMALPALFSLIIGFAFGALYWKKRQPSLTRAVENIQINEEDNEISMLQEKEREFQEV,mutated_sequence,1.0,273.0,NP_000890.1.a2m,NP_000890.1.npy,ClinVar +NP_000912.3,NP_000912.3.csv,MAVPGDAARVRDKPVHSGVSQAPTAGRDCHHRADPASPRDSGCRGCWGDLVLQPLRSSRKLSSALCAGSLSFLLALLVRLVRGEVGCDLEQCKEAAAAEEEEAAPGAEGGVFPGPRGGAPGGGARLSPWLQPSALLFSLLCAFFWMGLYLLRAGVRLPLAVALLAACCGGEALVQIGLGVGEDHLLSLPAAGVVLSCLAAATWLVLRLRLGVLMIALTSAVRTVSLISLERFKVAWRPYLAYLAGVLGILLARYVEQILPQSAEAAPREHLGSQLIAGTKEDIPVFKRRRRSSSVVSAEMSGCSSKSHRRTSLPCIPREQLMGHSEWDHKRGPRGSQSSGTSITVDIAVMGEAHGLITDLLADPSLPPNVCTSLRAVSNLLSTQLTFQAIHKPRVNPVTSLSENYTCSDSEESSEKDKLAIPKRLRRSLPPGLLRRVSSTWTTTTSATGLPTLEPAPVRRDRSTSIKLQEAPSSSPDSWNNPVMMTLTKSRSFTSSYAISAANHVKAKKQSRPGALAKISPLSSPCSSPLQGTPASSLVSKISAVQFPESADTTAKQSLGSHRALTYTQSAPDLSPQILTPPVICSSCGRPYSQGNPADEPLERSGVATRTPSRTDDTAQVTSDYETNNNSDSSDIVQNEDETECLREPLRKASACSTYAPETMMFLDKPILAPEPLVMDNLDSIMEQLNTWNFPIFDLVENIGRKCGRILSQVSYRLFEDMGLFEAFKIPIREFMNYFHALEIGYRDIPYHNRIHATDVLHAVWYLTTQPIPGLSTVINDHGSTSDSDSDSGFTHGHMGYVFSKTYNVTDDKYGCLSGNIPALELMALYVAAAMHDYDHPGRTNAFLVATSAPQAVLYNDRSVLENHHAAAAWNLFMSRPEYNFLINLDHVEFKHFRFLVIEAILATDLKKHFDFVAKFNGKVNDDVGIDWTNENDRLLVCQMCIKLADINGPAKCKELHLQWTDGIVNEFYEQGDEEASLGLPISPFMDRSAPQLANLQESFISHIVGPLCNSYDSAGLMPGKWVEDSDESGDTDDPEEEEEEAPAPNEEETCENNESPKKKTFKRRKIYCQITQHLLQNHKMWKKVIEEEQRLAGIENQSLDQTPQSHSSEQIQAIKEEEEEKGKPRGEEIPTQKPDQ,mutated_sequence,1.0,1141.0,NP_000912.3.a2m,NP_000912.3.npy,ClinVar +NP_000925.2,NP_000925.2.csv,MALLWGLLVLSWSCLQGPCSVFSPVSAMEPLGRQLTSGPNQEQVSPLTLLKLGNQEPGGQTALKSPPGVCSRDPTPEQTHRLARAMMAFTADLFSLVAQTSTCPNLILSPLSVALALSHLALGAQNHTLQRLQQVLHAGSGPCLPHLLSRLCQDLGPGAFRLAARMYLQKGFPIKEDFLEQSEQLFGAKPVSLTGKQEDDLANINQWVKEATEGKIQEFLSGLPEDTVLLLLNAIHFQGFWRNKFDPSLTQRDSFHLDEQFTVPVEMMQARTYPLRWFLLEQPEIQVAHFPFKNNMSFVVLVPTHFEWNVSQVLANLSWDTLHPPLVWERPTKVRLPKLYLKHQMDLVATLSQLGLQELFQAPDLRGISEQSLVVSGVQHQSTLELSEVGVEAAAATSIAMSRMSLSSFSVNRPFLFFIFEDTTGLPLFVGSVRNPNPSAPRELKEQQDSPGNKDFLQSLKGFPRGDKLFGPDLKLVPPMEEDYPQFGSPK,mutated_sequence,1.0,491.0,NP_000925.2.a2m,NP_000925.2.npy,ClinVar +NP_000928.1,NP_000928.1.csv,MHGGGPPSGDSACPLRTIKRVQFGVLSPDELKRMSVTEGGIKYPETTEGGRPKLGGLMDPRQGVIERTGRCQTCAGNMTECPGHFGHIELAKPVFHVGFLVKTMKVLRCVCFFCSKLLVDSNNPKIKDILAKSKGQPKKRLTHVYDLCKGKNICEGGEEMDNKFGVEQPEGDEDLTKEKGHGGCGRYQPRIRRSGLELYAEWKHVNEDSQEKKILLSPERVHEIFKRISDEECFVLGMEPRYARPEWMIVTVLPVPPLSVRPAVVMQGSARNQDDLTHKLADIVKINNQLRRNEQNGAAAHVIAEDVKLLQFHVATMVDNELPGLPRAMQKSGRPLKSLKQRLKGKEGRVRGNLMGKRVDFSARTVITPDPNLSIDQVGVPRSIAANMTFAEIVTPFNIDRLQELVRRGNSQYPGAKYIIRDNGDRIDLRFHPKPSDLHLQTGYKVERHMCDGDIVIFNRQPTLHKMSMMGHRVRILPWSTFRLNLSVTTPYNADFDGDEMNLHLPQSLETRAEIQELAMVPRMIVTPQSNRPVMGIVQDTLTAVRKFTKRDVFLERGEVMNLLMFLSTWDGKVPQPAILKPRPLWTGKQIFSLIIPGHINCIRTHSTHPDDEDSGPYKHISPGDTKVVVENGELIMGILCKKSLGTSAGSLVHISYLEMGHDITRLFYSNIQTVINNWLLIEGHTIGIGDSIADSKTYQDIQNTIKKAKQDVIEVIEKAHNNELEPTPGNTLRQTFENQVNRILNDARDKTGSSAQKSLSEYNNFKSMVVSGAKGSKINISQVIAVVGQQNVEGKRIPFGFKHRTLPHFIKDDYGPESRGFVENSYLAGLTPTEFFFHAMGGREGLIDTAVKTAETGYIQRRLIKSMESVMVKYDATVRNSINQVVQLRYGEDGLAGESVEFQNLATLKPSNKAFEKKFRFDYTNERALRRTLQEDLVKDVLSNAHIQNELEREFERMREDREVLRVIFPTGDSKVVLPCNLLRMIWNAQKIFHINPRLPSDLHPIKVVEGVKELSKKLVIVNGDDPLSRQAQENATLLFNIHLRSTLCSRRMAEEFRLSGEAFDWLLGEIESKFNQAIAHPGEMVGALAAQSLGEPATQMTLNTFHYAGVSAKNVTLGVPRLKELINISKKPKTPSLTVFLLGQSARDAERAKDILCRLEHTTLRKVTANTAIYYDPNPQSTVVAEDQEWVNVYYEMPDFDVARISPWLLRVELDRKHMTDRKLTMEQIAEKINAGFGDDLNCIFNDDNAEKLVLRIRIMNSDENKMQEEEEVVDKMDDDVFLRCIESNMLTDMTLQGIEQISKVYMHLPQTDNKKKIIITEDGEFKALQEWILETDGVSLMRVLSEKDVDPVRTTSNDIVEIFTVLGIEAVRKALERELYHVISFDGSYVNYRHLALLCDTMTCRGHLMAITRHGVNRQDTGPLMKCSFEETVDVLMEAAAHGESDPMKGVSENIMLGQLAPAGTGCFDLLLDAEKCKYGMEIPTNIPGLGAAGPTGMFFGSAPSPMGGISPAMTPWNQGATPAYGAWSPSVGSGMTPGAAGFSPSAASDASGFSPGYSPAWSPTPGSPGSPGPSSPYIPSPGGAMSPSYSPTSPAYEPRSPGGYTPQSPSYSPTSPSYSPTSPSYSPTSPNYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPNYSPTSPNYTPTSPSYSPTSPSYSPTSPNYTPTSPNYSPTSPSYSPTSPSYSPTSPSYSPSSPRYTPQSPTYTPSSPSYSPSSPSYSPTSPKYTPTSPSYSPSSPEYTPTSPKYSPTSPKYSPTSPKYSPTSPTYSPTTPKYSPTSPTYSPTSPVYTPTSPKYSPTSPTYSPTSPKYSPTSPTYSPTSPKGSTYSPTSPGYSPTSPTYSLTSPAISPDDSDEEN,mutated_sequence,1.0,1970.0,NP_000928.1.a2m,NP_000928.1.npy,ClinVar +NP_000966.2,NP_000966.2.csv,MAQDQGEKENPMRELRIRKLCLNICVGESGDRLTRAAKVLEQLTGQTPVFSKARYTVRSFGIRRNEKIAVHCTVRGAKAEEILEKGLKVREYELRKNNFSDTGNFGFGIQEHIDLGIKYDPSIGIYGLDFYVVLGRPGFSIADKKRRTGCIGAKHRISKEEAMRWFQQKYDGIILPGK,mutated_sequence,1.0,178.0,NP_000966.2.a2m,NP_000966.2.npy,ClinVar +NP_001001344.1,NP_001001344.1.csv,MGDMANSSIEFHPKPQQQRDVPQAGGFGCTLAELRTLMELRGAEALQKIEEAYGDVSGLCRRLKTSPTEGLADNTNDLEKRRQIYGQNFIPPKQPKTFLQLVWEALQDVTLIILEVAAIVSLGLSFYAPPGEESEACGNVSGGAEDEGEAEAGWIEGAAILLSVICVVLVTAFNDWSKEKQFRGLQSRIEQEQKFTVIRNGQLLQVPVAALVVGDIAQVKYGDLLPADGVLIQANDLKIDESSLTGESDHVRKSADKDPMLLSGTHVMEGSGRMVVTAVGVNSQTGIIFTLLGAGGEEEEKKDKKGKQQDGAMESSQTKAKKQDGAVAMEMQPLKSAEGGEMEEREKKKANAPKKEKSVLQGKLTKLAVQIGKAGLVMSAITVIILVLYFVIETFVVEGRTWLAECTPVYVQYFVKFFIIGVTVLVVAVPEGLPLAVTISLAYSVKKMMKDNNLVRHLDACETMGNATAICSDKTGTLTTNRMTVVQSYLGDTHYKEIPAPSALTPKILDLLVHAISINSAYTTKILPPEKEGALPRQVGNKTECALLGFVLDLKRDFQPVREQIPEDKLYKVYTFNSVRKSMSTVIRMPDGGFRLFSKGASEILLKKCTNILNSNGELRGFRPRDRDDMVRKIIEPMACDGLRTICIAYRDFSAGQEPDWDNENEVVGDLTCIAVVGIEDPVRPEVPEAIRKCQRAGITVRMVTGDNINTARAIAAKCGIIQPGEDFLCLEGKEFNRRIRNEKGEIEQERLDKVWPKLRVLARSSPTDKHTLVKGIIDSTTGEQRQVVAVTGDGTNDGPALKKADVGFAMGIAGTDVAKEASDIILTDDNFTSIVKAVMWGRNVYDSISKFLQFQLTVNVVAVIVAFTGACITQDSPLKAVQMLWVNLIMDTFASLALATEPPTESLLLRKPYGRDKPLISRTMMKNILGHAVYQLAIIFTLLFVGELFFDIDSGRNAPLHSPPSEHYTIIFNTFVMMQLFNEINARKIHGERNVFDGIFSNPIFCTIVLGTFGIQIVIVQFGGKPFSCSPLSTEQWLWCLFVGVGELVWGQVIATIPTSQLKCLKEAGHGPGKDEMTDEELAEGEEEIDHAERELRRGQILWFRGLNRIQTQIRVVKAFRSSLYEGLEKPESKTSIHNFMATPEFLINDYTHNIPLIDDTDVDENEERLRAPPPPSPNQNNNAIDSGIYLTTHVTKSATSSVFSSSPGSPLHSVETSL,mutated_sequence,1.0,1220.0,NP_001001344.1.a2m,NP_001001344.1.npy,ClinVar +NP_001003694.1,NP_001003694.1.csv,MGVDFDVKTFCHNLRATKPPYECPVETCRKVYKSYSGIEYHLYHYDHDNPPPPQQTPLRKHKKKGRQSRPANKQSPSPSEVSQSPGREVMSYAQAQRMVEVDLHGRVHRISIFDNLDVVSEDEEAPEEAPENGSNKENTETPAATPKSGKHKNKEKRKDSNHHHHHNVSASTTPKLPEVVYRELEQDTPDAPPRPTSYYRYIEKSAEELDEEVEYDMDEEDYIWLDIMNERRKTEGVSPIPQEIFEYLMDRLEKESYFESHNKGDPNALVDEDAVCCICNDGECQNSNVILFCDMCNLAVHQECYGVPYIPEGQWLCRRCLQSPSRAVDCALCPNKGGAFKQTDDGRWAHVVCALWIPEVCFANTVFLEPIDSIEHIPPARWKLTCYICKQRGSGACIQCHKANCYTAFHVTCAQQAGLYMKMEPVRETGANGTSFSVRKTAYCDIHTPPGSARRLPALSHSEGEEDEDEEEDEGKGWSSEKVKKAKAKSRIKMKKARKILAEKRAAAPVVSVPCIPPHRLSKITNRLTIQRKSQFMQRLHSYWTLKRQSRNGVPLLRRLQTHLQSQRNCDQVGRDSEDKNWALKEQLKSWQRLRHDLERARLLVELIRKREKLKRETIKVQQIAMEMQLTPFLILLRKTLEQLQEKDTGNIFSEPVPLSEVTELDEVPDYLDHIKKPMDFFTMKQNLEAYRYLNFDDFEEDFNLIVSNCLKYNAKDTIFYRAAVRLREQGGAVLRQARRQAEKMGIDFETGMHIPHSLAGDEATHHTEDAAEEERLVLLENQKHLPVEEQLKLLLERLDEVNASKQSVGRSRRAKMIKKEMTALRRKLAHQRETGRDGPERHGPSSRGSLTPHPAACDKDGQTDSAAEESSSQETSKGLGPNMSSTPAHEVGRRTSVLFSKKNPKTAGPPKRPGRPPKNRESQMTPSHGGSPVGPPQLPIMSSLRQRKRGRSPRPSSSSDSDSDKSTEDPPMDLPANGFSGGNQPVKKSFLVYRNDCSLPRSSSDSESSSSSSSSAASDRTSTTPSKQGRGKPSFSRGTFPEDSSEDTSGTENEAYSVGTGRGVGHSMVRKSLGRGAGWLSEDEDSPLDALDLVWAKCRGYPSYPALIIDPKMPREGMFHHGVPIPVPPLEVLKLGEQMTQEAREHLYLVLFFDNKRTWQWLPRTKLVPLGVNQDLDKEKMLEGRKSNIRKSVQIAYHRALQHRSKVQGEQSSETSDSD,mutated_sequence,1.0,1220.0,NP_001003694.1.a2m,NP_001003694.1.npy,ClinVar +NP_001003800.1,NP_001003800.1.csv,MSAPSEEEEYARLVMEAQPEWLRAEVKRLSHELAETTREKIQAAEYGLAVLEEKHQLKLQFEELEVDYEAIRSEMEQLKEAFGQAHTNHKKVAADGESREESLIQESASKEQYYVRKVLELQTELKQLRNVLTNTQSENERLASVAQELKEINQNVEIQRGRLRDDIKEYKFREARLLQDYSELEEENISLQKQVSVLRQNQVEFEGLKHEIKRLEEETEYLNSQLEDAIRLKEISERQLEEALETLKTEREQKNSLRKELSHYMSINDSFYTSHLHVSLDGLKFSDDAAEPNNDAEALVNGFEHGGLAKLPLDNKTSTPKKEGLAPPSPSLVSDLLSELNISEIQKLKQQLMQMEREKAGLLATLQDTQKQLEHTRGSLSEQQEKVTRLTENLSALRRLQASKERQTALDNEKDRDSHEDGDYYEVDINGPEILACKYHVAVAEAGELREQLKALRSTHEAREAQHAEEKGRYEAEGQALTEKVSLLEKASRQDRELLARLEKELKKVSDVAGETQGSLSVAQDELVTFSEELANLYHHVCMCNNETPNRVMLDYYREGQGGAGRTSPGGRTSPEARGRRSPILLPKGLLAPEAGRADGGTGDSSPSPGSSLPSPLSDPRREPMNIYNLIAIIRDQIKHLQAAVDRTTELSRQRIASQELGPAVDKDKEALMEEILKLKSLLSTKREQITTLRTVLKANKQTAEVALANLKSKYENEKAMVTETMMKLRNELKALKEDAATFSSLRAMFATRCDEYITQLDEMQRQLAAAEDEKKTLNSLLRMAIQQKLALTQRLELLELDHEQTRRGRAKAAPKTKPATPSVSHTCACASDRAEGTGLANQVFCSEKHSIYCD,mutated_sequence,1.0,855.0,NP_001003800.1.a2m,NP_001003800.1.npy,ClinVar +NP_001003891.1,NP_001003891.1.csv,MDVSGQETDWRSTAFRQKLVSQIEDAMRKAGVAHSKSSKDMESHVFLKAKTRDEYLSLVARLIIHFRDIHNKKSQASVSDPMNALQSLTGGPAAGAAGIGMPPRGPGQSLGGMGSLGAMGQPMSLSGQPPPGTSGMAPHSMAVVSTATPQTQLQLQQVALQQQQQQQQFQQQQQAALQQQQQQQQQQQFQAQQSAMQQQFQAVVQQQQQLQQQQQQQQHLIKLHHQNQQQIQQQQQQLQRIAQLQLQQQQQQQQQQQQQQQQALQAQPPIQQPPMQQPQPPPSQALPQQLQQMHHTQHHQPPPQPQQPPVAQNQPSQLPPQSQTQPLVSQAQALPGQMLYTQPPLKFVRAPMVVQQPPVQPQVQQQQTAVQTAQAAQMVAPGVQMITEALAQGGMHIRARFPPTTAVSAIPSSSIPLGRQPMAQVSQSSLPMLSSPSPGQQVQTPQSMPPPPQPSPQPGQPSSQPNSNVSSGPAPSPSSFLPSPSPQPSQSPVTARTPQNFSVPSPGPLNTPVNPSSVMSPAGSSQAEEQQYLDKLKQLSKYIEPLRRMINKIDKNEDRKKDLSKMKSLLDILTDPSKRCPLKTLQKCEIALEKLKNDMAVPTPPPPPVPPTKQQYLCQPLLDAVLANIRSPVFNHSLYRTFVPAMTAIHGPPITAPVVCTRKRRLEDDERQSIPSVLQGEVARLDPKFLVNLDPSHCSNNGTVHLICKLDDKDLPSVPPLELSVPADYPAQSPLWIDRQWQYDANPFLQSVHRCMTSRLLQLPDKHSVTALLNTWAQSVHQACLSAA,mutated_sequence,1.0,788.0,NP_001003891.1.a2m,NP_001003891.1.npy,ClinVar +NP_001004311.2,NP_001004311.2.csv,MDPAPGVLDPRAAPPALLGTPQAEVLEDVLREQFGPLPQLAAVCRLKRLPSGGYSSTENLQLVLERRRVANAKERERIKNLNRGFARLKALVPFLPQSRKPSKVDILKGATEYIQVLSDLLEGAKDSKKQDPDEQSYSNNSSESHTSSARQLSRNITQHISCAFGLKNEEEGPWADGGSGEPAHACRHSVMSTTEIISPTRSLDRFPEVELLSHRLPQV,mutated_sequence,1.0,219.0,NP_001004311.2.a2m,NP_001004311.2.npy,ClinVar +NP_001005273.1,NP_001005273.1.csv,MKAADTVILWARSKNDQLRISFPPGLCWGDRMPDKDDIRLLPSALGVKKRKRGPKKQKENKPGKPRKRKKRDSEEEFGSERDEYREKSESGGSEYGTGPGRKRRRKHREKKEKKTKRRKKGEGDGGQKQVEQKSSATLLLTWGLEDVEHVFSEEDYHTLTNYKAFSQFMRPLIAKKNPKIPMSKMMTILGAKWREFSANNPFKGSAAAVAAAAAAAAAAVAEQVSAAVSSATPIAPSGPPALPPPPAADIQPPPIRRAKTKEGKGPGHKRRSKSPRVPDGRKKLRGKKMAPLKIKLGLLGGKRKKGGSYVFQSDEGPEPEAEESDLDSGSVHSASGRPDGPVRTKKLKRGRPGRKKKKVLGCPAVAGEEEVDGYETDHQDYCEVCQQGGEIILCDTCPRAYHLVCLDPELDRAPEGKWSCPHCEKEGVQWEAKEEEEEYEEEGEEEGEKEEEDDHMEYCRVCKDGGELLCCDACISSYHIHCLNPPLPDIPNGEWLCPRCTCPVLKGRVQKILHWRWGEPPVAVPAPQQADGNPDVPPPRPLQGRSEREFFVKWVGLSYWHCSWAKELQLEIFHLVMYRNYQRKNDMDEPPPLDYGSGEDDGKSDKRKVKDPHYAEMEEKYYRFGIKPEWMTVHRIINHSVDKKGNYHYLVKWRDLPYDQSTWEEDEMNIPEYEEHKQSYWRHRELIMGEDPAQPRKYKKKKKELQGDGPPSSPTNDPTVKYETQPRFITATGGTLHMYQLEGLNWLRFSWAQGTDTILADEMGLGKTIQTIVFLYSLYKEGHTKGPFLVSAPLSTIINWEREFQMWAPKFYVVTYTGDKDSRAIIRENEFSFEDNAIKGGKKAFKMKREAQVKFHVLLTSYELITIDQAALGSIRWACLVVDEAHRLKNNQSKFFRVLNGYKIDHKLLLTGTPLQNNLEELFHLLNFLTPERFNNLEGFLEEFADISKEDQIKKLHDLLGPHMLRRLKADVFKNMPAKTELIVRVELSPMQKKYYKYILTRNFEALNSRGGGNQVSLLNIMMDLKKCCNHPYLFPVAAMESPKLPSGAYEGGALIKSSGKLMLLQKMLRKLKEQGHRVLIFSQMTKMLDLLEDFLDYEGYKYERIDGGITGALRQEAIDRFNAPGAQQFCFLLSTRAGGLGINLATADTVIIFDSDWNPHNDIQAFSRAHRIGQANKVMIYRFVTRASVEERITQVAKRKMMLTHLVVRPGLGSKAGSMSKQELDDILKFGTEELFKDENEGENKEEDSSVIHYDNEAIARLLDRNQDATEDTDVQNMNEYLSSFKVAQYVVREEDKIEEIEREIIKQEENVDPDYWEKLLRHHYEQQQEDLARNLGKGKRVRKQVNYNDAAQEDQDNQSEYSVGSEEEDEDFDERPEGRRQSKRQLRNEKDKPLPPLLARVGGNIEVLGFNTRQRKAFLNAVMRWGMPPQDAFTTQWLVRDLRGKTEKEFKAYVSLFMRHLCEPGADGSETFADGVPREGLSRQQVLTRIGVMSLVKKKVQEFEHINGRWSMPELMPDPSADSKRSSRASSPTKTSPTTPEASATNSPCTSKPATPAPSEKGEGIRTPLEKEEAENQEEKPEKNSRIGEKMETEADAPSPAPSLGERLEPRKIPLEDEVPGVPGEMEPEPGYRGDREKSATESTPGERGEEKPLDGQEHRERPEGETGDLGKREDVKGDRELRPGPRDEPRSNGRREEKTEKPRFMFNIADGGFTELHTLWQNEERAAISSGKLNEIWHRRHDYWLLAGIVLHGYARWQDIQNDAQFAIINEPFKTEANKGNFLEMKNKFLARRFKLLEQALVIEEQLRRAAYLNLSQEPAHPAMALHARFAEAECLAESHQHLSKESLAGNKPANAVLHKVLNQLEELLSDMKADVTRLPATLSRIPPIAARLQMSERSILSRLASKGTEPHPTPAYPPGPYATPPGYGAAFSAAPVGALAAAGANYSQMPAGSFITAATNGPPVLVKKEKEMVGALVSDGLDRKEPRAGEVICIDD,mutated_sequence,1.0,2000.0,NP_001005273.1.a2m,NP_001005273.1.npy,ClinVar +NP_001009899.3,NP_001009899.3.csv,MPEMTENETPTKKQHRKKNRETHNAVERHRKKKINAGINRIGELIPCSPALKQSKNMILDQAFKYITELKRQNDELLLNGGNNEQAEEIKKLRKQLEEIQKENGRYIELLKANDICLYDDPTIHWKGNLKNSKVSVVIPSDQVQKKIIVYSNGNQPGGNSQGTAVQGITFNVSHNLQKQTANVVPVQRTCNLVTPVSISGVYPSENKPWHQTTVPALATNQPVPLCLPAAISAQSILELPTSESESNVLGATSGSLIAVSIESEPHQHHSLHTCLNDQNSSENKNGQENPKVLKKMTPCVTNIPHSSSATATKVHHGNKSCLSIQDFRGDFQNTFVVSVTTTVCSQPPRTAGDSSPMSISKSADLTSTATVVASSAPGVGKATIPISTLSGNPLDNGWTLSCSLPSSSVSTSDLKNINSLTRISSAGNTQTTWTTLQLAGNTIQPLSQTPSSAVTPVLNESGTSPTTSNHSRYVATDINLNNSFPADGQPVEQVVVTLPSCPSLPMQPLIAQPQVKSQPPKNILPLNSAMQVIQMAQPVGSAVNSAPTNQNVIILQPPSTTPCPTVMRAEVSNQTVGQQIVIIQAANQNPLPLLPAPPPGSVRLPINGANTVIGSNNSVQNVPTPQTFGGKHLVHILPRPSSLSASNSTQTFSVTMSNQQPQTISLNGQLFALQPVMSSSGTTNQTPMQIIQPTTSEDPNTNVALNTFGALASLNQSISQMAGQSCVQLSISQPANSQTAANSQTTTANCVSLTTTAAPPVTTDSSATLASTYNLVSTSSMNTVACLPNMKSKRLNKKPGGRKHLAANKSACPLNSVRDVSKLDCPNTEGSAEPPCNDGLLESFPAVLPSVSVSQANSVSVSASHSLGVLSSESLIPESVSKSKSAEKSSPPSQESVTSEHFAMAAAKSKDSTPNLQQETSQDKPPSSLALSDAAKPCASANVLIPSPSDPHILVSQVPGLSSTTSTTSTDCVSEVEIIAEPCRVEQDSSDTMQTTGLLKGQGLTTLLSDLAKKKNPQKSSLSDQMDHPDFSSENPKIVDSSVNLHPKQELLLMNNDDRDPPQHHSCLPDQEVINGSLINGRQADSPMSTSSGSSRSFSVASMLPETTREDVTSNATTNTCDSCTFVEQTDIVALAARAIFDQENLEKGRVGLQADIREVASKPSEASLLEGDPPFKSQIPKESGTGQAEATPNEFNSQGSIEATMERPLEKPSCSLGIKTSNASLQDSTSQPPSITSLSVNNLIHQSSISHPLASCAGLSPTSEQTTVPATVNLTVSSSSYGSQPPGPSLMTEYSQEQLNTMTSTIPNSQIQEPLLKPSHESRKDSAKRAVQDDLLLSSAKRQKHCQPAPLRLESMSLMSRTPDTISDQTQMMVSQIPPNSSNSVVPVSNPAHGDGLTRLFPPSNNFVTPALRQTEVQCGSQPSVAEQQQTQASQHLQALQQHVPAQGVSHLHSNHLYIKQQQQQQQQQQQQQQQQQAGQLRERHHLYQMQHHVPHAESSVHSQPHNVHQQRTLQQEVQMQKKRNLVQGTQTSQLSLQPKHHGTDQSRSKTGQPHPHHQQMQQQMQQHFGSSQTEKSCENPSTSRNHHNHPQNHLNQDIMHQQQDVGSRQQGSGVSSEHVSGHNPMQRLLTSRGLEQQMVSQPSIVTRSSDMTCTPHRPERNRVSSYSAEALIGKTSSNSEQRMGISIQGSRVSDQLEMRSYLDVPRNKSLAIHNMQGRVDHTVASDIRLSDCQTFKPSGASQQPQSNFEVQSSRNNEIGNPVSSLRSMQSQAFRISQNTGPPPIDRQKRLSYPPVQSIPTGNGIPSRDSENTCHQSFMQSLLAPHLSDQVIGSQRSLSEHQRNTQCGPSSAIEYNCPPTHENVHIRRESESQNRESCDMSLGAINTRNSTLNIPFSSSSSSGDIQGRNTSPNVSVQKSNPMRITESHATKGHMNPPVTTNMHGVARPALPHPSVSHGNGDQGPAVRQANSSVPQRSRHPLQDSSGSKIRQPERNRSGNQRQSTVFDPSLPHLPLSTGGSMILGRQQPATEKRGSIVRFMPDSPQVPNDNSGPDQHTLSQNFGFSFIPEGGMNPPINANASFIPQVTQPSATRTPALIPVDPQNTLPSFYPPYSPAHPTLSNDISIPYFPNQMFSNPSTEKVNSGSLNNRFGSILSPPRPVGFAQPSFPLLPDMPPMHMTNSHLSNFNMTSLFPEIATALPDGSAMSPLLTIANSSASDSSKQSSNRPAHNISHILGHDCSSAV,mutated_sequence,1.0,2245.0,NP_001009899.3.a2m,NP_001009899.3.npy,ClinVar +NP_001009944.3,NP_001009944.3.csv,MPPAAPARLALALGLGLWLGALAGGPGRGCGPCEPPCLCGPAPGAACRVNCSGRGLRTLGPALRIPADATALDVSHNLLRALDVGLLANLSALAELDISNNKISTLEEGIFANLFNLSEINLSGNPFECDCGLAWLPRWAEEQQVRVVQPEAATCAGPGSLAGQPLLGIPLLDSGCGEEYVACLPDNSSGTVAAVSFSAAHEGLLQPEACSAFCFSTGQGLAALSEQGWCLCGAAQPSSASFACLSLCSGPPPPPAPTCRGPTLLQHVFPASPGATLVGPHGPLASGQLAAFHIAAPLPVTATRWDFGDGSAEVDAAGPAASHRYVLPGRYHVTAVLALGAGSALLGTDVQVEAAPAALELVCPSSVQSDESLDLSIQNRGGSGLEAAYSIVALGEEPARAVHPLCPSDTEIFPGNGHCYRLVVEKAAWLQAQEQCQAWAGAALAMVDSPAVQRFLVSRVTRSLDVWIGFSTVQGVEVGPAPQGEAFSLESCQNWLPGEPHPATAEHCVRLGPTGWCNTDLCSAPHSYVCELQPGGPVQDAENLLVGAPSGDLQGPLTPLAQQDGLSAPHEPVEVMVFPGLRLSREAFLTTAEFGTQELRRPAQLRLQVYRLLSTAGTPENGSEPESRSPDNRTQLAPACMPGGRWCPGANICLPLDASCHPQACANGCTSGPGLPGAPYALWREFLFSVPAGPPAQYSVTLHGQDVLMLPGDLVGLQHDAGPGALLHCSPAPGHPGPRAPYLSANASSWLPHLPAQLEGTWACPACALRLLAATEQLTVLLGLRPNPGLRLPGRYEVRAEVGNGVSRHNLSCSFDVVSPVAGLRVIYPAPRDGRLYVPTNGSALVLQVDSGANATATARWPGGSVSARFENVCPALVATFVPGCPWETNDTLFSVVALPWLSEGEHVVDVVVENSASRANLSLRVTAEEPICGLRATPSPEARVLQGVLVRYSPVVEAGSDMVFRWTINDKQSLTFQNVVFNVIYQSAAVFKLSLTASNHVSNVTVNYNVTVERMNRMQGLQVSTVPAVLSPNATLALTAGVLVDSAVEVAFLWTFGDGEQALHQFQPPYNESFPVPDPSVAQVLVEHNVMHTYAAPGEYLLTVLASNAFENLTQQVPVSVRASLPSVAVGVSDGVLVAGRPVTFYPHPLPSPGGVLYTWDFGDGSPVLTQSQPAANHTYASRGTYHVRLEVNNTVSGAAAQADVRVFEELRGLSVDMSLAVEQGAPVVVSAAVQTGDNITWTFDMGDGTVLSGPEATVEHVYLRAQNCTVTVGAASPAGHLARSLHVLVFVLEVLRVEPAACIPTQPDARLTAYVTGNPAHYLFDWTFGDGSSNTTVRGCPTVTHNFTRSGTFPLALVLSSRVNRAHYFTSICVEPEVGNVTLQPERQFVQLGDEAWLVACAWPPFPYRYTWDFGTEEAAPTRARGPEVTFIYRDPGSYLVTVTASNNISAANDSALVEVQEPVLVTSIKVNGSLGLELQQPYLFSAVGRGRPASYLWDLGDGGWLEGPEVTHAYNSTGDFTVRVAGWNEVSRSEAWLNVTVKRRVRGLVVNASRTVVPLNGSVSFSTSLEAGSDVRYSWVLCDRCTPIPGGPTISYTFRSVGTFNIIVTAENEVGSAQDSIFVYVLQLIEGLQVVGGGRYFPTNHTVQLQAVVRDGTNVSYSWTAWRDRGPALAGSGKGFSLTVLEAGTYHVQLRATNMLGSAWADCTMDFVEPVGWLMVAASPNPAAVNTSVTLSAELAGGSGVVYTWSLEEGLSWETSEPFTTHSFPTPGLHLVTMTAGNPLGSANATVEVDVQVPVSGLSIRASEPGGSFVAAGSSVPFWGQLATGTNVSWCWAVPGGSSKRGPHVTMVFPDAGTFSIRLNASNAVSWVSATYNLTAEEPIVGLVLWASSKVVAPGQLVHFQILLAAGSAVTFRLQVGGANPEVLPGPRFSHSFPRVGDHVVSVRGKNHVSWAQAQVRIVVLEAVSGLQVPNCCEPGIATGTERNFTARVQRGSRVAYAWYFSLQKVQGDSLVILSGRDVTYTPVAAGLLEIQVRAFNALGSENRTLVLEVQDAVQYVALQSGPCFTNRSAQFEAATSPSPRRVAYHWDFGDGSPGQDTDEPRAEHSYLRPGDYRVQVNASNLVSFFVAQATVTVQVLACREPEVDVVLPLQVLMRRSQRNYLEAHVDLRDCVTYQTEYRWEVYRTASCQRPGRPARVALPGVDVSRPRLVLPRLALPVGHYCFVFVVSFGDTPLTQSIQANVTVAPERLVPIIEGGSYRVWSDTRDLVLDGSESYDPNLEDGDQTPLSFHWACVASTQREAGGCALNFGPRGSSTVTIPRERLAAGVEYTFSLTVWKAGRKEEATNQTVLIRSGRVPIVSLECVSCKAQAVYEVSRSSYVYLEGRCLNCSSGSKRGRWAARTFSNKTLVLDETTTSTGSAGMRLVLRRGVLRDGEGYTFTLTVLGRSGEEEGCASIRLSPNRPPLGGSCRLFPLGAVHALTTKVHFECTGWHDAEDAGAPLVYALLLRRCRQGHCEEFCVYKGSLSSYGAVLPPGFRPHFEVGLAVVVQDQLGAAVVALNRSLAITLPEPNGSATGLTVWLHGLTASVLPGLLRQADPQHVIEYSLALVTVLNEYERALDVAAEPKHERQHRAQIRKNITETLVSLRVHTVDDIQQIAAALAQCMGPSRELVCRSCLKQTLHKLEAMMLILQAETTAGTVTPTAIGDSILNITGDLIHLASSDVRAPQPSELGAESPSRMVASQAYNLTSALMRILMRSRVLNEEPLTLAGEEIVAQGKRSDPRSLLCYGGAPGPGCHFSIPEAFSGALANLSDVVQLIFLVDSNPFPFGYISNYTVSTKVASMAFQTQAGAQIPIERLASERAITVKVPNNSDWAARGHRSSANSANSVVVQPQASVGAVVTLDSSNPAAGLHLQLNYTLLDGHYLSEEPEPYLAVYLHSEPRPNEHNCSASRRIRPESLQGADHRPYTFFISPGSRDPAGSYHLNLSSHFRWSALQVSVGLYTSLCQYFSEEDMVWRTEGLLPLEETSPRQAVCLTRHLTAFGASLFVPPSHVRFVFPEPTADVNYIVMLTCAVCLVTYMVMAAILHKLDQLDASRGRAIPFCGQRGRFKYEILVKTGWGRGSGTTAHVGIMLYGVDSRSGHRHLDGDRAFHRNSLDIFRIATPHSLGSVWKIRVWHDNKGLSPAWFLQHVIVRDLQTARSAFFLVNDWLSVETEANGGLVEKEVLAASDAALLRFRRLLVAELQRGFFDKHIWLSIWDRPPRSRFTRIQRATCCVLLICLFLGANAVWYGAVGDSAYSTGHVSRLSPLSVDTVAVGLVSSVVVYPVYLAILFLFRMSRSKVAGSPSPTPAGQQVLDIDSCLDSSVLDSSFLTFSGLHAEQAFVGQMKSDLFLDDSKSLVCWPSGEGTLSWPDLLSDPSIVGSNLRQLARGQAGHGLGPEEDGFSLASPYSPAKSFSASDEDLIQQVLAEGVSSPAPTQDTHMETDLLSSLSSTPGEKTETLALQRLGELGPPSPGLNWEQPQAARLSRTGLVEGLRKRLLPAWCASLAHGLSLLLVAVAVAVSGWVGASFPPGVSVAWLLSSSASFLASFLGWEPLKVLLEALYFSLVAKRLHPDEDDTLVESPAVTPVSARVPRVRPPHGFALFLAKEEARKVKRLHGMLRSLLVYMLFLLVTLLASYGDASCHGHAYRLQSAIKQELHSRAFLAITRSEELWPWMAHVLLPYVHGNQSSPELGPPRLRQVRLQEALYPDPPGPRVHTCSAAGGFSTSDYDVGWESPHNGSGTWAYSAPDLLGAWSWGSCAVYDSGGYVQELGLSLEESRDRLRFLQLHNWLDNRSRAVFLELTRYSPAVGLHAAVTLRLEFPAAGRALAALSVRPFALRRLSAGLSLPLLTSVCLLLFAVHFAVAEARTWHREGRWRVLRLGAWARWLLVALTAATALVRLAQLGAADRQWTRFVRGRPRRFTSFDQVAQLSSAARGLAASLLFLLLVKAAQQLRFVRQWSVFGKTLCRALPELLGVTLGLVVLGVAYAQLAILLVSSCVDSLWSVAQALLVLCPGTGLSTLCPAESWHLSPLLCVGLWALRLWGALRLGAVILRWRYHALRGELYRPAWEPQDYEMVELFLRRLRLWMGLSKVKEFRHKVRFEGMEPLPSRSSRGSKVSPDVPPPSAGSDASHPSTSSSQLDGLSVSLGRLGTRCEPEPSRLQAVFEALLTQFDRLNQATEDVYQLEQQLHSLQGRRSSRAPAGSSRGPSPGLRPALPSRLARASRGVDLATGPSRTPLRAKNKVHPSST,mutated_sequence,1.0,4303.0,NP_001009944.3.a2m,NP_001009944.3.npy,ClinVar +NP_001012632.1,NP_001012632.1.csv,MSGVRPPIMNGPLHPRPLVALLDGRDCTVEMPILKDVATVAFCDAQSTQEIHEKVLNEAVGALMYHTITLTREDLEKFKALRIIVRIGSGFDNIDIKSAGDLGIAVCNVPAASVEETADSTLCHILNLYRRATWLHQALREGTRVQSVEQIREVASGAARIRGETLGIIGLGRVGQAVALRAKAFGFNVLFYDPYLSDGVERALGLQRVSTLQDLLFHSDCVTLHCGLNEHNHHLINDFTVKQMRQGAFLVNTARGGLVDEKALAQALKEGRIRGAALDVHESEPFSFSQGPLKDAPNLICTPHAAWYSEQASIEMREEAAREIRRAITGRIPDSLKNCVNKDHLTAATHWASMDPAVVHPELNGAAYRYPPGVVGVAPTGIPAAVEGIVPSAMSLSHGLPPVAHPPHAPSPGQTVKPEADRDHASDQL,mutated_sequence,1.0,429.0,NP_001012632.1.a2m,NP_001012632.1.npy,ClinVar +NP_001013.1,NP_001013.1.csv,MPGVTVKDVNQQEFVRALAAFLKKSGKLKVPEWVDTVKLAKHKELAPYDENWFYTRAASTARHLYLRGGAGVGSMTKIYGGRQRNGVMPSHFSRGSKSVARRVLQALEGLKMVEKDQDGGRKLTPQGQRDLDRIAGQVAAANKKH,mutated_sequence,1.0,145.0,NP_001013.1.a2m,NP_001013.1.npy,ClinVar +NP_001013861.1,NP_001013861.1.csv,MIPPQEASARRREIEDKLKQEEETLSFIRDSLEKSDQLTKNMVSILSSFESRLMKLENSIIPVHKQTENLQRLQENVEKTLSCLDHVISYYHVASDTEKIIREGPTGRLEEYLGSMAKIQKAVEYFQDNSPDSPELNKVKLLFERGKEALESEFRSLMTRHSKVVSPVLILDLISGDDDLEAQEDVTLEHLPESVLQDVIRISRWLVEYGRNQDFMNVYYQIRSSQLDRSIKGLKEHFHKSSSSSGVPYSPAIPNKRKDTPTKKPVKRPGTIRKAQNLLKQYSQHGLDGKKGGSNLIPLEGRDDMLDVETDAYIHCVSAFVKLAQSEYQLLADIIPEHHQKKTFDSLIQDALDGLMLEGENIVSAARKAIVRHDFSTVLTVFPILRHLKQTKPEFDQVLQGTAASTKNKLPGLITSMETIGAKALEDFADNIKNDPDKEYNMPKDGTVHELTSNAILFLQQLLDFQETAGAMLASQETSSSATSYSSEFSKRLLSTYICKVLGNLQLNLLSKSKVYEDPALSAIFLHNNYNYILKSLEKSELIQLVAVTQKTAERSYREHIEQQIQTYQRSWLKVTDYIAEKNLPVFQPGVKLRDKERQIIKERFKGFNDGLEELCKIQKAWAIPDTEQRDRIRQAQKTIVKETYGAFLQKFGSVPFTKNPEKYIKYGVEQVGDMIDRLFDTSA,mutated_sequence,1.0,684.0,NP_001013861.1.a2m,NP_001013861.1.npy,ClinVar +NP_001015877.1,NP_001015877.1.csv,MSSSVEQKKGPTRQRKCGFCKSNRDKECGQLLISENQKVAAHHKCMLFSSALVSSHSDNESLGGFSIEDVQKEIKRGTKLMCSLCHCPGATIGCDVKTCHRTYHYHCALHDKAQIREKPSQGIYMVYCRKHKKTAHNSEADLEESFNEHELEPSSPKSKKKSRKGRPRKTNFKGLSEDTRSTSSHGTDEMESSSYRDRSPHRSSPSDTRPKCGFCHVGEEENEARGKLHIFNAKKAAAHYKCMLFSSGTVQLTTTSRAEFGDFDIKTVLQEIKRGKRMKCTLCSQPGATIGCEIKACVKTYHYHCGVQDKAKYIENMSRGIYKLYCKNHSGNDERDEEDEERESKSRGKVEIDQQQLTQQQLNGN,mutated_sequence,1.0,365.0,NP_001015877.1.a2m,NP_001015877.1.npy,ClinVar +NP_001015880.1,NP_001015880.1.csv,MSGIKKQKTENQQKSTNVVYQAHHVSRNKRGQVVGTRGGFRGCTVWLTGLSGAGKTTISFALEEYLVSHAIPCYSLDGDNVRHGLNRNLGFSPGDREENIRRIAEVAKLFADAGLVCITSFISPFAKDRENARKIHESAGLPFFEIFVDAPLNICESRDVKGLYKRARAGEIKGFTGIDSDYEKPETPERVLKTNLSTVSDCVHQVVELLQEQNIVPYTIIKDIHELFVPENKLDHVRAEAETLPSLSITKLDLQWVQVLSEGWATPLKGFMREKEYLQVMHFDTLLDGMALPDGVINMSIPIVLPVSAEDKTRLEGCSKFVLAHGGRRVAILRDAEFYEHRKEERCSRVWGTTCTKHPHIKMVMESGDWLVGGDLQVLEKIRWNDGLDQYRLTPLELKQKCKEMNADAVFAFQLRNPVHNGHALLMQDTRRRLLERGYKHPVLLLHPLGGWTKDDDVPLDWRMKQHAAVLEEGVLDPKSTIVAIFPSPMLYAGPTEVQWHCRSRMIAGANFYIVGRDPAGMPHPETKKDLYEPTHGGKVLSMAPGLTSVEIIPFRVAAYNKAKKAMDFYDPARHNEFDFISGTRMRKLAREGENPPDGFMAPKAWKVLTDYYRSLEKN,mutated_sequence,1.0,619.0,NP_001015880.1.a2m,NP_001015880.1.npy,ClinVar +NP_001018005.1,NP_001018005.1.csv,MDAIKKKMQMLKLDKENALDRAEQAEADKKAAEDRSKQLEDELVSLQKKLKGTEDELDKYSEALKDAQEKLELAEKKATDAEADVASLNRRIQLVEEELDRAQERLATALQKLEEAEKAADESERGMKVIESRAQKDEEKMEIQEIQLKEAKHIAEDADRKYEEVARKLVIIESDLERAEERAELSEGKCAELEEELKTVTNNLKSLEAQAEKYSQKEDRYEEEIKVLSDKLKEAETRAEFAERSVTKLEKSIDDLEDELYAQKLKYKAISEELDHALNDMTSI,mutated_sequence,1.0,284.0,NP_001018005.1.a2m,NP_001018005.1.npy,ClinVar +NP_001018125.1,NP_001018125.1.csv,MVSKRRLSKSEDKESLTEDASKTRKQPLSKKTKKSHIANEVEENDSIFVKLLKISGIILKTGESQNQLAVDQIAFQKKLFQTLRRHPSYPKIIEEFVSGLESYIEDEDSFRNCLLSCERLQDEEASMGASYSKSLIKLLLGIDILQPAIIKTLFEKLPEYFFENKNSDEINIPRLIVSQLKWLDRVVDGKDLTTKIMQLISIAPENLQHDIITSLPEILGDSQHADVGKELSDLLIENTSLTVPILDVLSSLRLDPNFLLKVRQLVMDKLSSIRLEDLPVIIKFILHSVTAMDTLEVISELREKLDLQHCVLPSRLQASQVKLKSKGRASSSGNQESSGQSCIILLFDVIKSAIRYEKTISEAWIKAIENTASVSEHKVFDLVMLFIIYSTNTQTKKYIDRVLRNKIRSGCIQEQLLQSTFSVHYLVLKDMCSSILSLAQSLLHSLDQSIISFGSLLYKYAFKFFDTYCQQEVVGALVTHICSGNEAEVDTALDVLLELVVLNPSAMMMNAVFVKGILDYLDNISPQQIRKLFYVLSTLAFSKQNEASSHIQDDMHLVIRKQLSSTVFKYKLIGIIGAVTMAGIMAADRSESPSLTQERANLSDEQCTQVTSLLQLVHSCSEQSPQASALYYDEFANLIQHEKLDPKALEWVGHTICNDFQDAFVVDSCVVPEGDFPFPVKALYGLEEYDTQDGIAINLLPLLFSQDFAKDGGPVTSQESGQKLVSPLCLAPYFRLLRLCVERQHNGNLEEIDGLLDCPIFLTDLEPGEKLESMSAKERSFMCSLIFLTLNWFREIVNAFCQETSPEMKGKVLTRLKHIVELQIILEKYLAVTPDYVPPLGNFDVETLDITPHTVTAISAKIRKKGKIERKQKTDGSKTSSSDTLSEEKNSECDPTPSHRGQLNKEFTGKEEKTSLLLHNSHAFFRELDIEVFSILHCGLVTKFILDTEMHTEATEVVQLGPPELLFLLEDLSQKLESMLTPPIARRVPFLKNKGSRNIGFSHLQQRSAQEIVHCVFQLLTPMCNHLENIHNYFQCLAAENHGVVDGPGVKVQEYHIMSSCYQRLLQIFHGLFAWSGFSQPENQNLLYSALHVLSSRLKQGEHSQPLEELLSQSVHYLQNFHQSIPSFQCALYLIRLLMVILEKSTASAQNKEKIASLARQFLCRVWPSGDKEKSNISNDQLHALLCIYLEHTESILKAIEEIAGVGVPELINSPKDASSSTFPTLTRHTFVVFFRVMMAELEKTVKKIEPGTAADSQQIHEEKLLYWNMAVRDFSILINLIKVFDSHPVLHVCLKYGRLFVEAFLKQCMPLLDFSFRKHREDVLSLLETFQLDTRLLHHLCGHSKIHQDTRLTQHVPLLKKTLELLVCRVKAMLTLNNCREAFWLGNLKNRDLQGEEIKSQNSQESTADESEDDMSSQASKSKATEDGEEDEVSAGEKEQDSDESYDDSD,mutated_sequence,1.0,1451.0,NP_001018125.1.a2m,NP_001018125.1.npy,ClinVar +NP_001019801.3,NP_001019801.3.csv,MASNSLFSTVTPCQQNFFWDPSTSRRFSPPSSSLQPGKMSDVSPVVAAQQQQQQQQQQQQQQQQQQQQQQQEAAAAAAAAAAAAAAAAAVPRLRPPHDNRTMVEIIADHPAELVRTDSPNFLCSVLPSHWRCNKTLPVAFKVVALGEVPDGTVVTVMAGNDENYSAELRNASAVMKNQVARFNDLRFVGRSGRGKSFTLTITVFTNPPQVATYHRAIKVTVDGPREPRRHRQKLDDSKPSLFSDRLSDLGRIPHPSMRVGVPPQNPRPSLNSAPSPFNPQGQSQITDPRQAQSSPPWSYDQSYPSYLSQMTSPSIHSTTPLSSTRGTGLPAITDVPRRISDDDTATSDFCLWPSTLSKKSQAGASELGPFSDPRQFPSISSLTESRFSNPRMHYPATFTYTPPVTSGMSLGMSATTHYHTYLPPPYPGSSQSQSGPFQTSSTPYLYYGTSSGSYQFPMVPGGDRSPSRMLPPCTTTSNGSTLLNPNLPNQNDGVDADGSHSSSPTVLNSSGRMDESVWRPY,mutated_sequence,1.0,521.0,NP_001019801.3.a2m,NP_001019801.3.npy,ClinVar +NP_001025067.1,NP_001025067.1.csv,MTQQPLRGVTSLRFNQDQSCFCCAMETGVRIYNVEPLMEKGHLDHEQVGSMGLVEMLHRSNLLALVGGGSSPKFSEISVLIWDDAREGKDSKEKLVLEFTFTKPVLSVRMRHDKIVIVLKNRIYVYSFPDNPRKLFEFDTRDNPKGLCDLCPSLEKQLLVFPGHKCGSLQLVDLASTKPGTSSAPFTINAHQSDIACVSLNQPGTVVASASQKGTLIRLFDTQSKEKLVELRRGTDPATLYCINFSHDSSFLCASSDKGTVHIFALKDTRLNRRSALARVGKVGPMIGQYVDSQWSLASFTVPAESACICAFGRNTSKNVNSVIAICVDGTFHKYVFTPDGNCNREAFDVYLDICDDDDF,mutated_sequence,1.0,360.0,NP_001025067.1.a2m,NP_001025067.1.npy,ClinVar +NP_001026.2,NP_001026.2.csv,MADGGEGEDEIQFLRTDDEVVLQCTATIHKEQQKLCLAAEGFGNRLCFLESTSNSKNVPPDLSICTFVLEQSLSVRALQEMLANTVEKSEGQVDVEKWKFMMKTAQGGGHRTLLYGHAILLRHSYSGMYLCCLSTSRSSTDKLAFDVGLQEDTTGEACWWTIHPASKQRSEGEKVRVGDDLILVSVSSERYLHLSYGNGSLHVDAAFQQTLWSVAPISSGSEAAQGYLIGGDVLRLLHGHMDECLTVPSGEHGEEQRRTVHYEGGAVSVHARSLWRLETLRVAWSGSHIRWGQPFRLRHVTTGKYLSLMEDKNLLLMDKEKADVKSTAFTFRSSKEKLDVGVRKEVDGMGTSEIKYGDSVCYIQHVDTGLWLTYQSVDVKSVRMGSIQRKAIMHHEGHMDDGISLSRSQHEESRTARVIRSTVFLFNRFIRGLDALSKKAKASTVDLPIESVSLSLQDLIGYFHPPDEHLEHEDKQNRLRALKNRQNLFQEEGMINLVLECIDRLHVYSSAAHFADVAGREAGESWKSILNSLYELLAALIRGNRKNCAQFSGSLDWLISRLERLEASSGILEVLHCVLVESPEALNIIKEGHIKSIISLLDKHGRNHKVLDVLCSLCVCHGVAVRSNQHLICDNLLPGRDLLLQTRLVNHVSSMRPNIFLGVSEGSAQYKKWYYELMVDHTEPFVTAEATHLRVGWASTEGYSPYPGGGEEWGGNGVGDDLFSYGFDGLHLWSGCIARTVSSPNQHLLRTDDVISCCLDLSAPSISFRINGQPVQGMFENFNIDGLFFPVVSFSAGIKVRFLLGGRHGEFKFLPPPGYAPCYEAVLPKEKLKVEHSREYKQERTYTRDLLGPTVSLTQAAFTPIPVDTSQIVLPPHLERIREKLAENIHELWVMNKIELGWQYGPVRDDNKRQHPCLVEFSKLPEQERNYNLQMSLETLKTLLALGCHVGISDEHAEDKVKKMKLPKNYQLTSGYKPAPMDLSFIKLTPSQEAMVDKLAENAHNVWARDRIRQGWTYGIQQDVKNRRNPRLVPYTLLDDRTKKSNKDSLREAVRTLLGYGYNLEAPDQDHAARAEVCSGTGERFRIFRAEKTYAVKAGRWYFEFETVTAGDMRVGWSRPGCQPDQELGSDERAFAFDGFKAQRWHQGNEHYGRSWQAGDVVGCMVDMNEHTMMFTLNGEILLDDSGSELAFKDFDVGDGFIPVCSLGVAQVGRMNFGKDVSTLKYFTICGLQEGYEPFAVNTNRDITMWLSKRLPQFLQVPSNHEHIEVTRIDGTIDSSPCLKVTQKSFGSQNSNTDIMFYRLSMPIECAEVFSKTVAGGLPGAGLFGPKNDLEDYDADSDFEVLMKTAHGHLVPDRVDKDKEATKPEFNNHKDYAQEKPSRLKQRFLLRRTKPDYSTSHSARLTEDVLADDRDDYDFLMQTSTYYYSVRIFPGQEPANVWVGWITSDFHQYDTGFDLDRVRTVTVTLGDEKGKVHESIKRSNCYMVCAGESMSPGQGRNNNGLEIGCVVDAASGLLTFIANGKELSTYYQVEPSTKLFPAVFAQATSPNVFQFELGRIKNVMPLSAGLFKSEHKNPVPQCPPRLHVQFLSHVLWSRMPNQFLKVDVSRISERQGWLVQCLDPLQFMSLHIPEENRSVDILELTEQEELLKFHYHTLRLYSAVCALGNHRVAHALCSHVDEPQLLYAIENKYMPGLLRAGYYDLLIDIHLSSYATARLMMNNEYIVPMTEETKSITLFPDENKKHGLPGIGLSTSLRPRMQFSSPSFVSISNECYQYSPEFPLDILKSKTIQMLTEAVKEGSLHARDPVGGTTEFLFVPLIKLFYTLLIMGIFHNEDLKHILQLIEPSVFKEAATPEEESDTLEKELSVDDAKLQGAGEEEAKGGKRPKEGLLQMKLPEPVKLQMCLLLQYLCDCQVRHRIEAIVAFSDDFVAKLQDNQRFRYNEVMQALNMSAALTARKTKEFRSPPQEQINMLLNFKDDKSECPCPEEIRDQLLDFHEDLMTHCGIELDEDGSLDGNSDLTIRGRLLSLVEKVTYLKKKQAEKPVESDSKKSSTLQQLISETMVRWAQESVIEDPELVRAMFVLLHRQYDGIGGLVRALPKTYTINGVSVEDTINLLASLGQIRSLLSVRMGKEEEKLMIRGLGDIMNNKVFYQHPNLMRALGMHETVMEVMVNVLGGGESKEITFPKMVANCCRFLCYFCRISRQNQKAMFDHLSYLLENSSVGLASPAMRGSTPLDVAAASVMDNNELALALREPDLEKVVRYLAGCGLQSCQMLVSKGYPDIGWNPVEGERYLDFLRFAVFCNGESVEENANVVVRLLIRRPECFGPALRGEGGNGLLAAMEEAIKIAEDPSRDGPSPNSGSSKTLDTEEEEDDTIHMGNAIMTFYSALIDLLGRCAPEMHLIHAGKGEAIRIRSILRSLIPLGDLVGVISIAFQMPTIAKDGNVVEPDMSAGFCPDHKAAMVLFLDRVYGIEVQDFLLHLLEVGFLPDLRAAASLDTAALSATDMALALNRYLCTAVLPLLTRCAPLFAGTEHHASLIDSLLHTVYRLSKGCSLTKAQRDSIEVCLLSICGQLRPSMMQHLLRRLVFDVPLLNEHAKMPLKLLTNHYERCWKYYCLPGGWGNFGAASEEELHLSRKLFWGIFDALSQKKYEQELFKLALPCLSAVAGALPPDYMESNYVSMMEKQSSMDSEGNFNPQPVDTSNITIPEKLEYFINKYAEHSHDKWSMDKLANGWIYGEIYSDSSKVQPLMKPYKLLSEKEKEIYRWPIKESLKTMLAWGWRIERTREGDSMALYNRTRRISQTSQVSVDAAHGYSPRAIDMSNVTLSRDLHAMAEMMAENYHNIWAKKKKMELESKGGGNHPLLVPYDTLTAKEKAKDREKAQDILKFLQINGYAVSRGFKDLELDTPSIEKRFAYSFLQQLIRYVDEAHQYILEFDGGSRGKGEHFPYEQEIKFFAKVVLPLIDQYFKNHRLYFLSAASRPLCSGGHASNKEKEMVTSLFCKLGVLVRHRISLFGNDATSIVNCLHILGQTLDARTVMKTGLESVKSALRAFLDNAAEDLEKTMENLKQGQFTHTRNQPKGVTQIINYTTVALLPMLSSLFEHIGQHQFGEDLILEDVQVSCYRILTSLYALGTSKSIYVERQRSALGECLAAFAGAFPVAFLETHLDKHNIYSIYNTKSSRERAALSLPTNVEDVCPNIPSLEKLMEEIVELAESGIRYTQMPHVMEVILPMLCSYMSRWWEHGPENNPERAEMCCTALNSEHMNTLLGNILKIIYNNLGIDEGAWMKRLAVFSQPIINKVKPQLLKTHFLPLMEKLKKKAATVVSEEDHLKAEARGDMSEAELLILDEFTTLARDLYAFYPLLIRFVDYNRAKWLKEPNPEAEELFRMVAEVFIYWSKSHNFKREEQNFVVQNEINNMSFLITDTKSKMSKAAVSDQERKKMKRKGDRYSMQTSLIVAALKRLLPIGLNICAPGDQELIALAKNRFSLKDTEDEVRDIIRSNIHLQGKLEDPAIRWQMALYKDLPNRTDDTSDPEKTVERVLDIANVLFHLEQKSKRVGRRHYCLVEHPQRSKKAVWHKLLSKQRKRAVVACFRMAPLYNLPRHRAVNLFLQGYEKSWIETEEHYFEDKLIEDLAKPGAEPPEEDEGTKRVDPLHQLILLFSRTALTEKCKLEEDFLYMAYADIMAKSCHDEEDDDGEEEVKSFEEKEMEKQKLLYQQARLHDRGAAEMVLQTISASKGETGPMVAATLKLGIAILNGGNSTVQQKMLDYLKEKKDVGFFQSLAGLMQSCSVLDLNAFERQNKAEGLGMVTEEGSGEKVLQDDEFTCDLFRFLQLLCEGHNSDFQNYLRTQTGNNTTVNIIISTVDYLLRVQESISDFYWYYSGKDVIDEQGQRNFSKAIQVAKQVFNTLTEYIQGPCTGNQQSLAHSRLWDAVVGFLHVFAHMQMKLSQDSSQIELLKELMDLQKDMVVMLLSMLEGNVVNGTIGKQMVDMLVESSNNVEMILKFFDMFLKLKDLTSSDTFKEYDPDGKGVISKRDFHKAMESHKHYTQSETEFLLSCAETDENETLDYEEFVKRFHEPAKDIGFNVAVLLTNLSEHMPNDTRLQTFLELAESVLNYFQPFLGRIEIMGSAKRIERVYFEISESSRTQWEKPQVKESKRQFIFDVVNEGGEKEKMELFVNFCEDTIFEMQLAAQISESDLNERSANKEESEKERPEEQGPRMAFFSILTVRSALFALRYNILTLMRMLSLKSLKKQMKKVKKMTVKDMVTAFFSSYWSIFMTLLHFVASVFRGFFRIICSLLLGGSLVEGAKKIKVAELLANMPDPTQDEVRGDGEEGERKPLEAALPSEDLTDLKELTEESDLLSDIFGLDLKREGGQYKLIPHNPNAGLSDLMSNPVPMPEVQEKFQEQKAKEEEKEEKEETKSEPEKAEGEDGEKEEKAKEDKGKQKLRQLHTHRYGEPEVPESAFWKKIIAYQQKLLNYFARNFYNMRMLALFVAFAINFILLFYKVSTSSVVEGKELPTRSSSENAKVTSLDSSSHRIIAVHYVLEESSGYMEPTLRILAILHTVISFFCIIGYYCLKVPLVIFKREKEVARKLEFDGLYITEQPSEDDIKGQWDRLVINTQSFPNNYWDKFVKRKVMDKYGEFYGRDRISELLGMDKAALDFSDAREKKKPKKDSSLSAVLNSIDVKYQMWKLGVVFTDNSFLYLAWYMTMSVLGHYNNFFFAAHLLDIAMGFKTLRTILSSVTHNGKQLVLTVGLLAVVVYLYTVVAFNFFRKFYNKSEDGDTPDMKCDDMLTCYMFHMYVGVRAGGGIGDEIEDPAGDEYEIYRIIFDITFFFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETKCFICGIGNDYFDTVPHGFETHTLQEHNLANYLFFLMYLINKDETEHTGQESYVWKMYQERCWEFFPAGDCFRKQYEDQLN,mutated_sequence,1.0,4967.0,NP_001026.2.a2m,NP_001026.2.npy,ClinVar +NP_001026895.2,NP_001026895.2.csv,MFVPRSLKIKRNANDDGKSCVAKIIKPDPEDLQLDKSRDVPVDAVATEAATIDRHISESCPFPSPGGQLAEVHSVSPEQGAKDSHPSEEPVKSFSKTQRWAEPGEPICVVCGRYGEYICDKTDEDVCSLECKAKHLLQVKEKEEKSKLSNPQKADSEPESPLNASYVYKEHPFILNLQEDQIENLKQQLGILVQGQEVTRPIIDFEHCSLPEVLNHNLKKSGYEVPTPIQMQMIPVGLLGRDILASADTGSGKTAAFLLPVIMRALFESKTPSALILTPTRELAIQIERQAKELMSGLPRMKTVLLVGGLPLPPQLYRLQQHVKVIIATPGRLLDIIKQSSVELCGVKIVVVDEADTMLKMGFQQQVLDILENIPNDCQTILVSATIPTSIEQLASQLLHNPVRIITGEKNLPCANVRQIILWVEDPAKKKKLFEILNDKKLFKPPVLVFVDCKLGADLLSEAVQKITGLKSISIHSEKSQIERKNILKGLLEGDYEVVVSTGVLGRGLDLISVRLVVNFDMPSSMDEYVHQIGRVGRLGQNGTAITFINNNSKRLFWDIAKRVKPTGSILPPQLLNSPYLHDQKRKEQQKDKQTQNDLVTGANLMDIIRKHDKSNSQK,mutated_sequence,1.0,619.0,NP_001026895.2.a2m,NP_001026895.2.npy,ClinVar +NP_001027392.1,NP_001027392.1.csv,MAPIGLKAVVGEKIMHDVIKKVKKKGEWKVLVVDQLSMRMLSSCCKMTDIMTEGITIVEDINKRREPLPSLEAVYLITPSEKSVHSLISDFKDPPTAKYRAAHVFFTDSCPDALFNELVKSRAAKVIKTLTEINIAFLPYESQVYSLDSADSFQSFYSPHKAQMKNPILERLAEQIATLCATLKEYPAVRYRGEYKDNALLAQLIQDKLDAYKADDPTMGEGPDKARSQLLILDRGFDPSSPVLHELTFQAMSYDLLPIENDVYKYETSGIGEARVKEVLLDEDDDLWIALRHKHIAEVSQEVTRSLKDFSSSKRMNTGEKTTMRDLSQMLKKMPQYQKELSKYSTHLHLAEDCMKHYQGTVDKLCRVEQDLAMGTDAEGEKIKDPMRAIVPILLDANVSTYDKIRIILLYIFLKNGITEENLNKLIQHAQIPPEDSEIITNMAHLGVPIVTDSTLRRRSKPERKERISEQTYQLSRWTPIIKDIMEDTIEDKLDTKHYPYISTRSSASFSTTAVSARYGHWHKNKAPGEYRSGPRLIIFILGGVSLNEMRCAYEVTQANGKWEVLIGSTHILTPQKLLDTLKKLNKTDEEISS,mutated_sequence,1.0,594.0,NP_001027392.1.a2m,NP_001027392.1.npy,ClinVar +NP_001027554.1,NP_001027554.1.csv,MPLPVALQTRLAKRGILKHLEPEPEEEIIAEDYDDDPVDYEATRLEGLPPSWYKVFDPSCGLPYYWNADTDLVSWLSPHDPNSVVTKSAKKLRSSNADAEEKLDRSHDKSDRGHDKSDRSHEKLDRGHDKSDRGHDKSDRDRERGYDKVDRERERDRERDRDRGYDKADREEGKERRHHRREELAPYPKSKKAVSRKDEELDPMDPSSYSDAPRGTWSTGLPKRNEAKTGADTTAAGPLFQQRPYPSPGAVLRANAEASRTKQQD,mutated_sequence,1.0,265.0,NP_001027554.1.a2m,NP_001027554.1.npy,ClinVar +NP_001034680.2,NP_001034680.2.csv,MTATTRGSPVGGNDNQGQAPDGQSQPPLQQNQTSSPDSSNENSPATPPDEQGQGDAPPQLEDEEPAFPHTDLAKLDDMINRPRWVVPVLPKGELEVLLEAAIDLSKKGLDVKSEACQRFFRDGLTISFTKILTDEAVSGWKFEIHRCIINNTHRLVELCVAKLSQDWFPLLELLAMALNPHCKFHIYNGTRPCESVSSSVQLPEDELFARSPDPRSPKGWLVDLLNKFGTLNGFQILHDRFINGSALNVQIIAALIKPFGQCYEFLTLHTVKKYFLPIIEMVPQFLENLTDEELKKEAKNEAKNDALSMIIKSLKNLASRVPGQEETVKNLEIFRLKMILRLLQISSFNGKMNALNEVNKVISSVSYYTHRHGNPEEEEWLTAERMAEWIQQNNILSIVLRDSLHQPQYVEKLEKILRFVIKEKALTLQDLDNIWAAQAGKHEAIVKNVHDLLAKLAWDFSPEQLDHLFDCFKASWTNASKKQREKLLELIRRLAEDDKDGVMAHKVLNLLWNLAHSDDVPVDIMDLALSAHIKILDYSCSQDRDTQKIQWIDRFIEELRTNDKWVIPALKQIREICSLFGEAPQNLSQTQRSPHVFYRHDLINQLQHNHALVTLVAENLATYMESMRLYARDHEDYDPQTVRLGSRYSHVQEVQERLNFLRFLLKDGQLWLCAPQAKQIWKCLAENAVYLCDREACFKWYSKLMGDEPDLDPDINKDFFESNVLQLDPSLLTENGMKCFERFFKAVNCREGKLVAKRRAYMMDDLELIGLDYLWRVVIQSNDDIASRAIDLLKEIYTNLGPRLQVNQVVIHEDFIQSCFDRLKASYDTLCVLDGDKDSVNCARQEAVRMVRVLTVLREYINECDSDYHEERTILPMSRAFRGKHLSFVVRFPNQGRQVDDLEVWSHTNDTIGSVRRCILNRIKANVAHTKIELFVGGELIDPADDRKLIGQLNLKDKSLITAKLTQISSNMPSSPDSSSDSSTGSPGNHGNHYSDGPNPEVESCLPGVIMSLHPRYISFLWQVADLGSSLNMPPLRDGARVLMKLMPPDSTTIEKLRAICLDHAKLGESSLSPSLDSLFFGPSASQVLYLTEVVYALLMPAGAPLADDSSDFQFHFLKSGGLPLVLSMLTRNNFLPNADMETRRGAYLNALKIAKLLLTAIGYGHVRAVAEACQPGVEGVNPMTQINQVTHDQAVVLQSALQSIPNPSSECMLRNVSVRLAQQISDEASRYMPDICVIRAIQKIIWASGCGSLQLVFSPNEEITKIYEKTNAGNEPDLEDEQVCCEALEVMTLCFALIPTALDALSKEKAWQTFIIDLLLHCHSKTVRQVAQEQFFLMCTRCCMGHRPLLFFITLLFTVLGSTARERAKHSGDYFTLLRHLLNYAYNSNINVPNAEVLLNNEIDWLKRIRDDVKRTGETGIEETILEGHLGVTKELLAFQTSEKKFHIGCEKGGANLIKELIDDFIFPASNVYLQYMRNGELPAEQAIPVCGSPPTINAGFELLVALAVGCVRNLKQIVDSLTEMYYIGTAITTCEALTEWEYLPPVGPRPPKGFVGLKNAGATCYMNSVIQQLYMIPSIRNGILAIEGTGSDVDDDMSGDEKQDNESNVDPRDDVFGYPQQFEDKPALSKTEDRKEYNIGVLRHLQVIFGHLAASRLQYYVPRGFWKQFRLWGEPVNLREQHDALEFFNSLVDSLDEALKALGHPAMLSKVLGGSFADQKICQGCPHRYECEESFTTLNVDIRNHQNLLDSLEQYVKGDLLEGANAYHCEKCNKKVDTVKRLLIKKLPPVLAIQLKRFDYDWERECAIKFNDYFEFPRELDMEPYTVAGVAKLEGDNVNPESQLIQQSEQSESETAGSTKYRLVGVLVHSGQASGGHYYSYIIQRNGGDGERNRWYKFDDGDVTECKMDDDEEMKNQCFGGEYMGEVFDHMMKRMSYRRQKRWWNAYILFYERMDTIDQDDELIRYISELAITTRPHQIIMPSAIERSVRKQNVQFMHNRMQYSMEYFQFMKKLLTCNGVYLNPPPGQDHLLPEAEEITMISIQLAARFLFTTGFHTKKVVRGSASDWYDALCILLRHSKNVRFWFAHNVLFNVSNRFSEYLLECPSAEVRGAFAKLIVFIAHFSLQDGPCPSPFASPGPSSQAYDNLSLSDHLLRAVLNLLRREVSEHGRHLQQYFNLFVMYANLGVAEKTQLLKLSVPATFMLVSLDEGPGPPIKYQYAELGKLYSVVSQLIRCCNVSSRMQSSINGNPPLPNPFGDPNLSQPIMPIQQNVADILFVRTSYVKKIIEDCSNSEETVKLLRFCCWENPQFSSTVLSELLWQVAYSYTYELRPYLDLLLQILLIEDSWQTHRIHNALKGIPDDRDGLFDTIQRSKNHYQKRAYQCIKCMVALFSNCPVAYQILQGNGDLKRKWTWAVEWLGDELERRPYTGNPQYTYNNWSPPVQSNETSNGYFLERSHSARMTLAKACELCPEEEPDDQDAPDEHESPPPEDAPLYPHSPGSQYQQNNHVHGQPYTGPAAHHMNNPQRTGQRAQENYEGSEEVSPPQTKDQ,mutated_sequence,1.0,2554.0,NP_001034680.2.a2m,NP_001034680.2.npy,ClinVar +NP_001035232.1,NP_001035232.1.csv,MAQSVLVPPGPDSFRFFTRESLAAIEQRIAEEKAKRPKQERKDEDDENGPKPNSDLEAGKSLPFIYGDIPPEMVSVPLEDLDPYYINKKTFIVLNKGKAISRFSATPALYILTPFNPIRKLAIKILVHSLFNMLIMCTILTNCVFMTMSNPPDWTKNVEYTFTGIYTFESLIKILARGFCLEDFTFLRDPWNWLDFTVITFAYVTEFVDLGNVSALRTFRVLRALKTISVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALIGLQLFMGNLRNKCLQWPPDNSSFEINITSFFNNSLDGNGTTFNRTVSIFNWDEYIEDKSHFYFLEGQNDALLCGNSSDAGQCPEGYICVKAGRNPNYGYTSFDTFSWAFLSLFRLMTQDFWENLYQLTLRAAGKTYMIFFVLVIFLGSFYLINLILAVVAMAYEEQNQATLEEAEQKEAEFQQMLEQLKKQQEEAQAAAAAASAESRDFSGAGGIGVFSESSSVASKLSSKSEKELKNRRKKKKQKEQSGEEEKNDRVRKSESEDSIRRKGFRFSLEGSRLTYEKRFSSPHQSLLSIRGSLFSPRRNSRASLFSFRGRAKDIGSENDFADDEHSTFEDNDSRRDSLFVPHRHGERRHSNVSQASRASRVLPILPMNGKMHSAVDCNGVVSLVGGPSTLTSAGQLLPEGTTTETEIRKRRSSSYHVSMDLLEDPTSRQRAMSIASILTNTMEELEESRQKCPPCWYKFANMCLIWDCCKPWLKVKHLVNLVVMDPFVDLAITICIVLNTLFMAMEHYPMTEQFSSVLSVGNLVFTGIFTAEMFLKIIAMDPYYYFQEGWNIFDGFIVSLSLMELGLANVEGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKISNDCELPRWHMHDFFHSFLIVFRVLCGEWIETMWDCMEVAGQTMCLTVFMMVMVIGNLVVLNLFLALLLSSFSSDNLAATDDDNEMNNLQIAVGRMQKGIDFVKRKIREFIQKAFVRKQKALDEIKPLEDLNNKKDSCISNHTTIEIGKDLNYLKDGNGTTSGIGSSVEKYVVDESDYMSFINNPSLTVTVPIAVGESDFENLNTEEFSSESDMEESKEKLNATSSSEGSTVDIGAPAEGEQPEVEPEESLEPEACFTEDCVRKFKCCQISIEEGKGKLWWNLRKTCYKIVEHNWFETFIVFMILLSSGALAFEDIYIEQRKTIKTMLEYADKVFTYIFILEMLLKWVAYGFQVYFTNAWCWLDFLIVDVSLVSLTANALGYSELGAIKSLRTLRALRPLRALSRFEGMRVVVNALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYHCINYTTGEMFDVSVVNNYSECKALIESNQTARWKNVKVNFDNVGLGYLSLLQVATFKGWMDIMYAAVDSRNVELQPKYEDNLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKFGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPANKFQGMVFDFVTKQVFDISIMILICLNMVTMMVETDDQSQEMTNILYWINLVFIVLFTGECVLKLISLRYYYFTIGWNIFDFVVVILSIVGMFLAELIEKYFVSPTLFRVIRLARIGRILRLIKGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYAIFGMSNFAYVKREVGIDDMFNFETFGNSMICLFQITTSAGWDGLLAPILNSGPPDCDPDKDHPGSSVKGDCGNPSVGIFFFVSYIIISFLVVVNMYIAVILENFSVATEESAEPLSEDDFEMFYEVWEKFDPDATQFIEFAKLSDFADALDPPLLIAKPNKVQLIAMDLPMVSGDRIHCLDILFAFTKRVLGESGEMDALRIQMEERFMASNPSKVSYEPITTTLKRKQEEVSAIIIQRAYRRYLLKQKVKKVSSIYKKDKGKECDGTPIKEDTLIDKLNENSTPEKTDMTPSTTSPPSYDSVTKPEKEKFEKDKSEKEDKGKDIRESKK,mutated_sequence,1.0,2005.0,NP_001035232.1.a2m,NP_001035232.1.npy,ClinVar +NP_001035526.1,NP_001035526.1.csv,MAAPILRSFSWGRWSGTLNLSVLLPLGLRKAHSGAQGLLAAQKARGLFKDFFPETGTKIELPELFDRGTASFPQTIYCGFDPTADSLHVGHLLALLGLFHLQRAGHNVIALVGGATARLGDPSGRTKEREALETERVRANARALRLGLEALAANHQQLFTDGRSWGSFTVLDNSAWYQKQHLVDFLAAVGGHFRMGTLLSRQSVQLRLKSPEGMSLAEFFYQVLQAYDFYYLFQRYGCRVQLGGSDQLGNIMSGYEFINKLTGEDVFGITVPLITSTTGAKLGKSAGNAVWLNRDKTSPFELYQFFVRQPDDSVERYLKLFTFLPLPEIDHIMQLHVKEPERRGPQKRLAAEVTKLVHGREGLDSAKRCTQALYHSSIDALEVMSDQELKELFKEAPFSEFFLDPGTSVLDTCRKANAIPDGPRGYRMITEGGVSINHQQVTNPESVLIVGQHILKNGLSLLKIGKRNFYIIKWLQL,mutated_sequence,1.0,477.0,NP_001035526.1.a2m,NP_001035526.1.npy,ClinVar +NP_001035806.1,NP_001035806.1.csv,MLKFRTVHGGLRLLGIRRTSTAPAASPNVRRLEYKPIKKVMVANRGEIAIRVFRACTELGIRTVAIYSEQDTGQMHRQKADEAYLIGRGLAPVQAYLHIPDIIKVAKENNVDAVHPGYGFLSERADFAQACQDAGVRFIGPSPEVVRKMGDKVEARAIAIAAGVPVVPGTDAPITSLHEAHEFSNTYGFPIIFKAAYGGGGRGMRVVHSYEELEENYTRAYSEALAAFGNGALFVEKFIEKPRHIEVQILGDQYGNILHLYERDCSIQRRHQKVVEIAPAAHLDPQLRTRLTSDSVKLAKQVGYENAGTVEFLVDRHGKHYFIEVNSRLQVEHTVTEEITDVDLVHAQIHVAEGRSLPDLGLRQENIRINGCAIQCRVTTEDPARSFQPDTGRIEVFRSGEGMGIRLDNASAFQGAVISPHYDSLLVKVIAHGKDHPTAATKMSRALAEFRVRGVKTNIAFLQNVLNNQQFLAGTVDTQFIDENPELFQLRPAQNRAQKLLHYLGHVMVNGPTTPIPVKASPSPTDPVVPAVPIGPPPAGFRDILLREGPEGFARAVRNHPGLLLMDTTFRDAHQSLLATRVRTHDLKKIAPYVAHNFSKLFSMENWGGATFDVAMRFLYECPWRRLQELRELIPNIPFQMLLRGANAVGYTNYPDNVVFKFCEVAKENGMDVFRVFDSLNYLPNMLLGMEAAGSAGGVVEAAISYTGDVADPSRTKYSLQYYMGLAEELVRAGTHILCIKDMAGLLKPTACTMLVSSLRDRFPDLPLHIHTHDTSGAGVAAMLACAQAGADVVDVAADSMSGMTSQPSMGALVACTRGTPLDTEVPMERVFDYSEYWEGARGLYAAFDCTATMKSGNSDVYENEIPGGQYTNLHFQAHSMGLGSKFKEVKKAYVEANQMLGDLIKVTPSSKIVGDLAQFMVQNGLSRAEAEAQAEELSFPRSVVEFLQGYIGVPHGGFPEPFRSKVLKDLPRVEGRPGASLPPLDLQALEKELVDRHGEEVTPEDVLSAAMYPDVFAHFKDFTATFGPLDSLNTRLFLQGPKIAEEFEVELERGKTLHIKALAVSDLNRAGQRQVFFELNGQLRSILVKDTQAMKEMHFHPKALKDVKGQIGAPMPGKVIDIKVVAGAKVAKGQPLCVLSAMKMETVVTSPMEGTVRKVHVTKDMTLEGDDLILEIE,mutated_sequence,1.0,1178.0,NP_001035806.1.a2m,NP_001035806.1.npy,ClinVar +NP_001035937.1,NP_001035937.1.csv,MRKRTEPVALEHERCAAAGSSSSGSAAAALDADCRLKQNLRLTGPAAAEPRCAADAGMKRALGRRKGVWLRLRKILFCVLGLYIAIPFLIKLCPGIQAKLIFLNFVRVPYFIDLKKPQDQGLNHTCNYYLQPEEDVTIGVWHTVPAVWWKNAQGKDQMWYEDALASSHPIILYLHGNAGTRGGDHRVELYKVLSSLGYHVVTFDYRGWGDSVGTPSERGMTYDALHVFDWIKARSGDNPVYIWGHSLGTGVATNLVRRLCERETPPDALILESPFTNIREEAKSHPFSVIYRYFPGFDWFFLDPITSSGIKFANDENVKHISCPLLILHAEDDPVVPFQLGRKLYSIAAPARSFRDFKVQFVPFHSDLGYRHKYIYKSPELPRILREFLGKSEPEHQH,mutated_sequence,1.0,398.0,NP_001035937.1.a2m,NP_001035937.1.npy,ClinVar +NP_001035957.1,NP_001035957.1.csv,MAAHRPVEWVQAVVSRFDEQLPIKTGQQNTHTKVSTEHNKECLINISKYKFSLVISGLTTILKNVNNMRIFGEAAEKNLYLSQLIILDTLEKCLAGQPKDTMRLDETMLVKQLLPEICHFLHTCREGNQHAAELRNSASGVLFSLSCNNFNAVFSRISTRLQELTVCSEDNVDVHDIELLQYINVDCAKLKRLLKETAFKFKALKKVAQLAVINSLEKAFWNWVENYPDEFTKLYQIPQTDMAECAEKLFDLVDGFAESTKRKAAVWPLQIILLILCPEIIQDISKDVVDENNMNKKLFLDSLRKALAGHGGSRQLTESAAIACVKLCKASTYINWEDNSVIFLLVQSMVVDLKNLLFNPSKPFSRGSQPADVDLMIDCLVSCFRISPHNNQHFKICLAQNSPSTFHYVLVNSLHRIITNSALDWWPKIDAVYCHSVELRNMFGETLHKAVQGCGAHPAIRMAPSLTFKEKVTSLKFKEKPTDLETRSYKYLLLSMVKLIHADPKLLLCNPRKQGPETQGSTAELITGLVQLVPQSHMPEIAQEAMEALLVLHQLDSIDLWNPDAPVETFWEISSQMLFYICKKLTSHQMLSSTEILKWLREILICRNKFLLKNKQADRSSCHFLLFYGVGCDIPSSGNTSQMSMDHEELLRTPGASLRKGKGNSSMDSAAGCSGTPPICRQAQTKLEVALYMFLWNPDTEAVLVAMSCFRHLCEEADIRCGVDEVSVHNLLPNYNTFMEFASVSNMMSTGRAALQKRVMALLRRIEHPTAGNTEAWEDTHAKWEQATKLILNYPKAKMEDGQAAESLHKTIVKRRMSHVSGGGSIDLSDTDSLQEWINMTGFLCALGGVCLQQRSNSGLATYSPPMGPVSERKGSMISVMSSEGNADTPVSKFMDRLLSLMVCNHEKVGLQIRTNVKDLVGLELSPALYPMLFNKLKNTISKFFDSQGQVLLTDTNTQFVEQTIAIMKNLLDNHTEGSSEHLGQASIETMMLNLVRYVRVLGNMVHAIQIKTKLCQLVEVMMARRDDLSFCQEMKFRNKMVEYLTDWVMGTSNQAADDDVKCLTRDLDQASMEAVVSLLAGLPLQPEEGDGVELMEAKSQLFLKYFTLFMNLLNDCSEVEDESAQTGGRKRGMSRRLASLRHCTVLAMSNLLNANVDSGLMHSIGLGYHKDLQTRATFMEVLTKILQQGTEFDTLAETVLADRFERLVELVTMMGDQGELPIAMALANVVPCSQWDELARVLVTLFDSRHLLYQLLWNMFSKEVELADSMQTLFRGNSLASKIMTFCFKVYGATYLQKLLDPLLRIVITSSDWQHVSFEVDPTRLEPSESLEENQRNLLQMTEKFFHAIISSSSEFPPQLRSVCHCLYQATCHSLLNKATVKEKKENKKSVVSQRFPQNSIGAVGSAMFLRFINPAIVSPYEAGILDKKPPPRIERGLKLMSKILQSIANHVLFTKEEHMRPFNDFVKSNFDAARRFFLDIASDCPTSDAVNHSLSFISDGNVLALHRLLWNNQEKIGQYLSSNRDHKAVGRRPFDKMATLLAYLGPPEHKPVADTHWSSLNLTSSKFEEFMTRHQVHEKEEFKALKTLSIFYQAGTSKAGNPIFYYVARRFKTGQINGDLLIYHVLLTLKPYYAKPYEIVVDLTHTGPSNRFKTDFLSKWFVVFPGFAYDNVSAVYIYNCNSWVREYTKYHERLLTGLKGSKRLVFIDCPGKLAEHIEHEQQKLPAATLALEEDLKVFHNALKLAHKDTKVSIKVGSTAVQVTSAERTKVLGQSVFLNDIYYASEIEEICLVDENQFTLTIANQGTPLTFMHQECEAIVQSIIHIRTRWELSQPDSIPQHTKIRPKDVPGTLLNIALLNLGSSDPSLRSAAYNLLCALTCTFNLKIEGQLLETSGLCIPANNTLFIVSISKTLAANEPHLTLEFLEECISGFSKSSIELKHLCLEYMTPWLSNLVRFCKHNDDAKRQRVTAILDKLITMTINEKQMYPSIQAKIWGSLGQITDLLDVVLDSFIKTSATGGLGSIKAEVMADTAVALASGNVKLVSSKVIGRMCKIIDKTCLSPTPTLEQHLMWDDIAILARYMLMLSFNNSLDVAAHLPYLFHVVTFLVATGPLSLRASTHGLVINIIHSLCTCSQLHFSEETKQVLRLSLTEFSLPKFYLLFGISKVKSAAVIAFRSSYRDRSFSPGSYERETFALTSLETVTEALLEIMEACMRDIPTCKWLDQWTELAQRFAFQYNPSLQPRALVVFGCISKRVSHGQIKQIIRILSKALESCLKGPDTYNSQVLIEATVIALTKLQPLLNKDSPLHKALFWVAVAVLQLDEVNLYSAGTALLEQNLHTLDSLRIFNDKSPEEVFMAIRNPLEWHCKQMDHFVGLNFNSNFNFALVGHLLKGYRHPSPAIVARTVRILHTLLTLVNKHRNCDKFEVNTQSVAYLAALLTVSEEVRSRCSLKHRKSLLLTDISMENVPMDTYPIHHGDPSYRTLKETQPWSSPKGSEGYLAATYPTVGQTSPRARKSMSLDMGQPSQANTKKLLGTRKSFDHLISDTKAPKRQEMESGITTPPKMRRVAETDYEMETQRISSSQQHPHLRKVSVSESNVLLDEEVLTDPKIQALLLTVLATLVKYTTDEFDQRILYEYLAEASVVFPKVFPVVHNLLDSKINTLLSLCQDPNLLNPIHGIVQSVVYHEESPPQYQTSYLQSFGFNGLWRFAGPFSKQTQIPDYAELIVKFLDALIDTYLPGIDEETSEESLLTPTSPYPPALQSQLSITANLNLSNSMTSLATSQHSPGIDKENVELSPTTGHCNSGRTRHGSASQVQKQRSAGSFKRNSIKKIV,mutated_sequence,1.0,2839.0,NP_001035957.1.a2m,NP_001035957.1.npy,ClinVar +NP_001036167.1,NP_001036167.1.csv,MFAAATKSFVKQVGDGGRLVPVPSLSEADKYQPLSLVVKKKRCFLFPRYKFTSTPFTLKDILLGDREISAGISSYQLLNYEDESDVSLYGRRGNHIVNDVGINVAGSDSIAVKASFGIVTKHEVEVSTLLKEITTRKINFDHSLIRQSRSSRKAVLCVVMESIRTTRQCSLSVHAGIRGEAMRFHFMDEQNPKGRDKAIVFPAHTTIAFSVFELFIYLDGAFDLCVTSVSKGGFEREETATFALLYRLRNILFERNRRVMDVISRSQLYLDDLFSDYYDKPLSMTDISLKEGTHIRVNLLNHNIPKGPCILCGMGNFKRETVYGCFQCSVDGQKYVRLHAVPCFDIWHKRMK,mutated_sequence,1.0,352.0,NP_001036167.1.a2m,NP_001036167.1.npy,ClinVar +NP_001041639.1,NP_001041639.1.csv,MRKPRAAVGSGHRKQAASQEGRQKHAKNNSQAKPSACDGLARQPEEVVLQASVSSYHLFRDVAEVTAFRGSLLSWYDQEKRDLPWRRRAEDEMDLDRRAYAVWVSEVMLQQTQVATVINYYTGWMQKWPTLQDLASASLEEVNQLWAGLGYYSRGRRLQEGARKVVEELGGHMPRTAETLQQLLPGVGRYTAGAIASIAFGQATGVVDGNVARVLCRVRAIGADPSSTLVSQQLWGLAQQLVDPARPGDFNQAAMELGATVCTPQRPLCSQCPVESLCRARQRVEQEQLLASGSLSGSPDVEECAPNTGQCHLCLPPSEPWDQTLGVVNFPRKASRKPPREESSATCVLEQPGALGAQILLVQRPNSGLLAGLWEFPSVTWEPSEQLQRKALLQELQRWAGPLPATHLRHLGEVVHTFSHIKLTYQVYGLALEGQTPVTTVPPGARWLTQEEFHTAAVSTAMKKVFRVYQGQQPGTCMGSKRSQVSSPCSRKKPRMGQQVLDNFFRSHISTDAHSLNSAAQ,mutated_sequence,1.0,521.0,NP_001041639.1.a2m,NP_001041639.1.npy,ClinVar +NP_001070833.1,NP_001070833.1.csv,MWGFLKRPVVVTADINLSLVALTGMGLLSRLWRLTYPRAVVFDEVYYGQYISFYMKQIFFLDDSGPPFGHMVLALGGYLGGFDGNFLWNRIGAEYSSNVPVWSLRLLPALAGALSVPMAYQIVLELHFSHCAAMGAALLMLIENALITQSRLMLLESVLIFFNLLAVLSYLKFFNCQKHSPFSLSWWFWLTLTGVACSCAVGIKYMGVFTYVLVLGVAAVHAWHLLGDQTLSNVCVFCHLLARAVALLVIPVVLYLLFFYVHLILVFRSGPHDQIMSSAFQASLEGGLARITQGQPLEVAFGSQVTLRNVFGKPVPCWLHSHQDTYPMIYENGRGSSHQQQVTCYPFKDVNNWWIVKDPRRHQLVVSSPPRPVRHGDMVQLVHGMTTRSLNTHDVAAPLSPHSQEVSCYIDYNISMPAQNLWRLEIVNRGSDTDVWKTILSEVRFVHVNTSAVLKLSGAHLPDWGYRQLEIVGEKLSRGYHGSTVWNVEEHRYGASQEQRERERELHSPAQVDVSRNLSFMARFSELQWRMLALRSDDSEHKYSSSPLEWVTLDTNIAYWLHPRTSAQIHLLGNIVIWVSGSLALAIYALLSLWYLLRRRRNVHDLPQDAWLRWVLAGALCAGGWAVNYLPFFLMEKTLFLYHYLPALTFQILLLPVVLQHISDHLCRSQLQRSIFSALVVAWYSSACHVSNTLRPLTYGDKSLSPHELKALRWKDSWDILIRKH,mutated_sequence,1.0,725.0,NP_001070833.1.a2m,NP_001070833.1.npy,ClinVar +NP_001071.1,NP_001071.1.csv,MATCIWLRSCGARRLGSTFPGCRLRPRAGGLVPASGPAPGPAQLRCYAGRLAGLSAALLRTDSFVGGRWLPAAATFPVQDPASGAALGMVADCGVREARAAVRAAYEAFCRWREVSAKERSSLLRKWYNLMIQNKDDLARIITAESGKPLKEAHGEILYSAFFLEWFSEEARRVYGDIIHTPAKDRRALVLKQPIGVAAVITPWNFPSAMITRKVGAALAAGCTVVVKPAEDTPFSALALAELASQAGIPSGVYNVIPCSRKNAKEVGEAICTDPLVSKISFTGSTTTGKILLHHAANSVKRVSMELGGLAPFIVFDSANVDQAVAGAMASKFRNTGQTCVCSNQFLVQRGIHDAFVKAFAEAMKKNLRVGNGFEEGTTQGPLINEKAVEKVEKQVNDAVSKGATVVTGGKRHQLGKNFFEPTLLCNVTQDMLCTHEETFGPLAPVIKFDTEEEAIAIANAADVGLAGYFYSQDPAQIWRVAEQLEVGMVGVNEGLISSVECPFGGVKQSGLGREGSKYGIDEYLELKYVCYGGL,mutated_sequence,1.0,535.0,NP_001071.1.a2m,NP_001071.1.npy,ClinVar +NP_001073911.1,NP_001073911.1.csv,MEGQTPGSRGLPEKPHPATAAATLSSMGAVFILMKSALGAGLLNFPWAFSKAGGVVPAFLVELVSLVFLISGLVILGYAAAVSGQATYQGVVRGLCGPAIGKLCEACFLLNLLMISVAFLRVIGDQLEKLCDSLLSGTPPAPQPWYADQRFTLPLLSVLVILPLSAPREIAFQKYTSILGTLAACYLALVITVQYYLWPQGLVRESHPSLSPASWTSVFSVFPTICFGFQCHEAAVSIYCSMRKRSLSHWALVSVLSLLACCLIYSLTGVYGFLTFGTEVSADVLMSYPGNDMVIIVARVLFAVSIVTVYPIVLFLGRSVMQDFWRRSCLGGWGPSALADPSGLWVRMPLTILWVTVTLAMALFMPDLSEIVSIIGGISSFFIFIFPGLCLICAMGVEPIGPRVKCCLEVWGVVSVLVGTFIFGQSTAAAVWEMF,mutated_sequence,1.0,435.0,NP_001073911.1.a2m,NP_001073911.1.npy,ClinVar +NP_001073936.1,NP_001073936.1.csv,MSVGELYSQCTRVWIPDPDEVWRSAELTKDYKEGDKSLQLRLEDETILEYPIDVQRNQLPFLRNPDILVGENDLTALSYLHEPAVLHNLKVRFLESNHIYTYCGIVLVAINPYEQLPIYGQDVIYTYSGQNMGDMDPHIFAVAEEAYKQMARDEKNQSIIVSGESGAGKTVSAKYAMRYFATVGGSASETNIEEKVLASSPIMEAIGNAKTTRNDNSSRFGKYIQIGFDKRYHIIGANMRTYLLEKSRVVFQADDERNYHIFYQLCAAAGLPEFKELALTSAEDFFYTSQGGDTSIEGVDDAEDFEKTRQAFTLLGVKESHQMSIFKIIASILHLGSVAIQAERDGDSCSISPQDVYLSNFCRLLGVEHSQMEHWLCHRKLVTTSETYVKTMSLQQVINARNALAKHIYAQLFGWIVEHINKALHTSLKQHSFIGVLDIYGFETFEVNSFEQFCINYANEKLQQQFNSHVFKLEQEEYMKEQIPWTLIDFYDNQPCIDLIEAKLGILDLLDEECKVPKGTDQNWAQKLYDRHSSSQHFQKPRMSNTAFIIVHFADKVEYLSDGFLEKNRDTVYEEQINILKASKFPLVADLFHDDKDPVPATTPGKGSSSKISVRSARPPMKVSNKEHKKTVGHQFRTSLHLLMETLNATTPHYVRCIKPNDEKLPFHFDPKRAVQQLRACGVLETIRISAAGYPSRWAYHDFFNRYRVLVKKRELANTDKKAICRSVLENLIKDPDKFQFGRTKIFFRAGQVAYLEKLRADKFRTATIMIQKTVRGWLQKVKYHRLKGATLTLQRYCRGHLARRLAEHLRRIRAAVVLQKHYRMQRARQAYQRVRRAAVVIQAFTRAMFVRRTYRQVLMEHKATTIQKHVRGWMARRHFQRLRDAAIVIQCAFRMLKARRELKALRIEARSAEHLKRLNVGMENKVVQLQRKIDEQNKEFKTLSEQLSVTTSTYTMEVERLKKELVHYQQSPGEDTSLRLQEEVESLRTELQRAHSERKILEDAHSREKDELRKRVADLEQENALLKDEKEQLNNQILCQSKDEFAQNSVKENLMKKELEEERSRYQNLVKEYSQLEQRYDNLRDEMTIIKQTPGHRRNPSNQSSLESDSNYPSISTSEIGDTEDALQQVEEIGLEKAAMDMTVFLKLQKRVRELEQERKKLQVQLEKREQQDSKKVQAEPPQTDIDLDPNADLAYNSLKRQELESENKKLKNDLNELRKAVADQATQNNSSHGSPDSYSLLLNQLKLAHEELEVRKEEVLILRTQIVSADQRRLAGRNAEPNINARSSWPNSEKHVDQEDAIEAYHGVCQTNSKTEDWGYLNEDGELGLAYQGLKQVARLLEAQLQAQSLEHEEEVEHLKAQLEALKEEMDKQQQTFCQTLLLSPEAQVEFGVQQEISRLTNENLDLKELVEKLEKNERKLKKQLKIYMKKAQDLEAAQALAQSERKRHELNRQVTVQRKEKDFQGMLEYHKEDEALLIRNLVTDLKPQMLSGTVPCLPAYILYMCIRHADYTNDDLKVHSLLTSTINGIKKVLKKHNDDFEMTSFWLSNTCRLLHCLKQYSGDEGFMTQNTAKQNEHCLKNFDLTEYRQVLSDLSIQIYQQLIKIAEGVLQPMIVSAMLENESIQGLSGVKPTGYRKRSSSMADGDNSYCLEAIIRQMNAFHTVMCDQGLDPEIILQVFKQLFYMINAVTLNNLLLRKDVCSWSTGMQLRYNISQLEEWLRGRNLHQSGAVQTMEPLIQAAQLLQLKKKTQEDAEAICSLCTSLSTQQIVKILNLYTPLNEFEERVTVAFIRTIQAQLQERNDPQQLLLDAKHMFPVLFPFNPSSLTMDSIHIPACLNLEFLNEV,mutated_sequence,1.0,1848.0,NP_001073936.1.a2m,NP_001073936.1.npy,ClinVar +NP_001076585.1,NP_001076585.1.csv,MAARLLLLGILLLLLPLPVPAPCHTAARSECKRSHKFVPGAWLAGEGVDVTSLRRSGSFPVDTQRFLRPDGTCTLCENALQEGTLQRLPLALTNWRAQGSGCQRHVTRAKVSSTEAVARDAARSIRNDWKVGLDVTPKPTSNVHVSVAGSHSQAANFAAQKTHQDQYSFSTDTVECRFYSFHVVHTPPLHPDFKRALGDLPHHFNASTQPAYLRLISNYGTHFIRAVELGGRISALTALRTCELALEGLTDNEVEDCLTVEAQVNIGIHGSISAEAKACEEKKKKHKMTASFHQTYRERHSEVVGGHHTSINDLLFGIQAGPEQYSAWVNSLPGSPGLVDYTLEPLHVLLDSQDPRREALRRALSQYLTDRARWRDCSRPCPPGRQKSPRDPCQCVCHGSAVTTQDCCPRQRGLAQLEVTFIQAWGLWGDWFTATDAYVKLFFGGQELRTSTVWDNNNPIWSVRLDFGDVLLATGGPLRLQVWDQDSGRDDDLLGTCDQAPKSGSHEVRCNLNHGHLKFRYHARCLPHLGGGTCLDYVPQMLLGEPPGNRSGAVW,mutated_sequence,1.0,555.0,NP_001076585.1.a2m,NP_001076585.1.npy,ClinVar +NP_001077083.1,NP_001077083.1.csv,MAALLRRLLQRERPSAASGRPVGRREANLGTDAGVAVRVRFAPSPTGFLHLGGLRTALYNYIFAKKYQGSFILRLEDTDQTRVVPGAAENIEDMLEWAGIPPDESPRRGGPAGPYQQSQRLELYAQATEALLKTGAAYPCFCSPQRLELLKKEALRNHQTPRYDNRCRNMSQEQVAQKLAKDPKPAIRFRLEQVVPAFQDLVYGWNRHEVASVEGDPVIMKSDGFPTYHLACVVDDHHMGISHVLRGSEWLVSTAKHLLLYQALGWQPPHFAHLPLLLNRDGSKLSKRQGDVFLEHFAADGFLPDSLLDIITNCGSGFAENQMGRTLPELITQFNLTQVTCHSALLDLEKLPEFNRLHLQRLVSNESQRRQLVGKLQVLVEEAFGCQLQNRDVLNPVYVERILLLRQGHICRLQDLVSPVYSYLWTRPAVGRAQLDAISEKVDVIAKRVLGLLERSSMSLTQDMLNGELKKLSEGLEGTKYSNVMKLLRMALSGQQQGPPVAEMMLALGPKEVRERIQKVVSS,mutated_sequence,1.0,523.0,NP_001077083.1.a2m,NP_001077083.1.npy,ClinVar +NP_001077431.1,NP_001077431.1.csv,MHHQQRMAALGTDKELSDLLDFSAMFSPPVSSGKNGPTSLASGHFTGSNVEDRSSSGSWGNGGHPSPSRNYGDGTPYDHMTSRDLGSHDNLSPPFVNSRIQSKTERGSYSSYGRESNLQGCHQQSLLGGDMDMGNPGTLSPTKPGSQYYQYSSNNPRRRPLHSSAMEVQTKKVRKVPPGLPSSVYAPSASTADYNRDSPGYPSSKPATSTFPSSFFMQDGHHSSDPWSSSSGMNQPGYAGMLGNSSHIPQSSSYCSLHPHERLSYPSHSSADINSSLPPMSTFHRSGTNHYSTSSCTPPANGTDSIMANRGSGAAGSSQTGDALGKALASIYSPDHTNNSFSSNPSTPVGSPPSLSAGTAVWSRNGGQASSSPNYEGPLHSLQSRIEDRLERLDDAIHVLRNHAVGPSTAMPGGHGDMHGIIGPSHNGAMGGLGSGYGTGLLSANRHSLMVGTHREDGVALRGSHSLLPNQVPVPQLPVQSATSPDLNPPQDPYRGMPPGLQGQSVSSGSSEIKSDDEGDENLQDTKSSEDKKLDDDKKDIKSITRSRSSNNDDEDLTPEQKAEREKERRMANNARERLRVRDINEAFKELGRMVQLHLKSDKPQTKLLILHQAVAVILSLEQQVRERNLNPKAACLKRREEEKVSSEPPPLSLAGPHPGMGDASNHMGQM,mutated_sequence,1.0,671.0,NP_001077431.1.a2m,NP_001077431.1.npy,ClinVar +NP_001091.1,NP_001091.1.csv,MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF,mutated_sequence,1.0,377.0,NP_001091.1.a2m,NP_001091.1.npy,ClinVar +NP_001092.1,NP_001092.1.csv,MDDDIAALVVDNGSGMCKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIVTNWDDMEKIWHHTFYNELRVAPEEHPVLLTEAPLNPKANREKMTQIMFETFNTPAMYVAIQAVLSLYASGRTTGIVMDSGDGVTHTVPIYEGYALPHAILRLDLAGRDLTDYLMKILTERGYSFTTTAEREIVRDIKEKLCYVALDFEQEMATAASSSSLEKSYELPDGQVITIGNERFRCPEALFQPSFLGMESCGIHETTFNSIMKCDVDIRKDLYANTVLSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWISKQEYDESGPSIVHRKCF,mutated_sequence,1.0,375.0,NP_001092.1.a2m,NP_001092.1.npy,ClinVar +NP_001093327.1,NP_001093327.1.csv,MNRHLWKSQLCEMVQPSGGPAADQDVLGEESPLGKPAMLHLPSEQGAPETLQRCLEENQELRDAIRQSNQILRERCEELLHFQASQREEKEFLMCKFQEARKLVERLGLEKLDLKRQKEQALREVEHLKRCQQQMAEDKASVKAQVTSLLGELQESQSRLEAATKECQALEGRARAASEQARQLESEREALQQQHSVQVDQLRMQGQSVEAALRMERQAASEEKRKLAQLQVAYHQLFQEYDNHIKSSVVGSERKRGMQLEDLKQQLQQAEEALVAKQEVIDKLKEEAEQHKIVMETVPVLKAQADIYKADFQAERQAREKLAEKKELLQEQLEQLQREYSKLKASCQESARIEDMRKRHVEVSQAPLPPAPAYLSSPLALPSQRRSPPEEPPDFCCPKCQYQAPDMDTLQIHVMECIE,mutated_sequence,1.0,419.0,NP_001093327.1.a2m,NP_001093327.1.npy,ClinVar +NP_001094896.1,NP_001094896.1.csv,MEAGPPGSARPAEPGPCLSGQRGADHTASASLQSVAGTEPGRHPQAVAAVLPAGGCGERMGVPTPKQFCPILERPLISYTLQALERVCWIKDIVVAVTGENMEVMKSIIQKYQHKRISLVEAGVTRHRSIFNGLKALAEDQINSKLSKPEVVIIHDAVRPFVEEGVLLKVVTAAKEHGAAGAIRPLVSTVVSPSADGCLDYSLERARHRASEMPQAFLFDVIYEAYQQCSDYDLEFGTECLQLALKYCCTKAKLVEGSPDLWKVTYKRDLYAAESIIKERISQEICVVMDTEEDNKHVGHLLEEVLKSELNHVKVTSEALGHAGRHLQQIILDQCYNFVCVNVTTSDFQETQKLLSMLEESSLCILYPVVVVSVHFLDFKLVPPSQKMENLMQIREFAKEVKERNILLYGLLISYPQDDQKLQESLRQGAIIIASLIKERNSGLIGQLLIA,mutated_sequence,1.0,451.0,NP_001094896.1.a2m,NP_001094896.1.npy,ClinVar +NP_001096034.1,NP_001096034.1.csv,MEDLLDLDEELRYSLATSRAKMGRRAQQESAQAENHLNGKNSSLTLTGETSSAKLPRCRQGGWAGDSVKASKFRRKASEEIEDFRLRPQSLNGSDYGGDIPIIPDLEEVQEEDFVLQVAAPPSIQIKRVMTYRDLDNDLMKYSAIQTLDGEIDLKLLTKVLAPEHEVREDDVGWDWDHLFTEVSSEVLTEWDPLQTEKEDPAGQARHT,mutated_sequence,1.0,208.0,NP_001096034.1.a2m,NP_001096034.1.npy,ClinVar +NP_001098101.1,NP_001098101.1.csv,MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT,mutated_sequence,1.0,809.0,NP_001098101.1.a2m,NP_001098101.1.npy,ClinVar +NP_001103348.1,NP_001103348.1.csv,MALSSRARAFSVEALVGRPSKRKLQDPIQAEQPELREKKGGEEEEERRSSAAGKSEPLEKQPKTEPSTSASSGCGSDSGYGNSSESLEEKDIQMELQGSELWKRFHDIGTEMIITKAGRRMFPSVRVKVKGLDPGKQYHVAIDVVPVDSKRYRYVYHSSQWMVAGNTDHLCIIPRFYVHPDSPCSGETWMRQIISFDRMKLTNNEMDDKGHIILQSMHKYKPRVHVIEQGSSVDLSQIQSLPTEGVKTFSFKETEFTTVTAYQNQQITKLKIERNPFAKGFRDTGRNRGVLDGLLETYPWRPSFTLDFKTFGADTQSGSSGSSPVTSSGGAPSPLNSLLSPLCFSPMFHLPTSSLGMPCPEAYLPNVNLPLCYKICPTNFWQQQPLVLPAPERLASSNSSQSLAPLMMEVPMLSSLGVTNSKSGSSEDSSDQYLQAPNSTNQMLYGLQSPGNIFLPNSITPEALSCSFHPSYDFYRYNFSMPSRLISGSNHLKVNDDSQVSFGEGKCNHVHWYPAINHYL,mutated_sequence,1.0,520.0,NP_001103348.1.a2m,NP_001103348.1.npy,ClinVar +NP_001103824.1,NP_001103824.1.csv,MELSYRLFICLLLWGSTELCYPQPLWLLQGGASHPETSVQPVLVECQEATLMVMVSKDLFGTGKLIRAADLTLGPEACEPLVSMDTEDVVRFEVGLHECGNSMQVTDDALVYSTFLLHDPRPVGNLSIVRTNRAEIPIECRYPRQGNVSSQAILPTWLPFRTTVFSEEKLTFSLRLMEENWNAEKRSPTFHLGDAAHLQAEIHTGSHVPLRLFVDHCVATPTPDQNASPYHTIVDFHGCLVDGLTDASSAFKVPRPGPDTLQFTVDVFHFANDSRNMIYITCHLKVTLAEQDPDELNKACSFSKPSNSWFPVEGSADICQCCNKGDCGTPSHSRRQPHVMSQWSRSASRNRRHVTEEADVTVGPLIFLDRRGDHEVEQWALPSDTSVVLLGVGLAVVVSLTLTAVILVLTRRCRTASHPVSASE,mutated_sequence,1.0,424.0,NP_001103824.1.a2m,NP_001103824.1.npy,ClinVar +NP_001104026.1,NP_001104026.1.csv,MSSSHSRAGQSAAGAAPGGGVDTRDAEMPATEKDLAEDAPWKKIQQNTFTRWCNEHLKCVSKRIANLQTDLSDGLRLIALLEVLSQKKMHRKHNQRPTFRQMQLENVSVALEFLDRESIKLVSIDSKAIVDGNLKLILGLIWTLILHYSISMPMWDEEEDEEAKKQTPKQRLLGWIQNKLPQLPITNFSRDWQSGRALGALVDSCAPGLCPDWDSWDASKPVTNAREAMQQADDWLGIPQVITPEEIVDPNVDEHSVMTYLSQFPKAKLKPGAPLRPKLNPKKARAYGPGIEPTGNMVKKRAEFTVETRSAGQGEVLVYVEDPAGHQEEAKVTANNDKNRTFSVWYVPEVTGTHKVTVLFAGQHIAKSPFEVYVDKSQGDASKVTAQGPGLEPSGNIANKTTYFEIFTAGAGTGEVEVVIQDPMGQKGTVEPQLEARGDSTYRCSYQPTMEGVHTVHVTFAGVPIPRSPYTVTVGQACNPSACRAVGRGLQPKGVRVKETADFKVYTKGAGSGELKVTVKGPKGEERVKQKDLGDGVYGFEYYPMVPGTYIVTITWGGQNIGRSPFEVKVGTECGNQKVRAWGPGLEGGVVGKSADFVVEAIGDDVGTLGFSVEGPSQAKIECDDKGDGSCDVRYWPQEAGEYAVHVLCNSEDIRLSPFMADIRDAPQDFHPDRVKARGPGLEKTGVAVNKPAEFTVDAKHGGKAPLRVQVQDNEGCPVEALVKDNGNGTYSCSYVPRKPVKHTAMVSWGGVSIPNSPFRVNVGAGSHPNKVKVYGPGVAKTGLKAHEPTYFTVDCAEAGQGDVSIGIKCAPGVVGPAEADIDFDIIRNDNDTFTVKYTPRGAGSYTIMVLFADQATPTSPIRVKVEPSHDASKVKAEGPGLSRTGVELGKPTHFTVNAKAAGKGKLDVQFSGLTKGDAVRDVDIIDHHDNTYTVKYTPVQQGPVGVNVTYGGDPIPKSPFSVAVSPSLDLSKIKVSGLGEKVDVGKDQEFTVKSKGAGGQGKVASKIVGPSGAAVPCKVEPGLGADNSVVRFLPREEGPYEVEVTYDGVPVPGSPFPLEAVAPTKPSKVKAFGPGLQGGSAGSPARFTIDTKGAGTGGLGLTVEGPCEAQLECLDNGDGTCSVSYVPTEPGDYNINILFADTHIPGSPFKAHVVPCFDASKVKCSGPGLERATAGEVGQFQVDCSSAGSAELTIEICSEAGLPAEVYIQDHGDGTHTITYIPLCPGAYTVTIKYGGQPVPNFPSKLQVEPAVDTSGVQCYGPGIEGQGVFREATTEFSVDARALTQTGGPHVKARVANPSGNLTETYVQDRGDGMYKVEYTPYEEGLHSVDVTYDGSPVPSSPFQVPVTEGCDPSRVRVHGPGIQSGTTNKPNKFTVETRGAGTGGLGLAVEGPSEAKMSCMDNKDGSCSVEYIPYEAGTYSLNVTYGGHQVPGSPFKVPVHDVTDASKVKCSGPGLSPGMVRANLPQSFQVDTSKAGVAPLQVKVQGPKGLVEPVDVVDNADGTQTVNYVPSREGPYSISVLYGDEEVPRSPFKVKVLPTHDASKVKASGPGLNTTGVPASLPVEFTIDAKDAGEGLLAVQITDPEGKPKKTHIQDNHDGTYTVAYVPDVTGRYTILIKYGGDEIPFSPYRVRAVPTGDASKCTVTVSIGGHGLGAGIGPTIQIGEETVITVDTKAAGKGKVTCTVCTPDGSEVDVDVVENEDGTFDIFYTAPQPGKYVICVRFGGEHVPNSPFQVTALAGDQPSVQPPLRSQQLAPQYTYAQGGQQTWAPERPLVGVNGLDVTSLRPFDLVIPFTIKKGEITGEVRMPSGKVAQPTITDNKDGTVTVRYAPSEAGLHEMDIRYDNMHIPGSPLQFYVDYVNCGHVTAYGPGLTHGVVNKPATFTVNTKDAGEGGLSLAIEGPSKAEISCTDNQDGTCSVSYLPVLPGDYSILVKYNEQHVPGSPFTARVTGDDSMRMSHLKVGSAADIPINISETDLSLLTATVVPPSGREEPCLLKRLRNGHVGISFVPKETGEHLVHVKKNGQHVASSPIPVVISQSEIGDASRVRVSGQGLHEGHTFEPAEFIIDTRDAGYGGLSLSIEGPSKVDINTEDLEDGTCRVTYCPTEPGNYIINIKFADQHVPGSPFSVKVTGEGRVKESITRRRRAPSVANVGSHCDLSLKIPEISIQDMTAQVTSPSGKTHEAEIVEGENHTYCIRFVPAEMGTHTVSVKYKGQHVPGSPFQFTVGPLGEGGAHKVRAGGPGLERAEAGVPAEFSIWTREAGAGGLAIAVEGPSKAEISFEDRKDGSCGVAYVVQEPGDYEVSVKFNEEHIPDSPFVVPVASPSGDARRLTVSSLQESGLKVNQPASFAVSLNGAKGAIDAKVHSPSGALEECYVTEIDQDKYAVRFIPRENGVYLIDVKFNGTHIPGSPFKIRVGEPGHGGDPGLVSAYGAGLEGGVTGNPAEFVVNTSNAGAGALSVTIDGPSKVKMDCQECPEGYRVTYTPMAPGSYLISIKYGGPYHIGGSPFKAKVTGPRLVSNHSLHETSSVFVDSLTKATCAPQHGAPGPGPADASKVVAKGLGLSKAYVGQKSSFTVDCSKAGNNMLLVGVHGPRTPCEEILVKHVGSRLYSVSYLLKDKGEYTLVVKWGDEHIPGSPYRVVVP,mutated_sequence,1.0,2647.0,NP_001104026.1.a2m,NP_001104026.1.npy,ClinVar +NP_001104262.1,NP_001104262.1.csv,MAAAAAAAPSGGGGGGEEERLEEKSEDQDLQGLKDKPLKFKKVKKDKKEEKEGKHEPVQPSAHHSAEPAEAGKAETSEGSGSAPAVPEASASPKQRRSIIRDRGPMYDDPTLPEGWTRKLKQRKSGRSAGKYDVYLINPQGKAFRSKVELIAYFEKVGDTSLDPNDFDFTVTGRGSPSRREQKPPKKPKSPKAPGTGRGRGRPKGSGTTRPKAATSEGVQVKRVLEKSPGKLLVKMPFQTSPGGKAEGGGATTSTQVMVIKRPGRKRKAEADPQAIPKKRGRKPGSVVAAAAAEAKKKAVKESSIRSVQETVLPIKKRKTRETVSIEVKEVVKPLLVSTLGEKSGKGLKTCKSPGRKSKESSPKGRSSSASSPPKKEHHHHHHHSESPKAPVPLLPPLPPPPPEPESSEDPTSPPEPQDLSSSVCKEEKMPRGGSLESDGCPKEPAKTQPAVATAATAAEKYKHRGEGERKDIVSSSMPRPNREEPVDSRTPVTERVS,mutated_sequence,1.0,498.0,NP_001104262.1.a2m,NP_001104262.1.npy,ClinVar +NP_001108225.1,NP_001108225.1.csv,MDRGTLPLAVALLLASCSLSPTSLAETVHCDLQPVGPERGEVTYTTSQVSKGCVAQAPNAILEVHVLFLEFPTGPSQLELTLQASKQNGTWPREVLLVLSVNSSVFLHLQALGIPLHLAYNSSLVTFQEPPGVNTTELPSFPKTQILEWAAERGPITSAAELNDPQSILLRLGQAQGSLSFCMLEASQDMGRTLEWRPRTPALVRGCHLEGVAGHKEAHILRVLPGHSAGPRTVTVKVELSCAPGDLDAVLILQGPPYVSWLIDANHNMQIWTTGEYSFKIFPEKNIRGFKLPDTPQGLLGEARMLNASIVASFVELPLASIVSLHASSCGGRLQTSPAPIQTTPPKDTCSPELLMSLIQTKCADDAMTLVLKKELVAHLKCTITGLTFWDPSCEAEDRGDKFVLRSAYSSCGMQVSASMISNEAVVNILSSSSPQRKKVHCLNMDSLSFQLGLYLSPHFLQASNTIEPGQQSFVQVRVSPSVSEFLLQLDSCHLDLGPEGGTVELIQGRAAKGNCVSLLSPSPEGDPRFSFLLHFYTVPIPKTGTLSCTVALRPKTGSQDQEVHRTVFMRLNIISPDLSGCTSKGLVLPAVLGITFGAFLIGALLTAALWYIYSHTRSPSKREPVVAVAAPASSESSSTNHSIGSTQSTPCSTSSMA,mutated_sequence,1.0,658.0,NP_001108225.1.a2m,NP_001108225.1.npy,ClinVar +NP_001116102.1,NP_001116102.1.csv,MERLVARGTFPVLVRTSACRSLFGPVDHEELSRELQARLAELNAEDQNRWDYDFQQDMPLRGPGRLQWTEVDSDSVPAFYRETVQVGRCRLLLAPRPVAVAVAVSPPLEPAAESLDGLEEAPEQLPSVPVPAPASTPPPVPVLAPAPAPAPAPVAAPVAAPVAVAVLAPAPAPAPAPAPAPAPVAAPAPAPAPAPAPAPAPAPAPDAAPQESAEQGANQGQRGQEPLADQLHSGISGRPAAGTAAASANGAAIKKLSGPLISDFFAKRKRSAPEKSSGDVPAPCPSPSAAPGVGSVEQTPRKRLR,mutated_sequence,1.0,305.0,NP_001116102.1.a2m,NP_001116102.1.npy,ClinVar +NP_001116427.1,NP_001116427.1.csv,MSTEKVDQKEEAGEKEVCGDQIKGPDKEEEPPAAASHGQGWRPGGRAARNARPEPGARHPALPAMVNDPPVPALLWAQEVGQVLAGRARRLLLQFGVLFCTILLLLWVSVFLYGSFYYSYMPTVSHLSPVHFYYRTDCDSSTTSLCSFPVANVSLTKGGRDRVLMYGQPYRVTLELELPESPVNQDLGMFLVTISCYTRGGRIISTSSRSVMLHYRSDLLQMLDTLVFSSLLLFGFAEQKQLLEVELYADYRENSYVPTTGAIIEIHSKRIQLYGAYLRIHAHFTGLRYLLYNFPMTCAFIGVASNFTFLSVIVLFSYMQWVWGGIWPRHRFSLQVNIRKRDNSRKEVQRRISAHQPGPEGQEESTPQSDVTEDGESPEDPSGTEGQLSEEEKPDQQPLSGEEELEPEASDGSGSWEDAALLTEANLPAPAPASASAPVLETLGSSEPAGGALRQRPTCSSS,mutated_sequence,1.0,462.0,NP_001116427.1.a2m,NP_001116427.1.npy,ClinVar +NP_001116857.1,NP_001116857.1.csv,MLSATPLYGNVHSWMNSERVRMCGASEDRKILVNDGDASKARLELREENPLNHNVVDASTAHRIDGLAALSMDRTGLIREGLRVPGNIVYSSLCGLGSEKGREAATSTLGGLGFSSERNPEMQFKPNTPETVEASAVSGKPPNGFSAIYKTPPGIQKSAVATAEALGLDRPASDKQSPLNINGASYLRLPWVNPYMEGATPAIYPFLDSPNKYSLNMYKALLPQQSYSLAQPLYSPVCTNGERFLYLPPPHYVGPHIPSSLASPMRLSTPSASPAIPPLVHCADKSLPWKMGVSPGNPVDSHAYPHIQNSKQPRVPSAKAVTSGLPGDTALLLPPSPRPSPRVHLPTQPAADTYSEFHKHYARISTSPSVALSKPYMTVSSEFPAARLSNGKYPKAPEGGEGAQPVPGHARKTAVQDRKDGSSPPLLEKQTVTKDVTDKPLDLSSKVVDVDASKADHMKKMAPTVLVHSRAGSGLVLSGSEIPKETLSPPGNGCAIYRSEIISTAPSSWVVPGPSPNEENNGKSMSLKNKALDWAIPQQRSSSCPRMGGTDAVITNVSGSVSSAGRPASASPAPNANADGTKTSRSSVETTPSVIQHVGQPPATPAKHSSSTSSKGAKASNPEPSFKANENGLPPSSIFLSPNEAFRSPPIPYPRSYLPYPAPEGIAVSPLSLHGKGPVYPHPVLLPNGSLFPGHLAPKPGLPYGLPTGRPEFVTYQDALGLGMVHPMLIPHTPIEITKEEKPERRSRSHERARYEDPTLRNRFSEILETSSTKLHPDVPTDKNLKPNPNWNQGKTVVKSDKLVYVDLLREEPDAKTDTNVSKPSFAAESVGQSAEPPKPSVEPALQQHRDFIALREELGRISDFHETYTFKQPVFTVSKDSVLAGTNKENLGLPVSTPFLEPPLGSDGPAVTFGKTQEDPKPFCVGSAPPSVDVTPTYTKDGADEAESNDGKVLKPKPSKLAKRIANSAGYVGDRFKCVTTELYADSSQLSREQRALQMEGLQEDSILCLPAAYCERAMMRFSELEMKEREGGHPATKDSEMCKFSPADWERLKGNQDKKPKSVTLEEAIAEQNESERCEYSVGNKHRDPFEAPEDKDLPVEKYFVERQPVSEPPADQVASDMPHSPTLRVDRKRKVSGDSSHTETTAEEVPEDPLLKAKRRRVSKDDWPEREMTNSSSNHLEDPHYSELTNLKVCIELTGLHPKKQRHLLHLRERWEQQVSAADGKPGRQSRKEVTQATQPEAIPQGTNITEEKPGRKRAEAKGNRSWSEESLKPSDNEQGLPVFSGSPPMKSLSSTSAGGKKQAQPSCAPASRPPAKQQKIKENQKTDVLCADEEEDCQAASLLQKYTDNSEKPSGKRLCKTKHLIPQESRRGLPLTGEYYVENADGKVTVRRFRKRPEPSSDYDLSPAKQEPKPFDRLQQLLPASQSTQLPCSSSPQETTQSRPMPPEARRLIVNKNAGETLLQRAARLGYEEVVLYCLENKICDVNHRDNAGYCALHEACARGWLNIVRHLLEYGADVNCSAQDGTRPLHDAVENDHLEIVRLLLSYGADPTLATYSGRTIMKMTHSELMEKFLTDYLNDLQGRNDDDASGTWDFYGSSVCEPDDESGYDVLANPPGPEDQDDDDDAYSDVFEFEFSETPLLPCYNIQVSVAQGPRNWLLLSDVLKKLKMSSRIFRCNFPNVEIVTIAEAEFYRQVSASLLFSCSKDLEAFNPESKELLDLVEFTNEIQTLLGSSVEWLHPSDLASDNYW,mutated_sequence,1.0,1755.0,NP_001116857.1.a2m,NP_001116857.1.npy,ClinVar +NP_001119580.2,NP_001119580.2.csv,MAELPTTETPGDATLCSGRFTISTLLSSDEPSPPAAYDSSHPSHLTHSSTFCMRTFGYNTIDVVPTYEHYANSTQPGEPRKVRPTLADLHSFLKQEGRHLHALAFDSRPSHEMTDGLVEGEAGTSSEKNPEEPVRFGWVKGVMIRCMLNIWGVILYLRLPWITAQAGIVLTWIIILLSVTVTSITGLSISAISTNGKVKSGGTYFLISRSLGPELGGSIGLIFAFANAVGVAMHTVGFAETVRDLLQEYGAPIVDPINDIRIIAVVSVTVLLAISLAGMEWESKAQVLFFLVIMVSFANYLVGTLIPPSEDKASKGFFSYRADIFVQNLVPDWRGPDGTFFGMFSIFFPSATGILAGANISGDLKDPAIAIPKGTLMAIFWTTISYLAISATIGSCVVRDASGVLNDTVTPGWGACEGLACSYGWNFTECTQQHSCHYGLINYYQTMSMVSGFAPLITAGIFGATLSSALACLVSAAKVFQCLCEDQLYPLIGFFGKGYGKNKEPVRGYLLAYAIAVAFIIIAELNTIAPIISNFFLCSYALINFSCFHASITNSPGWRPSFQYYNKWAALFGAIISVVIMFLLTWWAALIAIGVVLFLLLYVIYKKPEVNWGSSVQAGSYNLALSYSVGLNEVEDHIKNYRPQCLVLTGPPNFRPALVDFVGTFTRNLSLMICGHVLIGPHKQRMPELQLIANGHTKWLNKRKIKAFYSDVIAEDLRRGVQILMQAAGLGRMKPNILVVGFKKNWQSAHPATVEDYIGILHDAFDFNYGVCVMRMREGLNVSKMMQAHINPVFDPAEDGKEASARVDPKALVKEEQATTIFQSEQGKKTIDIYWLFDDGGLTLLIPYLLGRKRRWSKCKIRVFVGGQINRMDQERKAIISLLSKFRLGFHEVHILPDINQNPRAEHTKRFEDMIAPFRLNDGFKDEATVNEMRRDCPWKISDEEITKNRVKSLRQVRLNEIVLDYSRDAALIVITLPIGRKGKCPSSLYMAWLETLSQDLRPPVILIRGNQENVLTFYCQ,mutated_sequence,1.0,1021.0,NP_001119580.2.a2m,NP_001119580.2.npy,ClinVar +NP_001120694.1,NP_001120694.1.csv,MARFGDEMPARYGGGGSGAAAGVVVGSGGGRGAGGSRQGGQPGAQRMYKQSMAQRARTMALYNPIPVRQNCLTVNRSLFLFSEDNVVRKYAKKITEWPPFEYMILATIIANCIVLALEQHLPDDDKTPMSERLDDTEPYFIGIFCFEAGIKIIALGFAFHKGSYLRNGWNVMDFVVVLTGILATVGTEFDLRTLRAVRVLRPLKLVSGIPSLQVVLKSIMKAMIPLLQIGLLLFFAILIFAIIGLEFYMGKFHTTCFEEGTDDIQGESPAPCGTEEPARTCPNGTKCQPYWEGPNNGITQFDNILFAVLTVFQCITMEGWTDLLYNSNDASGNTWNWLYFIPLIIIGSFFMLNLVLGVLSGEFAKERERVENRRAFLKLRRQQQIERELNGYMEWISKAEEVILAEDETDGEQRHPFDALRRTTIKKSKTDLLNPEEAEDQLADIASVGSPFARASIKSAKLENSTFFHKKERRMRFYIRRMVKTQAFYWTVLSLVALNTLCVAIVHYNQPEWLSDFLYYAEFIFLGLFMSEMFIKMYGLGTRPYFHSSFNCFDCGVIIGSIFEVIWAVIKPGTSFGISVLRALRLLRIFKVTKYWASLRNLVVSLLNSMKSIISLLFLLFLFIVVFALLGMQLFGGQFNFDEGTPPTNFDTFPAAIMTVFQILTGEDWNEVMYDGIKSQGGVQGGMVFSIYFIVLTLFGNYTLLNVFLAIAVDNLANAQELTKDEQEEEEAANQKLALQKAKEVAEVSPLSAANMSIAVKEQQKNQKPAKSVWEQRTSEMRKQNLLASREALYNEMDPDERWKAAYTRHLRPDMKTHLDRPLVVDPQENRNNNTNKSRAAEPTVDQRLGQQRAEDFLRKQARYHDRARDPSGSAGLDARRPWAGSQEAELSREGPYGRESDHHAREGSLEQPGFWEGEAERGKAGDPHRRHVHRQGGSRESRSGSPRTGADGEHRRHRAHRRPGEEGPEDKAERRARHREGSRPARGGEGEGEGPDGGERRRRHRHGAPATYEGDARREDKERRHRRRKENQGSGVPVSGPNLSTTRPIQQDLGRQDPPLAEDIDNMKNNKLATAESAAPHGSLGHAGLPQSPAKMGNSTDPGPMLAIPAMATNPQNAASRRTPNNPGNPSNPGPPKTPENSLIVTNPSGTQTNSAKTARKPDHTTVDIPPACPPPLNHTVVQVNKNANPDPLPKKEEEKKEEEEDDRGEDGPKPMPPYSSMFILSTTNPLRRLCHYILNLRYFEMCILMVIAMSSIALAAEDPVQPNAPRNNVLRYFDYVFTGVFTFEMVIKMIDLGLVLHQGAYFRDLWNILDFIVVSGALVAFAFTGNSKGKDINTIKSLRVLRVLRPLKTIKRLPKLKAVFDCVVNSLKNVFNILIVYMLFMFIFAVVAVQLFKGKFFHCTDESKEFEKDCRGKYLLYEKNEVKARDREWKKYEFHYDNVLWALLTLFTVSTGEGWPQVLKHSVDATFENQGPSPGYRMEMSIFYVVYFVVFPFFFVNIFVALIIITFQEQGDKMMEEYSLEKNERACIDFAISAKPLTRHMPQNKQSFQYRMWQFVVSPPFEYTIMAMIALNTIVLMMKFYGASVAYENALRVFNIVFTSLFSLECVLKVMAFGILNYFRDAWNIFDFVTVLGSITDILVTEFGNNFINLSFLRLFRAARLIKLLRQGYTIRILLWTFVQSFKALPYVCLLIAMLFFIYAIIGMQVFGNIGIDVEDEDSDEDEFQITEHNNFRTFFQALMLLFRSATGEAWHNIMLSCLSGKPCDKNSGILTRECGNEFAYFYFVSFIFLCSFLMLNLFVAVIMDNFEYLTRDSSILGPHHLDEYVRVWAEYDPAAWGRMPYLDMYQMLRHMSPPLGLGKKCPARVAYKRLLRMDLPVADDNTVHFNSTLMALIRTALDIKIAKGGADKQQMDAELRKEMMAIWPNLSQKTLDLLVTPHKSTDLTVGKIYAAMMIMEYYRQSKAKKLQAMREEQDRTPLMFQRMEPPSPTQEGGPGQNALPSTQLDPGGALMAHESGLKESPSWVTQRAQEMFQKTGTWSPEQGPPTDMPNSQPNSQSVEMREMGRDGYSDSEHYLPMEGQGRAASMPRLPAENQRRRGRPRGNNLSTISDTSPMKRSASVLGPKARRLDDYSLERVPPEENQRHHQRRRDRSHRASERSLGRYTDVDTGLGTDLSMTTQSGDLPSKERDQERGRPKDRKHRQHHHHHHHHHHPPPPDKDRYAQERPDHGRARARDQRWSRSPSEGREHMAHRQGSSSVSGSPAPSTSGTSTPRRGRRQLPQTPSTPRPHVSYSPVIRKAGGSGPPQQQQQQQQQQQQQAVARPGRAATSGPRRYPGPTAEPLAGDRPPTGGHSSGRSPRMERRVPGPARSESPRACRHGGARWPASGPHVSEGPPGPRHHGYYRGSDYDEADGPGSGGGEEAMAGAYDAPPPVRHASSGATGRSPRTPRASGPACASPSRHGRRLPNGYYPAHGLARPRGPGSRKGLHEPYSESDDDWC,mutated_sequence,1.0,2506.0,NP_001120694.1.a2m,NP_001120694.1.npy,ClinVar +NP_001120864.1,NP_001120864.1.csv,MEVVDETEALQRFFEGHDINGALEPSNIDTSILEEYISKEDASDLCFPDISAPASSASYSHGQPAMPGSSGVHHLSPPGGGPSPGRHGPLPPPGYGTPLNCNNNNGMGAAPKPFPGGTGPPIKAEPKAPYAPGTLPDSPPDSGSEAYSPQQVNEPHLLRTITPETLCHVGVPSRLEHPPPPPAHLPGPPPPPPPPPHYPVLQRDLYMKAEPPIPHYAAMGQGLVPTDLHHTQQSQMLHQLLQQHGAELPTHPSKKRKHSESPPSTLNAQMLNGMIKQEPGTVTALPLHPTRAPSPPWPPQGPLSPGPGSLPLSIARVQTPPWHPPGAPSPGLLQDSDSLSGSYLDPNYQSIKWQPHQQNKWATLYDANYKELPMLTYRVDADKGFNFSVGDDAFVCQKKNHFQVTVYIGMLGEPKYVKTPEGLKPLDCFYLKLHGVKLEALNQSINIEQSQSDRSKRPFNPVTVNLPPEQVTKVTVGRLHFSETTANNMRKKGKPNPDQRYFMLVVALQAHAQNQNYTLAAQISERIIVRASNPGQFESDSDVLWQRAQVPDTVFHHGRVGINTDRPDEALVVHGNVKVMGSLMHPSDLRAKEHVQEVDTTEQLKRISRMRLVHYRYKPEFAASAGIEATAPETGVIAQEVKEILPEAVKDTGDMVFANGKTIENFLVVNKERIFMENVGAVKELCKLTDNLETRIDELERWSHKLAKLRRLDSLKSTGSSGAFSHAGSQFSRAGSVPHKKRPPKVASKSSSVVPDQACISQRFLQGTIIALVVVMAFSVVSMSTLYVLSLRTEEDLVDTDGSFAVSTSCLLALLRPQPPGGSEALCPWSSQSFGTTQLRQSPLTTGLPGIQPSLLLVTTSLTSSAPGSAVRTLDMCSSHPCPVICCSSPTTNPTTGPSLGPSFNPGHVLSPSPSPSTNRSGPSQMALLPVTNIRAKSWGLSVNGIGHSKHHKSLEPLASPAVPFPGGQGKAKNSPSLGFHGRARRGALQSSVGPAEPTWAQGQSASLLAEPVPSLTSIQVLENSMSITSQYCAPGDACRPGNFTYHIPVSSGTPLHLSLTLQMNSSSPVSVVLCSLRSKEEPCEEGSLPQSLHTHQDTQGTSHRWPITILSFREFTYHFRVALLGQANCSSEALAQPATDYHFHFYRLCD,mutated_sequence,1.0,1151.0,NP_001120864.1.a2m,NP_001120864.1.npy,ClinVar +NP_001121636.1,NP_001121636.1.csv,MKSNQERSNECLPPKKREIPATSRSSEEKAPTLPSDNHRVEGTAWLPGNPGGRGHGGGRHGPAGTSVELGLQQGIGLHKALSTGLDYSPPSAPRSVPVATTLPAAYATPQPGTPVSPVQYAHLPHTFQFIGSSQYSGTYASFIPSQLIPPTANPVTSAVASAAGATTPSQRSQLEAYSTLLANMGSLSQTPGHKAEQQQQQQQQQQQQHQHQQQQQQQQQQQQQQHLSRAPGLITPGSPPPAQQNQYVHISSSPQNTGRTASPPAIPVHLHPHQTMIPHTLTLGPPSQVVMQYADSGSHFVPREATKKAESSRLQQAIQAKEVLNGEMEKSRRYGAPSSADLGLGKAGGKSVPHPYESRHVVVHPSPSDYSSRDPSGVRASVMVLPNSNTPAADLEVQQATHREASPSTLNDKSGLHLGKPGHRSYALSPHTVIQTTHSASEPLPVGLPATAFYAGTQPPVIGYLSGQQQAITYAGSLPQHLVIPGTQPLLIPVGSTDMEASGAAPAIVTSSPQFAAVPHTFVTTALPKSENFNPEALVTQAAYPAMVQAQIHLPVVQSVASPAAAPPTLPPYFMKGSIIQLANGELKKVEDLKTEDFIQSAEISNDLKIDSSTVERIEDSHSPGVAVIQFAVGEHRAQVSVEVLVEYPFFVFGQGWSSCCPERTSQLFDLPCSKLSVGDVCISLTLKNLKNGSVKKGQPVDPASVLLKHSKADGLAGSRHRYAEQENGINQGSAQMLSENGELKFPEKMGLPAAPFLTKIEPSKPAATRKRRWSAPESRKLEKSEDEPPLTLPKPSLIPQEVKICIEGRSNVGK,mutated_sequence,1.0,815.0,NP_001121636.1.a2m,NP_001121636.1.npy,ClinVar +NP_001121697.2,NP_001121697.2.csv,MPGCPCPGCGMAGPRLLFLTALALELLERAGGSQPALRSRGTATACRLDNKESESWGALLSGERLDTWICSLLGSLMVGLSGVFPLLVIPLEMGTMLRSEAGAWRLKQLLSFALGGLLGNVFLHLLPEAWAYTCSASPGGEGQSLQQQQQLGLWVIAGILTFLALEKMFLDSKEEGTSQAPNKDPTAAAAALNGGHCLAQPAAEPGLGAVVRSIKVSGYLNLLANTIDNFTHGLAVAASFLVSKKIGLLTTMAILLHEIPHEVGDFAILLRAGFDRWSAAKLQLSTALGGLLGAGFAICTQSPKGVVGCSPAAEETAAWVLPFTSGGFLYIALVNVLPDLLEEEDPWRSLQQLLLLCAGIVVMVLFSLFVD,mutated_sequence,1.0,371.0,NP_001121697.2.a2m,NP_001121697.2.npy,ClinVar +NP_001123910.1,NP_001123910.1.csv,MDPSGVKVLETAEDIQERRQQVLDRYHRFKELSTLRRQKLEDSYRFQFFQRDAEELEKWIQEKLQIASDENYKDPTNLQGKLQKHQAFEAEVQANSGAIVKLDETGNLMISEGHFASETIRTRLMELHRQWELLLEKMREKGIKLLQAQKLVQYLRECEDVMDWINDKEAIVTSEELGQDLEHVEVLQKKFEEFQTDMAAHEERVNEVNQFAAKLIQEQHPEEELIKTKQDEVNAAWQRLKGLALQRQGKLFGAAEVQRFNRDVDETISWIKEKEQLMASDDFGRDLASVQALLRKHEGLERDLAALEDKVKALCAEADRLQQSHPLSATQIQVKREELITNWEQIRTLAAERHARLNDSYRLQRFLADFRDLTSWVTEMKALINADELASDVAGAEALLDRHQEHKGEIDAHEDSFKSADESGQALLAAGHYASDEVREKLTVLSEERAALLELWELRRQQYEQCMDLQLFYRDTEQVDNWMSKQEAFLLNEDLGDSLDSVEALLKKHEDFEKSLSAQEEKITALDEFATKLIQNNHYAMEDVATRRDALLSRRNALHERAMRRRAQLADSFHLQQFFRDSDELKSWVNEKMKTATDEAYKDPSNLQGKVQKHQAFEAELSANQSRIDALEKAGQKLIDVNHYAKDEVAARMNEVISLWKKLLEATELKGIKLREANQQQQFNRNVEDIELWLYEVEGHLASDDYGKDLTNVQNLQKKHALLEADVAAHQDRIDGITIQARQFQDAGHFDAENIKKKQEALVARYEALKEPMVARKQKLADSLRLQQLFRDVEDEETWIREKEPIAASTNRGKDLIGVQNLLKKHQALQAEIAGHEPRIKAVTQKGNAMVEEGHFAAEDVKAKLHELNQKWEALKAKASQRRQDLEDSLQAQQYFADANEAESWMREKEPIVGSTDYGKDEDSAEALLKKHEALMSDLSAYGSSIQALREQAQSCRQQVAPTDDETGKELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVNDRQGFVPAAYVKKLDPAQSASRENLLEEQGSIALRQEQIDNQTRITKEAGSVSLRMKQVEELYHSLLELGEKRKGMLEKSCKKFMLFREANELQQWINEKEAALTSEEVGADLEQVEVLQKKFDDFQKDLKANESRLKDINKVAEDLESEGLMAEEVQAVQQQEVYGMMPRDETDSKTASPWKSARLMVHTVATFNSIKELNERWRSLQQLAEERSQLLGSAHEVQRFHRDADETKEWIEEKNQALNTDNYGHDLASVQALQRKHEGFERDLAALGDKVNSLGETAERLIQSHPESAEDLQEKCTELNQAWSSLGKRADQRKAKLGDSHDLQRFLSDFRDLMSWINGIRGLVSSDELAKDVTGAEALLERHQEHRTEIDARAGTFQAFEQFGQQLLAHGHYASPEIKQKLDILDQERADLEKAWVQRRMMLDQCLELQLFHRDCEQAENWMAAREAFLNTEDKGDSLDSVEALIKKHEDFDKAINVQEEKIAALQAFADQLIAAGHYAKGDISSRRNEVLDRWRRLKAQMIEKRSKLGESQTLQQFSRDVDEIEAWISEKLQTASDESYKDPTNIQLSKLLSKHQKHQAFEAELHANADRIRGVIDMGNSLIERGACAGSEDAVKARLAALADQWQFLVQKSAEKSQKLKEANKQQNFNTGIKDFDFWLSEVEALLASEDYGKDLASVNNLLKKHQLLEADISAHEDRLKDLNSQADSLMTSSAFDTSQVKDKRDTINGRFQKIKSMAASRRAKLNESHRLHQFFRDMDDEESWIKEKKLLVGSEDYGRDLTGVQNLRKKHKRLEAELAAHEPAIQGVLDTGKKLSDDNTIGKEEIQQRLAQFVEHWKELKQLAAARGQRLEESLEYQQFVANVEEEEAWINEKMTLVASEDYGDTLAAIQGLLKKHEAFETDFTVHKDRVNDVCTNGQDLIKKNNHHEENISSKMKGLNGKVSDLEKAAAQRKAKLDENSAFLQFNWKADVVESWIGEKENSLKTDDYGRDLSSVQTLLTKQETFDAGLQAFQQEGIANITALKDQLLAAKHVQSKAIEARHASLMKRWSQLLANSAARKKKLLEAQSHFRKVEDLFLTFAKKASAFNSWFENAEEDLTDPVRCNSLEEIKALREAHDAFRSSLSSAQADFNQLAELDRQIKSFRVASNPYTWFTMEALEETWRNLQKIIKERELELQKEQRRQEENDKLRQEFAQHANAFHQWIQETRTYLLDGSCMVEESGTLESQLEATKRKHQEIRAMRSQLKKIEDLGAAMEEALILDNKYTEHSTVGLAQQWDQLDQLGMRMQHNLEQQIQARNTTGVTEEALKEFSMMFKHFDKDKSGRLNHQEFKSCLRSLGYDLPMVEEGEPDPEFEAILDTVDPNRDGHVSLQEYMAFMISRETENVKSSEEIESAFRALSSEGKPYVTKEELYQNLTREQADYCVSHMKPYVDGKGRELPTAFDYVEFTRSLFVN,mutated_sequence,1.0,2477.0,NP_001123910.1.a2m,NP_001123910.1.npy,ClinVar +NP_001127879.1,NP_001127879.1.csv,MGRVGYWTLLVLPALLVWRGPAPSAAAEKGPPALNIAVMLGHSHDVTERELRTLWGPEQAAGLPLDVNVVALLMNRTDPKSLITHVCDLMSGARIHGLVFGDDTDQEAVAQMLDFISSHTFVPILGIHGGASMIMADKDPTSTFFQFGASIQQQATVMLKIMQDYDWHVFSLVTTIFPGYREFISFVKTTVDNSFVGWDMQNVITLDTSFEDAKTQVQLKKIHSSVILLYCSKDEAVLILSEARSLGLTGYDFFWIVPSLVSGNTELIPKEFPSGLISVSYDDWDYSLEARVRDGIGILTTAASSMLEKFSYIPEAKASCYGQMERPEVPMHTLHPFMVNVTWDGKDLSFTEEGYQVHPRLVVIVLNKDREWEKVGKWENHTLSLRHAVWPRYKSFSDCEPDDNHLSIVTLEEAPFVIVEDIDPLTETCVRNTVPCRKFVKINNSTNEGMNVKKCCKGFCIDILKKLSRTVKFTYDLYLVTNGKHGKKVNNVWNGMIGEVVYQRAVMAVGSLTINEERSEVVDFSVPFVETGISVMVSRSNGTVSPSAFLEPFSASVWVMMFVMLLIVSAIAVFVFEYFSPVGYNRNLAKGKAPHGPSFTIGKAIWLLWGLVFNNSVPVQNPKGTTSKIMVSVWAFFAVIFLASYTANLAAFMIQEEFVDQVTGLSDKKFQRPHDYSPPFRFGTVPNGSTERNIRNNYPYMHQYMTKFNQKGVEDALVSLKTGKLDAFIYDAAVLNYKAGRDEGCKLVTIGSGYIFATTGYGIALQKGSPWKRQIDLALLQFVGDGEMEELETLWLTGICHNEKNEVMSSQLDIDNMAGVFYMLAAAMALSLITFIWEHLFYWKLRFCFTGVCSDRPGLLFSISRGIYSCIHGVHIEEKKKSPDFNLTGSQSNMLKLLRSAKNISSMSNMNSSRMDSPKRAADFIQRGSLIMDMVSDKGNLMYSDNRSFQGKESIFGDNMNELQTFVANRQKDNLNNYVFQGQHPLTLNESNPNTVEVAVSTESKANSRPRQLWKKSVDSIRQDSLSQNPVSQRDEATAENRTHSLKSPRYLPEEMAHSDISETSNRATCHREPDNSKNHKTKDNFKRSVASKYPKDCSEVERTYLKTKSSSPRDKIYTIDGEKEPGFHLDPPQFVENVTLPENVDFPDPYQDPSENFRKGDSTLPMNRNPLHNEEGLSNNDQYKLYSKHFTLKDKGSPHSETSERYRQNSTHCRSCLSNMPTYSGHFTMRSPFKCDACLRMGNLYDIDEDQMLQETGNPATGEQVYQQDWAQNNALQLQKNKLRISRQHSYDNIVDKPRELDLSRPSRSISLKDRERLLEGNFYGSLFSVPSSKLSGKKSSLFPQGLEDSKRSKSLLPDHTSDNPFLHSHRDDQRLVIGRCPSDPYKHSLPSQAVNDSYLRSSLRSTASYCSRDSRGHNDVYISEHVMPYAANKNNMYSTPRVLNSCSNRRVYKKMPSIESDV,mutated_sequence,1.0,1464.0,NP_001127879.1.a2m,NP_001127879.1.npy,ClinVar +NP_001128145.1,NP_001128145.1.csv,MYSPLCLTQDEFHPFIEALLPHVRAFAYTWFNLQARKRKYFKKHEKRMSKEEERAVKDELLSEKPEVKQKWASRLLAKLRKDIRPEYREDFVLTVTGKKPPCCVLSNPDQKGKMRRIDCLRQADKVWRLDLVMVILFKGIPLESTDGERLVKSPQCSNPGLCVQPHHIGVSVKELDLYLAYFVHAADSSQSESPSQPSDADIKDQPENGHLGFQDSFVTSGVFSVTELVRVSQTPIAAGTGPNFSLSDLESSSYYSMSPGAMRRSLPSTSSTSSTKRLKSVEDEMDSPGEEPFYTGQGRSPGSGSQSSGWHEVEPGMPSPTTLKKSEKSGFSSPSPSQTSSLGTAFTQHHRPVITGPRASPHATPSTLHFPTSPIIQQPGPYFSHPAIRYHPQETLKEFVQLVCPDAGQQAGQVGFLNPNGSSQGKVHNPFLPTPMLPPPPPPPMARPVPLPVPDTKPPTTSTEGGAASPTSPTYSTPSTSPANRFVSVGPRDPSFVNIPQQTQSWYLG,mutated_sequence,1.0,509.0,NP_001128145.1.a2m,NP_001128145.1.npy,ClinVar +NP_001130.1,NP_001130.1.csv,MATYKVRVATGTDLLSGTRDSISLTIVGTQGESHKQLLNHFGRDFATGAVGQYTVQCPQDLGELIIIRLHKERYAFFPKDPWYCNYVQICAPNGRIYHFPAYQWMDGYETLALREATGKTTADDSLPVLLEHRKEEIRAKQDFYHWRVFLPGLPSYVHIPSYRPPVRRHRNPNRPEWNGYIPGFPILINFKATKFLNLNLRYSFLKTASFFVRLGPMALAFKVRGLLDCKHSWKRLKDIRKIFPGKKSVVSEYVAEHWAEDTFFGYQYLNGVNPGLIRRCTRIPDKFPVTDDMVAPFLGEGTCLQAELEKGNIYLADYRIMEGIPTVELSGRKQHHCAPLCLLHFGPEGKMMPIAIQLSQTPGPDCPIFLPSDSEWDWLLAKTWVRYAEFYSHEAIAHLLETHLIAEAFCLALLRNLPMCHPLYKLLIPHTRYTVQINSIGRAVLLNEGGLSAKGMSLGVEGFAGVMVRALSELTYDSLYLPNDFVERGVQDLPGYYYRDDSLAVWNALEKYVTEIITYYYPSDAAVEGDPELQSWVQEIFKECLLGRESSGFPRCLRTVPELIRYVTIVIYTCSAKHAAVNTGQMEFTAWMPNFPASMRNPPIQTKGLTTLETFMDTLPDVKTTCITLLVLWTLSREPDDRRPLGHFPDIHFVEEAPRRSIEAFRQRLNQISHDIRQRNKCLPIPYYYLDPVLIENSISI,mutated_sequence,1.0,701.0,NP_001130.1.a2m,NP_001130.1.npy,ClinVar +NP_001133.1,NP_001133.1.csv,MGTWILFACLLGAAFAMPLPPHPGHPGYINFSYEVLTPLKWYQSIRPPYPSYGYEPMGGWLHHQIIPVLSQQHPPTHTLQPHHHIPVVPAQQPVIPQQPMMPVPGQHSMTPIQHHQPNLPPPAQQPYQPQPVQPQPHQPMQPQPPVHPMQPLPPQPPLPPMFPMQPLPPMLPDLTLEAWPSTDKTKREEVD,mutated_sequence,1.0,191.0,NP_001133.1.a2m,NP_001133.1.npy,ClinVar +NP_001136256.1,NP_001136256.1.csv,MSSSCSGLSRVLVAVATALVSASSPCPQAWGPPGVQYGQPGRSVKLCCPGVTAGDPVSWFRDGEPKLLQGPDSGLGHELVLAQADSTDEGTYICQTLDGALGGTVTLQLGYPPARPVVSCQAADYENFSCTWSPSQISGLPTRYLTSYRKKTVLGADSQRRSPSTGPWPCPQDPLGAARCVVHGAEFWSQYRINVTEVNPLGASTRLLDVSLQSILRPDPPQGLRVESVPGYPRRLRASWTYPASWPCQPHFLLKFRLQYRPAQHPAWSTVEPAGLEEVITDAVAGLPHAVRVSARDFLDAGTWSTWSPEAWGTPSTGTIPKEIPAWGQLHTQPEVEPQVDSPAPPRPSLQPHPRLLDHRDSVEQVAVLASLGILSFLGLVAGALALGLWLRLRRGGKDGSPKPGFLASVIPVDRRPGAPNL,mutated_sequence,1.0,422.0,NP_001136256.1.a2m,NP_001136256.1.npy,ClinVar +NP_001136272.1,NP_001136272.1.csv,MTDKSIVILSLMVFHSSFINGKTCRRQLVEEWHPQPSSYVVNWTLTENICLDFYRDCWFLGVNTKIDTSGNQAVPQICPLQIQLGDILVISSEPSLQFPEINLMNVSETSFVGCVQNTTTEDQLLFGCRLKGMHTVNSKWLSVGTHYFITVMASGPSPCPLGLRLNVTVKQQFCQESLSSEFCSGHGKCLSEAWSKTYSCHCQPPFSGKYCQELDACSFKPCKNNGSCINKRENWDEQAYECVCHPPFTGKNCSEIIGQCQPHVCFHGNCSNITSNSFICECDEQFSGPFCEVSAKPCVSLLFWKRGICPNSSSAYTYECPKGSSSQNGETDVSEFSLVPCQNGTDCIKISNDVMCICSPIFTDLLCKSIQTSCESFPLRNNATCKKCEKDYPCSCISGFTEKNCEKAIDHCKLLSINCLNEEWCFNIIGRFKYVCIPGCTKNPCWFLKNVYLIHQHLCYCGVTFHGICQDKGPAQFEYVWQLGFAGSEGEKCQGVIDAYFFLAANCTEDATYVNDPEDNNSSCWFPHEGTKEICANGCSCLSEEDSQEYRYLCFLRWAGNMYLENTTDDQENECQHEAVCKDEINRPRCSCSLSYIGRLCVVNVDYCLGNHSISVHGLCLALSHNCNCSGLQRYERNICEIDTEDCKSASCKNGTTSTHLRGYFFRKCVPGFKGTQCEIDIDECASHPCKNGATCIDQPGNYFCQCVPPFKVVDGFSCLCNPGYVGIRCEQDIDDCILNACEHNSTCKDLHLSYQCVCLSDWEGNFCEQESNECKMNPCKNNSTCTDLYKSYRCECTSGWTGQNCSEEINECDSDPCMNGGLCHESTIPGQFVCLCPPLYTGQFCHQRYNLCDLLHNPCRNNSTCLALVDANQHCICREEFEGKNCEIDVKDCLFLSCQDYGDCEDMVNNFRCICRPGFSGSLCEIEINECSSEPCKNNGTCVDLTNRFFCNCEPEYHGPFCELDVNKCKISPCLDEENCVYRTDGYNCLCAPGYTGINCEINLDECLSEPCLHDGVCIDGINHYTCDCKSGFFGTHCETNANDCLSNPCLHGRCTELINEYPCSCDADGTSTQCKIKINDCTSIPCMNEGFCQKSAHGFTCICPRGYTGAYCEKSIDNCAEPELNSVICLNGGICVDGPGHTFDCRCLPGFSGQFCEININECSSSPCLHGADCEDHINGYVCKCQPGWSGHHCENELECIPNSCVHELCMENEPGSTCLCTPGFMTCSIGLLCGDEIRRITCLTPIFQRTDPISTQTYTIPPSETLVSSFPSIKATRIPAIMDTYPVDQGPKQTGIVKHDILPTTGLATLRISTPLESYLLQELIVTRELSAKHSLLSSADVSSSRFLNFGIRDPAQIVQDKTSVSHMPIRTSAATLGFFFPDRRARTPFIMSSLMSDFIFPTQSLLFENCQTVALSATPTTSVIRSIPGADIELNRQSLLSRGFLLIAASISATPVVSRGAQEDIEEYSADSLISRREHWRLLSPSMSPIFPAKVIISKQVTILNSSALHRFSTKAFNPSEYQAITEASSNQRLTNIKSQAADSLRELSQTCATCSMTEIKSSREFSDQVLHSKQSHFYETFWMNSAILASWYALMGAQTITSGHSFSSATEITPSVAFTEVPSLFPSKKSAKRTILSSSLEESITLSSNLDVNLCLDKTCLSIVPSQTISSDLMNSDLTSKMTTDELSVSENILKLLKIRQYGITMGPTEVLNQESLLDMEKSKGSHTLFKLHPSDSSLDFELNLQIYPDVTLKTYSEITHANDFKNNLPPLTGSVPDFSEVTTNVAFYTVSATPALSIQTSSSMSVIRPDWPYFTDYMTSLKKEVKTSSEWSKWELQPSVQYQEFPTASRHLPFTRSLTLSSLESILAPQRLMISDFSCVRYYGDSYLEFQNVALNPQNNISLEFQTFSSYGLLLYVKQDSNLVDGFFIQLFIENGTLKYHFYCPGEAKFKSINTTVRVDNGQKYTLLIRQELDPCNAELTILGRNTQICESINHVLGKPLPKSGSVFIGGFPDLHGKIQMPVPVKNFTGCIEVIEINNWRSFIPSKAVKNYHINNCRSQGFMLSPTASFVDASDVTQGVDTMWTSVSPSVAAPSVCQQDVCHNGGTCHAIFLSSGIVSFQCDCPLHFTGRFCEKDAGLFFPSFNGNSYLELPFLKFVLEKEHNRTVTIYLTIKTNSLNGTILYSNGNNCGKQFLHLFLVEGRPSVKYGCGNSQNILTVSANYSINTNAFTPITIRYTTPVGSPGVVCMIEMTADGKPPVQKKDTEISHASQAYFESMFLGHIPANVQIHKKAGPVYGFRGCILDLQVNNKEFFIIDEARHGKNIENCHVPWCAHHLCRNNGTCISDNENLFCECPRLYSGKLCQFASCENNPCGNGATCVPKSGTDIVCLCPYGRSGPLCTDAINITQPRFSGTDAFGYTSFLAYSRISDISFHYEFHLKFQLANNHSALQNNLIFFTGQKGHGLNGDDFLAVGLLNGSVVYSYNLGSGIASIRSEPLNLSLGVHTVHLGKFFQEGWLKVDDHKNKSIIAPGRLVGLNVFSQFYVGGYSEYTPDLLPNGADFKNGFQGCIFTLQVRTEKDGHFRGLGNPEGHPNAGRSVGQCHASPCSLMKCGNGGTCIESGTSVYCNCTTGWKGSFCTETVSTCDPEHDPPHHCSRGATCISLPHGYTCFCPLGTTGIYCEQALSISDPSFRSNELSWMSFASFHVRKKTHIQLQFQPLAADGILFYAAQHLKAQSGDFLCISLVNSSVQLRYNLGDRTIILETLQKVTINGSTWHIIKAGRVGAEGYLDLDGINVTEKASTKMSSLDTNTDFYIGGVSSLNLVNPMAIENEPVGFQGCIRQVIINNQELQLTEFGAKGGSNVGDCDGTACGYNTCRNGGECTVNGTTFSCRCLPDWAGNTCNQSVSCLNNLCLHQSLCIPDQSFSYSCLCTLGWVGRYCENKTSFSTAKFMGNSYIKYIDPNYRMRNLQFTTISLNFSTTKTEGLIVWMGIAQNEENDFLAIGLHNQTLKIAVNLGERISVPMSYNNGTFCCNKWHHVVVIQNQTLIKAYINNSLILSEDIDPHKNFVALNYDGICYLGGFEYGRKVNIVTQEIFKTNFVGKIKDVVFFQEPKNIELIKLEGYNVYDGDEQNEVT,mutated_sequence,1.0,3144.0,NP_001136272.1.a2m,NP_001136272.1.npy,ClinVar +NP_001138830.1,NP_001138830.1.csv,MKRRLDDQESPVYAAQQRRIPGSTEAFPHQHRVLAPAPPVYEAVSETMQSATGIQYSVTPSYQVSAMPQSSGSHGPAIAAVHSSHHHPTAVQPHGGQVVQSHAHPAPPVAPVQGQQQFQRLKVEDALSYLDQVKLQFGSQPQVYNDFLDIMKEFKSQSIDTPGVISRVSQLFKGHPDLIMGFNTFLPPGYKIEVQTNDMVNVTTPGQVHQIPTHGIQPQPQPPPQHPSQPSAQSAPAPAQPAPQPPPAKVSKPSQLQAHTPASQQTPPLPPYASPRSPPVQPHTPVTISLGTAPSLQNNQPVEFNHAINYVNKIKNRFQGQPDIYKAFLEILHTYQKEQRNAKEAGGNYTPALTEQEVYAQVARLFKNQEDLLSEFGQFLPDANSSVLLSKTTAEKVDSVRNDHGGTVKKPQLNNKPQRPSQNGCQIRRHPTGTTPPVKKKPKLLNLKDSSMADASKHGGGTESLFFDKVRKALRSAEAYENFLRCLVIFNQEVISRAELVQLVSPFLGKFPELFNWFKNFLGYKESVHLETYPKERATEGIAMEIDYASCKRLGSSYRALPKSYQQPKCTGRTPLCKEVLNDTWVSFPSWSEDSTFVSSKKTQYEEHIYRCEDERFELDVVLETNLATIRVLEAIQKKLSRLSAEEQAKFRLDNTLGGTSEVIHRKALQRIYADKAADIIDGLRKNPSIAVPIVLKRLKMKEEEWREAQRGFNKVWREQNEKYYLKSLDHQGINFKQNDTKVLRSKSLLNEIESIYDERQEQATEENAGVPVGPHLSLAYEDKQILEDAAALIIHHVKRQTGIQKEDKYKIKQIMHHFIPDLLFAQRGDLSDVEEEEEEEMDVDEATGAVKKHNGVGGSPPKSKLLFSNTAAQKLRGMDEVYNLFYVNNNWYIFMRLHQILCLRLLRICSQAERQIEEENREREWEREVLGIKRDKSDSPAIQLRLKEPMDVDVEDYYPAFLDMVRSLLDGNIDSSQYEDSLREMFTIHAYIAFTMDKLIQSIVRQLQHIVSDEICVQVTDLYLAENNNGATGGQLNTQNSRSLLESTYQRKAEQLMSDENCFKLMFIQSQGQVQLTIELLDTEEENSDDPVEAERWSDYVERYMNSDTTSPELREHLAQKPVFLPRNLRRIRKCQRGREQQEKEGKEGNSKKTMENVDSLDKLECRFKLNSYKMVYVIKSEDYMYRRTALLRAHQSHERVSKRLHQRFQAWVDKWTKEHVPREMAAETSKWLMGEGLEGLVPCTTTCDTETLHFVSINKYRVKYGTVFKAP,mutated_sequence,1.0,1273.0,NP_001138830.1.a2m,NP_001138830.1.npy,ClinVar +NP_001153171.1,NP_001153171.1.csv,MASHRHSGPSSYKVGTMAEKFDCHYCRDPLQGKKYVQKDGHHCCLKCFDKFCANTCVECRKPIGADSKEVHYKNRFWHDTCFRCAKCLHPLANETFVAKDNKILCNKCTTREDSPKCKGCFKAIVAGDQNVEYKGTVWHKDCFTCSNCKQVIGTGSFFPKGEDFYCVTCHETKFAKHCVKCNKAITSGGITYQDQPWHADCFVCVTCSKKLAGQRFTAVEDQYYCVDCYKNFVAKKCAGCKNPITGFGKGSSVVAYEGQSWHDYCFHCKKCSVNLANKRFVFHQEQVYCPDCAKKL,mutated_sequence,1.0,296.0,NP_001153171.1.a2m,NP_001153171.1.npy,ClinVar +NP_001157749.1,NP_001157749.1.csv,MAAQGYGYYRTVIFSAMFGGYSLYYFNRKTFSFVMPSLVEEIPLDKDDLGFITSSQSAAYAISKFVSGVLSDQMSARWLFSSGLLLVGLVNIFFAWSSTVPVFAALWFLNGLAQGLGWPPCGKVLRKWFEPSQFGTWWAILSTSMNLAGGLGPILATILAQSYSWRSTLALSGALCVVVSFLCLLLIHNEPADVGLRNLDPMPSEGKKGSLKEESTLQELLLSPYLWVLSTGYLVVFGVKTCCTDWGQFFLIQEKGQSALVGSSYMSALEVGGLVGSIAAGYLSDRAMAKAGLSNYGNPRHGLLLFMMAGMTVSMYLFRVTVTSDSPKLWILVLGAVFGFSSYGPIALFGVIANESAPPNLCGTSHAIVGLMANVGGFLAGLPFSTIAKHYSWSTAFWVAEVICAASTAAFFLLRNIRTKMGRVSKKAE,mutated_sequence,1.0,429.0,NP_001157749.1.a2m,NP_001157749.1.npy,ClinVar +NP_001159435.1,NP_001159435.1.csv,MEQTVLVPPGPDSFNFFTRESLAAIERRIAEEKAKNPKPDKKDDDENGPKPNSDLEAGKNLPFIYGDIPPEMVSEPLEDLDPYYINKKTFIVLNKGKAIFRFSATSALYILTPFNPLRKIAIKILVHSLFSMLIMCTILTNCVFMTMSNPPDWTKNVEYTFTGIYTFESLIKIIARGFCLEDFTFLRDPWNWLDFTVITFAYVTEFVDLGNVSALRTFRVLRALKTISVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALIGLQLFMGNLRNKCIQWPPTNASLEEHSIEKNITVNYNGTLINETVFEFDWKSYIQDSRYHYFLEGFLDALLCGNSSDAGQCPEGYMCVKAGRNPNYGYTSFDTFSWAFLSLFRLMTQDFWENLYQLTLRAAGKTYMIFFVLVIFLGSFYLINLILAVVAMAYEEQNQATLEEAEQKEAEFQQMIEQLKKQQEAAQQAATATASEHSREPSAAGRLSDSSSEASKLSSKSAKERRNRRKKRKQKEQSGGEEKDEDEFQKSESEDSIRRKGFRFSIEGNRLTYEKRYSSPHQSLLSIRGSLFSPRRNSRTSLFSFRGRAKDVGSENDFADDEHSTFEDNESRRDSLFVPRRHGERRNSNLSQTSRSSRMLAVFPANGKMHSTVDCNGVVSLVGGPSVPTSPVGQLLPEVIIDKPATDDNGTTTETEMRKRRSSSFHVSMDFLEDPSQRQRAMSIASILTNTVEELEESRQKCPPCWYKFSNIFLIWDCSPYWLKVKHVVNLVVMDPFVDLAITICIVLNTLFMAMEHYPMTDHFNNVLTVGNLVFTGIFTAEMFLKIIAMDPYYYFQEGWNIFDGFIVTLSLVELGLANVEGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKDCVCKIASDCQLPRWHMNDFFHSFLIVFRVLCGEWIETMWDCMEVAGQAMCLTVFMMVMVIGNLVVLNLFLALLLSSFSADNLAATDDDNEMNNLQIAVDRMHKGVAYVKRKIYEFIQQSFIRKQKILDEIKPLDDLNNKKDSCMSNHTAEIGKDLDYLKDVNGTTSGIGTGSSVEKYIIDESDYMSFINNPSLTVTVPIAVGESDFENLNTEDFSSESDLEESKEKLNESSSSSEGSTVDIGAPVEEQPVVEPEETLEPEACFTEGCVQRFKCCQINVEEGRGKQWWNLRRTCFRIVEHNWFETFIVFMILLSSGALAFEDIYIDQRKTIKTMLEYADKVFTYIFILEMLLKWVAYGYQTYFTNAWCWLDFLIVDVSLVSLTANALGYSELGAIKSLRTLRALRPLRALSRFEGMRVVVNALLGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYHCINTTTGDRFDIEDVNNHTDCLKLIERNETARWKNVKVNFDNVGFGYLSLLQVATFKGWMDIMYAAVDSRNVELQPKYEESLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKFGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPGNKFQGMVFDFVTRQVFDISIMILICLNMVTMMVETDDQSEYVTTILSRINLVFIVLFTGECVLKLISLRHYYFTIGWNIFDFVVVILSIVGMFLAELIEKYFVSPTLFRVIRLARIGRILRLIKGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYAIFGMSNFAYVKREVGIDDMFNFETFGNSMICLFQITTSAGWDGLLAPILNSKPPDCDPNKVNPGSSVKGDCGNPSVGIFFFVSYIIISFLVVVNMYIAVILENFSVATEESAEPLSEDDFEMFYEVWEKFDPDATQFMEFEKLSQFAAALEPPLNLPQPNKLQLIAMDLPMVSGDRIHCLDILFAFTKRVLGESGEMDALRIQMEERFMASNPSKVSYQPITTTLKRKQEEVSAVIIQRAYRRHLLKRTVKQASFTYNKNKIKGGANLLIKEDMIIDRINENSITEKTDLTMSTAACPPSYDRVTKPIVEKHEQEGKDEKAKGK,mutated_sequence,1.0,2009.0,NP_001159435.1.a2m,NP_001159435.1.npy,ClinVar +NP_001162.5,NP_001162.5.csv,MAAPAEPCAGQGVWNQTEPEPAATSLLSLCFLRTAGVWVPPMYLWVLGPIYLLFIHHHGRGYLRMSPLFKAKMVLGFALIVLCTSSVAVALWKIQQGTPEAPEFLIHPTVWLTTMSFAVFLIHTERKKGVQSSGVLFGYWLLCFVLPATNAAQQASGAGFQSDPVRHLSTYLCLSLVVAQFVLSCLADQPPFFPEDPQQSNPCPETGAAFPSKATFWWVSGLVWRGYRRPLRPKDLWSLGRENSSEELVSRLEKEWMRNRSAARRHNKAIAFKRKGGSGMKAPETEPFLRQEGSQWRPLLKAIWQVFHSTFLLGTLSLIISDVFRFTVPKLLSLFLEFIGDPKPPAWKGYLLAVLMFLSACLQTLFEQQNMYRLKVLQMRLRSAITGLVYRKVLALSSGSRKASAVGDVVNLVSVDVQRLTESVLYLNGLWLPLVWIVVCFVYLWQLLGPSALTAIAVFLSLLPLNFFISKKRNHHQEEQMRQKDSRARLTSSILRNSKTIKFHGWEGAFLDRVLGIRGQELGALRTSGLLFSVSLVSFQVSTFLVALVVFAVHTLVAENAMNAEKAFVTLTVLNILNKAQAFLPFSIHSLVQARVSFDRLVTFLCLEEVDPGVVDSSSSGSAAGKDCITIHSATFAWSQESPPCLHRINLTVPQGCLLAVVGPVGAGKSSLLSALLGELSKVEGFVSIEGAVAYVPQEAWVQNTSVVENVCFGQELDPPWLERVLEACALQPDVDSFPEGIHTSIGEQGMNLSGGQKQRLSLARAVYRKAAVYLLDDPLAALDAHVGQHVFNQVIGPGGLLQGTTRILVTHALHILPQADWIIVLANGAIAEMGSYQELLQRKGALMCLLDQARQPGDRGEGETEPGTSTKDPRGTSAGRRPELRRERSIKSVPEKDRTTSEAQTEVPLDDPDRAGWPAGKDSIQYGRVKATVHLAYLRAVGTPLCLYALFLFLCQQVASFCRGYWLSLWADDPAVGGQQTQAALRGGIFGLLGCLQAIGLFASMAAVLLGGARASRLLFQRLLWDVVRSPISFFERTPIGHLLNRFSKETDTVDVDIPDKLRSLLMYAFGLLEVSLVVAVATPLATVAILPLFLLYAGFQSLYVVSSCQLRRLESASYSSVCSHMAETFQGSTVVRAFRTQAPFVAQNNARVDESQRISFPRLVADRWLAANVELLGNGLVFAAATCAVLSKAHLSAGLVGFSVSAALQVTQTLQWVVRNWTDLENSIVSVERMQDYAWTPKEAPWRLPTCAAQPPWPQGGQIEFRDFGLRYRPELPLAVQGVSFKIHAGEKVGIVGRTGAGKSSLASGLLRLQEAAEGGIWIDGVPIAHVGLHTLRSRISIIPQDPILFPGSLRMNLDLLQEHSDEAIWAALETVQLKALVASLPGQLQYKCADRGEDLSVGQKQLLCLARALLRKTQILILDEATAAVDPGTELQMQAMLGSWFAQCTVLLIAHRLRSVMDCARVLVMDKGQVAESGSPAQLLAQKGLFYRLAQESGLV,mutated_sequence,1.0,1503.0,NP_001162.5.a2m,NP_001162.5.npy,ClinVar +NP_001167560.1,NP_001167560.1.csv,MAAATRRVFHLQPCENSPTMSQNGYFEDSSYYKCDTDDTFEAREEILGDEAFDTANSSIVSGESIRFFVNVNLEMQATNTENEATSGGCVLLHTSRKYLKLKNFKEEIRAHRDLDGFLAQASIVLNETATSLDNVLRTMLRRFARDPDNNEPNCNLDLLMAMLFTDAGAPMRGKVHLLSDTIQGVTATVTGVRYQQSWLCIICTMKALQKRHVCISRLVRPQNWGENSCEVRFVILVLAPPKMKSTKTAMEVARTFATMFSDIAFRQKLLETRTEEEFKEALVHQRQLLTMVSHGPVAPRTKERSTVSLPAHRHPEPPKCKDFVPFGKGIREDIARRFPLYPLDFTDGIIGKNKAVGKYITTTLFLYFACLLPTIAFGSLNDENTDGAIDVQKTIAGQSIGGLLYALFSGQPLVILLTTAPLALYIQVIRVICDDYDLDFNSFYAWTGLWNSFFLALYAFFNLSLVMSLFKRSTEEIIALFISITFVLDAVKGTVKIFWKYYYGHYLDDYHTKRTSSLVSLSGLGASLNASLHTALNASFLASPTELPSATHSGQATAVLSLLIMLGTLWLGYTLYQFKKSPYLHPCVREILSDCALPIAVLAFSLISSHGFREIEMSKFRYNPSESPFAMAQIQSLSLRAVSGAMGLGFLLSMLFFIEQNLVAALVNAPENRLVKGTAYHWDLLLLAIINTGLSLFGLPWIHAAYPHSPLHVRALALVEERVENGHIYDTIVNVKETRLTSLGASVLVGLSLLLLPVPLQWIPKPVLYGLFLYIALTSLDGNQLVQRVALLLKEQTAYPPTHYIRRVPQRKIHYFTGLQVLQLLLLCAFGMSSLPYMKMIFPLIMIAMIPIRYILLPRIIEAKYLDVMDAEHRP,mutated_sequence,1.0,875.0,NP_001167560.1.a2m,NP_001167560.1.npy,ClinVar +NP_001167618.1,NP_001167618.1.csv,MDIATGPESLERCFPRGQTDCAKMLDGIKMEEHALRPGPATLGVLLGSDCPHPAVCEGCQRPISDRFLMRVNESSWHEECLQCAACQQALTTSCYFRDRKLYCKQDYQQLFAAKCSGCMEKIAPTEFVMRALECVYHLGCFCCCVCERQLRKGDEFVLKEGQLLCKGDYEKEKDLLSSVSPDESDSVKSEDEDGDMKPAKGQGSQSKGSGDDGKDPRRPKRPRTILTTQQRRAFKASFEVSSKPCRKVRETLAAETGLSVRVVQVWFQNQRAKMKKLARRHQQQQEQQNSQRLGQEVLSSRMEGMMASYTPLAPPQQQIVAMEQSPYGSSDPFQQGLTPPQMPGDHMNPYGNDSIFHDIDSDTSLTSLSDCFLGSSDVGSLQARVGNPIDRLYSMQSSYFAS,mutated_sequence,1.0,402.0,NP_001167618.1.a2m,NP_001167618.1.npy,ClinVar +NP_001171809.1,NP_001171809.1.csv,MESLLLPVLLLLAILWTQAAALINLKYSVEEEQRAGTVIANVAKDAREAGFALDPRQASAFRVVSNSAPHLVDINPSSGLLVTKQKIDRDLLCRQSPKCIISLEVMSSSMEICVIKVEIKDLNDNAPSFPAAQIELEISEAASPGTRIPLDSAYDPDSGSFGVQTYELTPNELFGLEIKTRGDGSRFAELVVEKSLDRETQSHYSFRITALDGGDPPRLGTVGLSIKVTDSNDNNPVFSESTYAVSVPENSPPNTPVIRLNASDPDEGTNGQVVYSFYGYVNDRTRELFQIDPHSGLVTVTGALDYEEGHVYELDVQAKDLGPNSIPAHCKVTVSVLDTNDNPPVINLLSVNSELVEVSESAPPGYVIALVRVSDRDSGLNGRVQCRLLGNVPFRLQEYESFSTILVDGRLDREQHDQYNLTIQARDGGVPMLQSAKSFTVLITDENDNHPHFSKPYYQVIVQENNTPGAYLLSVSARDPDLGLNGSVSYQIVPSQVRDMPVFTYVSINPNSGDIYALRSFNHEQTKAFEFKVLAKDGGLPSLQSNATVRVIILDVNDNTPVITAPPLINGTAEVYIPRNSGIGYLVTVVKAEDYDEGENGRVTYDMTEGDRGFFEIDQVNGEVRTTRTFGESSKSSYELIVVAHDHGKTSLSASALVLIYLSPALDAQESMGSVNLSLIFIIALGSIAGILFVTMIFVAIKCKRDNKEIRTYNCSNCLTITCLLGCFIKGQNSKCLHCISVSPISEEQDKKTEEKVSLRGKRIAEYSYGHQKKSSKKKKISKNDIRLVPRDVEETDKMNVVSCSSLTSSLNYFDYHQQTLPLGCRRSESTFLNVENQNTRNTSANHIYHHSFNSQGPQQPDLIINGVPLPETENYSFDSNYVNSRAHLIKSSSTFKDLEGNSLKDSGHEESDQTDSEHDVQRSLYCDTAVNDVLNTSVTSMGSQMPDHDQNEGFHCREECRILGHSDRCWMPRNPMPIRSKSPEHVRNIIALSIEATAADVEAYDDCGPTKRTFATFGKDVSDHPAEERPTLKGKRTVDVTICSPKVNSVIREAGNGCEAISPVTSPLHLKSSLPTKPSVSYTIALAPPARDLEQYVNNVNNGPTRPSEAEPRGADSEKVMHEVSPILKEGRNKESPGVKRLKDIVL,mutated_sequence,1.0,1148.0,NP_001171809.1.a2m,NP_001171809.1.npy,ClinVar +NP_001174.2,NP_001174.2.csv,MMAAMATARVRMGPRCAQALWRMPWLPVFLSLAAAAAAAAAEQQVPLVLWSSDRDLWAPAADTHEGHITSDLQLSTYLDPALELGPRNVLLFLQDKLSIEDFTAYGGVFGNKQDSAFSNLENALDLAPSSLVLPAVDWYAVSTLTTYLQEKLGASPLHVDLATLRELKLNASLPALLLIRLPYTASSGLMAPREVLTGNDEVIGQVLSTLKSEDVPYTAALTAVRPSRVARDVAVVAGGLGRQLLQKQPVSPVIHPPVSYNDTAPRILFWAQNFSVAYKDQWEDLTPLTFGVQELNLTGSFWNDSFARLSLTYERLFGTTVTFKFILANRLYPVSARHWFTMERLEVHSNGSVAYFNASQVTGPSIYSFHCEYVSSLSKKGSLLVARTQPSPWQMMLQDFQIQAFNVMGEQFSYASDCASFFSPGIWMGLLTSLFMLFIFTYGLHMILSLKTMDRFDDHKGPTISLTQIV,mutated_sequence,1.0,470.0,NP_001174.2.a2m,NP_001174.2.npy,ClinVar +NP_001177203.1,NP_001177203.1.csv,MNSVRAANRRPRRVSRPRPVQQQQQQPPQQPPPQPPQQQPPQQQPPPPPQQQQQQQPPPPPPPPPPLPQERNNVGERDDDVPADMVAEESGPGAQNSPYQLRRKTLLPKRTACPTKNSMEGASTSTTENFGHRAKRARVSGKSQDLSAAPAEQYLQEKLPDEVVLKIFSYLLEQDLCRAACVCKRFSELANDPILWKRLYMEVFEYTRPMMHPEPGKFYQINPEEYEHPNPWKESFQQLYKGAHVKPGFAEHFYSNPARYKGRENMLYYDTIEDALGGVQEAHFDGLIFVHSGIYTDEWIYIESPITMIGAAPGKVADKVIIENTRDSTFVFMEGSEDAYVGYMTIRFNPDDKSAQHHNAHHCLEITVNCSPIIDHCIIRSTCTVGSAVCVSGQGACPTIKHCNISDCENVGLYITDHAQGIYEDNEISNNALAGIWVKNHGNPIIRRNHIHHGRDVGVFTFDHGMGYFESCNIHRNRIAGFEVKAYANPTVVRCEIHHGQTGGIYVHEKGRGQFIENKIYANNFAGVWITSNSDPTIRGNSIFNGNQGGVYIFGDGRGLIEGNDIYGNALAGIQIRTNSCPIVRHNKIHDGQHGGIYVHEKGQGVIEENEVYSNTLAGVWVTTGSTPVLRRNRIHSGKQVGVYFYDNGHGVLEDNDIYNHMYSGVQIRTGSNPKIRRNKIWGGQNGGILVYNSGLGCIEDNEIFDNAMAGVWIKTDSNPTLRRNKIHDGRDGGICIFNGGRGLLEENDIFRNAQAGVLISTNSHPILRKNRIFDGFAAGIEITNHATATLEGNQIFNNRFGGLFLASGVNVTMKDNKIMNNQDAIEKAVSRGQCLYKISSYTSYPMHDFYRCHTCNTTDRNAICVNCIKKCHQGHDVEFIRHDRFFCDCGAGTLSNPCTLAGEPTHDTDTLYDSAPPIESNTLQHN,mutated_sequence,1.0,927.0,NP_001177203.1.a2m,NP_001177203.1.npy,ClinVar +NP_001182482.1,NP_001182482.1.csv,MELDFGHFDERDKTSRNMRGSRMNGLPSPTHSAHCSFYRTRTLQALSNEKKAKKVRFYRNGDRYFKGIVYAVSSDRFRSFDALLADLTRSLSDNINLPQGVRYIYTIDGSRKIGSMDELEEGESYVCSSDNFFKKVEYTKNVNPNWSVNVKTSANMKAPQSLASSNSAQARENKDFVRPKLVTIIRSGVKPRKAVRVLLNKKTAHSFEQVLTDITEAIKLETGVVKKLYTLDGKQVTCLHDFFGDDDVFIACGPEKFRYAQDDFSLDENECRVMKGNPSATAGPKASPTPQKTSAKSPGPMRRSKSPADSGNDQDANGTSSSQLSTPKSKQSPISTPTSPGSLRKHKVDLYLPLSLDDSDSLGDSM,mutated_sequence,1.0,366.0,NP_001182482.1.a2m,NP_001182482.1.npy,ClinVar +NP_001184033.1,NP_001184033.1.csv,MAHSCRWRFPARPGTTGGGGGGGRRGLGGAPRQRVPALLLPPGPPVGGGGPGAPPSPPAVAAAAAAAGSSGAGVPGGAAAASAASSSSASSSSSSSSSASSGPALLRVGPGFDAALQVSAAIGTNLRRFRAVFGESGGGGGSGEDEQFLGFGSDEEVRVRSPTRSPSVKTSPRKPRGRPRSGSDRNSAILSDPSVFSPLNKSETKSGDKIKKKDSKSIEKKRGRPPTFPGVKIKITHGKDISELPKGNKEDSLKKIKRTPSATFQQATKIKKLRAGKLSPLKSKFKTGKLQIGRKGVQIVRRRGRPPSTERIKTPSGLLINSELEKPQKVRKDKEGTPPLTKEDKTVVRQSPRRIKPVRIIPSSKRTDATIAKQLLQRAKKGAQKKIEKEAAQLQGRKVKTQVKNIRQFIMPVVSAISSRIIKTPRRFIEDEDYDPPIKIARLESTPNSRFSAPSCGSSEKSSAASQHSSQMSSDSSRSSSPSVDTSTDSQASEEIQVLPEERSDTPEVHPPLPISQSPENESNDRRSRRYSVSERSFGSRTTKKLSTLQSAPQQQTSSSPPPPLLTPPPPLQPASSISDHTPWLMPPTIPLASPFLPASTAPMQGKRKSILREPTFRWTSLKHSRSEPQYFSSAKYAKEGLIRKPIFDNFRPPPLTPEDVGFASGFSASGTAASARLFSPLHSGTRFDMHKRSPLLRAPRFTPSEAHSRIFESVTLPSNRTSAGTSSSGVSNRKRKRKVFSPIRSEPRSPSHSMRTRSGRLSSSELSPLTPPSSVSSSLSISVSPLATSALNPTFTFPSHSLTQSGESAEKNQRPRKQTSAPAEPFSSSSPTPLFPWFTPGSQTERGRNKDKAPEELSKDRDADKSVEKDKSRERDREREKENKRESRKEKRKKGSEIQSSSALYPVGRVSKEKVVGEDVATSSSAKKATGRKKSSSHDSGTDITSVTLGDTTAVKTKILIKKGRGNLEKTNLDLGPTAPSLEKEKTLCLSTPSSSTVKHSTSSIGSMLAQADKLPMTDKRVASLLKKAKAQLCKIEKSKSLKQTDQPKAQGQESDSSETSVRGPRIKHVCRRAAVALGRKRAVFPDDMPTLSALPWEEREKILSSMGNDDKSSIAGSEDAEPLAPPIKPIKPVTRNKAPQEPPVKKGRRSRRCGQCPGCQVPEDCGVCTNCLDKPKFGGRNIKKQCCKMRKCQNLQWMPSKAYLQKQAKAVKKKEKKSKTSEKKDSKESSVVKNVVDSSQKPTPSAREDPAPKKSSSEPPPRKPVEEKSEEGNVSAPGPESKQATTPASRKSSKQVSQPALVIPPQPPTTGPPRKEVPKTTPSEPKKKQPPPPESGPEQSKQKKVAPRPSIPVKQKPKEKEKPPPVNKQENAGTLNILSTLSNGNSSKQKIPADGVHRIRVDFKEDCEAENVWEMGGLGILTSVPITPRVVCFLCASSGHVEFVYCQVCCEPFHKFCLEENERPLEDQLENWCCRRCKFCHVCGRQHQATKQLLECNKCRNSYHPECLGPNYPTKPTKKKKVWICTKCVRCKSCGSTTPGKGWDAQWSHDFSLCHDCAKLFAKGNFCPLCDKCYDDDDYESKMMQCGKCDRWVHSKCENLSGTEDEMYEILSNLPESVAYTCVNCTERHPAEWRLALEKELQISLKQVLTALLNSRTTSHLLRYRQAAKPPDLNPETEESIPSRSSPEGPDPPVLTEVSKQDDQQPLDLEGVKRKMDQGNYTSVLEFSDDIVKIIQAAINSDGGQPEIKKANSMVKSFFIRQMERVFPWFSVKKSRFWEPNKVSSNSGMLPNAVLPPSLDHNYAQWQEREENSHTEQPPLMKKIIPAPKPKGPGEPDSPTPLHPPTPPILSTDRSREDSPELNPPPGIEDNRQCALCLTYGDDSANDAGRLLYIGQNEWTHVNCALWSAEVFEDDDGSLKNVHMAVIRGKQLRCEFCQKPGATVGCCLTSCTSNYHFMCSRAKNCVFLDDKKVYCQRHRDLIKGEVVPENGFEVFRRVFVDFEGISLRRKFLNGLEPENIHMMIGSMTIDCLGILNDLSDCEDKLFPIGYQCSRVYWSTTDARKRCVYTCKIVECRPPVVEPDINSTVEHDENRTIAHSPTSFTESSSKESQNTAEIISPPSPDRPPHSQTSGSCYYHVISKVPRIRTPSYSPTQRSPGCRPLPSAGSPTPTTHEIVTVGDPLLSSGLRSIGSRRHSTSSLSPQRSKLRIMSPMRTGNTYSRNNVSSVSTTGTATDLESSAKVVDHVLGPLNSSTSLGQNTSTSSNLQRTVVTVGNKNSHLDGSSSSEMKQSSASDLVSKSSSLKGEKTKVLSSKSSEGSAHNVAYPGIPKLAPQVHNTTSRELNVSKIGSFAEPSSVSFSSKEALSFPHLHLRGQRNDRDQHTDSTQSANSSPDEDTEVKTLKLSGMSNRSSIINEHMGSSSRDRRQKGKKSCKETFKEKHSSKSFLEPGQVTTGEEGNLKPEFMDEVLTPEYMGQRPCNNVSSDKIGDKGLSMPGVPKAPPMQVEGSAKELQAPRKRTVKVTLTPLKMENESQSKNALKESSPASPLQIESTSPTEPISASENPGDGPVAQPSPNNTSCQDSQSNNYQNLPVQDRNLMLPDGPKPQEDGSFKRRYPRRSARARSNMFFGLTPLYGVRSYGEEDIPFYSSSTGKKRGKRSAEGQVDGADDLSTSDEDDLYYYNFTRTVISSGGEERLASHNLFREEEQCDLPKISQLDGVDDGTESDTSVTATTRKSSQIPKRNGKENGTENLKIDRPEDAGEKEHVTKSSVGHKNEPKMDNCHSVSRVKTQGQDSLEAQLSSLESSRRVHTSTPSDKNLLDTYNTELLKSDSDNNNSDDCGNILPSDIMDFVLKNTPSMQALGESPESSSSELLNLGEGLGLDSNREKDMGLFEVFSQQLPTTEPVDSSVSSSISAEEQFELPLELPSDLSVLTTRSPTVPSQNPSRLAVISDSGEKRVTITEKSVASSESDPALLSPGVDPTPEGHMTPDHFIQGHMDADHISSPPCGSVEQGHGNNQDLTRNSSTPGLQVPVSPTVPIQNQKYVPNSTDSPGPSQISNAAVQTTPPHLKPATEKLIVVNQNMQPLYVLQTLPNGVTQKIQLTSSVSSTPSVMETNTSVLGPMGGGLTLTTGLNPSLPTSQSLFPSASKGLLPMSHHQHLHSFPAATQSSFPPNISNPPSGLLIGVQPPPDPQLLVSESSQRTDLSTTVATPSSGLKKRPISRLQTRKNKKLAPSSTPSNIAPSDVVSNMTLINFTPSQLPNHPSLLDLGSLNTSSHRTVPNIIKRSKSSIMYFEPAPLLPQSVGGTAATAAGTSTISQDTSHLTSGSVSGLASSSSVLNVVSMQTTTTPTSSASVPGHVTLTNPRLLGTPDIGSISNLLIKASQQSLGIQDQPVALPPSSGMFPQLGTSQTPSTAAITAASSICVLPSTQTTGITAASPSGEADEHYQLQHVNQLLASKTGIHSSQRDLDSASGPQVSNFTQTVDAPNSMGLEQNKALSSAVQASPTSPGGSPSSPSSGQRSASPSVPGPTKPKPKTKRFQLPLDKGNGKKHKVSHLRTSSSEAHIPDQETTSLTSGTGTPGAEAEQQDTASVEQSSQKECGQPAGQVAVLPEVQVTQNPANEQESAEPKTVEEEESNFSSPLMLWLQQEQKRKESITEKKPKKGLVFEISSDDGFQICAESIEDAWKSLTDKVQEARSNARLKQLSFAGVNGLRMLGILHDAVVFLIEQLSGAKHCRNYKFRFHKPEEANEPPLNPHGSARAEVHLRKSAFDMFNFLASKHRQPPEYNPNDEEEEEVQLKSARRATSMDLPMPMRFRHLKKTSKEAVGVYRSPIHGRGLFCKRNIDAGEMVIEYAGNVIRSIQTDKREKYYDSKGIGCYMFRIDDSEVVDATMHGNAARFINHSCEPNCYSRVINIDGQKHIVIFAMRKIYRGEELTYDYKFPIEDASNKLPCNCGAKKCRKFLN,mutated_sequence,1.0,3972.0,NP_001184033.1.a2m,NP_001184033.1.npy,ClinVar +NP_001193908.1,NP_001193908.1.csv,MGSKMNLIEHSHLPTTDEFSFSENLFGVLTEQVAGPLGQNLEVEPYSQYSNVQFPQVQPQISSSSYYSNLGFYPQQPEEWYSPGIYELRRMPAETLYQGETEVAEMPVTKKPRMGASAGRIKGDELCVVCGDRASGYHYNALTCEGCKGFFRRSITKNAVYKCKNGGNCVMDMYMRRKCQECRLRKCKEMGMLAECMYTGLLTEIQCKSKRLRKNVKQHADQTVNEDSEGRDLRQVTSTTKSCREKTELTPDQQTLLHFIMDSYNKQRMPQEITNKILKEEFSAEENFLILTEMATNHVQVLVEFTKKLPGFQTLDHEDQIALLKGSAVEAMFLRSAEIFNKKLPSGHSDLLEERIRNSGISDEYITPMFSFYKSIGELKMTQEEYALLTAIVILSPDRQYIKDREAVEKLQEPLLDVLQKLCKIHQPENPQHFACLLGRLTELRTFNHHHAEMLMSWRVNDHKFTPLLCEIWDVQ,mutated_sequence,1.0,476.0,NP_001193908.1.a2m,NP_001193908.1.npy,ClinVar +NP_001193927.1,NP_001193927.1.csv,MRPRSGGRPGATGRRRRRLRRRPRGLRCSRLPPPPPLPLLLGLLLAAAGPGAARAKETAFVEVVLFESSPSGDYTTYTTGLTGRFSRAGATLSAEGEIVQMHPLGLCNNNDEEDLYEYGWVGVVKLEQPELDPKPCLTVLGKAKRAVQRGATAVIFDVSENPEAIDQLNQGSEDPLKRPVVYVKGADAIKLMNIVNKQKVARARIQHRPPRQPTEYFDMGIFLAFFVVVSLVCLILLVKIKLKQRRSQNSMNRLAVQALEKMETRKFNSKSKGRREGSCGALDTLSSSSTSDCAICLEKYIDGEELRVIPCTHRFHRKCVDPWLLQHHTCPHCRHNIIEQKGNPSAVCVETSNLSRGRQQRVTLPVHYPGRVHRTNAIPAYPTRTSMDSHGNPVTLLTMDRHGEQSLYSPQTPAYIRSYPPLHLDHSLAAHRCGLEHRAYSPAHPFRRPKLSGRSFSKAACFSQYETMYQHYYFQGLSYPEQEGQSPPSLAPRGPARAFPPSGSGSLLFPTVVHVAPPSHLESGSTSSFSCYHGHRSVCSGYLADCPGSDSSSSSSSGQCHCSSSDSVVDCTEVSNQGVYGSCSTFRSSLSSDYDPFIYRSRSPCRASEAGGSGSSGRGPALCFEGSPPPEELPAVHSHGAGRGEPWPGPASPSGDQVSTCSLEMNYSSNSSLEHRGPNSSTSEVGLEASPGAAPDLRRTWKGGHELPSCACCCEPQPSPAGPSAGAAGSSTLFLGPHLYEGSGPAGGEPQSGSSQGLYGLHPDHLPRTDGVKYEGLPCCFYEEKQVARGGGGGSGCYTEDYSVSVQYTLTEEPPPGCYPGARDLSQRIPIIPEDVDCDLGLPSDCQGTHSLGSWGGTRGPDTPRPHRGLGATREEERALCCQARALLRPGCPPEEAGAVRANFPSALQDTQESSTTATEAAGPRSHSADSSSPGA,mutated_sequence,1.0,936.0,NP_001193927.1.a2m,NP_001193927.1.npy,ClinVar +NP_001194.1,NP_001194.1.csv,MLLRSAGKLNVGTKKEDGESTAPTPRPKVLRCKCHHHCPEDSVNNICSTDGYCFTMIEEDDSGLPVVTSGCLGLEGSDFQCRDTPIPHQRRSIECCTERNECNKDLHPTLPPLKNRDFVDGPIHHRALLISVTVCSLLLVLIILFCYFRYKRQETRPRYSIGLEQDETYIPPGESLRDLIEQSQSSGSGSGLPLLVQRTIAKQIQMVKQIGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDYLKSTTLDAKSMLKLAYSSVSGLCHLHTEIFSTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVKFISDTNEVDIPPNTRVGTKRYMPPEVLDESLNRNHFQSYIMADMYSFGLILWEVARRCVSGGIVEEYQLPYHDLVPSDPSYEDMREIVCIKKLRPSFPNRWSSDECLRQMGKLMTECWAHNPASRLTALRVKKTLAKMSESQDIKL,mutated_sequence,1.0,502.0,NP_001194.1.a2m,NP_001194.1.npy,ClinVar +NP_001195.2,NP_001195.2.csv,MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL,mutated_sequence,1.0,1038.0,NP_001195.2.a2m,NP_001195.2.npy,ClinVar +NP_001203.1,NP_001203.1.csv,MLPLLRCVPRVLGSSVAGLRAAAPASPFRQLLQPAPRLCTRPFGLLSVRAGSERRPGLLRPRGPCACGCGCGSLHTDGDKAFVDFLSDEIKEERKIQKHKTLPKMSGGWELELNGTEAKLVRKVAGEKITVTFNINNSIPPTFDGEEEPSQGQKVEEQEPELTSTPNFVVEVIKNDDGKKALVLDCHYPEDEVGQEDEAESDIFSIREVSFQSTGESEWKDTNYTLNTDSLDWALYDHLMDFLADRGVDNTFADELVELSTALEHQEYITFLEDLKSFVKSQ,mutated_sequence,1.0,282.0,NP_001203.1.a2m,NP_001203.1.npy,ClinVar +NP_001229825.1,NP_001229825.1.csv,MRTTKVYKLVIHKKGFGGSDDELVVNPKVFPHIKLGDIVEIAHPNDEYSPLLLQVKSLKEDLQKETISVDQTVTQVFRLRPYQDVYVNVVDPKDVTLDLVELTFKDQYIGRGDMWRLKKSLVSTCAYITQKVEFAGIRAQAGELWVKNEKVMCGYISEDTRVVFRSTSAMVYIFIQMSCEMWDFDIYGDLYFEKAVNGFLADLFTKWKEKNCSHEVTVVLFSRTFYDAKSVDEFPEINRASIRQDHKGRFYEDFYKVVVQNERREEWTSLLVTIKKLFIQYPVLVRLEQAEGFPQGDNSTSAQGNYLEAINLSFNVFDKHYINRNFDRTGQMSVVITPGVGVFEVDRLLMILTKQRMIDNGIGVDLVCMGEQPLHAVPLFKLHNRSAPRDSRLGDDYNIPHWINHSFYTSKSQLFCNSFTPRIKLAGKKPASEKAKNGRDTSLGSPKESENALPIQVDYDAYDAQVFRLPGPSRAQCLTTCRSVRERESHSRKSASSCDVSSSPSLPSRTLPTEEVRSQASDDSSLGKSANILMIPHPHLHQYEVSSSLGYTSTRDVLENMMEPPQRDSSAPGRFHVGSAESMLHVRPGGYTPQRALINPFAPSRMPMKLTSNRRRWMHTFPVGPSGEAIQIHHQTRQNMAELQGSGQRDPTHSSAELLELAYHEAAGRHSNSRQPGDGMSFLNFSGTEELSVGLLSNSGAGMNPRTQNKDSLEDSVSTSPDPILTLSAPPVVPGFCCTVGVDWKSLTTPACLPLTTDYFPDRQGLQNDYTEGCYDLLPEADIDRRDEDGVQMTAQQVFEEFICQRLMQGYQIIVQPKTQKPNPAVPPPLSSSPLYSRGLVSRNRPEEEDQYWLSMGRTFHKVTLKDKMITVTRYLPKYPYESAQIHYTYSLCPSHSDSEFVSCWVEFSHERLEEYKWNYLDQYICSAGSEDFSLIESLKFWRTRFLLLPACVTATKRITEGEAHCDIYGDRPRADEDEWQLLDGFVRFVEGLNRIRRRHRSDRMMRKGTAMKGLQMTGPISTHSLESTAPPVGKKGTSALSALLEMEASQKCLGEQQAAVHGGKSSAQSAESSSVAMTPTYMDSPRKDGAFFMEFVRSPRTASSAFYPQVSVDQTATPMLDGTSLGICTGQSMDRGNSQTFGNSQNIGEQGYSSTNSSDSSSQQLVASSLTSSSTLTEILEAMKHPSTGVQLLSEQKGLSPYCFISAEVVHWLVNHVEGIQTQAMAIDIMQKMLEEQLITHASGEAWRTFIYGFYFYKIVTDKEPDRVAMQQPATTWHTAGVDDFASFQRKWFEVAFVAEELVHSEIPAFLLPWLPSRPASYASRHSSFSRSFGGRSQAAALLAATVPEQRTVTLDVDVNNRTDRLEWCSCYYHGNFSLNAAFEIKLHWMAVTAAVLFEMVQGWHRKATSCGFLLVPVLEGPFALPSYLYGDPLRAQLFIPLNISCLLKEGSEHLFDSFEPETYWDRMHLFQEAIAHRFGFVQDKYSASAFNFPAENKPQYIHVTGTVFLQLPYSKRKFSGQQRRRRNSTSSTNQNMFCEERVGYNWAYNTMLTKTWRSSATGDEKFADRLLKDFTDFCINRDNRLVTFWTSCLEKMHASAP,mutated_sequence,1.0,1603.0,NP_001229825.1.a2m,NP_001229825.1.npy,ClinVar +NP_001230937.1,NP_001230937.1.csv,MAGASVKVAVRVRPFNSREMSRDSKCIIQMSGSTTTIVNPKQPKETPKSFSFDYSYWSHTSPEDINYASQKQVYRDIGEEMLQHAFEGYNVCIFAYGQTGAGKSYTMMGKQEKDQQGIIPQLCEDLFSRINDTTNDNMSYSVEVSYMEIYCERVRDLLNPKNKGNLRVREHPLLGPYVEDLSKLAVTSYNDIQDLMDSGNKARTVAATNMNETSSRSHAVFNIIFTQKRHDAETNITTEKVSKISLVDLAGSERADSTGAKGTRLKEGANINKSLTTLGKVISALAEMDSGPNKNKKKKKTDFIPYRDSVLTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADRAKQIRCNAVINEDPNNKLIRELKDEVTRLRDLLYAQGLGDITDTNTVPGGPKLTNALVGMSPSSSLSALSSRAASVSSLHERILFAPGSEEAIERLKETEKIIAELNETWEEKLRRTEAIRMEREALLAEMGVAMREDGGTLGVFSPKKTPHLVNLNEDPLMSECLLYYIKDGITRVGREDGERRQDIVLSGHFIKEEHCVFRSDSRGGSEAVVTLEPCEGADTYVNGKKVTEPSILRSGNRIIMGKSHVFRFNHPEQARQERERTPCAETPAEPVDWAFAQRELLEKQGIDMKQEMEQRLQELEDQYRREREEATYLLEQQRLDYESKLEALQKQMDSRYYPEVNEEEEEPEDEVQWTERECELALWAFRKWKWYQFTSLRDLLWGNAIFLKEANAISVELKKKVQFQFVLLTDTLYSPLPPDLLPPEAAKDRETRPFPRTIVAVEVQDQKNGATHYWTLEKLRQRLDLMREMYDRAAEVPSSVIEDCDNVVTGGDPFYDRFPWFRLVGSSAISGCNSYPLLNTCMSERMAALTPSPTFSSPDSDATEPAEEQSVGEEEEEEEEEEDEEEEDLEDDVFPEHALCDGRDPFYDRPPLFSLVGRAFVYLSNLLYPVPLVHRVAIVSEKGEVKGFLRVAVQAISADEEAPDYGSGVRQSGTAKISFDDQHFEKFQSESCPVVGMSRSGTSQEELRIVEGQGQGADVGPSADEVNNNTCSAVPPEGLLLDSSEKAALDGPLDAALDHLRLGNTFTFRVTVLQASSISAEYADIFCQFNFIHRHDEAFSTEPLKNTGRGPPLGFYHVQNIAVEVTKSFIEYIKSQPIVFEVFGHYQQHPFPPLCKDVLSPLRPSRRHFPRVMPLSKPVPATKLSTLTRPCPGPCHCKYDLLVYFEICELEANGDYIPAVVDHRGGMPCMGTFLLHQGIQRRITVTLLHETGSHIRWKEVRELVVGRIRNTPETDESLIDPNILSLNILSSGYIHPAQDDRTFYQFEAAWDSSMHNSLLLNRVTPYREKIYMTLSAYIEMENCTQPAVVTKDFCMVFYSRDAKLPASRSIRNLFGSGSLRASESNRVTGVYELSLCHVADAGSPGMQRRRRRVLDTSVAYVRGEENLAGWRPRSDSLILDHQWELEKLSLLQEVEKTRHYLLLREKLETAQRPVPEALSPAFSEDSESHGSSSASSPLSAEGRPSPLEAPNERQRELAVKCLRLLTHTFNREYTHSHVCVSASESKLSEMSVTLLRDPSMSPLGVATLTPSSTCPSLVEGRYGATDLRTPQPCSRPASPEPELLPEADSKKLPSPARATETDKEPQRLLVPDIQEIRVSPIVSKKGYLHFLEPHTSGWARRFVVVRRPYAYMYNSDKDTVERFVLNLATAQVEYSEDQQAMLKTPNTFAVCTEHRGILLQAASDKDMHDWLYAFNPLLAGTIRSKLSRRRSAQMRV,mutated_sequence,1.0,1791.0,NP_001230937.1.a2m,NP_001230937.1.npy,ClinVar +NP_001241.1,NP_001241.1.csv,MVRLPLQCVLWGCLLTAVHPEPPTACREKQYLINSQCCSLCQPGQKLVSDCTEFTETECLPCGESEFLDTWNRETHCHQHKYCDPNLGLRVQQKGTSETDTICTCEEGWHCTSEACESCVLHRSCSPGFGVKQIATGVSDTICEPCPVGFFSNVSSAFEKCHPWTSCETKDLVVQQAGTNKTDVVCGPQDRLRALVVIPIIFGILFAILLVLVFIKKVAKKPTNKAPHPKQEPQEINFPDDLPGSNTAAPVQETLHGCQPVTQEDGKESRISVQERQ,mutated_sequence,1.0,277.0,NP_001241.1.a2m,NP_001241.1.npy,ClinVar +NP_001243.1,NP_001243.1.csv,MPEEGSGCSVRRRPYGCVLRAALVPLVAGLVICLVVCIQRFAQAQQQLPLESLGWDVAELQLNHTGPQQDPRLYWQGGPALGRSFLHGPELDKGQLRIHRDGIYMVHIQVTLAICSSTTASRHHPTTLAVGICSPASRSISLLRLSFHQGCTIASQRLTPLARGDTLCTNLTGTLLPSRNTDETFFGVQWVRP,mutated_sequence,1.0,193.0,NP_001243.1.a2m,NP_001243.1.npy,ClinVar +NP_001243000.2,NP_001243000.2.csv,MECPSCQHVSKEETPKFCSQCGERLPPAAPIADSENNNSTMASASEGEMECGQELKEEGGPCLFPGSDSWQENPEEPCSKASWTVQESKKKKRKKKKKGNKSASSELASLPLSPASPCHLTLLSNPWPQDTALPHSQAQQSGPTGQPSQPPGTATTPLEGDGLSAPTEVGDSPLQAQALGEAGVATGSEAQSSPQFQDHTEGEDQDASIPSGGRGLSQEGTGPPTSAGEGHSRTEDAAQELLLPESKGGSSEPGTELQTTEQQAGASASMAVDAVAEPANAVKGAGKEMKEKTQRMKQPPATTPPFKTHCQEAETKTKDEMAAAEEKVGKNEQGEPEDLKKPEGKNRSAAAVKNEKEQKNQEADVQEVKASTLSPGGGVTVFFHAIISLHFPFNPDLHKVFIRGGEEFGESKWDSNICELHYTRDLGHDRVLVEGIVCISKKHLDKYIPYKYVIYNGESFEYEFIYKHQQKKGEYVNRCLFIKSSLLGSGDWHQYYDIVYMKPHGRLQKVMNHITDGPRKDLVKGKQIAAALMLDSTFSILQTWDTINLNSFFTQFEQFCFVLQQPMIYEGQAQLWTDLQYREKEVKRYLWQHLKKHVVPLPDGKSTDFLPVDCPVRSKLKTGLIVLFVVEKIELLLEGSLDWLCHLLTSDASSPDEFHRDLSHILGIPQSWRLYLVNLCQRCMDTRTYTWLGALPVLHCCMELAPRHKDAWRQPEDTWAALEGLSFSPFREQMLDTSSLLQFMREKQHLLSIDEPLFRSWFSLLPLSHLVMYMENFIEHLGRFPAHILDCLSGIYYRLPGLEQVLNTQDVQDVQNVQNILEMLLRLLDTYRDKIPEEALSPSYLTVCLKLHEAICSSTKLLKFYELPALSAEIVCRMIRLLSLVDSAGQRDETGNNSVQTVFQGTLAATKRWLREVFTKNMLTSSGASFTYVKEIEVWRRLVEIQFPAEHGWKESLLGDMEWRLTKEEPLSQITAYCNSCWDTKGLEDSVAKTFEKCIIEAVSSACQSQTSILQGFSYSDLRKFGIVLSAVITKSWPRTADNFDDILKHLLTLADVKHVFRLCGTDEKILANVTEDAKRLIAVADSVLTKVVGDLLSGTILVGQLELIIKHKNQFLDIWQLREKSLSPQDEQCAVEEALDWRREELLLLKKEKRCVDSLLKMCGNVKHLIQVDFGVLAVRHSQDLSSKRLNDTVTVRLSTSSNSQRATHYHLSSQVQEMAGKIDLLRDSHIFQLFWREAAEPLSEPKEDQEAAELLSEPEEESERHILELEEVYDYLYQPSYRKFIKLHQDLKSGEVTLAEIDVIFKDFVNKYTDLDSELKIMCTVDHQDQRDWIKDRVEQIKEYHHLHQAVHAAKVILQVKESLGLNGDFSVLNTLLNFTDNFDDFRRETLDQINQELIQAKKLLQDISEARCKGLQALSLRKEFICWVREALGGINELKVFVDLASISAGENDIDVDRVACFHDAVQGYASLLFKLDPSVDFSAFMKHLKKLWKALDKDQYLPRKLCDSARNLEWLKTVNESHGSVERSSLTLATAINQRGIYVIQAPKGGQKISPDTVLHLILPESPGSHEESREYSLEEVKELLNKLMLMSGKKDRNNTEVERFSEVFCSVQRLSQAFIDLHSAGNMLFRTWIAMAYCSPKQGVSLQMDFGLDLVTELKEGGDVTELLAALCRQMEHFLDSWKRFVTQKRMEHFYLNFYTAEQLVYLSTELRKQPPSDAALTMLSFIKSNCTLRDVLRASVGCGSEAARYRMRRVMEELPLMLLSEFSLVDKLRIIMEQSMRCLPAFLPDCLDLETLGHCLAHLAGMGGSPVERCLPRGLQVGQPNLVVCGHSEVLPAALAVYMQTPSQPLPTYDEVLLCTPATTFEEVALLLRRCLTLGSLGHKVYSLLFADQLSYEVARQAEELFHNLCTQQHREDYQLVMVCDGDWEHCYLPSAFSQHKVFVTPQAPLEAIQAYLAGHYRVPKQTLSAAAVFNDRLCVGIVASERAGVGKSLYVKRLHDKMKMQLNVKNVPLKTIRLIDPQVDESRVLGALLPFLDAQYQKVPVLFHLDVTSSVQTGIWVFLFKLLILQYLMDINGKMWLRNPCHLYIVEILERRTSVPSRSSSALRTRVPQFSFLDIFPKVTCRPPKEVIDMELSALRSDTEPGMDLWEFCSETFQRPYQYLRRFNQNQDLDTFQYQEGSVEGTPEECLQHFLFHCGVINPSWSELRNFARFLNYQLRDCEASLFCNPSFIGDTLRGFKKFVVTFMIFMARDFATPSLHTSDQSPGKHMVTMDGVREEDLAPFSLRKRWESEPHPYVFFNDDHTTMTFIGFHLQPNINGSVDAISHLTGKVIKRDVMTRDLYQGLLLQRVPFNVDFDKLPRHKKLERLCLTLGIPQATDPDKTYELTTDNMLKILAIEMRFRCGIPVIIMGETGCGKTRLIKFLSDLRRGGTNADTIKLVKVHGGTTADMIYSRVREAENVAFANKDQHQLDTILFFDEANTTEAISCIKEVLCDHMVDGQPLAEDSGLHIIAACNPYRKHSEEMICRLESAGLGYRVSMEETADRLGSIPLRQLVYRVHALPPSLIPLVWDFGQLSDVAEKLYIQQIVQRLVESISLDENGTRVITEVLCASQGFMRKTEDECSFVSLRDVERCVKVFRWFHEHSAMLLAQLNAFLSKSSVSKNHTERDPVLWSLMLAIGVCYHASLEKKDSYRKAIARFFPKPYDDSRLLLDEITRAQDLFLDGVPLRKTIAKNLALKENVFMMVVCIELKIPLFLVGKPGSSKSLAKTIVADAMQGPAAYSDLFRSLKQVHLVSFQCSPHSTPQGIISTFRQCARFQQGKDLQQYVSVVVLDEVGLAEDSPKMPLKTLHPLLEDGCIEDDPAPHKKVGFVGISNWALDPAKMNRGIFVSRGSPNETELIESAKGICSSDILVQDRVQGYFASFAKAYETVCKRQDKEFFGLRDYYSLIKMVFAAAKASNRKPSPQDIAQAVLRNFSGKDDIQALDIFLANLPEAKCSEEVSPMQLIKQNIFGPSQKVPGGEQEDAESRYLLVLTKNYVALQILQQTFFEGDQQPEIIFGSGFPKDQEYTQLCRNINRVKICMETGKMVLLLNLQNLYESLYDALNQYYVHLGGQKYVDLGLGTHRVKCRVHPNFRLIVIEEKDVVYKHFPIPLINRLEKHYLDINTVLEKWQKSIVEELCAWVEKFINVKAHHFQKRHKYSPSDVFIGYHSDACASVVLQVIERQGPRALTEELHQKVSEEAKSILLNCATPDAVVRLSAYSLGGFAAEWLSQEYFHRQRHNSFADFLQAHLHTADLERHAIFTEITTFSRLLTSHDCEILESEVTGRAPKPTLLWLQQFDTEYSFLKEVRNCLTNTAKCKILIFQTDFEDGIRSAQLIASAKYSVINEINKIRENEDRIFVYFITKLSRVGRGTAYVGFHGGLWQSVHIDDLRRSTLMVSDVTRLQHVTISQLFAPGDLPELGLEHRAEDGHEEAMETEASTSGEVAEVAEEAMETESSEKVGKETSELGGSDVSILDTTRLLRSCVQSAVGMLRDQNESCTRNMRRVVLLLGLLNEDDACHASFLRVSKMRLSVFLKKQEESQFHPLEWLAREACNQDALQEAGTFRHTLWKRVQGAVTPLLASMISFIDRDGNLELLTRPDTPPWARDLWMFIFSDTMLLNIPLVMNNERHKGEMAYIVVQNHMNLSENASNNVPFSWKIKDYLEELWVQAQYITDAEGLPKKFVDIFQQTPLGRFLAQLHGEPQQELLQCYLKDFILLTMRVSTEEELKFLQMALWSCTRKLKAASEAPEEEVSLPWVHLAYQRFRSRLQNFSRILTIYPQVLHSLMEARWNHELAGCEMTLDAFAAMACTEMLTRNTLKPSPQAWLQLVKNLSMPLELICSDEHMQGSGSLAQAVIREVRAQWSRIFSTALFVEHVLLGTESRVPELQGLVTEHVFLLDKCLRENSDVKTHGPFEAVMRTLCECKETASKTLSRFGIQPCSICLGDAKDPVCLPCDHVHCLRCLRAWFASEQMICPYCLTALPDEFSPAVSQAHREAIEKHARFRQMCNSFFVDLVSTICFKDNAPPEKEVIESLLSLLFVQKGRLRDAAQRHCEHTKSLSPFNDVVDKTPVIRSVILKLLLKYSFHDVKDYIQEYLTLLKKKAFITEDKTELYMLFINCLEDSILEKTSAYSRNDELNHLEEEGRFLKAYSPASRGREPANEASVEYLQEVARIRLCLDRAADFLSEPEGGPEMAKEKQCYLQQVKQFCIRVENDWHRVYLVRKLSSQRGMEFVQGLSKPGRPHQWVFPKDVVKQQGLRQDHPGQMDRYLVYGDEYKALRDAVAKAVLECKPLGIKTALKACKTPQSQQSAYFLLTLFREVAILYRSHNASLHPTPEQCEAVSKFIGECKILSPPDISRFATSLVDNSVPLLRAGPSDSNLDGTVTEMAIHAAAVLLCGQNELLEPLKNLAFSPATMAHAFLPTMPEDLLAQARRWKGLERVHWYTCPNGHPCSVGECGRPMEQSICIDCHAPIGGIDHKPRDGFHLVKDKADRTQTGHVLGNPQRRDVVTCDRGLPPVVFLLIRLLTHLALLLGASQSSQALINIIKPPVRDPKGFLQQHILKDLEQLAKMLGHSADETIGVVHLVLRRLLQEQHQLSSRRLLNFDTELSTKEMRNNWEKEIAAVISPELEHLDKTLPTMNNLISQDKRISSNPVAKIIYGDPVTFLPHLPRKSVVHCSKIWSCRKRITVEYLQHIVEQKNGKERVPILWHFLQKEAELRLVKFLPEILALQRDLVKQFQNVQQVEYSSIRGFLSKHSSDGLRQLLHNRITVFLSTWNKLRRSLETNGEINLPKDYCSTDLDLDTEFEILLPRRRGLGLCATALVSYLIRLHNEIVYAVEKLSKENNSYSVDAAEVTELHVISYEVERDLTPLILSNCQYQVEEGRETVQEFDLEKIQRQIVSRFLQGKPRLSLKGIPTLVYRHDWNYEHLFMDIKNKMAQDSLPSSVISAISGQLQSYSDACEVLSVVEVTLGFLSTAGGDPNMQLNVYTQDILQMGDQTIHVLKALNRCQLKHTIALWQFLSAHKSEQLLRLHKEPFGEISSRYKADLSPENAKLLSTFLNQTGLDAFLLELHEMIILKLKNPQTQTEERFRPQWSLRDTLVSYMQTKESEILPEMASQFPEEILLASCVSVWKTAAVLKWNREMR,mutated_sequence,1.0,5207.0,NP_001243000.2.a2m,NP_001243000.2.npy,ClinVar +NP_001243718.1,NP_001243718.1.csv,MSESEGGKDTTPEPSPANGAGPGPEWGLCPGPPAVEGESSGASGLGTPKRRNQHSKHKTVAVASAQRSPRALFCLTLANPLRRSCISIVEWKPFDILILLTIFANCVALGVYIPFPEDDSNTANHNLEQVEYVFLVIFTVETVLKIVAYGLVLHPSAYIRNGWNLLDFIIVVVGLFSVLLEQGPGRPGDAPHTGGKPGGFDVKALRAFRVLRPLRLVSGVPSLHIVLNSIMKALVPLLHIALLVLFVIIIYAIIGLELFLGRMHKTCYFLGSDMEAEEDPSPCASSGSGRACTLNQTECRGRWPGPNGGITNFDNFFFAMLTVFQCVTMEGWTDVLYWMQDAMGYELPWVYFVSLVIFGSFFVLNLVLGVLSGEFSKEREKAKARGDFQKQREKQQMEEDLRGYLDWITQAEELDMEDPSADDNLGPQLAELTNRRRGRLRWFSHSTRSTHSTSSHASLPASDTGSMTETQGDEDEEEGALASCTRCLNKIMKTRVCRRLRRANRVLRARCRRAVKSNACYWAVLLLVFLNTLTIASEHHGQPVWLTQIQEYANKVLLCLFTVEMLLKLYGLGPSAYVSSFFNRFDCFVVCGGILETTLVEVGAMQPLGISVLRCVRLLRIFKVTRHWASLSNLVASLLNSMKSIASLLLLLFLFIIIFSLLGMQLFGGKFNFDQTHTKRSTFDTFPQALLTVFQILTGEDWNVVMYDGIMAYGGPFFPGMLVCIYFIILFICGNYILLNVFLAIAVDNLASGDAGTAKDKGGEKSNEKDLPQENEGLVPGVEKEEEEGARREGADMEEEEEEEEEEEEEEEEEGAGGVELLQEVVPKEKVVPIPEGSAFFCLSQTNPLRKGCHTLIHHHVFTNLILVFIILSSVSLAAEDPIRAHSFRNHILGYFDYAFTSIFTVEILLKMTVFGAFLHRGSFCRSWFNMLDLLVVSVSLISFGIHSSAISVVKILRVLRVLRPLRAINRAKGLKHVVQCVFVAIRTIGNIMIVTTLLQFMFACIGVQLFKGKFYTCTDEAKHTPQECKGSFLVYPDGDVSRPLVRERLWVNSDFNFDNVLSAMMALFTVSTFEGWPALLYKAIDAYAEDHGPIYNYRVEISVFFIVYIIIIAFFMMNIFVGFVIITFRAQGEQEYQNCELDKNQRQCVEYALKAQPLRRYIPKNPHQYRVWATVNSAAFEYLMFLLILLNTVALAMQHYEQTAPFNYAMDILNMVFTGLFTIEMVLKIIAFKPKHYFTDAWNTFDALIVVGSIVDIAVTEVNNGGHLGESSEDSSRISITFFRLFRVMRLVKLLSKGEGIRTLLWTFIKSFQALPYVALLIAMIFFIYAVIGMQMFGKVALQDGTQINRNNNFQTFPQAVLLLFRCATGEAWQEIMLASLPGNRCDPESDFGPGEEFTCGSNFAIAYFISFFMLCAFLIINLFVAVIMDNFDYLTRDWSILGPHHLDEFKRIWSEYDPGAKGRIKHLDVVALLRRIQPPLGFGKLCPHRVACKRLVAMNMPLNSDGTVTFNATLFALVRTSLKIKTEGNLEQANQELRIVIKKIWKRMKQKLLDEVIPPPDEEEVTVGKFYATFLIQDYFRKFRRRKEKGLLGNDAAPSTSSALQAGLRSLQDLGPEMRQALTCDTEEEEEEGQEGVEEEDEKDLETNKATMVSQPSARRGSGISVSLPVGDRLPDSLSFGPSDDDRGTPTSSQPSVPQAGSNTHRRGSGALIFTIPEEGNSQPKGTKGQNKQDEDEEVPDRLSYLDEQAGTPPCSVLLPPHRAQRYMDGHLVPRRRLLPPTPAGRKPSFTIQCLQRQGSCEDLPIPGTYHRGRNSGPNRAQGSWATPPQRGRLLYAPLLLVEEGAAGEGYLGRSSGPLRTFTCLHVPGTHSDPSHGKRGSADSLVEAVLISEGLGLFARDPRFVALAKQEIADACRLTLDEMDNAASDLLAQGTSSLYSDEESILSRFDEEDLGDEMACVHAL,mutated_sequence,1.0,1966.0,NP_001243718.1.a2m,NP_001243718.1.npy,ClinVar +NP_001244025.1,NP_001244025.1.csv,MKFTLGLGSRAWRVSWEGAAAAAAGPGAGGSALRCRAQRVSSPRLGRRGSRLSGALPLCLSRGGGGAQALPDCAGPSPGHPGHPGARQLAGPLAMEQTYGEVNQLGGVFVNGRPLPNAIRLRIVELAQLGIRPCDISRQLRVSHGCVSKILARYNETGSILPGAIGGSKPRVTTPNVVKHIRDYKQGDPGIFAWEIRDRLLADGVCDKYNVPSVSSISRILRNKIGSLAQPGPYEASKQPPSQPTLPYNHIYQYPYPSPVSPTGAKMGSHPGVPGTAGHVSIPRSWPSAHSVSNILGIRTFMEQTGALAGSEGTAYSPKMEDWAGVNRTAFPATPAVNGLEKPALEADIKYTQSASTLSAVGGFLPACAYPASNQHGVYSAPGGGYLAPGPPWPPAQGPPLAPPGAGVAVHGGELAAAMTFKHPSREVADRKPPSSGSKAPDALSSLHGLPIPASTS,mutated_sequence,1.0,457.0,NP_001244025.1.a2m,NP_001244025.1.npy,ClinVar +NP_001245321.1,NP_001245321.1.csv,MLGSLVLRRKALAPRLLLRLLRSPTLRGHGGASGRNVTTGSLGEPQWLRVATGGRPGTSPALFSGRGAATGGRQGGRFDTKCLAAATWGRLPGPEETLPGQDSWNGVPSRAGLGMCALAAALVVHCYSKSPSNKDAALLEAARANNMQEVSRLLSEGADVNAKHRLGWTALMVAAINRNNSVVQVLLAAGADPNLGDDFSSVYKTAKEQGIHSLEVLITREDDFNNRLNNRASFKGCTALHYAVLADDYRTVKELLDGGANPLQRNEMGHTPLDYAREGEVMKLLRTSEAKYQEKQRKREAEERRRFPLEQRLKEHIIGQESAIATVGAAIRRKENGWYDEEHPLVFLFLGSSGIGKTELAKQTAKYMHKDAKKGFIRLDMSEFQERHEVAKFIGSPPGYVGHEEGGQLTKKLKQCPNAVVLFDEVDKAHPDVLTIMLQLFDEGRLTDGKGKTIDCKDAIFIMTSNVASDEIAQHALQLRQEALEMSRNRIAENLGDVQISDKITISKNFKENVIRPILKAHFRRDEFLGRINEIVYFLPFCHSELIQLVNKELNFWAKRAKQRHNITLLWDREVADVLVDGYNVHYGARSIKHEVERRVVNQLAAAYEQDLLPGGCTLRITVEDSDKQLLKSPELPSPQAEKRLPKLRLEIIDKDSKTRRLDIRAPLHPEKVCNTI,mutated_sequence,1.0,677.0,NP_001245321.1.a2m,NP_001245321.1.npy,ClinVar +NP_001258822.1,NP_001258822.1.csv,MEEGSSSPVSPVDSLGTSEEELERQPKRFGRKRRYSKKSSEDGSPTPGKRGKKGSPSAQSFEELQSQRILANVRERQRTQSLNEAFAALRKIIPTLPSDKLSKIQTLKLAARYIDFLYQVLQSDEMDNKMTSCSYVAHERLSYAFSVWRMEGAWSMSASH,mutated_sequence,1.0,160.0,NP_001258822.1.a2m,NP_001258822.1.npy,ClinVar +NP_001258867.1,NP_001258867.1.csv,MALGKVLAMALVLALAVLGSLSPGARAGDCKGQRQVLREAPGFVTDGAGNYSVNGNCEWLIEAPSPQHRILLDFLFLDTECTYDYLFVYDGDSPRGPLLASLSGSTRPPPIEASSGKMLLHLFSDANYNLLGFNASFRFSLCPGGCQSHGQCQPPGVCACEPGWGGPDCGLQECSAYCGSHGTCASPLGPCRCEPGFLGRACDLHLWENQGAGWWHNVSARDPAFSARIGAAGAFLSPPGLLAVFGGQDLNNALGDLVLYNFSANTWESWDLSPAPAARHSHVAVAWAGSLVLMGGELADGSLTNDVWAFSPLGRGHWELLAPPASSSSGPPGLAGHAAALVDDVWLYVSGGRTPHDLFSSGLFRFRLDSTSGGYWEQVIPAGGRPPAATGHSMVFHAPSRALLVHGGHRPSTARFSVRVNSTELFHVDRHVWTTLKGRDGLQGPRERAFHTASVLGNYMVVYGGNVHTHYQEEKCYEDGIFFYHLGCHQWVSGAELAPPGTPEGRAAPPSGRYSHVAAVLGGSVLLVAGGYSGRPRGDLMAYKVPPFVFQAPAPDYHLDYCSMYTDHSVCSRDPECSWCQGACQAAPPPGTPLGACPAASCLGLGRLLGDCQACLAFSSPTAPPRGPGTLGWCVHNESCLPRPEQARCRGEQISGTVGWWGPAPVFVTSLEACVTQSFLPGLHLLTFQQPPNTSQPDKVSIVRSTTITLTPSAETDVSLVYRGFIYPMLPGGPGGPGAEDVAVWTRAQRLHVLARMARGPDTENMEEVGRWVAHQEKETRRLQRPGSARLFPLPGRDHKYAVEIQGQLNGSAGPGHSELTLLWDRTGVPGGSEISFFFLEPYRSSSCTSYSSCLGCLADQGCGWCLTSATCHLRQGGAHCGDDGAGGSLLVLVPTLCPLCEEHRDCHACTQDPFCEWHQSTSRKGDAACSRRGRGRGALKSPEECPPLCSQRLTCEDCLANSSQCAWCQSTHTCFLFAAYLARYPHGGCRGWDDSVHSEPRCRSCDGFLTCHECLQSHECGWCGNEDNPTLGRCLQGDFSGPLGGGNCSLWVGEGLGLPVALPARWAYARCPDVDECRLGLARCHPRATCLNTPLSYECHCQRGYQGDGISHCNRTCLEDCGHGVCSGPPDFTCVCDLGWTSDLPPPTPAPGPPAPRCSRDCGCSFHSHCRKRGPGFCDECQDWTWGEHCERCRPGSFGNATGSRGCRPCQCNGHGDPRRGHCDNLSGLCFCQDHTEGAHCQLCSPGYYGDPRAGGSCFRECGGRALLTNVSSVALGSRRVGGLLPPGGGAARAGPGLSYCVWVVSATEELQPCAPGTLCPPLTLTFSPDSSTPCTLSYVLAFDGFPRFLDTGVVQSDRSLIAAFCGQRRDRPLTVQALSGLLVLHWEANGSSSWGFNASVGSARCGSGGPGSCPVPQECVPQDGAAGAGLCRCPQGWAGPHCRMALCPENCNAHTGAGTCNQSLGVCICAEGFGGPDCATKLDGGQLVWETLMDSRLSADTASRFLHRLGHTMVDGPDATLWMFGGLGLPQGLLGNLYRYSVSERRWTQMLAGAEDGGPGPSPRSFHAAAYVPAGRGAMYLLGGLTAGGVTRDFWVLNLTTLQWRQEKAPQTVELPAVAGHTLTARRGLSLLLVGGYSPENGFNQQLLEYQLATGTWVSGAQSGTPPTGLYGHSAVYHEATDSLYVFGGFRFHVELAAPSPELYSLHCPDRTWSLLAPSQGAKRDRMRNVRGSSRGLGQVPGEQPGSWGFREVRKKMALWAALAGTGGFLEEISPHLKEPRPRLFHASALLGDTMVVLGGRSDPDEFSSDVLLYQVNCNAWLLPDLTRSASVGPPMEESVAHAVAAVGSRLYISGGFGGVALGRLLALTLPPDPCRLLSSPEACNQSGACTWCHGACLSGDQAHRLGCGGSPCSPMPRSPEECRRLRTCSECLARHPRTLQPGDGEASTPRCKWCTNCPEGACIGRNGSCTSENDCRINQREVFWAGNCSEAACGAADCEQCTREGKCMWTRQFKRTGETRRILSVQPTYDWTCFSHSLLNVSPMPVESSPPLPCPTPCHLLPNCTSCLDSKGADGGWQHCVWSSSLQQCLSPSYLPLRCMAGGCGRLLRGPESCSLGCAQATQCALCLRRPHCGWCAWGGQDGGGRCMEGGLSGPRDGLTCGRPGASWAFLSCPPEDECANGHHDCNETQNCHDQPHGYECSCKTGYTMDNMTGLCRPVCAQGCVNGSCVEPDHCRCHFGFVGRNCSTECRCNRHSECAGVGARDHCLLCRNHTKGSHCEQCLPLFVGSAVGGGTCRPCHAFCRGNSHICISRKELQMSKGEPKKYSLDPEEIENWVTEGPSEDEAVCVNCQNNSYGEKCESCLQGYFLLDGKCTKCQCNGHADTCNEQDGTGCPCQNNTETGTCQGSSPSDRRDCYKYQCAKCRESFHGSPLGGQQCYRLISVEQECCLDPTSQTNCFHEPKRRALGPGRTVLFGVQPKFTNVDIRLTLDVTFGAVDLYVSTSYDTFVVRVAPDTGVHTVHIQPPPAPPPPPPPADGGPRGAGDPGGAGASSGPGAPAEPRVREVWPRGLITYVTVTEPSAVLVVRGVRDRLVITYPHEHHALKSSRFYLLLLGVGDPSGPGANGSADSQGLLFFRQDQAHIDLFVFFSVFFSCFFLFLSLCVLLWKAKQALDQRQEQRRHLQEMTKMASRPFAKVTVCFPPDPTAPASAWKPAGLPPPAFRRSEPFLAPLLLTGAGGPWGPMGGGCCPPAIPATTAGLRAGPITLEPTEDGMAGVATLLLQLPGGPHAPNGACLGSALVTLRHRLHEYCGGGGGAGGSGHGTGAGRKGLLSQDNLTSMSL,mutated_sequence,1.0,2845.0,NP_001258867.1.a2m,NP_001258867.1.npy,ClinVar +NP_001262.3,NP_001262.3.csv,MMRNKDKSQEEDSSLHSNASSHSASEEASGSDSGSQSESEQGSDPGSGHGSESNSSSESSESQSESESESAGSKSQPVLPEAKEKPASKKERIADVKKMWEEYPDVYGVRRSNRSRQEPSRFNIKEEASSGSESGSPKRRGQRQLKKQEKWKQEPSEDEQEQGTSAESEPEQKKVKARRPVPRRTVPKPRVKKQPKTQRGKRKKQDSSDEDDDDDEAPKRQTRRRAAKNVSYKEDDDFETDSDDLIEMTGEGVDEQQDNSETIEKVLDSRLGKKGATGASTTVYAIEANGDPSGDFDTEKDEGEIQYLIKWKGWSYIHSTWESEESLQQQKVKGLKKLENFKKKEDEIKQWLGKVSPEDVEYFNCQQELASELNKQYQIVERVIAVKTSKSTLGQTDFPAHSRKPAPSNEPEYLCKWMGLPYSECSWEDEALIGKKFQNCIDSFHSRNNSKTIPTRECKALKQRPRFVALKKQPAYLGGENLELRDYQLEGLNWLAHSWCKNNSVILADEMGLGKTIQTISFLSYLFHQHQLYGPFLIVVPLSTLTSWQREFEIWAPEINVVVYIGDLMSRNTIREYEWIHSQTKRLKFNALITTYEILLKDKTVLGSINWAFLGVDEAHRLKNDDSLLYKTLIDFKSNHRLLITGTPLQNSLKELWSLLHFIMPEKFEFWEDFEEDHGKGRENGYQSLHKVLEPFLLRRVKKDVEKSLPAKVEQILRVEMSALQKQYYKWILTRNYKALAKGTRGSTSGFLNIVMELKKCCNHCYLIKPPEENERENGQEILLSLIRSSGKLILLDKLLTRLRERGNRVLIFSQMVRMLDILAEYLTIKHYPFQRLDGSIKGEIRKQALDHFNADGSEDFCFLLSTRAGGLGINLASADTVVIFDSDWNPQNDLQAQARAHRIGQKKQVNIYRLVTKGTVEEEIIERAKKKMVLDHLVIQRMDTTGRTILENNSGRSNSNPFNKEELTAILKFGAEDLFKELEGEESEPQEMDIDEILRLAETRENEVSTSATDELLSQFKVANFATMEDEEELEERPHKDWDEIIPEEQRKKVEEEERQKELEEIYMLPRIRSSTKKAQTNDSDSDTESKRQAQRSSASESETEDSDDDKKPKRRGRPRSVRKDLVEGFTDAEIRRFIKAYKKFGLPLERLECIARDAELVDKSVADLKRLGELIHNSCVSAMQEYEEQLKENASEGKGPGKRRGPTIKISGVQVNVKSIIQHEEEFEMLHKSIPVDPEEKKKYCLTCRVKAAHFDVEWGVEDDSRLLLGIYEHGYGNWELIKTDPELKLTDKILPVETDKKPQGKQLQTRADYLLKLLRKGLEKKGAVTGGEEAKLKKRKPRVKKENKVPRLKEEHGIELSSPRHSDNPSEEGEVKDDGLEKSPMKKKQKKKENKENKEKQMSSRKDKEGDKERKKSKDKKEKPKSGDAKSSSKSKRSQGPVHITAGSEPVPIGEDEDDDLDQETFSICKERMRPVKKALKQLDKPDKGLNVQEQLEHTRNCLLKIGDRIAECLKAYSDQEHIKLWRRNLWIFVSKFTEFDARKLHKLYKMAHKKRSQEEEEQKKKDDVTGGKKPFRPEASGSSRDSLISQSHTSHNLHPQKPHLPASHGPQMHGHPRDNYNHPNKRHFSNADRGDWQRERKFNYGGGNNNPPWGSDRHHQYEQHWYKDHHYGDRRHMDAHRSGSYRPNNMSRKRPYDQYSSDRDHRGHRDYYDRHHHDSKRRRSDEFRPQNYHQQDFRRMSDHRPAMGYHGQGPSDHYRSFHTDKLGEYKQPLPPLHPAVSDPRSPPSQKSPHDSKSPLDHRSPLERSLEQKNNPDYNWNVRKT,mutated_sequence,1.0,1828.0,NP_001262.3.a2m,NP_001262.3.npy,ClinVar +NP_001263274.1,NP_001263274.1.csv,MSDIEEVVEEYEEEEQEEAAVEEEEDWREDEDEQEEAAEEDAEAEAETEETRAEEDEEEEEAKEAEDGPMEESKPKPRSFMPNLVPPKIPDGERVDFDDIHRKRMEKDLNELQALIEAHFENRKKEEEELVSLKDRIERRRAERAEQQRIRNEREKERQNRLAEERARREEEENRRKAEDEARKKKALSNMMHFGGYIQKQAQTERKSGKRQTEREKKKKILAERRKVLAIDHLNEDQLREKAKELWQSIYNLEAEKFDLQEKFKQQKYEINVLRNRINDNQKVSKTRGKAKVTGRWK,mutated_sequence,1.0,298.0,NP_001263274.1.a2m,NP_001263274.1.npy,ClinVar +NP_001264044.1,NP_001264044.1.csv,MAAQVAAREARDFREAPTLRLTSGAGLEAVGAVELEEEEENEEEAAARRARSFAQDARVRFLGGRLAMMLGFTEEKWSQYLESEDNRQVLGEFLESTSPACLVFSFAASGRLAASQEIPRDANHKLVFISKKITESIGVNDFSQVVLFGELPALSLGHVSAFLDEILVPVLSNKNNHKSWSCFTSQDMEYHIEVMKKKMYIFRGKMSRRTLLPIPTVAGKMDLDQNCSENKPPSNERIILHAIESVVIEWSHQIQEIIERDSVQRLLNGLHLSPQAELDFWMMRRENLSCIYDQLQAPVVLKMVKILTTKQSSYFPTLKDIFLAVENALLEAQDVELYLRPLRRHIQCLQETEFPQTRILIAPLFHTICLIWSHSKFYNTPARVIVLLQEFCNLFINQATAYLSPEDLLRGEIEESLEKVQVAVNILKTFKNSFFNYRKKLASYFMGRKLRPWDFQSHLVFCRFDKFLDRLIKIEDIFATTLEFEKLERLEFGGTKGAILNGQVHEMSEELMELCKLFKQSTYDPSDCTNMEFESDYVAFKSKTLEFDRRLGTIICEAFFNCNGLEAAFKLLTIFGNFLEKPVVMEIFSLHYSTLVHMFNTELDVCKQLYNEHMKQIECGHVVLNKNMPFTSGNMKWAQQVLQRLQMFWSNFASLRYLFLGNPDHALVYQKYVEMTTLLDQFESRIYNEWKSNVDEICEFNLNQPLVKFSAINGLLCVNFDPKLVAVLREVKYLLMLKKQDIPDSALAIFKKRNTILKYIGNLDLLVQGYNKLKQTLLEVEYPLIEDELRAIDEQLTAATTWLTWQDDCWGYIERVRAATSELEHRVERTQKNVKVIQQTMRGWARCVLPPRREHRREAAFTLEDKGDLFTKKYKLIQGDGCKIHNLVEENRKLFKANPSLDTWKIYVEFIDDIVVEGFFQAIMHDLDFFLKNTEKQLKPAPFFQAQMILLPPEIVFKPSLDREAGDGFYDLVEEMLCNSFRMSAQMNRIATHLEIKNYQNDMDNMLGLAEVRQEIMNRVVNVINKVLDFRNTLETHTYLWVDDRAEFMKHFLLYGHAVSSDEMDAHANEEIPEQPPTLEQFKEQIDIYEALYVQMSKFEDFRVFDSWFKVDMKPFKVSLLTIIKKWSWMFQEHLLRFVIDSLNELQEFIKETDSGLQRELNEGDHDGLVDIMVHLLAVRSRQRATDELFEPLKETITLLESYGQKMPEQVYIQLEELPERWETTKKIAATVRHEVSPLHNAEVTLIRKKCILFDAKQAEFRERFRHYAPLGFNAENPYTALDKANEELEALEEEMLQMQESTRLFEVALPEYKQMKQCRKEIKLLKGLWDVIIYVRRSIDNWTKTQWRQIHVEQMDVELRRFAKEIWSLNKEVRVWDAYTGLEGTVKDMTASLRAITELQSPALRDRHWHQLMKAIGVKFLINEATTLADLLALRLHRVEDDVRRIVDKAVKELGTEKVITEISQTWATMKFSYEVHYRTGIPLLKSDEQLFETLEHNQVQLQTLLQSKYVEYFIEQVLSWQNKLNIADLVIFTWMEVQRTWSHLESIFVCSEDIRIQLVKDARRFDGVDAEFKELMFKTAKVENVLEATCRPNLYEKLKDLQSRLSLCEKALAEYLETKRIAFPRFYFVSSADLLDILSKGAQPKQVTCHLAKLFDSIADLQFEDNQDVSAHRAVGMYSKEKEYVPFQAECECVGHVETWLLQLEQTMQETVRHSITEAIVAYEEKPRELWIFDFPAQVALTSSQIWWTTDVGIAFSRLEEGYETALKDFHKKQISQLNTLITLLLGELPPGDRQKIMTICTIDVHARDVVAKLISQKVVSPQAFTWLSQLRHRWEDTQKHCFVNICDAQFQYFYEYLGNSPRLVITPLTDRCYITLTQSLHLTMSGAPAGPAGTGKTETTKDLGRALGMMVYVFNCSEQMDYKSIGNIYKGLVQTGAWGCFDEFNRISVEVLSVVAVQVKMIHDAIRNRKKRFVFLGEAITLKPSVGIFITMNPGYAGRTELPENLKALFRPCAMVAPDIELICEILLVAEGFVDARALARKFITLYTLCKELLSKQDHYDWGLRAIKSVLVVAGSLKRGDKNRPEDQVLMRALRDFNMPKIVTDDIPVFLGLVGDLFPALDVPRRRKLHFEQMVRQSTLELRLQPEESFILKVVQLEELLAVRHSVFVVGNAGTGKSKILRTLNRTYVNMKQKPVWNDLNPKAVTTDELFGFIHHATREWKDGKIVYSYFIGLFSSILREQANLKHDGPKWIVLDGDIDPMWIESLNTVMDDNKVLTLASNERIALTPFMRLLFEIHHLRSATPATVSRAGILYVNPQDLGWNPYVASWIDRRRHQSEKANLTILFDKYVPACLDKLRTSFKTITSIPESSLVQTLCVLLECLLTPENVPSDSPKEVYEVYFVFACIWAFGGTLLQDQISDYQADFSRWWQKEMKAVKFPSQGTIFDYYVDHKTKKLLPWADKIAQFTMDPDVPLQTVLVHTTETARLRYFMELLLEKGKPLMLVGNAGVGKTVFVGDTLASLSEDYIVSRVPFNYYTTSTALQKILEKPLEKKAGHNYGPGGNKKLIYFIDDMNMPEVDLYGTVQPHTLIRQHIDYGHWYDRQKVMLKEIHNCQYVACMNPMVGSFTINPRLQRHFTVFAFNFPSLDALNTIYGQIFSFHFQQQAFAPSILRSGPTLIQATIAFHQTMMCNFLPTAIKFHYIFNLRDLSNVFQGILFASPECLKGPLDLIHLWLHESARVYGDKLIDKKDCDLFQRRMLETAYKYFEGIDSHMLLQQPLIYCHFADRGKDPHYMPVKDWEVLKTILTETLDNYNELNAAMHLVLFEDAMQHVCRISRILRTPQGCALLVGVGGSGKQSLSRLAAYLRGLEVFQITLTEGYGIQELRVDLANLYIRTGAKNMPTVFLLTDAQVLDESFLVLINDLLASGEIPDLFSDEDVDKIISGIHNEVHALGMVDSRENCWKFFMARVRLQLKIILCFSPVGRTLRVRARKFPAIVNCTAIDWFHAWPQEALVSVSRRFIEETKGIEPVHKDSISLFMAHVHTTVNEMSTRYYQNERRHNYTTPKSFLEQISLFKNLLKKKQNEVSEKKERLVNGIQKLKTTASQVGDLKARLASQEAELQLRNHDAEALITKIGLQTEKVSREKTIADAEERKVTAIQTEVFQKQRECEADLLKAEPALVAATAALNTLNRVNLSELKAFPNPPIAVTNVTAAVMVLLAPRGRVPKDRSWKAAKVFMGKVDDFLQALINYDKEHIPENCLKVVNEHYLKDPEFNPNLIRTKSFAAAGLCAWVINIIKFYEVYCDVEPKRQALAQANLELAAATEKLEAIRKKLVDLDRNLSRLTASFEKATAEKVRCQEEVNQTNKTIKLANRLVKELEAKKIRWGQSIKSFEAQEKTLCGDVLLTAAFVSYVGPFTRQYRQELVHCKWVPFLQQKVSIPLTEGLDLISMLTDDATIAAWNNEGLPSDRMSTENAAILTHCERWPLVIDPQQQGIKWIKNKYGMDLKVTHLGQKGFLNAIETALAFGDVILIENLEETIDPVLDPLLGRNTIKKGKYIRIGDKECEFNKNFRLILHTKLANPHYKPELQAQTTLLNFTVTEDGLEAQLLAEVVSIERPDLEKLKLVLTKHQNDFKIELKYLEDDLLLRLSAAEGSFLDDTKLVERLEATKTTVAEIEHKVIEAKENERKINEARECYRPVAARASLLYFVINDLQKINPLYQFSLKAFNVLFHRAIEQADKVEDMQGRISILMESITHAVFLYTSQALFEKDKLTFLSQMAFQILLRKKEIDPLELDFLLRFTVEHTHLSPVDFLTSQSWSAIKAIAVMEEFRGIDRDVEGSAKQWRKWVESECPEKEKLPQEWKKKSLIQKLILLRAMRPDRMTYALRNFVEEKLGAKYVERTRLDLVKAFEESSPATPIFFILSPGVDALKDLEILGKRLGFTIDSGKFHNVSLGQGQETVAEVALEKASKGGHWVILQNVHLVAKWLGTLEKLLERFSQGSHRDYRVFMSAESAPTPDEHIIPQGLLENSIKITNEPPTGMLANLHAALYNFDQDTLEICSKEQEFKSILFSLCYFHACVAGRLRFGPQGWSRSYPFNPGDLTICASVLYNYLEANSKVPWEDLRYLFGEIMYGGHITDDWDRKLCRVYLEEFMNPSLTEDELMLAPGFAAPPYLDYAGYHQYIEEMLPPESPALYGLHPNAEIEFLTVTSNTLFRTLLEMQPRNALSGDELGQSTEEKVKNVLDDILEKLPEEFNMAEIMQKNSNRSPYVLVCFQECERMNILIREIRISLEQLDLSLKGELALSPAVEAQQFALSYDTVPDTWSKLAYPSTYGLAQWFNDLLLRCRELDTWTQDLTLPAVVWLSGFFNPQSFLTAIMQTMARKNEWPLDKTRLTADVTKKTKEDYGHPPREGAYLHGLFMEGARWDTQAGTIVEARLKELACPMPVIFAKATPVDRQETKQTYECPVYRTKLRGPSYIWTFRLKSEEKTAKWVLAGVALLLEA,mutated_sequence,1.0,4516.0,NP_001264044.1.a2m,NP_001264044.1.npy,ClinVar +NP_001265441.1,NP_001265441.1.csv,MSAAPAYSEDKGGSAGPGEPEYGHDPASGGIFSSDYKRHDDLKEMLDTNKDSLKLEAMKRIVAMIARGKNASDLFPAVVKNVACKNIEVKKLVYVYLVRYAEEQQDLALLSISTFQRGLKDPNQLIRASALRVLSSIRVPIIVPIMMLAIKEAASDMSPYVRKTAAHAIPKLYSLDSDQKDQLIEVIEKLLADKTTLVAGSVVMAFEEVCPERIDLIHKNYRKLCNLLIDVEEWGQVVIISMLTRYARTQFLSPTQNESLLEENAEKAFYGSEEDEAKGAGSEETAAAAAPSRKPYVMDPDHRLLLRNTKPLLQSRSAAVVMAVAQLYFHLAPKAEVGVIAKALVRLLRSHSEVQYVVLQNVATMSIKRRGMFEPYLKSFYIRSTDPTQIKILKLEVLTNLANETNIPTVLREFQTYIRSMDKDFVAATIQAIGRCATNIGRVRDTCLNGLVQLLSNRDELVVAESVVVIKKLLQMQPAQHGEIIKHLAKLTDNIQVPMARASILWLIGEYCEHVPRIAPDVLRKMAKSFTAEEDIVKLQVINLAAKLYLTNSKQTKLLTQYVLSLAKYDQNYDIRDRARFTRQLIVPSEQGGALSRHAKKLFLAPKPAPVLESSFKDRDHFQLGSLSHLLNAKATGYQELPDWPEEAPDPSVRNVEEEDLSLIETHVGLLGEYTEVPEWTKCSNREKRKEKEKPFYSDSEGESGPTESADSDPESESESDSKSSSESGSGESSSESDNEDQDEDEEKGRGSESEQSEEDGKRKTKKKVPERKGEASSSDEGSDSSSSSSESEMTSESEEEQLEPASWSRKTPPSSKSAPATKEISLLDLEDFTPPSVQPVSPPAIVSTSLAADLEGLTLTDSTLVPSLLSPVSGVGRQELLHRVAGEGLAVDYTFSRQPFSGDPHMVSVHIHFSNSSDTPIKGLHVGTPKLPAGISIQEFPEIESLAPGESATAVMGINFCDSTQAANFQLCTQTRQFYVSIQPPVGELMAPVFMSENEFKKEQGKLMGMNEITEKLMLPDTCRSDHIVVQKVTATANLGRVPCGTSDEYRFAGRTLTGGSLVLLTLDARPAGAAQLTVNSEKMVIGTMLVKDVIQALTQ,mutated_sequence,1.0,1101.0,NP_001265441.1.a2m,NP_001265441.1.npy,ClinVar +NP_001265645.1,NP_001265645.1.csv,MSPVFPMLTVLTMFYYICLRRRARTATRGEMMNTHRAIESNSQTSPLNAEVVQYAKEVVDFSSHYGSENSMSYTMWNLAGVPNVFPSSGDFTQTAVFRTYGTWWDQCPSASLPFKRTPPNFQSQDYVELTFEQQVYPTAVHVLETYHPGAVIRILACSANPYSPNPPAEVRWEILWSERPTKVNASQARQFKPCIKQINFPTNLIRLEVNSSLLEYYTELDAVVLHGVKDKPVLSLKTSLIDMNDIEDDAYAEKDGCGMDSLNKKFSSAVLGEGPNNGYFDKLPYELIQLILNHLTLPDLCRLAQTCKLLSQHCCDPLQYIHLNLQPYWAKLDDTSLEFLQSRCTLVQWLNLSWTGNRGFISVAGFSRFLKVCGSELVRLELSCSHFLNETCLEVISEMCPNLQALNLSSCDKLPPQAFNHIAKLCSLKRLVLYRTKVEQTALLSILNFCSELQHLSLGSCVMIEDYDVIASMIGAKCKKLRTLDLWRCKNITENGIAELASGCPLLEELDLGWCPTLQSSTGCFTRLAHQLPNLQKLFLTANRSVCDTDIDELACNCTRLQQLDILGTRMVSPASLRKLLESCKDLSLLDVSFCSQIDNRAVLELNASFPKVFIKKSFTQ,mutated_sequence,1.0,621.0,NP_001265645.1.a2m,NP_001265645.1.npy,ClinVar +NP_001268669.1,NP_001268669.1.csv,MATLACRVQFLDDTDPFNSTNFPEPSRPPLFTFREDLALGTQLAGVHRLLQAPHKLDDCTLQLSHNGAYLDLEATLAEQRDELEGFQDDAGRGKKHSIILRTQLSVRVHACIEKLYNSSGRDLRRALFSLKQIFQDDKDLVHEFVVAEGLTCLIKVGAEADQNYQNYILRALGQIMLYVDGMNGVINRNETIQWLYTLIGSKFRLVVKTALKLLLVFVEYSESNAPLLIQAVTAVDTKRGVKPWSNIMEILEEKDGVDTELLVYAMTLVNKTLSGLPDQDTFYDVVDCLEELGIAAVSQRHLNKKGTDLDLVEQLNIYEVALRHEDGDETTEPPPSGCRDRRRASVCSSGGGEHRGLDRRRSRRHSVQSIKSTLSAPTSPCSQSAPSFKPNQVRDLREKEEEEEEEQPITEPSSEEEREDDASCQGKDSKVGAASGQSPTGRDAAPKSSALPAVSNASSQGKPLLVGTAGGTTWHSGSSGSEATPSALLSPPASAARPSSATPGSLKVSPTIDKLPYVPHSPFHLFSYDFEDSSLSTKEKEAESQKENSSSDSFSLSTYSASEPYHFRSFSSNRYSNFGNNSYHSSRPSSGSSVPTTPTSSVSPPQEARLERSSPSGLLTSSFRQHQESLAAERERRRQEREERLQRIEREERNKFSRDYLDKREEQRQAREERYKYLEQLAAEEHEKELRSRSVSRGRADLSLDLTSPAAPACLAPLSHSPSSSDSQEALTVSASSPGTPHHPQASAGDPEPESEAEPEAEAGAGQVADEAGQDIASAHEGAETEVEQALEQEPEERASLSEKERQNEGVNERDNCSASSVSSSSSTLEREEKEDKLSRDRTTGLWPAGVQDAGVNGQCGDILTNKRFMLDMLYAHNRKSPDDEEKGDGEAGRTQQEAEAVASLATRISTLQANSQTQDESVRRVDVGCLDNRGSVKAFAEKFNSGDLGRGSISPDAEPNDKVPETAPVQPKTESDYIWDQLMANPRELRIQDMDFTDLGEEDDIDVLDVDLGHREAPGPPPPPPPTFLGLPPPPPPPLLDSIPPPPVPGNLLVPPPPVFNAPQGLGWSQVPRGQPTFTKKKKTIRLFWNEVRPFDWPCKNNRRCREFLWSKLEPIKVDTSRLEHLFESKSKELSVSKKTAADGKRQEIIVLDSKRSNAINIGLTVLPPPRTIKIAILNFDEYALNKEGIEKILTMIPTDEEKQKIQEAQLANPEIPLGSAEQFLLTLSSISELSARLHLWAFKMDYETTEKEVAEPLLDLKEGIDQLENNKTLGFILSTLLAIGNFLNGTNAKAFELSYLEKVPEVKDTVHKQSLLHHVCTMVVENFPDSSDLYSEIGAITRSAKVDFDQLQDNLCQMERRCKASWDHLKAIAKHEMKPVLKQRMSEFLKDCAERIIILKIVHRRIINRFHSFLLFMGHPPYAIREVNINKFCRIISEFALEYRTTRERVLQQKQKRANHRERNKTRGKMITDTDEEEEVESGKFSGSSPAPPSQPQGLSYAEDAAEHENMKAVLKTSSPSVEDATPALGVRTRSRASRGSTSSWTMGTDDSPNVTDDAADEIMDRIVKSATQVPSQRVVPRERKRSRANRKSLRRTLKSGLTPEEARALGLVGTSELQL,mutated_sequence,1.0,1622.0,NP_001268669.1.a2m,NP_001268669.1.npy,ClinVar +NP_001269154.1,NP_001269154.1.csv,MLVDGPSERPALCFLLLAVAMSFFGSALSIDETRAHLLLKEKMMRLGGRLVLNTKEELANERLMTLKIAEMKEAMRTLIFPPSMHFFQAKHLIERSQVFNILRMMPKGAALHLHDIGIVTMDWLVRNVTYRPHCHICFTPRGIMQFRFAHPTPRPSEKCSKWILLEDYRKRVQNVTEFDDSLLRNFTLVTQHPEVIYTNQNVVWSKFETIFFTISGLIHYAPVFRDYVFRSMQEFYEDNVLYMEIRARLLPVYELSGEHHDEEWSVKTYQEVAQKFVETHPEFIGIKIIYSDHRSKDVAVIAESIRMAMGLRIKFPTVVAGFDLVGHEDTGHSLHDYKEALMIPAKDGVKLPYFFHAGETDWQGTSIDRNILDALMLNTTRIGHGFALSKHPAVRTYSWKKDIPIEVCPISNQVLKLVSDLRNHPVATLMATGHPMVISSDDPAMFGAKGLSYDFYEVFMGIGGMKADLRTLKQLAMNSIKYSTLLESEKNTFMEIWKKRWDKFIADVATK,mutated_sequence,1.0,511.0,NP_001269154.1.a2m,NP_001269154.1.npy,ClinVar +NP_001269646.1,NP_001269646.1.csv,MSSPLQRAVGDTKRALSASSSSSASLPFDDRDSNHTSEGNGDSLLADEDTDFEDSLNRNVKKRAAKRPPKTTPVAKHPKKGSRVVHRHSRKQSEPPANDLFNAVKAAKSDMQSLVDEWLDSYKQDQDAGFLELVNFFIQSCGCKGIVTPEMFKKMSNSEIIQHLTEQFNEDSGDYPLIAPGPSWKKFQGSFCEFVRTLVCQCQYSLLYDGFPMDDLISLLTGLSDSQVRAFRHTSTLAAMKLMTSLVKVALQLSVHQDNNQRQYEAERNKGPGQRAPERLESLLEKRKELQEHQEEIEGMMNALFRGVFVHRYRDVLPEIRAICIEEIGCWMQSYSTSFLTDSYLKYIGWTLHDKHREVRLKCVKALKGLYGNRDLTTRLELFTSRFKDRMVSMVMDREYDVAVEAVRLLILILKNMEGVLTDADCESVYPVVYASHRGLASAAGEFLYWKLFYPECEIRMMGGREQRQSPGAQRTFFQLLLSFFVESELHDHAAYLVDSLWDCAGARLKDWEGLTSLLLEKDQNLGDVQESTLIEILVSSARQASEGHPPVGRVTGRKGLTSKERKTQADDRVKLTEHLIPLLPQLLAKFSADAEKVTPLLQLLSCFDLHIYCTGRLEKHLELFLQQLQEVVVKHAEPAVLEAGAHALYLLCNPEFTFFSRADFARSQLVDLLTDRFQQELEELLQSSFLDEDEVYNLAATLKRLSAFYNTHDLTRWELYEPCCQLLQKAVDTGEVPHQVILPALTLVYFSILWTLTHISKSDASQKQLSSLRDRMVAFCELCQSCLSDVDTEIQEQAFVLLSDLLLIFSPQMIVGGRDFLRPLVFFPEATLQSELASFLMDHVFIQPGDLGSGDSQEDHLQIERLHQRRRLLAGFCKLLLYGVLEMDAASDVFKHYNKFYNDYGDIIKETLTRARQIDRSHCSRILLLSLKQLYTELLQEHGPQGLNELPAFIEMRDLARRFALSFGPQQLQNRDLVVMLHKEGIQFSLSELPPAGSSNQPPNLAFLELLSEFSPRLFHQDKQLLLSYLEKCLQHVSQAPGHPWGPVTTYCHSLSPVENTAETSPQVLPSSKRRRVEGPAKPNREDVSSSQEESLQLNSIPPTPTLTSTAVKSRQPLWGLKEMEEEDGSELDFAQGSQPVAGTERSRFLGPQYFQTPHNPSGPGLGNQLMRLSLMEEDEEEELEIQDESNEERQDTDMQASSYSSTSERGLDLLDSTELDIEDF,mutated_sequence,1.0,1226.0,NP_001269646.1.a2m,NP_001269646.1.npy,ClinVar +NP_001269938.1,NP_001269938.1.csv,MPKIVLNGVTVDFPFQPYKCQQEYMTKVLECLQQKVNGILESPTGTGKTLCLLCTTLAWREHLRDGISARKIAERAQGELFPDRALSSWGNAAAAAGDPIACYTDIPKIIYASRTHSQLTQVINELRNTSYRPKVCVLGSREQLCIHPEVKKQESNHLQIHLCRKKVASRSCHFYNNVEEKSLEQELASPILDIEDLVKSGSKHRVCPYYLSRNLKQQADIIFMPYNYLLDAKSRRAHNIDLKGTVVIFDEAHNVEKMCEESASFDLTPHDLASGLDVIDQVLEEQTKAAQQGEPHPEFSADSPSPGLNMELEDIAKLKMILLRLEGAIDAVELPGDDSGVTKPGSYIFELFAEAQITFQTKGCILDSLDQIIQHLAGRAGVFTNTAGLQKLADIIQIVFSVDPSEGSPGSPAGLGALQSYKVHIHPDAGHRRTAQRSDAWSTTAARKRGKVLSYWCFSPGHSMHELVRQGVRSLILTSGTLAPVSSFALEMQIPFPVCLENPHIIDKHQIWVGVVPRGPDGAQLSSAFDRRFSEECLSSLGKALGNIARVVPYGLLIFFPSYPVMEKSLEFWRARDLARKMEALKPLFVEPRSKGSFSETISAYYARVAAPGSTGATFLAVCRGKASEGLDFSDTNGRGVIVTGLPYPPRMDPRVVLKMQFLDEMKGQGGAGGQFLSGQEWYRQQASRAVNQAIGRVIRHRQDYGAVFLCDHRFAFADARAQLPSWVRPHVRVYDNFGHVIRDVAQFFRVAERTMPAPAPRATAPSVRGEDAVSEAKSPGPFFSTRKAKSLDLHVPSLKQRSSGSPAAGDPESSLCVEYEQEPVPARQRPRGLLAALEHSEQRAGSPGEEQAHSCSTLSLLSEKRPAEEPRGGRKKIRLVSHPEEPVAGAQTDRAKLFMVAVKQELSQANFATFTQALQDYKGSDDFAALAACLGPLFAEDPKKHNLLQGFYQFVRPHHKQQFEEVCIQLTGRGCGYRPEHSIPRRQRAQPVLDPTGRTAPDPKLTVSTAAAQQLDPQEHLNQGRPHLSPRPPPTGDPGSQPQWGSGVPRAGKQGQHAVSAYLADARRALGSAGCSQLLAALTAYKQDDDLDKVLAVLAALTTAKPEDFPLLHRFSMFVRPHHKQRFSQTCTDLTGRPYPGMEPPGPQEERLAVPPVLTHRAPQPGPSRSEKTGKTQSKISSFLRQRPAGTVGAGGEDAGPSQSSGPPHGPAASEWGEPHGRDIAGQQATGAPGGPLSAGCVCQGCGAEDVVPFQCPACDFQRCQACWQRHLQASRMCPACHTASRKQSVMQVFWPEPQ,mutated_sequence,1.0,1300.0,NP_001269938.1.a2m,NP_001269938.1.npy,ClinVar +NP_001278344.1,NP_001278344.1.csv,MKSCGVSLATAAAAAAAFGDEEKKMAAGKASGESEEASPSLTAEEREALGGLDSRLFGFVRFHEDGARTKALLGKAVRCYESLILKAEGKVESDFFCQLGHFNLLLEDYPKALSAYQRYYSLQSDYWKNAAFLYGLGLVYFHYNAFQWAIKAFQEVLYVDPSFCRAKEIHLRLGLMFKVNTDYESSLKHFQLALVDCNPCTLSNAEIQFHIAHLYETQRKYHSAKEAYEQLLQTENLSAQVKATVLQQLGWMHHTVDLLGDKATKESYAIQYLQKSLEADPNSGQSWYFLGRCYSSIGKVQDAFISYRQSIDKSEASADTWCSIGVLYQQQNQPMDALQAYICAVQLDHGHAAAWMDLGTLYESCNQPQDAIKCYLNATRSKSCSNTSALAARIKYLQAQLCNLPQGSLQNKTKLLPSIEEAWSLPIPAELTSRQGAMNTAQQACKPHHPNTEPVLGLSQTPISQQSLPLHMIPSSQVDDLSSPAKRKRTSSPTKNTSDNWSGGHAVSHPPVQQQAHSWCLTPQKLQHLEQLRANRNNLNPAQKLMLEQLESQFVLMQQHQMRPTGVAQVRSTGIPNGPTADSSLPTNSVSGQQPQLALTRVPSVSQPGVRPACPGQPLANGPFSAGHVPCSTSRTLGSTDTILIGNNHITGSGSNGNVPYLQRNALTLPHNRTNLTSSAEEPWKNQLSNSTQGLHKGQSSHSAGPNGERPLSSTGPSQHLQAAGSGIQNQNGHPTLPSNSVTQGAALNHLSSHTATSGGQQGITLTKESKPSGNILTVPETSRHTGETPNSTASVEGLPNHVHQMTADAVCSPSHGDSKSPGLLSSDNPQLSALLMGKANNNVGTGTCDKVNNIHPAVHTKTDNSVASSPSSAISTATPSPKSTEQTTTNSVTSLNSPHSGLHTINGEGMEESQSPMKTDLLLVNHKPSPQIIPSMSVSIYPSSAEVLKACRNLGKNGLSNSSILLDKCPPPRPPSSPYPPLPKDKLNPPTPSIYLENKRDAFFPPLHQFCTNPNNPVTVIRGLAGALKLDLGLFSTKTLVEANNEHMVEVRTQLLQPADENWDPTGTKKIWHCESNRSHTTIAKYAQYQASSFQESLREENEKRSHHKDHSDSESTSSDNSGRRRKGPFKTIKFGTNIDLSDDKKWKLQLHELTKLPAFVRVVSAGNLLSHVGHTILGMNTVQLYMKVPGSRTPGHQENNNFCSVNINIGPGDCEWFVVPEGYWGVLNDFCEKNNLNFLMGSWWPNLEDLYEANVPVYRFIQRPGDLVWINAGTVHWVQAIGWCNNIAWNVGPLTACQYKLAVERYEWNKLQSVKSIVPMVHLSWNMARNIKVSDPKLFEMIKYCLLRTLKQCQTLREALIAAGKEIIWHGRTKEEPAHYCSICEVEVFDLLFVTNESNSRKTYIVHCQDCARKTSGNLENFVVLEQYKMEDLMQVYDQFTLAPPLPSASS,mutated_sequence,1.0,1453.0,NP_001278344.1.a2m,NP_001278344.1.npy,ClinVar +NP_001289.1,NP_001289.1.csv,MAKINTQYSHPSRTHLKVKTSDRDLNRAENGLSRAHSSSEETSSVLQPGIAMETRGLADSGQGSFTGQGIARLSRLIFLLRRWAARHVHHQDQGPDSFPDRFRGAELKEVSSQESNAQANVGSQEPADRGRSAWPLAKCNTNTSNNTEEEKKTKKKDAIVVDPSSNLYYRWLTAIALPVFYNWYLLICRACFDELQSEYLMLWLVLDYSADVLYVLDVLVRARTGFLEQGLMVSDTNRLWQHYKTTTQFKLDVLSLVPTDLAYLKVGTNYPEVRFNRLLKFSRLFEFFDRTETRTNYPNMFRIGNLVLYILIIIHWNACIYFAISKFIGFGTDSWVYPNISIPEHGRLSRKYIYSLYWSTLTLTTIGETPPPVKDEEYLFVVVDFLVGVLIFATIVGNVGSMISNMNASRAEFQAKIDSIKQYMQFRKVTKDLETRVIRWFDYLWANKKTVDEKEVLKSLPDKLKAEIAINVHLDTLKKVRIFQDCEAGLLVELVLKLRPTVFSPGDYICKKGDIGKEMYIINEGKLAVVADDGVTQFVVLSDGSYFGEISILNIKGSKSGNRRTANIRSIGYSDLFCLSKDDLMEALTEYPEAKKALEEKGRQILMKDNLIDEELARAGADPKDLEEKVEQLGSSLDTLQTRFARLLAEYNATQMKMKQRLSQLESQVKGGGDKPLADGEVPGDATKTEDKQQ,mutated_sequence,1.0,694.0,NP_001289.1.a2m,NP_001289.1.npy,ClinVar +NP_001299838.1,NP_001299838.1.csv,MSCKKQRSRKHSVNEKCNMKIEHYFSPVSKEQQNNCSTSLMRMESRGDPRATTNTQAQRFHSPKKNPEDQTMPQNRTIYVTLKVNHRRNQDMKLKLTHSENSSLYMALNTLQAVRKEIETHQGQEMLVRGTEGIKEYINLGMPLSCFPEGGQVVITFSQSKSKQKEDNHIFGRQDKASTECVKFYIHAIGIGKCKRRIVKCGKLHKKGRKLCVYAFKGETIKDALCKDGRFLSFLENDDWKLIENNDTILESTQPVDELEGRYFQVEVEKRMVPSAAASQNPESEKRNTCVLREQIVAQYPSLKRESEKIIENFKKKMKVKNGETLFELHRTTFGKVTKNSSSIKVVKLLVRLSDSVGYLFWDSATTGYATCFVFKGLFILTCRHVIDSIVGDGIEPSKWATIIGQCVRVTFGYEELKDKETNYFFVEPWFEIHNEELDYAVLKLKENGQQVPMELYNGITPVPLSGLIHIIGHPYGEKKQIDACAVIPQGQRAKKCQERVQSKKAESPEYVHMYTQRSFQKIVHNPDVITYDTEFFFGASGSPVFDSKGSLVAMHAAGFAYTYQNETRSIIEFGSTMESILLDIKQRHKPWYEEVFVNQQDVEMMSDEDL,mutated_sequence,1.0,611.0,NP_001299838.1.a2m,NP_001299838.1.npy,ClinVar +NP_001308049.1,NP_001308049.1.csv,MLQDKGLSESEEAFRAPGPALGEASAANAPEPALAAPGLSGAALGSPPGPGADVVAAAAAEQTIENIKVGLHEKELWKKFHEAGTEMIITKAGRRMFPSYKVKVTGMNPKTKYILLIDIVPADDHRYKFCDNKWMVAGKAEPAMPGRLYVHPDSPATGAHWMRQLVSFQKLKLTNNHLDPFGHIILNSMHKYQPRLHIVKADENNAFGSKNTAFCTHVFPETSFISVTSYQNHKITQLKIENNPFAKGFRGSDDSDLRVARLQSKEYPVISKSIMRQRLISPQLSATPDVGPLLGTHQALQHYQHENGAHSQLAEPQDLPLSTFPTQRDSSLFYHCLKRRADGTRHLDLPCKRSYLEAPSSVGEDHYFRSPPPYDQQMLSPSYCSEVTPREACMYSGSGPEIAGVSGVDDLPPPPLSCNMWTSVSPYTSYSVQTMETVPYQPFPTHFTATTMMPRLPTLSAQSSQPPGNAHFSVYNQLSQSQVRERGPSASFPRERGLPQGCERKPPSPHLNAANEFLYSQTFSLSRESSLQYHSGMGTVENWTDG,mutated_sequence,1.0,546.0,NP_001308049.1.a2m,NP_001308049.1.npy,ClinVar +NP_001310218.1,NP_001310218.1.csv,MKIPNIGNVMNKFEILGVVGEGAYGVVLKCRHKETHEIVAIKKFKDSEENEEVKETTLRELKMLRTLKQENIVELKEAFRRRGKLYLVFEYVEKNMLELLEEMPNGVPPEKVKSYIYQLIKAIHWCHKNDIVHRDIKPENLLISHNDVLKLCDFGFARNLSEGNNANYTEYVATRWYRSPELLLGAPYGKSVDMWSVGCILGELSDGQPLFPGESEIDQLFTIQKVLGPLPSEQMKLFYSNPRFHGLRFPAVNHPQSLERRYLGILNSVLLDLMKNLLKLDPADRYLTEQCLNHPTFQTQRLLDRSPSRSAKRKPYHVESSTLSNRNQAGKSTALQSHHRSNSKDIQNLSVGLPRADEGLPANESFLNGNLAGASLSPLHTKTYQASSQPGSTSKDLTNNNIPHLLSPKEAKSKTEFDFNIDPKPSEGPGTKYLKSNSRSQQNRHSFMESSQSKAGTLQPNEKQSRHSYIDTIPQSSRSPSYRTKAKSHGALSDSKSVSNLSEARAQIAEPSTSRYFPSSCLDLNSPTSPTPTRHSDTRTLLSPSGRNNRNEGTLDSRRTTTRHSKTMEELKLPEHMDSSHSHSLSAPHESFSYGLGYTSPFSSQQRPHRHSMYVTRDKVRAKGLDGSLSIGQGMAARANSLQLLSPQPGEQLPPEMTVARSSVKETSREGTSSFHTRQKSEGGVYHDPHSDDGTAPKENRHLYNDPVPRRVGSFYRVPSPRPDNSFHENNVSTRVSSLPSESSSGTNHSKRQPAFDPWKSPENISHSEQLKEKEKQGFFRSMKKKKKKSQTVPNSDSPDLLTLQKSIHSASTPSSRPKEWRPEKISDLQTQSQPLKSLRKLLHLSSASNHPASSDPRFQPLTAQQTKNSFSEIRIHPLSQASGGSSNIRQEPAPKGRPALQLPGQMDPGWHVSSVTRSATEGPSYSEQLGAKSGPNGHPYNRTNRSRMPNLNDLKETAL,mutated_sequence,1.0,960.0,NP_001310218.1.a2m,NP_001310218.1.npy,ClinVar +NP_001317217.1,NP_001317217.1.csv,MAVRKKDGGPNVKYYEAADTVTQFDNVRLWLGKNYKKYIQAEPPTNKSLSSLVVQLLQFQEEVFGKHVSNAPLTKLPIKCFLDFKAGGSLCHILAAAYKFKSDQGWRRYDFQNPSRMDRNVEMFMTIEKSLVQNNCLSRPNIFLCPEIEPKLLGKLKDIIKRHQGTVTEDKNNASHVVYPVPGNLEEEEWVRPVMKRDKQVLLHWGYYPDSYDTWIPASEIEASVEDAPTPEKPRKVHAKWILDTDTFNEWMNEEDYEVNDDKNPVSRRKKISAKTLTDEVNSPDSDRRDKKGGNYKKRKRSPSPSPTPEAKKKNAKKGPSTPYTKSKRGHREEEQEDLTKDMDEPSPVPNVEEVTLPKTVNTKKDSESAPVKGGTMTDLDEQEDESMETTGKDEDENSTGNKGEQTKNPDLHEDNVTEQTHHIIIPSYAAWFDYNSVHAIERRALPEFFNGKNKSKTPEIYLAYRNFMIDTYRLNPQEYLTSTACRRNLAGDVCAIMRVHAFLEQWGLINYQVDAESRPTPMGPPPTSHFHVLADTPSGLVPLQPKTPQGRQVDADTKAGRKGKELDDLVPETAKGKPELQTSASQQMLNFPDKGKEKPTDMQNFGLRTDMYTKKNVPSKSKAAASATREWTEQETLLLLEALEMYKDDWNKVSEHVGSRTQDECILHFLRLPIEDPYLEDSEASLGPLAYQPIPFSQSGNPVMSTVAFLASVVDPRVASAAAKSALEEFSKMKEEVPTALVEAHVRKVEEAAKVTGKADPAFGLESSGIAGTTSDEPERIEESGNDEARVEGQATDEKKEPKEPREGGGAIEEEAKEKTSEAPKKDEEKGKEGDSEKESEKSDGDPIVDPEKEKEPKEGQEEVLKEVVESEGERKTKVERDIGEGNLSTAAAAALAAAAVKAKHLAAVEERKIKSLVALLVETQMKKLEIKLRHFEELETIMDREREALEYQRQQLLADRQAFHMEQLKYAEMRARQQHFQQMHQQQQQPPPALPPGSQPIPPTGAAGPPAVHGLAVAPASVVPAPAGSGAPPGSLGPSEQIGQAGSTAGPQQQQPAGAPQPGAVPPGVPPPGPHGPSPFPNQQTPPSMMPGAVPGSGHPGVAGNAPLGLPFGMPPPPPPPAPSIIPFGSLADSISINLPAPPNLHGHHHHLPFAPGTLPPPNLPVSMANPLHPNLPATTTMPSSLPLGPGLGSAAAQSPAIVAAVQGNLLPSASPLPDPGTPLPPDPTAPSPGTVTPVPPPQ,mutated_sequence,1.0,1245.0,NP_001317217.1.a2m,NP_001317217.1.npy,ClinVar +NP_001317629.1,NP_001317629.1.csv,MAKSGGCGAGAGVGGGNGALTWVTLFDQNNAAKKEESETANKNDSSKKLSVERVYQKKTQLEHILLRPDTYIGSVEPLTQFMWVYDEDVGMNCREVTFVPGLYKIFDEILVNAADNKQRDKNMTCIKVSIDPESNIISIWNNGKGIPVVEHKVEKVYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFSTKFTVETACKEYKHSFKQTWMNNMMKTSEAKIKHFDGEDYTCITFQPDLSKFKMEKLDKDIVALMTRRAYDLAGSCRGVKVMFNGKKLPVNGFRSYVDLYVKDKLDETGVALKVIHELANERWDVCLTLSEKGFQQISFVNSIATTKGGRHVDYVVDQVVGKLIEVVKKKNKAGVSVKPFQVKNHIWVFINCLIENPTFDSQTKENMTLQPKSFGSKCQLSEKFFKAASNCGIVESILNWVKFKAQTQLNKKCSSVKYSKIKGIPKLDDANDAGGKHSLECTLILTEGDSAKSLAVSGLGVIGRDRYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKSYDDAESLKTLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLKHGFLEEFITPIVKASKNKQELSFYSIPEFDEWKKHIENQKAWKIKYYKGLGTSTAKEAKEYFADMERHRILFRYAGPEDDAAITLAFSKKKIDDRKEWLTNFMEDRRQRRLHGLPEQFLYGTATKHLTYNDFINKELILFSNSDNERSIPSLVDGFKPGQRKVLFTCFKRNDKREVKVAQLAGSVAEMSAYHHGEQALMMTIVNLAQNFVGSNNINLLQPIGQFGTRLHGGKDAASPRYIFTMLSTLARLLFPAVDDNLLKFLYDDNQRVEPEWYIPIIPMVLINGAEGIGTGWACKLPNYDAREIVNNVRRMLDGLDPHPMLPNYKNFKGTIQELGQNQYAVSGEIFVVDRNTVEITELPVRTWTQVYKEQVLEPMLNGTDKTPALISDYKEYHTDTTVKFVVKMTEEKLAQAEAAGLHKVFKLQTTLTCNSMVLFDHMGCLKKYETVQDILKEFFDLRLSYYGLRKEWLVGMLGAESTKLNNQARFILEKIQGKITIENRSKKDLIQMLVQRGYESDPVKAWKEAQEKAAEEDETQNQHDDSSSDSGTPSGPDFNYILNMSLWSLTKEKVEELIKQRDAKGREVNDLKRKSPSDLWKEDLAAFVEELDKVESQEREDVLAGMSGKAIKGKVGKPKVKKLQLEETMPSPYGRRIIPEITAMKADASKKLLKKKKGDLDTAAVKVEFDEEFSGAPVEGAGEEALTPSVPINKGPKPKREKKEPGTRVRKTPTSSGKPSAKKVKKRNPWSDDESKSESDLEETEPVVIPRDSLLRRAAAERPKYTFDFSEEEDDDADDDDDDNNDLEELKVKASPITNDGEDEFVPSDGLDKDEYTFSPGKSKATPEKSLHDKKSQDFGNLFSFPSYSQKSEDDSAKFDSNEEDSASVFSPSFGLKQTDKVPSKTVAAKKGKPSSDTVPKPKRAPKQKKVVEAVNSDSDSEFGIPKKTTTPKGKGRGAKKRKASGSENEGDYNPGRKTSKTTSKKPKKTSFDQDSDVDIFPSDFPTEPPSLPRTGRARKEVKYFAESDEEEDDVDFAMFN,mutated_sequence,1.0,1626.0,NP_001317629.1.a2m,NP_001317629.1.npy,ClinVar +NP_001334650.1,NP_001334650.1.csv,MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS,mutated_sequence,1.0,754.0,NP_001334650.1.a2m,NP_001334650.1.npy,ClinVar +NP_001336813.1,NP_001336813.1.csv,MWWFQQGLSFLPSALVIWTSAAFIFSYITAVTLHHIDPALPYISDTGTVAPEKCLFGAMLNIAAVLCIATIYVRYKQVHALSPEENVIIKLNKAGLVLGILSCLGLSIVANFQKTTLFAAHVSGAVLTFGMGSLYMFVQTILSYQMQPKIHGKQVFWIRLLLVIWCGVSALSMLTCSSVLHSGNFGTDLEQKLHWNPEDKGYVLHMITTAAEWSMSFSFFGFFLTYIRDFQKISLRVEANLHGLTLYDTAPCPINNERTRLLSRDI,mutated_sequence,1.0,266.0,NP_001336813.1.a2m,NP_001336813.1.npy,ClinVar +NP_001340274.1,NP_001340274.1.csv,MENSHPPHHHHQQPPPQPGPSGERRNHHWRSYKLMIDPALKKGHHKLYRYDGQHFSLAMSSNRPVEIVEDPRVVGIWTKNKELELSVPKFKIDEFYVGPVPPKQVTFAKLNDNIRENFLRDMCKKYGEVEEVEILYNPKTKKHLGIAKVVFATVRGAKDAVQHLHSTSVMGNIIHVELDTKGETRMRFYELLVTGRYTPQTLPVGELDAVSPIVNETLQLSDALKRLKDGGLSAGCGSGSSSVTPNSGGTPFSQDTAYSSCRLDTPNSYGQGTPLTPRLGTPFSQDSSYSSRQPTPSYLFSQDPAVTFKARRHESKFTDAYNRRHEHHYVHNSPAVTAVAGATAAFRGSSDLPFGAVGGTGGSSGPPFKAQPQDSATFAHTPPPAQATPAPGFKSAFSPYQTPVAHFPPPPEEPTATAAFGARDSGEFRRAPAPPPLPPAEPLAKEKPGTPPGPPPPDTNSMELGGRPTFGWSPEPCDSPGTPTLESSPAGPEKPHDSLDSRIEMLLKEQRTKLLFLREPDSDTELQMEGSPISSSSSQLSPLAPFGTNSQPGFRGPTPPSSRPSSTGLEDISPTPLPDSDEDEELDLGLGPRPPPEPGPPDPAGLLSQTAEVALDLVGDRTPTSEKMDEGQQSSGEDMEISDDEMPSAPITSADCPKPMVVTPGAAAVAAPSVLAPTLPLPPPPGFPPLPPPPPPPPPQPGFPMPPPLPPPPPPPPPAHPAVTVPPPPLPAPPGVPPPPILPPLPPFPPGLFPVMQVDMSHVLGGQWGGMPMSFQMQTQVLSRLMTGQGACPYPPFMAAAAAAASAGLQFVNLPPYRGPFSLSNSGPGRGQHWPPLPKFDPSVPPPGYMPRQEDPHKATVDGVLLVVLKELKAIMKRDLNRKMVEVVAFRAFDEWWDKKERMAKASLTPVKSGEHKDEDRPKPKDRIASCLLESWGKGEGLGYEGLGLGIGLRGAIRLPSFKVKRKEPPDTTSSGDQKRLRPSTSVDEEDEESERERDRDMADTPCELAKRDPKGVGVRRRPARPLELDSGGEEDEKESLSASSSSSASSSSGSSTTSPSSSASDKEEEQESTEEEEEAEEEEEEEVPRSQLSSSSTSSTSDKDDDDDDSDDRDESENDDEDTALSEASEKDEGDSDEEETVSIVTSKAEATSSSESSESSEFESSSESSPSSSEDEEEVVAREEEEEEEEEEMVAEESMASAGPEDFEQDGEEAALAPGAPAVDSLGMEEEVDIETEAVAPEERPSMLDEPPLPVGVEEPADSREPPEEPGLSQEGAMLLSPEPPAKEVEARPPLSPERAPEHDLEVEPEPPMMLPLPLQPPLPPPRPPRPPSPPPEPETTDASHPSVPPEPLAEDHPPHTPGLCGSLAKSQSTETVPATPGGEPPLSGGSSGLSLSSPQVPGSPFSYPAPSPSLSSGGLPRTPGRDFSFTPTFSEPSGPLLLPVCPLPTGRRDERSGPLASPVLLETGLPLPLPLPLPLPLALPAVLRAQARAPTPLPPLLPAPLASCPPPMKRKPGRPRRSPPSMLSLDGPLVRPPAGAALGRELLLLPGQPQTPVFPSTHDPRTVTLDFRNAGIPAPPPPLPPQPPPPPPPPPVEPTKLPFKELDNQWPSEAIPPGPRGRDEVTEEYMELAKSRGPWRRPPKKRHEDLVPPAGSPELSPPQPLFRPRSEFEEMTILYDIWNGGIDEEDIRFLCVTYERLLQQDNGMDWLNDTLWVYHPSTSLSSAKKKKRDDGIREHVTGCARSEGFYTIDKKDKLRYLNSSRASTDEPPADTQGMSIPAQPHASTRAGSERRSEQRRLLSSFTGSCDSDLLKFNQLKFRKKKLKFCKSHIHDWGLFAMEPIAADEMVIEYVGQNIRQVIADMREKRYEDEGIGSSYMFRVDHDTIIDATKCGNFARFINHSCNPNCYAKVITVESQKKIVIYSKQHINVNEEITYDYKFPIEDVKIPCLCGSENCRGTLN,mutated_sequence,1.0,1966.0,NP_001340274.1.a2m,NP_001340274.1.npy,ClinVar +NP_001341533.1,NP_001341533.1.csv,MQSESGIVPDFEVGEEFHEEPKTYYELKSQPLKSSSSAEHPGASKPPISSSSMTSRILLRQQLMREQMQEQERREQQQKLQAAQFMQQRVPVSQTPAINVSVPTTLPSATQVPMEVLKVQTHLENPTKYHIQQAQRQQVKQYLSTTLANKHANQVLSLPCPNQPGDHVMPPVPGSSAPNSPMAMLTLNSNCEKEGFYKFEEQNRAESECPGMNTHSRASCMQMDDVIDDIISLESSYNEEILGLMDPALQMANTLPVSGNLIDLYGNQGLPPPGLTISNSCPANLPNIKRELTACIFPTESEARALAKERQKKDNHNLIERRRRFNINDRIKELGTLIPKSNDPDMRWNKGTILKASVDYIRKLQREQQRAKELENRQKKLEHANRHLLLRIQELEMQARAHGLSLIPSTGLCSPDLVNRIIKQEPVLENCSQDLLQHHADLTCTTTLDLTDGTITFNNNLGTGTEANQAYSVPTKMGSKLEDILMDDTLSPVGVTDPLLSSVSPGASKTSSRRSSMSMEETEHTC,mutated_sequence,1.0,526.0,NP_001341533.1.a2m,NP_001341533.1.npy,ClinVar +NP_001341569.1,NP_001341569.1.csv,MLLLLLLLLLLPPLVLRVAASRCLHDETQKSVSLLRPPFSQLPSKSRSSSLTLPSSRDPQPLRIQSCYLGDHISDGAWDPEGEGMRGGSRALAAVREATQRIQAVLAVQGPLLLSRDPAQYCHAVWGDPDSPNYHRCSLLNPGYKGESCLGAKIPDTHLRGYALWPEQGPPQLVQPDGPGVQNTDFLLYVRVAHTSKCHQETVSLCCPGWSTAAQSQLTAALTSWAQRRGFVMLPRLCLKLLGSSNLPTLASQSIRITGPSVIAYAACCQLDSEDRPLAGTIVYCAQHLTSPSLSHSDIVMATLHELLHALGFSGQLFKKWRDCPSGFSVRENCSTRQLVTRQDEWGQLLLTTPAVSLSLAKHLGVSGASLGVPLEEEEGLLSSHWEARLLQGSLMTATFDGAQRTRLDPITLAAFKDSGWYQVNHSAAEELLWGQGSGPEFGLVTTCGTGSSDFFCTGSGLGCHYLHLDKGSCSSDPMLEGCRMYKPLANGSECWKKENGFPAGVDNPHGEIYHPQSRCFFANLTSQLLPGDKPRHPSLTPHLKEAELMGRCYLHQCTGRGAYKVQVEGSPWVPCLPGKVIQIPGYYGLLFCPRGRLCQTNEDINAVTSPPVSLSTPDPLFQLSLELAGPPGHSLGKEQQEGLAEAVLEALASKGGTGRCYFHGPSITTSLVFTVHMWKSPGCQGPSVATLHKALTLTLQKKPLEVYHGGANFTTQPSKLLVTSDHNPSMTHLRLSMGLCLMLLILVGVMGTTAYQKRATLPVRPSASYHSPELHSTRVPVRGIREV,mutated_sequence,1.0,788.0,NP_001341569.1.a2m,NP_001341569.1.npy,ClinVar +NP_001341641.1,NP_001341641.1.csv,MTPNSMTENGLTAWDKPKHCPDREHDWKLVGMSEACLHRKSHSERRSTLKNEQSSPHLIQTTWTSSIFHLDHDDVNDQSVSSAQTFQTEEKKCKGYIPSYLDKDELCVVCGDKATGYHYRCITCEGCKGFFRRTIQKNLHPSYSCKYEGKCVIDKVTRNQCQECRFKKCIYVGMATDLVLDDSKRLAKRKLIEENREKRRREELQKSIGHKPEPTDEEWELIKTVTEAHVATNAQGSHWKQKRKFLPEDIGQAPIVNAPEGGKVDLEAFSHFTKIITPAITRVVDFAKKLPMFCELPCEDQIILLKGCCMEIMSLRAAVRYDPESETLTLNGEMAVTRGQLKNGGLGVVSDAIFDLGMSLSSFNLDDTEVALLQAVLLMSSDRPGLACVERIEKYQDSFLLAFEHYINYRKHHVTHFWPKLLMKVTDLRMIGACHASRFLHMKVECPTELFPPLFLEVFED,mutated_sequence,1.0,461.0,NP_001341641.1.a2m,NP_001341641.1.npy,ClinVar +NP_001346945.1,NP_001346945.1.csv,MAEQVALSRTQVCGILREELFQGDAFHQSDTHIFIIMGASGDLAKKKIYPTIWWLFRDGLLPENTFIVGYARSRLTVADIRKQSEPFFKATPEEKLKLEDFFARNSYVAGQYDDAASYQRLNSHMNALHLGSQANRLFYLALPPTVYEAVTKNIHESCMSQIGWNRIIVEKPFGRDLQSSDRLSNHISSLFREDQIYRIDHYLGKEMVQNLMVLRFANRIFGPIWNRDNIACVILTFKEPFGTEGRGGYFDEFGIIRDVMQNHLLQMLCLVAMEKPASTNSDDVRDEKVKVLKCISEVQANNVVLGQYVGNPDGEGEATKGYLDDPTVPRGSTTATFAAVVLYVENERWDGVPFILRCGKALNERKAEVRLQFHDVAGDIFHQQCKRNELVIRVQPNEAVYTKMMTKKPGMFFNPEESELDLTYGNRYKNVKLPDAYERLILDVFCGSQMHFVRSDELREAWRIFTPLLHQIELEKPKPIPYIYGSRGPTEADELMKRVGFQYEGTYKWVNPHKL,mutated_sequence,1.0,515.0,NP_001346945.1.a2m,NP_001346945.1.npy,ClinVar +NP_001347.3,NP_001347.3.csv,MSHVAVENALGLDQQFAGLDLNSSDNQSGGSTASKGRYIPPHLRNREATKGFYDKDSSGWSSSKDKDAYSSFGSRSDSRGKSSFFSDRGSGSRGRFDDRGRSDYDGIGSRGDRSGFGKFERGGNSRWCDKSDEDDWSKPLPPSERLEQELFSGGNTGINFEKYDDIPVEATGNNCPPHIESFSDVEMGEIIMGNIELTRYTRPTPVQKHAIPIIKEKRDLMACAQTGSGKTAAFLLPILSQIYSDGPGEALRAMKENGRYGRRKQYPISLVLAPTRELAVQIYEEARKFSYRSRVRPCVVYGGADIGQQIRDLERGCHLLVATPGRLVDMMERGKIGLDFCKYLVLDEADRMLDMGFEPQIRRIVEQDTMPPKGVRHTMMFSATFPKEIQMLARDFLDEYIFLAVGRVGSTSENITQKVVWVEESDKRSFLLDLLNATGKDSLTLVFVETKKGADSLEDFLYHEGYACTSIHGDRSQRDREEALHQFRSGKSPILVATAVAARGLDISNVKHVINFDLPSDIEEYVHRIGRTGRVGNLGLATSFFNERNINITKDLLDLLVEAKQEVPSWLENMAYEHHYKGSSRGRSKSSRFSGGFGARDYRQSSGASSSSFSSSRASSSRSGGGGHGSSRGFGGGGYGGFYNSDGYGGNYNSQGVDWWGN,mutated_sequence,1.0,662.0,NP_001347.3.a2m,NP_001347.3.npy,ClinVar +NP_001350047.1,NP_001350047.1.csv,MAAPTPARPVLTHLLVALFGMGSWAAVNGIWVELPVVVKELPEGWSLPSYVSVLVALGNLGLLVVTLWRRLAPGKDEQVPIRVVQVLGMVGTALLASLWHHVAPVAGQLHSVAFLALAFVLALACCASNVTFLPFLSHLPPRFLRSFFLGQGLSALLPCVLALVQGVGRLECPPAPINGTPGPPLDFLERFPASTFFWALTALLVASAAAFQGLLLLLPPPPSVPTGELGSGLQVGAPGAEEEVEESSPLQEPPSQAAGTTPGPDPKAYQLLSARSACLLGLLAATNALTNGVLPAVQSFSCLPYGRLAYHLAVVLGSAANPLACFLAMGVLCRSLAGLGGLSLLGVFCGGYLMALAVLSPCPPLVGTSAGVVLVVLSWVLCLGVFSYVKVAASSLLHGGGRPALLAAGVAIQVGSLLGAVAMFPPTSIYHVFHSRKDCADPCDS,mutated_sequence,1.0,445.0,NP_001350047.1.a2m,NP_001350047.1.npy,ClinVar +NP_001351.2,NP_001351.2.csv,MAAKSQPNIPKAKSLDGVTNDRTASQGQWGRAWEVDWFSLASVIFLLLFAPFIVYYFIMACDQYSCALTGPVVDIVTGHARLSDIWAKTPPITRKAAQLYTLWVTFQVLLYTSLPDFCHKFLPGYVGGIQEGAVTPAGVVNKYQINGLQAWLLTHLLWFANAHLLSWFSPTIIFDNWIPLLWCANILGYAVSTFAMVKGYFFPTSARDCKFTGNFFYNYMMGIEFNPRIGKWFDFKLFFNGRPGIVAWTLINLSFAAKQRELHSHVTNAMVLVNVLQAIYVIDFFWNETWYLKTIDICHDHFGWYLGWGDCVWLPYLYTLQGLYLVYHPVQLSTPHAVGVLLLGLVGYYIFRVANHQKDLFRRTDGRCLIWGRKPKVIECSYTSADGQRHHSKLLVSGFWGVARHFNYVGDLMGSLAYCLACGGGHLLPYFYIIYMAILLTHRCLRDEHRCASKYGRDWERYTAAVPYRLLPGIF,mutated_sequence,1.0,475.0,NP_001351.2.a2m,NP_001351.2.npy,ClinVar +NP_001352017.1,NP_001352017.1.csv,MHPPETTTKMASVRFMVTPTKIDDIPGLSDTSPDLSSRSSSRVRFSSRESVPETSRSEPMSEMSGATTSLATVALDPPSDRTSHPQDVIEDLSQNSITGEHSQLLDDGHKKARNAYLNNSNYEEGDEYFDKNLALFEEEMDTRPKVSSLLNRMANYTNLTQGAKEHEEAENITEGKKKPTKTPQMGTFMGVYLPCLQNIFGVILFLRLTWVVGTAGVLQAFAIVLICCCCTMLTAISMSAIATNGVVPAGGSYFMISRALGPEFGGAVGLCFYLGTTFAAAMYILGAIEIFLVYIVPRAAIFHSDDALKESAAMLNNMRVYGTAFLVLMVLVVFIGVRYVNKFASLFLACVIVSILAIYAGAIKSSFAPPHFPVCMLGNRTLSSRHIDVCSKTKEINNMTVPSKLWGFFCNSSQFFNATCDEYFVHNNVTSIQGIPGLASGIITENLWSNYLPKGEIIEKPSAKSSDVLGSLNHEYVLVDITTSFTLLVGIFFPSVTGIMAGSNRSGDLKDAQKSIPIGTILAILTTSFVYLSNVVLFGACIEGVVLRDKFGDAVKGNLVVGTLSWPSPWVIVIGSFFSTCGAGLQSLTGAPRLLQAIAKDNIIPFLRVFGHSKANGEPTWALLLTAAIAELGILIASLDLVAPILSMFFLMCYLFVNLACALQTLLRTPNWRPRFRYYHWALSFMGMSICLALMFISSWYYAIVAMVIAGMIYKYIEYQGAEKEWGDGIRGLSLSAARFALLRLEEGPPHTKNWRPQLLVLLKLDEDLHVKHPRLLTFASQLKAGKGLTIVGSVIVGNFLENYGEALAAEQTIKHLMEAEKVKGFCQLVVAAKLREGISHLIQSCGLGGMKHNTVVMGWPNGWRQSEDARAWKTFIGTVRVTTAAHLALLVAKNISFFPSNVEQFSEGNIDVWWIVHDGGMLMLLPFLLKQHKVWRKCSIRIFTVAQLEDNSIQMKKDLATFLYHLRIEAEVEVVEMHDSDISAYTYERTLMMEQRSQMLRHMRLSKTERDREAQLVKDRNSMLRLTSIGSDEDEETETYQEKVHMTWTKDKYMASRGQKAKSMEGFQDLLNMRPDQSNVRRMHTAVKLNEVIVNKSHEAKLVLLNMPGPPRNPEGDENYMEFLEVLTEGLERVLLVRGGGSEVITIYS,mutated_sequence,1.0,1150.0,NP_001352017.1.a2m,NP_001352017.1.npy,ClinVar +NP_001352465.1,NP_001352465.1.csv,MAMLPPPGPQSFVHFTKQSLALIEQRIAERKSKEPKEEKKDDDEEAPKPSSDLEAGKQLPFIYGDIPPGMVSEPLEDLDPYYADKKTFIVLNKGKTIFRFNATPALYMLSPFSPLRRISIKILVHSLFSMLIMCTILTNCIFMTMNNPPDWTKNVEYTFTGIYTFESLVKILARGFCVGEFTFLRDPWNWLDFVVIVFAYLTEFVNLGNVSALRTFRVLRALKTISVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALIGLQLFMGNLKHKCFRNSLENNETLESIMNTLESEEDFRKYFYYLEGSKDALLCGFSTDSGQCPEGYTCVKIGRNPDYGYTSFDTFSWAFLALFRLMTQDYWENLYQQTLRAAGKTYMIFFVVVIFLGSFYLINLILAVVAMAYEEQNQANIEEAKQKELEFQQMLDRLKKEQEEAEAIAAAAAEYTSIRRSRIMGLSESSSETSKLSSKSAKERRNRRKKKNQKKLSSGEEKGDAEKLSKSESEDSIRRKSFHLGVEGHRRAHEKRLSTPNQSPLSIRGSLFSARRSSRTSLFSFKGRGRDIGSETEFADDEHSIFGDNESRRGSLFVPHRPQERRSSNISQASRSPPMLPVNGKMHSAVDCNGVVSLVDGRSALMLPNGQLLPEVIIDKATSDDSGTTNQIHKKRRCSSYLLSEDMLNDPNLRQRAMSRASILTNTVEELEESRQKCPPWWYRFAHKFLIWNCSPYWIKFKKCIYFIVMDPFVDLAITICIVLNTLFMAMEHHPMTEEFKNVLAIGNLVFTGIFAAEMVLKLIAMDPYEYFQVGWNIFDSLIVTLSLVELFLADVEGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKINDDCTLPRWHMNDFFHSFLIVFRVLCGEWIETMWDCMEVAGQAMCLIVYMMVMVIGNLVVLNLFLALLLSSFSSDNLTAIEEDPDANNLQIAVTRIKKGINYVKQTLREFILKAFSKKPKISREIRQAEDLNTKKENYISNHTLAEMSKGHNFLKEKDKISGFGSSVDKHLMEDSDGQSFIHNPSLTVTVPIAPGESDLENMNAEELSSDSDSEYSKVRLNRSSSSECSTVDNPLPGEGEEAEAEPMNSDEPEACFTDGCVWRFSCCQVNIESGKGKIWWNIRKTCYKIVEHSWFESFIVLMILLSSGALAFEDIYIERKKTIKIILEYADKIFTYIFILEMLLKWIAYGYKTYFTNAWCWLDFLIVDVSLVTLVANTLGYSDLGPIKSLRTLRALRPLRALSRFEGMRVVVNALIGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYECINTTDGSRFPASQVPNRSECFALMNVSQNVRWKNLKVNFDNVGLGYLSLLQVATFKGWTIIMYAAVDSVNVDKQPKYEYSLYMYIYFVVFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPGNKIQGCIFDLVTNQAFDISIMVLICLNMVTMMVEKEGQSQHMTEVLYWINVVFIILFTGECVLKLISLRHYYFTVGWNIFDFVVVIISIVGMFLADLIETYFVSPTLFRVIRLARIGRILRLVKGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYAIFGMSNFAYVKKEDGINDMFNFETFGNSMICLFQITTSAGWDGLLAPILNSKPPDCDPKKVHPGSSVEGDCGNPSVGIFYFVSYIIISFLVVVNMYIAVILENFSVATEESTEPLSEDDFEMFYEVWEKFDPDATQFIEFSKLSDFAAALDPPLLIAKPNKVQLIAMDLPMVSGDRIHCLDILFAFTKRVLGESGEMDSLRSQMEERFMSANPSKVSYEPITTTLKRKQEDVSATVIQRAYRRYRLRQNVKNISSIYIKDGDRDDDLLNKKDMAFDNVNENSSPEKTDATSSTTSPPSYDSVTKPDKEKYEQDRTEKEDKGKDSKESKK,mutated_sequence,1.0,1988.0,NP_001352465.1.a2m,NP_001352465.1.npy,ClinVar +NP_001352928.1,NP_001352928.1.csv,MASERPEPEVEEAGQVFLLMKKDYRISRNVRLAWFLSHLHQTVQATPQEMLLQSEQELEVLSVLPPGWQPDEPVVPRPFLLVPSTRVTFLAWQYRFVIELDLSPSTGIVDDSTGEILFDEVFHALSRCLGGLLRPFRVPGSCIDFQPEIYVTIQAYSSIIGLQSHQVLVQGCLLDPSQREVFLQQIYEQLCLFEDKVATMLQQQYDPQSQAEDQSPDSGDLLGRKVGVSMVTADLGLVSMIRQGILALQLLPSNSSAGIIVITDGVTSVPDVAVCETLLNQLRSGTVACSFVQVGGVYSYDCSFGHVPNVELMKFIAMATFGSYLSTCPEPEPGNLGLTVYHRAFLLYSFLRSGEALNPEYYCGSQHRLFNEHLVSASSNPALALRRKKHTEKEVPADLVSTVSVRLREGYSVREVTLAKGGSQLEVKLVLLWKHNMRIEYVAMAPWPLEPEGPRVTRVEVTMEGGYDILHDVSCALRQPIRSLYRTHVIRRFWNTLQSINQTDQMLAHLQSFSSVPEHFTLPDSTKSGVPLFYIPPGSTTPVLSLQPSGSDSSHAQFAAYWKPVLSMDANSWQRWLHMHRLVLILEHDTPIPKHLHTPGSNGRYSTIQCRISHSSLTSLLRDWSSFVLVEGYSYVKLLSSAPDQPPNSFYMVRIISKAPCMVLRLGFPIGTPAPARHKIVSGLREEILRLRFPHRVQSKEPTPKVKRKGLGGAGGGSSPSKSPPVLGPQQALSDRPCLVVLHKPLDKLLIRYEKLPLDYRAPFLLTLEPPGPLPLVSGRSASSSLASLSRYLYHQRWLWSVPSGLAPALPLSAIAQLLSILTEVRLSEGFHFACSGEGIINMVLELPIQNEPPGQAAAEEKHTCVVQYILFPPHSTSTKDSFSTDDDNDVEVEALEGDSELNLVTEVWVEPQYGRVGPGPGIWKHLQDLTYSEIPQALHPRDAACIGSMLSFEYLIQLCQSKEWGPLPPEPRVSDGLDQGGDTCVHEIPFHFDLMGLLPQCQQLQMFFLLLAREPEGVPFAEGSCPANDMVLCLLHSCLGQELSDREIPLTPVDQAAFLSEVLRRTCHVPGAEGPLLGVHGIPKEQAVGSTQATGDSAFTSLSVGLPETLKPLISAQPPQWRCYARLVNPQHVFLTFLPATFSDVQRLAACGLEGPPQEETKPKFGDWSGAPSLKDLGGTGIKATKSHVPVLSVTLASDNAQNQGELSPPFRRDLQAYAGRQASQTESADGPRTRCPVYIYSCSLEALREQMVGMQPPQAPRDLIFRTQFLDHPSPSSAWMEPRYKEAANHCALLQEHAQRCYVRGLFRSLQQAQSVTSQDLLTAVDACEELLQEIDITPFLLALCGHTWGLPHAPPSPGPLSPGPFSSSMEEGAEPRERAILASESSIETEDLSEPEFQSTRVPGIPDPGPEISLTDVCQLRGEAHGALHSVIQEKFLEISRLHFRTVPSNPHYFFYCPPSSRREDEGPRDTVDRKISDLEFSEAELMGEEGDTSACCVVTESDPELEVEYRESRESDLGPAGLDSASLSDVDTVNPDEDSFSILGGDSPTGPESFLHDLPPLFLHLTCSVRLRGQHSSVPVCSLPTCLGQVLSSLEGPPVGGRVPLRDLSVTLDVFMLTLPLEVELPTASDPQHHRSTSESSASFPRSPGQPSSLRSDDGLGPPLPPPEEERHPGLSNLATPHRLAIETTMNEIRWLLEDEMVGALRRGGIPQSPALHRAAAHIHSSPGRSTCLRQTLPLSFVFGPERSLTQFKEEFRRLHLPGHVLLEDPDSGFFFVAAGQQPGGSHGEPSSAAWAWHSHEDRAEGIEGETLTASPQAPGSPEDSEGVPLISLPRVPQGGSQPGPSRGLSLMSSQGSVDSDHLGYDGGSSGSDSEGPNDTLGEKAPFTLRTPPGPAPPQPSLSGLPGPCLPDFWLIVRVLQDRVEVYAHARSLIREDGGPGTECRHLQQLLVRRVGEICREVNQRLLLQDLHDSHVCNSLLVAESEEDLWRSETPFHSRQRAPLPSDDYAADESCAPRGYLAATMQFVPGHFSCDVVWGTVIRVHSRLKMGPSMGVSRAIQALRSVLNAFSVVNRKNMFVYQERATKAVYYLRLLETSCSDRPWKGDALPPSLALSRSQEPIYSEEASGPRSPLDMVSSRSSDAARPVGQVDRHIQLLVHGVGQAGPEITDELVRVLCRRLDEATLDVITVMLVRNCKLTPADVEFIQPPGSLPSEVLHLALPTSCRPWLPALAWYLRQNLLIFLHSPKYTDSNSRNHFQHPLPPQGGLPDLDIYLYNKPGGQGTGGKGVACITLAFVDEGGAPLSLALWPPSSPGPPDPLREEEFEQLTQVIRCPVVVDSSSAQNGAPRLRLDVWEKGNISIVQLEEKLRGAARQALADAIIELQLLPASLCTEDTPTGSLRNGSLETKSSAGRASTFPPAPVPGEPVTPPSKAGRRSFWDMLSKTECGDLGSPKTTDDIVLDRPEDTRGRRRHKTESVRTPGGAERAPGSDSGAQRQKRRTTQLEEGEVGTLHPVFARVAQRWMEFMVQIGCASVSRSSAHMVSRFLLPSILSEFTALVTSMAGDTSVRIFEQHLGSEPEIFGPCSPGQLGPSPRPAAERHLLLLGRNFLQWRRPTQQAAKAMQRFEPGGDGSSGRNAPRQRLLLLEVVDKKLQLLTYNWAPDLGAALGRALVRLVQWQNARAHLIFCLLSQKLGLFHHYGQLDFPVRDEKEPNPFLLPTMEVETLIRSASPPLSREQGRLSGSSRGGGPLPLDTFPFDEALRDITAARPSSVLGPVPRPPDPVTYHGQQFLEIKMAERRELERQMKMENLFVTWQQRSTPATMPISAGELETLKQSSRLVHYCATAMLFDPAAWLHGPPETSGPPDGQRRHRPESGSGSREAPTSCESLDVSPPGAREEPWLKELSLAFLQQYVQYLQSIGFVLVPLRPPSPARSTSRPRAMAILGTEGRGSFSCPKTKTDGSPKSTSSPVTTYHLQRALPGGIILMELAFQGCYFCVKQFALECSRIPMGQAVNSQLSMLFTEECDKVRDLMHVHSFSYDFHLRLVHQHVLGAHLVLRHGYHLTTFLRHFLAHHPDGPHFGRNHIYQGTLELPTPLIAAHQLYNYVADHASSYHMKPLRMARPGGPEHNEYALVSAWHSSGSYLDSEGLRHQDDFDVSLLVCHCAAPFEEQGEAERHVLRLQFFVVLTSQRELFPRLTADMRRFRKPPRLPPEPEAPGSSAGSPGEASGLILAPGPAPLFPPLAAEVGMARARLAQLVRLAGGHCRRDTLWKRLFLLEPPGPDRLRLGGRLALAELEELLEAVHAKSIGDIDPQLDCFLSMTVSWYQSLIKVLLSRFPQSCRHFQSPDLGTQYLVVLNQKFTDCFVLVFLDSHLGKTSLTVVFREPFPVQPQDSESPPAQLVSTYHHLESVINTACFTLWTRLL,mutated_sequence,1.0,3432.0,NP_001352928.1.a2m,NP_001352928.1.npy,ClinVar +NP_001353314.1,NP_001353314.1.csv,MGELCRRDSALTALDEETLWEMMESHRHRIVRCICPSRLTPYLRQAKVLCQLDEEEVLHSPRLTNSAMRAGHLLDLLKTRGKNGAIAFLESLKFHNPDVYTLVTGLQPDVDFSNFSGLMETSKLTECLAGAIGSLQEELNQEKGQKEVLLRRCQQLQEHLGLAETRAEGLHQLEADHSRMKREVSAHFHEVLRLKDEMLSLSLHYSNALQEKELAASRCRSLQEELYLLKQELQRANMVSSCELELQEQSLRTASDQESGDEELNRLKEENEKLRSLTFSLAEKDILEQSLDEARGSRQELVERIHSLRERAVAAERQREQYWEEKEQTLLQFQKSKMACQLYREKVNALQAQVCELQKERDQAYSARDSAQREISQSLVEKDSLRRQVFELTDQVCELRTQLRQLQAEPPGVLKQEARTREPCPREKQRLVRMHAICPRDDSDCSLVSSTESQLLSDLSATSSRELVDSFRSSSPAPPSQQSLYKRVAEDFGEEPWSFSSCLEIPEGDPGALPGAKAGDPHLDYELLDTADLPQLESSLQPVSPGRLDVSESGVLMRRRPARRILSQVTMLAFQGDALLEQISVIGGNLTGIFIHRVTPGSAADQMALRPGTQIVMVDYEASEPLFKAVLEDTTLEEAVGLLRRVDGFCCLSVKVNTDGYKRLLQDLEAKVATSGDSFYIRVNLAMEGRAKGELQVHCNEVLHVTDTMFQGCGCWHAHRVNSYTMKDTAAHGTIPNYSRAQQQLIALIQDMTQQCTVTRKPSSGGPQKLVRIVSMDKAKASPLRLSFDRGQLDPSRMEGSSTCFWAESCLTLVPYTLVRPHRPARPRPVLLVPRAVGKILSEKLCLLQGFKKCLAEYLSQEEYEAWSQRGDIIQEGEVSGGRCWVTRHAVESLMEKNTHALLDVQLDSVCTLHRMDIFPIVIHVSVNEKMAKKLKKGLQRLGTSEEQLLEAARQEEGDLDRAPCLYSSLAPDGWSDLDGLLSCVRQAIADEQKKVVWTEQSPR,mutated_sequence,1.0,1004.0,NP_001353314.1.a2m,NP_001353314.1.npy,ClinVar +NP_001354.1,NP_001354.1.csv,MADAEVIILPKKHKKKKERKSLPEEDVAEIQHAEEFLIKPESKVAKLDTSQWPLLLKNFDKLNVRTTHYTPLACGSNPLKREIGDYIRTGFINLDKPSNPSSHEVVAWIRRILRVEKTGHSGTLDPKVTGCLIVCIERATRLVKSQQSAGKEYVGIVRLHNAIEGGTQLSRALETLTGALFQRPPLIAAVKRQLRVRTIYESKMIEYDPERRLGIFWVSCEAGTYIRTLCVHLGLLLGVGGQMQELRRVRSGVMSEKDHMVTMHDVLDAQWLYDNHKDESYLRRVVYPLEKLLTSHKRLVMKDSAVNAICYGAKIMLPGVLRYEDGIEVNQEIVVITTKGEAICMAIALMTTAVISTCDHGIVAKIKRVIMERDTYPRKWGLGPKASQKKLMIKQGLLDKHGKPTDSTPATWKQEYVDYSESAKKEVVAEVVKAPQVVAEAAKTAKRKRESESESDETPPAAPQLIKKEKKKSKKDKKAKAGLESGAEPGDGDSDTTKKKKKKKKAKEVELVSE,mutated_sequence,1.0,514.0,NP_001354.1.a2m,NP_001354.1.npy,ClinVar +NP_001354650.1,NP_001354650.1.csv,MADDDVLFEDVYELCEVIGKGPFSVVRRCINRETGQQFAVKIVDVAKFTSSPGLSTEDLKREASICHMLKHPHIVELLETYSSDGMLYMVFEFMDGADLCFEIVKRADAGFVYSEAVASHYMRQILEALRYCHDNNIIHRDVKPHCVLLASKENSAPVKLGGFGVAIQLGESGLVAGGRVGTPHFMAPEVVKREPYGKPVDVWGCGVILFILLSGCLPFYGTKERLFEGIIKGKYKMNPRQWSHISESAKDLVRRMLMLDPAERITVYEALNHPWLKERDRYAYKIHLPETVEQLRKFNARRKLKGAVLAAVSSHKFNSFYGDPPEELPDFSEDPTSSGLLAAERAVSQVLDSLEEIHALTDCSEKDLDFLHSVFQDQHLHTLLDLYDKINTKSSPQIRNPPSDAVQRAKEVLEEISCYPENNDAKELKRILTQPHFMALLQTHDVVAHEVYSDEALRVTPPPTSPYLNGDSPESANGDMDMENVTRVRLVQFQKNTDEPMGITLKMNELNHCIVARIMHGGMIHRQGTLHVGDEIREINGISVANQTVEQLQKMLREMRGSITFKIVPSYRTQSSSCERDSPSTSRQSPANGHSSTNNSVSDLPSTTQPKGRQIYVRAQFEYDPAKDDLIPCKEAGIRFRVGDIIQIISKDDHNWWQGKLENSKNGTAGLIPSPELQEWRVACIAMEKTKQEQQASCTWFGKKKKQYKDKYLAKHNAVFDQLDLVTYEEVVKLPAFKRKTLVLLGAHGVGRRHIKNTLITKHPDRFAYPIPHTTRPPKKDEENGKNYYFVSHDQMMQDISNNEYLEYGSHEDAMYGTKLETIRKIHEQGLIAILDVEPQALKVLRTAEFAPFVVFIAAPTITPGLNEDESLQRLQKESDILQRTYAHYFDLTIINNEIDETIRHLEEAVELVCTAPQWVPVSWVY,mutated_sequence,1.0,926.0,NP_001354650.1.a2m,NP_001354650.1.npy,ClinVar +NP_001355823.1,NP_001355823.1.csv,MQNSHSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQTHADAKVQVLDNQNVSNGCVSKILGRYYETGSIRPRAIGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEKQQMGADGMYDKLRMLNGQTGSWGTRPGWYPGTSVPGQPTQDGCQQQEGGGENTNSISSNGEDSDEAQMRLQLKRKLQRNRTSFTQEQIEALEKEFERTHYPDVFARERLAAKIDLPEARIQVWFSNRRAKWRREEKLRNQRRQASNTPSHIPISSSFSTSVYQPIPQPTTPVSSFTSGSMLGRTDTALTNTYSALPPMPSFTMANNLPMQPPVPSQTSSYSCMLPTSPSVNGRSYDTYTPPHMQTHMNSQPMGTSGTTSTGLISPGVSVPVQVPGSEPDMSQYWPRLQ,mutated_sequence,1.0,436.0,NP_001355823.1.a2m,NP_001355823.1.npy,ClinVar +NP_001357029.1,NP_001357029.1.csv,MARLTKRRQADTKAIQHLWAAIEIIRNQKQIANIDRITKYMSRVHGMHPKETTRQLSLAVKDGLIVETLTVGCKGSKAGIEQEGYWLPGDEIDWETENHDWYCFECHLPGEVLICDLCFRVYHSKCLSDEFRLRDSSSPWQCPVCRSIKKKNTNKQEMGTYLRFIVSRMKERAIDLNKKGKDNKHPMYRRLVHSAVDVPTIQEKVNEGKYRSYEEFKADAQLLLHNTVIFYGADSEQADIARMLYKDTCHELDELQLCKNCFYLSNARPDNWFCYPCIPNHELVWAKMKGFGFWPAKVMQKEDNQVDVRFFGHHHQRAWIPSENIQDITVNIHRLHVKRSMGWKKACDELELHQRFLREGRFWKSKNEDRGEEEAESSISSTSNEQLKVTQEPRAKKGRRNQSVEPKKEEPEPETEAVSSSQEIPTMPQPIEKVSVSTQTKKLSASSPRMLHRSTQTTNDGVCQSMCHDKYTKIFNDFKDRMKSDHKRETERVVREALEKLRSEMEEEKRQAVNKAVANMQGEMDRKCKQVKEKCKEEFVEEIKKLATQHKQLISQTKKKQWCYNCEEEAMYHCCWNTSYCSIKCQQEHWHAEHKRTCRRKR,mutated_sequence,1.0,602.0,NP_001357029.1.a2m,NP_001357029.1.npy,ClinVar +NP_001357188.2,NP_001357188.2.csv,MGLKAAQKTLFPLRSIDDVVRLFAAELGREEPDLVLLSLVLGFVEHFLAVNRVIPTNVPELTFQPSPAPDPPGGLTYFPVADLSIIAALYARFTAQIRGAVDLSLYPREGGVSSRELVKKVSDVIWNSLSRSYFKDRAHIQSLFSFITGTKLDSSGVAFAVVGACQALGLRDVHLALSEDHAWVVFGPNGEQTAEVTWHGKGNEDRRGQTVNAGVAERSWLYLKGSYMRCDRKMEVAFMVCAINPSIDLHTDSLELLQLQQKLLWLLYDLGHLERYPMALGNLADLEELEPTPGRPDPLTLYHKGIASAKTYYRDEHIYPYMYLAGYHCRNRNVREALQAWADTATVIQDYNYCREDEEIYKEFFEVANDVIPNLLKEAASLLEAGEERPGEQSQGTQSQGSALQDPECFAHLLRFYDGICKWEEGSPTPVLHVGWATFLVQSLGRFEGQVRQKVRIVSREAEAAEAEEPWGEEAREGRRRGPRRESKPEEPPPPKKPALDKGLGTGQGAVSGPPRKPPGTVAGTARGPEGGSTAQVPAPTASPPPEGPVLTFQSEKMKGMKELLVATKINSSAIKLQLTAQSQVQMKKQKVSTPSDYTLSFLKRQRKGL,mutated_sequence,1.0,610.0,NP_001357188.2.a2m,NP_001357188.2.npy,ClinVar +NP_001357524.1,NP_001357524.1.csv,MVVLRAGKKTFLPPLCRAFACRGCQLAPERGAERRDTAPSGVSRFCPPRKSCHDWIGPPDKYSNLRPVHFYIPENESPLEQKLRKLRQETQEWNQQFWANQNLTFSKEKEEFIHSRLKTKGLGLRTESGQKATLNAEEMADFYKEFLSKNFQKHMYYNRDWYKRNFAITFFMGKVALERIWNKLKQKQKKRSN,mutated_sequence,1.0,193.0,NP_001357524.1.a2m,NP_001357524.1.npy,ClinVar +NP_001357587.1,NP_001357587.1.csv,MSGARSKLALFLCGCYVVALGAHTGEESVADHHEAEYYVAAVYEHPSILSLNPLALISRQEALELMNQNLDIYEQQVMTAAQKDVQIIVFPEDGIHGFNFTRTSIYPFLDFMPSPQVVRWNPCLEPHRFNDTEVLQRLSCMAIRGDMFLVANLGTKEPCHSSDPRCPKDGRYQFNTNVVFSNNGTLVDRYRKHNLYFEAAFDVPLKVDLITFDTPFAGRFGIFTCFDILFFDPAIRVLRDYKVKHVVYPTAWMNQLPLLAAIEIQKAFAVAFGINVLAANVHHPVLGMTGSGIHTPLESFWYHDMENPKSHLIIAQVAKNPVGLIGAENATGETDPSHSKFLKILSGDPYCEKDAQEVHCDEATKWNVNAPPTFHSEMMYDNFTLVPVWGKEGYLHVCSNGLCCYLLYERPTLSKELYALGVFDGLHTVHGTYYIQVCALVRCGGLGFDTCGQEITEATGIFEFHLWGNFSTSYIFPLFLTSGMTLEVPDQLGWENDHYFLRKSRLSSGLVTAALYGRLYERD,mutated_sequence,1.0,523.0,NP_001357587.1.a2m,NP_001357587.1.npy,ClinVar +NP_001360.1,NP_001360.1.csv,MFRIGRRQLWKHSVTRVLTQRLKGEKEAKRALLDARHNYLFAIVASCLDLNKTEVEDAILEGNQIERIDQLFAVGGLRHLMFYYQDVEEAETGQLGSLGGVNLVSGKIKKPKVFVTEGNDVALTGVCVFFIRTDPSKAITPDNIHQEVSFNMLDAADGGLLNSVRRLLSDIFIPALRATSHGWGELEGLQDAANIRQEFLSSLEGFVNVLSGAQESLKEKVNLRKCDILELKTLKEPTDYLTLANNPETLGKIEDCMKVWIKQTEQVLAENNQLLKEADDVGPRAELEHWKKRLSKFNYLLEQLKSPDVKAVLAVLAAAKSKLLKTWREMDIRITDATNEAKDNVKYLYTLEKCCDPLYSSDPLSMMDAIPTLINAIKMIYSISHYYNTSEKITSLFVKVTNQIISACKAYITNNGTASIWNQPQDVVEEKILSAIKLKQEYQLCFHKTKQKLKQNPNAKQFDFSEMYIFGKFETFHRRLAKIIDIFTTLKTYSVLQDSTIEGLEDMATKYQGIVATIKKKEYNFLDQRKMDFDQDYEEFCKQTNDLHNELRKFMDVTFAKIQNTNQALRMLKKFERLNIPNLGIDDKYQLILENYGADIDMISKLYTKQKYDPPLARNQPPIAGKILWARQLFHRIQQPMQLFQQHPAVLSTAEAKPIIRSYNRMAKVLLEFEVLFHRAWLRQIEEIHVGLEASLLVKAPGTGELFVNFDPQILILFRETECMAQMGLEVSPLATSLFQKRDRYKRNFSNMKMMLAEYQRVKSKIPAAIEQLIVPHLAKVDEALQPGLAALTWTSLNIEAYLENTFAKIKDLELLLDRVNDLIEFRIDAILEEMSSTPLCQLPQEEPLTCEEFLQMTKDLCVNGAQILHFKSSLVEEAVNELVNMLLDVEVLSEEESEKISNENSVNYKNESSAKREEGNFDTLTSSINARANALLLTTVTRKKKETEMLGEEARELLSHFNHQNMDALLKVTRNTLEAIRKRIHSSHTINFRDSNSASNMKQNSLPIFRASVTLAIPNIVMAPALEDVQQTLNKAVECIISVPKGVRQWSSELLSKKKIQERKMAALQSNEDSDSDVEMGENELQDTLEIASVNLPIPVQTKNYYKNVSENKEIVKLVSVLSTIINSTKKEVITSMDCFKRYNHIWQKGKEEAIKTFITQSPLLSEFESQILYFQNLEQEINAEPEYVCVGSIALYTADLKFALTAETKAWMVVIGRHCNKKYRSEMENIFMLIEEFNKKLNRPIKDLDDIRIAMAALKEIREEQISIDFQVGPIEESYALLNRYGLLIAREEIDKVDTLHYAWEKLLARAGEVQNKLVSLQPSFKKELISAVEVFLQDCHQFYLDYDLNGPMASGLKPQEASDRLIMFQNQFDNIYRKYITYTGGEELFGLPATQYPQLLEIKKQLNLLQKIYTLYNSVIETVNSYYDILWSEVNIEKINNELLEFQNRCRKLPRALKDWQAFLDLKKIIDDFSECCPLLEYMASKAMMERHWERITTLTGHSLDVGNESFKLRNIMEAPLLKYKEEIEDICISAVKERDIEQKLKQVINEWDNKTFTFGSFKTRGELLLRGDSTSEIIANMEDSLMLLGSLLSNRYNMPFKAQIQKWVQYLSNSTDIIESWMTVQNLWIYLEAVFVGGDIAKQLPKEAKRFSNIDKSWVKIMTRAHEVPSVVQCCVGDETLGQLLPHLLDQLEICQKSLTGYLEKKRLCFPRFFFVSDPALLEILGQASDSHTIQAHLLNVFDNIKSVKFHEKIYDRILSISSQEGETIELDKPVMAEGNVEVWLNSLLEESQSSLHLVIRQAAANIQETGFQLTEFLSSFPAQVGLLGIQMIWTRDSEEALRNAKFDKKIMQKTNQAFLELLNTLIDVTTRDLSSTERVKYETLITIHVHQRDIFDDLCHMHIKSPMDFEWLKQCRFYFNEDSDKMMIHITDVAFIYQNEFLGCTDRLVITPLTDRCYITLAQALGMSMGGAPAGPAGTGKTETTKDMGRCLGKYVVVFNCSDQMDFRGLGRIFKGLAQSGSWGCFDEFNRIDLPVLSVAAQQISIILTCKKEHKKSFIFTDGDNVTMNPEFGLFLTMNPGYAGRQELPENLKINFRSVAMMVPDRQIIIRVKLASCGFIDNVVLARKFFTLYKLCEEQLSKQVHYDFGLRNILSVLRTLGAAKRANPMDTESTIVMRVLRDMNLSKLIDEDEPLFLSLIEDLFPNILLDKAGYPELEAAISRQVEEAGLINHPPWKLKVIQLFETQRVRHGMMTLGPSGAGKTTCIHTLMRAMTDCGKPHREMRMNPKAITAPQMFGRLDVATNDWTDGIFSTLWRKTLRAKKGEHIWIILDGPVDAIWIENLNSVLDDNKTLTLANGDRIPMAPNCKIIFEPHNIDNASPATVSRNGMVFMSSSILDWSPILEGFLKKRSPQEAEILRQLYTESFPDLYRFCIQNLEYKMEVLEAFVITQSINMLQGLIPLKEQGGEVSQAHLGRLFVFALLWSAGAALELDGRRRLELWLRSRPTGTLELPPPAGPGDTAFDYYVAPDGTWTHWNTRTQEYLYPSDTTPEYGSILVPNVDNVRTDFLIQTIAKQGKAVLLIGEQGTAKTVIIKGFMSKYDPECHMIKSLNFSSATTPLMFQRTIESYVDKRMGTTYGPPAGKKMTVFIDDVNMPIINEWGDQVTNEIVRQLMEQNGFYNLEKPGEFTSIVDIQFLAAMIHPGGGRNDIPQRLKRQFSIFNCTLPSEASVDKIFGVIGVGHYCTQRGFSEEVRDSVTKLVPLTRRLWQMTKIKMLPTPAKFHYVFNLRDLSRVWQGMLNTTSEVIKEPNDLLKLWKHECKRVIADRFTVSSDVTWFDKALVSLVEEEFGEEKKLLVDCGIDTYFVDFLRDAPEAAGETSEEADAETPKIYEPIESFSHLKERLNMFLQLYNESIRGAGMDMVFFADAMVHLVKISRVIRTPQGNALLVGVGGSGKQSLTRLASFIAGYVSFQITLTRSYNTSNLMEDLKVLYRTAGQQGKGITFIFTDNEIKDESFLEYMNNVLSSGEVSNLFARDEIDEINSDLASVMKKEFPRCLPTNENLHDYFMSRVRQNLHIVLCFSPVGEKFRNRALKFPALISGCTIDWFSRWPKDALVAVSEHFLTSYDIDCSLEIKKEVVQCMGSFQDGVAEKCVDYFQRFRRSTHVTPKSYLSFIQGYKFIYGEKHVEVRTLANRMNTGLEKLKEASESVAALSKELEAKEKELQVANDKADMVLKEVTMKAQAAEKVKAEVQKVKDRAQAIVDSISKDKAIAEEKLEAAKPALEEAEAALQTIRPSDIATVRTLGRPPHLIMRIMDCVLLLFQRKVSAVKIDLEKSCTMPSWQESLKLMTAGNFLQNLQQFPKDTINEEVIEFLSPYFEMPDYNIETAKRVCGNVAGLCSWTKAMASFFSINKEVLPLKANLVVQENRHLLAMQDLQKAQAELDDKQAELDVVQAEYEQAMTEKQTLLEDAERCRHKMQTASTLISGLAGEKERWTEQSQEFAAQTKRLVGDVLLATAFLSYSGPFNQEFRDLLLNDWRKEMKARKIPFGKNLNLSEMLIDAPTISEWNLQGLPNDDLSIQNGIIVTKASRYPLLIDPQTQGKIWIKNKESRNELQITSLNHKYFRNHLEDSLSLGRPLLIEDVGEELDPALDNVLERNFIKTGSTFKVKVGDKEVDVLDGFRLYITTKLPNPAYTPEISARTSIIDFTVTMKGLEDQLLGRVILTEKQELEKERTHLMEDVTANKRRMKELEDNLLYRLTSTQGSLVEDESLIVVLSNTKRTAEEVTQKLEISAETEVQINSAREEYRPVATRGSILYFLITEMRLVNEMYQTSLRQFLGLFDLSLARSVKSPITSKRIANIIEHMTYEVYKYAARGLYEEHKFLFTLLLTLKIDIQRNRVKHEEFLTLIKGGASLDLKACPPKPSKWILDITWLNLVELSKLRQFSDVLDQISRNEKMWKIWFDKENPEEEPLPNAYDKSLDCFRRLLLIRSWCPDRTIAQARKYIVDSMGEKYAEGVILDLEKTWEESDPRTPLICLLSMGSDPTDSIIALGKRLKIETRYVSMGQGQEVHARKLLQQTMANGGWALLQNCHLGLDFMDELMDIIIETELVHDAFRLWMTTEAHKQFPITLLQMSIKFANDPPQGLRAGLKRTYSGVSQDLLDVSSGSQWKPMLYAVAFLHSTVQERRKFGALGWNIPYEFNQADFNATVQFIQNHLDDMDVKKGVSWTTIRYMIGEIQYGGRVTDDYDKRLLNTFAKVWFSENMFGPDFSFYQGYNIPKCSTVDNYLQYIQSLPAYDSPEVFGLHPNADITYQSKLAKDVLDTILGIQPKDTSGGGDETREAVVARLADDMLEKLPPDYVPFEVKERLQKMGPFQPMNIFLRQEIDRMQRVLSLVRSTLTELKLAIDGTIIMSENLRDALDCMFDARIPAWWKKASWISSTLGFWFTELIERNSQFTSWVFNGRPHCFWMTGFFNPQGFLTAMRQEITRANKGWALDNMVLCNEVTKWMKDDISAPPTEGVYVYGLYLEGAGWDKRNMKLIESKPKVLFELMPVIRIYAENNTLRDPRFYSCPIYKKPVRTDLNYIAAVDLRTAQTPEHWVLRGVALLCDVK,mutated_sequence,1.0,4624.0,NP_001360.1.a2m,NP_001360.1.npy,ClinVar +NP_001361282.1,NP_001361282.1.csv,METSASATASEKQEAKSGILEAAGFPDPGKKASPLVVAAAAAAAVAAQGVPQHLLPPFHAPLPIDMRHQEGRYHYEPHSVHGVHGPPALSGSPVISDISLIRLSPHPAGPGESPFNAPHPYVNPHMEHYLRSVHSSPTLSMISAARGLSPADVAQEHLKERGLFGLPAPGTTPSDYYHQMTLVAGHPAPYGDLLMQSGGAASAPHLHDYLNPVDVSRFSSPRVTPRLSRKRALSISPLSDASLDLQRMIRTSPNSLVAYINNSRSSSAASGSYGHLSAGALSPAFTFPHPINPVAYQQILSQQRGLGSAFGHTPPLIQPSPTFLAQQPMALTSINATPTQLSSSSNCLSDTNQNKQSSESAVSSTVNPVAIHKRSKVKTEPEGLRPASPLALTQEQLADLKEDLDRDDCKQEAEVVIYETNCHWEDCTKEYDTQEQLVHHINNEHIHGEKKEFVCRWQACTREQKPFKAQYMLVVHMRRHTGEKPHKCTFEGCSKAYSRLENLKTHLRSHTGEKPYVCEHEGCNKAFSNASDRAKHQNRTHSNEKPYICKIPGCTKRYTDPSSLRKHVKTVHGPDAHVTKKQRNDVHLRTPLLKENGDSEAGTEPGGPESTEASSTSQAVEDCLHVRAIKTESSGLCQSSPGAQSSCSSEPSPLGSAPNNDSGVEMPGTGPGSLGDLTALDDTPPGADTSALAAPSAGGLQLRKHMTTMHRFEQLKKEKLKSLKDSCSWAGPTPHTRNTKLPPLPGSGSILENFSGSGGGGPAGLLPNPRLSELSASEVTMLSQLQERRDSSTSTVSSAYTVSRRSSGISPYFSSRRSSEASPLGAGRPHNASSADSYDPISTDASRRSSEASQCSGGSGLLNLTPAQQYSLRAKYAAATGGPPPTPLPGLERMSLRTRLALLDAPERTLPAGCPRPLGPRRGSDGPTYGHGHAGAAPAFPHEAPGGGARRASDPVRRPDALSLPRVQRFHSTHNVNPGPLPPCADRRGLRLQSHPSTDGGLARGAYSPRPPSISENVAMEAVAAGVDGAGPEADLGLPEDDLVLPDDVVQYIKAHASGALDEGTGQVYPTESTGFSDNPRLPSPGLHGQRRMVAADSNVGPSAPMLGGCQLGFGAPSSLNKNNMPVQWNEVSSGTVDALASQVKPPPFPQGNLAVVQQKPAFGQYPGYSPQGLQASPGGLDSTQPHLQPRSGAPSQGIPRVNYMQQLRQPVAGSQCPGMTTTMSPHACYGQVHPQLSPSTISGALNQFPQSCSNMPAKPGHLGHPQQTEVAPDPTTMGNRHRELGVPDSALAGVPPPHPVQSYPQQSHHLAASMSQEGYHQVPSLLPARQPGFMEPQTGPMGVATAGFGLVQPRPPLEPSPTGRHRGVRAVQQQLAYARATGHAMAAMPSSQETAEAVPKGAMGNMGSVPPQPPPQDAGGAPDHSMLYYYGQIHMYEQDGGLENLGSCQVMRSQPPQPQACQDSIQPQPLPSPGVNQVSSTVDSQLLEAPQIDFDAIMDDGDHSSLFSGALSPSLLHSLSQNSSRLTTPRNSLTLPSIPAGISNMAVGDMSSMLTSLAEESKFLNMMT,mutated_sequence,1.0,1569.0,NP_001361282.1.a2m,NP_001361282.1.npy,ClinVar +NP_001361314.1,NP_001361314.1.csv,MSTERDSETTFDEDSQPNDEVVPYSDDETEDELDDQGSAVEPEQNRVNREAEENREPFRKECTWQVKANDRKYHEQPHFMNTKFLCIKESKYANNAIKTYKYNAFTFIPMNLFEQFKRAANLYFLALLILQAVPQISTLAWYTTLVPLLVVLGVTAIKDLVDDVARHKMDKEINNRTCEVIKDGRFKVAKWKEIQVGDVIRLKKNDFVPADILLLSSSEPNSLCYVETAELDGETNLKFKMSLEITDQYLQREDTLATFDGFIECEEPNNRLDKFTGTLFWRNTSFPLDADKILLRGCVIRNTDFCHGLVIFAGADTKIMKNSGKTRFKRTKIDYLMNYMVYTIFVVLILLSAGLAIGHAYWEAQVGNSSWYLYDGEDDTPSYRGFLIFWGYIIVLNTMVPISLYVSVEVIRLGQSHFINWDLQMYYAEKDTPAKARTTTLNEQLGQIHYIFSDKTGTLTQNIMTFKKCCINGQIYGDHRDASQHNHNKIEQVDFSWNTYADGKLAFYDHYLIEQIQSGKEPEVRQFFFLLAVCHTVMVDRTDGQLNYQAASPDEGALVNAARNFGFAFLARTQNTITISELGTERTYNVLAILDFNSDRKRMSIIVRTPEGNIKLYCKGADTVIYERLHRMNPTKQETQDALDIFANETLRTLCLCYKEIEEKEFTEWNKKFMAASVASTNRDEALDKVYEEIEKDLILLGATAIEDKLQDGVPETISKLAKADIKIWVLTGDKKETAENIGFACELLTEDTTICYGEDINSLLHARMENQRNRGGVYAKFAPPVQESFFPPGGNRALIITGSWLNEILLEKKTKRNKILKLKFPRTEEERRMRTQSKRRLEAKKEQRQKNFVDLACECSAVICCRVTPKQKAMVVDLVKRYKKAITLAIGDGANDVNMIKTAHIGVGISGQEGMQAVMSSDYSFAQFRYLQRLLLVHGRWSYIRMCKFLRYFFYKNFAFTLVHFWYSFFNGYSAQTAYEDWFITLYNVLYTSLPVLLMGLLDQDVSDKLSLRFPGLYIVGQRDLLFNYKRFFVSLLHGVLTSMILFFIPLGAYLQTVGQDGEAPSDYQSFAVTIASALVITVNFQIGLDTSYWTFVNAFSIFGSIALYFGIMFDFHSAGIHVLFPSAFQFTGTASNALRQPYIWLTIILAVAVCLLPVVAIRFLSMTIWPSESDKIQKHRKRLKAEEQWQRRQQVFRRGVSTRRSAYAFSHQRGYADLISSGRSIRKKRSPLDAIVADGTAEYRRTGDS,mutated_sequence,1.0,1251.0,NP_001361314.1.a2m,NP_001361314.1.npy,ClinVar +NP_001361433.1,NP_001361433.1.csv,MPVAEAPQVAGGQGDGGDGEEAEPEGMFKACEDSKRKARGYLRLVPLFVLLALLVLASAGVLLWYFLGYKAEVMVSQVYSGSLRVLNRHFSQDLTRRESSAFRSETAKAQKMLKELITSTRLGTYYNSSSVYSFGEGPLTCFFWFILQIPEHRRLMLSPEVVQALLVEELLSTVNSSAAVPYRAEYEVDPEGLVILEASVKDIAALNSTLGCYRYSYVGQGQVLRLKGPDHLASSCLWHLQGPKDLMLKLRLEWTLAECRDRLAMYDVAGPLEKRLITSVYGCSRQEPVVEVLASGAIMAVVWKKGLHSYYDPFVLSVQPVVFQACEVNLTLDNRLDSQGVLSTPYFPSYYSPQTHCSWHLTVPSLDYGLALWFDAYALRRQKYDLPCTQGQWTIQNRRLCGLRILQPYAERIPVVATAGITINFTSQISLTGPGVRVHYGLYNQSDPCPGEFLCSVNGLCVPACDGVKDCPNGLDERNCVCRATFQCKEDSTCISLPKVCDGQPDCLNGSDEEQCQEGVPCGTFTFQCEDRSCVKKPNPQCDGRPDCRDGSDEEHCDCGLQGPSSRIVGGAVSSEGEWPWQASLQVRGRHICGGALIADRWVITAAHCFQEDSMASTVLWTVFLGKVWQNSRWPGEVSFKVSRLLLHPYHEEDSHDYDVALLQLDHPVVRSAAVRPVCLPARSHFFEPGLHCWITGWGALREGGPISNALQKVDVQLIPQDLCSEVYRYQVTPRMLCAGYRKGKKDACQGDSGGPLVCKALSGRWFLAGLVSWGLGCGRPNYFGVYTRITGVISWIQQVVT,mutated_sequence,1.0,802.0,NP_001361433.1.a2m,NP_001361433.1.npy,ClinVar +NP_001361552.1,NP_001361552.1.csv,MEEQVFKGDPDTPHSISFSGSGFLSFYQAGAVDALRDLAPRMLETAHRFAGTSAGAVIAALAICGIEMDEYLRVLNVGVAEVKKSFLGPLSPSCKMVQMMRQFLYRVLPEDSYKVTTGKLHVSLTRLTDGENVVVSEFTSKEELIEALYCSCFVPVYCGLIPPTYRGVRYIDGGFTGMQPCAFWTDAITISTFSGQQDICPRDCPAIFHDFRMFNCSFQFSLENIARMTHALFPPDLVILHDYYYRGYEDAVLYLRRLNAVYLNSSSKRVIFPRVEVYCQIELALGNECPERSQPSLRARQASLEGATQPHKEWVPKGDGRGSHGPPVSQPVQTLEFTCESPVSAPVSPLEQPPAQPLASSTPLSLSGMPPVSFPAVHKPPSSTPGSSLPTPPPGLSPLSPQQQVQPSGSPARSLHSQAPTSPRPSLGPSTVGAPQTLPRSSLSAFPAQPPVEELGQEQPQAVALLVSSKPKSAVPLVHVKETVSKPYVTESPAEDSNWVNKVFKKNKQKTSGTRKGFPRHSGSKKPSSKVQSAPCPLNFPLLSTSETVWVTYRPHPSRIQECCPEVWNSLG,mutated_sequence,1.0,572.0,NP_001361552.1.a2m,NP_001361552.1.npy,ClinVar +NP_001361757.1,NP_001361757.1.csv,MAARAAAAAAAAAARARARAGSGERRAPPGPRPAPGARDLEAGARGAAAAAAAPGPMLGGGGDGGGGLNSVHHHPLLPRHELNMAHNAGAAAAAGTHSAKSGGSEAALKEGGSAAALSSSSSSSAAAAAASSSSSSGPGSAMETGLLPNHKLKTVGEAPAAPPHQQHHHHHHAHHHHHHAHHLHHHHALQQQLNQFQQQQQQQQQQQQQQQQQQHPISNNNSLGGAGGGAPQPGPDMEQPQHGGAKDSAAGGQADPPGPPLLSKPGDEDDAPPKMGEPAGGRYEHPGLGALGTQQPPVAVPGGGGGPAAVPEFNNYYGSAAPASGGPGGRAGPCFDQHGGQQSPGMGMMHSASAAAAGAPGSMDPLQNSHEGYPNSQCNHYPGYSRPGAGGGGGGGGGGGGGSGGGGGGGGAGAGGAGAGAVAAAAAAAAAAAGGGGGGGYGGSSAGYGVLSSPRQQGGGMMMGPGGGGAASLSKAAAGSAAGGFQRFAGQNQHPSGATPTLNQLLTSPSPMMRSYGGSYPEYSSPSAPPPPPSQPQSQAAAAGAAAGGQQAAAGMGLGKDMGAQYAAASPAWAAAQQRSHPAMSPGTPGPTMGRSQGSPMDPMVMKRPQLYGMGSNPHSQPQQSSPYPGGSYGPPGPQRYPIGIQGRTPGAMAGMQYPQQQMPPQYGQQGVSGYCQQGQQPYYSQQPQPPHLPPQAQYLPSQSQQRYQPQQDMSQEGYGTRSQPPLAPGKPNHEDLNLIQQERPSSLPDLSGSIDDLPTGTEATLSSAVSASGSTSSQGDQSNPAQSPFSPHASPHLSSIPGGPSPSPVGSPVGSNQSRSGPISPASIPGSQMPPQPPGSQSESSSHPALSQSPMPQERGFMAGTQRNPQMAQYGPQQTGPSMSPHPSPGGQMHAGISSFQQSNSSGTYGPQMSQYGPQGNYSRPPAYSGVPSASYSGPGPGMGISANNQMHGQGPSQPCGAVPLGRMPSAGMQNRPFPGNMSSMTPSSPGMSQQGGPGMGPPMPTVNRKAQEAAAAVMQAAANSAQSRQGSFPGMNQSGLMASSSPYSQPMNNSSSLMNTQAPPYSMAPAMVNSSAASVGLADMMSPGESKLPLPLKADGKEEGTPQPESKSKDSYSSQGISQPPTPGNLPVPSPMSPSSASISSFHGDESDSISSPGWPKTPSSPKSSSSTTTGEKITKVYELGNEPERKLWVDRYLTFMEERGSPVSSLPAVGKKPLDLFRLYVCVKEIGGLAQVNKNKKWRELATNLNVGTSSSAASSLKKQYIQYLFAFECKIERGEEPPPEVFSTGDTKKQPKLQPPSPANSGSLQGPQTPQSTGSNSMAEVPGDLKPPTPASTPHGQMTPMQGGRSSTISVHDPFSDVSDSSFPKRNSMTPNAPYQQGMSMPDVMGRMPYEPNKDPFGGMRKVPGSSEPFMTQGQMPNSSMQDMYNQSPSGAMSNLGMGQRQQFPYGASYDRRHEPYGQQYPGQGPPSGQPPYGGHQPGLYPQQPNYKRHMDGMYGPPAKRHEGDMYNMQYSSQQQEMYNQYGGSYSGPDRRPIQGQYPYPYSRERMQGPGQIQTHGIPPQMMGGPLQSSSSEGPQQNMWAARNDMPYPYQNRQGPGGPTQAPPYPGMNRTDDMMVPDQRINHESQWPSHVSQRQPYMSSSASMQPITRPPQPSYQTPPSLPNHISRAPSPASFQRSLENRMSPSKSPFLPSMKMQKVMPTVPTSQVTGPPPQPPPIRREITFPPGSVEASQPVLKQRRKITSKDIVTPEAWRVMMSLKSGLLAESTWALDTINILLYDDSTVATFNLSQLSGFLELLVEYFRKCLIDIFGILMEYEVGDPSQKALDHNAARKDDSQSLADDSGKEEEDAECIDDDEEDEEDEEEDSEKTESDEKSSIALTAPDAAADPKEKPKQASKFDKLPIKIVKKNNLFVVDRSDKLGRVQEFNSGLLHWQLGGGDTTEHIQTHFESKMEIPPRRRPPPPLSSAGRKKEQEGKGDSEEQQEKSIIATIDDVLSARPGALPEDANPGPQTESSKFPFGIQQAKSHRNIKLLEDEPRSRDETPLCTIAHWQDSLAKRCICVSNIVRSLSFVPGNDAEMSKHPGLVLILGKLILLHHEHPERKRAPQTYEKEEDEDKGVACSKDEWWWDCLEVLRDNTLVTLANISGQLDLSAYTESICLPILDGLLHWMVCPSAEAQDPFPTVGPNSVLSPQRLVLETLCKLSIQDNNVDLILATPPFSRQEKFYATLVRYVGDRKNPVCREMSMALLSNLAQGDALAARAIAVQKGSIGNLISFLEDGVTMAQYQQSQHNLMHMQPPPLEPPSVDMMCRAAKALLAMARVDENRSEFLLHEGRLLDISISAVLNSLVASVICDVLFQIGQL,mutated_sequence,1.0,2372.0,NP_001361757.1.a2m,NP_001361757.1.npy,ClinVar +NP_001362309.1,NP_001362309.1.csv,MFGIQENIPRGGTTMKEEPLGSGMNPVRSWMHTAGVVDANTAAQSGVGLARAHFEKQPPSNLRKSNFFHFVLALYDRQGQPVEIERTAFVDFVEKEKEPNNEKTNNGIHYKLQLLYSNGVRTEQDLYVRLIDSMTKQAIVYEGQDKNPEMCRVLLTHEIMCSRCCDKKSCGNRNETPSDPVIIDRFFLKFFLKCNQNCLKNAGNPRDMRRFQVVVSTTVNVDGHVLAVSDNMFVHNNSKHGRRARRLDPSEGTAPSYLENATPCIKAISPSEGWTTGGATVIIIGDNFFDGLQVVFGTMLVWSELITPHAIRVQTPPRHIPGVVEVTLSYKSKQFCKGAPGRFVYTALNEPTIDYGFQRLQKVIPRHPGDPERLPKEVLLKRAADLVEALYGMPHNNQEIILKRAADIAEALYSVPRNHNQIPTLGNNPAHTGMMGVNSFSSQLAVNVSETSQANDQVGYSRNTSSVSPRGYVPSSTPQQSNYNTVSTSMNGYGSGAMASLGVPGSPGFLNGSSANSPYGIVPSSPTMAASSVTLPSNCSSTHGIFSFSPANVISAVKQKSAFAPVVRPQASPPPSCTSANGNGLQGSLLGAEDVAAEKTNWPFCEVGGIFHFDELMLKKGTGKLCLGW,mutated_sequence,1.0,629.0,NP_001362309.1.a2m,NP_001362309.1.npy,ClinVar +NP_001364194.1,NP_001364194.1.csv,MAEPRQEFEVMEDHAGTYGLGDRKDQGGYTMHQDQEGDTDAGLKESPLQTPTEDGSEEPGSETSDAKSTPTAEAEEAGIGDTPSLEDEAAGHVTQEELRVPGRQRKAPERPLANEISAHVQPGPCGEASGVSGPCLGEKEPEAPVPLTASLPQHRPVCPAPPPTGGPQEPSLEWGQKGGDWAEKGPAFPKPATTAYLHTEPESGKVVQEGFLREPGPPGLSHQLMSGMPGAPLLPEGPREATRQPSGTGPEDTEGGRHAPELLKHQLLGDLHQEGPPLKGAGGKERPGSKEEVDEDRDVDESSPQDSPPSKASPAQDGRPPQTAAREATSIPGFPAEGAIPLPVDFLSKVSTEIPASEPDGPSVGRAKGQDAPLEFTFHVEITPNVQKEQAHSEEHLGRAAFPGAPGEGPEARGPSLGEDTKEADLPEPSEKQPAAAPRGKPVSRVPQLKARMVSKSKDGTGSDDKKAKTSTRSSAKTLKNRPCLSPKHPTPGSSDPLIQPSSPAVCPEPPSSPKYVSSVTSRTGSSGAKEMKLKGADGKTKIATPRGAAPPGQKGQANATRIPAKTPPAPKTPPSSGEPPKSGDRSGYSSPGSPGTPGSRSRTPSLPTPPTREPKKVAVVRTPPKSPSSAKSRLQTAPVPMPDLKNVKSKIGSTENLKHQPGGGKVQIINKKLDLSNVQSKCGSKDNIKHVPGGGSVQIVYKPVDLSKVTSKCGSLGNIHHKPGGGQVEVKSEKLDFKDRVQSKIGSLDNITHVPGGGNKKIETHKLTFRENAKAKTDHGAEIVYKSPVVSGDTSPRHLSNVSSTGSIDMVDSPQLATLADEVSASLAKQGL,mutated_sequence,1.0,833.0,NP_001364194.1.a2m,NP_001364194.1.npy,ClinVar +NP_001365112.1,NP_001365112.1.csv,MASEVVCGLIFRLLLPICLAVACAFRYNGLSFVYLIYLLLIPLFSEPTKTTMQGHTGRLLKSLCFISLSFLLLHIIFHITLVSLEAQHRIAPGYNCSTWEKTFRQIGFESLKGADAGNGIRVFVPDIGMFIASLTIWLLCRNIVQKPVTDEAAQSNPEFENEELAEGEKIDSEEALIYEEDFNGGDGVEGELEESTKLKMFRRLASVASKLKEFIGNMITTAGKVVVTILLGSSGMMLPSLTSSVYFFVFLGLCTWWSWCRTFDPLLFSCLCVLLAIFTAGHLIGLYLYQFQFFQEAVPPNDYYARLFGIKSVIQTDCSSTWKIIVNPDLSWYHHANPILLLVMYYTLATLIRIWLQEPLVQDEGTKEEDKALACSPIQITAGRRRSLWYATHYPTDERKLLSMTQDDYKPSDGLLVTVNGNPVDYHTIHPSLPMENGPGKADLYSTPQYRWEPSDESSEKREEEEEEKEEFEEERSREEKRSIKVHAMVSVFQFIMKQSYICALIAMMAWSITYHSWLTFVLLIWSCTLWMIRNRRKYAMISSPFMVVYGNLLLILQYIWSFELPEIKKVPGFLEKKEPGELASKILFTITFWLLLRQHLTEQKALQEKEALLSEVKIGSQENEEKDEELQDIQVEGEPKEEEEEEAKEEKQERKKVEQEEAEEEDEQDIMKVLGNLVVAMFIKYWIYVCGGMFFFVSFEGKIVMYKIIYMVLFLFCVALYQVHYEWWRKILKYFWMSVVIYTMLVLIFIYTYQFENFPGLWQNMTGLKKEKLEDLGLKQFTVAELFTRIFIPTSFLLVCILHLHYFHDRFLELTDLKSIPSKEDNTIYSHAKVNGRVYLIINSIKKKLPIHQNELAHPEGSLPDLTMMHLTASLEKPEVRKLAEPGEEKLEGYSEKAQKGDLGKDSEESEEDGEEEEESEEEEETSDLRNKWHLVIDRLTVLFLKFLEYFHKLQVFMWWILELHIIKIVSSYIIWVSVKEVSLFNYVFLISWAFALPYAKLRRLASSVCTVWTCVIIVCKMLYQLQTIKPENFSVNCSLPNENQTNIPFNELNKSLLYSAPIDPTEWVGLRKSSPLLVYLRNNLLMLAILAFEVTIYRHQEYYRGRNNLTAPVSRTIFHDITRLHLDDGLINCAKYFINYFFYKFGLETCFLMSVNVIGQRMDFYAMIHACWLIAVLYRRRRKAIAEIWPKYCCFLACIITFQYFICIGIPPAPCRDYPWRFKGASFNDNIIKWLYFPDFIVRPNPVFLVYDFMLLLCASLQRQIFEDENKAAVRIMAGDNVEICMNLDAASFSQHNPVPDFIHCRSYLDMSKVIIFSYLFWFVLTIIFITGTTRISIFCMGYLVACFYFLLFGGDLLLKPIKSILRYWDWLIAYNVFVITMKNILSIGACGYIGTLVHNSCWLIQAFSLACTVKGYQMPAANSPCTLPSGEAGIIWDSICFAFLLLQRRVFMSYYFLHVVADIKASQILASRGAELFQATIVKAVKARIEEEKKSMDQLKRQMDRIKARQQKYKKGKERMLSLTQEPGEGQDMQKLSEEDDEREADKQKAKGKKKQWWRPWVDHASMVRSGDYYLFETDSEEEEEEELKKEDEEPPRRSAFQRAIGKFASAILALPKSVIKLPKTILQYLIRAAKFVYQAWITDPKTALRQRHKEKKRSAREERKRRRKGSKEGPVEWEDREDEPIKKKSDGPDNIIKRIFNILKFTWVLFLATVDSFTTWLNSISREHIDISTVLRIERCMLTREIKKGNVPTRESIHMYYQNHIMNLSRESGLDTIDEHPGAASGAQTAHRMDSLDSHDSISSCYTEATMLFSRQSTLDDLDGQEIPKTSERARPRLRKMLSMDMSSSSADSGSLASSEPTQCTMLYSRQGTTETIEEVEAEQEEEAGSTAPEPREAKEYEATGYDVGAMGAEEASLTPEEELTQFSTLDGDVEAPPSYSKAVSFEHLSFGSQDDSAGKNRMAVSPDDSRTDKLGSSILPPLTHELTASELLLKKMFHDDELEESEKFYVGQPRFLLLFYAMYNTLVARSEMVCYFVIILNHMVSASMITLLLPILIFLWAMLSVPRPSRRFWMMAIVYTEVAIVVKYFFQFGFFPWNKNVEVNKDKPYHPPNIIGVEKKEGYVLYDLIQLLALFFHRSILKCHGLWDEDDMTESGMAREESDDELSLGHGRRDSSDSLKSINLAASVESVHVTFPEQQTAVRRKRSGSSSEPSQRSSFSSNRSQRGSTSTRNSSQKGSSVLSIKQKGKRELYMEKLQEHLIKAKAFTIKKTLEIYVPIKQFFYNLIHPEYSAVTDVYVLMFLADTVDFIIIVFGFWAFGKHSAAADITSSLSEDQVPGPFLVMVLIQFGTMVVDRALYLRKTVLGKVIFQVILVFGIHFWMFFILPGVTERKFSQNLVAQLWYFVKCVYFGLSAYQIRCGYPTRVLGNFLTKSYNYVNLFLFQGFRLVPFLTELRAVMDWVWTDTTLSLSSWICVEDIYAHIFILKCWRESEKRYPQPRGQKKKKVVKYGMGGMIIVLLICIVWFPLLFMSLIKSVAGVINQPLDVSVTITLGGYQPIFTMSAQQSQLKVMDQQSFNKFIQAFSRDTGAMQFLENYEKEDITVAELEGNSNSLWTISPPSKQKMIHELLDPNSSFSVVFSWSIQRNLSLGAKSEIATDKLSFPLKNITRKNIAKMIAGNSTESSKTPVTIEKIYPYYVKAPSDSNSKPIKQLLSENNFMDITIILSRDNTTKYNSEWWVLNLTGNRIYNPNSQALELVVFNDKVSPPSLGFLAGYGIMGLYASVVLVIGKFVREFFSGISHSIMFEELPNVDRILKLCTDIFLVRETGELELEEDLYAKLIFLYRSPETMIKWTREKTN,mutated_sequence,1.0,2865.0,NP_001365112.1.a2m,NP_001365112.1.npy,ClinVar +NP_001365381.1,NP_001365381.1.csv,MSDKMSSFLHIGDICSLYAEGSTNGFISTLGLVDDRCVVQPETGDLNNPPKKFRDCLFKLCPMNRYSAQKQFWKAAKPGANSTTDAVLLNKLHHAADLEKKQNETENRKLLGTVIQYGNVIQLLHLKSNKYLTVNKRLPALLEKNAMRVTLDEAGNEGSWFYIQPFYKLRSIGDSVVIGDKVVLNPVNAGQPLHASSHQLVDNPGCNEVNSVNCNTSWKIVLFMKWSDNKDDILKGGDVVRLFHAEQEKFLTCDEHRKKQHVFLRTTGRQSATSATSSKALWEVEVVQHDPCRGGAGYWNSLFRFKHLATGHYLAAEVDPDFEEECLEFQPSVDPDQDASRSRLRNAQEKMVYSLVSVPEGNDISSIFELDPTTLRGGDSLVPRNSYVRLRHLCTNTWVHSTNIPIDKEEEKPVMLKIGTSPVKEDKEAFAIVPVSPAEVRDLDFANDASKVLGSIAGKLEKGTITQNERRSVTKLLEDLVYFVTGGTNSGQDVLEVVFSKPNRERQKLMREQNILKQIFKLLQAPFTDCGDGPMLRLEELGDQRHAPFRHICRLCYRVLRHSQQDYRKNQEYIAKQFGFMQKQIGYDVLAEDTITALLHNNRKLLEKHITAAEIDTFVSLVRKNREPRFLDYLSDLCVSMNKSIPVTQELICKAVLNPTNADILIETKLVLSRFEFEGVSSTGENALEAGEDEEEVWLFWRDSNKEIRSKSVRELAQDAKEGQKEDRDVLSYYRYQLNLFARMCLDRQYLAINEISGQLDVDLILRCMSDENLPYDLRASFCRLMLHMHVDRDPQEQVTPVKYARLWSEIPSEIAIDDYDSSGASKDEIKERFAQTMEFVEEYLRDVVCQRFPFSDKEKNKLTFEVVNLARNLIYFGFYNFSDLLRLTKILLAILDCVHVTTIFPISKMAKGEENKGNNDVEKLKSSNVMRSIHGVGELMTQVVLRGGGFLPMTPMAAAPEGNVKQAEPEKEDIMVMDTKLKIIEILQFILNVRLDYRISCLLCIFKREFDESNSQTSETSSGNSSQEGPSNVPGALDFEHIEEQAEGIFGGSEENTPLDLDDHGGRTFLRVLLHLTMHDYPPLVSGALQLLFRHFSQRQEVLQAFKQVQLLVTSQDVDNYKQIKQDLDQLRSIVEKSELWVYKGQGPDETMDGASGENEHKKTEEGNNKPQKHESTSSYNYRVVKEILIRLSKLCVQESASVRKSRKQQQRLLRNMGAHAVVLELLQIPYEKAEDTKMQEIMRLAHEFLQNFCAGNQQNQALLHKHINLFLNPGILEAVTMQHIFMNNFQLCSEINERVVQHFVHCIETHGRNVQYIKFLQTIVKAEGKFIKKCQDMVMAELVNSGEDVLVFYNDRASFQTLIQMMRSERDRMDENSPLMYHIHLVELLAVCTEGKNVYTEIKCNSLLPLDDIVRVVTHEDCIPEVKIAYINFLNHCYVDTEVEMKEIYTSNHMWKLFENFLVDICRACNNTSDRKHADSILEKYVTEIVMSIVTTFFSSPFSDQSTTLQTRQPVFVQLLQGVFRVYHCNWLMPSQKASVESCIRVLSDVAKSRAIAIPVDLDSQVNNLFLKSHSIVQKTAMNWRLSARNAARRDSVLAASRDYRNIIERLQDIVSALEDRLRPLVQAELSVLVDVLHRPELLFPENTDARRKCESGGFICKLIKHTKQLLEENEEKLCIKVLQTLREMMTKDRGYGEKLISIDELDNAELPPAPDSENATEELEPSPPLRQLEDHKRGEALRQVLVNRYYGNVRPSGRRESLTSFGNGPLSAGGPGKPGGGGGGSGSSSMSRGEMSLAEVQCHLDKEGASNLVIDLIMNASSDRVFHESILLAIALLEGGNTTIQHSFFCRLTEDKKSEKFFKVFYDRMKVAQQEIKATVTVNTSDLGNKKKDDEVDRDAPSRKKAKEPTTQITEEVRDQLLEASAATRKAFTTFRREADPDDHYQPGEGTQATADKAKDDLEMSAVITIMQPILRFLQLLCENHNRDLQNFLRCQNNKTNYNLVCETLQFLDCICGSTTGGLGLLGLYINEKNVALINQTLESLTEYCQGPCHENQNCIATHESNGIDIITALILNDINPLGKKRMDLVLELKNNASKLLLAIMESRHDSENAERILYNMRPKELVEVIKKAYMQGEVEFEDGENGEDGAASPRNVGHNIYILAHQLARHNKELQSMLKPGGQVDGDEALEFYAKHTAQIEIVRLDRTMEQIVFPVPSICEFLTKESKLRIYYTTERDEQGSKINDFFLRSEDLFNEMNWQKKLRAQPVLYWCARNMSFWSSISFNLAVLMNLLVAFFYPFKGVRGGTLEPHWSGLLWTAMLISLAIVIALPKPHGIRALIASTILRLIFSVGLQPTLFLLGAFNVCNKIIFLMSFVGNCGTFTRGYRAMVLDVEFLYHLLYLVICAMGLFVHEFFYSLLLFDLVYREETLLNVIKSVTRNGRSIILTAVLALILVYLFSIVGYLFFKDDFILEVDRLPNETAVPETGESLASEFLFSDVCRVESGENCSSPAPREELVPAEETEQDKEHTCETLLMCIVTVLSHGLRSGGGVGDVLRKPSKEEPLFAARVIYDLLFFFMVIIIVLNLIFGVIIDTFADLRSEKQKKEEILKTTCFICGLERDKFDNKTVTFEEHIKEEHNMWHYLCFIVLVKVKDSTEYTGPESYVAEMIKERNLDWFPRMRAMSLVSSDSEGEQNELRNLQEKLESTMKLVTNLSGQLSELKDQMTEQRKQKQRIGLLGHPPHMNVNPQQPA,mutated_sequence,1.0,2758.0,NP_001365381.1.a2m,NP_001365381.1.npy,ClinVar +NP_001365383.1,NP_001365383.1.csv,MEPEDLPWPGELEEEEEEEEEEEEEEEEAAAAAAANVDDVVVVEEVEEEAGRELDSDSHYGPQHLESIDDEEDEEAKAWLQAHPGRILPPLSPPQHRYSEGERTSLEKIVPLTCHVWQQIVYQGNSRTQISDTNVVCLETTAQRGSGDDQKTESWHCLPQEMDSSQTLDTSQTRFNVRTEDTEVTDFPSLEEGILTQSENQVKEPNRDLFCSPLLVIQDSFASPDLPLLTCLTQDQEFAPDSLFHQSELSFAPLRGIPDKSEDTEWSSRPSEVSEALFQATAEVASDLASSRFSVSQHPLIGSTAVGSQCPFLPSEQGNNEETISSVDELKIPKDCDRYDDLCSYMSWKTRKDTQWPENNLADKDQVSVATSFDITDENIATKRSDHFDAARSYGQYWTQEDSSKQAETYLTKGLQGKVESDVITLDGLNENAVVCSERVAELQRKPTRESEYHSSDLRMLRMSPDTVPKAPKHLKAGDTSKGGIAKVTQSNLKSGITTTPVDSDIGSHLSLSLEDLSQLAVSSPLETTTGQHTDTLNQKTLADTHLTEETLKVTAIPEPADQKTATPTVLSSSHSHRGKPSIFYQQGLPDSHLTEEALKVSAAPGLADQTTGMSTLTSTSYSHREKPGTFYQQELPESNLTEEPLEVSAAPGPVEQKTGIPTVSSTSHSHVEDLLFFYRQTLPDGHLTDQALKVSAVSGPADQKTGTATVLSTPHSHREKPGIFYQQEFADSHQTEETLTKVSATPGPADQKTEIPAVQSSSYSQREKPSILYPQDLADSHLPEEGLKVSAVAGPADQKTGLPTVPSSAYSHREKLLVFYQQALLDSHLPEEALKVSAVSGPADGKTGTPAVTSTSSASSSLGEKPSAFYQQTLPNSHLTEEALKVSIVPGPGDQKTGIPSAPSSFYSHREKPIIFSQQTLPDFLFPEEALKVSAVSVLAAQKTGTPTVSSNSHSHSEKSSVFYQQELPDSDLPRESLKMSAIPGLTDQKTVPTPTVPSGSFSHREKPSIFYQQEWPDSYATEKALKVSTGPGPADQKTEIPAVQSSSYPQREKPSVLYPQVLSDSHLPEESLKVSAFPGPADQMTDTPAVPSTFYSQREKPGIFYQQTLPESHLPKEALKISVAPGLADQKTGTPTVTSTSYSQHREKPSIFHQQALPGTHIPEEAQKVSAVTGPGNQKTWIPRVLSTFYSQREKPGIFYQQTLPGSHIPEEAQKVSPVLGPADQKTGTPTPTSASYSHTEKPGIFYQQVLPDNHPTEEALKISVASEPVDQTTGTPAVTSTSYSQYREKPSIFYQQSLPSSHLTEEAKNVSAVPGPADQKTVIPILPSTFYSHTEKPGVFYQQVLPHSHPTEEALKISVASEPVDQTTGTPTVTSTSYSQHTEKPSIFYQQSLPGSHLTEEAKNVSAVPGPGDRKTGIPTLPSTFYSHTEKPGSFYQQVLPHSHLPEEALEVSVAPGPVDQTIGTPTVTSPSSSFGEKPIVIYKQAFPEGHLPEESLKVSVAPGPVGQTTGAPTITSPSYSQHRAKSGSFYQLALLGSQIPEEALRVSSAPGPADQTTGIPTITSTSYSFGEKPIVNYKQAFPDGHLPEEALKVSIVSGPTEKKTDIPAGPLGSSALGEKPITFYRQALLDSPLNKEVVKVSAAPGPADQKTETLPVHSTSYSNRGKPVIFYQQTLSDSHLPEEALKVPPVPGPDAQKTETPSVSSSLYSYREKPIVFYQQALPDSELTQEALKVSAVPQPADQKTGLSTVTSSFYSHTEKPNISYQQELPDSHLTEEALKVSNVPGPADQKTGVSTVTSTSYSHREKPIVSYQRELPHFTEAGLKILRVPGPADQKTGINILPSNSYPQREHSVISYEQELPDLTEVTLKAIGVPGPADQKTGIQIASSSSYSNREKASIFHQQELPDVTEEALNVFVVPGQGDRKTEIPTVPLSYYSRREKPSVISQQELPDSHLTEEALKVSPVSIPAEQKTGIPIGLSSSYSHSHKEKLKISTVHIPDDQKTEFPAATLSSYSQIEKPKISTVIGPNDQKTPSQTAFHSSYSQTVKPNILFQQQLPDRDQSKGILKISAVPELTDVNTGKPVSLSSSYFHREKSNIFSPQELPGSHVTEDVLKVSTIPGPAGQKTVLPTALPSSFSHREKPDIFYQKDLPDRHLTEDALKISSALGQADQITGLQTVPSGTYSHGENHKLVSEHVQRLIDNLNSSDSSVSSNNVLLNSQADDRVVINKPESAGFRDVGSEEIQDAENSAKTLKEIRTLLMEAENMALKRCNFPAPLARFRDISDISFIQSKKVVCFKEPSSTGVSNGDLLHRQPFTEESPSSRCIQKDIGTQTNLKCRRGIENWEFISSTTVRSPLQEAESKVSMALEETLRQYQAAKSVMRSEPEGCSGTIGNKIIIPMMTVIKSDSSSDASDGNGSCSWDSNLPESLESVSDVLLNFFPYVSPKTSITDSREEEGVSESEDGGGSSVDSLAAHVKNLLQCESSLNHAKEILRNAEEEESRVRAHAWNMKFNLAHDCGYSISELNEDDRRKVEEIKAELFGHGRTTDLSKGLQSPRGMGCKPEAVCSHIIIESHEKGCFRTLTSEHPQLDRHPCAFRSAGPSEMTRGRQNPSSCRAKHVNLSASLDQNNSHFKVWNSLQLKSHSPFQNFIPDEFKISKGLRMPFDEKMDPWLSELVEPAFVPPKEVDFHSSSQMPSPEPMKKFTTSITFSSHRHSKCISNSSVVKVGVTEGSQCTGASVGVFNSHFTEEQNPPRDLKQKTSSPSSFKMHSNSQDKEVTILAEGRRQSQKLPVDFERSFQEEKPLERSDFTGSHSEPSTRANCSNFKEIQISDNHTLISMGRPSSTLGVNRSSSRLGVKEKNVTITPDLPSCIFLEQRELFEQSKAPRADDHVRKHHSPSPQHQDYVAPDLPSCIFLEQRELFEQCKAPYVDHQMRENHSPLPQGQDSIASDLPSPISLEQCQSKAPGVDDQMNKHHFPLPQGQDCVVEKNNQHKPKSHISNINVEAKFNTVVSQSAPNHCTLAASASTPPSNRKALSCVHITLCPKTSSKLDSGTLDERFHSLDAASKARMNSEFNFDLHTVSSRSLEPTSKLLTSKPVAQDQESLGFLGPKSSLDFQVVQPSLPDSNTITQDLKTIPSQNSQIVTSRQIQVNISDFEGHSNPEGTPVFADRLPEKMKTPLSAFSEKLSSDAVTQITTESPEKTLFSSEIFINAEDRGHEIIEPGNQKLRKAPVKFASSSSVQQVTFSRGTDGQPLLLPYKPSGSTKMYYVPQLRQIPPSPDSKSDTTVESSHSGSNDAIAPDFPAQVLGTRDDDLSATVNIKHKEGIYSKRVVTKASLPVGEKPLQNENADASVQVLITGDENLSDKKQQEIHSTRAVTEAAQAKEKESLQKDTADSSAAAAAEHSAQVGDPEMKNLPDTKAITQKEEIHRKKTVPEEAWPNNKESLQINIEESECHSEFENTTRSVFRSAKFYIHHPVHLPSDQDICHESLGKSVFMRHSWKDFFQHHPDKHREHMCLPLPYQNMDKTKTDYTRIKSLSINVNLGNKEVMDTTKSQVRDYPKHNGQISDPQRDQKVTPEQTTQHTVSLNELWNKYRERQRQQRQPELGDRKELSLVDRLDRLAKILQNPITHSLQVSESTHDDSRGERSVKEWSGRQQQRNKLQKKKRFKSLEKSHKNTGELKKSKVLSHHRAGRSNQIKIEQIKFDKYILSKQPGFNYISNTSSDCRPSEESELLTDTTTNILSGTTSTVESDILTQTDREVALHERSSSVSTIDTARLIQAFGHERVCLSPRRIKLYSSITNQQRRYLEKRSKHSKKVLNTGHPLVTSEHTRRRHIQVANHVISSDSISSSASSFLSSNSTFCNKQNVHMLNKGIQAGNLEIVNGAKKHTRDVGITFPTPSSSEAKLEENSDVTSWSEEKREEKMLFTGYPEDRKLKKNKKNSHEGVSWFVPVENVESRSKKENVPNTCGPGISWFEPITKTRPWREPLREQNCQGQHLDGRGYLAGPGREAGRDLLRPFVRATLQESLQFHRPDFISRSGERIKRLKLIVQERKLQSMLQTERDALFNIDRERQGHQNRMCPLPKRVFLAIQKNKPISKKEMIQRSKRIYEQLPEVQKKREEEKRKSEYKSYRLRAQLYKKRVTNQLLGRKVPWD,mutated_sequence,1.0,4168.0,NP_001365383.1.a2m,NP_001365383.1.npy,ClinVar +NP_001365544.1,NP_001365544.1.csv,MNPREEKVKIITEEFIENDEDADMGRQNKNSKVRRQPRKKQPPTAVPKEMVSEKSHLGNPQEPVQEEPKTRLLSMTVRRGPRSLPPIPSTSRTGFAEFSMRGRMREKLQAARSKAESALLQEIPTPRPRRLRSPSKKELETEFGTEPGKEVERTQQEVDSQSYSRVKFHDSARKIKPKPQVPPGFPSAEEAYNFFTFNFDPEPEGSEEKPKARHRAGTNQEEEEGEEEEPPAQGGGKEMDEEELLNGDDAEDFLLGLDHVADDFVAVRPADYESIHDRLQMEREMLFIPSRQTVPTYKKLPENVQPRFLEDEGLYTGVRPEVARTNQNIMENRLLMQDPERRWFGDDGRILALPNPIKPFPSRPPVLTQEQSIKAELETLYKKAVKYVHSSQHVIRSGDPPGNFQLDIDISGLIFTHHPCFSREHVLAAKLAQLYDQYLARHQRNKAKFLTDKLQALRNAVQTGLDPEKPHQSLDTIQKTINEYKSEIRQTRKFRDAEQEKDRTLLKTIIKVWKEMKSLREFQRFTNTPLKLVLRKEKADQKADEEAYEAEIQAEISELLEEHTEEYAQKMEEYRTSLQQWKAWRKVQRAKKKKRKQAAEEHPGDEIAEPYPEEDLVKPSPPEPTDRAVIEQEVRERAAQSRRRPWEPTLVPELSLAGSVTPNDQCPRAEVSRREDVKKRSVYLKVLFNNKEVSRTVSRPLGADFRVHFGQIFNLQIVNWPESLTLQVYETVGHSSPTLLAEVFLPIPETTVVTGRAPTEEVEFSSNQHVTLDHEGVGSGVPFSFEADGSNQLTLMTSGKVSHSVAWAIGENGIPLIPPLSQQNIGFRSALKKADAISSIGTSGLTDMKKLAKWAAESKLDPNDPNNAPLMQLISVATSGESYVPDFFRLEQLQQEFNFVSDQELNRSKRFRLLHLRSQEVPEFRNYKQVPVYDREIMEKVFQDYEKRLRDRNVIETKEHIDTHRAIVAKYLQQVRESVINRFLIAKQYFLLADMIVEEEVPNISILGLSLFKLAEQKRPLRPRRKGRKKVTAQNLSDGDIKLLVNIVRAYDIPVRKPAVSKFQQPSRSSRMFSEKHAASPSTYSPTHNADYPLGQVLVRPFVEVSFQRTVCHTTTAEGPNPSWNEELELPFRAPNGDYSTASLQSVKDVVFINIFDEVLHDVLEDDRERGSGIHTRIERHWLGCVKMPFSTIYFQARIDGTFKIDIPPVLLGYSKERNMILERGFDSVRSLSEGSYITLFITIEPQLVPGESIREKFESQEDEKLLQATEKFQAECALKFPNRQCLTTVIDISGKTVFITRYLKPLNPPQELLNVYPNNLQATAELVARYVSLIPFLPDTVSFGGICDLWSTSDQFLDLLAGDEEEHAVLLCNYFLSLGKKAWLLMGNAIPEGPTAYVLTWEQGRYLIWNPCSGHFYGQFDTFCPLKNVGCLIGPDNIWFNIQRYESPLRINFDVTRPKLWKSFFSRSLPYPGLSSVQPEELIYQRSDKAAAAELQDRIEKILKEKIMDWRPRHLTRWNRYCTSTLRHFLPLLEKSQGEDVEDDHRAELLKQLGDYRFSGFPLHMPYSEVKPLIDAVYSTGVHNIDVPNVEFALAVYIHPYPKNVLSVWIYVASLIRNR,mutated_sequence,1.0,1620.0,NP_001365544.1.a2m,NP_001365544.1.npy,ClinVar +NP_001365898.1,NP_001365898.1.csv,MAAGVAAWLPFARAAAIGWMPVANCPMPLAPADKNKRQDELIVLNVSGRRFQTWRTTLERYPDTLLGSTEKEFFFNEDTKEYFFDRDPEVFRCVLNFYRTGKLHYPRYECISAYDDELAFYGILPEIIGDCCYEEYKDRKRENAERLMDDNDSENNQESMPSLSFRQTMWRAFENPHTSTLALVFYYVTGFFIAVSVITNVVETVPCGTVPGSKELPCGERYSVAFFCLDTACVMIFTVEYLLRLFAAPSRYRFIRSVMSIIDVVAIMPYYIGLVMTNNEDVSGAFVTLRVFRVFRIFKFSRHSQGLRILGYTLKSCASELGFLLFSLTMAIIIFATVMFYAEKGSSASKFTSIPASFWYTIVTMTTLGYGDMVPKTIAGKIFGSICSLSGVLVIALPVPVIVSNFSRIYHQNQRADKRRAQKKARLARIRVAKTGSSNAYLHSKRNGLLNEALELTGTPEEEHMGKTTSLIESQHHHLLHCLEKTTGLSYLVDDPLLSVRTSTIKNHEFIDEQMFEQNCMESSMQNYPSTRSPSLSSHPGLTTTCCSRRSKKTTHLPNSNLPATRLRSMQELSTIHIQGSEQPSLTTSRSSLNLKADDGLRPNCKTSQITTAIISIPTPPALTPEGESRPPPASPGPNTNIPSIASNVVKVSAL,mutated_sequence,1.0,655.0,NP_001365898.1.a2m,NP_001365898.1.npy,ClinVar +NP_001365958.1,NP_001365958.1.csv,MSDNQSWNSSGSEEDPETESGPPVERCGVLSKWTNYIHGWQDRWVVLKNNALSYYKSEDETEYGCRGSICLSKAVITPHDFDECRFDISVNDSVWYLRAQDPDHRQQWIDAIEQHKTESGYGSESSLRRHGSMVSLVSGASGYSATSTSSFKKGHSLREKLAEMETFRDILCRQVDTLQKYFDACADAVSKDELQRDKVVEDDEDDFPTTRSDGDFLHSTNGNKEKLFPHVTPKGINGIDFKGEAITFKATTAGILATLSHCIELMVKREDSWQKRLDKETEKKRRTEEAYKNAMTELKKKSHFGGPDYEEGPNSLINEEEFFDAVEAALDRQDKIEEQSQSEKVRLHWPTSLPSGDAFSSVGTHRFVQKPYSRSSSMSSIDLVSASDDVHRFSSQVEEMVQNHMTYSLQDVGGDANWQLVVEEGEMKVYRREVEENGIVLDPLKATHAVKGVTGHEVCNYFWNVDVRNDWETTIENFHVVETLADNAIIIYQTHKRVWPASQRDVLYLSVIRKIPALTENDPETWIVCNFSVDHDSAPLNNRCVRAKINVAMICQTLVSPPEGNQEISRDNILCKITYVANVNPGGWAPASVLRAVAKREYPKFLKRFTSYVQEKTAGKPILF,mutated_sequence,1.0,624.0,NP_001365958.1.a2m,NP_001365958.1.npy,ClinVar +NP_001366039.1,NP_001366039.1.csv,MDEEIVSEKQAEESHRQDSANLLIFILLLTLTILTIWLFKHRRARFLHETGLAMIYGLLVGLVLRYGIHVPSDVNNVTLSCEVQSSPTTLLVNVSGKFYEYMLKGEISSHELNNVQDNEMLRKVTFDPEVFFNILLPPIIFYAGYSLKRRHFFRNLGSILAYAFLGTAISCFVIGSIMYGCVTLMKVTGQLAGDFYFTDCLLFGAIVSATDPVTVLAIFHELQVDVELYALLFGESVLNDAVAIVLSSSIVAYQPAGDNSHTFDVTAMFKSIGIFLGIFSGSFAMGAATGVVTALVTKFTKLREFQLLETGLFFLMSWSTFLLAEAWGFTGVVAVLFCGITQAHYTYNNLSTESQHRTKQLFELLNFLAENFIFSYMGLTLFTFQNHVFNPTFVVGAFVAIFLGRAANIYPLSLLLNLGRRSKIGSNFQHMMMFAGLRGAMAFALAIRDTATYARQMMFSTTLLIVFFTVWVFGGGTTAMLSCLHIRYCARLWGYRDELRHGLYPQGTHSLFGETDRVGVDSDQEHLGVPENERRTTKAESAWLFRMWYNFDHNYLKPLLTHSGPPLTTTLPACCGPIARCLTSPQAYENQEQLKDDDSDLILNDGDISLTYGDSTVNTEPATSSAPRRFMGNSSEDALDRELAFGDHELVIRGTRLVLPMDDSEPPLNLLDNTRHGPA,mutated_sequence,1.0,679.0,NP_001366039.1.a2m,NP_001366039.1.npy,ClinVar +NP_001366220.1,NP_001366220.1.csv,MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPPPPETSNPNKPKRQTNQLQYLLRVVLKTLWKHQFAWPFQQPVDAVKLNLPDYYKIIKTPMDMGTIKKRLENNYYWNAQECIQDFNTMFTNCYIYNKPGDDIVLMAEALEKLFLQKINELPTEETEIMIVQAKGRGRGRKETGTAKPGVSTVPNTTQASTPPQTQTPQPNPPPVQATPHPFPAVTPDLIVQTPVMTVVPPQPLQTPPPVPPQPQPPPAPAPQPVQSHPPIIAATPQPVKTKKGVKRKADTTTPTTIDPIHEPPSLPPEPKTTKLGQRRESSRPVKPPKKDVPDSQQHPAPEKSSKVSEQLKCCSGILKEMFAKKHAAYAWPFYKPVDVEALGLHDYCDIIKHPMDMSTIKSKLEAREYRDAQEFGADVRLMFSNCYKYNPPDHEVVAMARKLQDVFEMRFAKMPDEPEEPVVAVSSPAVPPPTKVVAPPSSSDSSSDSSSDSDSSTDDSEEERAQRLAELQEQLKAVHEQLAALSQPQQNKPKKKEKDKKEKKKEKHKRKEEVEENKKSKAKEPPPKKTKKNNSSNSNVSKKEPAPMKSKPPPTYESEEEDKCKPMSYEEKRQLSLDINKLPGEKLGRVVHIIQSREPSLKNSNPDEIEIDFETLKPSTLRELERYVTSCLRKKRKPQAEKVDVIAGSSKMKGFSSSESESSSESSSSDSEDSETEMAPKSKKKGHPGREQKKHHHHHHQQMQQAPAPVPQQPPPPPQQPPPPPPPQQQQQPPPPPPPPSMPQQAAPAMKSSPPPFIATQVPVLEPQLPGSVFDPIGHFTQPILHLPQPELPPHLPQPPEHSTPPHLNQHAVVSPPALHNALPQQPSRPSNRAAALPPKPARPPAVSPALTQTPLLPQPPMAQPPQVLLEDEEPPAPPLTSMQMQLYLQQLQKVQPPTPLLPSVKVQSQPPPPLPPPPHPSVQQQLQQQPPPPPPPQPQPPPQQQHQPPPRPVHLQPMQFSTHIQQPPPPQGQQPPHPPPGQQPPPPQPAKPQQVIQHHHSPRHHKSDPYSTGHLREAPSPLMIHSPQMSQFQSLTHQSPPQQNVQPKKQELRAASVVQPQPLVVVKEEKIHSPIIRSEPFSPSLRPEPPKHPESIKAPVHLPQRPEMKPVDVGRPVIRPPEQNAPPPGAPDKDKQKQEPKTPVAPKKDLKIKNMGSWASLVQKHPTTPSSTAKSSSDSFEQFRRAAREKEEREKALKAQAEHAEKEKERLRQERMRSREDEDALEQARRAHEEARRRQEQQQQQRQEQQQQQQQQAAAVAAAATPQAQSSQPQSMLDQQRELARKREQERRRREAMAATIDMNFQSDLLSIFEENLF,mutated_sequence,1.0,1362.0,NP_001366220.1.a2m,NP_001366220.1.npy,ClinVar +NP_001366332.1,NP_001366332.1.csv,MASLGEETLASASSSSDSDTGGASPPPRKKPRASAAEGVGEPGASAGRAGLSPPSSSSSSSSSSSSSVVVVVGLPPAAAPPAAAAVPHRSSGHSLVSGSIMQANGAGGGGGGGGGGGGGGGGGGGQGQTPELACLSAQNGESSPSSSSSAGDLAHANGLLPSAPSAASNNSNSLNVNNGVPGGAAAASSATVAAASATTAASSSLATPELGSSLKKKKRLSQSDEDVIRLIGQHLNGLGLNQTVDLLMQESGCRLEHPSATKFRNHVMEGDWDKAENDLNELKPLVHSPHAIVVRGALEISQTLLGIIVRMKFLLLQQKYLEYLEDGKVLEALQVLRCELTPLKYNTERIHVLSGYLMCSHAEDLRAKAEWEGKGTASRSKLLDKLQTYLPPSVMLPPRRLQTLLRQAVELQRDRCLYHNTKLDNNLDSVSLLIDHVCSRRQFPCYTQQILTEHCNEVWFCKFSNDGTKLATGSKDTTVIIWQVDPDTHLLKLLKTLEGHAYGVSYIAWSPDDNYLVACGPDDCSELWLWNVQTGELRTKMSQSHEDSLTSVAWNPDGKRFVTGGQRGQFYQCDLDGNLLDSWEGVRVQCLWCLSDGKTVLASDTHQRIRGYNFEDLTDRNIVQEDHPIMSFTISKNGRLALLNVATQGVHLWDLQDRVLVRKYQGVTQGFYTIHSCFGGHNEDFIASGSEDHKVYIWHKRSELPIAELTGHTRTVNCVSWNPQIPSMMASASDDGTVRIWGPAPFIDHQNIEEECSSMDS,mutated_sequence,1.0,761.0,NP_001366332.1.a2m,NP_001366332.1.npy,ClinVar +NP_001366539.1,NP_001366539.1.csv,MKVTGIFLLSALALLSLSGNTGADSLGREAKCYNELNGCTKIYDPVCGTDGNTYPNECVLCFENRKRQTSILIQKSGPC,mutated_sequence,1.0,79.0,NP_001366539.1.a2m,NP_001366539.1.npy,ClinVar +NP_001367.2,NP_001367.2.csv,MSEPGGGGGEDGSAGLEVSAVQNVADVSVLQKHLRKLVPLLLEDGGEAPAALEAALEEKSALEQMRKFLSDPQVHTVLVERSTLKEDVGDEGEEEKEFISYNINIDIHYGVKSNSLAFIKRTPVIDADKPVSSQLRVLTLSEDSPYETLHSFISNAVAPFFKSYIRESGKADRDGDKMAPSVEKKIAELEMGLLHLQQNIEIPEISLPIHPMITNVAKQCYERGEKPKVTDFGDKVEDPTFLNQLQSGVNRWIREIQKVTKLDRDPASGTALQEISFWLNLERALYRIQEKRESPEVLLTLDILKHGKRFHATVSFDTDTGLKQALETVNDYNPLMKDFPLNDLLSATELDKIRQALVAIFTHLRKIRNTKYPIQRALRLVEAISRDLSSQLLKVLGTRKLMHVAYEEFEKVMVACFEVFQTWDDEYEKLQVLLRDIVKRKREENLKMVWRINPAHRKLQARLDQMRKFRRQHEQLRAVIVRVLRPQVTAVAQQNQGEVPEPQDMKVAEVLFDAADANAIEEVNLAYENVKEVDGLDVSKEGTEAWEAAMKRYDERIDRVETRITARLRDQLGTAKNANEMFRIFSRFNALFVRPHIRGAIREYQTQLIQRVKDDIESLHDKFKVQYPQSQACKMSHVRDLPPVSGSIIWAKQIDRQLTAYMKRVEDVLGKGWENHVEGQKLKQDGDSFRMKLNTQEIFDDWARKVQQRNLGVSGRIFTIESTRVRGRTGNVLKLKVNFLPEIITLSKEVRNLKWLGFRVPLAIVNKAHQANQLYPFAISLIESVRTYERTCEKVEERNTISLLVAGLKKEVQALIAEGIALVWESYKLDPYVQRLAETVFNFQEKVDDLLIIEEKIDLEVRSLETCMYDHKTFSEILNRVQKAVDDLNLHSYSNLPIWVNKLDMEIERILGVRLQAGLRAWTQVLLGQAEDKAEVDMDTDAPQVSHKPGGEPKIKNVVHELRITNQVIYLNPPIEECRYKLYQEMFAWKMVVLSLPRIQSQRYQVGVHYELTEEEKFYRNALTRMPDGPVALEESYSAVMGIVSEVEQYVKVWLQYQCLWDMQAENIYNRLGEDLNKWQALLVQIRKARGTFDNAETKKEFGPVVIDYGKVQSKVNLKYDSWHKEVLSKFGQMLGSNMTEFHSQISKSRQELEQHSVDTASTSDAVTFITYVQSLKRKIKQFEKQVELYRNGQRLLEKQRFQFPPSWLYIDNIEGEWGAFNDIMRRKDSAIQQQVANLQMKIVQEDRAVESRTTDLLTDWEKTKPVTGNLRPEEALQALTIYEGKFGRLKDDREKCAKAKEALELTDTGLLSGSEERVQVALEELQDLKGVWSELSKVWEQIDQMKEQPWVSVQPRKLRQNLDALLNQLKSFPARLRQYASYEFVQRLLKGYMKINMLVIELKSEALKDRHWKQLMKRLHVNWVVSELTLGQIWDVDLQKNEAIVKDVLLVAQGEMALEEFLKQIREVWNTYELDLVNYQNKCRLIRGWDDLFNKVKEHINSVSAMKLSPYYKVFEEDALSWEDKLNRIMALFDVWIDVQRRWVYLEGIFTGSADIKHLLPVETQRFQSISTEFLALMKKVSKSPLVMDVLNIQGVQRSLERLADLLGKIQKALGEYLERERSSFPRFYFVGDEDLLEIIGNSKNVAKLQKHFKKMFAGVSSIILNEDNSVVLGISSREGEEVMFKTPVSITEHPKINEWLTLVEKEMRVTLAKLLAESVTEVEIFGKATSIDPNTYITWIDKYQAQLVVLSAQIAWSENVETALSSMGGGGDAAPLHSVLSNVEVTLNVLADSVLMEQPPLRRRKLEHLITELVHQRDVTRSLIKSKIDNAKSFEWLSQMRFYFDPKQTDVLQQLSIQMANAKFNYGFEYLGVQDKLVQTPLTDRCYLTMTQALEARLGGSPFGPAGTGKTESVKALGHQLGRFVLVFNCDETFDFQAMGRIFVGLCQVGAWGCFDEFNRLEERMLSAVSQQVQCIQEALREHSNPNYDKTSAPITCELLNKQVKVSPDMAIFITMNPGYAGRSNLPDNLKKLFRSLAMTKPDRQLIAQVMLYSQGFRTAEVLANKIVPFFKLCDEQLSSQSHYDFGLRALKSVLVSAGNVKRERIQKIKREKEERGEAVDEGEIAENLPEQEILIQSVCETMVPKLVAEDIPLLFSLLSDVFPGVQYHRGEMTALREELKKVCQEMYLTYGDGEEVGGMWVEKVLQLYQITQINHGLMMVGPSGSGKSMAWRVLLKALERLEGVEGVAHIIDPKAISKDHLYGTLDPNTREWTDGLFTHVLRKIIDSVRGELQKRQWIVFDGDVDPEWVENLNSVLDDNKLLTLPNGERLSLPPNVRIMFEVQDLKYATLATVSRCGMVWFSEDVLSTDMIFNNFLARLRSIPLDEGEDEAQRRRKGKEDEGEEAASPMLQIQRDAATIMQPYFTSNGLVTKALEHAFQLEHIMDLTRLRCLGSLFSMLHQACRNVAQYNANHPDFPMQIEQLERYIQRYLVYAILWSLSGDSRLKMRAELGEYIRRITTVPLPTAPNIPIIDYEVSISGEWSPWQAKVPQIEVETHKVAAPDVVVPTLDTVRHEALLYTWLAEHKPLVLCGPPGSGKTMTLFSALRALPDMEVVGLNFSSATTPELLLKTFDHYCEYRRTPNGVVLAPVQLGKWLVLFCDEINLPDMDKYGTQRVISFIRQMVEHGGFYRTSDQTWVKLERIQFVGACNPPTDPGRKPLSHRFLRHVPVVYVDYPGPASLTQIYGTFNRAMLRLIPSLRTYAEPLTAAMVEFYTMSQERFTQDTQPHYIYSPREMTRWVRGIFEALRPLETLPVEGLIRIWAHEALRLFQDRLVEDEERRWTDENIDTVALKHFPNIDREKAMSRPILYSNWLSKDYIPVDQEELRDYVKARLKVFYEEELDVPLVLFNEVLDHVLRIDRIFRQPQGHLLLIGVSGAGKTTLSRFVAWMNGLSVYQIKVHRKYTGEDFDEDLRTVLRRSGCKNEKIAFIMDESNVLDSGFLERMNTLLANGEVPGLFEGDEYATLMTQCKEGAQKEGLMLDSHEELYKWFTSQVIRNLHVVFTMNPSSEGLKDRAATSPALFNRCVLNWFGDWSTEALYQVGKEFTSKMDLEKPNYIVPDYMPVVYDKLPQPPSHREAIVNSCVFVHQTLHQANARLAKRGGRTMAITPRHYLDFINHYANLFHEKRSELEEQQMHLNVGLRKIKETVDQVEELRRDLRIKSQELEVKNAAANDKLKKMVKDQQEAEKKKVMSQEIQEQLHKQQEVIADKQMSVKEDLDKVEPAVIEAQNAVKSIKKQHLVEVRSMANPPAAVKLALESICLLLGESTTDWKQIRSIIMRENFIPTIVNFSAEEISDAIREKMKKNYMSNPSYNYEIVNRASLACGPMVKWAIAQLNYADMLKRVEPLRNELQKLEDDAKDNQQKANEVEQMIRDLEASIARYKEEYAVLISEAQAIKADLAAVEAKVNRSTALLKSLSAERERWEKTSETFKNQMSTIAGDCLLSAAFIAYAGYFDQQMRQNLFTTWSHHLQQANIQFRTDIARTEYLSNADERLRWQASSLPADDLCTENAIMLKRFNRYPLIIDPSGQATEFIMNEYKDRKITRTSFLDDAFRKNLESALRFGNPLLVQDVESYDPVLNPVLNREVRRTGGRVLITLGDQDIDLSPSFVIFLSTRDPTVEFPPDLCSRVTFVNFTVTRSSLQSQCLNEVLKAERPDVDEKRSDLLKLQGEFQLRLRQLEKSLLQALNEVKGRILDDDTIITTLENLKREAAEVTRKVEETDIVMQEVETVSQQYLPLSTACSSIYFTMESLKQIHFLYQYSLQFFLDIYHNVLYENPNLKGVTDHTQRLSIITKDLFQVAFNRVARGMLHQDHITFAMLLARIKLKGTVGEPTYDAEFQHFLRGNEIVLSAGSTPRIQGLTVEQAEAVVRLSCLPAFKDLIAKVQADEQFGIWLDSSSPEQTVPYLWSEETPATPIGQAIHRLLLIQAFRPDRLLAMAHMFVSTNLGESFMSIMEQPLDLTHIVGTEVKPNTPVLMCSVPGYDASGHVEDLAAEQNTQITSIAIGSAEGFNQADKAINTAVKSGRWVMLKNVHLAPGWLMQLEKKLHSLQPHACFRLFLTMEINPKVPVNLLRAGRIFVFEPPPGVKANMLRTFSSIPVSRICKSPNERARLYFLLAWFHAIIQERLRYAPLGWSKKYEFGESDLRSACDTVDTWLDDTAKGRQNISPDKIPWSALKTLMAQSIYGGRVDNEFDQRLLNTFLERLFTTRSFDSEFKLACKVDGHKDIQMPDGIRREEFVQWVELLPDTQTPSWLGLPNNAERVLLTTQGVDMISKMLKMQMLEDEDDLAYAETEKKTRTDSTSDGRPAWMRTLHTTASNWLHLIPQTLSHLKRTVENIKDPLFRFFEREVKMGAKLLQDVRQDLADVVQVCEGKKKQTNYLRTLINELVKGILPRSWSHYTVPAGMTVIQWVSDFSERIKQLQNISLAAASGGAKELKNIHVCLGGLFVPEAYITATRQYVAQANSWSLEELCLEVNVTTSQGATLDACSFGVTGLKLQGATCNNNKLSLSNAISTALPLTQLRWVKQTNTEKKASVVTLPVYLNFTRADLIFTVDFEIATKEDPRSFYERGVAVLCTE,mutated_sequence,1.0,4646.0,NP_001367.2.a2m,NP_001367.2.npy,ClinVar +NP_001368.2,NP_001368.2.csv,MANGTADVRKLFIFTTTQNYFGLMSELWDQPLLCNCLEINNFLDDGNQMLLRVQRSDAGISFSNTIEFGDTKDKVLVFFKLRPEVITDENLHDNILVSSMLESPISSLYQAVRQVFAPMLLKDQEWSRNFDPKLQNLLSELEAGLGIVLRRSDTNLTKLKFKEDDTRGILTPSDEFQFWIEQAHRGNKQISKERANYFKELFETIAREFYNLDSLSLLEVVDLVETTQDVVDDVWRQTEHDHYPESRMLHLLDIIGGSFGRFVQKKLGTLNLWEDPYYLVKESLKAGISICEQWVIVCNHLTGQVWQRYVPHPWKNEKYFPETLDKLGKRLEEVLAIRTIHEKFLYFLPASEEKIICLTRVFEPFTGLNPVQYNPYTEPLWKAAVSQYEKIIAPAEQKIAGKLKNYISEIQDSPQQLLQAFLKYKELVKRPTISKELMLERETLLARLVDSIKDFRLDFENRCRGIPGDASGPLSGKNLSEVVNSIVWVRQLELKVDDTIKIAEALLSDLPGFRCFHQSAKDLLDQLKLYEQEQFDDWSRDIQSGLSDSRSGLCIEASSRIMELDSNDGLLKVHYSDRLVILLREVRQLSALGFVIPAKIQQVANIAQKFCKQAIILKQVAHFYNSIDQQMIQSQRPMMLQSALAFEQIIKNSKAGSGGKSQITWDNPKELEGYIQKLQNAAERLATENRKLRKWHTTFCEKVVVLMNIDLLRQQQRWKDGLQELRTGLATVEAQGFQASDMHAWKQHWNHQLYKALEHQYQMGLEALNENLPEINIDLTYKQGRLQFRPPFEEIRAKYYREMKRFIGIPNQFKGVGEAGDESIFSIMIDRNASGFLTIFSKAEDLFRRLSAVLHQHKEWIVIGQVDMEALVEKHLFTVHDWEKNFKALKIKGKEVERLPSAVKVDCLNINCNPVKTVIDDLIQKLFDLLVLSLKKSIQAHLHEIDTFVTEAMEVLTIMPQSVEEIGDANLQYSKLQERKPEILPLFQEAEDKNRLLRTVAGGGLETISNLKAKWDKFELMMESHQLMIKDQIEVMKGNVKSRLQIYYQELEKFKARWDQLKPGDDVIETGQHNTLDKSAKLIKEKKIEFDDLEVTRKKLVDDCHHFRLEEPNFSLASSISKDIESCAQIWAFYEEFQQGFQEMANEDWITFRTKTYLFEEFLMNWHDRLRKVEEHSVMTVKLQSEVDKYKIVIPILKYVRGEHLSPDHWLDLFRLLGLPRGTSLEKLLFGDLLRVADTIVAKAADLKDLNSRAQGEVTIREALRELDLWGVGAVFTLIDYEDSQSRTMKLIKDWKDIVNQVGDNRCLLQSLKDSPYYKGFEDKVSIWERKLAELDEYLQNLNHIQRKWVYLEPIFGRGALPKEQTRFNRVDEDFRSIMTDIKKDNRVTTLTTHAGIRNSLLTILDQLQRCQKSLNEFLEEKRSAFPRFYFIGDDDLLEILGQSTNPSVIQSHLKKLFAGINSVCFDEKSKHITAMKSLEGEVVPFKNKVPLSNNVETWLNDLALEMKKTLEQLLKECVTTGRSSQGAVDPSLFPSQILCLAEQIKFTEDVENAIKDHSLHQIETQLVNKLEQYTNIDTSSEDPGNTESGILELKLKALILDIIHNIDVVKQLNQIQVHTTEDWAWKKQLRFYMKSDHTCCVQMVDSEFQYTYEYQGNASKLVYTPLTDKCYLTLTQAMKMGLGGNPYGPAGTGKTESVKALGGLLGRQVLVFNCDEGIDVKSMGRIFVGLVKCGAWGCFDEFNRLEESVLSAVSMQIQTIQDALKNHRTVCELLGKEVEVNSNSGIFITMNPAGKGYGGRQKLPDNLKQLFRPVAMSHPDNELIAEVILYSEGFKDAKVLSRKLVAIFNLSRELLTPQQHYDWGLRALKTVLRGSGNLLRQLNKSGTTQNANESHIVVQALRLNTMSKFTFTDCTRFDALIKDVFPGIELKEVEYDELSAALKQVFEEANYEIIPNQIKKALELYEQLCQRMGVVIVGPSGAGKSTLWRMLRAALCKTGKVVKQYTMNPKAMPRYQLLGHIDMDTREWSDGVLTNSARQVVREPQDVSSWIICDGDIDPEWIESLNSVLDDNRLLTMPSGERIQFGPNVNFVFETHDLSCASPATISRMGMIFLSDEETDLNSLIKSWLRNQPAEYRNNLENWIGDYFEKALQWVLKQNDYVVETSLVGTVMNGLSHLHGCRDHDEFIINLIRGLGGNLNMKSRLEFTKEVFHWARESPPDFHKPMDTYYDSTRGRLATYVLKKPEDLTADDFSNGLTLPVIQTPDMQRGLDYFKPWLSSDTKQPFILVGPEGCGKGMLLRYAFSQLRSTQIATVHCSAQTTSRHLLQKLSQTCMVISTNTGRVYRPKDCERLVLYLKDINLPKLDKWGTSTLVAFLQQVLTYQGFYDENLEWVGLENIQIVASMSAGGRLGRHKLTTRFTSIVRLCSIDYPEREQLQTIYGAYLEPVLHKNLKNHSIWGSSSKIYLLAGSMVQVYEQVRAKFTVDDYSHYFFTPCILTQWVLGLFRYDLEGGSSNHPLDYVLEIVAYEARRLFRDKIVGAKELHLFDIILTSVFQGDWGSDILDNMSDSFYVTWGARHNSGARAAPGQPLPPHGKPLGKLNSTDLKDVIKKGLIHYGRDNQNLDILLFHEVLEYMSRIDRVLSFPGGSLLLAGRSGVGRRTITSLVSHMHGAVLFSPKISRGYELKQFKNDLKHVLQLAGIEAQQVVLLLEDYQFVHPTFLEMINSLLSSGEVPGLYTLEELEPLLLPLKDQASQDGFFGPVFNYFTYRIQQNLHIVLIMDSANSNFMINCESNPALHKKCQVLWMEGWSNSSMKKIPEMLFSETGGGEKYNDKKRKEEKKKNSVDPDFLKSFLLIHESCKAYGATPSRYMTFLHVYSAISSSKKKELLKRQSHLQAGVSKLNEAKALVDELNRKAGEQSVLLKTKQDEADAALQMITVSMQDASEQKTELERLKHRIAEEVVKIEERKNKIDDELKEVQPLVNEAKLAVGNIKPESLSEIRSLRMPPDVIRDILEGVLRLMGIFDTSWVSMKSFLAKRGVREDIATFDARNISKEIRESVEELLFKNKGSFDPKNAKRASTAAAPLAAWVKANIQYSHVLERIHPLETEQAGLESNLKKTEDRKRKLEELLNSVGQKVSELKEKFQSRTSEAAKLEAEVSKAQETIKAAEVLINQLDREHKRWNAQVVEITEELATLPKRAQLAAAFITYLSAAPESLRKTCLEEWTKSAGLEKFDLRRFLCTESEQLIWKSEGLPSDDLSIENALVILQSRVCPFLIDPSSQATEWLKTHLKDSRLEVINQQDSNFITALELAVRFGKTLIIQEMDGVEPVLYPLLRRDLVAQGPRYVVQIGDKIIDYNEEFRLFLSTRNPNPFIPPDAASIVTEVNFTTTRSGLRGQLLALTIQHEKPDLEEQKTKLLQQEEDKKIQLAKLEESLLETLATSQGNILENKDLIESLNQTKASSALIQESLKESYKLQISLDQERDAYLPLAESASKMYFIISDLSKINNMYRFSLAAFLRLFQRALQNKQDSENTEQRIQSLISSLQHMVYEYICRCLFKADQLMFALHFVRGMHPELFQENEWDTFTGVVVGDMLRKADSQQKIRDQLPSWIDQERSWAVATLKIALPSLYQTLCFEDAALWRTYYNNSMCEQEFPSILAKKVSLFQQILVVQALRPDRLQSAMALFACKTLGLKEVSPLPLNLKRLYKETLEIEPILIIISPGADPSQELQELANAERSGECYHQVAMGQGQADLAIQMLKECARNGDWLCLKNLHLVVSWLPVLEKELNTLQPKDTFRLWLTAEVHPNFTPILLQSSLKITYESPPGLKKNLMRTYESWTPEQISKKDNTHRAHALFSLAWFHAACQERRNYIPQGWTKFYEFSLSDLRAGYNIIDRLFDGAKDVQWEFVHGLLENAIYGGRIDNYFDLRVLQSYLKQFFNSSVIDVFNQRNKKSIFPYSVSLPQSCSILDYRAVIEKIPEDDKPSFFGLPANIARSSQRMISSQVISQLRILGRSITAGSKFDREIWSNELSPVLNLWKKLNQNSNLIHQKVPPPNDRQGSPILSFIILEQFNAIRLVQSVHQSLAALSKVIRGTTLLSSEVQKLASALLNQKCPLAWQSKWEGPEDPLQYLRGLVARALAIQNWVDKAEKQALLSETLDLSELFHPDTFLNALRQETARAVGRSVDSLKFVASWKGRLQEAKLQIKISGLLLEGCSFDGNQLSENQLDSPSVSSVLPCFMGWIPQDACGPYSPDECISLPVYTSAERDRVVTNIDVPCGGNQDQWIQCGAALFLKNQ,mutated_sequence,1.0,4307.0,NP_001368.2.a2m,NP_001368.2.npy,ClinVar +NP_001371839.1,NP_001371839.1.csv,MGNVMEGKSVEELSSTECHQWYKKFMTECPSGQLTLYEFRQFFGLKNLSPSASQYVEQMFETFDFNKDGYIDFMEYVAALSLVLKGKVEQKLRWYFKLYDVDGNGCIDRDELLTIIQAIRAINPCSDTTMTAEEFTDTVFSKIDVNGDGELSLEEFIEGVQKDQMLLDTLTRSLDLTRIVRRLQNGEQDEEGADEAAEAAG,mutated_sequence,1.0,201.0,NP_001371839.1.a2m,NP_001371839.1.npy,ClinVar +NP_001373322.1,NP_001373322.1.csv,MGGLLGRQRLLLRMGGGRLGAPMERHGRASATSVSSAGEQAAGDPEGRRQEPLRRRASSASVPAVGASAEGTRRDRLGSYSGPTSVSRQRVESLRKKRPLFPWFGLDIGGTLVKLVYFEPKDITAEEEEEEVESLKSIRKYLTSNVAYGSTGIRDVHLELKDLTLCGRKGNLHFIRFPTHDMPAFIQMGRDKNFSSLHTVFCATGGGAYKFEQDFLTIGDLQLCKLDELDCLIKGILYIDSVGFNGRSQCYYFENPADSEKCQKLPFDLKNPYPLLLVNIGSGVSILAVYSKDNYKRVTGTSLGGGTFFGLCCLLTGCTTFEEALEMASRGDSTKVDKLVRDIYGGDYERFGLPGWAVASSFGNMMSKEKREAVSKEDLARATLITITNNIGSIARMCALNENINQVVFVGNFLRINTIAMRLLAYALDYWSKGQLKALFSEHEGYFGAVGALLELLKIP,mutated_sequence,1.0,460.0,NP_001373322.1.a2m,NP_001373322.1.npy,ClinVar +NP_001405.1,NP_001405.1.csv,MDDKELIEYFKSQMKEDPDMASAVAAIRTLLEFLKRDKGETIQGLRANLTSAIETLCGVDSSVAVSSGGELFLRFISLASLEYSDYSKCKKIMIERGELFLRRISLSRNKIADLCHTFIKDGATILTHAYSRVVLRVLEAAVAAKKRFSVYVTESQPDLSGKKMAKALCHLNVPVTVVLDAAVGYIMEKADLVIVGAEGVVENGGIINKIGTNQMAVCAKAQNKPFYVVAESFKFVRLFPLNQQDVPDKFKYKADTLKVAQTGQDLKEEHPWVDYTAPSLITLLFTDLGVLTPSAVSDELIKLYL,mutated_sequence,1.0,305.0,NP_001405.1.a2m,NP_001405.1.npy,ClinVar +NP_001420.2,NP_001420.2.csv,MAENVVEPGPPSAKRPKLSSPALSASASDGTDFGSLFDLEHDLPDELINSTELGLTNGGDINQLQTSLGMVQDAASKHKQLSELLRSGSSPNLNMGVGGPGQVMASQAQQSSPGLGLINSMVKSPMTQAGLTSPNMGMGTSGPNQGPTQSTGMMNSPVNQPAMGMNTGMNAGMNPGMLAAGNGQGIMPNQVMNGSIGAGRGRQNMQYPNPGMGSAGNLLTEPLQQGSPQMGGQTGLRGPQPLKMGMMNNPNPYGSPYTQNPGQQIGASGLGLQIQTKTVLSNNLSPFAMDKKAVPGGGMPNMGQQPAPQVQQPGLVTPVAQGMGSGAHTADPEKRKLIQQQLVLLLHAHKCQRREQANGEVRQCNLPHCRTMKNVLNHMTHCQSGKSCQVAHCASSRQIISHWKNCTRHDCPVCLPLKNAGDKRNQQPILTGAPVGLGNPSSLGVGQQSAPNLSTVSQIDPSSIERAYAALGLPYQVNQMPTQPQVQAKNQQNQQPGQSPQGMRPMSNMSASPMGVNGGVGVQTPSLLSDSMLHSAINSQNPMMSENASVPSLGPMPTAAQPSTTGIRKQWHEDITQDLRNHLVHKLVQAIFPTPDPAALKDRRMENLVAYARKVEGDMYESANNRAEYYHLLAEKIYKIQKELEEKRRTRLQKQNMLPNAAGMVPVSMNPGPNMGQPQPGMTSNGPLPDPSMIRGSVPNQMMPRITPQSGLNQFGQMSMAQPPIVPRQTPPLQHHGQLAQPGALNPPMGYGPRMQQPSNQGQFLPQTQFPSQGMNVTNIPLAPSSGQAPVSQAQMSSSSCPVNSPIMPPGSQGSHIHCPQLPQPALHQNSPSPVPSRTPTPHHTPPSIGAQQPPATTIPAPVPTPPAMPPGPQSQALHPPPRQTPTPPTTQLPQQVQPSLPAAPSADQPQQQPRSQQSTAASVPTPTAPLLPPQPATPLSQPAVSIEGQVSNPPSTSSTEVNSQAIAEKQPSQEVKMEAKMEVDQPEPADTQPEDISESKVEDCKMESTETEERSTELKTEIKEEEDQPSTSATQSSPAPGQSKKKIFKPEELRQALMPTLEALYRQDPESLPFRQPVDPQLLGIPDYFDIVKSPMDLSTIKRKLDTGQYQEPWQYVDDIWLMFNNAWLYNRKTSRVYKYCSKLSEVFEQEIDPVMQSLGYCCGRKLEFSPQTLCCYGKQLCTIPRDATYYSYQNRYHFCEKCFNEIQGESVSLGDDPSQPQTTINKEQFSKRKNDTLDPELFVECTECGRKMHQICVLHHEIIWPAGFVCDGCLKKSARTRKENKFSAKRLPSTRLGTFLENRVNDFLRRQNHPESGEVTVRVVHASDKTVEVKPGMKARFVDSGEMAESFPYRTKALFAFEEIDGVDLCFFGMHVQEYGSDCPPPNQRRVYISYLDSVHFFRPKCLRTAVYHEILIGYLEYVKKLGYTTGHIWACPPSEGDDYIFHCHPPDQKIPKPKRLQEWYKKMLDKAVSERIVHDYKDIFKQATEDRLTSAKELPYFEGDFWPNVLEESIKELEQEEEERKREENTSNESTDVTKGDSKNAKKKNNKKTSKNKSSLSRGNKKKPGMPNVSNDLSQKLYATMEKHKEVFFVIRLIAGPAANSLPPIVDPDPLIPCDLMDGRDAFLTLARDKHLEFSSLRRAQWSTMCMLVELHTQSQDRFVYTCNECKHHVETRWHCTVCEDYDLCITCYNTKNHDHKMEKLGLGLDDESNNQQAAATQSPGDSRRLSIQRCIQSLVHACQCRNANCSLPSCQKMKRVVQHTKGCKRKTNGGCPICKQLIALCCYHAKHCQENKCPVPFCLNIKQKLRQQQLQHRLQQAQMLRRRMASMQRTGVVGQQQGLPSPTPATPTTPTGQQPTTPQTPQPTSQPQPTPPNSMPPYLPRTQAAGPVSQGKAAGQVTPPTPPQTAQPPLPGPPPAAVEMAMQIQRAAETQRQMAHVQIFQRPIQHQMPPMTPMAPMGMNPPPMTRGPSGHLEPGMGPTGMQQQPPWSQGGLPQPQQLQSGMPRPAMMSVAQHGQPLNMAPQPGLGQVGISPLKPGTVSQQALQNLLRTLRSPSSPLQQQQVLSILHANPQLLAAFIKQRAAKYANSNPQPIPGQPGMPQGQPGLQPPTMPGQQGVHSNPAMQNMNPMQAGVQRAGLPQQQPQQQLQPPMGGMSPQAQQMNMNHNTMPSQFRDILRRQQMMQQQQQQGAGPGIGPGMANHNQFQQPQGVGYPPQQQQRMQHHMQQMQQGNMGQIGQLPQALGAEAGASLQAYQQRLLQQQMGSPVQPNPMSPQQHMLPNQAQSPHLQGQQIPNSLSNQVRSPQPVPSPRPQSQPPHSSPSPRMQPQPSPHHVSPQTSSPHPGLVAAQANPMEQGHFASPDQNSMLSQLASNPGMANLHGASATDLGLSTDNSDLNSNLSQSTLDIH,mutated_sequence,1.0,2414.0,NP_001420.2.a2m,NP_001420.2.npy,ClinVar +NP_001444.2,NP_001444.2.csv,MQARYSVSSPNSLGVVPYLGGEQSYYRAAAAAAGGGYTAMPAPMSVYSHPAHAEQYPGGMARAYGPYTPQPQPKDMVKPPYSYIALITMAIQNAPDKKITLNGIYQFIMDRFPFYRDNKQGWQNSIRHNLSLNECFVKVPRDDKKPGKGSYWTLDPDSYNMFENGSFLRRRRRFKKKDAVKDKEEKDRLHLKEPPPPGRQPPPAPPEQADGNAPGPQPPPVRIQDIKTENGTCPSPPQPLSPAAALGSGSAAAVPKIESPDSSSSSLSSGSSPPGSLPSARPLSLDGADSAPPPPAPSAPPPHHSQGFSVDNIMTSLRGSPQSAAAELSSGLLASAAASSRAGIAPPLALGAYSPGQSSLYSSPCSQTSSAGSSGGGGGGAGAAGGAGGAGTYHCNLQAMSLYAAGERGGHLQGAPGGAGGSAVDDPLPDYSLPPVTSSSSSSLSHGGGGGGGGGGQEAGHHPAAHQGRLTSWYLNQAGGDLGHLASAAAAAAAAGYPGQQQNFHSVREMFESQRIGLNNSPVNGNSSCQMAFPSSQSLYRTSGAFVYDCSKF,mutated_sequence,1.0,553.0,NP_001444.2.a2m,NP_001444.2.npy,ClinVar +NP_001483.3,NP_001483.3.csv,MPPPQQGPCGHHLLLLLALLLPSLPLTRAPVPPGPAAALLQALGLRDEPQGAPRLRPVPPVMWRLFRRRDPQETRSGSRRTSPGVTLQPCHVEELGVAGNIVRHIPDRGAPTRASEPASAAGHCPEWTVVFDLSAVEPAERPSRARLELRFAAAAAAAPEGGWELSVAQAGQGAGADPGPVLLRQLVPALGPPVRAELLGAAWARNASWPRSLRLALALRPRAPAACARLAEASLLLVTLDPRLCHPLARPRRDAEPVLGGGPGGACRARRLYVSFREVGWHRWVIAPRGFLANYCQGQCALPVALSGSGGPPALNHAVLRALMHAAAPGAADLPCCVPARLSPISVLFFDNSDNVVLRQYEDMVVDECGCR,mutated_sequence,1.0,372.0,NP_001483.3.a2m,NP_001483.3.npy,ClinVar +NP_001646.2,NP_001646.2.csv,MVLLAAAVCTKAGKAIVSRQFVEMTRTRIEGLLAAFPKLMNTGKQHTFVETESVRYVYQPMEKLYMVLITTKNSNILEDLETLRLFSRVIPEYCRALEENEISEHCFDLIFAFDEIVALGYRENVNLAQIRTFTEMDSHEEKVFRAVRETQEREAKAEMRRKAKELQQARRDAERQGKKAPGFGGFGSSAVSGGSTAAMITETIIETDKPKVAPAPARPSGPSKALKLGAKGKEVDNFVDKLKSEGETIMSSSMGKRTSEATKMHAPPINMESVHMKIEEKITLTCGRDGGLQNMELHGMIMLRISDDKYGRIRLHVENEDKKGVQLQTHPNVDKKLFTAESLIGLKNPEKSFPVNSDVGVLKWRLQTTEESFIPLTINCWPSESGNGCDVNIEYELQEDNLELNDVVITIPLPSGVGAPVIGEIDGEYRHDSRRNTLEWCLPVIDAKNKSGSLEFSIAGQPNDFFPVQVSFVSKKNYCNIQVTKVTQVDGNSPVRFSTETTFLVDKYEIL,mutated_sequence,1.0,511.0,NP_001646.2.a2m,NP_001646.2.npy,ClinVar +NP_001664.3,NP_001664.3.csv,MCGIWALFGSDDCLSVQCLSAMKIAHRGPDAFRFENVNGYTNCCFGFHRLAVVDPLFGMQPIRVKKYPYLWLCYNGEIYNHKKMQQHFEFEYQTKVDGEIILHLYDKGGIEQTICMLDGVFAFVLLDTANKKVFLGRDTYGVRPLFKAMTEDGFLAVCSEAKGLVTLKHSATPFLKVEPFLPGHYEVLDLKPNGKVASVEMVKYHHCRDVPLHALYDNVEKLFPGFEIETVKNNLRILFNNAVKKRLMTDRRIGCLLSGGLDSSLVAATLLKQLKEAQVQYPLQTFAIGMEDSPDLLAARKVADHIGSEHYEVLFNSEEGIQALDEVIFSLETYDITTVRASVGMYLISKYIRKNTDSVVIFSGEGSDELTQGYIYFHKAPSPEKAEEESERLLRELYLFDVLRADRTTAAHGLELRVPFLDHRFSSYYLSLPPEMRIPKNGIEKHLLRETFEDSNLIPKEILWRPKEAFSDGITSVKNSWFKILQEYVEHQVDDAMMANAAQKFPFNTPKTKEGYYYRQVFERHYPGRADWLSHYWMPKWINATDPSARTLTHYKSAVKA,mutated_sequence,1.0,561.0,NP_001664.3.a2m,NP_001664.3.npy,ClinVar +NP_001684.2,NP_001684.2.csv,MALRAMRGIVNGAAPELPVPTGGPAVGAREQALAVSRNYLSQPRLTYKTVSGVNGPLVILDHVKFPRYAEIVHLTLPDGTKRSGQVLEVSGSKAVVQVFEGTSGIDAKKTSCEFTGDILRTPVSEDMLGRVFNGSGKPIDRGPVVLAEDFLDIMGQPINPQCRIYPEEMIQTGISAIDGMNSIARGQKIPIFSAAGLPHNEIAAQICRQAGLVKKSKDVVDYSEENFAIVFAAMGVNMETARFFKSDFEENGSMDNVCLFLNLANDPTIERIITPRLALTTAEFLAYQCEKHVLVILTDMSSYAEALREVSAAREEVPGRRGFPGYMYTDLATIYERAGRVEGRNGSITQIPILTMPNDDITHPIPDLTGYITEGQIYVDRQLHNRQIYPPINVLPSLSRLMKSAIGEGMTRKDHADVSNQLYACYAIGKDVQAMKAVVGEEALTSDDLLYLEFLQKFERNFIAQGPYENRTVFETLDIGWQLLRIFPKEMLKRIPQSTLSEFYPRDSAKH,mutated_sequence,1.0,511.0,NP_001684.2.a2m,NP_001684.2.npy,ClinVar +NP_001724.4,NP_001724.4.csv,MWLLYLLVPALFCRAGGSIPIPQKLFGEVTSPLFPKPYPNNFETTTVITVPTGYRVKLVFQQFDLEPSEGCFYDYVKISADKKSLGRFCGQLGSPLGNPPGKKEFMSQGNKMLLTFHTDFSNEENGTIMFYKGFLAYYQAVDLDECASRSKSGEEDPQPQCQHLCHNYVGGYFCSCRPGYELQEDRHSCQAECSSELYTEASGYISSLEYPRSYPPDLRCNYSIRVERGLTLHLKFLEPFDIDDHQQVHCPYDQLQIYANGKNIGEFCGKQRPPDLDTSSNAVDLLFFTDESGDSRGWKLRYTTEIIKCPQPKTLDEFTIIQNLQPQYQFRDYFIATCKQGYQLIEGNQVLHSFTAVCQDDGTWHRAMPRCKIKDCGQPRNLPNGDFRYTTTMGVNTYKARIQYYCHEPYYKMQTRAGSRESEQGVYTCTAQGIWKNEQKGEKIPRCLPVCGKPVNPVEQRQRIIGGQKAKMGNFPWQVFTNIHGRGGGALLGDRWILTAAHTLYPKEHEAQSNASLDVFLGHTNVEELMKLGNHPIRRVSVHPDYRQDESYNFEGDIALLELENSVTLGPNLLPICLPDNDTFYDLGLMGYVSGFGVMEEKIAHDLRFVRLPVANPQACENWLRGKNRMDVFSQNMFCAGHPSLKQDACQGDSGGVFAVRDPNTDRWVATGIVSWGIGCSRGYGFYTKVLNYVDWIKKEMEEED,mutated_sequence,1.0,705.0,NP_001724.4.a2m,NP_001724.4.npy,ClinVar +NP_001725.1,NP_001725.1.csv,MWCIVLFSLLAWVYAEPTMYGEILSPNYPQAYPSEVEKSWDIEVPEGYGIHLYFTHLDIELSENCAYDSVQIISGDTEEGRLCGQRSSNNPHSPIVEEFQVPYNKLQVIFKSDFSNEERFTGFAAYYVATDINECTDFVDVPCSHFCNNFIGGYFCSCPPEYFLHDDMKNCGVNCSGDVFTALIGEIASPNYPKPYPENSRCEYQIRLEKGFQVVVTLRREDFDVEAADSAGNCLDSLVFVAGDRQFGPYCGHGFPGPLNIETKSNALDIIFQTDLTGQKKGWKLRYHGDPMPCPKEDTPNSVWEPAKAKYVFRDVVQITCLDGFEVVEGRVGATSFYSTCQSNGKWSNSKLKCQPVDCGIPESIENGKVEDPESTLFGSVIRYTCEEPYYYMENGGGGEYHCAGNGSWVNEVLGPELPKCVPVCGVPREPFEEKQRIIGGSDADIKNFPWQVFFDNPWAGGALINEYWVLTAAHVVEGNREPTMYVGSTSVQTSRLAKSKMLTPEHVFIHPGWKLLEVPEGRTNFDNDIALVRLKDPVKMGPTVSPICLPGTSSDYNLMDGDLGLISGWGRTEKRDRAVRLKAARLPVAPLRKCKEVKVEKPTADAEAYVFTPNMICAGGEKGMDSCKGDSGGAFAVQDPNDKTKFYAAGLVSWGPQCGTYGLYTRVKNYVDWIMKTMQENSTPRED,mutated_sequence,1.0,688.0,NP_001725.1.a2m,NP_001725.1.npy,ClinVar +NP_001796.2,NP_001796.2.csv,MSHGTYYECEPRGGQQPLEFSGGRAGPGELGDMCEHEASIDLSAYIESGEEQLLSDLFAVKPAPEARGLKGPGTPAFPHYLPPDPRPFAYPPHTFGPDRKALGPGIYSSPGSYDPRAVAVKEEPRGPEGSRAASRGSYNPLQYQVAHCGQTAMHLPPTLAAPGQPLRVLKAPLATAAPPCSPLLKAPSPAGPLHKGKKAVNKDSLEYRLRRERNNIAVRKSRDKAKRRILETQQKVLEYMAENERLRSRVEQLTQELDTLRNLFRQIPEAANLIKGVGGCS,mutated_sequence,1.0,281.0,NP_001796.2.a2m,NP_001796.2.npy,ClinVar +NP_001805.4,NP_001805.4.csv,MGAGPSLLLAALLLLLSGDGAVRCDTPANCTYLDLLGTWVFQVGSSGSQRDVNCSVMGPQEKKVVVYLQKLDTAYDDLGNSGHFTIIYNQGFEIVLNDYKWFAFFKYKEEGSKVTTYCNETMTGWVHDVLGRNWACFTGKKVGTASENVYVNIAHLKNSQEKYSNRLYKYDHNFVKAINAIQKSWTATTYMEYETLTLGDMIRRSGGHSRKIPRPKPAPLTAEIQQKILHLPTSWDWRNVHGINFVSPVRNQASCGSCYSFASMGMLEARIRILTNNSQTPILSPQEVVSCSQYAQGCEGGFPYLIAGKYAQDFGLVEEACFPYTGTDSPCKMKEDCFRYYSSEYHYVGGFYGGCNEALMKLELVHHGPMAVAFEVYDDFLHYKKGIYHHTGLRDPFNPFELTNHAVLLVGYGTDSASGMDYWIVKNSWGTGWGENGYFRIRRGTDECAIESIAVAATPIPKL,mutated_sequence,1.0,463.0,NP_001805.4.a2m,NP_001805.4.npy,ClinVar +NP_001835.3,NP_001835.3.csv,MIRLGAPQTLVLLTLLVAAVLRCQGQDVQEAGSCVQDGQRYNDKDVWKPEPCRICVCDTGTVLCDDIICEDVKDCLSPEIPFGECCPICPTDLATASGQPGPKGQKGEPGDIKDIVGPKGPPGPQGPAGEQGPRGDRGDKGEKGAPGPRGRDGEPGTPGNPGPPGPPGPPGPPGLGGNFAAQMAGGFDEKAGGAQLGVMQGPMGPMGPRGPPGPAGAPGPQGFQGNPGEPGEPGVSGPMGPRGPPGPPGKPGDDGEAGKPGKAGERGPPGPQGARGFPGTPGLPGVKGHRGYPGLDGAKGEAGAPGVKGESGSPGENGSPGPMGPRGLPGERGRTGPAGAAGARGNDGQPGPAGPPGPVGPAGGPGFPGAPGAKGEAGPTGARGPEGAQGPRGEPGTPGSPGPAGASGNPGTDGIPGAKGSAGAPGIAGAPGFPGPRGPPGPQGATGPLGPKGQTGEPGIAGFKGEQGPKGEPGPAGPQGAPGPAGEEGKRGARGEPGGVGPIGPPGERGAPGNRGFPGQDGLAGPKGAPGERGPSGLAGPKGANGDPGRPGEPGLPGARGLTGRPGDAGPQGKVGPSGAPGEDGRPGPPGPQGARGQPGVMGFPGPKGANGEPGKAGEKGLPGAPGLRGLPGKDGETGAAGPPGPAGPAGERGEQGAPGPSGFQGLPGPPGPPGEGGKPGDQGVPGEAGAPGLVGPRGERGFPGERGSPGAQGLQGPRGLPGTPGTDGPKGASGPAGPPGAQGPPGLQGMPGERGAAGIAGPKGDRGDVGEKGPEGAPGKDGGRGLTGPIGPPGPAGANGEKGEVGPPGPAGSAGARGAPGERGETGPPGPAGFAGPPGADGQPGAKGEQGEAGQKGDAGAPGPQGPSGAPGPQGPTGVTGPKGARGAQGPPGATGFPGAAGRVGPPGSNGNPGPPGPPGPSGKDGPKGARGDSGPPGRAGEPGLQGPAGPPGEKGEPGDDGPSGAEGPPGPQGLAGQRGIVGLPGQRGERGFPGLPGPSGEPGKQGAPGASGDRGPPGPVGPPGLTGPAGEPGREGSPGADGPPGRDGAAGVKGDRGETGAVGAPGAPGPPGSPGPAGPTGKQGDRGEAGAQGPMGPSGPAGARGIQGPQGPRGDKGEAGEPGERGLKGHRGFTGLQGLPGPPGPSGDQGASGPAGPSGPRGPPGPVGPSGKDGANGIPGPIGPPGPRGRSGETGPAGPPGNPGPPGPPGPPGPGIDMSAFAGLGPREKGPDPLQYMRADQAAGGLRQHDAEVDATLKSLNNQIESIRSPEGSRKNPARTCRDLKLCHPEWKSGDYWIDPNQGCTLDAMKVFCNMETGETCVYPNPANVPKKNWWSSKSKEKKHIWFGETINGGFHFSYGDDNLAPNTANVQMTFLRLLSTEGSQNITYHCKNSIAYLDEAAGNLKKALLIQGSNDVEIRAEGNSRFTYTALKDGCTKHTGKWGKTVIEYRSQKTSRLPIIDIAPMDIGGPEQEFGVDIGPVCFL,mutated_sequence,1.0,1487.0,NP_001835.3.a2m,NP_001835.3.npy,ClinVar +NP_001839.2,NP_001839.2.csv,MRAARALLPLLLQACWTAAQDEPETPRAVAFQDCPVDLFFVLDTSESVALRLKPYGALVDKVKSFTKRFIDNLRDRYYRCDRNLVWNAGALHYSDEVEIIQGLTRMPGGRDALKSSVDAVKYFGKGTYTDCAIKKGLEQLLVGGSHLKENKYLIVVTDGHPLEGYKEPCGGLEDAVNEAKHLGVKVFSVAITPDHLEPRLSIIATDHTYRRNFTAADWGQSRDAEEAISQTIDTIVDMIKNNVEQVCCSFECQPARGPPGLRGDPGFEGERGKPGLPGEKGEAGDPGRPGDLGPVGYQGMKGEKGSRGEKGSRGPKGYKGEKGKRGIDGVDGVKGEMGYPGLPGCKGSPGFDGIQGPPGPKGDPGAFGLKGEKGEPGADGEAGRPGSSGPSGDEGQPGEPGPPGEKGEAGDEGNPGPDGAPGERGGPGERGPRGTPGTRGPRGDPGEAGPQGDQGREGPVGVPGDPGEAGPIGPKGYRGDEGPPGSEGARGAPGPAGPPGDPGLMGERGEDGPAGNGTEGFPGFPGYPGNRGAPGINGTKGYPGLKGDEGEAGDPGDDNNDIAPRGVKGAKGYRGPEGPQGPPGHQGPPGPDECEILDIIMKMCSCCECKCGPIDLLFVLDSSESIGLQNFEIAKDFVVKVIDRLSRDELVKFEPGQSYAGVVQYSHSQMQEHVSLRSPSIRNVQELKEAIKSLQWMAGGTFTGEALQYTRDQLLPPSPNNRIALVITDGRSDTQRDTTPLNVLCSPGIQVVSVGIKDVFDFIPGSDQLNVISCQGLAPSQGRPGLSLVKENYAELLEDAFLKNVTAQICIDKKCPDYTCPITFSSPADITILLDGSASVGSHNFDTTKRFAKRLAERFLTAGRTDPAHDVRVAVVQYSGTGQQRPERASLQFLQNYTALASAVDAMDFINDATDVNDALGYVTRFYREASSGAAKKRLLLFSDGNSQGATPAAIEKAVQEAQRAGIEIFVVVVGRQVNEPHIRVLVTGKTAEYDVAYGESHLFRVPSYQALLRGVFHQTVSRKVALG,mutated_sequence,1.0,1028.0,NP_001839.2.a2m,NP_001839.2.npy,ClinVar +NP_001840.3,NP_001840.3.csv,MLQGTCSVLLLWGILGAIQAQQQEVISPDTTERNNNCPEKTDCPIHVYFVLDTSESVTMQSPTDILLFHMKQFVPQFISQLQNEFYLDQVALSWRYGGLHFSDQVEVFSPPGSDRASFIKNLQGISSFRRGTFTDCALANMTEQIRQDRSKGTVHFAVVITDGHVTGSPCGGIKLQAERAREEGIRLFAVAPNQNLKEQGLRDIASTPHELYRNDYATMLPDSTEIDQDTINRIIKVMKHEAYGECYKVSCLEIPGPSGPKGYRGQKGAKGNMGEPGEPGQKGRQGDPGIEGPIGFPGPKGVPGFKGEKGEFGADGRKGAPGLAGKNGTDGQKGKLGRIGPPGCKGDPGNRGPDGYPGEAGSPGERGDQGGKGDPGRPGRRGPPGEIGAKGSKGYQGNSGAPGSPGVKGAKGGPGPRGPKGEPGRRGDPGTKGSPGSDGPKGEKGDPGPEGPRGLAGEVGNKGAKGDRGLPGPRGPQGALGEPGKQGSRGDPGDAGPRGDSGQPGPKGDPGRPGFSYPGPRGAPGEKGEPGPRGPEGGRGDFGLKGEPGRKGEKGEPADPGPPGEPGPRGPRGVPGPEGEPGPPGDPGLTECDVMTYVRETCGCCDCEKRCGALDVVFVIDSSESIGYTNFTLEKNFVINVVNRLGAIAKDPKSETGTRVGVVQYSHEGTFEAIQLDDERIDSLSSFKEAVKNLEWIAGGTWTPSALKFAYDRLIKESRRQKTRVFAVVITDGRHDPRDDDLNLRALCDRDVTVTAIGIGDMFHEKHESENLYSIACDKPQQVRNMTLFSDLVAEKFIDDMEDVLCPDPQIVCPDLPCQTELSVAQCTQRPVDIVFLLDGSERLGEQNFHKARRFVEQVARRLTLARRDDDPLNARVALLQFGGPGEQQVAFPLSHNLTAIHEALETTQYLNSFSHVGAGVVHAINAIVRSPRGGARRHAELSFVFLTDGVTGNDSLHESAHSMRKQNVVPTVLALGSDVDMDVLTTLSLGDRAAVFHEKDYDSLAQPGFFDRFIRWIC,mutated_sequence,1.0,1019.0,NP_001840.3.a2m,NP_001840.3.npy,ClinVar +NP_001845.3,NP_001845.3.csv,MEPWSSRWKTKRWLWDFTVTTLALTFLFQAREVRGAAPVDVLKALDFHNSPEGISKTTGFCTNRKNSKGSDTAYRVSKQAQLSAPTKQLFPGGTFPEDFSILFTVKPKKGIQSFLLSIYNEHGIQQIGVEVGRSPVFLFEDHTGKPAPEDYPLFRTVNIADGKWHRVAISVEKKTVTMIVDCKKKTTKPLDRSERAIVDTNGITVFGTRILDEEVFEGDIQQFLITGDPKAAYDYCEHYSPDCDSSAPKAAQAQEPQIDEYAPEDIIEYDYEYGEAEYKEAESVTEGPTVTEETIAQTEANIVDDFQEYNYGTMESYQTEAPRHVSGTNEPNPVEEIFTEEYLTGEDYDSQRKNSEDTLYENKEIDGRDSDLLVDGDLGEYDFYEYKEYEDKPTSPPNEEFGPGVPAETDITETSINGHGAYGEKGQKGEPAVVEPGMLVEGPPGPAGPAGIMGPPGLQGPTGPPGDPGDRGPPGRPGLPGADGLPGPPGTMLMLPFRYGGDGSKGPTISAQEAQAQAILQQARIALRGPPGPMGLTGRPGPVGGPGSSGAKGESGDPGPQGPRGVQGPPGPTGKPGKRGRPGADGGRGMPGEPGAKGDRGFDGLPGLPGDKGHRGERGPQGPPGPPGDDGMRGEDGEIGPRGLPGEAGPRGLLGPRGTPGAPGQPGMAGVDGPPGPKGNMGPQGEPGPPGQQGNPGPQGLPGPQGPIGPPGEKGPQGKPGLAGLPGADGPPGHPGKEGQSGEKGALGPPGPQGPIGYPGPRGVKGADGVRGLKGSKGEKGEDGFPGFKGDMGLKGDRGEVGQIGPRGEDGPEGPKGRAGPTGDPGPSGQAGEKGKLGVPGLPGYPGRQGPKGSTGFPGFPGANGEKGARGVAGKPGPRGQRGPTGPRGSRGARGPTGKPGPKGTSGGDGPPGPPGERGPQGPQGPVGFPGPKGPPGPPGKDGLPGHPGQRGETGFQGKTGPPGPGGVVGPQGPTGETGPIGERGHPGPPGPPGEQGLPGAAGKEGAKGDPGPQGISGKDGPAGLRGFPGERGLPGAQGAPGLKGGEGPQGPPGPVGSPGERGSAGTAGPIGLPGRPGPQGPPGPAGEKGAPGEKGPQGPAGRDGVQGPVGLPGPAGPAGSPGEDGDKGEIGEPGQKGSKGDKGENGPPGPPGLQGPVGAPGIAGGDGEPGPRGQQGMFGQKGDEGARGFPGPPGPIGLQGLPGPPGEKGENGDVGPMGPPGPPGPRGPQGPNGADGPQGPPGSVGSVGGVGEKGEPGEAGNPGPPGEAGVGGPKGERGEKGEAGPPGAAGPPGAKGPPGDDGPKGNPGPVGFPGDPGPPGEPGPAGQDGVGGDKGEDGDPGQPGPPGPSGEAGPPGPPGKRGPPGAAGAEGRQGEKGAKGEAGAEGPPGKTGPVGPQGPAGKPGPEGLRGIPGPVGEQGLPGAAGQDGPPGPMGPPGLPGLKGDPGSKGEKGHPGLIGLIGPPGEQGEKGDRGLPGTQGSPGAKGDGGIPGPAGPLGPPGPPGLPGPQGPKGNKGSTGPAGQKGDSGLPGPPGSPGPPGEVIQPLPILSSKKTRRHTEGMQADADDNILDYSDGMEEIFGSLNSLKQDIEHMKFPMGTQTNPARTCKDLQLSHPDFPDGEYWIDPNQGCSGDSFKVYCNFTSGGETCIYPDKKSEGVRISSWPKEKPGSWFSEFKRGKLLSYLDVEGNSINMVQMTFLKLLTASARQNFTYHCHQSAAWYDVSSGSYDKALRFLGSNDEEMSYDNNPFIKTLYDGCASRKGYEKTVIEINTPKIDQVPIVDVMINDFGDQNQKFGFEVGPVCFLG,mutated_sequence,1.0,1806.0,NP_001845.3.a2m,NP_001845.3.npy,ClinVar +NP_001866.2,NP_001866.2.csv,MTRILTAFKVVRTLKTGFGFTNVTAHQKWKFSRPGIRLLSVKAQTAHIVLEDGTKMKGYSFGHPSSVAGEVVFNTGLGGYPEAITDPAYKGQILTMANPIIGNGGAPDTTALDELGLSKYLESNGIKVSGLLVLDYSKDYNHWLATKSLGQWLQEEKVPAIYGVDTRMLTKIIRDKGTMLGKIEFEGQPVDFVDPNKQNLIAEVSTKDVKVYGKGNPTKVVAVDCGIKNNVIRLLVKRGAEVHLVPWNHDFTKMEYDGILIAGGPGNPALAEPLIQNVRKILESDRKEPLFGISTGNLITGLAAGAKTYKMSMANRGQNQPVLNITNKQAFITAQNHGYALDNTLPAGWKPLFVNVNDQTNEGIMHESKPFFAVQFHPEVTPGPIDTEYLFDSFFSLIKKGKATTITSVLPKPALVASRVEVSKVLILGSGGLSIGQAGEFDYSGSQAVKAMKEENVKTVLMNPNIASVQTNEVGLKQADTVYFLPITPQFVTEVIKAEQPDGLILGMGGQTALNCGVELFKRGVLKEYGVKVLGTSVESIMATEDRQLFSDKLNEINEKIAPSFAVESIEDALKAADTIGYPVMIRSAYALGGLGSGICPNRETLMDLSTKAFAMTNQILVEKSVTGWKEIEYEVVRDADDNCVTVCNMENVDAMGVHTGDSVVVAPAQTLSNAEFQMLRRTSINVVRHLGIVGECNIQFALHPTSMEYCIIEVNARLSRSSALASKATGYPLAFIAAKIALGIPLPEIKNVVSGKTSACFEPSLDYMVTKIPRWDLDRFHGTSSRIGSSMKSVGEVMAIGRTFEESFQKALRMCHPSIEGFTPRLPMNKEWPSNLDLRKELSEPSSTRIYAIAKAIDDNMSLDEIEKLTYIDKWFLYKMRDILNMEKTLKGLNSESMTEETLKRAKEIGFSDKQISKCLGLTEAQTRELRLKKNIHPWVKQIDTLAAEYPSVTNYLYVTYNGQEHDVNFDDHGMMVLGCGPYHIGSSVEFDWCAVSSIRTLRQLGKKTVVVNCNPETVSTDFDECDKLYFEELSLERILDIYHQEACGGCIISVGGQIPNNLAVPLYKNGVKIMGTSPLQIDRAEDRSIFSAVLDELKVAQAPWKAVNTLNEALEFAKSVDYPCLLRPSYVLSGSAMNVVFSEDEMKKFLEEATRVSQEHPVVLTKFVEGAREVEMDAVGKDGRVISHAISEHVEDAGVHSGDATLMLPTQTISQGAIEKVKDATRKIAKAFAISGPFNVQFLVKGNDVLVIECNLRASRSFPFVSKTLGVDFIDVATKVMIGENVDEKHLPTLDHPIIPADYVAIKAPMFSWPRLRDADPILRCEMASTGEVACFGEGIHTAFLKAMLSTGFKIPQKGILIGIQQSFRPRFLGVAEQLHNEGFKLFATEATSDWLNANNVPATPVAWPSQEGQNPSLSSIRKLIRDGSIDLVINLPNNNTKFVHDNYVIRRTAVDSGIPLLTNFQVTKLFAEAVQKSRKVDSKSLFHYRQYSAGKAA,mutated_sequence,1.0,1500.0,NP_001866.2.a2m,NP_001866.2.npy,ClinVar +NP_001867.2,NP_001867.2.csv,MAEAHQAVAFQFTVTPDGIDLRLSHEALRQIYLSGLHSWKKKFIRFKNGIITGVYPASPSSWLIVVVGVMTTMYAKIDPSLGIIAKINRTLETANCMSSQTKNVVSGVLFGTGLWVALIVTMRYSLKVLLSYHGWMFTEHGKMSRATKIWMGMVKIFSGRKPMLYSFQTSLPRLPVPAVKDTVNRYLQSVRPLMKEEDFKRMTALAQDFAVGLGPRLQWYLKLKSWWATNYVSDWWEEYIYLRGRGPLMVNSNYYAMDLLYILPTHIQAARAGNAIHAILLYRRKLDREEIKPIRLLGSTIPLCSAQWERMFNTSRIPGEETDTIQHMRDSKHIVVYHRGRYFKVWLYHDGRLLKPREMEQQMQRILDNTSEPQPGEARLAALTAGDRVPWARCRQAYFGRGKNKQSLDAVEKAAFFVTLDETEEGYRSEDPDTSMDSYAKSLLHGRCYDRWFDKSFTFVVFKNGKMGLNAEHSWADAPIVAHLWEYVMSIDSLQLGYAEDGHCKGDINPNIPYPTRLQWDIPGECQEVIETSLNTANLLANDVDFHSFPFVAFGKGIIKKCRTSPDAFVQLALQLAHYKDMGKFCLTYEASMTRLFREGRTETVRSCTTESCDFVRAMVDPAQTVEQRLKLFKLASEKHQHMYRLAMTGSGIDRHLFCLYVVSKYLAVESPFLKEVLSEPWRLSTSQTPQQQVELFDLENNPEYVSSGGGFGPVADDGYGVSYILVGENLINFHISSKFSCPETDSHRFGRHLKEAMTDIITLFGLSSNSKK,mutated_sequence,1.0,773.0,NP_001867.2.a2m,NP_001867.2.npy,ClinVar +NP_001877.1,NP_001877.1.csv,MTLQCTKSAGPWKMVVWDEDGFQGRRHEFTAECPSVLELGFETVRSLKVLSGAWVGFEHAGFQGQQYILERGEYPSWDAWGGNTAYPAERLTSFRPAACANHRDSRLTIFEQENFLGKKGELSDDYPSLQAMGWEGNEVGSFHVHSGAWVCSQFPGYRGFQYVLECDHHSGDYKHFREWGSHAPTFQVQSIRRIQQ,mutated_sequence,1.0,196.0,NP_001877.1.a2m,NP_001877.1.npy,ClinVar +NP_001895.1,NP_001895.1.csv,MATQADLMELDMAMEPDRKAAVSHWQQQSYLDSGIHSGATTTAPSLSGKGNPEEEDVDTSQVLYEWEQGFSQSFTQEQVADIDGQYAMTRAQRVRAAMFPETLDEGMQIPSTQFDAAHPTNVQRLAEPSQMLKHAVVNLINYQDDAELATRAIPELTKLLNDEDQVVVNKAAVMVHQLSKKEASRHAIMRSPQMVSAIVRTMQNTNDVETARCTAGTLHNLSHHREGLLAIFKSGGIPALVKMLGSPVDSVLFYAITTLHNLLLHQEGAKMAVRLAGGLQKMVALLNKTNVKFLAITTDCLQILAYGNQESKLIILASGGPQALVNIMRTYTYEKLLWTTSRVLKVLSVCSSNKPAIVEAGGMQALGLHLTDPSQRLVQNCLWTLRNLSDAATKQEGMEGLLGTLVQLLGSDDINVVTCAAGILSNLTCNNYKNKMMVCQVGGIEALVRTVLRAGDREDITEPAICALRHLTSRHQEAEMAQNAVRLHYGLPVVVKLLHPPSHWPLIKATVGLIRNLALCPANHAPLREQGAIPRLVQLLVRAHQDTQRRTSMGGTQQQFVEGVRMEEIVEGCTGALHILARDVHNRIVIRGLNTIPLFVQLLYSPIENIQRVAAGVLCELAQDKEAAEAIEAEGATAPLTELLHSRNEGVATYAAAVLFRMSEDKPQDYKKRLSVELTSSLFRTEPMAWNETADLGLDIGAQGEPLGYRQDDPSYRSFHSGGYGQDALGMDPMMEHEMGGHHPGADYPVDGLPDLGHAQDLMDGLPPGDSNQLAWFDTDL,mutated_sequence,1.0,781.0,NP_001895.1.a2m,NP_001895.1.npy,ClinVar +NP_001900.1,NP_001900.1.csv,MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL,mutated_sequence,1.0,412.0,NP_001900.1.a2m,NP_001900.1.npy,ClinVar +NP_001918.3,NP_001918.3.csv,MSQAYSSSQRVSSYRRTFGGAPGFPLGSPLSSPVFPRAGFGSKGSSSSVTSRVYQVSRTSGGAGGLGSLRASRLGTTRTPSSYGAGELLDFSLADAVNQEFLTTRTNEKVELQELNDRFANYIEKVRFLEQQNAALAAEVNRLKGREPTRVAELYEEELRELRRQVEVLTNQRARVDVERDNLLDDLQRLKAKLQEEIQLKEEAENNLAAFRADVDAATLARIDLERRIESLNEEIAFLKKVHEEEIRELQAQLQEQQVQVEMDMSKPDLTAALRDIRAQYETIAAKNISEAEEWYKSKVSDLTQAANKNNDALRQAKQEMMEYRHQIQSYTCEIDALKGTNDSLMRQMRELEDRFASEASGYQDNIARLEEEIRHLKDEMARHLREYQDLLNVKMALDVEIATYRKLLEGEESRINLPIQTYSALNFRETSPEQRGSEVHTKKTVMIKTIETRDGEVVSEATQQQHEVL,mutated_sequence,1.0,470.0,NP_001918.3.a2m,NP_001918.3.npy,ClinVar +NP_001922.2,NP_001922.2.csv,MWRVCARRAQNVAPWAGLEARWTALQEVPGTPRVTSRSGPAPARRNSVTTGYGGVRALCGWTPSSGATPRNRLLLQLLGSPGRRYYSLPPHQKVPLPSLSPTMQAGTIARWEKKEGDKINEGDLIAEVETDKATVGFESLEECYMAKILVAEGTRDVPIGAIICITVGKPEDIEAFKNYTLDSSAAPTPQAAPAPTPAATASPPTPSAQAPGSSYPPHMQVLLPALSPTMTMGTVQRWEKKVGEKLSEGDLLAEIETDKATIGFEVQEEGYLAKILVPEGTRDVPLGTPLCIIVEKEADISAFADYRPTEVTDLKPQVPPPTPPPVAAVPPTPQPLAPTPSAPCPATPAGPKGRVFVSPLAKKLAVEKGIDLTQVKGTGPDGRITKKDIDSFVPSKVAPAPAAVVPPTGPGMAPVPTGVFTDIPISNIRRVIAQRLMQSKQTIPHYYLSIDVNMGEVLLVRKELNKILEGRSKISVNDFIIKASALACLKVPEANSSWMDTVIRQNHVVDVSVAVSTPAGLITPIVFNAHIKGVETIANDVVSLATKAREGKLQPHEFQGGTFTISNLGMFGIKNFSAIINPPQACILAIGASEDKLVPADNEKGFDVASMMSVTLSCDHRVVDGAVGAQWLAEFRKYLEKPITMLL,mutated_sequence,1.0,647.0,NP_001922.2.a2m,NP_001922.2.npy,ClinVar +NP_001944.1,NP_001944.1.csv,MAALMTPGTGAPPAPGDFSGEGSQGLPDPSPEPKQLPELIRMKRDGGRLSEADIRGFVAAVVNGSAQGAQIGAMLMAIRLRGMDLEETSVLTQALAQSGQQLEWPEAWRQQLVDKHSTGGVGDKVSLVLAPALAACGCKVPMISGRGLGHTGGTLDKLESIPGFNVIQSPEQMQVLLDQAGCCIVGQSEQLVPADGILYAARDVTATVDSLPLITASILSKKLVEGLSALVVDVKFGGAAVFPNQEQARELAKTLVGVGASLGLRVAAALTAMDKPLGRCVGHALEVEEALLCMDGAGPPDLRDLVTTLGGALLWLSGHAGTQAQGAARVAAALDDGSALGRFERMLAAQGVDPGLARALCSGSPAERRQLLPRAREQEELLAPADGTVELVRALPLALVLHELGAGRSRAGEPLRLGVGAELLVDVGQRLRRGTPWLRVHRDGPALSGPQSRALQEALVLSDRAPFAAPSPFAELVLPPQQ,mutated_sequence,1.0,482.0,NP_001944.1.a2m,NP_001944.1.npy,ClinVar +NP_001963.1,NP_001963.1.csv,MTLGRRLACLFLACVLPALLLGGTALASEIVGGRRARPHAWPFMVSLQLRGGHFCGATLIAPNFVMSAAHCVANVNVRAVRVVLGAHNLSRREPTRQVFAVQRIFENGYDPVNLLNDIVILQLNGSATINANVQVAQLPAQGRRLGNGVQCLAMGWGLLGRNRGIASVLQELNVTVVTSLCRRSNVCTLVRGRQAGVCFGDSGSPLVCNGLIHGIASFVRGGCASGLYPDAFAPVAQFVNWIDSIIQRSEDNPCPHPRDPDPASRTH,mutated_sequence,1.0,267.0,NP_001963.1.a2m,NP_001963.1.npy,ClinVar +NP_001978.1,NP_001978.1.csv,MSETPAQCSIKQERISYTPPESPVPSYASSTPLHVPVPRALRMEEDSIRLPAHLRLQPIYWSRDDVAQWLKWAENEFSLRPIDSNTFEMNGKALLLLTKEDFRYRSPHSGDVLYELLQHILKQRKPRILFSPFFHPGNSIHTQPEVILHQNHEEDNCVQRTPRPSVDNVHHNPPTIELLHRSRSPITTNHRPSPDPEQRPLRSPLDNMIRRLSPAERAQGPRPHQENNHQESYPLSVSPMENNHCPASSESHPKPSSPRQESTRVIQLMPSPIMHPLILNPRHSVDFKQSRLSEDGLHREGKPINLSHREDLAYMNHIMVSVSPPEEHAMPIGRIADCRLLWDYVYQLLSDSRYENFIRWEDKESKIFRIVDPNGLARLWGNHKNRTNMTYEKMSRALRHYYKLNIIRKEPGQRLLFRFMKTPDEIMSGRTDRLEHLESQELDEQIYQEDEC,mutated_sequence,1.0,452.0,NP_001978.1.a2m,NP_001978.1.npy,ClinVar +NP_001990.2,NP_001990.2.csv,MGRRRRLCLQLYFLWLGCVVLWAQGTAGQPQPPPPKPPRPQPPPQQVRSATAGSEGGFLAPEYREEGAAVASRVRRRGQQDVLRGPNVCGSRFHSYCCPGWKTLPGGNQCIVPICRNSCGDGFCSRPNMCTCSSGQISSTCGSKSIQQCSVRCMNGGTCADDHCQCQKGYIGTYCGQPVCENGCQNGGRCIGPNRCACVYGFTGPQCERDYRTGPCFTQVNNQMCQGQLTGIVCTKTLCCATIGRAWGHPCEMCPAQPQPCRRGFIPNIRTGACQDVDECQAIPGICQGGNCINTVGSFECRCPAGHKQSETTQKCEDIDECSIIPGICETGECSNTVGSYFCVCPRGYVTSTDGSRCIDQRTGMCFSGLVNGRCAQELPGRMTKMQCCCEPGRCWGIGTIPEACPVRGSEEYRRLCMDGLPMGGIPGSAGSRPGGTGGNGFAPSGNGNGYGPGGTGFIPIPGGNGFSPGVGGAGVGAGGQGPIITGLTILNQTIDICKHHANLCLNGRCIPTVSSYRCECNMGYKQDANGDCIDVDECTSNPCTNGDCVNTPGSYYCKCHAGFQRTPTKQACIDIDECIQNGVLCKNGRCVNTDGSFQCICNAGFELTTDGKNCVDHDECTTTNMCLNGMCINEDGSFKCICKPGFVLAPNGRYCTDVDECQTPGICMNGHCINSEGSFRCDCPPGLAVGMDGRVCVDTHMRSTCYGGIKKGVCVRPFPGAVTKSECCCANPDYGFGEPCQPCPAKNSAEFHGLCSSGVGITVDGRDINECALDPDICANGICENLRGSYRCNCNSGYEPDASGRNCIDIDECLVNRLLCDNGLCRNTPGSYSCTCPPGYVFRTETETCEDINECESNPCVNGACRNNLGSFNCECSPGSKLSSTGLICIDSLKGTCWLNIQDSRCEVNINGATLKSECCATLGAAWGSPCERCELDTACPRGLARIKGVTCEDVNECEVFPGVCPNGRCVNSKGSFHCECPEGLTLDGTGRVCLDIRMEQCYLKWDEDECIHPVPGKFRMDACCCAVGAAWGTECEECPKPGTKEYETLCPRGAGFANRGDVLTGRPFYKDINECKAFPGMCTYGKCRNTIGSFKCRCNSGFALDMEERNCTDIDECRISPDLCGSGICVNTPGSFECECFEGYESGFMMMKNCMDIDECERNPLLCRGGTCVNTEGSFQCDCPLGHELSPSREDCVDINECSLSDNLCRNGKCVNMIGTYQCSCNPGYQATPDRQGCTDIDECMIMNGGCDTQCTNSEGSYECSCSEGYALMPDGRSCADIDECENNPDICDGGQCTNIPGEYRCLCYDGFMASMDMKTCIDVNECDLNSNICMFGECENTKGSFICHCQLGYSVKKGTTGCTDVDECEIGAHNCDMHASCLNIPGSFKCSCREGWIGNGIKCIDLDECSNGTHQCSINAQCVNTPGSYRCACSEGFTGDGFTCSDVDECAENINLCENGQCLNVPGAYRCECEMGFTPASDSRSCQDIDECSFQNICVFGTCNNLPGMFHCICDDGYELDRTGGNCTDIDECADPINCVNGLCVNTPGRYECNCPPDFQLNPTGVGCVDNRVGNCYLKFGPRGDGSLSCNTEIGVGVSRSSCCCSLGKAWGNPCETCPPVNSTEYYTLCPGGEGFRPNPITIILEDIDECQELPGLCQGGNCINTFGSFQCECPQGYYLSEDTRICEDIDECFAHPGVCGPGTCYNTLGNYTCICPPEYMQVNGGHNCMDMRKSFCYRSYNGTTCENELPFNVTKRMCCCTYNVGKAWNKPCEPCPTPGTADFKTICGNIPGFTFDIHTGKAVDIDECKEIPGICANGVCINQIGSFRCECPTGFSYNDLLLVCEDIDECSNGDNLCQRNADCINSPGSYRCECAAGFKLSPNGACVDRNECLEIPNVCSHGLCVDLQGSYQCICHNGFKASQDQTMCMDVDECERHPCGNGTCKNTVGSYNCLCYPGFELTHNNDCLDIDECSSFFGQVCRNGRCFNEIGSFKCLCNEGYELTPDGKNCIDTNECVALPGSCSPGTCQNLEGSFRCICPPGYEVKSENCIDINECDEDPNICLFGSCTNTPGGFQCLCPPGFVLSDNGRRCFDTRQSFCFTNFENGKCSVPKAFNTTKAKCCCSKMPGEGWGDPCELCPKDDEVAFQDLCPYGHGTVPSLHDTREDVNECLESPGICSNGQCINTDGSFRCECPMGYNLDYTGVRCVDTDECSIGNPCGNGTCTNVIGSFECNCNEGFEPGPMMNCEDINECAQNPLLCAFRCMNTFGSYECTCPIGYALREDQKMCKDLDECAEGLHDCESRGMMCKNLIGTFMCICPPGMARRPDGEGCVDENECRTKPGICENGRCVNIIGSYRCECNEGFQSSSSGTECLDNRQGLCFAEVLQTICQMASSSRNLVTKSECCCDGGRGWGHQCELCPLPGTAQYKKICPHGPGYTTDGRDIDECKVMPNLCTNGQCINTMGSFRCFCKVGYTTDISGTSCIDLDECSQSPKPCNYICKNTEGSYQCSCPRGYVLQEDGKTCKDLDECQTKQHNCQFLCVNTLGGFTCKCPPGFTQHHTACIDNNECGSQPSLCGAKGICQNTPGSFSCECQRGFSLDATGLNCEDVDECDGNHRCQHGCQNILGGYRCGCPQGYIQHYQWNQCVDENECSNPNACGSASCYNTLGSYKCACPSGFSFDQFSSACHDVNECSSSKNPCNYGCSNTEGGYLCGCPPGYYRVGQGHCVSGMGFNKGQYLSLDTEVDEENALSPEACYECKINGYSKKDSRQKRSIHEPDPTAVEQISLESVDMDSPVNMKFNLSHLGSKEHILELRPAIQPLNNHIRYVISQGNDDSVFRIHQRNGLSYLHTAKKKLMPGTYTLEITSIPLYKKKELKKLEESNEDDYLLGELGEALRMRLQIQLY,mutated_sequence,1.0,2912.0,NP_001990.2.a2m,NP_001990.2.npy,ClinVar +NP_002026.1,NP_002026.1.csv,MLLLAAAFLVAFVLLLYMVSPLISPKPLALPGAHVVVTGGSSGIGKCIAIECYKQGAFITLVARNEDKLLQAKKEIEMHSINDKQVVLCISVDVSQDYNQVENVIKQAQEKLGPVDMLVNCAGMAVSGKFEDLEVSTFERLMSINYLGSVYPSRAVITTMKERRVGRIVFVSSQAGQLGLFGFTAYSASKFAIRGLAEALQMEVKPYNVYITVAYPPDTDTPGFAEENRTKPLETRLISETTSVCKPEQVAKQIVKDAIQGNFNSSLGSDGYMLSALTCGMAPVTSITEGLQQVVTMGLFRTIALFYLGSFDSIVRRCMMQREKSENADKTA,mutated_sequence,1.0,332.0,NP_002026.1.a2m,NP_002026.1.npy,ClinVar +NP_002058.2,NP_002058.2.csv,MTLESMMACCLSDEVKESKRINAEIEKQLRRDKRDARRELKLLLLGTGESGKSTFIKQMRIIHGAGYSEEDKRGFTKLVYQNIFTAMQAMIRAMETLKILYKYEQNKANALLIREVDVEKVTTFEHQYVSAIKTLWEDPGIQECYDRRREYQLSDSAKYYLTDVDRIATLGYLPTQQDVLRVRVPTTGIIEYPFDLENIIFRMVDVGGQRSERRKWIHCFENVTSIMFLVALSEYDQVLVESDNENRMEESKALFRTIITYPWFQNSSVILFLNKKDLLEDKILYSHLVDYFPEFDGPQRDAQAAREFILKMFVDLNPDSDKIIYSHFTCATDTENIRFVFAAVKDTILQLNLKEYNLV,mutated_sequence,1.0,359.0,NP_002058.2.a2m,NP_002058.2.npy,ClinVar +NP_002060.4,NP_002060.4.csv,MGCTLSAEDKAAVERSKMIDRNLREDGEKAAREVKLLLLGAGESGKSTIVKQMKIIHEAGYSEEECKQYKAVVYSNTIQSIIAIIRAMGRLKIDFGDSARADDARQLFVLAGAAEEGFMTAELAGVIKRLWKDSGVQACFNRSREYQLNDSAAYYLNDLDRIAQPNYIPTQQDVLRTRVKTTGIVETHFTFKDLHFKMFDVGGQRSERKKWIHCFEGVTAIIFCVALSDYDLVLAEDEEMNRMHESMKLFDSICNNKWFTDTSIILFLNKKDLFEEKIKKSPLTICYPEYAGSNTYEEAAAYIQCQFEDLNKRKDTKEIYTHFTCATDTKNVQFVFDAVTDVIIKNNLKDCGLF,mutated_sequence,1.0,354.0,NP_002060.4.a2m,NP_002060.4.npy,ClinVar +NP_002065.1,NP_002065.1.csv,MSELDQLRQEAEQLKNQIRDARKACADATLSQITNNIDPVGRIQMRTRRTLRGHLAKIYAMHWGTDSRLLVSASQDGKLIIWDSYTTNKVHAIPLRSSWVMTCAYAPSGNYVACGGLDNICSIYNLKTREGNVRVSRELAGHTGYLSCCRFLDDNQIVTSSGDTTCALWDIETGQQTTTFTGHTGDVMSLSLAPDTRLFVSGACDASAKLWDVREGMCRQTFTGHESDINAICFFPNGNAFATGSDDATCRLFDLRADQELMTYSHDNIICGITSVSFSKSGRLLLAGYDDFNCNVWDALKADRAGVLAGHDNRVSCLGVTDDGMAVATGSWDSFLKIWN,mutated_sequence,1.0,340.0,NP_002065.1.a2m,NP_002065.1.npy,ClinVar +NP_002066.1,NP_002066.1.csv,MGEMEQLRQEAEQLKKQIADARKACADVTLAELVSGLEVVGRVQMRTRRTLRGHLAKIYAMHWATDSKLLVSASQDGKLIVWDSYTTNKVHAIPLRSSWVMTCAYAPSGNFVACGGLDNMCSIYNLKSREGNVKVSRELSAHTGYLSCCRFLDDNNIVTSSGDTTCALWDIETGQQKTVFVGHTGDCMSLAVSPDFNLFISGACDASAKLWDVREGTCRQTFTGHESDINAICFFPNGEAICTGSDDASCRLFDLRADQELICFSHESIICGITSVAFSLSGRLLFAGYDDFNCNVWDSMKSERVGILSGHDNRVSCLGVTADGMAVATGSWDSFLKIWN,mutated_sequence,1.0,340.0,NP_002066.1.a2m,NP_002066.1.npy,ClinVar +NP_002070.1,NP_002070.1.csv,MAPPSVFAEVPQAQPVLVFKLTADFREDPDPRKVNLGVGAYRTDDCHPWVLPVVKKVEQKIANDNSLNHEYLPILGLAEFRSCASRLALGDDSPALKEKRVGGVQSLGGTGALRIGADFLARWYNGTNNKNTPVYVSSPTWENHNAVFSAAGFKDIRSYRYWDAEKRGLDLQGFLNDLENAPEFSIVVLHACAHNPTGIDPTPEQWKQIASVMKHRFLFPFFDSAYQGFASGNLERDAWAIRYFVSEGFEFFCAQSFSKNFGLYNERVGNLTVVGKEPESILQVLSQMEKIVRITWSNPPAQGARIVASTLSNPELFEEWTGNVKTMADRILTMRSELRARLEALKTPGTWNHITDQIGMFSFTGLNPKQVEYLVNEKHIYLLPSGRINVSGLTTKNLDYVATSIHEAVTKIQ,mutated_sequence,1.0,413.0,NP_002070.1.a2m,NP_002070.1.npy,ClinVar +NP_002071.2,NP_002071.2.csv,MALLHSGRVLPGIAAAFHPGLAAAASARASSWWTHVEMGPPDPILGVTEAFKRDTNSKKMNLGVGAYRDDNGKPYVLPSVRKAEAQIAAKNLDKEYLPIGGLAEFCKASAELALGENSEVLKSGRFVTVQTISGTGALRIGASFLQRFFKFSRDVFLPKPTWGNHTPIFRDAGMQLQGYRYYDPKTCGFDFTGAVEDISKIPEQSVLLLHACAHNPTGVDPRPEQWKEIATVVKKRNLFAFFDMAYQGFASGDGDKDAWAVRHFIEQGINVCLCQSYAKNMGLYGERVGAFTMVCKDADEAKRVESQLKILIRPMYSNPPLNGARIAAAILNTPDLRKQWLQEVKVMADRIIGMRTQLVSNLKKEGSTHNWQHITDQIGMFCFTGLKPEQVERLIKEFSIYMTKDGRISVAGVTSSNVGYLAHAIHQVTK,mutated_sequence,1.0,430.0,NP_002071.2.a2m,NP_002071.2.npy,ClinVar +NP_002100.2,NP_002100.2.csv,MAERAALEELVKLQGERVRGLKQQKASAELIEEEVAKLLKLKAQLGPDESKQKFVLKTPKGTRDYSPRQMAVREKVFDVIIRCFKRHGAEVIDTPVFELKETLMGKYGEDSKLIYDLKDQGGELLSLRYDLTVPFARYLAMNKLTNIKRYHIAKVYRRDNPAMTRGRYREFYQCDFDIAGNFDPMIPDAECLKIMCEILSSLQIGDFLVKVNDRRILDGMFAICGVSDSKFRTICSSVDKLDKVSWEEVKNEMVGEKGLAPEVADRIGDYVQQHGGVSLVEQLLQDPKLSQNKQALEGLGDLKLLFEYLTLFGIDDKISFDLSLARGLDYYTGVIYEAVLLQTPAQAGEEPLGVGSVAAGGRYDGLVGMFDPKGRKVPCVGLSIGVERIFSIVEQRLEALEEKIRTTETQVLVASAQKKLLEERLKLVSELWDAGIKAELLYKKNPKLLNQLQYCEEAGIPLVAIIGEQELKDGVIKLRSVTSREEVDVRREDLVEEIKRRTGQPLCIC,mutated_sequence,1.0,509.0,NP_002100.2.a2m,NP_002100.2.npy,ClinVar +NP_002172.2,NP_002172.2.csv,MSPARLRPRLHFCLVLLLLLVVPAAWGCGPGRVVGSRRRPPRKLVPLAYKQFSPNVPEKTLGASGRYEGKIARSSERFKELTPNYNPDIIFKDEENTGADRLMTQRCKDRLNSLAISVMNQWPGVKLRVTEGWDEDGHHSEESLHYEGRAVDITTSDRDRNKYGLLARLAVEAGFDWVYYESKAHVHCSVKSEHSAAAKTGGCFPAGAQVRLESGARVALSAVRPGDRVLAMGEDGSPTFSDVLIFLDREPHRLRAFQVIETQDPPRRLALTPAHLLFTADNHTEPAARFRATFASHVQPGQYVLVAGVPGLQPARVAAVSTHVALGAYAPLTKHGTLVVEDVVASCFAAVADHHLAQLAFWPLRLFHSLAWGSWTPGEGVHWYPQLLYRLGRLLLEEGSFHPLGMSGAGS,mutated_sequence,1.0,411.0,NP_002172.2.a2m,NP_002172.2.npy,ClinVar +NP_002221.1,NP_002221.1.csv,MEVMNLMEQPIKVTEWQQTYTYDSGIHSGANTCVPSVSSKGIMEEDEACGRQYTLKKTTTYTQGVPPSQGDLEYQMSTTARAKRVREAMCPGVSGEDSSLLLATQVEGQATNLQRLAEPSQLLKSAIVHLINYQDDAELATRALPELTKLLNDEDPVVVTKAAMIVNQLSKKEASRRALMGSPQLVAAVVRTMQNTSDLDTARCTTSILHNLSHHREGLLAIFKSGGIPALVRMLSSPVESVLFYAITTLHNLLLYQEGAKMAVRLADGLQKMVPLLNKNNPKFLAITTDCLQLLAYGNQESKLIILANGGPQALVQIMRNYSYEKLLWTTSRVLKVLSVCPSNKPAIVEAGGMQALGKHLTSNSPRLVQNCLWTLRNLSDVATKQEGLESVLKILVNQLSVDDVNVLTCATGTLSNLTCNNSKNKTLVTQNSGVEALIHAILRAGDKDDITEPAVCALRHLTSRHPEAEMAQNSVRLNYGIPAIVKLLNQPNQWPLVKATIGLIRNLALCPANHAPLQEAAVIPRLVQLLVKAHQDAQRHVAAGTQQPYTDGVRMEEIVEGCTGALHILARDPMNRMEIFRLNTIPLFVQLLYSSVENIQRVAAGVLCELAQDKEAADAIDAEGASAPLMELLHSRNEGTATYAAAVLFRISEDKNPDYRKRVSVELTNSLFKHDPAAWEAAQSMIPINEPYGDDMDATYRPMYSSDVPLDPLEMHMDMDGDYPIDTYSDGLRPPYPTADHMLA,mutated_sequence,1.0,745.0,NP_002221.1.a2m,NP_002221.1.npy,ClinVar +NP_002231.1,NP_002231.1.csv,MAKLTESMTNVLEGDSMDQDVESPVAIHQPKLPKQARDDLPRHISRDRTKRKIQRYVRKDGKCNVHHGNVRETYRYLTDIFTTLVDLKWRFNLLIFVMVYTVTWLFFGMIWWLIAYIRGDMDHIEDPSWTPCVTNLNGFVSAFLFSIETETTIGYGYRVITDKCPEGIILLLIQSVLGSIVNAFMVGCMFVKISQPKKRAETLVFSTHAVISMRDGKLCLMFRVGDLRNSHIVEASIRAKLIKSKQTSEGEFIPLNQTDINVGYYTGDDRLFLVSPLIISHEINQQSPFWEISKAQLPKEELEIVVILEGMVEATGMTCQARSSYITSEILWGYRFTPVLTLEDGFYEVDYNSFHETYETSTPSLSAKELAELASRAELPLSWSVSSKLNQHAELETEEEEKNLEEQTERNGDVANLENESKV,mutated_sequence,1.0,423.0,NP_002231.1.a2m,NP_002231.1.npy,ClinVar +NP_002263.3,NP_002263.3.csv,MIARQQCVRGGPRGFSCGSAIVGGGKRGAFSSVSMSGGAGRCSSGGFGSRSLYNLRGNKSISMSVAGSRQGACFGGAGGFGTGGFGGGFGGSFSGKGGPGFPVCPAGGIQEVTINQSLLTPLHVEIDPEIQKVRTEEREQIKLLNNKFASFIDKVQFLEQQNKVLETKWNLLQQQTTTTSSKNLEPLFETYLSVLRKQLDTLGNDKGRLQSELKTMQDSVEDFKTKYEEEINKRTAAENDFVVLKKDVDAAYLNKVELEAKVDSLNDEINFLKVLYDAELSQMQTHVSDTSVVLSMDNNRNLDLDSIIAEVRAQYEEIAQRSKAEAEALYQTKVQQLQISVDQHGDNLKNTKSEIAELNRMIQRLRAEIENIKKQCQTLQVSVADAEQRGENALKDAHSKRVELEAALQQAKEELARMLREYQELMSVKLALDIEIATYRKLLEGEEYRMSGECQSAVSISVVSGSTSTGGISGGLGSGSGFGLSSGFGSGSGSGFGFGGSVSGSSSSKIISTTTLNKRR,mutated_sequence,1.0,520.0,NP_002263.3.a2m,NP_002263.3.npy,ClinVar +NP_002372.1,NP_002372.1.csv,MPRPAPARRLPGLLLLLWPLLLLPSAAPDPVARPGFRRLETRGPGGSPGRRPSPAAPDGAPASGTSEPGRARGAGVCKSRPLDLVFIIDSSRSVRPLEFTKVKTFVSRIIDTLDIGPADTRVAVVNYASTVKIEFQLQAYTDKQSLKQAVGRITPLSTGTMSGLAIQTAMDEAFTVEAGAREPSSNIPKVAIIVTDGRPQDQVNEVAARAQASGIELYAVGVDRADMASLKMMASEPLEEHVFYVETYGVIEKLSSRFQETFCALDPCVLGTHQCQHVCISDGEGKHHCECSQGYTLNADKKTCSALDRCALNTHGCEHICVNDRSGSYHCECYEGYTLNEDRKTCSAQDKCALGTHGCQHICVNDRTGSHHCECYEGYTLNADKKTCSVRDKCALGSHGCQHICVSDGAASYHCDCYPGYTLNEDKKTCSATEEARRLVSTEDACGCEATLAFQDKVSSYLQRLNTKLDDILEKLKINEYGQIHR,mutated_sequence,1.0,486.0,NP_002372.1.a2m,NP_002372.1.npy,ClinVar +NP_002428.1,NP_002428.1.csv,MALWRAYQRALAAHPWKVQVLTAGSLMGLGDIISQQLVERRGLQEHQRGRTLTMVSLGCGFVGPVVGGWYKVLDRFIPGTTKVDALKKMLLDQGGFAPCFLGCFLPLVGALNGLSAQDNWAKLQRDYPDALITNYYLWPAVQLANFYLVPLHYRLAVVQCVAVIWNSYLSWKAHRL,mutated_sequence,1.0,176.0,NP_002428.1.a2m,NP_002428.1.npy,ClinVar +NP_002440.2,NP_002440.2.csv,MASPSKGNDLFSPDEEGPAVVAGPGPGPGGAEGAAEERRVKVSSLPFSVEALMSDKKPPKEASPLPAESASAGATLRPLLLSGHGAREAHSPGPLVKPFETASVKSENSEDGAAWMQEPGRYSPPPRHMSPTTCTLRKHKTNRKPRTPFTTSQLLALERKFRQKQYLSIAERAEFSSSLNLTETQVKIWFQNRRAKAKRLQEAELEKLKMAAKPMLPSSFSLPFPISSPLQAASIYGASYPFHRPVLPIPPVGLYATPVGYGMYHLS,mutated_sequence,1.0,267.0,NP_002440.2.a2m,NP_002440.2.npy,ClinVar +NP_002445.2,NP_002445.2.csv,MRRFLLLYATQQGQAKAIAEEICEQAVVHGFSADLHCISESDKYDLKTETAPLVVVVSTTGTGDPPDTARKFVKEIQNQTLPVDFFAHLRYGLLGLGDSEYTYFCNGGKIIDKRLQELGARHFYDTGHADDCVGLELVVEPWIAGLWPALRKHFRSSRGQEEISGALPVASPASSRTDLVKSELLHIESQVELLRFDDSGRKDSEVLKQNAVNSNQSNVVIEDFESSLTRSVPPLSQASLNIPGLPPEYLQVHLQESLGQEESQVSVTSADPVFQVPISKAVQLTTNDAIKTTLLVELDISNTDFSYQPGDAFSVICPNSDSEVQSLLQRLQLEDKREHCVLLKIKADTKKKGATLPQHIPAGCSLQFIFTWCLEIRAIPKKAFLRALVDYTSDSAEKRRLQELCSKQGAADYSRFVRDACACLLDLLLAFPSCQPPLSLLLEHLPKLQPRPYSCASSSLFHPGKLHFVFNIVEFLSTATTEVLRKGVCTGWLALLVASVLQPNIHASHEDSGKALAPKISISPRTTNSFHLPDDPSIPIIMVGPGTGIAPFIGFLQHREKLQEQHPDGNFGAMWLFFGCRHKDRDYLFRKELRHFLKHGILTHLKVSFSRDAPVGEEEAPAKYVQDNIQLHGQQVARILLQENGHIYVCGDAKNMAKDVHDALVQIISKEVGVEKLEAMKTLATLKEEKRYLQDIWS,mutated_sequence,1.0,698.0,NP_002445.2.a2m,NP_002445.2.npy,ClinVar +NP_002459.3,NP_002459.3.csv,MAAGGPGAGSAAPVSSTSSLPLAALNMRVRRRLSLFLNVRTQVAADWTALAEEMDFEYLEIRQLETQADPTGRLLDAWQGRPGASVGRLLELLTKLGRDDVLLELGPSIEEDCQKYILKQQQEEAEKPLQVAAVDSSVPRTAELAGITTLDDPLGHMPERFDAFICYCPSDIQFVQEMIRQLEQTNYRLKLCVSDRDVLPGTCVWSIASELIEKRCRRMVVVVSDDYLQSKECDFQTKFALSLSPGAHQKRLIPIKYKAMKKEFPSILRFITVCDYTNPCTKSWFWTRLAKALSLP,mutated_sequence,1.0,296.0,NP_002459.3.a2m,NP_002459.3.npy,ClinVar +NP_002461.2,NP_002461.2.csv,MSSDTEMEVFGIAAPFLRKSEKERIEAQNQPFDAKTYCFVVDSKEEYAKGKIKSSQDGKVTVETEDNRTLVVKPEDVYAMNPPKFDRIEDMAMLTHLNEPAVLYNLKDRYTSWMIYTYSGLFCVTVNPYKWLPVYNPEVVEGYRGKKRQEAPPHIFSISDNAYQFMLTDRENQSILITGESGAGKTVNTKRVIQYFATIAATGDLAKKKDSKMKGTLEDQIISANPLLEAFGNAKTVRNDNSSRFGKFIRIHFGTTGKLASADIETYLLEKSRVTFQLKAERSYHIFYQILSNKKPELIELLLITTNPYDYPFISQGEILVASIDDAEELLATDSAIDILGFTPEEKSGLYKLTGAVMHYGNMKFKQKQREEQAEPDGTEVADKTAYLMGLNSSDLLKALCFPRVKVGNEYVTKGQTVDQVHHAVNALSKSVYEKLFLWMVTRINQQLDTKLPRQHFIGVLDIAGFEIFEYNSLEQLCINFTNEKLQQFFNHHMFVLEQEEYKKEGIEWTFIDFGMDLAACIELIEKPMGIFSILEEECMFPKATDTSFKNKLYDQHLGKSNNFQKPKVVKGRAEAHFSLIHYAGTVDYSVSGWLEKNKDPLNETVVGLYQKSSNRLLAHLYATFATADADSGKKKVAKKKGSSFQTVSALFRENLNKLMSNLRTTHPHFVRCIIPNETKTPGAMEHSLVLHQLRCNGVLEGIRICRKGFPNRILYGDFKQRYRVLNASAIPEGQFIDSKKACEKLLASIDIDHTQYKFGHTKVFFKAGLLGTLEEMRDDRLAKLITRTQAVCRGFLMRVEFQKMVQRRESIFCIQYNIRSFMNVKHWPWMKLFFKIKPLLKSAETEKEMATMKEEFQKTKDELAKSEAKRKELEEKLVTLVQEKNDLQLQVQAESENLLDAEERCDQLIKAKFQLEAKIKEVTERAEDEEEINAELTAKKRKLEDECSELKKDIDDLELTLAKVEKEKHATENKVKNLTEELSGLDETIAKLTREKKALQEAHQQALDDLQAEEDKVNSLNKTKSKLEQQVEDLESSLEQEKKLRVDLERNKRKLEGDLKLAQESILDLENDKQQLDERLKKKDFEYCQLQSKVEDEQTLGLQFQKKIKELQARIEELEEEIEAERATRAKTEKQRSDYARELEELSERLEEAGGVTSTQIELNKKREAEFLKLRRDLEEATLQHEAMVAALRKKHADSVAELGEQIDNLQRVKQKLEKEKSEFKLEIDDLSSSMESVSKSKANLEKICRTLEDQLSEARGKNEEIQRSLSELTTQKSRLQTEAGELSRQLEEKESIVSQLSRSKQAFTQQTEELKRQLEEENKAKNALAHALQSSRHDCDLLREQYEEEQEGKAELQRALSKANSEVAQWRTKYETDAIQRTEELEEAKKKLAQRLQDSEEQVEAVNAKCASLEKTKQRLQGEVEDLMVDVERANSLAAALDKKQRNFDKVLAEWKTKCEESQAELEASLKESRSLSTELFKLKNAYEEALDQLETVKRENKNLEQEIADLTEQIAENGKTIHELEKSRKQIELEKADIQLALEEAEAALEHEEAKILRIQLELTQVKSEIDRKIAEKDEEIEQLKRNYQRTVETMQSALDAEVRSRNEAIRLKKKMEGDLNEIEIQLSHANRQAAETLKHLRSVQGQLKDTQLHLDDALRGQEDLKEQLAIVERRANLLQAEVEELRATLEQTERARKLAEQELLDSNERVQLLHTQNTSLIHTKKKLETDLMQLQSEVEDASRDARNAEEKAKKAITDAAMMAEELKKEQDTSAHLERMKKNLEQTVKDLQHRLDEAEQLALKGGKKQIQKLETRIRELEFELEGEQKKNTESVKGLRKYERRVKELTYQSEEDRKNVLRLQDLVDKLQVKVKSYKRQAEEADEQANAHLTKFRKAQHELEEAEERADIAESQVNKLRAKTRDFTSSRMVVHESEE,mutated_sequence,1.0,1940.0,NP_002461.2.a2m,NP_002461.2.npy,ClinVar +NP_002464.1,NP_002464.1.csv,MAQQAADKYLYVDKNFINNPLAQADWAAKKLVWVPSDKSGFEPASLKEEVGEEAIVELVENGKKVKVNKDDIQKMNPPKFSKVEDMAELTCLNEASVLHNLKERYYSGLIYTYSGLFCVVINPYKNLPIYSEEIVEMYKGKKRHEMPPHIYAITDTAYRSMMQDREDQSILCTGESGAGKTENTKKVIQYLAYVASSHKSKKDQGELERQLLQANPILEAFGNAKTVKNDNSSRFGKFIRINFDVNGYIVGANIETYLLEKSRAIRQAKEERTFHIFYYLLSGAGEHLKTDLLLEPYNKYRFLSNGHVTIPGQQDKDMFQETMEAMRIMGIPEEEQMGLLRVISGVLQLGNIVFKKERNTDQASMPDNTAAQKVSHLLGINVTDFTRGILTPRIKVGRDYVQKAQTKEQADFAIEALAKATYERMFRWLVLRINKALDKTKRQGASFIGILDIAGFEIFDLNSFEQLCINYTNEKLQQLFNHTMFILEQEEYQREGIEWNFIDFGLDLQPCIDLIEKPAGPPGILALLDEECWFPKATDKSFVEKVMQEQGTHPKFQKPKQLKDKADFCIIHYAGKVDYKADEWLMKNMDPLNDNIATLLHQSSDKFVSELWKDVDRIIGLDQVAGMSETALPGAFKTRKGMFRTVGQLYKEQLAKLMATLRNTNPNFVRCIIPNHEKKAGKLDPHLVLDQLRCNGVLEGIRICRQGFPNRVVFQEFRQRYEILTPNSIPKGFMDGKQACVLMIKALELDSNLYRIGQSKVFFRAGVLAHLEEERDLKITDVIIGFQACCRGYLARKAFAKRQQQLTAMKVLQRNCAAYLKLRNWQWWRLFTKVKPLLQVSRQEEEMMAKEEELVKVREKQLAAENRLTEMETLQSQLMAEKLQLQEQLQAETELCAEAEELRARLTAKKQELEEICHDLEARVEEEEERCQHLQAEKKKMQQNIQELEEQLEEEESARQKLQLEKVTTEAKLKKLEEEQIILEDQNCKLAKEKKLLEDRIAEFTTNLTEEEEKSKSLAKLKNKHEAMITDLEERLRREEKQRQELEKTRRKLEGDSTDLSDQIAELQAQIAELKMQLAKKEEELQAALARVEEEAAQKNMALKKIRELESQISELQEDLESERASRNKAEKQKRDLGEELEALKTELEDTLDSTAAQQELRSKREQEVNILKKTLEEEAKTHEAQIQEMRQKHSQAVEELAEQLEQTKRVKANLEKAKQTLENERGELANEVKVLLQGKGDSEHKRKKVEAQLQELQVKFNEGERVRTELADKVTKLQVELDNVTGLLSQSDSKSSKLTKDFSALESQLQDTQELLQEENRQKLSLSTKLKQVEDEKNSFREQLEEEEEAKHNLEKQIATLHAQVADMKKKMEDSVGCLETAEEVKRKLQKDLEGLSQRHEEKVAAYDKLEKTKTRLQQELDDLLVDLDHQRQSACNLEKKQKKFDQLLAEEKTISAKYAEERDRAEAEAREKETKALSLARALEEAMEQKAELERLNKQFRTEMEDLMSSKDDVGKSVHELEKSKRALEQQVEEMKTQLEELEDELQATEDAKLRLEVNLQAMKAQFERDLQGRDEQSEEKKKQLVRQVREMEAELEDERKQRSMAVAARKKLEMDLKDLEAHIDSANKNRDEAIKQLRKLQAQMKDCMRELDDTRASREEILAQAKENEKKLKSMEAEMIQLQEELAAAERAKRQAQQERDELADEIANSSGKGALALEEKRRLEARIAQLEEELEEEQGNTELINDRLKKANLQIDQINTDLNLERSHAQKNENARQQLERQNKELKVKLQEMEGTVKSKYKASITALEAKIAQLEEQLDNETKERQAACKQVRRTEKKLKDVLLQVDDERRNAEQYKDQADKASTRLKQLKRQLEEAEEEAQRANASRRKLQRELEDATETADAMNREVSSLKNKLRRGDLPFVVPRRMARKGAGDGSDEEVDGKADGAEAKPAE,mutated_sequence,1.0,1960.0,NP_002464.1.a2m,NP_002464.1.npy,ClinVar +NP_002476.2,NP_002476.2.csv,MWKLLPAAGPAGGEPYRLLTGVEYVVGRKNCAILIENDQSISRNHAVLTANFSVTNLSQTDEIPVLTLKDNSKYGTFVNEEKMQNGFSRTLKSGDGITFGVFGSKFRIEYEPLVACSSCLDVSGKTALNQAILQLGGFTVNNWTEECTHLVMVSVKVTIKTICALICGRPIVKPEYFTEFLKAVESKKQPPQIESFYPPLDEPSIGSKNVDLSGRQERKQIFKGKTFIFLNAKQHKKLSSAVVFGGGEARLITEENEEEHNFFLAPGTCVVDTGITNSQTLIPDCQKKWIQSIMDMLQRQGLRPIPEAEIGLAVIFMTTKNYCDPQGHPSTGLKTTTPGPSLSQGVSVDEKLMPSAPVNTTTYVADTESEQADTWDLSERPKEIKVSKMEQKFRMLSQDAPTVKESCKTSSNNNSMVSNTLAKMRIPNYQLSPTKLPSINKSKDRASQQQQTNSIRNYFQPSTKKRERDEENQEMSSCKSARIETSCSLLEQTQPATPSLWKNKEQHLSENEPVDTNSDNNLFTDTDLKSIVKNSASKSHAAEKLRSNKKREMDDVAIEDEVLEQLFKDTKPELEIDVKVQKQEEDVNVRKRPRMDIETNDTFSDEAVPESSKISQENEIGKKRELKEDSLWSAKEISNNDKLQDDSEMLPKKLLLTEFRSLVIKNSTSRNPSGINDDYGQLKNFKKFKKVTYPGAGKLPHIIGGSDLIAHHARKNTELEEWLRQEMEVQNQHAKEESLADDLFRYNPYLKRRR,mutated_sequence,1.0,754.0,NP_002476.2.a2m,NP_002476.2.npy,ClinVar +NP_002523.2,NP_002523.2.csv,MAAAEGPVGDGELWQTWLPNHVVFLRLREGLKNQSPTEAEKPASSSLPSSPPPQLLTRNVVFGLGGELFLWDGEDSSFLVVRLRGPSGGGEEPALSQYQRLLCINPPLFEIYQVLLSPTQHHVALIGIKGLMVLELPKRWGKNSEFEGGKSTVNCSTTPVAERFFTSSTSLTLKHAAWYPSEILDPHVVLLTSDNVIRIYSLREPQTPTNVIILSEAEEESLVLNKGRAYTASLGETAVAFDFGPLAAVPKTLFGQNGKDEVVAYPLYILYENGETFLTYISLLHSPGNIGKLLGPLPMHPAAEDNYGYDACAVLCLPCVPNILVIATESGMLYHCVVLEGEEEDDHTSEKSWDSRIDLIPSLYVFECVELELALKLASGEDDPFDSDFSCPVKLHRDPKCPSRYHCTHEAGVHSVGLTWIHKLHKFLGSDEEDKDSLQELSTEQKCFVEHILCTKPLPCRQPAPIRGFWIVPDILGPTMICITSTYECLIWPLLSTVHPASPPLLCTREDVEVAESPLRVLAETPDSFEKHIRSILQRSVANPAFLKASEKDIAPPPEECLQLLSRATQVFREQYILKQDLAKEEIQRRVKLLCDQKKKQLEDLSYCREERKSLREMAERLADKYEEAKEKQEDIMNRMKKLLHSFHSELPVLSDSERDMKKELQLIPDQLRHLGNAIKQVTMKKDYQQQKMEKVLSLPKPTIILSAYQRKCIQSILKEEGEHIREMVKQINDIRNHVNF,mutated_sequence,1.0,741.0,NP_002523.2.a2m,NP_002523.2.npy,ClinVar +NP_002537.3,NP_002537.3.csv,MNNLLCCALVFLDISIKWTTQETFPPKYLHYDEETSHQLLCDKCPPGTYLKQHCTAKWKTVCAPCPDHYYTDSWHTSDECLYCSPVCKELQYVKQECNRTHNRVCECKEGRYLEIEFCLKHRSCPPGFGVVQAGTPERNTVCKRCPDGFFSNETSSKAPCRKHTNCSVFGLLLTQKGNATHDNICSGNSESTQKCGIDVTLCEEAFFRFAVPTKFTPNWLSVLVDNLPGTKVNAESVERIKRQHSSQEQTFQLLKLWKHQNKDQDIVKKIIQDIDLCENSVQRHIGHANLTFEQLRSLMESLPGKKVGAEDIEKTIKACKPSDQILKLLSLWRIKNGDQDTLKGLMHALKHSKTYHFPKTVTQSLKKTIRFLHSFTMYKLYQKLFLEMIGNQVQSVKISCL,mutated_sequence,1.0,401.0,NP_002537.3.a2m,NP_002537.3.npy,ClinVar +NP_002600.1,NP_002600.1.csv,MRLPGAMPALALKGELLLLSLLLLLEPQISQGLVVTPPGPELVLNVSSTFVLTCSGSAPVVWERMSQEPPQEMAKAQDGTFSSVLTLTNLTGLDTGEYFCTHNDSRGLETDERKRLYIFVPDPTVGFLPNDAEELFIFLTEITEITIPCRVTDPQLVVTLHEKKGDVALPVPYDHQRGFSGIFEDRSYICKTTIGDREVDSDAYYVYRLQVSSINVSVNAVQTVVRQGENITLMCIVIGNEVVNFEWTYPRKESGRLVEPVTDFLLDMPYHIRSILHIPSAELEDSGTYTCNVTESVNDHQDEKAINITVVESGYVRLLGEVGTLQFAELHRSRTLQVVFEAYPPPTVLWFKDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL,mutated_sequence,1.0,1106.0,NP_002600.1.a2m,NP_002600.1.npy,ClinVar +NP_002606.3,NP_002606.3.csv,MQALVLLLCIGALLGHSSCQNPASPPEEGSPDPDSTGALVEEEDPFFKVPVNKLAAAVSNFGYDLYRVRSSTSPTTNVLLSPLSVATALSALSLGAEQRTESIIHRALYYDLISSPDIHGTYKELLDTVTAPQKNLKSASRIVFEKKLRIKSSFVAPLEKSYGTRPRVLTGNPRLDLQEINNWVQAQMKGKLARSTKEIPDEISILLLGVAHFKGQWVTKFDSRKTSLEDFYLDEERTVRVPMMSDPKAVLRYGLDSDLSCKIAQLPLTGSMSIIFFLPLKVTQNLTLIEESLTSEFIHDIDRELKTVQAVLTVPKLKLSYEGEVTKSLQEMKLQSLFDSPDFSKITGKPIKLTQVEHRAGFEWNEDGAGTTPSPGLQPAHLTFPLDYHLNQPFIFVLRDTDTGALLFIGKILDPRGP,mutated_sequence,1.0,418.0,NP_002606.3.a2m,NP_002606.3.npy,ClinVar +NP_002626.1,NP_002626.1.csv,MFSSVAHLARANPFNTPHLQLVHDGLGDLRSSSPGPTGQPRRPRNLAAAAVEEYSCEFGSAKYYALCGFGGVLSCGLTHTAVVPLDLVKCRMQVDPQKYKGIFNGFSVTLKEDGVRGLAKGWAPTFLGYSMQGLCKFGFYEVFKVLYSNMLGEENTYLWRTSLYLAASASAEFFADIALAPMEAAKVRIQTQPGYANTLRDAAPKMYKEEGLKAFYKGVAPLWMRQIPYTMMKFACFERTVEALYKFVVPKPRSECSKPEQLVVTFVAGYIAGVFCAIVSHPADSVVSVLNKEKGSSASLVLKRLGFKGVWKGLFARIIMIGTLTALQWFIYDSVKVYFRLPRPPPPEMPESLKKKLGLTQ,mutated_sequence,1.0,361.0,NP_002626.1.a2m,NP_002626.1.npy,ClinVar +NP_002632.1,NP_002632.1.csv,MACRGGAGNGHRASATLSRVSPGSLYTCRTRTHNICMVSDFFYPNMGGVESHIYQLSQCLIERGHKVIIVTHAYGNRKGIRYLTSGLKVYYLPLKVMYNQSTATTLFHSLPLLRYIFVRERVTIIHSHSSFSAMAHDALFHAKTMGLQTVFTDHSLFGFADVSSVLTNKLLTVSLCDTNHIICVSYTSKENTVLRAALNPEIVSVIPNAVDPTDFTPDPFRRHDSITIVVVSRLVYRKGIDLLSGIIPELCQKYPDLNFIIGGEGPKRIILEEVRERYQLHDRVRLLGALEHKDVRNVLVQGHIFLNTSLTEAFCMAIVEAASCGLQVVSTRVGGIPEVLPENLIILCEPSVKSLCEGLEKAIFQLKSGTLPAPENIHNIVKTFYTWRNVAERTEKVYDRVSVEAVLPMDKRLDRLISHCGPVTGYIFALLAVFNFLFLIFLRWMTPDSIIDVAIDATGPRGAWTNNYSHSKRGGENNEISETR,mutated_sequence,1.0,484.0,NP_002632.1.a2m,NP_002632.1.npy,ClinVar +NP_002668.1,NP_002668.1.csv,MSNKFLGTWKLVSSENFDDYMKALGVGLATRKLGNLAKPTVIISKKGDIITIRTESTFKNTEISFKLGQEFEETTADNRKTKSIVTLQRGSLNQVQRWDGKETTIKRKLVNGKMVAECKMKGVVCTRIYEKV,mutated_sequence,1.0,132.0,NP_002668.1.a2m,NP_002668.1.npy,ClinVar +NP_002682.2,NP_002682.2.csv,MDGKRRPGPGPGVPPKRARGGLWDDDDAPRPSQFEEDLALMEEMEAEHRLQEQEEEELQSVLEGVADGQVPPSAIDPRWLRPTPPALDPQTEPLIFQQLEIDHYVGPAQPVPGGPPPSRGSVPVLRAFGVTDEGFSVCCHIHGFAPYFYTPAPPGFGPEHMGDLQRELNLAISRDSRGGRELTGPAVLAVELCSRESMFGYHGHGPSPFLRITVALPRLVAPARRLLEQGIRVAGLGTPSFAPYEANVDFEIRFMVDTDIVGCNWLELPAGKYALRLKEKATQCQLEADVLWSDVVSHPPEGPWQRIAPLRVLSFDIECAGRKGIFPEPERDPVIQICSLGLRWGEPEPFLRLALTLRPCAPILGAKVQSYEKEEDLLQAWSTFIRIMDPDVITGYNIQNFDLPYLISRAQTLKVQTFPFLGRVAGLCSNIRDSSFQSKQTGRRDTKVVSMVGRVQMDMLQVLLREYKLRSYTLNAVSFHFLGEQKEDVQHSIITDLQNGNDQTRRRLAVYCLKDAYLPLRLLERLMVLVNAVEMARVTGVPLSYLLSRGQQVKVVSQLLRQAMHEGLLMPVVKSEGGEDYTGATVIEPLKGYYDVPIATLDFSSLYPSIMMAHNLCYTTLLRPGTAQKLGLTEDQFIRTPTGDEFVKTSVRKGLLPQILENLLSARKRAKAELAKETDPLRRQVLDGRQLALKVSANSVYGFTGAQVGKLPCLEISQSVTGFGRQMIEKTKQLVESKYTVENGYSTSAKVVYGDTDSVMCRFGVSSVAEAMALGREAADWVSGHFPSPIRLEFEKVYFPYLLISKKRYAGLLFSSRPDAHDRMDCKGLEAVRRDNCPLVANLVTASLRRLLIDRDPEGAVAHAQDVISDLLCNRIDISQLVITKELTRAASDYAGKQAHVELAERMRKRDPGSAPSLGDRVPYVIISAAKGVAAYMKSEDPLFVLEHSLPIDTQYYLEQQLAKPLLRIFEPILGEGRAEAVLLRGDHTRCKTVLTGKVGGLLAFAKRRNCCIGCRTVLSHQGAVCEFCQPRESELYQKEVSHLNALEERFSRLWTQCQRCQGSLHEDVICTSRDCPIFYMRKKVRKDLEDQEQLLRRFGPPGPEAW,mutated_sequence,1.0,1107.0,NP_002682.2.a2m,NP_002682.2.npy,ClinVar +NP_002684.1,NP_002684.1.csv,MSRLLWRKVAGATVGPGPVPAPGRWVSSSVPASDPSDGQRRRQQQQQQQQQQQQQPQQPQVLSSEGGQLRHNPLDIQMLSRGLHEQIFGQGGEMPGEAAVRRSVEHLQKHGLWGQPAVPLPDVELRLPPLYGDNLDQHFRLLAQKQSLPYLEAANLLLQAQLPPKPPAWAWAEGWTRYGPEGEAVPVAIPEERALVFDVEVCLAEGTCPTLAVAISPSAWYSWCSQRLVEERYSWTSQLSPADLIPLEVPTGASSPTQRDWQEQLVVGHNVSFDRAHIREQYLIQGSRMRFLDTMSMHMAISGLSSFQRSLWIAAKQGKHKVQPPTKQGQKSQRKARRGPAISSWDWLDISSVNSLAEVHRLYVGGPPLEKEPRELFVKGTMKDIRENFQDLMQYCAQDVWATHEVFQQQLPLFLERCPHPVTLAGMLEMGVSYLPVNQNWERYLAEAQGTYEELQREMKKSLMDLANDACQLLSGERYKEDPWLWDLEWDLQEFKQKKAKKVKKEPATASKLPIEGAGAPGDPMDQEDLGPCSEEEEFQQDVMARACLQKLKGTTELLPKRPQHLPGHPGWYRKLCPRLDDPAWTPGPSLLSLQMRVTPKLMALTWDGFPLHYSERHGWGYLVPGRRDNLAKLPTGTTLESAGVVCPYRAIESLYRKHCLEQGKQQLMPQEAGLAEEFLLTDNSAIWQTVEELDYLEVEAEAKMENLRAAVPGQPLALTARGGPKDTQPSYHHGNGPYNDVDIPGCWFFKLPHKDGNSCNVGSPFAKDFLPKMEDGTLQAGPGGASGPRALEINKMISFWRNAHKRISSQMVVWLPRSALPRAVIRHPDYDEEGLYGAILPQVVTAGTITRRAVEPTWLTASNARPDRVGSELKAMVQAPPGYTLVGADVDSQELWIAAVLGDAHFAGMHGCTAFGWMTLQGRKSRGTDLHSKTATTVGISREHAKIFNYGRIYGAGQPFAERLLMQFNHRLTQQEAAEKAQQMYAATKGLRWYRLSDEGEWLVRELNLPVDRTEGGWISLQDLRKVQRETARKSQWKKWEVVAERAWKGGTESEMFNKLESIATSDIPRTPVLGCCISRALEPSAVQEEFMTSRVNWVVQSSAVDYLHLMLVAMKWLFEEFAIDGRFCISIHDEVRYLVREEDRYRAALALQITNLLTRCMFAYKLGLNDLPQSVAFFSAVDIDRCLRKEVTMDCKTPSNPTGMERRYGIPQGEALDIYQIIELTKGSLEKRSQPGP,mutated_sequence,1.0,1239.0,NP_002684.1.a2m,NP_002684.1.npy,ClinVar +NP_002706.1,NP_002706.1.csv,MDEKVFTKELDQWIEQLNECKQLSESQVKSLCEKAKEILTKESNVQEVRCPVTVCGDVHGQFHDLMELFRIGGKSPDTNYLFMGDYVDRGYYSVETVTLLVALKVRYRERITILRGNHESRQITQVYGFYDECLRKYGNANVWKYFTDLFDYLPLTALVDGQIFCLHGGLSPSIDTLDHIRALDRLQEVPHEGPMCDLLWSDPDDRGGWGISPRGAGYTFGQDISETFNHANGLTLVSRAHQLVMEGYNWCHDRNVVTIFSAPNYCYRCGNQAAIMELDDTLKYSFLQFDPAPRRGEPHVTRRTPDYFL,mutated_sequence,1.0,309.0,NP_002706.1.a2m,NP_002706.1.npy,ClinVar +NP_002721.1,NP_002721.1.csv,MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF,mutated_sequence,1.0,351.0,NP_002721.1.a2m,NP_002721.1.npy,ClinVar +NP_002730.1,NP_002730.1.csv,MAGLGPGVGDSEGGPRPLFCRKGALRQKVVHEVKSHKFTARFFKQPTFCSHCTDFIWGIGKQGLQCQVCSFVVHRRCHEFVTFECPGAGKGPQTDDPRNKHKFRLHSYSSPTFCDHCGSLLYGLVHQGMKCSCCEMNVHRRCVRSVPSLCGVDHTERRGRLQLEIRAPTADEIHVTVGEARNLIPMDPNGLSDPYVKLKLIPDPRNLTKQKTRTVKATLNPVWNETFVFNLKPGDVERRLSVEVWDWDRTSRNDFMGAMSFGVSELLKAPVDGWYKLLNQEEGEYYNVPVADADNCSLLQKFEACNYPLELYERVRMGPSSSPIPSPSPSPTDPKRCFFGASPGRLHISDFSFLMVLGKGSFGKVMLAERRGSDELYAIKILKKDVIVQDDDVDCTLVEKRVLALGGRGPGGRPHFLTQLHSTFQTPDRLYFVMEYVTGGDLMYHIQQLGKFKEPHAAFYAAEIAIGLFFLHNQGIIYRDLKLDNVMLDAEGHIKITDFGMCKENVFPGTTTRTFCGTPDYIAPEIIAYQPYGKSVDWWSFGVLLYEMLAGQPPFDGEDEEELFQAIMEQTVTYPKSLSREAVAICKGFLTKHPGKRLGSGPDGEPTIRAHGFFRWIDWERLERLEIPPPFRPRPCGRSGENFDKFFTRAAPALTPPDRLVLASIDQADFQGFTYVNPDFVHPDARSPTSPVPVPVM,mutated_sequence,1.0,697.0,NP_002730.1.a2m,NP_002730.1.npy,ClinVar +NP_002736.3,NP_002736.3.csv,MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS,mutated_sequence,1.0,360.0,NP_002736.3.a2m,NP_002736.3.npy,ClinVar +NP_002746.1,NP_002746.1.csv,MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV,mutated_sequence,1.0,393.0,NP_002746.1.a2m,NP_002746.1.npy,ClinVar +NP_002769.1,NP_002769.1.csv,MYALFLLASLLGAALAGPVLGLKECTRGSAVWCQNVKTASDCGAVKHCLQTVWNKPTVKSLPCDICKDVVTAAGDMLKDNATEEEILVYLEKTCDWLPKPNMSASCKEIVDSYLPVILDIIKGEMSRPGEVCSALNLCESLQKHLAELNHQKQLESNKIPELDMTEVVAPFMANIPLLLYPQDGPRSKPQPKDNGDVCQDCIQMVTDIQTAVRTNSTFVQALVEHVKEECDRLGPGMADICKNYISQYSEIAIQMMMHMQPKEICALVGFCDEVKEMPMQTLVPAKVASKNVIPALELVEPIKKHEVPAKSDVYCEVCEFLVKEVTKLIDNNKTEKEILDAFDKMCSKLPKSLSEECQEVVDTYGSSILSILLEEVSPELVCSMLHLCSGTRLPALTVHVTQPKDGGFCEVCKKLVGYLDRNLEKNSTKQEILAALEKGCSFLPDPYQKQCDQFVAEYEPVLIEILVEVMDPSFVCLKIGACPSAHKPLLGTEKCIWGPSYWCQNTETAAQCNAVEHCKRHVWN,mutated_sequence,1.0,524.0,NP_002769.1.a2m,NP_002769.1.npy,ClinVar +NP_002779.1,NP_002779.1.csv,MSSIGTGYDLSASTFSPDGRVFQVEYAMKAVENSSTAIGIRCKDGVVFGVEKLVLSKLYEEGSNKRLFNVDRHVGMAVAGLLADARSLADIAREEASNFRSNFGYNIPLKHLADRVAMYVHAYTLYSAVRPFGCSFMLGSYSVNDGAQLYMIDPSGVSYGYWGCAIGKARQAAKTEIEKLQMKEMTCRDIVKEVAKIIYIVHDEVKDKAFELELSWVGELTNGRHEIVPKDIREEAEKYAKESLKEEDESDDDNM,mutated_sequence,1.0,255.0,NP_002779.1.a2m,NP_002779.1.npy,ClinVar +NP_002787.2,NP_002787.2.csv,MEAFLGSRSGLWAGGPAPGQFYRIPSTPDSFMDPASALYRGPITRTQNPMVTGTSVLGVKFEGGVVIAADMLGSYGSLARFRNISRIMRVNNSTMLGASGDYADFQYLKQVLGQMVIDEELLGDGHSYSPRAIHSWLTRAMYSRRSKMNPLWNTMVIGGYADGESFLGYVDMLGVAYEAPSLATGYGAYLAQPLLREVLEKQPVLSQTEARDLVERCMRVLYYRDARSYNRFQIATVTEKGVEIEGPLSTETNWDIAHMISGFE,mutated_sequence,1.0,264.0,NP_002787.2.a2m,NP_002787.2.npy,ClinVar +NP_002825.3,NP_002825.3.csv,MTSRRWFHPNITGVEAENLLLTRGVDGSFLARPSKSNPGDFTLSVRRNGAVTHIKIQNTGDYYDLYGGEKFATLAELVQYYMEHHGQLKEKNGDVIELKYPLNCADPTSERWFHGHLSGKEAEKLLTEKGKHGSFLVRESQSHPGDFVLSVRTGDDKGESNDGKSKVTHVMIRCQELKYDVGGGERFDSLTDLVEHYKKNPMVETLGTVLQLKQPLNTTRINAAEIESRVRELSKLAETTDKVKQGFWEEFETLQQQECKLLYSRKEGQRQENKNKNRYKNILPFDHTRVVLHDGDPNEPVSDYINANIIMPEFETKCNNSKPKKSYIATQGCLQNTVNDFWRMVFQENSRVIVMTTKEVERGKSKCVKYWPDEYALKEYGVMRVRNVKESAAHDYTLRELKLSKVGQGNTERTVWQYHFRTWPDHGVPSDPGGVLDFLEEVHHKQESIMDAGPVVVHCSAGIGRTGTFIVIDILIDIIREKGVDCDIDVPKTIQMVRSQRSGMVQTEAQYRFIYMAVQHYIETLQRRIEEEQKSKRKGHEYTNIKYSLADQTSGDQSPLPPCTPTPPCAEMREDSARVYENVGLMQQQKSFR,mutated_sequence,1.0,593.0,NP_002825.3.a2m,NP_002825.3.npy,ClinVar +NP_002829.3,NP_002829.3.csv,MTMYLWLKLLAFGFAFLDTEVFVTGQSPTPSPTGLTTAKMPSVPLSSDPLPTHTTAFSPASTFERENDFSETTTSLSPDNTSTQVSPDSLDNASAFNTTGVSSVQTPHLPTHADSQTPSAGTDTQTFSGSAANAKLNPTPGSNAISDVPGERSTASTFPTDPVSPLTTTLSLAHHSSAALPARTSNTTITANTSDAYLNASETTTLSPSGSAVISTTTIATTPSKPTCDEKYANITVDYLYNKETKLFTAKLNVNENVECGNNTCTNNEVHNLTECKNASVSISHNSCTAPDKTLILDVPPGVEKFQLHDCTQVEKADTTICLKWKNIETFTCDTQNITYRFQCGNMIFDNKEIKLENLEPEHEYKCDSEILYNNHKFTNASKIIKTDFGSPGEPQIIFCRSEAAHQGVITWNPPQRSFHNFTLCYIKETEKDCLNLDKNLIKYDLQNLKPYTKYVLSLHAYIIAKVQRNGSAAMCHFTTKSAPPSQVWNMTVSMTSDNSMHVKCRPPRDRNGPHERYHLEVEAGNTLVRNESHKNCDFRVKDLQYSTDYTFKAYFHNGDYPGEPFILHHSTSYNSKALIAFLAFLIIVTSIALLVVLYKIYDLHKKRSCNLDEQQELVERDDEKQLMNVEPIHADILLETYKRKIADEGRLFLAEFQSIPRVFSKFPIKEARKPFNQNKNRYVDILPYDYNRVELSEINGDAGSNYINASYIDGFKEPRKYIAAQGPRDETVDDFWRMIWEQKATVIVMVTRCEEGNRNKCAEYWPSMEEGTRAFGDVVVKINQHKRCPDYIIQKLNIVNKKEKATGREVTHIQFTSWPDHGVPEDPHLLLKLRRRVNAFSNFFSGPIVVHCSAGVGRTGTYIGIDAMLEGLEAENKVDVYGYVVKLRRQRCLMVQVEAQYILIHQALVEYNQFGETEVNLSELHPYLHNMKKRDPPSEPSPLEAEFQRLPSYRSWRTQHIGNQEENKSKNRNSNVIPYDYNRVPLKHELEMSKESEHDSDESSDDDSDSEEPSKYINASFIMSYWKPEVMIAAQGPLKETIGDFWQMIFQRKVKVIVMLTELKHGDQEICAQYWGEGKQTYGDIEVDLKDTDKSSTYTLRVFELRHSKRKDSRTVYQYQYTNWSVEQLPAEPKELISMIQVVKQKLPQKNSSEGNKHHKSTPLLIHCRDGSQQTGIFCALLNLLESAETEEVVDIFQVVKALRKARPGMVSTFEQYQFLYDVIASTYPAQNGQVKKNNHQEDKIEFDNEVDKVKQDANCVNPLGAPEKLPEAKEQAEGSEPTSGTEGPEHSVNGPASPALNQGS,mutated_sequence,1.0,1306.0,NP_002829.3.a2m,NP_002829.3.npy,ClinVar +NP_002878.2,NP_002878.2.csv,MDVLVSECSARLLQQEEEIKSLTAEIDRLKNCGCLGASPNLEQLQEENLKLKYRLNILRKSLQAERNKPTKNMINIISRLQEVFGHAIKAAYPDLENPPLLVTPSQQAKFGDYQCNSAMGISQMLKTKEQKVNPREIAENITKHLPDNECIEKVEIAGPGFINVHLRKDFVSEQLTSLLVNGVQLPALGENKKVIVDFSSPNIAKEMHVGHLRSTIIGESISRLFEFAGYDVLRLNHVGDWGTQFGMLIAHLQDKFPDYLTVSPPIGDLQVFYKESKKRFDTEEEFKKRAYQCVVLLQGKNPDITKAWKLICDVSRQELNKIYDALDVSLIERGESFYQDRMNDIVKEFEDRGFVQVDDGRKIVFVPGCSIPLTIVKSDGGYTYDTSDLAAIKQRLFEEKADMIIYVVDNGQSVHFQTIFAAAQMIGWYDPKVTRVFHAGFGVVLGEDKKKFKTRSGETVRLMDLLGEGLKRSMDKLKEKERDKVLTAEELNAAQTSVAYGCIKYADLSHNRLNDYIFSFDKMLDDRGNTAAYLLYAFTRIRSIARLANIDEEMLQKAARETKILLDHEKEWKLGRCILRFPEILQKILDDLFLHTLCDYIYELATAFTEFYDSCYCVEKDRQTGKILKVNMWRMLLCEAVAAVMAKGFDILGIKPVQRM,mutated_sequence,1.0,660.0,NP_002878.2.a2m,NP_002878.2.npy,ClinVar +NP_002896.2,NP_002896.2.csv,MWLPLLLGALLWAVLWLLRDRQSLPASNAFVFITGCDSGFGRLLALQLDQRGFRVLASCLTPSGAEDLQRVASSRLHTTLLDITDPQSVQQAAKWVEMHVKEAGLFGLVNNAGVAGIIGPTPWLTRDDFQRVLNVNTMGPIGVTLALLPLLQQARGRVINITSVLGRLAANGGGYCVSKFGLEAFSDSLRRDVAHFGIRVSIVEPGFFRTPVTNLESLEKTLQACWARLPPATQAHYGGAFLTKYLKMQQRIMNLICDPDLTKVSRCLEHALTARHPRTRYSPGWDAKLLWLPASYLPASLVDAVLTWVLPKPAQAVY,mutated_sequence,1.0,318.0,NP_002896.2.a2m,NP_002896.2.npy,ClinVar +NP_002991.2,NP_002991.2.csv,MAAVVALSLRRRLPATTLGGACLQASRGAQTAAATAPRIKKFAIYRWDPDKAGDKPHMQTYEVDLNKCGPMVLDALIKIKNEVDSTLTFRRSCREGICGSCAMNINGGNTLACTRRIDTNLNKVSKIYPLPHMYVIKDLVPDLSNFYAQYKSIEPYLKKKDESQEGKQQYLQSIEEREKLDGLYECILCACCSTSCPSYWWNGDKYLGPAVLMQAYRWMIDSRDDFTEERLAKLQDPFSLYRCHTIMNCTRTCPKGLNPGKAIAEIKKMMATYKEKKASV,mutated_sequence,1.0,280.0,NP_002991.2.a2m,NP_002991.2.npy,ClinVar +NP_002993.1,NP_002993.1.csv,MAVLWRLSAVCGALGGRALLLRTPVVRPAHISAFLQDRPIPEWCGVQHIHLSPSHHSGSKAASLHWTSERVVSVLLLGLLPAAYLNPCSAMDYSLAAALTLHGHWGLGQVVTDYVHGDALQKAAKAGLLALSALTFAGLCYFNYHDVGICKAVAMLWKL,mutated_sequence,1.0,159.0,NP_002993.1.a2m,NP_002993.1.npy,ClinVar +NP_003033.3,NP_003033.3.csv,MATNGSKVADGQISTEVSEAPVANDKPKTLVVKVQKKAADLPDRDTWKGRFDFLMSCVGYAIGLGNVWRFPYLCGKNGGGAFLIPYFLTLIFAGVPLFLLECSLGQYTSIGGLGVWKLAPMFKGVGLAAAVLSFWLNIYYIVIISWAIYYLYNSFTTTLPWKQCDNPWNTDRCFSNYSMVNTTNMTSAVVEFWERNMHQMTDGLDKPGQIRWPLAITLAIAWILVYFCIWKGVGWTGKVVYFSATYPYIMLIILFFRGVTLPGAKEGILFYITPNFRKLSDSEVWLDAATQIFFSYGLGLGSLIALGSYNSFHNNVYRDSIIVCCINSCTSMFAGFVIFSIVGFMAHVTKRSIADVAASGPGLAFLAYPEAVTQLPISPLWAILFFSMLLMLGIDSQFCTVEGFITALVDEYPRLLRNRRELFIAAVCIISYLIGLSNITQGGIYVFKLFDYYSASGMSLLFLVFFECVSISWFYGVNRFYDNIQEMVGSRPCIWWKLCWSFFTPIIVAGVFIFSAVQMTPLTMGNYVFPKWGQGVGWLMALSSMVLIPGYMAYMFLTLKGSLKQRIQVMVQPSEDIVRPENGPEQPQAGSSTSKEAYI,mutated_sequence,1.0,599.0,NP_003033.3.a2m,NP_003033.3.npy,ClinVar +NP_003051.1,NP_003051.1.csv,MRDYDEVTAFLGEWGPFQRLIFFLLSASIIPNGFTGLSSVFLIATPEHRCRVPDAANLSSAWRNHTVPLRLRDGREVPHSCRRYRLATIANFSALGLEPGRDVDLGQLEQESCLDGWEFSQDVYLSTIVTEWNLVCEDDWKAPLTISLFFVGVLLGSFISGQLSDRFGRKNVLFVTMGMQTGFSFLQIFSKNFEMFVVLFVLVGMGQISNYVAAFVLGTEILGKSVRIIFSTLGVCIFYAFGYMVLPLFAYFIRDWRMLLVALTMPGVLCVALWWFIPESPRWLISQGRFEEAEVIIRKAAKANGIVVPSTIFDPSELQDLSSKKQQSHNILDLLRTWNIRMVTIMSIMLWMTISVGYFGLSLDTPNLHGDIFVNCFLSAMVEVPAYVLAWLLLQYLPRRYSMATALFLGGSVLLFMQLVPPDLYYLATVLVMVGKFGVTAAFSMVYVYTAELYPTVVRNMGVGVSSTASRLGSILSPYFVYLGAYDRFLPYILMGSLTILTAILTLFLPESFGTPLPDTIDQMLRVKGMKHRKTPSHTRMLKDGQERPTILKSTAF,mutated_sequence,1.0,557.0,NP_003051.1.a2m,NP_003051.1.npy,ClinVar +NP_003063.2,NP_003063.2.csv,MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDESRHCSTGSGSASFAHTAPPPAGVNPDLEEPPLKEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED,mutated_sequence,1.0,1647.0,NP_003063.2.a2m,NP_003063.2.npy,ClinVar +NP_003064.2,NP_003064.2.csv,MMMMALSKTFGQKPVKFQLEDDGEFYMIGSEVGNYLRMFRGSLYKRYPSLWRRLATVEERKKIVASSHGKKTKPNTKDHGYTTLATSVTLLKASEVEEILDGNDEKYKAVSISTEPPTYLREQKAKRNSQWVPTLPNSSHHLDAVPCSTTINRNRMGRDKKRTFPLCFDDHDPAVIHENASQPEVLVPIRLDMEIDGQKLRDAFTWNMNEKLMTPEMFSEILCDDLDLNPLTFVPAIASAIRQQIESYPTDSILEDQSDQRVIIKLNIHVGNISLVDQFEWDMSEKENSPEKFALKLCSELGLGGEFVTTIAYSIRGQLSWHQKTYAFSENPLPTVEIAIRNTGDADQWCPLLETLTDAEMEKKIRDQDRNTRRMRRLANTAPAW,mutated_sequence,1.0,385.0,NP_003064.2.a2m,NP_003064.2.npy,ClinVar +NP_003065.3,NP_003065.3.csv,MAAAAGGGGPGTAVGATGSGIAAAAAGLAVYRRKDGGPATKFWESPETVSQLDSVRVWLGKHYKKYVHADAPTNKTLAGLVVQLLQFQEDAFGKHVTNPAFTKLPAKCFMDFKAGGALCHILGAAYKYKNEQGWRRFDLQNPSRMDRNVEMFMNIEKTLVQNNCLTRPNIYLIPDIDLKLANKLKDIIKRHQGTFTDEKSKASHHIYPYSSSQDDEEWLRPVMRKEKQVLVHWGFYPDSYDTWVHSNDVDAEIEDPPIPEKPWKVHVKWILDTDIFNEWMNEEDYEVDENRKPVSFRQRISTKNEEPVRSPERRDRKASANARKRKHSPSPPPPTPTESRKKSGKKGQASLYGKRRSQKEEDEQEDLTKDMEDPTPVPNIEEVVLPKNVNLKKDSENTPVKGGTVADLDEQDEETVTAGGKEDEDPAKGDQSRSVDLGEDNVTEQTNHIIIPSYASWFDYNCIHVIERRALPEFFNGKNKSKTPEIYLAYRNFMIDTYRLNPQEYLTSTACRRNLTGDVCAVMRVHAFLEQWGLVNYQVDPESRPMAMGPPPTPHFNVLADTPSGLVPLHLRSPQVPAAQQMLNFPEKNKEKPVDLQNFGLRTDIYSKKTLAKSKGASAGREWTEQETLLLLEALEMYKDDWNKVSEHVGSRTQDECILHFLRLPIEDPYLENSDASLGPLAYQPVPFSQSGNPVMSTVAFLASVVDPRVASAAAKAALEEFSRVREEVPLELVEAHVKKVQEAARASGKVDPTYGLESSCIAGTGPDEPEKLEGAEEEKMEADPDGQQPEKAENKVENETDEGDKAQDGENEKNSEKEQDSEVSEDTKSEEKETEENKELTDTCKERESDTGKKKVEHEISEGNVATAAAAALASAATKAKHLAAVEERKIKSLVALLVETQMKKLEIKLRHFEELETIMDREKEALEQQRQQLLTERQNFHMEQLKYAELRARQQMEQQQHGQNPQQAHQHSGGPGLAPLGAAGHPGMMPHQQPPPYPLMHHQMPPPHPPQPGQIPGPGSMMPGQHMPGRMIPTVAANIHPSGSGPTPPGMPPMPGNILGPRVPLTAPNGMYPPPPQQQPPPPPPADGVPPPPAPGPPASAAP,mutated_sequence,1.0,1105.0,NP_003065.3.a2m,NP_003065.3.npy,ClinVar +NP_003110.1,NP_003110.1.csv,MAVLLLLLRALRRGPGPGPRPLWGPGPAWSPGFPARPGRGRPYMASRPPGDLAEAGGRALQSLQLRLLTPTFEGINGLLLKQHLVQNPVRLWQLLGGTFYFNTSRLKQKNKEKDKSKGKAPEEDEEERRRRERDDQMYRERLRTLLVIAVVMSLLNALSTSGGSISWNDFVHEMLAKGEVQRVQVVPESDVVEVYLHPGAVVFGRPRLALMYRMQVANIDKFEEKLRAAEDELNIEAKDRIPVSYKRTGFFGNALYSVGMTAVGLAILWYVFRLAGMTGREGGFSAFNQLKMARFTIVDGKMGKGVSFKDVAGMHEAKLEVREFVDYLKSPERFLQLGAKVPKGALLLGPPGCGKTLLAKAVATEAQVPFLAMAGPEFVEVIGGLGAARVRSLFKEARARAPCIVYIDEIDAVGKKRSTTMSGFSNTEEEQTLNQLLVEMDGMGTTDHVIVLASTNRADILDGALMRPGRLDRHVFIDLPTLQERREIFEQHLKSLKLTQSSTFYSQRLAELTPGFSGADIANICNEAALHAAREGHTSVHTLNFEYAVERVLAGTAKKSKILSKEEQKVVAFHESGHALVGWMLEHTEAVMKVSITPRTNAALGFAQMLPRDQHLFTKEQLFERMCMALGGRASEALSFNEVTSGAQDDLRKVTRIAYSMVKQFGMAPGIGPISFPEAQEGLMGIGRRPFSQGLQQMMDHEARLLVAKAYRHTEKVLQDNLDKLQALANALLEKEVINYEDIEALIGPPPHGPKKMIAPQRWIDAQREKQDLGEEETEETQQPPLGGEEPTWPK,mutated_sequence,1.0,795.0,NP_003110.1.a2m,NP_003110.1.npy,ClinVar +NP_003117.2,NP_003117.2.csv,MEQFPKETVVESSGPKVLETAEEIQERRQEVLTRYQSFKERVAERGQKLEDSYHLQVFKRDADDLGKWIMEKVNILTDKSYEDPTNIQGKYQKHQSLEAEVQTKSRLMSELEKTREERFTMGHSAHEETKAHIEELRHLWDLLLELTLEKGDQLLRALKFQQYVQECADILEWIGDKEAIATSVELGEDWERTEVLHKKFEDFQVELVAKEGRVVEVNQYANECAEENHPDLPLIQSKQNEVNAAWERLRGLALQRQKALSNAANLQRFKRDVTEAIQWIKEKEPVLTSEDYGKDLVASEGLFHSHKGLERNLAVMSDKVKELCAKAEKLTLSHPSDAPQIQEMKEDLVSSWEHIRALATSRYEKLQATYWYHRFSSDFDELSGWMNEKTAAINADELPTDVAGGEVLLDRHQQHKHEIDSYDDRFQSADETGQDLVNANHEASDEVREKMEILDNNWTALLELWDERHRQYEQCLDFHLFYRDSEQVDSWMSRQEAFLENEDLGNSLGSAEALLQKHEDFEEAFTAQEEKIITVDKTATKLIGDDHYDSENIKAIRDGLLARRDALREKAATRRRLLKESLLLQKLYEDSDDLKNWINKKKKLADDEDYKDIQNLKSRVQKQQVFEKELAVNKTQLENIQKTGQEMIEGGHYASDNVTTRLSEVASLWEELLEATKQKGTQLHEANQQLQFENNAEDLQRWLEDVEWQVTSEDYGKGLAEVQNRLRKHGLLESAVAARQDQVDILTDLAAYFEEIGHPDSKDIRARQESLVCRFEALKEPLATRKKKLLDLLHLQLICRDTEDEEAWIQETEPSATSTYLGKDLIASKKLLNRHRVILENIASHEPRIQEITERGNKMVEEGHFAAEDVASRVKSLNQNMESLRARAARRQNDLEANVQFQQYLADLHEAETWIREKEPIVDNTNYGADEEAAGALLKKHEAFLLDLNSFGDSMKALRNQANACQQQQAAPVEGVAGEQRVMALYDFQARSPREVTMKKGDVLTLLSSINKDWWKVEAADHQGIVPAVYVRRLAHDEFPMLPQRRREEPGNITQRQEQIENQYRSLLDRAEERRRRLLQRYNEFLLAYEAGDMLEWIQEKKAENTGVELDDVWELQKKFDEFQKDLNTNEPRLRDINKVADDLLFEGLLTPEGAQIRQELNSRWGSLQRLADEQRQLLGSAHAVEVFHREADDTKEQIEKKCQALSAADPGSDLFSVQALQRRHEGFERDLVPLGDKVTILGETAERLSESHPDATEDLQRQKMELNEAWEDLQGRTKDRKESLNEAQKFYLFLSKARDLQNWISSIGGMVSSQELAEDLTGIEILLERHQEHRADMEAEAPTFQALEDFSAELIDSGHHASPEIEKKLQAVKLERDDLEKAWEKRKKILDQCLELQMFQGNCDQVESWMVARENSLRSDDKSSLDSLEALMKKRDDLDKAITAQEGKITDLEHFAESLIADEHYAKEEIATRLQRVLDRWKALKAQLIDERTKLGDYANLKQFYRDLEELEEWISEMLPTACDESYKDATNIQRKYLKHQTFAHEVDGRSEQVHGVINLGNSLIECSACDGNEEAMKEQLEQLKEHWDHLLERTNDKGKKLNEASRQQRFNTSIRDFEFWLSEAETLLAMKDQARDLASAGNLLKKHQLLEREMLAREDALKDLNTLAEDLLSSGTFNVDQIVKKKDNVNKRFLNVQELAAAHHEKLKEAYALFQFFQDLDDEESWIEEKLIRVSSQDYGRDLQGVQNLLKKHKRLEGELVAHEPAIQNVLDMAEKLKDKAAVGQEEIQLRLAQFVEHWEKLKELAKARGLKLEESLEYLQFMQNAEEEEAWINEKNALAVRGDCGDTLAATQSLLMKHEALENDFAVHETRVQNVCAQGEDILNKVLQEESQNKEISSKIEALNEKTPSLAKAIAAWKLQLEDDYAFQEFNWKADVVEAWIADKETSLKTNGNGADLGDFLTLLAKQDTLDASLQSFQQERLPEITDLKDKLISAQHNQSKAIEERYAALLKRWEQLLEASAVHRQKLLEKQLPLQKAEDLFVEFAHKASALNNWCEKMEENLSEPVHCVSLNEIRQLQKDHEDFLASLARAQADFKCLLELDQQIKALGVPSSPYTWLTVEVLERTWKHLSDIIEEREQELQKEEARQVKNFEMCQEFEQNASTFLQWILETRAYFLDGSLLKETGTLESQLEANKRKQKEIQAMKRQLTKIVDLGDNLEDALILDIKYSTIGLAQQWDQLYQLGLRMQHNLEQQIQAKDIKGVSEETLKEFSTIYKHFDENLTGRLTHKEFRSCLRGLNYYLPMVEEDEHEPKFEKFLDAVDPGRKGYVSLEDYTAFLIDKESENIKSSDEIENAFQALAEGKSYITKEDMKQALTPEQVSFCATHMQQYMDPRGRSHLSGYDYVGFTNSYFGN,mutated_sequence,1.0,2419.0,NP_003117.2.a2m,NP_003117.2.npy,ClinVar +NP_003127.1,NP_003127.1.csv,MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAIDLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSKLAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFKNENFEIIIVDTSGRHKQEDSLFEEMLQVANAIQPDNIVYVMDASIGQACEAQAKAFKDKVDVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDIEGLIDKVNELKLDDNEALIEKLKHGQFTLRDMYEQFQNIMKMGPFSQILGMIPGFGTDFMSKGNEQESMARLKKLMTIMDSMNDQELDSTDGAKVFSKQPGRIQRVARGSGVSTRDVQELLTQYTKFAQMVKKMGGIKGLFKGGDMSKNVSQSQMAKLNQQMAKMMDPRVLHHMGGMAGLQSMMRQFQQGAAGNMKGMMGFNNM,mutated_sequence,1.0,504.0,NP_003127.1.a2m,NP_003127.1.npy,ClinVar +NP_003163.1,NP_003163.1.csv,MAAVAALQLGLRAAGLGRAPASAAWRSVLRVSPRPGVAWRPSRCGSSAAEASATKAEDDSFLQWVLLLIPVTAFGLGTWQVQRRKWKLNLIAELESRVLAEPVPLPADPMELKNLEYRPVKVRGCFDHSKELYMMPRTMVDPVREAREGGLISSSTQSGAYVVTPFHCTDLGVTILVNRGFVPRKKVNPETRQKGQIEGEVDLIGMVRLTETRQPFVPENNPERNHWHYRDLEAMARITGAEPIFIDANFQSTVPGGPIGGQTRVTLRNEHLQYIVTWYGLSAATSYLWFKKFLRGTPGV,mutated_sequence,1.0,300.0,NP_003163.1.a2m,NP_003163.1.npy,ClinVar +NP_003273.1,NP_003273.1.csv,MGDEEKRNRAITARRQHLKSVMLQIAATELEKEESRREAEKQNYLAEHCPPLHIPGSMSEVQELCKQLHAKIDAAEEEKYDMEVRVQKTSKELEDMNQKLFDLRGKFKRPPLRRVRMSADAMLKALLGSKHKVCMDLRANLKQVKKEDTEKERDLRDVGDWRKNIEEKSGMEGRKKMFESES,mutated_sequence,1.0,182.0,NP_003273.1.a2m,NP_003273.1.npy,ClinVar +NP_003280.2,NP_003280.2.csv,MDAIKKKMQMLKLDKENAIDRAEQAEADKKQAEDRCKQLEEEQQALQKKLKGTEDEVEKYSESVKEAQEKLEQAEKKATDAEADVASLNRRIQLVEEELDRAQERLATALQKLEEAEKAADESERGMKVIENRAMKDEEKMELQEMQLKEAKHIAEDSDRKYEEVARKLVILEGELERSEERAEVAESKCGDLEEELKIVTNNLKSLEAQADKYSTKEDKYEEEIKLLEEKLKEAETRAEFAERSVAKLEKTIDDLEDEVYAQKMKYKAISEELDNALNDITSL,mutated_sequence,1.0,284.0,NP_003280.2.a2m,NP_003280.2.npy,ClinVar +NP_003292.1,NP_003292.1.csv,MENETVSELNQTQLQPRAVVALEYQVVTILLVLIICGLGIVGNIMVVLVVMRTKHMRTPTNCYLVSLAVADLMVLVAAGLPNITDSIYGSWVYGYVGCLCITYLQYLGINASSCSITAFTIERYIAICHPIKAQFLCTFSRAKKIIIFVWAFTSLYCMLWFFLLDLNISTYKDAIVISCGYKISRNYYSPIYLMDFGVFYVVPMILATVLYGFIARILFLNPIPSDPKENSKTWKNDSTHQNTNLNVNTSNRCFNSTVSSRKQVTKMLAVVVILFALLWMPYRTLVVVNSFLSSPFQENWFLLFCRICIYLNSAINPVIYNLMSQKFRAAFRKLCNCKQKPTEKPANYSVALNYSVIKESDHFSTELDDITVTDTYLSATKVSFDDTCLASEVSFSQS,mutated_sequence,1.0,398.0,NP_003292.1.a2m,NP_003292.1.npy,ClinVar +NP_003313.3,NP_003313.3.csv,MPLRDETLREVWASDSGHEEESLSPEAPRRPKQRPAPAQRLRKKRTEAPESPCPTGSKPRKPGAGRTGRPREEPSPDPAQARAPQTVYARFLRDPEAKKRDPRETFLVARAPDAEDEEEEEEEDEEDEEEEAEEKKEKILLPPKKPLREKSSADLKERRAKAQGPRGDLGSPDPPPKPLRVRNKEAPAGEGTKMRKTKKKGSGEADKDPSGSPASARKSPAAMFLVGEGSPDKKALKKKGTPKGARKEEEEEEEAATVIKKSNQKGKAKGKGKKKAKEERAPSPPVEVDEPREFVLRPAPQGRTVRCRLTRDKKGMDRGMYPSYFLHLDTEKKVFLLAGRKRKRSKTANYLISIDPTNLSRGGENFIGKLRSNLLGNRFTVFDNGQNPQRGYSTNVASLRQELAAVIYETNVLGFRGPRRMTVIIPGMSAENERVPIRPRNASDGLLVRWQNKTLESLIELHNKPPVWNDDSGSYTLNFQGRVTQASVKNFQIVHADDPDYIVLQFGRVAEDAFTLDYRYPLCALQAFAIALSSFDGKLACE,mutated_sequence,1.0,542.0,NP_003313.3.a2m,NP_003313.3.npy,ClinVar +NP_003327.2,NP_003327.2.csv,MSTPARRRLMRDFKRLQEDPPAGVSGAPSENNIMVWNAVIFGPEGTPFEDGTFKLTIEFTEEYPNKPPTVRFVSKMFHPNVYADGSICLDILQNRWSPTYDVSSILTSIQSLLDEPNPNSPANSQAAQLYQENKREYEKRVSAIVEQSWRDC,mutated_sequence,1.0,152.0,NP_003327.2.a2m,NP_003327.2.npy,ClinVar +NP_003352.2,NP_003352.2.csv,MGQPSLTWMLMVVVASWFITTAATDTSEARWCSECHSNATCTEDEAVTTCTCQEGFTGDGLTCVDLDECAIPGAHNCSANSSCVNTPGSFSCVCPEGFRLSPGLGCTDVDECAEPGLSHCHALATCVNVVGSYLCVCPAGYRGDGWHCECSPGSCGPGLDCVPEGDALVCADPCQAHRTLDEYWRSTEYGEGYACDTDLRGWYRFVGQGGARMAETCVPVLRCNTAAPMWLNGTHPSSDEGIVSRKACAHWSGHCCLWDASVQVKACAGGYYVYNLTAPPECHLAYCTDPSSVEGTCEECSIDEDCKSNNGRWHCQCKQDFNITDISLLEHRLECGANDMKVSLGKCQLKSLGFDKVFMYLSDSRCSGFNDRDNRDWVSVVTPARDGPCGTVLTRNETHATYSNTLYLADEIIIRDLNIKINFACSYPLDMKVSLKTALQPMVSALNIRVGGTGMFTVRMALFQTPSYTQPYQGSSVTLSTEAFLYVGTMLDGGDLSRFALLMTNCYATPSSNATDPLKYFIIQDRCPHTRDSTIQVVENGESSQGRFSVQMFRFAGNYDLVYLHCEVYLCDTMNEKCKPTCSGTRFRSGSVIDQSRVLNLGPITRKGVQATVSRAFSSLGLLKVWLPLLLSATLTLTFQ,mutated_sequence,1.0,640.0,NP_003352.2.a2m,NP_003352.2.npy,ClinVar +NP_003385.2,NP_003385.2.csv,MLEEPRPRPPPSGLAGLLFLALCSRALSNEILGLKLPGEPPLTANTVCLTLSGLSKRQLGLCLRNPDVTASALQGLHIAVHECQHQLRDQRWNCSALEGGGRLPHHSAILKRGFRESAFSFSMLAAGVMHAVATACSLGKLVSCGCGWKGSGEQDRLRAKLLQLQALSRGKSFPHSLPSPGPGSSPSPGPQDTWEWGGCNHDMDFGEKFSRDFLDSREAPRDIQARMRIHNNRVGRQVVTENLKRKCKCHGTSGSCQFKTCWRAAPEFRAVGAALRERLGRAIFIDTHNRNSGAFQPRLRPRRLSGELVYFEKSPDFCERDPTMGSPGTRGRACNKTSRLLDGCGSLCCGRGHNVLRQTRVERCHCRFHWCCYVLCDECKVTEWVNVCK,mutated_sequence,1.0,389.0,NP_003385.2.a2m,NP_003385.2.npy,ClinVar +NP_003403.2,NP_003403.2.csv,MLLDAGPQYPAIGVTTFGASRHHSAGDVAERDVGLGINPFADGMGAFKLNPSSHELASAGQTAFTSQAPGYAAAAALGHHHHPGHVGSYSSAAFNSTRDFLFRNRGFGDAAAAASAQHSLFAASAGGFGGPHGHTDAAGHLLFPGLHEQAAGHASPNVVNGQMRLGFSGDMYPRPEQYGQVTSPRSEHYAAPQLHGYGPMNVNMAAHHGAGAFFRYMRQPIKQELICKWIEPEQLANPKKSCNKTFSTMHELVTHVTVEHVGGPEQSNHICFWEECPREGKPFKAKYKLVNHIRVHTGEKPFPCPFPGCGKVFARSENLKIHKRTHTGEKPFKCEFEGCDRRFANSSDRKKHMHVHTSDKPYLCKMCDKSYTHPSSLRKHMKVHESSSQGSQPSPAASSGYESSTPPTIVSPSTDNPTTSSLSPSSSAVHHTAGHSALSSNFNEWYV,mutated_sequence,1.0,447.0,NP_003403.2.a2m,NP_003403.2.npy,ClinVar +NP_003404.1,NP_003404.1.csv,MTMLLDGGPQFPGLGVGSFGAPRHHEMPNREPAGMGLNPFGDSTHAAAAAAAAAAFKLSPAAAHDLSSGQSSAFTPQGSGYANALGHHHHHHHHHHHTSQVPSYGGAASAAFNSTREFLFRQRSSGLSEAASGGGQHGLFAGSASSLHAPAGIPEPPSYLLFPGLHEQGAGHPSPTGHVDNNQVHLGLRGELFGRADPYRPVASPRTDPYAAGAQFPNYSPMNMNMGVNVAAHHGPGAFFRYMRQPIKQELSCKWIDEAQLSRPKKSCDRTFSTMHELVTHVTMEHVGGPEQNNHVCYWEECPREGKSFKAKYKLVNHIRVHTGEKPFPCPFPGCGKIFARSENLKIHKRTHTGEKPFKCEFEGCDRRFANSSDRKKHMHVHTSDKPYICKVCDKSYTHPSSLRKHMKVHESQGSDSSPAASSGYESSTPPAIASANSKDTTKTPSAVQTSTSHNPGLPPNFNEWYV,mutated_sequence,1.0,467.0,NP_003404.1.a2m,NP_003404.1.npy,ClinVar +NP_003459.2,NP_003459.2.csv,MARPDPSAPPSLLLLLLAQLVGRAAAASKAPVCQEITVPMCRGIGYNLTHMPNQFNHDTQDEAGLEVHQFWPLVEIQCSPDLRFFLCSMYTPICLPDYHKPLPPCRSVCERAKAGCSPLMRQYGFAWPERMSCDRLPVLGRDAEVLCMDYNRSEATTAPPRPFPAKPTLPGPPGAPASGGECPAGGPFVCKCREPFVPILKESHPLYNKVRTGQVPNCAVPCYQPSFSADERTFATFWIGLWSVLCFISTSTTVATFLIDMERFRYPERPIIFLSACYLCVSLGFLVRLVVGHASVACSREHNHIHYETTGPALCTIVFLLVYFFGMASSIWWVILSLTWFLAAGMKWGNEAIAGYAQYFHLAAWLIPSVKSITALALSSVDGDPVAGICYVGNQNLNSLRGFVLGPLVLYLLVGTLFLLAGFVSLFRIRSVIKQGGTKTDKLEKLMIRIGIFTLLYTVPASIVVACYLYEQHYRESWEAALTCACPGHDTGQPRAKPEYWVLMLKYFMCLVVGITSGVWIWSGKTVESWRRFTSRCCCRPRRGHKSGGAMAAGDYPEASAALTGRTGPPGPAATYHKQVSLSHV,mutated_sequence,1.0,585.0,NP_003459.2.a2m,NP_003459.2.npy,ClinVar +NP_003473.3,NP_003473.3.csv,MDSQKLAGEDKDSEPAADGPAASEDPSATESDLPNPHVGEVSVLSSGSPRLQETPQDCSGGPVRRCALCNCGEPSLHGQRELRRFELPFDWPRCPVVSPGGSPGPNEAVLPSEDLSQIGFPEGLTPAHLGEPGGSCWAHHWCAAWSAGVWGQEGPELCGVDKAIFSGISQRCSHCTRLGASIPCRSPGCPRLYHFPCATASGSFLSMKTLQLLCPEHSEGAAYLEEARCAVCEGPGELCDLFFCTSCGHHYHGACLDTALTARKRAGWQCPECKVCQACRKPGNDSKMLVCETCDKGYHTFCLKPPMEELPAHSWKCKACRVCRACGAGSAELNPNSEWFENYSLCHRCHKAQGGQTIRSVAEQHTPVCSRFSPPEPGDTPTDEPDALYVACQGQPKGGHVTSMQPKEPGPLQCEAKPLGKAGVQLEPQLEAPLNEEMPLLPPPEESPLSPPPEESPTSPPPEASRLSPPPEELPASPLPEALHLSRPLEESPLSPPPEESPLSPPPESSPFSPLEESPLSPPEESPPSPALETPLSPPPEASPLSPPFEESPLSPPPEELPTSPPPEASRLSPPPEESPMSPPPEESPMSPPPEASRLFPPFEESPLSPPPEESPLSPPPEASRLSPPPEDSPMSPPPEESPMSPPPEVSRLSPLPVVSRLSPPPEESPLSPPPEESPTSPPPEASRLSPPPEDSPTSPPPEDSPASPPPEDSLMSLPLEESPLLPLPEEPQLCPRSEGPHLSPRPEEPHLSPRPEEPHLSPQAEEPHLSPQPEEPCLCAVPEEPHLSPQAEGPHLSPQPEELHLSPQTEEPHLSPVPEEPCLSPQPEESHLSPQSEEPCLSPRPEESHLSPELEKPPLSPRPEKPPEEPGQCPAPEELPLFPPPGEPSLSPLLGEPALSEPGEPPLSPLPEELPLSPSGEPSLSPQLMPPDPLPPPLSPIITAAAPPALSPLGELEYPFGAKGDSDPESPLAAPILETPISPPPEANCTDPEPVPPMILPPSPGSPVGPASPILMEPLPPQCSPLLQHSLVPQNSPPSQCSPPALPLSVPSPLSPIGKVVGVSDEAELHEMETEKVSEPECPALEPSATSPLPSPMGDLSCPAPSPAPALDDFSGLGEDTAPLDGIDAPGSQPEPGQTPGSLASELKGSPVLLDPEELAPVTPMEVYPECKQTAGQGSPCEEQEEPRAPVAPTPPTLIKSDIVNEISNLSQGDASASFPGSEPLLGSPDPEGGGSLSMELGVSTDVSPARDEGSLRLCTDSLPETDDSLLCDAGTAISGGKAEGEKGRRRSSPARSRIKQGRSSSFPGRRRPRGGAHGGRGRGRARLKSTASSIETLVVADIDSSPSKEEEEEDDDTMQNTVVLFSNTDKFVLMQDMCVVCGSFGRGAEGHLLACSQCSQCYHPYCVNSKITKVMLLKGWRCVECIVCEVCGQASDPSRLLLCDDCDISYHTYCLDPPLLTVPKGGWKCKWCVSCMQCGAASPGFHCEWQNSYTHCGPCASLVTCPICHAPYVEEDLLIQCRHCERWMHAGCESLFTEDDVEQAADEGFDCVSCQPYVVKPVAPVAPPELVPMKVKEPEPQYFRFEGVWLTETGMALLRNLTMSPLHKRRQRRGRLGLPGEAGLEGSEPSDALGPDDKKDGDLDTDELLKGEGGVEHMECEIKLEGPVSPDVEPGKEETEESKKRKRKPYRPGIGGFMVRQRKSHTRTKKGPAAQAEVLSGDGQPDEVIPADLPAEGAVEQSLAEGDEKKKQQRRGRKKSKLEDMFPAYLQEAFFGKELLDLSRKALFAVGVGRPSFGLGTPKAKGDGGSERKELPTSQKGDDGPDIADEESRGLEGKADTPGPEDGGVKASPVPSDPEKPGTPGEGMLSSDLDRISTEELPKMESKDLQQLFKDVLGSEREQHLGCGTPGLEGSRTPLQRPFLQGGLPLGNLPSSSPMDSYPGLCQSPFLDSRERGGFFSPEPGEPDSPWTGSGGTTPSTPTTPTTEGEGDGLSYNQRSLQRWEKDEELGQLSTISPVLYANINFPNLKQDYPDWSSRCKQIMKLWRKVPAADKAPYLQKAKDNRAAHRINKVQKQAESQINKQTKVGDIARKTDRPALHLRIPPQPGALGSPPPAAAPTIFIGSPTTPAGLSTSADGFLKPPAGSVPGPDSPGELFLKLPPQVPAQVPSQDPFGLAPAYPLEPRFPTAPPTYPPYPSPTGAPAQPPMLGASSRPGAGQPGEFHTTPPGTPRHQPSTPDPFLKPRCPSLDNLAVPESPGVGGGKASEPLLSPPPFGESRKALEVKKEELGASSPSYGPPNLGFVDSPSSGTHLGGLELKTPDVFKAPLTPRASQVEPQSPGLGLRPQEPPPAQALAPSPPSHPDIFRPGSYTDPYAQPPLTPRPQPPPPESCCALPPRSLPSDPFSRVPASPQSQSSSQSPLTPRPLSAEAFCPSPVTPRFQSPDPYSRPPSRPQSRDPFAPLHKPPRPQPPEVAFKAGSLAHTSLGAGGFPAALPAGPAGELHAKVPSGQPPNFVRSPGTGAFVGTPSPMRFTFPQAVGEPSLKPPVPQPGLPPPHGINSHFGPGPTLGKPQSTNYTVATGNFHPSGSPLGPSSGSTGESYGLSPLRPPSVLPPPAPDGSLPYLSHGASQRSGITSPVEKREDPGTGMGSSLATAELPGTQDPGMSGLSQTELEKQRQRQRLRELLIRQQIQRNTLRQEKETAAAAAGAVGPPGSWGAEPSSPAFEQLSRGQTPFAGTQDKSSLVGLPPSKLSGPILGPGSFPSDDRLSRPPPPATPSSMDVNSRQLVGGSQAFYQRAPYPGSLPLQQQQQQLWQQQQATAATSMRFAMSARFPSTPGPELGRQALGSPLAGISTRLPGPGEPVPGPAGPAQFIELRHNVQKGLGPGGTPFPGQGPPQRPRFYPVSEDPHRLAPEGLRGLAVSGLPPQKPSAPPAPELNNSLHPTPHTKGPTLPTGLELVNRPPSSTELGRPNPLALEAGKLPCEDPELDDDFDAHKALEDDEELAHLGLGVDVAKGDDELGTLENLETNDPHLDDLLNGDEFDLLAYTDPELDTGDKKDIFNEHLRLVESANEKAEREALLRGVEPGPLGPEERPPPAADASEPRLASVLPEVKPKVEEGGRHPSPCQFTIATPKVEPAPAANSLGLGLKPGQSMMGSRDTRMGTGPFSSSGHTAEKASFGATGGPPAHLLTPSPLSGPGGSSLLEKFELESGALTLPGGPAASGDELDKMESSLVASELPLLIEDLLEHEKKELQKKQQLSAQLQPAQQQQQQQQQHSLLSAPGPAQAMSLPHEGSSPSLAGSQQQLSLGLAGARQPGLPQPLMPTQPPAHALQQRLAPSMAMVSNQGHMLSGQHGGQAGLVPQQSSQPVLSQKPMGTMPPSMCMKPQQLAMQQQLANSFFPDTDLDKFAAEDIIDPIAKAKMVALKGIKKVMAQGSIGVAPGMNRQQVSLLAQRLSGGPSSDLQNHVAAGSGQERSAGDPSQPRPNPPTFAQGVINEADQRQYEEWLFHTQQLLQMQLKVLEEQIGVHRKSRKALCAKQRTAKKAGREFPEADAEKLKLVTEQQSKIQKQLDQVRKQQKEHTNLMAEYRNKQQQQQQQQQQQQQQHSAVLALSPSQSPRLLTKLPGQLLPGHGLQPPQGPPGGQAGGLRLTPGGMALPGQPGGPFLNTALAQQQQQQHSGGAGSLAGPSGGFFPGNLALRSLGPDSRLLQERQLQLQQQRMQLAQKLQQQQQQQQQQQHLLGQVAIQQQQQQGPGVQTNQALGPKPQGLMPPSSHQGLLVQQLSPQPPQGPQGMLGPAQVAVLQQQHPGALGPQGPHRQVLMTQSRVLSSPQLAQQGQGLMGHRLVTAQQQQQQQQHQQQGSMAGLSHLQQSLMSHSGQPKLSAQPMGSLQQLQQQQQLQQQQQLQQQQQQQLQQQQQLQQQQLQQQQQQQQLQQQQQQQLQQQQQQLQQQQQQQQQQFQQQQQQQQMGLLNQSRTLLSPQQQQQQQVALGPGMPAKPLQHFSSPGALGPTLLLTGKEQNTVDPAVSSEATEGPSTHQGGPLAIGTTPESMATEPGEVKPSLSGDSQLLLVQPQPQPQPSSLQLQPPLRLPGQQQQQVSLLHTAGGGSHGQLGSGSSSEASSVPHLLAQPSVSLGDQPGSMTQNLLGPQQPMLERPMQNNTGPQPPKPGPVLQSGQGLPGVGIMPTVGQLRAQLQGVLAKNPQLRHLSPQQQQQLQALLMQRQLQQSQAVRQTPPYQEPGTQTSPLQGLLGCQPQLGGFPGPQTGPLQELGAGPRPQGPPRLPAPPGALSTGPVLGPVHPTPPPSSPQEPKRPSQLPSPSSQLPTEAQLPPTHPGTPKPQGPTLEPPPGRVSPAAAQLADTLFSKGLGPWDPPDNLAETQKPEQSSLVPGHLDQVNGQVVPEASQLSIKQEPREEPCALGAQSVKREANGEPIGAPGTSNHLLLAGPRSEAGHLLLQKLLRAKNVQLSTGRGSEGLRAEINGHIDSKLAGLEQKLQGTPSNKEDAAARKPLTPKPKRVQKASDRLVSSRKKLRKEDGVRASEALLKQLKQELSLLPLTEPAITANFSLFAPFGSGCPVNGQSQLRGAFGSGALPTGPDYYSQLLTKNNLSNPPTPPSSLPPTPPPSVQQKMVNGVTPSEELGEHPKDAASARDSERALRDTSEVKSLDLLAALPTPPHNQTEDVRMESDEDSDSPDSIVPASSPESILGEEAPRFPHLGSGRWEQEDRALSPVIPLIPRASIPVFPDTKPYGALGLEVPGKLPVTTWEKGKGSEVSVMLTVSAAAAKNLNGVMVAVAELLSMKIPNSYEVLFPESPARAGTEPKKGEAEGPGGKEKGLEGKSPDTGPDWLKQFDAVLPGYTLKSQLDILSLLKQESPAPEPPTQHSYTYNVSNLDVRQLSAPPPEEPSPPPSPLAPSPASPPTEPLVELPTEPLAEPPVPSPLPLASSPESARPKPRARPPEEGEDSRPPRLKKWKGVRWKRLRLLLTIQKGSGRQEDEREVAEFMEQLGTALRPDKVPRDMRRCCFCHEEGDGATDGPARLLNLDLDLWVHLNCALWSTEVYETQGGALMNVEVALHRGLLTKCSLCQRTGATSSCNRMRCPNVYHFACAIRAKCMFFKDKTMLCPMHKIKGPCEQELSSFAVFRRVYIERDEVKQIASIIQRGERLHMFRVGGLVFHAIGQLLPHQMADFHSATALYPVGYEATRIYWSLRTNNRRCCYRCSIGENNGRPEFVIKVIEQGLEDLVFTDASPQAVWNRIIEPVAAMRKEADMLRLFPEYLKGEELFGLTVHAVLRIAESLPGVESCQNYLFRYGRHPLMELPLMINPTGCARSEPKILTHYKRPHTLNSTSMSKAYQSTFTGETNTPYSKQFVHSKSSQYRRLRTEWKNNVYLARSRIQGLGLYAAKDLEKHTMVIEYIGTIIRNEVANRREKIYEEQNRGIYMFRINNEHVIDATLTGGPARYINHSCAPNCVAEVVTFDKEDKIIIISSRRIPKGEELTYDYQFDFEDDQHKIPCHCGAWNCRKWMN,mutated_sequence,1.0,5537.0,NP_003473.3.a2m,NP_003473.3.npy,ClinVar +NP_003551.2,NP_003551.2.csv,MQFFGRLVNTFSGVTNLFSNPFRVKEVAVADYTSSDRVREEGQLILFQNTPNRTWDCVLVNPRNSQSGFRLFQLELEADALVNFHQYSSQLLPFYESSPQVLHTEVLQHLTDLIRNHPSWSVAHLAVELGIRECFHHSRIISCANCAENEEGCTPLHLACRKGDGEILVELVQYCHTQMDVTDYKGETVFHYAVQGDNSQVLQLLGRNAVAGLNQVNNQGLTPLHLACQLGKQEMVRVLLLCNARCNIMGPNGYPIHSAMKFSQKGCAEMIISMDSSQIHSKDPRYGASPLHWAKNAEMARMLLKRGCNVNSTSSAGNTALHVAVMRNRFDCAIVLLTHGANADARGEHGNTPLHLAMSKDNVEMIKALIVFGAEVDTPNDFGETPTFLASKIGRLVTRKAILTLLRTVGAEYCFPPIHGVPAEQGSAAPHHPFSLERAQPPPISLNNLELQDLMHISRARKPAFILGSMRDEKRTHDHLLCLDGGGVKGLIIIQLLIAIEKASGVATKDLFDWVAGTSTGGILALAILHSKSMAYMRGMYFRMKDEVFRGSRPYESGPLEEFLKREFGEHTKMTDVRKPKVMLTGTLSDRQPAELHLFRNYDAPETVREPRFNQNVNLRPPAQPSDQLVWRAARSSGAAPTYFRPNGRFLDGGLLANNPTLDAMTEIHEYNQDLIRKGQANKVKKLSIVVSLGTGRSPQVPVTCVDVFRPSNPWELAKTVFGAKELGKMVVDCCTDPDGRAVDRARAWCEMVGIQYFRLNPQLGTDIMLDEVSDTVLVNALWETEVYIYEHREEFQKLIQLLLSP,mutated_sequence,1.0,806.0,NP_003551.2.a2m,NP_003551.2.npy,ClinVar +NP_003562.1,NP_003562.1.csv,MSERRVVVDLPTSASSSMPLQRRRASFRGPRSSSSLESPPASRTNAMSGLVRAPGVYVGTAPSGCIGGLGARVTRRALGISSVFLQGLRSSGLATVPAPGLERDHGAVEDLGGCLVEYMAKVHALEQVSQELETQLRMHLESKATRSGNWGALRASWASSCQQVGEAVLENARLMLQTETIQAGADDFKERYENEQPFRKAAEEEINSLYKVIDEANLTKMDLESQIESLKEELGSLSRNYEEDVKLLHKQLAGCELEQMDAPIGTGLDDILETIRIQWERDVEKNRVEAGALLQAKQQAEVAHMSQTQEEKLAAALRVELHNTSCQVQSLQAETESLRALKRGLENTLHDAKHWHDMELQNLGAVVGRLEAELREIRAEAEQQQQERAHLLARKCQLQKDVASYHALLDREESG,mutated_sequence,1.0,415.0,NP_003562.1.a2m,NP_003562.1.npy,ClinVar +NP_003592.3,NP_003592.3.csv,MSSAAEPPPPPPPESAPSKPAASIASGGSNSSNKGGPEGVAAQAVASAASAGPADAEMEEIFDDASPGKQKEIQEPDPTYEEKMQTDRANRFEYLLKQTELFAHFIQPAAQKTPTSPLKMKPGRPRIKKDEKQNLLSVGDYRHRRTEQEEDEELLTESSKATNVCTRFEDSPSYVKWGKLRDYQVRGLNWLISLYENGINGILADEMGLGKTLQTISLLGYMKHYRNIPGPHMVLVPKSTLHNWMSEFKRWVPTLRSVCLIGDKEQRAAFVRDVLLPGEWDVCVTSYEMLIKEKSVFKKFNWRYLVIDEAHRIKNEKSKLSEIVREFKTTNRLLLTGTPLQNNLHELWSLLNFLLPDVFNSADDFDSWFDTNNCLGDQKLVERLHMVLRPFLLRRIKADVEKSLPPKKEVKIYVGLSKMQREWYTRILMKDIDILNSAGKMDKMRLLNILMQLRKCCNHPYLFDGAEPGPPYTTDMHLVTNSGKMVVLDKLLPKLKEQGSRVLIFSQMTRVLDILEDYCMWRNYEYCRLDGQTPHDERQDSINAYNEPNSTKFVFMLSTRAGGLGINLATADVVILYDSDWNPQVDLQAMDRAHRIGQTKTVRVFRFITDNTVEERIVERAEMKLRLDSIVIQQGRLVDQNLNKIGKDEMLQMIRHGATHVFASKESEITDEDIDGILERGAKKTAEMNEKLSKMGESSLRNFTMDTESSVYNFEGEDYREKQKIAFTEWIEPPKRERKANYAVDAYFREALRVSEPKAPKAPRPPKQPNVQDFQFFPPRLFELLEKEILFYRKTIGYKVPRNPELPNAAQAQKEEQLKIDEAESLNDEELEEKEKLLTQGFTNWNKRDFNQFIKANEKWGRDDIENIAREVEGKTPEEVIEYSAVFWERCNELQDIEKIMAQIERGEARIQRRISIKKALDTKIGRYKAPFHQLRISYGTNKGKNYTEEEDRFLICMLHKLGFDKENVYDELRQCIRNSPQFRFDWFLKSRTAMELQRRCNTLITLIERENMELEEKEKAEKKKRGPKPSTQKRKMDGAPDGRGRKKKLKL,mutated_sequence,1.0,1052.0,NP_003592.3.a2m,NP_003592.3.npy,ClinVar +NP_003602.1,NP_003602.1.csv,MMAQSNMFTVADVLSQDELRKKLYQTFKDRGILDTLKTQLRNQLIHELMHPVLSGELQPRSISVEGSSLLIGASNSLVADHLQRCGYEYSLSVFFPESGLAKEKVFTMQDLLQLIKINPTSSLYKSLVSGSDKENQKGFLMHFLKELAEYHQAKESCNMETQTSSTFNRDSLAEKLQLIDDQFADAYPQRIKFESLEIKLNEYKREIEEQLRAEMCQKLKFFKDTEIAKIKMEAKKKYEKELTMFQNDFEKACQAKSEALVLREKSTLERIHKHQEIETKEIYAQRQLLLKDMDLLRGREAELKQRVEAFELNQKLQEEKHKSITEALRRQEQNIKSFEETYDRKLKNELLKYQLELKDDYIIRTNRLIEDERKNKEKAVHLQEELIAINSKKEELNQSVNRVKELELELESVKAQSLAITKQNHMLNEKVKEMSDYSLLKEEKLELLAQNKLLKQQLEESRNENLRLLNRLAQPAPELAVFQKELRKAEKAIVVEHEEFESCRQALHKQLQDEIEHSAQLKAQILGYKASVKSLTTQVADLKLQLKQTQTALENEVYCNPKQSVIDRSVNGLINGNVVPCNGEISGDFLNNPFKQENVLARMVASRITNYPTAWVEGSSPDSDLEFVANTKARVKELQQEAERLEKAFRSYHRRVIKNSAKSPLAAKSPPSLHLLEAFKNITSSSPERHIFGEDRVVSEQPQVGTLEERNDVVEALTGSAASRLRGGTSSRRLSSTPLPKAKRSLESEMYLEGLGRSHIASPSPCPDRMPLPSPTESRHSLSIPPVSSPPEQKVGLYRRQTELQDKSEFSDVDKLAFKDNEEFESSFESAGNMPRQLEMGGLSPAGDMSHVDAAAAAVPLSYQHPSVDQKQIEEQKEEEKIREQQVKERRQREERRQSNLQEVLERERRELEKLYQERKMIEESLKIKIKKELEMENELEMSNQEIKDKSAHSENPLEKYMKIIQQEQDQESADKSSKKMVQEGSLVDTLQSSDKVESLTGFSHEELDDSW,mutated_sequence,1.0,1012.0,NP_003602.1.a2m,NP_003602.1.npy,ClinVar +NP_003713.3,NP_003713.3.csv,MNFETSRCATLQYCPDPYIQRFVETPAHFSWKESYYRSTMSQSTQTNEFLSPEVFQHIWDFLEQPICSVQPIDLNFVDEPSEDGATNKIEISMDCIRMQDSDLSDPMWPQYTNLGLLNSMDQQIQNGSSSTSPYNTDHAQNSVTAPSPYAQPSSTFDALSPSPAIPSNTDYPGPHSFDVSFQQSSTAKSATWTYSTELKKLYCQIAKTCPIQIKVMTPPPQGAVIRAMPVYKKAEHVTEVVKRCPNHELSREFNEGQIAPPSHLIRVEGNSHAQYVEDPITGRQSVLVPYEPPQVGTEFTTVLYNFMCNSSCVGGMNRRPILIIVTLETRDGQVLGRRCFEARICACPGRDRKADEDSIRKQQVSDSTKNGDGTKRPFRQNTHGIQMTSIKKRRSPDDELLYLPVRGRETYEMLLKIKESLELMQYLPQHTIETYRQQQQQQHQHLLQKQTSIQSPSSYGNSSPPLNKMNSMNKLPSVSQLINPQQRNALTPTTIPDGMGANIPMMGTHMPMAGDMNGLSPTQALPPPLSMPSTSHCTPPPPYPTDCSIVSFLARLGCSSCLDYFTTQGLTTIYQIEHYSMDDLASLKIPEQFRHAIWKGILDHRQLHEFSSPSHLLRTPSSASTVSVGSSETRGERVIDAVRFTLRQTISFPPRDEWNDFNFDMDARRNKQQRIKEEGE,mutated_sequence,1.0,680.0,NP_003713.3.a2m,NP_003713.3.npy,ClinVar +NP_003733.2,NP_003733.2.csv,MSDSVILRSIKKFGEENDGFESDKSYNNDKKSRLQDEKKGDGVRVGFFQLFRFSSSTDIWLMFVGSLCAFLHGIAQPGVLLIFGTMTDVFIDYDVELQELQIPGKACVNNTIVWTNSSLNQNMTNGTRCGLLNIESEMIKFASYYAGIAVAVLITGYIQICFWVIAAARQIQKMRKFYFRRIMRMEIGWFDCNSVGELNTRFSDDINKINDAIADQMALFIQRMTSTICGFLLGFFRGWKLTLVIISVSPLIGIGAATIGLSVSKFTDYELKAYAKAGVVADEVISSMRTVAAFGGEKREVERYEKNLVFAQRWGIRKGIVMGFFTGFVWCLIFLCYALAFWYGSTLVLDEGEYTPGTLVQIFLSVIVGALNLGNASPCLEAFATGRAAATSIFETIDRKPIIDCMSEDGYKLDRIKGEIEFHNVTFHYPSRPEVKILNDLNMVIKPGEMTALVGPSGAGKSTALQLIQRFYDPCEGMVTVDGHDIRSLNIQWLRDQIGIVEQEPVLFSTTIAENIRYGREDATMEDIVQAAKEANAYNFIMDLPQQFDTLVGEGGGQMSGGQKQRVAIARALIRNPKILLLDMATSALDNESEAMVQEVLSKIQHGHTIISVAHRLSTVRAADTIIGFEHGTAVERGTHEELLERKGVYFTLVTLQSQGNQALNEEDIKDATEDDMLARTFSRGSYQDSLRASIRQRSKSQLSYLVHEPPLAVVDHKSTYEEDRKDKDIPVQEEVEPAPVRRILKFSAPEWPYMLVGSVGAAVNGTVTPLYAFLFSQILGTFSIPDKEEQRSQINGVCLLFVAMGCVSLFTQFLQGYAFAKSGELLTKRLRKFGFRAMLGQDIAWFDDLRNSPGALTTRLATDASQVQGAAGSQIGMIVNSFTNVTVAMIIAFSFSWKLSLVILCFFPFLALSGATQTRMLTGFASRDKQALEMVGQITNEALSNIRTVAGIGKERRFIEALETELEKPFKTAIQKANIYGFCFAFAQCIMFIANSASYRYGGYLISNEGLHFSYVFRVISAVVLSATALGRAFSYTPSYAKAKISAARFFQLLDRQPPISVYNTAGEKWDNFQGKIDFVDCKFTYPSRPDSQVLNGLSVSISPGQTLAFVGSSGCGKSTSIQLLERFYDPDQGKVMIDGHDSKKVNVQFLRSNIGIVSQEPVLFACSIMDNIKYGDNTKEIPMERVIAAAKQAQLHDFVMSLPEKYETNVGSQGSQLSRGEKQRIAIARAIVRDPKILLLDEATSALDTESEKTVQVALDKAREGRTCIVIAHRLSTIQNADIIAVMAQGVVIEKGTHEELMAQKGAYYKLVTTGSPIS,mutated_sequence,1.0,1321.0,NP_003733.2.a2m,NP_003733.2.npy,ClinVar +NP_003784.2,NP_003784.2.csv,MAPWLQLLSLLGLLPGAVAAPAQPRAASFQAWGPPSPELLAPTRFALEMFNRGRAAGTRAVLGLVRGRVRRAGQGSLYSLEATLEEPPCNDPMVCRLPVSKKTLLCSFQVLDELGRHVLLRKDCGPVDTKVPGAGEPKSAFTQGSAMISSLSQNHPDNRNETFSSVISLLNEDPLSQDLPVKMASIFKNFVITYNRTYESKEEARWRLSVFVNNMVRAQKIQALDRGTAQYGVTKFSDLTEEEFRTIYLNTLLRKEPGNKMKQAKSVGDLAPPEWDWRSKGAVTKVKDQGMCGSCWAFSVTGNVEGQWFLNQGTLLSLSEQELLDCDKMDKACMGGLPSNAYSAIKNLGGLETEDDYSYQGHMQSCNFSAEKAKVYINDSVELSQNEQKLAAWLAKRGPISVAINAFGMQFYRHGISRPLRPLCSPWLIDHAVLLVGYGNRSDVPFWAIKNSWGTDWGEKGYYYLHRGSGACGVNTMASSAVVD,mutated_sequence,1.0,484.0,NP_003784.2.a2m,NP_003784.2.npy,ClinVar +NP_003892.2,NP_003892.2.csv,MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH,mutated_sequence,1.0,568.0,NP_003892.2.a2m,NP_003892.2.npy,ClinVar +NP_003910.1,NP_003910.1.csv,MQLPRWWELGDPCAWTGQGRGTRRMSPATTGTFLLTVYSIFSKVHSDRNVYPSAGVLFVHVLEREYFKGEFPPYPKPGEISNDPITFNTNLMGYPDRPGWLRYIQRTPYSDGVLYGSPTAENVGKPTIIEITAYNRRTFETARHNLIINIMSAEDFPLPYQAEFFIKNMNVEEMLASEVLGDFLGAVKNVWQPERLNAINITSALDRGGRVPLPINDLKEGVYVMVGADVPFSSCLREVENPQNQLRCSQEMEPVITCDKKFRTQFYIDWCKISLVDKTKQVSTYQEVIRGEGILPDGGEYKPPSDSLKSRDYYTDFLITLAVPSAVALVLFLILAYIMCCRREGVEKRNMQTPDIQLVHHSAIQKSTKELRDMSKNREIAWPLSTLPVFHPVTGEIIPPLHTDNYDSTNMPLMQTQQNLPHQTQIPQQQTTGKWYP,mutated_sequence,1.0,437.0,NP_003910.1.a2m,NP_003910.1.npy,ClinVar +NP_003912.1,NP_003912.1.csv,MEPTAPSLTEEDLTEVKKDALENLRVYLCEKIIAERHFDHLRAKKILSREDTEEISCRTSSRKRAGKLLDYLQENPKGLDTLVESIRREKTQNFLIQKITDEVLKLRNIKLEHLKGLKCSSCEPFPDGATNNLSRSNSDESNFSEKLRASTVMYHPEGESSTTPFFSTNSSLNLPVLEVGRTENTIFSSTTLPRPGDPGAPPLPPDLQLEEEGTCANSSEMFLPLRSRTVSRQ,mutated_sequence,1.0,233.0,NP_003912.1.a2m,NP_003912.1.npy,ClinVar +NP_003928.1,NP_003928.1.csv,MEPSSLELPADTVQRIAAELKCHPTDERVALHLDEEDKLRHFRECFYIPKIQDLPPVDLSLVNKDENAIYFLGNSLGLQPKMVKTYLEEELDKWAKIAAYGHEVGKRPWITGDESIVGLMKDIVGANEKEIALMNALTVNLHLLMLSFFKPTPKRYKILLEAKAFPSDHYAIESQLQLHGLNIEESMRMIKPREGEETLRIEDILEVIEKEGDSIAVILFSGVHFYTGQHFNIPAITKAGQAKGCYVGFDLAHAVGNVELYLHDWGVDFACWCSYKYLNAGAGGIAGAFIHEKHAHTIKPALVGWFGHELSTRFKMDNKLQLIPGVCGFRISNPPILLVCSLHASLEIFKQATMKALRKKSVLLTGYLEYLIKHNYGKDKAATKKPVVNIITPSHVEERGCQLTITFSVPNKDVFQELEKRGVVCDKRNPNGIRVAPVPLYNSFHDVYKFTNLLTSILDSAETKN,mutated_sequence,1.0,465.0,NP_003928.1.a2m,NP_003928.1.npy,ClinVar +NP_003968.3,NP_003968.3.csv,MADIIARLREDGIQKRVIQEGRGELPDFQDGTKATFHYRTLHSDDEGTVLDDSRARGKPMELIIGKKFKLPVWETIVCTMREGEIAQFLCDIKHVVLYPLVAKSLRNIAVGKDPLEGQRHCCGVAQMREHSSLGHADLDALQQNPQPLIFHMEMLKVESPGTYQQDPWAMTDEEKAKAVPLIHQEGNRLYREGHVKEAAAKYYDAIACLKNLQMKEQPGSPEWIQLDQQITPLLLNYCQCKLVVEEYYEVLDHCSSILNKYDDNVKAYFKRGKAHAAVWNAQEAQADFAKVLELDPALAPVVSRELRALEARIRQKDEEDKARFRGIFSH,mutated_sequence,1.0,330.0,NP_003968.3.a2m,NP_003968.3.npy,ClinVar +NP_003973.3,NP_003973.3.csv,MVDSTEYEVASQPEVETSPLGDGASPGPEQVKLKKEISLLNGVCLIVGNMIGSGIFVSPKGVLIYSASFGLSLVIWAVGGLFSVFGALCYAELGTTIKKSGASYAYILEAFGGFLAFIRLWTSLLIIEPTSQAIIAITFANYMVQPLFPSCFAPYAASRLLAAACICLLTFINCAYVKWGTLVQDIFTYAKVLALIAVIVAGIVRLGQGASTHFENSFEGSSFAVGDIALALYSALFSYSGWDTLNYVTEEIKNPERNLPLSIGISMPIVTIIYILTNVAYYTVLDMRDILASDAVAVTFADQIFGIFNWIIPLSVALSCFGGLNASIVAASRLFFVGSREGHLPDAICMIHVERFTPVPSLLFNGIMALIYLCVEDIFQLINYYSFSYWFFVGLSIVGQLYLRWKEPDRPRPLKLSVFFPIVFCLCTIFLVAVPLYSDTINSLIGIAIALSGLPFYFLIIRVPEHKRPLYLRRIVGSATRYLQVLCMSVAAEMDLEDGGEMPKQRDPKSN,mutated_sequence,1.0,511.0,NP_003973.3.a2m,NP_003973.3.npy,ClinVar +NP_003986.2,NP_003986.2.csv,MALPSLLLLVAALAGGVRPPGARNLTLAVVLPEHNLSYAWAWPRVGPAVALAVEALGRALPVDLRFVSSELEGACSEYLAPLSAVDLKLYHDPDLLLGPGCVYPAASVARFASHWRLPLLTAGAVASGFSAKNDHYRTLVRTGPSAPKLGEFVVTLHGHFNWTARAALLYLDARTDDRPHYFTIEGVFEALQGSNLSVQHQVYAREPGGPEQATHFIRANGRIVYICGPLEMLHEILLQAQRENLTNGDYVFFYLDVFGESLRAGPTRATGRPWQDNRTREQAQALREAFQTVLVITYREPPNPEYQEFQNRLLIRAREDFGVELGPSLMNLIAGCFYDGILLYAEVLNETIQEGGTREDGLRIVEKMQGRRYHGVTGLVVMDKNNDRETDFVLWAMGDLDSGDFQPAAHYSGAEKQIWWTGRPIPWVKGAPPSDNPPCAFDLDDPSCDKTPLSTLAIVALGTGITFIMFGVSSFLIFRKLMLEKELASMLWRIRWEELQFGNSERYHKGAGSRLTLSLRGSSYGSLMTAHGKYQIFANTGHFKGNVVAIKHVNKKRIELTRQVLFELKHMRDVQFNHLTRFIGACIDPPNICIVTEYCPRGSLQDILENDSINLDWMFRYSLINDLVKGMAFLHNSIISSHGSLKSSNCVVDSRFVLKITDYGLASFRSTAEPDDSHALYAKKLWTAPELLSGNPLPTTGMQKADVYSFGIILQEIALRSGPFYLEGLDLSPKEIVQKVRNGQRPYFRPSIDRTQLNEELVLLMERCWAQDPAERPDFGQIKGFIRRFNKEGGTSILDNLLLRMEQYANNLEKLVEERTQAYLEEKRKAEALLYQILPHSVAEQLKRGETVQAEAFDSVTIYFSDIVGFTALSAESTPMQVVTLLNDLYTCFDAIIDNFDVYKVETIGDAYMVVSGLPGRNGQRHAPEIARMALALLDAVSSFRIRHRPHDQLRLRIGVHTGPVCAGVVGLKMPRYCLFGDTVNTASRMESNGQALKIHVSSTTKDALDELGCFQLELRGDVEMKGKGKMRTYWLLGERKGPPGLL,mutated_sequence,1.0,1047.0,NP_003986.2.a2m,NP_003986.2.npy,ClinVar +NP_003995.2,NP_003995.2.csv,MDWGTLQTILGGVNKHSTSIGKIWLTVLFIFRIMILVVAAKEVWGDEQADFVCNTLQPGCKNVCYDHYFPISHIRLWALQLIFVSTPALLVAMHVAYRRHEKKRKFIKGEIKSEFKDIEEIKTQKVRIEGSLWWTYTSSIFFRVIFEAAFMYVFYVMYDGFSMQRLVKCNAWPCPNTVDCFVSRPTEKTVFTVFMIAVSGICILLNVTELCYLLIRYCSGKSKKPV,mutated_sequence,1.0,226.0,NP_003995.2.a2m,NP_003995.2.npy,ClinVar +NP_003997.2,NP_003997.2.csv,MLWWEEVEDCYEREDVQKKTFTKWVNAQFSKFGKQHIENLFSDLQDGRRLLDLLEGLTGQKLPKEKGSTRVHALNNVNKALRVLQNNNVDLVNIGSTDIVDGNHKLTLGLIWNIILHWQVKNVMKNIMAGLQQTNSEKILLSWVRQSTRNYPQVNVINFTTSWSDGLALNALIHSHRPDLFDWNSVVCQQSATQRLEHAFNIARYQLGIEKLLDPEDVDTTYPDKKSILMYITSLFQVLPQQVSIEAIQEVEMLPRPPKVTKEEHFQLHHQMHYSQQITVSLAQGYERTSSPKPRFKSYAYTQAAYVTTSDPTRSPFPSQHLEAPEDKSFGSSLMESEVNLDRYQTALEEVLSWLLSAEDTLQAQGEISNDVEVVKDQFHTHEGYMMDLTAHQGRVGNILQLGSKLIGTGKLSEDEETEVQEQMNLLNSRWECLRVASMEKQSNLHRVLMDLQNQKLKELNDWLTKTEERTRKMEEEPLGPDLEDLKRQVQQHKVLQEDLEQEQVRVNSLTHMVVVVDESSGDHATAALEEQLKVLGDRWANICRWTEDRWVLLQDILLKWQRLTEEQCLFSAWLSEKEDAVNKIHTTGFKDQNEMLSSLQKLAVLKADLEKKKQSMGKLYSLKQDLLSTLKNKSVTQKTEAWLDNFARCWDNLVQKLEKSTAQISQAVTTTQPSLTQTTVMETVTTVTTREQILVKHAQEELPPPPPQKKRQITVDSEIRKRLDVDITELHSWITRSEAVLQSPEFAIFRKEGNFSDLKEKVNAIEREKAEKFRKLQDASRSAQALVEQMVNEGVNADSIKQASEQLNSRWIEFCQLLSERLNWLEYQNNIIAFYNQLQQLEQMTTTAENWLKIQPTTPSEPTAIKSQLKICKDEVNRLSDLQPQIERLKIQSIALKEKGQGPMFLDADFVAFTNHFKQVFSDVQAREKELQTIFDTLPPMRYQETMSAIRTWVQQSETKLSIPQLSVTDYEIMEQRLGELQALQSSLQEQQSGLYYLSTTVKEMSKKAPSEISRKYQSEFEEIEGRWKKLSSQLVEHCQKLEEQMNKLRKIQNHIQTLKKWMAEVDVFLKEEWPALGDSEILKKQLKQCRLLVSDIQTIQPSLNSVNEGGQKIKNEAEPEFASRLETELKELNTQWDHMCQQVYARKEALKGGLEKTVSLQKDLSEMHEWMTQAEEEYLERDFEYKTPDELQKAVEEMKRAKEEAQQKEAKVKLLTESVNSVIAQAPPVAQEALKKELETLTTNYQWLCTRLNGKCKTLEEVWACWHELLSYLEKANKWLNEVEFKLKTTENIPGGAEEISEVLDSLENLMRHSEDNPNQIRILAQTLTDGGVMDELINEELETFNSRWRELHEEAVRRQKLLEQSIQSAQETEKSLHLIQESLTFIDKQLAAYIADKVDAAQMPQEAQKIQSDLTSHEISLEEMKKHNQGKEAAQRVLSQIDVAQKKLQDVSMKFRLFQKPANFEQRLQESKMILDEVKMHLPALETKSVEQEVVQSQLNHCVNLYKSLSEVKSEVEMVIKTGRQIVQKKQTENPKELDERVTALKLHYNELGAKVTERKQQLEKCLKLSRKMRKEMNVLTEWLAATDMELTKRSAVEGMPSNLDSEVAWGKATQKEIEKQKVHLKSITEVGEALKTVLGKKETLVEDKLSLLNSNWIAVTSRAEEWLNLLLEYQKHMETFDQNVDHITKWIIQADTLLDESEKKKPQQKEDVLKRLKAELNDIRPKVDSTRDQAANLMANRGDHCRKLVEPQISELNHRFAAISHRIKTGKASIPLKELEQFNSDIQKLLEPLEAEIQQGVNLKEEDFNKDMNEDNEGTVKELLQRGDNLQQRITDERKREEIKIKQQLLQTKHNALKDLRSQRRKKALEISHQWYQYKRQADDLLKCLDDIEKKLASLPEPRDERKIKEIDRELQKKKEELNAVRRQAEGLSEDGAAMAVEPTQIQLSKRWREIESKFAQFRRLNFAQIHTVREETMMVMTEDMPLEISYVPSTYLTEITHVSQALLEVEQLLNAPDLCAKDFEDLFKQEESLKNIKDSLQQSSGRIDIIHSKKTAALQSATPVERVKLQEALSQLDFQWEKVNKMYKDRQGRFDRSVEKWRRFHYDIKIFNQWLTEAEQFLRKTQIPENWEHAKYKWYLKELQDGIGQRQTVVRTLNATGEEIIQQSSKTDASILQEKLGSLNLRWQEVCKQLSDRKKRLEEQKNILSEFQRDLNEFVLWLEEADNIASIPLEPGKEQQLKEKLEQVKLLVEELPLRQGILKQLNETGGPVLVSAPISPEEQDKLENKLKQTNLQWIKVSRALPEKQGEIEAQIKDLGQLEKKLEDLEEQLNHLLLWLSPIRNQLEIYNQPNQEGPFDVKETEIAVQAKQPDVEEILSKGQHLYKEKPATQPVKRKLEDLSSEWKAVNRLLQELRAKQPDLAPGLTTIGASPTQTVTLVTQPVVTKETAISKLEMPSSLMLEVPALADFNRAWTELTDWLSLLDQVIKSQRVMVGDLEDINEMIIKQKATMQDLEQRRPQLEELITAAQNLKNKTSNQEARTIITDRIERIQNQWDEVQEHLQNRRQQLNEMLKDSTQWLEAKEEAEQVLGQARAKLESWKEGPYTVDAIQKKITETKQLAKDLRQWQTNVDVANDLALKLLRDYSADDTRKVHMITENINASWRSIHKRVSEREAALEETHRLLQQFPLDLEKFLAWLTEAETTANVLQDATRKERLLEDSKGVKELMKQWQDLQGEIEAHTDVYHNLDENSQKILRSLEGSDDAVLLQRRLDNMNFKWSELRKKSLNIRSHLEASSDQWKRLHLSLQELLVWLQLKDDELSRQAPIGGDFPAVQKQNDVHRAFKRELKTKEPVIMSTLETVRIFLTEQPLEGLEKLYQEPRELPPEERAQNVTRLLRKQAEEVNTEWEKLNLHSADWQRKIDETLERLRELQEATDELDLKLRQAEVIKGSWQPVGDLLIDSLQDHLEKVKALRGEIAPLKENVSHVNDLARQLTTLGIQLSPYNLSTLEDLNTRWKLLQVAVEDRVRQLHEAHRDFGPASQHFLSTSVQGPWERAISPNKVPYYINHETQTTCWDHPKMTELYQSLADLNNVRFSAYRTAMKLRRLQKALCLDLLSLSAACDALDQHNLKQNDQPMDILQIINCLTTIYDRLEQEHNNLVNVPLCVDMCLNWLLNVYDTGRTGRIRVLSFKTGIISLCKAHLEDKYRYLFKQVASSTGFCDQRRLGLLLHDSIQIPRQLGEVASFGGSNIEPSVRSCFQFANNKPEIEAALFLDWMRLEPQSMVWLPVLHRVAAAETAKHQAKCNICKECPIIGFRYRSLKHFNYDICQSCFFSGRVAKGHKMHYPMVEYCTPTTSGEDVRDFAKVLKNKFRTKRYFAKHPRMGYLPVQTVLEGDNMETPVTLINFWPVDSAPASSPQLSHDDTHSRIEHYASRLAEMENSNGSYLNDSISPNESIDDEHLLIQHYCQSLNQDSPLSQPRSPAQILISLESEERGELERILADLEEENRNLQAEYDRLKQQHEHKGLSPLPSPPEMMPTSPQSPRDAELIAEAKLLRQHKGRLEARMQILEDHNKQLESQLHRLRQLLEQPQAEAKVNGTTVSSPSTSLQRSDSSQPMLLRVVGSQTSDSMGEEDLLSPPQDTSTGLEEVMEQLNNSFPSSRGRNTPGKPMREDTM,mutated_sequence,1.0,3685.0,NP_003997.2.a2m,NP_003997.2.npy,ClinVar +NP_004077.1,NP_004077.1.csv,MSAAWIPALGLGVCLLLLPGPAGSEGAAPIAITCFTRGLDIRKEKADVLCPGGCPLEEFSVYGNIVYASVSSICGAAVHRGVISNSGGPVRVYSLPGRENYSSVDANGIQSQMLSRWSASFTVTKGKSSTQEATGQAVSTAHPPTGKRLKKTPEKKTGNKDCKADIAFLIDGSFNIGQRRFNLQKNFVGKVALMLGIGTEGPHVGLVQASEHPKIEFYLKNFTSAKDVLFAIKEVGFRGGNSNTGKALKHTAQKFFTVDAGVRKGIPKVVVVFIDGWPSDDIEEAGIVAREFGVNVFIVSVAKPIPEELGMVQDVTFVDKAVCRNNGFFSYHMPNWFGTTKYVKPLVQKLCTHEQMMCSKTCYNSVNIAFLIDGSSSVGDSNFRLMLEFVSNIAKTFEISDIGAKIAAVQFTYDQRTEFSFTDYSTKENVLAVIRNIRYMSGGTATGDAISFTVRNVFGPIRESPNKNFLVIVTDGQSYDDVQGPAAAAHDAGITIFSVGVAWAPLDDLKDMASKPKESHAFFTREFTGLEPIVSDVIRGICRDFLESQQ,mutated_sequence,1.0,550.0,NP_004077.1.a2m,NP_004077.1.npy,ClinVar +NP_004110.2,NP_004110.2.csv,MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS,mutated_sequence,1.0,993.0,NP_004110.2.a2m,NP_004110.2.npy,ClinVar +NP_004125.3,NP_004125.3.csv,MISASRAAAARLVGAAASRGPTAARHQDSWNGLSHEAFRLVSRRDYASEAIKGAVVGIDLGTTNSCVAVMEGKQAKVLENAEGARTTPSVVAFTADGERLVGMPAKRQAVTNPNNTFYATKRLIGRRYDDPEVQKDIKNVPFKIVRASNGDAWVEAHGKLYSPSQIGAFVLMKMKETAENYLGHTAKNAVITVPAYFNDSQRQATKDAGQISGLNVLRVINEPTAAALAYGLDKSEDKVIAVYDLGGGTFDISILEIQKGVFEVKSTNGDTFLGGEDFDQALLRHIVKEFKRETGVDLTKDNMALQRVREAAEKAKCELSSSVQTDINLPYLTMDSSGPKHLNMKLTRAQFEGIVTDLIRRTIAPCQKAMQDAEVSKSDIGEVILVGGMTRMPKVQQTVQDLFGRAPSKAVNPDEAVAIGAAIQGGVLAGDVTDVLLLDVTPLSLGIETLGGVFTKLINRNTTIPTKKSQVFSTAADGQTQVEIKVCQGEREMAGDNKLLGQFTLIGIPPAPRGVPQIEVTFDIDANGIVHVSAKDKGTGREQQIVIQSSGGLSKDDIENMVKNAEKYAEEDRRKKERVEAVNMAEGIIHDTETKMEEFKDQLPADECNKLKEEISKMRELLARKDSETGENIRQAASSLQQASLKLFEMAYKKMASEREGSGSSGTGEQKEDQKEEKQ,mutated_sequence,1.0,679.0,NP_004125.3.a2m,NP_004125.3.npy,ClinVar +NP_004161.4,NP_004161.4.csv,MGKPARKGCEWKRFLKNNWVLLSTVAAVVLGITTGVLVREHSNLSTLEKFYFAFPGEILMRMLKLIILPLIISSMITGVAALDSNVSGKIGLRAVVYYFCTTLIAVILGIVLVVSIKPGVTQKVGEIARTGSTPEVSTVDAMLDLIRNMFPENLVQACFQQYKTKREEVKPPSDPEMNMTEESFTAVMTTAISKNKTKEYKIVGMYSDGINVLGLIVFCLVFGLVIGKMGEKGQILVDFFNALSDATMKIVQIIMCYMPLGILFLIAGKIIEVEDWEIFRKLGLYMATVLTGLAIHSIVILPLIYFIVVRKNPFRFAMGMAQALLTALMISSSSATLPVTFRCAEENNQVDKRITRFVLPVGATINMDGTALYEAVAAVFIAQLNDLDLGIGQIITISITATSASIGAAGVPQAGLVTMVIVLSAVGLPAEDVTLIIAVDWLLDRFRTMVNVLGDAFGTGIVEKLSKKELEQMDVSSEVNIVNPFALESTILDNEDSDTKKSYVNGGFAVDKSDTISFTQTSQF,mutated_sequence,1.0,524.0,NP_004161.4.a2m,NP_004161.4.npy,ClinVar +NP_004165.2,NP_004165.2.csv,MWGLGARGPDRGLLLALALGGLARAGGVEVEPGGAHGESGGFQVVTFEWAHVQDPYVIALWILVASLAKIGFHLSHKVTSVVPESALLIVLGLVLGGIVWAADHIASFTLTPTVFFFYLLPPIVLDAGYFMPNRLFFGNLGTILLYAVVGTVWNAATTGLSLYGVFLSGLMGDLQIGLLDFLLFGSLMAAVDPVAVLAVFEEVHVNEVLFIIVFGESLLNDAVTVVLYNVFESFVALGGDNVTGVDCVKGIVSFFVVSLGGTLVGVVFAFLLSLVTRFTKHVRIIEPGFVFIISYLSYLTSEMLSLSAILAITFCGICCQKYVKANISEQSATTVRYTMKMLASSAETIIFMFLGISAVNPFIWTWNTAFVLLTLVFISVYRAIGVVLQTWLLNRYRMVQLEPIDQVVLSYGGLRGAVAFALVVLLDGDKVKEKNLFVSTTIIVVFFTVIFQGLTIKPLVQWLKVKRSEHREPRLNEKLHGRAFDHILSAIEDISGQIGHNYLRDKWSHFDRKFLSRVLMRRSAQKSRDRILNVFHELNLKDAISYVAEGERRGSLAFIRSPSTDNVVNVDFTPRSSTVEASVSYLLRENVSAVCLDMQSLEQRRRSIRDAEDMVTHHTLQQYLYKPRQEYKHLYSRHELTPTEDEKQDREIFHRTMRKRLESFKSTKLGLNQNKKAAKLYKRERAQKRRNSSIPNGKLPMESPAQNFTIKEKDLELSDTEEPPNYDEEMSGGIEFLASVTKDTASDSPAGIDNPVFSPDEALDRSLLARLPPWLSPGETVVPSQRARTQIPYSPGTFCRLMPFRLSSKSVDSFLQADGPEERPPAALPESTHM,mutated_sequence,1.0,834.0,NP_004165.2.a2m,NP_004165.2.npy,ClinVar +NP_004167.3,NP_004167.3.csv,MDEPPFSEAALEQALGEPCDLDAALLTDIEDMLQLINNQDSDFPGLFDPPYAGSGAGGTDPASPDTSSPGSLSPPPATLSSSLEAFLSGPQAAPSPLSPPQPAPTPLKMYPSMPAFSPGPGIKEESVPLSILQTPTPQPLPGALLPQSFPAPAPPQFSSTPVLGYPSPPGGFSTGSPPGNTQQPLPGLPLASPPGVPPVSLHTQVQSVVPQQLLTVTAAPTAAPVTTTVTSQIQQVPVLLQPHFIKADSLLLTAMKTDGATVKAAGLSPLVSGTTVQTGPLPTLVSGGTILATVPLVVDAEKLPINRLAAGSKAPASAQSRGEKRTAHNAIEKRYRSSINDKIIELKDLVVGTEAKLNKSAVLRKAIDYIRFLQHSNQKLKQENLSLRTAVHKSKSLKDLVSACGSGGNTDVLMEGVKTEVEDTLTPPPSDAGSPFQSSPLSLGSRGSGSGGSGSDSEPDSPVFEDSKAKPEQRPSLHSRGMLDRSRLALCTLVFLCLSCNPLASLLGARGLPSPSDTTSVYHSPGRNVLGTESRDGPGWAQWLLPPVVWLLNGLLVLVSLVLLFVYGEPVTRPHSGPAVYFWRHRKQADLDLARGDFAQAAQQLWLALRALGRPLPTSHLDLACSLLWNLIRHLLQRLWVGRWLAGRAGGLQQDCALRVDASASARDAALVYHKLHQLHTMGKHTGGHLTATNLALSALNLAECAGDAVSVATLAEIYVAAALRVKTSLPRALHFLTRFFLSSARQACLAQSGSVPPAMQWLCHPVGHRFFVDGDWSVLSTPWESLYSLAGNPVDPLAQVTQLFREHLLERALNCVTQPNPSPGSADGDKEFSDALGYLQLLNSCSDAAGAPAYSFSISSSMATTTGVDPVAKWWASLTAVVIHWLRRDEEAAERLCPLVEHLPRVLQESERPLPRAALHSFKAARALLGCAKAESGPASLTICEKASGYLQDSLATTPASSSIDKAVQLFLCDLLLVVRTSLWRQQQPPAPAPAAQGTSSRPQASALELRGFQRDLSSLRRLAQSFRPAMRRVFLHEATARLMAGASPTRTHQLLDRSLRRRAGPGGKGGAVAELEPRPTRREHAEALLLASCYLPPGFLSAPGQRVGMLAEAARTLEKLGDRRLLHDCQQMLMRLGGGTTVTSS,mutated_sequence,1.0,1147.0,NP_004167.3.a2m,NP_004167.3.npy,ClinVar +NP_004174.1,NP_004174.1.csv,MTITYTSQVANARLGSFSRLLLCWRGSIYKLLYGEFLIFLLCYYIIRFIYRLALTEEQQLMFEKLTLYCDSYIQLIPISFVLGFYVTLVVTRWWNQYENLPWPDRLMSLVSGFVEGKDEQGRLLRRTLIRYANLGNVLILRSVSTAVYKRFPSAQHLVQAGFMTPAEHKQLEKLSLPHNMFWVPWVWFANLSMKAWLGGRIRDPILLQSLLNEMNTLRTQCGHLYAYDWISIPLVYTQVVTVAVYSFFLTCLVGRQFLNPAKAYPGHELDLVVPVFTFLQFFFYVGWLKVAEQLINPFGEDDDDFETNWIVDRNLQVSLLAVDEMHQDLPRMEPDMYWNKPEPQPPYTAASAQFRRASFMGSTFNISLNKEEMEFQPNQEDEEDAHAGIIGRFLGLQSHDHHPPRANSRTKLLWPKRESLLHEGLPKNHKAAKQNVRGQEDNKAWKLKAVDAFKSAPLYQRPGYYSAPQTPLSPTPMFFPLEPSAPSKLHSVTGIDTKDKSLKTVSSGAKKSFELLSESDGALMEHPEVSQVRRKTVEFNLTDMPEIPENHLKEPLEQSPTNIHTTLKDHMDPYWALENRDEAHS,mutated_sequence,1.0,585.0,NP_004174.1.a2m,NP_004174.1.npy,ClinVar +NP_004195.2,NP_004195.2.csv,MVLKAFFPTCCVSTDSGLLVGRWVPEQSSAVVLAVLHFPFIPIQVKQLLAQVRQASQVGVAVLGTWCHCRQEPEESLGRFLESLGAVFPHEPWLRLCRERGGTFWSCEATHRQAPTAPGAPGEDQVMLIFYDQRQVLLSQLHLPTVLPDRQAGATTASTGGLAAVFDTVARSEVLFRSDRFDEGPVRLSHWQSEGVEASILAELARRASGPICLLLASLLSLVSAVSACRVFKLWPLSFLGSKLSTCEQLRHRLEHLTLIFSTRKAENPAQLMRKANTVASVLLDVALGLMLLSWLHGRSRIGHLADALVPVADHVAEELQHLLQWLMGAPAGLKMNRALDQVLGRFFLYHIHLWISYIHLMSPFVEHILWHVGLSACLGLTVALSLLSDIIALLTFHIYCFYVYGARLYCLKIHGLSSLWRLFRGKKWNVLRQRVDSCSYDLDQLFIGTLLFTILLFLLPTTALYYLVFTLLRLLVVAVQGLIHLLVDLINSLPLYSLGLRLCRPYRLAAGVKFRVLRHEAGRPLRLLMQINPLPYSRVVHTYRLPSCGCHPKHSWGALCRKLFLGELIYPWRQRGDKQD,mutated_sequence,1.0,581.0,NP_004195.2.a2m,NP_004195.2.npy,ClinVar +NP_004199.1,NP_004199.1.csv,MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED,mutated_sequence,1.0,613.0,NP_004199.1.a2m,NP_004199.1.npy,ClinVar +NP_004205.2,NP_004205.2.csv,MTSELDIFVGNTTLIDEDVYRLWLDGYSVTDAVALRVRSGILEQTGATAAVLQSDTMDHYRTFHMLERLLHAPPKLLHQLIFQIPPSRQALLIERYYAFDEAFVREVLGKKLSKGTKKDLDDISTKTGITLKSCRRQFDNFKRVFKVVEEMRGSLVDNIQQHFLLSDRLARDYAAIVFFANNRFETGKKKLQYLSFGDFAFCAELMIQNWTLGAVDSQMDDMDMDLDKEFLQDLKELKVLVADKDLLDLHKSLVCTALRGKLGVFSEMEANFKNLSRGLVNVAAKLTHNKDVRDLFVDLVEKFVEPCRSDHWPLSDVRFFLNQYSASVHSLDGFRHQALWDRYMGTLRGCLLRLYHD,mutated_sequence,1.0,357.0,NP_004205.2.a2m,NP_004205.2.npy,ClinVar +NP_004251.4,NP_004251.4.csv,MERLRDVRERLQAWERAFRRQRGRRPSQDDVEAAPEETRALYREYRTLKRTTGQAGGGLRSSESLPAAAEEAPEPRCWGPHLNRAATKSPQSTPGRSRQGSVPDYGQRLKANLKGTLQAGPALGRRPWPLGRASSKASTPKPPGTGPVPSFAEKVSDEPPQLPEPQPRPGRLQHLQASLSQRLGSLDPGWLQRCHSEVPDFLGAPKACRPDLGSEESQLLIPGESAVLGPGAGSQGPEASAFQEVSIRVGSPQPSSSGGEKRRWNEEPWESPAQVQQESSQAGPPSEGAGAVAVEEDPPGEPVQAQPPQPCSSPSNPRYHGLSPSSQARAGKAEGTAPLHIFPRLARHDRGNYVRLNMKQKHYVRGRALRSRLLRKQAWKQKWRKKGECFGGGGATVTTKESCFLNEQFDHWAAQCPRPASEEDTDAVGPEPLVPSPQPVPEVPSLDPTVLPLYSLGPSGQLAETPAEVFQALEQLGHQAFRPGQERAVMRILSGISTLLVLPTGAGKSLCYQLPALLYSRRSPCLTLVVSPLLSLMDDQVSGLPPCLKAACIHSGMTRKQRESVLQKIRAAQVHVLMLTPEALVGAGGLPPAAQLPPVAFACIDEAHCLSQWSHNFRPCYLRVCKVLRERMGVHCFLGLTATATRRTASDVAQHLAVAEEPDLHGPAPVPTNLHLSVSMDRDTDQALLTLLQGKRFQNLDSIIIYCNRREDTERIAALLRTCLHAAWVPGSGGRAPKTTAEAYHAGMCSRERRRVQRAFMQGQLRVVVATVAFGMGLDRPDVRAVLHLGLPPSFESYVQAVGRAGRDGQPAHCHLFLQPQGEDLRELRRHVHADSTDFLAVKRLVQRVFPACTCTCTRPPSEQEGAVGGERPVPKYPPQEAEQLSHQAAPGPRRVCMGHERALPIQLTVQALDMPEEAIETLLCYLELHPHHWLELLATTYTHCRLNCPGGPAQLQALAHRCPPLAVCLAQQLPEDPGQGSSSVEFDMVKLVDSMGWELASVRRALCQLQWDHEPRTGVRRGTGVLVEFSELAFHLRSPGDLTAEEKDQICDFLYGRVQARERQALARLRRTFQAFHSVAFPSCGPCLEQQDEERSTRLKDLLGRYFEEEEGQEPGGMEDAQGPEPGQARLQDWEDQVRCDIRQFLSLRPEEKFSSRAVARIFHGIGSPCYPAQVYGQDRRFWRKYLHLSFHALVGLATEELLQVAR,mutated_sequence,1.0,1208.0,NP_004251.4.a2m,NP_004251.4.npy,ClinVar +NP_004278.2,NP_004278.2.csv,MDPLFQQTHKQVHEIQSCMGRLETADKQSVHIVENEIQASIDQIFSRLERLEILSSKEPPNKRQNARLRVDQLKYDVQHLQTALRNFQHRRHAREQQERQREELLSRTFTTNDSDTTIPMDESLQFNSSLQKVHNGMDDLILDGHNILDGLRTQRLTLKGTQKKILDIANMLGLSNTVMRLIEKRAFQDKYFMIGGMLLTCVVMFLVVQYLT,mutated_sequence,1.0,212.0,NP_004278.2.a2m,NP_004278.2.npy,ClinVar +NP_004300.1,NP_004300.1.csv,MAEQEPTAEQLAQIAAENEEDEHSVNYKPPAQKSIQEIQELDKDDESLRKYKEALLGRVAVSADPNVPNVVVTGLTLVCSSAPGPLELDLTGDLESFKKQSFVLKEGVEYRIKISFRVNREIVSGMKYIQHTYRKGVKIDKTDYMVGSYGPRAEEYEFLTPVEEAPKGMLARGSYSIKSRFTDDDKTDHLSWEWNLTIKKDWKD,mutated_sequence,1.0,204.0,NP_004300.1.a2m,NP_004300.1.npy,ClinVar +NP_004320.2,NP_004320.2.csv,MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI,mutated_sequence,1.0,532.0,NP_004320.2.a2m,NP_004320.2.npy,ClinVar +NP_004324.2,NP_004324.2.csv,MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH,mutated_sequence,1.0,766.0,NP_004324.2.a2m,NP_004324.2.npy,ClinVar +NP_004351.1,NP_004351.1.csv,MGPWSRSLSALLLLLQVSSWLCQEPEPCHPGFDAESYTFTVPRRHLERGRVLGRVNFEDCTGRQRTAYFSLDTRFKVGTDGVITVKRPLRFHNPQIHFLVYAWDSTYRKFSTKVTLNTVGHHHRPPPHQASVSGIQAELLTFPNSSPGLRRQKRDWVIPPISCPENEKGPFPKNLVQIKSNKDKEGKVFYSITGQGADTPPVGVFIIERETGWLKVTEPLDRERIATYTLFSHAVSSNGNAVEDPMEILITVTDQNDNKPEFTQEVFKGSVMEGALPGTSVMEVTATDADDDVNTYNAAIAYTILSQDPELPDKNMFTINRNTGVISVVTTGLDRESFPTYTLVVQAADLQGEGLSTTATAVITVTDTNDNPPIFNPTTYKGQVPENEANVVITTLKVTDADAPNTPAWEAVYTILNDDGGQFVVTTNPVNNDGILKTAKGLDFEAKQQYILHVAVTNVVPFEVSLTTSTATVTVDVLDVNEAPIFVPPEKRVEVSEDFGVGQEITSYTAQEPDTFMEQKITYRIWRDTANWLEINPDTGAISTRAELDREDFEHVKNSTYTALIIATDNGSPVATGTGTLLLILSDVNDNAPIPEPRTIFFCERNPKPQVINIIDADLPPNTSPFTAELTHGASANWTIQYNDPTQESIILKPKMALEVGDYKINLKLMDNQNKDQVTTLEVSVCDCEGAAGVCRKAQPVEAGLQIPAILGILGGILALLILILLLLLFLRRRAVVKEPLLPPEDDTRDNVYYYDEEGGGEEDQDFDLSQLHRGLDARPEVTRNDVAPTLMSVPRYLPRPANPDEIGNFIDENLKAADTDPTAPPYDSLLVFDYEGSGSEAASLSSLNSSESDKDQDYDYLNEWGNRFKKLADMYGGGEDD,mutated_sequence,1.0,882.0,NP_004351.1.a2m,NP_004351.1.npy,ClinVar +NP_004355.2,NP_004355.2.csv,MESADFYEAEPRPPMSSHLQSPPHAPSSAAFGFPRGAGPAQPPAPPAAPEPLGGICEHETSIDISAYIDPAAFNDEFLADLFQHSRQQEKAKAAVGPTGGGGGGDFDYPGAPAGPGGAVMPGGAHGPPPGYGCAAAGYLDGRLEPLYERVGAPALRPLVIKQEPREEDEAKQLALAGLFPYQPPPPPPPSHPHPHPPPAHLAAPHLQFQIAHCGQTTMHLQPGHPTPPPTPVPSPHPAPALGAAGLPGPGSALKGLGAAHPDLRASGGSGAGKAKKSVDKNSNEYRVRRERNNIAVRKSRDKAKQRNVETQQKVLELTSDNDRLRKRVEQLSRELDTLRGIFRQLPESSLVKAMGNCA,mutated_sequence,1.0,358.0,NP_004355.2.a2m,NP_004355.2.npy,ClinVar +NP_004357.3,NP_004357.3.csv,MAAAAAEEGMEPRALQYEQTLMYGRYTQDLGAFAKEEAARIRLGGPEPWKGPPSSRAAPELLEYGRSRCARCRVCSVRCHKFLVSRVGEDWIFLVLLGLLMALVSWVMDYAIAACLQAQQWMSRGLNTSILLQYLAWVTYPVVLITFSAGFTQILAPQAVGSGIPEMKTILRGVVLKEYLTLKTFIAKVIGLTCALGSGMPLGKEGPFVHIASMCAALLSKFLSLFGGIYENESRNTEMLAAACAVGVGCCFAAPIGGVLFSIEVTSTFFAVRNYWRGFFAATFSAFIFRVLAVWNRDEETITALFKTRFRLDFPFDLQELPAFAVIGIASGFGGALFVYLNRKIVQVMRKQKTINRFLMRKRLLFPALVTLLISTLTFPPGFGQFMAGQLSQKETLVTLFDNRTWVRQGLVEELEPPSTSQAWNPPRANVFLTLVIFILMKFWMSALATTIPVPCGAFMPVFVIGAAFGRLVGESMAAWFPDGIHTDSSTYRIVPGGYAVVGAAALAGAVTHTVSTAVIVFELTGQIAHILPVMIAVILANAVAQSLQPSLYDSIIRIKKLPYLPELGWGRHQQYRVRVEDIMVRDVPHVALSCTFRDLRLALHRTKGRMLALVESPESMILLGSIERSQVVALLGAQLSPARRRQHMQERRATQTSPLSDQEGPPTPEASVCFQVNTEDSAFPAARGETHKPLKPALKRGPSVTRNLGESPTGSAESAGIALRSLFCGSPPPEAASEKLESCEKRKLKRVRISLASDADLEGEMSPEEILEWEEQQLDEPVNFSDCKIDPAPFQLVERTSLHKTHTIFSLLGVDHAYVTSIGRLIGIVTLKELRKAIEGSVTAQGVKVRPPLASFRDSATSSSDTETTEVHALWGPHSRHGLPREGSPSDSDDKCQ,mutated_sequence,1.0,898.0,NP_004357.3.a2m,NP_004357.3.npy,ClinVar +NP_004360.2,NP_004360.2.csv,MRKHRHLPLVAVFCLFLSGFPTTHAQQQQADVKNGAAADIIFLVDSSWTIGEEHFQLVREFLYDVVKSLAVGENDFHFALVQFNGNPHTEFLLNTYRTKQEVLSHISNMSYIGGTNQTGKGLEYIMQSHLTKAAGSRAGDGVPQVIVVLTDGHSKDGLALPSAELKSADVNVFAIGVEDADEGALKEIASEPLNMHMFNLENFTSLHDIVGNLVSCVHSSVSPERAGDTETLKDITAQDSADIIFLIDGSNNTGSVNFAVILDFLVNLLEKLPIGTQQIRVGVVQFSDEPRTMFSLDTYSTKAQVLGAVKALGFAGGELANIGLALDFVVENHFTRAGGSRVEEGVPQVLVLISAGPSSDEIRYGVVALKQASVFSFGLGAQAASRAELQHIATDDNLVFTVPEFRSFGDLQEKLLPYIVGVAQRHIVLKPPTIVTQVIEVNKRDIVFLVDGSSALGLANFNAIRDFIAKVIQRLEIGQDLIQVAVAQYADTVRPEFYFNTHPTKREVITAVRKMKPLDGSALYTGSALDFVRNNLFTSSAGYRAAEGIPKLLVLITGGKSLDEISQPAQELKRSSIMAFAIGNKGADQAELEEIAFDSSLVFIPAEFRAAPLQGMLPGLLAPLRTLSGTPEVHSNKRDIIFLLDGSANVGKTNFPYVRDFVMNLVNSLDIGNDNIRVGLVQFSDTPVTEFSLNTYQTKSDILGHLRQLQLQGGSGLNTGSALSYVYANHFTEAGGSRIREHVPQLLLLLTAGQSEDSYLQAANALTRAGILTFCVGASQANKAELEQIAFNPSLVYLMDDFSSLPALPQQLIQPLTTYVSGGVEEVPLAQPESKRDILFLFDGSANLVGQFPVVRDFLYKIIDELNVKPEGTRIAVAQYSDDVKVESRFDEHQSKPEILNLVKRMKIKTGKALNLGYALDYAQRYIFVKSAGSRIEDGVLQFLVLLVAGRSSDRVDGPASNLKQSGVVPFIFQAKNADPAELEQIVLSPAFILAAESLPKIGDLHPQIVNLLKSVHNGAPAPVSGEKDVVFLLDGSEGVRSGFPLLKEFVQRVVESLDVGQDRVRVAVVQYSDRTRPEFYLNSYMNKQDVVNAVRQLTLLGGPTPNTGAALEFVLRNILVSSAGSRITEGVPQLLIVLTADRSGDDVRNPSVVVKRGGAVPIGIGIGNADITEMQTISFIPDFAVAIPTFRQLGTVQQVISERVTQLTREELSRLQPVLQPLPSPGVGGKRDVVFLIDGSQSAGPEFQYVRTLIERLVDYLDVGFDTTRVAVIQFSDDPKVEFLLNAHSSKDEVQNAVQRLRPKGGRQINVGNALEYVSRNIFKRPLGSRIEEGVPQFLVLISSGKSDDEVDDPAVELKQFGVAPFTIARNADQEELVKISLSPEYVFSVSTFRELPSLEQKLLTPITTLTSEQIQKLLASTRYPPPAVESDAADIVFLIDSSEGVRPDGFAHIRDFVSRIVRRLNIGPSKVRVGVVQFSNDVFPEFYLKTYRSQAPVLDAIRRLRLRGGSPLNTGKALEFVARNLFVKSAGSRIEDGVPQHLVLVLGGKSQDDVSRFAQVIRSSGIVSLGVGDRNIDRTELQTITNDPRLVFTVREFRELPNIEERIMNSFGPSAATPAPPGVDTPPPSRPEKKKADIVFLLDGSINFRRDSFQEVLRFVSEIVDTVYEDGDSIQVGLVQYNSDPTDEFFLKDFSTKRQIIDAINKVVYKGGRHANTKVGLEHLRVNHFVPEAGSRLDQRVPQIAFVITGGKSVEDAQDVSLALTQRGVKVFAVGVRNIDSEEVGKIASNSATAFRVGNVQELSELSEQVLETLHDAMHETLCPGVTDAAKACNLDVILGFDGSRDQNVFVAQKGFESKVDAILNRISQMHRVSCSGGRSPTVRVSVVANTPSGPVEAFDFDEYQPEMLEKFRNMRSQHPYVLTEDTLKVYLNKFRQSSPDSVKVVIHFTDGADGDLADLHRASENLRQEGVRALILVGLERVVNLERLMHLEFGRGFMYDRPLRLNLLDLDYELAEQLDNIAEKACCGVPCKCSGQRGDRGPIGSIGPKGIPGEDGYRGYPGDEGGPGERGPPGVNGTQGFQGCPGQRGVKGSRGFPGEKGEVGEIGLDGLDGEDGDKGLPGSSGEKGNPGRRGDKGPRGEKGERGDVGIRGDPGNPGQDSQERGPKGETGDLGPMGVPGRDGVPGGPGETGKNGGFGRRGPPGAKGNKGGPGQPGFEGEQGTRGAQGPAGPAGPPGLIGEQGISGPRGSGGAAGAPGERGRTGPLGRKGEPGEPGPKGGIGNRGPRGETGDDGRDGVGSEGRRGKKGERGFPGYPGPKGNPGEPGLNGTTGPKGIRGRRGNSGPPGIVGQKGDPGYPGPAGPKGNRGDSIDQCALIQSIKDKCPCCYGPLECPVFPTELAFALDTSEGVNQDTFGRMRDVVLSIVNDLTIAESNCPRGARVAVVTYNNEVTTEIRFADSKRKSVLLDKIKNLQVALTSKQQSLETAMSFVARNTFKRVRNGFLMRKVAVFFSNTPTRASPQLREAVLKLSDAGITPLFLTRQEDRQLINALQINNTAVGHALVLPAGRDLTDFLENVLTCHVCLDICNIDPSCGFGSWRPSFRDRRAAGSDVDIDMAFILDSAETTTLFQFNEMKKYIAYLVRQLDMSPDPKASQHFARVAVVQHAPSESVDNASMPPVKVEFSLTDYGSKEKLVDFLSRGMTQLQGTRALGSAIEYTIENVFESAPNPRDLKIVVLMLTGEVPEQQLEEAQRVILQAKCKGYFFVVLGIGRKVNIKEVYTFASEPNDVFFKLVDKSTELNEEPLMRFGRLLPSFVSSENAFYLSPDIRKQCDWFQGDQPTKNLVKFGHKQVNVPNNVTSSPTSNPVTTTKPVTTTKPVTTTTKPVTTTTKPVTIINQPSVKPAAAKPAPAKPVAAKPVATKMATVRPPVAVKPATAAKPVAAKPAAVRPPAAAAAKPVATKPEVPRPQAAKPAATKPATTKPMVKMSREVQVFEITENSAKLHWERAEPPGPYFYDLTVTSAHDQSLVLKQNLTVTDRVIGGLLAGQTYHVAVVCYLRSQVRATYHGSFSTKKSQPPPPQPARSASSSTINLMVSTEPLALTETDICKLPKDEGTCRDFILKWYYDPNTKSCARFWYGGCGGNENKFGSQKECEKVCAPVLAKPGVISVMGT,mutated_sequence,1.0,3177.0,NP_004360.2.a2m,NP_004360.2.npy,ClinVar +NP_004371.2,NP_004371.2.csv,MAENLLDGPPNPKRAKLSSPGFSANDSTDFGSLFDLENDLPDELIPNGGELGLLNSGNLVPDAASKHKQLSELLRGGSGSSINPGIGNVSASSPVQQGLGGQAQGQPNSANMASLSAMGKSPLSQGDSSAPSLPKQAASTSGPTPAASQALNPQAQKQVGLATSSPATSQTGPGICMNANFNQTHPGLLNSNSGHSLINQASQGQAQVMNGSLGAAGRGRGAGMPYPTPAMQGASSSVLAETLTQVSPQMTGHAGLNTAQAGGMAKMGITGNTSPFGQPFSQAGGQPMGATGVNPQLASKQSMVNSLPTFPTDIKNTSVTNVPNMSQMQTSVGIVPTQAIATGPTADPEKRKLIQQQLVLLLHAHKCQRREQANGEVRACSLPHCRTMKNVLNHMTHCQAGKACQVAHCASSRQIISHWKNCTRHDCPVCLPLKNASDKRNQQTILGSPASGIQNTIGSVGTGQQNATSLSNPNPIDPSSMQRAYAALGLPYMNQPQTQLQPQVPGQQPAQPQTHQQMRTLNPLGNNPMNIPAGGITTDQQPPNLISESALPTSLGATNPLMNDGSNSGNIGTLSTIPTAAPPSSTGVRKGWHEHVTQDLRSHLVHKLVQAIFPTPDPAALKDRRMENLVAYAKKVEGDMYESANSRDEYYHLLAEKIYKIQKELEEKRRSRLHKQGILGNQPALPAPGAQPPVIPQAQPVRPPNGPLSLPVNRMQVSQGMNSFNPMSLGNVQLPQAPMGPRAASPMNHSVQMNSMGSVPGMAISPSRMPQPPNMMGAHTNNMMAQAPAQSQFLPQNQFPSSSGAMSVGMGQPPAQTGVSQGQVPGAALPNPLNMLGPQASQLPCPPVTQSPLHPTPPPASTAAGMPSLQHTTPPGMTPPQPAAPTQPSTPVSSSGQTPTPTPGSVPSATQTQSTPTVQAAAQAQVTPQPQTPVQPPSVATPQSSQQQPTPVHAQPPGTPLSQAAASIDNRVPTPSSVASAETNSQQPGPDVPVLEMKTETQAEDTEPDPGESKGEPRSEMMEEDLQGASQVKEETDIAEQKSEPMEVDEKKPEVKVEVKEEEESSSNGTASQSTSPSQPRKKIFKPEELRQALMPTLEALYRQDPESLPFRQPVDPQLLGIPDYFDIVKNPMDLSTIKRKLDTGQYQEPWQYVDDVWLMFNNAWLYNRKTSRVYKFCSKLAEVFEQEIDPVMQSLGYCCGRKYEFSPQTLCCYGKQLCTIPRDAAYYSYQNRYHFCEKCFTEIQGENVTLGDDPSQPQTTISKDQFEKKKNDTLDPEPFVDCKECGRKMHQICVLHYDIIWPSGFVCDNCLKKTGRPRKENKFSAKRLQTTRLGNHLEDRVNKFLRRQNHPEAGEVFVRVVASSDKTVEVKPGMKSRFVDSGEMSESFPYRTKALFAFEEIDGVDVCFFGMHVQEYGSDCPPPNTRRVYISYLDSIHFFRPRCLRTAVYHEILIGYLEYVKKLGYVTGHIWACPPSEGDDYIFHCHPPDQKIPKPKRLQEWYKKMLDKAFAERIIHDYKDIFKQATEDRLTSAKELPYFEGDFWPNVLEESIKELEQEEEERKKEESTAASETTEGSQGDSKNAKKKNNKKTNKNKSSISRANKKKPSMPNVSNDLSQKLYATMEKHKEVFFVIHLHAGPVINTLPPIVDPDPLLSCDLMDGRDAFLTLARDKHWEFSSLRRSKWSTLCMLVELHTQGQDRFVYTCNECKHHVETRWHCTVCEDYDLCINCYNTKSHAHKMVKWGLGLDDEGSSQGEPQSKSPQESRRLSIQRCIQSLVHACQCRNANCSLPSCQKMKRVVQHTKGCKRKTNGGCPVCKQLIALCCYHAKHCQENKCPVPFCLNIKHKLRQQQIQHRLQQAQLMRRRMATMNTRNVPQQSLPSPTSAPPGTPTQQPSTPQTPQPPAQPQPSPVSMSPAGFPSVARTQPPTTVSTGKPTSQVPAPPPPAQPPPAAVEAARQIEREAQQQQHLYRVNINNSMPPGRTGMGTPGSQMAPVSLNVPRPNQVSGPVMPSMPPGQWQQAPLPQQQPMPGLPRPVISMQAQAAVAGPRMPSVQPPRSISPSALQDLLRTLKSPSSPQQQQQVLNILKSNPQLMAAFIKQRTAKYVANQPGMQPQPGLQSQPGMQPQPGMHQQPSLQNLNAMQAGVPRPGVPPQQQAMGGLNPQGQALNIMNPGHNPNMASMNPQYREMLRRQLLQQQQQQQQQQQQQQQQQQGSAGMAGGMAGHGQFQQPQGPGGYPPAMQQQQRMQQHLPLQGSSMGQMAAQMGQLGQMGQPGLGADSTPNIQQALQQRILQQQQMKQQIGSPGQPNPMSPQQHMLSGQPQASHLPGQQIATSLSNQVRSPAPVQSPRPQSQPPHSSPSPRIQPQPSPHHVSPQTGSPHPGLAVTMASSIDQGHLGNPEQSAMLPQLNTPSRSALSSELSLVGDTTGDTLEKFVEGL,mutated_sequence,1.0,2442.0,NP_004371.2.a2m,NP_004371.2.npy,ClinVar +NP_004399.2,NP_004399.2.csv,MGNRGMEDLIPLVNRLQDAFSAIGQNADLDLPQIAVVGGQSAGKSSVLENFVGRDFLPRGSGIVTRRPLVLQLVNATTEYAEFLHCKGKKFTDFEEVRLEIEAETDRVTGTNKGISPVPINLRVYSPHVLNLTLVDLPGMTKVPVGDQPPDIEFQIRDMLMQFVTKENCLILAVSPANSDLANSDALKVAKEVDPQGQRTIGVITKLDLMDEGTDARDVLENKLLPLRRGYIGVVNRSQKDIDGKKDITAALAAERKFFLSHPSYRHLADRMGTPYLQKVLNQQLTNHIRDTLPGLRNKLQSQLLSIEKEVEEYKNFRPDDPARKTKALLQMVQQFAVDFEKRIEGSGDQIDTYELSGGARINRIFHERFPFELVKMEFDEKELRREISYAIKNIHGIRTGLFTPDMAFETIVKKQVKKIREPCLKCVDMVISELISTVRQCTKKLQQYPRLREEMERIVTTHIREREGRTKEQVMLLIDIELAYMNTNHEDFIGFANAQQRSNQMNKKKTSGNQDEILVIRKGWLTINNIGIMKGGSKEYWFVLTAENLSWYKDDEEKEKKYMLSVDNLKLRDVEKGFMSSKHIFALFNTEQRNVYKDYRQLELACETQEEVDSWKASFLRAGVYPERVGDKEKASETEENGSDSFMHSMDPQLERQVETIRNLVDSYMAIVNKTVRDLMPKTIMHLMINNTKEFIFSELLANLYSCGDQNTLMEESAEQAQRRDEMLRMYHALKEALSIIGDINTTTVSTPMPPPVDDSWLQVQSVPAGRRSPTSSPTPQRRAPAVPPARPGSRGPAPGPPPAGSALGGAPPVPSRPGASPDPFGPPPQVPSRPNRAPPGVPSRSGQASPSRPESPRPPFDL,mutated_sequence,1.0,864.0,NP_004399.2.a2m,NP_004399.2.npy,ClinVar +NP_004406.2,NP_004406.2.csv,MSCNGGSHPRINTLGRMIRAESGPDLRYEVTSGGGGTSRMYYSRRGVITDQNSDGYCQTGTMSRHQNQNTIQELLQNCSDCLMRAELIVQPELKYGDGIQLTRSRELDECFAQANDQMEILDSLIREMRQMGQPCDAYQKRLLQLQEQMRALYKAISVPRVRRASSKGGGGYTCQSGSGWDEFTKHVTSECLGWMRQQRAEMDMVAWGVDLASVEQHINSHRGIHNSIGDYRWQLDKIKADLREKSAIYQLEEEYENLLKASFERMDHLRQLQNIIQATSREIMWINDCEEEELLYDWSDKNTNIAQKQEAFSIRMSQLEVKEKELNKLKQESDQLVLNQHPASDKIEAYMDTLQTQWSWILQITKCIDVHLKENAAYFQFFEEAQSTEAYLKGLQDSIRKKYPCDKNMPLQHLLEQIKELEKEREKILEYKRQVQNLVNKSKKIVQLKPRNPDYRSNKPIILRALCDYKQDQKIVHKGDECILKDNNERSKWYVTGPGGVDMLVPSVGLIIPPPNPLAVDLSCKIEQYYEAILALWNQLYINMKSLVSWHYCMIDIEKIRAMTIAKLKTMRQEDYMKTIADLELHYQEFIRNSQGSEMFGDDDKRKIQSQFTDAQKHYQTLVIQLPGYPQHQTVTTTEITHHGTCQDVNHNKVIETNRENDKQETWMLMELQKIRRQIEHCEGRMTLKNLPLADQGSSHHITVKINELKSVQNDSQAIAEVLNQLKDMLANFRGSEKYCYLQNEVFGLFQKLENINGVTDGYLNSLCTVRALLQAILQTEDMLKVYEARLTEEETVCLDLDKVEAYRCGLKKIKNDLNLKKSLLATMKTELQKAQQIHSQTSQQYPLYDLDLGKFGEKVTQLTDRWQRIDKQIDFRLWDLEKQIKQLRNYRDNYQAFCKWLYDAKRRQDSLESMKFGDSNTVMRFLNEQKNLHSEISGKRDKSEEVQKIAELCANSIKDYELQLASYTSGLETLLNIPIKRTMIQSPSGVILQEAADVHARYIELLTRSGDYYRFLSEMLKSLEDLKLKNTKIEVLEEELRLARDANSENCNKNKFLDQNLQKYQAECSQFKAKLASLEELKRQAELDGKSAKQNLDKCYGQIKELNEKITRLTYEIEDEKRRRKSVEDRFDQQKNDYDQLQKARQCEKENLGWQKLESEKAIKEKEYEIERLRVLLQEEGTRKREYENELAKVRNHYNEEMSNLRNKYETEINITKTTIKEISMQKEDDSKNLRNQLDRLSRENRDLKDEIVRLNDSILQATEQRRRAEENALQQKACGSEIMQKKQHLEIELKQVMQQRSEDNARHKQSLEEAAKTIQDKNKEIERLKAEFQEEAKRRWEYENELSKVRNNYDEEIISLKNQFETEINITKTTIHQLTMQKEEDTSGYRAQIDNLTRENRSLSEEIKRLKNTLTQTTENLRRVEEDIQQQKATGSEVSQRKQQLEVELRQVTQMRTEESVRYKQSLDDAAKTIQDKNKEIERLKQLIDKETNDRKCLEDENARLQRVQYDLQKANSSATETINKLKVQEQELTRLRIDYERVSQERTVKDQDITRFQNSLKELQLQKQKVEEELNRLKRTASEDSCKRKKLEEELEGMRRSLKEQAIKITNLTQQLEQASIVKKRSEDDLRQQRDVLDGHLREKQRTQEELRRLSSEVEALRRQLLQEQESVKQAHLRNEHFQKAIEDKSRSLNESKIEIERLQSLTENLTKEHLMLEEELRNLRLEYDDLRRGRSEADSDKNATILELRSQLQISNNRTLELQGLINDLQRERENLRQEIEKFQKQALEASNRIQESKNQCTQVVQERESLLVKIKVLEQDKARLQRLEDELNRAKSTLEAETRVKQRLECEKQQIQNDLNQWKTQYSRKEEAIRKIESEREKSEREKNSLRSEIERLQAEIKRIEERCRRKLEDSTRETQSQLETERSRYQREIDKLRQRPYGSHRETQTECEWTVDTSKLVFDGLRKKVTAMQLYECQLIDKTTLDKLLKGKKSVEEVASEIQPFLRGAGSIAGASASPKEKYSLVEAKRKKLISPESTVMLLEAQAATGGIIDPHRNEKLTVDSAIARDLIDFDDRQQIYAAEKAITGFDDPFSGKTVSVSEAIKKNLIDRETGMRLLEAQIASGGVVDPVNSVFLPKDVALARGLIDRDLYRSLNDPRDSQKNFVDPVTKKKVSYVQLKERCRIEPHTGLLLLSVQKRSMSFQGIRQPVTVTELVDSGILRPSTVNELESGQISYDEVGERIKDFLQGSSCIAGIYNETTKQKLGIYEAMKIGLVRPGTALELLEAQAATGFIVDPVSNLRLPVEEAYKRGLVGIEFKEKLLSAERAVTGYNDPETGNIISLFQAMNKELIEKGHGIRLLEAQIATGGIIDPKESHRLPVDIAYKRGYFNEELSEILSDPSDDTKGFFDPNTEENLTYLQLKERCIKDEETGLCLLPLKEKKKQVQTSQKNTLRKRRVVIVDPETNKEMSVQEAYKKGLIDYETFKELCEQECEWEEITITGSDGSTRVVLVDRKTGSQYDIQDAIDKGLVDRKFFDQYRSGSLSLTQFADMISLKNGVGTSSSMGSGVSDDVFSSSRHESVSKISTISSVRNLTIRSSSFSDTLEESSPIAAIFDTENLEKISITEGIERGIVDSITGQRLLEAQACTGGIIHPTTGQKLSLQDAVSQGVIDQDMATRLKPAQKAFIGFEGVKGKKKMSAAEAVKEKWLPYEAGQRFLEFQYLTGGLVDPEVHGRISTEEAIRKGFIDGRAAQRLQDTSSYAKILTCPKTKLKISYKDAINRSMVEDITGLRLLEAASVSSKGLPSPYNMSSAPGSRSGSRSGSRSGSRSGSRSGSRRGSFDATGNSSYSYSYSFSSSSIGH,mutated_sequence,1.0,2871.0,NP_004406.2.a2m,NP_004406.2.npy,ClinVar +NP_004420.1,NP_004420.1.csv,MARPGQRWLGKWLVAMVVWALCRLATPLAKNLEPVSWSSLNPKFLSGKGLVIYPKIGDKLDIICPRAEAGRPYEYYKLYLVRPEQAAACSTVLDPNVLVTCNRPEQEIRFTIKFQEFSPNYMGLEFKKHHDYYITSTSNGSLEGLENREGGVCRTRTMKIIMKVGQDPNAVTPEQLTTSRPSKEADNTVKMATQAPGSRGSLGDSDGKHETVNQEEKSGPGASGGSSGDPDGFFNSKVALFAAVGAGCVIFLLIIIFLTVLLLKLRKRHRKHTQQRAAALSLSTLASPKGGSGTAGTEPSDIIIPLRTTENNYCPHYEKVSGDYGHPVYIVQEMPPQSPANIYYKV,mutated_sequence,1.0,346.0,NP_004420.1.a2m,NP_004420.1.npy,ClinVar +NP_004439.2,NP_004439.2.csv,MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV,mutated_sequence,1.0,1255.0,NP_004439.2.a2m,NP_004439.2.npy,ClinVar +NP_004444.2,NP_004444.2.csv,MLVPLAKLSCLAYQCFHALKIKKNYLPLCATRWSSTSTVPRITTHYTIYPRDKDKRWEGVNMERFAEEADVVIVGAGPAGLSAAVRLKQLAVAHEKDIRVCLVEKAAQIGAHTLSGACLDPGAFKELFPDWKEKGAPLNTPVTEDRFGILTEKYRIPVPILPGLPMNNHGNYIVRLGHLVSWMGEQAEALGVEVYPGYAAAEVLFHDDGSVKGIATNDVGIQKDGAPKATFERGLELHAKVTIFAEGCHGHLAKQLYKKFDLRANCEPQTYGIGLKELWVIDEKNWKPGRVDHTVGWPLDRHTYGGSFLYHLNEGEPLVALGLVVGLDYQNPYLSPFREFQRWKHHPSIRPTLEGGKRIAYGARALNEGGFQSIPKLTFPGGLLIGCSPGFMNVPKIKGTHTAMKSGILAAESIFNQLTSENLQSKTIGLHVTEYEDNLKNSWVWKELYSVRNIRPSCHGVLGVYGGMIYTGIFYWILRGMEPWTLKHKGSDFERLKPAKDCTPIEYPKPDGQISFDLLSSVALSGTNHEHDQPAHLTLRDDSIPVNRNLSIYDGPEQRFCPAGVYEFVPVEQGDGFRLQINAQNCVHCKTCDIKDPSQNINWVVPEGGGGPAYNGM,mutated_sequence,1.0,617.0,NP_004444.2.a2m,NP_004444.2.npy,ClinVar +NP_004447.2,NP_004447.2.csv,MGQTGKKSEKGPVCWRKRVKSEYMRLRQLKRFRRADEVKSMFSSNRQKILERTEILNQEWKQRRIQPVHILTSVSSLRGTRECSVTSDLDFPTQVIPLKTLNAVASVPIMYSWSPLQQNFMVEDETVLHNIPYMGDEVLDQDGTFIEELIKNYDGKVHGDRECGFINDEIFVELVNALGQYNDDDDDDDGDDPEEREEKQKDLEDHRDDKESRPPRKFPSDKIFEAISSMFPDKGTAEELKEKYKELTEQQLPGALPPECTPNIDGPNAKSVQREQSLHSFHTLFCRRCFKYDCFLHRKCNYSFHATPNTYKRKNTETALDNKPCGPQCYQHLEGAKEFAAALTAERIKTPPKRPGGRRRGRLPNNSSRPSTPTINVLESKDTDSDREAGTETGGENNDKEEEEKKDETSSSSEANSRCQTPIKMKPNIEPPENVEWSGAEASMFRVLIGTYYDNFCAIARLIGTKTCRQVYEFRVKESSIIAPAPAEDVDTPPRKKKRKHRLWAAHCRKIQLKKDGSSNHVYNYQPCDHPRQPCDSSCPCVIAQNFCEKFCQCSSECQNRFPGCRCKAQCNTKQCPCYLAVRECDPDLCLTCGAADHWDSKNVSCKNCSIQRGSKKHLLLAPSDVAGWGIFIKDPVQKNEFISEYCGEIISQDEADRRGKVYDKYMCSFLFNLNNDFVVDATRKGNKIRFANHSVNPNCYAKVMMVNGDHRIGIFAKRAIQTGEELFFDYRYSQADALKYVGIEREMEIP,mutated_sequence,1.0,751.0,NP_004447.2.a2m,NP_004447.2.npy,ClinVar +NP_004454.2,NP_004454.2.csv,MHGHRAPGGAGPSEPEHPATNPPGAAPPACADSDPGASEPGLLARRGSGSALGGPLDPQFVGPSDTSLGAAPGHRVLPCGPSPQHHRALRFSYHLEGSQPRPGLHQGNRILVKSLSLDPGQSLEPHPEGPQRLRSDPGPPTETPSQRPSPLKRAPGPKPQVPPKPSYLQMPRMPPPLEPIPPPPSRPLPADPRVAKGLAPRAEASPSSAAVSSLIEKFEREPVIVASDRPVPGPSPGPPEPVMLPQPTSQPPVPQLPEGEASRCLFLLAPGPRDGEKVPNRDSGIDSISSPSNSEETCFVSDDGPPSHSLCPGPPALASVPVALADPHRPGSQEVDSDLEEEDDEEEEEEKDREIPVPLMERQESVELTVQQKVFHIANELLQTEKAYVSRLHLLDQVFCARLLEEARNRSSFPADVVHGIFSNICSIYCFHQQFLLPELEKRMEEWDRYPRIGDILQKLAPFLKMYGEYVKNFDRAVELVNTWTERSTQFKVIIHEVQKEEACGNLTLQHHMLEPVQRIPRYELLLKDYLLKLPHGSPDSKDAQKSLELIATAAEHSNAAIRKMERMHKLLKVYELLGGEEDIVSPTKELIKEGHILKLSAKNGTTQDRYLILFNDRLLYCVPRLRLLGQKFSVRARIDVDGMELKESSNLNLPRTFLVSGKQRSLELQARTEEEKKDWVQAINSTLLKHEQTLETFKLLNSTNREDEDTPPNSPNVDLGKRAPTPIREKEVTMCMRCQEPFNSITKRRHHCKACGHVVCGKCSEFRARLVYDNNRSNRVCTDCYVALHGVPGSSPACSQHTPQRRRSILEKQASVAAENSVICSFLHYMEKGGKGWHKAWFVVPENEPLVLYIYGAPQDVKAQRSLPLIGFEVGPPEAGERPDRRHVFKITQSHLSWYFSPETEELQRRWMAVLGRAGRGDTFCPGPTLSEDREMEEAPVAALGATAEPPESPQTRDKT,mutated_sequence,1.0,961.0,NP_004454.2.a2m,NP_004454.2.npy,ClinVar +NP_004513.1,NP_004513.1.csv,MADPAECSIKVMCRFRPLNEAEILRGDKFIPKFKGDETVVIGQGKPYVFDRVLPPNTTQEQVYNACAKQIVKDVLEGYNGTIFAYGQTSSGKTHTMEGKLHDPQLMGIIPRIAHDIFDHIYSMDENLEFHIKVSYFEIYLDKIRDLLDVSKTNLAVHEDKNRVPYVKGCTERFVSSPEEVMDVIDEGKANRHVAVTNMNEHSSRSHSIFLINIKQENVETEKKLSGKLYLVDLAGSEKVSKTGAEGAVLDEAKNINKSLSALGNVISALAEGTKTHVPYRDSKMTRILQDSLGGNCRTTIVICCSPSVFNEAETKSTLMFGQRAKTIKNTVSVNLELTAEEWKKKYEKEKEKNKTLKNVIQHLEMELNRWRNGEAVPEDEQISAKDQKNLEPCDNTPIIDNIAPVVAGISTEEKEKYDEEISSLYRQLDDKDDEINQQSQLAEKLKQQMLDQDELLASTRRDYEKIQEELTRLQIENEAAKDEVKEVLQALEELAVNYDQKSQEVEDKTRANEQLTDELAQKTTTLTTTQRELSQLQELSNHQKKRATEILNLLLKDLGEIGGIIGTNDVKTLADVNGVIEEEFTMARLYISKMKSEVKSLVNRSKQLESAQMDSNRKMNASERELAACQLLISQHEAKIKSLTDYMQNMEQKRRQLEESQDSLSEELAKLRAQEKMHEVSFQDKEKEHLTRLQDAEEMKKALEQQMESHREAHQKQLSRLRDEIEEKQKIIDEIRDLNQKLQLEQEKLSSDYNKLKIEDQEREMKLEKLLLLNDKREQAREDLKGLEETVSRELQTLHNLRKLFVQDLTTRVKKSVELDNDDGGGSAAQKQKISFLENNLEQLTKVHKQLVRDNADLRCELPKLEKRLRATAERVKALESALKEAKENAMRDRKRYQQEVDRIKEAVRAKNMARRAHSAQIAKPIRPGHYPASSPTAVHAIRGGGGSSSNSTHYQK,mutated_sequence,1.0,957.0,NP_004513.1.a2m,NP_004513.1.npy,ClinVar +NP_004514.2,NP_004514.2.csv,MASQPNSSAKKKEEKGKNIQVVVRCRPFNLAERKASAHSIVECDPVRKEVSVRTGGLADKSSRKTYTFDMVFGASTKQIDVYRSVVCPILDEVIMGYNCTIFAYGQTGTGKTFTMEGERSPNEEYTWEEDPLAGIIPRTLHQIFEKLTDNGTEFSVKVSLLEIYNEELFDLLNPSSDVSERLQMFDDPRNKRGVIIKGLEEITVHNKDEVYQILEKGAAKRTTAATLMNAYSSRSHSVFSVTIHMKETTIDGEELVKIGKLNLVDLAGSENIGRSGAVDKRAREAGNINQSLLTLGRVITALVERTPHVPYRESKLTRILQDSLGGRTRTSIIATISPASLNLEETLSTLEYAHRAKNILNKPEVNQKLTKKALIKEYTEEIERLKRDLAAAREKNGVYISEENFRVMSGKLTVQEEQIVELIEKIGAVEEELNRVTELFMDNKNELDQCKSDLQNKTQELETTQKHLQETKLQLVKEEYITSALESTEEKLHDAASKLLNTVEETTKDVSGLHSKLDRKKAVDQHNAEAQDIFGKNLNSLFNNMEELIKDGSSKQKAMLEVHKTLFGNLLSSSVSALDTITTVALGSLTSIPENVSTHVSQIFNMILKEQSLAAESKTVLQELINVLKTDLLSSLEMILSPTVVSILKINSQLKHIFKTSLTVADKIEDQKKELDGFLSILCNNLHELQENTICSLVESQKQCGNLTEDLKTIKQTHSQELCKLMNLWTERFCALEEKCENIQKPLSSVQENIQQKSKDIVNKMTFHSQKFCADSDGFSQELRNFNQEGTKLVEESVKHSDKLNGNLEKISQETEQRCESLNTRTVYFSEQWVSSLNEREQELHNLLEVVSQCCEASSSDITEKSDGRKAAHEKQHNIFLDQMTIDEDKLIAQNLELNETIKIGLTKLNCFLEQDLKLDIPTGTTPQRKSYLYPSTLVRTEPREHLLDQLKRKQPELLMMLNCSENNKEETIPDVDVEEAVLGQYTEEPLSQEPSVDAGVDCSSIGGVPFFQHKKSHGKDKENRGINTLERSKVEETTEHLVTKSRLPLRAQINL,mutated_sequence,1.0,1056.0,NP_004514.2.a2m,NP_004514.2.npy,ClinVar +NP_004521.1,NP_004521.1.csv,MEALMARGALTGPLRALCLLGCLLSHAAAAPSPIIKFPGDVAPKTDKELAVQYLNTFYGCPKESCNLFVLKDTLKKMQKFFGLPQTGDLDQNTIETMRKPRCGNPDVANYNFFPRKPKWDKNQITYRIIGYTPDLDPETVDDAFARAFQVWSDVTPLRFSRIHDGEADIMINFGRWEHGDGYPFDGKDGLLAHAFAPGTGVGGDSHFDDDELWTLGEGQVVRVKYGNADGEYCKFPFLFNGKEYNSCTDTGRSDGFLWCSTTYNFEKDGKYGFCPHEALFTMGGNAEGQPCKFPFRFQGTSYDSCTTEGRTDGYRWCGTTEDYDRDKKYGFCPETAMSTVGGNSEGAPCVFPFTFLGNKYESCTSAGRSDGKMWCATTANYDDDRKWGFCPDQGYSLFLVAAHEFGHAMGLEHSQDPGALMAPIYTYTKNFRLSQDDIKGIQELYGASPDIDLGTGPTPTLGPVTPEICKQDIVFDGIAQIRGEIFFFKDRFIWRTVTPRDKPMGPLLVATFWPELPEKIDAVYEAPQEEKAVFFAGNEYWIYSASTLERGYPKPLTSLGLPPDVQRVDAAFNWSKNKKTYIFAGDKFWRYNEVKKKMDPGFPKLIADAWNAIPDNLDAVVDLQGGGHSYFFKGAYYLKLENQSLKSVKFGSIKSDWLGC,mutated_sequence,1.0,660.0,NP_004521.1.a2m,NP_004521.1.npy,ClinVar +NP_004535.1,NP_004535.1.csv,MALRLLKLAATSASARVVAAGAQRVRGIHSSVQCKLRYGMWHFLLGDKASKRLTERSRVITVDGNICTGKGKLAKEIAEKLGFKHFPEAGIHYPDSTTGDGKPLATDYNGNCSLEKFYDDPRSNDGNSYRLQSWLYSSRLLQYSDALEHLLTTGQGVVLERSIFSDFVFLEAMYNQGFIRKQCVDHYNEVKSVTICDYLPPHLVIYIDVPVPEVQRRIQKKGDPHEMKITSAYLQDIENAYKKTFLPEMSEKCEVLQYSAREAQDSKKVVEDIEYLKFDKGPWLKQDNRTLYHLRLLVQDKFEVLNYTSIPIFLPEVTIGAHQTDRVLHQFRELPGRKYSPGYNTEVGDKWIWLK,mutated_sequence,1.0,355.0,NP_004535.1.a2m,NP_004535.1.npy,ClinVar +NP_004553.2,NP_004553.2.csv,MIVFVRFNSSHGFPVEVDSDTSIFQLKEVVAKRQGVPADQLRVIFAGKELRNDWTVQNCDLDQQSIVHIVQRPWRKGQEMNATGGDDPRNAAGGCEREPQSLTRVDLSSSVLPGDSVGLAVILHTDSRKDSPPAGSPAGRSIYNSFYVYCKGPCQRVQPGKLRVQCSTCRQATLTLTQGPSCWDDVLIPNRMSGECQSPHCPGTSAEFFFKCGAHPTSDKETSVALHLIATNSRNITCITCTDVRSPVLVFQCNSRHVICLDCFHLYCVTRLNDRQFVHDPQLGYSLPCVAGCPNSLIKELHHFRILGEEQYNRYQQYGAEECVLQMGGVLCPRPGCGAGLLPEPDQRKVTCEGGNGLGCGFAFCRECKEAYHEGECSAVFEASGTTTQAYRVDERAAEQARWEAASKETIKKTTKPCPRCHVPVEKNGGCMHMKCPQPQCRLEWCWNCGCEWNRVCMGDHWFDV,mutated_sequence,1.0,465.0,NP_004553.2.a2m,NP_004553.2.npy,ClinVar +NP_004577.1,NP_004577.1.csv,MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVSIKEIAITHHVKEGHEKADPSQFELLKVLGQGSFGKVFLVKKISGSDARQLYAMKVLKKATLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKLTDFGLSKESIDHEKKAYSFCGTVEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGTLPFQGKDRKETMTMILKAKLGMPQFLSPEAQSLLRMLFKRNPANRLGAGPDGVEEIKRHSFFSTIDWNKLYRREIHPPFKPATGRPEDTFYFDPEFTAKTPKDSPGIPPSANAHQLFRGFSFVAITSDDESQAMQTVGVHSIVQQLHRNSIQFTDGYEVKEDIGVGSYSVCKRCIHKATNMEFAVKIIDKSKRDPTEEIEILLRYGQHPNIITLKDVYDDGKYVYVVTELMKGGELLDKILRQKFFSEREASAVLFTITKTVEYLHAQGVVHRDLKPSNILYVDESGNPESIRICDFGFAKQLRAENGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGVLLYTMLTGYTPFANGPDDTPEEILARIGSGKFSLSGGYWNSVSDTAKDLVSKMLHVDPHQRLTAALVLRHPWIVHWDQLPQYQLNRQDAPHLVKGAMAATYSALNRNQSPVLEPVGRSTLAQRRGIKKITSTAL,mutated_sequence,1.0,740.0,NP_004577.1.a2m,NP_004577.1.npy,ClinVar +NP_004585.1,NP_004585.1.csv,MLRAALSLLALPLAGAAEEPTQKPESPGEPPPGLELFRWQWHEVEAPYLVALWILVASLAKIVFHLSRKVTSLVPESCLLILLGLVLGGIVLAVAKKAEYQLEPGTFFLFLLPPIVLDSGYFMPSRLFFDNLGAILTYAVVGTLWNAFTTGAALWGLQQAGLVAPRVQAGLLDFLLFGSLISAVDPVAVLAVFEEVHVNETLFIIVFGESLLNDAVTVVLYKVCNSFVEMGSANVQATDYLKGVASLFVVSLGGAAVGLVFAFLLALTTRFTKRVRIIEPLLVFLLAYAAYLTAEMASLSAILAVTMCGLGCKKYVEANISHKSRTTVKYTMKTLASCAETVIFMLLGISAVDSSKWAWDSGLVLGTLIFILFFRALGVVLQTWVLNQFRLVPLDKIDQVVMSYGGLRGAVAFALVILLDRTKVPAKDYFVATTIVVVFFTVIVQGLTIKPLVKWLKVKRSEHHKPTLNQELHEHTFDHILAAVEDVVGHHGYHYWRDRWEQFDKKYLSQLLMRRSAYRIRDQIWDVYYRLNIRDAISFVDQGGHVLSSTGLTLPSMPSRNSVAETSVTNLLRESGSGACLDLQVIDTVRSGRDREDAVMHHLLCGGLYKPRRRYKASCSRHFISEDAQERQDKEVFQQNMKRRLESFKSTKHNICFTKSKPRPRKTGRRKKDGVANAEATNGKHRGLGFQDTAAVILTVESEEEEEESDSSETEKEDDEGIIFVARATSEVLQEGKVSGSLEVCPSPRIIPPSPTCAEKELPWKSGQGDLAVYVSSETTKIVPVDMQTGWNQSISSLESLASPPCNQAPILTCLPPHPRGTEEPQVPLHLPSDPRSSFAFPPSLAKAGRSRSESSADLPQQQELQPLMGHKDHTHLSPGTATSHWCIQFNRGSRL,mutated_sequence,1.0,896.0,NP_004585.1.a2m,NP_004585.1.npy,ClinVar +NP_004594.1,NP_004594.1.csv,MKDRTQELRTAKDSDDDDDVAVTVDRDRFMDEFFEQVEEIRGFIDKIAENVEEVKRKHSAILASPNPDEKTKEELEELMSDIKKTANKVRSKLKSIEQSIEQEEGLNRSSADLRIRKTQHSTLSRKFVEVMSEYNATQSDYRERCKGRIQRQLEITGRTTTSEELEDMLESGNPAIFASGIIMDSSISKQALSEIETRHSEIIKLENSIRELHDMFMDMAMLVESQGEMIDRIEYNVEHAVDYVERAVSDTKKAVKYQSKARRKKIMIIICCVILGIVIASTVGGIFA,mutated_sequence,1.0,288.0,NP_004594.1.a2m,NP_004594.1.npy,ClinVar +NP_004599.2,NP_004599.2.csv,MYHPRELYPSLGAGYRLGPAQPGADSSFPPALAEGYRYPELDTPKLDCFLSGMEAAPRTLAAHPPLPLLPPAMGTEPAPSAPEALHSLPGVSLSLENRELWKEFSSVGTEMIITKAGRRMFPACRVSVTGLDPEARYLFLLDVIPVDGARYRWQGRRWEPSGKAEPRLPDRVYIHPDSPATGAHWMRQPVSFHRVKLTNSTLDPHGHLILHSMHKYQPRIHLVRAAQLCSQHWGGMASFRFPETTFISVTAYQNPQITQLKIAANPFAKGFRENGRNCKRERDARVKRKLRGPEPAATEAYGSGDTPGGPCDSTLGGDIRESDPEQAPAPGEATAAPAPLCGGPSAEAYLLHPAAFHGAPSHLPTRSPSFPEAPDSGRSAPYSAAFLELPHGSGGSGYPAAPPAVPFAPHFLQGGPFPLPYTAPGGYLDVGSKPMY,mutated_sequence,1.0,436.0,NP_004599.2.a2m,NP_004599.2.npy,ClinVar +NP_004603.1,NP_004603.1.csv,MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM,mutated_sequence,1.0,503.0,NP_004603.1.a2m,NP_004603.1.npy,ClinVar +NP_004605.4,NP_004605.4.csv,MLLWPLRGWAARALRCFGPGSRGSPASGPGPRRVQRRAWPPDKEQEKEKKSVICVEGNIASGKTTCLEFFSNATDVEVLTEPVSKWRNVRGHNPLGLMYHDASRWGLTLQTYVQLTMLDRHTRPQVSSVRLMERSIHSARYIFVENLYRSGKMPEVDYVVLSEWFDWILRNMDVSVDLIVYLRTNPETCYQRLKKRCREEEKVIPLEYLEAIHHLHEEWLIKGSLFPMAAPVLVIEADHHMERMLELFEQNRDRILTPENRKHCP,mutated_sequence,1.0,265.0,NP_004605.4.a2m,NP_004605.4.npy,ClinVar +NP_004620.1,NP_004620.1.csv,MSRQTTSVGSSCLDLWREKNDRLVRQAKVAQNSGLTLRRQQLAQDALEGLRGLLHSLQGLPAAVPVLPLELTVTCNFIILRASLAQGFTEDQAQDIQRSLERVLETQEQQGPRLEQGLRELWDSVLRASCLLPELLSALHRLVGLQAALWLSADRLGDLALLLETLNGSQSGASKDLLLLLKTWSPPAEELDAPLTLQDAQGLKDVLLTAFAYRQGLQELITGNPDKALSSLHEAASGLCPRPVLVQVYTALGSCHRKMGNPQRALLYLVAALKEGSAWGPPLLEASRLYQQLGDTTAELESLELLVEALNVPCSSKAPQFLIEVELLLPPPDLASPLHCGTQSQTKHILASRCLQTGRAGDAAEHYLDLLALLLDSSEPRFSPPPSPPGPCMPEVFLEAAVALIQAGRAQDALTLCEELLSRTSSLLPKMSRLWEDARKGTKELPYCPLWVSATHLLQGQAWVQLGAQKVAISEFSRCLELLFRATPEEKEQGAAFNCEQGCKSDAALQQLRAAALISRGLEWVASGQDTKALQDFLLSVQMCPGNRDTYFHLLQTLKRLDRRDEATALWWRLEAQTKGSHEDALWSLPLYLESYLSWIRPSDRDAFLEEFRTSLPKSCDL,mutated_sequence,1.0,622.0,NP_004620.1.a2m,NP_004620.1.npy,ClinVar +NP_004634.1,NP_004634.1.csv,MAAAAAAAAAAGAAGGRGSGPGRRRHLVPGAGGEAGEGAPGGAGDYGNGLESEELEPEELLLEPEPEPEPEEEPPRPRAPPGAPGPGPGSGAPGSQEEEEEPGLVEGDPGDGAIEDPELEAIKARVREMEEEAEKLKELQNEVEKQMNMSPPPGNAGPVIMSIEEKMEADARSIYVGNVDYGATAEELEAHFHGCGSVNRVTILCDKFSGHPKGFAYIEFSDKESVRTSLALDESLFRGRQIKVIPKRTNRPGISTTDRGFPRARYRARTTNYNSSRSRFYSGFNSRPRGRVYRGRARATSWYSPY,mutated_sequence,1.0,306.0,NP_004634.1.a2m,NP_004634.1.npy,ClinVar +NP_004637.1,NP_004637.1.csv,MALGTTLRASLLLLGLLTEGLAQLAIPASVPRGFWALPENLTVVEGASVELRCGVSTPGSAVQWAKDGLLLGPDPRIPGFPRYRLEGDPARGEFHLHIEACDLSDDAEYECQVGRSEMGPELVSPRVILSILVPPKLLLLTPEAGTMVTWVAGQEYVVNCVSGDAKPAPDITILLSGQTISDISANVNEGSQQKLFTVEATARVTPRSSDNRQLLVCEASSPALEAPIKASFTVNVLFPPGPPVIEWPGLDEGHVRAGQSLELPCVARGGNPLATLQWLKNGQPVSTAWGTEHTQAVARSVLVMTVRPEDHGAQLSCEAHNSVSAGTQEHGITLQVTFPPSAIIILGSASQTENKNVTLSCVSKSSRPRVLLRWWLGWRQLLPMEETVMDGLHGGHISMSNLTFLARREDNGLTLTCEAFSEAFTKETFKKSLILNVKYPAQKLWIEGPPEGQKLRAGTRVRLVCLAIGGNPEPSLMWYKDSRTVTESRLPQESRRVHLGSVEKSGSTFSRELVLVTGPSDNQAKFTCKAGQLSASTQLAVQFPPTNVTILANASALRPGDALNLTCVSVSSNPPVNLSWDKEGERLEGVAAPPRRAPFKGSAAARSVLLQVSSRDHGQRVTCRAHSAELRETVSSFYRLNVLYRPEFLGEQVLVVTAVEQGEALLPVSVSANPAPEAFNWTFRGYRLSPAGGPRHRILSSGALHLWNVTRADDGLYQLHCQNSEGTAEARLRLDVHYAPTIRALQDPTEVNVGGSVDIVCTVDANPILPGMFNWERLGEDEEDQSLDDMEKISRGPTGRLRIHHAKLAQAGAYQCIVDNGVAPPARRLLRLVVRFAPQVEHPTPLTKVAAAGDSTSSATLHCRARGVPNIVFTWTKNGVPLDLQDPRYTEHTYHQGGVHSSLLTIANVSAAQDYALFTCTATNALGSDQTNIQLVSISRPDPPSGLKVVSLTPHSVGLEWKPGFDGGLPQRFCIRYEALGTPGFHYVDVVPPQATTFTLTGLQPSTRYRVWLLASNALGDSGLADKGTQLPITTPGLHQPSGEPEDQLPTEPPSGPSGLPLLPVLFALGGLLLLSNASCVGGVLWQRRLRRLAEGISEKTEAGSEEDRVRNEYEESQWTGERDTQSSTVSTTEAEPYYRSLRDFSPQLPPTQEEVSYSRGFTGEDEDMAFPGHLYDEVERTYPPSGAWGPLYDEVQMGPWDLHWPEDTYQDPRGIYDQVAGDLDTLEPDSLPFELRGHLV,mutated_sequence,1.0,1241.0,NP_004637.1.a2m,NP_004637.1.npy,ClinVar +NP_004691.2,NP_004691.2.csv,MAEAPPRRLGLGPPPGDAPRAELVALTAVQSEQGEAGGGGSPRRLGLLGSPLPPGAPLPGPGSGSGSACGQRSSAAHKRYRRLQNWVYNVLERPRGWAFVYHVFIFLLVFSCLVLSVLSTIQEHQELANECLLILEFVMIVVFGLEYIVRVWSAGCCCRYRGWQGRFRFARKPFCVIDFIVFVASVAVIAAGTQGNIFATSALRSMRFLQILRMVRMDRRGGTWKLLGSVVYAHSKELITAWYIGFLVLIFASFLVYLAEKDANSDFSSYADSLWWGTITLTTIGYGDKTPHTWLGRVLAAGFALLGISFFALPAGILGSGFALKVQEQHRQKHFEKRRMPAANLIQAAWRLYSTDMSRAYLTATWYYYDSILPSFRELALLFEHVQRARNGGLRPLEVRRAPVPDGAPSRYPPVATCHRPGSTSFCPGESSRMGIKDRIRMGSSQRRTGPSKQHLAPPTMPTSPSSEQVGEATSPTKVQKSWSFNDRTRFRASLRLKPRTSAEDAPSEEVAEEKSYQCELTVDDIMPAVKTVIRSIRILKFLVAKRKFKETLRPYDVKDVIEQYSAGHLDMLGRIKSLQTRVDQIVGRGPGDRKAREKGDKGPSDAEVVDEISMMGRVVKVEKQVQSIEHKLDLLLGFYSRCLRSGTSASLGAVQVPLFDPDITSDYHSPVDHEDISVSAQTLSISRSVSTNMD,mutated_sequence,1.0,695.0,NP_004691.2.a2m,NP_004691.2.npy,ClinVar +NP_004718.1,NP_004718.1.csv,MGKLIRMGPQERWLLRTKRLHWSRLLFLLGMLIIGSTYQHLRRPRGLSSLWAAVSSHQPIKLASRDLSSEEMMMMSSSPSKPSSEMGGKMLVPQASVGSDEATLSMTVENIPSMPKRTAKMIPTTTKNNYSPTAAGTERRKEDTPTSSRTLTYYTSTSSRQIVKKYTPTPRGEMKSYSPTQVREKVKYTPSPRGRRVGTYVPSTFMTMETSHAITPRTTVKDSDITATYKILETNSLKRIMEETTPTTLKGMFDSTPTFLTHEVEANVLTSPRSVMEKNNLFPPRRVESNSSAHPWGLVGKSNPKTPQGTVLLHTPATSEGQVTISTMTGSSPAETKAFTAAWSLRNPSPRTSVSAIKTAPAIVWRLAKKPSTAPSTSTTPTVRAKLTMQVHHCVVVKPTPAMLTTPSPSLTTALLPEELSPSPSVLPPSLPDLHPKGEYPPDLFSVEERRQGWVVLHVFGMMYVFVALAIVCDEYFVPALGVITDKLQISEDVAGATFMAAGGSAPELFTSLIGVFISHSNVGIGTIVGSAVFNILFVIGTCSLFSREILNLTWWPLFRDVSFYILDLIMLILFFLDSLIAWWESLLLLLAYAFYVFTMKWNKHIEVWVKEQLSRRPVAKVMALEDLSKPGDGAIAVDELQDNKKLKLPSLLTRGSSSTSLHNSTIRSTIYQLMLHSLDPLREVRLAKEKEEESLNQGARAQPQAKAESKPEEEEPAKLPAVTVTPAPVPDIKGDQKENPGGQEDVAEAESTGEMPGEEGETAGEGETEEKSGGETQPEGEGETETQGKGEECEDENEAEGKGDNEGEDEGEIHAEDGEMKGNEGETESQELSAENHGEAKNDEKGVEDGGGSDGGDSEEEEEEEEEQEEEEEEEEQEEEEEEEEEEEEKGNEEPLSLDWPETRQKQAIYLFLLPIVFPLWLTVPDVRRQESRKFFVFTFLGSIMWIAMFSYLMVWWAHQVGETIGISEEIMGLTILAAGTSIPDLITSVIVARKGLGDMAVSSSVGSNIFDITVGLPVPWLLFSLINGLQPVPVSSNGLFCAIVLLFLMLLFVISSIASCKWRMNKILGFTMFLLYFVFLIISVMLEDRIISCPVSV,mutated_sequence,1.0,1099.0,NP_004718.1.a2m,NP_004718.1.npy,ClinVar +NP_004735.2,NP_004735.2.csv,MKNPMLEVVSLLLEKLLLISNFTLFSSGAAGEDKGRNSFYETSSFHRGDVLEVPRTHLTHYGIYLGDNRVAHMMPDILLALTDDMGRTQKVVSNKRLILGVIVKVASIRVDTVEDFAYGANILVNHLDESLQKKALLNEEVARRAEKLLGFTPYSLLWNNCEHFVTYCRYGTPISPQSDKFCETVKIIIRDQRSVLASAVLGLASIVCTGLVSYTTLPAIFIPFFLWMAG,mutated_sequence,1.0,230.0,NP_004735.2.a2m,NP_004735.2.npy,ClinVar +NP_004804.2,NP_004804.2.csv,MEKLRLLGLRYQEYVTRHPAATAQLETAVRGFSYLLAGRFADSHELSELVYSASNLLVLLNDGILRKELRKKLPVSLSQQKLLTWLSVLECVEVFMEMGAAKVWGEVGRWLVIALVQLAKAVLRMLLLLWFKAGLQTSPPIVPLDRETQAQPPDGDHSPGNHEQSYVGKRSNRVVRTLQNTPSLHSRHWGAPQQREGRQQQHHEELSATPTPLGLQETIAEFLYIARPLLHLLSLGLWGQRSWKPWLLAGVVDVTSLSLLSDRKGLTRRERRELRRRTILLLYYLLRSPFYDRFSEARILFLLQLLADHVPGVGLVTRPLMDYLPTWQKIYFYSWG,mutated_sequence,1.0,336.0,NP_004804.2.a2m,NP_004804.2.npy,ClinVar +NP_004809.2,NP_004809.2.csv,MAGELADKKDRDASPSKEERKRSRTPDRERDRDRDRKSSPSKDRKRHRSRDRRRGGSRSRSRSRSKSAERERRHKERERDKERDRNKKDRDRDKDGHRRDKDRKRSSLSPGRGKDFKSRKDRDSKKDEEDEHGDKKPKAQPLSLEELLAKKKAEEEAEAKPKFLSKAEREAEALKRRQQEVEERQRMLEEERKKRKQFQDLGRKMLEDPQERERRERRERMERETNGNEDEEGRQKIREEKDKSKELHAIKERYLGGIKKRRRTRHLNDRKFVFEWDASEDTSIDYNPLYKERHQVQLLGRGFIAGIDLKQQKREQSRFYGDLMEKRRTLEEKEQEEARLRKLRKKEAKQRWDDRHWSQKKLDEMTDRDWRIFREDYSITTKGGKIPNPIRSWKDSSLPPHILEVIDKCGYKEPTPIQRQAIPIGLQNRDIIGVAETGSGKTAAFLIPLLVWITTLPKIDRIEESDQGPYAIILAPTRELAQQIEEETIKFGKPLGIRTVAVIGGISREDQGFRLRMGCEIVIATPGRLIDVLENRYLVLSRCTYVVLDEADRMIDMGFEPDVQKILEHMPVSNQKPDTDEAEDPEKMLANFESGKHKYRQTVMFTATMPPAVERLARSYLRRPAVVYIGSAGKPHERVEQKVFLMSESEKRKKLLAILEQGFDPPIIIFVNQKKGCDVLAKSLEKMGYNACTLHGGKGQEQREFALSNLKAGAKDILVATDVAGRGIDIQDVSMVVNYDMAKNIEDYIHRIGRTGRAGKSGVAITFLTKEDSAVFYELKQAILESPVSSCPPELANHPDAQHKPGTILTKKRREETIFA,mutated_sequence,1.0,820.0,NP_004809.2.a2m,NP_004809.2.npy,ClinVar +NP_004813.2,NP_004813.2.csv,MMRAVWEALAALAAVACLVGAVRGGPGLSMFAGQAAQPDPCSDENGHPRRCIPDFVNAAFGKDVRVSSTCGRPPARYCVVSERGEERLRSCHLCNASDPKKAHPPAFLTDLNNPHNLTCWQSENYLQFPHNVTLTLSLGKKFEVTYVSLQFCSPRPESMAIYKSMDYGRTWVPFQFYSTQCRKMYNRPHRAPITKQNEQEAVCTDSHTDMRPLSGGLIAFSTLDGRPSAHDFDNSPVLQDWVTATDIRVAFSRLHTFGDENEDDSELARDSYFYAVSDLQVGGRCKCNGHAARCVRDRDDSLVCDCRHNTAGPECDRCKPFHYDRPWQRATAREANECVACNCNLHARRCRFNMELYKLSGRKSGGVCLNCRHNTAGRHCHYCKEGYYRDMGKPITHRKACKACDCHPVGAAGKTCNQTTGQCPCKDGVTGITCNRCAKGYQQSRSPIAPCIKIPVAPPTTAASSVEEPEDCDSYCKASKGKLKINMKKYCKKDYAVQIHILKADKAGDWWKFTVNIISVYKQGTSRIRRGDQSLWIRSRDIACKCPKIKPLKKYLLLGNAEDSPDQSGIVADKSSLVIQWRDTWARRLRKFQQREKKGKCKKA,mutated_sequence,1.0,604.0,NP_004813.2.a2m,NP_004813.2.npy,ClinVar +NP_004850.1,NP_004850.1.csv,MAQILPIRFQEHLQLQNLGINPANIGFSTLTMESDKFICIREKVGEQAQVVIIDMNDPSNPIRRPISADSAIMNPASKVIALKAGKTLQIFNIEMKSKMKAHTMTDDVTFWKWISLNTVALVTDNAVYHWSMEGESQPVKMFDRHSSLAGCQIINYRTDAKQKWLLLTGISAQQNRVVGAMQLYSVDRKVSQPIEGHAASFAQFKMEGNAEESTLFCFAVRGQAGGKLHIIEVGTPPTGNQPFPKKAVDVFFPPEAQNDFPVAMQISEKHDVVFLITKYGYIHLYDLETGTCIYMNRISGETIFVTAPHEATAGIIGVNRKGQVLSVCVEEENIIPYITNVLQNPDLALRMAVRNNLAGAEELFARKFNALFAQGNYSEAAKVAANAPKGILRTPDTIRRFQSVPAQPGQTSPLLQYFGILLDQGQLNKYESLELCRPVLQQGRKQLLEKWLKEDKLECSEELGDLVKSVDPTLALSVYLRANVPNKVIQCFAETGQVQKIVLYAKKVGYTPDWIFLLRNVMRISPDQGQQFAQMLVQDEEPLADITQIVDVFMEYNLIQQCTAFLLDALKNNRPSEGPLQTRLLEMNLMHAPQVADAILGNQMFTHYDRAHIAQLCEKAGLLQRALEHFTDLYDIKRAVVHTHLLNPEWLVNYFGSLSVEDSLECLRAMLSANIRQNLQICVQVASKYHEQLSTQSLIELFESFKSFEGLFYFLGSIVNFSQDPDVHFKYIQAACKTGQIKEVERICRESNCYDPERVKNFLKEAKLTDQLPLIIVCDRFDFVHDLVLYLYRNNLQKYIEIYVQKVNPSRLPVVIGGLLDVDCSEDVIKNLILVVRGQFSTDELVAEVEKRNRLKLLLPWLEARIHEGCEEPATHNALAKIYIDSNNNPERFLRENPYYDSRVVGKYCEKRDPHLACVAYERGQCDLELINVCNENSLFKSLSRYLVRRKDPELWGSVLLESNPYRRPLIDQVVQTALSETQDPEEVSVTVKAFMTADLPNELIELLEKIVLDNSVFSEHRNLQNLLILTAIKADRTRVMEYINRLDNYDAPDIANIAISNELFEEAFAIFRKFDVNTSAVQVLIEHIGNLDRAYEFAERCNEPAVWSQLAKAQLQKGMVKEAIDSYIKADDPSSYMEVVQAANTSGNWEELVKYLQMARKKARESYVETELIFALAKTNRLAELEEFINGPNNAHIQQVGDRCYDEKMYDAAKLLYNNVSNFGRLASTLVHLGEYQAAVDGARKANSTRTWKEVCFACVDGKEFRLAQMCGLHIVVHADELEELINYYQDRGYFEELITMLEAALGLERAHMGMFTELAILYSKFKPQKMREHLELFWSRVNIPKVLRAAEQAHLWAELVFLYDKYEEYDNAIITMMNHPTDAWKEGQFKDIITKVANVELYYRAIQFYLEFKPLLLNDLLMVLSPRLDHTRAVNYFSKVKQLPLVKPYLRSVQNHNNKSVNESLNNLFITEEDYQALRTSIDAYDNFDNISLAQRLEKHELIEFRRIAAYLFKGNNRWKQSVELCKKDSLYKDAMQYASESKDTELAEELLQWFLQEEKRECFGACLFTCYDLLRPDVVLETAWRHNIMDFAMPYFIQVMKEYLTKVDKLDASESLRKEEEQATETQPIVYGQPQLMLTAGPSVAVPPQAPFGYGYTAPPYGQPQPGFGYSM,mutated_sequence,1.0,1675.0,NP_004850.1.a2m,NP_004850.1.npy,ClinVar +NP_004874.1,NP_004874.1.csv,MRQVCCSALPPPPLEKGRCSSYSDSSSSSSERSSSSSSSSSESGSSSRSSSNNSSISRPAAPPEPRPQQQPQPRSPAARRAAARSRAAAAGGMRRDPAPGFSMLLFGVSLACYSPSLKSVQDQAYKAPVVVEGKVQGLVPAGGSSSNSTREPPASGRVALVKVLDKWPLRSGGLQREQVISVGSCVPLERNQRYIFFLEPTEQPLVFKTAFAPLDTNGKNLKKEVGKILCTDCATRPKLKKMKSQTGQVGEKQSLKCEAAAGNPQPSYRWFKDGKELNRSRDIRIKYGNGRKNSRLQFNKVKVEDAGEYVCEAENILGKDTVRGRLYVNSVSTTLSSWSGHARKCNETAKSYCVNGGVCYYIEGINQLSCKCPNGFFGQRCLEKLPLRLYMPDPKQKAEELYQKRVLTITGICVALLVVGIVCVVAYCKTKKQRKQMHNHLRQNMCPAHQNRSLANGPSHPRLDPEEIQMADYISKNVPATDHVIRRETETTFSGSHSCSPSHHCSTATPTSSHRHESHTWSLERSESLTSDSQSGIMLSSVGTSKCNSPACVEARARRAAAYNLEERRRATAPPYHDSVDSLRDSPHSERYVSALTTPARLSPVDFHYSLATQVPTFEITSPNSAHAVSLPPAAPISYRLAEQQPLLRHPAPPGPGPGPGPGPGPGADMQRSYDSYYYPAAGPGPRRGTCALGGSLGSLPASPFRIPEDDEYETTQECAPPPPPRPRARGASRRTSAGPRRWRRSRLNGLAAQRARAARDSLSLSSGSGGGSASASDDDADDADGALAAESTPFLGLRGAHDALRSDSPPLCPAADSRTYYSLDSHSTRASSRHSRGPPPRAKQDSAPL,mutated_sequence,1.0,850.0,NP_004874.1.a2m,NP_004874.1.npy,ClinVar +NP_004915.2,NP_004915.2.csv,MVDYHAANQSYQYGPSSAGNGAGGGGSMGDYMAQEDDWDRDLLLDPAWEKQQRKTFTAWCNSHLRKAGTQIENIDEDFRDGLKLMLLLEVISGERLPKPERGKMRVHKINNVNKALDFIASKGVKLVSIGAEEIVDGNAKMTLGMIWTIILRFAIQDISVEETSAKEGLLLWCQRKTAPYKNVNVQNFHISWKDGLAFNALIHRHRPELIEYDKLRKDDPVTNLNNAFEVAEKYLDIPKMLDAEDIVNTARPDEKAIMTYVSSFYHAFSGAQKAETAANRICKVLAVNQENEHLMEDYEKLASDLLEWIRRTIPWLEDRVPQKTIQEMQQKLEDFRDYRRVHKPPKVQEKCQLEINFNTLQTKLRLSNRPAFMPSEGKMVSDINNGWQHLEQAEKGYEEWLLNEIRRLERLDHLAEKFRQKASIHEAWTDGKEAMLKHRDYETATLSDIKALIRKHEAFESDLAAHQDRVEQIAAIAQELNELDYYDSHNVNTRCQKICDQWDALGSLTHSRREALEKTEKQLEAIDQLHLEYAKRAAPFNNWMESAMEDLQDMFIVHTIEEIEGLISAHDQFKSTLPDADREREAILAIHKEAQRIAESNHIKLSGSNPYTTVTPQIINSKWEKVQQLVPKRDHALLEEQSKQQSNEHLRRQFASQANVVGPWIQTKMEEIGRISIEMNGTLEDQLSHLKQYERSIVDYKPNLDLLEQQHQLIQEALIFDNKHTNYTMEHIRVGWEQLLTTIARTINEVENQILTRDAKGISQEQMQEFRASFNHFDKDHGGALGPEEFKACLISLGYDVENDRQGEAEFNRIMSLVDPNHSGLVTFQAFIDFMSRETTDTDTADQVIASFKVLAGDKNFITAEELRRELPPDQAEYCIARMAPYQGPDAVPGALDYKSFSTALYGESDL,mutated_sequence,1.0,911.0,NP_004915.2.a2m,NP_004915.2.npy,ClinVar +NP_004928.2,NP_004928.2.csv,MIRNWLTIFILFPLKLVEKCESSVSLTVPPVVKLENGSSTNVSLTLRPPLNATLVITFEITFRSKNITILELPDEVVVPPGVTNSSFQVTSQNVGQLTVYLHGNHSNQTGPRIRFLVIRSSAISIINQVIGWIYFVAWSISFYPQVIMNWRRKSVIGLSFDFVALNLTGFVAYSVFNIGLLWVPYIKEQFLLKYPNGVNPVNSNDVFFSLHAVVLTLIIIVQCCLYERGGQRVSWPAIGFLVLAWLFAFVTMIVAAVGVTTWLQFLFCFSYIKLAVTLVKYFPQAYMNFYYKSTEGWSIGNVLLDFTGGSFSLLQMFLQSYNNDQWTLIFGDPTKFGLGVFSIVFDVVFFIQHFCLYRKRPGYDQLN,mutated_sequence,1.0,367.0,NP_004928.2.a2m,NP_004928.2.npy,ClinVar +NP_004950.2,NP_004950.2.csv,MDYSYDEDLDELCPVCGDKVSGYHYGLLTCESCKGFFKRTVQNNKHYTCTESQSCKIDKTQRKRCPFCRFQKCLTVGMRLEAVRADRMRGGRNKFGPMYKRDRALKQQKKAQIRANGFKLETGPPMGVPPPPPPAPDYVLPPSLHGPEPKGLAAGPPAGPLGDFGAPALPMAVPGAHGPLAGYLYPAFPGRAIKSEYPEPYASPPQPGLPYGYPEPFSGGPNVPELILQLLQLEPDEDQVRARILGCLQEPTKSRPDQPAAFGLLCRMADQTFISIVDWARRCMVFKELEVADQMTLLQNCWSELLVFDHIYRQVQHGKEGSILLVTGQEVELTTVATQAGSLLHSLVLRAQELVLQLLALQLDRQEFVCLKFIILFSLDLKFLNNHILVKDAQEKANAALLDYTLCHYPHCGDKFQQLLLCLVEVRALSMQAKEYLYHKHLGNEMPRNNLLIEMLQAKQT,mutated_sequence,1.0,461.0,NP_004950.2.a2m,NP_004950.2.npy,ClinVar +NP_004961.1,NP_004961.1.csv,MALRKGGLALALLLLSWVALGPRSLEGADPGTPGEAEGPACPAACVCSYDDDADELSVFCSSRNLTRLPDGVPGGTQALWLDGNNLSSVPPAAFQNLSSLGFLNLQGGQLGSLEPQALLGLENLCHLHLERNQLRSLALGTFAHTPALASLGLSNNRLSRLEDGLFEGLGSLWDLNLGWNSLAVLPDAAFRGLGSLRELVLAGNRLAYLQPALFSGLAELRELDLSRNALRAIKANVFVQLPRLQKLYLDRNLIAAVAPGAFLGLKALRWLDLSHNRVAGLLEDTFPGLLGLRVLRLSHNAIASLRPRTFKDLHFLEELQLGHNRIRQLAERSFEGLGQLEVLTLDHNQLQEVKAGAFLGLTNVAVMNLSGNCLRNLPEQVFRGLGKLHSLHLEGSCLGRIRPHTFTGLSGLRRLFLKDNGLVGIEEQSLWGLAELLELDLTSNQLTHLPHRLFQGLGKLEYLLLSRNRLAELPADALGPLQRAFWLDVSHNRLEALPNSLLAPLGRLRYLSLRNNSLRTFTPQPPGLERLWLEGNPWDCGCPLKALRDFALQNPSAVPRFVQAICEGDDCQPPAYTYNNITCASPPEVVGLDLRDLSEAHFAPC,mutated_sequence,1.0,605.0,NP_004961.1.a2m,NP_004961.1.npy,ClinVar +NP_004965.1,NP_004965.1.csv,MTVATGDPADEAAALPGHPQDTYDPEADHECCERVVINISGLRFETQLKTLAQFPETLLGDPKKRMRYFDPLRNEYFFDRNRPSFDAILYYYQSGGRLRRPVNVPLDIFSEEIRFYELGEEAMEMFREDEGYIKEEERPLPENEFQRQVWLLFEYPESSGPARIIAIVSVMVILISIVSFCLETLPIFRDENEDMHGSGVTFHTYSNSTIGYQQSTSFTDPFFIVETLCIIWFSFEFLVRFFACPSKAGFFTNIMNIIDIVAIIPYFITLGTELAEKPEDAQQGQQAMSLAILRVIRLVRVFRIFKLSRHSKGLQILGQTLKASMRELGLLIFFLFIGVILFSSAVYFAEADERESQFPSIPDAFWWAVVSMTTVGYGDMVPTTIGGKIVGSLCAIAGVLTIALPVPVIVSNFNYFYHRETEGEEQAQYLQVTSCPKIPSSPDLKKSRSASTISKSDYMEIQEGVNNSNEDFREENLKTANCTLANTNYVNITKMLTDV,mutated_sequence,1.0,499.0,NP_004965.1.a2m,NP_004965.1.npy,ClinVar +NP_004975.2,NP_004975.2.csv,MAETNNECSIKVLCRFRPLNQAEILRGDKFIPIFQGDDSVVIGGKPYVFDRVFPPNTTQEQVYHACAMQIVKDVLAGYNGTIFAYGQTSSGKTHTMEGKLHDPQLMGIIPRIARDIFNHIYSMDENLEFHIKVSYFEIYLDKIRDLLDVTKTNLSVHEDKNRVPFVKGCTERFVSSPEEILDVIDEGKSNRHVAVTNMNEHSSRSHSIFLINIKQENMETEQKLSGKLYLVDLAGSEKVSKTGAEGAVLDEAKNINKSLSALGNVISALAEGTKSYVPYRDSKMTRILQDSLGGNCRTTMFICCSPSSYNDAETKSTLMFGQRAKTIKNTASVNLELTAEQWKKKYEKEKEKTKAQKETIAKLEAELSRWRNGENVPETERLAGEEAALGAELCEETPVNDNSSIVVRIAPEERQKYEEEIRRLYKQLDDKDDEINQQSQLIEKLKQQMLDQEELLVSTRGDNEKVQRELSHLQSENDAAKDEVKEVLQALEELAVNYDQKSQEVEEKSQQNQLLVDELSQKVATMLSLESELQRLQEVSGHQRKRIAEVLNGLMKDLSEFSVIVGNGEIKLPVEISGAIEEEFTVARLYISKIKSEVKSVVKRCRQLENLQVECHRKMEVTGRELSSCQLLISQHEAKIRSLTEYMQSVELKKRHLEESYDSLSDELAKLQAQETVHEVALKDKEPDTQDADEVKKALELQMESHREAHHRQLARLRDEINEKQKTIDELKDLNQKLQLELEKLQADYEKLKSEEHEKSTKLQELTFLYERHEQSKQDLKGLEETVARELQTLHNLRKLFVQDVTTRVKKSAEMEPEDSGGIHSQKQKISFLENNLEQLTKVHKQLVRDNADLRCELPKLEKRLRATAERVKALEGALKEAKEGAMKDKRRYQQEVDRIKEAVRYKSSGKRGHSAQIAKPVRPGHYPASSPTNPYGTRSPECISYTNSLFQNYQNLYLQATPSSTSDMYFANSCTSSGATSSGGPLASYQKANMDNGNATDINDNRSDLPCGYEAEDQAKLFPLHQETAAS,mutated_sequence,1.0,1032.0,NP_004975.2.a2m,NP_004975.2.npy,ClinVar +NP_004976.2,NP_004976.2.csv,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM,mutated_sequence,1.0,188.0,NP_004976.2.a2m,NP_004976.2.npy,ClinVar +NP_004981.2,NP_004981.2.csv,MRLFVSDGVPGCLPVLAAAGRARGRAEVLISTVGPEDCVVPFLTRPKVPVLQLDSGNYLFSTSAICRYFFLLSGWEQDDLTNQWLEWEATELQPALSAALYYLVVQGKKGEDVLGSVRRALTHIDHSLSRQNCPFLAGETESLADIVLWGALYPLLQDPAYLPEELSALHSWFQTLSTQEPCQRAAETVLKQQGVLALRPYLQKQPQPSPAEGRAVTNEPEEEELATLSEEEIAMAVTAWEKGLESLPPLRPQQNPVLPVAGERNVLITSALPYVNNVPHLGNIIGCVLSADVFARYSRLRQWNTLYLCGTDEYGTATETKALEEGLTPQEICDKYHIIHADIYRWFNISFDIFGRTTTPQQTKITQDIFQQLLKRGFVLQDTVEQLRCEHCARFLADRFVEGVCPFCGYEEARGDQCDKCGKLINAVELKKPQCKVCRSCPVVQSSQHLFLDLPKLEKRLEEWLGRTLPGSDWTPNAQFITRSWLRDGLKPRCITRDLKWGTPVPLEGFEDKVFYVWFDATIGYLSITANYTDQWERWWKNPEQVDLYQFMAKDNVPFHSLVFPCSALGAEDNYTLVSHLIATEYLNYEDGKFSKSRGVGVFGDMAQDTGIPADIWRFYLLYIRPEGQDSAFSWTDLLLKNNSELLNNLGNFINRAGMFVSKFFGGYVPEMVLTPDDQRLLAHVTLELQHYHQLLEKVRIRDALRSILTISRHGNQYIQVNEPWKRIKGSEADRQRAGTVTGLAVNIAALLSVMLQPYMPTVSATIQAQLQLPPPACSILLTNFLCTLPAGHQIGTVSPLFQKLENDQIESLRQRFGGGQAKTSPKPAVVETVTTAKPQQIQALMDEVTKQGNIVRELKAQKADKNEVAAEVAKLLDLKKQLAVAEGKPPEAPKGKKKK,mutated_sequence,1.0,900.0,NP_004981.2.a2m,NP_004981.2.npy,ClinVar +NP_004997.4,NP_004997.4.csv,MLRIPVRKALVGLSKSPKGCVRTTATAASNLIEVFVDGQSVMVEPGTTVLQACEKVGMQIPRFCYHERLSVAGNCRMCLVEIEKAPKVVAACAMPVMKGWNILTNSEKSKKAREGVMEFLLANHPLDCPICDQGGECDLQDQSMMFGNDRSRFLEGKRAVEDKNIGPLVKTIMTRCIQCTRCIRFASEIAGVDDLGTTGRGNDMQVGTYIEKMFMSELSGNIIDICPVGALTSKPYAFTARPWETRKTESIDVMDAVGSNIVVSTRTGEVMRILPRMHEDINEEWISDKTRFAYDGLKRQRLTEPMVRNEKGLLTYTSWEDALSRVAGMLQSFQGKDVAAIAGGLVDAEALVALKDLLNRVDSDTLCTEEVFPTAGAGTDLRSNYLLNTTIAGVEEADVVLLVGTNPRFEAPLFNARIRKSWLHNDLKVALIGSPVDLTYTYDHLGDSPKILQDIASGSHPFSQVLKEAKKPMVVLGSSALQRNDGAAILAAVSSIAQKIRMTSGVTGDWKVMNILHRIASQVAALDLGYKPGVEAIRKNPPKVLFLLGADGGCITRQDLPKDCFIIYQGHHGDVGAPIADVILPGAAYTEKSATYVNTEGRAQQTKVAVTPPGLAREDWKIIRALSEIAGMTLPYDTLDQVRNRLEEVSPNLVRYDDIEGANYFQQANELSKLVNQQLLADPLVPPQLTIKDFYMTDSISRASQTMAKCVKAVTEGAQAVEEPSIC,mutated_sequence,1.0,727.0,NP_004997.4.a2m,NP_004997.4.npy,ClinVar +NP_005043.1,NP_005043.1.csv,MQAIKCVVVGDGAVGKTCLLISYTTNAFPGEYIPTVFDNYSANVMVDGKPVNLGLWDTAGQEDYDRLRPLSYPQTDVFLICFSLVSPASFENVRAKWYPEVRHHCPHTPILLVGTKLDLRDDKDTIERLRDKKLAPITYPQGLAMAREIGSVKYLECSALTQRGLKTVFDEAIRAVLCPPPVKKPGKKCTVF,mutated_sequence,1.0,192.0,NP_005043.1.a2m,NP_005043.1.npy,ClinVar +NP_005046.2,NP_005046.2.csv,MGQDQTKQQIEKGLQLYQSNQTEKALQVWTKVLEKSSDLMGRFRVLGCLVTAHSEMGRYKEMLKFAVVQIDTARELEDADFLLESYLNLARSNEKLCEFHKTISYCKTCLGLPGTRAGAQLGGQVSLSMGNAFLGLSVFQKALESFEKALRYAHNNDDAMLECRVCCSLGSFYAQVKDYEKALFFPCKAAELVNNYGKGWSLKYRAMSQYHMAVAYRLLGRLGSAMECCEESMKIALQHGDRPLQALCLLCFADIHRSRGDLETAFPRYDSAMSIMTEIGNRLGQVQALLGVAKCWVARKALDKALDAIERAQDLAEEVGNKLSQLKLHCLSESIYRSKGLQRELRAHVVRFHECVEETELYCGLCGESIGEKNSRLQALPCSHIFHLRCLQNNGTRSCPNCRRSSMKPGFV,mutated_sequence,1.0,412.0,NP_005046.2.a2m,NP_005046.2.npy,ClinVar +NP_005145.3,NP_005145.3.csv,MPAVASVPKELYLSSSLKDLNKKTEVKPEKISTKSYVHSALKIFKTAEECRLDRDEERAYVLYMKYVTVYNLIKKRPDFKQQQDYFHSILGPGNIKKAVEEAERLSESLKLRYEEAEVRKKLEEKDRQEEAQRLQQKRQETGREDGGTLAKGSLENVLDSKDKTQKSNGEKNEKCETKEKGAITAKELYTMMTDKNISLIIMDARRMQDYQDSCILHSLSVPEEAISPGVTASWIEAHLPDDSKDTWKKRGNVEYVVLLDWFSSAKDLQIGTTLRSLKDALFKWESKTVLRNEPLVLEGGYENWLLCYPQYTTNAKVTPPPRRQNEEVSISLDFTYPSLEESIPSKPAAQTPPASIEVDENIELISGQNERMGPLNISTPVEPVAASKSDVSPIIQPVPSIKNVPQIDRTKKPAVKLPEEHRIKSESTNHEQQSPQSGKVIPDRSTKPVVFSPTLMLTDEEKARIHAETALLMEKNKQEKELRERQQEEQKEKLRKEEQEQKAKKKQEAEENEITEKQQKAKEEMEKKESEQAKKEDKETSAKRGKEITGVKRQSKSEHETSDAKKSVEDRGKRCPTPEIQKKSTGDVPHTSVTGDSGSGKPFKIKGQPESGILRTGTFREDTDDTERNKAQREPLTRARSEEMGRIVPGLPSGWAKFLDPITGTFRYYHSPTNTVHMYPPEMAPSSAPPSTPPTHKAKPQIPAERDREPSKLKRSYSSPDITQAIQEEEKRKPTVTPTVNRENKPTCYPKAEISRLSASQIRNLNPVFGGSGPALTGLRNLGNTCYMNSILQCLCNAPHLADYFNRNCYQDDINRSNLLGHKGEVAEEFGIIMKALWTGQYRYISPKDFKITIGKINDQFAGYSQQDSQELLLFLMDGLHEDLNKADNRKRYKEENNDHLDDFKAAEHAWQKHKQLNESIIVALFQGQFKSTVQCLTCHKKSRTFEAFMYLSLPLASTSKCTLQDCLRLFSKEEKLTDNNRFYCSHCRARRDSLKKIEIWKLPPVLLVHLKRFSYDGRWKQKLQTSVDFPLENLDLSQYVIGPKNNLKKYNLFSVSNHYGGLDGGHYTAYCKNAARQRWFKFDDHEVSDISVSSVKSSAAYILFYTSLGPRVTDVAT,mutated_sequence,1.0,1118.0,NP_005145.3.a2m,NP_005145.3.npy,ClinVar +NP_005148.2,NP_005148.2.csv,MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR,mutated_sequence,1.0,1130.0,NP_005148.2.a2m,NP_005148.2.npy,ClinVar +NP_005150.1,NP_005150.1.csv,MCDDEETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVLSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWISKQEYDEAGPSIVHRKCF,mutated_sequence,1.0,377.0,NP_005150.1.a2m,NP_005150.1.npy,ClinVar +NP_005177.2,NP_005177.2.csv,MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA,mutated_sequence,1.0,714.0,NP_005177.2.a2m,NP_005177.2.npy,ClinVar +NP_005179.2,NP_005179.2.csv,MAGNVKKSSGAGGGSGSGGSGSGGLIGLMKDAFQPHHHHHHHLSPHPPGTVDKKMVEKCWKLMDKVVRLCQNPKLALKNSPPYILDLLPDTYQHLRTILSRYEGKMETLGENEYFRVFMENLMKKTKQTISLFKEGKERMYEENSQPRRNLTKLSLIFSHMLAELKGIFPSGLFQGDTFRITKADAAEFWRKAFGEKTIVPWKSFRQALHEVHPISSGLEAMALKSTIDLTCNDYISVFEFDIFTRLFQPWSSLLRNWNSLAVTHPGYMAFLTYDEVKARLQKFIHKPGSYIFRLSCTRLGQWAIGYVTADGNILQTIPHNKPLFQALIDGFREGFYLFPDGRNQNPDLTGLCEPTPQDHIKVTQEQYELYCEMGSTFQLCKICAENDKDVKIEPCGHLMCTSCLTSWQESEGQGCPFCRCEIKGTEPIVVDPFDPRGSGSLLRQGAEGAPSPNYDDDDDERADDTLFMMKELAGAKVERPPSPFSMAPQASLPPVPPRLDLLPQRVCVPSSASALGTASKAASGSLHKDKPLPVPPTLRDLPPPPPPDRPYSVGAESRPQRRPLPCTPGDCPSRDKLPPVPSSRLGDSWLPRPIPKVPVSAPSSSDPWTGRELTNRHSLPFSLPSQMEPRPDVPRLGSTFSLDTSMSMNSSPLVGPECDHPKIKPSSSANAIYSLAARPLPVPKLPPGEQCEGEEDTEYMTPSSRPLRPLDTSQSSRACDCDQQIDSCTYEAMYNIQSQAPSITESSTFGEGNLAAAHANTGPEESENEDDGYDVPKPPVPAVLARRTLSDISNASSSFGWLSLDGDPTTNVTEGSQVPERPPKPFPRRINSERKAGSCQQGSGPAASAATASPQLSSEIENLMSQGYSYQDIQKALVIAQNNIEMAKNILREFVSISSPAHVAT,mutated_sequence,1.0,906.0,NP_005179.2.a2m,NP_005179.2.npy,ClinVar +NP_005190.4,NP_005190.4.csv,MHGGQGPLLLLLLLAVCLGAQGRNQEERLLADLMQNYDPNLRPAERDSDVVNVSLKLTLTNLISLNEREEALTTNVWIEMQWCDYRLRWDPRDYEGLWVLRVPSTMVWRPDIVLENNVDGVFEVALYCNVLVSPDGCIYWLPPAIFRSACSISVTYFPFDWQNCSLIFQSQTYSTNEIDLQLSQEDGQTIEWIFIDPEAFTENGEWAIQHRPAKMLLDPAAPAQEAGHQKVVFYLLIQRKPLFYVINIIAPCVLISSVAILIHFLPAKAGGQKCTVAINVLLAQTVFLFLVAKKVPETSQAVPLISKYLTFLLVVTILIVVNAVVVLNVSLRSPHTHSMARGVRKVFLRLLPQLLRMHVRPLAPAAVQDTQSRLQNGSSGWSITTGEEVALCLPRSELLFQQWQRQGLVAAALEKLEKGPELGLSQFCGSLKQAAPAIQACVEACNLIACARHQQSHFDNGNEEWFLVGRVLDRVCFLAMLSLFICGTAGIFLMAHYNRVPALPFPGDPRPYLPSPD,mutated_sequence,1.0,517.0,NP_005190.4.a2m,NP_005190.4.npy,ClinVar +NP_005199.2,NP_005199.2.csv,METQAEQQELETLPTTKMAQTNPTPGSLGPWKITIYDQENFQGKRMEFTSSCPNVSERSFDNVRSLKVESGAWIGYEHTSFCGQQFILERGEYPRWDAWSGSNAYHIERLMSFRPICSANHKESKMTIFEKENFIGRQWEISDDYPSLQAMGWFNNEVGSMKIQSGAWVCYQYPGYRGYQYILECDHHGGDYKHWREWGSHAQTSQIQSIRRIQQ,mutated_sequence,1.0,215.0,NP_005199.2.a2m,NP_005199.2.npy,ClinVar +NP_005219.2,NP_005219.2.csv,MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA,mutated_sequence,1.0,1210.0,NP_005219.2.a2m,NP_005219.2.npy,ClinVar +NP_005240.3,NP_005240.3.csv,MLDMGDRKEVKMIPKSSFSINSLVPEAVQNDNHHASHGHHNSHHPQHHHHHHHHHHHPPPPAPQPPPPPQQQQPPPPPPPAPQPPQTRGAPAADDDKGPQQLLLPPPPPPPPAAALDGAKADGLGGKGEPGGGPGELAPVGPDEKEKGAGAGGEEKKGAGEGGKDGEGGKEGEKKNGKYEKPPFSYNALIMMAIRQSPEKRLTLNGIYEFIMKNFPYYRENKQGWQNSIRHNLSLNKCFVKVPRHYDDPGKGNYWMLDPSSDDVFIGGTTGKLRRRSTTSRAKLAFKRGARLTSTGLTFMDRAGSLYWPMSPFLSLHHPRASSTLSYNGTTSAYPSHPMPYSSVLTQNSLGNNHSFSTANGLSVDRLVNGEIPYATHHLTAAALAASVPCGLSVPCSGTYSLNPCSVNLLAGQTSYFFPHVPHPSMTSQSSTSMSARAASSSTSPQAPSTLPCESLRPSLPSFTTGLSGGLSDYFTHQNQGSSSNPLIH,mutated_sequence,1.0,489.0,NP_005240.3.a2m,NP_005240.3.npy,ClinVar +NP_005324.3,NP_005324.3.csv,MGLSPSAPAVAVQASNASASPPSGCPMHEGKMKGCPVNTEPSGPTCEKKTYSVPAHQERAYEYVECPIRGTAAENKENLDPSNLMPPPNQTPAPDQPFALSTVREESSIPRADSEKKWVYPSEQMFWNAMLKKGWKWKDEDISQKDMYNIIRIHNQNNEQAWKEILKWEALHAAECPCGPSLIRFGGKAKEYSPRARIRSWMGYELPFDRHDWIINRCGTEVRYVIDYYDGGEVNKDYQFTILDVRPALDSLSAVWDRMKVAWWRWTS,mutated_sequence,1.0,268.0,NP_005324.3.a2m,NP_005324.3.npy,ClinVar +NP_005334.1,NP_005334.1.csv,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS,mutated_sequence,1.0,189.0,NP_005334.1.a2m,NP_005334.1.npy,ClinVar +NP_005339.3,NP_005339.3.csv,MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVKDLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD,mutated_sequence,1.0,732.0,NP_005339.3.a2m,NP_005339.3.npy,ClinVar +NP_005350.1,NP_005350.1.csv,MDNMSITNTPTSNDACLSIVHSLMCHRQGGESETFAKRAIESLVKKLKEKKDELDSLITAITTNGAHPSKCVTIQRTLDGRLQVAGRKGFPHVIYARLWRWPDLHKNELKHVKYCQYAFDLKCDSVCVNPYHYERVVSPGIDLSGLTLQSNAPSSMMVKDEYVHDFEGQPSLSTEGHSIQTIQHPPSNRASTETYSTPALLAPSESNATSTANFPNIPVASTSQPASILGGSHSEGLLQIASGPQPGQQQNGFTGQPATYHHNSTTTWTGSRTAPYTPNLPHHQNGHLQHHPPMPPHPGHYWPVHNELAFQPPISNHPAPEYWCSIAYFEMDVQVGETFKVPSSCPIVTVDGYVDPSGGDRFCLGQLSNVHRTEAIERARLHIGKGVQLECKGEGDVWVRCLSDHAVFVQSYYLDREAGRAPGDAVHKIYPSAYIKVFDLRQCHRQMQQQAATAQAAAAAQAAAVAGNIPGPGSVGGIAPAISLSAAAGIGVDDLRRLCILRMSFVKGWGPDYPRQSIKETPCWIEIHLHRALQLLDEVLHTMPIADPQPLD,mutated_sequence,1.0,552.0,NP_005350.1.a2m,NP_005350.1.npy,ClinVar +NP_005369.2,NP_005369.2.csv,MPSCSTSTMPGMICKNPDLEFDSLQPCFYPDEDDFYFGGPDSTPPGEDIWKKFELLPTPPLSPSRGFAEHSSEPPSWVTEMLLENELWGSPAEEDAFGLGGLGGLTPNPVILQDCMWSGFSAREKLERAVSEKLQHGRGPPTAGSTAQSPGAGAASPAGRGHGGAAGAGRAGAALPAELAHPAAECVDPAVVFPFPVNKREPAPVPAAPASAPAAGPAVASGAGIAAPAGAPGVAPPRPGGRQTSGGDHKALSTSGEDTLSDSDDEDDEEEDEEEEIDVVTVEKRRSSSNTKAVTTFTITVRPKNAALGPGRAQSSELILKRCLPIHQQHNYAAPSPYVESEDAPPQKKIKSEASPRPLKSVIPPKAKSLSPRNSDSEDSERRRNHNILERQRRNDLRSSFLTLRDHVPELVKNEKAAKVVILKKATEYVHSLQAEEHQLLLEKEKLQARQQQLLKKIEHARTC,mutated_sequence,1.0,464.0,NP_005369.2.a2m,NP_005369.2.npy,ClinVar +NP_005436.1,NP_005436.1.csv,MYIKQVIIQGFRSYRDQTIVDPFSSKHNVIVGRNGSGKSNFFYAIQFVLSDEFSHLRPEQRLALLHEGTGPRVISAFVEIIFDNSDNRLPIDKEEVSLRRVIGAKKDQYFLDKKMVTKNDVMNLLESAGFSRSNPYYIVKQGKINQMATAPDSQRLKLLREVAGTRVYDERKEESISLMKETEGKREKINELLKYIEERLHTLEEEKEELAQYQKWDKMRRALEYTIYNQELNETRAKLDELSAKRETSGEKSRQLRDAQQDARDKMEDIERQVRELKTKISAMKEEKEQLSAERQEQIKQRTKLELKAKDLQDELAGNSEQRKRLLKERQKLLEKIEEKQKELAETEPKFNSVKEKEERGIARLAQATQERTDLYAKQGRGSQFTSKEERDKWIKKELKSLDQAINDKKRQIAAIHKDLEDTEANKEKNLEQYNKLDQDLNEVKARVEELDRKYYEVKNKKDELQSERNYLWREENAEQQALAAKREDLEKKQQLLRAATGKAILNGIDSINKVLDHFRRKGINQHVQNGYHGIVMNNFECEPAFYTCVEVTAGNRLFYHIVDSDEVSTKILMEFNKMNLPGEVTFLPLNKLDVRDTAYPETNDAIPMISKLRYNPRFDKAFKHVFGKTLICRSMEVSTQLARAFTMDCITLEGDQVSHRGALTGGYYDTRKSRLELQKDVRKAEEELGELEAKLNENLRRNIERINNEIDQLMNQMQQIETQQRKFKASRDSILSEMKMLKEKRQQSEKTFMPKQRSLQSLEASLHAMESTRESLKAELGTDLLSQLSLEDQKRVDALNDEIRQLQQENRQLLNERIKLEGIITRVETYLNENLRKRLDQVEQELNELRETEGGTVLTATTSELEAINKRVKDTMARSEDLDNSIDKTEAGIKELQKSMERWKNMEKEHMDAINHDTKELEKMTNRQGMLLKKKEECMKKIRELGSLPQEAFEKYQTLSLKQLFRKLEQCNTELKKYSHVNKKALDQFVNFSEQKEKLIKRQEELDRGYKSIMELMNVLELRKYEAIQLTFKQVSKNFSEVFQKLVPGGKATLVMKKGDVEGSQSQDEGEGSGESERGSGSQSSVPSVDQFTGVGIRVSFTGKQGEMREMQQLSGGQKSLVALALIFAIQKCDPAPFYLFDEIDQALDAQHRKAVSDMIMELAVHAQFITTTFRPELLESADKFYGVKFRNKVSHIDVITAEMAKDFVEDDTTHG,mutated_sequence,1.0,1217.0,NP_005436.1.a2m,NP_005436.1.npy,ClinVar +NP_005467.1,NP_005467.1.csv,MEKNGNNRKLRVCVATCNRADYSKLAPIMFGIKTEPEFFELDVVVLGSHLIDDYGNTYRMIEQDDFDINTRLHTIVRGEDEAAMVESVGLALVKLPDVLNRLKPDIMIVHGDRFDALALATSAALMNIRILHIEGGEVSGTIDDSIRHAITKLAHYHVCCTRSAEQHLISMCEDHDRILLAGCPSYDKLLSAKNKDYMSIIRMWLGDDVKSKDYIVALQHPVTTDIKHSIKMFELTLDALISFNKRTLVLFPNIDAGSKEMVRVMRKKGIEHHPNFRAVKHVPFDQFIQLVAHAGCMIGNSSCGVREVGAFGTPVINLGTRQIGRETGENVLHVRDADTQDKILQALHLQFGKQYPCSKIYGDGNAVPRILKFLKSIDLQEPLQKKFCFPPVKENISQDIDHILETLSALAVDLGGTNLRVAIVSMKGEIVKKYTQFNPKTYEERINLILQMCVEAAAEAVKLNCRILGVGISTGGRVNPREGIVLHSTKLIQEWNSVDLRTPLSDTLHLPVWVDNDGNCAALAERKFGQGKGLENFVTLITGTGIGGGIIHQHELIHGSSFCAAELGHLVVSLDGPDCSCGSHGCIEAYASGMALQREAKKLHDEDLLLVEGMSVPKDEAVGALHLIQAAKLGNAKAQSILRTAGTALGLGVVNILHTMNPSLVILSGVLASHYIHIVKDVIRQQALSSVQDVDVVVSDLVDPALLGAASMVLDYTTRRIY,mutated_sequence,1.0,722.0,NP_005467.1.a2m,NP_005467.1.npy,ClinVar +NP_005493.2,NP_005493.2.csv,MACWPQLRLLLWKNLTFRRRQTCQLLLEVAWPLFIFLILISVRLSYPPYEQHECHFPNKAMPSAGTLPWVQGIICNANNPCFRYPTPGEAPGVVGNFNKSIVARLFSDARRLLLYSQKDTSMKDMRKVLRTLQQIKKSSSNLKLQDFLVDNETFSGFLYHNLSLPKSTVDKMLRADVILHKVFLQGYQLHLTSLCNGSKSEEMIQLGDQEVSELCGLPREKLAAAERVLRSNMDILKPILRTLNSTSPFPSKELAEATKTLLHSLGTLAQELFSMRSWSDMRQEVMFLTNVNSSSSSTQIYQAVSRIVCGHPEGGGLKIKSLNWYEDNNYKALFGGNGTEEDAETFYDNSTTPYCNDLMKNLESSPLSRIIWKALKPLLVGKILYTPDTPATRQVMAEVNKTFQELAVFHDLEGMWEELSPKIWTFMENSQEMDLVRMLLDSRDNDHFWEQQLDGLDWTAQDIVAFLAKHPEDVQSSNGSVYTWREAFNETNQAIRTISRFMECVNLNKLEPIATEVWLINKSMELLDERKFWAGIVFTGITPGSIELPHHVKYKIRMDIDNVERTNKIKDGYWDPGPRADPFEDMRYVWGGFAYLQDVVEQAIIRVLTGTEKKTGVYMQQMPYPCYVDDIFLRVMSRSMPLFMTLAWIYSVAVIIKGIVYEKEARLKETMRIMGLDNSILWFSWFISSLIPLLVSAGLLVVILKLGNLLPYSDPSVVFVFLSVFAVVTILQCFLISTLFSRANLAAACGGIIYFTLYLPYVLCVAWQDYVGFTLKIFASLLSPVAFGFGCEYFALFEEQGIGVQWDNLFESPVEEDGFNLTTSVSMMLFDTFLYGVMTWYIEAVFPGQYGIPRPWYFPCTKSYWFGEESDEKSHPGSNQKRISEICMEEEPTHLKLGVSIQNLVKVYRDGMKVAVDGLALNFYEGQITSFLGHNGAGKTTTMSILTGLFPPTSGTAYILGKDIRSEMSTIRQNLGVCPQHNVLFDMLTVEEHIWFYARLKGLSEKHVKAEMEQMALDVGLPSSKLKSKTSQLSGGMQRKLSVALAFVGGSKVVILDEPTAGVDPYSRRGIWELLLKYRQGRTIILSTHHMDEADVLGDRIAIISHGKLCCVGSSLFLKNQLGTGYYLTLVKKDVESSLSSCRNSSSTVSYLKKEDSVSQSSSDAGLGSDHESDTLTIDVSAISNLIRKHVSEARLVEDIGHELTYVLPYEAAKEGAFVELFHEIDDRLSDLGISSYGISETTLEEIFLKVAEESGVDAETSDGTLPARRNRRAFGDKQSCLRPFTEDDAADPNDSDIDPESRETDLLSGMDGKGSYQVKGWKLTQQQFVALLWKRLLIARRSRKGFFAQIVLPAVFVCIALVFSLIVPPFGKYPSLELQPWMYNEQYTFVSNDAPEDTGTLELLNALTKDPGFGTRCMEGNPIPDTPCQAGEEEWTTAPVPQTIMDLFQNGNWTMQNPSPACQCSSDKIKKMLPVCPPGAGGLPPPQRKQNTADILQDLTGRNISDYLVKTYVQIIAKSLKNKIWVNEFRYGGFSLGVSNTQALPPSQEVNDAIKQMKKHLKLAKDSSADRFLNSLGRFMTGLDTKNNVKVWFNNKGWHAISSFLNVINNAILRANLQKGENPSHYGITAFNHPLNLTKQQLSEVALMTTSVDVLVSICVIFAMSFVPASFVVFLIQERVSKAKHLQFISGVKPVIYWLSNFVWDMCNYVVPATLVIIIFICFQQKSYVSSTNLPVLALLLLLYGWSITPLMYPASFVFKIPSTAYVVLTSVNLFIGINGSVATFVLELFTDNKLNNINDILKSVFLIFPHFCLGRGLIDMVKNQAMADALERFGENRFVSPLSWDLVGRNLFAMAVEGVVFFLITVLIQYRFFIRPRPVNAKLSPLNDEDEDVRRERQRILDGGGQNDILEIKELTKIYRRKRKPAVDRICVGIPPGECFGLLGVNGAGKSSTFKMLTGDTTVTRGDAFLNKNSILSNIHEVHQNMGYCPQFDAITELLTGREHVEFFALLRGVPEKEVGKVGEWAIRKLGLVKYGEKYAGNYSGGNKRKLSTAMALIGGPPVVFLDEPTTGMDPKARRFLWNCALSVVKEGRSVVLTSHSMEECEALCTRMAIMVNGRFRCLGSVQHLKNRFGDGYTIVVRIAGSNPDLKPVQDFFGLAFPGSVLKEKHRNMLQYQLPSSLSSLARIFSILSQSKKRLHIEDYSVSQTTLDQVFVNFAKDQSDDDHLKDLSLHKNQTVVDVAVLTSFLQDEKVKESYV,mutated_sequence,1.0,2261.0,NP_005493.2.a2m,NP_005493.2.npy,ClinVar +NP_005525.2,NP_005525.2.csv,MRPTLLWSLLLLLGVFAAAAAAPPDPLSQLPAPQHPKIRLYNAEQVLSWEPVALSNSTRPVVYQVQFKYTDSKWFTADIMSIGVNCTQITATECDFTAASPSAGFPMDFNVTLRLRAELGALHSAWVTMPWFQHYRNVTVGPPENIEVTPGEGSLIIRFSSPFDIADTSTAFFCYYVHYWEKGGIQQVKGPFRSNSISLDNLKPSRVYCLQVQAQLLWNKSNIFRVGHLSNISCYETMADASTELQQVILISVGTFSLLSVLAGACFFLVLKYRGLIKYWFHTPPSIPLQIEEYLKDPTQPILEALDKDSSPKDDVWDSVSIISFPEKEQEDVLQTL,mutated_sequence,1.0,337.0,NP_005525.2.a2m,NP_005525.2.npy,ClinVar +NP_005526.1,NP_005526.1.csv,MEPLVTWVVPLLFLFLLSRQGAACRTSECCFQDPPYPDADSGSASGPRDLRCYRISSDRYECSWQYEGPTAGVSHFLRCCLSSGRCCYFAAGSATRLQFSDQAGVSVLYTVTLWVESWARNQTEKSPEVTLQLYNSVKYEPPLGDIKVSKLAGQLRMEWETPDNQVGAEVQFRHRTPSSPWKLGDCGPQDDDTESCLCPLEMNVAQEFQLRRRQLGSQGSSWSKWSSPVCVPPENPPQPQVRFSVEQLGQDGRRRLTLKEQPTQLELPEGCQGLAPGTEVTYRLQLHMLSCPCKAKATRTLHLGKMPYLSGAAYNVAVISSNQFGPGLNQTWHIPADTHTEPVALNISVGTNGTTMYWPARAQSMTYCIEWQPVGQDGGLATCSLTAPQDPDPAGMATYSWSRESGAMGQEKCYYITIFASAHPEKLTLWSTVLSTYHFGGNASAAGTPHHVSVKNHSLDSVSVDWAPSLLSTCPGVLKEYVVRCRDEDSKQVSEHPVQPTETQVTLSGLRAGVAYTVQVRADTAWLRGVWSQPQRFSIEVQVSDWLIFFASLGSFLSILLVGVLGYLGLNRAARHLCPPLPTPCASSAIEFPGGKETWQWINPVDFQEEASLQEALVVEMSWDKGERTEPLEKTELPEGAPELALDTELSLEDGDRCKAKM,mutated_sequence,1.0,662.0,NP_005526.1.a2m,NP_005526.1.npy,ClinVar +NP_005535.1,NP_005535.1.csv,MASPPESDGFSDVRKVGYLRKPKSMHKRFFVLRAASEAGGPARLEYYENEKKWRHKSSAPKRSIPLESCFNINKRADSKNKHLVALYTRDEHFAIAADSEAEQDSWYQALLQLHNRAKGHHDGAAALGAGGGGGSCSGSSGLGEAGEDLSYGDVPPGPAFKEVWQVILKPKGLGQTKNLIGIYRLCLTSKTISFVKLNSEAAAVVLQLMNIRRCGHSENFFFIEVGRSAVTGPGEFWMQVDDSVVAQNMHETILEAMRAMSDEFRPRSKSQSSSNCSNPISVPLRRHHLNNPPPSQVGLTRRSRTESITATSPASMVGGKPGSFRVRASSDGEGTMSRPASVDGSPVSPSTNRTHAHRHRGSARLHPPLNHSRSIPMPASRCSPSATSPVSLSSSSTSGHGSTSDCLFPRRSSASVSGSPSDGGFISSDEYGSSPCDFRSSFRSVTPDSLGHTPPARGEEELSNYICMGGKGPSTLTAPNGHYILSRGGNGHRCTPGTGLGTSPALAGDEAASAADLDNRFRKRTHSAGTSPTITHQKTPSQSSVASIEEYTEMMPAYPPGGGSGGRLPGHRHSAFVPTRSYPEEGLEMHPLERRGGHHRPDSSTLHTDDGYMPMSPGVAPVPSGRKGSGDYMPMSPKSVSAPQQIINPIRRHPQRVDPNGYMMMSPSGGCSPDIGGGPSSSSSSSNAVPSGTSYGKLWTNGVGGHHSHVLPHPKPPVESSGGKLLPCTGDYMNMSPVGDSNTSSPSDCYYGPEDPQHKPVLSYYSLPRSFKHTQRPGEPEEGARHQHLRLSTSSGRLLYAATADDSSSSTSSDSLGGGYCGARLEPSLPHPHHQVLQPHLPRKVDTAAQTNSRLARPTRLSLGDPKASTLPRAREQQQQQQPLLHPPEPKSPGEYVNIEFGSDQSGYLSGPVAFHSSPSVRCPSQLQPAPREEETGTEEYMKMDLGPGRRAAWQESTGVEMGRLGPAPPGAASICRPTRAVPSSRGDYMTMQMSCPRQSYVDTSPAAPVSYADMRTGIAAEEVSLPRATMAAASSSSAASASPTGPQGAAELAAHSSLLGGPQGPGGMSAFTRVNLSPNRNQSAKVIRADPQGCRRRHSSETFSSTPSATRVGNTVPFGAGAAVGGGGGSSSSSEDVKRHSSASFENVWLRPGELGGAPKEPAKLCGAAGGLENGLNYIDLDLVKDFKQCPQECTPEPQPPPPPPPHQPLGSGESSSTRRSSEDLSAYASISFQKQPEDRQ,mutated_sequence,1.0,1242.0,NP_005535.1.a2m,NP_005535.1.npy,ClinVar +NP_005545.1,NP_005545.1.csv,MASTSTTIRSHSSSRRGFSANSARLPGVSRSGFSSVSVSRSRGSGGLGGACGGAGFGSRSLYGLGGSKRISIGGGSCAISGGYGSRAGGSYGFGGAGSGFGFGGGAGIGFGLGGGAGLAGGFGGPGFPVCPPGGIQEVTVNQSLLTPLNLQIDPTIQRVRAEEREQIKTLNNKFASFIDKVRFLEQQNKVLETKWTLLQEQGTKTVRQNLEPLFEQYINNLRRQLDSIVGERGRLDSELRGMQDLVEDFKNKYEDEINKRTAAENEFVTLKKDVDAAYMNKVELQAKADTLTDEINFLRALYDAELSQMQTHISDTSVVLSMDNNRNLDLDSIIAEVKAQYEEIAQRSRAEAESWYQTKYEELQVTAGRHGDDLRNTKQEIAEINRMIQRLRSEIDHVKKQCANLQAAIADAEQRGEMALKDAKNKLEGLEDALQKAKQDLARLLKEYQELMNVKLALDVEIATYRKLLEGEECRLNGEGVGQVNISVVQSTVSSGYGGASGVGSGLGLGGGSSYSYGSGLGVGGGFSSSSGRAIGGGLSSVGGGSSTIKYTTTSSSSRKSYKH,mutated_sequence,1.0,564.0,NP_005545.1.a2m,NP_005545.1.npy,ClinVar +NP_005546.2,NP_005546.2.csv,MASTSTTIRSHSSSRRGFSANSARLPGVSRSGFSSISVSRSRGSGGLGGACGGAGFGSRSLYGLGGSKRISIGGGSCAISGGYGSRAGGSYGFGGAGSGFGFGGGAGIGFGLGGGAGLAGGFGGPGFPVCPPGGIQEVTVNQSLLTPLNLQIDPAIQRVRAEEREQIKTLNNKFASFIDKVRFLEQQNKVLDTKWTLLQEQGTKTVRQNLEPLFEQYINNLRRQLDNIVGERGRLDSELRNMQDLVEDLKNKYEDEINKRTAAENEFVTLKKDVDAAYMNKVELQAKADTLTDEINFLRALYDAELSQMQTHISDTSVVLSMDNNRNLDLDSIIAEVKAQYEEIAQRSRAEAESWYQTKYEELQITAGRHGDDLRNTKQEIAEINRMIQRLRSEIDHVKKQCANLQAAIADAEQRGEMALKDAKNKLEGLEDALQKAKQDLARLLKEYQELMNVKLALDVEIATYRKLLEGEECRLNGEGVGQVNISVVQSTVSSGYGGASGVGSGLGLGGGSSYSYGSGLGVGGGFSSSSGRATGGGLSSVGGGSSTIKYTTTSSSSRKSYKH,mutated_sequence,1.0,564.0,NP_005546.2.a2m,NP_005546.2.npy,ClinVar +NP_005548.2,NP_005548.2.csv,MTTCSRQFTSSSSMKGSCGIGGGIGGGSSRISSVLAGGSCRAPSTYGGGLSVSSRFSSGGACGLGGGYGGGFSSSSSFGSGFGGGYGGGLGAGFGGGLGAGFGGGFAGGDGLLVGSEKVTMQNLNDRLASYLDKVRALEEANADLEVKIRDWYQRQRPSEIKDYSPYFKTIEDLRNKIIAATIENAQPILQIDNARLAADDFRTKYEHELALRQTVEADVNGLRRVLDELTLARTDLEMQIEGLKEELAYLRKNHEEEMLALRGQTGGDVNVEMDAAPGVDLSRILNEMRDQYEQMAEKNRRDAETWFLSKTEELNKEVASNSELVQSSRSEVTELRRVLQGLEIELQSQLSMKASLENSLEETKGRYCMQLSQIQGLIGSVEEQLAQLRCEMEQQSQEYQILLDVKTRLEQEIATYRRLLEGEDAHLSSQQASGQSYSSREVFTSSSSSSSRQTRPILKEQSSSSFSQGQSS,mutated_sequence,1.0,473.0,NP_005548.2.a2m,NP_005548.2.npy,ClinVar +NP_005576.3,NP_005576.3.csv,MFRSKRSGLVRRLWRSRVVPDREEGGSGGGGGGDEDGSLGSRAEPAPRAREGGGCGRSEVRPVAPRRPRDAVGQRGAQGAGRRRRAGGPPRPMSEPGAGAGSSLLDVAEPGGPGWLPESDCETVTCCLFSERDAAGAPRDASDPLAGAALEPAGGGRSREARSRLLLLEQELKTVTYSLLKRLKERSLDTLLEAVESRGGVPGGCVLVPRADLRLGGQPAPPQLLLGRLFRWPDLQHAVELKPLCGCHSFAAAADGPTVCCNPYHFSRLCGPESPPPPYSRLSPRDEYKPLDLSDSTLSYTETEATNSLITAPGEFSDASMSPDATKPSHWCSVAYWEHRTRVGRLYAVYDQAVSIFYDLPQGSGFCLGQLNLEQRSESVRRTRSKIGFGILLSKEPDGVWAYNRGEHPIFVNSPTLDAPGGRALVVRKVPPGYSIKVFDFERSGLQHAPEPDAADGPYDPNSVRISFAKGWGPCYSRQFITSCPCWLEILLNNPR,mutated_sequence,1.0,496.0,NP_005576.3.a2m,NP_005576.3.npy,ClinVar +NP_005600.1,NP_005600.1.csv,MSRPLSDQEKRKQISVRGLAGVENVTELKKNFNRHLHFTLVKDRNVATPRDYYFALAHTVRDHLVGRWIRTQQHYYEKDPKRIYYLSLEFYMGRTLQNTMVNLALENACDEATYQLGLDMEELEEIEEDAGLGNGGLGRLAACFLDSMATLGLAAYGYGIRYEFGIFNQKISGGWQMEEADDWLRYGNPWEKARPEFTLPVHFYGHVEHTSQGAKWVDTQVVLAMPYDTPVPGYRNNVVNTMRLWSAKAPNDFNLKDFNVGGYIQAVLDRNLAENISRVLYPNDNFFEGKELRLKQEYFVVAATLQDIIRRFKSSKFGCRDPVRTNFDAFPDKVAIQLNDTHPSLAIPELMRILVDLERMDWDKAWDVTVRTCAYTNHTVLPEALERWPVHLLETLLPRHLQIIYEINQRFLNRVAAAFPGDVDRLRRMSLVEEGAVKRINMAHLCIAGSHAVNGVARIHSEILKKTIFKDFYELEPHKFQNKTNGITPRRWLVLCNPGLAEVIAERIGEDFISDLDQLRKLLSFVDDEAFIRDVAKVKQENKLKFAAYLEREYKVHINPNSLFDIQVKRIHEYKRQLLNCLHVITLYNRIKREPNKFFVPRTVMIGGKAAPGYHMAKMIIRLVTAIGDVVNHDPAVGDRLRVIFLENYRVSLAEKVIPAADLSEQISTAGTEASGTGNMKFMLNGALTIGTMDGANVEMAEEAGEENFFIFGMRVEDVDKLDQRGYNAQEYYDRIPELRQVIEQLSSGFFSPKQPDLFKDIVNMLMHHDRFKVFADYEDYIKCQEKVSALYKNPREWTRMVIRNIATSGKFSSDRTIAQYAREIWGVEPSRQRLPAPDEAI,mutated_sequence,1.0,842.0,NP_005600.1.a2m,NP_005600.1.npy,ClinVar +NP_005620.1,NP_005620.1.csv,MAKKSAENGIYSVSGDEKKGPLIAPGPDGAPAKGDGPVGLGTPGGRLAVPPRETWTRQMDFIMSCVGFAVGLGNVWRFPYLCYKNGGGVFLIPYVLIALVGGIPIFFLEISLGQFMKAGSINVWNICPLFKGLGYASMVIVFYCNTYYIMVLAWGFYYLVKSFTTTLPWATCGHTWNTPDCVEIFRHEDCANASLANLTCDQLADRRSPVIEFWENKVLRLSGGLEVPGALNWEVTLCLLACWVLVYFCVWKGVKSTGKIVYFTATFPYVVLVVLLVRGVLLPGALDGIIYYLKPDWSKLGSPQVWIDAGTQIFFSYAIGLGALTALGSYNRFNNNCYKDAIILALINSGTSFFAGFVVFSILGFMAAEQGVHISKVAESGPGLAFIAYPRAVTLMPVAPLWAALFFFMLLLLGLDSQFVGVEGFITGLLDLLPASYYFRFQREISVALCCALCFVIDLSMVTDGGMYVFQLFDYYSASGTTLLWQAFWECVVVAWVYGADRFMDDIACMIGYRPCPWMKWCWSFFTPLVCMGIFIFNVVYYEPLVYNNTYVYPWWGEAMGWAFALSSMLCVPLHLLGCLLRAKGTMAERWQHLTQPIWGLHHLEYRAQDADVRGLTTLTPVSESSKVVVVESVM,mutated_sequence,1.0,635.0,NP_005620.1.a2m,NP_005620.1.npy,ClinVar +NP_005624.2,NP_005624.2.csv,MQAQQLPYEFFSEENAPKWRGLLVPALKKVQGQVHPTLESNDDALQYVEELILQLLNMLCQAQPRSASDVEERVQKSFPHPIDKWAIADAQSAIEKRKRRNPLSLPVEKIHPLLKEVLGYKIDHQVSVYIVAVLEYISADILKLVGNYVRNIRHYEITKQDIKVAMCADKVLMDMFHQDVEDINILSLTDEEPSTSGEQTYYDLVKAFMAEIRQYIRELNLIIKVFREPFVSNSKLFSANDVENIFSRIVDIHELSVKLLGHIEDTVEMTDEGSPHPLVGSCFEDLAEELAFDPYESYARDILRPGFHDRFLSQLSKPGAALYLQSIGEGFKEAVQYVLPRLLLAPVYHCLHYFELLKQLEEKSEDQEDKECLKQAITALLNVQSGMEKICSKSLAKRRLSESACRFYSQQMKGKQLAIKKMNEIQKNIDGWEGKDIGQCCNEFIMEGTLTRVGAKHERHIFLFDGLMICCKSNHGQPRLPGASNAEYRLKEKFFMRKVQINDKDDTNEYKHAFEIILKDENSVIFSAKSAEEKNNWMAALISLQYRSTLERMLDVTMLQEEKEEQMRLPSADVYRFAEPDSEENIIFEENMQPKAGIPIIKAGTVIKLIERLTYHMYADPNFVRTFLTTYRSFCKPQELLSLIIERFEIPEPEPTEADRIAIENGDQPLSAELKRFRKEYIQPVQLRVLNVCRHWVEHHFYDFERDAYLLQRMEEFIGTVRGKAMKKWVESITKIIQRKKIARDNGPGHNITFQSSPPTVEWHISRPGHIETFDLLTLHPIEIARQLTLLESDLYRAVQPSELVGSVWTKEDKEINSPNLLKMIRHTTNLTLWFEKCIVETENLEERVAVVSRIIEILQVFQELNNFNGVLEVVSAMNSSPVYRLDHTFEQIPSRQKKILEEAHELSEDHYKKYLAKLRSINPPCVPFFGIYLTNILKTEEGNPEVLKRHGKELINFSKRRKVAEITGEIQQYQNQPYCLRVESDIKRFFENLNPMGNSMEKEFTDYLFNKSLEIEPRNPKPLPRFPKKYSYPLKSPGVRPSNPRPGTMRHPTPLQQEPRKISYSRIPESETESTASAPNSPRTPLTPPPASGASSTTDVCSVFDSDHSSPFHSSNDTVFIQVTLPHGPRSASVSSISLTKGTDEVPVPPPVPPRRRPESAPAESSPSKIMSKHLDSPPAIPPRQPTSKAYSPRYSISDRTSISDPPESPPLLPPREPVRTPDVFSSSPLHLQPPPLGKKSDHGNAFFPNSPSPFTPPPPQTPSPHGTRRHLPSPPLTQEVDLHSIAGPPVPPRQSTSQHIPKLPPKTYKREHTHPSMHRDGPPLLENAHSS,mutated_sequence,1.0,1333.0,NP_005624.2.a2m,NP_005624.2.npy,ClinVar +NP_005630.1,NP_005630.1.csv,MVSESHHEALAAPPVTTVATVLPSNATEPASPGEGKEDAFSKLKEKFMNELHKIPLPPWALIAIAIVAVLLVLTCCFCICKKCLFKKKNKKKGKEKGGKNAINMKDVKDLGKTMKDQALKDDDAETGLTDGEEKEEPKEEEKLGKLQYSLDYDFQNNQLLVGIIQAAELPALDMGGTSDPYVKVFLLPDKKKKFETKVHRKTLNPVFNEQFTFKVPYSELGGKTLVMAVYDFDRFSKHDIIGEFKVPMNTVDFGHVTEEWRDLQSAEKEEQEKLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKIHLMQNGKRLKKKKTTIKKNTLNPYYNESFSFEVPFEQIQKVQVVVTVLDYDKIGKNDAIGKVFVGYNSTGAELRHWSDMLANPRRPIAQWHTLQVEEEVDAMLAVKK,mutated_sequence,1.0,422.0,NP_005630.1.a2m,NP_005630.1.npy,ClinVar +NP_005645.1,NP_005645.1.csv,MAMVVSSWRDPQDDVAGGNPGGPNPAAQAARGGGGGAGEQQQQAGSGAPHTPQTPGQPGAPATPGTAGDKGQGPPGSGQSQQHIECVVCGDKSSGKHYGQFTCEGCKSFFKRSVRRNLTYTCRANRNCPIDQHHRNQCQYCRLKKCLKVGMRREAVQRGRMPPTQPNPGQYALTNGDPLNGHCYLSGYISLLLRAEPYPTSRYGSQCMQPNNIMGIENICELAARLLFSAVEWARNIPFFPDLQITDQVSLLRLTWSELFVLNAAQCSMPLHVAPLLAAAGLHASPMSADRVVAFMDHIRIFQEQVEKLKALHVDSAEYSCLKAIVLFTSDACGLSDAAHIESLQEKSQCALEEYVRSQYPNQPSRFGKLLLRLPSLRTVSSSVIEQLFFVRLVGKTPIETLIRDMLLSGSSFNWPYMSIQCS,mutated_sequence,1.0,423.0,NP_005645.1.a2m,NP_005645.1.npy,ClinVar +NP_005651.1,NP_005651.1.csv,MAAVGAGGSTAAPGPGAVSAGALEPGTASAAHRRLKYISLAVLVVQNASLILSIRYARTLPGDRFFATTAVVMAEVLKGLTCLLLLFAQKRGNVKHLVLFLHEAVLVQYVDTLKLAVPSLIYTLQNNLQYVAISNLPAATFQVTYQLKILTTALFSVLMLNRSLSRLQWASLLLLFTGVAIVQAQQAGGGGPRPLDQNPGAGLAAVVASCLSSGFAGVYFEKILKGSSGSVWLRNLQLGLFGTALGLVGLWWAEGTAVATRGFFFGYTPAVWGVVLNQAFGGLLVAVVVKYADNILKGFATSLSIVLSTVASIRLFGFHVDPLFALGAGLVIGAVYLYSLPRGAAKAIASASASASGPCVHQQPPGQPPPPQLSSHRGDLITEPFLPKLLTKVKGS,mutated_sequence,1.0,396.0,NP_005651.1.a2m,NP_005651.1.npy,ClinVar +NP_005786.1,NP_005786.1.csv,MEKKCTLYFLVLLPFFMILVTAELEESPEDSIQLGVTRNKIMTAQYECYQKIMQDPIQQAEGVYCNRTWDGWLCWNDVAAGTESMQLCPDYFQDFDPSEKVTKICDQDGNWFRHPASNRTWTNYTQCNVNTHEKVKTALNLFYLTIIGHGLSIASLLISLGIFFYFKSLSCQRITLHKNLFFSFVCNSVVTIIHLTAVANNQALVATNPVSCKVSQFIHLYLMGCNYFWMLCEGIYLHTLIVVAVFAEKQHLMWYYFLGWGFPLIPACIHAIARSLYYNDNCWISSDTHLLYIIHGPICAALLVNLFFLLNIVRVLITKLKVTHQAESNLYMKAVRATLILVPLLGIEFVLIPWRPEGKIAEEVYDYIMHILMHFQGLLVSTIFCFFNGEVQAILRRNWNQYKIQFGNSFSNSEALRSASYTVSTISDGPGYSHDCPSEHLNGKSIHDIENVLLKPENLYN,mutated_sequence,1.0,461.0,NP_005786.1.a2m,NP_005786.1.npy,ClinVar +NP_005850.1,NP_005850.1.csv,MADRDSGSEQGGAALGSGGSLGHPGSGSGSGGGGGGGGGGGGSGGGGGGAPGGLQHETQELASKRVDIQNKRFYLDVKQNAKGRFLKIAEVGAGGNKSRLTLSMSVAVEFRDYLGDFIEHYAQLGPSQPPDLAQAQDEPRRALKSEFLVRENRKYYMDLKENQRGRFLRIRQTVNRGPGLGSTQGQTIALPAQGLIEFRDALAKLIDDYGVEEEPAELPEGTSLTVDNKRFFFDVGSNKYGVFMRVSEVKPTYRNSITVPYKVWAKFGHTFCKYSEEMKKIQEKQREKRAACEQLHQQQQQQQEETAAATLLLQGEEEGEED,mutated_sequence,1.0,322.0,NP_005850.1.a2m,NP_005850.1.npy,ClinVar +NP_005852.2,NP_005852.2.csv,MKGKEEKEGGARLGAGGGSPEKSPSAQELKEQGNRLFVGRKYPEAAACYGRAITRNPLVAVYYTNRALCYLKMQQHEQALADCRRALELDGQSVKAHFFLGQCQLEMESYDEAIANLQRAYSLAKEQRLNFGDDIPSALRIAKKKRWNSIEERRIHQESELHSYLSRLIAAERERELEECQRNHEGDEDDSHVRAQQACIEAKHDKYMADMDELFSQVDEKRKKRDIPDYLCGKISFELMREPCITPSGITYDRKDIEEHLQRVGHFDPVTRSPLTQEQLIPNLAMKEVIDAFISENGWVEDY,mutated_sequence,1.0,303.0,NP_005852.2.a2m,NP_005852.2.npy,ClinVar +NP_005893.1,NP_005893.1.csv,MSSILPFTPPIVKRLLGWKKGEQNGQEEKWCEKAVKSLVKKLKKTGQLDELEKAITTQNVNTKCITIPRSLDGRLQVSHRKGLPHVIYCRLWRWPDLHSHHELRAMELCEFAFNMKKDEVCVNPYHYQRVETPVLPPVLVPRHTEIPAEFPPLDDYSHSIPENTNFPAGIEPQSNIPETPPPGYLSEDGETSDHQMNHSMDAGSPNLSPNPMSPAHNNLDLQPVTYCEPAFWCSISYYELNQRVGETFHASQPSMTVDGFTDPSNSERFCLGLLSNVNRNAAVELTRRHIGRGVRLYYIGGEVFAECLSDSAIFVQSPNCNQRYGWHPATVCKIPPGCNLKIFNNQEFAALLAQSVNQGFEAVYQLTRMCTIRMSFVKGWGAEYRRQTVTSTPCWIELHLNGPLQWLDKVLTQMGSPSIRCSSVS,mutated_sequence,1.0,425.0,NP_005893.1.a2m,NP_005893.1.npy,ClinVar +NP_005948.3,NP_005948.3.csv,MVNEARGNSSLNPCLEGSASSGSESSKDSSRCSTPGLDPERHERLREKMRRRLESGDKWFSLEFFPPRTAEGAVNLISRFDRMAAGGPLYIDVTWHPAGDPGSDKETSSMMIASTAVNYCGLETILHMTCCRQRLEEITGHLHKAKQLGLKNIMALRGDPIGDQWEEEEGGFNYAVDLVKHIRSEFGDYFDICVAGYPKGHPEAGSFEADLKHLKEKVSAGADFIITQLFFEADTFFRFVKACTDMGITCPIVPGIFPIQGYHSLRQLVKLSKLEVPQEIKDVIEPIKDNDAAIRNYGIELAVSLCQELLASGLVPGLHFYTLNREMATTEVLKRLGMWTEDPRRPLPWALSAHPKRREEDVRPIFWASRPKSYIYRTQEWDEFPNGRWGNSSSPAFGELKDYYLFYLKSKSPKEELLKMWGEELTSEESVFEVFVLYLSGEPNRNGHKVTCLPWNDEPLAAETSLLKEELLRVNRQGILTINSQPNINGKPSSDPIVGWGPSGGYVFQKAYLEFFTSRETAEALLQVLKKYELRVNYHLVNVKGENITNAPELQPNAVTWGIFPGREIIQPTVVDPVSFMFWKDEAFALWIERWGKLYEEESPSRTIIQYIHDNYFLVNLVDNDFPLDNCLWQVVEDTLELLNRPTQNARETEAP,mutated_sequence,1.0,656.0,NP_005948.3.a2m,NP_005948.3.npy,ClinVar +NP_005951.1,NP_005951.1.csv,MQLLGLLGLLWMLKASPWATGTLSTATSISQVPFPRAEAASAVLSNSPHSRDLAGWPLGVPQLASPAPGHRENAPMTLTTSPHDTLISETLLNSPVSSNTSTTPTSKFAFKVETTPPTVLVYSATTECVYPTSFIITISHPTSICVTTTQVAFTSSYTSTPVTQKPVTTVTSTYSMTTTEKGTSAMTSSPSTTTARETPIVTVTPSSVSATDTTFHTTISSTTRTTERTPLPTGSIHTTTSPTPVFTTLKTAVTSTSPITSSITSTNTVTSMTTTASQPTATNTLSSPTRTILSSTPVLSTETITSGITNTTPLSTLVTTLPTTISRSTPTSETTYTTSPTSTVTDSTTKIAYSTSMTGTLSTETSLPPTSSSLPTTETATTPMTNLVTTTTEISSHSTPSFSSSTIYSTVSTSTTAISSLPPTSGTMVTSTTMTPSSLSTDIPFTTPTTITHHSVGSTGFLTTATDLTSTFTVSSSSAMSTSVIPSSPSIQNTETSSLVSMTSATTPNVRPTFVSTLSTPTSSLLTTFPATYSFSSSMSASSAGTTHTESISSPPASTSTLHTTAESTLAPTTTTSFTTSTTMEPPSTTAATTGTGQTTFTSSTATFPETTTPTPTTDMSTESLTTAMTSPPITSSVTSTNTVTSMTTTTSPPTTTNSFTSLTSMPLSSTPVPSTEVVTSGTINTIPPSILVTTLPTPNASSMTTSETTYPNSPTGPGTNSTTEITYPTTMTETSSTATSLPPTSPLVSTAKTAKTPTTNLVTTTTKTTSHSTTSFTSSTVYSTASTYTTAITSVPTTLGTMVTSTSMISSTVSTGIPTSQPTTITPSSVGISGSLPMMTDLTSVYTVSNMSARPTTVIPSSPTVQNTEISISVSMTSATTPSGGPTFTSTENTPTRSLLTSFPMTHSFSSSMSESSAGTTHTESISSPRGTTSTLHTTVESTPSPTTTTSFTTSTMMEPPSSTVSTTGRGQTTFPSSTATFPETTTLTPTTDISTVSLTTAMTSPPPVSSSITPTNTMTSMRTTTYWPTATNTLSPLTSSILSSTPVPSTEMITSHTTNTTPLSTLVTTLLTTITRSTPTSETTYPTSPTSIVSDSTTEITYSTSITGTLSTATTLPPTSSSLPTTETATMTPTTTLITTTPNTTSLSTPSFTSSTIYSTVSTSTTAISSASPTSGTMVTSTTMTPSSLSTDTPSTTPTTITYPSVGSTGFLTTATDLTSTFTVSSSSAMSTSVIPSSPSIQNTETSSLVSMTSATTPSLRPTITSTDSTLTSSLLTTFPSTYSFSSSMSASSAGTTHTETISSLPASTNTIHTTAESALAPTTTTSFTTSPTMEPPSTTVATTGTGQTTFPSSTATFLETTTLTPTTDFSTESLTTAMTSTPPITSSITPTDTMTSMRTTTSWPTATNTLSPLTSSILSSTPVPSTEVTTSHTTNTNPVSTLVTTLPITITRSTLTSETAYPSSPTSTVTESTTEITYPTTMTETSSTATSLPPTSSLVSTAETAKTPTTNLVTTTTKTTSHSTTSFTSSTIYSTASTPTTAITSVPTTLGTMVTSTSMIPSTVSTGIPTSQPTTITPSSVGISGSLPMMTDLTSVYTVSSMSARPTSVIPSSPTVQNTETSIFVSMMSATTPSGGPTFTSTENTPTRSLLTSFPVTHSFSSSMSASSVGTTHTQSISSPPAITSTLHTTAESTPSPTTTMSFTTFTKMETPSSTVATTGTGQTTFTSSTATSPKTTTLTPTSDISTGSFKTAVSSTPPITSSITSTYTVTSMTTTTPLGPTATNTLPSFTSSVSSSTPVPSTEAITSGTTNTTPLSTLVTTFSNSDTSSTPTSETTYPTSLTSALTDSTTRTTYSTNMTGTLSTVTSLRPTSSSLLTTVTATVPTTNLVTTTTKITSHSTPSFTSSIATTETPSHSTPRFTSSITTTETPSHSTPRFTSSITNTKTTSHSSPSFTSSITTTETTSHNTPSLTSSITTTKTTSHSTPSYTSLITTTTTTSHSTPSFTSSITTTETTSHNTPSLTSSITTTETTSHSTPSFTSSITTETTSHSTPSFTSLITITEITSHSTLSYTTSITTTETPSHSTLSFTSSITTTETTSHSTPSFTSSITTSEMPSHSTPSFTSSITTTENATHSTPNFTSSITTTETTSHSTPSFTSLITTTETTSHRWGTTETTSYSTPSFTSSNTITETTSHSTPSYITSITTTETPSSSTPSFSSSITTTETTSHSTPGFTSSITTTETTSHSTPSFTSSITTTETTSHDTPSFTSSITTSETPSHSTPSSTSLITTTKTTSHSTPSFTSSITTTETTSHSAHSFTSSITTTETTSHNTRSFTSSITTTETNSHSTTSFTSSITTTETTSHSTPSFSSSITTTETPLHSTPGLTSWVTTTKTTSHITPGLTSSITTTETTSHSTPGFTSSITTTETTSESTPSLSSSTIYSTVSTSTTAITSHFTTSETAVTPTPVTPSSLSTDIPTTSLRTLTPSSVGTSTSLTTTTDFPSIPTDISTLPTRTHIISSSPSIQSTETSSLVGTTSPTMSTVRMTLRITENTPISSFSTSIVVIPETPTQTPPVLTSATGTQTSPAPTTVTFGSTDSSTSTLHTLTPSTALSTIVSTSQVPIPSTHSSTLQTTPSTPSLQTSLTSTSEFTTESFTRGSTSTNAILTSFSTIIWSSTPTIIMSSSPSSASITPVFSTTIHSVPSSPYIFSTENVGSASITGFPSLSSSATTSTSSTSSSLTTALTEITPFSYISLPSTTPCPGTITITIVPASPTDPCVEMDPSTEATSPPTTPLTVFPFTTEMVTCPTSISIQTTLTTYMDTSSMMPESESSISPNASSSTGTGTVPTNTVFTSTRLPTSETWLSNSSVIPLPLPGVSTIPLTMKPSSSLPTILRTSSKSTHPSPPTTRTSETPVATTQTPTTLTSRRTTRITSQMTTQSTLTTTAGTCDNGGTWEQGQCACLPGFSGDRCQLQTRCQNGGQWDGLKCQCPSTFYGSSCEFAVEQVDLDVVETEVGMEVSVDQQFSPDLNDNTSQAYRDFNKTFWNQMQKIFADMQGFTFKGVEILSLRNGSIVVDYLVLLEMPFSPQLESEYEQVKTTLKEGLQNASQDVNSCQDSQTLCFKPDSIKVNNNSKTELTPAAICRRAAPTGYEEFYFPLVEATRLRCVTKCTSGVDNAIDCHQGQCVLETSGPTCRCYSTDTHWFSGPRCEVAVHWRALVGGLTAGAALLVLLLLALGVRAVRSGWWGGQRRGRSWDQDRKWFETWDEEVVGTFSNWGFEDDGTDKDTNFYVALENVDTTMKVHIKRPEMTSSSV,mutated_sequence,1.0,3323.0,NP_005951.1.a2m,NP_005951.1.npy,ClinVar +NP_005952.2,NP_005952.2.csv,MVQRWLLLSCCGALLSAGLANTSYTSPGLQRLKDSPQTAPDKGQCSTWGAGHFSTFDHHVYDFSGTCNYIFAATCKDAFPTFSVQLRRGPDGSISRIIVELGASVVTVSEAIISVKDIGVISLPYTSNGLQITPFGQSVRLVAKQLELELEVVWGPDSHLMVLVERKYMGQMCGLCGNFDGKVTNEFVSEEGKFLEPHKFAALQKLDDPGEICTFQDIPSTHVRQAQHARICTQLLTLVAPECSVSKEPFVLSCQADVAAAPQPGPQNSSCATLSEYSRQCSMVGQPVRRWRSPGLCSVGQCPANQVYQECGSACVKTCSNPQHSCSSSCTFGCFCPEGTVLNDLSNNHTCVPVTQCPCVLHGAMYAPGEVTIAACQTCRCTLGRWVCTERPCPGHCSLEGGSFVTTFDARPYRFHGTCTYILLQSPQLPEDGALMAVYDKSGVSHSETSLVAVVYLSRQDKIVISQDEVVTNNGEAKWLPYKTRNITVFRQTSTHLQMATSFGLELVVQLRPIFQAYVTVGPQFRGQTRGLCGNFNGDTTDDFTTSMGIAEGTASLFVDSWRAGNCPAALERETDPCSMSQLNKVCAETHCSMLLRTGTVFERCHATVNPAPFYKRCVYQACNYEETFPHICAALGDYVHACSLRGVLLWGWRSSVDNCTIPCTGNTTFSYNSQACERTCLSLSDRATECHHSAVPVDGCNCPDGTYLNQKGECVRKAQCPCILEGYKFILAEQSTVINGITCHCINGRLSCPQRPQMFLASCQAPKTFKSCSQSSENKFGAACAPTCQMLATGVACVPTKCEPGCVCAEGLYENADGQCVPPEECPCEFSGVSYPGGAELHTDCRTCSCSRGRWACQQGTHCPSTCTLYGEGHVITFDGQRFVFDGNCEYILATDVCGVNDSQPTFKILTENVICGNSGVTCSRAIKIFLGGLSVVLADRNYTVTGEEPHVQLGVTPGALSLVVDISIPGRYNLTLIWNRHMTILIRIARASQDPLCGLCGNFNGNMKDDFETRSRYVASSELELVNSWKESPLCGDVSFVTDPCSLNAFRRSWAERKCSVINSQTFATCHSKVYHLPYYEACVRDACGCDSGGDCECLCDAVAAYAQACLDKGVCVDWRTPAFCPIYCGFYNTHTQDGHGEYQYTQEANCTWHYQPCLCPSQPQSVPGSNIEGCYNCSQDEYFDHEEGVCVPCMPPTTPQPPTTPQLPTTGSRPTQVWPMTGTSTTIGLLSSTGPSPSSNHTPASPTQTPLLPATLTSSKPTASSGEPPRPTTAVTPQATSGLPPTATLRSTATKPTVTQATTRATASTASPATTSTAQSTTRTTMTLPTPATSGTSPTLPKSTNQELPGTTATQTTGPRPTPASTTGPTTPQPGQPTRPTATETTQTRTTTEYTTPQTPHTTHSPPTAGSPVPSTGPVTATSFHATTTYPTPSHPETTLPTHVPPFSTSLVTPSTHTVITPTHAQMATSASNHSAPTGTIPPPTTLKATGSTHTAPPITPTTSGTSQAHSSFSTNKTPTSLHSHTSSTHHPEVTPTSTTTITPNPTSTRTRTPVAHTNSATSSRPPPPFTTHSPPTGSSPFSSTGPMTATSFKTTTTYPTPSHPQTTLPTHVPPFSTSLVTPSTHTVITPTHAQMATSASIHSMPTGTIPPPTTLKATGSTHTAPTMTLTTSGTSQALSSLNTAKTSTSLHSHTSSTHHAEATSTSTTNITPNPTSTGTPPMTVTTSGTSQSRSSFSTAKTSTSLHSHTSSTHHPEVTSTSTTSITPNHTSTGTRTPVAHTTSATSSRLPTPFTTHSPPTGTTPISSTGPVTATSFQTTTTYPTPSHPHTTLPTHVPSFSTSLVTPSTHTVIIPTHTQMATSASIHSMPTGTIPPPTTIKATGSTHTAPPMTPTTSGTSQSPSSFSTAKTSTSLPYHTSSTHHPEVTPTSTTNITPKHTSTGTRTPVAHTTSASSSRLPTPFTTHSPPTGSSPFSSTGPMTATSFQTTTTYPTPSHPQTTLPTHVPPFSTSLVTPSTHTVIITTHTQMATSASIHSTPTGTVPPPTTLKATGSTHTAPPMTVTTSGTSQTHSSFSTATASSSFISSSSWLPQNSSSRPPSSPITTQLPHLSSATTPVSTTNQLSSSFSPSPSAPSTVSSYVPSSHSSPQTSSPSVGTSSSFVSAPVHSTTLSSGSHSSLSTHPTTASVSASPLFPSSPAASTTIRATLPHTISSPFTLSALLPISTVTVSPTPSSHLASSTIAFPSTPRTTASTHTAPAFSSQSTTSRSTSLTTRVPTSGFVSLTSGVTGIPTSPVTNLTTRHPGPTLSPTTRFLTSSLTAHGSTPASAPVSSLGTPTPTSPGVCSVREQQEEITFKGCMANVTVTRCEGACISAASFNIITQQVDARCSCCRPLHSYEQQLELPCPDPSTPGRRLVLTLQVFSHCVCSSVACGD,mutated_sequence,1.0,2439.0,NP_005952.2.a2m,NP_005952.2.npy,ClinVar +NP_005973.1,NP_005973.1.csv,MSMLPSFGFTQEQVACVCEVLQQGGNLERLGRFLWSLPACDHLHKNESVLKAKAVVAFHRGNFRELYKILESHQFSPHNHPKLQQLWLKAHYVEAEKLRGRPLGAVGKYRVRRKFPLPRTIWDGEETSYCFKEKSRGVLREWYAHNPYPSPREKRELAEATGLTTTQVSNWFKNRRQRDRAAEAKERENTENNNSSSNKQNQLSPLEGGKPLMSSSEEEFSPPQSPDQNSVLLLQGNMGHARSSNYSLPGLTASQPSHGLQTHQHQLQDSLLGPLTSSLVDLGS,mutated_sequence,1.0,284.0,NP_005973.1.a2m,NP_005973.1.npy,ClinVar +NP_005975.1,NP_005975.1.csv,MPAPRAPRALAAAAPASGKAKLTHPGKAILAGGLAGGIEICITFPTEYVKTQLQLDERSHPPRYRGIGDCVRQTVRSHGVLGLYRGLSSLLYGSIPKAAVRFGMFEFLSNHMRDAQGRLDSTRGLLCGLGAGVAEAVVVVCPMETIKVKFIHDQTSPNPKYRGFFHGVREIVREQGLKGTYQGLTATVLKQGSNQAIRFFVMTSLRNWYRGDNPNKPMNPLITGVFGAIAGAASVFGNTPLDVIKTRMQGLEAHKYRNTWDCGLQILKKEGLKAFYKGTVPRLGRVCLDVAIVFVIYDEVVKLLNKVWKTD,mutated_sequence,1.0,311.0,NP_005975.1.a2m,NP_005975.1.npy,ClinVar +NP_005996.2,NP_005996.2.csv,MDSNTAPLGPSCPQPPPAPQPQARSRLNATASLEQERSERPRAPGPQAGPGPGVRDAAAPAEPQAQHTRSRERADGTGPTKGDMEIPFEEVLERAKAGDPKAQTEVGKHYLQLAGDTDEELNSCTAVDWLVLAAKQGRREAVKLLRRCLADRRGITSENEREVRQLSSETDLERAVRKAALVMYWKLNPKKKKQVAVAELLENVGQVNEHDGGAQPGPVPKSLQKQRRMLERLVSSESKNYIALDDFVEITKKYAKGVIPSSLFLQDDEDDDELAGKSPEDLPLRLKVVKYPLHAIMEIKEYLIDMASRAGMHWLSTIIPTHHINALIFFFIVSNLTIDFFAFFIPLVIFYLSFISMVICTLKVFQDSKAWENFRTLTDLLLRFEPNLDVEQAEVNFGWNHLEPYAHFLLSVFFVIFSFPIASKDCIPCSELAVITGFFTVTSYLSLSTHAEPYTRRALATEVTAGLLSLLPSMPLNWPYLKVLGQTFITVPVGHLVVLNVSVPCLLYVYLLYLFFRMAQLRNFKGTYCYLVPYLVCFMWCELSVVILLESTGLGLLRASIGYFLFLFALPILVAGLALVGVLQFARWFTSLELTKIAVTVAVCSVPLLLRWWTKASFSVVGMVKSLTRSSMVKLILVWLTAIVLFCWFYVYRSEGMKVYNSTLTWQQYGALCGPRAWKETNMARTQILCSHLEGHRVTWTGRFKYVRVTDIDNSAESAINMLPFFIGDWMRCLYGEAYPACSPGNTSTAEEELCRLKLLAKHPCHIKKFDRYKFEITVGMPFSSGADGSRSREEDDVTKDIVLRASSEFKSVLLSLRQGSLIEFSTILEGRLGSKWPVFELKAISCLNCMAQLSPTRRHVKIEHDWRSTVHGAVKFAFDFFFFPFLSAA,mutated_sequence,1.0,890.0,NP_005996.2.a2m,NP_005996.2.npy,ClinVar +NP_006000.2,NP_006000.2.csv,MRECISIHVGQAGVQIGNACWELYCLEHGIQPDGQMPSDKTIGGGDDSFNTFFSETGAGKHVPRAVFVDLEPTVIDEVRTGTYRQLFHPEQLITGKEDAANNYARGHYTIGKEIIDLVLDRIRKLADQCTGLQGFLVFHSFGGGTGSGFTSLLMERLSVDYGKKSKLEFSIYPAPQVSTAVVEPYNSILTTHTTLEHSDCAFMVDNEAIYDICRRNLDIERPTYTNLNRLIGQIVSSITASLRFDGALNVDLTEFQTNLVPYPRIHFPLATYAPVISAEKAYHEQLSVAEITNACFEPANQMVKCDPRHGKYMACCLLYRGDVVPKDVNAAIATIKTKRTIQFVDWCPTGFKVGINYQPPTVVPGGDLAKVQRAVCMLSNTTAIAEAWARLDHKFDLMYAKRAFVHWYVGEGMEEGEFSEAREDMAALEKDYEEVGVDSVEGEGEEEGEEY,mutated_sequence,1.0,451.0,NP_006000.2.a2m,NP_006000.2.npy,ClinVar +NP_006006.3,NP_006006.3.csv,MAAQVAPAAASSLGNPPPPPPSELKKAEQQQREEAGGEAAAAAAAERGEMKAAAGQESEGPAVGPPQPLGKELQDGAESNGGGGGGGAGSGGGPGAEPDLKNSNGNAGPRPALNNNLTEPPGGGGGGSSDGVGAPPHSAAAALPPPAYGFGQPYGRSPSAVAAAAAAVFHQQHGGQQSPGLAALQSGGGGGLEPYAGPQQNSHDHGFPNHQYNSYYPNRSAYPPPAPAYALSSPRGGTPGSGAAAAAGSKPPPSSSASASSSSSSFAQQRFGAMGGGGPSAAGGGTPQPTATPTLNQLLTSPSSARGYQGYPGGDYSGGPQDGGAGKGPADMASQCWGAAAAAAAAAAASGGAQQRSHHAPMSPGSSGGGGQPLARTPQPSSPMDQMGKMRPQPYGGTNPYSQQQGPPSGPQQGHGYPGQPYGSQTPQRYPMTMQGRAQSAMGGLSYTQQIPPYGQQGPSGYGQQGQTPYYNQQSPHPQQQQPPYSQQPPSQTPHAQPSYQQQPQSQPPQLQSSQPPYSQQPSQPPHQQSPAPYPSQQSTTQQHPQSQPPYSQPQAQSPYQQQQPQQPAPSTLSQQAAYPQPQSQQSQQTAYSQQRFPPPQELSQDSFGSQASSAPSMTSSKGGQEDMNLSLQSRPSSLPDLSGSIDDLPMGTEGALSPGVSTSGISSSQGEQSNPAQSPFSPHTSPHLPGIRGPSPSPVGSPASVAQSRSGPLSPAAVPGNQMPPRPPSGQSDSIMHPSMNQSSIAQDRGYMQRNPQMPQYSSPQPGSALSPRQPSGGQIHTGMGSYQQNSMGSYGPQGGQYGPQGGYPRQPNYNALPNANYPSAGMAGGINPMGAGGQMHGQPGIPPYGTLPPGRMSHASMGNRPYGPNMANMPPQVGSGMCPPPGGMNRKTQETAVAMHVAANSIQNRPPGYPNMNQGGMMGTGPPYGQGINSMAGMINPQGPPYSMGGTMANNSAGMAASPEMMGLGDVKLTPATKMNNKADGTPKTESKSKKSSSSTTTNEKITKLYELGGEPERKMWVDRYLAFTEEKAMGMTNLPAVGRKPLDLYRLYVSVKEIGGLTQVNKNKKWRELATNLNVGTSSSAASSLKKQYIQCLYAFECKIERGEDPPPDIFAAADSKKSQPKIQPPSPAGSGSMQGPQTPQSTSSSMAEGGDLKPPTPASTPHSQIPPLPGMSRSNSVGIQDAFNDGSDSTFQKRNSMTPNPGYQPSMNTSDMMGRMSYEPNKDPYGSMRKAPGSDPFMSSGQGPNGGMGDPYSRAAGPGLGNVAMGPRQHYPYGGPYDRVRTEPGIGPEGNMSTGAPQPNLMPSNPDSGMYSPSRYPPQQQQQQQQRHDSYGNQFSTQGTPSGSPFPSQQTTMYQQQQQNYKRPMDGTYGPPAKRHEGEMYSVPYSTGQGQPQQQQLPPAQPQPASQQQAAQPSPQQDVYNQYGNAYPATATAATERRPAGGPQNQFPFQFGRDRVSAPPGTNAQQNMPPQMMGGPIQASAEVAQQGTMWQGRNDMTYNYANRQSTGSAPQGPAYHGVNRTDEMLHTDQRANHEGSWPSHGTRQPPYGPSAPVPPMTRPPPSNYQPPPSMQNHIPQVSSPAPLPRPMENRTSPSKSPFLHSGMKMQKAGPPVPASHIAPAPVQPPMIRRDITFPPGSVEATQPVLKQRRRLTMKDIGTPEAWRVMMSLKSGLLAESTWALDTINILLYDDNSIMTFNLSQLPGLLELLVEYFRRCLIEIFGILKEYEVGDPGQRTLLDPGRFSKVSSPAPMEGGEEEEELLGPKLEEEEEEEVVENDEEIAFSGKDKPASENSEEKLISKFDKLPVKIVQKNDPFVVDCSDKLGRVQEFDSGLLHWRIGGGDTTEHIQTHFESKTELLPSRPHAPCPPAPRKHVTTAEGTPGTTDQEGPPPDGPPEKRITATMDDMLSTRSSTLTEDGAKSSEAIKESSKFPFGISPAQSHRNIKILEDEPHSKDETPLCTLLDWQDSLAKRCVCVSNTIRSLSFVPGNDFEMSKHPGLLLILGKLILLHHKHPERKQAPLTYEKEEEQDQGVSCNKVEWWWDCLEMLRENTLVTLANISGQLDLSPYPESICLPVLDGLLHWAVCPSAEAQDPFSTLGPNAVLSPQRLVLETLSKLSIQDNNVDLILATPPFSRLEKLYSTMVRFLSDRKNPVCREMAVVLLANLAQGDSLAARAIAVQKGSIGNLLGFLEDSLAATQFQQSQASLLHMQNPPFEPTSVDMMRRAARALLALAKVDENHSEFTLYESRLLDISVSPLMNSLVSQVICDVLFLIGQS,mutated_sequence,1.0,2285.0,NP_006006.3.a2m,NP_006006.3.npy,ClinVar +NP_006010.2,NP_006010.2.csv,MGSMFRSEEVALVQLFLPTAAAYTCVSRLGELGLVEFRDLNASVSAFQRRFVVDVRRCEELEKTFTFLQEEVRRAGLVLPPPKGRLPAPPPRDLLRIQEETERLAQELRDVRGNQQALRAQLHQLQLHAAVLRQGHEPQLAAAHTDGASERTPLLQAPGGPHQDLRVNFVAGAVEPHKAPALERLLWRACRGFLIASFRELEQPLEHPVTGEPATWMTFLISYWGEQIGQKIRKITDCFHCHVFPFLQQEEARLGALQQLQQQSQELQEVLGETERFLSQVLGRVLQLLPPGQVQVHKMKAVYLALNQCSVSTTHKCLIAEAWCSVRDLPALQEALRDSSMEEGVSAVAHRIPCRDMPPTLIRTNRFTASFQGIVDAYGVGRYQEVNPAPYTIITFPFLFAVMFGDVGHGLLMFLFALAMVLAENRPAVKAAQNEIWQTFFRGRYLLLLMGLFSIYTGFIYNECFSRATSIFPSGWSVAAMANQSGWSDAFLAQHTMLTLDPNVTGVFLGPYPFGIDPIWSLAANHLSFLNSFKMKMSVILGVVHMAFGVVLGVFNHVHFGQRHRLLLETLPELTFLLGLFGYLVFLVIYKWLCVWAARAASAPSILIHFINMFLFSHSPSNRLLYPRQEVVQATLVVLALAMVPILLLGTPLHLLHRHRRRLRRRPADRQEENKAGLLDLPDASVNGWSSDEEKAGGLDDEEEAELVPSEVLMHQAIHTIEFCLGCVSNTASYLRLWALSLAHAQLSEVLWAMVMRIGLGLGREVGVAAVVLVPIFAAFAVMTVAILLVMEGLSAFLHALRLHWVEFQNKFYSGTGYKLSPFTFAATDD,mutated_sequence,1.0,830.0,NP_006010.2.a2m,NP_006010.2.npy,ClinVar +NP_006022.3,NP_006022.3.csv,MEVEQEQRRRKVEAGRTKLAHFRQRKTKGDSSHSEKKTAKRKGSAVDASVQEESPVTKEDSALCGGGDICKSTSCDDTPDGAGGAFAAQPEDCDGEKREDLEQLQQKQVNDHPPEQCGMFTVSDHPPEQHGMFTVGDHPPEQRGMFTVSDHPPEQHGMFTVSDHPPEQRGMFTISDHQPEQRGMFTVSDHTPEQRGIFTISDHPAEQRGMFTKECEQECELAITDLESGREDEAGLHQSQAVHGLELEALRLSLSNMHTAQLELTQANLQKEKETALTELREMLNSRRAQELALLQSRQQHELELLREQHAREKEEVVLRCGQEAAELKEKLQSEMEKNAQIVKTLKEDWESEKDLCLENLRKELSAKHQSEMEDLQNQFQKELAEQRAELEKIFQDKNQAERALRNLESHHQAAIEKLREDLQSEHGRCLEDLEFKFKESEKEKQLELENLQASYEDLKAQSQEEIRRLWSQLDSARTSRQELSELHEQLLARTSRVEDLEQLKQREKTQHESELEQLRIYFEKKLRDAEKTYQEDLTLLQQRLQGAREDALLDSVEVGLSCVGLEEKPEKGRKDHVDELEPERHKESLPRFQAELEESHRHQLEALESPLCIQHEGHVSDRCCVETSALGHEWRLEPSEGHSQELPWVHLQGVQDGDLEADTERAARVLGLETEHKVQLSLLQTELKEEIELLKIENRNLYGKLQHETRLKDDLEKVKHNLIEDHQKELNNAKQKTELMKQEFQRKETDWKVMKEELQREAEEKLTLMLLELREKAESEKQTIINKFELREAEMRQLQDQQAAQILDLERSLTEQQGRLQQLEQDLTSDDALHCSQCGREPPTAQDGELAALHVKEDCALQLMLARSRFLEERKEITEKFSAEQDAFLQEAQEQHARELQLLQERHQQQLLSVTAELEARHQAALGELTASLESKQGALLAARVAELQTKHAADLGALETRHLSSLDSLESCYLSEFQTIREEHRQALELLRADFEEQLWKKDSLHQTILTQELEKLKRKHEGELQSVRDHLRTEVSTELAGTVAHELQGVHQGEFGSEKKTALHEKEETLRLQSAQAQPFHQEEKESLSLQLQKKNHQVQQLKDQVLSLSHEIEECRSELEVLQQRRERENREGANLLSMLKADVNLSHSERGALQDALRRLLGLFGETLRAAVTLRSRIGERVGLCLDDAGAGLALSTAPALEETWSDVALPELDRTLSECAEMSSVAEISSHMRESFLMSPESVRECEQPIRRVFQSLSLAVDGLMEMALDSSRQLEEARQIHSRFEKEFSFKNEETAQVVRKHQELLECLKEESAAKAELALELHKTQGTLEGFKVETADLKEVLAGKEDSEHRLVLELESLRRQLQQAAQEQAALREECTRLWSRGEATATDAEAREAALRKEVEDLTKEQSETRKQAEKDRSALLSQMKILESELEEQLSQHRGCAKQAEAVTALEQQVASLDKHLRNQRQFMDEQAAEREHEREEFQQEIQRLEGQLRQAAKPQPWGPRDSQQAPLDGEVELLQQKLREKLDEFNELAIQKESADRQVLMQEEEIKRLEEMNINIRKKVAQLQEEVEKQKNIVKGLEQDKEVLKKQQMSSLLLASTLQSTLDAGRCPEPPSGSPPEGPEIQLEVTQRALLRRESEVLDLKEQLEKMKGDLESKNEEILHLNLKLDMQNSQTAVSLRELEEENTSLKVIYTRSSEIEELKATIENLQENQKRLQKEKAEEIEQLHEVIEKLQHELSLMGPVVHEVSDSQAGSLQSELLCSQAGGPRGQALQGELEAALEAKEALSRLLADQERRHSQALEALQQRLQGAEEAAELQLAELERNVALREAEVEDMASRIQEFEAALKAKEATIAERNLEIDALNQRKAAHSAELEAVLLALARIRRALEQQPLAAGAAPPELQWLRAQCARLSRQLQVLHQRFLRCQVELDRRQARRATAHTRVPGAHPQPRMDGGAKAQVTGDVEASHDAALEPVVPDPQGDLQPVLVTLKDAPLCKQEGVMSVLTVCQRQLQSELLLVKNEMRLSLEDGGKGKEKVLEDCQLPKVDLVAQVKQLQEKLNRLLYSMTFQNVDAADTKSLWPMASAHLLESSWSDDSCDGEEPDISPHIDTCDANTATGGVTDVIKNQAIDACDANTTPGGVTDVIKNWDSLIPDEMPDSPIQEKSECQDMSLSSPTSVLGGSRHQSHTAEAGPRKSPVGMLDLSSWSSPEVLRKDWTLEPWPSLPVTPHSGALSLCSADTSLGDRADTSLPQTQGPGLLCSPGVSAAALALQWAESPPADDHHVQRTAVEKDVEDFITTSFDSQETLSSPPPGLEGKADRSEKSDGSGFGARLSPGSGGPEAQTAGPVTPASISGRFQPLPEAMKEKEVRPKHVKALLQMVRDESHQILALSEGLAPPSGEPHPPRKEDEIQDISLHGGKTQEVPTACPDWRGDLLQVVQEAFEKEQEMQGVELQPRLSGSDLGGHSSLLERLEKIIREQGDLQEKSLEHLRLPDRSSLLSEIQALRAQLRMTHLQNQEKLQHLRTALTSAEARGSQQEHQLRRQVELLAYKVEQEKCIAGDLQKTLSEEQEKANSVQKLLAAEQTVVRDLKSDLCESRQKSEQLSRSLCEVQQEVLQLRSMLSSKENELKAALQELESEQGKGRALQSQLEEEQLRHLQRESQSAKALEELRASLETQRAQSSRLCVALKHEQTAKDNLQKELRIEHSRCEALLAQERSQLSELQKDLAAEKSRTLELSEALRHERLLTEQLSQRTQEACVHQDTQAHHALLQKLKEEKSRVVDLQAMLEKVQQQALHSQQQLEAEAQKHCEALRREKEVSATLKSTVEALHTQKRELRCSLEREREKPAWLQAELEQSHPRLKEQEGRKAARRSAEARQSPAAAEQWRKWQRDKEKLRELELQRQRDLHKIKQLQQTVRDLESKDEVPGSRLHLGSARRAAGSDADHLREQQRELEAMRQRLLSAARLLTSFTSQAVDRTVNDWTSSNEKAVMSLLHTLEELKSDLSRPTSSQKKMAAELQFQFVDVLLKDNVSLTKALSTVTQEKLELSRAVSKLEKLLKHHLQKGCSPSRSERSAWKPDETAPQSSLRRPDPGRLPPAASEEAHTSNVKMEKLYLHYLRAESFRKALIYQKKYLLLLIGGFQDSEQETLSMIAHLGVFPSKAERKITSRPFTRFRTAVRVVIAILRLRFLVKKWQEVDRKGALAQGKAPRPGPRARQPQSPPRTRESPPTRDVPSGHTRDPARGRRLAAAASPHSGGRATPSPNSRLERSLTASQDPEHSLTEYIHHLEVIQQRLGGVLPDSTSKKSCHPMIKQ,mutated_sequence,1.0,3336.0,NP_006022.3.a2m,NP_006022.3.npy,ClinVar +NP_006054.2,NP_006054.2.csv,MDSQRELAEELRLYQSTLLQDGLKDLLDEKKFIDCTLKAGDKSLPCHRLILSACSPYFREYFLSEIDEAKKKEVVLDNVDPAILDLIIKYLYSASIDLNDGNVQDIFALASRFQIPSVFTVCVSYLQKRLAPGNCLAILRLGLLLDCPRLAISAREFVSDRFVQICKEEDFMQLSPQELISVISNDSLNVEKEEAVFEAVMKWVRTDKENRVKNLSEVFDCIRFRLMTEKYFKDHVEKDDIIKSNPDLQKKIKVLKDAFAGKLPEPSKNAAKTGAGEVNGDVGDEDLLPGYLNDIPRHGMFVKDLILLVNDTAAVAYDPTENECYLTALAEQIPRNHSSIVTQQNQIYVVGGLYVDEENKDQPLQSYFFQLDSIASEWVGLPPLPSARCLFGLGEVDDKIYVVAGKDLQTEASLDSVLCYDPVAAKWNEVKKLPIKVYGHNVISHKGMIYCLGGKTDDKKCTNRVFIFNPKKGDWKDLAPMKIPRSMFGVAVHKGKIVIAGGVTEDGLSASVEAFDLTTNKWDVMTEFPQERSSISLVSLAGSLYAIGGFAMIQLESKEFAPTEVNDIWKYEDDKKEWAGMLKEIRYASGASCLATRLNLFKLSKL,mutated_sequence,1.0,606.0,NP_006054.2.a2m,NP_006054.2.npy,ClinVar +NP_006197.1,NP_006197.1.csv,MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL,mutated_sequence,1.0,1089.0,NP_006197.1.a2m,NP_006197.1.npy,ClinVar +NP_006205.1,NP_006205.1.csv,MEQLRAAARLQIVLGHLGRPSAGAVVAHPTSGTISSASFHPQQFQYTLDNNVLTLEQRKFYEENGFLVIKNLVPDADIQRFRNEFEKICRKEVKPLGLTVMRDVTISKSEYAPSEKMITKVQDFQEDKELFRYCTLPEILKYVECFTGPNIMAMHTMLINKPPDSGKKTSRHPLHQDLHYFPFRPSDLIVCAWTAMEHISRNNGCLVVLPGTHKGSLKPHDYPKWEGGVNKMFHGIQDYEENKARVHLVMEKGDTVFFHPLLIHGSGQNKTQGFRKAISCHFASADCHYIDVKGTSQENIEKEVVGIAHKFFGAENSVNLKDIWMFRARLVKGERTNL,mutated_sequence,1.0,338.0,NP_006205.1.a2m,NP_006205.1.npy,ClinVar +NP_006209.2,NP_006209.2.csv,MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIKHELFKEARKYPLHQLLQDESSYIFVSVTQEAEREEFFDETRRLCDLRLFQPFLKVIEPVGNREEKILNREIGFAIGMPVCEFDMVKDPEVQDFRRNILNVCKEAVDLRDLNSPHSRAMYVYPPNVESSPELPKHIYNKLDKGQIIVVIWVIVSPNNDKQKYTLKINHDCVPEQVIAEAIRKKTRSMLLSSEQLKLCVLEYQGKYILKVCGCDEYFLEKYPLSQYKYIRSCIMLGRMPNLMLMAKESLYSQLPMDCFTMPSYSRRISTATPYMNGETSTKSLWVINSALRIKILCATYVNVNIRDIDKIYVRTGIYHGGEPLCDNVNTQRVPCSNPRWNEWLNYDIYIPDLPRAARLCLSICSVKGRKGAKEEHCPLAWGNINLFDYTDTLVSGKMALNLWPVPHGLEDLLNPIGVTGSNPNKETPCLELEFDWFSSVVKFPDMSVIEEHANWSVSREAGFSYSHAGLSNRLARDNELRENDKEQLKAISTRDPLSEITEQEKDFLWSHRHYCVTIPEILPKLLLSVKWNSRDEVAQMYCLVKDWPPIKPEQAMELLDCNYPDPMVRGFAVRCLEKYLTDDKLSQYLIQLVQVLKYEQYLDNLLVRFLLKKALTNQRIGHFFFWHLKSEMHNKTVSQRFGLLLESYCRACGMYLKHLNRQVEAMEKLINLTDILKQEKKDETQKVQMKFLVEQMRRPDFMDALQGFLSPLNPAHQLGNLRLEECRIMSSAKRPLWLNWENPDIMSELLFQNNEIIFKNGDDLRQDMLTLQIIRIMENIWQNQGLDLRMLPYGCLSIGDCVGLIEVVRNSHTIMQIQCKGGLKGALQFNSHTLHQWLKDKNKGEIYDAAIDLFTRSCAGYCVATFILGIGDRHNSNIMVKDDGQLFHIDFGHFLDHKKKKFGYKRERVPFVLTQDFLIVISKGAQECTKTREFERFQEMCYKAYLAIRQHANLFINLFSMMLGSGMPELQSFDDIAYIRKTLALDKTEQEALEYFMKQMNDAHHGGWTTKMDWIFHTIKQHALN,mutated_sequence,1.0,1068.0,NP_006209.2.a2m,NP_006209.2.npy,ClinVar +NP_006227.1,NP_006227.1.csv,MATAASNPYLPGNSLLAAGSIVHSDAAGAGGGGGGGGGGGGGGAGGGGGGMQPGSAAVTSGAYRGDPSSVKMVQSDFMQGAMAASNGGHMLSHAHQWVTALPHAAAAAAAAAAAAVEASSPWSGSAVGMAGSPQQPPQPPPPPPQGPDVKGGAGRDDLHAGTALHHRGPPHLGPPPPPPHQGHPGGWGAAAAAAAAAAAAAAAAHLPSMAGGQQPPPQSLLYSQPGGFTVNGMLSAPPGPGGGGGGAGGGAQSLVHPGLVRGDTPELAEHHHHHHHHAHPHPPHPHHAQGPPHHGGGGGGAGPGLNSHDPHSDEDTPTSDDLEQFAKQFKQRRIKLGFTQADVGLALGTLYGNVFSQTTICRFEALQLSFKNMCKLKPLLNKWLEEADSSTGSPTSIDKIAAQGRKRKKRTSIEVSVKGALESHFLKCPKPSAQEITNLADSLQLEKEVVRVWFCNRRQKEKRMTPPGIQQQTPDDVYSQVGTVSADTPPPHHGLQTSVQ,mutated_sequence,1.0,500.0,NP_006227.1.a2m,NP_006227.1.npy,ClinVar +NP_006236.1,NP_006236.1.csv,MPYKLKKEKEPPKVAKCTAKPSSSGKDGGGENTEEAQPQPQPQPQPQAQSQPPSSNKRPSNSTPPPTQLSKIKYSGGPQIVKKERRQSSSRFNLSKNRELQKLPALKDSPTQEREELFIQKLRQCCVLFDFVSDPLSDLKFKEVKRAGLNEMVEYITHSRDVVTEAIYPEAVTMFSVNLFRTLPPSSNPTGAEFDPEEDEPTLEAAWPHLQLVYEFFLRFLESPDFQPNIAKKYIDQKFVLALLDLFDSEDPRERDFLKTILHRIYGKFLGLRAYIRRQINHIFYRFIYETEHHNGIAELLEILGSIINGFALPLKEEHKMFLIRVLLPLHKVKSLSVYHPQLAYCVVQFLEKESSLTEPVIVGLLKFWPKTHSPKEVMFLNELEEILDVIEPSEFSKVMEPLFRQLAKCVSSPHFQVAERALYYWNNEYIMSLISDNAARVLPIMFPALYRNSKSHWNKTIHGLIYNALKLFMEMNQKLFDDCTQQYKAEKQKGRFRMKEREEMWQKIEELARLNPQYPMFRAPPPLPPVYSMETETPTAEDIQLLKRTVETEAVQMLKDIKKEKVLLRRKSELPQDVYTIKALEAHKRAEEFLTASQEAL,mutated_sequence,1.0,602.0,NP_006236.1.a2m,NP_006236.1.npy,ClinVar +NP_006256.1,NP_006256.1.csv,MFYAHFVLSKRGPLAKIWLAAHWDKKLTKAHVFECNLESSVESIISPKVKMALRTSGHLLLGVVRIYHRKAKYLLADCNEAFIKIKMAFRPGVVDLPEENREAAYNAITLPEEFHDFDQPLPDLDDIDVAQQFSLNQSRVEEITMREEVGNISILQENDFGDFGMDDREIMREGSAFEDDDMLVSTTTSNLLLESEQSTSNLNEKINHLEYEDQYKDDNFGEGNDGGILDDKLISNNDGGIFDDPPALSEAGVMLPEQPAHDDMDEDDNVSMGGPDSPDSVDPVEPMPTMTDQTTLVPNEEEAFALEPIDITVKETKAKRKRKLIVDSVKELDSKTIRAQLSDYSDIVTTLDLAPPTKKLMMWKETGGVEKLFSLPAQPLWNNRLLKLFTRCLTPLVPEDLRKRRKGGEADNLDEFLKEFENPEVPREDQQQQHQQRDVIDEPIIEEPSRLQESVMEASRTNIDESAMPPPPPQGVKRKAGQIDPEPVMPPQQVEQMEIPPVELPPEEPPNICQLIPELELLPEKEKEKEKEKEDDEEEEDEDASGGDQDQEERRWNKRTQQMLHGLQRALAKTGAESISLLELCRNTNRKQAAAKFYSFLVLKKQQAIELTQEEPYSDIIATPGPRFHII,mutated_sequence,1.0,631.0,NP_006256.1.a2m,NP_006256.1.npy,ClinVar +NP_006261.1,NP_006261.1.csv,MSSGAASGTGRGRPRGGGPGPGDPPPSETHKLVVVGGGGVGKSALTIQFIQSYFVSDYDPTIEDSYTKICSVDGIPARLDILDTAGQEEFGAMREQYMRAGHGFLLVFAINDRQSFNEVGKLFTQILRVKDRDDFPVVLVGNKADLESQRQVPRSEASAFGASHHVAYFEASAKLRLNVDEAFEQLVRAVRKYQEQELPPSPPSAPRKKGGGCPCVLL,mutated_sequence,1.0,218.0,NP_006261.1.a2m,NP_006261.1.npy,ClinVar +NP_006297.2,NP_006297.2.csv,MGFLKLIEIENFKSYKGRQIIGPFQRFTAIIGPNGSGKSNLMDAISFVLGEKTSNLRVKTLRDLIHGAPVGKPAANRAFVSMVYSEEGAEDRTFARVIVGGSSEYKINNKVVQLHEYSEELEKLGILIKARNFLVFQGAVESIAMKNPKERTALFEEISRSGELAQEYDKRKKEMVKAEEDTQFNYHRKKNIAAERKEAKQEKEEADRYQRLKDEVVRAQVQLQLFKLYHNEVEIEKLNKELASKNKEIEKDKKRMDKVEDELKEKKKELGKMMREQQQIEKEIKEKDSELNQKRPQYIKAKENTSHKIKKLEAAKKSLQNAQKHYKKRKGDMDELEKEMLSVEKARQEFEERMEEESQSQGRDLTLEENQVKKYHRLKEEASKRAATLAQELEKFNRDQKADQDRLDLEERKKVETEAKIKQKLREIEENQKRIEKLEEYITTSKQSLEEQKKLEGELTEEVEMAKRRIDEINKELNQVMEQLGDARIDRQESSRQQRKAEIMESIKRLYPGSVYGRLIDLCQPTQKKYQIAVTKVLGKNMDAIIVDSEKTGRDCIQYIKEQRGEPETFLPLDYLEVKPTDEKLRELKGAKLVIDVIRYEPPHIKKALQYACGNALVCDNVEDARRIAFGGHQRHKTVALDGTLFQKSGVISGGASDLKAKARRWDEKAVDKLKEKKERLTEELKEQMKAKRKEAELRQVQSQAHGLQMRLKYSQSDLEQTKTRHLALNLQEKSKLESELANFGPRINDIKRIIQSREREMKDLKEKMNQVEDEVFEEFCREIGVRNIREFEEEKVKRQNEIAKKRLEFENQKTRLGIQLDFEKNQLKEDQDKVHMWEQTVKKDENEIEKLKKEEQRHMKIIDETMAQLQDLKNQHLAKKSEVNDKNHEMEEIRKKLGGANKEMTHLQKEVTAIETKLEQKRSDRHNLLQACKMQDIKLPLSKGTMDDISQEEGSSQGEDSVSGSQRISSIYAREALIEIDYGDLCEDLKDAQAEEEIKQEMNTLQQKLNEQQSVLQRIAAPNMKAMEKLESVRDKFQETSDEFEAARKRAKKAKQAFEQIKKERFDRFNACFESVATNIDEIYKALSRNSSAQAFLGPENPEEPYLDGINYNCVAPGKRFRPMDNLSGGEKTVAALALLFAIHSYKPAPFFVLDEIDAALDNTNIGKVANYIKEQSTCNFQAIVISLKEEFYTKAESLIGVYPEQGDCVISKVLTFDLTKYPDANPNPNEQ,mutated_sequence,1.0,1233.0,NP_006297.2.a2m,NP_006297.2.npy,ClinVar +NP_006336.3,NP_006336.3.csv,MLPGLAAAAAHRCSWSSLCRLRLRCRAAACNPSDRQEWQNLVTFGSFSNMVPCSHPYIGTLSQVKLYSTNVQKEGQGSQTLRVEKVPSFETAEGIGTELKAPLKQEPLQVRVKAVLKKREYGSKYTQNNFITGVRAINEFCLKSSDLEQLRKIRRRSPHEDTESFTVYLRSDVEAKSLEVWGSPEALAREKKLRKEAEIEYRERLFRNQKILREYRDFLGNTKPRSRTASVFFKGPGKVVMVAICINGLNCFFKFLAWIYTGSASMFSEAIHSLSDTCNQGLLALGISKSVQTPDPSHPYGFSNMRYISSLISGVGIFMMGAGLSWYHGVMGLLHPQPIESLLWAYCILAGSLVSEGATLLVAVNELRRNARAKGMSFYKYVMESRDPSTNVILLEDTAAVLGVIIAATCMGLTSITGNPLYDSLGSLGVGTLLGMVSAFLIYTNTEALLGRSIQPEQVQRLTELLENDPSVRAIHDVKATDLGLGKVRFKAEVDFDGRVVTRSYLEKQDFDQMLQEIQEVKTPEELETFMLKHGENIIDTLGAEVDRLEKELKKRNPEVRHVDLEIL,mutated_sequence,1.0,568.0,NP_006336.3.a2m,NP_006336.3.npy,ClinVar +NP_006339.4,NP_006339.4.csv,MEGGGGSVAVAGLGARGSGAAAATVRELLQDGCYSDFLNEDFDVKTYTSQSIHQAVIAEQLAKLAQGISQLDRELHLQVVARHEDLLAQATGIESLEGVLQMMQTRIGALQGAVDRIKAKIVEPYNKIVARTAQLARLQVACDLLRRIIRILNLSKRLQGQLQGGSREITKAAQSLNELDYLSQGIDLSGIEVIENDLLFIARARLEVENQAKRLLEQGLETQNPTQVGTALQVFYNLGTLKDTITSVVDGYCATLEENINSALDIKVLTQPSQSAVRGGPGRSTMPTPGNTAALRASFWTNMEKLMDHIYAVCGQVQHLQKVLAKKRDPVSHICFIEEIVKDGQPEIFYTFWNSVTQALSSQFHMATNSSMFLKQAFEGEYPKLLRLYNDLWKRLQQYSQHIQGNFNASGTTDLYVDLQHMEDDAQDIFIPKKPDYDPEKALKDSLQPYEAAYLSKSLSRLFDPINLVFPPGGRNPPSSDELDGIIKTIASELNVAAVDTNLTLAVSKNVAKTIQLYSVKSEQLLSTQGDASQVIGPLTEGQRRNVAVVNSLYKLHQSVTKVVSSQSSFPLAAEQTIISALKAIHALMENAVQPLLTSVGDAIEAIIITMHQEDFSGSLSSSGKPDVPCSLYMKELQGFIARVMSDYFKHFECLDFVFDNTEAIAQRAVELFIRHASLIRPLGEGGKMRLAADFAQMELAVGPFCRRVSDLGKSYRMLRSFRPLLFQASEHVASSPALGDVIPFSIIIQFLFTRAPAELKSPFQRAEWSHTRFSQWLDDHPSEKDRLLLIRGALEAYVQSVRSREGKEFAPVYPIMVQLLQKAMSALQ,mutated_sequence,1.0,829.0,NP_006339.4.a2m,NP_006339.4.npy,ClinVar +NP_006403.2,NP_006403.2.csv,MELWPCLAAALLLLLLLVQLSRAAEFYAKVALYCALCFTVSAVASLVCLLRHGGRTVENMSIIGWFVRSFKYFYGLRFEVRDPRRLQEARPCVIVSNHQSILDMMGLMEVLPERCVQIAKRELLFLGPVGLIMYLGGVFFINRQRSSTAMTVMADLGERMVRENLKVWIYPEGTRNDNGDLLPFKKGAFYLAVQAQVPIVPVVYSSFSSFYNTKKKFFTSGTVTVQVLEAIPTSGLTAADVPALVDTCHRAMRTTFLHISKTPQENGATAGSGVQPAQ,mutated_sequence,1.0,278.0,NP_006403.2.a2m,NP_006403.2.npy,ClinVar +NP_006406.1,NP_006406.1.csv,MATATEQWVLVEMVQALYEAPAYHLILEGILILWIIRLLFSKTYKLQERSDLTVKEKEELIEEWQPEPLVPPVPKDHPALNYNIVSGPPSHKTVVNGKECINFASFNFLGLLDNPRVKAAALASLKKYGVGTCGPRGFYGTFDVHLDLEDRLAKFMKTEEAIIYSYGFATIASAIPAYSKRGDIVFVDRAACFAIQKGLQASRSDIKLFKHNDMADLERLLKEQEIEDQKNPRKARVTRRFIVVEGLYMNTGTICPLPELVKLKYKYKARIFLEESLSFGVLGEHGRGVTEHYGINIDDIDLISANMENALASIGGFCCGRSFVIDHQRLSGQGYCFSASLPPLLAAAAIEALNIMEENPGIFAVLKEKCGQIHKALQGISGLKVVGESLSPAFHLQLEESTGSREQDVRLLQEIVDQCMNRSIALTQARYLEKEEKCLPPPSIRVVVTVEQTEEELERAASTIKEVAQAVLL,mutated_sequence,1.0,473.0,NP_006406.1.a2m,NP_006406.1.npy,ClinVar +NP_006436.3,NP_006436.3.csv,MAGVFPYRGPGNPVPGPLAPLPDYMSEEKLQEKARKWQQLQAKRYAEKRKFGFVDAQKEDMPPEHVRKIIRDHGDMTNRKFRHDKRVYLGALKYMPHAVLKLLENMPMPWEQIRDVPVLYHITGAISFVNEIPWVIEPVYISQWGSMWIMMRREKRDRRHFKRMRFPPFDDEEPPLDYADNILDVEPLEAIQLELDPEEDAPVLDWFYDHQPLRDSRKYVNGSTYQRWQFTLPMMSTLYRLANQLLTDLVDDNYFYLFDLKAFFTSKALNMAIPGGPKFEPLVRDINLQDEDWNEFNDINKIIIRQPIRTEYKIAFPYLYNNLPHHVHLTWYHTPNVVFIKTEDPDLPAFYFDPLINPISHRHSVKSQEPLPDDDEEFELPEFVEPFLKDTPLYTDNTANGIALLWAPRPFNLRSGRTRRALDIPLVKNWYREHCPAGQPVKVRVSYQKLLKYYVLNALKHRPPKAQKKRYLFRSFKATKFFQSTKLDWVEVGLQVCRQGYNMLNLLIHRKNLNYLHLDYNFNLKPVKTLTTKERKKSRFGNAFHLCREVLRLTKLVVDSHVQYRLGNVDAFQLADGLQYIFAHVGQLTGMYRYKYKLMRQIRMCKDLKHLIYYRFNTGPVGKGPGCGFWAAGWRVWLFFMRGITPLLERWLGNLLARQFEGRHSKGVAKTVTKQRVESHFDLELRAAVMHDILDMMPEGIKQNKARTILQHLSEAWRCWKANIPWKVPGLPTPIENMILRYVKAKADWWTNTAHYNRERIRRGATVDKTVCKKNLGRLTRLYLKAEQERQHNYLKDGPYITAEEAVAVYTTTVHWLESRRFSPIPFPPLSYKHDTKLLILALERLKEAYSVKSRLNQSQREELGLIEQAYDNPHEALSRIKRHLLTQRAFKEVGIEFMDLYSHLVPVYDVEPLEKITDAYLDQYLWYEADKRRLFPPWIKPADTEPPPLLVYKWCQGINNLQDVWETSEGECNVMLESRFEKMYEKIDLTLLNRLLRLIVDHNIADYMTAKNNVVINYKDMNHTNSYGIIRGLQFASFIVQYYGLVMDLLVLGLHRASEMAGPPQMPNDFLSFQDIATEAAHPIRLFCRYIDRIHIFFRFTADEARDLIQRYLTEHPDPNNENIVGYNNKKCWPRDARMRLMKHDVNLGRAVFWDIKNRLPRSVTTVQWENSFVSVYSKDNPNLLFNMCGFECRILPKCRTSYEEFTHKDGVWNLQNEVTKERTAQCFLRVDDESMQRFHNRVRQILMASGSTTFTKIVNKWNTALIGLMTYFREAVVNTQELLDLLVKCENKIQTRIKIGLNSKMPSRFPPVVFYTPKELGGLGMLSMGHVLIPQSDLRWSKQTDVGITHFRSGMSHEEDQLIPNLYRYIQPWESEFIDSQRVWAEYALKRQEAIAQNRRLTLEDLEDSWDRGIPRINTLFQKDRHTLAYDKGWRVRTDFKQYQVLKQNPFWWTHQRHDGKLWNLNNYRTDMIQALGGVEGILEHTLFKGTYFPTWEGLFWEKASGFEESMKWKKLTNAQRSGLNQIPNRRFTLWWSPTINRANVYVGFQVQLDLTGIFMHGKIPTLKISLIQIFRAHLWQKIHESIVMDLCQVFDQELDALEIETVQKETIHPRKSYKMNSSCADILLFASYKWNVSRPSLLADSKDVMDSTTTQKYWIDIQLRWGDYDSHDIERYARAKFLDYTTDNMSIYPSPTGVLIAIDLAYNLHSAYGNWFPGSKPLIQQAMAKIMKANPALYVLRERIRKGLQLYSSEPTEPYLSSQNYGELFSNQIIWFVDDTNVYRVTIHKTFEGNLTTKPINGAIFIFNPRTGQLFLKIIHTSVWAGQKRLGQLAKWKTAEEVAALIRSLPVEEQPKQIIVTRKGMLDPLEVHLLDFPNIVIKGSELQLPFQACLKVEKFGDLILKATEPQMVLFNLYDDWLKTISSYTAFSRLILILRALHVNNDRAKVILKPDKTTITEPHHIWPTLTDEEWIKVEVQLKDLILADYGKKNNVNVASLTQSEIRDIILGMEISAPSQQRQQIAEIEKQTKEQSQLTATQTRTVNKHGDEIITSTTSNYETQTFSSKTEWRVRAISAANLHLRTNHIYVSSDDIKETGYTYILPKNVLKKFICISDLRAQIAGYLYGVSPPDNPQVKEIRCIVMVPQWGTHQTVHLPGQLPQHEYLKEMEPLGWIHTQPNESPQLSPQDVTTHAKIMADNPSWDGEKTIIITCSFTPGSCTLTAYKLTPSGYEWGRQNTDKGNNPKGYLPSHYERVQMLLSDRFLGFFMVPAQSSWNYNFMGVRHDPNMKYELQLANPKEFYHEVHRPSHFLNFALLQEGEVYSADREDLYA,mutated_sequence,1.0,2335.0,NP_006436.3.a2m,NP_006436.3.npy,ClinVar +NP_006493.1,NP_006493.1.csv,MATGQDRVVALVDMDCFFVQVEQRQNPHLRNKPCAVVQYKSWKGGGIIAVSYEARAFGVTRSMWADDAKKLCPDLLLAQVRESRGKANLTKYREASVEVMEIMSRFAVIERASIDEAYVDLTSAVQERLQKLQGQPISADLLPSTYIEGLPQGPTTAEETVQKEGMRKQGLFQWLDSLQIDNLTSPDLQLTVGAVIVEEMRAAIERETGFQCSAGISHNKVLAKLACGLNKPNRQTLVSHGSVPQLFSQMPIRKIRSLGGKLGASVIEILGIEYMGELTQFTESQLQSHFGEKNGSWLYAMCRGIEHDPVKPRQLPKTIGCSKNFPGKTALATREQVQWWLLQLAQELEERLTKDRNDNDRVATQLVVSIRVQGDKRLSSLRRCCALTRYDAHKMSHDAFTVIKNCNTSGIQTEWSPPLTMLFLCATKFSASAPSSSTDITSFLSSDPSSLPKVPVTSSEAKTQGSGPAVTATKKATTSLESFFQKAAERQKVKEASLSSLTAPTQAPMSNSPSKPSLPFQTSQSTGTEPFFKQKSLLLKQKQLNNSSVSSPQQNPWSNCKALPNSLPTEYPGCVPVCEGVSKLEESSKATPAEMDLAHNSQSMHASSASKSVLEVTQKATPNPSLLAAEDQVPCEKCGSLVPVWDMPEHMDYHFALELQKSFLQPHSSNPQVVSAVSHQGKRNPKSPLACTNKRPRPEGMQTLESFFKPLTH,mutated_sequence,1.0,713.0,NP_006493.1.a2m,NP_006493.1.npy,ClinVar +NP_006505.4,NP_006505.4.csv,MEFPIGSLETNNFRRFTPESLVEIEKQIAAKQGTKKAREKHREQKDQEEKPRPQLDLKACNQLPKFYGELPAELIGEPLEDLDPFYSTHRTFMVLNKGRTISRFSATRALWLFSPFNLIRRTAIKVSVHSWFSLFITVTILVNCVCMTRTDLPEKIEYVFTVIYTFEALIKILARGFCLNEFTYLRDPWNWLDFSVITLAYVGTAIDLRGISGLRTFRVLRALKTVSVIPGLKVIVGALIHSVKKLADVTILTIFCLSVFALVGLQLFKGNLKNKCVKNDMAVNETTNYSSHRKPDIYINKRGTSDPLLCGNGSDSGHCPDGYICLKTSDNPDFNYTSFDSFAWAFLSLFRLMTQDSWERLYQQTLRTSGKIYMIFFVLVIFLGSFYLVNLILAVVTMAYEEQNQATTDEIEAKEKKFQEALEMLRKEQEVLAALGIDTTSLHSHNGSPLTSKNASERRHRIKPRVSEGSTEDNKSPRSDPYNQRRMSFLGLASGKRRASHGSVFHFRSPGRDISLPEGVTDDGVFPGDHESHRGSLLLGGGAGQQGPLPRSPLPQPSNPDSRHGEDEHQPPPTSELAPGAVDVSAFDAGQKKTFLSAEYLDEPFRAQRAMSVVSIITSVLEELEESEQKCPPCLTSLSQKYLIWDCCPMWVKLKTILFGLVTDPFAELTITLCIVVNTIFMAMEHHGMSPTFEAMLQIGNIVFTIFFTAEMVFKIIAFDPYYYFQKKWNIFDCIIVTVSLLELGVAKKGSLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTIILAIIVFVFALVGKQLLGENYRNNRKNISAPHEDWPRWHMHDFFHSFLIVFRILCGEWIENMWACMEVGQKSICLILFLTVMVLGNLVVLNLFIALLLNSFSADNLTAPEDDGEVNNLQVALARIQVFGHRTKQALCSFFSRSCPFPQPKAEPELVVKLPLSSSKAENHIAANTARGSSGGLQAPRGPRDEHSDFIANPTVWVSVPIAEGESDLDDLEDDGGEDAQSFQQEVIPKGQQEQLQQVERCGDHLTPRSPGTGTSSEDLAPSLGETWKDESVPQVPAEGVDDTSSSEGSTVDCLDPEEILRKIPELADDLEEPDDCFTEGCIRHCPCCKLDTTKSPWDVGWQVRKTCYRIVEHSWFESFIIFMILLSSGSLAFEDYYLDQKPTVKALLEYTDRVFTFIFVFEMLLKWVAYGFKKYFTNAWCWLDFLIVNISLISLTAKILEYSEVAPIKALRTLRALRPLRALSRFEGMRVVVDALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFWRCINYTDGEFSLVPLSIVNNKSDCKIQNSTGSFFWVNVKVNFDNVAMGYLALLQVATFKGWMDIMYAAVDSREVNMQPKWEDNVYMYLYFVIFIIFGGFFTLNLFVGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKFQGFVFDIVTRQAFDITIMVLICLNMITMMVETDDQSEEKTKILGKINQFFVAVFTGECVMKMFALRQYYFTNGWNVFDFIVVVLSIASLIFSAILKSLQSYFSPTLFRVIRLARIGRILRLIRAAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMSSFPHVRWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPNLPNSNGTRGDCGSPAVGIIFFTTYIIISFLIMVNMYIAVILENFNVATEESTEPLSEDDFDMFYETWEKFDPEATQFITFSALSDFADTLSGPLRIPKPNRNILIQMDLPLVPGDKIHCLDILFAFTKNVLGESGELDSLKANMEEKFMATNLSKSSYEPIATTLRWKQEDISATVIQKAYRSYVLHRSMALSNTPCVPRAEEEAASLPDEGFVAFTANENCVLPDKSETASATSFPPSYESVTRGLSDRVNMRTSSSIQNEDEATSMELIAPGP,mutated_sequence,1.0,1956.0,NP_006505.4.a2m,NP_006505.4.npy,ClinVar +NP_006507.2,NP_006507.2.csv,MEPSSKKLTGRLMLAVGGAVLGSLQFGYNTGVINAPQKVIEEFYNQTWVHRYGESILPTTLTTLWSLSVAIFSVGGMIGSFSVGLFVNRFGRRNSMLMMNLLAFVSAVLMGFSKLGKSFEMLILGRFIIGVYCGLTTGFVPMYVGEVSPTALRGALGTLHQLGIVVGILIAQVFGLDSIMGNKDLWPLLLSIIFIPALLQCIVLPFCPESPRFLLINRNEENRAKSVLKKLRGTADVTHDLQEMKEESRQMMREKKVTILELFRSPAYRQPILIAVVLQLSQQLSGINAVFYYSTSIFEKAGVQQPVYATIGSGIVNTAFTVVSLFVVERAGRRTLHLIGLAGMAGCAILMTIALALLEQLPWMSYLSIVAIFGFVAFFEVGPGPIPWFIVAELFSQGPRPAAIAVAGFSNWTSNFIVGMCFQYVEQLCGPYVFIIFTVLLVLFFIFTYFKVPETKGRTFDEIASGFRQGGASQSDKTPEELFHPLGADSQV,mutated_sequence,1.0,492.0,NP_006507.2.a2m,NP_006507.2.npy,ClinVar +NP_006508.2,NP_006508.2.csv,MALQSQASEEAKGPWQEADQEQQEPVGSPEPESEPEPEPEPEPVPVPPPEPQPEPQPLPDPAPLPELEFESERVHEPEPTPTVETRGTARGFQPPEGGFGWVVVFAATWCNGSIFGIHNSVGILYSMLLEEEKEKNRQVEFQAAWVGALAMGMIFFCSPIVSIFTDRLGCRITATAGAAVAFIGLHTSSFTSSLSLRYFTYGILFGCGCSFAFQPSLVILGHYFQRRLGLANGVVSAGSSIFSMSFPFLIRMLGDKIKLAQTFQVLSTFMFVLMLLSLTYRPLLPSSQDTPSKRGVRTLHQRFLAQLRKYFNMRVFRQRTYRIWAFGIAAAALGYFVPYVHLMKYVEEEFSEIKETWVLLVCIGATSGLGRLVSGHISDSIPGLKKIYLQVLSFLLLGLMSMMIPLCRDFGGLIVVCLFLGLCDGFFITIMAPIAFELVGPMQASQAIGYLLGMMALPMIAGPPIAGLLRNCFGDYHVAFYFAGVPPIIGAVILFFVPLMHQRMFKKEQRDSSKDKMLAPDPDPNGELLPGSPNPEEPI,mutated_sequence,1.0,539.0,NP_006508.2.a2m,NP_006508.2.npy,ClinVar +NP_006556.1,NP_006556.1.csv,MEGDAVEAIVEESETFIKGKERKTYQRRREGGQEEDACHLPQNQTDGGEVVQDVNSSVQMVMMEQLDPTLLQMKTEVMEGTVAPEAEAAVDDTQIITLQVVNMEEQPINIGELQLVQVPVPVTVPVATTSVEELQGAYENEVSKEGLAESEPMICHTLPLPEGFQVVKVGANGEVETLEQGELPPQEDPSWQKDPDYQPPAKKTKKTKKSKLRYTEEGKDVDVSVYDFEEEQQEGLLSEVNAEKVVGNMKPPKPTKIKKKGVKKTFQCELCSYTCPRRSNLDRHMKSHTDERPHKCHLCGRAFRTVTLLRNHLNTHTGTRPHKCPDCDMAFVTSGELVRHRRYKHTHEKPFKCSMCDYASVEVSKLKRHIRSHTGERPFQCSLCSYASRDTYKLKRHMRTHSGEKPYECYICHARFTQSGTMKMHILQKHTENVAKFHCPHCDTVIARKSDLGVHLRKQHSYIEQGKKCRYCDAVFHERYALIQHQKSHKNEKRFKCDQCDYACRQERHMIMHKRTHTGEKPYACSHCDKTFRQKQLLDMHFKRYHDPNFVPAAFVCSKCGKTFTRRNTMARHADNCAGPDGVEGENGGETKKSKRGRKRKMRSKKEDSSDSENAEPDLDDNEDEEEPAVEIEPEPEPQPVTPAPPPAKKRRGRPPGRTNQPKQNQPTAIIQVEDQNTGAIENIIVEVKKEPDAEPAEGEEEEAQPAATDAPNGDLTPEMILSMMDR,mutated_sequence,1.0,727.0,NP_006556.1.a2m,NP_006556.1.npy,ClinVar +NP_006570.1,NP_006570.1.csv,MTTNAGPLHPYWPQHLRLDNFVPNDRPTWHILAGLFSVTGVLVVTTWLLSGRAAVVPLGTWRRLSLCWFAVCGFIHLVIEGWFVLYYEDLLGDQAFLSQLWKEYAKGDSRYILGDNFTVCMETITACLWGPLSLWVVIAFLRQHPLRFILQLVVSVGQIYGDVLYFLTEHRDGFQHGELGHPLYFWFYFVFMNALWLVLPGVLVLDAVKHLTHAQSTLDAKATKAKSKKN,mutated_sequence,1.0,230.0,NP_006570.1.a2m,NP_006570.1.npy,ClinVar +NP_006584.1,NP_006584.1.csv,MQLEHCLSPSIMLSKKFLNVSSSYPHSGGSELVLHDHPIISTTDNLERSSPLKKITRGMTNQSDTDNFPDSKDSPGDVQRSKLSPVLDGVSELRHSFDGSAADRYLLSQSSQPQSAATAPSAMFPYPGQHGPAHPAFSIGSPSRYMAHHPVITNGAYNSLLSNSSPQGYPTAGYPYPQQYGHSYQGAPFYQFSSTQPGLVPGKAQVYLCNRPLWLKFHRHQTEMIITKQGRRMFPFLSFNISGLDPTAHYNIFVDVILADPNHWRFQGGKWVPCGKADTNVQGNRVYMHPDSPNTGAHWMRQEISFGKLKLTNNKGASNNNGQMVVLQSLHKYQPRLHVVEVNEDGTEDTSQPGRVQTFTFPETQFIAVTAYQNTDITQLKIDHNPFAKGFRDNYDTIYTGCDMDRLTPSPNDSPRSQIVPGARYAMAGSFLQDQFVSNYAKARFHPGAGAGPGPGTDRSVPHTNGLLSPQQAEDPGAPSPQRWFVTPANNRLDFAASAYDTATDFAGNAATLLSYAAAGVKALPLQAAGCTGRPLGYYADPSGWGARSPPQYCGTKSGSVLPCWPNSAAAAARMAGANPYLGEEAEGLAAERSPLPPGAAEDAKPKDLSDSSWIETPSSIKSIDSSDSGIYEQAKRRRISPADTPVSESSSPLKSEVLAQRDCEKNCAKDISGYYGFYSHS,mutated_sequence,1.0,682.0,NP_006584.1.a2m,NP_006584.1.npy,ClinVar +NP_006746.1,NP_006746.1.csv,MSSSPVKRQRMESALDQLKQFTTVVADTGDFHAIDEYKPQDATTNPSLILAAAQMPAYQELVEEAIAYGRKLGGSQEDQIKNAIDKLFVLFGAEILKKIPGRVSTEVDARLSFDKDAMVARARRLIELYKEAGISKDRILIKLSSTWEGIQAGKELEEQHGIHCNMTLLFSFAQAVACAEAGVTLISPFVGRILDWHVANTDKKSYEPLEDPGVKSVTKIYNYYKKFSYKTIVMGASFRNTGEIKALAGCDFLTISPKLLGELLQDNAKLVPVLSAKAAQASDLEKIHLDEKSFRWLHNEDQMAVEKLSDGIRKFAADAVKLERMLTERMFNAENGK,mutated_sequence,1.0,337.0,NP_006746.1.a2m,NP_006746.1.npy,ClinVar +NP_006763.2,NP_006763.2.csv,MSRSRASIHRGSIPAMSYAPFRDVRGPSMHRTQYVHSPYDRPGWNPRFCIISGNQLLMLDEDEIHPLLIRDRRSESSRNKLLRRTVSVPVEGRPHGEHEYHLGRSRRKSVPGGKQYSMEGAPAAPFRPSQGFLSRRLKSSIKRTKSQPKLDRTSSFRQILPRFRSADHDRARLMQSFKESHSHESLLSPSSAAEALELNLDEDSIIKPVHSSILGQEFCFEVTTSSGTKCFACRSAAERDKWIENLQRAVKPNKDNSRRVDNVLKLWIIEARELPPKKRYYCELCLDDMLYARTTSKPRSASGDTVFWGEHFEFNNLPAVRALRLHLYRDSDKKRKKDKAGYVGLVTVPVATLAGRHFTEQWYPVTLPTGSGGSGGMGSGGGGGSGGGSGGKGKGGCPAVRLKARYQTMSILPMELYKEFAEYVTNHYRMLCAVLEPALNVKGKEEVASALVHILQSTGKAKDFLSDMAMSEVDRFMEREHLIFRENTLATKAIEEYMRLIGQKYLKDAIGEFIRALYESEENCEVDPIKCTASSLAEHQANLRMCCELALCKVVNSHCVFPRELKEVFASWRLRCAERGREDIADRLISASLFLRFLCPAIMSPSLFGLMQEYPDEQTSRTLTLIAKVIQNLANFSKFTSKEDFLGFMNEFLELEWGSMQQFLYEISNLDTLTNSSSFEGYIDLGRELSTLHALLWEVLPQLSKEALLKLGPLPRLLNDISTALRNPNIQRQPSRQSERPRPQPVVLRGPSAEMQGYMMRDLNSSIDLQSFMARGLNSSMDMARLPSPTKEKPPPPPPGGGKDLFYVSRPPLARSSPAYCTSSSDITEPEQKMLSVNKSVSMLDLQGDGPGGRLNSSSVSNLAAVGDLLHSSQASLTAALGLRPAPAGRLSQGSGSSITAAGMRLSQMGVTTDGVPAQQLRIPLSFQNPLFHMAADGPGPPGGHGGGGGHGPPSSHHHHHHHHHHRGGEPPGDTFAPFHGYSKSEDLSSGVPKPPAASILHSHSYSDEFGPSGTDFTRRQLSLQDNLQHMLSPPQITIGPQRPAPSGPGGGSGGGSGGGGGGQPPPLQRGKSQQLTVSAAQKPRPSSGNLLQSPEPSYGPARPRQQSLSKEGSIGGSGGSGGGGGGGLKPSITKQHSQTPSTLNPTMPASERTVAWVSNMPHLSADIESAHIEREEYKLKEYSKSMDESRLDRVKEYEEEIHSLKERLHMSNRKLEEYERRLLSQEEQTSKILMQYQARLEQSEKRLRQQQAEKDSQIKSIIGRLMLVEEELRRDHPAMAEPLPEPKKRLLDAQERQLPPLGPTNPRVTLAPPWNGLAPPAPPPPPRLQITENGEFRNTADH,mutated_sequence,1.0,1343.0,NP_006763.2.a2m,NP_006763.2.npy,ClinVar +NP_006867.1,NP_006867.1.csv,MQMSYAIRCAFYQLLLAALMLVAMLQLLYLSLLSGLHGQEEQDQYFEFFPPSPRSVDQVKAQLRTALASGGVLDASGDYRVYRGLLKTTMDPNDVILATHASVDNLLHLSGLLERWEGPLSVSVFAATKEEAQLATVLAYALSSHCPDMRARVAMHLVCPSRYEAAVPDPREPGEFALLRSCQEVFDKLARVAQPGINYALGTNVSYPNNLLRNLAREGANYALVIDVDMVPSEGLWRGLREMLDQSNQWGGTALVVPAFEIRRARRMPMNKNELVQLYQVGEVRPFYYGLCTPCQAPTNYSRWVNLPEESLLRPAYVVPWQDPWEPFYVAGGKVPTFDERFRQYGFNRISQACELHVAGFDFEVLNEGFLVHKGFKEALKFHPQKEAENQHNKILYRQFKQELKAKYPNSPRRC,mutated_sequence,1.0,415.0,NP_006867.1.a2m,NP_006867.1.npy,ClinVar +NP_008816.3,NP_008816.3.csv,MEGCDSPVVSGKDNGCGIPQHQQWTELNSTHLPDKPSSMEQSTGESHGPLDSLRAPFNERLAESTASAGPPSEPASKEVTCNECSASFASLQTYMEHHCPSARPPPPLREESASDTGEEGDEESDVENLAGEIVYQPDGSAYIVESLSQLTQGGGACGSGSGSGPLPSLFLNSLPGAGGKQGDPSCAAPVYPQIINTFHIASSFGKWFEGPDQAFPNTSALAGLSPVLHSFRVFDVRHKSNKDYLNSDGSAKSSCVSKDVPNNVDLSKFDGFVLYGKRKPILMCFLCKLSFGYVRSFVTHAVHDHRMTLSEDERKILSNKNISAIIQGIGKDKEPLVSFLEPKNKNFQHPLVSTANLIGPGHSFYGKFSGIRMEGEEALPAGSAAGPEQPQAGLLTPSTLLNLGGLTSSVLKTPITSVPLGPLASSPTKSSEGKDSGAAEGEKQEVGDGDCFSEKVEPAEEEAEEEEEEEEAEEEEEEEEEEEEEEEDEGCKGLFPSELDEELEDRPHEEPGAAAGSSSKKDLALSNQSISNSPLMPNVLQTLSRGTASTSSNSASSFVVFDGANRRNRLSFNSEGVRANVAEGGRRLDFADESANKDNATAPEPNESTEGDDGGFVPHHQHAGSLCELGVGECPSGSGVECPKCDTVLGSSRSLGGHMTMMHSRNSCKTLKCPKCNWHYKYQQTLEAHMKEKHPEPGGSCVYCKSGQPHPRLARGESYTCGYKPFRCEVCNYSTTTKGNLSIHMQSDKHLNNMQNLQNGGGEQVFSHTAGAAAAAVAAAAAAANISSSCGAPSPTKPKTKPTWRCEVCDYETNVARNLRIHMTSEKHMHNMMLLQQNMTQIQHNRHLGLGSLPSPAEAELYQYYLAQNMNLPNLKMDSAASDAQFMMSGFQLDPAGPMAAMTPALVGGEIPLDMRLGGGQLVSEELMNLGESFIQTNDPSLKLFQCAVCNKFTTDNLDMLGLHMNVERSLSEDEWKAVMGDSYQCKLCRYNTQLKANFQLHCKTDKHVQKYQLVAHIKEGGKANEWRLKCVAIGNPVHLKCNACDYYTNSLEKLRLHTVNSRHEASLKLYKHLQQHESGVEGESCYYHCVLCNYSTKAKLNLIQHVRSMKHQRSESLRKLQRLQKGLPEEDEDLGQIFTIRRCPSTDPEEAIEDVEGPSETAADPEELAKDQEGGASSSQAEKELTDSPATSKRISFPGSSESPLSSKRPKTAEEIKPEQMYQCPYCKYSNADVNRLRVHAMTQHSVQPMLRCPLCQDMLNNKIHLQLHLTHLHSVAPDCVEKLIMTVTTPEMVMPSSMFLPAAVPDRDGNSNLEEAGKQPETSEDLGKNILPSASTEQSGDLKPSPADPGSVREDSGFICWKKGCNQVFKTSAALQTHFNEVHAKRPQLPVSDRHVYKYRCNQCSLAFKTIEKLQLHSQYHVIRAATMCCLCQRSFRTFQALKKHLETSHLELSEADIQQLYGGLLANGDLLAMGDPTLAEDHTIIVEEDKEEESDLEDKQSPTGSDSGSVQEDSGSEPKRALPFRKGPNFTMEKFLDPSRPYKCTVCKESFTQKNILLVHYNSVSHLHKLKRALQESATGQPEPTSSPDNKPFKCNTCNVAYSQSSTLEIHMRSVLHQTKARAAKLEAASGSSNGTGNSSSISLSSSTPSPVSTSGSNTFTTSNPSSAGIAPSSNLLSQVPTESVGMPPLGNPIGANIASPSEPKEANRKKLADMIASRQQQQQQQQQQQQQQQQQQQAQTLAQAQAQVQAHLQQELQQQAALIQSQLFNPTLLPHFPMTTETLLQLQQQQHLLFPFYIPSAEFQLNPEVSLPVTSGALTLTGTGPGLLEDLKAQVQVPQQSHQQILPQQQQNQLSIAQSHSALLQPSQHPEKKNKLVIKEKEKESQRERDSAEGGEGNTGPKETLPDALKAKEKKELAPGGGSEPSMLPPRIASDARGNATKALLENFGFELVIQYNENKQKVQKKNGKTDQGENLEKLECDSCGKLFSNILILKSHQEHVHQNYFPFKQLERFAKQYRDHYDKLYPLRPQTPEPPPPPPPPPPPPLPAAPPQPASTPAIPASAPPITSPTIAPAQPSVPLTQLSMPMELPIFSPLMMQTMPLQTLPAQLPPQLGPVEPLPADLAQLYQHQLNPTLLQQQNKRPRTRITDDQLRVLRQYFDINNSPSEEQIKEMADKSGLPQKVIKHWFRNTLFKERQRNKDSPYNFSNPPITSLEELKIDSRPPSPEPPKQEYWGSKRSSRTRFTDYQLRVLQDFFDANAYPKDDEFEQLSNLLNLPTRVIVVWFQNARQKARKNYENQGEGKDGERRELTNDRYIRTSNLNYQCKKCSLVFQRIFDLIKHQKKLCYKDEDEEGQDDSQNEDSMDAMEILTPTSSSCSTPMPSQAYSAPAPSANNTASSAFLQLTAEAEELATFNSKTEAGDEKPKLAEAPSAQPNQTQEKQGQPKPELQQQEQPEQKTNTPQQKLPQLVSLPSLPQPPPQAPPPQCPLPQSSPSPSQLSHLPLKPLHTSTPQQLANLPPQLIPYQCDQCKLAFPSFEHWQEHQQLHFLSAQNQFIHPQFLDRSLDMPFMLFDPSNPLLASQLLSGAIPQIPASSATSPSTPTSTMNTLKRKLEEKASASPGENDSGTGGEEPQRDKRLRTTITPEQLEILYQKYLLDSNPTRKMLDHIAHEVGLKKRVVQVWFQNTRARERKGQFRAVGPAQAHRRCPFCRALFKAKTALEAHIRSRHWHEAKRAGYNLTLSAMLLDCDGGLQMKGDIFDGTSFSHLPPSSSDGQGVPLSPVSKTMELSPRTLLSPSSIKVEGIEDFESPSMSSVNLNFDQTKLDNDDCSSVNTAITDTTTGDEGNADNDSATGIATETKSSSAPNEGLTKAAMMAMSEYEDRLSSGLVSPAPSFYSKEYDNEGTVDYSETSSLADPCSPSPGASGSAGKSGDSGDRPGQKRFRTQMTNLQLKVLKSCFNDYRTPTMLECEVLGNDIGLPKRVVQVWFQNARAKEKKSKLSMAKHFGINQTSYEGPKTECTLCGIKYSARLSVRDHIFSQQHISKVKDTIGSQLDKEKEYFDPATVRQLMAQQELDRIKKANEVLGLAAQQQGMFDNTPLQALNLPTAYPALQGIPPVLLPGLNSPSLPGFTPSNTALTSPKPNLMGLPSTTVPSPGLPTSGLPNKPSSASLSSPTPAQATMAMGPQQPPQQQQQQQQPQVQQPPPPPAAQPPPTPQLPLQQQQQRKDKDSEKVKEKEKAHKGKGEPLPVPKKEKGEAPTATAATISAPLPTMEYAVDPAQLQALQAALTSDPTALLTSQFLPYFVPGFSPYYAPQIPGALQSGYLQPMYGMEGLFPYSPALSQALMGLSPGSLLQQYQQYQQSLQEAIQQQQQRQLQQQQQQKVQQQQPKASQTPVPPGAPSPDKDPAKESPKPEEQKNTPREVSPLLPKLPEEPEAESKSADSLYDPFIVPKVQYKLVCRKCQAGFSDEEAARSHLKSLCFFGQSVVNLQEMVLHVPTGGGGGGSGGGGGGGGGGGGGGSYHCLACESALCGEEALSQHLESALHKHRTITRAARNAKEHPSLLPHSACFPDPSTASTSQSAAHSNDSPPPPSAAAPSSASPHASRKSWPQVVSRASAAKPPSFPPLSSSSTVTSSSCSTSGVQPSMPTDDYSEESDTDLSQKSDGPASPVEGPKDPSCPKDSGLTSVGTDTFRL,mutated_sequence,1.0,3703.0,NP_008816.3.a2m,NP_008816.3.npy,ClinVar +NP_008818.3,NP_008818.3.csv,MSTTLLSAFYDVDFLCKTEKSLANLNLNNMLDKKAVGTPVAAAPSSGFAPGFLRRHSASNLHALAHPAPSPGSCSPKFPGAANGSSCGSAAAGGPTSYGTLKEPSGGGGTALLNKENKFRDRSFSENGDRSQHLLHLQQQQKGGGGSQINSTRYKTELCRPFEESGTCKYGEKCQFAHGFHELRSLTRHPKYKTELCRTFHTIGFCPYGPRCHFIHNADERRPAPSGGASGDLRAFGTRDALHLGFPREPRPKLHHSLSFSGFPSGHHQPPGGLESPLLLDSPTSRTPPPPSCSSASSCSSSASSCSSASAASTPSGAPTCCASAAAAAAAALLYGTGGAEDLLAPGAPCAACSSASCANNAFAFGPELSSLITPLAIQTHNFAAVAAAAYYRSQQQQQQQGLAPPAQPPAPPSATLPAGAAAPPSPPFSFQLPRRLSDSPVFDAPPSPPDSLSDRDSYLSGSLSSGSLSGSESPSLDPGRRLPIFSRLSISDD,mutated_sequence,1.0,494.0,NP_008818.3.a2m,NP_008818.3.npy,ClinVar +NP_008846.2,NP_008846.2.csv,MGCFFSKRRKADKESRPENEEERPKQYSWDQREKVDPKDYMFSGLKDETVGRLPGTVAGQQFLIQDCENCNIYIFDHSATVTIDDCTNCIIFLGPVKGSVFFRNCRDCKCTLACQQFRVRDCRKLEVFLCCATQPIIESSSNIKFGCFQWYYPELAFQFKDAGLSIFNNTWSNIHDFTPVSGELNWSLLPEDAVVQDYVPIPTTEELKAVRVSTEANRSIVPISRGQRQKSSDESCLVVLFAGDYTIANARKLIDEMVGKGFFLVQTKEVSMKAEDAQRVFREKAPDFLPLLNKGPVIALEFNGDGAVEVCQLIVNEIFNGTKMFVSESKETASGDVDSFYNFADIQMGI,mutated_sequence,1.0,350.0,NP_008846.2.a2m,NP_008846.2.npy,ClinVar +NP_008853.3,NP_008853.3.csv,MAQALLVPPGPESFRLFTRESLAAIEKRAAEEKAKKPKKEQDNDDENKPKPNSDLEAGKNLPFIYGDIPPEMVSEPLEDLDPYYINKKTFIVMNKGKAIFRFSATSALYILTPLNPVRKIAIKILVHSLFSMLIMCTILTNCVFMTLSNPPDWTKNVEYTFTGIYTFESLIKILARGFCLEDFTFLRDPWNWLDFSVIVMAYVTEFVDLGNVSALRTFRVLRALKTISVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALIGLQLFMGNLRNKCLQWPPSDSAFETNTTSYFNGTMDSNGTFVNVTMSTFNWKDYIGDDSHFYVLDGQKDPLLCGNGSDAGQCPEGYICVKAGRNPNYGYTSFDTFSWAFLSLFRLMTQDYWENLYQLTLRAAGKTYMIFFVLVIFLGSFYLVNLILAVVAMAYEEQNQATLEEAEQKEAEFQQMLEQLKKQQEEAQAVAAASAASRDFSGIGGLGELLESSSEASKLSSKSAKEWRNRRKKRRQREHLEGNNKGERDSFPKSESEDSVKRSSFLFSMDGNRLTSDKKFCSPHQSLLSIRGSLFSPRRNSKTSIFSFRGRAKDVGSENDFADDEHSTFEDSESRRDSLFVPHRHGERRNSNVSQASMSSRMVPGLPANGKMHSTVDCNGVVSLVGGPSALTSPTGQLPPEGTTTETEVRKRRLSSYQISMEMLEDSSGRQRAVSIASILTNTMEELEESRQKCPPCWYRFANVFLIWDCCDAWLKVKHLVNLIVMDPFVDLAITICIVLNTLFMAMEHYPMTEQFSSVLTVGNLVFTGIFTAEMVLKIIAMDPYYYFQEGWNIFDGIIVSLSLMELGLSNVEGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKINDDCTLPRWHMNDFFHSFLIVFRVLCGEWIETMWDCMEVAGQTMCLIVFMLVMVIGNLVVLNLFLALLLSSFSSDNLAATDDDNEMNNLQIAVGRMQKGIDYVKNKMRECFQKAFFRKPKVIEIHEGNKIDSCMSNNTGIEISKELNYLRDGNGTTSGVGTGSSVEKYVIDENDYMSFINNPSLTVTVPIAVGESDFENLNTEEFSSESELEESKEKLNATSSSEGSTVDVVLPREGEQAETEPEEDLKPEACFTEGCIKKFPFCQVSTEEGKGKIWWNLRKTCYSIVEHNWFETFIVFMILLSSGALAFEDIYIEQRKTIKTMLEYADKVFTYIFILEMLLKWVAYGFQTYFTNAWCWLDFLIVDVSLVSLVANALGYSELGAIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYHCVNMTTGNMFDISDVNNLSDCQALGKQARWKNVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRDVKLQPVYEENLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKFGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPANKFQGMVFDFVTRQVFDISIMILICLNMVTMMVETDDQGKYMTLVLSRINLVFIVLFTGEFVLKLVSLRHYYFTIGWNIFDFVVVILSIVGMFLAEMIEKYFVSPTLFRVIRLARIGRILRLIKGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYAIFGMSNFAYVKKEAGIDDMFNFETFGNSMICLFQITTSAGWDGLLAPILNSAPPDCDPDTIHPGSSVKGDCGNPSVGIFFFVSYIIISFLVVVNMYIAVILENFSVATEESAEPLSEDDFEMFYEVWEKFDPDATQFIEFSKLSDFAAALDPPLLIAKPNKVQLIAMDLPMVSGDRIHCLDILFAFTKRVLGESGEMDALRIQMEDRFMASNPSKVSYEPITTTLKRKQEEVSAAIIQRNFRCYLLKQRLKNISSNYNKEAIKGRIDLPIKQDMIIDKLNGNSTPEKTDGSSSTTSPPSYDSVTKPDKEKFEKDKPEKESKGKEVRENQK,mutated_sequence,1.0,2000.0,NP_008853.3.a2m,NP_008853.3.npy,ClinVar +NP_008872.1,NP_008872.1.csv,MAEEQDLSEVELSPVGSEEPRCLSPGSAPSLGPDGGGGGSGLRASPGPGELGKVKKEQQDGEADDDKFPVCIREAVSQVLSGYDWTLVPMPVRVNGASKSKPHVKRPMNAFMVWAQAARRKLADQYPHLHNAELSKTLGKLWRLLNESDKRPFIEEAERLRMQHKKDHPDYKYQPRRRKNGKAAQGEAECPGGEAEQGGTAAIQAHYKSAHLDHRHPGEGSPMSDGNPEHPSGQSHGPPTPPTTPKTELQSGKADPKRDGRSMGEGGKPHIDFGNVDIGEISHEVMSNMETFDVAELDQYLPPNGHPGHVSSYSAAGYGLGSALAVASGHSAWISKPPGVALPTVSPPGVDAKAQVKTETAGPQGPPHYTDQPSTSQIAYTSLSLPHYGSAFPSISRPQFDYSDHQPSGPYYGHSGQASGLYSAFSYMGPSQRPLYTAISDPSPSGPQSHSPTHWEQPVYTTLSRP,mutated_sequence,1.0,466.0,NP_008872.1.a2m,NP_008872.1.npy,ClinVar +NP_008877.2,NP_008877.2.csv,MSSTLSPTDFDSLEIQGQYSDINNRWDLPDSDWDNDSSSARLFERSRIKALADEREAVQKKTFTKWVNSHLARVTCRVGDLYSDLRDGRNLLRLLEVLSGEILPKPTKGRMRIHCLENVDKALQFLKEQKVHLENMGSHDIVDGNHRLTLGLVWTIILRFQIQDISVETEDNKEKKSAKDALLLWCQMKTAGYPNVNVHNFTTSWRDGLAFNAIVHKHRPDLLDFESLKKCNAHYNLQNAFNLAEKELGLTKLLDPEDVNVDQPDEKSIITYVATYYHYFSKMKALAVEGKRIGKVLDHAMEAERLVEKYESLASELLQWIEQTIVTLNDRQLANSLSGVQNQLQSFNSYRTVEKPPKFTEKGNLEVLLFTIQSKLRANNQKVYTPREGRLISDINKAWERLEKAEHERELALRTELIRQEKLEQLAARFDRKAAMRETWLSENQRLVSQDNFGLELAAVEAAVRKHEAIETDIVAYSGRVQAVDAVAAELAAERYHDIKRIAARQHNVARLWDFLRQMVAARRERLLLNLELQKVFQDLLYLMDWMEEMKGRLQSQDLGRHLAGVEDLLQLHELVEADIAVQAERVRAVSASALRFCNPGKEYRPCDPQLVSERVAKLEQSYEALCELAAARRARLEESRRLWRFLWEVGEAEAWVREQQHLLASADTGRDLTGALRLLNKHTALRGEMSGRLGPLKLTLEQGQQLVAEGHPGASQASARAAELQAQWERLEALAEERAQRLAQAASLYQFQADANDMEAWLVDALRLVSSPELGHDEFSTQALARQHRALEEEIRSHRPTLDALREQAAALPPTLSRTPEVQSRVPTLERHYEELQARAGERARALEAALALYTMLSEAGACGLWVEEKEQWLNGLALPERLEDLEVVQQRFETLEPEMNTLAAQITAVNDIAEQLLKANPPGKDRIVNTQEQLNHRWQQFRRLADGKKAALTSALSIQNYHLECTETQAWMREKTKVIESTQGLGNDLAGVLALQRKLAGTERDLEAIAARVGELTREANALAAGHPAQAVAINARLREVQTGWEDLRATMRRREESLGEARRLQDFLRSLDDFQAWLGRTQTAVASEEGPATLPEAEALLAQHAALRGEVERAQSEYSRLRALGEEVTRDQADPQCLFLRQRLEALGTGWEELGRMWESRQGRLAQAHGFQGFLRDARQAEGVLSSQEYVLSHTEMPGTLQAADAAIKKLEDFMSTMDANGERIHGLLEAGRQLVSEGNIHADKIREKADSIERRHKKNQDAAQQFLGRLRDNREQQHFLQDCHELKLWIDEKMLTAQDVSYDEARNLHTKWQKHQAFMAELAANKDWLDKVDKEGRELTLEKPELKALVSEKLRDLHRRWDELETTTQAKARSLFDANRAELFAQSCCALESWLESLQAQLHSDDYGKDLTSVNILLKKQQMLEWEMAVREKEVEAIQAQAKALAQEDQGAGEVERTSRAVEEKFRALCQPMRERCRRLQASREQHQFHRDVEDEILWVTERLPMASSMEHGKDLPSVQLLMKKNQTLQKEIQGHEPRIADLRERQRALGAAAAGPELAELQEMWKRLGHELELRGKRLEDALRAQQFYRDAAEAEAWMGEQELHMMGQEKAKDELSAQAEVKKHQVLEQALADYAQTIHQLAASSQDMIDHEHPESTRISIRQAQVDKLYAGLKELAGERRERLQEHLRLCQLRRELDDLEQWIQEREVVAASHELGQDYEHVTMLRDKFREFSRDTSTIGQERVDSANALANGLIAGGHAARATVAEWKDSLNEAWADLLELLDTRGQVLAAAYELQRFLHGARQALARVQHKQQQLPDGTGRDLNAAEALQRRHCAYEHDIQALSPQVQQVQDDGHRLQKAYAGDKAEEIGRHMQAVAEAWAQLQGSSAARRQLLLDTTDKFRFFKAVRELMLWMDEVNLQMDAQERPRDVSSADLVIKNQQGIKAEIEARADRFSSCIDMGKELLARSHYAAEEISEKLSQLQARRQETAEKWQEKMDWLQLVLEVLVFGRDAGMAEAWLCSQEPLVRSAELGCTVDEVESLIKRHEAFQKSAVAWEERFCALEKLTALEEREKERKRKREEEERRKQPPAPEPTASVPPGDLVGGQTASDTTWDGTQPRPPPSTQAPSVNGVCTDGEPSQPLLGQQRLEHSSFPEGPGPGSGDEANGPRGERQTRTRGPAPSAMPQSRSTESAHAATLPPRGPEPSAQEQMEGMLCRKQEMEAFGKKAANRSWQNVYCVLRRGSLGFYKDAKAASAGVPYHGEVPVSLARAQGSVAFDYRKRKHVFKLGLQDGKEYLFQAKDEAEMSSWLRVVNAAIATASSASGEPEEPVVPSTTRGMTRAMTMPPVSPVGAEGPVVLRSKDGREREREKRFSFFKKNK,mutated_sequence,1.0,2390.0,NP_008877.2.a2m,NP_008877.2.npy,ClinVar +NP_008880.2,NP_008880.2.csv,MAPSGLKAVVGEKILSGVIRSVKKDGEWKVLIMDHPSMRILSSCCKMSDILAEGITIVEDINKRREPIPSLEAIYLLSPTEKSVQALIKDFQGTPTFTYKAAHIFFTDTCPEPLFSELGRSRLAKVVKTLKEIHLAFLPYEAQVFSLDAPHSTYNLYCPFRAEERTRQLEVLAQQIATLCATLQEYPAIRYRKGPEDTAQLAHAVLAKLNAFKADTPSLGEGPEKTRSQLLIMDRAADPVSPLLHELTFQAMAYDLLDIEQDTYRYETTGLSEAREKAVLLDEDDDLWVELRHMHIADVSKKVTELLRTFCESKRLTTDKANIKDLSQILKKMPQYQKELNKYSTHLHLADDCMKHFKGSVEKLCSVEQDLAMGSDAEGEKIKDSMKLIVPVLLDAAVPAYDKIRVLLLYILLRNGVSEENLAKLIQHANVQAHSSLIRNLEQLGGTVTNPGGSGTSSRLEPRERMEPTYQLSRWTPVIKDVMEDAVEDRLDRNLWPFVSDPAPTASSQAAVSARFGHWHKNKAGIEARAGPRLIVYVMGGVAMSEMRAAYEVTRATEGKWEVLIGSSHILTPTRFLDDLKALDKKLEDIALP,mutated_sequence,1.0,593.0,NP_008880.2.a2m,NP_008880.2.npy,ClinVar +NP_009060.2,NP_009060.2.csv,MLLDAGPQFPAIGVGSFARHHHHSAAAAAAAAAEMQDRELSLAAAQNGFVDSAAAHMGAFKLNPGAHELSPGQSSAFTSQGPGAYPGSAAAAAAAAALGPHAAHVGSYSGPPFNSTRDFLFRSRGFGDSAPGGGQHGLFGPGAGGLHHAHSDAQGHLLFPGLPEQHGPHGSQNVLNGQMRLGLPGEVFGRSEQYRQVASPRTDPYSAAQLHNQYGPMNMNMGMNMAAAAAHHHHHHHHHPGAFFRYMRQQCIKQELICKWIDPEQLSNPKKSCNKTFSTMHELVTHVSVEHVGGPEQSNHVCFWEECPREGKPFKAKYKLVNHIRVHTGEKPFPCPFPGCGKVFARSENLKIHKRTHTGEKPFQCEFEGCDRRFANSSDRKKHMHVHTSDKPYLCKMCDKSYTHPSSLRKHMKVHESSPQGSESSPAASSGYESSTPPGLVSPSAEPQSSSNLSPAAAAAAAAAAAAAAAVSAVHRGGGSGSGGAGGGSGGGSGSGGGGGGAGGGGGGSSGGGSGTAGGHSGLSSNFNEWYV,mutated_sequence,1.0,532.0,NP_009060.2.a2m,NP_009060.2.npy,ClinVar +NP_009167.1,NP_009167.1.csv,MGSRASTLLRDEELEEIKKETGFSHSQITRLYSRFTSLDKGENGTLSREDFQRIPELAINPLGDRIINAFFPEGEDQVNFRGFMRTLAHFRPIEDNEKSKDVNGPEPLNSRSNKLHFAFRLYDLDKDEKISRDELLQVLRMMVGVNISDEQLGSIADRTIQEADQDGDSAISFTEFVKVLEKVDVEQKMSIRFLH,mutated_sequence,1.0,195.0,NP_009167.1.a2m,NP_009167.1.npy,ClinVar +NP_009185.2,NP_009185.2.csv,MGEVEAPGRLWLESPPGGAPPIFLPSDGQALVLGRGPLTQVTDRKCSRTQVELVADPETRTVAVKQLGVNPSTTGTQELKPGLEGSLGVGDTLYLVNGLHPLTLRWEETRTPESQPDTPPGTPLVSQDEKRDAELPKKRMRKSNPGWENLEKLLVFTAAGVKPQGKVAGFDLDGTLITTRSGKVFPTGPSDWRILYPEIPRKLRELEAEGYKLVIFTNQMSIGRGKLPAEEFKAKVEAVVEKLGVPFQVLVATHAGLYRKPVTGMWDHLQEQANDGTPISIGDSIFVGDAAGRPANWAPGRKKKDFSCADRLFALNLGLPFATPEEFFLKWPAAGFELPAFDPRTVSRSGPLCLPESRALLSASPEVVVAVGFPGAGKSTFLKKHLVSAGYVHVNRDTLGSWQRCVTTCETALKQGKRVAIDNTNPDAASRARYVQCARAAGVPCRCFLFTATLEQARHNNRFREMTDSSHIPVSDMVMYGYRKQFEAPTLAEGFSAILEIPFRLWVEPRLGRLYCQFSEG,mutated_sequence,1.0,521.0,NP_009185.2.a2m,NP_009185.2.npy,ClinVar +NP_009193.2,NP_009193.2.csv,MASKRALVILAKGAEEMETVIPVDVMRRAGIKVTVAGLAGKDPVQCSRDVVICPDASLEDAKKEGPYDVVVLPGGNLGAQNLSESAAVKEILKEQENRKGLIAAICAGPTALLAHEIGFGSKVTTHPLAKDKMMNGGHYTYSENRVEKDGLILTSRGPGTSFEFALAIVEALNGKEVAAQVKAPLVLKD,mutated_sequence,1.0,189.0,NP_009193.2.a2m,NP_009193.2.npy,ClinVar +NP_009225.1,NP_009225.1.csv,MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDSSEDTVNKATYCSVGDQELLQITPQGTRDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTEKRAAERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVEKAEFCNKSKQPGLARSQHNRWAGSKETCNDRRTPSTEKKVDLNADPLCERKEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHDGESESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSKSVESNIEDKIFGKTYRKKASLPNLSHVTENLIIGAFVTEPQIIQERPLTNKLKRKRRPTSGLHPEDFIKKADLAVQKTPEMINQGTNQTEQNGQVMNITNSGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNIHNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEIKKKKYNQMPVRHSRNLQLMEGKEPATGAKKSNKPNEQTSKRHDSDTFPELKLTNAPGSFTKCSNTSELKEFVNPSLPREEKEEKLETVKVSNNAEDPKDLMLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNKCVSQCAAFENPKGLIHGCSKDNRNDTEGFKYPLGHEVNHSRETSIEMEESELDAQYLQNTFKVSKRQSFAPFSNPGNAEEECATFSAHSGSLKKQSPKVTFECEQKEENQGKNESNIKPVQTVNITAGFPVVGQKDKPVDNAKCSIKGGSRFCLSSQFRGNETGLITPNKHGLLQNPYRIPPLFPIKSFVKTKCKKNLLEENFEEHSMSPEREMGNENIPSTVSTISRNNIRENVFKEASSSNINEVGSSTNEVGSSINEIGSSDENIQAELGRNRGPKLNAMLRLGVLQPEVYKQSLPGSNCKHPEIKKQEYEEVVQTVNTDFSPYLISDNLEQPMGSSHASQVCSETPDDLLDDGEIKEDTSFAENDIKESSAVFSKSVQKGELSRSPSPFTHTHLAQGYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVNNIPSQSTRHSTVATECLSKNTEENLLSLKNSLNDCSNQVILAKASQEHHLSEETKCSASLFSSQCSELEDLTANTNTQDPFLIGSSKQMRHQSESQGVGLSDKELVSDDEERGTGLEENNQEEQSMDSNLGEAASGCESETSVSEDCSGLSSQSDILTTQQRDTMQHNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLRNPEQSTSEKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSKCPSLDDRWYMHSCSGSLQNRNYPSQEELIKVVDVEEQQLEESGPHDLTETSYLPRQDLEGTPYLESGISLFSDDPESDPSEDRAPESARVGNIPSSTSALKVPQLKVAESAQSPAAAHTTDTAGYNAMEESVSREKPELTASTERVNKRMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLIPQIPHSHY,mutated_sequence,1.0,1863.0,NP_009225.1.a2m,NP_009225.1.npy,ClinVar +NP_015564.5,NP_015564.5.csv,MARQKKMGQSVLRAVFFLVLGLLGHSHGGFPNTISIGGLFMRNTVQEHSAFRFAVQLYNTNQNTTEKPFHLNYHVDHLDSSNSFSVTNAFCSQFSRGVYAIFGFYDQMSMNTLTSFCGALHTSFVTPSFPTDADVQFVIQMRPALKGAILSLLGHYKWEKFVYLYDTERGFSILQAIMEAAVQNNWQVTARSVGNIKDVQEFRRIIEEMDRRQEKRYLIDCEVERINTILEQVVILGKHSRGYHYMLANLGFTDILLERVMHGGANITGFQIVNNENPMVQQFIQRWVRLDEREFPEAKNAPLKYTSALTHDAILVIAEAFRYLRRQRVDVSRRGSAGDCLANPAVPWSQGIDIERALKMVQVQGMTGNIQFDTYGRRTNYTIDVYEMKVSGSRKAGYWNEYERFVPFSDQQISNDSASSENRTIVVTTILESPYVMYKKNHEQLEGNERYEGYCVDLAYEIAKHVRIKYKLSIVGDGKYGARDPETKIWNGMVGELVYGRADIAVAPLTITLVREEVIDFSKPFMSLGISIMIKKPQKSKPGVFSFLDPLAYEIWMCIVFAYIGVSVVLFLVSRFSPYEWHLEDNNEEPRDPQSPPDPPNEFGIFNSLWFSLGAFMQQGCDISPRSLSGRIVGGVWWFFTLIIISSYTANLAAFLTVERMVSPIESAEDLAKQTEIAYGTLDSGSTKEFFRRSKIAVYEKMWSYMKSAEPSVFTKTTADGVARVRKSKGKFAFLLESTMNEYIEQRKPCDTMKVGGNLDSKGYGVATPKGSALRTPVNLAVLKLSEQGILDKLKNKWWYDKGECGAKDSGSKDKTSALSLSNVAGVFYILVGGLGLAMMVALIEFCYKSRAESKRMKLTKNTQNFKPAPATNTQNYATYREGYNVYGTESVKI,mutated_sequence,1.0,894.0,NP_015564.5.a2m,NP_015564.5.npy,ClinVar +NP_015566.1,NP_015566.1.csv,MSTMRLLTLALLFSCSVARAACDPKIVNIGAVLSTRKHEQMFREAVNQANKRHGSWKIQLNATSVTHKPNAIQMALSVCEDLISSQVYAILVSHPPTPNDHFTPTPVSYTAGFYRIPVLGLTTRMSIYSDKSIHLSFLRTVPPYSHQSSVWFEMMRVYSWNHIILLVSDDHEGRAAQKRLETLLEERESKAEKVLQFDPGTKNVTALLMEAKELEARVIILSASEDDAATVYRAAAMLNMTGSGYVWLVGEREISGNALRYAPDGILGLQLINGKNESAHISDAVGVVAQAVHELLEKENITDPPRGCVGNTNIWKTGPLFKRVLMSSKYADGVTGRVEFNEDGDRKFANYSIMNLQNRKLVQVGIYNGTHVIPNDRKIIWPGGETEKPRGYQMSTRLKIVTIHQEPFVYVKPTLSDGTCKEEFTVNGDPVKKVICTGPNDTSPGSPRHTVPQCCYGFCIDLLIKLARTMNFTYEVHLVADGKFGTQERVNNSNKKEWNGMMGELLSGQADMIVAPLTINNERAQYIEFSKPFKYQGLTILVKKEIPRSTLDSFMQPFQSTLWLLVGLSVHVVAVMLYLLDRFSPFGRFKVNSEEEEEDALTLSSAMWFSWGVLLNSGIGEGAPRSFSARILGMVWAGFAMIIVASYTANLAAFLVLDRPEERITGINDPRLRNPSDKFIYATVKQSSVDIYFRRQVELSTMYRHMEKHNYESAAEAIQAVRDNKLHAFIWDSAVLEFEASQKCDLVTTGELFFRSGFGIGMRKDSPWKQNVSLSILKSHENGFMEDLDKTWVRYQECDSRSNAPATLTFENMAGVFMLVAGGIVAGIFLIFIEIAYKRHKDARRKQMQLAFAAVNVWRKNLQDRKSGRAEPDPKKKATFRAITSTLASSFKRRRSSKDTSTGGGRGALQNQKDTVLPRRAIEREEGQLQLCSRHRES,mutated_sequence,1.0,938.0,NP_015566.1.a2m,NP_015566.1.npy,ClinVar +NP_036192.2,NP_036192.2.csv,MEALIPVINKLQDVFNTVGADIIQLPQIVVVGTQSSGKSSVLESLVGRDLLPRGTGIVTRRPLILQLVHVSQEDKRKTTGEENGVEAEEWGKFLHTKNKLYTDFDEIRQEIENETERISGNNKGVSPEPIHLKIFSPNVVNLTLVDLPGMTKVPVGDQPKDIELQIRELILRFISNPNSIILAVTAANTDMATSEALKISREVDPDGRRTLAVITKLDLMDAGTDAMDVLMGRVIPVKLGIIGVVNRSQLDINNKKSVTDSIRDEYAFLQKKYPSLANRNGTKYLARTLNRLLMHHIRDCLPELKTRINVLAAQYQSLLNSYGEPVDDKSATLLQLITKFATEYCNTIEGTAKYIETSELCGGARICYIFHETFGRTLESVDPLGGLNTIDILTAIRNATGPRPALFVPEVSFELLVKRQIKRLEEPSLRCVELVHEEMQRIIQHCSNYSTQELLRFPKLHDAIVEVVTCLLRKRLPVTNEMVHNLVAIELAYINTKHPDFADACGLMNNNIEEQRRNRLARELPSAVSRDKSSKVPSALAPASQEPSPAASAEADGKLIQDSRRETKNVASGGGGVGDGVQEPTTGNWRGMLKTSKAEELLAEEKSKPIPIMPASPQKGHAVNLLDVPVPVARKLSAREQRDCEVIERLIKSYFLIVRKNIQDSVPKAVMHFLVNHVKDTLQSELVGQLYKSSLLDDLLTESEDMAQRRKEAADMLKALQGASQIIAEIRETHLW,mutated_sequence,1.0,736.0,NP_036192.2.a2m,NP_036192.2.npy,ClinVar +NP_036286.2,NP_036286.2.csv,MYSGAGPALAPPAPPPPIQGYAFKPPPRPDFGTSGRTIKLQANFFEMDIPKIDIYHYELDIKPEKCPRRVNREIVEHMVQHFKTQIFGDRKPVFDGRKNLYTAMPLPIGRDKVELEVTLPGEGKDRIFKVSIKWVSCVSLQALHDALSGRLPSVPFETIQALDVVMRHLPSMRYTPVGRSFFTASEGCSNPLGGGREVWFGFHQSVRPSLWKMMLNIDVSATAFYKAQPVIEFVCEVLDFKSIEEQQKPLTDSQRVKFTKEIKGLKVEITHCGQMKRKYRVCNVTRRPASHQTFPLQQESGQTVECTVAQYFKDRHKLVLRYPHLPCLQVGQEQKHTYLPLEVCNIVAGQRCIKKLTDNQTSTMIRATARSAPDRQEEISKLMRSASFNTDPYVREFGIMVKDEMTDVTGRVLQPPSILYGGRNKAIATPVQGVWDMRNKQFHTGIEIKVWAIACFAPQRQCTEVHLKSFTEQLRKISRDAGMPIQGQPCFCKYAQGADSVEPMFRHLKNTYAGLQLVVVILPGKTPVYAEVKRVGDTVLGMATQCVQMKNVQRTTPQTLSNLCLKINVKLGGVNNILLPQGRPPVFQQPVIFLGADVTHPPAGDGKKPSIAAVVGSMDAHPNRYCATVRVQQHRQEIIQDLAAMVRELLIQFYKSTRFKPTRIIFYRDGVSEGQFQQVLHHELLAIREACIKLEKDYQPGITFIVVQKRHHTRLFCTDKNERVGKSGNIPAGTTVDTKITHPTEFDFYLCSHAGIQGTSRPSHYHVLWDDNRFSSDELQILTYQLCHTYVRCTRSVSIPAPAYYAHLVAFRARYHLVDKEHDSAEGSHTSGQSNGRDHQALAKAVQVHQDTLRTMYFA,mutated_sequence,1.0,859.0,NP_036286.2.a2m,NP_036286.2.npy,ClinVar +NP_036325.2,NP_036325.2.csv,MAWRGAGPSVPGAPGGVGLSLGLLLQLLLLLGPARGFGDEEERRCDPIRISMCQNLGYNVTKMPNLVGHELQTDAELQLTTFTPLIQYGCSSQLQFFLCSVYVPMCTEKINIPIGPCGGMCLSVKRRCEPVLKEFGFAWPESLNCSKFPPQNDHNHMCMEGPGDEEVPLPHKTPIQPGEECHSVGTNSDQYIWVKRSLNCVLKCGYDAGLYSRSAKEFTDIWMAVWASLCFISTAFTVLTFLIDSSRFSYPERPIIFLSMCYNIYSIAYIVRLTVGRERISCDFEEAAEPVLIQEGLKNTGCAIIFLLMYFFGMASSIWWVILTLTWFLAAGLKWGHEAIEMHSSYFHIAAWAIPAVKTIVILIMRLVDADELTGLCYVGNQNLDALTGFVVAPLFTYLVIGTLFIAAGLVALFKIRSNLQKDGTKTDKLERLMVKIGVFSVLYTVPATCVIACYFYEISNWALFRYSADDSNMAVEMLKIFMSLLVGITSGMWIWSAKTLHTWQKCSNRLVNSGKVKREKRGNGWVKPGKGSETVV,mutated_sequence,1.0,537.0,NP_036325.2.a2m,NP_036325.2.npy,ClinVar +NP_036345.2,NP_036345.2.csv,MRGFGPGLTARRLLPLRLPPRPPGPRLASGQAAGALERAMDELLRRAVPPTPAYELREKTPAPAEGQCADFVSFYGGLAETAQRAELLGRLARGFGVDHGQVAEQSAGVLHLRQQQREAAVLLQAEDRLRYALVPRYRGLFHHISKLDGGVRFLVQLRADLLEAQALKLVEGPDVREMNGVLKGMLSEWFSSGFLNLERVTWHSPCEVLQKISEAEAVHPVKNWMDMKRRVGPYRRCYFFSHCSTPGEPLVVLHVALTGDISSNIQAIVKEHPPSETEEKNKITAAIFYSISLTQQGLQGVELGTFLIKRVVKELQREFPHLGVFSSLSPIPGFTKWLLGLLNSQTKEHGRNELFTDSECKEISEITGGPINETLKLLLSSSEWVQSEKLVRALQTPLMRLCAWYLYGEKHRGYALNPVANFHLQNGAVLWRINWMADVSLRGITGSCGLMANYRYFLEETGPNSTSYLGSKIIKASEQVLSLVAQFQKNSKL,mutated_sequence,1.0,493.0,NP_036345.2.a2m,NP_036345.2.npy,ClinVar +NP_036382.2,NP_036382.2.csv,MAAAGWRDGSGQEKYRLVVVGGGGVGKSALTIQFIQSYFVTDYDPTIEDSYTKQCVIDDRAARLDILDTAGQEEFGAMREQYMRTGEGFLLVFSVTDRGSFEEIYKFQRQILRVKDRDEFPMILIGNKADLDHQRQVTQEEGQQLARQLKVTYMEASAKIRMNVDQAFHELVRVIRKFQEQECPPSPEPTRKEKDKKGCHCVIF,mutated_sequence,1.0,204.0,NP_036382.2.a2m,NP_036382.2.npy,ClinVar +NP_036470.1,NP_036470.1.csv,MAREDSVKCLRCLLYALNLLFWLMSISVLAVSAWMRDYLNNVLTLTAETRVEEAVILTYFPVVHPVMIAVCCFLIIVGMLGYCGTVKRNLLLLAWYFGSLLVIFCVELACGVWTYEQELMVPVQWSDMVTLKARMTNYGLPRYRWLTHAWNFFQREFKCCGVVYFTDWLEMTEMDWPPDSCCVREFPGCSKQAHQEDLSDLYQEGCGKKMYSFLRGTKQLQVLRFLGISIGVTQILAMILTITLLWALYYDRREPGTDQMMSLKNDNSQHLSCPSVELLKPSLSRIFEHTSMANSFNTHFEMEEL,mutated_sequence,1.0,305.0,NP_036470.1.a2m,NP_036470.1.npy,ClinVar +NP_036546.2,NP_036546.2.csv,MACSIVQFCYFQDLQAARDFLFPHLREEILSGALRRDPSKSTDWEDDGWGAWEENEPQEPEEEGNTCKTQKTSWLQDCVLSLSPTNDLMVIAREQKAVFLVPKWKYSDKGKEEMQFAVGWSGSLNVEEGECVTSALCIPLASQKRSSTGRPDWTCIVVGFTSGYVRFYTENGVLLLAQLLNEDPVLQLKCRTYEIPRHPGVTEQNEELSILYPAAIVTIDGFSLFQSLRACRNQVAKAAASGNENIQPPPLAYKKWGLQDIDTIIDHASVGIMTLSPFDQMKTASNIGGFNAAIKNSPPAMSQYITVGSNPFTGFFYALEGSTQPLLSHVALAVASKLTSALFNAASGWLGWKSKHEEEAVQKQKPKVEPATPLAVRFGLPDSRRHGESICLSPCNTLAAVTDDFGRVILLDVARGIAIRMWKGYRDAQIGWIQTVEDLHERVPEKADFSPFGNSQGPSRVAQFLVIYAPRRGILEVWSTQQGPRVGAFNVGKHCRLLYPGYKIMGLNNVTSQSWQPQTYQICLVDPVSGSVKTVNVPFHLALSDKKSERAKDMHLVKKLAALLKTKSPNLDLVETEIKELILDIKYPATKKQALESILASERLPFSCLRNITQTLMDTLKSQELESVDEGLLQFCANKLKLLQLYESVSQLNSLDFHLDTPFSDNDLALLLRLDEKELLKLQALLEKYKQENTRTNVRFSDDKDGVLPVKTFLEYLEYEKDVLNIKKISEEEYVALGSFFFWKCLHGESSTEDMCHTLESAGLSPQLLLSLLLSVWLSKEKDILDKPQSICCLHTMLSLLSKMKVAIDETWDSQSVSPWWQQMRTACIQSENNGAALLSAHVGHSVAAQISNNMTEKKFSQTVLGADSEALTDSWEALSLDTEYWKLLLKQLEDCLILQTLLHSKGNTQTSKVSSLQAEPLPRLSVKKLLEGGKGGIADSVAKWIFKQDFSPEVLKLANEERDAENPDEPKEGVNRSFLEVSEMEMDLGAIPDLLHLAYEQFPCSLELDVLHAHCCWEYVVQWNKDPEEARFFVRSIEHLKQIFNAHVQNGIALMMWNTFLVKRFSAATYLMDKVGKSPKDRLCRRDVGMSDTAMTSFLGSCLDLLQILMEADVSRDEIQVPVLDTEDAWLSVEGPISIVELALEQKHIHYPLVEHHSILCSILYAVMRFSLKTVKPLSLFDSKGKNAFFKDLTSIQLLPSGEMDPNFISVRQQFLLKVVSAAVQAQHSATKVKDPTEEATPTPFGKDQDWPALAVDLAHHLQVSEDVVRRHYVGELYNYGVDHLGEEAILQVHDKEVLASQLLVLTGQRLAHALLHTQTKEGMELLARLPPTLCTWLKAMDPQDLQNTEVPIATTAKLVNKVIELLPEKHGQYGLALHLIEAVEAISLPSL,mutated_sequence,1.0,1393.0,NP_036546.2.a2m,NP_036546.2.npy,ClinVar +NP_036601.2,NP_036601.2.csv,MNKKKKPFLGMPAPLGYVPGLGRGATGFTTRSDIGPARDANDPVDDRHAPPGKRTVGDQMKKNQAADDDDEDLNDTNYDEFNGYAGSLFSSGPYEKDDEEADAIYAALDKRMDERRKERREQREKEEIEKYRMERPKIQQQFSDLKRKLAEVTEEEWLSIPEVGDARNKRQRNPRYEKLTPVPDSFFAKHLQTGENHTSVDPRQTQFGGLNTPYPGGLNTPYPGGMTPGLMTPGTGELDMRKIGQARNTLMDMRLSQVSDSVSGQTVVDPKGYLTDLNSMIPTHGGDINDIKKARLLLKSVRETNPHHPPAWIASARLEEVTGKLQVARNLIMKGTEMCPKSEDVWLEAARLQPGDTAKAVVAQAVRHLPQSVRIYIRAAELETDIRAKKRVLRKALEHVPNSVRLWKAAVELEEPEDARIMLSRAVECCPTSVELWLALARLETYENARKVLNKARENIPTDRHIWITAAKLEEANGNTQMVEKIIDRAITSLRANGVEINREQWIQDAEECDRAGSVATCQAVMRAVIGIGIEEEDRKHTWMEDADSCVAHNALECARAIYAYALQVFPSKKSVWLRAAYFEKNHGTRESLEALLQRAVAHCPKAEVLWLMGAKSKWLAGDVPAARSILALAFQANPNSEEIWLAAVKLESENDEYERARRLLAKARSSAPTARVFMKSVKLEWVQDNIRAAQDLCEEALRHYEDFPKLWMMKGQIEEQKEMMEKAREAYNQGLKKCPHSTPLWLLLSRLEEKIGQLTRARAILEKSRLKNPKNPGLWLESVRLEYRAGLKNIANTLMAKALQECPNSGILWSEAIFLEARPQRRTKSVDALKKCEHDPHVLLAVAKLFWSQRKITKAREWFHRTVKIDSDLGDAWAFFYKFELQHGTEEQQEEVRKRCESAEPRHGELWCAVSKDIANWQKKIGDILRLVAGRIKNTF,mutated_sequence,1.0,941.0,NP_036601.2.a2m,NP_036601.2.npy,ClinVar +NP_037379.1,NP_037379.1.csv,MAAPRAGRGAGWSLRAWRALGGIRWGRRPRLTPDLRALLTSGTSDPRARVTYGTPSLWARLSVGVTEPRACLTSGTPGPRAQLTAVTPDTRTREASENSGTRSRAWLAVALGAGGAVLLLLWGGGRGPPAVLAAVPSPPPASPRSQYNFIADVVEKTAPAVVYIEILDRHPFLGREVPISNGSGFVVAADGLIVTNAHVVADRRRVRVRLLSGDTYEAVVTAVDPVADIATLRIQTKEPLPTLPLGRSADVRQGEFVVAMGSPFALQNTITSGIVSSAQRPARDLGLPQTNVEYIQTDAAIDFGNSGGPLVNLDGEVIGVNTMKVTAGISFAIPSDRLREFLHRGEKKNSSSGISGSQRRYIGVMMLTLSPSILAELQLREPSFPDVQHGVLIHKVILGSPAHRAGLRPGDVILAIGEQMVQNAEDVYEAVRTQSQLAVQIRRGRETLTLYVTPEVTE,mutated_sequence,1.0,458.0,NP_037379.1.a2m,NP_037379.1.npy,ClinVar +NP_037386.1,NP_037386.1.csv,MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL,mutated_sequence,1.0,729.0,NP_037386.1.a2m,NP_037386.1.npy,ClinVar +NP_037397.2,NP_037397.2.csv,MAAAAAAGPSPGSGPGDSPEGPEGEAPERRRKAHGMLKLYYGLSEGEAAGRPAGPDPLDPTDLNGAHFDPEVYLDKLRRECPLAQLMDSETDMVRQIRALDSDMQTLVYENYNKFISATDTIRKMKNDFRKMEDEMDRLATNMAVITDFSARISATLQDRHERITKLAGVHALLRKLQFLFELPSRLTKCVELGAYGQAVRYQGRAQAVLQQYQHLPSFRAIQDDCQVITARLAQQLRQRFREGGSGAPEQAECVELLLALGEPAEELCEEFLAHARGRLEKELRNLEAELGPSPPAPDVLEFTDHGGSGFVGGLCQVAAAYQELFAAQGPAGAEKLAAFARQLGSRYFALVERRLAQEQGGGDNSLLVRALDRFHRRLRAPGALLAAAGLADAATEIVERVARERLGHHLQGLRAAFLGCLTDVRQALAAPRVAGKEGPGLAELLANVASSILSHIKASLAAVHLFTAKEVSFSNKPYFRGEFCSQGVREGLIVGFVHSMCQTAQSFCDSPGEKGGATPPALLLLLSRLCLDYETATISYILTLTDEQFLVQDQFPVTPVSTLCAEARETARRLLTHYVKVQGLVISQMLRKSVETRDWLSTLEPRNVRAVMKRVVEDTTAIDVQVGLLYEEGVRKAQSSDSSKRTFSVYSSSRQQGRYAPSYTPSAPMDTNLLSNIQKLFSERIDVFSPVEFNKVSVLTGIIKISLKTLLECVRLRTFGRFGLQQVQVDCHFLQLYLWRFVADEELVHLLLDEVVASAALRCPDPVPMEPSVVEVICERG,mutated_sequence,1.0,782.0,NP_037397.2.a2m,NP_037397.2.npy,ClinVar +NP_037398.2,NP_037398.2.csv,MSAETPITLNIDPQDLQVQTFTVEKLLEPLIIQVTTLVNCPQNPSSRKKGRSKRASVLLASVEEATWNLLDKGEKIAQEATVLKDELTASLEEVRKESEALKVSAERFTDDPCFLPKREAVVQAARALLAAVTRLLILADMIDVMCLLQHVSAFQRTFESLKNVANKSDLQKTYQKLGKELENLDYLAFKRQQDLKSPNQRDEIAGARASLKENSPLLHSICSACLEHSDVASLKASKDTVCEEIQNALNVISNASQGIQNMTTPPEPQAATLGSALDELENLIVLNPLTVTEEEIRPSLEKRLEAIISGAALLADSSCTRDLHRERIIAECNAIRQALQDLLSEYMNNAGKKERSNTLNIALDNMCKKTRDLRRQLRKAIIDHVSDSFLDTTVPLLVLIEAAKNGREKEIKEYAAIFHEHTSRLVEVANLACSMSTNEDGIKIVKIAANHLETLCPQIINAALALAARPKSQAVKNTMEMYKRTWENHIHVLTEAVDDITSIDDFLAVSESHILEDVNKCIIALRDQDADNLDRAAGAIRGRAARVAHIVTGEMDSYEPGAYTEGVMRNVNFLTSTVIPEFVTQVNVALEALSKSSLNVLDDNQFVDISKKIYDTIHDIRCSVMMIRTPEELEDVSDLEEEHEVRSHTSIQTEGKTDRAKMTQLPEAEKEKIAEQVADFKKVKSKLDAEIEIWDDTSNDIIVLAKNMCMIMMEMTDFTRGKGPLKHTTDVIYAAKMISESGSRMDVLARQIANQCPDPSCKQDLLAYLEQIKFYSHQLKICSQVKAEIQNLGGELIMSALDSVTSLIQAAKNLMNAVVQTVKMSYIASTKIIRIQSPAGPRHPVVMWRMKAPAKKPLIKREKPEETCAAVRRGSAKKKIHPLQVMSEFRGRQIY,mutated_sequence,1.0,895.0,NP_037398.2.a2m,NP_037398.2.npy,ClinVar +NP_037407.4,NP_037407.4.csv,MPKGGCPKAPQQEELPLSSDMVEKQTGKKDKDKVSLTKTPKLERGDGGKEVRERASKRKLPFTAGANGEQKDSDTEKQGPERKRIKKEPVTRKAGLLFGMGLSGIRAGYPLSERQQVALLMQMTAEESANSPVDTTPKHPSQSTVCQKGTPNSASKTKDKVNKRNERGETRLHRAAIRGDARRIKELISEGADVNVKDFAGWTALHEACNRGYYDVAKQLLAAGAEVNTKGLDDDTPLHDAANNGHYKVVKLLLRYGGNPQQSNRKGETPLKVANSPTMVNLLLGKGTYTSSEESSTESSEEEDAPSFAPSSSVDGNNTDSEFEKGLKHKAKNPEPQKATAPVKDEYEFDEDDEQDRVPPVDDKHLLKKDYRKETKSNSFISIPKMEVKSYTKNNTIAPKKASHRILSDTSDEEDASVTVGTGEKLRLSAHTILPGSKTREPSNAKQQKEKNKVKKKRKKETKGREVRFGKRSDKFCSSESESESSESGEDDRDSLGSSGCLKGSPLVLKDPSLFSSLSASSTSSHGSSAAQKQNPSHTDQHTKHWRTDNWKTISSPAWSEVSSLSDSTRTRLTSESDYSSEGSSVESLKPVRKRQEHRKRASLSEKKSPFLSSAEGAVPKLDKEGKVVKKHKTKHKHKNKEKGQCSISQELKLKSFTYEYEDSKQKSDKAILLENDLSTENKLKVLKHDRDHFKKEEKLSKMKLEEKEWLFKDEKSLKRIKDTNKDISRSFREEKDRSNKAEKERSLKEKSPKEEKLRLYKEERKKKSKDRPSKLEKKNDLKEDKISKEKEKIFKEDKEKLKKEKVYREDSAFDEYCNKNQFLENEDTKFSLSDDQRDRWFSDLSDSSFDFKGEDSWDSPVTDYRDMKSDSVAKLILETVKEDSKERRRDSRAREKRDYREPFFRKKDRDYLDKNSEKRKEQTEKHKSVPGYLSEKDKKRRESAEAGRDRKDALESCKERRDGRAKPEEAHREELKECGCESGFKDKSDGDFGKGLEPWERHHPAREKEKKDGPDKERKEKTKPERYKEKSSDKDKSEKSILEKCQKDKEFDKCFKEKKDTKEKHKDTHGKDKERKASLDQGKEKKEKAFPGIISEDFSEKKDDKKGKEKSWYIADIFTDESEDDRDSCMGSGFKMGEASDLPRTDGLQEKEEGREAYASDRHRKSSDKQHPERQKDKEPRDRRKDRGAADAGRDKKEKVFEKHKEKKDKESTEKYKDRKDRASVDSTQDKKNKQKLPEKAEKKHAAEDKAKSKHKEKSDKEHSKERKSSRSADAEKSLLEKLEEEALHEYREDSNDKISEVSSDSFTDRGQEPGLTAFLEVSFTEPPGDDKPRESACLPEKLKEKERHRHSSSSSKKSHDRERAKKEKAEKKEKGEDYKEGGSRKDSGQYEKDFLEADAYGVSYNMKADIEDELDKTIELFSTEKKDKNDSEREPSKKIEKELKPYGSSAINILKEKKKREKHREKWRDEKERHRDRHADGLLRHHRDELLRHHRDEQKPATRDKDSPPRVLKDKSRDEGPRLGDAKLKEKFKDGAEKEKGDPVKMSNGNDKVAPSKDPGKKDARPREKLLGDGDLMMTSFERMLSQKDLEIEERHKRHKERMKQMEKLRHRSGDPKLKEKAKPADDGRKKGLDIPAKKPPGLDPPFKDKKLKESTPIPPAAENKLHPASGADSKDWLAGPHMKEVLPASPRPDQSRPTGVPTPTSVLSCPSYEEVMHTPRTPSCSADDYADLVFDCADSQHSTPVPTAPTSACSPSFFDRFSVASSGLSENASQAPARPLSTNLYRSVSVDIRRTPEEEFSVGDKLFRQQSVPAASSYDSPMPPSMEDRAPLPPVPAEKFACLSPGYYSPDYGLPSPKVDALHCPPAAVVTVTPSPEGVFSSLQAKPSPSPRAELLVPSLEGALPPDLDTSEDQQATAAIIPPEPSYLEPLDEGPFSAVITEEPVEWAHPSEQALASSLIGGTSENPVSWPVGSDLLLKSPQRFPESPKRFCPADPLHSAAPGPFSASEAPYPAPPASPAPYALPVAEPGLEDVKDGVDAVPAAISTSEAAPYAPPSGLESFFSNCKSLPEAPLDVAPEPACVAAVAQVEALGPLENSFLDGSRGLSHLGQVEPVPWADAFAGPEDDLDLGPFSLPELPLQTKDAADGEAEPVEESLAPPEEMPPGAPGVINGGDVSTVVAEEPPALPPDQASTRLPAELEPEPSGEPKLDVALEAAVEAETVPEERARGDPDSSVEPAPVPPEQRPLGSGDQGAEAEGPPAASLCAPDGPAPNTVAQAQAADGAGPEDDTEASRAAAPAEGPPGGIQPEAAEPKPTAEAPKAPRVEEIPQRMTRNRAQMLANQSKQGPPPSEKECAPTPAPVTRAKARGSEDDDAQAQHPRKRRFQRSTQQLQQQLNTSTQQTREVIQQTLAAIVDAIKLDAIEPYHSDRANPYFEYLQIRKKIEEKRKILCCITPQAPQCYAEYVTYTGSYLLDGKPLSKLHIPVIAPPPSLAEPLKELFRQQEAVRGKLRLQHSIEREKLIVSCEQEILRVHCRAARTIANQAVPFSACTMLLDSEVYNMPLESQGDENKSVRDRFNARQFISWLQDVDDKYDRMKTCLLMRQQHEAAALNAVQRMEWQLKVQELDPAGHKSLCVNEVPSFYVPMVDVNDDFVLLPA,mutated_sequence,1.0,2663.0,NP_037407.4.a2m,NP_037407.4.npy,ClinVar +NP_037471.2,NP_037471.2.csv,MEKWYLMTVVVLIGLTVRWTVSLNSYSGAGKPPMFGDYEAQRHWQEITFNLPVKQWYFNSSDNNLQYWGLDYPPLTAYHSLLCAYVAKFINPDWIALHTSRGYESQAHKLFMRTTVLIADLLIYIPAVVLYCCCLKEISTKKKIANALCILLYPGLILIDYGHFQYNSVSLGFALWGVLGISCDCDLLGSLAFCLAINYKQMELYHALPFFCFLLGKCFKKGLKGKGFVLLVKLACIVVASFVLCWLPFFTEREQTLQVLRRLFPVDRGLFEDKVANIWCSFNVFLKIKDILPRHIQLIMSFCSTFLSLLPACIKLILQPSSKGFKFTLVSCALSFFLFSFQVHEKSILLVSLPVCLVLSEIPFMSTWFLLVSTFSMLPLLLKDELLMPSVVTTMAFFIACVTSFSIFEKTSEEELQLKSFSISVRKYLPCFTFLSRIIQYLFLISVITMVLLTLMTVTLDPPQKLPDLFSVLVCFVSCLNFLFFLVYFNIIIMWDSKSGRNQKKIS,mutated_sequence,1.0,507.0,NP_037471.2.a2m,NP_037471.2.npy,ClinVar +NP_054728.2,NP_054728.2.csv,MPNPRPGKPSAPSLALGPSPGASPSWRAAPKASDLLGARGPGGTFQGRDLRGGAHASSSSLNPMPPSQLQLPTLPLVMVAPSGARLGPLPHLQALLQDRPHFMHQLSTVDAHARTPVLQVHPLESPAMISLTPPTTATGVFSLKARPGLPPGINVASLEWVSREPALLCTFPNPSAPRKDSTLSAVPQSSYPLLANGVCKWPGCEKVFEEPEDFLKHCQADHLLDEKGRAQCLLQREMVQSLEQQLVLEKEKLSAMQAHLAGKMALTKASSVASSDKGSCCIVAAGSQGPVVPAWSGPREAPDSLFAVRRHLWGSHGNSTFPEFLHNMDYFKFHNMRPPFTYATLIRWAILEAPEKQRTLNEIYHWFTRMFAFFRNHPATWKNAIRHNLSLHKCFVRVESEKGAVWTVDELEFRKKRSQRPSRCSNPTPGP,mutated_sequence,1.0,431.0,NP_054728.2.a2m,NP_054728.2.npy,ClinVar +NP_054859.2,NP_054859.2.csv,MSLPLTEEQRKKIEENRQKALARRAEKLLAEQHQRTSSGTSIAGNPFQAKQGPSQNFPRESCKPVSHGVIFKQQNLSSSSNADQRPHDSHSFQAKGIWKKPEEMPTACPGHSPRSQMALTGISPPLAQSPPEVPKQQLLSYELGQGHAQASPEIRFTPFANPTHKPLAKPKSSQETPAHSSGQPPRDAKLEAKTAKASPSGQNISYIHSSSESVTPRTEGRLQQKSGSSVQKGVNSQKGKCVRNGDRFQVLIGYNAELIAVFKTLPSKNYDPDTKTWNFSMNDYSALMKAAQSLPTVNLQPLEWAYGSSESPSTSSEGQAGLPSAPSLSFVKGRCMLISRAYFEADISYSQDLIALFKQMDSRRYDVKTRKWSFLLEEHSKLIAKVRCLPQVQLDPLPTTLTLAFASQLKKTSLSLTPDVPEADLSEVDPKLVSNLMPFQRAGVNFAIAKGGRLLLADDMGLGKTIQAICIAAFYRKEWPLLVVVPSSVRFTWEQAFLRWLPSLSPDCINVVVTGKDRLTAGLINIVSFDLLSKLEKQLKTPFKVVIIDESHFLKNSRTARCRAAMPVLKVAKRVILLSGTPAMSRPAELYTQIIAVKPTFFPQFHAFGLRYCDAKRMPWGWDYSGSSNLGELKLLLEEAVMLRRLKSDVLSQLPAKQRKIVVIAPGRINARTRAALDAAAKEMTTKDKTKQQQKDALILFFNRTAEAKIPSVIEYILDLLESGREKFLVFAHHKVVLDAITQELERKHVQHIRIDGSTSSAEREDLCQQFQLSERHAVAVLSITAANMGLTFSSADLVVFAELFWNPGVLIQAEDRVHRIGQTSSVGIHYLVAKGTADDYLWPLIQEKIKVLAEAGLSETNFSEMTESTDYLYKDPKQQKIYDLFQKSFEKEGSDMELLEAAESFDPGSASGTSGSSSQNMGDTLDESSLTASPQKKRRFEFFDNWDSFTSPL,mutated_sequence,1.0,954.0,NP_054859.2.a2m,NP_054859.2.npy,ClinVar +NP_054860.1,NP_054860.1.csv,MQAAPRAGCGAALLLWIVSSCLCRAWTAPSTSQKCDEPLVSGLPHVAFSSSSSISGSYSPGYAKINKRGGAGGWSPSDSDHYQWLQVDFGNRKQISAIATQGRYSSSDWVTQYRMLYSDTGRNWKPYHQDGNIWAFPGNINSDGVVRHELQHPIIARYVRIVPLDWNGEGRIGLRIEVYGCSYWADVINFDGHVVLPYRFRNKKMKTLKDVIALNFKTSESEGVILHGEGQQGDYITLELKKAKLVLSLNLGSNQLGPIYGHTSVMTGSLLDDHHWHSVVIERQGRSINLTLDRSMQHFRTNGEFDYLDLDYEITFGGIPFSGKPSSSSRKNFKGCMESINYNGVNITDLARRKKLEPSNVGNLSFSCVEPYTVPVFFNATSYLEVPGRLNQDLFSVSFQFRTWNPNGLLVFSHFADNLGNVEIDLTESKVGVHINITQTKMSQIDISSGSGLNDGQWHEVRFLAKENFAILTIDGDEASAVRTNSPLQVKTGEKYFFGGFLNQMNNSSHSVLQPSFQGCMQLIQVDDQLVNLYEVAQRKPGSFANVSIDMCAIIDRCVPNHCEHGGKCSQTWDSFKCTCDETGYSGATCHNSIYEPSCEAYKHLGQTSNYYWIDPDGSGPLGPLKVYCNMTEDKVWTIVSHDLQMQTPVVGYNPEKYSVTQLVYSASMDQISAITDSAEYCEQYVSYFCKMSRLLNTPDGSPYTWWVGKANEKHYYWGGSGPGIQKCACGIERNCTDPKYYCNCDADYKQWRKDAGFLSYKDHLPVSQVVVGDTDRQGSEAKLSVGPLRCQGDRNYWNAASFPNPSSYLHFSTFQGETSADISFYFKTLTPWGVFLENMGKEDFIKLELKSATEVSFSFDVGNGPVEIVVRSPTPLNDDQWHRVTAERNVKQASLQVDRLPQQIRKAPTEGHTRLELYSQLFVGGAGGQQGFLGCIRSLRMNGVTLDLEERAKVTSGFISGCSGHCTSYGTNCENGGKCLERYHGYSCDCSNTAYDGTFCNKDVGAFFEEGMWLRYNFQAPATNARDSSSRVDNAPDQQNSHPDLAQEEIRFSFSTTKAPCILLYISSFTTDFLAVLVKPTGSLQIRYNLGGTREPYNIDVDHRNMANGQPHSVNITRHEKTIFLKLDHYPSVSYHLPSSSDTLFNSPKSLFLGKVIETGKIDQEIHKYNTPGFTGCLSRVQFNQIAPLKAALRQTNASAHVHIQGELVESNCGASPLTLSPMSSATDPWHLDHLDSASADFPYNPGQGQAIRNGVNRNSAIIGGVIAVVIFTILCTLVFLIRYMFRHKGTYHTNEAKGAESAESADAAIMNNDPNFTETIDESKKEWLI,mutated_sequence,1.0,1331.0,NP_054860.1.a2m,NP_054860.1.npy,ClinVar +NP_055023.2,NP_055023.2.csv,MKIITYFCIWAVAWAIPVPQSKPLERHVEKSMNLHLLARSNVSVQDELNASGTIKESGVLVHEGDRGRQENTQDGHKGEGNGSKWAEVGGKSFSTYSTLANEEGNIEGWNGDTGKAETYGHDGIHGKEENITANGIQGQVSIIDNAGATNRSNTNGNTDKNTQNGDVGDAGHNEDVAVVQEDGPQVAGSNNSTDNEDEIIENSCRNEGNTSEITPQINSKRNGTKEAEVTPGTGEDAGLDNSDGSPSGNGADEDEDEGSGDDEDEEAGNGKDSSNNSKGQEGQDHGKEDDHDSSIGQNSDSKEYYDPEGKEDPHNEVDGDKTSKSEENSAGIPEDNGSQRIEDTQKLNHRESKRVENRITKESETHAVGKSQDKGIEIKGPSSGNRNITKEVGKGNEGKEDKGQHGMILGKGNVKTQGEVVNIEGPGQKSEPGNKVGHSNTGSDSNSDGYDSYDFDDKSMQGDDPNSSDESNGNDDANSESDNNSSSRGDASYNSDESKDNGNGSDSKGAEDDDSDSTSDTNNSDSNGNGNNGNDDNDKSDSGKGKSDSSDSDSSDSSNSSDSSDSSDSDSSDSNSSSDSDSSDSDSSDSSDSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSKSDSSKSESDSSDSDSKSDSSDSNSSDSSDNSDSSDSSNSSNSSDSSDSSDSSDSSSSSDSSNSSDSSDSSDSSNSSESSDSSDSSDSDSSDSSDSSNSNSSDSDSSNSSDSSDSSNSSDSSDSSDSSNSSDSSDSSDSSNSSDSSDSSDSSDSSDSSNSSDSNDSSNSSDSSDSSNSSDSSNSSDSSDSSDSSDSDSSNSSDSSNSSDSSDSSNSSDSSDSSDSSDGSDSDSSNRSDSSNSSDSSDSSDSSNSSDSSDSSDSNESSNSSDSSDSSNSSDSDSSDSSNSSDSSDSSNSSDSSESSNSSDNSNSSDSSNSSDSSDSSDSSNSSDSSNSSDSSNSSDSSDSNSSDSSDSSNSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSNSSDSSNSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSESSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSNSSDSSDSSESSDSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSNESSDSSDSSDSSDSSNSSDSSDSSDSSDSTSDSNDESDSQSKSGNGNNNGSDSDSDSEGSDSNHSTSDD,mutated_sequence,1.0,1301.0,NP_055023.2.a2m,NP_055023.2.npy,ClinVar +NP_055047.2,NP_055047.2.csv,MSATAATAPPAAPAGEGGPPAPPPNLTSNRRLQQTQAQVDEVVDIMRVNVDKVLERDQKLSELDDRADALQAGASQFETSAAKLKRKYWWKNLKMMIILGVICAIILIIIIVYFST,mutated_sequence,1.0,116.0,NP_055047.2.a2m,NP_055047.2.npy,ClinVar +NP_055064.1,NP_055064.1.csv,METRPTALMSSTVAAAAPAAGAASRKESPGRWGLGEDPTGVSPSLQCRVCGDSSSGKHYGIYACNGCSGFFKRSVRRRLIYRCQVGAGMCPVDKAHRNQCQACRLKKCLQAGMNQDAVQNERQPRSTAQVHLDSMESNTESRPESLVAPPAPAGRSPRGPTPMSAARALGHHFMASLITAETCAKLEPEDADENIDVTSNDPEFPSSPYSSSSPCGLDSIHETSARLLFMAVKWAKNLPVFSSLPFRDQVILLEEAWSELFLLGAIQWSLPLDSCPLLAPPEASAAGGAQGRLTLASMETRVLQETISRFRALAVDPTEFACMKALVLFKPETRGLKDPEHVEALQDQSQVMLSQHSKAHHPSQPVRFGKLLLLLPSLRFITAERIELLFFRKTIGNTPMEKLLCDMFKN,mutated_sequence,1.0,410.0,NP_055064.1.a2m,NP_055064.1.npy,ClinVar +NP_055067.1,NP_055067.1.csv,MKSNPAIQAAIDLTAGAAGGTACVLTGQPFDTMKVKMQTFPDLYRGLTDCCLKTYSQVGFRGFYKGTSPALIANIAENSVLFMCYGFCQQVVRKVAGLDKQAKLSDLQNAAAGSFASAFAALVLCPTELVKCRLQTMYEMETSGKIAKSQNTVWSVIKSILRKDGPLGFYHGLSSTLLREVPGYFFFFGGYELSRSFFASGRSKDELGPVPLMLSGGVGGICLWLAVYPVDCIKSRIQVLSMSGKQAGFIRTFINVVKNEGITALYSGLKPTMIRAFPANGALFLAYEYSRKLMMNQLEAY,mutated_sequence,1.0,301.0,NP_055067.1.a2m,NP_055067.1.npy,ClinVar +NP_055085.1,NP_055085.1.csv,MGDTGLRKRREDEKSIQSQEPKTTSLQKELGLISGISIIVGTIIGSGIFVSPKSVLSNTEAVGPCLIIWAACGVLATLGALCFAELGTMITKSGGEYPYLMEAYGPIPAYLFSWASLIVIKPTSFAIICLSFSEYVCAPFYVGCKPPQIVVKCLAAAAILFISTVNSLSVRLGSYVQNIFTAAKLVIVAIIIISGLVLLAQGNTKNFDNSFEGAQLSVGAISLAFYNGLWAYDGWNQLNYITEELRNPYRNLPLAIIIGIPLVTACYILMNVSYFTVMTATELLQSQAVAVTFGDRVLYPASWIVPLFVAFSTIGAANGTCFTAGRLIYVAGREGHMLKVLSYISVRRLTPAPAIIFYGIIATIYIIPGDINSLVNYFSFAAWLFYGLTILGLIVMRFTRKELERPIKVPVVIPVLMTLISVFLVLAPIISKPTWEYLYCVLFILSGLLFYFLFVHYKFGWAQKISKPITMHLQMLMEVVPPEEDPE,mutated_sequence,1.0,487.0,NP_055085.1.a2m,NP_055085.1.npy,ClinVar +NP_055086.1,NP_055086.1.csv,MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWSIDIKKYQVLVGEPVRIKCALFYGYIRTNYSLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGLYACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIREVREDDIGNYTCELKYGGFVVRRTTELTVTAPLTDKPPKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLHKRELMYTVELAGGLGAILLLLVCLVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYIEDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMPFKRIEPITHEQALDVSEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSREQNPDEAHTNSAILPLLPRETSISSVIW,mutated_sequence,1.0,696.0,NP_055086.1.a2m,NP_055086.1.npy,ClinVar +NP_055105.2,NP_055105.2.csv,MLEGDLVSKMLRAVLQSHKNGVALPRLQGEYRSLTGDWIPFKQLGFPTLEAYLRSVPAVVRIETSRSGEITCYAMACTETARIAQLVARQRSSKRKTGRQVNCQMRVKKTMPFFLEGKPKATLRQPGFASNFSVGKKPNPAPLRDKGNSVGVKPDAEMSPYMLHTTLGNEAFKDIPVQRHVTMSTNNRFSPKASLQPPLQMHLSRTSTKEMSDNLNQTVEKPNVKPPASYTYKMDEVQNRIKEILNKHNNGIWISKLPHFYKELYKEDLNQGILQQFEHWPHICTVEKPCSGGQDLLLYPAKRKQLLRSELDTEKVPLSPLPGPKQTPPLKGCPTVMAGDFKEKVADLLVKYTSGLWASALPKAFEEMYKVKFPEDALKNLASLSDVCSIDYISGNPQKAILYAKLPLPTDKIQKDAGQAHGDNDIKAMVEQEYLQVEESIAESANTFMEDITVPPLMIPTEASPSVLVVELSNTNEVVIRYVGKDYSAAQELMEDEMKEYYSKNPKITPVQAVNVGQLLAVNAEEDAWLRAQVISTEENKIKVCYVDYGFSENVEKSKAYKLNPKFCSLSFQATKCKLAGLEVLSDDPDLVKVVESLTCGKIFAVEILDKADIPLVVLYDTSGEDDININATCLKAICDKSLEVHLQVDAMYTNVKVTNICSDGTLYCQVPCKGLNKLSDLLRKIEDYFHCKHMTSECFVSLPFCGKICLFHCKGKWLRVEITNVHSSRALDVQFLDSGTVTSVKVSELREIPPRFLQEMIAIPPQAIKCCLADLPQSIGMWTPDAVLWLRDSVLNCSDCSIKVTKVDETRGIAHVYLFTPKNFPDPHRSINRQITNADLWKHQKDVFLSAISSGADSPNSKNGNMPMSGNTGENFRKNLTDVIKKSMVDHTSAFSTEELPPPVHLSKPGEHMDVYVPVACHPGYFVIQPWQEIHKLEVLMEEMILYYSVSEERHIAVEKDQVYAAKVENKWHRVLLKGILTNGLVSVYELDYGKHELVNIRKVQPLVDMFRKLPFQAVTAQLAGVKCNQWSEEASMVFRNHVEKKPLVALVQTVIENANPWDRKVVVYLVDTSLPDTDTWIHDFMSEYLIELSKVN,mutated_sequence,1.0,1098.0,NP_055105.2.a2m,NP_055105.2.npy,ClinVar +NP_055149.3,NP_055149.3.csv,MARPPRQHPGVWASLLLLLLTGPAACAASPADDGAGPGGRGPRGRARGDTGADEAVPRHDSSYGTFAGEFYDLRYLSEEGYPFPTAPPVDPFAKIKVDDCGKTKGCFRYGKPGCNAETCDYFLSYRMIGADVEFELSADTDGWVAVGFSSDKKMGGDDVMACVHDDNGRVRIQHFYNVGQWAKEIQRNPARDEEGVFENNRVTCRFKRPVNVPRDETIVDLHLSWYYLFAWGPAIQGSITRHDIDSPPASERVVSIYKYEDIFMPSAAYQTFSSPFCLLLIVALTFYLLMGTP,mutated_sequence,1.0,293.0,NP_055149.3.a2m,NP_055149.3.npy,ClinVar +NP_055177.2,NP_055177.2.csv,MGQREMWRLMSRFNAFKRTNTILHHLRMSKHTDAAEEVLLEKKGCTGVITLNRPKFLNALTLNMIRQIYPQLKKWEQDPETFLIIIKGAGGKAFCAGGDIRVISEAEKAKQKIAPVFFREEYMLNNAVGSCQKPYVALIHGITMGGGVGLSVHGQFRVATEKCLFAMPETAIGLFPDVGGGYFLPRLQGKLGYFLALTGFRLKGRDVYRAGIATHFVDSEKLAMLEEDLLALKSPSKENIASVLENYHTESKIDRDKSFILEEHMDKINSCFSANTVEEIIENLQQDGSSFALEQLKVINKMSPTSLKITLRQLMEGSSKTLQEVLTMEYRLSQACMRGHDFHEGVRAVLIDKDQSPKWKPADLKEVTEEDLNNHFKSLGSSDLKF,mutated_sequence,1.0,386.0,NP_055177.2.a2m,NP_055177.2.npy,ClinVar +NP_055178.3,NP_055178.3.csv,METKENRWVPVTVLPGCVGCRTVAALASWTVRDVKERIFAETGFPVSEQRLWRGGRELSDWIKIGDLTSKNCHLFVNLQSKGLKGGGRFGQTTPPLVDFLKDILRRYPEGGQILKELIQNAEDAGATEVKFLYDETQYGTETLWSKDMAPYQGPALYVYNNAVFTPEDWHGIQEIARSRKKDDPLKVGRFGIGFNSVYHITDVPCIFSGDQIGMLDPHQTLFGPHESGQCWNLKDDSKEISELSDQFAPFVGIFGSTKETFINGNFPGTFFRFPLRLQPSQLSSNLYNKQKVLELFESFRADADTVLLFLKSVQDVSLYVREADGTEKLVFRVTSSESKALKHERPNSIKILGTAISNYCKKTPSNNITCVTYHVNIVLEEESTKDAQKTSWLVCNSVGGRGISSKLDSLADELKFVPIIGIAMPLSSRDDEAKGATSDFSGKAFCFLPLPPGEESSTGLPVHISGFFGLTDNRRSIKWRELDQWRDPAALWNEFLVMNVVPKAYATLILDSIKRLEMEKSSDFPLSVDVIYKLWPEASKVKVHWQPVLEPLFSELLQNAVIYSISCDWVRLEQVYFSELDENLEYTKTVLNYLQSSGKQIAKVPGNVDAAVQLTAASGTTPVRKVTPAWVRQVLRKCAHLGCAEEKLHLLEFVLSDQAYSELLGLELLPLQNGNFVPFSSSVSDQDVIYITSAEYPRSLFPSLEGRFILDNLKPHLVAALKEAAQTRGRPCTQLQLLNPERFARLIKEVMNTFWPGRELIVQWYPFDENRNHPSVSWLKMVWKNLYIHFSEDLTLFDEMPLIPRTILEEGQTCVELIRLRIPSLVILDDESEAQLPEFLADIVQKLGGFVLKKLDASIQHPLIKKYIHSPLPSAVLQIMEKMPLQKLCNQITSLLPTHKDALRKFLASLTDSSEKEKRIIQELAIFKRINHSSDQGISSYTKLKGCKVLHHTAKLPADLRLSISVIDSSDEATIRLANMLKIEQLKTTSCLKLVLKDIENAFYSHEEVTQLMLWVLENLSSLKNENPNVLEWLTPLKFIQISQEQMVSAGELFDPDIEVLKDLFCNEEGTYFPPSVFTSPDILHSLRQIGLKNEASLKEKDVVQVAKKIEALQVGACPDQDVLLKKAKTLLLVLNKNHTLLQSSEGKMTLKKIKWVPACKERPPNYPGSLVWKGDLCNLCAPPDMCDVGHAILIGSSLPLVESIHVNLEKALGIFTKPSLSAVLKHFKIVVDWYSSKTFSDEDYYQFQHILLEIYGFMHDHLNEGKDSFRALKFPWVWTGKKFCPLAQAVIKPIHDLDLQPYLHNVPKTMAKFHQLFKVCGSIEELTSDHISMVIQKIYLKSDQDLSEQESKQNLHLMLNIIRWLYSNQIPASPNTPVPIHHSKNPSKLIMKPIHECCYCDIKVDDLNDLLEDSVEPIILVHEDIPMKTAEWLKVPCLSTRLINPENMGFEQSGQREPLTVRIKNILEEYPSVSDIFKELLQNADDANATECSFLIDMRRNMDIRENLLDPGMAACHGPALWSFNNSQFSDSDFVNITRLGESLKRGEVDKVGKFGLGFNSVYHITDIPIIMSREFMIMFDPNINHISKHIKDKSNPGIKINWSKQQKRLRKFPNQFKPFIDVFGCQLPLTVEAPYSYNGTLFRLSFRTQQEAKVSEVSSTCYNTADIYSLVDEFSLCGHRLIIFTQSVKSMYLKYLKIEETNPSLAQDTVIIKKKSCSSKALNTPVLSVLKEAAKLMKTCSSSNKKLPSDEPKSSCILQITVEEFHHVFRRIADLQSPLFRGPDDDPAALFEMAKSGQSKKPSDELSQKTVECTTWLLCTCMDTGEALKFSLSESGRRLGLVPCGAVGVQLSEIQDQKWTVKPHIGEVFCYLPLRIKTGLPVHINGCFAVTSNRKEIWKTDTKGRWNTTFMRHVIVKAYLQVLSVLRDLATSGELMDYTYYAVWPDPDLVHDDFSVICQGFYEDIAHGKGKELTKVFSDGSTWVSMKNVRFLDDSILKRRDVGSAAFKIFLKYLKKTGSKNLCAVELPSSVKLGFEEAGCKQILLENTFSEKQFFSEVFFPNIQEIEAELRDPLMIFVLNEKVDEFSGVLRVTPCIPCSLEGHPLVLPSRLIHPEGRVAKLFDIKDGRFPYGSTQDYLNPIILIKLVQLGMAKDDILWDDMLERAVSVAEINKSDHVAACLRSSILLSLIDEKLKIRDPRAKDFAAKYQTIRFLPFLTKPAGFSLDWKGNSFKPETMFAATDLYTAEHQDIVCLLQPILNENSHSFRGCGSVSLAVKEFLGLLKKPTVDLVINQLKEVAKSVDDGITLYQENITNACYKYLHEALMQNEITKMSIIDKLKPFSFILVENAYVDSEKVSFHLNFEAAPYLYQLPNKYKNNFRELFETVGVRQSCTVEDFALVLESIDQERGTKQITEENFQLCRRIISEGIWSLIREKKQEFCEKNYGKILLPDTNLMLLPAKSLCYNDCPWIKVKDTTVKYCHADIPREVAVKLGAVPKRHKALERYASNVCFTTLGTEFGQKEKLTSRIKSILNAYPSEKEMLKELLQNADDAKATEICFVFDPRQHPVDRIFDDKWAPLQGPALCVYNNQPFTEDDVRGIQNLGKGTKEGNPYKTGQYGIGFNSVYHITDCPSFISGNDILCIFDPHARYAPGATSISPGRMFRDLDADFRTQFSDVLDLYLGTHFKLDNCTMFRFPLRNAEMAKVSEISSVPASDRMVQNLLDKLRSDGAELLMFLNHMEKISICEIDKSTGALNVLYSVKGKITDGDRLKRKQFHASVIDSVTKKRQLKDIPVQQITYTMDTEDSEGNLTTWLICNRSGFSSMEKVSKSVISAHKNQDITLFPRGGVAACITHNYKKPHRAFCFLPLSLETGLPFHVNGHFALDSARRNLWRDDNGVGVRSDWNNSLMTALIAPAYVELLIQLKKRYFPGSDPTLSVLQNTPIHVVKDTLKKFLSFFPVNRLDLQPDLYCLVKALYNCIHEDMKRLLPVVRAPNIDGSDLHSAVIITWINMSTSNKTRPFFDNLLQDELQHLKNADYNITTRKTVAENVYRLKHLLLEIGFNLVYNCDETANLYHCLIDADIPVSYVTPADIRSFLMTFSSPDTNCHIGKLPCRLQQTNLKLFHSLKLLVDYCFKDAEENEIEVEGLPLLITLDSVLQTFDAKRPKFLTTYHELIPSRKDLFMNTLYLKYSNILLNCKVAKVFDISSFADLLSSVLPREYKTKSCTKWKDNFASESWLKNAWHFISESVSVKEDQEETKPTFDIVVDTLKDWALLPGTKFTVSANQLVVPEGDVLLPLSLMHIAVFPNAQSDKVFHALMKAGCIQLALNKICSKDSAFVPLLSCHTANIESPTSILKALHYMVQTSTFRAEKLVENDFEALLMYFNCNLNHLMSQDDIKILKSLPCYKSISGRYVSIGKFGTCYVLTKSIPSAEVEKWTQSSSSAFLEEKIHLKELYEVIGCVPVDDLEVYLKHLLPKIENLSYDAKLEHLIYLKNRLSSAEELSEIKEQLFEKLESLLIIHDANSRLKQAKHFYDRTVRVFEVMLPEKLFIPNDFFKKLEQLIKPKNHVTFMTSWVEFLRNIGLKYILSQQQLLQFAKEISVRANTENWSKETLQNTVDILLHHIFQERMDLLSGNFLKELSLIPFLCPERAPAEFIRFHPQYQEVNGTLPLIKFNGAQVNPKFKQCDVLQLLWTSCPILPEKATPLSIKEQEGSDLGPQEQLEQVLNMLNVNLDPPLDKVINNCRNICNITTLDEEMVKTRAKVLRSIYEFLSAEKREFRFQLRGVAFVMVEDGWKLLKPEEVVINLEYESDFKPYLYKLPLELGTFHQLFKHLGTEDIISTKQYVEVLSRIFKNSEGKQLDPNEMRTVKRVVSGLFRSLQNDSVKVRSDLENVRDLALYLPSQDGRLVKSSILVFDDAPHYKSRIQGNIGVQMLVDLSQCYLGKDHGFHTKLIMLFPQKLRPRLLSSILEEQLDEETPKVCQFGALCSLQGRLQLLLSSEQFITGLIRIMKHENDNAFLANEEKAIRLCKALREGLKVSCFEKLQTTLRVKGFNPIPHSRSETFAFLKRFGNAVILLYIQHSDSKDINFLLALAMTLKSATDNLISDTSYLIAMLGCNDIYRIGEKLDSLGVKYDSSEPSKLELPMPGTPIPAEIHYTLLMDPMNVFYPGEYVGYLVDAEGGDIYGSYQPTYTYAIIVQEVEREDADNSSFLGKIYQIDIGYSEYKIVSSLDLYKFSRPEESSQSRDSAPSTPTSPTEFLTPGLRSIPPLFSGRESHKTSSKHQSPKKLKVNSLPEILKEVTSVVEQAWKLPESERKKIIRRLYLKWHPDKNPENHDIANEVFKHLQNEINRLEKQAFLDQNADRASRRTFSTSASRFQSDKYSFQRFYTSWNQEATSHKSERQQQNKEKCPPSAGQTYSQRFFVPPTFKSVGNPVEARRWLRQARANFSAARNDLHKNANEWVCFKCYLSTKLALIAADYAVRGKSDKDVKPTALAQKIEEYSQQLEGLTNDVHTLEAYGVDSLKTRYPDLLPFPQIPNDRFTSEVAMRVMECTACIIIKLENFMQQKV,mutated_sequence,1.0,4579.0,NP_055178.3.a2m,NP_055178.3.npy,ClinVar +NP_055331.1,NP_055331.1.csv,MADKRKLQGEIDRCLKKVSEGVEQFEDIWQKLHNAANANQKEKYEADLKKEIKKLQRLRDQIKTWVASNEIKDKRQLIDNRKLIETQMERFKVVERETKTKAYSKEGLGLAQKVDPAQKEKEEVGQWLTNTIDTLNMQVDQFESEVESLSVQTRKKKGDKDKQDRIEGLKRHIEKHRYHVRMLETILRMLDNDSILVDAIRKIKDDVEYYVDSSQDPDFEENEFLYDDLDLEDIPQALVATSPPSHSHMEDEIFNQSSSTPTSTTSSSPIPPSPANCTTENSEDDKKRGRSTDSEVSQSPAKNGSKPVHSNQHPQSPAVPPTYPSGPPPAASALSTTPGNNGVPAPAAPPSALGPKASPAPSHNSGTPAPYAQAVAPPAPSGPSTTQPRPPSVQPSGGGGGGSGGGGSSSSSNSSAGGGAGKQNGATSYSSVVADSPAEVALSSSGGNNASSQALGPPSGPHNPPPSTSKEPSAAAPTGAGGVAPGSGNNSGGPSLLVPLPVNPPSSPTPSFSDAKAAGALLNGPPQFSTAPEIKAPEPLSSLKSMAERAAISSGIEDPVPTLHLTERDIILSSTSAPPASAQPPLQLSEVNIPLSLGVCPLGPVPLTKEQLYQQAMEEAAWHHMPHPSDSERIRQYLPRNPCPTPPYHHQMPPPHSDTVEFYQRLSTETLFFIFYYLEGTKAQYLAAKALKKQSWRFHTKYMMWFQRHEEPKTITDEFEQGTYIYFDYEKWGQRKKEGFTFEYRYLEDRDLQ,mutated_sequence,1.0,753.0,NP_055331.1.a2m,NP_055331.1.npy,ClinVar +NP_055400.1,NP_055400.1.csv,MTRAGDHNRQRGCCGSLADYLTSAKFLLYLGHSLSTWGDRMWHFAVSVFLVELYGNSLLLTAVYGLVVAGSVLVLGAIIGDWVDKNARLKVAQTSLVVQNVSVILCGIILMMVFLHKHELLTMYHGWVLTSCYILIITIANIANLASTATAITIQRDWIVVVAGEDRSKLANMNATIRRIDQLTNILAPMAVGQIMTFGSPVIGCGFISGWNLVSMCVEYVLLWKVYQKTPALAVKAGLKEEETELKQLNLHKDTEPKPLEGTHLMGVKDSNIHELEHEQEPTCASQMAEPFRTFRDGWVSYYNQPVFLAGMGLAFLYMTVLGFDCITTGYAYTQGLSGSILSILMGASAITGIMGTVAFTWLRRKCGLVRTGLISGLAQLSCLILCVISVFMPGSPLDLSVSPFEDIRSRFIQGESITPTKIPEITTEIYMSNGSNSANIVPETSPESVPIISVSLLFAGVIAARIGLWSFDLTVTQLLQENVIESERGIINGVQNSMNYLLDLLHFIMVILAPNPEAFGLLVLISVSFVAMGHIMYFRFAQNTLGNKLFACGPDAKEVRKENQANTSVV,mutated_sequence,1.0,571.0,NP_055400.1.a2m,NP_055400.1.npy,ClinVar +NP_055440.1,NP_055440.1.csv,MERRARSSSRESRGRGGRTPHKENKRAKAERSGGGRGRQEAGPEPSGSGRAGTPGEPRAPAATVVDVDEVRGSGEEGTEVVALLESERPEEGTKSSGLGACEWLLVLISLLFIIMTFPFSIWFCVKVVQEYERVIIFRLGHLLPGRAKGPGLFFFLPCLDTYHKVDLRLQTLEIPFHEIVTKDMFIMEIDAICYYRMENASLLLSSLAHVSKAVQFLVQTTMKRLLAHRSLTEILLERKSIAQDAKVALDSVTCIWGIKVERIEIKDVRLPAGLQHSLAVEAEAQRQAKVRMIAAEAEKAASESLRMAAEILSGTPAAVQLRYLHTLQSLSTEKPSTVVLPLPFDLLNCLSSPSNRTQGSLPFPSPSKPVEPLNPKKKDSPML,mutated_sequence,1.0,383.0,NP_055440.1.a2m,NP_055440.1.npy,ClinVar +NP_055542.1,NP_055542.1.csv,MAAAAGGGSCPGPGSARGRFPGRPRGAGGGGGRGGRGNGAERVRVALRRGGGATGPGGAEPGEDTALLRLLGLRRGLRRLRRLWAGPRVQRGRGRGRGRGWGPSRGCVPEEESSDGESDEEEFQGFHSDEDVAPSSLRSALRSQRGRAPRGRGRKHKTTPLPPPRLADVAPTPPKTPARKRGEEGTERMVQALTELLRRAQAPQAPRSRACEPSTPRRSRGRPPGRPAGPCRRKQQAVVVAEAAVTIPKPEPPPPVVPVKHQTGSWKCKEGPGPGPGTPRRGGQSSRGGRGGRGRGRGGGLPFVIKFVSRAKKVKMGQLSLGLESGQGQGQHEESWQDVPQRRVGSGQGGSPCWKKQEQKLDDEEEEKKEEEEKDKEGEEKEERAVAEEMMPAAEKEEAKLPPPPLTPPAPSPPPPLPPPSTSPPPPLCPPPPPPVSPPPLPSPPPPPAQEEQEESPPPVVPATCSRKRGRPPLTPSQRAEREAARAGPEGTSPPTPTPSTATGGPPEDSPTVAPKSTTFLKNIRQFIMPVVSARSSRVIKTPRRFMDEDPPKPPKVEVSPVLRPPITTSPPVPQEPAPVPSPPRAPTPPSTPVPLPEKRRSILREPTFRWTSLTRELPPPPPAPPPPPAPSPPPAPATSSRRPLLLRAPQFTPSEAHLKIYESVLTPPPLGAPEAPEPEPPPADDSPAEPEPRAVGRTNHLSLPRFAPVVTTPVKAEVSPHGAPALSNGPQTQAQLLQPLQALQTQLLPQALPPPQPQLQPPPSPQQMPPLEKARIAGVGSLPLSGVEEKMFSLLKRAKVQLFKIDQQQQQKVAASMPLSPGGQMEEVAGAVKQISDRGPVRSEDESVEAKRERPSGPESPVQGPRIKHVCRHAAVALGQARAMVPEDVPRLSALPLRDRQDLATEDTSSASETESVPSRSRRGKVEAAGPGGESEPTGSGGTLAHTPRRSLPSHHGKKMRMARCGHCRGCLRVQDCGSCVNCLDKPKFGGPNTKKQCCVYRKCDKIEARKMERLAKKGRTIVKTLLPWDSDESPEASPGPPGPRRGAGAGGPREEVVAHPGPEEQDSLLQRKSARRCVKQRPSYDIFEDSDDSEPGGPPAPRRRTPRENELPLPEPEEQSRPRKPTLQPVLQLKARRRLDKDALAPGPFASFPNGWTGKQKSPDGVHRVRVDFKEDCDLENVWLMGGLSVLTSVPGGPPMVCLLCASKGLHELVFCQVCCDPFHPFCLEEAERPLPQHHDTWCCRRCKFCHVCGRKGRGSKHLLECERCRHAYHPACLGPSYPTRATRKRRHWICSACVRCKSCGATPGKNWDVEWSGDYSLCPRCTQLYEKGNYCPICTRCYEDNDYESKMMQCAQCDHWVHAKCEGLSDEDYEILSGLPDSVLYTCGPCAGAAQPRWREALSGALQGGLRQVLQGLLSSKVVGPLLLCTQCGPDGKQLHPGPCGLQAVSQRFEDGHYKSVHSFMEDMVGILMRHSEEGETPDRRAGGQMKGLLLKLLESAFGWFDAHDPKYWRRSTRLPNGVLPNAVLPPSLDHVYAQWRQQEPETPESGQPPGDPSAAFQGKDPAAFSHLEDPRQCALCLKYGDADSKEAGRLLYIGQNEWTHVNCAIWSAEVFEENDGSLKNVHAAVARGRQMRCELCLKPGATVGCCLSSCLSNFHFMCARASYCIFQDDKKVFCQKHTDLLDGKEIVNPDGFDVLRRVYVDFEGINFKRKFLTGLEPDAINVLIGSIRIDSLGTLSDLSDCEGRLFPIGYQCSRLYWSTVDARRRCWYRCRILEYRPWGPREEPAHLEAAEENQTIVHSPAPSSEPPGGEDPPLDTDVLVPGAPERHSPIQNLDPPLRPDSGSAPPPAPRSFSGARIKVPNYSPSRRPLGGVSFGPLPSPGSPSSLTHHIPTVGDPDFPAPPRRSRRPSPLAPRPPPSRWASPPLKTSPQLRVPPPTSVVTALTPTSGELAPPGPAPSPPPPEDLGPDFEDMEVVSGLSAADLDFAASLLGTEPFQEEIVAAGAMGSSHGGPGDSSEEESSPTSRYIHFPVTVVSAPGLAPSATPGAPRIEQLDGVDDGTDSEAEAVQQPRGQGTPPSGPGVVRAGVLGAAGDRARPPEDLPSEIVDFVLKNLGGPGDGGAGPREESLPPAPPLANGSQPSQGLTASPADPTRTFAWLPGAPGVRVLSLGPAPEPPKPATSKIILVNKLGQVFVKMAGEGEPVPPPVKQPPLPPTISPTAPTSWTLPPGPLLGVLPVVGVVRPAPPPPPPPLTLVLSSGPASPPRQAIRVKRVSTFSGRSPPAPPPYKAPRLDEDGEASEDTPQVPGLGSGGFSRVRMKTPTVRGVLDLDRPGEPAGEESPGPLQERSPLLPLPEDGPPQVPDGPPDLLLESQWHHYSGEASSSEEEPPSPDDKENQAPKRTGPHLRFEISSEDGFSVEAESLEGAWRTLIEKVQEARGHARLRHLSFSGMSGARLLGIHHDAVIFLAEQLPGAQRCQHYKFRYHQQGEGQEEPPLNPHGAARAEVYLRKCTFDMFNFLASQHRVLPEGATCDEEEDEVQLRSTRRATSLELPMAMRFRHLKKTSKEAVGVYRSAIHGRGLFCKRNIDAGEMVIEYSGIVIRSVLTDKREKFYDGKGIGCYMFRMDDFDVVDATMHGNAARFINHSCEPNCFSRVIHVEGQKHIVIFALRRILRGEELTYDYKFPIEDASNKLPCNCGAKRCRRFLN,mutated_sequence,1.0,2715.0,NP_055542.1.a2m,NP_055542.1.npy,ClinVar +NP_055595.2,NP_055595.2.csv,MVGELRYREFRVPLGPGLHAYPDELIRQRVGHDGHPEYQIRWLILRRGDEGDGGSGQVDCKAEHILLWMSKDEIYANCHKMLGEDGQVIGPSQESAGEVGALDKSVLEEMETDVKSLIQRALRQLEECVGTIPPAPLLHTVHVLSAYASIEPLTGVFKDPRVLDLLMHMLSSPDYQIRWSAGRMIQALSSHDAGTRTQILLSLSQQEAIEKHLDFDSRCALLALFAQATLSEHPMSFEGIQLPQVPGRVLFSLVKRYLHVTSLLDQLNDSAAEPGAQNTSAPEELSGERGQLELEFSMAMGTLISELVQAMRWDQASDRPRSSARSPGSIFQPQLADVSPGLPAAQAQPSFRRSRRFRPRSEFASGNTYALYVRDTLQPGMRVRMLDDYEEISAGDEGEFRQSNNGVPPVQVFWESTGRTYWVHWHMLEILGFEEDIEDMVEADEYQGAVASRVLGRALPAWRWRPMTELYAVPYVLPEDEDTEECEHLTLAEWWELLFFIKKLDGPDHQEVLQILQENLDGEILDDEILAELAVPIELAQDLLLTLPQRLNDSALRDLINCHVYKKYGPEALAGNQAYPSLLEAQEDVLLLDAQAQAKDSEDAAKVEAKEPPSQSPNTPLQRLVEGYGPAGKILLDLEQALSSEGTQENKVKPLLLQLQRQPQPFLALMQSLDTPETNRTLHLTVLRILKQLVDFPEALLLPWHEAVDACMACLRSPNTDREVLQELIFFLHRLTSVSRDYAVVLNQLGARDAISKALEKHLGKLELAQELRDMVFKCEKHAHLYRKLITNILGGCIQMVLGQIEDHRRTHQPINIPFFDVFLRYLCQGSSVEVKEDKCWEKVEVSSNPHRASKLTDHNPKTYWESNGSAGSHYITLHMRRGILIRQLTLLVASEDSSYMPARVVVCGGDSTSSLHTELNSVNVMPSASRVILLENLTRFWPIIQIRIKRCQQGGIDTRIRGLEILGPKPTFWPVFREQLCRHTRLFYMVRAQAWSQDMAEDRRSLLHLSSRLNGALRQEQNFADRFLPDDEAAQALGKTCWEALVSPVVQNITSPDEDGISPLGWLLDQYLECQEAVFNPQSRGPAFFSRVRRLTHLLVHVEPCEAPPPVVATPRPKGRNRSHDWSSLATRGLPSSIMRNLTRCWRAVVEKQVNNFLTSSWRDDDFVPRYCEHFNILQNSSSELFGPRAAFLLALQNGCAGALLKLPFLKAAHVSEQFARHIDQQIQGSRIGGAQEMERLAQLQQCLQAVLIFSGLEIATTFEHYYQHYMADRLLGVVSSWLEGAVLEQIGPCFPNRLPQQMLQSLSTSKELQRQFHVYQLQQLDQELLKLEDTEKKIQVGLGASGKEHKSEKEEEAGAAAVVDVAEGEEEEEENEDLYYEGAMPEVSVLVLSRHSWPVASICHTLNPRTCLPSYLRGTLNRYSNFYNKSQSHPALERGSQRRLQWTWLGWAELQFGNQTLHVSTVQMWLLLYLNDLKAVSVESLLAFSGLSADMLNQAIGPLTSSRGPLDLHEQKDIPGGVLKIRDGSKEPRSRWDIVRLIPPQTYLQAEGEDGQNLEKRRNLLNCLIVRILKAHGDEGLHIDQLVCLVLEAWQKGPCPPRGLVSSLGKGSACSSTDVLSCILHLLGKGTLRRHDDRPQVLSYAVPVTVMEPHTESLNPGSSGPNPPLTFHTLQIRSRGVPYASCTATQSFSTFR,mutated_sequence,1.0,1698.0,NP_055595.2.a2m,NP_055595.2.npy,ClinVar +NP_055637.2,NP_055637.2.csv,MSQQGYVATPPYSQPQPGIGLSPPHYGHYGDPSHTASPTGMMKPAGPLGATATRGMLPPGPPPPGPHQFGQNGAHATGHPPQRFPGPPPVNNVASSHAPYQPSAQSSYPGPISTSSVTQLGSQLSAMQINSYGSGMAPPSQGPPGPLSATSLQTPPRPPQPSILQPGSQVLPPPPTTLNGPGASPLPLPMYRPDGLSGPPPPNAQYQPPPLPGQTLGAGYPPQQANSGPQMAGAQLSYPGGFPGGPAQMAGPPQPQKKLDPDSIPSPIQVIENDRASRGGQVYATNTRGQIPPLVTTDCMIQDQGNASPRFIRCTTYCFPCTSDMAKQAQIPLAAVIKPFATIPSNESPLYLVNHGESGPVRCNRCKAYMCPFMQFIEGGRRYQCGFCNCVNDVPPFYFQHLDHIGRRLDHYEKPELSLGSYEYVATLDYCRKSKPPNPPAFIFMIDVSYSNIKNGLVKLICEELKTMLEKIPKEEQEETSAIRVGFITYNKVLHFFNVKSNLAQPQMMVVTDVGEVFVPLLDGFLVNYQESQSVIHNLLDQIPDMFADSNENETVFAPVIQAGMEALKAADCPGKLFIFHSSLPTAEAPGKLKNRDDKKLVNTDKEKILFQPQTNVYDSLAKDCVAHGCSVTLFLFPSQYVDVASLGLVPQLTGGTLYKYNNFQMHLDRQQFLNDLRNDIEKKIGFDAIMRVRTSTGFRATDFFGGILMNNTTDVEMAAIDCDKAVTVEFKHDDKLSEDSGALIQCAVLYTTISGQRRLRIHNLGLNCSSQLADLYKSCETDALINFFAKSAFKAVLHQPLKVIREILVNQTAHMLACYRKNCASPSAASQLILPDSMKVLPVYMNCLLKNCVLLSRPEISTDERAYQRQLVMTMGVADSQLFFYPQLLPIHTLDVKSTMLPAAVRCSESRLSEEGIFLLANGLHMFLWLGVSSPPELIQGIFNVPSFAHINTDMTLLPEVGNPYSQQLRMIMGIIQQKRPYSMKLTIVKQREQPEMVFRQFLVEDKGLYGGSSYVDFLCCVHKEICQLLN,mutated_sequence,1.0,1032.0,NP_055637.2.a2m,NP_055637.2.npy,ClinVar +NP_055690.1,NP_055690.1.csv,MSLHSTHNRNNSGDILDIPSSQNSSSLNALTHSSRLKLHLKSDMSECENDDPLLRSAGKVRDINRTYVISASRKTADMPLTPNPVGRLALQRRTTRNKESSLLVSELEDTTEKTAETRLTLQRRAKTDSAEKWKTAEIDSVKMTLNVGGETENNGVSKESRTNVRIVNNAKNSFVASSVPLDEDPQVIEMMADKKYKETFSAPSRANENVALKYSSNRPPIASLSQTEVVRSGHLTTKPTQSKLDIKVLGTGNLYHRSIGKEIAKTSNKFGSLEKRTPTKCTTEHKLTTKCSLPQLKSPAPSILKNRMSNLQVKQRPKSSFLANKQERSAENTILPEEETVVQNTSAGKDPLKVENSQVTVAVRVRPFTKREKIEKASQVVFMSGKEITVEHPDTKQVYNFIYDVSFWSFDECHPHYASQTTVYEKLAAPLLERAFEGFNTCLFAYGQTGSGKSYTMMGFSEEPGIIPRFCEDLFSQVARKQTQEVSYHIEMSFFEVYNEKIHDLLVCKDENGQRKQPLRVREHPVYGPYVEALSMNIVSSYADIQSWLELGNKQRATAATGMNDKSSRSHSVFTLVMTQTKTEFVEGEEHDHRITSRINLIDLAGSERCSTAHTNGDRLKEGVSINKSLLTLGKVISALSEQANQRSVFIPYRESVLTWLLKESLGGNSKTAMIATISPAASNIEETLSTLRYANQARLIVNIAKVNEDMNAKLIRELKAEIAKLKAAQRNSRNIDPERYRLCRQEITSLRMKLHQQERDMAEMQRVWKEKFEQAEKRKLQETKELQKAGIMFQMDNHLPNLVNLNEDPQLSEMLLYMIKEGTTTVGKYKPNSSHDIQLSGVLIADDHCTIKNFGGTVSIIPVGEAKTYVNGKHILEITVLRHGDRVILGGDHYFRFNHPVEVQKGKRPSGRDTPISEGPKDFEFAKNELLMAQRSQLEAEIKEAQLKAKEEMMQGIQIAKEMAQQELSSQKAAYESKIKALEAELREESQRKKMQEINNQKANHKIEELEKAKQHLEQEIYVNKKRLEMETLATKQALEDHSIRHARILEALETEKQKIAKEVQILQQNRNNRDKTFTVQTTWSSMKLSMMIQEANAISSKLKTYYVFGRHDISDKSSSDTSIRVRNLKLGISTFWSLEKFESKLAAMKELYESNGSNRGEDAFCDPEDEWEPDITDAPVSSLSRRRSRSLMKNRRISGCLHDIQVHPIKNLHSSHSSGLMDKSSTIYSNSAESFLPGICKELIGSSLDFFGQSYDEERTIADSLINSFLKIYNGLFAISKAHEEQDEESQDNLFSSDRAIQSLTIQTACAFEQLVVLMKHWLSDLLPCTNIARLEDELRQEVKKLGGYLQLFLQGCCLDISSMIKEAQKNAIQIVQQAVKYVGQLAVLKGSKLHFLENGNNKAASVQEEFMDAVCDGVGLGMKILLDSGLEKAKELQHELFRQCTKNEVTKEMKTNAMGLIRSLENIFAESKIKSFRRQVQEENFEYQDFKRMVNRAPEFLKLKHCLEKAIEIIISALKGCHSDINLLQTCVESIRNLASDFYSDFSVPSTSVGSYESRVTHIVHQELESLAKSLLFCFESEESPDLLKPWETYNQNTKEEHQQSKSSGIDGSKNKGVPKRVYELHGSSPAVSSEECTPSRIQWV,mutated_sequence,1.0,1648.0,NP_055690.1.a2m,NP_055690.1.npy,ClinVar +NP_055761.2,NP_055761.2.csv,MNSPGGRGKKKGSGGASNPVPPRPPPPCLAPAPPAAGPAPPPESPHKRNLYYFSYPLFVGFALLRLVAFHLGLLFVWLCQRFSRALMAAKRSSGAAPAPASASAPAPVPGGEAERVRVFHKQAFEYISIALRIDEDEKAGQKEQAVEWYKKGIEELEKGIAVIVTGQGEQCERARRLQAKMMTNLVMAKDRLQLLEKMQPVLPFSKSQTDVYNDSTNLACRNGHLQSESGAVPKRKDPLTHTSNSLPRSKTVMKTGSAGLSGHHRAPSYSGLSMVSGVKQGSGPAPTTHKGTPKTNRTNKPSTPTTATRKKKDLKNFRNVDSNLANLIMNEIVDNGTAVKFDDIAGQDLAKQALQEIVILPSLRPELFTGLRAPARGLLLFGPPGNGKTMLAKAVAAESNATFFNISAASLTSKYVGEGEKLVRALFAVARELQPSIIFIDEVDSLLCERREGEHDASRRLKTEFLIEFDGVQSAGDDRVLVMGATNRPQELDEAVLRRFIKRVYVSLPNEETRLLLLKNLLCKQGSPLTQKELAQLARMTDGYSGSDLTALAKDAALGPIRELKPEQVKNMSASEMRNIRLSDFTESLKKIKRSVSPQTLEAYIRWNKDFGDTTV,mutated_sequence,1.0,616.0,NP_055761.2.a2m,NP_055761.2.npy,ClinVar +NP_055790.1,NP_055790.1.csv,MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP,mutated_sequence,1.0,1570.0,NP_055790.1.a2m,NP_055790.1.npy,ClinVar +NP_055861.3,NP_055861.3.csv,MSTCCWCTPGGASTIDFLKRYASNTPSGEFQTADEDLCYCLECVAEYHKARDELPFLHEVLWELETLRLINHFEKSMKAEIGDDDELYIVDNNGEMPLFDITGQDFENKLRVPLLEILKYPYLLLHERVNELCVEALCRMEQANCSFQVFDKHPGIYLFLVHPNEMVRRWAILTARNLGKVDRDDYYDLQEVLLCLFKVIELGLLESPDIYTSSVLEKGKLILLPSHMYDTTNYKSYWLGICMLLTILEEQAMDSLLLGSDKQNDFMQSILHTMEREADDDSVDPFWPALHCFMVILDRLGSKVWGQLMDPIVAFQTIINNASYNREIRHIRNSSVRTKLEPESYLDDMVTCSQIVYNYNPEKTKKDSGWRTAICPDYCPNMYEEMETLASVLQSDIGQDMRVHNSTFLWFIPFVQSLMDLKDLGVAYIAQVVNHLYSEVKEVLNQTDAVCDKVTEFFLLILVSVIELHRNKKCLHLLWVSSQQWVEAVVKCAKLPTTAFTRSSEKSSGNCSKGTAMISSLSLHSMPSNSVQLAYVQLIRSLLKEGYQLGQQSLCKRFWDKLNLFLRGNLSLGWQLTSQETHELQSCLKQIIRNIKFKAPPCNTFVDLTSACKISPASYNKEESEQMGKTSRKDMHCLEASSPTFSKEPMKVQDSVLIKADNTIEGDNNEQNYIKDVKLEDHLLAGSCLKQSSKNIFTERAEDQIKISTRKQKSVKEISSYTPKDCTSRNGPERGCDRGIIVSTRLLTDSSTDALEKVSTSNEDFSLKDDALAKTSKRKTKVQKDEICAKLSHVIKKQHRKSTLVDNTINLDENLTVSNIESFYSRKDTGVQKGDGFIHNLSLDPSGVLDDKNGEQKSQNNVLPKEKQLKNEELVIFSFHENNCKIQEFHVDGKELIPFTEMTNASEKKSSPFKDLMTVPESRDEEMSNSTSVIYSNLTREQAPDISPKSDTLTDSQIDRDLHKLSLLAQASVITFPSDSPQNSSQLQRKVKEDKRCFTANQNNVGDTSRGQVIIISDSDDDDDERILSLEKLTKQDKICLEREHPEQHVSTVNSKEEKNPVKEEKTETLFQFEESDSQCFEFESSSEVFSVWQDHPDDNNSVQDGEKKCLAPIANTTNGQGCTDYVSEVVKKGAEGIEEHTRPRSISVEEFCEIEVKKPKRKRSEKPMAEDPVRPSSSVRNEGQSDTNKRDLVGNDFKSIDRRTSTPNSRIQRATTVSQKKSSKLCTCTEPIRKVPVSKTPKKTHSDAKKGQNRSSNYLSCRTTPAIVPPKKFRQCPEPTSTAEKLGLKKGPRKAYELSQRSLDYVAQLRDHGKTVGVVDTRKKTKLISPQNLSVRNNKKLLTSQELQMQRQIRPKSQKNRRRLSDCESTDVKRAGSHTAQNSDIFVPESDRSDYNCTGGTEVLANSNRKQLIKCMPSEPETIKAKHGSPATDDACPLNQCDSVVLNGTVPTNEVIVSTSEDPLGGGDPTARHIEMAALKEGEPDSSSDAEEDNLFLTQNDPEDMDLCSQMENDNYKLIELIHGKDTVEVEEDSVSRPQLESLSGTKCKYKDCLETTKNQGEYCPKHSEVKAADEDVFRKPGLPPPASKPLRPTTKIFSSKSTSRIAGLSKSLETSSALSPSLKNKSKGIQSILKVPQPVPLIAQKPVGEMKNSCNVLHPQSPNNSNRQGCKVPFGESKYFPSSSPVNILLSSQSVSDTFVKEVLKWKYEMFLNFGQCGPPASLCQSISRPVPVRFHNYGDYFNVFFPLMVLNTFETVAQEWLNSPNRENFYQLQVRKFPADYIKYWEFAVYLEECELAKQLYPKENDLVFLAPERINEEKKDTERNDIQDLHEYHSGYVHKFRRTSVMRNGKTECYLSIQTQENFPANLNELVNCIVISSLVTTQRKLKAMSLLGSRNQLARAVLNPNPMDFCTKDLLTTTSERIIAYLRDFNEDQKKAIETAYAMVKHSPSVAKICLIHGPPGTGKSKTIVGLLYRLLTENQRKGHSDENSNAKIKQNRVLVCAPSNAAVDELMKKIILEFKEKCKDKKNPLGNCGDINLVRLGPEKSINSEVLKFSLDSQVNHRMKKELPSHVQAMHKRKEFLDYQLDELSRQRALCRGGREIQRQELDENISKVSKERQELASKIKEVQGRPQKTQSIIILESHIICCTLSTSGGLLLESAFRGQGGVPFSCVIVDEAGQSCEIETLTPLIHRCNKLILVGDPKQLPPTVISMKAQEYGYDQSMMARFCRLLEENVEHNMISRLPILQLTVQYRMHPDICLFPSNYVYNRNLKTNRQTEAIRCSSDWPFQPYLVFDVGDGSERRDNDSYINVQEIKLVMEIIKLIKDKRKDVSFRNIGIITHYKAQKTMIQKDLDKEFDRKGPAEVDTVDAFQGRQKDCVIVTCVRANSIQGSIGFLASLQRLNVTITRAKYSLFILGHLRTLMENQHWNQLIQDAQKRGAIIKTCDKNYRHDAVKILKLKPVLQRSLTHPPTIAPEGSRPQGGLPSSKLDSGFAKTSVAASLYHTPSDSKEITLTVTSKDPERPPVHDQLQDPRLLKRMGIEVKGGIFLWDPQPSSPQHPGATPPTGEPGFPVVHQDLSHIQQPAAVVAALSSHKPPVRGEPPAASPEASTCQSKCDDPEEELCHRREARAFSEGEQEKCGSETHHTRRNSRWDKRTLEQEDSSSKKRKLL,mutated_sequence,1.0,2677.0,NP_055861.3.a2m,NP_055861.3.npy,ClinVar +NP_055907.3,NP_055907.3.csv,MSRRAPGSRLSSGGGGGGTKYPRSWNDWQPRTDSASADPDNLKYSSSRDRGGSSSYGLQPSNSAVVSRQRHDDTRVHADIQNDEKGGYSVNGGSGENTYGRKSLGQELRVNNVTSPEFTSVQHGSRALATKDMRKSQERSMSYSDESRLSNLLRRITREDDRDRRLATVKQLKEFIQQPENKLVLVKQLDNILAAVHDVLNESSKLLQELRQEGACCLGLLCASLSYEAEKIFKWIFSKFSSSAKDEVKLLYLCATYKALETVGEKKAFSSVMQLVMTSLQSILENVDTPELLCKCVKCILLVARCYPHIFSTNFRDTVDILVGWHIDHTQKPSLTQQVSGWLQSLEPFWVADLAFSTTLLGQFLEDMEAYAEDLSHVASGESVDEDVPPPSVSLPKLAALLRVFSTVVRSIGERFSPIRGPPITEAYVTDVLYRVMRCVTAANQVFFSEAVLTAANECVGVLLGSLDPSMTIHCDMVITYGLDQLENCQTCGTDYIISVLNLLTLIVEQINTKLPSSFVEKLFIPSSKLLFLRYHKEKEVVAVAHAVYQAVLSLKNIPVLETAYKLILGEMTCALNNLLHSLQLPEACSEIKHEAFKNHVFNVDNAKFVVIFDLSALTTIGNAKNSLIGMWALSPTVFALLSKNLMIVHSDLAVHFPAIQYAVLYTLYSHCTRHDHFISSSLSSSSPSLFDGAVISTVTTATKKHFSIILNLLGILLKKDNLNQDTRKLLMTWALEAAVLMKKSETYAPLFSLPSFHKFCKGLLANTLVEDVNICLQACSSLHALSSSLPDDLLQRCVDVCRVQLVHSGTRIRQAFGKLLKSIPLDVVLSNNNHTEIQEISLALRSHMSKAPSNTFHPQDFSDVISFILYGNSHRTGKDNWLERLFYSCQRLDKRDQSTIPRNLLKTDAVLWQWAIWEAAQFTVLSKLRTPLGRAQDTFQTIEGIIRSLAAHTLNPDQDVSQWTTADNDEGHGNNQLRLVLLLQYLENLEKLMYNAYEGCANALTSPPKVIRTFFYTNRQTCQDWLTRIRLSIMRVGLLAGQPAVTVRHGFDLLTEMKTTSLSQGNELEVTIMMVVEALCELHCPEAIQGIAVWSSSIVGKNLLWINSVAQQAEGRFEKASVEYQEHLCAMTGVDCCISSFDKSVLTLANAGRNSASPKHSLNGESRKTVLSKPTDSSPEVINYLGNKACECYISIADWAAVQEWQNAIHDLKKSTSSTSLNLKADFNYIKSLSSFESGKFVECTEQLELLPGENINLLAGGSKEKIDMKKLLPNMLSPDPRELQKSIEVQLLRSSVCLATALNPIEQDQKWQSITENVVKYLKQTSRIAIGPLRLSTLTVSQSLPVLSTLQLYCSSALENTVSNRLSTEDCLIPLFSEALRSCKQHDVRPWMQALRYTMYQNQLLEKIKEQTVPIRSHLMELGLTAAKFARKRGNVSLATRLLAQCSEVQLGKTTTAQDLVQHFKKLSTQGQVDEKWGPELDIEKTKLLYTAGQSTHAMEMLSSCAISFCKSVKAEYAVAKSILTLAKWIQAEWKEISGQLKQVYRAQHQQNFTGLSTLSKNILTLIELPSVNTMEEEYPRIESESTVHIGVGEPDFILGQLYHLSSVQAPEVAKSWAALASWAYRWGRKVVDNASQGEGVRLLPREKSEVQNLLPDTITEEEKERIYGILGQAVCRPAGIQDEDITLQITESEDNEEDDMVDVIWRQLISSCPWLSELDESATEGVIKVWRKVVDRIFSLYKLSCSAYFTFLKLNAGQIPLDEDDPRLHLSHRVEQSTDDMIVMATLRLLRLLVKHAGELRQYLEHGLETTPTAPWRGIIPQLFSRLNHPEVYVRQSICNLLCRVAQDSPHLILYPAIVGTISLSSESQASGNKFSTAIPTLLGNIQGEELLVSECEGGSPPASQDSNKDEPKSGLNEDQAMMQDCYSKIVDKLSSANPTMVLQVQMLVAELRRVTVLWDELWLGVLLQQHMYVLRRIQQLEDEVKRVQNNNTLRKEEKIAIMREKHTALMKPIVFALEHVRSITAAPAETPHEKWFQDNYGDAIENALEKLKTPLNPAKPGSSWIPFKEIMLSLQQRAQKRASYILRLEEISPWLAAMTNTEIALPGEVSARDTVTIHSVGGTITILPTKTKPKKLLFLGSDGKSYPYLFKGLEDLHLDERIMQFLSIVNTMFATINRQETPRFHARHYSVTPLGTRSGLIQWVDGATPLFGLYKRWQQREAALQAQKAQDSYQTPQNPGIVPRPSELYYSKIGPALKTVGLSLDVSRRDWPLHVMKAVLEELMEATPPNLLAKELWSSCTTPDEWWRVTQSYARSTAVMSMVGYIIGLGDRHLDNVLIDMTTGEVVHIDYNVCFEKGKSLRVPEKVPFRMTQNIETALGVTGVEGVFRLSCEQVLHIMRRGRETLLTLLEAFVYDPLVDWTAGGEAGFAGAVYGGGGQQAESKQSKREMEREITRSLFSSRVAEIKVNWFKNRDEMLVVLPKLDGSLDEYLSLQEQLTDVEKLQGKLLEEIEFLEGAEGVDHPSHTLQHRYSEHTQLQTQQRAVQEAIQVKLNEFEQWITHYQAAFNNLEATQLASLLQEISTQMDLGPPSYVPATAFLQNAGQAHLISQCEQLEGEVGALLQQRRSVLRGCLEQLHHYATVALQYPKAIFQKHRIEQWKTWMEELICNTTVERCQELYRKYEMQYAPQPPPTVCQFITATEMTLQRYAADINSRLIRQVERLKQEAVTVPVCEDQLKEIERCIKVFLHENGEEGSLSLASVIISALCTLTRRNLMMEGAASSAGEQLVDLTSRDGAWFLEELCSMSGNVTCLVQLLKQCHLVPQDLDIPNPMEASETVHLANGVYTSLQELNSNFRQIIFPEALRCLMKGEYTLESMLHELDGLIEQTTDGVPLQTLVESLQAYLRNAAMGLEEETHAHYIDVARLLHAQYGELIQPRNGSVDETPKMSAGQMLLVAFDGMFAQVETAFSLLVEKLNKMEIPIAWRKIDIIREARSTQVNFFDDDNHRQVLEEIFFLKRLQTIKEFFRLCGTFSKTLSGSSSLEDQNTVNGPVQIVNVKTLFRNSCFSEDQMAKPIKAFTADFVRQLLIGLPNQALGLTLCSFISALGVDIIAQVEAKDFGAESKVSVDDLCKKAVEHNIQIGKFSQLVMNRATVLASSYDTAWKKHDLVRRLETSISSCKTSLQRVQLHIAMFQWQHEDLLINRPQAMSVTPPPRSAILTSMKKKLHTLSQIETSIATVQEKLAALESSIEQRLKWAGGANPALAPVLQDFEATIAERRNLVLKESQRASQVTFLCSNIIHFESLRTRTAEALNLDAALFELIKRCQQMCSFASQFNSSVSELELRLLQRVDTGLEHPIGSSEWLLSAHKQLTQDMSTQRAIQTEKEQQIETVCETIQNLVDNIKTVLTGHNRQLGDVKHLLKAMAKDEEAALADGEDVPYENSVRQFLGEYKSWQDNIQTVLFTLVQAMGQVRSQEHVEMLQEITPTLKELKTQSQSIYNNLVSFASPLVTDATNECSSPTSSATYQPSFAAAVRSNTGQKTQPDVMSQNARKLIQKNLATSADTPPSTVPGTGKSVACSPKKAVRDPKTGKAVQERNSYAVSVWKRVKAKLEGRDVDPNRRMSVAEQVDYVIKEATNLDNLAQLYEGWTAWV,mutated_sequence,1.0,3661.0,NP_055907.3.a2m,NP_055907.3.npy,ClinVar +NP_056110.2,NP_056110.2.csv,MAAADGGGPGGASVGTEEDGGGVGHRTVYLFDRREKESELGDRPLQVGERSDYAGFRACVCQTLGISPEEKFVITTTSRKEITCDNFDETVKDGVTLYLLQSVNQLLLTATKERIDFLPHYDTLVKSGMYEYYASEGQNPLPFALAELIDNSLSATSRNIGVRRIQIKLLFDETQGKPAVAVIDNGRGMTSKQLNNWAVYRLSKFTRQGDFESDHSGYVRPVPVPRSLNSDISYFGVGGKQAVFFVGQSARMISKPADSQDVHELVLSKEDFEKKEKNKEAIYSGYIRNRKPSDSVHITNDDERFLHHLIIEEKEKDSFTAVVITGVQPEHIQYLKNYFHLWTRQLAHIYHYYIHGPKGNEIRTSKEVEPFNNIDIEISMFEKGKVPKIVNLREIQDDMQTLYVNTAADSFEFKAHVEGDGVVEGIIRYHPFLYDRETYPDDPCFPSKLKDEDDEDDCFILEKAARGKRPIFECFWNGRLIPYTSVEDFDWCTPPKKRGLAPIECYNRISGALFTNDKFQVSTNKLTFMDLELKLKDKNTLFTRILNGQEQRMKIDREFALWLKDCHEKYDKQIKFTLFKGVITRPDLPSKKQGPWATYAAIEWDGKIYKAGQLVKTIKTLPLFYGSIVRFFLYGDHDGEVYATGGEVQIAMEPQALYDEVRTVPIAKLDRTVAEKAVKKYVEDEMARLPDRLSVTWPEGDELLPNEVRPAGTPIGALRIEILNKKGEAMQKLPGTSHGGSKKLLVELKVILHSSSGNKEIISHISQHGGKWPYWFKKMENIQKLGNYTLKLQVVLNESNADTYAGRPLPSKAIKFSVKEGKPEKFSFGLLDLPFRVGVPFNIPLEFQDEFGHTSQLVTDIQPVLEASGLSLHYEEITKGPNCVIRGVTAKGPVNSCQGKNYNLKVTLPGLKEDSQILKIRLLPGHPRRLKVKPDSEILVIENGTAFPFQVEVLDESDNITAQPKLIVHCKFSGAPNLPVYVVDCSSSGTSILTGSAIQVQNIKKDQTLKARIEIPSCKDVAPVEKTIKLLPSSHVARLQIFSVEGQKAIQIKHQDEVNWIAGDIMHNLIFQMYDEGEREINITSALAEKIKVNWTPEINKEHLLQGLLPDVQVPTSVKDMRYCQVSFQDDHVSLESAFTVRPLPDEPKHLKCEMKGGKTVQMGQELQGEVVIIITDQYGNQIQAFSPSSLSSLSIAGVGLDSSNLKTTFQENTQSISVRGIKFIPGPPGNKDLCFTWREFSDFIRVQLISGPPAKLLLIDWPELKESIPVINGRDLQNPIIVQLCDQWDNPAPVQHVKISLTKASNLKLMPSNQQHKTDEKGRANLGVFSVFAPRGEHTLQVKAIYNKSIIEGPIIKLMILPDPEKPVRLNVKYDKDASFLAGGLFTDFMISVISEDDSIIKNINPARISMKMWKLSTSGNRPPANAETFSCNKIKDNDKEDGCFYFRDKVIPNKVGTYCIQFGFMMDKTNILNSEQVIVEVLPNQPVKLVPKIKPPTPAVSNVRSVASRTLVRDLHLSITDDYDNHTGIDLVGTIIATIKGSNEEDTDTPLFIGKVRTLEFPFVNGSAEIMSLVLAESSPGRDSTEYFIVFEPRLPLLSRTLEPYILPFMFYNDVKKQQQMAALTKEKDQLSQSIVMYKSLFEASQQLLNEMKCQVEEARLKEAQLRNELKIHNIDIPTTQQVPHIEALLKRKLSEQEELKKKPRRSCTLPNYTKGSGDVLGKIAHLAQIEDDRAAMVISWHLASDMDCVVTLTTDAARRIYDETQGRQQVLPLDSIYKKTLPDWKRSLPHFRNGKLYFKPIGDPVFARDLLTFPDNVEHCETVFGMLLGDTIILDNLDAANHYRKEVVKITHCPTLLTRDGDRIRSNGKFGGLQNKAPPMDKLRGMVFGAPVPKQCLILGEQIDLLQQYRSAVCKLDSVNKDLNSQLEYLRTPDMRKKKQELDEHEKNLKLIEEKLGMTPIRKCNDSLRHSPKVETTDCPVPPKRMRREATRQNRIITKTDV,mutated_sequence,1.0,2005.0,NP_056110.2.a2m,NP_056110.2.npy,ClinVar +NP_056145.5,NP_056145.5.csv,MKKASRSVGSVPKVSAISKTQTAEKIKPENSSSASTGGKLVKPGTAASLSKTKSSDDLLAGMAGGVTVTNGVKGKKSTCPSAAPSASAPAMTTVENKSKISTGTASSTKRSTSTGNKESSSTRERLRERTRLNQSKKLPSAGQGANDMALAKRSRSRTATECDVRMSKSKSDNQISDRAALEAKVKDLLTLAKTKDVEILHLRNELRDMRAQLGINEDHSEGDEKSEKETIMAHQPTDVESTLLQLQEQNTAIREELNQLKNENRMLKDRLNALGFSLEQRLDNSEKLFGYQSLSPEITPGNQSDGGGTLTSSVEGSAPGSVEDLLSQDENTLMDHQHSNSMDNLDSECSEVYQPLTSSDDALDAPSSSESEGIPSIERSRKGSSGNASEVSVACLTERIHQMEENQHSTSEELQATLQELADLQQITQELNSENERLGEEKVILMESLCQQSDKLEHFSRQIEYFRSLLDEHHISYVIDEDVKSGRYMELEQRYMDLAENARFEREQLLGVQQHLSNTLKMAEQDNKEAQEMIGALKERSHHMERIIESEQKGKAALAATLEEYKATVASDQIEMNRLKAQLENEKQKVAELYSIHNSGDKSDIQDLLESVRLDKEKAETLASSLQEDLAHTRNDANRLQDAIAKVEDEYRAFQEEAKKQIEDLNMTLEKLRSDLDEKETERSDMKETIFELEDEVEQHRAVKLHDNLIISDLENTVKKLQDQKHDMEREIKTLHRRLREESAEWRQFQADLQTAVVIANDIKSEAQEEIGDLKRRLHEAQEKNEKLTKELEEIKSRKQEEERGRVYNYMNAVERDLAALRQGMGLSRRSSTSSEPTPTVKTLIKSFDSASQVPNPAAAAIPRTPLSPSPMKTPPAAAVSPMQRHSISGPISTSKPLTALSDKRPNYGEIPVQEHLLRTSSASRPASLPRVPAMESAKTLSVSRRSSEEVKRDISAQEGASPASLMAMGTTSPQLSLSSSPTASVTPTTRSRIREERKDPLSALAREYGGSKRNALLKWCQKKTEGYQNIDITNFSSSWNDGLAFCALLHTYLPAHIPYQELNSQDKRRNFMLAFQAAESVGIKSTLDINEMVRTERPDWQNVMLYVTAIYKYFET,mutated_sequence,1.0,1117.0,NP_056145.5.a2m,NP_056145.5.npy,ClinVar +NP_056161.2,NP_056161.2.csv,MNHPFGKEEAASQKQLFGFFCECLRRGEWELAQACVPQLQEGQGDIPKRVEDILQALVVCPNLLRCGQDINPQRVAWVWLLVLEKWLAREKKLLPVVFRRKLEFLLLSEDLQGDIPENILEELYETLTQGAVGHVPDGNPRRESWTPRLSSEAVSVLWDLLRQSPQPAQALLELLLEEDDGTGLCHWPLQNALVDLIRKALRALQGPDSVPPGVVDAIYGALRTLRCPAEPLGVELHLLCEELLEACRTEGSPLREERLLSCLLHKASRGLLSLYGHTYAEKVTEKPPRATASGKVSPDHLDPERAMLALFSNPNPAEAWKVAYFYCLSNNKHFLEQILVTALTLLKEEDFPNLGCLLDREFRPLSCLLVLLGWTHCQSLESAKRLLQTLHRTQGPGCDELLRDACDGLWAHLEVLEWCIQQSSNPIPKRDLLYHLHGGDSHSVLYTLHHLTNLPALREEDVLKLLQKVPAKDPQQEPDAVDAPVPEHLSQCQNLTLYQGFCAMKYAIYALCVNSHQHSQCQDCKDSLSEDLASATEPANDSLSSPGAANLFSTYLARCQQYLCSIPDSLCLELLENIFSLLLITSADLHPEPHLPEDYAEDDDIEGKSPSGLRSPSESPQHIAHPERKSERGSLGVPKTLAYTMPSHVKAEPKDSYPGPHRHSFLDLKHFTSGISGFLADEFAIGAFLRLLQEQLDEISSRSPPEKPKQESQSCSGSRDGLQSRLHRLSKVVSEAQWRHKVVTSNHRSEEQPSRRYQPATRHPSLRRGRRTRRSQADGRDRGSNPSLESTSSELSTSTSEGSLSAMSGRNELHSRLHPHPQSSLIPMMFSPPESLLASCILRGNFAEAHQVLFTFNLKSSPSSGELMFMERYQEVIQELAQVEHKIENQNSDAGSSTIRRTGSGRSTLQAIGSAAAAGMVFYSISDVTDKLLNTSGDPIPMLQEDFWISTALVEPTAPLREVLEDLSPPAMAAFDLACSQCQLWKTCKQLLETAERRLNSSLERRGRRIDHVLLNADGIRGFPVVLQQISKSLNYLLMSASQTKSESVEEKGGGPPRCSITELLQMCWPSLSEDCVASHTTLSQQLDQVLQSLREALELPEPRTPPLSSLVEQAAQKAPEAEAHPVQIQTQLLQKNLGKQTPSGSRQMDYLGTFFSYCSTLAAVLLQSLSSEPDHVEVKVGNPFVLLQQSSSQLVSHLLFERQVPPERLAALLAQENLSLSVPQVIVSCCCEPLALCSSRQSQQTSSLLTRLGTLAQLHASHCLDDLPLSTPSSPRTTENPTLERKPYSSPRDSSLPALTSSALAFLKSRSKLLATVACLGASPRLKVSKPSLSWKELRGRREVPLAAEQVARECERLLEQFPLFEAFLLAAWEPLRGSLQQGQSLAVNLCGWASLSTVLLGLHSPIALDVLSEAFEESLVARDWSRALQLTEVYGRDVDDLSSIKDAVLSCAVACDKEGWQYLFPVKDASLRSRLALQFVDRWPLESCLEILAYCISDTAVQEGLKCELQRKLAELQVYQKILGLQSPPVWCDWQTLRSCCVEDPSTVMNMILEAQEYELCEEWGCLYPIPREHLISLHQKHLLHLLERRDHDKALQLLRRIPDPTMCLEVTEQSLDQHTSLATSHFLANYLTTHFYGQLTAVRHREIQALYVGSKILLTLPEQHRASYSHLSSNPLFMLEQLLMNMKVDWATVAVQTLQQLLVGQEIGFTMDEVDSLLSRYAEKALDFPYPQREKRSDSVIHLQEIVHQAADPETLPRSPSAEFSPAAPPGISSIHSPSLRERSFPPTQPSQEFVPPATPPARHQWVPDETESICMVCCREHFTMFNRRHHCRRCGRLVCSSCSTKKMVVEGCRENPARVCDQCYSYCNKDVPEEPSEKPEALDSSKNESPPYSFVVRVPKADEVEWILDLKEEENELVRSEFYYEQAPSASLCIAILNLHRDSIACGHQLIEHCCRLSKGLTNPEVDAGLLTDIMKQLLFSAKMMFVKAGQSQDLALCDSYISKVDVLNILVAAAYRHVPSLDQILQPAAVTRLRNQLLEAEYYQLGVEVSTKTGLDTTGAWHAWGMACLKAGNLTAAREKFSRCLKPPFDLNQLNHGSRLVQDVVEYLESTVRPFVSLQDDDYFATLRELEATLRTQSLSLAVIPEGKIMNNTYYQECLFYLHNYSTNLAIISFYVRHSCLREALLHLLNKESPPEVFIEGIFQPSYKSGKLHTLENLLESIDPTLESWGKYLIAACQHLQKKNYYHILYELQQFMKDQVRAAMTCIRFFSHKAKSYTELGEKLSWLLKAKDHLKIYLQETSRSSGRKKTTFFRKKMTAADVSRHMNTLQLQMEVTRFLHRCESAGTSQITTLPLPTLFGNNHMKMDVACKVMLGGKNVEDGFGIAFRVLQDFQLDAAMTYCRAARQLVEKEKYSEIQQLLKCVSESGMAAKSDGDTILLNCLEAFKRIPPQELEGLIQAIHNDDNKVRAYLICCKLRSAYLIAVKQEHSRATALVQQVQQAAKSSGDAVVQDICAQWLLTSHPRGAHGPGSRK,mutated_sequence,1.0,2539.0,NP_056161.2.a2m,NP_056161.2.npy,ClinVar +NP_056183.2,NP_056183.2.csv,MAIAQLATEYVFSDFLLKEPTEPKFKGLRLELAVDKMVTCIAVGLPLLLISLAFAQEISIGTQISCFSPSSFSWRQAAFVDSYCWAAVQQKNSLQSESGNLPLWLHKFFPYILLLFAILLYLPPLFWRFAAAPHICSDLKFIMEELDKVYNRAIKAAKSARDLDMRDGACSVPGVTENLGQSLWEVSESHFKYPIVEQYLKTKKNSNNLIIKYISCRLLTLIIILLACIYLGYYFSLSSLSDEFVCSIKSGILRNDSTVPDQFQCKLIAVGIFQLLSVINLVVYVLLAPVVVYTLFVPFRQKTDVLKVYEILPTFDVLHFKSEGYNDLSLYNLFLEENISEVKSYKCLKVLENIKSSGQGIDPMLLLTNLGMIKMDVVDGKTPMSAEMREEQGNQTAELQGMNIDSETKANNGEKNARQRLLDSSC,mutated_sequence,1.0,426.0,NP_056183.2.a2m,NP_056183.2.npy,ClinVar +NP_056280.2,NP_056280.2.csv,MGQEPRTLPPSPNWYCARCSDAVPGGLFGFAARTSVFLVRVGPGAGESPGTPPFRVIGELVGHTERVSGFTFSHHPGQYNLCATSSDDGTVKIWDVETKTVVTEHALHQHTISTLHWSPRVKDLIVSGDEKGVVFCYWFNRNDSQHLFIEPRTIFCLTCSPHHEDLVAIGYKDGIVVIIDISKKGEVIHRLRGHDDEIHSIAWCPLPGEDCLSINQEETSEEAEITNGNAVAQAPVTKGCYLATGSKDQTIRIWSCSRGRGVMILKLPFLKRRGGGIDPTVKERLWLTLHWPSNQPTQLVSSCFGGELLQWDLTQSWRRKYTLFSASSEGQNHSRIVFNLCPLQTEDDKQLLLSTSMDRDVKCWDIATLECSWTLPSLGGFAYSLAFSSVDIGSLAIGVGDGMIRVWNTLSIKNNYDVKNFWQGVKSKVTALCWHPTKEGCLAFGTDDGKVGLYDTYSNKPPQISSTYHKKTVYTLAWGPPVPPMSLGGEGDRPSLALYSCGGEGIVLQHNPWKLSGEAFDINKLIRDTNSIKYKLPVHTEISWKADGKIMALGNEDGSIEIFQIPNLKLICTIQQHHKLVNTISWHHEHGSQPELSYLMASGSNNAVIYVHNLKTVIESSPESPVTITEPYRTLSGHTAKITSVAWSPHHDGRLVSASYDGTAQVWDALREEPLCNFRGHRGRLLCVAWSPLDPDCIYSGADDFCVHKWLTSMQDHSRPPQGKKSIELEKKRLSQPKAKPKKKKKPTLRTPVKLESIDGNEEESMKENSGPVENGVSDQEGEEQAREPELPCGLAPAVSREPVICTPVSSGFEKSKVTINNKVILLKKEPPKEKPETLIKKRKARSLLPLSTSLDHRSKEELHQDCLVLATAKHSRELNEDVSADVEERFHLGLFTDRATLYRMIDIEGKGHLENGHPELFHQLMLWKGDLKGVLQTAAERGELTDNLVAMAPAAGYHVWLWAVEAFAKQLCFQDQYVKAASHLLSIHKVYEAVELLKSNHFYREAIAIAKARLRPEDPVLKDLYLSWGTVLERDGHYAVAAKCYLGATCAYDAAKVLAKKGDAASLRTAAELAAIVGEDELSASLALRCAQELLLANNWVGAQEALQLHESLQGQRLVFCLLELLSRHLEEKQLSEGKSSSSYHTWNTGTEGPFVERVTAVWKSIFSLDTPEQYQEAFQKLQNIKYPSATNNTPAKQLLLHICHDLTLAVLSQQMASWDEAVQALLRAVVRSYDSGSFTIMQEVYSAFLPDGCDHLRDKLGDHQSPATPAFKSLEAFFLYGRLYEFWWSLSRPCPNSSVWVRAGHRTLSVEPSQQLDTASTEETDPETSQPEPNRPSELDLRLTEEGERMLSTFKELFSEKHASLQNSQRTVAEVQETLAEMIRQHQKSQLCKSTANGPDKNEPEVEAEQPLCSSQSQCKEEKNEPLSLPELTKRLTEANQRMAKFPESIKAWPFPDVLECCLVLLLIRSHFPGCLAQEMQQQAQELLQKYGNTKTYRRHCQTFCM,mutated_sequence,1.0,1508.0,NP_056280.2.a2m,NP_056280.2.npy,ClinVar +NP_056321.2,NP_056321.2.csv,MEPKVAELKQKIEDTLCPFGFEVYPFQVAWYNELLPPAFHLPLPGPTLAFLVLSTPAMFDRALKPFLQSCHLRMLTDPVDQCVAYHLGRVRESLPELQIEIIADYEVHPNRRPKILAQTAAHVAGAAYYYQRQDVEADPWGNQRISGVCIHPRFGGWFAIRGVVLLPGIEVPDLPPRKPHDCVPTRADRIALLEGFNFHWRDWTYRDAVTPQERYSEEQKAYFSTPPAQRLALLGLAQPSEKPSSPSPDLPFTTPAPKKPGNPSRARSWLSPRVSPPASPGP,mutated_sequence,1.0,282.0,NP_056321.2.a2m,NP_056321.2.npy,ClinVar +NP_056414.1,NP_056414.1.csv,MDLGAITKYSALHAKPNGLILQYGTAGFRTKAEHLDHVMFRMGLLAVLRSKQTKSTIGVMVTASHNPEEDNGVKLVDPLGEMLAPSWEEHATCLANAEEQDMQRVLIDISEKEAVNLQQDAFVVIGRDTRPSSEKLSQSVIDGVTVLGGQFHDYGLLTTPQLHYMVYCRNTGGRYGKATIEGYYQKLSKAFVELTKQASCSGDEYRSLKVDCANGIGALKLREMEHYFSQGLSVQLFNDGSKGKLNHLCGADFVKSHQKPPQGMEIKSNERCCSFDGDADRIVYYYHDADGHFHLIDGDKIATLISSFLKELLVEIGESLNIGVVQTAYANGSSTRYLEEVMKVPVYCTKTGVKHLHHKAQEFDIGVYFEANGHGTALFSTAVEMKIKQSAEQLEDKKRKAAKMLENIIDLFNQAAGDAISDMLVIEAILALKGLTVQQWDALYTDLPNRQLKVQVADRRVISTTDAERQAVTPPGLQEAINDLVKKYKLSRAFVRPSGTEDVVRVYAEADSQESADHLAHEVSLAVFQLAGGIGERPQPGF,mutated_sequence,1.0,542.0,NP_056414.1.a2m,NP_056414.1.npy,ClinVar +NP_056444.3,NP_056444.3.csv,MSLADELLADLEEAAEEEEGGSYGEEEEEPAIEDVQEETQLDLSGDSVKTIAKLWDSKMFAEIMMKIEEYISKQAKASEVMGPVEAAPEYRVIVDANNLTVEIENELNIIHKFIRDKYSKRFPELESLVPNALDYIRTVKELGNSLDKCKNNENLQQILTNATIMVVSVTASTTQGQQLSEEELERLEEACDMALELNASKHRIYEYVESRMSFIAPNLSIIIGASTAAKIMGVAGGLTNLSKMPACNIMLLGAQRKTLSGFSSTSVLPHTGYIYHSDIVQSLPPDLRRKAARLVAAKCTLAARVDSFHESTEGKVGYELKDEIERKFDKWQEPPPVKQVKPLPAPLDGQRKKRGGRRYRKMKERLGLTEIRKQANRMSFGEIEEDAYQEDLGFSLGHLGKSGSGRVRQTQVNEATKARISKTLQRTLQKQSVVYGGKSTIRDRSSGTASSVAFTPLQGLEIVNPQAAEKKVAEANQKYFSSMAEFLKVKGEKSGLMST,mutated_sequence,1.0,499.0,NP_056444.3.a2m,NP_056444.3.npy,ClinVar +NP_056477.1,NP_056477.1.csv,MHLKHLRTLLSPQDGAAKVTCMAWSQNNAKFAVCTVDRVVLLYDEHGERRDKFSTKPADMKYGRKSYMVKGMAFSPDSTKIAIGQTDNIIYVYKIGEDWGDKKVICNKFIQTSAVTCLQWPAEYIIVFGLAEGKVRLANTKTNKSSTIYGTESYVVSLTTNCSGKGILSGHADGTIVRYFFDDEGSGESQGKLVNHPCPPYALAWATNSIVAAGCDRKIVAYGKEGHMLQTFDYSRDPQEREFTTAVSSPGGQSVVLGSYDRLRVFNWIPRRSIWEEAKPKEITNLYTITALAWKRDGSRLCVGTLCGGVEQFDCCLRRSIYKNKFELTYVGPSQVIVKNLSSGTRVVLKSHYGYEVEEVKILGKERYLVAHTSETLLLGDLNTNRLSEIAWQGSGGNEKYFFENENVCMIFNAGELTLVEYGNNDTLGSVRTEFMNPHLISVRINERCQRGTEDNKKLAYLIDIKTIAIVDLIGGYNIGTVSHESRVDWLELNETGHKLLFRDRKLRLHLYDIESCSKTMILNFCSYMQWVPGSDVLVAQNRNSLCVWYNIEAPERVTMFTIRGDVIGLERGGGKTEVMVMEGVTTVAYTLDEGLIEFGTAIDDGNYIRATAFLETLEMTPETEAMWKTLSKLALEARQLHIAERCFSALGQVAKARFLHETNEIADQVSREYGGEGTDFYQVRARLAMLEKNYKLAEMIFLEQNAVEEAMGMYQELHRWDECIAVAEAKGHPALEKLRRSYYQWLMDTQQEERAGELQESQGDGLAAISLYLKAGLPAKAARLVLTREELLANTELVEHITAALIKGELYERAGDLFEKIHNPQKALECYRKGNAFMKAVELARLAFPVEVVKLEEAWGDHLVQQKQLDAAINHYIEARCSIKAIEAALGARQWKKAIYILDLQDRNTASKYYPLVAQHYASLQEYEIAEELYTKGDRTKDAIDMYTQAGRWEQAHKLAMKCMRPEDVSVLYITQAQEMEKQGKYREAERLYVTVQEPDLAITMYKKHKLYDDMIRLVGKHHPDLLSDTHLHLGKELEAEGRLQEAEYHYLEAQEWKATVNMYRASGLWEEAYRVARTQGGANAHKHVAYLWAKSLGGEAAVRLLNKLGLLEAAVDHAADNCSFEFAFELSRLALKHKTPEVHLKYAMFLEDEGKFEEAEAEFIRAGKPKEAVLMFVHNQDWEAAQRVAEAHDPDSVAEVLVGQARGALEEKDFQKAEGLLLRAQRPGLALNYYKEAGLWSDALRICKDYVPSQLEALQEEYEREATKKGARGVEGFVEQARHWEQAGEYSRAVDCYLKVRDSGNSGLAEKCWMKAAELSIKFLPPQRNMEVVLAVGPQLIGIGKHSAAAELYLNLDLVKEAIDAFIEGEEWNKAKRVAKELDPRYEDYVDQHYKEFLKNQGKVDSLVGVDVIAALDLYVEQGQWDKCIETATKQNYKILHKYVALYATHLIREGSSAQALALYVQHGAPANPQNFNIYKRIFTDMVSSPGTNCAEAYHSWADLRDVLFNLCENLVKSSEANSPAHEEFKTMLLIAHYYATRSAAQSVKQLETVAARLSVSLLRHTQLLPVDKAFYEAGIAAKAVGWDNMAFIFLNRFLDLTDAIEEGTLDGLDHSDFQDTDIPFEVPLPAKQHVPEAEREEVRDWVLTVSMDQRLEQVLPRDERGAYEASLVAASTGVRALPCLITGYPILRNKIEFKRPGKAANKDNWNKFLMAIKTSHSPVCQDVLKFISQWCGGLPSTSFSFQ,mutated_sequence,1.0,1749.0,NP_056477.1.a2m,NP_056477.1.npy,ClinVar +NP_056496.1,NP_056496.1.csv,MATASPSVFLLMVNGQVESAQFPEYDDLYCKYCFVYGQDWAPTAGLEEGISQITSKSQDVRQALVWNFPIDVTFKSTNPYGWPQIVLSVYGPDVFGNDVVRGYGAVHVPFSPGRHKRTIPMFVPESTSKLQKFTSWFMGRRPEYTDPKVVAQGEGREVTRVRSQGFVTLLFNVVTKDMRKLGYDTGPSDTQGVLGPSPPQSFPQ,mutated_sequence,1.0,204.0,NP_056496.1.a2m,NP_056496.1.npy,ClinVar +NP_056528.2,NP_056528.2.csv,MGDPERPEAAGLDQDERSSSDTNESEIKSNEEPLLRKSSRRFVIFPIQYPDIWKMYKQAQASFWTAEEVDLSKDLPHWNKLKADEKYFISHILAFFAASDGIVNENLVERFSQEVQVPEARCFYGFQILIENVHSEMYSLLIDTYIRDPKKREFLFNAIETMPYVKKKADWALRWIADRKSTFGERVVAFAAVEGVFFSGSFAAIFWLKKRGLMPGLTFSNELISRDEGLHCDFACLMFQYLVNKPSEERVREIIVDAVKIEQEFLTEALPVGLIGMNCILMKQYIEFVADRLLVELGFSKVFQAENPFDFMENISLEGKTNFFEKRVSEYQRFAVMAETTDNVFTLDADF,mutated_sequence,1.0,351.0,NP_056528.2.a2m,NP_056528.2.npy,ClinVar +NP_056993.2,NP_056993.2.csv,MAAPESGPALSPGTAEGEEETILYDLLVNTEWPPETEVQPRGNQKHGASFIITKAIRDRLLFLRQYIWYSPAPFLLPDGLVRLVNKQINWHLVLASNGKLLAAVQDQCVEIRSAKDDFTSIIGKCQVPKDPKPQWRRVAWSYDCTLLAYAESTGTVRVFDLMGSELFVISPASSFIGDLSYAIAGLIFLEYKASAQWSAELLVINYRGELRSYLVSVGTNQSYQESHCFSFSSHYPHGINTAIYHPGHRLLLVGGCETAEVGMSKASSCGLSAWRVLSGSPYYKQVTNGGDGVTAVPKTLGLLRMLSVKFYSRQGQEQDGIFKMSLSPDGMLLAAIHFSGKLSIWAIPSLKQQGEWGQNEQPGYDDLNPDWRLSTEKRKKIKDKESFYPLIDVNWWADSAVTLARCSGALTVSSVKTLKNLLGKSCEWFEPSPQVTATHDGGFLSLECEIKLAPKRSRLETRAGEEDEGEEDSDSDYEISAKARYFGYIKQGLYLVTEMERFAPPRKRPRTITKNYRLVSLRSTTPEELYQRKIESEEYEEALSLAHTYGLDTDLVYQRQWRKSAVNVASIQNYLSKIKKRSWVLHECLERVPENVDAAKELLQYGLKGTDLEALLAIGKGADDGRFTLPGEIDIDSISYEELSPPDEEPAKNKKEKELKKRQELLKLVNFSKLTLEQKELCRCRRKLLTYLDRLATYEEILGVPHASEQRYDAEFFKKFRNQNIVLSARTYAQESNVQALEILFTYHGSDLLPHRLAILSNFPETTSPHEYSVLLPEACFNGDSLMIIPWHEHKHRAKDWCEELACRMVVEPNLQDESEFLYAAQPELLRFRMTQLTVEKVMDWYQTRAEEIEHYARQVDCALSLIRLGMERNIPGLLVLCDNLVTLETLVYEARCDVTLTLKELQQMKDIEKLRLLMNSCSEDKYVTSAYQWMVPFLHRCEKQSPGVANELLKEYLVTLAKGDLKFPLKIFQHSKPDLQQKIIPDQDQLMAIALECIYTCERNDQLCLCYDLLECLPERGYGDKTEATTKLHDMVDQLEQILSVSELLEKHGLEKPISFVKNTQSSSEEARKLMVRLTRHTGRKQPPVSESHWRTLLQDMLTMQQNVYTCLDSDACYEIFTESLLCSSRLENIHLAGQMMHCSACSENPPAGIAHKGKPHYRVSYEKSIDLVLAASREYFNSSTNLTDSCMDLARCCLQLITDRPPAIQEELDLIQAVGCLEEFGVKILPLQVRLCPDRISLIKECISQSPTCYKQSTKLLGLAELLRVAGENPEERRGQVLILLVEQALRFHDYKAASMHCQELMATGYPKSWDVCSQLGQSEGYQDLATRQELMAFALTHCPPSSIELLLAASSSLQTEILYQRVNFQIHHEGGENISASPLTSKAVQEDEVGVPGSNSADLLRWTTATTMKVLSNTTTTTKAVLQAVSDGQWWKKSLTYLRPLQGQKCGGAYQIGTTANEDLEKQGCHPFYESVISNPFVAESEGTYDTYQHVPVESFAEVLLRTGKLAEAKNKGEVFPTTEVLLQLASEALPNDMTLALAYLLALPQVLDANRCFEKQSPSALSLQLAAYYYSLQIYARLAPCFRDKCHPLYRADPKELIKMVTRHVTRHEHEAWPEDLISLTKQLHCYNERLLDFTQAQILQGLRKGVDVQRFTADDQYKRETILGLAETLEESVYSIAISLAQRYSVSRWEVFMTHLEFLFTDSGLSTLEIENRAQDLHLFETLKTDPEAFHQHMVKYIYPTIGGFDHERLQYYFTLLENCGCADLGNCAIKPETHIRLLKKFKVVASGLNYKKLTDENMSPLEALEPVLSSQNILSISKLVPKIPEKDGQMLSPSSLYTIWLQKLFWTGDPHLIKQVPGSSPEWLHAYDVCMKYFDRLHPGDLITVVDAVTFSPKAVTKLSVEARKEMTRKAIKTVKHFIEKPRKRNSEDEAQEAKDSKVTYADTLNHLEKSLAHLETLSHSFILSLKNSEQETLQKYSHLYDLSRSEKEKLHDEAVAICLDGQPLAMIQQLLEVAVGPLDISPKDIVQSAIMKIISALSGGSADLGGPRDPLKVLEGVVAAVHASVDKGEELVSPEDLLEWLRPFCADDAWPVRPRIHVLQILGQSFHLTEEDSKLLVFFRTEAILKASWPQRQVDIADIENEENRYCLFMELLESSHHEAEFQHLVLLLQAWPPMKSEYVITNNPWVRLATVMLTRCTMENKEGLGNEVLKMCRSLYNTKQMLPAEGVKELCLLLLNQSLLLPSLKLLLESRDEHLHEMALEQITAVTTVNDSNCDQELLSLLLDAKLLVKCVSTPFYPRIVDHLLASLQQGRWDAEELGRHLREAGHEAEAGSLLLAVRGTHQAFRTFSTALRAAQHWV,mutated_sequence,1.0,2371.0,NP_056993.2.a2m,NP_056993.2.npy,ClinVar +NP_056999.2,NP_056999.2.csv,MAKNRRDRNSWGGFSEKTYEWSSEEEEPVKKAGPVQVLIVKDDHSFELDETALNRILLSEAVRDKEVVAVSVAGAFRKGKSFLMDFMLRYMYNQESVDWVGDYNEPLTGFSWRGGSERETTGIQIWSEIFLINKPDGKKVAVLLMDTQGTFDSQSTLRDSATVFALSTMISSIQVYNLSQNVQEDDLQHLQLFTEYGRLAMEETFLKPFQSLIFLVRDWSFPYEFSYGADGGAKFLEKRLKVSGNQHEELQNVRKHIHSCFTNISCFLLPHPGLKVATNPNFDGKLKEIDDEFIKNLKILIPWLLSPESLDIKEINGNKITCRGLVEYFKAYIKIYQGEELPHPKSMLQATAEANNLAAVATAKDTYNKKMEEICGGDKPFLAPNDLQTKHLQLKEESVKLFRGVKKMGGEEFSRRYLQQLESEIDELYIQYIKHNDSKNIFHAARTPATLFVVIFITYVIAGVTGFIGLDIIASLCNMIMGLTLITLCTWAYIRYSGEYRELGAVIDQVAAALWDQGSTNEALYKLYSAAATHRHLYHQAFPTPKSESTEQSEKKKM,mutated_sequence,1.0,558.0,NP_056999.2.a2m,NP_056999.2.npy,ClinVar +NP_057006.1,NP_057006.1.csv,MEPAVSEPMRDQVARTHLTEDTPKVNADIEKVNQNQAKRCTVIGGSGFLGQHMVEQLLARGYAVNVFDIQQGFDNPQVRFFLGDLCSRQDLYPALKGVNTVFHCASPPPSSNNKELFYRVNYIGTKNVIETCKEAGVQKLILTSSASVIFEGVDIKNGTEDLPYAMKPIDYYTETKILQERAVLGANDPEKNFLTTAIRPHGIFGPRDPQLVPILIEAARNGKMKFVIGNGKNLVDFTFVENVVHGHILAAEQLSRDSTLGGKAFHITNDEPIPFWTFLSRILTGLNYEAPKYHIPYWVAYYLALLLSLLVMVISPVIQLQPTFTPMRVALAGTFHYYSCERAKKAMGYQPLVTMDDAMERTVQSFRHLRRVK,mutated_sequence,1.0,373.0,NP_057006.1.a2m,NP_057006.1.npy,ClinVar +NP_057088.2,NP_057088.2.csv,MEKELRSTILFNAYKKEIFTTNNGYKSMQKKLRSNWKIQSLKDEITSEKLNGVKLWITAGPREKFTAAEFEILKKYLDTGGDVFVMLGEGGESRFDTNINFLLEEYGIMVNNDAVVRNVYHKYFHPKEALVSSGVLNREISRAAGKAVPGIIDEESSGNNAQALTFVYPFGATLSVMKPAVAVLSTGSVCFPLNRPILAFYHSKNQGGKLAVLGSCHMFSDQYLDKEENSKIMDVVFQWLTTGDIHLNQIDAEDPEISDYMMLPYTATLSKRNRECLQESDEIPRDFTTLFDLSIFQLDTTSFHSVIEAHEQLNVKHEPLQLIQPQFETPLPTLQPAVFPPSFRELPPPPLELFDLDETFSSEKARLAQITNKCTEEDLEFYVRKCGDILGVTSKLPKDQQDAKHILEHVFFQVVEFKKLNQEHDIDTSETAFQNNF,mutated_sequence,1.0,437.0,NP_057088.2.a2m,NP_057088.2.npy,ClinVar +NP_057119.3,NP_057119.3.csv,MATLLRPVLRRLCGLPGLQRPAAEMPLRARSDGAGPLYSHHLPTSPLQKGLLAAGSAAMALYNPYRHDMVAVLGETTGHRTLKVLRDQMRRDPEGAQILQERPRISTSTLDLGKLQSLPEGSLGREYLRFLDVNRVSPDTRAPTRFVDDEELAYVIQRYREVHDMLHTLLGMPTNILGEIVVKWFEAVQTGLPMCILGAFFGPIRLGAQSLQVLVSELIPWAVQNGRRAPCVLNLYYERRWEQSLRALREELGITAPPMHVQGLA,mutated_sequence,1.0,265.0,NP_057119.3.a2m,NP_057119.3.npy,ClinVar +NP_057122.2,NP_057122.2.csv,MSIFTPTNQIRLTNVAVVRMKRAGKRFEIACYKNKVVGWRSGVEKDLDEVLQTHSVFVNVSKGQVAKKEDLISAFGTDDQTEICKQILTKGEVQVSDKERHTQLEQMFRDIATIVADKCVNPETKRPYTVILIERAMKDIHYSVKTNKSTKQQALEVIKQLKEKMKIERAHMRLRFILPVNEGKKLKEKLKPLIKVIESEDYGQQLEIVCLIDPGCFREIDELIKKETKGKGSLEVLNLKDVEEGDEKFE,mutated_sequence,1.0,250.0,NP_057122.2.a2m,NP_057122.2.npy,ClinVar +NP_057206.2,NP_057206.2.csv,MVVSTFTDMDTFPNNFPPGGDSGLTGSQSEFQKMLIDERLRCEHHKANYQTLKAEHTRLQNEHVKLQNELKHLFNEKQTQQEKLQLLLEELRGELVEKTKDLEEMKLQILTPQKLELLRAQIQQELETPMRERFRNLDEEVEKYRAVYNKLRYEHTFLKSEFEHQKEEYARILDEGKIKYESEIARLEEDKEELRNQLLNVDLTKDSKRVEQLAREKVYLCQKLKGLEAEVAELKAEKENSEAQVENAQRIQVRQLAEMQATVRSLEAEKQSANLRAERLEKELQSSSEQNTFLINKLHKAEREINTLSSKVKELKHSNKLEITDIKLETARAKSELERERNKIQSELDGLQSDNEILKAAVEHHKVLLVEKDRELIRKVQAAKEEGYQKLVVLQDEKLELENRLADLEKMKVEHDVWRQSEKDQYEEKLRASQMAEEITRKELQSVRLKLQQQIVTIENAEKEKNENSDLKQQISSLQIQVTSLAQSENDLLNSNQMLKEMVERLKQECRNFRSQAEKAQLEAEKTLEEKQIQWLEEKHKLHERITDREEKYNQAKEKLQRAAIAQKKRKSLHENKLKRLQEKVEVLEAKKEELETENQVLNRQNVPFEDYTRLQKRLKDIQRRHNEFRSLILVPNMPPTASINPVSFQSSAMVPSMELPFPPHMQEEQHQRELSLLRKRLEELETTQRKQLEELGSSGE,mutated_sequence,1.0,701.0,NP_057206.2.a2m,NP_057206.2.npy,ClinVar +NP_057264.4,NP_057264.4.csv,MGSNSGQAGRHIYKSLADDGPFDSVEPPKRPTSRLIMHSMAMFGREFCYAVEAAYVTPVLLSVGLPSSLYSIVWFLSPILGFLLQPVVGSASDHCRSRWGRRRPYILTLGVMMLVGMALYLNGATVVAALIANPRRKLVWAISVTMIGVVLFDFAADFIDGPIKAYLFDVCSHQDKEKGLHYHALFTGFGGALGYLLGAIDWAHLELGRLLGTEFQVMFFFSALVLTLCFTVHLCSISEAPLTEVAKGIPPQQTPQDPPLSSDGMYEYGSIEKVKNGYVNPELAMQGAKNKNHAEQTRRAMTLKSLLRALVNMPPHYRYLCISHLIGWTAFLSNMLFFTDFMGQIVYRGDPYSAHNSTEFLIYERGVEVGCWGLCINSVFSSLYSYFQKVLVSYIGLKGLYFTGYLLFGLGTGFIGLFPNVYSTLVLCSLFGVMSSTLYTVPFNLITEYHREEEKERQQAPGGDPDNSVRGKGMDCATLTCMVQLAQILVGGGLGFLVNTAGTVVVVVITASAVALIGCCFVALFVRYVD,mutated_sequence,1.0,530.0,NP_057264.4.a2m,NP_057264.4.npy,ClinVar +NP_057287.2,NP_057287.2.csv,MGSAVMDTKKKKDVSSPGGSGGKKNASQKRRSLRVHIPDLSSFAMPLLDGDLEGSGKHSSRKVDSPFGPGSPSKGFFSRGPQPRPSSPMSAPVRPKTSPGSPKTVFPFSYQESPPRSPRRMSFSGIFRSSSKESSPNSNPATSPGGIRFFSRSRKTSGLSSSPSTPTQVTKQHTFPLESYKHEPERLENRIYASSSPPDTGQRFCPSSFQSPTRPPLASPTHYAPSKAAALAAALGPAEAGMLEKLEFEDEAVEDSESGVYMRFMRSHKCYDIVPTSSKLVVFDTTLQVKKAFFALVANGVRAAPLWESKKQSFVGMLTITDFINILHRYYKSPMVQIYELEEHKIETWRELYLQETFKPLVNISPDASLFDAVYSLIKNKIHRLPVIDPISGNALYILTHKRILKFLQLFMSDMPKPAFMKQNLDELGIGTYHNIAFIHPDTPIIKALNIFVERRISALPVVDESGKVVDIYSKFDVINLAAEKTYNNLDITVTQALQHRSQYFEGVVKCNKLEILETIVDRIVRAEVHRLVVVNEADSIVGIISLSDILQALILTPAGAKQKETETE,mutated_sequence,1.0,569.0,NP_057287.2.a2m,NP_057287.2.npy,ClinVar +NP_057323.3,NP_057323.3.csv,MAKEEDEEKKAKKGKKGKKAPEPEKPKRSLKGTSRLFMGFRDRTPKISKKGQFRSASAFFWGLHTGPQKTKRKRKARTVLKSTSKLMTQMRMGKKKRAMKGKKPSFMVIRFPGRRGYGRLRPRARSLSKASTAINWLTKKFLLKKAEESGSEQATVDAWLQRSSSRMGSRKLPFPSGAEILRPGGRLRRFPRSRSIYASGEPLGFLPFEDEAPFHHSGSRKSLYGLEGFQDLGEYYDYHRDGDDYYDRQSLHRYEEQEPYLAGLGPYSPAWPPYGDHYYGYPPEDPYDYYHPDYYGGPFDPGYTYGYGYDDYEPPYAPPSGYSSPYSYHDGYEGEAHPYGYYLDPYAPYDAPYPPYDLPYHTPYDVPYFDPYGVHYTVPYAEGVYGGGDEAIYPPEVPYFYPEESASAFVYPWVPPPIPSPHNPYAHAMDDIAELEEPEDAGVERQGTSFRLPSAAFFEQQGMDKPARSKLSLIRKFRLFPRPQVKLFGKEKLEVPLPPSLDIPLPLGDADEEEDEEELPPVSAVPYGHPFWGFLTPRQRNLQRALSAFGAHRGLGFGPEFGRPVPRPATSLARFLKKTLSEKKPIARLRGSQKARAGGPAVREAAYKRFGYKLAGMDPEKPGTPIVLRRAQPRARSSNDARRPPAPQPAPRTLSHWSALLSPPVPPRPPSSGPPPAPPLSPALSGLPRPASPYGSLRRHPPPWAAPAHVPPAPQASWWAFVEPPAVSPEVPPDLLAFPGPRPSFRGSRRRGAAFGFPGASPRASRRRAWSPLASPQPSLRSSPGLGYCSPLAPPSPQLSLRTGPFQPPFLPPARRPRSLQESPAPRRAAGRLGPPGSPLPGSPRPPSPPLGLCHSPRRSSLNLPSRLPHTWRRLSEPPTRAVKPQVRLPFHRPPRAGAWRAPLEHRESPREPEDSETPWTVPPLAPSWDVDMPPTQRPPSPWPGGAGSRRGFSRPPPVPENPFLQLLGPVPSPTLQPEDPAADMTRVFLGRHHEPGPGQLTKSAGPTPEKPEEEATLGDPQLPAETKPPTPAPPKDVTPPKDITPPKDVLPEQKTLRPSLSYPLAACDQTRATWPPWHRWGTLPQAAAPLAPIRAPEPLPKGGERRQAAPGRFAVVMPRVQKLSSFQRVGPATLKPQVQPIQDPKPRACSLRWSCLWLRADAYGPWPRVHTHPQSCHLGPGAACLSLRGSWEEVGPPSWRNKMHSIRNLPSMRFREQHGEDGVEDMTQLEDLQETTVLSNLKIRFERNLIYTYIGSILVSVNPYQMFGIYGPEQVQQYNGRALGENPPHLFAVANLAFAKMLDAKQNQCIIISGESGSGKTEATKLILRYLAAMNQKREVMQQIKILEATPLLESFGNAKTVRNDNSSRFGKFVEIFLEGGVISGAITSQYLLEKSRIVFQAKNERNYHIFYELLAGLPAQLRQAFSLQEAETYYYLNQGGNCEIAGKSDADDFRRLLAAMEVLGFSSEDQDSIFRILASILHLGNVYFEKYETDAQEVASVVSAREIQAVAELLQISPEGLQKAITFKVTETMREKIFTPLTVESAVDARDAIAKVLYALLFSWLITRVNALVSPRQDTLSIAILDIYGFEDLSFNSFEQLCINYANENLQYLFNKIVFQEEQEEYIREQIDWQEITFADNQPCINLISLKPYGILRILDDQCCFPQATDHTFLQKCHYHHGANPLYSKPKMPLPEFTIKHYAGKVTYQVHKFLDKNHDQVRQDVLDLFVRSRTRVVAHLFSSHAPQAAPQRLGKSSSVTRLYKAHTVAAKFQQSLLDLVEKMERCNPLFMRCLKPNHKKEPGLFEPDVVMAQLRYSGVLETVRIRKEGFPVRLPFQGFIDRYCCLVALKHDLPANGDMCVSVLSRLCKVMPNMYRVGVSKLFLKEHLYQLLESMREHVLNLAALTLQRCLRGFFIKRRFRSLRHKIILLQSRARGYLARQRYQQMRRSLVKFRSLVHAYVSRRRYLKLRAEWRCQVEGALLWEQEELSKREVVAVGHLEVPAELAGLLQAVAGLGLAQVPQVAPVRTPRLQAEPRVTLPLDINNYPMAKFVQCHFKEPAFGMLTVPLRTPLTQLPAEHHAEAVSIFKLILRFMGDPHLHGARENIFGNYIVQKGLAVPELRDEILAQLANQVWHNHNAHNAERGWLLLAACLSGFAPSPCFNKYLLKFVSDYGRNGFQAVCQHRLMQAMGRAQQQGSGAARTLPPTQLEWTATYEKASMALDVGCFNGDQFSCPVHSWSTGEEVAGDILRHRGLADGWRGWTVAMKNGVQWAELAGHDYVLDLVSDLELLRDFPRQKSYFIVGTEGPAASRGGPKVVFGNSWDSDEDMSTRPQPQEHMPKVLDSDGYSSHNQDGTNGETEAQRGTATHQESDSLGEPAVPHKGLDCYLDSLFDPVLSYGDADLEKPTAIAYRMKGGGQPGGGSSSGTEDTPRRPPEPKPIPGLDASTLALQQAFIHKQAVLLAREMTLQATALQQQPLSAALRSLPAEKPPAPEAQPTSVGTGPPAKPVLLRATPKPLAPAPLAKAPRLPIKPVAAPVLAQDQASPETTSPSPELVRYSTLNSEHFPQPTQQIKNIVRQYQQPFRGGRPEALRKDGGKVFMKRPDPHEEALMILKGQMTHLAAAPGTQVSREAVALVKPVTSAPRPSMAPTSALPSRSLEPPEELTQTRLHRLINPNFYGYQDAPWKIFLRKEVFYPKDSYSHPVQLDLLFRQILHDTLSEACLRISEDERLRMKALFAQNQLDTQKPLVTESVKRAVVSTARDTWEVYFSRIFPATGSVGTGVQLLAVSHVGIKLLRMVKGGQEAGGQLRVLRAYSFADILFVTMPSQNMLEFNLASEKVILFSARAHQVKTLVDDFILELKKDSDYVVAVRNFLPEDPALLAFHKGDIIHLQPLEPPRVGYSAGCVVRRKVVYLEELRRRGPDFGWRFGTIHGRVGRFPSELVQPAAAPDFLQLPTEPGRGRAAAVAAAVASAAAAQEVGRRREGPPVRARSADHGEDALALPPYTMLEFAQKYFRDPQRRPQDGLRLKSKEPRESRTLEDMLCFTKTPLQESLIELSDSSLSKMATDMFLAVMRFMGDAPLKGQSDLDVLCNLLKLCGDHEVMRDECYCQVVKQITDNTSSKQDSCQRGWRLLYIVTAYHSCSEVLHPHLTRFLQDVSRTPGLPFQGIAKACEQNLQKTLRFGGRLELPSSIELRAMLAGRSSKRQLFLLPGGLERHLKIKTCTVALDVVEEICAEMALTRPEAFNEYVIFVVTNRGQHVCPLSRRAYILDVASEMEQVDGGYMLWFRRVLWDQPLKFENELYVTMHYNQVLPDYLKGLFSSVPASRPSEQLLQQVSKLASLQHRAKDHFYLPSVREVQEYIPAQLYRTTAGSTWLNLVSQHRQQTQALSPHQARAQFLGLLSALPMFGSSFFFIQSCSNIAVPAPCILAINHNGLNFLSTETHELMVKFPLKEIQSTRTQRPTANSSYPYVEIALGDVAAQRTLQLQLEQGLELCRVVAVHVENLLSAHEKRLTLPPSEITLL,mutated_sequence,1.0,3530.0,NP_057323.3.a2m,NP_057323.3.npy,ClinVar +NP_057353.1,NP_057353.1.csv,MPQLSGGGGGGGGDPELCATDEMIPFKDEGDPQKEKIFAEISHPEEEGDLADIKSSLVNESEIIPASNGHEVARQAQTSQEPYHDKAREHPDDGKHPDGGLYNKGPSYSSYSGYIMMPNMNNDPYMSNGSLSPPIPRTSNKVPVVQPSHAVHPLTPLITYSDEHFSPGSHPSHIPSDVNSKQGMSRHPPAPDIPTFYPLSPGGVGQITPPLGWQGQPVYPITGGFRQPYPSSLSVDTSMSRFSHHMIPGPPGPHTTGIPHPAIVTPQVKQEHPHTDSDLMHVKPQHEQRKEQEPKRPHIKKPLNAFMLYMKEMRANVVAECTLKESAAINQILGRRWHALSREEQAKYYELARKERQLHMQLYPGWSARDNYGKKKKRKREKLQESASGTGPRMTAAYI,mutated_sequence,1.0,399.0,NP_057353.1.a2m,NP_057353.1.npy,ClinVar +NP_057425.3,NP_057425.3.csv,MTSEEMTASVLIPVTQRKVVSAQSAADESSEKVSDINISKAHTVRRSGETSHTISQLNKLKEEPSGSNLPKILSIAREKIVSDENSNEKCWEKIMPDSAKNLNINCNNILRNHQHGLPQRQFYEMYNSVAEEDLCLETGIPSPLERKVFPGIQLELDRPSMGISPLGNQSVIIETGRAHPDSRRAVFHFHYEVDRRMSDTFCTLSENLILDDCGNCVPLPGGEEKQKKNYVAYTCKLMELAKNCDNKNEQLQCDHCDTLNDKYFCFEGSCEKVDMVYSGDSFCRKDFTDSQAAKTFLSHFEDFPDNCDDVEEDAFKSKKERSTLLVRRFCKNDREVKKSVYTGTRAIVRTLPSGHIGLTAWSYIDQKRNGPLLPCGRVMEPPSTVEIRQDGSQRLSEAQWYPIYNAVRREETENTVGSLLHFLTKLPASETAHGRISVGPCLKQCVRDTVCEYRATLQRTSISQYITGSLLEATTSLGARSGLLSTFGGSTGRMMLKERQPGPSVANSNALPSSSAGISKELIDLQPLIQFPEEVASILMEQEQTIYRRVLPVDYLCFLTRDLGTPECQSSLPCLKASISASILTTQNGEHNALEDLVMRFNEVSSWVTWLILTAGSMEEKREVFSYLVHVAKCCWNMGNYNAVMEFLAGLRSRKVLKMWQFMDQSDIETMRSLKDAMAQHESSCEYRKVVTRALHIPGCKVVPFCGVFLKELCEVLDGASGLMKLCPRYNSQEETLEFVADYSGQDNFLQRVGQNGLKNSEKESTVNSIFQVIRSCNRSLETDEEDSPSEGNSSRKSSLKDKSRWQFIIGDLLDSDNDIFEQSKEYDSHGSEDSQKAFDHGTELIPWYVLSIQADVHQFLLQGATVIHYDQDTHLSARCFLQLQPDNSTLTWVKPTTASPASSKAKLGVLNNTAEPGKFPLLGNAGLSSLTEGVLDLFAVKAVYMGHPGIDIHTVCVQNKLGSMFLSETGVTLLYGLQTTDNRLLHFVAPKHTAKMLFSGLLELTRAVRKMRKFPDQRQQWLRKQYVSLYQEDGRYEGPTLAHAVELFGGRRWSARNPSPGTSAKNAEKPNMQRNNTLGISTTKKKKKILMRGESGEVTDDEMATRKAKMHKECRSRSGSDPQDINEQEESEVNAIANPPNPLPSRRAHSLTTAGSPNLAAGTSSPIRPVSSPVLSSSNKSPSSAWSSSSWHGRIKGGMKGFQSFMVSDSNMSFVEFVELFKSFSVRSRKDLKDLFDVYAVPCNRSGSESAPLYTNLTIDENTSDLQPDLDLLTRNVSDLGLFIKSKQQLSDNQRQISDAIAAASIVTNGTGIESTSLGIFGVGILQLNDFLVNCQGEHCTYDEILSIIQKFEPSISMCHQGLMSFEGFARFLMDKENFASKNDESQENIKELQLPLSYYYIESSHNTYLTGHQLKGESSVELYSQVLLQGCRSVELDCWDGDDGMPIIYHGHTLTTKIPFKEVVEAIDRSAFINSDLPIIISIENHCSLPQQRKMAEIFKTVFGEKLVTKFLFETDFSDDPMLPSPDQLRKKVLLKNKKLKAHQTPVDILKQKAHQLASMQVQAYNGGNANPRPANNEEEEDEEDEYDYDYESLSDDNILEDRPENKSCNDKLQFEYNEEIPKRIKKADNSACNKGKVYDMELGEEFYLDQNKKESRQIAPELSDLVIYCQAVKFPGLSTLNASGSSRGKERKSRKSIFGNNPGRMSPGETASFNKTSGKSSCEGIRQTWEESSSPLNPTTSLSAIIRTPKCYHISSLNENAAKRLCRRYSQKLTQHTACQLLRTYPAATRIDSSNPNPLMFWLHGIQLVALNYQTDDLPLHLNAAMFEANGGCGYVLKPPVLWDKNCPMYQKFSPLERDLDSMDPAVYSLTIVSGQNVCPSNSMGSPCIEVDVLGMPLDSCHFRTKPIHRNTLNPMWNEQFLFHVHFEDLVFLRFAVVENNSSAVTAQRIIPLKALKRGYRHLQLRNLHNEVLEISSLFINSRRMEENSSGNTMSASSMFNTEERKCLQTHRVTVHGVPGPEPFTVFTINGGTKAKQLLQQILTNEQDIKPVTTDYFLMEEKYFISKEKNECRKQPFQRAIGPEEEIMQILSSWFPEEGYMGRIVLKTQQENLEEKNIVQDDKEVILSSEEESFFVQVHDVSPEQPRTVIKAPRVSTAQDVIQQTLCKAKYSYSILSNPNPSDYVLLEEVVKDTTNKKTTTPKSSQRVLLDQECVFQAQSKWKGAGKFILKLKEQVQASREDKKKGISFASELKKLTKSTKQPRGLTSPSQLLTSESIQTKEEKPVGGLSSSDTMDYRQ,mutated_sequence,1.0,2302.0,NP_057425.3.a2m,NP_057425.3.npy,ClinVar +NP_057490.2,NP_057490.2.csv,MADEATRRVVSEIPVLKTNAGPRDRELWVQRLKEEYQSLIRYVENNKNADNDWFRLESNKEGTRWFGKCWYIHDLLKYEFDIEFDIPITYPTTAPEIAVPELDGKTAKMYRGGKICLTDHFKPLWARNVPKFGLAHLMALGLGPWLAVEIPDLIQKGVIQHKEKCNQ,mutated_sequence,1.0,167.0,NP_057490.2.a2m,NP_057490.2.npy,ClinVar +NP_057501.2,NP_057501.2.csv,MSGSLGRAAAALLRWGRGAGGGGLWGPGVRAAGSGAGGGGSAEQLDALVKKDKVVVFLKGTPEQPQCGFSNAVVQILRLHGVRDYAAYNVLDDPELRQGIKDYSNWPTIPQVYLNGEFVGGCDILLQMHQNGDLVEELKKLGIHSALLDEKKDQDSK,mutated_sequence,1.0,157.0,NP_057501.2.a2m,NP_057501.2.npy,ClinVar +NP_057688.3,NP_057688.3.csv,MADAAASPVGKRLLLLFADTAASASASAPAAAAASGDPGPALRTRAWRAGTVRAMSGAVPQDLAIFVEFDGCNWKQHSWVKVHAEEVIVLLLEGSLVWAPREDPVLLQGIRVSIAQWPALTFTPLVDKLGLGSVVPVEYLLDRELRFLSDANGLHLFQMGTDSQNQILLEHAALRETVNALISDQKLQEIFSRGPYSVQGHRVKIYQPEGEEGWLYGVVSHQDSITRLMEVSVTESGEIKSVDPRLIHVMLMDNSAPQSEGGTLKAVKSSKGKKKRESIEGKDGRRRKSASDSGCDPASKKLKGDRGEVDSNGSDGGEASRGPWKGGNASGEPGLDQRAKQPPSTFVPQINRNIRFATYTKENGRTLVVQDEPVGGDTPASFTPYSTATGQTPLAPEVGGAENKEAGKTLEQVGQGIVASAAVVTTASSTPNTVRISDTGLAAGTVPEKQKGSRSQASGENSRNSILASSGFGAPLPSSSQPLTFGSGRSQSNGVLATENKPLGFSFGCSSAQEAQKDTDLSKNLFFQCMSQTLPTSNYFTTVSESLADDSSSRDSFKQSLESLSSGLCKGRSVLGTDTKPGSKAGSSVDRKVPAESMPTLTPAFPRSLLNARTPENHENLFLQPPKLSREEPSNPFLAFVEKVEHSPFSSFASQASGSSSSATTVTSKVAPSWPESHSSADSASLAKKKPLFITTDSSKLVSGVLGSALTSGGPSLSAMGNGRSSSPTSSLTQPIEMPTLSSSPTEERPTVGPGQQDNPLLKTFSNVFGRHSGGFLSSPADFSQENKAPFEAVKRFSLDERSLACRQDSDSSTNSDLSDLSDSEEQLQAKTGLKGIPEHLMGKLGPNGERSAELLLGKSKGKQAPKGRPRTAPLKVGQSVLKDVSKVKKLKQSGEPFLQDGSCINVAPHLHKCRECRLERYRKFKEQEQDDSTVACRFFHFRRLIFTRKGVLRVEGFLSPQQSDPDAMNLWIPSSSLAEGIDLETSKYILANVGDQFCQLVMSEKEAMMMVEPHQKVAWKRAVRGVREMCDVCETTLFNIHWVCRKCGFGVCLDCYRLRKSRPRSETEEMGDEEVFSWLKCAKGQSHEPENLMPTQIIPGTALYNIGDMVHAARGKWGIKANCPCISRQNKSVLRPAVTNGMSQLPSINPSASSGNETTFSGGGGPAPVTTPEPDHVPKADSTDIRSEEPLKTDSSASNSNSELKAIRPPCPDTAPPSSALHWLADLATQKAKEETKEAGSLRSVLNKESHSPFGLDSFNSTAKVSPLTPKLFNSLLLGPTASNNKTEGSSLRDLLHSGPGKLPQTPLDTGIPFPPVFSTSSAGVKSKASLPNFLDHIIASVVENKKTSDASKRACNLTDTQKEVKEMVMGLNVLDPHTSHSWLCDGRLLCLHDPSNKNNWKIFRECWKQGQPVLVSGVHKKLKSELWKPEAFSQEFGDQDVDLVNCRNCAIISDVKVRDFWDGFEIICKRLRSEDGQPMVLKLKDWPPGEDFRDMMPTRFEDLMENLPLPEYTKRDGRLNLASRLPSYFVRPDLGPKMYNAYGLITAEDRRVGTTNLHLDVSDAVNVMVYVGIPIGEGAHDEEVLKTIDEGDADEVTKQRIHDGKEKPGALWHIYAAKDAEKIRELLRKVGEEQGQENPPDHDPIHDQSWYLDQTLRKRLYEEYGVQGWAIVQFLGDAVFIPAGAPHQVHNLYSCIKVAEDFVSPEHVKHCFRLTQEFRHLSNTHTNHEDKLQVKNIIYHAVKDAVGTLKAHESKLARS,mutated_sequence,1.0,1761.0,NP_057688.3.a2m,NP_057688.3.npy,ClinVar +NP_057737.2,NP_057737.2.csv,MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF,mutated_sequence,1.0,800.0,NP_057737.2.a2m,NP_057737.2.npy,ClinVar +NP_059830.1,NP_059830.1.csv,MPYEIKKVFASLPQVERGVSKIIGGDPKGNNFLYTNGKCVILRNIDNPALADIYTEHAHQVVVAKYAPSGFYIASGDVSGKLRIWDTTQKEHLLKYEYQPFAGKIKDIAWTEDSKRIAVVGEGREKFGAVFLWDSGSSVGEITGHNKVINSVDIKQSRPYRLATGSDDNCAAFFEGPPFKFKFTIGDHSRFVNCVRFSPDGNRFATASADGQIYIYDGKTGEKVCALGGSKAHDGGIYAISWSPDSTHLLSASGDKTSKIWDVSVNSVVSTFPMGSTVLDQQLGCLWQKDHLLSVSLSGYINYLDRNNPSKPLHVIKGHSKSIQCLTVHKNGGKSYIYSGSHDGHINYWDSETGENDSFAGKGHTNQVSRMTVDESGQLISCSMDDTVRYTSLMLRDYSGQGVVKLDVQPKCVAVGPGGYAVVVCIGQIVLLKDQRKCFSIDNPGYEPEVVAVHPGGDTVAIGGVDGNVRLYSILGTTLKDEGKLLEAKGPVTDVAYSHDGAFLAVCDASKVVTVFSVADGYSENNVFYGHHAKIVCLAWSPDNEHFASGGMDMMVYVWTLSDPETRVKIQDAHRLHHVSSLAWLDEHTLVTTSHDASVKEWTITY,mutated_sequence,1.0,606.0,NP_059830.1.a2m,NP_059830.1.npy,ClinVar +NP_060083.1,NP_060083.1.csv,MALSVPGYSPGFRKPPEVVRLRRKRARSRGAAASPPRELTEPAARRAALVAGLPLRPFPAAGGRGGGSGGGPAAARRNPFARLDNRPRVAAEPPDGPAREQPEAPVPFLDSNQENDLLWEEKFPERTTVTELPQTSHVSFSEPDIPSSKSTELPVDWSIKTRLLFTSSQPFTWADHLKAQEEAQGLVQHCRATEVTLPKSIQDPKLSSELRCTFQQSLIYWLHPALSWLPLFPRIGADRKMAGKTSPWSNDATLQHVLMSDWSVSFTSLYNLLKTKLCPYFYVCTYQFTVLFRAAGLAGSDLITALISPTTRGLREAMRNEGIEFSLPLIKESGHKKETASGTSLGYGEEQAISDEDEEESFSWLEEMGVQDKIKKPDILSIKLRKEKHEVQMDHRPESVVLVKGINTFTLLNFLINSKSLVATSGPQAGLPPTLLSPVAFRGATMQMLKARSVNVKTQALSGYRDQFSLEITGPIMPHSLHSLTMLLKSSQSGSFSAVLYPHEPTAVFNICLQMDKVLDMEVVHKELTNCGLHPNTLEQLSQIPLLGKSSLRNVVLRDYIYNWRS,mutated_sequence,1.0,566.0,NP_060083.1.a2m,NP_060083.1.npy,ClinVar +NP_060087.3,NP_060087.3.csv,MPPLLAPLLCLALLPALAARGPRCSQPGETCLNGGKCEAANGTEACVCGGAFVGPRCQDPNPCLSTPCKNAGTCHVVDRRGVADYACSCALGFSGPLCLTPLDNACLTNPCRNGGTCDLLTLTEYKCRCPPGWSGKSCQQADPCASNPCANGGQCLPFEASYICHCPPSFHGPTCRQDVNECGQKPGLCRHGGTCHNEVGSYRCVCRATHTGPNCERPYVPCSPSPCQNGGTCRPTGDVTHECACLPGFTGQNCEENIDDCPGNNCKNGGACVDGVNTYNCRCPPEWTGQYCTEDVDECQLMPNACQNGGTCHNTHGGYNCVCVNGWTGEDCSENIDDCASAACFHGATCHDRVASFYCECPHGRTGLLCHLNDACISNPCNEGSNCDTNPVNGKAICTCPSGYTGPACSQDVDECSLGANPCEHAGKCINTLGSFECQCLQGYTGPRCEIDVNECVSNPCQNDATCLDQIGEFQCICMPGYEGVHCEVNTDECASSPCLHNGRCLDKINEFQCECPTGFTGHLCQYDVDECASTPCKNGAKCLDGPNTYTCVCTEGYTGTHCEVDIDECDPDPCHYGSCKDGVATFTCLCRPGYTGHHCETNINECSSQPCRHGGTCQDRDNAYLCFCLKGTTGPNCEINLDDCASSPCDSGTCLDKIDGYECACEPGYTGSMCNINIDECAGNPCHNGGTCEDGINGFTCRCPEGYHDPTCLSEVNECNSNPCVHGACRDSLNGYKCDCDPGWSGTNCDINNNECESNPCVNGGTCKDMTSGYVCTCREGFSGPNCQTNINECASNPCLNQGTCIDDVAGYKCNCLLPYTGATCEVVLAPCAPSPCRNGGECRQSEDYESFSCVCPTGWQGQTCEVDINECVLSPCRHGASCQNTHGGYRCHCQAGYSGRNCETDIDDCRPNPCHNGGSCTDGINTAFCDCLPGFRGTFCEEDINECASDPCRNGANCTDCVDSYTCTCPAGFSGIHCENNTPDCTESSCFNGGTCVDGINSFTCLCPPGFTGSYCQHDVNECDSQPCLHGGTCQDGCGSYRCTCPQGYTGPNCQNLVHWCDSSPCKNGGKCWQTHTQYRCECPSGWTGLYCDVPSVSCEVAAQRQGVDVARLCQHGGLCVDAGNTHHCRCQAGYTGSYCEDLVDECSPSPCQNGATCTDYLGGYSCKCVAGYHGVNCSEEIDECLSHPCQNGGTCLDLPNTYKCSCPRGTQGVHCEINVDDCNPPVDPVSRSPKCFNNGTCVDQVGGYSCTCPPGFVGERCEGDVNECLSNPCDARGTQNCVQRVNDFHCECRAGHTGRRCESVINGCKGKPCKNGGTCAVASNTARGFICKCPAGFEGATCENDARTCGSLRCLNGGTCISGPRSPTCLCLGPFTGPECQFPASSPCLGGNPCYNQGTCEPTSESPFYRCLCPAKFNGLLCHILDYSFGGGAGRDIPPPLIEEACELPECQEDAGNKVCSLQCNNHACGWDGGDCSLNFNDPWKNCTQSLQCWKYFSDGHCDSQCNSAGCLFDGFDCQRAEGQCNPLYDQYCKDHFSDGHCDQGCNSAECEWDGLDCAEHVPERLAAGTLVVVVLMPPEQLRNSSFHFLRELSRVLHTNVVFKRDAHGQQMIFPYYGREEELRKHPIKRAAEGWAAPDALLGQVKASLLPGGSEGGRRRRELDPMDVRGSIVYLEIDNRQCVQASSQCFQSATDVAAFLGALASLGSLNIPYKIEAVQSETVEPPPPAQLHFMYVAAAAFVLLFFVGCGVLLSRKRRRQHGQLWFPEGFKVSEASKKKRREPLGEDSVGLKPLKNASDGALMDDNQNEWGDEDLETKKFRFEEPVVLPDLDDQTDHRQWTQQHLDAADLRMSAMAPTPPQGEVDADCMDVNVRGPDGFTPLMIASCSGGGLETGNSEEEEDAPAVISDFIYQGASLHNQTDRTGETALHLAARYSRSDAAKRLLEASADANIQDNMGRTPLHAAVSADAQGVFQILIRNRATDLDARMHDGTTPLILAARLAVEGMLEDLINSHADVNAVDDLGKSALHWAAAVNNVDAAVVLLKNGANKDMQNNREETPLFLAAREGSYETAKVLLDHFANRDITDHMDRLPRDIAQERMHHDIVRLLDEYNLVRSPQLHGAPLGGTPTLSPPLCSPNGYLGSLKPGVQGKKVRKPSSKGLACGSKEAKDLKARRKKSQDGKGCLLDSSGMLSPVDSLESPHGYLSDVASPPLLPSPFQQSPSVPLNHLPGMPDTHLGIGHLNVAAKPEMAALGGGGRLAFETGPPRLSHLPVASGTSTVLGSSSGGALNFTVGGSTSLNGQCEWLSRLQSGMVPNQYNPLRGSVAPGPLSTQAPSLQHGMVGPLHSSLAASALSQMMSYQGLPSTRLATQPHLVQTQQVQPQNLQMQQQNLQPANIQQQQSLQPPPPPPQPHLGVSSAASGHLGRSFLSGEPSQADVQPLGPSSLAVHTILPQESPALPTSLPSSLVPPVTAAQFLTPPSQHSYSSPVDNTPSHQLQVPEHPFLTPSPESPDQWSSSSPHSNVSDWSEGVSSPPTSMQSQIARIPEAFK,mutated_sequence,1.0,2555.0,NP_060087.3.a2m,NP_060087.3.npy,ClinVar +NP_060119.3,NP_060119.3.csv,MIGCGACEPKVKMAGGQAAAALPTWKMAARRSLSARGRGILQAAAGRLLPLLLLSCCCGAGGCAAVGENEETVIIGLRLEDTNDVSFMEGGALRVSERTRVKLRVYGQNINNETWSRIAFTEHERRRHSPGERGLGGPAPPEPDSGPQRCGIRTSDIIILPHIILNRRTSGIIEIEIKPLRKMEKSKSYYLCTSLSTPALGAGGSGSTGGAVGGKGGSGVAGLPPPPWAETTWIYHDGEDTKMIVGEEKKFLLPFWLQVIFISLLLCLSGMFSGLNLGLMALDPMELRIVQNCGTEKEKNYAKRIEPVRRQGNYLLCSLLLGNVLVNTTLTILLDDIAGSGLVAVVVSTIGIVIFGEIVPQAICSRHGLAVGANTIFLTKFFMMMTFPASYPVSKLLDCVLGQEIGTVYNREKLLEMLRVTDPYNDLVKEELNIIQGALELRTKTVEDVMTPLRDCFMITGEAILDFNTMSEIMESGYTRIPVFEGERSNIVDLLFVKDLAFVDPDDCTPLKTITKFYNHPLHFVFNDTKLDAMLEEFKKGKSHLAIVQRVNNEGEGDPFYEVLGIVTLEDVIEEIIKSEILDETDLYTDNRTKKKVAHRERKQDFSAFKQTDSEMKVKISPQLLLAMHRFLATEVEAFSPSQMSEKILLRLLKHPNVIQELKYDEKNKKAPEYYLYQRNKPVDYFVLILQGKVEVEAGKEGMKFEASAFSYYGVMALTASPVPLSLSRTFVVSRTELLAAGSPGENKSPPRPCGLNHSDSLSRSDRIDAVTPTLGSSNNQLNSSLLQVYIPDYSVRALSDLQFVKISRQQYQNALMASRMDKTPQSSDSENTKIELTLTELHDGLPDETANLLNEQNCVTHSKANHSLHNEGAI,mutated_sequence,1.0,875.0,NP_060119.3.a2m,NP_060119.3.npy,ClinVar +NP_060137.2,NP_060137.2.csv,MQKIKSLMTRQGLKSPQESLSDLGAIESLRVPGKEEFRELREQPSDPQAEQELINSIEQVYFSVDSFDIVKYELEKLPPVLNLQELEAYRDKLKQQQAAVSKKVADLILEKQPAYVKELERVTSLQTGLQLAAVICTNGRRHLNIAKEGFTQASLGLLANQRKRQLLIGLLKSLRTIKTLQRTDVRLSEMLEEEDYPGAIQLCLECQKAASTFKHYSCISELNSKLQDTLEQIEEQLDVALSKICKNFDINHYTKVQQAYRLLGKTQTAMDQLHMHFTQAIHNTVFQVVLGYVELCAGNTDTKFQKLQYKDLCTHVTPDSYIPCLADLCKALWEVMLSYYRTMEWHEKHDNEDTASASEGSNMIGTEETNFDRGYIKKKLEHGLTRIWQDVQLKVKTYLLGTDLSIFKYDDFIFVLDIISRLMQVGEEFCGSKSEVLQESIRKQSVNYFKNYHRTRLDELRMFLENETWELCPVKSNFSILQLHEFKFMEQSRSPSVSPSKQPVSTSSKTVTLFEQYCSGGNPFEIQANHKDEETEDVLASNGYESDEQEKSAYQEYDSDSDVPEELKRDYVDEQTGDGPVKSVSRETLKSRKKSDYSLNKVNAPILTNTTLNVIRLVGKYMQMMNILKPIAFDVIHFMSQLFDYYLYAIYTFFGRNDSLESTGLGLSSSRLRTTLNRIQESLIDLEVSADPTATLTAAEERKEKVPSPHLSHLVVLTSGDTLYGLAERVVATESLVFLAEQFEFLQPHLDAVMPAVKKPFLQQFYSQTVSTASELRKPIYWIVAGKALDYEQMLLLMANVKWDVKEIMSQHNIYVDALLKEFEQFNRRLNEVSKRVRIPLPVSNILWEHCIRLANRTIVEGYANVKKCSNEGRALMQLDFQQFLMKLEKLTDIRPIPDKEFVETYIKAYYLTENDMERWIKEHREYSTKQLTNLVNVCLGSHINKKARQKLLAAIDDIDRPKR,mutated_sequence,1.0,964.0,NP_060137.2.a2m,NP_060137.2.npy,ClinVar +NP_060141.3,NP_060141.3.csv,MLSSTDFTFASWELVVRVDHPNEEQQKDVTLRVSGDLHVGGVMLKLVEQINISQDWSDFALWWEQKHCWLLKTHWTLDKYGVQADAKLLFTPQHKMLRLRLPNLKMVRLRVSFSAVVFKAVSDICKILNIRRSEELSLLKPSGDYFKKKKKKDKNNKEPIIEDILNLESSPTASGSSVSPGLYSKTMTPIYDPINGTPASSTMTWFSDSPLTEQNCSILAFSQPPQSPEALADMYQPRSLVDKAKLNAGWLDSSRSLMEQGIQEDEQLLLRFKYYSFFDLNPKYDAVRINQLYEQARWAILLEEIDCTEEEMLIFAALQYHISKLSLSAETQDFAGESEVDEIEAALSNLEVTLEGGKADSLLEDITDIPKLADNLKLFRPKKLLPKAFKQYWFIFKDTSIAYFKNKELEQGEPLEKLNLRGCEVVPDVNVAGRKFGIKLLIPVADGMNEMYLRCDHENQYAQWMAACMLASKGKTMADSSYQPEVLNILSFLRMKNRNSASQVASSLENMDMNPECFVSPRCAKRHKSKQLAARILEAHQNVAQMPLVEAKLRFIQAWQSLPEFGLTYYLVRFKGSKKDDILGVSYNRLIKIDAATGIPVTTWRFTNIKQWNVNWETRQVVIEFDQNVFTAFTCLSADCKIVHEYIGGYIFLSTRSKDQNETLDEDLFHKLTGGQD,mutated_sequence,1.0,677.0,NP_060141.3.a2m,NP_060141.3.npy,ClinVar +NP_060206.2,NP_060206.2.csv,MAAPAQQTTQPGGGKRKGKAQYVLAKRARRCDAGGPRQLEPGLQGILITCNMNERKCVEEAYSLLNEYGDDMYGPEKFTDKDQQPSGSEGEDDDAEAALKKEVGDIKASTEMRLRRFQSVESGANNVVFIRTLGIEPEKLVHHILQDMYKTKKKKTRVILRMLPISGTCKAFLEDMKKYAETFLEPWFKAPNKGTFQIVYKSRNNSHVNREEVIRELAGIVCTLNSENKVDLTNPQYTVVVEIIKAVCCLSVVKDYMLFRKYNLQEVVKSPKDPSQLNSKQGNGKEAKLESADKSDQNNTAEGKNNQQVPENTEELGQTKPTSNPQVVNEGGAKPELASQATEGSKSNENDFS,mutated_sequence,1.0,353.0,NP_060206.2.a2m,NP_060206.2.npy,ClinVar +NP_060227.2,NP_060227.2.csv,MMDSENKPENDEDEKINKEAQDLTKLSSHNEDGGPVSDVIASFPENSMGKRGFSESSNSDSVVIGEDRNKHASKRRKLDEAEPLKSGKQGICRLETSESSVTEGGIALDETGKETFLSDCTVGGTCLPNALSPSCNFSTIDVVSLKTDTEKTSAQEMVSLDLERESPFPPKEISVSCTIGNVDTVLKCSICGHLFSSCSDLEKHAESHMQQPKEHTCCHCSHKAESSSALHMHIKQAHGPQKVFSCDLCGFQCSEENLLNAHYLGKTHLRRQNLAARGGFVQILTKQPFPKKSRTMATKNVHSKPRTSKSIAKNSDSKGLRNVGSTFKDFRGSISKQSGSSSELLVEMMPSRNTLSQEVEIVEEHVTSLGLAQNPENQSRKLDTLVTSEGLLEKLESTKNTLQAAHGNSVTSRPRPERNILVLGNSFRRRSSTFTLKGQAKKRFNLLGIKRGTSETQRMYMKHLRTQMKTHDAESVLKHLEACSSVQRVCVTTSETQEAEQGQGSARPPDSGLHSLTVKPASGSQTLCACTDCGQVATNRTDLEIHVKRCHAREMKFYCRTCDFSSMSRRDLDEHLHSNQHQQTASVLSCQCCSFISLDEINLRDHMKEKHNMHFLCTPCNLFFLSEKDVEEHKATEKHINSLVQPKTLQSSNSDLVLQTLPLSTLESENAKESMDDSGKASQEEPLKSRVSHGNEVRHSSKPQFQCKKCFYKTRSSTVLTRHIKLRHGQDYHFLCKACNLYSLSKEGMEKHIKRSKHLENAKKNNIGLSFEECIERVCIGANDKKEEFDVSGNGRIEGHIGVQLQEHSYLEKGMLASEELSQSGGSTKDDELASTTTPKRGRPKGNISRTCSHCGLLASSITNLTVHIRRKHSHQYSYLCKVCKYYTVTKGDMERHCATKKHKGRVEIEASGKHSSDIIVGPEGGSLEAGKKNAGSAVTMSDEHANKPAESPTSVLEKPDRGNSIEAEVENVFHSLDGEVNSHLLDKKEQISSEPEDFAQPGDVYSQRDVTGTGENKCLHCEFSAHSSASLELHVKRKHTKEFEFYCMACDYYAVTRREMTRHAATEKHKMKRQSYLNSANVEAGSADMSKNIIMPEEEHQQNSEEFQIISGQPSDTLKSRNAADCSILNENTNLDMSKVLCAADSVEVETEEESNFNEDHSFCETFQQAPVKDKVRKPEEMMSLTMSSNYGSPSRFQNENSGSSALNCETAKKNHEISNDAGELRVHCEGEGGNAGDGGGVVPHRHLCPVTLDGERSAESPVLVVTRITREQGNLESGGQNRVARGHGLEDLKGVQEDPVLGNKEILMNSQHETEFILEEDGPASDSTVESSDVYETIISIDDKGQAMYSFGRFDSSIIRIKNPEDGELIDQSEEGLIATGVRISELPLKDCAQGVKKKKSEGSSIGESTRIRCDDCGFLADGLSGLNVHIAMKHPTKEKHFHCLLCGKSFYTESNLHQHLASAGHMRNEQASVEELPEGGATFKCVKCTEPFDSEQNLFLHIKGQHEELLREVNKYIVEDTEQINREREENQGNVCKYCGKMCRSSNSMAFLAHIRTHTGSKPFKCKICHFATAQLGDARNHVKRHLGMREYKCHVCGVAFVMKKHLNTHLLGKHGVGTPKERKFTCHLCDRSFTEKWALNNHMKLHTGEKPFKCTWPTCHYSFLTASAMKDHYRTHTGEKSFLCDLCGFAGGTRHALTKHRRQHTGEKPFKCDECNFASTTQSHLTRHKRVHTGEKPYRCPWCDYRSNCAENIRKHILHTGKHEGVKMYNCPKCDYGTNVPVEFRNHLKEQHPDIENPDLAYLHAGIVSKSYECRLKGQGATFVETDSPFTAAALAEEPLVKEKPLRSSRRPAPPPEQVQQVIIFQGYDGEFALDPSVEETAAATLQTLAMAGQVARVVHITEDGQVIATSQSGAHVGSVVPGPILPEQLADGATQVVVVGGSMEGHGMDESLSPGGAVIQQVTKQEILNLSEAGVAPPEASSALDALLCAVTELGEVEGRAGLEEQGRPGAKDVLIQLPGQEVSHVAADPEAPEIQMFPQAQESPAAVEVLTQVVHPSAAMASQERAQVAFKKMVQGVLQFAVCDTAAAGQLVKDGVTQVVVSEEGAVHMVAGEGAQIIMQEAQGEHMDLVESDGEISQIIVTEELVQAMVQESSGGFSEGTTHYILTELPPGVQDEPGLYSHTVLETADSQELLQAGATLGTEAGAPSRAEQLASVVIYTQEGSSAAAAIQSQRESSELQEA,mutated_sequence,1.0,2248.0,NP_060227.2.a2m,NP_060227.2.npy,ClinVar +NP_060247.2,NP_060247.2.csv,MAETVWSTDTGEAVYRSRDPVRNLRLRVHLQRITSSNFLHYQPAAELGKDLIDLATFRPQPTASGHRPEEDEEEEIVIGWQEKLFSQFEVDLYQNETACQSPLDYQYRQEILKLENSGGKKNRRIFTYTDSDRYTNLEEHCQRMTTAASEVPSFLVERMANVRRRRQDRRGMEGGILKSRIVTWEPSEEFVRNNHVINTPLQTMHIMADLGPYKKLGYKKYEHVLCTLKVDSNGVITVKPDFTGLKGPYRIETEGEKQELWKYTIDNVSPHAQPEEEERERRVFKDLYGRHKEYLSSLVGTDFEMTVPGALRLFVNGEVVSAQGYEYDNLYVHFFVELPTAHWSSPAFQQLSGVTQTCTTKSLAMDKVAHFSYPFTFEAFFLHEDESSDALPEWPVLYCEVLSLDFWQRYRVEGYGAVVLPATPGSHTLTVSTWRPVELGTVAELRRFFIGGSLELEDLSYVRIPGSFKGERLSRFGLRTETTGTVTFRLHCLQQSRAFMESSSLQKRMRSVLDRLEGFSQQSSIHNVLEAFRRARRRMQEARESLPQDLVSPSGTLVS,mutated_sequence,1.0,559.0,NP_060247.2.a2m,NP_060247.2.npy,ClinVar +NP_060295.1,NP_060295.1.csv,MAAAAMAAAAGGGAGAARSLSRFRGCLAGALLGDCVGSFYEAHDTVDLTSVLRHVQSLEPDPGTPGSERTEALYYTDDTAMARALVQSLLAKEAFDEVDMAHRFAQEYKKDPDRGYGAGVVTVFKKLLNPKCRDVFEPARAQFNGKGSYGNGGAMRVAGISLAYSSVQDVQKFARLSAQLTHASSLGYNGAILQALAVHLALQGESSSEHFLKQLLGHMEDLEGDAQSVLDARELGMEERPYSSRLKKIGELLDQASVTREEVVSELGNGIAAFESVPTAIYCFLRCMEPDPEIPSAFNSLQRTLIYSISLGGDTDTIATMAGAIAGAYYGMDQVPESWQQSCEGYEETDILAQSLHRVFQKS,mutated_sequence,1.0,363.0,NP_060295.1.a2m,NP_060295.1.npy,ClinVar +NP_060334.2,NP_060334.2.csv,MSAEAADREAATSSRPCTPPQTCWFEFLLEESLLEKHLRKPCPDPAPVQLIVQFLEQASKPSVNEQNQVQPPPDNKRNRILKLLALKVAAHLKWDLDILEKSLSVPVLNMLLNELLCISKVPPGTKHVDMDLATLPPTTAMAVLLYNRWAIRTIVQSSFPVKQAKPGPPQLSVMNQMQQEKELTENILKVLKEQAADSILVLEAALKLNKDLYVHTMRTLDLLAMEPGMVNGETESSTAGLKVKTEEMQCQVCYDLGAAYFQQGSTNSAVYENAREKFFRTKELIAEIGSLSLHCTIDEKRLAGYCQACDVLVPSSDSTSQQLTPYSQVHICLRSGNYQEVIQIFIEDNLTLSLPVQFRQSVLRELFKKAQQGNEALDEICFKVCACNTVRDILEGRTISVQFNQLFLRPNKEKIDFLLEVCSRSVNLEKASESLKGNMAAFLKNVCLGLEDLQYVFMISSHELFITLLKDEERKLLVDQMRKRSPRVNLCIKPVTSFYDIPASASVNIGQLEHQLILSVDPWRIRQILIELHGMTSERQFWTVSNKWEVPSVYSGVILGIKDNLTRDLVYILMAKGLHCSTVKDFSHAKQLFAACLELVTEFSPKLRQVMLNEMLLLDIHTHEAGTGQAGERPPSDLISRVRGYLEMRLPDIPLRQVIAEECVAFMLNWRENEYLTLQVPAFLLQSNPYVKLGQLLAATCKELPGPKESRRTAKDLWEVVVQICSVSSQHKRGNDGRVSLIKQRESTLGIMYRSELLSFIKKLREPLVLTIILSLFVKLHNVREDIVNDITAEHISIWPSSIPNLQSVDFEAVAITVKELVRYTLSINPNNHSWLIIQADIYFATNQYSAALHYYLQAGAVCSDFFNKAVPPDVYTDQVIKRMIKCCSLLNCHTQVAILCQFLREIDYKTAFKSLQEQNSHDAMDSYYDYIWDVTILEYLTYLHHKRGETDKRQIAIKAIGQTELNASNPEEVLQLAAQRRKKKFLQAMAKLYF,mutated_sequence,1.0,995.0,NP_060334.2.a2m,NP_060334.2.npy,ClinVar +NP_060352.1,NP_060352.1.csv,MEATRRRQHLGATGGPGAQLGASFLQARHGSVSADEAARTAPFHLDLWFYFTLQNWVLDFGRPIAMLVFPLEWFPLNKPSVGDYFHMAYNVITPFLLLKLIERSPRTLPRSITYVSIIIFIMGASIHLVGDSVNHRLLFSGYQHHLSVRENPIIKNLKPETLIDSFELLYYYDEYLGHCMWYIPFFLILFMYFSGCFTASKAESLIPGPALLLVAPSGLYYWYLVTEGQIFILFIFTFFAMLALVLHQKRKRLFLDSNGLFLFSSFALTLLLVALWVAWLWNDPVLRKKYPGVIYVPEPWAFYTLHVSSRH,mutated_sequence,1.0,311.0,NP_060352.1.a2m,NP_060352.1.npy,ClinVar +NP_060387.2,NP_060387.2.csv,MDWKEVLRRRLATPNTCPNKKKSEQELKDEEMDLFTKYYSEWKGGRKNTNEFYKTIPRFYYRLPAEDEVLLQKLREESRAVFLQRKSRELLDNEELQNLWFLLDKHQTPPMIGEEAMINYENFLKVGEKAGAKCKQFFTAKVFAKLLHTDSYGRISIMQFFNYVMRKVWLHQTRIGLSLYDVAGQGYLRESDLENYILELIPTLPQLDGLEKSFYSFYVCTAVRKFFFFLDPLRTGKIKIQDILACSFLDDLLELRDEELSKESQETNWFSAPSALRVYGQYLNLDKDHNGMLSKEELSRYGTATMTNVFLDRVFQECLTYDGEMDYKTYLDFVLALENRKEPAALQYIFKLLDIENKGYLNVFSLNYFFRAIQELMKIHGQDPVSFQDVKDEIFDMVKPKDPLKISLQDLINSNQGDTVTTILIDLNGFWTYENREALVANDSENSADLDDT,mutated_sequence,1.0,453.0,NP_060387.2.a2m,NP_060387.2.npy,ClinVar +NP_060416.1,NP_060416.1.csv,MRLFLWNAVLTLFVTSLIGALIPEPEVKIEVLQKPFICHRKTKGGDLMLVHYEGYLEKDGSLFHSTHKHNNGQPIWFTLGILEALKGWDQGLKGMCVGEKRKLIIPPALGYGKEGKGKIPPESTLIFNIDLLEIRNGPRSHESFQEMDLNDDWKLSKDEVKAYLKKEFEKHGAVVNESHHDALVEDIFDKEDEDKDGFISAREFTYKHDEL,mutated_sequence,1.0,211.0,NP_060416.1.a2m,NP_060416.1.npy,ClinVar +NP_060420.2,NP_060420.2.csv,MAEPGGAAGRSHPEDGSASEGEKEGNNESHMVSPPEKDDGQKGEEAVGSTEHPEEVTTQAEAAIEEGEVETEGEAAVEGEEEAVSYGDAESEEEYYYTETSSPEGQISAADTTYPYFSPPQELPGEEAYDSVSGEAGLQGFQQEATGPPESRERRVTSPEPSHGVLGPSEQMGQVTSGPAVGRLTGSTEEPQGQVLPMGVQHRFRLSHGSDIESSDLEEFVSQEPVIPPGVPDAHPREGDLPVFQDQIQQPSTEEGAMAERVESEGSDEEAEDEGSQLVVLDPDHPLMVRFQAALKNYLNRQIEKLKLDLQELVVATKQSRAQRQELGVNLYEVQQHLVHLQKLLEKSHDRHAMASSERRQKEEELQAARALYTKTCAAANEERKKLAALQTEMENLALHLFYMQNIDQDMRDDIRVMTQVVKKAETERIRAEIEKKKQDLYVDQLTTRAQQLEEDIALFEAQYLAQAEDTRILRKAVSEACTEIDAISVEKRRIMQQWASSLVGMKHRDEAHRAVLEALRGCQHQAKSTDGEIEAYKKSIMKEEEKNEKLASILNRTETEATLLQKLTTQCLTKQVALQSQFNTYRLTLQDTEDALSQDQLEQMILTEELQAIRQAIQGELELRRKTDAAIREKLQEHMTSNKTTKYFNQLILRLQKEKTNMMTHLSKINGDIAQTTLDITHTSSRLDAHQKTLVELDQDVKKVNELITNSQSEISRRTILIERKQGLINFLNKQLERMVSELGGEEVGPLELEIKRLSKLIDEHDGKAVQAQVTWLRLQQEMVKVTQEQEEQLASLDASKKELHIMEQKKLRVESKIEQEKKEQKEIEHHMKDLDNDLKKLNMLMNKNRCSSEELEQNNRVTENEFVRSLKASERETIKMQDKLNQLSEEKATLLNQLVEAEHQIMLWEKKIQLAKEMRSSVDSEIGQTEIRAMKGEIHRMKVRLGQLLKQQEKMIRAMELAVARRETVTTQAEGQRKMDRKALTRTDFHHKQLELRRKIRDVRKATDECTKTVLELEETQRNVSSSLLEKQEKLSVIQADFDTLEADLTRLGALKRQNLSEIVALQTRLKHLQAVKEGRYVFLFRSKQSLVLERQRLDKRLALIATILDRVRDEYPQFQEALHKVSQMIANKLESPGPS,mutated_sequence,1.0,1142.0,NP_060420.2.a2m,NP_060420.2.npy,ClinVar +NP_060476.2,NP_060476.2.csv,MQALRHVVCALSGGVDSAVAALLLRRRGYQVTGVFMKNWDSLDEHGVCTADKDCEDAYRVCQILDIPFHQVSYVKEYWNDVFSDFLNEYEKGRTPNPDIVCNKHIKFSCFFHYAVDNLGADAIATGHYARTSLEDEEVFEQKHVKKPEGLFRNRFEVRNAVKLLQAADSFKDQTFFLSQVSQDALRRTIFPLGGLTKEFVKKIAAENRLHHVLQKKESMGMCFIGKRNFEHFLLQYLQPRPGHFISIEDNKVLGTHKGWFLYTLGQRANIGGLREPWYVVEKDSVKGDVFVAPRTDHPALYRDLLRTSRVHWIAEEPPAALVRDKMMECHFRFRHQMALVPCVLTLNQDGTVWVTAVQAVRALATGQFAVFYKGDECLGSGKILRLGPSAYTLQKGQRRAGMATESPSDSPEDGPGLSPLL,mutated_sequence,1.0,421.0,NP_060476.2.a2m,NP_060476.2.npy,ClinVar +NP_060521.4,NP_060521.4.csv,MEPGKRRTKDDTWKADDLRKHLWAIQSGGSKEERKHREKKLRKESEMDLPEHKEPRCRDPDQDARSRDRVAEVHTAKESPRGERDRDRQRERRRDAKDREKEKLKEKHREAEKSHSRGKDREKEKDRRARKEELRQTVAHHNLLGQETRDRQLLERAERKGRSVSKVRSEEKDEDSERGDEDRERRYRERKLQYGDSKDNPLKYWLYKEEGERRHRKPREPDRDNKHREKSSTREKREKYSKEKSNSFSDKGEERHKEKRHKEGFHFDDERHQSNVDRKEKSAKDEPRKRESQNGEHRNRGASSKRDGTSSQHAENLVRNHGKDKDSRRKHGHEEGSSVWWKLDQRPGGEETVEIEKEETDLENARADAYTASCEDDFEDYEDDFEVCDGDDDESSNEPESREKLEELPLAQKKEIQEIQRAINAENERIGELSLKLFQKRGRTEFEKEPRTDTNSSPSRASVCGIFVDFASASHRQKSRTQALKQKMRSTKLLRLIDLDFSFTFSLLDLPPVNEYDMYIRNFGKKNTKQAYVQCNEDNVERDIQTEEIETREVWTQHPGESTVVSGGSEQRDTSDAVVMPKIDTPRLCSFLRAACQVMAVLLEEDRLAAEPSWNLRAQDRALYFSDSSSQLNTSLPFLQNRKVSSLHTSRVQRQMVVSVHDLPEKSFVPLLDSKYVLCVWDIWQPSGPQKVLICESQVTCCCLSPLKAFLLFAGTAHGSVVVWDLREDSRLHYSVTLSDGFWTFRTATFSTDGILTSVNHRSPLQAVEPISTSVHKKQSFVLSPFSTQEEMSGLSFHIASLDESGVLNVWVVVELPKADIAGSISDLGLMPGGRVKLVHSALIQLGDSLSHKGNEFWGTTQTLNVKFLPSDPNHFIIGTDMGLISHGTRQDLRVAPKLFKPQQHGIRPVKVNVIDFSPFGEPIFLAGCSDGSIRLHQLSSAFPLLQWDSSTDSHAVTGLQWSPTRPAVFLVQDDTSNIYIWDLLQSDLGPVAKQQVSPNRLVAMAAVGEPEKAGGSFLALVLARASGSIDIQHLKRRWAAPEVDECNRLRLLLQEALWPEGKLHK,mutated_sequence,1.0,1066.0,NP_060521.4.a2m,NP_060521.4.npy,ClinVar +NP_060533.2,NP_060533.2.csv,MPAERPAGSGGSEAPAMVEQLDTAVITPAMLEEEEQLEAAGLERERKMLEKARMSWDRESTEIRYRRLQHLLEKSNIYSKFLLTKMEQQQLEEQKKKEKLERKKESLKVKKGKNSIDASEEKPVMRKKRGREDESYNISEVMSKEEILSVAKKNKKENEDENSSSTNLCVEDLQKNKDSNSIIKDRLSETVRQNTKFFFDPVRKCNGQPVPFQQPKHFTGGVMRWYQVEGMEWLRMLWENGINGILADEMGLGKTVQCIATIALMIQRGVPGPFLVCGPLSTLPNWMAEFKRFTPDIPTMLYHGTQEERQKLVRNIYKRKGTLQIHPVVITSFEIAMRDRNALQHCYWKYLIVDEGHRIKNMKCRLIRELKRFNADNKLLLTGTPLQNNLSELWSLLNFLLPDVFDDLKSFESWFDITSLSETAEDIIAKEREQNVLHMLHQILTPFLLRRLKSDVALEVPPKREVVVYAPLSKKQEIFYTAIVNRTIANMFGSSEKETIELSPTGRPKRRTRKSINYSKIDDFPNELEKLISQIQPEVDRERAVVEVNIPVESEVNLKLQNIMMLLRKCCNHPYLIEYPIDPVTQEFKIDEELVTNSGKFLILDRMLPELKKRGHKVLLFSQMTSMLDILMDYCHLRDFNFSRLDGSMSYSEREKNMHSFNTDPEVFIFLVSTRAGGLGINLTAADTVIIYDSDWNPQSDLQAQDRCHRIGQTKPVVVYRLVTANTIDQKIVERAAAKRKLEKLIIHKNHFKGGQSGLNLSKNFLDPKELMELLKSRDYEREIKGSREKVISDKDLELLLDRSDLIDQMNASGPIKEKMGIFKILENSEDSSPECLF,mutated_sequence,1.0,838.0,NP_060533.2.a2m,NP_060533.2.npy,ClinVar +NP_060552.4,NP_060552.4.csv,MDVLAEEFGNLTPEQLAAPIPTVEEKWRLLPAFLKVKGLVKQHIDSFNYFINVEIKKIMKANEKVTSDADPMWYLKYLNIYVGLPDVEESFNVTRPVSPHECRLRDMTYSAPITVDIEYTRGSQRIIRNALPIGRMPIMLRSSNCVLTGKTPAEFAKLNECPLDPGGYFIVKGVEKVILIQEQLSKNRIIVEADRKGAVGASVTSSTHEKKSRTNMAVKQGRFYLRHNTLSEDIPIVIIFKAMGVESDQEIVQMIGTEEHVMAAFGPSLEECQKAQIFTQMQALKYIGNKVRRQRMWGGGPKKTKIEEARELLASTILTHVPVKEFNFRAKCIYTAVMVRRVILAQGDNKVDDRDYYGNKRLELAGQLLSLLFEDLFKKFNSEMKKIADQVIPKQRAAQFDVVKHMRQDQITNGMVNAISTGNWSLKRFKMDRQGVTQVLSRLSYISALGMMTRISSQFEKTRKVSGPRSLQPSQWGMLCPSDTPEGEACGLVKNLALMTHITTDMEDGPIVKLASNLGVEDVNLLCGEELSYPNVFLVFLNGNILGVIRDHKKLVNTFRLMRRAGYINEFVSISTNLTDRCVYISSDGGRLCRPYIIVKKQKPAVTNKHMEELAQGYRNFEDFLHESLVEYLDVNEENDCNIALYEHTINKDTTHLEIEPFTLLGVCAGLIPYPHHNQSPRNTYQCAMGKQAMGTIGYNQRNRIDTLMYLLAYPQKPMVKTKTIELIEFEKLPAGQNATVAVMSYSGYDIEDALVLNKASLDRGFGRCLVYKNAKCTLKRYTNQTFDKVMGPMLDAATRKPIWRHEILDADGICSPGEKVENKQVLVNKSMPTVTQIPLEGSNVPQQPQYKDVPITYKGATDSYIEKVMISSNAEDAFLIKMLLRQTRRPEIGDKFSSRHGQKGVCGLIVPQEDMPFCDSGICPDIIMNPHGFPSRMTVGKLIELLAGKAGVLDGRFHYGTAFGGSKVKDVCEDLVRHGYNYLGKDYVTSGITGEPLEAYIYFGPVYYQKLKHMVLDKMHARARGPRAVLTRQPTEGRSRDGGLRLGEMERDCLIGYGASMLLLERLMISSDAFEVDVCGQCGLLGYSGWCHYCKSSCHVSSLRIPYACKLLFQELQSMNIIPRLKLSKYNE,mutated_sequence,1.0,1133.0,NP_060552.4.a2m,NP_060552.4.npy,ClinVar +NP_060575.1,NP_060575.1.csv,MVQSCSAYGCKNRYDKDKPVSFHKFPLTRPSLCKEWEAAVRRKNFKPTKYSSICSEHFTPDCFKRECNNKLLKENAVPTIFLCTEPHDKKEDLLEPQEQLPPPPLPPPVSQVDAAIGLLMPPLQTPVNLSVFCDHNYTVEDTMHQRKRIHQLEQQVEKLRKKLKTAQQRCRRQERQLEKLKEVVHFQKEKDDVSERGYVILPNDYFEIVEVPA,mutated_sequence,1.0,213.0,NP_060575.1.a2m,NP_060575.1.npy,ClinVar +NP_060597.4,NP_060597.4.csv,MWALCSLLRSAAGRTMSQGRTISQAPARRERPRKDPLRHLRTREKRGPSGCSGGPNTVYLQVVAAGSRDSGAALYVFSEFNRYLFNCGEGVQRLMQEHKLKVARLDNIFLTRMHWSNVGGLSGMILTLKETGLPKCVLSGPPQLEKYLEAIKIFSGPLKGIELAVRPHSAPEYEDETMTVYQIPIHSEQRRGKHQPWQSPERPLSRLSPERSSDSESNENEPHLPHGVSQRRGVRDSSLVVAFICKLHLKRGNFLVLKAKEMGLPVGTAAIAPIIAAVKDGKSITHEGREILAEELCTPPDPGAAFVVVECPDESFIQPICENATFQRYQGKADAPVALVVHMAPASVLVDSRYQQWMERFGPDTQHLVLNENCASVHNLRSHKIQTQLNLIHPDIFPLLTSFRCKKEGPTLSVPMVQGECLLKYQLRPRREWQRDAIITCNPEEFIVEALQLPNFQQSVQEYRRSAQDGPAPAEKRSQYPEIIFLGTGSAIPMKIRNVSATLVNISPDTSLLLDCGEGTFGQLCRHYGDQVDRVLGTLAAVFVSHLHADHHTGLPSILLQRERALASLGKPLHPLLVVAPNQLKAWLQQYHNQCQEVLHHISMIPAKCLQEGAEISSPAVERLISSLLRTCDLEEFQTCLVRHCKHAFGCALVHTSGWKVVYSGDTMPCEALVRMGKDATLLIHEATLEDGLEEEAVEKTHSTTSQAISVGMRMNAEFIMLNHFSQRYAKVPLFSPNFSEKVGVAFDHMKVCFGDFPTMPKLIPPLKALFAGDIEEMEERREKRELRQVRAALLSRELAGGLEDGEPQQKRAHTEEPQAKKVRAQ,mutated_sequence,1.0,826.0,NP_060597.4.a2m,NP_060597.4.npy,ClinVar +NP_060767.2,NP_060767.2.csv,MAAAALGSSSGSASPAVAELCQNTPETFLEASKLLLTYADNILRNPNDEKYRSIRIGNTAFSTRLLPVRGAVECLFEMGFEEGETHLIFPKKASVEQLQKIRDLIAIERSSRLDGSNKSHKVKSSQQPAASTQLPTTPSSNPSGLNQHTRNRQGQSSDPPSASTVAADSAILEVLQSNIQHVLVYENPALQEKALACIPVQELKRKSQEKLSRARKLDKGINISDEDFLLLELLHWFKEEFFHWVNNVLCSKCGGQTRSRDRSLLPSDDELKWGAKEVEDHYCDACQFSNRFPRYNNPEKLLETRCGRCGEWANCFTLCCRAVGFEARYVWDYTDHVWTEVYSPSQQRWLHCDACEDVCDKPLLYEIGWGKKLSYVIAFSKDEVVDVTWRYSCKHEEVIARRTKVKEALLRDTINGLNKQRQLFLSENRRKELLQRIIVELVEFISPKTPKPGELGGRISGSVAWRVARGEMGLQRKETLFIPCENEKISKQLHLCYNIVKDRYVRVSNNNQTISGWENGVWKMESIFRKVETDWHMVYLARKEGSSFAYISWKFECGSVGLKVDSISIRTSSQTFQTGTVEWKLRSDTAQVELTGDNSLHSYADFSGATEVILEAELSRGDGDVAWQHTQLFRQSLNDHEENCLEIIIKFSDL,mutated_sequence,1.0,654.0,NP_060767.2.a2m,NP_060767.2.npy,ClinVar +NP_060876.5,NP_060876.5.csv,MKGARWRRVPWVSLSCLCLCLLPHVVPGTTEDTLITGSKTAAPVTSTGSTTATLEGQSTAASSRTSNQDISASSQNHQTKSTETTSKAQTDTLTQMMTSTLFSSPSVHNVMETAPPDEMTTSFPSSVTNTLMMTSKTITMTTSTDSTLGNTEETSTAGTESSTPVTSAVSITAGQEGQSRTTSWRTSIQDTSASSQNHWTRSTQTTRESQTSTLTHRTTSTPSFSPSVHNVTGTVSQKTSPSGETATSSLCSVTNTSMMTSEKITVTTSTGSTLGNPGETSSVPVTGSLMPVTSAALVTFDPEGQSPATFSRTSTQDTTAFSKNHQTQSVETTRVSQINTLNTLTPVTTSTVLSSPSGFNPSGTVSQETFPSGETTTSSPSSVSNTFLVTSKVFRMPTSRDSTLGNTEETSLSVSGTISAITSKVSTIWWSDTLSTALSPSSLPPKISTAFHTQQSEGAETTGRPHERSSFSPGVSQEIFTLHETTTWPSSFSSKGHTTWSQTELPSTSTGAATRLVTGNPSTGTAGTIPRVPSKVSAIGEPGEPTTYSSHSTTLPKTTGAGAQTQWTQETGTTGEALLSSPSYSVTQMIKTATSPSSSPMLDRHTSQQITTAPSTNHSTIHSTSTSPQESPAVSQRGHTQAPQTTQESQTTRSVSPMTDTKTVTTPGSSFTASGHSPSEIVPQDAPTISAATTFAPAPTGDGHTTQAPTTALQAAPSSHDATLGPSGGTSLSKTGALTLANSVVSTPGGPEGQWTSASASTSPDTAAAMTHTHQAESTEASGQTQTSEPASSGSRTTSAGTATPSSSGASGTTPSGSEGISTSGETTRFSSNPSRDSHTTQSTTELLSASASHGAIPVSTGMASSIVPGTFHPTLSEASTAGRPTGQSSPTSPSASPQETAAISRMAQTQRTRTSRGSDTISLASQATDTFSTVPPTPPSITSTGLTSPQTETHTLSPSGSGKTFTTALISNATPLPVTYASSASTGHTTPLHVTDASSVSTGHATPLPVTSPSSVSTGHTTPLPVTDTSSESTGHVTPLPVTSFSSASTGDSTPLPVTDTSSASTGHVTPLPVTSLSSASTGDTTPLPVTDTSSASTGHATSLPVTDTSSVSTGHTTPLPVTDTSSASTGHATSLPVTDTSSVSTGHTTPLHVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDTSSASTGHATPLPVTDASSVSTDHATSLPVTIPSAASTGHTTPLPVTDTSSASTGQATSLLVTDTSSVSTGDTTPLPVTSTSSASTGHVTPLHVTSPSSASTGHATPLPVTSLSSASTGDTMPLPVTSPSSASTGDTTPLPVTDASSVSTGHTTPLHVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDTSSASTGHATPLPVTDASSVSTDHATSLPVTIPSAASTGHTTPLPVTDTSSASTGQATSLLVTDTSSVSTGDTTPLPVTSTSSASTGHVTPLHVTSPSSASTGHATPLPVTSLSSASTGDTMPLPVTSPSSASTGDTTPLPVTDASSVSTGHTTPLPVTSPSSASTGHTTPLPVTDTSSASKGDTTPLPVTSPSSASTGHTTPLPVTDTSSASTGDTTPLPVTNASSLSTGHATPLHVTSPSSASTGHATPLPVTSTSSASTGHATPLPVTGLSSATTDDTTRLPVTDVSSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHASPLLVTDASSASTGQATPLPVTDTSSVSTAHATPLPVTGLSSASTDDTTRLPVTDVSSASTGQAIPLPVTSPSSASTGDTTPLPVTDASSASTGDTTSLPVTIPSSASSGHTTSLPVTDASSVSTGHATSLLVTDASSVSTGDTTPLPVTDTNSASTGDTTPLHVTDASSVSTGHATSLPVTSLSSASTGDTTPLPVTSPSSASSGHTTPLPVTDASSVPTGHATSLPVTDASSVSTGHATPLPVTDASSVSTGHATPLPVTDTSSVSTGQATPLPVTSLSSASTGDTTPLPVTDTSSASTGQDTPLPVTSLSSVSTGDTTPLPVTNPSSASTGHATPLLVTDASSISTGHATSLLVTDASSVSTGHATALHDTDASSLSTGDTTPLPVTSPSSTSTGDTTPLPVTETSSVSTGHATSLPVTDTSSASTGHATSLPVTDTSSASTGHATPLPVTDTSSASTGQATPLPVTSPSSASTGHAIPLLVTDTSSASTGQATPLPVTSLSSASTGDTTPLPVTDASSVSTGHATSLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLHVTDASSASTGHATPLPVTSLSSASTGDTTPLPVTSPSSASTGHATPLHVTDASSVSTGDTTPLPVTSSSSASSGHTTPLPVTDASSASTGDTTPLPVTDTSSASTGHATHLPVTGLSSASTGDTTRLPVTNVSSASTGHATPLPVTSTSSASTGDTTPLPGTDTSSVSTGHTTPLLVTDASSVSTGDTTRLPVTSPSSASTGHTTPLPVTDTPSASTGDTTPLPVTNASSLSTRHATSLHVTSPSSASTGHATSLPVTDTSAASTGHATPLPVTSTSSASTGDTTPLPVTDTYSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATSLPVTDTSSASTGDTTSLPVTDTSSAYTGDTTSLPVTDTSSSSTGDTTPLLVTETSSVSTGDTTPLPVTDTSSASTGHATPLPVTNTSSVSTGHATPLHVTSPSSASTGHTTPLPVTDASSVSTGHATSLPVTDASSVFTGHATSLPVTIPSSASSGHTTPLPVTDASSVSTGHATSLPVTDASSVSTGHATPLPVTDASSVSTGHATPLPLTSLSSVSTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTDTSSASTGHATSLPVTDTSSASTGHATPLPDTDTSSASTGHATLLPVTDTSSASIGHATSLPVTDTSSISTGHATPLHVTSPSSASTGHATPLPVTDTSSASTGHANPLHVTSPSSASTGHATPLPVTDTSSASTGHATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHTTPLPVTDTSSASTGQATALPVTSTSSASTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATSLPVTDTSSASTGDTTSLPVTDTSSAYTGDTTSLPVTDTSSSSTGDTTPLLVTETSSVSTGHATPLLVTDASSASTGHATPLHVTSPSSASTGDTTPVPVTDTSSVSTGHATPLPVTGLSSASTGDTTRLPVTDISSASTGQATPLPVTNTSSVSTGDTMPLPVTSPSSASTGHATPLPVTSTSSASTGHATPVPVTSTSSASTGHTTPLPVTDTSSASTGDTTPLPVTSPSSASTGHTTPLHVTIPSSASTGDTSTLPVTGASSASTGHATPLPVTDTSSVSTGHATPLPVTSLSSVSTGDTTPLPVTDASSASTGQATPLPVTSLSSVSTGDTTPLLVTDASSVSTGHATPLPVTDTSSASTGDTTRLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLLVTDASSVSTGHATPLPVTDTSSASTGDTTRLPVTDTSSASTGQATPLPVTIPSSSSSGHTTPLPVTSTSSVSTGHVTPLHVTSPSSASTGHVTPLPVTSTSSASTGHATPLLVTDASSVSTGHATPLPVTDASSASTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTDASSASTGHATPLPVTIPSSVSTGDTMPLPVTSPSSASTGHATPLPVTGLSSASTGDTTPLPVTDTSSASTRHATPLPVTDTSSASTDDTTRLPVTDVSSASTGHATPLPVTSTSSASTGDTTPLPVTDTSSVSTGHATSLPVTSRSSASTGHATPLPVTDTSSVSTGHATPLPVTSTSSVSTGHATPLPVTSPSSASTGHATPVPVTSTSSASTGDTTPLPVTNASSLSTGHATPLHVTSPSSASRGDTSTLPVTDASSASTGHATPLPLTSLSSVSTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTIPSSASSGHTTSLPVTDASSVSTGHGTPLPVTSTSSASTGDTTPLPVTDTSSASTGHATPLPVTDTSSASTGHATPLPVTSLSSVSTGHATPLAVSSATSASTVSSDSPLKMETPGMTTPSLKTDGGRRTATSPPPTTSQTIISTIPSTAMHTRSTAAPIPILPERGVSLFPYGAGAGDLEFVRRTVDFTSPLFKPATGFPLGSSLRDSLYFTDNGQIIFPESDYQIFSYPNPLPTGFTGRDPVALVAPFWDDADFSTGRGTTFYQEYETFYGEHSLLVQQAESWIRKMTNNGGYKARWALKVTWVNAHAYPAQWTLGSNTYQAILSTDGSRSYALFLYQSGGMQWDVAQRSGNPVLMGFSSGDGYFENSPLMSQPVWERYRPDRFLNSNSGLQGLQFYRLHREERPNYRLECLQWLKSQPRWPSWGWNQVSCPCSWQQGRRDLRFQPVSIGRWGLGSRQLCSFTSWRGGVCCSYGPWGEFREGWHVQRPWQLAQELEPQSWCCRWNDKPYLCALYQQRRPHVGCATYRPPQPAWMFGDPHITTLDGVSYTFNGLGDFLLVGAQDGNSSFLLQGRTAQTGSAQATNFIAFAAQYRSSSLGPVTVQWLLEPHDAIRVLLDNQTVTFQPDHEDGGGQETFNATGVLLSRNGSEVSASFDGWATVSVIALSNILHASASLPPEYQNRTEGLLGVWNNNPEDDFRMPNGSTIPPGSPEEMLFHFGMTWQINGTGLLGKRNDQLPSNFTPVFYSQLQKNSSWAEHLISNCDGDSSCIYDTLALRNASIGLHTREVSKNYEQANATLNQYPPSINGGRVIEAYKGQTTLIQYTSNAEDANFTLRDSCTDLELFENGTLLWTPKSLEPFTLEILARSAKIGLASALQPRTVVCHCNAESQCLYNQTSRVGNSSLEVAGCKCDGGTFGRYCEGSEDACEEPCFPSVHCVPGKGCEACPPNLTGDGRHCAALGSSFLCQNQSCPVNYCYNQGHCYISQTLGCQPMCTCPPAFTDSRCFLAGNNFSPTVNLELPLRVIQLLLSEEENASMAEVNASVAYRLGTLDMRAFLRNSQVERIDSAAPASGSPIQHWMVISEFQYRPRGPVIDFLNNQLLAAVVEAFLYHVPRRSEEPRNDVVFQPISGEDVRDVTALNVSTLKAYFRCDGYKGYDLVYSPQSGFTCVSPCSRGYCDHGGQCQHLPSGPRCSCVSFSIYTAWGEHCEHLSMKLDAFFGIFFGALGGLLLLGVGTFVVLRFWGCSGARFSYFLNSAEALP,mutated_sequence,1.0,5412.0,NP_060876.5.a2m,NP_060876.5.npy,ClinVar +NP_060914.2,NP_060914.2.csv,MPAPTQLFFPLIRNCELSRIYGTACYCHHKHLCCSSSYIPQSRLRYTPHPAYATFCRPKENWWQYTQGRRYASTPQKFYLTPPQVNSILKANEYSFKVPEFDGKNVSSILGFDSNQLPANAPIEDRRSAATCLQTRGMLLGVFDGHAGCACSQAVSERLFYYIAVSLLPHETLLEIENAVESGRALLPILQWHKHPNDYFSKEASKLYFNSLRTYWQELIDLNTGESTDIDVKEALINAFKRLDNDISLEAQVGDPNSFLNYLVLRVAFSGATACVAHVDGVDLHVANTGDSRAMLGVQEEDGSWSAVTLSNDHNAQNERELERLKLEHPKSEAKSVVKQDRLLGLLMPFRAFGDVKFKWSIDLQKRVIESGPDQLNDNEYTKFIPPNYHTPPYLTAEPEVTYHRLRPQDKFLVLATDGLWETMHRQDVVRIVGEYLTGMHHQQPIAVGGYKVTLGQMHGLLTERRTKMSSVFEDQNAATHLIRHAVGNNEFGTVDHERLSKMLSLPEELARMYRDDITIIVVQFNSHVVGAYQNQE,mutated_sequence,1.0,537.0,NP_060914.2.a2m,NP_060914.2.npy,ClinVar +NP_060921.3,NP_060921.3.csv,MFLMPTSSELNSGQNFLTQWMTNPSRAGVILNRGFPILEADKEKRAAVDISTSFPIKGTHFSDSFSFINEEDSLLEEQKLESNNPYKPQSDKSETHTAFPCIKKGPQVAACHSAPGHQEENKNDFIPDLASEFKEGAYKDPLFKKLEQLKEVQQKKQEQLKRQQLEQLQRLMEEQEKLLTMVSGQCTLPGLSLLPDDQSQKHRSPGNTTTGERATCCFPSYVYPDPTQEETYPSNILSHEQSNFCRTAHGDFVLTSKRASPNLFSEAQYQEAPVEKNNLKEENRNHPTGESILCWEKVTEQIQEANDKNLQKHDDSSEVANIEERPIKAAIGERKQTFEDYLEEQIQLEEQELKQKQLKEAEGPLPIKAKPKQPFLKRGEGLARFTNAKSKFQKGKESKLVTNQSTSEDQPLFKMDRQQLQRKTALKNKELCADNPILKKDSKARTKSGSVTLSQKPKMLKCSNRKSLSPSGLKIQTGKKCDGQFRDQIKFENKVTSNNKENVTECPKPCDTGCTGWNKTQGKDRLPLSTGPASRLAAKSPIRETMKESESSLDVSLQKKLETWEREKEKENLELDEFLFLEQAADEISFSSNSSFVLKILERDQQICKGHRMSSTPVKAVPQKTNPADPISHCNRSEDLDHTAREKESECEVAPKQLHSLSSADELREQPCKIRKAVQKSTSENQTEWNARDDEGVPNSDSSTDSEEQLDVTIKPSTEDRERGISSREDSPQVCDDKGPFKDTRTQEDKRRDVDLDLSDKDYSSDESIMESIKHKVSEPSRSSSLSLSKMDFDDERTWTDLEENLCNHDVVLGNESTYGTPQTCYPNNEIGILDKTIKRKIAPVKRGEDLSKSRRSRSPPTSELMMKFFPSLKPKPKSDSHLGNELKLNISQDQPPGDNARSQVLREKIIELETEIEKFKAENASLAKLRIERESALEKLRKEIADFEQQKAKELARIEEFKKEEMRKLQKERKVFEKYTTAARTFPDKKEREEIQTLKQQIADLREDLKRKETKWSSTHSRLRSQIQMLVRENTDLREEIKVMERFRLDAWKRAEAIESSLEVEKKDKLANTSVRFQNSQISSGTQVEKYKKNYLPMQGNPPRRSKSAPPRDLGNLDKGQAASPREPLEPLNFPDPEYKEEEEDQDIQGEISHPDGKVEKVYKNGCRVILFPNGTRKEVSADGKTITVTFFNGDVKQVMPDQRVIYYYAAAQTTHTTYPEGLEVLHFSSGQIEKHYPDGRKEITFPDQTVKNLFPDGQEESIFPDGTIVRVQRDGNKLIEFNNGQRELHTAQFKRREYPDGTVKTVYANGHQETKYRSGRIRVKDKEGNVLMDTEL,mutated_sequence,1.0,1338.0,NP_060921.3.a2m,NP_060921.3.npy,ClinVar +NP_060956.1,NP_060956.1.csv,MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGDDDHPDSIEYGLGYDCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERILYVDLDLHHGDGVEDAFSFTSKVMTVSLHKFSPGFFPGTGDVSDVGLGKGRYYSVNVPIQDGIQDEKYYQICESVLKEVYQAFNPKAVVLQLGADTIAGDPMCSFNMTPVGIGKCLKYILQWQLATLILGGGGYNLANTARCWTYLTGVILGKTLSSEIPDHEFFTAYGPDYVLEITPSCRPDRNEPHRIQQILNYIKGNLKHVV,mutated_sequence,1.0,377.0,NP_060956.1.a2m,NP_060956.1.npy,ClinVar +NP_061154.1,NP_061154.1.csv,MADEQEIMCKLESIKEIRNKTLQMEKIKARLKAEFEALESEERHLKEYKQEMDLLLQEKMAHVEELRLIHADINVMENTIKQSENDLNKLLESTRRLHDEYKPLKEHVDALRMTLGLQRLPDLCEEEEKLSLDYFEKQKAEWQTEPQEPPIPESLAAAAAAAQQLQVARKQDTRQTATFRQQPPPMKACLSCHQQIHRNAPICPLCKAKSRSRNPKKPKRKQDE,mutated_sequence,1.0,224.0,NP_061154.1.a2m,NP_061154.1.npy,ClinVar +NP_061183.2,NP_061183.2.csv,MGRYSGKTCRLLFMLVLTVAFFVAELVSGYLGNSIALLSDSFNMLSDLISLCVGLSAGYIARRPTRGFSATYGYARAEVVGALSNAVFLTALCFTIFVEAVLRLARPERIDDPELVLIVGVLGLLVNVVGLLIFQDCAAWFACCLRGRSRRLQQRQQLAEGCVPGAFGGPQGAEDPRRAADPTAPGSDSAVTLRGTSVERKREKGATVFANVAGDSFNTQNEPEDMMKKEKKSEALNIRGVLLHVMGDALGSVVVVITAIIFYVLPLKSEDPCNWQCYIDPSLTVLMVIIILSSAFPLIKETAAILLQMVPKGVNMEELMSKLSAVPGISSVHEVHIWELVSGKIIATLHIKYPKDRGYQDASTKIREIFHHAGIHNVTIQFENVDLKEPLEQKDLLLLCNSPCISKGCAKQLCCPPGALPLAHVNGCAEHNGGPSLDTYGSDGLSRRDAREVAIEVSLDSCLSDHGQSLNKTQEDQCYVNRTHF,mutated_sequence,1.0,485.0,NP_061183.2.a2m,NP_061183.2.npy,ClinVar +NP_061193.2,NP_061193.2.csv,MNCEREQLRGNQEAAAAPDTMAQPYASAQFAPPQNGIPAEYTAPHPHPAPEYTGQTTVPEHTLNLYPPAQTHSEQSPADTSAQTVSGTATQTDDAAPTDGQPQTQPSENTENKSQPKRLHVSNIPFRFRDPDLRQMFGQFGKILDVEIIFNERGSKGFGFVTFENSADADRAREKLHGTVVEGRKIEVNNATARVMTNKKTVNPYTNGWKLNPVVGAVYSPEFYAGTVLLCQANQEGSSMYSAPSSLVYTSAMPGFPYPAATAAAAYRGAHLRGRGRTVYNTFRAAAPPPPIPAYGGVVYQDGFYGADIYGGYAAYRYAQPTPATAAAYSDSYGRVYAADPYHHALAPAPTYGVGAMNAFAPLTDAKTRSHADDVGLVLSSLQASIYRGGYNRFAPY,mutated_sequence,1.0,397.0,NP_061193.2.a2m,NP_061193.2.npy,ClinVar +NP_061496.2,NP_061496.2.csv,MDEEEDGAGAEESGQPRSFMRLNDLSGAGGRPGPGSAEKDPGSADSEAEGLPYPALAPVVFFYLSQDSRPRSWCLRTVCNPWFERISMLVILLNCVTLGMFRPCEDIACDSQRCRILQAFDDFIFAFFAVEMVVKMVALGIFGKKCYLGDTWNRLDFFIVIAGMLEYSLDLQNVSFSAVRTVRVLRPLRAINRVPSMRILVTLLLDTLPMLGNVLLLCFFVFFIFGIVGVQLWAGLLRNRCFLPENFSLPLSVDLERYYQTENEDESPFICSQPRENGMRSCRSVPTLRGDGGGGPPCGLDYEAYNSSSNTTCVNWNQYYTNCSAGEHNPFKGAINFDNIGYAWIAIFQVITLEGWVDIMYFVMDAHSFYNFIYFILLIIVGSFFMINLCLVVIATQFSETKQRESQLMREQRVRFLSNASTLASFSEPGSCYEELLKYLVYILRKAARRLAQVSRAAGVRVGLLSSPAPLGGQETQPSSSCSRSHRRLSVHHLVHHHHHHHHHYHLGNGTLRAPRASPEIQDRDANGSRRLMLPPPSTPALSGAPPGGAESVHSFYHADCHLEPVRCQAPPPRSPSEASGRTVGSGKVYPTVHTSPPPETLKEKALVEVAASSGPPTLTSLNIPPGPYSSMHKLLETQSTGACQSSCKISSPCLKADSGACGPDSCPYCARAGAGEVELADREMPDSDSEAVYEFTQDAQHSDLRDPHSRRQRSLGPDAEPSSVLAFWRLICDTFRKIVDSKYFGRGIMIAILVNTLSMGIEYHEQPEELTNALEISNIVFTSLFALEMLLKLLVYGPFGYIKNPYNIFDGVIVVISVWEIVGQQGGGLSVLRTFRLMRVLKLVRFLPALQRQLVVLMKTMDNVATFCMLLMLFIFIFSILGMHLFGCKFASERDGDTLPDRKNFDSLLWAIVTVFQILTQEDWNKVLYNGMASTSSWAALYFIALMTFGNYVLFNLLVAILVEGFQAEEISKREDASGQLSCIQLPVDSQGGDANKSESEPDFFSPSLDGDGDRKKCLALVSLGEHPELRKSLLPPLIIHTAATPMSLPKSTSTGLGEALGPASRRTSSSGSAEPGAAHEMKSPPSARSSPHSPWSAASSWTSRRSSRNSLGRAPSLKRRSPSGERRSLLSGEGQESQDEEESSEEERASPAGSDHRHRGSLEREAKSSFDLPDTLQVPGLHRTASGRGSASEHQDCNGKSASGRLARALRPDDPPLDGDDADDEGNLSKGERVRAWIRARLPACCLERDSWSAYIFPPQSRFRLLCHRIITHKMFDHVVLVIIFLNCITIAMERPKIDPHSAERIFLTLSNYIFTAVFLAEMTVKVVALGWCFGEQAYLRSSWNVLDGLLVLISVIDILVSMVSDSGTKILGMLRVLRLLRTLRPLRVISRAQGLKLVVETLMSSLKPIGNIVVICCAFFIIFGILGVQLFKGKFFVCQGEDTRNITNKSDCAEASYRWVRHKYNFDNLGQALMSLFVLASKDGWVDIMYDGLDAVGVDQQPIMNHNPWMLLYFISFLLIVAFFVLNMFVGVVVENFHKCRQHQEEEEARRREEKRLRRLEKKRRNLMLDDVIASGSSASAASEAQCKPYYSDYSRFRLLVHHLCTSHYLDLFITGVIGLNVVTMAMEHYQQPQILDEALKICNYIFTVIFVLESVFKLVAFGFRRFFQDRWNQLDLAIVLLSIMGITLEEIEVNASLPINPTIIRIMRVLRIARVLKLLKMAVGMRALLDTVMQALPQVGNLGLLFMLLFFIFAALGVELFGDLECDETHPCEGLGRHATFRNFGMAFLTLFRVSTGDNWNGIMKDTLRDCDQESTCYNTVISPIYFVSFVLTAQFVLVNVVIAVLMKHLEESNKEAKEEAELEAELELEMKTLSPQPHSPLGSPFLWPGVEGPDSPDSPKPGALHPAAHARSASHFSLEHPTDRQLFDTISLLIQGSLEWELKLMDELAGPGGQPSAFPSAPSLGGSDPQIPLAEMEALSLTSEIVSEPSCSLALTDDSLPDDMHTLLLSALESNMQPHPTELPGPDLLTVRKSGVSRTHSLPNDSYMCRHGSTAEGPLGHRGWGLPKAQSGSVLSVHSQPADTSYILQLPKDAPHLLQPHSAPTWGTIPKLPPPGRSPLAQRPLRRQAAIRTDSLDVQGLGSREDLLAEVSGPSPPLARAYSFWGQSSTQAQQHSRSHSKISKHMTPPAPCPGPEPNWGKGPPETRSSLELDTELSWISGDLLPPGGQEEPPSPRDLKKCYSVEAQSCQRRPTSWLDEQRRHSIAVSCLDSGSQPHLGTDPSNLGGQPLGGPGSRPKKKLSPPSITIDPPESQGPRTPPSPGICLRRRAPSSDSKDPLASGPPDSMAASPSPKKDVLSLSGLSSDPADLDP,mutated_sequence,1.0,2377.0,NP_061496.2.a2m,NP_061496.2.npy,ClinVar +NP_061764.2,NP_061764.2.csv,MNPASDGGTSESIFDLDYASWGIRSTLMVAGFVFYLGVFVVCHQLSSSLNATYRSLVAREKVFWDLAATRAVFGVQSTAAGLWALLGDPVLHADKARGQQNWCWFHITTATGFFCFENVAVHLSNLIFRTFDLFLVIHHLFAFLGFLGCLVNLQAGHYLAMTTLLLEMSTPFTCVSWMLLKAGWSESLFWKLNQWLMIHMFHCRMVLTYHMWWVCFWHWDGLVSSLYLPHLTLFLVGLALLTLIINPYWTHKKTQQLLNPVDWNFAQPEAKSRPEGNGQLLRKKRP,mutated_sequence,1.0,286.0,NP_061764.2.a2m,NP_061764.2.npy,ClinVar +NP_061820.1,NP_061820.1.csv,MGDVEKGKKIFIMKCSQCHTVEKGGKHKTGPNLHGLFGRKTGQAPGYSYTAANKNKGIIWGEDTLMEYLENPKKYIPGTKMIFVGIKKKEERADLIAYLKKATNE,mutated_sequence,1.0,105.0,NP_061820.1.a2m,NP_061820.1.npy,ClinVar +NP_061971.3,NP_061971.3.csv,MFKSLTKVNKVKPIGENNENEQSSRRNEEGSHPSNQSQQTTAQEENKGEEKSLKTKSTPVTSEEPHTNIQDKLSKKNSSGDLTTNPDPQNAAEPTGTVPEQKEMDPGKEGPNSPQNKPPAAPVINEYADAQLHNLVKRMRQRTALYKKKLVEGDLSSPEASPQTAKPTAVPPVKESDDKPTEHYYRLLWFKVKKMPLTEYLKRIKLPNSIDSYTDRLYLLWLLLVTLAYNWNCCFIPLRLVFPYQTADNIHYWLIADIICDIIYLYDMLFIQPRLQFVRGGDIIVDSNELRKHYRTSTKFQLDVASIIPFDICYLFFGFNPMFRANRMLKYTSFFEFNHHLESIMDKAYIYRVIRTTGYLLFILHINACVYYWASNYEGIGTTRWVYDGEGNEYLRCYYWAVRTLITIGGLPEPQTLFEIVFQLLNFFSGVFVFSSLIGQMRDVIGAATANQNYFRACMDDTIAYMNNYSIPKLVQKRVRTWYEYTWDSQRMLDESDLLKTLPTTVQLALAIDVNFSIISKVDLFKGCDTQMIYDMLLRLKSVLYLPGDFVCKKGEIGKEMYIIKHGEVQVLGGPDGTKVLVTLKAGSVFGEISLLAAGGGNRRTANVVAHGFANLLTLDKKTLQEILVHYPDSERILMKKARVLLKQKAKTAEATPPRKDLALLFPPKEETPKLFKTLLGGTGKASLARLLKLKREQAAQKKENSEGGEEEGKENEDKQKENEDKQKENEDKGKENEDKDKGREPEEKPLDRPECTASPIAVEEEPHSVRRTVLPRGTSRQSLIISMAPSAEGGEEVLTIEVKEKAKQ,mutated_sequence,1.0,809.0,NP_061971.3.a2m,NP_061971.3.npy,ClinVar +NP_063945.2,NP_063945.2.csv,MPSKAENLRPSEPAPQPPEGRTLQGQLPGAPPAQRAGSPPDAPGSESPALACSTPATPSGEDPPARAAPIAPRPPARPRLERALSLDDKGWRRRRFRGSQEDLEARNGTSPSRGSVQSEGPGAPAHSCSPPCLSTSLQEIPKSRGVLSSERGSPSSGGNPLSGVASSSPNLPHRDAAVAGSSPRLPSLLPPRPPPALSLDIASDSLRTANKVDSDLADYKLRAQPLLVRAHSSLGPGRPRSPLACDDCSLRSAKSSFSLLAPIRSKDVRSRSYLEGSLLASGALLGADELARYFPDRNVALFVATWNMQGQKELPPSLDEFLLPAEADYAQDLYVIGVQEGCSDRREWETRLQETLGPHYVLLSSAAHGVLYMSLFIRRDLIWFCSEVECSTVTTRIVSQIKTKGALGISFTFFGTSFLFITSHFTSGDGKVAERLLDYTRTVQALVLPRNVPDTNPYRSSAADVTTRFDEVFWFGDFNFRLSGGRTVVDALLCQGLVVDVPALLQHDQLIREMRKGSIFKGFQEPDIHFLPSYKFDIGKDTYDSTSKQRTPSYTDRVLYRSRHKGDICPVSYSSCPGIKTSDHRPVYGLFRVKVRPGRDNIPLAAGKFDRELYLLGIKRRISKEIQRQQALQSQNSSTICSVS,mutated_sequence,1.0,644.0,NP_063945.2.a2m,NP_063945.2.npy,ClinVar +NP_065175.4,NP_065175.4.csv,MPHLPLASFRPPFWGLRHSRGLPRFHSVSTQSEPHGSPISRRNREAKQKRLREKQATLEAEIAGESKSPAESIKAWRPKELVLYEIPTKPGEKKDVSGPLPPAYSPRYVEAAWYPWWVREGFFKPEYQARLPQATGETFSMCIPPPNVTGSLHIGHALTVAIQDALVRWHRMRGDQVLWVPGSDHAGIATQAVVEKQLWKERGVRRHELSREAFLREVWQWKEAKGGEICEQLRALGASLDWDRECFTMDVGSSVAVTEAFVRLYKAGLLYRNHQLVNWSCALRSAISDIEVENRPLPGHTQLRLPGCPTPVSFGLLFSVAFPVDGEPDAEVVVGTTRPETLPGDVAVAVHPDDSRYTHLHGRQLRHPLMGQPLPLITDYAVQPHVGTGAVKVTPAHSPADAEMGARHGLSPLNVIAEDGTMTSLCGDWLQGLHRFVAREKIMSVLSEWGLFRGLQNHPMVLPICSRSGDVIEYLLKNQWFVRCQEMGARAAKAVESGALELSPSFHQKNWQHWFSHIGDWCVSRQLWWGHQIPAYLVVEDHAQGEEDCWVVGRSEAEAREVAAELTGRPGAELTLERDPDVLDTWFSSALFPFSALGWPQETPDLARFYPLSLLETGSDLLLFWVGRMVMLGTQLTGQLPFSKVLLHPMVRDRQGRKMSKSLGNVLDPRDIISGVEMQVLQEKLRSGNLDPAELAIVAAAQKKDFPHGIPECGTDALRFTLCSHGVQAGDLHLSVSEVQSCRHFCNKIWNALRFILNALGEKFVPQPAEELSPSSPMDAWILSRLALAAQECERGFLTRELSLVTHALHHFWLHNLCDVYLEAVKPVLWHSPRPLGPPQVLFSCADLGLRLLAPLMPFLAEELWQRLPPRPGCPPAPSISVAPYPSACSLEHWRQPELERRFSRVQEVVQVLRALRATYQLTKARPRVLLQSSEPGDQGLFEAFLEPLGTLGYCGAVGLLPPGAAAPSGWAQAPLSDTAQVYMELQGLVDPQIQLPLLAARRYKLQKQLDSLTARTPSEGEAGTQRQQKLSSLQLELSKLDKAASHLRQLMDEPPAPGSPEL,mutated_sequence,1.0,1063.0,NP_065175.4.a2m,NP_065175.4.npy,ClinVar +NP_065184.2,NP_065184.2.csv,MGRARPGQRGPPSPGPAAQPPAPPRRRARSLALLGALLAAAAAAAVRVCARHAEAQAAARQELALKTLGTDGLFLFSSLDTDGDMYISPEEFKPIAEKLTGSCSVTQTGVQWCSHSSLQPQLPWLNUSSCLSLLRSTPAASCEEEELPPDPSEETLTIEARFQPLLPETMTKSKDGFLGVSRLALSGLRNWTAAASPSAVFATRHFQPFLPPPGQELGEPWWIIPSELSMFTGYLSNNRFYPPPPKGKEVIIHRLLSMFHPRPFVKTRFAPQGAVACLTAISDFYYTVMFRIHAEFQLSEPPDFPFWFSPAQFTGHIILSKDATHVRDFRLFVPNHRSLNVDMEWLYGASESSNMEVDIGYIPQMELEATGPSVPSVILDEDGSMIDSHLPSGEPLQFVFEEIKWQQELSWEEAARRLEVAMYPFKKVSYLPFTEAFDRAKAENKLVHSILLWGALDDQSCUGSGRTLRETVLESSPILTLLNESFISTWSLVKELEELQNNQENSSHQKLAGLHLEKYSFPVEMMICLPNGTVVHHINANYFLDITSVKPEEIESNLFSFSSTFEDPSTATYMQFLKEGLRRGLPLLQP,mutated_sequence,1.0,590.0,NP_065184.2.a2m,NP_065184.2.npy,ClinVar +NP_065394.1,NP_065394.1.csv,MTAPAGPRGSETERLLTPNPGYGTQAGPSPAPPTPPEEEDLRRRLKYFFMSPCDKFRAKGRKPCKLMLQVVKILVVTVQLILFGLSNQLAVTFREENTIAFRHLFLLGYSDGADDTFAAYTREQLYQAIFHAVDQYLALPDVSLGRYAYVRGGGDPWTNGSGLALCQRYYHRGHVDPANDTFDIDPMVVTDCIQVDPPERPPPPPSDDLTLLESSSSYKNLTLKFHKLVNVTIHFRLKTINLQSLINNEIPDCYTFSVLITFDNKAHSGRIPISLETQAHIQECKHPSVFQHGDNSFRLLFDVVVILTCSLSFLLCARSLLRGFLLQNEFVGFMWRQRGRVISLWERLEFVNGWYILLVTSDVLTISGTIMKIGIEAKNLASYDVCSILLGTSTLLVWVGVIRYLTFFHNYNILIATLRVALPSVMRFCCCVAVIYLGYCFCGWIVLGPYHVKFRSLSMVSECLFSLINGDDMFVTFAAMQAQQGRSSLVWLFSQLYLYSFISLFIYMVLSLFIALITGAYDTIKHPGGAGAEESELQAYIAQCQDSPTSGKFRRGSGSACSLLCCCGRDPSEEHSLLVN,mutated_sequence,1.0,580.0,NP_065394.1.a2m,NP_065394.1.npy,ClinVar +NP_065712.1,NP_065712.1.csv,MDSLLMNRRKFLYQFKNVRWAKGRRETYLCYVVKRRDSATSFSLDFGYLRNKNGCHVELLFLRYISDWDLDPGRCYRVTWFTSWSPCYDCARHVADFLRGNPNLSLRIFTARLYFCEDRKAEPEGLRRLHRAGVQIAIMTFKDYFYCWNTFVENHERTFKAWEGLHENSVRLSRQLRRILLPLYEVDDLRDAFRTLGL,mutated_sequence,1.0,198.0,NP_065712.1.a2m,NP_065712.1.npy,ClinVar +NP_065789.1,NP_065789.1.csv,MSVLISQSVINYVEEENIPALKALLEKCKDVDERNECGQTPLMIAAEQGNLEIVKELIKNGANCNLEDLDNWTALISASKEGHVHIVEELLKCGVNLEHRDMGGWTALMWACYKGRTDVVELLLSHGANPSVTGLYSVYPIIWAAGRGHADIVHLLLQNGAKVNCSDKYGTTPLVWAARKGHLECVKHLLAMGADVDQEGANSMTALIVAVKGGYTQSVKEILKRNPNVNLTDKDGNTALMIASKEGHTEIVQDLLDAGTYVNIPDRSGDTVLIGAVRGGHVEIVRALLQKYADIDIRGQDNKTALYWAVEKGNATMVRDILQCNPDTEICTKDGETPLIKATKMRNIEVVELLLDKGAKVSAVDKKGDTPLHIAIRGRSRKLAELLLRNPKDGRLLYRPNKAGETPYNIDCSHQKSILTQIFGARHLSPTETDGDMLGYDLYSSALADILSEPTMQPPICVGLYAQWGSGKSFLLKKLEDEMKTFAGQQIEPLFQFSWLIVFLTLLLCGGLGLLFAFTVHPNLGIAVSLSFLALLYIFFIVIYFGGRREGESWNWAWVLSTRLARHIGYLELLLKLMFVNPPELPEQTTKALPVRFLFTDYNRLSSVGGETSLAEMIATLSDACEREFGFLATRLFRVFKTEDTQGKKKWKKTCCLPSFVIFLFIIGCIISGITLLAIFRVDPKHLTVNAVLISIASVVGLAFVLNCRTWWQVLDSLLNSQRKRLHNAASKLHKLKSEGFMKVLKCEVELMARMAKTIDSFTQNQTRLVVIIDGLDACEQDKVLQMLDTVRVLFSKGPFIAIFASDPHIIIKAINQNLNSVLRDSNINGHDYMRNIVHLPVFLNSRGLSNARKFLVTSATNGDVPCSDTTGIQEDADRRVSQNSLGEMTKLGSKTALNRRDTYRRRQMQRTITRQMSFDLTKLLVTEDWFSDISPQTMRRLLNIVSVTGRLLRANQISFNWDRLASWINLTEQWPYRTSWLILYLEETEGIPDQMTLKTIYERISKNIPTTKDVEPLLEIDGDIRNFEVFLSSRTPVLVARDVKVFLPCTVNLDPKLREIIADVRAAREQISIGGLAYPPLPLHEGPPRAPSGYSQPPSVCSSTSFNGPFAGGVVSPQPHSSYYSGMTGPQHPFYNRPFFAPYLYTPRYYPGGSQHLISRPSVKTSLPRDQNNGLEVIKEDAAEGLSSPTDSSRGSGPAPGPVVLLNSLNVDAVCEKLKQIEGLDQSMLPQYCTTIKKANINGRVLAQCNIDELKKEMNMNFGDWHLFRSTVLEMRNAESHVVPEDPRFLSESSSGPAPHGEPARRASHNELPHTELSSQTPYTLNFSFEELNTLGLDEGAPRHSNLSWQSQTRRTPSLSSLNSQDSSIEISKLTDKVQAEYRDAYREYIAQMSQLEGGPGSTTISGRSSPHSTYYMGQSSSGGSIHSNLEQEKGKDSEPKPDDGRKSFLMKRGDVIDYSSSGVSTNDASPLDPITEEDEKSDQSGSKLLPGKKSSERSSLFQTDLKLKGSGLRYQKLPSDEDESGTEESDNTPLLKDDKDRKAEGKVERVPKSPEHSAEPIRTFIKAKEYLSDALLDKKDSSDSGVRSSESSPNHSLHNEVADDSQLEKANLIELEDDSHSGKRGIPHSLSGLQDPIIARMSICSEDKKSPSECSLIASSPEENWPACQKAYNLNRTPSTVTLNNNSAPANRANQNFDEMEGIRETSQVILRPSSSPNPTTIQNENLKSMTHKRSQRSSYTRLSKDPPELHAAASSESTGFGEERESIL,mutated_sequence,1.0,1771.0,NP_065789.1.a2m,NP_065789.1.npy,ClinVar +NP_065796.2,NP_065796.2.csv,MAASVAAAARRLRRAIRRSPAWRGLSHRPLSSEPPAAKASAVRAAFLNFFRDRHGHRLVPSASVRPRGDPSLLFVNAGMNQFKPIFLGTVDPRSEMAGFRRVANSQKCVRAGGHHNDLEDVGRDLSHHTFFEMLGNWAFGGEYFKEEACNMAWELLTQVYGIPEERLWISYFDGDPKAGLDPDLETRDIWLSLGVPASRVLSFGPQENFWEMGDTGPCGPCTEIHYDLAGGVGAPQLVELWNLVFMQHNREADGSLQPLPQRHVDTGMGLERLVAVLQGKHSTYDTDLFSPLLNAIQQGCRAPPYLGRVGVADEGRTDTAYRVVADHIRTLSVCISDGIFPGMSGPPLVLRRILRRAVRFSMEILKAPPGFLGSLVPVVVETLGDAYPELQRNSAQIANLVSEDEAAFLASLERGRRIIDRTLRTLGPSDMFPAEVAWSLSLCGDLGLPLDMVELMLEEKGVQLDSAGLERLAQEEAQHRARQAEPVQKQGLWLDVHALGELQRQGVPPTDDSPKYNYSLRPSGSYEFGTCEAQVLQLYTEDGTAVASVGKGQRCGLLLDRTNFYAEQGGQASDRGYLVRAGQEDVLFPVARAQVCGGFILHEAVAPECLRLGDQVQLHVDEAWRLGCMAKHTATHLLNWALRQTLGPGTEQQGSHLNPEQLRLDVTTQTPLTPEQLRAVENTVQEAVGQDEAVYMEEVPLALTAQVPGLRSLDEVYPDPVRVVSVGVPVAHALDPASQAALQTSVELCCGTHLLRTGAVGDLVIIGDRQLSKGTTRLLAVTGEQAQQARELGQSLAQEVKAATERLSLGSRDVAEALRLSKDIGRLIEAVETAVMPQWQRRELLATVKMLQRRANTAIRKLQMGQAAKKTQELLERHSKGPLIVDTVSAESLSVLVKVVRQLCEQAPSTSVLLLSPQPMGKVLCACQVAQGAMPTFTAEAWALAVCSHMGGKAWGSRVVAQGTGSTTDLEAALSIAQTYALSQL,mutated_sequence,1.0,985.0,NP_065796.2.a2m,NP_065796.2.npy,ClinVar +NP_065822.2,NP_065822.2.csv,MERAMEQLNRLTRSLRRARTVELPEDNETAVYTLMPMVMADQHRSVSELLSNSKFDVNYAFGRVKRSLLHIAANCGSVECLVLLLKKGANPNYQDISGCTPLHLAARNGQKKCMSKLLEYSADVNICNNEGLTAIHWLAVNGRTELLHDLVQHVSDVDVEDAMGQTALHVACQNGHKTTVQCLLDSGADINRPNVSGATPLYFACSHGQRDTAQILLLRGAKYLPDKNGVTPLDLCVQGGYGETCEVLIQYHPRLFQTIIQMTQNEDLRENMLRQVLEHLSQQSESQYLKILTSLAEVATTNGHKLLSLSSNYDAQMKSLLRIVRMFCHVFRIGPSSPSNGIDMGYNGNKTPRSQVFKPLELLWHSLDEWLVLIATELMKNKRDSTEITSILLKQKGQDQDAASIPPFEPPGPGSYENLSTGTRESKPDALAGRQEASADCQDVISMTANRLSAVIQAFYMCCSCQMPPGMTSPRFIEFVCKHDEVLKCFVNRNPKIIFDHFHFLLECPELMSRFMHIIKAQPFKDRCEWFYEHLHSGQPDSDMVHRPVNENDILLVHRDSIFRSSCEVVSKANCAKLKQGIAVRFHGEEGMGQGVVREWFDILSNEIVNPDYALFTQSADGTTFQPNSNSYVNPDHLNYFRFAGQILGLALNHRQLVNIYFTRSFYKHILGIPVNYQDVASIDPEYAKNLQWILDNDISDLGLELTFSVETDVFGAMEEVPLKPGGGSILVTQNNKAEYVQLVTELRMTRAIQPQINAFLQGFHMFIPPSLIQLFDEYELELLLSGMPEIDVSDWIKNTEYTSGYEREDPVIQWFWEVVEDITQEERVLLLQFVTGSSRVPHGGFANIMGGSGLQNFTIAAVPYTPNLLPTSSTCINMLKLPEYPSKEILKDRLLVALHCGSYGYTMA,mutated_sequence,1.0,909.0,NP_065822.2.a2m,NP_065822.2.npy,ClinVar +NP_065851.1,NP_065851.1.csv,MRLKISLLKEPKHQELVSCVGWTTAEELYSCSDDHQIVKWNLLTSETTQIVKLPDDIYPIDFHWFPKSLGVKKQTQAESFVLTSSDGKFHLISKLGRVEKSVEAHCGAVLAGRWNYEGTALVTVGEDGQIKIWSKTGMLRSTLAQQGTPVYSVAWGPDSEKVLYTAGKQLIIKPLQPNAKVLQWKAHDGIILKVDWNSVNDLILSAGEDCKYKVWDSYGRPLYNSQPHEHPITSVAWAPDGELFAVGSFHTLRLCDKTGWSYALEKPNTGSIFNIAWSIDGTQIAGACGNGHVVFAHVVEQHWEWKNFQVTLTKRRAMQVRNVLNDAVDLLEFRDRVIKASLNYAHLVVSTSLQCYVFSTKNWNTPIIFDLKEGTVSLILQAERHFLLVDGSSIYLYSYEGRFISSPKFPGMRTDILNAQTVSLSNDTIAIRDKADEKIIFLFEASTGKPLGDGKFLSHKNEILEIALDQKGLTNDRKIAFIDKNRDLCITSVKRFGKEEQIIKLGTMVHTLAWNDTCNILCGLQDTRFIVWYYPNTVYVDRDILPKTLYERDASEFSKNPHIVSFVGNQVTIRRADGSLVHISITPYPAILHEYVSSSKWEDAVRLCRFVKEQTMWACLAAMAVANRDMTTAEIAYAAIGEIDKVQYINSIKNLPSKESKMAHILLFSGNIQEAEIVLLQAGLVYQAIQININLYNWERALELAVKYKTHVDTVLAYRQKFLETFGKQETNKRYLHYAEGLQIDWEKIKAKIEMEITKEREQSSSSQSSKSIGLKP,mutated_sequence,1.0,777.0,NP_065851.1.a2m,NP_065851.1.npy,ClinVar +NP_066124.1,NP_066124.1.csv,MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS,mutated_sequence,1.0,1114.0,NP_066124.1.a2m,NP_066124.1.npy,ClinVar +NP_066192.1,NP_066192.1.csv,MASTGLELLGMTLAVLGWLGTLVSCALPLWKVTAFIGNSIVVAQVVWEGLWMSCVVQSTGQMQCKVYDSLLALPQDLQAARALCVIALLLALLGLLVAITGAQCTTCVEDEGAKARIVLTAGVILLLAGILVLIPVCWTAHAIIQDFYNPLVAEALKRELGASLYLGWAAAALLMLGGGLLCCTCPPPQVERPRGPRLGYSIPSRSGASGLDKRDYV,mutated_sequence,1.0,217.0,NP_066192.1.a2m,NP_066192.1.npy,ClinVar +NP_066268.1,NP_066268.1.csv,MGCTLSAEERAALERSKAIEKNLKEDGISAAKDVKLLLLGAGESGKSTIVKQMKIIHEDGFSGEDVKQYKPVVYSNTIQSLAAIVRAMDTLGIEYGDKERKADAKMVCDVVSRMEDTEPFSAELLSAMMRLWGDSGIQECFNRSREYQLNDSAKYYLDSLDRIGAADYQPTEQDILRTRVKTTGIVETHFTFKNLHFRLFDVGGQRSERKKWIHCFEDVTAIIFCVALSGYDQVLHEDETTNRMHESLMLFDSICNNKFFIDTSIILFLNKKDLFGEKIKKSPLTICFPEYTGPNTYEDAAAYIQAQFESKNRSPNKEIYCHMTCATDTNNIQVVFDAVTDIIIANNLRGCGLY,mutated_sequence,1.0,354.0,NP_066268.1.a2m,NP_066268.1.npy,ClinVar +NP_066285.1,NP_066285.1.csv,MAMVVSTWRDPQDEVPGSQGSQASQAPPVPGPPPGAPHTPQTPGQGGPASTPAQTAAGGQGGPGGPGSDKQQQQQHIECVVCGDKSSGKHYGQFTCEGCKSFFKRSVRRNLSYTCRANRNCPIDQHHRNQCQYCRLKKCLKVGMRREAVQRGRMPPTQPTHGQFALTNGDPLNCHSYLSGYISLLLRAEPYPTSRFGSQCMQPNNIMGIENICELAARMLFSAVEWARNIPFFPDLQITDQVALLRLTWSELFVLNAAQCSMPLHVAPLLAAAGLHASPMSADRVVAFMDHIRIFQEQVEKLKALHVDSAEYSCLKAIVLFTSDACGLSDVAHVESLQEKSQCALEEYVRSQYPNQPTRFGKLLLRLPSLRTVSSSVIEQLFFVRLVGKTPIETLIRDMLLSGSSFNWPYMAIQ,mutated_sequence,1.0,414.0,NP_066285.1.a2m,NP_066285.1.npy,ClinVar +NP_066288.2,NP_066288.2.csv,MEDSDSAAKQLGLAEAAAVAAAAAVAAAAAAAAGGEAEEPVLSRDEDSEEDADSEAERETPRVTAVAVMAAEPGHMDMGAEALPGPDEAAAAAAFAEVTTVTVANVGAAADNVFTTSVANAASISGHVLSGRTALQIGDSLNTEKATLIVVHTDGSIVETTGLKGPAAPLTPGPQSPPTPLAPGQEKGGTKYNWDPSVYDSELPVRCRNISGTLYKNRLGSGGRGRCIKQGENWYSPTEFEAMAGRASSKDWKRSIRYAGRPLQCLIQDGILNPHAASCTCAACCDDMTLSGPVRLFVPYKRRKKENELPTTPVKKDSPKNITLLPATAATTFTVTPSGQITTSGALTFDRASTVEATAVISESPAQGDVFAGATVQEASVQPPCRASHPEPHYPGYQDSCQIAPFPEAALPTSHPKIVLTSLPALAVPPPTPTKAAPPALVNGLELSEPRSWLYLEEMVNSLLNTAQQLKTLFEQAKHASTYREAATNQAKIHADAERKEQSCVNCGREAMSECTGCHKVNYCSTFCQRKDWKDHQHICGQSAAVTVQADEVHVAESVMEKVTV,mutated_sequence,1.0,565.0,NP_066288.2.a2m,NP_066288.2.npy,ClinVar +NP_067075.1,NP_067075.1.csv,MDEDVLTTLKILIIGESGVGKSSLLLRFTDDTFDPELAATIGVDFKVKTISVDGNKAKLAIWDTAGQERFRTLTPSYYRGAQGVILVYDVTRRDTFVKLDNWLNELETYCTRNDIVNMLVGNKIDKENREVDRNEGLKFARKHSMLFIEASAKTCDGVQCAFEELVEKIIQTPGLWESENQNKGVKLSHREEGQGGGACGGYCSVL,mutated_sequence,1.0,206.0,NP_067075.1.a2m,NP_067075.1.npy,ClinVar +NP_067627.3,NP_067627.3.csv,METPLQFQRGFFPEQPPPPPRSSHLHCQQQQQSQDKPCPPFAPLPHPHHHPHLAHQQPASGGSSPCLRCNSCASSGAPAAGAGDNLSLLLRTSSPGGAFRTRTSSPLSGSSCCCCCCSSRRGSQLNVSELTPSSHASALRQQYAQQSAQQSASASQYHQCHSLQPAASPTGSLGSLGSGPPLSHHHHHPHPAHHQHHQPQARRESNPFTEIAMSSCRYNGGVMRPLSNLSASRRNLHEMDSEAQPLQPPASVGGGGGASSPSAAAAAAAAVSSSAPEIVVSKPEHNNSNNLALYGTGGGGSTGGGGGGGGSGHGSSSGTKSSKKKNQNIGYKLGHRRALFEKRKRLSDYALIFGMFGIVVMVIETELSWGAYDKASLYSLALKCLISLSTIILLGLIIVYHAREIQLFMVDNGADDWRIAMTYERIFFICLEILVCAIHPIPGNYTFTWTARLAFSYAPSTTTADVDIILSIPMFLRLYLIARVMLLHSKLFTDASSRSIGALNKINFNTRFVMKTLMTICPGTVLLVFSISLWIIAAWTVRACERYHDQQDVTSNFLGAMWLISITFLSIGYGDMVPNTYCGKGVCLLTGIMGAGCTALVVAVVARKLELTKAEKHVHNFMMDTQLTKRVKNAAANVLRETWLIYKNTKLVKKIDHAKVRKHQRKFLQAIHQLRSVKMEQRKLNDQANTLVDLAKTQNIMYDMISDLNERSEDFEKRIVTLETKLETLIGSIHALPGLISQTIRQQQRDFIEAQMESYDKHVTYNAERSRSSSRRRRSSSTAPPTSSESS,mutated_sequence,1.0,791.0,NP_067627.3.a2m,NP_067627.3.npy,ClinVar +NP_067638.3,NP_067638.3.csv,MADSSEGPRAGPGEVAELPGDESGTPGGEAFPLSSLANLFEGEDGSLSPSPADASRPAGPGDGRPNLRMKFQGAFRKGVPNPIDLLESTLYESSVVPGPKKAPMDSLFDYGTYRHHSSDNKRWRKKIIEKQPQSPKAPAPQPPPILKVFNRPILFDIVSRGSTADLDGLLPFLLTHKKRLTDEEFREPSTGKTCLPKALLNLSNGRNDTIPVLLDIAERTGNMREFINSPFRDIYYRGQTALHIAIERRCKHYVELLVAQGADVHAQARGRFFQPKDEGGYFYFGELPLSLAACTNQPHIVNYLTENPHKKADMRRQDSRGNTVLHALVAIADNTRENTKFVTKMYDLLLLKCARLFPDSNLEAVLNNDGLSPLMMAAKTGKIGIFQHIIRREVTDEDTRHLSRKFKDWAYGPVYSSLYDLSSLDTCGEEASVLEILVYNSKIENRHEMLAVEPINELLRDKWRKFGAVSFYINVVSYLCAMVIFTLTAYYQPLEGTPPYPYRTTVDYLRLAGEVITLFTGVLFFFTNIKDLFMKKCPGVNSLFIDGSFQLLYFIYSVLVIVSAALYLAGIEAYLAVMVFALVLGWMNALYFTRGLKLTGTYSIMIQKILFKDLFRFLLVYLLFMIGYASALVSLLNPCANMKVCNEDQTNCTVPTYPSCRDSETFSTFLLDLFKLTIGMGDLEMLSSTKYPVVFIILLVTYIILTFVLLLNMLIALMGETVGQVSKESKHIWKLQWATTILDIERSFPVFLRKAFRSGEMVTVGKSSDGTPDRRWCFRVDEVNWSHWNQNLGIINEDPGKNETYQYYGFSHTVGRLRRDRWSSVVPRVVELNKNSNPDEVVVPLDSMGNPRCDGHQQGYPRKWRTDDAPL,mutated_sequence,1.0,871.0,NP_067638.3.a2m,NP_067638.3.npy,ClinVar +NP_067641.2,NP_067641.2.csv,MAVYRLCVTTGPYLRAGTLDNISVTLVGTCGESPKQRLDRMGRDFAPGSVQKYKVRCTAELGELLLLRVHKERYAFFRKDSWYCSRICVTEPDGSVSHFPCYQWIEGYCTVELRPGTARTICQDSLPLLLDHRTRELRARQECYRWKIYAPGFPCMVDVNSFQEMESDKKFALTKTTTCVDQGDSSGNRYLPGFPMKIDIPSLMYMEPNVRYSATKTISLLFNAIPASLGMKLRGLLDRKGSWKKLDDMQNIFWCHKTFTTKYVTEHWCEDHFFGYQYLNGVNPVMLHCISSLPSKLPVTNDMVAPLLGQDTCLQTELERGNIFLADYWILAEAPTHCLNGRQQYVAAPLCLLWLSPQGALVPLAIQLSQTPGPDSPIFLPTDSEWDWLLAKTWVRNSEFLVHENNTHFLCTHLLCEAFAMATLRQLPLCHPIYKLLLPHTRYTLQVNTIARATLLNPEGLVDQVTSIGRQGLIYLMSTGLAHFTYTNFCLPDSLRARGVLAIPNYHYRDDGLKIWAAIESFVSEIVGYYYPSDASVQQDSELQAWTGEIFAQAFLGRESSGFPSRLCTPGEMVKFLTAIIFNCSAQHAAVNSGQHDFGAWMPNAPSSMRQPPPQTKGTTTLKTYLDTLPEVNISCNNLLLFWLVSQEPKDQRPLGTYPDEHFTEEAPRRSIAAFQSRLAQISRDIQERNQGLALPYTYLDPPLIENSVSI,mutated_sequence,1.0,711.0,NP_067641.2.a2m,NP_067641.2.npy,ClinVar +NP_068741.1,NP_068741.1.csv,MATPDAGLPGAEGVEPAPWAQLEAPARLLLQALQAGPEGARRGLGVLRALGSRGWEPFDWGRLLEALCREEPVVQGPDGRLELKPLLLRLPRICQRNLMSLLMAVRPSLPESGLLSVLQIAQQDLAPDPDAWLRALGELLRRDLGVGTSMEGASPLSERCQRQLQSLCRGLGLGGRRLKSPQAPDPEEEENRDSQQPGKRRKDSEEEAASPEGKRVPKRLRCWEEEEDHEKERPEHKSLESLADGGSASPIKDQPVMAVKTGEDGSNLDDAKGLAESLELPKAIQDQLPRLQQLLKTLEEGLEGLEDAPPVELQLLHECSPSQMDLLCAQLQLPQLSDLGLLRLCTWLLALSPDLSLSNATVLTRSLFLGRILSLTSSASRLLTTALTSFCAKYTYPVCSALLDPVLQAPGTGPAQTELLCCLVKMESLEPDAQVLMLGQILELPWKEETFLVLQSLLERQVEMTPEKFSVLMEKLCKKGLAATTSMAYAKLMLTVMTKYQANITETQRLGLAMALEPNTTFLRKSLKAALKHLGP,mutated_sequence,1.0,536.0,NP_068741.1.a2m,NP_068741.1.npy,ClinVar +NP_068758.3,NP_068758.3.csv,MFPAGPPSHSLLRLPLLQLLLLVVQAVGRGLGRASPAGGPLEDVVIERYHIPRACPREVQMGDFVRYHYNGTFEDGKKFDSSYDRNTLVAIVVGVGRLITGMDRGLMGMCVNERRRLIVPPHLGYGSIGLAGLIPPDATLYFDVVLLDVWNKEDTVQVSTLLRPPHCPRMVQDGDFVRYHYNGTLLDGTSFDTSYSKGGTYDTYVGSGWLIKGMDQGLLGMCPGERRKIIIPPFLAYGEKGYGTVIPPQASLVFHVLLIDVHNPKDAVQLETLELPPGCVRRAGAGDFMRYHYNGSLMDGTLFDSSYSRNHTYNTYIGQGYIIPGMDQGLQGACMGERRRITIPPHLAYGENGTGDKIPGSAVLIFNVHVIDFHNPADVVEIRTLSRPSETCNETTKLGDFVRYHYNCSLLDGTQLFTSHDYGAPQEATLGANKVIEGLDTGLQGMCVGERRQLIVPPHLAHGESGARGVPGSAVLLFEVELVSREDGLPTGYLFVWHKDPPANLFEDMDLNKDGEVPPEEFSTFIKAQVSEGKGRLMPGQDPEKTIGDMFQNQDRNQDGKITVDELKLKSDEDEERVHEEL,mutated_sequence,1.0,582.0,NP_068758.3.a2m,NP_068758.3.npy,ClinVar +NP_071372.1,NP_071372.1.csv,MSADSSPLVGSTPTGYGTLTIGTSIDPLSSSVSSVRLSGYCGSPWRVIGYHVVVWMMAGIPLLLFRWKPLWGVRLRLRPCNLAHAETLVIEIRDKEDSSWQLFTVQVQTEAIGEGSLEPSPQSQAEDGRSQAAVGAVPEGAWKDTAQLHKSEEAVSVGQKRVLRYYLFQGQRYIWIETQQAFYQVSLLDHGRSCDDVHRSRHGLSLQDQMVRKAIYGPNVISIPVKSYPQLLVDEALNPYYGFQAFSIALWLADHYYWYALCIFLISSISICLSLYKTRKQSQTLRDMVKLSMRVCVCRPGGEEEWVDSSELVPGDCLVLPQEGGLMPCDAALVAGECMVNESSLTGESIPVLKTALPEGLGPYCAETHRRHTLFCGTLILQARAYVGPHVLAVVTRTGFCTAKGGLVSSILHPRPINFKFYKHSMKFVAALSVLALLGTIYSIFILYRNRVPLNEIVIRALDLVTVVVPPALPAAMTVCTLYAQSRLRRQGIFCIHPLRINLGGKLQLVCFDKTGTLTEDGLDVMGVVPLKGQAFLPLVPEPRRLPVGPLLRALATCHALSRLQDTPVGDPMDLKMVESTGWVLEEEPAADSAFGTQVLAVMRPPLWEPQLQAMEEPPVPVSVLHRFPFSSALQRMSVVVAWPGATQPEAYVKGSPELVAGLCNPETVPTDFAQMLQSYTAAGYRVVALASKPLPTVPSLEAAQQLTRDTVEGDLSLLGLLVMRNLLKPQTTPVIQALRRTRIRAVMVTGDNLQTAVTVARGCGMVAPQEHLIIVHATHPERGQPASLEFLPMESPTAVNGVKDPDQAASYTVEPDPRSRHLALSGPTFGIIVKHFPKLLPKVLVQGTVFARMAPEQKTELVCELQKLQYCVGMCGDGANDCGALKAADVGISLSQAEASVVSPFTSSMASIECVPMVIREGRCSLDTSFSVFKYMALYSLTQFISVLILYTINTNLGDLQFLAIDLVITTTVAVLMSRTGPALVLGRVRPPGALLSVPVLSSLLLQMVLVTGVQLGGYFLTLAQPWFVPLNRTVAAPDNLPNYENTVVFSLSSFQYLILAAAVSKGAPFRRPLYTNVPFLVALALLSSVLVGLVLVPGLLQGPLALRNITDTGFKLLLLGLVTLNFVGAFMLESVLDQCLPACLRRLRPKRASKKRFKQLERELAEQPWPPLPAGPLR,mutated_sequence,1.0,1180.0,NP_071372.1.a2m,NP_071372.1.npy,ClinVar +NP_071384.1,NP_071384.1.csv,MPKVVSRSVVCSDTRDREEYDDGEKPLHVYYCLCGQMVLVLDCQLEKLPMRPRDRSRVIDAAKHAHKFCNTEDEETMYLRRPEGIERQYRKKCAKCGLPLFYQSQPKNAPVTFIVDGAVVKFGQGFGKTNIYTQKQEPPKKVMMTKRTKDMGKFSSVTVSTIDEEEEEIEAREVADSYAQNAKVIEKQLERKGMSKRRLQELAELEAKKAKMKGTLIDNQFK,mutated_sequence,1.0,222.0,NP_071384.1.a2m,NP_071384.1.npy,ClinVar +NP_071407.4,NP_071407.4.csv,MGRHVATSCHVAWLLVLISGCWGQVNRLPFFTNHFFDTYLLISEDTPVGSSVTQLLAQDMDNDPLVFGVSGEEASRFFAVEPDTGVVWLRQPLDRETKSEFTVEFSVSDHQGVITRKVNIQVGDVNDNAPTFHNQPYSVRIPENTPVGTPIFIVNATDPDLGAGGSVLYSFQPPSQFFAIDSARGIVTVIRELDYETTQAYQLTVNATDQDKTRPLSTLANLAIIITDVQDMDPIFINLPYSTNIYEHSPPGTTVRIITAIDQDKGRPRGIGYTIVSGNTNSIFALDYISGVLTLNGLLDRENPLYSHGFILTVKGTELNDDRTPSDATVTTTFNILVIDINDNAPEFNSSEYSVAITELAQVGFALPLFIQVVDKDENLGLNSMFEVYLVGNNSHHFIISPTSVQGKADIRIRVAIPLDYETVDRYDFDLFANESVPDHVGYAKVKITLINENDNRPIFSQPLYNISLYENVTVGTSVLTVLATDNDAGTFGEVSYFFSDDPDRFSLDKDTGLIMLIARLDYELIQRFTLTIIARDGGGEETTGRVRINVLDVNDNVPTFQKDAYVGALRENEPSVTQLVRLRATDEDSPPNNQITYSIVSASAFGSYFDISLYEGYGVISVSRPLDYEQISNGLIYLTVMAMDAGNPPLNSTVPVTIEVFDENDNPPTFSKPAYFVSVVENIMAGATVLFLNATDLDRSREYGQESIIYSLEGSTQFRINARSGEITTTSLLDRETKSEYILIVRAVDGGVGHNQKTGIATVNITLLDINDNHPTWKDAPYYINLVEMTPPDSDVTTVVAVDPDLGENGTLVYSIQPPNKFYSLNSTTGKIRTTHAMLDRENPDPHEAELMRKIVVSVTDCGRPPLKATSSATVFVNLLDLNDNDPTFQNLPFVAEVLEGIPAGVSIYQVVAIDLDEGLNGLVSYRMPVGMPRMDFLINSSSGVVVTTTELDRERIAEYQLRVVASDAGTPTKSSTSTLTIHVLDVNDETPTFFPAVYNVSVSEDVPREFRVVWLNCTDNDVGLNAELSYFITGGNVDGKFSVGYRDAVVRTVVGLDRETTAAYMLILEAIDNGPVGKRHTGTATVFVTVLDVNDNRPIFLQSSYEASVPEDIPEGHSILQLKATDADEGEFGRVWYRILHGNHGNNFRIHVSNGLLMRGPRPLDRERNSSHVLIVEAYNHDLGPMRSSVRVIVYVEDINDEAPVFTQQQYSRLGLRETAGIGTSVIVVQATDRDSGDGGLVNYRILSGAEGKFEIDESTGLIITVNYLDYETKTSYMMNVSATDQAPPFNQGFCSVYITLLNELDEAVQFSNASYEAAILENLALGTEIVRVQAYSIDNLNQITYRFNAYTSTQAKALFKIDAITGVITVQGLVDREKGDFYTLTVVADDGGPKVDSTVKVYITVLDENDNSPRFDFTSDSAVSIPEDCPVGQRVATVKAWDPDAGSNGQVVFSLASGNIAGAFEIVTTNDSIGEVFVARPLDREELDHYILQVVASDRGTPPRKKDHILQVTILDINDNPPVIESPFGYNVSVNENVGGGTAVVQVRATDRDIGINSVLSYYITEGNKDMAFRMDRISGEIATRPAPPDRERQSFYHLVATVEDEGTPTLSATTHVYVTIVDENDNAPMFQQPHYEVLLDEGPDTLNTSLITIQALDLDEGPNGTVTYAIVAGNIVNTFRIDRHMGVITAAKELDYEISHGRYTLIVTATDQCPILSHRLTSTTTVLVNVNDINDNVPTFPRDYEGPFEVTEGQPGPRVWTFLAHDRDSGPNGQVEYSIMDGDPLGEFVISPVEGVLRVRKDVELDRETIAFYNLTICARDRGMPPLSSTMLVGIRVLDINDNDPVLLNLPMNITISENSPVSSFVAHVLASDADSGCNARLTFNITAGNRERAFFINATTGIVTVNRPLDRERIPEYKLTISVKDNPENPRIARRDYDLLLIFLSDENDNHPLFTKSTYQAEVMENSPAGTPLTVLNGPILALDADQDIYAVVTYQLLGAQSGLFDINSSTGVVTVRSGVIIDREAFSPPILELLLLAEDIGLLNSTAHLLITILDDNDNRPTFSPATLTVHLLENCPPGFSVLQVTATDEDSGLNGELVYRIEAGAQDRFLIHLVTGVIRVGNATIDREEQESYRLTVVATDRGTVPLSGTAIVTILIDDINDSRPEFLNPIQTVSVLESAEPGTVIANITAIDHDLNPKLEYHIVGIVAKDDTDRLVPNQEDAFAVNINTGSVMVKSPMNRELVATYEVTLSVIDNASDLPERSVSVPNAKLTVNVLDVNDNTPQFKPFGITYYMERILEGATPGTTLIAVAAVDPDKGLNGLVTYTLLDLVPPGYVQLEDSSAGKVIANRTVDYEEVHWLNFTVRASDNGSPPRAAEIPVYLEIVDINDNNPIFDQPSYQEAVFEDVPVGTIILTVTATDADSGNFALIEYSLGDGESKFAINPTTGDIYVLSSLDREKKDHYILTALAKDNPGDVASNRRENSVQVVIQVLDVNDCRPQFSKPQFSTSVYENEPAGTSVITMMATDQDEGPNGELTYSLEGPGVEAFHVDMDSGLVTTQRPLQSYEKFSLTVVATDGGEPPLWGTTMLLVEVIDVNDNRPVFVRPPNGTILHIREEIPLRSNVYEVYATDKDEGLNGAVRYSFLKTAGNRDWEFFIIDPISGLIQTAQRLDRESQAVYSLILVASDLGQPVPYETMQPLQVALEDIDDNEPLFVRPPKGSPQYQLLTVPEHSPRGTLVGNVTGAVDADEGPNAIVYYFIAAGNEEKNFHLQPDGCLLVLRDLDREREAIFSFIVKASSNRSWTPPRGPSPTLDLVADLTLQEVRVVLEDINDQPPRFTKAEYTAGVATDAKVGSELIQVLALDADIGNNSLVFYSILAIHYFRALANDSEDVGQVFTMGSMDGILRTFDLFMAYSPGYFVVDIVARDLAGHNDTAIIGIYILRDDQRVKIVINEIPDRVRGFEEEFIHLLSNITGAIVNTDNVQFHVDKKGRVNFAQTELLIHVVNRDTNRILDVDRVIQMIDENKEQLRNLFRNYNVLDVQPAISVRLPDDMSALQMAIIVLAILLFLAAMLFVLMNWYYRTVHKRKLKAIVAGSAGNRGFIDIMDMPNTNKYSFDGANPVWLDPFCRNLELAAQAEHEDDLPENLSEIADLWNSPTRTHGTFGREPAAVKPDDDRYLRAAIQEYDNIAKLGQIIREGPIKGSLLKVVLEDYLRLKKLFAQRMVQKASSCHSSISELIQTELDEEPGDHSPGQGSLRFRHKPPVELKGPDGIHVVHGSTGTLLATDLNSLPEEDQKGLGRSLETLTAAEATAFERNARTESAKSTPLHKLRDVIMETPLEITEL,mutated_sequence,1.0,3354.0,NP_071407.4.a2m,NP_071407.4.npy,ClinVar +NP_071415.1,NP_071415.1.csv,MWAVLRLALRPCARASPAGPRAYHGDSVASLGTQPDLGSALYQENYKQMKALVNQLHERVEHIKLGGGEKARALHISRGKLLPRERIDNLIDPGSPFLELSQFAGYQLYDNEEVPGGGIITGIGRVSGVECMIIANDATVKGGAYYPVTVKKQLRAQEIAMQNRLPCIYLVDSGGAYLPRQADVFPDRDHFGRTFYNQAIMSSKNIAQIAVVMGSCTAGGAYVPAMADENIIVRKQGTIFLAGPPLVKAATGEEVSAEDLGGADLHCRKSGVSDHWALDDHHALHLTRKVVRNLNYQKKLDVTIEPSEEPLFPADELYGIVGANLKRSFDVREVIARIVDGSRFTEFKAFYGDTLVTGFARIFGYPVGIVGNNGVLFSESAKKGTHFVQLCCQRNIPLLFLQNITGFMVGREYEAEGIAKDGAKMVAAVACAQVPKITLIIGGSYGAGNYGMCGRAYSPRFLYIWPNARISVMGGEQAANVLATITKDQRAREGKQFSSADEAALKEPIIKKFEEEGNPYYSSARVWDDGIIDPADTRLVLGLSFSAALNAPIEKTDFGIFRM,mutated_sequence,1.0,563.0,NP_071415.1.a2m,NP_071415.1.npy,ClinVar +NP_071900.2,NP_071900.2.csv,MDQTCELPRRNCLLPFSNPVNLDAPEDKDSPFGNGQSNFSEPLNGCTMQLSTVSGTSQNAYGQDSPSCYIPLRRLQDLASMINVEYLNGSADGSESFQDPEKSDSRAQTPIVCTSLSPGGPTALAMKQEPSCNNSPELQVKVTKTIKNGFLHFENFTCVDDADVDSEMDPEQPVTEDESIEEIFEETQTNATCNYETKSENGVKVAMGSEQDSTPESRHGAVKSPFLPLAPQTETQKNKQRNEVDGSNEKAALLPAPFSLGDTNITIEEQLNSINLSFQDDPDSSTSTLGNMLELPGTSSSSTSQELPFCQPKKKSTPLKYEVGDLIWAKFKRRPWWPCRICSDPLINTHSKMKVSNRRPYRQYYVEAFGDPSERAWVAGKAIVMFEGRHQFEELPVLRRRGKQKEKGYRHKVPQKILSKWEASVGLAEQYDVPKGSKNRKCIPGSIKLDSEEDMPFEDCTNDPESEHDLLLNGCLKSLAFDSEHSADEKEKPCAKSRARKSSDNPKRTSVKKGHIQFEAHKDERRGKIPENLGLNFISGDISDTQASNELSRIANSLTGSNTAPGSFLFSSCGKNTAKKEFETSNGDSLLGLPEGALISKCSREKNKPQRSLVCGSKVKLCYIGAGDEEKRSDSISICTTSDDGSSDLDPIEHSSESDNSVLEIPDAFDRTENMLSMQKNEKIKYSRFAATNTRVKAKQKPLISNSHTDHLMGCTKSAEPGTETSQVNLSDLKASTLVHKPQSDFTNDALSPKFNLSSSISSENSLIKGGAANQALLHSKSKQPKFRSIKCKHKENPVMAEPPVINEECSLKCCSSDTKGSPLASISKSGKVDGLKLLNNMHEKTRDSSDIETAVVKHVLSELKELSYRSLGEDVSDSGTSKPSKPLLFSSASSQNHIPIEPDYKFSTLLMMLKDMHDSKTKEQRLMTAQNLVSYRSPGRGDCSTNSPVGVSKVLVSGGSTHNSEKKGDGTQNSANPSPSGGDSALSGELSASLPGLLSDKRDLPASGKSRSDCVTRRNCGRSKPSSKLRDAFSAQMVKNTVNRKALKTERKRKLNQLPSVTLDAVLQGDRERGGSLRGGAEDPSKEDPLQIMGHLTSEDGDHFSDVHFDSKVKQSDPGKISEKGLSFENGKGPELDSVMNSENDELNGVNQVVPKKRWQRLNQRRTKPRKRMNRFKEKENSECAFRVLLPSDPVQEGRDEFPEHRTPSASILEEPLTEQNHADCLDSAGPRLNVCDKSSASIGDMEKEPGIPSLTPQAELPEPAVRSEKKRLRKPSKWLLEYTEEYDQIFAPKKKQKKVQEQVHKVSSRCEEESLLARGRSSAQNKQVDENSLISTKEEPPVLEREAPFLEGPLAQSELGGGHAELPQLTLSVPVAPEVSPRPALESEELLVKTPGNYESKRQRKPTKKLLESNDLDPGFMPKKGDLGLSKKCYEAGHLENGITESCATSYSKDFGGGTTKIFDKPRKRKRQRHAAAKMQCKKVKNDDSSKEIPGSEGELMPHRTATSPKETVEEGVEHDPGMPASKKMQGERGGGAALKENVCQNCEKLGELLLCEAQCCGAFHLECLGLTEMPRGKFICNECRTGIHTCFVCKQSGEDVKRCLLPLCGKFYHEECVQKYPPTVMQNKGFRCSLHICITCHAANPANVSASKGRLMRCVRCPVAYHANDFCLAAGSKILASNSIICPNHFTPRRGCRNHEHVNVSWCFVCSEGGSLLCCDSCPAAFHRECLNIDIPEGNWYCNDCKAGKKPHYREIVWVKVGRYRWWPAEICHPRAVPSNIDKMRHDVGEFPVLFFGSNDYLWTHQARVFPYMEGDVSSKDKMGKGVDGTYKKALQEAAARFEELKAQKELRQLQEDRKNDKKPPPYKHIKVNRPIGRVQIFTADLSEIPRCNCKATDENPCGIDSECINRMLLYECHPTVCPAGGRCQNQCFSKRQYPEVEIFRTLQRGWGLRTKTDIKKGEFVNEYVGELIDEEECRARIRYAQEHDITNFYMLTLDKDRIIDAGPKGNYARFMNHCCQPNCETQKWSVNGDTRVGLFALSDIKAGTELTFNYNLECLGNGKTVCKCGAPNCSGFLGVRPKNQPIATEEKSKKFKKKQQGKRRTQGEITKEREDECFSCGDAGQLVSCKKPGCPKVYHADCLNLTKRPAGKWECPWHQCDICGKEAASFCEMCPSSFCKQHREGMLFISKLDGRLSCTEHDPCGPNPLEPGEIREYVPPPVPLPPGPSTHLAEQSTGMAAQAPKMSDKPPADTNQMLSLSKKALAGTCQRPLLPERPLERTDSRPQPLDKVRDLAGSGTKSQSLVSSQRPLDRPPAVAGPRPQLSDKPSPVTSPSSSPSVRSQPLERPLGTADPRLDKSIGAASPRPQSLEKTSVPTGLRLPPPDRLLITSSPKPQTSDRPTDKPHASLSQRLPPPEKVLSAVVQTLVAKEKALRPVDQNTQSKNRAALVMDLIDLTPRQKERAASPHQVTPQADEKMPVLESSSWPASKGLGHMPRAVEKGCVSDPLQTSGKAAAPSEDPWQAVKSLTQARLLSQPPAKAFLYEPTTQASGRASAGAEQTPGPLSQSPGLVKQAKQMVGGQQLPALAAKSGQSFRSLGKAPASLPTEEKKLVTTEQSPWALGKASSRAGLWPIVAGQTLAQSCWSAGSTQTLAQTCWSLGRGQDPKPEQNTLPALNQAPSSHKCAESEQK,mutated_sequence,1.0,2696.0,NP_071900.2.a2m,NP_071900.2.npy,ClinVar +NP_071908.2,NP_071908.2.csv,MSGFLEELLGEKLVTGGGEEVDVHSLGARGISLLGLYFGCSLSAPCAQLSASLAAFYGRLRGDAAAGPGPGAGAGAAAEPEPRRRLEIVFVSSDQDQRQWQDFVRDMPWLALPYKEKHRKLKLWNKYRISNIPSLIFLDATTGKVVCRNGLLVIRDDPEGLEFPWGPKPFREVIAGPLLRNNGQSLESSSLEGSHVGVYFSAHWCPPCRSLTRVLVESYRKIKEAGQNFEIIFVSADRSEESFKQYFSEMPWLAVPYTDEARRSRLNRLYGIQGIPTLIMLDPQGEVITRQGRVEVLNDEDCREFPWHPKPVLELSDSNAAQLNEGPCLVLFVDSEDDGESEAAKQLIQPIAEKIIAKYKAKEEEAPLLFFVAGEDDMTDSLRDYTNLPEAAPLLTILDMSARAKYVMDVEEITPAIVEAFVNDFLAEKLKPEPI,mutated_sequence,1.0,435.0,NP_071908.2.a2m,NP_071908.2.npy,ClinVar +NP_071934.3,NP_071934.3.csv,MSVKEGAQRKWAALKEKLGPQDSDPTEANLESADPELCIRLLQMPSVVNYSGLRKRLEGSDGGWMVQFLEQSGLDLLLEALARLSGRGVARISDALLQLTCVSCVRAVMNSRQGIEYILSNQGYVRQLSQALDTSNVMVKKQVFELLAALCIYSPEGHVLTLDALDHYKTVCSQQYRFSIVMNELSGSDNVPYVVTLLSVINAVILGPEDLRARTQLRNEFIGLQLLDVLARLRDLEDADLLIQLEAFEEAKAEDEEELLRVSGGVDMSSHQEVFASLFHKVSCSPVSAQLLSVLQGLLHLEPTLRSSQLLWEALESLVNRAVLLASDAQECTLEEVVERLLSVKGRPRPSPLVKAHKSVQANLDQSQRGSSPQNTTTPKPSVEGQQPAAAAACEPVDHAQSESILKVSQPRALEQQASTPPPPPPPPLLPGSSAEPPPPPPPPPLPSVGAKALPTAPPPPPLPGLGAMAPPAPPLPPPLPGSCEFLPPPPPPLPGLGCPPPPPPLLPGMGWGPPPPPPPLLPCTCSPPVAGGMEEVIVAQVDHGLGSAWVPSHRRVNPPTLRMKKLNWQKLPSNVAREHNSMWASLSSPDAEAVEPDFSSIERLFSFPAAKPKEPTMVAPRARKEPKEITFLDAKKSLNLNIFLKQFKCSNEEVAAMIRAGDTTKFDVEVLKQLLKLLPEKHEIENLRAFTEERAKLASADHFYLLLLAIPCYQLRIECMLLCEGAAAVLDMVRPKAQLVLAACESLLTSRQLPIFCQLILRIGNFLNYGSHTGDADGFKISTLLKLTETKSQQNRVTLLHHVLEEAEKSHPDLLQLPRDLEQPSQAAGINLEIIRSEASSNLKKLLETERKVSASVAEVQEQYTERLQASISAFRALDELFEAIEQKQRELADYLCEDAQQLSLEDTFSTMKAFRDLFLRALKENKDRKEQAAKAERRKQQLAEEEARRPRGEDGKPVRKGPGKQEEVCVIDALLADIRKGFQLRKTARGRGDTDGGSKAASMDPPRATEPVATSNPAGDPVGSTRCPASEPGLDATTASESRGWDLVDAVTPGPQPTLEQLEEGGPRPLERRSSWYVDASDVLTTEDPQCPQPLEGAWPVTLGDAQALKPLKFSSNQPPAAGSSRQDAKDPTSLLGVLQAEADSTSEGLEDAVHSRGARPPAAGPGGDEDEDEEDTAPESALDTSLDKSFSEDAVTDSSGSGTLPRARGRASKGTGKRRKKRPSRSQEEVPPDSDDNKTKKLCVIQ,mutated_sequence,1.0,1249.0,NP_071934.3.a2m,NP_071934.3.npy,ClinVar +NP_072046.2,NP_072046.2.csv,MPAMPSSGPGDTSSSAAEREEDRKDGEEQEEPRGKEERQEPSTTARKVGRPGRKRKHPPVESGDTPKDPAVISKSPSMAQDSGASELLPNGDLEKRSEPQPEEGSPAGGQKGGAPAEGEGAAETLPEASRAVENGCCTPKEGRGAPAEAGKEQKETNIESMKMEGSRGRLRGGLGWESSLRQRPMPRLTFQAGDPYYISKRKRDEWLARWKREAEKKAKVIAGMNAVEENQGPGESQKVEEASPPAVQQPTDPASPTVATTPEPVGSDAGDKNATKAGDDEPEYEDGRGFGIGELVWGKLRGFSWWPGRIVSWWMTGRSRAAEGTRWVMWFGDGKFSVVCVEKLMPLSSFCSAFHQATYNKQPMYRKAIYEVLQVASSRAGKLFPVCHDSDESDTAKAVEVQNKPMIEWALGGFQPSGPKGLEPPEEEKNPYKEVYTDMWVEPEAAAYAPPPPAKKPRKSTAEKPKVKEIIDERTRERLVYEVRQKCRNIEDICISCGSLNVTLEHPLFVGGMCQNCKNCFLECAYQYDDDGYQSYCTICCGGREVLMCGNNNCCRCFCVECVDLLVGPGAAQAAIKEDPWNCYMCGHKGTYGLLRRREDWPSRLQMFFANNHDQEFDPPKVYPPVPAEKRKPIRVLSLFDGIATGLLVLKDLGIQVDRYIASEVCEDSITVGMVRHQGKIMYVGDVRSVTQKHIQEWGPFDLVIGGSPCNDLSIVNPARKGLYEGTGRLFFEFYRLLHDARPKEGDDRPFFWLFENVVAMGVSDKRDISRFLESNPVMIDAKEVSAAHRARYFWGNLPGMNRPLASTVNDKLELQECLEHGRIAKFSKVRTITTRSNSIKQGKDQHFPVFMNEKEDILWCTEMERVFGFPVHYTDVSNMSRLARQRLLGRSWSVPVIRHLFAPLKEYFACV,mutated_sequence,1.0,912.0,NP_072046.2.a2m,NP_072046.2.npy,ClinVar +NP_075044.2,NP_075044.2.csv,MSRRKQGKPQHLSKREFSPEPLEAILTDDEPDHGPLGAPEGDHDLLTCGQCQMNFPLGDILIFIEHKRKQCNGSLCLEKAVDKPPSPSPIEMKKASNPVEVGIQVTPEDDDCLSTSSRGICPKQEHIADKLLHWRGLSSPRSAHGALIPTPGMSAEYAPQGICKDEPSSYTCTTCKQPFTSAWFLLQHAQNTHGLRIYLESEHGSPLTPRVGIPSGLGAECPSQPPLHGIHIADNNPFNLLRIPGSVSREASGLAEGRFPPTPPLFSPPPRHHLDPHRIERLGAEEMALATHHPSAFDRVLRLNPMAMEPPAMDFSRRLRELAGNTSSPPLSPGRPSPMQRLLQPFQPGSKPPFLATPPLPPLQSAPPPSQPPVKSKSCEFCGKTFKFQSNLVVHRRSHTGEKPYKCNLCDHACTQASKLKRHMKTHMHKSSPMTVKSDDGLSTASSPEPGTSDLVGSASSALKSVVAKFKSENDPNLIPENGDEEEEEDDEEEEEEEEEEEEELTESERVDYGFGLSLEAARHHENSSRGAVVGVGDESRALPDVMQGMVLSSMQHFSEAFHQVLGEKHKRGHLAEAEGHRDTCDEDSVAGESDRIDDGTVNGRGCSPGESASGGLSKKLLLGSPSSLSPFSKRIKLEKEFDLPPAAMPNTENVYSQWLAGYAASRQLKDPFLSFGDSRQSPFASSSEHSSENGSLRFSTPPGELDGGISGRSGTGSGGSTPHISGPGPGRPSSKEGRRSDTCEYCGKVFKNCSNLTVHRRSHTGERPYKCELCNYACAQSSKLTRHMKTHGQVGKDVYKCEICKMPFSVYSTLEKHMKKWHSDRVLNNDIKTE,mutated_sequence,1.0,835.0,NP_075044.2.a2m,NP_075044.2.npy,ClinVar +NP_075555.1,NP_075555.1.csv,MMASYPEPEDAAGALLAPETGRTVKEPEGPPPSPGKGGGGGGGTAPEKPDPAQKPPYSYVALIAMAIRESAEKRLTLSGIYQYIIAKFPFYEKNKKGWQNSIRHNLSLNECFIKVPREGGGERKGNYWTLDPACEDMFEKGNYRRRRRMKRPFRPPPAHFQPGKGLFGAGGAAGGCGVAGAGADGYGYLAPPKYLQSGFLNNSWPLPQPPSPMPYASCQMAAAAAAAAAAAAAAGPGSPGAAAVVKGLAGPAASYGPYTRVQSMALPPGVVNSYNGLGGPPAAPPPPPHPHPHPHAHHLHAAAAPPPAPPHHGAAAPPPGQLSPASPATAAPPAPAPTSAPGLQFACARQPELAMMHCSYWDHDSKTGALHSRLDL,mutated_sequence,1.0,376.0,NP_075555.1.a2m,NP_075555.1.npy,ClinVar +NP_075598.2,NP_075598.2.csv,MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR,mutated_sequence,1.0,822.0,NP_075598.2.a2m,NP_075598.2.npy,ClinVar +NP_076872.1,NP_076872.1.csv,MDWKTLQALLSGVNKYSTAFGRIWLSVVFVFRVLVYVVAAERVWGDEQKDFDCNTKQPGCTNVCYDNYFPISNIRLWALQLIFVTCPSLLVILHVAYREERERRHRQKHGDQCAKLYDNAGKKHGGLWWTYLFSLIFKLIIEFLFLYLLHTLWHGFNMPRLVQCANVAPCPNIVDCYIARPTEKKIFTYFMVGASAVCIVLTICELCYLICHRVLRGLHKDKPRGGCSPSSSASRASTCRCHHKLVEAGEVDPDPGNNKLQASAPNLTPI,mutated_sequence,1.0,270.0,NP_076872.1.a2m,NP_076872.1.npy,ClinVar +NP_076932.1,NP_076932.1.csv,MRGNLALVGVLISLAFLSLLPSGHPQPAGDDACSVQILVPGLKGDAGEKGDKGAPGRPGRVGPTGEKGDMGDKGQKGSVGRHGKIGPIGSKGEKGDSGDIGPPGPNGEPGLPCECSQLRKAIGEMDNQVSQLTSELKFIKNAVAGVRETESKIYLLVKEEKRYADAQLSCQGRGGTLSMPKDEAANGLMAAYLAQAGLARVFIGINDLEKEGAFVYSDHSPMRTFNKWRSGEPNNAYDEEDCVEMVASGGWNDVACHTTMYFMCEFDKENM,mutated_sequence,1.0,271.0,NP_076932.1.a2m,NP_076932.1.npy,ClinVar +NP_077277.1,NP_077277.1.csv,MRLTRCQAALAAAITLNLLVLFYVSWLQHQPRNSRARGPRRASAAGPRVTVLVREFEAFDNAVPELVDSFLQQDPAQPVVVAADTLPYPPLALPRIPNVRLALLQPALDRPAAASRPETYVATEFVALVPDGARAEAPGLLERMVEALRAGSARLVAAPVATANPARCLALNVSLREWTARYGAAPAAPRCDALDGDAVVLLRARDLFNLSAPLARPVGTSLFLQTALRGWAVQLLDLTFAAARQPPLATAHARWKAEREGRARRAALLRALGIRLVSWEGGRLEWFGCNKETTRCFGTVVGDTPAYLYEERWTPPCCLRALRETARYVVGVLEAAGVRYWLEGGSLLGAARHGDIIPWDYDVDLGIYLEDVGNCEQLRGAEAGSVVDERGFVWEKAVEGDFFRVQYSESNHLHVDLWPFYPRNGVMTKDTWLDHRQDVEFPEHFLQPLVPLPFAGFVAQAPNNYRRFLELKFGPGVIENPQYPNPALLSLTGSG,mutated_sequence,1.0,495.0,NP_077277.1.a2m,NP_077277.1.npy,ClinVar +NP_077288.2,NP_077288.2.csv,MLFKLLQRQTYTCLSHRYGLYVCFLGVVVTIVSAFQFGEVVLEWSRDQYHVLFDSYRDNIAGKSFQNRLCLPMPIDVVYTWVNGTDLELLKELQQVREQMEEEQKAMREILGKNTTEPTKKSEKQLECLLTHCIKVPMLVLDPALPANITLKDLPSLYPSFHSASDIFNVAKPKNPSTNVSVVVFDSTKDVEDAHSGLLKGNSRQTVWRGYLTTDKEVPGLVLMQDLAFLSGFPPTFKETNQLKTKLPENLSSKVKLLQLYSEASVALLKLNNPKDFQELNKQTKKNMTIDGKELTISPAYLLWDLSAISQSKQDEDISASRFEDNEELRYSLRSIERHAPWVRNIFIVTNGQIPSWLNLDNPRVTIVTHQDVFRNLSHLPTFSSPAIESHIHRIEGLSQKFIYLNDDVMFGKDVWPDDFYSHSKGQKVYLTWPVPNCAEGCPGSWIKDGYCDKACNNSACDWDGGDCSGNSGGSRYIAGGGGTGSIGVGQPWQFGGGINSVSYCNQGCANSWLADKFCDQACNVLSCGFDAGDCGQDHFHELYKVILLPNQTHYIIPKGECLPYFSFAEVAKRGVEGAYSDNPIIRHASIANKWKTIHLIMHSGMNATTIHFNLTFQNTNDEEFKMQITVEVDTREGPKLNSTAQKGYENLVSPITLLPEAEILFEDIPKEKRFPKFKRHDVNSTRRAQEEVKIPLVNISLLPKDAQLSLNTLDLQLEHGDITLKGYNLSKSALLRSFLMNSQHAKIKNQAIITDETNDSLVAPQEKQVHKSILPNSLGVSERLQRLTFPAVSVKVNGHDQGQNPPLDLETTARFRVETHTQKTIGGNVTKEKPPSLIVPLESQMTKEKKITGKEKENSRMEENAENHIGVTEVLLGRKLQHYTDSYLGFLPWEKKKYFQDLLDEEESLKTQLAYFTDSKNTGRQLKDTFADSLRYVNKILNSKFGFTSRKVPAHMPHMIDRIVMQELQDMFPEEFDKTSFHKVRHSEDMQFAFSYFYYLMSAVQPLNISQVFDEVDTDQSGVLSDREIRTLATRIHELPLSLQDLTGLEHMLINCSKMLPADITQLNNIPPTQESYYDPNLPPVTKSLVTNCKPVTDKIHKAYKDKNKYRFEIMGEEEIAFKMIRTNVSHVVGQLDDIRKNPRKFVCLNDNIDHNHKDAQTVKAVLRDFYESMFPIPSQFELPREYRNRFLHMHELQEWRAYRDKLKFWTHCVLATLIMFTIFSFFAEQLIALKRKIFPRRRIHKEASPNRIRV,mutated_sequence,1.0,1256.0,NP_077288.2.a2m,NP_077288.2.npy,ClinVar +NP_077744.4,NP_077744.4.csv,MDFLLLQDPASTCVPEPASQHTLRSGPGCLQQPEQQGVRDPGGIWAKLGAAEASAERLQGRRSRGASGSEPQQMGSDVRDLNALLPAVPSLGGGGGCALPVSGAAQWAPVLDFAPPGASAYGSLGGPAPPPAPPPPPPPPPHSFIKQEPSWGGAEPHEEQCLSAFTVHFSGQFTGTAGACRYGPFGPPPPSQASSGQARMFPNAPYLPSCLESQPAIRNQGYSTVTFDGTPSYGHTPSHHAAQFPNHSFKHEDPMGQQGSLGEQQYSVPPPVYGCHTPTDSCTGSQALLLRTPYSSDNLYQMTSQLECMTWNQMNLGATLKGVAAGSSSSVKWTEGQSNHSTGYESDNHTTPILCGAQYRIHTHGVFRGIQDVRRVPGVAPTLVRSASETSEKRPFMCAYPGCNKRYFKLSHLQMHSRKHTGEKPYQCDFKDCERRFSRSDQLKRHQRRHTGVKPFQCKTCQRKFSRSDHLKTHTRTHTGKTSEKPFSCRWPSCQKKFARSDELVRHHNMHQRNMTKLQLAL,mutated_sequence,1.0,522.0,NP_077744.4.a2m,NP_077744.4.npy,ClinVar +NP_078853.2,NP_078853.2.csv,MGGCFCIPRERSLTRGPGKETPSKDPTVSSECIASSEYKEKCFLPQNINPDLTLSFCVKSRSRRCVNGPLQEAARRRLWALENEDQEVRMLFKDLSARLVSIQSQRAQFLITFKTMEEIWKFSTYLNLGYVSMCLEHLLFDHKYWLNCILVEDTEIQVSVDDKHLETIYLGLLIQEGHFFCRALCSVTPPAEKEGECLTLCKNELISVKMAEAGSELEGVSLVTGQRGLVLVSALEPLPLPFHQWFLKNYPGSCGLSRKRDWTGSYQIGRGRCKALTGYEPGEKDELNFYQGESIEIIGFVIPGLQWFIGKSTSSGQVGFVPTRNIDPDSYSPMSRNSAFLSDEERCSLLALGSDKQTECSSFLHTLARTDITSVYRLSGFESIQNPPNDLSASQPEGFKEVRPGRAWEEHQAVGSRQSSSSEDSSLEEELLSATSDSYRLPEPDDLDDPELLMDLSTGQEEEAENFAPILAFLDHEGYADHFKSLYDFSFSFLTSSFYSFSEEDEFVAYLEASRKWAKKSHMTWAHARLCFLLGRLSIRKVKLSQARVYFEEAIHILNGAFEDLSLVATLYINLAAIYLKQRLRHKGSALLEKAGALLACLPDRESSAKHELDVVAYVLRQGIVVGSSPLEARACFLAIRLLLSLGRHEEVLPFAERLQLLSGHPPASEAVASVLSFLYDKKYLPHLAVASVQQHGIQSAQGMSLPIWQVHLVLQNTTKLLGFPSPGWGEVSALACPMLRQALAACEELADRSTQRALCLILSKVYLEHRSPDGAIHYLSQALVLGQLLGEQESFESSLCLAWAYLLASQAKKALDVLEPLLCSLKETESLTQRGVIYNLLGLALQGEGRVNRAAKSYLRALNRAQEVGDVHNQAVAMANLGHLSLKSWAQHPARNYLLQAVRLYCELQASKETDMELVQVFLWLAQVLVSGHQLTHGLLCYEMALLFGLRHRHLKSQLQATKSLCHFYSSVSPNPEACITYHEHWLALAQQLRDREMEGRLLESLGQLYRNLNTARSLRRSLTCIKESLRIFIDLGETDKAAEAWLGAGRLHYLMQEDELVELCLQAAIQTALKSEEPLLALKLYEEAGDVFFNGTRHRHHAVEYYRAGAVPLARRLKAVRTELRIFNKLTELQISLEGYEKALEFATLAARLSTVTGDQRQELVAFHRLATVYYSLHMYEMAEDCYLKTLSLCPPWLQSPKEALYYAKVYYRLGRLTFCQLKDAHDATEYFLLALAAAVLLGDEELQDTIRSRLDNICQSPLWHSRPSGCSSERARWLSGGGLAL,mutated_sequence,1.0,1288.0,NP_078853.2.a2m,NP_078853.2.npy,ClinVar +NP_078916.3,NP_078916.3.csv,MSPARRCRGMRAAVAASVGLSEGPAGSRSGRLFRPPSPAPAAPGARLLRLPGSGAVQAASPERAGWTEALRAAVAELRAGAVVAVPTDTLYGLACAASCSAALRAVYRLKGRSEAKPLAVCLGRVADVYRYCRVRVPEGLLKDLLPGPVTLVMERSEELNKDLNPFTPLVGIRIPDHAFMQDLAQMFEGPLALTSANLSSQASSLNVEEFQDLWPQLSLVIDGGQIGDGQSPECRLGSTVVDLSVPGKFGIIRPGCALESTTAILQQKYGLLPSHASYL,mutated_sequence,1.0,279.0,NP_078916.3.a2m,NP_078916.3.npy,ClinVar +NP_078941.2,NP_078941.2.csv,MSISSDEVNFLVYRYLQESGFSHSAFTFGIESHISQSNINGALVPPAALISIIQKGLQYVEAEVSINEDGTLFDGRPIESLSLIDAVMPDVVQTRQQAYRDKLAQQQAAAAAAAAAAASQQGSAKNGENTANGEENGAHTIANNHTDMMEVDGDVEIPPNKAVVLRGHESEVFICAWNPVSDLLASGSGDSTARIWNLSENSTSGSTQLVLRHCIREGGQDVPSNKDVTSLDWNSEGTLLATGSYDGFARIWTKDGNLASTLGQHKGPIFALKWNKKGNFILSAGVDKTTIIWDAHTGEAKQQFPFHSAPALDVDWQSNNTFASCSTDMCIHVCKLGQDRPIKTFQGHTNEVNAIKWDPTGNLLASCSDDMTLKIWSMKQDNCVHDLQAHNKEIYTIKWSPTGPGTNNPNANLMLASASFDSTVRLWDVDRGICIHTLTKHQEPVYSVAFSPDGRYLASGSFDKCVHIWNTQTGALVHSYRGTGGIFEVCWNAAGDKVGASASDGSVCVLDLRK,mutated_sequence,1.0,514.0,NP_078941.2.a2m,NP_078941.2.npy,ClinVar +NP_078961.3,NP_078961.3.csv,MLSSMAAAGSVKAALQVAEVLEAIVSCCVGPEGRQVLCTKPTGEVLLSRNGGRLLEALHLEHPIARMIVDCVSSHLKKTGDGAKTFIIFLCHLLRGLHAITDREKDPLMCENIQTHGRHWKNCSRWKFISQALLTFQTQILDGIMDQYLSRHFLSIFSSAKERTLCRSSLELLLEAYFCGRVGRNNHKFISQLMCDYFFKCMTCKSGIGVFELVDDHFVELNVGVTGLPVSDSRIIAGLVLQKDFSVYRPADGDMRMVIVTETIQPLFSTSGSEFILNSEAQFQTSQFWIMEKTKAIMKHLHSQNVKLLISSVKQPDLVSYYAGVNGISVVECLSSEEVSLIRRIIGLSPFVPPQAFSQCEIPNTALVKFCKPLILRSKRYVHLGLISTCAFIPHSIVLCGPVHGLIEQHEDALHGALKMLRQLFKDLDLNYMTQTNDQNGTSSLFIYKNSGESYQAPDPGNGSIQRPYQDTVAENKDALEKTQTYLKVHSNLVIPDVELETYIPYSTPTLTPTDTFQTVETLTCLSLERNRLTDYYEPLLKNNSTAYSTRGNRIEISYENLQVTNITRKGSMLPVSCKLPNMGTSQSYLSSSMPAGCVLPVGGNFEILLHYYLLNYAKKCHQSEETMVSMIIANALLGIPKVLYKSKTGKYSFPHTYIRAVHALQTNQPLVSSQTGLESVMGKYQLLTSVLQCLTKILTIDMVITVKRHPQKVHNQDSEDEL,mutated_sequence,1.0,723.0,NP_078961.3.a2m,NP_078961.3.npy,ClinVar +NP_079033.4,NP_079033.4.csv,MAAADAEAVPARGEPQQDCCVKTELLGEETPMAADEGSAEKQAGEAHMAADGETNGSCENSDASSHANAAKHTQDSARVNPQDGTNTLTRIAENGVSERDSEAAKQNHVTADDFVQTSVIGSNGYILNKPALQAQPLRTTSTLASSLPGHAAKTLPGGAGKGRTPSAFPQTPAAPPATLGEGSADTEDRKLPAPGADVKVHRARKTMPKSVVGLHAASKDPREVREARDHKEPKEEINKNISDFGRQQLLPPFPSLHQSLPQNQCYMATTKSQTACLPFVLAAAVSRKKKRRMGTYSLVPKKKTKVLKQRTVIEMFKSITHSTVGSKGEKDLGASSLHVNGESLEMDSDEDDSEELEEDDGHGAEQAAAFPTEDSRTSKESMSEADRAQKMDGESEEEQESVDTGEEEEGGDESDLSSESSIKKKFLKRKGKTDSPWIKPARKRRRRSRKKPSGALGSESYKSSAGSAEQTAPGDSTGYMEVSLDSLDLRVKGILSSQAEGLANGPDVLETDGLQEVPLCSCRMETPKSREITTLANNQCMATESVDHELGRCTNSVVKYELMRPSNKAPLLVLCEDHRGRMVKHQCCPGCGYFCTAGNFMECQPESSISHRFHKDCASRVNNASYCPHCGEESSKAKEVTIAKADTTSTVTPVPGQEKGSALEGRADTTTGSAAGPPLSEDDKLQGAASHVPEGFDPTGPAGLGRPTPGLSQGPGKETLESALIALDSEKPKKLRFHPKQLYFSARQGELQKVLLMLVDGIDPNFKMEHQNKRSPLHAAAEAGHVDICHMLVQAGANIDTCSEDQRTPLMEAAENNHLEAVKYLIKAGALVDPKDAEGSTCLHLAAKKGHYEVVQYLLSNGQMDVNCQDDGGWTPMIWATEYKHVDLVKLLLSKGSDINIRDNEENICLHWAAFSGCVDIAEILLAAKCDLHAVNIHGDSPLHIAARENRYDCVVLFLSRDSDVTLKNKEGETPLQCASLNSQVWSALQMSKALQDSAPDRPSPVERIVSRDIARGYERIPIPCVNAVDSEPCPSNYKYVSQNCVTSPMNIDRNITHLQYCVCIDDCSSSNCMCGQLSMRCWYDKDGRLLPEFNMAEPPLIFECNHACSCWRNCRNRVVQNGLRARLQLYRTRDMGWGVRSLQDIPPGTFVCEYVGELISDSEADVREEDSYLFDLDNKDGEVYCIDARFYGNVSRFINHHCEPNLVPVRVFMAHQDLRFPRIAFFSTRLIEAGEQLGFDYGERFWDIKGKLFSCRCGSPKCRHSSAALAQRQASAAQEAQEDGLPDTSSAAAADPL,mutated_sequence,1.0,1298.0,NP_079033.4.a2m,NP_079033.4.npy,ClinVar +NP_079093.2,NP_079093.2.csv,MVSHFMGSLSVLCFLLLLGFQFVCPQPSTQHRKVPQRMAAEGAPEDDGGGGAPGVWGAWGPWSACSRSCSGGVMEQTRPCLPRSYRLRGGQRPGAPARAFADHVVSAVRTSVPLHRSRDETPALAGTDASRQGPTVLRGSRHPQPQGLEVTGDRRSRTRGTIGPGKYGYGKAPYILPLQTDTAHTPQRLRRQKLSSRHSRSQGASSARHGYSSPAHQVPQHGPLYQSDSGPRSGLQAAEAPIYQLPLTHDQGYPAASSLFHSPETSNNHGVGTHGATQSFSQPARSTAISCIGAYRQYKLCNTNVCPESSRSIREVQCASYNNKPFMGRFYEWEPFAEVKGNRKCELNCQAMGYRFYVRQAEKVIDGTPCDQNGTAICVSGQCKSIGCDDYLGSDKVVDKCGVCGGDNTGCQVVSGVFKHALTSLGYHRVVEIPEGATKINITEMYKSNNYLALRSRSGRSIINGNWAIDRPGKYEGGGTMFTYKRPNEISSTAGESFLAEGPTNEILDVYMIHQQPNPGVHYEYVIMGTNAISPQVPPHRRPGEPFNGQMVTEGRSQEEGEQKGRNEEKEDLRGEAPEMFTSESAQTFPVRHPDRFSPHRPDNLVPPAPQPPRRSRDHNWKQLGTTECSTTCGKGSQYPIFRCVHRSTHEEAPESYCDSSMKPTPEEEPCNIFPCPAFWDIGEWSECSKTCGLGMQHRQVLCRQVYANRSLTVQPYRCQHLEKPETTSTCQLKICSEWQIRTDWTSCSVPCGVGQRTRDVKCVSNIGDVVDDEECNMKLRPNDIENCDMGPCAKSWFLTEWSERCSAECGAGVRTRSVVCMTNHVSSLPLEGCGNNRPAEATPCDNGPCTGKVEWFAGSWSQCSIECGSGTQQREVICVRKNADTFEVLDPSECSFLEKPPSQQSCHLKPCGAKWFSTEWSMCSKSCQGGFRVREVRCLSDDMTLSNLCDPQLKPEERESCNPQDCVPEVDENCKDKYYNCNVVVQARLCVYNYYKTACCASCTRVANRQTGFLGSR,mutated_sequence,1.0,1018.0,NP_079093.2.a2m,NP_079093.2.npy,ClinVar +NP_079130.2,NP_079130.2.csv,MEAARPPPTAGKFVVVGGGIAGVTCAEQLATHFPSEDILLVTASPVIKAVTNFKQISKILEEFDVEEQSSTMLGKRFPNIKVIESGVKQLKSEEHCIVTEDGNQHVYKKLCLCAGAKPKLICEGNPYVLGIRDTDSAQEFQKQLTKAKRIMIIGNGGIALELVYEIEGCEVIWAIKDKAIGNTFFDAGAAEFLTSKLIAEKSEAKIAHKRTRYTTEGRKKEARSKSKADNVGSALGPDWHEGLNLKGTKEFSHKIHLETMCEVKKIYLQDEFRILKKKSFTFPRDHKSVTADTEMWPVYVELTNEKIYGCDFIVSATGVTPNVEPFLHGNSFDLGEDGGLKVDDHMHTSLPDIYAAGDICTTSWQLSPVWQQMRLWTQARQMGWYAAKCMAAASSGDSIDMDFSFELFAHVTKFFNYKVVLLGKYNAQGLGSDHELMLRCTKGREYIKVVMQNGRMMGAVLIGETDLEETFENLILNQMNLSSYGEDLLDPNIDIEDYFD,mutated_sequence,1.0,500.0,NP_079130.2.a2m,NP_079130.2.npy,ClinVar +NP_079265.2,NP_079265.2.csv,MFLHSVNLWNLAFYVFMVFLATLGLWDVFFGFEENKCSMSYMFEYPEYQKIELPKKLAKRYPAYELYLYGEGSYAEEHKILPLTGIPVLFLPGNAGSYKQVRSIGSIALRKAEDIDFKYHFDFFSVNFNEELVALYGGSLQKQTKFVHECIKTILKLYKGQEFAPKSVAIIGHSMGGLVARALLTLKNFKHDLINLLITQATPHVAPVMPLDRFITDFYTTVNNYWILNARHINLTTLSVAGGFRDYQVRSGLTFLPKLSHHTSALSVVSSAVPKTWVSTDHLSIVWCKQLQLTTVRAFFDLIDADTKQITQNSKKKLSVLYHHFIRHPSKHFEENPAIISDLTGTSMWVLVKVSKWTYVAYNESEKIYFTFPLENHRKIYTHVYCQSTMLDTNSWIFACINSTSMCLQGVDLSWKAELLPTIKYLTLRLQDYPSLSHLVVYVPSVRGSKFVVDCEFFKKEKRYIQLPVTHLFSFGLSSRKVVLNTNGLYYNLELLNFGQIYQAFKINVVSKCSAVKEEITSIYRLHIPWSYEDSLTIAQAPSSTEISLKLHIAQPENNTHVALFKMYTSSDCRYEVTVKTSFSQILGQVVRFHGGALPAYVVSNILLAYRGQLYSLFSTGCCLEYATMLDKEAKPYKVDPFVIIIKFLLGYKWFKELWDVLLLPELDAVILTCQSMCFPLISLILFLFGTCTAYWSGLLSSASVRLLSSLWLALKRPSELPKDIKMISPDLPFLTIVLIIVSWTTCGALAILLSYLYYVFKVVHLQASLTTFKNSQPVNPKHSRRSEKKSNHHKDSSIHHLRLSANDAEDSLRMHSTVINLLTWIVLLSMPSLIYWLKNLRYYFKLNPDPCKPLAFILIPTMAILGNTYTVSIKSSKLLKTTSQFPLPLAVGVIAFGSAHLYRLPCFVFIPLLLHALCNFM,mutated_sequence,1.0,922.0,NP_079265.2.a2m,NP_079265.2.npy,ClinVar +NP_079375.3,NP_079375.3.csv,MAAGRAQVPSSEQAWLEDAQVFIQKTLCPAVKEPNVQLTPLVIDCVKTVWLSQGRNQGSTLPLSYSFVSVQDLKTHQRLPCCSHLSWSSSAYQAWAQEAGPNGNPLPREQLLLLGTLTDLSADLEQECRNGSLYVRDNTGVLSCELIDLDLSWLGHLFLFPRWSYLPPARWNSSGEGHLELWDAPVPVFPLTISPGPVTPIPVLYPESASCLLRLRNKLRGVQRNLAGSLVRLSALVKSKQKAYFILSLGRSHPAVTHVSIIVQVPAQLVWHRALRPGTAYVLTELRVSKIRGQRQHVWMTSQSSRLLLLKPECVQELELELEGPLLEADPKPLPMPSNSEDKKDPESLVRYSRLLSYSGAVTGVLNEPAGLYELDGQLGLCLAYQQFRGLRRVMRPGVCLQLQDVHLLQSVGGGTRRPVLAPCLRGAVLLQSFSRQKPGAHSSRQAYGASLYEQLVWERQLGLPLYLWATKALEELACKLCPHVLRHHQFLQHSSPGSPSLGLQLLAPTLDLLAPPGSPVRNAHNEILEEPHHCPLQKYTRLQTPSSFPTLATLKEEGQRKAWASFDPKALLPLPEASYLPSCQLNRRLAWSWLCLLPSAFCPAQVLLGVLVASSHKGCLQLRDQSGSLPCLLLAKHSQPLSDPRLIGCLVRAERFQLIVERDVRSSFPSWKELSMPGFIQKQQARVYVQFFLADALILPVPRPCLHSATPSTPQTDPTGPEGPHLGQSRLFLLCHKEALMKRNFCVPPGASPEVPKPALSFYVLGSWLGGTQRKEGTGWGLPEPQGNDDNDQKVHLIFFGSSVRWFEFLHPGQVYRLIAPGPATPMLFEKDGSSCISRRPLELAGCASCLTVQDNWTLELESSQDIQDVLDANKSLPESSLTDLLSDNFTDSLVSFSAEILSRTLCEPLVASLWMKLGNTGAMRRCVKLTVALETAECEFPPHLDVYIEDPHLPPSLGLLPGARVHFSQLEKRVSRSHNVYCCFRSSTYVQVLSFPPETTISIPLPHIYLAELLQGGQSPFQATASCHIVSVFSLQLFWVCAYCTSICRQGKCTRLGSTCPTQTAISQAIIRLLVEDGTAEAVVTCRNHHVAAALGLCPREWASLLDFVQVPGRVVLQFAGPGAQLESSARVDEPMTMFLWTLCTSPSVLRPIVLSFELERKPSKIVPLEPPRLQRFQCGELPFLTHVNPRLRLSCLSIRESEYSSSLGILASSC,mutated_sequence,1.0,1217.0,NP_079375.3.a2m,NP_079375.3.npy,ClinVar +NP_079483.3,NP_079483.3.csv,MGWDLGTRLFQRQEQRSRLSRIWLEKTRVFLEGSTRTPALPHCLFWLLQVPSTQDPLFPGYGPQCPVDLAGPPCLRPLFGGLGGYWRALQRGREGRTMTSRASELSPGRSVTAGIIIVGDEILKGHTQDTNTFFLCRTLRSLGVQVCRVSVVPDEVATIAAEVTSFSNRFTHVLTAGGIGPTHDDVTFEAVAQAFGDELKPHPKLEAATKALGGEGWEKLSLVPSSARLHYGTDPCTGQPFRFPLVSVRNVYLFPGIPELLRRVLEGMKGLFQNPAVQFHSKELYVAADEASIAPILAEAQAHFGRRLGLGSYPDWGSNYYQVKLTLDSEEEGPLEECLAYLTARLPQGSLVPYMPNAVEQASEAVYKLAESGSSLGKKVAGALQTIETSLAQYSLTQLCVGFNGGKDCTALLHLFHAAVQRKLPDVPNPLQILYIRSISPFPELEQFLQDTIKRYNLQMLEAEGSMKQALGELQARHPQLEAVLMGTRRTDPYSCSLCPFSPTDPGWPAFMRINPLLDWTYRDIWDFLRQLFVPYCILYDRGYTSLGSRENTVRNPALKCLSPGGHPTYRPAYLLENEEEERNSRT,mutated_sequence,1.0,587.0,NP_079483.3.a2m,NP_079483.3.npy,ClinVar +NP_079495.1,NP_079495.1.csv,MADQRQRSLSTSGESLYHVLGLDKNATSDDIKKSYRKLALKYHPDKNPDNPEAADKFKEINNAHAILTDATKRNIYDKYGSLGLYVAEQFGEENVNTYFVLSSWWAKALFVFCGLLTCCYCCCCLCCCFNCCCGKCKPKAPEGEETEFYVSPEDLEAQLQSDEREATDTPIVIQPASATETTQLTADSHPSYHTDGFN,mutated_sequence,1.0,198.0,NP_079495.1.a2m,NP_079495.1.npy,ClinVar +NP_079519.1,NP_079519.1.csv,MDCYRTSLSSSWIYPTVILCLFGFFSMMRPSEPFLIPYLSGPDKNLTSAEITNEIFPVWTYSYLVLLLPVFVLTDYVRYKPVIILQGISFIITWLLLLFGQGVKTMQVVEFFYGMVTAAEVAYYAYIYSVVSPEHYQRVSGYCRSVTLAAYTAGSVLAQLLVSLANMSYFYLNVISLASVSVAFLFSLFLPMPKKSMFFHAKPSREIKKSSSVNPVLEETHEGEAPGCEEQKPTSEILSTSGKLNKGQLNSLKPSNVTVDVFVQWFQDLKECYSSKRLFYWSLWWAFATAGFNQVLNYVQILWDYKAPSQDSSIYNGAVEAIATFGGAVAAFAVGYVKVNWDLLGELALVVFSVVNAGSLFLMHYTANIWACYAGYLIFKSSYMLLITIAVFQIAVNLNVERYALVFGINTFIALVIQTIMTVIVVDQRGLNLPVSIQFLVYGSYFAVIAGIFLMRSMYITYSTKSQKDVQSPAPSENPDVSHPEEESNIIMSTKL,mutated_sequence,1.0,496.0,NP_079519.1.a2m,NP_079519.1.npy,ClinVar +NP_079541.1,NP_079541.1.csv,MAEAVFHAPKRKRRVYETYESPLPIPFGQDHGPLKEFKIFRAEMINNNVIVRNAEDIEQLYGKGYFGKGILSRSRPSFTISDPKLVAKWKDMKTNMPIITSKRYQHSVEWAAELMRRQGQDESTVRRILKDYTKPLEHPPVKRNEEAQVHDKLNSGMVSNMEGTAGGERPSVVNGDSGKSGGVGDPREPLGCLQEGSGCHPTTESFEKSVREDASPLPHVCCCKQDALILQRGLHHEDGSQHIGLLHPGDRGPDHEYVLVEEAECAMSEREAAPNEELVQRNRLICRRNPYRIFEYLQLSLEEAFFLVYALGCLSIYYEKEPLTIVKLWKAFTVVQPTFRTTYMAYHYFRSKGWVPKVGLKYGTDLLLYRKGPPFYHASYSVIIELVDDHFEGSLRRPLSWKSLAALSRVSVNVSKELMLCYLIKPSTMTDKEMESPECMKRIKVQEVILSRWVSSRERSDQDDL,mutated_sequence,1.0,465.0,NP_079541.1.a2m,NP_079541.1.npy,ClinVar +NP_085911.2,NP_085911.2.csv,MANETQKVGAIHFPFPFTPYSIQEDFMAELYRVLEAGKIGIFESPTGTGKSLSLICGALSWLRDFEQKKREEEARLLETGTGPLHDEKDESLCLSSSCEGAAGTPRPAGEPAWVTQFVQKKEERDLVDRLKAEQARRKQREERLQQLQHRVQLKYAAKRLRQEEEERENLLRLSREMLETGPEAERLEQLESGEEELVLAEYESDEEKKVASRVDEDEDDLEEEHITKIYYCSRTHSQLAQFVHEVKKSPFGKDVRLVSLGSRQNLCVNEDVKSLGSVQLINDRCVDMQRSRHEKKKGAEEEKPKRRRQEKQAACPFYNHEQMGLLRDEALAEVKDMEQLLALGKEARACPYYGSRLAIPAAQLVVLPYQMLLHAATRQAAGIRLQDQVVIIDEAHNLIDTITGMHSVEVSGSQLCQAHSQLLQYVERYGKRLKAKNLMYLKQILYLLEKFVAVLGGNIKQNPNTQSLSQTGTELKTINDFLFQSQIDNINLFKVQRYCEKSMISRKLFGFTERYGAVFSSREQPKLAGFQQFLQSLQPRTTEALAAPADESQASTLRPASPLMHIQGFLAALTTANQDGRVILSRQGSLSQSTLKFLLLNPAVHFAQVVKECRAVVIAGGTMQPVSDFRQQLLACAGVEAERVVEFSCGHVIPPDNILPLVICSGISNQPLEFTFQKRELPQMMDEVGRILCNLCGVVPGGVVCFFPSYEYLRQVHAHWEKGGLLGRLAARKKIFQEPKSAHQVEQVLLAYSRCIQACGQERGQVTGALLLSVVGGKMSEGINFSDNLGRCVVMVGMPFPNIRSAELQEKMAYLDQTLPRAPGQAPPGKALVENLCMKAVNQSIGRAIRHQKDFASVVLLDQRYARPPVLAKLPAWIRARVEVKATFGPAIAAVQKFHREKSASS,mutated_sequence,1.0,906.0,NP_085911.2.a2m,NP_085911.2.npy,ClinVar +NP_109587.1,NP_109587.1.csv,MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV,mutated_sequence,1.0,400.0,NP_109587.1.a2m,NP_109587.1.npy,ClinVar +NP_110426.4,NP_110426.4.csv,MSGFENLNTDFYQTSYSIDDQSQQSYDYGGSGGPYSKQYAGYDYSQQGRFVPPDMMQPQQPYTGQIYQPTQAYTPASPQPFYGNNFEDEPPLLEELGINFDHIWQKTLTVLHPLKVADGSIMNETDLAGPMVFCLAFGATLLLAGKIQFGYVYGISAIGCLGMFCLLNLMSMTGVSFGCVASVLGYCLLPMILLSSFAVIFSLQGMVGIILTAGIIGWCSFSASKIFISALAMEGQQLLVAYPCALLYGVFALISVF,mutated_sequence,1.0,257.0,NP_110426.4.a2m,NP_110426.4.npy,ClinVar +NP_112506.2,NP_112506.2.csv,MDEKTKKAEEMALSLTRAVAGGDEQVAMKCAIWLAEQRVPLSVQLKPEVSPTQDIRLWVSVEDAQMHTVTIWLTVRPDMTVASLKDMVFLDYGFPPVLQQWVIGQRLARDQETLHSHGVRQNGDSAYLYLLSARNTSLNPQELQRERQLRMLEDLGFKDLTLQPRGPLEPGPPKPGVPQEPGRGQPDAVPEPPPVGWQCPGCTFINKPTRPGCEMCCRARPEAYQVPASYQPDEEERARLAGEEEALRQYQQRKQQQQEGNYLQHVQLDQRSLVLNTEPAECPVCYSVLAPGEAVVLRECLHTFCRECLQGTIRNSQEAEVSCPFIDNTYSCSGKLLEREIKALLTPEDYQRFLDLGISIAENRSAFSYHCKTPDCKGWCFFEDDVNEFTCPVCFHVNCLLCKAIHEQMNCKEYQEDLALRAQNDVAARQTTEMLKVMLQQGEAMRCPQCQIVVQKKDGCDWIRCTVCHTEICWVTKGPRWGPGGPGDTSGGCRCRVNGIPCHPSCQNCH,mutated_sequence,1.0,510.0,NP_112506.2.a2m,NP_112506.2.npy,ClinVar +NP_112553.1,NP_112553.1.csv,METEQPEETFPNTETNGEFGKRPAEDMEEEQAFKRSRNTDEMVELRILLQSKNAGAVIGKGGKNIKALRTDYNASVSVPDSSGPERILSISADIETIGEILKKIIPTLEEGLQLPSPTATSQLPLESDAVECLNYQHYKGSDFDCELRLLIHQSLAGGIIGVKGAKIKELRENTQTTIKLFQECCPHSTDRVVLIGGKPDRVVECIKIILDLISESPIKGRAQPYDPNFYDETYDYGGFTMMFDDRRGRPVGFPMRGRGGFDRMPPGRGGRPMPPSRRDYDDMSPRRGPPPPPPGRGGRGGSRARNLPLPPPPPPRGGDLMAYDRRGRPGDRYDGMVGFSADETWDSAIDTWSPSEWQMAYEPQGGSGYDYSYAGGRGSYGDLGGPIITTQVTIPKDLAGSIIGKGGQRIKQIRHESGASIKIDEPLEGSEDRIITITGTQDQIQNAQYLLQNSVKQYADVEGF,mutated_sequence,1.0,464.0,NP_112553.1.a2m,NP_112553.1.npy,ClinVar +NP_112586.1,NP_112586.1.csv,MAEEMESSLEASFSSSGAVSGASGFLPPARSRIFKIIVIGDSNVGKTCLTYRFCAGRFPDRTEATIGVDFRERAVEIDGERIKIQLWDTAGQERFRKSMVQHYYRNVHAVVFVYDMTNMASFHSLPSWIEECKQHLLANDIPRILVGNKCDLRSAIQVPTDLAQKFADTHSMPLFETSAKNPNDNDHVEAIFMTLAHKLKSHKPLMLSQPPDNGIILKPEPKPAMTCWC,mutated_sequence,1.0,229.0,NP_112586.1.a2m,NP_112586.1.npy,ClinVar +NP_113584.3,NP_113584.3.csv,MKVDRTKLKKTPTEAPADCRALIDKLKVCNDEQLLLELQQIKTWNIGKCELYHWVDLLDRFDGILADAGQTVENMSWMLVCDRPEREQLKMLLLAVLNFTALLIEYSFSRHLYSSIEHLTTLLASSDMQVVLAVLNLLYVFSKRSNYITRLGSDKRTPLLTRLQHLAESWGGKENGFGLAECCRDLHMMKYPPSATTLHFEFYADPGAEVKIEKRTTSNTLHYIHIEQLDKISESPSEIMESLTKMYSIPKDKQMLLFTHIRLAHGFSNHRKRLQAVQARLHAISILVYSNALQESANSILYNGLIEELVDVLQITDKQLMEIKAASLRTLTSIVHLERTPKLSSIIDCTGTASYHGFLPVLVRNCIQAMIDPSMDPYPHQFATALFSFLYHLASYDAGGEALVSCGMMEALLKVIKFLGDEQDQITFVTRAVRVVDLITNLDMAAFQSHSGLSIFIYRLEHEVDLCRKECPFVIKPKIQRPNTTQEGEEMETDMDGVQCIPQRAALLKSMLNFLKKAIQDPAFSDGIRHVMDGSLPTSLKHIISNAEYYGPSLFLLATEVVTVFVFQEPSLLSSLQDNGLTDVMLHALLIKDVPATREVLGSLPNVFSALCLNARGLQSFVQCQPFERLFKVLLSPDYLPAMRRRRSSDPLGDTASNLGSAVDELMRHQPTLKTDATTAIIKLLEEICNLGRDPKYICQKPSIQKADGTATAPPPRSNHAAEEASSEDEEEEEVQAMQSFNSTQQNETEPNQQVVGTEERIPIPLMDYILNVMKFVESILSNNTTDDHCQEFVNQKGLLPLVTILGLPNLPIDFPTSAACQAVAGVCKSILTLSHEPKVLQEGLLQLDSILSSLEPLHRPIESPGGSVLLRELACAGNVADATLSAQATPLLHALTAAHAYIMMFVHTCRVGQSEIRSISVNQWGSQLGLSVLSKLSQLYCSLVWESTVLLSLCTPNSLPSGCEFGQADMQKLVPKDEKAGTTQGGKRSDGEQDGAAGSMDASTQGLLEGIGLDGDTLAPMETDEPTASDSKGKSKITPAMAARIKQIKPLLSASSRLGRALAELFGLLVKLCVGSPVRQRRSHHAASTTTAPTPAARSTASALTKLLTKGLSWQPPPYTPTPRFRLTFFICSVGFTSPMLFDERKYPYHLMLQKFLCSGGHNALFETFNWALSMGGKVPVSEGLEHSDLPDGTGEFLDAWLMLVEKMVNPTTVLESPHSLPAKLPGGVQNFPQFSALRFLVVTQKAAFTCIKNLWNRKPLKVYGGRMAESMLAILCHILRGEPVIRERLSKEKEGSRGEEDTGQEEGGSRREPQVNQQQLQQLMDMGFTREHAMEALLNTSTMEQATEYLLTHPPPIMGGVVRDLSMSEEDQMMRAIAMSLGQDIPMDQRAESPEEVACRKEEEERKAREKQEEEEAKCLEKFQDADPLEQDELHTFTDTMLPGCFHLLDELPDTVYRVCDLIMTAIKRNGADYRDMILKQVVNQVWEAADVLIKAALPLTTSDTKTVSEWISQMATLPQASNLATRILLLTLLFEELKLPCAWVVESSGILNVLIKLLEVVQPCLQAAKEQKEVQTPKWITPVLLLIDFYEKTAISSKRRAQMTKYLQSNSNNWRWFDDRSGRWCSYSASNNSTIDSAWKSGETSVRFTAGRRRYTVQFTTMVQVNEETGNRRPVMLTLLRVPRLNKNSKNSNGQELEKTLEESKEMDIKRKENKGNDTPLALESTNTEKETSLEETKIGEILIQGLTEDMVTVLIRACVSMLGVPVDPDTLHATLRLCLRLTRDHKYAMMFAELKSTRMILNLTQSSGFNGFTPLVTLLLRHIIEDPCTLRHTMEKVVRSAATSGAGSTTSGVVSGSLGSREINYILRVLGPAACRNPDIFTEVANCCIRIALPAPRGSGTASDDEFENLRIKGPNAVQLVKTTPLKPSPLPVIPDTIKEVIYDMLNALAAYHAPEEADKSDPKPGVMTQEVGQLLQDMGDDVYQQYRSLTRQSSDFDTQSGFSINSQVFAADGASTETSASGTSQGEASTPEESRDGKKDKEGDRASEEGKQKGKGSKPLMPTSTILRLLAELVRSYVGIATLIANYSYTVGQSELIKEDCSVLAFVLDHLLPHTQNAEDKDTPALARLFLASLAAAGSGTDAQVALVNEVKAALGRALAMAESTEKHARLQAVMCIISTIMESCPSTSSFYSSATAKTQHNGMNNIIRLFLKKGLVNDLARVPHSLDLSSPNMANTVNAALKPLETLSRIVNQPSSLFGSKSASSKNKSEQDAQGASQDSSSNQQDPGEPGEAEVQEEDHDVTQTEVADGDIMDGEAETDSVVIAGQPEVLSSQEMQVENELEDLIDELLERDGGSGNSTIIVSRSGEDESQEDVLMDEAPSNLSQASTLQANREDSMNILDPEDEEEHTQEEDSSGSNEDEDDSQDEEEEEEEDEEDDQEDDEGEEGDEDDDDDGSEMELDEDYPDMNASPLVRFERFDREDDLIIEFDNMFSSATDIPPSPGNIPTTHPLMVRHADHSSLTLGSGSSTTRLTQGIGRSQRTLRQLTANTGHTIHVHYPGNRQPNPPLILQRLLGPSAAADILQLSSSLPLQSRGRARLLVGNDDVHIIARSDDELLDDFFHDQSTATSQAGTLSSIPTALTRWTEECKVLDAESMHDCVSVVKVSIVNHLEFLRDEELEERREKRRKQLAEEETKITDKGKEDKENRDQSAQCTASKSNDSTEQNLSDGTPMPDSYPTTPSSTDAATSESKETLGTLQSSQQQPTLPTPPALGEVPQELQSPAGEGGSSTQLLMPVEPEELGPTRPSGEAETTQMELSPAPTITSLSPERAEDSDALTAVSSQLEGSPMDTSSLASCTLEEAVGDTSAAGSSEQPRAGSSTPGDAPPAVAEVQGRSDGSGESAQPPEDSSPPASSESSSTRDSAVAISGADSRGILEEPLPSTSSEEEDPLAGISLPEGVDPSFLAALPDDIRREVLQNQLGIRPPTRTAPSTNSSAPAVVGNPGVTEVSPEFLAALPPAIQEEVLAQQRAEQQRRELAQNASSDTPMDPVTFIQTLPSDLRRSVLEDMEDSVLAVMPPDIAAEAQALRREQEARQRQLMHERLFGHSSTSALSAILRSPAFTSRLSGNRGVQYTRLAVQRGGTFQMGGSSSHNRPSGSNVDTLLRLRGRLLLDHEALSCLLVLLFVDEPKLNTSRLHRVLRNLCYHAQTRHWVIRSLLSILQRSSESELCIETPKLTTSEEKGKKSSKSCGSSSHENRPLDLLHKMESKSSNQLSWLSVSMDAALGCRTNIFQIQRSGGRKHTEKHASGGSTVHIHPQAAPVVCRHVLDTLIQLAKVFPSHFTQQRTKETNCESDRERGNKACSPCSSQSSSSGICTDFWDLLVKLDNMNVSRKGKNSVKSVPVSAGGEGETSPYSLEASPLGQLMNMLSHPVIRRSSLLTEKLLRLLSLISIALPENKVSEAQANSGSGASSTTTATSTTSTTTTTAASTTPTPPTAPTPVTSAPALVAATAISTIVVAASTTVTTPTTATTTVSISPTTKGSKSPAKVSDGGSSSTDFKMVSSGLTENQLQLSVEVLTSHSCSEEGLEDAANVLLQLSRGDSGTRDTVLKLLLNGARHLGYTLCKQIGTLLAELREYNLEQQRRAQCETLSPDGLPEEQPQTTKLKGKMQSRFDMAENVVIVASQKRPLGGRELQLPSMSMLTSKTSTQKFFLRVLQVIIQLRDDTRRANKKAKQTGRLGSSGLGSASSIQAAVRQLEAEADAIIQMVREGQRARRQQQAATSESSQSEASVRREESPMDVDQPSPSAQDTQSIASDGTPQGEKEKEERPPELPLLSEQLSLDELWDMLGECLKELEESHDQHAVLVLQPAVEAFFLVHATERESKPPVRDTRESQLAHIKDEPPPLSPAPLTPATPSSLDPFFSREPSSMHISSSLPPDTQKFLRFAETHRTVLNQILRQSTTHLADGPFAVLVDYIRVLDFDVKRKYFRQELERLDEGLRKEDMAVHVRRDHVFEDSYRELHRKSPEEMKNRLYIVFEGEEGQDAGGLLREWYMIISREMFNPMYALFRTSPGDRVTYTINPSSHCNPNHLSYFKFVGRIVAKAVYDNRLLECYFTRSFYKHILGKSVRYTDMESEDYHFYQGLVYLLENDVSTLGYDLTFSTEVQEFGVCEVRDLKPNGANILVTEENKKEYVHLVCQMRMTGAIRKQLAAFLEGFYEIIPKRLISIFTEQELELLISGLPTIDIDDLKSNTEYHKYQSNSIQIQWFWRALRSFDQADRAKFLQFVTGTSKVPLQGFAALEGMNGIQKFQIHRDDRSTDRLPSAHTCFNQLDLPAYESFEKLRHMLLLAIQECSEGFGLA,mutated_sequence,1.0,4374.0,NP_113584.3.a2m,NP_113584.3.npy,ClinVar +NP_113663.2,NP_113663.2.csv,MALEQALQAARQGELDVLRSLHAAGLLGPSLRDPLDALPVHHAARAGKLHCLRFLVEEAALPAAARARNGATPAHDASATGHLACLQWLLSQGGCRVQDKDNSGATVLHLAARFGHPEVVNWLLHHGGGDPTAATDMGALPIHYAAAKGDFPSLRLLVEHYPEGVNAQTKNGATPLYLACQEGHLEVTQYLVQECGADPHARAHDGMTPLHAAAQMGHSPVIVWLVSCTDVSLSEQDKDGATAMHFAASRGHTKVLSWLLLHGGEISADLWGGTPLHDAAENGELECCQILVVNGAELDVRDRDGYTAADLSDFNGHSHCTRYLRTVENLSVEHRVLSRDPSAELEAKQPDSGMSSPNTTVSVQPLNFDLSSPTSTLSNYDSCSSSHSSIKGQHPPCGLSSARAADIQSYMDMLNPELGLPRGTIGKPTPPPPPPSFPPPPPPPGTQLPPPPPGYPAPKPPVGPQAADIYMQTKNKLRHVETEALKKELSSCDGHDGLRRQDSSRKPRAFSKQPSTGDYYRQLGRCPGETLAARPGMAHSEEVRARQPARAGCPRLGPAARGSLEGPSAPPQAALLPGNHVPNGCAADPKASRELPPPPPPPPPPLPEAASSPPPAPPLPLESAGPGCGQRRSSSSTGSTKSFNMMSPTGDNSELLAEIKAGKSLKPTPQSKGLTTVFSGIGQPAFQPDSPLPSVSPALSPVRSPTPPAAGFQPLLNGSLVPVPPTTPAPGVQLDVEALIPTHDEQGRPIPEWKRQVMVRKMQLKMQEEEEQRRKEEEEEARLASMPAWRRDLLRKKLEEEREQKRKEEERQKQEELRREKEQSEKLRTLGYDESKLAPWQRQVILKKGDIAKY,mutated_sequence,1.0,854.0,NP_113663.2.a2m,NP_113663.2.npy,ClinVar +NP_114032.2,NP_114032.2.csv,MSSSPVNVKKLKVSELKEELKKRRLSDKGLKAELMERLQAALDDEEAGGRPAMEPGNGSLDLGGDSAGRSGAGLEQEAAAGGDEEEEEEEEEEEGISALDGDQMELGEENGAAGAADSGPMEEEEAASEDENGDDQGFQEGEDELGDEEEGAGDENGHGEQQPQPPATQQQQPQQQRGAAKEAAGKSSGPTSLFAVTVAPPGARQGQQQAGGKKKAEGGGGGGRPGAPAAGDGKTEQKGGDKKRGVKRPREDHGRGYFEYIEENKYSRAKSPQPPVEEEDEHFDDTVVCLDTYNCDLHFKISRDRLSASSLTMESFAFLWAGGRASYGVSKGKVCFEMKVTEKIPVRHLYTKDIDIHEVRIGWSLTTSGMLLGEEEFSYGYSLKGIKTCNCETEDYGEKFDENDVITCFANFESDEVELSYAKNGQDLGVAFKISKEVLAGRPLFPHVLCHNCAVEFNFGQKEKPYFPIPEEYTFIQNVPLEDRVRGPKGPEEKKDCEVVMMIGLPGAGKTTWVTKHAAENPGKYNILGTNTIMDKMMVAGFKKQMADTGKLNTLLQRAPQCLGKFIEIAARKKRNFILDQTNVSAAAQRRKMCLFAGFQRKAVVVCPKDEDYKQRTQKKAEVEGKDLPEHAVLKMKGNFTLPEVAECFDEITYVELQKEEAQKLLEQYKEESKKALPPEKKQNTGSKKSNKNKSGKNQFNRGGGHRGRGGFNMRGGNFRGGAPGNRGGYNRRGNMPQRGGGGGGSGGIGYPYPRAPVFPGRGSYSNRGNYNRGGMPNRGNYNQNFRGRGNNRGYKNQSQGYNQWQQGQFWGQKPWSQHYHQGYY,mutated_sequence,1.0,825.0,NP_114032.2.a2m,NP_114032.2.npy,ClinVar +NP_115501.2,NP_115501.2.csv,MAEAEGSSLLLLPPPPPPPRMAEVEAPTAAETDMKQYQGSGGVAMDVERSRFPYCVVWTPIPVLTWFFPIIGHMGICTSTGVIRDFAGPYFVSEDNMAFGKPAKYWKLDPAQVYASGPNAWDTAVHDASEEYKHRMHNLCCDNCHSHVALALNLMRYNNSTNWNMVTLCFFCLLYGKYVSVGAFVKTWLPFILLLGIILTVSLVFNLR,mutated_sequence,1.0,208.0,NP_115501.2.a2m,NP_115501.2.npy,ClinVar +NP_115785.1,NP_115785.1.csv,MAVRQALGRGLQLGRALLLRFTGKPGRAYGLGRPGPAAGCVRGERPGWAAGPGAEPRRVGLGLPNRLRFFRQSVAGLAARLQRQFVVRAWGCAGPCGRAVFLAFGLGLGLIEEKQAESRRAVSACQEIQAIFTQKSKPGPDPLDTRRLQGFRLEEYLIGQSIGKGCSAAVYEATMPTLPQNLEVTKSTGLLPGRGPGTSAPGEGQERAPGAPAFPLAIKMMWNISAGSSSEAILNTMSQELVPASRVALAGEYGAVTYRKSKRGPKQLAPHPNIIRVLRAFTSSVPLLPGALVDYPDVLPSRLHPEGLGHGRTLFLVMKNYPCTLRQYLCVNTPSPRLAAMMLLQLLEGVDHLVQQGIAHRDLKSDNILVELDPDGCPWLVIADFGCCLADESIGLQLPFSSWYVDRGGNGCLMAPEVSTARPGPRAVIDYSKADAWAVGAIAYEIFGLVNPFYGQGKAHLESRSYQEAQLPALPESVPPDVRQLVRALLQREASKRPSARVAANVLHLSLWGEHILALKNLKLDKMVGWLLQQSAATLLANRLTEKCCVETKMKMLFLANLECETLCQAALLLCSWRAAL,mutated_sequence,1.0,581.0,NP_115785.1.a2m,NP_115785.1.npy,ClinVar +NP_115791.3,NP_115791.3.csv,MPGGGPEMDDYMETLKDEEDALWENVECNRHMLSRYINPAKLTPYLRQCKVIDEQDEDEVLNAPMLPSKINRAGRLLDILHTKGQRGYVVFLESLEFYYPELYKLVTGKEPTRRFSTIVVEEGHEGLTHFLMNEVIKLQQQMKAKDLQRCELLARLRQLEDEKKQMTLTRVELLTFQERYYKMKEERDSYNDELVKVKDDNYNLAMRYAQLSEEKNMAVMRSRDLQLEIDQLKHRLNKMEEECKLERNQSLKLKNDIENRPKKEQVLELERENEMLKTKNQELQSIIQAGKRSLPDSDKAILDILEHDRKEALEDRQELVNRIYNLQEEARQAEELRDKYLEEKEDLELKCSTLGKDCEMYKHRMNTVMLQLEEVERERDQAFHSRDEAQTQYSQCLIEKDKYRKQIRELEEKNDEMRIEMVRREACIVNLESKLRRLSKDSNNLDQSLPRNLPVTIISQDFGDASPRTNGQEADDSSTSEESPEDSKYFLPYHPPQRRMNLKGIQLQRAKSPISLKRTSDFQAKGHEEEGTDASPSSCGSLPITNSFTKMQPPRSRSSIMSITAEPPGNDSIVRRYKEDAPHRSTVEEDNDSGGFDALDLDDDSHERYSFGPSSIHSSSSSHQSEGLDAYDLEQVNLMFRKFSLERPFRPSVTSVGHVRGPGPSVQHTTLNGDSLTSQLTLLGGNARGSFVHSVKPGSLAEKAGLREGHQLLLLEGCIRGERQSVPLDTCTKEEAHWTIQRCSGPVTLHYKVNHEGYRKLVKDMEDGLITSGDSFYIRLNLNISSQLDACTMSLKCDDVVHVRDTMYQDRHEWLCARVDPFTDHDLDMGTIPSYSRAQQLLLVKLQRLMHRGSREEVDGTHHTLRALRNTLQPEEALSTSDPRVSPRLSRASFLFGQLLQFVSRSENKYKRMNSNERVRIISGSPLGSLARSSLDATKLLTEKQEELDPESELGKNLSLIPYSLVRAFYCERRRPVLFTPTVLAKTLVQRLLNSGGAMEFTICKSDIVTRDEFLRRQKTETIIYSREKNPNAFECIAPANIEAVAAKNKHCLLEAGIGCTRDLIKSNIYPIVLFIRVCEKNIKRFRKLLPRPETEEEFLRVCRLKEKELEALPCLYATVEPDMWGSVEELLRVVKDKIGEEQRKTIWVDEDQL,mutated_sequence,1.0,1154.0,NP_115791.3.a2m,NP_115791.3.npy,ClinVar +NP_115909.1,NP_115909.1.csv,MAAGLARLLLLLGLSAGGPAPAGAAKMKVVEEPNAFGVNNPFLPQASRLQAKRDPSPVSGPVHLFRLSGKCFSLVESTYKYEFCPFHNVTQHEQTFRWNAYSGILGIWHEWEIANNTFTGMWMRDGDACRSRSRQSKVELACGKSNRLAHVSEPSTCVYALTFETPLVCHPHALLVYPTLPEALQRQWDQVEQDLADELITPQGHEKLLRTLFEDAGYLKTPEENEPTQLEGGPDSLGFETLENCRKAHKELSKEIKRLKGLLTQHGIPYTRPTETSNLEHLGHETPRAKSPEQLRGDPGLRGSL,mutated_sequence,1.0,305.0,NP_115909.1.a2m,NP_115909.1.npy,ClinVar +NP_116027.2,NP_116027.2.csv,MEVAPEQPRWMAHPAVLNAQHPDSHHPGLAHNYMEPAQLLPPDEVDVFFNHLDSQGNPYYANPAHARARVSYSPAHARLTGGQMCRPHLLHSPGLPWLDGGKAALSAAAAHHHNPWTVSPFSKTPLHPSAAGGPGGPLSVYPGAGGGSGGGSGSSVASLTPTAAHSGSHLFGFPPTPPKEVSPDPSTTGAASPASSSAGGSAARGEDKDGVKYQVSLTESMKMESGSPLRPGLATMGTQPATHHPIPTYPSYVPAAAHDYSSGLFHPGGFLGGPASSFTPKQRSKARSCSEGRECVNCGATATPLWRRDGTGHYLCNACGLYHKMNGQNRPLIKPKRRLSAARRAGTCCANCQTTTTTLWRRNANGDPVCNACGLYYKLHNVNRPLTMKKEGIQTRNRKMSNKSKKSKKGAECFEELSKCMQEKSSPFSAAALAGHMAPVGHLPPFSHSGHILPTPTPIHPSSSLSFGHPHPSSMVTAMG,mutated_sequence,1.0,480.0,NP_116027.2.a2m,NP_116027.2.npy,ClinVar +NP_116250.3,NP_116250.3.csv,MSLAAYCVICCRRIGTSTSPPKSGTHWRDIRNIIKFTGSLILGGSLFLTYEVLALKKAVTLDTQVVEREKMKSYIYVHTVSLDKGENHGIAWQARKELHKAVRKVLATSAKILRNPFADPFSTVDIEDHECAVWLLLRKSKSDDKTTRLEAVREMSETHHWHDYQYRIIAQACDPKTLIGLARSEESDLRFFLLPPPLPSLKEDSSTEEELRQLLASLPQTELDECIQYFTSLALSESSQSLAAQKGGLWCFGGNGLPYAESFGEVPSATVEMFCLEAIVKHSEISTHCDKIEANGGLQLLQRLYRLHKDCPKVQRNIMRVIGNMALNEHLHSSIVRSGWVSIMAEAMKSPHIMESSHAARILANLDRETVQEKYQDGVYVLHPQYRTSQPIKADVLFIHGLMGAAFKTWRQQDSEQAVIEKPMEDEDRYTTCWPKTWLAKDCPALRIISVEYDTSLSDWRARCPMERKSIAFRSNELLRKLRAAGVGDRPVVWISHSMGGLLVKKMLLEASTKPEMSTVINNTRGIIFYSVPHHGSRLAEYSVNIRYLLFPSLEVKELSKDSPALKTLQDDFLEFAKDKNFQVLNFVETLPTYIGSMIKLHVVPVESADLGIGDLIPVDVNHLNICKPKKKDAFLYQRTLQFIREALAKDLEN,mutated_sequence,1.0,654.0,NP_116250.3.a2m,NP_116250.3.npy,ClinVar +NP_149078.1,NP_149078.1.csv,MAEEQGRERDSVPKPSVLFLHPDLGVGGAERLVLDAALALQARGCSVKIWTAHYDPGHCFAESRELPVRCAGDWLPRGLGWGGRGAAVCAYVRMVFLALYVLFLADEEFDVVVCDQVSACIPVFRLARRRKKILFYCHFPDLLLTKRDSFLKRLYRAPIDWIEEYTTGMADCILVNSQFTAAVFKETFKSLSHIDPDVLYPSLNVTSFDSVVPEKLDDLVPKGKKFLLLSINRYERKKNLTLALEALVQLRGRLTSQDWERVHLIVAGGYDERVLENVEHYQELKKMVQQSDLGQYVTFLRSFSDKQKISLLHSCTCVLYTPSNEHFGIVPLEAMYMQCPVIAVNSGGPLESIDHSVTGFLCEPDPVHFSEAIEKFIREPSLKATMGLAGRARVKEKFSPEAFTEQLYRYVTKLLV,mutated_sequence,1.0,416.0,NP_149078.1.a2m,NP_149078.1.npy,ClinVar +NP_203123.1,NP_203123.1.csv,MMAEEHTDLEAQIVKDIHCKEIDLVNRDPKNINEDIVKVDFEDVIAEPVGTYSFDGVWKVSYTTFTVSKYWCYRLLSTLLGVPLALLWGFLFACISFCHIWAVVPCIKSYLIEIQCISHIYSLCIRTFCNPLFAALGQVCSSIKVVLRKEV,mutated_sequence,1.0,151.0,NP_203123.1.a2m,NP_203123.1.npy,ClinVar +NP_203524.1,NP_203524.1.csv,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQRVEDAFYTLVREIRQYRLKKISKEEKTPGCVKIKKCIIM,mutated_sequence,1.0,189.0,NP_203524.1.a2m,NP_203524.1.npy,ClinVar +NP_203699.1,NP_203699.1.csv,MKLRGVSLAAGLFLLALSLWGQPAEAAACYGCSPGSKCDCSGIKGEKGERGFPGLEGHPGLPGFPGPEGPPGPRGQKGDDGIPGPPGPKGIRGPPGLPGFPGTPGLPGMPGHDGAPGPQGIPGCNGTKGERGFPGSPGFPGLQGPPGPPGIPGMKGEPGSIIMSSLPGPKGNPGYPGPPGIQGLPGPTGIPGPIGPPGPPGLMGPPGPPGLPGPKGNMGLNFQGPKGEKGEQGLQGPPGPPGQISEQKRPIDVEFQKGDQGLPGDRGPPGPPGIRGPPGPPGGEKGEKGEQGEPGKRGKPGKDGENGQPGIPGLPGDPGYPGEPGRDGEKGQKGDTGPPGPPGLVIPRPGTGITIGEKGNIGLPGLPGEKGERGFPGIQGPPGLPGPPGAAVMGPPGPPGFPGERGQKGDEGPPGISIPGPPGLDGQPGAPGLPGPPGPAGPHIPPSDEICEPGPPGPPGSPGDKGLQGEQGVKGDKGDTCFNCIGTGISGPPGQPGLPGLPGPPGSLGFPGQKGEKGQAGATGPKGLPGIPGAPGAPGFPGSKGEPGDILTFPGMKGDKGELGSPGAPGLPGLPGTPGQDGLPGLPGPKGEPGGITFKGERGPPGNPGLPGLPGNIGPMGPPGFGPPGPVGEKGIQGVAGNPGQPGIPGPKGDPGQTITQPGKPGLPGNPGRDGDVGLPGDPGLPGQPGLPGIPGSKGEPGIPGIGLPGPPGPKGFPGIPGPPGAPGTPGRIGLEGPPGPPGFPGPKGEPGFALPGPPGPPGLPGFKGALGPKGDRGFPGPPGPPGRTGLDGLPGPKGDVGPNGQPGPMGPPGLPGIGVQGPPGPPGIPGPIGQPGLHGIPGEKGDPGPPGLDVPGPPGERGSPGIPGAPGPIGPPGSPGLPGKAGASGFPGTKGEMGMMGPPGPPGPLGIPGRSGVPGLKGDDGLQGQPGLPGPTGEKGSKGEPGLPGPPGPMDPNLLGSKGEKGEPGLPGIPGVSGPKGYQGLPGDPGQPGLSGQPGLPGPPGPKGNPGLPGQPGLIGPPGLKGTIGDMGFPGPQGVEGPPGPSGVPGQPGSPGLPGQKGDKGDPGISSIGLPGLPGPKGEPGLPGYPGNPGIKGSVGDPGLPGLPGTPGAKGQPGLPGFPGTPGPPGPKGISGPPGNPGLPGEPGPVGGGGHPGQPGPPGEKGKPGQDGIPGPAGQKGEPGQPGFGNPGPPGLPGLSGQKGDGGLPGIPGNPGLPGPKGEPGFHGFPGVQGPPGPPGSPGPALEGPKGNPGPQGPPGRPGPTGFQGLPGPEGPPGLPGNGGIKGEKGNPGQPGLPGLPGLKGDQGPPGLQGNPGRPGLNGMKGDPGLPGVPGFPGMKGPSGVPGSAGPEGEPGLIGPPGPPGLPGPSGQSIIIKGDAGPPGIPGQPGLKGLPGPQGPQGLPGPTGPPGDPGRNGLPGFDGAGGRKGDPGLPGQPGTRGLDGPPGPDGLQGPPGPPGTSSVAHGFLITRHSQTTDAPQCPQGTLQVYEGFSLLYVQGNKRAHGQDLGTAGSCLRRFSTMPFMFCNINNVCNFASRNDYSYWLSTPEPMPMSMQPLKGQSIQPFISRCAVCEAPAVVIAVHSQTIQIPHCPQGWDSLWIGYSFMMHTSAGAEGSGQALASPGSCLEEFRSAPFIECHGRGTCNYYANSYSFWLATVDVSDMFSKPQSETLKAGDLRTRISRCQVCMKRT,mutated_sequence,1.0,1691.0,NP_203699.1.a2m,NP_203699.1.npy,ClinVar +NP_338599.1,NP_338599.1.csv,MGSQALPPGPMQTLIFFDMEATGLPFSQPKVTELCLLAVHRCALESPPTSQGPPPTVPPPPRVVDKLSLCVAPGKACSPAASEITGLSTAVLAAHGRQCFDDNLANLLLAFLRRQPQPWCLVAHNGDRYDFPLLQAELAMLGLTSALDGAFCVDSITALKALERASSPSEHGPRKSYSLGSIYTRLYGQSPPDSHTAEGDVLALLSICQWRPQALLRWVDAHARPFGTIRPMYGVTASARTKPRPSAVTTTAHLATTRNTSPSLGESRGTKDLPPVKDPGALSREGLLAPLGLLAILTLAVATLYGLSLATPGE,mutated_sequence,1.0,314.0,NP_338599.1.a2m,NP_338599.1.npy,ClinVar +NP_443077.1,NP_443077.1.csv,MAVCGLGSRLGLGSRLGLRGCFGAARLLYPRFQSRGPQGVEDGDRPQPSSKTPRIPKIYTKTGDKGFSSTFTGERRPKDDQVFEAVGTTDELSSAIGFALELVTEKGHTFAEELQKIQCTLQDVGSALATPCSSAREAHLKYTTFKAGPILELEQWIDKYTSQLPPLTAFILPSGGKISSALHFCRAVCRRAERRVVPLVQMGETDANVAKFLNRLSDYLFTLARYAAMKEGNQEKIYMKNDPSAESEGL,mutated_sequence,1.0,250.0,NP_443077.1.a2m,NP_443077.1.npy,ClinVar +NP_443106.1,NP_443106.1.csv,MKDRTQELRSAKDSDDEEEVVHVDRDHFMDEFFEQVEEIRGCIEKLSEDVEQVKKQHSAILAAPNPDEKTKQELEDLTADIKKTANKVRSKLKAIEQSIEQEEGLNRSSADLRIRKTQHSTLSRKFVEVMTEYNATQSKYRDRCKDRIQRQLEITGRTTTNEELEDMLESGKLAIFTDDIKMDSQMTKQALNEIETRHNEIIKLETSIRELHDMFVDMAMLVESQGEMIDRIEYNVEHSVDYVERAVSDTKKAVKYQSKARRKKIMIIICCVVLGVVLASSIGGTLGL,mutated_sequence,1.0,288.0,NP_443106.1.a2m,NP_443106.1.npy,ClinVar +NP_443715.1,NP_443715.1.csv,MRAVLTWRDKAEHCINDIAFKPDGTQLILAAGSRLLVYDTSDGTLLQPLKGHKDTVYCVAYAKDGKRFASGSADKSVIIWTSKLEGILKYTHNDAIQCVSYNPITHQLASCSSSDFGLWSPEQKSVSKHKSSSKIICCSWTNDGQYLALGMFNGIISIRNKNGEEKVKIERPGGSLSPIWSICWNPSSRWESFWMNRENEDAEDVIVNRYIQEIPSTLKSAVYSSQGSEAEEEEPEEEDDSPRDDNLEERNDILAVADWGQKVSFYQLSGKQIGKDRALNFDPCCISYFTKGEYILLGGSDKQVSLFTKDGVRLGTVGEQNSWVWTCQAKPDSNYVVVGCQDGTISFYQLIFSTVHGLYKDRYAYRDSMTDVIVQHLITEQKVRIKCKELVKKIAIYRNRLAIQLPEKILIYELYSEDLSDMHYRVKEKIIKKFECNLLVVCANHIILCQEKRLQCLSFSGVKEREWQMESLIRYIKVIGGPPGREGLLVGLKNGQILKIFVDNLFAIVLLKQATAVRCLDMSASRKKLAVVDENDTCLVYDIDTKELLFQEPNANSVAWNTQCEDMLCFSGGGYLNIKASTFPVHRQKLQGFVVGYNGSKIFCLHVFSISAVEVPQSAPMYQYLDRKLFKEAYQIACLGVTDTDWRELAMEALEGLDFETAKKAFIRVQDLRYLELISSIEERKKRGETNNDLFLADVFSYQGKFHEAAKLYKRSGHENLALEMYTDLCMFEYAKDFLGSGDPKETKMLITKQADWARNIKEPKAAVEMYISAGEHVKAIEICGDHGWVDMLIDIARKLDKAEREPLLLCATYLKKLDSPGYAAETYLKMGDLKSLVQLHVETQRWDEAFALGEKHPEFKDDIYMPYAQWLAENDRFEEAQKAFHKAGRQREAVQVLEQLTNNAVAESRFNDAAYYYWMLSMQCLDIAQDPAQKDTMLGKFYHFQRLAELYHGYHAIHRHTEDPFSVHRPETLFNISRFLLHSLPKDTPSGISKVKILFTLAKQSKALGAYRLARHAYDKLRGLYIPARFQKSIELGTLTIRAKPFHDSEELVPLCYRCSTNNPLLNNLGNVCINCRQPFIFSASSYDVLHLVEFYLEEGITDEEAISLIDLEVLRPKRDDRQLEIANNSSQILRLVETKDSIGDEDPFTAKLSFEQGGSEFVPVVVSRLVLRSMSRRDVLIKRWPPPLRWQYFRSLLPDASITMCPSCFQMFHSEDYELLVLQHGCCPYCRRCKDDPGP,mutated_sequence,1.0,1241.0,NP_443715.1.a2m,NP_443715.1.npy,ClinVar +NP_444504.1,NP_444504.1.csv,MAVEELQSIIKRCQILEEQDFKEEDFGLFQLAGQRCIEEGHTDQLLEIIQNEKNKVIIKNMGWNLVGPVVRCLLCKDKEDSKRKVYFLIFDLLVKLCNPKELLLGLLELIEEPSGKQISQSILLLLQPLQTVIQKLHNKAYSIGLALSTLWNQLSLLPVPYSKEQIQMDDYGLCQCCKALIEFTKPFVEEVIDNKENSLENEKLKDELLKFCFKSLKCPLLTAQFFEQSEEGGNDPFRYFASEIIGFLSAIGHPFPKMIFNHGRKKRTWNYLEFEEEENKQLADSMASLAYLVFVQGIHIDQLPMVLSPLYLLQFNMGHIEVFLQRTEESVISKGLELLENSLLRIEDNSLLYQYLEIKSFLTVPQGLVKVMTLCPIETLRKKSLAMLQLYINKLDSQGKYTLFRCLLNTSNHSGVEAFIIQNIKNQIDMSLKRTRNNKWFTGPQLISLLDLVLFLPEGAETDLLQNSDRIMASLNLLRYLVIKDNENDNQTGLWTELGNIENNFLKPLHIGLNMSKAHYEAEIKNSQEAQKSKDLCSITVSGEEIPNMPPEMQLKVLHSALFTFDLIESVLARVEELIEIKTKSTSEENIGIK,mutated_sequence,1.0,594.0,NP_444504.1.a2m,NP_444504.1.npy,ClinVar +NP_473368.1,NP_473368.1.csv,MVKFPALTHYWPLIRFLVPLGITNIAIDFGEQALNRGIAAVKEDAVEMLASYGLAYSLMKFFTGPMSDFKNVGLVFVNSKRDRTKAVLCMVVAGAIAAVFHTLIAYSDLGYYIINKLHHVDESVGSKTRRAFLYLAAFPFMDAMAWTHAGILLKHKYSFLVGCASISDVIAQVVFVAILLHSHLECREPLLIPILSLYMGALVRCTTLCLGYYKNIHDIIPDRSGPELGGDATIRKMLSFWWPLALILATQRISRPIVNLFVSRDLGGSSAATEAVAILTATYPVGHMPYGWLTEIRAVYPAFDKNNPSNKLVSTSNTVTAAHIKKFTFVCMALSLTLCFVMFWTPNVSEKILIDIIGVDFAFAELCVVPLRIFSFFPVPVTVRAHLTGWLMTLKKTFVLAPSSVLRIIVLIASLVVLPYLGVHGATLGVGSLLAGFVGESTMVAIAACYVYRKQKKKMENESATEGEDSAMTDMPPTEEVTDIVEMREENE,mutated_sequence,1.0,492.0,NP_473368.1.a2m,NP_473368.1.npy,ClinVar +NP_477352.3,NP_477352.3.csv,MAAAPARGGGGGGGGGGGCSGSGSSASRGFYFNTVLSLARSLAVQRPASLEKVQKLLCMCPVDFHGIFQLDERRRDAVIALGIFLIESDLQHKDCVVPYLLRLLKGLPKVYWVEESTARKGRGALPVAESFSFCLVTLLSDVAYRDPSLRDEILEVLLQVLHVLLGMCQALEIQDKEYLCKYAIPCLIGISRAFGRYSNMEESLLSKLFPKIPPHSLRVLEELEGVRRRSFNDFRSILPSNLLTVCQEGTLKRKTSSVSSISQVSPERGMPPPSSPGGSAFHYFEASCLPDGTALEPEYYFSTISSSFSVSPLFNGVTYKEFNIPLEMLRELLNLVKKIVEEAVLKSLDAIVASVMEANPSADLYYTSFSDPLYLTMFKMLRDTLYYMKDLPTSFVKEIHDFVLEQFNTSQGELQKILHDADRIHNELSPLKLRCQANAACVDLMVWAVKDEQGAENLCIKLSEKLQSKTSSKVIIAHLPLLICCLQGLGRLCERFPVVVHSVTPSLRDFLVIPSPVLVKLYKYHSQYHTVAGNDIKISVTNEHSESTLNVMSGKKSQPSMYEQLRDIAIDNICRCLKAGLTVDPVIVEAFLASLSNRLYISQESDKDAHLIPDHTIRALGHIAVALRDTPKVMEPILQILQQKFCQPPSPLDVLIIDQLGCLVITGNQYIYQEVWNLFQQISVKASSVVYSATKDYKDHGYRHCSLAVINALANIAANIQDEHLVDELLMNLLELFVQLGLEGKRASERASEKGPALKASSSAGNLGVLIPVIAVLTRRLPPIKEAKPRLQKLFRDFWLYSVLMGFAVEGSGLWPEEWYEGVCEIATKSPLLTFPSKEPLRSVLQYNSAMKNDTVTPAELSELRSTIINLLDPPPEVSALINKLDFAMSTYLLSVYRLEYMRVLRSTDPDRFQVMFCYFEDKAIQKDKSGMMQCVIAVADKVFDAFLNMMADKAKTKENEEELERHAQFLLVNFNHIHKRIRRVADKYLSGLVDKFPHLLWSGTVLKTMLDILQTLSLSLSADIHKDQPYYDIPDAPYRITVPDTYEARESIVKDFAARCGMILQEAMKWAPTVTKSHLQEYLNKHQNWVSGLSQHTGLAMATESILHFAGYNKQNTTLGATQLSERPACVKKDYSNFMASLNLRNRYAGEVYGMIRFSGTTGQMSDLNKMMVQDLHSALDRSHPQHYTQAMFKLTAMLISSKDCDPQLLHHLCWGPLRMFNEHGMETALACWEWLLAGKDGVEVPFMREMAGAWHMTVEQKFGLFSAEIKEADPLAASEASQPKPCPPEVTPHYIWIDFLVQRFEIAKYCSSDQVEIFSSLLQRSMSLNIGGAKGSMNRHVAAIGPRFKLLTLGLSLLHADVVPNATIRNVLREKIYSTAFDYFSCPPKFPTQGEKRLREDISIMIKFWTAMFSDKKYLTASQLVPPDNQDTRSNLDITVGSRQQATQGWINTYPLSSGMSTISKKSGMSKKTNRGSQLHKYYMKRRTLLLSLLATEIERLITWYNPLSAPELELDQAGENSVANWRSKYISLSEKQWKDNVNLAWSISPYLAVQLPARFKNTEAIGNEVTRLVRLDPGAVSDVPEAIKFLVTWHTIDADAPELSHVLCWAPTDPPTGLSYFSSMYPPHPLTAQYGVKVLRSFPPDAILFYIPQIVQALRYDKMGYVREYILWAASKSQLLAHQFIWNMKTNIYLDEEGHQKDPDIGDLLDQLVEEITGSLSGPAKDFYQREFDFFNKITNVSAIIKPYPKGDERKKACLSALSEVKVQPGCYLPSNPEAIVLDIDYKSGTPMQSAAKAPYLAKFKVKRCGVSELEKEGLRCRSDSEDECSTQEADGQKISWQAAIFKVGDDCRQDMLALQIIDLFKNIFQLVGLDLFVFPYRVVATAPGCGVIECIPDCTSRDQLGRQTDFGMYDYFTRQYGDESTLAFQQARYNFIRSMAAYSLLLFLLQIKDRHNGNIMLDKKGHIIHIDFGFMFESSPGGNLGWEPDIKLTDEMVMIMGGKMEATPFKWFMEMCVRGYLAVRPYMDAVVSLVTLMLDTGLPCFRGQTIKLLKHRFSPNMTEREAANFIMKVIQSCFLSNRSRTYDMIQYYQNDIPY,mutated_sequence,1.0,2102.0,NP_477352.3.a2m,NP_477352.3.npy,ClinVar +NP_510965.1,NP_510965.1.csv,MATATIALQVNGQQGGGSEPAAAAAVVAAGDKWKPPQGTDSIKMENGQSTAAKLGLPPLTPEQQEALQKAKKYAMEQSIKSVLVKQTIAHQQQQLTNLQMAAVTMGFGDPLSPLQSMAAQRQRALAIMCRVYVGSIYYELGEDTIRQAFAPFGPIKSIDMSWDSVTMKHKGFAFVEYEVPEAAQLALEQMNSVMLGGRNIKVGRPSNIGQAQPIIDQLAEEARAFNRIYVASVHQDLSDDDIKSVFEAFGKIKSCTLARDPTTGKHKGYGFIEYEKAQSSQDAVSSMNLFDLGGQYLRVGKAVTPPMPLLTPATPGGLPPAAAVAAAAATAKITAQEAVAGAAVLGTLGTPGLVSPALTLAQPLGTLPQAVMAAQAPGVITGVTPARPPIPVTIPSVGVVNPILASPPTLGLLEPKKEKEEEELFPESERPEMLSEQEHMSISGSSARHMVMQKLLRKQESTVMVLRNMVDPKDIDDDLEGEVTEECGKFGAVNRVIIYQEKQGEEEDAEIIVKIFVEFSIASETHKAIQALNGRWFAGRKVVAEVYDQERFDNSDLSA,mutated_sequence,1.0,559.0,NP_510965.1.a2m,NP_510965.1.npy,ClinVar +NP_542172.2,NP_542172.2.csv,MKLLRRAWRRRAALGLGTLALCGAALLYLARCAAEPGDPRAMSGRSPPPPAPARAAAFLAVLVASAPRAAERRSVIRSTWLARRGAPGDVWARFAVGTAGLGAEERRALEREQARHGDLLLLPALRDAYENLTAKVLAMLAWLDEHVAFEFVLKADDDSFARLDALLAELRAREPARRRRLYWGFFSGRGRVKPGGRWREAAWQLCDYYLPYALGGGYVLSADLVHYLRLSRDYLRAWHSEDVSLGAWLAPVDVQREHDPRFDTEYRSRGCSNQYLVTHKQSLEDMLEKHATLAREGRLCKREVQLRLSYVYDWSAPPSQCCQRREGIP,mutated_sequence,1.0,329.0,NP_542172.2.a2m,NP_542172.2.npy,ClinVar +NP_542411.2,NP_542411.2.csv,MERCSRCHRLLLLLPLVLGLSAAPGWAGAPPVDVLRALRFPSLPDGVRRAKGICPADVAYRVARPAQLSAPTRQLFPGGFPKDFSLLTVVRTRPGLQAPLLTLYSAQGVRQLGLELGRPVRFLYEDQTGRPQPPSQPVFRGLSLADGKWHRVAVAVKGQSVTLIVDCKKRVTRPLPRSARPVLDTHGVIIFGARILDEEVFEGDVQELAIVPGVQAAYESCEQKELECEGGQRERPQNQQPHRAQRSPQQQPSRLHRPQNQEPQSQPTESLYYDYEPPYYDVMTTGTTPDYQDPTPGEEEEILESSLLPPLEEEQTDLQVPPTADRFQAEEYGEGGTDPPEGPYDYTYGYGDDYREETELGPALSAETAHSGAAAHGPRGLKGEKGEPAVLEPGMLVEGPPGPEGPAGLIGPPGIQGNPGPVGDPGERGPPGRAGLPGSDGAPGPPGTSLMLPFRFGSGGGDKGPVVAAQEAQAQAILQQARLALRGPPGPMGYTGRPGPLGQPGSPGLKGESGDLGPQGPRGPQGLTGPPGKAGRRGRAGADGARGMPGDPGVKGDRGFDGLPGLPGEKGHRGDTGAQGLPGPPGEDGERGDDGEIGPRGLPGESGPRGLLGPKGPPGIPGPPGVRGMDGPQGPKGSLGPQGEPGPPGQQGTPGTQGLPGPQGAIGPHGEKGPQGKPGLPGMPGSDGPPGHPGKEGPPGTKGNQGPSGPQGPLGYPGPRGVKGVDGIRGLKGHKGEKGEDGFPGFKGDIGVKGDRGEVGVPGSRGEDGPEGPKGRTGPTGDPGPPGLMGEKGKLGVPGLPGYPGRQGPKGSLGFPGFPGASGEKGARGLSGKSGPRGERGPTGPRGQRGPRGATGKSGAKGTSGGDGPHGPPGERGLPGPQGPNGFPGPKGPPGPPGKDGLPGHPGQRGEVGFQGKTGPPGPPGVVGPQGAAGETGPMGERGHPGPPGPPGEQGLPGTAGKEGTKGDPGPPGAPGKDGPAGLRGFPGERGLPGTAGGPGLKGNEGPSGPPGPAGSPGERGAAGSGGPIGPPGRPGPQGPPGAAGEKGVPGEKGPIGPTGRDGVQGPVGLPGPAGPPGVAGEDGDKGEVGDPGQKGTKGNKGEHGPPGPPGPIGPVGQPGAAGADGEPGARGPQGHFGAKGDEGTRGFNGPPGPIGLQGLPGPSGEKGETGDVGPMGPPGPPGPRGPAGPNGADGPQGPPGGVGNLGPPGEKGEPGESGSPGIQGEPGVKGPRGERGEKGESGQPGEPGPPGPKGPTGDDGPKGNPGPVGFPGDPGPPGEGGPRGQDGAKGDRGEDGEPGQPGSPGPTGENGPPGPLGKRGPAGSPGSEGRQGGKGAKGDPGAIGAPGKTGPVGPAGPAGKPGPDGLRGLPGSVGQQGRPGATGQAGPPGPVGPPGLPGLRGDAGAKGEKGHPGLIGLIGPPGEQGEKGDRGLPGPQGSPGQKGEMGIPGASGPIGPGGPPGLPGPAGPKGAKGATGPGGPKGEKGVQGPPGHPGPPGEVIQPLPIQMPKKTRRSVDGSRLMQEDEAIPTGGAPGSPGGLEEIFGSLDSLREEIEQMRRPTGTQDSPARTCQDLKLCHPELPDGEYWVDPNQGCARDAFRVFCNFTAGGETCVTPRDDVTQFSYVDSEGSPVGVVQLTFLRLLSVSAHQDVSYPCSGAARDGPLRLRGANEDELSPETSPYVKEFRDGCQTQQGRTVLEVRTPVLEQLPVLDASFSDLGAPPRRGGVLLGPVCFMG,mutated_sequence,1.0,1736.0,NP_542411.2.a2m,NP_542411.2.npy,ClinVar +NP_570850.2,NP_570850.2.csv,MWRLRRAAVACEVCQSLVKHSSGIKGSLPLQKLHLVSRSIYHSHHPTLKLQRPQLRTSFQQFSSLTNLPLRKLKFSPIKYGYQPRRNFWPARLATRLLKLRYLILGSAVGGGYTAKKTFDQWKDMIPDLSEYKWIVPDIVWEIDEYIDFEKIRKALPSSEDLVKLAPDFDKIVESLSLLKDFFTSGHKLVSEVIGASDLLLLLGSPEETAFRATDRGSESDKHFRKGLLGELILLQQQIQEHEEEARRAAGQYSTSYAQQKRKVSDKEKIDQLQEELLHTQLKYQRILERLEKENKELRKLVLQKDDKGIHHRKLKKSLIDMYSEVLDVLSDYDASYNTQDHLPRVVVVGDQSAGKTSVLEMIAQARIFPRGSGEMMTRSPVKVTLSEGPHHVALFKDSSREFDLTKEEDLAALRHEIELRMRKNVKEGCTVSPETISLNVKGPGLQRMVLVDLPGVINTVTSGMAPDTKETIFSISKAYMQNPNAIILCIQDGSVDAERSIVTDLVSQMDPHGRRTIFVLTKVDLAEKNVASPSRIQQIIEGKLFPMKALGYFAVVTGKGNSSESIEAIREYEEEFFQNSKLLKTSMLKAHQVTTRNLSLAVSDCFWKMVRESVEQQADSFKATRFNLETEWKNNYPRLRELDRNELFEKAKNEILDEVISLSQVTPKHWEEILQQSLWERVSTHVIENIYLPAAQTMNSGTFNTTVDIKLKQWTDKQLPNKAVEVAWETLQEEFSRFMTEPKGKEHDDIFDKLKEAVKEESIKRHKWNDFAEDSLRVIQHNALEDRSISDKQQWDAAIYFMEEALQARLKDTENAIENMVGPDWKKRWLYWKNRTQEQCVHNETKNELEKMLKCNEEHPAYLASDEITTVRKNLESRGVEVDPSLIKDTWHQVYRRHFLKTALNHCNLCRRGFYYYQRHFVDSELECNDVVLFWRIQRMLAITANTLRQQLTNTEVRRLEKNVKEVLEDFAEDGEKKIKLLTGKRVQLAEDLKKVREIQEKLDAFIEALHQEK,mutated_sequence,1.0,1015.0,NP_570850.2.a2m,NP_570850.2.npy,ClinVar +NP_570854.1,NP_570854.1.csv,MATACKRSGEPQSDDIEASRMKRAAAKHLIERYYHQLTEGCGNEACTNEFCASCPTFLRMDNNAAAIKALELYKINAKLCDPHPSKKGASSAYLENSKGAPNNSCSEIKMNKKGARIDFKDVTYLTEEKVYEILELCREREDYSPLIRVIGRVFSSAEALVQSFRKVKQHTKEELKSLQAKDEDKDEDEKEKAACSAAAMEEDSEASSSRIGDSSQGDNNLQKLGPDDVSVDIDAIRRVYTRLLSNEKIETAFLNALVYLSPNVECDLTYHNVYSRDPNYLNLFIIVMENRNLHSPEYLEMALPLFCKAMSKLPLAAQGKLIRLWSKYNADQIRRMMETFQQLITYKVISNEFNSRNLVNDDDAIVAASKCLKMVYYANVVGGEVDTNHNEEDDEEPIPESSELTLQELLGEERRNKKGPRVDPLETELGVKTLDCRKPLIPFEEFINEPLNEVLEMDKDYTFFKVETENKFSFMTCPFILNAVTKNLGLYYDNRIRMYSERRITVLYSLVQGQQLNPYLRLKVRRDHIIDDALVRLEMIAMENPADLKKQLYVEFEGEQGVDEGGVSKEFFQLVVEEIFNPDIGMFTYDESTKLFWFNPSSFETEGQFTLIGIVLGLAIYNNCILDVHFPMVVYRKLMGKKGTFRDLGDSHPVLYQSLKDLLEYEGNVEDDMMITFQISQTDLFGNPMMYDLKENGDKIPITNENRKEFVNLYSDYILNKSVEKQFKAFRRGFHMVTNESPLKYLFRPEEIELLICGSRNLDFQALEETTEYDGGYTRDSVLIREFWEIVHSFTDEQKRLFLQFTTGTDRAPVGGLGKLKMIIAKNGPDTERLPTSHTCFNVLLLPEYSSKEKLKERLLKAITYAKGFGML,mutated_sequence,1.0,872.0,NP_570854.1.a2m,NP_570854.1.npy,ClinVar +NP_573566.2,NP_573566.2.csv,MAALLRSARWLLRAGAAPRLPLSLRLLPGGPGRLHAASYLPAARAGPVAGGLLSPARLYAIAAKEKDIQEESTFSSRKISNQFDWALMRLDLSVRRTGRIPKKLLQKVFNDTCRSGGLGGSHALLLLRSCGSLLPELKLEERTEFAHRIWDTLQKLGAVYDVSHYNALLKVYLQNEYKFSPTDFLAKMEEANIQPNRVTYQRLIASYCNVGDIEGASKILGFMKTKDLPVTEAVFSALVTGHARAGDMENAENILTVMRDAGIEPGPDTYLALLNAYAEKGDIDHVKQTLEKVEKSELHLMDRDLLQIIFSFSKAGYPQYVSEILEKVTCERRYIPDAMNLILLLVTEKLEDVALQILLACPVSKEDGPSVFGSFFLQHCVTMNTPVEKLTDYCKKLKEVQMHSFPLQFTLHCALLANKTDLAKALMKAVKEEGFPIRPHYFWPLLVGRRKEKNVQGIIEILKGMQELGVHPDQETYTDYVIPCFDSVNSARAILQENGCLSDSDMFSQAGLRSEAANGNLDFVLSFLKSNTLPISLQSIRSSLLLGFRRSMNINLWSEITELLYKDGRYCQEPRGPTEAVGYFLYNLIDSMSDSEVQAKEEHLRQYFHQLEKMNVKIPENIYRGIRNLLESYHVPELIKDAHLLVESKNLDFQKTVQLTSSELESTLETLKAENQPIRDVLKQLILVLCSEENMQKALELKAKYESDMVTGGYAALINLCCRHDKVEDALNLKEEFDRLDSSAVLDTGKYVGLVRVLAKHGKLQDAINILKEMKEKDVLIKDTTALSFFHMLNGAALRGEIETVKQLHEAIVTLGLAEPSTNISFPLVTVHLEKGDLSTALEVAIDCYEKYKVLPRIHDVLCKLVEKGETDLIQKAMDFVSQEQGEMVMLYDLFFAFLQTGNYKEAKKIIETPGIRARSARLQWFCDRCVANNQVETLEKLVELTQKLFECDRDQMYYNLLKLYKINGDWQRADAVWNKIQEENVIPREKTLRLLAEILREGNQEVPFDVPELWYEDEKHSLNSSSASTTEPDFQKDILIACRLNQKKGAYDIFLNAKEQNIVFNAETYSNLIKLLMSEDYFTQAMEVKAFAETHIKGFTLNDAANSRLIITQVRRDYLKEAVTTLKTVLDQQQTPSRLAVTRVIQALAMKGDVENIEVVQKMLNGLEDSIGLSKMVFINNIALAQIKNNNIDAAIENIENMLTSENKVIEPQYFGLAYLFRKVIEEQLEPAVEKISIMAERLANQFAIYKPVTDFFLQLVDAGKVDDARALLQRCGAIAEQTPILLLFLLRNSRKQGKASTVKSVLELIPELNEKEEAYNSLMKSYVSEKDVTSAKALYEHLTAKNTKLDDLFLKRYASLLKYAGEPVPFIEPPESFEFYAQQLRKLRENSS,mutated_sequence,1.0,1394.0,NP_573566.2.a2m,NP_573566.2.npy,ClinVar +NP_597677.2,NP_597677.2.csv,MNGDMPHVPITTLAGIASLTDLLNQLPLPSPLPATTTKSLLFNARIAEEVNCLLACRDDNLVSQLVHSLNQVSTDHIELKDNLGSDDPEGDIPVLLQAVLARSPNVFREKSMQNRYVQSGMMMSQYKLSQNSMHSSPASSNYQQTTISHSPSSRFVPPQTSSGNRFMPQQNSPVPSPYAPQSPAGYMPYSHPSSYTTHPQMQQASVSSPIVAGGLRNIHDNKVSGPLSGNSANHHADNPRHGSSEDYLHMVHRLSSDDGDSSTMRNAASFPLRSPQPVCSPAGSEGTPKGSRPPLILQSQSLPCSSPRDVPPDILLDSPERKQKKQKKMKLGKDEKEQSEKAAMYDIISSPSKDSTKLTLRLSRVRSSDMDQQEDMISGVENSNVSENDIPFNVQYPGQTSKTPITPQDINRPLNAAQCLSQQEQTAFLPANQVPVLQQNTSVAAKQPQTSVVQNQQQISQQGPIYDEVELDALAEIERIERESAIERERFSKEVQDKDKPLKKRKQDSYPQEAGGATGGNRPASQETGSTGNGSRPALMVSIDLHQAGRVDSQASITQDSDSIKKPEEIKQCNDAPVSVLQEDIVGSLKSTPENHPETPKKKSDPELSKSEMKQSESRLAESKPNENRLVETKSSENKLETKVETQTEELKQNESRTTECKQNESTIVEPKQNENRLSDTKPNDNKQNNGRSETTKSRPETPKQKGESRPETPKQKSDGHPETPKQKGDGRPETPKQKGESRPETPKQKNEGRPETPKHRHDNRRDSGKPSTEKKPEVSKHKQDTKSDSPRLKSERAEALKQRPDGRSVSESLRRDHDNKQKSDDRGESERHRGDQSRVRRPETLRSSSRNEHGIKSDSSKTDKLERKHRHESGDSRERPSSGEQKSRPDSPRVKQGDSNKSRSDKLGFKSPTSKDDKRTEGNKSKVDTNKAHPDNKAEFPSYLLGGRSGALKNFVIPKIKRDKDGNVTQETKKMEMKGEPKDKVEKIGLVEDLNKGAKPVVVLQKLSLDDVQKLIKDREDKSRSSLKPIKNKPSKSNKGSIDQSVLKELPPELLAEIESTMPLCERVKMNKRKRSTVNEKPKYAEISSDEDNDSDEAFESSRKRHKKDDDKAWEYEERDRRSSGDHRRSGHSHEGRRSSGGGRYRNRSPSDSDMEDYSPPPSLSEVARKMKKKEKQKKRKAYEPKLTPEEMMDSSTFKRFTASIENILDNLEDMDFTAFGDDDEIPQELLLGKHQLNELGSESAKIKAMGIMDKLSTDKTVKVLNILEKNIQDGSKLSTLLNHNNDTEEEERLWRDLIMERVTKSADACLTTINIMTSPNMPKAVYIEDVIERVIQYTKFHLQNTLYPQYDPVYRLDPHGGGLLSSKAKRAKCSTHKQRVIVMLYNKVCDIVSSLSELLEIQLLTDTTILQVSSMGITPFFVENVSELQLCAIKLVTAVFSRYEKHRQLILEEIFTSLARLPTSKRSLRNFRLNSSDMDGEPMYIQMVTALVLQLIQCVVHLPSSEKDSNAEEDSNKKIDQDVVITNSYETAMRTAQNFLSIFLKKCGSKQGEEDYRPLFENFVQDLLSTVNKPEWPAAELLLSLLGRLLVHQFSNKSTEMALRVASLDYLGTVAARLRKDAVTSKMDQGSIERILKQVSGGEDEIQQLQKALLDYLDENTETDPSLVFSRKFYIAQWFRDTTLETEKAMKSQKDEESSEGTHHAKEIETTGQIMHRAENRKKFLRSIIKTTPSQFSTLKMNSDTVDYDDACLIVRYLASMRPFAQSFDIYLTQILRVLGENAIAVRTKAMKCLSEVVAVDPSILARLDMQRGVHGRLMDNSTSVREAAVELLGRFVLCRPQLAEQYYDMLIERILDTGISVRKRVIKILRDICIEQPTFPKITEMCVKMIRRVNDEEGIKKLVNETFQKLWFTPTPHNDKEAMTRKILNITDVVAACRDTGYDWFEQLLQNLLKSEEDSSYKPVKKACTQLVDNLVEHILKYEESLADSDNKGVNSGRLVACITTLFLFSKIRPQLMVKHAMTMQPYLTTKCSTQNDFMVICNVAKILELVVPLMEHPSETFLATIEEDLMKLIIKYGMTVVQHCVSCLGAVVNKVTQNFKFVWACFNRYYGAISKLKSQHQEDPNNTSLLTNKPALLRSLFTVGALCRHFDFDLEDFKGNSKVNIKDKVLELLMYFTKHSDEEVQTKAIIGLGFAFIQHPSLMFEQEVKNLYNNILSDKNSSVNLKIQVLKNLQTYLQEEDTRMQQADRDWKKVAKQEDLKEMGDVSSGMSSSIMQLYLKQVLEAFFHTQSSVRHFALNVIALTLNQGLIHPVQCVPYLIAMGTDPEPAMRNKADQQLVEIDKKYAGFIHMKAVAGMKMSYQVQQAINTCLKDPVRGFRQDESSSALCSHLYSMIRGNRQHRRAFLISLLNLFDDTAKTDVTMLLYIADNLACFPYQTQEEPLFIMHHIDITLSVSGSNLLQSFKESMVKDKRKERKSSPSKENESSDSEEEVSRPRKSRKRVDSDSDSDSEDDINSVMKCLPENSAPLIEFANVSQGILLLLMLKQHLKNLCGFSDSKIQKYSPSESAKVYDKAINRKTGVHFHPKQTLDFLRSDMANSKITEEVKRSIVKQYLDFKLLMEHLDPDEEEEEGEVSASTNARNKAITSLLGGGSPKNNTAAETEDDESDGEDRGGGTSGSLRRSKRNSDSTELAAQMNESVDVMDVIAICCPKYKDRPQIARVVQKTSSGFSVQWMAGSYSGSWTEAKRRDGRKLVPWVDTIKESDIIYKKIALTSANKLTNKVVQTLRSLYAAKDGTSS,mutated_sequence,1.0,2804.0,NP_597677.2.a2m,NP_597677.2.npy,ClinVar +NP_598004.1,NP_598004.1.csv,MLKQSERRRSWSYRPWNTTENEGSQHRRSICSLGARSGSQASIHGWTEGNYNYYIEEDEDGEEEDQWKDDLAEEDQQAGEVTTAKPEGPSDPPALLSTLNVNVGGHSYQLDYCELAGFPKTRLGRLATSTSRSRQLSLCDDYEEQTDEYFFDRDPAVFQLVYNFYLSGVLLVLDGLCPRRFLEELGYWGVRLKYTPRCCRICFEERRDELSERLKIQHELRAQAQVEEAEELFRDMRFYGPQRRRLWNLMEKPFSSVAAKAIGVASSTFVLVSVVALALNTVEEMQQHSGQGEGGPDLRPILEHVEMLCMGFFTLEYLLRLASTPDLRRFARSALNLVDLVAILPLYLQLLLECFTGEGHQRGQTVGSVGKVGQVLRVMRLMRIFRILKLARHSTGLRAFGFTLRQCYQQVGCLLLFIAMGIFTFSAAVYSVEHDVPSTNFTTIPHSWWWAAVSISTVGYGDMYPETHLGRFFAFLCIAFGIILNGMPISILYNKFSDYYSKLKAYEYTTIRRERGEVNFMQRARKKIAECLLGSNPQLTPRQEN,mutated_sequence,1.0,545.0,NP_598004.1.a2m,NP_598004.1.npy,ClinVar +NP_612396.1,NP_612396.1.csv,MESTLGAGIVIAEALQNQLAWLENVWLWITFLGDPKILFLFYFPAAYYASRRVGIAVLWISLITEWLNLIFKWFLFGDRPFWWVHESGYYSQAPAQVHQFPSSCETGPGSPSGHCMITGAALWPIMTALSSQVATRARSRWVRVMPSLAYCTFLLAVGLSRIFILAHFPHQVLAGLITGAVLGWLMTPRVPMERELSFYGLTALALMLGTSLIYWTLFTLGLDLSWSISLAFKWCERPEWIHVDSRPFASLSRDSGAALGLGIALHSPCYAQVRRAQLGNGQKIACLVLAMGLLGPLDWLGHPPQISLFYIFNFLKYTLWPCLVLALVPWAVHMFSAQEAPPIHSS,mutated_sequence,1.0,346.0,NP_612396.1.a2m,NP_612396.1.npy,ClinVar +NP_612422.2,NP_612422.2.csv,MLGPQVWSSVRQGLSRSLSRNVGVWASGEGKKVDIAGIYPPVTTPFTATAEVDYGKLEENLHKLGTFPFRGFVVQGSNGEFPFLTSSERLEVVSRVRQAMPKNRLLLAGSGCESTQATVEMTVSMAQVGADAAMVVTPCYYRGRMSSAALIHHYTKVADLSPIPVVLYSVPANTGLDLPVDAVVTLSQHPNIVGMKDSGGDVTRIGLIVHKTRKQDFQVLAGSAGFLMASYALGAVGGVCALANVLGAQVCQLERLCCTGQWEDAQKLQHRLIEPNAAVTRRFGIPGLKKIMDWFGYYGGPCRAPLQELSPAEEEALRMDFTSNGWL,mutated_sequence,1.0,327.0,NP_612422.2.a2m,NP_612422.2.npy,ClinVar +NP_612486.2,NP_612486.2.csv,MAAVLESLLREEVSVAAVVRWIARSTQGSEDNAGEAAALSSLRALRKEFVPFLLNFLREQSSRVLPQGPPTPAKTPGASAALPGRPGGPPRGSRGARSQLFPPTEAQSTAAEAPLARRGGRRRGPGPARERGGRGLEEGVSGESLPGAGGRRLRGSGSPSRPSLTLSDPPNLSNLEEFPPVGSVPPGPTGTKPSRRINPTPVSEERSLSKPKTCFTSPPISCVPSSQPSALDTSPWGLGLPPGCRSLQEEREMLRKERSKQLQQSPTPTCPTPELGSPLPSRTGSLTDEPADPARVSSRQRLELVALVYSSCIAENLVPNLFLELFFVFQLLTARRMVTAKDSDPELSPAVLDSLESPLFQSIHDCVFFAVQVLECHFQVLSNLDKGTLKLLAENERLLCFSPALQGRLRAAYEGSVAKVSLVMPPSTQAVSFQPETDNRANFSSDRAFHTFKKQRDVFYEVLREWEDHHEEPGWDFEKGLGSRIRAMMGQLSAACSHSHFVRLFQKQLLQMCQSPGGAGGTVLGEAPDVLSMLGADKLGRLWRLQERLMAPQSSGGPCPPPTFPGCQGFFRDFILSASSFQFNQHLMDSLSLKIQELNGLALPQHEPNDEDGESDVDWQGERKQFAVVLLSLRLLAKFLGFVAFLPYRGPEPPPTGELQDSILALRSQVPPVLDVRTLLQRGLQARRAVLTVPWLVEFLSFADHVVPLLEYYRDIFTLLLRLHRSLVLSQESEGKMCFLNKLLLLAVLGWLFQIPTVPEDLFFLEEGPSYAFEVDTVAPEHGLDNAPVVDQQLLYTCCPYIGELRKLLASWVSGSSGRSGGFMRKITPTTTTSLGAQPSQTSQGLQAQLAQAFFHNQPPSLRRTVEFVAERIGSNCVKHIKATLVADLVRQAESLLQEQLVTQGEEGGDPAQLLEILCSQLCPHGAQALALGREFCQRKSPGAVRALLPEETPAAVLSSAENIAVGLATEKACAWLSANITALIRREVKAAVSRTLRAQGPEPAARGERRGCSRACEHHAPLPSHLISEIKDVLSLAVGPRDPDEGVSPEHLEQLLGQLGQTLRCRQFLCPPAEQHLAKCSVELASLLVADQIPILGPPAQYRLERGQARRLLHMLLSLWKEDFQGPVPLQLLLSPRNVGLLADTRPREWDLLLFLLRELVEKGLMGRMEIEACLGSLHQAQWPGDFAEELATLSNLFLAEPHLPEPQLRACELVQPNRGTVLAQS,mutated_sequence,1.0,1227.0,NP_612486.2.a2m,NP_612486.2.npy,ClinVar +NP_620130.2,NP_620130.2.csv,MDSNHQSNYKLSKTEKKFLRKQIKAKHTLLRHEGIETVSYATQSLVVANGGLGNGVSRNQLLPVLEKCGLVDALLMPPNKPYSFARYRTTEESKRAYVTLNGKEVVDDLGQKITLYLNFVEKVQWKELRPQALPPGLMVVEEIISSEEEKMLLESVDWTEDTDNQNSQKSLKHRRVKHFGYEFHYENNNVDKDKPLSGGLPDICESFLEKWLRKGYIKHKPDQMTINQYEPGQGIPAHIDTHSAFEDEIVSLSLGSEIVMDFKHPDGIAVPVMLPRRSLLVMTGESRYLWTHGITCRKFDTVQASESLKSGIITSDVGDLTLSKRGLRTSFTFRKVRQTPCNCSYPLVCDSQRKETPPSFPESDKEASRLEQEYVHQVYEEIAGHFSSTRHTPWPHIVEFLKALPSGSIVADIGCGNGKYLGINKELYMIGCDRSQNLVDICRERQFQAFVCDALAVPVRSGSCDACISIAVIHHFATAERRVAALQEIVRLLRPGGKALIYVWAMEQEYNKQKSKYLRGNRNSQGKKEEMNSDTSVQRSLVEQMRDMGSRDSASSVPRINDSQEGGCNSRQVSNSKLPVHVNRTSFYSQDVLVPWHLKGNPDKGKPVEPFGPIGSQDPSPVFHRYYHVFREGELEGACRTVSDVRILQSYYDQGNWCVILQKA,mutated_sequence,1.0,664.0,NP_620130.2.a2m,NP_620130.2.npy,ClinVar +NP_620596.2,NP_620596.2.csv,MHQRHPRARCPPLCVAGILACGFLLGCWGPSHFQQSCLQALEPQAVSSYLSPGAPLKGRPPSPGFQRQRQRQRRAAGGILHLELLVAVGPDVFQAHQEDTERYVLTNLNIGAELLRDPSLGAQFRVHLVKMVILTEPEGAPNITANLTSSLLSVCGWSQTINPEDDTDPGHADLVLYITRFDLELPDGNRQVRGVTQLGGACSPTWSCLITEDTGFDLGVTIAHEIGHSFGLEHDGAPGSGCGPSGHVMASDGAAPRAGLAWSPCSRRQLLSLLSAGRARCVWDPPRPQPGSAGHPPDAQPGLYYSANEQCRVAFGPKAVACTFAREHLDMCQALSCHTDPLDQSSCSRLLVPLLDGTECGVEKWCSKGRCRSLVELTPIAAVHGRWSSWGPRSPCSRSCGGGVVTRRRQCNNPRPAFGGRACVGADLQAEMCNTQACEKTQLEFMSQQCARTDGQPLRSSPGGASFYHWGAAVPHSQGDALCRHMCRAIGESFIMKRGDSFLDGTRCMPSGPREDGTLSLCVSGSCRTFGCDGRMDSQQVWDRCQVCGGDNSTCSPRKGSFTAGRAREYVTFLTVTPNLTSVYIANHRPLFTHLAVRIGGRYVVAGKMSISPNTTYPSLLEDGRVEYRVALTEDRLPRLEEIRIWGPLQEDADIQVYRRYGEEYGNLTRPDITFTYFQPKPRQAWVWAAVRGPCSVSCGAGLRWVNYSCLDQARKELVETVQCQGSQQPPAWPEACVLEPCPPYWAVGDFGPCSASCGGGLRERPVRCVEAQGSLLKTLPPARCRAGAQQPAVALETCNPQPCPARWEVSEPSSCTSAGGAGLALENETCVPGADGLEAPVTEGPGSVDEKLPAPEPCVGMSCPPGWGHLDATSAGEKAPSPWGSIRTGAQAAHVWTPAAGSCSVSCGRGLMELRFLCMDSALRVPVQEELCGLASKPGSRREVCQAVPCPARWQYKLAACSVSCGRGVVRRILYCARAHGEDDGEEILLDTQCQGLPRPEPQEACSLEPCPPRWKVMSLGPCSASCGLGTARRSVACVQLDQGQDVEVDEAACAALVRPEASVPCLIADCTYRWHVGTWMECSVSCGDGIQRRRDTCLGPQAQAPVPADFCQHLPKPVTVRGCWAGPCVGQGACGRQHLEPTGTIDMRGPGQADCAVAIGRPLGEVVTLRVLESSLNCSAGDMLLLWGRLTWRKMCRKLLDMTFSSKTNTLVVRQRCGRPGGGVLLRYGSQLAPETFYRECDMQLFGPWGEIVSPSLSPATSNAGGCRLFINVAPHARIAIHALATNMGAGTEGANASYILIRDTHSLRTTAFHGQQVLYWESESSQAEMEFSEGFLKAQASLRGQYWTLQSWVPEMQDPQSWKGKEGT,mutated_sequence,1.0,1371.0,NP_620596.2.a2m,NP_620596.2.npy,ClinVar +NP_644805.1,NP_644805.1.csv,MAQWNQLQQLDTRYLEQLHQLYSDSFPMELRQFLAPWIESQDWAYAASKESHATLVFHNLLGEIDQQYSRFLQESNVLYQHNLRRIKQFLQSRYLEKPMEIARIVARCLWEESRLLQTAATAAQQGGQANHPTAAVVTEKQQMLEQHLQDVRKRVQDLEQKMKVVENLQDDFDFNYKTLKSQGDMQDLNGNNQSVTRQKMQQLEQMLTALDQMRRSIVSELAGLLSAMEYVQKTLTDEELADWKRRQQIACIGGPPNICLDRLENWITSLAESQLQTRQQIKKLEELQQKVSYKGDPIVQHRPMLEERIVELFRNLMKSAFVVERQPCMPMHPDRPLVIKTGVQFTTKVRLLVKFPELNYQLKIKVCIDKDSGDVAALRGSRKFNILGTNTKVMNMEESNNGSLSAEFKHLTLREQRCGNGGRANCDASLIVTEELHLITFETEVYHQGLKIDLETHSLPVVVISNICQMPNAWASILWYNMLTNNPKNVNFFTKPPIGTWDQVAEVLSWQFSSTTKRGLSIEQLTTLAEKLLGPGVNYSGCQITWAKFCKENMAGKGFSFWVWLDNIIDLVKKYILALWNEGYIMGFISKERERAILSTKPPGTFLLRFSESSKEGGVTFTWVEKDISGKTQIQSVEPYTKQQLNNMSFAEIIMGYKIMDATNILVSPLVYLYPDIPKEEAFGKYCRPESQEHPEADPGSAAPYLKTKFICVTPTTCSNTIDLPMSPRTLDSLMQFGNNGEGAEPSAGGQFESLTFDMELTSECATSPM,mutated_sequence,1.0,770.0,NP_644805.1.a2m,NP_644805.1.npy,ClinVar +NP_653197.2,NP_653197.2.csv,MSSEMEPLLLAWSYFRRRKFQLCADLCTQMLEKSPYDQEPDPELPVHQAAWILKARALTEMVYIDEIDVDQEGIAEMMLDENAIAQVPRPGTSLKLPGTNQTGGPSQAVRPITQAGRPITGFLRPSTQSGRPGTMEQAIRTPRTAYTARPITSSSGRFVRLGTASMLTSPDGPFINLSRLNLTKYSQKPKLAKALFEYIFHHENDVKTALDLAALSTEHSQYKDWWWKVQIGKCYYRLGMYREAEKQFKSALKQQEMVDTFLYLAKVYVSLDQPVTALNLFKQGLDKFPGEVTLLCGIARIYEEMNNMSSAAEYYKEVLKQDNTHVEAIACIGSNHFYSDQPEIALRFYRRLLQMGIYNGQLFNNLGLCCFYAQQYDMTLTSFERALSLAENEEEAADVWYNLGHVAVGIGDTNLAHQCFRLALVNNNNHAEAYNNLAVLEMRKGHVEQARALLQTASSLAPHMYEPHFNFATISDKIGDLQRSYVAAQKSEAAFPDHVDTQHLIKQLRQHFAML,mutated_sequence,1.0,515.0,NP_653197.2.a2m,NP_653197.2.npy,ClinVar +NP_658985.2,NP_658985.2.csv,MSRLRALLGLGLLVAGSRVPRIKSQTIACRSGPTWWGPQRLNSGGRWDSEVMASTVVKYLSQEEAQAVDQELFNEYQFSVDQLMELAGLSCATAIAKAYPPTSMSRSPPTVLVICGPGNNGGDGLVCARHLKLFGYEPTIYYPKRPNKPLFTALVTQCQKMDIPFLGEMPAEPMTIDELYELVVDAIFGFSFKGDVREPFHSILSVLKGLTVPIASIDIPSGWDVEKGNAGGIQPDLLISLTAPKKSATQFTGRYHYLGGRFVPPALEKKYQLNLPPYPDTECVYRLQ,mutated_sequence,1.0,288.0,NP_658985.2.a2m,NP_658985.2.npy,ClinVar +NP_659434.2,NP_659434.2.csv,MNAIVALCHFCELHGPRTLFCTEVLHAPLPQGDGNEDSPGQGEQAEEEEGGIQMNSRMRAHSPAEGASVESSSPGPKKSDMCEGCRSLAAGHPGYISHDKETSIKYVSHQHPSHPQLFSIVRQACVRSLSCEVCPGREGPIFFGDEQHGFVFSHTFFIKDSLARGFQRWYSIITIMMDRIYLINSWPFLLGKVRGIIDELQGKALKVFEAEQFGCPQRAQRMNTAFTPFLHQRNGNAARSLTSLTSDDNLWACLHTSFAWLLKACGSRLTEKLLEGAPTEDTLVQMEKLADLEEESESWDNSEAEEEEKAPVLPESTEGRELTQGPAESSSLSGCGSWQPRKLPVFKSLRHMRQVLGAPSFRMLAWHVLMGNQVIWKSRDVDLVQSAFEVLRTMLPVGCVRIIPYSSQYEEAYRCNFLGLSPHVQIPPHVLSSEFAVIVEVHAAARSTLHPVGCEDDQSLSKYEFVVTSGSPVAADRVGPTILNKIEAALTNQNLSVDVVDQCLVCLKEEWMNKVKVLFKFTKVDSRPKEDTQKLLSILGASEEDNVKLLKFWMTGLSKTYKSHLMSTVRSPTASESRN,mutated_sequence,1.0,579.0,NP_659434.2.a2m,NP_659434.2.npy,ClinVar +NP_660208.2,NP_660208.2.csv,MSSKKNRKRLNQSAENGSSLPSAASSCAEARAPSAGSDFAATSGTLTVTNLLEKVDDKIPKTFQNSLIHLGLNTMKSANICIGRPVLLTSLNGKQEVYTAWPMAGFPGGKVGLSEMAQKNVGVRPGDAIQVQPLVGAVLQAEEMDVALSDKDMEINEEELTGCILRKLDGKIVLPGNFLYCTFYGRPYKLQVLRVKGADGMILGGPQSDSDTDAQRMAFEQSSMETSSLELSLQLSQLDLEDTQIPTSRSTPYKPIDDRITNKASDVLLDVTQSPGDGSGLMLEEVTGLKCNFESAREGNEQLTEEERLLKFSIGAKCNTDTFYFISSTTRVNFTEIDKNSKEQDNQFKVTYDMIGGLSSQLKAIREIIELPLKQPELFKSYGIPAPRGVLLYGPPGTGKTMIARAVANEVGAYVSVINGPEIISKFYGETEAKLRQIFAEATLRHPSIIFIDELDALCPKREGAQNEVEKRVVASLLTLMDGIGSEVSEGQVLVLGATNRPHALDAALRRPGRFDKEIEIGVPNAQDRLDILQKLLRRVPHLLTEAELLQLANSAHGYVGADLKVLCNEAGLCALRRILKKQPNLPDVKVAGLVKITLKDFLQAMNDIRPSAMREIAIDVPNVSWSDIGGLESIKLKLEQAVEWPLKHPESFIRMGIQPPKGVLLYGPPGCSKTMIAKALANESGLNFLAIKGPELMNKYVGESERAVRETFRKARAVAPSIIFFDELDALAVERGSSLGAGNVADRVLAQLLTEMDGIEQLKDVTILAATNRPDRIDKALMRPGRIDRIIYVPLPDAATRREIFKLQFHSMPVSNEVDLDELILQTDAYSGAEIVAVCREAALLALEEDIQANLIMKRHFTQALSTVTPRIPESLRRFYEDYQEKSGLHTL,mutated_sequence,1.0,893.0,NP_660208.2.a2m,NP_660208.2.npy,ClinVar +NP_660289.2,NP_660289.2.csv,MHGHGGYDSDFSDDERCGESSKRKKRTVEDDLLLQKPFQKEKHGKVAHKQVAAELLDREEARNRRFHLIAMDAYQRHTKFVNDYILYYGGKKEDFKRLGENDKTDLDVIRENHRFLWNEEDEMDMTWEKRLAKKYYDKLFKEYCIADLSKYKENKFGFRWRVEKEVISGKGQFFCGNKYCDKKEGLKSWEVNFGYIEHGEKRNALVKLRLCQECSIKLNFHHRRKEIKSKKRKDKTKKDCEESSHKKSRLSSAEEASKKKDKGHSSSKKSEDSLLRNSDEEESASESELWKGPLPETDEKSQEEEFDEYFQDLFL,mutated_sequence,1.0,315.0,NP_660289.2.a2m,NP_660289.2.npy,ClinVar +NP_663304.1,NP_663304.1.csv,MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS,mutated_sequence,1.0,606.0,NP_663304.1.a2m,NP_663304.1.npy,ClinVar +NP_689476.2,NP_689476.2.csv,MMEAIKKKMQMLKLDKENALDRAEQAEAEQKQAEERSKQLEDELAAMQKKLKGTEDELDKYSEALKDAQEKLELAEKKAADAEAEVASLNRRIQLVEEELDRAQERLATALQKLEEAEKAADESERGMKVIENRALKDEEKMELQEIQLKEAKHIAEEADRKYEEVARKLVIIEGDLERTEERAELAESKCSELEEELKNVTNNLKSLEAQAEKYSQKEDKYEEEIKILTDKLKEAETRAEFAERSVAKLEKTIDDLEDELYAQKLKYKAISEELDHALNDMTSI,mutated_sequence,1.0,285.0,NP_689476.2.a2m,NP_689476.2.npy,ClinVar +NP_689509.1,NP_689509.1.csv,MGDKKDDKDSPKKNKGKERRDLDDLKKEVAMTEHKMSVEEVCRKYNTDCVQGLTHSKAQEILARDGPNALTPPPTTPEWVKFCRQLFGGFSILLWIGAILCFLAYGIQAGTEDDPSGDNLYLGIVLAAVVIITGCFSYYQEAKSSKIMESFKNMVPQQALVIREGEKMQVNAEEVVVGDLVEIKGGDRVPADLRIISAHGCKVDNSSLTGESEPQTRSPDCTHDNPLETRNITFFSTNCVEGTARGVVVATGDRTVMGRIATLASGLEVGKTPIAIEIEHFIQLITGVAVFLGVSFFILSLILGYTWLEAVIFLIGIIVANVPEGLLATVTVCLTLTAKRMARKNCLVKNLEAVETLGSTSTICSDKTGTLTQNRMTVAHMWFDNQIHEADTTEDQSGTSFDKSSHTWVALSHIAGLCNRAVFKGGQDNIPVLKRDVAGDASESALLKCIELSSGSVKLMRERNKKVAEIPFNSTNKYQLSIHETEDPNDNRYLLVMKGAPERILDRCSTILLQGKEQPLDEEMKEAFQNAYLELGGLGERVLGFCHYYLPEEQFPKGFAFDCDDVNFTTDNLCFVGLMSMIDPPRAAVPDAVGKCRSAGIKVIMVTGDHPITAKAIAKGVGIISEGNETVEDIAARLNIPVSQVNPRDAKACVIHGTDLKDFTSEQIDEILQNHTEIVFARTSPQQKLIIVEGCQRQGAIVAVTGDGVNDSPALKKADIGVAMGIAGSDVSKQAADMILLDDNFASIVTGVEEGRLIFDNLKKSIAYTLTSNIPEITPFLLFIMANIPLPLGTITILCIDLGTDMVPAISLAYEAAESDIMKRQPRNPRTDKLVNERLISMAYGQIGMIQALGGFFSYFVILAENGFLPGNLVGIRLNWDDRTVNDLEDSYGQQWTYEQRKVVEFTCHTAFFVSIVVVQWADLIICKTRRNSVFQQGMKNKILIFGLFEETALAAFLSYCPGMDVALRMYPLKPSWWFCAFPYSFLIFVYDEIRKLILRRNPGGWVEKETYY,mutated_sequence,1.0,1013.0,NP_689509.1.a2m,NP_689509.1.npy,ClinVar +NP_689777.3,NP_689777.3.csv,MLESYVTPILMSYVNRYIKNLKPSDLQLSLWGGDVVLSKLELKLDVLEQELKLPFTFLSGHIHELRIHVPWTKLGSEPVVITINTMECILKLKDGIQDDHESCGSNSTNRSTAESTKSSIKPRRMQQAAPTDPDLPPGYVQSLIRRVVNNVNIVINNLILKYVEDDIVLSVNITSAECYTVGELWDRAFMDISATDLVLRKVINFSDCTVCLDKRNASGKIEFYQDPLLYKCSFRTRLHFTYENLNSKMPSVIKIHTLVESLKLSITDQQLPMFIRIMQLGIALYYGEIGNFKEGEIEDLTCHNKDMLGNITGSEDETRIDMQYPAQHKGQELYSQQDEEQPQGWVSWAWSFVPAIVSYDDGEEDFVGNDPASTMHQQKAQTLKDPIVSIGFYCTKATVTFKLTEMQVESSYYSPQKVKSKEVLCWEQEGTTVEALMMGEPFFDCQIGFVGCRAMCLKGIMGVKDFEENMNRSETEACFFICGDNLSTKGFTYLTNSLFDYRSPENNGTRAEFILDSTHHKETYTEIAGMQRFGAFYMDYLYTMENTSGKGSTNQQDFSSGKSEDLGTVQEKSTKSLVIGPLDFRLDSSAVHRILKMIVCALEHEYEPYSRLKSDIKDENETILNPEEVALLEEYIPTRHTSVTLLKCTCTISMAEFNLLDHLLPVIMGEKNSSNFMNTTNFQSLRPLPSIRILVDKINLEHSVPMYAEQLVHVVSSLTQPSDNLLHYCYVHCYLKIFGFQAGLTSLDCSGSYCLPVPVIPSFSTALYGKLLKLPTCWTKRSQIAITEGIFELPNLTIQATRAQTLLLQAIYQSWSHLGNVSSSAVIEALINEIFLSIGVKSKNPLPTLEGSIQNVELKYCSTSLVKCASGTMGSIKICAKAPVDSGKEKLIPLLQGPSDTKDLHSTKWLNESRKPESLLAPDLMAFTIQVPQYIDYCHNSGAVLLCSIQGLAVNIDPILYTWLIYQPQKRTSRHMQQQPVVAVPLVMPVCRRKEDEVSIGSAPLAKQQSYQASEYASSPVKTKTVTESRPLSVPVKAMLNISESCRSPEERMKEFIGIVWNAVKHLTLQLEVQSCCVFIPNDSLPSPSTIVSGDIPGTVRSWYHGQTSMPGTLVLCLPQIKIISAGHKYMEPLQEIPFVIPRPILEEGDAFPWTISLHNFSIYTLLGKQVTLCLVEPMGCTSTLAVTSQKLLATGPDTRHSFVVCLHVDLESLEIKCSNPQVQLFYELTDIMNKVWNKIQKRGNLNLSPTSPETMAGPVPTSPVRSSIGTAPPDTSTCSPSADIGTTTEGDSIQAGEESPFSDSVTLEQTTSNIGGTSGRVSLWMQWVLPKITIKLFAPDPENKGTEVCMVSELEDLSASIDVQDVYTKVKCKIESFNIDHYRSRPGEGWQSGHFEGVFLQCKEKSVTTTKLLDGTHQQHGFLSLTYTKAVTKNVRHKLTSRNERRSFHKLSEGLMDGSPHFLHEILLSAQAFDIVLYFPLLNAIASIFQAKLPKTQKEKRKSPGQPMRTHTLTSRNLPLIYVNTSVIRIFIPKTEEMQPTVEANQAAKEDTVVLKIGSVAMAPQADNPLGRSVLRKDIYQRALNLGILRDPGSEIEDRQYQIDLQSINIGTAQWHQLKPEKESVSGGVVTETERNSQNPALEWNMASSIRRHQERRAILTPVLTDFSVRITGAPAVIFTKVVSPENLHTEEILVCGHSLEVNITTNLDFFLSVAQVQLLHQLIVANMTGLEPSNKAAEISKQEQKKVDIFDGGMAETSSRYSGAQDSGIGSDSVKIRIVQIEQHSGASQHRIARPSRQSSIVKNLNFIPFDIFITASRISLMTYSCMALSKSKSQEQKNNEKTDKSSLNLPEVDSDVAKPNQACISTVTAEDLLRSSISFPSGKKIGVLSLESLHASTRSSARQALGITIVRQPGRRGTGDLQLEPFLYFIVSQPSLLLSCHHRKQRVEVSIFDAVLKGVASDYKCIDPGKTLPEALDYCTVWLQTVPGEIDSKSGIPPSFITLQIKDFLNGPADVNLDISKPLKANLSFTKLDQINLFLKKIKNAHSLAHSEETSAMSNTMVNKDDLPVSKYYRGKLSKPKIHGDGVQKISAQENMWRAVSCFQKISVQTTQIVISMETVPHTSKPCLLASLSNLNGSLSVKATQKVPGIILGSSFLLSINDFLLKTSLKERSRILIGPCCATANLEAKWCKHSGNPGPEQSIPKISIDLRGGLLQVFWGQEHLNCLVLLHELLNGYLNEEGNFEVQVSEPVPQMSSPVEKNQTFKSEQSSDDLRTGLFQYVQDAESLKLPGVYEVLFYNETEDCPGMMLWRYPEPRVLTLVRITPVPFNTTEDPDISTADLGDVLQVPCSLEYWDELQKVFVAFREFNLSESKVCELQLPDINLVNDQKKLVSSDLWRIVLNSSQNGADDQSSASESGSQSTCDPLVTPTALAACTRVDSCFTPWFVPSLCVSFQFAHLEFHLCHHLDQLGTAAPQYLQPFVSDRNMPSELEYMIVSFREPHMYLRQWNNGSVCQEIQFLAQADCKLLECRNVTMQSVVKPFSIFGQMAVSSDVVEKLLDCTVIVDSVFVNLGQHVVHSLNTAIQAWQQNKCPEVEELVFSHFVICNDTQETLRFGQVDTDENILLASLHSHQYSWRSHKSPQLLHICIEGWGNWRWSEPFSVDHAGTFIRTIQYRGRTASLIIKVQQLNGVQKQIIICGRQIICSYLSQSIELKVVQHYIGQDGQAVVREHFDCLTAKQKLPSYILENNELTELCVKAKGDEDWSRDVCLESKAPEYSIVIQVPSSNSSIIYVWCTVLTLEPNSQVQQRMIVFSPLFIMRSHLPDPIIIHLEKRSLGLSETQIIPGKGQEKPLQNIEPDLVHHLTFQAREEYDPSDCAVPISTSLIKQIATKVHPGGTVNQILDEFYGPEKSLQPIWPYNKKDSDRNEQLSQWDSPMRVKLSIWKPYVRTLLIELLPWALLINESKWDLWLFEGEKIVLQVPAGKIIIPPNFQEAFQIGIYWANTNTVHKSVAIKLVHNLTSPKWKDGGNGEVVTLDEEAFVDTEIRLGAFPGHQKLCQFCISSMVQQGIQIIQIEDKTTIINNTPYQIFYKPQLSVCNPHSGKEYFRVPDSATFSICPGGEQPAMKSSSLPCWDLMPDISQSVLDASLLQKQIMLGFSPAPGADSSQCWSLPAIVRPEFPRQSVAVPLGNFRENGFCTRAIVLTYQEHLGVTYLTLSEDPSPRVIIHNRCPVKMLIKENIKDIPKFEVYCKKIPSECSIHHELYHQISSYPDCKTKDLLPSLLLRVEPLDEVTTEWSDAIDINSQGTQVVFLTGFGYVYVDVVHQCGTVFITVAPEGKAGPILTNTNRAPEKIVTFKMFITQLSLAVFDDLTHHKASAELLRLTLDNIFLCVAPGAGPLPGEEPVAALFELYCVEICCGDLQLDNQLYNKSNFHFAVLVCQGEKAEPIQCSKMQSLLISNKELEEYKEKCFIKLCITLNEGKSILCDINEFSFELKPARLYVEDTFVYYIKTLFDTYLPNSRLAGHSTHLSGGKQVLPMQVTQHARALVNPVKLRKLVIQPVNLLVSIHASLKLYIASDHTPLSFSVFERGPIFTTARQLVHALAMHYAAGALFRAGWVVGSLDILGSPASLVRSIGNGVADFFRLPYEGLTRGPGAFVSGVSRGTTSFVKHISKGTLTSITNLATSLARNMDRLSLDEEHYNRQEEWRRQLPESLGEGLRQGLSRLGISLLGAIAGIVDQPMQNFQKTSEAQASAGHKAKGVISGVGKGIMGVFTKPIGGAAELVSQTGYGILHGAGLSQLPKQRHQPSDLHADQAPNSHVKYVWKMLQSLGRPEVHMALDVVLVRGSGQEHEGCLLLTSEVLFVVSVSEDTQQQAFPVTEIDCAQDSKQNNLLTVQLKQPRVACDVEVDGVRERLSEQQYNRLVDYITKTSCHLAPSCSSMQIPCPVVAAEPPPSTVKTYHYLVDPHFAQVFLSKFTMVKNKALRKGFP,mutated_sequence,1.0,3997.0,NP_689777.3.a2m,NP_689777.3.npy,ClinVar +NP_689831.2,NP_689831.2.csv,MVMACRVVNKRRHMGLQQLSSFAETGRTFLGPLKSSKFIIDEECHESVLISSTVRLLESLDLTSAVGQLLNEAVQAQNNTYRTGISTLLFLVGAWSSAVEECLHLGVPISIIVSVMSEGLNFCSEEVVSLHVPVHNIFDCMDSTKTFSQLETFSVSLCPFLQVPSDTDLIEELHGLKDVASQTLTISNLSGRPLKSYELFKPQTKVEADNNTSRTLKNSLLADTCCRQSILIHSRHFNRTDNTEGVSKPDGFQEHVTATHKTYRCNDLVELAVGLSHGDHSSMKLVEEAVQLQYQNACVQQGNCTKPFMFDISRIFTCCLPGLPETSSCVCPGYITVVSVSNNPVIKELQNQPVRIVLIEGDLTENYRHLGFNKSANIKTVLDSMRLQEDSSEELWANHVLQVLIQFKVNLVLVQGNVSERLIEKCINSKRLVIGSVNGSVMQAFAEAAGAVQVAYITQVNEDCVGDGVCVTFWRSSPLDVVDRNNRIAILLKTEGINLVTAVLTNPVTAQMQIKEDRFWTCAYRLYYALKEEKVFLGGGAVEFLCLSCLHILAEQSLKKENHACSGWLHNTSSWLASSLAIYRPTVLKFLANGWQKYLSTLLYNTANYSSEFEASTYIQHHLQNATDSGSPSSYILNEYSKLNSRIFNSDISNKLEQIPRVYDVVTPKIEAWRRALDLVLLVLQTDSEIITGHGHTQINSQELTGFLFL,mutated_sequence,1.0,710.0,NP_689831.2.a2m,NP_689831.2.npy,ClinVar +NP_689945.2,NP_689945.2.csv,MDADSLLLSLELASGSGQGLSPDRRASLLTSLMLVKRDYRYDRVLFWGRILGLVADYYIAQGLSEDQLAPRKTLYSLNCTEWSLLPPATEEMVAQSSVVKGRFMGDPSYEYEHTELQKVNEGEKVFEEEIVVQIKEETRLVSVIDQIDKAVAIIPRGALFKTPFGPTHVNRTFEGLSLSEAKKLSSYFHFREPVELKNKTLLEKADLDPSLDFMDSLEHDIPKGSWSIQMERGNALVVLRSLLWPGLTFYHAPRTKNYGYVYVGTGEKNMDLPFML,mutated_sequence,1.0,276.0,NP_689945.2.a2m,NP_689945.2.npy,ClinVar +NP_694950.2,NP_694950.2.csv,MAEAVLIDLFGLKLNSQKNCHQTLLKTLNAVQYHHAAKAKFLCIMCCSNISYERDGEQDNCEIETSNGLSALLEEFEIVSCPSMAATLYTIKQKIDEKNLSSIKVIVPRHRKTLMKAFIDQLFTDVYNFEFEDLQVTFRGGLFKQSIEINVITAQELRGIQNEIETFLRSLPALRGKLTIITSSLIPDIFIHGFTTRTGGISYIPTLSSFNLFSSSKRRDPKVVVQENLRRLANAAGFNVEKFYRIKTHHSNDIWIMGRKEPDSYDGITTNQRGVTIAALGADCIPIVFADPVKKACGVAHAGWKGTLLGVAMATVNAMIAEYGCSLEDIVVVLGPSVGPCCFTLPRESAEAFHNLHPACVQLFDSPNPCIDIRKATRILLEQGGILPQNIQDQNQDLNLCTSCHPDKFFSHVRDGLNFGTQIGFISIKE,mutated_sequence,1.0,430.0,NP_694950.2.a2m,NP_694950.2.npy,ClinVar +NP_694972.3,NP_694972.3.csv,MGTASSLVSPAGGEVIEDTYGAGGGEACEIPVEVKPKARLLRNSFRRGAGAAAGAGPGSLPRGVGAGGLLGASFKSTGSSVPELEYAAAEYERLRKEYEIFRVSKNQELLSMGRREAKLDTENKRLRAELQALQKTYQKILREKESALEAKYQAMERAATFEHDRDKVKRQFKIFRETKENEIQDLLRAKRELESKLQRLQAQGIQVFDPGESDSDDNCTDVTAAGTQCEYWTGGALGSEPSIGSMIQLQQSFRGPEFAHSSIDVEGPFANVNRDDWDIAVASLLQVTPLFSHSLWSNTVRCYLIYTDETQPEMDLFLKDYSPKLKRMCETMGYFFHAVYFPIDVENQYLTVRKWEIEKSSLVILFIHLTLPSLLLEDCEEAFLKNPEGKPRLIFHRLEDGKVSSDSVQQLIDQVSNLNKTSKAKIIDHSGDPAEGVYKTYICVEKIIKQDILGFENTDLETKDLGSEDSIPEEDDFGDVLWDIHDEQEQMETFQQASNSAHELGFEKYYQRLNDLVAAPAPIPPLLVSGGPGSGKSLLLSKWIQLQQKNSPNTLILSHFVGRPMSTSSESSLIIKRLTLKLMQHSWSVSALTLDPAKLLEEFPRWLEKLSARHQGSIIIVIDSIDQVQQVEKHMKWLIDPLPVNVRVIVSVNVETCPPAWRLWPTLHLDPLSPKDAKSIIIAECHSVDIKLSKEQEKKLERHCRSATTCNALYVTLFGKMIARAGRAGNLDKILHQCFQCQDTLSLYRLVLHSIRESMANDVDKELMKQILCLVNVSHNGVSESELMELYPEMSWTFLTSLIHSLYKMCLLTYGCGLLRFQHLQAWETVRLEYLEGPTVTSSYRQKLINYFTLQLSQDRVTWRSADELPWLFQQQGSKQKLHDCLLNLFVSQNLYKRGHFAELLSYWQFVGKDKSAMATEYFDSLKQYEKNCEGEDNMSCLADLYETLGRFLKDLGLLSQAIVPLQRSLEIRETALDPDHPRVAQSLHQLASVYVQWKKFGNAEQLYKQALEISENAYGADHPYTARELEALATLYQKQNKYEQAEHFRKKSFKIHQKAIKKKGNLYGFALLRRRALQLEELTLGKDTPDNARTLNELGVLYYLQNNLETADQFLKRSLEMRERVLGPDHPDCAQSLNNLAALCNEKKQYDKAEELYERALDIRRRALAPDHPSLAYTVKHLAILYKKMGKLDKAVPLYELAVEIRQKSFGPKHPSVATALVNLAVLYSQMKKHVEALPLYERALKIYEDSLGRMHPRVGETLKNLAVLSYEGGDFEKAAELYKRAMEIKEAETSLLGGKAPSRHSSSGDTFSLKTAHSPNVFLQQGQR,mutated_sequence,1.0,1330.0,NP_694972.3.a2m,NP_694972.3.npy,ClinVar +NP_710156.3,NP_710156.3.csv,MWGFRLLRSPPLLLLLPQLGIGNASSCSQARTMNPGGSGGARCSLSAEVRRRQCLQLSTVPGADPQRSNELLLLAAAGEGLERQDLPGDPAKEEPQPPPQHHVLYFPGDVQNYHEIMTRHPENYQWENWSLENVATILAHRFPNSYIWVIKCSRMHLHKFSCYDNFVKSNMFGAPEHNTDFGAFKHLYMLLVNAFNLSQNSLSKKSLNVWNKDSIASNCRSSPSHTTNGCQGEKVRTCEKSDESAMSFYPPSLNDASFTLIGFSKGCVVLNQLLFELKEAKKDKNIDAFIKSIRTMYWLDGGHSGGSNTWVTYPEVLKEFAQTGIIVHTHVTPYQVRDPMRSWIGKEHKKFVQILGDLGMQVTSQIHFTKEAPSIENHFRVHEVF,mutated_sequence,1.0,385.0,NP_710156.3.a2m,NP_710156.3.npy,ClinVar +NP_714915.3,NP_714915.3.csv,MATRGGAGVAMAVWSLLSARAVTAFLLLFLPRFLQAQTFSFPFQQPEKCDNNQYFDISALSCVPCGANQRQDARGTSCVCLPGFQMISNNGGPAIICKKCPENMKGVTEDGWNCISCPSDLTAEGKCHCPIGHILVERDINGTLLSQATCELCDGNENSFMVVNALGDRCVRCEPTFVNTSRSCACSEPNILTGGLCFSSTGNFPLRRISAARYGEVGMSLTSEWFAKYLQSSAAACWVYANLTSCQALGNMCVMNMNSYDFATFDACGLFQFIFENTAGLSTVHSISFWRQNLPWLFYGDQLGLAPQVLSSTSLPTNFSFKGENQNTKLKFVAASYDIRGNFLKWQTLEGGVLQLCPDTETRLNAAYSFGTTYQQNCEIPISKILIDFPTPIFYDVYLEYTDENQHQYILAVPVLNLNLQHNKIFVNQDSNSGKWLLTRRIFLVDAVSGRENDLGTQPRVIRVATQISLSVHLVPNTINGNIYPPLITIAYSDIDIKDANSQSVKVSFSVTYEMDHGEAHVQTDIALGVLGGLAVLASLLKTAGWKRRIGSPMIDLQTVVKFLVYYAGDLANVFFIITVGTGLYWLIFFKAQKSVSVLLPMPIQEERFVTYVGCAFALKALQFLHKLISQITIDVFFIDWERPKGKVLKAVEGEGGVRSATVPVSIWRTYFVANEWNEIQTVRKINSLFQVLTVLFFLEVVGFKNLALMDSSSSLSRNPPSYIAPYSCILRYAVSAALWLAIGIIQVVFFAVFYERFIEDKIRQFVDLCSMSNISVFLLSHKCFGYYIHGRSVHGHADTNMEEMNMNLKREAENLCSQRGLVPNTDGQTFEIAISNQMRQHYDRIHETLIRKNGPARLLSSSASTFEQSIKAYHMMNKFLGSFIDHVHKEMDYFIKDKLLLERILGMEFMEPMEKSIFYNDEGYSFSSVLYYGNEATLLIFDLLFFCVVDLACQNFILASFLTYLQQEIFRYIRNTVGQKNLASKTLVDQRFLI,mutated_sequence,1.0,995.0,NP_714915.3.a2m,NP_714915.3.npy,ClinVar +NP_714928.1,NP_714928.1.csv,MARGGAACKSDARLLLGRDALRPAPALLAPAVLLGAALGLGLGLWLGCRAGRQRTRHQKDDTQNLLKNLESNAQTPSETGSPSRRRKREVQMSKDKEAVDECEPPSNSNITAFALKAKVIYPINQKFRPLADGSSNPSLHENLKQAVLPHQPVEASPSSSLGSLSQGEKDDCSSSSSVHSATSDDRFLSRTFLRVNAFPEVLACESVDVDLCIYSLHLKDLLHLDTALRQEKHMMFIQIFKMCLLDLLPKKKSDDELYQKILSKQEKDLEELEKGLQVKLSNTEMSGAGDSEYITLADVEKKEREYSEQLIDNMEAFWKQMANIQHFLVDQFKCSSSKARQLMMTLTERMIAAEGLLCDSQELQALDALERTMGRAHMAKVIEFLKLQVQEETRCRLAAISHGLELLAGEGKLSGRQKEELLTQQHKAFWQEAERFSREFVQRGKDLVTASLAHQVEGTAKLTLAQEEEQRSFLAEAQPTADPEKFLEAFHEVLERQRLMQCDLEEEENVRATEAVVALCQELYFSTVDTFQKFVDALFLQTLPGMTGLPPEECDYLRQEVQENAAWQLGKSNRFRRQQWKLFQELLEQDQQVWMEECALSSVLQTHLREDHEGTIRGVLGRLGGLTEESTRCVLQGHDLLLRSALRRLALRGNALATLTQMRLSGKKHLLQELREQRALEQGSSQCLDEHQWQLLRALEARVLEEASRLEEEAQQTRLQLQQRLLAEAQEVGQLLQQHMECAIGQALLVHARNAATKSRAKDRDDFKRTLMEAAVESVYVTSAGVSRLVQAYYQQIGRIMEDHEERKLQHLKTLQGERMENYKLRKKQELSNPSSGSRTAGGAHETSQAVHQRMLSQQKRFLAQFPVHQQMRLHAQQQQAGVMDLLEAQLETQLQEAEQNFISELAALARVPLAESKLLPAKRGLLEKPLRTKRKKPLPQERGDLGVPNNEDLASGDQTSGSLSSKRLSQQESEAGDSGNSKKMLKRRSNL,mutated_sequence,1.0,992.0,NP_714928.1.a2m,NP_714928.1.npy,ClinVar +NP_733751.2,NP_733751.2.csv,MSSEEDKSVEQPQPPPPPPEEPGAPAPSPAAADKRPRGRPRKDGASPFQRARKKPRSRGKTAVEDEDSMDGLETTETETIVETEIKEQSAEEDAEAEVDNSKQLIPTLQRSVSEESANSLVSVGVEAKISEQLCAFCYCGEKSSLGQGDLKQFRITPGFILPWRNQPSNKKDIDDNSNGTYEKMQNSAPRKQRGQRKERSPQQNIVSCVSVSTQTASDDQAGKLWDELSLVGLPDAIDIQALFDSTGTCWAHHRCVEWSLGVCQMEEPLLVNVDKAVVSGSTERCAFCKHLGATIKCCEEKCTQMYHYPCAAGAGTFQDFSHIFLLCPEHIDQAPERSKEDANCAVCDSPGDLLDQFFCTTCGQHYHGMCLDIAVTPLKRAGWQCPECKVCQNCKQSGEDSKMLVCDTCDKGYHTFCLQPVMKSVPTNGWKCKNCRICIECGTRSSSQWHHNCLICDNCYQQQDNLCPFCGKCYHPELQKDMLHCNMCKRWVHLECDKPTDHELDTQLKEEYICMYCKHLGAEMDRLQPGEEVEIAELTTDYNNEMEVEGPEDQMVFSEQAANKDVNGQESTPGIVPDAVQVHTEEQQKSHPSESLDTDSLLIAVSSQHTVNTELEKQISNEVDSEDLKMSSEVKHICGEDQIEDKMEVTENIEVVTHQITVQQEQLQLLEEPETVVSREESRPPKLVMESVTLPLETLVSPHEESISLCPEEQLVIERLQGEKEQKENSELSTGLMDSEMTPTIEGCVKDVSYQGGKSIKLSSETESSFSSSADISKADVSSSPTPSSDLPSHDMLHNYPSALSSSAGNIMPTTYISVTPKIGMGKPAITKRKFSPGRPRSKQGAWSTHNTVSPPSWSPDISEGREIFKPRQLPGSAIWSIKVGRGSGFPGKRRPRGAGLSGRGGRGRSKLKSGIGAVVLPGVSTADISSNKDDEENSMHNTVVLFSSSDKFTLNQDMCVVCGSFGQGAEGRLLACSQCGQCYHPYCVSIKITKVVLSKGWRCLECTVCEACGKATDPGRLLLCDDCDISYHTYCLDPPLQTVPKGGWKCKWCVWCRHCGATSAGLRCEWQNNYTQCAPCASLSSCPVCYRNYREEDLILQCRQCDRWMHAVCQNLNTEEEVENVADIGFDCSMCRPYMPASNVPSSDCCESSLVAQIVTKVKELDPPKTYTQDGVCLTESGMTQLQSLTVTVPRRKRSKPKLKLKIINQNSVAVLQTPPDIQSEHSRDGEMDDSREGELMDCDGKSESSPEREAVDDETKGVEGTDGVKKRKRKPYRPGIGGFMVRQRSRTGQGKTKRSVIRKDSSGSISEQLPCRDDGWSEQLPDTLVDESVSVTESTEKIKKRYRKRKNKLEETFPAYLQEAFFGKDLLDTSRQSKISLDNLSEDGAQLLYKTNMNTGFLDPSLDPLLSSSSAPTKSGTHGPADDPLADISEVLNTDDDILGIISDDLAKSVDHSDIGPVTDDPSSLPQPNVNQSSRPLSEEQLDGILSPELDKMVTDGAILGKLYKIPELGGKDVEDLFTAVLSPANTQPTPLPQPPPPTQLLPIHNQDAFSRMPLMNGLIGSSPHLPHNSLPPGSGLGTFSAIAQSSYPDARDKNSAFNPMASDPNNSWTSSAPTVEGENDTMSNAQRSTLKWEKEEALGEMATVAPVLYTNINFPNLKEEFPDWTTRVKQIAKLWRKASSQERAPYVQKARDNRAALRINKVQMSNDSMKRQQQQDSIDPSSRIDSELFKDPLKQRESEHEQEWKFRQQMRQKSKQQAKIEATQKLEQVKNEQQQQQQQQFGSQHLLVQSGSDTPSSGIQSPLTPQPGNGNMSPAQSFHKELFTKQPPSTPTSTSSDDVFVKPQAPPPPPAPSRIPIQDSLSQAQTSQPPSPQVFSPGSSNSRPPSPMDPYAKMVGTPRPPPVGHSFSRRNSAAPVENCTPLSSVSRPLQMNETTANRPSPVRDLCSSSTTNNDPYAKPPDTPRPVMTDQFPKSLGLSRSPVVSEQTAKGPIAAGTSDHFTKPSPRADVFQRQRIPDSYARPLLTPAPLDSGPGPFKTPMQPPPSSQDPYGSVSQASRRLSVDPYERPALTPRPIDNFSHNQSNDPYSQPPLTPHPAVNESFAHPSRAFSQPGTISRPTSQDPYSQPPGTPRPVVDSYSQSSGTARSNTDPYSQPPGTPRPTTVDPYSQQPQTPRPSTQTDLFVTPVTNQRHSDPYAHPPGTPRPGISVPYSQPPATPRPRISEGFTRSSMTRPVLMPNQDPFLQAAQNRGPALPGPLVRPPDTCSQTPRPPGPGLSDTFSRVSPSAARDPYDQSPMTPRSQSDSFGTSQTAHDVADQPRPGSEGSFCASSNSPMHSQGQQFSGVSQLPGPVPTSGVTDTQNTVNMAQADTEKLRQRQKLREIILQQQQQKKIAGRQEKGSQDSPAVPHPGPLQHWQPENVNQAFTRPPPPYPGNIRSPVAPPLGPRYAVFPKDQRGPYPPDVASMGMRPHGFRFGFPGGSHGTMPSQERFLVPPQQIQGSGVSPQLRRSVSVDMPRPLNNSQMNNPVGLPQHFSPQSLPVQQHNILGQAYIELRHRAPDGRQRLPFSAPPGSVVEASSNLRHGNFIPRPDFPGPRHTDPMRRPPQGLPNQLPVHPDLEQVPPSQQEQGHSVHSSSMVMRTLNHPLGGEFSEAPLSTSVPSETTSDNLQITTQPSDGLEEKLDSDDPSVKELDVKDLEGVEVKDLDDEDLENLNLDTEDGKVVELDTLDNLETNDPNLDDLLRSGEFDIIAYTDPELDMGDKKSMFNEELDLPIDDKLDNQCVSVEPKKKEQENKTLVLSDKHSPQKKSTVTNEVKTEVLSPNSKVESKCETEKNDENKDNVDTPCSQASAHSDLNDGEKTSLHPCDPDLFEKRTNRETAGPSANVIQASTQLPAQDVINSCGITGSTPVLSSLLANEKSDNSDIRPSGSPPPPTLPASPSNHVSSLPPFIAPPGRVLDNAMNSNVTVVSRVNHVFSQGVQVNPGLIPGQSTVNHSLGTGKPATQTGPQTSQSGTSSMSGPQQLMIPQTLAQQNRERPLLLEEQPLLLQDLLDQERQEQQQQRQMQAMIRQRSEPFFPNIDFDAITDPIMKAKMVALKGINKVMAQNNLGMPPMVMSRFPFMGQVVTGTQNSEGQNLGPQAIPQDGSITHQISRPNPPNFGPGFVNDSQRKQYEEWLQETQQLLQMQQKYLEEQIGAHRKSKKALSAKQRTAKKAGREFPEEDAEQLKHVTEQQSMVQKQLEQIRKQQKEHAELIEDYRIKQQQQCAMAPPTMMPSVQPQPPLIPGATPPTMSQPTFPMVPQQLQHQQHTTVISGHTSPVRMPSLPGWQPNSAPAHLPLNPPRIQPPIAQLPIKTCTPAPGTVSNANPQSGPPPRVEFDDNNPFSESFQERERKERLREQQERQRIQLMQEVDRQRALQQRMEMEQHGMVGSEISSSRTSVSQIPFYSSDLPCDFMQPLGPLQQSPQHQQQMGQVLQQQNIQQGSINSPSTQTFMQTNERRQVGPPSFVPDSPSIPVGSPNFSSVKQGHGNLSGTSFQQSPVRPSFTPALPAAPPVANSSLPCGQDSTITHGHSYPGSTQSLIQLYSDIIPEEKGKKKRTRKKKRDDDAESTKAPSTPHSDITAPPTPGISETTSTPAVSTPSELPQQADQESVEPVGPSTPNMAAGQLCTELENKLPNSDFSQATPNQQTYANSEVDKLSMETPAKTEEIKLEKAETESCPGQEEPKLEEQNGSKVEGNAVACPVSSAQSPPHSAGAPAAKGDSGNELLKHLLKNKKSSSLLNQKPEGSICSEDDCTKDNKLVEKQNPAEGLQTLGAQMQGGFGCGNQLPKTDGGSETKKQRSKRTQRTGEKAAPRSKKRKKDEEEKQAMYSSTDTFTHLKQQNNLSNPPTPPASLPPTPPPMACQKMANGFATTEELAGKAGVLVSHEVTKTLGPKPFQLPFRPQDDLLARALAQGPKTVDVPASLPTPPHNNQEELRIQDHCGDRDTPDSFVPSSSPESVVGVEVSRYPDLSLVKEEPPEPVPSPIIPILPSTAGKSSESRRNDIKTEPGTLYFASPFGPSPNGPRSGLISVAITLHPTAAENISSVVAAFSDLLHVRIPNSYEVSSAPDVPSMGLVSSHRINPGLEYRQHLLLRGPPPGSANPPRLVSSYRLKQPNVPFPPTSNGLSGYKDSSHGIAESAALRPQWCCHCKVVILGSGVRKSFKDLTLLNKDSRESTKRVEKDIVFCSNNCFILYSSTAQAKNSENKESIPSLPQSPMRETPSKAFHQYSNNISTLDVHCLPQLPEKASPPASPPIAFPPAFEAAQVEAKPDELKVTVKLKPRLRAVHGGFEDCRPLNKKWRGMKWKKWSIHIVIPKGTFKPPCEDEIDEFLKKLGTSLKPDPVPKDYRKCCFCHEEGDGLTDGPARLLNLDLDLWVHLNCALWSTEVYETQAGALINVELALRRGLQMKCVFCHKTGATSGCHRFRCTNIYHFTCAIKAQCMFFKDKTMLCPMHKPKGIHEQELSYFAVFRRVYVQRDEVRQIASIVQRGERDHTFRVGSLIFHTIGQLLPQQMQAFHSPKALFPVGYEASRLYWSTRYANRRCRYLCSIEEKDGRPVFVIRIVEQGHEDLVLSDISPKGVWDKILEPVACVRKKSEMLQLFPAYLKGEDLFGLTVSAVARIAESLPGVEACENYTFRYGRNPLMELPLAVNPTGCARSEPKMSAHVKRFVLRPHTLNSTSTSKSFQSTVTGELNAPYSKQFVHSKSSQYRKMKTEWKSNVYLARSRIQGLGLYAARDIEKHTMVIEYIGTIIRNEVANRKEKLYESQNRGVYMFRMDNDHVIDATLTGGPARYINHSCAPNCVAEVVTFERGHKIIISSSRRIQKGEELCYDYKFDFEDDQHKIPCHCGAVNCRKWMN,mutated_sequence,1.0,4911.0,NP_733751.2.a2m,NP_733751.2.npy,ClinVar +NP_733765.1,NP_733765.1.csv,MENAHTKTVEEVLGHFGVNESTGLSLEQVKKLKERWGSNELPAEEGKTLLELVIEQFEDLLVRILLLAACISFVLAWFEEGEETITAFVEPFVILLILVANAIVGVWQERNAENAIEALKEYEPEMGKVYRQDRKSVQRIKAKDIVPGDIVEIAVGDKVPADIRLTSIKSTTLRVDQSILTGESVSVIKHTDPVPDPRAVNQDKKNMLFSGTNIAAGKAMGVVVATGVNTEIGKIRDEMVATEQERTPLQQKLDEFGEQLSKVISLICIAVWIINIGHFNDPVHGGSWIRGAIYYFKIAVALAVAAIPEGLPAVITTCLALGTRRMAKKNAIVRSLPSVETLGCTSVICSDKTGTLTTNQMSVCRMFILDRVEGDTCSLNEFTITGSTYAPIGEVHKDDKPVNCHQYDGLVELATICALCNDSALDYNEAKGVYEKVGEATETALTCLVEKMNVFDTELKGLSKIERANACNSVIKQLMKKEFTLEFSRDRKSMSVYCTPNKPSRTSMSKMFVKGAPEGVIDRCTHIRVGSTKVPMTSGVKQKIMSVIREWGSGSDTLRCLALATHDNPLRREEMHLEDSANFIKYETNLTFVGCVGMLDPPRIEVASSVKLCRQAGIRVIMITGDNKGTAVAICRRIGIFGQDEDVTSKAFTGREFDELNPSAQRDACLNARCFARVEPSHKSKIVEFLQSFDEITAMTGDGVNDAPALKKAEIGIAMGSGTAVAKTASEMVLADDNFSTIVAAVEEGRAIYNNMKQFIRYLISSNVGEVVCIFLTAALGFPEALIPVQLLWVNLVTDGLPATALGFNPPDLDIMNKPPRNPKEPLISGWLFFRYLAIGCYVGAATVGAAAWWFIAADGGPRVSFYQLSHFLQCKEDNPDFEGVDCAIFESPYPMTMALSVLVTIEMCNALNSLSENQSLLRMPPWENIWLVGSICLSMSLHFLILYVEPLPLIFQITPLNVTQWLMVLKISLPVILMDETLKFVARNYLEPGKECVQPATKSCSFSACTDGISWPFVLLIMPLVIWVYSTDTNFSDMFWS,mutated_sequence,1.0,1042.0,NP_733765.1.a2m,NP_733765.1.npy,ClinVar +NP_733775.1,NP_733775.1.csv,MAQRYDELPHYGGMDGVGVPASMYGDPHAPRPIPPVHHLNHGPPLHATQHYGAHAPHPNVMPASMGSAVNDALKRDKDAIYGHPLFPLLALVFEKCELATCTPREPGVAGGDVCSSDSFNEDIAVFAKQVRAEKPLFSSNPELDNLMIQAIQVLRFHLLELEKVHELCDNFCHRYISCLKGKMPIDLVIDERDGSSKSDHEELSGSSTNLADHNPSSWRDHDDATSTHSAGTPGPSSGGHASQSGDNSSEQGDGLDNSVASPGTGDDDDPDKDKKRQKKRGIFPKVATNIMRAWLFQHLTHPYPSEEQKKQLAQDTGLTILQVNNWFINARRRIVQPMIDQSNRAGFLLDPSVSQGAAYSPEGQPMGSFVLDGQQHMGIRPAGLQSMPGDYVSQGGPMGMSMAQPSYTPPQMTPHPTQLRHGPPMHSYLPSHPHHPAMMMHGGPPTHPGMTMSAQSPTMLNSVDPNVGGQVMDIHAQ,mutated_sequence,1.0,477.0,NP_733775.1.a2m,NP_733775.1.npy,ClinVar +NP_733821.1,NP_733821.1.csv,METPSQRRATRSGAQASSTPLSPTRITRLQEKEDLQELNDRLAVYIDRVRSLETENAGLRLRITESEEVVSREVSGIKAAYEAELGDARKTLDSVAKERARLQLELSKVREEFKELKARNTKKEGDLIAAQARLKDLEALLNSKEAALSTALSEKRTLEGELHDLRGQVAKLEAALGEAKKQLQDEMLRRVDAENRLQTMKEELDFQKNIYSEELRETKRRHETRLVEIDNGKQREFESRLADALQELRAQHEDQVEQYKKELEKTYSAKLDNARQSAERNSNLVGAAHEELQQSRIRIDSLSAQLSQLQKQLAAKEAKLRDLEDSLARERDTSRRLLAEKEREMAEMRARMQQQLDEYQELLDIKLALDMEIHAYRKLLEGEEERLRLSPSPTSQRSRGRASSHSSQTQGGGSVTKKRKLESTESRSSFSQHARTSGRVAVEEVDEEGKFVRLRNKSNEDQSMGNWQIKRQNGDDPLLTYRFPPKFTLKAGQVVTIWAAGAGATHSPPTDLVWKAQNTWGCGNSLRTALINSTGEEVAMRKLVRSVTVVEDDEDEDGDDLLHHHHGSHCSSSGDPAEYNLRSRTVLCGTCGQPADKASASGSGAQVGGPISSGSSASSVTVTRSYRSVGGSGGGSFGDNLVTRSYLLGNSSPRTQSPQNCSIM,mutated_sequence,1.0,664.0,NP_733821.1.a2m,NP_733821.1.npy,ClinVar +NP_742105.1,NP_742105.1.csv,MVQKSRNGGVYPGPSGEKKLKVGFVGLDPGAPDSTRDGALLIAGSEAPKRGSILSKPRAGGAGAGKPPKRNAFYRKLQNFLYNVLERPRGWAFIYHAYVFLLVFSCLVLSVFSTIKEYEKSSEGALYILEIVTIVVFGVEYFVRIWAAGCCCRYRGWRGRLKFARKPFCVIDIMVLIASIAVLAAGSQGNVFATSALRSLRFLQILRMIRMDRRGGTWKLLGSVVYAHSKELVTAWYIGFLCLILASFLVYLAEKGENDHFDTYADALWWGLITLTTIGYGDKYPQTWNGRLLAATFTLIGVSFFALPAGILGSGFALKVQEQHRQKHFEKRRNPAAGLIQSAWRFYATNLSRTDLHSTWQYYERTVTVPMYSSQTQTYGASRLIPPLNQLELLRNLKSKSGLAFRKDPPPEPSPSKGSPCRGPLCGCCPGRSSQKVSLKDRVFSSPRGVAAKGKGSPQAQTVRRSPSADQSLEDSPSKVPKSWSFGDRSRARQAFRIKGAASRQNSEEASLPGEDIVDDKSCPCEFVTEDLTPGLKVSIRAVCVMRFLVSKRKFKESLRPYDVMDVIEQYSAGHLDMLSRIKSLQSRVDQIVGRGPAITDKDRTKGPAEAELPEDPSMMGRLGKVEKQVLSMEKKLDFLVNIYMQRMGIPPTETEAYFGAKEPEPAPPYHSPEDSREHVDRHGCIVKIVRSSSSTGQKNFSAPPAAPPVQCPPSTSWQPQSHPRQGHGTSPVGDHGSLVRIPPPPAHERSLSAYGGGNRASMEFLRQEDTPGCRPPEGNLRDSDTSISIPSVDHEELERSFSGFSISQSKENLDALNSCYAAVAPCAKVRPYIAEGESDTDSDLCTPCGPPPRSATGEGPFGDVGWAGPRK,mutated_sequence,1.0,872.0,NP_742105.1.a2m,NP_742105.1.npy,ClinVar +NP_758440.1,NP_758440.1.csv,MASATEDPVLERYFKGHKAAITSLDLSPNGKQLATASWDTFLMLWNFKPHARAYRYVGHKDVVTSVQFSPHGNLLASASRDRTVRLWIPDKRGKFSEFKAHTAPVRSVDFSADGQFLATASEDKSIKVWSMYRQRFLYSLYRHTHWVRCAKFSPDGRLIVSCSEDKTIKIWDTTNKQCVNNFSDSVGFANFVDFNPSGTCIASAGSDQTVKVWDVRVNKLLQHYQVHSGGVNCISFHPSGNYLITASSDGTLKILDLLEGRLIYTLQGHTGPVFTVSFSKGGELFASGGADTQVLLWRTNFDELHCKGLTKRNLKRLHFDSPPHLLDIYPRTPHPHEEKVETVEINPKLEVIDLQISTPPVMDILSFDSTTTTETSGRTLPDKGEEACGYFLNPSLMSPECLPTTTKKKTEDMSDLPCESQRSIPLAVTDALEHIMEQLNVLTQTVSILEQRLTLTEDKLKDCLENQQKLFSAVQQKS,mutated_sequence,1.0,478.0,NP_758440.1.a2m,NP_758440.1.npy,ClinVar +NP_775109.2,NP_775109.2.csv,MASTSTTIRSHSSSRRGFSANSARLPGVSRSGFSSISVSRSRGSGGLGGACGGAGFGSRSLYGLGGSKRISIGGGSCAISGGYGSRAGGSYGFGGAGSGFGFGGGAGIGFGLGGGAGLAGGFGGPGFPVCPPGGIQEVTVNQSLLTPLNLQIDPAIQRVRAEEREQIKTLNNKFASFIDKVRFLEQQNKVLDTKWTLLQEQGTKTVRQNLEPLFEQYINNLRRQLDSIVGERGRLDSELRNMQDLVEDLKNKYEDEINKRTAAENEFVTLKKDVDAAYMNKVELQAKADTLTDEINFLRALYDAELSQMQTHISDTSVVLSMDNNRNLDLDSIIAEVKAQYEEIAQRSRAEAESWYQTKYEELQVTAGRHGDDLRNTKQEIAEINRMIQRLRSEIDHVKKQCASLQAAIADAEQRGEMALKDAKNKLEGLEDALQKAKQDLARLLKEYQELMNVKLALDVEIATYRKLLEGEECRLNGEGVGQVNVSVVQSTISSGYGGASGVGSGLGLGGGSSYSYGSGLGIGGGFSSSSGRAIGGGLSSVGGGSSTIKYTTTSSSSRKSYKH,mutated_sequence,1.0,564.0,NP_775109.2.a2m,NP_775109.2.npy,ClinVar +NP_775931.3,NP_775931.3.csv,MTEAALVEGQVKLRDGKKWKSRWLVLRKPSPVADCLLMLVYKDKSERIKGLRERSSLTLEDICGLEPGLPYEGLVHTLAIVCLSQAIMLGFDSHEAMCAWDARIRYALGEVHRFHVTVAPGTKLESGPATLHLCNDVLVLARDIPPAVTGQWKLSDLRRYGAVPSGFIFEGGTRCGYWAGVFFLSSAEGEQISFLFDCIVRGISPTKGPFGLRPVLPDPSPPGPSTVEERVAQEALETLQLEKRLSLLSHAGRPGSGGDDRSLSSSSSEASHLDVSASSRLTAWPEQSSSSASTSQEGPRPAAAQAAGEAMVGASRPPPKPLRPRQLQEVGRQSSSDSGIATGSHSSYSSSLSSYAGSSLDVWRATDELGSLLSLPAAGAPEPSLCTCLPGTVEYQVPTSLRAHYDTPRSLCLAPRDHSPPSQGSPGNSAARDSGGQTSAGCPSGWLGTRRRGLVMEAPQGSEATLPGPAPGEPWEAGGPHAGPPPAFFSACPVCGGLKVNPPP,mutated_sequence,1.0,504.0,NP_775931.3.a2m,NP_775931.3.npy,ClinVar +NP_777367.1,NP_777367.1.csv,MPSQQKKIIFCMAGVFSFACALGVVTALGTPLWIKATVLCKTGALLVNASGQELDKFMGEMQYGLFHGEGVRQCGLGARPFRFSFFPDLLKAIPVSIHVNVILFSAILIVLTMVGTAFFMYNAFGKPFETLHGPLGLYLLSFISGSCGCLVMILFASEVKIHHLSEKIANYKEGTYVYKTQSEKYTTSFWVIFFCFFVHFLNGLLIRLAGFQFPFAKSKDAETTNVAADLMY,mutated_sequence,1.0,232.0,NP_777367.1.a2m,NP_777367.1.npy,ClinVar +NP_787110.2,NP_787110.2.csv,MVSVNAPLGAPVESSYDTSPSEGTNLNAPNSLGVSALCAICGDRATGKHYGASSCDGCKGFFRRSVRKNHMYSCRFSRQCVVDKDKRNQCRYCRLKKCFRAGMKKEAVQNERDRISTRRSSYEDSSLPSINALLQAEVLSRQITSPVSGINGDIRAKKIASIADVCESMKEQLLVLVEWAKYIPAFCELPLDDQVALLRAHAGEHLLLGATKRSMVFKDVLLLGNDYIVPRHCPELAEMSRVSIRILDELVLPFQELQIDDNEYAYLKAIIFFDPDAKGLSDPGKIKRLRSQVQVSLEDYINDRQYDSRGRFGELLLLLPTLQSITWQMIEQIQFIKLFGMAKIDNLLQEMLLGGSPSDAPHAHHPLHPHLMQEHMGTNVIVANTMPTHLSNGQMCEWPRPRGQAATPETPQPSPPGGSGSEPYKLLPGAVATIVKPLSAIPQPTITKQEVI,mutated_sequence,1.0,452.0,NP_787110.2.a2m,NP_787110.2.npy,ClinVar +NP_789794.1,NP_789794.1.csv,MDLILNRMDYLQVGVTSQKTMKLIPASRHRATQKVVIGDHDGVVMCFGMKKGEAAAVFKTLPGPKIARLELGGVINTPQEKIFIAAASEIRGFTKRGKQFLSFETNLTESIKAMHISGSDLFLSASYIYNHYCDCKDQHYYLSGDKINDVICLPVERLSRITPVLACQDRVLRVLQGSDVMYAVEVPGPPTVLALHNGNGGDSGEDLLFGTSDGKLALIQITTSKPVRKWEIQNEKKRGGILCIDSFDIVGDGVKDLLVGRDDGMVEVYSFDNANEPVLRFDQMLSESVTSIQGGCVGKDSYDEIVVSTYSGWVTGLTTEPIHKESGPGEELKINQEMQNKISSLRNELEHLQYKVLQERENYQQSSQSSKAKSAVPSFGINDKFTLNKDDASYSLILEVQTAIDNVLIQSDVPIDLLDVDKNSAVVSFSSCDSESNDNFLLATYRCQADTTRLELKIRSIEGQYGTLQAYVTPRIQPKTCQVRQYHIKPLSLHQRTHFIDHDRPMNTLTLTGQFSFAEVHSWVVFCLPEVPEKPPAGECVTFYFQNTFLDTQLESTYRKGEGVFKSDNISTISILKDVLSKEATKRKINLNISYEINEVSVKHTLKLIHPKLEYQLLLAKKVQLIDALKELQIHEGNTNFLIPEYHCILEEADHLQEEYKKQPAHLERLYGMITDLFIDKFKFKGTNVKTKVPLLLEILDSYDQNALISFFDAA,mutated_sequence,1.0,715.0,NP_789794.1.a2m,NP_789794.1.npy,ClinVar +NP_796376.2,NP_796376.2.csv,MRNIFKRNQEPIVAPATTTATMPIGPVDNSTESGGAGESQEDMFAKLKEKLFNEINKIPLPPWALIAIAVVAGLLLLTCCFCICKKCCCKKKKNKKEKGKGMKNAMNMKDMKGGQDDDDAETGLTEGEGEGEEEKEPENLGKLQFSLDYDFQANQLTVGVLQAAELPALDMGGTSDPYVKVFLLPDKKKKYETKVHRKTLNPAFNETFTFKVPYQELGGKTLVMAIYDFDRFSKHDIIGEVKVPMNTVDLGQPIEEWRDLQGGEKEEPEKLGDICTSLRYVPTAGKLTVCILEAKNLKKMDVGGLSDPYVKIHLMQNGKRLKKKKTTVKKKTLNPYFNESFSFEIPFEQIQKVQVVVTVLDYDKLGKNEAIGKIFVGSNATGTELRHWSDMLANPRRPIAQWHSLKPEEEVDALLGKNK,mutated_sequence,1.0,419.0,NP_796376.2.a2m,NP_796376.2.npy,ClinVar +NP_803187.1,NP_803187.1.csv,MKSPALQPLSMAGLQLMTPASSPMGPFFGLPWQQEAIHDNIYTPRKYQVELLEAALDHNTIVCLNTGSGKTFIAVLLTKELSYQIRGDFSRNGKRTVFLVNSANQVAQQVSAVRTHSDLKVGEYSNLEVNASWTKERWNQEFTKHQVLIMTCYVALNVLKNGYLSLSDINLLVFDECHLAILDHPYREIMKLCENCPSCPRILGLTASILNGKCDPEELEEKIQKLEKILKSNAETATDLVVLDRYTSQPCEIVVDCGPFTDRSGLYERLLMELEEALNFINDCNISVHSKERDSTLISKQILSDCRAVLVVLGPWCADKVAGMMVRELQKYIKHEQEELHRKFLLFTDTFLRKIHALCEEHFSPASLDLKFVTPKVIKLLEILRKYKPYERQQFESVEWYNNRNQDNYVSWSDSEDDDEDEEIEEKEKPETNFPSPFTNILCGIIFVERRYTAVVLNRLIKEAGKQDPELAYISSNFITGHGIGKNQPRNKQMEAEFRKQEEVLRKFRAHETNLLIATSIVEEGVDIPKCNLVVRFDLPTEYRSYVQSKGRARAPISNYIMLADTDKIKSFEEDLKTYKAIEKILRNKCSKSVDTGETDIDPVMDDDDVFPPYVLRPDDGGPRVTINTAIGHINRYCARLPSDPFTHLAPKCRTRELPDGTFYSTLYLPINSPLRASIVGPPMSCVRLAERVVALICCEKLHKIGELDDHLMPVGKETVKYEEELDLHDEEETSVPGRPGSTKRRQCYPKAIPECLRDSYPRPDQPCYLYVIGMVLTTPLPDELNFRRRKLYPPEDTTRCFGILTAKPIPQIPHFPVYTRSGEVTISIELKKSGFMLSLQMLELITRLHQYIFSHILRLEKPALEFKPTDADSAYCVLPLNVVNDSSTLDIDFKFMEDIEKSEARIGIPSTKYTKETPFVFKLEDYQDAVIIPRYRNFDQPHRFYVADVYTDLTPLSKFPSPEYETFAEYYKTKYNLDLTNLNQPLLDVDHTSSRLNLLTPRHLNQKGKALPLSSAEKRKAKWESLQNKQILVPELCAIHPIPASLWRKAVCLPSILYRLHCLLTAEELRAQTASDAGVGVRSLPADFRYPNLDFGWKKSIDSKSFISISNSSSAENDNYCKHSTIVPENAAHQGANRTSSLENHDQMSVNCRTLLSESPGKLHVEVSADLTAINGLSYNQNLANGSYDLANRDFCQGNQLNYYKQEIPVQPTTSYSIQNLYSYENQPQPSDECTLLSNKYLDGNANKSTSDGSPVMAVMPGTTDTIQVLKGRMDSEQSPSIGYSSRTLGPNPGLILQALTLSNASDGFNLERLEMLGDSFLKHAITTYLFCTYPDAHEGRLSYMRSKKVSNCNLYRLGKKKGLPSRMVVSIFDPPVNWLPPGYVVNQDKSNTDKWEKDEMTKDCMLANGKLDEDYEEEDEEEESLMWRAPKEEADYEDDFLEYDQEHIRFIDNMLMGSGAFVKKISLSPFSTTDSAYEWKMPKKSSLGSMPFSSDFEDFDYSSWDAMCYLDPSKAVEEDDFVVGFWNPSEENCGVDTGKQSISYDLHTEQCIADKSIADCVEALLGCYLTSCGERAAQLFLCSLGLKVLPVIKRTDREKALCPTRENFNSQQKNLSVSCAAASVASSRSSVLKDSEYGCLKIPPRCMFDHPDADKTLNHLISGFENFEKKINYRFKNKAYLLQAFTHASYHYNTITDCYQRLEFLGDAILDYLITKHLYEDPRQHSPGVLTDLRSALVNNTIFASLAVKYDYHKYFKAVSPELFHVIDDFVQFQLEKNEMQGMDSELRRSEEDEEKEEDIEVPKAMGDIFESLAGAIYMDSGMSLETVWQVYYPMMRPLIEKFSANVPRSPVRELLEMEPETAKFSPAERTYDGKVRVTVEVVGKGKFKGVGRSYRIAKSAAARRALRSLKANQPQVPNS,mutated_sequence,1.0,1922.0,NP_803187.1.a2m,NP_803187.1.npy,ClinVar +NP_808592.2,NP_808592.2.csv,MPGRSCVALVLLAAAVSCAVAQHAPPWTEDCRKSTYPPSGPTYRGAVPWYTINLDLPPYKRWHELMLDKAPVLKVIVNSLKNMINTFVPSGKIMQVVDEKLPGLLGNFPGPFEEEMKGIAAVTDIPLGEIISFNIFYELFTICTSIVAEDKKGHLIHGRNMDFGVFLGWNINNDTWVITEQLKPLTVNLDFQRNNKTVFKASSFAGYVGMLTGFKPGLFSLTLNERFSINGGYLGILEWILGKKDVMWIGFLTRTVLENSTSYEEAKNLLTKTKILAPAYFILGGNQSGEGCVITRDRKESLDVYELDAKQGRWYVVQTNYDRWKHPFFLDDRRTPAKMCLNRTSQENISFETMYDVLSTKPVLNKLTVYTTLIDVTKGQFETYLRDCPDPCIGW,mutated_sequence,1.0,395.0,NP_808592.2.a2m,NP_808592.2.npy,ClinVar +NP_835361.1,NP_835361.1.csv,MDARWWAVVVLAAFPSLGAGGETPEAPPESWTQLWFFRFVVNAAGYASFMVPGYLLVQYFRRKNYLETGRGLCFPLVKACVFGNEPKASDEVPLAPRTEAAETTPMWQALKLLFCATGLQVSYLTWGVLQERVMTRSYGATATSPGERFTDSQFLVLMNRVLALIVAGLSCVLCKQPRHGAPMYRYSFASLSNVLSSWCQYEALKFVSFPTQVLAKASKVIPVMLMGKLVSRRSYEHWEYLTATLISIGVSMFLLSSGPEPRSSPATTLSGLILLAGYIAFDSFTSNWQDALFAYKMSSVQMMFGVNFFSCLFTVGSLLEQGALLEGTRFMGRHSEFAAHALLLSICSACGQLFIFYTIGQFGAAVFTIIMTLRQAFAILLSCLLYGHTVTVVGGLGVAVVFAALLLRVYARGRLKQRGKKAVPVESPVQKV,mutated_sequence,1.0,432.0,NP_835361.1.a2m,NP_835361.1.npy,ClinVar +NP_835464.1,NP_835464.1.csv,MEKYERIRVVGRGAFGIVHLCLRKADQKLVIIKQIPVEQMTKEERQAAQNECQVLKLLNHPNVIEYYENFLEDKALMIAMEYAPGGTLAEFIQKRCNSLLEEETILHFFVQILLALHHVHTHLILHRDLKTQNILLDKHRMVVKIGDFGISKILSSKSKAYTVVGTPCYISPELCEGKPYNQKSDIWALGCVLYELASLKRAFEAANLPALVLKIMSGTFAPISDRYSPELRQLVLSLLSLEPAQRPPLSHIMAQPLCIRALLNLHTDVGSVRMRRAEKSVAPSNTGSRTTSVRCRGIPRGPVRPAIPPPLSSVYAWGGGLGTPLRLPMLNTEVVQVAAGRTQKAGVTRSGRLILWEAPPLGAGGGSLLPGAVEQPQPQFISRFLEGQSGVTIKHVACGDFFTACLTDRGIIMTFGSGSNGCLGHGSLTDISQPTIVEALLGYEMVQVACGASHVLALSTERELFAWGRGDSGRLGLGTRESHSCPQQVPMPPGQEAQRVVCGIDSSMILTVPGQALACGSNRFNKLGLDHLSLGEEPVPHQQVEEALSFTLLGSAPLDQEPLLSIDLGTAHSAAVTASGDCYTFGSNQHGQLGTNTRRGSRAPCKVQGLEGIKMAMVACGDAFTVAIGAESEVYSWGKGARGRLGRRDEDAGLPRPVQLDETHPYTVTSVSCCHGNTLLAVRSVTDEPVPP,mutated_sequence,1.0,692.0,NP_835464.1.a2m,NP_835464.1.npy,ClinVar +NP_849191.3,NP_849191.3.csv,MRPVALLLLPSLLALLAHGLSLEAPTVGKGQAPGIEETDGELTAAPTPEQPERGVHFVTTAPTLKLLNHHPLLEEFLQEGLEKGDEELRPALPFQPDPPAPFTPSPLPRLANQDSRPVFTSPTPAMAAVPTQPQSKEGPWSPESESPMLRITAPLPPGPSMAVPTLGPGEIASTTPPSRAWTPTQEGPGDMGRPWVAEVVSQGAGIGIQGTITSSTASGDDEETTTTTTIITTTITTVQTPGPCSWNFSGPEGSLDSPTDLSSPTDVGLDCFFYISVYPGYGVEIKVQNISLREGETVTVEGLGGPDPLPLANQSFLLRGQVIRSPTHQAALRFQSLPPPAGPGTFHFHYQAYLLSCHFPRRPAYGDVTVTSLHPGGSARFHCATGYQLKGARHLTCLNATQPFWDSKEPVCIAACGGVIRNATTGRIVSPGFPGNYSNNLTCHWLLEAPEGQRLHLHFEKVSLAEDDDRLIIRNGDNVEAPPVYDSYEVEYLPIEGLLSSGKHFFVELSTDSSGAAAGMALRYEAFQQGHCYEPFVKYGNFSSSTPTYPVGTTVEFSCDPGYTLEQGSIIIECVDPHDPQWNETEPACRAVCSGEITDSAGVVLSPNWPEPYGRGQDCIWGVHVEEDKRIMLDIRVLRIGPGDVLTFYDGDDLTARVLGQYSGPRSHFKLFTSMADVTIQFQSDPGTSVLGYQQGFVIHFFEVPRNDTCPELPEIPNGWKSPSQPELVHGTVVTYQCYPGYQVVGSSVLMCQWDLTWSEDLPSCQRVTSCHDPGDVEHSRRLISSPKFPVGATVQYICDQGFVLMGSSILTCHDRQAGSPKWSDRAPKCLLEQLKPCHGLSAPENGARSPEKQLHPAGATIHFSCAPGYVLKGQASIKCVPGHPSHWSDPPPICRAASLDGFYNSRSLDVAKAPAASSTLDAAHIAAAIFLPLVAMVLLVGGVYFYFSRLQGKSSLQLPRPRPRPYNRITIESAFDNPTYETGSLSFAGDERI,mutated_sequence,1.0,994.0,NP_849191.3.a2m,NP_849191.3.npy,ClinVar +NP_851564.1,NP_851564.1.csv,MPRGWAAPLLLLLLQGGWGCPDLVCYTDYLQTVICILEMWNLHPSTLTLTWQDQYEELKDEATSCSLHRSAHNATHATYTCHMDVFHFMADDIFSVNITDQSGNYSQECGSFLLAESIKPAPPFNVTVTFSGQYNISWRSDYEDPAFYMLKGKLQYELQYRNRGDPWAVSPRRKLISVDSRSVSLLPLEFRKDSSYELQVRAGPMPGSSYQGTWSEWSDPVIFQTQSEELKEGWNPHLLLLLLLVIVFIPAFWSLKTHPLWRLWKKIWAVPSPERFFMPLYKGCSGDFKKWVGAPFTGSSLELGPWSPEVPSTLEVYSCHPPRSPAKRLQLTELQEPAELVESDGVPKPSFWPTAQNSGGSAYSEERDRPYGLVSIDTVTVLDAEGPCTWPCSCEDDGYPALDLDAGLEPSPGLEDPLLDAGTTVLSCGCVSAGSPGLGGPLGSLLDRLKPPLADGEDWAGGLPWGGRSPGGVSESEAGSPLAGLDMDTFDSGFVGSDCSSPVECDFTSPGDEGPPRSYLRQWVVIPPPLSSPGPQAS,mutated_sequence,1.0,538.0,NP_851564.1.a2m,NP_851564.1.npy,ClinVar +NP_852091.1,NP_852091.1.csv,MSSEFLAELHWEDGFAIPVANEENKLLEDQLSKLKDERASLQDELREYEERINSMTSHFKNVKQELSITQSLCKARERETESEEHFKAIAQRELGRVKDEIQRLENEMASILEKKSDKENGIFKATQKLDGLKCQMNWDQQALEAWLEESAHKDSDALTLQKYAQQDDNKIRALTLQLERLTLECNQKRKILDNELTETISAQLELDKAAQDFRKIHNERQELIKQWENTIEQMQKRDGDIDNCALELARIKQETREKENLVKEKIKFLESEIGNNTEFEKRISVADRKLLKCRTAYQDHETSRIQLKGELDSLKATVNRTSSDLEALRKNISKIKKDIHEETARLQKTKNHNEIIQTKLKEITEKTMSVEEKATNLEDMLKEEEKDVKEVDVQLNLIKGVLFKKAQELQTETMKEKAVLSEIEGTRSSLKHLNHQLQKLDFETLKQQEIMYSQDFHIQQVERRMSRLKGEINSEEKQALEAKIVELRKSLEEKKSTCGLLETQIKKLHNDLYFIKKAHSKNSDEKQSLMTKINELNLFIDRSEKELDKAKGFKQDLMIEDNLLKLEVKRTREMLHSKAEEVLSLEKRKQQLYTAMEERTEEIKVHKTMLASQIRYVDQERENISTEFRERLSKIEKLKNRYEILTVVMLPPEGEEEKTQAYYVIKAAQEKEELQREGDCLDAKINKAEKEIYALENTLQVLNSCNNNYKQSFKKVTPSSDEYELKIQLEEQKRAVDEKYRYKQRQIRELQEDIQSMENTLDVIEHLANNVKEKLSEKQAYSFQLSKETEEQKPKLERVTKQCAKLTKEIRLLKDTKDETMEEQDIKLREMKQFHKVIDEMLVDIIEENTEIRIILQTYFQQSGLELPTASTKGSRQSSRSPSHTSLSARSSRSTSTSTSQSSIKVLELKFPASSSLVGSPSRPSSASSSSSNVKSKKSSK,mutated_sequence,1.0,941.0,NP_852091.1.a2m,NP_852091.1.npy,ClinVar +NP_852259.1,NP_852259.1.csv,MADADEGFGLAHTPLEPDAKDLPCDSKPESALGAPSKSPSSPQAAFTQQGMEGIKVFLHERELWLKFHEVGTEMIITKAGRRMFPSYKVKVTGLNPKTKYILLMDIVPADDHRYKFADNKWSVTGKAEPAMPGRLYVHPDSPATGAHWMRQLVSFQKLKLTNNHLDPFGHIILNSMHKYQPRLHIVKADENNGFGSKNTAFCTHVFPETAFIAVTSYQNHKITQLKIENNPFAKGFRGSDDMELHRMSRMQSKEYPVVPRSTVRQKVASNHSPFSSESRALSTSSNLGSQYQCENGVSGPSQDLLPPPNPYPLPQEHSQIYHCTKRKEEECSTTDHPYKKPYMETSPSEEDSFYRSSYPQQQGLGASYRTESAQRQACMYASSAPPSEPVPSLEDISCNTWPSMPSYSSCTVTTVQPMDRLPYQHFSAHFTSGPLVPRLAGMANHGSPQLGEGMFQHQTSVAHQPVVRQCGPQTGLQSPGTLQPPEFLYSHGVPRTLSPHQYHSVHGVGMVPEWSDNS,mutated_sequence,1.0,518.0,NP_852259.1.a2m,NP_852259.1.npy,ClinVar +NP_852608.1,NP_852608.1.csv,MAFVPVIPESYSHVLAEFESLDPLLSALRLDSSRLKCTSIAVSRKWLALGSSGGGLHLIQKEGWKHRLFLSHREGAISQVACCLHDDDYVAVATSQGLVVVWELNQERRGKPEQMYVSSEHKGRRVTALCWDTAILRVFVGDHAGKVSAIKLNTSKQAKAAAAFVMFPVQTITTVDSCVVQLDYLDGRLLISSLTRSFLCDTEREKFWKIGNKERDGEYGACFFPGRCSGGQQPLIYCARPGSRMWEVNFDGEVISTHQFKKLLSLPPLPVITLRSEPQYDHTAGSSQSLSFPKLLHLSEHCVLTWTERGIYIFIPQNVQVLLWSEVKDIQDVAVCRNELFCLHLNGKVSHLSLISVERCVERLLRRGLWNLAARTCCLFQNSVIASRARKTLTADKLEHLKSQLDHGTYNDLISQLEELILKFEPLDSACSSRRSSISSHESFSILDSGIYRIISSRRGSQSDEDSCSLHSQTLSEDERFKEFTSQQEEDLPDQCCGSHGNEDNVSHAPVMFETDKNETFLPFGIPLPFRSPSPLVSLQAVKESVSSFVRKTTEKIGTLHTSPDLKVRPELRGDEQSCEEDVSSDTCPKEEDTEEEKEVTSPPPEEDRFQELKVATAEAMTKLQDPLVLFESESLRMVLQEWLSHLEKTFAMKDFSGVSDTDNSSMKLNQDVLLVNESKKGILDEDNEKEKRDSLGNEESVDKTACECVRSPRESLDDLFQICSPCAIASGLRNDLAELTTLCLELNVLNSKIKSTSGHVDHTLQQYSPEILACQFLKKYFFLLNLKRAKESIKLSYSNSPSVWDTFIEGLKEMASSNPVYMEMEKGDLPTRLKLLDDEVPFDSPLLVVYATRLYEKFGESALRSLIKFFPSILPSDIIQLCHHHPAEFLAYLDSLVKSRPEDQRSSFLESLLQPESLRLDWLLLAVSLDAPPSTSTMDDEGYPRPHSHLLSWGYSQLILHLIKLPADFITKEKMTDICRSCGFWPGYLILCLELERRREAFTNIVYLNDMSLMEGDNGWIPETVEEWKLLLHLIQSKSTRPAPQESLNGSLSDGPSPINVENVALLLAKAMGPDRAWSLLQECGLALELSEKFTRTCDILRIAEKRQRALIQSMLEKCDRFLWSQQA,mutated_sequence,1.0,1129.0,NP_852608.1.a2m,NP_852608.1.npy,ClinVar +NP_852664.1,NP_852664.1.csv,MSAEGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFSDGQEARPEEIGWLNGYNETTGERGDFPGTYVEYIGRKKISPPTPKPRPPRPLPVAPGSSKTEADVEQQALTLPDLAEQFAPPDIAPPLLIKLVEAIEKKGLECSTLYRTQSSSNLAELRQLLDCDTPSVDLEMIDVHVLADAFKRYLLDLPNPVIPAAVYSEMISLAPEVQSSEEYIQLLKKLIRSPSIPHQYWLTLQYLLKHFFKLSQTSSKNLLNARVLSEIFSPMLFRFSAASSDNTENLIKVIEILISTEWNERQPAPALPPKPPKPTTVANNGMNNNMSLQDAEWYWGDISREEVNEKLRDTADGTFLVRDASTKMHGDYTLTLRKGGNNKLIKIFHRDGKYGFSDPLTFSSVVELINHYRNESLAQYNPKLDVKLLYPVSKYQQDQVVKEDNIEAVGKKLHEYNTQFQEKSREYDRLYEEYTRTSQEIQMKRTAIEAFNETIKIFEEQCQTQERYSKEYIEKFKREGNEKEIQRIMHNYDKLKSRISEIIDSRRRLEEDLKKQAAEYREIDKRMNSIKPDLIQLRKTRDQYLMWLTQKGVRQKKLNEWLGNENTEDQYSLVEDDEDLPHHDEKTWNVGSSNRNKAENLLRGKRDGTFLVRESSKQGCYACSVVVDGEVKHCVINKTATGYGFAEPYNLYSSLKELVLHYQHTSLVQHNDSLNVTLAYPVYAQQRR,mutated_sequence,1.0,724.0,NP_852664.1.a2m,NP_852664.1.npy,ClinVar +NP_859056.2,NP_859056.2.csv,MGRAVKVLQLFKTLHRTRQQVFKNDARALEAARIKINEEFKNNKSETSSKKIEELMKIGSDVELLLRTSVIQGIHTDHNTLKLVPRKDLLVENVPYCDAPTQKQ,mutated_sequence,1.0,104.0,NP_859056.2.a2m,NP_859056.2.npy,ClinVar +NP_872282.1,NP_872282.1.csv,MAARLVSRCGAVRAAPHSGPLVSWRRWSGASTDTVYDVVVSGGGLVGAAMACALGYDIHFHDKKILLLEAGPKKVLEKLSETYSNRVSSISPGSATLLSSFGAWDHICNMRYRAFRRMQVWDACSEALIMFDKDNLDDMGYIVENDVIMHALTKQLEAVSDRVTVLYRSKAIRYTWPCPFPMADSSPWVHITLGDGSTFQTKLLIGADGHNSGVRQAVGIQNVSWNYDQSAVVATLHLSEATENNVAWQRFLPSGPIALLPLSDTLSSLVWSTSHEHAAELVSMDEEKFVDAVNSAFWSDADHTDFIDTAGAMLQYAVSLLKPTKVSARQLPPSVARVDAKSRVLFPLGLGHAAEYVRPRVALIGDAAHRVHPLAGQGVNMGFGDISSLAHHLSTAAFNGKDLGSVSHLTGYETERQRHNTALLAATDLLKRLYSTSASPLVLLRTWGLQATNAVSPLKEQIMAFASK,mutated_sequence,1.0,468.0,NP_872282.1.a2m,NP_872282.1.npy,ClinVar +NP_877435.3,NP_877435.3.csv,MRTSLQAVALWGQKAPPHSITAIMITDDQRTIVTGSQEGQLCLWNLSHELKISAKELLFGHSASVTCLARARDFSKQPYIVSAAENGEMCVWNVTNGQCMEKATLPYRHTAICYYHCSFRMTGEGWLLCCGEYQDVLIIDAKTLAVVHSFRSSQFPDWINCMCIVHSMRIQEDSLLVVSVAGELKVWDLSSSINSIQEKQDVYEKESKFLESLNCQTIRFCTYTERLLLVVFSKCWKVYDYCDFSLLLTEVSRNGQFFAGGEVIAAHRILIWTEDGHSYIYQLLNSGLSKSIYPADGRVLKETIYPHLLCSTSVQENKEQSRPFVMGYMNERKEPFYKVLFSGEVSGRITLWHIPDVPVSKFDGSPREIPVTATWTLQDNFDKHDTMSQSIIDYFSGLKDGAGTAVVTSSEYIPSLDKLICGCEDGTIIITQALNAAKARLLEGGSLVKDSPPHKVLKGHHQSVTSLLYPHGLSSKLDQSWMLSGDLDSCVILWDIFTEEILHKFFLEAGPVTSLLMSPEKFKLRGEQIICCVCGDHSVALLHLEGKSCLLHARKHLFPVRMIKWHPVENFLIVGCADDSVYIWEIETGTLERHETGERARIILNCCDDSQLVKSVLPIASETLKHKSIEQRSSSPYQLGPLPCPGLQVESSCKVTDAKFCPRPFNVLPVKTKWSNVGFHILLFDLENLVELLLPTPLSDVDSSSSFYGGEVLRRAKSTVEKKTLTLRKSKTACGPLSAEALAKPITESLAQGDNTIKFSEENDGIKRQKKMKISKKMQPKPSRKVDASLTIDTAKLFLSCLLPWGVDKDLDYLCIKHLNILKLQGPISLGISLNEDNFSLMLPGWDLCNSGMIKDYSGVNLFSRKVLDLSDKYTATLPNQVGIPRGLENNCDSLRESDTIVYLLSRLFLVNKLVNMPLELACRVGSSFRMESIHNKMRGAGNDILNMSSFYSCLRNGKNESHVPEADLSLLKLISCWRDQSVQVTEAIQAVLLAEVQQHMKSLGKIPVNSQPVSMAENGNCEMKQMLPKLEWTEELELQCVRNTLPLQTPVSPVKHDSNSNSANFQDVEDMPDRCALEESESPGEPRHHSWIAKVCPCKVS,mutated_sequence,1.0,1102.0,NP_877435.3.a2m,NP_877435.3.npy,ClinVar +NP_891555.2,NP_891555.2.csv,MQRGAALCLRLWLCLGLLDGLVSGYSMTPPTLNITEESHVIDTGDSLSISCRGQHPLEWAWPGAQEAPATGDKDSEDTGVVRDCEGTDARPYCKVLLLHEVHANDTGSYVCYYKYIKARIEGTTAASSYVFVRDFEQPFINKPDTLLVNRKDAMWVPCLVSIPGLNVTLRSQSSVLWPDGQEVVWDDRRGMLVSTPLLHDALYLQCETTWGDQDFLSNPFLVHITGNELYDIQLLPRKSLELLVGEKLVLNCTVWAEFNSGVTFDWDYPGKQAERGKWVPERRSQQTHTELSSILTIHNVSQHDLGSYVCKANNGIQRFRESTEVIVHENPFISVEWLKGPILEATAGDELVKLPVKLAAYPPPEFQWYKDGKALSGRHSPHALVLKEVTEASTGTYTLALWNSAAGLRRNISLELVVNVPPQIHEKEASSPSIYSRHSRQALTCTAYGVPLPLSIQWHWRPWTPCKMFAQRSLRRRQQQDLMPQCRDWRAVTTQDAVNPIESLDTWTEFVEGKNKTVSKLVIQNANVSAMYKCVVSNKVGQDERLIYFYVTTIPDGFTIESKPSEELLEGQPVLLSCQADSYKYEHLRWYRLNLSTLHDAHGNPLLLDCKNVHLFATPLAASLEEVAPGARHATLSLSIPRVAPEHEGHYVCEVQDRRSHDKHCHKKYLSVQALEAPRLTQNLTDLLVNVSDSLEMQCLVAGAHAPSIVWYKDERLLEEKSGVDLADSNQKLSIQRVREEDAGRYLCSVCNAKGCVNSSASVAVEGSEDKGSMEIVILVGTGVIAVFFWVLLLLIFCNMRRPAHADIKTGYLSIIMDPGEVPLEEQCEYLSYDASQWEFPRERLHLGRVLGYGAFGKVVEASAFGIHKGSSCDTVAVKMLKEGATASEHRALMSELKILIHIGNHLNVVNLLGACTKPQGPLMVIVEFCKYGNLSNFLRAKRDAFSPCAEKSPEQRGRFRAMVELARLDRRRPGSSDRVLFARFSKTEGGARRASPDQEAEDLWLSPLTMEDLVCYSFQVARGMEFLASRKCIHRDLAARNILLSESDVVKICDFGLARDIYKDPDYVRKGSARLPLKWMAPESIFDKVYTTQSDVWSFGVLLWEIFSLGASPYPGVQINEEFCQRLRDGTRMRAPELATPAIRRIMLNCWSGDPKARPAFSELVEILGDLLQGRGLQEEEEVCMAPRSSQSSEEGSFSQVSTMALHIAQADAEDSPPSLQRHSLAARYYNWVSFPGCLARGAETRGSSRMKTFEEFPMTPTTYKGSVDNQTDSGMVLASEEFEQIESRHRQESGFSCKGPGQNVAVTRAHPDSQGRRRRPERGARGGQVFYNSEYGELSEPSEEDHCSPSARVTFFTDNSY,mutated_sequence,1.0,1363.0,NP_891555.2.a2m,NP_891555.2.npy,ClinVar +NP_898888.1,NP_898888.1.csv,MGRVSGLVPSRFLTLLAHLVVVITLFWSRDSNIQACLPLTFTPEEYDKQDIQLVAALSVTLGLFAVELAGFLSGVSMFNSTQSLISIGAHCSASVALSFFIFERWECTTYWYIFVFCSALPAVTEMALFVTVFGLKKKPF,mutated_sequence,1.0,140.0,NP_898888.1.a2m,NP_898888.1.npy,ClinVar +NP_899058.1,NP_899058.1.csv,MSDGDYDYLIKFLALGDSGVGKTSVLYQYTDGKFNSKFITTVGIDFREKRVVYRASGPDGATGRGQRIHLQLWDTAGQERFRSLTTAFFRDAMGFLLLFDLTNEQSFLNVRNWISQLQMHAYCENPDIVLCGNKSDLEDQRVVKEEEAIALAEKYGIPYFETSAANGTNISQAIEMLLDLIMKRMERCVDKSWIPEGVVRSNGHASTDQLSEEKEKGACGC,mutated_sequence,1.0,221.0,NP_899058.1.a2m,NP_899058.1.npy,ClinVar +NP_899200.1,NP_899200.1.csv,MSGSKSVSPPGYAAQKTAAPAPRGGPEHRSAWGEADSRANGYPHAPGGSARGSTKKPGGAVTPQQQQRLASRWRSDDDDDPPLSGDDPLAGGFGFSFRSKSAWQERGGDDCGRGSRRQRRGAASGGSTRAPPAGGGGGSAAAAASAGGTEVRPRSVEVGLEERRGKGRAADELEAGAVEGGEGSGDGGSSADSGSGAGPGAVLSLGACCLALLQIFRSKKFPSDKLERLYQRYFFRLNQSSLTMLMAVLVLVCLVMLAFHAARPPLQLPYLAVLAAAVGVILIMAVLCNRAAFHQDHMGLACYALIAVVLAVQVVGLLLPQPRSASEGIWWTVFFIYTIYTLLPVRMRAAVLSGVLLSALHLAIALRTNAQDQFLLKQLVSNVLIFSCTNIVGVCTHYPAEVSQRQAFQETRECIQARLHSQRENQQQERLLLSVLPRHVAMEMKADINAKQEDMMFHKIYIQKHDNVSILFADIEGFTSLASQCTAQELVMTLNELFARFDKLAAENHCLRIKILGDCYYCVSGLPEARADHAHCCVEMGMDMIEAISLVREVTGVNVNMRVGIHSGRVHCGVLGLRKWQFDVWSNDVTLANHMEAGGKAGRIHITKATLNYLNGDYEVEPGCGGERNAYLKEHSIETFLILRCTQKRKEEKAMIAKMNRQRTNSIGHNPPHWGAERPFYNHLGGNQVSKEMKRMGFEDPKDKNAQESANPEDEVDEFLGRAIDARSIDRLRSEHVRKFLLTFREPDLEKKYSKQVDDRFGAYVACASLVFLFICFVQITIVPHSIFMLSFYLTCSLLLTLVVFVSVIYSCVKLFPSPLQTLSRKIVRSKMNSTLVGVFTITLVFLAAFVNMFTCNSRDLLGCLAQEHNISASQVNACHVAESAVNYSLGDEQGFCGSPWPNCNFPEYFTYSVLLSLLACSVFLQISCIGKLVLMLAIELIYVLIVEVPGVTLFDNADLLVTANAIDFFNNGTSQCPEHATKVALKVVTPIIISVFVLALYLHAQQVESTARLDFLWKLQATEEKEEMEELQAYNRRLLHNILPKDVAAHFLARERRNDELYYQSCECVAVMFASIANFSEFYVELEANNEGVECLRLLNEIIADFDEIISEDRFRQLEKIKTIGSTYMAASGLNDSTYDKVGKTHIKALADFAMKLMDQMKYINEHSFNNFQMKIGLNIGPVVAGVIGARKPQYDIWGNTVNVASRMDSTGVPDRIQVTTDMYQVLAANTYQLECRGVVKVKGKGEMMTYFLNGGPPLS,mutated_sequence,1.0,1261.0,NP_899200.1.a2m,NP_899200.1.npy,ClinVar +NP_919224.1,NP_919224.1.csv,MALLIHLKTVSELRGRGDRIAKVTFRGQSFYSRVLENCEDVADFDETFRWPVASSIDRNEMLEIQVFNYSKVFSNKLIGTFRMVLQKVVEESHVEVTDTLIDDNNAIIKTSLCVEVRYQATDGTVGSWDDGDFLGDESLQEEEKDSQETDGLLPGSRPSSRPPGEKSFRRAGRSVFSAMKLGKNRSHKEEPQRPDEPAVLEMEDLDHLAIRLGDGLDPDSVSLASVTALTTNVSNKRSKPDIKMEPSAGRPMDYQVSITVIEARQLVGLNMDPVVCVEVGDDKKYTSMKESTNCPYYNEYFVFDFHVSPDVMFDKIIKISVIHSKNLLRSGTLVGSFKMDVGTVYSQPEHQFHHKWAILSDPDDISSGLKGYVKCDVAVVGKGDNIKTPHKANETDEDDIEGNLLLPEGVPPERQWARFYVKIYRAEGLPRMNTSLMANVKKAFIGENKDLVDPYVQVFFAGQKGKTSVQKSSYEPLWNEQVVFTDLFPPLCKRMKVQIRDSDKVNDVAIGTHFIDLRKISNDGDKGFLPTLGPAWVNMYGSTRNYTLLDEHQDLNEGLGEGVSFRARLLLGLAVEIVDTSNPELTSSTEVQVEQATPISESCAGKMEEFFLFGAFLEASMIDRRNGDKPITFEVTIGNYGNEVDGLSRPQRPRPRKEPGDEEEVDLIQNASDDEAGDAGDLASVSSTPPMRPQVTDRNYFHLPYLERKPCIYIKSWWPDQRRRLYNANIMDHIADKLEEGLNDIQEMIKTEKSYPERRLRGVLEELSCGCCRFLSLADKDQGHSSRTRLDRERLKSCMRELENMGQQARMLRAQVKRHTVRDKLRLCQNFLQKLRFLADEPQHSIPDIFIWMMSNNKRVAYARVPSKDLLFSIVEEETGKDCAKVKTLFLKLPGKRGFGSAGWTVQAKVELYLWLGLSKQRKEFLCGLPCGFQEVKAAQGLGLHAFPPVSLVYTKKQAFQLRAHMYQARSLFAADSSGLSDPFARVFFINQSQCTEVLNETLCPTWDQMLVFDNLELYGEAHELRDDPPIIVIEIYDQDSMGKADFMGRTFAKPLVKMADEAYCPPRFPPQLEYYQIYRGNATAGDLLAAFELLQIGPAGKADLPPINGPVDVDRGPIMPVPMGIRPVLSKYRVEVLFWGLRDLKRVNLAQVDRPRVDIECAGKGVQSSLIHNYKKNPNFNTLVKWFEVDLPENELLHPPLNIRVVDCRAFGRYTLVGSHAVSSLRRFIYRPPDRSAPSWNTTVRLLRRCRVLCNGGSSSHSTGEVVVTMEPEVPIKKLETMVKLDATSEAVVKVDVAEEEKEKKKKKKGTAEEPEEEEPDESMLDWWSKYFASIDTMKEQLRQQEPSGIDLEEKEEVDNTEGLKGSMKGKEKARAAKEEKKKKTQSSGSGQGSEAPEKKKPKIDELKVYPKELESEFDNFEDWLHTFNLLRGKTGDDEDGSTEEERIVGRFKGSLCVYKVPLPEDVSREAGYDSTYGMFQGIPSNDPINVLVRVYVVRATDLHPADINGKADPYIAIRLGKTDIRDKENYISKQLNPVFGKSFDIEASFPMESMLTVAVYDWDLVGTDDLIGETKIDLENRFYSKHRATCGIAQTYSTHGYNIWRDPMKPSQILTRLCKDGKVDGPHFGPPGRVKVANRVFTGPSEIEDENGQRKPTDEHVALLALRHWEDIPRAGCRLVPEHVETRPLLNPDKPGIEQGRLELWVDMFPMDMPAPGTPLDISPRKPKKYELRVIIWNTDEVVLEDDDFFTGEKSSDIFVRGWLKGQQEDKQDTDVHYHSLTGEGNFNWRYLFPFDYLAAEEKIVISKKESMFSWDETEYKIPARLTLQIWDADHFSADDFLGAIELDLNRFPRGAKTAKQCTMEMATGEVDVPLVSIFKQKRVKGWWPLLARNENDEFELTGKVEAELHLLTAEEAEKNPVGLARNEPDPLEKPNRPDTSFIWFLNPLKSARYFLWHTYRWLLLKLLLLLLLLLLLALFLYSVPGYLVKKILGA,mutated_sequence,1.0,1997.0,NP_919224.1.a2m,NP_919224.1.npy,ClinVar +NP_919231.1,NP_919231.1.csv,MVPSSPAVEKQVPVEPGPDPELRSWRHLVCYLCFYGFMAQIRPGESFITPYLLGPDKNFTREQVTNEITPVLSYSYLAVLVPVFLLTDYLRYTPVLLLQGLSFVSVWLLLLLGHSVAHMQLMELFYSVTMAARIAYSSYIFSLVRPARYQRVAGYSRAAVLLGVFTSSVLGQLLVTVGRVSFSTLNYISLAFLTFSVVLALFLKRPKRSLFFNRDDRGRCETSASELERMNPGPGGKLGHALRVACGDSVLARMLRELGDSLRRPQLRLWSLWWVFNSAGYYLVVYYVHILWNEVDPTTNSARVYNGAADAASTLLGAITSFAAGFVKIRWARWSKLLIAGVTATQAGLVFLLAHTRHPSSIWLCYAAFVLFRGSYQFLVPIATFQIASSLSKELCALVFGVNTFFATIVKTIITFIVSDVRGLGLPVRKQFQLYSVYFLILSIIYFLGAMLDGLRHCQRGHHPRQPPAQGLRSAAEEKAAQALSVQDKGLGGLQPAQSPPLSPEDSLGAVGPASLEQRQSDPYLAQAPAPQAAEFLSPVTTPSPCTLCSAQASGPEAADETCPQLAVHPPGVSKLGLQCLPSDGVQNVNQ,mutated_sequence,1.0,591.0,NP_919231.1.a2m,NP_919231.1.npy,ClinVar +NP_919253.1,NP_919253.1.csv,MLHLKVQFLDDSQKIFVVDQKSSGKALFNLSCSHLNLAEKEYFGLEFCSHSGNNVWLELLKPITKQVKNPKEIVFKFMVKFFPVDPGHLREELTRYLFTLQIKKDLALGRLPCSDNCTALMVSHILQSELGDFHEETDRKHLAQTRYLPNQDCLEGKIMHFHQKHIGRSPAESDILLLDIARKLDMYGIRPHPASDGEGMQIHLAVAHMGVLVLRGNTKINTFNWAKIRKLSFKRKHFLIKLHANILVLCKDTLEFTMASRDACKAFWKTCVEYHAFFRLSEEPKSKPKTLLCSKGSSFRYSGRTQRQLLEYGRKGRLKSLPFERKHYPSQYHERQCRSSPDLLSDVSKQVEDLRLAYGGGYYQNVNGVHASEPVLESRRRNSALEVTFATELEHSKPEADPTLLHQSQSSSSFPFIYMDPVFNTEPNPNPDPRDIFSERSSLSSFQTSCKFSGNHMSIYSGLTSKVRPAKQLTYTDVPYIPCTGQQVGIMPPQVFFYVDKPPQVPRWSPIRAEERTSPHSYVEPTAMKPAERSPRNIRMKSFQQDLQVLQEAIARTSGRSNINVGLEEEDPNLEDAFVCNIQEQTPKRSQSQSDMKTIRFPFGSEFRPLGPCPALSHKADLFTDMFAEQELPAVLMDQSTAERYVASESSDSESEILKPDYYALYGKEIRSPMARIRLSSGSLQLDEEDEDAYFNTPTAEDRTSLKPCNYFLA,mutated_sequence,1.0,714.0,NP_919253.1.a2m,NP_919253.1.npy,ClinVar +NP_938148.1,NP_938148.1.csv,MAAVAAVAARRRRSWASLVLAFLGVCLGITLAVDRSNFKTCEESSFCKRQRSIRPGLSPYRALLDSLQLGPDSLTVHLIHEVTKVLLVLELQGLQKNMTRFRIDELEPRRPRYRVPDVLVADPPIARLSVSGRDENSVELTMAEGPYKIILTARPFRLDLLEDRSLLLSVNARGLLEFEHQRAPRVSQGSKDPAEGDGAQPEETPRDGDKPEETQGKAEKDEPGAWEETFKTHSDSKPYGPMSVGLDFSLPGMEHVYGIPEHADNLRLKVTEGGEPYRLYNLDVFQYELYNPMALYGSVPVLLAHNPHRDLGIFWLNAAETWVDISSNTAGKTLFGKMMDYLQGSGETPQTDVRWMSETGIIDVFLLLGPSISDVFRQYASLTGTQALPPLFSLGYHQSRWNYRDEADVLEVDQGFDDHNLPCDVIWLDIEHADGKRYFTWDPSRFPQPRTMLERLASKRRKLVAIVDPHIKVDSGYRVHEELRNLGLYVKTRDGSDYEGWCWPGSAGYPDFTNPTMRAWWANMFSYDNYEGSAPNLFVWNDMNEPSVFNGPEVTMLKDAQHYGGWEHRDVHNIYGLYVHMATADGLRQRSGGMERPFVLARAFFAGSQRFGAVWTGDNTAEWDHLKISIPMCLSLGLVGLSFCGADVGGFFKNPEPELLVRWYQMGAYQPFFRAHAHLDTGRREPWLLPSQHNDIIRDALGQRYSLLPFWYTLLYQAHREGIPVMRPLWVQYPQDVTTFNIDDQYLLGDALLVHPVSDSGAHGVQVYLPGQGEVWYDIQSYQKHHGPQTLYLPVTLSSIPVFQRGGTIVPRWMRVRRSSECMKDDPITLFVALSPQGTAQGELFLDDGHTFNYQTRQEFLLRRFSFSGNTLVSSSADPEGHFETPIWIERVVIIGAGKPAAVVLQTKGSPESRLSFQHDPETSVLVLRKPGINVASDWSIHLR,mutated_sequence,1.0,944.0,NP_938148.1.a2m,NP_938148.1.npy,ClinVar +NP_940908.3,NP_940908.3.csv,MHLFACLCIVLSFLEGVGCLCPSQCTCDYHGRNDGSGSRLVLCNDMDMNELPTNLPVDTVKLRIEKTVIRRISAEAFYYLVELQYLWVTYNSVASIDPSSFYNLKQLHELRLDGNSLAAFPWASLLDMPLLRTLDLHNNKITSVPNEALRYLKNLAYLDLSSNRLTTLPPDFLESWTHLVSTPSGVLDLSPSRIILGLQDNPWFCDCHISKMIELSKVVDPAIVLLDPLMTCSEPERLTGILFQRAELEHCLKPSVMTSATKIMSALGSNVLLRCDATGFPTPQITWTRSDSSPVNYTVIQESPEEGVRWSIMSLTGISSKDAGDYKCKAKNLAGMSEAVVTVTVLGITTTPIPPDTSERTGDHPEWDVQPGSGRSTSVSSASSYLWSSSFSPTSSFSASTLSPPSTASFSLSPFSSSTVSSTTTLSTSISASTTMANKRSFQLHQGGKRNLKVAKNGSKLPPASTSKKEELALLDQTMLTETNAAIENLRVVSETKESVTLTWNMINTTHNSAVTVLYSKYGGKDLLLLNADSSKNQVTIDGLEPGGQYMACVCPKGVPPQKDQCITFSTERVEGDDSQWSLLLVVTSTACVVILPLICFLLYKVCKLQCKSEPFWEDDLAKETYIQFETLFPRSQSVGELWTRSHRDDSEKLLLCSRSSVESQVTFKSEGSRPEYYC,mutated_sequence,1.0,679.0,NP_940908.3.a2m,NP_940908.3.npy,ClinVar +NP_940927.2,NP_940927.2.csv,MGLEAQRLPGAEEAPVRVALRVRPLLPKELLHGHQSCLQVEPGLGRVTLGRDRHFGFHVVLAEDAGQEAVYQACVQPLLEAFFEGFNATVFAYGQTGSGKTYTMGEASVASLLEDEQGIVPRAMAEAFKLIDENDLLDCLVHVSYLEVYKEEFRDLLEVGTASRDIQLREDERGNVVLCGVKEVDVEGLDEVLSLLEMGNAARHTGATHLNHLSSRSHTVFTVTLEQRGRAPSRLPRPAPGQLLVSKFHFVDLAGSERVLKTGSTGERLKESIQINSSLLALGNVISALGDPQRRGSHIPYRDSKITRILKDSLGGNAKTVMIACVSPSSSDFDETLNTLNYASRAQNIRNRATVNWRPEAERPPEETASGARGPPRHRSETRIIHRGRRAPGPATASAAAAMRLGAECARYRACTDAAYSLLRELQAEPGLPGAAARKVRDWLCAVEGERSALSSASGPDSGIESASVEDQAAQGAGGRKEDEGAQQLLTLQNQVARLEEENRDFLAALEDAMEQYKLQSDRLREQQEEMVELRLRLELVRPGWGGPRLLNGLPPGSFVPRPHTAPLGGAHAHVLGMVPPACLPGDEVGSEQRGEQVTNGREAGAELLTEVNRLGSGSSAASEEEEEEEEPPRRTLHLRRNRISNCSQRAGARPGSLPERKGPELCLEELDAAIPGSRAVGGSKARVQARQVPPATASEWRLAQAQQKIRELAINIRMKEELIGELVRTGKAAQALNRQHSQRIRELEQEAEQVRAELSEGQRQLRELEGKELQDAGERSRLQEFRRRVAAAQSQVQVLKEKKQATERLVSLSAQSEKRLQELERNVQLMRQQQGQLQRRLREETEQKRRLEAEMSKRQHRVKELELKHEQQQKILKIKTEEIAAFQRKRRSGSNGSVVSLEQQQKIEEQKKWLDQEMEKVLQQRRALEELGEELHKREAILAKKEALMQEKTGLESKRLRSSQALNEDIVRVSSRLEHLEKELSEKSGQLRQGSAQSQQQIRGEIDSLRQEKDSLLKQRLEIDGKLRQGSLLSPEEERTLFQLDEAIEALDAAIEYKNEAITCRQRVLRASASLLSQCEMNLMAKLSYLSSSETRALLCKYFDKVVTLREEQHQQQIAFSELEMQLEEQQRLVYWLEVALERQRLEMDRQLTLQQKEHEQNMQLLLQQSRDHLGEGLADSRRQYEARIQALEKELGRYMWINQELKQKLGGVNAVGHSRGGEKRSLCSEGRQAPGNEDELHLAPELLWLSPLTEGAPRTREETRDLVHAPLPLTWKRSSLCGEEQGSPEELRQREAAEPLVGRVLPVGEAGLPWNFGPLSKPRRELRRASPGMIDVRKNPL,mutated_sequence,1.0,1343.0,NP_940927.2.a2m,NP_940927.2.npy,ClinVar +NP_944494.1,NP_944494.1.csv,MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPLLRMFSFKAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL,mutated_sequence,1.0,475.0,NP_944494.1.a2m,NP_944494.1.npy,ClinVar +NP_945345.2,NP_945345.2.csv,MAGIRVTKVDWQRSRNGAAHHTQEYPCPELVVRRGQSFSLTLELSRALDCEEILIFTMETGPRASEALHTKAVFQTSELERGEGWTAAREAQMEKTLTVSLASPPSAVIGRYLLSIRLSSHRKHSNRRLGEFVLLFNPWCAEDDVFLASEEERQEYVLSDSGIIFRGVEKHIRAQGWNYGQFEEDILNICLSILDRSPGHQNNPATDVSCRHNPIYVTRVISAMVNSNNDRGVVQGQWQGKYGGGTSPLHWRGSVAILQKWLKGRYKPVKYGQCWVFAGVLCTVLRCLGIATRVVSNFNSAHDTDQNLSVDKYVDSFGRTLEDLTEDSMWNFHVWNESWFARQDLGPSYNGWQVLDATPQEESEGVFRCGPASVTAIREGDVHLAHDGPFVFAEVNADYITWLWHEDESRERVYSNTKKIGRCISTKAVGSDSRVDITDLYKYPEGSRKERQVYSKAVNRLFGVEASGRRIWIRRAGGRCLWRDDLLEPATKPSIAGKFKVLEPPMLGHDLRLALCLANLTSRAQRVRVNLSGATILYTRKPVAEILHESHAVRLGPQEEKRIPITISYSKYKEDLTEDKKILLAAMCLVTKGEKLLVEKDITLEDFITIKVLGPAMVGVAVTVEVTVVNPLIERVKDCALMVEGSGLLQEQLSIDVPTLEPQERASVQFDITPSKSGPRQLQVDLVSPHFPDIKGFVIVHVATAK,mutated_sequence,1.0,706.0,NP_945345.2.a2m,NP_945345.2.npy,ClinVar +NP_954712.1,NP_954712.1.csv,MATLLSHPQQRPPFLRQAIKIRRRRVRDLQDPPPQMAPEIQPPSHHFSPEQRALLYEDALYTVLHRLGHPEPNHVTEASELLRYLQEAFHVEPEEHQQTLQRVRELEKPIFCLKATVKQAKGILGKDVSGFSDPYCLLGIEQGVGVPGGSPGSRHRQKAVVRHTIPEEETHRTQVITQTLNPVWDETFILEFEDITNASFHLDMWDLDTVESVRQKLGELTDLHGLRRIFKEARKDKGQDDFLGNVVLRLQDLRCREDQWYPLEPRTETYPDRGQCHLQFQLIHKRRATSASRSQPSYTVHLHLLQQLVSHEVTQHEAGSTSWDGSLSPQAATVLFLHATQKDLSDFHQSMAQWLAYSRLYQSLEFPSSCLLHPITSIEYQWIQGRLKAEQQEELAASFSSLLTYGLSLIRRFRSVFPLSVSDSPARLQSLLRVLVQMCKMKAFGELCPNTAPLPQLVTEALQTGTTEWFHLKQQHHQPMVQGIPEAGKALLGLVQDVIGDLHQCQRTWDKIFHNTLKIHLFSMAFRELQWLVAKRVQDHTTVVGDVVSPEMGESLFQLYISLKELCQLRMSSSERDGVLALDNFHRWFQPAIPSWLQKTYNEALARVQRAVQMDELVPLGELTKHSTSAVDLSTCFAQISHTARQLDWPDPEEAFMITVKFVEDTCRLALVYCSLIKARARELSSGQKDQGQAANMLCVVVNDMEQLRLVIGKLPAQLAWEALEQRVGAVLEQGQLQNTLHAQLQSALAGLGHEIRTGVRTLAEQLEVGIAKHIQKLVGVRESVLPEDAILPLMKFLEVELCYMNTNLVQENFSSLLTLLWTHTLTVLVEAAASQRSSSLASNRLKIALQNLEICFHAEGCGLPPKALHTATFQALQRDLELQAASSRELIRKYFCSRIQQQAETTSEELGAVTVKASYRASEQKLRVELLSASSLLPLDSNGSSDPFVQLTLEPRHEFPELAARETQKHKKDLHPLFDETFEFLVPAEPCRKAGACLLLTVLDYDTLGADDLEGEAFLPLREVPGLSGSEEPGEVPQTRLPLTYPAPNGDPILQLLEGRKGDREAQVFVRLRRHRAKQASQHALRPAP,mutated_sequence,1.0,1090.0,NP_954712.1.a2m,NP_954712.1.npy,ClinVar +NP_955631.1,NP_955631.1.csv,MEAFPWAPRSPRRGRAPPPMALVPSARYVSAPGPAHPQPFSSWNDYLGLATLITKAVDGEPRFGCARGGNGGGGSPPSSSSSSCCSPHTGAGPGALGPALGPPDYDEDDDDDSDEPGSRGRYLGSALELRALELCAGPAEAGLLEERFAELSPFAGRAAAVLLGCAPAAAAAATTTSEATPREERAPAWAAEPRLHAASGAAAARLLKPELQVCVFCRNNKEAMALYTTHILKGPDGRVLCPVLRRYTCPLCGASGDNAHTIKYCPLSKVPPPPARPPPRSARDGPPGKKLR,mutated_sequence,1.0,292.0,NP_955631.1.a2m,NP_955631.1.npy,ClinVar +NP_957705.1,NP_957705.1.csv,MALKNINYLLIFYLSFSLLIYIKNSFCNKNNTRCLSNSCQNNSTCKDFSKDNDCSCSDTANNLDKDCDNMKDPCFSNPCQGSATCVNTPGERSFLCKCPPGYSGTICETTIGSCGKNSCQHGGICHQDPIYPVCICPAGYAGRFCEIDHDECASSPCQNGAVCQDGIDGYSCFCVPGYQGRHCDLEVDECASDPCKNEATCLNEIGRYTCICPHNYSGVNCELEIDECWSQPCLNGATCQDALGAYFCDCAPGFLGDHCELNTDECASQPCLHGGLCVDGENRYSCNCTGSGFTGTHCETLMPLCWSKPCHNNATCEDSVDNYTCHCWPGYTGAQCEIDLNECNSNPCQSNGECVELSSEKQYGRITGLPSSFSYHEASGYVCICQPGFTGIHCEEDVNECSSNPCQNGGTCENLPGNYTCHCPFDNLSRTFYGGRDCSDILLGCTHQQCLNNGTCIPHFQDGQHGFSCLCPSGYTGSLCEIATTLSFEGDGFLWVKSGSVTTKGSVCNIALRFQTVQPMALLLFRSNRDVFVKLELLSGYIHLSIQVNNQSKVLLFISHNTSDGEWHFVEVIFAEAVTLTLIDDSCKEKCIAKAPTPLESDQSICAFQNSFLGGLPVGMTSNGVALLNFYNMPSTPSFVGCLQDIKIDWNHITLENISSGSSLNVKAGCVRKDWCESQPCQSRGRCINLWLSYQCDCHRPYEGPNCLREYVAGRFGQDDSTGYVIFTLDESYGDTISLSMFVRTLQPSGLLLALENSTYQYIRVWLERGRLAMLTPNSPKLVVKFVLNDGNVHLISLKIKPYKIELYQSSQNLGFISASTWKIEKGDVIYIGGLPDKQETELNGGFFKGCIQDVRLNNQNLEFFPNPTNNASLNPVLVNVTQGCAGDNSCKSNPCHNGGVCHSRWDDFSCSCPALTSGKACEEVQWCGFSPCPHGAQCQPVLQGFECIANAVFNGQSGQILFRSNGNITRELTNITFGFRTRDANVIILHAEKEPEFLNISIQDSRLFFQLQSGNSFYMLSLTSLQSVNDGTWHEVTLSMTDPLSQTSRWQMEVDNETPFVTSTIATGSLNFLKDNTDIYVGDRAIDNIKGLQGCLSTIEIGGIYLSYFENVHGFINKPQEEQFLKISTNSVVTGCLQLNVCNSNPCLHGGNCEDIYSSYHCSCPLGWSGKHCELNIDECFSNPCIHGNCSDRVAAYHCTCEPGYTGVNCEVDIDNCQSHQCANGATCISHTNGYSCLCFGNFTGKFCRQSRLPSTVCGNEKTNLTCYNGGNCTEFQTELKCMCRPGFTGEWCEKDIDECASDPCVNGGLCQDLLNKFQCLCDVAFAGERCEVDLADDLISDIFTTIGSVTVALLLILLLAIVASVVTSNKRATQGTYSPSRQEKEGSRVEMWNLMPPPAMERLI,mutated_sequence,1.0,1406.0,NP_957705.1.a2m,NP_957705.1.npy,ClinVar +NP_958780.1,NP_958780.1.csv,MAGPLPDEQDFIQAYEEVREKYKDERDRVQKKTFTKWVNKHLIKAQRHISDLYEDLRDGHNLISLLEVLSGDSLPREKGRMRFHKLQNVQIALDYLRHRQVKLVNIRNDDIADGNPKLTLGLIWTIILHFQISDIQVSGQSEDMTAKEKLLLWSQRMVEGYQGLRCDNFTSSWRDGRLFNAIIHRHKPLLIDMNKVYRQTNLENLDQAFSVAERDLGVTRLLDPEDVDVPQPDEKSIITYVSSLYDAMPRVPDVQDGVRANELQLRWQEYRELVLLLLQWMRHHTAAFEERRFPSSFEEIEILWSQFLKFKEMELPAKEADKNRSKGIYQSLEGAVQAGQLKVPPGYHPLDVEKEWGKLHVAILEREKQLRSEFERLECLQRIVTKLQMEAGLCEEQLNQADALLQSDVRLLAAGKVPQRAGEVERDLDKADSMIRLLFNDVQTLKDGRHPQGEQMYRRVYRLHERLVAIRTEYNLRLKAGVAAPATQVAQVTLQSVQRRPELEDSTLRYLQDLLAWVEENQHRVDGAEWGVDLPSVEAQLGSHRGLHQSIEEFRAKIERARSDEGQLSPATRGAYRDCLGRLDLQYAKLLNSSKARLRSLESLHSFVAAATKELMWLNEKEEEEVGFDWSDRNTNMTAKKESYSALMRELELKEKKIKELQNAGDRLLREDHPARPTVESFQAALQTQWSWMLQLCCCIEAHLKENAAYFQFFSDVREAEGQLQKLQEALRRKYSCDRSATVTRLEDLLQDAQDEKEQLNEYKGHLSGLAKRAKAVVQLKPRHPAHPMRGRLPLLAVCDYKQVEVTVHKGDECQLVGPAQPSHWKVLSSSGSEAAVPSVCFLVPPPNQEAQEAVTRLEAQHQALVTLWHQLHVDMKSLLAWQSLRRDVQLIRSWSLATFRTLKPEEQRQALHSLELHYQAFLRDSQDAGGFGPEDRLMAEREYGSCSHHYQQLLQSLEQGAQEESRCQRCISELKDIRLQLEACETRTVHRLRLPLDKEPARECAQRIAEQQKAQAEVEGLGKGVARLSAEAEKVLALPEPSPAAPTLRSELELTLGKLEQVRSLSAIYLEKLKTISLVIRGTQGAEEVLRAHEEQLKEAQAVPATLPELEATKASLKKLRAQAEAQQPTFDALRDELRGAQEVGERLQQRHGERDVEVERWRERVAQLLERWQAVLAQTDVRQRELEQLGRQLRYYRESADPLGAWLQDARRRQEQIQAMPLADSQAVREQLRQEQALLEEIERHGEKVEECQRFAKQYINAIKDYELQLVTYKAQLEPVASPAKKPKVQSGSESVIQEYVDLRTHYSELTTLTSQYIKFISETLRRMEEEERLAEQQRAEERERLAEVEAALEKQRQLAEAHAQAKAQAEREAKELQQRMQEEVVRREEAAVDAQQQKRSIQEELQQLRQSSEAEIQAKARQAEAAERSRLRIEEEIRVVRLQLEATERQRGGAEGELQALRARAEEAEAQKRQAQEEAERLRRQVQDESQRKRQAEVELASRVKAEAEAAREKQRALQALEELRLQAEEAERRLRQAEVERARQVQVALETAQRSAEAELQSKRASFAEKTAQLERSLQEEHVAVAQLREEAERRAQQQAEAERAREEAERELERWQLKANEALRLRLQAEEVAQQKSLAQAEAEKQKEEAEREARRRGKAEEQAVRQRELAEQELEKQRQLAEGTAQQRLAAEQELIRLRAETEQGEQQRQLLEEELARLQREAAAATQKRQELEAELAKVRAEMEVLLASKARAEEESRSTSEKSKQRLEAEAGRFRELAEEAARLRALAEEAKRQRQLAEEDAARQRAEAERVLAEKLAAIGEATRLKTEAEIALKEKEAENERLRRLAEDEAFQRRRLEEQAAQHKADIEERLAQLRKASDSELERQKGLVEDTLRQRRQVEEEILALKASFEKAAAGKAELELELGRIRSNAEDTLRSKEQAELEAARQRQLAAEEERRRREAEERVQKSLAAEEEAARQRKAALEEVERLKAKVEEARRLRERAEQESARQLQLAQEAAQKRLQAEEKAHAFAVQQKEQELQQTLQQEQSVLDQLRGEAEAARRAAEEAEEARVQAEREAAQSRRQVEEAERLKQSAEEQAQARAQAQAAAEKLRKEAEQEAARRAQAEQAALRQKQAADAEMEKHKKFAEQTLRQKAQVEQELTTLRLQLEETDHQKNLLDEELQRLKAEATEAARQRSQVEEELFSVRVQMEELSKLKARIEAENRALILRDKDNTQRFLQEEAEKMKQVAEEAARLSVAAQEAARLRQLAEEDLAQQRALAEKMLKEKMQAVQEATRLKAEAELLQQQKELAQEQARRLQEDKEQMAQQLAEETQGFQRTLEAERQRQLEMSAEAERLKLRVAEMSRAQARAEEDAQRFRKQAEEIGEKLHRTELATQEKVTLVQTLEIQRQQSDHDAERLREAIAELEREKEKLQQEAKLLQLKSEEMQTVQQEQLLQETQALQQSFLSEKDSLLQRERFIEQEKAKLEQLFQDEVAKAQQLREEQQRQQQQMEQERQRLVASMEEARRRQHEAEEGVRRKQEELQQLEQQRRQQEELLAEENQRLREQLQLLEEQHRAALAHSEEVTASQVAATKTLPNGRDALDGPAAEAEPEHSFDGLRRKVSAQRLQEAGILSAEELQRLAQGHTTVDELARREDVRHYLQGRSSIAGLLLKATNEKLSVYAALQRQLLSPGTALILLEAQAASGFLLDPVRNRRLTVNEAVKEGVVGPELHHKLLSAERAVTGYKDPYTGQQISLFQAMQKGLIVREHGIRLLEAQIATGGVIDPVHSHRVPVDVAYRRGYFDEEMNRVLADPSDDTKGFFDPNTHENLTYLQLLERCVEDPETGLCLLPLTDKAAKGGELVYTDSEARDVFEKATVSAPFGKFQGKTVTIWEIINSEYFTAEQRRDLLRQFRTGRITVEKIIKIIITVVEEQEQKGRLCFEGLRSLVPAAELLESRVIDRELYQQLQRGERSVRDVAEVDTVRRALRGANVIAGVWLEEAGQKLSIYNALKKDLLPSDMAVALLEAQAGTGHIIDPATSARLTVDEAVRAGLVGPEFHEKLLSAEKAVTGYRDPYTGQSVSLFQALKKGLIPREQGLRLLDAQLSTGGIVDPSKSHRVPLDVACARGCLDEETSRALSAPRADAKAYSDPSTGEPATYGELQQRCRPDQLTGLSLLPLSEKAARARQEELYSELQARETFEKTPVEVPVGGFKGRTVTVWELISSEYFTAEQRQELLRQFRTGKVTVEKVIKILITIVEEVETLRQERLSFSGLRAPVPASELLASGVLSRAQFEQLKDGKTTVKDLSELGSVRTLLQGSGCLAGIYLEDTKEKVSIYEAMRRGLLRATTAALLLEAQAATGFLVDPVRNQRLYVHEAVKAGVVGPELHEQLLSAEKAVTGYRDPYSGSTISLFQAMQKGLVLRQHGIRLLEAQIATGGIIDPVHSHRVPVDVAYQRGYFSEEMNRVLADPSDDTKGFFDPNTHENLTYRQLLERCVEDPETGLRLLPLKGAEKAEVVETTQVYTEEETRRAFEETQIDIPGGGSHGGSTMSLWEVMQSDLIPEEQRAQLMADFQAGRVTKERMIIIIIEIIEKTEIIRQQGLASYDYVRRRLTAEDLFEARIISLETYNLLREGTRSLREALEAESAWCYLYGTGSVAGVYLPGSRQTLSIYQALKKGLLSAEVARLLLEAQAATGFLLDPVKGERLTVDEAVRKGLVGPELHDRLLSAERAVTGYRDPYTEQTISLFQAMKKELIPTEEALRLLDAQLATGGIVDPRLGFHLPLEVAYQRGYLNKDTHDQLSEPSEVRSYVDPSTDERLSYTQLLRRCRRDDGTGQLLLPLSDARKLTFRGLRKQITMEELVRSQVMDEATALQLREGLTSIEEVTKNLQKFLEGTSCIAGVFVDATKERLSVYQAMKKGIIRPGTAFELLEAQAATGYVIDPIKGLKLTVEEAVRMGIVGPEFKDKLLSAERAVTGYKDPYSGKLISLFQAMKKGLILKDHGIRLLEAQIATGGIIDPEESHRLPVEVAYKRGLFDEEMNEILTDPSDDTKGFFDPNTEENLTYLQLMERCITDPQTGLCLLPLKEKKRERKTSSKSSVRKRRVVIVDPETGKEMSVYEAYRKGLIDHQTYLELSEQECEWEEITISSSDGVVKSMIIDRRSGRQYDIDDAIAKNLIDRSALDQYRAGTLSITEFADMLSGNAGGFRSRSSSVGSSSSYPISPAVSRTQLASWSDPTEETGPVAGILDTETLEKVSITEAMHRNLVDNITGQRLLEAQACTGGIIDPSTGERFPVTDAVNKGLVDKIMVDRINLAQKAFCGFEDPRTKTKMSAAQALKKGWLYYEAGQRFLEVQYLTGGLIEPDTPGRVPLDEALQRGTVDARTAQKLRDVGAYSKYLTCPKTKLKISYKDALDRSMVEEGTGLRLLEAAAQSTKGYYSPYSVSGSGSTAGSRTGSRTGSRAGSRRGSFDATGSGFSMTFSSSSYSSSGYGRRYASGSSASLGGPESAVA,mutated_sequence,1.0,4533.0,NP_958780.1.a2m,NP_958780.1.npy,ClinVar +NP_958786.1,NP_958786.1.csv,MSQHQLRVPQPEGLGRKRTSSEDNLYLAVLRASEGKKDERDRVQKKTFTKWVNKHLIKAQRHISDLYEDLRDGHNLISLLEVLSGDSLPREKGRMRFHKLQNVQIALDYLRHRQVKLVNIRNDDIADGNPKLTLGLIWTIILHFQISDIQVSGQSEDMTAKEKLLLWSQRMVEGYQGLRCDNFTSSWRDGRLFNAIIHRHKPLLIDMNKVYRQTNLENLDQAFSVAERDLGVTRLLDPEDVDVPQPDEKSIITYVSSLYDAMPRVPDVQDGVRANELQLRWQEYRELVLLLLQWMRHHTAAFEERRFPSSFEEIEILWSQFLKFKEMELPAKEADKNRSKGIYQSLEGAVQAGQLKVPPGYHPLDVEKEWGKLHVAILEREKQLRSEFERLECLQRIVTKLQMEAGLCEEQLNQADALLQSDVRLLAAGKVPQRAGEVERDLDKADSMIRLLFNDVQTLKDGRHPQGEQMYRRVYRLHERLVAIRTEYNLRLKAGVAAPATQVAQVTLQSVQRRPELEDSTLRYLQDLLAWVEENQHRVDGAEWGVDLPSVEAQLGSHRGLHQSIEEFRAKIERARSDEGQLSPATRGAYRDCLGRLDLQYAKLLNSSKARLRSLESLHSFVAAATKELMWLNEKEEEEVGFDWSDRNTNMTAKKESYSALMRELELKEKKIKELQNAGDRLLREDHPARPTVESFQAALQTQWSWMLQLCCCIEAHLKENAAYFQFFSDVREAEGQLQKLQEALRRKYSCDRSATVTRLEDLLQDAQDEKEQLNEYKGHLSGLAKRAKAVVQLKPRHPAHPMRGRLPLLAVCDYKQVEVTVHKGDECQLVGPAQPSHWKVLSSSGSEAAVPSVCFLVPPPNQEAQEAVTRLEAQHQALVTLWHQLHVDMKSLLAWQSLRRDVQLIRSWSLATFRTLKPEEQRQALHSLELHYQAFLRDSQDAGGFGPEDRLMAEREYGSCSHHYQQLLQSLEQGAQEESRCQRCISELKDIRLQLEACETRTVHRLRLPLDKEPARECAQRIAEQQKAQAEVEGLGKGVARLSAEAEKVLALPEPSPAAPTLRSELELTLGKLEQVRSLSAIYLEKLKTISLVIRGTQGAEEVLRAHEEQLKEAQAVPATLPELEATKASLKKLRAQAEAQQPTFDALRDELRGAQEVGERLQQRHGERDVEVERWRERVAQLLERWQAVLAQTDVRQRELEQLGRQLRYYRESADPLGAWLQDARRRQEQIQAMPLADSQAVREQLRQEQALLEEIERHGEKVEECQRFAKQYINAIKDYELQLVTYKAQLEPVASPAKKPKVQSGSESVIQEYVDLRTHYSELTTLTSQYIKFISETLRRMEEEERLAEQQRAEERERLAEVEAALEKQRQLAEAHAQAKAQAEREAKELQQRMQEEVVRREEAAVDAQQQKRSIQEELQQLRQSSEAEIQAKARQAEAAERSRLRIEEEIRVVRLQLEATERQRGGAEGELQALRARAEEAEAQKRQAQEEAERLRRQVQDESQRKRQAEVELASRVKAEAEAAREKQRALQALEELRLQAEEAERRLRQAEVERARQVQVALETAQRSAEAELQSKRASFAEKTAQLERSLQEEHVAVAQLREEAERRAQQQAEAERAREEAERELERWQLKANEALRLRLQAEEVAQQKSLAQAEAEKQKEEAEREARRRGKAEEQAVRQRELAEQELEKQRQLAEGTAQQRLAAEQELIRLRAETEQGEQQRQLLEEELARLQREAAAATQKRQELEAELAKVRAEMEVLLASKARAEEESRSTSEKSKQRLEAEAGRFRELAEEAARLRALAEEAKRQRQLAEEDAARQRAEAERVLAEKLAAIGEATRLKTEAEIALKEKEAENERLRRLAEDEAFQRRRLEEQAAQHKADIEERLAQLRKASDSELERQKGLVEDTLRQRRQVEEEILALKASFEKAAAGKAELELELGRIRSNAEDTLRSKEQAELEAARQRQLAAEEERRRREAEERVQKSLAAEEEAARQRKAALEEVERLKAKVEEARRLRERAEQESARQLQLAQEAAQKRLQAEEKAHAFAVQQKEQELQQTLQQEQSVLDQLRGEAEAARRAAEEAEEARVQAEREAAQSRRQVEEAERLKQSAEEQAQARAQAQAAAEKLRKEAEQEAARRAQAEQAALRQKQAADAEMEKHKKFAEQTLRQKAQVEQELTTLRLQLEETDHQKNLLDEELQRLKAEATEAARQRSQVEEELFSVRVQMEELSKLKARIEAENRALILRDKDNTQRFLQEEAEKMKQVAEEAARLSVAAQEAARLRQLAEEDLAQQRALAEKMLKEKMQAVQEATRLKAEAELLQQQKELAQEQARRLQEDKEQMAQQLAEETQGFQRTLEAERQRQLEMSAEAERLKLRVAEMSRAQARAEEDAQRFRKQAEEIGEKLHRTELATQEKVTLVQTLEIQRQQSDHDAERLREAIAELEREKEKLQQEAKLLQLKSEEMQTVQQEQLLQETQALQQSFLSEKDSLLQRERFIEQEKAKLEQLFQDEVAKAQQLREEQQRQQQQMEQERQRLVASMEEARRRQHEAEEGVRRKQEELQQLEQQRRQQEELLAEENQRLREQLQLLEEQHRAALAHSEEVTASQVAATKTLPNGRDALDGPAAEAEPEHSFDGLRRKVSAQRLQEAGILSAEELQRLAQGHTTVDELARREDVRHYLQGRSSIAGLLLKATNEKLSVYAALQRQLLSPGTALILLEAQAASGFLLDPVRNRRLTVNEAVKEGVVGPELHHKLLSAERAVTGYKDPYTGQQISLFQAMQKGLIVREHGIRLLEAQIATGGVIDPVHSHRVPVDVAYRRGYFDEEMNRVLADPSDDTKGFFDPNTHENLTYLQLLERCVEDPETGLCLLPLTDKAAKGGELVYTDSEARDVFEKATVSAPFGKFQGKTVTIWEIINSEYFTAEQRRDLLRQFRTGRITVEKIIKIIITVVEEQEQKGRLCFEGLRSLVPAAELLESRVIDRELYQQLQRGERSVRDVAEVDTVRRALRGANVIAGVWLEEAGQKLSIYNALKKDLLPSDMAVALLEAQAGTGHIIDPATSARLTVDEAVRAGLVGPEFHEKLLSAEKAVTGYRDPYTGQSVSLFQALKKGLIPREQGLRLLDAQLSTGGIVDPSKSHRVPLDVACARGCLDEETSRALSAPRADAKAYSDPSTGEPATYGELQQRCRPDQLTGLSLLPLSEKAARARQEELYSELQARETFEKTPVEVPVGGFKGRTVTVWELISSEYFTAEQRQELLRQFRTGKVTVEKVIKILITIVEEVETLRQERLSFSGLRAPVPASELLASGVLSRAQFEQLKDGKTTVKDLSELGSVRTLLQGSGCLAGIYLEDTKEKVSIYEAMRRGLLRATTAALLLEAQAATGFLVDPVRNQRLYVHEAVKAGVVGPELHEQLLSAEKAVTGYRDPYSGSTISLFQAMQKGLVLRQHGIRLLEAQIATGGIIDPVHSHRVPVDVAYQRGYFSEEMNRVLADPSDDTKGFFDPNTHENLTYRQLLERCVEDPETGLRLLPLKGAEKAEVVETTQVYTEEETRRAFEETQIDIPGGGSHGGSTMSLWEVMQSDLIPEEQRAQLMADFQAGRVTKERMIIIIIEIIEKTEIIRQQGLASYDYVRRRLTAEDLFEARIISLETYNLLREGTRSLREALEAESAWCYLYGTGSVAGVYLPGSRQTLSIYQALKKGLLSAEVARLLLEAQAATGFLLDPVKGERLTVDEAVRKGLVGPELHDRLLSAERAVTGYRDPYTEQTISLFQAMKKELIPTEEALRLLDAQLATGGIVDPRLGFHLPLEVAYQRGYLNKDTHDQLSEPSEVRSYVDPSTDERLSYTQLLRRCRRDDGTGQLLLPLSDARKLTFRGLRKQITMEELVRSQVMDEATALQLREGLTSIEEVTKNLQKFLEGTSCIAGVFVDATKERLSVYQAMKKGIIRPGTAFELLEAQAATGYVIDPIKGLKLTVEEAVRMGIVGPEFKDKLLSAERAVTGYKDPYSGKLISLFQAMKKGLILKDHGIRLLEAQIATGGIIDPEESHRLPVEVAYKRGLFDEEMNEILTDPSDDTKGFFDPNTEENLTYLQLMERCITDPQTGLCLLPLKEKKRERKTSSKSSVRKRRVVIVDPETGKEMSVYEAYRKGLIDHQTYLELSEQECEWEEITISSSDGVVKSMIIDRRSGRQYDIDDAIAKNLIDRSALDQYRAGTLSITEFADMLSGNAGGFRSRSSSVGSSSSYPISPAVSRTQLASWSDPTEETGPVAGILDTETLEKVSITEAMHRNLVDNITGQRLLEAQACTGGIIDPSTGERFPVTDAVNKGLVDKIMVDRINLAQKAFCGFEDPRTKTKMSAAQALKKGWLYYEAGQRFLEVQYLTGGLIEPDTPGRVPLDEALQRGTVDARTAQKLRDVGAYSKYLTCPKTKLKISYKDALDRSMVEEGTGLRLLEAAAQSTKGYYSPYSVSGSGSTAGSRTGSRTGSRAGSRRGSFDATGSGFSMTFSSSSYSSSGYGRRYASGSSASLGGPESAVA,mutated_sequence,1.0,4547.0,NP_958786.1.a2m,NP_958786.1.npy,ClinVar +NP_976035.1,NP_976035.1.csv,MAASQAVEEMRSRVVLGEFGVRNVHTTDFPGNYSGYDDAWDQDRFEKNFRVDVVHMDENSLEFDMVGIDAAIANAFRRILLAEVPTMAVEKVLVYNNTSIVQDEILAHRLGLIPIHADPRLFEYRNQGDEEGTEIDTLQFRLQVRCTRNPHAAKDSSDPNELYVNHKVYTRHMTWIPLGNQADLFPEGTIRPVHDDILIAQLRPGQEIDLLMHCVKGIGKDHAKFSPVATASYRLLPDITLLEPVEGEAAEELSRCFSPGVIEVQEVQGKKVARVANPRLDTFSREIFRNEKLKKVVRLARVRDHYIFSVESTGVLPPDVLVSEAIKVLMGKCRRFLDELDAVQMD,mutated_sequence,1.0,346.0,NP_976035.1.a2m,NP_976035.1.npy,ClinVar +NP_981932.1,NP_981932.1.csv,MYFLTPILVAILCILVVWIFKNADRSMEKKKGEPRTRAEARPWVDEDLKDSSDLHQAEEDADEWQESEENVEHIPFSHNHYPEKEMVKRSQEFYELLNKRRSVRFISNEQVPMEVIDNVIRTAGTAPSGAHTEPWTFVVVKDPDVKHKIRKIIEEEEEINYMKRMGHRWVTDLKKLRTNWIKEYLDTAPILILIFKQVHGFAANGKKKVHYYNEISVSIACGILLAALQNAGLVTVTTTPLNCGPRLRVLLGRPAHEKLLMLLPVGYPSKEATVPDLKRKPLDQIMVTV,mutated_sequence,1.0,289.0,NP_981932.1.a2m,NP_981932.1.npy,ClinVar +NP_982301.1,NP_982301.1.csv,MATFSRQEFFQQLLQGCLLPTAQQGLDQIWLLLAICLACRLLWRLGLPSYLKHASTVAGGFFSLYHFFQLHMVWVVLLSLLCYLVLFLCRHSSHRGVFLSVTILIYLLMGEMHMVDTVTWHKMRGAQMIVAMKAVSLGFDLDRGEVGTVPSPVEFMGYLYFVGTIVFGPWISFHSYLQAVQGRPLSCRWLQKVARSLALALLCLVLSTCVGPYLFPYFIPLNGDRLLRNKKRKARGTMVRWLRAYESAVSFHFSNYFVGFLSEATATLAGAGFTEEKDHLEWDLTVSKPLNVELPRSMVEVVTSWNLPMSYWLNNYVFKNALRLGTFSAVLVTYAASALLHGFSFHLAAVLLSLAFITYVEHVLRKRLARILSACVLSKRCPPDCSHQHRLGLGVRALNLLFGALAIFHLAYLGSLFDVDVDDTTEEQGYGMAYTVHKWSELSWASHWVTFGCWIFYRLIG,mutated_sequence,1.0,461.0,NP_982301.1.a2m,NP_982301.1.npy,ClinVar +NP_991331.1,NP_991331.1.csv,MCPKGYEDSMEFPDHSRHLLQCLSEQRHQGFLCDCTVLVGDAQFRAHRAVLASCSMYFHLFYKDQLDKRDIVHLNSDIVTAPAFALLLEFMYEGKLQFKDLPIEDVLAAASYLHMYDIVKVCKKKLKEKATTEADSTKKEEDASSCSDKVESLSDGSSHIAGDLPSDEDEGEDEKLNILPSKRDLAAEPGNMWMRLPSDSAGIPQAGGEAEPHATAAGKTVASPCSSTESLSQRSVTSVRDSADVDCVLDLSVKSSLSGVENLNSSYFSSQDVLRSNLVQVKVEKEASCDESDVGTNDYDMEHSTVKESVSTNNRVQYEPAHLAPLREDSVLRELDREDKASDDEMMTPESERVQVEGGMESSLLPYVSNILSPAGQIFMCPLCNKVFPSPHILQIHLSTHFREQDGIRSKPAADVNVPTCSLCGKTFSCMYTLKRHERTHSGEKPYTCTQCGKSFQYSHNLSRHAVVHTREKPHACKWCERRFTQSGDLYRHIRKFHCELVNSLSVKSEALSLPTVRDWTLEDSSQELWK,mutated_sequence,1.0,531.0,NP_991331.1.a2m,NP_991331.1.npy,ClinVar +NP_996816.3,NP_996816.3.csv,MNCPVLSLGSGFLFQVIEMLIFAYFASISLTESRGLFPRLENVGAFKKVSIVPTQAVCGLPDRSTFCHSSAAAESIQFCTQRFCIQDCPYRSSHPTYTALFSAGLSSCITPDKNDLHPNAHSNSASFIFGNHKSCFSSPPSPKLMASFTLAVWLKPEQQGVMCVIEKTVDGQIVFKLTISEKETMFYYRTVNGLQPPIKVMTLGRILVKKWIHLSVQVHQTKISFFINGVEKDHTPFNARTLSGSITDFASGTVQIGQSLNGLEQFVGRMQDFRLYQVALTNREILEVFSGDLLRLHAQSHCRCPGSHPRVHPLAQRYCIPNDAGDTADNRVSRLNPEAHPLSFVNDNDVGTSWVSNVFTNITQLNQGVTISVDLENGQYQVFYIIIQFFSPQPTEIRIQRKKENSLDWEDWQYFARNCGAFGMKNNGDLEKPDSVNCLQLSNFTPYSRGNVTFSILTPGPNYRPGYNNFYNTPSLQEFVKATQIRFHFHGQYYTTETAVNLRHRYYAVDEITISGRCQCHGHADNCDTTSQPYRCLCSQESFTEGLHCDRCLPLYNDKPFRQGDQVYAFNCKPCQCNSHSKSCHYNISVDPFPFEHFRGGGGVCDDCEHNTTGRNCELCKDYFFRQVGADPSAIDVCKPCDCDTVGTRNGSILCDQIGGQCNCKRHVSGRQCNQCQNGFYNLQELDPDGCSPCNCNTSGTVDGDITCHQNSGQCKCKANVIGLRCDHCNFGFKFLRSFNDVGCEPCQCNLHGSVNKFCNPHSGQCECKKEAKGLQCDTCRENFYGLDVTNCKACDCDTAGSLPGTVCNAKTGQCICKPNVEGRQCNKCLEGNFYLRQNNSFLCLPCNCDKTGTINGSLLCNKSTGQCPCKLGVTGLRCNQCEPHRYNLTIDNFQHCQMCECDSLGTLPGTICDPISGQCLCVPNRQGRRCNQCQPGFYISPGNATGCLPCSCHTTGAVNHICNSLTGQCVCQDASIAGQRCDQCKDHYFGFDPQTGRCQPCNCHLSGALNETCHLVTGQCFCKQFVTGSKCDACVPSASHLDVNNLLGCSKTPFQQPPPRGQVQSSSAINLSWSPPDSPNAHWLTYSLLRDGFEIYTTEDQYPYSIQYFLDTDLLPYTKYSYYIETTNVHGSTRSVAVTYKTKPGVPEGNLTLSYIIPIGSDSVTLTWTTLSNQSGPIEKYILSCAPLAGGQPCVSYEGHETSATIWNLVPFAKYDFSVQACTSGGCLHSLPITVTTAQAPPQRLSPPKMQKISSTELHVEWSPPAELNGIIIRYELYMRRLRSTKETTSEESRVFQSSGWLSPHSFVESANENALKPPQTMTTITGLEPYTKYEFRVLAVNMAGSVSSAWVSERTGESAPVFMIPPSVFPLSSYSLNISWEKPADNVTRGKVVGYDINMLSEQSPQQSIPMAFSQLLHTAKSQELSYTVEGLKPYRIYEFTITLCNSVGCVTSASGAGQTLAAAPAQLRPPLVKGINSTTIHLRWFPPEELNGPSPIYQLERRESSLPALMTTMMKGIRFIGNGYCKFPSSTHPVNTDFTGIKASFRTKVPEGLIVFAASPGNQEEYFALQLKKGRLYFLFDPQGSPVEVTTTNDHGKQYSDGKWHEIIAIRHQAFGQITLDGIYTGSSAILNGSTVIGDNTGVFLGGLPRSYTILRKDPEIIQKGFVGCLKDVHFMKNYNPSAIWEPLDWQSSEEQINVYNSWEGCPASLNEGAQFLGAGFLELHPYMFHGGMNFEISFKFRTDQLNGLLLFVYNKDGPDFLAMELKSGILTFRLNTSLAFTQVDLLLGLSYCNGKWNKVIIKKEGSFISASVNGLMKHASESGDQPLVVNSPVYVGGIPQELLNSYQHLCLEQGFGGCMKDVKFTRGAVVNLASVSSGAVRVNLDGCLSTDSAVNCRGNDSILVYQGKEQSVYEGGLQPFTEYLYRVIASHEGGSVYSDWSRGRTTGAAPQSVPTPSRVRSLNGYSIEVTWDEPVVRGVIEKYILKAYSEDSTRPPRMPSASAEFVNTSNLTGILTGLLPFKNYAVTLTACTLAGCTESSHALNISTPQEAPQEVQPPVAKSLPSSLLLSWNPPKKANGIITQYCLYMDGRLIYSGSEENYIVTDLAVFTPHQFLLSACTHVGCTNSSWVLLYTAQLPPEHVDSPVLTVLDSRTIHIQWKQPRKISGILERYVLYMSNHTHDFTIWSVIYNSTELFQDHMLQYVLPGNKYLIKLGACTGGGCTVSEASEALTDEDIPEGVPAPKAHSYSPDSFNVSWTEPEYPNGVITSYGLYLDGILIHNSSELSYRAYGFAPWSLHSFRVQACTAKGCALGPLVENRTLEAPPEGTVNVFVKTQGSRKAHVRWEAPFRPNGLLTHSVLFTGIFYVDPVGNNYTLLNVTKVMYSGEETNLWVLIDGLVPFTNYTVQVNISNSQGSLITDPITIAMPPGAPDGVLPPRLSSATPTSLQVVWSTPARNNAPGSPRYQLQMRSGDSTHGFLELFSNPSASLSYEVSDLQPYTEYMFRLVASNGFGSAHSSWIPFMTAEDKPGPVVPPILLDVKSRMMLVTWQHPRKSNGVITHYNIYLHGRLYLRTPGNVTNCTVMHLHPYTAYKFQVEACTSKGCSLSPESQTVWTLPGAPEGIPSPELFSDTPTSVIISWQPPTHPNGLVENFTIERRVKGKEEVTTLVTLPRSHSMRFIDKTSALSPWTKYEYRVLMSTLHGGTNSSAWVEVTTRPSRPAGVQPPVVTVLEPDAVQVTWKPPLIQNGDILSYEIHMPDPHITLTNVTSAVLSQKVTHLIPFTNYSVTIVACSGGNGYLGGCTESLPTYVTTHPTVPQNVGPLSVIPLSESYVVISWQPPSKPNGPNLRYELLRRKIQQPLASNPPEDLNRWHNIYSGTQWLYEDKGLSRFTTYEYMLFVHNSVGFTPSREVTVTTLAGLPERGANLTASVLNHTAIDVRWAKPTVQDLQGEVEYYTLFWSSATSNDSLKILPDVNSHVIGHLKPNTEYWIFISVFNGVHSINSAGLHATTCDGEPQGMLPPEVVIINSTAVRVIWTSPSNPNGVVTEYSIYVNNKLYKTGMNVPGSFILRDLSPFTIYDIQVEVCTIYACVKSNGTQITTVEDTPSDIPTPTIRGITSRSLQIDWVSPRKPNGIILGYDLLWKTWYPCAKTQKLVQDQSDELCKAVRCQKPESICGHICYSSEAKVCCNGVLYNPKPGHRCCEEKYIPFVLNSTGVCCGGRIQEAQPNHQCCSGYYARILPGEVCCPDEQHNRVSVGIGDSCCGRMPYSTSGNQICCAGRLHDGHGQKCCGRQIVSNDLECCGGEEGVVYNRLPGMFCCGQDYVNMSDTICCSASSGESKAHIKKNDPVPVKCCETELIPKSQKCCNGVGYNPLKYVCSDKISTGMMMKETKECRILCPASMEATEHCGRCDFNFTSHICTVIRGSHNSTGKASIEEMCSSAEETIHTGSVNTYSYTDVNLKPYMTYEYRISAWNSYGRGLSKAVRARTKEDVPQGVSPPTWTKIDNLEDTIVLNWRKPIQSNGPIIYYILLRNGIERFRGTSLSFSDKEGIQPFQEYSYQLKACTVAGCATSSKVVAATTQGVPESILPPSITALSAVALHLSWSVPEKSNGVIKEYQIRQVGKGLIHTDTTDRRQHTVTGLQPYTNYSFTLTACTSAGCTSSEPFLGQTLQAAPEGVWVTPRHIIINSTTVELYWSLPEKPNGLVSQYQLSRNGNLLFLGGSEEQNFTDKNLEPNSRYTYKLEVKTGGGSSASDDYIVQTPMSTPEEIYPPYNITVIGPYSIFVAWIPPGILIPEIPVEYNVLLNDGSVTPLAFSVGHHQSTLLENLTPFTQYEIRIQACQNGSCGVSSRMFVKTPEAAPMDLNSPVLKALGSACIEIKWMPPEKPNGIIINYFIYRRPAGIEEESVLFVWSEGALEFMDEGDTLRPFTLYEYRVRACNSKGSVESLWSLTQTLEAPPQDFPAPWAQATSAHSVLLNWTKPESPNGIISHYRVVYQERPDDPTFNSPTVHAFTVKGTSHQAHLYGLEPFTTYRIGVVAANHAGEILSPWTLIQTLESSPSGLRNFIVEQKENGRALLLQWSEPMRTNGVIKTYNIFSDGFLEYSGLNRQFLFRRLDPFTLYTLTLEACTRAGCAHSAPQPLWTDEAPPDSQLAPTVHSVKSTSVELSWSEPVNPNGKIIRYEVIRRCFEGKAWGNQTIQADEKIVFTEYNTERNTFMYNDTGLQPWTQCEYKIYTWNSAGHTCSSWNVVRTLQAPPEGLSPPVISYVSMNPQKLLISWIPPEQSNGIIQSYRLQRNEMLYPFSFDPVTFNYTDEELLPFSTYSYALQACTSGGCSTSKPTSITTLEAAPSEVSPPDLWAVSATQMNVCWSPPTVQNGKITKYLVRYDNKESLAGQGLCLLVSHLQPYSQYNFSLVACTNGGCTASVSKSAWTMEALPENMDSPTLQVTGSESIEITWKPPRNPNGQIRSYELRRDGTIVYTGLETRYRDFTLTPGVEYSYTVTASNSQGGILSPLVKDRTSPSAPSGMEPPKLQARGPQEILVNWDPPVRTNGDIINYTLFIRELFERETKIIHINTTHNSFGMQSYIVNQLKPFHRYEIRIQACTTLGCASSDWTFIQTPEIAPLMQPPPHLEVQMAPGGFQPTVSLLWTGPLQPNGKVLYYELYRRQIATQPRKSNPVLIYNGSSTSFIDSELLPFTEYEYQVWAVNSAGKAPSSWTWCRTGPAPPEGLRAPTFHVISSTQAVVNISAPGKPNGIVSLYRLFSSSAHGAETVLSEGMATQQTLHGLQAFTNYSIGVEACTCFNCCSKGPTAELRTHPAPPSGLSSPQIGTLASRTASFRWSPPMFPNGVIHSYELQFHVACPPDSALPCTPSQIETKYTGLGQKASLGGLQPYTTYKLRVVAHNEVGSTASEWISFTTQKELPQYRAPFSVDSNLSVVCVNWSDTFLLNGQLKEYVLTDGGRRVYSGLDTTLYIPRTADKTFFFQVICTTDEGSVKTPLIQYDTSTGLGLVLTTPGKKKGSRSKSTEFYSELWFIVLMAMLGLILLAIFLSLILQRKIHKEPYIRERPPLVPLQKRMSPLNVYPPGENHMGLADTKIPRSGTPVSIRSNRSACVLRIPSQNQTSLTYSQGSLHRSVSQLMDIQDKKVLMDNSLWEAIMGHNSGLYVDEEDLMNAIKDFSSVTKERTTFTDTHL,mutated_sequence,1.0,5202.0,NP_996816.3.a2m,NP_996816.3.npy,ClinVar +NP_996820.1,NP_996820.1.csv,MAASQTSQTVASHVPFADLCSTLERIQKSKGRAEKIRHFREFLDSWRKFHDALHKNHKDVTDSFYPAMRLILPQLERERMAYGIKETMLAKLYIELLNLPRDGKDALKLLNYRTPTGTHGDAGDFAMIAYFVLKPRCLQKGSLTIQQVNDLLDSIASNNSAKRKDLIKKSLLQLITQSSALEQKWLIRMIIKDLKLGVSQQTIFSVFHNDAAELHNVTTDLEKVCRQLHDPSVGLSDISITLFSAFKPMLAAIADIEHIEKDMKHQSFYIETKLDGERMQMHKDGDVYKYFSRNGYNYTDQFGASPTEGSLTPFIHNAFKADIQICILDGEMMAYNPNTQTFMQKGTKFDIKRMVEDSDLQTCYCVFDVLMVNNKKLGHETLRKRYEILSSIFTPIPGRIEIVQKTQAHTKNEVIDALNEAIDKREEGIMVKQPLSIYKPDKRGEGWLKIKPEYVSGLMDELDILIVGGYWGKGSRGGMMSHFLCAVAEKPPPGEKPSVFHTLSRVGSGCTMKELYDLGLKLAKYWKPFHRKAPPSSILCGTEKPEVYIEPCNSVIVQIKAAEIVPSDMYKTGCTLRFPRIEKIRDDKEWHECMTLDDLEQLRGKASGKLASKHLYIGGDDEPQEKKRKAAPKMKKVIGIIEHLKAPNLTNVNKISNIFEDVEFCVMSGTDSQPKPDLENRIAEFGGYIVQNPGPDTYCVIAGSENIRVKNIILSNKHDVVKPAWLLECFKTKSFVPWQPRFMIHMCPSTKEHFAREYDCYGDSYFIDTDLNQLKEVFSGIKNSNEQTPEEMASLIADLEYRYSWDCSPLSMFRRHTVYLDSYAVINDLSTKNEGTRLAIKALELRFHGAKVVSCLAEGVSHVIIGEDHSRVADFKAFRRTFKRKFKILKESWVTDSIDKCELQEENQYLI,mutated_sequence,1.0,911.0,NP_996820.1.a2m,NP_996820.1.npy,ClinVar +NP_997244.4,NP_997244.4.csv,MHSAGTPGLSSRRTGNSTSFQPGPPPPPRLLLLLLLLLSLVSRVPAQPAAFGRALLSPGLAGAAGVPAEEAIVLANRGLRVPFGREVWLDPLHDLVLQVQPGDRCAVSVLDNDALAQRPGRLSPKRFPCDFGPGEVRYSHLGARSPSRDRVRLQLRYDAPGGAVVLPLVLEVEVVFTQLEVVTRNLPLVVEELLGTSNALDARSLEFAFQPETEECRVGILSGLGALPRYGELLHYPQVPGGAREGGAPETLLMDCKAFQELGVRYRHTAASRSPNRDWIPMVVELRSRGAPVGSPALKREHFQVLVRIRGGAENTAPKPSFVAMMMMEVDQFVLTALTPDMLAAEDAESPSDLLIFNLTSPFQPGQGYLVSTDDRSLPLSSFTQRDLRLLKIAYQPPSEDSDQERLFELELEVVDLEGAASDPFAFMVVVKPMNTMAPVVTRNTGLILYEGQSRPLTGPAGSGPQNLVISDEDDLEAVRLEVVAGLRHGHLVILGASSGSSAPKSFTVAELAAGQVVYQHDDRDGSLSDNLVLRMVDGGGRHQVQFLFPITLVPVDDQPPVLNANTGLTLAEGETVPILPLSLSATDMDSDDSLLLFVLESPFLTTGHLLLRQTHPPHEKQELLRGLWRKEGAFYERTVTEWQQQDITEGRLFYRHSGPHSPGPVTDQFTFRVQDNHDPPNQSGLQRFVIRIHPVDRLPPELGSGCPLRMVVQESQLTPLRKKWLRYTDLDTDDRELRYTVTQPPTDTDENHLPAPLGTLVLTDNPSVVVTHFTQAQINHHKIAYRPPGQELGVATRVAQFQFQVEDRAGNVAPGTFTLYLHPVDNQPPEILNTGFTIQEKGHHILSETELHVNDVDTDVAHISFTLTQAPKHGHMRVSGQILHVGGLFHLEDIKQGRVSYAHNGDKSLTDSCSLEVSDRHHVVPITLRVNVRPVDDEVPILSHPTGTLESYLDVLENGATEITANVIKGTNEETDDLMLTFLLEDPPLYGEILVNGIPAEQFTQRDILEGSVVYTHTSGEIGLLPKADSFNLSLSDMSQEWRIGGNTIQGVTIWVTILPVDSQAPEIFVGEQLIVMEGDKSVITSVHISAEDVDSLNDDILCTIVIQPTSGYVENISPAPGSEKSRAGIAISAFNLKDLRQGHINYVQSVHKGVEPVEDRFVFRCSDGINFSERQFFPIVIIPTNDEQPEMFMREFMVMEGMSLVIDTPILNAADADVPLDDLTFTITQFPTHGHIMNQLINGTVLVESFTLDQIIESSSIIYEHDDSETQEDSFVIKLTDGKHSVEKTVLIIVIPVDDETPRMTINNGLEIEIGDTKIINNKILMATDLDSEDKSLVYIIRYGPGHGLLQRRKPTGAFENITLGMNFTQDEVDRNLIQYVHLGQEGIRDLIKFDVTDGINPLIDRYFYVSIGSIDIVFPDVISKGVSLKEGGKVTLTTDLLSTSDLNSPDENLVFTITRAPMRGHLECTDQPGVSITSFTQLQLAGNKIYYIHTADDEVKMDSFEFQVTDGRNPVFRTFRISISDVDNKKPVVTIHKLVVSESENKLITPFELTVEDRDTPDKLLKFTITQVPIHGHLLFNNTRPVMVFTKQDLNENLISYKHDGTESSEDSFSFTVTDGTHTDFYVFPDTVFETRRPQVMKIQVLAVDNSVPQIAVNKGASTLRTLATGHLGFMITSKILKVEDRDSLHISLRFIVTEAPQHGYLLNLDKGNHSITQFTQADIDDMKICYVLREGANATSDMFYFAVEDGGGNKLTYQNFRLNWAWISFEKEYYLVNEDSKFLDVVLKRRGYLGETSFISIGTRDRTAEKDKDFKGKAQKQVQFNPGQTRATWRVRILSDGEHEQSETFQVVLSEPVLAALEFPTVATVEIVDPGDEPTVFIPQSKYSVEEDVGELFIPIRRSGDVSQELMVVCYTQQGTATGTVPTSVLSYSDYISRPEDHTSVVRFDKDEREKLCRIVIIDDSLYEEEETFHVLLSMPMGGRIGSEFPGAQVTIVPDKDDEPIFYFGDVEYSVDESAGYVEVQVWRTGTDLSKSSSVTVRSRKTDPPSADAGTDYVGISRNLDFAPGVNMQPVRVVILDDLGQPALEGIEKFELVLRMPMNAALGEPSKATVSINDSVSDLPKMQFKERIYTGSESDGQIVTMIHRTGDVQYRSSVRCYTRQGSAQVMMDFEERPNTDTSIITFLPGETEKPCILELMDDVLYEEVEELRLVLGTPQSNSPFGAAVGEQNETLIRIRDDADKTVIKFGETKFSVTEPKEPGESVVIRIPVIRQGDTSKVSIVRVHTKDGSATSGEDYHPVSEEIEFKEGETQHVVEIEVTFDGVREMREAFTVHLKPDENMIAEMQLTKAIVYIEEMSSMADVTFPSVPQIVSLLMYDDTSKAKESAEPMSGYPVICITACNPKYSDYDKTGSICASENINDTLTRYRWLISAPAGPDGVTSPMREVDFDTFFTSSKMVTLDSIYFQPGSRVQCAARAVNTNGDEGLELMSPIVTISREEGLCQPRVPGVVGAEPFSAKLRYTGPEDADYTNLIKLTVTMPHIDGMLPVISTRELSNFELTLSPDGTRVGNHKCSNLLDYTEVKTHYGFLTDATKNPEIIGETYPYQYSLSIRGSTTLRFYRNLNLEACLWEFVSYYDMSELLADCGGTIGTDGQVLNLVQSYVTLRVPLYVSYVFHSPVGVGGWQHFDLKSELRLTFVYDTAILWNDGIGSPPEAELQGSLYPTSMRIGDEGRLAVHFKTEAQFHGLFVLSHPASFTSSVIMSADHPGLTFSLRLIRSEPTYNQPVQQWSFVSDFAVRDYSGTYTVKLVPCTAPSHQEYRLPVTCNPREPVTFDLDIRFQQVSDPVAAEFSLNTQMYLLSKKSLWLSDGSMGFGQESDVAFAEGDIIYGRVMVDPVQNLGDSFYCSIEKVFLCTGADGYVPKYSPMNAEYGCLADSPSLLYRFKIVDKAQPETQATSFGNVLFNAKLAVDDPEAILLVNQPGSDGFKVDSTPLFQVALGREWYIHTIYTVRSKDNANRGIGKRSVEYHSLVSQGKPQSTTKSRKKREIRSTPSLAWEIGAENSRGTNIQHIALDRTKRQIPHGRAPPDGILPWELNSPSSAVSLVTVVGGTTVGLLTICLTVIAVLMCRGKESFRGKDAPKGSSSSEPMVPPQSHHNDSSEV,mutated_sequence,1.0,3169.0,NP_997244.4.a2m,NP_997244.4.npy,ClinVar +NP_997717.2,NP_997717.2.csv,MAAWSPAAAAPLLRGIRGLPLHHRMFATQTEGELRVTQILKEKFPRATAIKVTDISGGCGAMYEIKIESEEFKEKRTVQQHQMVNQALKEEIKEMHGLRIFTSVPKR,mutated_sequence,1.0,107.0,NP_997717.2.a2m,NP_997717.2.npy,ClinVar +UPI0000125CAE,UPI0000125CAE.csv,MAATGTAAAAATGRLLLLLLVGLTAPALALAGYIEALAANAGTGFAVAEPQIAMFCGKLNMHVNIQTGKWEPDPTGTKSCFETKEEVLQYCQEMYPELQITNVMEANQRVSIDNWCRRDKKQCKSRFVTPFKCLVGEFVSDVLLVPEKCQFFHKERMEVCENHQHWHTVVKEACLTQGMTLYSYGMLLPCGVDQFHGTEYVCCPQTKIIGSVSKEEEEEDEEEEEEEDEEEDYDVYKSEFPTEADLEDFTEAAVDEDDEDEEEGEEVVEDRDYYYDTFKGDDYNEENPTEPGSDGTMSDKEITHDVKAVCSQEAMTGPCRAVMPRWYFDLSKGKCVRFIYGGCGGNRNNFESEDYCMAVCKAMIPPTPLPTNDVDVYFETSADDNEHARFQKAKEQLEIRHRNRMDRVKKEWEEAELQAKNLPKAERQTLIQHFQAMVKALEKEAASEKQQLVETHLARVEAMLNDRRRMALENYLAALQSDPPRPHRILQALRRYVRAENKDRLHTIRHYQHVLAVDPEKAAQMKSQVMTHLHVIEERRNQSLSLLYKVPYVAQEIQEEIDELLQEQRADMDQFTASISETPVDVRVSSEESEEIPPFHPFHPFPALPENEDTQPELYHPMKKGSGVGEQDGGLIGAEEKVINSKNKVDENMVIDETLDVKEMIFNAERVGGLEEERESVGPLREDFSLSSSALIGLLVIAVAIATVIVISLVMLRKRQYGTISHGIVEVDPMLTPEERHLNKMQNHGYENPTYKYLEQMQI,mutated_sequence,1.0,763.0,UPI0000125CAE.a2m,UPI0000125CAE.npy,gnomAD +UPI000013C672,UPI000013C672.csv,MAKKVAVIGAGVSGLISLKCCVDEGLEPTCFERTEDIGGVWRFKENVEDGRASIYQSVVTNTSKEMSCFSDFPMPEDFPNFLHNSKLLEYFRIFAKKFDLLKYIQFQTTVLSVRKCPDFSSSGQWKVVTQSNGKEQSAVFDAVMVCSGHHILPHIPLKSFPGMERFKGQYFHSRQYKHPDGFEGKRILVIGMGNSGSDIAVELSKNAAQVFISTRHGTWVMSRISEDGYPWDSVFHTRFRSMLRNVLPRTAVKWMIEQQMNRWFNHENYGLEPQNKYIMKEPVLNDDVPSRLLCGAIKVKSTVKELTETSAIFEDGTVEENIDVIIFATGYSFSFPFLEDSLVKVENNMVSLYKYIFPAHLDKSTLACIGLIQPLGSIFPTAELQARWVTRVFKGLCSLPSERTMMMDIIKRNEKRIDLFGESQSQTLQTNYVDYLDELALEIGAKPDFCSLLFKDPKLAVRLYFGPCNSY,mutated_sequence,1.0,471.0,UPI000013C672.a2m,UPI000013C672.npy,gnomAD +UPI000035AA82,UPI000035AA82.csv,MLRYLLKTLLQMNLFADSLAGDISNSSELLLGFNSSLAALNHTLLPPGDPSLNGSRVGPEDAMPRIVEQPPDLLVSRGEPATLPCRAEGRPRPNIEWYKNGARVATVREDPRAHRLLLPSGALFFPRIVHGRRARPDEGVYTCVARNYLGAAASRNASLEVAVLRDDFRQSPGNVVVAVGEPAVLECVPPRGHPEPSVSWRKDGARLKEEEGRITIRGGKLMMSHTLKSDAGMYVCVASNMAGERESAAAEVMVLERPSFLRRPVNQVVLADAPVTFLCEVKGDPPPRLRWRKEDGELPTGRYEIRSDHSLWIGHVSAEDEGTYTCVAENSVGRAEASGSLSVHVPPQLVTQPQDQMAAPGESVAFQCETKGNPPPAIFWQKEGSQVLLFPSQSLQPTGRFSVSPRGQLNITAVQRGDAGYYVCQAVSVAGSILAKALLEIKGASLDGLPPVILQGPANQTLVLGSSVWLPCRVTGNPQPSVRWKKDGQWLQGDDLQFKTMANGTLYIANVQEMDMGFYSCVAKSSTGEATWSGWLKMREDWGVSPDPPTEPSSPPGAPSQPVVTEITKNSITLTWKPNPQTGAAVTSYVIEAFSPAAGNTWRTVADGVQLETHTVSGLQPNTIYLFLVRAVGAWGLSEPSPVSEPVRTQDSSPSRPVEDPWRGQQGLAEVAVRLQEPIVLGPRTLQVSWTVDGPVQLVQGFRVSWRVAGPEGGSWTMLDLQSPSQQSTVLRGLPPGTQIQIKVQAQGQEGLGAESLSVTRSIPEEAPSGPPQGVAVALGGDGNSSITVSWEPPLPSQQNGVITEYQIWCLGNESRFHLNRSAAGWARSAMLRGLVPGLLYRTLVAAATSAGVGVPSAPVLVQLPSPPDLEPGLEVGAGLAVRLARVLREPAFLAGSGAACGALLLGLCAALYWRRKQRKELSHYTASFAYTPAVSFPHSEGLSGASSRPPMGLGPAPYSWLADSWPHPSRSPSAQEPRGSCCPSNPDPDDRYYNEAGISLYLAQTARGTAAPGEGPVYSTIDPAGEELQTFHGGFPQHPSGDLGPWSQYAPPEWSQGDSGAKGGKVKLLGKPVQMPSLNWPEALPPPPPSCELSCLEGPEEELEGSSEPEEWCPPMPERSHLTEPSSSGGCLVTPSRRETPSPTPSYGQQSTATLTPSPPDPPQPPTDMPHLHQMPRRVPLGPSSPLSVSQPMLGIREARPAGLGAGPAASPHLSPSPAPSTASSAPGRTWQGNGEMTPPLQGPRARFRKKPKALPYRRENSPGDLPPPPLPPPEEEASWALELRAAGSMSSLERERSGERKAVQAVPLAAQRVLHPDEEAWLPYSRPSFLSRGQGTSTCSTAGSNSSRGSSSSRGSRGPGRSRSRSQSRSQSQRPGQKRREEPR,mutated_sequence,1.0,1386.0,UPI000035AA82.a2m,UPI000035AA82.npy,gnomAD +UPI00001D7B55,UPI00001D7B55.csv,MNYPGRGSPRSPEHNGRGGGGGAWELGSDARPAFGGGVCCFEHLPGGDPDDGDVPLALLRGEPGLHLAPGTDDHNHHLALDPCLSDENYDFSSAESGSSLRYYSEGESGGGGSSLSLHPPQQPPLVPTNSGGGGATGGSPGERKRTRLGGPAARHRYEVVTELGPEEVRWFYKEDKKTWKPFIGYDSLRIELAFRTLLQTTGARPQGGDRDGDHVCSPTGPASSSGEDDDEDRACGFCQSTTGHEPEMVELVNIEPVCVRGGLYEVDVTQGECYPVYWNQADKIPVMRGQWFIDGTWQPLEEEESNLIEQEHLNCFRGQQMQENFDIEVSKSIDGKDAVHSFKLSRNHVDWHSVDEVYLYSDATTSKIARTVTQKLGFSKASSSGTRLHRGYVEEATLEDKPSQTTHIVFVVHGIGQKMDQGRIIKNTAMMREAARKIEERHFSNHATHVEFLPVEWRSKLTLDGDTVDSITPDKVRGLRDMLNSSAMDIMYYTSPLYRDELVKGLQQELNRLYSLFCSRNPDFEEKGGKVSIVSHSLGCVITYDIMTGWNPVRLYEQLLQKEEELPDERWMSYEERHLLDELYITKRRLKEIEERLHGLKASSMTQTPALKFKVENFFCMGSPLAVFLALRGIRPGNTGSQDHILPREICNRLLNIFHPTDPVAYRLEPLILKHYSNISPVQIHWYNTSNPLPYEHMKPSFLNPAKEPTSVSENEGISTIPSPVTSPVLSRRHYGESITNIGKASILGAASIGKGLGGMLFSRFGRSSTTQSSETSKDSMEDEKKPVASPSATTVGTQTLPHSSSGFLDSAYFRLQESFFNLPQLLFPENVMQNKDNALVELDHRIDFELREGLVESRYWSAVTSHTAYWSSLDVALFLLTFMYKHEHDDDAKPNLDPI,mutated_sequence,1.0,900.0,UPI00001D7B55.a2m,UPI00001D7B55.npy,gnomAD +UPI000059D713,UPI000059D713.csv,MEAVKAEAWEGAAVAQDLLALGYGGVPGAASRGASCPDFRGLCVRLAAELATLGALEQQREAGAEVLSAGDGPGAEEDFLRQLGSLLRELHCPDRALCGGDGAAALREPGAGLRLLRFLCSELQATRLLCLRSLLDPSPRPPLGEGVVEGAGMVQELDLTLQALGLPRPAPGTPASQLLQELHAKISELQPSLPPGSLQPLLSCSLDAPRWEALESLSQSLRDQYRCRRCLLLKRLDLTTSAFHWSDRAEAQGEAMRAVLIPIREVLTPESDISIAHVLAARADLSCLVPATSVAVRRGTCCAINKVLMGNVPDRGGRPNELEPPMPTWRSRREDGGPQCWGRKKKKKK,mutated_sequence,1.0,349.0,UPI000059D713.a2m,UPI000059D713.npy,gnomAD +UPI0000246EE9,UPI0000246EE9.csv,MGNAAGSAEQPAGPAAPPPKQPAPPKQPMPAAGELEERFNRALNCMNLPPDKVQLLSQYDNEKKWELICDQERFQVKNPPAAYIQKLKSYVDTGGVSRKVAADWMSNLGFKRRVQESTQVLRELETSLRTNHIGWVQEFLNEENRGLDVLLEYLAFAQCSVTYDMESTDNGASNSEKNKPLEQSVEDLSKGPPSSVPKSRHLTIKLTPAHSRKALRNSRIVSQKDDVHVCIMCLRAIMNYQSGFSLVMNHPACVNEIALSLNNKNPRTKALVLELLAAVCLVRGGHDIILAAFDNFKEVCGEQHRFEKLMEYFRNEDSNIDFMVACMQFINIVVHSVENMNFRVFLQYEFTHLGLDLYLERLRLTESDKLQVQIQAYLDNIFDVGALLEDTETKNAVLEHMEELQEQVALLTERLRDAENESMAKIAELEKQLSQARKELETLRERFSESTAMGPSRRPPEPEKAPPAAPTRPSALELKVEELEEKGLIRILRGPGDAVSIEILPVAVATPSGGDAPTPGVPTGSPSPDLAPAAEPAPGAAPPPPPPLPGLPSPQEAPPSAPPQAPPLPGSPEPPPAPPLPGDLPPPPPPPPPPPGTDGPVPPPPPPPPPPPGGPPDALGRRDSELGPGVKAKKPIQTKFRMPLLNWVALKPSQITGTVFTELNDEKVLQELDMSDFEEQFKTKSQGPSLDLSALKSKAAQKAPSKATLIEANRAKNLAITLRKGNLGAERICQAIEAYDLQALGLDFLELLMRFLPTEYERSLITRFEREQRPMEELSEEDRFMLCFSRIPRLPERMTTLTFLGNFPDTAQLLMPQLNAIIAASMSIKSSDKLRQILEIVLAFGNYMNSSKRGAAYGFRLQSLDALLEMKSTDRKQTLLHYLVKVIAEKYPQLTGFHSDLHFLDKAGSVSLDSVLADVRSLQRGLELTQREFVRQDDCMVLKEFLRANSPTMDKLLADSKTAQEAFESVVEYFGENPKTTSPGLFFSLFSRFIKAYKKAEQEVEQWKKEAAAQEAGADTPGKGEPPAPKSPPKARRPQMDLISELKRRQQKEPLIYESDRDGAIEDIITVIKTVPFTARTGKRTSRLLCEASLGEEMPL,mutated_sequence,1.0,1100.0,UPI0000246EE9.a2m,UPI0000246EE9.npy,gnomAD +UPI000003C716,UPI000003C716.csv,MKFLLLVLAALGFLTQVIPASAGGSKCVSNTPGYCRTCCHWGETALFMCNASRKCCISYSFLPKPDLPQLIGNHWQSRRRNTQRKDKKQQTTVTS,mutated_sequence,1.0,95.0,UPI000003C716.a2m,UPI000003C716.npy,gnomAD +UPI0001D3B05C,UPI0001D3B05C.csv,AGLSPEPRAGVGSEFPAWFLGGSSQRRNMALLGSRAELEADEDVFEDALETISIWLKKKSGRREGKTSS,mutated_sequence,1.0,69.0,UPI0001D3B05C.a2m,UPI0001D3B05C.npy,gnomAD +UPI000003C48A,UPI000003C48A.csv,MKRPKLKKASKRMTCHKRYKIQKKVREHHRKLRKEAKKRGHKKPRKDPGVPNSAPFKEALLREAELRKQRLEELKQQQKLDRQKELEKKRKLETNPDIKPSNVEPMEKEFGLCKTENKAKSGKQNSKKLYCQELKKVIEASDVVLEVLDARDPLGCRCPQVEEAIVQSGQKKLVLILNKSDLVPKENLESWLNYLKKELPTVVFRASTKPKDKGKITKRVKAKKNAAPFRSEVCFGKEGLWKLLGGFQETCSKAIRVGVIGFPNVGKSSIINSLKQEQMCNVGVSMGLTRSMQVVPLDKQITIIDSPSFIVSPLNSSSALALRSPASIEVVKPMEAASAILSQADARQVVLKYTVPGYRNSLEFFTVLAQRRGMHQKGGIPNVEGAAKLLWSEWTGASLAYYCHPPTSWTPPPYFNESIVVDMKSGFNLEELEKNNAQSIRAIKGPHLANSILFQSSGLTNGIIEEKDIHEELPKRKERKQEEREDDKDSDQETVDEEVDENSSGMFAAEETGEALSEETTAGEQSTRSFILDKIIEEDDAYDFSTDYV,mutated_sequence,1.0,549.0,UPI000003C48A.a2m,UPI000003C48A.npy,gnomAD +UPI0000206D91,UPI0000206D91.csv,MAFSKGFRIYHKLDPPPFSLIVETRHKEECLMFESGAVAVLSSAEKEAIKGTYSKVLDAYGLLGVLRLNLGDTMLHYLVLVTGCMSVGKIQESEVFRVTSTEFISLRIDSSDEDRISEVRKVLNSGNFYFAWSASGISLDLSLNAHRSMQEQTTDNRFFWNQSLHLHLKHYGVNCDDWLLRLMCGGVEIRTIYAAHKQAKACLISRLSCERAGTRFNVRGTNDDGHVANFVETEQVVYLDDSVSSFIQIRGSVPLFWEQPGLQVGSHRVRMSRGFEANAPAFDRHFRTLKNLYGKQIIVNLLGSKEGEHMLSKAFQSHLKASEHAADIQMVNFDYHQMVKGGKAEKLHSVLKPQVQKFLDYGFFYFNGSEVQRCQSGTVRTNCLDCLDRTNSVQAFLGLEMLAKQLEALGLAEKPQLVTRFQEVFRSMWSVNGDSISKIYAGTGALEGKAKLKDGARSVTRTIQNNFFDSSKQEAIDVLLLGNTLNSDLADKARALLTTGSLRVSEQTLQSASSKVLKSMCENFYKYSKPKKIRVCVGTWNVNGGKQFRSIAFKNQTLTDWLLDAPKLAGIQEFQDKRSKPTDIFAIGFEEMVELNAGNIVSASTTNQKLWAVELQKTISRDNKYVLLASEQLVGVCLFVFIRPQHAPFIRDVAVDTVKTGMGGATGNKGAVAIRMLFHTTSLCFVCSHFAAGQSQVKERNEDFIEIARKLSFPMGRMLFSHDYVFWCGDFNYRIDLPNEEVKELIRQQNWDSLIAGDQLINQKNAGQVFRGFLEGKVTFAPTYKYDLFSDDYDTSEKCRTPAWTDRVLWRRRKWPFDRSAEDLDLLNASFQDESKILYTWTPGTLLHYGRAELKTSDHRPVVALIDIDIFEVEAEERQNIYKEVIAVQGPPDGTVLVSIKSSLPENNFFDDALIDELLQQFASFGEVILIRFVEDKMWVTFLEGSSALNVLSLNGKELLNRTITIALKSPDWIKNLEEEMSLEKISIALPSSTSSTLLGEDAEVAADFDMEGDVDDYSAEVEELLPQHLQPSSSSGLGTSPSSSPRTSPCQSPTISEGPVPSLPIRPSRAPSRTPGPPSAQSSPIDAQPATPLPQKDPAQPLEPKRPPPPRPVAPPTRPAPPQRPPPPSGARSPAPTRKEFGGIGAPPSPGVARREMEAPKSPGTTRKDNIGRSQPSPQAGLAGPGPAGYSTARPTIPPRAGVISAPQSHARASAGRLTPESQSKTSETSKGSTFLPEPLKPQAAFPPQSSLPPPAQRLQEPLVPVAAPMPQSGPQPNLETPPQPPPRSRSSHSLPSEASSQPQVKTNGISDGKRESPLKIDPFEDLSFNLLAVSKAQLSVQTSPVPTPDPKRLIQLPSATQSNVLSSVSCMPTMPPIPARSQSQENMRSSPNPFITGLTRTNPFSDRTAAPGNPFRAKSEESEATSWFSKEEPVTISPFPSLQPLGHNKSRASSSLDGFKDSFDLQGQSTLKISNPKGWVTFEEEEDFGVKGKSKSACSDLLGNQPSSFSGSNLTLNDDWNKGTNVSFCVLPSRRPPPPPVPLLPPGTSPPVDPFTTLASKASPTLDFTER,mutated_sequence,1.0,1573.0,UPI0000206D91.a2m,UPI0000206D91.npy,gnomAD +UPI00000721F7,UPI00000721F7.csv,MGDMKTPDFDDLLAAFDIPDIDANEAIHSGPEENEGPGGPGKPEPGVGSESEDTAAASAGDGPGVPAQASDHGLPPPDISVVSVIVKNTVCPEQSEALAGGSAGDGAQAAGVTKEGPVGPHRMQNGFGSPEPSLPGTPHSPAPPSGGTWKEKGMEGKTPLDLFAHFGPEPGDHSDPLPPSAPSPTREGALTPPPFPSSFELAQENGPGMQPPVSSPPLGALKQESCSPHHPQVLAQQGSGSSPKATDIPASASPPPVAGVPFFKQSPGHQSPLASPKVPVCQPLKEEDDDEGPVDKSSPGSPQSPSSGAEAADEDSNDSPASSSSRPLKVRIKTIKTSCGNITRTVTQVPSDPDPPAPLAEGAFLAEASLLKLSPATPTSEGPKVVSVQLGDGTRLKGTVLPVATIQNASTAMLMAASVARKAVVLPGGTATSPKMIAKNVLGLVPQALPKADGRAGLGTGGQKVNGASVVMVQPSKTATGPSTGGGTVISRTQSSLVEAFNKILNSKNLLPAYRPNLSPPAEAGLALPPTGYRCLECGDAFSLEKSLARHYDRRSMRIEVTCNHCARRLVFFNKCSLLLHAREHKDKGLVMQCSHLVMRPVALDQMVGQPDITPLLPVAVPPVSGPLALPALGKGEGAITSSAITTVAAEAPVLPLSTEPPAAPATSAYTCFRCLECKEQCRDKAGMAAHFQQLGPPAPGATSNVCPTCPMMLPNRCSFSAHQRMHKNRPPHVCPECGGNFLQANFQTHLREACLHVSRRVGYRCPSCSVVFGGVNSIKSHIQTSHCEVFHKCPICPMAFKSGPSAHAHLYSQHPSFQTQQAKLIYKCAMCDTVFTHKPLLSSHFDQHLLPQRVSVFKCPSCPLLFAQKRTMLEHLKNTHQSGRLEETAGKGAGGALLTPKTEPEELAVSQGGAAPATEESSSSSEEEEVPSSPEPPRPAKRPRRELGSKGLKGGGGGPGGWTCGLCHSWFPERDEYVAHMKKEHGKSVKKFPCRLCERSFCSAPSLRRHVRVNHEGIKRVYPCRYCTEGKRTFSSRLILEKHVQVRHGLQLGAQSPGRGTTLARGSSARAQGPGRKRRQSSDSCSEEPDSTTPPAKSPRGGPGSGGHGPLRYRSSSSTEQSLMMGLRVEDGAQQCLDCGLCFASPGSLSRHRFISHKKRRGVGKASALGLGDGEEEAPPSRSDPDGGDSPLPASGGPLTCKVCGKSCDSPLNLKTHFRTHGMAFIRARQGAVGDN,mutated_sequence,1.0,1237.0,UPI00000721F7.a2m,UPI00000721F7.npy,gnomAD +UPI000192952A,UPI000192952A.csv,MRVLACLLAALVGIQAVERLRLADGPHGCAGRLEVWHGGRWGTVCDDGWDLRDAAVACRQLGCGGALAAPGGAFFGEGAGPVWLSELACRGNEGQLGLCHHRGWKAHICSHEEDAGVVCAGQRVANSRDDSTSPLDGAPWPGLLLELSPSTEEPLVTHAPRPAGNPQNASRKKSPRPKQAKSTRAPLLTTGAPRQERLRLVSGPHRCAGRLEVWHGGRWGTVCDDGWDLRDAAVACRELGCGGALAAPGGARFGPGAGPVWMDDVGCGGGEQALRDCPRSPWGRSNCDHSEDAGLVCTGPAPRLRLADGPHGCAGRLEVWHGGRWGSVCDDAWDLRDAAVACRELGCGGALAAPGGAFFGEGSGPIILDDLRCRGNETALRFCPARPWGQHDCHHREDAGAVCDGMPLGYVPPTAPTDSNNSTPREAASRPPSTMTSQAPGTAGVSPPPASPTVLWEPGPEAGSPQLRLVAGPSKCSGRLEVWHDQRWGTVCDDSWDMRDSAVVCRELGCGGPQQPDPAAGRFGWGAGPIWLDDVGCVGTEASLSDCPAAPWGKHNCAHNEDVGVTCTGPPGLDSISDPFSWSWIPGLGRDRDAWLPGELATKPSASVTASVLEKTTTKAPGKMPKSTKKWVTKNAKRPTTQPPVMPTTKHSRAQSPPDLTSQTTAALTTEASRRPTSEFTRRPTTEAPQRWTSHTTATLTPQAPRERTTKTMAMLTTQGPQEMTSESTIKSIPQASLEPSAEIPEGSPESPKDPAPSPSVSTTGESGLFRVRLADGPNRCAGRLEVWHAGRWGTVCDDNWDLRDATVACWELGCGKVRPRVGKTHYGPGTGPIWLDDMGCKGSEASLSDCPSGAWGKHNCDHEEDVGLTCTGYTDYDDYPPWTWDPTSREDLAKGTTTAGVPGHTLPWRTTRRPGSSSPAIRRLPDTGSKDGYKLPWTWDTPSGRGLAEGTPTAGKLGPTLGAGTTRSPGSPPTLRVHGDTGSPRKPWPERRPPRPAATRTAPPTPSPGPSASPGPPGPALTSDSSRELTPHSALTSEATSDAPDTSPPTPDPASRTNPDLILTSPDFALSTPDSSVVPALTPEPSPTPLPTLPKELTSDPSTPSEVTSLSPTSEQVPESDTTPDLDTTPYSSTVSEYSRSPDPSPSPHPTTTPDPTMAPDPITTLNPTVTPHFPTTPHPTTTPHPTTITHSTMIPDPTTTPQPFTTITHSTMIPDPTTTPQPFTTMQPTTTPHSTTPHPTTTPHPTTITHSTMIPDPTTTPQPFTTMQPTTMPHPTTTPHPTTTPHPTTTPHPTTTPHPTMTPDPTTTPYPTTTPDPTTTPHPTTPDPSSTPVITTVSLPTSLGTELSSPTLAPTVKPSLHPQLTFTAPAPHTSTSQIPTLEPSPALESSPSRSSTATSMDPLSTEDFKPPRSQSPNLTPPPTHTPHSASDLTVSPDPLLSPTAHPLDHPPLDPLTLGPTPGQSPGPHGPCVAPTPPVRVMACEPPALVELVAAVRDVGGQLQRLTQVVEQERQERQALLLGLTQLVEAARGLGQLGEAVKRLAEMAWTTSMPAPTTTTPEEEERPLRGDV,mutated_sequence,1.0,1573.0,UPI000192952A.a2m,UPI000192952A.npy,gnomAD +UPI000006EC9A,UPI000006EC9A.csv,MGSMFRSEEVALVQLFLPTAAAYTCVSRLGELGLVEFRDLNASVSAFQRRFVVDVRRCEELEKTFTFLQEEVRRAGLVLPPPKGRLPAPPPRDLLRIQEETERLAQELRDVRGNQQALRAQLHQLQLHAAVLRQGHEPQLAAAHTDGASERTPLLQAPGGPHQDLRVNFVAGAVEPHKAPALERLLWRACRGFLIASFRELEQPLEHPVTGEPATWMTFLISYWGEQIGQKIRKITDCFHCHVFPFLQQEEARLGALQQLQQQSQELQEVLGETERFLSQVLGRVLQLLPPGQVQVHKMKAVYLALNQCSVSTTHKCLIAEAWCSVRDLPALQEALRDSSMEEGVSAVAHRIPCRDMPPTLIRTNRFTASFQGIVDAYGVGRYQEVNPAPYTIITFPFLFAVMFGDVGHGLLMFLFALAMVLAENRPAVKAAQNEIWQTFFRGRYLLLLMGLFSIYTGFIYNECFSRATSIFPSGWSVAAMANQSGWSDAFLAQHTMLTLDPNVTGVFLGPYPFGIDPIWSLAANHLSFLNSFKMKMSVILGVVHMAFGVVLGVFNHVHFGQRHRLLLETLPELTFLLGLFGYLVFLVIYKWLCVWAARAASAPSILIHFINMFLFSHSPSNRLLYPRQEVVQATLVVLALAMVPILLLGTPLHLLHRHRRRLRRRPADRQEENKAGLLDLPDASVNGWSSDEEKAGGLDDEEEAELVPSEVLMHQAIHTIEFCLGCVSNTASYLRLWALSLAHAQLSEVLWAMVMRIGLGLGREVGVAAVVLVPIFAAFAVMTVAILLVMEGLSAFLHALRLHWVEFQNKFYSGTGYKLSPFTFAATDD,mutated_sequence,1.0,830.0,UPI000006EC9A.a2m,UPI000006EC9A.npy,gnomAD +UPI00000726A9,UPI00000726A9.csv,MSSTLPALLCVGLCLSQRISAQQQTLPKPFIWAEPHFMVPKEKQVTICCQGNYGAVEYQLHFEGSLFAVDRPKPPERINKVKFYIPDMNSRMAGQYSCIYRVGELWSEPSNLLDLVVTEMYDTPTLSVHPGPEVISGEKVTFYCRLDTATSMFLLLKEGRSSHVQRGYGKVQAEFPLGPVTTAHRGTYRCFGSYNNHAWSFPSEPVKLLVTGDIENTSLAPEDPTFPADTWGTYLLTTETGLQKDHALWDHTAQNLLRMGLAFLVLVALVWFLVEDWLSRKRTRERASRASTWEGRRRLNTQTL,mutated_sequence,1.0,304.0,UPI00000726A9.a2m,UPI00000726A9.npy,gnomAD +UPI000173A2B0,UPI000173A2B0.csv,MGEKVSEAPEPVPRGCSGHGSRTPASALVAASSPGASSAESSSGSETLSEEGEPGGFSREHQPPPPPPLGGTLGARAPAAWAPASVLLERGVLALPPPLPGGAVPPAPRGSSASQEEQDEELDHILSPPPMPFRKCSNPDVASGPGKSLKYKRQLSEDGRQLRRGSLGGALTGRYLLPNPVAGQAWPASAETSNLVRMRSQALGQSAPSLTASLKELSLPRRGSFCRTSNRKSLIGNGQSPALPRPHSPLSAHAGNSPQDSPRNFSPSASAHFSFARRTDGRRWSLASLPSSGYGTNTPSSTVSSSCSSQEKLHQLPYQPTPDELHFLSKHFCTTESIATENRCRNTPMRPRSRSLSPGRSPACCDHEIIMMNHVYKERFPKATAQMEERLKEIITSYSPDNVLPLADGVLSFTHHQIIELARDCLDKSHQGLITSRYFLELQHKLDKLLQEAHDRSESGELAFIKQLVRKILIVIARPARLLECLEFDPEEFYYLLEAAEGHAKEGQGIKTDIPRYIISQLGLNKDPLEEMAHLGNYDSGTAETPETDESVSSSNASLKLRRKPRESDFETIKLISNGAYGAVYFVRHKESRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVSMYCSFETRRHLCMVMEYVEGGDCATLMKNMGPLPVDMARMYFAETVLALEYLHNYGIVHRDLKPDNLLVTSMGHIKLTDFGLSKVGLMSMTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDEINWPEKDEAPPPDAQDLITLLLRQNPLERLGTGGAYEVKQHRFFRSLDWNSLLRQKAEFIPQLESEDDTSYFDTRSEKYHHMETEEEDDTNDEDFNVEIRQFSSCSHRFSKVFSSIDRITQNSAEEKEDSVDKTKSTTLPSTETLSWSSEYSEMQQLSTSNSSDTESNRHKLSSGLLPKLAISTEGEQDEAASCPGDPHEEPGKPALPPEECAQEEPEVTTPASTISSSTLSVGSFSEHLDQINGRSECVDSTDNSSKPSSEPASHMARQRLESTEKKKISGKVTKSLSASALSLMIPGDMFAVSPLGSPMSPHSLSSDPSSSRDSSPSRDSSAASASPHQPIVIHSSGKNYGFTIRAIRVYVGDSDIYTVHHIVWNVEEGSPACQAGLKAGDLITHINGEPVHGLVHTEVIELLLKSGNKVSITTTPFENTSIKTGPARRNSYKSRMVRRSKKSKKKESLERRRSLFKKLAKQPSPLLHTSRSFSCLNRSLSSGESLPGSPTHSLSPRSPTPSYRSTPDFPSGTNSSQSSSPSSSAPNSPAGSGHIRPSTLHGLAPKLGGQRYRSGRRKSAGNIPLSPLARTPSPTPQPTSPQRSPSPLLGHSLGNSKIAQAFPSKMHSPPTIVRHIVRPKSAEPPRSPLLKRVQSEEKLSPSYGSDKKHLCSRKHSLEVTQEEVQREQSQREAPLQSLDENVCDVPPLSRARPVEQGCLKRPVSRKVGRQESVDDLDRDKLKAKVVVKKADGFPEKQESHQKSHGPGSDLENFALFKLEEREKKVYPKAVERSSTFENKASMQEAPPLGSLLKDALHKQASVRASEGAMSDGRVPAEHRQGGGDFRRAPAPGTLQDGLCHSLDRGISGKGEGTEKSSQAKELLRCEKLDSKLANIDYLRKKMSLEDKEDNLCPVLKPKMTAGSHECLPGNPVRPTGGQQEPPPASESRAFVSSTHAAQMSAVSFVPLKALTGRVDSGTEKPGLVAPESPVRKSPSEYKLEGRSVSCLKPIEGTLDIALLSGPQASKTELPSPESAQSPSPSGDVRASVPPVLPSSSGKKNDTTSARELSPSSLKMNKSYLLEPWFLPPSRGLQNSPAVSLPDPEFKRDRKGPHPTARSPGTVMESNPQQREGSSPKHQDHTTDPKLLTCLGQNLHSPDLARPRCPLPPEASPSREKPGLRESSERGPPTARSERSAARADTCREPSMELCFPETAKTSDNSKNLLSVGRTHPDFYTQTQAMEKAWAPGGKTNHKDGPGEARPPPRDNSSLHSAGIPCEKELGKVRRGVEPKPEALLARRSLQPPGIESEKSEKLSSFPSLQKDGAKEPERKEQPLQRHPSSIPPPPLTAKDLSSPAARQHCSSPSHASGREPGAKPSTAEPSSSPQDPPKPVAAHSESSSHKPRPGPDPGPPKTKHPDRSLSSQKPSVGATKGKEPATQSLGGSSREGKGHSKSGPDVFPATPGSQNKASDGIGQGEGGPSVPLHTDRAPLDAKPQPTSGGRPLEVLEKPVHLPRPGHPGPSEPADQKLSAVGEKQTLSPKHPKPSTVKDCPTLCKQTDNRQTDKSPSQPAANTDRRAEGKKCTEALYAPAEGDKLEAGLSFVHSENRLKGAERPAAGVGKGFPEARGKGPGPQKPPTEADKPNGMKRSPSATGQSSFRSTALPEKSLSCSSSFPETRAGVREASAASSDTSSAKAAGGMLELPAPSNRDHRKAQPAGEGRTHMTKSDSLPSFRVSTLPLESHHPDPNTMGGASHRDRALSVTATVGETKGKDPAPAQPPPARKQNVGRDVTKPSPAPNTDRPISLSNEKDFVVRQRRGKESLRSSPHKKAL,mutated_sequence,1.0,2623.0,UPI000173A2B0.a2m,UPI000173A2B0.npy,gnomAD +UPI0000D62424,UPI0000D62424.csv,MVDPVPEEEKAGAEPGDSGGDEAVASVPPDSQGAQEPAASSASASASAAVPRKAEVPCAAAEGGRREQSPLLHLDLFNFDCPEAEGSRYVLTSPRSLEACARCAVKPVELLPRALADLVREAPGRSMRVATGLYEAYEAERRAKLQQCRAERERIMREEKRRLFTPLSPAAAAAAAAAAASAPSAGSSSSCSSASLPASPAPRAARKASPSPSSARTQPPPAGSRTGRKSHSLDSLSRRREGALSSESGASSSSYSGESLRELRWPPRASARNSCPAGSASSTTNAPGRPSALTLVPITGRSFSLGDLSHSPQTAQHVERIVRQVRAERGLRGVPERDRKIAALMLARHQEELLLLEQRAAAHGQWELQRVHAKQRREREEREKQRALEQGRRAWAAQVEERRGRRGREEREAARRRQRQYERSEERRRELAERQGLLRRERAERAAREDRLRKLQQEQNLKQREEGLQEGRERAEQIRRERAQRAARAKQRQEGQLQREKRELSRAERARHEALLQGRTRQQRQEREGLRSSLEASLGRAQENYEHLVEQRTRELRERARREELQGRRAKEAAERKEREHQAHLEALARAGERRLQHATQVAEEAVQQKARRVGQSRLEKERAQRANKEKVERDEDCRRRELLQAIGRKLERSEQLTRERRSALESARSTARASFHVREKVREETNTRSFDRMVREAQLHASLDRK,mutated_sequence,1.0,707.0,UPI0000D62424.a2m,UPI0000D62424.npy,gnomAD +UPI000022DC3D,UPI000022DC3D.csv,MGVQGFQEFLEKRCPGAVVPVDLLKLARTVSRQQQQQHLHRQLPPTAALAPGAPRAARGSVPLQPPLPPAALGAYSGGAGPIRHHHPAHHFHHHGQAQPGLHPPLPPPPPPQLPGARVLVDAGSALPRLYGGYQTDWVCGGQWNAMLGYLSALCQACAYPGGDGLELVVMFPGGLGKDRLAEWGRRCQAERQTAQLIVGHVGNKGTPPPRAWFLPPACLSHCVRLALIRFRVKVFQSLEDHHLEVVAFFRENGFHGLLAHDSEYALYNIPSYYSSHALKLSWNGKNLTTNQFLMQEVAKQLGLKRMNFPIFAALLGNHILPDEDLAAFHWSLLGPEHPLASLKVRAHQLVLPPCDVVIKAVSEYVSSIKDPSNLDVVGKDVFKQSQSRTEDKIERFKKAVEYYSVTTKLSSLPVGPSFLGFRNNRLGNPPLPRNQVGTISAGKPMFSHQVPQKVKYPPPFPVGPNSSLLFSSHALGESHAFSEDPMLQNSPFANWAVSYDSSASQFPNYLPSKASPPLGPDSSHSSSSDGDEPNGASSDHITEAFHHQPEWGNPNRDRGSWAQPVDTGVSEASLGDGEPHIPSLLSMSTRNHMDITIPPLPPVAPEVLRVAEHRHRRGLMYPYIYHVLTKGEIKIPVCIEDECNMELPPAALLFRSARQYVYGVLFSLAETQRKMERLAMRRRLPVEVPSVILKEWSAYKGKSPQTPELVSALTFREWTCPNLKKLWLGKAVEDKNRRMRAFLACMKSDTPSMLNPANVPTHLLLMCCVLRYMVQWPGGRILHRHELDTFLAQAVSTQLYEPDRLQELKIEKLDARGIQLAALFMSGVDTALFANDACGQPVPWEHCCPWIYFDGKLFQSKLIKAGRERVSLVELCDGQADLATKVEKMRQSILEGVNMNHPPPSALLPSPTFVPPMVPSLYPVSLYSRAMGSMPLPPQGRSRGFAGLHPIPPQGGKLEIAGMVVGQWAGSRSSRGRGSFGMQVVSVGGPGKGHGKEQTGRGSKGHKKGNKQGSSDGVSKSLELHQGRSRSQVNGNSGALIKEEKSDHRLPAPSQCALSRDSNECNNGNRYLPMNNREKNHLQEQKLETVAQRKED,mutated_sequence,1.0,1096.0,UPI000022DC3D.a2m,UPI000022DC3D.npy,gnomAD +UPI000006DE38,UPI000006DE38.csv,MEPRAVAEAVETGEEDVIMEALRSYNQEHSQSFTFDDAQQEDRKRLAELLVSVLEQGLPPSHRVIWLQSVRILSRDRNCLDPFTSRQSLQALACYADISVSEGSVPESADMDVVLESLKCLCNLVLSSPVAQMLAAEARLVVKLTERVGLYRERSFPHDVQFFDLRLLFLLTALRTDVRQQLFQELKGVRLLTDTLELTLGVTPEGNPPPTLLPSQETERAMEILKVLFNITLDSIKGEVDEEDAALYRHLGTLLRHCVMIATAGDRTEEFHGHAVNLLGNLPLKCLDVLLTLEPHGDSTEFMGVNMDVIRALLIFLEKRLHKTHRLKESVAPVLSVLTECARMHRPARKFLKAQVLPPLRDVRTRPEVGEMLRNKLVRLMTHLDTDVKRVAAEFLFVLCSESVPRFIKYTGYGNAAGLLAARGLMAGGRPEGQYSEDEDTDTDEYKEAKASINPVTGRVEEKPPNPMEGMTEEQKEHEAMKLVTMFDKLSRNRVIQPMGMSPRGHLTSLQDAMCETMEQQLSSDPDSDPD,mutated_sequence,1.0,531.0,UPI000006DE38.a2m,UPI000006DE38.npy,gnomAD +UPI0000033466,UPI0000033466.csv,MDSDEGYNYEFDEDEECSEEDSGAEEEEDEDDDEPDDDTLDLGEVELVEPGLGVGGERDGLLCGETGGGGGSALGPGGGGGGGGGGGGGGPGHEQEEDYRYEVLTAEQILQHMVECIREVNEVIQNPATITRILLSHFNWDKEKLMERYFDGNLEKLFAECHVINPSKKSRTRQMNTRSSAQDMPCQICYLNYPNSYFTGLECGHKFCMQCWSEYLTTKIMEEGMGQTISCPAHGCDILVDDNTVMRLITDSKVKLKYQHLITNSFVECNRLLKWCPAPDCHHVVKVQYPDAKPVRCKCGRQFCFNCGENWHDPVKCKWLKKWIKKCDDDSETSNWIAANTKECPKCHVTIEKDGGCNHMVCRNQNCKAEFCWVCLGPWEPHGSAWYNCNRYNEDDAKAARDAQERSRAALQRYLFYCNRYMNHMQSLRFEHKLYAQVKQKMEEMQQHNMSWIEVQFLKKAVDVLCQCRATLMYTYVFAFYLKKNNQSIIFENNQADLENATEVLSGYLERDISQDSLQDIKQKVQDKYRYCESRRRVLLQHVHEGYEKDLWEYIED,mutated_sequence,1.0,557.0,UPI0000033466.a2m,UPI0000033466.npy,gnomAD +UPI000020E761,UPI000020E761.csv,MEPGKRRTKDDTWKADDLRKHLWAIQSGGSKEERKHREKKLRKESEMDLPEHKEPRCRDPDQDARSRDRVAEVHTAKESPRGERDRDRQRERRRDAKDREKEKLKEKHREAEKSHSRGKDREKEKDRRARKEELRQTVAHHNLLGQETRDRQLLERAERKGRSVSKVRSEEKDEDSERGDEDRERRYRERKLQYGDSKDNPLKYWLYKEEGERRHRKPREPDRDNKHREKSSTREKREKYSKEKSNSFSDKGEERHKEKRHKEGFHFDDERHQSNVDRKEKSAKDEPRKRESQNGEHRNRGASSKRDGTSSQHAENLVRNHGKDKDSRRKHGHEEGSSVWWKLDQRPGGEETVEIEKEETDLENARADAYTASCEDDFEDYEDDFEVCDGDDDESSNEPESREKLEELPLAQKKEIQEIQRAINAENERIGELSLKLFQKRGRTEFEKEPRTDTNSSPSRASVCGIFVDFASASHRQKSRTQALKQKMRSTKLLRLIDLDFSFTFSLLDLPPVNEYDMYIRNFGKKNTKQAYVQCNEDNVERDIQTEEIETREVWTQHPGESTVVSGGSEQRDTSDAVVMPKIDTPRLCSFLRAACQVMAVLLEEDRLAAEPSWNLRAQDRALYFSDSSSQLNTSLPFLQNRKVSSLHTSRVQRQMVVSVHDLPEKSFVPLLDSKYVLCVWDIWQPSGPQKVLICESQVTCCCLSPLKAFLLFAGTAHGSVVVWDLREDSRLHYSVTLSDGFWTFRTATFSTDGILTSVNHRSPLQAVEPISTSVHKKQSFVLSPFSTQEEMSGLSFHIASLDESGVLNVWVVVELPKADIAGSISDLGLMPGGRVKLVHSALIQLGDSLSHKGNEFWGTTQTLNVKFLPSDPNHFIIGTDMGLISHGTRQDLRVAPKLFKPQQHGIRPVKVNVIDFSPFGEPIFLAGCSDGSIRLHQLSSAFPLLQWDSSTDSHAVTGLQWSPTRPAVFLVQDDTSNIYIWDLLQSDLGPVAKQQVSPNRLVAMAAVGEPEKAGGSFLALVLARASGSIDIQHLKRRWAAPEVDECNRLRLLLQEALWPEGKLHK,mutated_sequence,1.0,1066.0,UPI000020E761.a2m,UPI000020E761.npy,gnomAD +UPI000013F0A1,UPI000013F0A1.csv,MLSRLSGLANVVLHELSGDDDTDQNMRAPLDPELHQESDMEFNNTTQEDVQERLAYAEQLVVELKDIIRQKDVQLQQKDEALQEERKAADNKIKKLKLHAKAKLTSLNKYIEEMKAQGGTVLPTEPQSEEQLSKHDKSSTEEEMEIEKIKHKLQEKEELISTLQAQLTQAQAEQPAQSSTEMEEFVMMKQQLQEKEEFISTLQAQLSQTQAEQAAQQVVREKDARFETQVRLHEDELLQLVTQADVETEMQQKLRVLQRKLEEHEESLVGRAQVVDLLQQELTAAEQRNQILSQQLQQMEAEHNTLRNTVETEREESKILLEKMELEVAERKLSFHNLQEEMHHLLEQFEQAGQAQAELESRYSALEQKHKAEMEEKTSHILSLQKTGQELQSACDALKDQNSKLLQDKNEQAVQSAQTIQQLEDQLQQKSKEISQFLNRLPLQQHETASQTSFPDVYNEGTQAVTEENIASLQKRVVELENEKGALLLSSIELEELKAENEKLSSQITLLEAQNRTGEADREVSEISIVDIANKRSSSAEESGQDVLENTFSQKHKELSVLLLEMKEAQEEIAFLKLQLQGKRAEEADHEVLDQKEMKQMEGEGIAPIKMKVFLEDTGQDFPLMPNEESSLPAVEKEQASTEHQSRTSEEISLNDAGVELKSTKQDGDKSLSAVPDIGQCHQDELERLKSQILELELNFHKAQEIYEKNLDEKAKEISNLNQLIEEFKKNADNNSSAFTALSEERDQLLSQVKELSMVTELRAQVKQLEMNLAEAERQRRLDYESQTAHDNLLTEQIHSLSIEAKSKDVKIEVLQNELDDVQLQFSEQSTLIRSLQSQLQNKESEVLEGAERVRHISSKVEELSQALSQKELEITKMDQLLLEKKRDVETLQQTIEEKDQQVTEISFSMTEKMVQLNEEKFSLGVEIKTLKEQLNLLSRAEEAKKEQVEEDNEVSSGLKQNYDEMSPAGQISKEELQHEFDLLKKENEQRKRKLQAALINRKELLQRVSRLEEELANLKDESKKEIPLSETERGEVEEDKENKEYSEKCVTSKCQEIEIYLKQTISEKEVELQHIRKDLEEKLAAEEQFQALVKQMNQTLQDKTNQIDLLQAEISENQAIIQKLITSNTDASDGDSVALVKETVVISPPCTGSSEHWKPELEEKILALEKEKEQLQKKLQEALTSRKAILKKAQEKERHLREELKQQKDDYNRLQEQFDEQSKENENIGDQLRQLQIQVRESIDGKLPSTDQQESCSSTPGLEEPLFKATEQHHTQPVLESNLCPDWPSHSEDASALQGGTSVAQIKAQLKEIEAEKVELELKVSSTTSELTKKSEEVFQLQEQINKQGLEIESLKTVSHEAEVHAESLQQKLESSQLQIAGLEHLRELQPKLDELQKLISKKEEDVSYLSGQLSEKEAALTKIQTEIIEQEDLIKALHTQLEMQAKEHDERIKQLQVELCEMKQKPEEIGEESRAKQQIQRKLQAALISRKEALKENKSLQEELSLARGTIERLTKSLADVESQVSAQNKEKDTVLGRLALLQEERDKLITEMDRSLLENQSLSSSCESLKLALEGLTEDKEKLVKEIESLKSSKIAESTEWQEKHKELQKEYEILLQSYENVSNEAERIQHVVEAVRQEKQELYGKLRSTEANKKETEKQLQEAEQEMEEMKEKMRKFAKSKQQKILELEEENDRLRAEVHPAGDTAKECMETLLSSNASMKEELERVKMEYETLSKKFQSLMSEKDSLSEEVQDLKHQIEGNVSKQANLEATEKHDNQTNVTEEGTQSIPGETEEQDSLSMSTRPTCSESVPSAKSANPAVSKDFSSHDEINNYLQQIDQLKERIAGLEEEKQKNKEFSQTLENEKNTLLSQISTKDGELKMLQEEVTKMNLLNQQIQEELSRVTKLKETAEEEKDDLEERLMNQLAELNGSIGNYCQDVTDAQIKNELLESEMKNLKKCVSELEEEKQQLVKEKTKVESEIRKEYLEKIQGAQKEPGNKSHAKELQELLKEKQQEVKQLQKDCIRYQEKISALERTVKALEFVQTESQKDLEITKENLAQAVEHRKKAQAELASFKVLLDDTQSEAARVLADNLKLKKELQSNKESVKSQMKQKDEDLERRLEQAEEKHLKEKKNMQEKLDALRREKVHLEETIGEIQVTLNKKDKEVQQLQENLDSTVTQLAAFTKSMSSLQDDRDRVIDEAKKWERKFSDAIQSKEEEIRLKEDNCSVLKDQLRQMSIHMEELKINISRLEHDKQIWESKAQTEVQLQQKVCDTLQGENKELLSQLEETRHLYHSSQNELAKLESELKSLKDQLTDLSNSLEKCKEQKGNLEGIIRQQEADIQNSKFSYEQLETDLQASRELTSRLHEEINMKEQKIISLLSGKEEAIQVAIAELRQQHDKEIKELENLLSQEEEENIVLEEENKKAVDKTNQLMETLKTIKKENIQQKAQLDSFVKSMSSLQNDRDRIVGDYQQLEERHLSIILEKDQLIQEAAAENNKLKEEIRGLRSHMDDLNSENAKLDAELIQYREDLNQVITIKDSQQKQLLEVQLQQNKELENKYAKLEEKLKESEEANEDLRRSFNALQEEKQDLSKEIESLKVSISQLTRQVTALQEEGTLGLYHAQLKVKEEEVHRLSALFSSSQKRIAELEEELVCVQKEAAKKVGEIEDKLKKELKHLHHDAGIMRNETETAEERVAELARDLVEMEQKLLMVTKENKGLTAQIQSFGRSMSSLQNSRDHANEELDELKRKYDASLKELAQLKEQGLLNRERDALLSETAFSMNSTEENSLSHLEKLNQQLLSKDEQLLHLSSQLEDSYNQVQSFSKAMASLQNERDHLWNELEKFRKSEEGKQRSAAQPSTSPAEVQSLKKAMSSLQNDRDRLLKELKNLQQQYLQINQEITELHPLKAQLQEYQDKTKAFQIMQEELRQENLSWQHELHQLRMEKSSWEIHERRMKEQYLMAISDKDQQLSHLQNLIRELRSSSSQTQPLKVQYQRQASPETSASPDGSQNLVYETELLRTQLNDSLKEIHQKELRIQQLNSNFSQLLEEKNTLSIQLCDTSQSLRENQQHYGDLLNHCAVLEKQVQELQAGPLNIDVAPGAPQEKNGVHRKSDPEELREPQQSFSEAQQQLCNTRQEVNELRKLLEEERDQRVAAENALSVAEEQIRRLEHSEWDSSRTPIIGSCGTQEQALLIDLTSNSCRRTRSGVGWKRVLRSLCHSRTRVPLLAAIYFLMIHVLLILCFTGHL,mutated_sequence,1.0,3259.0,UPI000013F0A1.a2m,UPI000013F0A1.npy,gnomAD +UPI0000129A56,UPI0000129A56.csv,MLRVFILYAENVHTPDTDISDAYCSAVFAGVKKRTKVIKNSVNPVWNEGFEWDLKGIPLDQGSELHVVVKDHETMGRNRFLGEAKVPLREVLATPSLSASFNAPLLDTKKQPTGASLVLQVSYTPLPGAVPLFPPPTPLEPSPTLPDLDVVADTGGEEDTEDQGLTGDEAEPFLDQSGGPGAPTTPRKLPSRPPPHYPGIKRKRSAPTSRKLLSDKPQDFQIRVQVIEGRQLPGVNIKPVVKVTAAGQTKRTRIHKGNSPLFNETLFFNLFDSPGELFDEPIFITVVDSRSLRTDALLGEFRMDVGTIYREPRHAYLRKWLLLSDPDDFSAGARGYLKTSLCVLGPGDEAPLERKDPSEDKEDIESNLLRPTGVALRGAHFCLKVFRAEDLPQMDDAVMDNVKQIFGFESNKKNLVDPFVEVSFAGKMLCSKILEKTANPQWNQNITLPAMFPSMCEKMRIRIIDWDRLTHNDIVATTYLSMSKISAPGGEIEEEPAGAVKPSKASDLDDYLGFLPTFGPCYINLYGSPREFTGFPDPYTELNTGKGEGVAYRGRLLLSLETKLVEHSEQKVEDLPADDILRVEKYLRRRKYSLFAAFYSATMLQDVDDAIQFEVSIGNYGNKFDMTCLPLASTTQYSRAVFDGCHYYYLPWGNVKPVVVLSSYWEDISHRIETQNQLLGIADRLEAGLEQVHLALKAQCSTEDVDSLVAQLTDELIAGCSQPLGDIHETPSATHLDQYLYQLRTHHLSQITEAALALKLGHSELPAALEQAEDWLLRLRALAEEPQNSLPDIVIWMLQGDKRVAYQRVPAHQVLFSRRGANYCGKNCGKLQTIFLKYPMEKVPGARMPVQIRVKLWFGLSVDEKEFNQFAEGKLSVFAETYENETKLALVGNWGTTGLTYPKFSDVTGKIKLPKDSFRPSAGWTWAGDWFVCPEKTLLHDMDAGHLSFVEEVFENQTRLPGGQWIYMSDNYTDVNGEKVLPKDDIECPLGWKWEDEEWSTDLNRAVDEQGWEYSITIPPERKPKHWVPAEKMYYTHRRRRWVRLRRRDLSQMEALKRHRQAEAEGEGWEYASLFGWKFHLEYRKTDAFRRRRWRRRMEPLEKTGPAAVFALEGALGGVMDDKSEDSMSVSTLSFGVNRPTISCIFDYGNRYHLRCYMYQARDLAAMDKDSFSDPYAIVSFLHQSQKTVVVKNTLNPTWDQTLIFYEIEIFGEPATVAEQPPSIVVELYDHDTYGADEFMGRCICQPSLERMPRLAWFPLTRGSQPSGELLASFELIQREKPAIHHIPGFEVQETSRILDESEDTDLPYPPPQREANIYMVPQNIKPALQRTAIEILAWGLRNMKSYQLANISSPSLVVECGGQTVQSCVIRNLRKNPNFDICTLFMEVMLPREELYCPPITVKVIDNRQFGRRPVVGQCTIRSLESFLCDPYSAESPSPQGGPDDVSLLSPGEDVLIDIDDKEPLIPIQEEEFIDWWSKFFASIGEREKCGSYLEKDFDTLKVYDTQLENVEAFEGLSDFCNTFKLYRGKTQEETEDPSVIGEFKGLFKIYPLPEDPAIPMPPRQFHQLAAQGPQECLVRIYIVRAFGLQPKDPNGKCDPYIKISIGKKSVSDQDNYIPCTLEPVFGKMFELTCTLPLEKDLKITLYDYDLLSKDEKIGETVVDLENRLLSKFGARCGLPQTYCVSGPNQWRDQLRPSQLLHLFCQQHRVKAPVYRTDRVMFQDKEYSIEEIEAGRIPNPHLGPVEERLALHVLQQQGLVPEHVESRPLYSPLQPDIEQGKLQMWVDLFPKALGRPGPPFNITPRRARRFFLRCIIWNTRDVILDDLSLTGEKMSDIYVKGWMIGFEEHKQKTDVHYRSLGGEGNFNWRFIFPFDYLPAEQVCTIAKKDAFWRLDKTESKIPARVVFQIWDNDKFSFDDFLGSLQLDLNRMPKPAKTAKKCSLDQLDDAFHPEWFVSLFEQKTVKGWWPCVAEEGEKKILAGKLEMTLEIVAESEHEERPAGQGRDEPNMNPKLEDPRRPDTSFLWFTSPYKTMKFILWRRFRWAIILFIILFILLLFLAIFIYAFPNYAAMKLVKPFS,mutated_sequence,1.0,2080.0,UPI0000129A56.a2m,UPI0000129A56.npy,gnomAD +UPI000013E95E,UPI000013E95E.csv,MYGRPQAEMEQEAGELSRWQAAHQAAQDNENSAPILNMSSSSGSSGVHTSWNQGLPSIQHFPHSAEMLGSPLVSVEAPGQNVNEGGPQFSMPLPERGMSYCPQATLTPSRMIYCQRMSPPQQEMTIFSGPQLMPVGEPNIPRVARPFGGNLRMPPNGLPVSASTGIPIMSHTGNPPVPYPGLSTVPSDETLLGPTVPSTEAQAVLPSMAQMLPPQDAHDLGMPPAESQSLLVLGSQDSLVSQPDSQEGPFLPEQPGPAPQTVEKNSRPQEGTGRRGSSEARPYCCNYENCGKAYTKRSHLVSHQRKHTGERPYSCNWESCSWSFFRSDELRRHMRVHTRYRPYKCDQCSREFMRSDHLKQHQKTHRPGPSDPQANNNNGEQDSPPAAGP,mutated_sequence,1.0,389.0,UPI000013E95E.a2m,UPI000013E95E.npy,gnomAD +UPI0000EA87E6,UPI0000EA87E6.csv,MEPPGGSLGPGRGTRDKKKGRSPDELPSAGGDGGKSKKFTLKRLMADELERFTSMRIKKEKEKPNSAHRNSSASYGDDPTAQSLQDVSDEQVLVLFEQMLLDMNLNEEKQQPLREKDIIIKREMVSQYLYTSKAGMSQKESSKSAMMYIQELRSGLRDMPLLSCLESLRVSLNNNPVSWVQTFGAEGLASLLDILKRLHDEKEETAGSYDSRNKHEIIRCLKAFMNNKFGIKTMLETEEGILLLVRAMDPAVPNMMIDAAKLLSALCILPQPEDMNERVLEAMTERAEMDEVERFQPLLDGLKSGTTIALKVGCLQLINALITPAEELDFRVHIRSELMRLGLHQVLQDLREIENEDMRVQLNVFDEQGEEDSYDLKGRLDDIRMEMDDFNEVFQILLNTVKDSKAEPHFLSILQHLLLVRNDYEARPQYYKLIEECISQIVLHKNGADPDFKCRHLQIEIEGLIDQMIDKTKVEKSEAKAAELEKKLDSELTARHELQVEMKKMESDFEQKLQDLQGEKDALHSEKQQIATEKQDLEAEVSQLTGEVAKLTKELEDAKKEMASLSAAAITVPPSVPSRAPVPPAPPLPGDSGTIIPPPPAPGDSTTPPPPPPPPPPPPPLPGGVCISSPPSLPGGTAISPPPPLSGDATIPPPPPLPEGVGIPSPSSLPGGTAIPPPPPLPGSARIPPPPPPLPGSAGIPPPPPPLPGEAGMPPPPPPLPGGPGIPPPPPFPGGPGIPPPPPGMGMPPPPPFGFGVPAAPVLPFGLTPKKLYKPEVQLRRPNWSKLVAEDLSQDCFWTKVKEDRFENNELFAKLTLTFSAQTKTSKAKKDQEGGEEKKSVQKKKVKELKVLDSKTAQNLSIFLGSFRMPYQEIKNVILEVNEAVLTESMIQNLIKQMPEPEQLKMLSELKDEYDDLAESEQFGVVMGTVPRLRPRLNAILFKLQFSEQVENIKPEIVSVTAACEELRKSESFSNLLEITLLVGNYMNAGSRNAGAFGFNISFLCKLRDTKSTDQKMTLLHFLAELCENDYPDVLKFPDELAHVEKASRVSAENLQKNLDQMKKQISDVERDVQNFPAATDEKDKFVEKMTSFVKDAQEQYNKLRMMHSNMETLYKELGEYFLFDPKKLSVEEFFMDLHNFRNMFLQAVKENQKRRETEEKMRRAKLAKEKAEKERLEKQQKREQLIDMNAEGDETGVMDSLLEALQSGAAFRRKRGPRQANRKAGCAVTSLLASELTKDDAMAAVPAKVSKNSETFPTILEEAKELVGRAS,mutated_sequence,1.0,1272.0,UPI0000EA87E6.a2m,UPI0000EA87E6.npy,gnomAD +UPI000020E4D9,UPI000020E4D9.csv,MASLSRPSLPSCLCSFLLLLLLQVSSSYAGQFRVIGPRHPIRALVGDEVELPCRISPGKNATGMEVGWYRPPFSRVVHLYRNGKDQDGDQAPEYRGRTELLKDAIGEGKVTLRIRNVRFSDEGGFTCFFRDHSYQEEAAMELKVEDPFYWVSPGVLVLLAVLPVLLLQITVGLIFLCLQYRLRGKLRAEIENLHRTFDPHFLRVPCWKITLFVIVPVLGPLVALIICYNWLHRRLAGQFLEELRNPF,mutated_sequence,1.0,247.0,UPI000020E4D9.a2m,UPI000020E4D9.npy,gnomAD +UPI0000072D37,UPI0000072D37.csv,MKPTSGPEEARRPASDIRVFASNCSMHGLGHVFGPGSLSLRRGMWAAAVVLSVATFLYQVAERVRYYREFHHQTALDERESHRLIFPAVTLCNINPLRRSRLTPNDLHWAGSALLGLDPAEHAAFLRALGRPPAPPGFMPSPTFDMAQLYARAGHSLDDMLLDCRFRGQPCGPENFTTIFTRMGKCYTFNSGADGAELLTTTRGGMGNGLDIMLDVQQEEYLPVWRDNEETPFEVGIRVQIHSQEEPPIIDQLGLGVSPGYQTFVSCQQQQLSFLPPPWGDCSSASLNPNYEPEPSDPLGSPSPSPSPPYTLMGCRLACETRYVARKCGCRMVYMPGDVPVCSPQQYKNCAHPAIDAMLRKDSCACPNPCASTRYAKELSMVRIPSRAAARFLARKLNRSEAYIAENVLALDIFFEALNYETVEQKKAYEMSELLGDIGGQMGLFIGASLLTILEILDYLCEVFRDKVLGYFWNRQHSQRHSSTNLLQEGLGSHRTQVPHLSLGPRPPTPPCAVTKTLSASHRTCYLVTQL,mutated_sequence,1.0,531.0,UPI0000072D37.a2m,UPI0000072D37.npy,gnomAD +UPI000059D6C5,UPI000059D6C5.csv,MNVMLENYKNLVFLAGIAVSKQDPITSLEQEKEPWNMKICEMVDESPAMCSSFTRDLWPEQDIKDSFQQVILRRHGKCEHENLQLRKGSANVVECKVYKKGYELNQCLTTTQSKIFPCDKYIKVFHKIFNSNRHKTRHTGEKPFKCKKCDESFCMLLHLHQHKRIHIRENSYQCEECDKVFKRFSTLTRHKRVHTGEKPFKCEECGKAFKHSSTLTTHKMIHTGEKPYRCEECGKAFYHSSHLTTHKVIHTGEKPFKCEECGKAFNHPSALTTHKFIHVKEKPYKCEECDKAFNRFSYLTKHKIIHSGEKSYKCEQCGKGFNWSSTLTKHKRIHTGEKPYKCEECGKAFNVSSHLTTHKMIHTGEKPYKCEECGKAFNHSSKLTIHKIIHTGEKPYKCEECGKAFNQSSNLTKHKIIHTGEKLYKCEECGKAFNRSSNLTTHKRIHTGEKPYKCEECGKAFNRSSNLTKHNIIHTGEKSYKCEECGKAFNQSSTLTKHRKIQQGMVAHACNPNTLRGLGEQIARSGVQDQPGQHGKTPSLLKIQKFAGCGGRRL,mutated_sequence,1.0,554.0,UPI000059D6C5.a2m,UPI000059D6C5.npy,gnomAD +UPI00001D7B9E,UPI00001D7B9E.csv,MIEPSEDSFETMMEHKNPSSKQMESSEGSSNTTEATSGSGVRGEAGPASGPAQEKKEPPSGPLQEMEELPTDLLQDMEEPSSGPRKEIEDPPNDLLQDLEESCNGSHQARGDPLSGASDRMKEASVNPSGAREEQEAHTDLKESGREETPQEQNQTEHSTAELMAMVRSIISLYFRMQDLKEQQRVAEEILIKGINAGQLPAPKHFSGDRREFHEFIVLCQLTLQSYPRMFYNDRLRVGYVINHLSGLALEWAKALLQENSPLIGDFPAFLEAMSEVFEYRQALRVAEEAMFTIRQGGRSATEYIDEFQSLVPILGWPDEVLQAHLCQGLNEEIRHYLFRVPQPDSLDSLIVLILQIEEKLAERRAMLRLPPEARPRNLTWIDSPAPERWMVSSWLPSEVHPDINRAHLFLLLMVRVNPYHSVAVQALVDSGADGNFMDEKFAQEHYVELYEKPYPQPVQSVDGSLIGNEPVWLYTEPLVCIHQNHQESIEFDIVPSPNFSVVLGIRWLRVHAPEVDWIKGRCTFHSPYCLKNCFRPPPPCIALERHGMSLLPGLPHPYSDLADVFNPKEADDETSDQPSSDGSDDLSESEPSELQQAGDSDHSETFYECPSTAPWEPVGARMQERARLQEEYWDLQDMLTNRQDYIQMIPELFDQLHGAEWFTKLELRGTIVEESVNGHRTEDVWKAAFGLELEEMKSYQPFALSPDPIIPQNVIHFILKDMLGFFVLSYGQEVLIYSMSQEEHLHHVRQVLVRFRHHNVYCSLDKSQFHRQTVEFLGFVVTPKGVKLNKNVMTIITGYPTPGSKLSLRNFIEFVFPYRHFVERFSIIAEPLVRQLLSSYQFYWGVEEQEAFECLKRAFRKAPLLHHPKPQNPFYLETGVTGTALHASLIQIDDQTGKRACCAFYSRNISPIEVEYSQAEMKILPIRAAFMVWCRYLENTEEPIMILLNTEDLASLNNDRLTVLLPGHWVFFFSHFNFDVMELPEQDGGRALPPVRNLRWRRAFQRNTAARQTLLLASRGFPRDPSTESGEEENEEQDELNEQILRQELLAMIPIDQILNSFLAHFSMAQIRAVILHFFRGLLYWKNTLALAAILVLLRVRQCLSLRPAPAMRVARPQPQRSLRLILDSSLIAGSSITTAITQLLTQMPALVGANTIPAQELAELFLGPGRWQRNALHSQAHRGLQFTPGFWLTLCEFFGVRVTPQEGHLPALRQNRYLELHVVGDEDVVLREALQDDLQRYRQCGLHDGLQDTSQDKQDNDVQEAPPSHTAATHPPRPRHLMDPQVLEFLGSRLLHIHSADGQLHLLSREQAARALSQFLTLIYRRALPIPAWESQPREQARLEELPDEDEDANLD,mutated_sequence,1.0,1358.0,UPI00001D7B9E.a2m,UPI00001D7B9E.npy,gnomAD +UPI0000D61A6B,UPI0000D61A6B.csv,MHQPPESTAAAAAAADISARKMAHPAMFPRRGSGSGSASALNAAGTGVGSNATSSEDFPPPSLLQPPPPAASSTSGPQPPPPQSLNLLSQAQLQAQPLAPGGTQMKKKSGFQITSVTPAQISASISSNNSIAEDTESYDDLDESHTEDLSSSEILDVSLSRATDLGEPERSSSEETLNNFQEAETPGAVSPNQPHLPQPHLPHLPQQNVVINGNAHPHHLHHHHQIHHGHHLQHGHHHPSHVAVASASITGGPPSSPVSRKLSTTGSSDSITPVAPTSAVSSSGSPASVMTNMRAPSTTGGIGINSVTGTSTVNNVNITAVGSFNPNVTSSMLGNVNISTSNIPSAAGVSVGPGVTSGVNVNILSGMGNGTISSSAAVSSVPNAAAGMTGGSVSSQQQQPTVNTSRFRVVKLDSSSEPFKKGRWTCTEFYEKENAVPATEGVLINKVVETVKQNPIEVTSERESTSGSSVSSSVSTLSHYTESVGSGEMGAPTVVVQQQQQQQQQQQQQPALQGVTLQQMDFGSTGPQSIPAVSIPQSISQSQISQVQLQSQELSYQQKQGLQPVPLQATMSAATGIQPSPVNVVGVTSALGQQPSISSLAQPQLPYSQAAPPVQTPLPGAPPPQQLQYGQQQPMVSTQMAPGHVKSVTQNPASEYVQQQPILQTAMSSGQPSSAGVGAGTTVIPVAQPQGIQLPVQPTAVPAQPAGASVQPVGQAPAAVSAVPTGSQIANIGQQANIPTAVQQPSTQVPPSVIQQGAPPSSQVVPPAQTGIIHQGVQTSAPSLPQQLVIASQSSLLTVPPQPQGVEPVAQGIVSQQLPAVSSLPSASSISVTSQVSSTGPSGMPSAPTNLVPPQNIAQTPATQNGNLVQSVSQPPLIATNTNLPLAQQIPLSSTQFSAQSLAQAIGSQIEDARRAAEPSLVGLPQTISGDSGGMSAVSDGSSSSLAASASLFPLKVLPLTTPLVDGEDESSSGASVVAIDNKIEQAMDLVKSHLMYAVREEVEVLKEQIKELIEKNSQLEQENNLLKTLASPEQLAQFQAQLQTGSPPATTQPQGTTQPPAQPASQGSGPTA,mutated_sequence,1.0,1073.0,UPI0000D61A6B.a2m,UPI0000D61A6B.npy,gnomAD +UPI000041A715,UPI000041A715.csv,MDDDSLDELVARSPGPDGHPQVGPADPAGDFEESSVGSSGDSGDDSDSEHGDGTDGEDEGASEEEDLEDRSGSEDSEDDGETLLEVAGTQGKLEAAGSFNSDDDAESCPICLNAFRDQAVGTPENCAHYFCLDCIVEWSKNANSCPVDRTLFKCICIRAQFGGKILRKIPVENTKASEEEEDPTFCEVCGRSDREDRLLLCDGCDAGYHMECLDPPLQEVPVDEWFCPECAAPGVVLAADAGPVSEEEVSLLLADVVPTTSRLRPRAGRTRAIARTRQSERVRATVNRNRISTARRVQHTPGRLGSSLLDEAIEAVATGLSTAVYQRPLTPRTPARRKRKTRRRKKVPGRKKTPSGPSAKSKSSATRSKKRQHRVKKRRGKKVKSEATTRSRIARTLGLRRPVHSSCIPSVLKPVEPSLGLLRADIGAASLSLFGDPYELDPFDSSEELSANPLSPLSAKRRALSRSALQSHQPVARPVSVGLSRRRLPAAVPEPDLEEEPVPDLLGSILSGQSLLMLGSSDVIIHRDGSLSAKRAAPVSFQRNSGSLSRGEEGFKGCLQPRALPSGSPAQGPSGNRPQSTGLSCQGRSRTPARTAGAPVRLDLPAAPGAVQARNLSNGSVPGFRQSHSPWFNGTNKHTLPLASAASKISSRDSKPPCRSVVPGPPLKPAPRRTDISELPRIPKIRRDDGGGRRDAAPAHGQSIEIPSACISRLTGREGTGQPGRGTRAESEASSRVPREPGVHTGSSRPPAPSSHGSLAPLGPSRGKGVGSTFESFRINIPGNMAHSSQLSSPGFCNTFRPVDDKEQRKENPSPLFSIKKTKQLRSEVYDPSDPTGSDSSAPGSSPERSGPGLLPSEITRTISINSPKAQTVQAVRCVTSYTVESIFGTEPEPPLGPSSAMSKLRGAVAAEGASDTEREEPTESQGLAARLRRPSPPEPWDEEDGASCSTFFGSEERTVTCVTVVEPEAPPSPDVLQAATHRVVELRPPSRSRSTSSSRSRKKAKRKRVSREHGRTRSGTRSESRDRSSRSASPSVGEERPRRQRSKAKSRRSSSDRSSSRERAKRKKAKDKSREHRRGPWGHSRRTSRSRSGSPGSSSYEHYESRKKKKRRSASRPRGRECSPTSSLERLCRHKHQRERSHERPDRKESVAWPRDRRKRRSRSPSSEHRAREHRRPRSREKWPQTRSHSPERKGAVREASPAPLAQGEPGREDLPTRLPALGEAHVSPEVATADKAPLQAPPVLEVAAECEPDDLDLDYGDSVEAGHVFDDFSSDAVFIQLDDMSSPPSPESTDSSPERDFPLKPALPPASLAVAAIQREVSLMHDEDPSQPPPLPEGTQEPHLLRPDAAEKAEAPSSPDVAPAGKEDSPSASGRVQEAARPEEVVSQTPLLRSRALVKRVTWNLQESESSAPAEDRAPRAPLHRPQKPREGAWDMEDVAPTGVRQVFSELPFPSHVLPEPGFPDTDPSQVYSPGLPPAPAQPSSIPPCALVSQPTVQFILQGSLPLVGCGAAQTLAPVPAALTPASEPASQATAASNSEEKTPAPRLAAEKTKKEEYMKKLHMQERAVEEVKLAIKPFYQKREVTKEEYKDILRKAVQKICHSKSGEINPVKVANLVKAYVDKYRHMRRHKKPEAGEEPPTQGAEG,mutated_sequence,1.0,1649.0,UPI000041A715.a2m,UPI000041A715.npy,gnomAD +UPI0000001BCE,UPI0000001BCE.csv,MAHEMIGTQIVTERLVALLESGTEKVLLIDSRPFVEYNTSHILEAININCSKLMKRRLQQDKVLITELIQHSAKHKVDIDCSQKVVVYDQSSQDVASLSSDCFLTVLLGKLEKSFNSVHLLAGGFAEFSRCFPGLCEGKSTLVPTCISQPCLPVANIGPTRILPNLYLGCQRDVLNKELMQQNGIGYVLNASNTCPKPDFIPESHFLRVPVNDSFCEKILPWLDKSVDFIEKAKASNGCVLVHCLAGISRSATIAIAYIMKRMDMSLDEAYRFVKEKRPTISPNFNFLGQLLDYEKKIKNQTGASGPKSKLKLLHLEKPNEPVPAVSEGGQKSETPLSPPCADSATSEAAGQRPVHPASVPSVPSVQPSLLEDSPLVQALSGLHLSADRLEDSNKLKRSFSLDIKSVSYSASMAASLHGFSSSEDALEYYKPSTTLDGTNKLCQFSPVQELSEQTPETSPDKEEASIPKKLQTARPSDSQSKRLHSVRTSSSGTAQRSLLSPLHRSGSVEDNYHTSFLFGLSTSQQHLTKSAGLGLKGWHSDILAPQTSTPSLTSSWYFATESSHFYSASAIYGGSASYSAYSCSQLPTCGDQVYSVRRRQKPSDRADSRRSWHEESPFEKQFKRRSCQMEFGESIMSENRSREELGKVGSQSSFSGSMEIIEVS,mutated_sequence,1.0,665.0,UPI0000001BCE.a2m,UPI0000001BCE.npy,gnomAD +UPI0001B79057,UPI0001B79057.csv,MALPQGLLTFRDVAIEFSQEEWKCLDPAQRTLYRDVMLENYRNLVSLGSCCAVEVGLDLLGSRHPPRPPKKLGLQAPTPVPNYWLLFLNFLHMCVLRANMSLLWTTSSWFLLIESVFAF,mutated_sequence,1.0,119.0,UPI0001B79057.a2m,UPI0001B79057.npy,gnomAD +UPI000006D1F4,UPI000006D1F4.csv,MKLNERSLAFYATCDAPVDNAGFLYKKGGRHAAYHRRWFVLRGNMLFYFEDAASREPVGVIILEGCTVELVEAAEEFAFAVRFAGTRARTYVLAAESQDAMEGWVKALSRASFDYLRLVVRELEQQLAAVRGGGGMALPQPQPQSLPLPPSLPSALAPVPSLPSAPAPVPALPLPRRPSALPPKENGCAVWSTEATFRPGPEPPPPPPRRRASAPHGPLDMAPFARLHECYGQEIRALRGQWLSSRVQP,mutated_sequence,1.0,249.0,UPI000006D1F4.a2m,UPI000006D1F4.npy,gnomAD +UPI00015FA087,UPI00015FA087.csv,MGNQDGKLKRSAGDALHEGGGGAEDALGPRDVEATKKGSGGKKALGKHGKGGGGGGGGGESGKKKSKSDSRASVFSNLRIRKNLSKGKGAGGSREDVLDSQALQTGELDSAHSLLTKTPDLSLSADEAGLSDTECADPFEVTGPGGPGPAEARVGGRPIAEDVETAAGAQDGQRTSSGSDTDIYSFHSATEQEDLLSDIQQAIRLQQQQQQQLQLQLQQQQQQQQLQGAEEPAAPPTAVSPQPGAFLGLDRFLLGPSGGAGEAPGSPDTEQALSALSDLPESLAAEPREPQQPPSPGGLPVSEAPSLPAAQPAAKDSPSSTAFPFPEAGPGEEAAGAPVRGAGDTDEEGEEDAFEDAPRGSPGEEWAPEVGEDAPQRLGEEPEEEAQGPDAPAAASLPGSPAPSQRCFKPYPLITPCYIKTTTRQLSSPNHSPSQSPNQSPRIKRRPEPSLSRGSRTALASVAAPAKKHRADGGLAAGLSRSADWTEELGARTPRVGGSAHLLERGVASDSGGGVSPALAAKASGAPAAADGFQNVFTGRTLLEKLFSQQENGPPEEAEKFCSRIIAMGLLLPFSDCFREPCNQNAQTNAASFDQDQLYTWAAVSQPTHSLDYSEGQFPRRVPSMGPPSKPPDEEHRLEDAETESQSAVSETPQKRSDAVQKEVVDMKSEGQATVIQQLEQTIEDLRTKIAELERQYPALDTEVASGHQGLENGVTASGDVCLEALRLEEKEVRHHRILEAKSIQTSPTEEGGVLTLPPVDGLPGRPPCPPGAESGPQTKFCSEISLIVSPRRISVQLDSHQPTQSISQPPPPPSLLWSAGQGQPGSQPPHSISTEFQTSHEHSVSSAFKNSCNIPSPPPLPCTESSSSMPGLGMVPPPPPPLPGMTVPTLPSTAIPQPPPLQGTEMLPPPPPPLPGAGIPPPPPLPGAGILPLPPLPGAGIPPPPPLPGAAIPPPPPLPGAGIPLPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGVGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPRVGIPPPPPLPGAGIPPPPPLPGAGIPPPPPLPGVGIPPPPPLPGVGIPPPPPLPGAGIPPPPPLPGMGIPPAPAPPLPPPGTGIPPPPLLPVSGPPLLPQVGSSTLPTPQVCGFLPPPLPSGLFGLGMNQDKGSRKQPIEPCRPMKPLYWTRIQLHSKRDSSTSLIWEKIEEPSIDCHEFEELFSKTAVKERKKPISDTISKTKAKQVVKLLSNKRSQAVGILMSSLHLDMKDIQHAVVNLDNSVVDLETLQALYENRAQSDELEKIEKHGRSSKDKENAKSLDKPEQFLYELSLIPNFSERVFCILFQSTFSESICSIRRKLELLQKLCETLKNGPGVMQVLGLVLAFGNYMNGGNKTRGQADGFGLDILPKLKDVKSSDNSRSLLSYIVSYYLRNFDEDAGKEQCLFPLPEPQDLFQASQMKFEDFQKDLRKLKKDLKACEVEAGKVYQVSSKEHMQPFKENMEQFIIQAKIDQEAEENSLTETHKCFLETTAYFFMKPKLGEKEVSPNAFFSIWHEFSSDFKDFWKKENKLLLQERVKEAEEVCRQKKGKSLYKIKPRHDSGIKAKISMKT,mutated_sequence,1.0,1722.0,UPI00015FA087.a2m,UPI00015FA087.npy,gnomAD +UPI000047099D,UPI000047099D.csv,MHGAARAPATSVSADCCIPAGLRLGPVPGTFKLGKYLSDRREPGPKKKVRMVRGELVDESGGSPLEWIGLIRAARNSQEQTLEAIADLPGGQIFYRALRDVQPGEELTVWYSNSLAQWFDIPTTATPTHDEKGEERYICWYCWRTFRYPNSLKAHLRFHCVFSGGGGGAFLHHEHAARQGAVPAADGLGLSPKPPAPDFAAPSQAGTLRPHPLGPPPVQACGAREGIKREASSAPSATSPTPGKWGQPKKGKEQLDRALDMSGAARGQGHFLGIVGGSSAGVGSLAFYPGVRSAFKPAGLARAAAAAHGDPYREESSSKQGAGLALGRLLGGGRACGRPGSGENSAAGGAGHHHHHHAHHHHHPKCLLAGDPPPPPPPGLPCSGALRGFPLLSVPPEEASAFKHVERAPPAAAALPGARYAQLPPAPGLPLERCALPPLDPGGLKAYPGGECSHLPAVMPAFTVYNGELLYGSPATTAYYPLKLHFGGLLKYPESISYFSGPAAAALSPAELGSLASIDREIAMHNQQLSEMAAGKGRGRLDSGTLPPAVAAAGGTGGGGSGGSGAGKPKTGHLCLYCGKLYSRKYGLKIHMRTHTGYKPLKCKVCLRPFGDPSNLNKHIRLHAEGNTPYRCEFCGKVLVRRRDLERHVKSRHPGQSLLAKAGDGPGAEPGYPPEPGDPKSDDSDVDVCFTDDQSDPEVGGGGERDL,mutated_sequence,1.0,707.0,UPI000047099D.a2m,UPI000047099D.npy,gnomAD +UPI000000D866,UPI000000D866.csv,MALGLEQAEEQRLYQQTLLQDGLKDMLDHGKFLDCVVRAGEREFPCHRLVLAACSPYFRARFLAEPERAGELHLEEVSPDVVAQVLHYLYTSEIALDEASVQDLFAAAHRFQIPSIFTICVSFLQKRLCLSNCLAVFRLGLLLDCARLAVAARDFICAHFTLVARDADFLGLSADELIAIISSDGLNVEKEEAVFEAVMRWAGSGDAEAQAERQRALPTVFESVRCRLLPRAFLESRVERHPLVRAQPELLRKVQMVKDAHEGRITTLRKKKKGKDGAGAKEADKGTSKAKAEEDEEAERILPGILNDTLRFGMFLQDLIFMISEEGAVAYDPAANECYCASLSNQVPKNHVSLVTKENQVFVAGGLFYNEDNKEDPMSAYFLQFDHLDSEWLGMPPLPSPRCLFGLGEALNSIYVVGGREIKDGERCLDSVMCYDRLSFKWGESDPLPYVVYGHTVLSHMDLVYVIGGKGSDRKCLNKMCVYDPKKFEWKELAPMQTARSLFGATVHDGRIIVAAGVTDTGLTSSAEVYSITDNKWAPFEAFPQERSSLSLVSLVGTLYAIGGFATLETESGELVPTELNDIWRYNEEEKKWEGVLREIAYAAGATFLPVRLNVLCLTKM,mutated_sequence,1.0,621.0,UPI000000D866.a2m,UPI000000D866.npy,gnomAD +UPI0000DBEE85,UPI0000DBEE85.csv,MSRRKPASGGLAASSSAPARQAVLSRFFQSTGSLKSTSSSTGAADQVDPGAAAAAAAAAAAAPPAPPAPAFPPQLPPHIATEIDRRKKRPLENDGPVKKKVKKVQQKEGGSDLGMSGNSEPKKCLRTRNVSKSLEKLKEFCCDSALPQSRVQTESLQERFAVLPKCTDFDDISLLHAKNAVSSEDSKRQINQKDTTLFDLSQFGSSNTSHENLQKTASKSANKRSKSIYTPLELQYIEMKQQHKDAVLCVECGYKYRFFGEDAEIAARELNIYCHLDHNFMTASIPTHRLFVHVRRLVAKGYKVGVVKQTETAALKAIGDNRSSLFSRKLTALYTKSTLIGEDVNPLIKLDDAVNVDEIMTDTSTSYLLCISENKENVRDKKKGNIFIGIVGVQPATGEVVFDSFQDSASRSELETRMSSLQPVELLLPSALSEQTEALIHRATSVSVQDDRIRVERMDNIYFEYSHAFQAVTEFYAKDTVDIKGSQIISGIVNLEKPVICSLAAIIKYLKEFNLEKMLSKPENFKQLSSKMEFMTINGTTLRNLEILQNQTDMKTKGSLLWVLDHTKTSFGRRKLKKWVTQPLLKLREINARLDAVSEVLHSESSVFGQIENHLRKLPDIERGLCSIYHKKCSTQEFFLIVKTLYHLKSEFQAIIPAVNSHIQSDLLRTVILEIPELLSPVEHYLKILNEQAAKVGDKTELFKDLSDFPLIKKRKDEIQGVIDEIRMHLQEIRKILKNPSAQYVTVSGQEFMIEIKNSAVSCIPTDWVKVGSTKAVSRFHSPFIVENYRHLNQLREQLVLDCSAEWLDFLEKFSEHYHSLCKAVHHLATVDCIFSLAKVAKQGDYCRPTVQEERKIVIKNGRHPVIDVLLGEQDQYVPNNTDLSEDSERVMIITGPNMGGKSSYIKQVALITIMAQIGSYVPAEEATIGIVDGIFTRMGAADNIYKGQSTFMEELTDTAEIIRKATSQSLVILDELGRGTSTHDGIAIAYATLEYFIRDVKSLTLFVTHYPPVCELEKNYSHQVGNYHMGFLVSEDESKLDPGAAEQVPDFVTFLYQITRGIAARSYGLNVAKLADVPGEILKKAAHKSKELEGLINTKRKRLKYFAKLWTMHNAQDLQKWTEEFNMEETQTSLLH,mutated_sequence,1.0,1137.0,UPI0000DBEE85.a2m,UPI0000DBEE85.npy,gnomAD +UPI000000DCCB,UPI000000DCCB.csv,MSASKIPLFKMKDLILILCLLEMSFAVPFFPQQSGTPGMASLSLETMRQLGSLQRLNTLSQYSRYGFGKSFNSLWMHGLLPPHSSLPWMRPREHETQQYEYSLPVHPPPLPSQPSLKPQQPGLKPFLQSAAATTNQATALKEALQPPIHLGHLPLQEGELPLVQQQVAPSDKPPKPELPGVDFADPQGPSLPGMDFPDPQGPSLPGLDFADPQGSTIFQIARLISHGPMPQNKQSPLYPGMLYVPFGANQLNAPARLGIMSSEEVAGGREDPMAYGAMFPGFGGMRPGFEGMPHNPAMGGDFTLEFDSPVAATKGPENEEGGAQGSPMPEANPDNLENPAFLTELEPAPHAGLLALPKDDIPGLPRSPSGKMKGLPSVTPAAADPLMTPELADVYRTYDADMTTSVDFQEEATMDTTMAPNSLQTSMPGNKAQEPEMMHDAWHFQEP,mutated_sequence,1.0,447.0,UPI000000DCCB.a2m,UPI000000DCCB.npy,gnomAD +UPI000013C495,UPI000013C495.csv,MEIPKLLPARGTLQGGGGGGIPAGGGRVHRGPDSPAGQVPTRRLLLLRGPQDGGPGRRREEASTASRGPGPSLLAPRTDQPSGGGGGGGDDFFLVLLDPVGGDVETAGSGQAAGPVLREEAEEGPGLQGGESGANPAGPTALGPRCLSAVPTPAPISAPGPAAAFAGTVTIHNQDLLLRFENGVLTLATPPPHAWEPGAAPAQQPGCLIAPQAGFPHAAHPGDCPELPPDLLLAEPAEPAPAPAPEEEAEGPAAALGPRGPLGSGPGVVLYLCPEAQCGQTFAKKHQLKVHLLTHSSSQGQRPFKCPLGGCGWTFTTSYKLKRHLQSHDKLRPFGCPAEGCGKSFTTVYNLKAHMKGHEQENSFKCEVCEESFPTQAKLSAHQRSHFEPERPYQCAFSGCKKTFITVSALFSHNRAHFREQELFSCSFPGCSKQYDKACRLKIHLRSHTGERPFLCDFDGCGWNFTSMSKLLRHKRKHDDDRRFMCPVEGCGKSFTRAEHLKGHSITHLGTKPFVCPVAGCCARFSARSSLYIHSKKHLQDVDTWKSRCPISSCNKLFTSKHSMKTHMVKRHKVGQDLLAQLEAANSLTPSSELTSQRQNDLSDAEIVSLFSDVPDSTSAALLDTALVNSGILTIDVASVSSTLAGHLPANNNNSVGQAVDPPSLMATSDPPQSLDTSLFFGTAATGFQQSSLNMDEVSSVSVGPLGSLDSLAMKNSSPEPQALTPSSKLTVDTDALTPSSTLCENSVSELLTPTKAEWNVHPDSDFFGQEGETQFGFPNAAGNHGSQKETDLITVTGSSFLV,mutated_sequence,1.0,803.0,UPI000013C495.a2m,UPI000013C495.npy,gnomAD +UPI000016A402,UPI000016A402.csv,MNAETCVSYCESPAAAMDAYYSPVSQSREGSSPFRAFPGGDKFGTTFLSAAAKAQGFGDAKSRARYGAGQQDLATPLESGAGARGSFNKFQPQPSTPQPQPPPQPQPQQQQPQPQPPAQPHLYLQRGACKTPPDGSLKLQEGSSGHSAALQVPCYAKESSLGEPELPPDSDTVGMDSSYLSVKEAGVKGPQDRASSDLPSPLEKADSESNKGKKRRNRTTFTSYQLEELEKVFQKTHYPDVYAREQLAMRTDLTEARVQVWFQNRRAKWRKRERFGQMQQVRTHFSTAYELPLLTRAENYAQIQNPSWLGNNGAASPVPACVVPCDPVPACMSPHAHPPGSGASSVTDFLSVSGAGSHVGQTHMGSLFGAASLSPGLNGYELNGEPDRKTSSIAALRMKAKEHSAAISWAT,mutated_sequence,1.0,411.0,UPI000016A402.a2m,UPI000016A402.npy,gnomAD +UPI00001AE452,UPI00001AE452.csv,MPPPSPDSENGFYPGLPSSMNPAFFPSFSPVSPHGCTGLSVPTSGGGGGGFGGPFSATAVPPPPPPAMNIPQQQPPPPAAPQQPQSRRSPVSPQLQQQHQAAAAAFLQQRNSYNHHQPLLKQSPWSNHQSSGWGTGSMSWGAMHGRDHRRTGNMGIPGTMNQISPLKKPFSGNVIAPPKFTRSTPSLTPKSWIEDNVFRTDNNSNTLLPLQVRSSLQLPAWGSDSLQDSWCTAAGTSRIDQDRSRMYDSLNMHSLENSLIDIMRAEHDPLKGRLSYPHPGTDNLLMLNGRSSLFPIDDGLLDDGHSDQVGVLNSPTCYSAHQNGERIERFSRKVFVGGLPPDIDEDEITASFRRFGPLVVDWPHKAESKSYFPPKGYAFLLFQEESSVQALIDACIEEDGKLYLCVSSPTIKDKPVQIRPWNLSDSDFVMDGSQPLDPRKTIFVGGVPRPLRAVELAMIMDRLYGGVCYAGIDTDPELKYPKGAGRVAFSNQQSYIAAISARFVQLQHGDIDKRVEVKPYVLDDQMCDECQGARCGGKFAPFFCANVTCLQYYCEFCWANIHSRAGREFHKPLVKEGADRPRQIHFRWN,mutated_sequence,1.0,589.0,UPI00001AE452.a2m,UPI00001AE452.npy,gnomAD +UPI0000070A05,UPI0000070A05.csv,MFVQEEKIFAGKVLRLHICASDGAEWLEEATEDTSVEKLKERCLKHCAHGSLEDPKSITHHKLIHAASERVLSDARTILEENIQDQDVLLLIKKRAPSPLPKMADVSAEEKKKQDQKAPDKEAILRATANLPSYNMDRAAVQTNMRDFQTELRKILVSLIEVAQKLLALNPDAVELFKKANAMLDEDEDERVDEAALRQLTEMGFPENRATKALQLNHMSVPQAMEWLIEHAEDPTIDTPLPGQAPPEAEGATAAASEAAAGASATDEEARDELTEIFKKIRRKREFRADARAVISLMEMGFDEKEVIDALRVNNNQQNAACEWLLGDRKPSPEELDKGIDPDSPLFQAILDNPVVQLGLTNPKTLLAFEDMLENPLNSTQWMNDPETGPVMLQISRIFQTLNRT,mutated_sequence,1.0,405.0,UPI0000070A05.a2m,UPI0000070A05.npy,gnomAD +UPI000013C9CD,UPI000013C9CD.csv,MNQNTTEPVAATETLAEVPEHVLRGLPEEVRLFPSAVDKTRIGVWATKPILKGKKFGPFVGDKKKRSQVKNNVYMWEVYYPNLGWMCIDATDPEKGNWLRYVNWACSGEEQNLFPLEINRAIYYKTLKPIAPGEELLVWYNGEDNPEIAAAIEEERASARSKRSSPKSRKGKKKSQENKNKGNKIQDIQLKTSEPDFTSANMRDSAEGPKEDEEKPSASALEQPATLQEVASQEVPPELATPAPAWEPQPEPDERLEAAACEVNDLGEEEEEEEEEDEEEEEDDDDDELEDEGEEEASMPNENSVKEPEIRCDEKPEDLLEEPKTTSEETLEDCSEVTPAMQIPRTKEEANGDVFETFMFPCQHCERKFTTKQGLERHMHIHISTVNHAFKCKYCGKAFGTQINRRRHERRHEAGLKRKPSQTLQPSEDLADGKASGENVASKDDSSPPSLGPDCLIMNSEKASQDTINSSVVEENGEVKELHPCKYCKKVFGTHTNMRRHQRRVHERHLIPKGVRRKGGLEEPQPPAEQAQATQNVYVPSTEPEEEGEADDVYIMDISSNISENLNYYIDGKIQTNNNTSNCDVIEMESASADLYGINCLLTPVTVEITQNIKTTQVPVTEDLPKEPLGSTNSEAKKRRTASPPALPKIKAETDSDPMVPSCSLSLPLSISTTEAVSFHKEKSVYLSSKLKQLLQTQDKLTPAGISATEIAKLGPVCVSAPASMLPVTSSRFKRRTSSPPSSPQHSPALRDFGKPSDGKAAWTDAGLTSKKSKLESHSDSPAWSLSGRDERETVSPPCFDEYKMSKEWTASSAFSSVCNQQPLDLSSGVKQKAEGTGKTPVQWESVLDLSVHKKHCSDSEGKEFKESHSVQPTCSAVKKRKPTTCMLQKVLLNEYNGIDLPVENPADGTRSPSPCKSLEAQPDPDLGPGSGFPAPTVESTPDVCPSSPALQTPSLSSGQLPPLLIPTDPSSPPPCPPVLTVATPPPPLLPTVPLPAPSSSASPHPCPSPLSNATAQSPLPILSPTVSPSPSPIPPVEPLMSAASPGPPTLSSSSSSSSSSSSFSSSSSSSSPSPPPLSAISSVVSSGDNLEASLPMISFKQEELENEGLKPREEPQSAAEQDVVVQETFNKNFVCNVCESPFLSIKDLTKHLSIHAEEWPFKCEFCVQLFKDKTDLSEHRFLLHGVGNIFVCSVCKKEFAFLCNLQQHQRDLHPDKVCTHHEFESGTLRPQNFTDPSKAHVEHMQSLPEDPLETSKEEEELNDSSEELYTTIKIMASGIKTKDPDVRLGLNQHYPSFKPPPFQYHHRNPMGIGVTATNFTTHNIPQTFTTAIRCTKCGKGVDNMPELHKHILACASASDKKRYTPKKNPVPLKQTVQPKNGVVVLDNSGKNAFRRMGQPKRLNFSVELSKMSSNKLKLNALKKKNQLVQKAILQKNKSAKQKADLKNACESSSHICPYCNREFTYIGSLNKHAAFSCPKKPLSPPKKKVSHSSKKGGHSSPASSDKNSNSNHRRRTADAEIKMQSMQTPLGKTRARSSGPTQVPLPSSSFRSKQNVKFAASVKSKKPSSSSLRNSSPIRMAKITHVEGKKPKAVAKNHSAQLSSKTSRSLHVRVQKSKAVLQSKSTLASKKRTDRFNIKSRERSGGPVTRSLQLAAAADLSENKREDGSAKQELKDFSYSLRLASRCSPPAAPYITRQYRKVKAPAAAQFQGPFFKE,mutated_sequence,1.0,1718.0,UPI000013C9CD.a2m,UPI000013C9CD.npy,gnomAD +UPI000004F239,UPI000004F239.csv,MEQSNYSVYADFILLGLFSNARFPWLLFALILLVFLTSIASNVVKIILIHIDSRLHTPMYFLLSQLSLRDILYISTIVPKMLVDQVMSQRAISFAGCTAQHFLYLTLAGAEFFLLGLMSYDRYVAICNPLHYPVLMSRKICWLIVAAAWLGGSIDGFLLTPVTMQFPFCASREINHFFCEVPALLKLSCTDTSAYETAMYVCCIMMLLIPFSVISGSYTRILITVYRMSEAEGRGKAVATCSSHMVVVSLFYGAAMYTYVLPHSYHTPEQDKAVSAFYTILTPMLNPLIYSLRNKDVTGALQKVVGRCVSSGKVTTF,mutated_sequence,1.0,317.0,UPI000004F239.a2m,UPI000004F239.npy,gnomAD +UPI0000129FBA,UPI0000129FBA.csv,MRALWVLGLCCVLLTFGSVRADDEVDVDGTVEEDLGKSREGSRTDDEVVQREEEAIQLDGLNASQIRELREKSEKFAFQAEVNRMMKLIINSLYKNKEIFLRELISNASDALDKIRLISLTDENALSGNEELTVKIKCDKEKNLLHVTDTGVGMTREELVKNLGTIAKSGTSEFLNKMTEAQEDGQSTSELIGQFGVGFYSAFLVADKVIVTSKHNNDTQHIWESDSNEFSVIADPRGNTLGRGTTITLVLKEEASDYLELDTIKNLVKKYSQFINFPIYVWSSKTETVEEPMEEEEAAKEEKEESDDEAAVEEEEEEKKPKTKKVEKTVWDWELMNDIKPIWQRPSKEVEEDEYKAFYKSFSKESDDPMAYIHFTAEGEVTFKSILFVPTSAPRGLFDEYGSKKSDYIKLYVRRVFITDDFHDMMPKYLNFVKGVVDSDDLPLNVSRETLQQHKLLKVIRKKLVRKTLDMIKKIADDKYNDTFWKEFGTNIKLGVIEDHSNRTRLAKLLRFQSSHHPTDITSLDQYVERMKEKQDKIYFMAGSSRKEAESSPFVERLLKKGYEVIYLTEPVDEYCIQALPEFDGKRFQNVAKEGVKFDESEKTKESREAVEKEFEPLLNWMKDKALKDKIEKAVVSQRLTESPCALVASQYGWSGNMERIMKAQAYQTGKDISTNYYASQKKTFEINPRHPLIRDMLRRIKEDEDDKTVLDLAVVLFETATLRSGYLLPDTKAYGDRIERMLRLSLNIDPDAKVEEEPEEEPEETAEDTTEDTEQDEDEEMDVGTDEEEETAKESTAEKDEL,mutated_sequence,1.0,803.0,UPI0000129FBA.a2m,UPI0000129FBA.npy,gnomAD +UPI0000141B94,UPI0000141B94.csv,MDPPRPALLALLALPALLLLLLAGARAEEEMLENVSLVCPKDATRFKHLRKYTYNYEAESSSGVPGTADSRSATRINCKVELEVPQLCSFILKTSQCTLKEVYGFNPEGKALLKKTKNSEEFAAAMSRYELKLAIPEGKQVFLYPEKDEPTYILNIKRGIISALLVPPETEEAKQVLFLDTVYGNCSTHFTVKTRKGNVATEISTERDLGQCDRFKPIRTGISPLALIKGMTRPLSTLISSSQSCQYTLDAKRKHVAEAICKEQHLFLPFSYKNKYGMVAQVTQTLKLEDTPKINSRFFGEGTKKMGLAFESTKSTSPPKQAEAVLKTLQELKKLTISEQNIQRANLFNKLVTELRGLSDEAVTSLLPQLIEVSSPITLQALVQCGQPQCSTHILQWLKRVHANPLLIDVVTYLVALIPEPSAQQLREIFNMARDQRSRATLYALSHAVNNYHKTNPTGTQELLDIANYLMEQIQDDCTGDEDYTYLILRVIGNMGQTMEQLTPELKSSILKCVQSTKPSLMIQKAAIQALRKMEPKDKDQEVLLQTFLDDASPGDKRLAAYLMLMRSPSQADINKIVQILPWEQNEQVKNFVASHIANILNSEELDIQDLKKLVKEALKESQLPTVMDFRKFSRNYQLYKSVSLPSLDPASAKIEGNLIFDPNNYLPKESMLKTTLTAFGFASADLIEIGLEGKGFEPTLEALFGKQGFFPDSVNKALYWVNGQVPDGVSKVLVDHFGYTKDDKHEQDMVNGIMLSVEKLIKDLKSKEVPEARAYLRILGEELGFASLHDLQLLGKLLLMGARTLQGIPQMIGEVIRKGSKNDFFLHYIFMENAFELPTGAGLQLQISSSGVIAPGAKAGVKLEVANMQAELVAKPSVSVEFVTNMGIIIPDFARSGVQMNTNFFHESGLEAHVALKAGKLKFIIPSPKRPVKLLSGGNTLHLVSTTKTEVIPPLIENRQSWSVCKQVFPGLNYCTSGAYSNASSTDSASYYPLTGDTRLELELRPTGEIEQYSVSATYELQREDRALVDTLKFVTQAEGAKQTEATMTFKYNRQSMTLSSEVQIPDFDVDLGTILRVNDESTEGKTSYRLTLDIQNKKITEVALMGHLSCDTKEERKIKGVISIPRLQAEARSEILAHWSPAKLLLQMDSSATAYGSTVSKRVAWHYDEEKIEFEWNTGTNVDTKKMTSNFPVDLSDYPKSLHMYANRLLDHRVPQTDMTFRHVGSKLIVAMSSWLQKASGSLPYTQTLQDHLNSLKEFNLQNMGLPDFHIPENLFLKSDGRVKYTLNKNSLKIEIPLPFGGKSSRDLKMLETVRTPALHFKSVGFHLPSREFQVPTFTIPKLYQLQVPLLGVLDLSTNVYSNLYNWSASYSGGNTSTDHFSLRARYHMKADSVVDLLSYNVQGSGETTYDHKNTFTLSYDGSLRHKFLDSNIKFSHVEKLGNNPVSKGLLIFDASSSWGPQMSASVHLDSKKKQHLFVKEVKIDGQFRVSSFYAKGTYGLSCQRDPNTGRLNGESNLRFNSSYLQGTNQITGRYEDGTLSLTSTSDLQSGIIKNTASLKYENYELTLKSDTNGKYKNFATSNKMDMTFSKQNALLRSEYQADYESLRFFSLLSGSLNSHGLELNADILGTDKINSGAHKATLRIGQDGISTSATTNLKCSLLVLENELNAELGLSGASMKLTTNGRFREHNAKFSLDGKAALTELSLGSAYQAMILGVDSKNIFNFKVSQEGLKLSNDMMGSYAEMKFDHTNSLNIAGLSLDFSSKLDNIYSSDKFYKQTVNLQLQPYSLVTTLNSDLKYNALDLTNNGKLRLEPLKLHVAGNLKGAYQNNEIKHIYAISSAALSASYKADTVAKVQGVEFSHRLNTDIAGLASAIDMSTNYNSDSLHFSNVFRSVMAPFTMTIDAHTNGNGKLALWGEHTGQLYSKFLLKAEPLAFTFSHDYKGSTSHHLVSRKSISAALEHKVSALLTPAEQTGTWKLKTQFNNNEYSQDLDAYNTKDKIGVELTGRTLADLTLLDSPIKVPLLLSEPINIIDALEMRDAVEKPQEFTIVAFVKYDKNQDVHSINLPFFETLQEYFERNRQTIIVVLENVQRNLKHINIDQFVRKYRAALGKLPQQANDYLNSFNWERQVSHAKEKLTALTKKYRITENDIQIALDDAKINFNEKLSQLQTYMIQFDQYIKDSYDLHDLKIAIANIIDEIIEKLKSLDEHYHIRVNLVKTIHDLHLFIENIDFNKSGSSTASWIQNVDTKYQIRIQIQEKLQQLKRHIQNIDIQHLAGKLKQHIEAIDVRVLLDQLGTTISFERINDILEHVKHFVINLIGDFEVAEKINAFRAKVHELIERYEVDQQIQVLMDKLVELAHQYKLKETIQKLSNVLQQVKIKDYFEKLVGFIDDAVKKLNELSFKTFIEDVNKFLDMLIKKLKSFDYHQFVDETNDKIREVTQRLNGEIQALELPQKAEALKLFLEETKATVAVYLESLQDTKITLIINWLQEALSSASLAHMKAKFRETLEDTRDRMYQMDIQQELQRYLSLVGQVYSTLVTYISDWWTLAAKNLTDFAEQYSIQDWAKRMKALVEQGFTVPEIKTILGTMPAFEVSLQALQKATFQTPDFIVPLTDLRIPSVQINFKDLKNIKIPSRFSTPEFTILNTFHIPSFTIDFVEMKVKIIRTIDQMLNSELQWPVPDIYLRDLKVEDIPLARITLPDFRLPEIAIPEFIIPTLNLNDFQVPDLHIPEFQLPHISHTIEVPTFGKLYSILKIQSPLFTLDANADIGNGTTSANEAGIAASITAKGESKLEVLNFDFQANAQLSNPKINPLALKESVKFSSKYLRTEHGSEMLFFGNAIEGKSNTVASLHTEKNTLELSNGVIVKINNQLTLDSNTKYFHKLNIPKLDFSSQADLRNEIKTLLKAGHIAWTSSGKGSWKWACPRFSDEGTHESQISFTIEGPLTSFGLSNKINSKHLRVNQNLVYESGSLNFSKLEIQSQVDSQHVGHSVLTAKGMALFGEGKAEFTGRHDAHLNGKVIGTLKNSLFFSAQPFEITASTNNEGNLKVRFPLRLTGKIDFLNNYALFLSPSAQQASWQVSARFNQYKYNQNFSAGNNENIMEAHVGINGEANLDFLNIPLTIPEMRLPYTIITTPPLKDFSLWEKTGLKEFLKTTKQSFDLSVKAQYKKNKHRHSITNPLAVLCEFISQSIKSFDRHFEKNRNNALDFVTKSYNETKIKFDKYKAEKSHDELPRTFQIPGYTVPVVNVEVSPFTIEMSAFGYVFPKAVSMPSFSILGSDVRVPSYTLILPSLELPVLHVPRNLKLSLPDFKELCTISHIFIPAMGNITYDFSFKSSVITLNTNAELFNQSDIVAHLLSSSSSVIDALQYKLEGTTRLTRKRGLKLATALSLSNKFVEGSHNSTVSLTTKNMEVSVATTTKAQIPILRMNFKQELNGNTKSKPTVSSSMEFKYDFNSSMLYSTAKGAVDHKLSLESLTSYFSIESSTKGDVKGSVLSREYSGTIASEANTYLNSKSTRSSVKLQGTSKIDDIWNLEVKENFAGEATLQRIYSLWEHSTKNHLQLEGLFFTNGEHTSKATLELSPWQMSALVQVHASQPSSFHDFPDLGQEVALNANTKNQKIRWKNEVRIHSGSFQSQVELSNDQEKAHLDIAGSLEGHLRFLKNIILPVYDKSLWDFLKLDVTTSIGRRQHLRVSTAFVYTKNPNGYSFSIPVKVLADKFIIPGLKLNDLNSVLVMPTFHVPFTDLQVPSCKLDFREIQIYKKLRTSSFALNLPTLPEVKFPEVDVLTKYSQPEDSLIPFFEITVPESQLTVSQFTLPKSVSDGIAALDLNAVANKIADFELPTIIVPEQTIEIPSIKFSVPAGIVIPSFQALTARFEVDSPVYNATWSASLKNKADYVETVLDSTCSSTVQFLEYELNVLGTHKIEDGTLASKTKGTFAHRDFSAEYEEDGKYEGLQEWEGKAHLNIKSPAFTDLHLRYQKDKKGISTSAASPAVGTVGMDMDEDDDFSKWNFYYSPQSSPDKKLTIFKTELRVRESDEETQIKVNWEEEAASGLLTSLKDNVPKATGVLYDYVNKYHWEHTGLTLREVSSKLRRNLQNNAEWVYQGAIRQIDDIDVRFQKAASGTTGTYQEWKDKAQNLYQELLTQEGQASFQGLKDNVFDGLVRVTQEFHMKVKHLIDSLIDFLNFPRFQFPGKPGIYTREELCTMFIREVGTVLSQVYSKVHNGSEILFSYFQDLVITLPFELRKHKLIDVISMYRELLKDLSKEAQEVFKAIQSLKTTEVLRNLQDLLQFIFQLIEDNIKQLKEMKFTYLINYIQDEINTIFSDYIPYVFKLLKENLCLNLHKFNEFIQNELQEASQELQQIHQYIMALREEYFDPSIVGWTVKYYELEEKIVSLIKNLLVALKDFHSEYIVSASNFTSQLSSQVEQFLHRNIQEYLSILTDPDGKGKEKIAELSATAQEIIKSQAIATKKIISDYHQQFRYKLQDFSDQLSDYYEKFIAESKRLIDLSIQNYHTFLIYITELLKKLQSTTVMNPYMKLAPGELTIIL,mutated_sequence,1.0,4563.0,UPI0000141B94.a2m,UPI0000141B94.npy,gnomAD +UPI000046D388,UPI000046D388.csv,MNMSQASVSFQDVTVEFTREEWQHLGPVERTLYRDVMLENYSHLISVGYCITKPKVISKLEKGEEPWSLEDEFLNQRYPGYFKVDHIKGIREKQEKPLWQEIFISDADKTLSKEGQKVLEKPFNLEIAPELSEKISCKCDSHRMNLPVASQLIISERKYSRKKTEYMNVCEKLQLDIKHEKAHAEEKSYEHGENAKAFSYKKDQHWKFQTLEESFECDGSGQGLYDKTICITPQSFLTGEKSCKDDEFRKNFDKITLFNHMRTDTRGKCSDLNEYGTSCDKTTAVEYNKVHMAMTHYECNERGINFSRKSPLTQSQRTITGWSAFESNKCEENFSQSSAHIVHQKTQAGDKFGEHNECTDALYQKLDFTAHQRIHTEDKFYLSDEHGKCRKSFYRKAHLIQHQRPHSGEKTYQYEECAKSFCSSSHPIQHPGTYVGFKLYECNECGKAFCQNSNLSKHLRIHTKEKPCDNNGCGRSYKSPLIGHQKTDAEMELCGGSEYGKTSHLKGHQRILMGEKPYECIECGKTFSKTSHLRAHQRIHTGEKPYECVECEKTFSHKTHLSVHQRVHTGEKPYECNDCGKSFTYNSALRAHQRIHTGEKPYECSDCEKTFAHNSALRAHHRIHTGEKPYECNECGRSFAHISVLKAHQRIHTGEKPYECNECGRSFTYNSALRAHQRIHTGRKPYECSDCEKTFAHNSALKIHQRIHTGEKPYECNECEKTFAHNSALRAHQNIHTGEKLYECSECGKTFFQKTRLSTHRRIHTGEKPYECSKCGKTFSQKSYLSGHERIHTGEKPYECNVCGKTFVYKAALIVHQRIHTGEKPYECNQCGKTFSQRTHLCAHQRIHTGEKPYECNECGKTFADNSALRAHHRIHTGEKPYECNDCGKTFSKTSHLRAHLRTRSGEKPYECSECGKTFSEKSYVSAHQRVHTGEKPYECNVCGKPFAHNSTLRVHQRIHTGEKSYECNDCGKTFSQKSHLSAHQRIHTGEKPYECNECGKAFAQNSTLRVHQRIHTGEKPYECDECGKTFVRKAALRVHHTRMHTREKTLACNGFGKS,mutated_sequence,1.0,1059.0,UPI000046D388.a2m,UPI000046D388.npy,gnomAD +UPI00001AEF8F,UPI00001AEF8F.csv,MKAIIHLTLLALLSVNTATNQGNSADAVTTTETATSGPTVAAADTTETNFPETASTTANTPFPTATSPAPPIISTHSSSTIPTPAPPIISTHSSSTIPIPTAADSESTTNVNSLATSDIITASSPNDGLITMVPSETQSNNEMSPTTEDNQSSGPPTGTALLETSTLNSTGPSNPCQDDPCADNSLCVKLHNTSFCLCLEGYYYNSSTCKKGKVFPGKISVTVSETFDPEEKHSMAYQDLHSEITSLFKDVFGTSVYGQTVILTVSTSLSPRSEMRADDKFVNVTIVTILAETTSDNEKTVTEKINKAIRSSSSNFLNYDLTLRCDYYGCNQTADDCLNGLACDCKSDLQRPNPQSPFCVASSLKCPDACNAQHKQCLIKKSGGAPECACVPGYQEDANGNCQKCAFGYSGLDCKDKFQLILTIVGTIAGIVILSMIIALIVTARSNNKTKHIEEENLIDEDFQNLKLRSTGFTNLGAEGSVFPKVRITASRDSQMQNPYSRHSSMPRPDY,mutated_sequence,1.0,511.0,UPI00001AEF8F.a2m,UPI00001AEF8F.npy,gnomAD +UPI000013ECF6,UPI000013ECF6.csv,MAGCIPEEKTYRRFLELFLGEFRGPCGGGEPEPEPEPEPEPEPESEPEPEPELVEAEAAEASVEEPGEEAATVAATEEGDQEQDPEPEEEAAVEGEEEEEGAATAAAAPGHSAVPPPPPQLPPLPPLPRPLSERITREEVEGESLDLCLQQLYKYNCPSFLAAALARATSDEVLQSDLSAHYIPKETDGTEGTVEIETVKLARSVFSKLHEICCSWVKDFPLRRRPQLYYETSIHAIKNMRRKMEDKHVCIPDFNMLFNLEDQEEQAYFAVFDGHGGVDAAIYASIHLHVNLVRQEMFPHDPAEALCRAFRVTDERFVQKAARESLRCGTTGVVTFIRGNMLHVAWVGDSQVMLVRKGQAVELMKPHKPDREDEKQRIEALGGCVVWFGAWRVNGSLSVSRAIGDAEHKPYICGDADSASTVLDGTEDYLILACDGFYDTVNPDEAVKVVSDHLKENNGDSSMVAHKLVASARDAGSSDNITVIVVFLRDMNKAVNVSEESDWTENSFQGGQEDGGDDKENHGECKRPWPQHQCSAPADLGYDGRVDSFTDRTSLSPGSQINVLEDPGYLDLTQIEASKPHSAQFLLPVEMFGPGAPKKANLINELMMEKKSVQSSLPEWSGAGEFPTAFNLGSTGEQIYRMQSLSPVCSGLENEQFKSPGNRVSRLSHLRHHYSKKWHRFRFNPKFYSFLSAQEPSHKIGTSLSSLTGSGKRNRIRSSLPWRQNSWKGYSENMRKLRKTHDIPCPDLPWSYKIE,mutated_sequence,1.0,755.0,UPI000013ECF6.a2m,UPI000013ECF6.npy,gnomAD +UPI0000061E6E,UPI0000061E6E.csv,MEGNKTWITDITLPRFQVGPALEILLCGLFSAFYTLTLLGNGVIFGIICLDCKLHTPMYFFLSHLAIVDISYASNYVPKMLTNLMNQESTISFFPCIMQTFLYLAFAHVECLILVVMSYDRYADICHPLRYNSLMSWRVCTVLAVASWVFSFLLALVPLVLILSLPFCGPHEINHFFCEILSVLKLACADTWLNQVVIFAACVFILVGPLCLVLVSYLRILAAILRIQSGEGRRKAFSTCSSHLCVVGLFFGSAIVTYMAPKSRHPEEQQKVLSLFYSLFNPMLNPLIYSLRNAEVKGALRRALRKERLT,mutated_sequence,1.0,310.0,UPI0000061E6E.a2m,UPI0000061E6E.npy,gnomAD +UPI00001FD815,UPI00001FD815.csv,MNLQAQPKAQNKRKRCLFGGQEPAPKEQPPPLQPPQQSIRVKEEQYLGHEGPGGAVSTSQPVELPPPSSLALLNSVVYGPERTSAAMLSQQVASVKWPNSVMAPGRGPERGGGGGVSDSSWQQQPGQPPPHSTWNCHSLSLYSATKGSPHPGVGVPTYYNHPEALKREKAGGPQLDRYVRPMMPQKVQLEVGRPQAPLNSFHAAKKPPNQSLPLQPFQLAFGHQVNRQVFRQGPPPPNPVAAFPPQKQQQQQQPQQQQQQQQAALPQMPLFENFYSMPQQPSQQPQDFGLQPAGPLGQSHLAHHSMAPYPFPPNPDMNPELRKALLQDSAPQPALPQVQIPFPRRSRRLSKEGILPPSALDGAGTQPGQEATGNLFLHHWPLQQPPPGSLGQPHPEALGFPLELRESQLLPDGERLAPNGREREAPAMGSEEGMRAVSTGDCGQVLRGGVIQSTRRRRRASQEANLLTLAQKAVELASLQNAKDGSGSEEKRKSVLASTTKCGVEFSEPSLATKRAREDSGMVPLIIPVSVPVRTVDPTEAAQAGGLDEDGKGPEQNPAEHKPSVIVTRRRSTRIPGTDAQAQAEDMNVKLEGEPSVRKPKQRPRPEPLIIPTKAGTFIAPPVYSNITPYQSHLRSPVRLADHPSERSFELPPYTPPPILSPVREGSGLYFNAIISTSTIPAPPPITPKSAHRTLLRTNSAEVTPPVLSVMGEATPVSIEPRINVGSRFQAEIPLMRDRALAAADPHKADLVWQPWEDLESSREKQRQVEDLLTAACSSIFPGAGTNQELALHCLHESRGDILETLNKLLLKKPLRPHNHPLATYHYTGSDQWKMAERKLFNKGIAIYKKDFFLVQKLIQTKTVAQCVEFYYTYKKQVKIGRNGTLTFGDVDTSDEKSAQEEVEVDIKTSQKFPRVPLPRRESPSEERLEPKREVKEPRKEGEEEVPEIQEKEEQEEGRERSRRAAAVKATQTLQANESASDILILRSHESNAPGSAGGQASEKPREGTGKSRRALPFSEKKKKTETFSKTQNQENTFPCKKCGR,mutated_sequence,1.0,1045.0,UPI00001FD815.a2m,UPI00001FD815.npy,gnomAD +UPI0000236D8A,UPI0000236D8A.csv,MGTFDLWTDYLGLAHLVRALSGKEGPETRLSPQPEPEPMLEPDQKRSLESSPAPERLCSFCKHNGESRAIYQSHVLKDEAGRVLCPILRDYVCPQCGATRERAHTRRFCPLTGQGYTSVYSHTTRNSAGKKLVRPDKAKTQDTGHRRGGGGGAGFRGAGKSEPSPSCSPSMST,mutated_sequence,1.0,173.0,UPI0000236D8A.a2m,UPI0000236D8A.npy,gnomAD +UPI0000237947,UPI0000237947.csv,MAKDSPSPLGASPKKPGCSSPAAAVLENQRRELEKLRAELEAERAGWRAERRRFAARERQLREEAERERRQLADRLRSKWEAQRSRELRQLQEEMQREREAEIRQLLRWKEAEQRQLQQLLHRERDGVVRQARELQRQLAEELVNRGHCSRPGASEVSAAQCRCRLQEVLAQLRWQTDGEQAARIRYLQAALEVERQLFLKYILAHFRGHPALSGSPDPQAVHSLEEPLPQTSSGSCHAPKPACQLGSLDSLSAEVGVRSRSLGLVSSACSSSPDGLLSTHASSLDCFAPACSRSLDSTRSLPKASKSEERPSSPDTSTPGSRRLSPPPSPLPPPPPPSAHRKLSNPRGGEGSESQPCEVLTPSPPGLGHHELIKLNWLLAKALWVLARRCYTLQEENKQLRRAGCPYQADEKVKRLKVKRAELTGLARRLADRARELQETNLRAVSAPIPGESCAGLELCQVFARQRARDLSEQASAPLAKDKQIEELRQECHLLQARVASGPCSDLHTGRGGPCTQWLNVRDLDRLQRESQREVLRLQRQLMLQQGNGGAWPEAGGQSATCEEVRRQMLALERELDQRRRECQELGTQAAPARRRGEEAETQLQAALLKNAWLAEENGRLQAKTDWVRKVEAENSEVRGHLGRACQERDASGLIAEQLLQQAARGQDRQQQLQRDPQKALCDLHPSWKEIQALQCRPGHPPEQPWETSQMPESQVKGSRRPKFHARPEDYAVSQPNRDIQEKREASLEESPVALGESASVPQVSETVPASQPLSKKTSSQSNSSSEGSMWATVPSSPTLDRDTASEVDDLEPDSVSLALEMGGSAAPAAPKLKIFMAQYNYNPFEGPNDHPEGELPLTAGDYIYIFGDMDEDGFYEGELDDGRRGLVPSNFVEQIPDSYIPGCLPAKSPDLGPSQLPAGQDEALEEDSLLSGKAQGMVDRGLCQMVRVGSKTEVATEILDTKTEACQLGLLQSMGKQGLSRPLLGTKGVLRMAPMQLHLQNVTATSANITWVYSSHRHPHVVYLDDREHALTPAGVSCYTFQGLCPGTHYRVRVEVRLPWDLLQVYWGTMSSTVTFDTLLAGPPYPPLDVLVERHASPGVLVVSWLPVTIDSAGSSNGVQVTGYAVYADGLKVCEVADATAGSTVLEFSQLQVPLTWQKVSVRTMSLCGESLDSVPAQIPEDFFMCHRWPETPPFSYTCGDPSTYRVTFPVCPQKLSLAPPSAKASPHNPGSCGEPQAKFLEAFFEEPPRRQSPVSNLGSEGECPSSGAGSQAQELAEAWEGCRKDLLFQKSPQNHRPPSVSDQPGEKENCYQHMGTSKSPAPGFIHLRTECGPRKEPCQEKAALERVLRQKQDAQGFTPPQLGASQQYASDFHNVLKEEQEALCLDLRGTERREERREPEPHSRQGQALGVKRGCQLHEPSSALCPAPSAKVIKMPRGGPQQLGTGANTPARVFVALSDYNPLVMSANLKAAEEELVFQKRQLLRVWGSQDTHDFYLSECNRQVGNIPGRLVAEMEVGTEQTDRRWRSPAQGHLPSVAHLEDFQGLIIPQGSSLVLQGNSKRLPLWTPKIMIAALDYDPGDGQMGGQGKGRLALRAGDVVMVYGPMDDQGFYYGELGGHRGLVPAHLLDHMSLHGH,mutated_sequence,1.0,1639.0,UPI0000237947.a2m,UPI0000237947.npy,gnomAD +UPI0000367804,UPI0000367804.csv,MSCQISCKSRGRGGGGGGFRGFSSGSAVVSGGSRRSTSSFSCLSRHGGGGGGFGGGGFGSRSLVGLGGTKSISISVAGGGGGFGAAGGFGGRGGGFGGGSSFGGGSGFSGGGFGGGGFGGGRFGGFGGPGGVGGLGGPGGFGPGGYPGGIHEVSVNQSLLQPLNVKVDPEIQNVKAQEREQIKTLNNKFASFIDKVRFLEQQNQVLQTKWELLQQMNVGTRPINLEPIFQGYIDSLKRYLDGLTAERTSQNSELNNMQDLVEDYKKKYEDEINKRTAAENDFVTLKKDVDNAYMIKVELQSKVDLLNQEIEFLKVLYDAEISQIHQSVTDTNVILSMDNSRNLDLDSIIAEVKAQYEEIAQRSKEEAEALYHSKYEELQVTVGRHGDSLKEIKIEISELNRVIQRLQGEIAHVKKQCKNVQDAIADAEQRGEHALKDARNKLNDLEEALQQAKEDLARLLRDYQELMNVKLALDVEIATYRKLLEGEECRMSGDLSSNVTVSVTSSTISSNVASKAAFGGSGGRGSSSGGGYSSGSSSYGSGGRQSGSRGGSGGGGSISGGGYGSGGGSGGRYGSGGGSKGGSISGGGYGSGGGKHSSGGGSRGGSSSGGGYGSGGGGSSSVKGSSGEAFGSSVTFSFR,mutated_sequence,1.0,639.0,UPI0000367804.a2m,UPI0000367804.npy,gnomAD +UPI00006C069B,UPI00006C069B.csv,MASPRASRWPPPLLLLLLPLLLLPPAAPGTRDPPPSPARRALSLAPLAGAGLELQLERRPEREPPPTPPRERRGPATPGPSYRAPEPGAATQRGPSGRAPRGGSADAAWKHWPESNTEAHVENITFYQNQEDFSTVSSKEGVMVQTSGKSHAASDAPENLTLLAETADARGRSGSSSRTNFTILPVGYSLEIATALTSQSGNLASESLHLPSSSSEFDERIAAFQTKSGTASEMGTERAMGLSEEWTVHSQEATTSAWSPSFLPALEMGELTTPSRKRNSSGPDLSWLHFYRTAASSPLLDLSSSSESTEKLNNSTGLQSSSVSQTKTMHVATVFTDGGPRTLRSLTVSLGPVSKTEGFPKDSRIATTSSSVLLSPSAVESRRNSRVTGNPGDEEFIEPSTENEFGLTSLRWQNDSPTFGEHQLASSSEVQNGSPMSQTETVSRSVAPMRGGEITAHWLLTNSTTSADVTGSSASYPEGVNASVLTQFSDSTVQSGGSHTALGDRSYSESSSTSSSESLNSSAPRGERSIAGISYGQVRGTAIEQRTSSDHTDHTYLSSTFTKGERALLSITDNSSSSDIVESSTSYIKISNSSHSEYSSFFHAQTERSNISSYDGEYAQPSTESPVLHTSNLPSYTPTINMPNTSVVLDTDAEFVSDSSSSSSSSSSSSSSGPPLPLPSVSQSHHLFSSILPSTRASVHLLKSTSDASTPWSSSPSPLPVSLTTSTSAPLSVSQTTLPQSSSTPVLPRARETPVTSFQTSTMTSFMTMLHSSQTADLKSQSTPHQEKVITESKSPSLVSLPTESTKAVTTNSPLPPSLTESSTEQTLPATSTNLAQMSPTFTTTILKTSQPLMTTPGTLSSTASLVTGPIAVQTTAGKQLSLTHPEILVPQISTEGGISTERNRVIVDATTGLIPLTSVPTSAKEMTTKLGVTAEYSPASRSLGTSPSPQTTVVSTAEDLAPKSATFAVQSSTQSPTTVSSSASVNSCAVNPCLHNGECVADNTSRGYHCRCPPSWQGDDCSVDVNECLSNPCPSTAMCNNTQGSFICKCPVGYQLEKGICNLVRTFVTEFKLKRTFLNTTVEKHSDLQEVENEITKTLNMCFSALPSYIRSTVHASRESNAVVISLQTTFSLASNVTLFDLADRMQKCVNSCKSSAEVCQLLGSQRRIFRAGSLCKRKSPECDKDTSICTDLDGVALCQCKSGYFQFNKMDHSCRACEDGYRLENETCMSCPFGLGGLNCGNPYQLITVVIAAAGGGLLLILGIALIVTCCRKNKNDISKLIFKSGDFQMSPYAEYPKNPRSQEWGREAIEMHENGSTKNLLQMTDVYYSPTSVRNPELERNGLYPAYTGLPGSRHSCIFPGQYNPSFISDESRRRDYF,mutated_sequence,1.0,1381.0,UPI00006C069B.a2m,UPI00006C069B.npy,gnomAD +UPI000013DBF5,UPI000013DBF5.csv,MRPKTFPATTYSGNSRQRLQEIREGLKQPSKSSVQGLPAGPNSDTSLDAKVLGSKDATRQQQQMRATPKFGPYQKALREIRYSLLPFANESGTSAAAEVNRQMLQELVNAGCDQEMAGRALKQTGSRSIEAALEYISKMGYLDPRNEQIVRVIKQTSPGKGLMPTPVTRRPSFEGTGDSFASYHQLSGTPYEGPSFGADGPTALEEMPRPYVDYLFPGVGPHGPGHQHQHPPKGYGASVEAAGAHFPLQGAHYGRPHLLVPGEPLGYGVQRSPSFQSKTPPETGGYASLPTKGQGGPPGAGLAFPPPAAGLYVPHPHHKQAGPAAHQLHVLGSRSQVFASDSPPQSLLTPSRNSLNVDLYELGSTSVQQWPAATLARRDSLQKPGLEAPPRAHVAFRPDCPVPSRTNSFNSHQPRPGPPGKAEPSLPAPNTVTAVTAAHILHPVKSVRVLRPEPQTAVGPSHPAWVPAPAPAPAPAPAPAAEGLDAKEEHALALGGAGAFPLDVEYGGPDRRCPPPPYPKHLLLRSKSEQYDLDSLCAGMEQSLRAGPNEPEGGDKSRKSAKGDKGGKDKKQIQTSPVPVRKNSRDEEKRESRIKSYSPYAFKFFMEQHVENVIKTYQQKVNRRLQLEQEMAKAGLCEAEQEQMRKILYQKESNYNRLKRAKMDKSMFVKIKTLGIGAFGEVCLACKVDTHALYAMKTLRKKDVLNRNQVAHVKAERDILAEADNEWVVKLYYSFQDKDSLYFVMDYIPGGDMMSLLIRMEVFPEHLARFYIAELTLAIESVHKMGFIHRDIKPDNILIDLDGHIKLTDFGLCTGFRWTHNSKYYQKGSHVRQDSMEPSDLWDDVSNCRCGDRLKTLEQRARKQHQRCLAHSLVGTPNYIAPEVLLRKGYTQLCDWWSVGVILFEMLVGQPPFLAPTPTETQLKVINWENTLHIPAQVKLSPEARDLITKLCCSADHRLGRNGADDLKAHPFFSAIDFSSDIRKQPAPYVPTISHPMDTSNFDPVDEESPWNDASEGSTKAWDTLTSPNNKHPEHAFYEFTFRRFFDDNGYPFRCPKPSGAEASQAESSDLESSDLVDQTEGCQPVYV,mutated_sequence,1.0,1088.0,UPI000013DBF5.a2m,UPI000013DBF5.npy,gnomAD +UPI00015CE746,UPI00015CE746.csv,MSHLPAVSPVFFQLPAPHPPTVLRPQLGLHPNPECDREKMSVRDHDPEVLTRNSACKPRGQLSGHLLKPRAPLEAA,mutated_sequence,,,UPI00015CE746.a2m,UPI00015CE746.npy,gnomAD +UPI0000EE57E0,UPI0000EE57E0.csv,MMTMTTMADGLEGQDSSKSAFMEFGQQQQQQQQQQQQQQQQQQQPPPPPPPPPQPHSQQSSPAMAGAHYPLHCLHSAAAAAAAGSHHHHHHQHHHHGSPYASGGGNSYNHRSLAAYPYMSHSQHSPYLQSYHNSSAAAQTRGDDTDQQKTTVIENGEIRFNGKGKKIRKPRTIYSSLQLQALNHRFQQTQYLALPERAELAASLGLTQTQVKIWFQNKRSKFKKLLKQGSNPHESDPLQGSAALSPRSPALPPVWDVSASAKGVSMPPNSYMPGYSHWYSSPHQDTMQRPQMM,mutated_sequence,1.0,293.0,UPI0000EE57E0.a2m,UPI0000EE57E0.npy,gnomAD +UPI000013E9AB,UPI000013E9AB.csv,MFARKPPGAAPLGAMPVPDQPSSASEKTSSLSPGLNTSNGDGSETETTSAILASVKEQELQFERLTRELEAERQIVASQLERCKLGSETGSMSSMSSAEEQFQWQSQDGQKDIEDELTTGLELVDSCIRSLQESGILDPQDYSTGERPSLLSQSALQLNSKPEGSFQYPASYHSNQTLALGETTPSQLPARGTQARATGQSFSQGTTSRAGHLAGPEPAPPPPPPPREPFAPSLGSAFHLPDAPPAAAAAALYYSSSTLPAPPRGGSPLAAPQGGSPTKLQRGGSAPEGATYAAPRGSSPKQSPSRLAKSYSTSSPINIVVSSAGLSPIRVTSPPTVQSTISSSPIHQLSSTIGTYATLSPTKRLVHASEQYSKHSQELYATATLQRPGSLAAGSRASYSSQHGHLGPELRALQSPEHHIDPIYEDRVYQKPPMRSLSQSQGDPLPPAHTGTYRTSTAPSSPGVDSVPLQRTGSQHGPQNAAAATFQRASYAAGPASNYADPYRQLQYCPSVESPYSKSGPALPPEGTLARSPSIDSIQKDPREFGWRDPELPEVIQMLQHQFPSVQSNAAAYLQHLCFGDNKIKAEIRRQGGIQLLVDLLDHRMTEVHRSACGALRNLVYGKANDDNKIALKNCGGIPALVRLLRKTTDLEIRELVTGVLWNLSSCDALKMPIIQDALAVLTNAVIIPHSGWENSPLQDDRKIQLHSSQVLRNATGCLRNVSSAGEEARRRMRECDGLTDALLYVIQSALGSSEIDSKTVENCVCILRNLSYRLAAETSQGQHMGTDELDGLLCGEANGKDAESSGCWGKKKKKKKSQDQWDGVGPLPDCAEPPKGIQMLWHPSIVKPYLTLLSECSNPDTLEGAAGALQNLAAGSWKWSVYIRAAVRKEKGLPILVELLRIDNDRVVCAVATALRNMALDVRNKELIGKYAMRDLVHRLPGGNNSNNTASKAMSDDTVTAVCCTLHEVITKNMENAKALRDAGGIEKLVGISKSKGDKHSPKVVKAASQVLNSMWQYRDLRSLYKKDGWSQYHFVASSSTIERDRQRPYSSSRTPSISPVRVSPNNRSASAPASPREMISLKERKTDYECTGSNATYHGAKGEHTSRKDAMTAQNTGISTLYRNSYGAPAEDIKHNQVSAQPVPQEPSRKDYETYQPFQNSTRNYDESFFEDQVHHRPPASEYTMHLGLKSTGNYVDFYSAARPYSELNYETSHYPASPDSWV,mutated_sequence,1.0,1225.0,UPI000013E9AB.a2m,UPI000013E9AB.npy,gnomAD +UPI0000070847,UPI0000070847.csv,MEEPQAGDAARFSCPPNFTAKPPASESPRFSLEALTGPDTELWLIQAPADFAPECFNGRHVPLSGSQIVKGKLAGKRHRYRVLSSCPQAGEATLLAPSTEAGGGLTCASAPQGTLRILEGPQQSLSGSPLQPIPASPPPQIPPGLRPRFCAFGGNPPVTGPRSALAPNLLTSGKKKKEMQVTEAPVTQEAVNGHGALEVDMALGSPEMDVRKKKKKKNQQLKEPEAAGPVGTEPTVETLEPLGVLFPSTTKKRKKPKGKETFEPEDKTVKQEQINTEPLEDTVLSPTKKRKRQKGTEGMEPEEGVTVESQPQVKVEPLEEAIPLPPTKKRKKEKGQMAMMEPGTEAMEPVEPEMKPLESPGGTMAPQQPEGAKPQAQAALAAPKKKTKKEKQQDATVEPETEVVGPELPDDLEPQAAPTSTKKKKKKKERGHTVTEPIQPLEPELPGEGQPEARATPGSTKKRKKQSQESRMPETVPQEEMPGPPLNSESGEEAPTGRDKKRKQQQQQPV,mutated_sequence,1.0,510.0,UPI0000070847.a2m,UPI0000070847.npy,gnomAD +UPI000044CC1A,UPI000044CC1A.csv,MEARSRSAEELRRAELVEIIVETEAQTGVSGINVAGGGKEGIFVRELREDSPAARSLSLQEGDQLLSARVFFENFKYEDALRLLQCAEPYKVSFCLKRTVPTGDLALRPGTVSGYEIKGPRAKVAKLNIQSLSPVKKKKMVPGALGVPADLAPVDVEFSFPKFSRLRRGLKAEAVKGPVPAAPARRRLQLPRLRVREVAEEAQAARLAAAAPPPRKAKVEAEVAAGARFTAPQVELVGPRLPGAEVGVPQVSAPKAAPSAEAAGGFALHLPTLGLGAPAPPAVEAPAVGIQVPQVELPALPSLPTLPTLPCLETREGAVSVVVPTLDVAAPTVGVDLALPGAEVEARGEAPEVALKMPRLSFPRFGARAKEVAEAKVAKVSPEARVKGPRLRMPTFGLSLLEPRPAAPEVVESKLKLPTIKMPSLGIGVSGPEVKVPKGPEVKLPKAPEVKLPKVPEAALPEVRLPEVELPKVSEMKLPKVPEMAVPEVRLPEVELPKVSEMKLPKVPEMAVPEVRLPEVQLLKVSEMKLPKVPEMAVPEVRLPEVQLPKVSEMKLPEVSEVAVPEVRLPEVQLPKVPEMKVPEMKLPKVPEMKLPEMKLPEVQLPKVPEMAVPDVHLPEVQLPKVPEMKLPEMKLPEVKLPKVPEMAVPDVHLPEVQLPKVPEMKLPKMPEMAVPEVRLPEVQLPKVSEMKLPKVPEMAVPDVHLPEVQLPKVCEMKVPDMKLPEIKLPKVPEMAVPDVHLPEVQLPKVSEIRLPEMQVPKVPDVHLPKAPEVKLPRAPEVQLKATKAEQAEGMEFGFKMPKMTMPKLGRAESPSRGKPGEAGAEVSGKLVTLPCLQPEVDGEAHVGVPSLTLPSVELDLPGALGLQGQVPAAKMGKGERVEGPEVAAGVREVGFRVPSVEIVTPQLPAVEIEEGRLEMIETKVKPSSKFSLPKFGLSGPKVAKAEAEGAGRATKLKVSKFAISLPKARVGAEAEAKGAGEAGLLPALDLSIPQLSLDAHLPSGKVEVAGADLKFKGPRFALPKFGVRGRDTEAAELVPGVAELEGKGWGWDGRVKMPKLKMPSFGLARGKEAEVQGDRASPGEKAESTAVQLKIPEVELVTLGAQEEGRAEGAVAVSGMQLSGLKVSTAGQVVTEGHDAGLRMPPLGISLPQVELTGFGEAGTPGQQAQSTVPSAEGTAGYRVQVPQVTLSLPGAQVAGGELLVGEGVFKMPTVTVPQLELDVGLSREAQAGEAATGEGGLRLKLPTLGARARVGGEGAEEQPPGAERTFCLSLPDVELSPSGGNHAEYQVAEGEGEAGHKLKVRLPRFGLVRAKEGAEEGEKAKSPKLRLPRVGFSQSEMVTGEGSPSPEEEEEEEEEGSGEGASGRRGRVRVRLPRVGLAAPSKASRGQEGDAAPKSPVREKSPKFRFPRVSLSPKARSGSGDQEEGGLRVRLPSVGFSETGAPGPARMEGAQAAAV,mutated_sequence,1.0,1461.0,UPI000044CC1A.a2m,UPI000044CC1A.npy,gnomAD +UPI0000036166,UPI0000036166.csv,MAFNFGAPSGTSGTAAATAAPAGGFGGFGTTSTTAGSAFSFSAPTNTGTTGLFGGTQNKGFGFGTGFGTTTGTSTGLGTGLGTGLGFGGFNTQQQQQTTLGGLFSQPTQAPTQSNQLINTASALSAPTLLGDERDAILAKWNQLQAFWGTGKGYFNNNIPPVEFTQENPFCRFKAVGYSCMPSNKDEDGLVVLVFNKKETEIRSQQQQLVESLHKVLGGNQTLTVNVEGTKTLPDDQTEVVIYVVERSPNGTSRRVPATTLYAHFEQANIKTQLQQLGVTLSMTRTELSPAQIKQLLQNPPAGVDPIIWEQAKVDNPDSEKLIPVPMVGFKELLRRLKVQDQMTKQHQTRLDIISEDISELQKNQTTSVAKIAQYKRKLMDLSHRTLQVLIKQEIQRKSGYAIQADEEQLRVQLDTIQGELNAPTQFKGRLNELMSQIRMQNHFGAVRSEERYYIDADLLREIKQHLKQQQEGLSHLISIIKDDLEDIKLVEHGLNETIHIRGGVFS,mutated_sequence,1.0,507.0,UPI0000036166.a2m,UPI0000036166.npy,gnomAD +UPI000015FC7D,UPI000015FC7D.csv,MRRSRSSAAAKLRGQKRSGASAAPAASAAAALAPSATRTRRSASQAGSKSQAVEKPPSEKPRLRRSSPRAQEEGPGEPPPPELALLPPPPPPPPTPATPTSSASNLDLGEQRERWETFQKRQKLTSEGAAKLLLDTFEYQGLVKHTGGCHCGAVRFEVWASADLHIFDCNCSICKKKQNRHFIVPASRFKLLKGAEHITTYTFNTHKAQHTFCKRCGVQSFYTPRSNPGGFGIAPHCLDEGTVRSMVTEEFNGSDWEKAMKEHKTIKNMSKE,mutated_sequence,1.0,272.0,UPI000015FC7D.a2m,UPI000015FC7D.npy,gnomAD +UPI0000061EA8,UPI0000061EA8.csv,MDGTNGSTQTHFILLGFSDRPHLERILFVVILIAYLLTLVGNTTIILVSRLDPHLHTPMYFFLAHLSFLDLSFTTSSIPQLLYNLNGCDKTISYMGCAIQLFLFLGLGGVECLLLAVMAYDRCVAICKPLHYMVIMNPRLCRGLVSVTWGCGVANSLAMSPVTLRLPRCGHHEVDHFLREMPALIRMACVSTVAIEGTVFVLAVGVVLSPLVFILLSYSYIVRAVLQIRSASGRQKAFGTCGSHLTVVSLFYGNIIYMYMQPGASSSQDQGMFLMLFYNIVTPLLNPLIYTLRNREVKGALGRLLLGKRELGKE,mutated_sequence,1.0,314.0,UPI0000061EA8.a2m,UPI0000061EA8.npy,gnomAD +UPI000014083E,UPI000014083E.csv,MFRQFYLWTCLASGIILGSLFEICLGQYDDDCKLARGGPPATIVAIDEESRNGTILVDNMLIKGTAGGPDPTIELSLKDNVDYWVLMDPVKQMLFLNSTGRVLDRDPPMNIHSIVVQVQCINKKVGTIIYHEVRIVVRDRNDNSPTFKHESYYATVNELTPVGTTIFTGFSGDNGATDIDDGPNGQIEYVIQYNPDDPTSNDTFEIPLMLTGNIVLRKRLNYEDKTRYFVIIQANDRAQNLNERRTTTTTLTVDVLDGDDLGPMFLPCVLVPNTRDCRPLTYQAAIPELRTPEELNPIIVTPPIQAIDQDRNIQPPSDRPGILYSILVGTPEDYPRFFHMHPRTAELSLLEPVNRDFHQKFDLVIKAEQDNGHPLPAFAGLHIEILDENNQSPYFTMPSYQGYILESAPVGATISDSLNLTSPLRIVALDKDIEDTKDPELHLFLNDYTSVFTVTQTGITRYLTLLQPVDREEQQTYTFSITAFDGVQESEPVIVNIQVMDANDNTPTFPEISYDVYVYTDMRPGDSVIQLTAVDADEGSNGEITYEILVGAQGDFIINKTTGLITIAPGVEMIVGRTYALTVQAADNAPPAERRNSICTVYIEVLPPNNQSPPRFPQLMYSLEISEAMRVGAVLLNLQATDREGDSITYAIENGDPQRVFNLSETTGILTLGKALDRESTDRYILIITASDGRPDGTSTATVNIVVTDVNDNAPVFDPYLPRNLSVVEEEANAFVGQVKATDPDAGINGQVHYSLGNFNNLFRITSNGSIYTAVKLNREVRDYYELVVVATDGAVHPRHSTLTLAIKVLDIDDNSPVFTNSTYTVLVEENLPAGTTILQIEAKDVDLGANVSYRIRSPEVKHFFALHPFTGELSLLRSLDYEAFPDQEASITFLVEAFDIYGTMPPGIATVTVIVKDMNDYPPVFSKRIYKGMVAPDAVKGTPITTVYAEDADPPGLPASRVRYRVDDVQFPYPASIFEVEEDSGRVITRVNLNEEPTTIFKLVVVAFDDGEPVMSSSATVKILVLHPGEIPRFTQEEYRPPPVSELATKGTMVGVISAAAINQSIVYSIVSGNEEDTFGINNITGVIYVNGPLDYETRTSYVLRVQADSLEVVLANLRVPSKSNTAKVYIEIQDENNHPPVFQKKFYIGGVSEDARMFTSVLRVKATDKDTGNYSVMAYRLIIPPIKEGKEGFVVETYTGLIKTAMLFHNMRRSYFKFQVIATDDYGKGLSGKADVLVSVVNQLDMQVIVSNVPPTLVEKKIEDLTEILDRYVQEQIPGAKVVVESIGARRHGDAFSLEDYTKCDLTVYAIDPQTNRAIDRNELFKFLDGKLLDINKDFQPYYGEGGRILEIRTPEAVTSIKKRGESLGYTEGALLALAFIIILCCIPAILVVLVSYRQFKVRQAECTKTARIQAALPAAKPAVPAPAPVAAPPPPPPPPPGAHLYEELGDSSILFLLYHFQQSRGNNSVSEDRKHQQVVMPFSSNTIEAHKSAHVDGSLKSNKLKSARKFTFLSDEDDLSAHNPLYKENISQVSTNSDISQRTDFVDPFSPKIQAKSKSLRGPREKIQRLWSQSVSLPRRLMRKVPNRPEIIDLQQWQGTRQKAENENTGICTNKRGSSNPLLTTEEANLTEKEEIRQGETLMIEGTEQLKSLSSDSSFCFPRPHFSFSTLPTVSRTVELKSEPNVISSPAECSLELSPSRPCVLHSSLSRRETPICMLPIETERNIFENFAHPPNISPSACPLPPPPPISPPSPPPAPAPLAPPPDISPFSLFCPPPSPPSIPLPLPPPTFFPLSVSTSGPPTPPLLPPFPTPLPPPPPSIPCPPPPSASFLSTECVCITGVKCTTNLMPAEKIKSSMTQLSTTTVCKTDPQREPKGILRHVKNLAELEKSVANMYSQIEKNYLRTNVSELQTMCPSEVTNMEITSEQNKGSLNNIVEGTEKQSHSQSTSL,mutated_sequence,1.0,1955.0,UPI000014083E.a2m,UPI000014083E.npy,gnomAD +UPI000012CF7F,UPI000012CF7F.csv,MTTSLLLHPRWPESLMYVYEDSAAESGIGGGGGGGGGGTGGAGGGCSGASPGKAPSMDGLGSSCPASHCRDLLPHPVLGRPPAPLGAPQGAVYTDIPAPEAARQCAPPPAPPTSSSATLGYGYPFGGSYYGCRLSHNVNLQQKPCAYHPGDKYPEPSGALPGDDLSSRAKEFAFYPSFASSYQAMPGYLDVSVVPGISGHPEPRHDALIPVEGYQHWALSNGWDSQVYCSKEQSQSAHLWKSPFPDVVPLQPEVSSYRRGRKKRVPYTKVQLKELEKEYAASKFITKEKRRRISATTNLSERQVTIWFQNRRVKEKKVVSKSKAPHLHST,mutated_sequence,1.0,330.0,UPI000012CF7F.a2m,UPI000012CF7F.npy,gnomAD +UPI000006EEBC,UPI000006EEBC.csv,MGRPLLLPLLLLLQPPAFLQPGGSTGSGPSYLYGVTQPKHLSASMGGSVEIPFSFYYPWELAIVPNVRISWRRGHFHGQSFYSTRPPSIHKDYVNRLFLNWTEGQESGFLRISNLRKEDQSVYFCRVELDTRRSGRQQLQSIKGTKLTITQAVTTTTTWRPSSTTTIAGLRVTESKGHSESWHLSLDTAIRVALAVAVLKTVILGLLCLLLLWWRRRKGSRAPSSDF,mutated_sequence,1.0,227.0,UPI000006EEBC.a2m,UPI000006EEBC.npy,gnomAD +UPI000003117A,UPI000003117A.csv,MQQDGLGVGTRNGSGKGRSVHPSWPWCAPRPLRYFGRDARARRAQTAAMALLAGGLSRGLGSHPAAAGRDAVVFVWLLLSTWCTAPARAIQVTVSNPYHVVILFQPVTLPCTYQMTSTPTQPIVIWKYKSFCRDRIADAFSPASVDNQLNAQLAAGNPGYNPYVECQDSVRTVRVVATKQGNAVTLGDYYQGRRITITGNADLTFDQTAWGDSGVYYCSVVSAQDLQGNNEAYAELIVLGRTSGVAELLPGFQAGPIEDWLFVVVVCLAAFLIFLLLGICWCQCCPHTCCCYVRCPCCPDKCCCPEALYAAGKAATSGVPSIYAPSTYAHLSPAKTPPPPAMIPMGPAYNGYPGGYPGDVDRSSSAGGQGSYVPLLRDTDSSVASEVRSGYRIQASQQDDSMRVLYYMEKELANFDPSRPGPPSGRVERAMSEVTSLHEDDWRSRPSRGPALTPIRDEEWGGHSPRSPRGWDQEPAREQAGGGWRARRPRARSVDALDDLTPPSTAESGSRSPTSNGGRSRAYMPPRSRSRDDLYDQDDSRDFPRSRDPHYDDFRSRERPPADPRSHHHRTRDPRDNGSRSGDLPYDGRLLEEAVRKKGSEERRRPHKEEEEEAYYPPAPPPYSETDSQASRERRLKKNLALSRESLVV,mutated_sequence,1.0,649.0,UPI000003117A.a2m,UPI000003117A.npy,gnomAD +UPI0000D61701,UPI0000D61701.csv,MFRSPGTLEILTPLRDTAFPAFDFQKCLVNIQALLMDPELHVGKCKIEFLTGEFIYRMYTIDMHSQLELTASLIPQPGTSLIPLVMVSNPHSLGFQATFYENGYTSDGNTKYKLDIFLKQQQHWGRTDSNFTSSLKKATMSTLTVDIANKEISCVDIKPLSTLISVGCDLDKKIVIQNKVSACSMGILDPLTLQDNYSFIIEKEFYDPGFQGQQSSEDLHVFYSYQQLGCPLLVYYDTLWKPVVELLESSGMNTVHCSLNLPGSSGPPASASQVAGTAVAYHNARLIFFFFFFFFFLRQSLALSPRLECSGVILAHRKLCLPGSRHSPASASRVAGITGARHCARLIFFIFSRDRVSPC,mutated_sequence,1.0,359.0,UPI0000D61701.a2m,UPI0000D61701.npy,gnomAD +UPI000003777D,UPI000003777D.csv,MELLQVTILFLLPSICSSNSTGVLEAANNSLVVTTTKPSITTPNTESLQKNVVTPTTGTTPKGTITNELLKMSLMSTATFLTSKDEGLKATTTDVRKNDSIISNVTVTSVTLPNAVSTLQSSKPKTETQSSIKTTEIPGSVLQPDASPSKTGTLTSIPVTIPENTSQSQVIGTEGGKNASTSATSRSYSSIILPVVIALIVITLSVFVLVGLYRMCWKADPGTPENGNDQPQSDKESVKLLTVKTISHESGEHSAQGKTKN,mutated_sequence,1.0,261.0,UPI000003777D.a2m,UPI000003777D.npy,gnomAD +UPI000000165C,UPI000000165C.csv,MSPSAKKRPKNSRVSKMQDEKLRDETEQPVSKVIERNRLRTVLKNLSLLKLLKSSNRRIQELHKLAKRCWHSLLSVPKILRISSGENSACNKTKQNNEEFQEIGCSEKELKSKKLESTGDPKKKEYKEWKSQVQSGMRNKEKTSLAAMPRKEKHIEPEVPRTSRDDSLNPGVQGRQPLTEGPRVIFIKPYRNRTPMGHMKQLDVADQWIWFEGLPTRIHLPAPRVMCRSSTLRWVKRRCTRFCSASLEMPMWHPYKVDVTWTRARGASRGWRSRHQLKGRNGWRNSRVYK,mutated_sequence,1.0,290.0,UPI000000165C.a2m,UPI000000165C.npy,gnomAD +UPI000013D46D,UPI000013D46D.csv,MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS,mutated_sequence,1.0,545.0,UPI000013D46D.a2m,UPI000013D46D.npy,gnomAD +UPI0000132189,UPI0000132189.csv,MEEAEELLLEGKKALQLAREPRLGLDLGWNPSGEGCTQGLKDVPPEPTRDILALKSLPRGLALGPSLAKEQRLGVWCVGDPLQPGLLWGPLEEESASKEKGEGVKPRQEENLSLGPWGDVCACEQSSGWTSLVQRGRLESEGNVAPVRISERLHLQVYQLVLPGSELLLWPQPSSEGPSLTQPGLDKEAAVAVVTEVESAVQQEVASPGEDAAEPCIDPGSQSPSGIQAENMVSPGLKFPTQDRISKDSQPLGPLLQDGDVDEECPAQAQMPPELQSNSATQQDPDGSGASFSSSARGTQPHGYLAKKLHSPSDQCPPRAKTPEPGAQQSGFPTLSRSPPGPAGSSPKQGRRYRCGECGKAFLQLCHLKKHAFVHTGHKPFLCTECGKSYSSEESFKAHMLGHRGVRPFPCPQCDKAYGTQRDLKEHQVVHSGARPFACDQCGKAFARRPSLRLHRKTHQVPAAPAPCPCPVCGRPLANQGSLRNHMRLHTGEKPFLCPHCGRAFRQRGNLRGHLRLHTGERPYRCPHCADAFPQLPELRRHLISHTGEAHLCPVCGKALRDPHTLRAHERLHSGERPFPCPQCGRAYTLATKLRRHLKSHLEDKPYRCPTCGMGYTLPQSLRRHQLSHRPEAPCSPPSVPSAASEPTVVLLQAEPQLLDTHREEEVSPARDVVEVTISESQEKCFVVPEEPDAAPSLVLIHKDMGLGAWAEVVEVEMGT,mutated_sequence,1.0,720.0,UPI0000132189.a2m,UPI0000132189.npy,gnomAD +UPI000012ADCB,UPI000012ADCB.csv,MTLSGGGSASDMSGQTVLTAEDVDIDVVGEGDDGLEEKDSDAGCDSPAGPPELRLDEADEVPPAAPHHGQPQPPHQQPLTLPKEAAGAGAGPGGDVGAPEADGCKGGVGGEEGGASGGGPGAGSGSAGGLAPSKPKNSLVKPPYSYIALITMAILQSPQKKLTLSGICEFISNRFPYYREKFPAWQNSIRHNLSLNDCFVKIPREPGNPGKGNYWTLDPQSEDMFDNGSFLRRRKRFKRHQQEHLREQTALMMQSFGAYSLAAAAGAAGPYGRPYGLHPAAAAGAYSHPAAAAAAAAAAALQYPYALPPVAPVLPPAVPLLPSGELGRKAAAFGSQLGPGLQLQLNSLGAAAAAAGTAGAAGTTASLIKSEPSARPSFSIENIIGGGPAAPGGSAVGAGVAGGTGGSGGGSTAQSFLRPPGTVQSAALMATHQPLSLSRTTATIAPILSVPLSGQFLQPAASAAAAAAAAAQAKWPAQ,mutated_sequence,1.0,478.0,UPI000012ADCB.a2m,UPI000012ADCB.npy,gnomAD +UPI000013E315,UPI000013E315.csv,MSEIPSTIVSKNMTNDKNSLESMNISSSSSTEENPKKQARKNEEHGPDPSANPFHLSGDVDFFLLRDQERNKALSERQQQKTMRVHQKMTYSSKVSAKHTSLRRQLQLEDKQEDLEARAEAEHQRAFRDYTTWKLTLTKEKNVEPENMSGYIKQKRQMFLLQYALDVKRREIQRLETLATKEEARLERAEKSLEKDAALFDEFVRENDCSSVQAMRAAEKETKAKIEKILEIRDLTTQIVNIKSEISRFEDTLKHYKVYKDFLYKLSPKEWLEEQEKKHSFLKKAKEVSEASKESSVNSTPGDKGPGIKGKASSMWAKEGQGTKKPWRFLQTMRLGRSPSYLSSPQQGSQPSESSGGDSRGSNSPIPPTQEDTDSDGEEPQLYFTEPQQLLDVFRELEEQNLSLIQNSQETEKTLEELSHTLKHTQIRMDREVNQLKQWVTTMMMSITKEEDTAAELELKARVFHFGEYKGDQQDKLLESLNCKVLDVYRHCTGTQQEANLGTVQMLTIIEHQLDELLENLEHVPQVKIEQAERAKEKERRIRLREEKLQMQKILQEEHLQRARARAQAEIKKKRGRTLVCRSRPPAHRIKQQSEHTLMDKEEEELLFFFT,mutated_sequence,1.0,611.0,UPI000013E315.a2m,UPI000013E315.npy,gnomAD +UPI00001402C1,UPI00001402C1.csv,MAASRLDFGEVETFLDRHPELFEDYLMRKGKQEMVEKWLQRHSQGQGALGPRPSLAGTSSLAHSTCRGGSSVGGGTGPNGSAHSQPLPGGGDCGGVPLSPSWAGGSRGDGNLQRRASQKELRKSFARSKAIHVNRTYDEQVTSRAQEPLSSVRRRALLRKASSLPPTTAHILSALLESRVNLPRYPPTAIDYKCHLKKHNERQFFLELVKDISNDLDLTSLSYKILIFVCLMVDADRCSLFLVEGAAAGKKTLVSKFFDVHAGTPLLPCSSTENSNEVQVPWGKGIIGYVGEHGETVNIPDAYQDRRFNDEIDKLTGYKTKSLLCMPIRSSDGEIIGVAQAINKIPEGAPFTEDDEKVMQMYLPFCGIAISNAQLFAASRKEYERSRALLEVVNDLFEEQTDLEKIVKKIMHRAQTLLKCERCSVLLLEDIESPVVKFTKSFELMSPKCSADAENSFKESMEKSSYSDWLINNSIAELVASTGLPVNISDAYQDPRFDAEADQISGFHIRSVLCVPIWNSNHQIIGVAQVLNRLDGKPFDDADQRLFEAFVIFCGLGINNTIMYDQVKKSWAKQSVALDVLSYHATCSKAEVDKFKAANIPLVSELAIDDIHFDDFSLDVDAMITAALRMFMELGMVQKFKIDYETLCRWLLTVRKNYRMVLYHNWRHAFNVCQLMFAMLTTAGFQDILTEVEILAVIVGCLCHDLDHRGTNNAFQAKSGSALAQLYGTSATLEHHHFNHAVMILQSEGHNIFANLSSKEYSDLMQLLKQSILATDLTLYFERRTEFFELVSKGEYDWNIKNHRDIFRSMLMTACDLGAVTKPWEISRQVAELVTSEFFEQGDRERLELKLTPSAIFDRNRKDELPRLQLEWIDSICMPLYQALVKVNVKLKPMLDSVATNRSKWEELHQKRLLASTASSSPASVMVAKEDRN,mutated_sequence,1.0,933.0,UPI00001402C1.a2m,UPI00001402C1.npy,gnomAD +UPI0000237200,UPI0000237200.csv,MSHSHPAGLLAAYNSLMDKHLAGYFNNTRIRRHLLRSGLITRSGRILSEKEYKLNMMKRDHQKYIRECLAQAIFHKVLDMERYHQLEIKKKLETLARKERIQRFKGEHTRRSVENNMPILSPHPPVGPKSNRGHSVLVDEGHSSPLALTAPRPYTAPGNMQPPIRLQPLPSNPAVETVPKVTSRSRSKTSLLENEALFPIGGKKAVMKFRNSIGNSQRMNSYQLPNINSYMMPIPPPLPPTGKITRENRSETWRRRRFRPTTAPNGLEPLLTKDSRRIHKTSLHSNAAITMIYLGKNVHLSSDNPDFRDEIKVYQQHCGGENLCVYKGKLLEKETFQFISKRHHGFPFSLTFFLNGMQVNRLSSCCEYKHRKGSRLGGKRGYFGFVCVERSSPCYKCIIAMGLDKKPSLPKSRKEKSTEKGEELKKAEGKVRKEREYVIPKRNEIKENKTSVSAKFSAQEIKTGLKEVVTAVEEMTSKGKPGQEVLEDDQENTLKYEYEEDFEVDEEKQGEKSNEEGQADVQMNGIPQSPLDDKKDNLDPEKESETSSQKAPDARDNVKDENDGCSESELEEDKQDMKTASSTSSRSHPYSSDSEDESAVGDREAHTDSSTDESARRSSSQELSENDKPRKSHLPIEESLEIEIEDQEITKADVETKPMPIDESFENVLKEGTEKGTQEIAEGLSEKSGKHVSAEEKEKDKSKLWEESTAQVKDKKAGLPGLEEGGKDSLPLAYVLALGAPTMNFMVDETAAINSNKESQQLVQKTYTLEKKEAMEEDEAPQHRDADIVQGKGEAALWGEAGAVHEAPLRAWKPTAEQPELAEEFTEKREIPPGIERGAEGAAEAEGVRRLGEGGSDPIGQAAAKDAVGLSKDEAPEKQALMLTVLETDKAASEGEQGLEKAVLANEAAALNLEHLHEVAALREAATSEEGEAEGGVAVSDVGESEEEASIDLEDTGPMEDTASKREDGSEEAILGGEEPAKERKEVMRTETRLSPFTGEAEASRMQVSEGSPEEGSLAKEAFLCKEDVEGEEMVTEAEANREDDRKEILPKELDLARERRKAERPKTSLRKTDSEREEVTRANALKDEDAFKEEQKLKAEEGETETEVRAEEETKAPPNEMGSDAENEAPVEASELSDNPGLLGEDSLKETVVPIFEATPGFEKSLENITALRKEGGGERLSEARDTEHKDREELSSRENRALKEGHRQDGEGALAAPEAEPAGKVQAPEGLIPATGQAEELAAKDHDSCAGLEGRAEGQGGVDVVLRTQEAVAEEDPIMAEKFREEAVDEDPEEEEDKECTLETEAMQDRNSEGDGDMEGEGNTQKNEGMGGGRVVAVEVLHGGGETAETAAEEREVLAGSETAEEKTIANKASSFSDVAEEETWHQQDELVGKTAAAGKVVVEELARSGEEVPAAEEMTVTYTTEAGVGTPGALERKTSGLGQEQEEGSEGQEAATGSGDGRQETGAAEKFRLGLSREGERELSPESLQAMATLPVKPDFTETREKQQHMVQGESETADVSPNNVQV,mutated_sequence,1.0,1530.0,UPI0000237200.a2m,UPI0000237200.npy,gnomAD +UPI000013E1E1,UPI000013E1E1.csv,MKQRFSALQLLKLLLLLQPPLPRALREALCPEPCNCVPDGALRCPGPTAGLTRLSLAYLPVKVIPSQAFRGLNEVIKIEISQIDSLERIEANAFDNLLNLSEILIQNTKNLRYIEPGAFINLPRLKYLSICNTGIRKFPDVTKVFSSESNFILEICDNLHITTIPGNAFQGMNNESVTLKLYGNGFEEVQSHAFNGTTLTSLELKENVHLEKMHNGAFRGATGPKTLDISSTKLQALPSYGLESIQRLIATSSYSLKKLPSRETFVNLLEATLTYPSHCCAFRNLPTKEQNFSHSISENFSKQCESTVRKVNNKTLYSSMLAESELSGWDYEYGFCLPKTPRCAPEPDAFNPCEDIMGYDFLRVLIWLINILAIMGNMTVLFVLLTSRYKLTVPRFLMCNLSFADFCMGLYLLLIASVDSQTKGQYYNHAIDWQTGSGCSTAGFFTVFASELSVYTLTVITLERWHTITYAIHLDQKLRLRHAILIMLGGWLFSSLIAMLPLVGVSNYMKVSICFPMDVETTLSQVYILTILILNVVAFFIICACYIKIYFAVRNPELMATNKDTKIAKKMAILIFTDFTCMAPISFFAISAAFKVPLITVTNSKVLLVLFYPINSCANPFLYAIFTKTFQRDFFLLLSKFGCCKRRAELYRRKDFSAYTSNCKNGFTGSNKPSQSTLKLSTLHCQGTALLDKTRYTEC,mutated_sequence,1.0,699.0,UPI000013E1E1.a2m,UPI000013E1E1.npy,gnomAD +UPI0000186945,UPI0000186945.csv,MLEGLGSPASPRAAASASVAGSSGPAACSPPSSSAPRSPESPAPRRGGVRASVPQKLAEMLSSQYGLIVFVAGLLLLLAWAVHAAGVSKSDLLCFLTALMLLQLLWMLWYVGRSSAHRRLFRLKDTHAGAGWLRGSITLFAVITVILGCLKIGYFIGFSECLSATEGVFPVTHSVHTLLQVYFLWGHAKDIIQSFKTLERFGVIHSVFTNLLLWANGVLNESKHQLNEHKERLITLGFGNITTVLDDHTPQCNCTPPTLCTAISHGIYYLYPFNIEYQILASTMLYVLWKNIGRKVDSHQHQKMQFKSDGVMVGAVLGLTVLAATIAVVVVYLIHIGRSKTKSESALIMFYLYAITLLMLMGAAGLAGIRIYRIDEKSLDESKNPARKLDSDLLVGTASGSWLISWGSILAILCAEGHPRYTWYNLPYSILAIVEKYIQNLFIFESIHREPEKLSEDIQTLRVVTVCNGNTMPLASSCPKSGGVARDVAPQGKDMPPAANGNVCMRESHDKEEEKQEESSWGGSPSPVRLPRFLQGNAKRKVLRNIAAFLFLCNISLWIPPAFGCRPEYDNGLEEIVFGFEPWIIVVNLAMPFSIFYRMHAAASLFEVYCKI,mutated_sequence,1.0,612.0,UPI0000186945.a2m,UPI0000186945.npy,gnomAD +UPI000013D6EF,UPI000013D6EF.csv,MAYSWQTDPNPNESHEKQYEHQEFLFVNQPHSSSQVSLGFDQIVDEISGKIPHYESEIDENTFFVPTAPKWDSTGHSLNEAHQISLNEFTSKSRELSWHQVSKAPAIGFSPSVLPKPQNTNKECSWGSPIGKHHGADDSRFSILAPSFTSLDKINLEKELENENHNYHIGFESSIPPTNSSFSSDFMPKEENKRSGHVNIVEPSLMLLKGSLQPGMWESTWQKNIESIGCSIQLVEVPQSSNTSLASFCNKVKKIRERYHAADVNFNSGKIWSTTTAFPYQLFSKTKFNIHIFIDNSTQPLHFMPCANYLVKDLIAEILHFCTNDQLLPKDHILSVCGSEEFLQNDHCLGSHKMFQKDKSVIQLHLQKSREAPGKLSRKHEEDHSQFYLNQLLEFMHIWKVSRQCLLTLIRKYDFHLKYLLKTQENVYNIIEEVKKICSVLGCVETKQITDAVNELSLILQRKGENFYQSSETSAKGLIEKVTTELSTSIYQLINVYCNSFYADFQPVNVPRCTSYLNPGLPSHLSFTVYAAHNIPETWVHRINFPLEIKSLPRESMLTVKLFGIACATNNANLLAWTCLPLFPKEKSILGSMLFSMTLQSEPPVEMITPGVWDVSQPSPVTLQIDFPATGWEYMKPDSEENRSNLEEPLKECIKHIARLSQKQTPLLLSEEKKRYLWFYRFYCNNENCSLPLVLGSAPGWDERTVSEMHTILRRWTFSQPLEALGLLTSSFPDQEIRKVAVQQLDNLLNDELLEYLPQLVQAVKFEWNLESPLVQLLLHRSLQSIQVAHRLYWLLKNAENEAYFKSWYQKLLAALQFCAGKALNDEFSKEQKLIKILGDIGERVKSASDHQRQEVLKKEIGRLEEFFQDVNTCHLPLNPALCIKGIDHDACSYFTSNALPLKITFINANPMGKNISIIFKAGDDLRQDMLVLQLIQVMDNIWLQEGLDMQMIIYRCLSTGKDQGLVQMVPDAVTLAKIHRHSGLIGPLKENTIKKWFSQHNHLKADYEKALRNFFYSCAGWCVVTFILGVCDRHNDNIMLTKSGHMFHIDFGKFLGHAQTFGGIKRDRAPFIFTSEMEYFITEGGKNPQHFQDFVELCCRAYNIIRKHSQLLLNLLEMMLYAGLPELSGIQDLKYVYNNLRPQDTDLEATSHFTKKIKESLECFPVKLNNLIHTLAQMSAISPAKSTSQTFPQESCLLSTTRSIERATILGFSKKSSNLYLIQVTHSNNETSLTEKSFEQFSKLHSQLQKQFASLTLPEFPHWWHLPFTNSDHRRFRDLNHYMEQILNVSHEVTNSDCVLSFFLSEAVQQTVEESSPVYLGEKFPDKKPKVQLVISYEDVKLTILVKHMKNIHLPDGSAPSAHVEFYLLPYPSEVRRRKTKSVPKCTDPTYNEIVVYDEVTELQGHVLMLIVKSKTVFVGAINIRLCSVPLDKEKWYPLGNSII,mutated_sequence,1.0,1445.0,UPI000013D6EF.a2m,UPI000013D6EF.npy,gnomAD +UPI00015DF949,UPI00015DF949.csv,WLTPVIPALGEAEVGGLLESRSSRRSCAMIAPLHSSLGDRARPRLKKKKKKKKRKKRSLILRPCLTLSPRLTCNGTILAHCNLRLPGSSDSPASASQVAGITGTHHHTRLIFVFLVESPCWSAIVSSA,mutated_sequence,1.0,128.0,UPI00015DF949.a2m,UPI00015DF949.npy,gnomAD +UPI0000072854,UPI0000072854.csv,MSESEGGKDTTPEPSPANGAGPGPEWGLCPGPPAVEGESSGASGLGTPKRRNQHSKHKTVAVASAQRSPRALFCLTLANPLRRSCISIVEWKPFDILILLTIFANCVALGVYIPFPEDDSNTANHNLEQVEYVFLVIFTVETVLKIVAYGLVLHPSAYIRNGWNLLDFIIVVVGLFSVLLEQGPGRPGDAPHTGGKPGGFDVKALRAFRVLRPLRLVSGVPSLHIVLNSIMKALVPLLHIALLVLFVIIIYAIIGLELFLGRMHKTCYFLGSDMEAEEDPSPCASSGSGRACTLNQTECRGRWPGPNGGITNFDNFFFAMLTVFQCVTMEGWTDVLYWMQDAMGYELPWVYFVSLVIFGSFFVLNLVLGVLSGEFSKEREKAKARGDFQKQREKQQMEEDLRGYLDWITQAEELDMEDPSADDNLGSMAEEGRAGHRPQLAELTNRRRGRLRWFSHSTRSTHSTSSHASLPASDTGSMTETQGDEDEEEGALASCTRCLNKIMKTRVCRRLRRANRVLRARCRRAVKSNACYWAVLLLVFLNTLTIASEHHGQPVWLTQIQEYANKVLLCLFTVEMLLKLYGLGPSAYVSSFFNRFDCFVVCGGILETTLVEVGAMQPLGISVLRCVRLLRIFKVTRHWASLSNLVASLLNSMKSIASLLLLLFLFIIIFSLLGMQLFGGKFNFDQTHTKRSTFDTFPQALLTVFQILTGEDWNVVMYDGIMAYGGPFFPGMLVCIYFIILFICGNYILLNVFLAIAVDNLASGDAGTAKDKGGEKSNEKDLPQENEGLVPGVEKEEEEGARREGADMEEEEEEEEEEEEEEEEEGAGGVELLQEVVPKEKVVPIPEGSAFFCLSQTNPLRKGCHTLIHHHVFTNLILVFIILSSVSLAAEDPIRAHSFRNHILGYFDYAFTSIFTVEILLKMTVFGAFLHRGSFCRSWFNMLDLLVVSVSLISFGIHSSAISVVKILRVLRVLRPLRAINRAKGLKHVVQCVFVAIRTIGNIMIVTTLLQFMFACIGVQLFKGKFYTCTDEAKHTPQECKGSFLVYPDGDVSRPLVRERLWVNSDFNFDNVLSAMMALFTVSTFEGWPALLYKAIDAYAEDHGPIYNYRVEISVFFIVYIIIIAFFMMNIFVGFVIITFRAQGEQEYQNCELDKNQRQCVEYALKAQPLRRYIPKNPHQYRVWATVNSAAFEYLMFLLILLNTVALAMQHYEQTAPFNYAMDILNMVFTGLFTIEMVLKIIAFKPKHYFTDAWNTFDALIVVGSIVDIAVTEVNNGGHLGESSEDSSRISITFFRLFRVMRLVKLLSKGEGIRTLLWTFIKSFQALPYVALLIAMIFFIYAVIGMQMFGKVALQDGTQINRNNNFQTFPQAVLLLFRCATGEAWQEIMLASLPGNRCDPESDFGPGEEFTCGSNFAIAYFISFFMLCAFLIINLFVAVIMDNFDYLTRDWSILGPHHLDEFKRIWSEYDPGAKGRIKHLDVVALLRRIQPPLGFGKLCPHRVACKRLVAMNMPLNSDGTVTFNATLFALVRTSLKIKTEGNLEQANQELRIVIKKIWKRMKQKLLDEVIPPPDEEEVTVGKFYATFLIQDYFRKFRRRKEKGLLGNDAAPSTSSALQAGLRSLQDLGPEMRQALTCDTEEEEEEGQEGVEEEDEKDLETNKATMVSQPSARRGSGISVSLPVGDRLPDSLSFGPSDDDRGTPTSSQPSVPQAGSNTHRRGSGALIFTIPEEGNSQPKGTKGQNKQDEDEEVPDRLSYLDEQAGTPPCSVLLPPHRAQRYMDGHLVPRRRLLPPTPAGRKPSFTIQCLQRQGSCEDLPIPGTYHRGRNSGPNRAQGSWATPPQRGRLLYAPLLLVEEGAAGEGYLGRSSGPLRTFTCLHVPGTHSDPSHGKRGSADSLVEAVLISEGLGLFARDPRFVALAKQEIADACRLTLDEMDNAASDLLAQGTSSLYSDEESILSRFDEEDLGDEMACVHAL,mutated_sequence,1.0,1977.0,UPI0000072854.a2m,UPI0000072854.npy,gnomAD +UPI00003E5903,UPI00003E5903.csv,MFSAGAESLLHQAREIQDEELKKFCSRICKLLQAEDLGPDTLDSLQRLFLIISATKYSRRLEKTCVDLLQATLGLPACPEQLQVLCAAILREMSPSDSLSLAWDHTQNSRQLSLVASVLLAQGDRNEEVRAVGQGVLRALESRQPEGPSLRHLLPVMAKVVVLSPGTLQEDQATLLSKRLVDWLRYASLQQGLPHSGGFFSTPRARQPGPVTEVDGAVATDFFTVLSSGHRFTDDQWLNVQAFSMLRAWLLHSGPEGPGTLDTDDRSEQEGSTLSVISATSSAGRLLPPRERLREVAFEYCQRLIEQSNRRALRKGDSDLQKACLVEAVLVLDVLCRQDPSFLYRSLSCLKALHGRVRGDPASVRVLLPLAHFFLSHGEAAAVDSEAVYQHLFTRIPVEQFHSPMLAFEFIQFCRDNLHLFSGHLSTLRLSFPNLFKFLAWNSPPLTSEFVALLPALVDAGTALEMLHALLDLPCLTAVLDLQLRSAPAASERPLWDTSLRAPSCLEAFRDPQFQGLFQYLLRPKASGATERLAPLHQLLQPMAGCARVAQCAQAVPTLLQAFFSAVTQVADGSLINQLALLLLGRSDSLYPAPGYAAGVHSVLSSQFLALCTLKPSLVVELARDLLEFLGSVNGLCSRASLVTSVVWAIGEYLSVTYDRRCTVEQINKFFEALEALLFEVTQCRPSAALPRCPPQVVTVLMTTLTKLASRSQDLIPRASLLLSKMRTLAHSPATSSTHSEEGAEAIRTRATELLTLLKMPSVAQFVLTPSTEVCSPRYHRDANTALPLALRTVSRLVEREAGLMPG,mutated_sequence,1.0,807.0,UPI00003E5903.a2m,UPI00003E5903.npy,gnomAD +UPI000015D1F5,UPI000015D1F5.csv,MTPSEGARAGTGRELEMLDSLLALGGLVLLRDSVEWEGRSLLKALVKKSALCGEQVHILGCEVSEEEFREGFDSDINNRLVYHDFFRDPLNWSKTEEAFPGGPLGALRAMCKRTDPVPVTIALDSLSWLLLRLPCTTLCQVLHAVSHQDSCPGETPPSLFPLIHLPLPRSVPLFLSTLE,mutated_sequence,1.0,179.0,UPI000015D1F5.a2m,UPI000015D1F5.npy,gnomAD +UPI000013EA48,UPI000013EA48.csv,DAEQPRGPSGAERGGLELGDAGAAGQLVLTNPWNIMIKHRQVQRRGRRSQMTTSFTDPAISMDLLRAVLQPSINEEIQTVFNKYMKFFQKAALNVRDNVGEEVDAEQLIQEACRSCLEQVRGGSSYHGPIWYHGDEHPAASLPLWCGQEA,mutated_sequence,1.0,150.0,UPI000013EA48.a2m,UPI000013EA48.npy,gnomAD +UPI0000137929,UPI0000137929.csv,MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKEEEEDTFIEEQQLEEEKLLERERQRLHEEWLLREQKAQEEFRIKKEKEEAAKKRQEEQERKLKEQWEEQQRKEREEEEQKRQEKKEKEEALQKMLDQAENELENGTTWQNPEPPVDFRVMEKDRANCPFYSKTGACRFGDRCSRKHNFPTSSPTLLIKSMFTTFGMEQCRRDDYDPDASLEYSEEETYQQFLDFYEDVLPEFKNVGKVIQFKVSCNLEPHLRGNVYVQYQSEEECQAALSLFNGRWYAGRQLQCEFCPVTRWKMAICGLFEIQQCPRGKHCNFLHVFRNPNNEFWEANRDIYLSPDRTGSSFGKNSERRERMGHHDDYYSRLRGRRNPSPDHSYKRNGESERKSSRHRGKKSHKRTSKSRERHNSRSRGRNRDRSRDRSRGRGSRSRSRSRSRRSRRSRSQSSSRSRSRGRRRSGNRDRTVQSPKSK,mutated_sequence,1.0,482.0,UPI0000137929.a2m,UPI0000137929.npy,gnomAD +UPI00001983C7,UPI00001983C7.csv,MAGAGRGAAVSRVQAGPGSPRRARGRQQVQPLGKQRPAPWPGLRSKEKKKVNCKPKNQDEQEIPFRLREIMRSRQEMKNPISNKKRKKAAQVTFRKTLEKEAKGEEPDIAVPKFKQRKGESDGAYIHRMQQEAQHVLFLSKNQAIRQPEVQAAPKEKSEQKKAKKAFQKRRLDKVRRKKEEKAADRLEQELLRDTVKFGEVVLQPPELTARPQRSVSKDQPGRRSQMLRMLLSPGGVSQPLTASLARQRIVEEERERAVQAYRALKQRQQQLHGERPHLTSRKKPEPQL,mutated_sequence,1.0,289.0,UPI00001983C7.a2m,UPI00001983C7.npy,gnomAD +UPI000022AA0A,UPI000022AA0A.csv,MAAAEPMGPAQVPMNSEVIVDPIQGQVNFEDVFVYFSQEEWVLLDEAQRLLYRDVMLENFALMASLGHTSFMSHIVASLVMGSEPWVPDWVDMTLAVATETPGGSDPGCWHGMEDEEIPFEQSFSIGMSQIRIPKGGPSTQKAYPCGTCGLVLKDILHLAEHQETHPGQKPYMCVLCGKQFCFSANLHQHQKQHSGEKPFRSDKSRPFLLNNCAVQSMEMSFVTGEACKDFLASSSIFEHHAPHNEWKPHSNTKCEEASHCGKRHYKCSECGKTFSRKDSLVQHQRVHTGERPYECGECGKTFSRKPILAQHQRIHTGEMPYECGICGKVFNHSSNLIVHQRVHTGARPYKCSECGKAYSHKSTLVQHESIHTGERPYECSECGKYFGHKYRLIKHWSVHTGARPYECIACGKFFSQSSDLIAHQRVHNGEKPYVCSECGKAFSHKHVLVQHHRIHTGERPYKCSECGKAFRQRASLIRHWKIHTGERP,mutated_sequence,1.0,489.0,UPI000022AA0A.a2m,UPI000022AA0A.npy,gnomAD +UPI000013D193,UPI000013D193.csv,MKLSRQFTVFGSAIFCVVIFSLYLMLDRGHLDYPRNPRREGSFPQGQLSMLQEKIDHLERLLAENNEIISNIRDSVINLSESVEDGPKSSQSNFSQGAGSHLLPSQLSLSVDTADCLFASQSGSHNSDVQMLDVYSLISFDNPDGGVWKQGFDITYESNEWDTEPLQVFVVPHSHNDPGWLKTFNDYFRDKTQYIFNNMVLKLKEDSRRKFIWSEISYLSKWWDIIDIQKKDAVKSLIENGQLEIVTGGWVMPDEATPHYFALIDQLIEGHQWLENNIGVKPRSGWAIDPFGHSPTMAYLLNRAGLSHMLIQRVHYAVKKHFALHKTLEFFWRQNWDLGSVTDILCHMMPFYSYDIPHTCGPDPKICCQFDFKRLPGGRFGCPWGVPPETIHPGNVQSRARMLLDQYRKKSKLFRTKVLLAPLGDDFRYCEYTEWDLQFKNYQQLFDYMNSQSKFKVKIQFGTLSDFFDALDKADETQRDKGQSMFPVLSGDFFTYADRDDHYWSGYFTSRPFYKRMDRIMESHLRAAEILYYFALRQAHKYKINKFLSSSLYTALTEARRNLGLFQHHDAITGTAKDWVVVDYGTRLFHSLMVLEKIIGNSAFLLILKDKLTYDSYSPDTFLEMDLKQKSQDSLPQKNIIRLSAEPRYLVVYNPLEQDRISLVSVYVSSPTVQVFSASGKPVEVQVSAVWDTANTISETAYEISFRAHIPPLGLKVYKILESASSNSHLADYVLYKNKVEDSGIFTIKNMINTEEGITLENSFVLLRFDQTGLMKQMMTKEDGKHHEVNVQFSWYGTTIKRDKSGAYLFLPDGNAKPYVYTTPPFVRVTHGRIYSEVTCFFDHVTHRVRLYHIQGIEGQSVEVSNIVDIRKVYNREIAMKISSDIKSQNRFYTDLNGYQIQPRMTLSKLPLQANVYPMTTMAYIQDAKHRLTLLSAQSLGVSSLNSGQIEVIMDRRLMQDDNRGLEQGIQDNKITANLFRILLEKRSAVNTEEEKKSVSYPSLLSHITSSLMNHPVIPMANKFSSPTLELQGEFSPLQSSLPCDIHLVNLRTIQSKVGNGHSNEAALILHRKGFDCRFSSKGTGLFCSTTQGKILVQKLLNKFIVESLTPSSLSLMHSPPGTQNISEINLSPMEISTFRIQLR,mutated_sequence,1.0,1144.0,UPI000013D193.a2m,UPI000013D193.npy,gnomAD +UPI000006CD59,UPI000006CD59.csv,MAVSRKDWSALSSLARQRTLEDEEEQERERRRRHRNLSSTTDDEAPRLSQNGDRQASASERLPSVEEAEVPKPLPPASKDEDEDIQSILRTRQERRQRRQVVEAAQAPIQERLEAEEGRNSLSPVQATQKPLVSKKELEIPPRRRLSREQRGPWALEEESLVGREPEERKKGVPEKSPVLEKSSMPKKTAPEKSLVSDKTSISEKVLASEKTSLSEKIAVSEKRNSSEKKSVLEKTSVSEKSLAPGMALGSGRRLVSEKASIFEKALASEKSPTADAKPAPKRATASEQPLAQEPPASGGSPATTKEQRGRALPGKNLPSLAKQGASDPPTVASRLPPVTLQVKIPSKEEEADMSSPTQRTYSSSLKRSSPRTISFRMKPKKENSETTLTRSASMKLPDNTVKLGEKLERYHTAIRRSESVKSRGLPCTELFVAPVGVASKRHLFEKELAGQSRAEPASSRKENLRLSGVVTSRLNLWISRTQESGDQDPQEAQKASSATERTQWGQKSDSSLDAEV,mutated_sequence,1.0,517.0,UPI000006CD59.a2m,UPI000006CD59.npy,gnomAD +UPI000006DC1A,UPI000006DC1A.csv,MEFDLGAALEPTSQKPGVGAGHGGDPKLSPHKVQGRSEAGAGPGPKQGHHSSSDSSSSSSDSDTDVKSHAAGSKQHESIPGKAKKPKVKKKEKGKKEKGKKKEAPH,mutated_sequence,1.0,106.0,UPI000006DC1A.a2m,UPI000006DC1A.npy,gnomAD +UPI00001AEEF8,UPI00001AEEF8.csv,MGVAGRNRPGAAWAVLLLLLLLPPLLLLAGAVPPGRGRAAGPQEDVDECAQGLDDCHADALCQNTPTSYKCSCKPGYQGEGRQCEDIDECGNELNGGCVHDCLNIPGNYRCTCFDGFMLAHDGHNCLDVDECLENNGGCQHTCVNVMGSYECCCKEGFFLSDNQHTCIHRSEEGLSCMNKDHGCSHICKEAPRGSVACECRPGFELAKNQRDCILTCNHGNGGCQHSCDDTADGPECSCHPQYKMHTDGRSCLEREDTVLEVTESNTTSVVDGDKRVKRRLLMETCAVNNGGCDRTCKDTSTGVHCSCPVGFTLQLDGKTCKDIDECQTRNGGCDHFCKNIVGSFDCGCKKGFKLLTDEKSCQDVDECSLDRTCDHSCINHPGTFACACNRGYTLYGFTHCGDTNECSINNGGCQQVCVNTVGSYECQCHPGYKLHWNKKDCVEVKGLLPTSVSPRVSLHCGKSGGGDGCFLRCHSGIHLSSDVTTIRTSVTFKLNEGKCSLKNAELFPEGLRPALPEKHSSVKESFRYVNLTCSSGKQVPGAPGRPSTPKEMFITVEFELETNQKEVTASCDLSCIVKRTEKRLRKAIRTLRKAVHREQFHLQLSGMNLDVAKKPPRTSERQAESCGVGQGHAENQCVSCRAGTYYDGARERCILCPNGTFQNEEGQMTCEPCPRPGNSGALKTPEAWNMSECGGLCQPGEYSADGFAPCHLCALGTFQPEAGRTSCFPCGGGLATKHQGATSFQDCETRVQCSPGHFYNTTTHRCIRCPVGTYQPEFGKNNCVSCPGNTTTDFDGSTNITQCKNRRCGGELGDFTGYIESPNYPGNYPANTECTWTINPPPKRRILIVVPEIFLPIEDDCGDYLVMRKTSSSNSVTTYETCQTYERPIAFTSRSKKLWIQFKSNEGNSARGFQVPYVTYDEDYQELIEDIVRDGRLYASENHQEILKDKKLIKALFDVLAHPQNYFKYTAQESREMFPRSFIRLLRSKVSRFLRPYK,mutated_sequence,1.0,999.0,UPI00001AEEF8.a2m,UPI00001AEEF8.npy,gnomAD +UPI0000D61944,UPI0000D61944.csv,CRQHHRSHPIQLASPPSPHTVGITTVTPYGQHHHRHPIWLASPPSPHAVGITEVTPYSWHHHRHPMQSASPKSPHTVGITAVTPCIGITEVTPYGRHHHRHPIQWASPPSPHTVGITAVTPYGRHHRRHPIQWASPPSPHTVGITAVTPCGRHHHRHPIQWASPKSPHAVGITLVAPCSQHHLGHPTQLASLPSPHAVGITTVTPYSGHHHHHPMQSAPLLSPHRVGITSVTPCSRHHLSHPMQS,mutated_sequence,,,UPI0000D61944.a2m,UPI0000D61944.npy,gnomAD +UPI0000127786,UPI0000127786.csv,MEQAGTRPAATEHPRLRRPMPWLLLLPLLLLLLLLLPGPAASQLRYSVPEEQAPGALVGNVARALGLELRRLGPGCLRINHLGAPSPRYLELDLTSGALFVNERIDREALCEQRPRCLLSLEVLAHNPVAVSAVEVEILDINDNSPRFPRPNYQLQVSESVAPGARFHIESAQDPDVGANSVQTYELSPSEHFELDLKPLQENSKVLELVLRKGLDREQAALHHLVLTAVDGGIPARSGTAQISVRVLDTNDNSPAFDQSTYRVQLREDSPPGTLVVKLNASDPDEGSNGELRYSLSSYTSDRERQLFSIDASTGEVRVIGGLDYEEASSYQIYVQATDRGPVPMAGHCKVLVDIVDVNDNAPEVVLTDLYSPVPENATPNTIVAVLSVNDQDSGPNRKVSLGLEATLPFRLNGFGNSYTLVVSGPLDRERVAVYNITVTATDGGIPQLTSLRTLKVEISDINDNPPSFLEDSYSIYIQENNLPGVLLCTVQATDPDEKENAEVTYSLLEREIQGLPVTSYVSINSASGSLYAVNSFDYEKFREFFVTVEAQDKGSPPLSSTVTANVYVVDMNDHAPHILYPTSTNSSAAFEMVPRTAPAGYLVTKVIAMDSDSGQNAWLFYHLAQTSDLDLFKVELHTGEIRTTRKMGDESGSTFNLTVVVRDNGEPSLSASVAITVAVVDRVSKILPDTQRHVKSPRTYSEITLYLIIALSTVSFIFLLTIIILSIIKCYRYTAYGTACCGGFCGVRERSPAELYKQANNNIDARIPHGLKVQPHFIEVRGNGSLTKTYCYKACLTAGSGSDTFMFYNTGAQTGPGPSGAQAAVTDSRNLTGQSGQNAGNLIILKNEAVSQNEPRQPNPDWRYSASLRAGMHSSVHLEEAGILRAGPGGPDQQWPTVSSATPEPEAGEVSPPVGAGVNSNSWTFKYGPGNPKQSGPGELPDKFIIPGSPAIISIRQEPTNSQIDKSDFITFGKKEETKKKKKKKKGNKTQEKKEKGNSTTDNSDQ,mutated_sequence,1.0,1007.0,UPI0000127786.a2m,UPI0000127786.npy,gnomAD +UPI0000458963,UPI0000458963.csv,MLAEWGACLLLAVALLGPGLQAQAMEGVKCGGVLSAPSGNFSSPNFPRLYPYNTECSWLIVVAEGSSVLLTFHAFDLEYHDTCSFDFLEIYNGASPDKGNLLGRFCGKVPPPPFTSSWHVMSVIFHSDKHVASHGFSAGYQKDVCGGVLTGLSGVLTSPEYPNNYPNSMECHWVIRAAGPAHVKLVFVDFQVEGNEECTYDYVAVLGGPGPTRGHHYCGSTRPPTLVSLGHELQVVFKSDFNIGGRGFKAYYFSGECQEVYMAMRGNFSSPQYPSSYPNNIRCHWTIRLPPGYQVKVFFLDLDLEEPNSLTKTCDFDHLAAFDGASEEAPLLGNWCGHHLPPPVTSSHNQLLLLLHTDRSTTRRGFSVAYIGGQLGCGSGSTEGEGEALQPQSLQSPSSIPPVCPAPPMNGLLQLLLHWLHPCPLSGPLRLDGTAPACFHYCRASFPSF,mutated_sequence,1.0,449.0,UPI0000458963.a2m,UPI0000458963.npy,gnomAD +UPI0000458979,UPI0000458979.csv,MGWGSRCCCPGRLDLLCVLALLGGCLLPVCRTRVYTNHWAVKIAGGFPEANRIASKYGFINIGQIGALKDYYHFYHSRTIKRSVISSRGTHSFISMEPKVEWIQQQVVKKRTKRDYDFSRAQSTYFNDPKWPSMWYMHCSDNTHPCQSDMNIEGAWKRGYTGKNIVVTILDDGIERTHPDLMQNYDALASCDVNGNDLDPMPRYDASNENKHGTRCAGEVAAAANNSHCTVGIAFNAKIGGVRMLDGDVTDMVEAKSVSFNPQHVHIYSASWGPDDDGKTVDGPAPLTRQAFENGVRMGRRGLGSVFVWASGNGGRSKDHCSCDGYTNSIYTISISSTAESGKKPWYLEECSSTLATTYSSGESYDKKIITTDLRQRCTDNHTGTSASAPMAAGIIALALEANPFLTWRDVQHVIVRTSRAGHLNANDWKTNAAGFKVSHLYGFGLMDAEAMVMEAEKWTTVPRQHVCVESTDRQIKTIRPNSAVRSIYKASGCSDNPNRHVNYLEHVVVRITITHPRRGDLAIYLTSPSGTRSQLLANRLFDHSMEGFKNWEFMTIHCWGERAAGDWVLEVYDTPSQLRNFKTPGKLKEWSLVLYGTSVQPYSPTNEFPKVERFRYSRVEDPTDDYGTEDYAGELASSGTQAKKRQLMHHPTTWENRMEWNGMKWNGMKWNGMEWNGMEWNRIESNRIE,mutated_sequence,1.0,690.0,UPI0000458979.a2m,UPI0000458979.npy,gnomAD +UPI000041EA63,UPI000041EA63.csv,MSSSFFNPSFAFSSHFDPDGAPLSELSWPSSLAVVAVSFSGLFAVIVLMLACLCCKKGGIGFKEFENAEGDEYAADLAQGSPATAAQNGPDVYVLPLTEVSLPMAKQPGRSVQLLKSTDVGRHSLLYLKEIGRGWFGKVFLGEVNSGISSAQVVVKELQASASVQEQMQFLEEVQPYRALKHSNLLQCLAQCAEVTPYLLVMEFCPLGDLKGYLRSCRVAESMAPDPRTLQRMACEVACGVLHLHRNNFVHSDLALRNCLLTADLTVKIGDYGLAHCKYREDYFVTADQLWVPLRWIAPELVDEVHSNLLVVDQTKSGNVWSLGVTIWELFELGTQPYPQHSDQQVLAYTVREQQLKLPKPQLQLTLSDRWYEVMQFCWLQPEQRPTAEEVHLLLSYLCAKGATEAEEEFERRWRSLRPGGGGVGPGPGAAGPMLGGVVELAAASSFPLLEQFAGDGFHADGDDVLTVTETSRGLNFEYKWEAGRGAEAFPATLSPGRTARLQELCAPDGAPPGVVPVLSAHSPSLGSEYFIRLEEAAPAAGHDPDCAGCAPSPPATADQDDDSDGSTAASLAMEPLLGHGPPVDVPWGRGDHYPRRSLARDPLCPSRSPSPSAGPLSLAEGGAEDADWGVAAFCPAFFEDPLGTSPLGSSGAPPLPLTGEDELEEVGARRAAQRGHWRSNVSANNNSGSRCPESWDPVSAGGHAEGCPSPKQTPRASPEPGYPGEPLLGLQAASAQEPGCCPGLPHLCSAQGLAPAPCLVTPSWTETASSGGDHPQAEPKLATEAEGTTGPRLPLPSVPSPSQEGAPLPSEEASAPDAPDALPDSPTPATGGEVSAIKLASALNGSSSSPEVEAPSSEDEDTAEATSGIFTDTSSDGLQARRPDVVPAFRSLQKQVGTPDSLDSLDIPSSASDGGYEVFSPSATGPSGGQPRALDSGYDTENYESPEFVLKEAQEGCEPQAFAELASEGEGPGPETRLSTSLSGLNEKNPYRDSAYFSDLEAEAEATSGPEKKCGGDRAPGPELGLPSTGQPSEQVCLRPGVSGEAQGSGPGEVLPPLLQLEGSSPEPSTCPSGLVPEPPEPQGPAKVRPGPSPSCSQFFLLTPVPLRSEGNSSEFQGPPGLLSGPAPQKRMGGPGTPRAPLRLALPGLPAALEGRPEEEEEDSEDSDESDEELRCYSVQEPSEDSEEEAPAVPVVVAESQSARNLRSLLKMPSLLSETFCEDLERKKKAVSFFDDVTVYLFDQESPTRELGEPFPGAKESPPTFLRGSPGSPSAPNRPQQADGSPNGSTAEEGGGFAWDDDFPLMTAKAAFAMALDPAAPAPAAPTPTPAPFSRFTVSPAPTSRFSITHVSDSDAESKRGPEAGAGGESKEA,mutated_sequence,1.0,1374.0,UPI000041EA63.a2m,UPI000041EA63.npy,gnomAD +UPI0000D4C11A,UPI0000D4C11A.csv,MWPQPRLPPRPAMSEETRQSKLAAAKKKLREYQQRNSPGVPTGAKKKKKIKNGSNPETTTSGGCHSPEDTPKDNAATLQPSDDTVLPGGVPSPGASLTSMAASQNHDADNVPNLMDETKTFSSTESLRQLSQQLNGLVCESATCVNGEGPASSANLKDLESRYQQLAVALDSSYVTNKQLNITIEKLKQQNQEITDQLEEEKKECHQKQGALREQLQVHIQTIGILVSEKAELQTALAHTQHAARQKEGESEDLASRLQYSRRRVGELERALSAVSTQQKKADRYNKELTKERDALRLELYKNTQSNEDLKQEKSELEEKLRVLVTEKAGMQLNLEELQKKLEMTELLLQQFSSRCEAPDANQQLQQAMEERAQLEAHLGQVMESVRQLQMERDKYAENLKGESAMWRQRMQQMSEQVHTLREEKECSMSRVQELETSLAELRNQMAEPPPPEPPAGPSEVEQQLQAEAEHLRKELEGLAGQLQAQVQDNEGLSRLNREQEERLLELERAAELWGEQAEARRQILETMQNDRTTISRALSQNRELKEQLAELQSGFVKLTNENMEITSALQSEQHVKRELGKKLGELQEKLSELKETVELKSQEAQSLQQQRDQYLGHLQQYVAAYQQLTSEKEVLHNQLLLQTQLVDQLQQQEAQGKAVAEMARQELQETQERLEAATQQNQQLRAQLSLMAHPGEGDGLDREEEEDEEEEEEEAVAVPQPMPSIPEDLESREAMVAFFNSAVASAEEEQARLRGQLKEQRVRCRRLAHLLASAQKEPEAAAPAPGTGGDSVCGETHRALQGAMEKLQSRFMELMQEKADLKERVEELEHRCIQLSGETDTIGEYIALYQSQRAVLKERHREKEEYISRLAQDKEEMKVKLLELQELVLRLVGDRNEWHGRFLAAAQNPADEPTSGAPAPQELGAANQQGDLCEVSLAGSVEPAQGEAREGSPRDNPTAQQIMQLLREMQNPRERPGLGSNPCIPFFYRADENDEVKITVI,mutated_sequence,1.0,1002.0,UPI0000D4C11A.a2m,UPI0000D4C11A.npy,gnomAD +UPI0000135F21,UPI0000135F21.csv,MGHHRPWLHASVLWAGVASLLLPPAMTQQLRGDGLGFRNRNNSTGVAGLSEEASAELRHHLHSPRDHPDENKDVSTENGHHFWSHPDREKEDEDVSKEYGHLLPGHRSQDHKVGDEGVSGEEVFAEHGGQARGHRGHGSEDTEDSAEHRHHLPSHRSHSHQDEDEDEVVSSEHHHHILRHGHRGHDGEDDEGEEEEEEEEEEEEASTEYGHQAHRHRGHGSEEDEDVSDGHHHHGPSHRHQGHEEDDDDDDDDDDDDDDDDVSIEYRHQAHRHQGHGIEEDEDVSDGHHHRDPSHRHRSHEEDDNDDDDVSTEYGHQAHRHQDHRKEEVEAVSGEHHHHVPDHRHQGHRDEEEDEDVSTERWHQGPQHVHHGLVDEEEEEEEITVQFGHYVASHQPRGHKSDEEDFQDEYKTEVPHHHHHRVPREEDEEVSAELGHQAPSHRQSHQDEETGHGQRGSIKEMSHHPPGHTVVKDRSHLRKDDSEEEKEKEEDPGSHEEDDESSEQGEKGTHHGSRDQEDEEDEEEGHGLSLNQEEEEEEDKEEEEEEEDEERREERAEVGAPLSPDHSEEEEEEEEGLEEDEPRFTIIPNPLDRREEAGGASSEEESGEDTGPQDAQEYGNYQPGSLCGYCSFCNRCTECESCHCDEENMGEHCDQCQHCQFCYLCPLVCETVCAPGSYVDYFSSSLYQALADMLETPEP,mutated_sequence,1.0,699.0,UPI0000135F21.a2m,UPI0000135F21.npy,gnomAD +UPI00006EB135,UPI00006EB135.csv,METRSSKTRRSLASRTNECQGTMWAPTSPPAGSSSPSQPTWKSSLYSSLAYSEAFHYSFAARPRRLTQLALAQRPEPQLLRLRPSSLRTQDISHLLTGVFRNLYSAEVIGDEVSASLIKARGSENERHEEFVDQLQQIRELYKQRLDEFEMLERHITQAQARAIAENERVMSQAGVQDLESLVRLPPVKSVSRWCIDSELLRKHHLISPEDYYTDTVPFHSAPKGISLPGCSKLTFSCEKRSVQKKELNKKLEDSCRKKLAEFEDELDHTVDSLTWNLTPKAKERTREPLKKASQPRNKNWMNHLRVPQRELDRLLLARMESRNHFLKNPRFFPPNTRYGGKSLVFPPKKPAPIGEFQSTEPEQSCADTPVFLAKPPIGFFTDYEIGPVYEMVIALQNTTTTSRYLRVLPPSTPYFALGLGMFPGKGGMVAPGMTCQYIVQFFPDCLGDFDDFILVETQSAHTLLIPLQARRPPPVLTLSPVLDCGYCLIGGVKMTRFICKNVGFSVGRFCIMPKTSWPPLSFKAIATVGFVEQPPFGILPSVFELAPGHAILVEVLFSPKSLGKAEQTFIIMCDNCQIKELVTIGIGQLIALDLIYISGEKSQPDPGELTDLTAQHFIRFEPENLRSTARKQLIIRNATHVELAFYWQIMKPNLQPLMPGETFSMDSIKCYPDKETAFSIMPRKGVLSPHTDHEFILSFSPHELRDFHSVLQMVLEEVPEPVSSEAESLGHSSYSVDDVIVLEIEVKGSVEPFQVLLEPYALIIPGENYIGINVKKAFKMWNNSKSPIRYLWGKISDCHIIEVEPGTGVIEPSEVGDFELNFTGGVPGPTSQDLLCEIEDSPSPVVLHIEAVFKGPALIINVSALQFGLLRLGQKATNSIQIRNVSQLPATWRMKESPVSLQERPEDVSPFDIEPSSGQLHSLGECRVDITLEALHCQHLETVLELEVENGAWSYLPVYAEVQKPHVYLQSSQVEVRNLYLGVPTKTTITLINGTLLPTQFHWGKLLGHQAEFCMVTVSPKHGLLGPSEECQLKLELTAHTQEELTHLALPCHVSGMKKPLVLGISGKPQGLQVAITISKESSDCSTEQWPGHPKELRLDFGSAVPLRTRVTRQLILTNRSPIRTRFSLKFEYFGSPQNSLSKKTSLPNMPPALLKTVRMQEHLAKREQLDFMESMLSHGKGAAFFPHFSQGMLGPYQQLCIDITGCANMWGEYWDNLICTVGDLLPEVIPVHMAAVGCPISSLRTTSYTIDQAQKEPAMRFGTQVSGGDTVTRTLRLNNSSPCDIRLDWETYVPEDKEDRLVELLVFYGPPFPLRDQAGNELVCPDTPEGGCLLWSPGPSSSSEFSHETDSSVEGSSSASNRVAQKLISVILQAHEGVPSGHLYCISPKQVVVPAGGSSTIYISFTPMVLSPEILHKVECTGYALGFMSLDSKVEREIPGKRHRLQDFAVGPLKLDLHSYVRPAQLSVELDYGGSMEFQCQASDLIPEQPCSGVLSELVTTHHLKLTNTTEIPHYFRLMVSRPFSVSQDGASQDHRAPGPGQKQECEEETASADKQLVLQAQENMLVNVSFSLSLELLSYQKLPADQTLPGVDIQQSASGEREMVFTQNLLLEYTNQTTQVVPLRAVVAVPELQLSTSWVDFGTCFVSQQRVREVYLMNLSGCRSYWTMLMGVVSCTSPRWWWKVCSVRSPAPCGSGAKAPMMRDTCCLTSPEAPPQPSAPGPSWRKNIAQGLGAALQHKDTDLGTWGPLGSSWNGRTPFHNGLSLGPHDMSSELT,mutated_sequence,1.0,1778.0,UPI00006EB135.a2m,UPI00006EB135.npy,gnomAD +UPI00003667EB,UPI00003667EB.csv,MPGPRGAAGGLAPEMRGAGAAGLLALLLLLLLLLLGLGGRVEGGPAGERGAGGGGALARERFKVVFAPVICKRTCLKGQCRDSCQQGSNMTLIGENGHSTDTLTGSGFRVVVCPLPCMNGGQCSSRNQCLCPPDFTGRFCQVPAGGAGGGTGGSGPGLSRTGALSTGALPPLAPEGDSVASKHAIYAVQVIADPPGPGEGPPAQHAAFLVPLGPGQISAEVQAPPPVVNVRVHHPPEASVQVHRIESSNAESAAPSQHLLPHPKPSHPRPPTQKPLGRCFQDTLPKQPCGSNPLPGLTKQEDCCGSIGTAWGQSKCHKCPQLQYTGVQKPGPVRGEVGADCPQGYKRLNSTHCQDINECAMPGVCRHGDCLNNPGSYRCVCPPGHSLGPSRTQCIADKPEEKSLCFRLVSPEHQCQHPLTTRLTRQLCCCSVGKAWGARCQRCPTDGTAAFKEICPAGKGYHILTSHQTLTIQGESDFSLFLHPDGPPKPQQLPESPSQAPPPEDTEEERGVTTDSPVSEERSVQQSHPTATTTPARPYPELISRPSPPTMRWFLPDLPPSRSAVEIAPTQVTETDECRLNQNICGHGECVPGPPDYSCHCNPGYRSHPQHRYCVDVNECEAEPCGPGRGICMNTGGSYNCHCNRGYRLHVGAGGRSCVDLNECAKPHLCGDGGFCINFPGHYKCNCYPGYRLKASRPPVCEDIDECRDPSSCPDGKCENKPGSFKCIACQPGYRSQGGGACRDVNECAEGSPCSPGWCENLPGSFRCTCAQGYAPAPDGRSCLDVDECEAGDVCDNGICSNTPGSFQCQCLSGYHLSRDRSHCEDIDECDFPAACIGGDCINTNGSYRCLCPQGHRLVGGRKCQDIDECSQDPSLCLPHGACKNLQGSYVCVCDEGFTPTQDQHGCEEVEQPHHKKECYLNFDDTVFCDSVLATNVTQQECCCSLGAGWGDHCEIYPCPVYSSAEFHSLCPDGKGYTQDNNIVNYGIPAHRDIDECMLFGSEICKEGKCVNTQPGYECYCKQGFYYDGNLLECVDVDECLDESNCRNGVCENTRGGYRCACTPPAEYSPAQRQCLSPEEMDVDECQDPAACRPGRCVNLPGSYRCECRPPWVPGPSGRDCQLPESPAERAPERRDVCWSQRGEDGMCAGPLAGPALTFDDCCCRQGRGWGAQCRPCPPRGAGSHCPTSQSESNSFWDTSPLLLGKPPRDEDSSEEDSDECRCVSGRCVPRPGGAVCECPGGFQLDASRARCVDIDECRELNQRGLLCKSERCVNTSGSFRCVCKAGFARSRPHGACVPQRRR,mutated_sequence,1.0,1303.0,UPI00003667EB.a2m,UPI00003667EB.npy,gnomAD +UPI000020198E,UPI000020198E.csv,MIATGGLLRISARKQDPLRPPSQIPKRKRKAKKRRKNDVVVVKGKLKLCSISGLIALCGILVLLVGIAMAVVGYWPKATGTNREGGKQLPPAGSSHRVPTTANSSSSGSKNRSRSHPRAPGGVNSSSAGAPRSTPPARAASPSSSSTSVGFFFRIFSGYLHSDKLKVFGPLIMGIGIFLFICANAVLHENRDKKTKIINLRDLYSTVIDVHSLRAKDLAAAAAAAAAAAASSSSSAPAAAPPGAIPLNGFLSYVQSRGLELKPGGCGGSGDAFGAAAMLAKGSWPPHPAAPSGGRPRGAASPPDLASSPRCPREPPSLAEAVYSVYRERSGVAGSRRAAAATAAAAASSCSSPAPCSPPESWGRQSTASSFVDSSLSAFALLPLQGGRDRGGDAEGASCSWQRPPGERGSQEIPRGELDLSMTNLRGAEGSMRGARREPEEPEGAVAARAARGQGGRLPRTGRYAALRRRSTSGLPDYRAPPSPEPPPSPGSADPDSSPLAKAASPSPPLRLEGSPPTRRDSGSSQSDDPSSSNKGYTPLREAGTSTESVLDAVAGQTRDSAVAAPVLGAEQSSPEGASQEPPTAEQPQPVQRQFTNKEKLIMISRSHAIGVEEELESTGI,mutated_sequence,1.0,621.0,UPI000020198E.a2m,UPI000020198E.npy,gnomAD +UPI00001C1FC7,UPI00001C1FC7.csv,MHRAVDPPGARAAREAFALGGLSCAGAWSSCPPHPPPRSAWLPGGRCSASIGQPPLPAPLPPSHGSSSGHPSKPYYAPGAPTPRPLHGKLESLHGCVQALLREPAQPGLWEQLGQLYESEHDSEEATRCYHSALRYGGSFAELGPRIGRLQQAQLWNFHTGSCQHRAKVLPPLEQVWNLLHLEHKRNYGAKRGGPPVKRAAEPPVVQPVPPAALSGPSGEEGLSPGGKRRRGCNSEQTGLPPGLPLPPPPLPPPPPPPPPPPPPLPGLATSPPFQLTKPGLWSTLHGDAWGPERKGSAPPERQEQRHSLPHPYPYPAPAYTAHPPGHRLVPAAPPGPGPRPPGAESHGCLPATRPPGSDLRESRVQRSRMDSSVSPAATTACVPYAPSRPPGLPGTTTSSSSSSSSNTGLRGVEPNPGIPGADHYQTPALEVSHHGRLGPSAHSSRKPFLGAPAATPHLSLPPGPSSPPPPPCPRLLRPPPPPAWLKGPACRAAREDGEILEELFFGTEGPPRPAPPPLPHREGFLGPPASRFSVGTQDSHTPPTPPTPTTSSSNSNSGSHSSSPAGPVSFPPPPYLARSIDPLPRPPSPAQNPQDPPLVPLTLALPPAPPSSCHQNTSGSFRRPESPRPRVSFPKTPEVGPGPPPGPLSKAPQPVPPGVGELPARGPRLFDFPPTPLEDQFEEPAEFKILPDGLANIMKMLDESIRKEEEQQQHEAGVAPQPPLKEPFASLQSPFPTDTAPTTTAPAVAVTTTTTTTTTTTATQEEEKKPPPALPPPPPLAKFPPPSQPQPPPPPPPSPASLLKSLASVLEGQKYCYRGTGAAVSTRPGPLPTTQYSPGPPSGATALPPTSAAPSAQGSPQPSASSSSQFSTSGGPWARERRAGEEPVPGPMTPTQPPPPLSLPPARSESEVLEEISRACETLVERVGRSATDPADPVDTAEPADSGTERLLPPAQAKEEAGGVAAVSGSCKRRQKEHQKEHRRHRRACKDSVGRRPREGRAKAKAKVPKEKSRRVLGNLDLQSEEIQGREKSRPDLGGASKAKPPTAPAPPSAPAPSAQPTPPSASVPGKKAREEAPGPPGVSRADMLKLRSLSEGPPKELKIRLIKVESGDKETFIASEVEERRLRMADLTISHCAADVVRASRNAKVKGKFRESYLSPAQSVKPKINTEEKLPREKLNPPTPSIYLESKRDAFSPVLLQFCTDPRNPITVIRGLAGSLRLNLGLFSTKTLVEASGEHTVEVRTQVQQPSDENWDLTGTRQIWPCESSRSHTTIAKYAQYQASSFQESLQEEKESEDEESEEPDSTTGTPPSSAPDPKNHHIIKFGTNIDLSDAKRWKPQLQELLKLPAFMRVTSTGNMLSHVGHTILGMNTVQLYMKVPGSRTPGHQENNNFCSVNINIGPGDCEWFAVHEHYWETISAFCDRHGVDYLTGSWWPILDDLYASNIPVYRFVQRPGDLVWINAGTVHWVQATGWCNNIAWNVGPLTAYQYQLALERYEWNEVKNVKSIVPMIHVSWNVARTVKISDPDLFKMIKFCLLQSMKHCQVQRESLVRAGKKIAYQGRVKDEPAYYCNECDVEVFNILFVTSENGSRNTYLVHCEGCARRRSAGLQGVVVLEQYRTEELAQAYDAFTLAPASTSR,mutated_sequence,1.0,1643.0,UPI00001C1FC7.a2m,UPI00001C1FC7.npy,gnomAD +UPI00005C3036,UPI00005C3036.csv,MESGPRAELGAGAPPAVVARTPPEPRPSPEGDPSPPPPPMSALVPDTPPDTPPAMKNATSSKQLPLEPESPSGQVGPRPAPPQEESPSSEAKSRGPTPPAMGPRDARPPRRSSQPSPTAVPASDSPPTKQEVKKAGERHKLAKERREERAKYLAAKKAVWLEKEEKAKALREKQLQERRRRLEEQRLKAEQRRAALEERQRQKLEKNKERYEAAIQRSVKKTWAEIRQQRWSWAGALHHSSPGHKTSGSRCSVSAVNLPKHVDSIINKRLSKSSATLWNSPSRNRSLQLSAWESSIVDRLMTPTLSFLARSRSAVTLPRNGRDQGRGCDPGRGPTWGRAGASLARGPQPDRTHPSAAVPVCPRSASASPLTPCSVTRSVHRCAPAGERGERRKPNAGGSPAPVRRRPEASPVQKKEKKDKERENEKEKSALARERSLKKRQSLPASPRARLSASTASELSPKSKARPSSPSTSWHRPASPCPSPGPGHTLPPKPPSPRGTTASPKGRVRRKEEAKESPSAAGPEDKSQSKRRASNEKESAAPASPAPSPAPSPTPAPPQKEQPPAETPTDAAVLTSPPAPAPPVTPSKPMAGTTDREEATRLLAEKRRQAREQREREEQERRLQAERDKRMREEQLAREAEARAEREAEARRREEQEAREKAQAEQEEQERLQKQKEEAEARSREEAERQRLEREKHFQQQEQERQERRKRLEEIMKRTRKSEVSETKQKQDSKEANANGSSPEPVKAVEARSPGLQKEAVQKEEPIPQEPQWSLPSKELPASLVNGLQPLPAHQENGFSTNGPSGDKSLSRTPETLLPFAEAEAFLKKAVVQSPQVTEVL,mutated_sequence,1.0,841.0,UPI00005C3036.a2m,UPI00005C3036.npy,gnomAD +UPI0001E92A31,UPI0001E92A31.csv,MRRGNISPAFWFLWLLLFGLLGPSSENTTAFTKGSDTTTASITGSETTMASTMASTSALTTGSKITTDSTTGSETTSASTMASTAAFTTGSETNTASTTDSGTTIASTRTFTTGSDTTTGSTAGSETIVASTTVSGTTTTFTIASTTVPETTMASSTTSTAGSEKTMASSIISETTMASTTGSETATVSTTGSETTTTSTASSEATKVSTTGSETTTASTAGSETTTTSTSMAGSEATTTSTADSKVITASSMSSETTVAPAAGSNTTTASTTGSETTTILIKASETTTASTAGSETTTPSPTGSQTTIVSISGSEITTTSTAGSENTTVSSAGSGTTTASMAGSETTVSTAGSETTTVSITGTETTMVSAMGSETTTNSTTSSETTVTSTAGSETTTVSTVGSETTTAYTADSETTAASTTGSEMTTVFTAGSETITPSTAGSETTTVSTAGSETTTVSTTGSETTTASTAHSETTAASTMGSETTKVSTAGSETTVSTAGSETTAASTEDSETNTAFTEDSKTTTASTTGFETTAASTTGSEPTMASTMGSETTMASTIGPETTKVSTASSEVTTVFAAGSETIRASTVGSETTTVSTTGSETTTASIMGSETSTDSTTGSETTTASTEGSETTTASTEGSEATTVSTTGSETTTVSITDSETTTTCTEGSEMTAVSTTVFETTTASTEGSEITIASTSDSETTTASTEGSETTTVTTAGSETKTAYTTGSETTTASNTGLETTTVFTIGSDTTTASTEGSETTAVSATGSEMTTVSTEGSENTTVSTTGSETTTVSTTGLETTTTSTEGSEMTTVSTTGAETTTDSTEGSGTTAASTAGSETTTVSTADSENTTASTADSETTSASTTGSETTTASTTSSETTTASTEGSETTTVSTTDSETTMVSTTGSERTITSTEGSETTTVSATGSETTVSTEGSGTTTVSITGSETTKVSTTGSETTTTSTEGSEITTASITGSETTTASTEGSETTTASTEGSETTSASTTGSETTTASTTSSETTMASIMGSETTMASTIGSETTKVSTASSKMTTVFTENSETTIASTTASETTTVSTAGSETIPASTAGSETTTTTSTEGSETTTASTEGSETTTASTESSETTTATTIGSETTTASTEGSETTTTSTEGSETTTASTEGSEITTVSTTGSETTTASTEGSETTTASTEGSELTTVSTTGSETITVSAEGSETTTVTTMGSETTTASTAGSETTTVSTAGSETTTASIEGSETTTVSSTGSETTTVSTTGTETTITSTEGSETTTVTTAGSETTAVYTTGSETTTTSTEGSETTTVSTTGSETTTASTADLETTTVSTSGSGTTTASTAGSETTTVYITGSKTTTASTEGSEATTVSTTSSETTTASTTGSEMTTVFTTVSETTTVSTIGSEATTSSAAGSEATTTSTEGSETTTASTAGSETTTASTAGSETTTASTSGSETNTACTTGSETSTPSSAGSETNTAFIIGSESTIASTASLEPTATSLTGSETTTVSITASGATAASTTVSSTTFVLTKATDVSIQPITNTPMSGTRTTGTRLTASSSVTMAPGMDFTASAASHTVPGIVLNTSGLGTSTMGASSTTSAHGVRTTTGSTREPTSSTFQETGPVSMGTNTVSMSHTPTNVIKPSGYLQPWAIILISLAAVVAAVGLSVGLSFCLRNLFFPLRYCGIYYPHGHSHSLGLDLNLGLGSGTFHSLGNALVHGGELEMGHGGTHGFGYGVGHGLSHIHGDGYGVNHGGHYGHGGGH,mutated_sequence,1.0,1773.0,UPI0001E92A31.a2m,UPI0001E92A31.npy,gnomAD +UPI00001D7CA5,UPI00001D7CA5.csv,MCSGNQTSQNQTASTDFTLTGLFAESKHAALLYTVTFLLFLMALTGNALLILLIHSEPRLHTPMYFFISQLALMDLMYLCVTVPKMLVGQVTGDDTISPSGCGIQMFFYLTLAGAEVFLLAAMAYDRYAAVCRPLHYPLLMNQRVCQLLVSACWVLGMVDGLLLTPITMSFPFCQSRKILSFFCETPALLKLSCSDVSLYKTLMYLCCILMLLAPIMVISSSYTLILHLIHRMNSAAGHRKALATCSSHMIIVLLLFGASFYTYMLPSSYHTAEQDMMVSAFYTIFTPVLNPLIYSLRNKDVTRALRSMMQSRMNQEK,mutated_sequence,1.0,318.0,UPI00001D7CA5.a2m,UPI00001D7CA5.npy,gnomAD +UPI00004DDA7C,UPI00004DDA7C.csv,MASPLRDEEEEEEEMVVSEEEEEEEEEGDEEEEEEVEAADEDDEEDDDEGVLGRGPGHDRGRDRHSPPGCHLFPPPPPPPPPLPPPPPPPPPDKDDIRLLPSALGVKKRKRGPKKQKENKPGKPRKRKKRDSEEEFGSERDEYREKSESGGSEYGTGPGRKRRRKHREKKEKKTKRRKKGEGDGGQKQVEQKSSATLLLTWGLEDVEHVFSEEDYHTLTNYKAFSQFMRPLIAKKNPKIPMSKMMTILGAKWREFSANNPFKGSAAAVAAAAAAAAAAVAEQVSAAVSSATPIAPSGPPALPPPPAADIQPPPIRRAKTKEGKGPGHKRRSKSPRVPDGRKKLRGKKMAPLKIKLGLLGGKRKKGGSYVFQSDEGPEPEAEESDLDSGSVHSASGRPDGPVRTKKLKRGRPGRKKKKVLGCPAVAGEEEVDGYETDHQDYCEVCQQGGEIILCDTCPRAYHLVCLDPELDRAPEGKWSCPHCEKEGVQWEAKEEEEEYEEEGEEEGEKEEEDDHMEYCRVCKDGGELLCCDACISSYHIHCLNPPLPDIPNGEWLCPRCTCPVLKGRVQKILHWRWGEPPVAVPAPQQADGNPDVPPPRPLQGRSEREFFVKWVGLSYWHCSWAKELQLEIFHLVMYRNYQRKNDMDEPPPLDYGSGEDDGKSDKRKVKDPHYAEMEEKYYRFGIKPEWMTVHRIINHSVDKKGNYHYLVKWRDLPYDQSTWEEDEMNIPEYEEHKQSYWRHRELIMGEDPAQPRKYKKKKKELQGDGPPSSPTNDPTVKYETQPRFITATGGTLHMYQLEGLNWLRFSWAQGTDTILADEMGLGKTIQTIVFLYSLYKEGHTKGPFLVSAPLSTIINWEREFQMWAPKFYVVTYTGDKDSRAIIRENEFSFEDNAIKGGKKAFKMKREAQVKFHVLLTSYELITIDQAALGSIRWACLVVDEAHRLKNNQSKFFRVLNGYKIDHKLLLTGTPLQNNLEELFHLLNFLTPERFNNLEGFLEEFADISKEDQIKKLHDLLGPHMLRRLKADVFKNMPAKTELIVRVELSPMQKKYYKYILTRNFEALNSRGGGNQVSLLNIMMDLKKCCNHPYLFPVAAMESPKLPSGAYEGGALIKSSGKLMLLQKMLRKLKEQGHRVLIFSQMTKMLDLLEDFLDYEGYKYERIDGGITGALRQEAIDRFNAPGAQQFCFLLSTRAGGLGINLATADTVIIFDSDWNPHNDIQAFSRAHRIGQANKVMIYRFVTRASVEERITQVAKRKMMLTHLVVRPGLGSKAGSMSKQELDDILKFGTEELFKDENEGENKEEDSSVIHYDNEAIARLLDRNQDATEDTDVQNMNEYLSSFKVAQYVVREEDKIEEIEREIIKQEENVDPDYWEKLLRHHYEQQQEDLARNLGKGKRVRKQVNYNDAAQEDQDNQSEYSVGSEEEDEDFDERPEGRRQSKRQLRNEKDKPLPPLLARVGGNIEVLGFNTRQRKAFLNAVMRWGMPPQDAFTTQWLVRDLRGKTEKEFKAYVSLFMRHLCEPGADGSETFADGVPREGLSRQQVLTRIGVMSLVKKKVQEFEHINGRWSMPELMPDPSADSKRSSRASSPTKTSPTTPEASATNSPCTSKPATPAPSEKGEGIRTPLEKEEAENQEEKPEKNSRIGEKMETEADAPSPAPSLGERLEPRKIPLEDEVPGVPGEMEPEPGYRGDREKSATESTPGERGEEKPLDGQEHRERPEGETGDLGKREDVKGDRELRPGPRDEPRSNGRREEKTEKPRFMFNIADGGFTELHTLWQNEERAAISSGKLNEIWHRRHDYWLLAGIVLHGYARWQDIQNDAQFAIINEPFKTEANKGNFLEMKNKFLARRFKLLEQALVIEEQLRRAAYLNLSQEPAHPAMALHARFAEAECLAESHQHLSKESLAGNKPANAVLHKVLNQLEELLSDMKADVTRLPATLSRIPPIAARLQMSERSILSRLASKGTEPHPTPAYPPGPYATPPGYGAAFSAAPVGALAAAGANYSQMPAGSFITAATNGPPVLVKKEKEMVGALVSDGLDRKEPRAGEVICIDD,mutated_sequence,1.0,2059.0,UPI00004DDA7C.a2m,UPI00004DDA7C.npy,gnomAD +UPI000013C519,UPI000013C519.csv,MCGPDDRCPARWPGPGRAVKCGKGLAAARPGRVERGGAQRGGAGLELHPLLGGRTWRAARDADGCEALGTVAVPFDDDDKIVGGYTCEENSLPYQVSLNSGSHFCGGSLISEQWVVSAAHCYKTRIQVRLGEHNIKVLEGNEQFINAAKIIRHPKYNRDTLDNDIMLIKLSSPAVINARVSTISLPTTPPAAGTECLISGWGNTLSFGADYPDELKCLDAPVLTQAECKASYPGKITNSMFCVGFLEGGKDSCQRDSGGPVVCNGQLQGVVSWGHGCAWKNRPGVYTKVYNYVDWIKDTIAANS,mutated_sequence,1.0,304.0,UPI000013C519.a2m,UPI000013C519.npy,gnomAD +UPI000015FEF2,UPI000015FEF2.csv,MYTVLTGTPPFMASPLSEMYQNIREGHYPEPAHLSANARRLIVHLLAPNPAERPSLDHLLQDDFFTQGFTPDRLPAHSCHSPPIFAIPPPLGRIFRKVGQRLLTQCRPPCPFTPKEASGPGEGGPDPDSMEWDGESSLSAKEVPCLEGPIHLVAQGTLQSDLAGPEGSRRPEVEAALRHLQLCLDVGPPATQDPLGEQQPILWAPKWVDYSSKYGFGYQLLDGGRTGRHPHGPATPRREGTLPTPVPPAGPGLCLLRFLASEHALLLLFSNGMVQVSFSGVPAQLVLSGEGEGLQLTLWEQGSPGTSYSLDVPRSHGCAPTTGQHLHHALRMLQSI,mutated_sequence,1.0,336.0,UPI000015FEF2.a2m,UPI000015FEF2.npy,gnomAD +UPI000012500C,UPI000012500C.csv,MASPALAAALAVAAAAGPNASGAGERGSGGVANASGASWGPPRGQYSAGAVAGLAAVVGFLIVFTVVGNVLVVIAVLTSRALRAPQNLFLVSLASADILVATLVMPFSLANELMAYWYFGQVWCGVYLALDVLFCTSSIVHLCAISLDRYWSVTQAVEYNLKRTPRRVKATIVAVWLISAVISFPPLVSLYRQPDGAAYPQCGLNDETWYILSSCIGSFFAPCLIMGLVYARIYRVAKLRTRTLSEKRAPVGPDGASPTTENGLGAAAGAGENGHCAPPPADVEPDESSAAAERRRRRGALRRGGRRRAGAEGGAGGADGQGAGPGAAESGALTASRSPGPGGRLSRASSRSVEFFLSRRRRARSSVCRRKVAQAREKRFTFVLAVVMGVFVLCWFPFFFSYSLYGICREACQVPGPLFKFFFWIGYCNSSLNPVIYTVFNQDFRRSFKHILFRRRRRGFRQ,mutated_sequence,1.0,462.0,UPI000012500C.a2m,UPI000012500C.npy,gnomAD +UPI000204AD4D,UPI000204AD4D.csv,SPLRRTFKSKVLAHYPQNIEWNPFDQDAVNMLCMPKGLSFRTQTDNKDPQFHSFIITREDGSRTYGFVLTFYEEVTSKQICTAMQTLYQMHNAEHYSSVYASSSCSMDSLASSLDEGDTTSLLKLQRYNSYDISRDTLYVSKSICLITPLPFMQACKKFLIQLYKAVTSQQPPPLPLESYIHNILYEVPLPPPGRSLKFYGVYEPVICQRPGPSELPLSDYPLREAFELLGLENLVQVFTCVLLEMQILLYSQDYQRLMTVAEGITTLLFPFQWQHVYVPILPASLLHFLDAPVPYLMGLQSKEGTDRSKLELPQEANLCFVDIDNHFIELPEEFPQFPNKVDFIQELSEVLVQFGIPPEGSLHCSESTSKLKNMVLKDLVNDKKNGNVCTNNISMYELLKGNETIARLQALAKRTGVAVEKMDLSASLGEKDKDLKLHCEEAELRDYQLNVQLREVFANRFTQMFADYEAFVIQTAQDMESWLTNREQMQNFDKVKRTIVFLLWLLVALLLILTFLNFLFFCAYAYPYIIDFVNLF,mutated_sequence,1.0,537.0,UPI000204AD4D.a2m,UPI000204AD4D.npy,gnomAD +UPI000013D247,UPI000013D247.csv,MEHPLFGCLRSPHATAQGLHPFSQSSLALHGRSDHMSYPELSTSSSSCIIAGYPNEEGMFASQHHRGHHHHHHHHHHHHHQQQQHQALQTNWHLPQMSSPPSAARHSLCLQPDSGGPPELGSSPPVLCSNSSSLGSSTPTGAACAPGDYGRQALSPAEAEKRSGGKRKSDSSDSQEGNYKSEVNSKPRKERTAFTKEQIRELEAEFAHHNYLTRLRRYEIAVNLDLTERQVKVWFQNRRMKWKRVKGGQQGAAAREKELVNVKKGTLLPSELSGIGAATLQQTGDSIANEDSHDSDHSSEHAHL,mutated_sequence,1.0,304.0,UPI000013D247.a2m,UPI000013D247.npy,gnomAD +UPI000012ECF8,UPI000012ECF8.csv,MFPVFPCTLLAPPFPVLGLDSRGVGGLMNSFPPPQGHAQNPLQVGAELQSRFFASQGCAQSPFQAAPAPPPTPQAPAAEPLQVDLLPVLAAAQESAAAAAAAAAAAAAVAAAPPAPAAASTVDTAALKQPPAPPPPPPPVSAPAAEAAPPASAATIAAAAATAVVAPTSTVAVAPVASALEKKTKSKGPYICALCAKEFKNGYNLRRHEAIHTGAKAGRVPSGAMKMPTMVPLSLLSVPQLSGAGGGGGEAGAGGGAAAVAAGGVVTTTASGKRIRKNHACEMCGKAFRDVYHLNRHKLSHSDEKPYQCPVCQQRFKRKDRMSYHVRSHDGAVHKPYNCSHCGKSFSRPDHLNSHVRQVHSTERPFKCEKCEAAFATKDRLRAHTVRHEEKVPCHVCGKMLSSAYISDHMKVHSQGPHHVCELCNKGTGEVCPMAAAAAAAAAAAAAAVAAPPTAVGSLSGAEGVPVSSQPLPSQPW,mutated_sequence,1.0,477.0,UPI000012ECF8.a2m,UPI000012ECF8.npy,gnomAD +UPI0000167B81,UPI0000167B81.csv,MAKSKNHTTHNQSRKWHRNGIKKPRSQRYESLKGVDPKFLRNMRFAKKHNKKGLKKMQANNAKAMSARAEAIKALVKPKEVKPKIPKGVSRKLDRLAYIAHPKLGKRARARIAKGLRLCRPKAKAKAKAKDQTKAQAAAPASVPAQAPKRTQAPTKASE,mutated_sequence,1.0,159.0,UPI0000167B81.a2m,UPI0000167B81.npy,gnomAD +UPI000013EF3A,UPI000013EF3A.csv,MKITRQKHAKKHLGFFRNNFGVREPYQILLDGTFCQAALRGRIQLREQLPRYLMGETQLCTTRCVLKELETLGKDLYGAKLIAQKCQVRNCPHFKNAVSGSECLLSMVEEGNPHHYFVATQDQNLSVKVKKKPGVPLMFIIQNTMVLDKPSPKTIAFVKAVESGQLVSVHEKESIKHLKEEQGLVKNTEQSRRKKRKKISGPNPLSCLKKKKKAPDTQSSASEKKRKRKRIRNRSNPKVLSEKQNAEGE,mutated_sequence,1.0,249.0,UPI000013EF3A.a2m,UPI000013EF3A.npy,gnomAD +UPI000046FEDB,UPI000046FEDB.csv,MTCFGPYPIGTGLQGITLCCFPSFCKMKSRMCMAISICQMLSMLSFVVCAFRYRHMFKRGWPMGTCCLFLPTAAPVLSCEAATQTERRLDLAAVTLRRGLRSRASRCRPRSLIDYKSYMDTKLLVARFLEQSSCTMTPDIHELVENIKSVLKSDEEHMEEAITSASFLEQIMAPLQPSTSRAHKLPSRRQPGLLHLQSCGDLHTFTPAGRPRAERRPRRVEAERPHSLIGVIRETVL,mutated_sequence,1.0,237.0,UPI000046FEDB.a2m,UPI000046FEDB.npy,gnomAD +UPI000007007F,UPI000007007F.csv,MLLPLLLLLPMCWAVEVKRPRGVSLTNHHFYDESKPFTCLDGSATIPFDQVNDDYCDCKDGSDEPGTAACPNGSFHCTNTGYKPLYIPSNRVNDGVCDCCDGTDEYNSGVICENTCKEKGRKERESLQQMAEVTREGFRLKKILIEDWKKAREEKQKKLIELQAGKKSLEDQVEMLRTVKEEAEKPEREAKEQHQKLWEEQLAAAKAQQEQELAADAFKELDDDMDGTVSVTELQTHPELDTDGDGALSEAEAQALLSGDTQTDATSFYDRVWAAIRDKYRSEALPTDLPAPSAPDLTEPKEEQPPVPSSPTEEEEEEEEEEEEEAEEEEEEEDSEEAPPPLSPPQPASPAEEDKMPPYDEQTQAFIDAAQEARNKFEEAERSLKDMEESIRNLEQEISFDFGPNGEFAYLYSQCYELTTNEYVYRLCPFKLVSQKPKLGGSPTSLGTWGSWIGPDHDKFSAMKYEQGTGCWQGPNRSTTVRLLCGKETMVTSTTEPSRCEYLMELMTPAACPEPPPEAPTEDDHDEL,mutated_sequence,1.0,528.0,UPI000007007F.a2m,UPI000007007F.npy,gnomAD +UPI0001D3B409,UPI0001D3B409.csv,XLHNFSARLWEQLVHFHVMRLTDSLFLWVGATPHLRNLAVAMCSRYVPCPHHLHDADLLGPPGKLFLQLCKETDPVSPGQAAHRGQDSSCSHSPGSPVRVPQKGERSFFFFFFFASG,mutated_sequence,1.0,117.0,UPI0001D3B409.a2m,UPI0001D3B409.npy,gnomAD +UPI00001BD8AE,UPI00001BD8AE.csv,MAAETQTLNFGPEWLRALSSGGSITSPPLSPALPKYKLADYRYGREEMLALFLKDNKIPSDLLDKEFLPILQEEPLPPLALVPFTEEEQRNFSMSVNSAAVLRLTGRGGGGTVVGAPRGRSSSRGRGRGRGECGFYQRSFDEVEGVFGRGGGREMHRSQSWEERGDRRFEKPGRKDVGRPNFEEGGPTSVGRKHEFIRSESENWRIFREEQNGEDEDGGWRLAGSRRDGERWRPHSPDGPRSAGWREHMERRRRFEFDFRDRDDERGYRRVRSGSGSIDDDRDSLPEWCLEDAEEEMGTFDSSGAFLSLKKVQKEPIPEEQEMDFRPVDEGEECSDSEGSHNEEAKEPDKTNKKEGEKTDRVGVEASEETPQTSSSSARPGTPSDHQSQEASQFERKDEPKTEQTEKAEEETRMENSLPAKVPSRGDEMVADVQQPLSQIPSDTASPLLILPPPVPNPSPTLRPVETPVVGAPGMGSVSTEPDDEEGLKHLEQQAEKMVAYLQDSALDDERLASKLQEHRAKGVSIPLMHEAMQKWYYKDPQGEIQGPFNNQEMAEWFQAGYFTMSLLVKRACDESFQPLGDIMKMWGRVPFSPGPAPPPHMGELDQERLTRQQELTALYQMQHLQYQQFLIQQQYAQVLAQQQKAALSSQQQQQLALLLQQFQTLKMRISDQNIIPSVTRSVSVPDTGSIWELQPTASQPTVWEGGSVWDLPLDTTTPGPALEQLQQLEKAKAAKLEQERREAEMRAKREEEERKRQEELRRQQEEILRRQQEEERKRREEEELARRKQEEALRRQREQEIALRRQREEEERQQQEEALRRLEERRREEEERRKQEELLRKQEEEAAKWAREEEEAQRRLEENRLRMEEEAARLRHEEEERKRKELEVQRQKELMRQRQQQQEALRRLQQQQQQQQLAQMKLPSSSTWGQQSNTTACQSQATLSLAEIQKLEEERERQLREEQRRQQRELMKALQQQQQQQQQKLSGWGNVSKPSGTTKSLLEIQQEEARQMQKQQQQQQQHQQPNRARNNTHSNLHTSIGNSVWGSINTGPPNQWASDLVSSIWSNADTKNSNMGFWDDAVKEVGPRNSTNKNKNNASLSKSVGVSNRQNKKVEEEEKLLKLFQGVNKAQDGFTQWCEQMLHALNTANNLDVPTFVSFLKEVESPYEVHDYIRAYLGDTSEAKEFAKQFLERRAKQKANQQRQQQQLPQQQQQQPPQQPPQQPQQQDSVWGMNHSTLHSVFQTNQSNNQQSNFEAVQSGKKKKKQKMVRADPSLLGFSVNASSERLNMGEIETLDDY,mutated_sequence,1.0,1299.0,UPI00001BD8AE.a2m,UPI00001BD8AE.npy,gnomAD +UPI00015ADD19,UPI00015ADD19.csv,MWLKPEEVLLKNALKLWVTQKSSCYFILQRRRGHGEGGGRLTGRLVGALDAVLDSNARVAPFRILLQVPGSQVYSPIACGATLEEINQHWDWLEQNLLHTLSVFDNKDDIASFVKGKVKALIAEETSSRLAEQEEEPEKFREALVKFEARFNFPEAEKLVTYYSCCCWKGRVPRQGWLYLSINHLCFYSFFLGKELKLVVPWVDIQKLERTSNVFLTDTIRITTQNKERDFSMFLNLDEVFKVMEQLADVTLRRLLDNEVFDLDPDLQEPSQITKRDLEARAQNEFFRAFFRLPRKEKLHAVVDCSLWTPFSRCHTTGRMFASDSYICFASREDGCCKIILPLREVVSIEKMEDTSLLPHPIIVSIRSKVAFQFIELRDRDSLVEALLARLKQVHANHPVHYDTSADDDMASLVFHSTSMCSDHRFGDLEMMSSQNSEESEKEKSPLMHPDALVTAFQQSGSQSPDSRMSREQIKISLWNDHFVEYGRTVCMFRTEKIRKLVAMGIPESLRGRLWLLFSDAVTDLASHPGYYGNLVEESLGKCCLVTEEIERDLHRSLPEHPAFQNETGIAALRRVLTAYAHRNPKIGYCQSMNILTSVLLLYTKEEEAFWLLVAVCERMLPDYFNHRVIGAQVDQSVFEELIKGHLPELAEHMNDLSALASVSLSWFLTLFLSIMPLESAVNVVDCFFYDGIKAIFQLGLAVLEANAEDLCSSKDDGQALMILSRFLDHIKNEDSPGPPVGSHHAFFSDDQEPYPVTDISDLIRDSYEKFGDQSVEQIEHLRYKHRIRVLQGHEDTTKQNVLRVVIPEVSILPEDLEELYDLFKREHMMSCYWEQPRPMASRHDPSRPYAEQYRIDARQFAHLFQLVSPWTCGAHTEILAERTFRLLDDNMDQLIEFKAFVSCLDIMYNGEMNEKIKLLYRLHIPPALTENDRDSQSPLRNPLLSTSRPLVFGKPNGDAVDYQKQLKQMIKDLAKEKDKTEKELPKMSQREFIQFCKTLYSMFHEDPEENDLYQAIATVTTLLLQIGEVGQRGSSSGSCSQECGEELRASAPSPEDSVFADTGKTPQDSQAFPEAAERDWTVSLEHILASLLTEQSLVNFFEKPLDMKSKLENAKINQYNLKTFEMSHQSQSELKLSNL,mutated_sequence,1.0,1140.0,UPI00015ADD19.a2m,UPI00015ADD19.npy,gnomAD +UPI00001604C8,UPI00001604C8.csv,MEAPTVETPPDPSPPSAPAPALVPLRAPDVARLREEQEKVVTNCQERIQHWKKVDNDYNALRERLSTLPDKLSYNIMVPFGPFAFMPGKLVHTNEVTVLLGDNWFAKCSAKQAVGLVEHRKEHVRKTIDDLKKVMKNFESRVEFTEDLQKMSDAAGDIVDIREEIKCDFEFKAKHRIAHKPHSKPKTSDIFEADIANDVKSKDLLADKELWARLEELERQEELLGELDSKPDTVIANGEDTTSSEEEKEDRNTNVNAMHQVTDSHTPCHKDVASSEPFSGQVNSQLNCSVNGSSSYHSDDDDDDDDDDDDDNIDDDDGDNDHEALGVGDNSIPTIYFSHTVEPKRVRINTGKNTTLKFSEKKEEAKRKRKNSTGSGHSAQELPTIRTPADIYRAFVDVVNGEYVPRKSILKSRSRENSVCSDTSESSAAEFDDRRGVLRSISCEEATCSDTSESILEEEPQENQKKLLPLSVTPEAFSGTVIEKEFVSPSLTPPPAIAHPALPTIPERKEVLLEASEETGKRVSKFKAARLQQKD,mutated_sequence,1.0,535.0,UPI00001604C8.a2m,UPI00001604C8.npy,gnomAD +UPI00005956CD,UPI00005956CD.csv,MPSGSSAALALAAAPAPLPQPPPPPPPPPPPLPPPSGGPELEGDGLLLRERLAALGLDDPSPAEPGAPALRAPAAAAQGQARRAAELSPEERAPPGRPGAPEAAELELEEDEEEGEEAELDGDLLEEEELEEAEEEDRSSLLLLSPPAATASQTQQIPGGSLGSVLLPAARFDAREAAAAAAAAGVLYGGDDAQGMMAAMLSHAYGPGGCGAAAAALNGEQAALLRRKSVNTTECVPVPSSEHVAEIVGRQGCKIKALRAKTNTYIKTPVRGEEPIFVVTGRKEDVAMAKREILSAAEHFSMIRASRNKNGPALGGLSCSPNLPGQTTVQVRVPYRVVGLVVGPKGATIKRIQQQTHTYIVTPSRDKEPVFEVTGMPENVDRAREEIEMHIAMRTGNYIELNEENDFHYNGTDVSFEGGTLGSAWLSSNPVPPSRARMISNYRNDSSSSLGSGSTDSYFGSNRLADFSPTSPFSTGNFWFGDTLPSVGSEDLAVDSPAFDSLPTSAQTIWTPFEPVNPLSGFGSDPSGNMKTQRRGSQPSTPRLSPTFPESIEHPLARRVRSDPPSTGNHVGLPIYIPAFSNGTNSYSSSNGGSTSSSPPESRRKHDCVICFENEVIAALVPCGHNLFCMECANKICEKRTPSCPVCQTAVTQAIQIHS,mutated_sequence,1.0,659.0,UPI00005956CD.a2m,UPI00005956CD.npy,gnomAD +UPI0000071CBA,UPI0000071CBA.csv,MSQKSWIESTLTKRECVYIIPSSKDPHRCLPGCQICQQLVRCFCGRLVKQHACFTASLAMKYSDVKLGDHFNQAIEEWSVEKHTEQSPTDAYGVINFQGGSHSYRAKYVRLSYDTKPEVILQLLLKEWQMELPKLVISVHGGMQKFELHPRIKQLLGKGLIKAAVTTGAWILTGGVNTGVAKHVGDALKEHASRSSRKICTIGIAPWGVIENRNDLVGRDVVAPYQTLLNPLSKLNVLNNLHSHFILVDDGTVGKYGAEVRLRRELEKTINQQRIHARIGQGVPVVALIFEGGPNVILTVLEYLQESPPVPVVVCEGTGRAADLLAYIHKQTEEGGNLPDAAEPDIISTIKKTFNFGQNEALHLFQTLMECMKRKELITVFHIGSDEHQDIDVAILTALLKGTNASAFDQLILTLAWDRVDIAKNHVFVYGQQWLVGSLEQAMLDALVMDRVAFVKLLIENGVSMHKFLTIPRLEELYNTKQGPTNPMLFHLVRDVKQGNLPPGYKITLIDIGLVIEYLMGGTYRCTYTRKRFRLIYNSLGGNNRRSGRNTSSSTPQLRKSHESFGNRADKKEKMRHNHFIKTAQPYRPKIDTVMEEGKKKRTKDEIVDIDDPETKRFPYPLNELLIWACLMKRQVMARFLWQHGEESMAKALVACKIYRSMAYEAKQSDLVDDTSEELKQYSNDFGQLAVELLEQSFRQDETMAMKLLTYELKNWSNSTCLKLAVSSRLRPFVAHTCTQMLLSDMWMGRLNMRKNSWYKVILSILVPPAILLLEYKTKAEMSHIPQSQDAHQMTMDDSENNFQNITEEIPMEVFKEVRILDSNEGKNEMEIQMKSKKLPITRKFYAFYHAPIVKFWFNTLAYLGFLMLYTFVVLVQMEQLPSVQEWIVIAYIFTYAIEKVREIFMSEAGKVNQKIKVWFSDYFNISDTIAIISFFIGFGLRFGAKWNFANAYDNHVFVAGRLIYCLNIIFWYVRLLDFLAVNQQAGPYVMMIGKMVANMFYIVVIMALVLLSFGVPRKAILYPHEAPSWTLAKDIVFHPYWMIFGEVYAYEIDVCANDSVIPQICGPGTWLTPFLQAVYLFVQYIIMVNLLIAFFNNVYLQVKAISNIVWKYQRYHFIMAYHEKPVLPPPLIILSHIVSLFCCICKRRKKDKTSDGPKLFLTEEDQKKLHDFEEQCVEMYFNEKDDKFHSGSEERIRVTFERVEQMCIQIKEVGDRVNYIKRSLQSLDSQIGHLQDLSALTVDTLKTLTAQKASEASKVHNEITRELSISKHLAQNLIDDGPVRPSVWKKHGVVNTLSSSLPQGDLESNNPFHCNILMKDDKDPQCNIFGQDLPAVPQRKEFNFPEAGSSSGALFPSAVSPPELRQRLHGVELLKIFNKNQKLGSSSTSIPHLSSPPTKFFVSTPSQPSCKSHLETGTKDQETVCSKATEGDNTEFGAFVGHRDSMDLQRFKETSNKIKILSNNNTSENTLKRVSSLAGFTDCHRTSIPVHSKQAEKISRRPSTEDTHEVDSKAALIPDWLQDRPSNREMPSEEGTLNGLTSPFKPAMDTNYYYSAVERNNLMRLSQSIPFTPVPPRGEPVTVYRLEESSPNILNNSMSSWSQLGLCAKIEFLSKEEMGGGLRRAVKVQCTWSEHDILKSGHLYIIKSFLPEVVNTWSSIYKEDTVLHLCLREIQQQRAAQKLTFAFNQMKPKSIPYSPRFLEVFLLYCHSAGQWFAVEECMTGEFRKYNNNNGDEIIPTNTLEEIMLAFSHWTYEYTRGELLVLDLQGVGENLTDPSVIKAEEKRSCDMVFGPANLGEDAIKNFRAKHHCNSCCRKLKLPDLKRNDYTPDKIIFPQDEPSDLNLQPGNSTKESESTNSVRLML,mutated_sequence,1.0,1865.0,UPI0000071CBA.a2m,UPI0000071CBA.npy,gnomAD +UPI000013E29D,UPI000013E29D.csv,MQLGEQLLVSSVNLPGAHFYPLESARGGSGGSAGHLPSAAPSPQKLDLDKASKKFSGSLSCEAVSGEPAAASAGAPAAMLSDTDAGDAFASAAAVAKPGPPDGRKGSPCGEEELPSAAAAAAAAAAAAAATARYSMDSLSSERYYLQSPGPQGSELAAPCSLFPYQAAAGAPHGPVYPAPNGARYPYGSMLPPGGFPAAVCPPGRAQFGPGAGAGSGAGGSSGGGGGPGTYQYSQGAPLYGPYPGAAAAGSCGGLGGLGVPGSGFRAHVYLCNRPLWLKFHRHQTEMIITKQGRRMFPFLSFNINGLNPTAHYNVFVEVVLADPNHWRFQGGKWVTCGKADNNMQGNKMYVHPESPNTGSHWMRQEISFGKLKLTNNKGANNNNTQMIVLQSLHKYQPRLHIVEVTEDGVEDLNEPSKTQTFTFSETQFIAVTAYQNTDITQLKIDHNPFAKGFRDNYDSSHQIVPGGRYGVQSFFPEPFVNTLPQARYYNGERTVPQTNGLLSPQQSEEVANPPQRWLVTPVQQPGTNKLDISSYESEYTSSTLLPYGIKSLPLQTSHALGYYPDPTFPAMAGWGGRGSYQRKMAAGLPWTSRTSPTVFSEDQLSKEKVKEEIGSSWIETPPSIKSLDSNDSGVYTSACKRRRLSPSNSSNENSPSIKCEDINAEEYSKDTSKGMGGYYAFYTTP,mutated_sequence,1.0,686.0,UPI000013E29D.a2m,UPI000013E29D.npy,gnomAD +UPI000003F57A,UPI000003F57A.csv,MFSVLSYGRLVARAVLGGLSQTDPRAGGGGGGDYGLVTAGCGFGKDFRKGLLKKGACYGDDACFVARHRSADVLGVADGVGGWRDYGVDPSQFSGTLMRTCERLVKEGRFVPSNPIGILTTSYCELLQNKVPLLGSSTACIVVLDRTSHRLHTANLGDSGFLVVRGGEVVHRSDEQQHYFNTPFQLSIAPPEAEGVVLSDSPDAADSTSFDVQLGDIILTATDGLFDNMPDYMILQELKKLKNSNYESIQQTARSIAEQAHELAYDPNYMSPFAQFACDNGLNVRGGKPDDITVLLSIVAEYTD,mutated_sequence,1.0,304.0,UPI000003F57A.a2m,UPI000003F57A.npy,gnomAD +UPI0000160243,UPI0000160243.csv,MELSSMKICAAIPTSRALPEVVRRMPRKRISGLEWLLQQDPGFSLVNTVKAGMIISFPSNNIYSSVCCCQSEIFKYEFSNSKKSSWIQEERHLGKNNVLYSAHDVSPEKVTSALKKTNKQTTTINNFPLQYLPGSKLLDRFLSLSRSLLCLNSWSSSLPLAPQVKKK,mutated_sequence,1.0,167.0,UPI0000160243.a2m,UPI0000160243.npy,gnomAD +UPI0001F784C6,UPI0001F784C6.csv,XFSSARLEEVGWLSGVGGLRSGGWCVQSSRTPGQSRVLGRGLGYKQQVRGWGHCEAVLVITLLIAAYLKLSVSLTHLKGRKETPFENLFVHLKVFTQVIHQVTHLLNQYH,mutated_sequence,1.0,110.0,UPI0001F784C6.a2m,UPI0001F784C6.npy,gnomAD +UPI00004708E6,UPI00004708E6.csv,MARACLIQRLPIKRDCTPVFVRGLSSPSAAMALLSEGLDEVPAACLSPCGPPNPTELFSESRRLALEELVAGGPEAFAAFLRRERLARFLNPDEVHAILRAAERPGEEGAAAAAAAEDSFGSSHDCSSGTYFPEQSDLEPPLLELGWPAFYQGAYRGATRVETHFQPRGAGEGGPYGCKDALRQQLRSAREVIAVVMDVFTDIDIFRDLQEICRKQGVAVYILLDQALLSQFLDMCMDLKVHPEQEKLMTVRTITGNIYYARSGTKIIGKVHEKFTLIDGIRVATGSYSFTWTDGKLNSSNLVILSGQVVEHFDLEFRILYAQSKPISPKLLSHFQSSNKFDHLTNRKPQSKELTLGNLLRMRLARLSSTPRKADLDPEMPAEGKAERKPHDCESSTVSEEDYFSSHRDELQSRKAIDAATQTEPGEEMPGLSVSEVGTQTSITTACAGTQTAVITRIASSQTTIWSRSTTTQTDMDENILFPRGTQSTEGSPVSKMSVSRSSSLKSSSSVSSQGSVASSTGSPASIRTTDFHNPGYPKYLGTPHLELYLSDSLRNLNKERQFHFAGIRSRLNHMLAMLSRRTLFTENHLGLHSGNFSRVNLLAVRDVALYPSYQ,mutated_sequence,1.0,615.0,UPI00004708E6.a2m,UPI00004708E6.npy,gnomAD +UPI00001AE6F8,UPI00001AE6F8.csv,MEESHFNSNPYFWPSIPTVSGQIENTMFINKMKDQLLPEKGCGLAPPHYPTLLTVPASVSLPSGISMDTESKSDQLTPHSQASVTQNITVVPVPSTGLMTAGVSCSQRWRREGSQSRGPGLVITSPSGSLVTTASSAQTFPISAPMIVSALPPGSQALQVVPDLSKKVASTLTEEGGGGGGGGGSVAPKPPRGRKKKRMLESGLPEMNDPYVLSPEDDDDHQKDGKTYRCRMCSLTFYSKSEMQIHSKSHTETKPHKCPHCSKTFANSSYLAQHIRIHSGAKPYSCNFCEKSFRQLSHLQQHTRIHSKMHTETIKPHKCPHCSKTFANTSYLAQHLRIHSGAKPYNCSYCQKAFRQLSHLQQHTRIHTGDRPYKCAHPGCEKAFTQLSNLQSHRRQHNKDKPFKCHNCHRAYTDAASLEVHLSTHTVKHAKVYTCTICSRAYTSETYLMKHMRKHNPPDLQQQVQAAAAAAAVAQAQAQAQAQAQAQAQAQAQAQASQASQQQQQQQQQQQQQQQQPPPHFQSPGAAPQGGGGGDSNPNPPPQCSFDLTPYKTAEHHKDICLTVTTSTIQVEHLASS,mutated_sequence,1.0,577.0,UPI00001AE6F8.a2m,UPI00001AE6F8.npy,gnomAD +UPI000013F990,UPI000013F990.csv,MIPGKYRSVSGRAANNVNCGLHLVIQTSSLPEKNKVEFKLNKDTSSFPGRLLQHDLERNYSSRQGDHINLVSSSLSSFPILQRSSEEKILYSDRLSLERQKLTVCPIINGEDHLRLLNFQHNFITRIQNISNLQKLISLDLYDNQIEEISGLSTLRCLRVLLLGKNRIKKISNLENLKSLDVLDLHGNQITKIENINHLCELRVLNLARNFLSHVDNLNGLDSLTELNLRHNQITFVRDVDNLPCLQHLFLSFNNISSFDSVSCLADSSSLSDITFDGNPIAQESWYKHTVLQNMMQLRQLDMKRITEEERRMASVLAKKEEEKKRESHKQSLLKEKKRLTINNVARQWDLQQQRVANIATNEDRKDSDSPQDPCQIDGSTLSAFPEETGPLDSGLNNALQGLSVIDTYLVEVDGDTLSLYGSGALESLDRNWSVQTAGMITTVSFTFIEFDEIVQVLPKLKIKFPNSLHLKFKETNLVMLQQFNALAQLRRIDQLTIDPQGNPVVNFTLWKYYVLFRLSHFSMQKINGTEVTQNDMIMAERLFGILAHVASSELPQYRLISILGDARKKQFRYLLESKGKKPGIINEENNDSKRLVGENTNRATLNYTTRDFYNEKLEEIKEKKKFCKTYIEDLVKEATEINMKNEALQKLWPQMFIELVRDAVIEIRNKNSYMKLCLQQITDQK,mutated_sequence,1.0,686.0,UPI000013F990.a2m,UPI000013F990.npy,gnomAD +UPI000156FA8B,UPI000156FA8B.csv,MMRRTLENRNAQTKQLQTAVSNVEKHFGELCQIFAAYVRKTARLRDKADLLVNEINAYAATETPHLKLGLMNFADEFAKLQDYRQAEVERLEAKVVEPLKTYGTIVKMKRDDLKATLTARNREAKQLTQLERTRQRNPSDRHVISQAETELQRAAMDASRTSRHLEETINNFERQKMKDIKTIFSEFITIEMLFHGKALEVYTAAYQNIQNIDEDEDLEVFRNSLYAPDYSSRLDIVRANSKSPLQRSLSAKCVSGTGQVSTCRLRKDQQAEDDEDDELDVTEEENFLK,mutated_sequence,1.0,289.0,UPI000156FA8B.a2m,UPI000156FA8B.npy,gnomAD +UPI00001FF5EB,UPI00001FF5EB.csv,MVFKLKTKEEQHSMLGSGFKAERLRVNLRLVINRLKLLEKKKTELAQKARKEIADYLAAGKDERARIRVEHIIREDYLVEAMEILELYCDLLLARFGLIQSMKELDSGLAESVSTLIWAAPRLQSEVAELKIVADQLCAKYSKEYGKLCRTNQIGTVNDRLMHKLSVEAPPKILVERYLIEIAKNYNVPYEPDSVVMAEAPPGVETDLIDVGFTDDVKKGGPGRGGSGGFTAPVGGPDGTVPMPMPMPMPMPSANTPFSYPLPKGPSDFNGLPMGTYQAFPNIHPPQIPATPPSYESVDDINADKNISSAQIVGPGPKPEASAKLPSRPADNYDNFVLPELPSVPDTLPTASAGASTSASEDIDFDDLSRRFEELKKKT,mutated_sequence,1.0,379.0,UPI00001FF5EB.a2m,UPI00001FF5EB.npy,gnomAD +UPI00016632FD,UPI00016632FD.csv,MDGRDFGPQRSVHGPPPPLLSGLAMDSHRVGAATAGRLPASGLPGPLPPGKYMAGLNLHPHPGEAFLGSFVASGMGPSASSHGSPVPLPSDLSFRSPTPSNLPMVQLWAAHAHEGFSHLPSGLYPSYLHLNHLEPPSSGSPLLSQLGQPSIFDTQKGQGPGGDGFYLPTAGAPGSLHSHAPSARTPGGGHSSGAPAKGSSSRDGPAKERAGRGGEPPPLFGKKDPRARGEEASGPRGVVDLTQEARAEGRQDRGPPRLAERLSPFLAESKTKNAALQPSVLTMCNGGAGDVGLPALVAEAGRGGAKEAARQDEGARLLRRTETLLPGPRPCPSPLPPPPAPPKGPPAPPAATPAGVYTVFREQGREHRVVAPTFVPSVEAFDERPGPIQIASQARDARAREREAGRPGVLQAPPGSPRPLDRPEGLREKNSVIRSLKRPPPADAPTVRATRASPDPRAYVPAKELLKPEADPRPCERAPRGPAGPAAQQAAKLFGLEPGRPPPTGPEHKWKPFELGNFAATQMAVLAAQHHHSRAEEEAAVVAASSSKKAYLDPGAVLPRSAATCGRPVADMHSAAHGSGEASAMQSLIKYSGSFARDAVAVRPGGCGKKSPFGGLGTMKPEPAPTSAGASRAQARLPHSGGPAAGGGRQLKRDPERPESAKAFGREGSGAQGEAEVRHPPVGIAVAVARQKDSGGSGRLGPGLVDQERSLSLSNVKGHGRADEDCVDDRARHREERLLGARLDRDQEKLLRESKELADLARLHPTSCAPNGLNPNLMVTGGPALAGSGRWSADPAAHLATHPWLPRSGNASMWLAGHPYGLGPPSLHQGMAPAFPPGLGGSLPSAYQFVRDPQSGQLVVIPSDHLPHFAELMERATVPPLWPALYPPGRSPLHHAQQLQLFSQQHFLRQQEFLYLQQQAAQALELQRSAQLVQERLKAQEHRAEMEEKGSKRGLEAAGKAGLATAGPGLLPRKPPGLAAGPAGTYGKAVSPPPSPRASPVAALKAKVIQKLEDVSKPPAYAYPATPSSHPTSPPPASPPPTPGITRKEEAPENVVEKKDLELEKEAPSPFQALFSDIPPRYPFQALPPHYGRPYPFLLQPTAAADADGLAPDVPLPADGPERLALSPEDKPIRLSPSKITEPLREGPEEEPLAEREVKAEVEDMDEGPTELPPLESPLPLPAAEAMATPSPAGGCGGGLLEAQALSATGQSCAEPSECPDFVEGPEPRVDSPGRTEPCTAALDLGVQLTPETLVEAKEEPVEVPVAVPVVEAVPEEGLAQVAPSESQPTLEMSDCDVPAGEGQCPSLEPQEAVPVLGSTCFLEEASSDQFLPSLEDPLAGMNALAAAAELPQARPLPSPGAAGAQALEKLEAAESLVLEQSFLHGITLLSEIAELELERRSQEMGGAERALVARPSLESLLAAGSHMLREVLDGPVVDPLKNLRLPRELKPNKKYSWMRKKEERMYAMKSSLEDMDALELDFRMRLAEVQRQYKEKQRELVKLQRRRDSEDRREEPHRSLARRGPGRPRKRTHAPSALSPPRKRGKSGHSSGKLSSKSLLTSDDYELGAGIRKRHKGSEEEHDALIGMGKARGRNQTWDEHEASSDFISQLKIKKKKMASDQEQLASKLDKALSLTKQDKLKSPFKFSDSAGGKSKTSGGCGRYLTPYDSLLGKNRKALAKGLGLSLKSSREGKHKRAAKTRKMEVGFKARGQPKSAHSPFASEVSSYSYNTDSEEDEEFLKDEWPAQGPSSSKLTPSLLCSMVAKNSKAAGGPKLTKRGLAAPRTLKPKPATSRKQPFCLLLREAEARSSFSDSSEESFDQDESSEEEDEEEELEEEDEASGGGYRLGARERALSPGLEESGLGLLARFAASALPSPTVGPSLSVVQLEAKQKARKKEERQSLLGTEFEYTDSESEVKVRKRSPAGLLRPKKGLGEPGPSLAAPTPGARGPDPSSPDKAKLAVEKGRKARKLRGPKEPGFEAGPEASDDDLWTRRRSERIFLHDASAAAPAPVSTAPATKTSRCAKGGPLSPRKDAGRAKDRKDPRKKKKGKEAGPGAGLPPPRAPALPSEARAPHASSLTAAKRSKAKAKGKEVKKENRGKGGAVSKLMESMAAEEDFEPNQDSSFSEDEHLPRGGAVERPLTPAPRSCIIDKDELKDGLRVLIPMDDKLLYAGHVQTVHSPDIYRVVVEGERGNRPHIYCLEQLLQEAIIDVRPASTRFLPQGTRIAAYWSQQYRCLYPGTVVRGLLDLEDDGDLITVEFDDGDTGRIPLSHIRLLPPDYKIQCAEPSPALLVPSAKRRSRKTSKDTGEGKDGGTAGSEEPGAKARGRGRKPSAKAKGDRAATLEEGNPTDEVPSTPLALEPSSTPGSKKSPPEPVDKRAKAPKARPAPPQPSPAPPAFTSCPAPEPFAELPAPATSLAPAPLITMPATRPKPKKARAAEESGAKGPRRPGEEAELLVKLDHEGVTSPKSKKAKEALLLREDPGAGGWQEPKSLLSLGSYPPAAGSSEPKAPWPKATDGDLAQEPGPGLTFEDSGNPKSPDKAQAEQDGAEESESSSSSSSGSSSSSSSSSSSGSETEGEEEGDKNGDGGCGTGGRNCSAASSRAASPASSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSTTDEDSSCSSDDEAAPAPTAGPSAQAALPTKATKQAGKARPSAHSPGKKTPAPQPQAPPPQPTQPLQPKAQAGAKSRPKKREGVHLPTTKELAKRQRLPSVENRPKIAAFLPARQLWKWFGKPTQRRGMKGKARKLFYKAIVRGKEMIRIGDCAVFLSAGRPNLPYIGRIQSMWESWGNNMVVRVKWFYHPEETSPGKQFHQGQHWDQKSSRSLPAALRVSSQRKDFMERALYQSSHVDENDVQTVSHKCLVVGLEQYEQMLKTKKYQDSEGLYYLAGTYEPTTGMIFSTDGVPVLC,mutated_sequence,1.0,2968.0,UPI00016632FD.a2m,UPI00016632FD.npy,gnomAD +UPI0000199CEF,UPI0000199CEF.csv,MSRPRNNPQTSSPQDSTKDGSSFHYFQGRFELSGKSRQYPADALEPQPGIGDVKVIEKATKSMLDPAQRSHFYLVTPSLVFLCFIFDGLHKALLSVGVSKRSNIVIGNENKETGTLYASKFEDVLPTFTALEMSSILRHCCDLIGIAAGSSDPICTNSLQVQRQFKAMMISIGRPLHSESADLLISYNAGPAIDWINSRPWVGGLMFTFLFGEFESPACELLDQVKVVASKAQMMTYYTVRMFLDQCVDGSTALPAVVLEIPVFEQKKPLAKKVLGDFFEFGGVLRHPVIGVLSPQMFPNLATAANYWAKRRNSTFSGFEALDIIPGSTITFPVLQMASAQKISRGSDMDPYTLNILRGYGISGFE,mutated_sequence,1.0,366.0,UPI0000199CEF.a2m,UPI0000199CEF.npy,gnomAD +UPI00021CF38C,UPI00021CF38C.csv,MRTAAGAVSPDSRPETRRQTRKNEEAAWGPRVCRAEREDNRKCPPSILKRSRPEHHRPEAKPQRTSRRVWFREPPAVTVHYIADKNATATVRAVCVRPARAGPGPILRPGQARGNGTGGPAGPAPRPCPAPAARGPHLLARPPAALTGRTGRTGRASRRAQALRGLRDSLSPGPAAW,mutated_sequence,1.0,177.0,UPI00021CF38C.a2m,UPI00021CF38C.npy,gnomAD +UPI000013D80E,UPI000013D80E.csv,MAAAVLSGPSAGSAAGVPGGTGGLSAVSSGPRLRLLLLESVSGLLQPRTGSAVAPVHPPNRSAPHLPGLMCLLRLHGSVGGAQNLSALGALVSLSNARLSSIKTRFEGLCLLSLLVGESPTELFQQHCVSWLRSIQQVLQTQDPPATMELAVAVLRDLLRYAAQLPALFRDISMNHLPGLLTSLLGLRPECEQSALEGMKACMTYFPRACGSLKGKLASFFLSRVDALSPQLQQLACECYSRLPSLGAGFSQGLKHTESWEQELHSLLASLHTLLGALYEGAETAPVQNEGPGVEMLLSSEDGDAHVLLQLRQRFSGLARCLGLMLSSEFGAPVSVPVQEILDFICRTLSVSSKNISLHGDGPLRLLLLPSIHLEALDLLSALILACGSRLLRFGILIGRLLPQVLNSWSIGRDSLSPGQERPYSTVRTKVYAILELWVQVCGASAGMLQGGASGEALLTHLLSDISPPADALKLRSPRGSPDGSLQTGKPSAPKKLKLDVGEAMAPPSHRKGDSNANSDVCAAALRGLSRTILMCGPLIKEETHRRLHDLVLPLVMGVQQGEVLGSSPYTSSRCRRELYCLLLALLLAPSPRCPPPLACALQAFSLGQREDSLEVSSFCSEALVTCAALTHPRVPPLQPMGPTCPTPAPVPPPEAPSPFRAPPFHPPGPMPSVGSMPSAGPMPSAGPMPSAGPVPSARPGPPTTANHLGLSVPGLVSVPPRLLPGPENHRAGSNEDPILAPSGTPPPTIPPDETFGGRVPRPAFVHYDKEEASDVEISLESDSDDSVVIVPEGLPPLPPPPPSGATPPPIAPTGPPTASPPVPAKEEPEELPAAPGPLPPPPPPPPPVPGPVTLPPPQLVPEGTPGGGGPPALEEDLTVININSSDEEEEEEEEEEEEEEEEEEEEEDFEEEEEDEEEYFEEEEEEEEEFEEEFEEEEGELEEEEEEEDEEEEEELEEVEDLEFGTAGGEVEEGAPPPPTLPPALPPPESPPKVQPEPEPEPGLLLEVEEPGTEEERGADTAPTLAPEALPSQGEVEREGESPAAGPPPQELVEEEPSAPPTLLEEETEDGSDKVQPPPETPAEEEMETETEAEALQEKEQDDTAAMLADFIDCPPDDEKPPPPTEPDS,mutated_sequence,1.0,1130.0,UPI000013D80E.a2m,UPI000013D80E.npy,gnomAD +UPI000013D6CB,UPI000013D6CB.csv,MRAAAAGGGVRTAALALLLGALHWAPARCEEYDYYGWQAEPLHGRSYSKPPQCLDIPADLPLCHTVGYKRMRLPNLLEHESLAEVKQQASSWLPLLAKRCHSDTQVFLCSLFAPVCLDRPIYPCRSLCEAVRAGCAPLMEAYGFPWPEMLHCHKFPLDNDLCIAVQFGHLPATAPPVTKICAQCEMEHSADGLMEQMCSSDFVVKMRIKEIKIENGDRKLIGAQKKKKLLKPGPLKRKDTKRLVLHMKNGAGCPCPQLDSLAGSFLVMGRKVDGQLLLMAVYRWDKKNKEMKFAVKFMFSYPCSLYYPFFYGAAEPH,mutated_sequence,1.0,317.0,UPI000013D6CB.a2m,UPI000013D6CB.npy,gnomAD +UPI000013D7E3,UPI000013D7E3.csv,MEQLTTLPRPGDPGAMEPWALPTWHSWTPGRGGEPSSAAPSIADTPPAALQLQELRSEESSKPKGDGSSRPVGGTDPEGAEACLPSLGQQASSSGPACQRPEDEEVEAFLKAKLNMSFGDRPNLELLRALGELRQRCAILKEENQMLRKSSFPETEEKVRRLKRKNAELAVIAKRLEERARKLQETNLRVVSAPLPRPGTSLELCRKALARQRARDLSETASALLAKDKQIAALQRECRELQARLTLVGKEGPQWLHVRDFDRLLRESQREVLRLQRQIALRNQRETLPLPPSWPPGPALQARAGAPAPGAPGEATPQEDADNLPVILGEPEKEQRVQQLESELSKKRKKCESLEQEARKKQRRCEELELQLRQAQNENARLVEENSRLSGRATEKEQVEWENAELRGQLLGVTQERDSALRKSQGLQSKLESLEQVLKHMREVAQRRQQLEVEHEQARLSLREKQEEVRRLQQAQAEAQREHEGAVQLLESTLDSMQARVRELEEQCRSQTEQFSLLAQELQAFRLHPGPLDLLTSALDCGSLGDCPPPPCCCSIPQPCRGSGPKDLDLPPGSPGRCTPKSSEPAPATLTGVPRRTAKKAESLSNSSHSESIHNSPKSCPTPEVDTASEVEELEADSVSLLPAAPEGSRGGARIQVFLARYSYNPFEGPNENPEAELPLTAGEYIYIYGNMDEDGFFEGELMDGRRGLVPSNFVERVSDDDLLTSLPPELADLSHSSGPELSFLSVGGGGSSSGGQSSVGRSQPRPEEEDAGDELSLSPSPEGLGEPPAVPYPRRLVVLKQLAHSVVLAWEPPPEQVELHGFHICVNGELRQALGPGAPPKAVLENLDLWAGPLHISVQALTSRGSSDPLRCCLAVGARAGVVPSQLRVHRLTATSAEITWVPGNSNLAHAIYLNGEECPPASPSTYWATFCHLRPGTPYQAQVEAQLPPQGPWEPGWERLEQRAATLQFTTLPAGPPDAPLDVQIEPGPSPGILIISWLPVTIDAAGTSNGVRVTGYAIYADGQKIMEVASPTAGSVLVELSQLQLLQVCREVVVRTMSPHGESADSIPAPITPALAPASLPARVSCPSPHPSPEARAPLASASPGPGDPSSPLQHPAPLGTQEPPGAPPASPSREMAKGSHEDPPAPCSQEEAGAAVLGTSEERTASTSTLGEKDPGPAAPSLAKQEAEWTAGEACPASSSTQGARAQQAPNTEMCQGGDPGSGLRPRAEKEDTAELGVHLVNSLVDHGRNSDLSDIQEEEEEEEEEEEEELGSRTCSFQKQVAGNSIRENGAKSQPDPFCETDSDEEILEQILELPLQQFCSKKLFSIPEEEEEEEEDEEEEKSGAGCSSRDPGPPEPALLGLGCDSGQPRRPGQCPLSPESSRAGDCLEDMPGLVGGSSRRRGGGSPEKPPSRRRPPDPREHCSRLLSNNGPQASGRLGPTRERGGLPVIEGPRTGLEASGRGRLGPSRRCSRGRALEPGLASCLSPKCLEISIEYDSEDEQEAGSGGISITSSCYPGDGEAWGTATVGRPRGPPKANSGPKPYPRLPAWEKGEPERRGRSATGRAKEPLSRATETGEARGQDGSGRRGPQKRGVRVLRPSTAELVPARSPSETLAYQHLPVRIFVALFDYDPVSMSPNPDAGEEELPFREGQILKVFGDKDADGFYQGEGGGRTGYIPCNMVAEVAVDSPAGRQQLLQRGYLSPDILLEGSGNGPFVYSTAHTTGPPPKPRRSKKAESEGPAQPCPGPPKLVPSADLKAPHSMVAAFDYNPQESSPNMDVEAELPFRAGDVITVFGGMDDDGFYYGELNGQRGLVPSNFLEGPGPEAGGLDREPRTPQAESQRTRRRRVQC,mutated_sequence,1.0,1857.0,UPI000013D7E3.a2m,UPI000013D7E3.npy,gnomAD +UPI000013CCDF,UPI000013CCDF.csv,MLGWVQRVLPQPPGTPRKTKMQEEEEVEPEPEMEAEVEPEPNPEEAETESESMPPEESFKEEEVAVADPSPQETKEAALTSTISLRAQGAEISEMNSPSRRVLTWLMKGVEKVIPQPVHSITEDPAQILGHGSTGDTGCTDEPNEALEAQDTRPGLRLLLWLEQNLERVLPQPPKSSEVWRDEPAVATGAASDPAPPGRPQEMGPKLQARETPSLPTPIPLQPKEEPKEAPAPEPQPGSQAQTSSLPPTRDPARLVAWVLHRLEMALPQPVLHGKIGEQEPDSPGICDVQTISILPGGQVEPDLVLEEVEPPWEDAHQDVSTSPQGTEVVPAYEEENKAVEKMPRELSRIEEEKEDEEEEEEEEEEEEEEEVTEVLLDSCVVSQVGVGQSEEDGTRPQSTSDQKLWEEVGEEAKKEAEEKAKEEAEEVAEEEAEKEPQDWAETKEEPEAEAEAASSGVPATKQHPEVQVEDTDADSCPLMAEENPPSTVLPPPSPAKSDTLIVPSSASGTHRKKLPSEDDEAEELKALSPAESPVVAWSDPTTPKDTDGQDRAASTASTNSAIINDRLQELVKLFKERTEKVKEKLIDPDVTSDEESPKPSPAKKAPEPAPDTKPAEAEPVEEEHYCDMLCCKFKHRPWKKYQFPQSIDPLTNLMYVLWLFFVVMAWNWNCWLIPVRWAFPYQTPDNIHHWLLMDYLCDLIYFLDITVFQTRLQFVRGGDIITDKKDMRNNYLKSRRFKMDLLSLLPLDFLYLKVGVNPLLRLPRCLKYMAFFEFNSRLESILSKAYVYRVIRTTAYLLYSLHLNSCLYYWASAYQGLGSTHWVYDGVGNSYIRCYYFAVKTLITIGGLPDPKTLFEIVFQLLNYFTGVFAFSVMIGQMRDVVGAATAGQTYYRSCMDSTVKYMNFYKIPKSVQNRVKTWYEYTWHSQGMLDESELMVQLPDKMRLDLAIDVNYNIVSKVALFQGCDRQMIFDMLKRLRSVVYLPNDYVCKKGEIGREMYIIQAGQVQVLGGPDGKSVLVTLKAGSVFGEISLLAVGGGNRRTANVVAHGFTNLFILDKKDLNEILVHYPESQKLLRKKARRMLRSNNKPKEEKSVLILPPRAGTPKLFNAALAMTGKMGGKGAKGGKLAHLRARLKELAALEAAAKQQELVEQAKSSQDVKGEEGSAAPDQHTHPKEAATDPPAPRTPPEPPGSPPSSPPPASLGRPEGEEEGPAEPEEHSVRICMSPGPEPGEQILSVKMPEEREEKAE,mutated_sequence,1.0,1251.0,UPI000013CCDF.a2m,UPI000013CCDF.npy,gnomAD +UPI00000503E3,UPI00000503E3.csv,MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLVLTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCCLVKCLFENVVPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTELMDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQGKNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQAGVQPALGVGL,mutated_sequence,1.0,332.0,UPI00000503E3.a2m,UPI00000503E3.npy,gnomAD +UPI000006D8B0,UPI000006D8B0.csv,MGARASGGPLARAGLLLLLLLLLLLGLLAPGAQGARGRGGAEKNSYRRTVNTFSQSVSSLFGEDNVRAAQKFLARLTERFVLGVDMFVETLWKVWTELLDVLGLDVSNLSQYFSPASVSSSPARALLLVGVVLLAYWFLSLTLGFTFSVLHVVFGRFFWIVRVVLFSMSCVYILHKYEGEPENAVLPLCFVVAVYFMTGPMGFYWRSSPSGPSNPSNPSVEEKLEHLEKQVRLLNIRLNRVLESLDRSKDK,mutated_sequence,1.0,251.0,UPI000006D8B0.a2m,UPI000006D8B0.npy,gnomAD +UPI0001B794AD,UPI0001B794AD.csv,XLPAPAAALPGAPPPPPGVRGYPARARGLQGASGGARFPSSLSLPVPGKGGWVVSRAKFPALDSCEITTAHPRLCFAVSPPFSCSLPLPSLFPHSPLLGRPKRQAKPAADEGFWDCSVCTFRNSAEAFKCSICDVRKGTSTRKPRINSQLVAQQVAQQYATPPPPKKEKKEKVEKQDKEKPEKDKEISPSVTKKNTNKKTKPKSDILKDPPSEANSIQSANATTKTSETNHTSRPRLKNVDRSTAQQLAVTVGNVTVIITDFKEKTRSSSTSSSTVTSSAGSEQQNQSSSGSESTDKGSSRSSTPKGDMSAVNDESF,mutated_sequence,1.0,317.0,UPI0001B794AD.a2m,UPI0001B794AD.npy,gnomAD +UPI000007413A,UPI000007413A.csv,MEGPSLRGPALRLAGLPTQQDCNIQEKIDLEIRMREGIWKLLSLSTQKDQVLHAVKNLMVCNARLMAYTSELQKLEEQIANQTGRCDVKFESKERTACKGKIAISDIRIPLMWKDSDHFSNKERSRRYAIFCLFKMGANVFDTDVVNVDKTITDICFENVTIFNEAGPDFQIKVEVYSCCTEESSITNTPKKLAKKLKTSISKATGKKISSVLQEEDDEMCLLLSSAVFGVKYNLLAHTTLTLESAEDSFKTHNLSINGNEESSFWLPLYGNMCCRLVAQPACMAEDAFAGFLNQQQMVEGLISWRRLYCVLRGGKLYCFYSPEEIEAKVEPALVVPINKETRIRAMDKDAKKRIHNFSVINPVPGQAITQIFAVDNREDLQKWMEAFWQHFFDLSQWKHCCEELMKIEIMSPRKPPLFLTKEATSVYHDMSIDSPMKLESLTDIIQKKIEETNGQFLIGQHEESLPPPWATLFDGNHQMVIQKKVLYPASEPLHDEKGKKRQAPLPPSDKLPFSLKSQSNTDQLVKDNWGKTSVSQTSSLDTKLSTLMHHLQKPMAAPRKLLPARRNRLSDGEHTDTKTNFEAKPVPAPRQKSIKDILDPRSWLQAQV,mutated_sequence,1.0,609.0,UPI000007413A.a2m,UPI000007413A.npy,gnomAD +UPI000012CF8B,UPI000012CF8B.csv,MSSYFVNPLYSKYKAAAAAAAAAGEAINPTYYDCHFAPEVGGRHAAAAAALQLYGNSAAGFPHAPPQAHAHPHPSPPPSGTGCGGREGRGQEYFHPGGGSPAAAYQAAPPPPPHPPPPPPPPPCGGIACHGEPAKFYGYDNLQRQPIFTTQQEAELVQYPDCKSSSGNIGEDPDHLNQSSSPSQMFPWMRPQAAPGRRRGRQTYSRFQTLELEKEFLFNPYLTRKRRIEVSHALALTERQVKIWFQNRRMKWKKENNKDKFPVSRQEVKDGETKKEAQELEEDRAEGLTN,mutated_sequence,1.0,290.0,UPI000012CF8B.a2m,UPI000012CF8B.npy,gnomAD +UPI000020A0AE,UPI000020A0AE.csv,MGDVKLVASSHISKTSLSVDPSRVDSMPLTEAPAFILPPRNLCIKEGATAKFEGRVRGYPEPQVTWHRNGQPITSGGRFLLDCGIRGTFSLVIHAVHEEDRGKYTCEATNGSGARQVTVELTVEGSFAKQLGQPVVSKTLGDRFSAPAVETRPSIWGECPPKFATKLGRVVVKEGQMGRFSCKITGRPQPQVTWLKGNVPLQPSARVSVSEKNGMQVLEIHGVNQDDVGVYTCLVVNGSGKASMSAELSIQGLDSANRSFVRETKATNSDVRKEVTNVISKESKLDSLEAAAKSKNCSSPQRGGSPPWAANSQPQPPRESKLESCKDSPRTAPQTPVLQKTSSSITLQAARVQPEPRAPGLGVLSPSGEERKRPAPPRPATFPTRQPGLGSQDVVSKAANRRIPMEGQRDSAFPKFESKPQSQEVKENQTVKFRCEVSGIPKPEVAWFLEGTPVRRQEGSIEVYEDAGSHYLCLLKARTRDSGTYSCTASNAQGQLSCSWTLQVERLAVMEVAPSFSSVLKDCAVIEGQDFVLQCSVRGTPVPRITWLLNGQPIQYARSTCEAGVAELHIQDALPEDHGTYTCLAENALGQVSCSAWVTVHEKKSSRKSEYLLPVAPSKPTAPIFLQGLSDLKVMDGSQVTMTVQVSGNPPPEVIWLHNGNEIQESEDFHFEQRGTQHSLCIQEVFPEDTGTYTCEAWNSAGEVRTQAVLTVQEPHDGTQPWFISKPRSVTASLGQSVLISCAIAGDPFPTVHWLRDGKALCKDTGHFEVLQNEDVFTLVLKKVQPWHAGQYEILLKNRVGECSCQVSLMLQNSSARALPRGREPASCEDLCGGGVGADGGGSDRYGSLRPGWPARGQGWLEEEDGEDVRGVLKRRVETRQHTEEAIRQQEVEQLDFRDLLGKKVSTKTLSEDDLKEIPAEQMDFRANLQRQVKPKTVSEEERKVHSPQQVDFRSVLAKKGTSKTPVPEKVPPPKPATPDFRSVLGGKKKLPAENGSSSAETLNAKAVESSKPLSNAQPSGPLKPVGNAKPAETLKPMGNAKPAETLKPMGNAKPDENLKSASKEELKKDVKNDVNCKRGHAGTTDNEKRSESQGTAPAFKQKLQDVHVAEGKKLLLQCQVSSDPPATIIWTLNGKTLKTTKFIILSQEGSLCSVSIEKALPEDRGLYKCVAKNDAGQAECSCQVTVDDAPASENTKAPEMKSRRPKSSLPPVLGTESDATVKKKPAPKTPPKAAMPPQIIQFPEDQKVRAGESVELFGKVTGTQPITCTWMKFRKQIQESEHMKVENSENGSKLTILAARQEHCGCYTLLVENKLGSRQAQVNLTVVDKPDPPAGTPCASDIRSSSLTLSWYGSSYDGGSAVQSYSIEIWDSANKTWKELATCRSTSFNVQDLLPDHEYKFRVRAINVYGTSEPSQESELTTVGEKPEEPKDEVEVSDDDEKEPEVDYRTVTINTEQKVSDFYDIEERLGSGKFGQVFRLVEKKTRKVWAGKFFKAYSAKEKENIRQEISIMNCLHHPKLVQCVDAFEEKANIVMVLEIVSGGELFERIIDEDFELTERECIKYMRQISEGVEYIHKQGIVHLDLKPENIMCVNKTGTRIKLIDFGLARRLENAGSLKVLFGTPEFVAPEVINYEPIGYATDMWSIGVICYILVSGLSPFMGDNDNETLANVTSATWDFDDEAFDEISDDAKDFISNLLKKDMKNRLDCTQCLQHPWLMKDTKNMEAKKLSKDRMKKYMARRKWQKTGNAVRAIGRLSSMAMISGLSGRKSSTGSPTSPLNAEKLESEEDVSQAFLEAVAEEKPHVKPYFSKTIRDLEVVEGSAARFDCKIEGYPDPEVVWFKDDQSIRESRHFQIDYDEDGNCSLIISDVCGDDDAKYTCKAVNSLGEATCTAELIVETMEEGEGEGEEEEE,mutated_sequence,1.0,1914.0,UPI000020A0AE.a2m,UPI000020A0AE.npy,gnomAD +UPI00001323E1,UPI00001323E1.csv,MIRAAPPPLFLLLLLLLLLVSWASRGEAAPDQDEIQRLPGLAKQPSFRQYSGYLKGSGSKHLHYWFVESQKDPENSPVVLWLNGGPGCSSLDGLLTEHGPFLVQPDGVTLEYNPYSWNLIANVLYLESPAGVGFSYSDDKFYATNDTEVAQSNFEALQDFFRLFPEYKNNKLFLTGESYAGIYIPTLAVLVMQDPSMNLQGLAVGNGLSSYEQNDNSLVYFAYYHGLLGNRLWSSLQTHCCSQNKCNFYDNKDLECVTNLQEVARIVGNSGLNIYNLYAPCAGGVPSHFRYEKDTVVVQDLGNIFTRLPLKRMWHQALLRSGDKVRMDPPCTNTTAASTYLNNPYVRKALNIPEQLPQWDMCNFLVNLQYRRLYRSMNSQYLKLLSSQKYQILLYNGDVDMACNFMGDEWFVDSLNQKMEVQRRPWLVKYGDSGEQIAGFVKEFSHIAFLTIKGAGHMVPTDKPLAAFTMFSRFLNKQPY,mutated_sequence,1.0,480.0,UPI00001323E1.a2m,UPI00001323E1.npy,gnomAD +UPI0000136846,UPI0000136846.csv,MPQLGGGGGGGGGGSGGGGGSSAGAAGGGDDLGANDELIPFQDEGGEEQEPSSDSASAQRDLDEVKSSLVNESENQSSSSDSEAERRPQPVRDTFQKPRDYFAEVRRPQDSAFFKGPPYPGYPFLMIPDLSSPYLSNGPLSPGGARTYLQMKWPLLDVPSSATVKDTRSPSPAHLSNKVPVVQHPHHMHPLTPLITYSNDHFSPGSPPTHLSPEIDPKTGIPRPPHPSELSPYYPLSPGAVGQIPHPLGWLVPQQGQPMYSLPPGGFRHPYPALAMNASMSSLVSSRFSPHMVAPAHPGLPTSGIPHPAIVSPIVKQEPAPPSLSPAVSVKSPVTVKKEEEKKPHVKKPLNAFMLYMKEMRAKVVAECTLKESAAINQILGRKWHNLSREEQAKYYELARKERQLHSQLYPTWSARDNYGKKKKRKREKQLSQTQSQQQVQEAEGALASKSKKPCVQYLPPEKPCDSPASSHGSMLDSPATPSAALASPAAPAATHSEQAQPLSLTTKPETRAQLALHSAAFLSAKAAASSSGQMGSQPPLLSRPLPLGSMPTALLASPPSFPATLHAHQALPVLQAQPLSLVTKSAH,mutated_sequence,1.0,588.0,UPI0000136846.a2m,UPI0000136846.npy,gnomAD +UPI00000742DC,UPI00000742DC.csv,MGKLRRRYNIKGRQQAGPGPSKGPPEPPPVQLELEDKDTLKGVDASNALVLPGKKKKKTKAPPLSKKEKKPLTKKEKKVLQKILEQKEKKSQRAEMLQKLSEVQASEAEMRLFYTTSKLGTGNRMYHTKEKADEVVAPGQEKISSLSGAHRKRRRWPSAEEEEEEEEESESELEEESELDEDPAAEPAEAGVGTTVAPLPPAPAPSSQPVPAGMTVPPPPAAAPPLPRALAKPAVFIPVNRSPEMQEERLKLPILSEEQVIMEAVAEHPIVIVCGETGSGKTTQVPQFLYEAGFSSEDSIIGVTEPRRVAAVAMSQRVAKEMNLSQRVVSYQIRYEGNVTEETRIKFMTDGVLLKEIQKDFLLLRYKVVIIDEAHERSVYTDILIGLLSRIVTLRAKRNLPLKLLIMSATLRVEDFTQNPRLFAKPPPVIKVESRQFPVTVHFNKRTPLEDYSGECFRKVCKIHRMLPAGGILVFLTGQAEVHALCRRLRKAFPPSRARPQEKDDDQKDSVEEMRKFKKSRARAKKARAEVLPQINLDHYSVLPAGEGDEDREAEVDEEEGALDSDLDLDLGDGGQDGGEQPDASLPLHVLPLYSLLAPEKQAQVFKPPPEGTRLCVVATNVAETSLTIPGIKYVVDCGKVKKRYYDRVTGVSSFRVTWVSQASADQRAGRAGRTEPGHCYRLYSSAVFGDFEQFPPPEITRRPVEDLILQMKALNVEKVINFPFPTPPSVEALLAAEELLIALGALQPPQKAERVKQLQENRLSCPITALGRTMATFPVAPRYAKMLALSRQHGCLPYAITIVASMTVRELFEELDRPAASDEELTRLKSKRARVAQMKRTWAGQGASLKLGDLMVLLGAVGACEYASCTPQFCEANGLRYKAMMEIRRLRGQLTTAVNAVCPEAELFVDPKMQPPTESQVTYLRQIVTAGLGDHLARRVQSEEMLEDKWRNAYKTPLLDDPVFIHPSSVLFKELPEFVVYQEIVETTKMYMKGVSSVEVQWIPALLPSYCQFDKPLEEPAPTYCPERGRVLCHRASVFYRVGWPLPAIEVDFPEGIDRYKHFARFLLEGQVFRKLASYRSCLLSSPGTMLKTWARLQPRTESLLRALVAEKADCHEALLAAWKKNPKYLLAEYCEWLPQAMHPDIEKAWPPTTVH,mutated_sequence,1.0,1157.0,UPI00000742DC.a2m,UPI00000742DC.npy,gnomAD +UPI0000203ED6,UPI0000203ED6.csv,MKEPLLGGECDKAVASQLGLLDEIKTEPDNAQEYCHRQQSRTQENELKINAVFSESASQLTAGIQLSLASSGVNKMLPSVSTTAIQVSCAGCKKILQKGQTAYQRKGSAQLFCSIPCITEYISSASSPVPSKRTCSNCSKDILNPKDVISVQLEDTTSCKTFCSLSCLSSYEEKRKPFVTICTNSILTKCSMCQKTAIIQYEVKYQNVKHNLCSNACLSKFHSANNFIMNCCENCGTYCYTSSSLSHILQMEGQSHYFNSSKSITAYKQKPAKPLISVPCKPLKPSDEMIETTSDLGKTELFCSINCFSAYSKAKMESSSVSVVSVVHDTSTELLSPKKDTTPVISNIVSLADTDVALPIMNTDVLQDTVSSVTATADVIVDLSKSSPSEPSNAVASSSTEQPSVSPSSSVFSQHAIGSSTEVQKDNMKSMKISDELCHPKCTSKVQKVKGKSRSIKKSCCADFECLENSKKDVAFCYSCQLFCQKYFSCGRESFATHGTSNWKKTLEKFRKHEKSEMHLKSLEFWREYQFCDGAVSDDLSIHSKQIEGNKKYLKLIIENILFLGKQCLPLRGNDQSVSSVNKGNFLELLEMRAKDKGEETFRLMNSQVDFYNSTQIQSDIIEIIKTEMLQDIVNEINDSSAFSIICDETINSAMKEQLSICVRYPQKSSKAILIKERFLGFVDTEEMTGTHLHRTIKTYLQQIGVDMDKIHGQAYDSTTNLKIKFNKIAAEFKKEEPRALYIHCYAHFLDLSIIRFCKEVKELRSALKTLSSLFNTICMSGEMLANFRNIYRLSQNKTCKKHISQSCWTVHDRTLLSVIDSLPEIIETLEVIASHSSNTSFADELSHLLTLVSKFEFVFCLKFLYRVLSVTGILSKELQNKTIDIFSLSSKIEAILECLSSERNDVYFKTIWDGTEEICQKITCKGFKVEKPSLQKRRKIQKSVDLGNSDNMFFPTSTEEQYKINIYYQGLDTILQNLKLCFSEFDYCKIKQISELLFKWNEPLNETTAKHVQEFYKLDEDIIPELRFYRHYAKLNFVIDDSCINFVSLGCLFIQHGLHSNIPCLSKLLYIALSWPITSASTENSFSTLPRLKTYLCNTMGQEKLTGPALMAVEQELVNKLMEPERLNEIVEKFISQMKEI,mutated_sequence,1.0,1142.0,UPI0000203ED6.a2m,UPI0000203ED6.npy,gnomAD +UPI000178DEEC,UPI000178DEEC.csv,MPTLSFWVCSATPVSPGFFALILLVFVTSIASNVVKIILIHIDSRLHTPMYFLLSQLSLRDILYISTIVPKMLVDQVMSQRAISFAGCTAQHFLYLTLAGAEFFLLGLMSCDRYVAICNPLHYPDLMSRKICWLIVAAAWLGGSIDGFLLTPVTMQFPFCASREINHFFCEVPALLKLSCTDTSAYETAMYVCCIMMLLIPFSVISGSYTRILITVYRMSEAEGRRKAVATCSSHMVVVSLFYGAAMYTYVLPHSYHTPEQDKAVSAFYTILTPMLNPLIYSLRNKDVTGALQKVVGRCVSSGKVTTF,mutated_sequence,1.0,308.0,UPI000178DEEC.a2m,UPI000178DEEC.npy,gnomAD +UPI000045725F,UPI000045725F.csv,MNNQKVVAVLLQECKQVLDQLLLEAPDVSEEDKSEDQRCRALLPSELRTLIQEAKEMKWPFVPEKWQYKQAVGPEDKTNLKDVIGAGLQQLLASLRASILARDCAAAAAIVFLVDRFLYGLDVSGKLLQVAKGLHKLQPATPIAPQVVIRQARISVNSGKLLKAEYILSSLISNNGATGTWLYRNESDKVLVQSVCIQIRGQILQKLGMWYEAAELIWASIVGYLALPQPDKKGLSTSLGILADIFVSMSKNDYEKFKNNPQINLSLLKEFDHHLLSAAEACKLAAAFSAYTPLFVLTAVNIRGTCLLSYSSSNDCPPELKNLHLCEAKEAFEIGLLTKRDDEPVTGKQELHSFVKAAFGLTTVHRRLHGETGTVHAASQLCKEAMGKLYNFSTSSRSQDREALSQEVMSVIAQVKEHLQVQSFSNVDDRSYVPESFECRLDKLILHGQGDFQKILDTYSQHHTSVCEVFESDCGNNKNEQKDAKTGVCITALKTEIKNIDTVSTTQEKPHCQRDTGISSSLMGKNVQRELRRGGRRNWTHSDAFRVSLDQDVETETEPSDYSNGEGAVFNKSLSGSQTSSAWSNLSGFSSSASWEEVNYHVDDRSARKEPGKEHLVDTQCSTALSEELENDREGRAMHSLHSQLHDLSLQEPNNDNLEPSQNQPQQQMPLTPFSPHNTPGIFLAPGAGLLEGAPEGIQEVRNMGPRNTSAHSRPSYRSASWSSDSGRPKNMGTHPSVQKEEAFEIIVEFPETNCDVKDRQGKEQGEEISERGAGPTFKASPSWVDPEGETAESTEDAPLDFHRVLHNSLGNISMLPCSSFTPNWPVQNPDSRKSGGPVAEQGIDPDASTVDEEGQLLDSMDVPCTNGHGSHRLCILRQPPGQRAETPNSSVSGNILFPVLSEDCTTTEEGNQPGNMLNCSQNSSSSSVWWLKSPAFSSGSSEGDSPWSYLNSSGSSWVSLPGKMRKEILEARTLQPDDFEKLLAGVRHDWLFQRLENTGVFKPSQLHRAHSALLLKYSKKSELWTAQETIVYLGDYLTVKKKGRQRNAFWVHHLHQEEILGRYVGKDYKEQKGLWHHFTDVERQMTAQHYVTEFNKRLYEQNIPTQIFYIPSTILLILEDKTIKGCISVEPYILGEFVKLSNNTKVVKTEYKATEYGLAYGHFSYEFSNHRDVVVDLQGWVTGNGKGLIYLTDPQIHSVDQKVFTTNFGKRGIFYFFNNQHVECNEICHRLSLTRPSMEKPCT,mutated_sequence,1.0,1244.0,UPI000045725F.a2m,UPI000045725F.npy,gnomAD +UPI00033351D1,UPI00033351D1.csv,XLGLERDVSRAVELLERLQRSGELPPQKLQALQRVLQSRFCSAIREVYEQLYDTLDITGSAEIRAHATAKVGPAPHCSWCLFNCLVELNISFMPVISVSSLMIFAKSMCHLTISK,mutated_sequence,1.0,115.0,UPI00033351D1.a2m,UPI00033351D1.npy,gnomAD +UPI00001D8160,UPI00001D8160.csv,MTTYRAIPSDGVDLAASCGARVGDVLPGPHTGDYAPLGFWAQNGSMSQPLGESPATATATATATTRPSPTTPAMPKMGVRARVADWPPKREALREHSNPSPSQDTDGTKATKMAHSMRSIQNGQPPTSTPASSGSKAFHRLSRRRSKDVEFQDGWPRSPGRAFLPLRHRSSSEITLSECDAEDAGEPRGARHTGALPLFREYGSTSSIDVQGMPEQSFFDILNEFRSEQPDARGCQALTELLRADPGPHLMGGGGGAKGDSHNGQPAKDSLLPLQPTKEKEKARKKPARGLGGGDTVDSSIFRKLRSSKPEGEAGRSPGEADEGRSPPEASRPWVCQKSFAHFDVQSMLFDLNEAAANRVSVSQRRNTTTGASAASAASAMASLTASRAHSLGGLDPAFTSTEDLNCKENLEQDLGDDNSNDLLLSCPHFRNEIGGECERNVSFSRASVGSPSSGEGHLAEPALSAYRTNASISVLEVPKEQQRTQSRPRQYSIEHVDLGARYYQDYFVGKEHANYFGVDEKLGPVAVSIKREKLEDHKEHGPQYQYRIIFRTRELITLRGSILEDATPTATKHGTGRGLPLKDALEYVIPELNIHCLRLALNTPKVTEQLLKLDEQGLCRKHKVGILYCKAGQSSEEEMYNNEEAGPAFEEFLSLIGEKVCLKGFTKYAAQLDVKTDSTGTHSLYTMYQDYEIMFHVSTLLPYTPNNRQQLLRKRHIGNDIVTIIFQEPGALPFTPKNIRSHFQHVFIIVRVHNPCTDNVCYSMAVTRSKDAPPFGPPIPSGTTFRKSDVFRDFLLAKVINAENAAHKSDKFHTMATRTRQEYLKDLAENCVSNTPIDSTGKFNLISLTSKKKEKTKARAGAEQHSAGAIAWRVVAQDYAQGVEIDCILGISNEFVVLLDLRTKEVVFNCYCGDVIGWTPDSSTLKIFYGRGDHIFLQATEGSVEDIREIVQRLKVMTSGWETVDMTLRRNGLGQLGFHVKYDGTVAEVEDYGFAWQAGLRQGSRLVEICKVAVVTLTHDQMIDLLRTSVTVKVVIIPPFEDGTPRRGWPETYDMNTSEPKTEQESITPGGRPPYRSNAPWQWSGPASHNSLPASKWATPTTPGHAQSLSRPLKQTPIVPFRESQPLHSKRPVSFPETPYTVSPAGADRVPPYRQPSGSFSTPGSATYVRYKPSPERYTAAPHPLLSLDPHFSHDGTSSGDSSSGGLTSQESTMERQKPEPLWHVPAQARLSAIAGSSGNKHPSRQDAAGKDSPNRHSKGEPQYSSHSSSNTLSSNASSSHSDDRWFDPLDPLEPEQDPLSKGGSSDSGIDTTLYTSSPSCMSLAKAPRPAKPHKPPGSMGLCGGGREAAGRSHHADRRREVSPAPAVAGQSKGYRPKLYSSGSSTPTGLAGGSRDPPRQPSDMGSRVGYPAQVYKTASAETPRPSQLAQPSPFQLSASVPKSFFSKQPVRNKHPTGWKRTEEPPPRPLPFSDPKKQVDTNTKNVFGQPRLRASLRDLRSPRKNYKSTIEDDLKKLIIMDNLGPEQERDTGQSPQKGLQRTLSDESLCSGRREPSFASPAGLEPGLPSDVLFTSTCAFPSSTLPARRQHQHPHPPVGPGATPAAGSGFPEKKSTISASELSLADGRDRPLRRLDPGLMPLPDTAAGLEWSSLVNAAKAYEVQRAVSLFSLNDPALSPDIPPAHSPVHSHLSLERGPPTPRTTPTMSEEPPLDLTGKVYQLEVMLKQLHTDLQKEKQDKVVLQSEVASLRQNNQRLQEESQAASEQLRKFAEIFCREKKEL,mutated_sequence,1.0,1781.0,UPI00001D8160.a2m,UPI00001D8160.npy,gnomAD +UPI0000038CE3,UPI0000038CE3.csv,MGIGRSEGGRRGAALGVLLALGAALLAVGSASEYDYVSFQSDIGPYQSGRFYTKPPQCVDIPADLRLCHNVGYKKMVLPNLLEHETMAEVKQQASSWVPLLNKNCHAGTQVFLCSLFAPVCLDRPIYPCRWLCEAVRDSCEPVMQFFGFYWPEMLKCDKFPEGDVCIAMTPPNATEASKPQGTTVCPPCDNELKSEAIIEHLCASEFALRMKIKEVKKENGDKKIVPKKKKPLKLGPIKKKDLKKLVLYLKNGADCPCHQLDNLSHHFLIMGRKVKSQYLLTAIHKWDKKNKEFKNFMKKMKNHECPTFQSVFK,mutated_sequence,1.0,314.0,UPI0000038CE3.a2m,UPI0000038CE3.npy,gnomAD +UPI0000070D27,UPI0000070D27.csv,MIIKEYRIPLPMTVEEYRIAQLYMIQKKSRNETYGEGSGVEILENRPYTDGPGGSGQYTHKVYHVGMHIPSWFRSILPKAALRVVEESWNAYPYTRTRFTCPFVEKFSIDIETFYKTDAGENPDVFNLSPVEKNQLTIDFIDIVKDPVPHNEYKTEEDPKLFQSTKTQRGPLSENWIEEYKKQVFPIMCAYKLCKVEFRYWGMQSKIERFIHDTGLRRVMVRAHRQAWCWQDEWYGLSMENIRELEKEAQLMLSRKMAQFNEDGEEATELVKHEAVSDQTSGEPPEPSSSNGEPLVGRGLKKQWSTSSKSSRSSKRGASPSRHSISEWRMQSIARDSDESSDDEFFDAHEDLSDTEEMFPKDITKWSSNDLMDKIESPEPEDTQDGLYRQGAPEFRVASSVEQLNIIEDEVSQPLAAPPSKIHVLLLVLHGGTILDTGAGDPSSKKGDANTIANVFDTVMRVHYPSALGRLAIRLVPCPPVCSDAFALVSNLSPYSHDEGCLSSSQDHIPLAALPLLATSSPQYQEAVATVIQRANLAYGDFIKSQEGMTFNGQVCLIGDCVGGILAFDALCYSNQPVSESQSSSRRGSVVSMQDNDLLSPGILMNAAHCCGGGGGGGGGGGSSGGGGSSGGSSLESSRHLSRSNVDIPRSNGTEDPKRQLPRKRSDSSTYELDTIQQHQAFLSSLHASVLRTEPCSRHSSSSTMLDGTGALGRFDFEITDLFLFGCPLGLVLALRKTVIPALDVFQLRPACQQVYNLFHPADPSASRLEPLLERRFHALPPFSVPRYQRYPLGDGCSTLLADVLQTHNAAFQEHGAPSSPGTAPASRGFRRASEISIASQVSGMAESYTASSIAQKAPDALSHTPSVRRLSLLALPAPSPTTPGPHPPARKASPGLERAPGLPELDIGEVAAKWWGQKRIDYALYCPDALTAFPTVALPHLFHASYWESTDVVSFLLRQVMRHDNSSILELDGKEVSVFTPSKPREKWQRKRTHVKLRNVTANHRINDALANEDGPQVLTGRFMYGPLDMVTLTGEKVDVHIMTQPPSGEWLYLDTLVTNNSGRVSYTIPESHRLGVGVYPIKMVVRGDHTFADSYITVLPKGTEFVVFSIDGSFAASVSIMGSDPKVRAGAVDVVRHWQDLGYLIIYVTGRPDMQKQRVVAWLAQHNFPHGVVSFCDGLVHDPLRHKANFLKLLISELHLRVHAAYGSTKDVAVYSAISLSPMQIYIVGRPTKKLQQQCQFITDGYAAHLAQLKYSHRARPARNTATRMALRKGSFGLPGQGDFLRSRNHLLRTISAQPSGPSHRHERTQSQADGEQRGQRSMSVAAGCWGRAMTGRLEPGAAAGPK,mutated_sequence,1.0,1349.0,UPI0000070D27.a2m,UPI0000070D27.npy,gnomAD +UPI0001B79456,UPI0001B79456.csv,MQGPDAPLLLSARELGPGRRGSASWYRQEGGAVCNWLRKPQPLEPRTSFPSARRSEFRPPRRLPWAGPASAQSEEGHAGGRCQAGSPAPARRLSGAFKTLVPGTHAQGASDTHPLGPPHTPVLDPSPQDGFPVLWRGSSKASHMNRLRNAKIYVERAVKQKKIFTIQGCYPVIRCLLRRRGWVEKKMVHRSGPTLLPPQKDLDSSAMGDSDTTEDEDEDEDEEFQPSQLFDFDDLLKFDDLDGTHALMVGLCLNLRNLPWFDEVDANSFFPRCYCLGAEDDKKAFIEDFWLTAARNVLKLVVKSEWKSYPIQAVEEEASGDKQPKKQEKNPVLVSPEFVDEALCACEEYLSNLAHMDIDKDLEAPLYLTPEGWSLFLQRYYQVVHEGAELRHLDTQVQRCEDILQQLQAVVPQIDMEGDRNIWIVKPGAKSRGRGIMCMDHLEEMLKLVNGNPVVMKDGKWVVQKYIERPLLIFGTKFDLRQWFLVTDWNPLTVWFYRDSYIRFSTQPFSLKNLDNSVHLCNNSIQKHLENSCHRHPLLPPDNMWSSQRFQAHLQEMGAPNAWSTIIVPGMKDAVIHALQTSQDTVQCRKASFELYGADFVFGEDFQPWLIEINASPTMAPSTAVTARLCAGVQADTLRVVIDRMLDRNCDTGAFELIYKQPAVEVPQYVGIRLLVEGFTIKKPMAMCHRRMGVRPAVPLLTQRGSGEARHHFPSLHTKAQLPSPHVLRHQGQVLRRQHSKLVGTKALSTTGKALRTLPTAKVFISLPPNLDFKVAPSILKPRKAPALLCLRGPQLEVPCCLCPLKSEQFLAPVGRSRPKANSRPDCDKPRAEACPMKRLSPLKPLPLVGTFQRRRGLGDMKLGKPLLRFPTALVLDPTPNKKKQVKYLGLDSIAVGGSRVDGARPCTPGSTARA,mutated_sequence,1.0,915.0,UPI0001B79456.a2m,UPI0001B79456.npy,gnomAD +UPI00002021CA,UPI00002021CA.csv,MAHELVMFRDVAIDVSQEEWECLNPAQRNLYKEVMLENYSNLVSLGLSVSKPAVISSLEQGKEPWMVVREETGRWCPGTWKTWGFHNNFLDNNEATDINADLASRDEPQKLSPKRDIYETELSQWVNMEEFKSHSPERSIFSAIWEGNCHFEQHQGQEEGYFRQLMINHENMPIFSQHTLLTQEFYDREKISECKKCRKIFSYHLFFSHHKRTHSKELSECKECTEIVNTPCLFKQQTIQNGDKCNECKECWKAFVHCSQLKHLRIHNGEKRYECNECGKAFNYGSELTLHQRIHTGEKPYECKECGKAFRQRSQLTQHQRLHTGEKPYECKQCGKAFIRGFQLTEHLRLHTGEKPYECKECGKTFRHRSHLTIHQRIHTGEKPYECRECGKAFSYHSSFSHHQKIHSGKKPYECHECGKAFCDGLQLTLHQRIHTGEKPYECKECGKTFRQCSHLKRHQRIHTGEKPHECMICGKAFRLHSHLIQHQRIHTGEKPYECKECGKAFSYHSSFSHHQRIHSGKKPYQCGKAFNHRLQLNLHQTLHTGEKPVRFPLLPPHPSLAS,mutated_sequence,1.0,563.0,UPI00002021CA.a2m,UPI00002021CA.npy,gnomAD +UPI000013CD7C,UPI000013CD7C.csv,MVLLSILRILFLCELVLFMEHRAQMAEGGQSSIALLAEAPTLPLIEELLEESPGEQPRKPRLLGHSLRYMLELYRRSADSHGHPRENRTIGATMVRLVKPLTNVARPHRGTWHIQILGFPLRPNRGLYQLVRATVVYRHHLQLTRFNLSCHVEPWVQKNPTNHFPSSEGDSSKPSLMSNAWKEMDITQLVQQRFWNNKGHRILRLRFMCQQQKDSGGLELWHGTSSLDIAFLLLYFNDTHKSIRKAKFLPRGMEEFMERESLLRRTRQADGISAEVTASSSKHSGPENNQCSLHPFQISFRQLGWDHWIIAPPFYTPNYCKGTCLRVLRDGLNSPNHAIIQNLINQLVDQSVPRPSCVPYKYVPISVLMIEANGSILYKEYEGMIAESCTCR,mutated_sequence,1.0,392.0,UPI000013CD7C.a2m,UPI000013CD7C.npy,gnomAD +UPI0000071891,UPI0000071891.csv,MEEEDESRGKTEESGEDRGDGPPDRDPTLSPSAFILRAIQQAVGSSLQGDLPNDKDGSRCHGLRWRRCRSPRSEPRSQESGGTDTATVLDMATDSFLAGLVSVLDPPDTWVPSRLDLRPGESEDMLELVAEVRIGDRDPIPLPVPSLLPRLRAWRTGKTVSPQSNSSRPTCARHLTLGTGDGGPAPPPAPSSASSSPSPSPSSSSPSPPPPPPPPAPPAPPAPRFDIYDPFHPTDEAYSPPPAPEQKYDPFEPTGSNPSSSAGTPSPEEEEEEEEEEEEEEEDEEEEEGLSQSISRISETLAGIYDDNSLSQDFPGDESPRPDAQPTQPTPAPGTPPQVDSTRADGAMRRRVFVVGTEAEACREGKVSVEVVTAGGAALPPPLLPPGDSEIEEGEIVQPEEEPRLALSLFRPGGRAARPTPAASATPTAQPLPQPPAPRAPEGDDFLSLHAESDGEGALQVDLGEPAPAPPAADSRWGGLDLRRKILTQRRERYRQRSPSPAPAPAPAAAAGPPTRKKSRRERKRSGEAKEAASSSSGTQPAPPAPASPWDSKKHRSRDRKPGSHASSSARRRSRSRSRSRSTRRRSRSTDRRRGGSRRSRSREKRRRRRRSASPPPATSSSSSSRRERHRGKHRDGGGSKKKKKRSRSRGEKRSGDGSEKAPAPAPPPSGSTSCGDRDSRRRGAVPPSIQDLTDHDLFAIKRTITVGRLDKSDPRGPSPAPASSPKREVLYDSEGLSGEERGGKSSQKDRRRSGAASSSSSSREKGSRRKALDGGDRDRDRDRDRDRDRSSKKARPPKESAPSSGPPPKPPVSSGSGSSSSSSSCSSRKVKLQSKVAVLIREGVSSTTPAKDAASAGLGSIGVKFSRDRESRSPFLKPDERAPTEMAKAAPGSTKPKKTKVKAKAGAKKTKGTKGKTKPSKTRKKVRSGGGSGGSGGQVSLKKSKADSCSQAAGTKGAEETSWSGEERAAKVPSTPPPKAAPPPPALTPDSQTVDSSCKTPEVSFLPEEATEEAGVRGGAEEEEEEEEEEEEEEEEEEQQPATTTATSTAAAAPSTAPSAGSTAGDSGAEDGPASRVSQLPTLPPPMPWNLPAGVDCTTSGVLALTALLFKMEEANLASRAKAQELIQATNQILSHRKPPSSLGMTPAPVPTSLGLPPGPSSYLLPGSLPLGGCGSTPPTPTGLAATSDKREGSSSSEGRGDTDKYLKKLHTQERAVEEVKLAIKPYYQKKDITKEEYKDILRKAVHKICHSKSGEINPVKVSNLVRAYVQRYRYFRKHGRKPGDPPGPPRPPKEPGPPDKGGPGLPLPPL,mutated_sequence,1.0,1312.0,UPI0000071891.a2m,UPI0000071891.npy,gnomAD +UPI000013D6B1,UPI000013D6B1.csv,MRWRTILLQYCFLLITCLLTALEAVPIDIDKTKVQNIHPVESAKIEPPDTGLYYDEYLKQVIDVLETDKHFREKLQKADIEEIKSGRLSKELDLVSHHVRTKLDELKRQEVGRLRMLIKAKLDSLQDIGMDHQALLKQFDHLNHLNPDKFESTDLDMLIKAATSDLEHYDKTRHEEFKKYEMMKEHERREYLKTLNEEKRKEEESKFEEMKKKHENHPKVNHPGSKDQLKEVWEETDGLDPNDFDPKTFFKLHDVNSDGFLDEQELEALFTKELEKVYDPKNEEDDMVEMEEERLRMREHVMSEVDTNKDRLVTLEEFLKATEKKEFLEPDSWETLDQQQFFTEEELKEYENIIALQENELKKKADELQKQKEELQRQHDQLEAQKLEYHQVIQQMEQKKLQQGIPPSGPAGELKFEPHI,mutated_sequence,1.0,420.0,UPI000013D6B1.a2m,UPI000013D6B1.npy,gnomAD +UPI000013DEA0,UPI000013DEA0.csv,MVKLANPLYTEWILEAIQKIKKQKQRPSEERICHAVSTSHGLDKKTVSEQLELSVQDGSVLKVTNKGLASYKDPDNPGRFSSVKPGTFPKSAKGSRGSCNDLRNVDWNKLLRRAIEGLEEPNGSSLKNIEKYLRSQSDLTSTTNNPAFQQRLRLGAKRAVNNGRLLKDGPQYRVNYGSLDGKGAPQYPSAFPSSLPPVSLLPHEKDQPRADPIPICSFCLGTKESNREKKPEELLSCADCGSSGHPSCLKFCPELTTNVKALRWQCIECKTCSACRVQGRNADNMLFCDSCDRGFHMECCDPPLSRMPKGMWICQVCRPKKKGRKLLHEKAAQIKRRYAKPIGRPKNKLKQRLLSVTSDEGSMNAFTGRGSPGRGQKTKVCTTPSSGHAASGKDSSSRLAVTDPTRPGATTKITTTSTYISASTLKVNKKTKGLIDGLTKFFTPSPDGRRSRGEIIDFSKHYRPRKKVSQKQSCTSHVLATGTTQKLKPPPSSLPPPTPISGQSPSSQKSSTATSSPSPQSSSSQCSVPSLSSLTTNSQLKALFDGLSHIYTTQGQSRKKGHPSYAPPKRMRRKTELSSTAKSKAHFFGKRDIRSRFISHSSSSSWGMARGSIFKAIAHFKRTTFLKKHRMLGRLKYKVTPQMGTPSPGKGSLTDGRIKPDQDDDTEIKINIKQESADVNVIGNKDVVTEEDLDVFKQAQELSWEKIECESGVEDCGRYPSVIEFGKYEIQTWYSSPYPQEYARLPKLYLCEFCLKYMKSKNILLRHSKKCGWFHPPANEIYRRKDLSVFEVDGNMSKIYCQNLCLLAKLFLDHKTLYYDVEPFLFYVLTKNDEKGCHLVGYFSKEKLCQQKYNVSCIMIMPQHQRQGFGRFLIDFSYLLSRREGQAGSPEKPLSDLGRLSYLAYWKSVILEYLYHHHERHISIKAISRATGMCPHDIATTLQHLHMIDKRDGRFVIIRREKLILSHMEKLKTCSRANELDPDSLRWTPILISNAAVSEEEREAEKEAERLMEQASCWEKEEQEILSTRANSRQSPAKVQSKNKYLHSPESRPVTGERGQLLELSKESSEEEEEEEDEEEEEEEEEEEEDEEEEEEEEEEEEEENIQSSPPRLTKPQSVAIKRKRPFVLKKKRGRKRRRINSSVTTETISETTEVLNEPFDNSDEERPMPQLEPTCEIEVEEDGRKPVLRKAFQHQPGKKRQTEEEEGKDNHCFKNADPCRNNMNDDSSNLKEGSKDNPEPLKCKQVWPKGTKRGLSKWRQNKERKTGFKLNLYTPPETPMEPDEQVTVEEQKETSEGKTSPSPIRIEEEVKETGEALLPQEENRREETCAPVSPNTSPGEKPEDDLIKPEEEEEEEEEEEEEEEEEEGEEEEGGGNVEKDPDGAKSQEKEEPEISTEKEDSARLDDHEEEEEEDEEPSHNEDHDADDEDDSHMESAEVEKEELPRESFKEVLENQETFLDLNVQPGHSNPEVLMDCGVDLTASCNSEPKELAGDPEAVPESDEEPPPGEQAQKQDQKNSKEVDTEFKEGNPATMEIDSETVQAVQSLTQESSEQDDTFQDCAETQEACRSLQNYTRADQSPQIATTLDDCQQSDHSSPVSSVHSHPGQSVRSVNSPSVPALENSYAQISPDQSAISVPSLQNMETSPMMDVPSVSDHSQQVVDSGFSDLGSIESTTENYENPSSYDSTMGGSICGNGSSQNSCSYSNLTSSSLTQSSCAVTQQMSNISGSCSMLQQTSISSPPTCSVKSPQGCVVERPPSSSQQLAQCSMAANFTPPMQLAEIPETSNANIGLYERMGQSDFGAGHYPQPSATFSLAKLQQLTNTLIDHSLPYSHSAAVTSYANSASLSTPLSNTGLVQLSQSPHSVPGGPQAQATMTPPPNLTPPPMNLPPPLLQRNMAASNIGISHSQRLQTQIASKGHISMRTKSASLSPAAATHQSQIYGRSQTVAMQGPARTLTMQRGMNMSVNLMPAPAYNVNSVNMNMNTLNAMNGYSMSQPMMNSGYHSNHGYMNQTPQYPMQMQMGMMGTQPYAQQPMQTPPHGNMMYTAPGHHGYMNTGMSKQSLNGSYMRR,mutated_sequence,1.0,2073.0,UPI000013DEA0.a2m,UPI000013DEA0.npy,gnomAD +UPI000000D799,UPI000000D799.csv,MESGDEAAIERHRVHLRSATLRDAVPATLHLLPCEVAVDGPAPVGRFFTPAIRQGPEGLEVSFRGRCLRGEEVAVPPGLVGYVMVTEEKKVSMGKPDPLRDSGTDDQEEEPLERDFDRFIGATANFSRFTLWGLETIPGPDAKVRGALTWPSLAAAIHAQVPED,mutated_sequence,1.0,164.0,UPI000000D799.a2m,UPI000000D799.npy,gnomAD +UPI000013C5D0,UPI000013C5D0.csv,MSRSLDSARSFLERLEARGGREGAVLAGEFSDIQACSAAWKADGVCSTVAGSRPENVRKNRYKDVLPYDQTRVILSLLQEEGHSDYINGNFIRGVDGSLAYIATQGPLPHTLLDFWRLVWEFGVKVILMACREIENGRKRCERYWAQEQEPLQTGLFCITLIKEKWLNEDIMLRTLKVTFQKESRSVYQLQYMSWPDRGVPSSPDHMLAMVEEARRLQGSGPEPLCVHCSAGCGRTGVLCTVDYVRQLLLTQMIPPDFSLFDVVLKMRKQRPAAVQTEEQYRFLYHTVAQMFCSTLQNASPHYQNIKENCAPLYDDALFLRTPQALLAIPRPPGGVLRSISVPGSPGHAMADTYAVVQKRGAPAGAGSGTQTGTGTGTGARSAEEAPLYSKVTPRAQRPGAHAEDARGTLPGRVPADQSPAGSGAYEDVAGGAQTGGLGFNLRIGRPKGPRDPPAEWTRV,mutated_sequence,1.0,460.0,UPI000013C5D0.a2m,UPI000013C5D0.npy,gnomAD +UPI000013C34D,UPI000013C34D.csv,MEGLLHYINPAHAISLLSALNEERLKGQLCDVLLIVGDQKFRAHKNVLAASSEYFQSLFTNKENESQTVFQLDFCEPDAFDNVLNYIYSSSLFVEKSSLAAVQELGYSLGISFLTNIVSKTPQAPFPTCPNRKKVFVEDDENSSQKRSVIVCQSRNEAQGKTVSQNQPDVSHTSRPSPSIAVKANTNKPHVPKPIEPLHNLSLTEKSWPKDSSVVYAKSLEHSGSLDDPNRISLVKRNAVLPSKPLQDREAMDDKPGVSGQLPKGKALELALKRPRPPVLSVCSSSETPYLLKETNKGNGQGEDRNLLYYSKLGLVIPSSGSGSGNQSIDRSGPLVKSLLRRSLSMDSQVPVYSPSIDLKSSQGSSSVSSDAPGNVLCALSQKSSLKDCSEKTALDDRPQVLQPHRLRSFSASQSTDREGASPVTEVRIKTEPSSPLSDPSDIIRVTVGDAATTAAASSSSVTRDLSLKTEDDQKDMSRLPAKRRFQADRRLPFKKLKVNEHGSPVSEDNFEEGSSPTLLDADFPDSDLNKDEFGELEGTRPNKKFKCKHCLKIFRSTAGLHRHVNMYHNPEKPYACDICHKRFHTNFKVWTHCQTQHGIVKNPSPASSSHAVLDEKFQRKLIDIVREREIKKALIIKLRRGKPGFQGQSSSQAQQVIKRNLRSRAKGAYICTYCGKAYRFLSQFKQHIKMHPGEKPLGVNKVAKPKEHAPLASPVENKEVYQCRLCNAKLSSLLEQGSHERLCRNAAVCPYCSLRFFSPELKQEHESKCEYKKLTCLECMRTFKSSFSIWRHQVEVHNQNNMAPTENFSLPVLDHNGDVTGSSRPQSQPEPNKVNHIVTTKDDNVFSDSSEQVNFDSEDSSCLPEDLSLSKQLKIQVKEEPVEEAEEEAPEASTAPKEAGPSKEASLWPCEKCGKMFTVHKQLERHQELLCSVKPFICHVCNKAFRTNFRLWSHFQSHMSQASEESAHKESEVCPVPTNSPSPPPLPPPPPLPKIQPLEPDSPTGLSENPTPATEKLFVPQESDTLFYHAPPLSAITFKRQFMCKLCHRTFKTAFSLWSHEQTHN,mutated_sequence,1.0,1066.0,UPI000013C34D.a2m,UPI000013C34D.npy,gnomAD +UPI00000743C2,UPI00000743C2.csv,MAPTKPSFQQDPSRRERITAQHPLPNQSECRKIYRYDGIYCESTYQNLQALRKEKSRDAARSRRGKENFEFYELAKLLPLPAAITSQLDKASIIRLTISYLKMRDFANQGDPPWNLRMEGPPPNTSVKVIGAQRRRSPSALAIEVFEAHLGSHILQSLDGFVFALNQEGKFLYISETVSIYLGLSQVELTGSSVFDYVHPGDHVEMAEQLGMKLPPGRGLLSQGTAEDGASSASSSSQSETPEPVESTSPSLLTTDNTLERSFFIRMKSTLTKRGVHIKSSGYKVIHITGRLRLRVSLSHGRTVPSQIMGLVVVAHALPPPTINEVRIDCHMFVTRVNMDLNIIYCENRISDYMDLTPVDIVGKRCYHFIHAEDVEGIRHSHLDLLNKGQCVTKYYRWMQKNGGYIWIQSSATIAINAKNANEKNIIWVNYLLSNPEYKDTPMDIAQLPHLPEKTSESSETSDSESDSKDTSGITEDNENSKSDEKGNQSENSEDPEPDRKKSGNACDNDMNCNDDGHSSSNPDSRDSDDSFEHSDFENPKAGEDGFGALGAMQIKVERYVESESDLRLQNCESLTSDSAKDSDSAGEAGAQASSKHQKRKKRRKRQKGGSASRRRLSSASSPGGLDAGLVEPPRLLSSPNSASVLKIKTEISEPINFDNDSSIWNYPPNREISRNESPYSMTKPPSSEHFPSPQGGGGGGGGGGGLHVAIPDSVLTPPGADGAAARKTQFGASATAALAPVASDPLSPPLSASPRDKHPGNGGGGGGGGGGAGGGGPSASNSLLYTGDLEALQRLQAGNVVLPLVHRVTGTLAATSTAAQRVYTTGTIRYAPAEVTLAMQSNLLPNAHAVNFVDVNSPGFGLDPKTPMEMLYHHVHRLNMSGPFGGAVSAASLTQMPAGNVFTTAEGLFSTLPFPVYSNGIHAAQTLERKED,mutated_sequence,1.0,933.0,UPI00000743C2.a2m,UPI00000743C2.npy,gnomAD +UPI0000000A01,UPI0000000A01.csv,MWNLLHETDSAVATARRPRWLCAGALVLAGGFFLLGFLFGWFIKSSNEATNITPKHNMKAFLDELKAENIKKFLYNFTQIPHLAGTEQNFQLAKQIQSQWKEFGLDSVELAHYDVLLSYPNKTHPNYISIINEDGNEIFNTSLFEPPPPGYENVSDIVPPFSAFSPQGMPEGDLVYVNYARTEDFFKLERDMKINCSGKIVIARYGKVFRGNKVKNAQLAGAKGVILYSDPADYFAPGVKSYPDGWNLPGGGVQRGNILNLNGAGDPLTPGYPANEYAYRRGIAEAVGLPSIPVHPIGYYDAQKLLEKMGGSAPPDSSWRGSLKVPYNVGPGFTGNFSTQKVKMHIHSTNEVTRIYNVIGTLRGAVEPDRYVILGGHRDSWVFGGIDPQSGAAVVHEIVRSFGTLKKEGWRPRRTILFASWDAEEFGLLGSTEWAEENSRLLQERGVAYINADSSIEGNYTLRVDCTPLMYSLVHNLTKELKSPDEGFEGKSLYESWTKKSPSPEFSGMPRISKLGSGNDFEVFFQRLGIASGRARYTKNWETNKFSGYPLYHSVYETYELVEKFYDPMFKYHLTVAQVRGGMVFELANSIVLPFDCRDYAVVLRKYADKIYSISMKHPQEMKTYSVSFDSLFSAVKNFTEIASKFSERLQDFDKSNPIVLRMMNDQLMFLERAFIDPLGLPDRPFYRHVIYAPSSHNKYAGESFPGIYDALFDIESKVDPSKAWGEVKRQIYVAAFTVQAAAETLSEVA,mutated_sequence,1.0,750.0,UPI0000000A01.a2m,UPI0000000A01.npy,gnomAD +UPI000013C6CB,UPI000013C6CB.csv,MEGGAYGAGKAGGAFDPYTLVRQPHTILRVVSWLFSIVVFGSIVNEGYLNSASEGEEFCIYNRNPNACSYGVAVGVLAFLTCLLYLALDVYFPQISSVKDRKKAVLSDIGVSAFWAFLWFVGFCYLANQWQVSKPKDNPLNEGTDAARAAIAFSFFSIFTWAGQAVLAFQRYQIGADSALFSQDYMDPSQDSSMPYAPYVEPTGPDPAGMGGTYQQPANTFDTEPQGYQSQGY,mutated_sequence,1.0,233.0,UPI000013C6CB.a2m,UPI000013C6CB.npy,gnomAD +UPI00001B4EFC,UPI00001B4EFC.csv,MGDTAPPQAPAGGLGGASGAGLLGGGSVTPRVHSAIVERLRARIAVCRQHHLSCEGRYERGRAESSDRERESTLQLLSLVQHGQGARKAGKHTKATATAATTTAPPPPPAAPPAASQAAATAAPPPPPDYHHHHQQHLLNSSNNGGSGGINGEQQPPASTPGDQRNSALIALQGSLKRKQVVNLSPANSKRPNGFVDNSFLDIKRIRVGENLSAGQGGLQINNGQSQIMSGTLPMSQAPLRKTNTLPSHTHSPGNGLFNMGLKEVKKEPGETLSCSKHMDGQMTQENIFPNRYGDDPGEQLMDPELQELFNELTNISVPPMSDLELENMINATIKQDDPFNIDLGQQSQRSTPRPSLPMEKIVIKSEYSPGLTQGPSGSPQLRPPSAGPAFSMANSALSTSSPIPSVPQSQAQPQTGSGASRALPSWQEVSHAQQLKQIAANRQQHARMQQHQQQHQPTNWSALPSSAGPSPGPFGQEKIPSPSFGQQTFSPQSSPMPGVAGGSGQSKVMANYMYKAGPSAQGGHLDVLMQQKPQDLSRSFINNPHPAMEPRQGNTKPLFHFNSDQANQQMPSVLPSQNKPSLLHYTQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQSSISAQQQQQQQSSISAQQQQQQQQQQQQQQQQQQQQQQQQQQQPSSQPAQSLPSQPLLRSPLPLQQKLLLQQMQNQPIAGMGYQVSQQQRQDQHSVVGQNTGPSPSPNPCSNPNTGSGYMNSQQSLLNQQLMGKKQTLQRQIMEQKQQLLLQQQMLADAEKIAPQDQINRHLSRPPPDYKDQRRNVGNMQPTAQYSGGSSTISLNSNQALANPVSTHTILTPNSSLLSTSHGTRMPSLSTAVQNMGMYGNLPCNQPNTYSVTSGMNQLTQQRNPKQLLANQNNPMMPRPPTLGPSNNNNVATFGAGSVGNSQQLRPNLTHSMASMPPQRTSNVMITSNTTAPNWASQEGTSKQQEALTSAGVRFPTGTPAAYTPNQSLQQAVGSQQFSQRAVAPPNQLTPAVQMRPMNQMSQTLNGQTMGPLRGLNLRPNQLSTQILPNLNQSGTGLNQSRTGINQPPSLTPSNFPSPNQSSRAFQGTDHSSDLAFDFLSQQNDNMGPALNSDADFIDSLLKTEPGNDDWMKDINLDEILGNNS,mutated_sequence,1.0,1156.0,UPI00001B4EFC.a2m,UPI00001B4EFC.npy,gnomAD +UPI00001FDCF7,UPI00001FDCF7.csv,MSATSWFLVSSSGARHRLPRELIFVGREECELMLQSRSVDKQHAVINYDQDRDEHWVKDLGSLNGTFVNDMRIPDQKYVTLKLNDVIRFGYDSNMYVLERVQHRVPEEALKHEKYTSQLQVSVKGLAPKRSEALPEHTPYCEASNPRPEKGDRRPGTEAASYRTPLYGQPSWWGEDDGSTLPDAQRQGEPYPERPKGPVQQDGELHGFRAPAEPQGCSFRREPSYFEIPTKETPQPSQPPEVPAHEMPTKDAEAGGGGAAPVVQSHASFTIEFDDCSPGKMKIKDHITKFSLRQRRPPGKEATPGEMVSAETKVADWLVQNDPSLLHRVGPGDDRHSTKSDLPVHTRTLKGHKHEDGTQSDSEDPLAKAASAAGVPLEASGEQVRLQRQIKRDPQELLHNQQAFVIEFFDEDTPRKKRSQSFTHSPSGDPKADKRRGPTPADRDRPSVPAPVQAGGRSSGPQRAGSLKREKTEERLGSPSPASRTPARPFGSVGRRSRLAQDFMAQCLRESSPAARPSPEKVPPVLPAPLTPHGTSPVGPPTPPPAPTDPQLTKARKQEEDDSLSDAGTYTIETEAQDTEVEEARKMIDQVFGVLESPELSRASSATFRPVIRGDRDESDDGGVAQRMALLQEFASRPLGAAPQAEHQGLPVPGSPGGQKWVSRWASLADSYSDPGLTEDGLGRRGGEPEGSLPVRMRRRLPQLPSERADSPAGPESSRRSGPGPPELDSEQPSRLFGQEELDPDSLSDASGSDGGRGPEPGVEPQDSRRRSPQEGPTWSRGRRSPRAPGEPTPASFFIGDQNGDAVLSRKPLAAPGDGEGLGQTAQPSPPARDGVYVSANGRMVIQLRPGRSPEPDGPAPAFLRQESFTKEPASGPPAPGKPPHISSHPLLQDLAATRAARMDFHSQDTHLILKETETALAALEARLLSNSVDAECEGGSTPRPPEDALSGDSDVDTASTVSLRSGKSGPSPTTPQPLRAQKEMSPSPPAAQDPGGTALVSAREQSSERQHHPLGPTDMGRGEPVRRSAIRRGHRPRGSLDWPSEERGPVLAHLPSSDVMASNHETPEATGAGRLGSRRKPAAPPPSPAAREEQSRSSASSQKGPQALTRSNSLSTPRPTRASRLRRARLGDASDTEAADGERGSLGNPEPVGRPAAEQAKKLSRLDILAMPRKRAGSFTGTSDPEAAPARTSFSGRSVELCCASRKPTMAEARAVSRKAANTATTTGPRQPFSRARSGSARYTSTTQTPRAGSSSRARSRAPGPRDTDDDEEEPDPYGFIVQTAEIAEIARLSQTLVKDVAILAQEIHDVAGDGDTLGSSEPAHSASLSNMPSTPASTISAREELVQRIPEASLNFQKVPPGSLNSRDFDQNMNDSCEDALANKTRPRNREEVIFDNLMLNPVSQLSQAIRENTEHLAEKMKILFQNTGRAWEDLEARINAENEVPILKTSNKEISSILKELRRVQKQLEVINAIVDPSGSLDLLTGNRSLASSAQPGLGKGRVAAQSPPSPASAEALLPALPLRNFPQRASCGPPSLPDPTFLPDAERFLI,mutated_sequence,1.0,1554.0,UPI00001FDCF7.a2m,UPI00001FDCF7.npy,gnomAD +UPI00002263B3,UPI00002263B3.csv,MPGGPSPRSPAPLLRPLLLLLCALAPGAPGPAPGRATEGRAALDIVHPVRVDAGGSFLSYELWPRALRKRDVSVRRDAPAFYELQYRGRELRFNLTANQHLLAPGFVSETRRRGGLGRAHIRAHTPACHLLGEVQDPELEGGLAAISACDGLKGVFQLSNEDYFIEPLDSAPARPGHAQPHVVYKRQAPERLAQRGDSSAPSTCGVQVYPELESRRERWEQRQQWRRPRLRRLHQRSVSKEKWVETLVVADAKMVEYHGQPQVESYVLTIMNMVAGLFHDPSIGNPIHITIVRLVLLEDEEEDLKITHHADNTLKSFCKWQKSINMKGDAHPLHHDTAILLTRKDLCAAMNRPCETLGLSHVAGMCQPHRSCSINEDTGLPLAFTVAHELGHSFGIQHDGSGNDCEPVGKRPFIMSPQLLYDAAPLTWSRCSRQYITRFLDRGWGLCLDDPPAKDIIDFPSVPPGVLYDVSHQCRLQYGAYSAFCEDMDNVCHTLWCSVGTTCHSKLDAAVDGTRCGENKWCLSGECVPVGFRPEAVDGGWSGWSAWSICSRSCGMGVQSAERQCTQPTPKYKGRYCVGERKRFRLCNLQACPAGRPSFRHVQCSHFDAMLYKGQLHTWVPVVNDVNPCELHCRPANEYFAEKLRDAVVDGTPCYQVRASRDLCINGICKNVGCDFEIDSGAMEDRCGVCHGNGSTCHTVSGTFEEAEGLGYVDVGLIPAGAREIRIQEVAEAANFLALRSEDPEKYFLNGGWTIQWNGDYQVAGTTFTYARRGNWENLTSPGPTKEPVWIQLLFQESNPGVHYEYTIHREAGGHDEVPPPVFSWHYGPWTKCTVTCGRGVQRQNVYCLERQAGPVDEEHCDPLGRPDDQQRKCSEQPCPARWWAGEWQLCSSSCGPGGLSRRAVLCIRSVGLDEQSALEPPACEHLPRPPTETPCNRHVPCPATWAVGNWSQCSVTCGEGTQRRNVLCTNDTGVPCDEAQQPASEVTCSLPLCRWPLGTLGPEGSGSGSSSHELFNEADFIPHHLAPRPSPASSPKPGTMGNAIEEEAPELDLPGPVFVDDFYYDYNFINFHEDLSYGPSEEPDLDLAGTGDRTPPPHSHPAAPSTGSPVPATEPPAAKEEGVLGPWSPSPWPSQAGRSPPPPSEQTPGNPLINFLPEEDTPIGAPDLGLPSLSWPRVSTDGLQTPATPESQNDFPVGKDSQSQLPPPWRDRTNEVFKDDEEPKGRGAPHLPPRPSSTLPPLSPVGSTHSSPSPDVAELWTGGTVAWEPALEGGLGPVDSELWPTVGVASLLPPPIAPLPEMKVRDSSLEPGTPSFPTPGPGSWDLQTVAVWGTFLPTTLTGLGHMPEPALNPGPKGQPESLSPEVPLSSRLLSTPAWDSPANSHRVPETQPLAPSLAEAGPPADPLVVRNAGWQAGNWSECSTTCGLGAVWRPVRCSSGRDEDCAPAGRPQPARRCHLRPCATWHSGNWSKCSRSCGGGSSVRDVQCVDTRDLRPLRPFHCQPGPAKPPAHRPCGAQPCLSWYTSSWRECSEACGGGEQQRLVTCPEPGLCEEALRPNTTRPCNTHPCTQWVVGPWGQCSGPCGGGVQRRLVKCVNTQTGLPEEDSDQCGHEAWPESSRPCGTEDCEPVEPPRCERDRLSFGFCETLRLLGRCQLPTIRTQCCRSCSPPSHGAPSRGHQRVARR,mutated_sequence,1.0,1686.0,UPI00002263B3.a2m,UPI00002263B3.npy,gnomAD +UPI0000197460,UPI0000197460.csv,MRVLCEQRYCAVCREELRQVVFGKKLPAFATIPIHQLQHEKKYDIYFADGKVYALYRQLLQHECPRCPELPPFSLFGDLEQHMRRQHELFCCRLCLQHLQIFTYERKWYSRKDLARHRMQGDPDDTSHRGHPLCKFCDERYLDNDELLKHLRRDHYFCHFCDSDGAQDYYSDYAYLREHFREKHFLCEEGRCSTEQFTHAFRTEIDLKAHRTACHSRSRAEARQNRHIDLQFSYAPRHSRRNEGVVGGEDYEEVDRYSRQGRVARAGTRGAQQSRRGSWRYKREEEDREVAAAVRASVAAQQQEEARRSEDQEEGGRPKKEEAAARGPEDPRGPRRSPRTQGEGPGPKETSTNGPVSQEAFSVTGPAAPGCVGVPGALPPPSPKLKDEDFPSLSASTSSSCSTAATPGPVGLALPYAIPARGRSAFQEEDFPALVSSVPKPGTAPTSLVSAWNSSSSSKKVAQPPLSAQATGSGQPTRKAGKGSRGGRKGGPPFTQEEEEDGGPALQELLSTRPTGSVSSTLGLASIQPSKVGKKKKVGSEKPGTTLPQPPPATCPPGALQAPEAPASRAEGPVAVVVNGHMEGPAPARSAPKEPPGLPRPLGSFPCPTPQEDFPALGGPCPPRMPPPPGFSAVVLLKGTPPPPPPGLVPPISKPPPGFSGLLPSPHPACVPSPATTTTTKAPRLLPAPRAYLVPENFRERNLQLIQSIRDFLQSDEARFSEFKSHSGEFRQGLISAAQYYKSCRDLLGENFQKVFNELLVLLPDTAKQQELLSAHTDFCNREKPLSTKSKKNKKSAWQATTQQAGLDCRVCPTCQQVLAHGDASSHQALHAARDDDFPSLQAIARIIT,mutated_sequence,1.0,849.0,UPI0000197460.a2m,UPI0000197460.npy,gnomAD +UPI000013E592,UPI000013E592.csv,MPRYGASLRQSCPRSGREQGQDGTAGAPGLLWMGLVLALALALALALALSDSRVLWAPAEAHPLSPQGHPARLHRIVPRLRDVFGWGNLTCPICKGLFTAINLGLKKEPNVARVGSVAIKLCNLLKIAPPAVCQSIVHLFEDDMVEVWRRSVLSPSEACGLLLGSTCGHWDIFSSWNISLPTVPKPPPKPPSPPAPGAPVSRILFLTDLHWDHDYLEGTDPDCADPLCCRRGSGLPPASRPGAGYWGEYSKCDLPLRTLESLLSGLGPAGPFDMVYWTGDIPAHDVWHQTRQDQLRALTTVTALVRKFLGPVPVYPAVGNHESTPVNSFPPPFIEGNHSSRWLYEAMAKAWEPWLPAEALRTLRIGGFYALSPYPGLRLISLNMNFCSRENFWLLINSTDPAGQLQWLVGELQAAEDRGDKVHIIGHIPPGHCLKSWSWNYYRIVARYENTLAAQFFGHTHVDEFEVFYDEETLSRPLAVAFLAPSATTYIGLNPGYRVYQIDGNYSGSSHVVLDHETYILNLTQANIPGAIPHWQLLYRARETYGLPNTLPTAWHNLVYRMRGDMQLFQTFWFLYHKGHPPSEPCGTPCRLATLCAQLSARADSPALCRHLMPDGSLPEAQSLWPRPLFC,mutated_sequence,1.0,631.0,UPI000013E592.a2m,UPI000013E592.npy,gnomAD +UPI00004DDD95,UPI00004DDD95.csv,MEKLGVEPEEEGGGDDDEEDAEAWAMELADVGAAASSQGVHDQVLPTPNASSRVIVHVDLDCFYAQVEMISNPELKDKPLGVQQKYLVVTCNYEARKLGVKKLMNVRDAKEKCPQLVLVNGEDLTRYREMSYKVTELLEEFSPVVERLGFDENFVDLTEMVEKRLQQLQSDELSAVTVSGHVYNNQSINLLDVLHIRLLVGSQIAAEMREAMYNQLGLTGCAGVASNKLLAKLVSGVFKPNQQTVLLPESCQHLIHSLNHIKEIPGIGYKTAKCLEALGINSVRDLQTFSPKILEKELGISVAQRIQKLSFGEDNSPVILSGPPQSFSEEDSFKKCSSEVEAKNKIEELLASLLNRVCQDGRKPHTVRLIIRRYSSEKHYGRESRQCPIPSHVIQKLGTGNYDVMTPMVDILMKLFRNMVNVKMPFHLTLLSVCFCNLKALNTAKKGLIDYYLMPSLSTTSRSGKHSFKMKDTHMEDFPKDKETNRDFLPSGRIESTRTRESPLDTTNFSKEKDINEFPLCSLPEGVDQEVFKQLPVDIQEEILSGKSREKFQGKGSVSCPLHASRGVLSFFSKKQMQDIPINPRDHLSSSKQVSSVSPCEPGTSGFNSSSSSYMSSQKDYSYYLDNRLKDERISQGPKEPQGFHFTNSNPAVSAFHSFPNLQSEQLFSRNHTTDSHKQTVATDSHEGLTENREPDSVDEKITFPSDIDPQVFYELPEAVQKELLAEWKRAGSDFHIGHK,mutated_sequence,1.0,740.0,UPI00004DDD95.a2m,UPI00004DDD95.npy,gnomAD +UPI0000D616C3,UPI0000D616C3.csv,MDPLETPIKDGILYQQHVKFGKKCWRKVWALLYAGGPSGVARLESWEVRDGGLGAAGDRSAGPGRRGERRVIRLADCVSVLPADGESCPRDTGAFLLTTTERSHLLAAQHRQAWMGPICQLAFPGTGEASSGSTDAQSPKRGLVPMEENSIYSSWQEVGEFPVVVQRTEAATRCQLKGPALLVLGPDAIQLREAKGTQALYSWPYHFLRKFGSDKILLGTPGVSLLICKGERTDDVSGIILDESLLRAYSVPGAGGHSRVQDSLGPVLREPTFQGERSFLKTSMLRSLLCSCSWRHPRSQPRTQASCLQGSDCPAPHRNSTSAAHTLGTS,mutated_sequence,1.0,330.0,UPI0000D616C3.a2m,UPI0000D616C3.npy,gnomAD +UPI0000129B6F,UPI0000129B6F.csv,MTILTYPFKNLPTASKWALRFSIRPLSCSSQLRAAPAVQTKTKKTLAKPNIRNVVVVDGVRTPFLLSGTSYKDLMPHDLARAALTGLLHRTSVPKEVVDYIIFGTVIQEVKTSNVAREAALGAGFSDKTPAHTVTMACISANQAMTTGVGLIASGQCDVIVAGGVELMSDVPIRHSRKMRKLMLDLNKAKSMGQRLSLISKFRFNFLAPELPAVSEFSTSETMGHSADRLAAAFAVSRLEQDEYALRSHSLAKKAQDEGLLSDVVPFKVPGKDTVTKDNGIRPSSLEQMAKLKPAFIKPYGTVTAANSSFLTDGASAMLIMAEEKALAMGYKPKAYLRDFMYVSQDPKDQLLLGPTYATPKVLEKAGLTMNDIDAFEFHEAFSGQILANFKAMDSDWFAENYMGRKTKVGLPPLEKFNNWGGSLSLGHPFGATGCRLVMAAANRLRKEGGQYGLVAACAAGGQGHAMIVEAYPK,mutated_sequence,1.0,474.0,UPI0000129B6F.a2m,UPI0000129B6F.npy,gnomAD +UPI00004588F6,UPI00004588F6.csv,MGDTAKPYFVKRTKDRGTMDDDDFRRGHPQQDYLIIDDHAKGHGSKMEKGLQKKKITPGNYGNTPRKGPCAVSSNPYAFKNPIYSQPAWMNDSHKDQSKRWLSDEHTGNSDNWREFKPGPRIPVINRQRKDSFQENEDGYRWQDTRGCRTVRRLFHKDLTSLETTSEMEAGSPENKKQRSRPRKPRKTRNEENEQDGDLEGPVIDESVLSTKELLGLQQAEERLKRDCIDRLKRRPRNYPTAKYTCRLCDVLIESIAFAHKHIKEKRHKKNIKEKQEEELLTTLPPPTPSQINAVGIAIDKVVQEFGLHNENLEQRLEIKRIMENVFQHKLPDCSLRLYGSSCSRLGFKNSDVNIDIQFPAIMSQPDVLLLVQECLKNSDSFIDVDADFHARVPVVVCREKQSGLLCKVSAGNENACLTTKHLTALGKLEPKLVPLVIAFRYWAKLCSIDRPEEGGLPPYVFALMAIFFLQQRKEPLLPVYLGSWIEGFSLSKLGNFNLQDIEKDVVIWEHTDSAAGDTGITKEEAPRETPIKRGQVSLILDVKHQPSVPVGQLWVELLRFYALEFNLADLVISIRVKELVSRELKDWPKKRIAIEDPYSVKRNVARTLNSQPVFEYILHCLRTTYKYFALPHKITKSSLLKPLNAITCISEHSKEVINHHPDVQTKDDKLKNSVLAQGPGATSSAANTCKVQPLTLKETAESFGSPPKEEMGNEHISVHPENSDCIQADVNSDDYKGDKVYHPETGRKNEKEKVGRKGKHLLTVDQKRGEHVVCGSTRNNESESTLDLEGFQNPTAKECEGLATLDNKADLDGESTEGTEELEDSLNHFTHSVQGQTSEMIPSDEEEEDDEEEEEEEEPRLTINQREDEDGMANEDELDNTYTGSGDEDALSEEDDELGEAAKYEDVKECGKHVERALLVELNKISLKEENVCEEKNSPVDQSDFFYEFSKLIFTKGKSPTVVCSLCKREGHLKKDCPEDFKRIQLEPLPPLTPKFLNILDQVCIQCYKDFSPTIIEDQAREHIRQNLESFIRQDFPGTKLSLFGSSKNGFGFKQSDLDVCMTINGLETAEGLDCVRTIEELARVLRKHSGLRNILPITTAKVPIVKFFHLRSGLEVDISLYNTLALHNTRLLSAYSAIDPRVKYLCYTMKVFTKMCDIGDASRGSLSSYAYTLMVLYFLQQRNPPVIPVLQEIYKGEKKPEIFVDGWNIYFFDQIDELPTYWSECGKNTESVGQLWLGLLRFYTEEFDFKEHVISIRRKSLLTTFKKQWTSKYIVIEDPFDLNHNLGAGLSRKMTNFIMKAFINGRRVFGIPVKGFPKDYPSKMEYFFDPDVLTEGELAPNDRCCRICGKIGHFMKDCPMRRKVRRRRDQEDALNQRYPENKEKRSKEDKEIHNKYTEREVSTKEDKPIQCTPQKAKPMRAAADLGREKILRPPVEKWKRQDDKDLREKRCFICGREGHIKKECPQFKGSSGSLSSKYMTQGKASAKRTQQES,mutated_sequence,1.0,1495.0,UPI00004588F6.a2m,UPI00004588F6.npy,gnomAD +UPI000035B018,UPI000035B018.csv,MRLDEITKSENEDGEKKMFTQQFESTDACVYEVYVKINSIVKSQSQCGLKRGSPETELWGAIVFRSQEENAKPVKETEVTASDADSGLYGFIEYSLYDGFLSYEAPQAFRIDPHDGQICVSQDIDRERDPATYDLLVEAKDGDEQRMTHLALVKGGLSAQAFVRVDLEDVNDNHPVFNPSTYVTSISDETQPGTEIINVLATDQDSGIYGTVAYELIPGNVSSLFTIDSTTGLYSPEVEILSAVNFSADKEVMNSLEMFLPLLRHFKKVERDEEAAEKKFEASRGWFMRFKGRRHLHYLKVQDEAASADGEAAARYVANLAKILGEGIIYLTLPLSHLESTTLSLMVSAQDGGGLTAVINADVTIHIFQTTLAPAEFERPKYTFLVYEDVPEDSPIGTVKAREPLNSSEPIFYRISSGDLGGKFSIHPRLGTIRTRKPLDHETQPVVVLTVQAQLGSAPACSSTEVNITVMDVNDNHPAFLRTSDEIRISQTTPPGTALYLARAEDRDSGRNGLIRYSIASPQPGVFAIDRALGVLFLNGSLGAGEQRELTLTLRAEDQGVHPQAALLVLTVVIEKREHSPSWTFEHLVYQVEVSESLSPMTQMLQTQAHPLGPQRAASPLRYSLEPSVDSAMFGIRPYTGWIYLRRQFDYESTQTYNFRVFAWIPEDGFLQNVSTTVIVRVWDENDNSPTFLHDVLFLKVEESPVPQGVIGKITAIDMDSGKNGQLLYFLLSDGKFFKMNPNTGPAGTIYVITWADGAAAFSGTDFAFSSDELQAFVLKSLFCELGEGELINWVALDREHRGHHEMTVLVTDRGSPPRNATMAVYVSVTDINDNRPFFPQCLPGKELHVKVLEGQPVNMLVTTVFAKDPDEGNNAEVTYSVSSEDSSDHFKIDANNGEIRTTTILSYDYRPSYRMSVIATDQGVPPLQGQAVVNIQVIPLSKGRAIMSQNIRHLIIPENLKPTKIMSLIKSSDHLQQHYNGKLHFSIVADDKDGHFEIDSSTGDLFLSKELDYETTSHYLFRVITTDHSKNLSLSSTVFLSIDVEDQNDHSPSFQDELIVISVEENVPIGTLVYVFNAKDDDGSFLNSRIQYYIESHNPGTNPFLIHPSFGTLVTVSRLDRESIPTVILTVTASDQAVNVTDRRLRSLTAQIVILDVNDHNPTFISFPNAHVKEDVTVGSLVHHITAHDPDEGRNGKVTYSILSGNENMTFMLDESSGLLTTTCPLDYEMKTQHILTVLALDDGTPALSSSQTLTVTVLDVNDEAPVFKQHLYEASVKENQNPGEFVTRVEALDRDSGVNSKLQFEIMPGASFELFEINSDTGEVVTTTILDREIQEVFTLRVLVRDGGFPSLSSTTTILCTVEDENDHAPEFIVSSYDIEVLENQEPEVVYTVLASDMDAGNNRAVEYHIIDGNTDECFTINEMSGELSTTRALDREQISNFTLVILCSDLGDPPRSSVIHLQVRVLDANDHSPSFPTLYYQSSVREDAEVGTVVLVLSAVDKDEGLNGQTEYFLTDEASGAFTIDPMSGTLKTSNTLDREARSQHTFSAVARDCSIQGSRSTTVIIKVYVTDVNDNDPVLEQNPFDVFLSPESPTNQTTVIVRADDLDLGPNGTVVFSFAETQSMFSIDKYTGEIQFQQNPSSEYFPIWLQLKVTDQGIPARTTTGLLVIHMEGEDVKISFSHHLYKGLVTENCEAGTSIVTVKAFAPDSIQDSMKYSIFSGNEDGVLSLCSKSGQLTVKEPKFLDFEVRNEVQLIVLAESSGHRAYCKVAVLIQDENDNSPCFEQSIYQASVSESQLYNAHVIQVFATDLDSGLNGLIEYSILSGNQEEAFQIDALSGVITTKAILDYELTSSYSLIVQATDKGMPRLSNTTVIKVQVTDINDNAPAFLPSEAVEITEDSLPGVIVTHVSVHDVDLNSAFIFSFAKESNPGTKFAIDQNTGVVVLVKTLDFEEMTEYELLIQISDSVHYTEGALVVRVLDVNDNPPVFSQDFYQVTVPESIPVGYSVLTLSATDLESNENISYRILSSSKEFSIDPKNGTIFTISPVLLLDTISTTQFLVEASDGGNPDLRALTLVEIGIEDMNNYAPEFTVKSYNLSLSEDALVGSTLVTFSNIDHDWTRENTYVEYSIISGNSQNNFHVETKFFHSEYPYKQVGYLVLLHSLDREASASHELVILASDSGCPPLSSTAVISIQVLDVNDNPPNFSSLSYHTHVKESTPLGSHITVVSANDRDTGSHAEIIYNIISGNEKGHFYLEENTGVLYLIKPLDYEKMTKFTLTVQASDAEKKHFSFAVVFVSVLDDNDHAPQFMFSSFSCIVPENLPISSTICSINALDFDAGPYGELTYSIVSPCFLTHGMSYDHDLFLIDPLTGDIHAKQILDYENGNKYCLTVQAKDKGDATASLVVWVDIEGIDEFEPIFTQDQYFFTLPEKNKDRQLIGRVEASDADAGIDGVILYSLGTSSPFFSVNKTNGNIYLIRALPLIKSQLNKEDTLEMKIIAHSPKSDSKFASCTVFVNVSFSSEGTPLAVFASSFSISLVVSFLVFLILICILIVMILRHKQKDTINNYEEKKTSSLDADLRVTRDASVLKAFQKTDDCSNEVVPVDATPEWLSLISIMEKDIVNLYRHSNSSGHCSVEGETAEDKEIQRINEHPYRKCSDSALSDHESRVPDSGIPRDSDQLSCLSGETDVMVTAETAEASQTFGEGDQGEGCSTTCAQNNVLPQTVQKREAKESILADVRKESVFISGDQEVRCAALSTQTTSDHDGKDNYHWNYLLSWEPKFQPLASVFNDIAKLKDEHLHMPGIPKEKKSFVFPPPLITAVAQPGIKAVPPRMPAVNLGQVPPKHPRSPIPYHLGSLPEGMTPNFSPSLSLLTMQPPALSPLLREGELLGTHISGTCHELKAEDEVQI,mutated_sequence,1.0,2916.0,UPI000035B018.a2m,UPI000035B018.npy,gnomAD +UPI0000071C18,UPI0000071C18.csv,MHACSSTALHPRPFIHGFSFTTFLQQLFPHGSSTALHPWPFIHGSSPMALHPRLFTHGPSSTALHPCPFTHGSSPMSLHPRLFTHSPSSMPLHPRPFVHASSPTALRPCLFTHGPSSMPLHPRPFVHASSPTALPPCLFTHGPSSMPLHPRPFLHASSPTALPPCLFTHGPSSMPLHPRPFVHASSPTALRPCLFTHGPSSMPLHPRPFVHASSPTALPPCLFTHGPSSMPLHPRPFVHASSPTALRPCLFTHGPSSMPLHPRPFLHASSPTALRPCLFTHGPSSMPLHPRPFVHASSPTALRPCLFTHGPSSMPLHPRPFVHASSSTSLHPRPFIHASSSKSLHLRLFSHSSCLVGFSQQGNVLLLSARATFNTCLLVSNMYFLIISY,mutated_sequence,1.0,389.0,UPI0000071C18.a2m,UPI0000071C18.npy,gnomAD +UPI00001C1EF8,UPI00001C1EF8.csv,MASDASHALEAALEQMDGIIAGTKTGADLSDGTCEPGLASPASYMNPFPVLHLIEDLRLALEMLELPQERAALLSQIPGPTAAYIKEWFEESLSQVNHHSAASNETYQERLARLEGDKESLILQVSVLTDQVEAQGEKIRDLEVCLEGHQVKLNAAEEMLQQELLSRTSLETQKLDLMTEVSELKLKLVGMEKEQREQEEKQRKAEELLQELRHLKIKVEELENERNQYEWKLKATKAEVAQLQEQVALKDAEIERLHSQLSRTAALHSESHTERDQEIQRLKMGMETLLLANEDKDRRIEELTGLLNQYRKVKEIVMVTQGPSERTLSINEEEPEGGFSKWNATNKDPEELFKQEMPPRCSSPTVGPPPLPQKSLETRAQKKLSCSLEDLRSESVDKCMDGNQPFPVLEPKDSPFLAEHKYPTLPGKLSGATPNGEAAKSPPTICQPDATGSSLLRLRDTESGWDDTAVVNDLSSTSSGTESGPQSPLTPDGKRNPKGIKKFWGKIRRTQSGNFYTDTLGMAEFRRGGLRATAGPRLSRTRDSKGQKSDANAPFAQWSTERVCAWLEDFGLAQYVIFARQWVSSGHTLLTATPQDMEKELGIKHPLHRKKLVLAVKAINTKQEEKSALLDHIWVTRWLDDIGLPQYKDQFHESRVDRRMLQYLTVNDLLFLKVTSQLHHLSIKCAIHVLHVNKFNPHCLHRRPADESNLSPSEVVQWSNHRVMEWLRSVDLAEYAPNLRGSGVHGGLIILEPRFTGDTLAMLLNIPPQKTLLRRHLTTKFNALIGPEAEQEKREKMASPAYTPLTTTAKVRPRKLGFSHFGNIRKKKFDESTDYICPMEPSDGVSDSHRVYSGYRGLSPLDAPELDGLDQVGQIS,mutated_sequence,1.0,876.0,UPI00001C1EF8.a2m,UPI00001C1EF8.npy,gnomAD +UPI0000405B22,UPI0000405B22.csv,MGSQVLQILRQGVWASLTGGWFFDPHQSTFSNCFHLYVWIFLLIFPFLLYMVLPPSLMVAGVYCLVVAVIFATIKTVNYRLHAMFDQGEIVEKRSSTMGELEEEPAQGDSNPPRDPGVEMTVFRKVSSTPPVRCSSQHSVFGFNQVSELLPRMEDSGPLRDIKELVREQGSNNVIVTSADREMLKLSSQEKLIGDLPQTPPGAVPDPSLASTDSSEPSPLAGDGAPWSGSSMADTPMSPLLKGSLSQELSKSFLTLTQPDRALVRTSSRREQRRGAGGYQPLDRRGSGEPTPQKAGSSDSCFSGTDRETLSSFKSEKTNSTHLDSPPGGPAPEGSDTDPPSEAELPASPDAGVPSDDTLRSFDTVIGAGTPPGLAEPLLVVRPKDLALLRPSKRQPPLRRHSPPGRAPRRPLLEGGGFFEDEDTSEGSELSPASSLRSQRRYSTDSSSSTSCYSPESSRGAAGGPRKRRAPHGAEEGTAVPPKRPYGTQRTPSTASAKTHARVLSMDGAGGDVLRPPLAGCKAELEAQVGVEQAASEPVVLPAEARRGPAANQPGWRGELQEEGAVGGAAEETGRRDRSSSVRRTQAIRRRHNAGSNPTPPASVMGSPPSSLQEAQRGRAASHSRALTLPSALHFASSLLLTRAGANVHEACTFDDTSEGAVHYFYDESGVRRSYTFGLAGGGYENPVGQQGEQTANGAWDRHSHSSSFHSADVPEATGGLNLLQPRPVVLQGMQVRRVPLEIPEEQTLMEEAPPRAQHSYKYWLLPGRWTSVRYERLALLALLDRTRGVLENIFGVGLSSLVAFLGYLLLLKGFFTDIWVFQFCLVIASCQYSLLKSVQPDAASPMHGHNWVIAYSRPVYFCICCLLIWLLDALGSAQPFPPVSLYGLTLFSASFFFCARDVATVFTLCFPFVFLLGLLPQVNTCLMYLLEQIDMHGFGGTAATSPLTAVFSLSRSLLAAALLYGFCLGAIKTPWPEQHVPVLFSVFCGLLVALSYHLSRQSSDPTVLWSLIRSKLFPELEERSLETARAEPPDPLPDKMRQSVREVLHSDLVMCVVIAVLTFAISASTVFIALKSVLGFVLYALAGAVGFFTHYLLPQLRKQLPWFCLSQPVLKPLEYSQYEVRGAAQVMWFEKLYAGLQCVEKYLIYPAVVLNALTVDAHTVVSHPDKYCFYCRALLMTVAGLKLLRSAFCCPPQQYLTLAFTVLLFHFDYPRLSQGFLLDYFLMSLLCSKLWDLLYKLRFVLTYIAPWQITWGSAFHAFAQPFAVPHSAMLFVQALLSGLFSTPLNPLLGSAVFIMSYARPLKFWERDYNTKRVDHSNTRLVTQLDRNPGADDNNLNSIFYEHLTRSLQHTLCGDLVLGRWGNYGPGDCFVLASDYLNALVHLIEVGNGLVTFQLRGLEFRGTYCQQREVEAITEGVEEDEGCCCCEPGHLPRVLSFNAAFGQRWLAWEVTASKYVLEGYSISDNNAASMLQVFDLRKILITYYVKSIIYYVSRSPKLEVWLSHEGITAALRPVRVPGYADSDPTFSLSVDEDYDLRLSGLSLPSFCAVHLEWIQYCASRRSQPVDQDWNSPLVTLCFGLCVLGRRALGTASHSMSASLEPFLYGLHALFKGDFRITSPRDEWVFADMDLLHRVVAPGVRMALKLHQDHFTSPDEYEEPAALYDAIAANEERLVISHEGDPAWRSAILSNTPSLLALRHVLDDASDEYKIIMLNRRHLSFRVIKVNRECVRGLWAGQQQELVFLRNRNPERGSIQNAKQALRNMINSSCDQPLGYPIYVSPLTTSLAGSHPQLRALWGGPISLGAIAHWLLRTWERLHKGCGAGCNSGGNVDDSDCSGGGGLTSLSNNPPVAHPTPENTAGNGDQPLPPGPGWGPRSSLSGSGDGRPPPLLQWPPPRLPGPPPASPIPTEGPRTSRPPGPGLLSSEGPSGKWSLGGRKGLGGSDGEPASGSPKGGTPKSQAPLDLSLSLSLSLSPDVSTEASPPRASQDIPCLDSSAPESGTPMGALGDWPAPIEERESPAAQPLLEHQY,mutated_sequence,1.0,2034.0,UPI0000405B22.a2m,UPI0000405B22.npy,gnomAD +UPI000012B3DF,UPI000012B3DF.csv,MAEAEGESLESWLNKATNPSNRQEDWEYIIGFCDQINKELEGPQIAVRLLAHKIQSPQEWEALQALTVLEACMKNCGRRFHNEVGKFRFLNELIKVVSPKYLGDRVSEKVKTKVIELLYSWTMALPEEAKIKDAYHMLKRQGIVQSDPPIPVDRTLIPSPPPRPKNPVFDDEEKSKLLAKLLKSKNPDDLQEANKLIKSMVKEDEARIQKVTKRLHTLEEVNNNVRLLSEMLLHYSQEDSSDGDRELMKELFDQCENKRRTLFKLASETEDNDNSLGDILQASDNLSRVINSYKTIIEGQVINGEVATLTLPDSEGNSQCSNQGTLIDLAELDTTNSLSSVLAPAPTPPSSGIPILPPPPQASGPPRSRSSSQAEATLGPSSTSNALSWLDEELLCLGLADPAPNVPPKESAGNSQWHLLQREQSDLDFFSPRPGTAACGASDAPLLQPSAPSSSSSQAPLPPPFPAPVVPASVPAPSAGSSLFSTGVAPALAPKVEPAVPGHHGLALGNSALHHLDALDQLLEEAKVTSGLVKPTTSPLIPTTTPARPLLPFSTGPGSPLFQPLSFQSQGSPPKGPELSLASIHVPLESIKPSSALPVTAYDKNGFRILFHFAKECPPGRPDVLVVVVSMLNTAPLPVKSIVLQAAVPKSMKVKLQPPSGTELSPFSPIQPPAAITQVMLLANPLKEKVRLRYKLTFALGEQLSTEVGEVDQFPPVEQWGNL,mutated_sequence,1.0,723.0,UPI000012B3DF.a2m,UPI000012B3DF.npy,gnomAD +UPI000006FBBD,UPI000006FBBD.csv,MKAGATSMWASCCGLLNEVMGTGAVRGQQSAFAGATGPFRFTPNPEFSTYPPAATEGPNIVCKACGLSFSVFRKKHVCCDCKKDFCSVCSVLQENLRRCSTCHLLQETAFQRPQLMRLKVKDLRQYLILRNIPIDTCREKEDLVDLVLCHHGLGSEDDMDTSSLNSSRSQTSSFFTRSFFSNYTAPSATMSSFQGELMDGDQTSRSGVPAQVQSEITSANTEDDDDDDDEDDDDEEENAEDRNPGLSKERVRASLSDLSSLDDVEGMSVRQLKEILARNFVNYSGCCEKWELVEKVNRLYKENEENQKSYGERLQLQDEEDDSLCRICMDAVIDCVLLECGHMVTCTKCGKRMSECPICRQYVVRAVHVFKS,mutated_sequence,1.0,372.0,UPI000006FBBD.a2m,UPI000006FBBD.npy,gnomAD +UPI0000161087,UPI0000161087.csv,MTGEVGSEVHLEINDPNVISQEEADSPSDSGQGSYETIGPLSEGDSDEEIFVSKKLKNRKVLQDSDSETEDTNASPEKTTYDSAEEENKENLYAGKNTKIKRIYKTVADSDESYMEKSLYQENLEAQVKPCLELSLQSGNSTDFTTDRKSSKKHIHDKEGTAGKAKVKSKRRLEKEERKMEKIRQLKKKETKNQEDDVEQPFNDSGCLLVDKDLFETGLEDENNSPLEDEESLESIRAAVKNKVKKHKKKEPSLESGVHSFEEGSELSKGTTRKERKAARLSKEALKQLHSETQRLIRESALNLPYHMPENKTIHDFFKRKPRPTCHGNAMALLKSSKYQSSHHKEIIDTANTTEMNSDHHSKGSEQTTGAENEVETNALPVVSKETQIITGSDESCRKDLVKNEELEIQEKQKQSDIRPSPGDSSVLQQESNFLGNNHSEECQVGGLVAFEPHALEGEGPQNPEETDEKVEEPEQQNKSSAVGPPEKVRRFTLDRLKQLGVDVSIKPRLGADEDSFVILEPETNRELEALKQRFWKHANPAAKPRAGQTVNVNVIVKDMGTDGKEELKADVVPVTLAPKKLDGASHTKPGEKLQVLKAKLQEAMKLRRFEERQKRQALFKLDNEDGFEEEEEEEEEMTDESEEDGEEKVEKEEKEEELEEEEEKEEEEEEEGNQETAEFLLSSEEIETKDEKEMDKENNDGSSEIGKAVGFLSVPKSLSSDSTLLLFKDSSSKMGYFPTEEKSETDENSGKQPSKLDEDDSCSLLTKESSHNSSFELIGSTIPSYQPCNRQTGRGTSFFPTAGGFRSPSPGLFRASLVSSASKSSGKLSEPSLPIEDSQDLYNASPEPKTLFLGAGDFQFCLEDDTQSQLLDADGFLNVRNHRNQYQALKPRLPLASMDENAMDANMDELLDLCTGKFTSQAEKHLPRKSDKKENMEELLNLCSGKFTSQDASTPASSELNKQEKESSMGDPMEEALALCSGSFPTDKEEEDEEEEFGDFRLVSNDNEFDSDEDEHSDSGNDLALEDHEDDDEEELLKRSEKLKRQMRLRKYLEDEAEVSGSDVGSEDEYDGEEIDEYEEDVIDEVLPSDEELQSQIKKIHMKTMLDDDKRQLRLYQERYLADGDLHSDGPGRMRKFRWKNIDDASQMDLFHRDSDDDQTEEQLDESEARWRKERIEREQWLRDMAQQGKITAEEEEEIGEDSQFMILAKKVTAKALQKNASRPMVIQESKSLLRNPFEAIRPGSAQQVKTGSLLNQPKAVLQKLAALSDHNPSAPRNSRNFVFHTLSPVKAEAAKESSKSQVKKRGPSFMTSPSPKHLKTDDSTSGLTRSIFKYLES,mutated_sequence,1.0,1339.0,UPI0000161087.a2m,UPI0000161087.npy,gnomAD +UPI000020630A,UPI000020630A.csv,MAAGSDLLDEVFFNSEVDEKVVSDLVGSLESQLAASAAHHHHLAPRTPEVRAAAAGALGNHVVSGSPAGAAGAGPAAPAEGAPGAAPEPPPAGRARPGGGGPQRPGPPSPRRPLVPAGPAPPAAKLRPPPEGSAGSCAPVPAAAAVAAGPEPAPAGPAKPAGPAALAARAGPGPGPGPGPGPGPGPGKPAGPGAAQTLNGSAALLNSHHAAAPAVSLVNNGPAALLPLPKPAAPGTVIQTPPFVGAAAPPAPAAPSPPAAPAPAAPAAAPPPPPPAPATLARPPGHPAGPPTAAPAVPPPAAAQNGGSAGAAPAPAPAAGGPAGVSGQPGPGAAAAAPAPGVKAESPKRVVQAAPPAAQTLAASGPASTAASMVIGPTMQGALPSPAAVPPPAPGTPTGLPKGAAGAVTQSLSRTPTATTSGIRATLTPTVLAPRLPQPPQNPTNIQNFQLPPGMVLVRSENGQLLMIPQQALAQMQAQAHAQPQTTMAPRPATPTSAPPVQISTVQAPGTPIIARQVTPTTIIKQVSQAQTTVQPSATLQRSPGVQPQLVLGGAAQTASLGTATAVQTGTPQRTVPGATTTSSAATETMENVKKCKNFLSTLIKLASSGKQSTETAANVKELVQNLLDGKIEAEDFTSRLYRELNSSPQPYLVPFLKRSLPALRQLTPDSAAFIQQSQQQPPPPTSQATTALTAVVLSSSVQRTAGKTAATVTSALQPPVLSLTQPTQVGVGKQGQPTPLVIQQPPKPGALIRPPQVTLTQTPMVALRQPHNRIMLTTPQQIQLNPLQPVPVVKPAVLPGTKALSAVSAQAAAAQKNKLKEPGGGSFRDDDDINDVASMAGVNLSEESARILATNSELVGTLTRSCKDETFLLQAPLQRRILEIGKKHGITELHPDVVSYVSHATQQRLQNLVEKISETAQQKNFSYKDDDRYEQASDVRAQLKFFEQLDQIEKQRKDEQEREILMRAAKSRSRQEDPEQLRLKQKAKEMQQQELAQMRQRDANLTALAAIGPRKKRKVDCPGPGSGAEGSGPGSVVPGSSGVGTPRQFTRQRITRVNLRDLIFCLENERETSHSLLLYKAFLK,mutated_sequence,1.0,1085.0,UPI000020630A.a2m,UPI000020630A.npy,gnomAD +UPI00003D7962,UPI00003D7962.csv,MELRSELPSVPGAATAAAATATGPPVASVASVAAAAAAAASLPVSVAGGLLRGPPLLLRAAEKYPRTPKCARCRNHGVVSALKGHKRYCRWKDCLCAKCTLIAERQRVMAAQVALRRQQAQEENEARELQLLYGTAEGLALAAANGIIPPRPAYEVFGSVCAADGGGPGAGAPAGTGGGAAGAGGSEAKLQKFDLFPKTLLQAGRPGSPLPPPVKPLSPDGADSGPGTSSPEVRPGSGSENGDGESFSGSPLARASKEAGGSCPGSAGPGGGGEEDSPGSASPLGSESGSEADKEEGEAAPAPGLGGGSGPRQRTPLDILTRVFPGHRRGVLELVLQGCGGDVVQAIEQVLNHHRGGLAAGLGPAAPPDKAAVGAAAAADDAWPSRVDAAAAAAAAAGGPGLPAPLQAGPAAPPHHRPLLAGAMAPGALGSLSSRSAFSPLQPNASHFGADAGAYPLGAPLGLSPLRLAYSAAAAHSRGLAFMAPYSTAGLVPTLGFRPPMDYAFSDLMRDRSAAAAAAVHKEPTYGGGLYGPMVNGAPEKQ,mutated_sequence,1.0,542.0,UPI00003D7962.a2m,UPI00003D7962.npy,gnomAD +UPI000020E164,UPI000020E164.csv,MIWRNNWKSTTGRLNVKLQSDKLQHGCGPDYSSAWLPANESLWQATTVPSNHRNNHIRRHSIASDSGDTGIGTSCSDSVEDHSTSSGTLSFKPSQSLITLPTAHVMPSNSSASISKLRESLTPDGSKWSTSLMQTLGNHSRGEQDSSLDMKDFRPLRKWSSLSKLTAPDNCGQGGTVCREESRNGLEKIGKAKALTSQLRTIGPSCLHDSMEMLRLEDKEINKKRSSTLDCKYKFESCSKEDFRASSSTLRRQPVDMTYSALPESKPIMTSSEAFEPPKYLMLGQQAVGGVPIQPSVRTQMWLTEQLRTNPLEGRNTEDSYSLAPWQQQQIEDFRQGSETPMQVLTGSSRQSYSPGYQDFSKWESMLKIKEGLLRQKEIVIDRQKQQITHLHERIRDNELRAQHAMLGHYVNCEDSYVASLQPQYENTSLQTPFSEESVSHSQQGEFEQKLASTEKEVLQLNEFLKQRLSLFSEEKKKLEEKVGFSNKVELGQQHFLSI,mutated_sequence,1.0,499.0,UPI000020E164.a2m,UPI000020E164.npy,gnomAD +UPI00000738BA,UPI00000738BA.csv,MSAAQVSSSRRQSCYLCDLPRMPWAMIWDFSEPVCRGCVNYEGADRIEFVIETARQLKRAHGCFQDGRSPGPPPPVGVKTVALSAKEAAAAAAAAAAAAAAAQQQQQQQQQQQQQQQQQQQQQQQQQLNHVDGSSKPAVLAAPSGLERYGLSAAAAAAAAAAAAVEQRSRFEYPPPPVSLGSSSHTARLPNGLGGPNGFPKPTPEEGPPELNRQSPNSSSAAASVASRRGTHGGLVTGLPNPGGGGGPQLTVPPNLLPQTLLNGPASAAVLPPPPPHALGSRGPPTPAPPGAPGGPACLGGTPGVSATSSSASSSTSSSVAEVGVGAGGKRPGSVSSTDQERELKEKQRNAEALAELSESLRNRAEEWASKPKMVRDTLLTLAGCTPYEVRFKKDHSLLGRVFAFDAVSKPGMDYELKLFIEYPTGSGNVYSSASGVAKQMYQDCMKDFGRGLSSGFKYLEYEKKHGSGDWRLLGDLLPEAVRFFKEGVPGADMLPQPYLDASCPMLPTALVSLSRAPSAPPGTGALPPAAPSGRGAAASLRKRKASPEPPDSAEGALKLGEEQQRQQWMANQSEALKLTMSAGGFAAPGHAAGGPPPPPPPLGPHSNRTTPPESAPQNGPSPMAALMSVADTLGTAHSPKDGSSVHSTTASARRNSSSPVSPASVPGQRRLASRNGDLNLQVAPPPPSAHPGMDQVHPQNIPDSPMANSGPLCCTICHERLEDTHFVQCPSVPSHKFCFPCSRESIKAQGATGEVYCPSGEKCPLVGSNVPWAFMQGEIATILAGDVKVKKERDP,mutated_sequence,1.0,796.0,UPI00000738BA.a2m,UPI00000738BA.npy,gnomAD +UPI0001B300F3,UPI0001B300F3.csv,MEPHVLGAVLYWLLLPCALLAACLLRFSGLSLVYLLFLLLLPWFPGPTRCGLQGHTGRLLRALLGLSLLFLVAHLALQICLHIVPRLDQLLGPSCSRWETLSRHIGVTRLDLKDIPNAIRLVAPDLGILVVSSVCLGICGRLARNTRQSPHPRELDDDERDVDASPTAGLQEAATLAPTRRSRLAARFRVTAHWLLVAAGRVLAVTLLALAGIAHPSALSSVYLLLFLALCTWWACHFPISTRGFSRLCVAVGCFGAGHLICLYCYQMPLAQALLPPAGIWARVLGLKDFVGPTNCSSPHALVLNTGLDWPVYASPGVLLLLCYATASLRKLRAYRPSGQRKEAAKGYEARELELAELDQWPQERESDQHVVPTAPDTEADNCIVHELTGQSSVLRRPVRPKRAEPREASPLHSLGHLIMDQSYVCALIAMMVWSITYHSWLTFVLLLWACLIWTVRSRHQLAMLCSPCILLYGMTLCCLRYVWAMDLRPELPTTLGPVSLRQLGLEHTRYPCLDLGAMLLYTLTFWLLLRQFVKEKLLKWAESPAALTEVTVADTEPTRTQTLLQSLGELVKGVYAKYWIYVCAGMFIVVSFAGRLVVYKIVYMFLFLLCLTLFQVYYSLWRKLLKAFWWLVVAYTMLVLIAVYTFQFQDFPAYWRNLTGFTDEQLGDLGLEQFSVSELFSSILVPGFFLLACILQLHYFHRPFMQLTDMEHVSLPGTRLPRWAHRQDAVSGTPLLREEQQEHQQQQQEEEEEEEDSRDEGLGVATPHQATQVPEGAAKWGLVAERLLELAAGFSDVLSRVQVFLRRLLELHVFKLVALYTVWVALKEVSVMNLLLVVLWAFALPYPRFRPMASCLSTVWTCVIIVCKMLYQLKVVNPQEYSSNCTEPFPNSTNLLPTEISQSLLYRGPVDPANWFGVRKGFPNLGYIQNHLQVLLLLVFEAIVYRRQEHYRRQHQLAPLPAQAVFASGTRQQLDQDLLGCLKYFINFFFYKFGLEICFLMAVNVIGQRMNFLVTLHGCWLVAILTRRHRQAIARLWPNYCLFLALFLLYQYLLCLGMPPALCIDYPWRWSRAVPMNSALIKWLYLPDFFRAPNSTNLISDFLLLLCASQQWQVFSAERTEEWQRMAGVNTDRLEPLRGEPNPVPNFIHCRSYLDMLKVAVFRYLFWLVLVVVFVTGATRISIFGLGYLLACFYLLLFGTALLQRDTRARLVLWDCLILYNVTVIISKNMLSLLACVFVEQMQTGFCWVIQLFSLVCTVKGYYDPKEMMDRDQDCLLPVEEAGIIWDSVCFFFLLLQRRVFLSHYYLHVRADLQATALLASRGFALYNAANLKSIDFHRRIEEKSLAQLKRQMERIRAKQEKHRQGRVDRSRPQDTLGPKDPGLEPGPDSPGGSSPPRRQWWRPWLDHATVIHSGDYFLFESDSEEEEEAVPEDPRPSAQSAFQLAYQAWVTNAQAVLRRRQQEQEQARQEQAGQLPTGGGPSQEVEPAEGPEEAAAGRSHVVQRVLSTAQFLWMLGQALVDELTRWLQEFTRHHGTMSDVLRAERYLLTQELLQGGEVHRGVLDQLYTSQAEATLPGPTEAPNAPSTVSSGLGAEEPLSSMTDDMGSPLSTGYHTRSGSEEAVTDPGEREAGASLYQGLMRTASELLLDRRLRIPELEEAELFAEGQGRALRLLRAVYQCVAAHSELLCYFIIILNHMVTASAGSLVLPVLVFLWAMLSIPRPSKRFWMTAIVFTEIAVVVKYLFQFGFFPWNSHVVLRRYENKPYFPPRILGLEKTDGYIKYDLVQLMALFFHRSQLLCYGLWDHEEDSPSKEHDKSGEEEQGAEEGPGVPAATTEDHIQVEARVGPTDGTPEPQVELRPRDTRRISLRFRRRKKEGPARKGAAAIEAEDREEEEGEEEKEAPTGREKRPSRSGGRVRAAGRRLQGFCLSLAQGTYRPLRRFFHDILHTKYRAATDVYALMFLADVVDFIIIIFGFWAFGKHSAATDITSSLSDDQVPEAFLVMLLIQFSTMVVDRALYLRKTVLGKLAFQVALVLAIHLWMFFILPAVTERMFNQNVVAQLWYFVKCIYFALSAYQIRCGYPTRILGNFLTKKYNHLNLFLFQGFRLVPFLVELRAVMDWVWTDTTLSLSSWMCVEDIYANIFIIKCSRETEKKYPQPKGQKKKKIVKYGMGGLIILFLIAIIWFPLLFMSLVRSVVGVVNQPIDVTVTLKLGGYEPLFTMSAQQPSIIPFTAQAYEELSRQFDPQPLAMQFISQYSPEDIVTAQIEGSSGALWRISPPSRAQMKRELYNGTADITLRFTWNFQRDLAKGGTVEYANEKHMLALAPNSTARRQLASLLEGTSDQSVVIPNLFPKYIRAPNGPEANPVKQLQPNEEADYLGVRIQLRREQGAGATGFLEWWVIELQECRTDCNLLPMVIFSDKVSPPSLGFLAGYGIMGLYVSIVLVIGKFVRGFFSEISHSIMFEELPCVDRILKLCQDIFLVRETRELELEEELYAKLIFLYRSPETMIKWTREKE,mutated_sequence,1.0,2521.0,UPI0001B300F3.a2m,UPI0001B300F3.npy,gnomAD +UPI00004193C9,UPI00004193C9.csv,MVSRMGWGGRRRRLGRWGDLGPGSVPLLPMPLPPPPPPSCRGPGGGRISIFSLSPAPHTRSSPSSFSPPTAGPPCSVLQGTGASQSCHSALPIPATPPTQAQPAMTPASASPSWGSHSTPPLAPATPTPSQQCPQDSPGLRVGPLIPEQDYERLEDCDPEGSQDSPIHGEEQQPLLHVPEGLRGSWHHIQNLDSFFTKIYSYHQRNGFACILLEDVFQLGQFIFIVTFTTFLLRCVDYNVLFANQPSNHTRPGPFHSKVTLSDAILPSAQCAERIRSSPLLVLLLVLAAGFWLVQLLRSVCNLFSYWDIQVFYREALHIPPGKGREDTGMYWRGQPGGLD,mutated_sequence,1.0,340.0,UPI00004193C9.a2m,UPI00004193C9.npy,gnomAD +UPI000013D01B,UPI000013D01B.csv,MEANWTAFLFQAHEASHHQQQAAQNSLLPLLSSAVEPPDQKPLLPIPITQKPQGAPETLKDAIGIKKEKPKTSFVCTYCSKAFRDSYHLRRHESCHTGIKLVSRPKKTPTTVVPLISTIAGDSSRTSLVSTIAGILSTVTTSSSGTNPSSSASTTAMPVTQSVKKPSKPVKKNHACEMCGKAFRDVYHLNRHKLSHSDEKPFECPICNQRFKRKDRMTYHVRSHEGGITKPYTCSVCGKGFSRPDHLSCHVKHVHSTERPFKCQTCTAAFATKDRLRTHMVRHEGKVSCNICGKLLSAAYITSHLKTHGQSQSINCNTCKQGISKTCMSEETSNQKQQQQQQQQQQQQQQQQQQHVTSWPGKQVETLRLWEEAVKARKKEAANLCQTSTAATTPVTLTTPFSITSSVSSGTMSNPVTVAAAMSMRSPVNVSSAVNITSPMNIGHPVTITSPLSMTSPLTLTTPVNLPTPVTAPVNIAHPVTITSPMNLPTPMTLAAPLNIAMRPVESMPFLPQALPTSPPW,mutated_sequence,1.0,521.0,UPI000013D01B.a2m,UPI000013D01B.npy,gnomAD +UPI0000366BAD,UPI0000366BAD.csv,MSAAKENPCRKFQANIFNKSKCQNCFKPRESHLLNDEDLTQAKPIYGGWLLLAPDGTDFDNPVHRSRKWQRRFFILYEHGLLRYALDEMPTTLPQGTINMNQCTDVVDGEGRTGQKFSLCILTPEKEHFIRAETKEIVSGWLEMLMVYPRTNKQNQKKKRKVEPPTPQEPGPAKVAVTSSSSSSSSSSSIPSAEKVPTTKSTLWQEEMRTKDQPDGSSLSPAQSPSQSQPPAASSLREPGLESKEEESAMSSDRMDCGRKVRVESGYFSLEKTKQDLKAEEQQLPPPLSPPSPSTPNHRRSQVIEKFEALDIEKAEHMETNAVGPSPSSDTRQGRSEKRAFPRKRDFTNEAPPAPLPDASASPLSPHRRAKSLDRRSTEPSVTPDLLNFKKGWLTKQYEDGQWKKHWFVLADQSLRYYRDSVAEEAADLDGEIDLSACYDVTEYPVQRNYGFQIHTKEGEFTLSAMTSGIRRNWIQTIMKHVHPTTAPDVTSSLPEEKNKSSCSFETCPRPTEKQEAELGEPDPEQKRSRARERRREGRSKTFDWAEFRPIQQALAQERVGGVGPADTHEPLRPEAEPGELERERARRREERRKRFGMLDATDGPGTEDAALRMEVDRSPGLPMSDLKTHNVHVEIEQRWHQVETTPLREEKQVPIAPVHLSSEDGGDRLSTHELTSLLEKELEQSQKEASDLLEQNRLLQDQLRVALGREQSAREGYVLQATCERGFAAMEETHQKKIEDLQRQHQRELEKLREEKDRLLAEETAATISAIEAMKNAHREEMERELEKSQRSQISSVNSDVEALRRQYLEELQSVQRELEVLSEQYSQKCLENAHLAQALEAERQALRQCQRENQELNAHNQELNNRLAAEITRLRTLLTGDGGGEATGSPLAQGKDAYELEVLLRVKESEIQYLKQEISSLKDELQTALRDKKYASDKYKDIYTELSIAKAKADCDISRLKEQLKAATEALGEKSPDSATVSGYDIMKSKSNPDFLKKDRSCVTRQLRNIRSKSVIEQVSWDT,mutated_sequence,1.0,1025.0,UPI0000366BAD.a2m,UPI0000366BAD.npy,gnomAD +UPI000012DA10,UPI000012DA10.csv,MPRAPAPLYACLLGLCALLPRLAGLNICTSGSATSCEECLLIHPKCAWCSKEDFGSPRSITSRCDLRANLVKNGCGGEIESPASSFHVLRSLPLSSKGSGSAGWDVIQMTPQEIAVNLRPGDKTTFQLQVRQVEDYPVDLYYLMDLSLSMKDDLDNIRSLGTKLAEEMRKLTSNFRLGFGSFVDKDISPFSYTAPRYQTNPCIGYKLFPNCVPSFGFRHLLPLTDRVDSFNEEVRKQRVSRNRDAPEGGFDAVLQAAVCKEKIGWRKDALHLLVFTTDDVPHIALDGKLGGLVQPHDGQCHLNEANEYTASNQMDYPSLALLGEKLAENNINLIFAVTKNHYMLYKNFTALIPGTTVEILDGDSKNIIQLIINAYNSIRSKVELSVWDQPEDLNLFFTATCQDGVSYPGQRKCEGLKIGDTASFEVSLEARSCPSRHTEHVFALRPVGFRDSLEVGVTYNCTCGCSVGLEPNSARCNGSGTYVCGLCECSPGYLGTRCECQDGENQSVYQNLCREAEGKPLCSGRGDCSCNQCSCFESEFGKIYGPFCECDNFSCARNKGVLCSGHGECHCGECKCHAGYIGDNCNCSTDISTCRGRDGQICSERGHCLCGQCQCTEPGAFGEMCEKCPTCPDACSTKRDCVECLLLHSGKPDNQTCHSLCRDEVITWVDTIVKDDQEAVLCFYKTAKDCVMMFTYVELPSGKSNLTVLREPECGNTPNAMTILLAVVGSILLVGLALLAIWKLLVTIHDRREFAKFQSERSRARYEMASNPLYRKPISTHTVDFTFNKFNKSYNGTVD,mutated_sequence,1.0,799.0,UPI000012DA10.a2m,UPI000012DA10.npy,gnomAD +UPI0001E8F4C1,UPI0001E8F4C1.csv,XGLGLGLWLGAGLSAARRLEQVPSLLGRRRKLHLPDPDLASWGPGRSGSGGGRWDCMCECLLRTDPVPEEGEDVAATISATETLSEEEQEELRRELAKVEEEIQTLSQVLAAKEKHLAEIKRKLGINSLQELKQNIAKGWQDVTATSA,mutated_sequence,1.0,148.0,UPI0001E8F4C1.a2m,UPI0001E8F4C1.npy,gnomAD +UPI0002A47099,UPI0002A47099.csv,MTGCFLWQYHLPKLKTGSLGPEETSAPVRMCPRHPEPVPLAHPLPVLKEALEKVDQILRQAMSAPGVAAMSAVVIHNDTVLWTGNFGKKNGSDPASGAPNEYTMYRISSISKIFPVLMLYRLWEEGIVASLDDPLERYASTFTINNPLGLASAEQQGLMDGLEQVGPAPRPSPVTLRRMASQLSGLPRRLRSTSLLWKGSTQEALNLLKDDVLVVDPGTRCHYSTLAFSLLAHVLAAHTAQGDYQRWVSENVLEPLGMADTGFDLTPDVRARLAAGFYGSGRPAPLYDLGWYRPSGQMYSTAADLAKLAVALLGGGPRRLLRPDAAKTLLAPLLACPGAYFANETGTPWEFHAQRGYRVVRKDGDLDGYAATFSLVPPLRLGLVLLLAGPRPPGPDLVARAYDELLPALERALREAEPGPAPPPTAHPFAGYFTFANLTFYEVRAGPAGELRLRQFGPRVEALVPPAFRTLALRHLHGRVFQLHVAHEFPCALPLGDAWLSLEAQHGQLVNFYPLDHHGLSPGFDVPGLNTYRVLRLRGKPVFKTQ,mutated_sequence,1.0,546.0,UPI0002A47099.a2m,UPI0002A47099.npy,gnomAD +UPI0002840CC6,UPI0002840CC6.csv,MFGSSRYLGSSEQPRANSLGPSDRTLVLCSLVEGEDKVNPSEPHGLRMEEKWLLKGKLRNQRNQNKLLSPNKKQRKNHTSKLQELALLLPIALKTGTKKLTKKEILVHVLQYIQYLQRNIDAAKALFKCHITTGEGGLAGLGQKPAWGPARRRRHSTPSSSPSSQKSCLQGACQKPRKKKLTQASESQTRTPKPRRSLALNKPEKLVAPSPDQKGSGTGGTTTPPRCPDSCGHPRPASSSPPGDRKGGQSQLTLLDLAEDTIHCDISSCWCQGSVQDDAPFPALLAQEDVARIHFLNKTQPHPRQKLVFYDSSEDVDKGSLDADPWLPAWTPENSPQGSPLFLGPPQIDVWSGTGHPSEILGLSPSLFSSPGKLLPDEILEDDMEYLTQAAFFEEVCLDLESSPSAYTQEAPQEKDTASKAPKDPPESHSLHRSSVSLDHCYLSLSGNSKAPSSSSSSSSSSSSSEDSDSEPLWKQREDMQANPVGTPGSSEEDEDTTWTPTRLASPLLAAEKKATKGQVARAPVKPKEKKKGPCPPQMKKKCVNGFIMFCRMNRKQYIRSCPGTASTAATKELAQLWRVMTQQERRPYCTKARRFSRQHNRIVKQDGSSSEAEDWETPKPFYQLLAEKALPLPPHLQ,mutated_sequence,1.0,638.0,UPI0002840CC6.a2m,UPI0002840CC6.npy,gnomAD +UPI0000160791,UPI0000160791.csv,MAGGRGAPGRGRDEPPESYPQRQDHELQALEAIYGADFQDLRPDACGPVKEPPEINLVLYPQGLTGEEVYVKVDLRVKCPPTYPDVVPEIELKNAKGLSNESVNLLKSRLEELAKKHCGEVMIFELAYHVQSFLSEHNKPPPKSFHEEMLERRAQEEQQRLLEAKRKEEQEQREILHEIQRRKEEIKEEKKRKEMAKQERLEIASLSNQDHTSKKDPGGHRTAAILHGGSPDFVGNGKHRANSSGRSRRERQYSVCNSEDSPGSCEILYFNMGSPDQLMVHKGKCIGSDEQLGKLVYNALETATGGFVLLYEWVLQWQKKMGPFLTSQEKEKIDKCKKQIQGTETEFNSLVKLSHPNVVRYLAMNLKEQDDSIVVDILVEHISGVSLAAHLSHSGPIPVHQLRRYTAQLLSGLDYLHSNSVVHKVLSASNVLVDAEGTVKITDYSISKRLADICKEDVFEQTRVRFSDNALPYKTGKKGDVWRLGLLLLSLSQGQECGEYPVTIPSDLPADFQDFLKKCVCLDDKERWSPQQLLKHSFINPQPKMPLVEQSPEDSEGQDYVETVIPSNRLPSAAFFSETQRQFSRYFIEFEELQLLGKGAFGAVIKVQNKLDGCCYAVKRIPINPASRQFRRIKGEVTLLSRLHHENIVRYYNAWIERHERPAGPGTPPPDSGPLAKDDRAARGQPASDTDGLDSVEAAAPPPILSSSVEWSTSGERSASARFPATGPGSSDDEDDDEDEHGGVFSQSFLPASDSESDIIFDNEDENSKSQNQDEDCNEKNGCHESEPSVTTEAVHYLYIQMEYCEKSTLRDTIDQGLYRDTVRLWRLFREILDGLAYIHEKGMIHRDLKPVNIFLDSDDHVKIGDFGLATDHLAFSADSKQDDQTGDLIKSDPSGHLTGMVGTALYVSPEVQGSTKSAYNQKVDLFSLGIIFFEMSYHPMVTASERIFVLNQLRDPTSPKFPEDFDDGEHAKQKSVISWLLNHDPAKRPTATELLKSELLPPPQMEESELHEVLHHTLTNVDGKAYRTMMAQIFSQRISPAIDYTYDSDILKGNFSIRTAKMQQHVCETIIRIFKRHGAVQLCTPLLLPRNRQIYEHNEAALFMDHSGMLVMLPFDLRIPFARYVARNNILNLKRYCIERVFRPRKLDRFHPKELLECAFDIVTSTTNSFLPTAEIIYTIYEIIQEFPALQERNYSIYLNHTMLLKAILLHCGIPEDKLSQVYIILYDAVTEKLTRREVEAKFCNLSLSSNSLCRLYKFIEQKGDLQDLMPTINSLIKQKTGIAQLVKYGLKDLEEVVGLLKKLGIKLQVLINLGLVYKVQQHNGIIFQFVAFIKRRQRAVPEILAAGGRYDLLIPQFRGPQALGPVPTAIGVSIAIDKISAAVLNMEESVTISSCDLLVVSVGQMSMSRAINLTQKLWTAGITAEIMYDWSQSQEELQEYCRHHEITYVALVSDKEGSHVKVKSFEKERQTEKRVLETELVDHVLQKLRTKVTDERNGREASDNLAVQNLKGSFSNASGLFEIHGATVVPIVSVLAPEKLSASTRRRYETQVQTRLQTSLANLHQKSSEIEILAVDLPKETILQFLSLEWDADEQAFNTTVKQLLSRLPKQRYLKLVCDEIYNIKVEKKVSVLFLYSYRDDYYRILF,mutated_sequence,1.0,1649.0,UPI0000160791.a2m,UPI0000160791.npy,gnomAD +UPI000041A871,UPI000041A871.csv,MWPQPRLPPHPAMSEKTQQGKLAAAKKKLKAYWQRKSPGIPAGANRKKKVNGSSPDTATSGGYHSPGDSATGIYGEGRASSTTLQDLESQYQELAVALDSSSAIISQLTENINSLVRTSKEEKKHEIHLVQKLGRSLFKLKNQTAEPLAPEPPAGPSKVEQLQDETNHLRKELESVGRQLQAEVENNQMLSLLNRRQEERLREQEERLHEQEERLHEQEERLCEQEERLCEQEERLREQEERLREQEERLHEQEERLCEQEERLCEQEERLCEQEKLPGQERLLEEVEKLLEQERRQEEQERLLERERLLDEVEELLEQERLRQQDERLWQQETLRELERLRELERLRELERLRELERMLELGWEALYEQRAEPRSGFEELNNENKSTLQLEQQVKELEKSGGAEEPRGSESAAAARPVPGAPVPQGAWMCGQAGWTPQEHPGLSGEAVGTGEAAGGAEEAACHSFRAAENRELNITII,mutated_sequence,1.0,479.0,UPI000041A871.a2m,UPI000041A871.npy,gnomAD +UPI000049DE26,UPI000049DE26.csv,MSRRKQSNPRQIKRSLGDMEAREEVQLVGASHMEQKATAPEAPSPPSADVNSPPPLPPPTSPGGPKELEGQEPEPRPTEEEPGSPWSGPDELEPVVQDGQRRIRARLSLATGLSWGPFHGSVQTRASSPRQAEPSPALTLLLVDEACWLRTLPQALTEAEANTEIHRKDDALWCRVTKPVPAGGLLSVLLTAEPHSTPGHPVKKEPAEPTCPAPAHDLQLLPQQAGMASILATAVINKDVFPCKDCGIWYRSERNLQAHLLYYCASRQGTGSPAAAATDEKPKETYPNERVCPFPQCRKSCPSASSLEIHMRSHSGERPFVCLICLSAFTTKANCERHLKVHTDTLSGVCHSCGFISTTRDILYSHLVTNHMVCQPGSKGEIYSPGAGHPATKLPPDSLGSFQQQHTALQGPLASADLGLAPTPSPGLDRKALAEATNGEARAEPLAQNGGSSEPPAAPRSIKVEAVEEPEAAPILGPGEPGPQAPSRTPSPRSPAPARVKAELSSPTPGSSPVPGELGLAGALFLPQYVFGPDAAPPASEILAKMSELVHSRLQQGAGAGAGGAQTGLFPGAPKGATCFECEITFSNVNNYYVHKRLYCSGRRAPEDAPAARRPKAPPGPARAPPGQPAEPDAPRSSPGPGAREEGAGGAATPEDGAGGRGSEGSQSPGSSVDDAEDDPSRTLCEACNIRFSRHETYTVHKRYYCASRHDPPPRRPAAPPGPPGPAAPPAPSPAAPVRTRRRRKLYELHAAGAPPPPPPGHAPAPESPRPGSGSGSGPGLAPARSPGPAADGPIDLSKKPRRPLPGAPAPALADYHECTACRVSFHSLEAYLAHKKYSCPAAPPPGALGLPAAACPYCPPNGPVRGDLLEHFRLAHGLLLGAPLAGPGVEARTPADRGPSPAPAPAASPQPGSRGPRDGLGPEPQEPPPGPPPSPAAAPEAVPPPPAPPSYSDKGVQTPSKGTPAPLPNGNHRYCRLCNIKFSSLSTFIAHKKYYCSSHAAEHVK,mutated_sequence,1.0,1006.0,UPI000049DE26.a2m,UPI000049DE26.npy,gnomAD +UPI000012DC9B,UPI000012DC9B.csv,MEPWPCSPGGGGGTRARHVIINVGGCRVRLAWAALARCPLARLERLRACRGHDDLLRVCDDYDVSRDEFFFDRSPCAFRAIVALLRAGKLRLLRGPCALAFRDELAYWGIDEARLERCCLRRLRRREEEAAEARAGPTERGAQGSPARALGPRGRLQRGRRRLRDVVDNPHSGLAGKLFACVSVSFVAVTAVGLCLSTMPDIRAEEERGECSPKCRSLFVLETVCVAWFSFEFLLRSLQAESKCAFLRAPLNIIDILALLPFYVSLLLGLAAGPGGTKLLERAGLVLRLLRALRVLYVMRLARHSLGLRSLGLTMRRCAREFGLLLLFLCVAMALFAPLVHLAERELGARRDFSSVPASYWWAVISMTTVGYGDMVPRSLPGQVVALSSILSGILLMAFPVTSIFHTFSRSYSELKEQQQRAASPEPALQEDSTHSATATEDSSQGPDSAGLADDSADALWVRAGR,mutated_sequence,1.0,466.0,UPI000012DC9B.a2m,UPI000012DC9B.npy,gnomAD +UPI000013FD3E,UPI000013FD3E.csv,MRGQGRRKGHRGSPARPSGAVLRVLRGCSTCGIPRAGSLLWRGKEGASGSEPSSAAHRSLPAQLAAPGGGARPGSRGVTWSSGRCNPGRLWGHVREVSYPCVSAGPLGHWELNEVVMSQVFSKIRTRHQTEEAGAPESTQGRVGEEGGRGCSAGAGRPQPRGTRRRAKTCHPAREGREGPREGAPFQPGETRVLAASLSSRQRAPVLTPAPFPRSMLLTKWPGEECRSPRGQALPKH,mutated_sequence,1.0,237.0,UPI000013FD3E.a2m,UPI000013FD3E.npy,gnomAD +UPI00004191D3,UPI00004191D3.csv,MPRAQLPEDSSAVDMDILFPLDSVIGTELCPSPIPQIIHFVLFVVFSLVILIILRLYIPREPSSVPPREEDSENDQAEVGEWLRIGNKYITLKDYRILLKELENLEIYTFLSKKCLKKLSREGSSHHLPRQVRPGPVYKPAPARNHRPRGGRGKASPTSFHVSPRAPLAPLASMPSSVPKTSVESLGSPSSLSSSKPREPLCPLKHPSHQPPASTLSPNPTSSTESLGETPHAGRWRQGSRFPQPGCANAAGRIRHQNPRHSHGHRISDIHEQLGIS,mutated_sequence,1.0,277.0,UPI00004191D3.a2m,UPI00004191D3.npy,gnomAD +UPI0000359605,UPI0000359605.csv,MAAAGARLSPGPGSGLRGRPRLCFHPGPPPLLPLLLLFLLLLPPPPLLAGATAAASREPDSPCRLKTVTVSTLPALRESDIGWSGARAGAGAGTGAGAAAAAASPGSPGSAGTAAESRLLLFVRNELPGRIAVQDDLDNTELPFFTLEMSGTAADISLVHWRQQWLENGTLYFHVSMSSSGQLAQATAPTLQEPSEIVEEQMHILHISVMGGLIALLLLLLVFTVALYAQRRWQKRRRIPQKSASTEATHEIHYIPSVLLGPQARESFRSSRLQTHNSVIGVPIRETPILDDYDCEEDEEPPRRANHVSREDEFGSQVTHTLDSLGHPGEEKVDFEKKAAAEATQETVESLMQKFKESFRANTPIEIGQLQPPLRSTSAGKRKRRSKSRGGISFGRAKGTSGSEADDETQLTFYTEQYRSRRRSKGLLKSPVNKTALTLIAVSSCILAMVCGSQMSCPLTVKVTLHVPEHFIADGSSFVVSEGSYLDISDWLNPAKLSLYYQINATSPWVRDLCGQRTTDACEQLCDPETGECSCHEGYAPDPVHRHLCVRSDWGQSEGPWPYTTLERGYDLVTGEQAPEKILRSTFSLGQGLWLPVSKSFVVPPVELSINPLASCKTDVLVTEDPADVREEAMLSTYFETINDLLSSFGPVRDCSRNNGGCTRNFKCVSDRQVDSSGCVCPEELKPMKDGSGCYDHSKGIDCSDGFNGGCEQLCLQQTLPLPYDATSSTIFMFCGCVEEYKLAPDGKSCLMLSDVCEGPKCLKPDSKFNDTLFGEMLHGYNNRTQHVNQGQVFQMTFRENNFIKDFPQLADGLLVIPLPVEEQCRGVLSEPLPDLQLLTGDIRYDEAMGYPMVQQWRVRSNLYRVKLSTITLAAGFTNVLKILTKESSREELLSFIQHYGSHYIAEALYGSELTCIIHFPSKKVQQQLWLQYQKETTELGSKKELKSMPFITYLSGLLTAQMLSDDQLISGVEIRCEEKGRCPSTCHLCRRPGKEQLSPTPVLLEINRVVPLYTLIQDNGTKEAFKSALMSSYWCSGKGDVIDDWCRCDLSAFDANGLPNCSPLLQPVLRLSPTVEPSSTVVSLEWVDVQPAIGTKVSDYILQHKKVDEYTDTDLYTGEFLSFADDLLSGLGTSCVAAGRSHGEVPEVSIYSVIFKCLEPDGLYKFTLYAVDTRGRHSELSTVTLRTACPLVDDNKAEEIADKIYNLYNGYTSGKEQQMAYNTLMEVSASMLFRVQHHYNSHYEKFGDFVWRSEDELGPRKAHLILRRLERVSSHCSSLLRSAYIQSRVETVPYLFCRSEEVRPAGMVWYSILKDTKITCEEKMVSMARNTYGESKGR,mutated_sequence,1.0,1339.0,UPI0000359605.a2m,UPI0000359605.npy,gnomAD +UPI000013CC65,UPI000013CC65.csv,MKRLSLVTTNRLSPHGNFLPLCTFPLAVDMAALFQEASSCPVCSDYLEKPMSLECGCAVCFKCINSLQKEPHGEDLLCCCCSMVSQKNKIRPSWQLERLASHIKELEPKLKKILQMNPRMRKFQVDMTLDADTANNFLLISDDLRSVRSGCITQNRQDLAERFDVSICILGSPRFTCGRHYWEVDVGTSTEWDLGVCRESVHRKGRIHLTTERGFWTVSLRDGSRLSASTVPLTFLFVDRKLQRVGIFLDMGMQNVSFFDAEGGSHVYTFRSVSAEEPLHLFFAPPSPPNGDKSVLSICPVINPGTTDAPVHPGEAK,mutated_sequence,1.0,317.0,UPI000013CC65.a2m,UPI000013CC65.npy,gnomAD +UPI00001B03C6,UPI00001B03C6.csv,MPLLLLGETEPLKLERDCRSPVDPWAAASPDLALACLCHCQDLSSGAFPDRGVLGGVLFPTVEMVIKVFVATSSGSIAIRKKQQEVVGFLEANKIDFKELDIAGDEDNRRWMRENVPGEKKPQNGIPLPPQIFNEEQYCGDFDSFFSAKEENIIYSFLGLAPPPDSKGSEKAEEGGETEAQKEGSEDVGNLPEAQEKNEEEGETATEETEEIAMEGAEGEAEEEEETAEGEEPGEDEDS,mutated_sequence,1.0,239.0,UPI00001B03C6.a2m,UPI00001B03C6.npy,gnomAD +UPI000059DB31,UPI000059DB31.csv,XVSCGMMEALLKVIKFLGDEQDQITFVTRAVRVVDLITNLDMAAFQSHSGLSIFIYRLEHEVDLCRKECPFVIKPKIQRPNTTQEGEEMETDMDVADVAMESSPGSSISMEHRLDVELRASGSSSSTNISSGPSPGPSPGTGPGPGPGPGPGPGPGPGPGPGPGPGPGPGPGPRPGVQCIPQRAALLKSMLNFLKKAIQDPAFSDGIRHVMDGSLPTSLKHIISNAEY,mutated_sequence,1.0,228.0,UPI000059DB31.a2m,UPI000059DB31.npy,gnomAD +UPI0000E5AEF3,UPI0000E5AEF3.csv,MSFSEMNRRTLAFRGGGLVTASGGGSTNNNAGGEASAWPPQPQPRQPPPPAPPALQPPNGRGADEEVELEGLEPQDLEASAGPAAGAAEEAKELLLPQDAGGPTSLGGGAGGPLLAERNRRTLAFRGGGGGGLGNNGSSRGRPETSVWPLRHFNGRGPATVDLELDALEGKELMQDGASLSDSTEDEEEGASLGDGSGAEGGSCSSSRRSGGDGGDEVEGSGVGAGEGETVQHFPLARPKSLMQKLQCSFQTSWLKDFPWLRYSKDTGLMSCGWCQKTPADGGSVDLPPVGHDELSRGTRNYKKTLLLRHHVSTEHKLHEANAQESEIPSEEGYCDFNSRPNENSYCYQLLRQLNEQRKKGILCDVSIVVSGKIFKAHKNILVAGSRFFKTLYCFSNKESPNQNNTTHLDIAAVQGFSVILDFLYSGNLVLTSQNAIEVMTVASYLQMSEVVQTCRNFIKDALNISIKSEAPESVVVDYNNRKPVNRDGLSSSRDQKIASFWATRNLTNLASNVKIENDGCNVDEGQIENYQMNDSSWVQDGSPEMAENESEGQTKVFIWNNMGSQGIQETGKTRRKNQTTKRFIYNIPPNNETNLEDCSVMQPPVAYPEENTLLIKEEPDLDGALLSGPDGDRNVNANLLAEAGTSQDGGDAGTSHDFKYGLMPGPSNDFKYGLIPGTSNDFKYGLIPGASNDFKYGLLPESWPKQETWENGESSLIMNKLKCPHCSYVAKYRRTLKRHLLIHTGVRSFSCDICGKLFTRREHVKRHSLVHKKDKKYKCMVCKKIFMLAASVGIRHGSRRYGVCVDCADKSQPGGQEGVDQGQDTEFPRDEEYEENEVGEADEELVDDGEDQNDPSRWDESGEVCMSLDD,mutated_sequence,1.0,871.0,UPI0000E5AEF3.a2m,UPI0000E5AEF3.npy,gnomAD +UPI000155B91C,UPI000155B91C.csv,MESRSVAQAGVQWCDLGSLQAPPPGFTLFSCLSLLSSWDYSSGFSGFCASPIEESHGALISSCNSRTMTDGLVTFRDVAIDFSQEEWECLDPAQRDLYVDVMLENYSNLVSLDLESKTYETKKIFSENDIFEINFSQWEMKDKSKTLGLEASIFRNNWKCKSIFEGLKGHQEGYFSQMIISYEKIPSYRKSKSLTPHQRIHNTEKSYVCKECGKACSHGSKLVQHERTHTAEKHFECKECGKNYLSAYQLNVHQRFHTGEKPYECKECGKTFSWGSSLVKHERIHTGEKPYECKECGKAFSRGYHLTQHQKIHTGVKSYKCKECGKAFFWGSSLAKHEIIHTGEKPYKCKECGKAFSRGYQLTQHQKIHTGKKPYECKICGKAFCWGYQLTRHQIFHTGEKPYECKECGKAFNCGSSLIQHERIHTGEKPYECKECGKAFSRGYHLSQHQKIHTGEKPFECKECGKAFSWGSSLVKHERVHTGEKSHECKECGKTFCSGYQLTRHQVFHTGEKPYECKECGKAFNCGSSLVQHERIHTGEKPYECKECGKAFSRGYHLTQHQKIHTGEKPFKCKECGKAFSWGSSLVKHERVHTNEKSYECKDCGKAFGSGYQLSVHQRFHTGEKLYQRKEFGKTFTCGSKLVHERTHSNDKPYKYNECGEAFLWTTYSNEKIDTDETL,mutated_sequence,1.0,679.0,UPI000155B91C.a2m,UPI000155B91C.npy,gnomAD +UPI000013E535,UPI000013E535.csv,MEASALTSSAVTSVAKVVRVASGSAVVLPLARIATVVIGGVVAVPMVLSAMGFTAAGIASSSIAAKMMSAAAIANGGGVASGSLVATLQSLGATGLSGLTKFILGSIGSAIAAVIARFY,mutated_sequence,1.0,119.0,UPI000013E535.a2m,UPI000013E535.npy,gnomAD +UPI0001B3CB30,UPI0001B3CB30.csv,MKGARWRRVPWVSLSCLCLCLLPHVVPGTTEDTLITGSKTAAPVTSTGSTTATLEGQSTAASSRTSNQDISASSQNHQTKSTETTSKAQTDTLTQMMTSTLFSSPSVHNVMETAPPDEMTTSFPSSVTNTLMMTSKTITMTTSTDSTLGNTEETSTAGTESSTPVTSAVSITAGQEGQSRTTSWRTSIQDTSASSQNHWTRSTQTTRESQTSTLTHRTTSTPSFSPSVHNVTGTVSQKTSPSGETATSSLCSVTNTSMMTSEKITVTTSTGSTLGNPGETSSVPVTGSLMPVTSAALVTFDPEGQSPATFSRTSTQDTTAFSKNHQTQSVETTRVSQINTLNTLTPVTTSTVLSSPSGFNPSGTVSQETFPSGETTTSSPSSVSNTFLVTSKVFRMPTSRDSTLGNTEETSLSVSGTISAITSKVSTIWWSDTLSTALSPSSLPPKISTAFHTQQSEGAETTGRPHERSSFSPGVSQEIFTLHETTTWPSSFSSKGHTTWSQTELPSTSTGAATRLVTGNPSTGTAGTIPRVPSKVSAIGEPGEPTTYSSHSTTLPKTTGAGAQTQWTQETGTTGEALLSSPSYSVTQMIKTATSPSSSPMLDRHTSQQITTAPSTNHSTIHSTSTSPQESPAVSQRGHTQAPQTTQESQTTRSVSPMTDTKTVTTPGSSFTASGHSPSEIVPQDAPTISAATTFAPAPTGDGHTTQAPTTALQAAPSSHDATLGPSGGTSLSKTGALTLANSVVSTPGGPEGQWTSASASTSPDTAAAMTHTHQAESTEASGQTQTSEPASSGSRTTSAGTATPSSSGASGTTPSGSEGISTSGETTRFSSNPSRDSHTTQSTTELLSASASHGAIPVSTGMASSIVPGTFHPTLSEASTAGRPTGQSSPTSPSASPQETAAISRMAQTQRTRTSRGSDTISLASQATDTFSTVPPTPPSITSTGLTSPQTETHTLSPSGSGKTFTTALISNATPLPVTYASSASTGHTTPLHVTDASSVSTGHATPLPVTSPSSVSTGHTTPLPVTDTSSESTGHVTPLPVTSFSSASTGDSTPLPVTDTSSASTGHVTPLPVTSLSSASTGDTTPLPVTDTSSASTGHATSLPVTDTSSVSTGHTTPLPVTDTSSASTGHATSLPVTDTSSVSTGHTTPLHVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDTSSASTGHATPLPVTDASSVSTDHATSLPVTIPSAASTGHTTPLPVTDTSSASTGQATSLLVTDTSSVSTGDTTPLPVTSTSSASTGHVTPLHVTSPSSASTGHATPLPVTSLSSASTGDTMPLPVTSPSSASTGDTTPLPVTDASSVSTGHTTPLHVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDTSSASTGHATPLPVTDASSVSTDHATSLPVTIPSAASTGHTTPLPVTDTSSASTGQATSLLVTDTSSVSTGDTTPLPVTSTSSASTGHVTPLHVTSPSSASTGHATPLPVTSLSSASTGDTMPLPVTSPSSASTGDTTPLPVTDASSVSTGHTTPLPVTSPSSASTGHTTPLPVTDTSSASKGDTTPLPVTSPSSASTGHTTPLPVTDTSSASTGDTTPLPVTNASSLSTGHATPLHVTSPSSASTGHATPLPVTSTSSASTGHATPLPVTGLSSATTDDTTRLPVTDVSSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHASPLLVTDASSASTGQATPLPVTDTSSVSTAHATPLPVTGLSSASTDDTTRLPVTDVSSASTGQAIPLPVTSPSSASTGDTTPLPVTDASSASTGDTTSLPVTIPSSASSGHTTSLPVTDASSVSTGHATSLLVTDASSVSTGDTTPLPVTDTNSASTGDTTPLHVTDASSVSTGHATSLPVTSLSSASTGDTTPLPVTSPSSASSGHTTPLPVTDASSVPTGHATSLPVTDASSVSTGHATPLPVTDASSVSTGHATPLPVTDTSSVSTGQATPLPVTSLSSASTGDTTPLPVTDTSSASTGQDTPLPVTSLSSVSTGDTTPLPVTNPSSASTGHATPLLVTDASSISTGHATSLLVTDASSVSTGHATALHDTDASSLSTGDTTPLPVTSPSSTSTGDTTPLPVTETSSVSTGHATSLPVTDTSSASTGHATSLPVTDTSSASTGHATPLPVTDTSSASTGQATPLPVTSPSSASTGHAIPLLVTDTSSASTGQATPLPVTSLSSASTGDTTPLPVTDASSVSTGHATSLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLHVTDASSASTGHATPLPVTSLSSASTGDTTPLPVTSPSSASTGHATPLHVTDASSVSTGDTTPLPVTSSSSASSGHTTPLPVTDASSASTGDTTPLPVTDTSSASTGHATHLPVTGLSSASTGDTTRLPVTNVSSASTGHATPLPVTSTSSASTGDTTPLPGTDTSSVSTGHTTPLLVTDASSVSTGDTTRLPVTSPSSASTGHTTPLPVTDTPSASTGDTTPLPVTNASSLSTRHATSLHVTSPSSASTGHATSLPVTDTSAASTGHATPLPVTSTSSASTGDTTPLPVTDTYSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATSLPVTDTSSASTGDTTSLPVTDTSSAYTGDTTSLPVTDTSSSSTGDTTPLLVTETSSVSTGDTTPLPVTDTSSASTGHATPLPVTNTSSVSTGHATPLHVTSPSSASTGHTTPLPVTDASSVSTGHATSLPVTDASSVFTGHATSLPVTIPSSASSGHTTPLPVTDASSVSTGHATSLPVTDASSVSTGHATPLPVTDASSVSTGHATPLPLTSLSSVSTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTDTSSASTGHATSLPVTDTSSASTGHATPLPDTDTSSASTGHATLLPVTDTSSASIGHATSLPVTDTSSISTGHATPLHVTSPSSASTGHATPLPVTDTSSASTGHANPLHVTSPSSASTGHATPLPVTDTSSASTGHATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHTTPLPVTDTSSASTGQATALPVTSTSSASTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATPLLVTDASSASTGQATPLPVTSLSSVSTGDTTPLPVTSPSSASTGHATSLPVTDTSSASTGDTTSLPVTDTSSAYTGDTTSLPVTDTSSSSTGDTTPLLVTETSSVSTGHATPLLVTDASSASTGHATPLHVTSPSSASTGDTTPVPVTDTSSVSTGHATPLPVTGLSSASTGDTTRLPVTDISSASTGQATPLPVTNTSSVSTGDTMPLPVTSPSSASTGHATPLPVTSTSSASTGHATPVPVTSTSSASTGHTTPLPVTDTSSASTGDTTPLPVTSPSSASTGHTTPLHVTIPSSASTGDTSTLPVTGASSASTGHATPLPVTDTSSVSTGHATPLPVTSLSSVSTGDTTPLPVTDASSASTGQATPLPVTSLSSVSTGDTTPLLVTDASSVSTGHATPLPVTDTSSASTGDTTRLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLLVTDASSVSTGHATPLPVTDTSSASTGDTTRLPVTDTSSASTGQATPLPVTIPSSSSSGHTTPLPVTSTSSVSTGHVTPLHVTSPSSASTGHVTPLPVTSTSSASTGHATPLLVTDASSVSTGHATPLPVTDASSASTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTDASSASTGHATPLPVTIPSSVSTGDTMPLPVTSPSSASTGHATPLPVTGLSSASTGDTTPLPVTDTSSASTRHATPLPVTDTSSASTDDTTRLPVTDVSSASTGHATPLPVTSTSSASTGDTTPLPVTDTSSVSTGHATSLPVTSRSSASTGHATPLPVTDTSSVSTGHATPLPVTSTSSVSTGHATPLPVTSPSSASTGHATPVPVTSTSSASTGDTTPLPVTNASSLSTGHATPLHVTSPSSASRGDTSTLPVTDASSASTGHATPLPLTSLSSVSTGDTTPLPVTDTSSASTGQATPLPVTSLSSVSTGDTTPLPVTIPSSASSGHTTSLPVTDASSVSTGHGTPLPVTSTSSASTGDTTPLPVTDTSSASTGHATPLPVTDTSSASTGHATPLPVTSLSSVSTGHATPLAVSSATSASTVSSDSPLKMETPGMTTPSLKTDGGRRTATSPPPTTSQTIISTIPSTAMHTRSTAAPIPILPERGVSLFPYGAGAGDLEFVRRTVDFTSPLFKPATGFPLGSSLRDSLYFTDNGQIIFPESDYQIFSYPNPLPTGFTGRDPVALVAPFWDDADFSTGRGTTFYQEYETFYGEHSLLVQQAESWIRKMTNNGGYKARWALKVTWVNAHAYPAQWTLGSNTYQAILSTDGSRSYALFLYQSGGMQWDVAQRSGNPVLMGFSSGDGYFENSPLMSQPVWERYRPDRFLNSNSGLQGLQFYRLHREERPNYRLECLQWLKSQPRWPSWGWNQVSCPCSWQQGRRDLRFQPVSIGRWGLGSRQLCSFTSWRGGVCCSYGPWGEFREGWHVQRPWQLAQELEPQSWCCRWNDKPYLCALYQQRRPHVGCATYRPPQPAWMFGDPHITTLDGVSYTFNGLGDFLLVGAQDGNSSFLLQGRTAQTGSAQATNFIAFAAQYRSSSLGPVTVQWLLEPHDAIRVLLDNQTVTFQPDHEDGGGQETFNATGVLLSRNGSEVSASFDGWATVSVIALSNILHASASLPPEYQNRTEGLLGVWNNNPEDDFRMPNGSTIPPGSPEEMLFHFGMTWQINGTGLLGKRNDQLPSNFTPVFYSQLQKNSSWAEHLISNCDGDSSCIYDTLALRNASIGLHTREVSKNYEQANATLNQYPPSINGGRVIEAYKGQTTLIQYTSNAEDANFTLRDSCTDLELFENGTLLWTPKSLEPFTLEILARSAKIGLASALQPRTVVCHCNAESQCLYNQTSRVGNSSLEVAGCKCDGGTFGRYCEGSEDACEEPCFPSVHCVPGKGCEACPPNLTGDGRHCAALGSSFLCQNQSCPVNYCYNQGHCYISQTLGCQPMCTCPPAFTDSRCFLAGNNFSPTVNLELPLRVIQLLLSEEENASMAEVNASVAYRLGTLDMRAFLRNSQVERIDSAAPASGSPIQHWMVISEFQYRPRGPVIDFLNNQLLAAVVEAFLYHVPRRSEEPRNDVVFQPISGEDVRDVTALNVSTLKAYFRCDGYKGYDLVYSPQSGFTCVSPCSRGYCDHGGQCQHLPSGPRCSCVSFSIYTAWGEHCEHLSMKLDAFFGIFFGALGGLLLLGVGTFVVLRFWGCSGARFSYFLNSAEALP,mutated_sequence,1.0,5412.0,UPI0001B3CB30.a2m,UPI0001B3CB30.npy,gnomAD +UPI0002064E1F,UPI0002064E1F.csv,MGEMWRKREEVIFREYSFSFYPLNIFAPFVLFPDYFCLLGFIWLSMNVLLFWKTFLLYNQGPEYHYLHQMLGLGLCLSRASASVLNLNCSLILLPMCRTLLAYLRGSQKVPSRRTRRLLDKSRTFHITCGVTICIFSGVHVAAHLVNALNFSVNYSEDFVELNAARYRDEDPRKLLFTTVPGLTGVCMVVVLFLMITASTYAIRVSNYDIFWYTHNLFFVFYMLLTLHVSGGLLKYQTNLDTHPPGCISLNRTSSQNISLPEYFSEHFHEPFPEGFSKPAEFTQHKFVKICMEEPRFQANFPQTWLWISGPLCLYCAERLYRYIRSNKPVTIISVMSHPSDVMEIRMVKENFKARPGQYITLHCPSVSALENHPFTLTMCPTETKATFGVHLKIVGDWTERFRDLLLPPSSQDSEILPFIQSRNYPKLYIDGPFGSPFEESLNYEVSLCVAGGIGVTPFASILNTLLDDWKPYKLRRLYFIWVCRDIQSFRWFADLLCMLHNKFWQENRPDYVNIQLYLSQTDGIQKIIGEKYHALNSRLFIGRPRWKLLFDEIAKYNRGKTVGVFCCGPNSLSKTLHKLSNQNNSYGTRFEYNKESFS,mutated_sequence,1.0,599.0,UPI0002064E1F.a2m,UPI0002064E1F.npy,gnomAD +UPI0000132184,UPI0000132184.csv,MMGSVLPAEALVLKTGLKAPGLALAEVITSDILHSFLYGRWRNVLGEQLFEDKSHHASPKTAFTAEVLAQSFSGEVQKLSSLVLPAEVIIAQSSIPGEGLGIFSKTWIKAGTEMGPFTGRVIAPEHVDICKNNNLMWEVFNEDGTVRYFIDASQEDHRSWMTYIKCARNEQEQNLEVVQIGTSIFYKAIEMIPPDQELLVWYGNSHNTFLGIPGVPGLEEDQKKNKHEDFHPADSAAGPAGRMRCVICHRGFNSRSNLRSHMRIHTLDKPFVCRFCNRRFSQSSTLRNHVRLHTGERPYKCQVCQSAYSQLAGLRAHQKSARHRPPSTALQAHSPALPAPHAHAPALAAAAAAAAAAAAHHLPAMVL,mutated_sequence,1.0,367.0,UPI0000132184.a2m,UPI0000132184.npy,gnomAD +UPI0000141565,UPI0000141565.csv,MARFGDEMPARYGGGGSGAAAGVVVGSGGGRGAGGSRQGGQPGAQRMYKQSMAQRARTMALYNPIPVRQNCLTVNRSLFLFSEDNVVRKYAKKITEWPPFEYMILATIIANCIVLALEQHLPDDDKTPMSERLDDTEPYFIGIFCFEAGIKIIALGFAFHKGSYLRNGWNVMDFVVVLTGILATVGTEFDLRTLRAVRVLRPLKLVSGIPSLQVVLKSIMKAMIPLLQIGLLLFFAILIFAIIGLEFYMGKFHTTCFEEGTDDIQGESPAPCGTEEPARTCPNGTKCQPYWEGPNNGITQFDNILFAVLTVFQCITMEGWTDLLYNSNDASGNTWNWLYFIPLIIIGSFFMLNLVLGVLSGEFAKERERVENRRAFLKLRRQQQIERELNGYMEWISKAEEVILAEDETDGEQRHPFDALRRTTIKKSKTDLLNPEEAEDQLADIASVGSPFARASIKSAKLENSTFFHKKERRMRFYIRRMVKTQAFYWTVLSLVALNTLCVAIVHYNQPEWLSDFLYYAEFIFLGLFMSEMFIKMYGLGTRPYFHSSFNCFDCGVIIGSIFEVIWAVIKPGTSFGISVLRALRLLRIFKVTKYWASLRNLVVSLLNSMKSIISLLFLLFLFIVVFALLGMQLFGGQFNFDEGTPPTNFDTFPAAIMTVFQILTGEDWNEVMYDGIKSQGGVQGGMVFSIYFIVLTLFGNYTLLNVFLAIAVDNLANAQELTKDEQEEEEAANQKLALQKAKEVAEVSPLSAANMSIAVKEQQKNQKPAKSVWEQRTSEMRKQNLLASREALYNEMDPDERWKAAYTRHLRPDMKTHLDRPLVVDPQENRNNNTNKSRAAEPTVDQRLGQQRAEDFLRKQARYHDRARDPSGSAGLDARRPWAGSQEAELSREGPYGRESDHHAREGSLEQPGFWEGEAERGKAGDPHRRHVHRQGGSRESRSGSPRTGADGEHRRHRAHRRPGEEGPEDKAERRARHREGSRPARGGEGEGEGPDGGERRRRHRHGAPATYEGDARREDKERRHRRRKENQGSGVPVSGPNLSTTRPIQQDLGRQDPPLAEDIDNMKNNKLATAESAAPHGSLGHAGLPQSPAKMGNSTDPGPMLAIPAMATNPQNAASRRTPNNPGNPSNPGPPKTPENSLIVTNPSGTQTNSAKTARKPDHTTVDIPPACPPPLNHTVVQVNKNANPDPLPKKEEEKKEEEEDDRGEDGPKPMPPYSSMFILSTTNPLRRLCHYILNLRYFEMCILMVIAMSSIALAAEDPVQPNAPRNNVLRYFDYVFTGVFTFEMVIKMIDLGLVLHQGAYFRDLWNILDFIVVSGALVAFAFTGNSKGKDINTIKSLRVLRVLRPLKTIKRLPKLKAVFDCVVNSLKNVFNILIVYMLFMFIFAVVAVQLFKGKFFHCTDESKEFEKDCRGKYLLYEKNEVKARDREWKKYEFHYDNVLWALLTLFTVSTGEGWPQVLKHSVDATFENQGPSPGYRMEMSIFYVVYFVVFPFFFVNIFVALIIITFQEQGDKMMEEYSLEKNERACIDFAISAKPLTRHMPQNKQSFQYRMWQFVVSPPFEYTIMAMIALNTIVLMMKFYGASVAYENALRVFNIVFTSLFSLECVLKVMAFGILNYFRDAWNIFDFVTVLGSITDILVTEFGNNFINLSFLRLFRAARLIKLLRQGYTIRILLWTFVQSFKALPYVCLLIAMLFFIYAIIGMQVFGNIGIDVEDEDSDEDEFQITEHNNFRTFFQALMLLFRSATGEAWHNIMLSCLSGKPCDKNSGILTRECGNEFAYFYFVSFIFLCSFLMLNLFVAVIMDNFEYLTRDSSILGPHHLDEYVRVWAEYDPAAWGRMPYLDMYQMLRHMSPPLGLGKKCPARVAYKRLLRMDLPVADDNTVHFNSTLMALIRTALDIKIAKGGADKQQMDAELRKEMMAIWPNLSQKTLDLLVTPHKSTDLTVGKIYAAMMIMEYYRQSKAKKLQAMREEQDRTPLMFQRMEPPSPTQEGGPGQNALPSTQLDPGGALMAHESGLKESPSWVTQRAQEMFQKTGTWSPEQGPPTDMPNSQPNSQSVEMREMGRDGYSDSEHYLPMEGQGRAASMPRLPAENQRRRGRPRGNNLSTISDTSPMKRSASVLGPKARRLDDYSLERVPPEENQRHHQRRRDRSHRASERSLGRYTDVDTGLGTDLSMTTQSGDLPSKERDQERGRPKDRKHRQHHHHHHHHHHPPPPDKDRYAQERPDHGRARARDQRWSRSPSEGREHMAHRQGSSSVSGSPAPSTSGTSTPRRGRRQLPQTPSTPRPHVSYSPVIRKAGGSGPPQQQQQQQQQQQQQAVARPGRAATSGPRRYPGPTAEPLAGDRPPTGGHSSGRSPRMERRVPGPARSESPRACRHGGARWPASGPHVSEGPPGPRHHGYYRGSDYDEADGPGSGGGEEAMAGAYDAPPPVRHASSGATGRSPRTPRASGPACASPSRHGRRLPNGYYPAHGLARPRGPGSRKGLHEPYSESDDDWC,mutated_sequence,1.0,2506.0,UPI0000141565.a2m,UPI0000141565.npy,gnomAD +UPI0001711D28,UPI0001711D28.csv,MGSRCRAWAWTRAWALAEFQARAEEGAAAAAAAAGGYPSTGRCRCSLRGMEGTAVAVFEILRFLIIHWKCDIDVSKGALLEGQLVISIEGLNSKHQANALHCVTTRWSLTLLPRPECSGAVSAHCNLHLPGSSDSHASVPRVAGITDAHHHAWLIMVASAGSLFGGMVLKKFLKEIQSILPGISAKLTWTSEEGSYSQDMTGVTPFQMIFEVDEKPRTLMTDCLVIKHFLRKIIMVHPKVRFHFSVKVNGILSTEIFGVENEPTLNLGNGIALLVDSQHYVRPNFGTIESHCSRIHPVLGHPVMLFIPEDVAGMDLLGELILTPAAALCPSPKVSSNQLNRISSVSIFLYGPLGLPLILSTWEQPMTTFFKDTSSLVDWKKYHLCMIPNLDLNLDRDLVLPDVSYQVESSEEDQSQTMDPQGQTLLLFLFVDFHSAFPVQQMEIWGVYTLLTTHLNAILVESHSVVQGSIQFTVDKVLEQHHQAAKAQQKLQASLSVAVNSIMSILTGSTRSSFRKMCLQTLQAADTQEFRTKLHKVFREITQHQFLHHCSCEVKQQLTLEKKDSAQGTEDAPDNSSLELLADTSGQAENKRLKRGSPRIEEMRALRSARAPSPSEAAPRRPEATAAPLTPRGREHREAHGRALAPGRASLGSRLEDVLWLQEVSNLSEWLSPSPGP,mutated_sequence,1.0,677.0,UPI0001711D28.a2m,UPI0001711D28.npy,gnomAD +UPI000059D669,UPI000059D669.csv,MRAGACARRPVGKWGVEWPRRLTWGGASRLCPAVKDRYGDRDSSSDSSSESDSSDERVEFDPQQERDFYKTLSLLKKKDPRIYQKDATFYNRTASSSDSEEDPEALEKQKKVRPMYLKDYERKVILEKAGKYVDEENSDGETSNHRLQETSSQSYVEEQKQLKESFRAFVEDSEDEDGAGEGGSSLLQKRAKTRQEKAQEEADYIEWLKGQKEIRNPDSLKELTHLKEYWNDPELDEGERFLRDYILNKRYEEEEEEEEDEEEMEEEEGVHGPPVQLAVDDSSDEGELFLKKQEDFEQKYNFRFEEPDSASVKTYPRSIASSVRRKDERRKEKREETRERKKREKAKKQEELKQLKNLKRKEILAKLEKLRKVTGNEMLGLEEGDLEDDFDPAQHDQLMQKCFGDEYYGAVEEEKPQFEEEEGLEDDWNWDTWDGPEQEGDWSQQELHCEDPNFNMDADYDPSQPRKKKREAPLTGKKKRKSPFAAAVGQEKPVFEPGDKTFEEYLDEYYRLDYEDIIDDLPCRFKYRTVVPCDFGLSTEEILAADDKELNRWCSLKKTCMYRSEQEELRDKRAYSQKAQNSWKKRQVFKSLCREEAETPAEATGKPQRDEAGPQRQLPALDGSLMGPESPPAQEEEAPVSPHKKPAPQKRRRAKKARLLGPTVMLGGCEFSRQRLQAFGLNPKRLHFRQLGRQRRKQQGPKNSS,mutated_sequence,1.0,705.0,UPI000059D669.a2m,UPI000059D669.npy,gnomAD +UPI000012DDC2,UPI000012DDC2.csv,MSQTQDYECRSHNVDLPESRIPGSNTRLEWVEIIEPRTRERMYANLVTGECVWDPPAGVRIKRTSENQWWELFDPNTSRFYYYNASTQRTVWHRPQGCDIIPLAKLQTLKQNTESPRASAESSPGRGSSVSREGSTSSSLEPEPDTEKAQELPARAGRPAAFGTVKEDSGSSSPPGVFLEKDYEIYRDYSADGQLLHYRTSSLRWNSGAKERMLIKVADREPSFLAAQGNGYAPDGPPGVRSRRPSGSQHSPSLQTFAPEADGTIFFPERRPSPFLKRAELPGSSSPLLAQPRKPSGDSQPSSPRYGYEPPLYEEPPVEYQAPIYDEPPMDVQFEAGGGYQAGSPQRSPGRKPRPFLQPNKQGPPSPCQQLVLTKQKCPERFLSLEYSPAGKEYVRQLVYVEQAGSSPKLRAGPRHKYAPNPGGGSYSLQPSPCLLRDQRLGVKSGDYSTMEGPELRHSQPPTPLPQAQEDAMSWSSQQDTLSSTGYSPGTRKRKSRKPSLCQATSATPTEGPGDLLVEQPLAEEQPPCGTSLAPVKRAEGEAEGARGAAEPFLAQARLAWEAQQAHFHMKQRSSWDSQQDGSGYESDGALPLPMPGPVVRAFSEDEALAQQENRHWRRGTFEKLGFPQILLEKSVSVQTNLASPEPYLHPSQSEDLAACAQFESSRQSRSGVPSSSCVFPTFTLRKPSSETDIENWASKHFNKHTQGLFRRKVSIANMLAWSSESIKKPMIVTSDRHVKKEACELFKLIQMYMGDRRAKADPLHVALEVATKGWSVQGLRDELYIQLCRQTTENFRLESLARGWELMAICLAFFPPTPKFHSYLEGYIYRHMDPVNDTKGVAISTYAKYCYHKLQKAALTGAKKGLKKPNVEEIRHAKNAVFSPSMFGSALQEVMGMQRERYPERQLPWVQTRLSEEVLALNGDQTEGIFRVPGDIDEVNALKLQVDQWKVPTGLEDPHVPASLLKLWYRELEEPLIPHEFYEQCIAHYDSPEAAVAVVHALPRINRMVLCYLIRFLQVFVQPANVAVTKMDVSNLAMVMAPNCLRCQSDDPRVIFENTRKEMSFLRVLIQHLDTSFMEGVL,mutated_sequence,1.0,1083.0,UPI000012DDC2.a2m,UPI000012DDC2.npy,gnomAD +UPI000013E37F,UPI000013E37F.csv,MNRKWEAKLKQIEERASHYERKPLSSVYRPRLSKPEEPPSIWRLFHRQAQAFNFVKSCKEDVHVFALECKVGDGQRIYLVTTYAEFWFYYKSRKNLLHCYEVIPENAVCKLYFDLEFNKPANPGADGKKMVALLIEYVCKALQELYGVNCSAEDVLNLDSSTDEKFSQHLIFQLHDVAFKDNIHVGNFLRKILQPALDLLGSEDDDSAPETTGHGFPHFSEAPARQGFSFNKMFTEKATEESWTSNSKKLERLGSAEQSSPDLSFLVVKNNMGEKHLFVDLGVYTRNRNFRLYKSSKIGKRVALEVTEDNKFFPIQSKDVSDEYQYFLSSLVSNVRFSDTLRILTCEPSQNKQKGVGYFNSIGTSVETIEGFQCSPYPEVDHFVLSLVNKDGIKGGIRRWNYFFPEELLVYDICKYRWCENIGRAHKSNNIMILVDLKNEVWYQKCHDPVCKAENFKSDCFPLPAEVCLLFLFKEEEEFTTDEADETRSNETQNPHKPSPSRLSTGASADAVWDNGIDDAYFLEATEDAELAEAAENSLLSYNSEVDEIPDELIIEVLQE,mutated_sequence,1.0,560.0,UPI000013E37F.a2m,UPI000013E37F.npy,gnomAD +UPI00000557D8,UPI00000557D8.csv,MPMPSRDGGLHPRHHHYGSHSPWSQLLSSPMETPSIKGLYYRRVRKVGALDASPVDLKKEILINVGGRRYLLPWSTLDRFPLSRLSKLRLCRSYEEIVQLCDDYDEDSQEFFFDRSPSAFGVIVSFLAAGKLVLLQEMCALSFQEELAYWGIEEAHLERCCLRKLLRKLEELEELAKLHREDVLRQQRETRRPASHSSRWGLCMNRLREMVENPQSGLPGKVFACLSILFVATTAVSLCVSTMPDLRAEEDQGECSRKCYYIFIVETICVAWFSLEFCLRFVQAQDKCQFFQGPLNIIDILAISPYYVSLAVSEEPPEDGERPSGSSYLEKVGLVLRVLRALRILYVMRLARHSLGLQTLGLTVRRCTREFGLLLLFLAVAITLFSPLVYVAEKESGRVLEFTSIPASYWWAIISMTTVGYGDMVPRSVPGQMVALSSILSGILIMAFPATSIFHTFSHSYLELKKEQEQLQARLRHLQNTGPASECELLDPHVASEHELMNDVNDLILEGPALPIMHM,mutated_sequence,1.0,519.0,UPI00000557D8.a2m,UPI00000557D8.npy,gnomAD +UPI000007206C,UPI000007206C.csv,MKHLKRWWSAGGGLLHLTLLLSLAGLRVDLDLYLLLPPPTLLQDELLFLGGPASSAYALSPFSASGGWGRAGHLHPKGRELDPAAPPEGQLLREVRALGVPFVPRTSVDAWLVHSVAAGSADEAHGLLGAAAASSTGGAGASVDGGSQAVQGGGGDPRAARSGPLDAGEEEKAPAEPTAQVPDAGGCASEENGVLREKHEAVDHSSQHEENEERVSAQKENSLQQNDDDENKIAEKPDWEAEKTTESRNERHLNGTDTSFSLEDLFQLLSSQPENSLEGISLGDIPLPGSISDGMNSSAHYHVNFSQAISQDVNLHEAILLCPNNTFRRDPTARTSQSQEPFLQLNSHTTNPEQTLPGTNLTGFLSPVDNHMRNLTSQDLLYDLDINIFDEINLMSLATEDNFDPIDVSQLFDEPDSDSGLSLDSSHNNTSVIKSNSSHSVCDEGAIGYCTDHESSSHHDLEGAVGGYYPEPSKLCHLDQSDSDFHGDLTFQHVFHNHTYHLQPTAPESTSEPFPWPGKSQKIRSRYLEDTDRNLSRDEQRAKALHIPFSVDEIVGMPVDSFNSMLSRYYLTDLQVSLIRDIRRRGKNKVAAQNCRKRKLDIILNLEDDVCNLQAKKETLKREQAQCNKAINIMKQKLHDLYHDIFSRLRDDQGRPVNPNHYALQCTHDGSILIVPKELVASGHKKETQKGKRK,mutated_sequence,1.0,694.0,UPI000007206C.a2m,UPI000007206C.npy,gnomAD +UPI00000723B9,UPI00000723B9.csv,MPSAKQRGSKGGHGAASPSEKGAHPSGGADDVAKKPPPAPQQPPPPPAPHPQQHPQQHPQNQAHGKGGHRGGGGGGGKSSSSSSASAAAAAAAASSSASCSRRLGRALNFLFYLALVAAAAFSGWCVHHVLEEVQQVRRSHQDFSRQREELGQGLQGVEQKVQSLQATFGTFESILRSSQHKQDLTEKAVKQGESEVSRISEVLQKLQNEILKDLSDGIHVVKDARERDFTSLENTVEERLTELTKSINDNIAIFTEVQKRSQKEINDMKAKVASLEESEGNKQDLKALKEAVKEIQTSAKSREWDMEALRSTLQTMESDIYTEVRELVSLKQEQQAFKEAADTERLALQALTEKLLRSEESVSRLPEEIRRLEEELRQLKSDSHGPKEDGGFRHSEAFEALQQKSQGLDSRLQHVEDGVLSMQVASARQTESLESLLSKSQEHEQRLAALQGRLEGLGSSEADQDGLASTVRSLGETQLVLYGDVEELKRSVGELPSTVESLQKVQEQVHTLLSQDQAQAARLPPQDFLDRLSSLDNLKASVSQVEADLKMLRTAVDSLVAYSVKIETNENNLESAKGLLDDLRNDLDRLFVKVEKIHEKV,mutated_sequence,1.0,602.0,UPI00000723B9.a2m,UPI00000723B9.npy,gnomAD +UPI000013C350,UPI000013C350.csv,MMKSQGLVSFKDVAVDFTQEEWQQLDPSQRTLYRDVMLENYSHLVSMGYPVSKPDVISKLEQGEEPWIIKGDISNWIYPDEYQADGRQDRKSNLHNSQSCILGTVSFHHKILKGVTRDGSLCSILKVCQGDGQLQRFLENQDKLFRQVTFVNSKTVTEASGHKYNPLGKIFQECIETDISIQRFHKYDAFKKNLKPNIDLPSCYKSNSRKKPDQSFGGGKSSSQSEPNSNLEKIHNGVIPFDDNQCGNVFRNTQSLIQYQNVETKEKSCVCVTCGKAFAKKSQLIVHQRIHTGKKPYDCGACGKAFSEKFHLVVHQRTHTGEKPYDCSECGKAFSQKSSLIIHQRVHTGEKPYECSECGKAFSQKSPLIIHQRIHTGEKPYECRECGKAFSQKSQLIIHHRAHTGEKPYECTECGKAFCEKSHLIIHKRIHTGEKPYKCAQCEEAFSRKTELITHQLVHTGEKPYECTECGKTFSRKSQLIIHQRTHTGEKPYKCSECGKAFCQKSHLIGHQRIHTGEKPYICTECGKAFSQKSHLPGHQRIHTGEKPYICAECGKAFSQKSDLVLHQRIHTGERPYQCAICGKAFIQKSQLTVHQRIHTVVKS,mutated_sequence,1.0,604.0,UPI000013C350.a2m,UPI000013C350.npy,gnomAD +UPI00001301E8,UPI00001301E8.csv,MMLPSPVTSTPFSVKDILNLEQQHQHFHGAHLQADLEHHFHSAPCMLAAAEGTQFSDGGEEDEEDEGEKLSYLNSLAAADGHGDSGLCPQGYVHTVLRDSCSEPKEHEEEPEVVRDRSQKSCQLKKSLETAGDCKAAEESERPKPRSRRKPRVLFSQAQVFELERRFKQQRYLSAPEREHLASSLKLTSTQVKIWFQNRRYKCKRQRQDKSLELGAHAPPPPPRRVAVPVLVRDGKPCVTPSAQAYGAPYSVGASAYSYNSFPAYGYGNSAAAAAAAAAAAAAAAAYSSSYGCAYPAGGGGGGGGTSAATTAMQPACSAAGGGPFVNVSNLGGFGSGGSAQPLHQGTAAGAACAQGTLQGIRAW,mutated_sequence,1.0,364.0,UPI00001301E8.a2m,UPI00001301E8.npy,gnomAD +UPI0000198666,UPI0000198666.csv,MLSLREQQLQVWFKNRRAKLARERRLQQQPQRVPGQRGRGARAAPLVPAASASAPQRGPSGILPAAEPTICSLHQAWGGPGCRAQKGIPAALSPGPGPIPAPIPGPAQIPGPLPGSIPGPIPGPAQIPSPIPAPIPGPISGPVQIPGPFRGPIPGPISGPAPIPGPISGPFSGPNPGPIPGPNPGPIPGPISGPIPGPISVPIPGPIPGPISGPISGPNPGPIPGPIPGPISGPNPGPIPGPISGPNPGLIPGPIPGPISGPGPIIGPIPSPAQIPGPGRLQGPGPILSPGRMRSPGSLPGLAPILGPGSGPGSGSVPAPIPGPGSLPAPAPLWPQSPDASDFLPDTQLFPHFTELLLPLDPLEGSSVSTMTSQYQEGDDSMGKKHSGSQPQEEGGSVNENHSGPRLLLDL,mutated_sequence,1.0,411.0,UPI0000198666.a2m,UPI0000198666.npy,gnomAD +UPI000013E667,UPI000013E667.csv,MHLALTTVLLWAWGLQAFEIVEKENIFQRTPCPAFLMFENAAYLADMSFELPCHCKPEEVPAVVWFYQKHLGSSHTKVLTDFDGRVLTEAAQVRVGSDMLTRFSIRMFSLLVFRAQSEDSGLYFCGTRKGDYFYAYDVDIQNSEGMVATFQDKGQEPFADEYYGHLHVFTTFWEWTPCDRCGVRGEQWRIGLCYLQSPDLSPRYLKAVPDVVSCGSRAVPRKLRTKARDHTPEVLVRSCLVPCEKTKTIREGVLAIINYVSKVGSRPWVPQVPIQFHQQRLGHGLIISCPGARPEHAVAWDKDRQHLYRTQYLKGVNRSMRVFIDHGNQLHIRFTQLDDRGIYYCWRQGVLVAGFRLGVTSHGHYPASFSDPETRSAVELTLIGYLLITAVFVTIHFCRCCCYLFHCCPSFSP,mutated_sequence,1.0,413.0,UPI000013E667.a2m,UPI000013E667.npy,gnomAD +UPI0000212176,UPI0000212176.csv,MECPEGQLPISSENDSTPTVSTSEVTSQQEPQILVDRGSETTYESSADIAGDEGTQIPADEDTQTDADSSAQAAAQAPENFQEGKDMSESQDEVPDEVENQFILRLPLEHACTVRNLARSQSVKMKDKLKIDLLPDGRHAVVEVEDVPLAAKLVDLPCVIESLRTLDKKTFYKTADISQMLVCTADGDIHLSPEEPAASTDPNIVRKKERGREEKCVWKHGITPPLKNVRKKRFRKTQKKVPDVKEMEKSSFTEYIESPDVENEVKRLLRSDAEAVSTRWEVIAEDGTKEIESQGSIPGFLISSGMSSHKQGHTSSEYDMLREMFSDSRSNNDDDEDEDDEDEDEDEDEDEDEDKEEEEEDCSEEYLERQLQAEFIESGQYRANEGTSSIVMEIQKQIEKKEKKLHKIQNKAQRQKDLIMKVENLTLKNHFQSVLEQLELQEKQKNEKLISLQEQLQRFLKK,mutated_sequence,1.0,462.0,UPI0000212176.a2m,UPI0000212176.npy,gnomAD +UPI00001615E1,UPI00001615E1.csv,MGTVSSRRSWWPLPLLLLLLLLLGPAGARAQEDEDGDYEELVLALRSEEDGLAEAPEHGTTATFHRCAKDPWRLPGTYVVVLKEETHLSQSERTARRLQAQAARRGYLTKILHVFHGLLPGFLVKMSGDLLELALKLPHVDYIEEDSSVFAQSIPWNLERITPPRYRADEYQPPDGGSLVEVYLLDTSIQSDHREIEGRVMVTDFENVPEEDGTRFHRQASKCDSHGTHLAGVVSGRDAGVAKGASMRSLRVLNCQGKGTVSGTLIGLEFIRKSQLVQPVGPLVVLLPLAGGYSRVLNAACQRLARAGVVLVTAAGNFRDDACLYSPASAPEVITVGATNAQDQPVTLGTLGTNFGRCVDLFAPGEDIIGASSDCSTCFVSQSGTSQAAAHVAGIAAMMLSAEPELTLAELRQRLIHFSAKDVINEAWFPEDQRVLTPNLVAALPPSTHGAGWQLFCRTVWSAHSGPTRMATAVARCAPDEELLSCSSFSRSGKRRGERMEAQGGKLVCRAHNAFGGEGVYAIARCCLLPQANCSVHTAPPAEASMGTRVHCHQQGHVLTGCSSHWEVEDLGTHKPPVLRPRGQPNQCVGHREASIHASCCHAPGLECKVKEHGIPAPQEQVTVACEEGWTLTGCSALPGTSHVLGAYAVDNTCVVRSRDVSTTGSTSEGAVTAVAICCRSRHLAQASQELQ,mutated_sequence,1.0,692.0,UPI00001615E1.a2m,UPI00001615E1.npy,gnomAD +UPI0000197DF0,UPI0000197DF0.csv,MKEGRGSFSVERGPRKERETAQSGMWKGNSPAGSQGAAMEGTGGELGGQGNWGPEDAPGLLARASLIMLPWPLPLASSALTLLFGALTSLFLWYCYRLGSQDMQALGAGSRAGGVRGGPVGCSEAGGPSPGGPGDPGEGPRTEGLVSRRLRAYARRYSWAGMGRVRRAAQGGPGPGRGPGVLGIQRPGLLFLPDLPSAPFVPRDAQRHDVELLESSFPAILRDFGAVSWDFSGTTPPPRGWSPPLAPGCYQLLLYQAGRCQPSNCRRCPGAYRALRGLRSFMSANTFGNAGFSVLLPGARLEGRCGPTNARVRCHLGLKIPPGCELVVGGEPQCWAEGHCLLVDDSFLHTVAHNGSPEDGPRVVFIVDLWHPNVAGAERQALDFVFAPDP,mutated_sequence,1.0,390.0,UPI0000197DF0.a2m,UPI0000197DF0.npy,gnomAD +UPI00003519AE,UPI00003519AE.csv,MEDAGAAGPGPEPEPEPEPEPEPAPEPEPEPKPGAGTSEAFSRLWTDVMGILDGSLGNIDDLAQQYADYYNTCFSDVCERMEELRKRRVSQDLEVEKPDASPTSLQLRSQIEESLGFCSAVSTPEVERKNPLHKSNSEDSSVGKGDWKKKNKYFWQNFRKNQKGIMRQTSKGEDVGYVASEITMSDEERIQLMMMVKEKMITIEEALARLKEYEAQHRQSAALDPADWPDGSYPTFDGSSNCNSREQSDDETEESVKFKRLHKLVNSTRRVRKKLIRVEEMKKPSTEGGEEHVFENSPVLDERSALYSGVHKKPLFFDGSPEKPPEDDSDSLTTSPSSSSLDTWGAGRKLVKTFSKGESRGLIKPPKKMGTFFSYPEEEKAQKVSRSLTEGEMKKGLGSLSHGRTCSFGGFDLTNRSLHVGSNNSDPMGKEGDFVYKEVIKSPTASRISLGKKVKSVKETMRKRMSKKYSSSVSEQDSGLDGMPGSPPPSQPDPEHLDKPKLKAGGSVESLRSSLSGQSSMSGQTVSTTDSSTSNRESVKSEDGDDEEPPYRGPFCGRARVHTDFTPSPYDTDSLKLKKGDIIDIISKPPMGTWMGLLNNKVGTFKFIYVDVLSEDEEKPKRPTRRRRKGRPPQPKSVEDLLDRINLKEHMPTFLFNGYEDLDTFKLLEEEDLDELNIRDPEHRAVLLTAVELLQEYDSNSDQSGSQEKLLVDSQGLSGCSPRDSGCYESSENLENGKTRKASLLSAKSSTEPSLKSFSRNQLGNYPTLPLMKSGDALKQGQEEGRLGGGLAPDTSKSCDPPGVTGLNKNRRSLPVSICRSCETLEGPQTVDTWPRSHSLDDLQVEPGAEQDVPTEVTEPPPQIVPEVPQKTTASSTKAQPLEQDSAVDNALLLTQSKRFSEPQKLTTKKLEGSIAASGRGLSPPQCLPRNYDAQPPGAKHGLARTPLEGHRKGHEFEGTHHPLGTKEGVDAEQRMQPKIPSQPPPVPAKKSRERLANGLHPVPMGPSGALPSPDAPCLPVKRGSPASPTSPSDCPPALAPRPLSGQAPGSPPSTRPPPWLSELPENTSLQEHGVKLGPALTRKVSCARGVDLETLTENKLHAEGIDLTEEPYSDKHGRCGIPEALVQRYAEDLDQPERDVAANMDQIRVKQLRKQHRMAIPSGGLTEICRKPVSPGCISSVSDWLISIGLPMYAGTLSTAGFSTLSQVPSLSHTCLQEAGITEERHIRKLLSAARLFKLPPGPEAM,mutated_sequence,1.0,1247.0,UPI00003519AE.a2m,UPI00003519AE.npy,gnomAD +UPI00001279CC,UPI00001279CC.csv,MAQALLVPPGPESFRLFTRESLAAIEKRAAEEKAKKPKKEQDNDDENKPKPNSDLEAGKNLPFIYGDIPPEMVSEPLEDLDPYYINKKTFIVMNKGKAIFRFSATSALYILTPLNPVRKIAIKILVHSLFSMLIMCTILTNCVFMTLSNPPDWTKNVEYTFTGIYTFESLIKILARGFCLEDFTFLRDPWNWLDFSVIVMAYVTEFVSLGNVSALRTFRVLRALKTISVIPGLKTIVGALIQSVKKLSDVMILTVFCLSVFALIGLQLFMGNLRNKCLQWPPSDSAFETNTTSYFNGTMDSNGTFVNVTMSTFNWKDYIGDDSHFYVLDGQKDPLLCGNGSDAGQCPEGYICVKAGRNPNYGYTSFDTFSWAFLSLFRLMTQDYWENLYQLTLRAAGKTYMIFFVLVIFLGSFYLVNLILAVVAMAYEEQNQATLEEAEQKEAEFQQMLEQLKKQQEEAQAVAAASAASRDFSGIGGLGELLESSSEASKLSSKSAKEWRNRRKKRRQREHLEGNNKGERDSFPKSESEDSVKRSSFLFSMDGNRLTSDKKFCSPHQSLLSIRGSLFSPRRNSKTSIFSFRGRAKDVGSENDFADDEHSTFEDSESRRDSLFVPHRHGERRNSNVSQASMSSRMVPGLPANGKMHSTVDCNGVVSLVGGPSALTSPTGQLPPEGTTTETEVRKRRLSSYQISMEMLEDSSGRQRAVSIASILTNTMEELEESRQKCPPCWYRFANVFLIWDCCDAWLKVKHLVNLIVMDPFVDLAITICIVLNTLFMAMEHYPMTEQFSSVLTVGNLVFTGIFTAEMVLKIIAMDPYYYFQEGWNIFDGIIVSLSLMELGLSNVEGLSVLRSFRLLRVFKLAKSWPTLNMLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKSYKECVCKINDDCTLPRWHMNDFFHSFLIVFRVLCGEWIETMWDCMEVAGQTMCLIVFMLVMVIGNLVVLNLFLALLLSSFSSDNLAATDDDNEMNNLQIAVGRMQKGIDYVKNKMRECFQKAFFRKPKVIEIHEGNKIDSCMSNNTGIEISKELNYLRDGNGTTSGVGTGSSVEKYVIDENDYMSFINNPSLTVTVPIAVGESDFENLNTEEFSSESELEESKEKLNATSSSEGSTVDVVLPREGEQAETEPEEDLKPEACFTEGCIKKFPFCQVSTEEGKGKIWWNLRKTCYSIVEHNWFETFIVFMILLSSGALAFEDIYIEQRKTIKTMLEYADKVFTYIFILEMLLKWVAYGFQTYFTNAWCWLDFLIVDVSLVSLVANALGYSELGAIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFYHCVNMTTGNMFDISDVNNLSDCQALGKQARWKNVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRDVKLQPVYEENLYMYLYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKFGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPANKFQGMVFDFVTRQVFDISIMILICLNMVTMMVETDDQGKYMTLVLSRINLVFIVLFTGEFVLKLVSLRHYYFTIGWNIFDFVVVILSIVGMFLAEMIEKYFVSPTLFRVIRLARIGRILRLIKGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYAIFGMSNFAYVKKEAGIDDMFNFETFGNSMICLFQITTSAGWDGLLAPILNSAPPDCDPDTIHPGSSVKGDCGNPSVGIFFFVSYIIISFLVVVNMYIAVILENFSVATEESAEPLSEDDFEMFYEVWEKFDPDATQFIEFSKLSDFAAALDPPLLIAKPNKVQLIAMDLPMVSGDRIHCLDILFAFTKRVLGESGEMDALRIQMEDRFMASNPSKVSYEPITTTLKRKQEEVSAAIIQRNFRCYLLKQRLKNISSNYNKEAIKGRIDLPIKQDMIIDKLNGNSTPEKTDGSSSTTSPPSYDSVTKPDKEKFEKDKPEKESKGKEVRENQK,mutated_sequence,1.0,2000.0,UPI00001279CC.a2m,UPI00001279CC.npy,gnomAD +UPI000006F3AC,UPI000006F3AC.csv,MPGANYRAGAGAGAGARRPRGARDREEDGGGLEPAAVARDLLRGTSNMSFEELLELQSQVGTKTYKQLVAGNSPKKQASRPPIQNACVADKHRPLEMSAKIRVPFLRQVVPISKKVARDPRFDDLSGEYNPEVFDKTYQFLNDIRAKEKELVKKQLKKHLSGEEHEKLQQLLQRMEQQEMAQQERKQQQELHLALKQERRAQAQQGHRPYFLKKSEQRQLALAEKFKELKRSKKLENFLSRKRRRNAGKDRRHLPLSKE,mutated_sequence,1.0,259.0,UPI000006F3AC.a2m,UPI000006F3AC.npy,gnomAD +UPI000040C55F,UPI000040C55F.csv,MEENPTLESEAWGSSRGWLAPREARGAPCSSPGPSLSSVLNELPSAATLRYRDPGVLPWGALEEEEEDGGRSRKAFTEVTQTELQDPHPSRELPWPMQARRAHRQRNASRDQVVYGSGTKTDRWARLLRRSKEKTKEGLRSLQPWAWTLKRIGGQFGAGTESYFSLLRFLLLLNVLASVLMACMTLLPTWLGGAPPGPPGPDISSPCGSYNPHSQGLVTFATQLFNLLSGEGYLEWSPLFYGFYPPRPRLAVTYLCWAFAVGLICLLLILHRSVSGLKQTLLAESEALTSYSHRVFSAWDFGLCGDVHVRLRQRIILYELKVELEETVVRRQAAVRTLGQQARVWLVRVLLNLLVVALLGAAFYGVYWATGCTVELQEMPLVQELPLLKLGVNYLPSIFIAGVNFVLPPVFKLIAPLEGYTRSRQIVFILLRTVFLRLASLVVLLFSLWNQITCGGDSEAEDCKTCGYNYKQLPCWETVLGQEMYKLLLFDLLTVLAVALLIQFPRKLLCGLCPGALGRLAGTQEFQVPDEVLGLIYAQTVVWVGSFFCPLLPLLNTVKFLLLFYLKKLTLFSTCSPAARTFRASAANFFFPLVLLLGLAISSVPLLYSIFLIPPSKLCGPFRGQSSIWAQIPESISSLPETTQNFLFFLGTQAFAVPLLLISSILMAYTVALANSYGRLISELKRQRQTEAQNKVFLARRAVALTSTKPAL,mutated_sequence,1.0,712.0,UPI000040C55F.a2m,UPI000040C55F.npy,gnomAD +UPI00001AE729,UPI00001AE729.csv,MDPPAGAARRLLCPALLLLLLLLPPPLLPPPPPPANARLAAAADPPGGPLGHGAERILAVPVRTDAQGRLVSHVVSAATSRAGVRARRAAPVRTPSFPGGNEEEPGSHLFYNVTVFGRDLHLRLRPNARLVAPGATMEWQGEKGTTRVEPLLGSCLYVGDVAGLAEASSVALSNCDGLAGLIRMEEEEFFIEPLEKGLAAQEAEQGRVHVVYRRPPTSPPLGGPQALDTGASLDSLDSLSRALGVLEEHANSSRRRARRHAADDDYNIEVLLGVDDSVVQFHGKEHVQKYLLTLMNIVNEIYHDESLGAHINVVLVRIILLSYGKSMSLIEIGNPSQSLENVCRWAYLQQKPDTGHDEYHDHAIFLTRQDFGPSGMQGYAPVTGMCHPVRSCTLNHEDGFSSAFVVAHETGHVLGMEHDGQGNRCGDEVRLGSIMAPLVQAAFHRFHWSRCSQQELSRYLHSYDCLLDDPFAHDWPALPQLPGLHYSMNEQCRFDFGLGYMMCTAFRTFDPCKQLWCSHPDNPYFCKTKKGPPLDGTMCAPGKHCFKGHCIWLTPDILKRDGSWGAWSPFGSCSRTCGTGVKFRTRQCDNPHPANGGRTCSGLAYDFQLCSRQDCPDSLADFREEQCRQWDLYFEHGDAQHHWLPHEHRDAKERCHLYCESRETGEVVSMKRMVHDGTRCSYKDAFSLCVRGDCRKVGCDGVIGSSKQEDKCGVCGGDNSHCKVVKGTFTRSPKKHGYIKMFEIPAGARHLLIQEVDATSHHLAVKNLETGKFILNEENDVDASSKTFIAMGVEWEYRDEDGRETLQTMGPLHGTITVLVIPVGDTRVSLTYKYMIHEDSLNVDDNNVLEEDSVVYEWALKKWSPCSKPCGGGSQFTKYGCRRRLDHKMVHRGFCAALSKPKAIRRACNPQECSQPVWVTGEWEPCSQTCGRTGMQVRSVRCIQPLHDNTTRSVHAKHCNDARPESRRACSRELCPGRWRAGPWSQCSVTCGNGTQERPVLCRTADDSFGICQEERPETARTCRLGPCPRNISDPSKKSYVVQWLSRPDPDSPIRKISSKGHCQGDKSIFCRMEVLSRYCSIPGYNKLCCKSCNLYNNLTNVEGRIEPPPGKHNDIDVFMPTLPVPTVAMEVRPSPSTPLEVPLNASSTNATEDHPETNAVDEPYKIHGLEDEVQPPNLIPRRPSPYEKTRNQRIQELIDEMRKKEMLGKF,mutated_sequence,1.0,1211.0,UPI00001AE729.a2m,UPI00001AE729.npy,gnomAD +UPI0000DA182F,UPI0000DA182F.csv,MRGPEPGPQPTMEGDVLDTLEALGYKGPLLEEQALTKAAEGGLSSPEFSELCIWLGSQIKSLCNLEESITSAGRDDLESFQLEISGFLKEMACPYSVLISGDIKDRLKKKEDCLKLLLFLSTELQASQILQNKKHKNSQLDKNSEVYQEVQAMFDTLGIPKSTTSDIPHMLNQVESKVKDILSKVQKNHVGKPLLKMDLNSEQAEQLERINDALSCEYECRRRMLMKRLDVTVQSFGWSDRAKVKTDDIARIYQPKRYALSPKTTITMAHLLAAREDLSKIIRTSSGTSREKTACAINKVLMGRVPDRGGRPNEIEPPPPEMPPWQKRQEGGGGRGGWGGGGGGGGRGGGGGGGGRGGWGGGGGGWGGGGGGGGGWGGGGGGGRGGFQGRGDYGGRGGYGGRGGYGGRGYGDPYGGGGGGGGGGGGGGGYRRY,mutated_sequence,1.0,433.0,UPI0000DA182F.a2m,UPI0000DA182F.npy,gnomAD +UPI0000E5ADF3,UPI0000E5ADF3.csv,MWKLGRGRVLLDEPPEEEDGLRGGPPPAAAAAAQAQVQGASFRGWKEVTSLFNKDDEQHLLERCKSPKSKGTNLRLKEELKAEKKSGFWDNLVLKQNIQSKKPDEIEGWEPPKLALEDISADPEDTVGGHPSWSGWEDDAKGSTKYTSLASSANSSRWSLRAAGRLVSIRRQSKGHLTDSPEEAE,mutated_sequence,1.0,185.0,UPI0000E5ADF3.a2m,UPI0000E5ADF3.npy,gnomAD +UPI0001D0434B,UPI0001D0434B.csv,MRDFGFGVLQTAPLRSSSPGPLFCGEAYGPYAVGSVNPLPSATPFGPLSPPPLPVTGFLEAASPFSVPLGGGAGSPAAAASSSSPFLAHQQTMQDELLLGLTQQPARPLSGAAATEKLPDHHPGGGTIAGVTHLLPSQDFKPSLHHPSSSSASSCCCCRTSSPQDFSKRQQQQLSSQKRKEFSPPHLPHPPDSKPPPPPPPLHCPGRFSPPPPPAGPLLQPAQLAQRQQQQPPQQFSLLHQQHLSPQDFAPRQRPADLPPLPQLPPSPPAAPRRRHGGAGSPRKTPAAGEGSAAESPNAGLASSTPVNPAPGSMESPNHPLLNSPSNLLPGGALGAGAFSSLQSPDLPHPGGGGGGGGGGPPGGGGGGGSASPPPLPGFGTPWSVQTASPPPQPQQPPPTQPQQQPPPPQQPPQPQPQPPGSSATTPGGGSGGSLSAMPPPSPDSENGFYPGLPSSMNPAFFPSFSPVSPHGCTGLSVPTSGGGGGGFGGPFSATAVPPPPPPAMNIPQQQPPPPAAPQQPQSRRSPVSPQLQQQHQAAAAAFLQQRNSYNHHQPLLKQSPWSNHQSSGWGTGSMSWGAMHGRDHRRTGNMGIPGTMNQISPLKKPFSGNVIAPPKFTRSTPSLTPKSWIEDNVFRTDNNSNTLLPLQVRSSLQLPAWGSDSLQDSWCTAAGTSRIDQDRSRMYDSLNMHSLENSLIDIMRAEHDPLKGRLSYPHPGTDNLLMLNARSYGRRRGRSSLFPIDDGLLDDGHSDQVGVLNSPTCYSAHQNGERIERFSRKVFVGGLPPDIDEDEITASFRRFGPLVVDWPHKAESKSYFPPKGYAFLLFQEESSVQALIDACIEEDGKLYLCVSSPTIKDKPVQIRPWNLSDSDFVMDGSQPLDPRKTIFVGGVPRPLRAVELAMIMDRLYGGVCYAGIDTDPELKYPKGAGRVAFSNQQSYIAAISARFVQLQHGDIDKRVEVKPYVLDDQMCDECQGARCGGKFAPFFCANVTCLQYYCEFCWANIHSRAGREFHKPLVKEGADRPRQIHFRWN,mutated_sequence,1.0,1034.0,UPI0001D0434B.a2m,UPI0001D0434B.npy,gnomAD +UPI0001662AC1,UPI0001662AC1.csv,MAESSLYRQRLEVIAEKRRLQEEIRAARREVEEEKLRVERLKRKSLRERWLMDGAAAVPEPSEDPTSKDPQSPEGQAQARIRNLEDSLFTLQSQLQLLQSASTGAQHKPSGRPSWRRQGHRPLSQSIVEAGSVGQTDLNKRASLPAGLVGTPPESPSEPREDVLGFLPGPRQVPGAAGDSSEANGPCPSPIPTPEQGLSQRAVPSEGRVGEAKGGGVVSVVWEGLRATEDCATGATGPELEAKVEEVVLEAIGDRKGAGSLELPAWVKEDRGIVEVVWEGVGGSDAEAMGEIGRVPEVVQTSSPRLQERLEAAASIEGEDVPQGSPEGDGQGGSGGEEGSFIWVERVTLSEEWEELLVEGLEGPEVAGRERGDESPLGAEGAKTGGGEETWEAEKRKAEESMGIGSEEKPGTGRDEAEMSPVVERKGGEKKLELESRGSAEKLGTEREGGEEPLGIERKVEGHLRAEKEGDEEKRGAEEEEVEEPLGVEKKGGEEEPEATKEPLEAERKGGEETLEAEKRGGEESLETEKTQGTEGDLNLEQGSREGSESQAEEMNEAGPPLEANTETRPEKEGPQPQEKPVGALEEEGVKPQTAAEGQGPLGDATPLLAETPAPEQPAECQPLLQGEGPSANPSAHPVPTYAPARQPEPSAPTEGEEASGPKQKTCQCCAVM,mutated_sequence,1.0,673.0,UPI0001662AC1.a2m,UPI0001662AC1.npy,gnomAD +UPI000011785A,UPI000011785A.csv,MAEEQPQVELFVKAGSDGAKIGNCPFSQRLFMVLWLKGVTFNVTTVDTKRRTETVQKLCPGGQLPFLLYGTEVHTDTNKIEEFLEAVLCPPRYPKLAALNPESNTAGLDIFAKFSAYIKNSNPALNDNLEKGLLKALKVLDNYLTSPLPEEVDETSAEDEGVSQRKFLDGNELTLADCNLLPKLHIVQVVCKKYRGFTIPEAFRGVHRYLSNAYAREEFASTCPDDEEIELAYEQVAKALK,mutated_sequence,1.0,241.0,UPI000011785A.a2m,UPI000011785A.npy,gnomAD +UPI0001837EA2,UPI0001837EA2.csv,MAERGQQPPPAKRLCCRPGGGGGGGGSSGGGGGAGGGYSSACRPGPRAGGAAAAAACGGGAALGLLPPGKTQSPESLLDIAARRVAEKWPFQRVEERFERIPEPVQRRIVYWSFPRSEREICMYSSFNTGGGAAGGPGDDSGGGGGAGGGGGGGSSSSPAATSAAATSAAAAAAAAAAAAAAAAGAGAPSVGAAGAADGGDETRLPFRRGIALLESGCVDNVLQVGFHLSGTVTEPAIQSEPETVCNVAISFDRCKITSVTCSCGNKDIFYCAHVVALSLYRIRKPDQVKLHLPISETLFQMNRDQLQKFVQYLITVHHTEVLPTAQKLADEILSQNSEINQVHGAPDPTAGASIDDENCWHLDEEQVQEQVKLFLSQGGYHGSGKQLNLLFAKVREMLKMRDSNGARMLTLITEQFMADPRLSLWRQQGTAMTDKYRQLWDELGALWMCIVLNPHCKLEQKASWLKQLKKWNSVDVCPWEDGNHGSELPNLTNALPQGANANQDSSNRPHRTVFTRAIEACDLHWQDSHLQHIISSDLYTNYCYHDDTENSLFDSRGWPLWHEHVPTACARVDALRSHGYPREALRLAIAIVNTLRRQQQKQLEMFRTQKKELPHKNITSITNLEGWVGHPLDPVGTLFSSLMEACRIDDENLSGFSDFTENMGQCKSLEYQHLPAHKFLEEGESYLTLAVEVALIGLGQQRIMPDGLYTQEKVCRNEEQLISKLQEIELDDTLVKIFRKQAVFLLEAGPYSGLGEIIHRESVPMHTFAKYLFTSLLPHDAELAYKIALRAMRLLVLESTAPSGDLTRPHHIASVVPNRYPRWFTLSHIESQQCELASTMLTAAKGDVRRLETVLESIQKNIHSSSHIFKLAQDAFKIATLMDSLPDITLLKVSLELGLQVMRMTLSTLNWRRREMVRWLVTCATEVGVYALDSIMQTWFTLFTPTEATSIVATTVMSNSTIVRLHLDCHQQEKLASSARTLALQCAMKDPQNCALSALTLCEKDHIAFETAYQIVLDAATTGMSYTQLFTIARYMEHRGYPMRAYKLATLAMTHLNLSYNQDTHPAINDVLWACALSHSLGKNELAAIIPLVVKSVKCATVLSDILRRCTLTTPGMVGLHGRRNSGKLMSLDKAPLRQLLDATIGAYINTTHSRLTHISPRHYSEFIEFLSKARETFLMAHDGHIQFTQFIDNLKQIYKGKKKLMMLVRERFG,mutated_sequence,1.0,1215.0,UPI0001837EA2.a2m,UPI0001837EA2.npy,gnomAD +UPI00001320B0,UPI00001320B0.csv,MEMEQEKMTMNKELSPDAAAYCCSACHGDETWSYNHPIRGRAKSRSLSASPALGSTKEFRRTRSLHGPCPVTTFGPKACVLQNPQTIMHIQDPASQRLTWNKSPKSVLVIKKMRDASLLQPFKELCTHLMEENMIVYVEKKVLEDPAIASDESFGAVKKKFCTFREDYDDISNQIDFIICLGGDGTLLYASSLFQGSVPPVMAFHLGSLGFLTPFSFENFQSQVTQVIEGNAAVVLRSRLKVRVVKELRGKKTAVHNGLGENGSQAAGLDMDVGKQAMQYQVLNEVVIDRGPSSYLSNVDVYLDGHLITTVQGDGVIVSTPTGSTAYAAAAGASMIHPNVPAIMITPICPHSLSFRPIVVPAGVELKIMLSPEARNTAWVSFDGRKRQEIRHGDSISITTSCYPLPSICVRDPVSDWFESLAQCLHWNVRKKQAHFEEEEEEEEEG,mutated_sequence,1.0,446.0,UPI00001320B0.a2m,UPI00001320B0.npy,gnomAD +UPI0000052A8C,UPI0000052A8C.csv,MVVLRSSLELHNHSAASATGSLDLSSDFLSLEHIGRRRLRSAGAAQKKPAATTAKAGDGSSVKEVETYHRTRALRSLRKDAQNSSDSSFEKNVEITEQLANGRHFTRQLARQQADKKKEEHREDKVIPVTRSLRARNIVQSTEHLHEDNGDVEVRRSCRIRSRYSGVNQSMLFDKLITNTAEAVLQKMDDMKKMRRQRMRELEDLGVFNETEESNLNMYTRGKQKDIQRTDEETTDNQEGSVESSEEGEDQEHEDDGEDEDDEDDDDDDDDDDDDDDEDDEDEEDGEEENQKRYYLRQRKATVYYQAPLEKPRHQRKPNIFYSGPASPARPRYRLSSAGPRSPYCKRMNRRRHAIHSSDSTSSSSSEDEQHFERRRKRSRNRAINRCLPLNFRKDELKGIYKDRMKIGASLADVDPMQLDSSVRFDSVGGLSNHIAALKEMVVFPLLYPEVFEKFKIQPPRGCLFYGPPGTGKTLVARALANECSQGDKRVAFFMRKGADCLSKWVGESERQLRLLFDQAYQMRPSIIFFDEIDGLAPVRSSRQDQIHSSIVSTLLALMDGLDSRGEIVVIGATNRLDSIDPALRRPGRFDREFLFSLPDKEARKEILKIHTRDWNPKPLDTFLEELAENCVGYCGADIKSICAEAALCALRRRYPQIYTTSEKLQLDLSSINISAKDFEVAMQKMIPASQRAVTSPGQALSTVVKPLLQNTVDKILEALQRVFPHAEFRTNKTLDSDISCPLLESDLAYSDDDVPSVYENGLSQKSSHKAKDNFNFLHLNRNACYQPMSFRPRILIVGEPGFGQGSHLAPAVIHALEKFTVYTLDIPVLFGVSTTSPEETCAQVIREAKRTAPSIVYVPHIHVWWEIVGPTLKATFTTLLQNIPSFAPVLLLATSDKPHSALPEEVQELFIRDYGEIFNVQLPDKEERTKFFEDLILKQAAKPPISKKKAVLQALEVLPVAPPPEPRSLTAEEVKRLEEQEEDTFRELRIFLRNVTHRLAIDKRFRVFTKPVDPDEVPDYVTVIKQPMDLSSVISKIDLHKYLTVKDYLRDIDLICSNALEYNPDRDPGDRLIRHRACALRDTAYAIIKEELDEDFEQLCEEIQESRKKRGCSSSKYAPSYYHVMPKQNSTLVGDKRSDPEQNEKLKTPSTPVACSTPAQLKRKIRKKSNWYLGTIKKRRKISQAKDDSQNAIDHKIESDTEETQDTSVDHNETGNTGESSVEENEKQQNASESKLELRNNSNTCNIENELEDSRKTTACTELRDKIACNGDASSSQIIHISDENEGKEMCVLRMTRARRSQVEQQQLITVEKALAILSQPTPSLVVDHERLKNLLKTVVKKSQNYNIFQLENLYAVISQCIYRHRKDHDKTSLIQKMEQEVENFSCSR,mutated_sequence,1.0,1390.0,UPI0000052A8C.a2m,UPI0000052A8C.npy,gnomAD +UPI00021CF3CC,UPI00021CF3CC.csv,RRLCPLHTHTHTHTHTHMHAHTRTHTKLSCLLVCPPEAGTCWSLRSTWSGPDVRCVYWLNLSRPIEEQGPLDVIIHKLTDVILEADQNDSQSLELVHRFQEYIDAHPETIVLDPLPAIRTLLDRSKSYELIRKIEAYMEDDRICSPPFMELTSLCGDDTMRLLEKNGLTFPFICKTRVAHGTNSHEMAIVFNQEGLNAIQPPCVVQNFINHNAVLYKVFVVGESYTVVQR,mutated_sequence,1.0,230.0,UPI00021CF3CC.a2m,UPI00021CF3CC.npy,gnomAD +UPI000002B371,UPI000002B371.csv,MARGRKMSKPRAVEAAAAAAAVAATAPGPEMVERRGPGRPRTDGENVFTGQSKIYSYMSPNKCSGMRFPLQEENSVTHHEVKCQGKPLAGIYRKREEKRNAGNAVRSAMKSEEQKIKDARKGPLVPFPNQKSEAAEPPKTPPSSCDSTNAAIAKQALKKPIKGKQAPRKKAQGKTQQNRKLTDFYPVRRSSRKSKAELQSEERKRIDELIESGKEEGMKIDLIDGKGRGVIATKQFSRGDFVVEYHGDLIEITDAKKREALYAQDPSTGCYMYYFQYLSKTYCVDATRETNRLGRLINHSKCGNCQTKLHDIDGVPHLILIASRDIAAGEELLYDYGDRSKASIEAHPWLKH,mutated_sequence,1.0,352.0,UPI000002B371.a2m,UPI000002B371.npy,gnomAD +UPI0002840938,UPI0002840938.csv,MLLPLLLLLPMCWAVEVKRPRGVSLTSESSCSPSRQAGGGRGQHWALGIGIYSLLTQFPGSAPLVGMGGQRWYLVEKALTCPGLSFLYPADHHFYDESKPFTCLDGSATIPFDQVNDDYCDCKDGSDEPGTA,mutated_sequence,1.0,132.0,UPI0002840938.a2m,UPI0002840938.npy,gnomAD +UPI0001747A7C,UPI0001747A7C.csv,MHNLYSITGYPDPPGTMEEEEEDDDYENSTPPYKDLPPKPGTMEEEEEDDDYENSTPPYKDLPPKPGTMEEEEEDDDYENSTPPYKDLPPKPGSSAPPRPPRAAKETEKPPLPCKPRNMTGLDLAAVTCPPPQLAVNLEPSPLQPSLAATPVPWLNQRSGGPGCCQKRWMVYLCLLVVTSLFLGCLGLTVTLIKYQELMEELRMLSFQQMTWRTNMTGMAGLAGLKHDIARVRADTNQSLVELWGLLDCRRITCPEGWLPFEGKCYYFSPSTKSWDEARMFCQENYSHLVIINSFAEHNFVAKAHGSPRVYWLGLNDRAQEGDWRWLDGSPVTLSFWEPEEPNNIHDEDCATMNKGGTWNDLSCYKTTYWICERKCSC,mutated_sequence,1.0,378.0,UPI0001747A7C.a2m,UPI0001747A7C.npy,gnomAD +UPI0001C0B37D,UPI0001C0B37D.csv,MASPLPSGFPARRNSRLDVFLRRHLPPEVYDAVRAYEPCIVVSNSENHILKYVVLSDRLVYLTENPPKSIRRVVALRDVVAIDLIDDYPEFLSSPDREISQHIRIIYSSTVLKKECKKSNSVRKFLFPFHHTKANNKKVKEEKNGLAFWRSKESRSLKESPLRDQQESSTPSKDSTLCPRPGLKKLSLHGQGAFRPLPSPSRRSSQSAPTTGKAVSEPSCTTNTKEPQGLPDHNSISEIPFKCNGNGNEFYLGNSLLDSPSQSNSNLEKKESELHLYVISTTSSIFLHLKSSWNNYIIKATLLQDPFYASEFSPAIGSQKPYRSEEKIKHFSQLKSELFLKDNSLRRILSLLMELKVAAQKNFILKRLFWKTSDLFYFIVNKLHEYLPESRDKNALQNQSQRVDELVACIEIIQTLVLMFRETETESSRLNTLAAKKGALFNLLVILISEPQIPKSCPVFDIQLVADSALVRMSFDAELQKLILEYTNTATALLYEILLVFQQGNLGLGSTKFAISWIMSFLQSCPPIITFVASIVKQVVRGLSASFQLLSPCQAVLLYQQFYILKSCLRHSRTLAEYIRNNYREEFRYFIHMPALQKRLPLCYPITQPTIQLFHEVLKLVE,mutated_sequence,1.0,622.0,UPI0001C0B37D.a2m,UPI0001C0B37D.npy,gnomAD +UPI0000136B58,UPI0000136B58.csv,MLRLQMTDGHISCTAVEFSYMSKISLNTPPGTKVKLSGIVDIKNGFLLLNDSNTTVLGGEVEHLIEKWELQRSLSKHNRSNIGTEGGPPPFVPFGQKCVSHVQVDSRELDRRKTLQVTMPVKPTNDNDEFEKQRTAAIAEVAKSKETKTFGGGGGGARSNLNMNAAGNRNREVLQKEKSTKSEGKHEGVYRELVDEKALKHITEMGFSKEASRQALMDNGNNLEAALNVLLTSNKQKPVMGPPLRGRGKGRGRIRSEDEEDLGNARPSAPSTLFDFLESKMGTLNVEEPKSQPQQLHQGQYRSSNTEQNGVKDNNHLRHPPRNDTRQPRNEKPPRFQRDSQNSKSVLEGSGLPRNRGSERPSTSSVSEVWAEDRIKCDRPYSRYDRTKDTSYPLGSQHSDGAFKKRDNSMQSRSGKGPSFAEAKENPLPQGSVDYNNQKRGKRESQTSIPDYFYDRKSQTINNEAFSGIKIEKHFNVNTDYQNPVRSNSFIGVPNGEVEMPLKGRRIGPIKPAGPVTAVPCDDKIFYNSGPKRRSGPIKPEKILESSIPMEYAKMWKPGDECFALYWEDNKFYRAEVEALHSSGMTAVVKFIDYGNYEEVLLSNIKPIQTEAWEEEGTYDQTLEFRRGGDGQPRRSTRPTQQFYQPPRARN,mutated_sequence,1.0,651.0,UPI0000136B58.a2m,UPI0000136B58.npy,gnomAD +UPI000013DF8A,UPI000013DF8A.csv,MGPRIGPAGEVPQVPDKETKATMGTENTPGGKASPDPQDVRPSVFHNIKLFVLCHSLLQLAQLMISGYLKSSISTVEKRFGLSSQTSGLLASFNEVGNTALIVFVSYFGSRVHRPRMIGYGAILVALAGLLMTLPHFISEPYRYDNTSPEDMPQDFKASLCLPTTSAPASAPSNGNCSSYTETQHLSVVGIMFVAQTLLGVGGVPIQPFGISYIDDFAHNSNSPLYLGILFAVTMMGPGLAFGLGSLMLRLYVDINQMPEGGISLTIKDPRWVGAWWLGFLIAAGAVALAAIPYFFFPKEMPKEKRELQFRRKVLAVTDSPARKGKDSPSKQSPGESTKKQDGLVQIAPNLTVIQFIKVFPRVLLQTLRHPIFLLVVLSQVCLSSMAAGMAIFLPKFLERQFSITASYANLLIGCLSFPSVIVGIVVGGVLVKRLHLGPVGCGALCLLGMLLCLFFSLPLFFIGCSSHQIAGITHQTSAHPGLELSPSCMEACSCPLDGFNPVCDPSTRVEYITPCHAGCSSWVVQDALDNSQVFYTNCSCVVEGNPVLAGSCDSTCSHLVVPFLLLVSLGSALACLTHTPSFMLILRGVKKEDKTLAVGIQFMFLRILAWMPSPVIHGSAIDTTCVHWALSCGRRAVCRYYNNDLLRNRFIGLQFFFKTGSVICFALVLAVLRQQDKEARTKESRSSPAVEQQLLVSGPGKKPEDSRV,mutated_sequence,1.0,709.0,UPI000013DF8A.a2m,UPI000013DF8A.npy,gnomAD +UPI000013EFE9,UPI000013EFE9.csv,MSSAPRRPAKGADSFCTPEPESLGPGTPGFPEQEEDELHRTLGVERFEEILQEAGSRGGEEPGRSYGEEDFEYHRQSSHHIHHPLSTHLPPDARRRKTPQGPGRKPRRRPGASPTGETPTIEEGEEDEDEASEAEGARALTQPSPVSTPSSVQFFLQEDDSADRKAERTSPSSPAPLPHQEATPRASKGAQAGTQVEEAEAEAVAVASGTAGGDDGGASGRPLPKAQPGHRSYNLQERRRIGSMTGAEQALLPRVPTDEIEAQTLATADLDLMKSHRFEDVPGVRRHLVRKNAKGSTQSGREGREPGPTPRARPRAPHKPHEVFVELNELLLDKNQEPQWRETARWIKFEEDVEEETERWGKPHVASLSFRSLLELRRTLAHGAVLLDLDQQTLPGVAHQVVEQMVISDQIKAEDRANVLRALLLKHSHPSDEKDFSFPRNISAGSLGSLLGHHHGQGAESDPHVTEPLMGGVPETRLEVERERELPPPAPPAGITRSKSKHELKLLEKIPENAEATVVLVGCVEFLSRPTMAFVRLREAVELDAVLEVPVPVRFLFLLLGPSSANMDYHEIGRSISTLMSDKQFHEAAYLADEREDLLTAINAFLDCSVVLPPSEVQGEELLRSVAHFQRQMLKKREEQGRLLPTGAGLEPKSAQDKALLQMVEAAGAAEDDPLRRTGRPFGGLIRDVRRRYPHYLSDFRDALDPQCLAAVIFIYFAALSPAITFGGLLGEKTQDLIGVSELIMSTALQGVVFCLLGAQPLLVIGFSGPLLVFEEAFFSFCSSNHLEYLVGRVWIGFWLVFLALLMVALEGSFLVRFVSRFTQEIFAFLISLIFIYETFYKLVKIFQEHPLHGCSASNSSEVDGGENMTWAGARPTLGPGNRSLAGQSGQGKPRGQPNTALLSLVLMAGTFFIAFFLRKFKNSRFFPGRIRRVIGDFGVPIAILIMVLVDYSIEDTYTQKLSVPSGFSVTAPEKRGWVINPLGEKSPFPVWMMVASLLPAILVFILIFMETQITTLIISKKERMLQKGSGFHLDLLLIVAMGGICALFGLPWLAAATVRSVTHANALTVMSKAVAPGDKPKIQEVKEQRVTGLLVALLVGLSIVIGDLLRQIPLAVLFGIFLYMGVTSLNGIQFYERLHLLLMPPKHHPDVTYVKKVRTLRMHLFTALQLLCLALLWAVMSTAASLAFPFILILTVPLRMVVLTRIFTDREMKCLDANEAEPVFDEREGVDEYNEMPMPV,mutated_sequence,1.0,1241.0,UPI000013EFE9.a2m,UPI000013EFE9.npy,gnomAD +UPI00015B3C70,UPI00015B3C70.csv,MTRGRAWGMRRAAAGAGGARAAGPTGGASRLHPNAGRRSGARAGAQGCGGPRVGSADSRALPAQPLACARGRSQRLVCDPKAASALPDLAPDVFVLRVRLEETGEMFRVANCRGDMTVRELKEELDLMVGIPFNLQRLQYLDEGVLMDDTTLKFHDVVPGGIISLCIWHHDGWTELVLAAVEGDPSKLSCLGLTEDSFYRTANSEHFEGEKWKHWTSQRAFVALYVASHRGHFDAVQYLLEHGASCLSRSPLGRTPLHVAAAMGRSDCIILLLQHGASIHDRDAKGETPISIAHRLNHTLSERQMVLLHRIAKSGIRDLNDLVMKNALQRVKSGFRSEKMTMTPH,mutated_sequence,1.0,345.0,UPI00015B3C70.a2m,UPI00015B3C70.npy,gnomAD +UPI00006E232B,UPI00006E232B.csv,MAQSPPPQSLLGHDHWIFAQGWGWAGHWDSTSPASSSDSSGSCPCDGARGLPQPQPPSCSSRAAEAAATTPRRARTGPAGGQRQSASEREKLRMRTLARALHELRRFLPPSLAPAGQSLTKIETLRLAIRYIGHLSAVLGLSEESLQCRRRQRGDAGSPWGCPLCPDRGPAEAQTQAEGQGQGQGQGQGQGQGQGQGQGQGQGQGRRPGLVSAVLAEASWGSPSACPGAQAAPERLGRGVHDTDPWATPPYCPKIQSPPYSSQGTTSDASLWTPPQGCPWTQSSPEPRNPPVPWTAAPATLELAAVYQGLSVSPEPCLSLGAPSLLPHPSCQRLQPQTPGRCWSHSAEVVPNSEDQGPGAAFQLSEASPPQSSGLRFSGCPELWQEDLEGARLGIFY,mutated_sequence,1.0,397.0,UPI00006E232B.a2m,UPI00006E232B.npy,gnomAD +UPI000016021B,UPI000016021B.csv,MAELSEPEGPVDWKERCVALESQLMKFRVQASKIRELLAEKMQQLERQVIDAERQAEKAFQQVQVMEDKLKAANIQTSESETRLYNKCQDLESLIQEKDDVIQNLELQLEEQKQIRIQEAKIIEEKAAKIKEWVTVKLNELELENQNLRLINQNQTEEIRTMQSKLQEVQGKKSSTVSTLKLSEGQRLSSLTFGCFLSRARSPPQVVKSEEMSKISSKEPEFTEGKDMEEMEIPEKSVDNQVLENNRGQRTLHQTPCGSEQNRKTRTSFATDGGISQNSGAPVSDWSSDEEDGSKGRSKSRCTSTLSSHTSEEGVQCSRMGSEMYLTASDDSSSIFEEETFGIKRPEHKKLYSWQQEAQWKALNSPLGKGNSELSKKEQDSSSDELNKKFQSQRLDYSSSSSEANTPSPILTPALMPKHPNSLSGKGTQLVPSSHLPPPKLRIPNVFSISVALAKRHLSQPQLSSDRMFGTNRNAISMIRPLRPQETDLDLVDGDSTEVLENMDTSCDDGLFSYDSLDSPNSDDQEHCDSAKKVAYSKPPTPPLHRFPSWESRIYAVAKSGIRMSEAFNMESVNKNSAATLSYTTSGLYTSLIYKNMTTPVYTTLKGKATQISSSPFLDDSSGSEEEDSSRSSSRTSESDSRSRSGPGSPRAMKRGVSLSSVASESDYAIPPDAYSTDTEYSQPEQKLPKTCSSSSDNGKNEPLEKSGYLLKMSGKVKSWKRRWFVLKGGELLYYKSPSDVIRKPQGHIELSASCSILRGDNKQTVQLTTEKHTYYLTADSPNILEEWIKVLQNVLRVQAANPLSLQPEGKPTMKGLLTKVKHGYSKRVWCTLIGKTLYYFRSQEDKFPLGQIKLWEAKVEEVDRSCDSDEDYEASGRSLLSTHYTIVIHPKDQGPTYLLIGSKHEKDTWLYHLTVAAGSNNVNVGSEFEQLVCKLLNIDGEPSSQIWRHPTLCHSKEGIISPLTTLPSEALQTEAIKLFKTCQLFINAAVDSPAIDYHISLAQSALQICLTHPELQNEICCQLIKQTRRRQPQNQPGPLQGWQLLALCVGLFLPHHPFLWLLRLHLKRNADSRTEFGKYAIYCQRCVERTQQNGDREARPSRMEILSTLLRNPYHHSLPFSIPVHFMNGIYQVVGFDASTTVEEFLNTLNQDTGMRKPAQSGFALFTDDPSGRDLEHCLQGNIKICDIISKWEQASKEQQPGKCEGTRTVRLTYKNRLYFSVQARGETDREKLLLMYQTNDQIINGLFPLNKDLALEMAALLSQVEIGDFERPFSTPAGHVTNQCKVNQTLKQVIEKFYPKRYRDGCSEEQLRQLCQRLSTRWMALRGHSAADCVRIYLTVARKWPFFGAKLFLAKPITPSSLGSTFLWLAVHEDGLSLLEYNSMRLIVSYVYKSLMTFGGYQDDFMVVINNTHSKDKPTEKLLFAMAKPKILEITLLIASYINNFHQQKAAFHHLSAPALLSAQTRGPQARMMGSQPLLSSSRPTKGPTLL,mutated_sequence,1.0,1493.0,UPI000016021B.a2m,UPI000016021B.npy,gnomAD +UPI000013D057,UPI000013D057.csv,MRGLEESGPRPTATPCGCVKPALETGNLLTEPVGYLESCFSAKNGTPRQPSICSYSRACLRIRKRIFNNPEHSLMGLEQFSHVWILFVFHKNGHLSCKAKVQPPRLNGAKTGVFSTRSPHRPNAIGLTLAKLEKVEGGAIYLSGIDMIHGTPVLDIKPYIAEYDSPQNVMEPLADFNLQNNQHTPNTVSQSDSKTDSCDQRQLSGCDEPQPHHSTKRKPKCPEDRTSEENYLTHSDTARIQQAFPMHREIAVDFGLESRRDQSSSVAEEQIGPYCPEKSFSEKGTDKKLERVEGAAVLQGSRAETQPMAPHCPAGRADGAPRSVVPAWVTEAPVATLEVRFTPHAEMDLGQLSSQDVGQASFKYFQSAEEAKRAIEAVLSADPRSVYRRKLCQDRLFYFTVDIAHVTCWFGDGFAEVLRIKPASEPVHMTGPVGSLVSLGS,mutated_sequence,1.0,441.0,UPI000013D057.a2m,UPI000013D057.npy,gnomAD +UPI000000127D,UPI000000127D.csv,MLVIPPGLSEEEEALQKKFNKLKKKKKALLALKKQSSSSTTSQGGVKRSLSEQPVMDTATATEQAKQLVKSGAISAIKAETKNSGFKRSRTLEGKLKDPEKGPVPTFQPFQRSISADDDLQESSRRPQRKSLYESFVSSSDRLRELGPDGEEAEGPGAGDGPPRSFDWGYEERSGAHSSASPPRSRSRDRSHERNRDRDRDRERDRDRDRDRDRERDRDRDRDRDRDRERDRDRERDRDRDREGPFRRSDSFPERRAPRKGNTLYVYGEDMTPTLLRGAFSPFGNIIDLSMDPPRNCAFVTYEKMESADQAVAELNGTQVESVQLKVNIARKQPMLDAATGKSVWGSLAVQNSPKGCHRDKRTQIVYSDDVYKENLVDGF,mutated_sequence,1.0,380.0,UPI000000127D.a2m,UPI000000127D.npy,gnomAD +UPI00003588F0,UPI00003588F0.csv,MAADKGPAAGPRSRAAMAQWRKKKGLRKRRGAASQARGSDSEDGEFEIQAEDDARARKLGPGRPLPTFPTSECTSDVEPDTREMVRAQNKKKKKSGGFQSMGLSYPVFKGIMKKGYKVPTPIQRKTIPVILDGKDVVAMARTGSGKTACFLLPMFERLKTHSAQTGARALILSPTRELALQTLKFTKELGKFTGLKTALILGGDRMEDQFAALHENPDIIIATPGRLVHVAVEMSLKLQSVEYVVFDEADRLFEMGFAEQLQEIIARLPGGHQTVLFSATLPKLLVEFARAGLTEPVLIRLDVDTKLNEQLKTSFFLVREDTKAAVLLHLLHNVVRPQDQTVVFVATKHHAEYLTELLTTQRVSCAHIYSALDPTARKINLAKFTLGKCSTLIVTDLAARGLDIPLLDNVINYSFPAKGKLFLHRVGRVARAGRSGTAYSLVAPDEIPYLLDLHLFLGRSLTLARPLKEPSGVAGVDGMLGRVPQSVVDEEDSGLQSTLEASLELRGLARVADNAQQQYVRSRPAPSPESIKRAKEMDLVGLGLHPLFSSRFEEEELQRLRLVDSIKNYRSRATIFEINASSRDLCSQVMRAKRQKDRKAIARFQQGQQGRQEQQEGPVGPAPSRPALQEKQPEKEEEEEAGESVEDIFSEVVGRKRQRSGPNRGAKRRREEARQRDQEFYIPYRPKDFDSERGLSISGEGGAFEQQAAGAVLDLMGDEAQNLTRGRQQLKWDRKKKRFVGQSGQEDKKKIKTESGRYISSSYKRDLYQKWKQKQKIDDRDSDEEGASDRRGPERRGGKRDRGQGASRPHAPGTPAGRVRPELKTKQQILKQRRRAQKLHFLQRGGLKQLSARNRRRVQELQQGAFGRGARSKKGKMRKRM,mutated_sequence,1.0,881.0,UPI00003588F0.a2m,UPI00003588F0.npy,gnomAD +UPI0000E671FE,UPI0000E671FE.csv,MGLKNKKNTEDPEEPLIASQSTEPEIGHLSPSKKETIMVTLHGATNLPACKDGSEPWPYVVVKSTSEEKNNQSSKAVTSVTSEPTRAPIWGDTVNVEIQAEDAGQEDVILKVVDNRKKQELLSYKIPIKYLRVFHPYHFELVKPTESGKADEATAKTQLYATVVRKSSFIPRYIGCNHMALEIFLRGVNEPLANNPNPIVVIARVVPNYKEFKVSQANRDLASVGLPITPLSFPIPSMMNFDVPRVSQNGCPQLSKPGGPPEQPLWNQSFLFQGRDGATSFSEDTALVLEYYSSTSMKGSQPWTLNQPLGISVLPLKSRLYQKMLTGKGLDGLHVERLPIMDTSLKTINDEAPTVALSFQLLSSERPENFLTPNNSKALPTLDPKILDKKLRTIQESWSKDTVSSTMDLSTSTPREAEEEPLVPEMSHDTEMNNYRRAMQKMAEDILSLRRQASILEGENRILRSRLAQQEEEEGQGKASEAQNTVSMKQKLLLSELDMKKLRDRVQHLQNELIRKNDREKELLLLYQAQQPQAALLKQYQGKLQKMKALEETVRHQEKVIEKMERVLEDRLQDRSKPPPLNRQQGKPYTGFPMLSASGLPLGSMGENLPVELYSVLLAENAKLRTELDKNRHQQAPIILQQQALPDLLSGTSDKFNLLAKLEHAQSRILSLESQLEDSARRWGREKQDLATRLQEQEKGFRHPSNSIIIEQPSALTHSMDLKQPSELEPLLPSSDSKLNKPLSPQKETANSQQT,mutated_sequence,1.0,755.0,UPI0000E671FE.a2m,UPI0000E671FE.npy,gnomAD +UPI0000DBEF37,UPI0000DBEF37.csv,MGKKSRAVPGRRPILQLSPPGPRGSTPGRDPEPEPDTEPDSTAAVPSQPAPSAATTTTTAVTAAAASDDSPSEDEQEAVQEVPRVVQNPPKPVMTTRPTAVKATGGLCLLGAYADSDDDDNDVSEKLAQSKETNGNQSTDIDSTLANFLAEIDAITAPQPAAPVGASAPPPTPPRPEPKEAATSTLSSSTSNGTDSTQTSGWQYDTQCSLAGVGIEMGDWQEVWDENTGCYYYWNTQTNEVTWELPQYLATQVQGLQHYQPSSVPGAETSFVVNTDIYSKEKTISVSSSKSGPVIAKREVKKEVNEGIQALSNSEEEKKGVAASLLAPLLPEGIKEEEERWRRKVICKEEPVSEVKETSTTVEEATTIVKPQEIMLDNIEDPSQEDLCSVVQSGESEEEEEQDTLELELVLERKKAELRALEEGDGSVSGSSPRSDISQPASQDGMRRLMSKRGKWKMFVRATSPESTSRSSSKTGRDTPENGETAIGAENSEKIDENSDKEMEVEESPEKIKVQTTPKVEEEQDLKFQIGELANTLTSKFEFLGINRQSISNFHVLLLQTETRIADWREGALNGNYLKRKLQDAAEQLKQYEINATPKGWSCHWDRDHRRYFYVNEQSGESQWEFPDGEEEEEESQAQENRDETLAKQTLKDKTGTDSNSTESSETSTGSLCKESFSGQVSSSSLMPLTPFWTLLQSNVPVLQPPLPLEMPPPPPPPPESPPPPPPPPPPAEDGEIQEVEMEDEGSEEPPAPGTEEDTPLKPSAQTTVVTSQSSVDSTISSSSSTKGIKRKATEISTAVVQRSATIGSSPVLYSQSAIATGHQAAGIGNQATGIGHQTIPVSLPAAGMGHQARGMSLQSNYLGLAAAPAIMSYAECSVPIGVTAPSLQPVQARGAVPTATIIEPPPPPPPPPPPPPPAPKMPPPEKTKKGRKDKAKKSKTKMPSLVKKWQSIQRELDEEDNSSSSEEDRESTAQKRIEEWKQQQLVSGMAERNANFEALPEDWRARLKRRKMAPNT,mutated_sequence,1.0,1017.0,UPI0000DBEF37.a2m,UPI0000DBEF37.npy,gnomAD +UPI00001A832D,UPI00001A832D.csv,MGEKSRRKGPAPRHADGKLGRTCDHPYAPWSFTPSSRAPTAWVRPPCPVWASRLQEHSPEPRRARAPPTRRAQAALYAPALRLRDHLDRFSILMTSCTSWLQAPQAPGLCRDEQSSRISVPQLSGAPILLPDLEGTKLSNFQESSPLPHKHERKDKRSTPEEEGRSAPEKIIQSLKLCPGGHRPASLSSGCPAGCRLSFNLPPSMLLSVQKCCMPSSLKTC,mutated_sequence,1.0,221.0,UPI00001A832D.a2m,UPI00001A832D.npy,gnomAD +UPI000004C619,UPI000004C619.csv,MRLLPEWFLLLFGPWLLRKAVSAQIPESGRPQYLGLRPAAAGAGAPGQQLPEPRSSDGLGVGRAWSWAWPTNHTGALARAGAAGALPAQRTKRKPSIKAARAKKIFGWGDFYFRVHTLKFSLLVTGKIVDHVNGTFSVYFRHNSSSLGNLSVSIVPPSKRVEFGGVWLPGPVPHPLQSTLALEGVLPGLGPPLGMAAAAAGPGLGGSLGGALAGPLGGALGVPGAKESRAFNCHVEYEKTNRARKHRPCLYDPSQVCFTEHTQSQAAWLCAKPFKVICIFVSFLSFDYKLVQKVCPDYNFQSEHPYFG,mutated_sequence,1.0,308.0,UPI000004C619.a2m,UPI000004C619.npy,gnomAD +UPI0000046406,UPI0000046406.csv,MASPSLPGSDCSQIIDHSHVPEFEVATWIKITLILVYLIIFVMGLLGNSATIRVTQVLQKKGYLQKEVTDHMVSLACSDILVFLIGMPMEFYSIIWNPLTTSSYTLSCKLHTFLFEACSYATLLHVLTLSFERYIAICHPFRYKAVSGPCQVKLLIGFVWVTSALVALPLLFAMGTEYPLVNVPSHRGLTCNRSSTRHHEQPETSNMSICTNLSSRWTVFQSSIFGAFVVYLVVLLSVAFMCWNMMQVLMKSQKGSLAGGTRPPQLRKSESEESRTARRQTIIFLRLIVVTLAVCWMPNQIRRIMAAAKPKHDWTRSYFRAYMILLPFSETFFYLSSVINPLLYTVSSQQFRRVFVQVLCCRLSLQHANHEKRLRVHAHSTTDSARFVQRPLLFASRRQSSARRTEKIFLSTFQSEAEPQSKSQSLSLESLEPNSGAKPANSAAENGFQEHEV,mutated_sequence,1.0,453.0,UPI0000046406.a2m,UPI0000046406.npy,gnomAD +UPI00003D4D6C,UPI00003D4D6C.csv,MAASTMSVCSSACSDSWQVDACPESCCEPHCCALSCCAPAPCLTLVCTPVSRVSSPCCQAACEPSPCQSGCTSSCTPSCCQQSSCQPACCTSSPCQQACCVPVCCKPVCCLPTCSKDSSSCCQQSSCQPTCCASSSSQQSCCVPVCCKPVCYVPTCSEDSSSCCQQSSCHPACCTSSPCQQACCVPVRCKPVCCKPICCVPVCSGASTSCCQQSSCQPACCTTSCCRPSSSVSLLCRPVCRPACCMPVSSCCAPASSCQASCCRPASCVSLLCRPACSRPAC,mutated_sequence,1.0,282.0,UPI00003D4D6C.a2m,UPI00003D4D6C.npy,gnomAD +UPI00002020EB,UPI00002020EB.csv,MKFQGPLACLLLALCLGSGEAGPLQSGEESTGTNIGEALGHGLGDALSEGVGKAIGKEAGGAAGSKVSEALGQGTREAVGTGVRQVPGFGVADALGNRVGEAAHALGNTGHEIGRQAEDVIRHGADAVRGSWQGVPGHNGAWETSGGHGIFGSQGGLGGQGQGNPGGLGTPWVHGYPGNSAGSFGMNPQGAPWGQGGNGGPPNFGTNTQGAVAQPGYGSVRASNQNEGCTNPPPSGSGGGSSNSGGGSGSQSGSSGSGSNGDNNNGSSSGGSSSGSSSGGSSGGSSGGSSGNSGGSRGDSGSESSWGSSTGSSSGNHGGSGGGNGHKPGCEKPGNEARGSGESGIQNSETSPGMFNFDTFWKNFKSKLGFINWDAINKNQVPPPSTRALLYFSRLWEDFKQNTPFLNWKAIIEGADASSLQKRAGRDDQNYNYNQHAYPTAYGGKYSVKTPAKGGVSPSSSASRVQPGLLQWVKFW,mutated_sequence,1.0,476.0,UPI00002020EB.a2m,UPI00002020EB.npy,gnomAD +UPI0000039B1A,UPI0000039B1A.csv,MSAYGMPMYKSGDLVFAKLKGYAHWPARIEHMTQPNRYQVFFFGTHETAFLSPKRLFPYKECKEKFGKPNKRRGFSAGLWEIENNPTVQASDCPLASEKGSGDGPWPEPEAAEGDEDKPTHAGGGGDELGKPDDDKPTEEEKGPLKRSAGDPPEDAPKRPKEAAPDQEEEAEAERAAEAERAAAAAAATAVDEESPFLVAVENGSAPSEPGLVCEPPQPEEEELREEEVADEEASQEWHAEAPGGGDRDSL,mutated_sequence,1.0,251.0,UPI0000039B1A.a2m,UPI0000039B1A.npy,gnomAD +UPI0000039E60,UPI0000039E60.csv,MLTEVMEVWHGLVIAVVSLFLQACFLTAINYLLSRHMAHKSEQILKAASLQVPRPSPGHHHPPAVKEMKETQTERDIPMSDSLYRHDSDTPSDSLDSSCSSPPACQATEDVDYTQVVFSDPGELKNDSPLDYENIKEITDYVNVNPERHKPSFWYFVNPALSEPAEYDQVAM,mutated_sequence,1.0,172.0,UPI0000039E60.a2m,UPI0000039E60.npy,gnomAD +UPI0000070C71,UPI0000070C71.csv,MTENVVCTGAVNAVKEVWEKRIKKLNEDLKREKEFQHKLVRIWEERVSLTKLREKVTREDGRVILKIEKEEWKTLPSSLLKLNQLQEWQLHRTGLLKIPEFIGRFQNLIVLDLSRNTISEIPPGIGLLTRLQELILSYNKIKTVPKELSNCASLEKLELAVNRDICDLPQELSNLLKLTHLDLSMNDFTTIPLAVLNMPALEWLDMGSNKLEQLPDTIERMQNLHTLWLQRNEITCLPQTISNMKNLGTLVLSNNKLQDIPVCMEEMANLRFVNFRDNPLKLKVSLPPSEGTDEEEERELFGLQFMHTYIQESRRRADHQVNGSTTLPISINTDG,mutated_sequence,1.0,335.0,UPI0000070C71.a2m,UPI0000070C71.npy,gnomAD +UPI0001612CC0,UPI0001612CC0.csv,MEEFLQRAKSKLNRSKRLEKVHVVIGPKSCDLDSLISTFTYAYFLDKVSPPGVLCLPVLNIPRTEFNYFTETRFILEELNISESFHIFRDEINLHQLNDEGKLSITLVGSSVLASEDKTLESAVVKVINPVEQSDANVEFRESSSSLVLKEILQEAPELITEQLAHRLRGSILFKWMTMESEKISEKQEEILSILEEKFPNLPPREDIINVLQETQFSAQGLSIEQTMLKDLKELSDGEIKVAISTVSMNLENCLFHSNITSDLKAFTDKFGFDVLILFSSYLSEEQQPRRQIAVYSENMELCSQICCELEECQNPCLELEPFDCGCDEILVYQQEDPSVTCDQVVLVVKEVINRRCPEMVSNSRTSSTEAVAGSAPLSQGSSGIMELYGSDIEPQPSSVNFIENPPDLNDSNQAQVDANVDLVSPDSGLATIRSSRSSKESSVFLSDDSPVGEGAGPHHTLLPGLDSYSPIPEGAVAEEHAWSGEHGEHFDLFNFDPAPMASGQSQQSSHSADYSPADDFFPNSDLSEGQLPAGPEGLDGMGTNMSNYSSSSLLSGAGKDSLVEHDEEFVQRQDSPRDNSERNLSLTDFVGDESPSPERLKNTGKRIPPTPMNSLVESSPSTEEPASLYTEDMTQKATDTGHMGPPQTHARCSSWWGGLEIDSKNIADAWSSSEQESVFQSPESWKEHKPSSIDRRASDSVFQPKSLEFTKSGPWESEFGQPELGSNDIQDKNEESLPFQNLPMEKSPLPNTSPQGTNHLIEDFASLWHSGRSPTAMPEPWGNPTDDGEPAAVAPFPAWSAFGKEDHDEALKNTWNLHPTSSKTPSVRDPNEWAMAKSGFAFSSSELLDNSPSEINNEAAPEIWGKKNNDSRDHIFAPGNPSSDLDHTWTNSKPPKEDQNGLVDPKTRGKVYEKVDSWNLFEENMKKGGSDVLVPWEDSFLSYKCSDYSASNLGEDSVPSPLDTNYSTSDSYTSPTFAGDEKETEHKPFAKEEGFESKDGNSTAEETDIPPQSLQQSSRNRISSGPGNLDMWASPHTDNSSEINTTHNLDENELKTEHTDGKNISMEDDVGESSQSSYDDPSMMQLYNETNRQLTLLHSSTNSRQTAPDSLDLWNRVILEDTQSTATISDMDNDLDWDDCSGGAAIPSDGQTEGYMAEGSEPETRFTVRQLEPWGLEYQEANQVDWELPASDEHTKDSAPSEHHTLNEKSGQLIANSIWDSVMRDKDMSSFMLPGSSHITDSEQRELPPEIPSHSANVKDTHSPDAPAASGTSESEALISHLDKQDTERETLQSDAASLATRLENPGYFPHPDPWKGHGDGQSESEKEAQGATDRGHLDEEEVIASGVENASGISEKGQSDQELSSLVASEHQEICIKSGKISSLAVTFSPQTEEPEEVLEYEEGSYNLDSRDVQTGMSADNLQPKDTHEKHLMSQRNSGETTETSDGMNFTKYVSVPEKDLEKTEECNFLEPENVGGGPPHRVPRSLDFGDVPIDSDVHVSSTCSEITKNLDVKGSENSLPGAGSSGNFDRDTISSEYTHSSASSPELNDSSVALSSWGQQPSSGYQEENQGNWSEQNHQESELITTDGQVEIVTKVKDLEKNRINEFEKSFDRKTPTFLEIWNDSVDGDSFSSLSSPETGKYSEHSGTHQESNLIASYQEKNEHDISATVQPEDARVISTSSGSDDDSVGGEESIEEEIQVANCHVAEDESRAWDSLNESNKFLVTADPKSENIYDYLDSSEPAENENKSNPFCDNQQSSPDPWTFSPLTETEMQITAVEKEKRSSPETGTTGDVAWQISPKASFPKNEDNSQLEMLGFSADSTEWWKASPQEGRLIESPFERELSDSSGVLEINSSVHQNASPWGVPVQGDIEPVETHYTNPFSDNHQSPFLEGNGKNSHEQLWNIQPRQPDPDADKFSQLVKLDQIKEKDSREQTFVSAAGDELTPETPTQEQCQDTMLPVCDHPDTAFTHAEENSCVTSNVSTNEGQETNQWEQEKSYLGEMTNSSIATENFPAVSSPTQLIMKPGSEWDGSTPSEDSRGTFVPDILHGNFQEGGQLASAAPDLWIDAKKPFSLKADGENPDILTHCEHDSNSQASDSPDICHDSEAKQETEKHLSACMGPEVESSELCLTEPEIDEEPIYEPGREFVPSNAELDSENATVLPPIGYQADIKGSSQPASHKGSPEPSEINGDNSTGLQVSEKGASPDMAPILEPVDRRIPRIENVATSIFVTHQEPTPEGDGSWISDSFSPESQPGARALFDGDPHLSTENPALVPDALLASDTCLDISEAAFDHSFSDASGLNTSTGTIDDMSKLTLSEGHPETPVDGDLGKQDICSSEASWGDFEYDVMGQNIDEDLLREPEHFLYGGDPPLEEDSLKQSLAPYTPPFDLSYLTEPAQSAETIEEAGSPEDESLGCRAAEIVLSALPDRRSEGNQAETKNRLPGSQLAVLHIREDPESVYLPVGAGSNILSPSNVDWEVETDNSDLPAGGDIGPPNGASKEISELEEEKTIPTKEPEQIKSEYKEERCTEKNEDRHALHMDYILVNREENSHSKPETCEERESIAELELYVGSKETGLQGTQLASFPDTCQPASLNERKGLSAEKMSSKSDTRSSFESPAQDQSWMFLGHSEVGDPSLDARDSGPGWSGKTVEPFSELGLGEGPQLQILEEMKPLESLALEEASGPVSQSQKSKSRGRAGPDAVTLQAVTHDNEWEMLSPQPVQKNMIPDTEMEEETEFLELGTRISRPNGLLSEDVGMDIPFEEGVLSPSAADMRPEPPNSLDLNDTHPRRIKLTAPNINLSLDQSEGSILSDDNLDSPDEIDINVDELDTPDEADSFEYTGHDPTANKDSGQESESIPEYTAEEEREDNRLWRTVVIGEQEQRIDMKVIEPYRRVISHGGYYGDGLNAIIVFAACFLPDSSRADYHYVMENLFLYVISTLELMVAEDYMIVYLNGATPRRRMPGLGWMKKCYQMIDRRLRKNLKSFIIVHPSWFIRTILAVTRPFISSKFSSKIKYVNSLSELSGLIPMDCIHIPESIIKLDEELREASEAAKTSCLYNDPEMSSMEKDIDLKLKEKP,mutated_sequence,1.0,3088.0,UPI0001612CC0.a2m,UPI0001612CC0.npy,gnomAD +UPI00001AE77D,UPI00001AE77D.csv,MSSNSFPYNEQSGGGEATELGQEATSTISPSGAFGLFSSDLKKNEDLKQMLESNKDSAKLDAMKRIVGMIAKGKNASELFPAVVKNVASKNIEIKKLVYVYLVRYAEEQQDLALLSISTFQRALKDPNQLIRASALRVLSSIRVPIIVPIMMLAIKEASADLSPYVRKNAAHAIQKLYSLDPEQKEMLIEVIEKLLKDKSTLVAGSVVMAFEEVCPDRIDLIHKNYRKLCNLLVDVEEWGQVVIIHMLTRYARTQFVSPWKEGDELEDNGKNFYESDDDQKEKTDKKKKPYTMDPDHRLLIRNTKPLLQSRNAAVVMAVAQLYWHISPKSEAGIISKSLVRLLRSNREVQYIVLQNIATMSIQRKGMFEPYLKSFYVRSTDPTMIKTLKLEILTNLANEANISTLLREFQTYVKSQDKQFAAATIQTIGRCATNILEVTDTCLNGLVCLLSNRDEIVVAESVVVIKKLLQMQPAQHGEIIKHMAKLLDSITVPVARASILWLIGENCERVPKIAPDVLRKMAKSFTSEDDLVKLQILNLGAKLYLTNSKQTKLLTQYILNLGKYDQNYDIRDRTRFIRQLIVPNVKSGALSKYAKKIFLAQKPAPLLESPFKDRDHFQLGTLSHTLNIKATGYLELSNWPEVAPDPSVRNVEVIELAKEWTPAGKAKQENSAKKFYSESEEEEDSSDSSSDSESESGSESGEQGESGEEGDSNEDSSEDSSSEQDSESGRESGLENKRTAKRNSKAKGKSDSEDGEKENEKSKTSDSSNDESSSIEDSSSDSESESEPESESESRRVTKEKEKKTKQDRTPLTKDVSLLDLDDFNPVSTPVALPTPALSPSLMADLEGLHLSTSSSVISVSTPAFVPTKTHVLLHRMSGKGLAAHYFFPRQPCIFGDKMVSIQITLNNTTDRKIENIHIGEKKLPIGMKMHVFNPIDSLEPEGSITVSMGIDFCDSTQTASFQLCTKDDCFNVNIQPPVGELLLPVAMSEKDFKKEQGVLTGMNETSAVIIAAPQNFTPSVIFQKVVNVANVGAVPSGQDNIHRFAAKTVHSGSLMLVTVELKEGSTAQLIINTEKTVIGSVLLRELKPVLSQG,mutated_sequence,1.0,1094.0,UPI00001AE77D.a2m,UPI00001AE77D.npy,gnomAD +UPI0001AE68B4,UPI0001AE68B4.csv,MEGTHCTLQLHKPITELCYISFCLPKGEVRGFSYKGTVTLDRSNKGFHNCYQVREESDIISLSQEPDEHPGDIFFKQTPTKDILTELYKLTTERERLLTNLLSSDHILGITMGNQEGKLQELSVSLAPEDDCFQSAGDWQGELPVGPLNKRSTHGNKKPRRSSGRRESFGALPQKRTKRKGRGGRESAPLMGKDKICSSHSLPLSRTRPNLWVLEEKGNLLPNGALACSLQRRESCPPDIPKTPDTDLGFGSFETAFKDTGLGREVLPPDCSSTEAGGDGIRRPPSGLEHQQTGLSESHQDPEKHPEAEKDEMEKPAKRTCKQKPVSKVVAKVQDLSSQVQRVVKTHSKGKETIAIRPAAHAEFVPKADLLTLPGAEAGAHGSRRQGKERQGDRSSQSPAGETASISSVSASAEGAVNKVPLKVIESEKLDEAPEGKRLGFPVHTSVPHTRPETRNKRRAGLPLGGHKSLFLDLPHKVGPDSSQPRGDKKKPSPPAPAALGKVFNNSASQSSTHKQTSPVPSPLSPRLPSPQQHHRILRLPALPGEREAALNDSPCRKSRVFSGCVSADTLEPPSSAKVTETKGASPAFLRAGQPRLVPGETLEKSLGPGKTTAEPQHQSPPGISSEGFPWDGFNEQTPKDLPNRDGGAWVLGYRAGPACPFLLHEEREKSNRSELYLDLHPDHSLTEQDDRTPGRLQAVWPPPKTKDTEEKVGLKYTEAEYQAAILHLKREHKEEIENLQAQFELRAFHIRGEHAMITARLEETIENLKHELEHRWRGGCEERKDVCISTDDDCPPKTFRNVCVQTDRETFLKPCESESKTTRSNQLVPKKLNISSLSQLSPPNDHKDIHAALQPMEGMASNQQKALPPPPASIPPPPPLPSGLGSLSPAPPMPPVSAGPPLPPPPPPPPPLPPPSSAGPPPPPPPPPLPNSPAPPNPGGPPPAPPPPGLAPPPPPGLFFGLGSSSSQCPRKPAIEPSCPMKPLYWTRIQISDRSQNATPTLWDSLEEPDIRDPSEFEYLFSKDTTQQKKKPLSETYEKKNKVKKIIKLLDGKRSQTVGILISSLHLEMKDIQQAIFNVDDSVVDLETLAALYENRAQEDELVKIRKYYETSKEEELKLLDKPEQFLHELAQIPNFAERAQCIIFRSVFSEGITSLHRKVEIITRASKDLLHVKSVKDILALILAFGNYMNGGNRTRGQADGYSLEILPKLKDVKSRDNGINLVDYVVKYYLRYYDQEAGTEKSVFPLPEPQDFFLASQVKFEDLIKDLRKLKRQLEASEKQMVVVCKESPKEYLQPFKDKLEEFFQKAKKEHKMEESHLENAQKSFETTVRYFGMKPKSGEKEITPSYVFMVWYEFCSDFKTIWKRESKNISKERLKMAQESVSKLTSEKKVETKKINPTASLKERLRQKEASVTTN,mutated_sequence,1.0,1419.0,UPI0001AE68B4.a2m,UPI0001AE68B4.npy,gnomAD +UPI000204A78B,UPI000204A78B.csv,MERPEAGINSNECENVSRKKKMSEEFEANTMDSLVDMPFATVDIQDDCGITDEPQINLKRSQENEWVKSDQVKKRKKKRKDYQPNYFLSIPITNKEIIKGIKILQNAIIQQDERLAKAMVSDGSFHITLLVMQLLNEDEVNIGIDALLELKPFIEELLQGKHLTLPFQGIGTFGNQVGFVKLAEGDHVNSLLEIAETANRTFQEKGILVGESRSFKPHLTFMKLSKSPWLRKNGVKKIDPDLYEKFISHRFGEEILYRIDLCSMLKKKQSNGYYHCESSIVIGKKPIGIRDLINEALHRETMGLKSKVKQIKELLLKPETQARIRRELFEGRLINNSNSANDVDFSTTLT,mutated_sequence,1.0,350.0,UPI000204A78B.a2m,UPI000204A78B.npy,gnomAD +UPI0000167E20,UPI0000167E20.csv,MNTNDAKEYLARREIPQLFESLLNGLMCSKPEDPVEYLESCLQKVKELGGCDKVKWDTFVSQEKKTLPPLNGGQSRRSFLRNVMPENSNFPYRRYDRLPPIHQFSIESDTDLSETAELIEEYEVFDPTRPRPKIILVIGGPGSGKGTQSLKIAERYGFQYISVGELLRKKIHSTSSNRKWSLIAKIITTGELAPQETTITEIKQKLMQIPDEEGIVIDGFPRDVAQALSFEDQICTPDLVVFLACANQRLKERLLKRAEQQGRPDDNVKATQRRLMNFKQNAAPLVKYFQEKGLIMTFDADRDEDEVFYDISMAVDNKLFPNKEAAAGSSDLDPSMILDTGEIIDTGSDYEDQGDDQLNVFGEDTMGGFMEDLRKCKIIFIIGGPGSGKGTQCEKLVEKYGFTHLSTGELLREELASESERSKLIRDIMERGDLVPSGIVLELLKEAMVASLGDTRGFLIDGYPREVKQGEEFGRRIGDPQLVICMDCSADTMTNRLLQRSRSSLPVDDTTKTIAKRLEAYYRASIPVIAYYETKTQLHKINAEGTPEDVFLQLCTAIDSIF,mutated_sequence,1.0,562.0,UPI0000167E20.a2m,UPI0000167E20.npy,gnomAD +UPI000003B115,UPI000003B115.csv,MGPVRLGILLFLFLAVHEAWAGMLKEEDDDTERLPSKCEVCKLLSTELQAELSRTGRSREVLELGQVLDTGKRKRHVPYSVSETRLEEALENLCERILDYSVHAERKGSLRYAKGQSQTMATLKGLVQKGVKVDLGIPLELWDEPSVEVTYLKKQCETMLEEFEDIVGDWYFHHQEQPLQNFLCEGHVLPAAETACLQETWTGKEITDGEEKTEGEEEQEEEEEEEEEEGGDKMTKTGSHPKLDREDL,mutated_sequence,1.0,248.0,UPI000003B115.a2m,UPI000003B115.npy,gnomAD +UPI000006EEEC,UPI000006EEEC.csv,MHPQVVILSLILHLADSVAGSVKVGGEAGPSVTLPCHYSGAVTSMCWNRGSCSLFTCQNGIVWTNGTHVTYRKDTRYKLLGDLSRRDVSLTIENTAVSDSGVYCCRVEHRGWFNDMKITVSLEIVPPKVTTTPIVTTVPTVTTVRTSTTVPTTTTVPMTTVPTTTVPTTMSIPTTTTVLTTMTVSTTTSVPTTTSIPTTTSVPVTTTVSTFVPPMPLPRQNHEPVATSPSSPQPAETHPTTLQGAIRREPTSSPLYSYTTDGNDTVTESSDGLWNNNQTQLFLEHSLLTANTTKGIYAGVCISVLVLLALLGVIIAKKYFFKKEVQQLSVSFSSLQIKALQNAVEKEVQAEDNIYIENSLYATD,mutated_sequence,1.0,364.0,UPI000006EEEC.a2m,UPI000006EEEC.npy,gnomAD +UPI00001BDC7C,UPI00001BDC7C.csv,MKKFSRMPKSEGGSGGGAAGGGAGGAGAGAGCGSGGSSVGVRVFAVGRHQVTLEESLAEGGFSTVFLVRTHGGIRCALKRMYVNNMPDLNVCKREITIMKELSGHKNIVGYLDCAVNSISDNVWEVLILMEYCRAGQVVNQMNKKLQTGFTEPEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLNDGGNYVLCDFGSATNKFLNPQKDGVNVVEEEIKKYTTLSYRAPEMINLYGGKPITTKADIWALGCLLYKLCFFTLPFGESQVAICDGNFTIPDNSRYSRNIHCLIRFMLEPDPEHRPDIFQVSYFAFKFAKKDCPVSNINNSSIPSALPEPMTASEAAARKSQIKARITDTIGPTETSIAPRQRPKANSATTATPSVLTIQSSATPVKVLAPGEFGNHRPKGALRPGNGPEILLGQGPPQQPPQQHRVLQQLQQGDWRLQQLHLQHRHPHQQQQQQQQQQQQQQQQQQQQQQQQQQQHHHHHHHHLLQDAYMQQYQHATQQQQMLQQQFLMHSVYQPQPSASQYPTMMPQYQQAFFQQQMLAQHQPSQQQASPEYLTSPQEFSPALVSYTSSLPAQVGTIMDSSYSANRSVADKEAIANFTNQKNISNPPDMSGWNPFGEDNFSKLTEEELLDREFDLLRSNRLEERASSDKNVDSLSAPHNHPPEDPFGSVPFISHSGSPEKKAEHSSINQENGTANPIKNGKTSPASKDQRTGKKTSVQGQVQKGNDESESDFESDPPSPKSSEEEEQDDEEVLQGEQGDFNDDDTEPENLGHRPLLMDSEDEEEEEKHSSDSDYEQAKAKYSDMSSVYRDRSGSGPTQDLNTILLTSAQLSSDVAVETPKQEFDVFGAVPFFAVRAQQPQQEKNEKNLPQHRFPAAGLEQEEFDVFTKAPFSKKVNVQECHAVGPEAHTIPGYPKSVDVFGSTPFQPFLTSTSKSESNEDLFGLVPFDEITGSQQQKVKQRSLQKLSSRQRRTKQDMSKSNGKRHHGTPTSTKKTLKPTYRTPERARRHKKVGRRDSQSSNEFLTISDSKENISVALTDGKDRGNVLQPEESLLDPFGAKPFHSPDLSWHPPHQGLSDIRADHNTVLPGRPRQNSLHGSFHSADVLKMDDFGAVPFTELVVQSITPHQSQQSQPVELDPFGAAPFPSKQ,mutated_sequence,1.0,1161.0,UPI00001BDC7C.a2m,UPI00001BDC7C.npy,gnomAD +UPI000045779F,UPI000045779F.csv,MNGGAERAMRSLPSLGGLALLCCAAAAAAAAVASAASAGNVTGGGGAAGQVDASPGPGLRGEPSHPFPRATAPTAQAPRTGPPRATVHRPLAATSPAQSPETTPLWATAGPSSTTFQAPLGPSPTTPPAAERTSTTSQAPTRPAPTTLSTTTGPAPTTPVATTVPAPTTPRTPTPDLPSSSNSSVLPTPPATEAPSSPPPEYVCNCSVVGSLNVNRCNQTTGQCECRPGYQGLHCETCKEGFYLNYTSGLCQPCDCSPHGALSIPCNSSGKCQCKVGVIGSICDRCQDGYYGFSKNGCLPCQCNNRSASCDALTGACLNCQENSKGNHCEECKEGFYQSPDATKECLRCPCSAVTSTGSCSIKSSELEPECDQCKDGYIGPNCNKCENGYYNFDSICRKCQCHGHVDPVKTPKICKPESGECINCLHNTTGFWCENCLEGYVHDLEGNCIKKEVILPTPEGSTILVSNASLTTSVPTPVINSTFTPTTLQTIFSVSTSENSTSALADVSWTQFNIIILTVIIIVVVLLMGFVGAVYMYREYQNRKLNAPFWTIELKEDNISFSSYHDSIPNADVSGLLEDDGNEVAPNGQLTLTTPIHNYKA,mutated_sequence,1.0,602.0,UPI000045779F.a2m,UPI000045779F.npy,gnomAD +UPI0002840E7F,UPI0002840E7F.csv,XIGLDSCKELLKPDRKSGALRCVRAF,mutated_sequence,1.0,26.0,UPI0002840E7F.a2m,UPI0002840E7F.npy,gnomAD +UPI000020ADBC,UPI000020ADBC.csv,MNLGDGLKLETELLDGKTKLILSPYEHKSKISVKMGNKAKIAKCPLRTKTGHILKSTQDTCIGSEKLLQKKPVGSETSQAKGEKNGMTFSSTKDLCKQCIDKDCLHIQKEISPATPNMQKTRNTVNTSLVGKQKPHKKHITAENMKSSLVCLTQDQLQQILMTVNQGNRSLSLTENGKEAKSQYSLYLNSISNQPKDENIMGLFKKTEMVSSVPAENKSVLNEHQETSKQCEQKIAIENEWKPADIFSTLGERECDRSSLEAKKAQWRKELDEQVALKKKEKEVSEKWNDPWKKSESDKIIWEKHQILDQSRETVLLEHPFSAVKQELQRKWIEELNKQIEDDRQRKIEEKIIYSKGEEHDRWAMHFDSLKSYPGSQSQLFSQSTHKQPEYFCVSPDTQELADVSSVCTPTTGSQVEPSEEEHIAKPIKDVVMANSKKTNFLRSMTALLDPAQIEERDRRRQKQLEHQKAITAQVEEKRRKKQLEEEQRKKEEQEEELRLAQEREEMQKQYEEDILKQKQKEEIMTLKTNELFQTMQRAQELAQRLKQEQRIRELAQKGHDTSRLIKNLGVDTIQMEYNASNISNSRHDSDEISGKMNTYMNSTTSKKDTGVQTDDLNIGIFTNAESHCGSLMERDITNCSSPEISAELIGQFSTKKNKQELTQDKGASLEKENNRCNDQCNQFTRIEKQTKHMKKYPKRPDWNINKPPKRYIPASEKYPKQLQKQREEKKVRRQMELLHLVEKNNPGHLSQNRGISPEIFHSSHQETESKLRWHLVKKEEEPLNIHSFSKERSPSSPVPVVKNRTQQTQNTLHLPLKNSSYERENLISGSNQTELSSGISESSHFIPYVRTNEIYYLDPDAPLSGPSTQDPQYQNSQDCGQKRQLFDSDCVRDPLLNPNMVKNRDRQQAILKGLSELRQGLLQKQKELESSLLPLAENQEESFGSSF,mutated_sequence,1.0,948.0,UPI000020ADBC.a2m,UPI000020ADBC.npy,gnomAD +UPI0000457614,UPI0000457614.csv,MEVLESGEQGVLQWDRKLSELSEPGDGEALMYHTHFSELLDEFSQNVLGQLLNDPFLSEKSVSMEVEPSPTSPAPLIQAEHSYSLCEEPRAQSPFTHITTSDSFNDDEVESEKWYLSTDFPSTSIKTEPVTDEPPPGLVPSVTLTITAISTPLEKEEPPLEMNTGVDSSCQTIIPKIKLEPHEVDQFLNFSPKEAPVDHLHLPPTPPSSHGSDSEGSLSPNPRLHPFSLPQTHSPSRAAPRAPSALSSSPLLTAPHKLQGSGPLVLTEEEKRTLIAEGYPIPTKLPLSKSEEKALKKIRRKIKNKISAQESRRKKKEYMDSLEKKVESCSTENLELRKKVEVLENTNRTLLQQLQKLQTLVMGKVSRTCKLAGTQTGTCLMVVVLCFAVAFGSFFQGYGPYPSATKMALPSQHSLQEPYTASVVRSRNLLIYEEHSPPEESSSPGSAGELGGWDRGSSLLRVSGLESRPDVDLPHFIISNETSLEKSVLLELQQHLVSAKLEGNETLKVVELDRRVNTTF,mutated_sequence,1.0,520.0,UPI0000457614.a2m,UPI0000457614.npy,gnomAD +UPI000013CF5D,UPI000013CF5D.csv,MLPAPAAPRWPPLLLLLLLLLPLARGAPARPAAGGQASELVVPTRLPGSAGELALHLSAFGKGFVLRLAPDDSFLAPEFKIERLGGSGRATGGERGLRGCFFSGTVNGEPESLAAVSLCRGLSGSFLLDGEEFTIQPQGAGGSLAQPHRLQRWGPAGARPLPRGPEWEVETGEGQRQERGDHQEDSEEESQEEEAEGASEPPPPLGATSRTKRFVSEARFVETLLVADASMAAFYGADLQNHILTLMSVAARIYKHPSIKNSINLMVVKVLIVEDEKWGPEVSDNGGLTLRNFCNWQRRFNQPSDRHPEHYDTAILLTRQNFCGQEGLCDTLGVADIGTICDPNKSCSVIEDEGLQAAHTLAHELGHVLSMPHDDSKPCTRLFGPMGKHHVMAPLFVHLNQTLPWSPCSAMYLTELLDGGHGDCLLDAPAAALPLPTGLPGRMALYQLDQQCRQIFGPDFRHCPNTSAQDVCAQLWCHTDGAEPLCHTKNGSLPWADGTPCGPGHLCSEGSCLPEEEVERPKPVADGGWAPWGPWGECSRTCGGGVQFSHRECKDPEPQNGGRYCLGRRAKYQSCHTEECPPDGKSFREQQCEKYNAYNYTDMDGNLLQWVPKYAGVSPRDRCKLFCRARGRSEFKVFEAKVIDGTLCGPETLAICVRGQCVKAGCDHVVDSPRKLDKCGVCGGKGNSCRKVSGSLTPTNYGYNDIVTIPAGATNIDVKQRSHPGVQNDGNYLALKTADGQYLLNGNLAISAIEQDILVKGTILKYSGSIATLERLQSFRPLPEPLTVQLLTVPGEVFPPKVKYTFFVPNDVDFSMQSSKERATTNIIQPLLHAQWVLGDWSECSSTCGAGWQRRTVECRDPSGQASATCNKALKPEDAKPCESQLCPL,mutated_sequence,1.0,889.0,UPI000013CF5D.a2m,UPI000013CF5D.npy,gnomAD +UPI0000070A3C,UPI0000070A3C.csv,MERGMHLGAAAAGEDDLFLHKSLSASTSKRLEAAFRSTPPGMDLSLAPPPRERPASSSSSPLGCFEPADPEGAGLLLPPPGGGGGGSAGSGGGGGGGVGVPGLLVGSAGVGGDPSLSSLPAGAALCLKYGESASRGSVAESSGGEQSPDDDSDGRCELVLRAGVADPRASPGAGGGGAKAAEGCSNAHLHGGASVPPGGLGGGGGGGSSSGSSGGGGGSGSGSGGSSSSSSSSSKKSKEQKALRLNINARERRRMHDLNDALDELRAVIPYAHSPSVRKLSKIATLLLAKNYILMQAQALEEMRRLVAYLNQGQAISAASLPSSAAAAAAAAALHPALGAYEQAAGYPFSAGLPPAASCPEKCALFNSVSSSLCKQCTEKP,mutated_sequence,1.0,381.0,UPI0000070A3C.a2m,UPI0000070A3C.npy,gnomAD +UPI0000198BEF,UPI0000198BEF.csv,MAVETRAELVGKRFLCVAVGDEARSERWESGRGWRSWRAGVIRAVSHRDSRNPDLAVYVEFDDLEWDKREWVKVYEDFSTFLVEYHLIWAKRNDPSQTQGSKSKQIQWPALTFKPLVERNIPSSVTAVEFLVDKQLDFLTEDSAFQPYQDDIDSLNPVLRDNPQLHEEVKVWVKEQKVQEIFMQGPYSLNGYRVRVYRQDSATQWFTGIITHHDLFTRTMIVMNDQVLEPQNVDPSMVQMTFLDDVVHSLLKGENIGITSRRRSRANQNVNAVHSHYTRAQANSPRPAMNSQAAVPKQNTHQQQQQRSIRPNKRKGSDSSIPDEEKMKEEKYDYISRGENPKGKNKHLMNKRRKPEEDEKKLNMKRLRTDNVSDFSESSDSENSNKRIIDNSSEQKPENELKNKNTSKINGEEGKPHNNEKAGEETLKNSQPPWDQIQEDKKHEEAEKRKSVDTQLQEDMIIHSSEQSTVSDHNSNDLLPQECNMDKTHTMELLPKEKFVSRPPTPKCVIDITNDTNLEKVAQENSSTFGLQTLQKMDPNVSDSKHSIANAKFLETAKKDSDQSWVSDVVKVDLTQSSVTNASSGNDHLNMEKEKYVSYISPLSAVSVMEDKLHKRSPPPETIKSKLNTSVDTHKIKSSPSPEVVKPKITHSPDSVKSKATYVNSQATGERRLANKIEHELSRCSFHPIPTRSSTLETTKSPLIIDKNEHFTVYRDPALIGSETGANHISPFLSQHPFPLHSSSHRTCLNPGTHHPALTPAPHLLAGSSSQTPLPTINTHPLTSGPHHAVHHPHLLPTVLPGVPTASLLGGHPRLESAHASSLSHLALAHQQQQQLLQHQSPHLLGQAHPSASYNQLGLYPIIWQYPNGTHAYSGLGLPSSKWVHPENAVNAEASLRRNSPSPWLHQPTPVTSADGIGLLSHIPVRPSSAEPHRPLKITAHSSPPLTKTLVDHHKEELERKAFMEPLRSVASTSAKNDLDLNRSQTGKDCHLHRHFVDPVLNQLQRPPQETGERLNKYKEEHRRILQESIDVAPFTTKIKGLEGERENYSRVASSSSSPKSHIIKQDMDVERSVSDLYKMKHSVPQSLPQSNYFTTLSNSVVNEPPRSYPSKEVSNIYGDKQSNALAAAAANPQTLTSFITSLSKPPPLIKHQPESEGLVGKIPEHLPHQIASHSVTTFRNDCRSPTHLTVSSTNTLRSMPALHRAPVFHPPIHHSLERKEGSYSSLSPPTLTPVMPVNAGGKVQESQKPPTLIPEPKDSQANFKSSSEQSLTEMWRPNNNLSKEKTEWHVEKSSGKLQAAMASVIVRPSSSTKTDSMPAMQLASKDRVSERSSAGAHKTDCLKLAEAGETGRIILPNVNSDSVHTKSEKNFQAVSQGSVPSSVMSAVNTMCNTKTDVITSAADTTSVSSWGGSEVISSLSNTILASTSSECVSSKSVSQPVAQKQECKVSTTAPVTLASSKTGSVVQPSSGFSGTTDFIHLKKHKAALAAAQYKSSNASETEPNAIKNQTLSASLPLDSTVICSTINKANSVGNGQASQTSQPNYHTKLKKAWLTRHSEEDKNTNKMENSGNSVSEIIKPCSVNLIASTSSDIQNSVDSKIIVDKYVKDDKVNRRKAKRTYESGSESGDSDESESKSEQRTKRQPKPTYKKKQNDLQKRKGEIEEDLKPNGVLSRSAKERSKLKLQSNSNTGIPRSVLKDWRKVKKLKQTGESFLQDDSCCEIGPNLQKCRECRLIRSKKGEEPAHSPVFCRFYYFRRLSFSKNGVVRIDGFSSPDQYDDEAMSLWTHENFEDDELDIETSKYILDIIGDKFCQLVTSEKTALSWVKKDAKIAWKRAVRGVREMCDACEATLFNIHWVCQKCGFVVCLDCYKAKERKSSRDKELYAWMKCVKGQPHDHKHLMPTQIIPGSVLTDLLDAMHTLREKYGIKSHCHCTNKQNLQVGNFPTMNGVSQVLQNVLNHSNKISLCMPESQQQNTPPKSEKNGGSSPESDVGTDNKLTPPESQSPLHWLADLAEQKAREEKKENKELTLENQIKEEREQDNSESPNGRTSPLVSQNNEQGSTLRDLLTTTAGKLRVGSTDAGIAFAPVYSMGAPSSKSGRTMPNILDDIIASVVENKIPPSKTSKINVKPELKEEPEESIISAVDENNKLYSDIPHSWICEKHILWLKDYKNSSNWKLFKECWKQGQPAVVSGVHKKMNISLWKAESISLDFGDHQADLLNCKDSIISNANVKEFWDGFEEVSKRQKNKSGETVVLKLKDWPSGEDFKTMMPARYEDLLKSLPLPEYCNPEGKFNLASHLPGFFVRPDLGPRLCSAYGVVAAKDHDIGTTNLHIEVSDVVNILVYVGIAKGNGILSKAGILKKFEEEDLDDILRKRLKDSSEIPGALWHIYAGKDVDKIREFLQKISKEQGLEVLPEHDPIRDQSWYVNKKLRQRLLEEYGVRTCTLIQFLGDAIVLPAGALHQVQNFHSCIQVTEDFVSPEHLVESFHLTQELRLLKEEINYDDKLQVKNILYHAVKEMVRALKIHEDEVEDMEEN,mutated_sequence,1.0,2540.0,UPI0000198BEF.a2m,UPI0000198BEF.npy,gnomAD +UPI0000136C3F,UPI0000136C3F.csv,MDQNNSLPPYAQGLASPQGAMTPGIPIFSPMMPYGTGLTPQPIQNTNSLSILEEQQRQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQAVAAAAVQQSTSQQATQGTSGQAPQLFHSQTLTTAPLPGTTPLYPSPMTPMTPITPATPASESSGIVPQLQNIVSTVNLGCKLDLKTIALRARNAEYNPKRFAAVIMRIREPRTTALIFSSGKMVCTGAKSEEQSRLAARKYARVVQKLGFPAKFLDFKIQNMVGSCDVKFPIRLEGLVLTHQQFSSYEPELFPGLIYRMIKPRIVLLIFVSGKVVLTGAKVRAEIYEAFENIYPILKGFRKTT,mutated_sequence,1.0,339.0,UPI0000136C3F.a2m,UPI0000136C3F.npy,gnomAD +UPI00000719CD,UPI00000719CD.csv,MRVVTIVILLCFCKAAELRKASPGSVRSRVNHGRAGGGRRGSNPVKRYAPGLPCDVYTYLHEKYLDCQERKLVYVLPGWPQDLLHMLLARNKIRTLKNNMFSKFKKLKSLDLQQNEISKIESEAFFGLNKLTTLLLQHNQIKVLTEEVFIYTPLLSYLRLYDNPWHCTCEIETLISMLQIPRNRNLGNYAKCESPQEQKNKKLRQIKSEQLCNEEEKEQLDPKPQVSGRPPVIKPEVDSTFCHNYVFPIQTLDCKRKELKKVPNNIPPDIVKLDLSYNKINQLRPKEFEDVHELKKLNLSSNGIEFIDPAAFLGLTHLEELDLSNNSLQNFDYGVLEDLYFLKLLWLRDNPWRCDYNIHYLYYWLKHHYNVHFNGLECKTPEEYKGWSVGKYIRSYYEECPKDKLPAYPESFDQDTEDDEWEKKHRDHTAKKQSVIITIVG,mutated_sequence,1.0,441.0,UPI00000719CD.a2m,UPI00000719CD.npy,gnomAD +UPI00000725EE,UPI00000725EE.csv,MDLPDESQWDETTCGLAVCQHPQCWATIRRIERGHPRILGSSCKTPLDAEDKLPVLTVVDILDSGFAAHHLPECTFTKAHSLLSQSSKFYSKFHGRPPKGLPDKSLINCTNRLPKFPVLNLNETQLPCPEDVRNMVVLWIPEETEIHVSQHGKKKRKNSAVKSKSFLGLSGNQSAGTRVGTPGMIVPPPTPVQLSEQFSSDFLPLWAQSEALPQDLLKELLPGGKQTMLCPEMKIKLAMMKKNLPLEKNRPDSVISSKMFLSIHRLTLERPALRYPERLKKLHNLKTEGYRKQQQRQQQQQQQQKKVKTPIKKQEAKKKAKSDPGIQSTSHKHPVTTVHDRLYGYRTLPGQNSDMKQQQQMEKGTTSKQDSTERPKMNYYDHADFHHSVKSPELYETEPTNKDISAPVDAVPEAQAARQKKISFNFSEIMASTGWNSELKLLRILQDTDDEDEEDQSSGAE,mutated_sequence,1.0,461.0,UPI00000725EE.a2m,UPI00000725EE.npy,gnomAD +UPI0000D617B1,UPI0000D617B1.csv,MGPPSACPHRECIPWQGLLLTASLLTFWNAPTTAWLFIASAPFEVAEGENVHLSVVYLPENLYSYGWYKGKTVEPNQLIAAYVIDTHVRTPGPAYSGRETISPSGDLHFQNVTLEDTGYYTLQVTYRNSQIEQASHHLRVYESVAQPSIQASSTTVTEKGSVVLTCHTNNTGTSFQWIFNNQRLQVTKRMKLSWFNHMLTIDPIRQEDAGEYQCEVSNPVSSNRSDPLKLTVKSDDNTLGILIGVLVGSLLVAALVCFLLLRKTGRASDQSDFREQQPPASTPGHGPSDSSIS,mutated_sequence,1.0,293.0,UPI0000D617B1.a2m,UPI0000D617B1.npy,gnomAD +UPI000003166A,UPI000003166A.csv,MRAHPGGGRCCPEQEEGESAAGGSGAGGDSAIEQGGQGSALAPSPVSGVRREGARGGGRGRGRWKQAGRGGGVCGRGRGRGRGRGRGRGRGRGRGRPPSGGSGLGGDGGGCGGGGSGGGGAPRREPVPFPSGSAGPGPRGPRATESGKRMDCPALPPGWKKEEVIRKSGLSAGKSDVYYFSPSGKKFRSKPQLARYLGNTVDLSSFDFRTGKMMPSKLQKNKQRLRNDPLNQNKGKPDLNTTLPIRQTASIFKQPVTKVTNHPSNKVKSDPQRMNEQPRQLFWEKRLQGLSASDVTEQIIKTMELPKGLQGVGPGSNDETLLSAVASALHTSSAPITGQVSAAVEKNPAVWLNTSQPLCKAFIVTDEDIRKQEERVQQVRKKLEEALMADILSRAADTEEMDIEMDSGDEA,mutated_sequence,1.0,411.0,UPI000003166A.a2m,UPI000003166A.npy,gnomAD +UPI000023271E,UPI000023271E.csv,MMQHASPAPALTMMATQNVPPPPYQDSPQMTATAQPPSKAQAVHISAPSAAASTPVPSAPIDPQAQLEADKRAVYRHPLFPLLTLLFEKCEQATQGSECITSASFDVDIENFVHQQEQEHKPFFSDDPELDNLMVKAIQVLRIHLLELEKVNELCKDFCNRYITCLKTKMHSDNLLRNDLGGPYSPNQPSINLHSQDLLQNSPNSMSGVSNNPQGIVVPASALQQGNIAMTTVNSQVVSGGALYQPVTMVTSQGQVVTQAIPQGAIQIQNTQVNLDLTSLLDNEDKKSKNKRGVLPKHATNIMRSWLFQHLMHPYPTEDEKRQIAAQTNLTLLQVNNWFINARRRILQPMLDASNPDPAPKAKKIKSQHRPTQRFWPNSIAAGVLQQQGGAPGTNPDGSINLDNLQSLSSDSATMAMQQAMMAAHDDSLDGTEEEDEDEMEEEEEEELEEEVDELQTTNVSDLGLEHSDSLE,mutated_sequence,1.0,472.0,UPI000023271E.a2m,UPI000023271E.npy,gnomAD +UPI0000129CA7,UPI0000129CA7.csv,MLTNLRIFAMSHQTIPSVYINNICCYKIRASLKRLKPHVPLGRNCSSLPGLIGNDIKSLHSIINPPIAKIRNIGIMAHIDAGKTTTTERILYYSGYTRSLGDVDDGDTVTDFMAQERERGITIQSAAVTFDWKGYRVNLIDTPGHVDFTLEVERCLRVLDGAVAVFDASAGVEAQTLTVWRQADKHNIPRICFLNKMDKTGASFKYAVESIREKLKAKPLLLQLPIGEAKTFKGVVDVVMKEKLLWNCNSNDGKDFERKPLLEMNDPELLKETTEARNALIEQVADLDDEFADLVLEEFSENFDLLPAEKLQTAIHRVTLAQTAVPVLCGSALKNKGIQPLLDAVTMYLPSPEERNYEFLQWYKDDLCALAFKVLHDKQRGPLVFMRIYSGTIKPQLAIHNINGNCTERISRLLLPFADQHVEIPSLTAGNIALTVGLKHTATGDTIVSSKSSALAAARRAEREGEKKHRQNNEAERLLLAGVEIPEPVFFCTIEPPSLSKQPDLEHALKCLQREDPSLKVRLDPDSGQTVLCGMGELHIEIIHDRIKREYGLETYLGPLQVAYRETILNSVRATDTLDRTLGDKRHLVTVEVEARPIETSSVMPVIEFEYAESINEGLLKVSQEAIENGIHSACLQGPLLGSPIQDVAITLHSLTIHPGTSTTMISACVSRCVQKALKKADKQVLEPLMNLEVTVARDYLSPVLADLAQRRGNIQEIQTRQDNKVVIGFVPLAEIMGYSTVLRTLTSGSATFALELSTYQAMNPQDQNTLLNRRSGLT,mutated_sequence,1.0,779.0,UPI0000129CA7.a2m,UPI0000129CA7.npy,gnomAD +UPI0000456F25,UPI0000456F25.csv,MVKLAKAGKNQGDPKKMAPPPKEVEEDSEDEEMSEDEEDDSSGEEVVIPQKKGKKAAATSAKKVVVSPTKKVAVATPAKKAAVTPGKKAAATPAKKTVTPAKAVTTPGKKGATPGKALVATPGKKGAAIPAKGAKNGKNAKKEDSDEEEDDDSEEDEEDDEDEDEDEDEIEPAAMKAAAAAPASEDEDDEDDEDDEDDDDDEEDDSEEEAMETTPAKGKKAAKVVPVKAKNVAEDEDEEEDDEDEDDDDDEDDEDDDDEDDEEEEEEEEEEPVKEAPGKRKKEMAKQKAAPEAKKQKVEGTEPTTAFNLFVGNLNFNKSAPELKTGISDVFAKNDLAVVDVRIGMTRKFGYVDFESAEDLEKALELTGLKVFGNEIKLEKPKGKDSKKERDARTLLAKNLPYKVTQDELKEVFEDAAEIRLVSKDGKSKGIAYIEFKTEADAEKTFEEKQGTEIDGRSISLYYTGEKGQNQDYRGGKNSTWSGESKTLVLSNLSYSATEETLQEVFEKATFIKVPQNQNGKSKGYAFIEFASFEDAKEALNSCNKREIEGRAIRLELQGPRGSPNARSQPSKTLFVKGLSEDTTEETLKESFDGSVRARIVTDRETGSSKGFGFVDFNSEEDAKAAKEAMEDGEIDGNKVTLDWAKPKGEGGFGGRGGGRGGFGGRGGGRGGRGGFGGRGRGGFGGRGGFRGGRGGGGDHKPQGKKTKFE,mutated_sequence,1.0,710.0,UPI0000456F25.a2m,UPI0000456F25.npy,gnomAD +UPI00001C1C97,UPI00001C1C97.csv,MIRHAGAPARGDPTGPVPVVGKGEEEEEEDGMRLCLPANPKNCLPHRRGISILEKLIKTCPVWLQLSLGQAEVARILHRVVAGMFLVRRDSSSKQLVLCVHFPSLNESSAEVLEYTIKEEKSILYLEGSALVFEDIFRLIAFYCVSRDLLPFTLRLPQAILEASSFTDLETIANLGLGFWDSSLNPPQERGKPAEPPRDRAPGFPLVSSLRPTAHDANCACEIELSVGNDRLWFVNPIFIEDCSSALPTDQPPLGNCPARPLPPTSDATSPTSRWAPRRPPPPPPVLPLQPCSPAQPPVLPALAPAPACPLPTSPPVPAPHVTPHAPGPPDHPNQPPMMTCERLPCPTAGLGPLREEAMKPGAASSPLQQVPAPPLPAKKNLPTAPPRRRVSERVSLEDQSPGMAAEGDQLSLPPQGTSDGPEDTPRESTEQGQDTEVKASDPHSMPELPRTAKQPPVPPPRKKRISRQLASTLPAPLENAELCTQAMALETPTPGPPREGQSPASQAGTQHPPAQATAHSQSSPEFKGSLASLSDSLGVSVMATDQDSYSTSSTEEELEQFSSPSVKKKPSMILGKARHRLSFASFSSMFHAFLSNNRKLYKKVVELAQDKGSYFGSLVQDYKVYSLEMMARQTSSTEMLQEIRTMMTQLKSYLLQSTELKALVDPALHSEEELEAIVESALYKCVLKPLKEAINSCLHQIHSKDGSLQQLKENQLVILATTTTDLGVTTSVPEVPMMEKILQKFTSMHKAYSPEKKISILLKTCKLIYDSMALGNPGKPYGADDFLPVLMYVLARSNLTEMLLNVEYMMELMDPALQLGEGSYYLTTTYGALEHIKSYDKITVTRQLSVEVQDSIHRWERRRTLNKARASRSSVQDFICVSYLEPEQQARTLASRADTQAQALCAQCAEKFAVERPQAHRLFVLVDGRCFQLADDALPHCIKGYLLRSEPKRDFHFVYRPLDGGGGGGGGSPPCLVVREPNFL,mutated_sequence,1.0,985.0,UPI00001C1C97.a2m,UPI00001C1C97.npy,gnomAD +UPI0000001C9F,UPI0000001C9F.csv,MKTLMRHGLAVCLALTTMCTSLLLVYSSLGGQKERPPQQQQQQQQQQQQASATGSSQPAAESSTQQRPGVPAGPRPLDGYLGVADHKPLKMHCRDCALVTSSGHLLHSRQGSQIDQTECVIRMNDAPTRGYGRDVGNRTSLRVIAHSSIQRILRNRHDLLNVSQGTVFIFWGPSSYMRRDGKGQVYNNLHLLSQVLPRLKAFMITRHKMLQFDELFKQETGKDRKISNTWLSTGWFTMTIALELCDRINVYGMVPPDFCRDPNHPSVPYHYYEPFGPDECTMYLSHERGRKGSHHRFITEKRVFKNWARTFNIHFFQPDWKPESLAINHPENKPVF,mutated_sequence,1.0,336.0,UPI0000001C9F.a2m,UPI0000001C9F.npy,gnomAD +UPI00000467CA,UPI00000467CA.csv,MNIRNAQPDDLMNMQHCNLLCLPENYQMKYYLYHGLSWPQLSYIAEDEDGKIVGYVLAKMEEEPDDVPHGHITSLAVKRSHRRLGLAQKLMDQASRAMIENFNAKYVSLHVRKSNRPALHLYSNTLNFQISEVEPKYYADGEDAYAMKRDLSQMADELRRQMDLKKGGYVVLGSRENQETQGSTLSDSEEACQQKNPATEESGSDSKEPKESVESTNVQDSSESSDSTS,mutated_sequence,1.0,229.0,UPI00000467CA.a2m,UPI00000467CA.npy,gnomAD +UPI0000EBB7D6,UPI0000EBB7D6.csv,MDLGTAEGTRCTDPPAGKPAMAPKRKGGLKLNAICAKLSRQVVVEKRADAGSHTEGSPSQPRDQERSGPESGAARAPRSEEDKRRAVIEKWVNGEYSEEPAPTPVLGRIAREGLELPPEGVYMVQPQGCSDEEDHAEEPSKDGGALEEKDSDGAASKEDSGPSTRQASGEASSLRDYAASTMTEFLGMFGYDDQNTRDELARKISFEKLHAGSTPEAATSSMLPTSEDTLSKRARFSKYEEYIRKLKAGEQLSWPAPSTKTEERVGKEVVGTLPGLRLPSSTAHLETKATILPLPSHSSVQMQNLVARASKYDFFIQKLKTGENLRPQNGSTYKKPSKYDLENVKYLHLFKPGEGSPDMGGAIAFKTGKVGRPSKYDVRGIQKPGPAKVPPTPSLAPAPLASVPSAPSAPGPGPEPPASLSFNTPEYLKSTFSKTDSITTGTVSTVKNGLPTDKPAVTEDVNIYQKYIARFSGSQHCGHIHCAYQYREHYHCLDPECNYQRFTSKQDVIRHYNMHKKRDNSLQHGFMRFSPLDDCSVYYHGCHLNGKSTHYHCMQVGCNKVYTSTSDVMTHENFHKKNTQLINDGFQRFRATEDCGTADCQFYGQKTTHFHCRRPGCTFTFKNKCDIEKHKSYHIKDDAYAKDGFKKFYKYEECKYEGCVYSKATNHFHCIRAGCGFTFTSTSQMTSHKRKHERRHIRSSGALGLPPSLLGAKDTEHEESSNDDLVDFSALSSKNSSLSASPTSQQSSASLAAATAATEAGPSATKPPNSKISGLLPQGLPGSIPLALALSNSGLPTPTPYFPILAGRGSTSLPVGTPSLLGAVSSGSAASATPDTPTLVASGAGDSAPVAAASVPAPPASIMERISASKGLISPMMARLAAAALKPSATFDPGSGQQVTPARFPPAQVKPEPGESTGAPGPHEASQDRSLDLTVKEPSNESNGHAVPANSSLLSSLMNKMSQGNPGLGSLLNIKAEAEGSPAAEPSPFLGKAVKALVQEKLAEPWKVYLRRFGTKDFCDGQCDFLHKAHFHCVVEECGALFSTLDGAIKHANFHFRTEGGAAKGNTEAAFPASAAETKPPMAPSSPPVPPVTTATVSSLEGPAPSPASVPSTPTLLAWKQLASTIPQMPQIPASVPHLPASPLATTSLENAKPQVKPGFLQFQENDPCLATDCKYANKFHFHCLFGNCKYVCKTSGKAESHCLDHINPNNNLVNVRDQFAYYSLQCLCPNQHCEFRMRGHYHCLRTGCYFVTNITTKLPWHIKKHEKAERRAANGFKYFTKREECGRLGCKYNQVNSHFHCIREGCQFSFLLKHQMTSHARKHMRRMLGKNFDRVPPSQGPPGLMDAETDECMDYTGCSPGAMSSESSTMDRSCSSTPVGNESTAAGNTISMPTASGAKKRFWIIEDMSPFGKRRKTASSRKMLDEGMMLEGFRRFDLYEDCKDAACQFSLKVTHYHCTRENCGYKFCGRTHMYKHAQHHDRVDNLVLDDFKRFKASLSCHFADCPFSGTSTHFHCLRCRFRCTDSTKVTAHRKHHGKQDVISAAGFCQFSSSADCAVPDCKYKLKCSHFHCTFPGCRHTVVGMSQMDSHKRKHEKQERGEPAAEGPAPGPPISLDGSLSLGAEPGSLLFLQSAAAGLGLALGDAGDPGPPDAAAPGPREGAAAAAAAAGESSQEDEEEELELPEEEAEDDEDEDDDEDDDDEDDDEDDDDEDLRTDSEESLPEAAAEAAGAGARTPALAALAALGAPGPAPTAASSP,mutated_sequence,1.0,1759.0,UPI0000EBB7D6.a2m,UPI0000EBB7D6.npy,gnomAD +UPI000016783D,UPI000016783D.csv,MTTKDYPSLWGFGTTKTFKIPIEHLDFKYIEKCSDVKHLEKILCVLRSGEEGYYPELTEFCEKHLQALAPESRALRKDKPAATAASFTAEEWEKIDGDIKSWVSEIKKEEDKMHFHETETFPAMKDNLPPVRGSNSCLHVGKEKYSKRPTKKKTPRDYAEWDKFDVEKECLKIDEDYKEKTVIDKSHLSKIETRIDTAGLTEKEKDFLATREKEKGNEAFNSGDYEEAVMYYTRSISALPTVVAYNNRAQAEIKLQNWNSAFQDCEKVLELEPGNVKALLRRATTYKHQNKLREATEDLSKVLDVEPDNDLAKKTLSEVERDLKNSEAASETQTKGKRMVIQEIENSEDEEGKSGRKHEDGGGDKKPAEPAGAARAAQPCVMGNIQKKLTGKAEGGKRPARGAPQRGQTPEAGADKRSPRRASAAAAAGGGATGHPGGGQGAENPAGLKSQGNELFRSGQFAEAAGKYSAAIALLEPAGSEIADDLSILYSNRAACYLKEGNCSGCIQDCNRALELHPFSMKPLLRRAMAYETLEQYGKAYVDYKTVLQIDCGLQLANDSVNRLSRILMELDGPNWREKLSPIPAVPASVPLQAWHPAKEMISKQAGDSSSHRQQGITDEKTFKALKEEGNQCVNDKNYKDALSKYSECLKINNKECAIYTNRALCYLKLCQFEEAKQDCDQALQLADGNVKAFYRRALAHKGLKNYQKSLIDLNKVILLDPSIIEAKMELEEVTRLLNLKDKTAPFNKEKERRKIEIQEVNEGKEEPGRPAGEVSMGCLASEKGGKSSRSPEDPEKLPIAKPNNAYEFGQIINALSTRKDKEACAHLLAITAPKDLPMFLSNKLEGDTFLLLIQSLKNNLIEKDPSLVYQHLLYLSKAERFKMMLTLISKGQKELIEQLFEDLSDTPNNHFTLEDIQALKRQYEL,mutated_sequence,1.0,926.0,UPI000016783D.a2m,UPI000016783D.npy,gnomAD +UPI00000437FE,UPI00000437FE.csv,MKALDEPPYLTVGTDVSAKYRGAFCEAKIKTAKRLVKVKVTFRHDSSTVEVQDDHIKGPLKVGAIVEVKNLDGAYQEAVINKLTDASWYTVVFDDGDEKTLRRSSLCLKGERHFAESETLDQLPLTNPEHFGTPVIGKKTNRGRRSNHIPEEESSSSSSDEDEDDRKQIDELLGKVVCVDYISLDKKKALWFPALVVCPDCSDEIAVKKDNILVRSFKDGKFTSVPRKDVHEITSDTAPKPDAVLKQAFEQALEFHKSRTIPANWKTELKEDSSSSEAEEEEEEEDDEKEKEDNSSEEEEEIEPFPEERENFLQQLYKFMEDRGTPINKRPVLGYRNLNLFKLFRLVHKLGGFDNIESGAVWKQVYQDLGIPVLNSAAGYNVKCAYKKYLYGFEEYCRSANIEFQMALPEKVVNKQCKECENVKEIKVKEENETEIKEIKMEEERNIIPREEKPIEDEIERKENIKPSLGSKKNLLESIPTHSDQEKEVNIKKPEDNENLDDKDDDTTRVDESLNIKVEAEEEKAKSGDETNKEEDEDDEEAEEEEEEEEEEEDEDDDDNNEEEEFECYPPGMKVQVRYGRGKNQKMYEASIKDSDVEGGEVLYLVHYCGWNVRYDEWIKADKIVRPADKNVPKIKHRKKIKNKLDKEKDKDEKYSPKNCKLRRLSKPPFQTNPSPEMVSKLDLTDAKNSDTAHIKSIEITSILNGLQASESSAEDSEQEDERGAQDMDNNGKEESKIDHLTNNRNDLISKEEQNSSSLLEENKVHADLVISKPVSKSPERLRKDIEVLSEDTDYEEDEVTKKRKDVKKDTTDKSSKPQIKRGKRRYCNTEECLKTGSPGKKEEKAKNKESLCMENSSNSSSDEDEEETKAKMTPTKKYNGLEEKRKSLRTTGFYSGFSEVAEKRIKLLNNSDERLQNSRAKDRKDVWSSIQGQWPKKTLKELFSDSDTEAAASPPHPAPEEGVAEESLQTVAEEESCSPSVELEKPPPVNVDSKPIEEKTVEVNDRKAEFPSSGSNSVLNTPPTTPESPSSVTVTEGSRQQSSVTVSEPLAPNQEEVRSIKSETDSTIEVDSVAGELQDLQSEGNSSPAGFDASVSSSSSNQPEPEHPEKACTGQKRVKDAQGGGSSSKKQKRSHKATVVNNKKKGKGTNSSDSEELSAGESITKSQPVKSVSTGMKSHSTKSPARTQSPGKCGKNGDKDPDLKEPSNRLPKVYKWSFQMSDLENMTSAERITILQEKLQEIRKHYLSLKSEVASIDRRRKRLKKKERESAATSSSSSSPSSSSITAAVMLTLAEPSMSSASQNGMSVECR,mutated_sequence,1.0,1312.0,UPI00000437FE.a2m,UPI00000437FE.npy,gnomAD +UPI00001B07C3,UPI00001B07C3.csv,MCAARLAAAAAAAQSVYAFSARPLAGGEPVSLGSLRGKVLLIENVASLUGTTVRDYTQMNELQRRLGPRGLVVLGFPCNQFGHQENAKNEEILNSLKYVRPGGGFEPNFMLFEKCEVNGAGAHPLFAFLREALPAPSDDATALMTDPKLITWSPVCRNDVAWNFEKFLVGPDGVPLRRYSRRFQTIDIEPDIEALLSQGPSCA,mutated_sequence,1.0,203.0,UPI00001B07C3.a2m,UPI00001B07C3.npy,gnomAD +UPI000013C3DC,UPI000013C3DC.csv,MLLDAGPQFPAIGVGSFARHHHHSAAAAAAAAAEMQDRELSLAAAQNGFVDSAAAHMGAFKLNPGAHELSPGQSSAFTSQGPGAYPGSAAAAAAAAALGPHAAHVGSYSGPPFNSTRDFLFRSRGFGDSAPGGGQHGLFGPGAGGLHHAHSDAQGHLLFPGLPEQHGPHGSQNVLNGQMRLGLPGEVFGRSEQYRQVASPRTDPYSAAQLHNQYGPMNMNMGMNMAAAAAHHHHHHHHHPGAFFRYMRQQCIKQELICKWIDPEQLSNPKKSCNKTFSTMHELVTHVSVEHVGGPEQSNHVCFWEECPREGKPFKAKYKLVNHIRVHTGEKPFPCPFPGCGKVFARSENLKIHKRTHTGEKPFQCEFEGCDRRFANSSDRKKHMHVHTSDKPYLCKMCDKSYTHPSSLRKHMKVHESSPQGSESSPAASSGYESSTPPGLVSPSAEPQSSSNLSPAAAAAAAAAAAAAAAVSAVHRGGGSGSGGAGGGSGGGSGSGGGGGGAGGGGGGSSGGGSGTAGGHSGLSSNFNEWYV,mutated_sequence,1.0,532.0,UPI000013C3DC.a2m,UPI000013C3DC.npy,gnomAD +UPI0000359594,UPI0000359594.csv,MRPGGALLALLASLLLLLLLRLLWCPADAPGRARILVEESREATHGTPAALRTLRSPATAVPRATNSTYLNEKSLQLTEKCKNLQYGIESFSNKTKGYSENDYLQIITDIQSCPWKRQAEEYANFRAKLASCCDAVQNFVVSQNNTPVGTNMSYEVESKKEIPIKKNIFHMFPVSQPFVDYPYNQCAVVGNGGILNKSLCGTEIDKSDFVFRCNLPPTTGDVSKDVGSKTNLVTINPSIITLKYGNLKEKKALFLEDIATYGDAFFLLPAFSFRANTGTSFKVYYTLEESKARQKVLFFHPKYLKDLALFWRTKGVTAYRLSTGLMITSVAVELCKNVKLYGFWPFSKTVEDIPVSHHYYDNKLPKHGFHQMPKEYSQILQLHMKGILKLQFSKCEVA,mutated_sequence,1.0,398.0,UPI0000359594.a2m,UPI0000359594.npy,gnomAD +UPI0000423DF4,UPI0000423DF4.csv,MAAPVLLRVSVPRWERVARYAVCAAGILLSIYAYHVEREKERDPEHRALCDLGPWVKCSAALASRHDSKRCGGFDPHDVLHHVGRGVPVPGLHSVLCAEGVLHHLHRHVRAELPSSHYQLQTTSLLERGLEAAAATQAGLTPDRLHPNSLKPLSIQFILQQVFIIIIIIIIHNRHFP,mutated_sequence,1.0,177.0,UPI0000423DF4.a2m,UPI0000423DF4.npy,gnomAD +UPI000012ADC5,UPI000012ADC5.csv,MQARYSVSSPNSLGVVPYLGGEQSYYRAAAAAAGGGYTAMPAPMSVYSHPAHAEQYPGGMARAYGPYTPQPQPKDMVKPPYSYIALITMAIQNAPDKKITLNGIYQFIMDRFPFYRDNKQGWQNSIRHNLSLNECFVKVPRDDKKPGKGSYWTLDPDSYNMFENGSFLRRRRRFKKKDAVKDKEEKDRLHLKEPPPPGRQPPPAPPEQADGNAPGPQPPPVRIQDIKTENGTCPSPPQPLSPAAALGSGSAAAVPKIESPDSSSSSLSSGSSPPGSLPSARPLSLDGADSAPPPPAPSAPPPHHSQGFSVDNIMTSLRGSPQSAAAELSSGLLASAAASSRAGIAPPLALGAYSPGQSSLYSSPCSQTSSAGSSGGGGGGAGAAGGAGGAGTYHCNLQAMSLYAAGERGGHLQGAPGGAGGSAVDDPLPDYSLPPVTSSSSSSLSHGGGGGGGGGGQEAGHHPAAHQGRLTSWYLNQAGGDLGHLASAAAAAAAAGYPGQQQNFHSVREMFESQRIGLNNSPVNGNSSCQMAFPSSQSLYRTSGAFVYDCSKF,mutated_sequence,1.0,553.0,UPI000012ADC5.a2m,UPI000012ADC5.npy,gnomAD +UPI00006C181B,UPI00006C181B.csv,MAEPGGAAGRSHPEDGSASEGEKEGNNESHMVSPPEKDDGQKGEEAVGSTEHPEEVTTQAEAAIEEGEVETEGEAAVEGEEEAVSYGDAESEEEYYYTETSSPEGQISAADTTYPYFSPPQELPGEEAYDSVSGEAGLQGFQQEATGPPESRERRVTSPEPSHGVLGPSEQMGQVTSGPAVGRLTGSTEEPQGQVLPMGVQHRFRLSHGSDIESSDLEEFVSQEPVIPPGVPDAHPREGDLPVFQDQIQQPSTEEGAMAERVESEGSDEEAEDEGSQLVVLDPDHPLMVRFQAALKNYLNRQIEKLKLDLQELVVATKQSRAQRQELGVNLYEVQQHLVHLQKLLEKSHDRHAMASSERRQKEEELQAARALYTKTCAAANEERKKLAALQTEMENLALHLFYMQNIDQDMRDDIRVMTQVVKKAETERIRAEIEKKKQDLYVDQLTTRAQQLEEDIALFEAQYLAQAEDTRILRKAVSEACTEIDAISVEKRRIMQQWASSLVGMKHRDEAHRAVLEALRGCQHQAKSTDGEIEAYKKSIMKEEEKNEKLASILNRTETEATLLQKLTTQCLTKQVALQSQFNTYRLTLQDTEDALSQDQLEQMILTEELQAIRQAIQGELELRRKTDAAIREKLQEHMTSNKTTKYFNQLILRLQKEKTNMMTHLSKINGDIAQTTLDITHTSSRLDAHQKTLVELDQDVKKVNELITNSQSEISRRTILIERKQGLINFLNKQLERMVSELGGEEVGPLELEIKRLSKLIDEHDGKAVQAQVTWLRLQQEMVKVTQEQEEQLASLDASKKELHIMEQKKLRVESKIEQEKKEQKEIEHHMKDLDNDLKKLNMLMNKNRCSSEELEQNNRVTENEFVRSLKASERETIKMQDKLNQLSEEKATLLNQLVEAEHQIMLWEKKIQLAKEMRSSVDSEIGQTEIRAMKGEIHRMKKYCRTTQDAHRHVHEQHGTHAGTCTKNTGRAQARARTTRDARGHVHEQHGTRAGTCTNNTGRAQARARTRDARRHVHEHRTHTARA,mutated_sequence,1.0,1030.0,UPI00006C181B.a2m,UPI00006C181B.npy,gnomAD +UPI000004A07B,UPI000004A07B.csv,MAPVSGSRSPDREASGSGGRRRSSSKSPKPSKSARSPRGRRSRSHSCSRSGDRNGLTHQLGGLSQGSRNQSYRSRSRSRSRERPSAPRGIPFASASSSVYYGSYSRPYGSDKPWPSLLDKEREESLRQKRLSERERIGELGAPEVWGLSPKNPEPDSDEHTPVEDEEPKKSTTSASTSEEEKKKKSSRSKERSKKRRKKKSSKRKHKKYSEDSDSDSDSETDSSDEDNKRRAKKAKKKEKKKKHRSKKYKKKRSKKSRKESSDSSSKESQEEFLENPWKDRTKAEEPSDLIGPEAPKTLTSQDDKPLNYGHALLPGEGAAMAEYVKAGKRIPRRGEIGLTSEEIASFECSGYVMSGSRHRRMEAVRLRKENQIYSADEKRALASFNQEERRKRENKILASFREMVYRKTKGKDDK,mutated_sequence,1.0,415.0,UPI000004A07B.a2m,UPI000004A07B.npy,gnomAD +UPI0000E5AFF9,UPI0000E5AFF9.csv,MKCVFVTVGTTSFDDLIACVSAPDSLQKIESLGYNRLILQIGRGTVVPEPFSTESFTLDVYRYKDSLKEDIQKADLVISHAGAGSCLETLEKGKPLVVVINEKLMNNHQLELAKQLHKEGHLFYCTCRVLTCPGQAKSIASAPGKCQDSAALTSTAFSGLDFGLLSGYLHKQALVTATHPTCTLLFPSCHAFFPLPLTPTLYKMHKGWKNYCSQKSLNEASMDEYLGSLGLFRKLTAKDASCLFRAISEQLFCSQVHHLEIRKACVSYMRENQQTFESYVEGSFEKYLERLGDPKESAGQLEIRALSLIYNRDFILYRFPGKPPTYVTDNGYEDKILLCYSSSGHYDSVYSKQFQSSAAVCQAVLYEILYKDVFVVDEEELKTAIKLFRSGSKKNRNNAVTGSEDAHTDYKSSNQNRMEEWGACYNAENIPEGYNKGTEETKSPENPSKMPFPYKVLKALDPEIYRNVEFDVWLDSRKELQKSDYMEYAGRQYYLGDKCQVCLESEGRYYNAHIQEVGNENNSVTVFIEELAEKHVVPLANLKPVTQVMSVPAWNAMPSRKGRGYQKMPGGYVPEIVISEMDIKQQKKMFKKIRGKEVYMTMAYGKGDPLLPPRLQHSMHYGHDPPMHYSQTAGNVMSNEHFHPQHPSPRQGRGYGMPRNSSRFINRHNMPGPKVDFYPGPGKRCCQSYDNFSYRSRSFRRSHRQMSCVNKESQYGFTPGNGQMPRGLEETITFYEVEEGDETAYPTLPNHGGPSTMVPATSGYCVGRRGHSSGKQTLNLEEGNGQSENGRYHEEYLYRAEPDYETSGVYSTTASTANLSLQDRKSCSMSPQDTVTSYNYPQKMMGNIAAVAASCANNVPAPVLSNGAAANQAISTTSVSSQNAIQPLFVSPPTHGRPVIASPSYPCHSAIPHAGASLPPPPPPPPPPPPPPPPPPPPPPPPPPPALDVGETSNLQPPPPLPPPPYSCDPSGSDLPQDTKVLQYYFNLGLQCYYHSYWHSMVYVPQMQQQLHVENYPVYTEPPLVDQTVPQCYSEVRREDGIQAEASANDTFPNADSSSVPHGAVYYPVMSDPYGQPPLPGFDSCLPVVPDYSCVPPWHPVGTAYGGSSQIHGAINPGPIGCIAPSPPASHYVPQGM,mutated_sequence,1.0,1137.0,UPI0000E5AFF9.a2m,UPI0000E5AFF9.npy,gnomAD +UPI000023281A,UPI000023281A.csv,MAHLRSPSGFGDPGKKDQKESEEELEEEEEEEEVEEEEEEVEEEEEEVEEEEEEVVEEELVGEEQELEAPETFSEEYLWKVTDIGDYDDDFPDVRPRLASIVSPSLTSTFVPSQSATSTETPSASPPSSTSSHKSFPKIFQTFRKDMSEMSIDRNIHRNLSPGIPVSVQTEESWLQDLSDKVQSRKKASKEKAEPECLASKLREKWVINPEESKLNILYELEFKEDFITLFEPSLRTLPSIGPPSILAYKEESSNLGINFKDEEEETSPKCEFCGSDLRAFFSNVDVSSEPKGHASCCIAFQNLIDYIYEEQIKTKPPKAELIAIDPHAAHGSEVDRLKAKEKALQRKQEQRMARHFAIISREQTHFSEDDSKRLKTISYQLSVDIPEKQIIDDIVFDFQLRNSNMSIICCDSRIACGKVVRNELLEKHYKHGSKFLTSFPDGTTQIFYPSGNLAIIRVPNKVNGFTCIVQEDMPTNPAILAVLDSSGRSSCYHPNGNVWVYINILGGQYSDQAGNRIRAWNWSNSITSSPFVSFKPVFLALNRYIGVRILEQDKISITFLAMGQQARISVGTKVKLPNPEEIPILRYVSGDDLLLLASLIKIRRLFHKLEGCVNFPSSQVWEKLKQPSYLSSLSLKLIALCHSSGIKQDIMKTIRNIINEEI,mutated_sequence,1.0,663.0,UPI000023281A.a2m,UPI000023281A.npy,gnomAD +UPI000014153E,UPI000014153E.csv,MTLGSCCCEIMSSESSPAALSEADADIDVVGGGSGGGELPARSGPRAPRDVLPHGHEPPAEEAEADLAEDEEESGGCSDGEPRALASRGAAAAAGSPGPGAAAARGAAGPGPGPPSGGAATRSPLVKPPYSYIALITMAILQSPKKRLTLSEICEFISGRFPYYREKFPAWQNSIRHNLSLNDCFVKIPREPGNPGKGNYWTLDPESADMFDNGSFLRRRKRFKRQPLPPPHPHPHPHPELLLRGGAAAAGDPGAFLPGFAAYGAYGYGYGLALPAYGAPPPGPAPHPHPHPHAFAFAAAAAAAPCQLSVPPGRAAAPPPGPPTASVFAGAGSAPAPAPASGSGPGPGPAGLPAFLGAELGCAKAFYAASLSPPAAGTAAGLPTALLRQGLKTDAGGGAGGGGAGAGQRPSFSIDHIMGHGGGGAAPPGAGEGSPGPPFAAAAGPGGQAQVLAMLTAPALAPVAGHIRLSHPGDALLSSGSRFASKVAGLSGCHF,mutated_sequence,1.0,495.0,UPI000014153E.a2m,UPI000014153E.npy,gnomAD +UPI000013CB8B,UPI000013CB8B.csv,MKSNQERSNECLPPKKREIPATSRSSEEKAPTLPSDNHRVEGTAWLPGNPGGRGHGGGRHGPAGTSVELGLQQGIGLHKALSTGLDYSPPSAPRSVPVATTLPAAYATPQPGTPVSPVQYAHLPHTFQFIGSSQYSGTYASFIPSQLIPPTANPVTSAVASAAGATTPSQRSQLEAYSTLLANMGSLSQTPGHKAEQQQQQQQQQQQQHQHQQQQQQQQQQQQQQHLSRAPGLITPGSPPPAQQNQYVHISSSPQNTGRTASPPAIPVHLHPHQTMIPHTLTLGPPSQVVMQYADSGSHFVPREATKKAESSRLQQAIQAKEVLNGEMEKSRRYGAPSSADLGLGKAGGKSVPHPYESRHVVVHPSPSDYSSRDPSGVRASVMVLPNSNTPAADLEVQQATHREASPSTLNDKSGLHLGKPGHRSYALSPHTVIQTTHSASEPLPVGLPATAFYAGTQPPVIGYLSGQQQAITYAGSLPQHLVIPGTQPLLIPVGSTDMEASGAAPAIVTSSPQFAAVPHTFVTTALPKSENFNPEALVTQAAYPAMVQAQIHLPVVQSVASPAAAPPTLPPYFMKGSIIQLANGELKKVEDLKTEDFIQSAEISNDLKIDSSTVERIEDSHSPGVAVIQFAVGEHRAQVSVEVLVEYPFFVFGQGWSSCCPERTSQLFDLPCSKLSVGDVCISLTLKNLKNGSVKKGQPVDPASVLLKHSKADGLAGSRHRYAEQENGINQGSAQMLSENGELKFPEKMGLPAAPFLTKIEPSKPAATRKRRWSAPESRKLEKSEDEPPLTLPKPSLIPQEVKICIEGRSNVGK,mutated_sequence,1.0,815.0,UPI000013CB8B.a2m,UPI000013CB8B.npy,gnomAD +UPI00004575C6,UPI00004575C6.csv,MVPPVWTLLLLVGAALFRKEKPPDQKLVVRSSRDNYVLTQCDFEDDAKPLCDWSQVSADDEDWVRASGPSPTGSTGAPGGYPNGEGSYLHMESNSFHRGGVARLLSPDLWEQGPLCVHFAHHMFGLSWGAQLRLLLLSGEEGRRPDVLWKHWNTQRPSWMLTTVTVPAGFTLPTRLMFEGTRGSTAYLDIALDALSIRRGSCNRVCMMQTCSFDIPNDLCDWTWIPTASGAKWTQKKGSSGKPGVGPDGDFSSPGSGCYMLLDPKNARPGQKAVLLSPVSLSSGCLSFSFHYILRGQSPGAALHIYASVLGSIRKHTLFSGQPGPNWQAVSVNYTAVGRIQFAVVGVFGKTPEPAVAVDATSIAPCGEGFPQCDFEDNAHPFCDWVQTSGDGGHWALGHKNGPVHGMGPAGGFPNAGGHYIYLEADEFSQAGQSVRLVSRPFCAPGDICVEFAYHMYGLGEGTMLELLLGSPAGSPPIPLWKRVGSQRPYWQNTSVTVPSGHQQPMQLIFKGIQGSNTASVVAMGFILINPGTCPVKVLPELPPVSPVSSTGPSETTGLTENPTISTKKPTVSIEKPSVTTEKPTVPKEKPTIPTEKPTISTEKPTIPSEKPNMPSEKPTIPSEKPTILTEKPTIPSEKPTIPSEKPTISTEKPTVPTEEPTTPTEETTTSMEEPVIPTEKPSIPTEKPSIPTEKPTISMEETIISTEKPTISPEKPTIPTEKPTIPTEKSTISPEKPTTPTEKPTIPTEKPTISPEKPTTPTEKPTISPEKLTIPTEKPTIPTEKPTIPTEKPTISTEEPTTPTEETTISTEKPSIPMEKPTLPTEETTTSVEETTISTEKLTIPMEKPTISTEKPTIPTEKPTISPEKLTIPTEKLTIPTEKPTIPIEETTISTEKLTIPTEKPTISPEKPTISTEKPTIPTEKPTIPTEETTISTEKLTIPTEKPTISPEKLTIPTEKPTISTEKPTIPTEKLTIPTEKPTIPTEKPTIPTEKLTALRPPHPSPTATGLAALVMSPHAPSTPMTSVILGTTTTSRSSTERCPPNARYESCACPASCKSPRPSCGPLCREGCVCNPGFLFSDNHCIQASSCNCFYNNDYYEPGAEWFSPNCTEHCRCWPGSRVECQISQCGTHTVCQLKNGQYGCHPYAGTATCLVYGDPHYVTFDGRHFGFMGKCTYILAQPCGNSTDPFFRVTAKNEEQGQEGVSCLSKVYVTLPESTVTLLKGRRTLVGGQQVTLPAIPSKGVFLGASGRFVELQTEFGLRVRWDGDQQLYVTVSSTYSGKLCGLCGNYDGNSDNDHLKLDGSPAGDKEELGNSWQTDQDEDQECQKYQVVNSPSCDSSLQSSMSGPGFCGRLVDTHGPFETCLLHVKAASFFDSCMLDMCGFQGLQHLLCTHMSTMTTTCQDAGHAVKPWREPHFCPMACPPNSKYSLCAKPCPDTCHSGFSGMFCSDRCVEACECNPGFVLSGLECIPRSQCGCLHPAGSYFKVGERWYKPGCKELCVCESNNRIRCQPWRCRAQEFCGQQDGIYGCHAQGAATCTASGDPHYLTFDGALHHFMGTCTYVLTRPCWSRSQDSYFVVSATNENRGGILEVSYIKAVHVTVFDLSISLLRGCKVMLNGHRVALPVWLAQGRVTIRLSSNLVLLYTNFGLQVRYDGSHLVEVTVPSSYGGQLCGLCGNYNNNSLDDNLRPDRKLAGDSMQLGAAWKLPESSEPGCFLVGGKPSSCQENSMADAWNKNCAILINPQGPFSQCHQVVPPQSSFASCVHGQCGTKGDTTALCRSLQAYASLCAQAGQAPAWRNRTFCPMRCPPGSSYSPCSSPCPDTCSSINNPRDCPKALPCAESCECQKGHILSGTSCVPLGQCGCTDPAGSYHPVGERWYTENTCTRLCTCSVHNNITCFQSTCKPNQICWALDGLLRRASGVGVCQLPGESHYVSFDGSNHSIPDACTLVLVKVCHPAMALPFFKISAKHEKEEGGTEAFRLHEVYIDIYDAQVTLQKGHRVLINSKQVTLPAISQIPGVSVKSSSIYSIVNIKIGVQVKFDGNHLLEIEIPTTYYGKVCGMCGNFNDEEEDELMMPSDEVANSDSEFVNSWKDKDIDPSCQSLLVDEQQIPAEQQENPSGNCRAADLRRAREKCEAALRAPVWAQCASRIDLTPFLVDCANTLCEFGGLYQALCQALQAFGATCQSQGLKPPLWRNSSFCPLECPAYSSYTNCLPSCSPSCWDLDGRCEGAKVPSACAEGCICQPGYVLSEDKCVPRSQCGCKDAHGGSIPLGKSWVSSGCTEKCVCTGGAIQCGDFRCPSGSHCQLTSDNSNSNCVSDKSEQCSVYGDPRYLTFDGFSYRLQGRMTYVLIKTVDVLPEGVEPLLVEGRNKMDPPRSSIFLQEVITTVYGYKVQLQAGLELVVNNQKMAVPYRPNEHLRVTLWGQRLYLVTDFELVVSFGGRKNAVISLPSMYEGLVSGLCGNYDKNRKNDMMLPSGALTQNLNTFGNSWEVKTEDALLRFPRAIPAEEEGQGAELGLRTGLQVSECSPEQLASNSTQACRVLADPQGPFAACHQTVAPEPFQEHCVLDLCSAQDPREQEELRCQVLSGYAILCQEAGAALAGWRDRTLCESPCLQNPCQNDGQCREQGATFTCECEVGYGGGLCMEPRDAPPPRKPASNLVGVLLGLLVPVVVVLLAVTRECIYRTRRKREKTQEGDRLARLVDTDTVLDCAC,mutated_sequence,1.0,2720.0,UPI00004575C6.a2m,UPI00004575C6.npy,gnomAD +UPI00022F843B,UPI00022F843B.csv,XFSTHLSPNPSWCFKRVKIPDRAPGWTRNYCPAGLHFHSGRWLVVPDKEDPEGNHTLPCRAPGFSSAKLTLTRLQEGKEPTPDSRLKGTRTREMRHTRAGQLWGSFQRGAEIRLPGGAPGPGGAPQCDWVKCFQRTRVEVGGISSRPCRHLPQNPRAPPSSLLNGNAGDVLSHPLSTHTSPLIWKFFSL,mutated_sequence,1.0,189.0,UPI00022F843B.a2m,UPI00022F843B.npy,gnomAD +UPI0000206B64,UPI0000206B64.csv,MDEEENHYVSQLREVYSSCDTTGTGFLDRQELTQLCLKLHLEQQLPVLLQTLLGNDHFARVNFEEFKEGFVAVLSSNAGVRPSDEDSSSLESAASSAIPPKYVNGSKWYGRRSRPELCDAATEARRVPEQQTQASLKSHLWRSASLESVESPKSDEEAESTKEAQNELFEAQGQLQTWDSEDFGSPQKSCSPSFDTPESQIRGVWEELGVGSSGHLSEQELAVVCQSVGLQGLEKEELEDLFNKLDQDGDGKVSLEEFQLGLFSHEPALLLESSTRVKPSKAWSHYQVPEESGCHTTTTSSLVSLCSSLRLFSSIDDGSGFAFPDQVLAMWTQEGIQNGREILQSLDFSVDEKVNLLELTWALDNELMTVDSAVQQAALACYHQELSYQQGQVEQLARERDKARQDLERAEKRNLEFVKEMDDCHSTLEQLTEKKIKHLEQGYRERLSLLRSEVEAERELFWEQAHRQRAALEWDVGRLQAEEAGLREKLTLALKENSRLQKEIVEVVEKLSDSERLALKLQKDLEFVLKDKLEPQSAELLAQEERFAAVLKEYELKCRDLQDRNDELQAELEGLWARLPKNRHSPSWSPDGRRRQLPGLGPAGISFLGNSAPVSIETELMMEQVKEHYQDLRTQLETKVNYYEREIAALKRNFEKERKDMEQARRREVSVLEGQKADLEELHEKSQEVIWGLQEQLQDTARGPEPEQMGLAPCCTQALCGLALRHHSHLQQIRREAEAELSGELSGLGALPARRDLTLELEEPPQGPLPRGSQRSEQLELERALKLQPCASEKRAQMCVSLALEEEELELARGKRVDGPSLEAEMQALPKDGLVAGSGQEGTRGLLPLRPGCGERPLAWLAPGDGRESEEAAGAGPRRRQAQDTEATQSPAPAPAPASHGPSERWSRMQPCGVDGDIVPKEPEPFGASAAGLEQPGARELPLLGTERDASQTQPRMWEPPLRPAASCRGQAERLQAIQEERARSWSRGTQEQASEQQARAEGALEPGCHKHSVEVARRGSLPSHLQLADPQGSWQEQLAAPEEGETKIALEREKDDMETKLLHLEDVVRALEKHVDLRENDRLEFHRLSEENTLLKNDLGRVRQELEAAESTHDAQRKEIEVLKKDKEKACSEMEVLNRQNQNYKDQLSQLNVRVLQLGQEASTHQAQNEEHRVTIQMLTQSLEEVVRSGQQQSDQIQKLRVELECLNQEHQSLQLPWSELTQTLEESQDQVQGAHLRLRQAQAQHLQEVRLVPQDRVAELHRLLSLQGEQARRRLDAQREEHEKQLKATEERVEEAEMILKNMEMLLQEKVDKLKEQFEKNTKSDLLLKELYVENAHLVRALQATEEKQRGAEKQSRLLEEKVRALNKLVSRIAPAALSV,mutated_sequence,1.0,1382.0,UPI0000206B64.a2m,UPI0000206B64.npy,gnomAD +UPI0000E599FA,UPI0000E599FA.csv,MCQCVRACVCVCVCACATQRASHSALPGTTISVKDWRLCLLDQFDACARSGLSEPRSLTLRVPSCGKPLPGPGARLGREVTPCLSFAFAWCWLKMCQEEQTSYMVVQTSEEGLAADAELPGPLLMLAQNCAVMHNLLGPACIFLRKGFAENRQPDRSLRPEEIEELREAFREFDKDKDGYINCRDLGNCMRTMGYMPTEMELIELSQQINMNLGGHVDFDDFVELMGPKLLAETADMIGVKELRDAFREFDTNGDGEISTSELREAMRKLLGHQVGHRDIEEIIRDVDLNGDGRVDFEEFVRMMSR,mutated_sequence,1.0,306.0,UPI0000E599FA.a2m,UPI0000E599FA.npy,gnomAD +UPI00015E00D0,UPI00015E00D0.csv,MLLLLGLCLGLSLCVGSQEEAQSWGHSSEQDGLRVPRQVRLLQRLKTKPLMTEFSVKSTIISRYAFTTVSCRMLNRASEDQDIEFQMQIPAAAFITNFTMLIGDKVYQGEITEREKKSGDRVKEKRNKTTEENGEKGTEIFRASAVIPSKDKAAFFLSYEELLQRRLGKYEHSISVRPQQLSGRLSVDVNILESAGIASLEVLPLHNSRQRGSGRGEDDSGPPPSTVINQNETFANIIFKPTVVQQARIAQNGILGDFIIRYDVNREQSIGDIQVLNGYFVHYFAPKDLPPLPKNVVFVLDSSASMVGTKLRQTKDALFTILHDLRPQDRFSIIGFSNRIKVWKDHLISVTPDSIRDGKVYIHHMSPTGGTDINGALQRAIRLLNKYVAHSGIGDRSVSLIVFLTDGKPTVGETHTLKILNNTREAARGQVCIFTIGIGNDVDFRLLEKLSLENCGLTRRVHEEEDAGSQLIGFYDEIRTPLLSDIRIDYPPSSVVQATKTLFPNYFNGSEIIIAGKLVDRKLDHLHVEVTASNSKKFIILKTDVPVRPQKAGKDVTGSPRPGGDGEGDTNHIERLWSYLTTKELLSSWLQSDDEPEKERLRQRAQALAVSYRFLTPFTSMKLRGPVPRMDGLEEAHGMSAAMGPEPVVQSVRGAGTQPGPLLKKPPNSVKKKQNKTKKRHGRDGVFPLHHLGIR,mutated_sequence,1.0,695.0,UPI00015E00D0.a2m,UPI00015E00D0.npy,gnomAD +UPI0000EE0485,UPI0000EE0485.csv,MFRQFYLWTCLASGIILGSLFEICLGQYDDDCKLARGGPPATIVAIDEESRNGTILVDNMLIKGTAGGPDPTIELSLKDNVDYWVLMDPVKQMLFLNSTGRVLDRDPPMNIHSIVVQVQCINKKVGTIIYHEVRIVVRDRNDNSPTFKHESYYATVNELTPVGTTIFTGFSGDNGATDIDDGPNGQIEYVIQYNPDDPTSNDTFEIPLMLTGNIVLRKRLNYEDKTRYFVIIQANDRAQNLNERRTTTTTLTVDVLDGDDLGPMFLPCVLVPNTRDCRPLTYQAAIPELRTPEELNPIIVTPPIQAIDQDRNIQPPSDRPGILYSILVGTPEDYPRFFHMHPRTAELSLLEPVNRDFHQKFDLVIKAEQDNGHPLPAFAGLHIEILDENNQSPYFTMPSYQGYILESAPVGATISDSLNLTSPLRIVALDKDIEDVPPSGVPTKDPELHLFLNDYTSVFTVTQTGITRYLTLLQPVDREEQQTYTFSITAFDGVQESEPVIVNIQVMDANDNTPTFPEISYDVYVYTDMRPGDSVIQLTAVDADEGSNGEITYEILVGAQGDFIINKTTGLITIAPGVEMIVGRTYALTVQAADNAPPAERRNSICTVYIEVLPPNNQSPPRFPQLMYSLEISEAMRVGAVLLNLQATDREGDSITYAIENGDPQRVFNLSETTGILTLGKALDRESTDRYILIITASDGRPDGTSTATVNIVVTDVNDNAPVFDPYLPRNLSVVEEEANAFVGQVKATDPDAGINGQVHYSLGNFNNLFRITSNGSIYTAVKLNREVRDYYELVVVATDGAVHPRHSTLTLAIKVLDIDDNSPVFTNSTYTVLVEENLPAGTTILQIEAKDVDLGANVSYRIRSPEVKHFFALHPFTGELSLLRSLDYEAFPDQEASITFLVEAFDIYGTMPPGIATVTVIVKDMNDYPPVFSKRIYKGMVAPDAVKGTPITTVYAEDADPPGLPASRVRYRVDDVQFPYPASIFEVEEDSGRVITRVNLNEEPTTIFKLVVVAFDDGEPVMSSSATVKILVLHPGEIPRFTQEEYRPPPVSELATKGTMVGVISAAAINQSIVYSIVSGNEEDTFGINNITGVIYVNGPLDYETRTSYVLRVQADSLEVVLANLRVPSKSNTAKVYIEIQDENNHPPVFQKKFYIGGVSEDARMFTSVLRVKATDKDTGNYSVMAYRLIIPPIKEGKEGFVVETYTGLIKTAMLFHNMRRSYFKFQVIATDDYGKGLSGKADVLVSVVNQLDMQVIVSNVPPTLVEKKIEDLTEILDRYVQEQIPGAKVVVESIGARRHGDAFSLEDYTKCDLTVYAIDPQTNRAIDRNELFKFLDGKLLDINKDFQPYYGEGGRILEIRTPEAVTSIKKRGESLGYTEGALLALAFIIILCCIPAILVVLVSYRQFKVRQAECTKTARIQAALPAAKPAVPAPAPVAAPPPPPPPPPGAHLYEELGDSSMHKYEMPQYGSRRRLLPPAGQEEYGEVVGEAEEEYEEEEWARKRMIKLVVDREYETSSTGEDSAPECQRNRLHHPSIHSNINGNIYIAQNGSVVRTRRACLTDNLKVASPVRLGGPFKKLDKLAVTHEENVPLNTLSKGPFSTEKMNARPTLVTFAPCPVGTDNTAVKPLRNRLKSTVEQESMIDSKNIKEALEFHSDHTQSDDEELWMGPWNNLHIPMTKL,mutated_sequence,1.0,1684.0,UPI0000EE0485.a2m,UPI0000EE0485.npy,gnomAD +UPI00004589BB,UPI00004589BB.csv,MAPPAHKSILERSENVLMSPWKGKLIVQDRMLCDIALWSTYGAMIPTQLPQELDFKYVMKVSSLKKRLPEAAFRKQNYLEEKVCFQDLCFNLYEVELSNRQGENIDKLTECIKNKQLAIIKCLEDRGFFILLTSSALLSEPDFGGKQMGLHGLHLFRSPLSTGVKDLKVEDDISMKVIPILSTLNCALLETKKSLPEERIHPNTLVKRHFQELYKADRSPSLSVAPQDRMKDPTFLGKLPSGFDLIPPAEKCPSESLTQLNSYFSDPSAYILEVSTALDLLAEHPQSPCVSDGICDAGFSLVMTPDPEFLVSEAEVRKETETKKDSEEMLKAKKRVFPLSPASNLRVQPKRKASMPHMVQSKKVNLCRPFPKRTASRADNSSDSPTTLKLVKGQFPQKRKRGAEVLTAQFVQKTKLDRKNQEAPISKDVPVPTNAKRARKQEKSPVKTVPRAKPPVKKSPQKQRVNIVKGNENPRNRKQLQPVKGETASKLQSEISRGCQEDGISINSVQPENTTAAHNDLPENSIVNYDSQALNMLADLALSSATSSTPVSEARNLHCSSELPQNDVLLSKENSLRGTSDHEYHRGVKTQKGELLPNPSSDRKSNSGSDLTVSQDEESLVPCSQAPAKAQSALTEEMLESSDASQSSSVSVEHSYALLLTEHSKKHLQEREILSPLFPRNGTKSPEAATPVGKVMPFRHQPGLLLQQKPPDDPVVKPKDRPPSARVKKSSCSRIVLSCDDSVKITFKCETEYAFSLDSKYTNNPLEKTVVRALHGPWNTDLPDNVEEVKLLLHMWVALFYSNQNKIIRSSRKVVEHSNPAKYVSINSTLESCELREIEESLGLEKCSADSLLETNEISRAHAAEVSFRDPNCLLPFIKTPLTQGLELCVQNEQKKTFARECDPDTQEDQNFICSYNNEVTGEEAKQESLETSNLVLSGIGSTQTNGPSVPSEEEIVQPLDSTRVASYSGTVTQATFTRTYDGPGSQPVICQSSVYGTLENKVDILDAAVQTKTGTLQDLIQHGSPINNECHPSLERKDDNMGCAVINPEPITLTFEKNAHVPIQTEGVNTADERTTFKKELIKQVSPAASLRHPVSTSENARTQGLRDIPSLVVAGQKGTKYLCASSVGGETLDKAVCSLQKETPLPVSLPSDKTMVMEALSLAKSSSHLSPSEEVRCTQDFLSQTQSLLGLSSEGLLELTQVEVDSSSASTTLGRQCSLNCISSGCHTSGDSLELRKNHKNGPNTENMNLEAFDSVFIKQTSLSVSREVSLELSEEDSDIDLALTISPPTSPREEMPAGEIEQFEEAPFSNLELQDVAEEIGEPEEVALTESREVSSADNVSVYPSVSEEPVENKERKGDNLQPVTLILSKENCTLEIAEEINVTSDFPFDSVIEEVSPASSPEPPVPVKETRPYQAVTPCILKLHGTQCEKSNQISQCESEDLGITEKENVFVGPTHPVGQDNFTQVQQMQVSAEMPLILTDHPGRTGRPTLPGKVTEEIVSSEHDEGLSFSGKVQCYGRELNQPASAAKCTGDFSPSPEKLVKSGNPLQPVSIENRNLDLKHLVLESSEPPFGPRNVIENKSLSDTLVSTTAPSGIVNVSVKQQTSPKSSQNHLFPGDLKTDEGIYLQVKSLTAASVDGAYSTQGCMCSVVPTLCSSSDNATLTHYVRPINAEPVFQAQEIPAGRMASLLKNGEPEAELHKETTGPGTAGPQSNTTSSLKGERKAIHTLQDVSTCETKELLNVGVSSLCAGPYQNTADTKENLSKEPLASFVSESFDTSVCGIATEHVEIENSGEGLRAEAGSETLGRDGEVGVNSDMHYELSGDSDLDLLGDCRNPRLDLEDSYTLRGSYTRKKDVPTDGYESSLNFHNNNQEDWGCSSWVPGMETSLPPGHWTAAVKKEEKCVPPYVQIRDLHGILRTYANFSITKELKDTMRTSHGLRRHPSFSANCGLPSSWTSTWQVADDLTQNTLDLEYLRFAHKLKQTIKNGDSQHSASSANVFPKESPTQISIGAFPSTKISEAPFLHPAPRSRSPLLVTVVESDPRPQGQPRRGYTASSLDSSSSWRERCSHNRDLRNSQRNHTVSFHLNKLKYNSTVKESRNDISLILNEYAEFNKVMKNSNQFIFQDKELNDVSGEATAQEMYLPFPGRSASYEDIIIDVCTNLHVKLRSVVKEACKSTFLFYLVETEDKSFFVRTKNLLRKGGHTEIEPQHFCQAFHRENDTLIIIIRNEDISSHLHQIPSLLKLKHFPSVIFAGVDSPGDVLDHTYQELFRAGGFVISDDKILEAVTLVQLKEIIKILEKLNGNGRWKWLLHYRENKKLKEDERVDSTAHKKNIMLKSFQSANIIELLHYHQCDSRSSTKAEILKCLLNLQIQHIDARFAVLLTDKPTIPREVFENSGILVTDVNNFIENIEKIAAPFRSSYW,mutated_sequence,1.0,2430.0,UPI00004589BB.a2m,UPI00004589BB.npy,gnomAD +UPI0000130C80,UPI0000130C80.csv,MYYAVSQARVNAVPGTMLRPQRPGDLQLGASLYELVGYRQPPSSSSSSTSSTSSTSSSSTTAPLLPKAAREKPEAPAEPPGPGPGSGAHPGGSARPDAKEEQQQQLRRKINSRERKRMQDLNLAMDALREVILPYSAAHCQGAPGRKLSKIATLLLARNYILLLGSSLQELRRALGEGAGPAAPRLLLAGLPLLAAAPGSVLLAPGAVGPPDALRPAKYLSLALDEPPCGQFALPGGGAGGPGLCTCAVCKFPHLVPASLGLAAVQAQFSK,mutated_sequence,1.0,271.0,UPI0000130C80.a2m,UPI0000130C80.npy,gnomAD +UPI00001C1E1E,UPI00001C1E1E.csv,MGDFAAPAAAANGSSICINSSLNSSLGGAGIGVNNTPNSTPAAPSSNHPAAGGCGGSGGPGGGSAAVPKHSTVVERLRQRIEGCRRHHVNCENRYQQAQVEQLELERRDTVSLYQRTLEQRAKKSGAGTGKQQHPSKPQQDAEAASAEQRNHTLIMLQETVKRKLEGARSPLNGDQQNGACDGNFSPTSKRIRKDISAGMEAINNLPSNMPLPSASPLHQLDLKPSLPLQNSGTHTPGLLEDLSKNGRLPEIKLPVNGCSDLEDSFTILQSKDLKQEPLDDPTCIDTSETSLSNQNKLFSDINLNDQEWQELIDELANTVPEDDIQDLFNEDFEEKKEPEFSQPATETPLSQESASVKSDPSHSPFAHVSMGSPQARPSSSGPPFSTVSTATSLPSVASTPAAPNPASSPANCAVQSPQTPNQAHTPGQAPPRPGNGYLLNPAAVTVAGSASGPVAVPSSDMSPAEQLKQMAAQQQQRAKLMQQKQQQQQQQQQQQQQQQQQQQQQQQQQHSNQTSNWSPLGPPSSPYGAAFTAEKPNSPMMYPQAFNNQNPIVPPMANNLQKTTMNNYLPQNHMNMINQQPNNLGTNSLNKQHNILTYGNTKPLTHFNADLSQRMTPPVANPNKNPLMPYIQQQQQQQQQQQQQQQQQQPPPPQLQAPRAHLSEDQKRLLLMKQKGVMNQPMAYAALPSHGQEQHPVGLPRTTGPMQSSVPPGSGGMVSGASPAGPGFLGSQPQAAIMKQMLIDQRAQLIEQQKQQFLREQRQQQQQQQQQILAEQQLQQSHLPRQHLQPQRNPYPVQQVNQFQGSPQDIAAVRSQAALQSMRTSRLMAQNAGMMGIGPSQNPGTMATAAAQSEMGLAPYSTTPTSQPGMYNMSTGMTQMLQHPNQSGMSITHNQAQGPRQPASGQGVGMVSGFGQSMLVNSAITQQHPQMKGPVGQALPRPQAPPRLQSLMGTVQQGAQSWQQRSLQGMPGRTSGELGPFNNGASYPLQAGQPRLTKQHFPQGLSQSVVDANTGTVRTLNPAAMGRQMMPSLPGQQGTSQARPMVMSGLSQGVPGMPAFSQPPAQQQIPSGSFAPSSQSQAYERNAPQDVSYNYSGDGAGGSFPGLPDGADLVDSIIKGGPGDEWMQELDELFGNP,mutated_sequence,1.0,1138.0,UPI00001C1E1E.a2m,UPI00001C1E1E.npy,gnomAD +UPI000006FF4C,UPI000006FF4C.csv,MADKMVRTPKCSRCRNHGFLVPVKGHAGKCRWKQCLCEKCYLISERQKIMAAQKVLKTQAAEEEQEAALCAQGPKQASGAAAAAPAPVPVPAASLRPLSPGTPSGDADPGPEGRAAACFFEQPPRGRNPGPRALQPVLGGRSHVEPSERAAVAMPSLAGPPFGAEAAGSGYPGPLDLRRPMRTVPGPLFTDFVRPLNINPDRALGPEYPGGSSMHPYCPFPLGYLDAPPGVPLQQGFRHVSRSQYQGGGLVSEPGGDFQPSYYLPPPPPPLPPLPPLPPQPQFLPPGYLSALHFLPPPPPPPPPSSFSLTVLFDTDKENTDDQDAEVLSGEPSQPSSQEQSD,mutated_sequence,1.0,342.0,UPI000006FF4C.a2m,UPI000006FF4C.npy,gnomAD +UPI0000071B6E,UPI0000071B6E.csv,MEAARPFAREWRAQSLPLAVGGVLKLRLCELWLLLLGSSLNARFLPDEEDVDFINEYVNLHNELRGDVIPRGSNLRFMTWDVALSRTARAWGKKCLFTHNIYLQDVQMVHPKFYGIGENMWVGPENEFTASIAIRSWHAEKKMYNFENGSCSGDCSNYIQLVWDHSYKVGCAVTPCSKIGHIIHAAIFICNYAPGGTLTRRPYEPGIFCTRCGRRDKCTDFLCSKIKKINMKKMHNGLDKKNKRLNTSFLWSC,mutated_sequence,1.0,253.0,UPI0000071B6E.a2m,UPI0000071B6E.npy,gnomAD +UPI000012ADEC,UPI000012ADEC.csv,MMQESATETISNSSMNQNGMSTLSSQLDAGSRDGRSSGDTSSEVSTVELLHLQQQQALQAARQLLLQQQTSGLKSPKSSDKQRPLQVPVSVAMMTPQVITPQQMQQILQQQVLSPQQLQALLQQQQAVMLQQQQLQEFYKKQQEQLHLQLLQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQHPGKQAKEQQQQQQQQQQLAAQQLVFQQQLLQMQQLQQQQHLLSLQRQGLISIPPGQAALPVQSLPQAGLSPAEIQQLWKEVTGVHSMEDNGIKHGGLDLTTNNSSSTTSSNTSKASPPITHHSIVNGQSSVLSARRDSSSHEETGASHTLYGHGVCKWPGCESICEDFGQFLKHLNNEHALDDRSTAQCRVQMQVVQQLEIQLSKERERLQAMMTHLHMRPSEPKPSPKPLNLVSSVTMSKNMLETSPQSLPQTPTTPTAPVTPITQGPSVITPASVPNVGAIRRRHSDKYNIPMSSEIAPNYEFYKNADVRPPFTYATLIRQAIMESSDRQLTLNEIYSWFTRTFAYFRRNAATWKNAVRHNLSLHKCFVRVENVKGAVWTVDEVEYQKRRSQKITGSPTLVKNIPTSLGYGAALNASLQAALAESSLPLLSNPGLINNASSGLLQAVHEDLNGSLDHIDSNGNSSPGCSPQPHIHSIHVKEEPVIAEDEDCPMSLVTTANHSPELEDDREIEEEPLSEDLE,mutated_sequence,1.0,715.0,UPI000012ADEC.a2m,UPI000012ADEC.npy,gnomAD +UPI000013C70B,UPI000013C70B.csv,MFYYPNVLQRHTGCFATIWLAATRGSRLVKREYLRVNVVKTCEEILNYVLVRVQPPQPGLPRPRFSLYLSAQLQIGVIRVYSQQCQYLVEDIQHILERLHRAQLQIRIDMETELPSLLLPNHLAMMETLEDAPDPFFGMMSVDPRLPSPFDIPQIRHLLEAAIPERVEEIPPEVPTEPREPERIPVTVLPPEAITILEAEPIRMLEIEGERELPEVSRRELDLLIAEEEAILLEIPRLPPPAPAEVEGIGEALGPEELRLTGWEPGALLMEVTPPEELRLPAPPSPERRPPVPPPPRRRRRRRLLFWDKETQISPEKFQEQLQTRAHCWECPMVQPPERTIRGPAELFRTPTLSGWLPPELLGLWTHCAQPPPKALRRELPEEAAAEEERRKIEVPSEIEVPREALEPSVPLMVSLEISLEAAEEEKSRISLIPPEERWAWPEVEAPEAPALPVVPELPEVPMEMPLVLPPELELLSLEAVHRAVALELQANREPDFSSLVSPLSPRRMAARVFYLLLVLSAQQILHVKQEKPYGRLLIQPGPRFH,mutated_sequence,1.0,546.0,UPI000013C70B.a2m,UPI000013C70B.npy,gnomAD +UPI0000131BD3,UPI0000131BD3.csv,MELWPCLAAALLLLLLLVQLSRAAEFYAKVALYCALCFTVSAVASLVCLLRHGGRTVENMSIIGWFVRSFKYFYGLRFEVRDPRRLQEARPCVIVSNHQSILDMMGLMEVLPERCVQIAKRELLFLGPVGLIMYLGGVFFINRQRSSTAMTVMADLGERMVRENLKVWIYPEGTRNDNGDLLPFKKGAFYLAVQAQVPIVPVVYSSFSSFYNTKKKFFTSGTVTVQVLEAIPTSGLTAADVPALVDTCHRAMRTTFLHISKTPQENGATAGSGVQPAQ,mutated_sequence,1.0,278.0,UPI0000131BD3.a2m,UPI0000131BD3.npy,gnomAD +UPI0000E5A38D,UPI0000E5A38D.csv,XCRSFLLRPSLCAVLILQFFLFHHFGTGSIRKKRFVSSHRYVETMLVADQSMAEFHGSGLKHYLLTLFSVAARLYKHPSIRNSVSLVVVKILVIHDEQKGPEVTSNAALTLRNFCNWQKQHNPPSDRDAEHYDTAILFTRQDLCGSQTCDTLGMADVGTVCDPSRSCSVIEDDGLQAAFTTAHELGHVFNMPHDDAKQCA,mutated_sequence,1.0,200.0,UPI0000E5A38D.a2m,UPI0000E5A38D.npy,gnomAD +UPI00001AE868,UPI00001AE868.csv,MGLGLLLPLLLLWTRGTQGSELDPKGQHVCVASSPSAELQCCAGWRQKDQECTIPICEGPDACQKDEVCVKPGLCRCKPGFFGAHCSSRCPGQYWGPDCRESCPCHPHGQCEPATGACQCQADRWGARCEFPCACGPHGRCDPATGVCHCEPGWWSSTCRRPCQCNTAAARCEQATGACVCKPGWWGRRCSFRCNCHGSPCEQDSGRCACRPGWWGPECQQQCECVRGRCSAASGECTCPPGFRGARCELPCPAGSHGVQCAHSCGRCKHNEPCSPDTGSCESCEPGWNGTQCQQPCLPGTFGESCEQQCPHCRHGEACEPDTGHCQRCDPGWLGPRCEDPCPTGTFGEDCGSTCPTCVQGSCDTVTGDCVCSAGYWGPSCNASCPAGFHGNNCSVPCECPEGLCHPVSGSCQPGSGSRDTALIAGSLVPLLLLFLGLACCACCCWAPRSDLKDRPARDGATVSRMKLQVWGTLTSLGSTLPCRSLSSHKLPWVTVSHHDPEVPFNHSFIEPPSAGWATDDSFSSDPESGEADEVPAYCVPPQEGMVPVAQAGSSEASLAAGAFPPPEDASTPFAIPRTSSLARAKRPSVSFAEGTKFAPQSRRSSGELSSPLRKPKRLSRGAQSGPEGREAEESTGPEEAEAPESFPAAASPGDSATGHRRPPLGGRTVAEHVEAIEGSVQESSGPVTTIYMLAGKPRGSEGPVRSVFRHFGSFQKGQAEAKVKRAIPKPPRQALNRKKGSPGLASGSVGQSPNSAPKAGLPGATGPMAVRPEEAVRGLGAGTESSRRAQEPVSGCGSPEQDPQKQAEEERQEEPEYENVVPISRPPEP,mutated_sequence,1.0,830.0,UPI00001AE868.a2m,UPI00001AE868.npy,gnomAD +UPI0000203C32,UPI0000203C32.csv,MERISAFFSSIWDTILTKHQEGIYNTICLGVLLGLPLLVIITLLFICCHCCWSPPGKRGQQPEKNKKKKKKKKKKDEEDLWISAQPKLLQMEKRPSLPV,mutated_sequence,1.0,99.0,UPI0000203C32.a2m,UPI0000203C32.npy,gnomAD +UPI000006F1B6,UPI000006F1B6.csv,MATVAELKAVLKDTLEKKGVLGHLKARIRAEVFNALDDDREPRPSLSHENLLINELIREYLEFNKYKYTASVLIAESGQPVVPLDRQFLIHELNAFEESKDNTIPLLYGILAHFLRGTKDGIQNAFLKGPSLQPSDPSLGRQPSRRKPMDDHLRKEEQKSTNIEDLHVSQAVNR,mutated_sequence,1.0,174.0,UPI000006F1B6.a2m,UPI000006F1B6.npy,gnomAD +UPI000004A077,UPI000004A077.csv,MAQEVSEYLSQNPRVAAWVEALRCDGETDKHWRHRRDFLLRNAGDLAPAGGAASASTDEAADAESGTRNRQLQQLISFSMAWANHVFLGCRYPQKVMDKILSMAEGIKVTDAPTYTTRDELVAKVKKRGISSSNEGVEEPSKKRVIEGKNSSAVEQDHAKTSAKTERASAQQENSSTCIGSAIKSESGNSARSSGISSQNSSTSDGDRSVSSQSSSSVSSQVTTAGSGKASEAEAPDKHGSASFVSLLKSSVNSHMTQSTDSRQQSGSPKKSALEGSSASASQSSSEIEVPLLGSSGSSEVELPLLSSKPSSETASSGLTSKTSSEASVSSSVAKNSSSSGTSLLTPKSSSSTNTSLLTSKSTSQVAASLLASKSSSQTSGSLVSKSTSLASVSQLASKSSSQTSTSQLPSKSTSQSSESSVKFSCKLTNEDVKQKQPFFNRLYKTVAWKLVAVGGFSPNVNHGELLNAAIEALKATLDVFFVPLKELADLPQNKSSQESIVCELRCKSVYLGTGCGKSKENAKAVASREALKLFLKKKVVVKICKRKYRGSEIEDLVLLDEESRPVNLPPALKHPQELL,mutated_sequence,1.0,580.0,UPI000004A077.a2m,UPI000004A077.npy,gnomAD +UPI000013E694,UPI000013E694.csv,MALAALMIALGSLGLHTWQAQAVPILPLGLAPDTFDDTYVGCAEEMEEKAAPLLKEEMAHHALLRESWEAAQETWEDKRRGLTLPPGFKAQNGIAIMVYTNSSNTLYWELNQAVRTGGGSRELYMRHFPFKALHFYLIRALQLLRGSGGCSRGPGEVVFRGVGSLRFEPKRLGDSVRLGQFASSSLDKAVAHRFGNATLFSLTTCFGAPIQAFSVFPKEREVLIPPHEVFLVTRFSQDGAQSLVTLWSYNQTCSHFNCAYLGGEKRRGCVSAPGALGTGDLHMTKRHLQQP,mutated_sequence,1.0,291.0,UPI000013E694.a2m,UPI000013E694.npy,gnomAD +UPI0000488BDC,UPI0000488BDC.csv,MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPELYTSTSPSSSSSSPLSSSSSSSSSSSS,mutated_sequence,1.0,733.0,UPI0000488BDC.a2m,UPI0000488BDC.npy,gnomAD +UPI0000071E24,UPI0000071E24.csv,MRLPLSHSPEHVEMALLSNILAAYSFVSENPERAALYFVSGVCIGLVLTLAALVIRISCHTDCRRRPGKKFLQDRESSSDSSDSEDGSEDTVSDLSVRRHRRFERTLNKNVFTSAEELERAQRLEERERIIREIWMNGQPEVPGTRSLNRYY,mutated_sequence,1.0,152.0,UPI0000071E24.a2m,UPI0000071E24.npy,gnomAD +UPI000059D33E,UPI000059D33E.csv,MRELEAKATKDVERNLSRDLVQEEEQLMEEKKKKKDDKKKKEAAQKKATEQKIKVPEQIKPSVSQPQPANSNNGTSTATSTNNNAKRATANNQQPQQQQQQQQPQQQQPQQQPQPQPQQQQPQQQPQALPRYPREVPPRFRHQEHKQLLKRGQHFPVIAANLGSAVKVLNSQSESSALTNQQPQNNGEVQNSKNQSDINHSTSGSHYENSQRGPVSSTSDSSTNCKNAVVSDLSEKEAWPSAPGSDPELASECMDADSASSSESERNITIMASGNTGGEKDGLRNSTGLGSQNKFVVGSSSNNVGHGSSTGPWGFSHGAIISTCQVSVDAPESKSESSNNRMNAWGTVSSSSNGGLNPSTLNSASNHGAWPVLENNGLALKGPVGSGSSGINIQCSTIGQMPNNQSINSKVSGGSTHGTWGSLQETCESEVSGTQKVSFSGQPQNITTEMTGPNNTTNFMTSSLPNSGSVQNNELPSSNTGAWRVSTMNHPQMQAPSGMNGTSLSHLSNGESKSGGSYGTTWGAYGSNYSGDKCSGPNGQANGDTVNATLMQPGVNGPMGTNFQVNTNKGGGVWESGAANSQSTSWGSGNGANSGGSRRGWGTPAQNTGTNLPSVEWNKLPSNQHSNDSANGNGKTFTNGWKSTEEEDQGSATSQTNEQSSVWAKTGGTVESDGSTESTGRLEEKGTGESQSRDRRKIDQHTLLQSIVNRTDLDPRVLSNSGWGQTPIKQNTAWDTETSPRGERKTDNGTEAWGSSATQTFNSGACIDKTSPNGNDTSSVSGWGDPKPALRWGDSKGSNCQGGWEDDSAATGMVKSNQWGNCKEEKAAWNDSQKNKQGWGDGQKSSQGWSVSASDNWGETSRNNHWGEANKKSSSGGSDSDRSVSGWNELGKTSSFTWGNNINPNNSSGWDESSKPTPSQGWGDPPKSNQSLGWGDSSKPVSSPDWNKQQDIVGSWGIPPATGKPPGTGWLGGPIPAPAKEEEPTGWEEPSPESIRRKMEIDDGTSAWGDPSKYNYKNVNMWNKNVPNGNSRSDQQAQVHQLLTPASAISNKEASSGSGWGEPWGEPSTPATTVDNGTSAWGKPIDSGPSWGEPIAAASSTSTWGSSSVGPQALSKSGPKSMQDGWCGDDMPLPGNRPTGWEEEEDVEIGMWNSNSSQELNSSLNWPPYTKKMSSKGLSGKKRRRERGMMKGGNKQEEAWINPFVKQFSNISFSRDSPEENVQSNKMDLSGGMLQDKRMEIDKHSLNIGDYNRTVGKGPGSRPQISKESSMERNPYFDKDGIVADESQNMQFMSSQSMKLPPSNSALPNQALGSIAGLGMQNLNSVRQNGNPSMFGVGNTAAQPRGMQQPPAQPLSSSQPNLRAQVPPPLLSPQVPVSLLKYAPNNGGLNPLFGPQQVAMLNQLSQLNQLSQISQLQRLLAQQQRAQSQRSVPSGNRPQQDQQGRPLSVQQQMMQQSRQLDPNLLVKQQTPPSQQQPLHQPAMKSFLDNVMPHTTPELQKGPSPINAFSNFPIGLNSNLNVNMDMNSIKEPQSRLRKWTTVDSISVNTSLDQNSSKHGAISSGFRLEESPFVPYDFMNSSTSPASPPGSIGDGWPRAKSPNGSSSVNWPPEFRPGEPWKGYPNIDPETDPYVTPGSVINNLSINTVREVDHLRDRNSGSSSSLNTTLPSTSAWSSIRASNYNVPLSSTAQSTSARNSDSKLTWSPGSVTNTSLAHELWKVPLPPKNITAPSRPPPGLTGQKPPLSTWDNSPLRIGGGWGNSDARYTPGSSWGESSSGRITNWLVLKNLTPQIDGSTLRTLCMQHGPLITFHLNLPHGNALVRYSSKEEVVKAQKSLHMCVLGNTTILAEFASEEEISRFFAQSQSLTPSPGWQSLGSSQSRLGSLDCSHSFSSRTDLNHWNGAGLSGTNCGDLHGTSLWGTPHYSTSLWGPPSSSDPRGISSPSPINAFLSVDHLGGGGESM,mutated_sequence,1.0,1962.0,UPI000059D33E.a2m,UPI000059D33E.npy,gnomAD +UPI000013D842,UPI000013D842.csv,MSVRYSSSKHYSSSRSGGGGGGGGCGGGGGVSSLRISSSKGSLGGGFSSGGFSGGSFSRGSSGGGCFGGSSGGYGGLGGFGGGSFRGSYGSSSFGGSYGGIFGGGSFGGGSFGGGSFGGGGFGGGGFGGGFGGGFGGDGGLLSGNEKVTMQNLNDRLASYLDKVRALEESNYELEGKIKEWYEKHGNSHQGEPRDYSKYYKTIDDLKNQILNLTTDNANILLQIDNARLAADDFRLKYENEVALRQSVEADINGLRRVLDELTLTKADLEMQIESLTEELAYLKKNHEEEMKDLRNVSTGDVNVEMNAAPGVDLTQLLNNMRSQYEQLAEQNRKDAEAWFNEKSKELTTEIDNNIEQISSYKSEITELRRNVQALEIELQSQLALKQSLEASLAETEGRYCVQLSQIQAQISALEEQLQQIRAETECQNTEYQQLLDIKIRLENEIQTYRSLLEGEGSSGGGGRGGGSFGGGYGGGSSGGGSSGGGHGGGHGGSSGGGYGGGSSGGGSSGGGYGGGSSSGGHGGSSSGGYGGGSSGGGGGGYGGGSSGGGSSSGGGYGGGSSSGGHKSSSSGSVGESSSKGPRY,mutated_sequence,1.0,584.0,UPI000013D842.a2m,UPI000013D842.npy,gnomAD +UPI00001378FC,UPI00001378FC.csv,MASGDTLYIATDGSEMPAEIVELHEIEVETIPVETIETTVVGEEEEEDDDDEDGGGGDHGGGGGHGHAGHHHHHHHHHHHPPMIALQPLVTDDPTQVHHHQEVILVQTREEVVGGDDSDGLRAEDGFEDQILIPVPAPAGGDDDYIEQTLVTVAAAGKSGGGGSSSSGGGRVKKGGGKKSGKKSYLSGGAGAAGGGGADPGNKKWEQKQVQIKTLEGEFSVTMWSSDEKKDIDHETVVEEQIIGENSPPDYSEYMTGKKLPPGGIPGIDLSDPKQLAEFARMKPRKIKEDDAPRTIACPHKGCTKMFRDNSAMRKHLHTHGPRVHVCAECGKAFVESSKLKRHQLVHTGEKPFQCTFEGCGKRFSLDFNLRTHVRIHTGDRPYVCPFDGCNKKFAQSTNLKSHILTHAKAKNNQ,mutated_sequence,1.0,414.0,UPI00001378FC.a2m,UPI00001378FC.npy,gnomAD +UPI0001838820,UPI0001838820.csv,MAPYPCGCHILLLLFCCLAAARANLLNLNWLWFNNEDTSHAATTIPEPQGPLPVQPTADTTTHVTPRNGSTEPATAPGSPEPPSELLEDGQDTPTSAESPDAPEENIAGVGAEILNVAKGIRSFVQLWNDTVPTESLARAETLVLETPVGPLALAGPSSTPQENGTTLWPSRGIPSSPGAHTTEAGTLPAPTPSPPSLGRPWAPLTGPSVPPPSSGRASLSSLLGGAPPWGSLQDPDSQGLSPAAAAPSQQLQRPDVRLRTPLLHPLVMGSLGKHAAPSAFSSGLPGALSQVAVTTLTRDSGAWVSHVANSVGPGLANNSALLGADPEAPAGRCLPLPPSLPVCGHLGISRFWLPNHLHHESGEQVRAGARAWGGLLQTHCHPFLAWFFCLLLVPPCGSVPPPAPPPCCQFCEALQDACWSRLGGGRLPVACASLPTQEDGYCVLIGPAAERISEEVGLLQLLGDPPPQQVTQTDDPDVGLAYVFGPDANSGQVARYHFPSLFFRDFSLLFHIRPATEGPGVLFAITDSAQAMVLLGVKLSGVQDGHQDISLLYTEPGAGQTHTAASFRLPAFVGQWTHLALSVAGGFVALYVDCEEFQRMPLARSSRGLELEPGAGLFVAQAGGADPDKFQGVIAELKVRRDPQVSPMHCLDEEGDDSDGASGDSGSGLGDARELLREETGAALKPRLPAPPPVTTPPLAGGSSTEDSRSEEVEEQTTVASLGAQTLPGSDSVSTWDGSVRTPGGRVKEGGLKGQKGEPGVPGPPGRAGPPGSPCLPGPPGLPCPVSPLGPAGPALQTVPGPQGPPGPPGRDGTPGRDGEPGDPGEDGKPGDTGPQGFPGTPGDVGPKGDKGDPGVGERGPPGPQGPPGPPGPSFRHDKLTFIDMEGSGFGGDLEALRGPRGFPGPPGPPGVPGLPGEPGRFGVNSSDVPGPAGLPGVPGREGPPGFPGLPGPPGPPGREGPPGRTGQKGSLGEAGAPGHKGSKGAPGPAGARGESGLAGAPGPAGPPGPPGPPGPPGPGLPAGFDDMEGSGGPFWSTARSADGPQGPPGLPGLKGDPGVPGLPGAKGEVGADGVPGFPGLPGREGIAGPQGPKGDRGSRGEKGDPGKDGVGQPGLPGPPGPPGPVVYVSEQDGSVLSVPGPEGRPGFAGFPGPAGPKGNLGSKGERGSPGPKGEKGEPGSIFSPDGGALGPAQKGAKGEPGFRGPPGPYGRPGYKGEIGFPGRPGRPGMNGLKGEKGEPGDASLGFGMRGMPGPPGPPGPPGPPGTPVYDSNVFAESSRPGPPGLPGNQGPPGPKGAKGEVGPPGPPGQFPFDFLQLEAEMKGEKGDRGDAGQKGERGEPGGGGFFGSSLPGPPGPPGPPGPRGYPGIPGPKGESIRGQPGPPGPQGPPGIGYEGRQGPPGPPGPPGPPSFPGPHRQTISVPGPPGPPGPPGPPGTMGASSGVRLWATRQAMLGQVHEVPEGWLIFVAEQEELYVRVQNGFRKVQLEARTPLPRGTDNEVAALQPPVVQLHDSNPYPRREHPHPTARPWRADDILASPPRLPEPQPYPGAPHHSSYVHLRPARPTSPPAHSHRDFQPVLHLVALNSPLSGGMRGIRGADFQCFQQARAVGLAGTFRAFLSSRLQDLYSIVRRADRAAVPIVNLKDELLFPSWEALFSGSEGPLKPGARIFSFDGKDVLRHPTWPQKSVWHGSDPNGRRLTESYCETWRTEAPSATGQASSLLGGRLLGQSAASCHHAYIVLCIENSFMTASK,mutated_sequence,1.0,1754.0,UPI0001838820.a2m,UPI0001838820.npy,gnomAD +UPI00001AFE92,UPI00001AFE92.csv,MGSSRAPWMGRVGGHGMMALLLAGLLLPGTLAKSIGTFSDPCKDPTRITSPNDPCLTGKGDSSGFSSYSGSSSSGSSISSARSSGGGSSGSSSGSSIAQGGSAGSFKPGTGYSQVSYSSGSGSSLQGASGSSQLGSSSSHSGNSGSHSGSSSSHSSSSSSFQFSSSSFQVGNGSALPTNDNSYRGILNPSQPGQSSSSSQTFGVSSSGQSVSSNQRPCSSDIPDSPCSGGPIVSHSGPYIPSSHSVSGGQRPVVVVVDQHGSGAPGVVQGPPCSNGGLPGKPCPPITSVDKSYGGYEVVGGSSDSYLVPGMTYSKGKIYPVGYFTKENPVKGSPGVPSFAAGPPISEGKYFSSNPIIPSQSAASSAIAFQPVGTGGVQLCGGGSTGSKGPCSPSSSRVPSSSSISSSSGLPYHPCGSASQSPCSPPGTGSFSSSSSSQSSGKIILQPCGSKSSSSGHPCMSVSSLTLTGGPDGSPHPDPSAGAKPCGSSSAGKIPCRSIRDILAQVKPLGPQLADPEVFLPQGELLNSP,mutated_sequence,1.0,529.0,UPI00001AFE92.a2m,UPI00001AFE92.npy,gnomAD +UPI000013C8B1,UPI000013C8B1.csv,MMFPGLLAPPAGYPSLLRPTPTLTLPQSLQSAFSGHSSFLVEDLIRISRPPAYLPRSVPTASMSPPRQGAPTALTDTGASDLGSPGPGSRRGGSPPTAFSPASETTFLKFGVNAILSSGPRTETSPALLQSVPPKTFAFPYFEGSFQPFIRSSYFPASSSVVPIPGTFSWPLAARGKPRRGMLRRAVFSDVQRKALEKMFQKQKYISKPDRKKLAAKLGLKDSQVKIWFQNRRMKWRNSKERELLSSGGCREQTLPTKLNPHPDLSDVGQKGPGNEEEEEGPGSPSHRLAYHASSDPQHLRDPRLPGPLPPSPAHSSSPGKPSDFSDSEEEEEGEEQEEITVS,mutated_sequence,1.0,343.0,UPI000013C8B1.a2m,UPI000013C8B1.npy,gnomAD +UPI000004B1EC,UPI000004B1EC.csv,MFLYLCFIFQRTCSEEMEEENATLLTEFVLTGFLHQPDCKIPLFLAFLVIYLITIMGNLGLIVLIWKDPHLHIPMYLFLGSLAFVDASLSSTVTPKMLINFLAKSKMISLSECMVQFFSLVTTVTTECFLLATMAYDRYVAICKALLYPVIMTNELCIQLLVLSFIGGLLHALIHEAFSFRLTFCNSNIIQHFYCDIIPLLKISCTDSSINFLMVFIFAGSVQVFTIGTILISYTIILFTILEKKSIKGIRKAVSTCGAHLLSVSLYYGPLTFKYLGSASPQADDQDMMESLFYTVIVPLLNPMIYSLRNKQVIASFTKMFKSNV,mutated_sequence,1.0,325.0,UPI000004B1EC.a2m,UPI000004B1EC.npy,gnomAD +UPI00001D7FAE,UPI00001D7FAE.csv,MEEGVQAPDWDSDETVIEGSVTESDLEEKELPWRRLLFDQDASLKSEFSLHPDTRGMCKGMPSPEIQLGFKLREDLQEQMNKNKMMPVLSEDTILQSQDETERNQALLQTRKNCSMFIGSFRQSGLSLNHQNIEGPEAESPEVLPHIEKELSEGRDSPEVSLLSGTAITVSDTVAVKETSLVEPEKILAAPNTFFEPRKEVTMTMTSEETKDEESSLETFVSALESLLTSPESTQEERLFELVSDFDRKELMNPLSDSLSSISIPLNSWSACHRDLLEDAKDDALPAELLEALNTLSEAKVETICHRKEGGSSLIARNECLEVEFNTSQTNEDCTQIAETLQDPNPSGLQTLAHQNITSCEPLSNKRNSNSVTNSSDQETACVLRRSSRLEKLKVSRDAKYSDHMYKMPEKILPKILGCEDLTNNNSSAQNFRMQDPALMIDGKEKNMHSARFKNGKQIRKNEQFSGKKEKMKVNKISLHSINRRNIFGENLVYKAALHDDADLVHHCIKKGGNVNQPSYAGWTALHEASVGGFYRTASELLKGGADVNIKGLYQITPLHDAVMNGHYKVAELLLLNGADPLFRNDDGKCALDEAKDLCMKRLLERYIPKHQKCLTSAQRSSIDPLDIEDVYQHKKPKFSSKSHIWHVYNENSNRQKLEHVKVNKGSKASLFINKEDVYEYYQKDPKNTKFGKSKHKQSTLDQIYSTGLRKGNLHNVKDPNTNVPKGIGRRKTQHKRTQVDDVDCNPRKILAVSPSRRINRLVTYQQHIPETHNDLPEELCEPSSLTLSSLRNGLDSSTEACSVSKEKHIQNLDLSDSQEVQCLELESVDQTEAVSFPGLLLHKEIKLPVVTTDKQPHTLQEQHHVLYKSHENSNLVPKDERFNKWENSFLSFVKENSDNDDDDDCSTSEKAITSKKVLCSTGGKKHYNFKENLTNKKEMGFQQFLLSEDHLSQENELKAVSLTTLPEQEAVNFSYSDNAVISEHVANYEQCIFGPSFDHSNGNPEQNSLACMRTLLTHEASKLTNHVELFKKPQDYIPRAPTFLMNQTDTHIVEKMAKNCDTERNYIDRDQKIIYSNEPLSIVAHSQVIETTKVEKRRQNHLESETIHNIDSHSTDNMSKELANISKLSQREKKEISHKPGMKAGRINKRNARGESQLHLAVRRGNLPLVKALIESGADVNLNDNAGWTPLHEASNEGSIDIIVELLKAGAKVNCENIDGILPLHDAVANNHLKAAEILLQNGANPNQKDQKQKSALDEADDEKMKELLRSYGAIETVNRDESDAIVNEKIPAVRSKRHKQCFCDDGKTIDSSSLSHQERSRESLSVHQTLSAILQDIEEKQEYLLEFEIRNPEDAEQYIEKMLKIKKIMDNVLAKQKAERDDLAKKYRVSIESFKHGALREQLANLAARQKSLLVVAKKQKKISLKIQNCRNVTSLPCLSLRKLPPRSEISSEKDSQELTSLENLEHPQSGSLSPVSGSMQETQLSLETWNYSQNTNICLNSEAVRRGEFSGNDMNSKQNGSDCTLDGFPKSRHSDGTEKNKLPSQPVAFIGQTEYSQKENDLTEATDKDHEFYVSSPVIGKLNISETASVLAENAAHPSNIICDQDLSNYDPKRGNRKTSSQQSPTGASESLAHQGIAVLGSDTVHQMKPYLKKSVSVVPCADDSQISSSSGSGQQDTIKKALNYSTAPKKKCIQIKDLILLGRINPGNNILEFKTQETTHKASILLNGKLKVESGQIYKNPVTWLKDLLGGNSYVTWNYAWSKVTYLGKELLRYVSEDAPILPEPNSVPQQYQPCLPEVACLDDPVQEPNKSMFEKTKFGQGTSRESMQSSPRYLQINEILLISDQEFLPCHIMDQHWKFCVECEELTP,mutated_sequence,1.0,1873.0,UPI00001D7FAE.a2m,UPI00001D7FAE.npy,gnomAD +UPI0000140198,UPI0000140198.csv,MLQTPESRGLPVPQAEGEKDGGHDGETRAPTASQERPKEELGAGREEGAAEPALTRKGARALAAKALARRRAYRRLNRTVAELVQFLLVKDKKKSPITRSEMVKYVIGDLKILFPDIIARAAEHLRYVFGFELKQFDRKHHTYILINKLKPLEEEEEEDLGGDGPRLGLLMMILGLIYMRGNSAREAQVWEMLRRLGVQPSKYHFLFGYPKRLIMEDFVQQRYLSYRRVPHTNPPEYEFSWGPRSNLEISKMEVLGFVAKLHKKEPQHWPVQYREALADEADRARAKARAEASMRARASARAGIHLW,mutated_sequence,1.0,307.0,UPI0000140198.a2m,UPI0000140198.npy,gnomAD +UPI0000135F5C,UPI0000135F5C.csv,MGSPAHRPALLLLLPPLLLLLLLRVPPSRSFPGSGDSPLEDDEVGYSHPRYKDTPWCSPIKVKYGDVYCRAPQGGYYKTALGTRCDIRCQKGYELHGSSLLICQSNKRWSDKVICKQKRCPTLAMPANGGFKCVDGAYFNSRCEYYCSPGYTLKGERTVTCMDNKAWSGRPASCVDMEPPRIKCPSVKERIAEPNKLTVRVSWETPEGRDTADGILTDVILKGLPPGSNFPEGDHKIQYTVYDRAENKGTCKFRVKVRVKRCGKLNAPENGYMKCSSDGDNYGATCEFSCIGGYELQGSPARVCQSNLAWSGTEPTCAAMNVNVGVRTAAALLDQFYEKRRLLIVSTPTARNLLYRLQLGMLQQAQCGLDLRHITVVELVGVFPTLIGRIGAKIMPPALALQLRLLLRIPLYSFSMVLVDKHGMDKERYVSLVMPVALFNLIDTFPLRKEEMVLQAEMSQTCNT,mutated_sequence,1.0,464.0,UPI0000135F5C.a2m,UPI0000135F5C.npy,gnomAD +UPI000000D824,UPI000000D824.csv,MNGDHMVLGSSVTDKKAIILVTILLLLRLVAIAGNGFITAALGVEWVLRRMLLPCDKLLVSLGASRFCLQSVVMGKTIYVFLHPMAFPYNPVLQFLAFQWDFLNAATLWSSTWLSVFYCVKIATFTHPVFFWLKHKLSGWLPWMLFSSVGLSSFTTILFFIGNHRMYQNYLRNHLQPWNVTGDSIRSYCEKFYLFPLKMITWTMPTAVFFICMILLITSLGRHRKKALLTTSGFREPSVQAHIKALLALLSFAMLFISYFLSLVFSAAGIFPPLDFKFWVWESVIYLCAAVHPIILLFSNCRLRAVLKSRRSSRCGTP,mutated_sequence,1.0,318.0,UPI000000D824.a2m,UPI000000D824.npy,gnomAD +UPI0001F7848D,UPI0001F7848D.csv,MKKNTSKTTMRINKQDALCTPHSHDPRDLQNMLDGGEYAPFVSPPMLESNFIQVNRRGESIYLHNRANWVTVGICFS,mutated_sequence,1.0,77.0,UPI0001F7848D.a2m,UPI0001F7848D.npy,gnomAD +UPI000004673A,UPI000004673A.csv,MGKGDVLEAAPTTTAYHSLMDEYGYEVGKAIGHGSYGSVYEAFYTKQKVMVAVKIISKKKASDDYLNKFLPREIQVMKVLRHKYLINFYRAIESTSRVYIILELAQGGDVLEWIQRYGACSEPLAGKWFSQLTLGIAYLHSKSIVHRDLKLENLLLDKWENVKISDFGFAKMVPSNQPVGCSPSYRQVNCFSHLSQTYCGSFAYACPEILRGLPYNPFLSDTWSMGVILYTLVVAHLPFDDTNLKKLLRETQKEVTFPANHTISQECKNLILQMLRQATKRATILDIIKDSWVLKFQPEQPTHEIRLLEAMCQLHNTTKQHQSLQITT,mutated_sequence,1.0,328.0,UPI000004673A.a2m,UPI000004673A.npy,gnomAD +UPI00001DFBF3,UPI00001DFBF3.csv,MPFAKRIVEPQWLCRQRRPAPGPAVDASGGSAEPPPPLQPPGRRDLDEVEAPGPEEPARAVPAPSGLPPPPPPLPAPADQTQPPHGEASVAGEESTAGIPEAAPAAGEASSAAAAAAVLLMLDLCAVSNAALARVLRQLSDVARHACSLFQELESDIQLTHRRVWALQGKLGGVQRVLSTLDPKQEAVPVSNLDIESKLSVYYRAPWHQQRNIFLPATRPPCVEELHRHARQSLQALRREHRSRSDRREQRAAAPLSIAAPPLPAYPPAHSQRRREFKDRHFLTSHPPEDEDTDVMLGQRPKNPIHNIPSTLDKQTNWSKALPLPTPEEKMKQDAQVISSCIIPINVTGVGFDREASIRCSLVHSQSVLQRRRKLRRRKTISGIPRRVQQEIDSDESPVARERNVIVHTNPDPSNTVNRISGTRDSECQTEDILIAAPSRRRIRAQRGQSIAASLSHSAGNISALADKGDTMFTPAVSSRTRSRSLPREGNRGGDAEPKVGAKPSAYEEGESFVGDHERTPNDFSEAPSSPSAQDHQPTLGLACSQHLHSPQHKLSERGRSRLSRMAADSGSCDISSNSDTFGSPIHCISTAGVLLSSHMDQKDDHQSSSGNWSGSSSTCPSQTSETIPPAASPPLTGSSHCDSELSLNTAPHANEDASVFVTEQYNDHLDKVRGHRANSFTSTVADLLDDPNNSNTSDSEWNYLHHHHDASCRQDFSPERPKADSLGCPSFTSMATYDSFLEKSPSDKADTSSHFSVDTEGYYTSMHFDCGLKGNKSYVCHYAALGPENGQGVGASPGLPDCAWQDYLDHKRQGRPSISFRKPKAKPTPPKRSSSLRKSDGNADISEKKEPKISSGQHLPHSSREMKLPLDFANTPSRMENANLPTKQEPSWINQSEQGIKEPQLDASDIPPFKDEVAESTHYADLWLLNDLKTNDPYRSLSNSSTATGTTVIECIKSPESSESQTSQSESRATTPSLPSVDNEFKLASPEKLAGLASPSSGYSSQSETPTSSFPTAFFSGPLSPGGSKRKPKVPERKSSLQQPSLKDGTISLSKDLELPIIPPTHLDLSALHNVLNKPFHHRHPLHVFTHNKQNTVGETLRSNPPPSLAITPTILKSVNLRSINKSEEVKQKEENNTDLPYLEESTLTTAALSPSKIRPHTANKSVSRQYSTEDTILSFLDSSAVEMGPDKLHLEKNSTFDVKNRCDPETITSAGSSLLDSNVTKDQVRTETEPIPENTPTKNCAFPTEGFQRVSAARPNDLDGKIIQYGPGPDETLEQVQKAPSAGLEEVAQPESVDVITSQSDSPTRATDVSNQFKHQFVMSRHHDKVPGTISYESEITSVNSFPEKCSKQENIASGISAKSASDNSKAEETQGNVDEASLKESSPSDDSIISPLSEDSQAEAEGVFVSPNKPRTTEDLFAVIHRSKRKVLGRKDSGDMSVRSKSRAPLSSSSSSASSITSPSSNVTTPNSQRSPGLIYRNAKKSNTSNEEFKLLLLKKGSRSDSSYRMSATEILKSPILPKPPGELTAESPQSTDDAHQGSQGAEALSPLSPCSPRVNAEGFSSKSFATSASARVGRSRAPPAASSSRYSVRCRLYNTPMQAISEGETENSDGSPHDDRSSQSST,mutated_sequence,1.0,1630.0,UPI00001DFBF3.a2m,UPI00001DFBF3.npy,gnomAD +UPI00001D68E9,UPI00001D68E9.csv,MEAFPWAPRSPRRGRAPPPMALVPSARYVSAPGPAHPQPFSSWNDYLGLATLITKAVDGEPRFGCARGGNGGGGSPPSSSSSSCCSPHTGAGPGALGPALGPPDYDEDDDDDSDEPGSRGRYLGSALELRALELCAGPAEAGLLEERFAELSPFAGRAAAVLLGCAPAAAAAATTTSEATPREERAPAWAAEPRLHAASGAAAARLLKPELQVCVFCRNNKEAMALYTTHILKGPDGRVLCPVLRRYTCPLCGASGDNAHTIKYCPLSKVPPPPARPPPRSARDGPPGKKLR,mutated_sequence,1.0,292.0,UPI00001D68E9.a2m,UPI00001D68E9.npy,gnomAD +UPI00001AED69,UPI00001AED69.csv,MEGGGKPNSSSNSRDDGNSVFPAKASATGAGPAAAEKRLGTPPGGGGAGAKEHGNSVCFKVDGGGGGGGGGGGGEEPAGGFEDAEGPRRQYGFMQRQFTSMLQPGVNKFSLRMFGSQKAVEKEQERVKTAGFWIIHPYSDFRFYWDLIMLIMMVGNLVIIPVGITFFTEQTTTPWIIFNVASDTVFLLDLIMNFRTGTVNEDSSEIILDPKVIKMNYLKSWFVVDFISSIPVDYIFLIVEKGMDSEVYKTARALRIVRFTKILSLLRLLRLSRLIRYIHQWEEIFHMTYDLASAVVRIFNLIGMMLLLCHWDGCLQFLVPLLQDFPPDCWVSLNEMVNDSWGKQYSYALFKAMSHMLCIGYGAQAPVSMSDLWITMLSMIVGATCYAMFVGHATALIQSLDSSRRQYQEKYKQVEQYMSFHKLPADMRQKIHDYYEHRYQGKIFDEENILNELNDPLREEIVNFNCRKLVATMPLFANADPNFVTAMLSKLRFEVFQPGDYIIREGAVGKKMYFIQHGVAGVITKSSKEMKLTDGSYFGEICLLTKGRRTASVRADTYCRLYSLSVDNFNEVLEEYPMMRRAFETVAIDRLDRIGKKNSILLQKFQKDLNTGVFNNQENEILKQIVKHDREMVQAIAPINYPQMTTLNSTSSTTTPTSRMRTQSPPVYTATSLSHSNLHSPSPSTQTPQPSAILSPCSYTTAVCSPPVQSPLAARTFHYASPTASQLSLMQQQPQQQVQQSQPPQTQPQQPSPQPQTPGSSTPKNEVHKSTQALHNTNLTREVRPLSASQPSLPHEVSTLISRPHPTVGESLASIPQPVTAVPGTGLQAGGRSTVPQRVTLFRQMSSGAIPPNRGVPPAPPPPAAALPRESSSVLNTDPDAEKPRFASNL,mutated_sequence,1.0,890.0,UPI00001AED69.a2m,UPI00001AED69.npy,gnomAD +UPI00004C3E05,UPI00004C3E05.csv,MRGGKCNMLSSLGCLLLCGSITLALGNAQKLPKGKRPNLKVHINTTSDSILLKFLRPSPNVKLEGLLLGYGSNVSPNQYFPLPAEGKFTEAIVDAEPKYLIVVRPAPPPSQKKSCSGKTRSRKPLQLVVGTLTPSSVFLSWGFLINPHHDWTLPSHCPNDRFYTIRYREKDKEKKWIFQICPATETIVENLKPNTVYEFGVKDNVEGGIWSKIFNHKTVVGSKKVNGKIQSTYDQDHTVPAYVPRKLIPITIIKQVIQNVTHKDSAKSPEKAPLGGVILVHLIIPGLNETTVKLPASLMFEISDALKTQLAKNETLALPAESKTPEVEKISARPTTVTPETVPRSTKPTTSSALDVSETTLVLSKRTPETLQTILIPQFELPLSTLAPKSLPEFPEAKTPFPFEKPRGTLASSEKPWIVPTAKISEDSKVLQPQTATYDVFSSPTTSDEPEISDSYTATSDRILDSIPPKTSRTLEQPRATLAPSETPFVPQKLEIFTSPEMQPTTPAPQQTTSIPSTPKRRPRPKPPRTKPERTTSAGTITPKISKSPEPTWTTPAPGKTQFISLKPKIPLSPEVTHTKPAPEPQTLLPSQSTIGPETPGTKPSTTLAPRKTKRPGRRPRPRPRPKTTPSPEVPKSKPALEPATIQPEPLVPTTASKPSERPKTTHRPDAPQIQPGSKPPKQLLPKPQTTAEPDMPPTKSVSEPVPFETEAPSMTIVPTTDIEPVTVRTEATVTTLAPKTSQRTRTRRPRPKHKTTPRPETLQTKLDFGPITPGTSSAPTTTTKRTRRPHPKPKTTPHPEVPQTKLATKTSKRTRPPRPRPKTTPSPQAPETKPVPATVLEPVTLRPEASTTLASKTSQRTRRPRLRTKTTPRPEAPESKPVPTAELKPVTLRTETWVTTQAPKTSQRTRRPRPKTKTTPSPEVPQTKLVPSTDLEPGTLRTEAPKTMVVTTVLEPDTFRTKFPETTLAPKTQRTRRPRPRPKTTSSPEVPQNKSGKCMNPWLK,mutated_sequence,1.0,1005.0,UPI00004C3E05.a2m,UPI00004C3E05.npy,gnomAD +UPI000012ADD0,UPI000012ADD0.csv,MNLPRAERPRSTPQRSLRDSDGEDGKIDVLGEEEDEDEVEDEEEEASQKFLEQSLQPGLQVARWGGVALPREHIEGGGPSDPSEFGTEFRAPPRSAAASEDARQPAKPPYSYIALITMAILQSPHKRLTLSGICAFISGRFPYYRRKFPAWQNSIRHNLSLNDCFVKIPREPGHPGKGTYWSLDPASQDMFDNGSFLRRRKRFKRHQLTPGAHLPHPFPLPAAHAALHNPRPGPLLGAPALPQPVPGAYPNTAPGRRPYALLHPHPPRYLLLSAPAYAGAPKKAEGADLATPGTLPVLQPSLGPQPWEEGKGLASPPGGGCISFSIESIMQGVRGAGTGAAQSLSPTAWSYCPLLQRPSSLSDNFAATAAASGGGLRQRLRSHQGRGAGRAPVGRVGAAAVSGGGRGL,mutated_sequence,1.0,408.0,UPI000012ADD0.a2m,UPI000012ADD0.npy,gnomAD +UPI000012BB5B,UPI000012BB5B.csv,MAARKGRRRTCETGEPMEAESGDTSSEGPAQVYLPGRGPPLREGEELVMDEEAYVLYHRAQTGAPCLSFDIVRDHLGDNRTELPLTLYLCAGTQAESAQSNRLMMLRMHNLHGTKPPPSEGSDEEEEEEDEEDEEERKPQLELAMVPHYGGINRVRVSWLGEEPVAGVWSEKGQVEVFALRRLLQVVEEPQALAAFLRDEQAQMKPIFSFAGHMGEGFALDWSPRVTGRLLTGDCQKNIHLWTPTDGGSWHVDQRPFVGHTRSVEDLQWSPTENTVFASCSADASIRIWDIRAAPSKACMLTTATAHDGDVNVISWSRREPFLLSGGDDGALKIWDLRQFKSGSPVATFKQHVAPVTSVEWHPQDSGVFAASGADHQITQWDLAVERDPEAGDVEADPGLADLPQQLLFVHQGETELKELHWHPQCPGLLVSTALSGFTIFRTISV,mutated_sequence,1.0,446.0,UPI000012BB5B.a2m,UPI000012BB5B.npy,gnomAD +UPI000003032B,UPI000003032B.csv,MSRSAAASGGPRRPERHLPPAPCGAPGPPETCRTEPDGAGTMNKLRQSLRRRKPAYVPEASRPHQWQADEDAVRKGTCSFPVRYLGHVEVEESRGMHVCEDAVKKLKAMGRKSVKSVLWVSADGLRVVDDKTKDLLVDQTIEKVSFCAPDRNLDKAFSYICRDGTTRRWICHCFLALKDSGERLSHAVGCAFAACLERKQRREKECGVTAAFDASRTSFAREGSFRLSGGGRPAEREAPDKKKAEAAAAPTVAPGPAQPGHVSPTPATTSPGEKGEAGTPVAAGTTAAAIPRRHAPLEQLVRQGSFRGFPALSQKNSPFKRQLSLRLNELPSTLQRRTDFQVKGTVPEMEPPGAGDSDSINALCTQISSSFASAGAPAPGPPPATTGTSAWGEPSVPPAAAFQPGHKRTPSEAERWLEEVSQVAKAQQQQQQQQQQQQQQQQQQQQAASVAPVPTMPPALQPFPAPVGPFDAAPAQVAVFLPPPHMQPPFVPAYPGLGYPPMPRVPVVGITPSQMVANAFCSAAQLQPQPATLLGKAGAFPPPAIPSAPGSQARPRPNGAPWPPEPAPAPAPELDPFEAQWAALEGKATVEKPSNPFSGDLQKTFEIEL,mutated_sequence,1.0,609.0,UPI000003032B.a2m,UPI000003032B.npy,gnomAD +UPI000004A951,UPI000004A951.csv,MSTAPSLSALRSSKHSGGGGGGGGGGGADPAWTSALSGNSSGPGPGSSPAGSTKPFVHAVPPSDPLRQANRLPIKVLKMLTARTGHILHPEYLQPLPSTPVSPIELDAKKSPLALLAQTCSQIGKPDPSPSSKLSSVASNGGGAGGAGGGAAGDKDTKSGPLKLSDIGVEDKSSFKPYSKPGSDKKEPGGGGGGGGGGGGGGGGVSSEKSGFRVPSATCQPFTPRTGSPSSSASACSPGGMLSSAGGAPEGKDDKKDTDVGGGGKGTGGASAEGGPTGLAHGRISCGGGINVDVNQHPDGGPGGKALGSDCGGSSGSSSGSGPSAPTSSSVLGSGLVAPVSPYKPGQTVFPLPPAGMTYPGSLAGAYAGYPPQFLPHGVALDPTKPGSLVGAQLAAAAAGSLGCSKPAGSSPLAGASPPSVMTASLCRDPYCLSYHCASHLAGAAAASASCAHDPAAAAAALKSGYPLVYPTHPLHGVHSSLTAAAAAGATPPSLAGHPLYPYGFMLPNDPLPHICNWVSANGPCDKRFATSEELLSHLRTHTAFPGTDKLLSGYPSSSSLASAAAAAMACHMHIPTSGAPGSPGTLALRSPHHALGLSSRYHPYSKSPLPTPGAPVPVPAATGPYYSPYALYGQRLTTASALGYQ,mutated_sequence,1.0,646.0,UPI000004A951.a2m,UPI000004A951.npy,gnomAD +UPI0000D61567,UPI0000D61567.csv,MESGAGKHWTEEEVKALLSVWAEKNIRKQLYGTLRNKGIFIYIAKRLQSLGVYRDWKQCWAKYKNLKYEYRTVKYAHNSGDSSKTMKFFHDLDVILQYEPATQFTEEDANGRYLETLSPSTAPETTEGKKKKKKKKKKKSTLVFLLFYSF,mutated_sequence,1.0,150.0,UPI0000D61567.a2m,UPI0000D61567.npy,gnomAD +UPI0000037B1F,UPI0000037B1F.csv,MPTNCAAAGCATTYNKHINISFHRFPLDPKRRKEWVRLVRRKNFVPGKHTFLCSKHFEASCFDLTGQTRRLKMDAVPTIFDFCTHIKSMKLKSRNLLKKNNSCSPAGPSNLKSNISSQQVLLEHSYAFRNPMEAKKRIIKLEKEIASLRRKMKTCLQKERRATRRWIKATCLVKNLEANSVLPKGTSEHMLPTALSSLPLEDFKILEQDQQDKTLLSLNLKQTKSTFI,mutated_sequence,1.0,228.0,UPI0000037B1F.a2m,UPI0000037B1F.npy,gnomAD +UPI00004565DA,UPI00004565DA.csv,MTRGGPGGRPGLPQPPPLLLLLLLLPLLLVTAEPPKPAGVYYATAYWMPAEKTVQVKNVMDKNGDAYGFYNNSVKTTGWGILEIRAGYGSQTLSNEIIMFVAGFLEGYLTAPHMNDHYTNLYPQLITKPSIMDKVQDFMEKQDKWTRKNIKEYKTDSFWRHTGYVMAQIDGLYVGAKKRAILEGTKPMTLFQIQFLNSVGDLLDLIPSLSPTKNGSLKVFKRWDMGHCSALIKVLPGFENILFAHSSWYTYAAMLRIYKHWDFNVIDKDTSSSRLSFSSYPGFLESLDDFYILSSGLILLQTTNSVFNKTLLKQVIPETLLSWQRVRVANMMADSGKRWADIFSKYNSGTYNNQYMVLDLKKVKLNHSLDKGTLYIVEQIPTYVEYSEQTDVLRKGYWPSYNVPFHEKIYNWSGYPLLVQKLGLDYSYDLAPRAKIFRRDQGKVTDTASMKYIMRYNNYKKDPYSRGDPCNTICCREDLNSPNPSPGGCYDTKVADIYLASQYTSYAISGPTVQGGLPVFRWDRFNKTLHQGMPEVYNFDFITMKPILKLDIK,mutated_sequence,1.0,553.0,UPI00004565DA.a2m,UPI00004565DA.npy,gnomAD +UPI000013DD75,UPI000013DD75.csv,MNGHSDEESVRNSSGESSQSDDDSGSASGSGSGSSSGSSSDGSSSQSGSSDSDSGSESGSQSESESDTSRENKVQAKPPKVDGAEFWKSSPSILAVQRSAILKKQQQQQQQQQHQASSNSGSEEDSSSSEDSDDSSSEVKRKKHKDEDWQMSGSGSPSQSGSDSESEEEREKSSCDETESDYEPKNKVKSRKPQNRSKSKNGKKILGQKKRQIDSSEEDDDEEDYDNDKRSSRRQATVNVSYKEDEEMKTDSDDLLEVCGEDVPQPEEEEFETIERFMDCRIGRKGATGATTTIYAVEADGDPNAGFEKNKEPGEIQYLIKWKGWSHIHNTWETEETLKQQNVRGMKKLDNYKKKDQETKRWLKNASPEDVEYYNCQQELTDDLHKQYQIVERIIAHSNQKSAAGYPDYYCKWQGLPYSECSWEDGALISKKFQACIDEYFSRNQSKTTPFKDCKVLKQRPRFVALKKQPSYIGGHEGLELRDYQLNGLNWLAHSWCKGNSCILADEMGLGKTIQTISFLNYLFHEHQLYGPFLLVVPLSTLTSWQREIQTWASQMNAVVYLGDINSRNMIRTHEWTHHQTKRLKFNILLTTYEILLKDKAFLGGLNWAFIGVDEAHRLKNDDSLLYKTLIDFKSNHRLLITGTPLQNSLKELWSLLHFIMPEKFSSWEDFEEEHGKGREYGYASLHKELEPFLLRRVKKDVEKSLPAKVEQILRMEMSALQKQYYKWILTRNYKALSKGSKGSTSGFLNIMMELKKCCNHCYLIKPPDNNEFYNKQEALQHLIRSSGKLILLDKLLIRLRERGNRVLIFSQMVRMLDILAEYLKYRQFPFQRLDGSIKGELRKQALDHFNAEGSEDFCFLLSTRAGGLGINLASADTVVIFDSDWNPQNDLQAQARAHRIGQKKQVNIYRLVTKGSVEEDILERAKKKMVLDHLVIQRMDTTGKTVLHTGSAPSSSTPFNKEELSAILKFGAEELFKEPEGEEQEPQEMDIDEILKRAETHENEPGPLTVGDELLSQFKVANFSNMDEDDIELEPERNSKNWEEIIPEDQRRRLEEEERQKELEEIYMLPRMRNCAKQISFNGSEGRRSRSRRYSGSDSDSISEGKRPKKRGRPRTIPRENIKGFSDAEIRRFIKSYKKFGGPLERLDAIARDAELVDKSETDLRRLGELVHNGCIKALKDSSSGTERTGGRLGKVKGPTFRISGVQVNAKLVISHEEELIPLHKSIPSDPEERKQYTIPCHTKAAHFDIDWGKEDDSNLLIGIYEYGYGSWEMIKMDPDLSLTHKILPDDPDKKPQAKQLQTRADYLIKLLSRDLAKKEALSGAGSSKRRKARAKKNKAMKSIKVKEEIKSDSSPLPSEKSDEDDDKLSESKSDGRERSKKSSVSDAPVHITASGEPVPISEESEELDQKTFSICKERMRPVKAALKQLDRPEKGLSEREQLEHTRQCLIKIGDHITECLKEYTNPEQIKQWRKNLWIFVSKFTEFDARKLHKLYKHAIKKRQESQQNSDQNSNLNPHVIRNPDVERLKENTNHDDSSRDSYSSDRHLTQYHDHHKDRHQGDSYKKSDSRKRPYSSFSNGKDHRDWDHYKQDSRYYSDREKHRKLDDHRSRDHRSNLEGSLKDRSHSDHRSHSDHRLHSDHRSSSEYTHHKSSRDYRYHSDWQMDHRASSSGPRSPLDQRSPYGSRSPFEHSVEHKSTPEHTWSSRKT,mutated_sequence,1.0,1710.0,UPI000013DD75.a2m,UPI000013DD75.npy,gnomAD +UPI00001C2000,UPI00001C2000.csv,MEAAPGTPPPPPSESPPPPSPPPPSTPSPPPCSPDARPATPHLLHHRLPLPDDREDGELEEGELEDDGAEETQDTSGGPERSRKEKGEKHHSDSDEEKSHRRLKRKRKKEREKEKRRSKKRRKSKHKRHASSSDDFSDFSDDSDFSPSEKGHRKYREYSPPYAPSHQQYPPSHATPLPKKAYSKMDSKSYGMYEDYENEQYGEYEGDEEEDMGKEDYDDFTKELNQYRRAKEGSSRGRGSRGRGRGYRGRGSRGGSRGRGMGRGSRGRGRGSMGGDHPEDEEDFYEEEMDYGESEEPMGDDDYDEYSKELNQYRRSKDSRGRGLSRGRGRGSRGRGKGMGRGRGRGGSRGGMNKGGMNDDEDFYDEDMGDGGGGSYRSRDHDKPHQQSDKKGKVICKYFVEGRCTWGDHCNFSHDIELPKKRELCKFYITGFCARAENCPYMHGDFPCKLYHTTGNCINGDDCMFSHDPLTEETRELLDKMLADDAEAGAEDEKEVEELKKQGINPLPKPPPGVGLLPTPPRPPGPQAPTSPNGRPMQGGPPPPPPPPPPPPGPPQMPMPVHEPLSPQQLQQQDMYNKKIPSLFEIVVRPTGQLAEKLGVRFPGPGGPPGPMGPGPNMGPPGPMGGPMHPDMHPDMHPDMHPDMHADMHADMPMGPGMNPGPPMGPGGPPMMPYGPGDSPHSGMMPPIPPAQNFYENFYQQQEGMEMEPGLLGDAEDYGHYEELPGEPGEHLFPEHPLEPDSFSEGGPPGRPKPGAGVPDFLPSAQRALYLRIQQKQQEEEERARRLAESSKQDRENEEGDTGNWYSSDEDEGGSSVTSILKTLRQQTSSRPPASVGELSSSGLGDPRLQKGHPTGSRLADPRLSRDPRLTRHVEASGGSGPGDSGPSDPRLARALPTSKPEGSLHSSPVGPSSSKGSGPPPTEEEEGERALREKAVNIPLDPLPGHPLRDPRSQLQQFSHIKKDVTLSKPSFARTVLWNPEDLIPLPIPKQDAVPPVPAALQSMPTLDPRLHRAATAGPPNARQRPGASTDSSTQGANLPDFELLSRILKTVNATGSSAAPGSSDKPSDPRVRKAPTDPRLQKPTDSTASSRAAKPGPAEAPSPTASPSGDASPPATAPYDPRVLAAGGLGQGGGGGQSSVLSGISLYDPRTPNAGGKATEPAADTGAQPKGAEGNGKSSASKAKEPPFVRKSALEQPETGKAGADGGTPTDRYNSYNRPRPKAAAAPAATTATPPPEGAPPQPGVHNLPVPTLFGTVKQTPKTGSGSPFAGNSPAREGEQDAASLKDVFKGFDPTASPFCQ,mutated_sequence,1.0,1303.0,UPI00001C2000.a2m,UPI00001C2000.npy,gnomAD +UPI0001A5E4A3,UPI0001A5E4A3.csv,MKKKQTVQGTFSKLFGKKHTTTPSTSLYATNPPWIFTQEAPEEGTGGFDGIYYGDNRFNTVSESGTATLKARPRVRPLLTFLPLNAQENHGLAVPTPSVPDDFADKEVTGTSSLVNGNLRLYSSVGDLRPGQYGQDLLIPPPPPGPAPGPPQDISEPPGGSPLPSPPSTAPPPPPLLLEPPPPPSMAPPPPPVLEALSPPHTLSSPSIPTPPDFIPPAPPLAFLAPPPPPVPAPAPPAPASPHTVGTRLFPPGGVTKWKSDVALNGRQAEATRASPPRSPAEPKGSALGPNPEPHLTFPRSFKVPPPTPVRTSSIPVQEAQEAPRKEEGATKKAPSRLPLPPSFHIRPASQVYPDRAPEPDCPGELKATAPASPRLGQSQSQADERAGTPPPAPPLPPPAPPLPPPAPPLPPAAPPLPCAQKAAHPPAGFTKTPKSSSPALKPKPNPPSPENTASSAPVDWRDPSQMEKLRNELAAYLCGSRREDRFLSHRPGPTVAPQSKEGKKGPRLPEKETLLSLPAKDTPPGVPEKSLGGSSLTETEAAPSLTLPSVDYIPQDSPTPSVRQIRNELEARLSSAAEKEAKPSIGSLPPKPRLEGGRICENGADDDKLSKPVAKNLPPQSTTLLPTTSLQPKAMLGPAIPPKATPEPAIPPKATLWPATPPKATLGPATPLKATSGPTTPLKATSGPAIASTATTLPTTTSQLMAEKDSGPAGQPEKPASQEVSTPSQARGEGSPSEATRLPTQGARSSAAFPPKTSPGGGEVPCLYKPHCHQSSLSREVAVVMPTLARGGAAGPGEPVEVKEPPGLPAKPPASAQPTDELLRHPVTGEVVERGSPMALLLAARQRAQKGRSVGAALGRSSLPGSLRDHSHQAEASSDSIFHSQGTPNSFTVVPKLPKEAEKDSPLTTEIPNKWGPRLGRDAEGTELSRRHNWTKPEPQAPVAWERVAPSNLPQGHPLPKSFSSPPSPSNKREEEEEEFNFEVIPPPPEFSNDPEPPAPALQYLGRQSSPPRNNYSDLRQLPNAGPGAPPALGFSRFPAGARYAGAGGLERFSGGGRSLIKKRLYVGEPHRGPGLPHGGTGRSLSSPNCFGPQPGGPEMRRVNSAGRAPPGGLHAPRLSLEGAARGAAEAKHKAPGSADYGFAPAAGRSPYTTTRYGSPINTFTVRPGTRHPISYVCSGAHRKATS,mutated_sequence,1.0,1188.0,UPI0001A5E4A3.a2m,UPI0001A5E4A3.npy,gnomAD +UPI00001AECDC,UPI00001AECDC.csv,MAPEMDQFYRSTMAIYKSIMEQFNPALENLVYLGNNYLRAFHALSEAAEVYFSAIQKIGERALQSPTSQILGEILVQMSDTQRHLNSDLEVVVQTFHGGLLQHMEKNTKLDMQFIKDSRQHYELEYRHRAANLEKCMSELWRMERKRDKNVREMKESVNRLHAQMQAFVSESQRAAELEEKRRYRFLAEKHLLLSNTFLQFFGRARGMLQNRVLLWKEQSEASRSPSRAHSPGLLGPALGPPYPSGRLTPTCLDMPPRPLGEFSSPRSRHGSGSYGTEPDARPASQLEPDRRSLPRTPSASSLYSGSAQSSRSNSFGERPGGGGGARRVRALVSHSEGANHTLLRFSAGDVVEVLVPEAQNGWLYGKLEGSSASGWFPEAYVKALEEGPVNPMTPVTPMTSMTSMSPMTPMNPGNELPSRSYPLRGSHSLDDLLDRPGNSIAPSEYWDGQSRSRTPSRVPSRAPSPAPPPLPSSRRSSMGSTAVATDVKKLMSSEQYPPQELFPRGTNPFATVKLRPTITNDRSAPLIR,mutated_sequence,1.0,529.0,UPI00001AECDC.a2m,UPI00001AECDC.npy,gnomAD +UPI000002ADD2,UPI000002ADD2.csv,MGRKKIQITRIMDERNRQVTFTKRKFGLMKKAYELSVLCDCEIALIIFNSSNKLFQYASTDMDKVLLKYTEYNEPHESRTNSDIVETLRKKGLNGCESPDADDYFEHSPLSEDRFSKLNEDSDFIFKRGPPGLPPQNFSMSVTVPVTSPNALSYTNPGSSLVSPSLAASSTLTDSSMLSPPQTTLHRNVSPGAPQRPPSTGNAGGMLSTTDLTVPNGAGSSPVGNGFVNSRASPNLIGATGANSLGKVMPTKSPPPPGGGNLGMNSRKPDLRVVIPPSSKGMMPPLSEEEELELNTQRISSSQATQPLATPVVSVTTPSLPPQGLVYSAMPTAYNTDYSLTSADLSALQGFNSPGMLSLGQVSAWQQHHLGQAALSSLVAGGQLSQGSNLSINTNQNISIKSEPISPPRDRMTPSGFQQQQQQQQQQQPPPPPQPQPQPPQPQPRQEMGRSPVDSLSSSSSSYDGSDREDPRGDFHSPIVLGRPPNTEDRESPSVKRMRMDAWVT,mutated_sequence,1.0,505.0,UPI000002ADD2.a2m,UPI000002ADD2.npy,gnomAD +UPI0000041CE2,UPI0000041CE2.csv,MANLTIVTEFILMGFSTNKNMCILHSILFLLIYLCALMGNVLIIMITTLDHHLHTPVYFFLKNLSFLDLCLISVTAPKSIANSLIHNNSISFLGCVSQVFLLLSSASAELLLLTVMSFDRYTAICHPLHYDVIMDRSTCVQRATVSWLYGGLIAVMHTAGTFSLSYCGSNMVHQFFCDIPQLLAISCSENLIREIALILINVVLDFCCFIVIIITYVHVFSTVKKIPSTEGQSKAYSICLPHLLVVLFLSTGFIAYLKPASESPSILDAVISVFYTMLPPTFNPIIYSLRNKAIKVALGMLIKGKLTKK,mutated_sequence,1.0,309.0,UPI0000041CE2.a2m,UPI0000041CE2.npy,gnomAD +UPI00001A9480,UPI00001A9480.csv,MDAPRASAAKPPTGRKMKARAPPPPGKAATLHVHSDQKPPHDGALGSQQNLVRMKEALRASTMDVTVVLPSGLEKRSVLNGSHAMMDLLVELCLQNHLNPSHHALEIRSSETQQPLSFKPNTLIGTLNVHTVFLKEKVPEEKVKPGPPKVPEKSVRLVVNYLRTQKAVVRVSPEVPLQNILPVICAKCEVSPEHVVLLRDNIAGEELELSKSLNELGIKELYAWDNRRETFRKSSLGNDETDKEKKKFLGFFKVNKRSNSKGCLTTPNSPSMHSRSLTLGPSLSLGSISGVSVKSEMKKRRAPPPPGSGPPVQDKASEKVSLGSQIDLQKKKRRAPAPPPPQPPPPSPLIPNRTEDKEENRKSTMVSLPLGSGSHCSPDGAPQVLSEAEETVSVGSCFASEDTTEDSGVMSSPSDIVSLDSQQDSMKYKDKWATDQEDCSDQDLAGTPDLGPQKSPLWEKNGSENSHLRTEKAVTASNDEEDLLIAGEFRKTLAELDEDLEEMEDSYETDTSSLTSSIHGASNHCPQDAMIPHGDTDAIPVTFIGEVSDDPVDSGLFSNRNNNAGSFDSEGVASRRDSLAPLQAEHSQPHEKAREEVPALHPASHDVGKGIRVALSNISKDGNLMETAPRVTSFASNLHTDNLNAKVKDKVYGCADGERTQATERVNSQPVNEKDSNDKNAALAPTSWHQRGQNPGKSYRLKHGLTTYKIIPPKSEMRCYDRDVSLSTGAIKIDELGNLVSPHATGIRIISLSSSVPEAESQPIGKVREFWRCNSVEKHLGRPSESSARGPPSTPVPTQTQNPESRLQADPKPISPQQKSAHHEGRNPLGEGRNQPPTMGMGHVRVPAAHTTEVTFLKPQRRTSSQYVASAIAKRIGAPKVHADVVRPHGYAEKGYAGKAPVLAAPPVTVKDDRTSSPHSETQGWKDGAQWPCVTPPNNHGEDLAVGAPPRGEVIGPHRKLSTQDRPAAIHRSSCFSLVQSSQRDRVSVGQSCGFSGKQSTSSQEASSASEPRRAPDGTDPPPPHTSDTQACSRELVNGSVRAPGHGEPSHPPGGSGTESHILLEREEKPSVFSTDGNETDSIWPPSIFGPKKKFKPVVQRPVPKDTSLHSALMEAIHSAGGKDRLRKTAEHTGEGRPAKLSYTEAEGERSALLAAIRGHSGTCSLRKVASSASEELQSFRDAALSAQGSESPLLEDLGLLSPPAIPPPPPPPSQALSAPRTASRFSTGTLSNTADARQALMDAIRSGTGAARLRKVPLLV,mutated_sequence,1.0,1261.0,UPI00001A9480.a2m,UPI00001A9480.npy,gnomAD +UPI00025A2EC3,UPI00025A2EC3.csv,MAPKKPEPKKEAAKPAPAPAPAPAPAPAPAPEAPKEPAFDPKSVKIDFTADQIEGEYGQPHLSVSIALSWRRRRRRKKRRSSCPHEFKEAFSLFDRTPTGEMKITYGQCGDVLRALGQNPTNAEVLRVLGKPKPEEMNVKMLDFETFLPILQHISRNKEQGTYEDFVEGLRVF,mutated_sequence,1.0,173.0,UPI00025A2EC3.a2m,UPI00025A2EC3.npy,gnomAD +UPI0001B79574,UPI0001B79574.csv,MTHEVLSTFIPSEGSSSKVEEVDLQDFKLDVQHVIEVDEGNTAVIACHLPESHPKAQVRYSVKQE,mutated_sequence,1.0,65.0,UPI0001B79574.a2m,UPI0001B79574.npy,gnomAD +UPI000022A768,UPI000022A768.csv,MKDSEGPQRPPLCFLSTLLSQKVPEKSDAVLRCIISGQPKPEVTWYKNGQAIDGSGIISNYEFFENQYIHVLHLSCCTKNDAAVYQISAKNSFGMICCSASVEVECSSENPQLSPNLEDDRDRGWKHETGTHEEERANQIDEKEHPYKEEESISPGTPRSADSSPSKSNHSLSLQSLGNLDISVSSSENPLGVKGTRHTGEAYDPSNTEEIANGLLFLNSSHIYEKQDRCCHKTVHSMASKFTDGDLNNDGPHDEGLRSSQQNPKVQKYISFSLPLSEATAHIYPGDSAVANKQPSPQLSSEDSDSDYELCPEITLTYTEEFSDDDLEYLECSDVMTDYSNAVWQRNLLGTEHVFLLESDDEEMEFGEHCLGGCEHFLSGMGCGSRVSGDAGPMVATAGFCGHHSQPQEVGVRSSRVSKHGPSSPQTGMTLILGPHQDGTSSVTEQGRYKLPTAPEAAENDYPGIQGETRDSHQAREEFASDNLLNMDESVRETEMKLLSGESENSGMSQCWETAADKRVGGKDLWSKRGSRKSARVRQPGMKGNPKKPNANLRESTTEGTLHLCSAKESAEPPLTQSDKRETSHTTAAATGRSSHADARECAISTQAEQEAKTLQTSTDSVSKEGNTNCKGEGMQVNTLFETSQVPDWSDPPQVQVQETVRETISCSQMPAFSEPAGEESPFTGTTTISFSNLGGVHKENASLAQHSEVKPCTCGPQHEEKQDRDGNIPDNFREDLKYEQSISEANDETMSPGVFSRHLPKDARADFREPVAVSVASPEPTDTALTLENVCDEPRDREAVCAMECFEAGDQGTCFDTIDSLVGRPVDKYSPQEICSVDTELAEGQNKVSDLCSSNDKTLEVFFQTQVSETSVSTCKSSKDGNSVMSPLFTSTFTLNISHTASEGATGENLAKVENSTYPLASTVHAGQEQPSPSNSGGLDETQLLSSENNPLVQFKEGGDKSPSPSAADTTATPASYSSIVSFPWEKPTTLTANNECFQATRETEDTSTVTIATEVHPAKYLAVSIPEDKHAGGTEERFPRASHEKVSQFPSQVQLDHILSGATIKSTKELLCRAPSVPGVPHHVLQLPEGEGFCSNSPLQVDNLSGDKSQTVDRADFRSYEENFQERGSETKQGVQQQSLSQQGSLSAPDFQQSLPTTSAAQEERNLVPTAHSPASSREGAGQRSGWGTRVSVVAETAGEEDSQALSNVPSLSDILLEESKEYRPGNWEAGNKLKIITLEASASEIWPPRQLTNSESKASDGGLIIPDKVWAVPDSLKADAVVPELAPSEIAALAHSPEDAESALADSRESHKGEEPTISVHWRSLSSRGFSQPRLLESSVDPVDEKELSVTDSLSAASETGGKENVNNVSQDQEEKQLKMDHTAFFKKFLTCPKILESSVDPIDEISVIEYTRAGKPEPSETTPQGAREGGQSNDGNMGHEAEIQPAILQVPCLQGTILSENRISRSQEGSMKQEAEQIQPEEAKTAIWQVLQPSEGGERIPSGCSIGQIQESSDGSLGEAEQSKKDKAELISPTSPLSSCLPIMTHASLGVDTHNSTGQIHDVPENDIVEPRKRQYVFPVSQKRGTIENERGKPLPSSPDLTRFPCTSSPEGNVTDFLISHKMEEPKIEVLQIGETKPPSSSSSSAKTLAFISGERELEKAPKLLQDPCQKGTLGCAKKSREREKSLEARAGKSPGTLTAVTGSEEVKRKPEAPGSGHLAEGVKKKILSRVAALRLKLEEKENIRKNSAFLKKMPKLETSLSHTEEKQDPKKPSCKREGRAPVLLKKIQAEMFPEHSGNVKLSCQFAEIHEDSTICWTKDSKSIAQVQRSAGDNSTVSFAIVQASPKDQGLYYCCIKNSYGKVTAEFNLTAEVLKQLSSRQDTKGCEEIEFSQLIFKEDFLHDSYFGGRLRGQIATEELHFGEGVHRKAFRSTVMHGLMPVFKPGHACVLKVHNAIAYGTRNNDELIQRNYKLAAQECYVQNTARYYAKIYAAEAQPLEGFGEVPEIIPIFLIHRPENNIPYATVEEELIGEFVKYSIRDGKEINFLRRESEAGQKCCTFQHWVYQKTSGCLLVTDMQGVGMKLTDVGIATLAKGYKGFKGNCSMTFIDQFKALHQCNKYCKMLGLKSLQNNNQKQKQPSIGKSKVQTNSMTIKKAGPETPGEKKT,mutated_sequence,1.0,2170.0,UPI000022A768.a2m,UPI000022A768.npy,gnomAD +UPI000046FD46,UPI000046FD46.csv,TEEEEKIKSQGQDVTSSVYFMKQTISNACGTIGLIHAIANNKDKMHFESGSTLKKFLEESVSMSPEERARYLENYDAIRVTHETSAHEGQTESSSPSSSQPHSSHCRTKASSLCHHASLPWVKRNHVGPAKATSPSLRLRRWRPFLRLPSLGLHSVAPSIDEKVDLHFIALVHVDGHLYELDGRKPFPINHGETSDETLLEDAIEVCKKFMERDPDELRFNAIALSAA,mutated_sequence,1.0,228.0,UPI000046FD46.a2m,UPI000046FD46.npy,gnomAD +UPI0000126640,UPI0000126640.csv,MSERAADDVRGEPRRAAAAAGGAAAAAARQQQQQQQQQQPPPPQPQRQQHPPPPPRRTRPEDGGPGAASTSAAAMATVGERRPLPSPEVMLGQSWNLWVEASKLPGKDGTELDESFKEFGKNREVMGLCREDMPIFGFCPAHDDFYLVVCNDCNQVVKPQAFQSHYERRHSSSSKPPLAVPPTSVFSFFPSLSKSKGGSASGSNRSSSGGVLSASSSSSKLLKSPKEKLQLRGNTRPMHPIQQSRVPHGRIMTPSVKVEKIHPKMDGTLLKSAVGPTCPATVSSLVKPGLNCPSIPKPTLPSPGQILNGKGLPAPPTLEKKPEDNSNNRKFLNKRLSEREFDPDIHCGVIDLDTKKPCTRSLTCKTHSLTQRRAVQGRRKRFDVLLAEHKNKTREKELIRHPDSQQPPQPLRDPHPAPPRTSQEPHQNPHGVIPSESKPFVASKPKPHTPSLPRPPGCPAQQGGSAPIDPPPVHESPHPPLPATEPASRLSSEEGEGDDKEESVEKLDCHYSGHHPQPASFCTFGSRQIGRGYYVFDSRWNRLRCALNLMVEKHLNAQLWKKIPPVPSTTSPISTRIPHRTNSVPTSQCGVSYLAAATVSTSPVLLSSTCISPNSKSVPAHGTTLNAQPAASGAMDPVCSMQSRQVSSSSSSPSTPSGLSSVPSSPMSRKPQKLKSSKSLRPKESSGNSTNCQNASSSTSGGSGKKRKNSSPLLVHSSSSSSSSSSSSHSMESFRKNCVAHSGPPYPSTVTSSHSIGLNCVTNKANAVNVRHDQSGRGPPTGSPAESIKRMSVMVNSSDSTLSLGPFIHQSNELPVNSHGSFSHSHTPLDKLIGKKRKCSPSSSSINNSSSKPTKVAKVPAVNNVHMKHTGTIPGAQGLMNSSLLHQPKARP,mutated_sequence,1.0,892.0,UPI0000126640.a2m,UPI0000126640.npy,gnomAD +UPI00000389E8,UPI00000389E8.csv,MGLFRGFVFLLVLCLLHQSNTSFIKLNNNGFEDIVIVIDPSVPEDEKIIEQIEDMVTTASTYLFEATEKRFFFKNVSILIPENWKENPQYKRPKHENHKHADVIVAPPTLPGRDEPYTKQFTECGEKGEYIHFTPDLLLGKKQNEYGPPGKLFVHEWAHLRWGVFDEYNEDQPFYRAKSKKIEATRCSAGISGRNRVYKCQGGSCLSRACRIDSTTKLYGKDCQFFPDKVQTEKASIMFMQSIDSVVEFCNEKTHNQEAPSLQNIKCNFRSTWEVISNSEDFKNTIPMVTPPPPPVFSLLKISQRIVCLVLDKSGSMGGKDRLNRMNQAAKHFLLQTVENGSWVGMVHFDSTATIVNKLIQIKSSDERNTLMAGLPTYPLGGTSICSGIKYAFQVIGELHSQLDGSEVLLLTDGEDNTASSCIDEVKQSGAIVHFIALGRAADEAVIEMSKITGGSHFYVSDEAQNNGLIDAFGALTSGNTDLSQKSLQLESKGLTLNSNAWMNDTVIIDSTVGKDTFFLITWNSLPPSISLWDPSGTIMENFTVDATSKMAYLSIPGTAKVGTWAYNLQAKANPETLTITVTSRAANSSVPPITVNAKMNKDVNSFPSPMIVYAEILQGYVPVLGANVTAFIESQNGHTEVLELLDNGAGADSFKNDGVYSRYFTAYTENGRYSLKVRAHGGANTARLKLRPPLNRAAYIPGWVVNGEIEANPPRPEIDEDTQTTLEDFSRTASGGAFVVSQVPSLPLPDQYPPSQITDLDATVHEDKIILTWTAPGDNFDVGKVQRYIIRISASILDLRDSFDDALQVNTTDLSPKEANSKESFAFKPENISEENATHIFIAIKSIDKSNLTSKVSNIAQVTLFIPQANPDDIDPTPTPTPTPTPDKSHNSGVNISTLVLSVIGSVVIVNFILSTTI,mutated_sequence,1.0,919.0,UPI00000389E8.a2m,UPI00000389E8.npy,gnomAD +UPI000020E71F,UPI000020E71F.csv,MAASRSAGEAGPGGSQGRVVRMKRRGGRGPRRGPAGGGEKALKRLKLAVEEFVHATSEGEAPGGCEGRGAPVSFRPGGRKSRKELRKEKRHLRKARRLQRTAGPEQGPGLGGRSGAEEASGHRQDTEERARPAPSRDPSPPRKPRPSRVKAKATAATAKTRPSAAATAAARKRALLAANEEEDREIRKLERCLGLNKRKKKDGSSSVPLSFARDGLDYILGALESGKNSGLYDSSGEEEEDAGQTLPESDLESDSQDESEEEEEGDVEKEKKAQEAEAQSEDDDEDTEEEQGEEKEKGAQEKRRGKRVRFAEDEEKSENSSEDGDITDKSLCGSGEKYIPPHVRQAEETVDFKKKEELERLKKHVKGLLNRLSEPNMASISGQLEELYMAHSRKDMNDTLTSALMGACVTASAMPSRLMMEHVLLVSILHHTVGIEVGAHFLEAVVRKFDAIYKYGSEGKECDNLFTVIAHLYNFHVVQSLLIFDILKKLIGTFTEKDIELILLMLKNVGFSLRKDDALSLKELITEAQTKASGAGSEFQDQTRIRFMLETMLALKNNDMRKIPGYDPEPVEKLRKLQRALVRNAGSGSETQLRVSWDSVLSAEQTGRWWIVGSAWSGAPMIDNSHHTHLQKQLVGTVSSKILELARKQRMNTDIRRNIFCTIMTSEDFLDAFEKLLKLGLKDQQEREIIHVLMDCCLQEKTYNPFYAFLASKFCEYERRFQMTFQFSIWDKFRDLENLPATNFSNLVHLVAHLLKTKSLSLSILKVVEFSELDKPRVRFLRKVLSILLMETEVEDLSLIFTRVSDNPKLGVLREGLKLFISHFLLKNAQAHRSADEANVLREKADLATKCLQGKASLRM,mutated_sequence,1.0,860.0,UPI000020E71F.a2m,UPI000020E71F.npy,gnomAD +UPI0000044645,UPI0000044645.csv,MPGRAEAGEAEEEAGAGSGSEAEEDALWERIEGVRHRLARALNPAKLTPYLRQCRVIDEQDEEEVLSTYRFPCRVNRTGRLMDILRCRGKRGYEAFLEALEFYYPEHFTLLTGQEPAQRCSMILDEEGPEGLTQFLMTEVRRLREARKSQLQREQQLQARGRVLEEERAGLEQRLRDQQQAQERCQRLREDWEAGSLELLRLKDENYMIAMRLAQLSEEKNSAVLRSRDLQLAVDQLKLKVSRLEEECALLRRARGPPPGAEEKEKEKEKEKEPDNVDLVSELRAENQRLTASLRELQEGLQQEASRPGAPGSERILLDILEHDWREAQDSRQELCQKLHAVQGELQWAEELRDQYLQEMEDLRLKHRTLQKDCDLYKHRMATVLAQLEEIEKERDQAIQSRDRIQLQYSQSLIEKDQYRKQVRGLEAERDELLTTLTSLEGTKALLEVQLQRAQGGTCLKACASSHSLCSNLSSTWSLSEFPSPLGGPEATGEAAVMGGPEPHNSEEATDSEKEINRLSILPFPPSAGSILRRQREEDPAPPKRSFSSMSDITGSVTLKPWSPGLSSSSSSDSVWPLGKPEGLLARGCGLDFLNRSLAIRVSGRSPPGGPEPQDKGPDGLSFYGDRWSGAVVRRVLSGPGSARMEPREQRVEAAGLEGACLEAEAQQRTLLWNQGSTLPSLMDSKACQSFHEALEAWAKGPGAEPFYIRANLTLPERADPHALCVKAQEILRLVDSAYKRRQEWFCTRVDPLTLRDLDRGTVPNYQRAQQLLEVQEKCLPSSRHRGPRSNLKKRALDQLRLVRPKPVGAPAGDSPDQLLLEPCAEPERSLRPYSLVRPLLVSALRPVVLLPECLAPRLIRNLLDLPSSRLDFQVCPAESLSGEELCPSSAPGAPKAQPATPGLGSRIRAIQESVGKKHCLLELGARGVRELVQNEIYPIVIHVEVTEKNVREVRGLLGRPGWRDSELLRQCRGSEQVLWGLPCSWVQVPAHEWGHAEELAKVVRGRILQEQARLVWVECGSSRGCPSSSEA,mutated_sequence,1.0,1032.0,UPI0000044645.a2m,UPI0000044645.npy,gnomAD +UPI0001AE67FB,UPI0001AE67FB.csv,MFQRAQELRRRAEDYHKCKIPPSARKALCNWVRMAAAEHRHSSGLPYWPYLTAETLKNRMGHQPPPPTQQHSITDNSLSLKTPPECLLTPLPPSADDNLKTPPECVLTPLPPSADDNLKTPPECVLTPLPPSADDNLKTPPECLLTPLPPSADDNLKTPPECLLTPLPPSALPSAPPSADDNLKTRAECLLHPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERRTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERHIMPPPSADDNINTPAERLRGPLPPSADDNLKTPSERQLTPLPPSADDNIKTPAERLRGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKIPAERLRIPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPGFHPERMIISRHLPSVSSLPFHPQLHPQQMIISRHLPSVCGGRFHPQRMIISRHLPSVSSLPFHPQLHPQQMIISRHLPSVCGGRFHPQQMIISRHLPSVSSLPFHPQLHPQQMIISRHLPSVCGGRFHPQRMIISRHLPSVSSLPFHPQLHPQQMIISRHLPSVCGGRFHPQRMIISRHLPSVSSLPFHPQLHPQQMIISRHLPSVCGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPSERQLTPLPPSAPPSADDNIKTPAERLRGPLPPSADDNLKTPPLATQEAEAEKPRKPKRQRAAEMEPPPEPKRRRVGDVEPSRKPKRRRAADVEPSSPEPKRRRVGDVEPSRKPKRRRAADVEPSSPEPKRRRVGDVEPSRKPKRRRAADVEPSLPEPKRRRLS,mutated_sequence,1.0,982.0,UPI0001AE67FB.a2m,UPI0001AE67FB.npy,gnomAD +UPI0000072AD5,UPI0000072AD5.csv,MAELGAGGDGHRGGDGAVRSETAPDSYKVQDKKNASSRPASAISGQNNNHSGNKPDPPPVLRVDDRQRLARERREEREKQLAAREIVWLEREERARQHYEKHLEERKKRLEEQRQKEERRRAAVEEKRRQRLEEDKERHEAVVRRTMERSQKPKQKHNRWSWGGSLHGSPSIHSADPDRRSVSTMNLSKYVDPVISKRLSSSSATLLNSPDRARRLQLSPWESSVVNRLLTPTHSFLARSKSTAALSGEAASCSPIIMPYKAAHSRNSMDRPKLFVTPPEGSSRRRIIHGTASYKKERERENVLFLTSGTRRAVSPSNPKARQPARSRLWLPSKSLPHLPGTPRPTSSLPPGSVKAAPAQVRPPSPGNIRPVKREVKVEPEKKDPEKEPQKVANEPSLKGRAPLVKVEEATVEERTPAEPEVGPAAPAMAPAPASAPAPASAPAPAPVPTPAMVSAPSSTVNASASVKTSAGTTDPEEATRLLAEKRRLAREQREKEERERREQEELERQKREELAQRVAEERTTRREEESRRLEAEQAREKEEQLQRQAEERALREREEAERAQRQKEEEARVREEAERVRQEREKHFQREEQERLERKKRLEEIMKRTRRTEATDKKTSDQRNGDIAKGALTGGTEVSALPCTTNAPGNGKPVGSPHVVTSHQSKVTVESTPDLEKQPNENGVSVQNENFEEIINLPIGSKPSRLDVTNSESPEIPLNPILAFDDEGTLGPLPQVDGVQTQQTAEVI,mutated_sequence,1.0,749.0,UPI0000072AD5.a2m,UPI0000072AD5.npy,gnomAD +UPI0000246F54,UPI0000246F54.csv,MTQQPQDDFDRSVEDAQAWMKAVQDQLQVNDNTQGPRAALEARLWETEKICQLEPEGRVRVDLVLRMAEALLACCPGDQKPGILARLKDIKAQWEETVTYMTHCHSRIEWVWLHWSEYLLARDEFYRWFQKMMVTLEPHIELQLGLKEKQWQLSHAQVLLHNVDNQAVLLDRLLEEAASLFNRIGDPSVDEDAQKRMKAEYDAVKAKAQKRVDLLEQVAREHEEYQAGVDEFQLWLKAVVEKVNGCLGRNCKLPITQRLSTLQDIAKDFPRGEESLETLEEQSAGVIRNTSPLGAEKITGELEEMRKVLEKLRALWEEEEERLRGLLRSRGAWEQQIKQLEAELSEFRMVLQRLAQEGLQPAAKAGTEDELVAHWRRYSATRAALASEEPRVDRLQAQLKELIVFPHNLKPLSDSVIATIQEYQSLKVKSARLRNAAAVELWQHFQRPLQDLQLWKALAQRLLEVTASLPDLPSLHTFLPQIEAALMESSRLKELLTMLQLKKDLLIGIFGQERATALLEQVAGSMRDRDLLHNSLLQRKSKLQSLLAQHKDFGAAFEPLQRKLLDLQVRVQAEKGLQRDLPGKQAQLSRLQGLQEEGLDLGAQMEAARPLVQENPNHQHKMDQLSSDFQALQRSLEDLVDRCRQSVQEHCTFSHQLLELRQWIVVTTQKLEAHRGEAGPGDAESQEAEFERLVAEFPEKEAQLSLVEAQGWLVMEKSSPEGAAVVQEELRELAESWRALRLLEESLLSLIRNWHLQRMEVDSGKKMVFTNNIPKSGFLINPMDPIPRHRRRANLLQEEEGSHEDFSQLLRNFGQWLQVENSKLVRIIAMRTSTAEDLRTRKSKLQELEARVPEGQHLFENLLRLGPARGTSDELEDLRYQWMLYKSKLKDSGHLLTQSSPGEPTGFQKTRRWRGLGSLFRRACCVALPLQLLLLLFLLLLFLLPIREEDRSCTLANNFARSFTLMLRYNGPPPT,mutated_sequence,1.0,975.0,UPI0000246F54.a2m,UPI0000246F54.npy,gnomAD +UPI0000E03260,UPI0000E03260.csv,MAFSQVQCLDDNHVNWRSSESKPEFFYSEEQRLALEALVARGRDAFYEVLKRENIRDFLSELELKRILETIEVYDPGSEDPRGTGPSQGPEDNGVGDGEEASGADGVPIEAEPLPSLEYWPQKSDRSIPQLDLGWPDTIAYRGVTRASVYMQPPIDGQAHIKEVVRKMISQAQKVIAVVMDMFTDVDIFKDLLDAGFKRKVAVYIIVDESNVKYFLHMCERACMHLGHLKNLRVRSSGGTEFFTRSATKFKGALAQKFMFVDGDRAVCGSYSFTWSAARTDRNVISVLSGQVVEMFDRQFQELYLMSHSVSLKGIPMEKEPEPEPIVLPSVVPLVPAGTVAKKLVNPKYALVKAKSVDEIAKISSEKQEAKKPLGLKGPALAEHPGELPELLPPIHPGLLHLERANMFEYLPTWVEPDPEPGSDILGYINIIDPNIWNPQPSQMNRIKIRDTSQASAQHQLWKQSQDSRPRPEPCPPPEPSAPQDGVPAENGLPQGDPEPLPPVPKPRTVPVADVLARDSSDIGWVLELPKEEAPQNGTDHRLPRMAGPGHAPLQRQLSVTQDDPESLGVGLPNGLDGVEEEDDDDYVTLSDQDSHSGSSGRGPGPRRPSVASSVSEEYFEVREHSVPLRRRHSEQVANGPTPPPRRQLSAPHITRGTFVGPQGGSPWAQSRGREEADALKRMQAQRSTDKEAQGQQFHHHRVPASGTRDKDGFPGPPRYRSAADSVQSSTRNAGPAMAGPHHWQAKGGQVPRLLPDPGSPRLAQNARPMTDGRATEEHPSPFGIPYSKLSQSKHLKARTGGSQWASSDSKRRAQAPRDRKDP,mutated_sequence,1.0,823.0,UPI0000E03260.a2m,UPI0000E03260.npy,gnomAD +UPI0000424CC7,UPI0000424CC7.csv,METCDSPPISRQENGQSTSKLCGTTQLDNEVPEKVAGMEPDRENSSTDDNLKTDERKSEALLGFSVENAAATQVTSAKEIPCNECATSFPSLQKYMEHHCPNARLPVLKDDNESEISELEDSDVENLTGEIVYQPDGSAYIIEDSKESGQNAQTGANSKLFSTAMFLDSLASAGEKSDQSASAPMSFYPQIINTFHIASSLGKPFTADQAFPNTSALAGVGPVLHSFRVYDLRHKREKDYLTSDGSAKNSCVSKDVPNNVDLSKFDGCVSDGKRKPVLMCFLCKLSFGYIRSFVTHAVHDHRMTLNDEEQKLLSNKCVSAIIQGIGKDKEPLISFLEPKKSTSVYPHFSTTNLIGPDPTFRGLWSAFHVENGDSLPAGFAFLKGSASTSSSAEQPLGITQMPKAEVNLGGLSSLVVNTPITSVSLSHSSSESSKMSESKDQENNCERPKESNVLHPNGECPVKSEPTEPGDEDEEDAYSNELDDEEVLGELTDSIGNKDFPLLNQSISPLSSSVLKFIEKGTSSSSATVSDDTEKKKQTAAVRASGSVASNYGISGKDFADASASKDSATAAHPSEIARGDEDSSATPHQHGFTPSTPGTPGPGGDGSPGSGIECPKCDTVLGSSRSLGGHMTMMHSRNSCKTLKCPKCNWHYKYQQTLEAHMKEKHPEPGGSCVYCKTGQPHPRLARGESYTCGYKPFRCEVCNYSTTTKGNLSIHMQSDKHLNNVQNLQNGNGEQVFGHSAPAPNTSLSGCGTPSPSKPKQKPTWRCEVCDYETNVARNLRIHMTSEKHMHNMMLLQQNMKQIQHNLHLGLAPAEAELYQYYLAQNIGLTGMKLENPADPQLMINPFQLDPATAAALAPGLVNNELPPEIRLASGQLMGDDLSLLTAGELSPYISDPALKLFQCAVCNKFTSDSLEALSVHVSSERSLPEEEWRAVIGDIYQCKLCNYNTQLKANFQLHCKTDKHMQKYQLVAHIKEGGKSNEWRLKCIAIGNPVHLKCNACDYYTNSVDKLRLHTTNHRHEAALKLYKHLQKQEGAVNPESCYYYCAVCDYTTKVKLNLVQHVRSVKHQQTEGLRKLQLHQQGLAPEEDNLSEIFFVKDCPPNELETASLGARTCDDDLTEQQLRSTSEEQSEEAEGAIKPTAVAEDDEKDTSERDNSEGKNSNKDSGIITPEKELKVSVAGGTQPLLLAKEEDVATKRSKPTEDNKFCHEQFYQCPYCNYNSRDQSRIQMHVLSQHSVQPVICCPLCQDVLSNKMHLQLHLTHLHSVSPDCVEKLLMTVPVPDVMMPNSMLLPAAASEKSERDTPAAVTAEGSGKYSGESPMDDKSMAGLEDSKANVEVKNEEQKPTKEPLEVSEWNKNSSKDVKIPDTLQDQLNEQQKRQPLSVSDRHVYKYRCNHCSLAFKTMQKLQIHSQYHAIRAATMCNLCQRSFRTFQALKKHLEAGHPELSEAELQQLYASLPVNGELWAESETMSQDDHGLEQEMEREYEVDHEGKASPVGSDSSSIPDDMGSEPKRTLPFRKGPNFTMEKFLDPSRPYKCTVCKESFTQKNILLVHYNSVSHLHKLKKVLQEASSPVPQETNSNTDNKPYKCSICNVAYSQSSTLEIHMRSVLHQTKARAAKLEPSGHVAGGHSIAANVNSPGQGMLDSMSLAAVNSKDTHLDAKELNKKQTPDLISAQPAHHPPQSPAQIQMQLQHELQQQAAFFQPQFLNPAFLPHFPMTPEALLQFQQPQFLFPFYIPGTEFSLGPDLGLPGSATFGMPGMTGMAGSLLEDLKQQIQTQHHVGQTQLQILQQQAQQYQATQPQLQPQKQQQQPPPPQQQQQQQASKLLKQEQSNIVSADCQIMKDVPSYKEAEDISEKPEKPKQEFISEGEGLKEGKDTKKQKSLEPSIPPPRIASGARGNAAKALLENFGFELVIQYNENRQKVQKKGKSGEGENTDKLECGTCGKLFSNVLILKSHQEHVHGQFFPYAALEKFARQYREAYDKLYPISPSSPETPPPPPPPPPLPPAPPQPSSMGPVKIPNTVSTPLQAPPPTPPPPPPPPPPPPPPPPPPPPSAPPQVQLPVSLDLPLFPSIMMQPVQHPALPPQLALQLPQMDALSADLTQLCQQQLGLDPNFLRHSQFKRPRTRITDDQLKILRAYFDINNSPSEEQIQEMAEKSGLSQKVIKHWFRNTLFKERQRNKDSPYNFSNPPITVLEDIRIDPQPTSLEHYKSDASFSKRSSRTRFTDYQLRVLQDFFDTNAYPKDDEIEQLSTVLNLPTRVIVVWFQNARQKARKSYENQAETKDNEKRELTNERYIRTSNMQYQCKKCNVVFPRIFDLITHQKKQCYKDEDDDAQDESQTEDSMDATDQVVYKHCTVSGQTDAAKNAAAPAASSGSGTSTPLIPSPKPEPEKTSPKPEYPAEKPKQSDPSPPSQGTKPALPLASTSSDPPQASTAQPQPQPQPPKQPQLIGRPPSASQTPVPSSPLQISMTSLQNSLPPQLLQYQCDQCTVAFPTLELWQEHQHMHFLAAQNQFLHSPFLERPMDMPYMIFDPNNPLMTGQLLGSSLTQMPPQASSSHTTAPTTVAASLKRKLDDKEDNNCSEKEGGNSGEDQHRDKRLRTTITPEQLEILYEKYLLDSNPTRKMLDHIAREVGLKKRVVQVWFQNTRARERKGQFRAVGPAQSHKRCPFCRALFKAKSALESHIRSRHWNEGKQAGYSLPPSPLISTEDGGESPQKYIYFDYPSLPLTKIDLSSENELASTVSTPVSKTAELSPKNLLSPSSFKAECSEDVENLNAPPAEAGYDQNKTDFDETSSINTAISDATTGDEGNTEMESTTGSSGDVKPALSPKEPKTLDTLPKPATTPTTEVCDDKFLFSLTSPSIHFNDKDGDHDQSFYITDDPDDNADRSETSSIADPSSPNPFGSSNPFKSKSNDRPGHKRFRTQMSNLQLKVLKACFSDYRTPTMQECEMLGNEIGLPKRVVQVWFQNARAKEKKFKINIGKPFMINQGGTEGTKPECTLCGVKYSARLSIRDHIFSKQHISKVRETVGSQLDREKDYLAPTTVRQLMAQQELDRIKKASDVLGLTVQQPGMMDSSSLHGISLPTAYPGLPGLPPVLLPGMNGPSSLPGFPQNSNTLTPPGAGMLGFPTSATSSPALSLSSAPTKPLLQTPPPPPPPPPPPPSSSLSGQQTEQQNKESEKKQTKPNKVKKIKEEELEATKPEKHPKKEEKISSALSVLGKVVGETHVDPIQLQALQNAIAGDPASFIGGQFLPYFIPGFASYFTPQLPGTVQGGYFPPVCGMESLFPYGPTMPQTLAGLSPGALLQQYQQYQQNLQESLQKQQKQQQEQQQKPVQAKTSKVESDQPQNSNDASETKEDKSTATESTKEEPQLESKSADFSDTYVVPFVKYEFICRKCQMMFTDEDAAVNHQKSFCYFGQPLIDPQETVLRVPVSKYQCLACDVAISGNEALSQHLQSSLHKEKTIKQAMRNAKEHVRLLPHSVCSPNPNTTSTSQSAASSNNTYPHLSCFSMKSWPNILFQASARRAASPPSSPPSLSLPSTVTSSLCSTSGVQTSLPTESCSDESDSELSQKLEDLDNSLEVKAKPASGLDGNFNSIRMDMFSV,mutated_sequence,1.0,3616.0,UPI0000424CC7.a2m,UPI0000424CC7.npy,gnomAD +UPI0000E5924D,UPI0000E5924D.csv,MRSPPCGLLRWFGGPLLASWCRCHLRFRAFGTSAGWYRAFPAPPPLLPPACPSPRDYRPHVSLSPFLSRPSRGGSSSSSSSRRRSPVAAVAGEPMAYSQGGGKKKVCYYYDGDIGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKATAEEMTKYHSDEYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVAGAVKLNRQQTDMAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNFPMRDGIDDESYGQIFKPIISKVMEMYQPSAVVLQCGADSLSGDRLGCFNLTVKGHAKCVEVVKTFNLPLLMLGGGGYTIRNVARCWTYETAVALDCEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTPEYMEKIKQRLFENLRMLPHAPGVQMQAIPEDAVHEDSGDEDGEDPDKRISIRASDKRIACDEEFSDSEDEGEGGRRNVADHKKGAKKARIEEDKKETEDKKTDVKEEDKSKDNSGEKTDTKGTKSEQLSNP,mutated_sequence,1.0,582.0,UPI0000E5924D.a2m,UPI0000E5924D.npy,gnomAD +UPI0000231C71,UPI0000231C71.csv,MARSRLPATSLRKPWKLDRQKLPSPDSGHSLLCGWSPGGKARPAGNTGAWAPAEQFFPASNRTREGGGLWPPLPLQSSPAAPTMLDSSAAEQVTRLTLKLLGQKLEQERQNVEGGPEGLHLEPGNEDRPDDALQTALKRRRDLLQRLREQHLLDELSRAQAWSGPSRGALGSALPPELPPTGILPTASPSPLAPDPPRIILPTVPQPPATIIQQLPQQPLIAQIPPPQAFPTQRSGSIKEDMVELLLLQNAQVHQLVLQNWMLKALPPALQDPPHVPPRVPRAARPRLPAVHHHHHHHHAVWPPGAATVLQPAPSLWTPGPP,mutated_sequence,1.0,322.0,UPI0000231C71.a2m,UPI0000231C71.npy,gnomAD +UPI000012666A,UPI000012666A.csv,MQAERGARGGRGRRPGRGRPGGDRHSERPGAAAAVARGGGGGGGGDGGGRRGRGRGRGFRGARGGRGGGGAPRGSRREPGGWGAGASAPVEDDSDAETYGEENDEQGNYSKRKIVSNWDRYQDIEKEVNNESGESQRGTDFSVLLSSAGDSFSQFRFAEEKEWDSEASCPKQNSAFYVDSELLVRALQELPLCLRLNVAAELVQGTVPLEVPQVKPKRTDDGKGLGMQLKGPLGPGGRGPIFELKSVAAGCPVLLGKDNPSPGPSRDSQKPTSPLQSAGDHLEEELDLLLNLDAPIKEGDNILPDQTSQDLKSKEDGEVVQEEEVCAKPSVTEEKNMEPEQPSTSKNVTEEELEDWLDSMIS,mutated_sequence,1.0,362.0,UPI000012666A.a2m,UPI000012666A.npy,gnomAD +UPI000013E87D,UPI000013E87D.csv,MERAISPGLLVRALLLLLLLLGLAARTVAAGRARGLPAPTAEAAFGLGAAAAPTSATRVPAAGAVAAAEVTVEDAEALPAAAGEQEPRGPEPDDETELRPRGRSLVIISTLDGRIAALDPENHGKKQWDLDVGSGSLVSSSLSKPEVFGNKMIIPSLDGALFQWDQDRESMETVPFTVESLLESSYKFGDDVVLVGGKSLTTYGLSAYSGKVRYICSALGCRQWDSDEMEQEEDILLLQRTQKTVRAVGPRSGNEKWNFSVGHFELRYIPDMETRAGFIESTFKPNENTEESKIISDVEEQEAAIMDIVIKVSVADWKVMAFSKKGGHLEWEYQFCTPIASAWLLKDGKVIPISLFDDTSYTSNDDVLEDEEDIVEAARGATENSVYLGMYRGQLYLQSSVRISEKFPSSPKALESVTNENAIIPLPTIKWKPLIHSPSRTPVLVGSDEFDKCLSNDKFSHEEYSNGALSILQYPYDNGYYLPYYKRERNKRSTQITVRFLDNPHYNKNIRKKDPVLLLHWWKEIVATILFCIIATTFIVRRLFHPHPHRQRKESETQCQTENKYDSVSGEANDSSWNDIKNSGYISRYLTDFEPIQCLGRGGFGVVFEAKNKVDDCNYAIKRIRLPNRELAREKVMREVKALAKLEHPGIVRYFNAWLEAPPEKWQEKMDEIWLKDESTDWPLSSPSPMDAPSVKIRRMDPFATKEHIEIIAPSPQRSRSFSVGISCDQTSSSESQFSPLEFSGMDHEDISESVDAAYNLQDSCLTDCDVEDGTMDGNDEGHSFELCPSEASPYVRSRERTSSSIVFEDSGCDNASSKEEPKTNRLHIGNHCANKLTAFKPTSSKSSSEATLSISPPRPTTLSLDLTKNTTEKLQPSSPKVYLYIQMQLCRKENLKDWMNGRCTIEERERSVCLHIFLQIAEAVEFLHSKGLMHRDLKPSNIFFTMDDVVKVGDFGLVTAMDQDEEEQTVLTPMPAYARHTGQVGTKLYMSPEQIHGNSYSHKVDIFSLGLILFELLYPFSTQMERVRTLTDVRNLKFPPLFTQKYPCEYVMVQDMLSPSPMERPEAINIIENAVFEDLDFPGKTVLRQRSRSLSSSGTKHSRQSNNSHSPLPSN,mutated_sequence,1.0,1116.0,UPI000013E87D.a2m,UPI000013E87D.npy,gnomAD +UPI0000072AAB,UPI0000072AAB.csv,MAAVKEPLEFHAKRPWRPEEAVEDPDEEDEDNTSEAENGFSLEEVLRLGGTKQDYLMLATLDENEEVIDGGKKGAIDDLQQGELEAFIQNLNLAKYTKASLVEEDEPAEKENSSKKEVKIPKINNKNTAESQRTSVNKVKNKNRPEPHSDENGSTTPKVKKDKQNIFEFFERQTLLLRPGGKWYDLEYSNEYSLKPQPQDVVSKYKTLAQKLYQHEINLFKSKTNSQKGASSTWMKAIVSSGTLGDRMAAMILLIQDDAVHTLQFVETLVNLVKKKGSKQQCLMALDTFKELLITDLLPDNRKLRIFSQRPFDKLEQLSSGNKDSRDRRLILWYFEHQLKHLVAEFVQVLETLSHDTLVTTKTRALTVAHELLCNKPEEEKALLVQVVNKLGDPQNRIATKASHLLETLLCKHPNMKGVVSGEVERLLFRSNISSKAQYYAICFLNQMALSHEESELANKLITVYFCFFRTCVKKKDVESKMLSALLTGVNRAYPYSQTGDDKVREQIDTLFKVLHIVNFNTSVQALMLLFQVMNSQQTISDRYYTALYRKMLDPGLMTCSKQAMFLNLVYKSLKADIVLRRVKAFVKRLLQVTCQQMPPFICGALYLVSEILKAKPGLRSQLDDHPESDDEENFIDANDDEDMEKFTDADKETEIVKKLETEETVPETDVETKKPEVASWVHFDNLKGGKQLNKYDPFSRNPLFCGAENTSLWELKKLSVHFHPSVALFAKTILQGNYIQYSGDPLQDFTLMRFLDRFVYRNPKPHKGKENTDSVVMQPKRKHFIKDIRHLPVNSKEFLAKEESQIPVDEVFFHRYYKKVAVKEKQKRDADEESIEDVDDEEFEELIDTFEDDNCFSSGKDDMDFAGNVKKRTKGAKDNTLDEDSEGSDDELGNLDDDEVSLGSMDDEEFAEVDEDGGTFMDVLDDESESVPELEVHSKVSTKKSKRKGTDDFDFAGSFQGPRKKKRNLNDSSLFVSAEEFGHLLDENMGSKFDNIGMNAMANKDNASLKQLRWEAERDDWLHNRDAKSIIKKKKHFKKKRIKTTQKTKKQRK,mutated_sequence,1.0,1054.0,UPI0000072AAB.a2m,UPI0000072AAB.npy,gnomAD +UPI0000205CB5,UPI0000205CB5.csv,MYHGMNPSNGDGFLEQQQQQQQPQSPQRLLAVILWFQLALCFGPAQLTGGFDDLQVCADPGIPENGFRTPSGGVFFEGSVARFHCQDGFKLKGATKRLCLKHFNGTLGWIPSDNSICVQEDCRIPQIEDAEIHNKTYRHGEKLIITCHEGFKIRYPDLHNMVSLCRDDGTWNNLPICQGCLRPLASSNGYVNISELQTSFPVGTVISYRCFPGFKLDGSAYLECLQNLIWSSSPPRCLALEVCPLPPMVSHGDFVCHPRPCERYNHGTVVEFYCDPGYSLTSDYKYITCQYGEWFPSYQVYCIKSEQTWPSTHETLLTTWKIVAFTATSVLLVLLLVILARMFQTKFKAHFPPRGPPRSSSSDPDFVVVDGVPVMLPSYDEAVSGGLSALGPGYMASVGQGCPLPVDDQSPPAYPGSGDTDTGPGESETCDSVSGSSELLQSLYSPPRCQESTHPASDNPDIIASTAEEVASTSPGIDIADEIPLMEEDP,mutated_sequence,1.0,490.0,UPI0000205CB5.a2m,UPI0000205CB5.npy,gnomAD +UPI0000212786,UPI0000212786.csv,MEPEDLPWPGELEEEEEEEEEEEEEEEEAAAAAAANVDDVVVVEEVEEEAGRELDSDSHYGPQHLESIDDEEDEEAKAWLQAHPGRILPPLSPPQHRYSEGERTSLEKIVPLTCHVWQQIVYQGNSRTQISDTNVVCLETTAQRGSGDDQKTESWHCLPQEMDSSQTLDTSQTRFNVRTEDTEVTDFPSLEEGILTQSENQVKEPNRDLFCSPLLVIQDSFASPDLPLLTCLTQDQEFAPDSLFHQSELSFAPLRGIPDKSEDTEWSSRPSEVSEALFQATAEVASDLASSRFSVSQHPLIGSTAVGSQCPFLPSEQGNNEETISSVDELKIPKDCDRYDDLCSYMSWKTRKDTQWPENNLADKDQVSVATSFDITDENIATKRSDHFDAARSYGQYWTQEDSSKQAETYLTKGLQGKVESDVITLDGLNENAVVCSERVAELQRKPTRESEYHSSDLRMLRMSPDTVPKAPKHLKAGDTSKGGIAKVTQSNLKSGITTTPVDSDIGSHLSLSLEDLSQLAVSSLETTTGQHTDTLNQKTLADTHLTEETLKVTAIPEPADQKTATPTVLSSSHSHRGKPSIFYQQGLPDSHLTEEALKVSAAPGLADQTTGMSTLTSTSYSHREKPGTFYQQELPESNLTEEPLEVSAAPGPVEQKTGIPTVSSTSHSHVEDLLFFYRQTLPDGHLTDQALKVSAVSGPADQKTGTATVLSTPHSHREKPGIFYQQEFADSHQTEETLTKVSATPGPADQKTEIPAVQSSSYSQREKPSILYPQDLADSHLPEEGLKVSAVAGPADQKTGLPTVPSSAYSHREKLLVFYQQALLDSHLPEEALKVSAVSGPADGKTGTPAVTSTSSASSSLGEKPSAFYQQTLPNSHLTEEALKVSIVPGPGDQKTGIPSAPSSFYSHREKPIIFSQQTLPDFLFPEEALKVSAVSVLAAQKTGTPTVSSNSHSHSEKSSVFYQQELPDSDLPRESLKMSAIPGLTDQKTVPTPTVPSGSFSHREKPSIFYQQEWPDSYATEKALKVSTGPGPADQKTEIPAVQSSSYPQREKPSVLYPQVLSDSHLPEESLKVSAFPGPADQMTDTPAVPSTFYSQREKPGIFYQQTLPESHLPKEALKISVAPGLADQKTGTPTVTSTSYSQHREKPSIFHQQALPGTHIPEEAQKVSAVTGPGNQKTWIPRVLSTFYSQREKPGIFYQQTLPGSHIPEEAQKVSPVLGPADQKTGTPTPTSASYSHTEKPGIFYQQVLPDNHPTEEALKISVASEPVDQTTGTPAVTSTSYSQYREKPSIFYQQSLPSSHLTEEAKNVSAVPGPADQKTVIPILPSTFYSHTEKPGVFYQQVLPHSHPTEEALKISVASEPVDQTTGTPTVTSTSYSQHTEKPSIFYQQSLPGSHLTEEAKNVSAVPGPGDRKTGIPTLPSTFYSHTEKPGSFYQQVLPHSHLPEEALEVSVAPGPVDQTIGTPTVTSPSSSFGEKPIVIYKQAFPEGHLPEESLKVSVAPGPVGQTTGAPTITSPSYSQHRAKSGSFYQLALLGSQIPEEALRVSSAPGPADQTTGIPTITSTSYSFGEKPIVNYKQAFPDGHLPEEALKVSIVSGPTEKKTDIPAGPLGSSALGEKPITFYRQALLDSPLNKEVVKVSAAPGPADQKTETLPVHSTSYSNRGKPVIFYQQTLSDSHLPEEALKVPPVPGPDAQKTETPSVSSSLYSYREKPIVFYQQALPDSELTQEALKVSAVPQPADQKTGLSTVTSSFYSHTEKPNISYQQELPDSHLTEEALKVSNVPGPADQKTGVSTVTSTSYSHREKPIVSYQRELPHFTEAGLKILRVPGPADQKTGINILPSNSYPQREHSVISYEQELPDLTEVTLKAIGVPGPADQKTGIQIASSSSYSNREKASIFHQQELPDVTEEALNVFVVPGQGDRKTEIPTVPLSYYSRREKPSVISQQELPDSHLTEEALKVSPVSIPAEQKTGIPIGLSSSYSHSHKEKLKISTVHIPDDQKTEFPAATLSSYSQIEKPKISTVIGPNDQKTPSQTAFHSSYSQTVKPNILFQQQLPDRDQSKGILKISAVPELTDVNTGKPVSLSSSYFHREKSNIFSPQELPGSHVTEDVLKVSTIPGPAGQKTVLPTALPSSFSHREKPDIFYQKDLPDRHLTEDALKISSALGQADQITGLQTVPSGTYSHGENHKLVSEHVQRLIDNLNSSDSSVSSNNVLLNSQADDRVVINKPESAGFRDVGSEEIQDAENSAKTLKEIRTLLMEAENMALKRCNFPAPLARFRDISDISFIQSKKVVCFKEPSSTGVSNGDLLHRQPFTEESPSSRCIQKDIGTQTNLKCRRGIENWEFISSTTVRSPLQEAESKVSMALEETLRQYQAAKSVMRSEPEGCSGTIGNKIIIPMMTVIKSDSSSDASDGNGSCSWDSNLPESLESVSDVLLNFFPYVSPKTSITDSREEEGVSESEDGGGSSVDSLAAHVKNLLQCESSLNHAKEILRNAEEEESRVRAHAWNMKFNLAHDCGYSISELNEDDRRKVEEIKAELFGHGRTTDLSKGLQSPRGMGCKPEAVCSHIIIESHEKGCFRTLTSEHPQLDRHPCAFRSAGPSEMTRGRQNPSSCRAKHVNLSASLDQNNSHFKVWNSLQLKSHSPFQNFIPDEFKISKGLRMPFDEKMDPWLSELVEPAFVPPKEVDFHSSSQMPSPEPMKKFTTSITFSSHRHSKCISNSSVVKVGVTEGSQCTGASVGVFNSHFTEEQNPPRDLKQKTSSPSSFKMHSNSQDKEVTILAEGRRQSQKLPVDFERSFQEEKPLERSDFTGSHSEPSTRANCSNFKEIQISDNHTLISMGRPSSTLGVNRSSSRLGVKEKNVTITPDLPSCIFLEQRELFEQSKAPRADDHVRKHHSPSPQHQDYVAPDLPSCIFLEQRELFEQCKAPYVDHQMRENHSPLPQGQDSIASDLPSPISLEQCQSKAPGVDDQMNKHHFPLPQGQDCVVEKNNQHKPKSHISNINVEAKFNTVVSQSAPNHCTLAASASTPPSNRKALSCVHITLCPKTSSKLDSGTLDERFHSLDAASKARMNSEFNFDLHTVSSRSLEPTSKLLTSKPVAQDQESLGFLGPKSSLDFQVVQPSLPDSNTITQDLKTIPSQNSQIVTSRQIQVNISDFEGHSNPEGTPVFADRLPEKMKTPLSAFSEKLSSDAVTQITTESPEKTLFSSEIFINAEDRGHEIIEPGNQKLRKAPVKFASSSSVQQVTFSRGTDGQPLLLPYKPSGSTKMYYVPQLRQIPPSPDSKSDTTVESSHSGSNDAIAPDFPAQVLGTRDDDLSATVNIKHKEGIYSKRVVTKASLPVGEKPLQNENADASVQVLITGDENLSDKKQQEIHSTRAVTEAAQAKEKESLQKDTADSSAAAAAEHSAQVGDPEMKNLPDTKAITQKEEIHRKKTVPEEAWPNNKESLQINIEESECHSEFENTTRSVFRSAKFYIHHPVHLPSDQDICHESLGKSVFMRHSWKDFFQHHPDKHREHMCLPLPYQNMDKTKTDYTRIKSLSINVNLGNKEVMDTTKSQVRDYPKHNGQISDPQRDQKVTPEQTTQHTVSLNELWNKYRERQRQQRQPELGDRKELSLVDRLDRLAKILQNPITHSLQVSESTHDDSRGERSVKEWSGRQQQRNKLQKKKRFKSLEKSHKNTGELKKSKVLSHHRAGRSNQIKIEQIKFDKYILSKQPGFNYISNTSSDCRPSEESELLTDTTTNILSGTTSTVESDILTQTDREVALHERSSSVSTIDTARLIQAFGHERVCLSPRRIKLYSSITNQQRRYLEKRSKHSKKVLNTGHPLVTSEHTRRRHIQVANHVISSDSISSSASSFLSSNSTFCNKQNVHMLNKGIQAGNLEIVNGAKKHTRDVGITFPTPSSSEAKLEENSDVTSWSEEKREEKMLFTGYPEDRKLKKNKKNSHEGVSWFVPVENVESRSKKENVPNTCGPGISWFEPITKTRPWREPLREQNCQGQHLDGRGYLAGPGREAGRDLLRPFVRATLQESLQFHRPDFISRSGERIKRLKLIVQERKLQSMLQTERDALFNIDRERQGHQNRMCPLPKRVFLAIQKNKPISKKEMIQRSKRIYEQLPEVQKKREEEKRKSEYKSYRLRAQLYKKRVTNQLLGRKVPWD,mutated_sequence,1.0,4167.0,UPI0000212786.a2m,UPI0000212786.npy,gnomAD +UPI00000421FD,UPI00000421FD.csv,MAALFLRGFVQIGNCKTGISKSKEAFIEAVERKKKDRLVLYFKSGKYSTFRLSDNIQNVVLKSYRGNQNHLHLTLQNNNGLFIEGLSSTDAEQLKIFLDRVHQNEVQPPVRPGKGGSVFSSTTQKEINKTSFHKVDEKSSSKSFEIAKGSGTGVLQRMPLLTSKLTLTCGELSENQHKKRKRMLSSSSEMNEEFLKENNSVEYKKSKADCSRCVSYNREKQLKLKELEENKKLECESSCIMNATGNPYLDDIGLLQALTEKMVLVFLLQQGYSDGYTKWDKLKLFFELFPEKICHGLPNLGNTCYMNAVLQSLLSIPSFADDLLNQSFPWGKIPLNALTMCLARLLFFKDTYNIEIKEMLLLNLKKAISAAAEIFHGNAQNDAHEFLAHCLDQLKDNMEKLNTIWKPKSEFGEDNFPKQVFADDPDTSGFSCPVITNFELELLHSIACKACGQVILKTELNNYLSINLPQRIKAHPSSIQSTFDLFFGAEELEYKCAKCEHKTSVGVHSFSRLPRILIVHLKRYSLNEFCALKKNDQEVIISKYLKVSSHCNEGTRPPLPLSEDGEITDFQLLKVIRKMTSGNISVSWPATKESKDILAPHIGSDKESEQKKGQTVFKGASRRQQQKYLGKNSKPNELESVYSGDRAFIEKEPLAHLMTYLEDTSLCQFHKAGGKPASSPGTPLSKVDFQTVPENPKRKKYVKTSKFVAFDRIINPTKDLYEDKNIRIPERFQKVSEQTQQCDGMRICEQAPQQALPQSFPKPGTQGHTKNLLRPTKLNLQKSNRNSLLALGSNKNPRNKDILDKIKSKAKETKRNDDKGDHTYRLISVVSHLGKTLKSGHYICDAYDFEKQIWFTYDDMRVLGIQEAQMQEDRRCTGYIFFYMHNEIFEEMLKREENAQLNSKEVEETLQKE,mutated_sequence,1.0,913.0,UPI00000421FD.a2m,UPI00000421FD.npy,gnomAD +UPI000013DDDC,UPI000013DDDC.csv,MALKKSSPSLDSGDSDSEELPTFAFLKKEPSSTKRRQPEREEKIVVVDISDCEASCPPAPELFSPPVPEIAETVTQTQPVRLLSSESEDEEEFIPLAQRLTCKFLTHKQLSPEDSSSPVKSVLDHQNNEGASCDWKKPFPKIPEVPLHDTPERSAADNKDLILDPCCQLPAYLSTCPGQSSSLAVTKTNSDILPPQKKTKPSQKVQGRGSHGCRQQRQARQKESTLRRQERKNAALVTRMKAQRPEECLKHIIVVLDPVLLQMEGGGQLLGALQTMECRCVIEAQAVPCSVTWRRRAGPSEDREDWVEEPTVLVLLRAEAFVSMIDNGKQGSLDSTMKGKETLQGFVTDITAKTAGKALSLVIVDQEKCFSAQNPPRRGKQGANKQTKKQQQRQPEASIGSMVSRVDAEEALVDLQLHTEAQAQIVQSWKELADFTCAFTKAVAEAPFKKLRDETTFSFCLESDWAGGVKVDLAGRGLALVWRRQIQQLNRVSLEMASAVVNAYPSPQLLVQAYQQCFSDKERQNLLADIQVRRGEGVTSTSRRIGPELSRRIYLQMTTLQPHLSLDSAD,mutated_sequence,1.0,570.0,UPI000013DDDC.a2m,UPI000013DDDC.npy,gnomAD +UPI0001662BAD,UPI0001662BAD.csv,MAAAATLRLSAQGTVTFEDVAVNFTWEEWNLLSEAQRCLYRDVTLENLALISSLGCWCGVEDEAAPSKQSIYIQRETQVRTPMAGVSPKKAHPCEMCGPILGDILHVADHQGTHHKQKLHRCEAWGNKLYDSGNFHQHQNEHIGEKPYRGSVEEALFAKRCKLHVSGESSVFSESGKDFLPRSGLLQQEASHTGEKSNSKTECVSPIQCGGAHYSCGESMKHFSTKHILSQHQRLLTREECYVCCECGKSFSKYASLSNHQRVHTEKKHECGECGKSFSKYVSFSNHQRVHTEKKHECGECGKSFSKYVSFSNHQRVHTGKRPYECGECGKSFSKYASFSNHQRVHTEKKHYECGECGKSFSKYVSFSNHQRVHTGKRPYECGECGKSFSKYASFSNHQRVHTDKKHYECGECGKSFSQKSSLIQHQRFHTGEKPYGCEECGKSFSSEGHLRSHQRVHAGERPFKCGECVKSFSHKRSLVHHQRVHSGERPYQCGECGKSFSQKGNLVLHQRVHTGARPYECGECGKSFSSKGHLRNHQQIHTGDRLYECGECGKSFSHKGTLILHQRVHPRERSYGCGECGKSFSSIGHLRSHQRVHTGERPYECGECGKSFSHKRSLVHHQRMHTGERPYKCGDCGKSFNEKGHLRNHQRVHTTERPFKCGECGKCFSHKGNLILHQHGHTGERPYVCRECGKLFKKKSHLLVHQRIHNGEKPYACEACQKFFRNKYQLIAHQRVHTGERPYECNDCGKSFTHSSTFCVHKRIHTGEKPYECSECGKSFAESSSFTKHKRVHTGEKPYECSECGKSFAESSSLTKHKRVHTGEKPYKCEKCGKLFNKKSHLLVHQSSHWRKAI,mutated_sequence,1.0,855.0,UPI0001662BAD.a2m,UPI0001662BAD.npy,gnomAD +UPI0000201E92,UPI0000201E92.csv,MVLAGLIRKLGHQLAEIRERALKSILCKIEHNLICYADLIQERQLFLHLLEWFNFPSVPMKEEVLNLLSRLVKYPPAVQHLVDVGAVEFLSKLRSNVEPNLQAEIDGILDGLFLLPSEVPALSSASYQTNQTELSKNPEILTGYFPQDKSNFQQMEVPPRPVVNQTVKCLKFSTFPWLPLTTTDRHVLSSNESSLRSSNHTLIWNTCELLKDVIMQDFPAEIFLQRPKIVQSLLSLLKLAFGDGKHRLALQSVSCLQQLCMYLRNRLNFHRDPGFFSNKHDTVSQNSSLSYCHEARGTHHSQNPSPGSSSPRPSVVGRTGQRPRGDGQDWDAASSSGSSSHAHVNSRISVHSPLDMGHIDLPELETEDTLELQFQQLSLPQFCVSILESAVPLLRTGSRQVIIRVLELLTEDMTLIGEAISTDIWDDSSLFGIDMKEKLLLVLGALGETMCYHKSSISLEQPEVMLVHHRMAFISISLFAVRLLQTLLPVEKASEFLSEPMSTALFLLSLDMPISLEYPNIHEAVVAYLEQLNSENYSIYKRTAEAVYSIECTCNFLSDIGKEGEKNLLELVELADQALRSFSYHQHFPLIKEIISICSKIWKSAQASPLLQGESQKVLLHMLSHPLPRVKAETYHCCLEITKECLGVHNVTKPVSSLCNGIHFLLHPKVLYEISVFGIQEPESEVNTAAKAILLYLLQGRLMMTALTWNKFIESLCPVIPILQGYADTEDPLGNCILLLSKASSDTEEMLPCTTRLKSMLRLLLVKKPSVRSLALKLLAFHLTSEEGADTKRPLIDARVLSRVTDLFIGKKPIELRLDDRRELVIKLETVEKVYEIFTSDDVDLVLRKSAAEQLAVIMQDIKMHAVVKKLCLIDKIIEYLNECVSQDGKVVECLVQPCLTLLRKVLCGDPVMRVSLSQQSSLLTVLFRVSLIFHEDCSVVTEVGALFCLLLFDEVSRMDMWSVNPSNKPSLPSVFSLPVSVFRRYHLPVHVIGHHAVSPYSIVLPLSADCLALKPVSDMLRIAWNLSWYHGSDNLLKQMNSETKTQEILDALKLSTEDILTLKITHMASGLQDCLHSIVQAATHREVRAAVTRMSFYLLNDRLSLKGCPGPCGVTLKSLAWHTALNRFLQVLPACTEDEKLLIDIIHFLNKLIKEQRKNSSLELLNWILELLLRHSANPLLDLLVLTESQAREETDDIRTAVRQQLQKELIALFDTLLLNFMEVTDRKCSELLYVFQTQLALKLLQCLKVTDAPHFYGLPSLERTLRGMANLTAFPGWSSHSPLTKPLDICVKYLSGLLEVITSFYVERGGNAMSFMGKGVTKSTILCLLHLSHEMMAQAGSLEWMSLWFLPLGSHSEEHIPTQQGLAWLIPLWVDRDPEVRFTSLGLGSALTTLETGCVALANSCQNISGGLWGTVVNILLDQSECSMVRREAAFILQNLLVIPMPTEIIKDYTWQGPCVHDEDSGLSLIGKPALQALLYHCHFYEHLNQMVKHCYLGRCMFDLNFSAFDRNSESNDLNGLDDSFKFWRAPSRTSQDRDPSSLSTSETTVAPSLGSTEFQPLVQSTTLLPEASHDQFVAQGHQESTSPRPPHDSSLSAPLPKLCVFVTPSLLSAMCSLLDNLLTIAPRDTAKAFRQAHLIELLCSIADATLIQTCVQELRALLPSSPPAEHTQAQVSFLLEYLSSLSRLLQSCLLVEPDLVIQDELVKPLITNIIGILTICTKDVLDKELISAFYHTWTHLFNLLAMLLRKAGAITLPFVTVALAKHWTAAIDMFCTCAGLSATCPALYTASLQFLSVLLTEEAKGHLQAKSKTHLCCSPTVASLLDDSQENQKSLEQLSDVILQCYEGKSSKDILKRVAANALMSLLAVSRRAQKHALKANLIDNCMEQMKHINAQLNLDSLRPGKAALKKKEDGVIKELSIAMQLLRNCLYQNEECKEAALEAHLVPVLHSLWPWILMDDSLMQISLQLLCVYTANFPNGCSSLCWSSCGQHPVQATHRGAVSNSLMLCILKLASQMPLENTTVQQMVFMLLSNLALSHDCKGVIQKSNFLQNFLSLALPKGGNKHLSNLTILWLKLLLNISSGEDGQQMILRLDGCLDLLTEMSKYKHKSSPLLPLLIFHNVCFSPANKPKILANEKVITVLAACLESENQNAQRIGAAALWALIYNYQKAKTALKSPSVKRRVDEAYSLAKKTFPNSEANPLNAYYLKCLENLVQLLNSS,mutated_sequence,1.0,2226.0,UPI0000201E92.a2m,UPI0000201E92.npy,gnomAD +UPI00001BDAC0,UPI00001BDAC0.csv,MMPLAEAGALAQGGGPSATEWACILRRKTPRHKQPTLLMVRASRRSGKTSAVLKAGRQSVSGRKNSTSKDLVTLGASSLREERGHPLHPRHRKAVHLRTRGRTRGWVQTLARMSRRTRGPVERAAAAAAAAAGGDAGHAPFPPPPAADGARAPRSPGQVTPRGLRLRLPRRESLLRGLCRPLRPLLGFRESDSAKPASLRLLQHTPSARRNYRIAGARLMRSNYPPPLSSAALRGAGPTRRN,mutated_sequence,1.0,242.0,UPI00001BDAC0.a2m,UPI00001BDAC0.npy,gnomAD +UPI000012ADD6,UPI000012ADD6.csv,MTTEGGPPPAPLRRACSPVPGALQAALMSPPPAAAAAAAAAPETTSSSSSSSSASCASSSSSSNSASAPSAACKSAGGGGAGAGSGGAKKASSGLRRPEKPPYSYIALIVMAIQSSPSKRLTLSEIYQFLQARFPFFRGAYQGWKNSVRHNLSLNECFIKLPKGLGRPGKGHYWTIDPASEFMFEEGSFRRRPRGFRRKCQALKPMYHRVVSGLGFGASLLPQGFDFQAPPSAPLGCHSQGGYGGLDMMPAGYDAGAGAPSHAHPHHHHHHHVPHMSPNPGSTYMASCPVPAGPGGVGAAGGGGGGDYGPDSSSSPVPSSPAMASAIECHSPYTSPAAHWSSPGASPYLKQPPALTPSSNPAASAGLHSSMSSYSLEQSYLHQNAREDLSVGLPRYQHHSTPVCDRKDFVLNFNGISSFHPSASGSYYHHHHQSVCQDIKPCVM,mutated_sequence,1.0,444.0,UPI000012ADD6.a2m,UPI000012ADD6.npy,gnomAD +UPI000022AC91,UPI000022AC91.csv,MSLPKTPSTPLNSTSTSESKKLKVSVAKEGTRGLPELKEKKNMVDRSKLLPTSLQNEFIPKEVLLSLTYAANAGPCPENLLPPKKIKTPKGTLPRLVDHVWHHPVRRNKFKYLIDHPVSLTGAGRDISFLYDVTYAKGQTREKAVCPPHLARSLQSHDGVIVPHKPKTLTDTLIPEEFHIVSSTGVSGLECYDDKYTTLLTDSENRLLLFPSMKPNKRVEVAQLNDVMDTMLERAGVENQEYTGPTKMHKLLHILKKEQTIYNMIFHELIRQVSVDCADRGELLSKVRERYVQMLDQIARQMIDFYKDLVTQRVMDQRILEELYNFKHVIEELTRELCLVRAHDVKLTKETEKAHKDLAQALLNAEKNAKIVEEYHDLYTLQRERMENDMKKLVAERDIWSSATYELALKVIERNRVILARRLYLNEKGWNKYTKHFIILLSNKDTEDLALLQKLTQKWRNLVNKLKQEVEQMEESTSETLKIVKDGLIKWQEFFNEKDILSPNKGNIFNSVLLDFKQWQKILNEKKEEFTGDVLLSKYDTLKIIKHLQENWADIGLGIFNRHKSLEGEMPSERQYMEEIIKNIQKLYKEYEIRINGDNGYSKILPSLISSLDFCSFKLENLEFPDTPLEEWQEIDEKINEMKSHLDILLNLTGIVPQHIDVDSVSVLQAYIFNMIQQWLLKIGNEINNGNIELQHHMDELHISMIQWMVNLLILMIPNFTDQDCLLKLEEESAEKHDIGVARLELDAIELTRKLYQYSSYLSSCCKGMVTAMALSKSTNSHKNATEDLYEVDKLKKECYEWINTCSCLLSNIKGRKITLLTYEEIERLLEEEAVKEFIEPEIDESFKEDEEESKEDRKLQEENKERAEEQPSTSTEKEKLIRFIGEDENVHSKPLFETDVLSSWRESAKQGTLAQKYLEAMAVIEHMQEKLLEVENRARQAEEKFEDAYEKLHHTLIKNKDLEELVMTSRKESKEEKENQDEREVKEEEEQQEEEEVRSAENSSKSPKKGH,mutated_sequence,1.0,1012.0,UPI000022AC91.a2m,UPI000022AC91.npy,gnomAD +UPI000006F1A1,UPI000006F1A1.csv,MKVFNPIPCFHTKDKESLNFPFPFFWAPIGSSIYNVSGLVGGRLSIEVSCVFTCLSCPISLVAINFLLLKYLDFWLPIWLPSLVFISVWF,mutated_sequence,1.0,90.0,UPI000006F1A1.a2m,UPI000006F1A1.npy,gnomAD +UPI00001376B5,UPI00001376B5.csv,MAAAAGGGSCPGPGSARGRFPGRPRGAGGGGGRGGRGNGAERVRVALRRGGGATGPGGAEPGEDTALLRLLGLRRGLRRLRRLWAGPRVQRGRGRGRGRGWGPSRGCVPEEESSDGESDEEEFQGFHSDEDVAPSSLRSALRSQRGRAPRGRGRKHKTTPLPPPRLADVAPTPPKTPARKRGEEGTERMVQALTELLRRAQAPQAPRSRACEPSTPRRSRGRPPGRPAGPCRRKQQAVVVAEAAVTIPKPEPPPPVVPVKHQTGSWKCKEGPGPGPGTPRRGGQSSRGGRGGRGRGRGGGLPFVIKFVSRAKKVKMGQLSLGLESGQGQGQHEESWQDVPQRRVGSGQGGSPCWKKQEQKLDDEEEEKKEEEEKDKEGEEKEERAVAEEMMPAAEKEEAKLPPPPLTPPAPSPPPPLPPPSTSPPPPLCPPPPPPVSPPPLPSPPPPPAQEEQEESPPPVVPATCSRKRGRPPLTPSQRAEREAARAGPEGTSPPTPTPSTATGGPPEDSPTVAPKSTTFLKNIRQFIMPVVSARSSRVIKTPRRFMDEDPPKPPKVEVSPVLRPPITTSPPVPQEPAPVPSPPRAPTPPSTPVPLPEKRRSILREPTFRWTSLTRELPPPPPAPPPPPAPSPPPAPATSSRRPLLLRAPQFTPSEAHLKIYESVLTPPPLGAPEAPEPEPPPADDSPAEPEPRAVGRTNHLSLPRFAPVVTTPVKAEVSPHGAPALSNGPQTQAQLLQPLQALQTQLLPQALPPPQPQLQPPPSPQQMPPLEKARIAGVGSLPLSGVEEKMFSLLKRAKVQLFKIDQQQQQKVAASMPLSPGGQMEEVAGAVKQISDRGPVRSEDESVEAKRERPSGPESPVQGPRIKHVCRHAAVALGQARAMVPEDVPRLSALPLRDRQDLATEDTSSASETESVPSRSRRGKVEAAGPGGESEPTGSGGTLAHTPRRSLPSHHGKKMRMARCGHCRGCLRVQDCGSCVNCLDKPKFGGPNTKKQCCVYRKCDKIEARKMERLAKKGRTIVKTLLPWDSDESPEASPGPPGPRRGAGAGGPREEVVAHPGPEEQDSLLQRKSARRCVKQRPSYDIFEDSDDSEPGGPPAPRRRTPRENELPLPEPEEQSRPRKPTLQPVLQLKARRRLDKDALAPGPFASFPNGWTGKQKSPDGVHRVRVDFKEDCDLENVWLMGGLSVLTSVPGGPPMVCLLCASKGLHELVFCQVCCDPFHPFCLEEAERPLPQHHDTWCCRRCKFCHVCGRKGRGSKHLLECERCRHAYHPACLGPSYPTRATRKRRHWICSACVRCKSCGATPGKNWDVEWSGDYSLCPRCTQLYEKGNYCPICTRCYEDNDYESKMMQCAQCDHWVHAKCEGLSDEDYEILSGLPDSVLYTCGPCAGAAQPRWREALSGALQGGLRQVLQGLLSSKVVGPLLLCTQCGPDGKQLHPGPCGLQAVSQRFEDGHYKSVHSFMEDMVGILMRHSEEGETPDRRAGGQMKGLLLKLLESAFGWFDAHDPKYWRRSTRLPNGVLPNAVLPPSLDHVYAQWRQQEPETPESGQPPGDPSAAFQGKDPAAFSHLEDPRQCALCLKYGDADSKEAGRLLYIGQNEWTHVNCAIWSAEVFEENDGSLKNVHAAVARGRQMRCELCLKPGATVGCCLSSCLSNFHFMCARASYCIFQDDKKVFCQKHTDLLDGKEIVNPDGFDVLRRVYVDFEGINFKRKFLTGLEPDAINVLIGSIRIDSLGTLSDLSDCEGRLFPIGYQCSRLYWSTVDARRRCWYRCRILEYRPWGPREEPAHLEAAEENQTIVHSPAPSSEPPGGEDPPLDTDVLVPGAPERHSPIQNLDPPLRPDSGSAPPPAPRSFSGARIKVPNYSPSRRPLGGVSFGPLPSPGSPSSLTHHIPTVGDPDFPAPPRRSRRPSPLAPRPPPSRWASPPLKTSPQLRVPPPTSVVTALTPTSGELAPPGPAPSPPPPEDLGPDFEDMEVVSGLSAADLDFAASLLGTEPFQEEIVAAGAMGSSHGGPGDSSEEESSPTSRYIHFPVTVVSAPGLAPSATPGAPRIEQLDGVDDGTDSEAEAVQQPRGQGTPPSGPGVVRAGVLGAAGDRARPPEDLPSEIVDFVLKNLGGPGDGGAGPREESLPPAPPLANGSQPSQGLTASPADPTRTFAWLPGAPGVRVLSLGPAPEPPKPATSKIILVNKLGQVFVKMAGEGEPVPPPVKQPPLPPTISPTAPTSWTLPPGPLLGVLPVVGVVRPAPPPPPPPLTLVLSSGPASPPRQAIRVKRVSTFSGRSPPAPPPYKAPRLDEDGEASEDTPQVPGLGSGGFSRVRMKTPTVRGVLDLDRPGEPAGEESPGPLQERSPLLPLPEDGPPQVPDGPPDLLLESQWHHYSGEASSSEEEPPSPDDKENQAPKRTGPHLRFEISSEDGFSVEAESLEGAWRTLIEKVQEARGHARLRHLSFSGMSGARLLGIHHDAVIFLAEQLPGAQRCQHYKFRYHQQGEGQEEPPLNPHGAARAEVYLRKCTFDMFNFLASQHRVLPEGATCDEEEDEVQLRSTRRATSLELPMAMRFRHLKKTSKEAVGVYRSAIHGRGLFCKRNIDAGEMVIEYSGIVIRSVLTDKREKFYDGKGIGCYMFRMDDFDVVDATMHGNAARFINHSCEPNCFSRVIHVEGQKHIVIFALRRILRGEELTYDYKFPIEDASNKLPCNCGAKRCRRFLN,mutated_sequence,1.0,2715.0,UPI00001376B5.a2m,UPI00001376B5.npy,gnomAD +UPI0000161FDC,UPI0000161FDC.csv,MAKRLCAGSALCVRGPRGPAPLLLVGLALLGAARAREEAGGGFSLHPPYFNLAEGARIAASATCGEEAPARGSPRPTEDLYCKLVGGPVAGGDPNQTIRGQYCDICTAANSNKAHPASNAIDGTERWWQSPPLSRGLEYNEVNVTLDLGQVFHVAYVLIKFANSPRPDLWVLERSMDFGRTYQPWQFFASSKRDCLERFGPQTLERITRDDAAICTTEYSRIVPLENGEIVVSLVNGRPGAMNFSYSPLLREFTKATNVRLRFLRTNTLLGHLMGKALRDPTVTRRYYYSIKDISIGGRCVCHGHADACDAKDPTDPFRLQCTCQHNTCGGTCDRCCPGFNQQPWKPATANSANECQSCNCYGHATDCYYDPEVDRRRASQSLDGTYQGGGVCIDCQHHTTGVNCERCLPGFYRSPNHPLDSPHVCRRCNCESDFTDGTCEDLTGRCYCRPNFSGERCDVCAEGFTGFPSCYPTPSSSNDTREQVLPAGQIVNCDCSAAGTQGNACRKDPRVGRCLCKPNFQGTHCELCAPGFYGPGCQPCQCSSPGVADDRCDPDTGQCRCRVGFEGATCDRCAPGYFHFPLCQLCGCSPAGTLPEGCDEAGRCLCQPEFAGPHCDRCRPGYHGFPNCQACTCDPRGALDQLCGAGGLCRCRPGYTGTACQECSPGFHGFPSCVPCHCSAEGSLHAACDPRSGQCSCRPRVTGLRCDTCVPGAYNFPYCEAGSCHPAGLAPVDPALPEAQVPCMCRAHVEGPSCDRCKPGFWGLSPSNPEGCTRCSCDLRGTLGGVAECQPGTGQCFCKPHVCGQACASCKDGFFGLDQADYFGCRSCRCDIGGALGQSCEPRTGVCRCRPNTQGPTCSEPARDHYLPDLHHLRLELEEAATPEGHAVRFGFNPLEFENFSWRGYAQMAPVQPRIVARLNLTSPDLFWLVFRYVNRGAMSVSGRVSVREEGRSATCANCTAQSQPVAFPPSTEPAFITVPQRGFGEPFVLNPGTWALRVEAEGVLLDYVVLLPSAYYEAALLQLRVTEACTYRPSAQQSGDNCLLYTHLPLDGFPSAAGLEALCRQDNSLPRPCPTEQLSPSHPPLITCTGSDVDVQLQVAVPQPGRYALVVEYANEDARQEVGVAVHTPQRAPQQGLLSLHPCLYSTLCRGTARDTQDHLAVFHLDSEASVRLTAEQARFFLHGVTLVPIEEFSPEFVEPRVSCISSHGAFGPNSAACLPSRFPKPPQPIILRDCQVIPLPPGLPLTHAQDLTPAMSPAGPRPRPPTAVDPDAEPTLLREPQATVVFTTHVPTLGRYAFLLHGYQPAHPTFPVEVLINAGRVWQGHANASFCPHGYGCRTLVVCEGQALLDVTHSELTVTVRVPKGRWLWLDYVLVVPENVYSFGYLREEPLDKSYDFISHCAAQGYHISPSSSSLFCRNAAASLSLFYNNGARPCGCHEVGATGPTCEPFGGQCPCHAHVIGRDCSRCATGYWGFPNCRPCDCGARLCDELTGQCICPPRTIPPDCLLCQPQTFGCHPLVGCEECNCSGPGIQELTDPTCDTDSGQCKCRPNVTGRRCDTCSPGFHGYPRCRPCDCHEAGTAPGVCDPLTGQCYCKENVQGPKCDQCSLGTFSLDAANPKGCTRCFCFGATERCRSSSYTRQEFVDMEGWVLLSTDRQVVPHERQPGTEMLRADLRHVPEAVPEAFPELYWQAPPSYLGDRVSSYGGTLRYELHSETQRGDVFVPMESRPDVVLQGNQMSITFLEPAYPTPGHVHRGQLQLVEGNFRHTETRNTVSREELMMVLASLEQLQIRALFSQISSAVFLRRVALEVASPAGQGALASNVELCLCPASYRGDSCQECAPGFYRDVKGLFLGRCVPCQCHGHSDRCLPGSGVCVDCQHNTEGAHCERCQAGFVSSRDDPSAPCVSCPCPLSVPSNNFAEGCVLRGGRTQCLCKPGYAGASCERCAPGFFGNPLVLGSSCQPCDCSGNGDPNLLFSDCDPLTGACRGCLRHTTGPRCEICAPGFYGNALLPGNCTRCDCTPCGTEACDPHSGHCLCKAGVTGRRCDRCQEGHFGFDGCGGCRPCACGPAAEGSECHPQSGQCHCRPGTMGPQCRECAPGYWGLPEQGCRRCQCPGGRCDPHTGRCNCPPGLSGERCDTCSQQHQVPVPGGPVGHSIHCEVCDHCVVLLLDDLERAGALLPAIHEQLRGINASSMAWARLHRLNASIADLQSQLRSPLGPRHETAQQLEVLEQQSTSLGQDARRLGGQAVGTRDQASQLLAGTEATLGHAKTLLAAIRAVDRTLSELMSQTGHLGLANASAPSGEQLLRTLAEVERLLWEMRARDLGAPQAAAEAELAAAQRLLARVQEQLSSLWEENQALATQTRDRLAQHEAGLMDLREALNRAVDATREAQELNSRNQERLEEALQRKQELSRDNATLQATLHAARDTLASVFRLLHSLDQAKEELERLAASLDGARTPLLQRMQTFSPAGSKLRLVEAAEAHAQQLGQLALNLSSIILDVNQDRLTQRAIEASNAYSRILQAVQAAEDAAGQALQQADHTWATVVRQGLVDRAQQLLANSTALEEAMLQEQQRLGLVWAALQGARTQLRDVRAKKDQLEAHIQAAQAMLAMDTDETSKKIAHAKAVAAEAQDTATRVQSQLQAMQENVERWQGQYEGLRGQDLGQAVLDAGHSVSTLEKTLPQLLAKLSILENRGVHNASLALSASIGRVRELIAQARGAASKVKVPMKFNGRSGVQLRTPRDLADLAAYTALKFYLQGPEPEPGQGTEDRFVMYMGSRQATGDYMGVSLRDKKVHWVYQLGEAGPAVLSIDEDIGEQFAAVSLDRTLQFGHMSVTVERQMIQETKGDTVAPGAEGLLNLRPDDFVFYVGGYPSTFTPPPLLRFPGYRGCIEMDTLNEEVVSLYNFERTFQLDTAVDRPCARSKSTGDPWLTDGSYLDGTGFARISFDSQISTTKRFEQELRLVSYSGVLFFLKQQSQFLCLAVQEGSLVLLYDFGAGLKKAVPLQPPPPLTSASKAIQVFLLGGSRKRVLVRVERATVYSVEQDNDLELADAYYLGGVPPDQLPPSLRRLFPTGGSVRGCVKGIKALGKYVDLKRLNTTGVSAGCTADLLVGRAMTFHGHGFLRLALSNVAPLTGNVYSGFGFHSAQDSALLYYRASPDGLCQVSLQQGRVSLQLLRTEVKTQAGFADGAPHYVAFYSNATGVWLYVDDQLQQMKPHRGPPPELQPQPEGPPRLLLGGLPESGTIYNFSGCISNVFVQRLLGPQRVFDLQQNLGSVNVSTGCAPALQAQTPGLGPRGLQATARKASRRSRQPARHPACMLPPHLRTTRDSYQFGGSLSSHLEFVGILARHRNWPSLSMHVLPRSSRGLLLFTARLRPGSPSLALFLSNGHFVAQMEGLGTRLRAQSRQRSRPGRWHKVSVRWEKNRILLVTDGARAWSQEGPHRQHQGAEHPQPHTLFVGGLPASSHSSKLPVTVGFSGCVKRLRLHGRPLGAPTRMAGVTPCILGPLEAGLFFPGSGGVITLDLPGATLPDVGLELEVRPLAVTGLIFHLGQARTPPYLQLQVTEKQVLLRADDGAGEFSTSVTRPSVLCDGQWHRLAVMKSGNVLRLEVDAQSNHTVGPLLAAAAGAPAPLYLGGLPEPMAVQPWPPAYCGCMRRLAVNRSPVAMTRSVEVHGAVGASGCPAA,mutated_sequence,1.0,3695.0,UPI0000161FDC.a2m,UPI0000161FDC.npy,gnomAD +UPI0000140E62,UPI0000140E62.csv,MGKTANSPGSGARPDPVRSFNRWKKKHSHRQNKKKQLRKQLKKPEWQVERESISRLMQNYEKINVNEITRFSDFPLSKKTLKGLQEAQYRLVTEIQKQTIGLALQGKDVLGAAKTGSGKTLAFLVPVLEALYRLQWTSTDGLGVLIISPTRELAYQTFEVLRKVGKNHDFSAGLIIGGKDLKHEAERINNINILVCTPGRLLQHMDETVSFHATDLQMLVLDEADRILDMGFADTMNAVIENLPKKRQTLLFSATQTKSVKDLARLSLKNPEYVWVHEKAKYSTPATLEQNYIVCELQQKISVLYSFLRSHLKKKSIVFFSSCKEVQYLYRVFCRLRPGVSILALHGRQQQMRRMEVYNEFVRKRAAVLFATDIAARGLDFPAVNWVLQFDCPEDANTYIHRAGRTARYKEDGEALLILLPSEKAMVQQLLQKKVPVKEIKINPEKLIDVQKKLESILAQDQDLKERAQRCFVSYVRSVYLMKDKEVFDVSKLPIPEYALSLGLAVAPRVRFLQKMQKQPTKELVRSQADKVIEPRAPSLTNDEVEEFRAYFNEKMSILQKGGKRLEGTEHRQDNDTGNEEQEEEEDDEEEMEEKLAKAKGSQAPSLPNTSEAQKIKEVPTQFLDRDEEEEDADFLKVKRHNVFGLDLKDEKTLQKKEPSKSSIKKKMTKVAEAKKVMKRNFKVNKKITFTDEGELVQQWPQMQKSAIKDAEEDDDTGGINLHKAKERLQEEDKFDKEEYRKKIKAKHREKRLKEREARREANKRQAKAKDEEEAFLDWSDDDDDDDDGFDPSTLPDPDKYRSSEDSDSEDMENKISDTKKKQGMKKRSNSEVEDVGPTSHNRKKARWDTLEPLDTGLSLAEDEELVLHLLRSQS,mutated_sequence,1.0,875.0,UPI0000140E62.a2m,UPI0000140E62.npy,gnomAD +UPI000013E44F,UPI000013E44F.csv,MLLRGVLLALQALQLAGALDLPAGSCAFEESTCGFDSVLASLPWILNEEGHYIYVDTSFGKQGEKAVLLSPDLQAEEWSCLRLVYQITTSSESLSDPSQLNLYMRFEDESFDRLLWSAKEPSDSWLIASLDLQNSSKKFKILIEGVLGQGNTASIALFEIKMTTGYCIECDFEENHLCGFVNRWNPNVNWFVGGGSIRNVHSILPQDHTFKSELGHYMYVDSVYVKHFQEVAQLISPLTTAPMAGCLSFYYQIQQGNDNVFSLYTRDVAGLYEEIWKADRPGNAAWNLAEVEFSAPYPMEVIFEVAFNGPKGGYVALDDISFSPVHCQNQTELLFSAVEASCNFEQDLCNFYQDKEGPGWTRVKVKPNMYRAGDHTTGLGYYLLANTKFTSQPGYIGRLYGPSLPGNLQYCLRFHYAIYGFLKMSDTLAVYIFEENHVVQEKIWSVLESPRGVWMQAEITFKKPMPTKVVFMSLCKSFWDCGLVALDDITIQLGSCSSSEKLPPPPGECTFEQDECTFTQEKRNRSSWHRRRGETPTSYTGPKGDHTTGVGYYMYIEASHMVYGQKARLLSRPLRGVSGKHCLTFFYHMYGGGTGLLSVYLKKEEDSEESLLWRRRGEQSISWLRALIEYSCERQHQIIFEAIRGVSIRSDIAIDDVKFQAGPCGEMEDTTQQSSGYSEDLNEIEY,mutated_sequence,1.0,686.0,UPI000013E44F.a2m,UPI000013E44F.npy,gnomAD +UPI0000209875,UPI0000209875.csv,MAAFGRQVLDWHRLIPLTWACMARQTPHLGEQRRTTASLLRKLTTASNGGVIEELSCVRSNNYVQEPECRRNLVQCLLEKQGTPVVQGSLELERVMSSLLDMGFSNAHINELLSVRRGASLQQLLDIISEFILLGLNPEPVCVVLKKSPQLLKLPIMQMRKRSSYLQKLGLGEGKLKRVLYCCPEIFTMRQQDINDTVRLLKEKCLFTVQQVTKILHSCPSVLREDLGQLEYKFQYAYFRMGIKHPDIVKSEYLQYSLTKIKQRHIYLERLGRYQTPDKKGQTQIPNPLLKDILRVSEAEFLARTACTSVEEFQVFKKLLAREEEESESSTSDDKRASLDEDEDDDDEEDNDEDDNDEDDDDEDDDEAEDNDEDEDDDEEE,mutated_sequence,1.0,381.0,UPI0000209875.a2m,UPI0000209875.npy,gnomAD +UPI0000251D78,UPI0000251D78.csv,MLREEATKKSKEKEPGMALPQGRLTFRDVAIEFSLEEWKCLNPAQRALYRAVMLENYRNLEFVDSSLKSMMEFSSTRHSITGEVIHTGTLQRHKSHHIGDFCFPEMKKDIHHFEFQWQEVERNGHEAPMTKIKKLTGSTDRSDHRHAGNKPIKDQLGLSFHSHLPELHMFQTKGKISNQLDKSIGASSASESQRISCRLKTHISNKYGKNFLHSSFTQIQEICMREKPCQSNECGKAFNYSSLLRRHHITHSREREYKCDVCGKIFNQKQYIVYHHRCHTGEKTYKCNECGKTFTQMSSLVCHRRLHTGEKPYKCNECGKTFSEKSSLRCHRRLHTGEKPYKCNECGKTFGRNSALVIHKAIHTGEKPYKCNECGKTFSQKSSLQCHHILHTGEKPYKCEECDNVYIRRSHLERHRKIHTGEGSYKCKVCDKVFRSDSYLAEHQRVHTGEKPYKCNKCGRSFSRKSSLQYHHTLHTGEKPYTCNECGKVFSRRENLARHHRLHAGEKPYKCEECDKVFSRRSHLERHRRIHTGEKPYKCKVCDKAFRSDSCLANHTRVHTGEKPYKCNKCAKVFNQKGILAQHQRVHTGEKPYKCNECGKVFNQKASLAKHQRVHTAEKPYKCNECGKAFTGQSTLIHHQAIHGCRETLQM,mutated_sequence,1.0,651.0,UPI0000251D78.a2m,UPI0000251D78.npy,gnomAD +UPI000059D3CC,UPI000059D3CC.csv,MSSAPEKQQPPHGGGGGGGGGGGAAMDPASSGPSKAKKTNAGIRRPEKPPYSYIALIVMAIQSSPTKRLTLSEIYQFLQSRFPFFRGSYQGWKNSVRHNLSLNECFIKLPKGLGRPGKGHYWTIDPASEFMFEEGSFRRRPRGFRRKCQALKPMYSMMNGLGFNHLPDTYGFQGSAGGLSCPPNSLALEGGLGMMNGHLPGNVDGMALPSHSVPHLPSNGGHSYMGGCGGAAAGEYPHHDSSVPASPLLPTGAGGVMEPHAVYSGSAAAWPPSASAALNSGASYIKQQPLSPCNPAANPLSGSLSTHSLEQPYLHQNSHNAPAELQGIPRYHSQSPSMCDRKEFVFSFNAMASSSMHSAGGGSYYHQQVTYQDIKPCVM,mutated_sequence,1.0,379.0,UPI000059D3CC.a2m,UPI000059D3CC.npy,gnomAD +UPI0001929538,UPI0001929538.csv,MSFSLTFTELANIAIPQCGVLNFKALHLLLHGILEHIHMAELKKVLSGDEDFLQTSQVVIMPREGDAQPILNPMKRLSNVFDHVVSRLDKLENQLALLQDLPSTAQLLEASQGTARPVQDLWHLIKLRKMVEGHDEVMAKSMQTLQDLLTDLHALQVTITALRKEVDMLKNMLDKVHPERMDIFAEDFKIQNWKMVALQREVASLQNKFKTIPKTEDMVLWSGLHDAMFTSEIGSSPLDLWQSVEQLPEAALAQTTKYLEATRAIQVSEPVQNPQLLQTVWHYEVPELLPEGSSAQAVSLSRAQEPAQPPALTPESAPGCTTEFAPGPAPGTEPVPGLELGLELEPVPALGPVPGPSVTPGSLPAPWPVLGPVPAPGAQPPPLGDWPALPRRWPLPQGWPRVGSWPLWDLGVLRPTQPQPSRAPPPATEFGSLWPRPLQPYQSRQGEALQLAAVQVKGEENDVPSLRGLRERARKDGAPKDRTRKDGVPKDRGGKDVDPKDRAHKDDVPKDRGGKDVDPKDRAHKDDVPKDRGGKDGDPKDRVGKDGAPKEAQPKAPQSALHRLKTTAAIAAAAAAAYAAATSSAAQAAKVAAKFVKDAPATKMAAIATDTAAAGPLGVFADVLGAGPSRGATESQILGDDSEIYEILSPSYSAASIGPDPALSQAMVATKQAMSPEDKKRAVKYSMSHIAQIPVKHDSLKEEFAQLSCNLNQRLSYLANMGGPSSLGTTVDILQKKIGSLQKSRLKEEELERIWGNQIEMMKDRYITLDKAVENLQIRMDEFKTLQAQIKRLEMNKVNKSTMEEELREKADRSALAGKASRVDLETVALELNEMIQGILFKVTIHEDSWKKAMEELSKDVNTKLVHSDLDPLKKEMEEVWKIVRKLLIEGLRLDPDSAAGFRRKLFKRVKCISCDRPVEMMTGPQLITIRKAHLLSRLRPASANSCEYLQRQQMREQQWLQLQDLGIQEDCQQDWGDGPQNATSLKCKSCNLLTLYPYGDPHVIDYDSAEVDILGVDGILYKGRVNSQRGAQPLAVAKELAAVKAPSPPSQSLYDRVHSSALFGAICPPLCPRSSACSAASGPHLTMPARPPSLPPLLLLPPLIPSLRDPQQAPGSTRLSRAPHIESRVGRKPPEEPANP,mutated_sequence,1.0,1141.0,UPI0001929538.a2m,UPI0001929538.npy,gnomAD +UPI000013E413,UPI000013E413.csv,MSNVSGILETAGVPLVSANWPQPSPPPAVPAGPQMDHMGNSSQGAPWLFLTSALARGVSGIFVWTALVLTCHQIYLHLRSYTVPQEQRYIIRLLLIVPIYAFDSWLSLLLLGDHQYYVYFDSVRDCYEAFVIYSFLSLCFQYLGGEGAIMAEIRGKPIKSSCLYGTCCLRGMTYSIGFLRFCKQATLQFCLVKPVMAVTTIILQAFGKYHDGDFNVRSGYLYVTLIYNASVSLALYALFLFYFTTRELLRPFQPVLKFLTIKAVIFLSFWQGLLLAILERCGVIPEVETSGGNKLGAGTLAAGYQNFIICVEMLFASVALRYAFPCQVYAEKKENSPAPPAPMQSISSGIRETVSPQDIVQDAIHNFSPAYQHYTQQATHEAPRPGTHPSGGSGGSRKSRSLEKRMLIPSEDL,mutated_sequence,1.0,413.0,UPI000013E413.a2m,UPI000013E413.npy,gnomAD +UPI000013D308,UPI000013D308.csv,MWRRKHPRTSGGTRGVLSGNRGVEYGSGRGHLGTFEGRWRKLPKMPEAVGTDPSTSRKMAELEEVTLDGKPLQALRVTDLKAALEQRGLAKSGQKSALVKRLKGALMLENLQKHSTPHAAFQPNSQIGEEMSQNSFIKQYLEKQQELLRQRLEREAREAAELEEASAESEDEMIHPEGVASLLPPDFQSSLERPELELSRHSPRKSSSISEEKGDSDDEKPRKGERRSSRVRQARAAKLSEGSQPAEEEEDQETPSRNLRVRADRNLKTEEEEEEEEEEEEDDEEEEGDDEGQKSREAPILKEFKEEGEEIPRVKPEEMMDERPKTRSQEQEVLERGGRFTRSQEEARKSHLARQQQEKEMKTTSPLEEEEREIKSSQGLKEKSKSPSPPRLTEDRKKASLVALPEQTASEEETPPPLLTKEASSPPPHPQLHSEEEIEPMEGPAPAVLIQLSPPNTDADTRELLVSQHTVQLVGGLSPLSSPSDTKAESPAEKVPEESVLPLVQKSTLADYSAQKDLEPESDRSAQPLPLKIEELALAKGITEECLKQPSLEQKEGRRASHTLLPSHRLKQSADSSSSRSSSSSSSSSRSRSRSPDSSGSRSHSPLRSKQRDVAQARTHANPRGRPKMGSRSTSESRSRSRSRSRSASSNSRKSLSPGVSRDSSTSYTETKDPSSGQEVATPPVPQLQVCEPKERTSTSSSSVQARRLSQPESAEKHVTQRLQPERGSPKKCEAEEAEPPAATQPQTSETQTSHLPESERIHHTVEEKEEVTMDTSENRPENDVPEPPMPIADQVSNDDRPEGSVEDEEKKESSLPKSFKRKISVVSATKGVPAGNSDTEGGQPGRKRRWGASTATTQKKPSISITTESLKSLIPDIKPLAGQEAVVDLHADDSRISEDETERNGDDGTHDKGLKICRTVTQVVPAEGQENGQREEEEEEKEPEAEPPVPPQVSVEVALPPPAEHEVKKVTLGDTLTRRSISQQKSGVSITIDDPVRTAQVPSPPRGKISNIVHISNLVRPFTLGQLKELLGRTGTLVEEAFWIDKIKSHCFVTYSTVEEAVATRTALHGVKWPQSNPKFLCADYAEQDELDYHRGLLVDRPSETKTEEQGIPRPLHPPPPPPVQPPQHPRAEQREQERAVREQWAEREREMERRERTRSEREWDRDKVREGPRSRSRSRDRRRKERAKSKEKKSEKKEKAQEEPPAKLLDDLFRKTKAAPCIYWLPLTDSQIVQKEAERAERAKEREKRRKEQEEEEQKEREKEAERERNRQLEREKRREHSRERDRERERERERDRGDRDRDRERDRERGRERDRRDTKRHSRSRSRSTPVRDRGGRR,mutated_sequence,1.0,1341.0,UPI000013D308.a2m,UPI000013D308.npy,gnomAD +UPI00001AF305,UPI00001AF305.csv,MRRRAARGPGPPPPGPGLSRLPLPLLLLLALGTRGGCAAPAPAPRAEDLSLGVEWLSRFGYLPPADPTTGQLQTQEELSKAITAMQQFGGLEATGILDEATLALMKTPRCSLPDLPVLTQARRRRQAPAPTKWNKRNLSWRVRTFPRDSPLGHDTVRALMYYALKVWSDIAPLNFHEVAGSAADIQIDFSKADHNDGYPFDGPGGTVAHAFFPGHHHTAGDTHFDDDEAWTFRSSDAHGMDLFAVAVHEFGHAIGLSHVAAAHSIMRPYYQGPVGDPLRYGLPYEDKVRVWQLYGVRESVSPTAQPEEPPLLPEPPDNRSSAPPRKDVPHRCSTHFDAVAQIRGEAFFFKGKYFWRLTRDRHLVSLQPAQMHRFWRGLPLHLDSVDAVYERTSDHKIVFFKGDRYWVFKDNNVEEGYPRPVSDFSLPPGGIDAAFSWAHNDRTYFFKDQLYWRYDDHTRHMDPGYPAQSPLWRGVPSTLDDAMRWSDGASYFFRGQEYWKVLDGELEVAPGYPQSTARDWLVCGDSQADGSVAAGVDAAEGPRAPPGQHDQSRSEDGYEVCSCTSGASSPPGAPGPLVAATMLLLLPPLSPGALWTAAQALTL,mutated_sequence,1.0,603.0,UPI00001AF305.a2m,UPI00001AF305.npy,gnomAD +UPI0001DD21C7,UPI0001DD21C7.csv,MGAPSACRTLVLALAAMLVVPQAETQGPVEPSWENAGHTMDGGAPTSSPTRRVSFVPPVTVFPSLSPLNPAHNGRVCSTWGDFHYKTFDGDVFRFPGLCNYVFSEHCRAAYEDFNVQLRRGLVGSRPVVTRVVIKAQGLVLEASNGSVLINGQREELPYSRTGLLVEQSGDYIKVSIRLVLTFLWNGEDSALLELDPKYANQTCGLCGDFNGLPAFNEFYAHNARLTPLQFGNLQKLDGPTEQCPDPLPLPAGNCTDEEGICHRTLLGPAFAECHALVDSTAYLAACAQDLCRCPTCPCATFVEYSRQCAHAGGQPRNWRCPELCPRTCPLNMQHQECGSPCTDTCSNPQRAQLCEDHCVDGCFCPPGTVLDDITHSGCLPLGQCPCTHGGRTYSPGTSFNTTCSSCTCSGGLWQCQDLPCPGTCSVQGGAHISTYDEKLYDLHGDCSYVLSKKCADSSFTVLAELRKCGLTDNENCLKAVTLSLDGGDTAIRVQADGGVFLNSIYTQLPLSAANITLFTPSSFFIVVQTGLGLQLLVQLVPLMQVFVRLDPAHQGQMCGLCGNFNQNQADDFTALSGVVEATGAAFANTWKAQAACANARNSFEDPCSLSVENENYARHWCSRLTDPNSAFSRCHSIINPKPFHSNCMFDTCNCERSEDCLCAALSSYVHACAAKGVQLSDWRDGVCTKYMQNCPKSQRYAYVVDACQPTCRGLSEADVTCSVSFVPVDGCTCPAGTFLNDAGACVPAQECPCYAHGTVLAPGEVVHDEGAVCSCTGGKLSCLGASLQKSTGCAAPMVYLDCSNSSAGTPGAECLRSCHTLDVGCFSTHCVSGCVCPPGLVSDGSGGCIAEEDCPCVHNEATYKPGETIRVDCNTCTCRNRRWECSHRLCLGTCVAYGDGHFITFDGDRYSFEGSCEYILAQDYCGDNTTHGTFRIVTENIPCGTTGTTCSKAIKLFVESYELILQEGTFKAVARGPGGDPPYKIRYMGIFLVIETHGMAVSWDRKTSVFIRLHQDYKGRVCGLCGNFDDNAINDFATRSRSVVGDALEFGNSWKLSPSCPDALAPKDPCTANPFRKSWAQKQCSILHGPTFAACRSQVDSTKYYEACVNDACACDSGGDCECFCTAVAAYAQACHDAGLCVSWRTPDTCPLFCDFYNPHGGCEWHYQPCGAPCLKTCRNPSGHCLVDLPGLEGCYPKCPPSQPFFNEDQMKCVAQCGCYDKDGNYYDVGARVPTAENCQSCNCTPSGIQCAHSLEACTCTYEDRTYSYQDVIYNTTDGLGACLIAICGSNGTIIRKAVACPGTPATTPFTFTTAWVPHSTTSPALPVSTVCVREVCRWSSWYNGHRPEPGLGGGDFETFENLRQRGYQVCPVLADIECRAAQLPDMPLEELGQQVDCDRMRGLMCANSQQSPPLCHDYELRVLCCEYVPCGPSPAPGTSPQPSLSASTEPAVPTPTQTTATEKTTLWVTPSIRSTAALTSQTGSSSGPVTVTPSAPGTTTCQPRCQWTEWFDEDYPKSEQLGGDVESYDKIRAAGGHLCQQPKDIECQAESFPNWTLAQVGQKVHCDVHFGLVCRNWEQEGVFKMCYNYRIRVLCCSDDHCRGRATTPPPTTELETATTTTTQALFSTPQPTSSPGLTRAPPASTTAVPTLSEGLTSPRYTSTLGTATTGGPTTPAGSTEPTVPGVATSTLPTRSALPGTTGSLGTWRPSQPPTLAPTTMATSRARPTGTASTASKEPLTTSLAPTLTSELSTSQAETSTPRTETTMSPLTNTTTSQGTTRCQPKCEWTEWFDVDFPTSGVAGGDMETFENIRAAGGKMCWAPKSIECRAENYPEVSIDQVGQVLTCSLETGLTCKNEDQTGRFNMCFNYNVRVLCCDDYSHCPSTPATSSTATPSSTPGTTWILTKPTTTATTTASTGSTATPTSTLRTAPPPKVLTTTATTPTVTSSKATPSSSPGTATALPALRSTATTPTATSVTPIPSSSLGTTWTRLSQTTTPTATMSTATPSSTPETAHTSTVLTATATTTGATGSVATPSSTPGTAHTTKVPTTTTTGFTATPSSSPGTALTPPVWISTTTTPTTRGSTVTPSSIPGTTHTATVLTTTTTTVATGSMATPSSSTQTSGTPPSLTTTATTITATGSTTNPSSTPGTTPIPPVLTTTATTPAATSNTVTPSSALGTTHTPPVPNTMATTHGRSLPPSSPHTVRTAWTSATSGILGTTHITEPSTVTSHTLAATTGTTQHSTPALSSPHPSSRTTESPPSPGTTTPGHTTATSRTTATATPSKTRTSTLLPSSPTSAPITTVVTMGCEPQCAWSEWLDYSYPMPGPSGGDFDTYSNIRAAGGAVCEQPLGLECRAQAQPGVPLRELGQVVECSLDFGLVCRNREQVGKFKMCFNYEIRVFCCNYGHCPSTPATSSTAMPSSTPGTTWILTELTTTATTTESTGSTATPSSTPGTTWILTEPSTTATVTVPTGSTATASSTQATAGTPHVSTTATTPTVTSSKATPFSSPGTATALPALRSTATTPTATSFTAIPSSSLGTTWTRLSQTTTPTATMSTATPSSTPETVHTSTVLTTTATTTGATGSVATPSSTPGTAHTTKVLTTTTTGFTATPSSSPGTARTLPVWISTTTTPTTRGSTVTPSSIPGTTHTPTVLTTTTTTVATGSMATPSSSTQTSGTPPSLTTTATTITATGSTTNPSSTPGTTPIPPVLTTTATTPAATSSTVTPSSALGTTHTPPVPNTTATTHGRSLSPSSPHTVRTAWTSATSGTLGTTHITEPSTGTSHTPAATTGTTQHSTPALSSPHPSSRTTESPPSPGTTTPGHTRATSRTTATATPSKTRTSTLLPSSPTSAPITTVVTMGCEPQCAWSEWLDYSYPMPGPSGGDFDTYSNIRAAGGAVCEQPLGLECRAQAQPGVPLRELGQVVECSLDFGLVCRNREQVGKFKMCFNYEIRVFCCNYGHCPSTPATSSTATPSSTPGTTWILTEQTTAATTTATTGSTAIPSSTPGTAPPPKVLTSTATTPTATSSKATSSSSPRTATTLPVLTSTATKSTATSFTPIPSFTLGTTGTLPEQTTTPMATMSTIHPSSTPETTHTSTVLTTKATTTRATSSMSTPSSTPGTTWILTELTTAATTTAATGPTATPSSTPGTTWILTEPSTTATVTVPTGSTATASSTRATAGTLKVLTSTATTPTVISSRATPSSSPGTATALPALRSTATTPTATSVTAIPSSSLGTAWTRLSQTTTPTATMSTATPSSTPETVHTSTVLTTTTTTTRATGSVATPSSTPGTAHTTKVPTTTTTGFTATPSSSPGTALTPPVWISTTTTPTTRGSTVTPSSIPGTTHTATVLTTTTTTVATGSMATPSSSTQTSGTPPSLTTTATTITATGSTTNPSSTPGTTPIPPVLTTTATTPAATSSTVTPSSALGTTHTPPVPNTTATTHGRSLPPSSPHTVRTAWTSATSGILGTTHITEPSTVTSHTPAATTSTTQHSTPALSSPHPSSRTTESPPSPGTTTPGHTRGTSRTTATATPSKTRTSTLLPSSPTSAPITTVVTTGCEPQCAWSEWLDYSYPMPGPSGGDFDTYSNIRAAGGAVCEQPLGLECRAQAQPGVPLRELGQVVECSLDFGLVCRNREQVGKFKMCFNYEIRVFCCNYGHCPSTPATSSTATPSSTPGTTWILTKLTTTATTTESTGSTATPSSTPGTTWILTEPSTTATVTVPTGSTATASSTQATAGTPHVSTTATTPTVTSSKATPFSSPGTATALPALRSTATTPTATSFTAIPSSSLGTTWTRLSQTTTPTATMSTATPSSTPETAHTSTVLTTTATTTRATGSVATPSSTPGTAHTTKVPTTTTTGFTVTPSSSPGTARTPPVWISTTTTPTTSGSTVTPSSVPGTTHTPTVLTTTTTTVATGSMATPSSSTQTSGTPPSLITTATTITATGSTTNPSSTPGTTPIPPVLTTTATTPAATSSTVTPSSALGTTHTPPVPNTTATTHGRSLSPSSPHTVRTAWTSATSGTLGTTHITEPSTGTSHTPAATTGTTQHSTPALSSPHPSSRTTESPPSPGTTTPGHTTATSRTTATATPSKTRTSTLLPSSPTSAPITTVVTTGCEPQCAWSEWLDYSYPMPGPSGGDFDTYSNIRAAGGAVCEQPLGLECRAQAQPGVPLGELGQVVECSLDFGLVCRNREQVGKFKMCFNYEIRVFCCNYGHCPSTPATSSTAMPSSTPGTTWILTELTTTATTTASTGSTATPSSTPGTAPPPKVLTSPATTPTATSSKATSSSSPRTATTLPVLTSTATKSTATSVTPIPSSTLGTTGTLPEQTTTPVATMSTIHPSSTPETTHTSTVLTTKATTTRATSSTSTPSSTPGTTWILTELTTAATTTAATGPTATPSSTPGTTWILTELTTTATTTASTGSTATPSSTPGTTWILTEPSTTATVTVPTGSTATASSTQATAGTPHVSTTATTPTVTSSKATPSSSPGTATALPALRSTATTPTATSFTAIPSSSLGTTWTRLSQTTTPTATMSTATPSSTPETVHTSTVLTATATTTGATGSVATPSSTPGTAHTTKVPTTTTTGFTATPSSSPGTALTPPVWISTTTTPTTTTPTTSGSTVTPSSIPGTTHTARVLTTTTTTVATGSMATPSSSTQTSGTPPSLTTTATTITATGSTTNPSSTPGTTPITPVLTSTATTPAATSSKATSSSSPRTATTLPVLTSTATKSTATSFTPIPSSTLWTTWTVPAQTTTPMSTMSTIHTSSTPETTHTSTVLTTTATMTRATNSTATPSSTLGTTRILTELTTTATTTAATGSTATLSSTPGTTWILTEPSTIATVMVPTGSTATASSTLGTAHTPKVVTTMATMPTATASTVPSSSTVGTTRTPAVLPSSLPTFSVSTVSSSVLTTLRPTGFPSSHFSTPCFCRAFGQFFSPGEVIYNKTDRAGCHFYAVCNQHCDIDRFQGACPTSPPPVSSAPLSSPSPAPGCDNAIPLRQVNETWTLENCTVARCVGDNRVVLLDPKPVANVTCVNKHLPIKVSDPSQPCDFHYECECICSMWGGSHYSTFDGTSYTFRGNCTYVLMREIHARFGNLSLYLDNHYCTASATAAAARCPRALSIHYKSMDIVLTVTMVHGKEEGLILFDQIPVSSGFSKNGVLVSVLGTTTMRVDIPALGVSVTFNGQVFQARLPYSLFHNNTEGQCGTCTNNQRDDCLQRDGTTAASCKDMAKTWLVPDSRKDGCWAPTGTPPTASPAAPVSSTPTPTPCPPQPLCDLMLSQVFAECHNLVPPGPFFNACISDHCRGRLEVPCQSLEAYAELCRARGVCSDWRGATGGLCDLTCPPTKVYKPCGPIQPATCNSRNQSPQLEGMAEGCFCPEDQILFNAHMGICVQACPCVGPDGFPKFPGERWVSNCQSCVCDEGSVSVQCKPLPCDAQGQPPPCNRPGFVTVTRPRAENPCCPETVCVCNTTTCPQSLPVCPPGQESICTQEEGDCCPTFRCRPQLCSYNGTFYGVGATFPGALPCHMCTCLSGDTQDPTVQCQEDACNNTTCPQGFEYKRVAGQCCGECVQTACLTPDGQPVQLNETWVNSHVDNCTVYLCEAEGGVHLLTPQPASCPDVSSCRGSLRKTGCCYSCEEDSCQVRINTTILWHQGCETEVNITFCEGSCPGASKYSAEAQAMQHQCTCCQERRVHEETVPLHCPNGSAILHTYTHVDECGCTPFCVPAPMAPPHTRGFPAQEATAV,mutated_sequence,1.0,5762.0,UPI0001DD21C7.a2m,UPI0001DD21C7.npy,gnomAD +UPI000004A10E,UPI000004A10E.csv,MLGGSAGRLKMSSSGTLSNYYVDSLIGHEGDEVFAARFGPPGPGAQGRPAGVADGPAATAAEFASCSFAPRSAVFSASWSAVPSQPPAAAAMSGLYHPYVPPPPLAASASEPGRYVRSWMEPLPGFPGGAGGGGGGGGGGPGRGPSPGPSGPANGRHYGIKPETRAAPAPATAASTTSSSSTSLSSSSKRTECSVARESQGSSGPEFSCNSFLQEKAAAATGGTGPGAGIGAATGTGGSSEPSACSDHPIPGCSLKEEEKQHSQPQQQQLDPNNPAANWIHARSTRKKRCPYTKYQTLELEKEFLFNMYLTRDRRYEVARILNLTERQVKIWFQNRRMKMKKMSKEKCPKGD,mutated_sequence,1.0,352.0,UPI000004A10E.a2m,UPI000004A10E.npy,gnomAD +UPI000007443D,UPI000007443D.csv,MPRPELPLPEGWEEARDFDGKVYYIDHTNRTTSWIDPRDRYTKPLTFADCISDELPLGWEEAYDPQVGDYFIDHNTKTTQIEDPRVQWRREQEHMLKDYLVVAQEALSAQKEIYQVKQQRLELAQQEYQQLHAVWEHKLGSQVSLVSGSSSSSKYDPEILKAEIATAKSRVNKLKREMVHLQHELQFKERGFQTLKKIDKKMSDAQGSYKLDEAQAVLRETKAIKKAITCGEKEKQDLIKSLAMLKDGFRTDRGSHSDLWSSSSSLESSSFPLPKQYLDVSSQTDISGSFGINSNNQLAEKVRLRLRYEEAKRRIANLKIQLAKLDSEAWPGVLDSERDRLILINEKEELLKEMRFISPRKWTQGEVEQLEMARKRLEKDLQAARDTQSKALTERLKLNSKRNQLVRELEEATRQVATLHSQLKSLSSSMQSLSSGSSPGSLTSSRGSLVASSLDSSTSASFTDLYYDPFEQLDSELQSKVEFLLLEGATGFRPSGCITTIHEDEVAKTQKAEGGGRLQALRSLSGTPKSMTSLSPRSSLSSPSPPCSPLMADPLLAGDAFLNSLEFEDPELSATLCELSLGNSAQERYRLEEPGTEGKQLGQAVNTAQGCGLKVACVSAAVSDESVAGDSGVYEASVQRLGASEAAAFDSDESEAVGATRIQIALKYDEKNKQFAILIIQLSNLSALLQQQDQKVNIRVAVLPCSESTTCLFRTRPLDASDTLVFNEVFWVSMSYPALHQKTLRVDVCTTDRSHLEECLGGAQISLAEVCRSGERSTRWYNLLSYKYLKKQSRELKPVGVMAPASGPASTDAVSALLEQTAVELEKRQEGRSSTQTLEDSWRYEETSENEAVAEEEEEEVEEEEGEEDVFTEKASPDMDGYPALKVDKETNTETPAPSPTVVRPKDRRVGTPSQGPFLRGSTIIRSKTFSPGPQSQYVCRLNRSDSDSSTLSKKPPFVRNSLERRSVRMKRPSSVKSLRSERLIRTSLDLELDLQATRTWHSQLTQEISVLKELKEQLEQAKSHGEKELPQWLREDERFRLLLRMLEKRQMDRAEHKGELQTDKMMRAAAKDVHRLRGQSCKEPPEVQSFREKMAFFTRPRMNIPALSADDV,mutated_sequence,1.0,1113.0,UPI000007443D.a2m,UPI000007443D.npy,gnomAD +UPI0000367134,UPI0000367134.csv,MVFRRFVEVGRVAYVSFGPHAGKLVAIVDVIDQNRALVDGPCTQVRRQAMPFKCMQLTDFILKFPHSAHQKYVRQAWQKADINTKWAATRWAKKIEARERKAKMTDFDRFKVMKAKKMRNRIIKNEVKKLQKAALLKASPKKAPGTKGTAAAAAAAAAAKVPAKKITAASKKAPAQKVPAQKATGQKAAPAPKAQKGQKAPAQKAPAPKASGKKA,mutated_sequence,1.0,215.0,UPI0000367134.a2m,UPI0000367134.npy,gnomAD +UPI0000048EB8,UPI0000048EB8.csv,MQPRRAQAPGAQLLPALALLLLLLGAGPRGSSLANPVPAAPLSAPGPCAAQPCRNGGVCTSRPEPDPQHPAPAGEPGYSCTCPAGISGANCQLVADPCASNPCHHGNCSSSSSSSSDGYLCICNEGYEGPNCEQALPSLPATGWTESMAPRQLQPVPATQEPDKILPRSQATVTLPTWQPKTGQKVVEMKWDQVEVIPDIACGNASSNSSAGGRLVSFEVPQNTSVKIRQDATASLILLWKVTATGFQQCSLIDGRSVTPLQASGGLVLLEEMLALGNNHFIGFVNDSVTKSIVALRLTLVVKVSTCVPGESHANDLECSGKGKCTTKPSEATFSCTCEEQYVGTFCEEYDACQRKPCQNNASCIDANEKQDGSNFTCVCLPGYTGELCQSKIDYCILDPCRNGATCISSLSGFTCQCPEGYFGSACEEKVDPCASSPCQNNGTCYVDGVHFTCNCSPGFTGPTCAQLIDFCALSPCAHGTCRSVGTSYKCLCDPGYHGLYCEEEYNECLSAPCLNAATCRDLVNGYECVCLAEYKGTHCELYKDPCANVSCLNGATCDSDGLNGTCICAPGFTGEECDIDINECDSNPCHHGGSCLDQPNGYNCHCPHGWVGANCEIHLQWKSGHMAESLTNMPRHSLYIIIGALCVAFILMLIILIVGICRISRIEYQGSSRPAYEEFYNCRSIDSEFSNAIASIRHARFGKKSRPAMYDVSPIAYEDYSPDDKPLVTLIKTKDL,mutated_sequence,1.0,737.0,UPI0000048EB8.a2m,UPI0000048EB8.npy,gnomAD +UPI00015E04B4,UPI00015E04B4.csv,RSPTLRKPLKHSTPEEAALGWSPRPSGGASYLSGSPMPAHFSQDLASHPAGVSPPATVRKRRLSTLWASKESSLDLSAPGEEPPTSASLTQRQRQRQQQQQQQESLRAKSWAQNPGLPGILNTTGRKRRDPKKRAAAMERVRQWEIYVLQNIEEATQHELTIEDD,mutated_sequence,1.0,165.0,UPI00015E04B4.a2m,UPI00015E04B4.npy,gnomAD +UPI00015E06EA,UPI00015E06EA.csv,MMAAAAAAAAGSSSSGGGGGGSGSSSSSSDTSSTGEEERMRRLFQTCDGDGDGYISRNDLLMVCRQLNMEESVAEIMNQLGADENGKISFQDFTRCRMQLVREIRKEEVDLSAKSDNSCTKKLRDRIASWPTSSDNSLGALSAARESWEYDSGARDLQSPDVQSQSALQKLLEYGGSSLHQQAALHKLLTQSPHIGNSVGGSYLELANTLHSAALASLKGDIVELNKRLQQTERERDLLEKKLAKAQCEQSHLMREHEDVQERTTLRYEERITELHSVIAELNKKIDRLQGTTIREEDEYSELRSELSQSQHEVNEDSRSMDQDQTSVSIPENQSTMVTADMDNCSDLNSELQRVLTGLENVVCGRKKSSCSLSVAEVDKHIEQLTTASEHCDLAIKTVEEIEGVLGRDLYPNLAEERSRWEKELAGLREENESLTAMLCSKEEELNRTKATMNAIREERDRLRRRVRELQTRLQSVQATGPSSPGRLTSTNRPINPSTGELSTSSSSNDIPIAKIAERVKLSKTRSESSSSDRPVLGSEISSIGVSSSVAEHLAHSLQDCSNIQEIFQTLYSHGSAISESKIREFEVETERLNSRIEHLKSQNDLLTITLEECKSNAERMSMLVGKYESNATALRLALQYSEQCIEAYELLLALAESEQSLILGQFRAAGVGSSPGDQSGDENITQMLKRAHDCRKTAENAAKALLMKLDGSCGGAFAVAGCSVQPWESLSSNSHTSTTSSTASSCDTEFTKEDEQRLKDYIQQLKNDRAAVKLTMLELESIHIDPLSYDVKPRGDSQRLDLENAVLMQELMAMKEEMAELKAQLYLLEKEKKALELKLSTREAQEQAYLVHIEHLKSEVEEQKEQRMRSLSSTSSGSKDKPGKECADAASPALSLAELRTTCSENELAAEFTNAIRREKKLKARVQELVSALERLTKSSEIRHQQSAEFVNDLKRANSNLVAAYEKAKKKHQNKLKKLESQMMAMVERHETQVRMLKQRIALLEEENSRPHTNETSL,mutated_sequence,1.0,1019.0,UPI00015E06EA.a2m,UPI00015E06EA.npy,gnomAD +UPI0002065974,UPI0002065974.csv,MSVSARSAAAEERSVNSSTMGQQKNLEGYVGFANLPNQVYRKSVKRGFEFTLMVVGESGLGKSTLINSLFLTDLYSPEYPGPSHRIKKTVQVEQSKVLIKEGGVQLLLTIVDTPGFGDAVDNSNCWQPVIDYIDSKFEDYLNAESRVNRRQMPDNRVQCCLYFIAPSGHGLKPLDIEFMKRLHEKVNIIPLIAKADTLTPEECQQFKKQIMKEIQEHKIKIYEFPETAKKKKKKK,mutated_sequence,1.0,235.0,UPI0002065974.a2m,UPI0002065974.npy,gnomAD +UPI0000073AA5,UPI0000073AA5.csv,MVSSVLPNPTSAECWAALLHDPMTLDMDAVLSDFVRSTGAEPGLARDLLEGKNWDLTAALSDYEQLRQVHTANLPHVFNEGRGPKQPEREPQPGHKVERPCLQRQDDIAQEKRLSRGISHASSAIVSLARSHVASECNNEQFPLEMPIYTFQLPDLSVYSEDFRSFIERDLIEQATMVALEQAGRLNWWSTVCTSCKRLLPLATTGDGNCLLHAASLGMWGFHDRDLVLRKALYTMMRTGAEREALKRRWRWQQTQQNKEEEWEREWTELLKLASSEPRTHFSKNGGTGGGVDNSEDPVYESLEEFHVFVLAHILRRPIVVVADTMLRDSGGEAFAPIPFGGIYLPLEVPPNRCHCSPLVLAYDQAHFSALVSMEQRDQQREQAVIPLTDSEHKLLPLHFAVDPGKDWEWGKDDNDNARLAHLILSLEAKLNLLHSYMNVTWIRIPSETRAPLAQPESPTASAGEDVQSLADSLDSDRDSVCSNSNSNNGKNGKDKEKEKQRKEKDKTRADSVANKLGSFSKTLGIKLKKNMGGLGGLVHGKMGRANSANGKNGDSAERGKEKKAKSRKGSKEESGASASTSPSEKTTPSPTDKAAGASPAEKGGGPRGDAWKYSTDVKLSLNILRAAMQGERKFIFAGLLLTSHRHQFHEEMIGYYLTSAQERFSAEQEQRRRDAATAAAAAAAAAAATAKRPPRRPETEGVPVPERASPGPPTQLVLKLKERPSPGPAAGRAARAAAGGTASPGGGARRASASGPVPGRSPPAPARQSVIHVQASGARDEACAPAVGALRPCATYPQQNRSLSSQSYSPARAAALRTVNTVESLARAVPGALPGAAGTAGAAEHKSQTYTNGFGALRDGLEFADADAPTARSNGECGRGGPGPVQRRCQRENCAFYGRAETEHYCSYCYREELRRRREARGARP,mutated_sequence,1.0,926.0,UPI0000073AA5.a2m,UPI0000073AA5.npy,gnomAD +UPI00001AFDEB,UPI00001AFDEB.csv,MVFRNVGRPPEEEDVEAAPEPGPSELLCPRHRCALDPKALPPGLALERTWGPAAGLEAQLAALGLGQPAGPGVKTVGGGCCPCPCPPQPPPPQPQPPAAAPQAGEDPTETSDALLVLEGLESEAESLETNSCSEEELSSPGRGGGGGGRLLLQPPGPELPPVPFPLQDLVPLGRLSRGEQQQQQQQQPPPPPPPPGPLRPLAGPSRKGSFKIRLSRLFRTKSCNGGSGGGDGTGKRPSGELAASAASLTDMGGSAGRELDAGRKPKLTRTQSAFSPVSFSPLFTGETVSLVDVDISQRGLTSPHPPTPPPPPRRSLSLLDDISGTLPTSVLVAPMGSSLQSFPLPPPPPPHAPDAFPRIAPIRAAESLHSQPPQHLQCPLYRPDSSSFAASLRELEKCGWYWGPMNWEDAEMKLKGKPDGSFLVRDSSDPRYILSLSFRSQGITHHTRMEHYRGTFSLWCHPKFEDRCQSVVEFIKRAIMHSKNGKFLYFLRSRVPGLPPTPVQLLYPVSRFSNVKSLQHLCRFRIRQLVRIDHIPDLPLPKPLISYIRKFYYYDPQEEVYLSLKEAQLISKQKQEVEPST,mutated_sequence,1.0,581.0,UPI00001AFDEB.a2m,UPI00001AFDEB.npy,gnomAD +UPI0000073F52,UPI0000073F52.csv,MLLLGLLLLLPLLAGARLLWNWWKLRSLHLPPLAPGFLHLLQPDLPIYLLGLTQKFGPIYRLHLGLQDVVVLNSKRTIEEAMVKKWADFAGRPEPLTYKLVSRNYPDLSLGDYSLLWKAHKKLTRSALLLGIRDSMEPVVEQLTQEFCERMRAQPGTPVAIEEEFSLLTCSIICYLTFGDKIKDDNLMPAYYKCIQEVLKTWSHWSIQIVDVIPFLRFFPNPGLRRLKQAIEKRDHIVEMQLRQHKESLVAGQWRDMMDYMLQGVAQPSMEEGSGQLLEGHVHMAAVDLLIGGTETTANTLSWAVVFLLHHPEIQQRLQEELDHELGPGASSSRVPYKDRARLPLLNATIAEVLRLRPVVPLALPHRTTRPSSISGYDIPEGTVIIPNLQGAHLDETVWERPHEFWPDRFLEPGKNSRALAFGCGARVCLGEPLARLELFVVLTRLLQAFTLLPSGDALPSLQPLPHCSVILKMQPFQVRLQPRGMGAHSPGQSQ,mutated_sequence,1.0,495.0,UPI0000073F52.a2m,UPI0000073F52.npy,gnomAD +UPI00002326B6,UPI00002326B6.csv,MPRLPVKKIRKQMKLLLLLLLLSCAAWLTYVHLGLVRQGRALRQRLGYGRDGEKLTSETDGRGVHAAPSTQRAEDSSESREEEQAPEGRDLDMLFPGGAGRLPLNFTHQTPPWREEYKGQVNLHVFEDWCGGAVGHLRRNLHFPLFPHTRTTVKKLAVSPKWKNYGLRIFGFIHPARDGDVQFSVASDDNSEFWLSLDESPAAAQLVAFVGKTGSEWTAPGEFTKFSSQVSKPRRLMASRRYYFELLHKQDDRGSDHVEVGWRAFLPGLKFEVISSAHISLYTDESALKMDHVAHVPQSPASHVGGRPPQEETSADMLRPDPRDTFFLTPRMESSSLENVLEPCAYAPTYVVKDFPIARYQGLQFVYLSFVYPNDYTRLTHMETDNKCFYRESPLYLERFGFYKYMKMDKEEGDEDEEDEVQRRAFLFLNPDDFLDDEDEGELLDSLEPTEAAPPRSGPQSPAPAAPAQPGATLAPPTPPRPRDGGTPRHSRALSWAARAARPLPLFLGRAPPPRPAVEQPPPKVYVTRVRPGQRASPRAPAPRAPWPPFPGVFLHPRPLPRVQLRAPPRPPRPHGRRTGGPQATQPRPPARAQATQGGREGQARTLGPAAPTVDSNLSSEARPVTSFLSLSQVSGPQLPGEGEEEEEGEDDGAPGDEAASEDSEEAAGPALGRWREDAIDWQRTFSVGAVDFELLRSDWNDLRCNVSGNLQLPEAEAVDVTAQYMERLNARHGGRFALLRIVNVEKRRDSARGSRFLLELELQERGGGRLRLSEYVFLRLPGARVGDADGESPEPAPAASVRPDGRPELCRPLRLAWRQDVMVHFIVPVKNQARWVAQFLADMAALHARTGDSRFSVVLVDFESEDMDVERALRAARLPRYQYLRRTGNFERSAGLQAGVDAVEDASSIVFLCDLHIHFPPNILDGIRKHCVEGRLAFAPVVMRLSCGSSPRDPHGYWEVNGFGLFGIYKSDFDRVGGMNTEEFRDQWGGEDWELLDRVLQAGLEVERLRLRNFYHHYHSKRGMWSVRSRKGSRTGAS,mutated_sequence,1.0,1039.0,UPI00002326B6.a2m,UPI00002326B6.npy,gnomAD +UPI0000129881,UPI0000129881.csv,MKLQAVMETLLQRQQRARQELEARQQLPPDPPAAPPGRARAAPDEDREPESARMQRAQMAALAAMRAAAAGLGHPASPGGSEDGPPGSEEEDAAREGTPGSPGRGREGPGEEHFEDMASDEDMKPKWEEEEMEEDLGEDEEEEEEDYEDEEEEEDEEGLGPPGPASLGTTALFPRKAQPPQAFRGDGVPRVLGGQERPGPGPAHPGGAAHVAPQLQPPDHGDWTYEEQFKQLYELDGDPKRKEFLDDLFSFMQKRGTPVNRIPIMAKQVLDLFMLYVLVTEKGGLVEVINKKLWREITKGLNLPTSITSAAFTLRTQYMKYLYPYECEKRGLSNPNELQAAIDSNRREGRRQSFGGSLFAYSPGGAHGMLSSPKLPVSSLGLAASTNGSSITPAPKIKKEEDSAIPITVPGRLPVSLAGHPVVAAQAAAVQAAAAQAAVAAQAAALEQLREKLESAEPPEKKMALVADEQQRLMQRALQQNFLAMAAQLPMSIRINSQASESRQDSAVNLTGTNGSNSISMSVEINGIMYTGVLFAQPPAPTPTSAPNKGGGGGGGSSSNAGGRGGNTGTSGGQAGPAGLSTPSTSTSNNSLP,mutated_sequence,1.0,593.0,UPI0000129881.a2m,UPI0000129881.npy,gnomAD +UPI000007385F,UPI000007385F.csv,MAAGGSAPEPRVLVCLGALLAGWVAVGLEAVVIGEVHENVTLHCGNISGLRGQVTWYRNNSEPVFLLSSNSSLRPAEPRFSLVDATSLHIESLSLGDEGIYTCQEILNVTQWFQVWLQVASGPYQIEVHIVATGTLPNGTLYAARGSQVDFSCNSSSRPPPVVEWWFQALNSSSESFGHNLTVNFFSLLLISPNLQGNYTCLALNQLSKRHRKVTTELLVYYPPPSAPQCWAQMASGSFMLQLTCRWDGGYPDPDFLWIEEPGGVIVGKSKLGVEMLSESQLSDGKKFKCVTSHIVGPESGASCMVQIRGPSLLSEPMKTCFTGGNVTLTCQVSGAYPPAKILWLRNLTQPEVIIQPSSRHLITQDGQNSTLTIHNCSQDLDEGYYICRADSPVGVREMEIWLSVKEPLNIGGIVGTIVSLLLLGLAIISGLLLHYSPVFCWKVGNTSRGQNMDDVMVLVDSEEEEEEEEEEEEDAAVGEQEGAREREELPKEIPKQDHIHRVTALVNGNIEQMGNGFQDLQDDSSEEQSDIVQEEDRPV,mutated_sequence,1.0,540.0,UPI000007385F.a2m,UPI000007385F.npy,gnomAD +UPI0001610E83,UPI0001610E83.csv,MLKPGDPGGSAFLKVDPAYLQHWQQLFPHGGAGPLKGSGAAGLLSAPQPLQPPPPPPPPERAEPPPDSLRPRPASLSSASSTPASSSTSASSASSCAAAAAAAALAGLSALPVSQLPVFAPLAAAAVAAEPLPPKELCLGATSGPGPVKCGGGGGGGGEGRGAPRFRCSAEELDYYLYGQQRMEIIPLNQHTSDPNNRCDMCADNRNGECPMHGPLHSLRRLVGTSSAAAAAPPPELPEWLRDLPREVCLCTSTVPGLAYGICAAQRIQQGTWIGPFQGVLLPPEKVQAGAVRNTQHLWEIYDQDGTLQHFIDGGEPSKSSWMRYIRCARHCGEQNLTVVQYRSNIFYRACIDIPRGTELLVWYNDSYTSFFGIPLQCIAQDENLNVPSTVMEAMCRQDALQPFNKSSKLAPTTQQRSVVFPQTPCSRNFSLLDKSGPIESGFNQINVKNQRVLASPTSTSQLHSEFSDWHLWKCGQCFKTFTQRILLQMHVCTQNPDRPYQCGHCSQSFSQPSELRNHVVTHSSDRPFKCGYCGRAFAGATTLNNHIRTHTGEKPFKCERCERSFTQATQLSRHQRMPNECKPITESPESIEVD,mutated_sequence,1.0,595.0,UPI0001610E83.a2m,UPI0001610E83.npy,gnomAD +UPI000006F554,UPI000006F554.csv,MKTRQNKDSMSMRSGRKKEAPGPREELRSRGRASPGGVSTSSSDGKAEKSRQTAKKARVEEASTPKVNKQGRSEEISESESEETNAPKKTKTEQELPRPQSPSDLDSLDGRSLNDDGSSDPRDIDQDNRSTSPSIYSPGSVENDSDSSSGLSQGPARPYHPPPLFPPSPQPPDSTPRQPEASFEPHPSVTPTGYHAPMEPPTSRMFQAPPGAPPPHPQLYPGGTGGVLSGPPMGPKGGGAASSVGGPNGGKQHPPPTTPISVSSSGASGAPPTKPPTTPVGGGNLPSAPPPANFPHVTPNLPPPPALRPLNNASASPPGLGAQPLPGHLPSPHAMGQGMGGLPPGPEKGPTLAPSPHSLPPASSSAPAPPMRFPYSSSSSSSAAASSSSSSSSSSASPFPASQALPSYPHSFPPPTSLSVSNQPPKYTQPSLPSQAVWSQGPPPPPPYGRLLANSNAHPGPFPPSTGAQSTAHPPVSTHHHHHQQQQQQQQQQQQQQQQQQQHHGNSGPPPPGAFPHPLEGGSSHHAHPYAMSPSLGSLRPYPPGPAHLPPPHSQVSYSQAGPNGPPVSSSSNSSSSTSQGSYPCSHPSPSQGPQGAPYPFPPVPTVTTSSATLSTVIATVASSPAGYKTASPPGPPPYGKRAPSPGAYKTATPPGYKPGSPPSFRTGTPPGYRGTSPPAGPGTFKPGSPTVGPGPLPPAGPSGLPSLPPPPAAPASGPPLSATQIKQEPAEEYETPESPVPPARSPSPPPKVVDVPSHASQSARFNKHLDRGFNSCARSDLYFVPLEGSKLAKKRADLVEKVRREAEQRAREEKEREREREREKEREREKERELERSVKLAQEGRAPVECPSLGPVPHRPPFEPGSAVATVPPYLGPDTPALRTLSEYARPHVMSPGNRNHPFYVPLGAVDPGLLGYNVPALYSSDPAAREREREARERDLRDRLKPGFEVKPSELEPLHGVPGPGLDPFPRHGGLALQPGPPGLHPFPFHPSLGPLERERLALAAGPALRPDMSYAERLAAERQHAERVAALGNDPLARLQMLNVTPHHHQHSHIHSHLHLHQQDAIHAASASVHPLIDPLASGSHLTRIPYPAGTLPNPLLPHPLHENEVLRHQLFAAPYRDLPASLSAPMSAAHQLQAMHAQSAELQRLALEQQQWLHAHHPLHSVPLPAQEDYYSHLKKESDKPL,mutated_sequence,1.0,1190.0,UPI000006F554.a2m,UPI000006F554.npy,gnomAD +UPI00015E05B9,UPI00015E05B9.csv,MWITSLITMKDEDIFDLAGDPQDSLIPGPSPTPKQNPVCNEAFFGHQTFSLRETGLKSVHCQGQEAENMEREKFQQKALKQTKQKKSKSAEFLMVKEDREATEGTGNPAFNMSSPDLSACQTAEKKVIRHDMPDRTLAAHQQKFRLPASAEPKGNEYGRNYFDPLMDEEINPRQCATEVSREDESGREETLNSEAPGSSNKSHEIHKEASEATTAHLEEFQRSQKTIILLGSSPLEQEIRSTSLHCMEDEMSHPWILLLKVTAVIRSRRYYREQRF,mutated_sequence,1.0,276.0,UPI00015E05B9.a2m,UPI00015E05B9.npy,gnomAD +UPI000004F638,UPI000004F638.csv,MEPRCPPPCGCCERLVLNVAGLRFETRARTLGRFPDTLLGDPARRGRFYDDARREYFFDRHRPSFDAVLYYYQSGGRLRRPAHVPLDVFLEEVAFYGLGAAALARLREDEGCPVPPERPLPRRAFARQLWLLFEFPESSQAARVLAVVSVLVILVSIVVFCLETLPDFRDDRDGTGLAAAAAAGPFPAPLNGSSQMPGNPPRLPFNDPFFVVETLCICWFSFELLVRLLVCPSKAIFFKNVMNLIDFVAILPYFVALGTELARQRGVGQQAMSLAILRVIRLVRVFRIFKLSRHSKGLQILGQTLRASMRELGLLIFFLFIGVVLFSSAVYFAEVDRVDSHFTSIPESFWWAVVTMTTVGYGDMAPVTVGGKIVGSLCAIAGVLTISLPVPVIVSNFSYFYHRETEGEEAGMFSHVDMQPCGPLEGKANGGLVDGEVPELPPPLWAPPGKHLVTEV,mutated_sequence,1.0,456.0,UPI000004F638.a2m,UPI000004F638.npy,gnomAD +UPI000006FECD,UPI000006FECD.csv,MKDAAEELSFARVLLQRVDELEKLFKDREQFLELVSRKLSLVPGAEEVTMVTWEELEQAITDGWRASQAGSETLMGFSKHGGFTSLTSPEGTLSGDSTKQPSIEQALDSASGLGPDRTASGSGGTAHPSDGVSSREQSKVPSGTGRQQQPRARDEAGVPRLHQSSTFQFKSDSDRHRSREKLTSTQPRRNARPGPVQQDLPLARDQPSSVPASQSQVHLRPDRRGLEPTGMNQPGLVPASTYPHGVVPLSMGQLGVPPPEMDDRELIPFVVDEQRMLPPSVPGRDQQGLELPSTDQHGLVSVSAYQHGMTFPGTDQRSMEPLGMDQRGCVISGMGQQGLVPPGIDQQGLTLPVVDQHGLVLPFTDQHGLVSPGLMPISADQQGFVQPSLEATGFIQPGTEQHDLIQSGRFQRALVQRGAYQPGLVQPGADQRGLVRPGMDQSGLAQPGADQRGLVWPGMDQSGLAQPGRDQHGLIQPGTGQHDLVQSGTGQGVLVQPGVDQPGMVQPGRFQRALVQPGAYQPGLVQPGADQIDVVQPGADQHGLVQSGADQSDLAQPGAVQHGLVQPGVDQRGLAQPRADHQRGLVPPGADQRGLVQPGADQHGLVQPGVDQHGLAQPGEVQRSLVQPGIVQRGLVQPGAVQRGLVQPGAVQRGLVQPGVDQRGLVQPGAVQRGLVQPGAVQHGLVQPGADQRGLVQPGVDQRGLVQPGVDQRGLVQPGMDQRGLIQPGADQPGLVQPGAGQLGMVQPGIGQQGMVQPQADPHGLVQPGAYPLGLVQPGAYLHDLSQSGTYPRGLVQPGMDQYGLRQPGAYQPGLIAPGTKLRGSSTFQADSTGFISVRPYQHGMVPPGREQYGQVSPLLASQGLASPGIDRRSLVPPETYQQGLMHPGTDQHSPIPLSTGLGSTHPDQQHVASPGPGEHDQVYPDAAQHGHAFSLFDSHDSMYPGYRGPGYLSADQHGQEGLDPNRTRASDRHGIPAQKAPGQDVTLFRSPDSVDRVLSEGSEVSSEVLSERRNSLRRMSSSFPTAVETFHLMGELSSLYVGLKESMKDLDEEQAGQTDLEKIQFLLAQMVKRTIPPELQEQLKTVKTLAKEVWQEKAKVERLQRILEGEGNQEAGKELKAGELRLQLGVLRVTVADIEKELAELRESQDRGKAAMENSVSEASLYLQDQLDKLRMIIESMLTSSSTLLSMSMAPHKAHTLAPGQIDPEATCPACSLDVSHQVSTLVRRYEQLQDMVNSLAVSRPSKKAKLQRQDEELLGRVQSAILQVQGDCEKLNITTSNLIEDHRQKQKDIAMLYQGLEKLEKEKANREHLEMEIDVKADKSALATKVSRVQFDATTEQLNHMMQELVAKMSGQEQDWQKMLDRLLTEMDNKLDRLELDPVKQLLEDRWKSLRQQLRERPPLYQADEAAAMRRQLLAHFHCLSCDRPLETPVTGHAIPVTPAGPGLPGHHSIRPYTVFELEQVRQHSRNLKLGSAFPRGDLAQMEQSVGRLRSMHSKMLMNIEKVQIHFGGSTKASSQIIRELLHAQCLGSPCYKRVTDMADYTYSTVPRRCGGSHTLTYPYHRSRPQHLPRGLYPTEEIQIAMKHDEVDILGLDGHIYKGRMDTRLPGILRKDSSGTSKRKSQQPRPHVHRPPSLSSNGQLPSRPQSAQISAGNTSER,mutated_sequence,1.0,1663.0,UPI000006FECD.a2m,UPI000006FECD.npy,gnomAD +UPI00025A2F44,UPI00025A2F44.csv,MGQPSLTWMLMVVVASWFITTAATDTSEARTKYNCPARWSWRTPQRGGDTEQGPDEDFTSQGSRKHKMCKEREGQQVHRKSYQRWCSECHSNATCTEDEAVTTCTCQEGFTGDGLTCVDLDECAIPGAHNCSANSSCVNTPGSFSCVCPEGFRLSPGLGCT,mutated_sequence,1.0,161.0,UPI00025A2F44.a2m,UPI00025A2F44.npy,gnomAD +UPI000013D104,UPI000013D104.csv,MESNFNQEGVPRPSYVFSADPIARPSEINFDGIKLDLSHEFSLVAPNTEANSFESKDYLQVCLRIRPFTQSEKELESEGCVHILDSQTVVLKEPQCILGRLSEKSSGQMAQKFSFSKVFGPATTQKEFFQGCIMQPVKDLLKGQSRLIFTYGLTNSGKTYTFQGTEENIGILPRTLNVLFDSLQERLYTKMNLKPHRSREYLRLSSEQEKEEIASKSALLRQIKEVTVHNDSDDTLYGSLTNSLNISEFEESIKDYEQANLNMANSIKFSVWVSFFEIYNEYIYDLFVPVSSKFQKRKMLRLSQDVKGYSFIKDLQWIQVSDSKEAYRLLKLGIKHQSVAFTKLNNASSRSHSIFTVKILQIEDSEMSRVIRVSELSLCDLAGSERTMKTQNEGERLRETGNINTSLLTLGKCINVLKNSEKSKFQQHVPFRESKLTHYFQSFFNGKGKICMIVNISQCYLAYDETLNVLKFSAIAQKVCVPDTLNSSQEKLFGPVKSSQDVSLDSNSNSKILNVKRATISWENSLEDLMEDEDLVEELENAEETQNVETKLLDEDLDKTLEENKAFISHEEKRKLLDLIEDLKKKLINEKKEKLTLEFKIREEVTQEFTQYWAQREADFKETLLQEREILEENAERRLAIFKDLVGKCDTREEAAKDICATKVETEETHNYVGFEDIIDSLQDNVADIKKQAEIAHLYIASLPDPQEATACLELKFNQIKAELAKTKGELIKTKEELKKRENESDSLIQELETSNKKIITQNQRIKELINIIDQKEDTINEFQNLKSHMENTFKCNDKADTSSLIINNKLICNETVEVPKDSKSKICSERKRVNENELQQDEPPAKKGSIHVSSAITEDQKKSEEVRPNIAEIEDIRVLQENNEGLRAFLLTIENELKNEKEEKAELNKQIVHFQQELSLSEKKNLTLSKEVQQIQSNYDIAIAELHVQKSKNQEQEEKIMKLSNEIETATRSITNNVSQIKLMHTKIDELRTLDSVSQISNIDLLNLRDLSNGSEEDNLPNTQLDLLGNDYLVSKQVKEYRIQEPNRENSFHSSIEAIWEECKEIVKASSKKSHQIEELEQQIEKLQAEVKGYKDENNRLKEKEHKNQDDLLKEKETLIQQLKEELQEKNVTLDVQIQHVVEGKRALSELTQGVTCYKAKIKELETILETQKVECSHSAKLEQDILEKESIILKLERNLKEFQEHLQDSVKNTKDLNVKELKLKEEITQLTNNLQDMKHLLQLKEEEEETNRQETEKLKEELSASSARTQNLKADLQRKEEDYADLKEKLTDAKKQIKQVQKEVSVMRDEDKLLRIKINELEKKKNQCSQELDMKQRTIQQLKEQLNNQKVEEAIQQYERACKDLNVKEKIIEDMRMTLEEQEQTQVEQDQVLEAKLEEVERLATELEKWKEKCNDLETKNNQRSNKEHENNTDVLGKLTNLQDELQESEQKYNADRKKWLEEKMMLITQAKEAENIRNKEMKKYAEDRERFFKQQNEMEILTAQLTEKDSDLQKWREERDQLVAALEIQLKALISSNVQKDNEIEQLKRIISETSKIETQIMDIKPKRISSADPDKLQTEPLSTSFEISRNKIEDGSVVLDSCEVSTENDQSTRFPKPELEIQFTPLQPNKMAVKHPGCTTPVTVKIPKARKRKSNEMEEDLVKCENKKNATPRTNLKFPISDDRNSSVKKEQKVAIRPSSKKTYSLRSQASIIGVNLATKKKEGTLQKFGDFLQHSPSILQSKAKKIIETMSSSKLSNVEASKENVSQPKRAKRKLYTSEISSPIDISGQVILMDQKMKESDHQIIKRRLRTKTAK,mutated_sequence,1.0,1820.0,UPI000013D104.a2m,UPI000013D104.npy,gnomAD +UPI000066DA6A,UPI000066DA6A.csv,MTAAAASNWGLITNIVNSIVGVSVLTMPFCFKQCGIVLGALLLVFCSWMTHQSCMFLVKSASLSKRRTYAGLAFHAYGKAGKMLVETSMIGLMLGTCIAFYVVIGDLGSNFFARLFGFQVGGTFRMFLLFAVSLCIVLPLSLQRNMMASIQSFSAMALLFYTVFMFVIVLSSLKHGLFSGQWLRRVSYVRWEGVFRCIPIFGMSFACQSQVLPTYDSLDEPSVKTMSSIFASSLNVVTTFYVMVGFFGYVSFTEATAGNVLMHFPSNLVTEMLRVGFMMSVAVGFPMMILPCRQALSTLLCEQQQKDGTFAAGGYMPPLRFKALTLSVVFGTMVGGILIPNVETILGLTGATMGSLICFICPALIYKKIHKNALSSQVVLWVGLGVLVVSTVTTLSVSEEVPEDLAEEAPGGRLGEAEGLMKVEAARLSAQDPVVAVAEDGREKPKLPKEREELEQAQIKGPVDVPGREDGKEAPEEAQLDRPGQGIAVPVGEAHRHEPPVPHDKVVVDEGQDREVPEENKPPSRHAGGKAPGVQGQMAPPLPDSEREKQEPEQGEVGKRPGQAQALEEAGDLPEDPQKVPEADGQPAVQPAKEDLGPGDRGLHPRPQAVLSEQQNGLAVGGGEKAKGGPPPGNAAGDTGQPAEDSDHGGKPPLPAEKPAPGPGLPPEPREQRDVERAGGNQAASQLEEAGRAEMLDHAVLLQVIKEQQVQQKRLLDQQEKLLAVIEEQHKEIHQQRQEDEEDKPRQVEVHQEPGAAVPRGQEAPEGKARETVENLPPLPLDPVLRAPGGRPAPSQDLNQRSLEHSEGPVGRDPAGPPDGGPDTEPRAAQAKLRDGQKDAAPRAAGTVKELPKGPEQVPVPDPAREAGGPEERLAEEFPGQSQDVTGGSQDRKKPGKEVAATGTSILKEANWLVAGPGAETGDPRMKPKQVSRDLGLAADLPGGAEGAAAQPQAVLRQPELRVISDGEQGGQQGHRLDHGGHLEMRKARGGDHVPVSHEQPRGGEDAAVQEPRQRPEPELGLKRAVPGGQRPDNAKPNRDLKLQAGSDLRRRRRDLGPHAEGQLAPRDGVIIGLNPLPDVQVNDLRGALDAQLRQAAGGALQVVHSRQLRQAPGPPEES,mutated_sequence,1.0,1119.0,UPI000066DA6A.a2m,UPI000066DA6A.npy,gnomAD +UPI00004C8020,UPI00004C8020.csv,MFGDLFEEEYSTVSNNQYGKGKKLKTKALEPPAPREFTNLSGIRNQGGTCYLNSLLQTLHFTPEFREALFSLGPEELGLFEDKDKPDAKVRIIPLQLQRLFAQLLLLDQEAASTADLTDSFGWTSNEEMRQHDVQELNRILFSALETSLVGTSGHDLIYRLYHGTIVNQIVCKECKNVSERQEDFLDLTVAVKNVSGLEDALWNMYVEEEVFDCDNLYHCGTCDRLVKAAKSAKLRKLPPFLTVSLLRFNFDFVKCERYKETSCYTFPLRINLKPFCEQSELDDLEYIYDLFSVIIHKGGCYGGHYHVYIKDVDHLGNWQFQEEKSKPDVNLKDLQSEEEIDHPLMILKAILLEENNLIPVDQLGQKLLKKIGISWNKKYRKQHGPLRKFLQLHSQIFLLSSDESTVRLLKNSSLQAESDFQRNDQQIFKMLPPESPGLNNSISCPHWFDINDSKVQPIREKDIEQQFQGKESAYMLFYRKSQLQRPPEARANPRYGVPCHLLNEMDAANIELQTKRAECDSANNTFELHLHLGPQYHFFNGALHPVVSQTESVWDLTFDKRKTLGDLRQSIFQLLEFWEGDMVLSVAKLVPAGLHIYQSLGGDELTLCETEIADGEDIFVWNGVEVGGVHIQTGIDCEPLLLNVLHLDTSSDGEKCCQVIESPHVFPANAEVGTVLTALAIPAGVIFINSAGCPGGEGWTAIPKEDMRKTFREQGLRNGSSILIQDSHDDNSLLTKEEKWVTSMNEIDWLHVKNLCQLESEEKQVKISATVNTMVFDIRIKAIKELKLMKELADNSCLRPIDRNGKLLCPVPDSYTLKEAELKMGSSLGLCLGKAPSSSQLFLFFAMGSDVQPGTEMEIVVEETISVRDCLKLMLKKSGLQGDAWHLRKMDWCYEAGEPLCEEDATLKELLICSGDTLLLIEGQLPPLGFLKVPIWWYQLQGPSGHWESHQDQTNCTSSWGRVWRATSSQGASGNEPAQVSLLYLGDIEISEDATLAELKSQAMTLPPFLEFGVPSPAHLRAWTVERKRPGRLLRTDRQPLREYKLGRRIEICLEPLQKGENLGPQDVLLRTQVRIPGERTYAPALDLVWNAAQGGTAGSLRQRVADFYRLPVEKIEIAKYFPEKFEWLPISSWNQQITKRKKKKKQDYLQGAPYYLKDGDTIGVKNLLIDDDDDFSTIRDDTGKEKQKQRALGRRKSQEALHEQSSYILSSAETPARPRAPETSLSIHVGSFR,mutated_sequence,1.0,1235.0,UPI00004C8020.a2m,UPI00004C8020.npy,gnomAD +UPI0000072C2E,UPI0000072C2E.csv,MSGGKSAQGPEEGGVCITEALITKRNLTFPEDGELSEKMFHTLDELQTVRLDREGITTIRNLEGLQNLHSLYLQGNKIQQIENLACIPSLRFLSLAGNQIRQVENLLDLPCLQFLDLSENLIETLKLDEFPQSLLILNLSGNSCTNQDGYRELVTEALPLLLDLDGQPVVERWISDEEDEASSDEEFPELSGPFCSERGFLKELEQELSRHREHRQQTALTEHLLRMEMQPTLTDLPLLPGVPMAGDSSPSATPAQGEETVPEAVSSPQASSPTKKPCSLIPRGHQSSFWGRKGARAATAPKASVAEAPSTTKTTAKRSKK,mutated_sequence,1.0,321.0,UPI0000072C2E.a2m,UPI0000072C2E.npy,gnomAD +UPI0000423C4C,UPI0000423C4C.csv,MKERKRHLGDTKHFCPVVLKENFILQPGNTEEAAKYREKIYYFSSAEAKEKFLEHPEDYVAHEEPLKAPPLRICLVGPQGSGKTMCGRQLAEKLNIFHIQFEEVLQEKLLLKTEKKVGPEFEEDSENEQAAKQELEELAIQANVKVEEENTKKQLPEVQLTEEEEVIKSSLMENEPLPPEILEVILSEWWLKEPIRSTGFILDGFPRYPEEAQFLGDRGFFPDAAVFIQVDDQDIFDRLLPAQIEKWKLKQKKKLERKKLIKDMKAKIRVDTIAKRRAELILERDKKRREPNKEKRKRGISENTTCYKCNHMSPWCEYAGLPYRGLLILYSATATATAGTKHCRSGAKVIVALSKQNIHNQK,mutated_sequence,1.0,362.0,UPI0000423C4C.a2m,UPI0000423C4C.npy,gnomAD +UPI00001C1E94,UPI00001C1E94.csv,MAKNKLRGPKSRNVFHIASQKNFKAKNKAKPVTTNLKKINIMNEEKVNRVNKAFVNVQKELAHFAKSISLEPLQKELIPQQRHESKPVDEATRLMALL,mutated_sequence,1.0,98.0,UPI00001C1E94.a2m,UPI00001C1E94.npy,gnomAD +UPI00001B647D,UPI00001B647D.csv,MGPSASSHGSPVPLPSDLSFRSPTPSNLPMVQLWAAHAHEGFSHLPSGLYPSYLHLNHLEPPSSGSPLLSQLGQPSIFDTQKGQGPGGGKTGVRGWGWHRGLLRGFWGLWQHPGSPQLCPGCWLFFFFFF,mutated_sequence,1.0,130.0,UPI00001B647D.a2m,UPI00001B647D.npy,gnomAD +UPI0000073CB8,UPI0000073CB8.csv,MRTHTRGAPSVFFIYLLCFVSAYITDENPEVMIPFTNANYDSHPMLYFSRAEVAELQLRAASSHEHIAARLTEAVHTMLSSPLEYLPPWDPKDYSARWNEIFGNNLGALAMFCVLYPENIEARDMAKDYMERMAAQPSWLVKDAPWDEVPLAHSLVGFATAYDFLYNYLSKTQQEKFLEVIANASGYMYETSYRRGWGFQYLHNHQPTNCMALLTGSLVLMNQGYLQEAYLWTKQVLTIMEKSLVLLREVTDGSLYEGVAYGSYTTRSLFQYMFLVQRHFNINHFGHPWLKQHFAFMYRTILPGFQRTVAIADSNYNWFYGPESQLVFLDKFVMRNGSGNWLADQIRRNRVVEGPGTPSKGQRWCTLHTEFLWYDGSLKSVPPPDFGTPTLHYFEDWGVVTYGSALPAEINRSFLSFKSGKLGGRAIYDIVHRNKYKDWIKGWRNFNAGHEHPDQNSFTFAPNGVPFITEALYGPKYTFFNNVLMFSPAVSKSCFSPWVGQVTEDCSSKWSKYKHDLAASCQGRVVAAEEKNGVVFIRGEGVGAYNPQLNLKNVQRNLILLHPQLLLLVDQIHLGEESPLETAASFFHNVDVPFEETVVDGVHGAFIRQRDGLYKMYWMDDTGYSEKATFASVTYPRGYPYNGTNYVNVTMHLRSPITRAAYLFIGPSIDVQSFTVHGDSQQLDVFIATSKHAYATYLWTGEATGQSAFAQVIADRHKILFDRNSAIKSSIVPEVKDYAAIVEQNLQHFKPVFQLLEKQILSRVRNTASFRKTAERLLRFSDKRQTEEAIDRIFAISQQQQQQSKSKKNRRAGKRYKFVDAVPDIFAQIEVNEKKIRQKAQILAQKELPIDEDEEMKDLLDFADVTYEKHKNGGLIKGRFGQARMVTTTHSRAPSLSASYTRLFLILNIAIFFVMLAMQLTYFQRAQSLHGQRCLYAVLLIDSCILLWLYSSCSQSQC,mutated_sequence,1.0,958.0,UPI0000073CB8.a2m,UPI0000073CB8.npy,gnomAD +UPI000013CDC7,UPI000013CDC7.csv,MGAGSSTEQRSPEQPPEGSSTPAEPEPSGGGPSAEAAPDTTADPAIAASDPATKLLQKNGQLSTINGVAEQDELSLQEGDLNGQKGALNGQGALNSQEEEEVIVTEVGQRDSEDVSKRDSDKEMATKSAVVHDITDDGQEETPEIIEQIPSSESNLEELTQPTESQANDIGFKKVFKFVGFKFTVKKDKTEKPDTVQLLTVKKDEGEGAAGAGDHKDPSLGAGEAASKESEPKQSTEKPEETLKREQSHAEISPPAESGQAVEECKEEGEEKQEKEPSKSAESPTSPVTSETGSTFKKFFTQGWAGWRKKTSFRKPKEDEVEASEKKKEQEPEKVDTEEDGKAEVASEKLTASEQAHPQEPAESAHEPRLSAEYEKVELPSEEQVSGSQGPSEEKPAPLATEVFDEKIEVHQEEVVAEVHVSTVEERTEEQKTEVEETAGSVPAEELVEMDAEPQEAEPAKELVKLKETCVSGEDPTQGADLSPDEKVLSKPPEGVVSEVEMLSSQERMKVQGSPLKKLFTSTGLKKLSGKKQKGKRGGGDEESGEHTQVPADSPDSQEEQKGESSASSPEEPEEITCLEKGLAEVQQDGEAEEGATSDGEKKREGVTPWASFKKMVTPKKRVRRPSESDKEDELDKVKSATLSSTESTASEMQEEMKGSVEEPKPEEPKRKVDTSVSWEALICVGSSKKRARRGSSSDEEGGPKAMGGDHQKADEAGKDKETGTDGILAGSQEHDPGQGSSSPEQAGSPTEGEGVSTWESFKRLVTPRKKSKSKLEEKSEDSIAGSGVEHSTPDTEPGKEESWVSIKKFIPGRRKKRPDGKQEQAPVEDAGPTGANEDDSDVPAVVPLSEYDAVEREKMEAQQAQKSAEQPEQKAATEVSKELSESQVHMMAAAVADGTRAATIIEERSPSWISASVTEPLEQVEAEAALLTEEVLEREVIAEEEPPTVTEPLPENREARGDTVVSEAELTPEAVTAAETAGPLGAEEGTEASAAEETTEMVSAVSQLTDSPDTTEEATPVQEVEGGVPDIEEQERRTQEVLQAVAEKVKEESQLPGTGGPEDVLQPVQRAEAERPEEQAEASGLKKETDVVLKVDAQEAKTEPFTQGKVVGQTTPESFEKAPQVTESIESSELVTTCQAETLAGVKSQEMVMEQAIPPDSVETPTDSETDGSTPVADFDAPGTTQKDEIVEIHEENEVASGTQSGGTEAEAVPAQKERPPAPSSFVFQEETKEQSKMEDTLEHTDKEVSVETVSILSKTEGTQEADQYADEKTKDVPFFEGLEGSIDTGITVSREKVTEVALKGEGTEEAECKKDDALELQSHAKSPPSPVEREMVVQVEREKTEAEPTHVNEEKLEHETAVTVSEEVSKQLLQTVNVPIIDGAKEVSSLEGSPPPCLGQEEAVCTKIQVQSSEASFTLTAAAEEEKVLGETANILETGETLEPAGAHLVLEEKSSEKNEDFAAHPGEDAVPTGPDCQAKSTPVIVSATTKKGLSSDLEGEKTTSLKWKSDEVDEQVACQEVKVSVAIEDLEPENGILELETKSSKLVQNIIQTAVDQFVRTEETATEMLTSELQTQAHVIKADSQDAGQETEKEGEEPQASAQDETPITSAKEESESTAVGQAHSDISKDMSEASEKTMTVEVEGSTVNDQQLEEVVLPSEEEGGGAGTKSVPEDDGHALLAERIEKSLVEPKEDEKGDDVDDPENQNSALADTDASGGLTKESPDTNGPKQKEKEDAQEVELQEGKVHSESDKAITPQAQEELQKQERESAKSELTES,mutated_sequence,1.0,1782.0,UPI000013CDC7.a2m,UPI000013CDC7.npy,gnomAD +UPI0001A48FC8,UPI0001A48FC8.csv,MTSQEKTEEYPFADIFDEDETERNFLLSKPVCFVVFGKPGVGKTTLARYITQAWKCIRVEALPILEEQIAAETESGVMLQSMLISGQSIPDELVIKLMLEKLNSPEVCHFGYIITEIPSLSQDAMTTLQQIELIKNLNLKPDVIINIKCPDYDLCQRISGQRQHNNTGYIYSRDQWDPEVIENHRKKKKEAQKDGKGEEEEEEEEQEEEEAFIAEMQMVAEILHHLVQRPEDYLENVENIVKLYKETILQTLEEVMAEHNPQYLIELNGNKPAEELFMIVMDRLKYLNLKRAAILTKLQGAEEEINDTMENDELFRTLASYKLIAPRYRWQRSKWGRTCPVNLKDGNIYSGLPDYSVSFLGKIYCLSSEEALKPFLLNPRPYLLPPMPGPPCKVFILGPQYSGKTTLCNMLAENYKGKVVDYAQLVQPRFDKARETLVENTIAEATAAAIKVVKEKLLRELQARKQAETALREFQRQYEKMEFGVFPMEATHSSIDEEGYIQGSQRDRGSSLVDTEEAKTKSENVLHDQAAKVDKDDGKETGETFTFKRHSQDASQDVKLYSDTAPTEDLIEEVTADHPEVVTMIEETIKMSQDINFEQPYEKHAEILQEVLGEVMEENKDRFPGAPKYGGWIVDNCPIVKELWMALIKKGIIPDLVIYLSDTENNGKCLFNRIYLQKKSEIDSKILERLLEELQKKKKEEEEARKATEEELRLEEENRRLLELMKVKAKEAEETDNEDEEEIEGDELEVHEEPEASHDTRGSWLPEEFEASEVPETEPEAVSEPIEETTVETEIPKGSKEGLEIEKLSETVVLPEFPEDSYPDVPEMEPFKEKIGSFIILWKQLEATISEAYIKILNLEIADRTPQELLQKVVETMEKPFQYTAWELTGEDYEEETEDYQTEAEVDEELEEEEEEEGEDKMKERKRHLGDTKHFCPVVLKENFILQPGNTEEAAKYREKIYYFSSAEAKEKFLEHPEDYVAHEEPLKAPPLRICLVGPQGSGKTMCGRQLAEKLNIFHIQFEEVLQEKLLLKTEKKVGPEFEEDSENEQAAKQELEELAIQANVKVEEENTKKQLPEVQLTEEEEVIKSSLMENEPLPPEILEVILSEWWLKEPIRSTGFILDGFPRYPEEAQFLGDRGFFPDAAVFIQVDDQDIFDRLLPAQIEKWKLKQKKKLERKKLIKDMKAKIRVDTIAKRRAELILERDKKRRENVVRDDEEISEEELEEDNDDIENILEDEFPKDEEEMSGEEDEEQETDAIERLRGELGEKFEADTHNLQIIQDELERYLIPIISINGARRNHIVQYTLNMKLKPLVENRASIFEKCHPIPAPLAQKMLTFTYKYISSFGYWDPVKLSEGETIKPVENAENPIYPVIHRQYIYFLSSKETKEKFMKNPIKYIRQPKPKPTVPIRIIIVGPPKSGKTTVAKKITSEYGLKHLSIGGALRYVLNNHPETELALMLNWHLHKGMTAPDELAIQALELSLMESVCNTAGVVIDGYPVTKHQMNLLEARSIIPMVIFELSVPSKEIFKRLLLEKENEQRLPYPLHNSAQIVAVNNVKYRKNIGEIRQYYQEQHQNWYVIDGFHSKWWVWNEVIKNVQMVNKYMQTYLERIKAGKAACIDKLCITPQELLSRLGEFEQFCPVSLAESQELFDCSATDSLEFAAEFRGHYYKMSSQEKLNKFLENPELYVPPLAPHPLPSADMIPKRLTLSELKSRFPKCAELQGYCPVTYKDGNQRYEALVPGSINYALEYHNRIYICENKEKLQKFLRSPLKYWEQKLPHKLPPLREPILLTSLPLPGYLEQGIATSLIKAMNAAGCLKPKFPFLSIRRSALLYIALHLKAFNPKGSEYTRKKYKKKMEQFMESCELITYLGAKMTRKYKEPQFRAIDFDHKLKTFLSLRNIDPING,mutated_sequence,1.0,1911.0,UPI0001A48FC8.a2m,UPI0001A48FC8.npy,gnomAD +UPI000195170D,UPI000195170D.csv,MSPPRLSSSLGAHLPCIVPSPVLDCIRPRLLRKWDLGSWPAPGLSAPHRHCKSLLSDFRDTVSVTRGEIEVWSYEALTVTSFQTKDFLRNIFLQITPSVT,mutated_sequence,,,UPI000195170D.a2m,UPI000195170D.npy,gnomAD +UPI0000141917,UPI0000141917.csv,MAGWPGAGPLCVLGGAALGVCLAGVAGQLVEPSTAPPKPKPPPLTKETVVFWDMRLWHVVGIFSLFVLSIIITLCCVFNCRVPRTRKEIEARYLQRKAAKMYTDKLETVPPLNELTEVPGEDKKKKKKKKKDSVDTVAIKVEEDEKNEAKKKKGEK,mutated_sequence,1.0,156.0,UPI0000141917.a2m,UPI0000141917.npy,gnomAD +UPI000013DFEA,UPI000013DFEA.csv,MEGAALLRVSVLCIWMSALFLGVGVRAEEAGARVQQNVPSGTDTGDPQSKPLGDWAAGTMDPESSIFIEDAIKYFKEKVSTQNLLLLLTDNEAWNGFVAAAELPRNEADELRKALDNLARQMIMKDKNWHDKGQQYRNWFLKEFPRLKSELEDNIRRLRALADGVQKVHKGTTIANVVSGSLSISSGILTLVGMGLAPFTEGGSLVLLEPGMELGITAALTGITSSTMDYGKKWWTQAQAHDLVIKSLDKLKEVREFLGENISNFLSLAGNTYQLTRGIGKDIRALRRARANLQSVPHASASRPRVTEPISAESGEQVERVNEPSILEMSRGVKLTDVAPVSFFLVLDVVYLVYESKHLHEGAKSETAEELKKVAQELEEKLNILNNNYKILQADQEL,mutated_sequence,1.0,398.0,UPI000013DFEA.a2m,UPI000013DFEA.npy,gnomAD +UPI00004700E1,UPI00004700E1.csv,MSSPLASLSKTRKVPLPSEPMNPGRRGIRIYGDEDEVDMLSDGCGSEEKISVPSCYGGIGAPVSRQVPASHDSELMAFMTRKLWDLEQQVKAQTDEILSKDQKIAALEDLVQTLRPHPAEATLQRQEELETMCVQLQRQVREMERFLSDYGLQWVGEPMDQEDSESKTVSEHGERDWMTAKKFWKPGDSLAPPEVDFDRLLASLQDLSELVVEGDTQVTPVPGGARLRTLEPIPLKLYRNGIMMFDGPFQPFYDPSTQRCLRDILDGFFPSELQRLYPNGVPFKVSDLRNQVYLEDGLDPFPGEGRVVGRQLMHKALDRVEEHPGSRMTAEKFLNRLPKFVIRQGEVIDIRGPIRDTLQNCCPLPARIQEIVVETPTLAAERERSQESPNTPAPPLSMLRIKSENGEQAFLLMMQPDNTIGDVRALLAQARVMDASAFEIFSTFPPTLYQDDTLTLQAAGLVPKAALLLRARRAPKSSLKFSPGPCPGPGPGPSPGPGPGPSPGPGPGPSPCPGPSPSPQ,mutated_sequence,1.0,520.0,UPI00004700E1.a2m,UPI00004700E1.npy,gnomAD +UPI000050ED33,UPI000050ED33.csv,MRNSEEQPSGGTTVLQRLLQEQLRYGNPSENRSLLAIHQQATGNGPPFPSGSGNPGPQSDVLSPQDHHQQLVAHAARQEPQGQEIQSENLIMEKQLSPRMQNNEELPTYEEAKVQSQYFRGQQHASVGAAFYVTGVTNQKMRTEGRPSVQRLNPGKMHQDEGLRDLKQGHVRSLSERLMQMSLATSGVKAHPPVTSAPLSPPQPNDLYKNPTSSSEFYKAQGPLPNQHSLKGMEHRGPPPEYPFKGMPPQSVVCKPQEPGHFYSEHRLNQPGRTEGQLMRYQHPPEYGAARPAQDISLPLSARNSQPHSPTSSLTSGGSLPLLQSPPSTRLSPARHPLVPNQGDHSAHLPRPQQHFLPNQAHQGDHYRLSQPGLSQQQQQQQQQHHHHHHHQQQQQQQPQQQPGEAYSAMPRAQPSSASYQPVPADPFAIVSRAQQMVEILSDENRNLRQELEGCYEKVARLQKVETEIQRVSEAYENLVKSSSKREALEKAMRNKLEGEIRRMHDFNRDLRERLETANKQLAEKEYEGSEDTRKTISQLFAKNKESQREKEKLEAELATARSTNEDQRRHIEIRDQALSNAQAKVVKLEEELKKKQVYVDKVEKMQQALVQLQAACEKREQLEHRLRTRLERELESLRIQQRQGNCQPTNVSEYNAAALMELLREKEERILALEADMTKWEQKYLEENVMRHFALDAAATVAAQRDTTVISHSPNTSYDTALEARIQKEEEEILMANKRCLDMEGRIKTLHAQIIEKDAMIKVLQQRSRKEPSKTEQLSCMRPAKSLMSISNAGSGLLSHSSTLTGSPIMEEKRDDKSWKGSLGILLGGDYRAEYVPSTPSPVPPSTPLLSAHSKTGSRDCSTQTERGTESNKTAAVAPISVPAPVAAAATAAAITATAATITTTMVAAAPVAVAAAAAPAAAAAPSPATAAATAAAVSPAAAGQIPAAASVASAAAVAPSAAAAAAVQVAPAAPAPVPAPALVPVPAPAAAQASAPAQTQAPTSAPAVAPTPAPTPTPAVAQAEVPASPATGPGPHRLSIPSLTCNPDKTDGPVFHSNTLERKTPIQILGQEPDAEMVEYLI,mutated_sequence,1.0,1084.0,UPI000050ED33.a2m,UPI000050ED33.npy,gnomAD +UPI000004E7FC,UPI000004E7FC.csv,MFPFYSCWRTGLLLLLLAVAVRESWQTEEKTCDLVGEKGKESEKELALVKRLKPLFNKSFESTVGQGSDTYIYIFRVCREAGNHTSGAGLVQINKSNGKETVVGRLNETHIFNGSNWIMLIYKGGDEYDNHCGKEQRRAVVMISCNRHTLADNFNPVSEERGKVQDCFYLFEMDSSLACSPEISHLSVGSILLVTFASLVAVYVVGGFLYQRLVVGAKGMEQFPHLAFWQDLGNLVADGCDFVCRSKPRNVPAAYRGVGDDQLGEESEERDDHLLPM,mutated_sequence,1.0,277.0,UPI000004E7FC.a2m,UPI000004E7FC.npy,gnomAD +UPI000204A77C,UPI000204A77C.csv,TLVNNVRLPRGHRLELSDGDLLTFGPEGPPGTSPSEFYFMFQQVRVKPQDFAAITIPRSRGEARVGAGFRPMLPSQGAPQRPLSTFSPAPKATLILNSIGSLSKLRPQPLTFSPSWGGPKSLPVPAPPGEMGTTPSAPPQRNRRKSVHRVLAELDDESEPPENPPPVLMEPRKKLRVDKAPLTPTGNWHIQGMQSIVKGQKERPWPREHLSGYEEAS,mutated_sequence,1.0,217.0,UPI000204A77C.a2m,UPI000204A77C.npy,gnomAD +UPI0001662BC1,UPI0001662BC1.csv,MSSPRLIPLWKDLKLLLNDTINKSKQPSEDPKNCLIVLSDRSQAVAWMKSKTEDMVEKRTFSMTERLPPIQSMVHAGSFHILVVYCGDLILRLFGDHFRAFKPLGKVPCRFNISCLCYDPEMKMLLSGILGAVVTWVIELGGTGLQIAHMVSMPGDELVQDIVLNGPSGSLLALCETVVRVLMHQGKGQLGEVKRFTSTSSGSSITCCFTCFDQGFLYAGNQAGEIQVWSLQQGHPLHSFQAHQSGVICIRSRPEAHTLLTAGSDSLIKEWNLTSGSLLRRLELGEELYRLQFIDSITFFCQTAHSFSLHRLPCFYSLFNVCGSAPQQLRRVCCGNNWFRILCTTEDGLLRFVSPVTGDLLVITWPFSILDQAVDWAYDPGKEELFVATGSSEVLVFDTTRCPCPAKYLLGTSPNSQDFVQCLAYGHFNLGRGLEGLIFSGHQSGVIRVLSQHSCARLEKFMHFGAVLALSTLSGGIFGGQGNSLLCSYGMDDYVHLSEAVLDGVKVQLRPLASILSSCHLTHLILLPKSVGAITETNCLRLWKFHDFLSSGSQNGLKFIETLPLHLCAITSFDVCLSLSLFVTGSADGSVRIWDFHGRLIGILDSSLHFGPVCFANDRGDLLVTFNQSLYLVSCLKLLPPALLTRLSFMSISDEVLEVPKPFIPSFFFSFETMFVPKYIYPGQAQQKLVGLEKLVNNRAIAFDHSVPHVIEEDEEGSPVLLRSSMHYSLQDMEDWMQVSKRYQCHYVLPPQLQLTSWDGLNPYQILRYYFGHGREWLFAPDCYIPNSVIRARLWPEGTPIYLQCNLHAPQRELEWDRSQEFFFWHSRVRAISNTEYPKNKEEDEHFLEMRLSKDVTYSVLTDGANRSWLGRKMSEITINSMIETMLNIMVHASLLKYQCCVGALGQIFASYQVSPALRSETARRLLNDTTNSNPLIRELAWEGLKRLGMITHLFAMPLAQGLMDKDERVRIKTLSLMAEIGIHSRTSLLQLTQKQETFREMQQQMIGEEPLDHLLGMRATDLQILSTQVEQRLNENLTLSHRDEKPAFSLDVSMPSELKSSLKPPTVSEESEVAIKPSKGQRRGQAGVKKHSQKWLRGLKKTKERDSKQMSTEPGLLEDESGTEAAPIEMEEASVYSQWSSSTSVIKLSKDVDSQEKDISKDHIALTLKRLQKIRDKRDKKATAQKLKKKHKKKGKEAKVINEETTPPVMEQPVTKKVKIQGRGASGISGRRSTAGDGSSWRDDLCRLMALRISGSQTKMSENLNAELVTFAQEMLVDRHPSWELFQEICPLLKKESKVLLEDLDWDVVPPEKKPIFIQEGAIREDMIQGVTQEVIRHKEVMPREEEQAQKKARDMLGLEETQVILKKGKKVIFLEPGNVTMGKEISKKEEKKTFQKSPKQGRKAVQKERKVGKIKREMTKEERDMSEEVEEMATLEEKVVKQEGKLVMIERTPSWQDWKKAWDEWKQVHGETRKSWKAWKEEWEKRLLQEEEKLHQAGEKLSPEEEMLQEDKKLKWEEWKQVWENMLSSKSKEQQYKDEEEVTLEEEVSREGEEKEQQVTEEQRHIQEEHKWARIHRKRARAEKKRAQEERKLAQEEEKLAQEERQLAQEERKLAQAYVKITQDDREMAQAEGKFAQKEETLAQRGEKLSQEAEKLAQKRKKLAKKWEKVAREEEKLAKKGGKLAEVKNILAQKVEELPQREQNLDWQEKELAQELEELEWDMEELSWKEEELNQEEGKLVEEKKKLAEEEEALAWQREKLSEEETKLAQEEELLIQEKEKLAQHKEKMPEEEERLGRKREQLIEKKMKLAQKRERWINSMEELTKNKMILYQKKNLAQEKKNLAQEKEKLAQRKENLLYNKERLTHSKKQLVQVKNKLGMFNKILAQVEEKLTQEKETVIKKKEKLAETEKKLVQVEDSLAKKQEKLAQEKMKLALEKAMVQGKKRLRGELDIAKEEKALNLEMKRLAEEKMRLVEGKETLSKGETPETSRQRKMTQVEQELFERKLSLEEKILLHEDRILAMEESEIAKGKLEFTRGQRIFVQGQRKLAKASRKLIKKRESLSKEPAKLNKILKALQKLTRDERKLTQEEIKMTKMKRALFVKERRLSIEQSKLDIKEWDFSEKRSELTKDEKKLARKQRKLANKMRRMINKEEKMTEEESKLARKHSEVILDDEEEGGIEEEEVIPFLKRRWRKRKEAKRGDKPKEKFSSQVDEVESEEHFSEEMESLLDELEKQESLSSEEEEEREEEEEREEEEVREEEEERKEEEEGEEKQVEKEEEEKKKKKKEKKKEEVQEKEEVFEEKEEIMSEEETESLSDEEEEEESCSLEEEVDREKEILKKEKQFKLQEQRRKSLRGRERVLSILRGVPHGKGRAIRLGVLKSPLKKLMSTALEMKEKTPVPVPEKQISWEDKKATVVEIPRKFLGTMDKEREVMGKYEPIPPHVLGTVLESQAQDLKTPFMSHILRRTVEAEELQHKPLGAWWKWFLQHPPLMGQTEVQLPLSQIPAKEQHADVSLSDVEWIRHVLERMEAGEQLSRDGFHRLCQLLKDLASKGNLEWLHLAKHEAIVYRHRQALESQDTRISSRQSMSPKYLKVIPPIKAKEKESWPKPLAVPTQKSPLATKRIPDPRAKNWHLLGEPYRSERAQQISIAHKEMEMQYFYPATRDIFPSAHASVEKQTLALMFQKDFWDFKDKRRFPKLPKLEKKTQPISKKKEELPLWETFVALYHVLRMLQQRYPKDSTAWMEQFYQLMDLYQLKSPRIQKLLQELLMREEPQPQEIIYEEALKATELVPGERLFCCLFCGSSHTPRSPQEFQGAVPLPWQNCVRTILPVGIARYGILELAWKSLPEADLHLTKALTHTVAPTL,mutated_sequence,1.0,2873.0,UPI0001662BC1.a2m,UPI0001662BC1.npy,gnomAD +UPI0000167B08,UPI0000167B08.csv,MEVQLGLGRVYPRPPSKTYRGAFQNLFQSVREVIQNPGPRHPEAASAAPPGASLLLLQQQQQQQQQQQQQQQQQQQQQQQETSPRQQQQQQGEDGSPQAHRRGPTGYLVLDEEQQPSQPQSALECHPERGCVPEPGAAVAASKGLPQQLPAPPDEDDSAAPSTLSLLGPTFPGLSSCSADLKDILSEASTMQLLQQQQQEAVSEGSSSGRAREASGAPTSSKDNYLGGTSTISDNAKELCKAVSVSMGLGVEALEHLSPGEQLRGDCMYAPLLGVPPAVRPTPCAPLAECKGSLLDDSAGKSTEDTAEYSPFKGGYTKGLEGESLGCSGSAAAGSSGTLELPSTLSLYKSGALDEAAAYQSRDYYNFPLALAGPPPPPPPPHPHARIKLENPLDYGSAWAAAAAQCRYGDLASLHGAGAAGPGSGSPSAAASSSWHTLFTAEEGQLYGPCGGGGGGGGGGGGGGGGGGGGGGGEAGAVAPYGYTRPPQGLAGQESDFTAPDVWYPGGMVSRVPYPSPTCVKSEMGPWMDSYSGPYGDMRLETARDHVLPIDYYFPPQKTCLICGDEASGCHYGALTCGSCKVFFKRAAEGKQKYLCASRNDCTIDKFRRKNCPSCRLRKCYEAGMTLGARKLKKLGNLKLQEEGEASSTTSPTEETTQKLTVSHIEGYECQPIFLNVLEAIEPGVVCAGHDNNQPDSFAALLSSLNELGERQLVHVVKWAKALPGFRNLHVDDQMAVIQYSWMGLMVFAMGWRSFTNVNSRMLYFAPDLVFNEYRMHKSRMYSQCVRMRHLSQEFGWLQITPQEFLCMKALLLFSIIPVDGLKNQKFFDELRMNYIKELDRIIACKRKNPTSCSRRFYQLTKLLDSVQPIARELHQFTFDLLIKSHMVSVDFPEMMAEIISVQVPKILSGKVKPIYFHTQ,mutated_sequence,1.0,920.0,UPI0000167B08.a2m,UPI0000167B08.npy,gnomAD +UPI0001EE4B77,UPI0001EE4B77.csv,MAVQISKKRKFVADGIFKAELNEFLTRELAEDGYSGVEVRVTPTRTEIIILATRTQNVLGEKGRRIRELTAVVQKRFGFPEGSVELKIMVMVTGYPLLPLKLYAEKVATRGLCAIAQAESLRYKLLGGLAVRRACYGVLRFIMESGAKGCEVVVSGKLRGQRAKSMKFVDGLMIHSGDPVNYYVDTAVRHVLLRQGVLGIKVKIMLPWDPTGKIGPKKPLPDHVSIVEPKDEILPTTPISEQKGGKPEPPAMPQPVPTA,mutated_sequence,1.0,259.0,UPI0001EE4B77.a2m,UPI0001EE4B77.npy,gnomAD +UPI000006FC41,UPI000006FC41.csv,MKKIFSKKGESPLGSFARRQRSSAGGGGEPGEGAYSQPGYHVRDRDLGKIHKAASAGNVAKVQQILLLRKNGLNDRDKMNRTALHLACANGHPEVVTLLVDRKCQLNVCDNENRTALMKAVQCQEEKCATILLEHGADPNLADVHGNTALHYAVYNEDISVATKLLLYDANIEAKNKDDLTPLLLAVSGKKQQMVEFLIKKKANVNAVDKLESSHQLISEYKEERIPKHSSQNSNSVDESSEDSLSRLSGKPGVDDSWPTSDDEDLNFDTKNVPKPSLAKLMTASQQSRKNLEATYGTVRTGNRTLFEDRDSDSQDEVVVESLPTTSIKVQCFSHPTYQSPDLLPKPSHKSLANPGLMKEEPTKPGIAKKENGIDIIESAPLEQTNNDNLTYVDEVHKNNRSDMMSALGLGQEEDIESPWDSESISENFPQKYVDPLAGAADGKEKNIGNEQAEDVFYIPSCMSGSRNFKMAKLEDTRNVGMPVAHMESPERYLHLKPTIEMKDSVPNKAGGMKDVQTSKAAEHDLEVASEEEQEREGSENNQPQVEEERKKHRNNEMEVSANIHDGATDDAEDDDDDDGLIQKRKSGETDHQQFPRKENKEYASSGPALQMKEVKSTEKEKRTSKESVNSPVFGKASLLTGGLLQVDDDSSLSEIDEDEGRPTKKTSNEKNKVKNQIQSMDDVDDLTQSSETASEDCELPHSSYKNFMLLIEQLGMECKDSVSLLKIQDAALSCERLLELKKNHCELLTVKIKKMEDKVNVLQRELSETKEIKSQLEHQKVEWERELCSLRFSLNQEEEKRRNADTLYEKIREQLRRKEEQYRKEVEVKQQLELSLQTLEMELRTVKSNLNQVVQERNDAQRQLSREQNARMLQDGILTNHLSKQKEIEMAQKKMNSENSHSHEEEKDLSHKNSMLQEEIAMLRLEIDTIKNQNQEKEKKCFEDLKIVKEKNEDLQKTIKQNEETLTQTISQYNGRLSVLTAENAMLNSKLENEKQSKERLEAEVESYHSRLAAAIHDRDQSETSKRELELAFQRARDECSRLQDKMNFDVSNLKDNNEILSQQLFKTESKLNSLEIEFHHTRDALREKTLGLERVQKDLSQTQCQMKEMEQKYQNEQVKVNKYIGKQESVEERLSQLQSENMLLRQQLDDAHNKADNKEKTVINIQDQFHAIVQKLQAESEKQSLLLEERNKELISECNHLKERQYQYENEKAEREVVVRQLQQELADTLKKQSMSEASLEVTSRYRINLEDETQDLKKKLGQIRNQLQEAQDRHTEAVRCAEKMQDHKQKLEKDNAKLKVTVKKQMDKIEELQKNLLNANLSEDEKEQLKKLMELKQSLECNLDQEMKKNVELEREITGFKNLLKMTRKKLNEYENGEFSFHGDLKTSQFEMDIQINKLKHKIDDLTAELETAGSKCLHLDTKNQILQEELLSMKTVQKKCEKLQKNKKKLEQEVINLRSHIERNMVELGQVKQYKQEIEERARQEIAEKLKEVNLFLQAQAASQENLEQFRENNFASMKSQMELRIKDLESELSKIKTSQEDFNKTELEKYKQLYLEELKVRKSLSSKLTKTNERLAEVNTKLLVEKQQSRSLFTTLTTRPVMEPPCVGNLNNSLDLNRKLIPRENLVISTSNPRASNNSMENYLSKMQQELEKNITRELKEAAAELESGSIASPLGSTDESNLNQDLVWKASREYVQVLKKNYMI,mutated_sequence,1.0,1710.0,UPI000006FC41.a2m,UPI000006FC41.npy,gnomAD +UPI0000070AF2,UPI0000070AF2.csv,MTCGFNSIGCGFRPGNFSCVSACGPRPSRCCITAAPYRGISCYRGLTGGFGSHSVCGGFRAGSCGRSFGYRSGGVCGPSPPCITTVSVNESLLTPLNLEIDPNAQCVKQEEKEQIKSLNSRFAAFIDKVRFLEQQNKLLETKLQFYQNRECCQSNLEPLFAGYIETLRREAECVEADSGRLASELNHVQEVLEGYKKKYEEEVALRATAENEFVALKKDVDCAYLRKSDLEANVEALIQEIDFLRRLYEEEIRILQSHISDTSVVVKLDNSRDLNMDCIVAEIKAQYDDIATRSRAEAESWYRSKCEEMKATVIRHGETLRRTKEEINELNRMIQRLTAEVENAKCQNSKLEAAVAQSEQQGEAALSDARCKLAELEGALQKAKQDMACLIREYQEVMNSKLGLDIEIATYRRLLEGEEQRLCEGVEAVNVCVSSSRGGVVCGDLCVSGSRPVTGSVCSAPCNGNLVVSTGLCKPCGQLNTTCGGGSCGQGRH,mutated_sequence,1.0,493.0,UPI0000070AF2.a2m,UPI0000070AF2.npy,gnomAD +UPI000034ECE0,UPI000034ECE0.csv,MSLRDKGGEEECFEYDCQDEERKPTHRQHDTQDLLEEVLCAERVGQMTKTYNDIDAVTRLLEEKERDLELAARIGQSLLKKNKTLTERNELLEEQVEHIREEVSQLRHELSMKDELLQFYTSAAEESEPESVCSTPLKRNESSSSVQNYFHLDSLQKKLKDLEEENVVLRSEASQLKTETITYEEKEQQLVNDCVKELRDANVQIASISEELAKKTEDAARQQEEITHLLSQIVDLQKKAKACAVENEELVQHLGAAKDAQRQLTAELRELEDKYAECMEMLHEAQEELKNLRNKTMPNTTSRRYHSLGLFPMDSLAAEIEGTMRKELQLEEAESPDITHQKRVFETVRNINQVVKQRSLTPSPMNIPGSNQSSAMNSLLSSCVSTPRSSFYGSDIGNVVLDNKTNSIILETEAADLGNDERSKKPGTPGTPGSHDLETALRRLSLRRENYLSERRFFEEEQERKLQELAEKGELRSGSLTPTESIMSLGTHSRFSEFTGFSGMSFSSRSYLPEKLQIVKPLEGSATLHHWQQLAQPHLGGILDPRPGVVTKGFRTLDVDLDEVYCLNDFEEDDTGDHISLPRLATSTPVQHPETSGERSQARVTVSGSRSYPSRPQASPEEMQEPPAATEEEEEEEEEEGSGEGTTISPVNLAPFPEAEFWAILTSVPGTIRSGSLSVASARLCG,mutated_sequence,1.0,686.0,UPI000034ECE0.a2m,UPI000034ECE0.npy,gnomAD +UPI000058E2EA,UPI000058E2EA.csv,MAHNAGAAAAAGTHSAKSGGSEAALKEGGSAAALSSSSSSSAAAAAASSSSSSGPGSAMETGLLPNHKLKTVGEAPAAPPHQQHHHHHHAHHHHHHAHHLHHHHALQQQLNQFQQQQQQQQQQQQQQQQQQHPISNNNSLGGAGGGAPQPGPDMEQPQHGGAKDSAAGGQADPPGPPLLSKPGDEDDAPPKMGEPAGGRYEHPGLGALGTQQPPVAVPGGGGGPAAVPEFNNYYGSAAPASGGPGGRAGPCFDQHGGQQSPGMGMMHSASAAAAGAPGSMDPLQNSHEGYPNSQCNHYPGYSRPGAGGGGGGGGGGGGGSGGGGGGGGAGAGGAGAGAVAAAAAAAAAAAGGGGGGGYGGSSAGYGVLSSPRQQGGGMMMGPGGGGAASLSKAAAGSAAGGFQRFAGQNQHPSGATPTLNQLLTSPSPMMRSYGGSYPEYSSPSAPPPPPSQPQSQAAAAGAAAGGQQAAAGMGLGKDMGAQYAAASPAWAAAQQRSHPAMSPGTPGPTMGRSQGSPMDPMVMKRPQLYGMGSNPHSQPQQSSPYPGGSYGPPGPQRYPIGIQGRTPGAMAGMQYPQQQMPPQYGQQGVSGYCQQGQQPYYSQQPQPPHLPPQAQYLPSQSQQRYQPQQDMSQEGYGTRSQPPLAPGKPNHEDLNLIQQERPSSLPDLSGSIDDLPTGTEATLSSAVSASGSTSSQGDQSNPAQSPFSPHASPHLSSIPGGPSPSPVGSPVGSNQSRSGPISPASIPGSQMPPQPPGSQSESSSHPALSQSPMPQERGFMAGTQRNPQMAQYGPQQTGPSMSPHPSPGGQMHAGISSFQQSNSSGTYGPQMSQYGPQGNYSRPPAYSGVPSASYSGPGPGMGISANNQMHGQGPSQPCGAVPLGRMPSAGMQNRPFPGNMSSMTPSSPGMSQQGGPGMGPPMPTVNRKAQEAAAAVMQAAANSAQSRQGSFPGMNQSGLMASSSPYSQPMNNSSSLMNTQAPPYSMAPAMVNSSAASVGLADMMSPGESKLPLPLKADGKEEGTPQPESKSKKSSSSTTTGEKITKVYELGNEPERKLWVDRYLTFMEERGSPVSSLPAVGKKPLDLFRLYVCVKEIGGLAQVNKNKKWRELATNLNVGTSSSAASSLKKQYIQYLFAFECKIERGEEPPPEVFSTGDTKKQPKLQPPSPANSGSLQGPQTPQSTGSNSMAEVPGDLKPPTPASTPHGQMTPMQGGRSSTISVHDPFSDVSDSSFPKRNSMTPNAPYQQGMSMPDVMGRMPYEPNKDPFGGMRKVPGSSEPFMTQGQMPNSSMQDMYNQSPSGAMSNLGMGQRQQFPYGASYDRRHEPYGQQYPGQGPPSGQPPYGGHQPGLYPQQPNYKRHMDGMYGPPAKRHEGDMYNMQYSSQQQEMYNQYGGSYSGPDRRPIQGQYPYPYSRERMQGPGQIQTHGIPPQMMGGPLQSSSSEGPQQNMWAARNDMPYPYQNRQGPGGPTQAPPYPGMNRTDDMMVPDQRINHESQWPSHVSQRQPYMSSSASMQPITRPPQPSYQTPPSLPNHISRAPSPASFQRSLENRMSPSKSPFLPSMKMQKVMPTVPTSQVTGPPPQPPPIRREITFPPGSVEASQPVLKQRRKITSKDIVTPEAWRVMMSLKSGLLAESTWALDTINILLYDDSTVATFNLSQLSGFLELLVEYFRKCLIDIFGILMEYEVGDPSQKALDHNAARKDDSQSLADDSGKEEEDAECIDDDEEDEEDEEEDSEKTESDEKSSIALTAPDAAADPKEKPKQASKFDKLPIKIVKKNNLFVVDRSDKLGRVQEFNSGLLHWQLGGGDTTEHIQTHFESKMEIPPRRRPPPPLSSAGRKKEQEGKGDSEEQQEKSIIATIDDVLSARPGALPEDANPGPQTESSKFPFGIQQAKSHRNIKLLEDEPRSRDETPLCTIAHWQDSLAKRCICVSNIVRSLSFVPGNDAEMSKHPGLVLILGKLILLHHEHPERKRAPQTYEKEEDEDKGVACSKDEWWWDCLEVLRDNTLVTLANISGQLDLSAYTESICLPILDGLLHWMVCPSAEAQDPFPTVGPNSVLSPQRLVLETLCKLSIQDNNVDLILATPPFSRQEKFYATLVRYVGDRKNPVCREMSMALLSNLAQGDALAARAIAVQKGSIGNLISFLEDGVTMAQYQQSQHNLMHMQPPPLEPPSVDMMCRAAKALLAMARVDENRSEFLLHEGRLLDISISAVLNSLVASVICDVLFQIGQL,mutated_sequence,1.0,2236.0,UPI000058E2EA.a2m,UPI000058E2EA.npy,gnomAD +UPI00004C2CAA,UPI00004C2CAA.csv,MAAAAAAATTAACSSGSAGTDAAGASGLQQPPPQPQPQPAAAAPAQPPPEPPRKPRMDPRRRQAALSFLTNISLDGRLPPQDAEWGGGEEGGAAKPGAGGACGARTRFSLLAAAERGGCIALAAPGTPAAGLAAGSGPCLPQPSSLPPLIPGGHATVSGPGVARGFASPLGAGRASGEQWQPPRPAPLAACAQLQLLDGSGAAGQEELEEDDAFISVQVPAAAFLGSGTPGSGSGSRGRLNSFTQGILPIAFSRPTSQNYCSLEQPGQGGSTSAFEQLQRSRRRLISQRSSLETLEDIEENAPLRRCRTLSGSPRPKNFKKIHFIKNMRQHDTRNGRIVLISGRRSFCSIFSVLPYRDSTQVGDLKLDGGRQSTGAVSLKEIIGLEGVELGADGKTVSYTQFLLPTNAFGARRNTIDSTSSFSQFRNLSHRSLSIGRASGTQGSLDTGSDLGDFMDYDPNLLDDPQWPCGKHKRVLIFPSYMTTVIDYVKPSDLKKDMNETFKEKFPHIKLTLSKIRSLKREMRKLAQEDCGLEEPTVAMAFVYFEKLALKGKLNKQNRKLCAGACVLLAAKIGSDLKKHEVKHLIDKLEEKFRLNRRELIAFEFPVLVALEFALHLPEHEVMPHYRRLVQSS,mutated_sequence,1.0,633.0,UPI00004C2CAA.a2m,UPI00004C2CAA.npy,gnomAD +UPI0000072F87,UPI0000072F87.csv,MESRKLISATDIQYSGSLLNSLNEQRGHGLFCDVTVIVEDRKFRAHKNILSASSTYFHQLFSVAGQVVELSFIRAEIFAEILNYIYSSKIVRVRSDLLDELIKSGQLLGVKFIAELGVPLSQVKSISGTAQDGNTEPLPPDSGDKNLVIQKSKDEAQDNGATIMPIITESFSLSAEDYEMKKIIVTDSDDDDDDVIFCSEILPTKETLPSNNTVAQVQSNPGPVAISDVAPSASNNSPPLTNITPTQKLPTPVNQATLSQTQGSEKLLVSSAPTHLTPNIILLNQTPLSTPPNVSSSLPNHMPSSINLLVQNQQTPNSAILTGNKANEEEEEEIIDDDDDTISSSPDSAVSNTSLVPQADTSQNTSFDGSLIQKMQIPTLLQEPLSNSLKISDIITRNTNDPGVGSKHLMEGQKIITLDTATEIEGLSTGCKVYANIGEDTYDIVIPVKDDPDEGEARLENEIPKTSGSEMANKRMKVKHDDHYELIVDGRVYYICIVCKRSYVCLTSLRRHFNIHSWEKKYPCRYCEKVFPLAEYRTKHEIHHTGERRYQCLACGKSFINYQFMSSHIKSVHSQDPSGDSKLYRLHPCRSLQIRQYAYLSDRSSTIPAMKDDGIGYKVDTGKEPPVGTTTSTQNKPMTWEDIFIQQENDSIFKQNVTDGSTEFEFIIPESY,mutated_sequence,1.0,672.0,UPI0000072F87.a2m,UPI0000072F87.npy,gnomAD +UPI000161137B,UPI000161137B.csv,MSPDVPLLNDYKQDFFLKRFPQTVLGGPRFKLGYCAPPYIYVNQIILFLMPWVWGGVGTLLYQLGILKDYYTAALSGGLMLFTAFVIQFTSLYAKNKSTTVERILTTDILAEEDEHEFTSCTGAETVKFLIPGKKYVANTVFHSILAGLACGLGTWYLLPNRITLLYGSTGGTALLFFFGWMTLCIAEYSLIVNTATETATFQTQDTYEIIPLMRPLYIFFFVSVDLAHRFVVNMPALEHMNQILHILFVFLPFLWALGTLPPPDALLLWAMEQVLEFGLGGSSMSTHLRLLVMFIMSAGTAIASYFIPSTVGVVLFMTGFGFLLSLNLSDMGHKIGTKSKDLPSGPEKHFSWKECLFYIIILVLALLETSLLHHFAGFSQISKSNSQAIVGYGLMILLIILWILREIQSVYIIGIFRNPFYPKDVQTVTVFFEKQTRLMKIGIVRRILLTLVSPFAMIAFLSLDSSLQGLHSVSVCIGFTRAFRMVWQNTENALLETVIVSTVHLISSTDIWWNRSLDTGLRLLLVGIIRDRLIQFISKLQFAVTVLLTSWTEKKQRRKTTATLCILNIVFSPFVLVIIVFSTLLSSPLLPLFTLPVFLVGFPRPIQSWPGAAGTTACVCADTVYYYQMVPRLTAVLQTAMAAGSLGLLLPGSHYLGRFQDRLMWIMILECGYTYCSINIKGLELQETSCHTAEARRVDEVFEDAFEQEYTRVCSLNEHFGNVLTPCTVLPVKLYSDARNVLSGIIDSHENLKEFKGDLIKVLVWILVQYCSKRPGMKENVHNTENKGKAPLMLPALNTLPPPKSPEDIDSLNSETFNDWSDDNIFDDEPTIKKVIEEKHQLKDLPGTNLFIPGSVESQRVGDHSTGTVPENDLYKAVLLGYPAVDKGKQEDMPYIPLMEFSCSHSHLVCLPAEWRTSCMPSSKMKEMSSLFPEDWYQFVLRQLECYHSEEKASNVLEEIAKDKVLKDFYVHTVMTCYFSLFGIDNMAPSPGHILRVYGGVLPWSVALDWLTEKPELFQLALKAFRYTLKLMIDKASLGPIEDFRELIKYLEEYERDWYIGLVSDEKWKEAILQEKPYLFSLGYDSNMGIYTGRVLSLQELLIQVGKLNPEAVRGQWANLSWELLYATNDDEERYSIQAHPLLLRNLTVQAAEPPLGYPIYSSKPLHIHLY,mutated_sequence,1.0,1172.0,UPI000161137B.a2m,UPI000161137B.npy,gnomAD +UPI00001294AC,UPI00001294AC.csv,MTGVFDSLVADMHSTQIAASSTYHQHQQPPSGGGAGPGGNSSSSSSLHKPQESPTLPVSTATDSSYYTNQQHPAGGGGGGGSPYAHMGSYQYQASGLNNVPYSAKSSYDLGYTAAYTSYAPYGTSSSPANNEPEKEDLEPEIRIVNGKPKKVRKPRTIYSSFQLAALQRRFQKTQYLALPERAELAASLGLTQTQVKIWFQNRRSKFKKMWKSGEIPSEQHPGASASPPCASPPVSAPASWDFGVPQRMAGGGGPGSGGSGAGSSGSSPSSAASAFLGNYPWYHQTSGSASHLQATAPLLHPTQTPQPHHHHHHHGGGGAPVSAGTIF,mutated_sequence,1.0,328.0,UPI00001294AC.a2m,UPI00001294AC.npy,gnomAD +UPI00001B6532,UPI00001B6532.csv,MEVKGPSGRSFCCESEGQFKSCLKRHTPSLLLPSSWKGNSGSCLMAKALHRMSPTPNSCPLPLPLCRMSGVLCSRNLFTFKFSLFQLDSGASGEPGHSLGLTLGFSHCGNCQTAVVSAQPEGMASNGAYPALGPGVTANPGTSLSVFTALPFTTPAPGPAHGPLLVTAGAPPGGPLVLSTLPSTPLVTEQDGCGPSGAGASNVFVQMRTEVGPVKAAQAQTLVLTQAPLVWQAPGALCGGVVCPPPLLLAAAPVVPVMAAQVVGGTQACEGGWSQGLPLPPPPPPAAQLPPIVSQGNAGPWPQGAHGEGSLASSQAKAPPDDSCNPRSVYENFRLWQHYKPLARRHLPQSPDTEALSCFLIPVLRSLARRKPTMTLEEGLWRAMREWQHTSNFDRMIFYEMAEKFLEFEAEEEMQIQKSQWMKGPQCLPPPATPRLEPRGPPAPEVVKQPVYLPSKAGPKAPTACLPPPRPQRPVTKARRPPPRPHRRAETKARLPPPRPQRPAETKVPEEIPPEVVQEYVDIMEELLGPSLGATGEPEKQREEGEVKQPQEEDWTPPDPGLLSYTDKLCSQKDFVTKVEAVIHPQFLEELLSPDPQMDFLALSQELEQEEGLTLAQLVEKRLLPLKEKQHARAAPSRGTARLDSSSSKFAAGQGAERDVPVPQQGVGMETCPPQTTARDSQGRGRAHTGMARSKDSVVLLGCQDSPGLRAARPTSPPQDHRPTCPGVGTKDALDLPGGSPVRESHGLAQGSSEEEELPSLAFLLGSQHKLLPWWLPQSPVPASGLLSPEKWGPQGTHQFPSAERRGLNLAPSPANKAKKRPLFGSLSPAEKTPHPGPGLRVSGEQSLTWGLGGPSQSQKRKGDPLVSRKEKKQRCSQ,mutated_sequence,1.0,878.0,UPI00001B6532.a2m,UPI00001B6532.npy,gnomAD +UPI0001881663,UPI0001881663.csv,MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL,mutated_sequence,1.0,961.0,UPI0001881663.a2m,UPI0001881663.npy,gnomAD +UPI000020187D,UPI000020187D.csv,MRSAAKPWNPAIRAGGHGPDRVRPLPAASSGMKSSKSSTSLAFESRLSRLKRASSEDTLNKPGSTAASGVVRLKKTATAGAISELTESRLRSGTGAFTTTKRTGIPAPREFSVTVSRERSVPRGPSNPRKSVSSPTSSNTPTPTKHLRTPSTKPKQENEGGEKAALESQVRELLAEAKAKDSEINRLRSELKKYKEKRTLNAEGTDALGPNVDGTSVSPGDTEPMIRALEEKNKNFQKELSDLEEENRVLKEKLIYLEHSPNSEGAASHTGDSSCPTSITQESSFGSPTGNQMSSDIDEYKKNIHGNALRTSGSSSSDVTKASLSPDASDFEHITAETPSRPLSSTSNPFKSSKCSTAGSSPNSVSELSLASLTEKIQKMEENHHSTAEELQATLQELSDQQQMVQELTAENEKLVDEKTILETSFHQHRERAEQLSQENEKLMNLLQERVKNEEPTTQEGKIIELEQKCTGILEQGRFEREKLLNIQQQLTCSLRKVEEENQGALEMIKRLKEENEKLNEFLELERHNNNMMAKTLEECRVTLEGLKMENGSLKSHLQGEKQKATEASAVEQTAESCEVQEMLKVARAEKDLLELSCNELRQELLKANGEIKHVSSLLAKVEKDYSYLKEICDHQAEQLSRTSLKLQEKASESDAEIKDMKETIFELEDQVEQHRAVKLHNNQLISELESSVIKLEEQKSDLERQLKTLTKQMKEETEEWRRFQADLQTAVVVANDIKCEAQQELRTVKRKLLEEEEKNARLQKELGDVQGHGRVVTSRAAPPPVDEEPESSEVDAAGRWPGVCVSRTSPTPPESATTVKSLIKSFDLGRPGGAGQNISVHKTPRSPLSGIPVRTAPAAAVSPMQRHSTYSSVRPASRGVTQRLDLPDLPLSDILKGRTETLKPDPHLRKSPSLESLSRPPSLGFGDTRLLSASTRAWKPQSKLSVERKDPLAALAREYGGSKRNALLKWCQKKTQGYANIDITNFSSSWSDGLAFCALLHTYLPAHIPYQELNSQEKKRNLLLAFEAAESVGIKPSLELSEMLYTDRPDWQSVMQYVAQIYKYFET,mutated_sequence,1.0,1068.0,UPI000020187D.a2m,UPI000020187D.npy,gnomAD +UPI000006D077,UPI000006D077.csv,MARTDQKPPCRGGCWGQPGHPNTGGAAAHPTYHPMGHRPRTCILLRGDQTTGGQAPSREISLGPWAAGTHFLAISTTPWGRKTPACISELPTSSGTAQPLANAVCEVQTVPGPGLRPQGTPAMRAPSHKGTPPTPNPWGPEQPQNRHKHPKKGVTGGPSPPPPAASRYGQTPGREPRVQAPGLGPCGRPASGRLLSLHLEKGDGKGTRQRIPLTDAAVGGDRTDIPSAIAAGPARTPDRHGLPIPGSTPTPMVGSGRLGAPVGRSGGGASARSSRPSCANVLLRADASLGTVLSVLWTGQLSRGWALLPPGDAGRHLETSVISAGVAAGIWLVEPGEAAQDPATRRTAPPRRTASPEPPAPGAPLPACPGRIPGAARFGPRSCPLGSPAVLAVTTGWSHRSV,mutated_sequence,1.0,402.0,UPI000006D077.a2m,UPI000006D077.npy,gnomAD +UPI000013DC6C,UPI000013DC6C.csv,MMMMSLNSKQAFSMPHGGSLHVEPKYSALHSTSPGSSAPIAPSASSPSSSSNAGGGGGGGGGGGGGGGRSSSSSSSGSSGGGGSEAMRRACLPTPPSNIFGGLDESLLARAEALAAVDIVSQSKSHHHHPPHHSPFKPDATYHTMNTIPCTSAASSSSVPISHPSALAGTHHHHHHHHHHHHQPHQALEGELLEHLSPGLALGAMAGPDGAVVSTPAHAPHMATMNPMHQAALSMAHAHGLPSHMGCMSDVDADPRDLEAFAERFKQRRIKLGVTQADVGSALANLKIPGVGSLSQSTICRFESLTLSHNNMIALKPILQAWLEEAEKSHREKLTKPELFNGAEKKRKRTSIAAPEKRSLEAYFAIQPRPSSEKIAAIAEKLDLKKNVVRVWFCNQRQKQKRMKYSAGI,mutated_sequence,1.0,409.0,UPI000013DC6C.a2m,UPI000013DC6C.npy,gnomAD +UPI000013C755,UPI000013C755.csv,MGPTLAVPTPYGCIGCKLPQPEYPPALIIFMFCAMVITIVVDLIGNSMVILAVTKNKKLRNSGNIFVVSLSVADMLVAIYPYPLMLHAMSIGGWDLSQLQCQMVGFITGLSVVGSIFNIVAIAINRYCYICHSLQYERIFSVRNTCIYLVITWIMTVLAVLPNMYIGTIEYDPRTYTCIFNYLNNPVFTVTIVCIHFVLPLLIVGFCYVRIWTKVLAARDPAGQNPDNQLAEVRNFLTMFVIFLLFAVCWCPINVLTVLVAVSPKEMAGKIPNWLYLAAYFIAYFNSCLNAVIYGLLNENFRREYWTIFHAMRHPIIFFSGLISDIREMQEARTLARARAHARDQAREQDRAHACPAVEETPMNVRNVPLPGDAAAGHPDRASGHPKPHSRSSSAYRKSASTHHKSVFSHSKAASGHLKPVSGHSKPASGHPKSATVYPKPASVHFKADSVHFKGDSVHFKPDSVHFKPASSNPKPITGHHVSAGSHSKSAFSAATSHPKPTTGHIKPATSHAEPTTADYPKPATTSHPKPTAADNPELSASHCPEIPAIAHPVSDDSDLPESASSPAAGPTKPAASQLESDTIADLPDPTVVTTSTNDYHDVVVIDVEDDPDEMAV,mutated_sequence,1.0,617.0,UPI000013C755.a2m,UPI000013C755.npy,gnomAD +UPI0000DAC777,UPI0000DAC777.csv,MNFQAGGGQSPQQQQSLAAPGGGGAAAQQLVCGGQFGGAGPGAGGGGGPSQQLAGGPPQQFALSNSAAIRAEIQRFESVHPNIYAIYDLIERIEDLALQNQIREHVISIEDSFVNSQEWTLSRSVPELKVGIVGNLSSGKSALVHRYLTGTYVQEESPEGGRFKKEIVVDGQSYLLLIRDEGGPPELQFAAWVDAVVFVFSLEDEISFQTVYNYFLRLCSFRNASEVPMVLVGTQDAISAANPRVIDDSRARKLSTDLKRCTYYETCATYGLNVERVFQDVAQKVVALRKKQQLAIGPCKSLPNSPSHSAVSAASIPAVHINQATNGGGSAFSDYSSSVPSTPSISQRELRIETIAASSTPTPIRKQSKRRSNIFTSRKGADLDREKKAAECKVDSIGSGRAIPIKQGILLKRSGKSLNKEWKKKYVTLCDNGLLTYHPSLHDYMQNIHGKEIDLLRTTVKVPGKRLPRATPATAPGTSPRANGLSVERSNTQLGGGTGAPHSASSASLHSERPLSSSAWAGPRPEGLHQRSCSVSSADQWSEATTSLPPGMQHPASGPAEVLSSSPKLDPPPSPHSNRKKHRRKKSTGTPRPDGPSSATEEAEESFEFVVVSLTGQTWHFEASTAEERELWVQSVQAQILASLQGCRSAKDKTRLGNQNAALAVQAVRTVRGNSFCIDCDAPNPDWASLNLGALMCIECSGIHRHLGAHLSRVRSLDLDDWPPELLAVMTAMGNALANSVWEGALGGYSKPGPDACREEKERWIRAKYEQKLFLAPLPSSDVPLGQQLLRAVVEDDLRLLVMLLAHGSKEEVNETYGDGDGRTALHLSSAMANVVFTQLLIWYGVDVRSRDARGLTPLAYARRAGSQECADILIQHGCPGEGCGLAPTPNREPANGTNPSAELHRSPSLL,mutated_sequence,1.0,911.0,UPI0000DAC777.a2m,UPI0000DAC777.npy,gnomAD +UPI000042467C,UPI000042467C.csv,MRCALALSALLLLLSTPPLLPSSPSPSPSPSQNATQTTTDSSNKTAPTPASSVTIMATDTAQQSTVPTSKANEILASVKATTLGVSSDSPGTTTLAQQVSGPVNTTVARGGGSGNPTTTIESPKSTKSADTTTVATSTATAKPNTTSSQNGAEDTTNSGGKSSHSVTTDLTSTKAEHLTTPHPTSPLSPRQPTSTHPVATPTSSGHDHLMKISSSSSTVAIPGYTFTSPGMTTTLLETVFHHVSQAGLELLTSGDLPTLASQSAGITASSVISQRTQQTSSQMPASSTAPSSQETVQPTSPATALRTPTLPETMSSSPTAASTTHRYPKTPSPTVAHESNWAKCEDLETQTQSEKQLVLNLTGNTLCAGGASDEKLISLICRAVKATFNPAQDKCGIRLASVPGSQTVVVKEITIHTKLPAKDVYERLKDKWDELKEAGVSDMKLGDQGPPEEAEDRFSMPLIITIVCMASFLLLVAALYGCCHQRLSQRKDQQRLTEELQTVENGYHDNPTLEVMETSSEMQEKKVVSLNGELGDSWIVPLDNLTKDDLDEEEDTHL,mutated_sequence,1.0,558.0,UPI000042467C.a2m,UPI000042467C.npy,gnomAD +UPI00006AB830,UPI00006AB830.csv,METNESTEGSRSRSRSLDIQPSSEGLGPTSEPFPSSDDSPRSALAAATAAAAAAASAAAATAAFTTAKAAALSTKTPAPCSEFMEPSSDPSLLGEPCAGPGFTHNIAHGSLGFEPVYVSCIAQDTCTTTDHSSNPGPVPGSSSGPVLGSSSGAGHGSGSGSGPGCGSVPGSGSGPGPGSGPGSGPGHGSGSHPGPASGPGPDTGPDSELSPCIPPGFRNLVADRVPNYTSWSQHCPWEPQKQPPWEFLQVLEPGARGLWKPPDIKGKLMVCYETLPRGQCLLYNWEEERATNHLDQVPSMQDGSESFFFRHGHRGLLTMQLKSPMPSSTTQKDSYQPPGNVYWPLRGKREAMLEMLLQHQICKEVQAEQEPTRKLFEVESVTHHDYRMELAQAGTPAPTKPHDYRQEQPETFWIQRAPQLPGVSNIRTLDTPFRKNCSFSTPVPLSLGKLLPYEPENYPYQLGEISSLPCPGGRLGGGGGRMTPF,mutated_sequence,1.0,485.0,UPI00006AB830.a2m,UPI00006AB830.npy,gnomAD +UPI00002077A2,UPI00002077A2.csv,MALLTAETFRLQFNNKRRLRRPYYPRKALLCYQLTPQNGSTPTRGYFENKKKCHAEICFINEIKSMGLDETQCYQVTCYLTWSPCSSCAWELVDFIKAHDHLNLGIFASRLYYHWCKPQQKGLRLLCGSQVPVEVMGFPKFADCWENFVDHEKPLSFNPYKMLEELDKNSRAIKRRLERIKIPGVRAQGRYMDILCDAEV,mutated_sequence,1.0,200.0,UPI00002077A2.a2m,UPI00002077A2.npy,gnomAD +UPI0001AE641D,UPI0001AE641D.csv,MLAGKHVALTPLKAYSQDAGVICPENLHLRRNSREGGDQKEKKKGLQGPLQFRDVAIEFSLEEWHCLDMAQRNLYRDVMLENYRNLVFLGIVVSKPDLITHLEQGKKPSTMQRHEMVANPSVLCSHFNQDLWPEQSIKDSFQKLILRRHKKCGHDNLQLKKGCESVDKCKVHKRGYNGLNQCLTTTQSKMFQCDKHGKVFHQFSNTNRHKIRHTGKNPCKFTECGKAFNRSSTFTTHKKIHTGEKPYKCIECGKAFNRSSHLTTHKIIHTGEKRYKCEDCGKAFNRSSNLTTHKKIHTGEKPYKCEECGKAFKRSSILTTHKRIHTGEKPYKCEECGKVFKYLSSLSTHKIIHTGEKPYKCEECGKAFNWSSHLTTHKRIHTGEKPYKCEECGKGFKYSSTLTKHKIIHTGEKPYKCEECRSLRSQCDQLEERVSVMEDEMNEMK,mutated_sequence,1.0,445.0,UPI0001AE641D.a2m,UPI0001AE641D.npy,gnomAD +UPI0000229786,UPI0000229786.csv,MMFRDQVGVLAGWFKGWNECEQTVALLSLLKRVSQTQARFLQLCLEHSLADCAELHVLEREANSPGIINQWQQESKDKVISLLLTHLPLLKPGNLDAKVEYMKLLPKILAHSIEHNQHIEESRQLLSYALIHPATSLEDRSALAMWLNHLEDRTSTSFGGQNRGRSDSVDYGQTHYYHQRQNSDDKLNGWQNSRDSGICINASNWQDKSMGCENGHVPLYSSSSVPTTINTIGTSTSTILSGQAHHSPLKRSVSLTPPMNVPNQPLGHGWMSHEDLRARGPQCLPSDHAPLSPQSSVASSGSGGSEHLEDQTTARNTFQEEGSGMKDVPAWLKSLRLHKYAALFSQMTYEEMMALTECQLEAQNVTKGARHKIVISIQKLKERQNLLKSLERDIIEGGSLRIPLQELHQMILTPIKAYSSPSTTPEARRREPQAPRQPSLMGPESQSPDCKDGAAATGATATPSAGASGGLQPHQLSSCDGELAVAPLPEGDLPGQFTRVMGKVCTQLLVSRPDEENISSYLQLIDKCLIHEAFTETQKKRLLSWKQQVQKLFRSFPRKTLLDISGYRQQRNRGFGQSNSLPTAGSVGGGMGRRNPRQYQIPSRNVPSARLGLLGTSGFVSSNQRNTTATPTIMKQGRQNLWFANPGGSNSMPSRTHSSVQRTRSLPVHTSPQNMLMFQQPEFQLPVTEPDINNRLESLCLSMTEHALGDGVDRTSTI,mutated_sequence,1.0,718.0,UPI0000229786.a2m,UPI0000229786.npy,gnomAD +UPI00018848F1,UPI00018848F1.csv,MARMPGSGDCNTSAGGSASAAAAAAENNGERGEGERGAGGRGRRHSRPHYCSAGEEEEEEEEEDEIQEVQITGDEEEEEDGGGGLEEDEEEEEEEEMGLDWDEPLEPEDSAGEELEPEPVHMINMDQSAALEPEAPPRLLAPRARGGPPGDGSELDPDVLQRPERARLSENTRLATRYAVRIFREYLSEKAQSPDFETMDKGALCRVLRSFYAEARSKSGQLYSKSSLISIRSSLNRYLNEPPYCRTLDLTKDPELRSANLTLAAVIRKLEEQGAGPVVQKQAITRADLRKLYTSSVFSTNTPFGLLNKVWFETCMYFCTRGRENQRELEEDSFGLAMDEDGRKFVYFKSLGPYHKSRSSSWSKKRAESSDEENLPRMYETGTEFCPYASFVKYLSKRNPLCKAFFQRPRDHCSEGDVTWYENKAIGKNLLGTRMQMLSKAAKLSKTYTNHCIGAVSIATLNSIAGIGTKLGSPAPQGCYAEALNGAARHHSHHPPTHPSHHHRPQPPSLGNTYILPKDSQVGPDVKSEAAPKRALYESVFGSGEICGPTSPKRLCIRPSEPVDAVVVVSVKHDPLPLLPEANGHRSTNSPTIVSPAIVSPTQDSRPNMSRPLITRSPASPLNNQGIPTPAQLTKSNAPVHIDVGGHMYTSSLATLTKYPESRIGRLFDGTEPIVLDSLKQHYFIDRDGQMFRYILNFLRTSKLLIPDDFKDYTLLYEEAKYFQLQPMLLEMERWKQDRETGRFSRPCECLVVRVAPDLGERITLSGDKSLIEEVFPEIGDVMCNSVNAGWNHDSTHVIRFPLNGYCHLNSVQVLERLQQRGFEIVGSCGGGVDSSQFSEYVLRRELRRTPRVPSVIRIKQEPLD,mutated_sequence,1.0,865.0,UPI00018848F1.a2m,UPI00018848F1.npy,gnomAD +UPI00001AEE5A,UPI00001AEE5A.csv,MSSAMLVTCLPDPSSSFREDAPRPPVPGEEGETPPCQPGVGKGQVTKPMPVSSNTRRNEDGLGEPEGRASPDSPLTRWTKSLHSLLGDQDGAYLFRTFLEREKCVDTLDFWFACNGFRQMNLKDTKTLRVAKAIYKRYIENNSIVSKQLKPATKTYIRDGIKKQQIDSIMFDQAQTEIQSVMEENAYQMFLTSDIYLEYVRSGGENTAYMSNGGLGSLKVVCGYLPTLNEEEEWTCADFKCKLSPTVVGLSSKTLRATASVRSTETVDSGYRSFKRSDPVNPYHIGSGYVFAPATSANDSEISSDALTDDSMSMTDSSVDGIPPYRVGSKKQLQREMHRSVKANGQVSLPHFPRTHRLPKEMTPVEPATFAAELISRLEKLKLELESRHSLEERLQQIREDEEREGSELTLNSREGAPTQHPLSLLPSGSYEEDPQTILDDHLSRVLKTPGCQSPGVGRYSPRSRSPDHHHHHHSQYHSLLPPGGKLPPAAASPGACPLLGGKGFVTKQTTKHVHHHYIHHHAVPKTKEEIEAEATQRVHCFCPGGSEYYCYSKCKSHSKAPETMPSEQFGGSRGSTLPKRNGKGTEPGLALPAREGGAPGGAGALQLPREEGDRSQDVWQWMLESERQSKPKPHSAQSTKKAYPLESARSSPGERASRHHLWGGNSGHPRTTPRAHLFTQDPAMPPLTPPNTLAQLEEACRRLAEVSKPPKQRCCVASQQRDRNHSATVQTGATPFSNPSLAPEDHKEPKKLAGVHALQASELVVTYFFCGEEIPYRRMLKAQSLTLGHFKEQLSKKGNYRYYFKKASDEFACGAVFEEIWEDETVLPMYEGRILGKVERID,mutated_sequence,1.0,843.0,UPI00001AEE5A.a2m,UPI00001AEE5A.npy,gnomAD +UPI00017A8741,UPI00017A8741.csv,MLRLAQTPRRLPSPFRVTYRPWGTRAFGAFPVFGTQISSPPENLDTAHLPHPEGAFEEWMRSGWKKRDLTQQAKDIQNITVQETNKNNSESIECSKITMDLKFNNSRKYISITVPSKTQTMSPHIKSVDDVVVLGMNLSKFNKLTQFFICVAGVFVFYLIYGYLQELIFSVEGFKSCGWYLTLVQFAFYSIFGLIELQLIQDKRRRYVVCFYFLIFLY,mutated_sequence,1.0,218.0,UPI00017A8741.a2m,UPI00017A8741.npy,gnomAD +UPI0000141054,UPI0000141054.csv,MDPKLGRMAASLLAVLLLLLERGMFSSPSPPPALLEKVFQYIDLHQDEFVQTLKEWVAIESDSVQPVPRFRQELFRMMAVAADTLQRLGARVASVDMGPQQLPDGQSLPIPPIILAELGSDPTKGTVCFYGHLDVQPADRGDGWLTDPYVLTEVDGKLYGRGATDNKGPVLAWINAVSAFRALEQDLPVNIKFIIEGMEEAGSVALEELVEKEKDRFFSGVDYIVISDNLWISQRKPAITYGTRGNSYFMVEVKCRDQDFHSGTFGGILHEPMADLVALLGSLVDSSGHILVPGIYDEVVPLTEEEINTYKAIHLDLEEYRNSSRVEKFLFDTKEEILMHLWRYPSLSIHGIEGAFDEPGTKTVIPGRVIGKFSIRLVPHMNVSAVEKQVTRHLEDVFSKRNSSNKMVVSMTLGLHPWIANIDDTQYLAAKRAIRTVFGTEPDMIRDGSTIPIAKMFQEIVHKSVVLIPLGAVDDGEHSQNEKINRWNYIEGTKLFAAFFLEMAQLH,mutated_sequence,1.0,507.0,UPI0000141054.a2m,UPI0000141054.npy,gnomAD +UPI000013D1EC,UPI000013D1EC.csv,MSRLIVKNLPNGMKEERFRQLFAAFGTLTDCSLKFTKDGKFRKFGFIGFKSEEEAQKAQKHFNKSFIDTSRITVEFCKSFGDPAKPRAWSKHAQKPSQPKQPPKDSTTPEIKKDEKKKKVAGQLEKLKEDTEFQEFLSVHQRRAQAATWANDGLDAEPSKGKSKPASDYLNFDSDSGQESEEEGAGEDLEEEASLEPKAAVQKELSDMDYLKSKMVKAGSSSSSEEEESEDEAVHCDEGSEAEEEDSSATPVLQERDSKGAGQEQGMPAGKKRPPEARAETEKPANQKEPTTCHTVKLRGAPFNVTEKNVMEFLAPLKPVAIRIVRNAHGNKTGYIFVDFSNEEEVKQALKCNREYMGGRYIEVFREKNVPTTKGAPKNTTKSWQGRILGENEEEEDLAESGRLFVRNLPYTSTEEDLEKLFSKYGPLSELHYPIDSLTKKPKGFAFITFMFPEHAVKAYSEVDGQVFQGRMLHVLPSTIKKEASEDASALGSSSYKKKKEAQDKANSASSHNWNTLFMGPNAVADAIAQKYNATKSQVFDHETKGSVAVRVALGETQLVQEVRRFLIDNGVSLDSFSQAAAERSKTVILVKNLPAGTLAAQLQETFGHFGSLGRVLLPEGGITAIVEFLEPLEARKAFRHLAYSKFHHVPLYLEWAPVGVFSSTAPQKKKLQDTPSEPMEKDPAEPETVPDGETPEDENPTEEGADNSSAKMEEEEEEEEEEEESLPGCTLFIKNLNFDTTEEKLKEVFSKVGTVKSCSISKKKNKAGVLLSMGFGFVEYRKPEQAQKALKQLQGHVVDGHKLEVRISERATKPAVTLARKKQVPRKQTTSKILVRNIPFQAHSREIRELFSTFGELKTVRLPKKMTGTGTHRGFGFVDFLTKQDAKRAFNALCHSTHLYGRRLVLEWADSEVTLQALRRKTAAHFHEPPKKKRSVVLDEILEQLEGSDSDSEEQTLQL,mutated_sequence,1.0,960.0,UPI000013D1EC.a2m,UPI000013D1EC.npy,gnomAD +UPI0000169571,UPI0000169571.csv,MPPPQQGPCGHHLLLLLALLLPSLPLTRAPVPPGPAAALLQALGLRDEPQGAPRLRPVPPVMWRLFRRRDPQETRSGSRRTSPGVTLQPCHVEELGVAGNIVRHIPDRGAPTRASEPASAAGHCPEWTVVFDLSAVEPAERPSRARLELRFAAAAAAAPEGGWELSVAQAGQGAGADPGPVLLRQLVPALGPPVRAELLGAAWARNASWPRSLRLALALRPRAPAACARLAEASLLLVTLDPRLCHPLARPRRDAEPVLGGGPGGACRARRLYVSFREVGWHRWVIAPRGFLANYCQGQCALPVALSGSGGPPALNHAVLRALMHAAAPGAADLPCCVPARLSPISVLFFDNSDNVVLRQYEDMVVDECGCR,mutated_sequence,1.0,372.0,UPI0000169571.a2m,UPI0000169571.npy,gnomAD +UPI0002065925,UPI0002065925.csv,MERYKALEQLLTELDDFLKILDQENLSSTALVKKSCLAELLRLYTKSSSSDEEYIYMNKVTINKQQNAESQGKAPEEQGLLPNGEPSQHSSAPQKSLPDLPPPKMRILTLGQISGAQSFSPDGTHLTGTEFLGGWSWRPSQIRLMSPGTHWGDLTATEIPERKQLAIPKTESPEGYYEEAEPYDTSLNEDGEAVSSSYESYDEEDGSKGKSAPYQWPSPEAGIELMRDARICAFLWRKKWLGQWAKQLCVIKDNRLLCYKSSKDHSPQLDVNLLGSSVIHKEKQVRKKEHKLKITPMNADVIVLGLQSKDQAEQWLRVIQEVSGLPSEGASEGNQYTPDAQRFNCQKPDIAEKYLSASEYGSSVDGHPEVPETKDVKKKCSAGLKLSNLMNLGRKKSTSLEPVERSLETSSYLNVLVNSQWKSRWCSVRDNHLHFYQDRNRSKVAQQPLSLVGCEVVPDPSPDHLYSFRILHKGEELAKLEAKSSEEMGHWLGLLLSESGSKTDPEEFTYDYVDADRVSCIVSAAKNSLLLMQRKFSEPNTYIDGLPSQDRQEELYDDVDLSELTAAVEPTEEATPVADDPNERESDRVYLDLTPVKSFLHGPSSAQAQASSPTLSCLDNATEALPADSGPGPTPDEPCIKCPENLGEQQLESLEPEDPSLRITTVKIQTEQQRISFPPSCPDAVVATPPGASPPVKDRLRVTSAEIKLGKNRTEAEVKRYTEEKERLEKKKEEIRGHLAQLRKEKRELKETLLKCTDKEVLASLEQKLKEIDEECRGEESRRVDLELSIMEVKDNLKKAEAGPVTLGTTVDTTHLENVSPRPKAVTPASAPDCTPVNSATTLKNRPLSVVVTGKGTVLQKAKEWEKKGAS,mutated_sequence,1.0,871.0,UPI0002065925.a2m,UPI0002065925.npy,gnomAD +UPI000067CB88,UPI000067CB88.csv,MEEVPGDALCEHFEANILTQNRCQNCFHPEEAHGARYQELRSPSGAEVPYCDLPRCPPAPEDPLSASTSGCQSVVDPGLRPGPKRGPSPSAGLPEEGPTAAPRSRSRELEAVPYLEGLTTSLCGSCNEDPGSDPTSSPDSATPDDTSNSSSVDWDTVERQEEEAPSWDELAVMIPRRPREGPRADSSQRAPSLLTRSPVGGDAAGQKKEDTGGGGRSAGQHWARLRGESGLSLERHRSTLTQASSMTPHSGPRSTTSQASPAQRDTAQAASTREIPRASSPHRITQRDTSRASSTQQEISRASSTQQETSRASSTQEDTPRASSTQEDTPRASSTQWNTPRASSPSRSTQLDNPRTSSTQQDNPQTSFPTCTPQRENPRTPCVQQDDPRASSPNRTTQRENSRTSCAQRDNPKASRTSSPNRATRDNPRTSCAQRDNPRASSPSRATRDNPTTSCAQRDNPRASRTSSPNRATRDNPRTSCAQRDNPRASSPSRATRDNPTTSCAQRDNPRASRTSSPNRATRDNPRTSCAQRDNPRASSPNRAARDNPTTSCAQRDNPRASRTSSPNRATRDNPRTSCAQRDNPRASSPNRATRDNPTTSCAQRDNPRASRTSSPNRATRDNPRTSCAQRDNPRASSPNRTTQQDSPRTSCARRDDPRASSPNRTIQQENPRTSCALRDNPRASSPSRTIQQENPRTSCAQRDDPRASSPNRTTQQENPRTSCARRDNPRASSRNRTIQRDNPRTSCAQRDNPRASSPNRTIQQENLRTSCTRQDNPRTSSPNRATRDNPRTSCAQRDNLRASSPIRATQQDNPRTCIQQNIPRSSSTQQDNPKTSCTKRDNLRPTCTQRDRTQSFSFQRDNPGTSSSQCCTQKENLRPSSPHRSTQWNNPRNSSPHRTNKDIPWASFPLRPTQSDGPRTSSPSRSKQSEVPWASIALRPTQGDRPQTSSPSRPAQHDPPQSSFGPTQYNLPSRATSSSHNPGHQSTSRTSSPVYPAAYGAPLTSPEPSQPPCAVCIGHRDAPRASSPPRYLQHDPFPFFPEPRAPESEPPHHEPPYIPPAVCIGHRDAPRASSPPRHTQFDPFPFLPDTSDAEHQCQSPQHEPLQLPAPVCIGYRDAPRASSPPRQAPEPSLLFQDLPRASTESLVPSMDSLHECPHIPTPVCIGHRDAPSFSSPPRQAPEPSLFFQDPPGTSMESLAPSTDSLHGSPVLIPQVCIGHRDAPRASSPPRHPPSDLAFLAPSPSPGSSGGSRGSAPPGETRHNLEREEYTVLADLPPPRRLAQRQPGPQAQCSSGGRTHSPGRAEVERLFGQERRKSEAAGAFQAQDEGRSQQPSQGQSQLLRRQSSPAPSRQVTMLPAKQAELTRRSQAEPPHPWSPEKRPEGDRQLQGSPLPPRTSARTPERELRTQRPLESGQAGPRQPLGVWQSQEEPPGSQGPHRHLERSWSSQEGGLGPGGWWGCGEPSLGAAKAPEGAWGGTSREYKESWGQPEAWEEKPTHELPRELGKRSPLTSPPENWGGPAESSQSWHSGTPTAVGWGAEGACPYPRGSERRPELDWRDLLGLLRAPGEGVWARVPSLDWEGLLELLQARLPRKDPAGHRDDLARALGPELGPPGTNDVPEQESHSQPEGWAEATPVNGHSPALQSQSPVQLPSPACTSTQWPKIKVTRGPATATLAGLEQTGPLGSRSTAKGPSLPELQFQPEEPEESEPSRGQDPLTDQKQADSADKRPAEGKAGSPLKGRLVTSWRMPGDRPTLFNPFLLSLGVLRWRRPDLLNFKKGWMSILDEPGEPPSPSLTTTSTSQWKKHWFVLTDSSLKYYRDSTAEEADELDGEIDLRSCTDVTEYAVQRNYGFQIHTKDAVYTLSAMTSGIRRNWIEALRKTVRPTSAPDVTKLSDSNKENALHSYSTQKGPLKAGEQRAGSEVISRGGPRKADGQRQALDYVELSPLTQASPQRARTPARTPDRLAKQEELERDLAQRSEERRKWFEATDSRTPEVPAGEGPRRGLGAPLTEDQQNRLSEEIEKKWQELEKLPLRENKRVPLTALLNQSRGERRGPPSDGHEALEKEVQALRAQLEAWRLQGEAPQSALRSQEDGHIPPGYISQEACERSLAEMESSHQQVMEELQRHHERELQRLQQEKEWLLAEETAATASAIEAMKKAYQEELSRELSKTRSLQQGPDGLRKQHQSDVEALKRELQVLSEQYSQKCLEIGALMRQAEEREHTLRRCQQEGQELLRHNQELHGRLSEEIDQLRGFIASQGMGNGCGRSNERSSCELEVLLRVKENELQYLKKEVQCLRDELQMMQKDKRFTSGKYQDVYVELSHIKTRSEREIEQLKEHLRLAMAALQEKESMRNSLAE,mutated_sequence,1.0,2365.0,UPI000067CB88.a2m,UPI000067CB88.npy,gnomAD +UPI000006F773,UPI000006F773.csv,MGSGAGELGRAERLPVLFLFLLSLFCPALCEQIRYRIPEEMPKGSVVGNLATDLGFSVQELPTRKLRVSSEKPYFTVSAESGELLVSSRLDREEICGKKPACALEFEAVAENPLNFYHVNVEIEDINDHTPKFTQNSFELQISESAQPGTRFILGSAHDADIGSNTLQNYQLSPSDHFSLINKEKSDGSKYPEMVLKTPLDREKQKSYHLTLTALDFGAPPLSSTAQIHVLVTDANDNAPVFSQDVYRVSLSENVYPGTTVLQVTATDQDEGVNAEITFSFSEASQITQFDLNSNTGEITVLNTLDFEEVKEYSIVLEARDGGGMIAQCTVEVEVIDENDNAPEVIFQSLPNLIMEDAELGTHIALLKVRDKDSRHNGEVTCKLEGDVPFKILTSSRNTYKLVTDAVLDREQNPEYNITVTATDRGKPPLSSSSSITLHIGDVNDNAPVFSQSSYIVHVAENNPPGASISQVRASDPDLGPNGQVSYCIMASDLEQRELSSYVSISAESGVVFAQRAFDHEQLRAFELTLQARDQGSPALSANVSLRVLVDDRNDNAPRVLYPALGPDGSALFDMVPHAAEPGYLVTKVVAVDADSGHNAWLSYHVLQASEPGLFSLGLRTGEVRTARALGDRDAVRQRLLVAVRDGGQPPLSATATLHLVFADSLQEVLPDITDRPDPSDLQAELQFYLVVALALISVLFLVAMILAIALRLRRSSSPASWSCFQPGLCVKSESVVPPNYSEGTLPYSYNLCVAHTGKTEFNFLKCSEQLSSGQDILCGDSSGALFPLCNSSELTSHQQAPPNTDWRFSQAQRPGTSGSQNGDDTGTWPNNQFDTEMLQAMILASASEAADGSSTLGGGAGTMGLSARYGPQFTLQHVPDYRQNVYIPGSNATLTNAAGKRDGKAPAGGNGNKKKSGKKEKK,mutated_sequence,1.0,923.0,UPI000006F773.a2m,UPI000006F773.npy,gnomAD +UPI0000251DD8,UPI0000251DD8.csv,MGCTPSHSDLVNSVAKSGIQFLKKPKAIRPGCQGGSERGSIPLLVKNSTCYDAGEGLAEEQPSPRRNQTTAKGLCQLMGDPASGKRKDMEGLIPGTKTSSSQLNKSQSHMAKDIPFKTQGSHGSQGADFSGDESEESSTQDTSKWKRTAKCHTSSTQSHCYQTIHPAHEPEGKVDFPEPLVKAHQQAYTYLHSSLSKYEAILCIIHQATQTRELLQPMVSFLLLCFEEISQLLGEISKDGEVLLQEVREDLAWPLKKREPQEQPNLLQQLLQYTVSKLQVLNGTVASLTGSFLEGSSSYLHSTATHLENKLSTKRNVDERLLRALRQLESLASGCGDPGVQGLPLCSEDSGIGADNESVQSVDKLGKQTSWDLAPEPEEWKSVTSPHTEARQSGHTWQQSPFCLGSGRPQDCLLSGAPMAKVQPRAQDEARSPCLSSTSPENITSPPLKLGTSTPCDSFGIGVSVEPHLSKTSRPMDASSLSDSEDSSPEEEEEDKMSSMSLCAWQEKTPHSRPQSSPADRESPFQARTRRLRSLQAQEMILKMKESISERIKFVPVPCGHQDWSEEEEGRTVVPPRPSTVSGSRRAPERQTRSQSESCLQSHVEDPTFQELRRVQRDLSQKLEAFYALGAKGQGQSQEQILQPRAAAVWPNGTCRVSPSNTTSRLKASLTKNFSILPSQDKSILQKCNPHPEDEQGKAGKLPNAIPSGEVSEAAKATDWNVRGCPTRTSVKKLIETFSPTESLRMLGDSKDAGASPCLRNCIMPPRFPKYTGLAPLYPKPQISPASGRESLKMGIGWKPLAPIFPPLPKAEAAKSEELSCEMEGNLEHLPPPPMEVLMDKSFASLESPESSKSTENSPKETQEPGPGEAGPTRRTWASPKLRASVSPLDLLPSKSTASLTKPHSTGPGSGRSSCQPRKPALDLSSPPATSQSPEVKGGTWSQAEKATSLYRQPRKAIAWHHSGPPSGQNRTSESSLARPRQSRERSPPVGRKASPTRTHWVPQADKRRRSLPSSYRPAQPSPSAVQTPPSPPVSPRVLSPPTTKRRTSPPHQPKLPNPPPESAPAQCKVPSPPTQHPEASPPFSIPSPSPPMSPSQEHKETRDSEDSQAVIAKVSGNTHSIFCPATSSLFEAKPPLSTAHPLTPPSLPPEAGGPLGNPAECWKNSSGPWLRADSQRRAALCALNPLPFLRRTASDRQPGGRPQPPTLDPTSTSYESQLGQNSSSEESPKKDTEPGSSPCSPELQGGTRRASPPEFCVLGHGLQPEPRTGHIQDKSQPEAQPQQEEVS,mutated_sequence,1.0,1288.0,UPI0000251DD8.a2m,UPI0000251DD8.npy,gnomAD +UPI0000470B60,UPI0000470B60.csv,MNSASVDGHLSGCRLFLFLSPLFRFYCDYCDTYLTHDSPSVRKTHCSGRKHKENVKDYYQKWMEEQAQSLIDKTTAAFQQGKIPPTPFSAPPPAGAMIPPPPSLPGPPRPGMMPAPHMGGPPMMPMMGPPPPGMMPVGPAPGMRPPMGGHMPMMPGPPMMRPPARPMMVPTRPGMTRPDR,mutated_sequence,1.0,180.0,UPI0000470B60.a2m,UPI0000470B60.npy,gnomAD +UPI0000073246,UPI0000073246.csv,MEAAPPGPPWPLLLLLLLLLALCGCPAPAAASPLLLFANRRDVRLVDAGGVKLESTIVVSGLEDAAAVDFQFSKGAVYWTDVSEEAIKQTYLNQTGAAVQNVVISGLVSPDGLACDWVGKKLYWTDSETNRIEVANLNGTSRKVLFWQDLDQPRAIALDPAHGYMYWTDWGETPRIERAGMDGSTRKIIVDSDIYWPNGLTIDLEEQKLYWADAKLSFIHRANLDGSFRQKVVEGSLTHPFALTLSGDTLYWTDWQTRSIHACNKRTGGKRKEILSALYSPMDIQVLSQERQPFFHTRCEEDNGGCSHLCLLSPSEPFYTCACPTGVQLQDNGRTCKAGAEEVLLLARRTDLRRISLDTPDFTDIVLQVDDIRHAIAIDYDPLEGYVYWTDDEVRAIRRAYLDGSGAQTLVNTEINDPDGIAVDWVARNLYWTDTGTDRIEVTRLNGTSRKILVSEDLDEPRAIALHPVMGLMYWTDWGENPKIECANLDGQERRVLVNASLGWPNGLALDLQEGKLYWGDAKTDKIEVINVDGTKRRTLLEDKLPHIFGFTLLGDFIYWTDWQRRSIERVHKVKASRDVIIDQLPDLMGLKAVNVAKVVGTNPCADRNGGCSHLCFFTPHATRCGCPIGLELLSDMKTCIVPEAFLVFTSRAAIHRISLETNNNDVAIPLTGVKEASALDFDVSNNHIYWTDVSLKTISRAFMNGSSVEHVVEFGLDYPEGMAVDWMGKNLYWADTGTNRIEVARLDGQFRQVLVWRDLDNPRSLALDPTKGYIYWTEWGGKPRIVRAFMDGTNCMTLVDKVGRANDLTIDYADQRLYWTDLDTNMIESSNMLGQERVVIADDLPHPFGLTQYSDYIYWTDWNLHSIERADKTSGRNRTLIQGHLDFVMDILVFHSSRQDGLNDCMHNNGQCGQLCLAIPGGHRCGCASHYTLDPSSRNCSPPTTFLLFSQKSAISRMIPDDQHSPDLILPLHGLRNVKAIDYDPLDKFIYWVDGRQNIKRAKDDGTQPFVLTSLSQGQNPDRQPHDLSIDIYSRTLFWTCEATNTINVHRLSGEAMGVVLRGDRDKPRAIVVNAERGYLYFTNMQDRAAKIERAALDGTEREVLFTTGLIRPVALVVDNTLGKLFWVDADLKRIESCDLSGANRLTLEDANIVQPLGLTILGKHLYWIDRQQQMIERVEKTTGDKRTRIQGRVAHLTGIHAVEEVSLEEFSAHPCARDNGGCSHICIAKGDGTPRCSCPVHLVLLQNLLTCGEPPTCSPDQFACATGEIDCIPGAWRCDGFPECDDQSDEEGCPVCSAAQFPCARGQCVDLRLRCDGEADCQDRSDEADCDAICLPNQFRCASGQCVLIKQQCDSFPDCIDGSDELMCEITKPPSDDSPAHSSAIGPVIGIILSLFVMGGVYFVCQRVVCQRYAGANGPFPHEYVSGTPHVPLNFIAPGGSQHGPFTGIACGKSMMSSVSLMGGRGGVPLYDRNHVTGASSSSSSSTKATLYPPILNPPPSPATDPSLYNMDMFYSSNIPATARPYRPYIIRGMAPPTTPCSTDVCDSDYSASRWKASKYYLDLNSDSDPYPPPPTPHSQYLSAEDSCPPSPATERSYFHLFPPPPSPCTDSS,mutated_sequence,1.0,1615.0,UPI0000073246.a2m,UPI0000073246.npy,gnomAD +UPI000013E18E,UPI000013E18E.csv,MSDVSTSVQSKFARLAKKKENITYMKREQLTETDKDIAPVLDLKCKDVSAIMNKFKVLMEIQDLMFEEMRETLKNDLKAVLGGKATIPEVKNSENSSSRTEFQQIINLALQKTGMVGKIEGENSKIGDDNENLTFKLEVNELSGKLDNTNEYNSNDGKKLPQGESRSYEVMGSMEETLCNIDDRDGNRNVHLEFTERESRKDGEDEFVKEMREERKFQKLKNKEEVLKASREEKVLMDEGAVLTLVADLSSATLDISKQWSNVFNILRENDFEPKFLCEVKLAFKCDGEIKTFSDLQSLRKFASQKSSVKELLKDVLPQKEEINQGGRKYGIQEKRDKTLIDSKHRAGEITSDGLSFLFLKEVKVAKPEEMKNLETQEEEFSELEELDEEASGMEDDEDTSGLEEEEEEPSGLEEEEEEEASGLEEDEASGLEEEEEQTSEQDSTFQGHTLVDAKHEVEITSDGMETTFIDSVEDSESEEEEEGKSSETGKVKTTSLTEKKASRRQKEIPFSYLVGDSGKKKLVKHQVVHKTQEEEETAVPTSQGTGTPCLTLCLASPSKSLEMSHDEHKKHSHTNLSISTGVTKLKKTEEKKHRTLHTEELTSKEADLTEETEENLRSSVINSIREIKEEIGNLKSSHSGVLEIENSVDDLSSRMDILEERIDSLEDQIEEFSKDTMQMTKQIISKERQRDIEERSRSCNIRLIGIPEKESYENRAEDIIKEIIDENFAELKKGSSLEIVSACRVPSKIDEKRLTPRHILVKFWNSSDKEKIIRASRERREITYQGTRIRLTADLSLDTLDARSKWSNVFKVLLEKGFNPRILYPAKMAFDFRGKTKVFLSIEEFRDYVLHMPTLRELLGNNIP,mutated_sequence,1.0,865.0,UPI000013E18E.a2m,UPI000013E18E.npy,gnomAD +UPI00001C1F20,UPI00001C1F20.csv,MATEHPEPPKAELQLPPPPPPGHYGAWAAQELQAKLAEIGAPIQGNREELVERLQSYTRQTGIVLNRPVLRGEDGDKAAPPPMSAQLPGIPMPPPPLGLPPLQPPPPPPPPPPGLGLGFPMAHPPNLGPPPPLRVGEPVALSEEERLKLAQQQAALLMQQEERAKQQGDHSLKEHELLEQQKRAAVLLEQERQQEIAKMGTPVPRPPQDMGQIGVRTPLGPRVAAPVGPVGPTPTVLPMGAPVPRPRGPPPPPGDENREMDDPSVGPKIPQALEKILQLKESRQEEMNSQQEEEEMETDARSSLGQSASETEEDTVSVSKKEKNRKRRNRKKKKKPQRVRGVSSESSGDREKDSTRSRGSDSPAADVEIEYVTEEPEIYEPNFIFFKRIFEAFKLTDDVKKEKEKEPEKLDKLENSAAPKKKGFEEEHKDSDDDSSDDEQEKKPEAPKLSKKKLRRMNRFTVAELKQLVARPDVVEMHDVTAQDPKLLVHLKATRNSVPVPRHWCFKRKYLQGKRGIEKPPFELPDFIKRTGIQEMREALQEKEEQKTMKSKMREKVRPKMGKIDIDYQKLHDAFFKWQTKPKLTIHGDLYYEGKEFETRLKEKKPGDLSDELRISLGMPVGPNAHKVPPPWLIAMQRYGPPPSYPNLKIPGLNSPIPESCSFGYHAGGWGKPPVDETGKPLYGDVFGTNAAEFQTKTEEEEIDRTPWGELEPSDEESSEEEEEEESDEDKPDETGFITPADSGLITPGGFSSVPAGMETPELIELRKKKIEEAMDGSETPQLFTVLPEKRTATVGGAMMGSTHIYDMSTVMSRKGPAPELQGVEVALAPEELELDPMAMTQKYEEHVREQQAQVEKEDFSDMVAEHAAKQKQKKRKAQPQDSRGGSKKYKEFKF,mutated_sequence,1.0,895.0,UPI00001C1F20.a2m,UPI00001C1F20.npy,gnomAD +UPI0000185F04,UPI0000185F04.csv,MKGMSHEPKSPSLGMLSTATRTTATVNPLTPSPLNGALVPSGSPATSSALSAQAAPSSSFAAALRKLAKQAEEPRGSSLSSESSPVSSPATNHSSPASTPKRVPMGPIIVPPGGHSVPSTPPVVTIAPTKTVNGVWRSESRQDAGSRSSSGGRERLIVEPPLPQEKAGGPAIPSHLLSTPYPFGLSPSSVVQDSRFPPLNLQRPVHHVVPPSTVTEDYLRSFRPYHTTDDLRMSSLPPLGLDPATAAAYYHPSYLAPHPFPHPAFRMDDSYCLSALRSPFYPIPTPGSLPPLHPSAMHLHLSGVRYPPELSHSSLAALHSERMSGLSAERLQMDEELRREREREREREREREADREREKEREREREKEREQEKEREREKERERELERQREQRAREKELLAAKALEPSFLPVAELHGLRGHATEERGKPSEQLTPTRAEKLKDAGLQAPKPVQHPLHPVPTPHHTVPSLISNHGIFSLPSSSAATALLIQRTNEEEKWLARQRRLRQEKEDRQSQVSEFRQQVLEQHLDMGRPPVPAEAEHRPESTTRPGPNRHEPGGRDPPQHFGGPPPLISPKPQLHAAPTALWNPVSLMDNTLETRRAESHSLHSHPAAFEPSRQAAVPLVKVERVFCPEKAEEGPRKREPAPLDKYQPPPPPPREGGSLEHQPFLPGPGPFLAELEKSTQTILGQQRASLPQAATFGELSGPLKPGSPYRPPVPRAPDPAYIYDEFLQQRRRLVSKLDLEERRRREAQEKGYYYDLDDSYDESDEEEVRAHLRCVAEQPPLKLDTSSEKLEFLQLFGLTTQQQKEELVAQKRRKRRRMLRERSPSPPTIQSKRQTPSPRLALSTRYSPDEMNNSPNFEEKKKFLTIFNLTHISAEKRKDKERLVEMLRAMKQKALSAAVADSLTNSPRDSPAVSLSEPATQQASLDVEKPVGVAASLSDIPKAAEPGKLEQVRPQELSRVQELAPASGEKARLSEAPGGKKSLSMLHYIRGAAPKDIPVPLSHSTNGKSKPWEPFVAEEFAHQFHESVLQSTQKALQKHKGSVAVLSAEQNHKVDTSVHYNIPELQSSSRAPPPQHNGQQEPPTARKGPPTQELDRDSEEEEEEDDEDGEDEEEVPKRKWQGIEAVFEAYQEHIEEQNLERQVLQTQCRRLEARHYSLSLTAEQLSHSVAELRSQKQKMVSERERLQAELDHLRKCLALPAMHWPRGYLKGYPR,mutated_sequence,1.0,1217.0,UPI0000185F04.a2m,UPI0000185F04.npy,gnomAD +UPI000004A2BE,UPI000004A2BE.csv,MARMGLAGAAGRWWGLALGLTAFFLPGVHSQVVQVNDSMYGFIGTDVVLHCSFANPLPSVKITQVTWQKSTNGSKQNVAIYNPSMGVSVLAPYRERVEFLRPSFTDGTIRLSRLELEDEGVYICEFATFPTGNRESQLNLTVMAKPTNWIEGTQAVLRAKKGQDDKVLVATCTSANGKPPSVVSWETRLKGEAEYQEIRNPNGTVTVISRYRLVPSREAHQQSLACIVNYHMDRFKESLTLNVQYEPEVTIEGFDGNWYLQRMDVKLTCKADANPPATEYHWTTLNGSLPKGVEAQNRTLFFKGPINYSLAGTYICEATNPIGTRSGQVEVNITEFPYTPSPPEHGRRAGPVPTAIIGGVAGSILLVLIVVGGIVVALRRRRHTFKGDYSTKKHVYGNGYSKAGIPQHHPPMAQNLQYPDDSDDEKKAGPLGGSSYEEEEEEEEGGGGGERKVGGPHPKYDEDAKRPYFTVDEAEARQDGYGDRTLGYQYDPEQLDLAENMVSQNDGSFISKKEWYV,mutated_sequence,1.0,517.0,UPI000004A2BE.a2m,UPI000004A2BE.npy,gnomAD +UPI0000127442,UPI0000127442.csv,MSDASLRSTSTMERLVARGTFPVLVRTSACRSLFGPVDHEELSRELQARLAELNAEDQNRWDYDFQQDMPLRGPGRLQWTEVDSDSVPAFYRETVQVGRCRLLLAPRPVAVAVAVSPPLEPAAESLDGLEEAPEQLPSVPVPAPASTPPPVPVLAPAPAPAPAPVAAPVAAPVAVAVLAPAPAPAPAPAPAPAPVAAPAPAPAPAPAPAPAPAPAPDAAPQESAEQGANQGQRGQEPLADQLHSGISGRPAAGTAAASANGAAIKKLSGPLISDFFAKRKRSAPEKSSGDVPAPCPSPSAAPGVGSVEQTPRKRLR,mutated_sequence,1.0,316.0,UPI0000127442.a2m,UPI0000127442.npy,gnomAD +UPI000013CDEE,UPI000013CDEE.csv,MSSPTTSSLDTPLPGNGPPQPGAPSSSPTVKEEGPEPWPGGPDPDVPGTDEASSACSTDWVIPDPEEEPERKRKKGPAPKMLGHELCRVCGDKASGFHYNVLSCEGCKGFFRRSVVRGGARRYACRGGGTCQMDAFMRRKCQQCRLRKCKEAGMREQCVLSEEQIRKKKIRKQQQESQSQSQSPVGPQGSSSSASGPGASPGGSEAGSQGSGEGEGVQLTAAQELMIQQLVAAQLQCNKRSFSDQPKVTPWPLGADPQSRDARQQRFAHFTELAIISVQEIVDFAKQVPGFLQLGREDQIALLKASTIEIMLLETARRYNHETECITFLKDFTYSKDDFHRAGLQVEFINPIFEFSRAMRRLGLDDAEYALLIAINIFSADRPNVQEPGRVEALQQPYVEALLSYTRIKRPQDQLRFPRMLMKLVSLRTLSSVHSEQVFALRLQDKKLPPLLSEIWDVHE,mutated_sequence,1.0,460.0,UPI000013CDEE.a2m,UPI000013CDEE.npy,gnomAD +UPI00001D7B21,UPI00001D7B21.csv,MSVGELYSQCTRVWIPDPDEVWRSAELTKDYKEGDKSLQLRLEDETILEYPIDVQRNQLPFLRNPDILVGENDLTALSYLHEPAVLHNLKVRFLESNHIYTYCGIVLVAINPYEQLPIYGQDVIYTYSGQNMGDMDPHIFAVAEEAYKQMARDEKNQSIIVSGESGAGKTVSAKYAMRYFATVGGSASETNIEEKVLASSPIMEAIGNAKTTRNDNSSRFGKYIQIGFDKRYHIIGANMRTYLLEKSRVVFQADDERNYHIFYQLCAAAGLPEFKELALTSAEDFFYTSQGGDTSIEGVDDAEDFEKTRQAFTLLGVKESHQMSIFKIIASILHLGSVAIQAERDGDSCSISPQDVYLSNFCRLLGVEHSQMEHWLCHRKLVTTSETYVKTMSLQQVINARNALAKHIYAQLFGWIVEHINKALHTSLKQHSFIGVLDIYGFETFEVNSFEQFCINYANEKLQQQFNSHVFKLEQEEYMKEQIPWTLIDFYDNQPCIDLIEAKLGILDLLDEECKVPKGTDQNWAQKLYDRHSSSQHFQKPRMSNTAFIIVHFADKVEYLSDGFLEKNRDTVYEEQINILKASKFPLVADLFHDDKDPVPATTPGKGSSSKISVRSARPPMKVSNKEHKKTVGHQFRTSLHLLMETLNATTPHYVRCIKPNDEKLPFHFDPKRAVQQLRACGVLETIRISAAGYPSRWAYHDFFNRYRVLVKKRELANTDKKAICRSVLENLIKDPDKFQFGRTKIFFRAGQVAYLEKLRADKFRTATIMIQKTVRGWLQKVKYHRLKGATLTLQRYCRGHLARRLAEHLRRIRAAVVLQKHYRMQRARQAYQRVRRAAVVIQAFTRAMFVRRTYRQVLMEHKATTIQKHVRGWMARRHFQRLRDAAIVIQCAFRMLKARRELKALRIEARSAEHLKRLNVGMENKVVQLQRKIDEQNKEFKTLSEQLSVTTSTYTMEVERLKKELVHYQQSPGEDTSLRLQEEVESLRTELQRAHSERKILEDAHSREKDELRKRVADLEQENALLKDEKEQLNNQILCQSKDEFAQNSVKENLMKKELEEERSRYQNLVKEYSQLEQRYDNLRDEMTIIKQTPGHRRNPSNQSSLESDSNYPSISTSEIGDTEDALQQVEEIGLEKAAMDMTVFLKLQKRVRELEQERKKLQVQLEKREQQDSKKVQAEPPQTDIDLDPNADLAYNSLKRQELESENKKLKNDLNELRKAVADQATQNNSSHGSPDSYSLLLNQLKLAHEELEVRKEEVLILRTQIVSADQRRLAGRNAEPNINARSSWPNSEKHVDQEDAIEAYHGVCQTNSKTEDWGYLNEDGELGLAYQGLKQVARLLEAQLQAQSLEHEEEVEHLKAQLEALKEEMDKQQQTFCQTLLLSPEAQVEFGVQQEISRLTNENLDLKELVEKLEKNERKLKKQLKIYMKKAQDLEAAQALAQSERKRHELNRQVTVQRKEKDFQGMLEYHKEDEALLIRNLVTDLKPQMLSGTVPCLPAYILYMCIRHADYTNDDLKVHSLLTSTINGIKKVLKKHNDDFEMTSFWLSNTCRLLHCLKQYSGDEGFMTQNTAKQNEHCLKNFDLTEYRQVLSDLSIQIYQQLIKIAEGVLQPMIVSAMLENESIQGLSGVKPTGYRKRSSSMADGDNSYCLEAIIRQMNAFHTVMCDQGLDPEIILQVFKQLFYMINAVTLNNLLLRKDVCSWSTGMQLRYNISQLEEWLRGRNLHQSGAVQTMEPLIQAAQLLQLKKKTQEDAEAICSLCTSLSTQQIVKILNLYTPLNEFEERVTVAFIRTIQAQLQERNDPQQLLLDAKHMFPVLFPFNPSSLTMDSIHIPACLNLEFLNEV,mutated_sequence,1.0,1848.0,UPI00001D7B21.a2m,UPI00001D7B21.npy,gnomAD +UPI000013F27D,UPI000013F27D.csv,MDARRMKKEGLTENTGLPRKLLEKHDPWPAYVTYTSQTVKRLIEKSKTRELECMRALEERPWASRQNKPSSVIQPKRRKSSKSSGKAVFRDTLSESTLSMWGAYSVLAMAPTMIPEPTHLHADSRDCPTENYNKIIFARKPMMRMLPTVRY,mutated_sequence,1.0,151.0,UPI000013F27D.a2m,UPI000013F27D.npy,gnomAD +UPI0000037B54,UPI0000037B54.csv,MALRSRFWGLFSVCRNPGCRFAALSTSSEPAAKPEVDPVENEAVAPEFTNRNPRNLELLSVARKERGWRTVFPSREFWHRLRVIRTQHHVEALVEHQNGKVVVSASTREWAIKKHLYSTRNVVACESIGRVLAQRCLEAGINFMVYQPTPWEAASDSMKRLQSAMTEGGVVLREPQRIYE,mutated_sequence,1.0,180.0,UPI0000037B54.a2m,UPI0000037B54.npy,gnomAD +UPI0000E0A787,UPI0000E0A787.csv,MFYGTHFIMSPPTKSKLKRQSQLLSSMLSRTLSYKYRDLDSTFSSLGASDDPAELSTQLSAPGVLKVFGDSVCTGTHYKSVLATGTSSARELVKEALERYALDPRQAGQYVLCDVVGQAGDAGQRWQARCFRVFGDSEKPLLIQELWKPREGLSRRFELRKRSDVEELAAKEVDTITAGINAQARRLQRSRAKGTPTPALGDARSSPPPRLRRTVSETSLSPVNALPAAAQGPEEPGPDAMRYSLYQSPHLLLLQGYSQQHDSLVYVLNRDRHTVGQRTPSSKPSISLSAPDILPLHCTIRRQPLPDSGQAAGRLVLEPIPGAHISVNFSEVGHRTVVLHHGDLLSLGLYYLLLFKDPAQAQPLPARALARLRAVPQSCRLCGAALGARGAASPTQAALPRRQQLLLEFEPHLEDTLLQRIMTLIEPGGDDHKLTPAFLLCLCIQHSATHFQPGTFGQLLLKIARLIRETVWEKTKELAEKQAQLQEPISLASCAMADLVPDLQPILFWMSNSIELLYFIQQKCPLYMQSMEEQLDITGSKESLFSCTLTASEEAMAVLEEVVLYAFQQCVYYVSKSLYICLPALLECPPFQTERRESWSSAPELPEELRRVVSVYQAALDLLRQLQVHPEVASQMLAYLFFFSGTLLLNQLLDRGPSLSCFHWPRGVQACARLQQLLEWMRSAGFGAAGEHFFQKLSCTLNLLATPRAQLIQMSWTALRAAFPALSPAQLHRLLTHYQLASAMGPMSTWEPGAQDSPEAFRSEDVLESYENPPPIVLPSDGFQVDLEANCLDDSIYQHLLYVRHFLWGLRSRASPGSPGRPGSGASQPVCPEGMHHVVLDGHLEAPSCPLAPRDPGPAAREVAPERTLPLRGAPWAQAPPGRQPSRGGSQAGPPHTDSSCLLTPPSTPLGPEPGDPDWPESGGPCGKALPERQRNGLSGLRGAAPEGDSAALAEESPPAPSSRSSSTEDFCYVFTVELERGPSGLGMGLIDGMHTHLGAPGLYIQTLLPGSPAAADGRLSLGDRILEVNGSSLLGLGYLRAVDLIRHGGKKMRFLVAKSDVETAKKIHFRTPPL,mutated_sequence,1.0,1075.0,UPI0000E0A787.a2m,UPI0000E0A787.npy,gnomAD +UPI00001C1FDF,UPI00001C1FDF.csv,MKEWKSKMEISEEKKSARAASEKLQRQITQECELVETSNSEDRLLKHWVSPLKDAMRHLPSQESGIREMHIIPQKAIVGEIGHGCNEGEKILSAGESSHRYEVSGQNFKQKSGLTEHQKIHNINKTYECKECEKTFNRSSNLIIHQRIHTGNKPYVCNECGKDSNQSSNLIIHQRIHTGKKPYICHECGKDFNQSSNLVRHKQIHSGGNPYECKECGKAFKGSSNLVLHQRIHSRGKPYLCNKCGKAFSQSTDLIIHHRIHTGEKPYECYDCGQMFSQSSHLVPHQRIHTGEKPLKCNECEKAFRQHSHLTEHQRLHSGEKPYECHRCGKTFSGRTAFLKHQRLHAGEKIEECEKTFSKDEELREEQRIHQEEKAYWCNQCGRNFQGTSDLIRHQVTHTGEKPYECKECGKTFNQSSDLLRHHRIHSGEKPCVCSKCGKSFRGSSDLIRHHRVHTGEKPYECSECGKAFSQRSHLVTHQKIHTGEKPYQCTECGKAFRRRSLLIQHRRIHSGEKPYECKECGKLFIWRTAFLKHQSLHTGEKLECEKTFSQDEELRGEQKIHQEAKAYWCNQCGRAFQGSSDLIRHQVTHTREKPYECKECGKTFNQSSDLLRHHRIHSGEKPYVCNKCGKSFRGSSDLIKHHRIHTGEKPYECSECGKAFSQRSHLATHQKIHTGEKPYQCSECGNAFRRRSLLIQHRRLHSGEKPYECKECGKLFMWHTAFLKHQRLHAGEKLEECEKTFSKDEELRKEQRTHQEKKVYWCNQCSRTFQGSSDLIRHQVTHTREKPYECKECGKTQSELRPSETS,mutated_sequence,1.0,807.0,UPI00001C1FDF.a2m,UPI00001C1FDF.npy,gnomAD +UPI00002004B5,UPI00002004B5.csv,MPKLQGFEFWSRTLRGARHVVAPMVDQSELAWRLLSRRHGAQLCYTPMLHAQVFVRDANYRKENLYCEVCPEDRPLIVQFCANDPEVFVQAALLAQDYCDAIDLNLGCPQMIAKRGHYGAFLQDEWDLLQRMILLAHEKLSVPVTCKIRVFPEIDKTVRYAQMLEKAGCQLLTVHGRTKEQKGPLSGAASWEHIKAVRKAVAIPVFANGNIQCLQDVERCLRDTGVQGVMSAEGNLHNPALFEGRSPAVWELAEEYLDIVREHPCPLSYVRAHLFKLWHHTLQVHQELREELAKVKTLEGIAAVSQELKLRCQEEISRQEGAKPTGDLPFHWICQPYIRPGPREGSKEKAGARSKRALEEEEGGTEVLSKNKQKKQLRNPHKTFDPSLKPKYAKCDQCGNPKGNRCVFSLCRGCCKKRASKETADCPGHGLLFKTKLEKSLAWKEAQPELQEPQPAAPGTPGGFSEVMGSALA,mutated_sequence,1.0,473.0,UPI00002004B5.a2m,UPI00002004B5.npy,gnomAD +UPI0000041DD3,UPI0000041DD3.csv,MSDSNLSDNHLPDTFFLTGIPGLEAAHFWIAIPFCAMYLVALVGNAALILVIAMDNALHAPMYLFLCLLSLTDLALSSTTVPKMLAILWLHAGEISFGGCLAQMFCVHSIYALESSILLAMAFDRYVAICNPLRYTTILNHAVIGRIGFVGLFRSVAIVSPFIFLLRRLPYCGHRVMTHTYCEHMGIARLACANITVNIVYGLTVALLAMGLDSILIAISYGFILHAVFHLPSHDAQHKALSTCGSHIGIILVFYIPAFFSFLTHRFGHHEVPKHVHIFLANLYVLVPPVLNPILYGARTKEIRSRLLKLLHLGKTSI,mutated_sequence,1.0,318.0,UPI0000041DD3.a2m,UPI0000041DD3.npy,gnomAD +UPI0000073ADE,UPI0000073ADE.csv,MEPAGPAPGRLGPLLCLLLAASCAWSGVAGEEELQVIQPDKSVLVAAGETATLRCTATSLIPVGPIQWFRGAGPGRELIYNQKEGHFPRVTTVSDLTKRNNMDFSIRIGNITPADAGTYYCVKFRKGSPDDVEFKSGAGTELSVRAKPSAPVVSGPAARATPQHTVSFTCESHGFSPRDITLKWFKNGNELSDFQTNVDPVGESVSYSIHSTAKVVLTREDVHSQVICEVAHVTLQGDPLRGTANLSETIRVPPTLEVTQQPVRAENQVNVTCQVRKFYPQRLQLTWLENGNVSRTETASTVTENKDGTYNWMSWLLVNVSAHRDDVKLTCQVEHDGQPAVSKSHDLKVSAHPKEQGSNTAAENTGSNERNIYIVVGVVCTLLVALLMAALYLVRIRQKKAQGSTSSTRLHEPEKNAREITQDTNDITYADLNLPKGKKPAPQAAEPNNHTEYASIQTSPQPASEDTLTYADLDMVHLNRTPKQPAPKPEPSFSEYASVQVPRK,mutated_sequence,1.0,504.0,UPI0000073ADE.a2m,UPI0000073ADE.npy,gnomAD +UPI000013DE11,UPI000013DE11.csv,MVTWLYRFLPTSNMAAKLRSLLPPDLRLQFWLHARLQKCFLSRGCGSYCAGAKASPLPGKMAMGLMCGRRELLRLLQSGRRVHSVAGPSQWLGKPLTTRLLFPAAPCCCRPHYLFLAASGPRSLSTSAISFAEVQVQAPPVVAATPSPTAVPEVASGETADVVQTAAEQSFAELGLGSYTPVGLIQNLLEFMHVDLGLPWWGAIAACTVFARCLIFPLIVTGQREAARIHNHLPEIQKFSSRIREAKLAGDHIEYYKASSEMALYQKKHGIKLYKPLILPVTQAPIFISFFIALREMANLPVPSLQTGGLWWFQDLTVSDPIYILPLAVTATMWAVLELGAETGVQSSDLQWMRNVIRMMPLITLPITMHFPTAVFMYWLSSNLFSLVQVSCLRIPAVRTVLKIPQRVVHDLDKLPPREGFLESFKKGWKNAEMTRQLREREQRMRNQLELAARGPLRQTFTHNPLLQPGKDNPPNIPSSSSKPKSKYPWHDTLG,mutated_sequence,1.0,495.0,UPI000013DE11.a2m,UPI000013DE11.npy,gnomAD +UPI00015E09B8,UPI00015E09B8.csv,SRRRSKSSRRSSRRSSRRSRSKRSRSRRRSKSSRRSSRRSRRRSRTRRSRSSSRRSSTNRSSSK,mutated_sequence,,,UPI00015E09B8.a2m,UPI00015E09B8.npy,gnomAD +UPI0000049802,UPI0000049802.csv,MANASEPGGSGGGEAAALGLKLATLSLLLCVSLAGNVLFALLIVRERSLHRAPYYLLLDLCLADGLRALACLPAVMLAARRAAAAAGAPPGALGCKLLAFLAALFCFHAAFLLLGVGVTRYLAIAHHRFYAERLAGWPCAAMLVCAAWALALAAAFPPVLDGGGDDEDAPCALEQRPDGAPGALGFLLLLAVVVGATHLVYLRLLFFIHDRRKMRPARLVPAVSHDWTFHGPGATGQAAANWTAGFGRGPTPPALVGIRPAGPGRGARRLLVLEEFKTEKRLCKMFYAVTLLFLLLWGPYVVASYLRVLVRPGAVPQAYLTASVWLTFAQAGINPVVCFLFNRELRDCFRAQFPCCQSPRTTQATHPCDLKGIGL,mutated_sequence,1.0,375.0,UPI0000049802.a2m,UPI0000049802.npy,gnomAD +UPI000013DA0D,UPI000013DA0D.csv,MRLTHICCCCLLYQLGFLSNGIVSELQFAPDREEWEVVFPALWRREPVDPAGGSGGSADPGWVRGVGGGGSARAQAAGSSREVRSVAPVPLEEPVEGRSESRLRPPPPSEGEEDEELESQELPRGSSGAAALSPGAPASWQPPPPPQPPPSPPPAQHAEPDGDEVLLRIPAFSRDLYLLLRRDGRFLAPRFAVEQRPNPGPGPTGAASAPQPPAPPDAGCFYTGAVLRHPGSLASFSTCGGGLMGFIQLNEDFIFIEPLNDTMAITGHPHRVYRQKRSMEEKVTEKSALHSHYCGIISDKGRPRSRKIAESGRGKRYSYKLPQEYNIETVVVADPAMVSYHGADAARRFILTILNMVFNLFQHKSLSVQVNLRVIKLILLHETPPELYIGHHGEKMLESFCKWQHEEFGKKNDIHLEMSTNWGEDMTSVDAAILITRKDFCVHKDEPCDTVGIAYLSGMCSEKRKCIIAEDNGLNLAFTIAHEMGHNMGINHDNDHPSCADGLHIMSGEWIKGQNLGDVSWSRCSKEDLERFLRSKASNCLLQTNPQSVNSVMVPSKLPGMTYTADEQCQILFGPLASFCQEMQHVICTGLWCKVEGEKECRTKLDPPMDGTDCDLGKWCKAGECTSRTSAPEHLAGEWSLWSPCSRTCSAGISSRERKCPGLDSEARDCNGPRKQYRICENPPCPAGLPGFRDWQCQAYSVRTSSPKHILQWQAVLDEEKPCALFCSPVGKEQPILLSEKVMDGTSCGYQGLDICANGRCQKVGCDGLLGSLAREDHCGVCNGNGKSCKIIKGDFNHTRGAGYVEVLVIPAGARRIKVVEEKPAHSYLALRDAGKQSINSDWKIEHSGAFNLAGTTVHYVRRGLWEKISAKGPTTAPLHLLVLLFQDQNYGLHYEYTIPSDPLPENQSSKAPEPLFMWTHTSWEDCDATCGGGERKTTVSCTKIMSKNISIVDNEKCKYLTKPEPQIRKCNEQPCQTRWMMTEWTPCSRTCGKGMQSRQVACTQQLSNGTLIRARERDCIGPKPASAQRCEGQDCMTVWEAGVWSECSVKCGKGIRHRTVRCTNPRKKCVLSTRPREAEDCEDYSKCYVWRMGDWSKCSITCGKGMQSRVIQCMHKITGRHGNECFSSEKPAAYRPCHLQPCNEKINVNTITSPRLAALTFKCLGDQWPVYCRVIREKNLCQDMRWYQRCCETCRDFYAQKLQQKS,mutated_sequence,1.0,1207.0,UPI000013DA0D.a2m,UPI000013DA0D.npy,gnomAD +UPI0000374562,UPI0000374562.csv,MLLLLSDQLLLTALRKPNPQAMAALFLSAPPQAEVTFEDVAVYLSREEWGRLGPAQRGLYRDVMLETYGNLVSLGVGPAGPKPGVISQLERGDEPWVLDVQGTSGKEHLRVNSPALGTRTEYKELTSQETFGEEDPQGSEPVEACDHISKSEGSLEKLVEQRGPRAVTLTNGESSRESGGNLRLLSRPVPDQRPHKCDICEQSFEQRSYLNNHKRVHRSKKTNTVRNSGEIFSANLVVKEDQKIPTGKKLHYCSYCGKTFRYSANLVKHQRLHTEEKPYKCDECGKAFSQSCEFINHRRMHSGEIPYRCDECGKTFTRRPNLMKHQRIHTGEKPYKCGECGKHFSAYSSLIYHQRIHTGEKPYKCNDCGKAFSDGSILIRHRRTHTGEKPFECKECGKGFTQSSNLIQHQRIHTGEKPYKCNECEKAFIQKTKLVEHQRSHTGEKPYECNDCGKVFSQSTHLIQHQRIHTGEKPYKCSECGKAFHNSSRLIHHQRLHHGEKPYRCSDCKKAFSQSTYLIQHRRIHTGEKPYKCSECGKAFRHSSNMCQHQRIHLREDFSM,mutated_sequence,1.0,560.0,UPI0000374562.a2m,UPI0000374562.npy,gnomAD +UPI000013CCD8,UPI000013CCD8.csv,MSRRKQAKPQHFQSDPEVASLPRRDGDTEKGQPSRPTKSKDAHVCGRCCAEFFELSDLLLHKKNCTKNQLVLIVNENPASPPETFSPSPPPDNPDEQMNDTVNKTDQVDCSDLSEHNGLDREESMEVEAPVANKSGSGTSSGSHSSTAPSSSSSSSSSSGGGGSSSTGTSAITTSLPQLGDLTTLGNFSVINSNVIIENLQSTKVAVAQFSQEARCGGASGGKLAVPALMEQLLALQQQQIHQLQLIEQIRHQILLLASQNADLPTSSSPSQGTLRTSANPLSTLSSHLSQQLAAAAGLAQSLASQSASISGVKQLPPIQLPQSSSGNTIIPSNSGSSPNMNILAAAVTTPSSEKVASSAGASHVSNPAVSSSSSPAFAISSLLSPASNPLLPQQASANSVFPSPLPNIGTTAEDLNSLSALAQQRKSKPPNVTAFEAKSTSDEAFFKHKCRFCAKVFGSDSALQIHLRSHTGERPFKCNICGNRFSTKGNLKVHFQRHKEKYPHIQMNPYPVPEHLDNIPTSTGIPYGMSIPPEKPVTSWLDTKPVLPTLTTSVGLPLPPTLPSLIPFIKTEEPAPIPISHSATSPPGSVKSDSGGPESATRNLGGLPEEAEGSTLPPSGGKSEESGMVTNSVPTASSSVLSSPAADCGPAGSATTFTNPLLPLMSEQFKAKFPFGGLLDSAQASETSKLQQLVENIDKKATDPNECIICHRVLSCQSALKMHYRTHTGERPFKCKICGRAFTTKGNLKTHYSVHRAMPPLRVQHSCPICQKKFTNAVVLQQHIRMHMGGQIPNTPVPDSYSESMESDTGSFDEKNFDDLDNFSDENMEDCPEGSIPDTPKSADASQDSLSSSPLPLEMSSIAALENQMKMINAGLAEQLQASLKSVENGSIEGDVLTNDSSSVGGDMESQSAGSPAISESTSSMQALSPSNSTQEFHKSPSIEEKPQRAVPSEFANGLSPTPVNGGALDLTSSHAEKIIKEDSLGILFPFRDRGKFKNTACDICGKTFACQSALDIHYRSHTKERPFICTVCNRGFSTKGNLKQHMLTHQMRDLPSQLFEPSSNLGPNQNSAVIPANSLSSLIKTEVNGFVHVSPQDSKDTPTSHVPSGPLSSSATSPVLLPALPRRTPKQHYCNTCGKTFSSSSALQIHERTHTGEKPFACTICGRAFTTKGNLKVHMGTHMWNSTPARRGRRLSVDGPMTFLGGNPVKFPEMFQKDLAARSGSGDPSSFWNQYAAALSNGLAMKANEISVIQNGGIPPIPGSLGSGNSSPVSGLTGNLERLQNSEPNAPLAGLEKMASSENGTNFRFTRFVEDSKEIVTS,mutated_sequence,1.0,1324.0,UPI000013CCD8.a2m,UPI000013CCD8.npy,gnomAD +UPI0000073D30,UPI0000073D30.csv,MSDSPAGSNPRTPESSGSGSGGGGKRPAVPAAVSLLPPADPLRQANRLPIRVLKMLSAHTGHLLHPEYLQPLSSTPVSPIELDAKKSPLALLAQTCSQIGKPDPPPSSKLNSVAAAANGLGAEKDPGRSAPGAASAAAALKQLGDSPAEDKSSFKPYSKGSGGGDSRKDSGSSSVSSTSSSSSSSPGDKAGFRVPSAACPPFPPHGAPVSASSSSSSPGGSRGGSPHHSDCKNGGGVGGGELDKKDQEPKPSPEPAAVSRGGGGEPGAHGGAESGASGRKSEPPSALVGAGHVAPVSPYKPGHSVFPLPPSSIGYHGSIVGAYAGYPSQFVPGLDPSKSGLVGGQLSGGLGLPPGKPPSSSPLTGASPPSFLQGLCRDPYCLGGYHGASHLGGSSCSTCSAHDPAGPSLKAGGYPLVYPGHPLQPAALSSSAAQAALPGHPLYTYGFMLQNEPLPHSCNWVAASGPCDKRFATSEELLSHLRTHTALPGAEKLLAAYPGASGLGSAAAAAAAAASCHLHLPPPAAPGSPGSLSLRNPHTLGLSRYHPYGKSHLSTAGGLAVPSLPTAGPYYSPYALYGQRLASASALGYQ,mutated_sequence,1.0,590.0,UPI0000073D30.a2m,UPI0000073D30.npy,gnomAD +UPI000015F97E,UPI000015F97E.csv,MHYDGHVRFDLPPQGSVLARNVSTRSCPPRTSPAVDLEEEEEESSVDGKGDRKSTGLKLSKKKARRRHTDDPSKECFTLKFDLNVDIETEIVPAMKKKSLGEVLLPVFERKGIALGKVDIYLDQSNTPLSLTFEAYRFGGHYLRVKAPAKPGDEGKVEQGMKDSKSLSLPILRPAGTGPPALERVDAQSRRESLDILAPGRRRKNMSEFLGEASIPGQEPPTPSSCSLPSGSSGSTNTGDSWKNRAASRFSGFFSSGPSTSAFGREVDKMEQLEGKLHTYSLFGLPRLPRGLRFDHDSWEEEYDEDEDEDNACLRLEDSWRELIDGHEKLTRRQCHQQEAVWELLHTEASYIRKLRVIINLFLCCLLNLQESGLLCEVEAERLFSNIPEIAQLHRRLWASVMAPVLEKARRTRALLQPGDFLKGFKMFGSLFKPYIRYCMEEEGCMEYMRGLLRDNDLFRAYITWAEKHPQCQRLKLSDMLAKPHQRLTKYPLLLKSVLRKTEEPRAKEAVVAMIGSVERFIHHVNACMRQRQERQRLAAVVSRIDAYEVVESSSDEVDKLLKEFLHLDLTAPIPGASPEETRQLLLEGSLRMKEGKDSKMDVYCFLFTDLLLVTKAVKKAERTRVIRPPLLVDKIVCRELRDPGSFLLIYLNEFHSAVGAYTFQASGQALCRGWVDTIYNAQNQLQQLRAQEPPGSQQPLQSLEEEEDEQEEEEEEEEEEEEGEDSGTSAASSPTIMRKSSGSPDSQHCASDGSTETLAMVVVEPGDTLSSPEFDSGPFSSQSDETSLSTTASSATPTSELLPLGPVDGRSCSMDSAYGTLSPTSLQDFVAPGPMAELVPRAPESPRVPSPPPSPRLRRRTPVQLLSCPPHLLKSKSEASLLQLLAGAGTHGTPSAPSRSLSELCLAVPAPGIRTQGSPQEAGPSWDCRGAPSPGSGPGLVGCLAGEPAGSHRKRCGDLPSGASPRVQPEPPPGVSAQHRKLTLAQLYRIRTTLLLNSTLTASEV,mutated_sequence,1.0,1006.0,UPI000015F97E.a2m,UPI000015F97E.npy,gnomAD +UPI00001D818D,UPI00001D818D.csv,MLRRGHLAFRDVAIEFPQEEWKCLDPAQRTLYREVMVENYRNLVFLGICLPDLSVISMLEQRRDPRNLQSEVKIANNPGGRECIKGVNAESSSKLGSNAGNKSLKNQLGLTFQLHLSELQLFQAERNISGCKHVEKPINNSLVSPLQKIYSSVKSHILNKYRNDFDDSPFLPQEQKAQIREKPCECNEHGKAFRVSSRLANNQVIHTADNPYKCNECDKVFSNSSNLVQHQRIHTGEKPYKCHECGKLFNRISLLARHQRIHTGEKPYKCHECGKVFTQNSHLANHHRIHTGEKPYKCNECGKVFNRNAHLARHQKIHSGEKPYKCKECGKAFSGGSGLTAHLVIHTGEKLYKCNKCGKVFNRNAHLTRHQRIHTGEKPYECKECGKVFRHKFCLTNHHRMHTGEQPYKCNECGKAFRDCSGLTAHLLIHTGEKPYKCKECAKVFRHRLSLSNHQRFHTGEKPYRCDECGKDFTRNSNLANHHRIHTGEKPYKCSECHKVFSHNSHLARHRQIHTGEKSYKCNECGKVFSHKLYLKKHERIHTGEKPYRCHECGKDFTRNSNLANHHRIHTGEKPYR,mutated_sequence,1.0,577.0,UPI00001D818D.a2m,UPI00001D818D.npy,gnomAD +UPI00001FEBF9,UPI00001FEBF9.csv,MRAVPLPAPLLPLLLLALLAAPAARASRAESVSAPWPEPERESRPPPGPGPGNTTRFGSGAAGGSGSSSSNSSGDALVTRISILLRDLPTLKAAVIVAFAFTTLLIACLLLRVFRSGKRLKKTRKYDIITTPAERVEMAPLNEEDDEDEDSTVFDIKYR,mutated_sequence,1.0,159.0,UPI00001FEBF9.a2m,UPI00001FEBF9.npy,gnomAD +UPI0000073E41,UPI0000073E41.csv,MAATLDLKSKEEKDAELDKRIEALRRKNEALIRRYQEIEEDRKKAELEGVAVTAPRKGRSVEKENVAVESEKNLGPSRRSPGTPRPPGASKGGRTPPQQGGRAGMGRASRSWEGSPGEQPRGGGAGGRGRRGRGRGSPHLSGAGDTSISDRKSKEWEERRRQNIEKMNEEMEKIAEYERNQREGVLEPNPVRNFLDDPRRRSGPLEESERDRREESRRHGRNWGGPDFERVRCGLEHERQGRRAGLGSAGDMTLSMTGRERSEYLRWKQEREKIDQERLQRHRKPTGQWRREWDAEKTDGMFKDGPVPAHEPSHRYDDQAWARPPKPPTFGEFLSQHKAEASSRRRRKSSRPQAKAAPRAYSDHDDRWETKEGAASPAPETPQPTSPETSPKETPMQPPEIPAPAHRPPEDEGEENEGEEDEEWEDISEDEEEEEIEVEEGDEEEPAQDHQAPEAAPTGIPCSEQAHGVPFSPEEPLLEPQAPGTPSSPFSPPSGHQPVSDWGEEVELNSPRTTHLAGALSPGEAWPFESV,mutated_sequence,1.0,531.0,UPI0000073E41.a2m,UPI0000073E41.npy,gnomAD +UPI0000246F73,UPI0000246F73.csv,MYQASAVSLLPRDIPSCHSPSPGFSHLPTSSSQLAPDLLQFPLGQDPSFLAIPILTLPPSDSLVPPYIVWYIVWPSALISFLGCTLTVQFSNGKLQSPGNMRFTLYENKDSTNPRKRNQRILAAETDRLSYVGNNFGTGALKCNTLCRHFVGILNKTSGQMEVYDAELFNMQPLFSDVSVESELALESQTKTYREKMDSCIEAFGTTKQKRALNTRRMNRVGNESLNRAVAKAAETIIDTKGVTALVSDAIHNDLQDDSLYLPPCYDDAAKPEDVYKFEDLLSPAEYEALQSPSEAFRNVTSEEILKMIEENSHCTFVIEALKSLPSDVESRDRQARCIWFLDTLIKFRAHRVVKRKSALGPGVPHIINTKLLKHFTCLTYNNGRLRNLISDSMKAKITAYVIILALHIHDFQIDLTVLQRDLKLSEKRMMEIAKAMRLKISKRRVSVAAGSEEDHKLGTLSLPLPPAQTSDRLAKRRKIT,mutated_sequence,1.0,481.0,UPI0000246F73.a2m,UPI0000246F73.npy,gnomAD +UPI0001642876,UPI0001642876.csv,MGNSYAGQLKSARFEEALHNSIEASLRCSSVVPRPIFSQLYLDPDQHPFSSADVKPKVEDLDKDLVNRYTQNGSLDFSNNLTVNEMEDDEDDEEMSDSNSPPIPYSQKPAPEGSCTTDGFCQAGKDLRLVSLCMEQIDIPAGFLLVGAKSPNLPEHILVCAVDKRFLPDDHGKNALLGFSGNCIGCGERGFRYFTEFSNHINLKLTTQPKKQKHLKYYLVRSSQGVLSKGPLICWKECRSRQSSASCHSIKPSSSVSSTVTPENGTTNGYKSGFTQTDAANGNSSHGGKGSASSSTPAHTGNYSLSPRPSYASGDQATMFISGPPKKRHRGWYPGSPLPQPGLVVPVPTVRPLSRTEPLLSAPVPQTPLTGILQPRPIPAGETVIVPENLLSNSGVRPVILIGYGTLPYFYGNVGDIVVSPLLVNCYKIPQLENKDLEKLGLTGSQFLSVENMILLTIQYLVRLGPDQVPLREEFEQIMLKAMQEFTLRERALQIGAQCVPVSPGQLPWLARLIASVSQDLVHVVVTQNSLAEGISETLRTLSEMRHYQRLPDYVVVICASKIRGNEFCVVVLGQHQSRALAESMLTTSEFLKEISYELITGKVSFLASHFKTTSLGDDLDKLLEKMQQRRGDSVVTPFDGDLNECVSPQEAAAMIPTQNLDLDNETFHIYQPQLTVARKLLSQVCAIADSGSQSLDLGHFSKVDFIIIVPRSEVLVQQTLQRIRQSGVLVDLGLEENGTAHQRAEKYVVRLDNEIQTKFEVFMRRVKQNPYTLFVLVHDNSHVELTSVISGSLSHSEPSHGLADRVINCREVLEAFNLLVLQVSSFPYTLQTQQSRISSSNEVHWIQLDTGEDVGCEEKLYFGLSEYSKSLQWGITSPLLRCDETFEKMVNTLLERYPRLHSMVVRCYLLIQQYSEALMALTTMASLRDHSTPETLSIMDDLISSPGKNKSGRGHMLIIRVPSVQLAMLAKERLQEVRDKLGLQYRFEIILGNPATELSVATHFVARLKSWRGNEPEEWIPRTYQDLDGLPCIVILTGKDPLGETFPRSLKYCDLRLIDSSYLTRTALEQEVGLACCYVSKEVIRGPTVALDLSGKEQERAAVSENDSDELLIDLERPQSNSSAVTGTSGSIMENGVSSSSTADKSQKQSLTPSFQSPATSLGLDEGVSASSAGAGAGETLKQECDSLGPQMASSTTSKPSSSSSGPRTLPWPGQPIRGCRGPQAALPPVVILSKAAYSLLGSQKSGKLPSSSSLLPHADVAWVSSLRPLLNKDMSSEEQSLYYRQWTLARQHHADYSNQLDPASGTRNFHPRRLLLTGPPQVGKTGSYLQFLRILFRMLIRLLEVDVYDEEEINTDHNESSEVSQSEGEPWPDIESFSKMPFDVSVHDPKYSLMSLVYTEKLAGVKQEVIKESKVEEPRKRETVSIMLTKYAAYNTFHHCEQCRQYMDFTSASQMSDSTLHAFTFSSSMLGEEVQLYFIIPKSKESHFVFSKQGKHLESMRLPLVSDKNLNAVKSPIFTPSSGRHEHGLLNLFHAMEGISHLHLLVVKEYEMPLYRKYWPNHIMLVLPGMFNNAGVGAARFLIKELSYHNLELERNRLEELGIKRQCVWPFIVMMDDSCVLWNIHSVQEPSSQPMEVGVSSKNVSLKTVLQHIEATPKIVHYAILGIQKWSSKLTSQSLKAPFSRCHVHDFILLNTDLTQNVQYDFNRYFCEDADFNLRTNSSGLLICRFNNFSLMKKHVQVGGQRDFIIKPKIMVSESLAPILPLQYICAPDSEHTLLAAPAQFLLEKFLQHASYKLFPKAIHNFRSPVLAIDCYLNIGPEVAICYISSRPHSSNVNCEGVFFSGLLLYLCDSFVGADLKKFKFLKGATLCVICQDRSSLRQTIVRLELEDEWQFRLRDEFQTANSSDDKPLYFLTGRHV,mutated_sequence,1.0,1923.0,UPI0001642876.a2m,UPI0001642876.npy,gnomAD +UPI00001D771D,UPI00001D771D.csv,MASNGAYPVLGPGVTVNPGTSLSVFTALPFATPAPGPAHRPPLVTAVVPPAGPLVLSAFPSTPLVAGQDGRGPSGAGASNVFVQMRTEVGPVKPPQAQTLILTQAPLVWQAPGTLCGGVMCPPPLLLAAAPGVPVTSAQVVGGTQACEGGWSHGLPLPPPPPAAQVAPIVSPGNARPWPQGAHGEGSLAPSQAKARPDDSCKPKSVYENFRLWQHYKPLARRHLPQSPDTEALSCFLIPVLRSLARRKPTMTLEEGLWQAMREWQHTSNFDRMIFYEMAEKFLEFEAEEEMQIQKSQWMKGPQSLPPPAPPRLEPRGPPAPEVVKQPVYLPSKDGPKAPTACLPPPRPQRPAETKAHLPPPRPQRPAETNAHLPPPRPQRPAETKVPEEIPPEVVQEYVDIMEELLGSHPGDTGEPEGQREKGKVEQPQEEDGITSDPGLLSYIDKLCSQEDFVTKVEAVIHPRFLEELLSPDPQMDFLALSQELEQEEGLTLAQLVEKRLLSLKEKGCGRAAPRHGTARLDSSPSEFAAGQEAAREVPDPQQRVSVETSPPQTAAQDPQGQGRVRTGMARSEDPAVLLGCQDSPRLKAVRPTSPPQDHRPTCPGLGTKDALGLPGESPVKESHGLAKGSSEETELPGMVYVVGSHHRLRPWRLSQSPVPSSGLLSPGGRGPQGALQSPSAQKRGLSPSPSPASKSKKRPLFGSPSPAEKTPHPGPGLRVSGEQSLAWGLGGPSQSQKRKGDPLASRRKKKRHCSQ,mutated_sequence,1.0,756.0,UPI00001D771D.a2m,UPI00001D771D.npy,gnomAD +UPI000013E4EC,UPI000013E4EC.csv,MDVDAEREKITQEIKELERILDPGSSGSHVEISESSLESDSEADSLPSEDLDPADPPISEEERWGEASNDEDDPKDKTLPEDPETCLQLNMVYQEVIQEKLAEANLLLAQNREQQEELMRDLAGSKGTKVKDGKSLPPSTYMGHFMKPYFKDKVTGVGPPANEDTREKAAQGIKAFEELLVTKWKNWEKALLRKSVVSDRLQRLLQPKLLKLEYLHQKQSKVSSELERQALEKQGREAEKEIQDINQLPEEALLGNRLDSHDWEKISNINFEGSRSAEEIRKFWQNSEHPSINKQEWSREEEERLQAIAAAHGHLEWQKIAEELGTSRSAFQCLQKFQQHNKALKRKEWTEEEDRMLTQLVQEMRVGSHIPYRRIVYYMEGRDSMQLIYRWTKSLDPGLKKGYWAPEEDAKLLQAVAKYGEQDWFKIREEVPGRSDAQCRDRYLRRLHFSLKKGRWNLKEEEQLIELIEKYGVGHWAKIASELPHRSGSQCLSKWKIMMGKKQGLRRRRRRARHSVRWSSTSSSGSSSGSSGGSSSSSSSSSEEDEPEQAQAGEGDRALLSPQYMVPDMDLWVPARQSTSQPWRGGAGAWLGGPAASLSPPKGSSASQGGSKEASTTAAAPGEETSPVQVPARAHGPVPRSAQASHSADTRPAGAEKQALEGGRRLLTVPVETVLRVLRANTAARSCTQKEQLRQPPLPTSSPGVSSGDSVARSHVQWLRHRATQSGQRRWRHALHRRLLNRRLLLAVTPWVGDVVVPCTQASQRPAVVQTQADGLREQLQQARLASTPVFTLFTQLFHIDTAGCLEVVRERKALPPRLPQAGARDPPVHLLQASSSAQSTPGHLFPNVPAQEASKSASHKGSRRLASSRVERTLPQASLLASTGPRPKPKTVSELLQEKRLQEARAREATRGPVVLPSQLLVSSSVILQPPLPHTPHGRPAPGPTVLNVPLSGPGAPAAAKPGTSGSWQEAGTSAKDKRLSTMQALPLAPVFSEAEGTAPAASQAPALGPGQISVSCPESGLGQSQAPAASRKQGLPEAPPFLPAAPSPTPLPVQPLSLTHIGGPHVATSVPLPVTWVLTAQGLLPVPVPAVVSLPRPAGTPGPAGLLATLLPPLTETRAAQGPRAPALSSSWQPPANMNREPEPSCRTDTPAPPTHALSQSPAEADGSVAFVPGEAQVAREIPEPRTSSHADPPEAEPPWSGRLPAFGGVIPATEPRGTPGSPSGTQEPRGPLGLEKLPLRQPGPEKGALDLEKPPLPQPGPEKGALDLGLLSQEGEAATQQWLGGQRGVRVPLLGSRLPYQPPALCSLRALSGLLLHKKALEHKATSLVVGGEAERPAGALQASLGLVRGQLQDNPAYLLLRARFLAAFTLPALLATLAPQGVRTTLSVPSRVGSESEDEDLLSELELADRDGQPGCTTATCPIQGAPDSGKCSASSCLDTSNDPDDLDVLRTRHARHTRKRRRLV,mutated_sequence,1.0,1469.0,UPI000013E4EC.a2m,UPI000013E4EC.npy,gnomAD +UPI00001C1FA8,UPI00001C1FA8.csv,MARAGSCGGAAAGAGRPEPWELSLEEVLKAYEQPLNEEQAWAVCFQGCRGLRGSPGRRLRDTGDLLLRGDGSVGAREPEAAEPATMVVPLASSEAQTVQSLGFAIYRALDWGLDESEERELSPQLERLIDLMANNDSEDSGCGAADEGYGGPEEEEEAEGVPRSVRTFAQAMRLCAARLTDPRGAQAHYQAVCRALFVETLELRAFLARVREAKEMLQKLREDEPHLETPRAELDSLGHTDWARLWVQLMRELRRGVKLKKVQEQEFNPLPTEFQLTPFEMLMQDIRARNYKLRKVMVDGDIPPRVKKDAHELILDFIRSRPPLKQVSERRLRPLPPKQRSLHEKILEEIKQERRLRPVRGEGWAARGFGSLPCILNACSGDAKSTSCINLSVTDAGGSAQRPRPRVLLKAPTLAEMEEMNTSEEEESPCGEVTLKRDRSFSEHDLAQLRSEVASGLQSATHPPGGTEPPRPRAGSAHVWRPGSRDQGTCPASVSDPSHPLLSNRGSSGDRPEASMTPDAKHLWLEFSHPVESLALTVEEVMDVRRVLVKAEMEKFLQNKELFSSLKKGKICCCCRAKFPLFSWPPSCLFCKRAVCTSCSIKMKMPSKKFGHIPVYTLGFESPQRVSAAKTAPIQRRDIFQSLQGPQWQSVEEAFPHIYSHGCVLKDVCSECTSFVADVVRSSRKSVDVLNTTPRRSRQTQSLYIPNTRTLDFK,mutated_sequence,1.0,714.0,UPI00001C1FA8.a2m,UPI00001C1FA8.npy,gnomAD +UPI0000458A54,UPI0000458A54.csv,MLRRVTVAAVCATRRKLCEAGRELAALWGIETRGRCEDSAAARPFPILAMPGRNKAKSTCSCPDLQPNGQDLGENSRVARLGADESEEEGRRGSLSNAGDPEIVKSPSDPKQYRYIKLQNGLQALLISDLSNMEGKTGNTTDDEEEEEVEEEEEDDDEDSGAEIEDDDEEGFDDEDEFDDEHDDDLDTEDNELEELEERAEARKKTTEKQSAAALCVGVGSFADPDDLPGLAHFLEHMVFMGSLKYPDENGFDAFLKKHGGSDNASTDCERTVFQFDVQRKYFKEALDRWAQFFIHPLMIRDAIDREVEAVDSEYQLARPSDANRKEMLFGSLARPGHPMGKFFWGNAETLKHEPRKNNIDTHARLREFWMRYYSSHYMTLVVQSKETLDTLEKWVTEIFSQIPNNGLPRPNFGHLTDPFDTPAFNKLYRVVPIRKIHALTITWALPPQQQHYRVKPLHYISWLVGHEGKGSILSFLRKKCWALALFGGNGETGFEQNSTYSVFSISITLTDEGYEHFYEVAYTVFQYLKMLQKLGPEKRIFEEIRKIEDNEFHYQEQTDPVEYVENMCENMQLYPLQDILTGDQLLFEYKPEVIGEALNQLVPQKANLVLLSGANEGKCDLKEKWFGTQYSIEDIENSWAELWNSNFELNPDLHLPAENKYIATDFTLKAFDCPETEYPVKIVNTPQGCLWYKKDNKFKIPKAYIRFHLISPLIQKSAANVVLFDIFVNILTHNLAEPAYEADVAQLEYKLVAGEHGLIIRVKGFNHKLPLLFQLIIDYLAEFNSTPAVFTMITEQLKKTYFNILIKPETLAKDVRLLILEYARWSMIDKYQALMDGLSLESLLSFVKEFKSQLFVEGLVQGNVTSTESMDFLKYVVDKLNFKPLEQEMPVQFQVVELPSGHHLCKVKALNKGDANSEVTVYYQSGTRSLREYTLMELLVMHMEEPCFDFLRTKQTLGYHVYPTCRNTSGILGFSVTVGTQATKYNSEVVDKKIEEFLSSFEEKIENLTEEAFNTQVTALIKLKECEDTHLGEEVDRNWNEVVTQQYLFDRLAHEIEALKSFSKSDLVNWFKAHRGPGSKMLSVHVVGYGKYELEEDGTPSSEDSNSSCEVMQLTYLPTSPLLADCIIPITDIRAFTTTLNLLPYHKIVK,mutated_sequence,1.0,1151.0,UPI0000458A54.a2m,UPI0000458A54.npy,gnomAD +UPI000066D9E3,UPI000066D9E3.csv,MATNPQPQPPPPAPPPPPPQPQPQPPPPPPGPGAGPGAGGAGGAGAGAGDPQLVAMIVNHLKSQGLFDQFRRDCLADVDTKPAYQNLRQRVDNFVANHLATHTWSPHLNKNQLRNNIRQQVLKSGMLESGIDRIISQVVDPKINHTFRPQVEKAVHEFLATLNHKEEGSGNTAPDDEKPDTSLITQGVPTPGPSANVANDAMSILETITSLNQEASAARASTETSNAKTSERASKKLPSQPTTDTSTDKERTSEDMADKEKSTADSGGEGLETAPKSEEFSDLPCPVEEIKNYTKEHNNLILLNKDVQQESSEQKNKSTDKGEKKPDSNEKGERKKEKKEKTEKKFDHSKKSEDTQKVKDEKQAKEKEVESLKLPSEKNSNKAKTVEGTKEDFSLIDSDVDGLTDITVSSVHTSDLSSFEEDTEEEVVTSDSMEEGEITSDDEEKNKQNKTKTQTSDSSEGKTKSVRHAYVHKPYLYSKYYSDSDDELTVEQRRQSIAKEKEERLLRRQINREKLEEKRKQKAEKTKSSKTKGQGRSSVDLEESSTKSLEPKAARIKEVLKERKVLEKKVALSKKRKKDSRNVEENSKKKQQYEEDSKETLKTSEHCEKEKISSSKELKHVHAKSEPSKPARRLSESLHVVDENKNESKLEREHKRRTSTPVIMEGVQEETDTRDVKRQVERSEICTEEPQKQKSTLKNEKHLKKDDSETPHLKSLLKKEVKSSKEKPEREKTPSEDKLSVKHKYKGDCMHKTGDETELHSSEKGLKVEENIQKQSQQTKLSSDDKTERKSKHRNERKLSVLGKDGKPVSEYIIKTDENVRKENNKKERRLSAEKTKAEHKSRRSSDSKIQKDSLGSKQHGITLQRRSESYSEDKCDMDSTNMDSNLKPEEVVHKEKRRTKSLLEEKLVLKSKSKTQGKQVKVVETELQEGATKQATTPKPDKEKNTEENDSEKQRKSKVEDKPFEETGVEPVLETASSSAHSTQKDSSHRAKLPLAKEKYKSDKDSTSTRLERKLSDGHKSRSLKHSSKDIKKKDENKSDDKDGKEVDSSHEKARGNSSLMEKKLSRRLCENRRGSLSQEMAKGEEKLAANTLSTPSGSSLQRPKKSGDMTLIPEQEPMEIDSEPGVENVFEVSKTQDNRNNNSQQDIDSENMKQKTSATVQKDELRTCTADSKATAPAYKPGRGTGVNSNSEKHADHRSTLTKKMHIQSAVSKMNPGEKEPIHRGTTEVNIDSETVHRMLLSAPSENDRVQKNLKNTAAEEHVAQGDATLEHSTNLDSSPSLSSVTVVPLRESYDPDVIPLFDKRTVLEGSTASTSPADHSALPNQSLTVRESEVLKTSDSKEGGEGFTVDTPAKASITSKRHIPEAHQATLLDGKQGKVIMPLGSKLTGVIVENENITKEGGLVDMAKKENDLNAEPNLKQTIKATVENGKKDGIAVDHVVGLNTEKYAETVKLKHKRSPGKVKDISIDVERRNENSEVDTSAGSGSAPSVLHQRNGQTEDVATGPRRAEKTSVATSTEGKDKDVTLSPVKAGPATTTSSETRQSEVALPCTSIEADEGLIIGTHSRNNPLHVGAEASECTVFAAAEEGGAVVTEGFAESETFLTSTKEGESGECAVAESEDRAADLLAVHAVKIEANVNSVVTEEKDDAVTSAGSEEKCDGSLSRDSEIVEGTITFISEVESDGAVTSAGTEIRAGSISSEEVDGSQGNMMRMGPKKETEGTVTCTGAEGRSDNFVICSVTGAGPREERMVTGAGVVLGDNDAPPGTSASQEGDGSVNDGTEGESAVTSTGITEDGEGPASCTGSEDSSEGFAISSESEENGESAMDSTVAKEGTNVPLVAAGPCDDEGIVTSTGAKEEDEEGEDVVTSTGRGNEIGHASTCTGLGEESEGVLICESAEGDSQIGTVVEHVEAEAGAAIMNANENNVDSMSGTEKGSKDTDICSSAKGIVESSVTSAVSGKDEVTPVPGGCEGPMTSAASDQSDSQLEKVEDTTISTGLVGGSYDVLVSGEVPECEVAHTSPSEKEDEDIITSVENEECDGLMATTASGDITNQNSLAGGKNQGKVLIISTSTTNDYTPQVSAITDVEGGLSDALRTEENMEGTRVTTEEFEAPMPSAVSGDDSQLTASRSEEKDECAMISTSIGEEFELPISSATTIKCAESLQPVAAAVEERATGPVLISTADFEGPMPSAPPEAESPLASTSKEEKDECALISTSIAEECEASVSGVVVESENERAGTVMEEKDGSGIISTSSVEDCEGPVSSAVPQEEGDPSVTPAEEMGDTAMISTSTSEGCEAVMIGAVLQDEDRLTITRVEDLSDAAIISTSTAECMPISASIDRHEENQLTADNPEGNGDLSATEVSKHKVPMPSLIAENNCRCPGPVRGGKEPGPVLAVSTEEGHNGPSVHKPSAGQGHPSAVCAEKEEKHGKECPEIGPFAGRGQKESTLHLINAEEKNVLLNSLQKEDKSPETGTAGGSSTASYSAGRGLEGNANSPAHLRGPEQTSGQTAKDPSVSIRYLAAVNTGAIKADDMPPVQGTVAEHSFLPAEQQGSEDNLKTSTTKCITGQESKIAPSHTMIPPATYSVALLAPKCEQDLTIKNDYSGKWTDQASAEKTGDDNSTRKSFPEEGDIMVTVSSEENVCDIGNEESPLNVLGGLKLKANLKMEAYVPSEEEKNGEILAPPESLCGGKPSGIAELQREPLLVNESLNVENSGFRTNEEIHSESYNKGEISSGRKDNAEAISGHSVEADPKEVEEEERHMPKRKRKQHYLSSEDEPDDNPDVLDSRIETAQRQCPETEPHDTKEENSRDLEELPKTSSETNSTTSRVMEEKDEYSSSETTGEKPEQNDDDTIKSQEEDQPIIIKRKRGRPRKYPVETTLKMKDDSKTDTGIVTVEQSPSSSKLKVMQTDESNKETANLQERSISNDDGEEKIVTSVRRRGRKPKRSLTVSDDAESSEPERKRQKSVSDPVEDKKEQESDEEEEEEEEDEPSGATTRSTTRSEAQRSKTQLSPSIKRKREVSPPGARTRGQQRVEEAPVKKAKR,mutated_sequence,1.0,3051.0,UPI000066D9E3.a2m,UPI000066D9E3.npy,gnomAD +UPI0000126665,UPI0000126665.csv,MDGPTRGHGLRKKRRSRSQRDRERRSRGGLGAGAAGGGGAGRTRALSLASSSGSDKEDNGKPPSSAPSRPRPPRRKRRESTSAEEDIIDGFAMTSFVTFEALEKDVALKPQERVEKRQTPLTKKKREALTNGLSFHSKKSRLSHPHHYSSDRENDRNLCQHLGKRKKMPKALRQLKPGQNSCRDSDSESASGESKGFHRSSSRERLSDSSAPSSLGTGYFCDSDSDQEEKASDASSEKLFNTVIVNKDPELGVGTLPEHDSQDAGPIVPKISGLERSQEKSQDCCKEPIFEPVVLKDPCPQVAQPIPQPQTEPQLRAPSPDPDLVQRTEAPPQPPPLSTQPPQGPPEAQLQPAPQPQVQRPPRPQSPTQLLHQNLPPVQAHPSAQSLSQPLSAYNSSSLSLNSLSSSRSSTPAKTQPAPPHISHHPSASPFPLSLPNHSPLHSFTPTLQPPAHSHHPNMFAPPTALPPPPPLTSGSLQVAGHPAGSTYSEQDILRQELNTRFLASQSADRGASLGPPPYLRTEFHQHQHQHQHTHQHTHQHTFTPFPHAIPPTAIMPTPAPPMFDKYPTKVDPFYRHSLFHSYPPAVSGIPPMIPPTGPFGSLQGAFQPKTSNPIDVAARPGTVPHTLLQKDPRLTDPFRPMLRKPGKWCAMHVHIAWQIYHHQQKVKKQMQSDPHKLDFGLKPEFLSRPPGPSLFGAIHHPHDLARPSTLFSAAGAAHPTGTPFGPPPHHSNFLNPAAHLEPFNRPSTFTGLAAVGGNAFGGLGNPSVTPNSMFGHKDGPSVQNFSNPHEPWNRLHRTPPSFPTPPPWLKPGELERSASAAAHDRDRDVDKRDSSVSKDDKERESVEKRHSSHPSPAPVLPVNALGHTRSSTEQIRAHLNTEAREKDKPKERERDHSESRKDLAADEHKAKEGHLPEKDGHGHEGRAAGEEAKQLARVPSPYVRTPVVESARPNSTSSREAEPRKGEPAYENPKKSSEVKVKEERKEDHDLPPEAPQTHRASEPPPPNSSSSVHPGPLASMPMTVGVTGIHPMNSISSLDRTRMMTPFMGISPLPGGERFPYPSFHWDPIRDPLRDPYRELDIHRRDPLGRDFLLRNDPLHRLSTPRLYEADRSFRDREPHDYSHHHHHHHHPLSVDPRREHERGGHLDERERLHMLREDYEHTRLHSVHPASLDGHLPHPSLITPGLPSMHYPRISPTAGNQNGLLNKTPPTAALSAPPPLISTLGGRPVSPRRTTPLSAEIRERPPSHTLKDIEAR,mutated_sequence,1.0,1259.0,UPI0000126665.a2m,UPI0000126665.npy,gnomAD +UPI00000015B6,UPI00000015B6.csv,MRSPATGVPLPTPPPPLLLLLLLLLPPPLLGDQVGPCRSLGSRGRGSSGACAPMGWLCPSSASNLWLYTSRCRDAGTELTGHLVPHHDGLRVWCPESEAHIPLPPAPEGCPWSCRLLGIGGHLSPQGKLTLPEEHPCLKAPRLRCQSCKLAQAPGLRAGERSPEESLGGRRKRNVNTAPQFQPPSYQATVPENQPAGTPVASLRAIDPDEGEAGRLEYTMDALFDSRSNQFFSLDPVTGAVTTAEELDRETKSTHVFRVTAQDHGMPRRSALATLTILVTDTNDHDPVFEQQEYKESLRENLEVGYEVLTVRATDGDAPPNANILYRLLEGSGGSPSEVFEIDPRSGVIRTRGPVDREEVESYQLTVEASDQGRDPGPRSTTAAVFLSVEDDNDNAPQFSEKRYVVQVREDVTPGAPVLRVTASDRDKGSNAVVHYSIMSGNARGQFYLDAQTGALDVVSPLDYETTKEYTLRVRAQDGGRPPLSNVSGLVTVQVLDINDNAPIFVSTPFQATVLESVPLGYLVLHVQAIDADAGDNARLEYRLAGVGHDFPFTINNGTGWISVAAELDREEVDFYSFGVEARDHGTPALTASASVSVTVLDVNDNNPTFTQPEYTVRLNEDAAVGTSVVTVSAVDRDAHSVITYQITSGNTRNRFSITSQSGGGLVSLALPLDYKLERQYVLAVTASDGTRQDTAQIVVNVTDANTHRPVFQSSHYTVNVNEDRPAGTTVVLISATDEDTGENARITYFMEDSIPQFRIDADTGAVTTQAELDYEDQVSYTLAITARDNGIPQKSDTTYLEILVNDVNDNAPQFLRDSYQGSVYEDVPPFTSVLQISATDRDSGLNGRVFYTFQGGDDGDGDFIVESTSGIVRTLRRLDRENVAQYVLRAYAVDKGMPPARTPMEVTVTVLDVNDNPPVFEQDEFDVFVEENSPIGLAVARVTATDPDEGTNAQIMYQIVEGNIPEVFQLDIFSGELTALVDLDYEDRPEYVLVIQATSAPLVSRATVHVRLLDRNDNPPVLGNFEILFNNYVTNRSSSFPGGAIGRVPAHDPDISDSLTYSFERGNELSLVLLNASTGELKLSRALDNNRPLEAIMSVLVSDGVHSVTAQCALRVTIITDEMLTHSITLRLEDMSPERFLSPLLGLFIQAVAATLATPPDHVVVFNVQRDTDAPGGHILNVSLSVGQPPGPGGGPPFLPSEDLQERLYLNRSLLTAISAQRVLPFDDNICLREPCENYMRCVSVLRFDSSAPFIASSSVLFRPIHPVGGLRCRCPPGFTGDYCETEVDLCYSRPCGPHGRCRSREGGYTCLCRDGYTGEHCEVSARSGRCTPGVCKNGGTCVNLLVGGFKCDCPSGDFEKPYCQVTTRSFPAHSFITFRGLRQRFHFTLALSFATKERDGLLLYNGRFNEKHDFVALEVIQEQVQLTFSAGESTTTVSPFVPGGVSDGQWHTVQLKYYNKPLLGQTGLPQGPSEQKVAVVTVDGCDTGVALRFGSVLGNYSCAAQGTQGGSKKSLDLTGPLLLGGVPDLPESFPVRMRQFVGCMRNLQVDSRHIDMADFIANNGTVPGCPAKKNVCDSNTCHNGGTCVNQWDAFSCECPLGFGGKSCAQEMANPQHFLGSSLVAWHGLSLPISQPWYLSLMFRTRQADGVLLQAITRGRSTITLQLREGHVMLSVEGTGLQASSLRLEPGRANDGDWHHAQLALGASGGPGHAILSFDYGQQRAEGNLGPRLHGLHLSNITVGGIPGPAGGVARGFRGCLQGVRVSDTPEGVNSLDPSHGESINVEQGCSLPDPCDSNPCPANSYCSNDWDSYSCSCDPGYYGDNCTNVCDLNPCEHQSVCTRKPSAPHGYTCECPPNYLGPYCETRIDQPCPRGWWGHPTCGPCNCDVSKGFDPDCNKTSGECHCKENHYRPPGSPTCLLCDCYPTGSLSRVCDPEDGQCPCKPGVIGRQCDRCDNPFAEVTTNGCEVNYDSCPRAIEAGIWWPRTRFGLPAAAPCPKGSFGTAVRHCDEHRGWLPPNLFNCTSITFSELKGFAERLQRNESGLDSGRSQQLALLLRNATQHTAGYFGSDVKVAYQLATRLLAHESTQRGFGLSATQDVHFTENLLRVGSALLDTANKRHWELIQQTEGGTAWLLQHYEAYASALAQNMRHTYLSPFTIVTPNIVISVVRLDKGNFAGAKLPRYEALRGEQPPDLETTVILPESVFRETPPVVRPAGPGEAQEPEELARRQRRHPELSQGEAVASVIIYRTLAGLLPHNYDPDKRSLRVPKRPIINTPVVSISVHDDEELLPRALDKPVTVQFRLLETEERTKPICVFWNHSILVSGTGGWSARGCEVVFRNESHVSCQCNHMTSFAVLMDVSRRENGEILPLKTLTYVALGVTLAALLLTFFFLTLLRILRSNQHGIRRNLTAALGLAQLVFLLGINQADLPFACTVIAILLHFLYLCTFSWALLEALHLYRALTEVRDVNTGPMRFYYMLGWGVPAFITGLAVGLDPEGYGNPDFCWLSIYDTLIWSFAGPVAFAVSMSVFLYILAARASCAAQRQGFEKKGPVSGLQPSFAVLLLLSATWLLALLSVNSDTLLFHYLFATCNCIQGPFIFLSYVVLSKEVRKALKLACSRKPSPDPALTTKSTLTSSYNCPSPYADGRLYQPYGDSAGSLHSTSRSGKSQPSYIPFLLREESALNPGQGPPGLGDPGSLFLEGQDQQHDPDTDSDSDLSLEDDQSGSYASTHSSDSEEEEEEEEEEAAFPGEQGWDSLLGPGAERLPLHSTPKDGGPGPGKAPWPGDFGTTAKESSGNGAPEERLRENGDALSREGSLGPLPGSSAQPHKGILKKKCLPTISEKSSLLRLPLEQCTGSSRGSSASEGSRGGPPPRPPPRQSLQEQLNGVMPIAMSIKAGTVDEDSSGSEFLFFNFLH,mutated_sequence,1.0,2923.0,UPI00000015B6.a2m,UPI00000015B6.npy,gnomAD +UPI0000070598,UPI0000070598.csv,MVVPEKEQSWIPKIFKKKTCTTFIVDSTDPGGTLCQCGRPRTAHPAVAMEDAFGAAVVTVWDSDAHTTEKPTDAYGELDFTGAGRKHSNFLRLSDRTDPAAVYSLVTRTWGFRAPNLVVSVLGGSGGPVLQTWLQDLLRRGLVRAAQSTGAWIVTGGLHTGIGRHVGVAVRDHQMASTGGTKVVAMGVAPWGVVRNRDTLINPKGSFPARYRWRGDPEDGVQFPLDYNYSAFFLVDDGTHGCLGGENRFRLRLESYISQQKTGVGGTGIDIPVLLLLIDGDEKMLTRIENATQAQLPCLLVAGSGGAADCLAETLEDTLAPGSGGARQGEARDRIRRFFPKGDLEVLQAQVERIMTRKELLTVYSSEDGSEEFETIVLKALVKACGSSEASAYLDELRLAVAWNRVDIAQSELFRGDIQWRSFHLEASLMDALLNDRPEFVRLLISHGLSLGHFLTPMRLAQLYSAAPSNSLIRNLLDQASHSAGTKAPALKGGAAELRPPDVGHVLRMLLGKMCAPRYPSGGAWDPHPGQGFGESMYLLSDKATSPLSLDAGLGQAPWSDLLLWALLLNRAQMAMYFWEMGSNAVSSALGACLLLRVMARLEPDAEEAARRKDLAFKFEGMGVDLFGECYRSSEVRAARLLLRRCPLWGDATCLQLAMQADARAFFAQDGVQSLLTQKWWGDMASTTPIWALVLAFFCPPLIYTRLITFRKSEEEPTREELEFDMDSVINGEGPVGTADPAEKTPLGVPRQSGRPGCCGGRCGGRRCLRRWFHFWGAPVTIFMGNVVSYLLFLLLFSRVLLVDFQPAPPGSLELLLYFWAFTLLCEELRQGLSGGGGSLASGGPGPGHASLSQRLRLYLADSWNQCDLVALTCFLLGVGCRLTPGLYHLGRTVLCIDFMVFTVRLLHIFTVNKQLGPKIVIVSKMMKDVFFFLFFLGVWLVAYGVATEGLLRPRDSDFPSILRRVFYRPYLQIFGQIPQEDMDVALMEHSNCSSEPGFWAHPPGAQAGTCVSQYANWLVVLLLVIFLLVANILLVNLLIAMFSYTFGKVQGNSDLYWKAQRYRLIREFHSRPALAPPFIVISHLRLLLRQLCRRPRSPQPSSPALEHFRVYLSKEAERKLLTWESVHKENFLLARARDKRESDSERLKRTSQKVDLALKQLGHIREYEQRLKVLEREVQQCSRVLGWVAEALSRSALLPPGGPPPPDLPGSKD,mutated_sequence,1.0,1214.0,UPI0000070598.a2m,UPI0000070598.npy,gnomAD +UPI00003FEC88,UPI00003FEC88.csv,MKCFFPVLSCLAVLGVVSAQRQVTVQEGPLYRTEGSHITIWCNVSGYQGPSEQNFQWSIYLPSSPEREVQIVSTMDSSFPYAIYTQRVRGGKIFIERVQGNSTLLHITDLQARDAGEYECHTPSTDKQYFGSYSAKMNLVVIPDSLQTTAMPQTLHRVEQDPLELTCEVASETIQHSHLSVAWLRQKVGEKPVEVISLSRDFMLHSSSEYAQRQSLGEVRLDKLGRTTFRLTIFHLQPSDQGEFYCEAAEWIQDPDGSWYAMTRKRSEGAVVNVQPTDKEFTVRLETEKRLHTVGEPVEFRCILEAQNVPDRYFAVSWAFNSSLIATMGPNAVPVLNSEFAHREARGQLKVAKESDSVFVLKIYHLRQEDSGKYNCRVTEREKTVTGEFIDKESKRPKNIPIIVLPLKSSISVEVASNASVILEGEDLRFSCSVRTAGRPQGRFSVIWQLVDRQNRRSNIMWLDRDGTVQPGSSYWERSSFGGVQMEQVQPNSFSLGIFNSRKEDEGQYECHVTEWVRAVDGEWQIVGERRASTPISITALEMGFAVTAISRTPGVTYSDSFDLQCIIKPHYPAWVPVSVTWRFQPVGTVEFHDLVTFTRDGGVQWGDRSSSFRTRTAIEKAESSNNVRLSISRASDTEAGKYQCVAELWRKNYNNTWTRLAERTSNLLEIRVLQPVTKLQVSKSKRTLTLVENKPIQLNCSVKSQTSQNSHFAVLWYVHKPSDADGKLILKTTHNSAFEYGTYAEEEGLRARLQFERHVSGGLFSLTVQRAEVSDSGSYYCHVEEWLLSPNYAWYKLAEEVSGRTEVTVKQPDSRLRLSQAQGNLSVLETRQVQLECVVLNRTSITSQLMVEWFVWKPNHPERETVARLSRDATFHYGEQAAKNNLKGRLHLESPSPGVYRLFIQNVAVQDSGTYSCHVEEWLPSPSGMWYKRAEDTAGQTALTVMRPDASLQVDTVVPNATVSEKAAFQLDCSIVSRSSQDSRFAVAWYSLRTKAGGKRSSPGLEEQEEEREEEEEEDDDDDDDPTERTALLSVGPDAVFGPEGSPWEGRLRFQRLSPVLYRLTVLQASPQDTGNYSCHVEEWLPSPQKEWYRLTEEESAPIGIRVLDTSPTLQSIICSNDALFYFVFFYPFPIFGILIITILLVRFKSRNSSKNSDGKNGVPLLWIKEPHLNYSPTCLEPPVLSIHPGAID,mutated_sequence,1.0,1194.0,UPI00003FEC88.a2m,UPI00003FEC88.npy,gnomAD +UPI0001CE93AE,UPI0001CE93AE.csv,MVVSAGPWSSEKAEMNILEINETLRPQLAEKKQQFRNLKEKCFLTQLAGFLANRQKKYKYEECKDLIKFMLRNERQFKEEKLAEQLKQAEELRQYKVLVHSQERELTQLREKLREGRDASRSLYEHLQALLTPDEPDKSQGQDLQEQLAEGCRLAQHLVQKLSPENDEDEDEDVQVEEAEKVLESSAPREVQKAEESKVPEDSLEECAITCSNSHGPCDSNQPHKNIKITFEEDEVNSTLVVDRESSHDECQDALNILPVPGPTSSATNVSMVVSAGPLSSEKAEMNILEINEKLRPQLAEKKQQFRNLKEKCFLTQLSGFLANQQKKYKYEECKDLIKFMLRNERQFKEEKLAEQLKQAEELRQYKVLVHAQERELTQLKEKLREGRDASRSLNEHLQALLTPYEPDKSQGQDLQEQLAEGCRLAQHLVQKLSPENDNDDDEDVQVEVAEKVQKSSAPREMQKAEEKEVPEDSLEECAITYSNSHGSYDSNQPHRKTKITFEEDKVDSTLIGSSSHVEWEDAVHIIPENESDDEEEEEKGPVSPRNLQESEEEEVPQESWDEGYSTLSIPPEMLASYQSYSSTFHSLEEQQVCMAVDIGRHRWDQVKKEDQEATGPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQRVGFAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQRVGFAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDEEEEEDQDPPCPRLSRELLEVVAPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQRVGMAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLNRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDEDQDPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEENHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEENHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEENHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEENHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEENHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELTDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPKVLQDSLDRCYSTPSGCLELCDSCQPYRSAFYVLEQQRVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELCDSCQPYRSAFYILEQQRVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDRIKKDQEEEEDQGPPCPRLSRELLEVVEPEVLQDSLDRCYSTPSSCLEQPDSCQPYGSSFYALEEKHVGFSLDVGEIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLSRELLDEKGPEVLQDSLDRCYSTPSGCLELCDSCQPYRSAFYVLEQQHVGLAVDMDEIEKYQEVEEDQDPSCPRLSRELLDEKEPEVLQDSLDRCYSTPSGYLELPDLGQPYSSAVYSLEEQYLGLALDVDKIEKKGKGKKRRGRRSKKERRRGRKEGEEDQNPPCPRLNGVLMEVEEREVLQDSLDRCYSTPSMYFELPDSFQHYRSVFYSFEEQHISFALYVDNRFFTLTVTSLHLVFQMGVIFPQ,mutated_sequence,1.0,3553.0,UPI0001CE93AE.a2m,UPI0001CE93AE.npy,gnomAD +UPI00004CEC5B,UPI00004CEC5B.csv,MKEHIIYQKLYGLILMSSFIFLSDTLSLKGKKLDFFGRGDTYVSLIDTIPELSRFTACIDLVFMDDNSRYWMAFSYITNNALLGREDIDLGLAGDHQQLILYRLGKTFSIRHHLASFQWHTICLIWDGVKGKLELFLNKERILEVTDQPHNLTPHGTLFLGHFLKNESSEVKSMMRSFPGSLYYFQLWDHILENEEFMKCLDGNIVSWEEDVWLVNKIIPTVDRTLRCFVPENMTIQEKSTTVSQQIDMTTPSQITGVKPQNTAHSSTLLSQSIPIFATDYTTISYSNTTSPPLETMTAQKILKTLVDETATFAVDVLSTSSAISLPTQSISIDNTTNSMKKTKSPSSESTKTTKMVEAMATEIFQPPTPSNFLSTSRFTKNSVVSTTSAIKSQSAVTKTTSLFSTIESTSMSTTPCLKQKSTNTGALPISTAGQEFIESTAAGTVPWFTVEKTSPASTHVGTASSFPPEPVLISTAAPVDSVFPRNQTAFPLATTDMKIAFTVHSLTLPTRLIETTPAPRTAETELTSTNFQDVSLPRVEDAMSTSMSKETSSKTFSFLTSFSFTGTESVQTVIDAEATRTALTPEITLASTVAETMLSSTITGRVYTQNTPTADGHLLTLMSTRSASTSKAPESGPTSTTDEAAHLFSSNETIWTSRPDQALLASMNTTTILTFVPNENFTSAFHENTTYTEYLSATTNITPLKASPEGKGTTANDATTARYTTAVSKLTSPWFANFSIVSGTTSITNMPEFKLTTLLLKTIPMSTKPANELPLTPRETVVPSVDIISTLACIQPNFSTEESASETTQTEINGAIVFGGTTTPVPKSATTQRLNATVTRKEATSHYLMRKSTIAAVAEVSPFSTMLEVTDESAQRVTASVTVSSFPDIEKLSTPLDNKTATTEVRESWLLTKLVKTTPRSSYNEMTEMFNFNHTYVAHWTSETSEGISAGSPTSGSTHIFGEPLGASTTRISETSFSTTPTDRTATSLSDGILPPQPTAAHSSATPVPVTHMFSLPVNGSSVVAEETEVTMSEPSTLARAFSTSVLSDVSNLSSTTMTTALVPPLDQTASTTIVIVPTHGDLIRTTSEATVISVRKTSMAVPSLTETPFHSLRLSTPVTAKAETTLFSTSVDTVTPSTHTLVCSKPPPDNIPPASSTHVISTTSTPEATQPISQVEETSTYALSFPYTFSGGGVVASLATGTTETSVVDETTPSHISANKLTTSVNSHISSSATYRVHTPVSIQLVTSTSVLSSDKDQMTISLGKTPRTMEVTEMSPSKNSFISYSRGTPSLEMTDTGFPETTKISSHQTHSPSEIPLGTPSDGNLASSPTSGSTQITPTLTSSNTVGVHIPEMSTSLGKTALPSQALTITTFLCPEKESTSALPAYTPRTVEMIVNSTYVTHSVSYGQDTSFVDTTTSSSTRISNPMDINTTFSHLHSLRTQPEVTSVASFISESTQTFPESLSLSTAGLYNDGFTVLSDRITTAFSVPNVPTMLPRESSMATSTPIYQMSSLPVNVTAFTSKKVSDTPPIVITKSSKTMHPGCLKSPCTATSGPMSEMSSIPVNNSAFTPATVSSDTSTRVGLFSTLLSSVTPRTTMTMQTSTLDVTPVIYAGATSKNKMVSSAFTTEMIEAPSRITPTTFLSPTEPTLPFVKTVPTTIMAGIVTPFVGTTAFSPLSSKSTGAISSIPKTTFSPFLSATQQSSQADEATTLGILSGITNRSLSTVNSGTGVALTDTYSRITVPENMLSPTHADSLHTSFNIQVSPSLTSFKSASGPTKNVKTTTNCFSSNTRKMTSLLEKTSLTNYATSLNTPVSYPPWTPSSATLPSLTSFVYSPHSTEAEISTPKTSPPPTSQMVEFPVLGTRMTSSNTQPLLMTSWNIPTAEGSQFPISTTINVPTSNEMETETLHLVPGPLSTFTASQTGLVSKDVMAMSSIPMSGILPNHGLSENPSLSTSLRAITSTLADVKHTFEKMTTSVTPGTTLPSILSGATSGSVISKSPILTWLLSSLPSGSPPATVSNAPHVMTSSTVEVSKSTFLTSDMISAHPFTNLTTLPSATMSTILTRTIPTPTLGGITTGFPTSLPMSINVTDDIVYISTHPEASSRTTITANPRTVSHPSSFSRKTMSPSTTDHTLSVGAMPLPSSTITSSWNRIPTASSPSTLIIPKPTLDSLLNIMTTTSTVPGASFPLISTGVTYPFTATVSSPISSFFETTWLDSTPSFLSTEASTSPTATKSTVSFYNVEMSFSVFVEEPRIPITSVINEFTENSLNSIFQNSEFSLATLETQIKSRDISEEEMVMDRAILEQREGQEMATISYVPYSCVCQVIIKASSSLASSELMRKIKSKIHGNFTHGNFTQDQLTLLVNCEHVAVKKLEPGNCKADETASKYKGTYKWLLTNPTETAQTRCIKNEDGNATRFCSISINTGKSQWEKPKFKQCKLLQELPDKIVDLANITISDENAEDVAEHILNLINESPALGKEETKIIVSKISDISQCDEISMNLTHVMLQIINVVLEKQNNSASDLHEISNEILRIIERTGHKMEFSGQIANLTVAGLALAVLRGDHTFDGMAFSIHSYEEGTDPEIFLGNVPVGGILASIYLPKSLTERIPLSNLQTILFNFFGQTSLFKTKNVTKALTTYVVSASISDDMFIQNLADPVVITLQHIGGNQNYGQVHCAFWDFENNNGLGGWNSSGCKVKETNVNYTICQCDHLTHFGVLMDLSRSTVDSVNEQILALITYTGCGISSIFLGVAVVTYIAFHKLRKDYPAKILINLCTALLMLNLVFLINSWLSSFQKVGVCITAAVALHYFLLVSFTWMGLEAVHMYLALVKVFNIYIPNYILKFCLVGWGIPAIMVAITVSVKKDLYGTLSPTTPFCWIKDDSIFYISVVAYFCLIFLMNLSMFCTVLVQLNSVKSQIQKTRRKMILHDLKGTMSLTFLLGLTWGFAFFAWGPMRNFFLYLFAIFNTLQGFFIFVFHCVMKESVREQWQIHLCCGWLRLDNSSDGSSRCQIKVGYKQEGLKKIFEHKLLTPSLKSTATSSTFKSLGSAQGTPSEISFPNDDFDKDPYCSSP,mutated_sequence,1.0,3080.0,UPI00004CEC5B.a2m,UPI00004CEC5B.npy,gnomAD +UPI000007315E,UPI000007315E.csv,MKGSNRNKDHSAEGEGVGKRPKRKCLQWHPLLAKKLLDFSEEEEEEDEEEDIDKVQLLGADGLEQDVGETEDDESPEQRARRPMNAFLLFCKRHRSLVRQEHPRLDNRGATKILADWWAVLDPKEKQKYTDMAKEYKDAFMKANPGYKWCPTTNKPVKSPTPTVNPRKKLWAFPSDSSRDLPSPKKAKTEEMPQLNFGMADPTQMGGLSMLLLAGEHALGTPEVSSGTCRPDVSESPELRQKSPLFQFAEISSSTSHSDASTKQCQTSALFQFAEISSNTSQLGGAEPVKRCGKSALFQLAEMCLASEGMKMEESKLIKAKESDGGRIKELEKGKEEKEIKMEKTDETRLQKEAEFEKSAKENLRDSKELRNFEALQIDDIMAIKMEDPKEIRKEELEEDHKCSHFPDFSYSASSKIIISDVPSRKDHMCHPHGIMIIEDPAALNKPEKLKKKKKKSKMDRHGNDKSTPKKTCKKRQSSESDIESVIYTIEAVAKGDWGIEKLGDTPRKKVRTSSSGKGSILDAKPPKKKVKSREKKMSKEKSSDTTKESRPPDFISISASKNISGETPEGIKAEPLTPMEDALPPSLSGQAKPEDSDCHRKIETCGSRKSERSCKGALYKTLVSEGMLTSLRANVDRGKRSSGKGNSSDHEGCWNEESWTFSQSGTSGSKKFKKTKPKEDCLLGSAKLDEEFEKKFNSLPQYSPVTFDRKCVPVPRKKKKTGNVSSEPTKTSKGPFQSQKKNLFHKIVSKYKHKKEKPNVPEKGSGDKWSNKQLFLDAIHPTEAIFSEDRNTMEPVHKVKNIPSIFNTPEPTTTQEPLVGSQKRKARKTKITHLVRTADGRVSPAGGTLDDKPKEQLQRSLPKATETDCNDKCSHNTEVGETRSSTPEMPAVSAFFSLAALAEVAAMENVHRGQRSTPLTHDGQPKEMPQAPVLISCADQ,mutated_sequence,1.0,941.0,UPI000007315E.a2m,UPI000007315E.npy,gnomAD +UPI0000E0BF7B,UPI0000E0BF7B.csv,MAAAASVTGRVTWAASPMRSLGLGRRLSLPGPRLDAVTAAVNPSLSDHGNGLGRGTRGSGCSGGSLVADWGGGAAAAAAVALALAPALSTMRRGSSESELAARWEAEAVAAAKAAAKAEAEATAETVAEQVRVDAGAAGEPECKAGEEQPKVLAPAPAQPSAAEEGNTQVLQRPPPTLPPSKPKPVQGLCPHGKPRDKGRSCKRSSGHGSGENGSQRPVTVDSSKARTSLDALKISIRQLKWKEFPFGRRLPCDIYWHGVSFHDNDIFSGQVNKFPGMTEMVRKITLSRAVRTMQNLFPEEYNFYPRSWILPDEFQLFVAQVQMVKDDDPSWKPTFIVKPDGGCQGDGIYLIKDPSDIRLAGTLQSRPAVVQEYICKPLLIDKLKFDIRLYVLLKSLDPLEIYIAKDGLSRFCTEPYQEPTPKNLHRIFMHLTNYSLNIHSGNFIHSDSASTGSKRTFSSILCRLSSKGVDIKKVWSDIISVVIKTVIALTPELKVFYQSDIPTGRPGPTCFQILGFDILLMKNLKPILLEVNANPSMRIEHEHELSPGVFENVPSLVDEEVKVAVIRDTLRLMDPLKKKRENQSQQLEKPFAGKEDALDGELTSAPDCNANPEAHLPSICLKQVFPKYAKQFNYLRLVDRMANLFIRFLGIKGTMKLGPTGFRTFIRSCKLSSSSLSMAAVDILYIDITRRWNSMTLDQRDSGMCLQAFVEAFFFLAQRKFKMLPLHEQVASLIDLCEYHLSLLDEKRLVCGRGVPSGGRPPHRGPPQEPSPSAQPAGDNPPPRTSCANKLSHPRHTLS,mutated_sequence,1.0,800.0,UPI0000E0BF7B.a2m,UPI0000E0BF7B.npy,gnomAD +UPI00001F9669,UPI00001F9669.csv,MEGVAVVTAGSVGAAKTEGAAALPPPPPVSPPALTPAPAAGEEGPAPLSETGAPGCSGSRPPELEPERSLGRFRGRFEDEDEELEEEEELEEEEEEEEEDMSHFSLRLEGGRQDSEDEEERLINLSELTPYILCSICKGYLIDATTITECLHTFCKSCIVRHFYYSNRCPKCNIVVHQTQPLYNIRLDRQLQDIVYKLVINLEEREKKQMHDFYKERGLEVPKPAVPQPVPSSKGRSKKVLESVFRIPPELDMSLLLEFIGANEGTGHFKPLEKKFVRVSGEATIGHVEKFLRRKMGLDPACQVDIICGDHLLEQYQTLREIRRAIGDAAMQDGLLVLHYGLVVSPLKIT,mutated_sequence,1.0,350.0,UPI00001F9669.a2m,UPI00001F9669.npy,gnomAD +UPI000004FDF5,UPI000004FDF5.csv,MAKFALNQNLPDLGGPRLCPVPAAGGARSPSSPYSVETPYGFHLDLDFLKYIEELERGPAARRAPGPPTSRRPRAPRPGLAGARSPGAWTSSESLASDDGGAPGILSQGAPSGLLMQPLSPRAPVRNPRVEHTLRETSRRLELAQTHERAPSPGRGVPRSPRGSGRSSPAPNLAPASPGPAQLQLVREQMAAALRRLRELEDQARTLPELQEQVRALRAEKARLLAGRAQPEPDGEAETRPDKLAQLRRLTERLATSERGGRARASPRADSPDGLAAGRSEGALQVLDGEVGSLDGTPQTREVAAEAVPETREAGAQAVPETREAGVEAAPETVEADAWVTEALLGLPAAAERELELLRASLEHQRGVSELLRGRLRELEEAREAAEEAAAGARAQLREATTQTPWSCAEKAAQTESPAEAPSLTQESSPGSMDGDRAVAPAGILKSIMKKRDGTPGAQPSSGPKSLQFVGVLNGEYESSSSEDASDSDGDSENGGAEPPGSSSGSGDDSGGGSDSGTPGPPSGGDIRDPEPEAEAEPQQVAQGRCELSPRLREACVALQRQLSRPRGVASDGGAVRLVAQEWFRVSSQRRSQAEPVARMLEGVRRLGPELLAHVVNLADGNGNTALHYSVSHGNLAIASLLLDTGACEVNRQNRAGYSALMLAALTSVRQEEEDMAVVQRLFCMGDVNAKASQTGQTALMLAISHGRQDMVATLLACGADVNAQDADGATALMCASEYGRLDTVRLLLTQPGCDPAILDNEGTSALAIALEAEQDEVAALLHAHLSSGQPDTQSESPPGSQTATPGEGECGDNGENPQVQ,mutated_sequence,1.0,821.0,UPI000004FDF5.a2m,UPI000004FDF5.npy,gnomAD +UPI000000CC09,UPI000000CC09.csv,MASDDFDIVIEAMLEAPYKKEEDEQQRKEVKKDYPSNTTSSTSNSGNETSGSSTIGETSKKKRSRSHNKSRDRKRSRSRDRDRYRRRNSRSRSPGRQCRHRSRSWDRRHGSESRSRDHRREDRVHYRSPPLATGYRYGHSKSPHFREKSPVREPVDNLSPEERDARTVFCMQLAARIRPRDLEDFFSAVGKVRDVRIISDRNSRRSKGIAYVEFCEIQSVPLAIGLTGQRLLGVPIIVQASQAEKNRLAAMANNLQKGNGGPMRLYVGSLHFNITEDMLRGIFEPFGKIDNIVLMKDSDTGRSKGYGFITFSDSECARRALEQLNGFELAGRPMRVGHVTERLDGGTDITFPDGDQELDLGSAGGRFQLMAKLAEGAGIQLPSTAAAAAAAAAQAAALQLNGAVPLGALNPAALTALSPALNLASQCFQLSSLFTPQTM,mutated_sequence,1.0,439.0,UPI000000CC09.a2m,UPI000000CC09.npy,gnomAD +UPI000022D996,UPI000022D996.csv,MFGAGDEDDTDFLSPSGGARLASLFGLDQAAAGHGNEFFQYTAPKQPKKGQGTAATGNQATPKTAPATMSTPTILVATAVHAYRYTNGQYVKQGKFGAAVLGNHTAREYRILLYISQQQPVTVARIHVNFELMVRPNNYSTFYDDQRQNWSIMFESEKAAVEFNKQVCIAKCNSTSSLDAVLSQDLIVADGPAVEVGDSLEVAYTGWLFQNHVLGQVFDSTANKDKLLRLKLGSGKVIKGWEDGMLGMKKGGKRLLIVPPACAVGSEGVIGWTQATDSILVFEVEVRRVKFARDSGSDGHSVSSRDSAAPSPIPGADNLSADPVVSPPTSIPFKSGEPALRTKSNSLSEQLAINTSPDAVKAKLISRMAKMGQPMLPILPPQLDSNDSEIEDVNTLQGGGQPVVTPSVQPSLHPAHPALPQMTSQAPQPSVTGLQAPSAALMQVSSLDSHSAVSGNAQSFQPYAGMQAYAYPQASAVTSQLQPVRPLYPAPLSQPPHFQGSGDMASFLMTEARQHNTEIRMAVSKVADKMDHLMTKVEELQKHSAGNSMLIPSMSVTMETSMIMSNIQRIIQENERLKQEILEKSNRIEEQNDKISELIERNQRYVEQSNLMMEKRNNSLQTATENTQARVLHAEQEKAKVTEELAAATAQVSHLQLKMTAHQKKETELQMQLTESLKETDLLRGQLTKVQAKLSELQETSEQAQSKFKSEKQNRKQLELKVTSLEEELTDLRVEKESLEKNLSERKKKSAQERSQAEEEIDEIRKSYQEELDKLRQLLKKTRVSTDQAAAEQLSLVQAELQTQWEAKCEHLLASAKDEHLQQYQEVCAQRDAYQQKLVQLQEKCLALQAQITALTKQNEQHIKELEKNKSQMSGVEAAASDPSEKVKKIMNQVFQSLRREFELEESYNGRTILGTIMNTIKMVTLQLLNQQEQEKEESSSEEEEEKAEERPRRPSQEQSASASSGQPQAPLNRERPESPMVPSEQVVEEAVPLPPQALTTSQDGHRRKGDSEAEALSEIKDGSLPPELSCIPSHRVLGPPTSIPPEPLGPVSMDSECEESLAASPMAAKPDNPSGKVCVREVAPDGPLQESSTRLSLTSDPEEGDPLALGPESPGEPQPPQLKKDDVTSSTGPHKELSSTEAGSTVAGAALRPSHHSQRSSLSGDEEDELFKGATLKALRPKAQPEEEDEDEVSMKGRPPPTPLFGDDDDDDDIDWLG,mutated_sequence,1.0,1219.0,UPI000022D996.a2m,UPI000022D996.npy,gnomAD +UPI0000231C2D,UPI0000231C2D.csv,MGYFLKLYAYVNSHSLFVWVCDRSYKRSFRPMILNKIKELSRNQFSTMSHLRKDSQPSSPGDDAMDRSGLPDLQGRFELSGKNRQYPLDALEPQPSIGDIKDIKKAAKSMLDPAHKSHFHPVTPSLVFLCFIFDGLHQALLSVGVSKRSNTVVGNENEERGTPYASRFKDMPNFIALEKSSVLRHCCDLLIGIAAGSSDKICTSSLQVQRRFKAMMASIGRLSHGESADLLISCNAESAIGWISSRPWVGELMFTLLFGDFESPLHKLRKSS,mutated_sequence,1.0,272.0,UPI0000231C2D.a2m,UPI0000231C2D.npy,gnomAD +UPI000015F240,UPI000015F240.csv,MTEFHLQSQMPSIRLIFRRLSLGRIKPSQSPRCSTSFMVVPSFSIAEHWRRMKGANLSQGMEFELLGLTTDPQLQRLLFVVFLGMYTATLLGNLVMFLLIHVSATLHTPMYSLLKSLSFLDFCYSSTVVPQTLVNFLAKRKVISYFGCMTQMFFYAGFATSECYLIAAMAYDRYAAICNPLLYSTIMSPEVCASLIVGSYSAGFLNSLIHTGCIFSLKFCGAHVVTHFFCDGPPILSLSCVDTSLCEILLFIFAGFNLLSCTLTILISYFLILNTILKMSSAQGRFKAFSTCASHLTAICLFFGTTLFMYLRPRSSYSLTQDRTVAVIYTVVIPVLNPLMYSLRNKDVKKALIKVWGRKTME,mutated_sequence,1.0,362.0,UPI000015F240.a2m,UPI000015F240.npy,gnomAD +UPI00021CF3BF,UPI00021CF3BF.csv,XPTPGIVINRPNGTDVYQGVPKDYTGEDVTPQNFLAVLRGGVRSLRCSGPPGEPPLQGSLQDLLHKMTCSLLPASPVFSEKLQIRVGKALY,mutated_sequence,1.0,91.0,UPI00021CF3BF.a2m,UPI00021CF3BF.npy,gnomAD +UPI000011D62A,UPI000011D62A.csv,MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM,mutated_sequence,1.0,503.0,UPI000011D62A.a2m,UPI000011D62A.npy,gnomAD +UPI000013E507,UPI000013E507.csv,MEGSRPRAPSGHLAPSPPAFDGELDLQRYSNGPAVSAGSLGMGAVSWSESRAGERRFPCPVCGKRFRFNSILALHLRAHPGAQAFQCPHCGHRAAQRALLRSHLRTHQPERPRSPAARLLLELEERALLREARLGRARSSGGMQATPATEGLARPQAPSSSAFRCPYCKGKFRTSAERERHLHILHRPWKCGLCSFGSSQEEELLHHSLTAHGAPERPLAATSAAPPPQPQPQPPPQPEPRSVPQPEPEPEPEREATPTPAPAAPEEPPAPPEFRCQVCGQSFTQSWFLKGHMRKHKASFDHACPVCGRCFKEPWFLKNHMKVHASKLGPLRAPGPASGPARAPQPPDLGLLAYEPLGPALLLAPAPTPAERREPPSLLGYLSLRAGEGRPNGEGAEPGPGRSFGGFRPLSSALPARARRHRAEEPEEEEEVVEAEEETWARGRSLGSLASLHPRPGEGPGHSASAAGAQARSTATQEENGLLVGGTRPEGGRGATGKDCPFCGKSFRSAHHLKVHLRVHTGERPYKCPHCDYAGTQSGSLKYHLQRHHREQRSGAGPGPPPEPPPPSQRGSAPQSGAKPSPQPATWVEGASSPRPPSSGAGPGSRRKPASPGRTLRNGRGGEAEPLDLSLRAGPGGEAGPGGALHRCLFCPFATGAPELMALHLQVHHSRRARGRRPPQADASPPYARVPSGETPPSPSQEGEEGSGLSRPGEAGLGGQER,mutated_sequence,1.0,722.0,UPI000013E507.a2m,UPI000013E507.npy,gnomAD +UPI000013DCDA,UPI000013DCDA.csv,MSTTLLSAFYDVDFLCKTEKSLANLNLNNMLDKKAVGTPVAAAPSSGFAPGFLRRHSASNLHALAHPAPSPGSCSPKFPGAANGSSCGSAAAGGPTSYGTLKEPSGGGGTALLNKENKFRDRSFSENGDRSQHLLHLQQQQKGGGGSQINSTRYKTELCRPFEESGTCKYGEKCQFAHGFHELRSLTRHPKYKTELCRTFHTIGFCPYGPRCHFIHNADERRPAPSGGASGDLRAFGTRDALHLGFPREPRPKLHHSLSFSGFPSGHHQPPGGLESPLLLDSPTSRTPPPPSCSSASSCSSSASSCSSASAASTPSGAPTCCASAAAAAAAALLYGTGGAEDLLAPGAPCAACSSASCANNAFAFGPELSSLITPLAIQTHNFAAVAAAAYYRSQQQQQQQGLAPPAQPPAPPSATLPAGAAAPPSPPFSFQLPRRLSDSPVFDAPPSPPDSLSDRDSYLSGSLSSGSLSGSESPSLDPGRRLPIFSRLSISDD,mutated_sequence,1.0,494.0,UPI000013DCDA.a2m,UPI000013DCDA.npy,gnomAD +UPI0001881A85,UPI0001881A85.csv,MAQGSGGREGALRTPAGGWHSPPSPDMQELLRSVERDLSIDPRQLAPAPGGTHVVALVPARWLASLRDRRLPLGPCPRAEGLGEAEVRTLLQRSVQRLPAGWTRVEVHGLRKRRLSYPLGGGLPFEDGSCGPETLTRFMQEVAAQNYRNLWRHAYHTYGQPYSHSPAPSAVPALDSVRQALQRVYGCSFLPVGETTQCPSYAREGPCPPRGSPACPSLLRAEALLESPEMLYVVHPYVQFSLHDVVTFSPAKLTNSQAKVLFILFRVLRAMDACHRQGLACGALSLYHIAVDEKLCSELRLDLSAYERPEEDENEEAPVARDEAGIVSQEEQGGQPGQPTGQEELRSLVLDWVHGRISNFHYLMQLNRLAGRRQGDPNYHPVLPWVVDFTTPHGRFRDLRKSKFRLNKGDKQLDFTYEMTRQAFVAGGAGGGEPPHVPHHISDVLSDITYYVYKARRTPRSVLCGHVRAQWEPHEYPASMERMQNWTPDECIPEFYTDPSIFRSIHPDMPDLDVPAWCSSSQEFVAAHRALLESREVSRDLHHWIDLTFGYKLQGKEAVKEKNVCLHLVDAHTHLASYGVVQLFDQPHPQRLAGAPALAPEPPLIPKLLVQTIQETTGREDFTENPGQLPNGVGRPVLEATPCEASWTRDRPVAGEDDLEQATEALDSISLAGKAGDQLGSSSQASPGLLSFSVASASRPGRRNKAAGADPGEGEEGRILLPEGFNPMQALEELEKTGNFLAKGLGGLLEVPEQPRVQPAVPLQCLLHRDMQALGVLLAEMVFATRVRTLQPDAPLWVRFQAVRGLCTRHPKEVPVSLQPVLDTLLQMSGPEVPMGAERGKLDQLFEYRPVSQGLPPPCPSQLLSPFSSVVPFPPYFPALHRFILLYQARRVEDEAQGRELVFALWQQLGAVLKDITPEGLEILLPFVLSLMSEEHTAVYTAWYLFEPVAKALGPKNANKYLLKPLIGAYESPCQLHGRFYLYTDCFVAQLMVRLGLQAFLTHLLPHVLQVLAGAEASQEESKDLAGAAEEEESGLPGAGPGSCAFGEEIPMDGEPPASSGLGLPDYTSGVSFHDQADLPETEDFQAGLYVTESPQPQEAEAVSLGRLSDKSSTSETSLGEERAPDEGGAPVDKSSLRSGDSSQDLKQSEGSEEEEEEEDSCVVLEEEEGEQEEVTGASELTLSDTVLSMETVVAGGSGGDGEEEEEALPEQSEGKEQKILLDTACKMVRWLSAKLGPTVASRHVARNLLRLLTSCYVGPTRQQFTVSSGESPPLSAGNIYQKRPVLGDIVSGPVLSCLLHIARLYGEPVLTYQYLPYISYLVAPGSASGPSRLNSRKEAGLLAAVTLTQKIIVYLSDTTLMDILPRISHEVLLPVLSFLTSLVTGFPSGAQARTILCVKTISLIALICLRIGQEMVQQHLSEPVATFFQVFSQLHELRQQDLKLDPAGRGEGQLPQVVFSDGQQRPVDPALLDELQKVFTLEMAYTIYVPFSCLLGDIIRKIIPNHELVGELAALYLESISPSSRNPASVEPTMPGTGPEWDPHGGGCPQDDGHSGTFGSVLVGNRIQIPNDSRPENPGPLGPISGVGGGGLGSGSDDNALKQELPRSVHGLSGNWLAYWQYEIGVSQQDAHFHFHQIRLQSFPGHSGAVKCVAPLSSEDFFLSGSKDRTVRLWPLYNYGDGTSETAPRLVYTQHRKSVFFVGQLEAPQHVVSCDGAVHVWDPFTGKTLRTVEPLDSRVPLTAVAVMPAPHTSITMASSDSTLRFVDCRKPGLQHEFRLGGGLNPGLVRALAISPSGRSVVAGFSSGFMVLLDTRTGLVLRGWPAHEGDILQIKAVEGSVLVSSSSDHSLTVWKELEQKPTHHYKSASDPIHTFDLYGSEVVTGTVSNKIGVCSLLEPPSQATTKLSSENFRGTLTSLALLPTKRHLLLGSDNGVIRLLA,mutated_sequence,1.0,1941.0,UPI0001881A85.a2m,UPI0001881A85.npy,gnomAD +UPI0001AE62FE,UPI0001AE62FE.csv,MHSWTWDPHSSEQLDLAQSLSEEDPLSSLRGSIQPQPPAEAPDKGGSQANNDPGGWDPGCSSSQVLGRTRGPCLGVRVREEEAGTRVKENLPVWTVTGELQGKPLGNPAAGTMNPESSIFIEDYLKYFQDQVSRENLLQLLTDDEAWNGFVAAAELPRDEADELRKALNKLASHMVMKDKNRHDKDQQHRQWFLKEFPRLKRELEDHIRKLRALAEEVEQVHRGTTIANVVSNSVGTTSGILTLLGLGLAPFTEGISFVLLDTGMGLGAAAAVAGITCSVVELVNKLRARAQARNLDQSGTNVAKVMKEFVGGNTPNVLTLVDNWYQVTQGIGRNIRAIRRARANPQLGAYAPPPHIIGRISAEGGEQVERVVEGPAQAMSRGTMIVGAATGGILLLLDVVSLAYESKHLLEGAKSESAEELKKRAQELEGKLNFLTKIHEMLQPGQDQ,mutated_sequence,1.0,449.0,UPI0001AE62FE.a2m,UPI0001AE62FE.npy,gnomAD +UPI0000418EA1,UPI0000418EA1.csv,MRVKPQGLVVTSSAVCSSPDYLREPKYYPGGPPTPRPLLPTRPPASPPDKAFSTHAFSENPRPPPRRDPSTRRPPVLAKGDDPLPPRAARPVSQARCPTPVGDGSSSRRCWDNGRVNLRPVVQLIDIMKDLTRLSQDLQHSGVHLDCGGLRLSRPPAPPPGDLQYSFFSSPSLANSIRSPEERATPHAKSERPSHPLYEPEPEPRDSPQPGQGHSPGATAAATGLPPEPEPDSTDYSELADADILSELASLTCPEAQLLEAQALEPPSPEPEPQLLDPQPRFLDPQALEPLGEALELPPLQPLADPLGLPGLALQALDTLPDSLESQLLDPQALDPLPKLLDVPGRRLEPQQPLGHCPLAEPLRLDLCSPHGPPGPEGHPKYALRRTDRPKILCRRRKAGRGRKADAGPEGRLLPLPMPTGLVAALAEPPPPPPPPPPALPGPGPVSVPELKPESSQTPVVSTRKGKCRGVRRMVVKMAKIPVSLGRRNKTTYKVSSLSSSLSVEGKELGLRVSAEPTPLLKMKNNGRNVVVVFPPGEMPIILKRKRGRPPKNLLLGPGKPKEPAVVAAEAATVAAATMAMPEVKKRRRRKQKLASPQPSYAADANDSKAEYSDVLAKLAFLNRQSQCAGRCSPPRCWTPSEPESVHQAPDTQSISHFLHRVQGFRRRGGKAGGFGGRGGGHAAKSARCSFSDFFEGIGKKKKVVAVAAAGVGGPGLTELGHPRKRGRGEVDAVTGKPKRKRRSRKNGTLFPEQVPSGPGFGEAGAEWAGDKGGGWAPHHGHPGGQAGRNCGFQGTEARAFASTGLESGASGRGSYYSTGAPSGQTELSQERQNLFTGYFRSLLDSDDSSDLLDFALSASRPESRKASGTYAGPPTSALPAQRGLATFPSRGAKASPVAVGSSGAGADPSFQPVLSARQTFPPGRAASYGLTPAASDCRAAETFPKLVPPPSAMARSPTTHPPANTYLPQYGGYGAGQSVFAPTKPFTGQDCANSKDCSFAYGSGNSLPASPSSAHSAGYAPPPTGGPCLPPSKASFFSSSEGAPFSGSAPTPLRCDSRASTVSPGGYMVPKGTTASATSAASAASSSSSSFQPSPENCRQFAGASQWPFRQGYGGLDWASEAFSQLYNPSFDCHVSEPNVILDISNYTPQKVKQQTAVSETFSESSSDSTQFNQPVGGGGFRRANSEASSSEGQSSLSSLEKLMMDWNEASSAPGYNWNQSVLFQSSSKPGRGRRKKVDLFEASHLGFPTSASAAASGYPSKRSTGPRQPRGGRGGGACSAKKERGGAAAKAKFIPKPQPVNPLFQDSPDLGLDYYSGDSSMSPLPSQSRAFGVGERDPCDFIGPYSMNPSTPSDGTFGQGFHCDSPSLGAPELDGKHFPPLAHPPTVFDAGLQKAYSPTCSPTLGFKEELRPPPTKLAACEPLKHGLQGASLGHAAAAQAHLSCRDLPLGQPHYDSPSCKGTAYWYPPGSAARSPPYEGKVGTGLLADFLGRTEAACLSAPHLASPPATPKADKEPLEMARPPGPPRGPAAAAAGYGCPLLSDLTLSPVPRDSLLPLQDTAYRYPGFMPQAHPGLGGGPKSGFLGPMAEPHPEDTFTVTSL,mutated_sequence,1.0,1603.0,UPI0000418EA1.a2m,UPI0000418EA1.npy,gnomAD +UPI00001D9756,UPI00001D9756.csv,MGTAAAAAAAAAAAAAGEGARSPSPAAVSLGLGVAVVSSLVNGSTFVLQKKGIVRAKRRGTSYLTDIVWWAGTIAMAVGQIGNFLAYTAVPTVLVTPLGALGVPFGSILASYLLKEKLNILGKLGCLLSCAGSVVLIIHSPKSESVTTQAELEEKLTNPVFVGYLCIVLLMLLLLIFWIAPAHGPTNIMVYISICSLLGSFTVPSTKGIGLAAQDILHNNPSSQRALCLCLVLLAVLGCSIIVQFRYINKALECFDSSVFGAIYYVVFTTLVLLASAILFREWSNVGLVDFLGMACGFTTVSVGIVLIQVFKEFNFNLGEMNKSNMKTD,mutated_sequence,1.0,329.0,UPI00001D9756.a2m,UPI00001D9756.npy,gnomAD +UPI000013E280,UPI000013E280.csv,MEEPPQEALAEPLKHESPAAPSSAGHTKGQEEDDQKNQAERKADNHTAHRIADQTALRVPSQAESSIFSQATNGVAEQNGHSTPGQAGRRASNPADVSDLRADDQVNQTPSEQTKGKASSQANNVQHEQSDGQVSGLTEERTAEQTERRLPTQAERRTSGQIDGRLAMPSDQRGSRQTDHRMAGQSERRASEQMDRRMSGEAERRTSEQITHRLSKLSERRPSVQIDSGSSVPSDQSPSVQIDSGSSVPSDQRPSVQIDRRMSGKVRRRSSEKTDYRLAGLADPGTSEQTDLRLYGLVDHKTSVKTHHQVYGQATELAEHQAIDQAHSNADQPPVDNAHYTESDQTDHLADRQANHKDQLSYYETRGQSEDRIFPQLGNSKEDKEADYRVQPCKFEDSQVDLNSKPSVEMETQNATTIPPYNPVDARFTSNFQAKDQALFPRLPSISSKLNYTSSQEKTQAIVTKSDEFSEIDQGKGYHIRNQTYRRFPSIVYEDPYQVSLQYMEKHHILQIFQQITENLVYEKPEDPLNFMLCQV,mutated_sequence,1.0,536.0,UPI000013E280.a2m,UPI000013E280.npy,gnomAD +UPI000016864A,UPI000016864A.csv,MGLGRCIWEGWTLESEALRRDMGTWLLACICICTCVCLGVSVTGEGQGPRSRTFTCLTNNILRIDCHWSAPELGQGSSPWLLFTSNQAPGGTHKCILRGSECTVVLPPEAVLVPSDNFTITFHHCMSGREQVSLVDPEYLPRRHVKLDPPSDLQSNISSGHCILTWSISPALEPMTTLLSYELAFKKQEEAWEQAQHRDHIVGVTWLILEAFELDPGFIHEARLRVQMATLEDDVVEEERYTGQWSEWSQPVCFQAPQRQGPLIPPWGWPGNTLVAVSIFLLLTGPTYLLFKLSPRVKRIFYQNVPSPAMFFQPLYSVHNGNFQTWMGAHGAGVLLSQDCAGTPQGALEPCVQEATALLTCGPARPWKSVALEEEQEGPGTRLPGNLSSEDVLPAGCTEWRVQTLAYLPQEDWAPTSLTRPAPPDSEGSRSSSSSSSSNNNNYCALGCYGGWHLSALPGNTQSSGPIPALACGLSCDHQGLETQQGVAWVLAGHCQRPGLHEDLQGMLLPSVLSKARSWTF,mutated_sequence,1.0,521.0,UPI000016864A.a2m,UPI000016864A.npy,gnomAD +UPI00001AE786,UPI00001AE786.csv,MAHHLPAAMESHQDFRSIKAKFQASQPEPSDLPKKPPKPEFGKLKKFSQPELSEHPKKAPLPEFGAVSLKPPPPEVTDLPKKPPPPEVTDLPKKPPPPEVTDLPKKPPPPEVTDLPKKPSKLELSDLSKKFPQLGATPFPRKPLQPEVGEAPLKASLPEPGAPARKPLQPDELSHPARPPSEPKSGAFPRKLWQPEAGEATPRSPQPELSTFPKKPAQPEFNVYPKKPPQPQVGGLPKKSVPQPEFSEAAQTPLWKPQSSEPKRDSSAFPKKASQPPLSDFPKKPPQPELGDLTRTSSEPEVSVLPKRPRPAEFKALSKKPPQPELGGLPRTSSEPEFNSLPRKLLQPERRGPPRKFSQPEPSAVLKRHPQPEFFGDLPRKPPLPSSASESSLPAAVAGFSSRHPLSPGFGAAGTPRWRSGGLVHSGGARPGLRPSHPPRRRPLPPASSLGHPPAKPPLPPGPVDMQSFRRPSAASIDLRRTRSAAGLHFQDRQPEDIPQVPDEIYELYDDVEPRDDSSPSPKGRDEAPSVQQAARRPPQDPALRKEKDPQPQQLPPMDPKLLKQLRKAEKAEREFRKKFKFEGEIVVHTKMMIDPNAKTRRGGGKHLGIRRGEILEVIEFTSNEEMLCRDPKGKYGYVPRTALLPLETEVYDDVDFCDPLENQPLPLGR,mutated_sequence,1.0,670.0,UPI00001AE786.a2m,UPI00001AE786.npy,gnomAD +UPI000166C19F,UPI000166C19F.csv,MPQIIKMTRILTAFKVVRTLKTGFGFTNVTAHQKWKFSRPGIRLLSVKAQTAHIVLEDGTKMKGYSFGHPSSVAGEVVFNTGLGGYPEAITDPAYKGQILTMANPIIGNGGAPDTTALDELGLSKYLESNGIKVSGLLVLDYSKDYNHWLATKSLGQWLQEEKVPAIYGVDTRMLTKIIRDKGTMLGKIEFEGQPVDFVDPNKQNLIAEVSTKDVKVYGKGNPTKVVAVDCGIKNNVIRLLVKRGAEVHLVPWNHDFTKMEYDGILIAGGPGNPALAEPLIQNVRKILESDRKEPLFGISTGNLITGLAAGAKTYKMSMANRGQNQPVLNITNKQAFITAQNHGYALDNTLPAGWKPLFVNVNDQTNEGIMHESKPFFAVQFHPEVTPGPIDTEYLFDSFFSLIKKGKATTITSVLPKPALVASRVEVSKVLILGSGGLSIGQAGEFDYSGSQAVKAMKEENVKTVLMNPNIASVQTNEVGLKQADTVYFLPITPQFVTEVIKAEQPDGLILGMGGQTALNCGVELFKRGVLKEYGVKVLGTSVESIMATEDRQLFSDKLNEINEKIAPSFAVESIEDALKAADTIGYPVMIRSAYALGGLGSGICPNRETLMDLSTKAFAMTNQILVEKSVTGWKEIEYEVVRDADDNCVTVCNMENVDAMGVHTGDSVVVAPAQTLSNAEFQMLRRTSINVVRHLGIVGECNIQFALHPTSMEYCIIEVNARLSRSSALASKATGYPLAFIAAKIALGIPLPEIKNVVSGKTSACFEPSLDYMVTKIPRWDLDRFHGTSSRIGSSMKSVGEVMAIGRTFEESFQKALRMCHPSIEGFTPRLPMNKEWPSNLDLRKELSEPSSTRIYAIAKAIDDNMSLDEIEKLTYIDKWFLYKMRDILNMEKTLKGLNSESMTEETLKRAKEIGFSDKQISKCLGLTEAQTRELRLKKNIHPWVKQIDTLAAEYPSVTNYLYVTYNGQEHDVNFDDHGMMVLGCGPYHIGSSVEFDWCAVSSIRTLRQLGKKTVVVNCNPETVSTDFDECDKLYFEELSLERILDIYHQEACGGCIISVGGQIPNNLAVPLYKNGVKIMGTSPLQIDRAEDRSIFSAVLDELKVAQAPWKAVNTLNEALEFAKSVDYPCLLRPSYVLSGSAMNVVFSEDEMKKFLEEATRVSQEHPVVLTKFVEGAREVEMDAVGKDGRVISHAISEHVEDAGVHSGDATLMLPTQTISQGAIEKVKDATRKIAKAFAISGPFNVQFLVKGNDVLVIECNLRASRSFPFVSKTLGVDFIDVATKVMIGENVDEKHLPTLDHPIIPADYVAIKAPMFSWPRLRDADPILRCEMASTGEVACFGEGIHTAFLKAMLSTGFKIPQKGILIGIQQSFRPRFLGVAEQLHNEGFKLFATEATSDWLNANNVPATPVAWPSQEGQNPSLSSIRKLIRDGSIDLVINLPNNNTKFVHDNYVIRRTAVDSGIPLLTNFQVTKLFAEAVQKSRKVDSKSLFHYRQYSAGKAA,mutated_sequence,1.0,1506.0,UPI000166C19F.a2m,UPI000166C19F.npy,gnomAD +UPI000022BFB0,UPI000022BFB0.csv,MDISKGLPGMQGGLHIWISENRKMVPVPEGAYGNFFEEHCYVILHVPQSPKATQGASSDLHYWVGKQAGAEAQGAAEAFQQRLQDELGGQTVLHREAQGHESDCFCSYFRPGIIYRKGGLASDLKHVETNLFNIQRLLHIKGRKHVSATEVELSWNSFNKGDIFLLDLGKMMIQWNGPKTSISEKARGLALTYSLRDRERGGGRAQIGVVDDEAKAPDLMQIMEAVLGRRVGSLRAATPSKDINQLQKANVRLYHVYEKGKDLVVLELATPPLTQDLLQEEDFYILDQGGFKIYVWQGRMSSLQERKAAFSRAVGFIQAKGYPTYTNVEVVNDGAESAAFKQLFRTWSEKRRRNQKLGGRDKSIHVKLDVGKLHTQPKLAAQLRMVDDGSGKVEVWCIQDLHRQPVDPKRHGQLCAGNCYLVLYTYQRLGRVQYILYLWQGHQATADEIEALNSNAEELDVMYGGVLVQEHVTMGSEPPHFLAIFQGQLVIFQERAGHHGKGQSASTTRLFQVQGTDSHNTRTMEVPARASSLNSSDIFLLVTASVCYLWFGKGCNGDQREMARVVVTVISRKNEETVLEGQEPPHFWEALGGRAPYPSNKRLPEEVPSFQPRLFECSSHMGCLVLAEVGFFSQEDLDKYDIMLLDTWQEIFLWLGEAASEWKEAVAWGQEYLKTHPAGRSPATPIVLVKQGHEPPTFIGWFFTWDPYKWTSHPSHKEVVDGSPAAASTISEITAEVNNLRLSRWPGNGRAGAVALQALKGSQDSSENDLVRSPKSAGSRTSSSVSSTSATINGGLRREQLMHQAVEDLPEGVDPARREFYLSDSDFQDIFGKSKEEFYSMATWRQRQEKKQLGFF,mutated_sequence,1.0,856.0,UPI000022BFB0.a2m,UPI000022BFB0.npy,gnomAD +UPI0000413F35,UPI0000413F35.csv,MALAAAAAAAAAGVSQAAVLGFLQEHGGKVRNSELLSRFKPLLDAGDPRGRAARRDRFKQFVNNVAVVKELDGVKFVVLRKKPRPPEPEPAPFGPPGAAAQPSKPTSTVLPRSASAPGAPPLVRVPRPVEPPGDLGLPTEPQDTPGGPASEPAQPPGERSADPPLPALELAQATERPSADAAPPPRAPSEAASPCSDPPDAEPGPGAAKGPPQQKPCMLPVRCVPAPATLRLRAEEPGLRRQLSEEPSPRSSPLLLRRLSVEESGLGLGLGPGRSPHLRRLSRAGPRLLSPDAEELPAAPPPSAVPLEPSEHEWLVRTAGGRWTHQLHGLLLRDRGLAAKRDFMSGFTALHWAAKSGDGEMALQLVEVARRSGAPVDVNARSHGGYTPLHLAALHGHEDAAVLLVVRLGAQVHVRDHSGRRAYQYLRPGSSYALRRLLGDPGLRGTTEPDATGGGSGSLAARRPVQVAATILSSTTSAFLGVLADDLMLQDLARGLKKSSSFSKFLSASPMAPRKKTKIRGGLPAFSEISRRPTPGPLAGLVPSFPPTT,mutated_sequence,1.0,549.0,UPI0000413F35.a2m,UPI0000413F35.npy,gnomAD +UPI000013CFAC,UPI000013CFAC.csv,MKPNFSLRLRIFNLNCWGIPYLSKHRADRMRRLGDFLNQESFDLALLEEVWSEQDFQYLRQKLSPTYPAAHHFRSGIIGSGLCVFSKHPIQELTQHIYTLNGYPYMIHHGDWFSGKAVGLLVLHLSGMVLNAYVTHLHAEYNRQKDIYLAHRVAQAWELAQFIHHTSKKADVVLLCGDLNMHPEDLGCCLLKEWTGLHDAYLETRDFKGSEEGNTMVPKNCYVSQQELKPFPFGVRIDYVLYKAVSGFYISCKSFETTTGFDPHRGTPLSDHEALMATLFVRHSPPQQNPSSTHGPAERSPLMCVLKEAWTELGLGMAQARWWATFASYVIGLGLLLLALLCVLAAGGGAGEAAILLWTPSVGLVLWAGAFYLFHVQEVNGLYRAQAELQHVLGRAREAQDLGPEPQPALLLGQQEGDRTKEQ,mutated_sequence,1.0,423.0,UPI000013CFAC.a2m,UPI000013CFAC.npy,gnomAD +UPI0000205DDE,UPI0000205DDE.csv,MAIEGGERTCGVHELICIRKVSPEAVGFLSAVGVFIILMLLLFLYINKKFCFENVGGFPDLGSEYSTRKNSQDKIYNSYMDKDEHGSSSESEDEALGKYHEALSRTHNSRLPLADSRQRNYAWETRQKYSPLSAEYDGYSSEASIDEGNCIQRMRRTPPLDELQPPPYQDDSGSPHLSCTPSEIGDSKCEFSHCSNSPRCSYNKCPSEGSTGHEIESFHNKGYEEDVPSDSTAVLSPEDMSAQGSSSQLPKPFDPEPEAKYGTLDVTFDYDSQEQKLLVTVTAVTDIPTYNRTGGNSWQVHLVLLPIKKQRAKTSIQRGPCPVFTETFKFNHVESEMIGNYAVRFRLYGVHRMKKEKIVGEKIFYLTKLNLQGKMSLPVILEPSYNHSGCDSQMSVSEMSCSESTSSCQSLEHGSVPEILIGLLYNATTGRLSAEVIKGSHFKNLAANRPPNTYVKLTLLNSMGQEMSKCKTSIRRGQPNPVYKETFVFQVALFQLSDVTLILSVYNKRSMKRKEMIGWISLGLNSSGEEELNHWTEMKESKGQQVCRWHALLES,mutated_sequence,1.0,555.0,UPI0000205DDE.a2m,UPI0000205DDE.npy,gnomAD +UPI0000046C44,UPI0000046C44.csv,MKLLLLHPAFQSCLLLTLLGLWRTTPEAHASSLGAPAISAASFLQDLIHRYGEGDSLTLQQLKALLNHLDVGVGRGNVTQHVQGHRNLSTCFSSGDLFTAHNFSEQSRIGSSELQEFCPTILQQLDSRACTSENQENEENEQTEEGRPSAVEVWGYGLLCVTVISLCSLLGASVVPFMKKTFYKRLLLYFIALAIGTLYSNALFQLIPEAFGFNPLEDYYVSKSAVVFGGFYLFFFTEKILKILLKQKNEHHHGHSHYASESLPSKKDQEEGVMEKLQNGDLDHMIPQHCSSELDGKAPMVDEKVIVGSLSVQDLQASQSACYWLKGVRYSDIGTLAWMITLSDGLHNFIDGLAIGASFTVSVFQGISTSVAILCEEFPHELGDFVILLNAGMSIQQALFFNFLSACCCYLGLAFGILAGSHFSANWIFALAGGMFLYISLADMFPEMNEVCQEDERKGSILIPFIIQNLGLLTGFTIMVVLTMYSGQIQIG,mutated_sequence,1.0,492.0,UPI0000046C44.a2m,UPI0000046C44.npy,gnomAD +UPI0000037465,UPI0000037465.csv,MGRWCQTVARGQRPRTSAPSRAGALLLLLLLLRSAGCWGAGEAPGALSTADPADQSVQCVPKATCPSSRPRLLWQTPTTQTLPSTTMETQFPVSEGKVDPYRSCGFSYEQDPTLRDPEAVARRWPWMVSVRANGTHICAGTIIASQWVLTVAHCLIWRDVIYSVRVGSPWIDQMTQTASDVPVLQVIMHSRYRAQRFWSWVGQANDIGLLKLKQELKYSNYVRPICLPGTDYVLKDHSRCTVTGWGLSKADGMWPQFRTIQEKEVIILNNKECDNFYHNFTKIPTLVQIIKSQMMCAEDTHREKFCYELTGEPLVCSMEGTWYLVGLVSWGAGCQKSEAPPIYLQVSSYQHWIWDCLNGQALALPAPSRTLLLALPLPLSLLAAL,mutated_sequence,1.0,385.0,UPI0000037465.a2m,UPI0000037465.npy,gnomAD +UPI0000130507,UPI0000130507.csv,MRQVCCSALPPPPLEKGRCSSYSDSSSSSSERSSSSSSSSSESGSSSRSSSNNSSISRPAAPPEPRPQQQPQPRSPAARRAAARSRAAAAGGMRRDPAPGFSMLLFGVSLACYSPSLKSVQDQAYKAPVVVEGKVQGLVPAGGSSSNSTREPPASGRVALVKVLDKWPLRSGGLQREQVISVGSCVPLERNQRYIFFLEPTEQPLVFKTAFAPLDTNGKNLKKEVGKILCTDCATRPKLKKMKSQTGQVGEKQSLKCEAAAGNPQPSYRWFKDGKELNRSRDIRIKYGNGRKNSRLQFNKVKVEDAGEYVCEAENILGKDTVRGRLYVNSVSTTLSSWSGHARKCNETAKSYCVNGGVCYYIEGINQLSCKCPNGFFGQRCLEKLPLRLYMPDPKQKAEELYQKRVLTITGICVALLVVGIVCVVAYCKTKKQRKQMHNHLRQNMCPAHQNRSLANGPSHPRLDPEEIQMADYISKNVPATDHVIRRETETTFSGSHSCSPSHHCSTATPTSSHRHESHTWSLERSESLTSDSQSGIMLSSVGTSKCNSPACVEARARRAAAYNLEERRRATAPPYHDSVDSLRDSPHSERYVSALTTPARLSPVDFHYSLATQVPTFEITSPNSAHAVSLPPAAPISYRLAEQQPLLRHPAPPGPGPGPGPGPGPGADMQRSYDSYYYPAAGPGPRRGTCALGGSLGSLPASPFRIPEDDEYETTQECAPPPPPRPRARGASRRTSAGPRRWRRSRLNGLAAQRARAARDSLSLSSGSGGGSASASDDDADDADGALAAESTPFLGLRGAHDALRSDSPPLCPAADSRTYYSLDSHSTRASSRHSRGPPPRAKQDSAPL,mutated_sequence,1.0,850.0,UPI0000130507.a2m,UPI0000130507.npy,gnomAD +UPI000013E60F,UPI000013E60F.csv,MALSLWPLLLLLLLLLLLSFAVTLAPTGPHSLDPGLSFLKSLLSTLDQAPQGSLSRSRFFTFLANISSSFEPGRMGEGPVGEPPPLQPPALRLHDFLVTLRGSPDWEPMLGLLGDMLALLGQEQTPRDFLVHQAGVLGGLVEVLLGALVPGGPPTPTRPPCTRDGPSDCVLAADWLPSLLLLLEGTRWQALVQVQPSVDPTNATGLDGREAAPHFLQGLLGLLTPTGELGSKEALWGGLLRTVGAPLYAAFQEGLLRVTHSLQDEVFSILGQPEPDTNGQCQGGNLQQLLLWGVRHNLSWDVQALGFLSGSPPPPPALLHCLSTGVPLPRASQPSAHISPRQRRAITVEALCENHLGPAPPYSISNFSIHLLCQHTKPATPQPHPSTTAICQTAVWYAVSWAPGAQGWLQACHDQFPDEFLDAICSNLSFSALSGSNRRLVKRLCAGLLPPPTSCPEGLPPVPLTPDIFWGCFLENETLWAERLCGEASLQAVPPSNQAWVQHVCQGPTPDVTASPPCHIGPCGERCPDGGSFLVMVCANDTMYEVLVPFWPWLAGQCRISRGGNDTCFLEGLLGPLLPSLPPLGPSPLCLTPGPFLLGMLSQLPRCQSSVPALAHPTRLHYLLRLLTFLLGPGAGGAEAQGMLGRALLLSSLPDNCSFWDAFRPEGRRSVLRTIGEYLEQDEEQPTPSGFEPTVNPSSGISKMELLACFSPVLWDLLQREKSVWALQILVQAYLHMPPENLQQLVLSAEREAAQGFLTLMLQGKLQGKLQVPPSEEQALGRLTALLLQRYPRLTSQLFIDLSPLIPFLAVSDLMRFPPSLLANDSVLAAIRDYSPGMRPEQKEALAKRLLAPELFGEVPAWPQELLWAVLPLLPHLPLENFLQLSPHQIQALEDSWPAAGLGPGHARHVLRSLVNQSVQDGEEQVRRLGPLACFLSPEELQSLVPLSDPTGPVERGLLECAANGTLSPEGRVAYELLGVLRSSGGAVLSPRELRVWAPLFSQLGLRFLQELSEPQLRAMLPVLQGTSVTPAQAVLLLGRLLPRHDLSLEELCSLHLLLPGLSPQTLQAIPRRVLVGACSCLAPELSRLSACQTAALLQTFRVKDGVKNMGTTGAGPAVCIPGQPIPTTWPDCLLPLLPLKLLQLDSLALLANRRRYWELPWSEQQAQFLWKKMQVPTNLTLRNLQALGTLAGGMSCEFLQQINSMVDFLEVVHMIYQLPTRVRGSLRACIWAELQRRMAMPEPEWTTVGPELNGLDSKLLLDLPIQLMDRLSNESIMLVVELVQRAPEQLLALTPLHQAALAERALQNLAPKETPVSGEVLETLGPLVGFLGTESTRQIPLQILLSHLSQLQGFCLGETFATELGWLLLQESVLGKPELWSQDEVEQAGRLVFTLSTEAISLIPREALGPETLERLLEKQQSWEQSRVGQLCREPQLAAKKAALVAGVVRPAAEDLPEPVPNCADVRGTFPAAWSATQIAEMELSDFEDCLTLFAGDPGLGPEELRAAMGKAKQLWGPPRGFRPEQILQLGRLLIGLGDRELQELILVDWGVLSTLGQIDGWSTTQLRIVVSSFLRQSGRHVSHLDFVHLTALGYTLCGLRPEELQHISSWEFSQAALFLGTLHLQCSEEQLEVLAHLLVLPGGFGPISNWGPEIFTEIGTIAAGIPDLALSALLRGQIQGVTPLAISVIPPPKFAVVFSPIQLSSLTSAQAVAVTPEQMAFLSPEQRRAVAWAQHEGKESPEQQGRSTAWGLQDWSRPSWSLVLTISFLGHLL,mutated_sequence,1.0,1775.0,UPI000013E60F.a2m,UPI000013E60F.npy,gnomAD +UPI0000041BDE,UPI0000041BDE.csv,MVATNNVTEIIFVGFSQNWSEQRVISVMFLLMYTAVVLGNGLIVVTILASKVLTSPMYFFLSYLSFVEICYCSVMAPKLIFDSFIKRKVISLKGCLTQMFSLHFFGGTEAFLLMVMAYDRYVAICKPLHYMAIMNQRMCGLLVRIAWGGGLLHSVGQTFLIFQLPFCGPNIMDHYFCDVHPVLELACADTFFISLLIITNGGSISVVSFFVLMASYLIILHFLRSHNLEGQHKALSTCASHVTVVDLFFIPCSLVYIRPCVTLPADKIVAVFYTVVTPLLNPVIYSFRNAEVKNAMRRFIGGKVI,mutated_sequence,1.0,305.0,UPI0000041BDE.a2m,UPI0000041BDE.npy,gnomAD +UPI000006EEED,UPI000006EEED.csv,MLQAADFIERTETAGELSRGLIGVLSSQISWCLLNVNLSKLPTRLQRLSCSVLNSSPAMRGGARGRPQLTLERPLRPGCRLHSCSEAEKGGFVRRKEIILFPPCEDPARGWLSANPGREPSPGICWHLNLGLPSLHNCEE,mutated_sequence,1.0,140.0,UPI000006EEED.a2m,UPI000006EEED.npy,gnomAD +UPI0000EADF0B,UPI0000EADF0B.csv,MEIEVSVAECKSVPGITSTPHPMDHPSAFYSPPHNGLLTDHHESLDNDVAREIRYLDEVLEANCCDSAVDGTYNGTSSPEPGAVVLVGGLSPPVHEATQPEPTERTASRQAPPHIELSNSSPDPMAEAERTNGHSPSQPRDALGDSLQVPVSPSSTTSSRCSSRDGEFTLTTLKKEAKFELRAFHEDKKPSKLFEDDEHEKEQYCIRKVRPSEEMLELEKERRELIRSQAVKKNPGIAAKWWNPPQEKTIEEQLDEEHLESHKKYKERKERRAQQEQLLLQKQLQQQQQQPPSQLCTAPASSHERASMIDKAKEDIVTEQIDFSAARKQFQLMENSRQAVAKGQSTPRLFSIKPFYRPLGSVNSDKPLTNPRPPSVGGPPEDSGASAAKGQKSPGALETPSAAGSQGNTASQGKEGPYSEPSKRGPLSKLWAEDGEFTSARAVLTVVKDDDHGILDQFSRSVNVSLTQEELDSGLDELSVRSQDTTVLETLSNDFSMDNISDSGASNETTNALQENSLADFSLPQTPQTDNPSEGRGEGVSKSFSDHGFYSPSSTLGDSPLVDDPLEYQAGLLVQNAIQQAIAEQVDKAVSKTSRDGAEQQGPEATVEEAEAAAFGSEKPQSMFEPPQVSSPVQEKRDVLPKILPAEDRALRERGPPQPLPAVQPSGPINMEETRPEGSYFSKYSEAAELRSTASLLATQESDVMVGPFKLRSRKQRTLSMIEEEIRAAQEREEELKRQRQVLQSTQSPRTKNAPSLPSRTCYKTAPGKIEKVKPPPSPTTEGPSLQPDLAPEEAAGTQRPKNLMQTLMEDYETHKSKRRERMDDSSVLEATRVNRRKSALALRWEAGIYANQEEEDNE,mutated_sequence,1.0,859.0,UPI0000EADF0B.a2m,UPI0000EADF0B.npy,gnomAD +UPI0000D820CC,UPI0000D820CC.csv,MTEITAEGNASTTTTVIDSKNGSVPKSPGKVLKRTVTEDIVTTFSSPAAWLLVIALIITWSAVAIVMFDLVDYKNFSASSIAKIGSDPLKLVRDAMEETTDWIYGFFSLLSDIISSEDEEDDDGDEDTDKGEIDEPPLRKKEIHKDKTEKQEKPERKIQTKVTHKEKEKGKEKVREKEKPEKKATHKEKIEKKEKPETKTLAKEQKKAKTAEKSEEKTKKEVKGGKQEKVKQTAAKVKEVQKTPSKPKEKEDKEKAAVSKHEQKDQYAFCRYMIDIFVHGDLKPGQSPAIPPPLPTEQASRPTPASPALEEKEGEKKKAEKKVTSETKKKEKEDIKKKSEKETAIDVEKKEPGKASETKQGTVKIAAQAAAKKDEKKEDSKKTKKPAEVEQPKGKKQEKKEKHVEPAKSPKKEHSVPSDKQVKAKTERAKEEIGAVSIKKAVPGKKEEKTTKTVEQEIRKEKSGKTSSILKDKEPIKGKEEKVPASLKEKEPETKKDEKMSKAGKEVKPKPPQLQGKKEEKPEPQIKKEAKPAISEKVQIHKQDIVKPEKTVSHGKPEEKVLKQVKAVTIEKTAKPKPTKKAEHREREPPSIKTDKPKPTPKGTSEVTESGKKKTEISEKESKEKADMKHLREEKVSTRKESLQLHNVTKAEKPARVSKDVEDVPASKKAKEGTEDVSPTKQKSPISFFQCVYLDGYNGYGFQFPFTPADRPGESSGQANSPGQKQQGQ,mutated_sequence,1.0,729.0,UPI0000D820CC.a2m,UPI0000D820CC.npy,gnomAD +UPI000013CE13,UPI000013CE13.csv,MTHCCSPCCQPTCCRTTCCRTTCWKPTTVTTCSSTPCCQPACCVSSCCQPCCRPTCCQNTCCRTTCCQPTCVTSCCQPSCCSTPCCQPTCCGSSCCGQTSCGSSCGQSSSCAPVYCRRTCYYPTTVCLPGCLNQSCGSNCCQPCCRPACCETTCCRTTCFQPTCVSSCCQPSCC,mutated_sequence,1.0,174.0,UPI000013CE13.a2m,UPI000013CE13.npy,gnomAD +UPI0000074578,UPI0000074578.csv,MSGGFELQPRDGGPRVALAPGETVIGRGPLLGITDKRVSRRHAILEVAGGQLRIKPIHTNPCFYQSSEKSQLLPLKPNLWCYLNPGDSFSLLVDKYIFRILSIPSEVEMQCTLRNSQVLDEDNILNETPKSPVINLPHETTGASQLEGSTEIAKTQMTPTNSVSFLGENRDCNKQQPILAERKRILPTWMLAEHLSDQNLSVPAISGGNVIQGSGKEEICKDKSQLNTTQQGRRQLISSGSSENTSAEQDTGEECKNTDQEESTISSKEMPQSFSAITLSNTEMNNIKTNAQRNKLPIEELGKVSKHKIATKRTPHKEDEAMSCSENCSSAQGDSLQDESQGSHSESSSNPSNPETLHAKATDSVLQGSEGNKVKRTSCMYGANCYRKNPVHFQHFSHPGDSDYGGVQIVGQDETDDRPECPYGPSCYRKNPQHKIEYRHNTLPVRNVLDEDNDNVGQPNEYDLNDSFLDDEEEDYEPTDEDSDWEPGKEDEEKEDVEELLKEAKRFMKRK,mutated_sequence,1.0,511.0,UPI0000074578.a2m,UPI0000074578.npy,gnomAD +UPI000005104D,UPI000005104D.csv,MAEEEAPKKSRAAGGGASWELCAGALSARLAEEGSGDAGGRRRPPVDPRRLARQLLLLLWLLEAPLLLGVRAQAAGQGPGQGPGPGQQPPPPPQQQQSGQQYNGERGISVPDHGYCQPISIPLCTDIAYNQTIMPNLLGHTNQEDAGLEVHQFYPLVKVQCSAELKFFLCSMYAPVCTVLEQALPPCRSLCERARQGCEALMNKFGFQWPDTLKCEKFPVHGAGELCVGQNTSDKGTPTPSLLPEFWTSNPQHGGGGHRGGFPGGAGASERGKFSCPRALKVPSYLNYHFLGEKDCGAPCEPTKVYGLMYFGPEELRFSRTWIGIWSVLCCASTLFTVLTYLVDMRRFSYPERPIIFLSGCYTAVAVAYIAGFLLEDRVVCNDKFAEDGARTVAQGTKKEGCTILFMMLYFFSMASSIWWVILSLTWFLAAGMKWGHEAIEANSQYFHLAAWAVPAIKTITILALGQVDGDVLSGVCFVGLNNVDALRGFVLAPLFVYLFIGTSFLLAGFVSLFRIRTIMKHDGTKTEKLEKLMVRIGVFSVLYTVPATIVIACYFYEQAFRDQWERSWVAQSCKSYAIPCPHLQAGGGAPPHPPMSPDFTVFMIKYLMTLIVGITSGFWIWSGKTLNSWRKFYTRLTNSKQGETTV,mutated_sequence,1.0,647.0,UPI000005104D.a2m,UPI000005104D.npy,gnomAD +UPI00001C2011,UPI00001C2011.csv,MAEVVAEVAEMPTQMSPGAVEMSTPMSAEMMEMSTEVTEMTPGEALASSLFFQHHQFMCSECGSLYNTLEEVLSHQEQHMLAVSEEEALTTQNVGLEPELVPGAEGPFQCGECSQLILSPGELLAHQDAHLRESANQIQYQCWDCQELFPSPELWVAHRKAQHLSATVAEPPVPPPLPPPTPLPPPSPPSEVKMEPYECPECSTLCATPEEFLEHQGTHFDSLEKEERNGLEEEEEDDEEDEEDDEEMEDEEAMAEVGDDAVGGDESTAGWAQGCGDCPQHQPSAGARRQHRRTAHSPASATHPFHCSQCQRSFSSANRLQAHGRAHVGGTHECTTCSKVFKKAASLEQHLRLHRGEARYLCVDCGRGFGTELTLVAHRRAHTANPLHRCRCGKTFSNMTKFLYHRRTHAGKSGAPPTGATAPPAPAEPTPPPPPPAPPAQLPCPQCSKSFASASRLSRHRRAVHGPPERRHRCGVCGKGFKKLIHVRNHLRTHTGERPFQCHSCGKTFASLANLSRHQLTHTGARPYQCLDCGKRFTQSSNLQQHRRLHLRPVAFARAPRLPITGLYNKSPYYCGTCGRWFRAMAGLRLHQRVHARARTLTLQPPRSPSPAPPPPPEPQQTIMCTELGETIAIIETSQPLALEDTLQLCQAALGASEAGGLLQLDTAFV,mutated_sequence,1.0,670.0,UPI00001C2011.a2m,UPI00001C2011.npy,gnomAD +UPI000198C8A7,UPI000198C8A7.csv,MACRRRYFVEGEAPSSETGTSLDSPSAYPQGPLVPGSSLSPDHYEHTSVGAYGLYSGPPGQQQRTRRPKLQHSTSILRKQAEEEAIKRSRSLSESYELSSDLQDKQVEMLERKYGGRLVTRHAARTIQTAFRQYQMNKNFERLRSSMSENRMSRRIVLSNMRMQFSFEGPEKVHSSYFEGKQVSVTNDGSQLGALVSPECGDLSEPTTLKSPAPSSDFADAITELEDAFSRQVKSLAESIDDALNCRSLHTEEAPALDAARARDTEPQTALHGMDHRKLDEMTASYSDVTLYIDEEELSPPLPLSQAGDRPSSTESDLRLRAGGAAPDYWALAHKEDKADTDTSCRSTPSLERQEQRLRVEHLPLLTIEPPSDSSVDLSDRSERGSLKRQSAYERSLGGQQGSPKHGPHSGAPKSLPREEPELRPRPPRPLDSHLAINGSANRQSKSESDYSDGDNDSINSTSNSNDTINCSSESSSRDSLREQTLSKQTYHKEARNSWDSPAFSNDVIRKRHYRIGLNLFNKKPEKGVQYLIERGFVPDTPVGVAHFLLQRKGLSRQMIGEFLGNRQKQFNRDVLDCVVDEMDFSTMELDEALRKFQAHIRVQGEAQKVERLIEAFSQRYCICNPGVVRQFRNPDTIFILAFAIILLNTDMYSPNVKPERKMKLEDFIKNLRGVDDGEDIPREMLMGIYERIRKRELKTNEDHVSQVQKVEKLIVGKKPIGSLHPGLGCVLSLPHRRLVCYCRLFEVPDPNKPQKLGLHQREIFLFNDLLVVTKIFQKKKNSVTYSFRQSFSLYGMQVLLFENQYYPNGIRLTSSVPGADIKVLINFNAPNPQDRKKFTDDLRESIAEVQEMEKHRIESELEKQKGVVRPSMSQCSSLKKESGNGTLSRACLDDSYASGEGLKRSALSSSLRDLSEAGKRGRRSSAGSLESNVEGSIISSPHMRRRATSTRECPSRPHQTMPNSSSLLGSLFGSKRGKPPPQAHLPSAPALATPPPTGGPASLAALCGWPPPGAPRGAAAGRHARASHPVLPHAEPSPVPPSPPPPPTPAHPARTPVPPRPPWGPPSLRGPCPRPPAAALGPRGAHSAPPWAAPCPAAPHQQQGQTQRHQHNCVDSLGRGPRLPETPAHHTGHARGSPAAHQTRGTSVAISPLCPSRPN,mutated_sequence,1.0,1160.0,UPI000198C8A7.a2m,UPI000198C8A7.npy,gnomAD +UPI0002065B81,UPI0002065B81.csv,MAGASVKVAVRVRPFNSREMSRDSKCIIQMSGSTTTIVNPKQPKETPKSFSFDYSYWSHTSPEDINYASQKQVYRDIGEEMLQHAFEGYNVCIFAYGQTGAGKSYTMMGKQEKDQQGIIPQLCEDLFSRINDTTNDNMSYSVEVSYMEIYCERVRDLLNPKNKGNLRVREHPLLGPYVEDLSKLAVTSYNDIQDLMDSGNKARTVAATNMNETSSRSHAVFNIIFTQKRHDAETNITTEKVSKISLVDLAGSERADSTGAKGTRLKEGANINKSLTTLGKVISALAEMDSGPNKNKKKKKTDFIPYRDSVLTWLLRENLGGNSRTAMVAALSPADINYDETLSTLRYADRAKQIRCNAVINEDPNNKLIRELKDEVTRLRDLLYAQGLGDITDTNTVPGGPKLTNALVGMSPSSSLSALSSRAASVSSLHERILFAPGSEEAIERLKETEKIIAELNETWEEKLRRTEAIRMEREALLAEMGVAMREDGGTLGVFSPKKTPHLVNLNEDPLMSECLLYYIKDGITRVGREDGERRQDIVLSGHFIKEEHCVFRSDSRGGSEAVVTLEPCEGADTYVNGKKVTEPSILRSGNRIIMGKSHVFRFNHPEQARQERERTPCAETPAEPVDWAFAQRELLEKQGIDMKQEMEQRLQELEDQYRREREEATYLLEQQRLDYESKLEALQKQMDSRYYPEVNEEEEEPEDEVQWTERECELALWAFRKWKWYQFTSLRDLLWGNAIFLKEANAISVELKKKVQFQFVLLTDTLYSPLPPDLLPPEAAKDRETRPFPRTIVAVEVQDQKNGATHYWTLEKLRQRLDLMREMYDRAAEVPSSVIEDCDNVVTGGDPFYDRFPWFRLVGSSAISGCNSYPLLNTCMSERMAALTPSPTFSSPDSDATEPAEEQSVGEEEEEEEEEEDEEEEDLEDDVFPEHALCDGRDPFYDRPPLFSLVGRAFVYLSNLLYPVPLVHRVAIVSEKGEVKGFLRVAVQAISADEEAPDYGSGVRQSGTAKISFDDQHFEKFQSESCPVVGMSRSGTSQEELRIVEGQGQGADVGPSADEVNNNTCSAVPPEGLLLDSSEKAALDGPLDAALDHLRLGNTFTFRVTVLQASSISAEYADIFCQFNFIHRHDEAFSTEPLKNTGRGPPLGFYHVQNIAVEVTKSFIEYIKSQPIVFEVFGHYQQHPFPPLCKDVLSPLRPSRRHFPRVMPLSKPVPATKLSTLTRPCPGPCHCKYDLLVYFEICELEANGDYIPAVVDHRGGMPCMGTFLLHQGIQRRITVTLLHETGSHIRWKEVRELVVGRIRNTPETDESLIDPNILSLNILSSGYIHPAQDDRTFYQFEAAWDSSMHNSLLLNRVTPYREKIYMTLSAYIEMENCTQPAVVTKDFCMVFYSRDAKLPASRSIRNLFGSGSLRASESNRVTGVYELSLCHVADAGSPGMQRRRRRVLDTSVAYVRGEENLAGWRPRSDSLILDHQWELEKLSLLQEVEKTRHYLLLREKLETAQRPVPEALSPAFSEDSESHGSSSASSPLSAEGRPSPLEAPNERQRELAVKCLRLLTHTFNREYTHSHVCVSASESKLSEMSVTLLRDPSMSPLGVATLTPSSTCPSLVEGRYGATDLRTPQPCSRPASPEPELLPEADSKKLPSPARATETDKEPQRLLVPDIQEIRVSPIVSKKGYLHFLEPHTSGWARRFVVVRRPYAYMYNSDKDTVERFVLNLATAQVEYSEDQQAMLKTPNTFAVCTEHRGILLQAASDKDMHDWLYAFNPLLAGTIRSKLSRRRSAQMRV,mutated_sequence,1.0,1791.0,UPI0002065B81.a2m,UPI0002065B81.npy,gnomAD +UPI0001AE795F,UPI0001AE795F.csv,KERKKERKKERKEREREKERKKKYPKSSLSSSKFLRFLGQSQN,mutated_sequence,,,UPI0001AE795F.a2m,UPI0001AE795F.npy,gnomAD +UPI000013811E,UPI000013811E.csv,MMAAMATARVRMGPRCAQALWRMPWLPVFLSLAAAAAAAAAEQQVPLVLWSSDRDLWAPAADTHEGHITSDLQLSTYLDPALELGPRNVLLFLQDKLSIEDFTAYGGVFGNKQDSAFSNLENALDLAPSSLVLPAVDWYAVSTLTTYLQEKLGASPLHVDLATLRELKLNASLPALLLIRLPYTASSGLMAPREVLTGNDEVIGQVLSTLKSEDVPYTAALTAVRPSRVARDVAVVAGGLGRQLLQKQPVSPVIHPPVSYNDTAPRILFWAQNFSVAYKDQWEDLTPLTFGVQELNLTGSFWNDSFARLSLTYERLFGTTVTFKFILANRLYPVSARHWFTMERLEVHSNGSVAYFNASQVTGPSIYSFHCEYVSSLSKKGSLLVARTQPSPWQMMLQDFQIQAFNVMGEQFSYASDCASFFSPGIWMGLLTSLFMLFIFTYGLHMILSLKTMDRFDDHKGPTISLTQIV,mutated_sequence,1.0,470.0,UPI000013811E.a2m,UPI000013811E.npy,gnomAD +UPI000006D7C1,UPI000006D7C1.csv,MADNLQATFGESLLSDMLDDFPDTLPSPEALKFKILVKNKKIGTLKETHERKGSDKRGKVEEWEEEVADGEEEEEEEEEEEEEEEDKFKESEVLESVLGDNQDKETGVKKLPGVMLFKKKKTRKLKIALALSDLVIYTKAEKFKSFQHSRLYQQFNENNSIGETQARKLSKLRVHEFIFHTRKFITRIYPKATRADSSNFNPQEFWNIGCQMVALNFQTPGLPMDLQNGKFLDNGGSGYILKPHFLRESKSYFNPSNIKEGMPITLTIRLISGIQLPLTHSSSNKGDSLVIIEVFGVPNDQMKQQTRVIKKNAFSPRWNETFTFIIHVPELALIRFVVEGQGLIAGNEFLGQYTLPLLCMNKGYRRIPLFSRMGESLEPASLFVYVWYVR,mutated_sequence,1.0,390.0,UPI000006D7C1.a2m,UPI000006D7C1.npy,gnomAD +UPI000013EFF9,UPI000013EFF9.csv,MAFLIILITCFVIILATSQPCQTPDDFVAATSPGHIIIGGLFAIHEKMLSSEDSPRRPQIQECVGFEISVFLQTLAMIHSIEMINNSTLLPGVKLGYEIYDTCTEVTVAMAATLRFLSKFNCSRETVEFKCDYSSYMPRVKAVIGSGYSEITMAVSRMLNLQLMPQVGYESTAEILSDKIRFPSFLRTVPSDFHQIKAMAHLIQKSGWNWIGIITTDDDYGRLALNTFIIQAEANNVCIAFKEVLPAFLSDNTIEVRINRTLKKIILEAQVNVIVVFLRQFHVFDLFNKAIEMNINKMWIASDNWSTATKITTIPNVKKIGKVVGFAFRRGNISSFHSFLQNLHLLPSDSHKLLHEYAMHLSACAYVKDTDLSQCIFNHSQRTLAYKANKAIERNFVMRNDFLWDYAEPGLIHSIQLAVFALGYAIRDLCQARDCQNPNAFQPWELLGVLKNVTFTDGWNSFHFDAHGDLNTGYDVVLWKEINGHMTVTKMAEYDLQNDVFIIPDQETKNEFRNLKQIQSKCSKECSPGQMKKTTRSQHICCYECQNCPENHYTNQTDMPHCLLCNNKTHWAPVRSTMCFEKEVEYLNWNDSLAILLLILSLLGIIFVLVVGIIFTRNLNTPVVKSSGGLRVCYVILLCHFLNFASTSFFIGEPQDFTCKTRQTMFGVSFTLCISCILTKSLKILLAFSFDPKLQKFLKCLYRPILIIFTCTGIQVVICTLWLIFAAPTVEVNVSLPRVIILECEEGSILAFGTMLGYIAILAFICFIFAFKGKYENYNEAKFITFGMLIYFIAWITFIPIYATTFGKYVPAVEIIVILISNYGILYCTFIPKCYVIICKQEINTKSAFLKMIYSYSSHSVSSIALSPASLDSMSGNVTMTNPSSSGKSATWQKSKDLQAQAFAHICRENATSVSKTLPRKRMSSI,mutated_sequence,1.0,926.0,UPI000013EFF9.a2m,UPI000013EFF9.npy,gnomAD +UPI000013D82E,UPI000013D82E.csv,MELPAVGEHVFAVESIEKKRIRKGRVEYLVKWRGWSPKYNTWEPEENILDPRLLIAFQNRERQEQLMGYRKRGPKPKPLVVQVPTFARRSNVLTGLQDSSTDNRAKLDLGAQGKGQGHQYELNSKKHHQYQPHSKERAGKPPPPGKSGKYYYQLNSKKHHPYQPDPKMYDLQYQGGHKEAPSPTCPDLGAKSHPPDKWAQGAGAKGYLGAVKPLAGAAGAPGKGSEKGPPNGMMPAPKEAVTGNGIGGKMKIVKNKNKNGRIVIVMSKYMENGMQAVKIKSGEVAEGEARSPSHKKRAADERHPPADRTFKKAAGAEEKKVEAPPKRREEEVSGVSDPQPQDAGSRKLSPTKEAFGEQPLQLTTKPDLLAWDPARNTHPPSHHPHPHPHHHHHHHHHHHHAVGLNLSHVRKRCLSETHGEREPCKKRLTARSISTPTCLGGSPAAERPADLPPAAALPQPEVILLDSDLDEPIDLRCVKTRSEAGEPPSSLQVKPETPASAAVAVAAAAAPTTTAEKPPAEAQDEPAESLSEFKPFFGNIIITDVTANCLTVTFKEYVTV,mutated_sequence,1.0,560.0,UPI000013D82E.a2m,UPI000013D82E.npy,gnomAD +UPI00004EC29C,UPI00004EC29C.csv,MEKEETTRELLLPNWQGSGSHGLTIAQRDDGVFVQEVTQNSPAARTGVVKEGDQIVGATIYFDNLQSGEVTQLLNTMGHHTVGLKLHRKGDRSPEPGQTWTREVFSSCSSEVVLSGDDEEYQRIYTTKIKPRLKSEDGVEGDLGETQSRTITVTRRVTAYTVDVTGREGAKDIDISSPEFKIKIPRHELTEISNVDVETQSGKTVIRLPSGSGAASPTGSAVDIRAGAISASGPELQGAGHSKLQVTMPGIKVGGSGVNVNAKGLDLGGRGGVQVPAVDISSSLGGRAVEVQGPSLESGDHGKIKFPTMKVPKFGVSTGREGQTPKAGLRVSAPEVSVGHKGGKPGLTIQAPQLEVSVPSANIEGLEGKLKGPQITGPSLEGDLGLKGAKPQGHIGVDASAPQIGGSITGPSVEVQAPDIDVQGPGSKLNVPKMKVPKFSVSGAKGEETGIDVTLPTGEVTVPGVSGDVSLPEIATGGLEGKMKGTKVKTPEMIIQKPKISMQDVDLSLGSPKLKGDIKVSAPGVQGDVKGPQVALKGSRVDIETPNLEGTLTGPRLGSPSGKTGTCRISMSEVDLNVAAPKVKGGVDVTLPRVEGKVKVPEVDVRGPKVDVSAPDVEAHGPEWNLKMPKMKMPTFSTPGAKGEGPDVHMTLPKGDISISGPKVNVEAPDVNLEGLGGKLKGPDVKLPDMSVKTPKISMPDVDLHVKGTKVKGEYDVTVPKLEGELKGPKVDIDAPDVDVHGPDWHLKMPKMKMPKFSVPGFKAEGPEVDVNLPKADVDISGPKIDVTAPDVSIEEPEGKLKGPKFKMPEMNIKVPKISMPDVDLHLKGPNVKGEYDVTMPKVESEIKVPDVELKSAKMDIDVPDVEVQGPDWHLKMPKMKMPKFSMPGFKAEGPEVDVNLPKADVDISGPKVGVEVPDVNIEGPEGKLKGPKFKMPEMNIKAPKISMPDVDLHMKGPKVKGEYDMTVPKLEGDLKGPKVDVSAPDVEMQGPDWNLKMPKIKMPKFSMPSLKGEGPEFDVNLSKANVDISAPKVDTNAPDLSLEGPEGKLKGPKFKMPEMHFRAPKMSLPDVDLDLKGPKMKGNVDISAPKIEGEMQVPDVDIRGPKVDIKAPDVEGQGLDWSLKIPKMKMPKFSMPSLKGEGPEVDVNLPKADVVVSGPKVDIEAPDVSLEGPEGKLKGPKFKMPEMHFKTPKISMPDVDLHLKGPKVKGDVDVSVPKVEGEMKVPDVEIKGPKMDIDAPDVEVQGPDWHLKMPKMKMPKFSMPGFKGEGREVDVNLPKADIDVSGPKVDVEVPDVSLEGPEGKLKGPKFKMPEMHFKAPKISMPDVDLNLKGPKLKGDVDVSLPEVEGEMKVPDVDIKGPKVDISAPDVDVHGPDWHLKMPKVKMPKFSMPGFKGEGPEVDVKLPKADVDVSGPKMDAEVPDVNIEGPDAKLKGPKFKMPEMSIKPQKISIPDVGLHLKGPKMKGDYDVTVPKVEGEIKAPDVDIKGPKVDINAPDVEVHGPDWHLKMPKVKMPKFSMPGFKGEGPEVDMNLPKADLGVSGPKVDIDVPDVNLEAPEGKLKGPKFKMPSMNIQTHKISMPDVGLNLKAPKLKTDVDVSLPKVEGDLKGPEIDVKAPKMDVNVGDIDIEGPEGKLKGPKFKMPEMHFKAPKISMPDVDLHLKGPKVKGDMDVSVPKVEGEMKVPDVDIKGPKVDIDAPDVEVHDPDWHLKMPKMKMPKFSMPGFKAEGPEVDVNLPKADIDVSGPSVDTDAPDLDIEGPEGKLKGSKFKMPKLNIKAPKVSMPDVDLNLKGPKLKGEIDASVPELEGDLRGPQVDVKGPFVEAEVPDVDLECPDAKLKGPKFKMPEMHFKAPKISMPDVDLHLKGPKVKGDADVSVPKLEGDLTGPSVGVEVPDVELECPDAKLKGPKFKMPDMHFKAPKISMPDVDLHLKGPKVKGDVDVSVPKLEGDLTGPSVGVEVPDVELECPDAKLKGPKFKMPEMHFKTPKISMPDVDLHLKGPKVKGDMDVSVPKVEGEMKVPDVDIKGPKMDIDAPDVDVHGPDWHLKMPKMKMPKFSMPGFKAEGPEVDVNLPKADVVVSGPKVDVEVPDVSLEGPEGKLKGPKLKMPEMHFKAPKISMPDVDLHLKGPKVKGDVDVSLPKLEGDLTGPSVDVEVPDVELECPDAKLKGPKFKMPEMHFKTPKISMPDVNLNLKGPKVKGDMDVSVPKVEGEMKVPDVDIRGPKVDIDAPDVDVHGPDWHLKMPKMKMPKFSMPGFKGEGPEVDVNLPKADVDVSGPKVDVEVPDVSLEGPEGKLKGPKFKMPEMHFKTPKISMPDVDFNLKGPKIKGDVDVSAPKLEGELKGPELDVKGPKLDADMPEVAVEGPNGKWKTPKFKMPDMHFKAPKISMPDLDLHLKSPKAKGEVDVDVPKLEGDLKGPHVDVSGPDIDIEGPEGKLKGPKFKMPDMHFKAPNISMPDVDLNLKGPKIKGDVDVSVPEVEGKLEVPDMNIRGPKVDVNAPDVQAPDWHLKMPKMKMPKFSMPGFKAEGPEVDVNLPKADVDISGPKVDIEGPDVNIEGPEGKLKGPKLKMPEMNIKAPKISMPDFDLHLKGPKVKGDVDVSLPKVEGDLKGPEVDIKGPKVDINAPDVGVQGPDWHLKMPKVKMPKFSMPGFKGEGPDGDVKLPKADIDVSGPKVDIEGPDVNIEGPEGKLKGPKFKMPEMNIKAPKISMPDIDLNLKGPKVKGDVDVSLPKVEGDLKGPEVDIKGPKVDIDAPDVDVHGPDWHLKMPKIKMPKISMPGFKGEGPDVDVNLPKADIDVSGPKVDVECPDVNIEGPEGKWKSPKFKMPEMHFKTPKISMPDIDLNLTGPKIKGDVDVTGPKVEGDLKGPEVDLKGPKVDIDVPDVNVQGPDWHLKMPKMKMPKFSMPGFKAEGPEVDVNLPKADVDVSGPKVDVEGPDVNIEGPEGKLKGPKFKMPEMNIKAPKIPMPDFDLHLKGPKVKGDVDISLPKVEGDLKGPEVDIRGPQVDIDVPDVGVQGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVNLPKADLDVSGPKVDIDVPDVNIEGPEGKLKGPKFKMPEMNIKAPKISMPDIDLNLKGPKVKGDMDVSLPKVEGDMKVPDVDIKGPKVDINAPDVDVQGPDWHLKMPKIKMPKISMPGFKGEGPEVDVNLPKADLDVSGPKVDVDVPDVNIEGPDAKLKGPKFKMPEMNIKAPKISMPDLDLNLKGPKMKGEVDVSLANVEGDLKGPALDIKGPKIDVDAPDIDIHGPDAKLKGPKLKMPDMHVNMPKISMPEIDLNLKGSKLKGDVDVSGPKLEGDIKAPSLDIKGPEVDVSGPKLNIEGKSKKSRFKLPKFNFSGSKVQTPEVDVKGKKPDIDITGPKVDINAPDVEVQGKVKGSKFKMPFLSISSPKVSMPDVELNLKSPKVKGDLDIAGPNLEGDFKGPKVDIKAPEVNLNAPDVDVHGPDWNLKMPKMKMPKFSVSGLKAEGPDVAVDLPKGDINIEGPSMNIEGPDLNVEGPEGGLKGPKFKMPDMNIKAPKISMPDIDLNLKGPKVKGDVDISLPKLEGDLKGPEVDIKGPKVDINAPDVDVHGPDWHLKMPKVKMPKFSMPGFKGEGPEVDVTLPKADIDISGPNVDVDVPDVNIEGPDAKLKGPKFKMPEMNIKAPKISMPDFDLNLKGPKMKGDVVVSLPKVEGDLKGPEVDIKGPKVDIDTPDINIEGSEGKFKGPKFKIPEMHLKAPKISMPDIDLNLKGPKVKGDVDVSLPKMEGDLKGPEVDIKGPKVDINAPDVDVQGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVNLPKADLDVSGPKVDIDVPDVNIEGPEGKLKGPKFKMPEMNIKAPKISMPDIDLNLKGPKVKGDMDVSLPKVEGDMQVPDLDIKGPKVDINAPDVDVRGPDWHLKMPKIKMPKISMPGFKGEGPEVDVNLPKADLDVSGPKVDVDVPDVNIEGPDAKLKGPKFKMPEMNIKAPKISMPDFDLHLKGPKVKGDVDVSLPKMEGDLKAPEVDIKGPKVDIDAPDVDVHGPDWHLKMPKVKMPKFSMPGFKGEGPEVDVNLPKADIDVSGPKVDIDTPDIDIHGPEGKLKGPKFKMPDLHLKAPKISMPEVDLNLKGPKMKGDVDVSLPKVEGDLKGPEVDIKGPKVDIDVPDVDVQGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVNLPKADLDVSGPKVDIDVPDVNIEGPDAKLKGPKFKMPEMNIKAPKISMPDFDLHLKGPKVKGDVDVSLPKVEGDLKGPEVDIKGPKVDIDAPDVDVHGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVTLPKADIEISGPKVDIDAPDVSIEGPDAKLKGPKFKMPEMNIKAPKISMPDIDFNLKGPKVKGDVDVSLPKVEGDLKGPEIDIKGPSLDIDTPDVNIEGPEGKLKGPKFKMPEMNIKAPKISMPDFDLHLKGPKVKGDVDVSLPKVESDLKGPEVDIEGPEGKLKGPKFKMPDVHFKSPQISMSDIDLNLKGPKIKGDMDISVPKLEGDLKGPKVDVKGPKVGIDTPDIDIHGPEGKLKGPKFKMPDLHLKAPKISMPEVDLNLKGPKVKGDMDISLPKVEGDLKGPEVDIRDPKVDIDVPDVDVQGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVNLPKADIDVSGPKVDVDVPDVNIEGPDAKLKGPKFKMPEMSIKAPKISMPDIDLNLKGPKVKGDVDVTLPKVEGDLKGPEADIKGPKVDINTPDVDVHGPDWHLKMPKVKMPKFSMPGFKGEGPDVDVSLPKADIDVSGPKVDVDIPDVNIEGPDAKLKGPKFKMPEINIKAPKISIPDVDLDLKGPKVKGDFDVSVPKVEGTLKGPEVDLKGPRLDFEGPDAKLSGPSLKMPSLEISAPKVTAPDVDLHLKAPKIGFSGPKLEGGEVDLKGPKVEAPSLDVHMDSPDINIEGPDVKIPKFKKPKFGFGAKSPKADIKSPSLDVTVPEAELNLETPEISVGGKGKKSKFKMPKIHMSGPKIKAKKQGFDLNVPGGEIDASLKAPDVDVNIAGPDAALKVDVKSPKTKKTMFGKMYFPDVEFDIKSPKFKAEAPLPSPKLEGELQAPDLELSLPAIHVEGLDIKAKAPKVKMPDVDISVPKIEGDLKGPKVQANLGAPDINIEGLDAKVKTPSFGISAPQVSIPDVNVNLKGPKIKGDVPSVGLEGPDVDLQGPEAKIKFPKFSMPKIGIPGVKMEGGGAEVHAQLPSLEGDLRGPDVKLEGPDVSLKGPGVDLPSVNLSMPKVSGPDLDLNLKGPSLKGDLDASVPSMKVHAPGLNLSGVGGKMQVGGDGVKVPGIDATTKLNVGAPDVTLRGPSLQGDLAVSGDIKCPKVSVGAPDLSLEASEGSIKLPKMKLPQFGISTPGSDLHVNAKGPQVSGELKGPGVDVNLKGPRISAPNVDFNLEGPKVKGSLGATGEIKGPTVGGGLPGIGVQGLEGNLQMPGIKSSGCDVNLPGVNVKLPTGQISGPEIKGGLKGSEVGFHGAAPDISVKGPAFNMASPESDFGINLKGPKIKGGADVSGGVSAPDISLGEGHLSVKGSGGEWKGPQVSSALNLDTSKFAGGLHFSGPKVEGGVKGGQIGLQAPGLSVSGPQGHLESGSGKVTFPKMKIPKFTFSGRELVGREMGVDVHFPKAEASIQAGAGDGEWEESEVKLKKSKIKMPKFNFSKPKGKGGVTGSPEASISGSKGDLKSSKASLGSLEGEAEAEASSPKGKFSLFKSKKPRHRSNSFSDEREFSGPSTPTGTLEFEGGEVSLEGGKVKGKHGKLKFGTFGGLGSKSKGHYEVTGSDDETGKLQGSGVSLASKKSRLSSSSSNDSGNKVGIQLPEVELSVSTKKE,mutated_sequence,1.0,5890.0,UPI00004EC29C.a2m,UPI00004EC29C.npy,gnomAD +UPI000051AE2E,UPI000051AE2E.csv,MEPAAAATVQRLPELGREDRASAPAAAAAAAAAAAAAAAALAAAAGGGRSPEPALTPAAPSGGNGSGSGAREEAPGEAPPGPLPGRAGGAGRRRRRGAPQPIAGGAAPVPGAGGGANSLLLRRGRLKRNLSAAAAAASSSSSSSAAAASHSPGAAGLPASCSASASLCTRSLDRKTLLLKHRQTLQLQPSDRDWVRHQLQRGCVHVFDRHMASTYLRPVLCTLDTTAGEVAARLLQLGHKGGGVVKVLGQGPGAAAAREPAEPPPEAGPRLAPPEPRDSEVPPARSAPGAFGGPPRAPPADLPLPVGGPGGWSRRASPAPSDSSPGEPFVGGPVSSPRAPRPVVSDTESFSLSPSAESVSDRLDPYSSGGGSSSSSEELEADAASAPTGVPGQPRRPGHPAQPLPLPQTASSPQPQQKAPRAIDSPGGAVREGSCEEKAAAAVAPGGLQSTPGRSGVTAEKAPPPPPPPTLYVQLHGETTRRLEAEEKPLQIQNDYLFQLGFGELWRVQEEGMDSEIGCLIRFYAGKPHSTGSSERIQLSGMYNVRKGKMQLPVNRWTRRQVILCGTCLIVSSVKDSLTGKMHVLPLIGGKVEEVKKHQHCLAFSSSGPQSQTYYICFDTFTEYLRWLRQVSKVASQRISSVDLSCCSLEHLPANLFYSQDLTHLNLKQNFLRQNPSLPAARGLNELQRFTKLKSLNLSNNHLGDFPLAVCSIPTLAELNVSCNALRSVPAAVGVMHNLQTFLLDGNFLQSLPAELENMKQLSYLGLSFNEFTDIPEVLEKLTAVDKLCMSGNCVETLRLQALRKMPHIKHVDLRLNVIRKLIADEVDFLQHVTQLDLRDNKLGDLDAMIFNNIEVLHCERNQLVTLDICGYFLKALYASSNELVQLDVYPVPNYLSYMDVSRNRLENVPEWVCESRKLEVLDIGHNQICELPARLFCNSSLRKLLAGHNQLARLPERLERTSVEVLDVQHNQLLELPPNLLMKADSLRFLNASANKLESLPPATLSEETNSILQELYLTNNSLTDKCVPLLTGHPHLKILHMAYNRLQSFPASKMAKLEELEEIDLSGNKLKAIPTTIMNCRRMHTVIAHSNCIEVFPEVMQLPEIKCVDLSCNELSEVTLPENLPPKLQELDLTGNPRLVLDHKTLELLNNIRCFKIDQPSTGDASGAPAVWSHGYTEASGVKNKLCVAALSVNNFCDNREALYGVFDGDRNVEVPYLLQCTMSDILAEELQKTKNEEEYMVNTFIVMQRKLGTAGQKLGGAAVLCHIKHDPVDPGGSFTLTSANVGKCQTVLCRNGKPLPLSRSYIMSCEEELKRIKQHKAIITEDGKVNGVTESTRILGYTFLHPSVVPRPHVQSVLLTPQDEFFILGSKGLWDSLSVEEAVEAVRNVPDALAAAKKLCTLAQSYGCHDSISAVVVQLSVTEDSFCCCELSAGGAVPPPSPGIFPPSVNMVIKDRPSDGLGVPSSSSGMASEISSELSTSEMSSEVGSTASDEPPPGALSENSPAYPSEQRCMLHPICLSNSFQRQLSSATFSSAFSDNGLDSDDEEPIEGVFTNGSRVEVEVDIHCSRAKEKEKQQHLLQVPAEASDEGIVISANEDEPGLPRKADFSAVGTIGRRRANGSVAPQERSHNVIEVATDAPLRKPGGYFAAPAQPDPDDQFIIPPELEEEVKEIMKHHQEQQQQQQPPPPPQLQPQLPRHYQLDQLPDYYDTPL,mutated_sequence,1.0,1717.0,UPI000051AE2E.a2m,UPI000051AE2E.npy,gnomAD +UPI000013D704,UPI000013D704.csv,MRRAPAAERLLELGFPPRCGRQEPPFPLGVTRGWGRWPIQKRREGARPVPFSERSQEDGRGPAARSSGTLWRIRTRLSLCRDPEPPPPLCLLRVSLLCALRAGGRGSRWGEDGARLLLLPPARAAGNGEAEPSGGPSYAGRMLESSGCKALKEGVLEKRSDGLLQLWKKKCCILTEEGLLLIPPKQLQHQQQQQQQQQQQQQQQPGQGPAEPSQPSGPAVASLEPPVKLKELHFSNMKTVDCVERKGKYMYFTVVMAEGKEIDFRCPQDQGWNAEITLQMVQYKNRQAILAVKSTRQKQQHLVQQQPPSQPQPQPQLQPQPQPQPQPQPQPQSQPQPQPQPKPQPQQLHPYPHPHPHPHSHPHSHPHPHPHPHPHQIPHPHPQPHSQPHGHRLLRSTSNSA,mutated_sequence,1.0,401.0,UPI000013D704.a2m,UPI000013D704.npy,gnomAD +UPI000016ABE3,UPI000016ABE3.csv,MKLVFLVLLFLGALGLCLAGRRRSVQWCAVSQPEATKCFQWQRNMRKVRGPPVSCIKRDSPIQCIQAIAENRADAVTLDGGFIYEAGLAPYKLRPVAAEVYGTERQPRTHYYAVAVVKKGGSFQLNELQGLKSCHTGLRRTAGWNVPIGTLRPFLNWTGPPEPIEAAVARFFSASCVPGADKGQFPNLCRLCAGTGENKCAFSSQEPYFSYSGAFKCLRDGAGDVAFIRESTVFEDLSDEAERDEYELLCPDNTRKPVDKFKDCHLARVPSHAVVARSVNGKEDAIWNLLRQAQEKFGKDKSPKFQLFGSPSGQKDLLFKDSAIGFSRVPPRIDSGLYLGSGYFTAIQNLRKSEEEVAARRARVVWCAVGEQELRKCNQWSGLSEGSVTCSSASTTEDCIALVLKGEADAMSLDGGYVYTAGKCGLVPVLAENYKSQQSSDPDPNCVDRPVEGYLAVAVVRRSDTSLTWNSVKGKKSCHTAVDRTAGWNIPMGLLFNQTGSCKFDEYFSQSCAPGSDPRSNLCALCIGDEQGENKCVPNSNERYYGYTGAFRCLAENAGDVAFVKDVTVLQNTDGNNNEAWAKDLKLADFALLCLDGKRKPVTEARSCHLAMAPNHAVVSRMDKVERLKQVLLHQQAKFGRNGSDCPDKFCLFQSETKNLLFNDNTECLARLHGKTTYEKYLGPQYVAGITNLKKCSTSPLLEACEFLRK,mutated_sequence,1.0,710.0,UPI000016ABE3.a2m,UPI000016ABE3.npy,gnomAD +UPI000019B3C1,UPI000019B3C1.csv,MNRQVCKKSFSGRSQGFSGRSAVVSGSSRMSCVARSGGAGGGACGFRSGAGSFGSRSLYNLGSNKSISISVAAGSSRAGGFGGGRSSCGFAGGYGGGFGGSYGGGFGGGRGVGSGFGGAGGFGGAGGFGGPGVFGGPGSFGGPGGFGPGGFPGGIQEVIVNQSLLQPLNVEIDPQIGQVKAQEREQIKTLNNKFASFIDKVRFLEQQNKVLETKWELLQQQTTGSGPSSLEPCFESYISFLCKQLDSLLGERGNLEGELKSMQDLVEDFKKKYEDEINKRTAAENEFVGLKKDVDAAFMNKVELQAKVDSLTDEVSFLRTLYEMELSQMQSHASDTSVVLSMDNNRCLDLGSIIAEVRAQYEEIAQRSKSEAEALYQTKLGELQTTAGRHGDDLRNTKSEIMELNRMIQRLRAEIENVKKQNANLQTAIAEAEQRGEMALKDANAKLQDLQTALQKAKDDLARLLRDYQELMNVKLALDVEIATYRKLLEGEECRMSGECQSAVCISVVSNVTSTSGSSGSSRGVFGGVSGSGSGGYKGGSSSSSSSGYGVSGGSGSGYGGVSSGSTGGRGSSGSYQSSSSGSRLGGAGSISVSHSGMGSSSGSIQTSGGSGYKSGGGGSTSIRFSQTTSSSQHSSTK,mutated_sequence,1.0,638.0,UPI000019B3C1.a2m,UPI000019B3C1.npy,gnomAD +UPI000013C57B,UPI000013C57B.csv,MEVPAAGRVPAEGAPTAAVAEVRCPGPAPLRLLEWRVAAGAAVRIGSVLAVFEAAASAQSSGASQSRVASGGCVRPARPERRLRSERAGVVRELCAQPGQVVAPGAVLVRLEGCSHPVVMKGLCAECGQDLTQLQSKNGKQQVPLSTATVSMVHSVPELMVSSEQAEQLGREDQQRLHRNRKLVLMVDLDQTLIHTTEQHCQQMSNKGIFHFQLGRGEPMLHTRLRPHCKDFLEKIAKLYELHVFTFGSRLYAHTIAGFLDPEKKLFSHRILSRDECIDPFSKTGNLRNLFPCGDSMVCIIDDREDVWKFAPNLITVKKYVYFQGTGDMNAPPGSRESQTRKKVNHSRGTEVSEPSPPVRDPEGVTQAPGVEPSNGLEKPARELNGSEAATPRDSPRPGKPDERDIWPPAQAPTSSQELAGAPEPQGSCAQGGRVAPGQRPAQGATGTDLDFDLSSDSESSSESEGTKSSSSASDGESEGKRGRQKPKAAPEGAGALAQGSSLEPGRPAAPSLPGEAEPGAHAPDKEPELGGQEEGERDGLCGLGNGCADRKEAETESQNSELSGVTAGESLDQSMEEEEEEDTDEDDHLIYLEEILVRVHTDYYAKYDRYLNKEIEEAPDIRKIVPELKSKVLADVAIIFSGLHPTNFPIEKTREHYHATALGAKILTRLVLSPDAPDRATHLIAARAGTEKVLQAQECGHLHVVNPDWLWSCLERWDKVEEQLFPLRDDHTKAQRENSPAAFPDREGVPPTALFHPMPVLPKAQPGPEVRIYDSNTGKLIRTGARGPPAPSSSLPIRQEPSSFRAVPPPQPQMFGEELPDAQDGEQPGPSRRKRQPSMSETMPLYTLCKEDLESMDKEVDDILGEGSDDSDSEKRRPEEQEEEPQPRKPGTRRERTLGAPASSERSAAGGRGPRGHKRKLNEEDAASESSRESSNEDEGSSSEADEMAKALEAELNDLM,mutated_sequence,1.0,961.0,UPI000013C57B.a2m,UPI000013C57B.npy,gnomAD +UPI0000141A8E,UPI0000141A8E.csv,MTHDKSWRRCSISGSTKCRCGSRIAGPNALGSGGSRSSSSSSRSILSSSILSSSIPSSSSSSSSPSSSPSSSSPSSSHSSSSPSSSSSTSSPSSSSSSSSSSPSSSNSSSSSSSSSPSSSSSSSSSSPSSSSSSPSSSSSSSSSSPSSSSSSPSSSSSSSSSSSSSPSSSSPSSSGSSPSSSNSSPSSSSSSPSSSSSSPSPRSSSPSSSSSSTSSPSSSSPSSSSPSSSCPSAALGRRPQSPQSSHCAPFP,mutated_sequence,1.0,252.0,UPI0000141A8E.a2m,UPI0000141A8E.npy,gnomAD +UPI000020E56F,UPI000020E56F.csv,MSDRSGPTAKGKDGKKYSSLNLFDTYKGKSLEIQKPAVAPRHGLQSLGKVAIARRMPPPANLPSLKAENKGNDPNVSLVPKDGTGWASKQEQSDPKSSDASTAQPPESQPLPASQTPASNQPKRPPAAPENTPLVPSGVKSWAQASVTHGAHGDGGRASSLLSRFSREEFPTLQAAGDQDKAAKERESAEQSSGPGPSLRPQNSTTWRDGGGRGPDELEGPDSKLHHGHDPRGGLQPSGPPQFPPYRGMMPPFMYPPYLPFPPPYGPQGPYRYPTPDGPSRFPRVAGPRGSGPPMRLVEPVGRPSILKEDNLKEFDQLDQENDDGWAGAHEEVDYTEKLKFSDEEDGRDSDEEGAEGHRDSQSASGEERPPEADGKKGNSPNSEPPTPKTAWAETSRPPETEPGPPAPKPPLPPPHRGPAGNWGPPGDYPDRGGPPCKPPAPEDEDEAWRQRRKQSSSEISLAVERARRRREEEERRMQEERRAACAEKLKRLDEKFGAPDKRLKAEPAAPPAAPSTPAPPPAVPKELPAPPAPPPASAPTPETEPEEPAQAPPAQSTPTPGVAAAPTLVSGGGSTSSTSSGSFEASPVEPQLPSKEGPEPPEEVPPPTTPPVPKVEPKGDGIGPTRQPPSQGLGYPKYQKSLPPRFQRQQQEQLLKQQQQHQWQQHQQGSAPPTPVPPSPPQPVTLGAVPAPQAPPPPPKALYPGALGRPPPMPPMNFDPRWMMIPPYVDPRLLQGRPPLDFYPPGVHPSGLVPRERSDSGGSSSEPFDRHAPAMLRERGTPPVDPKLAWVGDVFTATPAEPRPLTSPLRQAADEDDKGMRSETPPVPPPPPYLASYPGFPENGAPGPPISRFPLEEPGPRPLPWPPGSDEVAKIQTPPPKKEPPKEETAQLTGPEAGRKPARGVGSGGQGPPPPRRESRTETRWGPRPGSSRRGIPPEEPGAPPRRAGPIKKPPPPTKVEELPPKPLEQGDETPKPPKPDPLKITKGKLGGPKETPPNGNLSPAPRLRRDYSYERVGPTSCRGRGRGEYFARGRGFRGTYGGRGRGARSREFRSYREFRGDDGRGGGTGGPNHPPAPRGRTASETRSEGSEYEEIPKRRRQRGSETGSETHESDLAPSDKEAPTPKEGTLTQVPLAPPPPGAPPSPAPARFTARGGRVFTPRGVPSRRGRGGGRPPPQVCPGWSPPAKSLAPKKPPTGPLPPSKEPLKEKLIPGPLSPVARGGSNGGSNVGMEDGERPRRRRHGRAQQQDKPPRFRRLKQERENAARGSEGKPSLTLPASAPGPEEALTTVTVAPAPRRAAAKSPDLSNQNSDQANEEWETASESSDFTSERRGDKEAPPPVLLTPKAVGTPGGGGGGAVPGISAMSRGDLSQRAKDLSKRSFSSQRPGMERQNRRPGPGGKAGSSGSSSGGGGGGPGGRTGPGRGDKRSWPSPKNRSRPPEERPPGLPLPPPPPSSSAVFRLDQVIHSNPAGIQQALAQLSSRQGSVTAPGGHPRHKPGLPQAPQGPSPRPPTRYEPQRVNSGLSSDPHFEEPGPMVRGVGGTPRDSAGVSPFPPKRRERPPRKPELLQEESLPPPHSSGFLGSKPEGPGPQAESRDTGTEALTPHIWNRLHTATSRKSYRPSSMEPWMEPLSPFEDVAGTEMSQSDSGVDLSGDSQVSSGPCSQRSSPDGGLKGAAEGPPKRPGGSSPLNAVPCEGPPGSEPPRRPPPAPHDGDRKELPREQPLPPGPIGTERSQRTDRGTEPGPIRPSHRPGPPVQFGTSDKDSDLRLVVGDSLKAEKELTASVTEAIPVSRDWELLPSAAASAEPQSKNLDSGHCVPEPSSSGQRLYPEVFYGSAGPSSSQISGGAMDSQLHPNSGGFRPGTPSLHPYRSQPLYLPPGPAPPSALLSGLALKGQFLDFSTMQATELGKLPAGGVLYPPPSFLYSPAFCPSPLPDTSLLQVRQDLPSPSDFYSTPLQPGGQSGFLPSGAPAQQMLLPMVDSQLPVVNFGSLPPAPPPAPPPLSLLPVGPALQPPSLAVRPPPAPATRVLPSPARPFPASLGRAELHPVELKPFQDYQKLSSNLGGPGSSRTPPTGRSFSGLNSRLKATPSTYSGVFRTQRVDLYQQASPPDALRWIPKPWERTGPPPREGPSRRAEEPGSRGDKEPGLPPPR,mutated_sequence,1.0,2157.0,UPI000020E56F.a2m,UPI000020E56F.npy,gnomAD +UPI00001AADF3,UPI00001AADF3.csv,MLAATCNKIGSPSPSPSSLSDSSSSFGKGFHPWKRSSSSSSASCNVVGSSLSSFGVSGASRNGGSSSAAAAAAAAAAAAAALVSDSFSCGGSPGSSAFSLTSSSAAAAAAAAAAAASSSPFANDYSVFQAPGVSGGSGGGGGGGGGGSSAHSQDGSHQPVFISKVHTSVDGLQGIYPRVGMAHPYESWFKPSHPGLGAAGEVGSAGASSWWDVGAGWIDVQNPNSAAALPGSLHPAAGGLQTSLHSPLGGYNSDYSGLSHSAFSSGASSHLLSPAGQHLMDGFKPVLPGSYPDSAPSPLAGAGGSMLSAGPSAPLGGSPRSSARRYSGRATCDCPNCQEAERLGPAGASLRRKGLHSCHIPGCGKVYGKTSHLKAHLRWHTGERPFVCNWLFCGKRFTRSDELQRHLRTHTGEKRFACPVCNKRFMRSDHLSKHVKTHSGGGGGGGSAGSGSGGKKGSDTDSEHSAAGSPPCHSPELLQPPEPGHRNGLE,mutated_sequence,1.0,490.0,UPI00001AADF3.a2m,UPI00001AADF3.npy,gnomAD +UPI0000071679,UPI0000071679.csv,MEMKKKINLELRNRSPEEVTELVLDNCLCVNGEIEGLNDTFKELEFLSMANVELSSLARLPSLNKLRKLELSDNIISGGLEVLAEKCPNLTYLNLSGNKIKDLSTVEALQNLKNLKSLDLFNCEITNLEDYRESIFELLQQITYLDGFDQEDNEAPDSEEEDDEDGDEDDEEEEENEAGPPEGYEEEEEEEEEEDEDEDEDEDEAGSELGEGEEEVGLSYLMKEEIQDEEDDDDYVEEGEEEEEEEEGGLRGEKRKRDAEDDGEEEDD,mutated_sequence,1.0,268.0,UPI0000071679.a2m,UPI0000071679.npy,gnomAD +UPI0000126A2D,UPI0000126A2D.csv,MPGLGRRAQWLCWWWGLLCSCCGPPPLRPPLPAAAAAAAGGQLLGDGGSPGRTEQPPPSPQSSSGFLYRRLKTQEKREMQKEILSVLGLPHRPRPLHGLQQPQPPALRQQEEQQQQQQLPRGEPPPGRLKSAPLFMLDLYNALSADNDEDGASEGERQQSWPHEAASSSQRRQPPPGAAHPLNRKSLLAPGSGSGGASPLTSAQDSAFLNDADMVMSFVNLVEYDKEFSPRQRHHKEFKFNLSQIPEGEVVTAAEFRIYKDCVMGSFKNQTFLISIYQVLQEHQHRDSDLFLLDTRVVWASEEGWLEFDITATSNLWVVTPQHNMGLQLSVVTRDGVHVHPRAAGLVGRDGPYDKQPFMVAFFKVSEVHVRTTRSASSRRRQQSRNRSTQSQDVARVSSASDYNSSELKTACRKHELYVSFQDLGWQDWIIAPKGYAANYCDGECSFPLNAHMNATNHAIVQTLVHLMNPEYVPKPCCAPTKLNAISVLYFDDNSNVILKKYRNMVVRACGCH,mutated_sequence,1.0,513.0,UPI0000126A2D.a2m,UPI0000126A2D.npy,gnomAD +UPI0000EE4A53,UPI0000EE4A53.csv,MVRGWEPPPGLDCAISEGHKSEGTMPPNKEASGLSSSPAGLICLPPISEELQLVWTQAAQTSELDSNEHLLKTFSYFPYPSLADIALLCLRYGLQMEKVKTWFMAQRLRCGISWSSEEIEETRARVVYRRDQLHFKSLLSFTHHAGRPPEEVPPPPVPAPEQVGIGIGPPTLSKPTQTKGLKVEPEEPSQMPPLPQSHQKLKESLMTPGSGAFPYQSDFWQHLQSSGLSKEQAGRGPNQSHGIGTASWNHSTTVPQPQARDKPPPIALIASSCKEESASSVTPSSSSTSSSFQVLANGATAASKPLQPLGCVPQSVSPSEQALPPHLEPAWPQGLRHNSVPGRVGPTEYLSPDMQRQRKTKRKTKEQLAILKSFFLQCQWARREDYQKLEQITGLPRPEIIQWFGDTRYALKHGQLKWFRDNAVPGAPSFQDPAIPTPPPSTRSLNERAETPPLPIPPPPPDIQPLERYWAAHQQLRETDIPQLSQASRLSTQQVLDWFDSRLPQPAEVVVCLDEEEEEEEEELPEDDEEEEEEEEEDDDDDDDDVIIQD,mutated_sequence,1.0,550.0,UPI0000EE4A53.a2m,UPI0000EE4A53.npy,gnomAD +UPI00001A96B8,UPI00001A96B8.csv,MSCVHYKFSSKLNYDTVTFDGLHISLCDLKKQIMGREKLKAADCDLQITNAQTKEEYTDDNALIPKNSSVIVRRIPIGGVKSTSKTYVISRTEPAMATTKAIDDSSASISLAQLTKTANLAEANASEEDKIKAMMSQSGHEYDPINYMKKPLGPPPPSYTCFRCGKPGHYIKNCPTNGDKNFESGPRIKKSTGIPRSFMMEVKDPNMKGAMLTNTGKYAIPTIDAEAYAIGKKEKPPFLPEEPSSSSEEDDPIPDELLCLICKDIMTDAVVIPCCGNSYCDECIRTALLESDEHTCPTCHQNDVSPDALIANKFLRQAVNNFKNETGYTKRLRKQLPPPPPPIPPPRPLIQRNLQPLMRSPISRQQDPLMIPVTSSSTHPAPSISSLTSNQSSLAPPVSGNPSSAPAPVPDITATVSISVHSEKSDGPFRDSDNKILPAAALASEHSKGTSSIAITALMEEKGYQVPVLGTPSLLGQSLLHGQLIPTTGPVRINTARPGGGRPGWEHSNKLGYLVSPPQQIRRGERSCYRSINRGRHHSERSQRTQGPSLPATPVFVPVPPPPLYPPPPHTLPLPPGVPPPQFSPQFPPGQPPPAGYSVPPPGFPPAPANLSTPWVSSGVQTAHSNTIPTTQAPPLSREEFYREQRRLKEEEKKKSKLDEFTNDFAKELMEYKKIQKERRRSFSRSKSPYSGSSYSRSSYTYSKSRSGSTRSRSYSRSFSRSHSRSYSRSPPYPRRGRGKSRNYRSRSRSHGYHRSRSRSPPYRRYHSRSRSPQAFRGQSPNKRNVPQGETEREYFNRYREVPPPYDMKAYYGRSVDFRDPFEKERYREWERKYREWYEKYYKGYAAGAQPRPSANRENFSPERFLPLNIRNSPFTRGRREDYVGGQSHRSRNIGSNYPEKLSARDGHNQKDNTKSKEKESENAPGDGKGNKHKKHRKRRKGEESEGFLNPELLETSRKSREPTGVEENKTDSLFVLPSRDDATPVRDEPMDAESITFKSVSEKDKRERDKPKAKGDKTKRKNDGSAVSKKENIVKPAKGPQEKVDGERERSPRSEPPIKKAKEETPKTDNTKSSSSSQKDEKITGTPRKAHSKSAKEHQETKPVKEEKVKKDYSKDVKSEKLTTKEEKAKKPNEKNKPLDNKGEKRKRKTEEKGVDKDFESSSMKISKLEVTEIVKPSPKRKMEPDTEKMDRTPEKDKISLSAPAKKIKLNRETGKKIGSTENISNTKEPSEKLESTSSKVKQEKVKGKVRRKVTGTEGSSSTLVDYTSTSSTGGSPVRKSEEKTDTKRTVIKTMEEYNNDNTAPAEDVIIMIQVPQSKWDKDDFESEEEDVKSTQPISSVGKPASVIKNVSTKPSNIVKYPEKESEPSEKIQKFTKDVSHEIIQHEVKSSKNSASSEKGKTKDRDYSVLEKENPEKRKNSTQPEKESNLDRLNEQGNFKSLSQSSKEARTSDKHDSTRASSNKDFTPNRDKKTDYDTREYSSSKRRDEKNELTRRKDSPSRNKDSASGQKNKPREERDLPKKGTGDSKKSNSSPSRDRKPHDHKATYDTKRPNEETKSVDKNPCKDREKHVLEARNNKESSGNKLLYILNPPETQVEKEQITGQIDKSTVKPKPQLSHSSRLSSDLTRETDEAAFEPDYNESDSESNVSVKEEESSGNISKDLKDKIVEKAKESLDTAAVVQVGISRNQSHSSPSVSPSRSHSPSGSQTRSHSSSASSAESQDSKKKKKKKEKKKHKKHKKHKKHKKHAGTEVELEKSQKHKHKKKKSKKNKDKEKEKEKDDQKVKSVTV,mutated_sequence,1.0,1792.0,UPI00001A96B8.a2m,UPI00001A96B8.npy,gnomAD +UPI0000161631,UPI0000161631.csv,MARLCRRVPCTLLLGLAVVLLKARLVPAAARAELSRSDLSLIQQQQQQQQQQQQQQKQLEEAEEERTEVPGATSTLTVPVSVFMLKVQVNDIISRQYLSQAVVEVFVNYTKTNSTVTKSNGAVLIKVPYKLGLSLTIIAYKDGYVLTPLPWKTRRMPIYSSVTLSLFPQSQANIWLFEDTVLITGKLADAKSQPSVQFSKALIKLPDNHHISNVTGYLTVLQQFLKVDNFLHTTGITLNKPGFENIELTPLAAICVKIYSGGKELKVNGSIQVSLPLLRLNDISAGDRIPAWTFDMNTGAWVNHGRGMVKEHNNHLIWTYDAPHLGYWIAAPLPGTRGSGINEDSKDITAYHTVFLTAILGGTIVIVIGFFAVLLCYCRDKCGTPQKRERNITKLEVLKRDQTTSTTHINHISTVKVALKAEDKSQLFNAKNSSYSPQKKEPSKAETEERVSMVKTRDDFKIYNEDVSFLSVNQNNYSRNPTQSLEPNVGSKQPKHINNNLSSSLGDAQDEKRYLTGNEEAYGRSHIPEQLMHIYSQPIAILQTSDLFSTPEQLHTAKSATLPRKGQLVYGQLMEPVNRENFTQTLPKMPIHSHAQPPDAREEDIILEGQQSLPSQASDWSRYSSSLLESVSVPGTLNEAVVMTPFSSELQGISEQTLLELSKGKPSPHPRAWFVSLDGKPVAQVRHSFIDLKKGKRTQSNDTSLDSGVDMNELHSSRKLEREKTFIKSMHQPKILYLEDLDLSSSESGTTVCSPEDPALRHILDGGSGVIMEHPGEESPGRKSTVEDFEANTSPTKRRGRPPLAKRDSKTNIWKKREERPLIPIN,mutated_sequence,1.0,826.0,UPI0000161631.a2m,UPI0000161631.npy,gnomAD +UPI00015E0572,UPI00015E0572.csv,MLETLRERLLSVQQDFTSGLKTLSDKSREAKVKSKPRTVPFLPKYSAGLELLSRYEDTWAALHRRAKDCASAGELVDSEVVMLSAHWEKKKTSLVELQEQLQQLPALIADLESMTANLTHLEASFEEVENNLLHLEDLCGQCELERCKHMQSQQLENYKKNKRKELEVTFRSELDAEHAQKVLEMEHTQQMKLKERQKFFEEAFQQDMEQYLSTGYLQIAERREPIGSMSSMEVNVDMLEQMDLMDISDQEALDVFLNSGGEENTVLSPALGPESSTCQNEITLQVPNPSELRAKPPSSSSTCTDSATRDISEGGESPVVQSDEEEVQVDTALATSHTDREATPDGGEDSDS,mutated_sequence,1.0,352.0,UPI00015E0572.a2m,UPI00015E0572.npy,gnomAD +UPI0002065466,UPI0002065466.csv,VHQEIHTIEKTFECSHGKKSFCQESHFTEHHRTCTREKPCESSDCKKRFCHSSVLRVHQRTHTGEKPYECKECRKSFHVKPNLTKHQKTHIGEKPFKCTACGRTFFQQSTLNVHQRTHTGEKPCGCNECEKSFYNKDALIIHQRTHTGEKPYKCNECEKSYLKKSHLNIHLRNHTGKRPHVCNECGKAFSMKSTLTVHQRTHGEKPYKCNECRKSFYMKSALSQHQRIHIWEEHCECKECGKTYQRSHLAKHHRKYTKKPYECKQCGKTFQKSHLIEHQRTHPGEIPHECNKCGKSFCYKSPLTIHQRTHIEEKPYKCSKSVTYFCMKSHLTVHQRPHTRKNPFECNECRKMFYVKSNLINHQRTHTGEKPYEGNTCGKS,mutated_sequence,1.0,380.0,UPI0002065466.a2m,UPI0002065466.npy,gnomAD +UPI0000167B2F,UPI0000167B2F.csv,MTAESGPPPPQPEVLATVKEERGETAAGAGVPGEATGRGAGGRRRKRPLQRGKPPYSYIALIAMAIAHAPERRLTLGGIYKFITERFPFYRDNPKKWQNSIRHNLTLNDCFLKIPREAGRPGKGNYWALDPNAEDMFESGSFLRRRKRFKRSDLSTYPAYMHDAAAAAAAAAAAAAAAAIFPGAVPAARPPYPGAVYAGYAPPSLAAPPPVYYPAASPGPCRVFGLVPERPLSPELGPAPSGPGGSCAFASAGAPATTTGYQPAGCTGARPANPSAYAAAYAGPDGAYPQGAGSAIFAAAGRLAGPASPPAGGSSGGVETTVDFYGRTSPGQFGALGACYNPGGQLGGASAGAYHARHAAAYPGGIDRFVSAM,mutated_sequence,1.0,373.0,UPI0000167B2F.a2m,UPI0000167B2F.npy,gnomAD +UPI0000456EEB,UPI0000456EEB.csv,MSVSRTMEDSCELDLVYVTERIIAVSFPSTANEENFRSNLREVAQMLKSKHGGNYLLFNLSERRPDITKLHAKVLEFGWPDLHTPALEKICSICKAMDTWLNADPHNVVVLHNKGNRGRIGVVIAAYMHYSNISASADQALDRFAMKRFYEDKIVPIGQPSQRRYVHYFSGLLSGSIKMNNKPLFLHHVIMHGIPNFESKGGCRPFLRIYQAMQPVYTSGIYNIPGDSQTSVCITIEPGLLLKGDILLKCYHKKFRSPARDVIFRVQFHTCAIHDLGVVFGKEDLDDAFKDDRFPEYGKVEFVFSYGPEKIQGMEHLENGPSVSVDYNTSDPLIRWDSYDNFSGHRDDGMEEVVGHTQGPLDGSLYAKVKKKDSLHGSTGAVNATRPTLSATPNHVEHTLSVSSDSGNSTASTKTDKTDEPVPGASSATAALSPQEKRELDRLLSGFGLEREKQGAMYHTQHLRSRPAGGSAVPSSGRHVVPAQVHVNGGALASERETDILDDELPNQDGHSAGSMGTLSSLDGVTNTSEGGYPEALSPLTNGLDKSYPMEPMVNGGGYPYESASRAGPAHAGHTAPMRPSYSAQEGLAGYQREGPHPAWPQPVTTSHYAHDPSGMFRSQSFSEAEPQLPPAPVRGGSSREAVQRGLNSWQQQQQQQQQPRPPPRQQERAHLESLVASRPSPQPLAETPIPSLPEFPRAASQQEIEQSIETLNMLMLDLEPASAAAPLHKSQSVPGAWPGASPLSSQPLSGSSRQSHPLTQSRSGYIPSGHSLGTPEPAPRASLESVPPGRSYSPYDYQPCLAGPNQDFHSKSPASSSLPAFLPTTHSPPGPQQPPASLPGLTAQPLLSPKEATSDPSRTPEEEPLNLEGLVAHRVAGVQAREKQPAEPPAPLRRRAASDGQYENQSPEATSPRSPGVRSPVQCVSPELALTIALNPGGRPKEPHLHSYKEAFEEMEGTSPSSPPPSGVRSPPGLAKTPLSALGLKPHNPADILLHPTGVTRRRIQPEEDEGKVVVRLSEEPRSYVESVARTAVAGPRAQDSEPKSFSAPATQAYGHEIPLRNGTLGGSFVSPSPLSTSSPILSADSTSVGSFPSGESSDQGPRTPTQPLLESGFRSGSLGQPSPSAQRNYQSSSPLPTVGSSYSSPDYSLQHFSSSPESQARAQFSVAGVHTVPGSPQARHRTVGTNTPPSPGFGWRAINPSMAAPSSPSLSHHQMMGPPGTGFHGSTVSSPQSSAATTPGSPSLCRHPAGVYQVSGLHNKVATTPGSPSLGRHPGAHQGNLASGLHSNAIASPGSPSLGRHLGGSGSVVPGSPCLDRHVAYGGYSTPEDRRPTLSRQSSASGYQAPSTPSFPVSPAYYPGLSSPATSPSPDSAAFRQGSPTPALPEKRRMSVGDRAGSLPNYATINGKVSSPVASGMSSPSGGSTVSFSHTLPDFSKYSMPDNSPETRAKVKFVQDTSKYWYKPEISREQAIALLKDQEPGAFIIRDSHSFRGAYGLAMKVSSPPPTIMQQNKKGDMTHELVRHFLIETGPRGVKLKGCPNEPNFGSLSALVYQHSIIPLALPCKLVIPNRDPTDESKDSSGPANSTADLLKQGAACNVLFVNSVDMESLTGPQAISKATSETLAADPTPAATIVHFKVSAQGITLTDNQRKLFFRRHYPLNTVTFCDLDPQERKWMKTEGGAPAKLFGFVARKQGSTTDNACHLFAELDPNQPASAIVNFVSKVMLNAGQKR,mutated_sequence,1.0,1735.0,UPI0000456EEB.a2m,UPI0000456EEB.npy,gnomAD +UPI00006E2246,UPI00006E2246.csv,MSPQGPAVLSLGSLCLDTNQAPNWTGLQTLLQQLPPQDIDERYCLALGEEERAELQLFCARRKQEALGQGVARLVLPKLEGHTCEKCRELLKPGEYGVFAARAGEQRCWHQPCFACQACGQALINLIYFYHDGQLYCGRHHAELLRPRCPACDQLIFSWRCTEAEGQRWHENHFCCQDCAGPLGGGRYALPGGSPCCPSCFENRYSDAGSSWAGALEGQAFLGETGLDRTEGRDQTSVNSATLSRTLLAAAGGSSLQTQRGLPGSSPQQENRPGDKAEAPKGQEQCRLETIRDPKDTPFSTCSSSSDSEPEGFFLGERLPQSWKTPGSLQAEDSNASKTHCTMC,mutated_sequence,1.0,344.0,UPI00006E2246.a2m,UPI00006E2246.npy,gnomAD +UPI000013C98D,UPI000013C98D.csv,MLPRVGCPALPLPPPPLLPLLLLLLGASGGGGGARAEVLFRCPPCTPERLAACGPPPVAPPAAVAAVAGGARMPCAELVREPGCGCCSVCARLEGEACGVYTPRCGQGLRCYPHPGSELPLQALVMGEGTCEKRRDAEYGASPEQVADNGDDHSEGGLVENHVDSTMNMLGGGGSAGRKPLKSGMKELAVFREKVTEQHRQMGKGGKHHLGLEEPKKLRPPPARTPCQQELDQVLERISTMRLPDERGPLEHLYSLHIPNCDKHGLYNLKQCKMSLNGQRGECWCVNPNTGKLIQGAPTIRGDPECHLFYNEQQEARGVHTQRMQ,mutated_sequence,1.0,325.0,UPI000013C98D.a2m,UPI000013C98D.npy,gnomAD +UPI000015153B,UPI000015153B.csv,MAAAAGNRASSSGFPGARATSPEAGGGGGALKASSAPAAAAGLLREAGSGGRERADWRRRQLRKVRSVELDQLPEQPLFLAASPPASSTSPSPEPADAAGSGTGFQPVAVPPPHGAASRGGAHLTESVAAPDSGASSPAAAEPGEKRAPAAEPSPAAAPAGREMENKETLKGLHKMDDRPEERMIREKLKATCMPAWKHEWLERRNRRGPVVVKPIPVKGDGSEMNHLAAESPGEVQASAASPASKGRRSPSPGNSPSGRTVKSESPGVRRKRVSPVPFQSGRITPPRRAPSPDGFSPYSPEETNRRVNKVMRARLYLLQQIGPNSFLIGGDSPDNKYRVFIGPQNCSCARGTFCIHLLFVMLRVFQLEPSDPMLWRKTLKNFEVESLFQKYHSRRSSRIKAPSRNTIQKFVSRMSNSHTLSSSSTSTSSSENSIKDEEEQMCPICLLGMLDEESLTVCEDGCRNKLHHHCMSIWAEECRRNREPLICPLCRSKWRSHDFYSHELSSPVDSPSSLRAAQQQTVQQQPLAGSRRNQESNFNLTHYGTQQIPPAYKDLAEPWIQVFGMELVGCLFSRNWNVREMALRRLSHDVSGALLLANGESTGNSGGSSGSSPSGGATSGSSQTSISGDVVEACCSVLSMVCADPVYKVYVAALKTLRAMLVYTPCHSLAERIKLQRLLQPVVDTILVKCADANSRTSQLSISTLLELCKGQAGELAVGREILKAGSIGIGGVDYVLNCILGNQTESNNWQELLGRLCLIDRLLLEFPAEFYPHIVSTDVSQAEPVEIRYKKLLSLLTFALQSIDNSHSMVGKLSRRIYLSSARMVTTVPHVFSKLLEMLSVSSSTHFTRMRRRLMAIADEVEIAEAIQLGVEDTLDGQQDSFLQASVPNNYLETTENSSPECTVHLEKTGKGLCATKLSASSEDISERLASISVGPSSSTTTTTTTTEQPKPMVQTKGRPHSQCLNSSPLSHHSQLMFPALSTPSSSTPSVPAGTATDVSKHRLQGFIPCRIPSASPQTQRKFSLQFHRNCPENKDSDKLSPVFTQSRPLPSSNIHRPKPSRPTPGNTSKQGDPSKNSMTLDLNSSSKCDDSFGCSSNSSNAVIPSDETVFTPVEEKCRLDVNTELNSSIEDLLEASMPSSDTTVTFKSEVAVLSPEKAENDDTYKDDVNHNQKCKEKMEAEEEEALAIAMAMSASQDALPIVPQLQVENGEDIIIIQQDTPETLPGHTKAKQPYREDTEWLKGQQIGLGAFSSCYQAQDVGTGTLMAVKQVTYVRNTSSEQEEVVEALREEIRMMSHLNHPNIIRMLGATCEKSNYNLFIEWMAGGSVAHLLSKYGAFKESVVINYTEQLLRGLSYLHENQIIHRDVKGANLLIDSTGQRLRIADFGAAARLASKGTGAGEFQGQLLGTIAFMAPEVLRGQQYGRSCDVWSVGCAIIEMACAKPPWNAEKHSNHLALIFKIASATTAPSIPSHLSPGLRDVALRCLELQPQDRPPSRELLKHPVFRTTW,mutated_sequence,1.0,1512.0,UPI000015153B.a2m,UPI000015153B.npy,gnomAD +UPI0000070FB1,UPI0000070FB1.csv,MPTVDDILEQVGESGWFQKQAFLILCLLSAAFAPICVGIVFLGFTPDHHCQSPGVAELSQRCGWSPAEELNYTVPGLGPAGEAFLGQCRRYEVDWNQSALSCVDPLASLATNRSHLPLGPCQDGWVYDTPGSSIVTEFNLVCADSWKLDLFQSCLNAGFLFGSLGVGYFADRFGRKLCLLGTVLVNAVSGVLMAFSPNYMSMLLFRLLQGLVSKGNWMAGYTLITEFVGSGSRRTVAIMYQMAFTVGLVALTGLAYALPHWRWLQLAVSLPTFLFLLYYWCVPESPRWLLSQKRNTEAIKIMDHIAQKNGKLPPADLKMLSLEEDVTEKLSPSFADLFRTPRLRKRTFILMYLWFTDSVLYQGLILHMGATSGNLYLDFLYSALVEIPGAFIALITIDRVGRIYPMAMSNLLAGAACLVMIFISPDLHWLNIIIMCVGRMGITIAIQMICLVNAELYPTFVRNLGVMVCSSLCDIGGIITPFIVFRLREVWQALPLILFAVLGLLAAGVTLLLPETKGVALPETMKDAENLGRKAKPKENTIYLKVQTSEPSGT,mutated_sequence,1.0,554.0,UPI0000070FB1.a2m,UPI0000070FB1.npy,gnomAD +UPI00001293CB,UPI00001293CB.csv,MSRPQLRRWRLVSSPPSGVPGLALLALLALLALRLAAGTDCPCPEPELCRPIRHHPDFEVFVFDVGQKTWKSYDWSQITTVATFGKYDSELMCYAHSKGARVVLKGDVSLKDIIDPAFRASWIAQKLNLAKTQYMDGINIDIEQEVNCLSPEYDALTALVKETTDSFHREIEGSQVTFDVAWSPKNIDRRCYNYTGIADACDFLFVMSYDEQSQIWSECIAAANAPYNQTLTGYNDYIKMSINPKKLVMGVPWYGYDYTCLNLSEDHVCTIAKVPFRGAPCSDAAGRQVPYKTIMKQINSSISGNLWDKDQRAPYYNYKDPAGHFHQVWYDNPQSISLKATYIQNYRLRGIGMWNANCLDYSGDAVAKQQTEEMWEVLKPKLLQR,mutated_sequence,1.0,385.0,UPI00001293CB.a2m,UPI00001293CB.npy,gnomAD +UPI00001F9E3B,UPI00001F9E3B.csv,MDRNPSPPPPGRDKEEEEEVAGGDCIGSTVYSKHWLFGVLSGLIQIVSPENTKSSSDDEEQLTELDEEMENEICRVWDMSMDEDVALFLQEFNAPDIFMGVLAKSKCPRLREICVGILGNMACFQEICVSISSDKNLGQVLLHCLYDSDPPTLLETSRLLLTCLSQAEVASVWVERIQEHPAIYDSICFIMSSSTNVDLLVKVGEVVDKLFDLDEKLMLEWVRNGAAQPLDQPQEESEEQPVFRLVPCILEAAKQVRSENPEWLDVYMHILQLLTTVDDGIQAIVHCPDTGKDIWNLLFDLVCHEFCQSDDPPIILQEQKTVLASVFSVLSAIYASQTEQEYLKIEKVDLPLIDSLIRVLQNMEQCQKKPENSAESNTEETKRTDLTQDDFHLKILKDILCEFLSNIFQALTKETVAQGVKEGQLSKQKCSSAFQNLLPFYSPVVEDFIKILREVDKALADDLEKNFPSLKVQT,mutated_sequence,1.0,474.0,UPI00001F9E3B.a2m,UPI00001F9E3B.npy,gnomAD +UPI0000208B29,UPI0000208B29.csv,MDLSAAAALCLWLLSACRPRDGLEAAAVLRAAGAGPVRSPGGGGGGGGGGRTLAQAAGAAAVPAAAVPRARAARRAAGSGFRNGSVVPHHFMMSLYRSLAGRAPAGAAAVSASGHGRADTITGFTDQATQDESAAETGQSFLFDVSSLNDADEVVGAELRVLRRGSPESGPGSWTSPPLLLLSTCPGAARAPRLLYSRAAEPLVGQRWEAFDVADAMRRHRREPRPPRAFCLLLRAVAGPVPSPLALRRLGFGWPGGGGSAAEERAVLVVSSRTQRKESLFREIRAQARALGAALASEPLPDPGTGTASPRAVIGGRRRRRTALAGTRTAQGSGGGAGRGHGRRGRSRCSRKPLHVDFKELGWDDWIIAPLDYEAYHCEGLCDFPLRSHLEPTNHAIIQTLLNSMAPDAAPASCCVPARLSPISILYIDAANNVVYKQYEDMVVEACGCR,mutated_sequence,1.0,450.0,UPI0000208B29.a2m,UPI0000208B29.npy,gnomAD +UPI00001B4B18,UPI00001B4B18.csv,MECYYIVISSTHLSNGHFRNIKGVFRGPLSKNGNKTLDYAEKENTIAKALEDLKANFYCELCDKQYYKHQEFDNHINSYDHAHKQRLKELKQREFARNVASKSRKDERKQEKALQRLHKLAELRKETVCAPGSGPMFKSTTVTVRENCNEISQRVVVDSVNNQQDFKYTLIHSEENTKDATTVAEDPESANNYTAKNNQVGDQAQGIHRHKIGFSFAFPKKASVKLESSAAAFSEYSDDASVGKGFSRKSRFVPSACHLQQSSPTDVLLSSEEKTNSFHPPEAMCRDKETVQTQEIKEVSSEKDALLLPSFCKFQLQLSSDADNCQNSVPLADQIPLESVVINEDIPVSGNSFELLGNKSTVLDMSNDCISVQATTEENVKHNEASTTEVENKNGPETLAPSNTEEVNITIHKKTNFCKRQCEPFVPVLNKHRSTVLQWPSEMLVYTTTKPSISYSCNPLCFDFKSTKVNNNLDKNKPDLKDLCSQQKQEDICMGPLSDYKDVSTEGLTDYEIGSSKNKCSQVTPLLADDILSSSCDSGKNENTGQRYKNISCKIRETEKYNFTKSQIKQDTLDEKYNKIRLKETHEYWFHKSRRKKKRKKLCQHHHMEKTKESETRCKMEAENSYTENAGKYLLEPISEKQYLAAEQLLDSHQLLDKRPKSESISLSDNEEMCKTWNTEYNTYDTISSKNHCKKNTILLNGQSNATMIHSGKHNLTYSRTYCCWKTKMSSCSQDHRSLVLQNDMKHMSQNQAVKRGYNSVMNESERFYRKRRQHSHSYSSDESLNRQNHLPEEFLRPPSTSVAPCKPKKKRRRKRGRFHPGFETLELKENTDYPVKDNSSLNPLDRLISEDKKEKMKPQEVAKIERNSEQTNQLRNKLSFHPNNLLPSETNGETEHLEMETTSGELSDVSNDPTTSVCVASAPTKEAIDNTLLEHKERSENINLNEKQIPFQVPNIERNFRQSQPKSYLCHYELAEALPQGKMNETPTEWLRYNSGILNTQPPLPFKEAHVSGHTFVTAEQILAPLALPEQALLIPLENHDKFKNVPCEVYQHILQPNMLANKVKFTFPPAALPPPSTPLQPLPLQQSLCSTSVTTIHHTVLQQHAAAAAAAAAAAAAGTFKVLQPHQQFLSQIPALTRTSLPQLSVGPVGPRLCPGNQPTFVAPPQMPIIPASVLHPSHLAFPSLPHALFPSLLSPHPTVIPLQPLF,mutated_sequence,1.0,1209.0,UPI00001B4B18.a2m,UPI00001B4B18.npy,gnomAD +UPI00001273CC,UPI00001273CC.csv,MEFSWGSGQESRRLLLLLLLLAAWEAGNGQLHYSVSEEAKHGTFVGRIAQDLGLELAELVPRLFRVASKGRGGLLEVNLQNGILFVNSRIDREELCRRSAECSIHLEVIVDRPLQVFHVDVEVRDINDNPPVFPATQKNLSIAESRPLDSRFPLEGASDADIGENALLTYRLSPNEYFSLEKPPDDELVKGLGLILRKSLDREEAPEIFLVLTATDGGKPELTGTVQLLITVLDANDNAPAFDRTIYKVRLLENVPNGTLVIKLNASDLDEGLNGDIVYSFSNDISPNVKSKFHIDPITGQIIVKGYIDFEESKSYEIIVEGIDKGQLPLSGHCRVIVEVEDNNDNVPDLEFKSLSLPIREDAPLGTVIALISVSDKDMGVNGLVTCSLTSHVPFKLVSTFKNYYSLVLDSALDRESVSAYELVVTARDGGSPSLWATASVSVEVADVNDNAPAFAQPEYTVFVKENNPPGCHIFTVSAWDADAQENALVSYSLVERRVGERALSSYVSVHAESGKVYALQPLDHEELELLQFQVTARDAGVPPLGSNVTLQVFVLDENDNAPALLAPRAGGTGGAVSELVPWSVGVGHVVAKVRAVDADSGYNAWLSYELQPGTGGARIPFRVGLYTGEISTTRALDETDAPRHRLLVLVKDHGEPALTATATVLVSLVESGQAPKASSRALVGAVGPDAALVDVNVYLIIAICAVSSLLVLTLLLYTALRCSALPTEGACAPGKPTLVCSSAVGSWSYSQQRRPRVCSGEGPPKTDLMAFSPSLPDSRDREDQLQTTEESFAKPRQPNPDWRYSASLRAGMHSSVHLEEAGILRAGPGGPDQQWPTVSSATPEPEAGEVSPPVGAGVNSNSWTFKYGPGNPKQSGPGELPDKFIIPGSPAIISIRQEPTNSQIDKSDFITFGKKEETKKKKKKKKGNKTQEKKEKGNSTTDNSDQ,mutated_sequence,1.0,947.0,UPI00001273CC.a2m,UPI00001273CC.npy,gnomAD +UPI0000470BBD,UPI0000470BBD.csv,MSYQKKQPTPQPPVDCVKTSGGGGGGGGSGGGGCGFFGGGGSGGGSSGSGCGYSGGGGYSGGGCGGGSSGGGGGGGIGGCGGGSGGSVKYSGGGGSSGGGSGCFSSGGGGSGCFSSGGGGSSGGGSGCFSSGGGGSSGGGSGCFSSGGGGFSGQAVQCQSYGGVSSGGSSGGGSGCFSSGGGGGSVCGYSGGGSGCGGGSSGGSGSGYVSSQQVTQTSCAPQPSYGGGSSGGGGSGGSGCFSSGGGGGSSGCGGGSSGIGSGCIISGGGSVCGGGSSGGGGGGSSVGGSGSGKGVPICHQTQQKQAPTWPSK,mutated_sequence,1.0,312.0,UPI0000470BBD.a2m,UPI0000470BBD.npy,gnomAD +UPI00000718AE,UPI00000718AE.csv,MEVVTFGDVAVHFSREEWQCLDPGQRALYREVMLENHSSVAGLAGFLVFKPELISRLEQGEEPWVLDLQGAEGTEAPRTSKTGFLGRPTMGQEPRHPHAPPATPVPGLPKHCSQRLTLPPPGLSSSPLGHFLVHDQDRRRGTSAIWMV,mutated_sequence,1.0,148.0,UPI00000718AE.a2m,UPI00000718AE.npy,gnomAD +UPI000013D567,UPI000013D567.csv,MATLEKLMKAFESLKSFQQQQQQQQQQQQQQQQQQQQQPPPPPPPPPPPQLPQPPPQAQPLLPQPQPPPPPPPPPPGPAVAEEPLHRPKKELSATKKDRVNHCLTICENIVAQSVRNSPEFQKLLGIAMELFLLCSDDAESDVRMVADECLNKVIKALMDSNLPRLQLELYKEIKKNGAPRSLRAALWRFAELAHLVRPQKCRPYLVNLLPCLTRTSKRPEESVQETLAAAVPKIMASFGNFANDNEIKVLLKAFIANLKSSSPTIRRTAAGSAVSICQHSRRTQYFYSWLLNVLLGLLVPVEDEHSTLLILGVLLTLRYLVPLLQQQVKDTSLKGSFGVTRKEMEVSPSAEQLVQVYELTLHHTQHQDHNVVTGALELLQQLFRTPPPELLQTLTAVGGIGQLTAAKEESGGRSRSGSIVELIAGGGSSCSPVLSRKQKGKVLLGEEEALEDDSESRSDVSSSALTASVKDEISGELAASSGVSTPGSAGHDIITEQPRSQHTLQADSVDLASCDLTSSATDGDEEDILSHSSSQVSAVPSDPAMDLNDGTQASSPISDSSQTTTEGPDSAVTPSDSSEIVLDGTDNQYLGLQIGQPQDEDEEATGILPDEASEAFRNSSMALQQAHLLKNMSHCRQPSDSSVDKFVLRDEATEPGDQENKPCRIKGDIGQSTDDDSAPLVHCVRLLSASFLLTGGKNVLVPDRDVRVSVKALALSCVGAAVALHPESFFSKLYKVPLDTTEYPEEQYVSDILNYIDHGDPQVRGATAILCGTLICSILSRSRFHVGDWMGTIRTLTGNTFSLADCIPLLRKTLKDESSVTCKLACTAVRNCVMSLCSSSYSELGLQLIIDVLTLRNSSYWLVRTELLETLAEIDFRLVSFLEAKAENLHRGAHHYTGLLKLQERVLNNVVIHLLGDEDPRVRHVAAASLIRLVPKLFYKCDQGQADPVVAVARDQSSVYLKLLMHETQPPSHFSVSTITRIYRGYNLLPSITDVTMENNLSRVIAAVSHELITSTTRALTFGCCEALCLLSTAFPVCIWSLGWHCGVPPLSASDESRKSCTVGMATMILTLLSSAWFPLDLSAHQDALILAGNLLAASAPKSLRSSWASEEEANPAATKQEEVWPALGDRALVPMVEQLFSHLLKVINICAHVLDDVAPGPAIKAALPSLTNPPSLSPIRRKGKEKEPGEQASVPLSPKKGSEASAASRQSDTSGPVTTSKSSSLGSFYHLPSYLKLHDVLKATHANYKVTLDLQNSTEKFGGFLRSALDVLSQILELATLQDIGKCVEEILGYLKSCFSREPMMATVCVQQLLKTLFGTNLASQFDGLSSNPSKSQGRAQRLGSSSVRPGLYHYCFMAPYTHFTQALADASLRNMVQAEQENDTSGWFDVLQKVSTQLKTNLTSVTKNRADKNAIHNHIRLFEPLVIKALKQYTTTTCVQLQKQVLDLLAQLVQLRVNYCLLDSDQVFIGFVLKQFEYIEVGQFRESEAIIPNIFFFLVLLSYERYHSKQIIGIPKIIQLCDGIMASGRKAVTHAIPALQPIVHDLFVLRGTNKADAGKELETQKEVVVSMLLRLIQYHQVLEMFILVLQQCHKENEDKWKRLSRQIADIILPMLAKQQMHIDSHEALGVLNTLFEILAPSSLRPVDMLLRSMFVTPNTMASVSTVQLWISGILAILRVLISQSTEDIVLSRIQELSFSPYLISCTVINRLRDGDSTSTLEEHSEGKQIKNLPEETFSRFLLQLVGILLEDIVTKQLKVEMSEQQHTFYCQELGTLLMCLIHIFKSGMFRRITAAATRLFRSDGCGGSFYTLDSLNLRARSMITTHPALVLLWCQILLLVNHTDYRWWAEVQQTPKRHSLSSTKLLSPQMSGEEEDSDLAAKLGMCNREIVRRGALILFCDYVCQNLHDSEHLTWLIVNHIQDLISLSHEPPVQDFISAVHRNSAASGLFIQAIQSRCENLSTPTMLKKTLQCLEGIHLSQSGAVLTLYVDRLLCTPFRVLARMVDILACRRVEMLLAANLQSSMAQLPMEELNRIQEYLQSSGLAQRHQRLYSLLDRFRLSTMQDSLSPSPPVSSHPLDGDGHVSLETVSPDKDWYVHLVKSQCWTRSDSALLEGAELVNRIPAEDMNAFMMNSEFNLSLLAPCLSLGMSEISGGQKSALFEAAREVTLARVSGTVQQLPAVHHVFQPELPAEPAAYWSKLNDLFGDAALYQSLPTLARALAQYLVVVSKLPSHLHLPPEKEKDIVKFVVATLEALSWHLIHEQIPLSLDLQAGLDCCCLALQLPGLWSVVSSTEFVTHACSLIYCVHFILEAVAVQPGEQLLSPERRTNTPKAISEEEEEVDPNTQNPKYITAACEMVAEMVESLQSVLALGHKRNSGVPAFLTPLLRNIIISLARLPLVNSYTRVPPLVWKLGWSPKPGGDFGTAFPEIPVEFLQEKEVFKEFIYRINTLGWTSRTQFEETWATLLGVLVTQPLVMEQEESPPEEDTERTQINVLAVQAITSLVLSAMTVPVAGNPAVSCLEQQPRNKPLKALDTRFGRKLSIIRGIVEQEIQAMVSKRENIATHHLYQAWDPVPSLSPATTGALISHEKLLLQINPERELGSMSYKLGQVSIHSVWLGNSITPLREEEWDEEEEEEADAPAPSSPPTSPVNSRKHRAGVDIHSCSQFLLELYSRWILPSSSARRTPAILISEVVRSLLVVSDLFTERNQFELMYVTLTELRRVHPSEDEILAQYLVPATCKAAAVLGMDKAVAEPVSRLLESTLRSSHLPSRVGALHGVLYVLECDLLDDTAKQLIPVISDYLLSNLKGIAHCVNIHSQQHVLVMCATAFYLIENYPLDVGPEFSASIIQMCGVMLSGSEESTPSIIYHCALRGLERLLLSEQLSRLDAESLVKLSVDRVNVHSPHRAMAALGLMLTCMYTGKEKVSPGRTSDPNPAAPDSESVIVAMERVSVLFDRIRKGFPCEARVVARILPQFLDDFFPPQDIMNKVIGEFLSNQQPYPQFMATVVYKVFQTLHSTGQSSMVRDWVMLSLSNFTQRAPVAMATWSLSCFFVSASTSPWVAAILPHVISRMGKLEQVDVNLFCLVATDFYRHQIEEELDRRAFQSVLEVVAAPGSPYHRLLTCLRNVHKVTTC,mutated_sequence,1.0,3142.0,UPI000013D567.a2m,UPI000013D567.npy,gnomAD +UPI0000140E19,UPI0000140E19.csv,MILNKALLLGALALTTVMSPCGGEDIVADHVASCGVNLYQFYGPSGQYTHEFDGDEQFYVDLERKETAWRWPEFSKFGGFDPQGALRNMAVAKHNLNIMIKRYNSTAATNEVPEVTVFSKSPVTLGQPNTLICLVDNIFPPVVNITWLSNGQSVTEGVSETSFLSKSDHSFFKISYLTFLPSADEIYDCKVEHWGLDQPLLKHWEPEIPAPMSELTETVVCALGLSVGLMGIVVGTVFIIQGLRSVGASRHQGPL,mutated_sequence,1.0,255.0,UPI0000140E19.a2m,UPI0000140E19.npy,gnomAD +UPI0000141657,UPI0000141657.csv,MDPQPPPPAQGSPPHRGRGRGRGRGRGRGRGRGRGGAGAPRAPLPCPTCGRLFRFPYYLSRHRLSHSGLRPHACPLCPKAFRRPAHLSRHLRGHGPQPPLRCAACPRTFPEPAQLRRHLAQEHAGGEVELAIERVAKETAEPSWGPQDEGSEPPTTAAAGATEEEAVAAWPETWPAGEPSTLAAPTSAAEPRESESEEAEAGAAELRAELALAAGRQEEKQVLLQADWTLLCLRCREAFATKGELKAHPCLRPEGEQEGEGGPPPRPKRHQCSICLKAFARPWSLSRHRLVHSTDRPFVCPDCGLAFRLASYLRQHRRVHGPLSLLAPLPAAGKKDDKASGARNSAKGPEGGEGAECGGASEGGEGQNGGDAAPARPPAGEPRFWCPECGKGFRRRAHLRQHGVTHSGARPFQCVRCQREFKRLADLARHAQVHAGGPAPHPCPRCPRRFSRAYSLLRHQRCHRAELERAAALQALQAQAPTSPPPPPPPLKAEQEEEGLPLPLANIKEEPPSPGTPPQSPPAPPVFLSASCFDSQDHSAFEMEEEEVDSKAHLRGLGGLAS,mutated_sequence,1.0,562.0,UPI0000141657.a2m,UPI0000141657.npy,gnomAD +UPI000013D783,UPI000013D783.csv,MGSGPLSLPLALSPPRLLLLLLLSLLPVARASEAEHRLFERLFEDYNEIIRPVANVSDPVIIHFEVSMSQLVKVDEVNQIMETNLWLKQIWNDYKLKWNPSDYGGAEFMRVPAQKIWKPDIVLYNNAVGDFQVDDKTKALLKYTGEVTWIPPAIFKSSCKIDVTYFPFDYQNCTMKFGSWSYDKAKIDLVLIGSSMNLKDYWESGEWAIIKAPGYKHDIKYNCCEEIYPDITYSLYIRRLPLFYTINLIIPCLLISFLTVLVFYLPSDCGEKVTLCISVLLSLTVFLLVITETIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHYRTPTTHTMPSWVKTVFLNLLPRVMFMTRPTSNEGNAQKPRPLYGAELSNLNCFSRAESKGCKEGYPCQDGMCGYCHHRRIKISNFSANLTRSSSSESVDAVLSLSALSPEIKEAIQSVKYIAENMKAQNEAKEIQDDWKYVAMVIDRIFLWVFTLVCILGTAGLFLQPLMAREDA,mutated_sequence,1.0,505.0,UPI000013D783.a2m,UPI000013D783.npy,gnomAD +UPI000013EF6E,UPI000013EF6E.csv,MLTVALLALLCASASGNAIQARSSSYSGEYGGGGGKRFSHSGNQLDGPITALRVRVNTYYIVGLQVRYGKVWSDYVGGRNGDLEEIFLHPGESVIQVSGKYKWYLKKLLFVTDKGRYLSFGKDSGTSFNAVPLHPNTVLRFISGRSGSLIDAIGLHWDVYPSSCSRC,mutated_sequence,1.0,167.0,UPI000013EF6E.a2m,UPI000013EF6E.npy,gnomAD +UPI00001AEBA4,UPI00001AEBA4.csv,MGGALGPALLLTSLFGAWAGLGPGQGEQGMTVAVVFSSSGPPQAQFRARLTPQSFLDLPLEIQPLTVGVNTTNPSSLLTQICGLLGAAHVHGIVFEDNVDTEAVAQILDFISSQTHVPILSISGGSAVVLTPKEPGSAFLQLGVSLEQQLQVLFKVLEEYDWSAFAVITSLHPGHALFLEGVRAVADASHVSWRLLDVVTLELGPGGPRARTQRLLRQLDAPVFVAYCSREEAEVLFAEAAQAGLVGPGHVWLVPNLALGSTDAPPATFPVGLISVVTESWRLSLRQKVRDGVAILALGAHSYWRQHGTLPAPAGDCRVHPGPVSPAREAFYRHLLNVTWEGRDFSFSPGGYLVQPTMVVIALNRHRLWEMVGRWEHGVLYMKYPVWPRYSASLQPVVDSRHLTVATLEERPFVIVESPDPGTGGCVPNTVPCRRQSNHTFSSGDVAPYTKLCCKGFCIDILKKLARVVKFSYDLYLVTNGKHGKRVRGVWNGMIGEVYYKRADMAIGSLTINEERSEIVDFSVPFVETGISVMVARSNGTVSPSAFLEPYSPAVWVMMFVMCLTVVAITVFMFEYFSPVSYNQNLTRGKKSGGPAFTIGKSVWLLWALVFNNSVPIENPRGTTSKIMVLVWAFFAVIFLASYTANLAAFMIQEQYIDTVSGLSDKKFQRPQDQYPPFRFGTVPNGSTERNIRSNYRDMHTHMVKFNQRSVEDALTSLKMGKLDAFIYDAAVLNYMAGKDEGCKLVTIGSGKVFATTGYGIAMQKDSHWKRAIDLALLQFLGDGETQKLETVWLSGICQNEKNEVMSSKLDIDNMAGVFYMLLVAMGLALLVFAWEHLVYWKLRHSVPNSSQLDFLLAFSRGIYSCFSGVQSLASPPRQASPDLTASSAQASVLKMLQAARDMVTTAGVSSSLDRATRTIENWGGGRRAPPPSPCPTPRSGPSPCLPTPDPPPEPSPTGWGPPDGGRAALVRRAPQPPGRPPTPGPPLSDVSRVSRRPAWEARWPVRTGHCGRHLSASERPLSPARCHYSSFPRADRSGRPFLPLFPELEDLPLLGPEQLARREALLHAAWARGSRPRHASLPSSVAEAFARPSSLPAGCTGPACARPDGHSACRRLAQAQSMCLPIYREACQEGEQAGAPAWQHRQHVCLHAHAHLPFCWGAVCPHLPPCASHGSWLSGAWGPLGHRGRTLGLGTGYRDSGGLDEISRVARGTQGFPGPCTWRRISSLESEV,mutated_sequence,1.0,1233.0,UPI00001AEBA4.a2m,UPI00001AEBA4.npy,gnomAD +UPI00000711D1,UPI00000711D1.csv,MDGDGDPESVGQPEEASPEEQPEEASAEEERPEDQQEEEAAAAAAYLDELPEPLLLRVLAALPAAELVQACRLVCLRWKELVDGAPLWLLKCQQEGLVPEGGVEEERDHWQQFYFLSKRRRNLLRNPCGEEDLEGWCDVEHGGDGWRVEELPGDSGVEFTHDESVKKYFASSFEWCRKAQVIDLQAEGYWEELLDTTQPAIVVKDWYSGRSDAGCLYELTVKLLSEHENVLAEFSSGQVAVPQDSDGGGWMEISHTFTDYGPGVRFVRFEHGGQDSVYWKGWFGARVTNSSVWVEP,mutated_sequence,1.0,296.0,UPI00000711D1.a2m,UPI00000711D1.npy,gnomAD +UPI00001313D9,UPI00001313D9.csv,MAAYSYRPGPGAGPGPAAGAALPDQSFLWNVFQRVDKDRSGVISDTELQQALSNGTWTPFNPVTVRSIISMFDRENKAGVNFSEFTGVWKYITDWQNVFRTYDRDNSGMIDKNELKQALSGFGYRLSDQFHDILIRKFDRQGRGQIAFDDFIQGCIVLQRLTDIFRRYDTDQDGWIQVSYEQYLSMVFSIV,mutated_sequence,1.0,191.0,UPI00001313D9.a2m,UPI00001313D9.npy,gnomAD +UPI0000D6DA73,UPI0000D6DA73.csv,MSEARGEPGSGPEAGARFFCTAGRGLEPFVMREVRARLAATQVEYISGKVFFTTCSDLNMLKKLKSAERLFLLIKKQFPLIISSVSKGKIFNEMQRLINEDPGSWLNAISIWKNLLELDAKKEKLSQRDDNQLKRKVGENEIIAKKLKIEQMQKIEENRDCQLEKQIKEETLEQRDFTTKSEKFQEEEFQNDIEKAIDTHNQNDLTFRVSCRCSGTIGKAFTAQEVGKVIGIAIMKHFGWKADLRNPQLEIFIHLNDIYSVVGIPVFRVSLASRAYIKTAGLRSTIAWAMASLADIKAGAFVLDPMCGLGTILLEAAKEWPDVYYVGADVSDSQLLGTWDNLKAAGLEDKIELLKISVIELPLPSESVDIIISDIPFGKKFKLGKDIKSILQEMERVLHVGGTIVLLLSEDHHRRLTDCKESNIPFNSKDSHTDEPGIKKCLNPEEKTGAFKTASTSFEASNHKFLDRMSPFGSLVPVECYKVSLGKTDAFICKYKKSHSSGL,mutated_sequence,1.0,503.0,UPI0000D6DA73.a2m,UPI0000D6DA73.npy,gnomAD +UPI00015E040A,UPI00015E040A.csv,MPEDLVTFDDVAWYLTTREWFKLDPEQRALEYYGNVTSVAGVPVLNPALVPHLAQGQVLLVSDPSPNTDPAKYSESTSATRHQMKGEDAQPQEMASTSFPRASGPSPEFRQHGDSDGKRGSPQNLPIEHHFACKECGDTFRLKVLLVQHQRVHSEEKGWECGDCGKVFRGVAEFNEHRKSHVAAEPQPGPSRALENAAEKREQMEREAKPFECEECGKRFKKNAGLSQHLRVHSREKPFDCEECGRSFKVNTHLFRHQKLHTSEKPFACKACSRDFLDRQELLKHQRMHTGHLPFDCDDCGKSFRGVNGLAEHQRIHSGAKPYGCPHCGKLFRRSSELTKHRRIHTGEKPYACGQCGKAFRQSSSLLEHARIHSGERPYACGECGKAFRGPSDLIKHRRIHSGLKPYECDKCGKAFRRSSGLSRHRRIHSGARRCECSQCGRVFKRRSALQKHQPTHHE,mutated_sequence,1.0,459.0,UPI00015E040A.a2m,UPI00015E040A.npy,gnomAD 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ESAEITITTQTGPHGATSQDTFTMDPSNTTPQAGIHSAMTHGFSQLDVTTLMSRIPQDVSWTSPPSVDKTSSPSSFLSSPAMTTPSLISSTLPEDKLSSPMTSLLTSGLVKITDILRTRLEPVTSSLPNFSSTSDKILATSKDSKDTKEIFPSINTEETNVKANNSGHESHSPALADSETPKATTQMVITTTVGDPAPSTSMPVHGSSETTNIKREPTYFLTPRLRETSTSQESSFPTDTSFLLSKVPTGTITEVSSTGVNSSSKISTPDHDKSTVPPDTFTGEIPRVFTSSIKTKSAEMTITTQASPPESASHSTLPLDTSTTLSQGGTHSTVTQGFPYSEVTTLMGMGPGNVSWMTTPPVEETSSVSSLMSSPAMTSPSPVSSTSPQSIPSSPLPVTALPTSVLVTTTDVLGTTSPESVTSSPPNLSSITHERPATYKDTAHTEAAMHHSTNTAVTNVGTSGSGHKSQSSVLADSETSKATPLMSTTSTLGDTSVSTSTPNISQTNQIQTEPTASLSPRLRESSTSEKTSSTTETNTAFSYVPTGAITQASRTEISSSRTSISDLDRPTIAPDISTGMITRLFTSPIMTKSAEMTVTTQTTTPGATSQGILPWDTSTTLFQGGTHSTVSQGFPHSEITTLRSRTPGDVSWMTTPPVEETSSGFSLMSPSMTSPSPVSSTSPESIPSSPLPVTALLTSVLVTTTNVLGTTSPEPVTSSPPNLSSPTQERLTTYKDTAHTEAMHASMHTNTAVANVGTSISGHESQSSVPADSHTSKATSPMGITFAMGDTSVSTSTPAFFETRIQTESTSSLIPGLRDTRTSEEINTVTETSTVLSEVPTTTTTEVSRTEVITSSRTTISGPDHSKMSPYISTETITRLSTFPFVTGSTEMAITNQTGPIGTISQATLTLDTSSTASWEGTHSPVTQRFPHSEETTTMSRSTKGVSWQSPPSVEETSSPSSPVPLPAITSHSSLYSAVSGSSPTSALPVTSLLTSGRRKTIDMLDTHSELVTSSLPSASSFSGEILTSEASTNTETIHFSENTAETNMGTTNSMHKLHSSVSIHSQPSGHTPPKVTGSMMEDAIVSTSTPGSPETKNVDRDSTSPLTPELKEDSTALVMNSTTESNTVFSSVSLDAATEVSRAEVTYYDPTFMPASAQSTKSPDISPEASSSHSNSPPLTISTHKTIATQTGPSGVTSLGQLTLDTSTIATSAGTPSARTQDFVDSETTSVMNNDLNDVLKTSPFSAEEANSLSSQAPLLVTTSPSPVTSTLQEHSTSSLVSVTSVPTPTLAKITDMDTNLEPVTRSPQNLRNTLATSEATTDTHTMHPSINTAVANVGTTSSPNEFYFTVSPDSDPYKATSAVVITSTSGDSIVSTSMPRSSAMKKIESETTFSLIFRLRETSTSQKIGSSSDTSTVFDKAFTAATTEVSRTELTSSSRTSIQGTEKPTMSPDTSTRSVTMLSTFAGLTKSEERTIATQTGPHRATSQGTLTWDTSITTSQAGTHSAMTHGFSQLDLSTLTSRVPEYISGTSPPSVEKTSSSSSLLSLPAITSPSPVPTTLPESRPSSPVHLTSLPTSGLVKTTDMLASVASLPPNLGSTSHKIPTTSEDIKDTEKMYPSTNIAVTNVGTTTSEKESYSSVPAYSEPPKVTSPMVTSFNIRDTIVSTSMPGSSEITRIEMESTFSLAHGLKGTSTSQDPIVSTEKSAVLHKLTTGATETSRTEVASSRRTSIPGPDHSTESPDISTEVIPSLPISLGITESSNMTIITRTGPPLGSTSQGTFTLDTPTTSSRAGTHSMATQEFPHSEMTTVMNKDPEILSWTIPPSIEKTSFSSSLMPSPAMTSPPVSSTLPKTIHTTPSPMTSLLTPSLVMTTDTLGTSPEPTTSSPPNLSSTSHEILTTDEDTTAIEAMHPSTSTAATNVETTSSGHGSQSSVLADSEKTKATAPMDTTSTMGHTTVSTSMSVSSETTKIKRESTYSLTPGLRETSISQNASFSTDTSIVLSEVPTGTTAEVSRTEVTSSGRTSIPGPSQSTVLPEISTRTMTRLFASPTMTESAEMTIPTQTGPSGSTSQDTLTLDTSTTKSQAKTHSTLTQRFPHSEMTTLMSRGPGDMSWQSSPSLENPSSLPSLLSLPATTSPPPISSTLPVTISSSPLPVTSLLTSSPVTTTDMLHTSPELVTSSPPKLSHTSDERLTTGKDTTNTEAVHPSTNTAASNVEIPSSGHESPSSALADSETSKATSPMFITSTQEDTTVAISTPHFLETSRIQKESISSLSPKLRETGSSVETSSAIETSAVLSEVSIGATTEISRTEVTSSSRTSISGSAESTMLPEISTTRKIIKFPTSPILAESSEMTIKTQTSPPGSTSESTFTLDTSTTPSLVITHSTMTQRLPHSEITTLVSRGAGDVPRPSSLPVEETSPPSSQLSLSAMISPSPVSSTLPASSHSSSASVTSLLTPGQVKTTEVLDASAEPETSSPPSLSSTSVEILATSEVTTDTEKIHPFSNTAVTKVGTSSSGHESPSSVLPDSETTKATSAMGTISIMGDTSVSTLTPALSNTRKIQSEPASSLTTRLRETSTSEETSLATEANTVLSKVSTGATTEVSRTEAISFSRTSMSGPEQSTMSQDISIGTIPRISASSVLTESAKMTITTQTGPSESTLESTLNLNTATTPSWVETHSIVIQGFPHPEMTTSMGRGPGGVSWPSPPFVKETSPPSSPLSLPAVTSPHPVSTTFLAHIPPSPLPVTSLLTSGPATTTDILGTSTEPGTSSSSSLSTTSHERLTTYKDTAHTEAVHPSTNTGGTNVATTSSGYKSQSSVLADSSPMCTTSTMGDTSVLTSTPAFLETRRIQTELASSLTPGLRESSGSEGTSSGTKMSTVLSKVPTGATTEISKEDVTSIPGPAQSTISPDISTRTVSWFSTSPVMTESAEITMNTHTSPLGATTQGTSTLDTSSTTSLTMTHSTISQGFSHSQMSTLMRRGPEDVSWMSPPLLEKTRPSFSLMSSPATTSPSPVSSTLPESISSSPLPVTSLLTSGLAKTTDMLHKSSEPVTNSPANLSSTSVEILATSEVTTDTEKTHPSSNRTVTDVGTSSSGHESTSFVLADSQTSKVTSPMVITSTMEDTSVSTSTPGFFETSRIQTEPTSSLTLGLRKTSSSEGTSLATEMSTVLSGVPTGATAEVSRTEVTSSSRTSISGFAQLTVSPETSTETITRLPTSSIMTESAEMMIKTQTDPPGSTPESTHTVDISTTPNWVETHSTVTQRFSHSEMTTLVSRSPGDMLWPSQSSVEETSSASSLLSLPATTSPSPVSSTLVEDFPSASLPVTSLLNPGLVITTDRMGISREPGTSSTSNLSSTSHERLTTLEDTVDTEDMQPSTHTAVTNVRTSISGHESQSSVLSDSETPKATSPMGTTYTMGETSVSISTSDFFETSRIQIEPTSSLTSGLRETSSSERISSATEGSTVLSEVPSGATTEVSRTEVISSRGTSMSGPDQFTISPDISTEAITRLSTSPIMTESAESAITIETGSPGATSEGTLTLDTSTTTFWSGTHSTASPGFSHSEMTTLMSRTPGDVPWPSLPSVEEASSVSSSLSSPAMTSTSFFSTLPESISSSPHPVTALLTLGPVKTTDMLRTSSEPETSSPPNLSSTSAEILATSEVTKDREKIHPSSNTPVVNVGTVIYKHLSPSSVLADLVTTKPTSPMATTSTLGNTSVSTSTPAFPETMMTQPTSSLTSGLREISTSQETSSATERSASLSGMPTGATTKVSRTEALSLGRTSTPGPAQSTISPEISTETITRISTPLTTTGSAEMTITPKTGHSGASSQGTFTLDTSSRASWPGTHSAATHRSPHSGMTTPMSRGPEDVSWPSRPSVEKTSPPSSLVSLSAVTSPSPLYSTPSESSHSSPLRVTSLFTPVMMKTTDMLDTSLEPVTTSPPSMNITSDESLATSKATMETEAIQLSENTAVTQMGTISARQEFYSSYPGLPEPSKVTSPVVTSSTIKDIVSTTIPASSEITRIEMESTSTLTPTPRETSTSQEIHSATKPSTVPYKALTSATIEDSMTQVMSSSRGPSPDQSTMSQDISTEVITRLSTSPIKTESTEMTITTQTGSPGATSRGTLTLDTSTTFMSGTHSTASQGFSHSQMTALMSRTPGDVPWLSHPSVEEASSASFSLSSPVMTSSSPVSSTLPDSIHSSSLPVTSLLTSGLVKTTELLGTSSEPETSSPPNLSSTSAEILAITEVTTDTEKLEMTNVVTSGYTHESPSSVLADSVTTKATSSMGITYPTGDTNVLTSTPAFSDTSRIQTKSKLSLTPGLMETSISEETSSATEKSTVLSSVPTGATTEVSRTEAISSSRTSIPGPAQSTMSSDTSMETITRISTPLTRKESTDMAITPKTGPSGATSQGTFTLDSSSTASWPGTHSATTQRFPQSVVTTPMSRGPEDVSWPSPLSVEKNSPPSSLVSSSSVTSPSPLYSTPSGSSHSSPVPVTSLFTSIMMKATDMLDASLEPETTSAPNMNITSDESLAASKATTETEAIHVFENTAASHVETTSATEELYSSSPGFSEPTKVISPVVTSSSIRDNMVSTTMPGSSGITRIEIESMSSLTPGLRETRTSQDITSSTETSTVLYKMPSGATPEVSRTEVMPSSRTSIPGPAQSTMSLDISDEVVTRLSTSPIMTESAEITITTQTGYSLATSQVTLPLGTSMTFLSGTHSTMSQGLSHSEMTNLMSRGPESLSWTSPRFVETTRSSSSLTSLPLTTSLSPVSSTLLDSSPSSPLPVTSLILPGLVKTTEVLDTSSEPKTSSSPNLSSTSVEIPATSEIMTDTEKIHPSSNTAVAKVRTSSSVHESHSSVLADSETTITIPSMGITSAVDDTTVFTSNPAFSETRRIPTEPTFSLTPGFRETSTSEETTSITETSAVLYGVPTSATTEVSMTEIMSSNRIHIPDSDQSTMSPDIITEVITRLSSSSMMSESTQMTITTQKSSPGATAQSTLTLATTTAPLARTHSTVPPRFLHSEMTTLMSRSPENPSWKSSLFVEKTSSSSSLLSLPVTTSPSVSSTLPQSIPSSSFSVTSLLTPGMVKTTDTSTEPGTSLSPNLSGTSVEILAASEVTTDTEKIHPSSSMAVTNVGTTSSGHELYSSVSIHSEPSKATYPVGTPSSMAETSISTSMPANFETTGFEAEPFSHLTSGFRKTNMSLDTSSVTPTNTPSSPGSTHLLQSSKTDFTSSAKTSSPDWPPASQYTEIPVDIITPFNASPSITESTGITSFPESRFTMSVTESTHHLSTDLLPSAETISTGTVMPSLSEAMTSFATTGVPRAISGSGSPFSRTESGPGDATLSTIAESLPSSTPVPFSSSTFTTTDSSTIPALHEITSSSATPYRVDTSLGTESSTTEGRLVMVSTLDTSSQPGRTSSSPILDTRMTESVELGTVTSAYQVPSLSTRLTRTDGIMEHITKIPNEAAHRGTIRPVKGPQTSTSPASPKGLHTGGTKRMETTTTALKTTTTALKTTSRATLTTSVYTPTLGTLTPLNASMQMASTIPTEMMITTPYVFPDVPETTSSLATSLGAETSTALPRTTPSVFNRESETTASLVSRSGAERSPVIQTLDVSSSEPDTTASWVIHPAETIPTVSKTTPNFFHSELDTVSSTATSHGADVSSAIPTNISPSELDALTPLVTISGTDTSTTFPTLTKSPHETETRTTWLTHPAETSSTIPRTIPNFSHHESDATPSIATSPGAETSSAIPIMTVSPGAEDLVTSQVTSSGTDRNMTIPTLTLSPGEPKTIASLVTHPEAQTSSAIPTSTISPAVSRLVTSMVTSLAAKTSTTNRALTNSPGEPATTVSLVTHPAQTSPTVPWTTSIFFHSKSDTTPSMTTSHGAESSSAVPTPTVSTEVPGVVTPLVTSSRAVISTTIPILTLSPGEPETTPSMATSHGEEASSAIPTPTVSPGVPGVVTSLVTSSRAVTSTTIPILTFSLGEPETTPSMATSHGTEAGSAVPTVLPEVPGMVTSLVASSRAVTSTTLPTLTLSPGEPETTPSMATSHGAEASSTVPTVSPEVPGVVTSLVTSSSGVNSTSIPTLILSPGELETTPSMATSHGAEASSAVPTPTVSPGVSGVVTPLVTSSRAVTSTTIPILTLSSSEPETTPSMATSHGVEASSAVLTVSPEVPGMVTSLVTSSRAVTSTTIPTLTISSDEPETTTSLVTHSEAKMISAIPTLAVSPTVQGLVTSLVTSSGSETSAFSNLTVASSQPETIDSWVAHPGTEASSVVPTLTVSTGEPFTNISLVTHPAESSSTLPRTTSRFSHSELDTMPSTVTSPEAESSSAISTTISPGIPGVLTSLVTSSGRDISATFPTVPESPHESEATASWVTHPAVTSTTVPRTTPNYSHSEPDTTPSIATSPGAEATSDFPTITVSPDVPDMVTSQVTSSGTDTSITIPTLTLSSGEPETTTSFITYSETHTSSAIPTLPVSPGASKMLTSLVISSGTDSTTTFPTLTETPYEPETTAIQLIHPAETNTMVPRTTPKFSHSKSDTTLPVAITSPGPEASSAVSTTTISPDMSDLVTSLVPSSGTDTSTTFPTLSETPYEPETTATWLTHPAETSTTVSGTIPNFSHRGSDTAPSMVTSPGVDTRSGVPTTTIPPSIPGVVTSQVTSSATDTSTAIPTLTPSPGEPETTASSATHPGTQTGFTVPIRTVPSSEPDTMASWVTHPPQTSTPVSRTTSSFSHSSPDATPVMATSPRTEASSAVLTTISPGAPEMVTSQITSSGAATSTTVPTLTHSPGMPETTALLSTHPRTETSKTFPASTVFPQVSETTASLTIRPGAETSTALPTQTTSSLFTLLVTGTSRVDLSPTASPGVSAKTAPLSTHPGTETSTMIPTSTLSLGLLETTGLLATSSSAETSTSTLTLTVSPAVSGLSSASITTDKPQTVTSWNTETSPSVTSVGPPEFSRTVTGTTMTLIPSEMPTPPKTSHGEGVSPTTILRTTMVEATNLATTGSSPTVAKTTTTFNTLAGSLFTPLTTPGMSTLASESVTSRTSYNHRSWISTTSSYNRRYWTPATSTPVTSTFSPGISTSSIPSSTAATVPFMVPFTLNFTITNLQYEEDMRHPGSRKFNATERELQGLLKPLFRNSSLEYLYSGCRLASLRPEKDSSATAVDAICTHRPDPEDLGLDRERLYWELSNLTNGIQELGPYTLDRNSLYVNGFTHRSSMPTTSTPGTSTVDVGTSGTPSSSPSPTTAGPLLMPFTLNFTITNLQYEEDMRRTGSRKFNTMESVLQGLLKPLFKNTSVGPLYSGCRLTLLRPEKDGAATGVDAICTHRLDPKSPGLNREQLYWELSKLTNDIEELGPYTLDRNSLYVNGFTHQSSVSTTSTPGTSTVDLRTSGTPSSLSSPTIMAAGPLLVPFTLNFTITNLQYGEDMGHPGSRKFNTTERVLQGLLGPIFKNTSVGPLYSGCRLTSLRSEKDGAATGVDAICIHHLDPKSPGLNRERLYWELSQLTNGIKELGPYTLDRNSLYVNGFTHRTSVPTSSTPGTSTVDLGTSGTPFSLPSPATAGPLLVLFTLNFTITNLKYEEDMHRPGSRKFNTTERVLQTLLGPMFKNTSVGLLYSGCRLTLLRSEKDGAATGVDAICTHRLDPKSPGVDREQLYWELSQLTNGIKELGPYTLDRNSLYVNGFTHWIPVPTSSTPGTSTVDLGSGTPSSLPSPTTAGPLLVPFTLNFTITNLKYEEDMHCPGSRKFNTTERVLQSLLGPMFKNTSVGPLYSGCRLTLLRSEKDGAATGVDAICTHRLDPKSPGVDREQLYWELSQLTNGIKELGPYTLDRNSLYVNGFTHQTSAPNTSTPGTSTVDLGTSGTPSSLPSPTSAGPLLVPFTLNFTITNLQYEEDMHHPGSRKFNTTERVLQGLLGPMFKNTSVGLLYSGCRLTLLRPEKNGAATGMDAICSHRLDPKSPGLNREQLYWELSQLTHGIKELGPYTLDRNSLYVNGFTHRSSVAPTSTPGTSTVDLGTSGTPSSLPSPTTAVPLLVPFTLNFTITNLQYGEDMRHPGSRKFNTTERVLQGLLGPLFKNSSVGPLYSGCRLISLRSEKDGAATGVDAICTHHLNPQSPGLDREQLYWQLSQMTNGIKELGPYTLDRNSLYVNGFTHRSSGLTTSTPWTSTVDLGTSGTPSPVPSPTTTGPLLVPFTLNFTITNLQYEENMGHPGSRKFNITESVLQGLLKPLFKSTSVGPLYSGCRLTLLRPEKDGVATRVDAICTHRPDPKIPGLDRQQLYWELSQLTHSITELGPYTLDRDSLYVNGFTQRSSVPTTSTPGTFTVQPETSETPSSLPGPTATGPVLLPFTLNFTITNLQYEEDMRRPGSRKFNTTERVLQGLLMPLFKNTSVSSLYSGCRLTLLRPEKDGAATRVDAVCTHRPDPKSPGLDRERLYWKLSQLTHGITELGPYTLDRHSLYVNGFTHQSSMTTTRTPDTSTMHLATSRTPASLSGPMTASPLLVLFTINFTITNLRYEENMHHPGSRKFNTTERVLQGLLRPVFKNTSVGPLYSGCRLTLLRPKKDGAATKVDAICTYRPDPKSPGLDREQLYWELSQLTHSITELGPYTLDRDSLYVNGFTQRSSVPTTSIPGTPTVDLGTSGTPVSKPGPSAASPLLVLFTLNFTITNLRYEENMQHPGSRKFNTTERVLQGLLRSLFKSTSVGPLYSGCRLTLLRPEKDGTATGVDAICTHHPDPKSPRLDREQLYWELSQLTHNITELGPYALDNDSLFVNGFTHRSSVSTTSTPGTPTVYLGASKTPASIFGPSAASHLLILFTLNFTITNLRYEENMWPGSRKFNTTERVLQGLLRPLFKNTSVGPLYSGCRLTLLRPEKDGEATGVDAICTHRPDPTGPGLDREQLYLELSQLTHSITELGPYTLDRDSLYVNGFTHRSSVPTTSTGVVSEEPFTLNFTINNLRYMADMGQPGSLKFNITDNVMQHLLSPLFQRSSLGARYTGCRVIALRSVKNGAETRVDLLCTYLQPLSGPGLPIKQVFHELSQQTHGITRLGPYSLDKDSLYLNGYNEPGPDEPPTTPKPATTFLPPLSEATTAMGYHLKTLTLNFTISNLQYSPDMGKGSATFNSTEGVLQHLLRPLFQKSSMGPFYLGCQLISLRPEKDGAATGVDTTCTYHPDPVGPGLDIQQLYWELSQLTHGVTQLGFYVLDRDSLFINGYAPQNLSIRGEYQINFHIVNWNLSNPDPTSSEYITLLRDIQDKVTTLYKGSQLHDTFRFCLVTNLTMDSVLVTVKALFSSNLDPSLVEQVFLDKTLNASFHWLGSTYQLVDIHVTEMESSVYQPTSSSSTQHFYLNFTITNLPYSQDKAQPGTTNYQRNKRNIEDALNQLFRNSSIKSYFSDCQVSTFRSVPNRHHTGVDSLCNFSPLARRVDRVAIYEEFLRMTRNGTQLQNFTLDRSSVLVDGYSPNRNEPLTGNSDLPFWAVILIGLAGLLGVITCLICGVLVTTRRRKKEGEYNVQQQCPGYYQSHLDLEDLQ,mutated_sequence,1.0,14507.0,UPI000065CA24.a2m,UPI000065CA24.npy,gnomAD +UPI000006F76F,UPI000006F76F.csv,MNALLEQKEQQERLREAAALGDIREVQKLVESGVDVNSQNEVNGWTCLHWACKRNHGQVVSYLLKSGADKEILTTKGEMPVQLTSRREIRKIMGVEEEDDDDDDDDNLPQLKKESELPFVPNYLANPAFPFIYTPTAEDSAQMQNGGPSTPPASPPADGSPPLLPPGEPPLLGTFPRDHTSLALVQNGDVSAPSAILRTPESTKPGPVCQPPVSQSRSLFSSVPSKPPMSLEPQNGTYAGPAPAFQPFFFTGAFPFNMQELVLKVRIQNPSLRENDFIEIELDRQELTYQELLRVCCCELGVNPDQVEKIRKLPNTLLRKDKDVARLQDFQELELVLMISENNFLFRNAASTLTERPCYNRRASKLTY,mutated_sequence,1.0,368.0,UPI000006F76F.a2m,UPI000006F76F.npy,gnomAD +UPI00001BE8EA,UPI00001BE8EA.csv,MTAVGVQAQRPLGQRQPRRSFFESFIRTLIITCVALAVVLSSVSICDGHWLLAEDRLFGLWHFCTTTNQTICFRDLGQAHVPGLAVGMGLVRSVGALAVVAAIFGLEFLMVSQLCEDKHSQCKWVMGSILLLVSFVLSSGGLLGFVILLRNQVTLIGFTLMFWCEFTASFLLFLNAISGLHINSITHPWE,mutated_sequence,1.0,190.0,UPI00001BE8EA.a2m,UPI00001BE8EA.npy,gnomAD +UPI00001313B6,UPI00001313B6.csv,MQKELGIVPSCPGMKSPRPHLLLPLLLLLLLLLGAGVPGAWGQAGSLDLQIDEEQPAGTLIGDISAGLPAGTAAPLMYFISAQEGSGVGTDLAIDEHSGVVRTARVLDREQRDRYRFTAVTPDGATVEVTVRVADINDHAPAFPQARAALQVPEHTAFGTRYPLEPARDADAGRLGTQGYALSGDGAGETFRLETRPGPDGTPVPELVVTGELDRENRSHYMLQLEAYDGGSPPRRAQALLDVTLLDINDHAPAFNQSRYHAVVSESLAPGSPVLQVFASDADAGVNGAVTYEINRRQSEGDGPFSIDAHTGLLQLERPLDFEQRRVHELVVQARDGGAHPELGSAFVTVHVRDANDNQPSMTVIFLSADGSPQVSEAAPPGQLVARISVSDPDDGDFAHVNVSLEGGEGHFALSTQDSVIYLVCVARRLDREERDAYNLRVTATDSGSPPLRAEAAFVLHVTDVNDNAPAFDRQLYRPEPLPEVALPGSFVVRVTARDPDQGTNGQVTYSLAPGAHTHWFSIDPTSGIITTAASLDYELEPQPQLIVVATDGGLPPLASSATVSVALQDVNDNEPQFQRTFYNASLPEGTQPGTCFLQVTATDADSGPFGLLSYSLGAGLGSSGSPPFRIDAHSGDVCTTRTLDRDQGPSSFDFTVTAVDGGGLKSMVYVKVFLSDENDNPPQFYPREYAASISAQSPPGTAVLRLRAHDPDQGSHGRLSYHILAGNSPPLFTLDEQSGLLTVAWPLARRANSVVQLEIGAEDGGGLQAEPSARVDISIVPGTPTPPIFEQLQYVFSVPEDVAPGTSVGIVQAHNPPGRLAPVTLSLSGGDPRGLFSLDAVSGLLQTLRPLDRELLGPVLELEVRAGSGVPPAFAVARVRVLLDDVNDNSPAFPAPEDTVLLPPNTAPGTPIYTLRALDPDSGVNSRVTFTLLAGGGGAFTVDPTTGHVRLMRPLGPSGGPAHELELEARDGGSPPRTSHFRLRVVVQDVGTRGLAPRFNSPTYRVDLPSGTTAGTQVLQVQAQAPDGGPITYHLAAEGASSPFGLEPQSGWLWVRAALDREAQELYILKVMAVSGSKAELGQQTGTATVRVSILNQNEHSPRLSEDPTFLAVAENQPPGTSVGRVFATDRDSGPNGRLTYSLQQLSEDSKAFRIHPQTGEVTTLQTLDREQQSSYQLLVQVQDGGSPPRSTTGTVHVAVLDLNDNSPTFLQASGAAGGGLPIQVPDRVPPGTLVTTLQAKDPDEGENGTILYTLTGPGSELFSLHPHSGELLTAAPLIRAERPHYVLTLSAHDQGSPPRSASLQLLVQVLPSARLAEPPPDLAERDPAAPVPVVLTVTAAEGLRPGSLLGSVAAPEPAGVGALTYTLVGGADPEGTFALDAASGRLYLARPLDFEAGPPWRALTVRAEGPGGAGARLLRVQVQVQDENEHAPAFARDPLALALPENPEPGAALYTFRASDADGPGPNSDVRYRLLRQEPPVPALRLDARTGALSAPRGLDRETTPALLLLVEATDRPANASRRRAARVSARVFVTDENDNAPVFASPSRVRLPEDQPPGPAALHVVARDPDLGEAARVSYRLASGGDGHFRLHSSTGALSVVRPLDREQRAEHVLTVVASDHGSPPRSATQVLTVSVADVNDEAPTFQQQEYSVLLRENNPPGTSLLTLRATDPDVGANGQVTYGGVSSESFSLDPDTGVLTTLRALDREEQEEINLTVYAQDRGSPPQLTHVTVRVAVEDENDHAPTFGSAHLSLEVPEGQDPQTLTMLRASDPDVGANGQLQYRILDGDPSGAFVLDLASGEFGTMRPLDREVEPAFQLRIEARDGGQPALSATLLLTVTVLDANDHAPAFPVPAYSVEVPEDVPAGTLLLQLQAHDPDAGANGHVTYYLGAGTAGAFLLEPSSGELRTAAALDREQCPSYTFSVSAVDGAAAGPLSTTVSVTITVRDVNDHAPTFPTSPLRLRLPRPGPSFSTPTLALATLRAEDRDAGANASILYRLAGTPPPGTTVDSYTGEIRVARSPVALGPRDRVLFIVATDLGRPARSATGVIIVGLQGEAERGPRFPRASSEATIRENAPPGTPIVSPRAVHAGGTNGPITYSILSGNEKGTFSIQPSTGAITVRSAEGLDFEVSPRLRLVLQAESGGAFAFTVLTLTLQDANDNAPRFLRPHYVAFLPESRPLEGPLLQVEADDLDQGSGGQISYSLAASQPARGLFHVDPTTGTITTTAILDREIWAETRLVLMATDRGSPALVGSATLTVMVIDTNDNRPTIPQPWELRVSEDALLGSEIAQVTGNDVDSGPVLWYVLSPSGPQDPFSVGRYGGRVSLTGPLDFEQCDRYQLQLLAHDGPHEGRANLTVLVEDVNDNAPAFSQSLYQVMLLEHTPPGSAILSVSATDRDSGANGHISYHLASPADGFSVDPNNGTLFTIVGTVALGHDGSGAVDVVLEARDHGAPGRAARATVHVQLQDQNDHAPSFTLSHYRVAVTEDLPPGSTLLTLEATDADGSRSHAAVDYSIISGNWGRVFQLEPRLAEAGESAGPGPRALGCLVLLEPLDFESLTQYNLTVAAADRGQPPQSSVVPVTVTVLDVNDNPPVFTRASYRVTVPEDTPVGAELLHVEASDADPGPHGLVRFTVSSGDPSGLFELDESSGTLRLAHALDCETQARHQLVVQAADPAGAHFALAPVTIEVQDVNDHGPAFPLNLLSTSVAENQPPGTLVTTLHAIDGDAGAFGRLRYSLLEAGPGPEGREAFALNSSTGELRARVPFDYEHTESFRLLVGAADAGNLSASVTVSVLVTGEDEYDPVFLAPAFHFQVPEGARRGHSLGHVQATDEDGGADGLVLYSLATSSPYFGINQTTGALYLRVDSRAPGSGTATSGGGGRTRREAPRELRLEVIARGPLPGSRSATVPVTVDITHTALGLAPDLNLLLVGAVAASLGVVVVLALAALVLGLVRARSRKAEAAPGPMSQAAPLASDSLQKLGREPPSPPPSEHLYHQTLPSYGGPGAGGPYPRGGSLDPSHSSGRGSAEAAEDDEIRMINEFPRVASVASSLAARGPDSGIQQDADGLSDTSCEPPAPDTWYKGRKAGLLLPGAGATLYREEGPPATATAFLGGCGLSPAPTGDYGFPADGKPCVAGALTAIVAGEEELRGSYNWDYLLSWCPQFQPLASVFTEIARLKDEARPCPPAPRIDPPPLITAVAHPGAKSVPPKPANTAAARAIFPPASHRSPISHEGSLSSAAMSPSFSPSLSPLAARSPVVSPFGVAQGPSASALSAESGLEPPDDTELHI,mutated_sequence,1.0,3298.0,UPI00001313B6.a2m,UPI00001313B6.npy,gnomAD +UPI000013D92B,UPI000013D92B.csv,MAVYCYALNSLVIMNSANEMKSGGGPGPSGSETPPPPRRAVLSPGSVFSPGRGASFLFPPAESLSPEEPRSPGGWRSGRRRLNSSSGSGSGSSGSSVSSPSWAGRLRGDRQQVVAAGTLSPPGPEEAKRKLRILQRELQNVQVNQKVGMFEAHIQAQSSAIQAPRSPRLGRARSPSPCPFRSSSQPPGRVLVQGARSEERRTKSWGEQCPETSGTDSGRKGGPSLCSSQVKKGMPPLPGRAAPTGSEAQGPSAFVRMEKGIPASPRCGSPTAMEIDKRGSPTPGTRSCLAPSLGLFGASLTMATEVAARVTSTGPHRPQDLALTEPSGRARELEDLQPPEALVERQGQFLGSETSPAPERGGPRDGEPPGKMGKGYLPCGMPGSGEPEVGKRPEETTVSVQSAESSDSLSWSRLPRALASVGPEEARSGAPVGGGRWQLSDRVEGGSPTLGLLGGSPSAQPGTGNVEAGIPSGRMLEPLPCWDAAKDLKEPQCPPGDRVGVQPGNSRVWQGTMEKAGLAWTRGTGVQSEGTWESQRQDSDALPSPELLPQDPDKPFLRKACSPSNIPAVIITDMGTQEDGALEETQGSPRGNLPLRKLSSSSASSTGFSSSYEDSEEDISSDPERTLDPNSAFLHTLDQQKPRVSKSWRKIKNMVHWSPFVMSFKKKYPWIQLAGHAGSFKAAANGRILKKHCESEQRCLDRLMVDVLRPFVPAYHGDVVKDGERYNQMDDLLADFDSPCVMDCKMGIRTYLEEELTKARKKPSLRKDMYQKMIEVDPEAPTEEEKAQRAVTKPRYMQWRETISSTATLGFRIEGIKKEDGTVNRDFKKTKTREQVTEAFREFTKGNHNILIAYRDRLKAIRTTLEVSPFFKCHEVIGSSLLFIHDKKEQAKVWMIDFGKTTPLPEGQTLQHDVPWQEGNREDGYLSGLNNLVDILTEMSQDAPLA,mutated_sequence,1.0,946.0,UPI000013D92B.a2m,UPI000013D92B.npy,gnomAD +UPI000004B634,UPI000004B634.csv,MVAACRSVAGLLPRRRRCFPARAPLLRVALCLLCWTPAAVRAVPELGLWLETVNDKSGPLIFRKTMFNSTDIKLSVKSFHCSGPVKFTIVWHLKYHTCHNEHSNLEELFQKHKLSVDEDFCHYLKNDNCWTTKNENLDCNSDSQVFPSLNNKELINIRNVSNQERSMDVVARTQKDGFHIFIVSIKTENTDASWNLNVSLSMIGPHGYISASDWPLMIFYMVMCIVYILYGILWLTWSACYWKDILRIQFWIAAVIFLGMLEKAVFYSEYQNISNTGLSTQGLLIFAELISAIKRTLARLLVIIVSLGYGIVKPRLGTVMHRVIGLGLLYLIFAAVEGVMRVIGGSNHLAVVLDDIILAVIDSIFVWFIFISLAQTMKTLRLRKNTVKFSLYRHFKNTLIFAVLASIVFMGWTTKTFRIAKCQSDWMERWVDDAFWSFLFSLILIVIMFLWRPSANNQRYAFMPLIDDSDDEIEEFMVTSENLTEGIKLRASKSVSNGTAKPATSENFDEDLKWVEENIPSSFTDVALPVLVDSDEEIMTRSEMAEKMFSSEKIM,mutated_sequence,1.0,555.0,UPI000004B634.a2m,UPI000004B634.npy,gnomAD +UPI00004574E1,UPI00004574E1.csv,MREAAAALVPPPAFAVTPAAAMEEPPPPPPPPPPPPEPETESEPECCLAARQEGTLGDSACKSPESDLEDFSDETNTENLYGTSPPSTPRQMKRMSTKHQRNNVGRPASRSNLKEKMNAPNQPPHKDTGKTVENVEEYSYKQEKKIRAALRTTERDRKKNVQCSFMLDSVGGSLPKKSIPDVDLNKPYLSLGCSNAKLPVSVPMPIARPARQTSRTDCPADRLKFFETLRLLLKLTSVSKKKDREQRGQENTSGFWLNRSNELIWLELQAWHAGRTINDQDFFLYTARQAIPDIINEILTFKVDYGSFAFVRDRAGFNGTSVEGQCKATPGTKIVGYSTHHEHLQRQRVSFEQVKRIMELLEYIEALYPSLQALQKDYEKYAAKDFQDRVQALCLWLNITKDLNQKLRIMGTVLGIKNLSDIGWPVFEIPSPRPSKGNEPEYEGDDTEGELKELESSTDESEEEQISDPRVPEIRQPIDNSFDIQSRDCISKKLERLESEDDSLGWGAPDWSTEAGFSRHCLTSIYRPFVDKALKQMGLRKLILRLHKLMDGSLQRARIALVKNDRPVEFSEFPDPMWGSDYVQLSRTPPSSEEKCSAVSWEELKAMDLPSFEPAFLVLCRVLLNVIHECLKLRLEQRPAGEPSLLSIKQLVRECKEVLKGGLLMKQYYQFMLQEVLEDLEKPDCNIDAFEEDLHKMLMVYFDYMRSWIQMLQQLPQASHSLKNLLEEEWNFTKEITHYIRGGEAQAGKLFCDIAGMLLKSTGSFLEFGLQESCAEFWTSADDSSASDEIRRSVIEISRALKELFHEARERASKALGFAKMLRKDLEIAAEFRLSAPVRDLLDVLKSKQYVKVQIPGLENLQMFVPDTLAEEKSIILQLLNAAAGKDCSKDSDDVLIDAYLLLTKHGDRARDSEDSWGTWEAQPVKVVPQVETVDTLRSMQVDNLLLVVMQSAHLTIQRKAFQQSIEGLMTLCQEQTSSQPVIAKALQQLKNDALELCNRISNAIDRVDHMFTSEFDAEVDESESVTLQQYYREAMIQGYNFGFEYHKEVVRLMSGEFRQKIGDKYISFARKWMNYVLTKCESGRGTRPRWATQGFDFLQAIEPAFISALPEDDFLSLQALMNECIGHVIGKPHSPVTGLYLAIHRNSPRPMKVPRCHSDPPNPHLIIPTPEGFSTRSMPSDARSHGSPAAAAAAAAAAVAASRPSPSGGDSVLPKSISSAHDTRGSSVPENDRLASIAAELQFRSLSRHSSPTEERDEPAYPRGDSSGSTRRSWELRTLISQSKDTASKLGPIEAIQKSVRLFEEKRYREMRRKNIIGQVCDTPKSYDNVMHVGLRKVTFKWQRGNKIGEGQYGKVYTCISVDTGELMAMKEIRFQPNDHKTIKETADELKIFEGIKHPNLVRYFGVELHREEMYIFMEYCDEGTLEEVSRLGLQEHVIRLYSKQITIAINVLHEHGIVHRDIKGANIFLTSSGLIKLGDFGCSVKLKNNAQTMPGEVNSTLGTAAYMAPEVITRAKGEGHGRAADIWSLGCVVIEMVTGKRPWHEYEHNFQIMYKVGMGHKPPIPERLSPEGKDFLSHCLESDPKMRWTASQLLDHSFVKVCTDEE,mutated_sequence,1.0,1608.0,UPI00004574E1.a2m,UPI00004574E1.npy,gnomAD +UPI00001B4B16,UPI00001B4B16.csv,MDNRNTQMYTEEEKTVNPFLPSTPGPKKAKGGGEAVETHPAPGPLPPPEVRDIGERREPDRAQQQPQKPAVAAGTQSLGNFRQGFMKCLLEVEKMEASHRRASKARSQTAQKSPRTLTPVPTSAPSLPQTPASVPASGPSWARLPAPGPEPAPMGAPVPTSMPCPVLLGPALDLGWRRMELLHQSSERTLSYAKARQEPEEQSLQKLYQNREKSEEQLTLKQEEAFRSYFEIFNGPGEVDAQSLKNILLLMGFSVTLAQVEDALMSADVNGDGRVDFKDFLAVMTDTRRFFCSVEQNALSDMAPHNPHTLLFEILSLLVEMLALPEAVLEEITNYYQKKLKEGTCKAQEMEAAVGRLRLQKLPYNPQQEESSEVPERKVLSILSRLKQQNYAPNLQSPYAQVPCILLCPQLDKKMVRRQPSNHYALDQCTPPGLDPDIRSPFFQSGSQGNREHNSDSRKWLSSVPARTH,mutated_sequence,1.0,469.0,UPI00001B4B16.a2m,UPI00001B4B16.npy,gnomAD +UPI000013D915,UPI000013D915.csv,MDTSGHFHDSGVGDLDEDPKCPCPSSGDEQQQQQQQQQQQQPPPPAPPAAPQQPLGPSLQPQPPQLQQQQQQQQQQQQQQPPHPLSQLAQLQSQPVHPGLLHSSPTAFRAPPSSNSTAILHPSSRQGSQLNLNDHLLGHSPSSTATSGPGGGSRHRQASPLVHRRDSNPFTEIAMSSCKYSGGVMKPLSRLSASRRNLIEAETEGQPLQLFSPSNPPEIVISSREDNHAHQTLLHHPNATHNHQHAGTTASSTTFPKANKRKNQNIGYKLGHRRALFEKRKRLSDYALIFGMFGIVVMVIETELSWGLYSKDSMFSLALKCLISLSTIILLGLIIAYHTREVQLFVIDNGADDWRIAMTYERILYISLEMLVCAIHPIPGEYKFFWTARLAFSYTPSRAEADVDIILSIPMFLRLYLIARVMLLHSKLFTDASSRSIGALNKINFNTRFVMKTLMTICPGTVLLVFSISLWIIAAWTVRVCERYHDQQDVTSNFLGAMWLISITFLSIGYGDMVPHTYCGKGVCLLTGIMGAGCTALVVAVVARKLELTKAEKHVHNFMMDTQLTKRIKNAAANVLRETWLIYKHTKLLKKIDHAKVRKHQRKFLQAIHQLRSVKMEQRKLSDQANTLVDLSKMQNVMYDLITELNDRSEDLEKQIGSLESKLEHLTASFNSLPLLIADTLRQQQQQLLSAIIEARGVSVAVGTTHTPISDSPIGVSSTSFPTPYTSSSSC,mutated_sequence,1.0,731.0,UPI000013D915.a2m,UPI000013D915.npy,gnomAD +UPI00001B0366,UPI00001B0366.csv,MAAELAMGAELPSSPLAIEYVNDFDLMKFEVKKEPPEAERFCHRLPPGSLSSTPLSTPCSSVPSSPSFCAPSPGTGGGGGAGGGGGSSQAGGAPGPPSGGPGAVGGTSGKPALEDLYWMSGYQHHLNPEALNLTPEDAVEALIGSGHHGAHHGAHHPAAAAAYEAFRGPGFAGGGGADDMGAGHHHGAHHAAHHHHAAHHHHHHHHHHGGAGHGGGAGHHVRLEERFSDDQLVSMSVRELNRQLRGFSKEEVIRLKQKRRTLKNRGYAQSCRFKRVQQRHILESEKCQLQSQVEQLKLEVGRLAKERDLYKEKYEKLAGRGGPGSAGGAGFPREPSPPQAGPGGAKGTADFFL,mutated_sequence,1.0,353.0,UPI00001B0366.a2m,UPI00001B0366.npy,gnomAD +UPI00015B3B88,UPI00015B3B88.csv,MSQRVRRNGSPTPAGSLGGGAVATAGGPGSRLQPMRATVPFQLKQQQQQQHGSPTRSGGGGGGNNNGGCCGGASGPAGGGGGGGPRTASRSTSPTRGGGNAAARTSPTVATQTGASATSTRGTSPTRSAAPGARGSPPRPPPPPPLLGTVSSPSSSPTHLWTGEVSAAPPPARVRHRRRSPEQSRSSPEKRSPSAPVCKAGDKTRQPSSSPSSIIRRTSSLDTLAAPYLAGHWPRDSHGQAAPCMRDKATQTESAWAEEYSEKKKGSHKRSASWGSTDQLKEIAKLRQQLQRSKHSSRHHRDKERQSPFHGNHAAINQCQAPVPKSALIPVIPITKSTGSRFRNSVEGLNQEIEIIIKETGEKEEQLIPQDIPDGHRAPPPLVQRSSSTRSIDTQTPGGADRGSNNSSRSQSVSPTSFLTISNEGSEESPCSADDLLVDPRDKENGNNSPLPKYATSPKPNNSYMFKREPPEGCERVKVFEECSPKQLHEIPAFYCPDKNKVNFIPKSGSAFCLVSILKPLLPTPDLTLKGSGHSLTVTTGMTTTLLQPIAVASLSTNTEQDRVSRGTSTVMPSASLLPPPEPIEEAEG,mutated_sequence,1.0,589.0,UPI00015B3B88.a2m,UPI00015B3B88.npy,gnomAD +UPI000004CB0B,UPI000004CB0B.csv,MEIISSKLFILLTLATSSLLTSNIFCADELVISNLHSKENYDKYSEPRGYPKGERSLNFEELKDWGPKNVIKMSTPAVNKMPHSFANLPLRFGRNVQEERSAGATANLPLRSGRNMEVSLVRRVPNLPQRFGRTTTAKSVCRMLSDLCQGSMHSPCANDLFYSMTCQHQEIQNPDQKQSRRLLFKKIDDAELKQEK,mutated_sequence,1.0,196.0,UPI000004CB0B.a2m,UPI000004CB0B.npy,gnomAD +UPI00001A95DE,UPI00001A95DE.csv,MDPFTEKLLERTRARRENLQRKMAERPTAAPRSMTHAKRARQPLSEASNQQPLSGGEEKSCTKPSPSKKRCSDNTEVEVSNLENKQPVESTSAKSCSPSPVSPQVQPQAADTISDSVAVPASLLGMRRGLNSRLEATAASSVKTRMQKLAEQRRRWDNDDMTDDIPESSLFSPMPSEEKAASPPRPLLSNASATPVGRRGRLANLAATICSWEDDVNHSFAKQNSVQEQPGTACLSKFSSASGASARINSSSVKQEATFCSQRDGDASLNKALSSSADDASLVNASISSSVKATSPVKSTTSITDAKSCEGQNPELLPKTPISPLKTGVSKPIVKSTLSQTVPSKGELSREICLQSQSKDKSTTPGGTGIKPFLERFGERCQEHSKESPARSTPHRTPIITPNTKAIQERLFKQDTSSSTTHLAQQLKQERQKELACLRGRFDKGNIWSAEKGGNSKSKQLETKQETHCQSTPLKKHQGVSKTQSLPVTEKVTENQIPAKNSSTEPKGFTECEMTKSSPLKITLFLEEDKSLKVTSDPKVEQKIEVIREIEMSVDDDDINSSKVINDLFSDVLEEGELDMEKSQEEMDQALAESSEEQEDALNISSMSLLAPLAQTVGVVSPESLVSTPRLELKDTSRSDESPKPGKFQRTRVPRAESGDSLGSEDRDLLYSIDAYRSQRFKETERPSIKQVIVRKEDVTSKLDEKNNAFPCQVNIKQKMQELNNEINMQQTVIYQASQALNCCVDEEHGKGSLEEAEAERLLLIATGKRTLLIDELNKLKNEGPQRKNKASPQSEFMPSKGSVTLSEIRLPLKADFVCSTVQKPDAANYYYLIILKAGAENMVATPLASTSNSLNGDALTFTTTFTLQDVSNDFEINIEVYSLVQKKDPSGLDKKKKTSKSKAITPKRLLTSITTKSNIHSSVMASPGGLSAVRTSNFALVGSYTLSLSSVGNTKFVLDKVPFLSSLEGHIYLKIKCQVNSSVEERGFLTIFEDVSGFGAWHRRWCVLSGNCISYWTYPDDEKRKNPIGRINLANCTSRQIEPANREFCARRNTFELITVRPQREDDRETLVSQCRDTLCVTKNWLSADTKEERDLWMQKLNQVLVDIRLWQPDACYKPIGKP,mutated_sequence,1.0,1124.0,UPI00001A95DE.a2m,UPI00001A95DE.npy,gnomAD +UPI000003B052,UPI000003B052.csv,MASYLYGVLFAVGLCAPIYCVSPANAPSAYPRPSSTKSTPASQVYSLNTDFAFRLYRRLVLETPSQNIFFSPVSVSTSLAMLSLGAHSVTKTQILQGLGFNLTHTPESAIHQGFQHLVHSLTVPSKDLTLKMGSALFVKKELQLQANFLGNVKRLYEAEVFSTDFSNPSIAQARINSHVKKKTQGKVVDIIQGLDLLTAMVLVNHIFFKAKWEKPFHPEYTRKNFPFLVGEQVTVHVPMMHQKEQFAFGVDTELNCFVLQMDYKGDAVAFFVLPSKGKMRQLEQALSARTLRKWSHSLQKRWIEVFIPRFSISASYNLETILPKMGIQNVFDKNADFSGIAKRDSLQVSKATHKAVLDVSEEGTEATAATTTKFIVRSKDGPSYFTVSFNRTFLMMITNKATDGILFLGKVENPTKS,mutated_sequence,1.0,417.0,UPI000003B052.a2m,UPI000003B052.npy,gnomAD +UPI000017DF81,UPI000017DF81.csv,MGLCYSLRPLLFGGPGDDPCAASEPPVEDAQPAPAPALAPVRAAARDTARTLLPRGGEGSPACARPKADKPKEKRQRTEQLSAEEREAAKEREAVKEARKVSRGIDRMLRDQKRDLQQTHRLLLLGAGESGKSTIVKQMRILHVNGFNPEEKKQKILDIRKNVKDAIVTIVSAMSTIIPPVPLANPENQFRSDYIKSIAPITDFEYSQEFFDHVKKLWDDEGVKACFERSNEYQLIDCAQYFLERIDSVSLVDYTPTDQDLLRCRVLTSGIFETRFQVDKVNFHMFDVGGQRDERRKWIQCFNDVTAIIYVAACSSYNMVIREDNNTNRLRESLDLFESIWNNRWLRTISIILFLNKQDMLAEKVLAGKSKIEDYFPEYANYTVPEDATPDAGEDPKVTRAKFFIRDLFLRISTATGDGKHYCYPHFTCAVDTENIRRVFNDCRDIIQRMHLKQYELL,mutated_sequence,1.0,458.0,UPI000017DF81.a2m,UPI000017DF81.npy,gnomAD +UPI0001611034,UPI0001611034.csv,XVSAACQRRNTGTVGLKLSKVVVVGDLYVGKTSLIHRFCKNVFDRDYKATIGVDFEIERFEIAGIPYSLQIWDTAGQEKFKCIASAYYRGAQGEQHATSGLNCMQQRGPCRAPQWESRVLCTGTSFLSSKTH,mutated_sequence,1.0,132.0,UPI0001611034.a2m,UPI0001611034.npy,gnomAD +UPI0000202F63,UPI0000202F63.csv,MLRSTGFFRAIDCPYWSGAPGGPCRRPYCHFRHRGARGSGAPGDGGEAPPAAGLGYDPYNPELPKPPAQRENGTLGLGEEPRPDVLELELVNQAIEAVRSEVELEQRRYRELLETTREHRSAEAPALAPRGPNASPTVGPDEDAFPLAFDYSPGSHGLLSPDAGYQPTPLAAPAEPGSKYSLASLDRGQGRGGGGGGALEYVPKAVSQPRRHSRPVPSGKYVVDNSRPPTDLEYDPLSNYSARHLSRASSRDERAAKRPRGSRGSEPYTPAPKKLCDPFGSCDARFSDSEDEAATVPGNEPTTASTPKARADPEIKATGQPPSKEGLEAEGGGLRETKETAVQCDVGDLQPPPAKPASPAQVQSSQDGGCPKEGKPKKKKTGAPPAPSCKDGAQGKDKTKDKGRGRPVEKPRADKKGPQASSPRRKAERPEGTKKKPSSATPVATSGKGRPDRPARRPSPTSGDSRPAAGRGPPRPLQLPDRKSTKAPSGKLVERKARSLDEGASQDAPKLKKRALSHADLFGDESEDEAAGPGVPSVWPSALPSLSSDSDSDSDSSLGFPEAQGPPKRLKASPPPSPAPSSSSSSSSSTSSAGADVDYSALEKEVDFDSDPMEECLRIFNESTSVKTEDRGRLARQPPKEEKSEEKGLSGLTTLFPGQKRRISHLSKQGQEVEPPRRGPAVPPARPPTAQEVCYLRAQQAQRASASLLQAPARLAEKSPSVHISAPGEKRRIAHIPNPRLAAAPTGAKRTLAASGSQSSNGPEPGGQQLKTRTLSGMASKTTTTIIPKRIAHSPSLQSLKKPIIPKEFGGKVPTVIRQRYLNLFIEECLKFCTSNQEAIEKALNEEKVAYDRSPSKNIYLNVAVNTLKKLRGLAPSAVPGLSKTSGRRVVSHEVVLGGRLAAKTSFSLSRPSSPRVEDLKGAALYSRLREYLLTQDQLKENGYPFPHPERPGGAIIFTAEEKRPKDSSCRTCCRCGTEYLVSSSGRCIRDEECYYHWGRLRRNRVAGGWETQYMCCSAAAGSVGCQVAKQHVQDGRKERLEGFVKTFEKELSGDTHPGIYALDCEMSYTTYGLELTRVTVVDTDVHVVYDTFVKPDNEIVDYNTRFSGVTEADLADTSVTLRDVQAVLLSMFSADTILIGHSLESDLLALKVIHSTVVDTSVLFPHRLGLPYKRSLRNLMADYLRQIIQDNVDGHSSSEDAGACMHLVIWKVREDAKTKR,mutated_sequence,1.0,1221.0,UPI0000202F63.a2m,UPI0000202F63.npy,gnomAD +UPI00000728B5,UPI00000728B5.csv,MARGPLAARGLRLLLPLLPLLPLLPLPQVALGFADGSCDPSDQCPPQARWSSLWHVGLILLAVLLLLLCGVTAGCVRFCCLRKQAQAQPHLPPARQPCDVAVIPMDSDSPVHSTVTSYSSVQYPLGMRLPLPFGELDLDSMAPPAYSLYTPEPPPSYDEAVKMAKPREEGPALSQKPSPLLGASGLETTPVPQESGPNTQLPPCSPGAP,mutated_sequence,1.0,209.0,UPI00000728B5.a2m,UPI00000728B5.npy,gnomAD +UPI0002A47704,UPI0002A47704.csv,XDSGSDEEEGEAEASSSTPATGVFLKSWVYQPGEDTEEEEDEDSDTGSAEDEREAETSASTPPASAFLKAWVYRPGEDTEEEEDEDVDSEDKEDDSEAALGEAESDPHPSHPDQRAHFRGWGYRPGKETEEEEAAEDWGEAEPCPFRVAIYVPGEKPPPPWAPPRLPLRLQRRLKRPETPTHDPDPETPLKARKVGAESPDSIFFFFLIELESWAVTQAGVQRHDLGSLPTSAAQVQAILVPQPPEYLGLQAHDTTPS,mutated_sequence,1.0,258.0,UPI0002A47704.a2m,UPI0002A47704.npy,gnomAD +UPI0000074451,UPI0000074451.csv,MGLTLLLLLLLGLEGQGIVGSLPEVLQAPVGSSILVQCHYRLQDVKAQKVWCRFLPEGCQPLVSSAVDRRAPAGRRTFLTDLGGGLLQVEMVTLQEEDAGEYGCMVDGARGPQILHRVSLNILPPEEEEETHKIGSLAENAFSDPAGSANPLEPSQDEKSIPLIWGAVLLVGLLVAAVVLFAVMAKRKQGNRLGVCGRFLSSRVSGMNPSSVVHHVSDSGPAAELPLDVPHIRLDSPPSFDNTTYTSLPLDSPSGKPSLPAPSSLPPLPPKVLVCSKPVTYATVIFPGGNKGGGTSCGPAQNPPNNQTPSS,mutated_sequence,1.0,311.0,UPI0000074451.a2m,UPI0000074451.npy,gnomAD +UPI000049DE01,UPI000049DE01.csv,MDPGSGGGGGGGGGGGSSSGSSSSDSAPDCWDQADMEAPGPGPCGGGGSLAAAAEAQRENLSAAFSRQLNVNAKPFVPNVHAAEFVPSFLRGPAAPPPPVGGAANNHGAGSGAGGRAAPVESSQEEQSLCEGSNSAVSMELSEPIVENGETEMSPEESWEHKEEISEAEPGGGSLGDGRPPEESAHEMMEEEEEIPKPKSVVAPPGAPKKEHVNVVFIGHVDAGKSTIGGQIMYLTGMVDKRTLEKYEREAKEKNRETWYLSWALDTNQEERDKGKTVEVGRAYFETEKKHFTILDAPGHKSFVPNMIGGASQADLAVLVISARKGEFETGFEKGGQTREHAMLAKTAGVKHLIVLINKMDDPTVNWSNERYEECKEKLVPFLKKVGFNPKKDIHFMPCSGLTGANLKEQSDFCPWYIGLPFIPYLDNLPNFNRSVDGPIRLPIVDKYKDMGTVVLGKLESGSICKGQQLVMMPNKHNVEVLGILSDDVETDTVAPGENLKIRLKGIEEEEILPGFILCDPNNLCHSGRTFDAQIVIIEHKSIICPGYNAVLHIHTCIEEVEITALICLVDKKSGEKSKTRPRFVKQDQVCIARLRTAGTICLETFKDFPQMGRFTLRDEGKTIAIGKVLKLVPEKD,mutated_sequence,1.0,637.0,UPI000049DE01.a2m,UPI000049DE01.npy,gnomAD +UPI0000073639,UPI0000073639.csv,MVLPPPDRRHVCLTTLVIMGSMAVMDAYLVEQNQGPRKIGVCIIVLVGDVCFLLVLRYVAVWVGAEVRTAKRGYAMILWFLYIFVLEIKLYFIFQNYKAARRGAADPVARKALTLLLSVCVPGLFLLLVALDRMEYVRTFRKREDLRGRLFWVALDLLDLLDMQASLWEPPRSGLPLWAEGLTFFYCYMLLLVLPCVALSEVSMQGEHIAPQKMMLYPVLSLATVNVVAVLARAANMALFRDSRVSAIFVGKNVVALATKACTFLEYRRQVRDFPPPALSLELQPPPPQRNSVPPPPPPLHGPPGRPHMSSPTRDPLDT,mutated_sequence,1.0,319.0,UPI0000073639.a2m,UPI0000073639.npy,gnomAD +UPI0000E59EF5,UPI0000E59EF5.csv,MNESASQEELRPAQENRKEDKERKWNLTEVKELHETLQSVPDVPVKEDTNSVVEKAMDEIKSQELNLEGQRKISPGSIKDSKTEASGNIAIRKSAKVIFALDETELKSKPEHTWKKNLFERMEARAQAMQQKIIDKENLKKELEKKAEKKLPRDNLAKEWFNTDSMTLNNTAYLLDKLLPTLVPGVENMLTQVEKKKVLTEADTPSKFDPINYLGEYLIRNNPNYIKDPGMSGYQRLMKEVTEDLKIYVPDTICNRVSKMKENVKQNRKQRESIDKIIVKVANTRKQALQEQFDEWILDPKGMIPKSVIQNVLQEFFQNPDFKLGSHCKQLDITDSTEPRLNKMEFTEYISSHIKDLKSEMFEELLKHLCHSADEFREVIKADMRRQMFAELFLHCDHGKVGFLDRQRTLALLELFYDHSSQMLRSLLRNPRQWPFIEFEEINLTELWGDMDNQKHIYEGFDKVLLEMNTLLSANHASKTQSKLLESPDQPKLNEQRTSTPSPNPPEQQRGVTAEQGPQRISIEEQQQGKKPTAEQELYIESVIEPGTHTESTLEQGSSRRLLTEQETHRESTTEQGQHKGSIEGQGPRRVSVSEQGSSRESVAEQGSRRESIAEQDRHKGSVAEQGSRRMSAAEQGSLRESVIEEPYQKSEQGPYGEIISEEQEDIGSTSQSRKDSILKSTKYGEPITSEYIEVPLQEKRSWEQTYEEEIFLSSELQEEVPTLSRKDHFPETTKKEVQKDKPCEPKSQKIEGKSWSGEFFTCNWKMKYVTFEDEEQANLIYGNSRFTDLHSIIRNIQSCKEVKGRTAFNGVSFNLLQFVQLLETFVGEDAPLSVSETLTSFFKEGYVETEQEKMNALEQFSQNAFQVRQRLLLEAIFQKWDSDGSGFLDLKEVDELLYTYKEGMEKESMKKAKLHIQFPKPHPGHEVRLSSKQFQNYIELVVSELRGNEDQVLESVVEFLMNALERSHIESLRNSARRKWLHQIQCAAETSGVSLEPVYSETFKALMQDAEAHGNKKISAHISLLEENLLLPEKGNVLLRNVACTLDDAQFVLNRVLYRDMKGISFTVVDEGKPIHVPQVQYHGNIFFWNQSRNKHDYNGSFLALPLQDAYMRIFGVLAVDTLRDPHEINIFLPHEIRFYQGVANVFSTAYHYVHSREHILHIVITGIGWLYDVTSSITSITTYFVEPSPAQDSDYVLRNMMVTGQLGLTEIHKNPPTIHRKSCIFRDFLFKCTDSSEVVLASACGETHIVVPLRERTGEALGVLDFNIGQNRMLLCQEYKDLQKMMKVVQVACYEILGEFSGEIKKKYILEIENVREVQRAGILFFRIMLLELQESIQLLNSMEFVSLLLYDHTLVTEPNSPQDSKSMELEANVKLVRDILKAVILFFHPELEFSSDFGSWDKCKFYVNKYLVNNICAFDPTAKHVEVNVQLIDEYIRDHSRTEVWKFGNVVIEHLYHWIHICSALMKITKQLNSGITPPLPSKTDNYMYAKMPGEGLQEK,mutated_sequence,1.0,1503.0,UPI0000E59EF5.a2m,UPI0000E59EF5.npy,gnomAD +UPI000023731A,UPI000023731A.csv,MNPREEKVKIITEEFIENDEDADMGRQNKNSKVRRQPRKKQPPTAVPKEMVSEKSHLGNPQEPVQEEPKTRLLSMTVRRGPRSLPPIPSTSRTGFAEFSMRGRMREKLQAARSKAESALLQEIPTPRPRRLRSPSKKELETEFGTEPGKEVERTQQEVDSQSYSRVKFHDSARKIKPKPQVPPGFPSAEEAYNFFTFNFDPEPEGSEEKPKARHRAGTNQEEEEGEEEEPPAQGGGKEMDEEELLNGDDAEDFLLGLDHVADDFVAVRPADYESIHDRLQMEREMLFIPSRQTVPTYKKLPENVQPRFLEDEGLYTGVRPEVARTNQNIMENRLLMQDPERRWFGDDGRILALPNPIKPFPSRPPVLTQEQSIKAELETLYKKAVKYVHSSQHVIRSGDPPGNFQLDIDISGLIFTHHPCFSREHVLAAKLAQLYDQYLARHQRNKAKFLTDKLQALRNAVQTGLDPEKPHQSLDTIQKTINEYKSEIRQTRKFRDAEQEKDRTLLKTIIKVWKEMKSLREFQRFTNTPLKLVLRKEKADQKADEEAYEAEIQAEISELLEEHTEEYAQKMEEYRTSLQQWKAWRKVQRAKKKKRKQAAEEHPGDEIAEPYPEEDLVKPSPPEPTDRAVIEQEVRERAAQSRRRPWEPTLVPELSLAGSVTPNDQCPRAEVSRREDVKKRSVYLKVLFNNKEVSRTVSRPLGADFRVHFGQIFNLQIVNWPESLTLQVYETVGHSSPTLLAEVFLPIPETTVVTGRAPTEEVEFSSNQHVTLDHEGVGSGVPFSFEADGSNQLTLMTSGKVSHSVAWAIGENGIPLIPPLSQQNIGFRSALKKADAISSIGTSGLTDMKKLAKWAAESKLDPNDPNNAPLMQLISVATSGESYVPDFFRLEQLQQEFNFVSDQELNRSKRFRLLHLRSQEVPEFRNYKQVPVYDREIMEKVFQDYEKRLRDRNVIETKEHIDTHRAIVAKYLQQVRESVINRFLIAKQYFLLADMIVEEEVPNISILGLSLFKLAEQKRPLRPRRKGRKKVTAQNLSDGDIKLLVNIVRAYDIPVRKPAVSKFQQPSRSSRMFSEKHAASPSTYSPTHNADYPLGQVLVRPFVEVSFQRTVCHTTTAEGPNPSWNEELELPFRAPNGDYSTASLQSVKDVVFINIFDEVLHDVLEDDRERGSGIHTRIERHWLGCVKMPFSTIYFQARIDGTFKIDIPPVLLGYSKERNMILERGFDSVRSLSEGSYITLFITIEPQLVPGESIREKFESQEDEKLLQATEKFQAECALKFPNRQCLTTVIDISGKTVFITRYLKPLNPPQELLNVYPNNLQATAELVARYVSLIPFLPDTVSFGGICDLWSTSDQFLDLLAGDEEEHAVLLCNYFLSLGKKAWLLMGNAIPEGPTAYVLTWEQGRYLIWNPCSGHFYGQFDTFCPLKNVGCLIGPDNIWFNIQRYESPLRINFDVTRPKLWKSFFSRSLPYPGLSSVQPEELIYQRSDKAAAAELQDRIEKILKEKIMDWRPRHLTRWNRYCTSTLRHFLPLLEKSQGEDVEDDHRAELLKQLGDYRFSGFPLHMPYSEVKPLIDAVYSTGVHNIDVPNVEFALAVYIHPYPKNVLSVWIYVASLIRNR,mutated_sequence,1.0,1620.0,UPI000023731A.a2m,UPI000023731A.npy,gnomAD +UPI0000000B17,UPI0000000B17.csv,MLVMAPRTVLLLLSAALALTETWAGSHSMRYFYTSVSRPGRGEPRFISVGYVDDTQFVRFDSDAASPREEPRAPWIEQEGPEYWDRNTQIYKAQAQTDRESLRNLRGYYNQSEAGSHTLQSMYGCDVGPDGRLLRGHDQYAYDGKDYIALNEDLRSWTAADTAAQITQRKWEAAREAEQRRAYLEGECVEWLRRYLENGKDKLERADPPKTHVTHHPISDHEATLRCWALGFYPAEITLTWQRDGEDQTQDTELVETRPAGDRTFQKWAAVVVPSGEEQRYTCHVQHEGLPKPLTLRWEPSSQSTVPIVGIVAGLAVLAVVVIGAVVAAVMCRRKSSGGKGGSYSQAACSDSAQGSDVSLTA,mutated_sequence,1.0,362.0,UPI0000000B17.a2m,UPI0000000B17.npy,gnomAD +UPI000013D56A,UPI000013D56A.csv,MSSCRYNGGVMRPLSNLSASRRNLHEMDSEAQPLQPPASVGGGGGASSPSAAAAAAAAVSSSAPEIVVSKPEHNNSNNLALYGTGGGGSTGGGGGGGGSGHGSSSGTKSSKKKNQNIGYKLGHRRALFEKRKRLSDYALIFGMFGIVVMVIETELSWGAYDKASLYSLALKCLISLSTIILLGLIIVYHAREIQLFMVDNGADDWRIAMTYERIFFICLEILVCAIHPIPGNYTFTWTARLAFSYAPSTTTADVDIILSIPMFLRLYLIARVMLLHSKLFTDASSRSIGALNKINFNTRFVMKTLMTICPGTVLLVFSISLWIIAAWTVRACERYHDQQDVTSNFLGAMWLISITFLSIGYGDMVPNTYCGKGVCLLTGIMGAGCTALVVAVVARKLELTKAEKHVHNFMMDTQLTKRVKNAAANVLRETWLIYKNTKLVKKIDHAKVRKHQRKFLQAIHQLRSVKMEQRKLNDQANTLVDLAKTQNIMYDMISDLNERSEDFEKRIVTLETKLETLIGSIHALPGLISQTIRQQQRDFIEAQMESYDKHVTYNAERSRSSSRRRRSSSTAPPTSSESS,mutated_sequence,1.0,579.0,UPI000013D56A.a2m,UPI000013D56A.npy,gnomAD +UPI00005D3C95,UPI00005D3C95.csv,MREPEELMPDSGAVFTFGKSKFAENNPGKFWFKNDVPVHLSCGDEHSAVVTGNNKLYMFGSNNWGQLGLGSKSAISKPTCVKALKPEKVKLAACGRNHTLVSTEGGNVYATGGNNEGQLGLGDTEERNTFHVISFFTSEHKIKQLSAGSNTSAALTEDGRLFMWGDNSEGQIGLKNVSNVCVPQQVTIGKPVSWISCGYYHSAFVTTDGELYVFGEPENGKLGLPNQLLGNHRTPQLVSEIPEKVIQVACGGEHTVVLTENAVYTFGLGQFGQLGLGTFLFETSEPKVIENIRDQTISYISCGENHTALITDIGLMYTFGDGRHGKLGLGLENFTNHFIPTLCSNFLRFIVKLVACGGCHMVVFAAPHRGVAKEIEFDEINDTCLSVATFLPYSSLTSGNVLQRTLSARMRRRERERSPDSFSMRRTLPPIEGTLGLSACFLPNSVFPRCSERNLQESVLSEQDLMQPEEPDYLLDEMTKEAEIDNSSTVESLGETTDILNMTHIMSLNSNEKSLKLSPVQKQKKQQTIGELTQDTALTENDDSDEYEEMSEMKEGKACKQHVSQGIFMTQPATTIEAFSDEEVEIPEEKEGAEDSKGNGIEEQEVEANEENVKVHGGRKEKTEILSDDLTDKAEVSEGKAKSVGEAEDGPEGRGDGTCEEGSSGAEHWQDEEREKGEKDKGRGEMERPGEGEKELAEKEEWKKRDGEEQEQKEREQGHQKERNQEMEEGGEEEHGEGEEEEGDREEEEEKEGEGKEEGEGEEVEGEREKEEGERKKEERAGKEEKGEEEGDQGEGEEEETEGRGEEKEEGGEVEGGEVEEGKGEREEEEEEGEGEEEEGEGEEEEGEGEEEEGEGKGEEEGEEGEGEEEGEEGEGEGEEEEGEGEGEEEGEGEGEEEEGEGEGEEEGEGEGEEEEGEGKGEEEGEEGEGEGEEEEGEGEGEDGEGEGEEEEGEWEGEEEEGEGEGEEEGEGEGEEGEGEGEEEEGEGEGEEEEGEEEGEEEGEGEEEGEGEGEEEEEGEVEGEVEGEEGEGEGEEEEGEEEGEEREKEGEGEENRRNREEEEEEEGKYQETGEEENERQDGEEYKKVSKIKGSVKYGKHKTYQKKSVTNTQGNGKEQRSKMPVQSKRLLKNGPSGSKKFWNNVLPHYLELK,mutated_sequence,1.0,1152.0,UPI00005D3C95.a2m,UPI00005D3C95.npy,gnomAD +UPI00000467E6,UPI00000467E6.csv,MSLKLQASNVTNKNDPKSINSRVFIGNLNTALVKKSDVETIFSKYGRVAGCSVHKGYAFVQYSNERHARAAVLGENGRVLAGQTLDINMAGEPKPDRPKGLKRAASAIYSGYIFDYDYYRDDFYDRLFDYRGRLSPVPVPRAVPVKRPRVTVPLVRRVKTNVPVKLFARSTAVTTSSAKIKLKSSELQAIKTELTQIKSNIDALLSRLEQIAAEQKANPDGKKKGDGGGAGGGGGGGGSGGGGSGGGGGGGSSRPPAPQENTTSEAGLPQGEARTRDDGDEEGLLTHSEEELEHSQDTDADDGALQ,mutated_sequence,1.0,306.0,UPI00000467E6.a2m,UPI00000467E6.npy,gnomAD +UPI00015386B3,UPI00015386B3.csv,MGDLKSGFEEVDGVRLGYLIIKGKQMFALSQVFTDLLKNIPRTTVHKRMDHLKVKKHHCDLEELRKLKAINSIAFHAAKCTLISREDVEALYTSCKTERVLKTKRRRVGRALATKAPPPERAAAASPRPGFWKDKHQLWRGLSGAARPLPISAQSQRPGAAAARPAAHLPQIFSKYPGSHYPEIVRSPCKPPLNYETAPLQGNYVAFPSDPAYFRSLLCSKHPAAAAAAAAAAAAAAAAAAAAAYYQVSAAGPQPKAAAGAGGPGSLSYRCKRKRGGAKDCLLAPHAGARRLLLLPRSYKAKAAAAAAAAAAAAAAAAGATCLERFHLVNGFCPPPHHHHHHHHHHHHHHHRAQPPQQSHHPPHHHRPQPHLGSFPESCSSDSESSSYSDHAANDSDFGSSLSSSSNSVSSEEEEEEGEEEEEEEEEEGGSGASDSSEVSSEEEDSSTESDSSSGSSQVSVQSIRFRRTSFCKPPSVQAQANFLYHLASAAAATKPAAFEDAGRLPDLKSSVKAESPAEWNLQSWAPKASPVYCPASLGSCFAEIRNDRVSEITFPHSEISNAVKRTDLTINCLAEGASSPSPKTNNAFPQQRILREARKCLQTTPTTHCADNNTIAARFLNNDSSGAEANSEKYSKILHCPEFATDLPSSQTDPEVNAAGAAATKAENPCTDTGDKTLPFLHNIKIKVEDSSANEEYEPHLFTNKLKCECNDTKGEFYSVTESKEEDALLTTAKEGFACPEKETPSLNPLAQSQGLSCTLGSPKPEDGEYKFGARVRKNYRTLVLGKRPVLQTPPVKPNLKSARSPRPTGKTETNEGTLDDFTVINRRKKVASNVASAVKRPFHFMANFPCPPSLIIGRDGDLWPAYSLNTTKDSQTPHKAHPIWKWQLGGSAIPLPPSHKFRKFNS,mutated_sequence,1.0,908.0,UPI00015386B3.a2m,UPI00015386B3.npy,gnomAD +UPI000006EA15,UPI000006EA15.csv,MTASAQPRGRRPGVGVGVVVTSCKHPRCVLLGKRKGSVGAGSFQLPGGHLEFGETWEECAQRETWEEAALHLKNVHFASVVNSFIEKENYHYVTILMKGEVDVTHDSEPKNVEPEKNESWEWVPWEELPPLDQLFWGLRCLKEQGYDPFKEDLNHLVGYKGNHL,mutated_sequence,1.0,164.0,UPI000006EA15.a2m,UPI000006EA15.npy,gnomAD +UPI00000715E9,UPI00000715E9.csv,MAAGSEATTPVIVAAGAGGEEGEHVKPFKPEKAKEIIMSLQQPAIFCNMVFDWPARHWNAKYLSQVLHGKQIRFRMGMKSMSTVPQFETTCNYVEATLEEFLTWNCDQSSISGPFRDYDHSKFWAYADYKYFVSLFEDKTDLFQDVKWSDFGFPGRNGQESTLWIGSLGAHTPCHLDSYGCNLVFQVQGRKRWHLFPPEDTPFLYPTRIPYEESSVFSKINVVNPDLKRFPQFRKAQRHAVTLSPGQVLFVPRHWWHYVESIDPVTVSINSWIELEEDHLARVEEAITRMLVCALKTAENPQNTRAWLNPTEVEETSHAVNCCYLNAAVSAFFDRCRTSEVVEIQALRTDGEHMKKEELNVCNHMEVGQTGSQNLTTGTDKPEAASPFGPDLVPVAQRSEEPPSERGGIFGSDGKDFVDKDGEHFGKLHCAKRQQIMSNSENAIEEQIASNTTTTPQTFISTDDLLDCLVNPQVTRIVAQLLIQGRSL,mutated_sequence,1.0,488.0,UPI00000715E9.a2m,UPI00000715E9.npy,gnomAD +UPI000013D4C2,UPI000013D4C2.csv,MEENLISMREDHSFHVRYRMEASCLELALEGERLCKSGDCRAGVSFFEAAVQVGTEDLKTLSAIYSQLGNAYFYLHDYAKALEYHHHDLTLARTIGDQLGEAKASGNLGNTLKVLGNFDEAIVCCQRHLDISRELNDKVGEARALYNLGNVYHAKGKSFGCPGPQDVGEFPEEVRDALQAAVDFYEENLSLVTALGDRAAQGRAFGNLGNTHYLLGNFRDAVIAHEQRLLIAKEFGDKAAERRAYSNLGNAYIFLGEFETASEYYKKTLLLARQLKDRAVEAQSCYSLGNTYTLLQDYEKAIDYHLKHLAIAQELNDRIGEGRACWSLGNAYTALGNHDQAMHFAEKHLEISREVGDKSGELTARLNLSDLQMVLGLSYSTNNSIMSENTEIDSSLNGVRPKLGRRHSMENMELMKLTPEKVQNWNSEILAKQKPLIAKPSAKLLFVNRLKGKKYKTNSSTKVLQDASNSIDHRIPNSQRKISADTIGDEGFFDLLSRFQSNRMDDQRCCLQEKNCHTASTTTSSTPPKMMLKTSSVPVVSPNTDEFLDLLASSQSRRLDDQRASFSNLPGLRLTQNSQSVLSHLMTNDNKEADEDFFDILVKCQGSRLDDQRCAPPPATTKGPTVPDEDFFSLILRSQGKRMDEQRVLLQRDQNRDTDFGLKDFLQNNALLEFKNSGKKSADH,mutated_sequence,1.0,684.0,UPI000013D4C2.a2m,UPI000013D4C2.npy,gnomAD +UPI0000160D96,UPI0000160D96.csv,MEEKEILRRQIRLLQGLIDDYKTLHGNAPAPGTPAASGWQPPTYHSGRAFSARYPRPSRRGYSSHHGPSWRKKYSLVNRPPGPSDPPADHAVRPLHGARGGQPPVPQQHVLERQVQLSQGQNVVIKVKPPSKSGSASASGAQRGSLEEFEETPWSDQRPREGEGEPPRGQLQPSRPTRARGTCSVEDPLLVCQKEPGKPRMVKSVGSVGDSPREPRRTVSESVIAVKASFPSSALPPRTGVALGRKLGSHSVASCAPQLLGDRRVDAGHTDQPVPSGSVGGPARPASGPRQAREASLVVTCRTNKFRKNNYKWVAASSKSPRVARRALSPRVAAENVCKASAGMANKVEKPQLIADPEPKPRKPATSSKPGSAPSKYKWKASSPSASSSSSFRWQSEASSKDHASQLSPVLSRSPSGDRPAVGHSGLKPLSGETPLSAYKVKSRTKIIRRRSSTSLPGDKKSGTSPAATAKSHLSLRRRQALRGKSSPVLKKTPNKGLVQVTTHRLCRLPPSRAHLPTKEASSLHAVRTAPTSKVIKTRYRIVKKTPASPLSAPPFPLSLPSWRARRLSLSRSLVLNRLRPVASGGGKAQPGSPWWRSKGYRCIGGVLYKVSANKLSKTSGQPSDAGSRPLLRTGRLDPAGSCSRSLASRAVQRSLAIIRQARQRREKRKEYCMYYNRFGRCNRGERCPYIHDPEKVAVCTRFVRGTCKKTDGTCPFSHHVSKEKMPVCSYFLKGICSNSNCPYSHVYVSRKAEVCSDFLKGYCPLGAKCKKKHTLLCPDFARRGACPRGAQCQLLHRTQKRHSRRAATSPAPGPSDATARSRVSASHGPRKPSASQRPTRQTPSSAALTAAAVAAPPHCPGGSASPSSSKASSSSSSSSSPPASLDHEAPSLQEAALAAACSNRLCKLPSFISLQSSPSPGAQPRVRAPRAPLTKDSGKPLHIKPRL,mutated_sequence,1.0,948.0,UPI0000160D96.a2m,UPI0000160D96.npy,gnomAD +UPI000006CF77,UPI000006CF77.csv,MSLPESPHSPATLDYALEDPHQGQRSREKSKATEVMADMFDGRLEPIVFPPPRLPEEGVAPQDPADGGHTFHILVDAGRSHGAIKAGQEVTPPPAEGLEAASASLTTDGSLKNGFPGEETHGLGGEKALETCGAGRSESEVIAEGKAEDVKPEECAMFSAPVDEKPGGEEMDVAEENRAIDEVNREAGPGPGPGPLNVGLHLNPLESIQLELDSVNAEADRALLQVERRFGQIHEYYLEQRNDIIRNIPGFWVTAFRHHPQLSAMIRGQDAEMLSYLTNLEVKELRHPRTGCKFKFFFQRNPYFRNKLIVKVYEVRSFGQVVSFSTLIMWRRGHGPQSFIHRNRHVICSFFTWFSDHSLPESDRIAQIIKEDLWSNPLQYYLLGEDAHRARRRLVREPVEIPRPFGFQCG,mutated_sequence,1.0,410.0,UPI000006CF77.a2m,UPI000006CF77.npy,gnomAD +UPI000013DCB6,UPI000013DCB6.csv,MKMEEAVGKVEELIESEAPPKASEQETAKEEDGSVELESQVQKDGVADSTVISSMPCLLMELRRDSSESQLASTESDKPTTGRVYESDSSNHCMLSPSSSGHLADSDTLSSAEENEPSQAETAVEGDPSGVSGATVGRKSRRSRSESETSTMAAKKNRQSSDKQNGRVAKVKGHRSQKHKERIRLLRQKREAAARKKYNLLQDSSTSDSDLTCDSSTSSSDDDEEVSGSSKTITAEIPDGPPVVAHYDMSDTNSDPEVVNVDNLLAAAVVQEHSNSVGGQDTGATWRTSGLLEELNAEAGHLDPGFLASDKTSGNAPLNEEINIASSDSEVEIVGVQEHARCVHPRGGVIQSVSSWKHGSGTQYVSTRQTQSWTAVTPQQTWASPAEVVDLTLDEDSRRKYLL,mutated_sequence,1.0,403.0,UPI000013DCB6.a2m,UPI000013DCB6.npy,gnomAD +UPI000015FD15,UPI000015FD15.csv,MGTGDFICISMTGGAPWGFRLQGGKEQKQPLQVAKIRNQSKASGSGLCEGDEVVSINGNPCADLTYPEVIKLMESITDSLQMLIKRPSSGISEALISENENKNLEHLTHGGYVESTTLQIRPATKTQCTEFFLAPVKTEVPLAENQRSGPDCAGSLKEETGPSYQRAPQMPDSQRGRVAEELILREKVEAVQPGPVVELQLSLSQERHKGASGPLVALPGAEKSKSPDPDPNLSHDRIVHINSIPTNEKADPFLRSSKIIQISSGRELRVIQESEAGDAGLPRVEVILDCSDRQKTEGCRLQAGKECVDSPVEGGQSEAPPSLVSFAVSSEGTEQGEDPRSEKDHSRPHKHRARHARLRRSESLSEKQVKEAKSKCKSIALLLTDAPNPNSKGVLMFKKRRRRARKYTLVSYGTGELEREADEEEEGDKEDTCEVAFLGASESEVDEELLSDVDDNTQVVNFDWDSGLVDIEKKLNRGDKMEMLPDTTGKGALMFAKRRERMDQITAQKEEDKVGGTPSREQDAAQTDGLRTTTSYQRKEEESVRTQSSVSKSYIEVSHGLGHVPQQNGFSGTSETANIQRMVPMNRTAKPFPGSVNQPATPFSPTRNMTSPIADFPAPPPYSAVTPPPDAFSRGVSSPIAGPAQPPPWPQPAPWSQPAFYDSSERIASRDERISVPAKRTGILQEAKRRSTTKPMFTFKEPKVSPNPELLSLLQNSEGKRGTGAGGDSGPEEDYLSLGAEACNFMQSSSAKQKTPPPVAPKPAVKSSSSQPVTPVSPVWSPGVAPTQPPAFPTSNPSKGTVVSSIKIAQPSYPPARPASTLNVAGPFKGPQAAVASQNYTPKPTVSTPTVNAVQPGAVGPSNELPGMSGRGAQLFAKRQSRMEKYVVDSDTVQAHAARAQSPTPSLPASWKYSSNVRAPPPVAYNPIHSPSYPLAALKSQPSAAQPSKMGKKKGKKPLNALDVMKHQPYQLNASLFTFQPPDAKDGLPQKSSVKVNSALAMKQALPPRPVNAASPTNVQASSVYSVPAYTSPPSFFAEASSPVSASPVPVGIPTSPKQESASSSYFVAPRPKFSAKKSGVTIQVWKPSVVEE,mutated_sequence,1.0,1093.0,UPI000015FD15.a2m,UPI000015FD15.npy,gnomAD +UPI0001823FDC,UPI0001823FDC.csv,MAGSDVDSEGPARRGGAARRPGAPGGPGSEAAAGCPEPLSTAEAPAESATLPAWMRLYFYGMHGITLDVLVSSARRFARSPDLRMLGFSSPYRCLLHSLTHFALEKVYLQQRRCPNAFVFNFLLYPSAHVGLQTLAGQALLLSLGGGAGVAVAPGALDLALQYVLALYHCQVFLKRFLRLRYGRQRRRQQQQQQQQQQQQRRGALPVPPGARVPTAAGARRRRPRGPRGAGGAPSQGLPDLPRFLFFGMHGFLDEIFFTFFFNVLGQGDGTTSGHTSLWSFFMYGSCSFVVEKLYFHLHYSRGWGTWKRVPIYVIFIYVWELSWGLGLRTCGACSWDYSHYPLNFMGLITLMYLPGWIFLSVYQDLISNVLWRVQYVPAN,mutated_sequence,1.0,380.0,UPI0001823FDC.a2m,UPI0001823FDC.npy,gnomAD +UPI0000071834,UPI0000071834.csv,MLERRCRGPLAMGLAQPRLLSGPSQESPQTLGKESRGLRQQGTSVAQSGAQAPGRAHRCAHCRRHFPGWVALWLHTRRCQARLPLPCPECGRRFRHAPFLALHRQVHAAATPDLGFACHLCGQSFRGWVALVLHLRAHSAAKRPIACPKCERRFWRRKQLRAHLRRCHPPAPEARPFICGNCGRSFAQWDQLVAHKRVHVAEALEEAAAKALGPRPRGRPAVTAPRPGGDAVDRPFQCACCGKRFRHKPNLIAHRRVHTGERPHQCPECGKRFTNKPYLTSHRRIHTGEKPYPCKECGRRFRHKPNLLSHSKIHKRSEGSAQAAPGPGSPQLPAGPQESAAEPTPAVPLKPAQEPPPGAPPEHPQDPIEAPPSLYSCDDCGRSFRLERFLRAHQRQHTGERPFTCAECGKNFGKKTHLVAHSRVHSGERPFACEECGRRFSQGSHLAAHRRDHAPDRPFVCPDCGKAFRHKPYLAAHRRIHTGEKPYVCPDCGKAFSQKSNLVSHRRIHTGERPYACPDCDRSFSQKSNLITHRKSHIRDGAFCCAICGQTFDDEERLLAHQKKHDV,mutated_sequence,1.0,567.0,UPI0000071834.a2m,UPI0000071834.npy,gnomAD +UPI000006FF0C,UPI000006FF0C.csv,MVRETRHLWVGNLPENVREEKIIEHFKRYGRVESVKILPKRGSEGGVAAFVDFVDIKSAQKAHNSVNKMGDRDLRTDYNEPGTIPSAARGLDDTVSIASRSREVSGFRGGGGGPAYGPPPSLHAREGRYERRLDGASDNRERAYEHSAYGHHERGTGGFDRTRHYDQDYYRDPRERTLQHGLYYASRSRSPNRFDAHDPRYEPRAREQFTLPSVVHRDIYRDDITREVRGRRPERNYQHSRSRSPHSSQSRNQSPQRLASQASRPTRSPSGSGSRSRSSSSDSISSSSSTSSDSSDSSSSSSDDSPARSVQSAAVPAPTSQLLSSLEKDEPRKSFGIKVQNLPVRSTDTSLKDGLFHEFKKFGKVTSVQIHGTSEERYGLVFFRQQEDQEKALTASKGKLFFGMQIEVTAWIGPETESENEFRPLDERIDEFHPKATRTLFIGNLEKTTTYHDLRNIFQRFGEIVDIDIKKVNGVPQYAFLQYCDIASVCKAIKKMDGEYLGNNRLKLGFGKSMPTNCVWLDGLSSNVSDQYLTRHFCRYGPVVKVVFDRLKGMALVLYNEIEYAQAAVKETKGRKIGGNKIKVDFANRESQLAFYHCMEKSGQDIRDFYEMLAERREERRASYDYNQDRTYYESVRTPGTYPEDSRRDYPARGREFYSEWETYQGDYYESRYYDDPREYRDYRNDPYEQDIREYSYRQRERERERERFESDRDRDHERRPIERSQSPVHLRRPQSPGASPSQAERLPSDSERRLYSRSSDRSGSCSSLSPPRYEKLDKSRLERYTKNEKTDKERTFDPERVERERRLIRKEKVEKDKTDKQKRKGKVHSPSSQSSETDQENEREQSPEKPRSCNKLSREKADKEGIAKNRLELMPCVVLTRVKEKEGKVIDHTPVEKLKAKLDNDTVKSSALDQKLQVSQTEPAKSDLSKLESVRMKVPKEKGLSSHVEVVEKEGRLKARKHLKPEQPADGVSAVDLEKLEARKRRFADSNLKAEKQKPEVKKSSPEMEDARVLSKKQPDVSSREVILLREGEAERKPVRKEILKRESKKIKLDRLNTVASPKDCQELASISVGSGSRPSSDLQARLGELAGESVENQEVQSKKPIPSKPQLKQLQVLDDQGPEREDVRKNYCSLRDETPERKSGQEKSHSVNTEEKIGIDIDHTQSYRKQMEQSRRKQQMEMEIAKSEKFGSPKKDVDEYERRSLVHEVGKPPQDVTDDSPPSKKKRMDHVDFDICTKRERNYRSSRQISEDSERTGGSPSVRHGSFHEDEDPIGSPRLLSVKGSPKVDEKVLPYSNITVREESLKFNPYDSSRREQMADMAKIKLSVLNSEDELNRWDSQMKQDAGRFDVSFPNSIIKRDSLRKRSVRDLEPGEVPSDSDEDGEHKSHSPRASALYESSRLSFLLRDREDKLRERDERLSSSLERNKFYSFALDKTITPDTKALLERAKSLSSSREENWSFLDWDSRFANFRNNKDKEKVDSAPRPIPSWYMKKKKIRTDSEGKMDDKKEDHKEEEQERQELFASRFLHSSIFEQDSKRLQHLERKEEDSDFISGRIYGKQTSEGANSTTDSIQEPVVLFHSRFMELTRMQQKEKEKDQKPKEVEKQEDTENHPKTPESAPENKDSELKTPPSVGPPSVTVVTLESAPSALEKTTGDKTVEAPLVTEEKTVEPATVSEEAKPASEPAPAPVEQLEQVDLPPGADPDKEAAMMPAGVEEGSSGDQPPYLDAKPPTPGASFSQAESNVDPEPDSTQPLSKPAQKSEEANEPKAEKPDATADAEPDANQKAEAAPESQPPASEDLEVDPPVAAKDKKPNKSKRSKTPVQAAAVSIVEKPVTRKSERIDREKLKRSNSPRGEAQKLLELKMEAEKITRTASKNSAADLEHPEPSLPLSRTRRRNVRSVYATMGDHENRSPVKEPVEQPRVTRKRLERELQEAAAVPTTPRRGRPPKTRRRADEEEENEAKEPAETLKPPEGWRSPRSQKTAAGGGPQGKKGKNEPKVDATRPEATTEVGPQIGVKESSMEPKAAEEEAGSEQKRDRKDAGTDKNPPETAPVEVVEKKPAPEKNSKSKRGRSRNSRLAVDKSASLKNVDAAVSPRGAAAQAGERESGVVAVSPEKSESPQKEDGLSSQLKSDPVDPDKEPEKEDVSASGPSPEATQLAKQMELEQAVEHIAKLAEASASAAYKADAPEGLAPEDRDKPAHQASETELAAAIGSIINDISGEPENFPAPPPYPGESQTDLQPPAGAQALQPSEEGMETDEAVSGILETEAATESSRPPVNAPDPSAGPTDTKEARGNSSETSHSVPEAKGSKEVEVTLVRKDKGRQKTTRSRRKRNTNKKVVAPVESHVPESNQAQGESPAANEGTTVQHPEAPQEEKQSEKPHSTPPQSCTSDLSKIPSTENSSQEISVEERTPTKASVPPDLPPPPQPAPVDEEPQARFRVHSIIESDPVTPPSDPSIPIPTLPSVTAAKLSPPVASGGIPHQSPPTKVTEWITRQEEPRAQSTPSPALPPDTKASDVDTSSSTLRKILMDPKYVSATSVTSTSVTTAIAEPVSAAPCLHEAPPPPVDSKKPLEEKTAPPVTNNSEIQASEVLVAADKEKVAPVIAPKITSVISRMPVSIDLENSQKITLAKPAPQTLTGLVSALTGLVNVSLVPVNALKGPVKGSVTTLKSLVSTPAGPVNVLKGPVNVLTGPVNVLTTPVNATVGTVNAAPGTVNAAASAVNATASAVTVTAGAVTAASGGVTATTGTVTMAGAVIAPSTKCKQRASANENSRFHPGSMPVIDDRPADAGSGAGLRVNTSEGVVLLSYSGQKTEGPQRISAKISQIPPASAMDIEFQQSVSKSQVKPDSVTASQPPSKGPQAPAGYANVATHSTLVLTAQTYNASPVISSVKADRPSLEKPEPIHLSVSTPVTQGGTVKVLTQGINTPPVLVHNQLVLTPSIVTTNKKLADPVTLKIETKVLQPANLGSTLTPHHPPALPSKLPTEVNHVPSGPSIPADRTVSHLAAAKLDAHSPRPSGPGPSSFPRASHPSSTASTALSTNATVMLAAGIPVPQFISSIHPEQSVIMPPHSITQTVSLSHLSQGEVRMNTPTLPSITYSIRPEALHSPRAPLQPQQIEVRAPQRASTPQPAPAGVPALASQHPPEEEVHYHLPVARATAPVQSEVLVMQSEYRLHPYTVPRDVRIMVHPHVTAVSEQPRAADGVVKVPPASKAPQQPGKEAAKTPDAKAAPTPTPAPVPVPVPLPAPAPAPHGEARILTVTPSNQLQGLPLTPPVVVTHGVQIVHSSGELFQEYRYGDIRTYHPPAQLTHTQFPAASSVGLPSRTKTAAQGPPPEGEPLQPPQPVQSTQPAQPAPPCPPSQLGQPGQPPSSKMPQVSQEAKGTQTGVEQPRLPAGPANRPPEPHTQVQRAQAETGPTSFPSPVSVSMKPDLPVSLPTQTAPKQPLFVPTTSGPSTPPGLVLPHTEFQPAPKQDSSPHLTSQRPVDMVQLLKKYPIVWQGLLALKNDTAAVQLHFVSGNNVLAHRSLPLSEGGPPLRIAQRMRLEATQLEGVARRMTVETDYCLLLALPCGRDQEDVVSQTESLKAAFITYLQAKQAAGIINVPNPGSNQPAYVLQIFPPCEFSESHLSRLAPDLLASISNISPHLMIVIASV,mutated_sequence,1.0,3664.0,UPI000006FF0C.a2m,UPI000006FF0C.npy,gnomAD +UPI0000505465,UPI0000505465.csv,MALGEEKAEAEASEDTKAQSYGRGSCRERELDIPGPMSGEQPPRLEAEGGLISPVWGAEGIPAPTCWIGTDPGGPSRAHQPQASDANREPVAERSEPALSGLPPATMGSGDLLLSGESQVEKTKLSSSEEFPQTLSLPRTTTICSGHDADTEDDPSLADLPQALDLSQQPHSSGLSCLSQWKSVLSPGSAAQPSSCSISASSTGSSLQGHQERAEPRGGSLAKVSSSLEPVVPQEPSSVVGLGPRPQWSPQPVFSGGDASGLGRRRLSFQAEYWACVLPDSLPPSPDRHSPLWNPNKEYEDLLDYTYPLRPGPQLPKHLDSRVPADPVLQDSGVDLDSFSVSPASTLKSPTNVSPNCPPAEATALPFSGPREPSLKQWPSRVPQKQGGMGLASWSQLASTPRAPGSRDARWERREPALRGAKDRLTIGKHLDMGSPQLRTRDRGWPSPRPEREKRTSQSARRPTCTESRWKSEEEVESDDEYLALPARLTQVSSLVSYLGSISTLVTLPTGDIKGQSPLEVSDSDGPASFPSSSSQSQLPPGAALQGSGDPEGQNPCFLRSFVRAHDSAGEGSLGSSQALGVSSGLLKTRPSLPARLDRWPFSDPDVEGQLPRKGGEQGKESLVQCVKTFCCQLEELICWLYNVADVTDHGTAARSNLTSLKSSLQLYRQFKKDIDEHQSLTESVLQKGEILLQCLLENTPVLEDVLGRIAKQSGELESHADRLYDSILASLDMLAGCTLIPDKKPMAAMEHPCEGV,mutated_sequence,1.0,757.0,UPI0000505465.a2m,UPI0000505465.npy,gnomAD +UPI0000E5B01A,UPI0000E5B01A.csv,MAEGGEGGEDEIQFLRTEDEVVLQCIATIHKEQRKFCLAAEGLGNRLCFLEPTSEAKYIPPDLCVCNFVLEQSLSVRALQEMLANTGENGGEGAAQGGGHRTLLYGHAVLLRHSFSGMYLTCLTTSRSQTDKLAFDVGLREHATGEACWWTIHPASKQRSEGEKVRIGDDLILVSVSSERYLHLSVSNGNIQVDASFMQTLWNVHPTCSGSSIEEGYLLGGHVVRLFHGHDECLTIPSTDQNDSQHRRIFYEAGGAGTRARSLWRVEPLRISWSGSNIRWGQAFRLRHLTTGHYLALTEDQGLILQDRAKSDTKSTAFSFRASKELKEKLDSSHKRDIEGMGVPEIKYGDSVCFVQHIASGLWVTYKAQDAKTSRLGPLKRKVILHQEGHMDDGLTLQRCQREESQAARIIRNTTALFSQFVSGNNRTAAPITLPIEEVLQTLQDLIAYFQPPEEEMRHEDKQNKLRSLKNRQNLFKEEGMLALVLNCIDRLNVYNSVAHFAGIAREESGMAWKEILNLLYKLLAALIRGNRNNCAQFSNNLDWLISKLDRLESSSGILEVLHCILTESPEALNLIAEGHIKSIISLLDKHGRNHKVLDILCSLCLCNGVAVRANQNLICDNLLPRRNLLLQTRLINDVTSIRPNIFLGVAEGSAQYKKWYFELIIDQVDPFLTAEPTHLRVGWASSSGYAPYPGGGEGWGGNGVGDDLYSYGFDGLHLWSGRIPRAVASINQHLLRSDDVVSCCLDLGVPSISFRINGQPVQGMFENFNTDGLFFPVMSFSAGVKVRFLMGGRHGEFKFLPPSGYAPCYEALLPKEKMRLEPVKEYKRDADGIRDLLGTTQFLSQASFIPCPVDTSQVILPPHLEKIRDRLAENIHELWGMNKIELGWTFGKIRDDNKRQHPCLVEFSKLPETEKNYNLQMSTETLKTLLALGCHIAHVNPAAEEDLKKVKLPKNYMMSNGYKPAPLDLSDVKLLPPQEILVDKLAENAHNVWAKDRIKQGWTYGIQQDLKNKRNPRLVPYALLDERTKKSNRDSLREAVRTFVGYGYNIEPSDQELADSAVEKVSIDKIRFFRVERSYAVRSGKWYFEFEVVTGGDMRVGWARPGCRPDVELGADDQAFVFEGNRGQRWHQGSGYFGRTWQPGDVVGCMINLDDASMIFTLNGELLITNKGSELAFADYEIENGFVPICCLGLSQIGRMNLGTDASTFKFYTMCGLQEGFEPFAVNMNRDVAMWFSKRLPTFVNVPKDHPHIEVMRIDGTMDSPPCLKVTHKTFGTQNSNADMIYCRLSMPVECHSSFSHSPCLDSEAFQKRKQMQEILSHTTTQCYYAIRIFAGQDPSCVWVGWVTPDYHLYSEKFDLNKNCTVTVTLGDERGRVHESVKRSNCYMVWGGDIVASSQRSNRSNVDLEIGCLVDLAMGMLSFSANGKELGTCYQVEPNTKVFPAVFLQPTSTSLFQFELGKLKNAMPLSAAIFRSEEKNPVPQCPPRLDVQTIQPVLWSRMPNSFLKVETERVSERHGWVVQCLEPLQMMALHIPEENRCVDILELCEQEDLMRFHYHTLRLYSAVCALGNSRVAYALCSHVDLSQLFYAIDNKYLPGLLRSGFYDLLISIHLASAKERKLMMKNEYIIPITSTTRNIRLFPDESKRHGLPGVGLRTCLKPGFRFSTPCFVVTGEDHQKQSPEIPLESLRTKALSMLTEAVQCSGAHIRDPVGGSVEFQFVPVLKLIGTLLVMGVFDDDDVRQILLLIDPSVFGEHSAGTEEGAEKEEVTQVEEKAVEAGEKAGKEAPVKGLLQTRLPESVKLQMCELLSYLCDCELQHRVEAIVAFGDIYVSKLQANQKFRYNELMQALNMSAALTARKTKEFRSPPQEQINMLLNFQLGENCPCPEEIREELYDFHEDLLLHCGVPLEEEEEEEEDTSWTGKLCALVYKIKGPPKPEKEQPTEEEERCPTTLKELISQTMICWAQEDQIQDSELVRMMFNLLRRQYDSIGELLQALRKTYTISHTSVSDTINLLAALGQIRSLLSVRMGKEEELLMINGLGDIMNNKVFYQHPNLMRVLGMHETVMEVMVNVLGTEKSQIAFPKMVASCCRFLCYFCRISRQNQKAMFEHLSYLLENSSVGLASPSMRGSTPLDVAASSVMDNNELALSLEEPDLEKVVTYLAGCGLQSCPMLLAKGYPDVGWNPIEGERYLSFLRFAVFVNSESVEENASVVVKLLIRRPECFGPALRGEGGNGLLAAMQGAIKISENPALDLPSQGYKREVSTGDDEEEEEIVHMGNAIMSFYSALIDLLGRCAPEMHLIQTGKGEAIRIRSILRSLVPTEDLVGIISIPLKLPSLNKDGSVSEPDMAANFCPDHKAPMVLFLDRVYGIKDQTFLLHLLEVGFLPDLRASASLDTVSLSTTEAALALNRYICSAVLPLLTRCAPLFAGTEHCTSLIDSTLQTIYRLSKGRSLTKAQRDTIEECLLAICNHLRPSMLQQLLRRLVFDVPQLNEYCKMPLKLLTNHYEQCWKYYCLPSGWGSYGLAVEEELHLTEKLFWGIFDSLSHKKYDPDLFRMALPCLSAIAGALPPDYLDTRITATLEKQISVDADGNFDPKPINTMNFSLPEKLEYIVTKYAEHSHDKWACDKSQSGWKYGISLDENVKTHPLIRPFKTLTEKEKEIYRWPARESLKTMLAVGWTVERTKEGEALVQQRENEKLRSVSQANQGNSYSPAPLDLSNVVLSRELQGMVEVVAENYHNIWAKKKKLELESKGGGSHPLLVPYDTLTAKEKFKDREKAQDLFKFLQVNGIIVSRGMKDMELDASSMEKRFAYKFLKKILKYVDSAQEFIAHLEAIVSSGKTEKSPRDQEIKFFAKVLLPLVDQYFTSHCLYFLSSPLKPLSSSGYASHKEKEMVAGLFCKLAALVRHRISLFGSDSTTMVSCLHILAQTLDTRTVMKSGSELVKAGLRAFFENAAEDLEKTSENLKLGKFTHSRTQIKGVSQNINYTTVALLPILTSIFEHVTQHQFGMDLLLGDVQISCYHILCSLYSLGTGKNIYVERQRPALGECLASLAAAIPVAFLEPTLNRYNPLSVFNTKTPRERSILGMPDTVEDMCPDIPQLEGLMKEINDLAESGARYTEMPHVIEVILPMLCNYLSYWWERGPENLPPSTGPCCTKVTSEHLSLILGNILKIINNNLGIDEASWMKRIAVYAQPIISKARPDLLRSHFIPTLEKLKKKAVKTVQEEEQLKADGKGDTQEAELLILDEFAVLCRDLYAFYPMLIRYVDNNRSNWLKSPDADSDQLFRMVAEVFILWCKSHNFKREEQNFVIQNEINNLAFLTGDSKSKMSKAMQVKSGGQDQERKKTKRRGDLYSIQTSLIVAALKKMLPIGLNMCTPGDQELISLAKSRYSHRDTDEEVREHLRNNLHLQEKSDDPAVKWQLNLYKDVLKSEEPFNPEKTVERVQRISAAVFHLEQVEQPLRSKKAVWHKLLSKQRKRAVVACFRMAPLYNLPRHRSINLFLHGYQRFWIETEEYSFEEKLVQDLAKSPKVEEEEEEETEKQPDPLHQIILYFSRNALTERSKLEDDPLYTSYSSMMAKSCQSGEDEEEDEDKEKTFEEKEMEKQKTLYQQARLHERGAAEMVLQMISASKGEMSPMVVETLKLGIAILNGGNAGVQQKMLDYLKEKKDAGFFQSLSGLMQSCSVLDLNAFERQNKAEGLGMVTEEGTLIVRERGEKVLQNDEFTRDLFRFLQLLCEGHNSDFQNFLRTQMGNTTTVNVIISTVDYLLRLQESISDFYWYYSGKDIIDESGQHNFSKALAVTKQIFNSLTEYIQGPCIGNQQSLAHSRLWDAVVGFLHVFANMQMKLSQDSSQIELLKELLDLLQDMVVMLLSLLEGNVVNGTIGKQMVDTLVESSTNVEMILKFFDMFLKLKDLTSSDTFKEYDPDGKGIISKKEFQKAMEGQKQYTQSEIDFLLSCAEADENDMFNYVDFVDRFHEPAKDIGFNVAVLLTNLSEHMPNDSRLKCLLDPAESVLNYFEPYLGRIEIMGGAKKIERVYFEISESSRTQWEKPQVKESKRQFIFDVVNEGGEQEKMELFVNFCEDTIFEMQLASQISESDSADRPEEEEEDEDSSYVLEIAGEEEEDGSLEPASAFAMACASVKRNVTDFLKRATLKNLRKQYRNVKKMTAKELVKVLFSFFWMLFVGLFQLLFTILGGIFQILWSTVFGGGLVEGAKNIRVTKILGDMPDPTQFGIHDDTMEAERAEVMEPGITTELVHFIKGEKGDTDIMSDLFGLHPKKEGSLKHGPEVGLGDLSEIIGKDEPPTLESTVQKKRKAQAAEMKAANEAEGKVESEKADMEDGEKEDKDKEEEQAEYLWTEVTKKKKRRCGQKVEKPEAFTANFFKGLEIYQTKLLHYLARNFYNLRFLALFVAFAINFILLFYKVTEEPLEEETEDVANLWNSFNDEEEEEAMVFFVLQESTGYMAPTLRALAIIHTIISLVCVVGYYCLKVPLVVFKREKEIARKLEFDGLYITEQPSEDDIKGQWDRLVINTPSFPNNYWDKFVKRKVINKYGDLYGAERIAELLGLDKNALDFSPVEETKAEAASLVSWLSSIDMKYHIWKLGVVFTDNSFLYLAWYTTMSVLGHYNNFFFAAHLLDIAMGFKTLRTILSSVTHNGKQLVLTVGLLAVVVYLYTVVAFNFFRKFYNKSEDDDEPDMKCDDMMTCYLFHMYVGVRAGGGIGDEIEDPAGDPYEMYRIVFDITFFFFVIVILLAIIQGLIIDAFGELRDQQEQVREDMETKCFICGIGNDYFDTTPHGFETHTLQEHNLANYLFFLMYLINKDETEHTGQESYVWKMYQERCWDFFPAGDCFRKQYEDQLG,mutated_sequence,1.0,4870.0,UPI0000E5B01A.a2m,UPI0000E5B01A.npy,gnomAD +UPI000045882C,UPI000045882C.csv,MTRGAWMCRQYDDGLKIWLAAPRENEKPFIDSERAQKWRLSLASLLFFTVLLSDHLWFCAEAKLTRARDKEHQQQQRQQQQQQQQQRQRQQQQQQRRQQEPSWPALLASMGESSPAAQAHRLLSASSSPTLPPSPGDGGGGGGKGNRGKDDRGKALFLGNSAKPVWRLETCYPQGASSGQCFTVENADAVCARNWSRGAAGGDGQEVRSKHPTPLWNLSDFYLSFCNSYTLWELFSGLSSPNTLNCSLDVVLKEGGEMTTCRQCVEAYQDYDHHAQEKYEEFESVLHKYLQSEEYSVKSCPEDCKIVYKAWLCSQYFEVTQFNCRKTIPCKQYCLEVQTRCPFILPDNDEVIYGGLSSFICTGLYETFLTNDEPECCDVRREEKSNNPSKGTVEKSGSCHRTSLTVSSATRLCNSRLKLCVLVLILLHTVLTASAAQNTAGLSFGGINTLEENSTNEE,mutated_sequence,1.0,458.0,UPI000045882C.a2m,UPI000045882C.npy,gnomAD +UPI0000031D01,UPI0000031D01.csv,MRPGPALLLLGVGLSLSVGRLPLPPVPRGAQAAVSGAPGGLLRGAPGLGVRGGRALLSLRPSAVRAGGAVLSGRGSLCFPHGGTGRRWYCLDLRVLLSAQRLPWPAAPALALVDLQLSARGGRLSLTWSVRLPRSPGRLAWAFRLRLLGPGAARPASPAARVSPRSAAPGPRPQQGFVARTECPTDGPARVMLQAVNSSSHRAVESSVSCQINACVIQRVRINTDQKGAPVRLSMQAEATINASVQLDCPAARAIAQYWQVFSVPAVGQAPDWTQPLDLPQLEIRNSPLFIHIPNNSLQWGVYVFNFTVSITTGNPKMPEVKDSDAVYVWIVRSSLQAVMLGDANITANFTEQLILDGSTSSDPDADSPLQGLQFFWYCTTDPRNYGGDRIILGSKEVCHPEQANLKWPWASGPVLTLLPETLKGDHVYFFRMVIRKDSRTAFSDKRVHVLQGPKAIAHITCIENCERNFIVSDRFSLFLNCTNCASRDFYKWSILSSSGGEMLFDWMGETVTGRNGAYLSIKAFAFRHFLEAEFSISLYLACWSGVTSVFRHSFIINHGPQIGECKINPAKGIALITKFVVQCSNFRDKHVPLTYKIIVSDLHSVGEISSVKENTLGTILYLGPQSTVPPSFLPVGMLASQYGLKIYAQVYDSLGAFSQVTLHATAQAPTDKNSSKTVLNQLLSFTVGPSSLLSTLIQKKDFLPAGYLLYIVASVLNNMKTELPLRDDRVNLRKHLIDQSFLLPVSTLVEIGQVVMTITKLTQKPSEFTWDAQKRATMRVWQANQALQEYQQKDKRFRSEQIEIVSTGILMSLSNILKMTSPHQVVKDPFYVIESLSDTILANKVPGNKTTSMRTPNFNMYVKKVEKWGINQLFRNEKHCRNCFYPTLNVSSVPGLSANGPISTMFCDFTNDLFPWLNDQENTSVEVSGFRMTGVADNGSVLEITPDVAEVYLVRKNLTFAAFNLTVGPNSEVDGSLKKTTGGFSFQVDSTVLREVLVHIVTEVMVLFTVLVYTGSQITPTALVATFLVPHDIPPFASQSALFDPACTVKKARVVCLPVSLLQLIAQHSHSPHCTVSIVLQAPRFVMKLNDKLVRISIFSVQCLDMYGIQSEWREGYCILGEKTSWYEVHCICKNVVRARRQLGTIGLTGIHLHTHYVMAKVIVIPNPVDLRLNIIKSLHQNPVTLFTVLFIILLYVGLAFWALYRDEMDQHLRGHVIVLPDNDPYDNLCYLVTIFTGSRWGSGTRANVFVQLRGTVSTSDVHCLSHPHFTTLYRGSINTFLLTTKSDLGDIHSIRVWHNNEGRSPSWYLSRIKVENLFSRHIWLFICQKWLSVDTTLDRTFHVTHPDERLTRKDFFFIDVSSNLRKNHMWFSIFASVVAKTFNRLQRLSCCLAMLLSSLLCNIMFFNLNRQEQTESRERKYMRSMMIGIESVLITIPVQLLITFLFTCSQRKPQADLKEVSPQKHPLMSEASEHWEEYLRKWHAYETAKVHPREVAKPASKGKPRLPKASPKATSKPKHRHRKAQIKTPETLGPNTNSNNNIEDDQDVHSEQHPSQKDLQQLKKKPRIVLPWWCVYVAWFLVFATSSISSFFIVFYGLTYGYDKSIEWLFASFCSFCQSVLLVQPSKIILLSGFRTNKPKYCKNLSWSTKYKYTEIRLDGMRMHPEEMQRIHDQIVRIRGTRMYQPLTEDEIRIFKRKKRIKRRALLFLSYILTHFIFLALLLILIVLLRHTDCFYYNQFIRDRFSMDLATVTKLEDIYRWLNSVLLPLLHNDLNPTFLPESSSKILGLPLMRQVRAKSSEKMCLPAEKFVQNSIRREIHCHPKYGIDPEDTKNYSGFWNEVDKQAIDESTNGFTYKPQGTQWLYYSYGLLHTYGSGGYALYFFPEQQRFNSTLRLKELQESNWLDEKTWAVVLELTTFNPDINLFCSISVIFEVSQLGVVNTSISLHSFSLADFDRKASAEIYLYVAILIFFLAYVVDEGCIIMQERASYVRSVYNLLNFALKCIFTVLIVLFLRKHFLATGIIRFYLSNPEDFIPFHAVSQVDHIMRIILGFLLFLTILKTLRYSRFFYDVRLAQRAIQAALPGICHMAFVVSVYFFVYMAFGYLVFGQHEWNYSNLIHSTQTVFSYCVSAFQNTEFSNNRILGVLFLSSFMLVMICVLINLFQAVILSAYEEMKQPVYEEPSDEVEAMTYLCRKLRTMFSFLTSQSKAKDEPEFFIDMLYGQPEKNSHRYLGLKTRNINGKKMVYLVV,mutated_sequence,1.0,2253.0,UPI0000031D01.a2m,UPI0000031D01.npy,gnomAD +UPI000020F9BB,UPI000020F9BB.csv,MRILKRFLACIQLLCVCRLDWANGYYRQQRKLVEEIGWSYTGALNQKNWGKKYPTCNSPKQSPINIDEDLTQVNVNLKKLKFQGWDKTSLENTFIHNTGKTVEINLTNDYRVSGGVSEMVFKASKITFHWGKCNMSSDGSEHSLEGQKFPLEMQIYCFDADRFSSFEEAVKGKGKLRALSILFEVGTEENLDFKAIIDGVESVSRFGKQAALDPFILLNLLPNSTDKYYIYNGSLTSPPCTDTVDWIVFKDTVSISESQLAVFCEVLTMQQSGYVMLMDYLQNNFREQQYKFSRQVFSSYTGKEEIHEAVCSSEPENVQADPENYTSLLVTWERPRVVYDTMIEKFAVLYQQLDGEDQTKHEFLTDGYQDLGAILNNLLPNMSYVLQIVAICTNGLYGKYSDQLIVDMPTDNPELDLFPELIGTEEIIKEEEEGKDIEEGAIVNPGRDSATNQIRKKEPQISTTTHYNRIGTKYNEAKTNRSPTRGSEFSGKGDVPNTSLNSTSQPVTKLATEKDISLTSQTVTELPPHTVEGTSASLNDGSKTVLRSPHMNLSGTAESLNTVSITEYEEESLLTSFKLDTGAEDSSGSSPATSAIPFISENISQGYIFSSENPETITYDVLIPESARNASEDSTSSGSEESLKDPSMEGNVWFPSSTDITAQPDVGSGRESFLQTNYTEIRVDESEKTTKSFSAGPVMSQGPSVTDLEMPHYSTFAYFPTEVTPHAFTPSSRQQDLVSTVNVVYSQTTQPVYNGETPLQPSYSSEVFPLVTPLLLDNQILNTTPAASSSDSALHATPVFPSVDVSFESILSSYDGAPLLPFSSASFSSELFRHLHTVSQILPQVTSATESDKVPLHASLPVAGGDLLLEPSLAQYSDVLSTTHAASETLEFGSESGVLYKTLMFSQVEPPSSDAMMHARSSGPEPSYALSDNEGSQHIFTVSYSSAIPVHDSVGVTYQGSLFSGPSHIPIPKSSLITPTASLLQPTHALSGDGEWSGASSDSEFLLPDTDGLTALNISSPVSVAEFTYTTSVFGDDNKALSKSEIIYGNETELQIPSFNEMVYPSESTVMPNMYDNVNKLNASLQETSVSISSTKGMFPGSLAHTTTKVFDHEISQVPENNFSVQPTHTVSQASGDTSLKPVLSANSEPASSDPASSEMLSPSTQLLFYETSASFSTEVLLQPSFQASDVDTLLKTVLPAVPSDPILVETPKVDKISSTMLHLIVSNSASSENMLHSTSVPVFDVSPTSHMHSASLQGLTISYASEKYEPVLLKSESSHQVVPSLYSNDELFQTANLEINQAHPPKGRHVFATPVLSIDEPLNTLINKLIHSDEILTSTKSSVTGKVFAGIPTVASDTFVSTDHSVPIGNGHVAITAVSPHRDGSVTSTKLLFPSKATSELSHSAKSDAGLVGGGEDGDTDDDGDDDDDDRGSDGLSIHKCMSCSSYRESQEKVMNDSDTHENSLMDQNNPISYSLSENSEEDNRVTSVSSDSQTGMDRSPGKSPSANGLSQKHNDGKEENDIQTGSALLPLSPESKAWAVLTSDEESGSGQGTSDSLNENETSTDFSFADTNEKDADGILAAGDSEITPGFPQSPTSSVTSENSEVFHVSEAEASNSSHESRIGLAEGLESEKKAVIPLVIVSALTFICLVVLVGILIYWRKCFQTAHFYLEDSTSPRVISTPPTPIFPISDDVGAIPIKHFPKHVADLHASSGFTEEFETLKEFYQEVQSCTVDLGITADSSNHPDNKHKNRYINIVAYDHSRVKLAQLAEKDGKLTDYINANYVDGYNRPKAYIAAQGPLKSTAEDFWRMIWEHNVEVIVMITNLVEKGRRKCDQYWPADGSEEYGNFLVTQKSVQVLAYYTVRNFTLRNTKIKKGSQKGRPSGRVVTQYHYTQWPDMGVPEYSLPVLTFVRKAAYAKRHAVGPVVVHCSAGVGRTGTYIVLDSMLQQIQHEGTVNIFGFLKHIRSQRNYLVQTEEQYVFIHDTLVEAILSKETEVLDSHIHAYVNALLIPGPAGKTKLEKQFQLLSQSNIQQSDYSAALKQCNREKNRTSSIIPVERSRVGISSLSGEGTDYINASYIMGYYQSNEFIITQHPLLHTIKDFWRMIWDHNAQLVVMIPDGQNMAEDEFVYWPNKDEPINCESFKVTLMAEEHKCLSNEEKLIIQDFILEATQDDYVLEVRHFQCPKWPNPDSPISKTFELISVIKEEAANRDGPMIVHDEHGGVTAGTFCALTTLMHQLEKENSVDVYQVAKMINLMRPGVFADIEQYQFLYKVILSLVSTRQEENPSTSLDSNGAALPDGNIAESLESLV,mutated_sequence,1.0,2315.0,UPI000020F9BB.a2m,UPI000020F9BB.npy,gnomAD +UPI00001AAC44,UPI00001AAC44.csv,MEYEVKKGKKGFVSPIRRLVFPKAGRRAACRSSVSRRPLHSMPLYPPDYLIDPQILLCDYLEKEVKFLGHLTWVTSSLNPSSRDELLQLLDTARQLKELPLKTTAEQDSILSLSARCLLLTWRDNEELILRIPTHEIAAASYLQDDALHLLVLKTGLGVDPVPAGVDASPGGAGRDPGPPGGAPEKRRVGTAERRHTICSLDWRMGWGGGAAEARAGGGGGGSLERQRAGARASGSWERRQTFSGSWERRHGGGGGGGGAGKPGGSWERRQAGSGGGGSWERRHPGPNPLDPQDPSPDAYCNLVILAVANRDAAEESCALICQVFQIIYGDQSIECVDRAGYHYTSTPERPWLCSRSESCHTDGTYAYDADFSCCSSFNGSQDTFEACYSGTSTPSFHGSHCSGSDHSSLGLEQLQDYMVTLRSKLGPLEIQQFAMLLREYRLGLPIQDYCTGLLKLYGDRRKFLLLGMRPFIPDQDIGYFEGFLEGVGIREGGILTDSFGRIKRSMSSTSASAVRSYDGAAQRPEAQAFHRLLADITHDIEALAPDDDDDDEDEPRGSRGGSDAAEDNYL,mutated_sequence,1.0,571.0,UPI00001AAC44.a2m,UPI00001AAC44.npy,gnomAD +UPI00024672CE,UPI00024672CE.csv,MWPQPHLPPHPMMSEKTRQNKLAEAKKKFTDYRQWNIAGVGTRATDTKKKKINNGTNPETTTSEGCHSPEDTQQNRAQLKEEKKASHQHQEALRREIEAQDHTIRILTCQKTELETALYYSQDAARKFEDGNLGTPSSFNLALSQAFRGSPLGCVSTSLIPGESKDLAGRLHHSWHFAGELQRALSAVSTWHKKADRYIEELTKERDALSLELYRNTITNEELKKKNAELQEKLRLAESEKSEIQLNVKELKRKLERAKFLLPQVQTNTLQEEMWRQEEELREQEKKIRKQEEKMWRQEERLREQEGKMREQEEKMRRQEKRLREQEKELREQEKELREQKKLREQEEQMQEQEEKMWEQEEKMREQEEKMWRQEERLWEQEKQMREQEQKMRDQEERMWEQDERLREKEERMREQEKMWEQVEKMREEKKMQEQEKKTRDQEEKMQEEERIREREKKMREEEETMREQEEKMQKQEENMWEQEEKEWQQQRLPEQKEKLWEQEKMQEQEEKIWEQEEKIRDQEEMWGQEKKMWRQEKMREQEDVETGGEAAGAGEADVGAGGEDAGSGAEDVGPGGEDVGAGREAAGEGGENAGAEEDVAAGGEDAGGEEDAGAGEEDMGPGGEDARGGEDAGAGEEDAGGGGDDAGAGGEDAGAGREDAGAGGEDVGAGREDAGAGGEDVGAGGEDVGAGRRRCGSSRGCRNRRRSCGNTRRCRSRRSGAEDVGPEGEDVGAGREAAGEGGENAGAEDVAAGGEDAGEEEDAGGEDAGAAREDAGAGGDDVGAGREDAGAGGEDVGAGGEDAGAGGEDAGAGGEDAGPGGEDAGAGGEDAGPGGEDAGAGGEDAGPGGEDVGPGGEDVGAGGEDVGAGGDAREGGEDTRSEREDAGEAARARGAVLRALPPSLQSSL,mutated_sequence,1.0,909.0,UPI00024672CE.a2m,UPI00024672CE.npy,gnomAD +UPI000000DA9E,UPI000000DA9E.csv,MPAGRRGPAAQSARRPPPLLPLLLLLCVLGAPRAGSGAHTAVISPQDPTLLIGSSLLATCSVHGDPPGATAEGLYWTLNGRRLPPELSRVLNASTLALALANLNGSRQRSGDNLVCHARDGSILAGSCLYVGLPPEKPVNISCWSKNMKDLTCRWTPGAHGETFLHTNYSLKYKLRWYGQDNTCEEYHTVGPHSCHIPKDLALFTPYEIWVEATNRLGSARSDVLTLDILDVVTTDPPPDVHVSRVGGLEDQLSVRWVSPPALKDFLFQAKYQIRYRVEDSVDWKVVDDVSNQTSCRLAGLKPGTVYFVQVRCNPFGIYGSKKAGIWSEWSHPTAASTPRSERPGPGGGACEPRGGEPSSGPVRRELKQFLGWLKKHAYCSNLSFRLYDQWRAWMQKSHKTRNQDEGILPSGRRGTARGPAR,mutated_sequence,1.0,422.0,UPI000000DA9E.a2m,UPI000000DA9E.npy,gnomAD +UPI00001AE937,UPI00001AE937.csv,MEGCDSPVVSGKDNGCGIPQHQQWTELNSTHLPDKPSSMEQSTGESHGPLDSLRAPFNERLAESTASAGPPSEPASKEVTCNECSASFASLQTYMEHHCPSARPPPPLREESASDTGEEGDEESDVENLAGEIVYQPDGSAYIVESLSQLTQGGGACGSGSGSGPLPSLFLNSLPGAGGKQGDPSCAAPVYPQIINTFHIASSFGKWFEGPDQAFPNTSALAGLSPVLHSFRVFDVRHKSNKDYLNSDGSAKSSCVSKDVPNNVDLSKFDGFVLYGKRKPILMCFLCKLSFGYVRSFVTHAVHDHRMTLSEDERKILSNKNISAIIQGIGKDKEPLVSFLEPKNKNFQHPLVSTANLIGPGHSFYGKFSGIRMEGEEALPAGSAAGPEQPQAGLLTPSTLLNLGGLTSSVLKTPITSVPLGPLASSPTKSSEGKDSGAAEGEKQEVGDGDCFSEKVEPAEEEAEEEEEEEEAEEEEEEEEEEEEEEEDEGCKGLFPSELDEELEDRPHEEPGAAAGSSSKKDLALSNQSISNSPLMPNVLQTLSRGTASTSSNSASSFVVFDGANRRNRLSFNSEGVRANVAEGGRRLDFADESANKDNATAPEPNESTEGDDGGFVPHHQHAGSLCELGVGECPSGSGVECPKCDTVLGSSRSLGGHMTMMHSRNSCKTLKCPKCNWHYKYQQTLEAHMKEKHPEPGGSCVYCKSGQPHPRLARGESYTCGYKPFRCEVCNYSTTTKGNLSIHMQSDKHLNNMQNLQNGGGEQVFSHTAGAAAAAVAAAAAAANISSSCGAPSPTKPKTKPTWRCEVCDYETNVARNLRIHMTSEKHMHNMMLLQQNMTQIQHNRHLGLGSLPSPAEAELYQYYLAQNMNLPNLKMDSAASDAQFMMSGFQLDPAGPMAAMTPALVGGEIPLDMRLGGGQLVSEELMNLGESFIQTNDPSLKLFQCAVCNKFTTDNLDMLGLHMNVERSLSEDEWKAVMGDSYQCKLCRYNTQLKANFQLHCKTDKHVQKYQLVAHIKEGGKANEWRLKCVAIGNPVHLKCNACDYYTNSLEKLRLHTVNSRHEASLKLYKHLQQHESGVEGESCYYHCVLCNYSTKAKLNLIQHVRSMKHQRSESLRKLQRLQKGLPEEDEDLGQIFTIRRCPSTDPEEAIEDVEGPSETAADPEELAKDQEGGASSSQAEKELTDSPATSKRISFPGSSESPLSSKRPKTAEEIKPEQMYQCPYCKYSNADVNRLRVHAMTQHSVQPMLRCPLCQDMLNNKIHLQLHLTHLHSVAPDCVEKLIMTVTTPEMVMPSSMFLPAAVPDRDGNSNLEEAGKQPETSEDLGKNILPSASTEQSGDLKPSPADPGSVREDSGFICWKKGCNQVFKTSAALQTHFNEVHAKRPQLPVSDRHVYKYRCNQCSLAFKTIEKLQLHSQYHVIRAATMCCLCQRSFRTFQALKKHLETSHLELSEADIQQLYGGLLANGDLLAMGDPTLAEDHTIIVEEDKEEESDLEDKQSPTGSDSGSVQEDSGSEPKRALPFRKGPNFTMEKFLDPSRPYKCTVCKESFTQKNILLVHYNSVSHLHKLKRALQESATGQPEPTSSPDNKPFKCNTCNVAYSQSSTLEIHMRSVLHQTKARAAKLEAASGSSNGTGNSSSISLSSSTPSPVSTSGSNTFTTSNPSSAGIAPSSNLLSQVPTESVGMPPLGNPIGANIASPSEPKEANRKKLADMIASRQQQQQQQQQQQQQQQQQQQAQTLAQAQAQVQAHLQQELQQQAALIQSQLFNPTLLPHFPMTTETLLQLQQQQHLLFPFYIPSAEFQLNPEVSLPVTSGALTLTGTGPGLLEDLKAQVQVPQQSHQQILPQQQQNQLSIAQSHSALLQPSQHPEKKNKLVIKEKEKESQRERDSAEGGEGNTGPKETLPDALKAKEKKELAPGGGSEPSMLPPRIASDARGNATKALLENFGFELVIQYNENKQKVQKKNGKTDQGENLEKLECDSCGKLFSNILILKSHQEHVHQNYFPFKQLERFAKQYRDHYDKLYPLRPQTPEPPPPPPPPPPPPLPAAPPQPASTPAIPASAPPITSPTIAPAQPSVPLTQLSMPMELPIFSPLMMQTMPLQTLPAQLPPQLGPVEPLPADLAQLYQHQLNPTLLQQQNKRPRTRITDDQLRVLRQYFDINNSPSEEQIKEMADKSGLPQKVIKHWFRNTLFKERQRNKDSPYNFSNPPITSLEELKIDSRPPSPEPPKQEYWGSKRSSRTRFTDYQLRVLQDFFDANAYPKDDEFEQLSNLLNLPTRVIVVWFQNARQKARKNYENQGEGKDGERRELTNDRYIRTSNLNYQCKKCSLVFQRIFDLIKHQKKLCYKDEDEEGQDDSQNEDSMDAMEILTPTSSSCSTPMPSQAYSAPAPSANNTASSAFLQLTAEAEELATFNSKTEAGDEKPKLAEAPSAQPNQTQEKQGQPKPELQQQEQPEQKTNTPQQKLPQLVSLPSLPQPPPQAPPPQCPLPQSSPSPSQLSHLPLKPLHTSTPQQLANLPPQLIPYQCDQCKLAFPSFEHWQEHQQLHFLSAQNQFIHPQFLDRSLDMPFMLFDPSNPLLASQLLSGAIPQIPASSATSPSTPTSTMNTLKRKLEEKASASPGENDSGTGGEEPQRDKRLRTTITPEQLEILYQKYLLDSNPTRKMLDHIAHEVGLKKRVVQVWFQNTRARERKGQFRAVGPAQAHRRCPFCRALFKAKTALEAHIRSRHWHEAKRAGYNLTLSAMLLDCDGGLQMKGDIFDGTSFSHLPPSSSDGQGVPLSPVSKTMELSPRTLLSPSSIKVEGIEDFESPSMSSVNLNFDQTKLDNDDCSSVNTAITDTTTGDEGNADNDSATGIATETKSSSAPNEGLTKAAMMAMSEYEDRLSSGLVSPAPSFYSKEYDNEGTVDYSETSSLADPCSPSPGASGSAGKSGDSGDRPGQKRFRTQMTNLQLKVLKSCFNDYRTPTMLECEVLGNDIGLPKRVVQVWFQNARAKEKKSKLSMAKHFGINQTSYEGPKTECTLCGIKYSARLSVRDHIFSQQHISKVKDTIGSQLDKEKEYFDPATVRQLMAQQELDRIKKANEVLGLAAQQQGMFDNTPLQALNLPTAYPALQGIPPVLLPGLNSPSLPGFTPSNTALTSPKPNLMGLPSTTVPSPGLPTSGLPNKPSSASLSSPTPAQATMAMGPQQPPQQQQQQQQPQVQQPPPPPAAQPPPTPQLPLQQQQQRKDKDSEKVKEKEKAHKGKGEPLPVPKKEKGEAPTATAATISAPLPTMEYAVDPAQLQALQAALTSDPTALLTSQFLPYFVPGFSPYYAPQIPGALQSGYLQPMYGMEGLFPYSPALSQALMGLSPGSLLQQYQQYQQSLQEAIQQQQQRQLQQQQQQKVQQQQPKASQTPVPPGAPSPDKDPAKESPKPEEQKNTPREVSPLLPKLPEEPEAESKSADSLYDPFIVPKVQYKLVCRKCQAGFSDEEAARSHLKSLCFFGQSVVNLQEMVLHVPTGGGGGGSGGGGGGGGGGGGGGSYHCLACESALCGEEALSQHLESALHKHRTITRAARNAKEHPSLLPHSACFPDPSTASTSQSAAHSNDSPPPPSAAAPSSASPHASRKSWPQVVSRASAAKPPSFPPLSSSSTVTSSSCSTSGVQPSMPTDDYSEESDTDLSQKSDGPASPVEGPKDPSCPKDSGLTSVGTDTFRL,mutated_sequence,1.0,3703.0,UPI00001AE937.a2m,UPI00001AE937.npy,gnomAD +UPI00001285F3,UPI00001285F3.csv,MQQTRTEAVAGAFSRCLGFCGMRLGLLLLARHWCIAGVFPQKFDGDSAYVGMSDGNPELLSTSQTYNGQSENNEDYEIPPITPPNLPEPSLLHLGDHEASYHSLCHGLTPNGLLPAYSYQAMDLPAIMVSNMLAQDSHLLSGQLPTIQEMVHSEVAAYDSGRPGPLLGRPAMLASHMSALSQSQLISQMGIRSSIAHSSPSPPGSKSATPSPSSSTQEEESEVHFKISGEKRPSADPGKKAKNPKKKKKKDPNEPQKPVSAYALFFRDTQAAIKGQNPSATFGDVSKIVASMWDSLGEEQKQSSPDQGETKSTQANPPAKMLPPKQPMYAMPGLASFLTPSDLQAFRSGASPASLARTLGSKSLLPGLSASPPPPPSFPLSPTLHQQLSLPPHAQGALLSPPVSMSPAPQPPVLPTPMALQVQLAMSPSPPGPQDFPHISEFPSSSGSCSPGPSNPTSSGDWDSSYPSGECGISTCSLLPRDKSLYLT,mutated_sequence,1.0,488.0,UPI00001285F3.a2m,UPI00001285F3.npy,gnomAD +UPI00001D822A,UPI00001D822A.csv,MQTLGSFFGSLPGFSSARNLVANAHSSARARPAADPTGAPAAEAAQPQAQVAAHPEQTAPWTEKELQPSEKQMVSGAKDLVCSKMSRAKDAVSSGVASVVDVAKGVVQGGLDTTRSALTGTKEVVSSGVTGAMDMAKGAVQGGLDTSKAVLTGTKDTVSTGLTGAVNVAKGTVQAGVDTTKTVLTGTKDTVTTGVMGAVNLAKGTVQTGVETSKAVLTGTKDAVSTGLTGAVNVARGSIQTGVDTSKTVLTGTKDTVCSGVTGAMNVAKGTIQTGVDTSKTVLTGTKDTVCSGVTGAMNVAKGTIQTGVDTSKTVLTGTKDTVCSGVTGAMNVAKGTIQTGVDTTKTVLTGTKNTVCSGVTGAVNLAKEAIQGGLDTTKSMVMGTKDTMSTGLTGAANVAKGAMQTGLNTTQNIATGTKDTVCSGVTGAMNLARGTIQTGVDTTKIVLTGTKDTVCSGVTGAANVAKGAVQGGLDTTKSVLTGTKDAVSTGLTGAVNVAKGTVQTGVDTTKTVLTGTKDTVCSGVTSAVNVAKGAVQGGLDTTKSVVIGTKDTMSTGLTGAANVAKGAVQTGVDTAKTVLTGTKDTVTTGLVGAVNVAKGTVQTGMDTTKTVLTGTKDTIYSGVTSAVNVAKGAVQTGLKTTQNIATGTKNTFGSGVTSAVNVAKGAAQTGVDTAKTVLTGTKDTVTTGLMGAVNVAKGTVQTSVDTTKTVLTGTKDTVCSGVTGAANVAKGAIQGGLDTTKSVLTGTKDAVSTGLTGAVKLAKGTVQTGMDTTKTVLTGTKDAVCSGVTGAANVAKGAVQMGVDTAKTVLTGTKDTVCSGVTGAANVAKGAVQTGLKTTQNIATGTKNTLGSGVTGAAKVAKGAVQGGLDTTKSVLTGTKDAVSTGLTGAVNLAKGTVQTGVDTSKTVLTGTKDTVCSGVTGAVNVAKGTVQTGVDTAKTVLSGAKDAVTTGVTGAVNVAKGTVQTGVDASKAVLMGTKDTVFSGVTGAMSMAKGAVQGGLDTTKTVLTGTKDAVSAGLMGSGNVATGATHTGLSTFQNWLPSTPATSWGGLTSSRTTDNGGEQTALSPQEAPFSGISTPPDVLSVGPEPAWEAAATTKGLATDVATFTQGAAPGREDTGLLATTHGPEEAPRLAMLQNELEGLGDIFHPMNAEEQAQLAASQPGPKVLSAEQGSYFVRLGDLGPSFRQRAFEHAVSHLQHGQFQARDTLAQLQDCFRLIEKAQQAPEGQPRLDQGSGASAEDAAVQEERDAGVLSRVCGLLRQLHTAYSGLVSSLQGLPAELQQPVGRARHSLCELYGIVASAGSVEELPAERLVQSREGVHQAWQGLEQLLEGLQHNPPLSWLVGPFALPAGGQ,mutated_sequence,1.0,1357.0,UPI00001D822A.a2m,UPI00001D822A.npy,gnomAD +UPI0000052952,UPI0000052952.csv,MGDQQLYKTNHVAHGSENLFYQQPPLGVHSGLNHNYGNAVTGGGMDAPQASPISPHFPQDTRDGLGLPVGSKNLGQMDTSRQGGWGSHAGPGNHVQLRGNLANSNMMWGAPAQAEPTDGYQYTYSQASEIRTQKLTSGVLHKLDSFTQVFANQNLRIQVNNMAQVLHTQSAVMDGAPDSALRQLLSQKPMEPPAPAIPSRYQQVPQQPHPGFTGGLSKPALQVGQHPTQGHLYYDYQQPLAQVPVQGGQPLQAPQMLSQHMQQMQQHQYYPPQQQQQAGQQRISMQEIQTQPQQIRPSQPQPPPQQQQPQQLQLQQRQGSMQIPQYYQPQPMMQHLQEQQQQQMHLQPPSYHRDPHQYTPEQAHTVQLIPLGSMSQYYYQEPQQPYSHPLYQQSHLSQHQQREDSQLKTYSSDRQAQAMLSSHGDLGPPDTGMGDPASSDLTRVSSTLPHRPLLSPSGIHLNNMGPQHQQLSPSAMWPQMHLPDGRAQPGSPESSGQPKGAFGEQFDAKNKLTCSICLKEFKNLPALNGHMRSHGGMRASPNLKQEEGEKVLPPQPQPPLPPPPPPPPPPQLPPEAESLTPMVMPVSVPVKLLPPKPSSQGFTNSTVAAPSARDKPASSMSDDEMPVLEIPRKHQPSVPKAEEPLKTVQEKKKFRHRPEPLFIPPPPSYNPNPAASYSGATLYQSQLRSPRVLGDHLLLDPTHELPPYTPPPMLSPVRQGSGLFSNVLISGHGPGAHPQLPLTPLTPTPRVLLCRSNSIDGSNVTVTPGPGEQTVDVEPRINIGLRFQAEIPELQDISALAQDTHKATLVWKPWPELENHDLQQRVENLLNLCCSSALPGGGTNSEFALHSLFEAKGDVMVALEMLLLRKPVRLKCHPLANYHYAGSDKWTSLERKLFNKALATYSKDFIFVQKMVKSKTVAQCVEYYYTWKKIMRLGRKHRTRLAEIIDDCVTSEEEEELEEEEEEDPEEDRKSTKEEESEVPKSPEPPPVPVLAPTEGPPLQALGQPSGSFICEMPNCGAVFSSRQALNGHARIHGGTNQVTKARGAIPSGKQKPGGTQSGYCSVKSSPSHSTTSGETDPTTIFPCKECGKVFFKIKSRNAHMKTHRQQEEQQRQKAQKAAFAAEMAATIERTTGPVGAPGLLPLDQLSLIKPIKDVDILDDDVVQQLGGVMEEAEVVDTDLLLDDQDSVLLQGDAEL,mutated_sequence,1.0,1200.0,UPI0000052952.a2m,UPI0000052952.npy,gnomAD +UPI00004A3B76,UPI00004A3B76.csv,MGNWVVNHWFSVLFLVVWLGLNVFLFVDAFLKYEKADKYYYTRKILGSTLACARASALCLNFNSTLILLPVCRNLLSFLRGTCSFCSRTLRKQLDHNLTFHKLVAYMICLHTAIHIIAHLFNFDCYSRSRQATDGSLASILSSLSHDEKKGGSWLNPIHPHITPTVYMFTVTFDMVLSSVNSNLFLFLLIKK,mutated_sequence,1.0,192.0,UPI00004A3B76.a2m,UPI00004A3B76.npy,gnomAD +UPI0000164A41,UPI0000164A41.csv,MSEASSEDLVPPLEAGAAPYREEEEAAKKKKEKKKKSKGLANVFCVFTKGKKKKGQPSSAEPEDAAGSRQGLDGPPPTVEELKAALERGQLEAARPLLALERELAAAAAAGGVSEEELVRRQSKVEALYELLRDQVLGVLRRPLEAPPERLRQALAVVAEQEREDRQAAAAGPGTSGLAATRPRRWLQLWRRGVAEAAEERMGQRPAAGAEVPESVFLHLGRTMKEDLEAVVERLKPLFPAEFGVVAAYAESYHQHFAAHLAAVAQFELCERDTYMLLLWVQNLYPNDIINSPKLVGELQGMGLGSLLPPRQIRLLEATFLSSEAANVRELMDRALELEARRWAEDVPPQRLDGHCHSELAIDIIQITSQAQAKAESITLDLGSQIKRVLLVELPAFLRSYQRAFNEFLERGKQLTNYRANVIANINNCLSFRMSMEQNWQVPQDTLSLLLGPLGELKSHGFDTLLQNLHEDLKPLFKRFTHTRWAAPVETLENIIATVDTRLPEFSELQGCFREELMEALHLHLVKEYIIQLSKGRLVLKTAEQQQQLAGYILANADTIQHFCTQHGSPATWLQPALPTLAEIIRLQDPSAIKIEVATYATCYPDFSKGHLSAILAIKGNLSNSEVKRIRSILDVSMGAQEPSRPLFSLIKVG,mutated_sequence,1.0,654.0,UPI0000164A41.a2m,UPI0000164A41.npy,gnomAD +UPI0000D62427,UPI0000D62427.csv,MEPSRALLGCLASAAAAAPPGEDGAGAGAEEEEEEEEEAAAAVGPGELGCDAPLPYWTAVFEYEAAGEDELTLRLGDVVEVLSKDSQVSGDEGWWTGQLNQRVGIFPSNYVTPRSAFSSRCQPGGEDPSCYPPIQLLEIDFAELTLEEIIGIGGFGKVYRAFWIGDEVAVKAARHDPDEDISQTIENVRQEAKLFAMLKHPNIIALRGVCLKEPNLCLVMEFARGGPLNRVLSGKRIPPDILVNWAVQIARGMNYLHDEAIVPIIHRDLKSSNILILQKVENGDLSNKILKITDFGLAREWHRTTKMSAAGTYAWMAPEVIRASMFSKGSDVWSYGVLLWELLTGEVPFRGIDGLAVAYGVAMNKLALPIPSTCPEPFAKLMEDCWNPDPHSRPSFTNILDQLTTIEESGFFEMPKDSFHCLQDNWKHEIQEMFDQLRAKEKELRTWEEELTRAALQQKNQEELLRRREQELAEREIDILERELNIIIHQLCQEKPRVKKRKGKFRKSRLKLKDGNRISLPSDFQHKFTVQASPTMDKRKSLINSRSSPPASPTIIPRLRAIQLTPGESSKTWGRSSVVPKEEGEEEEKRAPKKKGRTWGPGTLGQKELASGDEGSPQRREKANGLSTPSESPHFHLGLKSLVDGYKQWSSSAPNLVKGPRSSPALPGFTSLMEMEDEDSEGPGSGESRLQHSPSQSYLCIPFPRGEDGDGPSSDGIHEEPTPVNSATSTPQLTPTNSLKRGGAHHRRCEVALLGCGAVLAATGLGFDLLEAGKCQLLPLEEPEPPAREEKKRREGLFQRSSRPRRSTSPPSRKLFKKEEPMLLLGDPSASLTLLSLSSISECNSTRSLLRSDSDEIVVYEMPVSPVEAPPLSPCTHNPLVNVRVERFKRDPNQSLTPTHVTLTTPSQPSSHRRTPSDGALKPETLLASRSPSSNGLSPSPGAGMLKTPSPSRDPGEFPRLPDPNVVFPPTPRRWNTQQDSTLERPKTLEFLPRPRPSANRQRLDPWWFVSPSHARSTSPANSSSTETPSNLDSCFASSSSTVEERPGLPALLPFQAGPLPPTERTLLDLDAEGQSQDSTVPLCRAELNTHRPAPYEIQQEFWS,mutated_sequence,1.0,1104.0,UPI0000D62427.a2m,UPI0000D62427.npy,gnomAD +UPI000059DA57,UPI000059DA57.csv,XTASSEPAPASAAKQEKPAEKPAETPVATSPTATDSTSGDSSRSNLFEDATSALVTGQSYENMVTEIMSMGYEREQVIAALRASFNNPDRAVEYLLMIIVKTGNKKKKQPLLGK,mutated_sequence,1.0,114.0,UPI000059DA57.a2m,UPI000059DA57.npy,gnomAD +UPI000012D193,UPI000012D193.csv,MKAVSPVRPSGRKAPSGCGGGELALRCLAEHGHSLGGSAAAAAAAAAARCKAAEAAADEPALCLQCDMNDCYSRLRRLVPTIPPNKKVSKVEILQHVIDYILDLQLALETHPALLRQPPPPAPPHHPAGTCPAAPPRTPLTALNTDPAGAVNKQGDSILCR,mutated_sequence,1.0,161.0,UPI000012D193.a2m,UPI000012D193.npy,gnomAD +UPI0000DA5AF5,UPI0000DA5AF5.csv,MMAKSALRENGTNSETFRQRFRRFHYQEVAGPREAFSQLWELCCRWLRPEVRTKEQIVELLVLEQFLTVLPGEIQNWVQEQCPENGEEAVTLVEDLEREPGRPRSSVTVSVKGQEVRLEKMTPPKSSQELLSVRQESVEPQPRGVPKKERARSPDLGPQEQMNPKEKLKPFQRSGLPFPKSGVVSRLEQGEPWIPDLLGSKEKELPSGSHIGDRRVHADLLPSKKDRRSWVEQDHWSFEDEKVAGVHWGYEETRTLLAILSQTEFYEALRNCHRNSQVYGAVAERLREYGFLRTLEQCRTKFKGLQKSYRKVKSGHPPETCPFFEEMEALMSAQVIALPSNGLEAAASHSGLVGSDAETEEPGQRGWQHEEGAEEAVAQESDSDDMDLEATPQDPNSAAPVVFRSPGGVHWGYEETKTYLAILSETQFYEALRNCHRNSQLYGAVAERLWEYGFLRTPEQCRTKFKSLQTSYRKVKNGQAPETCPFFEEMDALVSVRVAAPPNDGQEETASCPVQGTSEAEAQKQAEEADEATEEDSDDDEEDTEIPPGAVITRAPVLFQSPRGFEAGFENEDNSKRDISEEVQLHRTLLARSERKIPRYLHQGKGNESDCRSGRQWAKTSGEKRGKLTLPEKSLSEVLSQQRPCLGERPYKYLKYSKSFGPNSLLMHQVSHQVENPYKCADCGKSFSRSARLIRHRRIHTGEKPYKCLDCGKSFRDSSNFITHRRIHTGEKPYQCGECGKCFNQSSSLIIHQRTHTGEKPYQCEECGKSFNNSSHFSAHRRIHTGERPHVCPDCGKSFSKSSDLRAHHRTHTGEKPYGCHDCGKCFSKSSALNKHGEIHAREKLLTQSAPK,mutated_sequence,1.0,852.0,UPI0000DA5AF5.a2m,UPI0000DA5AF5.npy,gnomAD +UPI000000D72E,UPI000000D72E.csv,MACTGPSLPSAFDILGAAGQDKLLYLKHKLKTPRPGCQGQDLLHAMVLLKLGQETEARISLEALKADAVARLVARQWAGVDSTEDPEEPPDVSWAVARLYHLLAEEKLCPASLRDVAYQEAVRTLSSRDDHRLGELQDEARNRCGWDIAGDPGSIRTLQSNLGCLPPSSALPSGTRSLPRPIDGVSDWSQGCSLRSTGSPASLASNLEISQSPTMPFLSLHRSPHGPSKLCDDPQASLVPEPVPGGCQEPEEMSWPPSGEIASPPELPSSPPPGLPEVAPDATSTGLPDTPAAPETSTNYPVECTEGSAGPQSLPLPILEPVKNPCSVKDQTPLQLSVEDTTSPNTKPCPPTPTTPETSPPPPPPPPSSTPCSAHLTPSSLFPSSLESSSEQKFYNFVILHARADEHIALRVREKLEALGVPDGATFCEDFQVPGRGELSCLQDAIDHSAFIILLLTSNFDCRLSLHQVNQAMMSNLTRQGSPDCVIPFLPLESSPAQLSSDTASLLSGLVRLDEHSQIFARKVANTFKPHRLQARKAMWRKEQDTRALREQSQHLDGERMQAAALNAAYSAYLQSYLSYQAQMEQLQVAFGSHMSFGTGAPYGARMPFGGQVPLGAPPPFPTWPGCPQPPPLHAWQAGTPPPPSPQPAAFPQSLPFPQSPAFPTASPAPPQSPGLQPLIIHHAQMVQLGLNNHMWNQRGSQAPEDKTQEAE,mutated_sequence,1.0,712.0,UPI000000D72E.a2m,UPI000000D72E.npy,gnomAD +UPI000006E083,UPI000006E083.csv,MSRRKQGNPQHLSQRELITPEADHVEAAILEEDEGLEIEEPSGLGLMVGGPDPDLLTCGQCQMNFPLGDILVFIEHKRKQCGGSLGACYDKALDKDSPPPSSRSELRKVSEPVEIGIQVTPDEDDHLLSPTKGICPKQENIAGPCRPAQLPAVAPIAASSHPHSSVITSPLRALGALPPCLPLPCCSARPVSGDGTQGEGQTEAPFGCQCQLSGKDEPSSYICTTCKQPFNSAWFLLQHAQNTHGFRIYLEPGPASSSLTPRLTIPPPLGPEAVAQSPLMNFLGDSNPFNLLRMTGPILRDHPGFGEGRLPGTPPLFSPPPRHHLDPHRLSAEEMGLVAQHPSAFDRVMRLNPMAIDSPAMDFSRRLRELAGNSSTPPPVSPGRGNPMHRLLNPFQPSPKSPFLSTPPLPPMPPGGTPPPQPPAKSKSCEFCGKTFKFQSNLIVHRRSHTGEKPYKCQLCDHACSQASKLKRHMKTHMHKAGSLAGRSDDGLSAASSPEPGTSELAGEGLKAADGDFRHHESDPSLGHEPEEEDEEEEEEEEELLLENESRPESSFSMDSELSRNRENGGGGVPGVPGAGGGAAKALADEKALVLGKVMENVGLGALPQYGELLADKQKRGAFLKRAAGGGDAGDDDDAGGCGDAGAGGAVNGRGGGFAPGTEPFPGLFPRKPAPLPSPGLNSAAKRIKVEKDLELPPAALIPSENVYSQWLVGYAASRHFMKDPFLGFTDARQSPFATSSEHSSENGSLRFSTPPGDLLDGGLSGRSGTASGGSTPHLGGPGPGRPSSKEGRRSDTCEYCGKVFKNCSNLTVHRRSHTGERPYKCELCNYACAQSSKLTRHMKTHGQIGKEVYRCDICQMPFSVYSTLEKHMKKWHGEHLLTNDVKIEQAERS,mutated_sequence,1.0,894.0,UPI000006E083.a2m,UPI000006E083.npy,gnomAD +UPI000004980B,UPI000004980B.csv,MADGAPRPQLYRSVSFKLLERWSGGPGLREEDTDTPGLRRRASCRPTTAARGQPSRRVSKLASGPLAAPAQPRPLRSLSPSVRQLSRRFDAPRLDDGSAGTRDGGVLPAAAEEAAEGPARGAWPSVTEMRKLFGGPGSRRPSADSESPGTPSPDGAAWEPPARESRQPPTPPPRTCFPLAGLRSARPLTGPETEGRLRRPQQQQERAQRPADGLHSWHIFSQPQAGARASCSSSSIAASYPVSRSRAASSSEEEEEGPPQLPGAQSPAYHGGHSSGSDDDRDGEGGHRWGGRPGLRPGSSLLDQDCRPDSDGLNLSSMNSAGVSGSPEPPTSPRAPREEGLREWGSGSPPCVPGPQEGLRPMSDSVGGAFRVAKVSFPSYLASPAGSRGSSRYSSTETLKDDDLWSSRGSGGWGVYRSPSFGAGEGLLRSQARTRAKGPGGTSRALRDGGFEPEKSRQRKSLSNPDIASETLTLLSFLRSDLSELRVRKPGGSSGDRGSNPLDGRDSPSAGGPVGQLEPIPIPAPASPGTRPTLKDLTATLRRAKSFTCSEKPMARRLPRTSALKSSSSELLLTGPGAEEDPLPLIVQDQYVQEARQVFEKIQRMGAQQDDGSDAPPGSPDWAGDVTRGQRSQEELSGPESSLTDEGIGADPEPPVAAFCGLGTTGMWRPLSSSSAQTNHHGPGTEDSLGGWALVSPETPPTPGALRRRRKVPPSGSGGSELSNGEAGEAYRSLSDPIPQRHRAATSEEPTGFSVDSNLLGSLSPKTGLPATSAMDEGLTSGHSDWSVGSEESKGYQEVIQSIVQGPGTLGRVVDDRIAGKAPKKKSLSDPSRRGELAGPGFEGPGGEPIREVEPMLPPSSSEPILVEQRAEPEEPGATRSRAQSERALPEALPPPATAHRNFHLDPKLADILSPRLIRRGSKKRPARSSHQELRRDEGSQDQTGSLSRARPSSRHVRHASVPATFMPIVVPEPPTSVGPPVAVPEPIGFPTRAHPTLQAPSLEDVTKQYMLNLHSGEVPAPVPVDMPCLPLAAPPSAEAKPPEAARPADEPTPASKCCSKPQVDMRKHVAMTLLDTEQSYVESLRTLMQGYMQPLKQPENSVLCDPSLVDEIFDQIPELLEHHEQFLEQVRHCMQTWHAQQKVGALLVQSFSKDVLVNIYSAYIDNFLNAKDAVRVAKEARPAFLKFLEQSMRENKEKQALSDLMIKPVQRIPRYELLVKDLLKHTPEDHPDHPLLLEAQRNIKQVAERINKGVRSAEEAERHARVLQEIEAHIEGMEDLQAPLRRFLRQEMVIEVKAIGGKKDRSLFLFTDLIVCTTLKRKSGSLRRSSMSLYTAASVIDTASKYKMLWKLPLEDADIIKGASQATNRENIQKAISRLDEDLTTLGQMSKLSESLGFPHQSLDDALRDLSAAMHRDLSEKQALCYALSFPPTKLELCATRPEGTDSYIFEFPHPDARLGFEQAFDEAKRKLASSKSCLDPEFLKAIPIMKTRSGMQFSCAAPTLNSCPEPSPEVWVCNSDGYVGQVCLLSLRAEPDVEACIAVCSARILCIGAVPGLQPRCHREPPPSLRSPPETAPEPAGPELDVEAAADEEAATLAEPGPQPCLHISIAGSGLEMTPGLGEGDPRPELVPFDSDSDDESSPSPSGTLQSQASRSTISSSFGNEETPSSKEATAETTSSEEEQEPGFLPLSGSFGPGGPCGTSPMDGRALRRSSHGSFTRGSLEDLLSVDPEAYQSSVWLGTEDGCVHVYQSSDSIRDRRNSMKLQHAASVTCILYLNNQVFVSLANGELVVYQREAGHFWDPQNFKSVTLGTQGSPITKMVSVGGRLWCGCQNRVLVLSPDTLQLEHMFYVGQDSSRCVACMVDSSLGVWVTLKGSAHVCLYHPDTFEQLAEVDVTPPVHRMLAGSDAIIRQHKAACLRITALLVCEELLWVGTSAGVVLTMPTSPGTVSCPRAPLSPTGLGQGHTGHVRFLAAVQLPDGFNLLCPTPPPPPDTGPEKLPSLEHRDSPWHRGPAPARPKMLVISGGDGYEDFRLSSGGGSSSETVGRDDSTNHLLLWRV,mutated_sequence,1.0,2063.0,UPI000004980B.a2m,UPI000004980B.npy,gnomAD +UPI000013C68E,UPI000013C68E.csv,MSALCWGRGAAGLKRALRPCGRPGLPGKEGTAGGVCGPRRSSSASPQEQDQDRRKDWGHVELLEVLQARVRQLQAESVSEVVVNRVDVARLPECGSGDGSLQPPRKVQMGAKDATPVPCGRWAKILEKDKRTQQMRMQRLKAKLQMPFQSGEFKALTRRLQVEPRLLSKQMAGCLEDCTRQAPESPWEEQLARLLQEAPGKLSLDVEQAPSGQHSQAQLSGQQQRLLAFFKCCLLTDQLPLAHHLLVVHHGQRQKRKLLTLDMYNAVMLGWARQGAFKELVYVLFMVKDAGLTPDLLSYAAALQCMGRQDQDAGTIERCLEQMSQEGLKLQALFTAVLLSEEDRATVLKAVHKVKPTFSLPPQLPPPVNTSKLLRDVYAKDGRVSYPKLHLPLKTLQCLFEKQLHMELASRVCVVSVEKPTLPSKEVKHARKTLKTLRDQWEKALCRALRETKNRLEREVYEGRFSLYPFLCLLDEREVVRMLLQVLQALPAQGESFTTLARELSARTFSRHVVQRQRVSGQVQALQNHYRKYLCLLASDAEVPEPCLPRQYWEELGAPEALREQPWPLPVQMELGKLLAEMLVQATQMPCSLDKPHRSSRLVPVLYHVYSFRNVQQIGILKPHPAYVQLLEKAAEPTLTFEAVDVPMLCPPLPWTSPHSGAFLLSPTKLMRTVEGATQHQELLETCPPTALHGALDALTQLGNCAWRVNGRVLDLVLQLFQAKGCPQLGVPAPPSEAPQPPEAHLPHSAAPARKAELRRELAHCQKVAREMHSLRAEALYRLSLAQHLRDRVFWLPHNMDFRGRTYPCPPHFNHLGSDVARALLEFAQGRPLGPHGLDWLKIHLVNLTGLKKREPLRKRLAFAEEVMDDILDSADQPLTGRKWWMGAEEPWQTLACCMEVANAVRASDPAAYVSHLPVHQDGSCNGLQHYAALGRDSVGAASVNLEPSDVPQDVYSGVAAQVEVFRRQDAQRGMRVAQVLEGFITRKVVKQTVMTVVYGVTRYGGRLQIEKRLRELSDFPQEFVWEASHYLVRQVFKSLQEMFSGTRAIQHWLTESARLISHMGSVVEWVTPLGVPVIQPYRLDSKVKQIGGGIQSITYTHNGDISRKPNTRKQKNGFPPNFIHSLDSSHMMLTALHCYRKGLTFVSVHDCYWTHAADVSVMNQVCREQFVRLHSEPILQDLSRFLVKRFCSEPQKILEASQLKETLQAVPKPGAFDLEQVKRSTYFFS,mutated_sequence,1.0,1230.0,UPI000013C68E.a2m,UPI000013C68E.npy,gnomAD +UPI0000041E80,UPI0000041E80.csv,MERGNQTEVGNFLLLGFAEDSDMQLLLHGLFLSMYLVTIIGNLLIILTISSDSHLHTPMYFFLSNLSFADICFTSTTVPKMLVNIQTQSKMITFAGCLTQIFFFIAFGCLDNLLLTMTAYDRFVAICYPLHYTVIMNPRLCGLLVLGSWCISVMGSLLETLTILRLSFCTNMEIPHFFCDPSEVLKLACSDTFINNIVMYFVTIVLGVFPLCGILFSYSQIFSSVLRVSARGQHKAFSTCGSHLSVVSLFYGTGLGVYLSSAVTPPSRTSLAASVMYTMVTPMLNPFIYSLRNKDMKGSLGRLLLRATSLKEGTIAKLS,mutated_sequence,1.0,319.0,UPI0000041E80.a2m,UPI0000041E80.npy,gnomAD +UPI0000073D24,UPI0000073D24.csv,MAVVIRLQGLPIVAGTMDIRHFFSGLTIPDGGVHIVGGELGEAFIVFATDEDARLGMMRTGGTIKGSKVTLLLSSKTEMQNMIELSRRRFETANLDIPPANASRSGPPPSSGMSSRVNLPTTVSNFNNPSPSVVTATTSVHESNKNIQTFSTASVGTAPPNMGASFGSPTFSSTVPSTASPMNTVPPPPIPPIPAMPSLPPMPSIPPIPVPPPVPTLPPVPPVPPIPPVPSVPPMTPLPPMSGMPPLNPPPVAPLPAGMNGSGAPMNLNNNLNPMFLGPLNPVNPIQMNSQSSVKPLPINPDDLYVSVHGMPFSAMENDVRDFFHGLRVDAVHLLKDHVGRNNGNGLVKFLSPQDTFEALKRNRMLMIQRYVEVSPATERQWVAAGGHITFKQNMGPSGQTHPPPQTLPRSKSPSGQKRSRSRSPHEAGFCVYLKGLPFEAENKHVIDFFKKLDIVEDSIYIAYGPNGKATGEGFVEFRNEADYKAALCRHKQYMGNRFIQVHPITKKGMLEKIDMIRKRLQNFSYDQREMILNPEGDVNSAKVCAHITNIPFSITKMDVLQFLEGIPVDENAVHVLVDNNGQGLGQALVQFKNEDDARKSERLHRKKLNGREAFVHVVTLEDMREIEKNPPAQGKKGLKMPVPGNPAVPGMPNAGLPGVGLPSAGLPGAGLPSTGLPGSAITSAGLPGAGMPSAGIPSAGGEEHAFLTVGSKEANNGPPFNFPGNFGGSNAFGPPIPPPGLGGGAFGDARPGMPSVGNSGLPGLGLDVPGFGGGPNNLSGPSGFGGGPQNFGNGPGSLGGPPGFGSGPPGLGSAPGHLGGPPAFGPGPGPGPGPGPIHIGGPPGFASSSGKPGPTVIKVQNMPFTVSIDEILDFFYGYQVIPGSVCLKYNEKGMPTGEAMVAFESRDEATAAVIDLNDRPIGSRKVKLVLG,mutated_sequence,1.0,932.0,UPI0000073D24.a2m,UPI0000073D24.npy,gnomAD +UPI000012FE45,UPI000012FE45.csv,MSGLGENLDPLASDSRKRKLPCDTPGQGLTCSGEKRRREQESKYIEELAELISANLSDIDNFNVKPDKCAILKETVRQIRQIKEQGKTISNDDDVQKADVSSTGQGVIDKDSLGPLLLQALDGFLFVVNRDGNIVFVSENVTQYLQYKQEDLVNTSVYNILHEEDRKDFLKNLPKSTVNGVSWTNETQRQKSHTFNCRMLMKTPHDILEDINASPEMRQRYETMQCFALSQPRAMMEEGEDLQSCMICVARRITTGERTFPSNPESFITRHDLSGKVVNIDTNSLRSSMRPGFEDIIRRCIQRFFSLNDGQSWSQKRHYQEAYLNGHAETPVYRFSLADGTIVTAQTKSKLFRNPVTNDRHGFVSTHFLQREQNGYRPNPNPVGQGIRPPMAGCNSSVGGMSMSPNQGLQMPSSRAYGLADPSTTGQMSGARYGGSSNIASLTPGPGMQSPSSYQNNNYGLNMSSPPHGSPGLAPNQQNIMISPRNRGSPKIASHQFSPVAGVHSPMASSGNTGNHSFSSSSLSALQAISEGVGTSLLSTLSSPGPKLDNSPNMNITQPSKVSNQDSKSPLGFYCDQNPVESSMCQSNSRDHLSDKESKESSVEGAENQRGPLESKGHKKLLQLLTCSSDDRGHSSLTNSPLDSSCKESSVSVTSPSGVSSSTSGGVSSTSNMHGSLLQEKHRILHKLLQNGNSPAEVAKITAEATGKDTSSITSCGDGNVVKQEQLSPKKKENNALLRYLLDRDDPSDALSKELQPQVEGVDNKMSQCTSSTIPSSSQEKDPKIKTETSEEGSGDLDNLDAILGDLTSSDFYNNSISSNGSHLGTKQQVFQGTNSLGLKSSQSVQSIRPPYNRAVSLDSPVSVGSSPPVKNISAFPMLPKQPMLGGNPRMMDSQENYGSSMGGPNRNVTVTQTPSSGDWGLPNSKAGRMEPMNSNSMGRPGGDYNTSLPRPALGGSIPTLPLRSNSIPGARPVLQQQQQMLQMRPGEIPMGMGANPYGQAAASNQLGSWPDGMLSMEQVSHGTQNRPLLRNSLDDLVGPPSNLEGQSDERALLDQLHTLLSNTDATGLEEIDRALGIPELVNQGQALEPKQDAFQGQEAAVMMDQKAGLYGQTYPAQGPPMQGGFHLQGQSPSFNSMMNQMNQQGNFPLQGMHPRANIMRPRTNTPKQLRMQLQQRLQGQQFLNQSRQALELKMENPTAGGAAVMRPMMQPQVSSQQGFLNAQMVAQRSRELLSHHFRQQRVAMMMQQQQQQQQQQQQQQQQQQQQQQQQQQQQQTQAFSPPPNVTASPSMDGLLAGPTMPQAPPQQFPYQPNYGMGQQPDPAFGRVSSPPNAMMSSRMGPSQNPMMQHPQAASIYQSSEMKGWPSGNLARNSSFSQQQFAHQGNPAVYSMVHMNGSSGHMGQMNMNPMPMSGMPMGPDQKYC,mutated_sequence,1.0,1424.0,UPI000012FE45.a2m,UPI000012FE45.npy,gnomAD +UPI00001A8223,UPI00001A8223.csv,MYLSLWVTINTVNLRNTLSGLRGAVTTVGMIKSDVPGTQEWLDERRRQGDLPLPTNSNPVLSLELCDPGQGPAPFQAVVVLIQPGRGLALRPPPSCLFPPDPTPSPPAGQIRVKPDRTGVVTDGVKHSMNPFCEIAVEEAVRLKEKKLVKEVIAVSCGPAQCQETIRTALAMGADRGIHVEVPPAEAERLGPLQVARVLAKLAEKEKVDLVLLGKQAIDDDCNQTGQMTAGFLDWPQGTFASQVTLEGDKLKVEREIDGGLETLRLKLPAVVTADLRLNEPRYATLPNIMKAKKKKIEVIKPGDLGVDLTSKLSVISVEDPPQRTAGVKVETTEDLVAKLKEIGRI,mutated_sequence,1.0,346.0,UPI00001A8223.a2m,UPI00001A8223.npy,gnomAD +UPI00001612A6,UPI00001612A6.csv,MPEPGPRMNGFSLGELCWLFCCPPCPSRIAAKLAFLPPEPTYTVLAPEQRGAGASAPAPAQATAAAAAAQPAPQQPEEGAGAGPGACSLHLSERADWQYSQRELDAVEVFFSRTARDNRLGCMFVRCAPSSRYTLLFSHGNAVDLGQMCSFYIGLGSRINCNIFSYDYSGYGVSSGKPSEKNLYADIDAAWQALRTRYGVSPENIILYGQSIGTVPTVDLASRYECAAVILHSPLMSGLRVAFPDTRKTYCFDAFPSIDKISKVTSPVLVIHGTEDEVIDFSHGLAMYERCPRAVEPLWVEGAGHNDIELYAQYLERLKQFISHELPNS,mutated_sequence,1.0,329.0,UPI00001612A6.a2m,UPI00001612A6.npy,gnomAD +UPI000006D9E4,UPI000006D9E4.csv,MEGSGEQPGPQPQHPGDHRIRDGDFVVLKREDVFKAVQVQRRKKVTFEKQWFYLDNVIGHSYGTAFEVTSGGSLQPKKKREEPTAETKEAGTDNRNIVDDGKSQKLTQDDIKALKDKGIKGEEIVQQLIENSTTFRDKTEFAQDKYIKKKKKKYEAIITVVKPSTRILSIMYYAREPGKINHMRYDTLAQMLTLGNIRAGNKMIVMETCAGLVLGAMMERMGGFGSIIQLYPGGGPVRAATACFGFPKSFLSGLYEFPLNKVDSLLHGTFSAKMLSSEPKDSALVEESNGTLEEKQASEQENEDSMAEAPESNHPEDQETMETISQDPEHKGPKERGSKKDYIQEKQRRQEEQRKRHLEAAALLSERNADGLIVASRFHPTPLLLSLLDFVAPSRPFVVYCQYKEPLLECYTKLRERGGVINLRLSETWLRNYQVLPDRSHPKLLMSGGGGYLLSGFTVAMDNLKADTSLKSNASTLESHETEEPAAKKRKCPESDS,mutated_sequence,1.0,497.0,UPI000006D9E4.a2m,UPI000006D9E4.npy,gnomAD +UPI0000047F3B,UPI0000047F3B.csv,MEWGYLLEVTSLLAALALLQRSSGAAAASAKELACQEITVPLCKGIGYNYTYMPNQFNHDTQDEAGLEVHQFWPLVEIQCSPDLKFFLCSMYTPICLEDYKKPLPPCRSVCERAKAGCAPLMRQYGFAWPDRMRCDRLPEQGNPDTLCMDYNRTDLTTAAPSPPRRLPPPPPGEQPPSGSGHGRPPGARPPHRGGGRGGGGGDAAAPPARGGGGGGKARPPGGGAAPCEPGCQCRAPMVSVSSERHPLYNRVKTGQIANCALPCHNPFFSQDERAFTVFWIGLWSVLCFVSTFATVSTFLIDMERFKYPERPIIFLSACYLFVSVGYLVRLVAGHEKVACSGGAPGAGGAGGAGGAAAGAGAAGAGAGGPGGRGEYEELGAVEQHVRYETTGPALCTVVFLLVYFFGMASSIWWVILSLTWFLAAGMKWGNEAIAGYSQYFHLAAWLVPSVKSIAVLALSSVDGDPVAGICYVGNQSLDNLRGFVLAPLVIYLFIGTMFLLAGFVSLFRIRSVIKQQDGPTKTHKLEKLMIRLGLFTVLYTVPAAVVVACLFYEQHNRPRWEATHNCPCLRDLQPDQARRPDYAVFMLKYFMCLVVGITSGVWVWSGKTLESWRSLCTRCCWASKGAAVGGGAGATAAGGGGGPGGGGGGGPGGGGGPGGGGGSLYSDVSTGLTWRSGTASSVSYPKQMPLSQV,mutated_sequence,1.0,694.0,UPI0000047F3B.a2m,UPI0000047F3B.npy,gnomAD +UPI000020B8EF,UPI000020B8EF.csv,MTLQELVHKAASCYMDRVAVCFDECNNQLPVYYTYKTVVNAASELSNFLLLHCDFQGIREIGLYCQPGIDLPSWILGILQVPAAYVPIEPDSPPSLSTHFMKKCNLKYILVEKKQINKFKSFHETLLNYDTFTVEHNDLVLFRLHWKNTEVNLMLNDGKEKYEKEKIKSISSEHVNEEKAEEHMDLRLKHCLAYVLHTSGTTGIPKIVRVPHKCIVPNIQHFRVLFDITQEDVLFLASPLTFDPSVVEIFLALSSGASLLIVPTSVKLLPSKLASVLFSHHRVTVLQATPTLLRRFGSQLIKSTVLSATTSLRVLALGGEAFPSLTVLRSWRGEGNKTQIFNVYGITEVSSWATIYRIPEKTLNSTLKCELPVQLGFPLLGTVVEVRDTNGFTIQEGSGQVFLGGRNRVCFLDDEVTVPLGTMRATGDFVTVKDGEIFFLGRKDSQIKRHGKRLNIELVQQVAEELQQVESCAVTWYNQEKLILFMVSKDASVKEYIFKELQKYLPSHAVPDELVLIDSLPFTSHGKIDVSELNKIYLNYINLKSENKLSGKEDLWEKLQYLWKSTLNLPEDLLRVPDESLFLNSGGDSLKSIRLLSEIEKLVGTSVPGLLEIILSSSILEIYNHILQTVVPDEDVTFRKSCATKRKLSDINQEEASGTSLHQKAIMTFTCHNEINAFVVLSRGSQILSLNSTRFLTKLGHCSSACPSDSVSQTNIQNLKGLNSPVLIGKSKDPSCVAKVSEEGKPAIGTQKMELHVRWRSDTGKCVDASPLVVIPTFDKSSTTVYIGSHSHRMKAVDFYSGKVKWEQILGDRIESSACVSKCGNFIVVGCYNGLVYVLKSNSGEKYWMFTTEDAVKSSATMDPTTGLIYIGSHDQHAYALDIYRKKCVWKSKCGGTVFSSPCLNLIPHHLYFATLGGLLLAVNPATGNVIWKHSCGKPLFSSPQCCSQYICIGCVDGNLLCFTHFGEQVWQFSTSGPIFSSPCTSPSEQKIFFGSHDCFIYCCNMKGHLQWKFETTSRVYATPFAFHNYNGSNEMLLAAASTDGKVWILESQSGQLQSVYELPGEVFSSPVVLESMLIIGCRDNYVYCLDLLGGNQK,mutated_sequence,1.0,1098.0,UPI000020B8EF.a2m,UPI000020B8EF.npy,gnomAD +UPI0000137078,UPI0000137078.csv,MRGHPSLLLLYMALTTCLDTSPSEETDQEVFLGPPEAQSFLSSHTRIPRANHWDLELLTPGNLERECLEERCSWEEAREYFEDNTLTERFWESYIYNGKGGRGRVDVASLAVGLTGGILLIVLAGLGAFWYLRWRQHRGQQPCPQEAGLISPLSPLNPLGPPTPLPPPPPPPPGLPTYEQALAASGVHDAPPPPYTSLRRPH,mutated_sequence,1.0,202.0,UPI0000137078.a2m,UPI0000137078.npy,gnomAD +UPI0000246C98,UPI0000246C98.csv,MSHQVKGLKEEARGGVKGRVKSGSPHTGDRLGRRSSSKRALKAEGTPGRRGAQRSQKERAGGSPSPGSPRRKQTGRRRHREELGEQERGEAERTCEGRRKRDERASFQERTAAPKREKEIPRREEKSKRQKKPRSSSLASSASGGESLSEEELAQILEQVEEKKKLIATMRSKPWPMAKKLTELREAQEFVEKYEGALGKGKGKQLYAYKMLMAKKWVKFKRDFDNFKTQCIPWEMKIKDIESHFGSSVASYFIFLRWMYGVNLVLFGLIFGLVIIPEVLMGMPYGSIPRKTVPRAEEEKAMDFSVLWDFEGYIKYSALFYGYYNNQRTIGWLRYRLPMAYFMVGVSVFGYSLIIVIRSMASNTQGSTGEGESDNFTFSFKMFTSWDYLIGNSETADNKYASITTSFKESIVDEQESNKEENIHLTRFLRVLANFLIICCLCGSGYLIYFVVKRSQQFSKMQNVSWYERNEVEIVMSLLGMFCPPLFETIAALENYHPRTGLKWQLGRIFALFLGNLYTFLLALMDDVHLKLANEETIKNITHWTLFNYYNSSGWNESVPRPPLHPADVPRGSCWETAVGIEFMRLTVSDMLVTYITILLGDFLRACFVRFMNYCWCWDLEAGFPSYAEFDISGNVLGLIFNQGMIWMGSFYAPGLVGINVLRLLTSMYFQCWAVMSSNVPHERVFKASRSNNFYMGLLLLVLFLSLLPVAYTIMSLPPSFDCGPFSGKNRMYDVLQETIENDFPTFLGKIFAFLANPGLIIPAILLMFLAIYYLNSVSKSLSRANAQLRKKIQVLREVEKSHKSVKGKATARDSEDTPKSSSKNATQLQLTKEETTPPSASQSQAMDKKAQGPGTSNSASRTTLPASGHLPISRPPGIGPDSGHAPSQTHPWRSASGKSAQRPPH,mutated_sequence,1.0,906.0,UPI0000246C98.a2m,UPI0000246C98.npy,gnomAD +UPI00001D82A6,UPI00001D82A6.csv,MHQTLCLNPESLKMSACSDFVEHIWKPGSCKNCFCLRSDHQLVAGPPQPRAGSLPPPPRLPPRPENCRLEDEGVNSSPYSKPTIAVKPTMMSSEASDVWTEANLSAEVSQVIWRRAPGKLPLPKQEDAPVVYLGSFRGVQKPAGPSTSPDGNSRCPPAYTMVGLHNLEPRGERNIAFHPVSFPEEKAVHKEKPSFPYQDRPSTQESFRQKLAAFAGTTSGCHQGPGPLRESLPSEDDSDQRCSPSGDSEGGEYCSILDCCPGSPVAKAASQTAGSRGRHGGRDCSPTCWEQGKCSGPAEQEKRGPSFPKECCSQGPTAHPSCLGPKKLSLTSEAAISSDGLSCGSGSGSGASSPFVPHLESDYCSLMKEPAPEKQQDPGCPGVTPSRCLGLTGEPQPPAHPREATQPEPIYAESTKRKKAAPVPSKSQAKIEHAAAAQGQGQVCTGNAWAQKAASGWGRDSPDPTPQVSATITVMAAHPEEDHRTIYLSSPDSAVGVQWPRGPVSQNSEVGEEETSAGQGLSSRESHAHSASESKPKERPAIPPKLSKSSPVGSPVSPSAGGPPVSPLADLSDGSSGGSSIGPQPPSQGPADPAPSCRTNGVAISDPSRCPQPAASSASEQRRPRFQAGTWSRQCRIEEEEEVEQELLSHSWGRETKNGPTDHSNSTTWHRLHPTDGSSGQNSKVGTGMSKSASFAFEFPKDRSGIETFSPPPPPPKSRHLLKMNKSSSDLEKVSQGSAESLSPSFRGVHVSFTTGSTDSLASDSRTCSDGGPSSELAHSPTNSGKKLFAPVPFPSGSTEDVSPSGPQQPPPLPQKKIVSRAASSPDGFFWTQGSPKPGTASPKLNLSHSETNVHDESHFSYSLSPGNRHHPVFSSSDPLEKAFKGSGHWLPAAGLAGNRGGCGSPGLQCKGAPSASSSQLSVSSQASTGSTQLQLHGLLSNISSKEGTYAKLGGLYTQSLARLVAKCEDLFMGGQKKELHFNENNWSLFKLTCNKPCCDSGDAIYYCATCSEDPGSTYAVKICKAPEPKTVSYCSPSVPVHFNIQQDCGHFVASVPSSMLSSPDAPKDPVPALPTHPPAQEQDCVVVITREVPHQTASDFVRDSAASHQAEPEAYERRVCFLLLQLCNGLEHLKEHGIIHRDLCLENLLLVHCTLQAGPGPAPAPAPAPAAAAPPCSSAAPPAGGTLSPAAGPASPEGPREKQLPRLIISNFLKAKQKPGGTPNLQQKKSQARLAPEIVSASQYRKFDEFQTGILIYELLHQPNPFEVRAQLRERDYRQEDLPPLPALSLYSPGLQQLAHLLLEADPIKRIRIGEAKRVLQCLLWGPRRELVQQPGTSEEALCGTLHNWIDMKRALMMMKFAEKAVDRRRGVELEDWLCCQYLASAEPGALLQSLKLLQLL,mutated_sequence,1.0,1402.0,UPI00001D82A6.a2m,UPI00001D82A6.npy,gnomAD +UPI0001533DAA,UPI0001533DAA.csv,MANCSQEELDEEFEQFMKELSDDSFENSDKTARQSKKEMKKKDTVPWWITEDDFKDDGLLGTNVSYLKTKKTSQPVMEIEEESAEKIQFLKSSGTSLLSTDSLETNELVVSELNHSSLGVGLDTLEEQEEKEQFFARLEKGLTSSIDYSRLNKELDSNDSTHFKALHSNQANAELTDDEHENESKHEELAENYSDDFEDEYVGAPLTTKDEEMPSKENSKSEKISVPKQEEEKTGMLANVVLLDSLDSVAEVNLDEQDKITPKPRCLPEMTENEMTGTGVSYGQSSSDVEALHQAYCHIAHSLGDEDKQKIESNTVEDIKSSVKGHPQENEENSKNISTMESDLPTVEELMKPIRIDSFGISGFDLQPVSSEKVAERKETEFFSSLPLKMNPNILSQDSQHVNLFFDKNDENVILQKTTNESMENSCPQVTEVTATEEHVDKMYLNILRKKITVNSSSLSQDDKINKTYRSQLSSEEEGAVMGKQVPYKKARSAPPLLKRKPQSGLYASVRSSGYGKPSSPLKMFSTLEKKTSEDIIKSKNLRSISTSNQPRKKEILSGTKLIKPAALDKPAHKTESCLSTRKKSENPTETDSCIQFQTDSLGYCGENKEKKLLMFKRVQEAEDKWRGAQALIEQIKATFSEKEKELENKLEELKKQQEKELFKLNQDNYILQAKLSSFEETNKKQRWLHFGEAADPVTGEKLKQIQKEIQEQETLLQGYQQENERLYNQVKDLQEQNKKNEERMFKENQSLFSEVASLKEQMHKSRFLSQVVEDSEPTRNQNFTDLLAELRMAQKEKDSLLEDIKRLKQDKQALEVDFEKMKKERDQAKDQIAYVTGEKLYEIKILEETHKQEISRLQKRLQWYAENQELLDKDALRLREANEEIEKLKLEIEKLKAESGNPSIRQKIRLKDKAADAKKIQDLERQVKEMEGILKRRYPNSLPALILAASAAGDTVDKNTVEFMEKRIKKLEADLEGKDEDAKKSLRTMEQQFQKMKIQYEQRLEQQEQLLACKLNQHDSPRIKALEKELDDIKEAHQITVRNLEAEIDVLKHQNAELDVKKNDKDDEDFQSIEFQVEQAHAKAKLVRLNEELAAKKREIQDLSKTVERLQKDRRMMLSNQNSKGREEMSAKRAKKDVLHSSKGNANSFPGTLDSKLYQPHTFTDSHVSEVLQENYRLKNELEGLISEKNELKMKSEAVMNQFENSMRRVKEDTAAHIASLKASHQREIEKLLCQNAVENSSSKVAELNRKIATQEVLIRHFQSQVNELQSKQESLVVSEVREEILQKEITKLLEELREAKENHTPEMKHFVGLEKKIKQMEMRHAQREQELQQIIQQTHQVVETEQNKEVEKWKRLAQLKNRELEKFRTELDSILDVLRELHRQGVVVPVAFADEMNAPEY,mutated_sequence,1.0,1403.0,UPI0001533DAA.a2m,UPI0001533DAA.npy,gnomAD +UPI00000398BB,UPI00000398BB.csv,MPIVDKLKEALKPGRKDSADDGELGKLLASSAKKVLLQKIEFEPASKSFSYQLEALKSKYVLLNPKTEGASRHKSGDDPPARRQGSEHTYESCGDGVPAPQKVLFPTERLSLRWERVFRVGAGLHNLGNTCFLNATIQCLTYTPPLANYLLSKEHARSCHQGSFCMLCVMQNHIVQAFANSGNAIKPVSFIRDLKKIARHFRFGNQEDAHEFLRYTIDAMQKACLNGCAKLDRQTQATTLVHQIFGGYLRSRVKCSVCKSVSDTYDPYLDVALEIRQAANIVRALELFVKADVLSGENAYMCAKCKKKVPASKRFTIHRTSNVLTLSLKRFANFSGGKITKDVGYPEFLNIRPYMSQNNGDPVMYGLYAVLVHSGYSCHAGHYYCYVKASNGQWYQMNDSLVHSSNVKVVLNQQAYVLFYLRIPGSKKSPEGLISRTGSSSLPGRPSVIPDHSKKNIGNGIISSPLTGKRQDSGTMKKPHTTEEIGVPISRNGSTLGLKSQNGCIPPKLPSGSPSPKLSQTPTHMPTILDDPGKKVKKPAPPQHFSPRTAQGLPGTSNSNSSRSGSQRQGSWDSRDVVLSTSPKLLATATANGHGLKGNDESAGLDRRGSSSSSPEHSASSDSTKAPQTPRSGAAHLCDSQETNCSTAGHSKTPPSGADSKTVKLKSPVLSNTTTEPASTMSPPPAKKLALSAKKASTLWRATGNDLRPPPPSPSSDLTHPMKTSHPVVASTWPVHRARAVSPAPQSSSRLQPPFSPHPTLLSSTPKPPGTSEPRSCSSISTALPQVNEDLVSLPHQLPEASEPPQSPSEKRKKTFVGEPQRLGSETRLPQHIREATAAPHGKRKRKKKKRPEDTAASALQEGQTQRQPGSPMYRREGQAQLPAVRRQEDGTQPQVNGQQVGCVTDGHHASSRKRRRKGAEGLGEEGGLHQDPLRHSCSPMGDGDPEAMEESPRKKKKKKRKQETQRAVEEDGHLKCPRSAKPQDAVVPESSSCAPSANGWCPGDRMGLSQAPPVSWNGERESDVVQELLKYSSDKAYGRKVLTWDGKMSAVSQDAIEDSRQARTETVVDDWDEEFDRGKEKKIKKFKREKRRNFNAFQKLQTRRNFWSVTHPAKAASLSYRR,mutated_sequence,1.0,1123.0,UPI00000398BB.a2m,UPI00000398BB.npy,gnomAD +UPI0001E8F37E,UPI0001E8F37E.csv,MLQWGLDHSPRPALQEFAPSATGLAQHTGCLVRRLPTAPFLNSVGIETHLLFLCLSLPDPRPFS,mutated_sequence,,,UPI0001E8F37E.a2m,UPI0001E8F37E.npy,gnomAD +UPI0000190820,UPI0000190820.csv,MDSTKEKCDSYKDDLLLRMGLNDNKAGMEGLDKEKINKIIMEATKGSRFYGNELKKEKQVNQRIENMMQQKAQITSQQLRKAQLQVDRFAMELEQSRNLSNTIVHIDMDAFYAAVEMRDNPELKDKPIAVGSMSMLSTSNYHARRFGVRAAMPGFIAKRLCPQLIIVPPNFDKYRAVSKEVKEILADYDPNFMAMSLDEAYLNITKHLEERQNWPEDKRRYFIKMGSSVENDNPGKEVNKLSEHERSISPLLFEESPSDVQPPGDPFQVNFEEQNNPQILQNSVVFGTSAQEVVKEIRFRIEQKTTLTASAGIAPNTMLAKVCSDKNKPNGQYQILPNRQAVMDFIKDLPIRKVSGIGKVTEKMLKALGIITCTELYQQRALLSLLFSETSWHYFLHISLGLGSTHLTRDGERKSMSVERTFSEINKAEEQYSLCQELCSELAQDLQKERLKVLYFDMVSLVFKFFNSKMLP,mutated_sequence,1.0,472.0,UPI0000190820.a2m,UPI0000190820.npy,gnomAD +UPI000013DE67,UPI000013DE67.csv,MENDPSRRRESISLTPVAKGLENMGADFLESLEEGQLPRSDLSPAEIRSSWSEAAPKPFSRWRNLQPALRARSFCREHMQLFRWIGTGLLCTGLSAFLLVACLLDFQRALALFVLTCVVLTFLGHRLLKRLLGPKLRRFLKPQGHPRLLLWFKRGLALAAFLGLVLWLSLDTSQRPEQLVSFAGICVFVALLFACSKHHCAVSWRAVSWGLGLQFVLGLLVIRTEPGFIAFEWLGEQIRIFLSYTKAGSSFVFGEALVKDVFAFQVLPIIVFFSCVISVLYHVGLMQWVILKIAWLMQVTMGTTATETLSVAGNIFVSQTEAPLLIRPYLADMTLSEVHVVMTGGYATIAGSLLGAYISFGIDATSLIAASVMAAPCALALSKLVYPEVEESKFRREEGVKLTYGDAQNLIEAASTGAAISVKVVANIAANLIAFLAVLDFINAALSWLGDMVDIQGLSFQLICSYILRPVAFLMGVAWEDCPVVAELLGIKLFLNEFVAYQDLSKYKQRRLAGAEEWVGDRKQWISVRAEVLTTFALCGFANFSSIGIMLGGLTSMVPQRKSDFSQIVLRALFTGACVSLVNACMAGILYMPRGAEVDCMSLLNTTLSSSSFEIYQCCREAFQSVNPEFSPEALDNCCRFYNHTICAQ,mutated_sequence,1.0,649.0,UPI000013DE67.a2m,UPI000013DE67.npy,gnomAD +UPI0000073FE5,UPI0000073FE5.csv,MTLRAAVFDLDGVLALPAVFGVLGRTEEALALPRGLLNDAFQKGGPEGATTRLMKGEITLSQWIPLMEENCRKCSETAKVCLPKNFSIKEIFDKAISARKINRPMLQAALMLRKKGFTTAILTNTWLDDRAERDGLAQLMCELKMHFDFLIESCQVGMVKPEPQIYKFLLDTLKASPSEVVFLDDIGANLKPARDLGMVTILVQDTDTALKELEKVTGIQLLNTPAPLPTSCNPSDMSHGYVTVKPRVRLHFVELGSGPAVCLCHGFPESWYSWRYQIPALAQAGYRVLAMDMKGYGESSAPPEIEEYCMEVLCKEMVTFLDKLGLSQAVFIGHDWGGMLVWYMALFYPERVRAVASLNTPFIPANPNMSPLESIKANPVFDYQLYFQEPGVAEAELEQNLSRTFKSLFRASDESVLSMHKVCEAGGLFVNSPEEPSLSRMVTEEEIQFYVQQFKKSGFRGPLNWYRNMERNWKWACKSLGRKILIPALMVTAEKDFVLVPQMSQHMEDWIPHLKRGHIEDCGHWTQMDKPTEVNQILIKWLDSDARNPPVVSKM,mutated_sequence,1.0,555.0,UPI0000073FE5.a2m,UPI0000073FE5.npy,gnomAD +UPI0000073BBA,UPI0000073BBA.csv,MAAYPESCVDTTVLDFVADLSLASPRRPLLCDFAPGVSLGDPALALREGRPRRMARFEEGDPEEEECEVDQGDGEEEEEEERGRGVSLLGRPKRKRVITYAQRQAANIRERKRMFNLNEAFDQLRRKVPTFAYEKRLSRIETLRLAIVYISFMTELLESCEKKESG,mutated_sequence,1.0,166.0,UPI0000073BBA.a2m,UPI0000073BBA.npy,gnomAD +UPI000041512B,UPI000041512B.csv,MQCLAAALKDETNMSGGGEQADILPANYVVKDRWKVLKKIGGGGFGEIYEAMDLLTRENVALKVESAQQPKQVLKMEVAVLKKLQGKDHVCRFIGCGRNEKFNYVVMQLQGRNLADLRRSQPRGTFTLSTTLRLGKQILESIEAIHSVGFLHRDIKPSNFAMGRLPSTYRKCYMLDFGLARQYTNTTGDVRPPRNVAGFRGTVRYASVNAHKNREMGRHDDLWSLFYMLVEFAVGQLPWRKIKDKEQVGMIKEKYEHRMLLKHMPSEFHLFLDHIASLDYFTKPDYQLIMSVFENSMKERGIAENEAFDWEKAGTDALLSTSTSTPPQQNTRQTAAMFGVVNVTPVPGDLLRENTEDVLQGEHLSDQENAPPILPGRPSEGLGPSPHLVPHPGGPEAEVWEETDVNRNKLRINIGKSPCVEEEQSRGMGVPSSPVRAPPDSPTTPVRSLRYRRVNSPESERLSTADGRVELPERRSRMDLPGSPSRQACSSQPAQMLSVDTGHADRQASGRMDVSASVEQEALSNAFRSVPLAEEEDFDSKEWVIIDKETELKDFPPGAEPSTSGTTDEEPEELRPLPEEGEERRRLGAEPTVRPRGRSMQALAEEDLQHLPPQPLPPQLSQGDGRSETSQPPTPGSPSHSPLHSGPRPRRRESDPTGPQRQVFSVAPPFEVNGLPRAVPLSLPYQDFKRDLSDYRERARLLNRVRRVGFSHMLLTTPQVPLAPVQPQANGKEEEEEEEEDEEEEEEDEEEEEEEEEEEEEEEEEEEEEEEAAAAVALGEVLGPRSGSSSEGSERSTDRSQEGAPSTLLADDQKESRGRASMADGDLEPEEGSKTLVLVSPGDMKKSPVTAELAPDPDLGTLAALTPQHERPQPTGSQLDVSEPGTLSSVLKSEPKPPGPGAGLGAGTVTTGVGGVAVTSSPFTKVERTFVHIAEKTHLNVMSSGGQALRSEEFSAGGELGLELASDGGAVEEGARAPLENGLALSGLNGAEIEGSALSGAPRETPSEMATNSLPNGPALADGPAPVSPLEPSPEKVATISPRRHAMPGSRPRSRIPVLLSEEDTGSEPSGSLSAKERWSKRARPQQDLARLVMEKRQGRLLLRLASGASSSSSEEQRRASETLSGTGSEEDTPASEPAAALPRKSGRAAATRSRIPRPIGLRMPMPVAAQQPASRSHGAAPALDTAITSRLQLQTPPGSATAADLRPKQPPGRGLGPGRAQAGARPPAPRSPRLPASTSAARNASASPRSQSLSRRESPSPSHQARPGVPPPRGVPPARAQPDGTPSPGGSKKGPRGKLQAQRATTKGRAGGAEGRAGAR,mutated_sequence,1.0,1321.0,UPI000041512B.a2m,UPI000041512B.npy,gnomAD +UPI000013EA1F,UPI000013EA1F.csv,MSSEQKSQHCKPEEGVEAQEEALGLVGAQAPTTEEQEAAVSSSSPLVPGTLEEVPAAESAGPPQSPQGASALPTTISFTCWRQPNEGSSSQEEEGPSTSPDAESLFREALSNKVDELAHFLLRKYRAKELVTKAEMLERVIKNYKRCFPVIFGKASESLKMIFGIDVKEVDPASNTYTLVTCLGLSYDGLLGNNQIFPKTGLLIIVLGTIAMEGDSASEEEIWEELGVMGVYDGREHTVYGEPRKLLTQDWVQENYLEYRQVPGSNPARYEFLWGPRALAETSYVKVLEHVVRVNARVRIAYPSLREAALLEEEEGV,mutated_sequence,1.0,317.0,UPI000013EA1F.a2m,UPI000013EA1F.npy,gnomAD +UPI0000072D52,UPI0000072D52.csv,MAGYATTPSPMQTLQEEAVCAICLDYFKDPVSISCGHNFCRGCVTQLWSKEDEEDQNEEEDEWEEEEDEEAVGAMDGWDGSIREVLYRGNADEELFQDQDDDELWLGDSGITNWDNVDYMWDEEEEEEEEDQDYYLGGLRPDLRIDVYREEEILEAYDEDEDEELYPDIHPPPSLPLPGQFTCPQCRKSFTRRSFRPNLQLANMVQIIRQMCPTPYRGNRSNDQGMCFKHQEALKLFCEVDKEAICVVCRESRSHKQHSVLPLEEVVQEYQEIKLETTLVGILQIEQESIHSKAYNQ,mutated_sequence,1.0,297.0,UPI0000072D52.a2m,UPI0000072D52.npy,gnomAD +UPI00001FEDD8,UPI00001FEDD8.csv,MASPDRSKRKILKAKKTMPLSCRKQVEMLNKSRNVEALKTAIGSNVPSGNQSFSPSVITRTTEITKCSPSENGASSLDSNKNSISEKSKVFSQNCIKPVEEIVHSETKLEQVVCSYQKPSRTTESPSRVFTEEAKDSLNTSENDSEHQTNVTRSLFEHEGACSLKSSCCPPSVLSGVVQMPESTVTSTVGDKKTDQMVFHLETNSNSESHDKRQSDNILCSEDSGFVPVEKTPNLVNSVTSNNCADDILKTDECSRTSISNCESADSTWQSSLDTNNNSHYQKKRMFSENEENVKRMKTSEQINENICVSLERQTAFLEQVRHLIQQEIYSINYELFDKKLKELNQRIGKTECRNKHEGIADKLLAKIAKLQRRIKTVLLFQRNCLKPNMLSSNGASKVANSEAMILDKNLESVNSPIEKSSVNYEPSNPSEKGSKKINLSSDQNKSVSESNNDDVMLISVESPNLTTPITSNPTDTRKITSGNSSNSPNAEVMAVQKKLDSIIDLTKEGLSNCNTESPVSPLESHSKAASNSKETTPLAQNAVQVPESFEHLPPLPEPPAPLPELVDKTRDTLPPQKPELKVKRVFRPNGIALTWNITKINPKCAPVESYHLFLCHENSNNKLIWKKIGEIKALPLPMACTLSQFLASNRYYFTVQSKDIFGRYGPFCDIKSIPGFSENLT,mutated_sequence,1.0,682.0,UPI00001FEDD8.a2m,UPI00001FEDD8.npy,gnomAD +UPI0000071E9E,UPI0000071E9E.csv,MVGVLAMAAAAAPPPVKDCEIEPCKKRKKDDDTSTCKTITKYLSPLGKTRDRVFAPPKPSNILDYFRKTSPTNEKTQLGKECKIKSPESVPVDSNKDCTTPLEMFSNVEFKKKRKRVNLSHQLNNIKTENEAPIEISSDDSKEDYSLNNDFVESSTSVLRYKKQVEVLAENIQDTKSQPNTMTSLQNSKKVNPKQGTTKNDFKKLRKRKCRDVVDLSESLPLAEELNLLKKDGKDTKQMENTTSHANSRDNVTEAAQLNDSIITVSYEEFLKSHKENKVEEIPDSTMSICVPSETVDEIVKSGYISESENSEISQQVRFKTVTVLAQVHPIPPKKTGKIPRIFLKQKQFEMENSLSDPENEQTVQKRKSNVVIQEEELELAVLEAGSSEAVKPKCTLEERQQFMKAFRQPASDALKNGVKKSSDKQKDLNEKCLYEVGRDDNSKKIMENSGIQMVSKNGNLQLHTDKGSFLKEKNKKLKKKNKKTLDTGAIPGKNREGNTQKKETTFFLKEKQYQNRMSLRQRKTEFFKSSTLFNNESLVYEDIANDDLLKVSSLCNNNKLSRKTSIPVKDIKLTQSKAESEASLLNVSTPKSTRRSGRISSTPTTETIRGIDSDDVQDNSQLKASTQKAANLSEKHSLYTAELITVPFDSESPIRMKFTRISTPKKSKKKSNKRSEKSEATDGGFTSQIRKASNTSKNISKAKQLIEKAKALHISRSKVTEEIAIPLRRSSRHQTLPERKKLSETEDSVIIIDSSPTALKHPEKNQKKLQCLNDVLGKKLNTSTKNVPGKMKVAPLFLVRKAQKAADPVPSFDESSQDTSEKSQDCDVQCKAKRDFLMSGLPDLLKRQIAKKAAALDVYNAVSTSFQRVVHVQQKDDGCCLWHLKPPSCPLLTKFKELNTKVIDLSKCGIALGEFSTLNSKLKSGNSAAVFMRTRKEFTEEVRNLLLEEIRWSNPEFSLKKYFPLLLKKQIEHQVLSSECHSKQELEADVSHKETKRKLVEAENSKSKRKKPNEYSKNLEKTNRKSEELSKRNNSSGIKLDSSKDSGTEDMLWTEKYQPQTASELIGNELAIKKLHSWLKDWKRRAELEERQNLKGKRDEKHEDFSGGIDFKGSSDDEEESRLCNTVLITGPTGVGKTAAVYACAQELGFKIFEVNASSQRSGRQILSQLKEATQSHQVDKQGVNSQKPCFFNSYYIGKSPKKISSPKKVVTSPRKVPPPSPKSSGPKRALPPKTLANYFKVSPKPKNNEEIGMLLENNKGIKNSFEQKQITQTKSTNATNSNVKDVGAEEPSRKNATSLILFEEVDVIFDEDAGFLNAIKTFMATTKRPVILTTSDPTFSLMFDGCFEEIKFSTPSLLNVASYLQMICLTENFRTDVKDFVTLLTANTCDIRKSILYLQFWIRSGGGVLEERPLTLYRGNSRNVQLVCSEHGLDNKIYPKNTKKKRVDLPKCDSGCAETLFGLKNIFSPSEDLFSFLKHKITMKEEWHKFIQLLTEFQMRNVDFLYSNLEFILPLPVDTIPETKNFCGPSVTVDASAATKSMNCLARKHSEREQPLKKSQKKKQKKTLVILDDSDLFDTDLDFPDQSISLSSVSSSSNAEESKTGDEESKARDKGNNPETKKSIPCPPKTTAGKKCSALVSHCLNSLSEFMDNMSFLDALLTDVREQNKYGRNDFSWTNGKVTSGLCDEFSLESNDGWTSQSSGELKAAAEALSFTKCSSAISKALETLNSCKKLGRDPTNDLTFYVSQKRNNVYFSQSAANLDNAWKRISVIKSVFSSRSLLYVGNRQASIIEYLPTLRNICKTEKLKEQGKSKRRFLHYFEGIHLDIPKETVNTLAADFP,mutated_sequence,1.0,1844.0,UPI0000071E9E.a2m,UPI0000071E9E.npy,gnomAD +UPI0000201AC8,UPI0000201AC8.csv,MALTDGGWCLPKRFGAAGADASDSRAFPAREPSTPPSPISSSSSSCSRGGERGPGGASNCGTPQLDTEAAAGPPARSLLLSSYASHPFGAPHGPSAPGVAGPGGNLSSWEDLLLFTDLDQAATASKLLWSSRGAKLSPFAPEQPEEMYQTLAALSSQGPAAYDGAPGGFVHSAAAAAAAAAAASSPVYVPTTRVGSMLPGLPYHLQGSGSGPANHAGGAGAHPGWPQASADSPPYGSGGGAAGGGAAGPGGAGSAAAHVSARFPYSPSPPMANGAAREPGGYAAAGSGGAGGVSGGGSSLAAMGGREPQYSSLSAARPLNGTYHHHHHHHHHHPSPYSPYVGAPLTPAWPAGPFETPVLHSLQSRAGAPLPVPRGPSADLLEDLSESRECVNCGSIQTPLWRRDGTGHYLCNACGLYSKMNGLSRPLIKPQKRVPSSRRLGLSCANCHTTTTTLWRRNAEGEPVCNACGLYMKLHGVPRPLAMKKEGIQTRKRKPKNINKSKTCSGNSNNSIPMTPTSTSSNSDDCSKNTSPTTQPTASGAGAPVMTGAGESTNPENSELKYSGQDGLYIGVSLASPAEVTSSVRPDSWCALALA,mutated_sequence,1.0,595.0,UPI0000201AC8.a2m,UPI0000201AC8.npy,gnomAD +UPI00017A6F19,UPI00017A6F19.csv,MREKEQEREEQLMEDKKRKKEDKKKKEATQKVTEQKTKVPEVTKPSLSQPTAASPIGSSPSPPVNGGNNAKRVAVPNGQPPSAARYMPREVPPRFRCQQDHKVLLKRGQPPPPSCMLLGGGAGPPPCTAPGANPNNAQVTGALLQSESGTAPDSTLGGAAASNYANSTWGSGASSNNGTSPNPIHIWDKVIVDGSDMEEWPCIASKDTESSSENTTDNNSASNPGSEKSTLPGSTTSNKGKGSQCQSASSGNECNLGVWKSDPKAKSVQSSNSTTENNNGLGNWRNVSGQDRIGPGSGFSNFNPNSNPSAWPALVQEGTSRKGALETDNSNSSAQVSTVGQTSREQQSKMENAGVNFVVSGREQAQIHNTDGPKNGNTNSLNLSSPNPMENKGMPFGMGLGNTSRSTDAPSQSTGDRKTGSVGSWGAARGPSGTDTVSGQSNSGNNGNNGKEREDSWKGASVQKSTGSKNDSWDNNNRSTGGSWNFGPQDSNDNKWGEGNKMTSGVSQGEWKQPTGSDELKIGEWSGPNQPNSSTGAWDNQKGHPLPENQGNAQAPCWGRSSSSTGSEVGGQSTGSNHKAGSSDSHNSGRRSYRPTHPDCQAVLQTLLSRTDLDPRVLSNTGWGQTQIKQDTVWDIEEVPRPEGKSDKGTEGWESAATQTKNSGGWGDAPSQSNQMKSGWGELSASTEWKDPKNTGGWNDYKNNNSSNWGGGRPDEKTPSSWNENPSKDQGWGGGRQPNQGWSSGKNGWGEEVDQTKNSNWESSASKPVSGWGEGGQNEIGTWGNGGNASLASKGGWEDCKRSPAWNETGRQPNSWNKQHQQQQPPQQPPPPQPEASGSWGGPPPPPPGNVRPSNSSWSSGPQPATPKDEEPSGWEEPSPQSISRKMDIDDGTSAWGDPNSYNYKNVNLWDKNSQGGPAPREPNLPTPMTSKSASVWSKSTPPAPDNGTSAWGEPNESSPGWGEMDDTGASTTGWGNTPANAPNAMKPNSKSMQDGWGESDGPVTGARHPSWEEEEDGGVWNTTGSQGSASSHNSASWGQGGKKQMKCSLKGGNNDSWMNPLAKQFSNMGLLSQTEDNPSSKMDLSVGSLSDKKFDVDKRAMNLGDFNDIMRKDRSGFRPPNSKDMGTTDSGPYFEKLTLPFSNQDGCLGDEAPCSPFSPSPSYKLSPSGSTLPNVSLGAIGTGLNPQNFAARQGGSHGLFGNSTAQSRGLHTPVQPLNSSPSLRAQVPPQFISPQVSASMLKQFPNSGLSPGLFNVGPQLSPQQIAMLSQLPQIPQFQLACQLLLQQQQQQQLLQNQRKISQAVRQQQEQQLARMVSALQQQQQQQQRQPGMKHSPSHPVGPKPHLDNMVPNALNVGLPDLQTKGPIPGYGSGFSSGGMDYGMVGGKEAGTESRFKQWTSMMEGLPSVATQEANMHKNGAIVAPGKTRGGSPYNQFDIIPGDTLGGHTGPAGDSWLPAKSPPTNKIGSKSSNASWPPEFQPGVPWKGIQNIDPESDPYVTPGSVLGGTATSPIVDTDHQLLRDNTTGSNSSLNTSLPSPGAWPYSASDNSFTNVHSTSAKFPDYKSTWSPDPIGHNPTHLSNKMWKNHISSRNTTPLPRPPPGLTNPKPSSPWSSTAPRSVRGWGTQDSRLASASTWSDGGSVRPSYWLVLHNLTPQIDGSTLRTICMQHGPLLTFHLNLTQGTALIRYSTKQEAAKAQTALHMCVLGNTTILAEFATDDEVSRFLAQAQPPTPAATPSAPAAGWQSLETGQNQSDPVGPALNLFGGSTGLGQWSSSAGGSSGADLAGASLWGPPNYSSSLWGVPTVEDPHRMGSPAPLLPGDLLGGGSDSI,mutated_sequence,1.0,1833.0,UPI00017A6F19.a2m,UPI00017A6F19.npy,gnomAD +UPI0000070494,UPI0000070494.csv,MFNPHALDSPAVIFDNGSGFCKAGLSGEFGPRHMVSSIVGHLKFQAPSAEANQKKYFVGEEALYKQEALQLHSPFERGLITGWDDVERLWKHLFEWELGVKPSDQPLLATEPSLNPRENREKMAEVMFENFGVPAFYLSDQAVLALYASACVTGLVVDSGDAVTCTVPIFEGYSLPHAVTKLHVAGRDITELLMQLLLASGHTFPCQLDKGLVDDIKKKLCYVALEPEKELSRRPEEVLREYKLPDGNIISLGDPLHQAPEALFVPQQLGSQSPGLSNMVSSSITKCDTDIQKILFGEIVLSGGTTLFHGLDDRLLKELEQLASKDTPIKITAPPDRWFSTWIGASIVTSLSSFKQMWVTAADFKEFGTSVVQRRCF,mutated_sequence,1.0,377.0,UPI0000070494.a2m,UPI0000070494.npy,gnomAD +UPI00006C19FA,UPI00006C19FA.csv,MLTLLDGLPSGIIGLMSRLSPDDGLNPNRCSCCIYAPPEPPHLPFFKWTYSFLLKSINLKKLLYTA,mutated_sequence,,,UPI00006C19FA.a2m,UPI00006C19FA.npy,gnomAD +UPI000012ADEA,UPI000012ADEA.csv,MAEAPASPAPLSPLEVELDPEFEPQSRPRSCTWPLQRPELQASPAKPSGETAADSMIPEEEDDEDDEDGGGRAGSAMAIGGGGGSGTLGSGLLLEDSARVLAPGGQDPGSGPATAAGGLSGGTQALLQPQQPLPPPQPGAAGGSGQPRKCSSRRNAWGNLSYADLITRAIESSPDKRLTLSQIYEWMVRCVPYFKDKGDSNSSAGWKNSIRHNLSLHSRFMRVQNEGTGKSSWWIINPDGGKSGKAPRRRAVSMDNSNKYTKSRGRAAKKKAALQTAPESADDSPSQLSKWPGSPTSRSSDELDAWTDFRSRTNSNASTVSGRLSPIMASTELDEVQDDDAPLSPMLYSSSASLSPSVSKPCTVELPRLTDMAGTMNLNDGLTENLMDDLLDNITLPPSQPSPTGGLMQRSSSFPYTTKGSGLGSPTSSFNSTVFGPSSLNSLRQSPMQTIQENKPATFSSMSHYGNQTLQDLLTSDSLSHSDVMMTQSDPLMSQASTAVSAQNSRRNVMLRNDPMMSFAAQPNQGSLVNQNLLHHQHQTQGALGGSRALSNSVSNMGLSESSSLGSAKHQQQSPVSQSMQTLSDSLSGSSLYSTSANLPVMGHEKFPSDLDLDMFNGSLECDMESIIRSELMDADGLDFNFDSLISTQNVVGLNVGNFTGAKQASSQSWVPG,mutated_sequence,1.0,673.0,UPI000012ADEA.a2m,UPI000012ADEA.npy,gnomAD +UPI0000456F39,UPI0000456F39.csv,MRKHRHLPLVAVFCLFLSGFPTTHAQQQQADVKNGAAADIIFLVDSSWTIGEEHFQLVREFLYDVVKSLAVGENDFHFALVQFNGNPHTEFLLNTYRTKQEVLSHISNMSYIGGTNQTGKGLEYIMQSHLTKAAGSRAGDGVPQVIVVLTDGHSKDGLALPSAELKSADVNVFAIGVEDADEGALKEIASEPLNMHMFNLENFTSLHDIVGNLVSCVHSSVSPERAGDTETLKDITAQDSADIIFLIDGSNNTGSVNFAVILDFLVNLLEKLPIGTQQIRVGVVQFSDEPRTMFSLDTYSTKAQVLGAVKALGFAGGELANIGLALDFVVENHFTRAGGSRVEEGVPQVLVLISAGPSSDEIRYGVVALKQASVFSFGLGAQAASRAELQHIATDDNLVFTVPEFRSFGDLQEKLLPYIVGVAQRHIVLKPPTIVTQVIEVNKRDIVFLVDGSSALGLANFNAIRDFIAKVIQRLEIGQDLIQVAVAQYADTVRPEFYFNTHPTKREVITAVRKMKPLDGSALYTGSALDFVRNNLFTSSAGYRAAEGIPKLLVLITGGKSLDEISQPAQELKRSSIMAFAIGNKGADQAELEEIAFDSSLVFIPAEFRAAPLQGMLPGLLAPLRTLSGTPEVHSNKRDIIFLLDGSANVGKTNFPYVRDFVMNLVNSLDIGNDNIRVGLVQFSDTPVTEFSLNTYQTKSDILGHLRQLQLQGGSGLNTGSALSYVYANHFTEAGGSRIREHVPQLLLLLTAGQSEDSYLQAANALTRAGILTFCVGASQANKAELEQIAFNPSLVYLMDDFSSLPALPQQLIQPLTTYVSGGVEEVPLAQPESKRDILFLFDGSANLVGQFPVVRDFLYKIIDELNVKPEGTRIAVAQYSDDVKVESRFDEHQSKPEILNLVKRMKIKTGKALNLGYALDYAQRYIFVKSAGSRIEDGVLQFLVLLVAGRSSDRVDGPASNLKQSGVVPFIFQAKNADPAELEQIVLSPAFILAAESLPKIGDLHPQIVNLLKSVHNGAPAPVSGEKDVVFLLDGSEGVRSGFPLLKEFVQRVVESLDVGQDRVRVAVVQYSDRTRPEFYLNSYMNKQDVVNAVRQLTLLGGPTPNTGAALEFVLRNILVSSAGSRITEGVPQLLIVLTADRSGDDVRNPSVVVKRGGAVPIGIGIGNADITEMQTISFIPDFAVAIPTFRQLGTVQQVISERVTQLTREELSRLQPVLQPLPSPGVGGKRDVVFLIDGSQSAGPEFQYVRTLIERLVDYLDVGFDTTRVAVIQFSDDPKVEFLLNAHSSKDEVQNAVQRLRPKGGRQINVGNALEYVSRNIFKRPLGSRIEEGVPQFLVLISSGKSDDEVDDPAVELKQFGVAPFTIARNADQEELVKISLSPEYVFSVSTFRELPSLEQKLLTPITTLTSEQIQKLLASTRYPPPAVESDAADIVFLIDSSEGVRPDGFAHIRDFVSRIVRRLNIGPSKVRVGVVQFSNDVFPEFYLKTYRSQAPVLDAIRRLRLRGGSPLNTGKALEFVARNLFVKSAGSRIEDGVPQHLVLVLGGKSQDDVSRFAQVIRSSGIVSLGVGDRNIDRTELQTITNDPRLVFTVREFRELPNIEERIMNSFGPSAATPAPPGVDTPPPSRPEKKKADIVFLLDGSINFRRDSFQEVLRFVSEIVDTVYEDGDSIQVGLVQYNSDPTDEFFLKDFSTKRQIIDAINKVVYKGGRHANTKVGLEHLRVNHFVPEAGSRLDQRVPQIAFVITGGKSVEDAQDVSLALTQRGVKVFAVGVRNIDSEEVGKIASNSATAFRVGNVQELSELSEQVLETLHDAMHETLCPGVTDAAKACNLDVILGFDGSRDQNVFVAQKGFESKVDAILNRISQMHRVSCSGGRSPTVRVSVVANTPSGPVEAFDFDEYQPEMLEKFRNMRSQHPYVLTEDTLKVYLNKFRQSSPDSVKVVIHFTDGADGDLADLHRASENLRQEGVRALILVGLERVVNLERLMHLEFGRGFMYDRPLRLNLLDLDYELAEQLDNIAEKACCGVPCKCSGQRGDRGPIGSIGPKGIPGEDGYRGYPGDEGGPGERGPPGVNGTQGFQGCPGQRGVKGSRGFPGEKGEVGEIGLDGLDGEDGDKGLPGSSGEKGNPGRRGDKGPRGEKGERGDVGIRGDPGNPGQDSQERGPKGETGDLGPMGVPGRDGVPGGPGETGKNGGFGRRGPPGAKGNKGGPGQPGFEGEQGTRGAQGPAGPAGPPGLIGEQGISGPRGSGGAAGAPGERGRTGPLGRKGEPGEPGPKGGIGNRGPRGETGDDGRDGVGSEGRRGKKGERGFPGYPGPKGNPGEPGLNGTTGPKGIRGRRGNSGPPGIVGQKGDPGYPGPAGPKGNRGDSIDQCALIQSIKDKCPCCYGPLECPVFPTELAFALDTSEGVNQDTFGRMRDVVLSIVNDLTIAESNCPRGARVAVVTYNNEVTTEIRFADSKRKSVLLDKIKNLQVALTSKQQSLETAMSFVARNTFKRVRNGFLMRKVAVFFSNTPTRASPQLREAVLKLSDAGITPLFLTRQEDRQLINALQINNTAVGHALVLPAGRDLTDFLENVLTCHVCLDICNIDPSCGFGSWRPSFRDRRAAGSDVDIDMAFILDSAETTTLFQFNEMKKYIAYLVRQLDMSPDPKASQHFARVAVVQHAPSESVDNASMPPVKVEFSLTDYGSKEKLVDFLSRGMTQLQGTRALGSAIEYTIENVFESAPNPRDLKIVVLMLTGEVPEQQLEEAQRVILQAKCKGYFFVVLGIGRKVNIKEVYTFASEPNDVFFKLVDKSTELNEEPLMRFGRLLPSFVSSENAFYLSPDIRKQCDWFQGDQPTKNLVKFGHKQVNVPNNVTSSPTSNPVTTTKPVTTTKPVTTTTKPVTTTTKPVTIINQPSVKPAAAKPAPAKPVAAKPVATKMATVRPPVAVKPATAAKPVAAKPAAVRPPAAAAAKPVATKPEVPRPQAAKPAATKPATTKPMVKMSREVQVFEITENSAKLHWERAEPPGPYFYDLTVTSAHDQSLVLKQNLTVTDRVIGGLLAGQTYHVAVVCYLRSQVRATYHGSFSTKKSQPPPPQPARSASSSTINLMVSTEPLALTETDICKLPKDEGTCRDFILKWYYDPNTKSCARFWYGGCGGNENKFGSQKECEKVCAPVLAKPGVISVMGT,mutated_sequence,1.0,3177.0,UPI0000456F39.a2m,UPI0000456F39.npy,gnomAD +UPI00025A2DFB,UPI00025A2DFB.csv,QLKQQRDKLRQYQKRIAQQLERERALARQLLRDGRKERAKLLLKKKRYQEQLLDRTENQISSLEAMVQSIEFTQIEMKVMEGLQFGNECLNKMHQVMSIEEVERILDETQEAVEYQRQIDELLAGSFTQEDEDAILEELSAITQEQIELPEVPSEPLPEKIPGISMFDSFEWLEFAGEKWILEGNKTECSQAFLGRGQALLPGCLAWGRACLALGLGLGFPGVR,mutated_sequence,1.0,224.0,UPI00025A2DFB.a2m,UPI00025A2DFB.npy,gnomAD +UPI0000E06BD6,UPI0000E06BD6.csv,MAAAPARGGGGGGGGGGGCSGSGSSASRGFYFNTVLSLARSLAVQRPASLEKVQKLLCMCPVDFHGIFQLDERRRDAVIALGIFLIESDLQHKDCVVPYLLRLLKGLPKVYWVEESTARKGRGALPVAESFSFCLVTLLSDVAYRDPSLRDEILEVLLQVLHVLLGMCQALEIQDKEYLCKYAIPCLIGISRAFGRYSNMEESLLSKLFPKIPPHSLRVLEELEGVRRRSFNDFRSILPSNLLTVCQEGTLKRKTSSVSSISQVSPERGMPPPSSPGGSAFHYFEASCLPDGTALEPEYYFSTISSSFSVSPLFNGVTYKEFNIPLEMLRELLNLVKKIVEEAVLKSLDAIVASVMEANPSADLYYTSFSDPLYLTMFKMLRDTLYYMKDLPTSFVKEIHDFVLEQFNTSQGELQKILHDADRIHNELSPLKLRCQANAACVDLMVWAVKDEQGAENLCIKLSEKLQSKTSSKVIIAHLPLLICCLQGLGRLCERFPVVVHSVTPSLRDFLVIPSPVLVKLYKYHSQYHTVAGNDIKISVTNEHSESTLNVMSGKKSQPSMYEQLRDIAIDNICRCLKAGLTVDPVIVEAFLASLSNRLYISQESDKDAHLIPDHTIRALGHIAVALRDTPKVMEPILQILQQKFCQPPSPLDVLIIDQLGCLVITGNQYIYQEVWNLFQQISVKASSVVYSATKDYKDHGYRHCSLAVINALANIAANIQDEHLVDELLMNLLELFVQLGLEGKRASERASEKGPALKASSSAGNLGVLIPVIAVLTRRLPPIKEAKPRLQKLFRDFWLYSVLMGFAVEGSGLWPEEWYEGVCEIATKSPLLTFPSKEPLRSVLQYNSAMKNDTVTPAELSELRSTIINLLDPPPEVSALINKLDFAMSTYLLSVYRLEYMRVLRSTDPDRFQVMFCYFEDKAIQKDKSGMMQCVIAVADKVFDAFLNMMADKAKTKENEEELERHAQFLLVNFNHIHKRIRRVADKYLSGLVDKFPHLLWSGTVLKTMLDILQTLSLSLSADIHKDQPYYDIPDAPYRITVPDTYEARESIVKDFAARCGMILQEAMKWAPTVTKSHLQEYLNKHQNWVSGLSQHTGLAMATESILHFAGYNKQNTTLGATQLSERPACVKKDYSNFMASLNLRNRYAGEVYGMIRFSGTTGQMSDLNKMMVQDLHSALDRSHPQHYTQAMFKLTAMLISSKDCDPQLLHHLCWGPLRMFNEHGMETALACWEWLLAGKDGVEVPFMREMAGAWHMTVEQKFGLFSAEIKEADPLAASEASQPKPCPPEVTPHYIWIDFLVQRFEIAKYCSSDQVEIFSSLLQRSMSLNIGGAKGSMNRHVAAIGPRFKLLTLGLSLLHADVVPNATIRNVLREKIYSTAFDYFSCPPKFPTQGEKRLREDISIMIKFWTAMFSDKKYLTASQLVPPDNQDTRSNLDITVGSRQQATQGWINTYPLSSGMSTISKKSGMSKKTNRGSQLHKYYMKRRTLLLSLLATEIERLITWYNPLSAPELELDQAGENSVANWRSKYISLSEKQWKDNVNLAWSISPYLAVQLPARFKNTEAIGNEVTRLVRLDPGAVSDVPEAIKFLVTWHTIDADAPELSHVLCWAPTDPPTGLSYFSSMYPPHPLTAQYGVKVLRSFPPDAILFYIPQIVQALRYDKMGYVREYILWAASKSQLLAHQFIWNMKTNIYLDEEGHQKDPDIGDLLDQLVEEITGSLSGPAKDFYQREFDFFNKITNVSAIIKPYPKGDERKKACLSALSEVKVQPGCYLPSNPEAIVLDIDYKSGTPMQSAAKAPYLAKFKVKRCGVSELEKEGLRCRSDSEDECSTQEADGQKISWQAAIFKVGDDCRQDMLALQIIDLFKNIFQLVGLDLFVFPYRVVATAPGCGVIECIPDCTSRDQLGRQTDFGMYDYFTRQYGDESTLAFQQARYNFIRSMAAYSLLLFLLQIKDRHNGNIMLDKKGHIIHIDFGFMFESSPGGNLGWEPDIKLTDEMVMIMGGKMEATPFKWFMEMCVRGYLAVRPYMDAVVSLVTLMLDTGLPCFRGQTIKLLKHRFSPNMTEREAANFIMKVIQSCFLSNRSRTYDMIQYYQNDIPY,mutated_sequence,1.0,2102.0,UPI0000E06BD6.a2m,UPI0000E06BD6.npy,gnomAD +UPI0001573469,UPI0001573469.csv,MGNEASLEGEGLPEGLAAAAAAGGGASGAGSPSHTAIPAGMEADLSQLSEEERRQIAAVMSRAQGLPKGSVPPAAAESPSMHRKQELDSSHPPKQSGRPPDPGRPAQPGLSKSRTTDTFRSEQKLPGRSPSTISLKESKSRTDLKEEHKSSMMPGFLSEVNALSAVSSVVNKFNPFDLISDSEASQEETTKKQKVVQKEQGKPEGIIKPPLQQQPPKPIPKQQGPGRDPLQQDGTPKSISSQQPEKIKSQPPGTGKPIQGPTQTPQTDHAKLPLQRDASRPQTKQADIVRGESVKPSLPSPSKPPIQQPTPGKPPAQQPGHEKSQPGPAKPPAQPSGLTKPLAQQPGTVKPPVQPPGTTKPPAQPLGPAKPPAQQTGSEKPSSEQPGPKALAQPPGVGKTPAQQPGPAKPPTQQVGTPKPLAQQPGLQSPAKAPGPTKTPVQQPGPGKIPAQQAGPGKTSAQQTGPTKPPSQLPGPAKPPPQQPGPAKPPPQQPGSAKPPSQQPGSTKPPPQQPGPAKPSPQQPGSTKPPSQQPGSAKPSAQQPSPAKPSAQQSTKPVSQTGSGKPLQPPTVSPSAKQPPSQGLPKTICPLCNTTELLLHVPEKANFNTCTECQTTVCSLCGFNPNPHLTEVKEWLCLNCQMKRALGGDLAPVPSSPQPKLKTAPVTTTSAVSKSSPQPQQTSPKKDAAPKQDLSKAPEPKKPPPLVKQPTLHGSPSAKAKQPPEADSLSKPAPPKEPSVPSEQDKAPVADDKPKQPKMVKPTTDLVSSSSATTKPDIPSSKVQSQAEEKTTPPLKTDSAKPSQSFPPTGEKVSPFDSKAIPRPASDSKIISHPGPSSESKGQKQVDPVQKKEEPKKAQTKMSPKPDAKPMPKGSPTPPGPRPTAGQTVPTPQQSPKPQEQSRRFSLNLGSITDAPKSQPTTPQETVTGKLFGFGASIFSQASNLISTAGQPGPHSQSGPGAPMKQAPAPSQPPTSQGPPKSTGQAPPAPAKSIPVKKETKAPAAEKLEPKAEQAPTVKRTETEKKPPPIKDSKSLTAEPQKAVLPTKLEKSPKPESTCPLCKTELNIGSKDPPNFNTCTECKNQVCNLCGFNPTPHLTEIQEWLCLNCQTQRAISGQLGDIRKMPPAPSGPKASPMPVPTESSSQKTAVPPQVKLVKKQEQEVKTEAEKVILEKVKETLSMEKIPPMVTTDQKQEESKLEKDKASALQEKKPLPEEKKLIPEEEKIRSEEKKPLLEEKKPTPEDKKLLPEAKTSAPEEQKHDLLKSQVQIAEEKLEGRVAPKTVQEGKQPQTKMEGLPSGTPQSLPKEDDKTTKTIKEQPQPPCTAKPDQVEPGKEKTEKEDDKSDTSSSQQPKSPQGLSDTGYSSDGISSSLGEIPSLIPTDEKDILKGLKKDSFSQESSPSSPSDLAKLESTVLSILEAQASTLADEKSEKKTQPHEVSPEQPKDQEKTQSLSETLEITISEEEIKESQEERKDTFKKDSQQDIPSSKDHKEKSEFVDDITTRREPYDSVEESSESENSPVPQRKRRTSVGSSSSDEYKQEDSQGSGEEEDFIRKQIIEMSADEDASGSEDDEFIRNQLKEISSSTESQKKEETKGKGKITAGKHRRLTRKSSTSIDEDAGRRHSWHDEDDEAFDESPELKYRETKSQESEELVVTGGGGLRRFKTIELNSTIADKYSAESSQKKTSLYFDEEPELEMESLTDSPEDRSRGEGSSSLHASSFTPGTSPTSVSSLDEDSDSSPSHKKGESKQQRKARHRPHGPLLPTIEDSSEEEELREEEELLKEQEKQREIEQQQRKSSSKKSKKDKDELRAQRRRERPKTPPSNLSPIEDASPTEELRQAAEMEELHRSSCSEYSPSIESDPEGFEISPEKIIEVQKVYKLPTAVSLYSPTDEQSIMQKEGSQKALKSAEEMYEEMMHKTHKYKAFPAANERDEVFEKEPLYGGMLIEDYIYESLVEDTYNGSVDGSLLTRQEEENGFMQQKGREQKIRLSEQIYEDPMQKITDLQKEFYELESLHSVVPQEDIVSSSFIIPESHEIVDLGTMVTSTEEERKLLDADAAYEELMKRQQMQLTPGSSPTQAPIGEDMTESTMDFDRMPDASLTSSVLSGASLTDSTSSATLSIPDVKITQHFSTEEIEDEYVTDYTREIQEIIAHESLILTYSEPSESATSVPPSDTPSLTSSVSSVCTTDSSSPITTLDSITTVYTEPVDMITKFEDSEEISSSTYFPGSIIDYPEEISVSLDRTAPPDGRASADHIVISLSDMASSIIESVVPKPEGPVADTVSTDLLISEKDPVKKAKKETGNGIILEVLEAYRDKKELEAERTKSSLSETVFDHPPSSVIALPMKEQLSTTYFTSGETFGQEKPASQLPSGSPSVSSLPAKPRPFFRSSSLDISAQPPPPPPPPPPPPPPPPPPPPPPLPPPTSPKPTILPKKKLTVASPVTTATPLFDAVTTLETTAVLRSNGLPVTRICTTAPPPVPPKPSSIPSGLVFTHRPEPSKPPIAPKPVIPQLPTTTQKPTDIHPKPTGLSLTSSMTLNLVTSADYKLPSPTSPLSPHSNKSSPRFSKSLTETYVVITLPSEPGTPTDSSASQAITSWPLGSPSKDLVSVEPVFSVVPPVTAVEIPISSEQTFYISGALQTFSATPVTAPSSFQAAPTSVTQFLTTEVSKTEVSATRSTAPSVGLSSISITIPPEPLALDNIHLEKPQYKEDGKLQLVGDVIDLRTVPKVEVKTTDKCIDLSASTMDVKRQITANEVYGKQISAVQPSIINLSVTSSIVTPVSLATETVTFVTCTASASYTTGTESLVGAEHAMTTPLQLTTSKHAEPPYRIPSDQVFPIAREEAPINLSLGTPAHAVTLAITKPVTVPPVGVTNGWTDSTVSQGITDGEVVDLSTTKSHRTVVTMDESTSSVMTKIIEDEKPVDLTAGRRAVCCDVVYKLPFGRSCTAQQPATTLPEDRFGYRDDHYQYDRSGPYGYRGIGGMKPSMSDTNLAEAGHFFYKSKNAFDYSEGTDTAVDLTSGRVTTGEVMDYSSKTTGPYPETRQVISGAGISTPQYSTARMTPPPGPQYCVGSVLRSSNGVVYSSVATPTPSTFAITTQPGSIFSTTVRDLSGIHTADAVTSLPAMHHSQPMPRSYFITTGASETDIAVTGIDISASLQTITMESLTAETIDSVPTLTTASEVFPEVVGDESALLIVPEEDKQQQQLDLERELLELEKIKQQRFAEELEWERQEIQRFREQEKIMVQKKLEELQSMKQHLLFQQEEERQAQFMMRQETLAQQQLQLEQIQQLQQQLHQQLEEQKIRQIYQYNYDPSGTASPQTTTEQAILEGQYAALEGSQFWATEDATTTASAVVAIEIPQSQGWYTVQSDGVTQYIAPPGILSTVSEIPLTDVVVKEEKQPKKRSSGAKVRGQYDDMGENMTDDPRSFKKIVDSGVQTDDEDATDRSYVSRRRRTKKSVDTSVQTDDEDQDEWDMPTRSRRKARVGKYGDSMTEADKTKPLSKVSSIAVQTVAEISVQTEPVGTIRTPSIRARVDAKVEIIKHISAPEKTYKGGSLGCQTEADSDTQSPQYLSATSPPKDKKRPTPLEIGYSSHLRADSTVQLAPSPPKSPKVLYSPISPLSPGKALESAFVPYEKPLPDDISPQKVLHPDMAKVPPASPKTAKMMQRSMSDPKPLSPTADESSRAPFQYTEGYTTKGSQTMTSSGAQKKVKRTLPNPPPEEISTGTQSTFSTMGTVSRRRICRTNTMARAKILQDIDRELDLVERESAKLRKKQAELDEEEKEIDAKLRYLEMGINRRKEALLKEREKRERAYLQGVAEDRDYMSDSEVSSTRPTRIESQHGIERPRTAPQTEFSQFIPPQTQTESQLVPPTSPYTQYQYSSPALPTQAPTSYTQQSHFEQQTLYHQQVSPYQTQPTFQAVATMSFTPQVQPTPTPQPSYQLPSQMMVIQQKPRQTTLYLEPKITSNYEVIRNQPLMIAPVSTDNTFAVSHLGSKYNSLDLRIGLEERSSMASSPISSISADSFYADIDHHTPRNYVLIDDIGEITKGTAALSTAFSLHEKDLSKTDRLLRTTETRRSQEVTDFLAPLQSSSRLHSYVKAEEDPMEDPYELKLLKHQIKQEFRRGTESLDHLAGLSHYYHADTSYRHFPKSEKYSISRLTLEKQAAKQLPAAILYQKQSKHKKSLIDPKMSKFSPIQESRDLEPDYSSYMTSSTSSIGGISSRARLLQDDITFGLRKNITDQQKFMGSSLGTGLGTLGNTIRSALQDEADKPYSSGSRSRPSSRPSSVYGLDLSIKRDSSSSSLRLKAQEAEALDVSFSHASSSARTKPTSLPISQSRGRIPIVAQNSEEESPLSPVGQPMGMARAAAGPLPPISADTRDQFGSSHSLPEVQQHMREESRTRGYDRDIAFIMDDFQHAMSDSEAYHLRREETDWFDKPRESRLENGHGLDRKLPERLVHSRPLSQHQEQIIQMNGKTMHYIFPHARIKITRDSKDHTVSGNGLGIRIVGGKEIPGHSGEIGAYIAKILPGGSAEQTGKLMEGMQVLEWNGIPLTSKTYEEVQSIISQQSGEAEICVRLDLNMLSDSENSQHLELHEPPKAVDKAKSPGVDPKQLAAELQKVSLQQSPLVLSSVVEKGSHVHSGPTSAGSSSVPSPGQPGSPSVSKKKHGSSKPTDGTKVVSHPITGEIQLQINYDLGNLIIHILQARNLVPRDNNGYSDPFVKVYLLPGRGQVMVVQNASAEYKRRTKHVQKSLNPEWNQTVIYKSISMEQLKKKTLEVTVWDYDRFSSNDFLGEVLIDLSSTSHLDNTPRWYPLKEQTESIDHGKSHSSQSSQQSPKPSVIKSRSHGIFPDPSKDMQVPTIEKSHSSPGSSKSSSEGHLRSHGPSRSQSKTSVTQTHLEDAGAAIAAAEAAVQQLRIQPTKPPNHRPAESSVSTGSSGSSFGSGYSVDSEGSSSTAGETNLFPIPRIGKMGQNGQEPVKQPGVGVGLADTEAKTQVMGEIKIALKKEMKTDGEQLIVEILQCRNITYKFKSPDHLPDLYVKIYVMNISTQKKVIKKKTRVCRHDREPSFNETFRFSLSPAGHSLQILLFSNGGKFMKKTLIGEACIWLDKVDLRKRIVNWHKLLVSPTQTH,mutated_sequence,1.0,5142.0,UPI0001573469.a2m,UPI0001573469.npy,gnomAD +UPI000002A38D,UPI000002A38D.csv,MEDEERQKKLEAGKAKLAQFRQRKAQSDGQSPSKKQKKKRKTSSSKHDVSAHHDLNIDQSQCNEMYINSSQRVESTVIPESTIMRTLHSGEITSHEQGFSVELESEISTTADDCSSEVNGCSFVMRTGKPTNLLREEEFGVDDSYSEQGAQDSPTHLEMMESELAGKQHEIEELNRELEEMRVTYGTEGLQQLQEFEAAIKQRDGIITQLTANLQQARREKDETMREFLELTEQSQKLQIQFQQLQASETLRNSTHSSTAADLLQAKQQILTHQQQLEEQDHLLEDYQKKKEDFTMQISFLQEKIKVYEMEQDKKVENSNKEEIQEKETIIEELNTKIIEEEKKTLELKDKLTTADKLLGELQEQIVQKNQEIKNMKLELTNSKQKERQSSEEIKQLMGTVEELQKRNHKDSQFETDIVQRMEQETQRKLEQLRAELDEMYGQQIVQMKQELIRQHMAQMEEMKTRHKGEMENALRSYSNITVNEDQIKLMNVAINELNIKLQDTNSQKEKLKEELGLILEEKCALQRQLEDLVEELSFSREQIQRARQTIAEQESKLNEAHKSLSTVEDLKAEIVSASESRKELELKHEAEVTNYKIKLEMLEKEKNAVLDRMAESQEAELERLRTQLLFSHEEELSKLKEDLEIEHRINIEKLKDNLGIHYKQQIDGLQNEMSQKIETMQFEKDNLITKQNQLILEISKLKDLQQSLVNSKSEEMTLQINELQKEIEILRQEEKEKGTLEQEVQELQLKTELLEKQMKEKENDLQEKFAQLEAENSILKDEKKTLEDMLKIHTPVSQEERLIFLDSIKSKSKDSVWEKEIEILIEENEDLKQQCIQLNEEIEKQRNTFSFAEKNFEVNYQELQEEYACLLKVKDDLEDSKNKQELEYKSKLKALNEELHLQRINPTTVKMKSSVFDEDKTFVAETLEMGEVVEKDTTELMEKLEVTKREKLELSQRLSDLSEQLKQKHGEISFLNEEVKSLKQEKEQVSLRCRELEIIINHNRAENVQSCDTQVSSLLDGVVTMTSRGAEGSVSKVNKSFGEESKIMVEDKVSFENMTVGEESKQEQLILDHLPSVTKESSLRATQPSENDKLQKELNVLKSEQNDLRLQMEAQRICLSLVYSTHVDQVREYMENEKDKALCSLKEELIFAQEEKIKELQKIHQLELQTMKTQETGDEGKPLHLLIGKLQKAVSEECSYFLQTLCSVLGEYYTPALKCEVNAEDKENSGDYISENEDPELQDYRYEVQDFQENMHTLLNKVTEEYNKLLVLQTRLSKIWGQQTDGMKLEFGEENLPKEETEFLSIHSQMTNLEDIDVNHKSKLSSLQDLEKTKLEEQVQELESLISSLQQQLKETEQNYEAEIHCLQKRLQAVSESTVPPSLPVDSVVITESDAQRTMYPGSCVKKNIDGTIEFSGEFGVKEETNIVKLLEKQYQEQLEEEVAKVIVSMSIAFAQQTELSRISGGKENTASSKQAHAVCQQEQHYFNEMKLSQDQIGFQTFETVDVKFKEEFKPLSKELGEHGKEILLSNSDPHDIPESKDCVLTISEEMFSKDKTFIVRQSIHDEISVSSMDASRQLMLNEEQLEDMRQELVRQYQEHQQATELLRQAHMRQMERQREDQEQLQEEIKRLNRQLAQRSSIDNENLVSERERVLLEELEALKQLSLAGREKLCCELRNSSTQTQNGNENQGEVEEQTFKEKELDRKPEDVPPEILSNERYALQKANNRLLKILLEVVKTTAAVEETIGRHVLGILDRSSKSQSSASLIWRSEAEASVKSCVHEEHTRVTDESIPSYSGSDMPRNDINMWSKVTEEGTELSQRLVRSGFAGTEIDPENEELMLNISSRLQAAVEKLLEAISETSSQLEHAKVTQTELMRESFRQKQEATESLKCQEELRERLHEESRAREQLAVELSKAEGVIDGYADEKTLFERQIQEKTDIIDRLEQELLCASNRLQELEAEQQQIQEERELLSRQKEAMKAEAGPVEQQLLQETEKLMKEKLEVQCQAEKVRDDLQKQVKALEIDVEEQVSRFIELEQEKNTELMDLRQQNQALEKQLEKMRKFLDEQAIDREHERDVFQQEIQKLEQQLKVVPRFQPISEHQTREVEQLANHLKEKTDKCSELLLSKEQLQRDIQERNEEIEKLEFRVRELEQALLVSADTFQKVEDRKHFGAVEAKPELSLEVQLQAERDAIDRKEKEITNLEEQLEQFREELENKNEEVQQLHMQLEIQKKESTTRLQELEQENKLFKDDMEKLGLAIKESDAMSTQDQHVLFGKFAQIIQEKEVEIDQLNEQVTKLQQQLKITTDNKVIEEKNELIRDLETQIECLMSDQECVKRNREEEIEQLNEVIEKLQQELANIGQKTSMNAHSLSEEADSLKHQLDVVIAEKLALEQQVETANEEMTFMKNVLKETNFKMNQLTQELFSLKRERESVEKIQSIPENSVNVAIDHLSKDKPELEVVLTEDALKSLENQTYFKSFEENGKGSIINLETRLLQLESTVSAKDLELTQCYKQIKDMQEQGQFETEMLQKKIVNLQKIVEEKVAAALVSQIQLEAVQEYAKFCQDNQTISSEPERTNIQNLNQLREDELGSDISALTLRISELESQVVEMHTSLILEKEQVEIAEKNVLEKEKKLLELQKLLEGNEKKQREKEKKRSPQDVEVLKTTTELFHSNEESGFFNELEALRAESVATKAELASYKEKAEKLQEELLVKETNMTSLQKDLSQVRDHLAEAKEKLSILEKEDETEVQESKKACMFEPLPIKLSKSIASQTDGTLKISSSNQTPQILVKNAGIQINLQSECSSEEVTEIISQFTEKIEKMQELHAAEILDMESRHISETETLKREHYVAVQLLKEECGTLKAVIQCLRSKEGSSIPELAHSDAYQTREICSSDSGSDWGQGIYLTHSQGFDIASEGRGEESESATDSFPKKIKGLLRAVHNEGMQVLSLTESPYSDGEDHSIQQVSEPWLEERKAYINTISSLKDLITKMQLQREAEVYDSSQSHESFSDWRGELLLALQQVFLEERSVLLAAFRTELTALGTTDAVGLLNCLEQRIQEQGVEYQAAMECLQKADRRSLLSEIQALHAQMNGRKITLKREQESEKPSQELLEYNIQQKQSQMLEMQVELSSMKDRATELQEQLSSEKMVVAELKSELAQTKLELETTLKAQHKHLKELEAFRLEVKDKTDEVHLLNDTLASEQKKSRELQWALEKEKAKLGRSEERDKEELEDLKFSLESQKQRNLQLNLLLEQQKQLLNESQQKIESQRMLYDAQLSEEQGRNLELQVLLESEKVRIREMSSTLDRERELHAQLQSSDGTGQSRPPLPSEDLLKELQKQLEEKHSRIVELLNETEKYKLDSLQTRQQMEKDRQVHRKTLQTEQEANTEGQKKMHELQSKVEDLQRQLEEKRQQVYKLDLEGQRLQGIMQEFQKQELEREEKRESRRILYQNLNEPTTWSLTSDRTRNWVLQQKIEGETKESNYAKLIEMNGGGTGCNHELEMIRQKLQCVASKLQVLPQKASERLQFETADDEDFIWVQENIDEIILQLQKLTGQQGEEPSLVSPSTSCGSLTERLLRQNAELTGHISQLTEEKNDLRNMVMKLEEQIRWYRQTGAGRDNSSRFSLNGGANIEAIIASEKEVWNREKLTLQKSLKRAEAEVYKLKAELRNDSLLQTLSPDSEHVTLKRIYGKYLRAESFRKALIYQKKYLLLLLGGFQECEDATLALLARMGGQPAFTDLEVITNRPKGFTRFRSAVRVSIAISRMKFLVRRWHRVTGSVSININRDGFGLNQGAEKTDSFYHSSGGLELYGEPRHTTYRSRSDLDYIRSPLPFQNRYPGTPADFNPGSLACSQLQNYDPDRALTDYITRLEALQRRLGTIQSGSTTQFHAGMRR,mutated_sequence,1.0,3907.0,UPI000002A38D.a2m,UPI000002A38D.npy,gnomAD +UPI000050BBEF,UPI000050BBEF.csv,MDMASESVGGKILFATDDFFAPAENLIKSDSPCFKEHEYTEFGKWMDGWETRRKRIPGHDWCVLRLGIQGVIRGFDVDVSYFTGDYAPRVSIQAANLEEDKLPEIPERGTRTGAAATPEEFEAIAELKSDDWSYLVPMTELKPGNPASGHNYFLVNSQQRWTHIRLNIFPDGGIARLRVFGTGQKDWTATDPKEPADLVAIAFGGVCVGFSNAKFGHPNNIIGVGGAKSMADGWETARRLDRPPILENDENGILLVPGCEWAVFRLAHPGVITRIEIDTKYFEGNAPDSCKVDGCILTTQEEEAVIRQKWILPAHKWKPLLPVTKLSPNQSHLFDSLTLELQDVITHARLTIVPDGGVSRLRLRGFPSSICLLRPREKPMLKFSVSFKANP,mutated_sequence,1.0,391.0,UPI000050BBEF.a2m,UPI000050BBEF.npy,gnomAD +UPI00001419CC,UPI00001419CC.csv,MTADKDKDKDKEKDRDRDRDREREKRDKARESENSRPRRSCTLEGGAKNYAESDHSEDEDNDNNSATAEESTKKNKKKPPKKKSRYERTDTGEITSYITEDDVVYRPGDCVYIESRRPNTPYFICSIQDFKLVHNSQACCRSPTPALCDPPACSLPVASQPPQHLSEAGRGPVGSKRDHLLMNVKWYYRQSEVPDSVYQHLVQDRHNENDSGRELVITDPVIKNRELFISDYVDTYHAAALRGKCNISHFSDIFAAREFKARVDSFFYILGYNPETRRLNSTQGEIRVGPSHQAKLPDLQPFPSPDGDTVTQHEELVWMPGVNDCDLLMYLRAARSMAAFAGMCDGGSTEDGCVAASRDDTTLNALNTLHESGYDAGKALQRLVKKPVPKLIEKCWTEDEVKRFVKGLRQYGKNFFRIRKELLPNKETGELITFYYYWKKTPEAASSRAHRRHRRQAVFRRIKTRTASTPVNTPSRPPSSEFLDLSSASEDDFDSEDSEQELKGYACRHCFTTTSKDWHHGGRENILLCTDCRIHFKKYGELPPIEKPVDPPPFMFKPVKEEDDGLSGKHSMRTRRSRGSMSTLRSGRKKQPASPDGRTSPINEDIRSSGRNSPSAASTSSNDSKAETVKKSAKKVKEEASSPLKSNKRQREKVASDTEEADRTSSKKTKTQEISRPNSPSEGEGESSDSRSVNDEGSSDPKDIDQDNRSTSPSIPSPQDNESDSDSSAQQQMLQAQPPALQAPTGVTPAPSSAPPGTPQLPTPGPTPSATAVPPQGSPTASQAPNQPQAPTAPVPHTHIQQAPALHPQRPPSPHPPPHPSPHPPLQPLTGSAGQPSAPSHAQPPLHGQGPPGPHSLQAGPLLQHPGPPQPFGLPPQASQGQAPLGTSPAAAYPHTSLQLPASQSALQSQQPPREQPLPPAPLAMPHIKPPPTTPIPQLPAPQAHKHPPHLSGPSPFSMNANLPPPPALKPLSSLSTHHPPSAHPPPLQLMPQSQPLPSSPAQPPGLTQSQNLPPPPASHPPTGLHQVAPQPPFAQHPFVPGGPPPITPPTCPSTSTPPAGPGTSAQPPCSGAAASGGSIAGGSSCPLPTVQIKEEALDDAEEPESPPPPPRSPSPEPTVVDTPSHASQSARFYKHLDRGYNSCARTDLYFMPLAGSKLAKKREEAIEKAKREAEQKAREEREREKEKEKEREREREREREAERAAKASSSAHEGRLSDPQLSGPGHMRPSFEPPPTTIAAVPPYIGPDTPALRTLSEYARPHVMSPTNRNHPFYMPLNPTDPLLAYHMPGLYNVDPTIRERELREREIREREIRERELRERMKPGFEVKPPELDPLHPAANPMEHFARHSALTIPPTAGPHPFASFHPGLNPLERERLALAGPQLRPEMSYPDRLAAERIHAERMASLTSDPLARLQMFNVTPHHHQHSHIHSHLHLHQQDPLHQGSAGPVHPLVDPLTAGPHLARFPYPPGTLPNPLLGQPPHEHEMLRHPVFGTPYPRDLPGAIPPPMSAAHQLQAMHAQSAELQRLAMEQQWLHGHPHMHGGHLPSQEDYYSRLKKEGDKQL,mutated_sequence,1.0,1566.0,UPI00001419CC.a2m,UPI00001419CC.npy,gnomAD +UPI0000074015,UPI0000074015.csv,MSDEGSRGSRLPLALPPASQGCSSGGGGGGSSAGGSGNSRPPRNLQGLLQMAITAGSEEPDPPPEPMSEERRQWLQEAMSAAFRGQREEVEQMKSCLRVLSQPMPPTAGEAEQAADQQEREGALELLADLCENMDNAADFCQLSGMHLLVGRYLEAGAAGLRWRAAQLIGTCSQNVAAIQEQVLGLGALRKLLRLLDRDACDTVRVKALFAISCLVREQEAGLLQFLRLDGFSVLMRAMQQQVQKLKVKSAFLLQNLLVGHPEHKGTLCSMGMVQQLVALVRTEHSPFHEHVLGALCSLVTDFPQGVRECREPELGLEELLRHRCQLLQQHEEYQEELEFCEKLLQTCFSSPADDSMDR,mutated_sequence,1.0,359.0,UPI0000074015.a2m,UPI0000074015.npy,gnomAD +UPI00001C2068,UPI00001C2068.csv,MENRPGSFQYVPVQLQGGAPWGFTLKGGLEHCEPLTVSKIEDGGKAALSQKMRTGDELVNINGTPLYGSRQEALILIKGSFRILKLIVRRRNAPVSRPHSWHVAKLLEGCPEAATTMHFPSEAFSLSWHSGCNTSDVCVQWCPLSRHCSTEKSSSIGSMESLEQPGQATYESHLLPIDQNMYPNQRDSAYSSFSASSNASDCALSLRPEEPASTDCIMQGPGPTKAPSGRPNVAETSGGSRRTNGGHLTPSSQMSSRPQEGYQSGPAKAVRGPPQPPVRRDSLQASRAQLLNGEQRRASEPVVPLPQKEKLSLEPVLPARNPNRFCCLSGHDQVTSEGHQNCEFSQPPESSQQGSEHLLMQASTKAVGSPKACDRASSVDSNPLNEASAELAKASFGRPPHLIGPTGHRHSAPEQLLASHLQHVHLDTRGSKGMELPPVQDGHQWTLSPLHSSHKGKKSPCPPTGGTHDQSSKERKTRQVDDRSLVLGHQSQSSPPHGEADGHPSEKGFLDPNRTSRAASELANQQPSASGSLVQQATDCSSTTKAASGTEAGEEGDSEPKECSRMGGRRSGGTRGRSIQNRRKSERFATNLRNEIQRRKAQLQKSKGPLSQLCDTKEPVEETQEPPESPPLTASNTSLLSSCKKPPSPRDKLFNKSMMLRARSSECLSQAPESHESRTGLEGRISPGQRPGQSSLGLNTWWKAPDPSSSDPEKAHAHCGVRGGHWRWSPEHNSQPLVAAAMEGPSNPGDNKELKASTAQAGEDAILLPFADRRKFFEESSKSLSTSHLPGLTTHSNKTFTQRPKPIDQNFQPMSSSCRELRRHPMDQSYHSADQPYHATDQSYHSMSPLQSETPTYSECFASKGLENSMCCKPLHCGDFDYHRTCSYSCSVQGALVHDPCIYCSGEICPALLKRNMMPNCYNCRCHHHQCIRCSVCYHNPQHSALEDSSLAPGNTWKPRKLTVQEFPGDKWNPITGNRKTSQSGREMAHSKTSFSWATPFHPCLENPALDLSSYRAISSLDLLGDFKHALKKSEETSVYEEGSSLASMPHPLRSRAFSESHISLAPQSTRAWGQHRRELFSKGDETQSDLLGARKKAFPPPRPPPPNWEKYRLFRAAQQQKQQQQQQKQQEEEEEEEEEEEEEEEEEEEEAEEEEEELPPQYFSSETSGSCALNPEEVLEQPQPLSFGHLEGSRQGSQSVPAEQESFALHSSDFLPPIRGHLGSQPEQAQPPCYYGIGGLWRTSGQEATESAKQEFQHFSPPSGAPGIPTSYSAYYNISVAKAELLNKLKDQPEMAEIGLGEEEVDHELAQKKIQLIESISRKLSVLREAQRGLLEDINANSALGEEVEANLKAVCKSNEFEKYHLFVGDLDKVVNLLLSLSGRLARVENALNSIDSEANQEKLVLIEKKQQLTGQLADAKELKEHVDRREKLVFGMVSRYLPQDQLQDYQHFVKMKSALIIEQRELEEKIKLGEEQLKCLRESLLLGPSNF,mutated_sequence,1.0,1493.0,UPI00001C2068.a2m,UPI00001C2068.npy,gnomAD +UPI00005788EA,UPI00005788EA.csv,MTMFKEAVTFKDVAVVFTEEELGLLDVSQRKLYRDVMLENFRNLLSVGHQLSHRDTFHFQREEKFWIMETATQREGNSGGKIQTELESVPETGPHEEWSCQQIWEQTASELTRPQDSISSSQFSTQGDVPSQVDAGLSIIHIGETPSEHGKCKKFFSDVSILDLHQQLHSGKISHTCNEYRKRFCYSSALCLHQKVHMGEKRYKCDVCSKAFSQNSQLQTHQRIHTGEKPFKCEQCGKSFSRRSGMYVHCKLHTGEKPHICEECGKAFIHNSQLREHQRIHTGEKPFKCYICGKSFHSRSNLNRHSMVHMQEKSFRCDTCSNSFGQRSALNSHCMDHTKEKLYKCEECGRSFTCRQDLCKHQMDHTGDKPYNCNVCGKGFRWSSCLSRHQRVHNGETTFKCDGCGKRFYMNSQGHSHQRAYREEELYKCQKCGKGYISKFNLDLHQRVHTGERPYNCKECGKSFRWASGILRHKRLHTGEKPFKCEECGKRFTENSKLRFHQRIHTGEKPYKCEECGKGFRWASTHLTHQRLHSREKLFQCEDCGKSSEHSSCLQDQQSDHSGEKTSKCEDCGKRYERRLNLDMILSLFLNDI,mutated_sequence,1.0,593.0,UPI00005788EA.a2m,UPI00005788EA.npy,gnomAD +UPI00006BFF57,UPI00006BFF57.csv,MKIITYFCIWAVAWAIPVPQSKPLERHVEKSMNLHLLARSNVSVQDELNASGTIKESGVLVHEGDRGRQENTQDGHKGEGNGSKWAEVGGKSFSTYSTLANEEGNIEGWNGDTGKAETYGHDGIHGKEENITANGIQGQVSIIDNAGATNRSNTNGNTDKNTQNGDVGDAGHNEDVAVVQEDGPQVAGSNNSTDNEDEIIENSCRNEGNTSEITPQINSKRNGTKEAEVTPGTGEDAGLDNSDGSPSGNGADEDEDEGSGDDEDEEAGNGKDSSNNSKGQEGQDHGKEDDHDSSIGQNSDSKEYYDPEGKEDPHNEVDGDKTSKSEENSAGIPEDNGSQRIEDTQKLNHRESKRVENRITKESETHAVGKSQDKGIEIKGPSSGNRNITKEVGKGNEGKEDKGQHGMILGKGNVKTQGEVVNIEGPGQKSEPGNKVGHSNTGSDSNSDGYDSYDFDDKSMQGDDPNSSDESNGNDDANSESDNNSSSRGDASYNSDESKDNGNGSDSKGAEDDDSDSTSDTNNSDSNGNGNNGNDDNDKSDSGKGKSDSSDSDSSDSSNSSDSSDSSDSDSSDSNSSSDSDSSDSDSSDSSDSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSKSDSSKSESDSSDSDSKSDSSDSNSSDSSDNSDSSDSSNSSNSSDSSDSSDSSDSSSSSDSSNSSDSSDSSDSSNSSESSDSSDSSDSDSSDSSDSSNSNSSDSDSSNSSDSSDSSNSSDSSDSSDSSNSSDSSDSSDSSNSSDSSDSSDSSDSSDSSNSSDSNDSSNSSDSSDSSNSSDSSNSSDSSDSSDSSDSDSSNSSDSSNSSDSSDSSNSSDSSDSSDSSDGSDSDSSNRSDSSNSSDSSDSSDSSNSSDSSDSSDSNESSNSSDSSDSSNSSDSDSSDSSNSSDSSDSSNSSDSSESSNSSDNSNSSDSSNSSDSSDSSDSSNSSDSSNSSDSSNSSDSSDSNSSDSSDSSNSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSNSSDSSNSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSESSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSNSSDSSDSSESSDSSDSSDSSDSSDSSDSSDSSDSSDSSNSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSSDSNESSDSSDSSDSSDSSNSSDSSDSSDSSDSTSDSNDESDSQSKSGNGNNNGSDSDSDSEGSDSNHSTSDD,mutated_sequence,1.0,1301.0,UPI00006BFF57.a2m,UPI00006BFF57.npy,gnomAD +UPI000013D050,UPI000013D050.csv,MGDSRDLCPHLDSIGEVTKEDLLLKSKGTCQSCGVTGPNLWACLQVACPYVGCGESFADHSTIHAQAKKHNLTVNLTTFRLWCYACEKEVFLEQRLAAPLLGSSSKFSEQDSPPPSHPLKAVPIAVADEGESESEDDDLKPRGLTGMKNLGNSCYMNAALQALSNCPPLTQFFLECGGLVRTDKKPALCKSYQKLVSEVWHKKRPSYVVPTSLSHGIKLVNPMFRGYAQQDTQEFLRCLMDQLHEELKEPVVATVALTEARDSDSSDTDEKREGDRSPSEDEFLSCDSSSDRGEGDGQGRGGGSSQAETELLIPDEAGRAISEKERMKDRKFSWGQQRTNSEQVDEDADVDTAMAALDDQPAEAQPPSPRSSSPCRTPEPDNDAHLRSSSRPCSPVHHHEGHAKLSSSPPRASPVRMAPSYVLKKAQVLSAGSRRRKEQRYRSVISDIFDGSILSLVQCLTCDRVSTTVETFQDLSLPIPGKEDLAKLHSAIYQNVPAKPGACGDSYAAQGWLAFIVEYIRRFVVSCTPSWFWGPVVTLEDCLAAFFAADELKGDNMYSCERCKKLRNGVKYCKVLRLPEILCIHLKRFRHEVMYSFKINSHVSFPLEGLDLRPFLAKECTSQITTYDLLSVICHHGTAGSGHYIAYCQNVINGQWYEFDDQYVTEVHETVVQNAEGYVLFYRKSSEEAMRERQQVVSLAAMREPSLLRFYVSREWLNKFNTFAEPGPITNQTFLCSHGGIPPHKYHYIDDLVVILPQNVWEHLYNRFGGGPAVNHLYVCSICQVEIEALAKRRRIEIDTFIKLNKAFQAEESPGVIYCISMQWFREWEAFVKGKDNEPPGPIDNSRIAQVKGSGHVQLKQGADYGQISEETWTYLNSLYGGGPEIAIRQSVAQPLGPENLHGEQKIEAETRAV,mutated_sequence,1.0,914.0,UPI000013D050.a2m,UPI000013D050.npy,gnomAD +UPI000011F17B,UPI000011F17B.csv,MGNRSTADADGLLAGRGPAAGASAGASAGLAGQGAAALVGGVLLIGAVLAGNSLVCVSVATERALQTPTNSFIVSLAAADLLLALLVLPLFVYSEVQGGAWLLSPRLCDALMAMDVMLCTASIFNLCAISVDRFVAVAVPLRYNRQGGSRRQLLLIGATWLLSAAVAAPVLCGLNDVRGRDPAVCRLEDRDYVVYSSVCSFFLPCPLMLLLYWATFRGLQRWEVARRAKLHGRAPRRPSGPGPPSPTPPAPRLPQDPCGPDCAPPAPGLPRGPCGPDCAPAAPSLPQDPCGPDCAPPAPGLPPDPCGSNCAPPDAVRAAALPPQTPPQTRRRRRAKITGRERKAMRVLPVVVGAFLLCWTPFFVVHITQALCPACSVPPRLVSAVTWLGYVNSALNPVIYTVFNAEFRNVFRKALRACC,mutated_sequence,1.0,419.0,UPI000011F17B.a2m,UPI000011F17B.npy,gnomAD +UPI000004064B,UPI000004064B.csv,MEFVRALWLGLALALGPGSAGGHPQPCGVLARLGGSVRLGALLPRAPLARARARAALARAALAPRLPHNLSLELVVAAPPARDPASLTRGLCQALVPPGVAALLAFPEARPELLQLHFLAAATETPVLSLLRREARAPLGAPNPFHLQLHWASPLETLLDVLVAVLQAHAWEDVGLALCRTQDPGGLVALWTSRAGRPPQLVLDLSRRDTGDAGLRARLAPMAAPVGGEAPVPAAVLLGCDIARARRVLEAVPPGPHWLLGTPLPPKALPTAGLPPGLLALGEVARPPLEAAIHDIVQLVARALGSAAQVQPKRALLPAPVNCGDLQPAGPESPGRFLARFLANTSFQGRTGPVWVTGSSQVHMSRHFKVWSLRRDPRGAPAWATVGSWRDGQLDLEPGGASARPPPPQGAQVWPKLRVVTLLEHPFVFARDPDEDGQCPAGQLCLDPGTNDSATLDALFAALANGSAPRALRKCCYGYCIDLLERLAEDTPFDFELYLVGDGKYGALRDGRWTGLVGDLLAGRAHMAVTSFSINSARSQVVDFTSPFFSTSLGIMVRARDTASPIGAFMWPLHWSTWLGVFAALHLTALFLTVYEWRSPYGLTPRGRNRSTVFSYSSALNLCYAILFRRTVSSKTPKCPTGRLLMNLWAIFCLLVLSSYTANLAAVMVGDKTFEELSGIHDPKLHHPAQGFRFGTVWESSAEAYIKKSFPDMHAHMRRHSAPTTPRGVAMLTSDPPKLNAFIMDKSLLDYEVSIDADCKLLTVGKPFAIEGYGIGLPQNSPLTSNLSEFISRYKSSGFIDLLHDKWYKMVPCGKRVFAVTETLQMSIYHFAGLFVLLCLGLGSALLSSLGEHAFFRLALPRIRKGSRLQYWLHTSQKIHRALNTEPPEGSKEETAEAEPSGPEVEQQQQQQDQPTAPEGWKRARRAVDKERRVRFLLEPAVVVAPEADAEAEAAPREGPVWLCSYGRPPAARPTGAPQPGELQELERRIEVARERLRQALVRRGQLLAQLGDSARHRPRRLLQARAAPAEAPPHSGRPGSQE,mutated_sequence,1.0,1043.0,UPI000004064B.a2m,UPI000004064B.npy,gnomAD +UPI0000127E5B,UPI0000127E5B.csv,MPRSCCSRSGALLLALLLQASMEVRGWCLESSQCQDLTTESNLLECIRACKPDLSAETPMFPGNGDEQPLTENPRKYVMGHFRWDRFGRRNSSSSGSSGAGQKREDVSAGEDCGPLPEGGPEPRSDGAKPGPREGKRSYSMEHFRWGKPVGKKRRPVKVYPNGAEDESAEAFPLEFKRELTGQRLREGDGPDGPADDGAGAQADLEHSLLVAAEKKDEGPYRMEHFRWGSPPKDKRYGGFMTSEKSQTPLVTLFKNAIIKNAYKKGE,mutated_sequence,1.0,267.0,UPI0000127E5B.a2m,UPI0000127E5B.npy,gnomAD +UPI00001BB2B9,UPI00001BB2B9.csv,MSGIKRTIKETDPDYEDVSVALPNKRHKAIENSARDAAVQKIETIIKEQFALEMKNKEHEIEVIDQRLIEARRMMDKLRACIVANYYASAGLLKVSEGSKTCDTMVFNHPAIKKFLESPSRSSSPANQRAETPSANHSESDSLSQHNDFLSDKDNNSNMDIEERLSNNMEQRPSRNTGRDTSRITGSHKTEQRNADLTDETSRLFVKKTIVVGNVSKYIPPDKREENDQSTHKWMVYVRGSRREPSINHFVKKVWFFLHPSYKPNDLVEVREPPFHLTRRGWGEFPVRVQVHFKDSQNKRIDIIHNLKLDRTYTGLQTLGAETVVDVELHRHSLGEDCIYPQSSESDISDAPPSLPLTIPAPVKASSPIKQSHEPVPDTSVEKGFPASTEAERHTPFYALPSSLERTPTKMTTSQKVTFCSHGNSAFQPIASSCKIVPQSQVPNPESPGKSFQPITMSCKIVSGSPISTPSPSPLPRTPTSTPVHVKQGTAGSVINNPYVIMDKQPGQVIGATTPSTGSPTNKISTASQVSQGTGSPVPKIHGSSFVTSTVKQEDSLFASMPPLCPIGSHPKVQSPKPITGGLGAFTKVIIKQEPGEAPHVPATGAASQSPLPQYVTVKGGHMIAVSPQKQVITPGEGIAQSAKVQPSKVVGVPVGSALPSTVKQAVAISGGQILVAKASSSVSKAVGPKQVVTQGVAKAIVSGGGGTIVAQPVQTLTKAQVTAAGPQKSGSQGSVMATLQLPATNLANLANLPPGTKLYLTTNSKNPSGKGKLLLIPQGAILRATNNANLQSGSAASGGSGAGGGGGGGGGGGSGSGGGGSTGGGGGTAGGGTQSTAGPGGISQHLTYTSYILKQTPQGTFLVGQPSPQTSGKQLTTGSVVQGTLGVSTSSAQGQQTLKVISGQKTTLFTQAAHGGQASLMKISDSTLKTVPATSQLSKPGTTMLRVAGGVITTATSPAVALSANGPAQQSEGMAPVSSSTVSSVTKTSGQQQVCVSQATVGTCKAATPTVVSATSLVPTPNPISGKATVSGLLKIHSSQSSPQQAVLTIPSQLKPLSVNTSGGVQTILMPVNKVVQSFSTSKPPAILPVAAPTPVVPSSAPAAVAKVKTEPETPGPSCLSQEGQTAVKTEESSELGNYVIKIDHLETIQQLLTAVVKKIPLITAKSEDASCFSAKSVEQYYGWNIGKRRAAEWQRAMTMRKVLQEILEKNPRFHHLTPLKTKHIAHWCRCHGYTPPDPESLRNDGDSIEDVLTQIDSEPECPSSFSSADNLCRKLEDLQQFQKREPENEEEVDILSLSEPVKINIKKEQEEKQEEVKFYLPPTPGSEFIGDVTQKIGITLQPVALHRNVYASVVEDMILKATEQLVNDILRQALAVGYQTASHNRIPKEITVSNIHQAICNIPFLDFLTNKHMGILNEDQ,mutated_sequence,1.0,1422.0,UPI00001BB2B9.a2m,UPI00001BB2B9.npy,gnomAD +UPI000008AB1D,UPI000008AB1D.csv,MAVMAPRTLLLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASQRMEPRAPWIEQEGPEYWDQETRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQIMYGCDVGSDGRFLRGYRQDAYDGKDYIALNEDLRSWTAADMAAQITKRKWEAAHEAEQLRAYLDGTCVEWLRRYLENGKETLQRTDPPKTHMTHHPISDHEATLRCWALGFYPAEITLTWQRDGEDQTQDTELVETRPAGDGTFQKWAAVVVPSGEEQRYTCHVQHEGLPKPLTLRWELSSQPTIPIVGIIAGLVLLGAVITGAVVAAVMWRRKSSDRKGGSYTQAASSDSAQGSDVSLTACKV,mutated_sequence,1.0,365.0,UPI000008AB1D.a2m,UPI000008AB1D.npy,gnomAD +UPI0000456761,UPI0000456761.csv,MSAPPVLRPPSPLLPVAAAAAAAAAALVPGSGPGPAPFLAPVAAPVGGISFHLQIGLSREPVLLLQDSSGDYSLAHVREMACSIVDQKFPECGFYGMYDKILLFRHDPTSENILQLVKAASDIQEGDLIEVVLSASATFEDFQIRPHALFVHSYRAPAFCDHCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRRRRLSNVSLTGVSTIRTSSAELSTSAPDEPLLQKSPSESFIGREKRSNSQSYIGRPIHLDKILMSKVKVPHTFVIHSYTRPTVCQYCKKLLKGLFRQGLQCKDCRFNCHKRCAPKVPNNCLGEVTINGDLLSPGAESDVVMEEGSDDNDSERNSGLMDDMEEAMVQDAEMAMAECQNDSGEMQDPDPDHEDANRTISPSTSNNIPLMRVVQSVKHTKRKSSTVMKEGWMVHYTSKDTLRKRHYWRLDSKCITLFQNDTGSRYYKEIPLSEILSLEPVKTSALIPNGANPHCFEITTANVVYYVGENVVNPSSPSPNNSVLTSGVGADVARMWEIAIQHALMPVIPKGSSVGTGTNLHRDISVSISVSNCQIQENVDISTVYQIFPDEVLGSGQFGIVYGGKHRKTGRDVAIKIIDKLRFPTKQESQLRNEVAILQNLHHPGVVNLECMFETPERVFVVMEKLHGDMLEMILSSEKGRLPEHITKFLITQILVALRHLHFKNIVHCDLKPENVLLASADPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRNKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDIHDQIQNAAFMYPPNPWKEISHEAIDLINNLLQVKMRKRYSVDKTLSHPWLQDYQTWLDLRELECKIGERYITHESDDLRWEKYAGEQGLQYPTHLINPSASHSDTPETEETEMKALGERVSIL,mutated_sequence,1.0,912.0,UPI0000456761.a2m,UPI0000456761.npy,gnomAD 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+NP_001121897.1,MTPLVSRLSRLWAIMRKPRAAVGSGHRKQAASQEGRQKHAKNNSQAKPSACDACAGMIAECPGAPAGLARQPEEVVLQASVSSYHLFRDVAEVTAFRGSLLSWYDQEKRDLPWRRRAEDEMDLDRRAYAVWVSEVMLQQTQVATVINYYTGWMQKWPTLQDLASASLEEVNQLWAGLGYYSRGRRLQEGARKVVEELGGHMPRTAETLQQLLPGVGRYTAGAIASIAFGQATGVVDGNVARVLCRVRAIGADPSSTLVSQQLWGLAQQLVDPARPGDFNQAAMELGATVCTPQRPLCSQCPVESLCRARQRVEQEQLLASGSLSGSPDVEECAPNTGQCHLCLPPSEPWDQTLGVVNFPRKASRKPPREESSATCVLEQPGALGAQILLVQRPNSGLLAGLWEFPSVTWEPSEQLQRKALLQELQRWAGPLPATHLRHLGEVVHTFSHIKLTYQVYGLALEGQTPVTTVPPGARWLTQEEFHTAAVSTAMKKVFRVYQGQQPGTCMGSKRSQVSSPCSRKKPRMGQQVLDNFFRSHISTDAHSLNSAAQ,549,NP_001121897.1.csv,refseq-MUTYH-NM_001128425.1_clinical_seed_0_final,refseq-MUTYH-NM_001128425.1.a2m,Invitae,refseq-MUTYH-NM_001128425.1.npy,1,549,549 +NP_001121901.1,MNLFNLDRFRFEKRNKIEEAPEATPQPSQPGPSSPISLSAEEENAEGEVSRANTPDSDITEKTEDSSVPETPDNERKASISYFKNQRGIQYIDLSSDSEDVVSPNCSNTVQEKTFNKDTVIIVSEPSEDEESQGLPTMARRNDDISELEDLSELEDLKDAKLQTLKELFPQRSDNDLLKLIESTSTMDGAIAAALLMFGDAGGGPRKRKLSSSSEPYEEDEFNDDQSIKKTRLDHGEESNESAESSSNWEKQESIVLKLQKEFPNFDKQELREVLKEHEWMYTEALESLKVFAEDQDMQYVSQSEVPNGKEVSSRSQNYPKNATKTKLKQKFSMKAQNGFNKKRKKNVFNPKRVVEDSEYDSGSDVGSSLDEDYSSGEEVMEDGYKGKILHFLQDASIGELTLIPQCSQKKAQKITELRPFNSWEALFTKMSKTNGLSEDLIWHCKTLIQERDVVIRLMNKCEDISNKLTKQVTMLTGNGGGWNIEQPSILNQSLSLKPYQKVGLNWLALVHKHGLNGILADEMGLGKTIQAIAFLAYLYQEGNNGPHLIVVPASTIDNWLREVNLWCPTLKVLCYYGSQEERKQIRFNIHSRYEDYNVIVTTYNCAISSSDDRSLFRRLKLNYAIFDEGHMLKNMGSIRYQHLMTINANNRLLLTGTPVQNNLLELMSLLNFVMPHMFSSSTSEIRRMFSSKTKSADEQSIYEKERIAHAKQIIKPFILRRVKEEVLKQLPPKKDRIELCAMSEKQEQLYLGLFNRLKKSINNLVTEKNTEMCNVMMQLRKMANHPLLHRQYYTAEKLKEMSQLMLKEPTHCEANPDLIFEDMEVMTDFELHVLCKQYRHINNFQLDMDLILDSGKFRVLGCILSELKQKGDRVVLFSQFTMMLDILEVLLKHHQHRYLRLDGKTQISERIHLIDEFNTDMDIFVFLLSTKAGGLGINLTSANVVILHDIDCNPYNDKQAEDRCHRVGQTKEVLVIKLISQGTIEESMLKINQQKLKLEQDMTTVDEGDEGSMPADIATLLKTSMGL,1028,NP_001121901.1.csv,refseq-SMARCAD1-NM_001128429.2_clinical_seed_0_final,refseq-SMARCAD1-NM_001128429.2.a2m,Invitae,refseq-SMARCAD1-NM_001128429.2.npy,1,1028,1028 +NP_001121903.1,MKLLLLHPAFQSCLLLTLLGLWRTTPEAHASSLGAPAISAASFLQDLIHRYGEGDSLTLQQLKALLNHLDVGVGRGNVTQHVQGHRNLSTCFSSGDLFTAHNFSEQSRIGSSELQEFCPTILQQLDSRACTSENQENEENEQTEEGRPSAVEVWGYGLLCVTVISLCSLLGASVVPFMKKTFYKRLLLYFIALAIGTLYSNALFQLIPEAFGFNPLEDYYVSKSAVVFGGFYLFFFTEKILKILLKQKNEHHHGHSHYASESLPSKKDQEEGVMEKLQNGDLDHMIPQHCSSELDGKAPMVDEKVIVGSLSVQDLQASQSACYWLKGVRYSDIGTLAWMITLSDGLHNFIDGLAIGASFTVSVFQGISTSVAILCEEFPHELGDFVILLNAGMSIQQALFFNFLSACCCYLGLAFGILAGSHFSANWIFALAGGMFLYISLADMFPEMNEVCQEDERKGSILIPFIIQNLGLLTGFTIMVVLTMYSGQIQIG,492,NP_001121903.1.csv,refseq-SLC39A14-NM_001128431.2_clinical_seed_0_final,refseq-SLC39A14-NM_001128431.2.a2m,Invitae,refseq-SLC39A14-NM_001128431.2.npy,1,492,492 +NP_001122092.1,MSNNGLDIQDKPPAPPMRNTSTMIGAGSKDAGTLNHGSKPLPPNPEEKKKKDRFYRSILPGDKTNKKKEKERPEISLPSDFEHTIHVGFDAVTGEFTGMPEQWARLLQTSNITKSEQKKNPQAVLDVLEFYNSKKTSNSQKYMSFTDKSAEDYNSSNALNVKAVSETPAVPPVSEDEDDDDDDATPPPVIAPRPEHTKSVYTRSVIEPLPVTPTRDVATSPISPTENNTTPPDALTRNTEKQKKKPKMSDEEILEKLRSIVSVGDPKKKYTRFEKIGQGASGTVYTAMDVATGQEVAIKQMNLQQQPKKELIINEILVMRENKNPNIVNYLDSYLVGDELWVVMEYLAGGSLTDVVTETCMDEGQIAAVCRECLQALEFLHSNQVIHRDIKSDNILLGMDGSVKLTDFGFCAQITPEQSKRSTMVGTPYWMAPEVVTRKAYGPKVDIWSLGIMAIEMIEGEPPYLNENPLRALYLIATNGTPELQNPEKLSAIFRDFLNRCLEMDVEKRGSAKELLQVRKLRFQVFSNFSMIAASIPEDCQAPLQPHSTDCCS,553,NP_001122092.1.csv,refseq-PAK1-NM_001128620.1_clinical_seed_0_final,refseq-PAK1-NM_001128620.1.a2m,Invitae,refseq-PAK1-NM_001128620.1.npy,1,553,553 +NP_001122317.1,MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDEEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKTLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED,1617,NP_001122317.1.csv,refseq-SMARCA4-NM_001128845.2_clinical_seed_0_final,refseq-SMARCA4-NM_001128845.2.a2m,Invitae,refseq-SMARCA4-NM_001128845.2.npy,1,1617,1617 +NP_001123292.1,MESLKTDTEMPYPEVIVDVGRVIFGEENRKKMTNSCLKRSENSRIIRAICALLNSGGGVIKAEIDDKTYSYQCHGLGQDLETSFQKLLPSGSQKYLDYMQQGHNLLIFVKSWSPDVFSLPLRICSLRSNLYRRDVTSAINLSASSALELLREKGFRAQRGRPRVKKLHPQQVLNRCIQEEEDMRILASEFFKKDKLMYKEKLNFTESTHVEFKRFTTKKVIPRIKEMLPHYVSAFANTQGGYVLIGVDDKSKEVVGCKWEKVNPDLLKKEIENCIEKLPTFHFCCEKPKVNFTTKILNVYQKDVLDGYVCVIQVEPFCCVVFAEAPDSWIMKDNSVTRLTAEQWVVMMLDTQSAPPSLVTDYNSCLISSASSARKSPGYPIKVHKFKEALQRHLFPVTQEEVQFKPESLCKKLFSDHKELEGLMKTLIHPCSQGIVIFSRSWAGDVGFRKEQNVLCDALLIAVNSPVVLYTILIDPNWPGGLEYARNTAHQLKQKLQTVGGYTGKVCIIPRLIHLSSTQSRPGEIPLRYPRSYRLADEEEMEDLLQALVVVSLSSRSLLSDQMGCEFFNLLIMEQSQLLSESLQKTRELFIYCFPGVRKTALAIKIMEKIKDLFHCKPKEILYVCESDSLKDFVTQQTTCQAVTRKTFMQGEFLKIKHIVMDETENFCSKYGNWYMKAKNITHPKAKGTGSENLHHGILWLFLDPFQIHHADVNGLPPPSAQFPRKTITSGIHCALEIAKVMKEEMKRIKENPPSNMSPDTLALFSETAYEEATCAQALPGVCETKTNLTTEQIANYVARKCHSLFQCGYLPKDIAILCRRGEDRGRYRLALLKAMELIETHRPSEVVFSPATGVWGSHIVLDSIQQFSGLERTVVFGLSPECDQSEEFHKLCFASRAIKHLYLLYEKRAAY,912,NP_001123292.1.csv,refseq-SLFN14-NM_001129820.1_clinical_seed_0_final,refseq-SLFN14-NM_001129820.1.a2m,Invitae,refseq-SLFN14-NM_001129820.1.npy,1,912,912 +NP_001123476.1,MDHYDSQQTNDYMQPEEDWDRDLLLDPAWEKQQRKTFTAWCNSHLRKAGTQIENIEEDFRDGLKLMLLLEVISGERLAKPERGKMRVHKISNVNKALDFIASKGVKLVSIGAEEIVDGNVKMTLGMIWTIILRFAIQDISVEETSAKEGLLLWCQRKTAPYKNVNIQNFHISWKDGLGFCALIHRHRPELIDYGKLRKDDPLTNLNTAFDVAEKYLDIPKMLDAEDIVGTARPDEKAIMTYVSSFYHAFSGAQKAETAANRICKVLAVNQENEQLMEDYEKLASDLLEWIRRTIPWLENRVPENTMHAMQQKLEDFRDYRRLHKPPKVQEKCQLEINFNTLQTKLRLSNRPAFMPSEGRMVSDINNAWGCLEQVEKGYEEWLLNEIRRLERLDHLAEKFRQKASIHEAWTDGKEAMLRQKDYETATLSEIKALLKKHEAFESDLAAHQDRVEQIAAIAQELNELDYYDSPSVNARCQKICDQWDNLGALTQKRREALERTEKLLETIDQLYLEYAKRAAPFNNWMEGAMEDLQDTFIVHTIEEIQGLTTAHEQFKATLPDADKERLAILGIHNEVSKIVQTYHVNMAGTNPYTTITPQEINGKWDHVRQLVPRRDQALTEEHARQQHNERLRKQFGAQANVIGPWIQTKMEEIGRISIEMHGTLEDQLSHLRQYEKSIVNYKPKIDQLEGDHQLIQEALIFDNKHTNYTMEHIRVGWEQLLTTIARTINEVENQILTRDAKGISQEQMNEFRASFNHFDRDHSGTLGPEEFKACLISLGYDIGNDPQKKTGMMDTDDFRACLISMGYNMGEAEFARIMSIVDPNRLGVVTFQAFIDFMSRETADTDTADQVMASFKILAGDKNYITMDELRRELPPDQAEYCIARMAPYTGPDSVPGALDYMSFSTALYGESDL,914,NP_001123476.1.csv,refseq-ACTN1-NM_001130004.1_clinical_seed_0_final,refseq-ACTN1-NM_001130004.1.a2m,Invitae,refseq-ACTN1-NM_001130004.1.npy,1,914,914 +NP_001123482.1,MILTKAQYDEIAQCLVSVPPTRQSLRKLKQRFPSQSQATLLSIFSQEYQKHIKRTHAKHHTSEAIESYYQRYLNGVVKNGAAPVLLDLANEVDYAPSLMARLILERFLQEHEETPPSKSIINSMLRDPSQIPDGVLANQVYQCIVNDCCYGPLVDCIKHAIGHEHEVLLRDLLLEKNLSFLDEDQLRAKGYDKTPDFILQVPVAVEGHIIHWIESKASFGDECSHHAYLHDQFWSYWNRFGPGLVIYWYGFIQELDCNRERGILLKACFPTNIVTLCHSIA,281,NP_001123482.1.csv,refseq-C15orf41-NM_001130010.2_clinical_seed_0_final,refseq-C15orf41-NM_001130010.2.a2m,Invitae,refseq-C15orf41-NM_001130010.2.npy,1,281,281 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+NP_001123616.1,MPGPRGAAGGLAPEMRGAGAAGLLALLLLLLLLLLGLGGRVEGGPAGERGAGGGGALARERFKVVFAPVICKRTCLKGQCRDSCQQGSNMTLIGENGHSTDTLTGSGFRVVVCPLPCMNGGQCSSRNQCLCPPDFTGRFCQVPAGGAGGGTGGSGPGLSRTGALSTGALPPLAPEGDSVASKHAIYAVQVIADPPGPGEGPPAQHAAFLVPLGPGQISAEVQAPPPVVNVRVHHPPEASVQVHRIESSNAESAAPSQHLLPHPKPSHPRPPTQKPLGRCFQDTLPKQPCGSNPLPGLTKQEDCCGSIGTAWGQSKCHKCPQLQYTGVQKPGPVRGEVGADCPQGYKRLNSTHCQDINECAMPGVCRHGDCLNNPGSYRCVCPPGHSLGPSRTQCIADKPEEKSLCFRLVSPEHQCQHPLTTRLTRQLCCCSVGKAWGARCQRCPTDGTAAFKEICPAGKGYHILTSHQTLTIQGESDFSLFLHPDGPPKPQQLPESPSQAPPPEDTEEERGVTTDSPVSEERSVQQSHPTATTTPARPYPELISRPSPPTMRWFLPDLPPSRSAVEIAPTQVTETDECRLNQNICGHGECVPGPPDYSCHCNPGYRSHPQHRYCVDVNECEAEPCGPGRGICMNTGGSYNCHCNRGYRLHVGAGGRSCVDLNECAKPHLCGDGGFCINFPGHYKCNCYPGYRLKASRPPVCEDIDECRDPSSCPDGKCENKPGSFKCIACQPGYRSQGGGACRDVNECAEGSPCSPGWCENLPGSFRCTCAQGYAPAPDGRSCLDVDECEAGDVCDNGICSNTPGSFQCQCLSGYHLSRDRSHCEDIDECDFPAACIGGDCINTNGSYRCLCPQGHRLVGGRKCQDIDECSQDPSLCLPHGACKNLQGSYVCVCDEGFTPTQDQHGCEEVEQPHHKKECYLNFDDTVFCDSVLATNVTQQECCCSLGAGWGDHCEIYPCPVYSSAEFHSLCPDGKGYTQDNNIVNYGIPAHRDIDECMLFGSEICKEGKCVNTQPGYECYCKQGFYYDGNLLECVDVDECLDESNCRNGVCENTRGGYRCACTPPAEYSPAQRQCLSPEEMDVDECQDPAACRPGRCVNLPGSYRCECRPPWVPGPSGRDCQLPESPAERAPERRDVCWSQRGEDGMCAGPLAGPALTFDDCCCRQGRGWGAQCRPCPPRGAGSHCPTSQSESNSFWDTSPLLLGKPPRDEDSSEEDSDECRCVSGRCVPRPGGAVCECPGGFQLDASRARCVDIDECRELNQRGLLCKSERCVNTSGSFRCVCKAGFARSRPHGACVPQRRR,1303,NP_001123616.1.csv,refseq-LTBP3-NM_001130144.2_clinical_seed_0_final,refseq-LTBP3-NM_001130144.2.a2m,Invitae,refseq-LTBP3-NM_001130144.2.npy,1,1303,1303 +NP_001123617.1,MDPGQQPPPQPAPQGQGQPPSQPPQGQGPPSGPGQPAPAATQAAPQAPPAGHQIVHVRGDSETDLEALFNAVMNPKTANVPQTVPMRLRKLPDSFFKPPEPKSHSRQASTDAGTAGALTPQHVRAHSSPASLQLGAVSPGTLTPTGVVSGPAATPTAQHLRQSSFEIPDDVPLPAGWEMAKTSSGQRYFLNHIDQTTTWQDPRKAMLSQMNVTAPTSPPVQQNMMNSASGPLPDGWEQAMTQDGEIYYINHKNKTTSWLDPRLDPRFAMNQRISQSAPVKQPPPLAPQSPQGGVMGGSNSNQQQQMRLQQLQMEKERLRLKQQELLRQAMRNINPSTANSPKCQELALRSQLPTLEQDGGTQNPVSSPGMSQELRTMTTNSSDPFLNSGTYHSRDESTDSGLSMSSYSVPRTPDDFLNSVDEMDTGDTINQSTLPSQQNRFPDYLEAIPGTNVDLGTLEGDGMNIEGEELMPSLQEALSSDILNDMESVLAATKLDKESFLTWL,504,NP_001123617.1.csv,refseq-YAP1-NM_001130145.2_clinical_seed_0_final,refseq-YAP1-NM_001130145.2.a2m,Invitae,refseq-YAP1-NM_001130145.2.npy,1,504,504 +NP_001123990.1,MMMVRRGLLAWISRVVVLLVLLCCAISVLYMLACTPKGDEEQLALPRANSPTGKEGYQAVLQEWEEQHRNYVSSLKRQIAQLKEELQERSEQLRNGQYQASDAAGLGLDRSPPEKTQADLLAFLHSQVDKAEVNAGVKLATEYAAVPFDSFTLQKVYQLETGLTRHPEEKPVRKDKRDELVEAIESALETLNSPAENSPNHRPYTASDFIEGIYRTERDKGTLYELTFKGDHKHEFKRLILFRPFGPIMKVKNEKLNMANTLINVIVPLAKRVDKFRQFMQNFREMCIEQDGRVHLTVVYFGKEEINEVKGILENTSKAANFRNFTFIQLNGEFSRGKGLDVGARFWKGSNVLLFFCDVDIYFTSEFLNTCRLNTQPGKKVFYPVLFSQYNPGIIYGHHDAVPPLEQQLVIKKETGFWRDFGFGMTCQYRSDFINIGGFDLDIKGWGGEDVHLYRKYLHSNLIVVRTPVRGLFHLWHEKRCMDELTPEQYKMCMQSKAMNEASHGQLGMLVFRHEIEAHLRKQKQKTSSKKT,532,NP_001123990.1.csv,refseq-CSGALNACT1-NM_001130518.1_clinical_seed_0_final,refseq-CSGALNACT1-NM_001130518.1.a2m,Invitae,refseq-CSGALNACT1-NM_001130518.1.npy,1,532,532 +NP_001124162.1,MEDGPSNNASCFRRLTECFLSPSLTDEKVKAYLSLHPQVLDEFVSESVSAETVEKWLKRKNNKSEDESAPKEVSRYQDTNMQGVVYELNSYIEQRLDTGGDNQLLLYELSSIIKIATKADGFALYFLGECNNSLCIFTPPGIKEGKPRLIPAGPITQGTTVSAYVAKSRKTLLVEDILGDERFPRGTGLESGTRIQSVLCLPIVTAIGDLIGILELYRHWGKEAFCLSHQEVATANLAWASVAIHQVQVCRGLAKQTELNDFLLDVSKTYFDNIVAIDSLLEHIMIYAKNLVNADRCALFQVDHKNKELYSDLFDIGEEKEGKPVFKKTKEIRFSIEKGIAGQVARTGEVLNIPDAYADPRFNREVDLYTGYTTRNILCMPIVSRGSVIGVVQMVNKISGSAFSKTDENNFKMFAVFCALALHCANMYHRIRHSECIYRVTMEKLSYHSICTSEEWQGLMQFTLPVRLCKEIELFHFDIGPFENMWPGIFVYMVHRSCGTSCFELEKLCRFIMSVKKNYRRVPYHNWKHAVTVAHCMYAILQNNHTLFTDLERKGLLIACLCHDLDHRGFSNSYLQKFDHPLAALYSTSTMEQHHFSQTVSILQLEGHNIFSTLSSSEYEQVLEIIRKAIIATDLALYFGNRKQLEEMYQTGSLNLNNQSHRDRVIGLMMTACDLCSVTKLWPVTKLTANDIYAEFWAEGDEMKKLGIQPIPMMDRDKKDEVPQGQLGFYNAVAIPCYTTLTQILPPTEPLLKACRDNLSQWEKVIRGEETATWISSPSVAQKAAASED,789,NP_001124162.1.csv,refseq-PDE10A-NM_001130690.2_clinical_seed_0_final,refseq-PDE10A-NM_001130690.2.a2m,Invitae,refseq-PDE10A-NM_001130690.2.npy,1,789,789 +NP_001124295.1,MPARTAPARVPTLAVPAISLPDDVRRRLKDLERDSLTEKECVKEKLNLLHEFLQTEIKNQLCDLETKLRKEELSEEGYLAKVKSLLNKDLSLENGAHAYNREVNGRLENGNQARSEARRVGMADANSPPKPLSKPRTPRRSKSDGEAKRSRDPPASASQVTGIRAEPSPSPRITRKSTRQTTITSHFAKGPAKRKPQEESERAKSDESIKEEDKDQDEKRRRVTSRERVARPLPAEEPERAKSGTRTEKEEERDEKEEKRLRSQTKEPTPKQKLKEEPDREARAGVQADEDEDGDEKDEKKHRSQPKDLAAKRRPEEKEPEKVNPQISDEKDEDEKEEKRRKTTPKEPTEKKMARAKTVMNSKTHPPKCIQCGQYLDDPDLKYGQHPPDAVDEPQMLTNEKLSIFDANESGFESYEALPQHKLTCFSVYCKHGHLCPIDTGLIEKNIELFFSGSAKPIYDDDPSLEGGVNGKNLGPINEWWITGFDGGEKALIGFSTSFAEYILMDPSPEYAPIFGLMQEKIYISKIVVEFLQSNSDSTYEDLINKIETTVPPSGLNLNRFTEDSLLRHAQFVVEQVESYDEAGDSDEQPIFLTPCMRDLIKLAGVTLGQRRAQARRQTIRHSTREKDRGPTKATTTKLVYQIFDTFFAEQIEKDDREDKENAFKRRRCGVCEVCQQPECGKCKACKDMVKFGGSGRSKQACQERRCPNMAMKEADDDEEVDDNIPEMPSPKKMHQGKKKKQNKNRISWVGEAVKTDGKKSYYKKVCIDAETLEVGDCVSVIPDDSSKPLYLARVTALWEDSSNGQMFHAHWFCAGTDTVLGATSDPLELFLVDECEDMQLSYIHSKVKVIYKAPSENWAMEGGMDPESLLEGDDGKTYFYQLWYDQDYARFESPPKTQPTEDNKFKFCVSCARLAEMRQKEIPRVLEQLEDLDSRVLYYSATKNGILYRVGDGVYLPPEAFTFNIKLSSPVKRPRKEPVDEDLYPEHYRKYSDYIKGSNLDAPEPYRIGRIKEIFCPKKSNGRPNETDIKIRVNKFYRPENTHKSTPASYHADINLLYWSDEEAVVDFKAVQGRCTVEYGEDLPECVQVYSMGGPNRFYFLEAYNAKSKSFEDPPNHARSPGNKGKGKGKGKGKPKSQACEPSEPEIEIKLPKLRTLDVFSGCGGLSEGFHQAGISDTLWAIEMWDPAAQAFRLNNPGSTVFTEDCNILLKLVMAGETTNSRGQRLPQKGDVEMLCGGPPCQGFSGMNRFNSRTYSKFKNSLVVSFLSYCDYYRPRFFLLENVRNFVSFKRSMVLKLTLRCLVRMGYQCTFGVLQAGQYGVAQTRRRAIILAAAPGEKLPLFPEPLHVFAPRACQLSVVVDDKKFVSNITRLSSGPFRTITVRDTMSDLPEVRNGASALEISYNGEPQSWFQRQLRGAQYQPILRDHICKDMSALVAARMRHIPLAPGSDWRDLPNIEVRLSDGTMARKLRYTHHDRKNGRSSSGALRGVCSCVEAGKACDPAARQFNTLIPWCLPHTGNRHNHWAGLYGRLEWDGFFSTTVTNPEPMGKQGRVLHPEQHRVVSVRECARSQGFPDTYRLFGNILDKHRQVGNAVPPPLAKAIGLEIKLCMLAKARESASAKIKEEEAAKD,1632,NP_001124295.1.csv,refseq-DNMT1-NM_001130823.1_clinical_seed_0_final,refseq-DNMT1-NM_001130823.1.a2m,Invitae,refseq-DNMT1-NM_001130823.1.npy,1,1632,1632 +NP_001124497.1,MAMRELVEAECGGANPLMKLAGHFTQDKALRQEGLRPGPWPPGAPASEAASKPLGVASEDELVAEFLQDQNAPLVSRAPQTFKMDDLLAEMQQIEQSNFRQAPQRAPGVADLALSENWAQEFLAAGDAVDVTQDYNETDWSQEFISEVTDPLSVSPARWAEEYLEQSEEKLWLGEPEGTATDRWYDEYHPEEDLQHTASDFVAKVDDPKLANSEFLKFVRQIGEGQVSLESGAGSGRAQAEQWAAEFIQQQGTSDAWVDQFTRPVNTSALDMEFERAKSAIESDVDFWDKLQAELEEMAKRDAEAHPWLSDYDDLTSATYDKGYQFEEENPLRDHPQPFEEGLRRLQEGDLPNAVLLFEAAVQQDPKHMEAWQYLGTTQAENEQELLAISALRRCLELKPDNQTALMALAVSFTNESLQRQACETLRDWLRYTPAYAHLVTPAEEGAGGAGLGPSKRILGSLLSDSLFLEVKELFLAAVRLDPTSIDPDVQCGLGVLFNLSGEYDKAVDCFTAALSVRPNDYLLWNKLGATLANGNQSEEAVAAYRRALELQPGYIRSRYNLGISCINLGAHREAVEHFLEALNMQRKSRGPRGEGGAMSENIWSTLRLALSMLGQSDAYGAADARDLSTLLTMFGLPQ,639,NP_001124497.1.csv,refseq-PEX5-NM_001131025.1_clinical_seed_0_final,refseq-PEX5-NM_001131025.1.a2m,Invitae,refseq-PEX5-NM_001131025.1.npy,1,639,639 +NP_001125.1,MKWVESIFLIFLLNFTESRTLHRNEYGIASILDSYQCTAEISLADLATIFFAQFVQEATYKEVSKMVKDALTAIEKPTGDEQSSGCLENQLPAFLEELCHEKEILEKYGHSDCCSQSEEGRHNCFLAHKKPTPASIPLFQVPEPVTSCEAYEEDRETFMNKFIYEIARRHPFLYAPTILLWAARYDKIIPSCCKAENAVECFQTKAATVTKELRESSLLNQHACAVMKNFGTRTFQAITVTKLSQKFTKVNFTEIQKLVLDVAHVHEHCCRGDVLDCLQDGEKIMSYICSQQDTLSNKITECCKLTTLERGQCIIHAENDEKPEGLSPNLNRFLGDRDFNQFSSGEKNIFLASFVHEYSRRHPQLAVSVILRVAKGYQELLEKCFQTENPLECQDKGEEELQKYIQESQALAKRSCGLFQKLGEYYLQNAFLVAYTKKAPQLTSSELMAITRKMAATAATCCQLSEDKLLACGEGAADIIIGHLCIRHEMTPVNPGVGQCCTSSYANRRPCFSSLVVDETYVPPAFSDDKFIFHKDLCQAQGVALQTMKQEFLINLVKQKPQITEEQLEAVIADFSGLLEKCCQGQEQEVCFAEEGQKLISKTRAALGV,609,NP_001125.1.csv,refseq-AFP-NM_001134.3_clinical_seed_0_final,refseq-AFP-NM_001134.3.a2m,Invitae,refseq-AFP-NM_001134.3.npy,1,609,609 +NP_001127835.2,MVLAAAMSQDADPSGPEQPDRVACSVPGARASPAPSGPRGMQQPPPPPQPPPPPQAGLPQIIQNAAKLLDKNPFSVSNPNPLLPSPASLQLAQLQAQLTLHRLKLAQTAVTNNTAAATVLNQVLSKVAMSQPLFNQLRHPSVITGPHGHAGVPQHAAAIPSTRFPSNAIAFSPPSQTRGPGPSMNLPNQPPSAMVMHPFTGVMPQTPGQPAVILGIGKTGPAPATAGFYEYGKASSGQTYGPETDGQPGFLPSSASTSGSVTYEGHYSHTGQDGQAAFSKDFYGPNSQGSHVASGFPAEQAGGLKSEVGPLLQGTNSQWESPHGFSGQSKPDLTAGPMWPPPHNQPYELYDPEEPTSDRTPPSFGGRLNNSKQGFIGAGRRAKEDQALLSVRPLQAHELNDFHGVAPLHLPHICSICDKKVFDLKDWELHVKGKLHAQKCLVFSENAGIRCILGSAEGTLCASPNSTAVYNPAGNEDYASNLGTSYVPIPARSFTQSSPTFPLASVGTTFAQRKGAGRVVHICNLPEGSCTENDVINLGLPFGKVTNYILMKSTNQAFLEMAYTEAAQAMVQYYQEKSAVINGEKLLIRMSKRYKELQLKKPGKAVAAIIQDIHSQRERDMFREADRYGPERPRSRSPVSRSLSPRSHTPSFTSCSSSHSPPGPSRADWGNGRDSWEHSPYARREEERDPAPWRDNGDDKRDRMDPWAHDRKHHPRQLDKAELDERPEGGRPHREKYPRSGSPNLPHSVSSYKSREDGYYRKEPKAKSDKYLKQQQDAPGRSRRKDEARLRESRHPHPDDSGKEDGLGPKVTRAPEGAKAKQNEKNKTKRTDRDQEGADDRKENTMAENEAGKEEQEGMEESPQSVGRQEKEAEFSDPENTRTKKEQDWESESEAEGESWYPTNMEELVTVDEVGEEEDFIVEPDIPELEEIVPIDQKDKICPETCLCVTTTLDLDLAQDFPKEGVKAVGNGAAEISLKSPRELPSASTSCPSDMDVEMPGLNLDAERKPAESETGLSLEDSDCYEKEAKGVESSDVHPAPTVQQMSSPKPAEERARQPSPFVDDCKTRGTPEDGACEGSPLEEKASPPIETDLQNQACQEVLTPENSRYVEMKSLEVRSPEYTEVELKQPLSLPSWEPEDVFSELSIPLGVEFVVPRTGFYCKLCGLFYTSEETAKMSHCRSAVHYRNLQKYLSQLAEEGLKETEGADSPRPEDSGIVPRFERKKL,1227,NP_001127835.2.csv,NP_001127835.2_colabfold_clinical_seed_0_final,NP_001127835.2_colabfold.a2m,colabfold,NP_001127835.2_colabfold_theta_0.2.npy,1,1227,1227 +NP_001128243.1,MSRRFTVTSLPPAGPARSPDPESRRHSVADPRHLPGEDVKGDGNPKESSPFINSTDTEKGKEYDGKNMALFEEEMDTSPMVSSLLSGLANYTNLPQGSREHEEAENNEGGKKKPVQAPRMGTFMGVYLPCLQNIFGVILFLRLTWVVGIAGIMESFCMVFICCSCTMLTAISMSAIATNGVVPAGGSYYMISRSLGPEFGGAVGLCFYLGTTFAGAMYILGTIEILLAYLFPAMAIFKAEDASGEAAAMLNNMRVYGTCVLTCMATVVFVGVKYVNKFALVFLGCVILSILAIYAGVIKSAFDPPNFPICLLGNRTLSRHGFDVCAKLAWEGNETVTTRLWGLFCSSRFLNATCDEYFTRNNVTEIQGIPGAASGLIKENLWSSYLTKGVIVERSGMTSVGLADGTPIDMDHPYVFSDMTSYFTLLVGIYFPSVTGIMAGSNRSGDLRDAQKSIPTGTILAIATTSAVYISSVVLFGACIEGVVLRDKFGEAVNGNLVVGTLAWPSPWVIVIGSFFSTCGAGLQSLTGAPRLLQAISRDGIVPFLQVFGHGKANGEPTWALLLTACICEIGILIASLDEVAPILSMFFLMCYMFVNLACAVQTLLRTPNWRPRFRYYHWTLSFLGMSLCLALMFICSWYYALVAMLIAGLIYKYIEYRGAEKEWGDGIRGLSLSAARYALLRLEEGPPHTKNWRPQLLVLVRVDQDQNVVHPQLLSLTSQLKAGKGLTIVGSVLEGTFLENHPQAQRAEESIRRLMEAEKVKGFCQVVISSNLRDGVSHLIQSGGLGGLQHNTVLVGWPRNWRQKEDHQTWRNFIELVRETTAGHLALLVTKNVSMFPGNPERFSEGSIDVWWIVHDGGMLMLLPFLLRHHKVWRKCKMRIFTVAQMDDNSIQMKKDLTTFLYHLRITAEVEVVEMHESDISAYTYEKTLVMEQRSQILKQMHLTKNEREREIQSITDESRGSIRRKNPANTRLRLNVPEETAGDSEEKPEEEVQLIHDQSAPSCPSSSPSPGEEPEGEGETDPEKVHLTWTKDKSVAEKNKGPSPVSSEGIKDFFSMKPEWENLNQSNVRRMHTAVRLNEVIVKKSRDAKLVLLNMPGPPRNRNGDENYMEFLEVLTEHLDRVMLVRGGGREVITIYS,1139,NP_001128243.1.csv,S12A5_HUMAN_b07_clinical_seed_0_final,S12A5_HUMAN_b07.a2m,EVE,S12A5_HUMAN_b07_theta_0.2.npy,1,1139,1139 +NP_001128493.1,MNEKSCSFHSKEELRDGQGERLSAGYSPSYDKDKSVLAFRGIPISELKNHGILQALTTEAYEWEPRVVSTEVVRAQEEWEAVDTIQPETGSQASSEQPGQLISFSEALQHFQTVDLSPFKKRIQPTIRRTGLAALRHYLFGPPKLHQRLREERDLVLTIAQCGLDSQDPVHGRVLQTIYKKLTGSKFDCALHGNHWEDLGFQGANPATDLRGAGFLALLHLLYLVMDSKTLPMAQEIFRLSRHHIQQFPFCLMSVNITHIAIQALREECLSRECNRQQKVIPVVNSFYAATFLHLAHVWRTQRKTISDSGFVLKELEVLAKKSPRRLLKTLELYLARVSKGQASLLGAQKCYGPEAPPFKDLTFTGESDLQSHSSEGVWLI,381,NP_001128493.1.csv,refseq-ELMOD3-NM_001135021.1_clinical_seed_0_final,refseq-ELMOD3-NM_001135021.1.a2m,Invitae,refseq-ELMOD3-NM_001135021.1.npy,1,381,381 +NP_001128715.1,MAEARKRRELLPLIYHHLLRAGYVRAAREVKEQSGQKCFLAQPVTLLDIYTHWQQTSELGRKRKAEEDAALQAKKTRVSDPISTSESSEEEEEAEAETAKATPRLASTNSSVLGADLPSSMKEKAKAETEKAGKTGNSMPHPATGKTVANLLSGKSPRKSAEPSANTTLVSETEEEGSVPAFGAAAKPGMVSAGQADSSSEDTSSSSDETDVEGKPSVKPAQVKASSVSTKESPARKAAPAPGKVGDVTPQVKGGALPPAKRAKKPEEESESSEEGSESEEEAPAGTRSQVKASEKILQVRAASAPAKGTPGKGATPAPPGKAGAVASQTKAGKPEEDSESSSEESSDSEEETPAAKALLQAKASGKTSQVGAASAPAKESPRKGAAPAPPGKTGPAVAKAQAGKREEDSQSSSEESDSEEEAPAQAKPSGKAPQVRAASAPAKESPRKGAAPAPPRKTGPAAAQVQVGKQEEDSRSSSEESDSDREALAAMNAAQVKPLGKSPQVKPASTMGMGPLGKGAGPVPPGKVGPATPSAQVGKWEEDSESSSEESSDSSDGEVPTAVAPAQEKSLGNILQAKPTSSPAKGPPQKAGPVAVQVKAEKPMDNSESSEESSDSADSEEAPAAMTAAQAKPALKIPQTKACPKKTNTTASAKVAPVRVGTQAPRKAGTATSPAGSSPAVAGGTQRPAEDSSSSEESDSEEEKTGLAVTVGQAKSVGKGLQVKAASVPVKGSLGQGTAPVLPGKTGPTVTQVKAEKQEDSESSEEESDSEEAAASPAQVKTSVKKTQAKANPAAARAPSAKGTISAPGKVVTAAAQAKQRSPSKVKPPVRNPQNSTVLARGPASVPSVGKAVATAAQAQTGPEEDSGSSEEESDSEEEAETLAQVKPSGKTHQIRAALAPAKESPRKGAAPTPPGKTGPSAAQAGKQDDSGSSSEESDSDGEAPAAVTSAQVIKPPLIFVDPNRSPAGPAATPAQAQAASTPRKARASESTARSSSSESEDEDVIPATQCLTPGIRTNVVTMPTAHPRIAPKASMAGASSSKESSRISDGKKQEGPATQVSKKNPASLPLTQAALKVLAQKASEAQPPVARTQPSSGVDSAVGTLPATSPQSTSVQAKGTNKLRKPKLPEVQQATKAPESSDDSEDSSDSSSGSEEDGEGPQGAKSAHTLGPTPSRTETLVEETAAESSEDDVVAPSQSLLSGYMTPGLTPANSQASKATPKLDSSPSVSSTLAAKDDPDGKQEAKPQQAAGMLSPKTGGKEAASGTTPQKSRKPKKGAGNPQASTLALQSNITQCLLGQPWPLNEAQVQASVVKVLTELLEQERKKVVDTTKESSRKGWESRKRKLSGDQPAARTPRSKKKKKLGAGEGGEASVSPEKTSTTSKGKAKRDKASGDVKEKKGKGSLGSQGAKDEPEEELQKGMGTVEGGDQSNPKSKKEKKKSDKRKKDKEKKEKKKKAKKASTKDSESPSQKKKKKKKKTAEQTV,1488,NP_001128715.1.csv,refseq-TCOF1-NM_001135243.1_clinical_seed_0_final,refseq-TCOF1-NM_001135243.1.a2m,Invitae,refseq-TCOF1-NM_001135243.1.npy,1,1488,1488 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+NP_001928.2,MDLSLLWVLLPLVTMAWGQYGDYGYPYQQYHDYSDDGWVNLNRQGFSYQCPQGQVIVAVRSIFSKKEGSDRQWNYACMPTPQSLGEPTECWWEEINRAGMEWYQTCSNNGLVAGFQSRYFESVLDREWQFYCCRYSKRCPYSCWLTTEYPGHYGEEMDMISYNYDYYIRGATTTFSAVERDRQWKFIMCRMTEYDCEFANV,201,NP_001928.2.csv,refseq-DPT-NM_001937.4_clinical_seed_0_final,refseq-DPT-NM_001937.4.a2m,Invitae,refseq-DPT-NM_001937.4.npy,1,201,201 +NP_001934.2,MARSPGRAYALLLLLICFNVGSGLHLQVLSTRNENKLLPKHPHLVRQKRAWITAPVALREGEDLSKKNPIAKIHSDLAEERGLKITYKYTGKGITEPPFGIFVFNKDTGELNVTSILDREETPFFLLTGYALDARGNNVEKPLELRIKVLDINDNEPVFTQDVFVGSVEELSAAHTLVMKINATDADEPNTLNSKISYRIVSLEPAYPPVFYLNKDTGEIYTTSVTLDREEHSSYTLTVEARDGNGEVTDKPVKQAQVQIRILDVNDNIPVVENKVLEGMVEENQVNVEVTRIKVFDADEIGSDNWLANFTFASGNEGGYFHIETDAQTNEGIVTLIKEVDYEEMKNLDFSVIVANKAAFHKSIRSKYKPTPIPIKVKVKNVKEGIHFKSSVISIYVSESMDRSSKGQIIGNFQAFDEDTGLPAHARYVKLEDRDNWISVDSVTSEIKLAKLPDFESRYVQNGTYTVKIVAISEDYPRKTITGTVLINVEDINDNCPTLIEPVQTICHDAEYVNVTAEDLDGHPNSGPFSFSVIDKPPGMAEKWKIARQESTSVLLQQSEKKLGRSEIQFLISDNQGFSCPEKQVLTLTVCECLHGSGCREAQHDSYVGLGPAAIALMILAFLLLLLVPLLLLMCHCGKGAKGFTPIPGTIEMLHPWNNEGAPPEDKVVPSFLPVDQGGSLVGRNGVGGMAKEATMKGSSSASIVKGQHEMSEMDGRWEEHRSLLSGRATQFTGATGAIMTTETTKTARATGASRDMAGAQAAAVALNEEFLRNYFTDKAASYTEEDENHTAKDCLLVYSQEETESLNASIGCCSFIEGELDDRFLDDLGLKFKTLAEVCLGQKIDINKEIEQRQKPATETSMNTASHSLCEQTMVNSENTYSSGSSFPVPKSLQEANAEKVTQEIVTERSVSSRQAQKVATPLPDPMASRNVIATETSYVTGSTMPPTTVILGPSQPQSLIVTERVYAPASTLVDQPYANEGTVVVTERVIQPHGGGSNPLEGTQHLQDVPYVMVRERESFLAPSSGVQPTLAMPNIAVGQNVTVTERVLAPASTLQSSYQIPTENSMTARNTTVSGAGVPGPLPDFGLEESGHSNSTITTSSTRVTKHSTVQHSYS,1118,NP_001934.2.csv,refseq-DSG2-NM_001943.3_clinical_seed_0_final,refseq-DSG2-NM_001943.3.a2m,Invitae,refseq-DSG2-NM_001943.3.npy,1,1118,1118 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+NP_055059.2,MDPPAGAARRLLCPALLLLLLLLPPPLLPPPPPPANARLAAAADPPGGPLGHGAERILAVPVRTDAQGRLVSHVVSAATSRAGVRARRAAPVRTPSFPGGNEEEPGSHLFYNVTVFGRDLHLRLRPNARLVAPGATMEWQGEKGTTRVEPLLGSCLYVGDVAGLAEASSVALSNCDGLAGLIRMEEEEFFIEPLEKGLAAQEAEQGRVHVVYRRPPTSPPLGGPQALDTGASLDSLDSLSRALGVLEEHANSSRRRARRHAADDDYNIEVLLGVDDSVVQFHGKEHVQKYLLTLMNIVNEIYHDESLGAHINVVLVRIILLSYGKSMSLIEIGNPSQSLENVCRWAYLQQKPDTGHDEYHDHAIFLTRQDFGPSGMQGYAPVTGMCHPVRSCTLNHEDGFSSAFVVAHETGHVLGMEHDGQGNRCGDEVRLGSIMAPLVQAAFHRFHWSRCSQQELSRYLHSYDCLLDDPFAHDWPALPQLPGLHYSMNEQCRFDFGLGYMMCTAFRTFDPCKQLWCSHPDNPYFCKTKKGPPLDGTMCAPGKHCFKGHCIWLTPDILKRDGSWGAWSPFGSCSRTCGTGVKFRTRQCDNPHPANGGRTCSGLAYDFQLCSRQDCPDSLADFREEQCRQWDLYFEHGDAQHHWLPHEHRDAKERCHLYCESRETGEVVSMKRMVHDGTRCSYKDAFSLCVRGDCRKVGCDGVIGSSKQEDKCGVCGGDNSHCKVVKGTFTRSPKKHGYIKMFEIPAGARHLLIQEVDATSHHLAVKNLETGKFILNEENDVDASSKTFIAMGVEWEYRDEDGRETLQTMGPLHGTITVLVIPVGDTRVSLTYKYMIHEDSLNVDDNNVLEEDSVVYEWALKKWSPCSKPCGGGSQFTKYGCRRRLDHKMVHRGFCAALSKPKAIRRACNPQECSQPVWVTGEWEPCSQTCGRTGMQVRSVRCIQPLHDNTTRSVHAKHCNDARPESRRACSRELCPGRWRAGPWSQCSVTCGNGTQERPVLCRTADDSFGICQEERPETARTCRLGPCPRNISDPSKKSYVVQWLSRPDPDSPIRKISSKGHCQGDKSIFCRMEVLSRYCSIPGYNKLCCKSCNLYNNLTNVEGRIEPPPGKHNDIDVFMPTLPVPTVAMEVRPSPSTPLEVPLNASSTNATEDHPETNAVDEPYKIHGLEDEVQPPNLIPRRPSPYEKTRNQRIQELIDEMRKKEMLGKF,1211,NP_055059.2.csv,refseq-ADAMTS2-NM_014244.4_clinical_seed_0_final,refseq-ADAMTS2-NM_014244.4.a2m,Invitae,refseq-ADAMTS2-NM_014244.4.npy,1,1211,1211 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+NP_055066.1,MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP,675,NP_055066.1.csv,refseq-SLC25A13-NM_014251.2_clinical_seed_0_final,refseq-SLC25A13-NM_014251.2.a2m,Invitae,refseq-SLC25A13-NM_014251.2.npy,1,675,675 +NP_055067.1,MKSNPAIQAAIDLTAGAAGGTACVLTGQPFDTMKVKMQTFPDLYRGLTDCCLKTYSQVGFRGFYKGTSPALIANIAENSVLFMCYGFCQQVVRKVAGLDKQAKLSDLQNAAAGSFASAFAALVLCPTELVKCRLQTMYEMETSGKIAKSQNTVWSVIKSILRKDGPLGFYHGLSSTLLREVPGYFFFFGGYELSRSFFASGRSKDELGPVPLMLSGGVGGICLWLAVYPVDCIKSRIQVLSMSGKQAGFIRTFINVVKNEGITALYSGLKPTMIRAFPANGALFLAYEYSRKLMMNQLEAY,301,NP_055067.1.csv,refseq-SLC25A15-NM_014252.3_clinical_seed_0_final,refseq-SLC25A15-NM_014252.3.a2m,Invitae,refseq-SLC25A15-NM_014252.3.npy,1,301,301 +NP_055069.1,MRLTRKRLCSFLIALYCLFSLYAAYHVFFGRRRQAPAGSPRGLRKGAAPARERRGREQSTLESEEWNPWEGDEKNEQQHRFKTSLQILDKSTKGKTDLSVQIWGKAAIGLYLWEHIFEGLLDPSDVTAQWREGKSIVGRTQYSFITGPAVIPGYFSVDVNNVVLILNGREKAKIFYATQWLLYAQNLVQIQKLQHLAVVLLGNEHCDNEWINPFLKRNGGFVELLFIIYDSPWINDVDVFQWPLGVATYRNFPVVEASWSMLHDERPYLCNFLGTIYENSSRQALMNILKKDGNDKLCWVSAREHWQPQETNESLKNYQDALLQSDLTLCPVGVNTECYRIYEACSYGSIPVVEDVMTAGNCGNTSVHHGAPLQLLKSMGAPFIFIKNWKELPAVLEKEKTIILQEKIERRKMLLQWYQHFKTELKMKFTNILESSFLMNNKS,443,NP_055069.1.csv,refseq-RXYLT1-NM_014254.2_clinical_seed_0_final,refseq-RXYLT1-NM_014254.2.a2m,Invitae,refseq-RXYLT1-NM_014254.2.npy,1,443,443 +NP_055085.1,MGDTGLRKRREDEKSIQSQEPKTTSLQKELGLISGISIIVGTIIGSGIFVSPKSVLSNTEAVGPCLIIWAACGVLATLGALCFAELGTMITKSGGEYPYLMEAYGPIPAYLFSWASLIVIKPTSFAIICLSFSEYVCAPFYVGCKPPQIVVKCLAAAAILFISTVNSLSVRLGSYVQNIFTAAKLVIVAIIIISGLVLLAQGNTKNFDNSFEGAQLSVGAISLAFYNGLWAYDGWNQLNYITEELRNPYRNLPLAIIIGIPLVTACYILMNVSYFTVMTATELLQSQAVAVTFGDRVLYPASWIVPLFVAFSTIGAANGTCFTAGRLIYVAGREGHMLKVLSYISVRRLTPAPAIIFYGIIATIYIIPGDINSLVNYFSFAAWLFYGLTILGLIVMRFTRKELERPIKVPVVIPVLMTLISVFLVLAPIISKPTWEYLYCVLFILSGLLFYFLFVHYKFGWAQKISKPITMHLQMLMEVVPPEEDPE,487,NP_055085.1.csv,refseq-SLC7A9-NM_014270.4_clinical_seed_0_final,refseq-SLC7A9-NM_014270.4.a2m,Invitae,refseq-SLC7A9-NM_014270.4_theta_0.2.npy,1,487,487 +NP_055086.1,MKAPIPHLILLYATFTQSLKVVTKRGSADGCTDWSIDIKKYQVLVGEPVRIKCALFYGYIRTNYSLAQSAGLSLMWYKSSGPGDFEEPIAFDGSRMSKEEDSIWFRPTLLQDSGLYACVIRNSTYCMKVSISLTVGENDTGLCYNSKMKYFEKAELSKSKEISCRDIEDFLLPTREPEILWYKECRTKTWRPSIVFKRDTLLIREVREDDIGNYTCELKYGGFVVRRTTELTVTAPLTDKPPKLLYPMESKLTIQETQLGDSANLTCRAFFGYSGDVSPLIYWMKGEKFIEDLDENRVWESDIRILKEHLGEQEVSISLIVDSVEEGDLGNYSCYVENGNGRRHASVLLHKRELMYTVELAGGLGAILLLLVCLVTIYKCYKIEIMLFYRNHFGAEELDGDNKDYDAYLSYTKVDPDQWNQETGEEERFALEILPDMLEKHYGYKLFIPDRDLIPTGTYIEDVARCVDQSKRLIIVMTPNYVVRRGWSIFELETRLRNMLVTGEIKVILIECSELRGIMNYQEVEALKHTIKLLTVIKWHGPKCNKLNSKFWKRLQYEMPFKRIEPITHEQALDVSEQGPFGELQTVSAISMAAATSTALATAHPDLRSTFHNTYHSQMRQKHYYRSYEYDVPPTGTLPLTSIGNQHTYCNIPMTLINGQRPQTKSSREQNPDEAHTNSAILPLLPRETSISSVIW,696,NP_055086.1.csv,refseq-IL1RAPL1-NM_014271.3_clinical_seed_0_final,refseq-IL1RAPL1-NM_014271.3.a2m,Invitae,refseq-IL1RAPL1-NM_014271.3.npy,1,696,696 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+NP_055151.3,MDAALLLNVEGVKKTILHGGTGELPNFITGSRVIFHFRTMKCDEERTVIDDSRQVGQPMHIIIGNMFKLEVWEILLTSMRVHEVAEFWCDTIHTGVYPILSRSLRQMAQGKDPTEWHVHTCGLANMFAYHTLGYEDLDELQKEPQPLVFVIELLQVDAPSDYQRETWNLSNHEKMKAVPVLHGEGNRLFKLGRYEEASSKYQEAIICLRNLQTKEKPWEVQWLKLEKMINTLILNYCQCLLKKEEYYEVLEHTSDILRHHPGIVKAYYVRARAHAEVWNEAEAKADLQKVLELEPSMQKAVRRELRLLENRMAEKQEEERLRCRNMLSQGATQPPAEPPTEPPAQSSTEPPAEPPTAPSAELSAGPPAEPATEPPPSPGHSLQH,384,NP_055151.3.csv,refseq-AIPL1-NM_014336.4_clinical_seed_0_final,refseq-AIPL1-NM_014336.4.a2m,Invitae,refseq-AIPL1-NM_014336.4.npy,1,384,384 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+NP_056201.2,MGTKMADLDSPPKLSGVQQPSEGVGGGRCSEISAELIRSLTELQELEAVYERLCGEEKVVERELDALLEQQNTIESKMVTLHRMGPNLQLIEGDAKQLAGMITFTCNLAENVSSKVRQLDLAKNRLYQAIQRADDILDLKFCMDGVQTALRSEDYEQAAAHTHRYLCLDKSVIELSRQGKEGSMIDANLKLLQEAEQRLKAIVAEKFAIATKEGDLPQVERFFKIFPLLGLHEEGLRKFSEYLCKQVASKAEENLLMVLGTDMSDRRAAVIFADTLTLLFEGIARIVETHQPIVETYYGPGRLYTLIKYLQVECDRQVEKVVDKFIKQRDYHQQFRHVQNNLMRNSTTEKIEPRELDPILTEVTLMNARSELYLRFLKKRISSDFEVGDSMASEEVKQEHQKCLDKLLNNCLLSCTMQELIGLYVTMEEYFMRETVNKAVALDTYEKGQLTSSMVDDVFYIVKKCIGRALSSSSIDCLCAMINLATTELESDFRDVLCNKLRMGFPATTFQDIQRGVTSAVNIMHSSLQQGKFDTKGIESTDEAKMSFLVTLNNVEVCSENISTLKKTLESDCTKLFSQGIGGEQAQAKFDSCLSDLAAVSNKFRDLLQEGLTELNSTAIKPQVQPWINSFFSVSHNIEEEEFNDYEANDPWVQQFILNLEQQMAEFKASLSPVIYDSLTGLMTSLVAVELEKVVLKSTFNRLGGLQFDKELRSLIAYLTTVTTWTIRDKFARLSQMATILNLERVTEILDYWGPNSGPLTWRLTPAEVRQVLALRIDFRSEDIKRLRL,789,NP_056201.2.csv,refseq-COG4-NM_015386.2_clinical_seed_0_final,refseq-COG4-NM_015386.2.a2m,Invitae,refseq-COG4-NM_015386.2.npy,1,789,789 +NP_056227.2,MMAAVPPGLEPWNRVRIPKAGNRSAVTVQNPGAALDLCIAAVIKECHLVILSLKSQTLDAETDVLCAVLYSNHNRMGRHKPHLALKQVEQCLKRLKNMNLEGSIQDLFELFSSNENQPLTTKVCVVPSQPVVELVLMKVLGACKLLLRLLDCCCKTFLLTVKHLGLQEFIILNLVMVGLVSRLWVLYKGVLKRLILLYEPLFGLLQEVARIQPMPYFKDFTFPSDITEFLGQPYFEAFKKKMPIAFAAKGINKLLNKLFLINEQSPRASEETLLGISKKAKQMKINVQNNVDLGQPVKNKRVFKEESSEFDVRAFCNQLKHKATQETSFDFKCSQSRLKTTKYSSQKVIGTPHAKSFVQRFREAESFTQLSEEIQMAVVWCRSKKLKAQAIFLGNKLLKSNRLKHLEAQGTSLPKKLECIKTSICNHLLRGSGIKTSKHHLRQRRSQNKFLRRQRKPQRKLQSTLLREIQQFSQGTRKSATDTSAKWRLSHCTVHRTDLYPNSKQLLNSGVSMPVIQTKEKMIHENLRGIHENETDSWTVMQINKNSTSGTIKETDDIDDIFALMGV,567,NP_056227.2.csv,refseq-NEPRO-NM_015412.3_clinical_seed_0_final,refseq-NEPRO-NM_015412.3.a2m,Invitae,refseq-NEPRO-NM_015412.3.npy,1,567,567 +NP_056240.2,MLISKNMPWRRLQGISFGMYSAEELKKLSVKSITNPRYLDSLGNPSANGLYDLALGPADSKEVCSTCVQDFSNCSGHLGHIELPLTVYNPLLFDKLYLLLRGSCLNCHMLTCPRAVIHLLLCQLRVLEVGALQAVYELERILNRFLEENPDPSASEIREELEQYTTEIVQNNLLGSQGAHVKNVCESKSKLIALFWKAHMNAKRCPHCKTGRSVVRKEHNSKLTITFPAMVHRTAGQKDSEPLGIEEAQIGKRGYLTPTSAREHLSALWKNEGFFLNYLFSGMDDDGMESRFNPSVFFLDFLVVPPSRYRPVSRLGDQMFTNGQTVNLQAVMKDVVLIRKLLALMAQEQKLPEEVATPTTDEEKDSLIAIDRSFLSTLPGQSLIDKLYNIWIRLQSHVNIVFDSEMDKLMMDKYPGIRQILEKKEGLFRKHMMGKRVDYAARSVICPDMYINTNEIGIPMVFATKLTYPQPVTPWNVQELRQAVINGPNVHPGASMVINEDGSRTALSAVDMTQREAVAKQLLTPATGAPKPQGTKIVCRHVKNGDILLLNRQPTLHRPSIQAHRARILPEEKVLRLHYANCKAYNADFDGDEMNAHFPQSELGRAEAYVLACTDQQYLVPKDGQPLAGLIQDHMVSGASMTTRGCFFTREHYMELVYRGLTDKVGRVKLLSPSILKPFPLWTGKQVVSTLLINIIPEDHIPLNLSGKAKITGKAWVKETPRSVPGFNPDSMCESQVIIREGELLCGVLDKAHYGSSAYGLVHCCYEIYGGETSGKVLTCLARLFTAYLQLYRGFTLGVEDILVKPKADVKRQRIIEESTHCGPQAVRAALNLPEAASYDEVRGKWQDAHLGKDQRDFNMIDLKFKEEVNHYSNEINKACMPFGLHRQFPENSLQMMVQSGAKGSTVNTMQISCLLGQIELEGRRPPLMASGKSLPCFEPYEFTPRAGGFVTGRFLTGIKPPEFFFHCMAGREGLVDTAVKTSRSGYLQRCIIKHLEGLVVQYDLTVRDSDGSVVQFLYGEDGLDIPKTQFLQPKQFPFLASNYEVIMKSQHLHEVLSRADPKKALHHFRAIKKWQSKHPNTLLRRGAFLSYSQKIQEAVKALKLESENRNGRSPGTQEMLRMWYELDEESRRKYQKKAAACPDPSLSVWRPDIYFASVSETFETKVDDYSQEWAAQTEKSYEKSELSLDRLRTLLQLKWQRSLCEPGEAVGLLAAQSIGEPSTQMTLNTFHFAGRGEMNVTLGIPRLREILMVASANIKTPMMSVPVLNTKKALKRVKSLKKQLTRVCLGEVLQKIDVQESFCMEEKQNKFQVYQLRFQFLPHAYYQQEKCLRPEDILRFMETRFFKLLMESIKKKNNKASAFRNVNTRRATQRDLDNAGELGRSRGEQEGDEEEEGHIVDAEAEEGDADASDAKRKEKQEEEVDYESEEEEEREGEENDDEDMQEERNPHREGARKTQEQDEEVGLGTEEDPSLPALLTQPRKPTHSQEPQGPEAMERRVQAVREIHPFIDDYQYDTEESLWCQVTVKLPLMKINFDMSSLVVSLAHGAVIYATKGITRCLLNETTNNKNEKELVLNTEGINLPELFKYAEVLDLRRLYSNDIHAIANTYGIEAALRVIEKEIKDVFAVYGIAVDPRHLSLVADYMCFEGVYKPLNRFGIRSNSSPLQQMTFETSFQFLKQATMLGSHDELRSPSACLVVGKVVRGGTGLFELKQPLR,1720,NP_056240.2.csv,refseq-POLR1A-NM_015425.5_clinical_seed_0_final,refseq-POLR1A-NM_015425.5.a2m,Invitae,refseq-POLR1A-NM_015425.5_theta_0.2.npy,1,1720,1720 +NP_056241.3,MAAPCAEDPSLERHFKGHRDAVTCVDFSINTKQLASGSMDSCLMVWHMKPQSRAYRFTGHKDAVTCVNFSPSGHLLASGSRDKTVRIWVPNVKGESTVFRAHTATVRSVHFCSDGQSFVTASDDKTVKVWATHRQKFLFSLSQHINWVRCAKFSPDGRLIVSASDDKTVKLWDKSSRECVHSYCEHGGFVTYVDFHPSGTCIAAAGMDNTVKVWDVRTHRLLQHYQLHSAAVNGLSFHPSGNYLITASSDSTLKILDLMEGRLLYTLHGHQGPATTVAFSRTGEYFASGGSDEQVMVWKSNFDIVDHGEVTKVPRPPATLASSMGNLPEVDFPVPPGRGRSVESVQSQPQEPVSVPQTLTSTLEHIVGQLDVLTQTVSILEQRLTLTEDKLKQCLENQQLIMQRATP,407,NP_056241.3.csv,refseq-POC1A-NM_015426.4_clinical_seed_0_final,refseq-POC1A-NM_015426.4.a2m,Invitae,refseq-POC1A-NM_015426.4.npy,1,407,407 +NP_056265.2,MSLVPATNYIYTPLNQLKGGTIVNVYGVVKFFKPPYLSKGTDYCSVVTIVDQTNVKLTCLLFSGNYEALPIIYKNGDIVRFHRLKIQVYKKETQGITSSGFASLTFEGTLGAPIIPRTSSKYFNFTTEDHKMVEALRVWASTHMSPSWTLLKLCDVQPMQYFDLTCQLLGKAEVDGASFLLKVWDGTRTPFPSWRVLIQDLVLEGDLSHIHRLQNLTIDILVYDNHVHVARSLKVGSFLRIYSLHTKLQSMNSENQTMLSLEFHLHGGTSYGRGIRVLPESNSDVDQLKKDLESANLTANQHSDVICQSEPDDSFPSSGSVSLYEVERCQQLSATILTDHQYLERTPLCAILKQKAPQQYRIRAKLRSYKPRRLFQSVKLHCPKCHLLQEVPHEGDLDIIFQDGATKTPDVKLQNTSLYDSKIWTTKNQKGRKVAVHFVKNNGILPLSNECLLLIEGGTLSEICKLSNKFNSVIPVRSGHEDLELLDLSAPFLIQGTIHHYGCKQCSSLRSIQNLNSLVDKTSWIPSSVAEALGIVPLQYVFVMTFTLDDGTGVLEAYLMDSDKFFQIPASEVLMDDDLQKSVDMIMDMFCPPGIKIDAYPWLECFIKSYNVTNGTDNQICYQIFDTTVAEDVI,634,NP_056265.2.csv,refseq-POT1-NM_015450.2_clinical_seed_0_final,refseq-POT1-NM_015450.2.a2m,Invitae,refseq-POT1-NM_015450.2.npy,1,634,634 +NP_056280.2,MGQEPRTLPPSPNWYCARCSDAVPGGLFGFAARTSVFLVRVGPGAGESPGTPPFRVIGELVGHTERVSGFTFSHHPGQYNLCATSSDDGTVKIWDVETKTVVTEHALHQHTISTLHWSPRVKDLIVSGDEKGVVFCYWFNRNDSQHLFIEPRTIFCLTCSPHHEDLVAIGYKDGIVVIIDISKKGEVIHRLRGHDDEIHSIAWCPLPGEDCLSINQEETSEEAEITNGNAVAQAPVTKGCYLATGSKDQTIRIWSCSRGRGVMILKLPFLKRRGGGIDPTVKERLWLTLHWPSNQPTQLVSSCFGGELLQWDLTQSWRRKYTLFSASSEGQNHSRIVFNLCPLQTEDDKQLLLSTSMDRDVKCWDIATLECSWTLPSLGGFAYSLAFSSVDIGSLAIGVGDGMIRVWNTLSIKNNYDVKNFWQGVKSKVTALCWHPTKEGCLAFGTDDGKVGLYDTYSNKPPQISSTYHKKTVYTLAWGPPVPPMSLGGEGDRPSLALYSCGGEGIVLQHNPWKLSGEAFDINKLIRDTNSIKYKLPVHTEISWKADGKIMALGNEDGSIEIFQIPNLKLICTIQQHHKLVNTISWHHEHGSQPELSYLMASGSNNAVIYVHNLKTVIESSPESPVTITEPYRTLSGHTAKITSVAWSPHHDGRLVSASYDGTAQVWDALREEPLCNFRGHRGRLLCVAWSPLDPDCIYSGADDFCVHKWLTSMQDHSRPPQGKKSIELEKKRLSQPKAKPKKKKKPTLRTPVKLESIDGNEEESMKENSGPVENGVSDQEGEEQAREPELPCGLAPAVSREPVICTPVSSGFEKSKVTINNKVILLKKEPPKEKPETLIKKRKARSLLPLSTSLDHRSKEELHQDCLVLATAKHSRELNEDVSADVEERFHLGLFTDRATLYRMIDIEGKGHLENGHPELFHQLMLWKGDLKGVLQTAAERGELTDNLVAMAPAAGYHVWLWAVEAFAKQLCFQDQYVKAASHLLSIHKVYEAVELLKSNHFYREAIAIAKARLRPEDPVLKDLYLSWGTVLERDGHYAVAAKCYLGATCAYDAAKVLAKKGDAASLRTAAELAAIVGEDELSASLALRCAQELLLANNWVGAQEALQLHESLQGQRLVFCLLELLSRHLEEKQLSEGKSSSSYHTWNTGTEGPFVERVTAVWKSIFSLDTPEQYQEAFQKLQNIKYPSATNNTPAKQLLLHICHDLTLAVLSQQMASWDEAVQALLRAVVRSYDSGSFTIMQEVYSAFLPDGCDHLRDKLGDHQSPATPAFKSLEAFFLYGRLYEFWWSLSRPCPNSSVWVRAGHRTLSVEPSQQLDTASTEETDPETSQPEPNRPSELDLRLTEEGERMLSTFKELFSEKHASLQNSQRTVAEVQETLAEMIRQHQKSQLCKSTANGPDKNEPEVEAEQPLCSSQSQCKEEKNEPLSLPELTKRLTEANQRMAKFPESIKAWPFPDVLECCLVLLLIRSHFPGCLAQEMQQQAQELLQKYGNTKTYRRHCQTFCM,1508,NP_056280.2.csv,refseq-GEMIN5-NM_015465.4_clinical_seed_0_final,refseq-GEMIN5-NM_015465.4.a2m,Invitae,refseq-GEMIN5-NM_015465.4_theta_0.2.npy,1,1508,1508 +NP_056289.2,MQRADSEQPSKRPRCDDSPRTPSNTPSAEADWSPGLELHPDYKTWGPEQVCSFLRRGGFEEPVLLKNIRENEITGALLPCLDESRFENLGVSSLGERKKLLSYIQRLVQIHVDTMKVINDPIHGHIELHPLLVRIIDTPQFQRLRYIKQLGGGYYVFPGASHNRFEHSLGVGYLAGCLVHALGEKQPELQISERDVLCVQIAGLCHDLGHGPFSHMFDGRFIPLARPEVKWTHEQGSVMMFEHLINSNGIKPVMEQYGLIPEEDICFIKEQIVGPLESPVEDSLWPYKGRPENKSFLYEIVSNKRNGIDVDKWDYFARDCHHLGIQNNFDYKRFIKFARVCEVDNELRICARDKEVGNLYDMFHTRNSLHRRAYQHKVGNIIDTMITDAFLKADDYIEITGAGGKKYRISTAIDDMEAYTKLTDNIFLEILYSTDPKLKDAREILKQIEYRNLFKYVGETQPTGQIKIKREDYESLPKEVASAKPKVLLDVKLKAEDFIVDVINMDYGMQEKNPIDHVSFYCKTAPNRAIRITKNQVSQLLPEKFAEQLIRVYCKKVDRKSLYAARQYFVQWCADRNFTKPQDGDVIAPLITPQKKEWNDSTSVQNPTRLREASKSRVQLFKDDPM,626,NP_056289.2.csv,refseq-SAMHD1-NM_015474.3_clinical_seed_0_final,refseq-SAMHD1-NM_015474.3.a2m,Invitae,refseq-SAMHD1-NM_015474.3.npy,1,626,626 +NP_056303.3,MAAVVAATALKGRGARNARVLRGILAGATANKASHNRTRALQSHSSPEGKEEPEPLSPELEYIPRKRGKNPMKAVGLAWYSLYTRTWLGYLFYRQQLRRARNRYPKGHSKTQPRLFNGVKVLPIPVLSDNYSYLIIDTQAQLAVAVDPSDPRAVQASIEKEGVTLVAILCTHKHWDHSGGNRDLSRRHRDCRVYGSPQDGIPYLTHPLCHQDVVSVGRLQIRALATPGHTQGHLVYLLDGEPYKGPSCLFSGDLLFLSGCGRTFEGNAETMLSSLDTVLGLGDDTLLWPGHEYAEENLGFAGVVEPENLARERKMQWVQRQRLERKGTCPSTLGEERSYNPFLRTHCLALQEALGPGPGPTGDDDYSRAQLLEELRRLKDMHKSK,385,NP_056303.3.csv,refseq-PNKD-NM_015488.4_clinical_seed_0_final,refseq-PNKD-NM_015488.4.a2m,Invitae,refseq-PNKD-NM_015488.4.npy,1,385,385 +NP_056321.2,MEPKVAELKQKIEDTLCPFGFEVYPFQVAWYNELLPPAFHLPLPGPTLAFLVLSTPAMFDRALKPFLQSCHLRMLTDPVDQCVAYHLGRVRESLPELQIEIIADYEVHPNRRPKILAQTAAHVAGAAYYYQRQDVEADPWGNQRISGVCIHPRFGGWFAIRGVVLLPGIEVPDLPPRKPHDCVPTRADRIALLEGFNFHWRDWTYRDAVTPQERYSEEQKAYFSTPPAQRLALLGLAQPSEKPSSPSPDLPFTTPAPKKPGNPSRARSWLSPRVSPPASPGP,282,NP_056321.2.csv,refseq-MMACHC-NM_015506.2_clinical_seed_0_final,refseq-MMACHC-NM_015506.2.a2m,Invitae,refseq-MMACHC-NM_015506.2.npy,1,282,282 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+NP_060021.1,MEALTLWLLPWICQCVSVRADSIIHIGAIFEENAAKDDRVFQLAVSDLSLNDDILQSEKITYSIKVIEANNPFQAVQEACDLMTQGILALVTSTGCASANALQSLTDAMHIPHLFVQRNPGGSPRTACHLNPSPDGEAYTLASRPPVRLNDVMLRLVTELRWQKFVMFYDSEYDIRGLQSFLDQASRLGLDVSLQKVDKNISHVFTSLFTTMKTEELNRYRDTLRRAILLLSPQGAHSFINEAVETNLASKDSHWVFVNEEISDPEILDLVHSALGRMTVVRQIFPSAKDNQKCTRNNHRISSLLCDPQEGYLQMLQISNLYLYDSVLMLANAFHRKLEDRKWHSMASLNCIRKSTKPWNGGRSMLDTIKKGHITGLTGVMEFREDSSNPYVQFEILGTTYSETFGKDMRKLATWDSEKGLNGSLQERPMGSRLQGLTLKVVTVLEEPFVMVAENILGQPKRYKGFSIDVLDALAKALGFKYEIYQAPDGRYGHQLHNTSWNGMIGELISKRADLAISAITITPERESVVDFSKRYMDYSVGILIKKPEEKISIFSLFAPFDFAVWACIAAAIPVVGVLIFVLNRIQAVRAQSAAQPRPSASATLHSAIWIVYGAFVQQGGESSVNSMAMRIVMGSWWLFTLIVCSSYTANLAAFLTVSRMDNPIRTFQDLSKQVEMSYGTVRDSAVYEYFRAKGTNPLEQDSTFAELWRTISKNGGADNCVSSPSEGIRKAKKGNYAFLWDVAVVEYAALTDDDCSVTVIGNSISSKGYGIALQHGSPYRDLFSQRILELQDTGDLDVLKQKWWPHMGRCDLTSHASAQADGKSLKLHSFAGVFCILAIGLLLACLVAALELWWNSNRCHQETPKEDKEVNLEQVHRRMNSLMDEDIAHKQISPASIELSALEMGGLAPTQTLEPTREYQNTQLSVSTFLPEQSSHGTSRTLSSGPSSNLPLPLSSSATMPSMQCKHRSPNGGLFRQSPVKTPIPMSFQPVPGGVLPEALDTSHGTSI,1009,NP_060021.1.csv,refseq-GRID1-NM_017551.2_clinical_seed_0_final,refseq-GRID1-NM_017551.2.a2m,Invitae,refseq-GRID1-NM_017551.2.npy,1,1009,1009 +NP_060033.3,MAPWLQLCSVFFTVNACLNGSQLAVAAGGSGRARGADTCGWRGVGPASRNSGLYNITFKYDNCTTYLNPVGKHVIADAQNITISQYACHDQVAVTILWSPGALGIEFLKGFRVILEELKSEGRQCQQLILKDPKQLNSSFKRTGMESQPFLNMKFETDYFVKVVPFPSIKNESNYHPFFFRTRACDLLLQPDNLACKPFWKPRNLNISQHGSDMQVSFDHAPHNFGFRFFYLHYKLKHEGPFKRKTCKQEQTTETTSCLLQNVSPGDYIIELVDDTNTTRKVMHYALKPVHSPWAGPIRAVAITVPLVVISAFATLFTVMCRKKQQENIYSHLDEESSESSTYTAALPRERLRPRPKVFLCYSSKDGQNHMNVVQCFAYFLQDFCGCEVALDLWEDFSLCREGQREWVIQKIHESQFIIVVCSKGMKYFVDKKNYKHKGGGRGSGKGELFLVAVSAIAEKLRQAKQSSSAALSKFIAVYFDYSCEGDVPGILDLSTKYRLMDNLPQLCSHLHSRDHGLQEPGQHTRQGSRRNYFRSKSGRSLYVAICNMHQFIDEEPDWFEKQFVPFHPPPLRYREPVLEKFDSGLVLNDVMCKPGPESDFCLKVEAAVLGATGPADSQHESQHGGLDQDGEARPALDGSAALQPLLHTVKAGSPSDMPRDSGIYDSSVPSSELSLPLMEGLSTDQTETSSLTESVSSSSGLGEEEPPALPSKLLSSGSCKADLGCRSYTDELHAVAPL,739,NP_060033.3.csv,refseq-IL17RD-NM_017563.4_clinical_seed_0_final,refseq-IL17RD-NM_017563.4.a2m,Invitae,refseq-IL17RD-NM_017563.4.npy,1,739,739 +NP_060035.2,MPGLRRDRLLTLLLLGALLSADLYFHLWPQVQRQLRPRERPRGCPCTGRASSLARDSAAAASDPGTIVHNFSRTEPRTEPAGGSHSGSSSKLQALFAHPLYNVPEEPPLLGAEDSLLASQEALRYYRRKVARWNRRHKMYREQMNLTSLDPPLQLRLEASWVQFHLGINRHGLYSRSSPVVSKLLQDMRHFPTISADYSQDEKALLGACDCTQIVKPSGVHLKLVLRFSDFGKAMFKPMRQQRDEETPVDFFYFIDFQRHNAEIAAFHLDRILDFRRVPPTVGRIVNVTKEILEVTKNEILQSVFFVSPASNVCFFAKCPYMCKTEYAVCGNPHLLEGSLSAFLPSLNLAPRLSVPNPWIRSYTLAGKEEWEVNPLYCDTVKQIYPYNNSQRLLNVIDMAIFDFLIGNMDRHHYEMFTKFGDDGFLIHLDNARGFGRHSHDEISILSPLSQCCMIKKKTLLHLQLLAQADYRLSDVMRESLLEDQLSPVLTEPHLLALDRRLQTILRTVEGCIVAHGQQSVIVDGPVEQLAPDSGQANLTS,541,NP_060035.2.csv,refseq-FAM20A-NM_017565.3_clinical_seed_0_final,refseq-FAM20A-NM_017565.3.a2m,Invitae,refseq-FAM20A-NM_017565.3.npy,1,541,541 +NP_060083.1,MALSVPGYSPGFRKPPEVVRLRRKRARSRGAAASPPRELTEPAARRAALVAGLPLRPFPAAGGRGGGSGGGPAAARRNPFARLDNRPRVAAEPPDGPAREQPEAPVPFLDSNQENDLLWEEKFPERTTVTELPQTSHVSFSEPDIPSSKSTELPVDWSIKTRLLFTSSQPFTWADHLKAQEEAQGLVQHCRATEVTLPKSIQDPKLSSELRCTFQQSLIYWLHPALSWLPLFPRIGADRKMAGKTSPWSNDATLQHVLMSDWSVSFTSLYNLLKTKLCPYFYVCTYQFTVLFRAAGLAGSDLITALISPTTRGLREAMRNEGIEFSLPLIKESGHKKETASGTSLGYGEEQAISDEDEEESFSWLEEMGVQDKIKKPDILSIKLRKEKHEVQMDHRPESVVLVKGINTFTLLNFLINSKSLVATSGPQAGLPPTLLSPVAFRGATMQMLKARSVNVKTQALSGYRDQFSLEITGPIMPHSLHSLTMLLKSSQSGSFSAVLYPHEPTAVFNICLQMDKVLDMEVVHKELTNCGLHPNTLEQLSQIPLLGKSSLRNVVLRDYIYNWRS,566,NP_060083.1.csv,refseq-DONSON-NM_017613.3_clinical_seed_0_final,refseq-DONSON-NM_017613.3.a2m,Invitae,refseq-DONSON-NM_017613.3_theta_0.2.npy,1,566,566 +NP_060106.2,MVVPEKEQSWIPKIFKKKTCTTFIVDSTDPGGTLCQCGRPRTAHPAVAMEDAFGAAVVTVWDSDAHTTEKPTDAYGELDFTGAGRKHSNFLRLSDRTDPAAVYSLVTRTWGFRAPNLVVSVLGGSGGPVLQTWLQDLLRRGLVRAAQSTGAWIVTGGLHTGIGRHVGVAVRDHQMASTGGTKVVAMGVAPWGVVRNRDTLINPKGSFPARYRWRGDPEDGVQFPLDYNYSAFFLVDDGTHGCLGGENRFRLRLESYISQQKTGVGGTGIDIPVLLLLIDGDEKMLTRIENATQAQLPCLLVAGSGGAADCLAETLEDTLAPGSGGARQGEARDRIRRFFPKGDLEVLQAQVERIMTRKELLTVYSSEDGSEEFETIVLKALVKACGSSEASAYLDELRLAVAWNRVDIAQSELFRGDIQWRSFHLEASLMDALLNDRPEFVRLLISHGLSLGHFLTPMRLAQLYSAAPSNSLIRNLLDQASHSAGTKAPALKGGAAELRPPDVGHVLRMLLGKMCAPRYPSGGAWDPHPGQGFGESMYLLSDKATSPLSLDAGLGQAPWSDLLLWALLLNRAQMAMYFWEMGSNAVSSALGACLLLRVMARLEPDAEEAARRKDLAFKFEGMGVDLFGECYRSSEVRAARLLLRRCPLWGDATCLQLAMQADARAFFAQDGVQSLLTQKWWGDMASTTPIWALVLAFFCPPLIYTRLITFRKSEEEPTREELEFDMDSVINGEGPVGTADPAEKTPLGVPRQSGRPGCCGGRCGGRRCLRRWFHFWGAPVTIFMGNVVSYLLFLLLFSRVLLVDFQPAPPGSLELLLYFWAFTLLCEELRQGLSGGGGSLASGGPGPGHASLSQRLRLYLADSWNQCDLVALTCFLLGVGCRLTPGLYHLGRTVLCIDFMVFTVRLLHIFTVNKQLGPKIVIVSKMMKDVFFFLFFLGVWLVAYGVATEGLLRPRDSDFPSILRRVFYRPYLQIFGQIPQEDMDVALMEHSNCSSEPGFWAHPPGAQAGTCVSQYANWLVVLLLVIFLLVANILLVNLLIAMFSYTFGKVQGNSDLYWKAQRYRLIREFHSRPALAPPFIVISHLRLLLRQLCRRPRSPQPSSPALEHFRVYLSKEAERKLLTWESVHKENFLLARARDKRESDSERLKRTSQKVDLALKQLGHIREYEQRLKVLEREVQQCSRVLGWVAEALSRSALLPPGGPPPPDLPGSKD,1214,NP_060106.2.csv,refseq-TRPM4-NM_017636.3_clinical_seed_0_final,refseq-TRPM4-NM_017636.3.a2m,Invitae,refseq-TRPM4-NM_017636.3.npy,1,1214,1214 +NP_060111.2,MLGAPDESSVRVAVRIRPQLAKEKIEGCHICTSVTPGEPQVFLGKDKAFTFDYVFDIDSQQEQIYIQCIEKLIEGCFEGYNATVFAYGQTGAGKTYTMGTGFDVNIVEEELGIISRAVKHLFKSIEEKKHIAIKNGLPAPDFKVNAQFLELYNEEVLDLFDTTRDIDAKSKKSNIRIHEDSTGGIYTVGVTTRTVNTESEMMQCLKLGALSRTTASTQMNVQSSRSHAIFTIHVCQTRVCPQIDADNATDNKIISESAQMNEFETLTAKFHFVDLAGSERLKRTGATGERAKEGISINCGLLALGNVISALGDKSKRATHVPYRDSKLTRLLQDSLGGNSQTIMIACVSPSDRDFMETLNTLKYANRARNIKNKVMVNQDRASQQINALRSEITRLQMELMEYKTGKRIIDEEGVESINDMFHENAMLQTENNNLRVRIKAMQETVDALRSRITQLVSDQANHVLARAGEGNEEISNMIHSYIKEIEDLRAKLLESEAVNENLRKNLTRATARAPYFSGSSTFSPTILSSDKETIEIIDLAKKDLEKLKRKEKRKKKSVAGKEDNTDTDQEKKEEKGVSERENNELEVEESQEVSDHEDEEEEEEEEEDDIDGGESSDESDSESDEKANYQADLANITCEIAIKQKLIDELENSQKRLQTLKKQYEEKLMMLQHKIRDTQLERDQVLQNLGSVESYSEEKAKKVRSEYEKKLQAMNKELQRLQAAQKEHARLLKNQSQYEKQLKKLQQDVMEMKKTKVRLMKQMKEEQEKARLTESRRNREIAQLKKDQRKRDHQLRLLEAQKRNQEVVLRRKTEEVTALRRQVRPMSDKVAGKVTRKLSSSDAPAQDTGSSAAAVETDASRTGAQQKMRIPVARVQALPTPATNGNRKKYQRKGLTGRVFISKTARMKWQLLERRVTDIIMQKMTISNMEADMNRLLKQREELTKRREKLSKRREKIVKENGEGDKNVANINEEMESLTANIDYINDSISDCQANIMQMEEAKEEGETLDVTAVINACTLTEARYLLDHFLSMGINKGLQAAQKEAQIKVLEGRLKQTEITSATQNQLLFHMLKEKAELNPELDALLGHALQDLDSVPLENVEDSTDEDAPLNSPGSEGSTLSSDLMKLCGEVKPKNKARRRTTTQMELLYADSSELASDTSTGDASLPGPLTPVAEGQEIGMNTETSGTSAREKELSPPPGLPSKIGSISRQSSLSEKKIPEPSPVTRRKAYEKAEKSKAKEQKHSDSGTSEASLSPPSSPPSRPRNELNVFNRLTVSQGNTSVQQDKSDESDSSLSEVHRSSRRGIINPFPASKGIRAFPLQCIHIAEGHTKAVLCVDSTDDLLFTGSKDRTCKVWNLVTGQEIMSLGGHPNNVVSVKYCNYTSLVFTVSTSYIKVWDIRDSAKCIRTLTSSGQVTLGDACSASTSRTVAIPSGENQINQIALNPTGTFLYAASGNAVRMWDLKRFQSTGKLTGHLGPVMCLTVDQISSGQDLIITGSKDHYIKMFDVTEGALGTVSPTHNFEPPHYDGIEALTIQGDNLFSGSRDNGIKKWDLTQKDLLQQVPNAHKDWVCALGVVPDHPVLLSGCRGGILKVWNMDTFMPVGEMKGHDSPINAICVNSTHIFTAADDRTVRIWKARNLQDGQISDTGDLGEDIASN,1661,NP_060111.2.csv,refseq-KIF21A-NM_017641.3_clinical_seed_0_final,refseq-KIF21A-NM_017641.3.a2m,Invitae,refseq-KIF21A-NM_017641.3.npy,1,1661,1661 +NP_060116.2,MASVAAARAVPVGSGLRGLQRTLPLVVILGATGTGKSTLALQLGQRLGGEIVSADSMQVYEGLDIITNKVSAQEQRICRHHMISFVDPLVTNYTVVDFRNRATALIEDIFARDKIPIVVGGTNYYIESLLWKVLVNTKPQEMGTEKVIDRKVELEKEDGLVLHKRLSQVDPEMAAKLHPHDKRKVARSLQVFEETGISHSEFLHRQHTEEGGGPLGGPLKFSNPCILWLHADQAVLDERLDKRVDDMLAAGLLEELRDFHRRYNQKNVSENSQDYQHGIFQSIGFKEFHEYLITEGKCTLETSNQLLKKGIEALKQVTKRYARKQNRWVKNRFLSRPGPIVPPVYGLEVSDVSKWEESVLEPALEIVQSFIQGHKPTATPIKMPYNEAENKRSYHLCDLCDRIIIGDREWAAHIKSKSHLNQLKKRRRLDSDAVNTIESQSVSPDHNKEPKEKGSPGQNDQELKCSV,467,NP_060116.2.csv,refseq-TRIT1-NM_017646.5_clinical_seed_0_final,refseq-TRIT1-NM_017646.5.a2m,Invitae,refseq-TRIT1-NM_017646.5.npy,1,467,467 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+NP_060289.2,MAAFLKMSVSVNFFRPFTRFLVPFTLHRKRNNLTILQRYMSSKIPAVTYPKNESTPPSEELELDKWKTTMKSSVQEECVSTISSSKDEDPLAATREFIEMWRLLGREVPEHITEEELKTLMECVSNTAKKKYLKYLYTKEKVKKARQIKKEMKAAAREEAKNIKLLETTEEDKQKNFLFLRLWDRNMDIAMGWKGAQAMQFGQPLVFDMAYENYMKRKELQNTVSQLLESEGWNRRNVDPFHIYFCNLKIDGALHRELVKRYQEKWDKLLLTSTEKSHVDLFPKDSIIYLTADSPNVMTTFRHDKVYVIGSFVDKSMQPGTSLAKAKRLNLATECLPLDKYLQWEIGNKNLTLDQMIRILLCLKNNGNWQEALQFVPKRKHTGFLEISQHSQEFINRLKKAKT,403,NP_060289.2.csv,refseq-TRMT10C-NM_017819.3_clinical_seed_0_final,refseq-TRMT10C-NM_017819.3.a2m,Invitae,refseq-TRMT10C-NM_017819.3.npy,1,403,403 +NP_060297.1,MAASMARRLWPLLTRRGFRPRGGCISNDSPRRSFTTEKRNRNLLYEYAREGYSALPQLDIERFCACPEEAAHALELRKGELRSADLPAIISTWQELRQLQEQIRSLEEEKAAVTEAVRALLANQDSGEVQQDPKYQGLRARGREIRKELVHLYPREAQLEEQFYLQALKLPNQTHPDVPVGDESQARVLHMVGDKPVFSFQPRGHLEIGEKLDIIRQKRLSHVSGHRSYYLRGAGALLQHGLVNFTFNKLLRRGFTPMTVPDLLRGAVFEGCGMTPNANPSQIYNIDPARFKDLNLAGTAEVGLAGYFMDHTVAFRDLPVRMVCSSTCYRAETNTGQEPRGLYRVHHFTKVEMFGVTGPGLEQSSQLLEEFLSLQMEILTELGLHFRVLDMPTQELGLPAYRKFDIEAWMPGRGRFGEVTSASNCTDFQSRRLHIMFQTEAGELQFAHTVNATACAVPRLLIALLESNQQKDGSVLVPPALQSYLGTDRITAPTHVPLQYIGPNQPRKPGLPGQPAVS,518,NP_060297.1.csv,refseq-SARS2-NM_017827.3_clinical_seed_0_final,refseq-SARS2-NM_017827.3.a2m,Invitae,refseq-SARS2-NM_017827.3.npy,1,518,518 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+NP_060342.2,MWGACKVKVHDSLATISITLRRYLRLGATMAKSKFEYVRDFEADDTCLAHCWVVVRLDGRNFHRFAEKHNFAKPNDSRALQLMTKCAQTVMEELEDIVIAYGQSDEYSFVFKRKTNWFKRRASKFMTHVASQFASSYVFYWRDYFEDQPLLYPPGFDGRVVVYPSNQTLKDYLSWRQADCHINNLYNTVFWALIQQSGLTPVQAQGRLQGTLAADKNEILFSEFNINYNNELPMYRKGTVLIWQKVDEVMTKEIKLPTEMEGKKMAVTRTRTKPVPLHCDIIGDAFWKEHPEILDEDS,298,NP_060342.2.csv,refseq-THG1L-NM_017872.4_clinical_seed_0_final,refseq-THG1L-NM_017872.4.a2m,Invitae,refseq-THG1L-NM_017872.4.npy,1,298,298 +NP_060345.2,MIQNSRPSLLQPQDVGDTVETLMLHPVIKAFLCGSISGTCSTLLFQPLDLLKTRLQTLQPSDHGSRRVGMLAVLLKVVRTESLLGLWKGMSPSIVRCVPGVGIYFGTLYSLKQYFLRGHPPTALESVMLGVGSRSVAGVCMSPITVIKTRYESGKYGYESIYAALRSIYHSEGHRGLFSGLTATLLRDAPFSGIYLMFYNQTKNIVPHDQVDATLIPITNFSCGIFAGILASLVTQPADVIKTHMQLYPLKFQWIGQAVTLIFKDYGLRGFFQGGIPRALRRTLMAAMAWTVYEEMMAKMGLKS,304,NP_060345.2.csv,refseq-SLC25A38-NM_017875.2_clinical_seed_0_final,refseq-SLC25A38-NM_017875.2.a2m,Invitae,refseq-SLC25A38-NM_017875.2.npy,1,304,304 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+NP_060379.2,MPATLLRAVARSHHILSKAHQCRRIGHLMLKPLKEFENTTCSTLTIRQSLDLFLPDKTASGLNKSQILEMNQKKSDTSMLSPLNAARCQDEKAHLPTMKSFGTHRRVTHKPNLLGSKWFIKILKRHFSSVSTETFVPKQDFPQVKRPLKASRTRQPSRTNLPVLSVNEDLMHCTAFATADEYHLGNLSQDLASHGYVEVTSLPRDAANILVMGVENSAKEGDPGTIFFFREGAAVFWNVKDKTMKHVMKVLEKHEIQPYEIALVHWENEELNYIKIEGQSKLHRGEIKLNSELDLDDAILEKFAFSNALCLSVKLAIWEASLDKFIESIQSIPEALKAGKKVKLSHEEVMQKIGELFALRHRINLSSDFLITPDFYWDRENLEGLYDKTCQFLSIGRRVKVMNEKLQHCMELTDLMRNHLNEKRALRLEWMIVILITIEVMFELGRVFF,449,NP_060379.2.csv,refseq-RMND1-NM_017909.3_clinical_seed_0_final,refseq-RMND1-NM_017909.3.a2m,Invitae,refseq-RMND1-NM_017909.3.npy,1,449,449 +NP_060399.1,MKSDSSTSAAPLRGLGGPLRSSEPVRAVPARAPAVDLLEEAADLLVVHLDFRAALETCERAWQSLANHAVAEEPAGTSLEVKCSLCVVGIQALAEMDRWQEVLSWVLQYYQVPEKLPPKVLELCILLYSKMQEPGAVLDVVGAWLQDPANQNLPEYGALAEFHVQRVLLPLGCLSEAEELVVGSAAFGEERRLDVLQAIHTARQQQKQEHSGSEEAQKPNLEGSVSHKFLSLPMLVRQLWDSAVSHFFSLPFKKSLLAALILCLLVVRFDPASPSSLHFLYKLAQLFRWIRKAAFSRLYQLRIRD,305,NP_060399.1.csv,refseq-PEX26-NM_017929.5_clinical_seed_0_final,refseq-PEX26-NM_017929.5.a2m,Invitae,refseq-PEX26-NM_017929.5.npy,1,305,305 +NP_060404.4,MSCERKGLSELRSELYFLIARFLEDGPCQQAAQVLIREVAEKELLPRRTDWTGKEHPRTYQNLVKYYRHLAPDHLLQICHRLGPLLEQEIPQSVPGVQTLLGAGRQSLLRTNKSCKHVVWKGSALAALHCGRPPESPVNYGSPPSIADTLFSRKLNGKYRLERLVPTAVYQHMKMHKRILGHLSSVYCVTFDRTGRRIFTGSDDCLVKIWATDDGRLLATLRGHAAEISDMAVNYENTMIAAGSCDKMIRVWCLRTCAPLAVLQGHSASITSLQFSPLCSGSKRYLSSTGADGTICFWLWDAGTLKINPRPAKFTERPRPGVQMICSSFSAGGMFLATGSTDHIIRVYFFGSGQPEKISELEFHTDKVDSIQFSNTSNRFVSGSRDGTARIWQFKRREWKSILLDMATRPAGQNLQGIEDKITKMKVTMVAWDRHDNTVITAVNNMTLKVWNSYTGQLIHVLMGHEDEVFVLEPHPFDPRVLFSAGHDGNVIVWDLARGVKIRSYFNMIEGQGHGAVFDCKCSPDGQHFACTDSHGHLLIFGFGSSSKYDKIADQMFFHSDYRPLIRDANNFVLDEQTQQAPHLMPPPFLVDVDGNPHPSRYQRLVPGRENCREEQLIPQMGVTSSGLNQVLSQQANQEISPLDSMIQRLQQEQDLRRSGEAVISNTSRLSRGSISSTSEVHSPPNVGLRRSGQIEGVRQMHSNAPRSEIATERDLVAWSRRVVVPELSAGVASRQEEWRTAKGEEEIKTYRSEEKRKHLTVPKENKIPTVSKNHAHEHFLDLGESKKQQTNQHNYRTRSALEETPRPSEEIENGSSSSDEGEVVAVSGGTSEEEERAWHSDGSSSDYSSDYSDWTADAGINLQPPKKVPKNKTKKAESSSDEEEESEKQKQKQIKKEKKKVNEEKDGPISPKKKKPKERKQKRLAVGELTENGLTLEEWLPSTWITDTIPRRCPFVPQMGDEVYYFRQGHEAYVEMARKNKIYSINPKKQPWHKMELREQELMKIVGIKYEVGLPTLCCLKLAFLDPDTGKLTGGSFTMKYHDMPDVIDFLVLRQQFDDAKYRRWNIGDRFRSVIDDAWWFGTIESQEPLQLEYPDSLFQCYNVCWDNGDTEKMSPWDMELIPNNAVFPEELGTSVPLTDGECRSLIYKPLDGEWGTNPRDEECERIVAGINQLMTLDIASAFVAPVDLQAYPMYCTVVAYPTDLSTIKQRLENRFYRRVSSLMWEVRYIEHNTRTFNEPGSPIVKSAKFVTDLLLHFIKDQTCYNIIPLYNSMKKKVLSDSEDEEKDADVPGTSTRKRKDHQPRRRLRNRAQSYDIQAWKKQCEELLNLIFQCEDSEPFRQPVDLLEYPDYRDIIDTPMDFATVRETLEAGNYESPMELCKDVRLIFSNSKAYTPSKRSRIYSMSLRLSAFFEEHISSVLSDYKSALRFHKRNTITKRRKKRNRSSSVSSSAASSPERKKRILKPQLKSESSTSAFSTPTRSIPPRHNAAQINGKTESSSVVRTRSNRVVVDPVVTEQPSTSSAAKTFITKANASAIPGKTILENSVKHSKALNTLSSPGQSSFSHGTRNNSAKENMEKEKPVKRKMKSSVLPKASTLSKSSAVIEQGDCKNNALVPGTIQVNGHGGQPSKLVKRGPGRKPKVEVNTNSGEIIHKKRGRKPKKLQYAKPEDLEQNNVHPIRDEVLPSSTCNFLSETNNVKEDLLQKKNRGGRKPKRKMKTQKLDADLLVPASVKVLRRSNRKKIDDPIDEEEEFEELKGSEPHMRTRNQGRRTAFYNEDDSEEEQRQLLFEDTSLTFGTSSRGRVRKLTEKAKANLIGW,1821,NP_060404.4.csv,refseq-PHIP-NM_017934.6_clinical_seed_0_final,refseq-PHIP-NM_017934.6.a2m,Invitae,refseq-PHIP-NM_017934.6.npy,1,1821,1821 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+NP_065857.1,MATEGMILTNHDHQIRVGVLTVSDSCFRNLAEDRSGINLKDLVQDPSLLGGTISAYKIVPDEIEEIKETLIDWCDEKELNLILTTGGTGFAPRDVTPEATKEVIEREAPGMALAMLMGSLNVTPLGMLSRPVCGIRGKTLIINLPGSKKGSQECFQFILPALPHAIDLLRDAIVKVKEVHDELEDLPSPPPPLSPPPTTSPHKQTEDKGVQCEEEEEEKKDSGVASTEDSSSSHITAAAIAAKKHPFYTSPAVVMAHGEQPIPGLINYSHHSTDERIPDSIISRGVQVLPRDTASLSTTPSESPRAQATSRLSTASCPTPKVQSRCSSKENILRASHSAVDITKVARRHRMSPFPLTSMDKAFITVLEMTPVLGTEIINYRDGMGRVLAQDVYAKDNLPPFPASVKDGYAVRAADGPGDRFIIGESQAGEQPTQTVMPGQVMRVTTGAPIPCGADAVVQVEDTELIRESDDGTEELEVRILVQARPGQDIRPIGHDIKRGECVLAKGTHMGPSEIGLLATVGVTEVEVNKFPVVAVMSTGNELLNPEDDLLPGKIRDSNRSTLLATIQEHGYPTINLGIVGDNPDDLLNALNEGISRADVIITSGGVSMGEKDYLKQVLDIDLHAQIHFGRVFMKPGLPTTFATLDIDGVRKIIFALPGNPVSAVVTCNLFVVPALRKMQGILDPRPTIIKARLSCDVKLDPRPEYHRCILTWHHQEPLPWAQSTGNQMSSRLMSMRSANGLLMLPPKTEQYVELHKGEVVDVMVIGRL,769,NP_065857.1.csv,refseq-GPHN-NM_020806.4_clinical_seed_0_final,refseq-GPHN-NM_020806.4.a2m,Invitae,refseq-GPHN-NM_020806.4.npy,1,769,769 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+NP_112224.1,MARLADYFIVVGYDHEKPGSGEGLGKIIQRFPQKDWDDTPFPQGIELFCQPGGWQLSRERKQPTFFVVVLTDIDSDRHYCSCLTFYEAEINLQGTKKEEIEGEAKVSGLIQPAEVFAPKSLVLVSRLYYPEIFRACLGLIYTVYVDSLNVSLESLIANLCACLVPAAGGSQKLFSLGAGDRQLIQTPLHDSLPITGTSVALLFQQLGIQNVLSLFCAVLTENKVLFHSASFQRLSDACRALESLMFPLKYSYPYIPILPAQLLEVLSSPTPFIIGVHSVFKTDVHELLDVIIADLDGGTIKIPECIHLSSLPEPLLHQTQSALSLILHPDLEVADHAFPPPRTALSHSKMLDKEVRAVFLRLFAQLFQGYRSCLQLIRIHAEPVIHFHKTAFLGQRGLVENDFLTKVLSGMAFAGFVSERGPPYRSCDLFDELVAFEVERIKVEENNPVKMIKHVRELAEQLFKNENPNPHMAFQKVPRPTEGSHLRVHILPFPEINEARVQELIQENVAKNQNAPPATRIEKKCVVPAGPPVVSIMDKVTTVFNSAQRLEVVRNCISFIFENKILETEKTLPAALRALKGKAARQCLTDELGLHVQQNRAILDHQQFDYIIRMMNCTLQDCSSLEEYNIAAALLPLTSAFYRKLAPGVSQFAYTCVQDHPIWTNQQFWETTFYNAVQEQVRSLYLSAKEDNHAPHLKQKDKLPDDHYQEKTAMDLAAEQLRLWPTLSKSTQQELVQHEESTVFSQAIHFANLMVNLLVPLDTSKNKLLRTSAPGDWESGSNSIVTNSIAGSVAESYDTESGFEDSENTDIANSVVRFITRFIDKVCTESGVTQDHIKSLHCMIPGIVAMHIETLEAVHRESRRLPPIQKPKILRPALLPGEEIVCEGLRVLLDPDGREEATGGLLGGPQLLPAEGALFLTTYRILFRGTPHDQLVGEQTVVRSFPIASITKEKKITMQNQLQQNMQEGLQITSASFQLIKVAFDEEVSPEVVEIFKKQLMKFRYPQSIFSTFAFAAGQTTPQIILPKQKEKNTSFRTFSKTIVKGAKRAGKMTIGRQYLLKKKTGTIVEERVNRPGWNEDDDVSVSDESELPTSTTLKASEKSTMEQLVEKACFRDYQRLGLGTISGSSSRSRPEYFRITASNRMYSLCRSYPGLLVVPQAVQDSSLPRVARCYRHNRLPVVCWKNSRSGTLLLRSGGFHGKGVVGLFKSQNSPQAAPTSSLESSSSIEQEKYLQALLNAVSVHQKLRGNSTLTVRPAFALSPGVWASLRSSTRLISSPTSFIDVGARLAGKDHSASFSNSSYLQNQLLKRQAALYIFGEKSQLRNFKVEFALNCEFVPVEFHEIRQVKASFKKLMRACIPSTIPTDSEVTFLKALGDSEWFPQLHRIMQLAVVVSEVLENGSSVLVCLEEGWDITAQVTSLVQLLSDPFYRTLEGFQMLVEKEWLSFGHKFSQRSSLTLNCQGSGFAPVFLQFLDCVHQVHNQYPTEFEFNLYYLKFLAFHYVSNRFKTFLLDSDYERLEHGTLFDDKGEKHAKKGVCIWECIDRMHKRSPIFFNYLYSPLEIEALKPNVNVSSLKKWDYYIEETLSTGPSYDWMMLTPKHFPSEDSDLAGEAGPRSQRRTVWPCYDDVSCTQPDALTSLFSEIEKLEHKLNQAPEKWQQLWERVTVDLKEEPRTDRSQRHLSRSPGIVSTNLPSYQKRSLLHLPDSSMGEEQNSSISPSNGVERRAATLYSQYTSKNDENRSFEGTLYKRGALLKGWKPRWFVLDVTKHQLRYYDSGEDTSCKGHIDLAEVEMVIPAGPSMGAPKHTSDKAFFDLKTSKRVYNFCAQDGQSAQQWMDKIQSCISDA,1849,NP_112224.1.csv,refseq-SBF2-NM_030962.3_clinical_seed_0_final,refseq-SBF2-NM_030962.3.a2m,Invitae,refseq-SBF2-NM_030962.3.npy,1,1849,1849 +NP_112235.2,MVPGSEGPARAGSVVADVVFVIEGTANLGPYFEGLRKHYLLPAIEYFNGGPPAETDFGGDYGGTQYSLVVFNTVDCAPESYVQCHAPTSSAYEFVTWLDGIKFMGGGGESCSLIAEGLSTALQLFDDFKKMREQIGQTHRVCLLICNSPPYLLPAVESTTYSGCTTENLVQQIGERGIHFSIVSPRKLPALRLLFEKAAPPALLEPLQPPTDVSQDPRHMVLVRGLVLPVGGGSAPGPLQSKQPVPLPPAAPSGATLSAAPQQPLPPVPPQYQVPGNLSAAQVAAQNAVEAAKNQKAGLGPRFSPITPLQQAAPGVGPPFSQAPAPQLPPGPPGAPKPPPASQPSLVSTVAPGSGLAPTAQPGAPSMAGTVAPGGVSGPSPAQLGAPALGGQQSVSNKLLAWSGVLEWQEKPKPASVDANTKLTRSLPCQVYVNHGENLKTEQWPQKLIMQLIPQQLLTTLGPLFRNSRMVQFHFTNKDLESLKGLYRIMGNGFAGCVHFPHTAPCEVRVLMLLYSSKKKIFMGLIPYDQSGFVNGIRQVITNHKQVQQQKLEQQQRGMGGQQAPPGLGPILEDQARPSQNLLQLRPPQPQPQGTVGASGATGQPQPQGTAQPPPGAPQGPPGAASGPPPPGPILRPQNPGANPQLRSLLLNPPPPQTGVPPPQASLHHLQPPGAPALLPPPHQGLGQPQLGPPLLHPPPAQSWPAQLPPRAPLPGQMLLSGGPRGPVPQPGLQPSVMEDDILMDLI,747,NP_112235.2.csv,refseq-MED25-NM_030973.3_clinical_seed_0_final,refseq-MED25-NM_030973.3.a2m,Invitae,refseq-MED25-NM_030973.3.npy,1,747,747 +NP_112420.1,MSKSESPKEPEQLRKLFIGGLSFETTDESLRSHFEQWGTLTDCVVMRDPNTKRSRGFGFVTYATVEEVDAAMNARPHKVDGRVVEPKRAVSREDSQRPGAHLTVKKIFVGGIKEDTEEHHLRDYFEQYGKIEVIEIMTDRGSGKKRGFAFVTFDDHDSVDKIVIQKYHTVNGHNCEVRKALSKQEMASASSSQRGRSGSGNFGGGRGGGFGGNDNFGRGGNFSGRGGFGGSRGGGGYGGSGDGYNGFGNDGGYGGGGPGYSGGSRGYGSGGQGYGNQGSGYGGSGSYDSYNNGGGGGFGGGSGSNFGGGGSYNDFGNYNNQSSNFGPMKGGNFGGRSSGPYGGGGQYFAKPRNQGGYGGSSSSSSYGSGRRF,372,NP_112420.1.csv,refseq-HNRNPA1-NM_031157.2_clinical_seed_0_final,refseq-HNRNPA1-NM_031157.2.a2m,Invitae,refseq-HNRNPA1-NM_031157.2.npy,1,372,372 +NP_112483.1,MSWESGAGPGLGSQGMDLVWSAWYGKCVKGKGSLPLSAHGIVVAWLSRAEWDQVTVYLFCDDHKLQRYALNRITVWRSRSGNELPLAVASTADLIRCKLLDVTGGLGTDELRLLYGMALVRFVNLISERKTKFAKVPLKCLAQEVNIPDWIVDLRHELTHKKMPHINDCRRGCYFVLDWLQKTYWCRQLENSLRETWELEEFREGIEEEDQEEDKNIVVDDITEQKPEPQDDGKSTESDVKADGDSKGSEEVDSHCKKALSHKELYERARELLVSYEEEQFTVLEKFRYLPKAIKAWNNPSPRVECVLAELKGVTCENREAVLDAFLDDGFLVPTFEQLAALQIEYEDGQTEVQRGEGTDPKSHKNVDLNDVLVPKPFSQFWQPLLRGLHSQNFTQALLERMLSELPALGISGIRPTYILRWTVELIVANTKTGRNARRFSAGQWEARRGWRLFNCSASLDWPRMVESCLGSPCWASPQLLRIIFKAMGQGLPDEEQEKLLRICSIYTQSGENSLVQEGSEASPIGKSPYTLDSLYWSVKPASSSFGSEAKAQQQEEQGSVNDVKEEEKEEKEVLPDQVEEEEENDDQEEEEEDEDDEDDEEEDRMEVGPFSTGQESPTAENARLLAQKRGALQGSAWQVSSEDVRWDTFPLGRMPGQTEDPAELMLENYDTMYLLDQPVLEQRLEPSTCKTDTLGLSCGVGSGNCSNSSSSNFEGLLWSQGQLHGLKTGLQLF,734,NP_112483.1.csv,refseq-LAS1L-NM_031206.4_clinical_seed_0_final,refseq-LAS1L-NM_031206.4.a2m,Invitae,refseq-LAS1L-NM_031206.4.npy,1,734,734 +NP_112503.1,MVLEMLNPIHYNITSIVPEAMPAATMPVLLLTGLFLLVWNYEGTSSIPGPGYCMGIGPLISHGRFLWMGIGSACNYYNRVYGEFMRVWISGEETLIISKSSSMFHIMKHNHYSSRFGSKLGLQCIGMHEKGIIFNNNPELWKTTRPFFMKALSGPGLVRMVTVCAESLKTHLDRLEEVTNESGYVDVLTLLRRVMLDTSNTLFLRIPLDESAIVVKIQGYFDAWQALLIKPDIFFKISWLYKKYEKSVKDLKDAIEVLIAEKRRRISTEEKLEECMDFATELILAEKRGDLTRENVNQCILEMLIAAPDTMSVSLFFMLFLIAKHPNVEEAIIKEIQTVIGERDIKIDDIQKLKVMENFIYESMRYQPVVDLVMRKALEDDVIDGYPVKKGTNIILNIGRMHRLEFFPKPNEFTLENFAKNVPYRYFQPFGFGPRGCAGKYIAMVMMKAILVTLLRRFHVKTLQGQCVESIQKIHDLSLHPDETKNMLEMIFTPRNSDRCLEH,503,NP_112503.1.csv,refseq-CYP19A1-NM_031226.2_clinical_seed_0_final,refseq-CYP19A1-NM_031226.2.a2m,Invitae,refseq-CYP19A1-NM_031226.2.npy,1,503,503 +NP_112506.2,MDEKTKKAEEMALSLTRAVAGGDEQVAMKCAIWLAEQRVPLSVQLKPEVSPTQDIRLWVSVEDAQMHTVTIWLTVRPDMTVASLKDMVFLDYGFPPVLQQWVIGQRLARDQETLHSHGVRQNGDSAYLYLLSARNTSLNPQELQRERQLRMLEDLGFKDLTLQPRGPLEPGPPKPGVPQEPGRGQPDAVPEPPPVGWQCPGCTFINKPTRPGCEMCCRARPEAYQVPASYQPDEEERARLAGEEEALRQYQQRKQQQQEGNYLQHVQLDQRSLVLNTEPAECPVCYSVLAPGEAVVLRECLHTFCRECLQGTIRNSQEAEVSCPFIDNTYSCSGKLLEREIKALLTPEDYQRFLDLGISIAENRSAFSYHCKTPDCKGWCFFEDDVNEFTCPVCFHVNCLLCKAIHEQMNCKEYQEDLALRAQNDVAARQTTEMLKVMLQQGEAMRCPQCQIVVQKKDGCDWIRCTVCHTEICWVTKGPRWGPGGPGDTSGGCRCRVNGIPCHPSCQNCH,510,NP_112506.2.csv,refseq-RBCK1-NM_031229.3_clinical_seed_0_final,refseq-RBCK1-NM_031229.3.a2m,Invitae,refseq-RBCK1-NM_031229.3.npy,1,510,510 +NP_112533.1,MEKTLETVPLERKKREKEQFRKLFIGGLSFETTEESLRNYYEQWGKLTDCVVMRDPASKRSRGFGFVTFSSMAEVDAAMAARPHSIDGRVVEPKRAVAREESGKPGAHVTVKKLFVGGIKEDTEEHHLRDYFEEYGKIDTIEIITDRQSGKKRGFGFVTFDDHDPVDKIVLQKYHTINGHNAEVRKALSRQEMQEVQSSRSGRGGNFGFGDSRGGGGNFGPGPGSNFRGGSDGYGSGRGFGDGYNGYGGGPGGGNFGGSPGYGGGRGGYGGGGPGYGNQGGGYGGGYDNYGGGNYGSGNYNDFGNYNQQPSNYGPMKSGNFGGSRNMGGPYGGGNYGPGGSGGSGGYGGRSRY,353,NP_112533.1.csv,refseq-HNRNPA2B1-NM_031243.2_clinical_seed_0_final,refseq-HNRNPA2B1-NM_031243.2.a2m,Invitae,refseq-HNRNPA2B1-NM_031243.2.npy,1,353,353 +NP_112740.1,MEVPPRLSHVPPPLFPSAPATLASRSLSHWRPRPPRQLAPLLPSLAPSSARQGARRAQRHVTAQQPSRLAGGAAIKGGRRRRPDLFRRHFKSSSIQRSAAAAAATRTARQHPPADSSVTMEDMNEYSNIEEFAEGSKINASKNQQDDGKMFIGGLSWDTSKKDLTEYLSRFGEVVDCTIKTDPVTGRSRGFGFVLFKDAASVDKVLELKEHKLDGKLIDPKRAKALKGKEPPKKVFVGGLSPDTSEEQIKEYFGAFGEIENIELPMDTKTNERRGFCFITYTDEEPVKKLLESRYHQIGSGKCEIKVAQPKEVYRQQQQQQKGGRGAAAGGRGGTRGRGRGQGQNWNQGFNNYYDQGYGNYNSAYGGDQNYSGYGGYDYTGYNYGNYGYGQGYADYSGQQSTYGKASRGGGNHQNNYQPY,420,NP_112740.1.csv,refseq-HNRNPDL-NM_031372.3_clinical_seed_0_final,refseq-HNRNPDL-NM_031372.3.a2m,Invitae,refseq-HNRNPDL-NM_031372.3.npy,1,420,420 +NP_113606.2,MVHHSGSIQSFKQQKGMNISKSEITKETSLKPSRRSLPCLAQSYAYSKSLSQSTSLFQSTESESQAPTSITLISTDKAEQVNTEENKNDSVLRCSFADLSDFCLALGKDKDYTDESEHATYDRSRLINDFVIKDKSEFKTKLSKNDMNYIASSGPLFKDGKKRIDYILVYRKTNIQYDKRNTFEKNLRAEGLMLEKEPAIASPDIMFIKIHIPWDTLCKYAERLNIRMPFRKKCYYTDGRSKSMGRMQTYFRRIKNWMAQNPMVLDKSAFPDLEESDCYTGPFSRARIHHFIINNKDTFFSNATRSRIVYHMLERTKYENGISKVGIRKLINNGSYIAAFPPHEGAYKSSQPIKTHGPQNNRHLLYERWARWGMWYKHQPLDLIRLYFGEKIGLYFAWLGWYTGMLIPAAIVGLCVFFYGLFTMNNSQVSQEICKATEVFMCPLCDKNCSLQRLNDSCIYAKVTYLFDNGGTVFFAIFMAIWATVFLEFWKRRRSILTYTWDLIEWEEEEETLRPQFEAKYYKMEIVNPITGKPEPHQPSSDKVTRLLVSVSGIFFMISLVITAVFGVVVYRLVVMEQFASFKWNFIKQYWQFATSAAAVCINFIIIMLLNLAYEKIAYLLTNLEYPRTESEWENSFALKMFLFQFVNLNSSIFYIAFFLGRFVGHPGKYNKLFDRWRLEECHPSGCLIDLCLQMGVIMFLKQIWNNFMELGYPLIQNWWSRHKIKRGIHDASIPQWENDWNLQPMNLHGLMDEYLEMVLQFGFTTIFVAAFPLAPLLALLNNIIEIRLDAYKFVTQWRRPLPARATDIGIWLGILEGIGILAVITNAFVIAITSDYIPRFVYEYKYGPCANHVEPSENCLKGYVNNSLSFFDLSELGMGKSGYCRYRDYRGPPWSSKPYEFTLQYWHILAARLAFIIVFEHLVFGIKSFIAYLIPDVPKGLHDRIRREKYLVQEMMYEAELEHLQQQRRKSGQPVHHEWP,981,NP_113606.2.csv,refseq-ANO3-NM_031418.2_clinical_seed_0_final,refseq-ANO3-NM_031418.2.a2m,Invitae,refseq-ANO3-NM_031418.2.npy,1,981,981 +NP_113621.1,MKDFSDVILCMEATESSKTEFCNPAFEPESGPPCPPPVFPEDASYSVPAPWHGRRPRGLRPDCRFSWLCVLLLSSLLLLLLGLLVAIILAQLQAAPPSGASHSPLPAGGLTTTTTTPTITTSQAAGTPKGQQESGVSPSPQSTCGGLLSGPRGFFSSPNYPDPYPPNTHCVWHIQVATDHAIQLKIEALSIESVASCLFDRLELSPEPEGPLLRVCGRVPPPTLNTNASHLLVVFVSDSSVEGFGFHAWYQAMAPGRGSCAHDEFRCDQLICLLPDSVCDGFANCADGSDETNCSAKFSGCGGNLTGLQGTFSTPSYLQQYPHQLLCTWHISVPAGHSIELQFHNFSLEAQDECKFDYVEVYETSSSGAFSLLGRFCGAEPPPHLVSSHHELAVLFRTDHGISSGGFSATYLAFNATENPCGPSELSCQAGGCKGVQWMCDMWRDCTDGSDDNCSGPLFPPPELACEPVQVEMCLGLSYNTTAFPNIWVGMITQEEVVEVLSGYKSLTSLPCYQHFRRLLCGLLVPRCTPLGSVLPPCRSVCQEAEHQCQSGLALLGTPWPFNCNRLPEAADLEACAQP,579,NP_113621.1.csv,refseq-MFRP-NM_031433.3_clinical_seed_0_final,refseq-MFRP-NM_031433.3.a2m,Invitae,refseq-MFRP-NM_031433.3.npy,1,579,579 +NP_113631.1,MEEEGKKGKKPGIVSPFKRVFLKGEKSRDKKAHEKVTERRPLHTVVLSLPERVEPDRLLSDYIEKEVKYLGQLTSIPGYLNPSSRTEILHFIDNAKRAHQLPGHLTQEHDAVLSLSAYNVKLAWRDGEDIILRVPIHDIAAVSYVRDDAAHLVVLKTAQDPGISPSQSLCAESSRGLSAGSLSESAVGPVEACCLVILAAESKVAAEELCCLLGQVFQVVYTESTIDFLDRAIFDGASTPTHHLSLHSDDSSTKVDIKETYEVEASTFCFPESVDVGGASPHSKTISESELSASATELLQDYMLTLRTKLSSQEIQQFAALLHEYRNGASIHEFCINLRQLYGDSRKFLLLGLRPFIPEKDSQHFENFLETIGVKDGRGIITDSFGRHRRALSTTSSSTTNGNRATGSSDDRSAPSEGDEWDRMISDISSDIEALGCSMDQDSA,444,NP_113631.1.csv,refseq-CCM2-NM_031443.3_clinical_seed_0_final,refseq-CCM2-NM_031443.3.a2m,Invitae,refseq-CCM2-NM_031443.3_theta_0.2.npy,1,444,444 +NP_113663.2,MALEQALQAARQGELDVLRSLHAAGLLGPSLRDPLDALPVHHAARAGKLHCLRFLVEEAALPAAARARNGATPAHDASATGHLACLQWLLSQGGCRVQDKDNSGATVLHLAARFGHPEVVNWLLHHGGGDPTAATDMGALPIHYAAAKGDFPSLRLLVEHYPEGVNAQTKNGATPLYLACQEGHLEVTQYLVQECGADPHARAHDGMTPLHAAAQMGHSPVIVWLVSCTDVSLSEQDKDGATAMHFAASRGHTKVLSWLLLHGGEISADLWGGTPLHDAAENGELECCQILVVNGAELDVRDRDGYTAADLSDFNGHSHCTRYLRTVENLSVEHRVLSRDPSAELEAKQPDSGMSSPNTTVSVQPLNFDLSSPTSTLSNYDSCSSSHSSIKGQHPPCGLSSARAADIQSYMDMLNPELGLPRGTIGKPTPPPPPPSFPPPPPPPGTQLPPPPPGYPAPKPPVGPQAADIYMQTKNKLRHVETEALKKELSSCDGHDGLRRQDSSRKPRAFSKQPSTGDYYRQLGRCPGETLAARPGMAHSEEVRARQPARAGCPRLGPAARGSLEGPSAPPQAALLPGNHVPNGCAADPKASRELPPPPPPPPPPLPEAASSPPPAPPLPLESAGPGCGQRRSSSSTGSTKSFNMMSPTGDNSELLAEIKAGKSLKPTPQSKGLTTVFSGIGQPAFQPDSPLPSVSPALSPVRSPTPPAAGFQPLLNGSLVPVPPTTPAPGVQLDVEALIPTHDEQGRPIPEWKRQVMVRKMQLKMQEEEEQRRKEEEEEARLASMPAWRRDLLRKKLEEEREQKRKEEERQKQEELRREKEQSEKLRTLGYDESKLAPWQRQVILKKGDIAKY,854,NP_113663.2.csv,refseq-ESPN-NM_031475.2_clinical_seed_0_final,refseq-ESPN-NM_031475.2.a2m,Invitae,refseq-ESPN-NM_031475.2.npy,1,854,854 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+NP_116023.2,MQKASVLLFLAWVCFLFYAGIALFTSGFLLTRLELTNHSSCQEPPGPGSLPWGSQGKPGACWMASRFSRVVLVLIDALRFDFAQPQHSHVPREPPVSLPFLGKLSSLQRILEIQPHHARLYRSQVDPPTTTMQRLKALTTGSLPTFIDAGSNFASHAIVEDNLIKQLTSAGRRVVFMGDDTWKDLFPGAFSKAFFFPSFNVRDLDTVDNGILEHLYPTMDSGEWDVLIAHFLGVDHCGHKHGPHHPEMAKKLSQMDQVIQGLVERLENDTLLVVAGDHGMTTNGDHGGDSELEVSAALFLYSPTAVFPSTPPEEPEVIPQVSLVPTLALLLGLPIPFGNIGEVMAELFSGGEDSQPHSSALAQASALHLNAQQVSRFLHTYSAATQDLQAKELHQLQNLFSKASADYQWLLQSPKGAEATLPTVIAELQQFLRGARAMCIESWARFSLVRMAGGTALLAASCFICLLASQWAISPGFPFCPLLLTPVAWGLVGAIAYAGLLGTIELKLDLVLLGAVAAVSSFLPFLWKAWAGWGSKRPLATLFPIPGPVLLLLLFRLAVFFSDSFVVAEARATPFLLGSFILLLVVQLHWEGQLLPPKLLTMPRLGTSATTNPPRHNGAYALRLGIGLLLCTRLAGLFHRCPEETPVCHSSPWLSPLASMVGGRAKNLWYGACVAALVALLAAVRLWLRRYGNLKSPEPPMLFVRWGLPLMALGTAAYWALASGADEAPPRLRVLVSGASMVLPRAVAGLAASGLALLLWKPVTVLVKAGAGAPRTRTVLTPFSGPPTSQADLDYVVPQIYRHMQEEFRGRLERTKSQGPLTVAAYQLGSVYSAAMVTALTLLAFPLLLLHAERISLVFLLLFLQSFLLLHLLAAGIPVTTPGPFTVPWQAVSAWALMATQTFYSTGHQPVFPAIHWHAAFVGFPEGHGSCTWLPALLVGANTFASHLLFAVGCPLLLLWPFLCESQGLRKRQQPPGNEADARVRPEEEEEPLMEMRLRDAPQHFYAALLQLGLKYLFILGIQILACALAASILRRHLMVWKVFAPKFIFEAVGFIVSSVGLLLGIALVMRVDGAVSSWFRQLFLAQQR,1089,NP_116023.2.csv,refseq-PIGO-NM_032634.3_clinical_seed_0_final,refseq-PIGO-NM_032634.3.a2m,Invitae,refseq-PIGO-NM_032634.3.npy,1,1089,1089 +NP_116027.2,MEVAPEQPRWMAHPAVLNAQHPDSHHPGLAHNYMEPAQLLPPDEVDVFFNHLDSQGNPYYANPAHARARVSYSPAHARLTGGQMCRPHLLHSPGLPWLDGGKAALSAAAAHHHNPWTVSPFSKTPLHPSAAGGPGGPLSVYPGAGGGSGGGSGSSVASLTPTAAHSGSHLFGFPPTPPKEVSPDPSTTGAASPASSSAGGSAARGEDKDGVKYQVSLTESMKMESGSPLRPGLATMGTQPATHHPIPTYPSYVPAAAHDYSSGLFHPGGFLGGPASSFTPKQRSKARSCSEGRECVNCGATATPLWRRDGTGHYLCNACGLYHKMNGQNRPLIKPKRRLSAARRAGTCCANCQTTTTTLWRRNANGDPVCNACGLYYKLHNVNRPLTMKKEGIQTRNRKMSNKSKKSKKGAECFEELSKCMQEKSSPFSAAALAGHMAPVGHLPPFSHSGHILPTPTPIHPSSSLSFGHPHPSSMVTAMG,480,NP_116027.2.csv,refseq-GATA2-NM_032638.4_clinical_seed_0_final,refseq-GATA2-NM_032638.4.a2m,Invitae,refseq-GATA2-NM_032638.4.npy,1,480,480 +NP_116045.2,MGKLRRRYNIKGRQQAGPGPSKGPPEPPPVQLELEDKDTLKGVDASNALVLPGKKKKKTKAPPLSKKEKKPLTKKEKKVLQKILEQKEKKSQRAEMLQKLSEVQASEAEMRLFYTTSKLGTGNRMYHTKEKADEVVAPGQEKISSLSGAHRKRRRWPSAEEEEEEEEESESELEEESELDEDPAAEPAEAGVGTTVAPLPPAPAPSSQPVPAGMTVPPPPAAAPPLPRALAKPAVFIPVNRSPEMQEERLKLPILSEEQVIMEAVAEHPIVIVCGETGSGKTTQVPQFLYEAGFSSEDSIIGVTEPRRVAAVAMSQRVAKEMNLSQRVVSYQIRYEGNVTEETRIKFMTDGVLLKEIQKDFLLLRYKVVIIDEAHERSVYTDILIGLLSRIVTLRAKRNLPLKLLIMSATLRVEDFTQNPRLFAKPPPVIKVESRQFPVTVHFNKRTPLEDYSGECFRKVCKIHRMLPAGGILVFLTGQAEVHALCRRLRKAFPPSRARPQEKDDDQKDSVEEMRKFKKSRARAKKARAEVLPQINLDHYSVLPAGEGDEDREAEVDEEEGALDSDLDLDLGDGGQDGGEQPDASLPLHVLPLYSLLAPEKQAQVFKPPPEGTRLCVVATNVAETSLTIPGIKYVVDCGKVKKRYYDRVTGVSSFRVTWVSQASADQRAGRAGRTEPGHCYRLYSSAVFGDFEQFPPPEITRRPVEDLILQMKALNVEKVINFPFPTPPSVEALLAAEELLIALGALQPPQKAERVKQLQENRLSCPITALGRTMATFPVAPRYAKMLALSRQHGCLPYAITIVASMTVRELFEELDRPAASDEELTRLKSKRARVAQMKRTWAGQGASLKLGDLMVLLGAVGACEYASCTPQFCEANGLRYKAMMEIRRLRGQLTTAVNAVCPEAELFVDPKMQPPTESQVTYLRQIVTAGLGDHLARRVQSEEMLEDKWRNAYKTPLLDDPVFIHPSSVLFKELPEFVVYQEIVETTKMYMKGVSSVEVQWIPALLPSYCQFDKPLEEPAPTYCPERGRVLCHRASVFYRVGWPLPAIEVDFPEGIDRYKHFARFLLEGQVFRKLASYRSCLLSSPGTMLKTWARLQPRTESLLRALVAEKADCHEALLAAWKKNPKYLLAEYCEWLPQAMHPDIEKAWPPTTVH,1157,NP_116045.2.csv,refseq-DHX37-NM_032656.3_clinical_seed_0_final,refseq-DHX37-NM_032656.3.a2m,Invitae,refseq-DHX37-NM_032656.3.npy,1,1157,1157 +NP_116056.3,MVNDPPVPALLWAQEVGQVLAGRARRLLLQFGVLFCTILLLLWVSVFLYGSFYYSYMPTVSHLSPVHFYYRTDCDSSTTSLCSFPVANVSLTKGGRDRVLMYGQPYRVTLELELPESPVNQDLGMFLVTISCYTRGGRIISTSSRSVMLHYRSDLLQMLDTLVFSSLLLFGFAEQKQLLEVELYADYRENSYVPTTGAIIEIHSKRIQLYGAYLRIHAHFTGLRYLLYNFPMTCAFIGVASNFTFLSVIVLFSYMQWVWGGIWPRHRFSLQVNIRKRDNSRKEVQRRISAHQPGPEGQEESTPQSDVTEDGESPEDPSGTEGQLSEEEKPDQQPLSGEEELEPEASDGSGSWEDAALLTEANLPAPAPASASAPVLETLGSSEPAGGALRQRPTCSSS,398,NP_116056.3.csv,refseq-BSCL2-NM_032667.6_clinical_seed_0_final,refseq-BSCL2-NM_032667.6.a2m,Invitae,refseq-BSCL2-NM_032667.6.npy,1,398,398 +NP_116071.2,MMQESGTETKSNGSAIQNGSGGSNHLLECGGLREGRSNGETPAVDIGAADLAHAQQQQQQALQVARQLLLQQQQQQQVSGLKSPKRNDKQPALQVPVSVAMMTPQVITPQQMQQILQQQVLSPQQLQVLLQQQQALMLQQQQLQEFYKKQQEQLQLQLLQQQHAGKQPKEQQQVATQQLAFQQQLLQMQQLQQQHLLSLQRQGLLTIQPGQPALPLQPLAQGMIPTELQQLWKEVTSAHTAEETTGNNHSSLDLTTTCVSSSAPSKTSLIMNPHASTNGQLSVHTPKRESLSHEEHPHSHPLYGHGVCKWPGCEAVCEDFQSFLKHLNSEHALDDRSTAQCRVQMQVVQQLELQLAKDKERLQAMMTHLHVKSTEPKAAPQPLNLVSSVTLSKSASEASPQSLPHTPTTPTAPLTPVTQGPSVITTTSMHTVGPIRRRYSDKYNVPISSADIAQNQEFYKNAEVRPPFTYASLIRQAILESPEKQLTLNEIYNWFTRMFAYFRRNAATWKNAVRHNLSLHKCFVRVENVKGAVWTVDEVEFQKRRPQKISGNPSLIKNMQSSHAYCTPLNAALQASMAENSIPLYTTASMGNPTLGNLASAIREELNGAMEHTNSNESDSSPGRSPMQAVHPVHVKEEPLDPEEAEGPLSLVTTANHSPDFDHDRDYEDEPVNEDME,677,NP_116071.2.csv,refseq-FOXP1-NM_032682.5_clinical_seed_0_final,refseq-FOXP1-NM_032682.5.a2m,Invitae,refseq-FOXP1-NM_032682.5.npy,1,677,677 +NP_116119.2,MEFLKTCVLRRNACTAVCFWRSKVVQKPSVRRISTTSPRSTVMPAWVIDKYGKNEVLRFTQNMMMPIIHYPNEVIVKVHAASVNPIDVNMRSGYGATALNMKRDPLHVKIKGEEFPLTLGRDVSGVVMECGLDVKYFKPGDEVWAAVPPWKQGTLSEFVVVSGNEVSHKPKSLTHTQAASLPYVALTAWSAINKVGGLNDKNCTGKRVLILGASGGVGTFAIQVMKAWDAHVTAVCSQDASELVRKLGADDVIDYKSGSVEEQLKSLKPFDFILDNVGGSTETWAPDFLKKWSGATYVTLVTPFLLNMDRLGIADGMLQTGVTVGSKALKHFWKGVHYRWAFFMASGPCLDDIAELVDAGKIRPVIEQTFPFSKVPEAFLKVERGHARGKTVINVV,396,NP_116119.2.csv,refseq-RTN4IP1-NM_032730.4_clinical_seed_0_final,refseq-RTN4IP1-NM_032730.4.a2m,Invitae,refseq-RTN4IP1-NM_032730.4.npy,1,396,396 +NP_116145.1,MAAPALRLCHIAFHVPAGQPLARNLQRLFGFQPLASREVDGWRQLALRSGDAVFLVNEGAGSGEPLYGLDPRHAVPSATNLCFDVADAGAATRELAALGCSVPVPPVRVRDAQGAATYAVVSSPAGILSLTLLERAGYRGPFLPGFRPVSSAPGPGWVSRVDHLTLACTPGSSPTLLRWFHDCLGFCHLPLSPGEDPELGLEMTAGFGLGGLRLTALQAQPGSIVPTLVLAESLPGATTRQDQVEQFLARHKGPGLQHVGLYTPNIVEATEGVATAGGQFLAPPGAYYQQPGKERQIRAAGHEPHLLARQGILLDGDKGKFLLQVFTKSLFTEDTFFLELIQRQGATGFGQGNIRALWQSVQEQSARSQEA,371,NP_116145.1.csv,refseq-HPDL-NM_032756.2_clinical_seed_0_final,refseq-HPDL-NM_032756.2.a2m,Invitae,refseq-HPDL-NM_032756.2.npy,1,371,371 +NP_116190.3,MALRRPPRLRLCARLPDFFLLLLFRGCLIGAVNLKSSNRTPVVQEFESVELSCIITDSQTSDPRIEWKKIQDEQTTYVFFDNKIQGDLAGRAEILGKTSLKIWNVTRRDSALYRCEVVARNDRKEIDEIVIELTVQVKPVTPVCRVPKAVPVGKMATLHCQESEGHPRPHYSWYRNDVPLPTDSRANPRFRNSSFHLNSETGTLVFTAVHKDDSGQYYCIASNDAGSARCEEQEMEVYDLNIGGIIGGVLVVLAVLALITLGICCAYRRGYFINNKQDGESYKNPGKPDGVNYIRTDEEGDFRHKSSFVI,310,NP_116190.3.csv,refseq-JAM3-NM_032801.4_clinical_seed_0_final,refseq-JAM3-NM_032801.4.a2m,Invitae,refseq-JAM3-NM_032801.4.npy,1,310,310 +NP_116195.2,MHLSAVFNALLVSVLAAVLWKHVRLREHAATLEEELALSRQATEPAPALRIDYPKALQILMEGGTHMVCTGRTHTDRICRFKWLCYSNEAEEFIFFHGNTSVMLPNLGSRRFQPALLDLSTVEDHNTQYFNFVELPAAALRFMPKPVFVPDVALIANRFNPDNLMHVFHDDLLPLFYTLRQFPGLAHEARLFFMEGWGEGAHFDLYKLLSPKQPLLRAQLKTLGRLLCFSHAFVGLSKITTWYQYGFVQPQGPKANILVSGNEIRQFARFMTEKLNVSHTGVPLGEEYILVFSRTQNRLILNEAELLLALAQEFQMKTVTVSLEDHTFADVVRLVSNASMLVSMHGAQLVTTLFLPRGATVVELFPYAVNPDHYTPYKTLAMLPGMDLQYVAWRNMMPENTVTHPERPWDQGGITHLDRAEQARILQSREVPRHLCCRNPEWLFRIYQDTKVDIPSLIQTIRRVVKGRPGPRKQKWTVGLYPGKVREARCQASVHGASEARLTVSWQIPWNLKYLKVREVKYEVWLQEQGENTYVPYILALQNHTFTENIKPFTTYLVWVRCIFNKILLGPFADVLVCNT,580,NP_116195.2.csv,refseq-POMGNT2-NM_032806.5_clinical_seed_0_final,refseq-POMGNT2-NM_032806.5.a2m,Invitae,refseq-POMGNT2-NM_032806.5.npy,1,580,580 +NP_116222.4,MEPGTGGSRKRLGPRAGFRFWPPFFPRRSQAGSSKFPTPLGPENSGNPTLLSSAQPETRVSYWTKLLSQLLAPLPGLLQKVLIWSQLFGGMFPTRWLDFAGVYSALRALKGREKPAAPTAQKSLSSLQLDSSDPSVTSPLDWLEEGIHWQYSPPDLKLELKAKGSALDPAAQAFLLEQQLWGVELLPSSLQSRLYSNRELGSSPSGPLNIQRIDNFSVVSYLLNPSYLDCFPRLEVSYQNSDGNSEVVGFQTLTPESSCLREDHCHPQPLSAELIPASWQGCPPLSTEGLPEIHHLRMKRLEFLQQASKGQDLPTPDQDNGYHSLEEEHSLLRMDPKHCRDNPTQFVPAAGDIPGNTQESTEEKIELLTTEVPLALEEESPSEGCPSSEIPMEKEPGEGRISVVDYSYLEGDLPISARPACSNKLIDYILGGASSDLETSSDPEGEDWDEEAEDDGFDSDSSLSDSDLEQDPEGLHLWNSFCSVDPYNPQNFTATIQTAARIVPEEPSDSEKDLSGKSDLENSSQSGSLPETPEHSSGEEDDWESSADEAESLKLWNSFCNSDDPYNPLNFKAPFQTSGENEKGCRDSKTPSESIVAISECHTLLSCKVQLLGSQESECPDSVQRDVLSGGRHTHVKRKKVTFLEEVTEYYISGDEDRKGPWEEFARDGCRFQKRIQETEDAIGYCLTFEHRERMFNRLQGTCFKGLNVLKQC,713,NP_116222.4.csv,NP_116222.4_colabfold_clinical_seed_0_final,NP_116222.4_colabfold.a2m,colabfold,NP_116222.4_colabfold_theta_0.2.npy,1,713,713 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+NP_597725.1,MLWFSGVGALAERYCRRSPGITCCVLLLLNCSGVPMSLASSFLTGSVAKCENEGEVLQIPFITDNPCIMCVCLNKEVTCKREKCPVLSRDCALAIKQRGACCEQCKGCTYEGNTYNSSFKWQSPAEPCVLRQCQEGVVTESGVRCVVHCKNPLEHLGMCCPTCPGCVFEGVQYQEGEEFQPEGSKCTKCSCTGGRTQCVREVCPILSCPQHLSHIPPGQCCPKCLGQRKVFDLPFGSCLFRSDVYDNGSSFLYDNCTACTCRDSTVVCKRKCSHPGGCDQGQEGCCEECLLRVPPEDIKVCKFGNKIFQDGEMWSSINCTICACVKGRTECRNKQCIPISSCPQGKILNRKGCCPICTEKPGVCTVFGDPHYNTFDGRTFNFQGTCQYVLTKDCSSPASPFQVLVKNDARRTRSFSWTKSVELVLGESRVSLQQHLTVRWNGSRIALPCRAPHFHIDLDGYLLKVTTKAGLEISWDGDSFVEVMAAPHLKGKLCGLCGNYNGHKRDDLIGGDGNFKFDVDDFAESWRVESNEFCNRPQRKPVPELCQGTVKVKLRAHRECQKLKSWEFQTCHSTVDYATFYRSCVTDMCECPVHKNCYCESFLAYTRACQREGIKVHWEPQQNCAATQCKHGAVYDTCGPGCIKTCDNWNEIGPCNKPCVAGCHCPANLVLHKGRCIKPVLCPQR,685,NP_597725.1.csv,refseq-BMPER-NM_133468.4_clinical_seed_0_final,refseq-BMPER-NM_133468.4.a2m,Invitae,refseq-BMPER-NM_133468.4.npy,1,685,685 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+NP_619729.1,MEQWDHFHNQQEDTDSCSESVKFDARSMTALLPPNPKNSPSLQEKLKSFKAALIALYLLVFAVLIPLIGIVAAQLLKWETKNCSVSSTNANDITQSLTGKGNDSEEEMRFQEVFMEHMSNMEKRIQHILDMEANLMDTEHFQNFSMTTDQRFNDILLQLSTLFSSVQGHGNAIDEISKSLISLNTTLLDLQLNIENLNGKIQENTFKQQEEISKLEERVYNVSAEIMAMKEEQVHLEQEIKGEVKVLNNITNDLRLKDWEHSQTLRNITLIQGPPGPPGEKGDRGPTGESGPRGFPGPIGPPGLKGDRGAIGFPGSRGLPGYAGRPGNSGPKGQKGEKGSGNTLTPFTKVRLVGGSGPHEGRVEILHSGQWGTICDDRWEVRVGQVVCRSLGYPGVQAVHKAAHFGQGTGPIWLNEVFCFGRESSIEECKIRQWGTRACSHSEDAGVTCTL,451,NP_619729.1.csv,refseq-MSR1-NM_138715.2_clinical_seed_0_final,refseq-MSR1-NM_138715.2.a2m,Invitae,refseq-MSR1-NM_138715.2.npy,1,451,451 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+NP_689966.2,MGSGRVPGLCLLVLLVHARAAQYSKAAQDVDECVEGTDNCHIDAICQNTPRSYKCICKSGYTGDGKHCKDVDECEREDNAGCVHDCVNIPGNYRCTCYDGFHLAHDGHNCLDVDECAEGNGGCQQSCVNMMGSYECHCREGFFLSDNQHTCIQRPEEGMNCMNKNHGCAHICRETPKGGIACECRPGFELTKNQRDCKLTCNYGNGGCQHTCDDTEQGPRCGCHIKFVLHTDGKTCIETCAVNNGGCDSKCHDAATGVHCTCPVGFMLQPDRKTCKDIDECRLNNGGCDHICRNTVGSFECSCKKGYKLLINERNCQDIDECSFDRTCDHICVNTPGSFQCLCHRGYLLYGITHCGDVDECSINRGGCRFGCINTPGSYQCTCPAGQGRLHWNGKDCTEPLKCQGSPGASKAMLSCNRSGKKDTCALTCPSRARFLPESENGFTVSCGTPSPRAAPARAGHNGNSTNSNHCHEAAVLSIKQRASFKIKDAKCRLHLRNKGKTEEAGRITGPGGAPCSECQVTFIHLKCDSSRKGKGRRARTPPGKEVTRLTLELEAEVRAEETTASCGLPCLRQRMERRLKGSLKMLRKSINQDRFLLRLAGLDYELAHKPGLVAGERAEPMESCRPGQHRAGTKCVSCPQGTYYHGQTEQCVPCPAGTFQEREGQLSCDLCPGSDAHGPLGATNVTTCAGQCPPGQHSVDGFKPCQPCPRGTYQPEAGRTLCFPCGGGLTTKHEGAISFQDCDTKVQCSPGHYYNTSIHRCIRCAMGSYQPDFRQNFCSRCPGNTSTDFDGSTSVAQCKNRQCGGELGEFTGYIESPNYPGNYPAGVECIWNINPPPKRKILIVVPEIFLPSEDECGDVLVMRKNSSPSSITTYETCQTYERPIAFTARSRKLWINFKTSEANSARGFQIPYVTYDEDYEQLVEDIVRDGRLYASENHQEILKDKKLIKAFFEVLAHPQNYFKYTEKHKEMLPKSFIKLLRSKVSSFLRPYK,993,NP_689966.2.csv,refseq-SCUBE3-NM_152753.3_clinical_seed_0_final,refseq-SCUBE3-NM_152753.3.a2m,Invitae,refseq-SCUBE3-NM_152753.3.npy,1,993,993 +NP_689967.2,MNANKDERLKARSQDFHLFPALMMLSMTMLFLPVTGTLKQNIPRLKLTYKDLLLSNSCIPFLGSSEGLDFQTLLLDEERGRLLLGAKDHIFLLSLVDLNKNFKKIYWPAAKERVELCKLAGKDANTECANFIRVLQPYNKTHIYVCGTGAFHPICGYIDLGVYKEDIIFKLDTHNLESGRLKCPFDPQQPFASVMTDEYLYSGTASDFLGKDTAFTRSLGPTHDHHYIRTDISEHYWLNGAKFIGTFFIPDTYNPDDDKIYFFFRESSQEGSTSDKTILSRVGRVCKNDVGGQRSLINKWTTFLKARLICSIPGSDGADTYFDELQDIYLLPTRDERNPVVYGVFTTTSSIFKGSAVCVYSMADIRAVFNGPYAHKESADHRWVQYDGRIPYPRPGTCPSKTYDPLIKSTRDFPDDVISFIKRHSVMYKSVYPVAGGPTFKRINVDYRLTQIVVDHVIAEDGQYDVMFLGTDIGTVLKVVSISKEKWNMEEVVLEELQIFKHSSIILNMELSLKQQQLYIGSRDGLVQLSLHRCDTYGKACADCCLARDPYCAWDGNACSRYAPTSKRRARRQDVKYGDPITQCWDIEDSISHETADEKVIFGIEFNSTFLECIPKSQQATIKWYIQRSGDEHREELKPDERIIKTEYGLLIRSLQKKDSGMYYCKAQEHTFIHTIVKLTLNVIENEQMENTQRAEHEEGKVKDLLAESRLRYKDYIQILSSPNFSLDQYCEQMWHREKRRQRNKGGPKWKHMQEMKKKRNRRHHRDLDELPRAVAT,777,NP_689967.2.csv,refseq-SEMA3D-NM_152754.2_clinical_seed_0_final,refseq-SEMA3D-NM_152754.2.a2m,Invitae,refseq-SEMA3D-NM_152754.2.npy,1,777,777 +NP_689991.1,MAGLRNESEQEPLLGDTPGSREWDILETEEHYKSRWRSIRILYLTMFLSSVGFSVVMMSIWPYLQKIDPTADTSFLGWVIASYSLGQMVASPIFGLWSNYRPRKEPLIVSILISVAANCLYAYLHIPASHNKYYMLVARGLLGIGAGNVAVVRSYTAGATSLQERTSSMANISMCQALGFILGPVFQTCFTFLGEKGVTWDVIKLQINMYTTPVLLSAFLGILNIILILAILREHRVDDSGRQCKSINFEEASTDEAQVPQGNIDQVAVVAINVLFFVTLFIFALFETIITPLTMDMYAWTQEQAVLYNGIILAALGVEAVVIFLGVKLLSKKIGERAILLGGLIVVWVGFFILLPWGNQFPKIQWEDLHNNSIPNTTFGEIIIGLWKSPMEDDNERPTGCSIEQAWCLYTPVIHLAQFLTSAVLIGLGYPVCNLMSYTLYSKILGPKPQGVYMGWLTASGSGARILGPMFISQVYAHWGPRWAFSLVCGIIVLTITLLGVVYKRLIALSVRYGRIQE,518,NP_689991.1.csv,refseq-MFSD8-NM_152778.2_clinical_seed_0_final,refseq-MFSD8-NM_152778.2.a2m,Invitae,refseq-MFSD8-NM_152778.2.npy,1,518,518 +NP_689996.4,MLPRRPLAWPAWLLRGAPGAAGSWGRPVGPLARRGCCSAPGTPEVPLTRERYPVRRLPFSTVSKQDLAAFERIVPGGVVTDPEALQAPNVDWLRTLRGCSKVLLRPRTSEEVSHILRHCHERNLAVNPQGGNTGMVGGSVPVFDEIILSTARMNRVLSFHSVSGILVCQAGCVLEELSRYVEERDFIMPLDLGAKGSCHIGGNVATNAGGLRFLRYGSLHGTVLGLEVVLADGTVLDCLTSLRKDNTGYDLKQLFIGSEGTLGIITTVSILCPPKPRAVNVAFLGCPGFAEVLQTFSTCKGMLGEILSAFEFMDAVCMQLVGRHLHLASPVQESPFYVLIETSGSNAGHDAEKLGHFLEHALGSGLVTDGTMATDQRKVKMLWALRERITEALSRDGYVYKYDLSLPVERLYDIVTDLRARLGPHAKHVVGYGHLGDGNLHLNVTAEAFSPSLLAALEPHVYEWTAGQQGSVSAEHGVGFRKRDVLGYSKPPGALQLMQQLKALLDPKGILNPYKTLPSQA,521,NP_689996.4.csv,refseq-D2HGDH-NM_152783.4_clinical_seed_0_final,refseq-D2HGDH-NM_152783.4.a2m,Invitae,refseq-D2HGDH-NM_152783.4.npy,1,521,521 +NP_690870.3,MCIIFFKFDPRPVSKNAYRLILAANRDEFYSRPSKLADFWGNNNEILSGLDMEEGKEGGTWLGISTRGKLAALTNYLQPQLDWQARGRGELVTHFLTTDVDSLSYLKKVSMEGHLYNGFNLIAADLSTAKGDVICYYGNRGEPDPIVLTPGTYGLSNALLETPWRKLCFGKQLFLEAVERSQALPKDVLIASLLDVLNNEEAQLPDPAIEDQGGEYVQPMLSKYAAVCVRCPGYGTRTNTIILVDADGHVTFTERSMMDKDLSHWETRTYEFTLQS,276,NP_690870.3.csv,refseq-TANGO2-NM_152906.6_clinical_seed_0_final,refseq-TANGO2-NM_152906.6.a2m,Invitae,refseq-TANGO2-NM_152906.6.npy,1,276,276 +NP_694551.1,MATALMAVVLRAAAVAPRLRGRGGTGGARRLSCGARRRAARGTSPGRRLSTAWSQPQPPPEEYAGADDVSQSPVAEEPSWVPSPRPPVPHESPEPPSGRSLVQRDIQAFLNQCGASPGEARHWLTQFQTCHHSADKPFAVIEVDEEVLKCQQGVSSLAFALAFLQRMDMKPLVVLGLPAPTAPSGCLSFWEAKAQLAKSCKVLVDALRHNAAAAVPFFGGGSVLRAAEPAPHASYGGIVSVETDLLQWCLESGSIPILCPIGETAARRSVLLDSLEVTASLAKALRPTKIIFLNNTGGLRDSSHKVLSNVNLPADLDLVCNAEWVSTKERQQMRLIVDVLSRLPHHSSAVITAASTLLTELFSNKGSGTLFKNAERMLRVRSLDKLDQGRLVDLVNASFGKKLRDDYLASLRPRLHSIYVSEGYNAAAILTMEPVLGGTPYLDKFVVSSSRQGQGSGQMLWECLRRDLQTLFWRSRVTNPINPWYFKHSDGSFSNKQWIFFWFGLADIRDSYELVNHAKGLPDSFHKPASDPGS,534,NP_694551.1.csv,refseq-NAGS-NM_153006.2_clinical_seed_0_final,refseq-NAGS-NM_153006.2.a2m,Invitae,refseq-NAGS-NM_153006.2.npy,1,534,534 +NP_694571.2,MPLEMEPKMSKLAFGCQRSSTSDDDSGCALEEYAWVPPGLRPEQIQLYFACLPEEKVPYVNSPGEKHRIKQLLYQLPPHDNEVRYCQSLSEEEKKELQVFSAQRKKEALGRGTIKLLSRAVMHAVCEQCGLKINGGEVAVFASRAGPGVCWHPSCFVCFTCNELLVDLIYFYQDGKIHCGRHHAELLKPRCSACDEIIFADECTEAEGRHWHMKHFCCLECETVLGGQRYIMKDGRPFCCGCFESLYAEYCETCGEHIGVDHAQMTYDGQHWHATEACFSCAQCKASLLGCPFLPKQGQIYCSKTCSLGEDVHASDSSDSAFQSARSRDSRRSVRMGKSSRSADQCRQSLLLSPALNYKFPGLSGNADDTLSRKLDDLSLSRQGTSFASEEFWKGRVEQETPEDPEEWADHEDYMTQLLLKFGDKSLFQPQPNEMDIRASEHWISDNMVKSKTELKQNNQSLASKKYQSDMYWAQSQDGLGDSAYGSHPGPASSRRLQELELDHGASGYNHDETQWYEDSLECLSDLKPEQSVRDSMDSLALSNITGASVDGENKPRPSLYSLQNFEEMETEDCEKMSNMGTLNSSMLHRSAESLKSLSSELCPEKILPEEKPVHLPVLRRSKSQSRPQQVKFSDDVIDNGNYDIEIRQPPMSERTRRRVYNFEERGSRSHHHRRRRSRKSRSDNALNLVTERKYSPKDRLRLYTPDNYEKFIQNKSAREIQAYIQNADLYGQYAHATSDYGLQNPGMNRFLGLYGEDDDSWCSSSSSSSDSEEEGYFLGQPIPQPRPQRFAYYTDDLSSPPSALPTPQFGQRTTKSKKKKGHKGKNCIIS,831,NP_694571.2.csv,refseq-PRICKLE1-NM_153026.2_clinical_seed_0_final,refseq-PRICKLE1-NM_153026.2.a2m,Invitae,refseq-PRICKLE1-NM_153026.2.npy,1,831,831 +NP_694944.1,MNWAFLQGLLSGVNKYSTVLSRIWLSVVFIFRVLVYVVAAEEVWDDEQKDFVCNTKQPGCPNVCYDEFFPVSHVRLWALQLILVTCPSLLVVMHVAYREERERKHHLKHGPNAPSLYDNLSKKRGGLWWTYLLSLIFKAAVDAGFLYIFHRLYKDYDMPRVVACSVEPCPHTVDCYISRPTEKKVFTYFMVTTAAICILLNLSEVFYLVGKRCMEIFGPRHRRPRCRECLPDTCPPYVLSQGGHPEDGNSVLMKAGSAPVDAGGYP,266,NP_694944.1.csv,refseq-GJB4-NM_153212.2_clinical_seed_0_final,refseq-GJB4-NM_153212.2.a2m,Invitae,refseq-GJB4-NM_153212.2.npy,1,266,266 +NP_694955.2,MVSKSDQLLIVVSILEGRHFPKRPKHMLVVEAKFDGEQLATDPVDHTDQPEFATELAWEIDRKALHQHRLQRTPIKLQCFALDPVTSAKETIGYIVLDLRTAQETKQAPKWYQLLSNKYTKFKSEIQISIALETDTKPPVDSFKAKGAPPRDGKVPAILAGLDPRDIVAVLNEEGGYHQIGPAEYCTDSFIMSVTIAFATQLEQLIPCTMKLPERQPEFFFYYSLLGNDVTNEPFNDLINPNFEPERASVRIRSSVEILRVYLALQSKLQIHLCCGDQSLGSTEIPLTGLLKKGSTEINQHPVTVEGAFTLDPPNRAKQKLAPIPVELAPTVGVSVALQREGIDSQSLIELKTQNEHEPEHSKKKVLTPIKEKTLTGPKSPTVSPVPSHNQSPPTKDDATESEVESLQYDKDTKPNPKASSSVPASLAQLVTTSNASEVASGQKIAVPATSHHFCFSIDLRSIHALEIGFPINCILRYSYPFFGSAAPIMTNPPVEVRKNMEVFLPQSYCAFDFATMPHQLQDTFLRIPLLVELWHKDKMSKDLLLGIARIQLSNILSSEKTRFLGSNGEQCWRQTYSESVPVIAAQGSNNRIADLSYTVTLEDYGLVKMREIFISDSSQGVSAVQQKPSSLPPAPCPSEIQTEPRETLEYKAALELEMWKEMQEDIFENQLKQKELAHMQALAEEWKKRDRERESLVKKKVAEYTILEGKLQKTLIDLEKREQQLASVESELQREKKELQSERQRNLQELQDSIRRAKEDCIHQVELERLKIKQLEEDKHRLQQQLNDAENKYKILEKEFQQFKDQQNNKPEIRLQSEINLLTLEKVELERKLESATKSKLHYKQQWGRALKELARLKQREQESQMARLKKQQEELEQMRLRYLAAEEKDTVKTERQELLDIRNELNRLRQQEQKQYQDSTEIASGKKDGPHGSVLEEGLDDYLTRLIEERDTLMRTGVYNHEDRIISELDRQIREILAKSNASN,986,NP_694955.2.csv,refseq-CEP120-NM_153223.3_clinical_seed_0_final,refseq-CEP120-NM_153223.3.a2m,Invitae,refseq-CEP120-NM_153223.3.npy,1,986,986 +NP_694972.3,MGTASSLVSPAGGEVIEDTYGAGGGEACEIPVEVKPKARLLRNSFRRGAGAAAGAGPGSLPRGVGAGGLLGASFKSTGSSVPELEYAAAEYERLRKEYEIFRVSKNQELLSMGRREAKLDTENKRLRAELQALQKTYQKILREKESALEAKYQAMERAATFEHDRDKVKRQFKIFRETKENEIQDLLRAKRELESKLQRLQAQGIQVFDPGESDSDDNCTDVTAAGTQCEYWTGGALGSEPSIGSMIQLQQSFRGPEFAHSSIDVEGPFANVNRDDWDIAVASLLQVTPLFSHSLWSNTVRCYLIYTDETQPEMDLFLKDYSPKLKRMCETMGYFFHAVYFPIDVENQYLTVRKWEIEKSSLVILFIHLTLPSLLLEDCEEAFLKNPEGKPRLIFHRLEDGKVSSDSVQQLIDQVSNLNKTSKAKIIDHSGDPAEGVYKTYICVEKIIKQDILGFENTDLETKDLGSEDSIPEEDDFGDVLWDIHDEQEQMETFQQASNSAHELGFEKYYQRLNDLVAAPAPIPPLLVSGGPGSGKSLLLSKWIQLQQKNSPNTLILSHFVGRPMSTSSESSLIIKRLTLKLMQHSWSVSALTLDPAKLLEEFPRWLEKLSARHQGSIIIVIDSIDQVQQVEKHMKWLIDPLPVNVRVIVSVNVETCPPAWRLWPTLHLDPLSPKDAKSIIIAECHSVDIKLSKEQEKKLERHCRSATTCNALYVTLFGKMIARAGRAGNLDKILHQCFQCQDTLSLYRLVLHSIRESMANDVDKELMKQILCLVNVSHNGVSESELMELYPEMSWTFLTSLIHSLYKMCLLTYGCGLLRFQHLQAWETVRLEYLEGPTVTSSYRQKLINYFTLQLSQDRVTWRSADELPWLFQQQGSKQKLHDCLLNLFVSQNLYKRGHFAELLSYWQFVGKDKSAMATEYFDSLKQYEKNCEGEDNMSCLADLYETLGRFLKDLGLLSQAIVPLQRSLEIRETALDPDHPRVAQSLHQLASVYVQWKKFGNAEQLYKQALEISENAYGADHPYTARELEALATLYQKQNKYEQAEHFRKKSFKIHQKAIKKKGNLYGFALLRRRALQLEELTLGKDTPDNARTLNELGVLYYLQNNLETADQFLKRSLEMRERVLGPDHPDCAQSLNNLAALCNEKKQYDKAEELYERALDIRRRALAPDHPSLAYTVKHLAILYKKMGKLDKAVPLYELAVEIRQKSFGPKHPSVATALVNLAVLYSQMKKHVEALPLYERALKIYEDSLGRMHPRVGETLKNLAVLSYEGGDFEKAAELYKRAMEIKEAETSLLGGKAPSRHSSSGDTFSLKTAHSPNVFLQQGQR,1330,NP_694972.3.csv,refseq-NPHP3-NM_153240.4_clinical_seed_0_final,refseq-NPHP3-NM_153240.4.a2m,Invitae,refseq-NPHP3-NM_153240.4.npy,1,1330,1330 +NP_694984.5,MAAAPTQIEAELYYLIARFLQSGPCNKSAQVLVQELEEHQLIPRRLDWEGKEHRRSFEDLVAANAHIPPDYLLKICERIGPLLDKEIPQSVPGVQTLLGVGRQSLLRDAKDCKSTLWNGSAFAALHRGRPPELPVNYVKPPNVVNITSARQLTGCSRFGHIFPSSAYQHIKMHKRILGHLSSVYCVAFDRSGRRIFTGSDDCLVKIWATDDGRLLATLRGHSAEISDMAVNYENTLIAAGSCDKVVRVWCLRTCAPVAVLQGHSASITSIQFCPSTKGTNRYLTSTGADGTICFWQWHVKTMKFRDRPVKFTERSRPGVQISCSSFSSGGMFITTGSTDHVIRIYYLGSEVPEKIAELESHTDKVVAVQFCNNGDSLRFVSGSRDGTARIWQYQQQEWKSIVLDMATKMTGNNLPSGEDKITKLKVTMVAWDRYDTTVITAVNNFLLKVWNSITGQLLHTLSGHDDEVFVLEAHPFDQRIILSAGHDGNIFIWDLDRGTKIRNYFNMIEGQGHGAVFDCKFSPDGNHFACTDSHGHLLLFGFGCSKYYEKIPDQMFFHTDYRPLIRDANNYVLDEQTQQAPHLMPPPFLVDVDGNPHPTKFQRLVPGRENCKDEQLIPQLGYVANGDGEVVEQVIGQQTNDQDESILDGIIRELQREQDLRLINEGDVPHLPVNRAYSVNGALRSPNMDISSSPNIRLRRHSSQIEGVRQMHNNAPRSQMATERDLMAWSRRVVVNELNNGVSRVQEECRTAKGDIEISLYTVEKKKKPSYTTQRNDYEPSCGRSLRRTQRKRQHTYQTRSNIEHNSQASCQNSGVQEDSDSSSEEDETVGTSDASVEDPVVEWQSESSSSDSSSEYSDWTADAGINLQPPKRQTRQTTRKICSSSDEENLKSLEERQKKPKQTRKKKGGLVSIAGEPNEEWFAPQWILDTIPRRSPFVPQMGDELIYFRQGHEAYVRAVRKSKIYSVNLQKQPWNKMDLREQEFVKIVGIKYEVGPPTLCCLKLAFLDPISGKMTGESFSIKYHDMPDVIDFLVLHQFYNEAKERNWQIGDRFRSIIDDAWWFGTVESQQPFQPEYPDSSFQCYSVHWDNNEREKMSPWDMEPIPEGTAFPDEVGAGVPVSQEELTALLYKPQEGEWGAHSRDEECERVIQGINHLLSLDFASPFAVPVDLSAYPLYCTVVAYPTDLNTIRRRLENRFYRRISALMWEVRYIEHNARTFNEPDSPIVKAAKIVTDVLLRFIGDQSCTDILDTYNKIKAEERNSTDAEEDTEIVDLDSDGPGTSSGRKVKCRGRRQSLKCNPDAWKKQCKELLSLIYEREDSEPFRQPADLLSYPGHQEQEGESSESVVPERQQDSSLSEDYQDVIDTPVDFSTVKETLEAGNYGSPLEFYKDVRQIFNNSKAYTSNKKSRIYSMMLRLSALFESHIKNIISEYKSAIQSQKRRRPRYRKRLRSSSSSLSSSGAPSPKGKQKQMKLQPKNDQNTSVSHARTSSPFSSPVSDAAEGLSLYLLDDEPDGPFSSSSFGGYSRSGNSHDPGKAKSFRNRVLPVKQDHSLDGPLTNGDGREPRTGIKRKLLSASEEDENMGGEDKEKKETKEKSHLSTSESGELGSSLSSESTCGSDSDSESTSRTDQDYVDGDHDYSKFIQTRPKRKLRKQHGNGKRNWKTRGTGGRGRWGRWGRWSRGGRGRGGRGRGSRGRGGGGTRGRGRGRGGRGASRGATRAKRARIADDEFDTMFSGRFSRLPRIKTRNQGRRTVLYNDDSDNDNFVSTEDPLNLGTSRSGRVRKMTEKARVSHLMGWNY,1802,NP_694984.5.csv,refseq-BRWD3-NM_153252.5_clinical_seed_0_final,refseq-BRWD3-NM_153252.5.a2m,Invitae,refseq-BRWD3-NM_153252.5_theta_0.2.npy,1,1802,1802 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+NP_705831.1,MDFSKFLADDFDVKEWINAAFRAGSKEAASGKADGHAATLVMKLQLFIQEVNHAVEETSHQALQNMPKVLRDVEALKQEASFLKEQMILVKEDIKKFEQDTSQSMQVLVEIDQVKSRMQLAAESLQEADKWSTLSADIEETFKTQDIAVISAKLTGMQNSLMMLVDTPDYSEKCVHLEALKNRLEALASPQIVAAFTSQAVDQSKVFVKVFTEIDRMPQLLAYYYKCHKVQLLAAWQELCQSDLSLDRQLTGLYDALLGAWHTQIQWATQVFQKPHEVVMVLLIQTLGALMPSLPSCLSNGVERAGPEQELTRLLEFYDATAHFAKGLEMALLPHLHEHNLVKVTELVDAVYDPYKPYQLKYGDMEESNLLIQMSAVPLEHGEVIDCVQELSHSVNKLFGLASAAVDRCVRFTNGLGTCGLLSALKSLFAKYVSDFTSTLQSIRKKCKLDHIPPNSLFQEDWTAFQNSIRIIATCGELLRHCGDFEQQLANRILSTAGKYLSDSCSPRSLAGFQESILTDKKNSAKNPWQEYNYLQKDNPAEYASLMEILYTLKEKGSSNHNLLAAPRAALTRLNQQAHQLAFDSVFLRIKQQLLLISKMDSWNTAGIGETLTDELPAFSLTPLEYISNIGQYIMSLPLNLEPFVTQEDSALELALHAGKLPFPPEQGDELPELDNMADNWLGSIARATMQTYCDAILQIPELSPHSAKQLATDIDYLINVMDALGLQPSRTLQHIVTLLKTRPEDYRQVSKGLPRRLATTVATMRSVNY,770,NP_705831.1.csv,refseq-COG7-NM_153603.3_clinical_seed_0_final,refseq-COG7-NM_153603.3.a2m,Invitae,refseq-COG7-NM_153603.3.npy,1,770,770 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+NP_777577.2,MLPHVVLTFRRLGCALASCRLAPARHRGSGLLHTAPVARSDRSAPVFTRALAFGDRIALVDQHGRHTYRELYSRSLRLSQEICRLCGCVGGDLREERVSFLCANDASYVVAQWASWMSGGVAVPLYRKHPAAQLEYVICDSQSSVVLASQEYLELLSPVVRKLGVPLLPLTPAIYTGAVEEPAEVPVPEQGWRNKGAMIIYTSGTTGRPKGVLSTHQNIRAVVTGLVHKWAWTKDDVILHVLPLHHVHGVVNALLCPLWVGATCVMMPEFSPQQVWEKFLSSETPRINVFMAVPTIYTKLMEYYDRHFTQPHAQDFLRAVCEEKIRLMVSGSAALPLPVLEKWKNITGHTLLERYGMTEIGMALSGPLTTAVRLPGSVGTPLPGVQVRIVSENPQREACSYTIHAEGDERGTKVTPGFEEKEGELLVRGPSVFREYWNKPEETKSAFTLDGWFKTGDTVVFKDGQYWIRGRTSVDIIKTGGYKVSALEVEWHLLAHPSITDVAVIGVPDMTWGQRVTAVVTLREGHSLSHRELKEWARNVLAPYAVPSELVLVEEIPRNQMGKIDKKALIRHFHPS,576,NP_777577.2.csv,ACSF3_HUMAN_b07_clinical_seed_0_final,ACSF3_HUMAN_b07.a2m,EVE,ACSF3_HUMAN_b07_theta_0.2.npy,1,576,576 +NP_777596.2,MGTVSSRRSWWPLPLLLLLLLLLGPAGARAQEDEDGDYEELVLALRSEEDGLAEAPEHGTTATFHRCAKDPWRLPGTYVVVLKEETHLSQSERTARRLQAQAARRGYLTKILHVFHGLLPGFLVKMSGDLLELALKLPHVDYIEEDSSVFAQSIPWNLERITPPRYRADEYQPPDGGSLVEVYLLDTSIQSDHREIEGRVMVTDFENVPEEDGTRFHRQASKCDSHGTHLAGVVSGRDAGVAKGASMRSLRVLNCQGKGTVSGTLIGLEFIRKSQLVQPVGPLVVLLPLAGGYSRVLNAACQRLARAGVVLVTAAGNFRDDACLYSPASAPEVITVGATNAQDQPVTLGTLGTNFGRCVDLFAPGEDIIGASSDCSTCFVSQSGTSQAAAHVAGIAAMMLSAEPELTLAELRQRLIHFSAKDVINEAWFPEDQRVLTPNLVAALPPSTHGAGWQLFCRTVWSAHSGPTRMATAVARCAPDEELLSCSSFSRSGKRRGERMEAQGGKLVCRAHNAFGGEGVYAIARCCLLPQANCSVHTAPPAEASMGTRVHCHQQGHVLTGCSSHWEVEDLGTHKPPVLRPRGQPNQCVGHREASIHASCCHAPGLECKVKEHGIPAPQEQVTVACEEGWTLTGCSALPGTSHVLGAYAVDNTCVVRSRDVSTTGSTSEGAVTAVAICCRSRHLAQASQELQ,692,NP_777596.2.csv,refseq-PCSK9-NM_174936.3_clinical_seed_0_final,refseq-PCSK9-NM_174936.3.a2m,Invitae,refseq-PCSK9-NM_174936.3.npy,1,692,692 +NP_778243.1,MMRVCWLVRQDSRHQRIRLPHLEAVVIGRGPETKITDKKCSRQQVQLKAECNKGYVKVKQVGVNPTSIDSVVIGKDQEVKLQPGQVLHMVNELYPYIVEFEEEAKNPGLETHRKRKRSGNSDSIERDAAQEAEAGTGLEPGSNSGQCSVPLKKGKDAPIKKESLGHWSQGLKISMQDPKMQVYKDEQVVVIKDKYPKARYHWLVLPWTSISSLKAVAREHLELLKHMHTVGEKVIVDFAGSSKLRFRLGYHAIPSMSHVHLHVISQDFDSPCLKNKKHWNSFNTEYFLESQAVIEMVQEAGRVTVRDGMPELLKLPLRCHECQQLLPSIPQLKEHLRKHWTQ,342,NP_778243.1.csv,refseq-APTX-NM_175073.2_clinical_seed_0_final,refseq-APTX-NM_175073.2.a2m,Invitae,refseq-APTX-NM_175073.2.npy,1,342,342 +NP_783328.1,MPAMPSSGPGDTSSSAAEREEDRKDGEEQEEPRGKEERQEPSTTARKVGRPGRKRKHPPVESGDTPKDPAVISKSPSMAQDSGASELLPNGDLEKRSEPQPEEGSPAGGQKGGAPAEGEGAAETLPEASRAVENGCCTPKEGRGAPAEAGKEQKETNIESMKMEGSRGRLRGGLGWESSLRQRPMPRLTFQAGDPYYISKRKRDEWLARWKREAEKKAKVIAGMNAVEENQGPGESQKVEEASPPAVQQPTDPASPTVATTPEPVGSDAGDKNATKAGDDEPEYEDGRGFGIGELVWGKLRGFSWWPGRIVSWWMTGRSRAAEGTRWVMWFGDGKFSVVCVEKLMPLSSFCSAFHQATYNKQPMYRKAIYEVLQVASSRAGKLFPVCHDSDESDTAKAVEVQNKPMIEWALGGFQPSGPKGLEPPEEEKNPYKEVYTDMWVEPEAAAYAPPPPAKKPRKSTAEKPKVKEIIDERTRERLVYEVRQKCRNIEDICISCGSLNVTLEHPLFVGGMCQNCKNCFLECAYQYDDDGYQSYCTICCGGREVLMCGNNNCCRCFCVECVDLLVGPGAAQAAIKEDPWNCYMCGHKGTYGLLRRREDWPSRLQMFFANNHDQEFDPPKVYPPVPAEKRKPIRVLSLFDGIATGLLVLKDLGIQVDRYIASEVCEDSITVGMVRHQGKIMYVGDVRSVTQKHIQEWGPFDLVIGGSPCNDLSIVNPARKGLYEGTGRLFFEFYRLLHDARPKEGDDRPFFWLFENVVAMGVSDKRDISRFLESNPVMIDAKEVSAAHRARYFWGNLPGMNRPLASTVNDKLELQECLEHGRIAKFSKVRTITTRSNSIKQGKDQHFPVFMNEKEDILWCTEMERVFGFPVHYTDVSNMSRLARQRLLGRSWSVPVIRHLFAPLKEYFACV,912,NP_783328.1.csv,refseq-DNMT3A-NM_175629.2_clinical_seed_0_final,refseq-DNMT3A-NM_175629.2.a2m,Invitae,refseq-DNMT3A-NM_175629.2.npy,1,912,912 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mode 100644 index 0000000..2288fac --- /dev/null +++ b/reference_files/reference_files_description.md @@ -0,0 +1,32 @@ +## ProteinGym reference files + +In the reference files, we provide detailed information about all DMS assays included in ProteinGym. There are two reference files: one for the substitution benchmark and one for the indel benchmark. + +The meaning of each column in the ProteinGym reference files is provided below: +- DMS_id (str): Uniquely identifies each DMS assay in ProteinGym. It is obtained as the concatenation of the UniProt ID of the mutated protein, the first author name and the year of publication. If there are several datasets with the same characteristics, another defining attribute of the assay is added to preserve unicity. +- DMS_filename (str): Name of the processed DMS file. +- target_seq (str): Sequence of the target protein (reference sequence mutated in the assay). +- seq_len (int): Length of the target protein sequence. +- includes_multiple_mutants (bool): Indicates whether the DMS contains mutations that are multiple mutants. Substitution benchmark only. +- DMS_total_number_mutants (int): Number of rows of the DMS in ProteinGym. +- DMS_number_single_mutants (int): Number of single amino acid substitutions in the DMS. Substitution benchmark only. +- DMS_number_multiple_mutants (int): Number of multiple amino acid substitutions in the DMS. Substitution benchmark only. +- DMS_binarization_cutoff_ProteinGym (float): Cutoff used to divide fitness scores into binary labels. +- DMS_binarization_method (str): Method used to decide the binarization cutoff (manual or median). +- region_mutated (str): Region of the target protein that is mutated in the DMS. +- MSA_filename (str): Name of the MSA file generated based on the reference sequence mutated during the DMS experiment. Note that different reference sequences may be used in different DMS experiments for the same protein. For example, Giacomelli et al. (2018) and Kotler et al. (2018) used slightly different reference sequences in their respective DMS experiments for the P53 protein. We generated different MSAs accordingly. +- MSA_start (int): Locates the beginning of the first sequence in the MSA with respect to the target sequence. For example, if the MSA covers from position 10 to position 60 of the target sequence, then MSA_start is 10. +- MSA_end (int): Locates the end of the first sequence in the MSA with respect to the target sequence. For example, if the MSA covers from position 10 to position 60 of the target sequence, then MSA_end is 60. +- MSA_bitscore (float): Bitscore threshold used to generate the alignment divided by the length of the target protein. +- MSA_theta (float): Hamming distance cutoff for sequence re-weighting. +- MSA_num_seqs (int): Number of sequences in the Multiple Sequence Alignment (MSA) used in this work for this DMS. +- MSA_perc_cov (float): Percentage of positions of the MSA that had a coverage higher than 70% (less than 30% gaps). +- MSA_num_cov (int): Number of positions of the MSA that had a coverage higher than 70% (less than 30% gaps). +- MSA_N_eff (float): The effective number of sequences in the MSA defined as the sum of the different sequence weights. +- MSA_N_eff_L (float): Neff / num_cov. +- MSA_num_significant (int): Number of evolutionary couplings that are considered significant. Significance is defined by having more than 90% probability of belonging to the log-normal distribution in a Gaussian Mixture Model of normal and log-normal distributions. +- MSA_num_significant_L (float): MSA_num_significant / num_cov. +- raw_DMS_filename (str): Name of the raw DMS file. +- raw_DMS_phenotype_name (str): Name of the column in the raw DMS that we used as fitness score. +- raw_DMS_directionality (int): Sign of the correlation between the DMS_phenotype column values and protein fitness in the raw DMS files. In any given DMS, the directionality is 1 if higher values of the measurement are associated with higher fitness, and -1 otherwise. For simplicity, we adjusted directionality in the final ProteinGym benchmarks so that a higher value of DMS_score is always associated with higher fitness. Consequently, correlations between model scores and the final DMS_score values should always be positive (unless the predictions from the considered model are worse than random for that DMS). +- raw_DMS_mutant_column (str): Name of the column in the raw DMS that indicates which mutants were assayed. \ No newline at end of file diff --git a/scripts/scoring_DMS_supervised/performance_indels.sh b/scripts/scoring_DMS_supervised/performance_indels.sh new file mode 100644 index 0000000..572e96a --- /dev/null +++ b/scripts/scoring_DMS_supervised/performance_indels.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export output_performance_file_folder=../../benchmarks/DMS_supervised/indels +export input_scoring_file=../../benchmarks/raw_score_files/DMS_supervised_indels.csv + +python3 ../../proteingym/performance_DMS_supervised_benchmarks.py \ + --input_scoring_file ${input_scoring_file} \ + --output_performance_file_folder ${output_performance_file_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_DMS_supervised/performance_substitutions.sh b/scripts/scoring_DMS_supervised/performance_substitutions.sh new file mode 100644 index 0000000..66ea6c1 --- /dev/null +++ b/scripts/scoring_DMS_supervised/performance_substitutions.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export output_performance_file_folder=../../benchmarks/DMS_supervised/substitutions +export input_scoring_file=../../benchmarks/raw_score_files/DMS_supervised_substitutions.csv +export DMS_data_folder="path to supervised DMS data" + +python ../../proteingym/performance_DMS_supervised_benchmarks.py \ + --input_scoring_file ${input_scoring_file} \ + --output_performance_file_folder ${output_performance_file_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/calc_weights_eve.sh b/scripts/scoring_DMS_zero_shot/calc_weights_eve.sh new file mode 100644 index 0000000..365d252 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/calc_weights_eve.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh + +# EVE example +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" + +python ../../proteingym/baselines/EVE/calc_weights.py \ + --MSA_data_folder ${DMS_MSA_data_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_index "${DMS_index}" \ + --MSA_weights_location ${DMS_MSA_weights_folder} \ + --num_cpus -1 \ + --calc_method evcouplings \ + --threshold_focus_cols_frac_gaps 1 \ + --skip_existing + #--overwrite + diff --git a/scripts/scoring_DMS_zero_shot/evotune_UniRep_substitutions.sh b/scripts/scoring_DMS_zero_shot/evotune_UniRep_substitutions.sh new file mode 100644 index 0000000..9e5cb96 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/evotune_UniRep_substitutions.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export savedir="folder to save evotuned models to" +export initial_weights_dir="initial weights checkpoint for UniRep model" +export DMS_reference_file_path=$DMS_reference_file_path_subs +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" +# uncomment below to run for indels +# export DMS_reference_file_path=$DMS_reference_file_path_indels +export steps=13000 #Same as Unirep paper + +python ../../proteingym/baselines/unirep/unirep_evotune.py \ + --seqs_fasta_path $DMS_MSA_data_folder \ + --save_weights_dir $savedir \ + --initial_weights_dir $initial_weights_dir \ + --num_steps $steps \ + --batch_size 128 \ + --mapping_path $DMS_reference_file_path \ + --DMS_index $DMS_index \ + --max_seq_len 500 \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/merge_all_scores.sh b/scripts/scoring_DMS_zero_shot/merge_all_scores.sh new file mode 100644 index 0000000..bf75f95 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/merge_all_scores.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export mutation_type='substitutions' + +python ../../proteingym/merge.py \ +--DMS_assays_location ${DMS_data_folder_subs} \ +--model_scores_location ${DMS_output_score_folder_subs} \ +--merged_scores_dir ${DMS_merged_score_folder_subs} \ +--mutation_type ${mutation_type} diff --git a/scripts/scoring_DMS_zero_shot/merge_all_scores_indels.sh b/scripts/scoring_DMS_zero_shot/merge_all_scores_indels.sh new file mode 100644 index 0000000..9263c3f --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/merge_all_scores_indels.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +source ../zero_shot_config.sh + +export mutation_type='indels' +python ../../proteingym/merge.py \ +--DMS_assays_location ${DMS_data_folder_indels} \ +--model_scores_location ${DMS_output_score_folder_indels} \ +--merged_scores_dir ${DMS_merged_score_folder_indels} \ +--mutation_type ${mutation_type} \ +--DMS_reference_file ${DMS_reference_file_path_indels} diff --git a/scripts/scoring_DMS_zero_shot/performance_indels.sh b/scripts/scoring_DMS_zero_shot/performance_indels.sh new file mode 100644 index 0000000..03e82c8 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/performance_indels.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +source ../zero_shot_config.sh + +export output_performance_file_folder=../../benchmarks/DMS_zero_shot/indels +python ../../proteingym/performance_DMS_benchmarks.py \ + --input_scoring_files_folder ${DMS_merged_score_folder_indels} \ + --output_performance_file_folder ${output_performance_file_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --DMS_data_folder ${DMS_data_folder_indels} \ + --indel_mode + ##--performance_by_depth \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/performance_substitutions.sh b/scripts/scoring_DMS_zero_shot/performance_substitutions.sh new file mode 100644 index 0000000..1e7d9fe --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/performance_substitutions.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +source ../zero_shot_config.sh + +export output_performance_file_folder=../../benchmarks/DMS_zero_shot/substitutions +python ../../proteingym/performance_DMS_benchmarks.py \ +--input_scoring_files_folder ${DMS_merged_score_folder_subs} \ +--output_performance_file_folder ${output_performance_file_folder} \ +--DMS_reference_file_path ${DMS_reference_file_path_subs} \ +--DMS_data_folder ${DMS_data_folder_subs} \ +--performance_by_depth diff --git a/scripts/scoring_DMS_zero_shot/scoring_CARP_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_CARP_substitutions.sh new file mode 100644 index 0000000..ea7b973 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_CARP_substitutions.sh @@ -0,0 +1,16 @@ +source ../zero_shot_config.sh +source activate proteingym_env + +export model_name="carp_640M" #[carp_600k|carp_38M|carp_76M|carp_640M] +export model_path="Path to CARP checkpoints" +export DMS_output_score_folder=${DMS_output_score_folder_subs}/CARP +export performance_file='CARP_640M_performance.csv' + +srun python3 ../../proteingym/baselines/carp_mif/compute_fitness.py \ + --model_name ${model_name} \ + --model_path ${model_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $SLURM_ARRAY_TASK_ID \ + --output_scores_folder ${DMS_output_score_folder} \ + --performance_file ${performance_file} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_ESM1b_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ESM1b_substitutions.sh new file mode 100644 index 0000000..6fec61d --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ESM1b_substitutions.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# ESM1b parameters +# checkpoint for ESM1b +export model_checkpoint="path to ESM1b checkpoint" + +export dms_output_folder="${DMS_output_score_folder_subs}/ESM1b/" + +export model_type="ESM1b" +export scoring_strategy="wt-marginals" +export scoring_window="overlapping" + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/esm/compute_fitness.py \ + --model-location ${model_checkpoint} \ + --model_type ${model_type} \ + --dms_index ${DMS_index} \ + --dms_mapping ${DMS_reference_file_path_subs} \ + --dms-input ${DMS_data_folder_subs} \ + --dms-output ${dms_output_folder} \ + --scoring-strategy ${scoring_strategy} \ + --scoring-window ${scoring_window} diff --git a/scripts/scoring_DMS_zero_shot/scoring_ESM1v_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ESM1v_substitutions.sh new file mode 100644 index 0000000..0c544d2 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ESM1v_substitutions.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# ESM-1v parameters +# Five checkpoints for ESM-1v +export model_checkpoint1="path to seed 1 model checkpoint" +export model_checkpoint2="path to seed 2 model checkpoint" +export model_checkpoint3="path to seed 3 model checkpoint" +export model_checkpoint4="path to seed 4 model checkpoint" +export model_checkpoint5="path to seed 5 model checkpoint" +# combine all five into one string +export model_checkpoint="${model_checkpoint1} ${model_checkpoint2} ${model_checkpoint3} ${model_checkpoint4} ${model_checkpoint5}" + +export dms_output_folder="${DMS_output_score_folder_subs}/ESM1v/" + +export model_type="ESM1v" +export scoring_strategy="masked-marginals" # MSATransformer only uses masked-marginals +export scoring_window="optimal" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/esm/compute_fitness.py \ + --model-location ${model_checkpoint} \ + --model_type ${model_type} \ + --dms_index ${DMS_index} \ + --dms_mapping ${DMS_reference_file_path_subs} \ + --dms-input ${DMS_data_folder_subs} \ + --dms-output ${dms_output_folder} \ + --scoring-strategy ${scoring_strategy} \ + --scoring-window ${scoring_window} diff --git a/scripts/scoring_DMS_zero_shot/scoring_ESM2_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ESM2_substitutions.sh new file mode 100644 index 0000000..0c20534 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ESM2_substitutions.sh @@ -0,0 +1,40 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# Run whichever size of ESM2 by uncommenting the appropriate pair of lines below + +export model_checkpoint="path to 8M ESM2 checkpoint" +export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/8M + +# export model_checkpoint="path to 35M ESM2 checkpoint" +# export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/35M + +# export model_checkpoint="path to 150M ESM2 checkpoint" +# export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/150M + +# export model_checkpoint="path to 650M ESM2 checkpoint" +# export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/650M + +# export model_checkpoint="path to 3B ESM2 checkpoint" +# export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/3B + +# export model_checkpoint="path to 15B ESM2 checkpoint" +# export dms_output_folder=${DMS_output_score_folder_subs}/ESM2/15B + +## Regression weights are at: https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50S-contact-regression.pt +#https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50S-contact-regression.pt + +export model_type="ESM2" +export scoring_strategy="masked-marginals" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/esm/compute_fitness.py \ + --model-location ${model_checkpoint} \ + --dms_index $DMS_index \ + --dms_mapping ${DMS_reference_file_path_subs} \ + --dms-input ${DMS_data_folder_subs} \ + --dms-output ${dms_output_folder} \ + --scoring-strategy ${scoring_strategy} \ + --model_type ${model_type} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_ESM_IF1_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ESM_IF1_substitutions.sh new file mode 100644 index 0000000..07d6fb6 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ESM_IF1_substitutions.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +## Regression weights are at: https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50S-contact-regression.pt +#https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t33_650M_UR50S-contact-regression.pt + +export model_checkpoint="path to ESM-IF1 checkpoint" +export DMS_output_score_folder=${DMS_output_score_folder_subs}/ESM-IF1/ +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/esm/compute_fitness_esm_if1.py \ + --model_location ${model_checkpoint} \ + --structure_folder ${DMS_structure_folder} \ + --DMS_index $DMS_index \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --output_scores_folder ${DMS_output_score_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_EVE_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_EVE_substitutions.sh new file mode 100644 index 0000000..90194c5 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_EVE_substitutions.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export model_parameters_location='../../proteingym/baselines/EVE/EVE/default_model_params.json' +export training_logs_location='../../proteingym/baselines/EVE/logs/' +export computation_mode='DMS' +export output_score_folde="${DMS_output_score_folder_subs}/EVE/" +export num_samples_compute_evol_indices=20000 +export batch_size=1024 # Pushing batch size to limit of GPU memory +export random_seeds="0 1 2 3 4" + +python ../../proteingym/baselines/EVE/compute_evol_indices_DMS.py \ + --MSA_data_folder ${DMS_MSA_data_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --protein_index ${DMS_index} \ + --VAE_checkpoint_location ${DMS_EVE_model_folder} \ + --model_parameters_location ${model_parameters_location} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --output_evol_indices_location ${output_score_folder} \ + --num_samples_compute_evol_indices ${num_samples_compute_evol_indices} \ + --batch_size ${batch_size} \ + --aggregation_method "full" \ + --threshold_focus_cols_frac_gaps 1 \ + --skip_existing \ + --MSA_weights_location ${DMS_MSA_weights_folder} \ + --random_seeds ${random_seeds} diff --git a/scripts/scoring_DMS_zero_shot/scoring_EVmutation_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_EVmutation_substitutions.sh new file mode 100644 index 0000000..7606b05 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_EVmutation_substitutions.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate evcouplings_env + +export output_score_folder=${DMS_output_score_folder_subs}/EVmutation/ +export model_folder="path to folder containing EVCouplings models" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +python ../../proteingym/baselines/EVmutation/score_mutants.py \ + --DMS_index $DMS_index \ + --DMS_data_folder $DMS_data_folder_subs \ + --model_folder $model_folder \ + --output_scores_folder $output_score_folder \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_GEMME_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_GEMME_substitutions.sh new file mode 100644 index 0000000..e10c658 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_GEMME_substitutions.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +export GEMME_LOCATION="path to GEMME installation" +export JET2_LOCATION="path to JET2 installation" +export TEMP_FOLDER="./gemme_tmp/" +export DMS_output_score_folder="${DMS_output_score_folder_subs}/GEMME/" + +python ../../proteingym/baselines/gemme/compute_fitness.py --DMS_index=$DMS_index --DMS_reference_file_path=$DMS_reference_file_path_subs \ +--DMS_data_folder=$DMS_data_folder_subs --MSA_folder=$DMS_MSA_data_folder --output_scores_folder=$DMS_output_score_folder \ +--GEMME_path=$GEMME_LOCATION --JET_path=$JET2_LOCATION --temp_folder=$TEMP_FOLDER \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_HMM_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_HMM_indels.sh new file mode 100644 index 0000000..de62690 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_HMM_indels.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export TEMP_FOLDER="./HMM_temp" +export output_score_folder="${DMS_output_score_folder_indels}/HMM/" +export HMMER_PATH="path to HMMER installation" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/HMM/score_hmm.py \ +--DMS_reference_file=$DMS_reference_file_path_indels --DMS_folder=$DMS_data_folder_indels \ +--DMS_index=$DMS_index \ +--hmmer_path=$HMMER_PATH \ +--MSA_folder=$DMS_MSA_data_folder \ +--output_scores_folder=$output_score_folder --intermediate_outputs_folder=$TEMP_FOLDER diff --git a/scripts/scoring_DMS_zero_shot/scoring_MIF_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_MIF_substitutions.sh new file mode 100644 index 0000000..e66b97a --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_MIF_substitutions.sh @@ -0,0 +1,17 @@ +source ../zero_shot_config.sh +source activate proteingym_env + +export model_name="mifst" #[mif|mifst] +export model_path="Path to MIFST checkpoints" +export DMS_output_score_folder=${DMS_output_score_folder_subs}/MIFST +export performance_file='MIFST_performance.csv' + +srun python3 ../../proteingym/baselines/carp_mif/compute_fitness.py \ + --model_name ${model_name} \ + --model_path ${model_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $SLURM_ARRAY_TASK_ID \ + --output_scores_folder ${DMS_output_score_folder} \ + --performance_file ${performance_file} \ + --structure_data_folder ${DMS_structure_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_MSA_transformer_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_MSA_transformer_substitutions.sh new file mode 100644 index 0000000..0255ce6 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_MSA_transformer_substitutions.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# MSA transformer checkpoint +export model_checkpoint="path to MSA transformer checkpoint" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export dms_output_folder="${DMS_output_score_folder_subs}/MSA_Transformer/" +export scoring_strategy=masked-marginals # MSA transformer only supports "masked-marginals" +export model_type=MSA_transformer +export scoring_window="optimal" +export random_seeds="1 2 3 4 5" +export DMS_MSA_weights_for_MSA_Transformer_folder="${DMS_MSA_weights_folder}/DMS_msa_weights_for_MSA_Transformer" # Use weights recomputed post MSA filtering used in MSA Transformer + +python ../../proteingym/baselines/esm/compute_fitness.py \ + --model-location ${model_checkpoint} \ + --model_type ${model_type} \ + --dms_index ${DMS_index} \ + --dms_mapping ${DMS_reference_file_path_subs} \ + --dms-input ${DMS_data_folder_subs} \ + --dms-output ${dms_output_folder} \ + --scoring-strategy ${scoring_strategy} \ + --scoring-window ${scoring_window} \ + --msa-path ${DMS_MSA_data_folder} \ + --msa-weights-folder ${DMS_MSA_weights_for_MSA_Transformer_folder} \ + --seeds ${random_seeds} diff --git a/scripts/scoring_DMS_zero_shot/scoring_Progen2_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_Progen2_indels.sh new file mode 100644 index 0000000..85011b0 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Progen2_indels.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export Progen2_model_name_or_path="path to progen2 small model" +# export output_scores_folder="${DMS_output_score_folder_indels}/Progen2/small" + +# export Progen2_model_name_or_path="path to progen2 medium model" +# export output_scores_folder="${DMS_output_score_folder_indels}/Progen2/medium" + +# export Progen2_model_name_or_path="path to progen2 base model" +# export output_scores_folder="${DMS_output_score_folder_indels}/Progen2/base" + +# export Progen2_model_name_or_path="path to progen2 large model" +# export output_scores_folder="${DMS_output_score_folder_indels}/Progen2/large" + +export Progen2_model_name_or_path="/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/progen2/progen2-xlarge" +export output_scores_folder="${DMS_output_score_folder_indels}/Progen2/xlarge" + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/progen2/compute_fitness.py \ + --Progen2_model_name_or_path ${Progen2_model_name_or_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --DMS_data_folder ${DMS_data_folder_indels} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_Progen2_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_Progen2_substitutions.sh new file mode 100644 index 0000000..10ec4e5 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Progen2_substitutions.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export Progen2_model_name_or_path="path to progen2 small model" +export output_scores_folder="${DMS_output_score_folder_subs}/Progen2/small" + +# export Progen2_model_name_or_path="path to progen2 medium model" +# export output_scores_folder="${DMS_output_score_folder_subs}/Progen2/medium" + +# export Progen2_model_name_or_path="path to progen2 base model" +# export output_scores_folder="${DMS_output_score_folder_subs}/Progen2/base" + +# export Progen2_model_name_or_path="path to progen2 large model" +# export output_scores_folder="${DMS_output_score_folder_subs}/Progen2/large" + +# export Progen2_model_name_or_path="path to progen2 xlarge model" +# export output_scores_folder="${DMS_output_score_folder_subs}/Progen2/xlarge" + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/progen2/compute_fitness.py \ + --Progen2_model_name_or_path ${Progen2_model_name_or_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_indels.sh new file mode 100644 index 0000000..3d2af8c --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_indels.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export ProtGPT2_model_name_or_path="path to ProtGPT2 model checkpoint" +export output_scores_folder="${DMS_output_score_folder_indels}/ProtGPT2" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/protgpt2/compute_fitness.py \ + --ProtGPT2_model_name_or_path ${ProtGPT2_model_name_or_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_substitutions.sh new file mode 100644 index 0000000..2496c66 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ProtGPT2_substitutions.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export ProtGPT2_model_name_or_path="path to ProtGPT2 model checkpoint" +export output_scores_folder="${DMS_output_score_folder_subs}/ProtGPT2" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/protgpt2/compute_fitness.py \ + --ProtGPT2_model_name_or_path ${ProtGPT2_model_name_or_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_ProtSSN_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ProtSSN_substitutions.sh new file mode 100644 index 0000000..08d00bd --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ProtSSN_substitutions.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protssn + +# please download and unzip the following files to a folder: https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/ProteinGym_substitutions_pdb-csv_checked.zip +# eg. data/mutant_example/ProteinGym_substitutions_pdb-csv_checked +export DMS_and_structure_folder="Path to unzipped data folder" + +# model checkpoint is at: https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/ProtSSN.model.tar +# please download and unzip the files to a folder +export model_checkpoint="Path to untarred model checkpoints" + +# To ensemble all models use: "k10_h512 k20_h512 k30_h512 k10_h768 k20_h768 k30_h768 k10_h1280 k20_h1280 k30_h1280" +# To use a single model, only reference a single model string via the model_name argument (eg., k20_h512) +export model_name="k10_h512 k20_h512 k30_h512 k10_h768 k20_h768 k30_h768 k10_h1280 k20_h1280 k30_h1280" +export gnn_config=../../proteingym/baselines/protssn/src/config/egnn.yaml +export score_info=../../protssn_scores.csv + +export DMS_output_score_folder="Path to folder where all model predictions should be stored" + +python ../../proteingym/baselines/protssn/compute_fitness.py \ + --gnn_config ${gnn_config} \ + --gnn_model_dir ${model_checkpoint} \ + --gnn_model_name ${model_name} \ + --use_ensemble \ + --mutant_dataset_dir ${DMS_and_structure_folder} \ + --output_scores_folder ${DMS_output_score_folder} \ + --score_info ${score_info} diff --git a/scripts/scoring_DMS_zero_shot/scoring_ProteinMPNN_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_ProteinMPNN_substitutions.sh new file mode 100644 index 0000000..b09dfef --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_ProteinMPNN_substitutions.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export output_scores_folder=${DMS_output_score_folder_subs}/ProteinMPNN + +export model_checkpoint="Path to ProteinMPNN model checkpoint" +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/protein_mpnn/compute_fitness.py \ + --checkpoint ${model_checkpoint} \ + --structure_folder ${DMS_structure_folder} \ + --DMS_index $DMS_index \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_RITA_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_RITA_indels.sh new file mode 100644 index 0000000..ad1d9aa --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_RITA_indels.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export RITA_model_path="path to RITA small model" +# export output_scores_folder="${DMS_output_score_folder_indels}/RITA/small" + +# export RITA_model_path="path to RITA medium model" +# export output_scores_folder="${DMS_output_score_folder_indels}/RITA/medium" + +# export RITA_model_path="path to RITA large model" +# export output_scores_folder="${DMS_output_score_folder_indels}/RITA/large" + +export RITA_model_path="path to RITA xlarge model" +export output_scores_folder="${DMS_output_score_folder_indels}/RITA/xlarge" + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/rita/compute_fitness.py \ + --RITA_model_name_or_path ${RITA_model_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_RITA_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_RITA_substitutions.sh new file mode 100644 index 0000000..9faaddd --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_RITA_substitutions.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export RITA_model_path="path to RITA small model" +# export output_scores_folder="${DMS_output_score_folder_subs}/RITA/small" + +# export RITA_model_path="path to RITA medium model" +# export output_scores_folder="${DMS_output_score_folder_subs}/RITA/medium" + +# export RITA_model_path="path to RITA large model" +# export output_scores_folder="${DMS_output_score_folder_subs}/RITA/large" + +export RITA_model_path="path to RITA xlarge model" +export output_scores_folder="${DMS_output_score_folder_subs}/RITA/xlarge" + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../../proteingym/baselines/rita/compute_fitness.py \ + --RITA_model_name_or_path ${RITA_model_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_SaProt_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_SaProt_substitutions.sh new file mode 100644 index 0000000..60a2d4d --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_SaProt_substitutions.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +source ../zero_shot_config.sh + +export SaProt_model_path="path to SaProt model location" #Path where you have downloaded all SaProt model/tokenizer files from the HF hub (https://huggingface.co/westlake-repl/SaProt_650M_AF2) +export output_scores_folder="${DMS_output_score_folder_subs}/SaProt/SaProt_650M_AF2" +export foldseek_bin="path to foldseek binaries" #(Download from here: https://github.com/steineggerlab/foldseek?tab=readme-ov-file) + +#export DMS_index="Experiment index to run (e.g. 0,1,...216)" +export DMS_index=0 + +python ../../proteingym/baselines/saprot/compute_fitness.py \ + --foldseek_bin ${foldseek_bin} \ + --SaProt_model_name_or_path ${SaProt_model_path} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --structure_data_folder ${DMS_structure_folder} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_indels.sh new file mode 100644 index 0000000..b134c82 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_indels.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export checkpoint="path to Tranception small checkpoint" +export output_scores_folder=${DMS_output_score_folder_indels}/TranceptEVE/TranceptEVE_S + +# export checkpoint="path to Tranception medium checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/TranceptEVE/TranceptEVE_M + +# export checkpoint="path to Tranception large checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/TranceptEVE/TranceptEVE_L + +export DMS_reference_file_path_indels=$DMS_reference_file_path_indels +export DMS_data_folder_indels=$DMS_data_folder_indels +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export inference_time_retrieval_type="TranceptEVE" +export EVE_num_samples_log_proba=200000 +export EVE_model_parameters_location="../../proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json" +export EVE_seeds="0 1 2 3 4" +export scoring_window="optimal" +export clustal_omega_location="path to clustal omega executable" +export batch_size_inference=1 +python ../../proteingym/baselines/trancepteve/score_trancepteve.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --DMS_data_folder ${DMS_data_folder_indels} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --inference_time_retrieval_type ${inference_time_retrieval_type} \ + --indel_mode \ + --batch_size_inference ${batch_size_inference} \ + --clustal_omega_location ${clustal_omega_location} \ + --MSA_folder ${DMS_MSA_data_folder} \ + --MSA_weights_folder ${DMS_MSA_weights_folder} \ + --EVE_num_samples_log_proba ${EVE_num_samples_log_proba} \ + --EVE_model_parameters_location ${EVE_model_parameters_location} \ + --EVE_model_folder ${DMS_EVE_model_folder} \ + --scoring_window ${scoring_window} \ + --EVE_seeds ${EVE_seeds} \ + --EVE_recalibrate_probas \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_substitutions.sh new file mode 100644 index 0000000..56fffb5 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_TranceptEVE_substitutions.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export checkpoint="path to Tranception_Small checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/TranceptEVE/TranceptEVE_S + +export checkpoint="path to Tranception_Medium checkpoint" +export output_scores_folder=${DMS_output_score_folder_subs}/TranceptEVE/TranceptEVE_M + +# export checkpoint="path to Tranception_Large checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/TranceptEVE/TranceptEVE_L + +# Replace the following paths based on where you store models and data +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export inference_time_retrieval_type="TranceptEVE" +export EVE_num_samples_log_proba=200000 +export EVE_model_parameters_location="../../proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json" +export EVE_seeds="0 1 2 3 4" +export scoring_window="optimal" +python ../../proteingym/baselines/trancepteve/score_trancepteve.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --inference_time_retrieval_type ${inference_time_retrieval_type} \ + --MSA_folder ${DMS_MSA_data_folder} \ + --MSA_weights_folder ${DMS_MSA_weights_folder} \ + --EVE_num_samples_log_proba ${EVE_num_samples_log_proba} \ + --EVE_model_parameters_location ${EVE_model_parameters_location} \ + --EVE_model_folder ${DMS_EVE_model_folder} \ + --scoring_window ${scoring_window} \ + --EVE_seeds ${EVE_seeds} \ + --EVE_recalibrate_probas \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels.sh new file mode 100644 index 0000000..4e14fa1 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate /n/groups/marks/software/anaconda_o2/envs/proteingym_env + +# export checkpoint="path to Tranception Large checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/Tranception/Tranception_L + +# export checkpoint="path to Tranception Medium checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/Tranception/Tranception_M + +export checkpoint="path to Tranception Small checkpoint" +export output_scores_folder=${DMS_output_score_folder_indels}/Tranception/Tranception_S + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export clustal_omega_location="path to clustal omega executable" +# Leveraging retrieval when scoring indels require batch size of 1 (no retrieval can use any batch size fitting in memory) +export batch_size_inference=1 + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --batch_size_inference ${batch_size_inference} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --DMS_data_folder ${DMS_data_folder_indels} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ + --clustal_omega_location ${clustal_omega_location} \ + --inference_time_retrieval \ + --MSA_folder ${DMS_MSA_data_folder} \ + --MSA_weights_folder ${DMS_MSA_weights_folder} \ + --scoring_window 'optimal' \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels_no_retrieval.sh b/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels_no_retrieval.sh new file mode 100644 index 0000000..0183164 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Tranception_indels_no_retrieval.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export checkpoint="path to Tranception Large checkpoint" +export output_scores_folder=${DMS_output_score_folder_indels}/Tranception_no_retrieval/Tranception_L + +# export checkpoint="path to Tranception Medium checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/Tranception_no_retrieval/Tranception_M + +# export checkpoint="path to Tranception Small checkpoint" +# export output_scores_folder=${DMS_output_score_folder_indels}/Tranception_no_retrieval/Tranception_S + +export DMS_data_folder=$DMS_data_folder_indels +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${DMS_reference_file_path_indels} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions.sh new file mode 100644 index 0000000..de0543b --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export checkpoint="path to Tranception Small checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/Tranception/Tranception_S + +# export checkpoint="path to Tranception Medium checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/Tranception/Tranception_M + +export checkpoint="path to Tranception Large checkpoint" +export output_scores_folder=${DMS_output_score_folder_subs}/Tranception/Tranception_L + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --inference_time_retrieval \ + --MSA_folder ${DMS_MSA_data_folder} \ + --MSA_weights_folder ${DMS_MSA_weights_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions_no_retrieval.sh b/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions_no_retrieval.sh new file mode 100644 index 0000000..d55820d --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_Tranception_substitutions_no_retrieval.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export checkpoint="path to Tranception Small checkpoint" +export output_scores_folder=${DMS_output_score_folder_subs}/Tranception_no_retrieval/Tranception_S + +# export checkpoint="path to Tranception Medium checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/Tranception_no_retrieval/Tranception_M + +# export checkpoint="path to Tranception Large checkpoint" +# export output_scores_folder=${DMS_output_score_folder_subs}/Tranception_no_retrieval/Tranception_L + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${DMS_reference_file_path_subs} \ + --DMS_data_folder ${DMS_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_indels.sh new file mode 100644 index 0000000..cef6984 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_indels.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to folder containing evotuned UniRep models" +export output_dir=${DMS_output_score_folder_indels}/UniRep_evotuned/ +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $DMS_data_folder_indels \ + --output_dir $output_dir \ + --mapping_path $DMS_reference_file_path_indels \ + --DMS_index $DMS_index \ + --batch_size 32 \ + --evotune \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_substitutions.sh new file mode 100644 index 0000000..290e856 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_UniRep_evotune_substitutions.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to folder containing evotuned UniRep models" +export output_dir=${DMS_output_score_folder_subs}/UniRep_evotuned +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $DMS_data_folder_subs \ + --output_dir $output_dir \ + --mapping_path $DMS_reference_file_path_subs \ + --DMS_index $DMS_index \ + --batch_size 32 \ + --evotune \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_UniRep_indels.sh b/scripts/scoring_DMS_zero_shot/scoring_UniRep_indels.sh new file mode 100644 index 0000000..bfc0d0b --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_UniRep_indels.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to initial unirep weight checkpoint" +export output_dir=${DMS_data_folder_indels}/UniRep/ +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $DMS_data_folder_indels \ + --output_dir $output_dir \ + --mapping_path $DMS_reference_file_path_indels \ + --DMS_index $DMS_index \ + --batch_size 32 \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_UniRep_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_UniRep_substitutions.sh new file mode 100644 index 0000000..3934bbe --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_UniRep_substitutions.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to initial unirep weight checkpoint" +export output_dir=${DMS_output_score_folder_subs}/UniRep +export DMS_index="Experiment index to run (E.g. 0,1,2,...,217)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $DMS_data_folder_subs \ + --output_dir $output_dir \ + --mapping_path $DMS_reference_file_path_subs \ + --DMS_index $DMS_index \ + --batch_size 8 \ No newline at end of file diff --git a/scripts/scoring_DMS_zero_shot/scoring_VESPA_substitutions.sh b/scripts/scoring_DMS_zero_shot/scoring_VESPA_substitutions.sh new file mode 100644 index 0000000..316ac15 --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/scoring_VESPA_substitutions.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# Ranges of DMSs to score in one run of VESPA +export DMS_index_range_start="starting index" +export DMS_index_range_end="ending index" +export vespa_cache="cache to place ProtT5 checkpoint" + +python ../../proteingym/baselines/vespa/compute_fitness.py \ + --cache_location $vespa_cache \ + --DMS_reference_file_path $DMS_reference_file_path_subs \ + --MSA_data_folder $DMS_MSA_data_folder \ + --DMS_data_folder $DMS_data_folder_subs \ + --DMS_index_range_start $DMS_index_range_start \ + --DMS_index_range_end $DMS_index_range_end diff --git a/scripts/scoring_DMS_zero_shot/training_EVE_models.sh b/scripts/scoring_DMS_zero_shot/training_EVE_models.sh new file mode 100755 index 0000000..0fd582c --- /dev/null +++ b/scripts/scoring_DMS_zero_shot/training_EVE_models.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="Experiment index to run (e.g. 1,2,...217)" +export seed="random seed value" + +export model_parameters_location='../../proteingym/baselines/EVE/EVE/default_model_params.json' +export training_logs_location='../../proteingym/baselines/EVE/logs/' +export DMS_reference_file_path=$DMS_reference_file_path_subs +# export DMS_reference_file_path=$DMS_reference_file_path_indels + +python ../../proteingym/baselines/EVE/train_VAE.py \ + --MSA_data_folder ${DMS_MSA_data_folder} \ + --DMS_reference_file_path ${DMS_reference_file_path} \ + --protein_index "${DMS_index}" \ + --MSA_weights_location ${DMS_MSA_weights_folder} \ + --VAE_checkpoint_location ${DMS_EVE_model_folder} \ + --model_parameters_location ${model_parameters_location} \ + --training_logs_location ${training_logs_location} \ + --threshold_focus_cols_frac_gaps 1 \ + --seed ${seed} \ + --skip_existing \ + --experimental_stream_data \ + --force_load_weights + diff --git a/scripts/scoring_clinical_zero_shot/score_GEMME_substitutions.sh b/scripts/scoring_clinical_zero_shot/score_GEMME_substitutions.sh new file mode 100644 index 0000000..79ad276 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/score_GEMME_substitutions.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +source activate proteingym_env + +DMS_index="variant index to run (e.g. 1,2,...2525)" +OUTPUT_SCORES_FOLDER="${clinical_output_score_folder_subs}/GEMME" +GEMME_LOCATION="path to GEMME installation" +JET2_LOCATION="path to JET2 installation" +TEMP_FOLDER="./gemme_tmp/" + +python ../../proteingym/baselines/gemme/compute_fitness.py --DMS_index=$DMS_index --DMS_reference_file_path=$clinical_reference_file_path_subs \ +--DMS_data_folder=$clinical_data_folder_subs --MSA_folder=$clinical_MSA_data_folder_subs --output_scores_folder=$OUTPUT_SCORES_FOLDER \ +--GEMME_path=$GEMME_LOCATION --JET_path=$JET2_LOCATION --temp_folder=$TEMP_FOLDER \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_ESM1b_substitutions.sh b/scripts/scoring_clinical_zero_shot/scoring_ESM1b_substitutions.sh new file mode 100644 index 0000000..26d3aae --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_ESM1b_substitutions.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +# ESM-1v +# export model_checkpoint1="path to seed 1 model checkpoint" +# export model_checkpoint2="path to seed 2 model checkpoint" +# export model_checkpoint3="path to seed 3 model checkpoint" +# export model_checkpoint4="path to seed 4 model checkpoint" +# export model_checkpoint5="path to seed 5 model checkpoint" +# combine all five into one string +# export model_checkpoint="${model_checkpoint1} ${model_checkpoint2} ${model_checkpoint3} ${model_checkpoint4} ${model_checkpoint5}" +# export dms_output_folder="${clinical_output_score_folder_subs}/ESM1v/" +# export scoring_window="optimal" +# export model_type="ESM1v" + +# ESM1b: +export model_checkpoint=/n/groups/marks/projects/marks_lab_and_oatml/protein_transformer/baseline_models/ESM1b/esm1b_t33_650M_UR50S.pt + +export dms_output_folder="${clinical_output_score_folder_subs}/ESM1b/" + +export scoring_strategy="wt-marginals" +export scoring_window="overlapping" # For long proteins +export model_type="ESM1b" + +python ../../proteingym/baselines/esm/compute_fitness.py \ + --model-location ${model_checkpoint} \ + --model_type ${model_type} \ + --dms-input ${clinical_data_folder_subs} \ + --dms-output ${dms_output_folder} \ + --scoring-strategy ${scoring_strategy} \ + --scoring-window ${scoring_window} \ + --dms_mapping ${clinical_reference_file_path_subs} \ + --dms_index ${dms_index} diff --git a/scripts/scoring_clinical_zero_shot/scoring_EVE_substitutions.sh b/scripts/scoring_clinical_zero_shot/scoring_EVE_substitutions.sh new file mode 100644 index 0000000..f246e88 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_EVE_substitutions.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +export model_parameters_location='../../proteingym/baselines/EVE/EVE/default_model_params.json' +export training_logs_location='../../proteingym/baselines/EVE/logs/' + +export computation_mode='DMS' +export output_scores_folder="${clinical_output_score_folder_subs}/EVE" # Custom output location +export num_samples_compute_evol_indices=20000 +export batch_size=1024 # Pushing batch size to limit of GPU memory +export random_seeds="random seed values" + +python ../../proteingym/baselines/EVE/compute_evol_indices_DMS.py \ + --MSA_data_folder ${clinical_MSA_data_folder_subs} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --protein_index ${DMS_index} \ + --VAE_checkpoint_location ${clinical_EVE_model_folder} \ + --model_parameters_location ${model_parameters_location} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --output_scores_folder ${output_scores_folder} \ + --num_samples_compute_evol_indices ${num_samples_compute_evol_indices} \ + --batch_size ${batch_size} \ + --aggregation_method "full" \ + --threshold_focus_cols_frac_gaps 1 \ + --MSA_weights_location ${clinical_MSA_weights_folder_subs} \ + --random_seeds ${random_seeds} diff --git a/scripts/scoring_clinical_zero_shot/scoring_PoET.sh b/scripts/scoring_clinical_zero_shot/scoring_PoET.sh new file mode 100644 index 0000000..8381657 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_PoET.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +source ../zero_shot_config.sh +conda activate poet + +export checkpoint="path to checkpoint" +export output_scores_folder=${clinical_output_score_folder_subs}/PoET + +export DMS_index="variant index to run (e.g. 0,1,...2524)" + +python ../../proteingym/baselines/PoET/scripts/score.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --MSA_folder ${clinical_MSA_data_folder_subs} \ + --context_lengths 49152 \ + --batch_size 8 \ + --relative_to_wt diff --git a/scripts/scoring_clinical_zero_shot/scoring_PoET_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_PoET_indels.sh new file mode 100644 index 0000000..e575e18 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_PoET_indels.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +source ../zero_shot_config.sh +conda activate poet + +export checkpoint="path to checkpoint" +export output_scores_folder=${clinical_output_score_folder_indels}/PoET + +export DMS_index="variant index to run (e.g. 0,1,...2524)" + +python ../../proteingym/baselines/PoET/scripts/score.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${clinical_reference_file_path_indels} \ + --DMS_data_folder ${clinical_data_folder_indels} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --MSA_folder ${clinical_MSA_data_folder_indels} \ + --context_lengths 49152 \ + --batch_size 8 \ + --relative_to_wt diff --git a/scripts/scoring_clinical_zero_shot/scoring_Progen2.sh b/scripts/scoring_clinical_zero_shot/scoring_Progen2.sh new file mode 100644 index 0000000..1a05d74 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_Progen2.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym + +#export Progen2_model_name_or_path="path to progen2 small model" +#export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/small" + +#export Progen2_model_name_or_path="path to progen2 medium model" +#export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/medium" + +export Progen2_model_name_or_path="path to progen2 base model" +export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/base" + +#export Progen2_model_name_or_path="path to progen2 large model" +#export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/large" + +#export Progen2_model_name_or_path="path to progen2 xlarge model" +#export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/xlarge" + +#export Progen2_model_name_or_path="path to progen2 oas model" +#export output_scores_folder="${clinical_output_score_folder_subs}/Progen2/oas" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/progen2/compute_fitness.py \ + --Progen2_model_name_or_path ${Progen2_model_name_or_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_Progen2_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_Progen2_indels.sh new file mode 100644 index 0000000..2d1d576 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_Progen2_indels.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +#export Progen2_model_name_or_path="path to progen2 small model" +#export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/small" + +#export Progen2_model_name_or_path="path to progen2 medium model" +#export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/medium" + +export Progen2_model_name_or_path="path to progen2 base model" +export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/base" + +#export Progen2_model_name_or_path="path to progen2 large model" +#export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/large" + +#export Progen2_model_name_or_path="path to progen2 xlarge model" +#export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/xlarge" + +#export Progen2_model_name_or_path="path to progen2 oas model" +#export output_scores_folder="${clinical_output_score_folder_indels}/Progen2/oas" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/progen2/compute_fitness.py \ + --Progen2_model_name_or_path ${Progen2_model_name_or_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_indels} \ + --DMS_data_folder ${clinical_data_folder_indels} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2.sh b/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2.sh new file mode 100644 index 0000000..ff65223 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export ProtGP2_model_name_or_path="path to ProtGPT2 checkpoint" +export output_scores_folder="${clinical_output_score_folder_subs}/ProtGPT2" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/protgpt2/compute_fitness.py \ + --ProtGP2_model_name_or_path ${ProtGP2_model_name_or_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2_indels.sh new file mode 100644 index 0000000..20ac6e7 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_ProtGPT2_indels.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export ProtGPT2_model_name_or_path="path to ProtGPT2 checpoint" +export output_scores_folder="${clinical_output_score_folder_indels}/ProtGPT2" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../baselines/protgpt2/compute_fitness.py \ + --ProtGP2_model_name_or_path ${ProtGPT2_model_name_or_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_indels} \ + --DMS_data_folder ${clinical_data_folder_indels} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_RITA.sh b/scripts/scoring_clinical_zero_shot/scoring_RITA.sh new file mode 100644 index 0000000..76c136b --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_RITA.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +source activate proteingym_env + +export RITA_model_path="path to RITA small checkpoint" +export output_scores_folder="${clinical_output_score_folder_subs}/RITA/small" + +#export RITA_model_path="path to RITA medium checkpoint" +#export output_scores_folder="${clinical_output_score_folder_subs}/RITA/medium" + +#export RITA_model_path="path to RITA large checkpoint" +#export output_scores_folder="${clinical_output_score_folder_subs}/RITA/large" + +#export RITA_model_path="path to RITA xlarge checkpoint" +#export output_scores_folder="${clinical_output_score_folder_subs}/RITA/xlarge" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/rita/compute_fitness.py \ + --RITA_model_path ${RITA_model_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_RITA_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_RITA_indels.sh new file mode 100644 index 0000000..0deca2e --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_RITA_indels.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export RITA_model_path="path to RITA small checkpoint" +export output_scores_folder="${clinical_output_score_folder_indels}/RITA/small" + +#export RITA_model_path="path to RITA medium checkpoint" +#export output_scores_folder="${clinical_output_score_folder_indels}/RITA/medium" + +#export RITA_model_path="path to RITA large checkpoint" +#export output_scores_folder="${clinical_output_score_folder_indels}/RITA/large" + +#export RITA_model_path="path to RITA xlarge checkpoint" +#export output_scores_folder="${clinical_output_score_folder_indels}/RITA/xlarge" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/rita/compute_fitness.py \ + --RITA_model_path ${RITA_model_path} \ + --DMS_reference_file_path ${clinical_reference_file_path_indels} \ + --DMS_data_folder ${clinical_reference_file_path_indels} \ + --DMS_index $DMS_index \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_TranceptEVE_substitutions.sh b/scripts/scoring_clinical_zero_shot/scoring_TranceptEVE_substitutions.sh new file mode 100644 index 0000000..cedb2ee --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_TranceptEVE_substitutions.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +# export checkpoint="path to Tranception small checkpoint" +# export output_scores_folder=${clinical_output_score_folder_subs}/TranceptEVE/TranceptEVE_S + +export checkpoint="path to Tranception medium checkpoint" +export output_scores_folder=${clinical_output_score_folder_subs}/TranceptEVE/TranceptEVE_M + +# export checkpoint="path to Tranception large checkpoint" +# export output_scores_folder=${clinical_output_score_folder_subs}/TranceptEVE/TranceptEVE_L + +export DMS_index="variant index to run (e.g. 1,2,...2525)" +export DMS_reference_file_path="../../reference_files/clinical_substitutions.csv" +export inference_time_retrieval_type="TranceptEVE" +export EVE_num_samples_log_proba=200000 +export EVE_model_parameters_location="../../proteingym/baselines/trancepteve/trancepteve/utils/eve_model_default_params.json" +export EVE_seeds="random seed values for EVE models" +export scoring_window="optimal" +python ../../proteingym/baselines/trancepteve/score_trancepteve.py \ + --checkpoint ${checkpoint} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --inference_time_retrieval_type ${inference_time_retrieval_type} \ + --MSA_folder ${clinical_MSA_data_folder_subs} \ + --MSA_weights_folder ${clinical_MSA_weights_folder_subs} \ + --EVE_num_samples_log_proba ${EVE_num_samples_log_proba} \ + --EVE_model_parameters_location ${EVE_model_parameters_location} \ + --EVE_model_folder ${clinical_EVE_model_folder} \ + --scoring_window ${scoring_window} \ + --EVE_seeds ${EVE_seeds} \ + --EVE_recalibrate_probas \ + --clinvar_scoring \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_Tranception.sh b/scripts/scoring_clinical_zero_shot/scoring_Tranception.sh new file mode 100644 index 0000000..beab12c --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_Tranception.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate tranception_env + +# Replace the following paths based on where you store models and data +export checkpoint="path to Tranception large checkpoint" +export output_scores_folder="${clinical_output_score_folder_subs}/Tranception/Tranception_L" + +# export checkpoint="path to Tranception medium checkpoint" +# export output_scores_folder="${clinical_output_score_folder_subs}/Tranception/Tranception_M" + +# export checkpoint="path to Tranception small checkpoint" +# export output_scores_folder="${clinical_output_score_folder_subs}/Tranception/Tranception_S" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +export batch_size_inference=10 + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --batch_size_inference ${batch_size_inference} \ + --DMS_reference_file_path ${clinical_reference_file_path_subs} \ + --DMS_data_folder ${clinical_data_folder_subs} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --inference_time_retrieval \ + --MSA_folder ${clinical_MSA_data_folder_subs} \ + --MSA_weights_folder ${clinical_MSA_weights_folder_subs} \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_Tranception_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_Tranception_indels.sh new file mode 100644 index 0000000..6981773 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_Tranception_indels.sh @@ -0,0 +1,34 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export checkpoint="path to Tranception large checkpoint" +export output_scores_folder="${clinical_output_score_folder_indels}/Tranception/Tranception_L" + +# export checkpoint="path to Tranception medium checkpoint" +# export output_scores_folder="${clinical_output_score_folder_indels}/Tranception/Tranception_M" + +# export checkpoint="path to Tranception small checkpoint" +# export output_scores_folder="${clinical_output_score_folder_indels}/Tranception/Tranception_S" + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +# Clustal Omega is required when scoring indels with retrieval (not needed if scoring indels with no retrieval) +export clustal_omega_location="path to clustal omega executable" + +# Leveraging retrieval when scoring indels require batch size of 1 (no retrieval can use any batch size fitting in memory) +export batch_size_inference=1 + +python ../../proteingym/baselines/tranception/score_tranception_proteingym.py \ + --checkpoint ${checkpoint} \ + --batch_size_inference ${batch_size_inference} \ + --DMS_reference_file_path ${clinical_reference_file_path_indels} \ + --DMS_data_folder ${clinical_data_folder_indels} \ + --DMS_index ${DMS_index} \ + --output_scores_folder ${output_scores_folder} \ + --indel_mode \ + --clustal_omega_location ${clustal_omega_location} \ + --inference_time_retrieval \ + --MSA_folder ${clinical_MSA_data_folder_indels} \ + --MSA_weights_folder ${clinical_MSA_weights_folder_indels} \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_UniRep.sh b/scripts/scoring_clinical_zero_shot/scoring_UniRep.sh new file mode 100644 index 0000000..4af4220 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_UniRep.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to unirep weights checkpoint" +export output_dir="${clinical_output_score_folder_subs}/Unirep" +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $clinical_data_folder_subs \ + --output_dir $output_dir \ + --mapping_path $clinical_reference_file_path_subs \ + --DMS_index $DMS_index \ + --batch_size 32 \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune.sh b/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune.sh new file mode 100644 index 0000000..7c281c8 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 +export model_path="path to folder containing unirep evotune models" +export output_dir="${clinical_output_score_folder_subs}/Unirep_evotune" +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $clinical_data_folder_subs \ + --output_dir $output_dir \ + --mapping_path $clinical_reference_file_path_subs \ + --DMS_index $DMS_index \ + --batch_size 32 \ + --evotune \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune_indels.sh new file mode 100644 index 0000000..e699eb3 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_UniRep_evotune_indels.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to folder containing unirep evotuned models" +export output_dir="${clinical_output_score_folder_indels}/Unirep_evotune" +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $clinical_data_folder_indels \ + --output_dir $output_dir \ + --mapping_path $clinical_reference_file_path_indels \ + --DMS_index $DMS_index \ + --batch_size 32 \ + --evotune \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/scoring_UniRep_indels.sh b/scripts/scoring_clinical_zero_shot/scoring_UniRep_indels.sh new file mode 100644 index 0000000..15bc74d --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/scoring_UniRep_indels.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate protein_fitness_prediction_hsu + +export OMP_NUM_THREADS=1 + +export model_path="path to unirep weights checkpoint" +export output_dir="${clinical_output_score_folder_indels}/Unirep" +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +python ../../proteingym/baselines/unirep/unirep_inference.py \ + --model_path $model_path \ + --data_path $clinical_data_folder_indels \ + --output_dir $output_dir \ + --mapping_path $clinical_reference_file_path_indels \ + --DMS_index $DMS_index \ + --batch_size 32 \ No newline at end of file diff --git a/scripts/scoring_clinical_zero_shot/training_EVE_models.sh b/scripts/scoring_clinical_zero_shot/training_EVE_models.sh new file mode 100755 index 0000000..743a443 --- /dev/null +++ b/scripts/scoring_clinical_zero_shot/training_EVE_models.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +source ../zero_shot_config.sh +source activate proteingym_env + +export DMS_index="variant index to run (e.g. 1,2,...2525)" + +export model_parameters_location='../../proteingym/baselines/EVE/EVE/default_model_params.json' +export training_logs_location='../../proteingym/baselines/EVE/logs/' +export clinical_reference_file_path=$clinical_reference_file_path_subs + +python ../../proteingym/baselines/EVE/train_VAE.py \ + --MSA_data_folder ${clinical_MSA_data_folder_subs} \ + --DMS_reference_file_path ${clinical_reference_file_path} \ + --protein_index "${DMS_index}" \ + --MSA_weights_location ${clinical_MSA_weights_folder_subs} \ + --VAE_checkpoint_location ${clinical_EVE_model_folder} \ + --model_parameters_location ${model_parameters_location} \ + --training_logs_location ${training_logs_location} \ + --threshold_focus_cols_frac_gaps 1 \ + --seed 0 \ + --skip_existing \ + --experimental_stream_data \ + --force_load_weights + diff --git a/scripts/zero_shot_config.sh b/scripts/zero_shot_config.sh new file mode 100644 index 0000000..5c50203 --- /dev/null +++ b/scripts/zero_shot_config.sh @@ -0,0 +1,60 @@ +# This file has all general filepaths and directories used in the scoring pipeline. The individual scripts may have +# additional parameters specific to each method + +# DMS zero-shot parameters + +# Folders containing the csvs with the variants for each DMS assay +export DMS_data_folder_subs="Folder containing DMS substitution csvs" +export DMS_data_folder_indels="Folder containing DMS indel csvs" + +# Folders containing multiple sequence alignments and MSA weights for all DMS assays +export DMS_MSA_data_folder="folder containing DMS MSA files" +export DMS_MSA_weights_folder="folder containing DMS MSA weights" + +# Reference files for substitution and indel assays +export DMS_reference_file_path_subs=../../reference_files/DMS_substitutions.csv +export DMS_reference_file_path_indels=../../reference_files/DMS_indels.csv + +# Folders where fitness predictions for baseline models are saved +export DMS_output_score_folder_subs="folder for DMS substitution scores" +export DMS_output_score_folder_indels="folder for DMS indel scores" + +# Folder containing EVE models for each DMS assay +export DMS_EVE_model_folder="folder for DMS assay specific EVE models" + +# Folders containing merged score files for each DMS assay +export DMS_merged_score_folder_subs="folder for merged scores for DMS substitutions" +export DMS_merged_score_folder_indels="folder for merged score for DMS indels" + +# Folders containing predicted structures for the DMSs +export DMS_structure_folder="folder containing predicted structures for each DMS assay" + + +# Clinical parameters + +# Folder containing variant csvs +export clinical_data_folder_subs="folder containing clinical substitution csvs" +export clinical_data_folder_indels="folder containing clinical indel csvs" + +# Folders containing multiple sequence alignments and MSA weights for all clinical datasets +export clinical_MSA_data_folder_subs="folder containing clinical MSA files for substitutions" +export clinical_MSA_data_folder_indels="folder containing clinical MSA files for indels" + +# Folder containing MSA weights for all clinical datasets +export clinical_MSA_weights_folder_subs="folder containing clinical MSA weights for substitutions" +export clinical_MSA_weights_folder_indels="folder containing clinical MSA weights for indels" + +# reference files for substitution and indel clinical variants +export clinical_reference_file_path_subs=../../reference_files/clinical_substitutions.csv +export clinical_reference_file_path_indels=../../reference_files/clinical_indels.csv + +# Folder where clinical benchmark fitness predictions for baseline models are saved +export clinical_output_score_folder_subs="folder for clinical substitution scores" +export clinical_output_score_folder_indels="folder for clinical indel scores" + +# Folder containing EVE models for each clinical variant +export clinical_EVE_model_folder="folder for clinical EVE models" + +# Folder containing merged score files for each clinical variant +export clinical_merged_score_folder_subs="folder for merged scores for clinical substitutions" +export clinical_merged_score_folder_indels="folder for merged score for clinical indels" diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..e20a486 --- /dev/null +++ b/setup.py @@ -0,0 +1,16 @@ +from setuptools import setup + +with open("README.md") as f: + readme = f.read() + +setup( + name="proteingym", + description="ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction", + long_description=readme, + long_description_content_type="text/markdown", + author="OATML-Markslab", + version="1.1", + license="MIT", + url="https://github.com/OATML-Markslab/ProteinGym", + packages=["proteingym"] +) \ No newline at end of file