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DeepGlyco Tutorial: Training New Models for iRT

Training new models for glycopeptide iRT prediction.

Prerequisites

System Requirements

In this tutorial, model training has been tested on a workstation with Intel Core i9-12900K CPU, 64 GB RAM, and Microsoft Windows 10 Version 22H2 (OS Build 19045.2604) operating system. A NVIDIA GeForce RTX 3090 GPU is needed, with CUDA Version 11.6.

Software Dependency

The following software and packages are required:

  • Python (version 3.9.16, Anaconda distribution is recommended)
  • PyTorch (version 1.12.1)
  • DGL (version 1.0.1)
  • numpy (version 1.23.5)
  • pandas (version 1.5.2)
  • scipy (version 1.10.1)
  • scikit-learn (version 1.2.1)
  • statsmodels (version 0.13.2)
  • h5py (version 3.7.0)
  • pymzml (version 2.5.2)
  • matplotlib (version 3.6.2)
  • tensorboard (version 2.10.0)

Later versions may be compatible, but have not been tested.

Tutorial Data

Starting Materials

Starting materials of this tutorial are available at ProteomeXchange and iProX with identifier PXD045248 or IPX0007075004.

A StrucGP search result file:

  • MouseFiveTissues_result.xlsx

Peptide retention time extracted from Byonic search results:

  • MouseFiveTissues_Byonic.zip
  • PXD031032_MouseFiveTissues_Byonic_peptides.rt.csv

A pretrained model for peptide iRT prediction:

  • PXD004452_peprt_model.pt

Train a iRT model

Prepare Training Data

Create a directory for the project, and place the files in the folder.

Open Anaconda PowerShell Prompt, set the project folder as working directory, and set path to the scripts as global parameter.

cd "Path_to_project_data"
$script_path = "Path_to_DeepGlyco\src"

Retention Time Extraction

Create a subdirectory named gprt in the project directory. Extract experimental retention time values from the StrucGP result.

mkdir "gprt" -f | Out-Null

python "$script_path\build_gprtlib.py" `
--in "mzML\MouseFiveTissues_result.xlsx" `
--out "gprt\PXD025859_MouseFiveTissues_StrucGP.rtlib.h5"

A subdirectory named gprt is created in the project directory. The extracted retention time values are imported into a HDF5 file (*.rtlib.h5).

iRT Calibration

Predict iRT of the peptides identified in the same experiment.

python "$script_path\predict_rt.py" `
--no-validate `
--model "PXD004452_peprt_model.pt" `
--in "PXD031032_MouseFiveTissues_Byonic_peptides.rt.csv" `
--out "PXD031032_MouseFiveTissues_Byonic_peptides_prediction.rt.csv"

These peptides are used as anchors for iRT calibration of glycopeptides. Combine replicate iRT entries into consensus entries (one entry per glycopeptide).

python "$script_path\build_consensus_gprtib.py" `
--in "gprt\PXD025859_MouseFiveTissues_StrucGP.rtlib.h5" `
--reference "PXD031032_MouseFiveTissues_Byonic_peptides_prediction.rt.csv" `
--out "gprt\PXD025859_MouseFiveTissues_StrucGP_consensus.rtlib.h5"

The consensus library is saved in a HDF5 file (*_consensus.rtlib.h5), which will be used as training data.

Model Training

Create a subdirectory named training in the project directory, and place the pretrained model in the project folder:

  • training\PXD004452_peprt_model.pt

Start model training.

python "$script_path\train_gprt.py" `
--in "gprt\PXD025859_MouseFiveTissues_StrucGP_consensus.rtlib.h5" `
--wkdir "training\deepgprt\PXD025859_MouseFiveTissues" `
--pretrained "training\PXD004452_peprt_model.pt"

The trained model checkpoints are saved in Python binary files (epoch_*.pt) in the path training\deepgprt\PXD025859_MouseFiveTissues\checkpoints.

Open another Anaconda PowerShell Prompt, start TensorBoard to monitor the training process.

tensorboard --logdir=training\deepgprt\PXD025859_MouseFiveTissues

Select the best model based on the metrics on training and validation set.

Model Testing

Test the trained model.

python "$script_path\predict_gprt.py" `
--validate `
--pretrained "training\deepgprt\PXD025859_MouseFiveTissues\checkpoints\epoch_265.pt" `
--in "gprt\PXD025859_MouseFiveTissues_StrucGP_consensus.rtlib.h5" `
--out "training\deepgprt\PXD025859_MouseFiveTissues\PXD025859_MouseFiveTissues_StrucGP_consensus_prediction.rt.csv"

Note that epoch_265.pt should be replaced with the model selected for testing. The --in argument should be changed when testing on other data (*.rtlib.h5).

The predicted and target data are saved as a CSV file(*_prediction.rt.csv).

Model finetuning

When finetuning a pretrained model with other data, change the config and epoch number.

python "$script_path\train_gprt.py" `
--in "gprt\FinetuningData_consensus.rtlib.h5" `
--wkdir "training\deepgprt\FinetuningData" `
--pretrained "training\deepgprt\PXD025859_MouseFiveTissues\checkpoints\epoch_265.pt" `
--config "$script_path\deepglyco\deeplib\gpep\rt\gprtmodel_finetune.yaml" `
--epochs 50