Renamed Rank Sequences -> Score Sequences

source/resources/tutorials-examples.rst

@@ -19,7 +19,7 @@ Explore our platform capabilities
                         <ul>
                             <li><a href="../web-app/poet/prompts.html">Prompt and prompt sampling methods</a></li>
                             <li><a href="../web-app/poet/scoring-log-likelihood.html">PoET scoring and log-likelihood</a></li>
-                            <li><a href="../web-app/poet/rank-sequences.html">Rank Sequences tool</a></li>
+                            <li><a href="../web-app/poet/score-sequences.html">Score Sequences tool</a></li>
                             <li><a href="../web-app/poet/substitution-analysis.html">PoET Substitution Analysis tool</a></li>
                             <li><a href="../web-app/poet/generate-sequences.html">Generate Sequences tool</a></li>
                         </ul>

source/walkthroughs/enzyme-engineering.rst

@@ -172,7 +172,7 @@ Designing and ranking a combinatorial variant library
 
 By considering the overall fitness landscape and introducing multiple mutations per variant, we can explore a much larger sequence design space not limited to mutating to one amino acid per position at a time. In this section, we will create a library of combinatorial variants using the PSSM generated above in Exploring the Fitness Landscape of EstA, where variants can have more than one amino acid substitution per position as well as multiple substitutions per variant. The PSSM derived from our PoET substitution scores provides the frequencies of each amino acid at each position that can be randomly combined to create a large combinatorial variant library.
 
-Although the PSSM is an additive model, we can explore potential epistatic interactions between residues and to design specific combinatorial variants that are likely to be fitter than the wild-type. To do this, we can sample sequences from our PSSM and score these full combinatorial variants using the **Rank Sequences** tool. By comparing the full PoET scores with the additive scores, we can find variants with positive or negative epistasis and prioritize specific combinatorial variants based on their fitness relative to the WT sequence.
+Although the PSSM is an additive model, we can explore potential epistatic interactions between residues and to design specific combinatorial variants that are likely to be fitter than the wild-type. To do this, we can sample sequences from our PSSM and score these full combinatorial variants using the **Score Sequences** tool. By comparing the full PoET scores with the additive scores, we can find variants with positive or negative epistasis and prioritize specific combinatorial variants based on their fitness relative to the WT sequence.
 
 First, we’ll sample a large number of candidate combinatorial variants from the PSSM with the following Python script. You can download the py file `here <https://drive.google.com/file/d/1h6a066f5Y0MGDrIcdQ41m3S8GdQRmESA/view?usp=drive_link>`__.
 
@@ -229,7 +229,7 @@ First, we’ll sample a large number of candidate combinatorial variants from th
 
 
 
-This Python code generates a CSV file with the scores column and header removed, and can be found `here <https://drive.google.com/file/d/16QxerujZD-EhSHfOArbcLIHSs5CxYrQL/view?usp=drive_link>`__ for your reference. We’ll upload this `CSV file <https://drive.google.com/file/d/1cWRI8eG_Yhjt-p0m95oCfIgH007lkqt_/view?usp=drive_link>`_ by clicking on **select a file**. In the **Prompt Definition** field, we’ll use the wild-type sequence shown below. Then, we’ll run **Rank Sequences** with **Number of prompts to ensemble** set to 3 and the default parameters. Note that the CSV file should only have 2 columns containing the name and sequence, without headers.
+This Python code generates a CSV file with the scores column and header removed, and can be found `here <https://drive.google.com/file/d/16QxerujZD-EhSHfOArbcLIHSs5CxYrQL/view?usp=drive_link>`__ for your reference. We’ll upload this `CSV file <https://drive.google.com/file/d/1cWRI8eG_Yhjt-p0m95oCfIgH007lkqt_/view?usp=drive_link>`_ by clicking on **select a file**. In the **Prompt Definition** field, we’ll use the wild-type sequence shown below. Then, we’ll run **Score Sequences** with **Number of prompts to ensemble** set to 3 and the default parameters. Note that the CSV file should only have 2 columns containing the name and sequence, without headers.
 
 .. code-block:: python
 
@@ -240,11 +240,11 @@ This Python code generates a CSV file with the scores column and header removed,
 .. image:: /_static/walkthroughs/enzymes/PoETW1-P8.png
 
 
-We will then **Export** the results, which are shown here sorted by their log-likelihood scores. We can compare the additive log-likelihood scores from the PSSM versus the full PoET log-likelihood scores from **Rank sequences**.
+We will then **Export** the results, which are shown here sorted by their log-likelihood scores. We can compare the additive log-likelihood scores from the PSSM versus the full PoET log-likelihood scores from **Score Sequences**.
 
 .. image:: /_static/walkthroughs/enzymes/PoETW1-P9.png
 
-As seen below, from plotting the expected log-likelihood score calculated from the PSSM and the log-likelihood score from PoET **Rank Sequence**, there are differences when considering each position additively (PSSM score) versus the mutations in the context of the full length protein (PoET score). The differences highlight the potential epistatic effects which amino acids at different positions can have on each other, and highlights the importance of running generated variants through PoET’s **Rank Sequence** tool to determine the best combinatorial variants to screen. Certain pairs or groups of amino acids that are in contact may need to be conserved or engineered together.
+As seen below, from plotting the expected log-likelihood score calculated from the PSSM and the log-likelihood score from PoET **Score Sequences**, there are differences when considering each position additively (PSSM score) versus the mutations in the context of the full length protein (PoET score). The differences highlight the potential epistatic effects which amino acids at different positions can have on each other, and highlights the importance of running generated variants through PoET’s **Score Sequences** tool to determine the best combinatorial variants to screen. Certain pairs or groups of amino acids that are in contact may need to be conserved or engineered together.
 
 .. image:: /_static/walkthroughs/enzymes/PoETW1-P10-1.png
 
@@ -261,7 +261,7 @@ For further refinement of the prompt, you can look at the tutorial found `here <
 
 Consider selecting at least 10 to 30 designs designed using PoET as an initial library to serve as proof of concept for experimental validation. The data generated from the library can then be used to further enhance the enzyme using **OP Models** following the tutorial found `here <../web-app/opmodels/index.rst>`__.
 
-You can also perform **Structure Prediction** on the specific variants, such as those with PoET **Rank Sequence** log-likelihood scores higher than the expected log-likelihood score calculated from the PSSM. This could indicate potential positive epistasis. You can align the sequences to compare or compare the predicted structures to examine if there are specific key residues.
+You can also perform **Structure Prediction** on the specific variants, such as those with PoET **Score Sequences** log-likelihood scores higher than the expected log-likelihood score calculated from the PSSM. This could indicate potential positive epistasis. You can align the sequences to compare or compare the predicted structures to examine if there are specific key residues.
 
 
 References

source/web-app/index.rst

@@ -25,7 +25,7 @@ Our tools are standalone or can be used together at any point in your project.
          * Introduce diversity
          * Identify mutagenesis hotspots
          * Predict variant effects
-         * Rank sequence designs 
+         * Score sequence designs
 
       `Learn more <./poet/index.rst>`__
 

source/web-app/poet/index.rst

@@ -25,7 +25,7 @@ Learn more and get started with our tutorials
 - `Ensembling <./ensembling.rst>`_
 - `Interpreting PoET Results Table <./results-table.rst>`_
 - `PoET history <./history.rst>`_
-- `Using the Rank Sequences tool <./rank-sequences.rst>`_
+- `Using the Score Sequences tool <./score-sequences.rst>`_
 - `Using the Generate Sequences tool <./generate-sequences.rst>`_
 - `Using the PoET Substitution Analysis tool <./substitution-analysis.rst>`_
 
@@ -61,7 +61,7 @@ Learn more about what makes PoET state of the art in machine learning for protei
   Ensembling <ensembling>
   Interpreting PoET Results Table <results-table>
   PoET history <history>
-  Rank sequences <rank-sequences>
+  Score sequences <score-sequences>
   Generate sequences <generate-sequences>
   Substitution analysis with PoET <substitution-analysis>
   Indel Analysis <indel-analysis>

source/web-app/poet/results-table.rst

@@ -1,7 +1,7 @@
 Interpreting PoET Results Table
 ================================
 
-This tutorial explains how to interpret the results generated by the PoET Generate Sequences and Rank Sequences tools.
+This tutorial explains how to interpret the results generated by the PoET Generate Sequences and Score Sequences tools.
 
 Results Table
 ^^^^^^^^^^^^^^

source/web-app/poet/rank-sequences.rst → source/web-app/poet/score-sequences.rst

@@ -1,7 +1,7 @@
-Using The Rank Sequences Tool
+Using The Score Sequences Tool
 ===============================
 
-This tutorial teaches you how to assess protein fitness by using the Rank Sequences tool to score your input sequences relative to a prompt. Use this as a starting point for predicting the outcomes of a specific sequence or prioritizing variants for further analysis.
+This tutorial teaches you how to assess protein fitness by using the Score Sequences tool to score your input sequences relative to a prompt. Use this as a starting point for predicting the outcomes of a specific sequence or prioritizing variants for further analysis.
 
 On this page, you will learn how to score sequences to predict fitness and rank variants, then interpret and fine-tune the results.
 
@@ -16,10 +16,10 @@ This tool requires a multiple sequence alignment (MSA), from which it builds a p
 You also need an input sequence, or list of sequences you want to score against the prompt.
 
 
-Rank Your Sequences
--------------------
+Score Your Sequences
+--------------------
 
-Navigate to the tool by opening the **PoET** dropdown menu, then selecting **Rank Sequences.** You can choose the model used to run the job. We recommend using PoET-2 for most use cases.
+Navigate to the tool by opening the **PoET** dropdown menu, then selecting **Score Sequences.** You can choose the model used to run the job. We recommend using PoET-2 for most use cases.
 
 Step 1: Input Sequences
 ^^^^^^^^^^^^^^^^^^^^^^^^
@@ -35,7 +35,7 @@ If you choose to upload a CSV file, please note the following requirements:
 
 You can choose the default structure prediction model to generate the sequence structures after the job completes.
 
-.. image:: /_static/tools/poet/rank-sequences-1.png
+.. image:: /_static/tools/poet/score-sequences-1.png
   :alt: Input Sequences
 
 
@@ -50,7 +50,7 @@ Step 3: Prompt Context
 
 Refer to `Creating a Context <./prompts.rst#creating-a-context>`_ to learn about Prompt Context.
 
-You're ready to rank your sequences! Click **Run.** The job may take a few minutes depending on how busy the service is, how long your sequences are, and how many sequences you want to score.
+You're ready to score your sequences! Click **Run.** The job may take a few minutes depending on how busy the service is, how long your sequences are, and how many sequences you want to score.
 
 A 400 (Bad request) error code may be due to the following: