______   ______   .__   __. .___________..______          ___   .___________.  ______ .______      
 /      | /  __  \  |  \ |  | |           ||   _  \        /   \  |           | /      ||   _  \     
|  ,----'|  |  |  | |   \|  | `---|  |----`|  |_)  |      /  ^  \ `---|  |----`|  ,----'|  |_)  |    
|  |     |  |  |  | |  . `  |     |  |     |      /      /  /_\  \    |  |     |  |     |      /     
|  `----.|  `--'  | |  |\   |     |  |     |  |\  \----./  _____  \   |  |     |  `----.|  |\  \----.
 \______| \______/  |__| \__|     |__|     | _| `._____/__/     \__\  |__|      \______|| _| `._____|

ContraTCR: Predicting TCR-Epitope Binding with Features Generated by Fine-Tuned Protein Language Model and Contrastive Learning

Welcome to ContraTCR, a tool designed for training and predicting T-cell receptor (TCR) and epitope binding using contrastive learning techniques. This guide will walk you through the steps required to run the project, from training the model to making predictions.

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Table of Contents

1. Introduction
2. Project Structure
3. Running the Project
   • Step 1: Train the ESM-2 Model with Projection Head
   • Step 2: Extract Features Using the Trained Model
   • Step 3: Predict Binding Specificity with XGBoost
4. References

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Introduction

ContraTCR leverages contrastive learning to model the interaction between T-cell receptors and epitopes. By training on the PyTDC dataset, it aims to predict binding specificity with high accuracy. This guide provides a step-by-step walkthrough for users to:

• Train the ESM-2 model along with the projection head using PyTDC data.
• Extract features using the trained model.
• Use the extracted features to train an XGBoost model for binding specificity prediction.

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Project Structure

The project consists of several key files and directories:

• run.py: Main script to run different modes (train, extract, predict).
• config/: Contains configuration files (e.g., midterm400_clean.yaml).
• model.py: Defines the ESM-2 model and projection head.
• data.py: Data loading and preprocessing utilities.
• train.py: Training routines for different contrastive modes.
• extract.py: Feature extraction functions.
• xgb.py: Functions for training and evaluating the XGBoost model.
• result/: Default directory where results, logs, and checkpoints are saved.

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Running the Project

The project operates in three primary modes: train, extract, and predict. Below are detailed instructions for each step, including how to incorporate your own data.

Step 1: Train the ESM-2 Model with Projection Head

Description: Train the ESM-2 model along with a projection head.

Command:

!python ./run.py --config_path './config/default/your_config.yaml' --mode train

Instructions:

1. Prepare Your Configuration File:

   • Copy the example configuration file midterm400_clean.yaml and rename it (e.g., your_config.yaml).
   • Open your_config.yaml and update the hyperparameters

2. Run Training:

   • Execute the command in your terminal or Colab notebook, replacing your_config.yaml with the name of your configuration file.
   • This will start the training process using your specified settings.

3. Monitor Training:

   • Training logs will be printed to the console and saved in the log directory specified in your configuration.
   • Model checkpoints will be saved in the checkpoint directory.

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Step 2: Extract Features Using the Trained Model

Description: Utilize the trained ESM-2 model and projection head to extract features from the dataset.

Command:

!python ./run.py --config_path ./config/default/your_config.yaml --resume_path '/path/to/your/model_checkpoint.pth' --mode extract

Instructions:

1. Ensure Model Checkpoint Exists:

   • After training, the model checkpoint should be saved in the checkpoint directory specified in your configuration file.
   • Locate the checkpoint file (e.g., model_triplet.pth or a similarly named file).

2. Run Feature Extraction:

   • Replace /path/to/your/model_checkpoint.pth with the actual path to your model checkpoint.

3. Verify Extraction:

   • The extracted features will be saved to the paths within the result directory.
   • Ensure that the feature files are generated successfully.

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Step 3: Predict Binding Specificity with XGBoost

Description: Use the extracted features to train an XGBoost model and perform binding specificity prediction.

Command:

!python ./run.py --config_path './config/default/your_config.yaml' \
--train_feature_path '/path/to/your/feature_data_train.csv' \
--test_feature_path '/path/to/your/feature_data_test.csv' \
--mode predict

Instructions:

1. Locate Extracted Features:

   • Identify where the feature extraction step saved your feature files.
   • Typically, these are named feature_data_train.csv and feature_data_test.csv.

2. Update Paths:

   • Replace /path/to/your/feature_data_train.csv and /path/to/your/feature_data_test.csv with the actual paths to your feature files.

3. Run Prediction:

   • Execute the command to start training the XGBoost model and perform predictions.
   • The script will output performance metrics such as precision, recall, F1 score, and ROC AUC.

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References

• ESM-2 Model: Evolutionary Scale Modeling (ESM) GitHub Repository
• Contrastive Learning: A Simple Framework for Contrastive Learning of Visual Representations
• XGBoost: XGBoost Documentation