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TCR-Epitope Binding - Usage Guide

Overview

Predict TCR-epitope specificity using deep learning models and database matching to identify antigen-reactive T-cells.

Prerequisites

pip install pandas torch scikit-learn
# ERGO-II: git clone https://github.com/IdoSpringer/ERGO-II

Quick Start

Tell your AI agent what you want to do:

  • "Predict what antigens this TCR sequence recognizes"
  • "Match my TCRs to known epitopes in VDJdb"
  • "Cluster TCRs that likely share specificity"

Example Prompts

Specificity Prediction

"What epitopes might this CDR3 beta sequence recognize?"

"Predict binding between my TCRs and these candidate epitopes"

Database Matching

"Find matches for my TCRs in VDJdb"

"Identify TCRs recognizing viral epitopes"

Repertoire Analysis

"What fraction of my repertoire recognizes known antigens?"

"Cluster TCRs by predicted specificity"

What the Agent Will Do

  1. Parse TCR sequence data (CDR3 alpha/beta)
  2. Match against VDJdb or other databases
  3. Run ERGO-II prediction if available
  4. Cluster similar TCRs
  5. Report potential epitope specificities

Tips

  • CDR3 beta - Most informative for specificity; alpha adds ~20%
  • VDJdb - Curated database of known TCR-epitope pairs
  • Clustering - TCRs within 1-3 edit distance often share specificity
  • ERGO-II - Deep learning provides better predictions than simple matching
  • Validation - Predicted specificities should be validated experimentally