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MHC Binding Prediction - Usage Guide

Overview

Predict peptide-MHC binding affinity using neural network models to identify potential T-cell epitopes.

Prerequisites

pip install mhcflurry
mhcflurry-downloads fetch

Quick Start

Tell your AI agent what you want to do:

  • "Predict MHC binding for these peptides with HLA-A*02:01"
  • "Scan this protein for potential epitopes"
  • "Find strong binders for my patient's HLA type"

Example Prompts

Single Prediction

"What is the binding affinity of SIINFEKL to HLA-A*02:01?"

"Is this peptide a strong MHC binder?"

Protein Scanning

"Find all 9-mer epitopes in this spike protein"

"Scan my antigen for epitopes binding common HLA-A alleles"

Multiple Alleles

"Predict binding for these peptides against all common HLA types"

"Which of my patient's HLA alleles bind this peptide best?"

What the Agent Will Do

  1. Load MHCflurry prediction model
  2. Accept peptide sequences and HLA alleles
  3. Predict binding affinity (IC50) and percentile rank
  4. Classify as strong/moderate/weak binder
  5. Return ranked results

Tips

  • Peptide length - MHC-I: 8-11aa (most common 9aa); MHC-II: 13-25aa
  • Threshold - IC50 <500nM or percentile <2% for binders
  • Patient-specific - Use actual HLA typing for personalized predictions
  • Presentation score - Includes processing; more biologically relevant
  • Population coverage - 5-6 common alleles cover ~85% of population