/bio-immunoinformatics-mhc-binding-prediction

name: bio-immunoinformatics-mhc-binding-prediction
description: Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.
tool_type: python
primary_tool: mhcflurry

Version Compatibility

Reference examples tested with: MHCflurry 2.1+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: `pip show <package>` then `help(module.function)` to check signatures
• CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

MHC Binding Prediction

**"Predict which peptides bind to MHC"** → Predict peptide-MHC class I and II binding affinity using neural network models to identify potential T-cell epitopes from protein sequences.

• Python: `mhcflurry.Class1PresentationPredictor().predict()` for MHC-I
• CLI: `netMHCpan` for alternative MHC-I/II predictions

MHCflurry Setup

**Goal:** Install MHCflurry and download pre-trained prediction models.

**Approach:** Install via pip and fetch model weights for class I pan-allele or specific allele predictions.

# Install MHCflurry
pip install mhcflurry

# Download prediction models
mhcflurry-downloads fetch

# Download models for specific alleles
mhcflurry-downloads fetch models_class1_pan

MHCflurry Python API

**Goal:** Predict peptide-MHC binding affinity and presentation scores for a set of peptides.

**Approach:** Load the Class1PresentationPredictor and call predict() with peptide-allele pairs to obtain IC50, percentile rank, and presentation scores.

from mhcflurry import Class1PresentationPredictor

# Load predictor (includes binding and processing scores)
predictor = Class1PresentationPredictor.load()

# Predict for single allele
result = predictor.predict(
    peptides=['SIINFEKL', 'GILGFVFTL', 'NLVPMVATV'],
    alleles=['HLA-A*02:01', 'HLA-A*02:01', 'HLA-A*02:01']
)

# Result columns:
# - mhcflurry_affinity: Predicted IC50 (nM)
# - mhcflurry_affinity_percentile: Percentile rank
# - mhcflurry_presentation_score: Combined binding + processing

print(result)

Interpret Binding Predictions

**Goal:** Classify peptide-MHC binding strength from predicted IC50 values.

**Approach:** Apply standard affinity thresholds (strong <50nM, moderate <500nM, weak <5000nM) to categorize binding.

def interpret_binding(ic50_nm):
    '''Interpret MHC binding affinity

    IC50 thresholds (commonly used):
    - <50 nM: Strong binder (high confidence epitope)
    - 50-500 nM: Moderate binder (potential epitope)
    - 500-5000 nM: Weak binder (unlikely epitope)
    - >5000 nM: Non-binder

    Percentile rank (recommended):
    - <0.5%: Strong binder
    - 0.5-2%: Moderate binder
    - >2%: Weak/non-binder
    '''
    if ic50_nm < 50:
        return 'strong'
    elif ic50_nm < 500:
        return 'moderate'
    elif ic50_nm < 5000:
        return 'weak'
    else:
        return 'non-binder'

Batch Prediction

**Goal:** Predict binding for all peptide-allele combinations in a batch.

**Approach:** Iterate over peptide-allele pairs, call MHCflurry for each combination, and concatenate results into a single DataFrame.

from mhcflurry import Class1PresentationPredictor
import pandas as pd

def predict_binding_batch(peptides, alleles):
    '''Predict binding for multiple peptides and alleles

    Args:
        peptides: List of peptide sequences
        alleles: List of HLA alleles (4-digit format)

    Returns:
        DataFrame with predictions for all combinations
    '''
    predictor = Class1PresentationPredictor.load()

    # Create all combinations
    results = []
    for peptide in peptides:
        for allele in alleles:
            pred = predictor.predict(
                peptides=[peptide],
                alleles=[allele]
            )
            pred['peptide'] = peptide
            pred['allele'] = allele
            results.append(pred)

    return pd.concat(results, ignore_index=True)


# Example usage
peptides = ['SIINFEKL', 'GILGFVFTL', 'NLVPMVATV', 'YMLDLQPETT']
alleles = ['HLA-A*02:01', 'HLA-A*03:01', 'HLA-B*07:02']

predictions = predict_binding_batch(peptides, alleles)
print(predictions[['peptide', 'allele', 'mhcflurry_affinity', 'mhcflurry_affinity_percentile']])

Scan Protein Sequence

**Goal:** Identify all potential MHC-I epitopes within a protein by scanning overlapping peptide windows.

**Approach:** Generate all k-mers (8-11aa) from the protein, predict binding for each against target alleles, and retain those below the 2% percentile rank cutoff.

def scan_protein_for_epitopes(protein_seq, alleles, peptide_lengths=[8, 9, 10, 11]):
    '''Scan protein for potential MHC epitopes

    MHC-I typically binds 8-11mer peptides
    Most common: 9-mers

    Returns all peptides with predicted binding
    '''
    from mhcflurry import Class1PresentationPredictor

    predictor = Class1PresentationPredictor.load()

    epitopes = []
    for length in peptide_lengths:
        for i in range(len(protein_seq) - length + 1):
            peptide = protein_seq[i:i + length]

            for allele in alleles:
                pred = predictor.predict(peptides=[peptide], alleles=[allele])

                if pred['mhcflurry_affinity_percentile'].values[0] < 2.0:
                    epitopes.append({
                        'peptide': peptide,
                        'position': i + 1,
                        'length': length,
                        'allele': allele,
                        'affinity_nM': pred['mhcflurry_affinity'].values[0],
                        'percentile': pred['mhcflurry_affinity_percentile'].values[0]
                    })

    return pd.DataFrame(e