bio-immunoinformatics-immunogenicity-scoring

name	bio-immunoinformatics-immunogenicity-scoring
description	Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.
tool_type	python
primary_tool	mhcflurry

Version Compatibility

Reference examples tested with: MHCflurry 2.1+, numpy 1.26+, 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

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

Immunogenicity Scoring

"Rank my neoantigen candidates by immunogenicity" → Score and prioritize epitopes using multi-factor models combining MHC binding, proteasomal processing, expression level, and sequence foreignness for vaccine candidate selection.

• Python: mhcflurry for binding + processing predictions, custom scoring pipeline

Multi-Factor Scoring

Goal: Calculate a composite immunogenicity score from multiple weighted factors (binding, agretopicity, processing, expression, clonality, foreignness).

Approach: Score each factor on a 0-1 scale, then combine via weighted sum with domain-informed weights.

import pandas as pd
import numpy as np

def calculate_immunogenicity_score(peptide_data):
    '''Calculate composite immunogenicity score

    Factors considered:
    1. MHC binding affinity (IC50)
    2. Agretopicity (MT vs WT binding ratio)
    3. Proteasomal processing
    4. TAP transport
    5. Expression level
    6. Clonality (VAF for neoantigens)
    7. Self-similarity (avoid tolerance)

    Each factor scored 0-1, then weighted and combined.
    '''
    scores = {}

    # 1. Binding affinity (lower IC50 = better)
    # Transform to 0-1: 1 at 0nM, 0 at 5000nM
    ic50 = peptide_data.get('ic50_nM', 500)
    scores['binding'] = 1 - min(ic50 / 5000, 1)

    # 2. Agretopicity (MT binds better than WT)
    # Ratio of WT/MT IC50, capped at 10
    agretopicity = peptide_data.get('agretopicity', 1.0)
    scores['agretopicity'] = min(agretopicity / 10, 1)

    # 3. Processing score (from MHCflurry)
    processing = peptide_data.get('processing_score', 0.5)
    scores['processing'] = processing

    # 4. Expression (log scale, capped)
    expression = peptide_data.get('expression_tpm', 10)
    scores['expression'] = min(np.log10(expression + 1) / 3, 1)

    # 5. Clonality (for neoantigens)
    vaf = peptide_data.get('vaf', 0.5)
    scores[] = vaf

    
    self_sim = peptide_data.get(, )
    scores[] =  - self_sim

    
    weights = {
        : ,
        : ,
        : ,
        : ,
        : ,
        : 
    }

    total = (scores[k] * weights[k]  k  weights)

     total, scores

Processing Prediction

Goal: Predict proteasomal cleavage and TAP transport probability for candidate peptides.

Approach: Use MHCflurry's Class1ProcessingPredictor to score peptide processing likelihood.

from mhcflurry import Class1ProcessingPredictor

def predict_processing_score(peptides):
    '''Predict proteasomal cleavage and TAP transport

    Processing score reflects probability that peptide will be:
    1. Cleaved from protein by proteasome
    2. Transported by TAP into ER
    3. Loaded onto MHC

    Higher processing score = more likely to be presented
    '''
    predictor = Class1ProcessingPredictor.load()

    results = []
    for peptide in peptides:
        # Need surrounding sequence context for processing
        # In practice, extract from protein context
        pred = predictor.predict(peptides=[peptide])
        results.append({
            'peptide': peptide,
            'processing_score': pred['processing_score'].values[0]
        })

    return pd.DataFrame(results)

Self-Similarity Assessment

Goal: Determine whether a candidate peptide resembles self-peptides, indicating potential T-cell tolerance.

Approach: Compute pairwise sequence identity against a proteome peptide set and flag high-similarity matches.

def calculate_self_similarity(peptide, proteome_peptides, threshold=0.8):
    '''Check if peptide is similar to self-peptides

    High similarity to self-peptides suggests:
    - T-cells may be tolerized (deleted during development)
    - Lower likelihood of immune response

    Threshold 0.8 = 80% identity considered "self-like"
    '''
    def sequence_identity(seq1, seq2):
        if len(seq1) != len(seq2):
            return 0
        matches = sum(1 for a, b in zip(seq1, seq2) if a == b)
        return matches / len(seq1)

    max_similarity = 0
    most_similar = None

    for self_peptide in proteome_peptides:
        sim = sequence_identity(peptide, self_peptide)
        if sim > max_similarity:
            max_similarity = sim
            most_similar = self_peptide

    return {
        'similarity': max_similarity,
        'is_self_like': max_similarity >= threshold,
        'closest_self': most_similar
    }

Hydrophobicity at Position 2

Goal: Assess MHC anchor residue quality by checking hydrophobicity at key positions.

Approach: Check whether position 2 and C-terminal residues fall within the hydrophobic amino acid set preferred by HLA-A*02:01-like alleles.

def check_anchor_hydrophobicity(peptide):
    '''Check hydrophobicity at MHC anchor positions

    For HLA-A*02:01 and similar alleles:
    - Position 2: Prefers hydrophobic (L, I, V, M)
    - Position 9 (C-terminus): Prefers hydrophobic (L, V, I)

    Strong anchors improve binding stability.
    '''
    hydrophobic = set('LIVMFYW')

    pos2 = peptide[1] if len(peptide) > 1 else ''
    pos_last = peptide[-1]

    return {
        'pos2_hydrophobic': pos2 in hydrophobic,
        'pos_last_hydrophobic': pos_last in hydrophobic,
        'anchor_score': (pos2 in hydrophobic) + (pos_last in hydrophobic)
    }

Rank Epitopes

Goal: Rank epitopes by composite immunogenicity score and assign confidence tiers for prioritization.

Approach: Score all candidates, sort by immunogenicity, and assign high/medium/low tiers based on percentile ranking.

def rank_epitopes(epitope_df, top_n=20):
    '''Rank epitopes by immunogenicity

    Returns top candidates with scores and confidence tiers.

    Confidence tiers:
    - High: Top 5%, all factors favorable
    - Medium: Top 20%, most factors favorable
    - Low: Remaining, some factors favorable
    '''
    epitope_df = epitope_df.copy()

    # Calculate scores
    scores = []
    factor_scores = []
    for _, row in epitope_df.iterrows():
        total, factors = calculate_immunogenicity_score(row.to_dict())
        scores.append(total)
        factor_scores.append(factors)

    epitope_df['immunogenicity_score'] = scores
    factor_df = pd.DataFrame(factor_scores)

    # Combine
    result = pd.concat([epitope_df, factor_df], axis=1)

    # Rank
    result = result.sort_values('immunogenicity_score', ascending=False)

    # Assign tiers
    n = len(result)
    result['tier'] = 'low'
    result.iloc[:int(n * 0.20), result.columns.get_loc('tier')] = 'medium'
    result.iloc[:int(n * 0.05), result.columns.get_loc('tier')] = 'high'

    return result.head(top_n)

Compare Candidates

Goal: Select a diverse set of vaccine candidates with broad HLA coverage and non-overlapping positions.

Approach: Iterate through ranked candidates, selecting those with non-overlapping genomic positions to maximize epitope diversity.

def compare_vaccine_candidates(candidates_df):
    '''Compare and select vaccine candidates

    Vaccine design typically selects:
    - Multiple epitopes (5-20)
    - Diverse HLA coverage
    - High immunogenicity scores
    - Non-overlapping sequences
    '''
    # Group by HLA coverage
    hla_coverage = candidates_df.groupby('allele').size()

    # Select diverse set
    selected = []
    used_positions = set()

    for _, candidate in candidates_df.iterrows():
        # Check for overlap with selected
        pos = candidate.get('position', 0)
        if not any(abs(pos - p) < 5 for p in used_positions):
            selected.append(candidate)
            used_positions.add(pos)

        if len(selected) >= 20:
            break

    return pd.DataFrame(selected)

Related Skills

• immunoinformatics/mhc-binding-prediction - Binding affinity component
• immunoinformatics/neoantigen-prediction - Input candidates
• immunoinformatics/epitope-prediction - Epitope identification