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
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Immunogenicity Scoring
Multi-Factor Scoring
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['clonality'] = vaf
# 6. Self-similarity (lower = better, less tolerance)
self_sim = peptide_data.get('self_similarity', 0.5)
scores['foreignness'] = 1 - self_sim
# Weighted combination
weights = {
'binding': 0.25,
'agretopicity': 0.20,
'processing': 0.10,
'expression': 0.15,
'clonality': 0.15,
'foreignness': 0.15
}
total = sum(scores[k] * weights[k] for k in weights)
return total, scores
Processing Prediction
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
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
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
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
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
By