TCR-Epitope Binding
ERGO-II Model
# ERGO-II uses deep learning to predict TCR-epitope binding
# GitHub: https://github.com/IdoSpringer/ERGO-II
def setup_ergo():
'''Setup ERGO-II for TCR-epitope prediction
Requirements:
- PyTorch
- Pre-trained models from ERGO-II repository
ERGO-II features:
- Uses both CDR3 alpha and beta chains
- Incorporates MHC context
- Trained on VDJdb and IEDB data
'''
print('ERGO-II setup:')
print('1. Clone: git clone https://github.com/IdoSpringer/ERGO-II')
print('2. Install: pip install torch pandas scikit-learn')
print('3. Download models from repository')
TCR Input Format
def parse_tcr_data(tcr_file):
'''Parse TCR sequence data
Required columns:
- cdr3_beta: CDR3 beta chain sequence (most informative)
- cdr3_alpha: CDR3 alpha chain (optional, improves accuracy)
- v_beta: V gene usage (optional)
- j_beta: J gene usage (optional)
CDR3 is the primary determinant of antigen specificity.
Alpha chain provides ~20% additional specificity.
'''
import pandas as pd
df = pd.read_csv(tcr_file, sep='\t')
# Validate CDR3 sequences
valid_aa = set('ACDEFGHIKLMNPQRSTVWY')
def is_valid_cdr3(seq):
if pd.isna(seq):
return False
return all(aa in valid_aa for aa in seq.upper())
df['valid_beta'] = df['cdr3_beta'].apply(is_valid_cdr3)
return df[df['valid_beta']]
Predict TCR-Epitope Binding
def predict_binding_simple(cdr3_beta, epitope):
'''Simple TCR-epitope compatibility score
This is a simplified heuristic. For accurate predictions,
use ERGO-II or other deep learning models.
Features considered:
- CDR3 length compatibility
- Amino acid composition
- Hydrophobicity matching
'''
# Length compatibility
# TCRs recognizing similar epitopes often have similar CDR3 lengths
optimal_length = len(epitope) + 5 # Rough heuristic
length_score = 1 - abs(len(cdr3_beta) - optimal_length) / 10
# Charge complementarity
positive = set('RKH')
negative = set('DE')
tcr_charge = sum(1 if aa in positive else -1 if aa in negative else 0
for aa in cdr3_beta)
epitope_charge = sum(1 if aa in positive else -1 if aa in negative else 0
for aa in epitope)
# Opposite charges suggest complementarity
charge_score = 0.5 + (tcr_charge * -epitope_charge) / 20
return {
'cdr3_beta': cdr3_beta,
'epitope': epitope,
'length_score': max(0, min(1, length_score)),
'charge_score': max(0, min(1, charge_score)),
'combined': (length_score + charge_score) / 2
}
Match TCRs to Known Epitopes
def match_to_vdjdb(tcr_sequences, vdjdb_path='vdjdb.tsv'):
'''Match TCRs to known epitopes in VDJdb
VDJdb is a curated database of TCR-epitope pairs.
Download from: https://vdjdb.cdr3.net/
Matching approaches:
- Exact CDR3 match
- Similar CDR3 (edit distance ≤1)
- Cluster-based (group similar TCRs)
'''
import pandas as pd
from difflib import SequenceMatcher
vdjdb = pd.read_csv(vdjdb_path, sep='\t')
matches = []
for tcr in tcr_sequences:
# Exact match
exact = vdjdb[vdjdb['cdr3'] == tcr]
if len(exact) > 0:
matches.append({
'query_tcr': tcr,
'match_type': 'exact',
'epitopes': exact['antigen.epitope'].tolist(),
'species': exact['antigen.species'].tolist()
})
continue
# Fuzzy match (1 mismatch)
for _, row in vdjdb.iterrows():
similarity = SequenceMatcher(None, tcr, row['cdr3']).ratio()
if similarity > 0.9: # >90% similar
matches.append({
'query_tcr': tcr,
'match_type': 'similar',
'similarity': similarity,
'db_tcr': row['cdr3'],
'epitope': row['antigen.epitope'],
'species': row['antigen.species']
})
return pd.DataFrame(matches)
TCR Clustering
def cluster_tcrs_by_specificity(tcr_sequences, method='levenshtein'):
'''Cluster TCRs likely to share specificity
TCRs recognizing the same epitope often have:
- Similar CDR3 length
- Shared motifs
- Similar V gene usage
Methods:
- levenshtein: Edit distance clustering
- tcrdist: TCRdist3 distance metric
- deep: Deep learning embeddings
'''
from scipy.cluster.hierarchy import linkage, fcluster
from scipy.spatial.distance import pdist, squareform
import numpy as np
def levenshtein_distance(s1, s2):
if len(s1) < len(s2):
return levenshtein_distance(s2, s1)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
# Calculate pairwise distances
n = len(tcr_sequences)
distances = np.zeros((n, n))
for i in range(n):
for j in range(i + 1, n):
d = levenshtein_distance(tcr_sequences[i], tcr_sequences[j])
distances[i, j] = distances[j, i] = d
# Cluster
condensed = squareform(distances)
Z = linkage(condensed, method='average')
clusters = fcluster(Z, t=3, criterion='distance') # Max 3 edits
return dict(zip(tcr_sequences, clusters))
Analyze Repertoire Specificity
def analyze_repertoire_specificity(tcr_df, epitope_db):
'''Analyze antigen specificity of TCR repertoire
Reports:
- Fraction matching known epitopes
- Epitope diversity
- Potential public TCRs (shared across individuals)
'''
results = {
'total_tcrs': len(tcr_df),
'unique_cdr3': tcr_df['cdr3_beta'].nunique(),
'matched_epitopes': 0,
'epitope_distribution': {}
}
# Match to database
matched = match_to_vdjdb(tcr_df['cdr3_beta'].unique(), epitope_db)
if len(matched) > 0:
results['matched_epitopes'] = len(matched['query_tcr'].unique())
results['epitope_distribution'] = matched['epitope'].value_counts().to_dict()
return results
Related Skills
- tcr-bcr-analysis/mixcr-analysis - TCR repertoire sequencing analysis
- immunoinformatics/mhc-binding-prediction - Epitope context
- single-cell/clustering - Single-cell TCR analysis
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