Explore which antigen fragments T-cell receptors may recognize. The skill covers ERGO-II setup, matching receptor sequences against known examples in VDJdb, grouping similar sequences, and summarizing candidate recognition patterns across a receptor collection.
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Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Reference examples tested with: MiXCR 4.6+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Predict which epitopes my TCRs recognize" → Match T-cell receptors to their cognate epitopes using deep learning models for TCR antigen specificity prediction.
# 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')
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']]
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
}
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)
Goal: Group TCRs that likely recognize the same epitope based on CDR3 sequence similarity, enabling specificity group discovery from large repertoire datasets.
Approach: Compute pairwise Levenshtein distances between CDR3 sequences, apply hierarchical clustering with average linkage, and cut the dendrogram at a maximum edit distance threshold to define specificity groups.
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))
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
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