Bio Immunoinformatics Tcr Epitope Binding

Version Compatibility

Reference examples tested with: tcrdist3 0.2+, pandas 2.2+, scipy 1.12+

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.

Notes specific to this skill: tcrdist3 expects IMGT-style columns (cdr3_b_aa, v_b_gene, j_b_gene, and the _a_ analogs, plus count). Tools disagree on whether CDR3 keeps the leading Cys / trailing Phe-Trp — a common silent input mismatch. Supervised predictors (ERGO-II, NetTCR, pMTnet) are separate repos with pretrained weights; their reported AUCs depend heavily on the train/test split and negative-sampling scheme. Re-verify before trusting any number.

TCR-Epitope Binding

"What antigen does this TCR recognize / which TCRs share specificity?" -> Annotate specificity by clustering + database lookup; predict de-novo binding only as a validation-bound hypothesis.

• Python: tcrdist3 (TCRrep distance + meta-clonotypes), GLIPH2, clusTCR, GIANA for clustering
• Python: ERGO-II / NetTCR-2.x / pMTnet for supervised scoring (caveated); VDJdb/IEDB/McPAS-TCR for lookup

The Single Most Important Modern Insight -- general prediction for unseen epitopes does not work; clustering does

Every supervised TCR-epitope predictor performs respectably on epitopes seen in training and collapses to near-random on epitopes it has never seen (Grazioli 2022; IMMREP22, Meysman 2023 across 23 models). The cause is the data, not the architecture: the labeled TCR-pMHC universe is dominated by a few immunodominant epitopes (NLVPMVATV/CMV, GILGFVFTL/influenza M1, SARS-CoV-2 spike), so a model learns "is this an anti-CMV TCR" rather than the rules of TCR-peptide docking. Compounding this, there is no true negative set — experiments report binders, and absence of a measured non-binder is not non-binding — so every supervised model manufactures negatives, and that choice dominates the reported metric more than the architecture (Dens 2023). The honest, defensible task is unsupervised specificity clustering: "these TCRs are sequence-similar enough to likely share a specificity," a discovery statement used within one dataset and propagated by guilt-by-association to a known member. Clustering is honest because it never extrapolates into unseen-epitope space; per-pair prediction is dishonest when it pretends to. Route the user to the honest task and refuse to let a supervised per-pair probability substitute for a tetramer.

Tool Taxonomy

Reference Databases (training set AND lookup table)

Decision Tree by Scenario

Cluster TCRs by Specificity (tcrdist3)

Goal: Group TCRs likely to share an antigen, for discovery within one cohort.

Approach: Build a TCRrep (which computes the position-weighted multi-loop distance, 3x on CDR3), then cluster the pairwise matrix and annotate clusters containing a known-specificity member. Keep HLA as an explicit covariate — the same CDR3 on a different allele is a different specificity.

from tcrdist.repertoire import TCRrep
from scipy.cluster.hierarchy import linkage, fcluster
from scipy.spatial.distance import squareform

def cluster_tcrs(df, max_dist=50):
    '''df needs IMGT columns: cdr3_b_aa, v_b_gene, j_b_gene (+ _a_ analogs), count.
    Returns cluster labels; annotate clusters that contain a database/tetramer hit.'''
    tr = TCRrep(cell_df=df, organism='human', chains=['beta'])
    condensed = squareform(tr.pw_beta, checks=False)
    return fcluster(linkage(condensed, method='average'), t=max_dist, criterion='distance')

Annotate by Database Lookup

Goal: Assign specificity to TCRs that match known TCR-pMHC pairs.

Approach: Match exactly or near-exactly against VDJdb/IEDB/McPAS, filtering VDJdb on its confidence score, and report the database hit and HLA restriction driving each annotation — not a per-pair probability dressed as certainty.

import pandas as pd

def lookup_vdjdb(query_cdr3b, vdjdb, min_confidence=1):
    '''Exact CDR3b match against a confidence-filtered VDJdb. Near-matches (edit
    distance 1) belong to the clustering route, not a binding claim.'''
    db = vdjdb[vdjdb['vdjdb.score'] >= min_confidence]
    hits = db[db['cdr3'].isin(set(query_cdr3b))]
    return hits[['cdr3', 'antigen.epitope', 'antigen.species', 'mhc.a']]

Per-Method Failure Modes

Unseen-epitope collapse

Trigger: using a supervised model on an epitope absent from training. Mechanism: models learn a few well-sampled specificities, not docking rules. Symptom: great benchmark AUC, near-random on novel epitopes. Fix: route de-novo questions to ranking-plus-validation; never report a confident per-pair call.

Negative-sampling artifact

Trigger: trusting a headline AUC. Mechanism: manufactured negatives (shuffled or random-TCR) create artificial separability; repeated-negative leakage lets the model count TCR frequency. Symptom: AUC > 0.85 with no discussion of negatives/splits. Fix: read the negative-sampling sentence first; require epitope-disjoint evaluation.

CDR3β-only ceiling

Trigger: strong claims from a β-only model. Mechanism: alpha chain and V/J carry heavy signal; bulk β-only is information-poor. Symptom: big AUC from the least informative input (i.e. from artifacts). Fix: prefer paired-chain data; add V/J; discount β-only headline numbers.

Clustering confounds (HLA + background)

Trigger: pooling multi-donor repertoires and clustering naively. Mechanism: same CDR3 on different HLA is a different specificity; motif enrichment depends on the reference background. Symptom: merged unrelated TCRs; spurious "enriched" motifs. Fix: cluster within a coherent cohort, keep HLA as a covariate, match the background repertoire.

Metrics that lie

Trigger: a single global or per-epitope-averaged AUC. Mechanism: averaging over seen epitopes hides the novel-epitope collapse. Symptom: one trustworthy-looking number, no split description. Fix: demand epitope-disjoint (TPP-II/III) splits reported per-epitope with a peptide-distance decay analysis.

Quantitative Thresholds

Common Errors

References

• Dash P, Fiore-Gartland AJ, Hertz T, et al. 2017. Quantifiable predictive features define epitope-specific T cell receptor repertoires (TCRdist). Nature 547(7661):89-93.
• Mayer-Blackwell K, Schattgen S, Cohen-Lavi L, et al. 2021. TCR meta-clonotypes for biomarker discovery with tcrdist3. eLife 10:e68605.
• Glanville J, Huang H, Nau A, et al. 2017. Identifying specificity groups in the T cell receptor repertoire (GLIPH). Nature 547(7661):94-98.
• Huang H, Wang C, Rubelt F, Scriba TJ, Davis MM. 2020. Analyzing the M. tuberculosis immune response by T-cell receptor clustering with GLIPH2. Nature Biotechnology 38:1194-1202.
• Valkiers S, Van Houcke M, Laukens K, Meysman P. 2021. clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences. Bioinformatics 37(24):4865-4867.
• Zhang H, Zhan X, Li B. 2021. GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation. Nature Communications 12:4699.
• Springer I, Tickotsky N, Louzoun Y. 2021. Contribution of T cell receptor alpha and beta CDR3, MHC typing, V and J genes to peptide binding prediction (ERGO-II). Frontiers in Immunology 12:664514.
• Montemurro A, Schuster V, Povlsen HR, et al. 2021. NetTCR-2.0 enables accurate prediction of TCR-peptide binding using paired TCRα and β sequence data. Communications Biology 4:1060.
• Lu T, Zhang Z, Zhu J, et al. 2021. Deep learning-based prediction of the T cell receptor-antigen binding specificity (pMTnet). Nature Machine Intelligence 3:864-875.
• Meysman P, Barton J, Bravi B, et al. 2023. Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report. ImmunoInformatics 9:100024.
• Grazioli F, Mösch A, Machart P, et al. 2022. On TCR binding predictors failing to generalize to unseen peptides. Frontiers in Immunology 13:1014256.
• Dens C, Bittremieux W, Affaticati F, Laukens K, Meysman P. 2023. The pitfalls of negative data bias for the T-cell epitope specificity challenge. Nature Machine Intelligence 5:1063-1065.
• Shugay M, Bagaev DV, Zvyagin IV, et al. 2018. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Research 46(D1):D419-D427.
• Bradley P. 2023. Structure-based prediction of T cell receptor:peptide-MHC interactions (TCRdock). eLife 12:e82813.

Related Skills

• immunoinformatics/mhc-binding-prediction - the pMHC context a TCR recognizes (HLA restriction)
• immunoinformatics/neoantigen-prediction - de-novo neoantigen TCRs are the unseen-epitope case where prediction fails
• tcr-bcr-analysis/mixcr-analysis - upstream TCR repertoire extraction from sequencing
• single-cell/clustering - paired single-cell TCR data and embedding-based grouping
• workflows/tcr-pipeline - end-to-end repertoire processing