tcren

tcren — structure-based prediction of TCR–epitope recognition

python

TCRen predicts which epitopes a T-cell receptor recognises from a single TCR–peptide–MHC structure (experimental or modelled). It extracts the TCR–peptide contact map and scores every candidate peptide with a residue-level statistical potential derived from contact preferences in TCR:pMHC crystal structures — answering not "what fancy complex can a model draw?" but "is this binding physically plausible?".

This is a documented, tested, CLI-driven Python library. TCR chains are annotated with the sibling arda; MHC chains are mapped and the groove partitioned against a curated reference; structures are oriented into one canonical frame; and the original contact maps, potential, and scores are reproduced numerically (validated against committed oracles to floating-point precision).

While the original tcren focused on TCR:peptide contacts, the new version brings in features to score TCR:MHC and peptide:MHC interactions, required to get full picture of TCR:pMHC binding mechanics and estimate ddG values.

What it does

From one TCR–peptide–MHC structure (crystal or model), each task is one command or one call:

task	command	library
Score candidate epitopes for a TCR	`tcren score`	`score_peptides`
Percentile-rank a peptide vs background	`tcren rank`	`percentile_rank`
ΔΔG of mutations (alanine scan / neoantigen)	`tcren ddg`	`alanine_scan`, `neoantigen_ddg`
Binder vs non-binder for a TCR model	`tcren binder`	`binder_score`
All interface descriptors + joint P(real)	`tcren recognize`	`recognition_features`, `real_probability`
Three-interface energy breakdown + total	`tcren pipeline`	`run_pipeline`
Annotate chains + region markup	`tcren annotate`	`classify_chains`, `annotate_mhc`
Interface contact table (5/8/12 Å)	`tcren contacts`	`ContactMap`, `multi_contacts`
Orient into the canonical MHC frame	`tcren superimpose` / `orient`	`superimpose`, `canonicalize_structure`
Graft a TCR onto another pMHC (chimera)	`tcren substitute-tcr`	`substitute_tcr`
Wrong-TCR decoy set (recognition negatives)	`tcren shuffle`	`make_decoys`, `graft_tcr`
Substitute a peptide + refine its pose	`tcren refine`	`substitute_peptide`, `refine_peptide`
DOPE interface energy (ΔΔG `e_native`)	`tcren energy`	`interface_energy`
Interface mechanics — koff proxies (stiffness / rupture)	`tcren mechanics`	`stiffness_tensor`, `rupture`, `coupling_residues`
Re-derive the statistical potential	`tcren derive-potential`	`derive_tcren`
Steric-clash / wrong-register QC	—	`interface_clashes`, `check_register`
2D complementarity map + 3D pocket/CDR view	—	`render_complementarity_map`, `view_pocket_cdr`

Scope — ranking, not affinity. TCRen ranks peptide/TCR specificity for a given receptor (and the ddg matrix is a fast triage, not a free energy). It is not an affinity model: on the ATLAS SPR benchmark neither the raw contact energy nor its poly-alanine difference predicts Kd/ΔG/koff/kon (|ρ|≤0.3). The one affinity-adjacent quantity a structure predicts is the off-rate koff, via interface mechanics (tcren mechanics) — not the contact sum.

Install

pip install tcren          # from PyPI — binary wheels ship the C++ extension; pulls in arda-mapper

For development (editable install, conda env with the build toolchain, and the reference data fetched into data/):

bash setup.sh              # creates the `tcren` conda env, installs arda + tcren, fetches data/
conda activate tcren

tcren ships five small pybind11/C++ extensions, built on install by scikit-build-core: tcren._align (MHC-pseudosequence fitting alignment; a Biopython fallback runs if unbuilt), tcren._refine (DOPE atom-level Monte-Carlo peptide refinement), tcren._relax (DOPE interface energy for tcren energy / ΔΔG), tcren._fold (CCD loop closure) and tcren._geom (interface geometry for tcren binder). TCR annotation is provided by arda, a runtime dependency published to PyPI as arda-mapper (it imports as arda); pip/setup.sh pull it automatically, and from arda-mapper >= 2.0.3 it auto-fetches its own reference on first use (no ARDA_HOME to set). setup.sh also runs tcren fetch-data to populate data/ with the reference structure sets (Native2026, Canonical2026) used by orient/superimpose (set TCREN_NO_FETCH=1 to skip).

Command line

# Full pipeline: annotate -> superimpose -> resmarkup / canonical Cα / contacts -> per-interface
# energies (TCRen for TCR↔peptide, MJ for TCR↔MHC and peptide↔MHC) + total
tcren pipeline -s complex.pdb -o scores.csv

# Configurable per-interface potential: swap a bundled name (tcren|mj|keskin), a CSV, or
# None for any interface; default reproduces the built-in per-interface families exactly.
tcren pipeline -s complex.pdb -o scores.csv --tcr-mhc-potential keskin

# Opt-in TCR framework regions: --regions {all,cdr,cdr+fr} chooses which TCR regions
# contribute on the TCR side (cdr = CDR1-3 only; cdr+fr adds FR1-3; all = unfiltered, default).
tcren score -s complex.pdb -c candidates.txt -o ranked.csv --regions cdr+fr

# Percentile-rank the native (or candidate) peptide's TCRen energy against a random pMHC
# background — small rank_pct = the peptide scores among the best binders.
tcren rank -s complex.pdb -o rank.csv

# Fast ΔΔG of peptide point mutations (virtual-matrix path: no atoms move, no re-docking).
tcren ddg -s complex.pdb -o ddg.csv

# Binder vs non-binder P(binder) from AF-orthogonal interface geometry + the CDR1/2-vs-CDR3a
# TCRen term — ranks candidate TCRs against a fixed pMHC, beating AlphaFold/TCRmodel2 confidence
# (denoised AUC 0.928 vs 0.872) with no external tool. See tcren.binder.binder_score.
tcren binder -s complex.pdb -o binder.csv

# One TSV per structure: every interface descriptor (geometry + energies) + joint P(real).
tcren recognize -s my_pdbs/ -o recognize.tsv          # descriptors + p_real + p_real_bn, one row/PDB

# End-to-end candidate-epitope scoring from a structure
tcren score -s complex.pdb -c candidates.txt -o ranked.csv

# Wrong-TCR decoys: keep each ORIENTED complex's pMHC, graft on 10 other complexes' TCRs (within
# MHC class, no real pairing). Real-vs-decoy trains a label-free TCR-recognition classifier.
tcren orient -s natives/ -o oriented/          # inputs must share the canonical MHC frame
tcren shuffle -s oriented/ -o shuffled/ --n 10

# Substitute a peptide and refine its pose (knowledge-based MC scored by the DOPE atom-level
# statistical potential — independent of the TCRen/MJ scoring potentials, restrained to the input).
# Not physics relaxation — use Rosetta FlexPepDock for that.
tcren refine -s complex.pdb -o refined/ --substitute KQWLVWLFL

# Structures: any of .pdb / .cif / .pdb.gz / .cif.gz, a directory, or a .tar.gz batch
tcren contacts -s batch.tar.gz -o contacts.csv --interface tcr_peptide

# Per-residue markup: TCR (CDR/FR) + MHC groove (helix/floor) + peptide in one table.
# --regions all|tcr|mhc|peptide filters; --pseudo also marks NetMHCpan groove residues (MPS).
tcren annotate -s complex.cif.gz -o markup.csv --regions mhc --pseudo

# Superimpose structure(s) onto the canonical frame, by MHC, against the canonical database
# (data/Canonical2026, fetched at install). Detects MHC class + species and averages the
# superposition over every database structure of that class/species. Chains -> A=Vα B=Vβ
# C=peptide D=MHCα E=MHCβ/β2m. -s takes a file / directory / .tar.gz / glob; -o is a directory,
# or a single structure file (one input) whose extension must match --mmCIF/--compress; -t threads.
tcren superimpose -s complex.pdb -o oriented.pdb           # single file
tcren superimpose -s 'data/*.pdb' -o oriented/ -t 8        # glob -> directory, threaded

# Build a canonical database from native complexes (how Canonical2026 is produced). Annotation
# is one batched mmseqs call; -t threads only the structural alignment + write.
tcren orient -s data/Native2026 -o data/Canonical2026 -t 8

# Structure outputs are plain .pdb by default; add --mmCIF for .cif and --compress for .gz.
tcren superimpose -s complex.pdb -o oriented/ --mmCIF --compress   # -> oriented/<id>.cif.gz

# Fetch recent TCR-pMHC structures from RCSB -> data/pdb_recent (mmCIF .cif.gz, 5-chain validated)
tcren fetch-recent --discover --after 2024-01-01

# Build the MHC reference once (IMGT/HLA + mouse H-2; cached, not committed)
tcren build-mhc-ref

tcren info
tcren --install-completion        # shell tab-completion (bash/zsh/fish)

tcren orient and tcren superimpose need the reference sets in data/ (Native2026, Canonical2026); setup.sh fetches them at install via tcren fetch-data (re-run it any time).

One table per structure: descriptors, energies & the joint recognizer

Give tcren recognize a list of complexes (a file, directory, .tar.gz, or glob) and it writes one TSV row per structure with the full interface descriptor set and the joint recognition probability P(real):

tcren recognize -s my_pdbs/ -o recognize.tsv               # 35 descriptors + p_real + p_real_bn
tcren recognize -s my_pdbs/ -o feats.tsv --features-only   # descriptors only, skip the models

what you want	columns in `recognize.tsv`
(a) energy — TCRen/MJ `F` per interface + poly-alanine `dF` + loop parts	`F_tcr_pep`, `F_tcr_mhc`, `F_pep_mhc`, `dF_tcr_pep`, `dF_pep_mhc`, `e_cdr12`, `e_cdr3a`, `e_cdr3b`, `e_tcr_mhc`
(b) geometry — every docking + interface descriptor	`pitch`, `crossing`, `dock_d`, `dock_torsion`, `dock_{tcr,mhc}_u{y,z}`, `extent`, `chain_balance`, `burial`, `n_contacts_{tp,tm}`, `n_pep_contacted`, `ct_{tp,tm}_*`
(d) joint P(real) ~ Bayesian model over energy + geometry	`p_real` — distribution-aware Bayesian logistic (5-fold CV AUC 0.885); `p_real_bn` — the Gaussian BN variant

Where the joint model lives. p_real is the frozen recognizer we derive from real crystals vs wrong-TCR shuffled decoys: code in tcren.recognition (recognition_features → real_probability), coefficients shipped in src/tcren/data/shuffle_logistic.json.gz, and the full derivation (PyMC fit, encoding, ROC/PR, posterior forest) in the appendix appendix/logistic_stan/. Decoys come from tcren shuffle; the Gaussian-BN companion is appendix/shuffle_bn/.

(c) physics of the interaction is heavier and mutation-/energy-specific, so it stays in its own commands on the same inputs:

tcren ddg       -s complex.pdb -o ddg.csv     # per-residue alanine / neoantigen ΔΔF (fast virtual matrix)
tcren mechanics -s complex.pdb -o mech.csv    # koff proxies: interface stiffness tensor + steered rupture

(Per the affinity scope caveat above, structures predict the off-rate koff via tcren mechanics, not Kd/ΔG/kon.) From Python:

from tcren.recognition import recognition_features, real_probability
feats = recognition_features("complex.pdb")    # dict of the 35 descriptors (RECOGNITION_FEATURES)
p = real_probability(feats)                     # {"logistic": P(real), "bn": P(real)}

Library

from tcren import run_pipeline, parse_structure, import_structure, ContactMap, score_peptides
from tcren.annotation import classify_chains
from tcren.potential import tcren

# One call: annotate -> superimpose -> contacts -> per-interface energies + total
res = run_pipeline("complex.pdb")              # res.scores, res.markup, res.contacts, res.oriented

# Oracle facade: one structure -> a bundle of ready-to-tabulate frames for the paper
# notebooks (scores, percentile rank, ΔΔG alanine scan, markup, contacts). Configurable
# per-interface potentials and TCR-region selection are forwarded to every milestone.
from tcren import summarize_structure
bundle = summarize_structure("complex.pdb", alanine=True)   # bundle["scores"], ["rank"], ["ddg"], …

# …or the individual steps:
s = parse_structure("complex.pdb.gz")          # also .cif/.cif.gz; import_structure trims the C-gene
classify_chains(s, organism="human")           # TRA/TRB via arda, peptide, MHC
cm = ContactMap.from_structure(s)              # 5 Å contacts + interface partitioning
ranked = score_peptides(cm, ["KQWLVWLFL", "RLLHPHHPL"], tcren())

Batch inputs, gzip, archives

from tcren.structure import iter_structures
for pdb_id, structure in iter_structures("batch.tar.gz"):   # file | directory | .tar.gz
    classify_chains(structure, organism="human")
    ...

Canonical orientation, contacts, docking geometry

from tcren.mhc import annotate_mhc
from tcren.orient import canonicalize_structure, superimpose, docking_angles
from tcren.contacts import multi_contacts, ContactDefinition

annotate_mhc(s)
oriented, info = canonicalize_structure(s)     # frame: z=MHC→TCR, y=peptide, x=thin; chains A–E
oriented, info = superimpose(s)                # orient onto data/Canonical2026 by MHC (class+species ensemble)
layers = multi_contacts(s, ContactDefinition(d1=5, d2=8, d3=12))   # heavy-atom / Cβ / Cα
d = docking_angles(s)                          # crossing (~20–70° αβ) + incident angle

2D complementarity maps & region-pair contacts

from tcren.project2d import (project_structure, residue_markup_table, contacts_table,
                             region_pair_summary)
from tcren.viz import render_complementarity_map, view_pocket_cdr

proj = project_structure(s)                                   # canonical groove plane
svg  = render_complementarity_map(residue_markup_table(s, proj),
                                  contacts=contacts_table(s, threshold=5.0))
region_pair_summary(s, kind="closest")        # contacts per region pair + bond types (cb/ca too)
view_pocket_cdr(s).show()                      # interactive 3D pocket + CDR overlay (py3Dmol)

Modules

module	what it does
`tcren.structure`	parse/write `.pdb`/`.cif`(`.gz`)/`.tar.gz`; the `Atom`/`Residue`/`Chain`/`Structure` model; `iter_structures`
`tcren.annotation`	chain typing — TCR loci/CDRs via `arda`, peptide, MHC; αβ/γδ C-gene call
`tcren.mhc`	map MHC chains to allele/class/role; partition the groove (helices/floor); NetMHCpan pseudosequence
`tcren.contacts` / `contactmap`	closest-atom 5 Å contacts, Cα distances, multi-layer (5/8/12 Å) contact tables, interface partitioning
`tcren.potential`	`Potential` (TCRen/MJ/Keskin); `derive_tcren` (classic/AM/LOO) with non-redundancy filtering
`tcren.scoring` / `scoring_rank`	substitution scoring of candidate peptides; percentile rank vs a background
`tcren.ddg`	fast virtual-matrix ΔΔG — alanine scan, neoantigen mutants
`tcren.binder`	binder/non-binder classifier from AF-orthogonal interface geometry
`tcren.recognition`	35-descriptor extractor (`recognition_features`) + frozen real-vs-shuffled recognizers — distribution-aware Bayesian logistic + Gaussian BN — for joint `P(real)`
`tcren.orient`	canonical frame, `superimpose` onto the canonical DB, docking angles, reverse-dock detection
`tcren.refine`	peptide substitution + refinement (DOPE MC; CCD/OpenMM/ProMod3/FlexPepDock engines); register QC
`tcren.clashes` / `mechanics`	steric-clash report; interface spring-network stiffness + rupture model
`tcren.project2d` / `viz`	project the interface onto the groove plane; SVG complementarity maps + 3D pocket/CDR views
`tcren.pipeline` / `oracle`	one-call end-to-end runs (`run_pipeline`, `summarize_structure`)
`tcren.paper`	Nat Comput Sci 2022 reproduction (HF bootstrap, batch annotation, legacy comparison)

Data

Structures live in the Hugging Face dataset isalgo/tcren_structures, all gzipped:

folder	contents
`Native2022`	the 2022 paper set (oracle)
`Native2026`	the comprehensive 2026 TCR:pMHC set the current potential is derived from
`Canonical2026`	`Native2026` re-oriented into the canonical frame (`tcren orient`)

tcren reads .pdb/.cif/.pdb.gz/.cif.gz and .tar.gz batches; an installed library lazily fetches the canonical reference structures from the Hub when orienting a new complex. The root data/ holds Native2026 (+ Canonical2026, gitignored, fetched on demand), PDB_date.tsv, orient_metadata.json, and TCRen_potential.csv — the current potential derived from the Native2026 set (use it with tcren score -p data/TCRen_potential.csv).

Notebooks

Runnable examples under notebooks/ (rendered in the docs):

complementarity_map_2d — 2D interface maps, multiple structural + map views of 1ao7
contact_thresholds_and_bondtypes — region-pair contact counts (closest/Cβ/Cα) + bond types
canonical_frame_figures — canonical-frame QC across the Native2026 set
pymol_canonical_figures — ray-traced PyMOL panels (overlay, groove, interface) by class/species
mhc_pseudosequence_mps — NetMHCpan MHC pseudosequence (MPS) residues vs. peptide contacts
example_gil_a02_rs_motif — GILGFVFTL/HLA-A*02 and the public CDR3β Arg–Ser motif
natcompsci2022/ — full reproduction of the Nat Comput Sci 2022 analyses

Performance

Per-stage wall time (best of n) on a TCR-pMHC complex (1ao7), Apple M-series, single thread (RUN_BENCHMARK=1 pytest -k benchmark -s to reproduce the core stages):

stage	time	notes
parse a gzipped structure	~17 ms	`.pdb.gz` / `.cif.gz`
contact map (5 Å, cKDTree)	~9 ms	per structure
score 1000 candidate peptides	~11 ms	~10 µs/peptide (vectorised)
ΔΔG alanine scan (9-mer)	~11 ms	virtual-matrix; no atoms move
binder P(bind) (features + model)	~49 ms	native geometry, no external tool
peptide refine (2000-step DOPE MC)	~320 ms	knowledge-based rigid-body refinement
annotate (MHC map, 1 structure)	~670 ms	one mmseqs2 search
annotate (TCR + MHC), batched	~0.2 s/structure	one mmseqs2 call for the whole set; vs ~1.5 s/structure unbatched
superimpose onto the canonical DB (per query)	~2.8 s	aligns to every same-class DB structure

peak RSS	value	notes
single-structure pipeline (no orient)	~200 MB	parse → annotate → contacts → score → refine
+ `superimpose` (loads canonical DB)	~780 MB	holds Canonical2026 in RAM; skip with `--no-superimpose`

Annotation is the only network/compute-heavy step and is always batched (one mmseqs2 search over all chains; mmseqs2 parallelises internally — never per-structure, never Python-threaded). Threads are used only for the embarrassingly-parallel, mmseqs-free stages (structural alignment, write, rendering): tcren orient -t N. Screening a peptide/TCR panel is embarrassingly parallel — references are annotated and oriented once, so the hot loop is just refine + contacts + score per complex.

Tests

pytest -m "not slow"          # unit + fast regression (the CI gate)
pytest                        # add the arda/mmseqs-backed regression tests
RUN_BENCHMARK=1 pytest -k benchmark -s

Methods appendix

The coordinate-level extensions — backbone-preserving peptide substitution and the potential-guided Monte-Carlo refinement kernel (energy function, the restraint-necessity argument, sampler, and citations) — are written up in the technical appendix appendix/tcren.tex (built with make -C appendix → appendix/tcren.pdf).

Citing

TCRen is free for academic and non-commercial use. If you use it, please cite our latest Nature Computational Science 2024 paper:

Karnaukhov VK, Shcherbinin DS, Chugunov AO, Chudakov DM, Efremov RG, Zvyagin IV, Shugay M. Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen. Nat Comput Sci. 2024 Jul;4(7):510-521. doi: 10.1038/s43588-024-00653-0. Epub 2024 Jul 10. PMID: 38987378.

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Release history Release notifications | RSS feed

2.5.0

Jul 28, 2026

2.4.0

Jul 28, 2026

2.3.2

Jul 24, 2026

2.3.1

Jul 24, 2026

2.2.3

Jul 19, 2026

2.2.2

Jul 17, 2026

2.2.1

Jul 15, 2026

This release

2.2.0

Jul 13, 2026

2.1.2

Jul 2, 2026

2.1.1

Jul 2, 2026

0.1.0

Jun 17, 2026

tcren 2.2.0

tcren — structure-based prediction of TCR–epitope recognition

What it does

Install

Command line

One table per structure: descriptors, energies & the joint recognizer

Library

Batch inputs, gzip, archives

Canonical orientation, contacts, docking geometry

2D complementarity maps & region-pair contacts

Modules

Data

Notebooks

Performance

Tests

Methods appendix

Citing

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

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