tcren — structure-based prediction of TCR–epitope recognition

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:

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 (a repo-local .venv via uv, an editable install, and the reference data fetched into data/):

bash setup.sh                    # uv venv + editable install + arda + fetch data/ (no conda)
source .venv/bin/activate

setup.sh needs only uv and a C++ compiler (macOS: xcode-select --install); it never touches conda. Pass --tests to run the fast suite after install.

tcren ships five small pybind11/C++ extensions, built on install by scikit-build-core (which fetches cmake+ninja automatically): 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); uv/setup.sh pull it automatically, and from arda-mapper >= 2.5.7 it auto-fetches both its own reference and a static mmseqs2 binary on first use — so no conda/bioconda and no ARDA_HOME to set (override the binary with $ARDA_MMSEQS). 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

# Score structures: the three interface contact energies (TCRen for TCR↔peptide, MJ for
# TCR↔MHC and peptide↔MHC) and their total Φ. One row per structure.
tcren scoring -s complex.pdb.gz -o scores.csv

# Inputs: a file, a directory, a .tar.gz, a quoted glob, a .txt manifest (one path per line),
# a comma-separated list, or a repeated -s. Mix freely.
tcren scoring -s a.pdb.gz -s b.pdb.gz -o scores.csv
tcren scoring -s 'models/*.pdb.gz' -o scores.csv
tcren scoring -s models/ --delta --geometry -t 8 -o scores.csv   # a directory, 8 workers
tcren scoring -s models.txt -o scores.csv

# --delta adds the poly-alanine reference ΔΦ per interface (ΔΦ_TCR:MHC is identically 0).
# Use ΔΦ, not Φ, when each candidate carries its OWN generated pose: raw Φ then partly reads
# the pose the predictor chose rather than the peptide.
tcren scoring -s 'models/*.pdb.gz' --delta -o scores.csv

# --geometry adds the interface descriptors and Q, the directional decorrelated
# interface-quality score (native-crystal calibrated, so it is defined for a single structure).
tcren scoring -s complex.pdb.gz --delta --geometry -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 scoring -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).
# Requires --native (the peptide) and exactly one mode: --alanine-scan or --mutant.
# ddG = E(native) - E(mutant), and lower energy binds better, so POSITIVE = stabilising.
tcren ddg -s complex.pdb --native EPITOPE --alanine-scan -o ddg.csv

# Predict a combinatorial-peptide-library (CPL) response matrix from ONE template TCR:pMHC
# structure: every peptide position x all 20 residues, threaded on the template's own contact map.
# Every cell sums BOTH peptide-bearing interfaces (TCRen over TCR:peptide + Miyazawa-Jernigan over
# peptide:MHC), because the assay reads activation, which needs presentation as well as engagement.
tcren cpl -s complex.pdb -o cpl_matrix.csv
# Two reference states, both emitted, and a cell means nothing except against one of them:
#   effect_equimolar  vs the 1/20 mixture  -> the CPL background; compare against a measured matrix
#   effect_wild_type  vs the template residue -> the mutation-scan / neoantigen question
# Positive is favourable on both. Three narrower questions off the same matrix:
tcren cpl -s complex.pdb --position 5                  # every substitution at position 5, best first
tcren cpl -s complex.pdb --position 5 --mutation W     # just that one cell
tcren cpl -s complex.pdb --position 5 --to-mixture     # cost of giving position 5 up to the mixture

# Binder vs non-binder from AF-orthogonal interface geometry + the CDR1/2-vs-CDR3a TCRen term —
# ranks candidate TCRs against a fixed pMHC, on par with AlphaFold/TCRmodel2 confidence with no
# external tool (raw-label macro AUC ~0.80 vs AF ipTM 0.79). PREFER the fit-free Q = tcren.cohort.
# q_score, which matches this and generalises across cohorts where the fitted p_bind does not; with
# ipTM, z(ipTM)+z(Q) is the fit-free synergy (macro 0.83 vs 0.79). `tcren binder` emits the fitted
# p_bind (retained for reproducibility); `tcren recognize --scores` adds q_bind + s_strain.
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)

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 scored.tsv --scores         # + q_bind, s_strain (recommended) + p_bind, p_forced
tcren recognize -s my_pdbs/ -o feats.tsv --features-only   # descriptors only, skip the models

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 technical appendix, which lives with the manuscript rather than here — logistic_stan/, with the Gaussian-BN companion in shuffle_bn/. Decoys come from tcren shuffle. See Technical appendix below.

(c) physics of the interaction. The koff proxies fold into the same table with --mechanics; only the mutation scan, which is per-residue rather than per-structure, needs its own command:

tcren recognize -s models/ --scores --mechanics -t 0 -o out.tsv   # every per-structure descriptor, one table
tcren ddg       -s complex.pdb -o ddg.csv     # per-residue alanine / neoantigen ΔΔF (fast virtual matrix)

--mechanics is how to ask for the stiffness tensor, steered rupture and coupling residues on a cohort. tcren mechanics still exists and gives the same numbers, but as a second command it repeats the parse and both mmseqs searches to return a second table — CSV, keyed pdb.id rather than complex.id — that then has to be joined. Inside recognize the structures are already annotated, so the flag costs only the mechanics arithmetic (12 crystals: 19.0 s → 19.5 s, against 22.5 s for the two commands).

(Per the affinity scope caveat above, structures predict the off-rate koff via the mechanics columns, 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
res = run_pipeline("complex.pdb", reference_aa="A")  # + delta_* : the poly-alanine ΔΦ per interface

# 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())

CPL response matrices from one template structure

A positional-scanning combinatorial peptide library fixes position i to residue a and leaves every other position an equimolar 1/20 mixture, so a measured cell is an ensemble mean, R[i,a] = E[response | x_i = a]. tcren.cpl predicts that matrix from a single template complex — each of the twenty residues threaded through the template's own contact map, nothing re-docked, nothing fitted to any assay.

from tcren import (ContactMap, parse_structure, response_matrix,
                   mutation_effect, position_scan, equimolar_effect)
from tcren.annotation import classify_chains
from tcren.mhc import annotate_mhc

s = parse_structure("3HG1.pdb", pdb_id="3HG1")
classify_chains(s, organism="human")
annotate_mhc(s)                       # REQUIRED: without it peptide:MHC is empty and anchors zero out
rm = response_matrix(ContactMap.from_structure(s, cutoff=5.0))

rm.to_frame()                         # the whole matrix, one row per (position, amino acid) cell
position_scan(rm, 5)                  # every substitution at position 5, best first
mutation_effect(rm, 5, "W")           # one cell
equimolar_effect(rm, 5)               # cost of giving position 5 up to the 1/20 mixture

Every cell sums both peptide-bearing interfaces — TCRen over TCR:peptide plus Miyazawa–Jernigan over peptide:MHC — because the assay reads activation, which needs the peptide presented as well as the receptor engaged. A position the receptor never touches is an anchor; its TCR term is constant along the row, so the sum degrades to presentation alone rather than to a special case.

Two reference states, and a cell is meaningless except against one of them. A raw Φ carries a large per-position offset that says only how many contacts the position makes:

They differ by a per-position constant — how far the template's residue sits above its column mean. Positive is favourable on both, since lower energy is the better binder. Under "wild_type" the template's own cell is identically zero; under "equimolar" it is an ordinary measurement.

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)

Publication figures

tcren.viz.pymol drives a headless PyMOL to ray-trace figure panels of oriented complexes. Three scenes cover the usual views, and every panel carries a labelled axis gizmo in its corner:

Figures need the viz extra (pip install "tcren[viz]") for Pillow, plus a pymol binary on PATH — PyMOL is a separate install, not a Python dependency.

from tcren.viz.pymol import render, overlay_scene, groove_scene, interface_scene
render(groove_scene("1ao7", "data/Canonical2026"), "groove.png")            # peptide in the cleft
render(groove_scene("1ao7", "data/Canonical2026", surface=True), "s.png")   # + molecular surface
render(overlay_scene(ids, "data/Canonical2026"), "overlay.png")             # ensemble, side-on
render(interface_scene("1ao7", "data/Canonical2026", cdr), "iface.png")     # peptide + CDR loops

A canonically-oriented structure is only interpretable if the reader can tell which way the frame points, and x/y/z does not tell them — so the arrows are named for what they mean:

The triad is thin, arrow-headed, and turns with the camera. An axis pointing at the viewer foreshortens to a dot and its label drops to the lower left of it, the usual convention for an axis normal to the page. These are the three directions the docking-geometry literature uses (SwiftTCR, TCR3d); only the principal-component ranking differs, because tcren.orient.frame fits the whole complex where those fit the MHC groove alone.

Colour by which residues carry the score. Φ is a sum over residue–residue contacts, so it decomposes exactly: a residue's share is the sum of φ(a_i, a_j) over the contacts it makes. The total says how large the score is; this says what it is made of.

from tcren.viz.pymol import residue_importance, importance_scene
imp = residue_importance(structure)                 # phi + n_contacts, per residue
render(importance_scene("1ao7", CANON, imp), "importance.png")                     # energy share
render(importance_scene("1ao7", CANON, imp, by="n_contacts",
                        spectrum="white_red"), "contacts.png")                     # geometric share

CDR3 and peptide residues become sticks on a ramp, everything else stays pale. Blue is favourable and red unfavourable — the ramp is centred on zero rather than fitted to the range, so those words keep their meaning even when every contact in an interface is stabilising. Each contact is attributed to both residues it joins, so the per-residue values sum to twice Φ: an attribution, not a partition.

render() is deliberately not a tcren subcommand: a figure is a handful of styling choices that want editing, not a fixed flag set. Pass any PyMOL script body as the scene.

Explore it interactively with the marimo app — pick a structure and scene, swing the camera and watch the gizmo follow, restyle it, colour by importance with the numbers beside the render, and rotate a live 3Dmol.js view with the mouse:

pip install "tcren[marimo]"
marimo run notebooks/pymol_interactive.py       # or `marimo edit` to change the code

Worked examples of every view, with images: Figure gallery.

Modules

Data

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

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, and TCRen_potential.csv — the current potential derived from the Native2026 set (use it with tcren score -p data/TCRen_potential.csv). Canonical2026's orient_metadata.json ships inside the package (src/tcren/data/), because the fetch brings down structures only and an installed library has no repo data/.

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):

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

Technical 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 tcren.tex, alongside the PyMC derivation of the shipped shuffle_logistic.json.gz (logistic_stan/) and the Gaussian-BN companion for shuffle_bn.json.gz (shuffle_bn/).

That appendix is not in this repo. It is manuscript material, it was 2.4 MB of the source distribution, and it moved to the manuscript repository on 2026-07-28 (2026-tcren/archive/tcren-appendix/, with a dated tarball beside it). Every file remains in this repository's git history — git log --all -- appendix/ — if the derivations are ever needed here again.

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.