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README: dataset card — layout, gz/tar.gz+md rule, three-task framing, as_case

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metadata

license: cc-by-4.0
tags:
  - immunology
  - tcr
  - pmhc
  - structural-biology

isalgo/tcren_structures

TCR:peptide:MHC structure sets and benchmarks for TCRen2 (structure-based prediction of TCR recognition). Fetch with tcren / the manuscript scripts/bootstrap_data.py.

Contents rule: structures as .gz/.tar.gz (LFS) and .txt/.md descriptions only — no notebooks, figures, or analysis tables.

Layout

folder	task	contents
`Native2026/`, `Canonical2026/`	derivation / ergodicity	non-redundant TCR:pMHC structures (`.gz`)
`Native2022/`, `PolyV2022/`	2022-paper reproduction	structure sets (`.gz`)
`tcrvdb/`	TCR-ranking (specificity)	618 TCRmodel2 models (`<hash>.pdb`)
`cpl/pdb_cpl/`	peptide-ranking (epitope)	peptide-swap best/worst, 5 TCRs
`as_case/`	MHC-ranking (B*27:05 vs :02)	`as_case.tar.gz` + `content.md`
`Bobisse/`, `Bigot/`	neoantigen	cohort structures (`.gz`)

The three tasks

TCRen2 scores one interface-decomposed recognition energy Φ (TCRen for TCR:peptide, Miyazawa–Jernigan for the presentation interfaces) and reads it three ways: rank peptides for a TCR:MHC (epitope / neoantigen), rank TCRs for a pMHC (specificity), rank MHC alleles for a TCR:peptide (allele restriction, e.g. ankylosing spondylitis B*27:05 vs :02).