Authors
Alex Ascunce-París, Miguel Romero-Durana, Alfonso Valencia, Roc Farriol-Duran, Víctor Guallar
Published in
Frontiers in immunology. Volume 17. Pages 1869810. Epub Jul 24, 2026.
Abstract
T cell receptor (TCR) recognition of peptide-MHC complexes (pMHCs) is central to adaptive immunity. Structural insights into TCR-pMHC interactions are critical for understanding antigen specificity and T-cell function. However, progress remains limited by the scarcity of experimentally resolved structures (275 TCR-pMHC class I structures in the PDB, Jan 2026). Although protein structure modelling tools have advanced rapidly, accurate structural modelling of TCRs remains challenging due to CDR loop hypervariability and conformational flexibility. In addition, there is a lack of reliable quality assessment strategies that do not rely on comparisons with experimental references. To address this, we benchmarked four general-purpose (AlphaFold2.3-Multimer, AlphaFold3, Boltz-2, Chai-1) and three TCR-specific (TCRmodel2, tFold-TCR, TCRdock) protein modelling algorithms by recalculating all experimentally determined TCR-pMHC class I complexes in the PDB. AlphaFold3 demonstrated superior performance across metrics (mean TCR-iRMSD = 3.59 Å and DockQ = 0.54), whereas the other algorithms displayed lower accuracy. Built on AlphaFold3 structures, we present a scalable and interpretable ML framework for the quality assessment of TCR-pMHC structural models without matched experimental references. We trained a random forest classifier integrating multiple confidence metrics (pLDDT, ipTM, ipSAE, iPAE, iPDE, pDockQv1-2) derived from 1325 modelled structures of 265 experimentally determined PDB TCR-pMHC class I complexes. The classifier reliably stratifies structural models into low-, acceptable-, medium-, and high-quality tiers defined by comparisons to their experimental reference structures, outperforming single metrics. These quality-tier predictions further enable the prioritization of high-confidence TCR-pMHC interactions. This was demonstrated across two held-out datasets comprising a total of 4,090 AlphaFold3-modelled TCR-pMHC complexes (20,450 models, 5 models per complex): a re-evaluated set of TCR-pMHC class I complexes from VDJdb (n = 606) and the reference IMMREP23 dataset (n = 3,484). We profiled the first dataset to reduce false-positive TCR-pMHC interactions erroneously annotated in VDJdb, and the second to enrich for biologically validated TCR-pMHC interactions amongst higher-quality structural models over their synthetic negative counterparts. Altogether, our structural quality-tier framework provides a scalable and interpretable approach that complements structural modelling and functional analyses of TCR-pMHC class I complexes, with direct translational applications in T-cell immunology and TCR-based immunotherapies.
PMID:
42568581
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.
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