{"id":"doi:10.1371/journal.pcbi.1014396","kind":"article","title":"TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity","authors":["Dong W","Yang Q","Xu L","Li X","Wang K","Dong S","Luo G","Zhang X","Yang T","Gao X","Wang G"],"author_count":11,"year":2026,"published":"2026-06-22","venue":"PLoS computational biology","cited_by":0,"doi":"10.1371/journal.pcbi.1014396","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1371/journal.pcbi.1014396","archive_url":"https://space-kitty.com/papers/doi:10.1371/journal.pcbi.1014396","updated":null,"pmid":"42330011","pmcid":"PMC13298993","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC13298993/pdf","abstract":"Deciphering how human T cells recognise peptide-HLA (pHLA) complexes underpins next-generation vaccines and personalised immunotherapies, yet extreme sequence diversity and paired-chains interdependence still hamper reliable in silico prediction of T-cell receptor (TCR) specificity. To overcome these hurdles, we built TCRBinder, a paired-chain-aware deep model with a multi-branch encoder that routes each molecular component through dedicated transformer-based modules to capture contextual signals in both HLA pseudo-sequences and antigenic peptides while simultaneously processing the TCR [Formula: see text] and [Formula: see text] chains. This design captures the synergistic interaction between paired chains to emulate peptide-HLA-TCR (PHT) interactions and expose residue-level contact motifs. Across PHT and peptide-TCR (pTCR) benchmarks, the model delivered state-of-the-art performance (AUC-ROC = 0.911, AUPR = 0.791 for the PHT task) and remained superior on multiple independent datasets. We tracked the dynamics of clonal expansion and, in a large SARS-CoV-2 repertoire containing completely unseen peptides, improved the AUC-ROC by up to 16.3% over the leading alternatives. Moreover, TCRBinder provided mechanistic insights by pinpointing contact hotspots and quantifying residue contributions to binding probability. These capabilities position TCRBinder as a versatile tool for rational antigen discovery, immunotherapy stratification, and neoantigen vaccine design.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity\" by Dong W, Yang Q, Xu L et al., https://doi.org/10.1371/journal.pcbi.1014396, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"mrna-cancer-vaccines","name":"mRNA Therapeutics & Cancer Vaccines","domain_slug":"medicine"}],"cited_in_dossiers":[]}