# TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity

Article · Dong W, Yang Q, Xu L, Li X, Wang K, Dong S, Luo G, Zhang X, Yang T, Gao X, Wang G

- Archive id: doi:10.1371/journal.pcbi.1014396
- Published: 2026-06-22
- Venue: PLoS computational biology
- DOI: https://doi.org/10.1371/journal.pcbi.1014396
- PMID: 42330011
- PMCID: PMC13298993
- Citations: 0
- Source URL: https://doi.org/10.1371/journal.pcbi.1014396
- PDF: https://europepmc.org/articles/PMC13298993/pdf
- Provenance: europepmc

## 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 reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [mRNA Therapeutics & Cancer Vaccines](https://space-kitty.com/topics/mrna-cancer-vaccines)

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