TRIOPS: A deep learning framework for prediction of T cell receptor-MHC binding specificity

Authors

Rose, N. R., Ramirez, C. M., Mok, L., Wong, C. K., Jonsson, V. D.

Abstract

T cell receptor (TCR) recognition is MHC-restricted, yet accurately predicting a TCR's restricting HLA allele remains an open problem. We present TRIOPS, a dual-branch convolutional model with soft cross-attention that predicts TCR-MHC restriction from amino acid sequence alone. TRIOPS uses cross-reactivity-aware negative sampling by HLA pseudosequence similarity to reduce allele-boundary label noise, extending prediction to alleles absent from training. TRIOPS reaches a held-out AUC of 0.97 for paired TCR; and 0.92 for TCR-only inputs, generalizes to unseen receptors and HLA alleles, and after locus-specific calibration, assigns TCR clonotypes to their likeliest restricting allele across an individual's HLA genotype. In TCGA tumors, TCR repertoires preferentially engage the expression-lost allele at HLA-A and HLA-B and the retained allele at HLA-C, recapitulating from bulk tumor RNA-seq the allele specific HLA loss previously linked to immune escape.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 05 Jul 2026.

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