PRISM : Peptide-specificity annotation of T-cell receptors with uncertainty quantification

Abstract

Mapping T-cell receptor (TCR) sequences to their cognate peptide-major histocompatibility complex (pMHC) ligands underlies both basic immunology and T-cell target discovery, yet current models aimed at predicting TCR specificity are limited by sparse labels, viral-biased training data, and an inability to recognize receptors outside their training distribution. We present PRISM, an uncertainty-aware metric-learning framework for TCR{beta} sequence representation. PRISM embeds receptors into a peptide-organized latent space, returns top-k peptides by nearest-neighbor retrieval, and abstains on out-of-distribution receptors by modeling an intrinsic uncertainty that tracks annotation correctness. To offset the viral bias of public databases, PRISM augments training data with structure-guided synthetic receptors that diversify TCR sequences while preserving the energetics of the TCR-pMHC interface. Across a held-out set of 923 peptides and the independent IMMREP23 benchmark, PRISM matches or exceeds sequence-based models, with largest gains on rare epitopes. Finally, PRISM learns attention weights on TCR residues that concentrate on the CDR3{beta} salt-bridge and hydrophobic contacts central to peptide recognition, linking PRISMs positional focus to the biochemical properties of TCR-pMHC structures.