TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved generalizability.

ABSTRACT: The binding of T cell receptors (TCRs) to peptide-MHC I (pMHC) complexes is critical for triggering adaptive immune responses to potential health threats. Developing highly accurate machine learning (ML) models to predict TCR-pMHC binding could significantly accelerate immunotherapy advancements. However, existing ML models for TCR-pMHC binding prediction often underperform with unseen epitopes, severely limiting their applicability. We introduce TRAP, which leverages contrastive learning to enhance model performance by aligning structural and sequence features of pMHC with TCR sequences. TRAP outperforms previous state-of-the-art models in both random and unseen epitope scenarios, achieving an AUPR of 0.84 (a 22% improvement over the second-best model) and an AUC of 0.92 in the random sce

SUBMITTER: Ge J 

PROVIDER: S-EPMC12046420 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature