Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

ABSTRACT: The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases. Here, we propose an attention-aware framework comprising TranspMHC for pMHC-I binding prediction and TransTCR for TCR-pMHC-I recognition prediction. Leveraging the attention mechanism, TranspMHC surpasses existing algorithms on in

SUBMITTER: Niu R 

PROVIDER: S-EPMC11781218 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature