SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.

ABSTRACT: T-cell receptors (TCRs) recognize peptide-MHC (pMHC) complexes through intricate structural interactions, which is a core component of adaptive immunity. However, the diverse and cross-reactive nature of TCRs poses great challenges for accurate prediction of TCR-epitope interactions, hampering the advancement and broad application of TCR-related therapies. Here, we present SageTCR, a bi-level graph neural network (GNN) framework that leverages structural data to predict TCR-pMHC binding possibilities. Harnessing the pretrained language models, SageTCR encodes detailed structural arrangement at both residue-level and atomic-level and effectively integrates the bimodal representations via attention mechanisms. To tackle the deficiency of experimental structures, we explore comprehensive data

SUBMITTER: Li X 

PROVIDER: S-EPMC12454268 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature