A Biologically Informed Vision-Guided Framework for Interpretable T Cell Receptor-Epitope Binding Prediction.

ABSTRACT: Accurate identification of the interactions between T-cell receptors (TCRs) and antigenic epitopes presented by major histocompatibility complex (MHC) molecules is fundamental to advancing cancer immunotherapy. Nevertheless, predictive modeling of TCR-epitope binding remains challenging, as existing models struggle to generalize to unseen epitopes while often overlooking key physicochemical properties governing immune recognition. Here, a biologically informed vision-guided deep learning framework (DAISY) is proposed for robust and interpretable TCR-epitope binding prediction. DAISY integrates hierarchical physicochemical features via a biologically inspired Condition-Adaptive Fusion module, jointly modeling residue-level spatial interactions and global biochemical context. DAISY consisten

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC12822471 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature