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

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 consistently outperforms state-of-the-art models across four generalization scenarios, notably improving ROC-AUC by 11% and PR-AUC by 16% over the strongest competitor in the most challenging Unseen-Pair setting. DAISY also offers intuitive interpretability by localizing interaction-relevant residues via Score-CAM visualizations. Furthermore, its computational predictions are bridged to key immunological and clinical outcomes, demonstrating utility in correlating with T-cell clonal expansion, identifying functional TCRs, and robustly forecasting patient survival. Together, DAISY can serve as a powerful tool for broad translational immunology and introduces a scalable modeling paradigm for next-generation immune modeling.