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

作者：Yajing Yuan, Junwei Chen, Yufang Zhang, Yitian Fang, Zhongcheng Fang, Yanyi Chu, Jiayi Li, Chen Zhang, Yuzhe Li, Dong‐Qing Wei · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202512544 · 被引用次数：3 · 研究领域：vaccines and immunoinformatics approaches、Immunotherapy and Immune Responses、Cancer Immunotherapy and Biomarkers

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-genera...