Deciphering T-cell receptor-antigen recognition through interpretable residue-level interaction modeling.
Accurate identification of interactions between T-cell receptors (TCRs) and antigenic peptides presented by major histocompatibility complex (MHC) molecules is essential for advancing precision immunotherapy. However, existing approaches often exhibit limited generalization to unseen peptides and struggle to capture the complex interaction patterns underlying immune recognition. Here, we present TCR-IFNet, a biologically informed deep learning framework for interpretable TCR-peptide interaction prediction. The model integrates global contextual representations from protein language models with local motif refinement via a gated convolutional module. To model cross-sequence dependencies, we introduce a Fast Kolmogorov-Arnold Network (FastKAN)-based cross-attention mechanism for nonlinear interaction modeling, together with a bilinear attention network to aggregate residue-level features into compact interface representations. Evaluation across multiple settings indicates that TCR-IFNet achieves competitive performance compared with existing methods, with higher AUPRC observed on both antigen-specific and healthy-sourced datasets, as well as improved results on independent test sets. The model also shows consistent generalization to unseen peptides under different negative sampling strategies. In addition, TCR-IFNet provides biologically meaningful interpretability by identifying key residue-level interaction patterns consistent with structural binding interfaces. Collectively, these findings demonstrate that TCR-IFNet provides a robust and generalizable computational framework for characterizing TCR-peptide interactions.