TEPCAM: Prediction of T‐cell receptor–epitope binding specificity via interpretable deep learning

Abstract The recognition of T‐cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR–epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR–epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR–EPitope identification based on Cross‐Attention and Multi‐channel convolution), a deep learning model that incorporates self‐attention, cross‐attention mechanism, and multi‐channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state‐of‐the‐art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross‐attention layer and mapping them to the three‐dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM .