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

SUBMITTER: Chen J 

PROVIDER: S-EPMC10731497 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature