MFTEP: A Multimodal Fusion Deep Learning Framework for T Cell Receptor-epitope Interaction Prediction

Accurately predicting immunogenic peptides recognized by T cell receptors (TCR) is a crucial step toward personalized immunotherapy. However, prediction of TCR-epitope interactions is still a challenging task. Early works, like molecular dynamics simulation-based methods, suffer from slow speed and poor generalization capabilities. It is necessary to develop novel computational methods to predict TCR-epitope interactions precisely. With the development of high-throughput sequencing technologies, more and more TCR-epitope interaction data have been recorded in public databases. With the help of these databases, many in silico predictive methods have shown promising performance. However, current methods still perform poorly on unseen TCRs and epitopes. Moreover, most current models still accept single-modal information about TCRs and epitopes, such as sequences or physicochemical information. Effectively utilizing the multimodal information of TCRs and epitopes, such as molecular graphs and 3D structure, may enhance the model's prediction performance. To address the above issues, we presented MFTEP, a multimodal fusion method to predict TCR-epitope interactions by fusing the sequence features, molecular graph features, and 3D structure features of TCRs and epitopes. The ablation study highlights the importance of the multi-modal fusion module in enhancing the model's performance. Several datasets were collected and utilized to evaluate the generality and robustness of the proposed model. According to the experimental results, MFTEP performs better than other state-of-the-art methods, indicating its high predictive power. Overall, the results demonstrate that MFTEP can learn general TCR-epitope interaction patterns and is a powerful prediction tool to apply to real-world scenarios. All the data and code are available at: https://github.com/skybluewhy/MFTEP