Mkdban-Tei: a Multi-Level Knowledge Distillation-Based Deep Learning Architecture for Predicting T Cell Receptor-Epitope Binding Specificity

Understanding the underlying mechanisms of TCR-epitope interactions is crucial for studying the adaptive immune system and promoting the field of immunotherapy. Given the high cost of traditional experimental methods, it is urgent to develop computational methods to predict TCRepitope binding. With the advancement of experimental technology, an increasing number of TCR-epitope binding pairs have been archived in public databases, creating opportunities for the advancement of computational methods. In this study, we propose a novel framework called MKDBAN-TEI for predicting TCR-epitope binding. We encode TCR and epitope sequences using a learnable residue embedding matrix and employ CNN layers to extract features. An interpretable bilinear attention network is then used to capture the interaction patterns between TCR and epitope. To improve the model's performance and generalization capability, we introduce a multi-level knowledge distillation framework: first, we cluster epitopes in the training set based on sequence similarity to define distinct domains; second, for each domain, we integrate three types of protein sequence features (protein language embeddings, physicochemical information, and evolutionary information) to train domain-specific teacher models via internal multi-feature knowledge distillation, capturing domain-specific binding patterns; finally, we distill knowledge from all domain-specific teachers to a universal student model through inter-domain knowledge distillation, enhancing generalization to unseen epitopes. Compared to several state-of-the-art models, MKDBAN-TEI demonstrates superior performance and generalization capability. Further experiments illustrate the effectiveness of the model in realworld scenarios. Visualizing the attention maps learned by MKDBAN-TEI provides new insights into TCR-epitope interactions. The code is available at: https://github.com/X/MKDBAN-TEI.