TSpred: a robust prediction framework for TCR-epitope interactions based on an ensemble deep learning approach using paired chain TCR sequence data

Abstract

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications such as cancer immunotherapy. However, due to the scarcity of available data, it is known to be a challenging task particularly for novel epitopes. Here, we propose TSpred, a new ensemble deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. This method combines the predictive power of CNN and the attention mechanism to capture the patterns underlying TCR-epitope interactions. In particular, we design a reciprocal attention mechanism which contributes to higher model generalizability to unseen epitopes. We perform a comprehensive evaluation of our model and observe that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Our model performs consistently well across both of the two widely used negative sampling strategies, while avoiding the potential bias associated with each strategy. Also, compared to other predictors, it is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.