TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data.

ABSTRACT:

Motivation

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data.

Results

We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find th

SUBMITTER: Kim HY 

PROVIDER: S-EPMC11297499 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature