Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.
Journal:
Computer methods and programs in biomedicine
Published Date:
May 10, 2025
Abstract
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to epitopes presented on Major Histocompatibility Complex (MHC) molecules. However, experimentally identifying antigens that could be recognizable by T cells and possess immunogenic properties is resource-intensive, with most candidates proving non-immunogenic, underscoring the need for computational tools to predict peptide-MHC (pMHC) and TCR binding. Despite extensive efforts, accurately predicting TCR-antigen binding pairs remains challenging due to the vast diversity of TCRs.