Understanding the mechanisms of T cell antigen recognition that underpin adaptive immune responses is critical for developing vaccines, immunotherapies and treatments against autoimmune diseases. Despite extensive research efforts, accurate prediction of T cell receptor (TCR)–antigen binding pairs remains a great challenge due to the vast diversity and cross-reactivity of TCRs. Here we propose a deep-learning-based framework termed epitope-anchored contrastive transfer learning (EPACT) tailored to paired human CD8+ TCRs. Harnessing the pretrained representations and co-embeddings of peptide–major histocompatibility complex (pMHC) and TCR, EPACT demonstrated generalizability in predicting binding specificity for unseen epitopes and distinct TCR repertoires. Contrastive learning enabled highly precise predictions for immunodominant epitopes and interpretable analysis of epitope-specific T cells. We applied EPACT to SARS-CoV-2-responsive T cells, and the predicted binding strength aligned well with the surge in spike-specific immune responses after vaccination. We further fine-tuned EPACT on structural data to decipher the residue-level interactions involved in TCR–antigen recognition. EPACT was capable of quantifying interchain distance matrices and identifying contact residues, corroborating the presence of TCR cross-reactivity across multiple tumour-associated antigens. Together, EPACT can serve as a useful artificial intelligence approach with important potential in practical applications and contribute towards the development of TCR-based immunotherapies.
This is a preview of subscription content, access via your institution
Access options