Neoantigens play a key role in the recognition of tumour cells by T cells; however,
proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which neoantigens
are recognized by which T-cell receptors (TCRs). We built a transfer learning-based model named the
pMHC–TCR binding prediction network (pMTnet) to predict TCR binding specificities of the
neoantigens—and T cell antigens in general—presented by class I major histocompatibility complexes.
pMTnet was comprehensively validated by a series of analyses and exhibited great advances over
Predicting TCR–neoantigen/antigen pairing is one of the most daunting challenges in modern immunology; however, we achieved an accurate prediction of the pairing using only the TCR sequence (CDR3β), antigen sequence and class I major histocompatibility complex allele, and our work revealed unique insights into the interactions between TCRs and major histocompatibility complexes in human tumours, using pMTnet as a discovery tool.
Lu, T., Zhang, Z., Zhu, J.et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity. Nat Mach Intell 3, 864–875 (2021). link