Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques (preprint)

ABSTRACT

The molecular mechanisms underlying the recognition of epitopes by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a comprehensive strategy that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms of TCRs. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919-927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790-1798), for a TCR expressed by 204 T cells (TCR-204). According to the molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. This strategy will facilitate the discovery and optimization of TCR-based therapeutics. In addition, the comprehensive strategy can also promote the development of cancer vaccines in virtue of the ability to discover neoepitopes and epitope-recognition mechanisms.