PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure - Hall-Swan, Sarah; Slone, Jared; Rigo, Mauricio M.; Antunes, Dinler A.; Lizée, Gregory; Kavraki, Lydia E. (Frontiers in Immunology, 2023)
Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity, and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. We show our method could accurately separate cross-reactive from non-cross-reactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. Also, our method can be generalized to work on any dataset of class I peptide-HLAs. PepSim is available as a web server at pepsim.kavrakilab.org.