PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure

• Published in: Frontiers in immunology 2023-04, Vol.14, p.1108303-1108303
• Main Authors: Hall-Swan, Sarah, Slone, Jared, Rigo, Mauricio M, Antunes, Dinler A, Lizée, Gregory, Kavraki, Lydia E
• Format: Article
• Language: English
• Subjects: Amino Acid Sequence; Clone Cells; Immunology; immunotherapy; peptide-HLA; Peptides; sequence similarity; structure comparison; T-cell cross-reactivity; T-Lymphocytes, Cytotoxic
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2023.1108303

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 prediction 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 can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.