S. Hall-Swan, J. Slone, M. M. Rigo, D. A. Antunes, G. Lizée, and L. E. Kavraki, “PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure,” Frontiers in Immunology, vol. 14, 2023.
Introduction: 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. Methods: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. Results and discussion: 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.
Publisher: http://dx.doi.org/10.3389/fimmu.2023.1108303
PDF preprint: http://kavrakilab.org/publications/hall-swan2023pepsim.pdf