摘要：SummaryWe describe a physics-based learning model for predicting the immunogenicity of cytotoxic T lymphocyte (CTL) epitopes derived from diverse pathogens including SARS-CoV-2. The model was trained and optimized on the relative immunodominance of CTL epitopes in human immunodeficiency virus infection. Its accuracy was tested against experimental data from patients with COVID-19. Our model predicts that only some SARS-CoV-2 epitopes predicted to bind to HLA molecules are immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but those from the SARS-CoV-2 spike protein alone are unlikely to do so. Our model also predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to seasonal coronaviruses circulating in the population and such cross-reactive CD8+T cells can indeed be detected in prepandemic blood donors, suggesting that some level of CTL immunity against COVID-19 may be present in some individuals before SARS-CoV-2 infection.Graphical abstractDisplay OmittedHighlights•A physics-based learning model to predict CTL epitope immunogenicity across viruses•Trained on relative CTL epitope immunodominance in HIV and applied to SARS-CoV-2•Only a fraction of SARS-CoV-2 peptides that bind to HLA molecules is immunogenic•Immunogenic SARS-CoV-2 epitopes identical to seasonal coronaviruses were identifiedImmunology; Immune Respons; In Silico Biology; Artificial Intelligence
