Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2

We 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 corona-viruses 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.

Automated summary

A physics-based learning model was used to predict the immunogenicity of CTL epitopes derived from SARS-CoV-2, showing that only some epitopes are immunogenic, with spike protein epitopes being less likely to provide broad immune coverage. Additionally, some immunogenic SARS-CoV-2 CTL epitopes were found to be identical to those of seasonal coronaviruses, suggesting existing CTL immunity against COVID-19 in some individuals prior to infection.