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

• Main Authors: Gao, Ang (Author), Amitai, Assaf (Author), Doelger, Julia (Author), Chakraborty, Arup K (Author), Julg, Boris D. (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor), Massachusetts Institute of Technology. Department of Chemistry (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
• Format: Article
• Language: English
• Published: Elsevier BV, 2021-04-01T17:22:23Z.
• Subjects: Article
• Online Access: Get fulltext

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 COVID-19 patients. 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 prior to SARS-CoV-2 infection.
National Science Foundation (U.S.) (Grant PHY-2026995)
Frederick National Laboratory for Cancer Research (Contract HHSN261200800001E)
National Institutes of Health (U.S.) (Grant AI138790)