Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2
Author(s)Gao, Ang; Amitai, Assaf; Doelger, Julia; Chakraborty, Arup K; Julg, Boris D.
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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.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Department of Physics; Massachusetts Institute of Technology. Department of Chemistry; Massachusetts Institute of Technology. Department of Biological Engineering
Gao, Ang et al. “Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2.” iScience, 24, 4 (April 2021): 102311 © 2021 The Author(s)
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