1. Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2
- Author
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Ang Gao, Zhilin Chen, Assaf Amitai, Julia Doelger, Vamsee Mallajosyula, Emily Sundquist, Florencia Pereyra Segal, Mary Carrington, Mark M. Davis, Hendrik Streeck, Arup K. Chakraborty, and Boris Julg
- Subjects
Immunology ,Immune Respons ,In Silico Biology ,Artificial Intelligence ,Science - Abstract
Summary: 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 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.
- Published
- 2021
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