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

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iScience


. 2021 Mar 15;102311.
doi: 10.1016/j.isci.2021.102311. Online ahead of print.
Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2


Ang Gao ^{1 2} , Zhilin Chen ³ , Assaf Amitai ¹ , Julia Doelger ¹ , Vamsee Mallajosyula ⁴ , Emily Sundquist ³ , Florencia Pereyra Segal ⁵ , Mary Carrington ^{3 6} , Mark M Davis ^{4 7 8} , Hendrik Streeck ⁹ , Arup K Chakraborty ^{1 2 3 10 11} , Boris Julg ³



Affiliations

• PMID: 33748696
• PMCID: PMC7956900
• DOI: 10.1016/j.isci.2021.102311

Abstract

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.