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10.1016/j.isci.2021.102311

http://scihub22266oqcxt.onion/10.1016/j.isci.2021.102311
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suck abstract from ncbi


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pmid33748696      iScience 2021 ; 24 (4): 102311
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  • Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2 #MMPMID33748696
  • Gao A; Chen Z; Amitai A; Doelger J; Mallajosyula V; Sundquist E; Pereyra Segal F; Carrington M; Davis MM; Streeck H; Chakraborty AK; Julg B
  • iScience 2021[Apr]; 24 (4): 102311 PMID33748696show ga
  • 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.
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