tetano
Editor, Senior Moderator
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[SUP] 1 2 [/SUP], Zhilin Chen[SUP] 3 [/SUP], Assaf Amitai[SUP] 1 [/SUP], Julia Doelger[SUP] 1 [/SUP], Vamsee Mallajosyula[SUP] 4 [/SUP], Emily Sundquist[SUP] 3 [/SUP], Florencia Pereyra Segal[SUP] 5 [/SUP], Mary Carrington[SUP] 3 6 [/SUP], Mark M Davis[SUP] 4 7 8 [/SUP], Hendrik Streeck[SUP] 9 [/SUP], Arup K Chakraborty[SUP] 1 2 3 10 11 [/SUP], Boris Julg[SUP] 3 [/SUP]
Affiliations
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[SUP]+[/SUP] 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.
. 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[SUP] 1 2 [/SUP], Zhilin Chen[SUP] 3 [/SUP], Assaf Amitai[SUP] 1 [/SUP], Julia Doelger[SUP] 1 [/SUP], Vamsee Mallajosyula[SUP] 4 [/SUP], Emily Sundquist[SUP] 3 [/SUP], Florencia Pereyra Segal[SUP] 5 [/SUP], Mary Carrington[SUP] 3 6 [/SUP], Mark M Davis[SUP] 4 7 8 [/SUP], Hendrik Streeck[SUP] 9 [/SUP], Arup K Chakraborty[SUP] 1 2 3 10 11 [/SUP], Boris Julg[SUP] 3 [/SUP]
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[SUP]+[/SUP] 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.