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

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; Boris Julg

doi:10.1016/j.isci.2021.102311

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

iScience. 2021 Apr 23;24(4):102311. doi: 10.1016/j.isci.2021.102311. Epub 2021 Mar 15.

Authors

Ang Gao^{1

2}, Zhilin Chen³, Assaf Amitai¹, Julia Doelger¹, Vamsee Mallajosyula⁴, Emily Sundquist³, Florencia Pereyra Segal⁵, Mary Carrington^{3

6}, Mark M Davis^{4

7

8}, Hendrik Streeck⁹, Arup K Chakraborty^{1

2

3

10

11}, Boris Julg³

Affiliations

¹ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
² Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
³ Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA.
⁴ Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁵ Brigham and Women's Hospital, Boston, MA 02115, USA.
⁶ Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
⁷ Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁸ Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁹ Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany.
¹⁰ Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
¹¹ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

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 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.

Keywords: Artificial Intelligence; Immune Respons; Immunology; In Silico Biology.

Abstract

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