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
Affiliations
Ang Gao
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Zhilin Chen
Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
Assaf Amitai
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
Julia Doelger
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
Vamsee Mallajosyula
Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA
Emily Sundquist
Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA
Florencia Pereyra Segal
Brigham and Women's Hospital, Boston, MA 02115, USA
Mary Carrington
Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA; Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
Mark M. Davis
Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
Hendrik Streeck
Institut für Virologie, Universitätsklinikum Bonn, 53127 Bonn, Germany
Arup K. Chakraborty
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Corresponding author
Boris Julg
Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, 400 Technology Sq., Cambridge, MA 02139, USA; Corresponding author
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.
