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.
| Original language | English |
|---|---|
| Article number | 102311 |
| Journal | iScience |
| Volume | 24 |
| Issue number | 4 |
| DOIs | |
| State | Published - 23 Apr 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 The Author(s)
Funding
This work was supported by the National Institutes of Health ( AI138790 to B.J. and AI057229 to M.M.D.), NSF grant # PHY-2026995 to A.G. and A.K.C. A.G., B.J., and A.K.C. were supported by the Ragon Institute of MGH , MIT , & Harvard . This project has been funded in part with federal funds from the Frederick National Laboratory for Cancer Research , under Contract No. HHSN261200800001E . Neither does the content of this publication necessarily reflect the views or policies of the Department of Health and Human Services nor does the mention of trade names, commercial products, or organizations imply endorsement by the US Government. This research was supported in part by the Intramural Research Program of the NIH , Frederick National Lab , Center for Cancer Research . This work was supported by the National Institutes of Health (AI138790 to B.J. and AI057229 to M.M.D.), NSF grant # PHY-2026995 to A.G. and A.K.C. A.G. B.J. and A.K.C. were supported by the Ragon Institute of MGH, MIT, & Harvard. This project has been funded in part with federal funds from the Frederick National Laboratory for Cancer Research, under Contract No. HHSN261200800001E. Neither does the content of this publication necessarily reflect the views or policies of the Department of Health and Human Services nor does the mention of trade names, commercial products, or organizations imply endorsement by the US Government. This research was supported in part by the Intramural Research Program of the NIH, Frederick National Lab, Center for Cancer Research. Project conceptualizing and planning was performed by A.G. A.K.C. and B.J.; HIV and SARS-CoV-2 ELISpot data were generated by Z.C. E.S. F.P.S. and H.S.; HLA data were generated by M.C.; the model development and validation were done by A.G. A.A. J.D. and A.K.C.; V.M. generated the tetramer data under the supervision of M.M.D.; and the manuscript was written by A.G. Z.C. A.K.C.V.M. M.M.D. and B.J. The authors declare no competing interests.
| Funders | Funder number |
|---|---|
| National Science Foundation | 2026995, PHY-2026995 |
| National Institutes of Health | AI138790, AI057229 |
| Frederick National Laboratory for Cancer Research | HHSN261200800001E |
| Ragon Institute of MGH, MIT and Harvard | |
| Government of South Australia |
Keywords
- Artificial Intelligence
- Immune Respons
- Immunology
- In Silico Biology
