Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2.
Artificial Intelligence
Immune Respons
Immunology
In Silico Biology
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
23 Apr 2021
23 Apr 2021
Historique:
received:
12
01
2021
revised:
22
02
2021
accepted:
10
03
2021
pubmed:
23
3
2021
medline:
23
3
2021
entrez:
22
3
2021
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 33748696
doi: 10.1016/j.isci.2021.102311
pii: S2589-0042(21)00279-0
pmc: PMC7956900
doi:
Types de publication
Journal Article
Langues
eng
Pagination
102311Subventions
Organisme : CCR NIH HHS
ID : HHSN261200800001C
Pays : United States
Organisme : NCI NIH HHS
ID : HHSN261200800001E
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI138790
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI057229
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2021 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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