Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2.
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
15 May 2020
15 May 2020
Historique:
entrez:
9
6
2020
pubmed:
9
6
2020
medline:
9
6
2020
Statut:
epublish
Résumé
We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.
Identifiants
pubmed: 32511339
doi: 10.1101/2020.05.14.095885
pmc: PMC7241102
pii:
doi:
Types de publication
Preprint
Langues
eng
Commentaires et corrections
Type : UpdateIn
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