tetano
Editor, Senior Moderator
Genome Med
. 2020 Aug 13;12(1):70.
doi: 10.1186/s13073-020-00767-w.
Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes
Asaf Poran[SUP] 1 [/SUP], Dewi Harjanto[SUP] 2 [/SUP], Matthew Malloy[SUP] 3 [/SUP], Christina M Arieta[SUP] 3 [/SUP], Daniel A Rothenberg[SUP] 3 [/SUP], Divya Lenkala[SUP] 3 [/SUP], Marit M van Buuren[SUP] 3 [/SUP], Terri A Addona[SUP] 3 [/SUP], Michael S Rooney[SUP] 3 [/SUP], Lakshmi Srinivasan[SUP] 3 [/SUP], Richard B Gaynor[SUP] 3 [/SUP]
Affiliations
Abstract
Background: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2.
Methods: We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8[SUP]+[/SUP] T cell responses.
Results: We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8[SUP]+[/SUP] T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity.
Conclusions: Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4[SUP]+[/SUP] and CD8[SUP]+[/SUP] T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8[SUP]+[/SUP] T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.
Keywords: COVID-19; Computational biology; HLA-I binding prediction; HLA-II binding prediction; SARS-CoV-2 T cell epitopes; T cell assay; Vaccine design.
. 2020 Aug 13;12(1):70.
doi: 10.1186/s13073-020-00767-w.
Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes
Asaf Poran[SUP] 1 [/SUP], Dewi Harjanto[SUP] 2 [/SUP], Matthew Malloy[SUP] 3 [/SUP], Christina M Arieta[SUP] 3 [/SUP], Daniel A Rothenberg[SUP] 3 [/SUP], Divya Lenkala[SUP] 3 [/SUP], Marit M van Buuren[SUP] 3 [/SUP], Terri A Addona[SUP] 3 [/SUP], Michael S Rooney[SUP] 3 [/SUP], Lakshmi Srinivasan[SUP] 3 [/SUP], Richard B Gaynor[SUP] 3 [/SUP]
Affiliations
- PMID: 32791978
- DOI: 10.1186/s13073-020-00767-w
Abstract
Background: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2.
Methods: We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8[SUP]+[/SUP] T cell responses.
Results: We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8[SUP]+[/SUP] T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity.
Conclusions: Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4[SUP]+[/SUP] and CD8[SUP]+[/SUP] T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8[SUP]+[/SUP] T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.
Keywords: COVID-19; Computational biology; HLA-I binding prediction; HLA-II binding prediction; SARS-CoV-2 T cell epitopes; T cell assay; Vaccine design.