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Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes.
Genome Med. 2020 08 13; 12(1):70.GM

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+ 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+ 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+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ 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.

Authors+Show Affiliations

BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA. asaf.poran@biontech.us.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA. dewi.harjanto@biontech.us.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.BioNTech US, Inc., 40 Erie Street, Suite 110, Cambridge, MA, 02139, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32791978

Citation

Poran, Asaf, et al. "Sequence-based Prediction of SARS-CoV-2 Vaccine Targets Using a Mass Spectrometry-based Bioinformatics Predictor Identifies Immunogenic T Cell Epitopes." Genome Medicine, vol. 12, no. 1, 2020, p. 70.
Poran A, Harjanto D, Malloy M, et al. Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes. Genome Med. 2020;12(1):70.
Poran, A., Harjanto, D., Malloy, M., Arieta, C. M., Rothenberg, D. A., Lenkala, D., van Buuren, M. M., Addona, T. A., Rooney, M. S., Srinivasan, L., & Gaynor, R. B. (2020). Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes. Genome Medicine, 12(1), 70. https://doi.org/10.1186/s13073-020-00767-w
Poran A, et al. Sequence-based Prediction of SARS-CoV-2 Vaccine Targets Using a Mass Spectrometry-based Bioinformatics Predictor Identifies Immunogenic T Cell Epitopes. Genome Med. 2020 08 13;12(1):70. PubMed PMID: 32791978.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes. AU - Poran,Asaf, AU - Harjanto,Dewi, AU - Malloy,Matthew, AU - Arieta,Christina M, AU - Rothenberg,Daniel A, AU - Lenkala,Divya, AU - van Buuren,Marit M, AU - Addona,Terri A, AU - Rooney,Michael S, AU - Srinivasan,Lakshmi, AU - Gaynor,Richard B, Y1 - 2020/08/13/ PY - 2020/05/20/received PY - 2020/07/21/accepted PY - 2020/8/15/entrez PY - 2020/8/15/pubmed PY - 2020/8/29/medline KW - COVID-19 KW - Computational biology KW - HLA-I binding prediction KW - HLA-II binding prediction KW - SARS-CoV-2 T cell epitopes KW - T cell assay KW - Vaccine design SP - 70 EP - 70 JF - Genome medicine JO - Genome Med VL - 12 IS - 1 N2 - 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+ 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+ 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+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ 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. SN - 1756-994X UR - https://www.unboundmedicine.com/medline/citation/32791978/Sequence_based_prediction_of_SARS_CoV_2_vaccine_targets_using_a_mass_spectrometry_based_bioinformatics_predictor_identifies_immunogenic_T_cell_epitopes_ L2 - https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-020-00767-w DB - PRIME DP - Unbound Medicine ER -