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Rigshospitalet - a part of Copenhagen University Hospital
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Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools

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The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vaccine development. Stimulation of a proper immune response that leads to protection is highly dependent on presentation of epitopes to circulating T-cells via the HLA complex. SARS-CoV-2 is a large RNA virus and testing of all of its overlapping peptides in vitro to deconvolute an immune response is not feasible. Therefore HLA-binding prediction tools are often used to narrow down the number of peptides to test. We tested NetMHC suite tools' predictions by using an in vitro peptide-MHC stability assay. We assessed 777 peptides that were predicted to be good binders across 11 MHC alleles in a complex-stability assay and tested a selection of 19 epitope-HLA-binding prediction tools against the assay. In this investigation of potential SARS-CoV-2 epitopes we found that current prediction tools vary in performance when assessing binding stability, and they are highly dependent on the MHC allele in question. Designing a COVID-19 vaccine where only a few epitope targets are included is therefore a very challenging task. Here, we present 174 SARS-CoV-2 epitopes with high prediction binding scores, validated to bind stably to 11 HLA alleles. Our findings may contribute to the design of an efficacious vaccine against COVID-19.

Original languageEnglish
JournalScientific Reports
Volume10
Issue number1
Pages (from-to)20465
ISSN2045-2322
DOIs
Publication statusPublished - 24 Nov 2020

    Research areas

  • Alleles, Base Sequence, COVID-19/prevention & control, COVID-19 Vaccines/immunology, Computational Biology/methods, Epitopes, T-Lymphocyte/immunology, HLA Antigens/genetics, Histocompatibility Antigens Class I/genetics, Histocompatibility Antigens Class II/genetics, Humans, Machine Learning, Peptides/genetics, SARS-CoV-2/immunology, Spike Glycoprotein, Coronavirus/genetics

ID: 62073945