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.
【저자키워드】 viral infection, machine learning, Software, Antigen processing and presentation, MHC, Proteome informatics, 【초록키워드】 COVID-19, SARS-CoV-2, Vaccine, Vaccine development, COVID-19 vaccine, immune response, 2019-nCoV, peptide, in vitro, virus, stability, outbreak, target, T-cell, RNA virus, epitope, binding, Stimulation, overlapping, HLA alleles, allele, circulating, binding scores, tested, predicted, question, contribute, dependent on, feasible, MHC allele, SARS-CoV-2 epitope, the HLA complex, 【제목키워드】 COVID-19 vaccine, epitope, identification, reveal,