The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.
【저자키워드】 immunology, Applied immunology, Vaccines, Immunotherapy, Virtual drug screening, Biotechnology, Computational biology and bioinformatics, Data integration, immunization, MHC class I, MHC class II, Antigen processing and presentation, 【초록키워드】 COVID-19, coronavirus disease, severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, Coronavirus disease 2019, Vaccine, coronavirus, pandemic, Mutation, artificial intelligence, virus, database, coronavirus 2, Novel coronavirus, Protein, surface antigen, Epitopes, Region, Coverage, Viral, cells, SARS-CoV-2 mutations, proteome, HLA, predictor, Antigen presentation, respiratory, T-cell epitopes, epitope, predict, HLA-DR, T-cell epitope, HLA-B, broad spectrum, Combination, Sequence conservation, Analysis, Nonsynonymous mutations, human population, HLA-A, acute respiratory syndrome coronavirus, chance, individual, sequence, help, SARS-CoV-2 proteome, host immune system, immunogenic, homology, allele, input, hotspot, HLA haplotypes, approach, effective, Cell, autoimmune responses, robust, nonsynonymous mutation, predicted, analyzed, identify, develop, occurred, caused, reduced, reduce, subset, statistically significant, HLA haplotype, NEC, 【제목키워드】 SARS-CoV-2, artificial intelligence, Vaccine design, predict, immunogenic,