TY - JOUR T1 - Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. AU - Malone,Brandon, AU - Simovski,Boris, AU - Moliné,Clément, AU - Cheng,Jun, AU - Gheorghe,Marius, AU - Fontenelle,Hugues, AU - Vardaxis,Ioannis, AU - Tennøe,Simen, AU - Malmberg,Jenny-Ann, AU - Stratford,Richard, AU - Clancy,Trevor, Y1 - 2020/12/23/ PY - 2020/06/21/received PY - 2020/11/30/accepted PY - 2020/12/28/entrez PY - 2020/12/29/pubmed PY - 2021/1/8/medline SP - 22375 EP - 22375 JF - Scientific reports JO - Sci Rep VL - 10 IS - 1 N2 - 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. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/33361777/Artificial_intelligence_predicts_the_immunogenic_landscape_of_SARS_CoV_2_leading_to_universal_blueprints_for_vaccine_designs_ DB - PRIME DP - Unbound Medicine ER -