Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Brandon Malone; Boris Simovski; Clement Moline; Jun Cheng; Marius Gheorghe; Hugues Fontenelle; Ioannis Vardaxis; Simen Tennoe; Jenny-Ann Malmberg; Richard Stratford; Trevor Clancy

In order to effectively combat the SARS-CoV-2 pandemic a vaccine will need to protect the vast majority of the human population, and stimulate diverse T cell responses, against multiple viral targets including but not limited to the Sprotein. To help achieve this ambitious aim, we have profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population and generated comprehensive epitope maps. We subsequently used these epitope maps as the basis for modeling the specific genetic HLA background of individual persons in a diverse set of different human populations using the most significant CD8 and CD4 T cell "epitope hotspots" in the virus. To the best of our knowledge this is the first computational approach that generates comprehensive vaccine design blueprints from large-scale epitope maps of SARS-CoV-2, in a manner that optimizes for diverse T cell immune responses across the global population. Underlying this approach are two novel methods that when integrated together result in a solution that is uniquely suited to achieving the objective of the study i.e. designing blueprints for universal vaccines. Firstly, a framework that leverages Monte Carlo simulations was developed to identify statistically significant epitope hotspot regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. Secondly, a novel person-specific or "digital twin" type simulation using the actual HLA genotypes of approximately 22, 000 individuals prioritizes these epitope hotspots, to identify the optimal constellation of vaccine hotspots in the SARS-CoV-2 proteome that are most likely to promote a robust T cell immune response in the global population.