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

Prior to applying the NEC Immune Profiler suite of tools to map the SARS-CoV-2 viral proteome, it was important to first validate, to the extent that is possible from the limited number of validated SARS-CoV viral epitopes, that the T cell based AP and IP scores are predicting viable targets. We identified class I epitopes from the original SARS-CoV virus (that first emerged in the Guangdong province in China in 2002) that shared ≥90% sequence identity with the current SARS-CoV-2. Unfortunately, many of the published epitopes were identified using ELISPOT on PBMCs from convalescent patients and/or healthy donors (or humanised mouse models) where the restricting HLA was not explicitly deconvoluted. In order to circumvent this problem, we identified a subset of 5 epitopes where the minimal epitopes and HLA restriction had been identified using tetramers [6] . Four out of the 5 epitopes tested were identified as positive i.e. had an IP score of above 0.5 (see Table 1 ) demonstrating an accuracy of 80%.