Author: Brandon Malone; Boris Simovski; Clement Moline; Jun Cheng; Marius Gheorghe; Hugues Fontenelle; Ioannis Vardaxis; Simen Tennoe; Jenny-Ann Malmberg; Richard Stratford; Trevor Clancy
Title: Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs Document date: 2020_4_21
ID: cm30gyd8_13
Snippet: In order to rank-prioritize these potential universal epitope hotspots, and the peptides that underlie them at high resolution, the baseline peptide predictions are then taken through a graph based "digital twin" type simulation [32] , to prioritize hotspots and the specific overlapping peptides that they comprise at a patient specific and population specific level. In addition, epitope hotspots containing viral epitopes that had high similarity .....
Document: In order to rank-prioritize these potential universal epitope hotspots, and the peptides that underlie them at high resolution, the baseline peptide predictions are then taken through a graph based "digital twin" type simulation [32] , to prioritize hotspots and the specific overlapping peptides that they comprise at a patient specific and population specific level. In addition, epitope hotspots containing viral epitopes that had high similarity with human peptides, especially those expressed in critical organs were removed from the blueprints.
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