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_41
Snippet: A genomic analysis of approximately 3400 SARS-COV-2 sequences revealed that the epitope hotspots that we predict are robust across different evolving clades of the virus, which may be important in the design of universal vaccine blueprints. However, on average, mutations in the virus that cause amino acid changes in peptides seem to reduce their potential to be presented on the cell surface and consequently detected by the host immune system. We .....
Document: A genomic analysis of approximately 3400 SARS-COV-2 sequences revealed that the epitope hotspots that we predict are robust across different evolving clades of the virus, which may be important in the design of universal vaccine blueprints. However, on average, mutations in the virus that cause amino acid changes in peptides seem to reduce their potential to be presented on the cell surface and consequently detected by the host immune system. We therefore apply filters on the vaccine blueprints that discard less sequence conserved hotspots, and hotspots that harbor peptides that have an exact match in the human proteome, before performing our digital twin simulation.
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