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_85
Snippet: For each protein within the viral genome, the set of unique amino acid sequences was compiled from all the strains available in the GISAID database [36] as of 29 .03.2020. These sets were individually processed using the Clustal Omega The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.21.052084 doi: bioRxiv preprint settings. The software outputs a consensus sequence that contains conserv.....
Document: For each protein within the viral genome, the set of unique amino acid sequences was compiled from all the strains available in the GISAID database [36] as of 29 .03.2020. These sets were individually processed using the Clustal Omega The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.21.052084 doi: bioRxiv preprint settings. The software outputs a consensus sequence that contains conservation information for each amino acid within the protein sequence. As such, an amino acid depicted as an "*" at position i within the consensus sequence translates to that amino acid being conserved at position i among all the input sequences [40] The hotspot offsets were then used to extract their respective consensus subsequence. For each hotspot, the conservation score was calculated as the ratio of "*" within its consensus sub-sequence to the total length of the sub-sequence.
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