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_50
Snippet: The data sets inputted into the statistical framework are epitope maps generated for each amino-acid position in all the proteins in the SARS-CoV-2 proteome, for all of the studied 100 HLA alleles. A score for any given amino acid was determined as the maximum AP or IP score that a peptide overlapping that amino acid holds in the epitope map. All peptide lengths of size 8-11 amino acids for class I, and 15 for class II were processed, generating .....
Document: The data sets inputted into the statistical framework are epitope maps generated for each amino-acid position in all the proteins in the SARS-CoV-2 proteome, for all of the studied 100 HLA alleles. A score for any given amino acid was determined as the maximum AP or IP score that a peptide overlapping that amino acid holds in the epitope map. All peptide lengths of size 8-11 amino acids for class I, and 15 for class II were processed, generating one HLA dataset per viral protein. Each row in the dataset represents the amino acid epitope scores predicted for one HLA type.
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