Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

Santrupti Nerli; Nikolaos G Sgourakis

Identification of SARS-CoV-2 peptide epitopes 82 The SARS-CoV-2 protein sequences (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2) were 83 obtained from NCBI and used to generate all possible peptides of lengths 9 and 10 (9,621 9mer 84 and 9,611 10mer peptides). We used NetMHCpan-4.0 (27) to derive binding scores to HLA-85 A*02:01, and retained only peptides classified as strong or weak binders (selected using the default 86 percentile rank cut-off values). The binding classification was performed using eluted ligand 87 likelihood predictions. While in this study we use NetMHCpan-4.0 predictions as inputs to select 88 candidate epitopes for structure modeling, our workflow is fully compatible with any alternative 89 epitope prediction method. The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.23.004176 doi: bioRxiv preprint 4 made available through an online database (see data availability). The website that hosts our 120 database was constructed using the Django web framework.