Candidate targets for immune responses to 2019-Novel Coronavirus (nCoV): sequence homology- and bioinformatic-based predictions

Alba Grifoni; John Sidney; Yun Zhang; Richard H Scheuermann; Bjoern Peters; Alessandro Sette

Epitope prediction was carried out using the ten proteins predicted for the reference The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.02.12.946087 doi: bioRxiv preprint For CD4 T cell epitope prediction, we applied a previously described algorithm that was developed to predict dominant HLA class II epitopes, using a median consensus percentile of prediction cutoff ≤ 20 as recommended (18) . For CD8 T cell epitope prediction, we selected the 12 most frequent HLA class I alleles in the worldwide population (20, 21) (17) . For each HLA class I allele analyzed, we selected the top 1% epitopes ranked based on prediction score. To generate a final set for synthesis, duplicate peptides (i.e., those selected for multiple alleles) were reduced to a single occurance, and nested peptides were ensconced within longer sequences, up to 14 residues in length, before assigning the multiple corresponding HLA restrictions for each region.