Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON

Asaf Poran; Dewi Harjanto; Matthew Malloy; Michael S. Rooney; Lakshmi Srinivasan; Richard B. Gaynor

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.06.027805 doi: bioRxiv preprint pathogen data from internally curated data, researcher submissions and data from various 1 external sources. Our approach provides a significant improvement in both the breadth of 2 predictions, and their validity, compared with a recent study that had a similar aim (31). We used 3 the HLA-I and HLA-II binding predictors from RECON to predict the binding potential of 4 peptide sequences from across the entire SARS-CoV-2 genome for a broad set of HLA-I and 5 HLA-II alleles, covering the vast majority of USA, European, and Asian populations 6 (Supplementary Table 3 if a given peptide-allele pair was assayed multiple times by a specific assay type and was 18 determined to be positive in any single one of the assays, the peptide-allele pair was classified as 19 positive. Specifically, the priority was given by the following order: Positive-High > Positive-20 Intermediate > Positive-Low > Positive > Negative (e.g., a peptide allele pairing that was 21 assayed three times with the results Positive-High, Positive, and Negative were assigned a 22 Positive-High result). 23 author/funder. All rights reserved. No reuse allowed without permission.