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The copyright holder for this preprint (which was not peer-reviewed) is the .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 CoV-2 that had previously been assayed in ViPR were confirmed as non-binding. We therefore 1 concluded that using RECON's HLA binding predictors to predict T cell epitopes from the ORFs 2 of SARS-CoV-2 provides a significantly expanded, novel set of high-quality vaccine targets for 3 the virus. These sequences can be exploited for vaccines in various formats, including RNA or 4 peptides. 5 6 This application of our prediction algorithms has clearly identified many candidate epitopes that 7 can be included in a vaccine to induce cellular responses against this novel virus. Immune Although short lived, antibody responses are essential to control the persistent phase of CoV 16 infection by preventing subsequent viral entry. We thus propose that a combination of B and T 17 cell epitopes could provide long-lasting immunity from SARS-CoV-2 or mitigate the severity of 18 disease when protection is partial. 19 The strength of our prediction is two-fold: first, we have validated predictors for both HLA-I and 20 HLA-II binders, which potentially could be leveraged to induce both long-term CD4 + and CD8 +