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The copyright holder for this preprint (which was not peer-reviewed) is the .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 13 merged the "Negative" and "Positive-Low" groups into one group and compared their percent 1 ranks with either the "Positive-Intermediate" or the "Positive-High" groups ( Figure 1B) . This 2 analysis revealed a trend similar to that observed with HLA-I predictions, indicating that stronger 3 MHC-binding assay results are associated with a lower predicted percent rank for HLA-II binders, 4 as we expect for a robust predictor. Similar to the HLA-I T cell assays, there were too few recorded 5 HLA-II T cell assays (31) in our validation dataset to determine percent rank differences between We detected a total of 11,776 unique SARS-CoV-2 peptides that were predicted to bind at least 21 one HLA-I allele with a percent rank score of 1% or lower (Supplementary Table 4 ). 14 of these 22 author/funder. All rights reserved. No reuse allowed without permission.