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The copyright holder for this preprint (which was not peer-reviewed) is the .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 5 infections. Given the homology between SARS-CoV and SARS-CoV-2, as well as emerging 1 data on SARS-CoV-2 (23), cellular immune mechanisms might play a critical role in providing 2 protection against SARS-CoV-2. 3 Here, we used T cell epitope prediction tools from the bioinformatic pipeline RECON ® (Real- 4 time Epitope Computation for ONcology) (26, 27) to identify SARS-CoV-2 epitopes recognized 5 by CD4 + and CD8 + T cells. RECON was trained on high-quality mono-allelic major 6 histocompatibility complex (MHC) immunopeptidome data generated via mass spectrometry. 7 The use of mass spectrometry allows for the high throughput, and relatively unbiased, collection 8 of MHC binding data compared to traditional binding affinity assays, as well as the inclusion of 9 important chaperone molecules. Additionally, the use of engineered mono-allelic cell lines 10 avoids dependence on in-silico deconvolution techniques and allows for allele coverage to be 11 expanded in a targeted manner. 12 With this approach, we generated data for 74 human leukocyte antigen (HLA)-I and 83 HLA-II 13 alleles (Supplementary Tables 1 and 2 ). This mass spectrometry data enabled us to train neural 14 network-based binding predictors that outperform the leading affinity-based predictors for both 15 HLA-I (26) and . Furthermore, we demonstrated in (27) that this improved binding 16 prediction leads to improved immunogenicity prediction by validating on a data set of tetramer 17 responses to a diverse collection of pathogens and allergens (28,29). Although RECON was 18 originally developed to prioritize neoantigens for immunotherapy applications, it is agnostic to 19 the source of peptide sequences evaluated and can be easily applied to peptides derived from 20 pathogens as well. As validation to that end, the binding predictors from RECON were used to 21 score Coronaviridae family peptides that had been assayed for T cell reactivity or MHC binding 22 from the Virus Pathogen Resource (ViPR) database (30). The ViPR database integrates viral 23 author/funder. All rights reserved. No reuse allowed without permission.