Apr 8, 2020

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

BioRxiv : the Preprint Server for Biology
Asaf PoranR. B. Gaynor

Abstract

Background: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Consistent with observations for SARS-CoV, a closely related coronavirus responsible for the 2003 SARS outbreak, early reports identify a protective role for both humoral and cell-mediated immunity for SARS CoV-2. Methods: In this study, we leveraged HLA-I and HLA-II T cell epitope prediction tools from RECON (R) (Real-time Epitope Computation for ONcology), our bioinformatic pipeline that was developed using proteomic profiling of individual HLA-I and HLA-II alleles to predict rules for peptide binding to a diverse set of such alleles. We applied these binding predictors to viral genomes from the Coronaviridae family, and specifically to identify SARS-CoV-2 T cell epitopes. Results: To test the suitability of these tools to identify viral T cell epitopes, we first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then use our HLA-I and HLA-II predictors to identify 11,776 HLA-I and 7,991 HLA-II candidate binding peptides across all 12 open reading frames (ORFs) of SARS-CoV-2. This ex...Continue Reading

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Mentioned in this Paper

Study
Pseudo brand of pseudoephedrine
Reverse Transcriptase Polymerase Chain Reaction
Gene Expression
Sequencing
Oligonucleotide Primers
Nonclinical Randomization
cDNA Library
Transcriptome
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