In Silico Modeling and Immunoinformatics Probing Disclose the Epitope Based PeptideVaccine Against Zika Virus Envelope Glycoprotein

Abstract

Zika virus (ZIKV) is an aedes mosquito borne pathogen belonging to the member of flaviviridae subgroup is the causative agent of an emerging disease called Zika fever, known as a benign infection usually presenting as influenza like illness with cutaneous rash. Due to recent epidemic outbreaks it is realized as a major health risk which need enhanced surveillance, but no attempt has been made to design an epitope based peptide vaccine against Zika virus. Viral envelope proteins are derived from host cell membrane proteins with some viral glycoproteins and are used to cover their protective protein capsid, help the viruses to enter host cells and help them to avoid the host immune response. In this study, amino acid sequence of ZIKV envelope glycoprotein was obtained from a protein database and examined with in silico approaches to determine the most immunogenic epitopes for B cell and T cell which could induce humoral as well as cell mediated immune response. Both the linear and conformational epitopes for B cell were predicted by immunoinformatics tools housed in IEDB resources. The peptide sequence DAHAKRQTVVVLGSQEGAV from position 121 and peptide sequence from 117-137 amino acids were predicted as most potential B cell linear and conformational epitopes respectively. Epitopes for CD4+ and CD8+ T cell were also predicted by using tools within IEDB resource and peptide sequence MMLELDPPF from position 250-258 amino acids was predicted as most immunogenic CD8+ T cell epitope with immune response evoking ability prediction score (I pMHC) of 0.09139 and conservancy of 52.17%. The innate immune response for ZIKV envelope glycoprotein was determined by interferon (IFN)-gamma effectuation and mimicking capacity by immunoinformatics and molecular docking study respectively. However, this is an introductory approach to design an epitope based peptide vaccine against Zika virus; we hope this model will be very much helpful in designing and predicting novel vaccine candidate.