In-Silico Design of an E2 Envelope Glycoprotein targeted Multi Epitope vaccine against Chikungunya Virus (CHIKV)

Abstract

The mosquito-borne chikungunya virus has caused numerous outbreaks globally over the years and its transmission is expeditious. Due to the lack of an effective vaccine, chikungunya infection prevention mainly focuses on preventing mosquito bites. Chikungunya infection causes debilitating symptoms. An effective vaccine can provide stronger protection in future outbreaks. In this study, in-silico approach was taken to construct a multi-epitope vaccine against the chikungunya virus and in-silico biochemical analysis of the designed vaccine was performed. The E2 envelope glycoprotein of chikungunya virus, collected from Vipr database, was selected as primary protein. Its antigenicity (0.5679) was found using Vaxijen v2.0. Cytotoxic T lymphocyte epitopes, helper T lymphocyte epitopes, and B cell epitopes were identified using NetCTL-1.2, NetMHCIIpan 4.0, and Bepipred servers respectively. The helper T lymphocyte epitopes were screened using IFNepitope, IL-4Pred, and IL-10Pred. Linkers were used according to literature studies in order to construct the vaccine. Biochemical analysis of the final vaccine represented promising results. ProtParam was used to predict the instability index (35.68), grand average of hydropathy (-0.553), and molecular weight (63.72038 kDa) of the final vaccine. AllergenOnline and T3DB predicted the vaccine as non-allergen and non-toxic respectively. ProSAweb was applied to assess Z-score (-5.16) and SWISS-MODEL generated Ramachandran plots. C IMMSIM predicted desirable responses of immune cells and antibodies in accordance with three doses of the vaccine. As this study was based on in-silico computational methods, future investigations incorporating in vitro and in vivo methods are needed to validate the safety and efficacy of the constructed chikungunya vaccine.