In silico identification of vaccine candidate from various screening methods against hepatitis C virus

Abstract

Hepatitis C virus (HCV) being an infectious disease is prevalent in most parts of the world. Till date, no vaccine is being developed in the market for HCV. This paper focuses mainly on developing a peptide-based vaccine against HCV. The purpose for this study is taken to determine the suitable epitope with the help of Bioinformatics tools developed for designing a vaccine against infectious diseases such as HCV. In present work, T-cell epitope is taken into consideration, as it recognises the antigen that helps to generate peptide with the help of antigen presenting cell. With respect to T-cell epitope selection, high binding energy must be required for binding major histocompatibility complex molecule. Moreover, T-cell epitope were considered on the basis of conserved site, protease cleavage site, motif, as well as an excellent hydrophobic binding pocket with a high half-life of dissociation. In consideration to the mentioned criteria, the required bioinformatics tools are used which are designed to predict the epitopes from different envelope and non-structural proteins of HCV virus. On an average, 1,000 epitopes from various databases and tools were extracted, from which 11 adept epitopes were withdrawn virtually with a base of binding energy using MHC I and II molecule protein interaction. The best epitope predicted during study was IMYAPTIWV peptide of NS5A protein. The T-cell predicted epitope can be further used for later chore in vaccine discovery for HCV.