Computational approach for selection of epitope-based dengue vaccine targets

High antigenic variability in the envelope (E) protein of different virus strains has been a major obstacle in designing effective vaccines for Dengue virus (DENV). To maintain their biological function, some parts of viral proteins remain stable during evolution thus one possible approach to solve this problem is to recognize specific regions within different protein sequences of E that have the tendency to stay constant through evolution. These regions may possess some special attributes to become a vaccine candidate against dengue virus. In this study, a computational approach was utilized to identify and analyze highly conserved amino acid sequences of the DENV E protein. Sequences of 9 amino acids or more were specifically focused due to their immune-relevant as T-cell determinants. Different bioinformatics tools were responsible for revealing conserved regions in the DENV E protein and constructing the phylogenetic tree from the sequence database. The tools also predicted immunogenicity of the identified vaccine targets. Ultimately, two peptide regions of at least 9 amino acids were chosen due to their high conserved attribute in more than 95% of all collected DENV sequences. Moreover, both of them was found to be immune-relevant by their correspondence to known or putative HLA-restricted T cell determinants. The conserved attribute of these sequences through the entire analysis of this study supports their potential as candidates for further in vitro experiments for rational design a universal vaccine which has longer and broader impact