Prediction of T cell epitopes for the utility of vaccine development from structural proteins of dengue virus variants using in silico methods

The spread of dengue virus, dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) has increased significantly in the past two decades and thus has been a major concern of public health globally. Dengue virus infection can lead to a wide range of manifestations in the form of undifferentiated fever, classic dengue fever, DHF with plasma leakage, which leads to hypovelmic shock DSS. A new strategy for developing prophylactic and therapeutic application of pathogenspecific immunity was provided from epitope-based vaccines; it is a critical requirement for the identification and selection of T cell epitopes that act as vaccine target. Immunoinformatics serves as a valuable tool to screen and select antigenic peptide sequences as potential T cell epitopes for binding affinity with HLA alleles. We studied dengue variants conserved epitopes in three structural proteins, capsid, envelope and precursor membrane, which recognize some highest binding affinity HLA. A total of 45 promiscuous nanomer candidate epitopes for HTL are recognized against MHC Class II and 28 promiscuous epitopes are recognized against CTL for MHC class I. This computational prediction analysis will improve our understanding of T cell immune response and help in identifying the antigenic peptide for formulation of antigen based diagnostic kit and peptide based subunit vaccine design against dengue virus.