Computational modeling and analysis of prominent T-cell epitopes for assisting in designing vaccine of ZIKA virus

Abstract

The Zika virus disease or Zika fever, regularly shows no or just mellow side effects, like an exceptionally gentle type of dengue fever. It spread eastwards from 2007-16 over the Pacific Ocean to the Americas, whereas in 2015 to 2016, Zika virus scourge achieved epidemic levels. In this study, the antigenic epitopes of Zika virus (ZIKV) were predicted and modeled. The highest binding scorers among the predicted ones and their correlating MHC class II alleles were further subjected to binding simulation studies. Immunoinformatics tools were applied to analyze the viral antigenic proteins that may be helpful in designing vaccine for ZIKV. The promiscuous epitopes of MHC class II were predicted from the viral proteins using ProPred, an immunoinformatics tool. The chosen epitopes and MHC alleles were modeled molecularly using PEP-FOLD3 and CPH model 3.2 servers respectively. The viral glycoprotein having epitope/peptide YRIMLSVHG bound to DRB1*01:01 MHC class II allele demonstrated the most noteworthy binding score. The anticipated peptide has high possibility of inducing T cell mediated immune response and it might be helpful in designing vaccines based on epitopes after continued future trials.