In silico Prediction of Epitope Based Vaccine Candidates against Powassan Virus Infection

Abstract

Powassan virus (POWV) is responsible for encephalitis and severe neurological sequelae globally. Peptide target based designing offers a promising therapeutic invention for the eradication of viral infection. Immunoinformatics serves as a powerful tool to screen and select antigenic peptide sequences as potential epitopes for binding affinity with HLA alleles. In the present study, a computational pipeline was developed for the identification of B-cell and T-cell epitopes for suitable vaccine candidates. Further, immunogenicity and physico-chemical prediction studies enable the discrimination between antigens and non-antigens. Considering the population setting globally, population coverage analysis was also performed for the identification of possible binding alleles (MHC class-I and MHC class-II) of T-cell epitopes.This computational prediction analysis will enhance our understanding of B-cell/T-cell immune response and assist in selecting the antigenic peptide(s) for the formulation of antigen based diagnostic kit or peptide based subunit vaccine design against POWV.