Identification of Potent Vaccine Candidates Against Campylobacter jejuni Using Immunoinformatics Approach

Abstract

Infection from Campylobacter jejuni causes intense enteritis with diarrhea, fever and abdominal pain. The peptide-based vaccine could be the best way to deal with the immune response especially when antibiotic resistance is increasing in bacteria. In this study, Vaxign Server was used to predict the vaccine targets by computing proteome of diarrheagenic Campylobacter jejuni strain NCTC 11168 using default parameters. NetMHC II 2.3 server and Vaxijen tool were used to screen epitopes from protein and to identify the most antigenic epitopes binding with MHC class II molecules, respectively. Three potent T cell epitopes YFYGLAGGG, PFIMFSMLM and YIILNNFKI were screened binding with HLA-DRB1*10:01, HLA-DRB1*01:03, HLA-DRB1*04:01 and HLA-DRB1*07:01 MHC class II alleles, respectively. PepstrMod and Swiss-Model server were used to build a 3D structure model of epitopes and alleles, respectively. Identified epitopes were further docked with HLA alleles using AutoDock Vina tool to confirm binding ability. The epitopes YFYGLAGGG and PFIMFSMLM had a binding affinity of − 6.3 kcal/mol and − 6.1 kcal/mol with HLA-DRB1*10:01 and HLA-DRB1*01:03 alleles, respectively. Epitope YIILNNFKI had binding energy − 6.1 kcal/mol with two alleles HLA-DRB1*04:01 and HLA-DRB1*07:01. The predicted epitope-allele docked complex structure was optimized using molecular dynamics simulation and stability of complex structure was evaluated. These predicted epitopes were found to cover a maximum number of populations coverage in India as well as worldwide. Therefore, these epitopes had the most potential to induce T cell-mediated immune response and potent vaccine candidates against Campylobacter jejuni after testing by experimental study.