Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Mycobacterium Tuberculosis PPE65 family Protein

Abstract

AbstractIntroductionTuberculosis (TB) is a serious disease with varying rates of mortality and morbidity among infected individuals which estimates for approximately two million deaths/year. The number of deaths could increase by 60% if left untreated. It mainly affects immune-compromised individuals and people of third world, due to poverty, low health standards, and inadequate medical care. It has varying range of manifestations that is affected by the host immune system response, the strain causing the infection, its virulence, and transmissibility.Materials and methodsA total of 1750Mycobacterium TuberculosisPPE65 family protein strains were retrieved from National Center for Biotechnology Information (NCBI) database on March 2019 and several tools were used for the analysis of the T- and B-cell peptides and homology modelling.Results and conclusionFour strong epitope candidates had been predicted in this study for having good binding affinity to HLA alleles, good global population coverage percentages. These peptides are YAGPGSGPM, AELDASVAM, GRAFNNFAAPRYGFK and a single B-cell peptide YAGP.This study uses immunoinformatics approach for the design of peptide based vaccines for M. tuberculosis. Peptide based vaccines are safer, more stable and less hazardous/allergenic when compared to conventional vaccines. In addition, peptide vaccines are less labouring, time consuming and cost efficient. The only weakness is the need to introduce an adjuvant to increase immunogenic stimulation of the vaccine recipient.