Reverse vaccinology based in silico analysis of Epitope prediction in cya, lef and pagA genes from Bacillus anthracis against Anthrax infected species: An Immunoinformatics approach

Abstract

Bacillus anthracis is a Gram-positive spore-forming bacterium that causes the zoonotic disease: anthrax, an abrupt illness that disproportionately impacts grazing livestock and wild ruminants. The anthrax’s geographical reach despite years of research on anthrax epizootic and epidemics behaviour, till date remains to be elucidated. Existing therapeutics, however, are ineffective in combating this infectious disease, necessitating the development of a better vaccine to pause the pandemic using immunoinformatics approaches, this study intended to predict an efficient epitope for vaccine against the anthrax in animals and humans of the toxin genes such as cya, lef and pagA of B.anthracis against anthrax. The B-cell and T-cell epitopes were predicted utilizing various bioinformatics tools/software and docking analysis was performed. Consequently, it was found that the evaluated epitopes had no allergenicity, no toxicity and had high antigenicity that provides an effectual and most rapid technique to estimate peptide synthetic vaccines to impede the anthrax.