Record Details

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

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title 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
Not Available
 
Creator Indrabalan UB
Puttahonnappa SK
Beelagi MS
Patil SS
Shivamallu C
Pappana M
Amachawadi R
 
Subject Google Anthrax
cya
lef
pag
B-cell
T-cell epitopes
 
Description Not Available
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 halt 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 allegenicity, no toxicity and had high antigenicity that provides an effectual and most rapid technique to estimate peptide synthetic vaccines to impede the anthrax.
Not Available
 
Date 2023-04-12T07:23:47Z
2023-04-12T07:23:47Z
2022-02-27
 
Type Research Paper
 
Identifier Indrabalan UB, Puttahonnappa SK, Beelagi MS, Patil SS, Shivamallu C, Pappana M and Amachawadi R. (2022). 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. Chemical Biology Letters, 9(2): 295-295.
2347–9825
http://krishi.icar.gov.in/jspui/handle/123456789/76826
 
Language English
 
Relation Not Available;
 
Publisher ScienceIn Publishing