Modeling of biological network by using proteoinformatics analysis for identification of vaccine candidate antigen(s) of Mycobacterium tuberculosis

Abstract

According to WHO reports, tuberculosis is considered as one of the ten top causes of death worldwide. In 1921, Bacillus Calmette-Guerin (BCG) was introduced as a prophylactic vaccine against tuberculosis, which induces high protection in the children. On the other hand, the BCG showed questionable efficacy and variable levels of protection in adults, immunocompromised individuals, and inefficiency on drug-resistant strains. Therefore, the development of a new and more efficient vaccine against TB has the utmost importance. Using the latest techniques like next-generation sequencing (NGS) and virtual screening of proteins to find appropriate vaccine candidates can be a suitable solution for TB vaccine challenges. Aim: We aimed to utilize computational and immunoinformatic tools to identify new antigens as potential vaccine candidates for designing an epitope-based vaccine against Mycobacterium tuberculosis. Methods For generating a reliable and appropriate protein-protein interaction network, The STRING database was used to gather data for establishing a PPI network. Using topological analyzer tools facilitates identifying hub proteins of generated networks. Consequently, filtration tools have been applied to make a shortened vaccine antigen list by identifying hub proteins. We used concepts including subcellular localization, antigenicity, virulence factor, homology, allergenicity, and essentiality to achieve this goal. Result From 3993 proteins of M. tuberculosis's reference strain, 283 proteins have been considered as hub proteins. The results were filtered to reach eight antigen proteins for possible vaccine design using all in-silico methods mentioned above. Conclusion Our study confirmed the potency of topological analysis and immunoinformatic tools for effective antigen prediction and vaccine development.