A soft-computation hybrid method for search of the antibiotic-resistant gene in Mycobacterium tuberculosis for promising drug target identification and antimycobacterial lead discovery.

Abstract

Tuberculosis (TB) control programs were already piloted before the COVID-19 pandemic commenced and the global TB response was amplified by the pandemic. To combat the global TB epidemic, drug repurposing, novel drug discovery, identification and targeting of the antimicrobial resistance (AMR) genes, and addressing social determinants of TB are required. The study aimed to identify AMR genes in Mycobacterium tuberculosis (MTB) and a new anti-mycobacterial drug candidate. In this research, we used a few software to explore some AMR genes as a target protein in MTB and identified some potent antimycobacterial agents. We used Maestro v12.8 software, along with STRING v11.0, KEGG and Pass Server databases to gain a deeper understanding of MTB AMR genes as drug targets. Computer-aided analysis was used to identify mtrA and katG AMR genes as potential drug targets to depict some antimycobacterial drug candidates. Based on docking scores of -4.218 and -6.161, carvacrol was identified as a potent inhibitor against both drug targets. This research offers drug target identification and discovery of antimycobacterial leads, a unique and promising approach to combating the challenge of antibiotic resistance in Mycobacterium, and contributes to the development of a potential futuristic solution.