Designing Novel and Potent Inhibitors for Multi Drug-Resistant Tuberculosis: A Computational Approach

Abstract

Tuberculosis is a major global health problem and is still among the top 10 causes of death. The increasing rate of drug resistance to infectious agents has provoked an urgent need to discover novel anti-tuberculosis agents with novel modes of action. In this study, small molecule inhibitors of the proteins encoded by the drug resistant genes, i.e., katG, gyrA, pncA and rpoB of Mycobacterium tuberculosis (M. tuberculosis), were identified using computational methods. In the ligand base pharmacophore, an already reported four ligands for the four proteins encoded by the resistant genes of M. tuberculosis were selected for the generation of pharmacophores. The validated pharmacophores model of all the four proteins, generated on the basis of ligand base, were selected for further screening of ZINC drug like database. As screening results 486 structurally diverse hits for katG, 542 for PncA, 112 for rpoB and 365 for gyrA were mapped and filtered via Lipinskis rule of five. Finally, on the basis of docking score and binding interactions, ten small molecules were selected for each protein as novel inhibitors. These selected novel inhibitors have significant interaction with the active site of the protein and a strong possibility to act as an additional starting opinion in the development of new and potential inhibitors. The result indicates that novel inhibitors could be a promising lead compound and be effective in treating sensitive as well as multi drug resistant tuberculosis.