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

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 Lipinski’s 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.


49
Introduction 50 Tuberculosis is a major global health concern and can be transmitted through droplet aerosol 51 from person to person (Khan et al., 2013). The causative agent of the disastrous tuberculosis  Multidrug resistant TB develops due to mutations in the genes or a change in the titration of the 57 drug (Migliori et al., 2007). The resistant strains are known as multiple drug resistance 58 tuberculosis (MDR-TB) strains when they develop resistance to at least two of the first line 59 treatment (isoniazid and rifampicin) (Prasad, 2005). When in addition to MDR, M. tuberculosis 60 strain develops resistance to at least one fluoroquinolone and at least one of the three second-line 61 injectable drugs, viz., amikacin, kanamycin, and capreomycin, is called extensively drug-62 resistant tuberculosis (XDR-TB) (Hamilton et al., 2007). 63 Isoniazid resistance is a complex process and develops as a result of mutations in inhA, 64 katG,kasA, ahpC and ndh genes (Da Silva and Palomino, 2011). katG codes for the enzyme 65 catalase/peroxidase, which is involved in the activation of isoniazid. The reduced or absent 66 activity of this enzyme occurs due to the mutations in katG gene, which is responsible for 67 isoniazid resistance (Zhang et al., 1992).Rifampicin works by binding with RNA polymerase β-68 subunit encoded by rpoBgene and thus blocking the elongation of messenger RNA (Blanchard, 69 1996). Pyrazinamide is equivalent to nicotinamide in terms of structure and hence naturally gets  Identifying the disease causing target protein is very critical in an in silico drug designing 78 method. The method used for investigating the protein ligand interactions is molecular docking.

79
The insilico drug designing approach has been getting recognition as an important tool to 80 identify potential novel drugs for various diseases (Gore and Desai, 2014). Computer Aided 81 Drug Designing (CADD) has effectively been used in molecular biology, nanotechnology, and 82 biochemistry and sooner it will be a dominant and commonly used technique in modern medical 83 sciences.

84
The in-silico screening is also known as virtual screening (VS). It exploits computer aided fitting well into the target binding site and hence increasing the chances of binding the target. In 91 the present study, an effort was made to find out powerful novel small molecule inhibitors of the 92 proteins encoded by drug resistant genes using an in-silico approach.

94
Computer aided drug designing tools were applied to find out the small molecule inhibitors of 95 the proteins encoded by drug resistant genes, i.e., katG, gyrA, pncA and rpoBof M. tuberculosis.

96
An HP Z620 Workstations with NVIDIA DDR5, 4GB GTX-980 graphics card was used on   The rpoB gene codes for a protein named DNA-directed RNA polymerase β-subunit, which is 132 linked to rifampicin resistance. The crystal structure of RNA polymerase β-subunit was obtained 133 from RCSB PDB with PDB ID 5UHC at a resolution of 3.8 angstrom.             Lipinski's rule. All screening outputs from ZINC database were evaluated by London dG scoring 296 method in MOE2016 and resulting five conformations were selected for each ligand using the 297 proxy triangle algorithm. Of these docked conformers 15% were selected on the basis of docking 298 score, which was further analyzed for binding interactions. Finally, on the basis of docking score 299 and binding interactions, ten small molecules were selected for each protein as novel inhibitors.  Table-1-4. 306

307
In this study, proteins encoded by katG, pncA, rpoB and gyrA gene of M. tuberculosis were 308 selected and targeted by drug like small compounds from ZINC drug like databases using an in-309 silico approach. The data analysis generated numerous novel and potent small molecules against 310 the proteins of katG, pncA, rpoB and gyrA gene.

311
Virtual screening based on the structure and ligand is an important tool in medicinal chemistry, 312 which plays a significant role in identification and chemo informatics. These virtual screening 313 methods are found to be widely applied to many therapeutic targets. . Another related study was performed to discover novel inhibitors of M. tuberculosis inhA 333 enzyme using in silico approach (Pauli et al., 2013). The present study reveals that the 334 pharmacophore-based approach of screening base ligand can be convenient in the discovery of 335 structurally diverse hits. These hits were structurally diverse with significant docking score and 336 good binding affinity with the targeted proteins' 3D structures.

337
The purpose of this study was to obtain a ligand having superior characteristics to inhibit the