A Computational Study on the Structural Prediction of InhA Inhibitors as Antimycobacterial Agents

Abstract

InhA is an enoyl acyl carrier reductase that catalyzes the chemo-selective reduction of its 2-trans-enoyl-ACP substrate. The pharmacological effects of the frontline medications used in the treatment of tuberculosis are elicited by inhibiting the enzyme InhA, which disrupts the mycolic acid biosynthesis pathway. The present study involves development of ligand-based pharmacophore model, docking studies, generation of 3D-QSAR model, and molecular dynamic simulation studies of virtually screened putative InhA inhibitors. The best field-based 3D-QSAR model was validated using partial least-square regression (PLS) method with high regression coefficient for training set (R 2) = 0.9256 and test set (R 2) = 0.7542, cross-validated coefficient (r cv 2) = 0.72 and R 2 pred = 0.9764. Also, virtual screening yielded compounds 4, 25, 1, and 30 exhibiting appreciable interactions (docking scores −5.972, −3.819, −3.801, and −3.701, respectively with InhA synthetase; PDB ID: 1BVR). Further, ADMET studies supported drug-like potential of compounds 25, 1, and 30 whereas compound 4 showed negligible human oral absorption. The molecular dynamic simulation studies of the top scored molecule 25 suggested stability of 25-1BVR complex over the course of simulation run. Therefore, this work can be helpful for future discovery of novel InhA inhibitors against drug-resistant tuberculosis.