STRUCTURE-BASED OPTIMIZATION OF DRUG CANDIDATES TARGETING INHA AND DPRE1 IN MYCOBACTERIUM TUBERCULOSIS

Abstract

Tuberculosis treatment has become challenging because of the increasing prevalence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains. Here, we used an integrated structure-based computational workflow combining ligand design, protein preparation, ensemble molecular docking, ADMET prediction, and 100-ns molecular dynamics (MD) simulation to investigate inhibitors of enoyl-ACP reductase (InhA) and decaprenyl phosphoryl-β-D-ribose 2′-epimerase (DprE1). Eight marketed anti-tubercular drugs and five designed ligands were evaluated against multiple crystallographic structures. Among the marketed drugs, Drug 2 showed the most favorable DprE1 docking score (−851.183) against PDB 4FDO, whereas Drug 8 showed the most favorable InhA score (−685.026) against 5UGU. Among the designed compounds, Ligand 5 showed the most favorable DprE1 docking score (−983.573) against 4FDO. Ligand 4 produced the strongest InhA docking score (−796.963) against 4OYR. To assess the dynamic behavior of the prioritized DprE1–Ligand 5 complex, a 100-ns GROMACS trajectory was analyzed using RMSD, radius of gyration (Rg), RMSF, and solvent-accessible surface area (SASA). The complex reached an apparently stable RMSD regime after the initial equilibration phase, while Rg showed no sustained drift. Residue-level fluctuations were generally low, with higher mobility localized to specific regions, and SASA remained within a relatively consistent range throughout the trajectory. Collectively, the docking and MD results provide complementary evidence that Ligand 5 can maintain a stable association with DprE1 over the simulated 100-ns period. Experimental validation and additional independent simulations are required to establish inhibitory activity and reproducibility.