Identification of Potential Antituberculosis Drugs Through Docking and Virtual Screening

Tuberculosis is the most prominent contagious disease and needs the new targets and drugs identification. Target identification and validation is a crucial step in drug discovery process. In Mycobacterium tuberculosis, decaprenyl-phosphoryl-β-D-ribose 2'-oxidase is a potential target for antitubercular chemotherapy. It is encoded by genes dprE1 (Rv3790) and dprE2 (Rv3791). Three-dimensional (3D) structure prediction of selected target (461 amino acid residues) was intent by homology modeling using multitemplate approach based on crystal structure of 2EXR.pdb and 2Q4W.pdb with score = 48.9 bits and identities = 42/148 (29 %). A computed model was subjected to refinement based on consensus generated from two-dimensional structures and then evaluated using Structural Analysis and Verification Server to get reliable model. The final optimized model after analysis of Ramachandran plot revealed 86.5 % Core, 78.1 % Verify 3D score and 75.302 Errat values. Structure-based virtual screening against ZINC database was performed through molecular docking approach using Molegro Virtual Docker 4.2.0. The best 10 docked ligands were enumerated and validated based on their AutoDock Vina docking energy, scoring function and absorption, distribution, metabolism, excretion properties. The complex scoring function, docking energies and binding affinities revealed that these ligand molecules could be promising inhibitors against decaprenyl-phosphoryl-β-D-ribose 2'-oxidase. The present work also investigates the potential of computational molecular modeling.