Identification of Potential Antituberculosis Drugs Through Docking and Virtual Screening

Area of Science:

• Computational biology
• Drug discovery
• Structural bioinformatics

Background:

• Tuberculosis (TB) remains a major global health challenge, necessitating novel therapeutic targets and drugs.
• Decaprenyl-phosphoryl-β-D-ribose 2 ime-oxidase, encoded by dprE1 and dprE2 genes in Mycobacterium tuberculosis, is a promising target for anti-TB chemotherapy.

Purpose of the Study:

• To predict the 3D structure of decaprenyl-phosphoryl-β-D-ribose 2 ime-oxidase using homology modeling.
• To perform structure-based virtual screening to identify potential inhibitors of this TB target.

Main Methods:

• Homology modeling using a multi-template approach with available crystal structures.
• Model refinement and validation using Ramachandran plot analysis and other structural verification tools.
• Structure-based virtual screening of the ZINC database via molecular docking.

Main Results:

• A reliable 3D model of the target enzyme was generated and validated.
• Virtual screening identified 10 potential inhibitor molecules based on docking energy and scoring.
• Ligand validation considered ADMET properties, confirming their potential as inhibitors.

Conclusions:

• Computational molecular modeling successfully predicted the target enzyme's structure and identified promising drug candidates.
• The identified ligands show potential for development into novel antitubercular agents targeting decaprenyl-phosphoryl-β-D-ribose 2 ime-oxidase.