Title:In-Silico Analysis of Imidazo[2,1-b][1,3,4]thiadiazole Analogs as Putative Mycobacterium tuberculosis Enoyl Reductase Inhibitors
Volume: 12
Issue: 1
Author(s): Preeti Wadhwa, Sourav Bagchi and Anuj Sharma*
Affiliation:
- Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee- 247667,India
Keywords:
Tuberculosis, InhA, molecular docking, 3D-QSAR, CoMFA, CoMSIA, imidazo[2, 1-b][1, 3, 4]
thiadiazoles, InhA inhibitors.
Abstract: Background: Trans-2-Enoyl-ACP reductase (InhA) is an established target
towards anti-tuberculosis therapy.
Objective: Computational studies on imidazo[2,1-b][1,3,4]thiadiazole derivatives as
putative InhA inhibitors.
Methods: Combined molecular docking and three-dimensional quantitative structureactivity
relationship (3D-QSAR) comprising comparative molecular field analysis (CoMFA)
and comparative molecular similarity indices analysis (CoMSIA) were performed on
imidazo[2,1-b][1,3,4]thiadiazole derivatives as putative InhA inhibitors.
Results: Docking analysis reveals that hydrogen bonding, α-π and hydrophobic
interactions are the governing factors for anti-TB activity. Furthermore, their best poses
were used as an alignment tool for the development of 3D-QSAR models. The CoMFA
model exhibited statistically significant results where Leave One Out (LOO) crossvalidated
(q2), non-cross validated (r2) and predicted correlation coefficient (r2
pred) values
were found to be 0.812, 0.982 and 0.667 respectively, therefore unveiled the important key
structural requirements for InhA inhibition.
Conclusion: The active site residues GLN100, PRO156, TYR158, LEU197, ALA198,
MET199, and LEU218 were identified as crucial binding residues responsible for
interactions between inhibitors and InhA. Further, CoMFA analysis theorized that more
potent InhA inhibitor could be developed based on proper substitution pattern around the
phenyl ring at R2 and R3 position. Conclusively, this comprehensive study, an integration
of molecular docking and CoMFA analysis provided insights and new predictive tools for
structure-based design and optimization of InhA inhibitors.