Discovery of multi-target mur enzymes inhibitors with anti-mycobacterial activity through a Scaffold approach

Abstract

In the present study, we generated a ligand-based scaffold model from a known bioactive datasets of mur enzymes of other species to identify multi-targeting inhibitors as antitubercular agents. Compounds in the ChEMBL database were first filtered to screen for substructure molecules ofMtb’s multi-target enzymes. 5′-O-(5-Amino-5-deoxy-β-D-ribofuranosyl)uridine has been identified as scaffold to develop compounds targeting Mtb’s mur enzymes. A library of Murcko scaffolds was extracted and evaluated for their in-silico antitubercular activity against Mtb’s mur enzymes. The screened compounds were subjected to molecular docking, molecular dynamics simulations, MM/PBSA calculation with Mtb’s mur enzymes to evaluate the mechanism of interaction to assess inhibitory activity against the target protein. The results revealed that 15 compounds have higher docking scores and good interactions with multiple mur enzymes of Mtb. From the docking analysis, compound HPT had the best score and binding affinity with the all mur enzymes. Further, protein-ligand interactions were evaluated by molecular dynamics simulations to assess their stability throughout 100 ns period. From the MD trajectory, we calculated RMSD, RMSF, Rg, PCA, DCCM, FEL, hydrogen bonding, and vector motion. Furthermore, the binding free energies of the all nine mur enzymes with compound HPT exhibited good binding affinity might show the anti-mycobacterial activity. The compound HPT revealed from this computational study could act as potent anti-mycobacterial inhibitors and further serve as lead scaffolds to develop more potent pharmaceutical molecules targeting multiple mur enzymes of Mtb based on 5′-O-(5-Amino-5-deoxy-β-D-ribofuranosyl)uridine in the future.

Communicated by Ramaswamy H. Sarma

Keywords:

• Scaffold
• Murcko scaffold
• mur enzymes
• Mycobacterium tuberculosis
• virtual screening
• molecular docking
• molecular dynamic simulations
• dynamic cross-correlation matrices
• free energy landscape

Acknowledgements

Bioinformatics infrastructure Facility supported by the Department of Biotechnology of Government of India and Madhulata Kumari Research Associate funded by Indian Council of Medical Research, New Delhi, India.

Disclosure statement

The authors declare that they have no conflict of interest in relation to the study in this paper.

Additional information

Funding

Centres of Excellence in Bioinformatics supported by Department ofBiotechnology of Government of India and funded by Indian Council ofMedical Research, New Delhi, India.