Exploring the interaction mechanism between potential inhibitor and multi-target Mur enzymes of mycobacterium tuberculosis using molecular docking, molecular dynamics simulation, principal component analysis, free energy landscape, dynamic cross-correlation matrices, vector movements, and binding free energy calculation

Abstract

Multi-targeting enzyme approaches are considered to be the most significant in suppressing pathogen growth and disease control for MDR and XDR-resistant Mycobacterium tuberculosis. The multiple Mur enzymes involved in peptidoglycan biosynthesis play a key role in a cell’s growth. Firstly, homology modeling was employed to construct the 3 D structure of the Mur enzymes. The computational approaches, including molecular docking and molecular dynamics simulations and MM-PBSA methods, were performed to explore the detailed interaction mechanism to evaluate the inhibitory activity against targeted proteins. The computational calculations revealed that the best-docked phytochemical compound (gallomyricitrin) inhibits the selected targets: Mur enzymes by forming stable hydrogen bonds. The analysis of RMSD, RMSF, Rg, PCA, DCCM, cross-correlation network, FEL, H-bond, and vector movement reveal that the docked complex of MurA, MurI, MurG, MurC, and MurE is more stable compared to MurB, MurF, MurD, and MurX docked complexes during MD simulations. Moreover, FEL exposed that gallomyricitrin stabilized to the minimum global energy of Mur Enzymes. The PCA, DCCM, and vector movements and binding free energy results provided further evidence for the stability of gallomyricitrin’s interactions inside the binding sites by forming hydrogen bonds. The cross-correlation analysis reveals that Mur enzymes exhibit a positive and negative correlated motion between residues in different protein domains. The computational results contribute in several ways to our understanding of inhibition activity and provide a basic insight into the binding activity of gallomyricitrin as a multi-target drug for tuberculosis.

Communicated by Ramaswamy H. Sarma

Keywords:

• Mur enzymes
• multi-target
• Mycobacterium tuberculosis
• virtual screening
• MD simulation
• DCCM
• FEL
• cross-correlation network
• vector movement
• porcupine plot

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

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.