Identification of potential inhibitors against Escherichia coli Mur D enzyme to combat rising drug resistance: an in-silico approach

Abstract

Indiscriminate use of anti-microbial agents has resulted in the inception, frequency, and spread of antibiotic resistance among targeted bacterial pathogens and the commensal flora. Mur enzymes, playing a crucial role in cell-wall synthesis, are one of the most appropriate targets for developing novel inhibitors against antibiotic-resistant bacterial pathogens. In the present study, in-silico high-throughput virtual (HTVS) and Standard-Precision (SP) screening was carried out with 0.3 million compounds from several small-molecule libraries against the E. coli Mur D enzyme (PDB ID 2UUP). The docked complexes were further subjected to extra-precision (XP) docking calculations, and highest Glide-score compound was further subjected to molecular simulation studies. The top six virtual hits (S1–S6) displayed a glide score (G-score) within the range of −9.013 to −7.126 kcal/mol and compound S1 was found to have the highest stable interactions with the Mur D enzyme (2UUP) of E. coli. The stability of compound S1 with the Mur D (2UUP) complex was validated by a 100-ns molecular dynamics simulation. Binding free energy calculation by the MM-GBSA strategy of the S1-2UUP (Mur D) complex established van der Waals, hydrogen bonding, lipophilic, and Coulomb energy terms as significant favorable contributors for ligand binding. The final lead molecules were subjected to ADMET predictions to study their pharmacokinetic properties and displayed promising results, except for certain modifications required to improve QPlogHERG values. So, the compounds screened against the Mur D enzyme can be further studied as preparatory points for in-vivo studies to develop potential drugs.

HIGHLIGHTS

• E.coli is a common cause of urinary tract infections.

• E.coli MurD enzyme is a suitable target for drug development.

• Novel inhibitors against E.coli MurD enzyme were identified.

• Molecular dynamics studies identified in-silico potential of identified compound.

• ADMET predictions and Lipinski’s rule of five studies showed promising results.

Communicated by Ramaswamy H. Sarma

Keywords:

• E.coli
• hTVS
• XP
• MD simulation
• MM-gbsa
• ADMET predictions
• Mur enzymes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgements

The author wishes to thank Lovely professional University for their help and necessary support for carrying out the present work. The work did not receive any specific grant from funding agencies in the publc, commercial, or not-for-profit sectors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.