Identification of potential non-nucleoside MraY inhibitors for tuberculosis chemotherapy using structure-based virtual screening

Abstract

The efforts to limit the spread of the tuberculosis epidemic have been challenged by the rise of drug-resistant strains of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. It is critical to discover new chemical scaffolds acting on novel or unexploited targets to beat this drug-resistant pathogen. MraY (phospho-MurNAc-pentapeptide translocase or translocase I) is an in vivo validated target for antibacterials-discovery. MraY is inhibited by nucleoside-based natural products that suffer from poor in vivo efficacy. The current study is focused on discovering novel chemical entities, particularly, non-nucleoside small molecules, as MraY_Mtb inhibitors possessing antituberculosis activity. In the absence of any reported X-ray crystal structures of MraY_Mtb, we used a homology model-based virtual screening approach combined with the ligand-based e-pharmacophore screening. We screened ∼12 million commercially available compounds from the ZINC15 database using GOLD software. The resulting hits were filtered using a 2-pronged screening method comprising e-pharmacophore hypotheses and docking against the MraY_Mtb homology model using Glide. Further clustering based on Glide scores and optimal binding interactions resulted in 15 in silico hits. We performed molecular dynamics (MD) simulations for the three best-ranking compounds and one other poorer-ranking compound, out of the 15 in silico hits, to analyze the interaction modes in detail. The MD simulations indicated stable interactions between the compounds and key residues in the MraY active site that are crucial for maintaining the enzymatic activity. These in silico hits could advance the antibacterial drug discovery campaign to find new MraY inhibitors for tuberculosis treatment.

Communicated by Ramaswamy H. Sarma

Keywords:

• MraY
• tuberculosis
• homology modeling
• docking
• virtual screening
• molecular dynamics

Acknowledgements

This work used the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges at the Pittsburgh Supercomputing Center using the allocation CHE190048P, which is supported by the National Science Foundation grant number ACI-1548562.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This research was funded by grant number R21AI142210 from the National Institute of Allergy and Infectious Diseases (NIAID), a component of the National Institutes of Health (NIH), and in part by P20GM103460 from the NIH National Institute of General Medical Sciences (NIGMS). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIAID or NIH. This investigation was conducted in part in a facility constructed with support from the Research Facilities Improvements Program (C06RR14503) from the National Institutes of Health (NIH) National Center for Research Resources.