Unraveling the ligand specificity and promiscuity of the Staphylococcus aureus NorA efflux pump: a computational study

Abstract

Staphylococcus aureus, a gram-positive bacterial pathogen, develops antibiotic resistance partly through enhanced activity of transmembrane multi-drug efflux pump proteins like NorA. Being a prominent member of the Major Facilitator Superfamily (MFS), NorA transports various small molecules including hydrophilic fluoroquinolone antibiotics across the cell membrane. Intriguingly, NorA is inhibited by a structurally diverse set of small molecule inhibitors as well, indicating a highly promiscuous ligand/inhibitor recognition. Our study aims to elucidate the structural facets of this promiscuity. Known NorA inhibitors were grouped into five clusters based on chemical class and docked into ligand binding pockets on NorA conformations generated via molecular dynamics simulations. We discovered that several key residues, such as I23, E222, and F303, are involved in inhibitor binding. Additionally, residues I244, T223, F303, and F140 were identified as prominent in interactions with specific ligand clusters. Our findings suggest that NorA's substrate binding site, encompassing residues aiding ligand recognition based on chemical nature, facilitates the recognition of chemically diverse ligands. This insight into NorA's structural promiscuity in ligand recognition not only enhances understanding of antibiotic resistance mechanisms in S. aureus but also sets the stage for the development of more effective efflux pump inhibitors, vital for combating multidrug resistance.

Communicated by Ramaswamy H. Sarma

Keywords:

• Staphylococcus aureus
• multidrug efflux pumps
• ligand promiscuity
• NorA
• antibiotic resistance
• antibiotic adjuvants
• molecular dynamics
• molecular docking
• protein-ligand interactions
• major facilitator superfamily

Author contributions

EBI: Data analysis, writing, OS: Study design, data analysis, and writing. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

The authors declare no conflict of interests.

Additional information

Funding

This project was supported by a TUBITAK BIDEB 2210/A Scholarship to EBI. The computational analyses were accomplished by using resources from TRUBA, GTU Faculty of Engineering TEI Lab, and BVU Big Data and Bioinformatics Laboratory. No specific funding was available for this study.