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Mol Pharm. 2018 Nov 5;15(11):5410-5426. doi: 10.1021/acs.molpharmaceut.8b00905. Epub 2018 Oct 18.

In Silico Screen and Structural Analysis Identifies Bacterial Kinase Inhibitors which Act with β-Lactams To Inhibit Mycobacterial Growth.

Author information

1
Department of Medicine , University of Wisconsin-Madison , 3341 Microbial Sciences Building, 1550 Linden Dr. , Madison , Wisconsin 53706 , United States.
2
Department of Medical Microbiology and Immunology , University of Wisconsin-Madison , 4203 Microbial Sciences Building, 1550 Linden Dr. , Madison , Wisconsin 53706 , United States.
3
Small Molecule Screening Facility, Carbone Cancer Center , University of Wisconsin-Madison , 1111Highland Ave. , Madison , Wisconsin 53705 , United States.
4
School of Medicine and Dentistry , University of Rochester Medical Center , 601 Elmwood Ave. , Rochester , New York 14620 , United States.
5
Eshelman School of Pharmacy , University of North Carolina at Chapel Hill , SGC Center for Chemical Biology, 120 Mason Farm Rd. , Chapel Hill , North Carolina 27599 , United States.
6
William S. Middleton Memorial Veterans Hospital , 2500 Overlook Terr. , Madison , Wisconsin 53705 , United States.

Abstract

New tools and concepts are needed to combat antimicrobial resistance. Actinomycetes and firmicutes share several eukaryotic-like Ser/Thr kinases (eSTK) that offer antibiotic development opportunities, including PknB, an essential mycobacterial eSTK. Despite successful development of potent biochemical PknB inhibitors by many groups, clinically useful microbiologic activity has been elusive. Additionally, PknB kinetics are not fully described, nor are structures with specific inhibitors available to inform inhibitor design. We used computational modeling with available structural information to identify human kinase inhibitors predicted to bind PknB, and we selected hits based on drug-like characteristics intended to increase the likelihood of cell entry. The computational model suggested a family of inhibitors, the imidazopyridine aminofurazans (IPAs), bind PknB with high affinity. We performed an in-depth characterization of PknB and found that these inhibitors biochemically inhibit PknB, with potency roughly following the predicted models. A novel X-ray structure confirmed that the inhibitors bound as predicted and made favorable protein contacts with the target. These inhibitors also have antimicrobial activity toward mycobacteria and nocardia. We demonstrated that the inhibitors are uniquely potentiated by β-lactams but not antibiotics traditionally used to treat mycobacteria, consistent with PknB's role in sensing cell wall stress. This is the first demonstration in the phylum actinobacteria that some β-lactam antibiotics could be more effective if paired with a PknB inhibitor. Collectively, our data show that in silico modeling can be used as a tool to discover promising drug leads, and the inhibitors we discovered can act with clinically relevant antibiotics to restore their efficacy against bacteria with limited treatment options.

KEYWORDS:

antibiotics; bacterial protein kinase; computer modeling; docking; mycobacteria; structural biology; structure−activity relationship; tuberculosis; β-lactam

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