Bacterial cell wall transglycosylases (TGs) and transpeptidases (TPs) are ideal drug targets due to their essentiality, accessibility and lack of mammalian homologs. Although antibacterial therapy using the beta-lactam family of TP inhibitors has been successful for decades, potent TG inhibitors which are suitable for development into antibiotics for human use have yet to be identified. We sought to further understand the molecular interactions required to inhibit bacterial transglycosylation by characterizing the active site of Staphylococcus aureus (Sa) monofunctional transglycosylase (Mtg). Ten mutants were tested for their ability to polymerize Lipid II and to crystallize in the presence of moenomycin. Five of six putative active site mutants (E100Q, D101N, Q136E, E156T, and Y176F) were found to be catalytically inactive whereas a F104Y mutation did not affect activity. Four mutants generated to enhance crystal formation (F143T, V154T, L157T, and F158T) also retained activity. Here we also report the crystal structure of Sa Mtg E100Q mutant in complex with the inhibitor moenomycin to 2.1A resolution. The co-crystal structure revealed detailed interactions between the protein and inhibitor including portions of the polycarbon tail of moenomycin. The structure also contained an ordered phosphate ion which helped to identify the Lipid II binding site.
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http://dx.doi.org/10.1016/j.jsb.2009.04.010 | DOI Listing |
Arch Biochem Biophys
January 2025
Department of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States; Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States. Electronic address:
The mitochondrial flavoenzymes proline dehydrogenase (PRODH) and hydroxyproline dehydrogenase (PRODH2) catalyze the first steps of proline and hydroxyproline catabolism, respectively. The enzymes are targets for chemical probe development because of their roles in cancer cell metabolism (PRODH) and primary hyperoxaluria (PRODH2). Mechanism-based inactivators of PRODH target the FAD by covalently modifying the N5 atom, with N-propargylglycine (NPPG) being the current best-in-class of this type of probe.
View Article and Find Full Text PDFActa Pharmacol Sin
January 2025
Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method.
View Article and Find Full Text PDFJ Neurointerv Surg
January 2025
Department of Neurology, UTHealth Houston McGovern Medical School, Houston, Texas, USA
Background: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized.
Methods: This analysis was conducted as a pre-planned post-hoc analysis of a randomized prospective clinical trial.
In the leucine (Leu) biosynthesis pathway, homeostasis is achieved through a feedback regulatory mechanism facilitated by the binding of the end-product Leu at the C-terminal regulatory domain of the first committed enzyme, isopropylmalate synthase (IPMS). In vitro studies have shown that removing the regulatory domain abolishes the feedback regulation on plant IPMS while retaining its catalytic activity. However, the physiological consequences and underlying molecular regulation on Leu flux upon removing the IPMS C-terminal domain remain to be explored in plants.
View Article and Find Full Text PDFAcc Chem Res
January 2025
The Department of Chemistry, State University of New York at Binghamton, Binghamton, New York 13902, United States.
ConspectusIn the search for efficient and selective electrocatalysts capable of converting greenhouse gases to value-added products, enzymes found in naturally existing bacteria provide the basis for most approaches toward electrocatalyst design. Ni,Fe-carbon monoxide dehydrogenase (Ni,Fe-CODH) is one such enzyme, with a nickel-iron-sulfur cluster named the C-cluster, where CO binds and is converted to CO at high rates near the thermodynamic potential. In this Account, we divide the enzyme's catalytic contributions into three categories based on location and function.
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