Active site binding modes of inhibitors of Staphylococcus aureus mevalonate diphosphate decarboxylase from docking and molecular dynamics simulations

被引:0
作者
James K. Addo
D. Andrew Skaff
Henry M. Miziorko
机构
[1] University of Missouri-Kansas City,Division of Molecular Biology and Biochemistry, School of Biological Sciences
来源
Journal of Molecular Modeling | 2016年 / 22卷
关键词
ATP; Hydrogen bonding interaction; Mevalonate diphosphate decarboxylase; Molecular docking and dynamics; Small molecule inhibitor;
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摘要
Bacterial mevalonate diphosphate decarboxylase (MDD) is an attractive therapeutic target for antibacterial drug development. In this work, we discuss a combined docking and molecular dynamics strategy toward inhibitor binding to bacterial MDD. The docking parameters utilized in this study were first validated with observations for the inhibitors 6-fluoromevalonate diphosphate (FMVAPP) and diphosphoglycolylproline (DPGP) using existing structures for the Staphylococcus epidermidis enzyme. The validated docking protocol was then used to predict structures of the inhibitors bound to Staphylococcus aureus MDD using the unliganded crystal structure of Staphylococcus aureus MDD. We also investigated a possible interactions improvement by combining this docking method with molecular dynamics simulations. Thus, the predicted docking structures were analyzed in a molecular dynamics trajectory to generate dynamic models and reinforce the predicted binding modes. FMVAPP is predicted to make more extensive contacts with S. aureus MDD, forming stable hydrogen bonds with Arg144, Arg193, Lys21, Ser107, and Tyr18, as well as making stable hydrophobic interactions with Tyr18, Trp19, and Met196. The differences in predicted binding are supported by experimentally determined Ki values of 0.23 ± 0.02 and 34 ± 8 μM, for FMVAPP and DPGP, respectively. The structural information coupled with the kinetic characterization obtained from this study should be useful in defining the requirements for inhibition as well as in guiding the selection of active compounds for inhibitor optimization.
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