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首页> 外文期刊>Current topics in medicinal chemistry >Statistical Analysis, Optimization, and Prioritization of Virtual Screening Parameters for Zinc Enzymes Including the Anthrax Toxin Lethal Factor
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Statistical Analysis, Optimization, and Prioritization of Virtual Screening Parameters for Zinc Enzymes Including the Anthrax Toxin Lethal Factor

机译:包括炭疽毒素致死因子在内的锌酶的虚拟筛选参数的统计分析,优化和优先级排序

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The anthrax toxin lethal factor (LF) and matrix metalloproteinase-3 (MMP-3, stromelysin-1) are popular zinc metalloenzyme drug targets, with LF primarily responsible for anthrax-related toxicity and host death, while MMP-3 is involved in cancer-and rheumatic disease-related tissue remodeling. A number of in silico screening techniques, most notably docking and scoring, have proven useful for identifying new potential drug scaffolds targeting LF and MMP-3, as well as for optimizing lead compounds and investigating mechanisms of action. However, virtual screening outcomes can vary significantly depending on the specific docking parameters chosen, and systematic statistical significance analyses are needed to prioritize key parameters for screening small molecules against these zinc systems. In the current work, we present a series of chi-square statistical analyses of virtual screening outcomes for cocrystallized LF and MMP-3 inhibitors docked into their respective targets, evaluated by predicted enzyme-inhibitor dissociation constant and root-meansquare deviation (RMSD) between predicted and experimental bound configurations, and we present a series of preferred parameters for use with these systems in the industry-standard Surflex-Dock screening program, for use by researchers utilizing in silico techniques to discover and optimize new scaffolds.
机译:炭疽毒素致死因子(LF)和基质金属蛋白酶3(MMP-3,stromelysin-1)是流行的锌金属酶药物靶标,其中LF主要负责与炭疽相关的毒性和宿主死亡,而MMP-3参与癌症和风湿性疾病相关的组织重塑。事实证明,许多计算机筛查技术(尤其是对接和评分)可用于识别靶向LF和MMP-3的新的潜在药物支架,以及优化前导化合物和研究作用机制。但是,虚拟筛选的结果可能会因所选的特定对接参数而有很大不同,因此需要系统的统计显着性分析,以优先针对这些锌系统筛选小分子的关键参数。在当前的工作中,我们提供了一系列对通过共沉淀的LF和MMP-3抑制剂对接入各自靶标的虚拟筛选结果进行的卡方统计分析,并通过预测的酶-抑制剂解离常数和均方根偏差(RMSD)进行了评估。预测的和实验的结合构型,我们在行业标准的Surflex-Dock筛选程序中提供了与这些系统一起使用的一系列优选参数,供研究人员利用计算机技术来发现和优化新的支架。

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