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Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors

机译:药理学建模,虚拟筛选,分子对接研究和密度泛函理论方法,用于鉴定新型酮己酮激酶(KHK)抑制剂

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Fructose catabolism starts with phosphorylation of D-fructose to fructose 1-phosphate, which is performed by ketohexokinase (KHK). Fructose metabolism may be the key to understand the long-term consumption of fructose in human's obesity, diabetes and metabolic states in western populations. The inhibition of KHK has medicinally potential roles in fructose metabolism and the metabolic syndrome. To identify the essential chemical features for KHK inhibition, a three-dimensional (3D) chemical-feature-based QSAR pharmacophore model was developed for the first time by using Discovery Studio v2.5 (DS). The best pharmacophore hypothesis (Hypo1) consisting two hydrogen bond donor, two hydrophobic features and has exhibited high correlation co-efficient (0.97), cost difference (76.1) and low RMS (0.66) value. The robustness and predictability of Hypo1 was validated by fisher's randomization method, test set, and the decoy set. Subsequently, chemical databases like NCI, Chembridge and Maybridge were screened for validated Hypo1. The screened compounds were further analyzed by applying drug-like filters such as Lipinski's rule of five, ADME properties, and molecular docking studies. Further, the highest occupied molecular orbital, lowest unoccupied molecular orbital and energy gap values were calculated for the hits compounds using density functional theory. Finally, 3 hit compounds were selected based on their good molecular interactions with key amino acids in the KHK active site, GOLD fitness score, and lowest energy gaps. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译:果糖分解代谢开始于D-果糖磷酸化为果糖1-磷酸,这是通过酮己酮激酶(KHK)进行的。果糖代谢可能是了解长期摄入果糖在西方人群中肥胖,糖尿病和代谢状态的关键。 KHK的抑制在果糖代谢和代谢综合征中具有医学上的潜在作用。为了确定抑制KHK的基本化学特征,使用Discovery Studio v2.5(DS)首次开发了基于三维(3D)化学特征的QSAR药效团模型。最好的药效基团假说(Hypo1)由两个氢键供体,两个疏水特征组成,并显示出高相关系数(0.97),成本差异(76.1)和低RMS(0.66)值。 Hypo1的鲁棒性和可预测性已通过费舍尔的随机方法,测试集和诱饵集得到了验证。随后,对化学数据库(如NCI,Chembridge和Maybridge)进行了筛选,以获取经过验证的Hypo1。通过应用类似毒品的过滤器(如Lipinski的5律,ADME特性和分子对接研究)进一步分析了筛选出的化合物。此外,使用密度泛函理论计算了命中化合物的最高占据分子轨道,最低未占据分子轨道和能隙值。最后,根据它们与KHK活性位点中关键氨基酸的良好分子相互作用,GOLD适应度评分和最低能隙,选择了3种命中化合物。 (C)2015 Elsevier Ireland Ltd.保留所有权利。

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