Quantitative structure–activity relationship studies of mushroom tyrosinase inhibitors
Quantitative structure–activity relationship studies of mushroom tyrosinase inhibitors
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DOI：
10.1007/s10822-008-9187-6
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发表时间：
2008-02
影响因子：
3.5
通讯作者：
Chaobin Xue;Wan-Chun Luo;Qi Ding;Shou-Zhu Liu;Xingming Gao
中科院分区：
文献类型：
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作者：
Chaobin Xue;Wan-Chun Luo;Qi Ding;Shou-Zhu Liu;Xingming Gao
在这里，我们报告我们的结果从酪氨酸酶抑制剂的定量构效关系的研究。苯甲酸衍生物和酪氨酸酶活性位点之间的相互作用也进行了研究，使用分子对接方法。这些研究表明，苯甲酸衍生物与酪氨酸酶活性中心相互作用的一种可能机制是Tyr 98的羟基（aOH）和羰基氧原子之间形成氢键，从而稳定了Tyr 98的位置，阻止了Tyr 98参与酪氨酸酶与ORF 378的相互作用。酪氨酸酶（Tyrosinase）又称酚氧化酶（phenoloxidase），是动物、植物和昆虫体内催化酪氨酸羟基化为邻苯二酚和邻苯二酚氧化为邻醌的关键酶。以苯甲醛、苯甲酸和肉桂酸等48种化合物的生物活性为研究对象，采用比较分子场（CoMFA）和比较分子相似性指数（CoMSIA）分析方法，构建了3D-QSAR模型。通过常用的基于亚结构的比对方法进行叠加，得到了具有6个最优组分的CoMFA（q2= 0.855，r2 = 0.978）和CoMSIA（q2= 0.841，r2 = 0.946）的3D-QSAR模型。采用电子参数（Hammett σ）、疏水参数（π）、空间位阻参数（MR）、氢键受体参数（H-acc）和指示变量（I）等化学参数构建了2D-QSAR模型。QSAR结果表明，π，MR和H-acc分别占计算的生物学方差的34.9%，31.6%和26.7%。采用柔性对接方法（FlexX）研究了配体与靶分子之间的相互作用。最好的得分候选人灵活对接，苯甲酸衍生物和酪氨酸酶活性位点之间的相互作用进行了详细阐述。我们相信，这里建立的定量构效关系模型提供了重要的信息，为设计新的酪氨酸酶抑制剂。
Here, we report our results from quantitative structure–activity relationship studies on tyrosinase inhibitors. Interactions between benzoic acid derivatives and tyrosinase active sites were also studied using a molecular docking method. These studies indicated that one possible mechanism for the interaction between benzoic acid derivatives and the tyrosinase active site is the formation of a hydrogen-bond between the hydroxyl (aOH) and carbonyl oxygen atoms of Tyr98, which stabilized the position of Tyr98and prevented Tyr98from participating in the interaction between tyrosinase and ORF378. Tyrosinase, also known as phenoloxidase, is a key enzyme in animals, plants and insects that is responsible for catalyzing the hydroxylation of tyrosine intoo-diphenols and the oxidation ofo-diphenols intoo-quinones. In the present study, the bioactivities of 48 derivatives of benzaldehyde, benzoic acid, and cinnamic acid compounds were used to construct three-dimensional quantitative structure–activity relationship (3D-QSAR) models using comparative molecular field (CoMFA) and comparative molecular similarity indices (CoMSIA) analyses. After superimposition using common substructure-based alignments, robust and predictive 3D-QSAR models were obtained from CoMFA (q2= 0.855,r2= 0.978) and CoMSIA (q2= 0.841,r2= 0.946), with 6 optimum components. Chemical descriptors, including electronic (Hammett σ), hydrophobic (π), and steric (MR) parameters, hydrogen bond acceptor (H-acc), and indicator variable (I), were used to construct a 2D-QSAR model. The results of this QSAR indicated that π, MR, and H-acc account for 34.9, 31.6, and 26.7% of the calculated biological variance, respectively. The molecular interactions between ligand and target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the benzoic acid derivatives and the tyrosinase active site was elucidated in detail. We believe that the QSAR models built here provide important information necessary for the design of novel tyrosinase inhibitors.