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CCR3 受体拮抗剂的首个药效基团模型及其同源模建辅助的逐步虚拟筛选。

First pharmacophore model of CCR3 receptor antagonists and its homology model-assisted, stepwise virtual screening.

机构信息

Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar- 160 062, Punjab, India.

出版信息

Chem Biol Drug Des. 2011 May;77(5):373-87. doi: 10.1111/j.1747-0285.2011.01088.x. Epub 2011 Mar 1.

Abstract

CCR3, a G protein-coupled receptor, plays a central role in allergic inflammation and is an important drug target for inflammatory diseases. To understand the structure-function relationship of CCR3 receptor, different computational techniques were employed, which mainly include: (i) homology modeling of CCR3 receptor, (ii) 3D-quantitative pharmacophore model of CCR3 antagonists, (iii) virtual screening of small compound databases, and (iv) finally, molecular docking at the binding site of the CCR3 receptor homology model. Pharmacophore model was developed for the first time, on a training data set of 22 CCR3 antagonists, using CATALYST HypoRefine program. Best hypothesis (Hypo1) has three different chemical features: two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic. Hypo1 model was further validated using (i) 87 test set CCR3 antagonists, (ii) Cat Scramble randomization technique, and (iii) Decoy data set. Molecular docking studies were performed on modeled CCR3 receptor using 303 virtually screened hits, obtained from small compound database virtual screening. Finally, five hits were identified as potential leads against CCR3 receptor, which exhibited good estimated activities, favorable binding interactions, and high docking scores. These studies provided useful information on the structurally vital residues of CCR3 receptor involved in the antagonist binding, and their unexplored potential for the future development of potent CCR3 receptor antagonists.

摘要

CCR3,一种 G 蛋白偶联受体,在过敏炎症中发挥核心作用,是炎症性疾病的重要药物靶标。为了了解 CCR3 受体的结构-功能关系,采用了不同的计算技术,主要包括:(i)CCR3 受体的同源建模,(ii)CCR3 拮抗剂的 3D 定量药效团模型,(iii)小分子化合物数据库的虚拟筛选,以及(iv)最后,在 CCR3 受体同源模型的结合位点进行分子对接。首次使用 CATALYST HypoRefine 程序,针对 22 个 CCR3 拮抗剂的训练数据集,开发了药效团模型。最佳假设(Hypo1)具有三个不同的化学特征:两个氢键受体、一个疏水性和一个环芳香性。Hypo1 模型进一步通过(i)87 个测试集 CCR3 拮抗剂、(ii)Cat Scramble 随机化技术和(iii)诱饵数据集进行了验证。使用从小分子化合物数据库虚拟筛选获得的 303 个虚拟筛选命中物,对建模的 CCR3 受体进行了分子对接研究。最后,鉴定出五个命中物作为潜在的 CCR3 受体配体,它们表现出良好的估计活性、有利的结合相互作用和高对接分数。这些研究提供了有关 CCR3 受体中参与拮抗剂结合的结构关键残基的有用信息,以及它们在未来开发有效 CCR3 受体拮抗剂方面的未探索潜力。

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