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GABAAα3受体苯二氮䓬结合位点配体识别的结构基础及基于药效团的虚拟筛选方法

Structural basis for ligand recognition at the benzodiazepine binding site of GABAA alpha 3 receptor, and pharmacophore-based virtual screening approach.

作者信息

Vijayan R S K, Ghoshal Nanda

机构信息

Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, 4 Raja S.C. Mullick Road, Jadavpur, Kolkata 700032, India.

出版信息

J Mol Graph Model. 2008 Oct;27(3):286-98. doi: 10.1016/j.jmgm.2008.05.003. Epub 2008 May 9.

DOI:10.1016/j.jmgm.2008.05.003
PMID:18565775
Abstract

Given the heterogeneity of GABA(A) receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA(A) alpha(3) receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure-activity relationship) studies on a series of novel GABA(A) alpha(3) subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to "Message-Address" concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on "Drug likeness" and "Similarity-based" methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.

摘要

鉴于GABA(A)受体的异质性,识别亚型选择性调节剂的药理学意义日益受到认可。因此,预计对GABA(A)α(3)受体具有选择性的药物将比目前临床使用的药物显示出更少的副作用。因此,我们对一系列新型GABA(A)α(3)亚型选择性调节剂进行了三维定量构效关系(3D QSAR)研究,以更深入地了解亚型亲和力。为了确定亚型选择性所需的三维功能属性,生成了一种基于化学特征的药效团,主要基于代表不同结构类别的选择性配体。所获得的苯二氮䓬结合位点的伪受体模型揭示了一种类似于“信息-地址”概念的结合模式。通过多构象的May Bridge数据库进行骨架跳跃,以识别新型化学类型。此外,采用了一种聚焦的数据缩减方法,基于“类药性质”和“基于相似性”的方法选择一组富集化合物。综合这些结果可为合理设计和优化更具选择性和高亲和力的先导化合物提供动力,这些先导化合物有可能减少不良反应。

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