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G蛋白偶联受体(GPCRs)三维结构建模:进展及其在药物发现中的应用

Modeling the 3D structure of GPCRs: advances and application to drug discovery.

作者信息

Becker Oren M, Shacham Sharon, Marantz Yael, Noiman Silvia

机构信息

Predix Pharmaceuticals Ltd, SAP Building, 3 Hayetzira Street, Ramat Gan 52521, Israel.

出版信息

Curr Opin Drug Discov Devel. 2003 May;6(3):353-61.

Abstract

G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.

摘要

G蛋白偶联受体(GPCRs)是负责信号转导的膜嵌入蛋白;因此,这些受体是最重要的药物靶点之一。在缺乏X射线结构的情况下,人们进行了大量尝试来模拟GPCRs的三维(3D)结构。在这篇综述中,对GPCR建模的现状进行了评估,重点介绍了基于视紫红质的同源建模和从头建模技术方面的最新进展。对最近基于视紫红质的同源建模研究的评估表明,尽管取得了显著进展,但这些模型在计算机模拟筛选中的命中率并不足够高(筛选已知结合剂时为10%至40%)。相比之下,独立于视紫红质结构的PREDICT建模算法现已在药物发现背景下得到充分验证。PREDICT模型成功用于药物发现,产生了优异的命中率(筛选已知结合剂时为85%至100%),从而发现了针对多种GPCR靶点的纳摩尔级新化学实体。因此,GPCRs的3D模型现在应该允许使用基于结构的有效方法进行药物发现。

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