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设计针对G蛋白偶联受体(GPCR)的文库和筛选集的策略。

Strategies for designing GPCR-focused libraries and screening sets.

作者信息

Jimonet Patrick, Jäger Robert

机构信息

Chemical Biology GPCRs, High Throughput Medicinal Chemistry, Aventis Pharmaceuticals Inc, 1041 Route 202-206, PO Box 6800, N103B, Bridgewater, NJ 08807-0800, USA.

出版信息

Curr Opin Drug Discov Devel. 2004 May;7(3):325-33.

Abstract

In recent years drug discovery has progressively moved away from a traditional single-target focus toward a family-based approach. The development of knowledge relating to targets and ligands of the same protein family has been actively pursued to support more predictive and efficient pharmaceutical research. The design of focused libraries and screening sets for the G protein-coupled receptor (GPCR) family has been undertaken along several different routes. A first approach has been ligand-based, relying either on physicochemical properties or on privileged substructures of GPCR ligands, but despite some success this approach has suffered from the near absence of knowledge coming from the receptor. To strengthen the weak link between the chemical and biological aspects, new databases have been developed and have steadily moved toward integrated information systems. Several research groups have reported novel approaches to library design and compound selection based on two- or three-dimensional mapping of the ligand-receptor interaction sites. The development of homology models derived from the rhodopsin crystal structure, the use of site-directed mutagenesis in relation to ligand structure-activity relationships (SARs), and the integration of informatics analyses have been critical elements for driving new designs in a modern chemogenomics environment.

摘要

近年来,药物研发已逐渐从传统的单一靶点聚焦转向基于家族的方法。为支持更具预测性和高效的药物研究,人们积极探索同一蛋白家族靶点和配体相关知识的发展。针对G蛋白偶联受体(GPCR)家族的聚焦文库和筛选集的设计已沿着几条不同的途径展开。第一种方法是基于配体的,要么依赖于物理化学性质,要么依赖于GPCR配体的特权亚结构,但尽管取得了一些成功,这种方法仍因几乎缺乏来自受体的知识而受到影响。为加强化学和生物学方面之间的薄弱联系,已开发了新的数据库,并稳步朝着综合信息系统发展。几个研究小组报告了基于配体-受体相互作用位点的二维或三维映射进行文库设计和化合物选择的新方法。源自视紫红质晶体结构的同源模型的开发、与配体结构-活性关系(SAR)相关的定点诱变的使用以及信息学分析的整合,一直是在现代化学基因组学环境中推动新设计的关键要素。

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