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G蛋白偶联受体类别的预测。

Prediction of G-protein-coupled receptor classes.

作者信息

Chou Kuo-Chen

机构信息

Gordon Life Science Institute, 13784 Torrey Del Mar, San Diego, CA 92130, USA.

出版信息

J Proteome Res. 2005 Jul-Aug;4(4):1413-8. doi: 10.1021/pr050087t.

DOI:10.1021/pr050087t
PMID:16083294
Abstract

Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. The functions of many of GPCRs are unknown, and it is both time-consuming and expensive to determine their ligands and signaling pathways. This forces us to face a critical challenge: how to develop an automated method for classifying the family of GPCRs so as to help us in classifying drugs and expedite the process of drug discovery. Owing to their highly divergent nature, it is difficult to predict the classification of GPCRs by means of conventional sequence alignment approaches. To cope with such a situation, the CD (Covariant Discriminant) predictor was introduced to predict the families of GPCRs. The overall success rate thus obtained by jack-knife test for 1238 GPCRs classified into three main families, i.e., class A-"rhodopsin like", class B-"secretin like", and class C-"metabotrophic/glutamate/pheromone", was over 97%. The high success rate suggests that the CD predictor holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.

摘要

作为细胞表面受体中最大的家族,G蛋白偶联受体(GPCRs)是治疗药物最常见的靶点之一。许多GPCRs的功能尚不清楚,确定它们的配体和信号通路既耗时又昂贵。这迫使我们面临一个关键挑战:如何开发一种自动方法来对GPCRs家族进行分类,以帮助我们对药物进行分类并加快药物发现的进程。由于其高度的多样性,通过传统的序列比对方法很难预测GPCRs的分类。为了应对这种情况,引入了CD(协变判别)预测器来预测GPCRs家族。通过留一法检验对1238个GPCRs进行分类,分为三个主要家族,即A类——“视紫红质样”、B类——“促胰液素样”和C类——“代谢型/谷氨酸/信息素”,总体成功率超过97%。如此高的成功率表明,CD预测器极有可能成为一种有用的工具,用于理解靶向GPCRs的药物的作用,并设计出副作用更少、疗效更高的新药物。

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