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表征GPCR蛋白亚家族配体的常见子结构。

Characterizing common substructures of ligands for GPCR protein subfamilies.

作者信息

Erguner Bekir, Hattori Masahiro, Goto Susumu, Kanehisa Minoru

机构信息

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.

出版信息

Genome Inform. 2010;24:31-41.

Abstract

The G-protein coupled receptor (GPCR) superfamily is the largest class of proteins with therapeutic value. More than 40% of present prescription drugs are GPCR ligands. The high therapeutic value of GPCR proteins and recent advancements in virtual screening methods gave rise to many virtual screening studies for GPCR ligands. However, in spite of vast amounts of research studying their functions and characteristics, 3D structures of most GPCRs are still unknown. This makes target-based virtual screenings of GPCR ligands extremely difficult, and successful virtual screening techniques rely heavily on ligand information. These virtual screening methods focus on specific features of ligands on GPCR protein level, and common features of ligands on higher levels of GPCR classification are yet to be studied. Here we extracted common substructures of GPCR ligands of GPCR protein subfamilies. We used the SIMCOMP, a graph-based chemical structure comparison program, and hierarchical clustering to reveal common substructures. We applied our method to 850 GPCR ligands and we found 53 common substructures covering 439 ligands. These substructures contribute to deeper understanding of structural features of GPCR ligands which can be used in new drug discovery methods.

摘要

G蛋白偶联受体(GPCR)超家族是具有治疗价值的最大一类蛋白质。目前超过40%的处方药是GPCR配体。GPCR蛋白的高治疗价值以及虚拟筛选方法的最新进展引发了许多针对GPCR配体的虚拟筛选研究。然而,尽管对其功能和特性进行了大量研究,但大多数GPCR的三维结构仍然未知。这使得基于靶点的GPCR配体虚拟筛选极其困难,而成功的虚拟筛选技术严重依赖配体信息。这些虚拟筛选方法侧重于GPCR蛋白水平上配体的特定特征,而GPCR更高分类水平上配体的共同特征尚未得到研究。在此,我们提取了GPCR蛋白亚家族的GPCR配体的共同子结构。我们使用基于图形的化学结构比较程序SIMCOMP和层次聚类来揭示共同子结构。我们将我们的方法应用于850个GPCR配体,发现了53个覆盖439个配体的共同子结构。这些子结构有助于更深入地了解GPCR配体的结构特征,可用于新药发现方法。

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