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通过对接自动生成的GPCR模型进行基于靶点的虚拟筛选。

Target based virtual screening by docking into automatically generated GPCR models.

作者信息

Tautermann Christofer S

机构信息

Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany.

出版信息

Methods Mol Biol. 2012;914:255-70. doi: 10.1007/978-1-62703-023-6_15.

Abstract

Target based virtual screening (VS) combined with high-throughput measurements is an extremely useful tool to identify small molecule hits for proteins and in particular for G-protein coupled receptors (GPCRs). However, this is a quite difficult process for GPCRs due to the paucity of 3D structural information on these receptors. Therefore, the only possibility for target based VS is to build a structural model of the GPCR to be used for docking. However, GPCR model building is a very time consuming process, if the model should be able to explain all experimental findings and this investment is not always justified, if the model is only used for VS. Thus, a fully automated workflow is presented here, where a large number of GPCR models is built, and the best model is identified to be used for docking. The workflow leads to moderate enrichments with a very low effort. The inputs required are the sequence of the targeted GPCR, a reference ligand with experimental information and a database of small molecules to be used for docking. Manual intervention is recommended at various points, but it is strictly speaking not necessary.

摘要

基于靶点的虚拟筛选(VS)结合高通量测量是一种极其有用的工具,可用于识别针对蛋白质尤其是G蛋白偶联受体(GPCR)的小分子命中物。然而,由于这些受体的三维结构信息匮乏,对于GPCR来说这是一个相当困难的过程。因此,基于靶点的VS的唯一可能性是构建用于对接的GPCR结构模型。然而,如果模型要能够解释所有实验结果,GPCR模型构建是一个非常耗时的过程,而且如果该模型仅用于VS,这种投入并不总是合理的。因此,本文提出了一种全自动工作流程,其中构建了大量GPCR模型,并识别出最佳模型用于对接。该工作流程只需付出很少的努力就能实现适度富集。所需的输入是目标GPCR的序列、具有实验信息的参考配体以及用于对接的小分子数据库。建议在各个环节进行人工干预,但严格来说并非必需。

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