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重新审视自动G蛋白偶联受体建模:额外模板结构对神经激肽-1受体模型的益处。

Revisiting automated G-protein coupled receptor modeling: the benefit of additional template structures for a neurokinin-1 receptor model.

作者信息

Kneissl Benny, Leonhardt Bettina, Hildebrandt Andreas, Tautermann Christofer S

机构信息

Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.

出版信息

J Med Chem. 2009 May 28;52(10):3166-73. doi: 10.1021/jm8014487.

Abstract

The feasibility of automated procedures for the modeling of G-protein coupled receptors (GPCR) is investigated on the example of the human neurokinin-1 (NK1) receptor. We use a combined method of homology modeling and molecular docking and analyze the information content of the resulting docking complexes regarding the binding mode for further refinements. Moreover, we explore the impact of different template structures, the bovine rhodopsin structure, the human beta(2) adrenergic receptor, and in particular a combination of both templates to include backbone flexibility in the target conformational space. Our results for NK1 modeling demonstrate that model selection from a set of decoys can in general not solely rely on docking experiments but still requires additional mutagenesis data. However, an enrichment factor of 2.6 in a nearly fully automated approach indicates that reasonable models can be created automatically if both available templates are used for model construction. Thus, the recently resolved GPCR structures open new ways to improve the model building fundamentally.

摘要

以人类神经激肽-1(NK1)受体为例,研究了G蛋白偶联受体(GPCR)建模自动化程序的可行性。我们采用同源建模和分子对接相结合的方法,并分析所得对接复合物关于结合模式的信息内容,以进行进一步优化。此外,我们探讨了不同模板结构、牛视紫红质结构、人类β2肾上腺素能受体,特别是两种模板的组合对目标构象空间中主链灵活性的影响。我们对NK1建模的结果表明,从一组诱饵中选择模型通常不能仅仅依赖对接实验,还需要额外的诱变数据。然而,在几乎完全自动化的方法中2.6的富集因子表明,如果将两个可用模板都用于模型构建,可以自动创建合理的模型。因此,最近解析的GPCR结构为从根本上改进模型构建开辟了新途径。

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