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SuperBiHelix 方法预测 G 蛋白偶联受体构象的多效集合。

SuperBiHelix method for predicting the pleiotropic ensemble of G-protein-coupled receptor conformations.

机构信息

Materials and Process Simulation Center, California Institute of Technology, Pasadena, CA 91125.

出版信息

Proc Natl Acad Sci U S A. 2014 Jan 7;111(1):E72-8. doi: 10.1073/pnas.1321233111. Epub 2013 Dec 16.

Abstract

There is overwhelming evidence that G-protein-coupled receptors (GPCRs) exhibit several distinct low-energy conformations, each of which might favor binding to different ligands and/or lead to different downstream functions. Understanding the function of such proteins requires knowledge of the ensemble of low-energy configurations that might play a role in this pleiotropic functionality. We earlier reported the BiHelix method for efficiently sampling the (12)(7) = 35 million conformations resulting from 30° rotations about the axis (η) of all seven transmembrane helices (TMHs), showing that the experimental structure is reliably selected as the best conformation from this ensemble. However, various GPCRs differ sufficiently in the tilts of the TMHs that this method need not predict the optimum conformation starting from any other template. In this paper, we introduce the SuperBiHelix method in which the tilt angles (θ, ϕ) are optimized simultaneously with rotations (η) efficiently enough that it is practical and sufficient to sample (5 × 3 × 5)(7) = 13 trillion configurations. This method can correctly identify the optimum structure of a GPCR starting with the template from a different GPCR. We have validated this method by predicting known crystal structure conformations starting from the template of a different protein structure. We find that the SuperBiHelix conformational ensemble includes the higher energy conformations associated with the active protein in addition to those associated with the more stable inactive protein. This methodology was then applied to design and experimentally confirm structures of three mutants of the CB1 cannabinoid receptor associated with different functions.

摘要

有大量证据表明,G 蛋白偶联受体(GPCR)表现出几种不同的低能量构象,每种构象可能有利于与不同的配体结合,并/或导致不同的下游功能。要理解此类蛋白质的功能,就需要了解在这种多功能性中可能起作用的低能量构象的整体情况。我们之前曾报道过 BiHelix 方法,该方法可以有效地采样 30°围绕所有七个跨膜螺旋(TMH)的轴(η)旋转产生的(12)(7)=3500 万种构象,结果表明实验结构可从该构象整体中可靠地选择为最佳构象。然而,各种 GPCR 的 TMH 倾斜度差异足够大,以至于这种方法不一定需要从任何其他模板开始预测最佳构象。在本文中,我们引入了 SuperBiHelix 方法,该方法可以高效地同时优化倾斜角(θ,ϕ)和旋转(η),足以进行(5×3×5)(7)=13 万亿种构象的采样。这种方法可以从不同 GPCR 的模板开始,正确识别 GPCR 的最佳结构。我们通过从不同蛋白质结构的模板开始预测已知晶体结构构象来验证了这种方法。我们发现,SuperBiHelix 构象整体包括与活性蛋白相关的更高能量构象,以及与更稳定的非活性蛋白相关的构象。然后,我们将这种方法应用于设计和实验证实与不同功能相关的三种 CB1 大麻素受体突变体的结构。

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