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Progress in structure prediction of alpha-helical membrane proteins.α-螺旋膜蛋白结构预测的进展
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Understanding recombinant expression of membrane proteins.理解膜蛋白的重组表达。
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Structure modeling of all identified G protein-coupled receptors in the human genome.人类基因组中所有已鉴定的G蛋白偶联受体的结构建模。
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Multipass membrane protein structure prediction using Rosetta.使用Rosetta预测多次跨膜蛋白结构
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Solving the membrane protein folding problem.解决膜蛋白折叠问题。
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Gating charge displacement in voltage-gated ion channels involves limited transmembrane movement.电压门控离子通道中的门控电荷位移涉及有限的跨膜移动。
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Computational analysis of membrane proteins: genomic occurrence, structure prediction and helix interactions.膜蛋白的计算分析:基因组出现情况、结构预测及螺旋相互作用
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Modeling membrane proteins based on low-resolution electron microscopy maps: a template for the TM domains of the oxalate transporter OxlT.基于低分辨率电子显微镜图谱对膜蛋白进行建模:草酸盐转运蛋白OxlT跨膜结构域的模板
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Computational analysis of alpha-helical membrane protein structure: implications for the prediction of 3D structural models.α-螺旋膜蛋白结构的计算分析:对三维结构模型预测的启示
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借助电子显微镜图谱辅助的螺旋膜蛋白原子结构的计算预测。

Computational prediction of atomic structures of helical membrane proteins aided by EM maps.

作者信息

Kovacs Julio A, Yeager Mark, Abagyan Ruben

机构信息

Department of Molecular Biology, Department of Cell Biology, The Scripps Research Institute, La Jolla, CA, USA.

出版信息

Biophys J. 2007 Sep 15;93(6):1950-9. doi: 10.1529/biophysj.106.102137. Epub 2007 May 11.

DOI:10.1529/biophysj.106.102137
PMID:17496035
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1959528/
Abstract

Integral membrane proteins pose a major challenge for protein-structure prediction because only approximately 100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane alpha-helices. However, when an intermediate-resolution electron microscopy (EM) map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: 1), generation of an ensemble of alpha-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; 2), fast optimization of side chains and scoring of the resulting conformations; and 3), refinement of the lowest-scoring conformations with internal coordinate mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional, and solvation energy contributions. In addition, our method implements a penalty term through a so-called tethering map, derived from the EM map, which restrains the positions of the alpha-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL.

摘要

整合膜蛋白对蛋白质结构预测构成了重大挑战,因为目前仅有大约100个高分辨率结构,这阻碍了预测跨膜α螺旋堆积的规则或经验势的发展。然而,当有中等分辨率的电子显微镜(EM)图谱时,它可用于提供约束条件,结合合适的计算方案,使结构预测成为可能。在这项工作中,我们提出了这样一种方案,它分三个阶段进行:1)通过灵活拟合到低分辨率EM图谱中的每个密度棒来生成α螺旋集合,跨越围绕主螺旋轴的一系列旋转角度以及沿密度棒的平移;2)快速优化侧链并对所得构象进行评分;3)通过优化范德华力、静电、氢键、扭转和溶剂化能贡献,用内部坐标力学对得分最低的构象进行细化。此外,我们的方法通过一个从EM图谱导出的所谓拴系图谱实现了一个惩罚项,它限制了α螺旋的位置。该方案在三个测试案例上得到了验证:GpA、KcsA和MscL。