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本文引用的文献

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Computational protein structure refinement: Almost there, yet still so far to go.计算蛋白质结构优化:已近完成,却仍任重道远。
Wiley Interdiscip Rev Comput Mol Sci. 2017 May-Jun;7(3). doi: 10.1002/wcms.1307. Epub 2017 Mar 28.
2
Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model.用 HDGB 隐式膜模型测定膜蛋白的疏水性长度。
J Chem Inf Model. 2017 Dec 26;57(12):3032-3042. doi: 10.1021/acs.jcim.7b00510. Epub 2017 Dec 1.
3
What makes it difficult to refine protein models further via molecular dynamics simulations?是什么使得通过分子动力学模拟进一步优化蛋白质模型变得困难?
Proteins. 2018 Mar;86 Suppl 1(Suppl 1):177-188. doi: 10.1002/prot.25393. Epub 2017 Oct 16.
4
Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.由冷冻电镜密度引导的迭代分子动力学-罗塞塔膜蛋白结构优化
J Chem Theory Comput. 2017 Oct 10;13(10):5131-5145. doi: 10.1021/acs.jctc.7b00464. Epub 2017 Sep 26.
5
Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions.基于隐式膜评分函数的膜蛋白类天然状态判别
J Chem Theory Comput. 2017 Jun 13;13(6):3049-3059. doi: 10.1021/acs.jctc.7b00254. Epub 2017 May 11.
6
Heterogeneous dielectric generalized Born model with a van der Waals term provides improved association energetics of membrane-embedded transmembrane helices.带有范德华项的非均匀介电广义 Born 模型可改善跨膜螺旋在膜中的缔合能。
J Comput Chem. 2017 Jun 15;38(16):1308-1320. doi: 10.1002/jcc.24691. Epub 2017 Feb 4.
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Galaxy7TM: flexible GPCR-ligand docking by structure refinement.Galaxy7TM:通过结构优化实现灵活的G蛋白偶联受体-配体对接
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9
Assessment of refinement of template-based models in CASP11.在蛋白质结构预测技术关键评估第11轮(CASP11)中基于模板的模型优化评估
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10
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通过分子动力学模拟对膜蛋白进行结构精修。

Structure refinement of membrane proteins via molecular dynamics simulations.

机构信息

Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA.

出版信息

Proteins. 2018 Jul;86(7):738-750. doi: 10.1002/prot.25508. Epub 2018 May 6.

DOI:10.1002/prot.25508
PMID:29675899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6013386/
Abstract

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

摘要

针对水溶性蛋白质建立的基于物理技术的细化方案被用于膜蛋白结构。初始结构通过同源建模生成,并在含有明确脂质双层和水相溶剂的系统中通过分子动力学模拟进行采样。基于知识或隐式基于膜的评分函数对模拟中的快照进行选择,并对其进行平均以获得细化模型。该方案导致膜蛋白结构的一致且显著细化,类似于可溶性蛋白质细化方法的性能。在存在脂质双层和水相溶剂的情况下进行采样的效果相似,但脂质双层的存在可能有利于改善面向脂质的残基。事实证明,基于知识的函数(DFIRE 和 RWplus)的评分与使用隐式基于膜的评分函数的评分一样好,这表明在膜蛋白同源模型的细化过程中,内部堆积的差异比相对于膜的取向更为重要。