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A Sidekick for Membrane Simulations: Automated Ensemble Molecular Dynamics Simulations of Transmembrane Helices

机译:膜模拟的辅助工具:跨膜螺旋的自动集成分子动力学模拟

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摘要

The interactions of transmembrane (TM) α-helices with the phospholipid membrane and with one another are central to understanding the structure and stability of integral membrane proteins. These interactions may be analysed via coarse-grained molecular dynamics (CGMD) simulations. To obtain statistically meaningful analysis of TM helix interactions, large (N ca. 100) ensembles of CGMD simulations are needed. To facilitate the running and analysis of such ensembles of simulations we have developed Sidekick, an automated pipeline software for performing high throughput CGMD simulations of α-helical peptides in lipid bilayer membranes. Through an end-to-end approach, which takes as input a helix sequence and outputs analytical metrics derived from CGMD simulations, we are able to predict the orientation and likelihood of insertion into a lipid bilayer of a given helix of family of helix sequences. We illustrate this software via analysis of insertion into a membrane of short hydrophobic TM helices containing a single cationic arginine residue positioned at different positions along the length of the helix. From analysis of these ensembles of simulations we estimate apparent energy barriers to insertion which are comparable to experimentally determined values. In a second application we use CGMD simulations to examine self-assembly of dimers of TM helices from the ErbB1 receptor tyrosine kinase, and analyse the numbers of simulation repeats necessary to obtain convergence of simple descriptors of the mode of packing of the two helices within a dimer. Our approach offers proof-of-principle platform for the further employment of automation in large ensemble CGMD simulations of membrane proteins.
机译:跨膜(TM)α螺旋与磷脂膜的相互作用以及彼此之间的相互作用对于理解整体膜蛋白的结构和稳定性至关重要。可通过粗粒度分子动力学(CGMD)模拟来分析这些相互作用。为了获得对TM螺旋相互作用的统计意义的分析,需要CGMD模拟的大型(N约100个)集合。为了促进此类模拟的运行和分析,我们开发了Sidekick,这是一种自动管线软件,用于对脂质双层膜中的α-螺旋肽进行高通量CGMD模拟。通过一种端到端方法,该方法以螺旋序列作为输入并输出从CGMD模拟得出的分析指标,我们能够预测定向到螺旋序列家族的给定螺旋的脂质双层中的方向和可能性。我们通过分析插入短疏水疏水性TM螺旋膜的膜来说明该软件,该螺旋包含一个单一的阳离子精氨酸残基,沿着螺旋线的长度位于不同位置。通过对这些模拟集合的分析,我们估计了明显的能量插入障碍,其与实验确定的值相当。在第二个应用程序中,我们使用CGMD模拟检查来自ErbB1受体酪氨酸激酶的TM螺旋二聚体的自组装,并分析必要的模拟重复次数,以获得两个螺旋堆积模式的简单描述符的收敛。二聚体。我们的方法为进一步在膜蛋白的大型整体CGMD模拟中进一步利用自动化提供了原理验证平台。

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