Quantifying Changes in Intrinsic Molecular Motion Using Support Vector Machines
Quantifying Changes in Intrinsic Molecular Motion Using Support Vector Machines
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DOI：
10.1021/ct300694e
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发表时间：
2013-02-01
影响因子：
5.5
通讯作者：
Varma, Sameer
中科院分区：
文献类型：
--
作者：
Leighty, Ralph E.;Varma, Sameer
一个分子所表现出的三维（3-D）构型的集合，即其内在运动，可以被几个环境因素改变，也可以被其他分子的结合改变。定量这种诱导的内在运动的变化是很重要的，因为它提供了一个基础，热力学变化的分子运动的变化。然而，这项任务具有挑战性，因为它需要比较两个高维数据集。传统上，当分析分子模拟时，这个问题是通过首先分别减少两个集合的维度，然后比较两个集合的汇总统计量来避免的。然而，由于降维是在集成比较之前进行的，因此这种策略容易受到来自信息丢失的人为偏差的影响。在这里，我们介绍了一种基于支持向量机的方法，该方法在将两个集合直接进行比较后，对两个集合之间的差异进行归一化的定量估计。虽然这种方法可以应用于任何分子系统，包括非生物分子和晶体，在这里，我们展示了如何将其应用于识别副粘病毒G蛋白的特定区域，这些区域受到其优选的人类受体Ephrin B2结合的影响。这种蛋白质-蛋白质相互作用引发病毒与宿主细胞的融合。具体而言，对于G蛋白中的每个残基，我们分别获得它们在存在和不存在肝配蛋白B2的情况下采样的构型集合之间的定量差异。这些合奏使用分子动力学模拟产生。对运动变化最大的残基进行排序，然后将其映射到G蛋白的3-D结构上，结果表明它们主要聚集在蛋白质的单个连续面上，并且包括实验上已知在调节病毒融合中起重要作用的集合。
The ensemble of three-dimensional (3-D) configurations exhibited by a molecule, that is, its intrinsic motion, can be altered by several environmental factors, and also by the binding of other molecules. Quantification of such induced changes in intrinsic motion is important because it provides a basis for relating thermodynamic changes to changes in molecular motion. This task is, however, challenging because it requires comparing two high-dimensional data sets. Traditionally, when analyzing molecular simulations, this problem is circumvented by first reducing the dimensions of the two ensembles separately, and then comparing summary statistics from the two ensembles against each other. However, since dimensionality reduction is carried out prior to ensemble comparison, such strategies are susceptible to artifactual biases from information loss. Here, we introduce a method based on support vector machines that yields a normalized quantitative estimate for the difference between two ensembles after comparing them directly against one another. While this method can be applied to any molecular system, including nonbiological molecules and crystals, here, we show how it can be applied to identify the specific regions of a paramyxovirus G protein that are affected by the binding of its preferred human receptor, Ephrin B2. This protein-protein interaction initiates the fusion of the virus with the host cell. Specifically, for every residue in the G protein, we obtain separately a quantitative difference between the ensemble of configurations they sample in the presence and in the absence of Ephrin B2. These ensembles were generated using molecular dynamics simulations. Rank-ordering and then mapping the residues that undergo the greatest change in motion onto the 3-D structure of the G protein reveals that they are clustered primarily on a single contiguous facet of the protein and include the set that is known experimentally to play a vital role in regulating viral fusion.