Validating clustering of molecular dynamics simulations using polymer models.
Validating clustering of molecular dynamics simulations using polymer models.
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DOI：
10.1186/1471-2105-12-445
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发表时间：
2011-11-14
影响因子：
3
通讯作者：
Newsam S
中科院分区：
文献类型：
--
作者：
Phillips JL;Colvin ME;Newsam S
分子动力学（MD）模拟是对蛋白质和其他生物分子的亚稳态和过渡构象进行采样的有力技术。计算数据聚类已经成为一种有用的，自动化的技术，从分子动力学模拟数据中提取构象状态。尽管广泛的应用，相对较少的工作已经完成，以确定是否聚类算法实际上是提取有用的信息。因此，本文的一个主要目标是提供这样一个理解，通过详细的数据聚类分析应用到一系列日益复杂的生物聚合物模型。我们开发了一系列新的模型，使用基本的聚合物理论，具有直观的，明确定义的动力学和表现出的基本属性，我们正在寻求确定在MD模拟的真实的生物分子。然后，我们应用谱聚类，一种算法，特别适合于聚类聚合物结构，我们的模型和MD模拟的几个本质上无序的蛋白质。聚合物模型的聚类结果提供了明确的证据，亚稳态和过渡构象的算法检测。聚合物模型的结果还有助于通过比较和对比提取的簇的统计特性来指导无序蛋白质模拟的分析。我们已经开发了一个框架，用于验证聚类算法的性能和效用，研究分子生物聚合物模拟，利用几个分析和动态聚合物模型表现良好的动态，包括：亚稳态，过渡态，螺旋结构，和随机动力学。我们表明，谱聚类对结构比对引入的异常具有鲁棒性，并且可以从聚类结果中可靠地区分本质无序蛋白质的不同结构类别。据我们所知，我们的框架是第一个利用模型聚合物来严格测试聚类算法用于研究生物聚合物的效用。
Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.
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影响因子：
2.9
作者：
KARPEN, ME;TOBIAS, DJ;BROOKS, CL
通讯作者：
BROOKS, CL
影响因子：
56.9
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
2.1
作者：
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通讯作者：
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DOI：
10.1109/34.868688
发表时间：
2000-08-01
影响因子：
23.6
作者：
Shi, JB;Malik, J
通讯作者：
Malik, J