Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods
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DOI：
10.3389/fmolb.2019.00042
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发表时间：
2019-06-12
影响因子：
5
通讯作者：
Butts, Carter T.
中科院分区：
文献类型：
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作者：
Grazioli, Gianmarc;Martin, Rachel W.;Butts, Carter T.
对内在无序蛋白质(IDPs)的模拟给比较分析带来了许多挑战，主要包括高度动态的构象状态和缺乏明确的二级结构。机器学习(ML)算法在区分差异极其细微的高维输入方面特别有效，使其非常适合于国内流离失所者的研究。在这项工作中，我们应用了各种最大似然技术，包括支持向量机(SVM)和聚类，以及相关的方法，如主成分分析(PCA)和蛋白质结构网络(PSN)分析，以揭示来自同一IDP的两个变体的分子动力学模拟的构型数据之间的差异。我们研究了野生型淀粉样β蛋白(Aβ(1-40))及其“北极”变体(E22G)的分子动力学(MD)轨迹，这两个系统在阿尔茨海默病的病因学中发挥着核心作用。我们的分析展示了ML和相关方法可以用来阐明这些蛋白质之间的细微差异的方法，包括传统度量方法很难捕捉到的瞬时结构。
Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problemof uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (A beta(1-40)) and its "Arctic" variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.