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• W2801476319 endingPage "241726" @default.
• W2801476319 startingPage "241726" @default.
• W2801476319 abstract "Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E's PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulator-bound and regulator-free states. The employed protocol reproduces methyl deuterium order parameters from NMR. Results from unsupervised clustering of Cij combined with flow analyses of weighted graphs of Cij show that regulator binding significantly alters the global signaling network in the protein; however, not by altering the spatial arrangement of strongly interacting amino acid clusters but by modifying the connectivity between clusters. Additionally, we find that regulator-induced shifts in conformational ensembles, which we evaluate by repartitioning ensembles using supervised learning, are, in fact, correlated. This correlation Δij is less extensive compared to Cij, but in contrast to Cij, Δij depends inversely on the distance from the regulator binding site. Assuming that Δij is an indicator of the transduction of the regulatory signal leads to the conclusion that the regulatory signal weakens with distance from the regulatory site. Overall, this work provides new approaches to analyze high-dimensional molecular simulation data and also presents applications that yield new insight into dynamic allostery." @default.
• W2801476319 created "2018-05-17" @default.
• W2801476319 creator A5002987852 @default.
• W2801476319 creator A5004495836 @default.
• W2801476319 creator A5027761662 @default.
• W2801476319 date "2018-06-28" @default.
• W2801476319 modified "2023-10-12" @default.
• W2801476319 title "Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain" @default.
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• W2801476319 doi "https://doi.org/10.1063/1.5022469" @default.
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• W2801476319 hasPublicationYear "2018" @default.
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