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      Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

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      1 , 1 , 2 , *
      PLoS Computational Biology
      Public Library of Science

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          Abstract

          A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

          Author Summary

          Functional versatility and structural adaptability of the Hsp90 chaperones are regulated by allosteric interactions that allow for diverse functions including modulation of ATP hydrolysis and binding with cochaperones and client proteins. By integrating molecular simulations and network-based approaches we have characterized conformational dynamics and allosteric interactions in different functional forms of Hsp90. The network centrality analysis and structural mapping of allosteric communications have revealed a small-world organization of the interaction network that is mediated by functionally important residues of the Hsp90 activity. We have found that effective allosteric communications in the Hsp90 chaperone may be provided by structurally stable residues that exhibit high centrality properties. Nucleotide-specific rewiring of the network topology and assortative organization of functional residues may protect the active form of the chaperone from random perturbations and detrimental mutations. These results have confirmed that allosteric interactions in the Hsp90 chaperone may be determined by a small-world network of functional residues that can regulate the network efficiency and resiliency by modulating the statistical ensemble of communication pathways in response to functional requirements of the ATPase cycle.

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          Most cited references103

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          Uncovering the overlapping community structure of complex networks in nature and society

          Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
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            Assortative mixing in networks

            M. Newman (2002)
            A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. We define a measure of assortative mixing for networks and use it to show that social networks are often assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortative network, which we study both analytically and numerically. Within the framework of this model we find that assortative networks tend to percolate more easily than their disassortative counterparts and that they are also more robust to vertex removal.
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              Specificity and stability in topology of protein networks

              Molecular networks guide the biochemistry of a living cell on multiple levels: its metabolic and signalling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks we quantify correlations between connectivities of interacting nodes and compare them to a null model of a network, in which al links were randomly rewired. We find that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, while those between a highly-connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations.

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                June 2014
                12 June 2014
                : 10
                : 6
                : e1003679
                Affiliations
                [1 ]School of Computational Sciences and Crean School of Health and Life Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
                [2 ]Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America
                University of Houston, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GMV. Performed the experiments: KB GMV. Analyzed the data: GMV. Contributed reagents/materials/analysis tools: KB GMV. Wrote the paper: GMV.

                [¤]

                Current address: Current address: Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University, Piscataway, New Jersey, United States of America

                Article
                PCOMPBIOL-D-14-00093
                10.1371/journal.pcbi.1003679
                4055421
                24922508
                d59e4fbf-1e18-47dd-9117-e9f0ca09a656
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 January 2014
                : 5 May 2014
                Page count
                Pages: 21
                Funding
                This work is supported by funding from Chapman University. No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Folding
                Biomacromolecule-Ligand Interactions
                Computational Biology
                Biophysics
                Biophysical Simulations
                Biophysics Theory
                Molecular Biology
                Macromolecular Structure Analysis
                Macromolecular Complex Analysis
                Molecular Complexes
                Computer and Information Sciences
                Computer Modeling
                Physical Sciences
                Chemistry
                Computational Chemistry
                Molecular Dynamics
                Molecular Mechanics
                Physical Chemistry
                Physics
                Classical Mechanics
                Motion
                Newton's Laws of Motion
                Thermodynamics

                Quantitative & Systems biology
                Quantitative & Systems biology

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