Network analysis of protein dynamics.
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TLDR
The results of the topological analysis of protein structures as molecular networks describing their small‐world character are reviewed, and the role of hubs and central network elements in governing enzyme activity, allosteric regulation, protein motor function, signal transduction and protein stability is reviewed.About:
This article is published in FEBS Letters.The article was published on 2007-06-19 and is currently open access. It has received 217 citations till now. The article focuses on the topics: Network dynamics & Network topology.read more
Citations
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Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review
Peter Csermely,Tamas Korcsmaros,Tamas Korcsmaros,Huba Kiss,Gábor London,Ruth Nussinov,Ruth Nussinov +6 more
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.
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Induced fit, conformational selection and independent dynamic segments: an extended view of binding events
TL;DR: It is argued that protein segments whose dynamics are distinct from the rest of the protein ('discrete breathers') can govern conformational transitions and allosteric propagation that accompany binding processes and, as such, might be more sensitive to mutational events.
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Allosteric regulation and catalysis emerge via a common route.
TL;DR: In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed, and recent experimental data and the resulting insights into allostery within proteins are summarized.
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Topological analysis and interactive visualization of biological networks and protein structures
TL;DR: This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks, to perform a topological analysis of biological networks.
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Molecular dynamics simulations and novel drug discovery.
TL;DR: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets.
References
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Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Complex networks: Structure and dynamics
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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Network biology: understanding the cell's functional organization
TL;DR: This work states that rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize the view of biology and disease pathologies in the twenty-first century.
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Uncovering the overlapping community structure of complex networks in nature and society
TL;DR: After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.