Literature DB >> 23441894

The topology and dynamics of protein complexes: insights from intra- molecular network theory.

Guang Hu1, Jianhong Zhou, Wenying Yan, Jiajia Chen, Bairong Shen.   

Abstract

Intra-molecular interactions within complex systems play a pivotal role in the biological function. They form a major challenge to computational structural proteomics. The network paradigm treats any system as a set of nodes linked by edges corresponding to the relations existing between the nodes. It offers a computationally efficient tool to meet this challenge. Here, we review the recent advances in the use of network theory to study the topology and dynamics of protein- ligand and protein-nucleic acid complexes. The study of protein complexes networks not only involves the topological classification in term of network parameters, but also reveals the consistent picture of intrinsic functional dynamics. Current dynamical analysis focuses on a plethora of functional phenomena: the process of allosteric communication, the binding induced conformational changes, prediction and identification of binding sites of protein complexes, which will give insights into intra-protein complexes interactions. Furthermore, such computational results may elucidate a variety of known biological processes and experimental data, and thereby demonstrate a huge potential for applications such as drug design and functional genomics. Finally we describe some web-based resources for protein complexes, as well as protein network servers and related bioinformatics tools.

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Year:  2013        PMID: 23441894     DOI: 10.2174/1389203711314020004

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  5 in total

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2.  Molecular Dynamics Simulations of Kir2.2 Interactions with an Ensemble of Cholesterol Molecules.

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Journal:  Biophys J       Date:  2018-08-23       Impact factor: 4.033

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Journal:  Biomed Res Int       Date:  2017-01-22       Impact factor: 3.411

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5.  Transduction motif analysis of gastric cancer based on a human signaling network.

Authors:  G Liu; D Z Li; C S Jiang; W Wang
Journal:  Braz J Med Biol Res       Date:  2014-05       Impact factor: 2.590

  5 in total

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