Literature DB >> 21826754

Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions.

Nitin Bhardwaj1, Alexej Abyzov, Declan Clarke, Chong Shou, Mark B Gerstein.   

Abstract

The integration of molecular networks with other types of data, such as changing levels of gene expression or protein-structural features, can provide richer information about interactions than the simple node-and-edge representations commonly used in the network community. For example, the mapping of 3D-structural data onto networks enables classification of proteins into singlish- or multi-interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi-interface hubs display a greater degree of conformational change than do singlish-interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.
Copyright © 2011 The Protein Society.

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Year:  2011        PMID: 21826754      PMCID: PMC3218368          DOI: 10.1002/pro.710

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  28 in total

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3.  Relating three-dimensional structures to protein networks provides evolutionary insights.

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4.  Similar binding sites and different partners: implications to shared proteins in cellular pathways.

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5.  Is the intrinsic disorder of proteins the cause of the scale-free architecture of protein-protein interaction networks?

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Journal:  Proteomics       Date:  2007-03       Impact factor: 3.984

6.  Identification of transient hub proteins and the possible structural basis for their multiple interactions.

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Journal:  Protein Sci       Date:  2008-01       Impact factor: 6.725

7.  Structural changes involved in protein binding correlate with intrinsic motions of proteins in the unbound state.

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9.  Towards inferring time dimensionality in protein-protein interaction networks by integrating structures: the p53 example.

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10.  The role of disorder in interaction networks: a structural analysis.

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Journal:  Mol Syst Biol       Date:  2008-03-25       Impact factor: 11.429

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  14 in total

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Review 2.  Reads meet rotamers: structural biology in the age of deep sequencing.

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Review 3.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

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5.  Novel insights through the integration of structural and functional genomics data with protein networks.

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Review 6.  From systems to structure: bridging networks and mechanism.

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Review 7.  Standardization and quality control in quantifying non-enzymatic oxidative protein modifications in relation to ageing and disease: Why is it important and why is it hard?

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8.  Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles.

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9.  Interpretation of genomic variants using a unified biological network approach.

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10.  The human proteome - a scientific opportunity for transforming diagnostics, therapeutics, and healthcare.

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