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Extreme multifunctional proteins identified from a human protein interaction network

Author

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  • Charles E. Chapple

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090)

  • Benoit Robisson

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090)

  • Lionel Spinelli

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090
    Aix-Marseille University, CIML
    CNRS, UMR 7280)

  • Céline Guien

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090
    Present address: Aix-Marseille University, GMGF, Marseille, F-13005 France; INSERM UMR_S910, Marseille F-13005, France)

  • Emmanuelle Becker

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090
    Present address: Rennes I University, IRSET, Rennes F-35042, France; INSERM U1085, Rennes F-35042, France)

  • Christine Brun

    (Aix-Marseille University, TAGC
    INSERM UMR_S1090
    CNRS)

Abstract

Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of ‘extreme multifunctional’ proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins.

Suggested Citation

  • Charles E. Chapple & Benoit Robisson & Lionel Spinelli & Céline Guien & Emmanuelle Becker & Christine Brun, 2015. "Extreme multifunctional proteins identified from a human protein interaction network," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
  • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8412
    DOI: 10.1038/ncomms8412
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    Cited by:

    1. Naomi S Hachiya, 2017. "Unfoldin, A Novel Tool for the Analysis of Protein Misfolding or Neurodegenerative Diseases," Open Access Journal of Neurology & Neurosurgery, Juniper Publishers Inc., vol. 6(3), pages 40-44, October.

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