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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Discovering essential proteins based on PPI network and protein complex.

Jun Ren, Jianxin Wang, Min Li

    International Journal of Data Mining and Bioinformatics
    |October 23, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces harmonic centrality (HC), a novel method for identifying essential proteins by integrating protein complex information with network topology. HC improves essential protein prediction, especially in incomplete protein-protein interaction networks.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Current essential protein identification methods rely on protein-protein interaction (PPI) network topology.
    • These methods struggle with proteins of low centrality and incomplete network data.

    Purpose of the Study:

    • To propose a new centrality measure that incorporates protein complex information for improved essential protein identification.
    • To evaluate the performance of the proposed measure against existing methods.

    Main Methods:

    • Developed a novel centrality measure, harmonic centrality (HC), by combining complex centrality and subgraph centrality.
    • Integrated protein complex data with PPI network topology.
    • Generated a weighted PPI network using cellular localization and biological process information.

    Main Results:

    • Harmonic centrality (HC) demonstrates superior performance in predicting essential proteins compared to traditional topological centrality measures.
    • The performance improvement of HC is particularly significant in incomplete PPI networks.
    • Further enhancement of HC performance by 5% was achieved using a weighted PPI network.

    Conclusions:

    • Protein complex information is a crucial factor in determining protein essentiality.
    • Harmonic centrality (HC) offers a more robust approach for identifying essential proteins, outperforming existing methods.
    • The integration of diverse biological data enhances the accuracy of essential protein prediction.