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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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

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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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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Prioritizing protein complexes implicated in human diseases by network optimization.

Yong Chen, Thibault Jacquemin, Shuyan Zhang

    BMC Systems Biology
    |February 26, 2014
    PubMed
    Summary
    This summary is machine-generated.

    We developed MAXCOM, a novel computational method to identify disease-related protein complexes. MAXCOM effectively prioritizes candidate complexes by optimizing network relationships, aiding disease mechanism and drug discovery.

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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Genomics

    Background:

    • Understanding protein complex roles in inherited diseases is crucial for disease mechanism elucidation.
    • Protein complex dysfunctions, stemming from member disturbances, are linked to various diseases.
    • Existing computational methods primarily focus on individual disease proteins, lacking systematic approaches for protein complexes.

    Purpose of the Study:

    • To introduce MAXCOM, a computational method for prioritizing candidate disease-related protein complexes.
    • To systematically investigate associations between protein complexes and human inherited diseases using network optimization.

    Main Methods:

    • MAXCOM utilizes a maximum information flow algorithm within a heterogeneous network.
    • The network integrates protein-protein interactions and disease phenotypic similarities.
    • Candidate protein complexes are prioritized based on their optimized relationships with query diseases.

    Main Results:

    • Cross-validation on 539 protein complexes showed MAXCOM ranked 70.87% correctly compared to random.
    • Permutation experiments confirmed MAXCOM's robustness to network structure and parameters.
    • Analysis of top-ranked complexes for breast cancer suggested a potential association with the SWI/SNF complex.

    Conclusions:

    • MAXCOM is an effective network optimization method for discovering disease-related protein complexes.
    • The approach demonstrates high performance and robustness, facilitating disease pathology studies.
    • MAXCOM can aid in designing drugs that target multiple proteins involved in disease.