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Related Concept Videos

Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.2K
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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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A Granularity-Related Network Refinement Method Based on Module Division and Biological Information for Identifying

Li Pan, Chenyang Xiao, Jinliang Yang

    IEEE Transactions on Computational Biology and Bioinformatics
    |August 27, 2025
    PubMed
    Summary

    Identifying essential proteins is crucial for understanding life and disease. This study introduces a new method that refines protein-protein interaction networks (PINs) by considering modular structures, significantly improving essential protein discovery accuracy.

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    An Integrated Approach for Microprotein Identification and Sequence Analysis
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    Area of Science:

    • Bioinformatics
    • Systems Biology
    • Computational Biology

    Background:

    • Essential proteins are vital for biological functions and disease research.
    • Existing methods often struggle with noisy protein-protein interaction networks (PINs).
    • Network refinement, particularly using modularity, enhances essential protein identification.

    Purpose of the Study:

    • To develop a novel granularity-aware network refinement framework for improved essential protein discovery.
    • To integrate hierarchical module division and multi-source biological evidence for more accurate PINs.
    • To enhance the identification of critical proteins in biological systems.

    Main Methods:

    • Constructed weighted PINs using gene expression and Gene Ontology (GO) data.
    • Employed the Louvain algorithm for hierarchical module division to optimize granularity.
    • Integrated evolutionary conservation and nuclear localization enrichment for critical module detection, creating a Granularity-Modulated PIN (GM-PIN).

    Main Results:

    • GM-PIN demonstrated superior performance compared to baseline networks across multiple evaluation metrics.
    • The method showed significant improvements in identifying essential proteins in yeast and human datasets.
    • Comparative evaluations confirmed the effectiveness of the granularity-modulated refinement approach.

    Conclusions:

    • The proposed granularity-aware framework effectively refines protein-interaction networks.
    • This refinement substantially enhances the accuracy and efficiency of essential protein discovery.
    • The GM-PIN approach offers a promising strategy for advancing biological systems research.