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Protein Networks

Protein Networks

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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Protein Networks

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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.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Identifying protein complexes based on density and modularity in protein-protein interaction network.

Jun Ren, Jianxin Wang, Min Li

BMC Systems Biology

|February 26, 2014

Summary

This study introduces LF_PIN, a new algorithm for identifying protein complexes by combining density and modularity. LF_PIN effectively detects complexes with varying densities and modularities, outperforming existing methods.

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein complex identification is vital for understanding cellular functions.
Existing methods often focus on either subgraph density or modularity in PPI networks.
This focus limits the detection of complexes with mixed or low density/modularity.

Purpose of the Study:

To develop a novel algorithm for identifying protein complexes with diverse density and modularity characteristics.
To address the limitations of existing density- or modularity-focused approaches.

Main Methods:

Proposed a novel subgraph fitness function: fρ = (density)^(ρ) * (modularity)^(1-ρ).
Developed the LF_PIN algorithm to identify protein complexes by expanding seed edges to subgraphs with maximum local fitness.
Evaluated LF_PIN using protein-protein interaction (PPI) networks.

Main Results:

LF_PIN demonstrated superior performance in identifying known protein complexes compared to methods using only density (ρ=1) or modularity (ρ=0).
Comparative analysis against seven established methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, NFC) in S. cerevisiae and E. coli showed LF_PIN's effectiveness.
LF_PIN achieved better matching with known complexes and superior functional enrichment, particularly for complexes with low density or low modularity.

Conclusions:

The LF_PIN algorithm, by integrating both density and modularity, offers a more comprehensive approach to protein complex detection.
LF_PIN significantly outperforms existing methods, especially in identifying challenging protein complexes characterized by low density or low modularity.