Identifying protein complexes from interaction networks based on clique percolation and distance restriction
Jianxin Wang1, Binbin Liu, Min Li
1School of Information Science and Engineering, Central South University, Changsha 410083, China. jxwang@mail.csu.edu.cn
BMC Genomics
|November 5, 2010
Summary
A new algorithm, CP-DR, improves protein complex identification in biological networks by extending the clique percolation method with distance restrictions. This method enhances accuracy and identifies meso-scale complexes more effectively than existing approaches.
Area of Science:
- Systems Biology
- Bioinformatics
- Network Science
Background:
- Protein complex identification is vital for understanding cellular organization and protein function in the post-genomic era.
- Protein-protein interaction networks often feature proteins belonging to multiple complexes, necessitating methods for identifying overlapping structures.
- Identifying overlapping protein complexes from large interaction networks remains a significant research challenge.
Purpose of the Study:
- To develop a novel algorithm for identifying overlapping protein complexes in protein-protein interaction networks.
- To address the limitations of the clique percolation method (CPM) in terms of accuracy and suitability for meso-scale complexes.
- To improve the identification of dense subgraphs corresponding to known protein complexes.
Main Methods:
- Extended the k-clique community definition from the clique percolation method (CPM).
- Introduced a distance restriction to the topological model.
- Developed a novel algorithm, CP-DR, based on the extended model and distance restriction.
Main Results:
- The CP-DR algorithm was applied to the Saccharomyces cerevisiae protein interaction network.
- CP-DR successfully identified numerous well-characterized protein complexes.
- The algorithm demonstrated improved performance compared to the standard CPM.
Conclusions:
- The CP-DR algorithm effectively identifies dense subgraphs in protein interaction networks, many of which represent known protein complexes.
- The integration of clique percolation with distance restriction enhances the ability to detect protein complexes.
- CP-DR exhibits superior performance over the conventional CPM for protein complex identification.
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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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02:26Protein 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.
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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02:04Protein-protein Interfaces
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 polypeptide...
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