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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
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Exploiting locational and topological overlap model to identify modules in protein interaction networks
Lixin Cheng1,2, Pengfei Liu3, Dong Wang4
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. lxcheng@cse.cuhk.edu.hk.
BMC Bioinformatics
|January 16, 2019
Summary
This study introduces a new model for identifying protein complexes in molecular networks by considering protein location. This approach improves the accuracy of functional module detection in biological systems.
Area of Science:
- Systems Biology
- Bioinformatics
- Molecular Biology
Background:
- Molecular network clustering is vital for predicting protein complexes and functional modules in system biology.
- Biological molecules are dynamically regulated in space and time, with interactions often occurring in specific cellular locations.
Purpose of the Study:
- To investigate the relationship between subcellular localization and biological functions.
- To develop a novel model for identifying functional modules in protein interaction networks (PINs) by integrating spatial and topological information.
Main Methods:
- Constructed a co-localization human protein interaction network (PIN) by considering subcellular localization.
- Proposed the Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN.
- LTOM requires shared protein partners (topological overlaps) to be co-localized with the proteins themselves.
Main Results:
- The LTOM demonstrated improved correspondence with known protein complexes.
- The model showed increased relevance to cancer-related functions.
- Results were validated using both human and yeast datasets with ClusterONE and MCL clustering algorithms.
Conclusions:
- Integrating protein localization and topological overlap enhances the performance of module detection in protein interaction networks.
- This approach offers a more accurate method for identifying functional modules within dynamic cellular networks.
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