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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
Four-dimensional visualisation and analysis of protein-protein interaction networks
Apurv Goel1, Simone S Li, Marc R Wilkins
1Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
Proteomics
|June 2, 2011
Summary
This study introduces software for dynamic, four-dimensional (4-D) protein interaction networks, visualizing temporal changes in protein interactions. This approach reveals how temporal gene expression controls protein interactions, particularly for hub proteins.
Area of Science:
- Systems Biology
- Computational Biology
- Molecular Interactomics
Background:
- Protein-protein interaction networks are typically static, composite representations of known interactions.
- Existing network models do not account for the dynamic nature of protein interactions within a cell.
- Understanding dynamic interactions is crucial for comprehending cellular processes.
Purpose of the Study:
- To develop and present software for generating and analyzing dynamic, four-dimensional (4-D) protein interaction networks.
- To visualize temporal changes in protein interactions using time-course abundance data.
- To provide tools for real-time navigation, manipulation, and querying of dynamic networks.
Main Methods:
- Mapping time-course abundance data onto three-dimensional (3-D) protein interaction networks to create dynamic network visualizations ('network movies').
- Generating two types of 4-D networks: one mapping expression data onto protein nodes, and another using 'real-time rendering' for dynamic appearance/disappearance of nodes and interactions.
- Applying the software to analyze hub protein interactions during the yeast cell cycle.
Main Results:
- Demonstrated the utility of the 4-D network software in analyzing yeast cell cycle interactions.
- Identified strict temporal control over the expression of interaction partners for proteins MLC1 and YPT52.
- Observed that temporal control of gene expression may regulate competition at interaction interfaces of hub proteins.
Conclusions:
- The developed software enables the creation and analysis of dynamic, 4-D protein interaction networks.
- Temporal control of gene expression plays a significant role in regulating protein-protein interactions and managing competition at interaction interfaces.
- The findings provide new insights into the dynamic regulation of cellular processes through protein interactions.
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