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Updated: Aug 13, 2026
08:38
Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
Published on: March 3, 2015
A simple physical model for scaling in protein-protein interaction networks
Eric J Deeds1, Orr Ashenberg, Eugene I Shakhnovich
1Department of Molecular and Cellular Biology, Harvard University, 7 Divinity Avenue, Cambridge, MA 02138, USA.
Summary
Biological networks, including protein-protein interactions (PPIs), often show scale-free topology. A new physical model explains this, linking protein interactions to desolvation and hydrophobic residues.
Area of Science:
- Systems biology
- Biophysics
- Network science
Background:
- Many biological networks exhibit scale-free topology, characterized by a power-law distribution of node connections (p(k) ~ k^-gamma).
- Evolutionary models have successfully reproduced this scale-free property in biological networks.
Purpose of the Study:
- To investigate the topology of protein-protein interaction (PPI) networks.
- To propose and validate a physical model for PPIs that explains their scale-free nature and higher-order correlations.
Main Methods:
- Analysis of two independent protein-protein interaction datasets.
- Development of a physical model based on protein desolvation principles.
- Correlation analysis between protein interaction counts and surface hydrophobic residue fractions.
Main Results:
- Independent PPI datasets show weak correlation in specific interactions but similar scale-free topology.
- The proposed physical model accurately reproduces the scale-free topology of PPI networks.
- A significant correlation was found between a protein's interaction count and its surface's hydrophobic residue fraction.
Conclusions:
- Desolvation is a key physical factor driving protein-protein interactions and network topology.
- The physical model provides a comprehensive explanation for the structure of experimentally determined PPI networks.
- Findings have broad implications for understanding PPIs and other scale-free biological networks.
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