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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.
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,...

Protein Networks

Protein Networks

Protein-protein Interfaces

Protein-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...

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Related Experiment Video

Updated: Apr 30, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Applied graph-mining algorithms to study biomolecular interaction networks.

Ru Shen¹, Chittibabu Guda²

¹Department of Computer Science, University at Albany, 1400 Washington Avenue, Albany, NY 12222, USA.

Biomed Research International

|May 7, 2014

Summary

This summary is machine-generated.

This review explores computational methods for analyzing protein-protein interaction (PPI) networks. It highlights graph comparison and module detection techniques, including a novel frequent subgraph method for cancer PPI networks.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Area of Science:

Bioinformatics
Computational Biology
Network Science

Background:

Protein-protein interaction (PPI) networks are crucial for understanding cellular organization.
Identifying functional modules within these networks is a key challenge in biological network analysis.
Computational methods are essential for analyzing large-scale biomolecular interaction networks.

Purpose of the Study:

To provide a comprehensive review of computational methods for analyzing PPI networks.
To summarize current literature on graph comparison and module detection strategies.
To present a novel frequent subgraph method for detecting shared functional modules across cancer PPI networks.

Main Methods:

Graph comparison methods: graph kernel and graph alignment.
Module detection methods: seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.
Review of major algorithms within each category.

Main Results:

Detailed summary of existing literature on graph comparison and module detection algorithms.
Introduction of a new frequent subgraph method for identifying functional modules.
Application of these methods to detect shared modules in cancer PPI networks.

Conclusions:

Computational analysis, particularly graph comparison and module detection, is vital for understanding PPI networks.
The reviewed methods offer diverse approaches to biological network analysis.
The proposed frequent subgraph method shows promise for identifying cancer-specific functional modules.