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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

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-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 polypeptide...

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Related Experiment Video

Updated: May 16, 2026

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Identifying protein interaction subnetworks by a bagging Markov random field-based method.

Li Chen¹, Jianhua Xuan, Rebecca B Riggins

¹Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.

Nucleic Acids Research

|November 20, 2012

Summary

This summary is machine-generated.

This study introduces a new Bagging Markov Random Field (BMRF) method for identifying protein-protein interaction subnetworks in breast cancer. The BMRF approach improves accuracy and reveals biologically meaningful subnetworks relevant to cancer progression and tamoxifen resistance.

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Last Updated: May 16, 2026

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Published on: May 17, 2020

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Area of Science:

Computational biology
Bioinformatics
Cancer research

Background:

Identifying differentially expressed subnetworks from protein-protein interaction (PPI) networks is crucial for understanding cancer mechanisms.
Existing methods often overlook gene dependencies, failing to identify key hub genes.

Purpose of the Study:

To develop an improved method for PPI subnetwork identification in breast cancer research.
To explicitly consider pairwise gene interactions for more accurate subnetwork discovery.

Main Methods:

A novel Bagging Markov Random Field (BMRF) framework was developed.
The method utilizes a maximum a posteriori principle for a new network score that considers gene interactions.
A bagging scheme with bootstrapping enhances subnetwork robustness and statistical selection.

Main Results:

The BMRF-based method demonstrated superior performance compared to existing approaches on simulation data.
Application to breast cancer data identified PPI subnetworks linked to cancer progression and tamoxifen resistance.
The approach achieved improved prediction performance on independent datasets.

Conclusions:

The BMRF method offers enhanced prediction performance for PPI subnetwork identification.
It effectively reveals biologically meaningful subnetworks relevant to breast cancer and tamoxifen resistance.
This approach advances mechanistic studies in cancer research.