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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.
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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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Updated: Jun 21, 2026

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

Published on: June 15, 2018

A max-flow based approach to the identification of protein complexes using protein interaction and microarray data.

Jianxing Feng¹, Rui Jiang, Tao Jiang

¹Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. fengjx06@mails.tsinghua.edu.cn

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference

|August 1, 2009

Summary

This study introduces a novel Graph Fragmentation Algorithm (GFA) to identify protein complexes by integrating protein-protein interaction (PPI) data and gene expression profiles. GFA enhances accuracy and predicts over 200 novel protein complexes.

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Last Updated: Jun 21, 2026

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Published on: June 15, 2018

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Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

High-throughput technologies generate vast protein-protein interaction (PPI) data and gene expression profiles.
Computational methods can identify protein complexes, but integrating diverse data types can improve accuracy.

Purpose of the Study:

To develop a novel computational method for protein complex identification.
To leverage both PPI data and gene expression profiles for enhanced accuracy.

Main Methods:

A Graph Fragmentation Algorithm (GFA) was developed, adapted from a max-flow algorithm.
GFA identifies dense subgraphs in PPI networks and fragments them using gene expression data (log fold changes).

Main Results:

The GFA method demonstrated superior performance in accuracy and efficiency across three PPI datasets.
The algorithm successfully predicted a significant number of novel protein complexes with high specificity.

Conclusions:

The proposed GFA method effectively integrates PPI and gene expression data for robust protein complex identification.
The study predicts over 200 novel protein complexes, highlighting the method's potential for biological discovery.