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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...

Protein-Protein Interfaces

Protein-Protein Interfaces

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

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Related Experiment Video

Updated: May 3, 2026

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

Integration, visualization and analysis of human interactome.

Chiara Pastrello¹, Elisa Pasini², Max Kotlyar¹

¹Princess Margaret Cancer Centre, University Health Network and TECHNA Institute for the Advancement of Technology for Health, TMDT, Room 11-314, 101 College Street, Toronto, ON M5G 1L7, Canada.

Biochemical and Biophysical Research Communications

|February 5, 2014

Summary

This summary is machine-generated.

Network analysis is key for integrating diverse omics data, revealing insights into biological functions and interactions. This approach enhances data visualization and hypothesis generation, particularly in cancer research.

Keywords:

Gastric cancer Interactome Network analysis Omics Visual data mining

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Related Experiment Videos

Last Updated: May 3, 2026

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Published on: March 3, 2015

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

Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

The diversity of 'omics' fields generates large volumes of heterogeneous and distributed data.
Meaningful hypotheses require effective integration and visualization of this complex data.

Purpose of the Study:

To review network analysis as a key technique for data integration and visualization in 'omics' research.
To explore network properties for gaining insights into network function and element relationships.
To demonstrate the value of network analysis in cancer research using an example integration.

Main Methods:

Systematic exploration of network properties.
Analysis of the interactome for connecting experimental data.
Focus on context-specific features and dynamic data in signaling pathways.

Main Results:

Network analysis facilitates the integration and visualization of diverse 'omics' data.
Understanding network structures reveals relationships between network components and their functions.
The interactome plays a crucial role in linking data from different experiments.
Network analysis can identify context-specific interaction features.

Conclusions:

Network analysis is a powerful approach for extracting meaningful insights from complex 'omics' data.
This technique is valuable for hypothesis generation and understanding biological systems, with applications in areas like cancer research.
Dynamic data and interaction context are critical for a comprehensive understanding of biological networks.