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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-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...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Related Experiment Video

Updated: Oct 10, 2025

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Improving Analysis and Annotation of Microarray Data with Protein Interactions.

Max Kotlyar^1,2, Serene W H Wong^1,2, Chiara Pastrello^1,2

¹Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.

Methods in Molecular Biology (Clifton, N.J.)

|December 13, 2021

Summary

This summary is machine-generated.

Integrating protein-protein interactions with gene expression microarrays enhances disease mechanism discovery and improves gene signature reliability. This approach addresses limitations in traditional microarray analysis for better biological insights.

Keywords:

Gene expression Gene expression analysis Network analysis Protein–protein interactions

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Last Updated: Oct 10, 2025

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

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

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

Published on: March 3, 2015

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Molecular Biology
Bioinformatics
Systems Biology

Background:

Gene expression microarrays are key high-throughput tools in molecular biology.
Current microarray analysis often overlooks complex gene and protein interactions.
This limitation hinders accurate disease mechanism identification and diagnostic/prognostic gene signature development.

Purpose of the Study:

To review the integration of protein-protein interaction (PPI) data with microarray analysis.
To highlight the benefits of incorporating PPI data for enhanced biological interpretation.
To demonstrate how PPIs improve gene prioritization and the generalizability of gene signatures.

Main Methods:

Review of existing literature and methodologies.
Conceptual framework for integrating PPI networks with gene expression data.
Case studies illustrating the application of integrated approaches.

Main Results:

Incorporating PPI data provides a more comprehensive understanding of biological pathways.
Enhanced explanations of disease mechanisms through network-based analysis.
Improved gene annotations and more robust gene signatures with better predictive power.

Conclusions:

Protein-protein interactions are crucial for unlocking the full potential of microarray data.
Integrating PPIs significantly advances the application of microarrays in disease research and biomarker discovery.
This integrated approach leads to more reliable and interpretable biological findings.