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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...

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: Feb 27, 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

Protein network construction using reverse phase protein array data.

Rency S Varghese¹, Yiming Zuo², Yi Zhao³

¹Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.

Methods (San Diego, Calif.)

|June 28, 2017

Summary

This summary is machine-generated.

We developed a new computational method using reverse phase protein array (RPPA) data to build protein networks. This approach identifies key signaling proteins and complex patterns in breast cancer cell lines.

Keywords:

Breast cancer MANOVA Network construction RPPA Topology analysis

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Related Experiment Videos

Last Updated: Feb 27, 2026

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

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Published on: October 19, 2021

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

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Published on: January 22, 2013

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

Area of Science:

Computational biology
Systems biology
Cancer research

Background:

Protein signaling networks are crucial for understanding cellular processes and disease mechanisms.
Reverse Phase Protein Array (RPPA) provides high-throughput proteomic profiling data.
Breast cancer progression involves complex alterations in signaling pathways.

Purpose of the Study:

To introduce a novel computational method for constructing protein networks from RPPA data.
To identify complex patterns in protein signaling relevant to breast cancer.
To pinpoint key proteins associated with significant expression changes in cancer cell lines.

Main Methods:

Utilized RPPA data from three breast cancer cell lines (MCF7, LCC1, LCC9) with phosphoproteomic profiles.
Acquired temporal data at 48h, 96h, and 144h post-gene knockdown.
Constructed interaction networks using a multivariate analysis of variance model and a novel scoring criterion.

Main Results:

Successfully applied the method to phosphoproteomic profiles of 76 key signaling proteins.
Identified complex patterns in protein signaling across different experimental conditions.
Network topology analysis revealed key proteins associated with significant expression changes.

Conclusions:

The novel computational method enables the construction of protein networks from RPPA data.
This approach facilitates the identification of critical signaling pathways and proteins in breast cancer.
The findings contribute to a deeper understanding of breast cancer cell signaling and survival determinants.