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Related Concept Videos

Protein Networks02:26

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 Networks02:26

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,...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Proteomics01:33

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 proteomics...
Protein-protein Interfaces02:04

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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

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

Published on: October 19, 2021

Protein co-expression network analysis (ProCoNA).

David L Gibbs1, Arie Baratt, Ralph S Baric

  • 1Division of Bioinformatics and Computational Biology, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Rd, Portland, OR 97239, USA. gibbsd@ohsu.edu.

Journal of Clinical Bioinformatics
|June 4, 2013
PubMed
Summary

We developed a new method using weighted gene co-expression network analysis (WGCNA) to build protein co-expression networks from proteomics data. This approach reveals biologically meaningful networks for systems biology and disease research.

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Area of Science:

  • Systems Biology
  • Proteomics
  • Network Biology

Background:

  • Biological networks are crucial for understanding complex diseases and biological systems.
  • Proteomics data offers valuable insights but lacks robust de novo network construction methods.
  • Systems biology approaches require advanced network modeling for high-dimensional data.

Purpose of the Study:

  • To evaluate weighted gene co-expression network analysis (WGCNA) for constructing de novo protein co-expression networks.
  • To assess the biological meaningfulness and feasibility of peptide networks derived from proteomics data.
  • To explore novel applications of protein co-expression networks in biological interpretation and biomarker discovery.

Main Methods:

  • Application of weighted gene co-expression network analysis (WGCNA) principles to quantitative proteomics data.
  • Construction and analysis of peptide co-expression networks using mouse lung and human plasma datasets.
  • Statistical evaluation of network properties, module significance, and functional enrichment.

Main Results:

  • Feasible construction of approximately scale-free peptide networks with statistically significant modules.
  • Peptides from the same protein exhibit higher topological overlap and abundance concordance within networks.
  • Network modules (eigenpeptides) significantly correlate with biological phenotypes and show enrichment for Gene Ontology and protein-protein interactions.

Conclusions:

  • Protein co-expression networks constructed via WGCNA provide a powerful tool for biological interpretation.
  • This method enables improved quality control, protein abundance inference, and biomarker signature discovery.
  • The approach offers a framework for resolving peptide-protein mappings and advancing systems biology research.