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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,...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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...
Protein Complexes with Interchangeable Parts01:57

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 to...
Protein Complexes with Interchangeable Parts01:57

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 to...

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Related Experiment Video

Updated: May 11, 2026

Exploring Biomolecular Interaction Between the Molecular Chaperone Hsp90 and Its Client Protein Kinase Cdc37 using Field-Effect Biosensing Technology
09:39

Exploring Biomolecular Interaction Between the Molecular Chaperone Hsp90 and Its Client Protein Kinase Cdc37 using Field-Effect Biosensing Technology

Published on: March 31, 2022

Modular biological function is most effectively captured by combining molecular interaction data types.

Ryan M Ames1, Jamie I Macpherson, John W Pinney

  • 1Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom. ryan.ames@manchester.ac.uk

Plos One
|May 10, 2013
PubMed
Summary
This summary is machine-generated.

Integrating diverse molecular interaction networks, including protein-protein interaction (PPI) and genetic interaction data, is crucial for a comprehensive understanding of biological functions. Combining network data improves the representation of complex cellular processes and Gene Ontology (GO) annotations.

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

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Related Experiment Videos

Last Updated: May 11, 2026

Exploring Biomolecular Interaction Between the Molecular Chaperone Hsp90 and Its Client Protein Kinase Cdc37 using Field-Effect Biosensing Technology
09:39

Exploring Biomolecular Interaction Between the Molecular Chaperone Hsp90 and Its Client Protein Kinase Cdc37 using Field-Effect Biosensing Technology

Published on: March 31, 2022

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

Area of Science:

  • Systems biology
  • Network biology
  • Computational biology

Background:

  • Molecular interactions are fundamental to biological function.
  • Biological functions emerge from complex networks of molecular interactions.
  • Understanding these functions requires integrating diverse data types.

Purpose of the Study:

  • To identify functional modules within yeast molecular interaction networks.
  • To assess the utility of different network data types (PPI, genetic interaction, gene co-regulation) for capturing biological functions.
  • To evaluate the coverage and accuracy of Gene Ontology (GO) annotations using network-derived subgraphs.

Main Methods:

  • Applied graph partitioning algorithms to identify subnetworks in yeast PPI, genetic interaction, and gene co-regulation networks.
  • Identified cohesive subgraphs within these networks as potential functional modules.
  • Analyzed overlaps between subgraphs from different data types and their correspondence with GO terms.

Main Results:

  • Significant overlap was observed between subgraphs derived from different molecular interaction data types.
  • These overlapping subgraphs effectively represent related biological functions, as validated by GO annotations.
  • Protein-protein interaction networks showed high enrichment for GO terms (84% biological process, 58% molecular function, 93% cellular component).
  • Integrating multiple interaction data types into a combined network enhanced the coverage of GO annotations.
  • No single interaction data type predominantly captured all GO annotation types.

Conclusions:

  • The accurate representation of biological functions using network data is dependent on both the specific function and the type of interaction data used.
  • Individual data types have limitations in capturing the full spectrum of biological functions.
  • Integrating subnetworks across different data types is essential for a comprehensive understanding of complex, emergent biological functions.