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Related Concept Videos

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 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-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 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,...

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Related Experiment Video

Updated: Jul 15, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork.

Sarah Lawson1, Diane Donovan1, James Lefevre1

  • 1ARC Centre of Excellence, Plant Success in Nature and Agriculture, School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia.

Plos One
|October 3, 2024
PubMed
Summary

Hypergraph centrality offers a powerful alternative to traditional methods for analyzing biological networks. This study extends hypergraph centrality models to identify essential proteins and classify protein complexes, revealing new insights into biological systems.

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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

Published on: October 19, 2021

Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Traditional graph centrality measures in biological networks are limited to dyadic interactions.
  • Biological interactions are often polyadic, requiring more complex network representations.
  • Hypergraphs provide a framework to model these polyadic interactions effectively.

Purpose of the Study:

  • To extend a nonlinear hypergraph centrality model for analyzing biological networks.
  • To apply the extended model to a Saccharomyces Cerevisiae protein complex hypernetwork.
  • To assess the model's ability to identify essential proteins and classify protein complexes.

Main Methods:

  • Review and extension of a nonlinear hypergraph centrality model incorporating mutually dependent node and edge centralities.
  • Application of the model to a hypernetwork representing protein complexes in Saccharomyces Cerevisiae.
  • Analysis of node and edge rankings to determine biological essentiality and complex classification.

Main Results:

  • Certain variations of the hypergraph centrality model accurately predict protein and complex essentiality.
  • The degree-based hypergraph centrality variation demonstrates the extension of the centrality-lethality rule.
  • The model successfully identifies small sets of proteins enriched with essential members and classifies protein complexes.

Conclusions:

  • Hypergraph centrality provides a robust framework for analyzing complex biological interactions beyond dyadic relationships.
  • The extended model offers a novel approach for identifying key proteins and classifying protein complexes.
  • This method enhances our understanding of biological network organization and essentiality.