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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,...
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-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 Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...

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Updated: Jun 8, 2026

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

Diffusion model based spectral clustering for protein-protein interaction networks.

Kentaro Inoue1, Weijiang Li, Hiroyuki Kurata

  • 1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan.

Plos One
|September 11, 2010
PubMed
Summary
This summary is machine-generated.

We developed Adjustable Diffusion Matrix-based Spectral Clustering (ADMSC) to divide protein-protein interaction (PPI) networks. This method effectively identifies biologically significant clusters in complex networks.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 8, 2026

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

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

Area of Science:

  • Systems biology
  • Network analysis
  • Bioinformatics

Background:

  • Analyzing large-scale molecular networks, including gene expressions and protein-protein interactions (PPIs), is crucial in systems biology.
  • Understanding the relationship between network topology and biological function requires effective network decomposition.
  • Heterogeneous and scale-free properties of PPI networks pose challenges for clear modular decomposition.

Purpose of the Study:

  • To develop a novel algorithm for robust and efficient decomposition of protein-protein interaction networks.
  • To address the limitations of existing methods in handling heterogeneous network properties.
  • To identify biologically significant modules within complex PPI networks.

Main Methods:

  • Proposed a diffusion model-based spectral clustering algorithm, Adjustable Diffusion Matrix-based Spectral Clustering (ADMSC).
  • Incorporated a power factor to adjust the diffusion matrix, accommodating network heterogeneity.
  • Applied the algorithm to a yeast PPI network for modular decomposition.

Main Results:

  • ADMSC successfully decomposed the yeast PPI network into biologically significant clusters of approximately equal size.
  • The algorithm demonstrated clear and fast decomposition compared to established methods.
  • The introduction of the power factor effectively adjusted the diffusion matrix for heterogeneous networks.

Conclusions:

  • ADMSC provides an effective method for partitioning PPI networks into biologically meaningful modules.
  • The algorithm is fast, robust, and simple to implement.
  • ADMSC's ability to handle network heterogeneity leads to improved modular decomposition.