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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 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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Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
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Protein complex detection with semi-supervised learning in protein interaction networks.

Lei Shi1, Xiujuan Lei, Aidong Zhang

  • 1Computer Science & Engineering Department, State University of New York at Buffalo, Buffalo, NY, USA. lshi2@buffalo.edu.

Proteome Science
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-supervised neural network for detecting protein complexes from noisy protein-protein interaction data. The method improves precision and recall, identifying complexes missed by traditional approaches.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for biological processes.
  • Analyzing PPI networks aids understanding of cellular organization and function.
  • Detecting protein complexes from noisy PPI data is challenging due to overlapping complexes and data noise.

Purpose of the Study:

  • To develop an effective method for protein complex detection from noisy protein interaction data.
  • To address the limitations of traditional unsupervised graph clustering methods.
  • To improve the accuracy and comprehensiveness of protein complex identification.

Main Methods:

  • Developed a novel "semi-supervised" learning approach utilizing neural networks.
  • Redefined properties and features for protein complexes.
  • Employed recursive retraining of the neural network to optimize parameters.
  • Utilized a weighted network representation for protein interactions.
  • Integrated biological and topological features for complex representation.

Main Results:

  • The proposed semi-supervised method successfully detects protein complexes.
  • The algorithm identifies complexes missed by other existing methods.
  • Achieved superior precision and recall rates compared to traditional methods.
  • Demonstrated the framework's extensibility for future enhancements.

Conclusions:

  • Weighted networks are more suitable than unweighted networks for PPI analysis.
  • Integrating biological and topological features enhances protein complex representation over dense subgraphs.
  • Semi-supervised learning models show promise for protein complex detection with rich feature integration.