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Related Concept Videos

Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.0K
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...
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Protein complexes detection based on node local properties and gene expression in PPI weighted networks.

Yang Yu1, Dezhou Kong2

  • 1Software College, Shenyang Normal University, Shenyang, 110034, People's Republic of China. yuyangsd1204@126.com.

BMC Bioinformatics
|January 7, 2022
PubMed
Summary

This study introduces NRAGE-WPN, a novel method for identifying protein complexes in protein-protein interaction (PPI) networks. It effectively utilizes node resource allocation and gene expression data, outperforming existing approaches.

Keywords:
Protein complexProtein–protein interaction (PPI)Resource allocationWeighted graph construction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Protein complex identification is vital for understanding cellular functions.
  • Existing protein-protein interaction (PPI) network algorithms often overlook crucial data like resource allocation and second-order neighbors.
  • Effective utilization of these ignored factors is key to improving protein complex detection.

Purpose of the Study:

  • To develop an improved algorithm for identifying protein complexes within PPI networks.
  • To leverage node resource allocation and gene expression data for enhanced network weighting.
  • To incorporate second-order neighbors and core-attachment principles for more accurate complex detection.

Main Methods:

  • Proposed a novel method named NRAGE-WPN (Node Resource Allocation and Gene Expression-Weighted Protein Network).
  • Integrated node resource allocation and gene expression information to weight the PPI network.
  • Detected protein complexes using core-attachment strategies and considering second-order neighbors.

Main Results:

  • The NRAGE-WPN algorithm demonstrated superior performance compared to eleven existing methods on Yeast and Human PPI networks.
  • Achieved a higher f-measure+ score in over 75% of comparisons.
  • Exhibited ideal overall performance based on a composite score derived from five distinct performance measures.

Conclusions:

  • NRAGE-WPN offers a simple yet accurate approach for identifying protein complexes.
  • The method effectively utilizes underutilized network information, leading to improved detection capabilities.
  • Experimental validation confirms the algorithm's enhanced accuracy and overall performance in complex identification.