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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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Related Experiment Video

Updated: May 7, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Functional module identification in protein interaction networks by interaction patterns.

Yijie Wang1, Xiaoning Qian

  • 1Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)
|October 3, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms, LCP(2) (low two-hop conductance sets), to identify both dense and sparse functional modules in protein-protein interaction networks. These methods outperform existing approaches in detecting biologically meaningful protein groupings.

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Related Experiment Videos

Last Updated: May 7, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Identifying Protein-protein Interaction Sites Using Peptide Arrays
07:44

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

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Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

Published on: October 7, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Network Science

Background:

  • Protein-protein interaction (PPI) networks are crucial for understanding cellular organization and mechanisms.
  • Existing module identification methods often focus on dense protein groups, potentially missing functionally significant sparse modules.
  • Previous blockmodel algorithms face computational challenges and lack global optimum guarantees for large-scale PPI networks.

Purpose of the Study:

  • To develop novel algorithms for identifying both dense and sparse functional modules in PPI networks.
  • To address limitations of existing methods by considering protein interaction patterns beyond simple connectivity.
  • To provide a computationally feasible approach for analyzing large-scale PPI data.

Main Methods:

  • Proposed a new optimization formulation, LCP(2) (low two-hop conductance sets), utilizing Markov random walks on graphs.
  • Developed a spectral approximate algorithm (SLCP(2)) for non-overlapping module identification.
  • Extended LCP(2) to a greedy algorithm (GLCP(2)) for overlapping functional module detection.

Main Results:

  • The proposed LCP(2) formulation enables simultaneous identification of dense and sparse modules.
  • SLCP(2) and GLCP(2) were compared against state-of-the-art algorithms on synthetic and real-world PPI networks.
  • Algorithms based on LCP(2) demonstrated superior performance in protein complex prediction, Gene Ontology term prediction, and sparse module detection.

Conclusions:

  • The LCP(2) approach offers a robust framework for functional module identification in PPI networks.
  • SLCP(2) and GLCP(2) provide effective solutions for both non-overlapping and overlapping module detection.
  • These novel algorithms advance the analysis of cellular functional organization through improved PPI network analysis.