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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: Jun 23, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Protein complex prediction based on mutually exclusive interactions in protein interaction network.

Suk Hoon Jung1, Woo-Hyuk Jang, Hee-Yung Hur

  • 1School of Engineering, Information and Communications University, Yuseong-gu, Daejeon, Korea. jsh@icu.ac.kr

Genome Informatics. International Conference on Genome Informatics
|May 9, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to predict protein complexes by integrating protein-protein interaction data with structural information. This approach refines existing clustering methods, reducing false positives while accurately identifying true protein complexes.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

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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:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Protein-Protein Interaction (PPI) data is rapidly expanding, enabling scalable protein complex prediction.
  • Protein complexes, stable groups of proteins, often correspond to dense sub-graphs in PPI Networks (PPINs).
  • Conventional graph-theoretic clustering methods on PPINs suffer from high false positive rates due to dynamic interactions.

Purpose of the Study:

  • To develop an improved approach for protein complex prediction.
  • To integrate PPI data with mutually exclusive interaction information from structural interface data.
  • To reduce false positives in protein complex prediction.

Main Methods:

  • Proposed an approach integrating PPI data and mutually exclusive interaction information from protein domain structural interfaces.
  • Introduced the concept of Simultaneous Protein Interaction Cluster (SPIC) to exclude conflicting interactions within network clusters.
  • Applied SPIC to conventional graph-theoretic clustering algorithms (MCODE, LCMA) to evaluate cluster density.

Main Results:

  • SPIC-based methods effectively refined false positives from original graph-theoretic clustering algorithms.
  • The approach successfully converted false positives into true positive protein complexes.
  • No loss of true positive predictions was observed compared to the original methods.

Conclusions:

  • The integration of PPI data with mutually exclusive structural information significantly improves protein complex prediction accuracy.
  • SPIC represents a novel concept for enhancing network cluster analysis by ensuring interaction consistency.
  • This refined clustering approach offers a more reliable method for identifying stable protein complexes from large-scale interaction data.