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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...

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Related Experiment Video

Updated: May 20, 2026

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

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative

Jonathan Q Jiang1, Maoying Wu

  • 1School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.

BMC Bioinformatics
|July 5, 2012
PubMed
Summary

Proteins that interact physically often share the same subcellular location. This study introduces novel graph-based algorithms to predict protein localization using protein-protein interaction networks, improving accuracy.

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Last Updated: May 20, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

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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

Area of Science:

  • Cell Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Proteins interacting in vivo often reside in the same or adjacent subcellular compartments.
  • Protein-protein interaction (PPI) networks offer potential for predicting subcellular localization.
  • Previous efforts to leverage PPI networks for localization prediction were limited.

Purpose of the Study:

  • To systematically validate the hypothesis that interacting proteins share common subcellular localizations.
  • To introduce and evaluate graph-based semi-supervised learning algorithms for predicting "multiplex localization" of proteins.
  • To build an ensemble classifier for assigning subcellular localizations to unannotated proteins.

Main Methods:

  • Application of four graph-based semi-supervised learning algorithms (Majority, χ2-score, GenMultiCut, FunFlow) for protein localization prediction.
  • Large-scale cross-validation on the Saccharomyces cerevisiae proteome from BioGRID.
  • Comparison of algorithm performance across 22 protein subcellular localizations.
  • Development of an ensemble classifier integrating multiple algorithms.

Main Results:

  • Validated the hypothesis that physically interacting proteins share subcellular localizations.
  • Demonstrated the effectiveness of graph-based algorithms for assigning multiplex localization.
  • Achieved high accuracy in predicting localizations for unlabeled and ambiguously-annotated proteins.
  • Identified subcellular localizations for 529 unlabeled and 137 ambiguously-annotated proteins, with many findings supported by prior experimental studies.

Conclusions:

  • Physical protein interactions are a strong indicator of co-localization.
  • Global graph-based algorithms significantly outperform local approaches in predicting protein subcellular localization.
  • The developed methods provide a robust framework for inferring protein localization from PPI networks.