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Related Concept Videos

Protein Networks02:26

Protein Networks

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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.
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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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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Signing protein-protein interaction networks.

Lorenzo Federico Signorini1,2, Martin Kupiec2, Roded Sharan1

  • 1Blavatnik School of Computer Science and AI, Tel Aviv University, Tel Aviv 6997801, Israel.

Bioinformatics (Oxford, England)
|December 22, 2025
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Summary
This summary is machine-generated.

We developed SIGNAL, a new algorithm to annotate protein-protein interaction networks with activation/repression signs. This method uses network propagation and machine learning to predict functional roles in cell signaling pathways.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular signaling pathways.
  • Experimental PPI data lacks functional information like activation or repression (sign).
  • Annotating interaction signs is crucial for building logical models of cell signaling.

Purpose of the Study:

  • To develop a computational method for annotating PPI networks with interaction signs.
  • To enable prediction of functional roles within signaling pathways.

Main Methods:

  • Developed the SIGNAL (SIGN Annotation aLgorithm) method.
  • Utilized a multiplicative model for pathway effects.
  • Employed network propagation to assess edge influence on gene expression.
  • Used a classifier for sign prediction based on network features.

Main Results:

  • SIGNAL successfully annotates PPI networks with activation/repression signs.
  • Validated the method using existing annotations.
  • Demonstrated SIGNAL's utility in predicting knockout effects on gene expression and telomere length.

Conclusions:

  • SIGNAL provides a novel approach to functionally annotate PPI networks.
  • The method enhances the understanding of cell signaling pathways.
  • SIGNAL aids in predicting cellular responses to perturbations.