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Related Concept Videos

Protein Networks02:26

Protein Networks

4.4K
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,...
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Protein Networks02:26

Protein Networks

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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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...
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Related Experiment Video

Updated: Dec 11, 2025

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

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SARS-CoV-2-human protein-protein interaction network.

Babak Khorsand1, Abdorreza Savadi1, Mahmoud Naghibzadeh1

  • 1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Informatics in Medicine Unlocked
|August 25, 2020
PubMed
Summary
This summary is machine-generated.

A new computational method predicts Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-human protein-protein interactions (PPIs). This aids in understanding viral behavior and designing antiviral drugs for the ongoing pandemic.

Keywords:
COVID-19CoronavirusHost-pathogen protein interactionProtein interaction predictionProtein-protein interactionSARS-CoV-2

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Area of Science:

  • Virology
  • Computational Biology
  • Network Science

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic with high mortality.
  • Understanding virus-host interactions is crucial for developing diagnostics and treatments.
  • Experimental identification of protein-protein interactions (PPIs) is time-consuming and costly.

Purpose of the Study:

  • To develop a novel computational method for predicting SARS-CoV-2-human PPIs.
  • To identify potential drug targets by analyzing virus-host interactions.
  • To provide a faster and more cost-effective alternative to experimental PPI identification.

Main Methods:

  • A three-layer network model was developed.
  • The first layer identified Alphainfluenzavirus proteins similar to SARS-CoV-2 proteins.
  • The second layer mapped Alphainfluenzavirus-human PPIs.
  • The third layer predicted SARS-CoV-2-human PPIs using network clustering coefficients.
  • Analysis included identifying central human proteins and integrating differentially expressed genes.

Main Results:

  • A prediction network for SARS-CoV-2-human PPIs was successfully constructed.
  • Key human proteins targeted by SARS-CoV-2 were identified.
  • PPIs involving upregulated human genes were reported, suggesting potential therapeutic pathways.

Conclusions:

  • The developed computational method offers an efficient approach for predicting SARS-CoV-2-human PPIs.
  • The findings contribute to understanding SARS-CoV-2 pathogenesis and identifying potential therapeutic targets.
  • This method can accelerate the development of antiviral strategies against SARS-CoV-2.