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Related Concept Videos

Protein Networks02:26

Protein Networks

4.6K
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

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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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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Drug Repurposing by Simulating Flow Through Protein-Protein Interaction Networks.

M Manczinger1,2, V Á Bodnár1, B T Papp1,3

  • 1Department of Dermatology and Allergology, University of Szeged, Hungary.

Clinical Pharmacology and Therapeutics
|June 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for drug repurposing, combining intracellular network simulations and machine learning to identify new uses for existing drugs, specifically for psoriasis treatment.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug development is costly, making drug repurposing a financially viable strategy.
  • Existing algorithms for drug repurposing lack reliability, particularly those integrating intracellular network simulations and machine learning.

Purpose of the Study:

  • To develop and validate a novel algorithm for drug repurposing by simulating drug effects on protein-protein interaction networks.
  • To identify potential new indications for marketed and investigational drugs, focusing on psoriasis as a model disease.

Main Methods:

  • Developed a computational algorithm to simulate drug effects on information flow within protein-protein interaction networks.
  • Employed support vector machine (SVM) learning to predict drug efficacy.
  • Screened approximately 1,500 substances and selected 51 potential candidates for further validation.

Main Results:

  • Identified 51 potentially effective drugs for psoriasis out of 1,500 screened substances.
  • Experimental validation confirmed that selected drugs inhibited tumor necrosis factor alpha-induced nuclear factor kappa B activity in vitro.
  • In vivo studies demonstrated that these drugs significantly reduced imiquimod-induced ear thickening and inflammation in a mouse model.

Conclusions:

  • The developed algorithm demonstrates high predictive performance for identifying effective drugs for psoriasis.
  • The findings support the potential of the identified drugs for treating psoriasis in humans.
  • This approach offers a reliable and efficient method for drug repurposing.