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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 Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules.

Willam Hendrix1, Andrea M Rocha, Kanchana Padmanabhan

  • 1Department of Computer Science, North Carolina State University, Raleigh, 27695, USA.

BMC Systems Biology
|October 26, 2011
PubMed
Summary

This study introduces DENSE, a method to identify functional gene modules related to phenotypes using biologist-provided protein information. It helps uncover biological relationships and hypothesize protein functions.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Identifying cellular subsystems linked to phenotypes is crucial but existing methods may yield irrelevant results.
  • Incorporating prior biological knowledge can improve the relevance of identified subsystems.

Purpose of the Study:

  • To develop a method that integrates biologist's prior knowledge for identifying phenotype-related cellular subsystems.
  • To ensure identified subsystems are not only relevant to the phenotype but also contain biologically interesting information.

Main Methods:

  • Introduced DENSE (Dense and ENriched Subgraph Enumeration), a fast and theoretically guaranteed algorithm.
  • DENSE accepts a set of query proteins (biologist's prior knowledge) as input.
  • Identifies dense functional modules in biological networks containing parts of the query proteins, with tunable density (γ) and enrichment (μ) parameters.

Main Results:

  • The DENSE algorithm was applied to the protein functional association network of Clostridium acetobutylicum ATCC 824.
  • Successfully verified known biological relationships and uncovered previously unknown ones, including regulatory and signaling functions.
  • Hypothesized associations of uncharacterized proteins with the target phenotype.

Conclusions:

  • DENSE provides a valuable tool for dissecting complex biological networks and phenotype-related subsystems.
  • The method aids in discovering novel biological insights and prioritizing research on uncharacterized proteins.
  • The DENSE code is publicly available for broader scientific application.