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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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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.
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Protein-protein Interfaces02:04

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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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Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
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GECluster: a novel protein complex prediction method.

Lingtao Su1, Guixia Liu1, Han Wang2

  • 1College of Computer Science and Technology, Jilin University , Changchun , P. R. China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University , Changchun , P. R. China.

Biotechnology, Biotechnological Equipment
|May 29, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces GECluster, a novel method for predicting protein complexes by integrating protein-protein interaction networks with Gene Ontology and gene expression data. GECluster accurately predicts complexes and reveals how yeast protein complexes evolve under environmental stress like increasing alcohol concentration.

Keywords:
GOPPIcore and attachment proteinevolutiongene expression valueprotein complex

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Protein complex identification is crucial for understanding cellular functions.
  • Traditional methods often overlook biological information like Gene Ontology (GO) and gene expression.
  • Environmental influences on protein complex evolution remain underexplored due to data limitations.

Purpose of the Study:

  • To develop a novel computational method for predicting protein complexes.
  • To integrate protein-protein interaction (PPI) networks with GO and gene expression data.
  • To investigate the evolution of protein complexes in yeast under varying environmental conditions.

Main Methods:

  • A combined PPI network integrating GO and gene expression data was constructed.
  • A novel seed node expansion strategy-based method, GECluster (Gene Expression Cluster), was proposed.
  • GECluster was applied to training and test datasets, including yeast response to stress during wine fermentation.

Main Results:

  • GECluster demonstrated higher accuracy in predicting protein complexes compared to peer methods.
  • The integration of a combined PPI network significantly improved prediction accuracy.
  • Yeast protein complexes were observed to evolve dynamically with rising alcohol concentration, showing changes in core and attachment proteins.

Conclusions:

  • Integrating diverse biological data, including GO and gene expression, enhances protein complex prediction.
  • GECluster provides a robust approach for accurate protein complex identification.
  • Environmental factors, such as alcohol concentration, drive significant evolutionary changes in cellular protein complexes.