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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.
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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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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An efficient algorithm for pairwise local alignment of protein interaction networks.

Wenbin Chen1, Matthew Schmidt, Wenhong Tian

  • 1Department of Computer Science, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China , Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433, P. R. China , State Key Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, 210093, P. R. China.

Journal of Bioinformatics and Computational Biology
|December 6, 2014
PubMed
Summary
This summary is machine-generated.

Researchers developed a fast algorithm to find conserved protein interaction networks, identifying larger functional modules more efficiently than existing methods.

Keywords:
Network alignmentconserved functional modulesgraph optimizationgraph theory

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Understanding organismal traits involves studying conserved protein interaction patterns.
  • These conserved modules are crucial for trait expression and evolutionary significance.

Purpose of the Study:

  • To develop an efficient algorithm for identifying conserved protein interaction modules.
  • To compare the new algorithm's performance against existing network alignment methods.

Main Methods:

  • Formulating conserved pattern identification as a graph optimization problem.
  • Developing a fast heuristic algorithm for network alignment.
  • Comparing performance with the MaWISh algorithm using precision and recall metrics.

Main Results:

  • The novel algorithm identified conserved modules with more proteins.
  • The algorithm operated an order of magnitude faster than MaWISh.
  • Achieved comparable precision and recall rates to MaWISh.

Conclusions:

  • The developed heuristic algorithm is a more efficient tool for discovering conserved functional modules in protein interaction networks.
  • This advancement aids in understanding, modifying, and creating beneficial traits in organisms through network analysis.