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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

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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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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
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Noncovalent Attractions in Biomolecules02:35

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Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Probabilistic graphlets capture biological function in probabilistic molecular networks.

Sergio Doria-Belenguer1,2, Markus K Youssef1,2, René Böttcher1

  • 1Barcelona Supercomputing Center, Barcelona 08034, Spain.

Bioinformatics (Oxford, England)
|December 31, 2020
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Summary

Probabilistic graphlets analyze weighted molecular networks, outperforming unweighted methods. This approach captures more biological information and identifies condition-specific functions more effectively.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Network analysis

Background:

  • Molecular interactions are modeled as networks, where network structure correlates with biological function.
  • Graphlets are sensitive measures for unweighted network structure analysis.
  • Existing graphlet methods discard crucial information from weighted molecular networks.

Purpose of the Study:

  • Introduce probabilistic graphlets for analyzing probabilistic networks.
  • Evaluate probabilistic graphlets against unweighted counterparts.
  • Apply probabilistic graphlets to real-world molecular interaction networks.

Main Methods:

  • Developed probabilistic graphlets for weighted network analysis.
  • Generated synthetic networks to compare probabilistic and unweighted graphlets.
  • Applied weighted graphlet-based methods to molecular interaction networks.

Main Results:

  • Probabilistic graphlets outperform unweighted graphlets in distinguishing network structures.
  • Probabilistic graphlet methods more robustly capture biological information from weighted networks.
  • Probabilistic graphlet methods show higher sensitivity in identifying condition-specific functions.

Conclusions:

  • Probabilistic graphlets offer a powerful tool for analyzing weighted molecular networks.
  • This method overcomes limitations of traditional unweighted graphlet approaches.
  • Probabilistic graphlets enhance the understanding of molecular interactions and their functions.