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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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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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ExpoPath: A method for identifying and annotating exposure pathways from chemical co-occurrence networks.

Michael A Zurek-Ost1, Katherine A Phillips2, Antony J Williams2

  • 1Oak Ridge Institute for Science and Education, 299 Bethel Valley Rd, Oak Ridge, TN 37830, USA; Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, NC 27709, USA.

The Science of the Total Environment
|April 26, 2025
PubMed
Summary

Network analysis and graph machine learning reveal chemical exposure pathways by analyzing co-occurrence data. This approach helps identify environmental risks from industrial and commercial chemical sources.

Keywords:
Chemical co-occurrenceEnvironmental monitoringExpoCastExposure modelingExposure pathwaysNetwork analysis

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Area of Science:

  • Environmental Science
  • Computational Chemistry
  • Toxicology

Background:

  • Accurate environmental and human health risk assessment is crucial for the U.S. Environmental Protection Agency (EPA).
  • Identifying chemical transport from sources to environmental media is key to understanding exposure pathways.
  • Traditional methods may not fully capture complex chemical interactions and transport patterns.

Purpose of the Study:

  • To develop and apply network analysis and graph machine learning for identifying chemical exposure pathways.
  • To construct and analyze a global chemical co-occurrence network.
  • To empirically derive and validate exposure pathways using data-driven methods.

Main Methods:

  • Aggregated data from chemical source databases to build a chemical co-occurrence network.
  • Employed network analysis and community detection algorithms to identify chemical clusters.
  • Annotated identified communities using presence-in-media, physicochemical properties, and functional use data.

Main Results:

  • Successfully constructed a global chemical co-occurrence network linking sources to environmental media.
  • Identified distinct communities of chemicals representing likely exposure pathways.
  • Detected pathways associated with pharmaceuticals, consumer products, pesticides, and persistent chemicals.

Conclusions:

  • Network analysis and graph machine learning offer a novel and effective approach to exposure science.
  • This method empirically identifies patterns of chemical connectivity and potential exposure routes.
  • Findings support improved risk evaluation for environmental and human health by elucidating exposure pathways.