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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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BIND: the Biomolecular Interaction Network Database.

Gary D Bader1, Doron Betel, Christopher W V Hogue

  • 1Department of Biochemistry, Samuel Lunenfeld Research Institute, University of Toronto, Toronto M5G 1X5, Canada.

Nucleic Acids Research
|January 10, 2003
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Summary
This summary is machine-generated.

The Biomolecular Interaction Network Database (BIND) archives crucial biomolecular interaction data. New tools enhance analysis of protein domains and interaction networks, aiding biological research.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Systems Biology

Background:

  • The Biomolecular Interaction Network Database (BIND) is a key resource for archiving biomolecular interaction, complex, and pathway information.
  • BIND facilitates data submission, querying, and viewing through a web-based system.
  • The database continuously expands with new data from individual submissions, Protein Data Bank (PDB), and large-scale experimental mapping projects.

Purpose of the Study:

  • To describe the ongoing development and new features of the Biomolecular Interaction Network Database (BIND).
  • To introduce novel analysis tools for exploring protein domain composition and interaction networks.
  • To highlight improvements in the BIND data specification, including enhanced support for genetic interactions.

Main Methods:

  • Archiving and integrating biomolecular interaction, complex, and pathway data.
  • Developing a web-based system for data access and submission.
  • Implementing a graphical analysis tool for protein domain composition in interaction records.
  • Creating an interaction network clustering tool for focused analysis.
  • Updating the BIND data specification based on user input and experimental data.

Main Results:

  • BIND has grown significantly, incorporating diverse interaction data from various sources and experimental techniques (yeast two-hybrid, mass spectrometry, genetic interactions, phage display).
  • New graphical and clustering tools enable users to visualize domain composition and identify key regions within interaction networks.
  • The BIND data specification has been enhanced to include detailed genetic interaction information.
  • Data specification is available in ASN.1 and XML DTD formats.

Conclusions:

  • BIND serves as a comprehensive and evolving repository for biomolecular interaction data.
  • The integrated analysis tools enhance the utility of BIND for understanding protein function and biological pathways.
  • Continued user input and data integration ensure BIND remains a valuable resource for the scientific community.