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A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding

S Bradbury1, V Kamenska, P Schmieder

  • 1U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Boulevard, Duluth, Minnesota 55804, USA. bradbury.steven@epa.gov

Toxicological Sciences : an Official Journal of the Society of Toxicology
|December 2, 2000
PubMed
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The COREPA approach, a 3-D QSAR technique, was used to define stereoelectronic requirements for human estrogen receptor alpha (hERalpha) binding affinity. This method aids in ranking compounds by their binding potential, improving drug discovery screening.

Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Toxicology

Background:

  • Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting chemical compound activity.
  • Existing 3-D QSAR methods often lack flexibility in handling ligand-receptor interactions and conformational changes.
  • The Common Reactivity Pattern (COREPA) approach offers a dynamic alternative for analyzing chemical-biological interactions.

Purpose of the Study:

  • To explore the application of the COREPA algorithm in defining reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity.
  • To establish stereoelectronic requirements for ligands binding to hERalpha.
  • To develop a computational tool for ranking the binding affinity of large chemical datasets.

Main Methods:

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  • Utilized the COREPA approach, a 3-D QSAR technique incorporating dynamic conformational flexibility.
  • Analyzed 45 steroidal and nonsteroidal ligands for their binding affinity to hERalpha.
  • Established reactivity patterns based on global nucleophilicity, interatomic distances, and heteroatom electron donor capabilities.

    • Main Results:

    • Defined reactivity patterns for relative hERalpha binding affinity (RBA), using 17ss-estradiol as a reference (100%).
    • Developed descriptor profiles to categorize compounds by RBA levels (>150%, 100-10%, 10-1%, 1-0.1%).
    • Observed increased specificity in reactivity patterns for ligands with RBAs above 10%.

    Conclusions:

    • The COREPA approach effectively defines stereoelectronic requirements for hERalpha binding.
    • An exploratory expert system was developed for ranking relative ER binding affinity potential.
    • This method enhances the screening of chemical datasets for potential endocrine disruptors or therapeutic agents.