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Simulating alpha/beta selectivity at the human thyroid hormone receptor: consensus scoring using multidimensional

Angelo Vedani1, Martin Zumstein, Markus A Lill

  • 1Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland. angelo.vedani@unibas.ch

Chemmedchem
|November 11, 2006
PubMed
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This study developed consensus scoring models for thyroid hormone receptors alpha and beta, achieving high predictive accuracy for ligand binding. These models accurately predict compound activity, aiding drug discovery for thyroid hormone-related conditions.

Area of Science:

  • Endocrinology
  • Computational Chemistry
  • Structural Biology

Background:

  • Thyroid hormone receptors (TRs) are crucial nuclear receptors regulating metabolism and development.
  • TR subtypes alpha (TRα) and beta (TRβ) have distinct physiological roles.
  • Accurate prediction of ligand binding to TRs is vital for developing targeted therapeutics.

Purpose of the Study:

  • To develop and validate consensus scoring models for human TRα and TRβ.
  • To simulate induced fit effects in receptor-ligand interactions.
  • To predict the binding affinity (K(i)) of agonists and antagonists to TRs.

Main Methods:

  • Utilized multidimensional quantitative structure-activity relationship (QSAR) modeling with Quasar and Raptor software.
  • Employed flexible docking (Yeti software) to identify binding modes from X-ray crystal structures.

Related Experiment Videos

  • Generated 4D datasets representing ligand conformations and binding modes.

    • Main Results:

    • Achieved high cross-validated (r²) values for TRα (0.846/0.919) and TRβ (0.823/0.909).
    • Demonstrated strong predictive performance with test set r² values of 0.812/0.814 for TRα and 0.665/0.796 for TRβ.
    • Consensus scoring showed good agreement between Quasar and Raptor predictions (factor of 2.2 for training, 2.8 for test set K(i)).

    Conclusions:

    • The developed consensus scoring models provide reliable predictions of ligand activity for TRα and TRβ.
    • These computational models effectively capture induced fit effects in receptor-ligand interactions.
    • The findings support the use of these models in structure-based drug design targeting thyroid hormone receptors.