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Related Experiment Video

Updated: Jun 23, 2026

Reverse Yeast Two-hybrid System to Identify Mammalian Nuclear Receptor Residues that Interact with Ligands and/or Antagonists

Reverse Yeast Two-hybrid System to Identify Mammalian Nuclear Receptor Residues that Interact with Ligands and/or Antagonists

Published on: November 15, 2013

Understanding nuclear receptors using computational methods.

Ni Ai¹, Matthew D Krasowski, William J Welsh

¹Department of Pharmacology, Robert Wood Johnson Medical School, University of Medicine & Dentistry of New Jersey, Piscataway, NJ 08854, USA.

Drug Discovery Today

|May 12, 2009

Summary

This summary is machine-generated.

Computational methods aid drug discovery by revealing nuclear receptor (NR) interactions. This review highlights their use in identifying new drugs and predicting toxic side effects of xenobiotics, focusing on pregnane X receptor (PXR).

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Last Updated: Jun 23, 2026

Reverse Yeast Two-hybrid System to Identify Mammalian Nuclear Receptor Residues that Interact with Ligands and/or Antagonists

Reverse Yeast Two-hybrid System to Identify Mammalian Nuclear Receptor Residues that Interact with Ligands and/or Antagonists

Published on: November 15, 2013

Area of Science:

Pharmacology and Toxicology
Computational Biology
Molecular Biology

Background:

Nuclear receptors (NRs) are crucial drug targets regulating gene expression.
NRs function by binding ligands and interacting with regulatory proteins.
Understanding these interactions is key for drug development and safety assessment.

Purpose of the Study:

To review the application of computational methods in studying clinically important NRs.
To emphasize the role of computational approaches in drug discovery and toxicity prediction.
To focus on the pregnane X receptor (PXR) as a case study.

Main Methods:

Literature review of computational techniques applied to NRs.
Analysis of computational insights into ligand-receptor interactions.
Examination of computational predictions for protein-protein interactions.

Main Results:

Computational methods facilitate the discovery of novel NR agonists and antagonists.
These methods enhance the specificity and affinity of potential drug candidates.
Computational approaches aid in predicting drug toxicity by identifying off-target interactions.

Conclusions:

Computational strategies are invaluable for advancing NR-targeted drug discovery.
Predictive toxicology using computational tools can mitigate risks associated with xenobiotics.
Further application of these methods, especially for PXR, promises significant therapeutic and safety benefits.