Ligand-induced shifts in conformational ensembles that describe transcriptional activation

Sabab Hasan Khan1, Sean M Braet2, Stephen John Koehler2

  • 1Department of Biochemistry and Molecular Biology, Pennsylvania State University, State College, United States.

Elife
|October 12, 2022
PubMed

Insights

Researchers engineered ancestral nuclear receptors to understand how ligands alter their function. Molecular dynamics simulations revealed that receptor conformational ensembles accurately predict ligand responses, aiding in the design of new drugs.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Structural Biology

Background:

  • Nuclear receptors are crucial ligand-regulated transcription factors.
  • Ligand binding induces conformational changes that dictate receptor function.
  • Understanding these changes is key to designing targeted therapies.

Purpose of the Study:

  • To investigate ligand-induced conformational changes in nuclear receptors.
  • To engineer receptor variants with modified ligand specificities.
  • To correlate structural dynamics with transcriptional activity.

Main Methods:

  • Utilized a reconstructed ancestral nuclear receptor and engineered variants.
  • Combined cellular assays, biophysical experiments, and atomistic molecular dynamics (MD) simulations.
  • Employed enhanced sampling techniques for MD simulations.
  • Analyzed conformational ensembles and correlated predictions with functional data.

Main Results:

  • Engineered receptor variants exhibited altered ligand specificities.
  • Conformational ensembles generated by MD simulations accurately reflected ligand responses.
  • Observed population shifts in receptor conformations correlated with transcriptional activity.

Conclusions:

  • Conformational ensembles provide an accurate model for predicting nuclear receptor-ligand interactions.
  • This approach enables the structural characterization of physiologically relevant receptor conformations.
  • The findings offer a platform for designing novel ligands and predicting their transcriptional effects.

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