Ligand-induced shifts in conformational ensembles that describe transcriptional activation

• Published in: eLife Vol. 11
• Main Authors: Khan, Sabab Hasan, Braet, Sean M, Koehler, Stephen John, Elacqua, Elizabeth, Anand, Ganesh Srinivasan, Okafor, C Denise
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 12.10.2022; eLife Sciences Publications, Ltd
• Subjects: Biochemistry and Chemical Biology; Conformational analysis; conformational ensembles; Estrogens; Hormones; Ligands; Molecular Conformation; molecular dynamics; Molecular Dynamics Simulation; Mutation; Nuclear receptors; Population; Population studies; Protein Conformation; protein dynamics; Proteins; Receptor mechanisms; Receptors, Cytoplasmic and Nuclear; Simulation; Steroids; Structural Biology and Molecular Biophysics; Transcription activation; Transcription Factors; Transcriptional Activation; nuclear receptors; conformational ensembles; chemical biology; biochemistry; protein dynamics; structural biology; none; molecular biophysics; molecular dynamics; transcriptional activation
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.80140

Nuclear receptors function as ligand-regulated transcription factors whose ability to regulate diverse physiological processes is closely linked with conformational changes induced upon ligand binding. Understanding how conformational populations of nuclear receptors are shifted by various ligands could illuminate strategies for the design of synthetic modulators to regulate specific transcriptional programs. Here, we investigate ligand-induced conformational changes using a reconstructed, ancestral nuclear receptor. By making substitutions at a key position, we engineer receptor variants with altered ligand specificities. We combine cellular and biophysical experiments to characterize transcriptional activity, as well as elucidate mechanisms underlying altered transcription in receptor variants. We then use atomistic molecular dynamics (MD) simulations with enhanced sampling to generate ensembles of wildtype and engineered receptors in combination with multiple ligands, followed by conformational analysis and correlation of MD-based predictions with functional ligand profiles. We determine that conformational ensembles accurately describe ligand responses based on observed population shifts. These studies provide a platform which will allow structural characterization of physiologically-relevant conformational ensembles, as well as provide the ability to design and predict transcriptional responses in novel ligands.