Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor alpha (ERR alpha), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERR alpha have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERR alpha determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERR alpha agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERR alpha with EC50 values ranging from 1.11 to 21.70 mu M, with novel scaffolds different from known ERR alpha agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERR alpha with an IC50 of 0.82 mu M. The predicted binding modes showed that these compounds were anchored in ERR alpha-LBP via interactions with several residues of ERR alpha. Overall, this study not only identified the novel potent ERR alpha agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERR alpha agonists.

Automated summary

This study describes the discovery of novel potent agonists or inverse agonist of ERR alpha using a combined virtual screening approach. These compounds showed activity against ERR alpha in biological investigations and have different chemical structures from known agonists. The study also demonstrates the successful application of molecular dynamics-guided virtual screening in identifying ERR alpha agonists.