Mixed-model QSAR at the human mineralocorticoid receptor: Predicting binding mode and affinity of anabolic steroidsAuthors: Ourania Peristera, Morena Spreafico, Martin Smiesko, Beat Ernst and Angelo Vedani
Journal: Toxocology Letters
Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland.
We present a computational study on the human mineralocorticoid receptor (hMR) that is based on multi-dimensional quantitative structure-activity relationships (mQSAR). Therein, we identified the binding mode of 48 steroid and non-steroid homologues by flexible docking to the crystal structure (software Yeti) and quantified it using 6D-QSAR (software Quasar). The receptor surrogate, evolved using a genetic algorithm, converged at a cross-validated r2 of 0.810, and yielded a predictive r2 of 0.661. The model was challenged by a series of scramble tests and by consensus scoring (software Raptor: r2 = 0.844, predictive r2 = 0.620). The model was then employed to predict the binding affinity of 26 anabolic steroids, demonstrating to which extent they might disrupt the endocrine system via binding to the hMR. The model for the hMR was added to the VirtualToxLab™, a technology developed by the Biographics Laboratory 3R, allows the identification of the endocrine-disrupting potential of drugs, chemicals and natural products in silico.
Quasar model of the mineraolcorticoid receptor with bound progesterone.
Comparison of experimental and predicted Ki values. Green dots mark the ligand molecules of the training set, red dots identify those belonging to the test set; dashed lines mark a factor of five and ten from the experimental value, respectively.