Predicting estrogen receptor binding of chemicals using a suite of in silico methods – Complementary approaches of (Q)SAR, molecular docking and molecular dynamics

• Published in: Toxicology and applied pharmacology 2019-09, Vol.378, p.114630, Article 114630
• Main Authors: Cotterill, J.V., Palazzolo, L., Ridgway, C., Price, N., Rorije, E., Moretto, A., Peijnenburg, A., Eberini, I.
• Format: Article
• Language: English
• Subjects: Estrogen receptor; Estrogen Receptor alpha - metabolism; Estrogens - metabolism; Humans; In Silico; Low-mode molecular dynamics simulation; Molecular docking; Molecular Docking Simulation; Molecular Dynamics Simulation; Protein Binding - physiology; QSAR; Quantitative Structure-Activity Relationship
• ISSN: 0041-008X; 1096-0333
• DOI: 10.1016/j.taap.2019.114630

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest. •Estrogen receptor binding predicted well using suite of in silico models.•Majority consensus of QSARs gives high accuracy and balanced sensitivity/specificity.•Molecular docking and QSARs can be combined to reduce false negative predictions.