Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Abstract

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure-activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

Keywords: 17β-estradiol; AD; ADMET; AR; AUC; AhR; Docking; E(2); EDCs; EDKB; EDSP; EF; EPA; ER; Endocrine disrupting chemicals; Estrogen receptor; MTL; Multi-task learning; PDB; Protein Data Bank; QSAR; Quantitative structure–activity relationships modeling; RBA; ROC; RP; SE; SP; STL; US Environmental Protection Agency; Virtual screening; absorption, distribution, metabolism, excretion, and toxicity; androgen receptor; applicability domain; area under the curve; aryl hydrocarbon receptor; endocrine disrupting chemicals; endocrine disruptor knowledge base; endocrine disruptor screening program; enrichment factor; estrogen receptor; k-nearest neighbors; kNN; multi-task learning; quantitative structure–activity relationships; receiver operating characteristic; relative binding affinity; relative potency; sensitivity; single-task learning; specificity.