Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists

Abstract

BackgroundEndocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders.MethodsThe approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands.ResultsThe performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis.ConclusionThis approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.

Hazard assessment through hybrid in vitro / in silico approach: The case of zearalenone

“…For this purpose, ZEN and metabolites were selected as a case study since they are structurally and biologically very similar (EFSA, 2011;WHO, 2000). Also, several works already pointed toward the feasibility of modeling the estrogenic activity of ligands through the evaluation of interaction with ER-LBD in silico (McRobb et al, 2014;Ng et al, 2014b). In the first step, available experimental data were reviewed to identify the most relevant endpoint to be used for the formation/characterization of a ZEN analog group.…”

QSAR Models at the US FDA/NCTR

“…This QSAR model consists of two separate docking models (SDMs), one constructed using known agonists and the other was built from known antagonists [16]. Figure 8 shows the study design.…”

“…By the end of last century, the advancement in computer technology and the generation of a huge amount of scientific data [5,6] progressed the field of QSAR to a new height. A lot of QSAR methods have been developed and applied by the scientific research community and in regulatory sciences [7][8][9][10], e.g., pharmacophore modeling [11][12][13][14][15], molecular docking [16][17][18][19], CoMFA [19], classification tree model [20], decision forest [21][22][23][24][25][26][27], and support vector machine [28], to name a few.…”

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

“…The potential of these applications is impressive, considering their applicability in risk assessment on potential EDCs, but also for the identification of potential agonists and antagonists in drug discovery projects. [233] Alternariol and alternariol methyl ether, produced by different Alternaria species, have been recognized as another set of emerging micotoxins. In silico docking, scoring and re-scoring strategies were applied to investigate first whether these molecules could generate genotoxic effects by the activation of DNA topoisomerase I and/or II [229].…”