Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders

An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions' reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and useable at QsarDB.org (http://dx.doi.org/10.15152/QDB.259) according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

The Role of Estrogen Receptors in Health and Disease

Many biological and physiological events, including growth, development, and metabolism of reproductive and non-reproductive tissues in men and women, are regulated by estrogens and estrogen receptors (ERs) [...].

A molecular dynamics perspective into estrogen receptor inhibition by selective flavonoids as alternative therapeutic options

Zearalenone is an estrogenic mycotoxin which is a common food contaminant and has been implicated in increasing the incidence of carcinogenesis and other reproductive health ailments through the estrogen receptor alpha (ERα) pathway. Competitive ERα blockers such as 4-Hydroxytamoxifen (OHT), are synthetic FDA approved drugs which, albeit being an effective anticancer agent, induces life altering side effects. For this reason, there is an increased interest in the use of naturally occurring medicinal plant products such as flavonoids. This study aimed to identity flavonoid ERα inhibitors and provide insights into the mechanism of inhibition using computational techniques. The Molecular Mechanics/Generalized Born Surface Area calculations revealed that quercetrin, hesperidin, epigallocatechin 3-gallate and kaempferol 7-O-glucoside out of 14 flavonoids had higher binding affinity for ERα than OHT. The structural analysis revealed that the binding of the compounds to the receptor lead to dynamic alterations, which induced conformational shift in the structure and orientation of the receptor resulting in stabilised, compact and low energy systems. The results of this study provide imperative information that supports the use of flavonoids in the inhibition of ERα to prevent or ameliorate the consequential adverse effects associated with zearalenone exposure.Communicated by Ramaswamy H. Sarma.