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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
In silico study of molecular mechanisms of action: Estrogenic disruptors among phthalate esters
Qian Zhu1, Lanhua Liu1, Xiaohong Zhou1
1State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Abstract:
Phthalate esters (PAEs), as widely used plasticizers, have been concerned for their possible disruption of estrogen functions via binding to and activating the transcription of estrogen receptors (ERs). Nevertheless, the computational interpretation of the mechanism of ERs activities modulated by PAEs at the molecular level is still insufficient, which hinders the reliable screening of the ERs-active PAEs with high speed and high throughput. To bridge the gap, the in silico simulations considering the effects of coactivators were accomplished to explore the molecular mechanism of action for the purpose of predicting the estrogenic potencies of PAEs. The transcriptional activation functions of human ERα (hERα) modulated by PAEs is predicted via the simulations including binding interaction of PAEs and hERα, conformational changes of PAEs-hERα complexes and recruitment of coactivators. Molecular insight into the diverse estrogen mechanism of action among PAEs with regard to hERα agonists and selective estrogen receptor modulators (SERMs) is provided. Agonist-modulated conformational change of hERα leads to the optimal exposure of its Activation Function 2 (AF-2) surface which, in turn, facilitates the recruitment of coactivators, therefore promoting the transcriptional activation functions of hERα. Conversely, binding interaction of hERα with SERMs among PAEs leads to the conformational change with blocked AF-2 surface, thus preventing the recruitment of coactivators and consequently inhibiting the AF-2 activity. The two-hybrid recombinant yeast is experimentally used for verification. The established in silico evaluation methodology exhibits great promise to speed up the prediction of chemicals which work as hERα agonist or SERMs.
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