Multiple-Targeting and Conformational Selection in the Estrogen Receptor: Computation and Experiment

Authors

  • Peng Yuan,

    1. State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
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  • Kaiwei Liang,

    1. State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
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  • Buyong Ma,

    1. Center for Cancer Research Nanobiology Program, SAIC-Frederick, National Cancer Institute, Frederick, MD 21702, USA
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  • Nan Zheng,

    1. State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
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  • Ruth Nussinov,

    1. Center for Cancer Research Nanobiology Program, SAIC-Frederick, National Cancer Institute, Frederick, MD 21702, USA
    2. Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
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  • Jian Huang

    Corresponding author
    1. State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
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Corresponding author: Jian Huang, jianhuang@whu.edu.cn

Abstract

Conformational selection is a primary mechanism in biomolecular recognition. The conformational ensemble may determine the ability of a drug to compete with a native ligand for a receptor target. Traditional docking procedures which use one or few protein structures are limited and may not be able to represent a complex competition among closely related protein receptors in agonist and antagonist ensembles. Here, we test a protocol aimed at selecting a drug candidate based on its ability to synergistically bind to distinct conformational states. We demonstrate, for the case of estrogen receptor α (ERα) and estrogen receptor β (ERβ), that the functional outcome of ligand binding can be inferred from its ability to simultaneously bind both ERα and ERβ in agonist and antagonist conformations as calculated docking scores. Combining a conformational selection method with an experimental reporter gene system in yeast, we propose that several phytoestrogens can be novel estrogen receptor β selective agonists. Our work proposes a computational protocol to select estrogen receptor subtype selective agonists. Compared with other models, present method gives the best prediction in ligands’ function.

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