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Molecular basis of agonism and antagonism in the oestrogen receptor

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Listed:
  • Andrzej M. Brzozowski

    (Protein Structure Group, University of York)

  • Ashley C. W. Pike

    (Protein Structure Group, University of York)

  • Zbigniew Dauter

    (Protein Structure Group, University of York)

  • Roderick E. Hubbard

    (Protein Structure Group, University of York)

  • Tomas Bonn

    (Karo Bio AB, NOVUM)

  • Owe Engström

    (Karo Bio AB, NOVUM)

  • Lars Öhman

    (Karo Bio AB, NOVUM)

  • Geoffrey L. Greene

    (The Ben May Institute for Cancer Research, The University of Chicago)

  • Jan-Åke Gustafsson

    (Karolinska Institute)

  • Mats Carlquist

    (Karo Bio AB, NOVUM)

Abstract

Oestrogens are involved in the growth, development and homeostasis of a number of tissues1. The physiological effects of these steroids are mediated by a ligand-inducible nuclear transcription factor, the oestrogen receptor (ER)2. Hormone binding to the ligand-binding domain (LBD) of the ER initiates a series of molecular events culminating in the activation or repression of target genes. Transcriptional regulation arises from the direct interaction of the ER with components of the cellular transcription machinery3,4. Here we report the crystal structures of the LBD of ER in complex with the endogenous oestrogen, 17β-oestradiol, and the selective antagonist raloxifene5, at resolutions of 3.1 and 2.6 Å, respectively. The structures provide a molecular basis for the distinctive pharmacophore of the ER and its catholic binding properties. Agonist and antagonist bind at the same site within the core of the LBD but demonstrate different binding modes. In addition, each class of ligand induces a distinct conformation in the transactivation domain of the LBD, providing structural evidence of the mechanism of antagonism.

Suggested Citation

  • Andrzej M. Brzozowski & Ashley C. W. Pike & Zbigniew Dauter & Roderick E. Hubbard & Tomas Bonn & Owe Engström & Lars Öhman & Geoffrey L. Greene & Jan-Åke Gustafsson & Mats Carlquist, 1997. "Molecular basis of agonism and antagonism in the oestrogen receptor," Nature, Nature, vol. 389(6652), pages 753-758, October.
  • Handle: RePEc:nat:nature:v:389:y:1997:i:6652:d:10.1038_39645
    DOI: 10.1038/39645
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    Cited by:

    1. Michele Samorani & Manuel Laguna & Robert Kirk DeLisle & Daniel C. Weaver, 2011. "A Randomized Exhaustive Propositionalization Approach for Molecule Classification," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 331-345, August.
    2. Xueyan Chen & Ugur Uzuner & Man Li & Weibing Shi & Joshua S. Yuan & Susie Y. Dai, 2016. "Phytoestrogens and Mycoestrogens Induce Signature Structure Dynamics Changes on Estrogen Receptor α," IJERPH, MDPI, vol. 13(9), pages 1-14, August.
    3. Wei He & Wenhui Zhang & Zhenhua Chu & Yu Li, 2021. "Mitigating the Adverse Effects of Polychlorinated Biphenyl Derivatives on Estrogenic Activity via Molecular Modification Techniques," IJERPH, MDPI, vol. 18(9), pages 1-19, May.
    4. Henrieta Hlisníková & Ida Petrovičová & Branislav Kolena & Miroslava Šidlovská & Alexander Sirotkin, 2020. "Effects and Mechanisms of Phthalates’ Action on Reproductive Processes and Reproductive Health: A Literature Review," IJERPH, MDPI, vol. 17(18), pages 1-37, September.
    5. Benjamin M. Steiner & Abigail M. Benvie & Derek Lee & Yuwei Jiang & Daniel C. Berry, 2024. "Cxcr4 regulates a pool of adipocyte progenitors and contributes to adiposity in a sex-dependent manner," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    6. Peixuan Sun & Wenjin Zhao, 2021. "Strategies to Control Human Health Risks Arising from Antibiotics in the Environment: Molecular Modification of QNs for Enhanced Plant–Microbial Synergistic Degradation," IJERPH, MDPI, vol. 18(20), pages 1-26, October.
    7. Haigang Zhang & Chengji Zhao & Hui Na, 2020. "Enhanced Biodegradation of Phthalic Acid Esters’ Derivatives by Plasticizer-Degrading Bacteria ( Burkholderia cepacia , Archaeoglobus fulgidus , Pseudomonas aeruginosa ) Using a Correction 3D-QSAR Mod," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    8. Fenglei Li & Qiaoyu Hu & Xianglei Zhang & Renhong Sun & Zhuanghua Liu & Sanan Wu & Siyuan Tian & Xinyue Ma & Zhizhuo Dai & Xiaobao Yang & Shenghua Gao & Fang Bai, 2022. "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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