Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Virtual and biomolecular screening converge on a selective agonist for GPR30


Estrogen is a hormone critical in the development, normal physiology and pathophysiology1 of numerous human tissues2. The effects of estrogen have traditionally been solely ascribed to estrogen receptor α (ERα) and more recently ERβ, members of the soluble, nuclear ligand–activated family of transcription factors3. We have recently shown that the seven-transmembrane G protein–coupled receptor GPR30 binds estrogen with high affinity and resides in the endoplasmic reticulum, where it activates multiple intracellular signaling pathways4. To differentiate between the functions of ERα or ERβ and GPR30, we used a combination of virtual and biomolecular screening to isolate compounds that selectively bind to GPR30. Here we describe the identification of the first GPR30-specific agonist, G-1 (1), capable of activating GPR30 in a complex environment of classical and new estrogen receptors. The development of compounds specific to estrogen receptor family members provides the opportunity to increase our understanding of these receptors and their contribution to estrogen biology.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Structure and ligand-binding properties of G-1.
Figure 2: G-1 agonism in the mobilization of intracellular calcium by GPR30.
Figure 3: G-1 agonism in PI3K activation by GPR30.
Figure 4: Selective targeting of GPR30 versus ERα or ERβ by G-1.
Figure 5: G-1 and 17β-estradiol structural overlap and docking to ERα.

Similar content being viewed by others


  1. Osborne, C.K. & Schiff, R. Estrogen-receptor biology: continuing progress and therapeutic implications. J. Clin. Oncol. 23, 1616–1622 (2005).

    Article  CAS  Google Scholar 

  2. Hall, J.M., Couse, J.F. & Korach, K.S. The multifaceted mechanisms of estradiol and estrogen receptor signaling. J. Biol. Chem. 276, 36869–36872 (2001).

    Article  CAS  Google Scholar 

  3. Korach, K.S. et al. Update on animal models developed for analyses of estrogen receptor biological activity. J. Steroid Biochem. Mol. Biol. 86, 387–391 (2003).

    Article  CAS  Google Scholar 

  4. Revankar, C.M., Cimino, D.F., Sklar, L.A., Arterburn, J.B. & Prossnitz, E.R. A transmembrane intracellular estrogen receptor mediates rapid cell signaling. Science 307, 1625–1630 (2005).

    Article  CAS  Google Scholar 

  5. Thomas, P., Pang, Y., Filardo, E.J. & Dong, J. Identity of an estrogen membrane receptor coupled to a G-protein in human breast cancer cells. Endocrinology 146, 624–632 (2005).

    Article  CAS  Google Scholar 

  6. Filardo, E.J., Quinn, J.A., Bland, K.I. & Frackelton, A.R., Jr. Estrogen-induced activation of Erk-1 and Erk-2 requires the G protein-coupled receptor homolog, GPR30, and occurs via trans-activation of the epidermal growth factor receptor through release of HB-EGF. Mol. Endocrinol. 14, 1649–1660 (2000).

    Article  CAS  Google Scholar 

  7. Walters, W.P., Stahl, M.T. & Murcko, M.A. Virtual screening—an overview. Drug Discov. Today 3, 160–178 (1998).

    Article  CAS  Google Scholar 

  8. Oprea, T.I. & Matter, H. Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol. 8, 349–358 (2004).

    Article  CAS  Google Scholar 

  9. Oprea, T.I., Li, J., Muresan, S. & Mattes, K.C. High throughput and virtual screening: choosing the appropriate leads, in EuroQSAR 2002. In Designing Drugs and Crop Protectants: Processes, Problems and Solutions (eds. Ford, M., Livingstone, D., Dearden, J. and Van de Waterbeemd, H.) (Blackwell Publishing, New York, 2003).

    Google Scholar 

  10. Savchuk, N.P., Tkachenko, S.E. & Balakin, K.V. Rational design of GPCR-specific combinatorial libraries based on the concept of privileged substructures. in Cheminformatics in Drug Discovery (ed. Oprea, T.I.) 287–313 (Wiley VCH, Weinheim, 2005).

    Chapter  Google Scholar 

  11. Edwards, B.S., Oprea, T., Prossnitz, E.R. & Sklar, L.A. Flow cytometry for high-throughput, high-content screening. Curr. Opin. Chem. Biol. 8, 392–398 (2004).

    Article  CAS  Google Scholar 

  12. Waller, A. et al. Techniques: GPCR assembly, pharmacology and screening by flow cytometry. Trends Pharmacol. Sci. 25, 663–669 (2004).

    Article  CAS  Google Scholar 

  13. Zhang, J.H., Chung, T.D. & Oldenburg, K.R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 67–73 (1999).

    Article  CAS  Google Scholar 

  14. Balla, T. & Varnai, P. Visualizing cellular phosphoinositide pools with GFP-fused protein-modules. Sci. STKE 2002, PL3 (2002).

    PubMed  Google Scholar 

  15. Rochefort, H. et al. Estrogen receptor mediated inhibition of cancer cell invasion and motility: an overview. J. Steroid Biochem. Mol. Biol. 65, 163–168 (1998).

    Article  CAS  Google Scholar 

  16. Grant, J.A., Pickup, B.T. & Nicholls, A. A smooth permittivity function for Poisson-Boltzmann solvation methods. J. Comput. Chem. 22, 608–640 (2001).

    Article  CAS  Google Scholar 

  17. Rich, R.L. et al. Kinetic analysis of estrogen receptor/ligand interactions. Proc. Natl. Acad. Sci. USA 99, 8562–8567 (2002).

    Article  CAS  Google Scholar 

  18. Olah, M., Bologa, C.G. & Oprea, T.I. Strategies for compound selection. Curr. Drug Discov. Technol. 1, 211–220 (2004).

    Article  CAS  Google Scholar 

  19. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Article  CAS  Google Scholar 

  20. Durant, J.L., Leland, B.A., Henry, D.R. & Nourse, J.G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).

    Article  CAS  Google Scholar 

  21. Tanimoto, T.T. Non-linear model for a computer assisted medical diagnostic procedure. Trans. NY Acad. Sci. 23, 576–580 (1961).

    Article  Google Scholar 

  22. Tversky, A. Features of similarity. Psychol. Rev. 84, 327–352 (1977).

    Article  Google Scholar 

  23. Jackson, J.E. A Users Guide to Principal Components (Wiley-VCH, New York, 1991).

    Book  Google Scholar 

  24. Pastor, M., Cruciani, G., McLay, I., Pickett, S. & Clementi, S. GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J. Med. Chem. 43, 3233–3243 (2000).

    Article  CAS  Google Scholar 

  25. Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).

    Article  CAS  Google Scholar 

  26. Morris, G.M. et al. Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J. Comp. Chem. 19, 1639–1662 (1998).

    Article  CAS  Google Scholar 

  27. Ramirez, S., Aiken, C.T., Andrzejewski, B., Sklar, L.A. & Edwards, B.S. High-throughput flow cytometry: validation in microvolume bioassays. Cytometry A 53, 55–65 (2003).

    Article  Google Scholar 

  28. Deryugina, E.I. et al. MT1-MMP initiates activation of pro-MMP-2 and integrin alphavbeta3 promotes maturation of MMP-2 in breast carcinoma cells. Exp. Cell Res. 263, 209–223 (2001).

    Article  CAS  Google Scholar 

  29. Baudelle, R., Melnyk, P., Deprez, B. & Tartar, A. Parallel synthesis of polysubstituted tetrahydroquinolines. Tetrahedron 54, 4125–4140 (1998).

    Article  CAS  Google Scholar 

  30. Sartori, G., Bigi, F., Maggi, R., Mazzacani, A. & Oppici, G. Clay/water mixtures—a heterogeneous and ecologically efficient catalyst for the three-component stereoselective synthesis of tetrahydroquinolines. Eur. J. Org. Chem. 2001, 2513–2518 (2001).

    Article  Google Scholar 

Download references


This work was supported by US National Institutes of Health (NIH) grant AI36357 and a University of New Mexico Cancer Research and Treatment Center Translational Research Grant to E.R.P., by NIH grant EB00264 to L.A.S., and by support from the New Mexico Tobacco Settlement fund to C.G.B. and T.I.O. Additional support was provided by the New Mexico Cancer Research and Treatment Center (CRTC; NIH 1. P30 CA118100), the New Mexico Molecular Libraries Screening Center (NIH MH074425) and the New Mexico Center for Environmental Health Sciences (NIH ES012072). Flow cytometry data and confocal images in this study were generated in the Flow Cytometry and Fluorescence Microscopy Facilities, respectively, at the University of New Mexico Health Sciences Center, which received support from National Center for Research Resources (NCRR) 1 S10 RR14668, National Science Foundation MCB9982161, NCRR P20 RR11830, National Cancer Institute R24 CA88339, NCRR S10 RR19287, NCRR S10 RR016918, the University of New Mexico Health Sciences Center, and the University of New Mexico CRTC.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Tudor I Oprea or Eric R Prossnitz.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Calculated and experimental affinity constants for ERα. (PDF 39 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bologa, C., Revankar, C., Young, S. et al. Virtual and biomolecular screening converge on a selective agonist for GPR30. Nat Chem Biol 2, 207–212 (2006).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing