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.

GPCRmd uncovers the dynamics of the 3D-GPCRome

A Publisher Correction to this article was published on 23 July 2020

This article has been updated


G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (, an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1: GPCRmd framework.
Fig. 2: The 3D-GPCRome.
Fig. 3: The GPCRmd viewer.
Fig. 4: Interaction network tool.
Fig. 5: A water bridge signature revealed by comparative analysis using the GPCRmd.
Fig. 6: Allosteric Na+ ion interaction in class A GPCRs.

Data availability

The MD data have been deposited in the GPCRmd database (

Code availability

Set-up, simulation and analysis protocols are openly available at

Change history


  1. Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: mew agents, targets and indications. Nat. Rev. Drug Discov. 16, 829–842 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Munk, C. et al. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res. 46, 440–446 (2017).

    Google Scholar 

  3. Munk, C. et al. An online resource for GPCR structure determination and analysis. Nat. Methods 16, 151–162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Latorraca, N. R., Venkatakrishnan, A. J. & Dror, R. O. GPCR dynamics: structures in motion. Chem. Rev. 117, 139–155 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Hildebrand, P. W., Rose, A. S. & Tiemann, J. K. S. Bringing molecular dynamics simulation data into view. Trends Biochem. Sci. 44, 902–913 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Rose, A. S. & Hildebrand, P. W. NGL Viewer: a web application for molecular visualization. Nucleic Acids Res. 43, W576–W579 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tiemann, J. K. S., Guixà-González, R., Hildebrand, P. W. P. W. & Rose, A. S. MDsrv: viewing and sharing molecular dynamics simulations on the web. Nat. Methods 14, 1123–1124 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Carrillo-Tripp, M. et al. HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data. J. Comput. Aided Mol. Des. 32, 869–876 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hauser, A. S. et al. Pharmacogenomics of GPCR drug targets. Cell 172, 41–54 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Munk, C., Harpsøe, K., Hauser, A. S., Isberg, V. & Gloriam, D. E. Integrating structural and mutagenesis data to elucidate GPCR ligand binding. Curr. Opin. Pharmacol. 30, 51–58 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Isberg, V. et al. Generic GPCR residue numbers - aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Venkatakrishnan, A. J. et al. Uncovering patterns of atomic interactions in static and dynamic structures of proteins. Preprint at bioRxiv (2019).

  14. Liu, W. et al. Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337, 232–236 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yuan, S., Filipek, S., Palczewski, K. & Vogel, H. Activation of G-protein-coupled receptors correlates with the formation of a continuous internal water pathway. Nat. Commun. 5, 4733 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Hildebrand, P. W. et al. A ligand channel through the G protein coupled receptor opsin. PloS ONE 4, e4382 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Guixà-González, R. et al. Membrane cholesterol access into a G-protein-coupled receptor. Nat. Commun. 8, 14505 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Venkatakrishnan, A. J. et al. Diverse GPCRs exhibit conserved water networks for stabilization and activation. Proc. Natl Acad. Sci. USA 116, 3288–3293 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Alexander, S. P. et al. The concise guide to pharmacology 2017/18: G protein-coupled receptors. Br. J. Pharmacol. 174, S17–S129 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Roth, C. B., Hanson, M. A. & Stevens, R. C. Stabilization of the human β2-adrenergic receptor TM4-TM3-TM5 helix interface by mutagenesis of Glu1223.41, a critical residue in GPCR structure. J. Mol. Biol. 376, 1305–1319 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Selent, J., Sanz, F., Pastor, M. & De Fabritiis, G. Induced effects of sodium ions on dopaminergic G-protein coupled receptors. PLoS Comput. Biol. 6, e1000884 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zarzycka, B., Zaidi, S. A., Roth, B. L. & Katritch, V. Harnessing ion-binding sites for GPCR pharmacology. Pharmacol. Rev. 71, 571–595 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Selvam, B., Shamsi, Z. & Shukla, D. Universality of the sodium ion binding mechanism in class A G-protein-coupled receptors. Angew. Chem. 130, 3102–3107 (2018).

    Article  Google Scholar 

  24. Yuan, S., Vogel, H. & Filipek, S. The role of water and sodium ions in the activation of the μ-Opioid receptor. Angew. Chem. 52, 1–5 (2013).

    Article  Google Scholar 

  25. Gutiérrez-De-Terán, H. et al. The role of a sodium ion binding site in the allosteric modulation of the A2A adenosine G protein-coupled receptor. Structure 21, 2175–2185 (2013).

    Article  PubMed  Google Scholar 

  26. Bostock, M. J., Solt, A. S. & Nietlispach, D. The role of NMR spectroscopy in mapping the conformational landscape of GPCRs. Curr. Opin. Struct. Biol. 57, 145–156 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Wingler, L. M. et al. Angiotensin analogs with divergent bias stabilize distinct receptor conformations. Cell 176, 468–478 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gregorio, G. G. et al. Single-molecule analysis of ligand efficacy in β2AR–G-protein activation. Nature 547, 68–73 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sommer, M. E. et al. The European Research Network on Signal Transduction (ERNEST): toward a multidimensional holistic understanding of G protein-coupled receptor signaling. ACS Pharmacol. Transl. Sci. 3, 361–370 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ballesteros, J. A. et al. Activation of the β2-adrenergic receptor involves disruption of an ionic lock between the cytoplasmic ends of transmembrane segments 3 and 6. J. Biol. Chem. 276, 29171–29177 (2001).

    Article  CAS  PubMed  Google Scholar 

  31. Mayol, E. et al. HomolWat: a web server tool to incorporate ‘homologous’ water molecules into GPCR structures. Nucleic Acids Res. (in the press);

  32. Buch, I., Harvey, M. J., Giorgino, T., Anderson, D. P. & De Fabritiis, G. High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. Model. 50, 397–403 (2010).

    Article  CAS  PubMed  Google Scholar 

  33. Heller, S. R., McNaught, A., Pletnev, I., Stein, S. & Tchekhovskoi, D. InChI, the IUPAC International Chemical Identifier. J. Cheminformatics 7, 23 (2015).

    Article  Google Scholar 

  34. Southan, C. et al. The IUPHAR/BPS Guide to pharmacology in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res. 44, D1054–D1068 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Gilson, M. K. et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 44, D1045–D1053 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Karczewski, K. J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at bioRxiv 531210 (2019).

  38. Gowers, R. J. et al. MDAnalysis: a python package for the rapid analysis of molecular dynamics simulations. In Proc. 15th Python Sci. Conference 98–105 (2016).

  39. McGibbon, R. T. et al. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109, 1528–1532 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article  CAS  PubMed  Google Scholar 

  41. Chovancova, E. et al. CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. PLoS Comput. Biol. 8, 23–30 (2012).

    Article  Google Scholar 

  42. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


The GPCRmd consortium acknowledges the support of COST Action CA18133, the European Research Network on Signal Transduction ( and COST Action CM1207 GLISTEN. We thank R. Fonseca and A.J. Venkatakrishnan for their help implementing Flareplots into the GPCRmd toolkit. We also thank the volunteers of GPUGRID for donating their computing time for the simulations. M.T.-F. acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities (FPU16/01209). T.M.S. acknowledges support from the National Center of Science, Poland (grant no. 2017/27/N/NZ2/02571). I.R.-E. acknowledges Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (2015 FI_B00145) for its financial support. X.D. and R.G.-G. acknowledge support from the Swiss National Science Foundation (grant no. 192780). P.K. thanks the German Research Foundation DFG for the Heisenberg professorship grant nos. KO4095/4-1 and KO4095/5-1 as well as project KO4095/3-1 (funding M.M.-S.). G.D.F. acknowledges support from MINECO (Unidad de Excelencia María de Maeztu, funded by the AEI (CEX2018-000782-M) and BIO2017-82628-P) and FEDER and from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 823712 (CompBioMed2 Project). D.L. acknowledges support from the National Centre of Science in Poland (DEC-2012/07/D/NZ1/04244). P.W.H. thanks the DFG (Hi 1502, project number 168703014; SFB1423, project number 421152132, subproject Z04), the Stiftung Charité and the Einstein Foundation. S.F. thanks the National Science Centre Poland grant no. 2017/25/B/NZ7/02788. M.F. acknowledges support by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD018522 and S10OD026880, as well as the Extreme Science and Engineering Discovery Environment (XSEDE) under MCB080077, which is supported by National Science Foundation grant number ACI-1548562. J.K.S.T. acknowledges support from HPC-EUROPA3 (INFRAIA-2016-1-730897) and the EC Research Innovation Action under the H2020 Programme. The work was supported by grants from the Swedish Research Council (2017-4676), the Swedish strategic research program eSSENCE and the Science for Life Laboratory to J.C. H.W. and G.K. acknowledge support from NSF grant no. 1740990 for In Situ Data Analytics for Next Generation Molecular Dynamics Workflows, and the 1923 Fund. D.E.G. acknowledges the Lundbeck Foundation (R163-2013-16327) and European Research Council (639125) for support. F.S. received support from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 802750 (FAIRplus) with the support of the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA Companies. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and FEDER (PT17/0009/0014). The DCEXS is a ‘Unidad de Excelencia María de Maeztu’, funded by the AEI (CEX2018-000782-M). The GRIB is also supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (2017 SGR 00519). Finally, J.S. acknowledges financial support from the Instituto de Salud Carlos III FEDER (PI15/00460 and PI18/00094) and the ERA-NET NEURON and Ministry of Economy, Industry and Competitiveness (AC18/00030).

Author information

Authors and Affiliations



Conceptualization came from J.S., R.G.-G., I.R.-E. and M.T.-F. Database structure was designed by I.R.-E. GPCRmd workbench was by M.T.-F. with support from N.W., A.V.-R., J.K.S.T. and F.S. Meta-analysis tool was developed by D.A.-G. with support from M.T.-F. and I.R.-E. Submission system was developed by J.M.A.R. with support from I.R.-E. Query system was developed by A.V.-R. with support from I.R.-E. Server maintenance was performed by M.T.-F. Simulation standard protocol, original draft was written by R.G.-G. and J.S.; Revised simulation standard protocol was written by G.D.F., A.C., I.R.-E., J.C., H.G.-d.-T., W.J., M.M.-S., P.K., J.K.S.T., P.W.H., T.M.S., S.F., T.G. and M.J.-R. Protein curation and modeling of missing loops was done by G.P.-S. and D.E.G. Protein curation, placement of internal water molecules was done by E.M., J.K.S.T., P.W.H., R.G.-G., M.O. and A.C. Expert knowledge for final protein curation (for example, protonation states, disulfide bridges and so on) was done by I.R.-E., M.T.-F., J.K.S.T., D.A.G., J.M.R.-A., T.M.S., N.W., A.V.-R., A.M.-P., B.M.-L., G.P.-S., E.M., T.G., J.C., X.D., S.F., J.C.G.-T., A.G., H.G.-d.-T., M.J.-R., W.J., J.K., P.K., D.L., M.M.-S., P. Matricon, M.-T.M., P. Miszta, M.O., L.P.-B., S.R., I.R.T., J.S., A.S., S.V., P.W.H., G.D.F., F.S., D.E.G., A.C., R.G.-G. and J.S. Coordination of data exchange was done by T.M.S. Preparation of solvated receptor-membrane systems was done by I.R.-E. with support from T.M.S. MD simulation was done by G.D.F., I.R.-E. and B.M.-L. MD data curation and submission was done by I.R.-E., A.M.-P., M.T.-F., D.A.-G., T.M.S. and J.S. Individual contribution of MD data was given by G.K., H.W., U.Z., NV, D.P. and M.F. The original draft of the manuscript was written by R.G.-G., J.S. with input from I.R.-E., M.T.-F. and J.K.S.T. The manuscript was reviewed and edited by all authors with important contributions from D.E.G., T.G. and P.K. Project supervision and administration was done by J.S.

Corresponding authors

Correspondence to Ramon Guixà-González or Jana Selent.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Figs. 1–8.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodríguez-Espigares, I., Torrens-Fontanals, M., Tiemann, J.K.S. et al. GPCRmd uncovers the dynamics of the 3D-GPCRome. Nat Methods 17, 777–787 (2020).

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