GPCRmd uncovers the dynamics of the 3D-GPCRome


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.

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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

  • 23 July 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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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




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.

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Correspondence to Ramon Guixà-González or Jana Selent.

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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.

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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).

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