GPCRmd uncovers the dynamics of the 3D-GPCRome.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
08
01
2020
accepted:
29
05
2020
pubmed:
15
7
2020
medline:
5
11
2020
entrez:
15
7
2020
Statut:
ppublish
Résumé
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 (http://gpcrmd.org/), 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.
Identifiants
pubmed: 32661425
doi: 10.1038/s41592-020-0884-y
pii: 10.1038/s41592-020-0884-y
doi:
Substances chimiques
Receptors, G-Protein-Coupled
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
777-787Subventions
Organisme : NIH HHS
ID : S10 OD018522
Pays : United States
Organisme : NIH HHS
ID : S10 OD026880
Pays : United States
Commentaires et corrections
Type : ErratumIn
Références
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).
pubmed: 6882681
pmcid: 6882681
Munk, C. et al. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res. 46, 440–446 (2017).
Munk, C. et al. An online resource for GPCR structure determination and analysis. Nat. Methods 16, 151–162 (2019).
doi: 10.1038/s41592-018-0302-x
Latorraca, N. R., Venkatakrishnan, A. J. & Dror, R. O. GPCR dynamics: structures in motion. Chem. Rev. 117, 139–155 (2017).
doi: 10.1021/acs.chemrev.6b00177
Hildebrand, P. W., Rose, A. S. & Tiemann, J. K. S. Bringing molecular dynamics simulation data into view. Trends Biochem. Sci. 44, 902–913 (2019).
doi: 10.1016/j.tibs.2019.06.004
Rose, A. S. & Hildebrand, P. W. NGL Viewer: a web application for molecular visualization. Nucleic Acids Res. 43, W576–W579 (2015).
doi: 10.1093/nar/gkv402
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).
doi: 10.1038/nmeth.4497
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).
doi: 10.1007/s10822-018-0141-y
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
doi: 10.1038/sdata.2016.18
Hauser, A. S. et al. Pharmacogenomics of GPCR drug targets. Cell 172, 41–54 (2018).
doi: 10.1016/j.cell.2017.11.033
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).
doi: 10.1016/j.coph.2016.07.003
Isberg, V. et al. Generic GPCR residue numbers - aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).
doi: 10.1016/j.tips.2014.11.001
Venkatakrishnan, A. J. et al. Uncovering patterns of atomic interactions in static and dynamic structures of proteins. Preprint at bioRxiv https://doi.org/10.1101/840694 (2019).
Liu, W. et al. Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337, 232–236 (2012).
doi: 10.1126/science.1219218
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).
doi: 10.1038/ncomms5733
Hildebrand, P. W. et al. A ligand channel through the G protein coupled receptor opsin. PloS ONE 4, e4382 (2009).
doi: 10.1371/journal.pone.0004382
Guixà-González, R. et al. Membrane cholesterol access into a G-protein-coupled receptor. Nat. Commun. 8, 14505 (2017).
doi: 10.1038/ncomms14505
Venkatakrishnan, A. J. et al. Diverse GPCRs exhibit conserved water networks for stabilization and activation. Proc. Natl Acad. Sci. USA 116, 3288–3293 (2019).
doi: 10.1073/pnas.1809251116
Alexander, S. P. et al. The concise guide to pharmacology 2017/18: G protein-coupled receptors. Br. J. Pharmacol. 174, S17–S129 (2017).
doi: 10.1111/bph.13878
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).
doi: 10.1016/j.jmb.2007.12.028
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).
doi: 10.1371/journal.pcbi.1000884
Zarzycka, B., Zaidi, S. A., Roth, B. L. & Katritch, V. Harnessing ion-binding sites for GPCR pharmacology. Pharmacol. Rev. 71, 571–595 (2019).
doi: 10.1124/pr.119.017863
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).
doi: 10.1002/ange.201708889
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).
doi: 10.1002/anie.201209858
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).
doi: 10.1016/j.str.2013.09.020
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).
doi: 10.1016/j.sbi.2019.03.030
Wingler, L. M. et al. Angiotensin analogs with divergent bias stabilize distinct receptor conformations. Cell 176, 468–478 (2019).
doi: 10.1016/j.cell.2018.12.005
Gregorio, G. G. et al. Single-molecule analysis of ligand efficacy in β2AR–G-protein activation. Nature 547, 68–73 (2017).
doi: 10.1038/nature22354
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).
doi: 10.1021/acsptsci.0c00024
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).
doi: 10.1074/jbc.M103747200
Mayol, E. et al. HomolWat: a web server tool to incorporate ‘homologous’ water molecules into GPCR structures. Nucleic Acids Res. (in the press); https://doi.org/10.1093/nar/gkaa440
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).
doi: 10.1021/ci900455r
Heller, S. R., McNaught, A., Pletnev, I., Stein, S. & Tchekhovskoi, D. InChI, the IUPAC International Chemical Identifier. J. Cheminformatics 7, 23 (2015).
doi: 10.1186/s13321-015-0068-4
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).
doi: 10.1093/nar/gkv1037
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).
doi: 10.1093/nar/gkv1072
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
doi: 10.1093/nar/28.1.235
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).
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).
McGibbon, R. T. et al. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109, 1528–1532 (2015).
doi: 10.1016/j.bpj.2015.08.015
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).
doi: 10.1016/0263-7855(96)00018-5
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).
doi: 10.1371/journal.pcbi.1002708
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
doi: 10.1038/s41592-019-0686-2