Article
(PDB) under accession number 6ORV and Electron Microscopy Data
Bank (EMDB) accession EMD-20179.
- Liang, Y.-L. et al. Dominant negative G proteins enhance formation and purification of
agonist-GPCR-G protein complexes for structure determination. ACS Pharmacol. Transl.
Sci. 1 , 9 (2018). - Furness, S. G. B. et al. Ligand-dependent modulation of G protein conformation alters
drug efficacy. Cell 167 , 739–749.e711 (2016). - Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for
improved cryo-electron microscopy. Nat. Methods 14 , 331–332 (2017). - Zhang, K. Gctf: Real-time CTF determination and correction. J. Struct. Biol. 193 , 1–12 (2016).
- Nakane, T., Kimanius, D., Lindahl, E. & Scheres, S. H. Characterisation of molecular
motions in cryo-EM single-particle data by multi-body refinement in RELION. eLife 7 ,
e36861 (2018). - Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure
determination in RELION-3. eLife 7 , e42166 (2018). - Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid
unsupervised cryo-EM structure determination. Nat. Methods 14 , 290–296 (2017). - Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM
density maps. Nat. Methods 11 , 63–65 (2014). - Chan, K. Y., Trabuco, L. G., Schreiner, E. & Schulten, K. Cryo-electron microscopy
modeling by the molecular dynamics flexible fitting method. Biopolymers 97 , 678–686
(2012). - Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot.
Acta Crystallogr. D 66 , 486–501 (2010). - Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular
structure solution. Acta Crystallogr. D 66 , 213–221 (2010). - Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D. electronic Ligand Builder and
Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation.
Acta Crystallogr. D 65 , 1074–1080 (2009). - Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography.
Acta Crystallogr. D 74 , 531–544 (2018). - Jacobson, M. P. et al. A hierarchical approach to all-atom protein loop prediction. Proteins
55 , 351–367 (2004). - Eswar, N. et al. Comparative protein structure modeling using Modeller. Curr Protoc
Bioinformatics Chapter 5, Unit 5.6 (2006). - Vohra, S. et al. Similarity between class A and class B G-protein-coupled receptors
exemplified through calcitonin gene-related peptide receptor modelling and
mutagenesis studies. J. R. Soc. Interface 10 , 20120846 (2012). - Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph 14 ,
33–38 (1996). - Rasmussen, S. G. et al. Crystal structure of the β 2 adrenergic receptor–Gs protein
complex. Nature 477 , 549–555 (2011). - Carpenter, B., Nehmé, R., Warne, T., Leslie, A. G. & Tate, C. G. Structure of the adenosine
A2A receptor bound to an engineered G protein. Nature 536 , 104–107 (2016). - Huang, J. & MacKerell, A. D. Jr. CHARMM36 all-atom additive protein force field: validation
based on comparison to NMR data. J. Comput. Chem. 34 , 2135–2145 (2013). - Doerr, S., Harvey, M. J., Noé, F. & De Fabritiis, G. HTMD: high-throughput molecular
dynamics for molecular discovery. J. Chem. Theory Comput. 12 , 1845–1852 (2016). - Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. & Baker, N. A. PDB2PQR: an automated
pipeline for the setup of Poisson–Boltzmann electrostatics calculations. Nucleic Acids
Res. 32 , W665–W667 (2004). - Olsson, M. H., Søndergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent
treatment of internal and surface residues in empirical pka predictions. J. Chem. Theory
Comput. 7 , 525–537 (2011). - Lomize, M. A., Lomize, A. L., Pogozheva, I. D. & Mosberg, H. I. OPM: orientations of
proteins in membranes database. Bioinformatics 22 , 623–625 (2006). - Sommer, B. Membrane packing problems: a short review on computational membrane
modeling methods and tools. Comput. Struct. Biotechnol. J. 5 , e201302014 (2013). - Vanommeslaeghe, K. et al. CHARMM general force field: a force field for drug-like
molecules compatible with the CHARMM all-atom additive biological force fields.
J. Comput. Chem. 31 , 671–690 (2010). - Vanommeslaeghe, K. & MacKerell, A. D. Jr. Automation of the CHARMM General Force
Field (CGenFF) I: bond perception and atom typing. J. Chem. Inf. Model. 52 , 3144–3154
(2012).
57. Vanommeslaeghe, K., Raman, E. P. & MacKerell, A. D. Jr. Automation of the CHARMM
General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic
charges. J. Chem. Inf. Model. 52 , 3155–3168 (2012).
58. Harvey, M. J., Giupponi, G. & Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics
in the Microsecond Time Scale. J. Chem. Theory Comput. 5 , 1632–1639 (2009).
59. Loncharich, R. J., Brooks, B. R. & Pastor, R. W. Langevin dynamics of peptides: the
frictional dependence of isomerization rates of N-acetylalanyl-N'-methylamide.
Biopolymers 32 , 523–535 (1992).
60. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R.
Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81 , 3684 (1984).
61. Kräutler, V. G., van Gunsteren, W. F. & Hünenberger, P. H. A fast SHAKE algorithm to solve
distance constraint equations for small molecules in molecular dynamics simulations.
J. Comput. Chem. 22 , 501–508 (2001).
62. Essmann, U. P. & Berkowitz, L. M.L. A smooth particle mesh Ewald method. J. Chem. Phys.
103 , 8577 (1995).
63. Cuzzolin, A., Deganutti, G., Salmaso, V., Sturlese, M. & Moro, S. AquaMMapS: an
alternative tool to monitor the role of water molecules during protein-ligand association.
ChemMedChem 13 , 522–531 (2018).
64. Wall, M. E., Calabró, G., Bayly, C. I., Mobley, D. L. & Warren, G. L. Biomolecular solvation
structure revealed by molecular dynamics simulations. J. Am. Chem. Soc. 141 , 4711–4720
(2019).
65. Koole, C. et al. Allosteric ligands of the glucagon-like peptide 1 receptor (GLP-1R)
differentially modulate endogenous and exogenous peptide responses in a pathway-
selective manner: implications for drug screening. Mol. Pharmacol. 78 , 456–465
(2010).
66. Savage, E. E., Wootten, D., Christopoulos, A., Sexton, P. M. & Furness, S. G. A simple
method to generate stable cell lines for the analysis of transient protein-protein
interactions. Biotechniques 54 , 217–221 (2013).
67. Jun, L. S. et al. A novel humanized GLP-1 receptor model enables both affinity purification
and Cre-LoxP deletion of the receptor. PLoS One 9 , e93746 (2014).
Acknowledgements The work was supported by the Monash University Ramaciotti Centre for
Cryo-Electron Microscopy, the Monash MASSIVE high-performance computing facility, the
National Health and Medical Research Council of Australia (NHMRC) project grants (1061044,
1065410, 1120919 and 1126857) and NHMRC program grants (1055134 and 1150083), the Japan
Society for the Promotion of Science (JSPS) KAKENHI no. 18H06043 and Japan Science and
Technology Agency (JST) PRESTO no. 18069571 (to R.D.). P.M.S. and A.C. are NHMRC Senior
Principal Research Fellows and D.W. is an NHMRC Senior Research Fellow. S.G.B.F. is an ARC
Future Fellow. A.I. was funded by the PRIME JP17gm5910013 and the LEAP JP17gm0010004
from the Japan Agency for Medical Research and Development, and JSPS KAKENHI 17K08264.
We are grateful to G. Christopoulos, V. Julita, T. Fields, C. Lafuente, J. M. Minguez, G. C. Sanz
and F. Qu for assay and technical support.
Author contributions P.Z. designed and performed most of the pharmacological studies
with assistance from T.T.T.; Y.-L.L. expressed and purified the complex; R.D. performed
cryo-sample preparation and imaging to acquire electron microscopy data; M.J.B. and
R.D. processed the electron microscopy data and performed electron microscopy map
calculations; M.J.B. built the model and performed refinement; M.M.F. performed the
mutagenesis studies, L.C. performed studies in the HEK293 CRISPR-knockout cells;
G.D. and C.A.R. designed, performed and analysed the molecular dynamics simulations;
F.S.W. and M.G.B. provided TT-OAD2. M.E.C., M.G.B. and K.W.S. designed and oversaw the
in vivo studies; P.Z., Y.-L.L., M.J.B., G.D., C.A.R., F.S.W., K.W.S., R.D., P.M.S. and D.W. performed
data analysis; P.Z., Y.-L.L., M.J.B., G.D., C.A.R., F.S.W., K.W.S., A.C., L.J.M., M.-W.W. and
R.D. assisted with data interpretation, figure and manuscript preparation; P.M.S. and
D.W. designed and supervised the project, interpreted the data and wrote the manuscript.
Competing interests F.W.S., M.E.C. and K.W.S. are employees of Eli Lilly and Company.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41586-
019-1902-z.
Correspondence and requests for materials should be addressed to R.D., P.M.S. or D.W.
Peer review information Nature thanks Doryen Bubeck, Dave D’Alessio, Nita R. Shah and the
other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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