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Diverse activation pathways in class A GPCRs converge near the G-protein-coupling region

Nature volume 536, pages 484–487 (2016)Cite this article

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Abstract

Class A G-protein-coupled receptors (GPCRs) are a large family of membrane proteins that mediate a wide variety of physiological functions, including vision, neurotransmission and immune responses1,2,3,4. They are the targets of nearly one-third of all prescribed medicinal drugs5 such as beta blockers and antipsychotics. GPCR activation is facilitated by extracellular ligands and leads to the recruitment of intracellular G proteins3,6. Structural rearrangements of residue contacts in the transmembrane domain serve as ‘activation pathways’ that connect the ligand-binding pocket to the G-protein-coupling region within the receptor. In order to investigate the similarities in activation pathways across class A GPCRs, we analysed 27 GPCRs from diverse subgroups for which structures of active, inactive or both states were available. Here we show that, despite the diversity in activation pathways between receptors, the pathways converge near the G-protein-coupling region. This convergence is mediated by a highly conserved structural rearrangement of residue contacts between transmembrane helices 3, 6 and 7 that releases G-protein-contacting residues. The convergence of activation pathways may explain how the activation steps initiated by diverse ligands enable GPCRs to bind a common repertoire of G proteins.

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Figure 1: Comparison of residue contacts in inactive- and active-state structures of class A GPCRs.
Figure 2: Patterns of residue contacts across inactive and active state structures of GPCRs.
Figure 3: Conserved rearrangement of residue contacts between inactive and active state structures of GPCRs.
Figure 4: Conserved rearrangement of residue contacts between inactive- and active-state structures across diverse class A GPCRs.

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  • 16 August 2016

    A minor change was made to Extended Data Fig. 2e.

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Acknowledgements

We thank C. Chothia, G. Chalancon, N. Latysheva, R. Henderson, A. Sente, K. Kruse, A. Singhal and S. Chavali for their comments on this manuscript. We acknowledge LMB International Scholarship (in support of A.J.V.), St. John’s College Benefactor Scholarship (A.J.V.), MRC Centenary Early Career Award (A.J.V.), the Medical Research Council (MC_U105185859) (M.M.B., S.B., T.F.); (MC_U105197215) (C.G.T., G.L.), Boehringer Ingelheim Fonds (T.F.), Heptares Therapeutics Ltd (G.L.), ATIP-Avenir program (G.L.), Swiss National Science Foundation (grant number 310030_153145) (G.F.X.S.); (31003A_146520) (X.D.); (31003A_159748) (D.B.V.), Swiss National Science Foundation Doc.Mobility fellowship P1EZP3_165219 (F.M.H.), the Human Frontiers Science Program (RGP0034/2014) (G.F.X.S.), the Canadian Institute for Health Research (MOP-10501) (M.B.) and the COST Action CM1207 (GLISTEN) (X.D., G.F.X.S., C.G.T., G.L.). M.M.B. is a Lister Institute Research Prize Fellow. M.B. holds a Canada Research Chair.

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Authors and Affiliations

  1. MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, United Kingdom

    A. J. Venkatakrishnan, Tilman Flock, Santhanam Balaji, Christopher G. Tate & M. Madan Babu

  2. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, 279 Campus Drive, Stanford, 94305, California, USA

    A. J. Venkatakrishnan

  3. Department of Computer Science, Stanford University, 318 Campus Drive, Stanford, 94305, California, USA

    A. J. Venkatakrishnan

  4. Institute for Computational and Mathematical Engineering, Stanford University, 475 Via Ortega, Stanford, 94305, California, USA

    A. J. Venkatakrishnan

  5. Paul Scherrer Institute, Villigen, 5232, Switzerland

    Xavier Deupi, Franziska M. Heydenreich, Tamara Miljus, Dmitry B. Veprintsev & Gebhard F. X. Schertler

  6. CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la cardonille, Montpellier, F-34094, Cedex 05, France

    Guillaume Lebon

  7. INSERM U1191, Montpellier, F-34094, France

    Guillaume Lebon

  8. Université Montpellier, Montpellier, F-34094, France

    Guillaume Lebon

  9. Department of Biology, ETH Zurich, Wolfgang-Pauli-Str. 27, Zurich, 8093, Switzerland

    Franziska M. Heydenreich, Tamara Miljus, Dmitry B. Veprintsev & Gebhard F. X. Schertler

  10. Department of Biochemistry & Institute for Research in Immunology and Cancer, University of Montreal, Montreal, H3C 3J7, Canada

    Franziska M. Heydenreich & Michel Bouvier

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Contributions

A.J.V. and M.M.B. designed the project, analysed the data, interpreted the results and wrote the manuscript, with inputs from other authors. A.J.V. collected data, wrote scripts and performed the contact fingerprinting analyses. X.D. and G.L. helped with interpretation of the activation pathways. T.F. helped with the collection of disease mutations. S.B. performed the sequence analysis of GPCRs. D.B.V., M.B., F.M.H. and T.M. designed and performed the mutagenesis and BRET assays and analysed the data. G.F.X.S. and C.G.T. provided general input on the project. M.M.B. supervised the project.

Corresponding authors

Correspondence to A. J. Venkatakrishnan or M. Madan Babu.

Additional information

Reviewer Information Nature thanks D. Gloriam and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Sequence analysis of positions involved in the conserved rearrangement during receptor activation.

The alignment of 311 non-olfactory class A human GPCRs were obtained from GPCRdb. Percentage of residue pairs making a contact in the inactive state only (a, orange spectrum; five bins) and the active state only (b, green spectrum; five bins). The set of large hydrophobic/aromatic residues was defined to include the following amino acids: Val, Leu, Ile, Met, Phe, Tyr, Trp. c, The percentage of amino acids occupying residue positions 3x46, 6x37 and 7x53 involved in the conserved rearrangement is shown. d, The odds of finding a large hydrophobic or aromatic residue at a pair/triplet of positions that form a contact during the conserved rearrangement. See Methods for details.

Extended Data Figure 2 Signalling profile of Vasopressin V2 receptor mutants using BRET-based biosensors.

a, b, Activation of Gs (a) and Gq (b) by wild-type and mutant vasopressin V2 receptors. The residues at positions 3x46, 6x37 and 7x53 were mutated to alanine separately and in combination and their G protein signalling response was measured using BRET-based biosensors. The data points represent the mean ± SEM of n = 3–4 experiments. Gs is the primary and Gq the secondary cognate G protein of the vasopressin V2 receptor. For both G proteins, the activation is monitored by the decrease in BRET resulting from the separation between Gα and Gβγ. The G protein response was markedly reduced for all mutants, pointing towards reduced receptor activity. The expression levels of the mutant receptors were quantified using a fluorescent dye bound to the SNAP tag of surface-expressed receptors. While the Y325A (Y7x53A), T273A (T6x37A) and T273A/Y325A (T6x37A/Y7x53A) proteins were expressed at wild-type levels, the expression of M133A (M3x46A) and its combinations showed reduced expression levels. As a comparison, the signalling of reduced levels of wild-type vasopressin V2 receptor (% of DNA transfected) for Gs and Gq is shown in c and d, respectively. This indicates that the reduction in expression level alone cannot explain the reduced signalling of Y325A (Y7x53A), T273A (T6x37A) and the Y325A (Y7x53A)/T273A (T6x37A) combination. Thus, the reduced signalling activity of Y325A (Y7x53A), T273A (T6x37A) and T273A/Y325A (T6x37A/Y7x53A) is due to a change in the ability of the receptor to promote G protein activation. In the case of receptor mutants (single, double and triple) involving M133A (M3x46A), both the G protein response and the expression level of the receptor are markedly reduced. This may be because position 3x46 is involved in mediating contacts that are important for both the active and the inactive state, and mutating this position might simultaneously affect receptor biogenesis and downstream response. e, The increases in EC50 (along with the decrease in maximal responses) are consistent with a reduced ability of the receptor to activate G proteins.

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Venkatakrishnan, A., Deupi, X., Lebon, G. et al. Diverse activation pathways in class A GPCRs converge near the G-protein-coupling region. Nature 536, 484–487 (2016). https://doi.org/10.1038/nature19107

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