Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Selectivity determinants of GPCR–G-protein binding

Nature volume 545, pages 317–322 (2017)Cite this article

Subjects

Abstract

The selective coupling of G-protein-coupled receptors (GPCRs) to specific G proteins is critical to trigger the appropriate physiological response. However, the determinants of selective binding have remained elusive. Here we reveal the existence of a selectivity barcode (that is, patterns of amino acids) on each of the 16 human G proteins that is recognized by distinct regions on the approximately 800 human receptors. Although universally conserved positions in the barcode allow the receptors to bind and activate G proteins in a similar manner, different receptors recognize the unique positions of the G-protein barcode through distinct residues, like multiple keys (receptors) opening the same lock (G protein) using non-identical cuts. Considering the evolutionary history of GPCRs allows the identification of these selectivity-determining residues. These findings lay the foundation for understanding the molecular basis of coupling selectivity within individual receptors and G proteins.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Selectivity in GPCR–G-protein signalling.
Figure 2: Asymmetric evolution of the GPCR and Gα protein repertoire.
Figure 3: Subtype-specific residues and Gα selectivity barcode.
Figure 4: Residue contacts at the GPCR–G-protein interface.
Figure 5: Evolutionary history of GPCRs and selectivity-determining positions on the receptor.
Figure 6: Lock and key analogy for GPCR–G-protein selectivity.

Similar content being viewed by others

References

  1. Bjarnadóttir, T. K. et al. Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse. Genomics 88, 263–273 (2006)

    PubMed  Google Scholar 

  2. Anantharaman, V., Abhiman, S., de Souza, R. F. & Aravind, L. Comparative genomics uncovers novel structural and functional features of the heterotrimeric GTPase signaling system. Gene 475, 63–78 (2011)

    CAS  PubMed  Google Scholar 

  3. 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 (D1), D1054–D1068 (2016)

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  5. Neves, S. R., Ram, P. T. & Iyengar, R. G protein pathways. Science 296, 1636–1639 (2002)

    ADS  CAS  PubMed  Google Scholar 

  6. Marinissen, M. J. & Gutkind, J. S. G-protein-coupled receptors and signaling networks: emerging paradigms. Trends Pharmacol. Sci. 22, 368–376 (2001)

    CAS  PubMed  Google Scholar 

  7. Oldham, W. M. & Hamm, H. E. Heterotrimeric G protein activation by G-protein-coupled receptors. Nature Rev. Mol. Cell Biol. 9, 60–71 (2008)

    CAS  Google Scholar 

  8. Frielle, T. et al. Cloning of the cDNA for the human β1-adrenergic receptor. Proc. Natl Acad. Sci. USA 84, 7920–7924 (1987)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ruat, M. et al. A novel rat serotonin (5-HT6) receptor: molecular cloning, localization and stimulation of cAMP accumulation. Biochem. Biophys. Res. Commun. 193, 268–276 (1993)

    CAS  PubMed  Google Scholar 

  10. Li, F., De Godoy, M. & Rattan, S. Role of adenylate and guanylate cyclases in β1-, β2-, and β3-adrenoceptor-mediated relaxation of internal anal sphincter smooth muscle. J. Pharmacol. Exp. Ther. 308, 1111–1120 (2004)

    CAS  PubMed  Google Scholar 

  11. Wess, J. Molecular basis of receptor/G-protein-coupling selectivity. Pharmacol. Ther. 80, 231–264 (1998)

    CAS  PubMed  Google Scholar 

  12. Horn, F., van der Wenden, E. M., Oliveira, L., IJzerman, A. P. & Vriend, G. Receptors coupling to G proteins: is there a signal behind the sequence? Proteins 41, 448–459 (2000)

    CAS  PubMed  Google Scholar 

  13. Wong, S. K. G protein selectivity is regulated by multiple intracellular regions of GPCRs. Neurosignals 12, 1–12 (2003)

    CAS  PubMed  Google Scholar 

  14. Kruse, A. C. et al. Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature 482, 552–556 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rasmussen, S. G. et al. Crystal structure of the β2 adrenergic receptor–Gs protein complex. Nature 477, 549–555 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. 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)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Masuho I. et al. Distinct profiles of functional discrimination among G proteins determine the actions of G protein-coupled receptors. Sci. Signal. 8, ra123 (2015)

    PubMed  PubMed Central  Google Scholar 

  18. Krishnan, A. et al. Evolutionary hierarchy of vertebrate-like heterotrimeric G protein families. Mol. Phylogenet. Evol. 91, 27–40 (2015)

    CAS  PubMed  Google Scholar 

  19. de Mendoza, A., Sebé-Pedrós, A. & Ruiz-Trillo, I. The evolution of the GPCR signaling system in eukaryotes: modularity, conservation, and the transition to metazoan multicellularity. Genome Biol. Evol. 6, 606–619 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Krishnan, A., Almén, M. S., Fredriksson, R. & Schiöth, H. B. The origin of GPCRs: identification of mammalian like Rhodopsin, Adhesion, Glutamate and Frizzled GPCRs in fungi. PLoS ONE 7, e29817 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mirny, L. A. & Gelfand, M. S. Using orthologous and paralogous proteins to identify specificity determining residues. Genome Biol. 3, http://dx.doi.org/10.1186/gb-2002-3-3-preprint0002 (2002)

  22. Flock, T. et al. Universal allosteric mechanism for Gα activation by GPCRs. Nature 524, 173–179 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sun, D. et al. Probing Gαi1 protein activation at single-amino acid resolution. Nature Struct. Mol. Biol. 22, 686–694 (2015)

    CAS  Google Scholar 

  24. Conklin, B. R., Farfel, Z., Lustig, K. D., Julius, D. & Bourne, H. R. Substitution of three amino acids switches receptor specificity of Gqα to that of Giα. Nature 363, 274–276 (1993)

    ADS  CAS  PubMed  Google Scholar 

  25. Komatsuzaki, K. et al. A novel system that reports the G-proteins linked to a given receptor: a study of type 3 somatostatin receptor. FEBS Lett. 406, 165–170 (1997)

    CAS  PubMed  Google Scholar 

  26. Sasamura, H. et al. Analysis of Gα protein recognition profiles of angiotensin II receptors using chimeric Gα proteins. Mol. Cell. Endocrinol. 170, 113–121 (2000)

    CAS  PubMed  Google Scholar 

  27. Janin, J. & Chothia, C. The structure of protein-protein recognition sites. J. Biol. Chem. 265, 16027–16030 (1990)

    CAS  PubMed  Google Scholar 

  28. Venkatakrishnan, A. J. et al. Molecular signatures of G-protein-coupled receptors. Nature 494, 185–194 (2013)

    ADS  CAS  PubMed  Google Scholar 

  29. Reichmann, D. et al. The modular architecture of protein-protein binding interfaces. Proc. Natl Acad. Sci. USA 102, 57–62 (2005)

    ADS  CAS  PubMed  Google Scholar 

  30. Kleinau, G. et al. Principles and determinants of G-protein coupling by the rhodopsin-like thyrotropin receptor. PLoS ONE 5, e9745 (2010)

    ADS  PubMed  PubMed Central  Google Scholar 

  31. Isberg, V. et al. GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res. 44 (D1), D356–D364 (2016)

    CAS  PubMed  Google Scholar 

  32. Furness, S. G. et al. Ligand-dependent modulation of G protein conformation alters drug efficacy. Cell 167, 739–749 (2016)

    CAS  PubMed  Google Scholar 

  33. Rose, A. S. et al. Position of transmembrane helix 6 determines receptor G protein coupling specificity. J. Am. Chem. Soc. 136, 11244–11247 (2014)

    CAS  PubMed  Google Scholar 

  34. Wichard, J. D. et al. Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signalling. PLoS ONE 6, e16811 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pawson, A. J. et al. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 42, D1098–D1106 (2014)

    CAS  PubMed  Google Scholar 

  36. Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44 (D1), D279–D285 (2016)

    CAS  PubMed  Google Scholar 

  37. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015)

  38. Wuster, A., Venkatakrishnan, A. J., Schertler, G. F. & Babu, M. M. Spial: analysis of subtype-specific features in multiple sequence alignments of proteins. Bioinformatics 26, 2906–2907 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Fredriksson, R. & Schiöth, H. B. The repertoire of G-protein-coupled receptors in fully sequenced genomes. Mol. Pharmacol. 67, 1414–1425 (2005)

    CAS  PubMed  Google Scholar 

  40. Altenhoff, A. M., Schneider, A., Gonnet, G. H. & Dessimoz, C. OMA 2011: orthology inference among 1000 complete genomes. Nucleic Acids Res. 39, D289–D294 (2011)

    CAS  PubMed  Google Scholar 

  41. Vilella, A. J. et al. EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Res. 19, 327–335 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Finn, R. D. et al. HMMER web server: 2015 update. Nucleic Acids Res. 43 (W1), W30–W38 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Putnam, N. H. et al. Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science 317, 86–94 (2007)

    ADS  CAS  PubMed  Google Scholar 

  44. Srivastava, M. et al. The Trichoplax genome and the nature of placozoans. Nature 454, 955–960 (2008)

    ADS  CAS  PubMed  Google Scholar 

  45. Paradis, E ., Claude, J . & Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289–290 (2004)

    CAS  PubMed  Google Scholar 

  46. Ruan, J. et al. TreeFam: 2008 Update. Nucleic Acids Res. 36, D735–D740 (2008)

    CAS  PubMed  Google Scholar 

  47. Pei, J., Sadreyev, R. & Grishin, N. V. PCMA: fast and accurate multiple sequence alignment based on profile consistency. Bioinformatics 19, 427–428 (2003)

    CAS  PubMed  Google Scholar 

  48. Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007)

    CAS  PubMed  Google Scholar 

  49. Rost, B. & Sander, C. Conservation and prediction of solvent accessibility in protein families. Proteins 20, 216–226 (1994)

    CAS  PubMed  Google Scholar 

  50. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Shannon, P. T., Grimes, M., Kutlu, B., Bot, J. J. & Galas, D. J. RCytoscape: tools for exploratory network analysis. BMC Bioinformatics 14, 217 (2013)

    PubMed  PubMed Central  Google Scholar 

  52. Su, G ., Kuchinsky, A ., Morris, J. H ., States, D. J . & Meng, F. GLay: community structure analysis of biological networks. Bioinformatics 26, 3135–3137 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Morris, J. H. et al. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics 12, 436 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Doncheva, N. T., Assenov, Y., Domingues, F. S. & Albrecht, M. Topological analysis and interactive visualization of biological networks and protein structures. Nature Protocols 7, 670–685 (2012)

    CAS  PubMed  Google Scholar 

  55. Liu, Y., Schmidt, B. & Maskell, D. L. MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities. Bioinformatics 26, 1958–1964 (2010)

    CAS  PubMed  Google Scholar 

  56. Konagurthu, A. S ., Whisstock, J. C ., Stuckey, P. J . & Lesk, A. M. MUSTANG: a multiple structural alignment algorithm. Proteins 64, 559–574 (2006)

    CAS  PubMed  Google Scholar 

  57. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010)

    ADS  PubMed  PubMed Central  Google Scholar 

  58. Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44 (W1), W242–W245 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Grant, B. J., Rodrigues, A. P., ElSawy, K. M., McCammon, J. A. & Caves, L. S. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22, 2695–2696 (2006)

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank U. F. Lang, D. Veprintsev, C. Ravarani, H. Harbrecht, G. De Baets, D. Prado, X. Deupi, C. G. Tate and N. S. Latysheva for their comments on this work, and J. Westmoreland for assistance with Fig. 6. We thank S. Chavali and B. Lang for help with compiling mutation and expression data. We thank M. Mounir and C. Munk for help with the GPCRdb web service. This work was supported by the Medical Research Council (MC_U105185859; M.M.B., T.F., S.B.), the Boehringer Ingelheim Fond (T.F.), European Research Council (DE-ORPHAN 639125; D.E.G., A.S.H., N.L.) and the Lundbeck Foundation (R163-2013-16327; D.E.G.). T.F. is a Research Fellow of Fitzwilliam College, University of Cambridge, UK. M.M.B. is a Lister Institute Research Prize Fellow and is supported by a European Research Council Consolidator Grant.

Author information

Authors and Affiliations

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

    Tilman Flock, Santhanam Balaji & M. Madan Babu

  2. Fitzwilliam College, Cambridge, CB3 0DG, UK

    Tilman Flock

  3. Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, Copenhagen, 2100, Denmark

    Alexander S. Hauser, Nadia Lund & David E. Gloriam

Authors
  1. Tilman Flock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander S. Hauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nadia Lund
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David E. Gloriam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Santhanam Balaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Madan Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.F. and M.M.B. designed the project, analysed the data, interpreted the results and wrote the manuscript, with inputs from all authors. T.F. collected data, wrote scripts and performed all the analyses. S.B. performed orthologue detection, receptor alignment, tree building and ancestral reconstruction with help from T.F.; D.E.G., N.L. and A.S.H. performed the analysis on GPCR sequence patterns, and developed the web services. M.M.B. supervised the project.

Corresponding authors

Correspondence to Tilman Flock or M. Madan Babu.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks M. Lassig, A. B. Tobin and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Figure 1 G-protein coupling properties of human GPCRs.

a, Number of GPCRs with distinct primary signal transduction (G-protein coupling) for each GPCR family as annotated in the IUPHAR/BPS Guide to Pharmacology database (GtoPdb). Only ‘primary transduction’, as defined by the database, is shown here. Note that Fig. 1c, d shows both primary and secondary coupling. b, Number of GPCRs with distinct primary signal transduction properties grouped by GPCR class.

Extended Data Figure 2 Gene expression profile of human GPCRs and G proteins.

The gene expression level (transcriptome) of human G proteins (top) and GPCRs (bottom) across 84 healthy tissues or cell types is shown. The right insets show histograms of the number of G proteins (blue) or GPCRs (red) that are expressed in one or multiple tissues. This highlights that at least one member of each G-protein subfamily (Gαs, Gαi/o, Gαq/11, Gα12/13) is ubiquitously expressed in most tissues. Other subtypes, such as Gαt, are more tissue-specific. GPCRs, on the other hand, seem to be much more tissue-specific and are only expressed in single or few tissues, except for some ubiquitously expressed GPCRs such as chemokine receptors. Normalized expression data were derived from BioGPS (http://biogps.org).

Extended Data Figure 3 Asymmetric evolution of GPCR and Gα protein repertoires.

a, The GPCR and Gα protein repertoires (unique genes) across 13 representative organisms determined using Pfam domain annotations (see Methods and Supplementary Table). The number of class A receptors slightly differs from the IUPHAR/BPS Guide to Pharmacology database as class A taste receptors are classified as a separate Pfam family. b, The lineage-specific expansion and differentiation of the GPCR and G-protein repertoires during evolution. The numbers of G proteins and GPCRs are shown for C. owczarzaki (an early-branching unicellular sister group of metazoans), T. adhaerens (one of the oldest known multicellular organism) and humans.

Extended Data Figure 4 ‘Phylogenetic age’ of human GPCRs and Gα proteins.

a, Estimation of the ‘phylogenetic age’ of human GPCRs and G proteins by identifying the most distant one-to-one orthologues (dark grey) or any orthologue (light grey) from 215 organisms in the OMA database. The ‘phylogenetic age’ was determined by the branching times of humans and the oldest organisms that share either a one-to-one orthologue or any orthologue (one–many or many–one or many–many) with the human gene (Methods). The classification of GPCRs follows the IUPHAR receptor classification. b, Complete table of the GPCR and G-protein repertoire and the phylogenetic ‘overlap’ of the protein repertoires. The Jaccard similarity index (Methods) was used for the GPCR and G-protein repertoires in the 12 completely sequenced genomes from the different eukaryotic lineages. The subscripts ‘u’ and ‘s’ for organisms A and B refer to the number of unique and shared genes, respectively.

Extended Data Figure 5 Conservation of residue positions among orthologues and paralogues in Gα proteins.

a, Jitterplots showing the degree of sequence conservation (sequence identity) of each CGN position in Gα proteins. The plots show the degree of conservation in each one-to-one orthologue alignment for each Gα subtype versus the conservation of the human paralogue alignment (alignments are provided as Supplementary Data and can be visualized to identify which amino acids were fixed at what time points during evolution). b, Boxplot showing the distribution of the relative accessible surface area of residue positions in each group for Gαs (mapped onto Gαi with PDB accession number 1GP2). c, The conserved positions at the interface of the β2AR–Gαs (PDB accession number 3SN6) form central clusters (magenta) and tend to be surrounded by selectivity-determining positions (blue). The average distances among positions are conserved-to-conserved: 9.84 Å, conserved-to-specific: 11.23 Å, specific-to-specific: 12.20 Å.

Extended Data Figure 6 Integration of sequence- and structure-derived information to understand how GPCRs read the G-protein selectivity barcode.

a, G-protein selectivity barcode (Fig. 3d) mapped onto the GPCR–G-protein interface clusters obtained using the β2AR–Gαs complex structure (Fig. 4 and Methods) highlights which regions of the GPCR contact selectivity-determining residues on the G protein. Nodes represent GPCR (rounded squares) and G-protein (circles) positions. The edges and their width represent the number of atomic contacts between residues. The size of the nodes is relative to their node degree (number of contacts to other nodes, which is a measure of how central a node is). Residues within the cluster are grouped and coloured differently in the background (red, blue, green, brown and yellow). b, Statistics highlighting the results from integrating the G-protein barcode analysis (sequence-based analysis) with the structural clustering analysis (structure-based analysis). The number of residues in Gαs with a particular sequence conservation property in each cluster (that is, universally conserved, neutrally evolving, selectivity-determining position) is shown. The numbers of residues that map to the different GPCR and G-protein secondary structure elements are shown both for GPCR and for G protein on the basis of the β2AR–Gαs complex structure (PDB accession number 3SN6).

Extended Data Figure 7 Comparison of the interface contacts and the contacting residues between β2AR–Gαs and A2AR–mini Gαs.

a, Comparison of the overall structure of both complex structures shows that the two receptors bind the G protein in a similar binding mode. Root mean square deviation values are provided in the figure. b, Detailed comparison of the residue contacts between equivalent positions of β2AR and A2AR receptor with equivalent positions of Gαs and the mini Gαs construct used to obtain the complex structures. The exact residue and the GPCRdb numbering scheme for the receptor and the CGN system for the G protein are shown on the axes. Contacts (coloured cells in the matrix) and positions (horizontal and vertical coloured bars next to the axes) that are common or unique to the β2AR–Gαs or A2AR–mini Gαs complex are shown in different colours. The G-protein selectivity barcode as in Fig. 3 is shown in the bottom of the matrix. This analysis suggests that while the same positions of the G protein and GPCRs may be involved in the recognition, distinct residues (both positions and the amino-acid residue) on the two different receptors contact them. In other words, the same selectivity barcode presented by Gαs is read differently by receptors belonging to different subtypes.

Extended Data Figure 8 Phylogenetic tree of GPCRs and mapping of ancestral reconstruction of coupling selectivity.

A phylogenetic tree of human class A, B and C GPCRs was derived from a full-length GPCR multiple sequence alignment that was created in-house (Methods). Concentric circles illustrate the G-protein coupling selectivity of each GPCR: the four dots depict both primary and secondary G-protein coupling (from inside to outside: Gαs, Gαi/o, Gαq/11, Gα12/13). The inset on the top left shows a magnification of one clade in the phylogenetic tree. G-protein coupling of each ancestral node was reconstructed by considering only the primary coupling of the receptors (Methods).

Extended Data Figure 9 Selectivity patterns at the GPCR–G-protein interface.

a, Using the phylogenetic history to define receptor clades with a common ancestor uncovers distinct conserved properties of amino acids at specific interface positions on the receptor. The figure shows molecular property signatures (ability of residues at a given G-protein interface position to mediate a distinct type of molecular interaction) on the intracellular interface of GPCRs. Each circle represents a property (coloured) and its distinctiveness (sizing) within the receptors that couple to the given G-protein subtype (versus those that do not). There is no conserved sequence pattern in all the receptors that couple to the same Gα protein. b, Receptors that form a phylogenetic clade exhibit distinct molecular property signatures (Methods). The legend (bottom) shows the colour scheme used for amino acids with different properties. c, Sequence pattern determined by Spial (Methods) of the interface positions (left). Top: clades of vasopressin 2 receptor (V2R) and β-adrenoreceptors (βARs), which belong to different groups, both couple to Gαs. However, the common ancestor of the V2R-related receptor coupled to Gαq (suggesting alteration of selectivity) whereas the common ancestor of aminergic receptors coupled to Gαs proteins (suggesting inheritance of selectivity). An analysis of the equivalent interface positions on the receptor that contact the Gα protein shows that V2R independently accumulated a different set of mutations in the same region to selectively couple to Gαs and hence arrived at a different sequence pattern to read the selectivity barcode on Gαs. Bottom: adenosine-clade and βARs (which belong to different groups) that both couple to Gαs and have complex evolutionary histories (Extended Data Fig. 8). An analysis of the equivalent interface positions on the receptor that contact the Gα protein shows that A2AR independently accumulated a different set of mutations in the same region to couple to Gαs and hence arrived at a different sequence pattern to read the same selectivity barcode on Gαs (see also Extended Data Fig. 7b). Mutagenesis of the A2B receptor has shown that the positions 3x50, 3x54, 5x69, 6x36 and 6x37 affect the coupling of Gα proteins Gαq, Gα12, Gα13, Gα14, Gαi1, Gαi2 and Gα15 (see also Supplementary Table 1).

Extended Data Figure 10 Webserver for the analysis of GPCR–G-protein selectivity.

Summary of the features in GPCRdb, describing the receptor–G-protein binding interface. These features allow users to investigate various aspects of receptor–G-protein binding selectivity and G-protein-specific information for all the human GPCRs and G proteins.

Supplementary information

Supplementary Data

This zipped file contains Supplementary Table 1, Supplementary Data files 1-2, Supplementary Figure 1 and 2 additional Supplementary Data files – see the Supplementary Guide file within the zipped folder for more details. (ZIP 1207 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flock, T., Hauser, A., Lund, N. et al. Selectivity determinants of GPCR–G-protein binding. Nature 545, 317–322 (2017). https://doi.org/10.1038/nature22070

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature22070

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research