Common coupling map advances GPCR-G protein selectivity

  1. Alexander Sebastian Hauser
  2. Charlotte Avet
  3. Claire Normand
  4. Arturo Mancini
  5. Asuka Inoue
  6. Michel Bouvier  Is a corresponding author
  7. David E Gloriam  Is a corresponding author
  1. University of Copenhagen, Denmark
  2. University of Montreal, Canada
  3. Domain Therapeutics North America, Canada
  4. Tohoku University, Japan

Abstract

Two-thirds of human hormones and one-third of clinical drugs act on membrane receptors that couple to G proteins to achieve appropriate functional responses. While G protein transducers from literature are annotated in the Guide to Pharmacology database, two recent large-scale datasets now expand the receptor-G protein 'couplome'. However, these three datasets differ in scope and reported G protein couplings giving different coverage and conclusions on GPCR-G protein signaling. Here, we report a common coupling map uncovering novel couplings supported by both large-scale studies, the selectivity/promiscuity of GPCRs and G proteins, and how the co-coupling and co-expression of G proteins compare to the families from phylogenetic relationships. The coupling map and insights on GPCR-G protein selectivity will catalyze advances in receptor research and cellular signaling towards the exploitation of G protein signaling bias in design of safer drugs.

Data availability

All underlying data are available in Spreadsheets S1-5. The obtained common coupling map is available in the online database GproteinDb at https://gproteindb.org/signprot/couplings.

The following previously published data sets were used

Article and author information

Author details

  1. Alexander Sebastian Hauser

    Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1098-6419
  2. Charlotte Avet

    Department of Biochemistry and Molecular Medicine, University of Montreal, Montréal, Canada
    Competing interests
    No competing interests declared.
  3. Claire Normand

    Domain Therapeutics North America, Montréal, Canada
    Competing interests
    Claire Normand, was an employees of Domain Therapeutics North America during part or all of this research..
  4. Arturo Mancini

    Domain Therapeutics North America, Montréal, Canada
    Competing interests
    Arturo Mancini, was an employees of Domain Therapeutics North America during part or all of this research..
  5. Asuka Inoue

    Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
    Competing interests
    No competing interests declared.
  6. Michel Bouvier

    Department of Biochemistry and Molecular Medicine, University of Montreal, Montréal, Canada
    For correspondence
    michel.bouvier@umontreal.ca
    Competing interests
    Michel Bouvier, is the president of Domain Therapeutics scientific advisory board..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1128-0100
  7. David E Gloriam

    Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    david.gloriam@sund.ku.dk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4299-7561

Funding

Canadian Institutes of Health Research (FDN-148431)

  • Michel Bouvier

Lundbeckfonden (R218-2016-1266)

  • David E Gloriam

Lundbeckfonden (R313-2019-526)

  • David E Gloriam

Novo Nordisk Fonden (NNF18OC0031226)

  • David E Gloriam

Basis for Supporting Innovative Drug Discovery and Life Science Research (JP20am0101095)

  • Asuka Inoue

Leading Asia's Private Infrastructure Fund (JP20gm0010004)

  • Asuka Inoue

Japan Agency for Medical Research and Development

  • Asuka Inoue

Takeda Science Foundation

  • Asuka Inoue

Uehara Memorial Foundation

  • Asuka Inoue

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Hauser et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,731
    views
  • 979
    downloads
  • 100
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Alexander Sebastian Hauser
  2. Charlotte Avet
  3. Claire Normand
  4. Arturo Mancini
  5. Asuka Inoue
  6. Michel Bouvier
  7. David E Gloriam
(2022)
Common coupling map advances GPCR-G protein selectivity
eLife 11:e74107.
https://doi.org/10.7554/eLife.74107

Share this article

https://doi.org/10.7554/eLife.74107

Further reading

    1. Computational and Systems Biology
    Masaaki Uematsu, Jeremy M Baskin
    Tools and Resources

    Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.