Common coupling map advances GPCR-G protein selectivity
- University of Copenhagen, Denmark
- University of Montreal, Canada
- Domain Therapeutics North America, Canada
- 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.
-
Quantitative G protein and B-arrestin profiles of 100 therapeutically relevant GPCRsbioRxiv https://www.biorxiv.org/.
-
Quantitative G protein profiles of 150 GPCRsCell https://www.cell.com/.
-
Qualitative G protein profiles of 254 GPCRsNucleic Acids Research https://academic.oup.com/nar.
Article and author information
Author details
Alexander Sebastian Hauser
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.
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Common coupling map advances GPCR-G protein selectivity
eLife 11:e74107.
https://doi.org/10.7554/eLife.74107
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