Common activation mechanism of class A GPCRs

  1. Qingtong Zhou
  2. Dehua Yang
  3. Meng Wu
  4. Yu Guo
  5. Wangjing Guo
  6. Li Zhong
  7. Xiaoqing Cai
  8. Antao Dai
  9. Wonjo Jang
  10. Eugene I Shakhnovich
  11. Zhi-Jie Liu
  12. Raymond C Stevens
  13. Nevin A Lambert
  14. M Madan Babu  Is a corresponding author
  15. Ming-Wei Wang  Is a corresponding author
  16. Suwen Zhao  Is a corresponding author
  1. ShanghaiTech University, China
  2. Shanghai Institute of Materia Medica, China
  3. Augusta University, United States
  4. Harvard University, United States
  5. MRC Laboratory of Molecular Biology, United Kingdom

Abstract

Class A G protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. Understanding receptor activation mechanism is critical for discovering novel therapeutics since about one-third of all marketed drugs target members of this family. GPCR activation is an allosteric process that couples agonist binding to G protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue level changes of this movement remain less well understood. Here, we report a framework to quantify conformational changes. By analyzing the conformational changes in 234 structures from 45 class A GPCRs, we discovered a common GPCR activation pathway comprising of 34 residue pairs and 35 residues. The pathway unifies previous findings into a common activation mechanism and strings together the scattered key motifs such as CWxP, DRY, Na+ pocket, NPxxY and PIF, thereby directly linking the bottom of ligand-binding pocket with G protein coupling region. Site-directed mutagenesis experiments support this proposition and reveal that rational mutations of residues in this pathway can be used to obtain receptors that are constitutively active or inactive. The common activation pathway provides the mechanistic interpretation of constitutively activating, inactivating and disease mutations. As a module responsible for activation, the common pathway allows for decoupling of the evolution of the ligand binding site and G protein binding region. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1, 2, 6 and 7.

Article and author information

Author details

  1. Qingtong Zhou

    iHuman Institute, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Dehua Yang

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3028-3243
  3. Meng Wu

    iHuman Institute, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Yu Guo

    iHuman Institute, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Wangjing Guo

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Li Zhong

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Xiaoqing Cai

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Antao Dai

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Wonjo Jang

    Department of Pharmacology and Toxicology, Augusta University, Augusta, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Eugene I Shakhnovich

    Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4769-2265
  11. Zhi-Jie Liu

    iHuman Institute, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7279-2893
  12. Raymond C Stevens

    iHuman Institute, ShanghaiTech University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Nevin A Lambert

    Department of Pharmacology and Toxicology, Augusta University, Augusta, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. M Madan Babu

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    For correspondence
    madanm@mrc-lmb.cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  15. Ming-Wei Wang

    The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Shanghai, China
    For correspondence
    mwwang@simm.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
  16. Suwen Zhao

    iHuman Institute, ShanghaiTech University, Shanghai, China
    For correspondence
    zhaosw@shanghaitech.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5609-434X

Funding

Medical Research Council (MC_U105185859)

  • M Madan Babu

National Mega R&D Program for Drug Discovery (2018ZX09735-001)

  • Ming-Wei Wang

National Key R&D Program of China (2016YFC0905900)

  • Suwen Zhao

National Key R&D Program of China (2018YFA0507000)

  • Suwen Zhao

National Key R&D Program of China (2018YFA0507000)

  • Ming-Wei Wang

National Natural Science Foundation of China (31971178)

  • Suwen Zhao

National Natural Science Foundation of China (81872915)

  • Ming-Wei Wang

Novo Nordisk-CAS Research (NNCAS-2017-1-CC)

  • Dehua Yang

Young Talent Program of Shanghai

  • Suwen Zhao

Shanghai Science and Technology Development Fund (16ZR1448500)

  • Suwen Zhao

Shanghai Science and Technology Development Fund (16ZR1407100)

  • Antao Dai

National Natural Science Foundation of China (21704064)

  • Qingtong Zhou

National Natural Science Foundation of China (81573479)

  • Dehua Yang

National Natural Science Foundation of China (81773792)

  • Dehua Yang

National Mega R&D Program for Drug Discovery (2018ZX09711002-002-005)

  • Dehua Yang

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

Reviewing Editor

  1. Yibing Shan, DE Shaw Research, United States

Version history

  1. Received: July 17, 2019
  2. Accepted: December 19, 2019
  3. Accepted Manuscript published: December 19, 2019 (version 1)
  4. Version of Record published: January 10, 2020 (version 2)
  5. Version of Record updated: February 25, 2020 (version 3)

Copyright

© 2019, Zhou 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

  • 24,041
    views
  • 3,596
    downloads
  • 346
    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. Qingtong Zhou
  2. Dehua Yang
  3. Meng Wu
  4. Yu Guo
  5. Wangjing Guo
  6. Li Zhong
  7. Xiaoqing Cai
  8. Antao Dai
  9. Wonjo Jang
  10. Eugene I Shakhnovich
  11. Zhi-Jie Liu
  12. Raymond C Stevens
  13. Nevin A Lambert
  14. M Madan Babu
  15. Ming-Wei Wang
  16. Suwen Zhao
(2019)
Common activation mechanism of class A GPCRs
eLife 8:e50279.
https://doi.org/10.7554/eLife.50279

Share this article

https://doi.org/10.7554/eLife.50279

Further reading

    1. Computational and Systems Biology
    Natalie R Davidson, Casey S Greene
    Research Article

    Science journalism is a critical way for the public to learn about and benefit from scientific findings. Such journalism shapes the public’s view of the current state of science and legitimizes experts. Journalists can only cite and quote a limited number of sources, who they may discover in their research, including recommendations by other scientists. Biases in either process may influence who is identified and ultimately included as a source. To examine potential biases in science journalism, we analyzed 22,001 non-research articles published by Nature and compared these with Nature-published research articles with respect to predicted gender and name origin. We extracted cited authors’ names and those of quoted speakers. While citations and quotations within a piece do not reflect the entire information-gathering process, they can provide insight into the demographics of visible sources. We then predicted gender and name origin of the cited authors and speakers. We compared articles with a comparator set made up of first and last authors within primary research articles in Nature and a subset of Springer Nature articles in the same time period. In our analysis, we found a skew toward quoting men in Nature science journalism. However, quotation is trending toward equal representation at a faster rate than authorship rates in academic publishing. Gender disparity in Nature quotes was dependent on the article type. We found a significant over-representation of names with predicted Celtic/English origin and under-representation of names with a predicted East Asian origin in both in extracted quotes and journal citations but dampened in citations.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Chananchida Sang-aram, Robin Browaeys ... Yvan Saeys
    Research Article

    Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).