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An online GPCR structure analysis platform

Nature Structural & Molecular Biology volume 28, pages 875–878 (2021)Cite this article

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Abstract

We present an online, interactive platform for comparative analysis of all available G-protein coupled receptor (GPCR) structures while correlating to functional data. The comprehensive platform encompasses structure similarity, secondary structure, protein backbone packing and movement, residue–residue contact networks, amino acid properties and prospective design of experimental mutagenesis studies. This lets any researcher tap the potential of sophisticated structural analyses enabling a plethora of basic and applied receptor research studies.

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Fig. 1: Structure comparison tool.
Fig. 2: Structure similarity trees.

Data availability

All data is available in GPCRdb (https://review.gpcrdb.org) and GitHub (https://github.com/protwis/gpcrdb_data). Documentation is available at https://docs.gpcrdb.org.

Code availability

All open-source code can be obtained from GitHub (https://github.com/protwis/protwis) under the permissive Apache 2.0 License (https://www.apache.org/licenses/LICENSE-2.0).

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Acknowledgements

This work was supported by the Lundbeck Foundation (grant nos R163-2013-16327 and R218-2016-1266), the Novo Nordisk Foundation (grant no NNF18OC0031226) and Independent Research Fund Denmark | Natural Sciences (grant no 8021-00173B) to D.E.G.

Author information

Author notes

  1. Christian Munk

    Present address: Data Tools Department, Novozymes A/S, Copenhagen, Denmark

  2. These authors contributed equally: Albert J. Kooistra, Christian Munk.

Authors and Affiliations

  1. Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark

    Albert J. Kooistra, Christian Munk, Alexander S. Hauser & David E. Gloriam

Authors
  1. Albert J. Kooistra
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  2. Christian Munk
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Contributions

D.E.G. conceptualized the study. D.E.G., C.M. and A.J.K. developed the methodology. A.J.K. curated the data. A.J.K. and D.E.G. carried out the investigation. A.J.K. and D.E.G. validated the data. D.E.G. wrote the original draft of the manuscript. A.J.K and A.S.H. reviewed and edited the manuscript. D.E.G., A.S.H. and A.J.K. visualized the study. D.E.G. acquired the funding. A.J.K. and C.M. developed the software. D.E.G. supervised the study.

Corresponding authors

Correspondence to Albert J. Kooistra or David E. Gloriam.

Ethics declarations

Competing interests

After the completion of this study, C.M. moved to become an employee of Novozymes A/S.

The other authors declare no competing interests.

Additional information

Peer review information Nature Structural & Molecular Biology thanks Peter Hildebrand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Florian Ullrich was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 State-stabilizing mutation design tool, residue positions and experimental data.

a, The ‘State-stabilizing mutation design tool’ presents data-driven suggestions of mutagenesis experiments for all human GPCRs (https://review.gpcrdb.org/mutations/state_stabilizing). The tool ranks receptor positions by calculating a net sum of residue contacts expected to be gained or removed upon mutation. b, Suggested state-stabilizing positions for classes A and B1, respectively. These are limited to the 30 generic residue positions with the largest inactive/active state contact sum difference. The rightmost column indicates state stabilizers with high-frequency contacts5. c, Percent coverage of suggested state-stabilizing versus all other generic residue positions by experimentally determined mutations that are ligand activity-altering (>5-fold effect), thermostabilizing (540 data points) or expression increasing (100% would mean that all determinants or non-determinants, respectively are covered by experimental mutations). For ligand activity mutations (34,648 data points in GPCRdb), we required an effect in at least two receptors. For class A GPCRs, 27/30 residue positions have experimental support (avg. 1.8 functional associations). In class B1, 8 positions are supported by functional data (avg. 0.3 associations). We compared the percentages of residue positions covered by experimental effects for the class A and B1 determinants suggested in the mutation design tool (top 30) versus all other generic residue positions. This shows a near double representation of such data for suggested determinants than other generic residue positions in class B1. For class A GPCRs, we find stronger determinant overlaps spanning 2.1-, 2.9-, 3.1 and 8.8-fold ratios for mutations shown to influence thermostability, expression in ligand activity studies, ligand activity and expression of structure constructs, respectively. Notably, the top and third positions for class A GPCRs are two well-characterized residue microswitches, R3x50 and Y5x58 and the second position is a conserved proline causing the hinge of TM7. Notably, in both classes 13 out of 30 (43%) of suggested mutagenesis positions are unique from this tool (not in5.

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Kooistra, A.J., Munk, C., Hauser, A.S. et al. An online GPCR structure analysis platform. Nat Struct Mol Biol 28, 875–878 (2021). https://doi.org/10.1038/s41594-021-00675-6

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