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Computational design of G Protein-Coupled Receptor allosteric signal transductions

Nature Chemical Biology volume 16, pages 77–86 (2020)Cite this article

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Abstract

Membrane receptors sense and transduce extracellular stimuli into intracellular signaling responses but the molecular underpinnings remain poorly understood. We report a computational approach for designing protein allosteric signaling functions. By combining molecular dynamics simulations and design calculations, the method engineers amino-acid ‘microswitches’ at allosteric sites that modulate receptor stability or long-range coupling, to reprogram specific signaling properties. We designed 36 dopamine D2 receptor variants, whose constitutive and ligand-induced signaling agreed well with our predictions, repurposed the D2 receptor into a serotonin biosensor and predicted the signaling effects of more than 100 known G-protein-coupled receptor (GPCR) mutations. Our results reveal the existence of distinct classes of allosteric microswitches and pathways that define an unforeseen molecular mechanism of regulation and evolution of GPCR signaling. Our approach enables the rational design of allosteric receptors with enhanced stability and function to facilitate structural characterization, and reprogram cellular signaling in synthetic biology and cell engineering applications.

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Fig. 1: Rational design of GPCR allosteric regulation.
Fig. 2: Design of single microswitches modulating stability or long-range structural coupling.
Fig. 3: Designed microswitches stabilize distinct local active state structures.
Fig. 4: Designed allosteric microswitches reprogram ligand sensing and signaling responses.
Fig. 5: A high-resolution mechanism for the regulation and evolution of GPCR allosteric signaling properties.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Protein modeling and design applications (IPHoLD, RosettaMembrane) are available in the latest version of the Rosetta software package (https://www.rosettacommons.org/software) or at https://doi.org/10.5281/zenodo.3460811. Bio3D is available at: http://thegrantlab.org/bio3d/index.php.

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Acknowledgements

We thank R. Sharma for setting up some NMA calculations, T. Wensel and his laboratory for sharing experimental protocols and members of the Barth laboratory for helpful discussion. This work was supported by funding from EPFL and the Ludwig Institute for Cancer Research, partially supported by a grant from the National Institute of Health (no. 1R01GM097207), and by a supercomputer allocation from XSEDE (no. MCB120101) to P.B.

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Author notes

  1. Kuang-Yui Michael Chen

    Present address: Department of Cell Biology and Human Anatomy, University of California at Davis, Davis, CA, USA

  2. These authors contributed equally: Kuang-Yui Michael Chen, Daniel Keri.

Authors and Affiliations

  1. Swiss Federal Institute of Technology (EPFL), Institute of Bioengineering, Lausanne, Switzerland

    Kuang-Yui Michael Chen, Daniel Keri & Patrick Barth

  2. Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA

    Kuang-Yui Michael Chen & Patrick Barth

  3. Department of Pharmacology, Baylor College of Medicine, Houston, TX, USA

    Patrick Barth

  4. Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, TX, USA

    Patrick Barth

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Contributions

P.B. designed the studies. P.B., K.-Y.M.C. and D.K. performed the calculations. K.-Y.M.C. and D.K. performed experiments on D2. P.B., D.K. and K.-Y.M.C. analyzed the data. P.B. wrote the manuscript.

Corresponding author

Correspondence to Patrick Barth.

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Competing interests

P.B. declares the following patent application: patent applicant, Ecole Polytechnique Fédérale de Lausanne. Name of inventor(s), Patrick Barth. Application number, EP 19189259.5. Status of application, priority year. Specific aspect of manuscript covered in patent application, methods and protein variants.

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Chen, KY.M., Keri, D. & Barth, P. Computational design of G Protein-Coupled Receptor allosteric signal transductions. Nat Chem Biol 16, 77–86 (2020). https://doi.org/10.1038/s41589-019-0407-2

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