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Computational design of ligand-binding membrane receptors with high selectivity

Nature Chemical Biology volume 13, pages 715723 (2017) | Download Citation

Abstract

Accurate modeling and design of protein–ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein–ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding. In structure modeling tests, we blindly predicted, with near-atomic accuracy, ligand conformations bound to G-protein-coupled receptors (GPCRs) that have rarely been identified using traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally uncharacterized GPCRs. We then applied this technique to design functional human dopamine receptors with novel ligand-binding selectivity. Most blindly predicted ligand-binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand-binding profile of membrane receptors that remain difficult to crystallize.

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Acknowledgements

We thank the members of the Barth lab for insightful discussions during this study and critical comments on the manuscript. This work was supported by a grant from the National Institute of Health (1R01GM097207) and by a supercomputer allocation from XSEDE (MCB120101) to P.B. and a training fellowship from the NIH NIGMS T32GM008280 to M.Y. and K.-Y.M.C.

Author information

Author notes

    • Joaquin Ambia
    •  & Kuang-Yui M Chen

    Present addresses: Center for Petroleum and Geosystems Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas, USA (J.A.); Department of Cell Biology and Human Anatomy, University of California at Davis, Davis, California, USA (K.-Y.M.C.).

Affiliations

  1. Department of Pharmacology, Baylor College of Medicine, Houston, Texas, USA.

    • Xiang Feng
    • , Joaquin Ambia
    •  & Patrick Barth
  2. Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA.

    • Kuang-Yui M Chen
    • , Melvin Young
    •  & Patrick Barth
  3. Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas, USA.

    • Patrick Barth

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Contributions

P.B. designed the study; J.A. and P.B. developed the integrated homology-modeling and ligand-docking protocol; X.F. optimized the parameters for the ligand-docking simulations and ligand pose selection; X.F. and K.-Y.M.C. performed the blind and benchmark structure predictions; X.F. performed the ligand-binding selectivity prediction benchmark and the design calculations; X.F. and M.Y. performed the experimental validations; P.B. and X.F. analyzed and discussed the results; P.B. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Patrick Barth.

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    Supplementary Results, Supplementary Tables 1–6 and Supplementary Figures 1–8

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DOI

https://doi.org/10.1038/nchembio.2371

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