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Computational design of ligand-binding membrane receptors with high selectivity

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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Figure 1: Integrated homology modeling and ligand docking.
Figure 2: Accurate prediction of ligand conformations bound to GPCRs using IPHoLD.
Figure 3: Near-atomic accuracy prediction of GPCR-bound ligand poses.
Figure 4: Prediction and design of ligand-binding selectivity to structurally uncharacterized GPCRs.

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References

  1. 1

    Kolb, P. et al. Structure-based discovery of β2-adrenergic receptor ligands. Proc. Natl. Acad. Sci. USA 106, 6843–6848 (2009).

    CAS  Article  Google Scholar 

  2. 2

    Deupi, X. & Kobilka, B.K. Energy landscapes as a tool to integrate GPCR structure, dynamics, and function. Physiology (Bethesda) 25, 293–303 (2010).

    CAS  Google Scholar 

  3. 3

    Katritch, V., Cherezov, V. & Stevens, R.C. Diversity and modularity of G protein-coupled receptor structures. Trends Pharmacol. Sci. 33, 17–27 (2012).

    CAS  Article  Google Scholar 

  4. 4

    Manglik, A. et al. Structural insights into the dynamic process of β2-adrenergic receptor signaling. Cell 161, 1101–1111 (2015).

    CAS  Article  Google Scholar 

  5. 5

    Nygaard, R. et al. The dynamic process of β(2)-adrenergic receptor activation. Cell 152, 532–542 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Kahsai, A.W. et al. Multiple ligand-specific conformations of the β2-adrenergic receptor. Nat. Chem. Biol. 7, 692–700 (2011).

    CAS  Article  Google Scholar 

  7. 7

    Katritch, V., Cherezov, V. & Stevens, R.C. Structure-function of the G-protein-coupled receptor superfamily. Annu. Rev. Pharmacol. Toxicol. 53, 531–556 (2013).

    CAS  Article  Google Scholar 

  8. 8

    Kim, T.H. et al. The role of ligands on the equilibria between functional states of a G-protein-coupled receptor. J. Am. Chem. Soc. 135, 9465–9474 (2013).

    CAS  Article  Google Scholar 

  9. 9

    Stevens, R.C. et al. The GPCR Network: a large-scale collaboration to determine human GPCR structure and function. Nat. Rev. Drug Discov. 12, 25–34 (2013).

    CAS  Article  Google Scholar 

  10. 10

    Pieper, U. et al. Coordinating the impact of structural genomics on the human α-helical transmembrane proteome. Nat. Struct. Mol. Biol. 20, 135–138 (2013).

    CAS  Article  Google Scholar 

  11. 11

    Eswar, N., Eramian, D., Webb, B., Shen, M.Y. & Sali, A. Protein structure modeling with MODELLER. Methods Mol. Biol. 426, 145–159 (2008).

    CAS  Article  Google Scholar 

  12. 12

    Kelm, S., Shi, J. & Deane, C.M. MEDELLER: homology-based coordinate generation for membrane proteins. Bioinformatics 26, 2833–2840 (2010).

    CAS  Article  Google Scholar 

  13. 13

    Chen, K.Y., Sun, J., Salvo, J.S., Baker, D. & Barth, P. High-resolution modeling of transmembrane helical protein structures from distant homologues. PLoS Comput. Biol. 10, e1003636 (2014).

    Article  Google Scholar 

  14. 14

    Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).

    CAS  Article  Google Scholar 

  15. 15

    Davis, I.W. & Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol. 385, 381–392 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Repasky, M.P., Shelley, M. & Friesner, R.A. in Current Protocols in Bioinformatics. Ch. 8, Unit 8.12 (John Wiley and Sons, Inc., 2007).

  17. 17

    Zhou, Z., Felts, A.K., Friesner, R.A. & Levy, R.M. Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets. J. Chem. Inf. Model. 47, 1599–1608 (2007).

    CAS  Article  Google Scholar 

  18. 18

    Moustakas, D.T. et al. Development and validation of a modular, extensible docking program: DOCK 5. J. Comput. Aided Mol. Des. 20, 601–619 (2006).

    CAS  Article  Google Scholar 

  19. 19

    Trott, O. & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Huang, X.P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015).

    CAS  Article  Google Scholar 

  21. 21

    Michino, M. et al. Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat. Rev. Drug Discov. 8, 455–463 (2009).

    CAS  Article  Google Scholar 

  22. 22

    Kufareva, I., Rueda, M., Katritch, V., Stevens, R.C. & Abagyan, R. Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19, 1108–1126 (2011).

    CAS  Article  Google Scholar 

  23. 23

    Kufareva, I., Katritch, V., Stevens, R.C. & Abagyan, R. Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure 22, 1120–1139 (2014).

    CAS  Article  Google Scholar 

  24. 24

    Forrest, L.R., Tang, C.L. & Honig, B. On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys. J. 91, 508–517 (2006).

    CAS  Article  Google Scholar 

  25. 25

    Stamm, M. & Forrest, L.R. Structure alignment of membrane proteins: accuracy of available tools and a consensus strategy. Proteins 83, 1720–1732 (2015).

    CAS  Article  Google Scholar 

  26. 26

    Qian, B. et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450, 259–264 (2007).

    CAS  Article  Google Scholar 

  27. 27

    Combs, S.A. et al. Small-molecule ligand docking into comparative models with Rosetta. Nat. Protoc. 8, 1277–1298 (2013).

    CAS  Article  Google Scholar 

  28. 28

    Fischer, M., Coleman, R.G., Fraser, J.S. & Shoichet, B.K. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat. Chem. 6, 575–583 (2014).

    CAS  Article  Google Scholar 

  29. 29

    Spyrakis, F., BidonChanal, A., Barril, X. & Luque, F.J. Protein flexibility and ligand recognition: challenges for molecular modeling. Curr. Top. Med. Chem. 11, 192–210 (2011).

    CAS  Article  Google Scholar 

  30. 30

    Cavasotto, C.N. & Abagyan, R.A. Protein flexibility in ligand docking and virtual screening to protein kinases. J. Mol. Biol. 337, 209–225 (2004).

    CAS  Article  Google Scholar 

  31. 31

    Conklin, B.R. et al. Engineering GPCR signaling pathways with RASSLs. Nat. Methods 5, 673–678 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Roth, B.L. DREADDs for neuroscientists. Neuron 89, 683–694 (2016).

    CAS  Article  Google Scholar 

  33. 33

    Nguyen, E.D., Norn, C., Frimurer, T.M. & Meiler, J. Assessment and challenges of ligand docking into comparative models of G-protein-coupled receptors. PLoS One 8, e67302 (2013).

    CAS  Article  Google Scholar 

  34. 34

    Rodriguez, G.J., Yao, R., Lichtarge, O. & Wensel, T.G. Evolution-guided discovery and recoding of allosteric pathway specificity determinants in psychoactive bioamine receptors. Proc. Natl. Acad. Sci. USA 107, 7787–7792 (2010).

    CAS  Article  Google Scholar 

  35. 35

    Liu, T., Lin, Y., Wen, X., Jorissen, R.N. & Gilson, M.K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).

    CAS  Article  Google Scholar 

  36. 36

    Wang, C. et al. Structural basis for molecular recognition at serotonin receptors. Science 340, 610–614 (2013).

    CAS  Article  Google Scholar 

  37. 37

    Barth, P., Schonbrun, J. & Baker, D. Toward high-resolution prediction and design of transmembrane helical protein structures. Proc. Natl. Acad. Sci. USA 104, 15682–15687 (2007).

    CAS  Article  Google Scholar 

  38. 38

    Lemmon, G. & Meiler, J. Towards ligand docking including explicit interface water molecules. PLoS One 8, e67536 (2013).

    CAS  Article  Google Scholar 

  39. 39

    Söding, J., Biegert, A. & Lupas, A.N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244–W248 (2005).

    Article  Google Scholar 

  40. 40

    Isberg, V. et al. GPCRdb: an information system for G-protein-coupled receptors. Nucleic Acids Res. 44, D1, D356–D364 (2016).

    Article  Google Scholar 

  41. 41

    Isberg, V. et al. GPCRDB: an information system for G-protein-coupled receptors. Nucleic Acids Res. 42, D422–D425 (2014).

    CAS  Article  Google Scholar 

  42. 42

    Yarov-Yarovoy, V., Schonbrun, J. & Baker, D. Multipass membrane protein structure prediction using Rosetta. Proteins 62, 1010–1025 (2006).

    CAS  Article  Google Scholar 

  43. 43

    Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of data clusters via the Gap statistic. J. R. Statist. Soc. B 63, 411–423 (2001).

    Article  Google Scholar 

  44. 44

    Hawkins, P.C.D., Skillman, A.G., Warren, G.L., Ellingson, B.A. & Stahl, M.T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 50, 572–584 (2010).

    CAS  Article  Google Scholar 

  45. 45

    Venkatakrishnan, A.J. et al. Molecular signatures of G-protein-coupled receptors. Nature 494, 185–194 (2013).

    CAS  Article  Google Scholar 

  46. 46

    Tinberg, C.E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

    CAS  Article  Google Scholar 

  47. 47

    Chen, K.Y., Zhou, F., Fryszczyn, B.G. & Barth, P. Naturally evolved G-protein-coupled receptors adopt metastable conformations. Proc. Natl. Acad. Sci. USA 109, 13284–13289 (2012).

    CAS  Article  Google Scholar 

  48. 48

    Luo, J., Zhu, Y., Zhu, M.X. & Hu, H. Cell-based calcium assay for medium to high throughput screening of TRP channel functions using FlexStation 3. J. Vis. Exp. http://dx.doi.org/10.3791/3149 (2011).

  49. 49

    Sung, Y.M., Wilkins, A.D., Rodriguez, G.J., Wensel, T.G. & Lichtarge, O. Intramolecular allosteric communication in dopamine D2 receptor revealed by evolutionary amino acid covariation. Proc. Natl. Acad. Sci. USA 113, 3539–3544 (2016).

    CAS  Article  Google Scholar 

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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.

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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.

Corresponding author

Correspondence to Patrick Barth.

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The authors declare no competing financial interests.

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Supplementary Results, Supplementary Tables 1–6 and Supplementary Figures 1–8 (PDF 6264 kb)

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Feng, X., Ambia, J., Chen, KY. et al. Computational design of ligand-binding membrane receptors with high selectivity. Nat Chem Biol 13, 715–723 (2017). https://doi.org/10.1038/nchembio.2371

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