Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Rational design of proteins that exchange on functional timescales

Abstract

Proteins are intrinsically dynamic molecules that can exchange between multiple conformational states, enabling them to carry out complex molecular processes with extreme precision and efficiency. Attempts to design novel proteins with tailored functions have mostly failed to yield efficiencies matching those found in nature because standard methods do not allow the design of exchange between necessary conformational states on a functionally relevant timescale. Here we developed a broadly applicable computational method to engineer protein dynamics that we term meta-multistate design. We used this methodology to design spontaneous exchange between two novel conformations introduced into the global fold of Streptococcal protein G domain β1. The designed proteins, named DANCERs, for dynamic and native conformational exchangers, are stably folded and switch between predicted conformational states on the millisecond timescale. The successful introduction of defined dynamics on functional timescales opens the door to new applications requiring a protein to spontaneously access multiple conformational states.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The meta-MSD framework for design of conformational exchange.
Figure 2: DANCER variants undergo conformational exchange.
Figure 3: Structural analysis of DANCER-1 and DANCER-3.

Similar content being viewed by others

Accession codes

Primary accessions

Biological Magnetic Resonance Data Bank

Protein Data Bank

Referenced accessions

Protein Data Bank

References

  1. Dahiyat, B.I. & Mayo, S.L. De novo protein design: fully automated sequence selection. Science 278, 82–87 (1997).

    Article  CAS  PubMed  Google Scholar 

  2. Malakauskas, S.M. & Mayo, S.L. Design, structure and stability of a hyperthermophilic protein variant. Nat. Struct. Biol. 5, 470–475 (1998).

    Article  CAS  PubMed  Google Scholar 

  3. Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).

    Article  CAS  PubMed  Google Scholar 

  4. Koga, N. et al. Principles for designing ideal protein structures. Nature 491, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Marcos, E. et al. Principles for designing proteins with cavities formed by curved β sheets. Science 355, 201–206 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ambroggio, X.I. & Kuhlman, B. Computational design of a single amino acid sequence that can switch between two distinct protein folds. J. Am. Chem. Soc. 128, 1154–1161 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Jiang, L. et al. De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Privett, H.K. et al. Iterative approach to computational enzyme design. Proc. Natl. Acad. Sci. USA 109, 3790–3795 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Bhabha, G. et al. A dynamic knockout reveals that conformational fluctuations influence the chemical step of enzyme catalysis. Science 332, 234–238 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kerns, S.J. et al. The energy landscape of adenylate kinase during catalysis. Nat. Struct. Mol. Biol. 22, 124–131 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tzeng, S.R. & Kalodimos, C.G. Dynamic activation of an allosteric regulatory protein. Nature 462, 368–372 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Tuinstra, R.L. et al. Interconversion between two unrelated protein folds in the lymphotactin native state. Proc. Natl. Acad. Sci. USA 105, 5057–5062 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Allen, B.D., Nisthal, A. & Mayo, S.L. Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles. Proc. Natl. Acad. Sci. USA 107, 19838–19843 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Davey, J.A. & Chica, R.A. Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles. Proteins 82, 771–784 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Davey, J.A., Damry, A.M., Euler, C.K., Goto, N.K. & Chica, R.A. Prediction of stable globular proteins using negative design with non-native backbone ensembles. Structure 23, 2011–2021 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Henzler-Wildman, K. & Kern, D. Dynamic personalities of proteins. Nature 450, 964–972 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Crowhurst, K.A. & Mayo, S.L. NMR-detected conformational exchange observed in a computationally designed variant of protein Gβ1. Protein Eng. Des. Sel. 21, 577–587 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Bouvignies, G. et al. Identification of slow correlated motions in proteins using residual dipolar and hydrogen-bond scalar couplings. Proc. Natl. Acad. Sci. USA 102, 13885–13890 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Derrick, J.P. & Wigley, D.B. The third IgG-binding domain from streptococcal protein G. An analysis by X-ray crystallography of the structure alone and in a complex with Fab. J. Mol. Biol. 243, 906–918 (1994).

    Article  CAS  PubMed  Google Scholar 

  20. Gallagher, T., Alexander, P., Bryan, P. & Gilliland, G.L. Two crystal structures of the B1 immunoglobulin-binding domain of streptococcal protein G and comparison with NMR. Biochemistry 33, 4721–4729 (1994).

    Article  CAS  PubMed  Google Scholar 

  21. Gronenborn, A.M. et al. A novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G. Science 253, 657–661 (1991).

    Article  CAS  PubMed  Google Scholar 

  22. Wylie, B.J. et al. Ultrahigh resolution protein structures using NMR chemical shift tensors. Proc. Natl. Acad. Sci. USA 108, 16974–16979 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tomlinson, J.H., Green, V.L., Baker, P.J. & Williamson, M.P. Structural origins of pH-dependent chemical shifts in the B1 domain of protein G. Proteins 78, 3000–3016 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. Wilton, D.J., Tunnicliffe, R.B., Kamatari, Y.O., Akasaka, K. & Williamson, M.P. Pressure-induced changes in the solution structure of the GB1 domain of protein G. Proteins 71, 1432–1440 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Strop, P., Marinescu, A.M. & Mayo, S.L. Structure of a protein G helix variant suggests the importance of helix propensity and helix dipole interactions in protein design. Protein Sci. 9, 1391–1394 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Saio, T., Ogura, K., Yokochi, M., Kobashigawa, Y. & Inagaki, F. Two-point anchoring of a lanthanide-binding peptide to a target protein enhances the paramagnetic anisotropic effect. J. Biomol. NMR 44, 157–166 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Jee, J., Ishima, R. & Gronenborn, A.M. Characterization of specific protein association by 15N CPMG relaxation dispersion NMR: the GB1(A34F) monomer-dimer equilibrium. J. Phys. Chem. B 112, 6008–6012 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Kuszewski, J., Gronenborn, A.M. & Clore, G.M. Improving the packing and accuracy of NMR structures with a pseudopotential for the radius of gyration. J. Am. Chem. Soc. 121, 2337–2338 (1999).

    Article  CAS  Google Scholar 

  29. Wei, G., Xi, W., Nussinov, R. & Ma, B. Protein ensembles: how does nature harness thermodynamic fluctuations for life? The diverse functional roles of conformational ensembles in the cell. Chem. Rev. 116, 6516–6551 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Davey, J.A. & Chica, R.A. Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design. Protein Sci. 24, 545–560 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Davey, J.A. & Chica, R.A. Multistate computational protein design with backbone ensembles. Methods Mol. Biol. 1529, 161–179 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Myers, J.K., Pace, C.N. & Scholtz, J.M. Denaturant m values and heat capacity changes: relation to changes in accessible surface areas of protein unfolding. Protein Sci. 4, 2138–2148 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kleckner, I.R. & Foster, M.P. An introduction to NMR-based approaches for measuring protein dynamics. Biochim. Biophys. Acta 1814, 942–968 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Kellogg, E.H., Leaver-Fay, A. & Baker, D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 79, 830–838 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. Reeve, S.M. et al. Protein design algorithms predict viable resistance to an experimental antifolate. Proc. Natl. Acad. Sci. USA 112, 749–754 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Roberts, K.E., Cushing, P.R., Boisguerin, P., Madden, D.R. & Donald, B.R. Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity. PLoS Comput. Biol. 8, e1002477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bouvignies, G. et al. Solution structure of a minor and transiently formed state of a T4 lysozyme mutant. Nature 477, 111–114 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jee, J., Byeon, I.J., Louis, J.M. & Gronenborn, A.M. The point mutation A34F causes dimerization of GB1. Proteins 71, 1420–1431 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Campbell, E. et al. The role of protein dynamics in the evolution of new enzyme function. Nat. Chem. Biol. 12, 944–950 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Labute, P. Protonate3D: assignment of ionization states and hydrogen coordinates to macromolecular structures. Proteins 75, 187–205 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Davis, I.W., Arendall, W.B. III., Richardson, D.C. & Richardson, J.S. The backrub motion: how protein backbone shrugs when a sidechain dances. Structure 14, 265–274 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Lauck, F., Smith, C.A., Friedland, G.F., Humphris, E.L. & Kortemme, T. RosettaBackrub--a web server for flexible backbone protein structure modeling and design. Nucleic Acids Res. 38, W569–W575 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Nash, S.G. A survey of truncated-Newton methods. J. Comput. Appl. Math. 124, 45–59 (2000).

    Article  Google Scholar 

  45. Wang, J., Cieplak, P. & Kollman, P.A. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem. 21, 1049–1074 (2000).

    Article  CAS  Google Scholar 

  46. Chica, R.A., Moore, M.M., Allen, B.D. & Mayo, S.L. Generation of longer emission wavelength red fluorescent proteins using computationally designed libraries. Proc. Natl. Acad. Sci. USA 107, 20257–20262 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Allen, B.D. & Mayo, S.L. Dramatic performance enhancements for the FASTER optimization algorithm. J. Comput. Chem. 27, 1071–1075 (2006).

    Article  CAS  PubMed  Google Scholar 

  48. Allen, B.D. & Mayo, S.L. An efficient algorithm for multistate protein design based on FASTER. J. Comput. Chem. 31, 904–916 (2010).

    CAS  PubMed  Google Scholar 

  49. Dunbrack, R.L. Jr. & Cohen, F.E. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 6, 1661–1681 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mayo, S.L., Olafson, B.D. & Goddard, W.A. Dreiding: a generic force field for molecular simulations. J. Phys. Chem. 94, 8897–8909 (1990).

    Article  CAS  Google Scholar 

  51. Lazaridis, T. & Karplus, M. Discrimination of the native from misfolded protein models with an energy function including implicit solvation. J. Mol. Biol. 288, 477–487 (1999).

    Article  CAS  PubMed  Google Scholar 

  52. Koepf, E.K., Petrassi, H.M., Sudol, M. & Kelly, J.W. WW: an isolated three-stranded antiparallel beta-sheet domain that unfolds and refolds reversibly; evidence for a structured hydrophobic cluster in urea and GdnHCl and a disordered thermal unfolded state. Protein Sci. 8, 841–853 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Delaglio, F. et al. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).

    Article  CAS  PubMed  Google Scholar 

  54. Johnson, B.A. & Blevins, R.A. NMR view: a computer program for the visualization and analysis of NMR data. J. Biomol. NMR 4, 603–614 (1994).

    CAS  PubMed  Google Scholar 

  55. Wishart, D.S., Sykes, B.D. & Richards, F.M. The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy. Biochemistry 31, 1647–1651 (1992).

    Article  CAS  PubMed  Google Scholar 

  56. Farrow, N.A., Zhang, O., Forman-Kay, J.D. & Kay, L.E. A heteronuclear correlation experiment for simultaneous determination of 15N longitudinal decay and chemical exchange rates of systems in slow equilibrium. J. Biomol. NMR 4, 727–734 (1994).

    Article  CAS  PubMed  Google Scholar 

  57. Shen, Y., Delaglio, F., Cornilescu, G. & Bax, A. TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J. Biomol. NMR 44, 213–223 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Güntert, P. in Protein NMR Techniques (ed. Downing, A.K.) 353–378 (Humana Press, 2004).

Download references

Acknowledgements

R.A.C. acknowledges an Early Researcher Award from the Ontario Ministry of Economic Development & Innovation (ER14-10-139), and grants from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2016-04831) and the Canada Foundation for Innovation (26503). N.K.G. acknowledges a grant from NSERC (RGPIN-2011-20298378). J.A.D. is the recipient of an Ontario Graduate Scholarship and A.M.D. is the recipient of a NSERC postgraduate scholarship. We acknowledge G. Facey, Y. Aubin, and S. Sauvé for assistance with NMR experiments, as well as Y. Mou for helpful discussions.

Author information

Authors and Affiliations

Authors

Contributions

J.A.D. performed all computational experiments. A.M.D. and J.A.D. performed biophysical characterization experiments. A.M.D. performed all NMR experiments. N.K.G. and A.M.D. designed NMR experiments and analyzed data. J.A.D. and R.A.C. conceived the project, designed computational experiments and analyzed data. All authors wrote the manuscript.

Corresponding authors

Correspondence to Natalie K Goto or Roberto A Chica.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Tables 1–3, Supplementary Figures 1–12 (PDF 4157 kb)

Life Sciences Reporting Summary (PDF 129 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Davey, J., Damry, A., Goto, N. et al. Rational design of proteins that exchange on functional timescales. Nat Chem Biol 13, 1280–1285 (2017). https://doi.org/10.1038/nchembio.2503

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.2503

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing