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:

Probing the transition state in enzyme catalysis by high-pressure NMR dynamics

Nature Catalysis volume 2, pages 726–734 (2019)Cite this article

Subjects

Abstract

Protein conformational changes are often essential for enzyme catalysis and, in several cases, are shown to be the limiting factor for overall catalytic speed. However, a structural understanding of the corresponding transition states, needed to rationalize the kinetics, remains obscure due to their transient nature. Here, we determine the transition state ensemble of the rate-limiting conformational transition in the enzyme adenylate kinase through a synergistic approach between experimental high-pressure NMR relaxation during catalysis and molecular dynamics simulations. By comparing homologous kinases that evolved under ambient or high pressure in the deep sea, we detail transition state ensembles that differ in solvation as directly measured by the pressure dependence of catalysis. Capturing transition state ensembles begins to complete the catalytic energy landscape that is generally characterized by the structures of all intermediates and the frequencies of transitions among them.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Investigating the pressure dependence of turnover for mesoAdk versus piezoAdk to gain insight into transition states.
Fig. 2: The dynamic and structural differences of Adk under pressure.
Fig. 3: The putative TSE identified from differential solvation in TMD simulations.
Fig. 4: Triple mutation induces full pressure activation in mesoAdk.

Similar content being viewed by others

Data availability

Structure factors and refined model of piezoAdk has been deposited in the PDB under the accession code 4K46. The data that support the plots within this paper and other findings of this study are available from the corresponding author on reasonable request.

Code availability

In-house Python scripts are available from the corresponding author on request.

References

  1. Boehr, D. D., McElheny, D., Dyson, H. J. & Wright, P. E. The dynamic energy landscape of dihydrofolate reductase catalysis. Science 313, 1638–1642 (2006).

    Article  CAS  Google Scholar 

  2. Sekhar, A. & Kay, L. E. NMR paves the way for atomic level descriptions of sparsely populated, transiently formed biomolecular conformers. Proc. Natl Acad. Sci. USA 110, 12867–12874 (2013).

    Article  CAS  Google Scholar 

  3. Kornev, A. P. & Taylor, S. S. Dynamics-driven allostery in protein kinases. Trends Biochem. Sci. 40, 628–647 (2015).

    Article  CAS  Google Scholar 

  4. Baldwin, A. J. & Kay, L. E. NMR spectroscopy brings invisible protein states into focus. Nat. Chem. Biol. 5, 808–814 (2009).

    Article  CAS  Google Scholar 

  5. Schramm, V. L. Enzymatic transition states, transition-state analogs, dynamics, thermodynamics, and lifetimes. Annu. Rev. Biochem. 80, 703–732 (2011).

    Article  CAS  Google Scholar 

  6. Laidler, K. J. & King, M. C. The development of transition-state theory. J. Phys. Chem. 87, 2657–2664 (1983).

    Article  CAS  Google Scholar 

  7. Royer, C. A. The nature of the transition state ensemble and the mechanisms of protein folding: a review. Arch. Biochem. Biophys. 469, 34–45 (2008).

    Article  CAS  Google Scholar 

  8. Zhang, Y. et al. High pressure ZZ-exchange NMR reveals key features of protein folding transition states. J. Am. Chem. Soc. 138, 15260–15266 (2016).

    Article  CAS  Google Scholar 

  9. Korzhnev, D. M. et al. Probing the transition state ensemble of a protein folding reaction by pressure-dependent NMR relaxation dispersion. J. Am. Chem. Soc. 128, 5262–5269 (2006).

    Article  CAS  Google Scholar 

  10. Akasaka, K. Probing conformational fluctuation of proteins by pressure perturbation. Chem. Rev. 106, 1814–1835 (2006).

    Article  CAS  Google Scholar 

  11. Roche, J. et al. Effect of internal cavities on folding rates and routes revealed by real-time pressure-jump NMR spectroscopy. J. Am. Chem. Soc. 135, 14610–14618 (2013).

    Article  CAS  Google Scholar 

  12. Royer, C. A. Revisiting volume changes in pressure-induced protein unfolding. Biochim. Biophys. Acta 1595, 201–209 (2002).

    Article  CAS  Google Scholar 

  13. Mitra, L., Smolin, N., Ravindra, R., Royer, C. & Winter, R. Pressure perturbation calorimetric studies of the solvation properties and the thermal unfolding of proteins in solution–experiments and theoretical interpretation. Phys. Chem. Chem. Phys. 8, 1249–1265 (2006).

    Article  CAS  Google Scholar 

  14. Vezzi, A. et al. Life at depth: Photobacterium profundum genome sequence and expression analysis. Science 307, 1459–1461 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Masson, P. & Balny, C. Linear and non-linear pressure dependence of enzyme catalytic parameters. Biochim. Biophys. Acta 1724, 440–450 (2005).

    Article  CAS  Google Scholar 

  17. Hay, S. et al. Are the catalytic properties of enzymes from piezophilic organisms pressure adapted? Chembiochem 10, 2348–2353 (2009).

    Article  CAS  Google Scholar 

  18. Hay, S., Sutcliffe, M. J. & Scrutton, N. S. Promoting motions in enzyme catalysis probed by pressure studies of kinetic isotope effects. Proc. Natl Acad. Sci. USA 104, 507–512 (2007).

    Article  CAS  Google Scholar 

  19. Loria, J. P., Rance, M. & Palmer, A. G. III. A TROSY CPMG sequence for characterizing chemical exchange in large proteins. J. Biomol. NMR 15, 151–155 (1999).

    Article  CAS  Google Scholar 

  20. Berry, M. B., Bae, E., Bilderback, T. R., Glaser, M. & Phillips, G. N. Jr Crystal structure of ADP/AMP complex of Escherichia coli adenylate kinase. Proteins 62, 555–556 (2006).

    Article  CAS  Google Scholar 

  21. Muller, C. W. & Schulz, G. E. Structure of the complex between adenylate kinase from Escherichia coli and the inhibitor Ap5a refined at 1.9 a resolution—a model for a catalytic transition-state. J. Mol. Biol. 224, 159–177 (1992).

    Article  CAS  Google Scholar 

  22. Schlitter, J., Engels, M. & Kruger, P. Targeted molecular dynamics: a new approach for searching pathways of conformational transitions. J. Mol. Graph 12, 84–89 (1994).

    Article  CAS  Google Scholar 

  23. Dill, K. A. Dominant forces in protein folding. Biochemistry 29, 7133–7155 (1990).

    Article  CAS  Google Scholar 

  24. Li, D., Liu, M. S. & Ji, B. Mapping the dynamics landscape of conformational transitions in enzyme: the adenylate kinase case. Biophys. J. 109, 647–660 (2015).

    Article  CAS  Google Scholar 

  25. Arora, K. & Brooks, C. L. 3rd Large-scale allosteric conformational transitions of adenylate kinase appear to involve a population-shift mechanism. Proc. Natl Acad. Sci. USA 104, 18496–18501 (2007).

    Article  CAS  Google Scholar 

  26. Lee, J., Joo, K., Brooks, B. R. & Lee, J. The atomistic mechanism of conformational transition of adenylate kinase investigated by Lorentzian structure-based potential. J. Chem. Theory Comput. 11, 3211–3224 (2015).

    Article  CAS  Google Scholar 

  27. Unan, H., Yildirim, A. & Tekpinar, M. Opening mechanism of adenylate kinase can vary according to selected molecular dynamics force field. J. Comput. Aided Mol. Des. 29, 655–665 (2015).

    Article  CAS  Google Scholar 

  28. Wang, Y., Gan, L., Wang, E. & Wang, J. Exploring the dynamic functional landscape of adenylate kinase modulated by substrates. J. Chem. Theory Comput. 9, 84–95 (2013).

    Article  Google Scholar 

  29. Charlier, C. et al. Study of protein folding under native conditions by rapidly switching the hydrostatic pressure inside an NMR sample cell. Proc. Natl Acad. Sci. USA 115, E4169–E4178 (2018).

    Article  Google Scholar 

  30. Kremer, W. et al. Pulsed pressure perturbations, an extra dimension in NMR spectroscopy of proteins. J. Am. Chem. Soc. 133, 13646–13651 (2011).

    Article  CAS  Google Scholar 

  31. Hay, S., Johannissen, L. O., Hothi, P., Sutcliffe, M. J. & Scrutton, N. S. Pressure effects on enzyme-catalyzed quantum tunneling events arise from protein-specific structural and dynamic changes. J. Am. Chem. Soc. 134, 9749–9754 (2012).

    Article  CAS  Google Scholar 

  32. Pudney, C. R. et al. Parallel pathways and free-energy landscapes for enzymatic hydride transfer probed by hydrostatic pressure. Chembiochem 10, 1379–1384 (2009).

    Article  CAS  Google Scholar 

  33. Kalbitzer, H. R. et al. Intrinsic allosteric inhibition of signaling proteins by targeting rare interaction states detected by high-pressure NMR spectroscopy. Angew. Chem. Int. Ed. 52, 14242–14246 (2013).

    Article  CAS  Google Scholar 

  34. Fersht, A. R., Matouschek, A. & Serrano, L. The folding of an enzyme. I. Theory of protein engineering analysis of stability and pathway of protein folding. J. Mol. Biol. 224, 771–782 (1992).

    Article  CAS  Google Scholar 

  35. Mitra, L. et al. V i-value analysis: a pressure-based method for mapping the folding transition state ensemble of proteins. J. Am. Chem. Soc. 129, 14108–14109 (2007).

    Article  CAS  Google Scholar 

  36. Theobald, D. L. & Wuttke, D. S. Accurate structural correlations from maximum likelihood superpositions. PLoS Comput. Biol. 4, e43 (2008).

    Article  Google Scholar 

  37. Wolf-Watz, M. et al. Linkage between dynamics and catalysis in a thermophilic mesophilic enzyme pair. Nat. Struct. Mol. Biol. 11, 945–949 (2004).

    Article  CAS  Google Scholar 

  38. Peterson, R. W. & Wand, A. J. Self contained high pressure cell, apparatus and procedure for the preparation of encapsulated proteins dissolved in low viscosity fluids for NMR spectroscopy. Rev. Sci. Instrum. 76, 1–7 (2005).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  40. Vranken, W. F. et al. The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 59, 687–696 (2005).

    Article  CAS  Google Scholar 

  41. Ahlner, A., Carlsson, M., Jonsson, B. H. & Lundstrom, P. PINT: a software for integration of peak volumes and extraction of relaxation rates. J. Biomol. NMR 56, 191–202 (2013).

    Article  CAS  Google Scholar 

  42. Carver, J. P. & Richards, R. E. General two-site solution for chemical exchange produced dependence of T 2 upon Carr–Purcell pulse separation. J. Magn. Reson. 6, 89 (1972).

    CAS  Google Scholar 

  43. Newville, M., Stensitzki, T., Allen, D. B. & Ingargiola, A. A. LMFIT: non-linear least-square minimization and curve-fitting for Python (Zenodo, 2014); http:/lmfit.github.io/lmfit-py

  44. Battye, T. G. G., Kontogiannis, L., Johnson, O., Powell, H. R. & Leslie, A. G. W. iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM. Acta Cryst. D 67, 271–281 (2011).

    Article  CAS  Google Scholar 

  45. Winn, M. D. et al. Overview of the CCP4 suite and current developments. Acta Cryst. D 67, 235–242 (2011).

    Article  CAS  Google Scholar 

  46. Mccoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    Article  CAS  Google Scholar 

  47. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Cryst. D 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  48. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Cryst. D 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  49. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25 (2015).

    Article  Google Scholar 

  50. Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C. & Bussi, G. PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014).

    Article  CAS  Google Scholar 

  51. Biasini, M. et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252–W258 (2014).

    Article  CAS  Google Scholar 

  52. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).

    Article  CAS  Google Scholar 

  53. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    Article  CAS  Google Scholar 

  54. Nosé, S. A molecular dynamics method for simulations in the canonical ensemble. Mol. Phys. 52, 255–268 (2006).

    Article  Google Scholar 

  55. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985).

    Article  CAS  Google Scholar 

  56. Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl Phys. 52, 7182–7190 (1981).

    Article  CAS  Google Scholar 

  57. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 27–38 (1996).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful for D. V. Pachov’s preliminary TMD simulations on mesoAdk and piezoAdk and discussions, F. Pontiggia with guidance for the TMD simulations and J. Wand and R. Peterson for the initial exploratory pressure NMR experiments. Finally, we thank D. Bartlett for generously gifting P. profundom genomic DNA. This work was supported by the Howard Hughes Medical Institute, the Office of Basic Energy Sciences, the Catalysis Science Program, the US Department of Energy (grant no. DE-FG02-05ER15699 to D.K. and NIH; grant no. GM100966 to M.F.H and D.K). R.O. was supported as an HHMI fellow of the Damon Runyon Cancer Research Foundation (grant no. DRG-2114-12). Computational resources were provided by NSF XSEDE computing resources (grant no. TG-MCB090163) and the Brandeis HPCC, which is partially supported by grant no. DMR-1420382. We thank the Advanced Light Source (ALS) for beamline use. The Berkeley Center for Structural Biology is supported in part by the National Institutes of Health, National Institute of General Medical Sciences and the HHMI. The ALS is supported by the director (Office of Science, Office of Basic Energy Sciences) of the US Department of Energy under contract no. DE-AC02-05CH11231.

Author information

Author notes

  1. S. Jordan Kerns

    Present address: Emulate Inc., Boston, MA, USA

  2. These authors contributed equally: John B. Stiller, S. Jordan Kerns.

Authors and Affiliations

  1. Department of Biochemistry and Howard Hughes Medical Institute, Brandeis University, Waltham, MA, USA

    John B. Stiller, S. Jordan Kerns, Marc Hoemberger, Young-Jin Cho, Renee Otten & Dorothee Kern

  2. Department of Physics, Brandeis University, Waltham, MA, USA

    Michael F. Hagan

Authors
  1. John B. Stiller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Jordan Kerns
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marc Hoemberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Young-Jin Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Renee Otten
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael F. Hagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dorothee Kern
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.K. conceived the project idea. D.K., S.J.K, and M.F.H. developed the research plan and experimental strategy. S.J.K performed pressure-variable kinetic experiments, which included purifying the wild-type and mutant proteins, designing and assembling the high-pressure spectrophotometer, performing pressure-variable experiments and analysing the results. S.J.K, with help from R.O., also performed all NMR experiments. J.B.S, S.J.K. and R.O. performed the NMR data analyses, fitting and interpretation. Y.J.-C. purified piezoAdk for crystallization and solved the X-ray structure. M.H. performed the TMD simulations. M.H. and J.B.S analysed the TMD simulations with assistance by M.F.H. and D.K. S.J.K. and D.K. designed mutations on the basis of experimental data and TMD simulations. All authors discussed the results, which led to the overall scientific findings. J.B.S. and D.K wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Dorothee Kern.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–10, Supplementary Tables 1 and 2, Supplementary Note 1 and Supplementary References

Reporting Summary

Supplementary Data 1

Triple mutant mesoAdk final structure

Supplementary Data 2

MesoAdk final structure

Supplementary Data 3

MesoAdk initial structure

Supplementary Data 4

PiezoAdk final structure

Supplementary Data 5

PiezoAdk initial structure

Supplementary Data 6

Triple mutant mesoAdk initial structure

Supplementary Video 1

Hydration of AMP-lid interface in piezoAdk

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stiller, J.B., Jordan Kerns, S., Hoemberger, M. et al. Probing the transition state in enzyme catalysis by high-pressure NMR dynamics. Nat Catal 2, 726–734 (2019). https://doi.org/10.1038/s41929-019-0307-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41929-019-0307-6

This article is cited by

Search

Advanced 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