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.

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

Nature Chemistry volume 9, pages 1005–1011 (2017)Cite this article

Subjects

Abstract

Protein–protein association is fundamental to many life processes. However, a microscopic model describing the structures and kinetics during association and dissociation is lacking on account of the long lifetimes of associated states, which have prevented efficient sampling by direct molecular dynamics (MD) simulations. Here we demonstrate protein–protein association and dissociation in atomistic resolution for the ribonuclease barnase and its inhibitor barstar by combining adaptive high-throughput MD simulations and hidden Markov modelling. The model reveals experimentally consistent intermediate structures, energetics and kinetics on timescales from microseconds to hours. A variety of flexibly attached intermediates and misbound states funnel down to a transition state and a native basin consisting of the loosely bound near-native state and the tightly bound crystallographic state. These results offer a deeper level of insight into macromolecular recognition and our approach opens the door for understanding and manipulating a wide range of macromolecular association processes.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Simulation of protein–protein association and dissociation.
Figure 2: Kinetic model of barnase–barstar association.
Figure 3: Late steps in binding from late intermediates to the loosely bound state.
Figure 4: Native substate conformational dynamics.
Figure 5: Comparison of kinetics between wild type and E73A mutant.

References

  1. Scott, D. E., Bayly, A. R., Abell, C. & Skidmore, J. Small molecules, big targets: drug discovery faces the protein–protein interaction challenge. Nat. Rev. Drug. Discov. 15, 533–550 (2016).

    CAS  PubMed  Google Scholar 

  2. Doench, J. G. et al. Rational design of highly active sgrnas for crispr–cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. King, N. P. et al. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336, 1171–1174 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Schreiber, G., Haran, G. & Zhou, H.-X. Fundamental aspects of protein–protein association kinetics. Chem. Rev. 109, 839–860 (2009).

    CAS  PubMed  Google Scholar 

  5. Tang, C., Iwahara, J. & Clore, G. M. Visualization of transient encounter complexes in protein–protein association. Nature 444, 383–386 (2006).

    CAS  PubMed  Google Scholar 

  6. Gabdoulline, R. R. & Wade, R. C. Simulation of the diffusional association of barnase and barstar. Biophys. J. 72, 1917–1929 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Spaar, A., Dammer, C., Gabdoulline, R. R., Wade, R. C. & Helms, V. Diffusional encounter of barnase and barstar. Biophys. J. 90, 1913–1924 (2006).

    CAS  PubMed  Google Scholar 

  8. Levy, Y., Wolynes, P. G. & Onuchic, J. N. Protein topology determines binding mechanism. Proc. Natl Acad. Sci. USA 101, 511–516 (2004).

    CAS  PubMed  Google Scholar 

  9. Schluttig, J., Alamanova, D., Helms, V. & Schwarz, U. S. Dynamics of protein–protein encounter: a langevin equation approach with reaction patches. J. Chem. Phys. 129, 155106 (2008).

    PubMed  Google Scholar 

  10. Gumbart, J. C., Roux, B. & Chipot, C. Efficient determination of protein–protein standard binding free energies from first principles. J. Chem. Theory Comput. 9, 3789–3798 (2013).

    CAS  Google Scholar 

  11. Barducci, A., Bonomi, M., Prakash, M. K. & Parrinello, M. Free-energy landscape of protein oligomerization from atomistic simulations. Proc. Natl Acad. Sci. USA 110, E4708–E4713 (2013).

    CAS  PubMed  Google Scholar 

  12. Tiwary, P. & Parrinello, M. From metadynamics to dynamics. Phys. Rev. Lett. 111, 230602 (2013).

    PubMed  Google Scholar 

  13. Wu, H., Paul, F., Wehmeyer, C. & Noé, F. Multiensemble Markov models of molecular thermodynamics and kinetics. Proc. Natl Acad. Sci. USA 113, E3221–E3230 (2016).

    CAS  PubMed  Google Scholar 

  14. Prinz, J.-H. et al. Markov models of molecular kinetics: generation and validation. J. Chem. Phys. 134, 174105 (2011).

    PubMed  Google Scholar 

  15. Lindorff-Larsen, K., Piana, S., Dror, R. O. & Shaw, D. E. How fast-folding proteins fold. Science 334, 517–520 (2011).

    CAS  PubMed  Google Scholar 

  16. Buch, I., Giorgino, T. & De Fabritiis, G. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc. Natl Acad. Sci. USA 108, 10184–10189 (2011).

    CAS  PubMed  Google Scholar 

  17. Kohlhoff, K. J. et al. Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat. Chem. 6, 15–21 (2014).

    CAS  PubMed  Google Scholar 

  18. Plattner, N. & Noé, F. Protein conformational plasticity and complex ligand binding kinetics explored by atomistic simulations and Markov models. Nat. Commun. 6, 7653 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. Silva, D.-A., Bowman, G. R., Sosa-Peinado, A. & Huang, X. A role for both conformational selection and induced fit in ligand binding by the Lao protein. PLoS Comput. Biol. 7, e1002054 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Piana, S., Lindorff-Larsen, K. & Shaw, D. E. Atomistic description of the folding of a dimeric protein. J. Phys. Chem. B 117, 12935–12942 (2013).

    CAS  PubMed  Google Scholar 

  21. Ahmad, M., Gu, W., Geyer, T. & Helms, V. Adhesive water networks facilitate binding of protein interfaces. Nat. Commun. 2, 261 (2011).

    PubMed  Google Scholar 

  22. Schreiber, G. & Fersht, A. R. Rapid, electrostatically assisted association of proteins. Nat. Struct. Biol. 3, 427–431 (1996).

    CAS  PubMed  Google Scholar 

  23. Schreiber, G. & Fersht, A. R. Interaction of barnase with its polypeptide inhibitor barstar studied by protein engineering. Biochemistry 32, 5145–5150 (1993).

    CAS  PubMed  Google Scholar 

  24. Hartley, R. W. Directed mutagenesis and barnase–barstar recognition. Biochemistry 32, 5978–5984 (1993).

    CAS  PubMed  Google Scholar 

  25. Doerr, S. & Fabritiis, G. D. On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations. J. Chem. Theory Comput. 10, 2064–2069 (2014).

    CAS  PubMed  Google Scholar 

  26. Bowman, G. R., Ensign, D. L. & Pande, V. S. Enhanced modeling via network theory: adaptive sampling of Markov state models. J. Chem. Theory Comput. 6, 787–794 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Preto, J. & Clementi, C. Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics. Phys. Chem. Chem. Phys. 16, 19181–19191 (2014).

    CAS  PubMed  Google Scholar 

  28. Bowman, G. R., Pande, V. S. & Noé, F. (eds.) An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (Vol. 797 of Advances in Experimental Medicine and Biology, Springer, 2014).

    Google Scholar 

  29. Sarich, M. & Schütte, C. Metastability and Markov State Models in Molecular Dynamics (Courant Lecture Notes, American Mathematical Society, 2013).

    Google Scholar 

  30. Noé, F., Wu, H., Prinz, J.-H. & Plattner, N. Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules. J. Chem. Phys. 139, 184114 (2013).

    PubMed  Google Scholar 

  31. Northrup, S. H., Allison, S. & McCammon, J. Brownian dynamics of diffusion-influenced bimolecular reactions. J. Chem. Phys. 80, 1517–1524 (1984).

    CAS  Google Scholar 

  32. Schreiber, G. & Fersht, A. R. Energetics of protein–protein interactions: analysis of the barnase–barstar interface by single mutations and double mutant cycles. J. Mol. Biol. 248, 478–486 (1995).

    CAS  PubMed  Google Scholar 

  33. Matysiak, S. & Clementi, C. Optimal combination of theory and experiment for the characterization of the protein folding landscape of S6: how far can a minimalist model go? J. Mol. Biol. 343, 235–248 (2004).

    CAS  PubMed  Google Scholar 

  34. Frisch, C., Fersht, A. R. & Schreiber, G. Experimental assignment of the structure of the transition state for the association of barnase and barstar. J. Mol. Biol. 308, 69–77 (2001).

    CAS  PubMed  Google Scholar 

  35. Harel, M., Cohen, M. & Schreiber, G. On the dynamic nature of the transition state for protein–protein association as determined by double-mutant cycle analysis and simulation. J. Mol. Biol. 371, 180–196 (2007).

    CAS  PubMed  Google Scholar 

  36. Chung, H. S., Louis, J. M. & Eaton, W. A. Single-molecule fluorescence experiments determine protein folding transition path times. Science 335, 981–984 (2012).

    CAS  PubMed  Google Scholar 

  37. Anunciado, D., Dhar, A., Gruebele, M. & Baranger, A. M. Multistep kinetics of the U1A–SL2 RNA complex dissociation. J. Mol. Biol. 408, 896–908 (2011).

    CAS  PubMed  Google Scholar 

  38. Buckle, A. M., Schreiber, G. & Fersht, A. R. Protein–protein recognition: crystal structural analysis of a barnase–barstar complex at 2.0-Å resolution. Biochemistry 33, 8878–8889 (1994).

    CAS  PubMed  Google Scholar 

  39. Case, D. A. et al. The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668–1688 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65, 712–725 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    CAS  Google Scholar 

  42. Harvey, M. J., Giupponi, G. & De Fabritiis, G. ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale. J. Chem. Theory Comp. 5, 1632–1639 (2009).

    CAS  Google Scholar 

  43. Buch, I., Harvey, M. J., Giorgino, T., Anderson, D. P. & De Fabritiis, G. High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. Model. 50, 397–403 (2010).

    CAS  PubMed  Google Scholar 

  44. 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. Theo. Comp. 8, 3257–3273 (2012).

    CAS  Google Scholar 

  45. Doerr, S., Harvey, M. J., Noé, F. & Fabritiis, G. D. HTMD: high-throughput molecular dynamics for molecular discovery. J. Chem. Theory Comput. 12, 1845–1852 (2016).

    CAS  PubMed  Google Scholar 

  46. Scherer, M. K. et al. PyEMMA 2: a software package for estimation, validation and analysis of Markov models. J. Chem. Theory Comput. 11, 5525–5542 (2015).

    CAS  PubMed  Google Scholar 

  47. Perez-Hernandez, G., Paul, F., Giogino, T., De Fabritiis, G. & Noé, F. Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 139, 015102 (2013).

    PubMed  Google Scholar 

  48. Molgedey, L. & Schuster, H. G. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634–3637 (1994).

    CAS  PubMed  Google Scholar 

  49. Trendelkamp-Schroer, B., Wu, H., Paul, F. & Noé, F. Estimation and uncertainty of reversible Markov models. J. Chem. Phys. 143, 174101 (2015).

    PubMed  Google Scholar 

  50. Noé, F. & Nüske, F. A variational approach to modeling slow processes in stochastic dynamical systems. Multiscale Model. Simul. 11, 635–655 (2013).

    Google Scholar 

Download references

Acknowledgements

We are grateful to the following researchers for inspiring discussions and extensive feedback on the manuscript: J. Clark, C. Clementi, O. Daumke, W. A. Eaton, M. Gruebele, K. Lindorff-Larsen, S. Olsson, F. Paul, B. Roux, U. Schwarz, S. Sukenik, R. Wade and T. Weikl. Funding is acknowledged from the European Commission (ERC StG pcCells and PRACE project 2014102337 to F.N.), Deutsche Forschungsgemeinschaft (NO 825/3-1, SFB 958/A4, SFB 740/D7 to F.N.), Einstein Foundation Berlin (project SoOPic to N.P.), MINECO (BIO2014-53095-P) and FEDER (to G.D.F.), Acellera Ltd. (to S.D.). We thank the volunteers of GPUGRID for donating computing time for simulations.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, Berlin, 14195, Germany

    Nuria Plattner & Frank Noé

  2. Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C/ Doctor Aiguader 88, Barcelona, 08003, Spain

    Stefan Doerr & Gianni De Fabritiis

  3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona, 08010, Spain

    Gianni De Fabritiis

Authors
  1. Nuria Plattner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Doerr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gianni De Fabritiis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank Noé
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.P., S.D., G.D.F and F.N. designed research and developed/implemented software. N.P. and S.D. conducted simulations. N.P., S.D. and F.N. analysed simulations. F.N. developed methods. N.P. and F.N. wrote the paper.

Corresponding authors

Correspondence to Nuria Plattner or Frank Noé.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary information (PDF 4823 kb)

Supplementary information

Supplementary Movie 1 (MP4 1533 kb)

Supplementary information

Supplementary Movie 2 (MP4 1352 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Plattner, N., Doerr, S., De Fabritiis, G. et al. Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nature Chem 9, 1005–1011 (2017). https://doi.org/10.1038/nchem.2785

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchem.2785

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