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

Nature Chemistry

Volumn 9, Issue 10, 2017, Pages 1005-1011

  Plattner, Nuria ^a   Doerr, Stefan ^b   De Fabritiis, Gianni ^b,c   Noé, Frank ^a  

^a FREIE UNIVERSITÄT BERLIN   (Germany)

^b UNIVERSITAT POMPEU FABRA   (Spain)

^c INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN; PROTEIN BINDING;

CHEMISTRY; KINETICS; MACROMOLECULE; MARKOV CHAIN; MOLECULAR DYNAMICS; THERMODYNAMICS;

KINETICS; MACROMOLECULAR SUBSTANCES; MARKOV CHAINS; MOLECULAR DYNAMICS SIMULATION; PROTEIN BINDING; PROTEINS; THERMODYNAMICS;

EID: 85029770667     PISSN: 17554330     EISSN: 17554349     Source Type: Journal    
DOI: 10.1038/nchem.2785     Document Type: Article

References (50)

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).
2. Doench, J. G. et al. Rational design of highly active sgrnas for crispr-cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262-1267 (2014).
3. King, N. P. et al. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336, 1171-1174 (2012).
4. Schreiber, G., Haran, G. & Zhou, H.-X. Fundamental aspects of protein-protein association kinetics. Chem. Rev. 109, 839-860 (2009).
5. Tang, C., Iwahara, J. & Clore, G. M. Visualization of transient encounter complexes in protein-protein association. Nature 444, 383-386 (2006).
6. Gabdoulline, R. R. & Wade, R. C. Simulation of the diffusional association of barnase and barstar. Biophys. J. 72, 1917-1929 (1997).
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).
8. Levy, Y., Wolynes, P. G. & Onuchic, J. N. Protein topology determines binding mechanism. Proc. Natl Acad. Sci. USA 101, 511-516 (2004).
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).
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).
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).
12. Tiwary, P. & Parrinello, M. From metadynamics to dynamics. Phys. Rev. Lett. 111, 230602 (2013).
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).
14. Prinz, J.-H. et al. Markov models of molecular kinetics: generation and validation. J. Chem. Phys. 134, 174105 (2011).
15. Lindorff-Larsen, K., Piana, S., Dror, R. O. & Shaw, D. E. How fast-folding proteins fold. Science 334, 517-520 (2011).
16. Buch, I., Giorgino, T. & De Fabritiis, G. Complete reconstruction of an enzymeinhibitor binding process by molecular dynamics simulations. Proc. Natl Acad. Sci. USA 108, 10184-10189 (2011).
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).
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).
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).
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).
21. Ahmad, M., Gu, W., Geyer, T. & Helms, V. Adhesive water networks facilitate binding of protein interfaces. Nat. Commun. 2, 261 (2011).
22. Schreiber, G. & Fersht, A. R. Rapid, electrostatically assisted association of proteins. Nat. Struct. Biol. 3, 427-431 (1996).
23. Schreiber, G. & Fersht, A. R. Interaction of barnase with its polypeptide inhibitor barstar studied by protein engineering. Biochemistry 32, 5145-5150 (1993).
24. Hartley, R. W. Directed mutagenesis and barnase-barstar recognition. Biochemistry 32, 5978-5984 (1993).
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).
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).
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).
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).
29. Sarich, M. & Schütte, C. Metastability and Markov State Models in Molecular Dynamics (Courant Lecture Notes, American Mathematical Society, 2013).
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).
31. Northrup, S. H., Allison, S. & McCammon, J. Brownian dynamics of diffusioninfluenced bimolecular reactions. J. Chem. Phys. 80, 1517-1524 (1984).
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).
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).
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).
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).
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).
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).
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).
39. Case, D. A. et al. The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668-1688 (2005).
40. Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65, 712-725 (2006).
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).
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).
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).
44. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone , and side-chain 1 and 2 dihedral angles. J. Chem. Theo. Comp. 8, 3257-3273 (2012).
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).
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).
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).
48. Molgedey, L. & Schuster, H. G. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634-3637 (1994).
49. Trendelkamp-Schroer, B., Wu, H., Paul, F. & Noé, F. Estimation and uncertainty of reversible Markov models. J. Chem. Phys. 143, 174101 (2015).
50. Noé, F. & Nüske, F. A variational approach to modeling slow processes in stochastic dynamical systems. Multiscale Model. Simul. 11, 635-655 (2013).