Protein Folding Dynamics: Bridging the Gap between Theory and Experiment
- Fabio Pietrucci (Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland)
- Ruhong Zhou (IBM Watson Research Center, Yorktown Heights, USA and Columbia University, New York, USA)
Recently knowledge-based methods for the in-silico prediction of protein structure reached a considerable success, in part due to the growing wealth of experimentally resolved protein structures. In the future these methods may further replace expensive structural biology experiments leading to the efficient exploitation of genomes in drug discovery. Physics-based methods for the de-novo prediction of the native state are instead still very expensive: e.g. all-atom simulations are applied just to peptides and few small proteins. However, the latter approaches have the major advantage of providing atomic-scale information on the dynamics of the folding process [1-3].
In the last years, the timescales reachable by accurate simulations enlarged considerably (thanks e.g. to distributed computing , enhanced sampling techniques [5-8], specialized hardware , etc.), while those probed by experiments reduced (fast probes [10-14], synthesis of fast folding peptides [15-16], etc.). Nowadays the two timescales start to overlap sizably, with a consequent great advance in the comprehension of folding mechanisms [16-20]. It emerges that proteins can fold through a complicated network of pathways including several intermediates on- or off-pathway [21-23]. Unfolded and intermediate states are often characterized by structural heterogeneity. The prevailing pathway depends on conditions like pH, denaturants, temperature, and is affected also by mutations or ligand binding. Even when simple exponential kinetics is observed, it does not necessarily imply a simple one-route two-state folding pathway . Nanoscale dewetting (water drying) can also play a significant role in the protein folding kinetics. 
Simulations can help answering the relevant open questions: How the amino acid sequence determines the native structure and folding pathways? In which precise way the structural heterogeneity reduces during folding (i.e., what is the shape of the folding funnel)? What are the physical driving forces of folding? To which extent the existing simplified models of folding mechanisms are realistic? To answer these questions, simulations face challenges: How to sample efficiently the space of protein conformations? How to extract relevant information from the huge amount of data stored in atomic trajectories? How to compare the complex kinetic schemes emerging from simulations with the limited information obtained from experiments? An emerging paradigm for the interpretation of protein folding dynamics is that of Markovian master equations [4, 8, 24]. These approaches provide a coarse-grained visualization of folding pathways as a sequence of transitions among intermediate states, and in some cases they can be validated by the most advanced experimental probes. Thanks to the combined use of theories, experiments, and simulations, the features of the free-energy landscapes through which proteins fold are getting clearer: insight is emerging about the effective size and heterogeneity of the conformational space, the nature of the unfolded ensemble, and the reaction coordinates which drive folding [1, 25-33].
1. Dill, K.A., et al., The protein folding problem. Annual Review of Biophysics, 2008. 37: p. 289-316.
2. Bolhuis, P.G., Two-state protein folding kinetics through all-atom molecular dynamics based sampling. Frontiers in Bioscience, 2009. 14: p. 2801-2828.
3. Karplus, M. and J.A. McCammon, Molecular dynamics simulations of biomolecules. Nature Structural Biology, 2002. 9(9): p. 646-652.
4. Jayachandran, G., V. Vishal, and V.S. Pande, Using massively parallel simulation and Markovian models to study protein folding: Examining the dynamics of the villin headpiece. Journal of Chemical Physics, 2006. 124(16).
5. Zhou, R.H., Trp-cage: Folding free energy landscape in explicit water. Proceedings of the National Academy of Sciences of the United States of America, 2003. 100(23): p. 13280-13285.
6. Juraszek, J. and P.G. Bolhuis, Sampling the multiple folding mechanisms of Trp-cage in explicit solvent. Proceedings of the National Academy of Sciences of the United States of America, 2006. 103(43): p. 15859-15864.
7. Bussi, G., et al., Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. Journal of the American Chemical Society, 2006. 128(41): p. 13435-13441.
8. Marinelli, F., et al., A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations. PLoS Comput Biol, 2009. 5(8): p. e1000452.
9. Klepeis, J.L., et al., Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology, 2009. 19(2): p. 120-127.
10. Kim, S.J., et al., Real-time detection of protein-water dynamics upon protein folding by terahertz absorption spectroscopy. Angewandte Chemie-International Edition, 2008. 47(34): p. 6486-6489.
11. Korzhnev, D.M., et al., Low-populated folding intermediates of Fyn SH3 characterized by relaxation dispersion NMR. Nature, 2004. 430(6999): p. 586-590.
12. Nettels, D., et al., Ultrafast dynamics of protein collapse from single-molecule photon statistics. Proceedings of the National Academy of Sciences of the United States of America, 2007. 104(8): p. 2655-2660.
13. Schuler, B. and W.A. Eaton, Protein folding studied by single-molecule FRET. Current Opinion in Structural Biology, 2008. 18(1): p. 16-26.
14. Chung, H.S., J.M. Louis, and W.A. Eaton, Experimental determination of upper bound for transition path times in protein folding from single-molecule photon-by-photon trajectories. Proceedings of the National Academy of Sciences of the United States of America, 2009. 106(29): p. 11837-11844.
15. Dyer, R.B., Ultrafast and downhill protein folding. Current Opinion in Structural Biology, 2007. 17(1): p. 38-47.
16. Kubelka, J., J. Hofrichter, and W.A. Eaton, The protein folding 'speed limit'. Current Opinion in Structural Biology, 2004. 14(1): p. 76-88.
17. Friel, C.T., et al., The mechanism of folding of Im7 reveals competition between functional and kinetic evolutionary constraints. Nature Structural & Molecular Biology, 2009. 16(3): p. 318-324.
18. Ihalainen, J.A., et al., alpha-Helix folding in the presence of structural constraints. Proceedings of the National Academy of Sciences of the United States of America, 2008. 105(28): p. 9588-9593.
19. Snow, C.D., et al., Absolute comparison of simulated and experimental protein-folding dynamics. Nature, 2002. 420(6911): p. 102-106.
20. Teplow, D.B., et al., Elucidating amyloid beta-protein folding and assembly: A multidisciplinary approach. Accounts of Chemical Research, 2006. 39(9): p. 635-645.
21. Udgaonkar, J.B., Multiple routes and structural heterogeneity in protein folding. Annual Review of Biophysics, 2008. 37: p. 489-510.
22. Gianni, S., et al., Identification and characterization of protein folding intermediates. Biophysical Chemistry, 2007. 128(2-3): p. 105-113.
23. Lindberg, M.O. and M. Oliveberg, Malleability of protein folding pathways: a simple reason for complex behaviour. Current Opinion in Structural Biology, 2007. 17(1): p. 21-29.
24. Best, R.B. and G. Hummer, Reaction coordinates and rates from transition paths. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(19): p. 6732-6737.
25. Zhou, R.H., et al., Hydrophobic collapse in multidomain protein folding. Science, 2004. 305(5690): p. 1605-1609.
26. Hua, L., et al., Urea denaturation by stronger dispersion interactions with proteins than water implies a 2-stage unfolding. Proceedings of the National Academy of Sciences of the United States of America, 2008. 105(44): p. 16928-16933.
27. Banavar, J.R. and A. Maritan, Physics of proteins. Annual Review of Biophysics and Biomolecular Structure, 2007. 36: p. 261-280.
28. Yang, J.S., S. Wallin, and E.I. Shakhnovich, Universality and diversity of folding mechanics for three-helix bundle proteins. Proceedings of the National Academy of Sciences of the United States of America, 2008. 105(3): p. 895-900.
29. Pietrucci, F. and A. Laio, A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1. Journal of Chemical Theory and Computation, 2009.
30. Wirmer, J., W. Peti, and H. Schwalbe, Motional properties of unfolded ubiquitin: a model for a random coil protein. Journal of Biomolecular Nmr, 2006. 35(3): p. 175-186.
31. Mills, I.A., et al., Folding and stability of the isolated Greek key domains of the long-lived human lens proteins gamma D-crystallin and gamma S-crystallin. Protein Science, 2007. 16(11): p. 2427-2444.
32. Larios, E., et al., Multiple probes reveal a native-like intermediate during low-temperature refolding of ubiquitin. Journal of Molecular Biology, 2004. 340(1): p. 115-125.
33. Wales, D.J. and T.V. Bogdan, Potential energy and free energy landscapes. Journal of Physical Chemistry B, 2006. 110(42): p. 20765-20776.