Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics

International Journal of Molecular Sciences

Volumn 14, Issue 5, 2013, Pages 9893-9905

  Wabik, Jacek ^a   Kmiecik, Sebastian ^a   Gront, Dominik ^a   Kouza, Maksim ^a   Koliński, Andrzej ^a  

^a UNIVERSITY OF WARSAW   (Poland)

Author keywords

CABS; Multiscale modeling; Protein folding; Replica exchange molecular dynamics

Indexed keywords

PROTEIN G; PROTEIN;

ARTICLE; MOLECULAR DYNAMICS; MONTE CARLO METHOD; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; REPLICA EXCHANGE MOLECULAR DYNAMICS; THERMODYNAMICS; CHEMISTRY;

MOLECULAR DYNAMICS SIMULATION; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS;

EID: 84877811168     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms14059893     Document Type: Article

References (75)

1. Kolinski, A.; Bujnicki, J.M. Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins 2005, 61, 84-90.
2. Scheraga, H.A.; Khalili, M.; Liwo, A. Protein-folding dynamics: Overview of molecular simulation techniques. Annu. Rev. Phys. Chem. 2007, 58, 57-83.
3. Kouza, M.; Hu, C.K.; Zung, H.; Li, M.S. Protein mechanical unfolding: Importance of non-native interactions. J. Chem. Phys. 2009, 131, doi:10.1063/1.3272275.
4. Malolepsza, E.; Boniecki, M.; Kolinski, A.; Piela, L. Theoretical model of prion propagation: A misfolded protein induces misfolding. Proc. Natl. Acad. Sci. USA 2005, 102, 7835-7840.
5. Kmiecik, S.; Jamroz, M.; Kolinski, A. Multiscale Approach to Protein Folding Dynamics. In Multiscale Approaches to Protein Modeling; Kolinski, A., Ed.; Springer: New York, NY, USA, 2011; pp. 281-293.
6. Shakhnovich, E. Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chem. Rev. 2006, 106, 1559-1588.
7. Liwo, A.; He, Y.; Scheraga, H.A. Coarse-grained force field: General folding theory. PCCP 2011, 13, 16890-16901.
8. Kolinski, A. Protein modeling and structure prediction with a reduced representation. Acta Biochim. Pol. 2004, 51, 349-371.
9. Tozzini, V. Coarse-grained models for proteins. Curr. Opin. Struct. Biol. 2005, 15, 144-150.
10. Kmiecik, S.; Kolinski, A. Characterization of protein-folding pathways by reduced-space modeling. Proc. Natl. Acad. Sci. USA 2007, 104, 12330-12335.
11. Kmiecik, S.; Kolinski, A. Folding pathway of the B1 domain of protein G explored by multiscale modeling. Biophys. J. 2008, 94, 726-736.
12. Kmiecik, S.; Kolinski, A. Simulation of chaperonin effect on protein folding: A shift from nucleation-condensation to framework mechanism. J. Am. Chem. Soc. 2011, 133, 10283-10289.
13. Kmiecik, S.; Gront, D.; Kouza, M.; Kolinski, A. From coarse-grained to atomic-level characterization of protein dynamics: Transition state for the folding of B domain of protein A. J. Phys. Chem. B 2012, 116, 7026-7032.
14. Jamroz, M.; Orozco, M.; Kolinski, A.; Kmiecik, S. Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J. Chem. Theory. Comput. 2013, 9, 119-125.
15. Cornell, W.D.; Cieplak, P.; Bayly, C.I.; Gould, I.R.; Merz, K.M.; Ferguson, D.M.; Spellmeyer, D.C.; Fox, T.; Caldwell, J.W.; Kollman, P.A. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 1995, 117, 5179-5197.
16. Van der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701-1718.
17. Brooks, B.R.; Brooks, C.L.; Mackerell, A.D.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545-1614.
18. Jorgensen, W.L.; Tirado-Rives, J. The OPLS potential functions for proteins. Energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 1988, 110, 1657-1666.
19. Boczko, E.M.; Brooks, C.L. First-principles calculation of the folding free-energy of a 3-helix bundle protein. Science 1995, 269, 393-396.
20. Berg, B.A.; Neuhaus, T. Multicanonical algorithms for 1st order phase-transitions. Phys. Lett. B 1991, 267, 249-253.
21. Berne, B.J.; Straub, J.E. Novel methods of sampling phase space in the simulation of biological systems. Curr. Opin. Struct. Biol. 1997, 7, 181-189.
22. Hansmann, U.H.E. Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 1997, 281, 140-150.
23. Sugita, Y.; Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 1999, 314, 141-151.
24. Pokarowski, P.; Kolinski, A.; Skolnick, J. A minimal physically realistic protein-like lattice model: Designing an energy landscape that ensures all-or-none folding to a unique native state. Biophys. J. 2003, 84, 1518-1526.
25. Kouza, M.; Hu, C.K.; Li, M.S. New force replica exchange method and protein folding pathways probed by force-clamp technique. J Chem Phys 2008, 128, 045103.
26. Gront, D.; Kolinski, A.; Skolnick, J. A new combination of replica exchange Monte Carlo and histogram analysis for protein folding and thermodynamics. J. Chem. Phys. 2001, 115, 1569-1574.
27. Kouza, M.; Hansmann, U.H.E. Velocity scaling for optimizing replica exchange molecular dynamics. J. Chem. Phys. 2011, 134, 044124.
28. Chaudhury, S.; Olson, M.A.; Tawa, G.; Wallqvist, A.; Lee, M.S. Efficient conformational sampling in explicit solvent using a hybrid replica exchange molecular dynamics method. J. Chem. Theory. Comput. 2012, 8, 677-687.
29. Nguyen, P.; Stock, G.; Mittag, E.; Hu, C.-K.; Li, M. Free energy landscape and folding mechanism of a β-hairpin in explicit water: A replica exchange molecular dynamics study. Proteins 2005, 61, 795-808.
30. Okur, A.; Roe, D.R.; Cui, G.; Hornak, V.; Simmerling, C. Improving convergence of replica-exchange simulations through coupling to a high-temperature structure reservoir. J. Chem. Theory. Comput. 2007, 3, 557-568.
31. Roitberg, A.E.; Okur, A.; Simmerling, C. Coupling of replica exchange simulations to a non-boltzmann structure reservoir. J. Phys. Chem. B 2007, 111, 2415-2418.
32. Schlick, T. Molecular dynamics-based approaches for enhanced sampling of long-time, large-scale conformational changes in biomolecules. F1000 Biol. Rep. 2009, 1, doi:10.3410/B1-51.
33. Meli, M.; Morra, G.; Colombo, G. Investigating the mechanism of peptide aggregation: Insights from mixed monte carlo-molecular dynamics simulations. Biophys. J. 2008, 94, 4414-4426.
34. De Mori, G.M.S.; Micheletti, C.; Colombo, G. All-atom folding simulations of the villin headpiece from stochastically selected coarse-grained structures. J. Phys. Chem. B 2004, 108, 12267-12270.
35. Colombo, G.; Micheletti, C. Protein folding simulations: combining coarse-grained models and all-atom molecular dynamics. Theor. Chem. Acc. 2006, 116, 75-86.
36. Thorpe, I.F.; Zhou, J.; Voth, G.A. Peptide folding using multiscale coarse-grained models. J. Phys. Chem. B 2008, 112, 13079-13090.
37. Chang, C.-E.A.; Trylska, J.; Tozzini, V.; Andrew Mccammon, J. Binding pathways of ligands to HIV-1 Protease: Coarse-grained and atomistic simulations. Chem. Biol. Drug Des. 2007, 69, 5-13.
38. Best, R.B.; Mittal, J. Free-energy landscape of the GB1 hairpin in all-atom explicit solvent simulations with different force fields: Similarities and differences. Proteins 2011, 79, 1318-1328.
39. Kolinski, A.; Ilkowski, B.; Skolnick, J. Dynamics and thermodynamics of beta-hairpin assembly: Insights from various simulation techniques. Biophys. J. 1999, 77, 2942-2952.
40. García, A.; Sanbonmatsu, K. Exploring the energy landscape of a β hairpin in explicit solvent. Proteins 2001, 42, 345-354.
41. Lwin, T.Z.; Luo, R. Force field influences in β-hairpin folding simulations. Protein Sci. 2006, 15, 2642-2655.
42. Shao, Q.; Yang, L.; Gao, Y.Q. Structure change of beta-hairpin induced by turn optimization: An enhanced sampling molecular dynamics simulation study. J. Chem. Phys. 2011, 135, 235104-235110.
43. Bhattacharya, A.; Best, R.B.; Mittal, J. Smoothing of the GB1 hairpin folding landscape by interfacial confinement. Biophys. J. 2012, 103, 596-600.
44. Cao, Z.; Wang, J. A comparative study of two different force fields on structural and thermodynamics character of H1 peptide via molecular dynamics simulations. J. Biomol. Struct. Dyn. 2010, 27, 651-661.
45. Blanco, F.; Rivas, G.; Serrano, L. A short linear peptide that folds into a native stable β-hairpin in aqueous solution. Nat. Struct. Mol. Biol. 1994, 1, 584-590.
46. Du, D.; Zhu, Y.; Huang, C.-Y.; Gai, F. Understanding the key factors that control the rate of beta-hairpin folding. Proc. Natl. Acad. Sci. USA 2004, 101, 15915-15920.
47. Lewandowska, A.; Ołdziej, S.; Liwo, A.; Scheraga, H.A. Mechanism of formation of the C-terminal beta-hairpin of the B3 domain of the immunoglobulin-binding protein G from Streptococcus. IV. Implication for the mechanism of folding of the parent protein. Biopolymers 2010, 93, 469-480.
48. Skwierawska, A.; Makowska, J.; Oldziej, S.; Liwo, A.; Scheraga, H.A. Mechanism of formation of the C-terminal beta-hairpin of the B3 domain of the immunoglobulin binding protein G from Streptococcus. I. Importance of hydrophobic interactions in stabilization of beta-hairpin structure. Proteins 2009, 75, 931-953.
49. Skwierawska, A.; Zmudzinska, W.; Oldziej, S.; Liwo, A.; Scheraga, H.A. Mechanism of formation of the C-terminal beta-hairpin of the B3 domain of the immunoglobulin binding protein G from Streptococcus. II. Interplay of local backbone conformational dynamics and long-range hydrophobic interactions in hairpin formation. Proteins 2009, 76, 637-654.
50. Munoz, V.; Thompson, P.A.; Hofrichter, J.; Eaton, W.A. Folding dynamics and mechanism of [beta]-hairpin formation. Nature 1997, 390, 196-199.
51. Paschek, D.; Day, R.; Garcia, A.E. Influence of water-protein hydrogen bonding on the stability of Trp-cage miniprotein. A comparison between the TIP3P and TIP4P-Ew water models. Phys. Chem. Chem. Phys. 2011, 13, 19840-19847.
52. Paschek, D.; Hempel, S.; García, A.E. Computing the stability diagram of the Trp-cage miniprotein. Proc. Natl. Acad. Sci. USA 2008, 105, 17754-17759.
53. Kim, E.; Jang, S.; Pak, Y. Consistent free energy landscapes and thermodynamic properties of small proteins based on a single all-atom force field employing an implicit solvation. J. Chem. Phys. 2007, 127, 145104.
54. Garcia, A.E.; Paschek, D. Simulation of the pressure and temperature folding/unfolding equilibrium of a small RNA hairpin. J. Am. Chem. Soc. 2007, 130, 815-817.
55. Zhou, R.; Berne, B.; Germain, R. The free energy landscape for β hairpin folding in explicit water. Proc. Natl. Acad. Sci. USA 2001, 98, 14931-14936.
56. Best, R.B.; Mittal, J. Balance between α and β Structures in Ab initio protein folding. J. Phys. Chem. B 2010, 114, 8790-8798.
57. Piana, S.; Lindorff-Larsen, K.; Shaw, D.E. How robust are protein folding simulations with respect to force field parameterization? Biophys. J. 2011, 100, L47-L49.
58. CABSfold. Available online: http://www.biocomp.chem.uw.edu.pl/CABSfold/ (accesed on 1 Febury 2013)
59. Kmiecik, S.; Gront, D.; Kolinski, A. Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field. BMC Struct. Biol. 2007, 7, doi:10.1186/1472-6807-7-43.
60. Gront, D.; Kmiecik, S.; Kolinski, A. Backbone building from quadrilaterals: A fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J. Comput. Chem. 2007, 28, 1593-1597.
61. Canutescu, A.; Shelenkov, A.; Dunbrack, R. A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 2003, 12, 2001-2014.
62. Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; Hermans, J. Interaction models for water in relation to protein hydration. Intermolecular Forces 1981, 331-342.
63. Hess, B.; Bekker, H.; Berendsen, H.J.C.; Fraaije, J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J Comput Chem 1997, 18, 1463-1472.
64. van Gunsteren, W.F.; Berendsen H.J.C. A leap-frog algorithm for stochastic dynamics. Mol. Simul. 1988, 1, 173-185.
65. Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. The Journal of Chemical Physics 1995, 103, 8577-8593.
66. Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006, 65, 712-725.
67. Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory. Comput. 2008, 4, 435-447.
68. Gront, D.; Kolinski, A. BioShell-a package of tools for structural biology computations. Bioinformatics 2006, 22, 621-622.
69. Gront, D.; Kolinski, A. Utility library for structural bioinformatics. Bioinformatics 2008, 24, 584-585.
70. Kabsch, W.; Sander, C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22, 2577-2637.
71. DeLano, W.L. The PyMOL Molecular Graphics System, Version 1.4.1; Schrodinger, LLC: Portland, OR, USA, 2010.
72. Gront, D.; Kolinski, A. Efficient scheme for optimization of parallel tempering Monte Carlo method. J. Phys. Cond. Mat. 2007, 19, doi:10.1088/0953-8984/19/3/036225.
73. Gront, D.; Kmiecik, S.; Blaszczyk, M.; Ekonomiuk, D.; Koliński, A. Optimization of protein models. WIREs Comp. Mol. Sci. 2012, 2, 479-493.
74. Cao, Z.; Liu, L.; Wang, J. Why the OPLS-AA force field cannot produce the beta-hairpin structure of H1 peptide in solution when comparing with the GROMOS 43A1 force field? J. Biomol. Struct. Dyn. 2011, 29, 527-539.
75. Seibert, M.M.; Patriksson, A.; Hess, B.; van der Spoel, D. Reproducible polypeptide folding and structure prediction using molecular dynamics simulations. J. Mol. Biol. 2005, 354, 173-183.