Constructing multi-resolution markov state models (MSMS) to elucidate RNA hairpin folding mechanisms

Pacific Symposium on Biocomputing 2010, PSB 2010

Volumn , Issue , 2010, Pages 228-239

  Huang, Xuhui ^a,b   Yao, Yuan ^c,d   Bowman, Gregory R ^d   Sun, Jian ^b   Guibas, Leonidas J ^b   Carlsson, Gunnar ^d   Pande, Vijay S ^d  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b Stanford University   (United States)

^c PEKING UNIVERSITY   (China)

^d STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATOMIC RESOLUTION; ATOMISTIC SIMULATIONS; COARSE GRAINING; COARSE GRAINS; CONFORMATIONAL SPACE; DENSITY LEVEL SET; FOLDED STATE; FOLDING DYNAMICS; FOLDING MECHANISM; HIER-ARCHICAL CLUSTERING; INTERMEDIATE STATE; LONG-TIME DYNAMICS; MARKOV STATE MODELS; META-STABLE STATE; MULTI-RESOLUTIONS; MULTIPLE RESOLUTIONS; ORDERS OF MAGNITUDE; PHASE SPACES; SIMULATION DATA; SMALL RNA; TIME-SCALES;

COMPUTER SIMULATION; DYNAMICS; PHASE SPACE METHODS; RNA;

CLUSTERING ALGORITHMS;

RNA;

ALGORITHM; ARTICLE; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; CONFORMATION; MOLECULAR DYNAMICS; PROBABILITY; RNA FOLDING; VALIDATION STUDY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; MARKOV CHAINS; MODELS, MOLECULAR; MOLECULAR DYNAMICS SIMULATION; NUCLEIC ACID CONFORMATION; RNA; RNA FOLDING;

EID: 84873038722     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (50)

1. Dobson, CM., Protein folding and misfolding. Nature, 2003. 426(6968): p. 884-90.
2. Brion, P. and E. Westhof, Hierarchy and dynamics of RNA folding. Annual review of biophysics and biomolecular structure, 1997. 26: p. 113-37.
3. Kornberg, R.D., The molecular basis ofeukaryotic transcription. Proc Natl Acad Sci USA, 2007. 104(32): p. 12955-61.
4. Song, J.J. and L. Joshua-Tor, Argonaute and RNA-getting into the groove. Curr Opin Struct Biol, 2006. 16(1): p. 5-11.
5. Marshall, R.A., et al., Translation at the single-molecule level. Annu Rev Biochem, 2008. 77: p. 177-203.
6. Levitt, M. and A. Warshel, Computer simulation of protein folding. Nature, 1975. 253(5494): p. 694-8.
7. Tozzini, V., Coarse-grained models for proteins. Curr Opin Struct Biol, 2005. 15(2): p. 144-50.
8. Shelley, J.C, et al., Coarse Grain Model for Phospholipid Simulations. The Journal of Physical Chemistry B, 2001.105(19): p. 4464-4470.
9. Marrink, S.J., A.H. de Vries, and A.E. Mark, Coarse Grained Model for Semiquantitative Lipid Simulations. The Journal of Physical Chemistry B, 2003.108(2): p. 750-760.
10. Friedel, M. and J.-E. Shea, Self-assembly of peptides into a beta-barrel motif. The Journal of Chemical Physics, 2004. 120(12): p. 5809-5823.
11. Brown, S., N.J. Fawzi, and T. Head-Gordon, Coarse-grained sequences for protein folding and design. Proc Natl Acad Sci U S A, 2003. 100(19): p. 10712-7.
12. Buchete, N.V., J.E. Straub, and D. Thirumalai, Orientational potentials extracted from protein structures improve native fold recognition. Protein Sci, 2004. 13(4): p. 862-74.
13. Noe, F. and S. Fischer, Transition networks for modeling the kinetics of conformational change in macromolecules. Curr Opin Struct Biol, 2008.18(2): p. 154-62.
14. Chodera, J.D., et al., Automatic discovery ofmetastable states for the construction of Markov models of macromolecular conformational dynamics. J Chem Phys, 2007. 126(15): p. 155101.
15. Buchete, N.V. and G. Hummer, Coarse master equations for peptide folding dynamics. J Phys Chem B, 2008. 112(19): p. 6057-69.
16. Swope, W.C, J.W. Pitera, and F. Suits, Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory. J. Phys. Chem. B 2004. 108: p. 6571-6581.
17. Sriraman, S., I.G. Kevrekidis, and G. Hummer, Coarse master equation from Bayesian analysis of replica molecular dynamics simulations. J Phys Chem B, 2005. 109(14): p. 6479-84.
18. Bowman, G.R., X. Huang, and V.S. Pande, Using generalized ensemble simulations and Markov state models to identify conformational states. Methods, 2009.
19. Faradjian, A.K. and R. Elber, Computing time scales from reaction coordinates by milestoning. J Chem Phys, 2004. 120(23): p. 10880-9.
20. Sun, J., et al., A Fast Geometric Clustering Method on Conformation Space of Biomolecules. In prepration, 2009.
21. Uhlenbeck, O.C, Tetraloops and RNA folding. Nature, 1990. 346(6285): p. 613-4.
22. Bowman, G.R., et al., Structural insight into RNA hairpin folding intermediates. J Am Chem Soc, 2008. 130(30): p. 9676-8.
23. Ma, H., et al., Exploring the energy landscape of a small RNA hairpin. J Am Chem Soc, 2006. 128(5): p. 1523-30.
24. Ma, H., et al., DNA folding and melting observed in real time redefine the energy landscape. Proc Natl Acad Sci USA, 2007. 104(3): p. 712-6.
25. Ansari, A., S.V. Kuznetsov, and Y. Shen, Conflgurational diffusion down a folding funnel describes the dynamics of DNA hairpins. Proc Natl Acad Sci USA, 2001. 98(14): p. 7771-6.
26. Sorin, E.J., Y.M. Rhee, and V.S. Pande, Does water play a structural role in the folding of small nucleic acids? Biophys J, 2005. 88(4): p. 2516-24.
27. Stancik, A.L. and E.B. Brauns, Rearrangement of Partially Ordered Stacked Conformations Contributes to the Rugged Energy Landscape of a Small RNA Hairpin. Biochemistry, 2008. 47(41): p. 10834-10840.
28. Garcia, A.E. and D. Paschek, Simulation of the pressure and temperature folding/unfolding equilibrium of a small RNA hairpin, in J Am Chem Soc. 2008. p. 815-+.
29. Marinari, E. and G. Parisi, Simulated Tempering: a New Monte Carlo Scheme. Europhysics Letters, 1992. 19: p. 451-458.
30. Huang, X., G.R. Bowman, and V.S. Pande, Convergence of folding free energy landscapes via application of enhanced sampling methods in a distributed computing environment. J. Chem. Phys, 2008. 128(20): p. 205106.
31. Huang, X., et al., Rapid Equilibrium Sampling Initiated from Non-equilibrium Data. Proc Natl Acad Sci U S A, 2009. In Press.
32. Das, P., et al., Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction. Proc Natl Acad Sci U S A, 2006. 103(26): p. 9885-90.
33. Yao, Y., et al., Topological methods for exploring low-density states in biomolecular folding pathways. J Chem Phys, 2009. 130(14): p. 144115.
34. Singh, G., F. Memoli, and G. Carlsson, Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition, in Eurographics Symposium on Point-Based Graphics. 2007.
35. Schütte, C. and W. Huisinga, Biomolecular Conformations can be Identified as Metastable Sets of Molecular Dynamics. Handbook of Numerical Analysis X, 2003: p. 699-744.
36. Schütte, C. and W. Huisinga, Biomolecular Conformations as Metastable Sets of Markov Chains. Proceedings of the 38th Annual Allerton Conference on Communication, Control, and Computing, 2000: p. 1106-1115.
37. Deuflhard, P., et al., Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains. Lin. Alg. Appl., 2000. 315: p. 39-59.
38. Deuflhard, P. and M. Weber, Robust Perron cluster analysis in conformation dynamics. Linear Algebra and Its Applications, 2005. 398: p. 161-184.
39. Jung, J. and A. Van Orden, A three-state mechanism for DNA hairpin folding characterized by multiparameter fluorescence fluctuation spectroscopy. J Am Chem Soc, 2006. 128(4): p. 1240-9.
40. Sarkar, K., et al., Folding of an RNA tetraloop on a rugged energy landscape using a stacking-sensitive probe. Submitted, 2009.
41. Singhal, N., C.D. Snow, and V.S. Pande, Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. J Chem Phys, 2004. 121(1): p. 415-25.
42. DeLano, W.L., The PyMOL Molecular Graphics System. 2002. DeLano Scientific, Palo Alto, CA, USA.
43. Bacallado, S., J.D. Chodera, and V.S. Pande, Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint. Submitted, 2009.
44. Shirts, M. and V.S. Pande, COMPUTING: Screen Savers of the World Unite! Science, 2000. 290(5498): p. 1903-1904.
45. DUAN, Y., et al., A Point-Charge Force Field for Molecular Mechanics Simulations of Proteins Based on Condensed-Phase Quantum Mechanical Calculations. J. Comp. Chem., 2003. 24: p. 1999-2012.
46. Wang, J., P. Cieplak, and P.A. Kollman, How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? 2000. p. 1049-1074.
47. Jorgensen, W.L.C., J.; Madura, J. D.; Impey, R. W.; Klein, M. L., J. Chem. Phys, 1983. 79(926-935).
48. Hoover, W., Phys. Rev. A, 1985. 31: p. 1695-1697.
49. Darden, T., D. York, and L. Pedersen., A smooth particle mesh Ewald potential. J. Chem. Phys., 1995. 103: p. 3014-3021.
50. Hess, B., H. Bekker, H. J. C. Berendsen, and J. G. E. M. Fraaije., LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem., 1997. 18: p. 1463-1472.