Building Markov state models with solvent dynamics

BMC Bioinformatics

Volumn 14, Issue , 2013, Pages

  Gu, Chen ^a   Chang, Huang Wei ^a   Maibaum, Lutz ^b   Pande, Vijay S ^c   Carlsson, Gunnar E ^c   Guibas, Leonidas J ^a  

^a Stanford University   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; CARBON NANOTUBES; CLUSTERING ALGORITHMS; DEGREES OF FREEDOM (MECHANICS); DYNAMICAL SYSTEMS; MOLECULES; YARN;

BIOLOGICAL MACROMOLECULE; BIOMOLECULAR SIMULATION; CONFORMATIONAL CHANGE; GEOMETRIC CLUSTERING; HIGH DIMENSIONAL DATA; INDISTINGUISHABILITY; MOLECULAR SIMULATIONS; TIME-SCALE SIMULATION;

SOLVENTS;

SOLVENT;

ALGORITHM; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; COMPUTER SIMULATION; PROBABILITY; SOLUTION AND SOLUBILITY;

ALGORITHMS; COMPUTER SIMULATION; MARKOV CHAINS; MODELS, MOLECULAR; SOLUTIONS; SOLVENTS;

EID: 84882419152     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-S2-S8     Document Type: Article

References (33)

1. Dobson CM: Protein folding and misfolding. Nature 2003, 426(6968):884-90.
2. Levitt M, Warshel A: Computer simulation of protein folding. Nature 1975, 253(5494):694-8.
3. Noé F, Fischer S: Transition networks for modeling the kinetics of conformational change in macromolecules. Current Opinion in Structural Biology 2008, 18(2):154-162.
4. Chodera JD, Singhal N, Pande VS, Dill KA, Swope WC: Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. The Journal of Chemical Physics 2007, 126(15):155101+.
5. Buchete NV, Hummer G: Coarse master equations for peptide folding dynamics. The Journal of Physical Chemistry B 2008, 112(19):6057-6069.
6. Bowman GR, Huang X, Pande VS: Using generalized ensemble simulations and Markov state models to identify conformational states. Methods 2009, 49(2):197-201.
7. Bowman G, Beauchamp K, Boxer G, Pande V: Progress and challenges in the automated construction of Markov state models for full protein systems. The Journal of Chemical Physics 2009, 131(12):124101.
8. Huang X, Yao Y, Bowman GR, Sun J, Guibas LJ, Carlsson G, Pande VS: Constructing multi-resolution Markov state models (MSMs) to elucidate RNA hairpin folding mechanisms. Proceedings of the Pacific Symposium on Biocomputing 2010, 228-239.
9. Gu C, Jiang X, Guibas LJ: Kinetically-aware conformational distances in molecular dynamics. Proceedings of the 23rd Canadian Conference on Computational Geometry 2011, 217-222.
10. Swope WC, Pitera JW, Suits F, Pitman M, Eleftheriou M, Fitch BG, Germain RS, Rayshubski A, Ward TJC, Zhestkov Y, Zhou R: Describing protein folding kinetics by molecular dynamics simulations. 2. Example applications to alanine dipeptide and a ß-hairpin peptide. The Journal of Physical Chemistry B 2004, 108(21):6582-6594.
11. Sriraman S, Kevrekidis IG, Hummer G: Coarse master equation from Bayesian analysis of replica molecular dynamics simulations. The Journal of Physical Chemistry B 2005, 109(14):6479-6484.
12. Sorin EJ, Pande VS: Exploring the helix-coil transition via all-atom equilibrium ensemble simulations. Biophysical Journal 2005, 88(4):2472-2493.
13. Elmer SP, Park S, Pande VS: Foldamer dynamics expressed via Markov state models. II. State space decomposition. The Journal of Chemical Physics 2005, 123(11):114903.
14. Chodera J, Swope W, Pitera J, Dill K: Long-time protein folding dynamics from short-time molecular dynamics simulations. Multiscale Modeling & Simulation 2006, 5(4):1214-1226.
15. de Groot BL, Daura X, Mark AE, Grubmüller H: Essential dynamics of reversible peptide folding: memory-free conformational dynamics governed by internal hydrogen bonds. Journal of Molecular Biology 2001, 309:299-313.
16. Singhal N, Snow CD, Pande VS: Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. The Journal of Chemical Physics 2004, 121:415-425.
17. Andrec M, Felts AK, Gallicchio E, Levy RM: Protein folding pathways from replica exchange simulations and a kinetic network model. Proceedings of the National Academy of Sciences of the United States of America 2005, 102(19):6801-6806.
18. Karpen ME, Tobias DJ, Brooks CL: Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. Biochemistry 1993, 32(2):412-420.
19. Sorin EJ, Rhee YM, Pande VS: Does water play a structural role in the folding of small nucleic acids? Biophysical Journal 2005, 88(4):2516-2524.
20. Bolhuis PG, Dellago C, Chandler D: Reaction coordinates of biomolecular isomerization. Proceedings of the National Academy of Sciences of the United States of America 2000, 97(11):5877-5882.
21. Hua L, Huang X, Zhou R, Berne BJ: Dynamics of water confined in the interdomain region of a multidomain protein. The Journal of Physical Chemistry B 2006, 110(8):3704-3711.
22. Ma A, Dinner AR: Automatic method for identifying reaction coordinates in complex systems. The Journal of Physical Chemistry B 2005, 109(14):6769-6779.
23. Hummer G, Rasaiah JC, Noworyta JP: Water conduction through the hydrophobic channel of a carbon nanotube. Nature 2001, 414(6860):188-90.
24. Huang X, Margulis CJ, Berne BJ: Dewetting-induced collapse of hydrophobic particles. Proceedings of the National Academy of Sciences of the United States of America 2003, 100(21):11953-11958.
25. Berne BJ, Weeks JD, Zhou R: Dewetting and hydrophobic interaction in physical and biological systems. Annual Review of Physical Chemistry 2009, 60:85-103.
26. Kabsch W: A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A 1976, 32(5):922-923.
27. Coutsias EA, Seok C, Dill KA: Using quaternions to calculate RMSD. Journal of Computational Chemistry 2004, 25(15):1849-1857.
28. Ahuja RK, Magnanti TL, Orlin JB: Network Flows: Theory, Algorithms, and Applications Prentice Hall; 1993.
29. Kuhn HW: The Hungarian method for the assignment problem. Naval Research Logistics 1955, 2(1-2):83-97.
30. Hochbaum DS, Shmoys DB: A best possible heuristic for the k-center problem. Mathematics of Operations Research 1985, 10:180-184.
31. Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis Wiley-Interscience; 1990.
32. Kirkpatrick S, Gelatt CD, Vecchi MP: Optimization by simulated annealing. Science 1983, 220(4598):671-680.
33. Jolliffe IT: Principal Component Analysis Springer; 2002.