The free energy landscape of small peptides as obtained from metadynamics with umbrella sampling corrections

Journal of Chemical Physics

Volumn 125, Issue 20, 2006, Pages

  Babin, Volodymyr ^a   Roland, Christopher ^a   Darden, Thomas A ^b   Sagui, Celeste ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FREE ENERGY LANDSCAPE; METADYNAMICS; PEPTIDES; UMBRELLA SAMPLING CORRECTIONS;

COMPUTER SIMULATION; ERROR CORRECTION; FREE ENERGY; MOLECULAR DYNAMICS; PROBES; SOLVENTS;

AMINO ACIDS;

PEPTIDE;

ALGORITHM; ARTICLE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; ENERGY TRANSFER; KINETICS; PROTEIN CONFORMATION;

ALGORITHMS; COMPUTER SIMULATION; ENERGY TRANSFER; KINETICS; MODELS, CHEMICAL; MODELS, MOLECULAR; PEPTIDES; PROTEIN CONFORMATION;

EID: 33751558641     PISSN: 00219606     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.2393236     Document Type: Article

References (45)

1. E. Weinan and E. Vanden-Eijnden, in Lecture Notes in Computational Science and Engineering, edited by, S. Attinger, and, P. Koumoutsakos, (Springer, Berlin, 2004).
2. E. Darve and A. Pohorille, J. Chem. Phys. 115, 9169 (2001).
3. Y. Sugita, A. Kitao, and Y. Okamoto, J. Phys. Chem. 113, 6042 (2000).
4. U. Hansmann, Chem. Phys. Lett. 281, 140 (1997).
5. Y. Sugita and Y. Okamoto, Chem. Phys. Lett. 314, 141 (1999).
6. S. Kumar, D. Bouzida, R. H. Swendsen, P. A. Kollman, and J. Rosenberg, J. Comput. Chem. 13, 1011 (1992).
7. C. Bartels, M. Schaefer, and M. Karplus, J. Chem. Phys. 111, 8048 (1999).
8. M. Mezei, J. Comput. Phys. 68, 237 (1987).
9. R. W. W. Hooft, J. Chem. Phys. 97, 6690 (1992).
10. C. Bartels and M. Karplus, J. Comput. Chem. 18, 1450 (1997).
11. A. Laio and M. Parrinello, Proc. Natl. Acad. Sci. U.S.A. 99, 12562 (2002).
12. M. Iannuzzi, A. Laio, and M. Parrinello, Phys. Rev. Lett. 90, 238302 (2003).
13. R. Car and M. Parrinello, Phys. Rev. Lett. 55, 2471 (1985).
14. H. C. Andersen, J. Chem. Phys. 72, 2384 (1980).
15. S. Nose, Mol. Phys. 52, 255 (1984).
16. T. Huber, A. E. Torda, and W. F. van Gunsteren, J. Comput.-Aided Mol. Des. 8, 695 (1994).
17. G. Hummer and I. Kevrekidis, J. Chem. Phys. 118, 10762 (2003).
18. F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001).
19. B. Ensing, A. Laio, F. L. Gervasio, M. Parrinello, and M. L. Klein, J. Am. Chem. Soc. 126, 9492 (2004).
20. S. V. Churakov, M. Ianuzzi, and M. Parrinello, J. Phys. Chem. B 108, 11567 (2004).
21. F. Gervasio, A. Laio, and M. Parrinello, J. Am. Chem. Soc. 124, 2600 (2005).
22. M. Ceccarelli, C. Danelon, A. Laio, and M. Parrinello, Biophys. J. 87, 58 (2004).
23. M. Iannuzzi and M. Parrinello, Phys. Rev. Lett. 93, 025901 (2004).
24. A. L. A. Stirling, M. Ianuzzi, and M. Parrinello, ChemPhysChem 5, 1558 (2004).
25. E. Asciutto and C. Sagui, J. Phys. Chem. A 109, 7682 (2005).
26. J. G. Lee, E. Asciutto, V. Babin, C. Sagui, T. A. Darden, and C. Roland, J. Phys. Chem. B 110, 2325 (2006).
27. T. Ikeda, M. Hirata, and T. Kimura, J. Chem. Phys. 122, 244507 (2005).
28. D. A. Case, T. A. Darden, T. E. Cheatham III, AMBER 8 (University of California, San Francisco, 2004).
29. S. Gnanakaran, H. Nymeyer, J. Portman, K. Y. Sanbonmatsu, and A. E. García, Curr. Opin. Struct. Biol. 13, 168 (2003).
30. A. E. García and K. Y. Sanbonmatsu, Proteins 42, 345 (2001).
31. R. Zhou and B. Berne, Proc. Natl. Acad. Sci. U.S.A. 96, 14931 (2001).
32. C. Chipot and J. H́nin, J. Chem. Phys. 123, 244906 (2005).
33. E. Weinan and E. Vanden-Eijnden, http://www.cims.nyu.edu/~eve2/ metastable.pdf
34. J. L. Bentley, Commun. ACM 18, 509 (1975).
35. B. Ensing, A. Laio, M. Parrinello, and M. Klein, J. Phys. Chem. B 109, 6676 (2005).
36. G. Bussi, A. Laio, and M. Parrinello, Phys. Rev. Lett. 96, 090601 (2006).
37. C. Bartels and M. Karplus, J. Phys. Chem. B 102, 865 (1998).
38. W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell, and P. A. Kollman, J. Am. Chem. Soc. 117, 5179 (1995).
39. H. J. C. Berendsen, J. P. M. Postma, W. F. van Gunsteren, A. Di Nola, and J. R. Haak, J. Chem. Phys. 81, 3684 (1984).
40. T. A. Darden, D. M. York, and L. G. Pedersen, J. Chem. Phys. 98, 10089 (1993).
41. U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee, and L. G. Pedersen, J. Chem. Phys. 103, 8577 (1995).
42. W. L. Jorgensen, J. Chandrasekhar, J. Madura, and M. L. Klein, J. Chem. Phys. 79, 926 (1983).
43. M. N. Kobrak, J. Comput. Chem. 24, 1437 (2003).
44. A. Preusser, ACM Trans. Math. Softw. 15, 79 (1989).
45. This method is different from other adaptive umbrella sampling methods that use the potential energy, such as that introduced in Ref.. In that work, the authors use the potential energy as the collective variable, and successive biasing potentials are built using the probability distributions. This requires the partition of the energy collective variable into bins and the use of the WHAM and extrapolation techniques. The potential energy as collective variable has the advantage that it does not depend on the molecular geometry; however, due to practical reasons the range of potential energies that are sampled has to be restricted. The method can be costly since it requires many updates of the umbrella potential.