Skip to main content

Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fields

Abstract

The parametrization of coarse-grained (CG) simulation models for molecular systems often aims at reproducing static properties alone. The reduced molecular friction of the CG representation usually results in faster, albeit inconsistent, dynamics. In this work, we rely on Markov state models to simultaneously characterize the static and kinetic properties of two CG peptide force fields—one top-down and one bottom-up. Instead of a rigorous evolution of CG dynamics (e.g., using a generalized Langevin equation), we attempt to improve the description of kinetics by simply altering the existing CG models, which employ standard Langevin dynamics. By varying masses and relevant force-field parameters, we can improve the timescale separation of the slow kinetic processes, achieve a more consistent ratio of mean-first-passage times between metastable states, and refine the relative free-energies between these states. Importantly, we show that the incorporation of kinetic information into a structure-based parametrization improves the description of the helix-coil transition sampled by a minimal CG model. While structure-based models understabilize the helical state, kinetic constraints help identify CG models that improve the ratio of forward/backward timescales by effectively hindering the sampling of spurious conformational intermediate states.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    W.F. van Gunsteren, H.J. Berendsen, Angew. Chem. Int. Ed. Engl. 29, 992 (1990)

    Article  Google Scholar 

  2. 2.

    M. Karplus, J.A. McCammon, Nat. Struct. Mol. Biol. 9, 646 (2002)

    Article  Google Scholar 

  3. 3.

    T.J. Lane, D. Shukla, K.A. Beauchamp, V.S. Pande, Curr. Opin. Struct. Biol. 23, 58 (2013)

    Article  Google Scholar 

  4. 4.

    C. Neale, W.D. Bennett, D.P. Tieleman, R. Pomès, J. Chem. Theory Comput. 7, 4175 (2011)

    Article  Google Scholar 

  5. 5.

    W. Tschöp, K. Kremer, J. Batoulis, T. Bürger, O. Hahn, Acta Poly. 49, 61 (1998)

    Article  Google Scholar 

  6. 6.

    K. Kremer, F. Müller-Plathe, Mol. Sim. 28, 729 (2002)

    Article  Google Scholar 

  7. 7.

    C. Peter, K. Kremer, Soft Matter 5, 4357 (2009)

    ADS  Article  Google Scholar 

  8. 8.

    G.A. Voth (ed.), Coarse-Graining of Condensed Phase and Biomolecular Systems (CRC Press, Boca Raton, FL USA, 2009)

  9. 9.

    S. Riniker, J.R. Allison, W.F. van Gunsteren, Phys. Chem. Chem. Phys. 14, 12423 (2012)

    Article  Google Scholar 

  10. 10.

    W. Noid, J. Chem. Phys. 139, 090901 (2013)

    ADS  Article  Google Scholar 

  11. 11.

    V.A. Harmandaris, D. Reith, N.F.A. Van der Vegt, K. Kremer, Macromol. Chem. Physic. 208, 2109 (2007)

    Article  Google Scholar 

  12. 12.

    V.A. Harmandaris, K. Kremer, Soft Matter 5, 3920 (2009)

    ADS  Article  Google Scholar 

  13. 13.

    K.M. Salerno, A. Agrawal, D. Perahia, G.S. Grest, Phys. Rev. Lett. 116, 058302 (2016)

    ADS  Article  Google Scholar 

  14. 14.

    R. Zwanzig, Phys. Rev. 124, 983 (1961)

    ADS  Article  Google Scholar 

  15. 15.

    H. Mori, Prog. Theor. Phys. 33, 423 (1965)

    ADS  Article  Google Scholar 

  16. 16.

    S. Izvekov, G.A. Voth, J. Chem. Phys. 125, 151101 (2006)

    ADS  Article  Google Scholar 

  17. 17.

    C. Hijon, P. Español, E. Vanden-Eijnden, R. Delgado-Buscalioni, Faraday Disc. 144, 301 (2010)

    ADS  Article  Google Scholar 

  18. 18.

    S. Markutsya, M.H. Lamm, J. Chem. Phys. 141, 174107 (2014)

    ADS  Article  Google Scholar 

  19. 19.

    S. Izvekov, B.M. Rice, J. Chem. Phys. 140 (2014)

  20. 20.

    G. Deichmann, V. Marcon, N.F.A. van der Vegt, J. Chem. Phys. 141, 224109 (2014)

    ADS  Article  Google Scholar 

  21. 21.

    A. Davtyan, J.F. Dama, G.A. Voth, H.C. Andersen, J. Chem. Phys. 142, 154104 (2015)

    ADS  Article  Google Scholar 

  22. 22.

    I. Lyubimov, M. Guenza, J. Chem. Phys. 138, 12A546 (2013)

    Article  Google Scholar 

  23. 23.

    N. Guttenberg, J.F. Dama, M.G. Saunders, G.A. Voth, J. Weare, A.R. Dinner, J. Chem. Phys. 138, 094111 (2013)

    ADS  Article  Google Scholar 

  24. 24.

    J.D. Chodera, W.C. Swope, J.W. Pitera, K.A. Dill, Multiscale Model. Simul. 5, 1214 (2006)

    MathSciNet  Article  Google Scholar 

  25. 25.

    F. Noé, C. Schütte, E. Vanden-Eijnden, L. Reich, T.R. Weikl, Proc. Natl. Acad. Sci. USA 106, 19011 (2009)

    ADS  Article  Google Scholar 

  26. 26.

    G.R. Bowman, V.S. Pande, F. Noé (eds.), An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (Springer Science and Business Media, Dordrecht, Netherlands, 2014)

  27. 27.

    J.D. Chodera, V.S. Pande, Proc. Natl. Acad. Sci. USA 108, 12969 (2011)

    ADS  Article  Google Scholar 

  28. 28.

    T.J. Lane, G.R. Bowman, K. Beauchamp, V.A. Voelz, V.S. Pande, J. Am. Chem. Soc. 133, 18413 (2011)

    Article  Google Scholar 

  29. 29.

    G.R. Bowman, V.A. Voelz, V.S. Pande, J. Am. Chem. Soc. 133, 664 (2011)

    Article  Google Scholar 

  30. 30.

    I. Buch, T. Giorgino, G. De Fabritiis, Proc. Natl. Acad. Sci. USA 108, 10184 (2011)

    ADS  Article  Google Scholar 

  31. 31.

    G.R. Bowman, P.L. Geissler, Proc. Natl. Acad. Sci. USA 109, 11681 (2012)

    ADS  Article  Google Scholar 

  32. 32.

    N. Plattner, F. Noé, Nat. Commun. 6, 7653 (2015)

    ADS  Article  Google Scholar 

  33. 33.

    D. Shukla, A. Peck, V.S. Pande, Nat. Commun. 7, 10910 (2016)

    ADS  Article  Google Scholar 

  34. 34.

    W. Humphrey, A. Dalke, K. Schulten, J. Mol. Graphics 14, 33 (1996)

    Article  Google Scholar 

  35. 35.

    J.F. Rudzinski, K. Kremer, T. Bereau, J. Chem. Phys. 144, 051102 (2016)

    ADS  Article  Google Scholar 

  36. 36.

    J.F. Rudzinski, W.G. Noid, J. Chem. Theor. Comp. 11, 1278 (2015)

    Article  Google Scholar 

  37. 37.

    W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, J. Am. Chem. Soc. 118, 11225 (1996)

    Article  Google Scholar 

  38. 38.

    H. Berendsen, J. Grigera, T. Straatsma, J. Phys. Chem. 91, 6269 (1987)

    Article  Google Scholar 

  39. 39.

    T. Bereau, M. Deserno, J. Chem. Phys. 130, 235106 (2009)

    ADS  Article  Google Scholar 

  40. 40.

    G.T. Ramachandran, V. Sasisekharan, Adv. Protein Chem. 23, 283 (1968)

    Article  Google Scholar 

  41. 41.

    T. Bereau, M. Bachmann, M. Deserno, J. Am. Chem. Soc. 132, 13129 (2010)

    Article  Google Scholar 

  42. 42.

    T. Bereau, M. Deserno, M. Bachmann, Biophys. J. 100, 2764 (2011)

    ADS  Article  Google Scholar 

  43. 43.

    T. Bereau, M. Deserno, J. Membrane Biol. 248, 395 (2014)

    Article  Google Scholar 

  44. 44.

    T. Bereau, W.D. Bennett, J. Pfaendtner, M. Deserno, M. Karttunen, J. Chem. Phys. 143, 243127 (2015)

    ADS  Article  Google Scholar 

  45. 45.

    T. Bereau, C. Globisch, M. Deserno, C. Peter, J. Chem. Theory Comput. 8, 3750 (2012)

    Article  Google Scholar 

  46. 46.

    K. Osborne, M. Bachmann, B. Strodel, in From Computational Biophysics to Systems Biology (CBSB11) 2012 (Proceedings, 20–22 July 2011, Julich, Germany), p. 151

  47. 47.

    K.L. Osborne, M. Bachmann, B. Strodel, Proteins: Struct., Funct., Bioinf. 81, 1141 (2013)

    Article  Google Scholar 

  48. 48.

    K.L. Osborne, B. Barz, M. Bachmann, B. Strodel, Phys. Proc. 53, 90 (2014)

    ADS  Article  Google Scholar 

  49. 49.

    G.O. Rutter, A.H. Brown, D. Quigley, T.R. Walsh, M.P. Allen, Phys. Chem. Chem. Phys. 17, 31741 (2015)

    Article  Google Scholar 

  50. 50.

    Z.J. Wang, M. Deserno, J. Phys. Chem. B 114, 11207 (2010)

    Article  Google Scholar 

  51. 51.

    T. Bereau, Z.J. Wang, M. Deserno, J. Chem. Phys. 140, 115101 (2014)

    ADS  Article  Google Scholar 

  52. 52.

    H.J. Limbach, A. Arnold, B.A. Mann, C. Holm, Comput. Phys. Comm. 174, 704 (2006)

    ADS  Article  Google Scholar 

  53. 53.

    H.M. Cho, J.W. Chu, J. Chem. Phys. 131, 134107 (2009)

    ADS  Article  Google Scholar 

  54. 54.

    J.F. Rudzinski, W.G. Noid, J. Phys. Chem. B 118, 8295 (2014)

    Article  Google Scholar 

  55. 55.

    J.H. Prinz, H. Wu, M. Sarich, B. Keller, M. Senne, M. Held, J.D. Chodera, C. Schütte, F. Noé, J. Chem. Phys. 134, 174105 (2011)

    ADS  Article  Google Scholar 

  56. 56.

    G.R. Bowman, K.A. Beauchamp, G. Boxer, V.S. Pande, J. Chem. Phys. 131, 124101 (2009)

    ADS  Article  Google Scholar 

  57. 57.

    F. Noé, coworkers, Pyemma, https://github.com/markovmodel/PyEMMA/ (2015)

  58. 58.

    M. Senne, B. Trendelkamp-Schroer, A.S.J.S. Mey, C. Schütte, F. Noé, J. Chem. Theor. Comp. 8, 2223 (2012)

    Article  Google Scholar 

  59. 59.

    M.K. Scherer, B. Trendelkamp-Schroer, F. Paul, G. Pérez-Hernández, M. Hoffmann, N. Plattner, C. Wehmeyer, J.H. Prinz, F. Noé J. Chem. Theor. Comp. 11, 5525 (2015)

    Article  Google Scholar 

  60. 60.

    S. Kullback, R.A. Leibler, Ann. Math. Stat. 22, 79 (1951)

    MathSciNet  Article  Google Scholar 

  61. 61.

    T. Bereau, Unconstrained Structure Formation in Coarse-grained Protein Simulations, Ph.D. thesis, Carnegie Mellon University, 2011

  62. 62.

    S.T. Walsh, H. Cheng, J.W. Bryson, H. Roder, W.F. DeGrado, Proc. Natl. Acad. Sci. USA 96, 5486 (1999)

    ADS  Article  Google Scholar 

  63. 63.

    R.L. Henderson, Phys. Lett. A 49, 197 (1974)

    ADS  Article  Google Scholar 

  64. 64.

    J.T. Chayes, L. Chayes, E.H. Lieb, Comm. Math. Phys. 93, 57 (1984)

    ADS  MathSciNet  Article  Google Scholar 

  65. 65.

    J.F. Rudzinski, W.G. Noid, J. Chem. Phys. 135, 214101 (2011)

    ADS  Article  Google Scholar 

  66. 66.

    H.C. Andersen, D. Chandler, J.D. Weeks, Adv. Chem. Phys. 34, 105 (1976)

    Google Scholar 

  67. 67.

    F. Müller-Plathe, Chem. Phys. Chem. 3, 754 (2002)

    Google Scholar 

  68. 68.

    J.F. Rudzinski, W.G. Noid, J. Phys. Chem. B 116, 8621 (2012)

    Article  Google Scholar 

  69. 69.

    P. Ganguly, D. Mukherji, C. Junghans, N.F.A. van der Vegt, J. Chem. Theor. Comp. 8, 1802 (2012)

    Article  Google Scholar 

  70. 70.

    N.J.H. Dunn, W.G. Noid, J. Chem. Phys. 143 (2015)

Download references

Author information

Affiliations

Authors

Corresponding authors

Correspondence to J.F. Rudzinski or T. Bereau.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rudzinski, J., Bereau, T. Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fields. Eur. Phys. J. Spec. Top. 225, 1373–1389 (2016). https://doi.org/10.1140/epjst/e2016-60114-5

Download citation