Bayesian energy landscape tilting: Towards concordant models of molecular ensembles

Biophysical Journal

Volumn 106, Issue 6, 2014, Pages 1381-1390

  Beauchamp, Kyle A ^a   Pande, Vijay S ^b   Das, Rhiju ^b  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALANYL-ALANYL-ALANINE; OLIGOPEPTIDE;

ALGORITHM; BAYES THEOREM; CHEMICAL MODEL; CHEMISTRY; MONTE CARLO METHOD; POLYMERIZATION; PROBABILITY;

ALGORITHMS; BAYES THEOREM; MARKOV CHAINS; MODELS, CHEMICAL; MONTE CARLO METHOD; OLIGOPEPTIDES; POLYMERIZATION;

EID: 84896519419     PISSN: 00063495     EISSN: 15420086     Source Type: Journal    
DOI: 10.1016/j.bpj.2014.02.009     Document Type: Article

References (62)

1. H.M. Berman, and J. Westbrook P.E. Bourne The Protein Data Bank Nucleic Acids Res. 28 2000 235 242 (Pubitemid 30047768)
2. E.A. Dethoff, and K. Petzold H.M. Al-Hashimi Visualizing transient low-populated structures of RNA Nature 491 2012 724 728
3. A.L. Fink Natively unfolded proteins Curr. Opin. Struct. Biol. 15 2005 35 41 (Pubitemid 40249507)
4. D.M. Korzhnev, and X. Salvatella L.E. Kay Low-populated folding intermediates of Fyn SH3 characterized by relaxation dispersion NMR Nature 430 2004 586 590 (Pubitemid 38998635)
5. K. Lindorff-Larsen, and R.B. Best M. Vendruscolo Simultaneous determination of protein structure and dynamics Nature 433 2005 128 132
6. O.F. Lange, and N.-A. Lakomek B.L. de Groot Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution Science 320 2008 1471 1475 (Pubitemid 351929422)
7. M.A. DePristo, P.I. de Bakker, and T.L. Blundell Heterogeneity and inaccuracy in protein structures solved by x-ray crystallography Structure 12 2004 831 838 (Pubitemid 38595771)
8. P.T. Lang, and H.-L. Ng T. Alber Automated electron-density sampling reveals widespread conformational polymorphism in proteins Protein Sci. 19 2010 1420 1431
9. R.B. Best, N.V. Buchete, and G. Hummer Are current molecular dynamics force fields too helical? Biophys. J. 95 2008 L07 L09
10. K. Lindorff-Larsen, and P. Maragakis D.E. Shaw Systematic validation of protein force fields against experimental data PLoS ONE 7 2012 e32131
11. K.A. Beauchamp, and Y.S. Lin V.S. Pande Are protein force fields getting better? a systematic benchmark on 524 diverse NMR measurements J. Chem. Theory Comput. 8 2012 1409 1414
12. D.-W. Li, and R. Bruschweiler Iterative optimization of molecular mechanics force fields from NMR data of full-length proteins J. Chem. Theory Comput. 7 2011 1773
13. 2 dihedral angles J. Chem. Theory Comput. 8 2012 3257 3273
14. Nerenberg, P., and T. Head-Gordon. 2011. Optimizing protein- solvent force fields to reproduce intrinsic conformational preferences of model peptides. J. Chem. Theory Comput. 7:1220-1230. http://pubs.acs.org/doi/abs/10. 1021/ct2000183.
15. J. Pitera, and J. Chodera On the use of experimental observations to bias simulated ensembles J. Chem. Theory Comput. 8 2012 3445 3451
16. A. Patil, D. Huard, and C.J. Fonnesbeck PYMC: Bayesian stochastic modeling in PYTHON J. Stat. Softw. 35 2010 1 81
17. G.E. Crooks Beyond Boltzmann-Gibbs statistics: maximum entropy hyperensembles out of equilibrium Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 75 2007 041119
18. B. Różycki, Y.C. Kim, and G. Hummer SAXS ensemble refinement of ESCRT-III CHMP3 conformational transitions Structure 19 2011 109 116
19. J. Graf, and P.H. Nguyen H. Schwalbe Structure and dynamics of the homologous series of alanine peptides: a joint molecular dynamics/NMR study J. Am. Chem. Soc. 129 2007 1179 1189 (Pubitemid 46208587)
20. B. Vögeli, and J. Ying A. Bax Limits on variations in protein backbone dynamics from precise measurements of scalar couplings J. Am. Chem. Soc. 129 2007 9377 9385 (Pubitemid 47218806)
21. W. Rieping, M. Habeck, and M. Nilges Inferential structure determination Science 309 2005 303 306
22. J. Friedman, T. Hastie, and R. Tibshirani The Elements of Statistical Learning Vol. 1 2001 Springer Series in Statistics. Springer New York
23. S. Kullback, and R.A. Leibler On information and sufficiency Ann. Math. Statist. 22 1951 79 86
24. C.K. Fisher, A. Huang, and C.M. Stultz Modeling intrinsically disordered proteins with Bayesian statistics J. Am. Chem. Soc. 132 2010 14919 14927
25. C.K. Fisher, and C.M. Stultz Constructing ensembles for intrinsically disordered proteins Curr. Opin. Struct. Biol. 21 2011 426 431
26. D. Rubin The Bayesian bootstrap Ann. Stat. 9 1981 130 134
27. P. Kollman Advances and continuing challenges in achieving realistic and predictive simulations of the properties of organic and biological molecules Acc. Chem. Res. 29 1996 461 469
28. J. Wang, P. Cieplak, and P. Kollman How well does a restrained electrostatic potential(resp) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem. 21 2000 1049 1074
29. D.W. Li, and R. Brüschweiler NMR-based protein potentials Angew. Chem. Int. Ed. Engl. 49 2010 6778 6780
30. K. Lindorff-Larsen, and S. Piana D. Shaw Improved side-chain torsion potentials for the AMBER ff99sb protein force field Proteins 78 2010 1950 1958
31. A.D. MacKerell Jr., M. Feig, and C.L. Brooks 3rd Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations J. Comput. Chem. 25 2004 1400 1415
32. P. Bjelkmar, and P. Larsson E. Lindahl Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction maps, virtual interaction sites, and water models J. Chem. Theory Comput. 6 2010 459 466
33. G. Kaminski, and R. Friesner W. Jorgensen Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides J. Phys. Chem. B 105 2001 6474 6487 (Pubitemid 35339015)
34. B. Hess, and C. Kutzner E. Lindahl GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation J. Chem. Theory Comput. 4 2008 435 447
35. H.W. Horn, and W.C. Swope T. Head-Gordon Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew J. Chem. Phys. 120 2004 9665 9678
36. B. Han, and Y. Liu D.S. Wishart SHIFTX2: significantly improved protein chemical shift prediction J. Biomol. NMR 50 2011 43 57
37. Y. Shen, and A. Bax SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network J. Biomol. NMR 48 2010 13 22
38. D.-W. Li, and R. Brüschweiler PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles J. Biomol. NMR 54 2012 257 265
39. 3J coupling analysis for the joint calibration of Karplus coefficients and evaluation of torsion angles J. Biomol. NMR 14 1999 1 12 (Pubitemid 29258281)
40. J. Grdadolnik, and V. Mohacek-Grosev F. Avbelj Populations of the three major backbone conformations in 19 amino acid dipeptides Proc. Natl. Acad. Sci. USA 108 2011 1794 1798
41. A.K. Jha, and A. Colubri K.F. Freed Helix, sheet, and polyproline II frequencies and strong nearest neighbor effects in a restricted coil library Biochemistry 44 2005 9691 9702 (Pubitemid 40994363)
42. F. Avbelj, and S.G. Grdadolnik R.L. Baldwin Intrinsic backbone preferences are fully present in blocked amino acids Proc. Natl. Acad. Sci. USA 103 2006 1272 1277 (Pubitemid 43191155)
43. S. Pronk, and S. Pall D. van der Spoel GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit Bioinformatics 29 2013 845 854
44. P. Eastman, and M.S. Friedrichs V.S. Pande OPENMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation J. Chem. Theory Comput. 9 2013 461 469
45. P. Eastman, and V. Pande OPENMM: a hardware-independent framework for molecular simulations Comput. Sci. Eng. 12 2010 34 39
46. D. Shaw, and M. Deneroff J. Chao ANTON, a special-purpose machine for molecular dynamics simulation Commun. ACM 51 2008 91 97
47. M. Senne, and B. Trendelkamp-Schroer F. Noé EMMA - a software package for Markov model building and analysis J. Chem. Theory Comput. 8 2012 2223 2238
48. K.A. Beauchamp, and G.R. Bowman V.S. Pande MSMBUILDER2: modeling conformational dynamics at the picosecond to millisecond scale J. Chem. Theory Comput. 7 2011 3412 3419
49. V.A. Voelz, and G.R. Bowman V.S. Pande Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39) J. Am. Chem. Soc. 132 2010 1526 1528
50. G.R. Bowman, V.A. Voelz, and V.S. Pande Atomistic folding simulations of the five-helix bundle protein λ(6-85) J. Am. Chem. Soc. 133 2011 664 667
51. D.E. Shaw, and P. Maragakis W. Wriggers Atomic-level characterization of the structural dynamics of proteins Science 330 2010 341 346
52. K. Lindorff-Larsen, and S. Piana D.E. Shaw How fast-folding proteins fold Science 334 2011 517 520
53. C.K. Fisher, O. Ullman, and C.M. Stultz Efficient construction of disordered protein ensembles in a Bayesian framework with optimal selection of conformations Pac. Symp. Biocomput. 2012 2012 82 93
54. L.-P. Wang, J. Chen, and T. van Voorhis Systematic parametrization of polarizable force fields from quantum chemistry data J. Chem. Theory Comput. 9 2012 452 460
55. L.-P. Wang, and T.L. Head-Gordon V.S. Pande Systematic improvement of a classical molecular model of water J. Phys. Chem. B 117 2013 9956 9972
56. B. Richter, and J. Gsponer M. Vendruscolo The MUMO (minimal under-restraining minimal over-restraining) method for the determination of native state ensembles of proteins J. Biomol. NMR 37 2007 117 135 (Pubitemid 46157977)
57. S. Olsson, and J. Frellsen T. Hamelryck Inference of structure ensembles of flexible biomolecules from sparse, averaged data PLoS ONE 8 2013 e79439
58. 3J scalar couplings J. Biomol. NMR 45 2009 17 21
59. A. Raval, and S. Piana D.E. Shaw Refinement of protein structure homology models via long, all-atom molecular dynamics simulations Proteins 80 2012 2071 2079
60. E.T. Jaynes Information theory and statistical mechanics Phys. Rev. 106 1957 620
61. H. Flyvbjerg, and H.G. Petersen Error estimates on averages of correlated data J. Chem. Phys. 91 1989 461
62. M.R. Shirts, and J.D. Chodera Statistically optimal analysis of samples from multiple equilibrium states J. Chem. Phys. 129 2008 124105