Unconstrained enhanced sampling for free energy calculations of biomolecules: a review

Molecular Simulation

Volumn 42, Issue 13, 2016, Pages 1046-1055

  Miao, Yinglong ^a,b   McCammon, J Andrew ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b San Diego   (United States)

Author keywords

Biomolecules; enhanced sampling; free energy; unconstrained

Indexed keywords

BIOMOLECULES; MOLECULAR DYNAMICS;

ACCELERATED MOLECULAR DYNAMICS; ADAPTIVE BIASING FORCES; BIOMOLECULAR FREE ENERGY; BIOMOLECULAR SIMULATION; COLLECTIVE VARIABLES; ENHANCED SAMPLINGS; FREE-ENERGY CALCULATIONS; UNCONSTRAINED;

FREE ENERGY;

EID: 84977614030     PISSN: 08927022     EISSN: 10290435     Source Type: Journal    
DOI: 10.1080/08927022.2015.1121541     Document Type: Article

References (118)

1. J.A.McCammon, B.R.Gelin, M.Karplus. Dynamics of folded proteins. Nature. 1977;267:585–590.10.1038/267585a0
2. M.Karplus, J.A.McCammon. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 2002;9:646–652.10.1038/nsb0902-646
3. Y.Miao, Z.Yi, C.Cantrell, C.Glass Dennis, J.Baudry, N.Jain, C.Smith Jeremy. Coupled flexibility change in cytochrome P450cam substrate binding determined by neutron scattering, NMR, and molecular dynamics simulation. Biophys. J. 2012;103:2167–2176.10.1016/j.bpj.2012.10.013
4. P.Maragakis, K.Lindorff-Larsen, M.P.Eastwood, R.O.Dror, J.L.Klepeis, I.T.Arkin, M.O.Jensen, H.Xu, N.Trbovic, R.A.Friesner, A.G.Iii, D.E.Shaw. Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins. J. Phys. Chem. B. 2008;112:6155–6158. doi:10.1021/jp077018h.
5. S.Yang, L.Salmon. A-Hashimi HM. Measuring similarity between dynamic ensembles of biomolecules. Nat. Methods. 2014;11:552–554. doi:10.1038/nmeth.2921.
6. J.Smith, K.Kuczera, M.Karplus. Dynamics of myoglobin: comparison of simulation results with neutron scattering spectra. Proc. Nat. Acad. Sci. 1990;87:1601–1605. doi:10.1073/pnas.87.4.1601.
7. Y.L.Miao, Z.Yi, D.C.Glass, L.Hong, M.Tyagi, J.Baudry, N.T.Jain, J.C.Smith. Temperature-dependent dynamical transitions of different classes of amino acid residue in a globular protein. J. Am. Chem. Soc. 2012;134:19576–19579. doi:10.1021/ja3097898.
8. K.Y.Chan, L.G.Trabuco, E.Schreiner, K.Schulten. Cryo-electron microscopy modeling by the molecular dynamics flexible fitting method. Biopolymers. 2012;134:678–686. doi:10.1002/bip.22042.
9. L.G.Trabuco, E.Villa, K.Mitra, J.Frank, K.Schulten. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure. 2008;97:673–683. doi:10.1016/j.str.2008.03.005.
10. K.Lindorff-Larsen, S.Piana, R.O.Dror, D.E.Shaw. How fast-folding proteins fold. Science. 2011;334:517–520. doi:10.1126/science.1208351.
11. A.T.Brunger, J.Kuriyan, M.Karplus. Crystallographic R factor refinement by molecular dynamics. Science. 1987;235:458–460. doi:10.1126/science.235.4787.458.
12. R.McGreevy, A.Singharoy, Q.Li, J.Zhang, D.Xu, E.Perozo, K.Schulten. xMDFF: molecular dynamics flexible fitting of low-resolution X-ray structures. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2014;70:2344–2355.10.1107/S1399004714013856
13. A.E.Torda, W.F.van Gunsteren. The refinement of NMR structures by molecular dynamics simulation. Comput. Phys. Commun. 1991;62:289–296.10.1016/0010-4655(91)90101-P
14. J.Lehtivarjo, K.Tuppurainen, T.Hassinen, R.Laatikainen, M.Perakyla. Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction. J. Biomol. NMR. 2012;52:257–267. doi:10.1007/s10858-012-9609-6.
15. P.Kollman. Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev. 1993;93:2395–2417.10.1021/cr00023a004
16. E.Vanden-Eijnden. Some recent techniques for free energy calculations. J. Comput. Chem. 2009;30:1737–1747.10.1002/jcc.v30:11
17. C.D.Christ, A.E.Mark, W.F.van Gunsteren. Basic ingredients of free energy calculations: a review. J. Comput. Chem. 2010;31:1569–1582.
18. A.Pohorille, C.Jarzynski, C.Chipot. Good practices in free-energy calculations. J. Phys. Chem. B. 2010;114:10235–10253. doi:10.1021/jp102971x.
19. J.M.Johnston, M.Filizola. Showcasing modern molecular dynamics simulations of membrane proteins through G protein-coupled receptors. Curr. Opin. Struct. Biol. 2011;21:552–558.10.1016/j.sbi.2011.06.008
20. K.Henzler-Wildman, D.Kern. Dynamic personalities of proteins. Nature. 2007;450:964–972.10.1038/nature06522
21. J.R.Lewandowski, M.E.Halse, M.Blackledge, L.Emsley. Direct observation of hierarchical protein dynamics. Science. 2015;348:578–581. doi:10.1126/science.aaa6111.
22. C.Abrams, G.Bussi. Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy. 2014;16:163–199.
23. V.Spiwok, Z.Sucur, P.Hosek. Enhanced sampling techniques in biomolecular simulations. Biotechnol. Adv. 2015;33:1130–1140. doi:10.1016/j.biotechadv.2014.11.011.
24. C.Dellago, P.G.Bolhuis. Transition path sampling and other advanced simulation techniques for rare events. Adv. Comput. Simul. Approach. Soft Matter Sci. III. 2009;221:167–233.10.1007/978-3-540-87706-6
25. Y.Q.Gao, L.J.Yang, Y.B.Fan, Q.Shao. Thermodynamics and kinetics simulations of multi-time-scale processes for complex systems. Int. Rev. Phys. Chem. 2008;27:201–227. doi:10.1080/01442350801920334.
26. A.Liwo, C.Czaplewski, S.Oldziej, H.A.Scheraga. Computational techniques for efficient conformational sampling of proteins. Curr. Opin. Struct. Biol. 2008;18:134–139. doi:10.1016/j.sbi.2007.12.001.
27. M.Christen, W.F.van Gunsteren. On searching in, sampling of, and dynamically moving through conformational space of biomolecular systems: a review. J. Comput. Chem. 2008;29:157–166.10.1002/(ISSN)1096-987X
28. J.Kästner. Umbrella sampling. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2011;1:932–942.10.1002/wcms.66
29. G.M.Torrie, J.P.Valleau. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 1977;23:187–199.10.1016/0021-9991(77)90121-8
30. G.Ciccotti, R.Kapral, E.Vanden-Eijnden. Blue moon sampling, vectorial reaction coordinates, and unbiased constrained dynamics. ChemPhysChem. 2005;6:1809–1814.
31. E.Darve, D.Rodriguez-Gomez, A.Pohorille. Adaptive biasing force method for scalar and vector free energy calculations. J. Chem. Phys. 2008;128:144120.10.1063/1.2829861
32. E.Darve, A.Pohorille. Calculating free energies using average force. J. Chem. Phys. 2001;115:9169–9183.
33. A.Laio, M.Parrinello. Escaping free-energy minima. Proc. Nat. Acad. Sci. 2002;99:12562–12566.10.1073/pnas.202427399
34. A.Laio, F.L.Gervasio. Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys. 2008;71:126601.10.1088/0034-4885/71/12/126601
35. H.Grubmüller. Predicting slow structural transitions in macromolecular systems: conformational flooding. Phys. Rev. E. 1995;52:2893–2906.10.1103/PhysRevE.52.2893
36. L.Maragliano, A.Fischer, E.Vanden-Eijnden, G.Ciccotti. String method in collective variables: minimum free energy paths and isocommittor surfaces. J. Chem. Phys. 2006;125:024106.10.1063/1.2212942
37. A.C.Pan, D.Sezer, B.Roux. Finding transition pathways using the string method with swarms of trajectories. J. Phys. Chem. B. 2008;112:3432–3440.10.1021/jp0777059
38. J.L.Knight, C.L.Brooks. λ-Dynamics free energy simulation methods. J. Comput. Chem.2009;30:1692–1700.10.1002/jcc.v30:11
39. L.Rosso, M.E.Tuckerman. An adiabatic molecular dynamics method for the calculation of free energy profiles. Mol. Simul. 2002;28:91–112.10.1080/08927020211977
40. C.F.Abrams, E.Vanden-Eijnden. Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics. Proc. Nat. Acad. Sci. 2010;107:4961–4966.10.1073/pnas.0914540107
41. J.Schlitter, M.Engels, P.Kruger, E.Jacoby, A.Wollmer. Targeted molecular dynamics simulation of conformational change-application to the T ↔ R transition in insulin. Mol. Simul. 1993;10:291–308.10.1080/08927029308022170
42. P.Ferrara, J.Apostolakis, A.Caflisch. Targeted molecular dynamics simulations of protein unfolding. J. Phys. Chem. B. 2000;104:4511–4518.10.1021/jp9943878
43. J.Schlitter, M.Engels, P.Kruger. Targeted molecular dynamics: a new approach for searching pathways of conformational transitions. J. Mol. Graphics. 1994;12:84–89.10.1016/0263-7855(94)80072-3
44. L.Q.Zheng, M.G.Chen, W.Yang. Random walk in orthogonal space to achieve efficient free-energy simulation of complex systems. Proc. Nat. Acad. Sci. 2008;105:20227–20232.10.1073/pnas.0810631106
45. L.Q.Zheng, M.G.Chen, W.Yang. Simultaneous escaping of explicit and hidden free energy barriers: application of the orthogonal space random walk strategy in generalized ensemble based conformational sampling. J. Chem. Phys. 2009;130:234105.10.1063/1.3153841
46. Y.Sugita, Y.Okamoto. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 1999;314:141–151.10.1016/S0009-2614(99)01123-9
47. Y.Okamoto. Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J. Mol. Graphics Modell. 2004;22:425–439.
48. U.H.E.Hansmann. Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 1997;281:140–150.10.1016/S0009-2614(97)01198-6
49. X.Wu, S.Wang. Crossing the time scale of protein folding through self-guided molecular dynamics simulation. J. Mol. Graphics Modell. 1998;16:290–290.
50. X.W.Wu, S.M.Wang. Self-guided molecular dynamics simulation for efficient conformational search. J. Phys. Chem. B. 1998;102:7238–7250.10.1021/jp9817372
51. X.W.Wu, B.R.Brooks. Self-guided Langevin dynamics simulation method. Chem. Phys. Lett. 2003;381:512–518.10.1016/j.cplett.2003.10.013
52. X.Wu, B.R.Brooks, E.Vanden-Eijnden. Self-guided Langevin dynamics via generalized Langevin equation. J. Comput. Chem. 2015;. doi:10.1002/jcc.24015.
53. H.Li, D.Min, Y.Liu, W.Yang. Essential energy space random walk via energy space metadynamics method to accelerate molecular dynamics simulations. J. Chem. Phys. 2007;127:094101.10.1063/1.2769356
54. L.Q.Zheng, W.Yang. Essential energy space random walks to accelerate molecular dynamics simulations: convergence improvements via an adaptive-length self-healing strategy. J. Chem. Phys. 2008;129:014105.10.1063/1.2949815
55. C.Lv, L.Q.Zheng, W.Yang. Generalized essential energy space random walks to more effectively accelerate solute sampling in aqueous environment. J. Chem. Phys. 2012;136:044103.10.1063/1.3678220
56. D.Hamelberg, J.Mongan, J.A.McCammon. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J. Chem. Phys. 2004;120:11919–11929.10.1063/1.1755656
57. Y.Miao, V.A.Feher, J.A.McCammon. Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation. J. Chem. Theory Comput. 2015;11:3584–3595.10.1021/acs.jctc.5b00436
58. E.Marinari, G.Parisi. Simulated tempering: a new Monte Carlo scheme. Europhys. Lett. 1992;19:451–458.10.1209/0295-5075/19/6/002
59. A.P.Lyubartsev, A.A.Martsinovski, S.V.Shevkunov, P.N.Vorontsovvelyaminov. New approach to Monte Carlo calculation of the free energy: method of expanded ensembles. J. Chem. Phys. 1992;96:1776–1783.10.1063/1.462133
60. B.A.Berg, T.Neuhaus. Multicanonical algorithms for first order phase transitions. Phys. Lett. B. 1991;267:249–253.10.1016/0370-2693(91)91256-U
61. Y.Sugita, Y.Okamoto. Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape. Chem. Phys. Lett. 2000;329:261–270.10.1016/S0009-2614(00)00999-4
62. A.Mitsutake, Y.Sugita, Y.Okamoto. Replica-exchange multicanonical and multicanonical replica-exchange Monte Carlo simulations of peptides. I. Formulation and benchmark test. J. Chem. Phys. 2003;118:6664–6675.10.1063/1.1555847
63. A.Mitsutake, Y.Okamoto. Replica-exchange simulated tempering method for simulations of frustrated systems. Chem. Phys. Lett. 2000;332:131–138.10.1016/S0009-2614(00)01262-8
64. Y.Ishikawa, Y.Sugita, T.Nishikawa, Y.Okamoto. Ab initio replica-exchange Monte Carlo method for cluster studies. Chem. Phys. Lett. 2001;333:199–206.10.1016/S0009-2614(00)01342-7
65. Y.Sugita, A.Kitao, Y.Okamoto. Multidimensional replica-exchange method for free-energy calculations. J. Chem. Phys. 2000;113:6042–6051.10.1063/1.1308516
66. K.Murata, Y.Sugita, Y.Okamoto. Molecular dynamics simulations of DNA dimers based on replica-exchange umbrella sampling. I. Test of sampling efficiency. J. Theor. Comput. Chem. 2005;4:411–432.
67. J.Curuksu, M.Zacharias. Enhanced conformational sampling of nucleic acids by a new Hamiltonian replica exchange molecular dynamics approach. J. Chem. Phys. 2009;130:104110.10.1063/1.3086832
68. M.Meli, G.Colombo. A Hamiltonian replica exchange molecular dynamics (MD) method for the study of folding, based on the analysis of the stabilization determinants of proteins. Int. J. Mol. Sci. 2013;14:12157–12169.10.3390/ijms140612157
69. T.Nishikawa, H.Ohtsuka, Y.Sugita, M.Mikami, Y.Okamoto. Replica – exchange Monte Carlo method for Ar fluid. Prog. Theor. Phys. Suppl. 2000:270–271.10.1143/PTPS.138.270
70. A.Mitsutake, Y.Sugita, Y.Okamoto. Replica-exchange multicanonical and multicanonical replica-exchange Monte Carlo simulations of peptides. II. Application to a more complex system. J. Chem. Phys. 2003;118:6676–6688.10.1063/1.1555849
71. G.La Penna, A.Mitsutake, M.Masuya, Y.Okamoto. Molecular dynamics of C-peptide of ribonuclease A studied by replica-exchange Monte Carlo method and diffusion theory. Chem. Phys. Lett. 2003;380:609–619.10.1016/j.cplett.2003.07.031
72. J.W.Pitera, W.Swope. Understanding folding and design: replica-exchange simulations of ‘Trp-cage’ miniproteins. Proc. Nat. Acad. Sci. 2003;100:7587–7592.10.1073/pnas.1330954100
73. Y.Sugita, Y.Okamoto. An analysis on protein folding problem by replica-exchange method. Prog. Theor. Phys. Suppl. 2000:402–403.10.1143/PTPS.138.402
74. P.H.Nguyen, G.Stock, E.Mittag, C.K.Hu, M.S.Li. Free energy landscape and folding mechanism of a β-hairpin in explicit water: a replica exchange molecular dynamics study. Proteins: Struct. Funct. Bioinf. 2005;61:795–808.10.1002/prot.20696
75. S.Kannan, M.Zacharias. Application of biasing-potential replica-exchange simulations for loop modeling and refinement of proteins in explicit solvent. Proteins: Struct. Funct. Bioinf. 2010;78:2809–2819.10.1002/prot.v78:13
76. K.Murata, Y.Sugita, Y.Okamoto. Free energy calculations for DNA base stacking by replica-exchange umbrella sampling. Chem. Phys. Lett. 2004;385:1–7.10.1016/j.cplett.2003.10.159
77. A.Mitsutake, Y.Okamoto. Replica-exchange extensions of simulated tempering method. J. Chem. Phys. 2004;121:2491–2504.10.1063/1.1766015
78. X.W.Wu, A.Damjanovic, B.R.Brooks. Efficient and unbiased sampling of biomolecular systems in the canonical ensemble: a review of self-guided Langevin dynamics. Adv. Chem. Phys. 2012;150:255–326.
79. X.W.Wu, B.R.Brooks. Force-momentum-based self-guided Langevin dynamics: a rapid sampling method that approaches the canonical ensemble. J. Chem. Phys. 2011;135:204101.10.1063/1.3662489
80. Y.B.Sheng, W.wang, P.Chen. Adsorption of an ionic complementary peptide on the hydrophobic graphite surface. J. Phys. Chem. C. 2010;114:454–459.10.1021/jp908629g
81. X.W.Wu, S.Subramaniam, D.A.Case, K.W.Wu, B.R.Brooks. Targeted conformational search with map-restrained self-guided Langevin dynamics: application to flexible fitting into electron microscopic density maps. J. Struct. Biol. 2013;183:429–440.10.1016/j.jsb.2013.07.006
82. V.Chandrasekaran, C.J.Lee, P.Lin, R.E.Duke, L.G.Pedersen. A computational modeling and molecular dynamics study of the Michaelis complex of human protein Z-dependent protease inhibitor (ZPI) and factor Xa (FXa). J. Mol. Model. 2009;15:897–911.10.1007/s00894-008-0444-3
83. C.Y.Yang, Z.Nikolovska-Coleska, P.Li, P.Roller, S.M.Wang. Solution conformations of wild-type and mutated Bak BH3 peptides via dynamical conformational sampling and implication to their binding to antiapoptotic Bcl-2 proteins. J. Phys. Chem. B. 2004;108:1467–1477.10.1021/jp036009f
84. J.Varady, X.W.Wu, S.M.Wang. Competitive and reversible binding of a guest molecule to its host in aqueous solution through molecular dynamics simulation: benzyl alcohol/β-cyclodextrin system. J. Phys. Chem. B. 2002;106:4863–4872.10.1021/jp0131469
85. D.Choudhary, P.Clancy. Application of accelerated molecular dynamics schemes to the production of amorphous silicon. J. Chem. Phys. 2005;122:154509.
86. D.Hamelberg, C.A.F.de Oliveira,J.A.McCammon Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. J. Chem. Phys. 2007;127:155102.10.1063/1.2789432
87. P.R.L.Markwick, J.A.McCammon. Studying functional dynamics in bio-molecules using accelerated molecular dynamics. PCCP. 2011;13:20053–20065.10.1039/c1cp22100k
88. W.Sinko, C.A.F.de Oliveira,L.C.T.Pierce, J.A.McCammon. Protecting high energy barriers: a new equation to regulate boost energy in accelerated molecular dynamics simulations. J. Chem. Theory Comput. 2012;8:17–23.10.1021/ct200615k
89. P.R.L.Markwick, L.C.T.Pierce, D.B.Goodin, J.A.McCammon. Adaptive accelerated molecular dynamics (Ad-AMD) revealing the molecular plasticity of P450cam. J. Phys. Chem. Lett. 2011;2:158–164.10.1021/jz101462n
90. U.Doshi, D.Hamelberg. Improved statistical sampling and accuracy with accelerated molecular dynamics on rotatable torsions. J. Chem. Theory Comput. 2012;8:4004–4012.10.1021/ct3004194
91. J.Wereszczynski, J.A.McCammon. Using selectively applied accelerated molecular dynamics to enhance free energy calculations. J. Chem. Theory Comput. 2010;6:3285–3292.10.1021/ct100322t
92. C.A.F.de Oliveira, D.Hamelberg, J.A.McCammon. Coupling accelerated molecular dynamics methods with thermodynamic integration simulations. J. Chem. Theory Comput. 2008;4:1516–1525.10.1021/ct800160q
93. M.Fajer, D.Hamelberg, J.A.McCammon. Replica-exchange accelerated molecular dynamics (REXAMD) applied to thermodynamic integration. J. Chem. Theory Comput. 2008;4:1565–1569.10.1021/ct800250m
94. M.Fajer, R.V.Swift, J.A.McCammon. Using multistate free energy techniques to improve the efficiency of replica exchange accelerated molecular dynamics. J. Comput. Chem. 2009;30:1719–1725.10.1002/jcc.v30:11
95. M.Arrar, C.A.F.de Oliveira, M.Fajer, W.Sinko, J.A.McCammon. w-REXAMD: a Hamiltonian replica exchange approach to improve free energy calculations for systems with kinetically trapped conformations. J. Chem. Theory Comput. 2013;9:18–23.10.1021/ct300896h
96. S.L.Williams, C.A.F.de Oliveira, J.A.McCammon. Coupling constant pH molecular dynamics with accelerated molecular dynamics. J. Chem. Theory Comput. 2010;6:560–568.10.1021/ct9005294
97. D.Bucher, L.C.T.Pierce, J.A.McCammon, P.R.L.Markwick. On the use of accelerated molecular dynamics to enhance configurational sampling in Ab Initio simulations. J. Chem. Theory Comput. 2011;7:890–897.10.1021/ct100605v
98. L.C.T.Pierce, P.R.L.Markwick, J.A.McCammon, N.L.Doltsinis. Accelerating chemical reactions: exploring reactive free-energy surfaces using accelerated ab initio molecular dynamics. J. Chem. Phys. 2011;134:174107.10.1063/1.3581093
99. D.Hamelberg, T.Shen, J.A.McCammon. Phosphorylation effects on cis/trans isomerization and the backbone conformation of serine−proline motifs: accelerated molecular dynamics analysis. J. Am. Chem. Soc. 2005;127:1969–1974.10.1021/ja0446707
100. C.A.F.De Oliveira, D.Hamelberg, J.A.McCammon. Estimating kinetic rates from accelerated molecular dynamics simulations: alanine dipeptide in explicit solvent as a case study. J. Chem. Phys. 2007;127:175105.
101. Y.Wang, P.R.L.Markwick, C.A.F.de Oliveira, J.A.McCammon. Enhanced lipid diffusion and mixing in accelerated molecular dynamics. J. Chem. Theory Comput. 2011;7:3199–3207.10.1021/ct200430c
102. R.Baron, S.E.Wong, C.A.F.de Oliveira, J.A.McCammon. E9-Im9 colicin DNase-immunity protein biomolecular association in water: a multiple-copy and accelerated molecular dynamics simulation study. J. Phys. Chem. B. 2008;112:16802–16814.10.1021/jp8061543
103. B.J.Grant, A.A.Gorfe, J.A.McCammon. Ras conformational switching: simulating nucleotide-dependent conformational transitions with accelerated molecular dynamics. PLoS Comput. Biol. 2009;5:e1000325.10.1371/journal.pcbi.1000325
104. D.Bucher, B.J.Grant, P.R.Markwick, J.A.McCammon. Accessing a hidden conformation of the maltose binding protein using accelerated molecular dynamics. PLoS Comput. Biol. 2011;7:e1002034.10.1371/journal.pcbi.1002034
105. C.A.F.de Oliveira, B.J.Grant, M.Zhou, J.A.McCammon Large-scale conformational changes of trypanosoma cruzi proline racemase predicted by accelerated molecular dynamics simulation. PLoS Comput. Biol. 2011;7:e1002178.10.1371/journal.pcbi.1002178
106. U.Doshi, D.Hamelberg. Achieving rigorous accelerated conformational sampling in explicit solvent. J. Phys. Chem. Lett. 2014;5:1217–1224.10.1021/jz500179a
107. Y.Miao, F.Feixas, C.Eun, J.A.McCammon. Accelerated molecular dynamics simulations of protein folding. J. Comput. Chem. 2015;36:1536–1549.10.1002/jcc.v36.20
108. K.Kappel, Y.Miao, J.A.McCammon. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein coupled receptor. Q. Rev. Biophys. 2015;48:479–487. doi:10.1017/S0033583515000153.
109. L.C.T.Pierce, R.Salomon-Ferrer, C.A.F.de Oliveira, J.A.McCammon, R.C.Walker. Routine access to millisecond time scale events with accelerated molecular dynamics. J. Chem. Theory Comput. 2012;8:2997–3002.10.1021/ct300284c
110. Y.Miao, S.E.Nichols, P.M.Gasper, V.T.Metzger, J.A.McCammon. Activation and dynamic network of the M2 muscarinic receptor. Proc. Nat. Acad. Sci. 2013;110:10982–10987.10.1073/pnas.1309755110
111. Y.Miao, S.E.Nichols, J.A.McCammon. Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics. PCCP. 2014;16:6398–6406.10.1039/c3cp53962h
112. Y.Miao, A.D.Caliman, J.A.McCammon. Allosteric effects of sodium ion binding on activation of the M3 muscarinic G-protein coupled receptor. Biophys. J. 2015;108:1796–1806.10.1016/j.bpj.2015.03.003
113. T.Y.Shen, D.Hamelberg. A statistical analysis of the precision of reweighting-based simulations. J. Chem. Phys. 2008:034103.10.1063/1.2944250
114. W.Sinko, Y.Miao, C.A.F.de Oliveira, J.A.McCammon. Population based reweighting of scaled molecular dynamics. J. Phys. Chem. B. 2013;117:12759–12768.10.1021/jp401587e
115. Y.Miao, W.Sinko, L.Pierce, D.Bucher, J.A.McCammon. Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J. Chem. Theory Comput. 2014;10:2677–2689.10.1021/ct500090q
116. G.Bussi, F.L.Gervasio, A.Laio, M.Parrinello. Free-energy landscape for β hairpin folding from combined parallel tempering and metadynamics. J. Am. Chem. Soc. 2006;128:13435–13441.10.1021/ja062463w
117. C.Jarzynski. Nonequilibrium equality for free energy differences. Phys. Rev. Lett. 1997;78:2690–2693.10.1103/PhysRevLett.78.2690
118. J.Gore, F.Ritort, C.Bustamante. Bias and error in estimates of equilibrium free-energy differences from nonequilibrium measurements. Proc. Nat. Acad. Sci. 2003;100:12564–12569.10.1073/pnas.1635159100