Long-timescale molecular dynamics simulations of protein structure and function

Current Opinion in Structural Biology

Volumn 19, Issue 2, 2009, Pages 120-127

  Klepeis, John L ^a   Lindorff Larsen, Kresten ^a   Dror, Ron O ^a   Shaw, David E ^a,b  

^a D E SHAW RESEARCH   (United States)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ATOM; COMPUTER; COMPUTER PROGRAM; CONFORMATIONAL TRANSITION; MOLECULAR DYNAMICS; MOLECULAR MECHANICS; PRIORITY JOURNAL; PROTEIN FUNCTION; PROTEIN STRUCTURE; REVIEW; SIMULATION; VALIDATION PROCESS;

COMPUTER SIMULATION; MODELS, MOLECULAR; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; SOFTWARE; STRUCTURE-ACTIVITY RELATIONSHIP; TIME FACTORS;

EID: 64649101249     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sbi.2009.03.004     Document Type: Review

References (61)

1. McCammon J.A., Gelin B.R., and Karplus M. Dynamics of folded proteins. Nature 267 (1977) 585-590
2. Schaeffer R.D., Fersht A., and Daggett V. Combining experiment and simulation in protein folding: closing the gap for small model systems. Curr Opin Struct Biol 18 (2008) 4-9
3. Duan Y., and Kollman P.A. Pathways to protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282 (1998) 740-744
4. Freddolino P.L., Liu F., Gruebele M., and Schulten K. Ten-microsecond molecular dynamics simulation of a fast-folding WW domain. Biophys J 94 (2008) L75-L77. The paper describes a combined experimental and computational study of a fast-folding WW domain, featuring a 10 μs all-atom MD simulation.
5. Ensign D.L., Kasson P.M., and Pande V.S. Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. J Mol Biol 374 (2007) 806-816
6. Pérez A., Luque F.J., and Orozco M. Dynamics of B-DNA on the microsecond time scale. J Am Chem Soc 129 (2007) 14739-14745
7. Seibert M.M., Patriksson A., Hess B., and van der Spoel D. Reproducible polypeptide folding and structure prediction using molecular dynamics simulations. J Mol Biol 354 (2005) 173-183
8. Maragakis P., Lindorff-Larsen K., Eastwood M.P., Dror R.O., Klepeis J.L., Arkin I.T., Jensen M.Ø., Xu H., Trbovic N., Friesner R.A., et al. Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins. J Phys Chem B 112 (2008) 6155-6158
9. Martinez-Mayorga K., Pitman M.C., Grossfield A., Feller S.E., and Brown M.F. Retinal counterion switch mechanism in vision evaluated by molecular simulations. J Am Chem Soc 128 (2006) 16502-16503
10. Grossfield A., Pitman M.C., Feller S.E., Soubias O., and Gawrisch K. Internal hydration increases during activation of the G-protein-coupled receptor rhodopsin. J Mol Biol 381 (2008) 478-486. The authors perform several microsecond-length MD simulations of the first steps of rhodopsin activation, emulating the effect of light by the forced isomerization of retinal. They observe a dramatic increase of internal water in the protein and test this hypothesis using NMR spectroscopy.
11. 2-adrenergic receptor exists in equilibrium between two inactive conformations, only one of which has been observed crystallographically; in the other, an 'ionic lock' connects helices 3 and 6, in agreement with biochemical data. Previously unobserved secondary structure elements also formed during the simulations.
12. Bhatelé A., Kumar S., Mei C., Phillips J.C., Zheng G., and Kalé L.V. Overcoming scaling challenges in biomolecular simulations across multiple platforms. Proceedings of the IEEE International Parallel and Distributed Processing Symposium (2008), IEEE Computer Society Press 1-12
13. Fitch B.G., Rayshubsky A., Elefitheriou M., Ward T.J.C., Giampapa M.E., Pitman M.C., Pitera J.W., Swope W.C., and Germain R.S. Blue matter: scaling of N-body simulations to one atom per node. IBM J Res Dev 52 (2008) 145-158
14. Hess B., Kutzner C., van der Spoel D., and Lindahl E. GROMACS 4: algorithms for highly efficient load-balanced, and scalable molecular simulation. J Chem Theory Comput 4 (2008) 435-447
15. Bowers K.J., Chow E., Xu H., Dror R.O., Eastwood M.P., Gregersen B.A., Klepeis J.L., Kolossváry I., Moraes M.A., Sacerdoti F.D., et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC06) (2006), ACM Press
16. Chow E, Rendleman CA, Bowers KJ, Dror RO, Hughes DH, Gullingsrud J, Sacerdoti FD, Shaw DE: Desmond performance on a cluster of multicore processors. D. E. Shaw Research Technical Report DESRES/TR - 2008-01; 2008. http://www.DEShawResearch.com.
17. Shaw D.E. A fast, scalable method for the parallel evaluation of distance-limited pairwise particle interactions. J Comput Chem 26 (2005) 1318-1328
18. Bowers K.J., Dror R.O., and Shaw D.E. Zonal methods for the parallel execution of range-limited N-body simulations. J Comput Phys 221 (2006) 303-329
19. Kalé L.V., Skeel R., Bhandarka M., Brunner R., Gursoy A., Krawetz N., Phillips J.C., Shinozaki A., Varadarajan K., and Schulten K. NAMD2: greater scalability for parallel molecular dynamics. J Comput Phys 151 (1999) 283-312
20. Luttmann E., Ensign D.L., Vaidyanathan V., Houston M., Rimon N., Oland J., Jayachandran G., Friedrichs M., and Pande V.S. Accelerating molecular dynamic simulations on the cell processor and PlayStation 3. J Comput Chem 30 (2009) 268-274
21. Friedrichs M.S., Eastman P., Vaidyanathan V., Houston M., Legrand S., Beberg A.L., Ensign D.L., Bruns C.M., and Pande V.S. Accelerating molecular dynamic simulation on graphics processing units. J Comput Chem 30 (2009) 864-872
22. Phillips J.C., Stone J.E., and Schulten K. Adapting a message-driven parallel application to GPU-accelerated cluster. Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (SC08) (2008), ACM Press
23. Chodera J.D., Singhal N., Pande V.S., Dill K.A., and Swope W.C. Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. J Chem Phys 126 (2007) 155101
24. Narumi T., Ohno Y., Okimoto N., Koishi T., Suenaga A., Futatsugi N., Yanai R., Himeno R., Fujikawa S., and Taiji M. A 55 TFLOPS simulation of amyloid-forming peptides from yeast prion Sup35 with the special-purpose computer system MDGRAPE-3. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC06) (2006), ACM Press
25. Shaw D.E., Deneroff M.M., Dror R.O., Kuskin J.S., Larson R.H., Salmon J.K., Young C., Batson B., Bowers K.J., Chao J.C., et al. Anton, a special-purpose machine for molecular dynamics simulation. Proceedings of the 34th Annual International Symposium on Computer Architecture (ISCA07) (2007), ACM Press
26. Monticelli L., Kandasamy S.K., Periole X., Larson R.G., Tieleman D.P., and Marrink S.J. The MARTINI coarse-grained force field: extensions to proteins. J Chem Theory Comput 4 (2008) 819-834. The authors present a coarse-grained model for the simulation of proteins based on the earlier MARTINI force field for lipids.
27. Buchete N.V., and Hummer G. Peptide folding kinetics from replica exchange molecular dynamics. Phys Rev Lett 77 (2008) 030902. The authors present a method to obtain kinetic information from generalized ensemble simulations using transition path data and a master equation approach. The method is applied to a short helical peptide in explicit water.
28. Piana S., and Laio A. A bias-exchange approach to protein folding. J Phys Chem B 111 (2007) 4553-4559
29. Zoete V., Michielin O., and Karplus M. Relation between sequence and structure of HIV-1 protease inhibitor complexes: a model system for the analysis of protein flexibility. J Mol Biol 31 (2002) 21-52
30. Best R.B., Lindorff-Larsen K., DePristo M.A., and Vendruscolo M. Relation between native ensembles and experimental structures of proteins. Proc Natl Acad Sci U S A 103 (2006) 10901-10906
31. Lange O.F., Lakomek N.A., Farès C., Schröder G.F., Walter K.F., Becker S., Meiler J., Grubmüller H., Griesinger C., and de Groot B.L. Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320 (2008) 1471-1475
32. Henzler-Wildman K.A., Thai V., Lei M., Ott M., Wolf-Watz M., Fenn T., Pozharski E., Wilson M.A., Petsko G.A., Karplus M., et al. Intrinsic motions along an enzymatic reaction trajectory. Nature 450 (2007) 838-844
33. Kubelka J., Chiu T.K., Davies D.R., Eaton W.A., and Hofrichter J. Sub-microsecond protein folding. J Mol Biol 359 (2006) 546-553
34. Best R.B., Buchete N.V., and Hummer G. Are current molecular dynamics force fields too helical?. Biophys J 95 (2008) L07-L09. The authors perform long MD simulations of a penta-alanine peptide using 12 different force fields. Comparison between the simulations and NMR-derived scalar couplings are used to evaluate the ability of the different force fields to reproduce the conformational ensemble of this peptide.
35. Freddolino PL, Park S, Roux B, Schulten K: Force field bias in protein folding simulations. Biophys J, in press.
36. Vásquez V., Sotomayor M., Cordero-Morales J., Schulten K., and Perozo E. A structural mechanism for MscS gating in lipid bilayers. Science 321 (2008) 1210-1214
37. + channel in a membrane environment. Biophys J 93 (2007) 3070-3082
38. Khalili-Araghi F, Gumbart J, Wen P-C, Sotomayor M, Tajkhorshid E, Schulten K: Molecular dynamics simulations of membrane channels and transporters. Curr Opin Struct Biol, doi:10.1016/j.sbi.2009.02.011, in press.
39. Henin J., Tajkhorshid E., Schulten K., and Chipot C. Diffusion of glycerol through Escherichia coli aqua glyceroporin GlpF. Biophys J 94 (2008) 832-839
40. Hub J.S., and de Groot B.L. Mechanism of selectivity in aquaporins and aquaglyceroprins. Proc Natl Acad Sci U S A 105 (2008) 1198-1203
41. Jensen M.Ø., Dror R.O., Xu H., Borhani D.W., Arkin I.T., Eastwood M.P., and Shaw D.E. Dynamic control of slow water transport by aquaporin 0: implications for hydration and junction stability in the eye lens. Proc Natl Acad Sci U S A 105 (2008) 14430-14435
42. Shan Y., Seeliger M.A., Eastwood M.P., Frank F., Xu H., Jensen M.Ø., Dror R.O., Kuriyan J., and Shaw D.E. A conserved protonation-dependent switch controls drug binding in the Abl kinase. Proc Natl Acad Sci U S A 106 (2009) 139-144
43. Faraldo-Gómez R.B. On the importance of a funneled energy landscape for the assembly and regulation of multidomain Src tyrosine kinases. Proc Natl Acad Sci U S A 104 (2007) 13643-13648. The authors combine long simulations and free-energy calculations to study the regulation of tyrosine kinases by SH2/SH3 domains. They suggest that the connector region in the kinase can play an active role in shaping the free energy landscape of kinase activation.
44. + antiporting. Science 317 (2007) 799-803
45. Jardetzky O. Simple allosteric model for membrane pumps. Nature 211 (1966) 969-970
46. Mackerell Jr. A.D., Feig M., and Brooks III C.L. 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
47. Hornak V., Abel R., Okur A., Strockbine B., Roitberg A., and Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65 (2006) 712-725. The authors describe the parameterization of a new version of the Amber-ff99 force field based on quantum-level calculations of energies of tripeptides. The new force field was tested using several methods including comparison of MD simulations with experimental NMR order parameters.
48. Pérez A., Marchán I., Svozil D., Sponer J., Cheatham III T.E., Laughton C.A., and Orozco M. Refinement of the AMBER force field for nucleic acids: improving the description of alpha/gamma conformers. Biophys J 92 (2007) 3817-3829
49. Klauda J.B., Brooks B.R., MacKerell Jr. A.D., Venable R.M., and Pastor R.W. An ab initio study on the torsional surface of alkanes and its effect on molecular simulations of alkanes and a DPPC bilayer. J Phys Chem B 109 (2005) 5300-5311
50. Buck M., Bouguet-Bonnet S., Pastor R.W., and MacKerell Jr. A.D. Importance of the CMAP correction to the CHARMM22 protein force field: dynamics of hen lysozome. Biophys J 90 (2006) L36-L38
51. Showalter S.A., and Brüschweiler R. Quantitative molecular ensemble interpretation of NMR dipolar couplings without restraints. J Am Chem Soc 129 (2007) 4158-4159
52. Trbovic N., Kim B., Friesner R.A., and Palmer III A.G. Structural analysis of protein dynamics by MD simulations and NMR spin-relaxation. Proteins 71 (2008) 684-694
53. Koller A.N., Schwalbe H., and Gohlke H. Starting structure dependence of NMR order parameters derived from MD simulations: implications for judging force-field quality. Biophys J 95 (2008) L04-L06
54. Norgaard A.B., Ferkinghoff-Borg J., and Lindorff-Larsen K. Experimental parameterization of an energy function for the simulation of unfolded proteins. Biophys J 94 (2008) 182-192
55. Friesner R.A. Modeling polarization in proteins and protein-ligand complexes: Methods and preliminary results. Adv Protein Chem 72 (2006) 79-104
56. Anisimov V.M., Vorobyov I.V., Roux B., and Mackerell Jr. A.D. Polarizable empirical force field for the primary and secondary alcohol series based on the classical Drude model. J Chem Theory Comput 3 (2007) 1927-1946
57. Schnieders M.J., Baker N.A., Ren P., and Ponder J.W. Polarizable atomic multipole solutes in a Poisson-Boltzmann continuum. J Chem Phys 126 (2007) 124114
58. Monticelli L., Sorin E.J., Tieleman D.P., Pande V.S., and Colombo G. Molecular simulation of multistate peptide dynamics: a comparison between microsecond timescale sampling and multiple shorter trajectories. J Comput Chem 29 (2008) 1740-1752
59. Markwick P.R., Bouvignies G., and Blackledge M. Exploring multiple timescale motions in protein dynamics and NMR spectroscopy. J Am Chem Soc 129 (2007) 4724-4730
60. Hedges L.O., Jack R.L., Garrahan J.P., and Chandler D. Dynamic order-disorder in atomistic models of structural glass formers. Science 323 (2009) 1309-1313
61. Tu T., Rendleman C.A., Borhani D.W., Dror R.O., Gullingsrud J., Jensen M.Ø., Klepeis J.L., Maragakis P., Miller P., Stafford K.A., and Shaw D.E. A scalable parallel framework for analyzing terascale molecular dynamics trajectories. Proceedings of the 2008 ACM/IEEE Conference on Supercomputing (SC08) (2008), ACM Press. The authors describe a new framework for the parallel analysis of long molecular dynamics simulations that is inspired by Google's MapReduce programming interface.