Computational models of protein kinematics and dynamics: Beyond simulation

Annual Review of Analytical Chemistry

Volumn 5, Issue , 2012, Pages 273-291

  Gipson, Bryant ^a   Hsu, David ^c   Kavraki, Lydia E ^a,b   Latombe, Jean Claude ^d  

^a Rice University   (United States)

^b RICE UNIVERSITY   (United States)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d Stanford University   (United States)

Author keywords

conformation sampling; geometric methods; graph based methods; kinematic models; protein motion

Indexed keywords

ATOMIC MOVEMENTS; COMPUTATIONAL MODEL; DYNAMIC PROTEINS; GEOMETRIC METHOD; GRAPH-BASED METHODS; HIGH FREQUENCY HF; KINEMATIC MODEL; KINEMATICS AND DYNAMICS; PHYSICS-BASED SIMULATION; PROTEIN DYNAMICS; PROTEIN MOTION; PROTEIN SYSTEM; SPACE AND TIME; TEMPORAL RESOLUTION; TIME VARYING;

DYNAMICS; KINEMATICS;

PROTEINS;

PROTEIN;

ANIMAL; BIOLOGICAL MODEL; BIOMECHANICS; CHEMISTRY; COMPUTER SIMULATION; HUMAN; MOLECULAR DYNAMICS; PROTEIN CONFORMATION; REVIEW;

ANIMALS; BIOMECHANICS; COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; MOLECULAR DYNAMICS SIMULATION; PROTEIN CONFORMATION; PROTEINS;

EID: 84863307801     PISSN: 19361327     EISSN: 19361335     Source Type: Book Series    
DOI: 10.1146/annurev-anchem-062011-143024     Document Type: Review

References (111)

1. Adcock SA,McCammon JA. 2006. Molecular dynamics: survey of methods for simulating the activity of proteins. Chem. Rev. 106:1589-615 (Pubitemid 43792773)
2. Day R, Daggett V. 2003. All-atom simulations of protein folding and unfolding. Adv. Protein Chem. 66:373-403
3. Scheraga HA, Khalili M, Liwo A. 2007. Protein-folding dynamics: overview of molecular simulation techniques. Annu. Rev. Phys. Chem. 58:57-83 (Pubitemid 46877585)
4. Moll M, SchwarzD, Kavraki L. 2008. Roadmapmethods for protein folding. MethodsMol. Biol. 413:219-39
5. Chodera JD, SinghalN, Pande VS, Dill KA, Swope WC. 2007. Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. J. Chem. Phys. 126:155101-18
6. Henzler-Wildman K, Kern D. 2007. Dynamic personalities of proteins. Nature 450:964-72 (Pubitemid 350273626)
7. Anfinsen CB. 1973. Principles that govern the folding of protein chains. Science 181:223-30
8. Ahmed A, Kazemi S, Gohlke H. 2007. Protein flexibility and mobility in structure-based drug design. Front. Drug Des. Discov. Struct. Based Drug Des. Century 3:455-76
9. Carlson HA. 2002. Protein flexibility is an important component of structure-based drug discovery. Curr. Pharm. Des. 8:1571-78 (Pubitemid 34727543)
10. Tsai J, Bonneau R, Morozov AV, Kuhlman B, Rohl CA, Baker D. 2003. An improved protein decoy set for testing energy functions for protein structure prediction. Proteins Struct. Funct. Bioinform. 53:76-87 (Pubitemid 37059546)
11. Jacobs DJ. 2010. Ensemble-based methods for describing protein dynamics. Curr. Opin. Pharmacol. 10:760-69
12. Vorobjev YN, Hermans J. 2001. Free energies of protein decoys provide insight into determinants of protein stability. Protein Sci. 10:2498-506 (Pubitemid 33091573)
13. Van Den Bedem H, Dhanik A, Latombe JC, Deacon AM. 2009. Modeling discrete heterogeneity in X-ray diffraction data by fitting multi-conformers. Acta Crystallogr. Sect. D Biol. Crystallogr. 65:1107-17
14. Levin EJ, Kondrashov DA, Wesenberg GE, Phillips GN. 2007. Ensemble refinement of protein crystal structures: validation and application. Structure 15:1040-52 (Pubitemid 47362686)
15. Dantas G, Kuhlman B,Callender D, Wong M, BakerD. 2003. A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins. J. Mol. Biol. 332:449-60 (Pubitemid 37013512)
16. Chiti F,Dobson C. 2006. Proteinmisfolding, functional amyloid, and human disease. Annu. Rev. Biochem. 75:333-66 (Pubitemid 44118036)
17. Berman HM,Westbrook J, Feng Z, GillilandG, Bhat T, et al. 2000. The protein data bank. Nucleic Acids Res. 28:235-42 (Pubitemid 30047768)
18. Burling F, Brunger A. 1994. Thermal motion and conformational disorder in protein crystal structures: comparison of multi-conformer and time-averaging models. Isr. J. Chem. 34:165-75
19. Kuriyan J, Osapay K, Burley SK, Brünger AT, HendricksonWA, KarplusM. 1991. Probing disorder in high resolution protein structures by simulated annealing. Proteins Struct. Funct. Genet. 10:340-58
20. Baker ML, Zhang J, Ludtke SJ, ChiuW. 2010. Cryo-EM of macromolecular assemblies at near-atomic resolution. Nat. Protoc. 5:1697-708
21. Schlick T. 1999. Algorithmic challenges in computational molecular biophysics. J. Comput. Phys. 151:9-48
22. Kumar S,Huang C, ZhengG, Bohm E, Bhatele A, et al. 2008. Scalable molecular dynamics withNAMD on Blue Gene/L system. IBM J. Res. Dev. 52:177-88 (Pubitemid 351386010)
23. ShawDE, Deneroff MM,Dror RO,Kuskin JS, Larson RH, et al. 2007. Anton, a special-purposemachine for molecular dynamics simulation. ACM SIGARCH Comput. Arch. News 35:1-12 (Pubitemid 47582086)
24. Stone JE, Hardy DJ, Ufimtsev IS, Schulten K. 2010. GPU-accelerated molecular modeling coming of age. J. Mol. Graph. Model. 29:116-25
25. Tozzini V. 2005. Coarse-grained models for proteins. Curr. Opin. Struct. Biol. 15:144-50
26. Sherwood P, Brooks BR, Sansom MSP. 2008. Multiscale methods formacromolecular simulations. Curr. Opin. Struct. Biol. 18:630-40
27. Lei H, Duan Y. 2007. Improved sampling methods for molecular simulation. Curr. Opin. Struct. Biol. 17:187-91 (Pubitemid 46575250)
28. Sugita Y, Okamoto Y. 1999. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314:141-51 (Pubitemid 129556751)
29. Skjaerven L, Hollup SM, Reuter N. 2009. Normal mode analysis for proteins. J. Mol. Struct. 898:42-48
30. Case DA. 1994. Normal mode analysis of protein dynamics. Curr. Opin. Struct. Biol. 4:285-90 (Pubitemid 24116915)
31. Levitt M, Sander C, Stern PS. 1985. Protein normal-mode dynamics: trypsin inhibitor, crambin, ribonuclease and lysozyme. J. Mol. Biol. 181:423-47
32. Haliloglu T, Bahar I, Erman B. 1997. Gaussian dynamics of folded proteins. Phys. Rev. Lett. 79:3090-93 (Pubitemid 127645089)
33. Schroüder GF, Brunger AT, Levitt M. 2007. Combining efficient conformational sampling with a deformable elastic network model facilitates structure refinement at low resolution. Structure 15:1630-41 (Pubitemid 350213409)
34. Thorpe MF. 2007. Comment on elastic network models and proteins. Phys. Biol. 4:60-65
35. Binder K, Heermann DW. 2010. Monte Carlo Simulation in Statistical Physics: An Introduction. New York: Springer. 2nd e.d.
36. Hansmann UHE, Okamoto Y. 1999. New Monte Carlo algorithms for protein folding. Curr. Opin. Struct. Biol. 9:177-83 (Pubitemid 29452895)
37. Csermely P, PalotaiR,NussinovR. 2010. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends Biochem. Sci. 35:539-46
38. Hammes GG, Chang YC, Oas TG. 2009. Conformational selection or induced fit: a flux description of reaction mechanism. Proc. Natl. Acad. Sci. USA 106:13737-41
39. Zhou H-X. 2010. From induced fit to conformational selection: a continuum of binding mechanism controlled by the timescale of conformational transitions. Biophys. J. 98:L15-17
40. Kavraki LE, Svestka P, Latombe JC, Overmars MH. 1996. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat. 12:566-80 (Pubitemid 126774162)
41. Krogh A, BrownM,Mian IS, Sjoülander K, Haussler D. 1994. HiddenMarkov models in computational biology. Applications to protein modeling. J. Mol. Biol. 235:1501-31
42. Callender RH, Dyer RB, Gilmanshin R, Woodruff WH. 1998. Fast events in protein folding: the time evolution of primary processes. Annu. Rev. Phys. Chem. 49:173-202 (Pubitemid 128436889)
43. Brown ID. 2009. Recent developments in the methods and applications of the bond valence model. Chem. Rev. 109:6858-919
44. ZhangM, Kavraki LE. 2002. A new method for fast and accurate derivation of molecular conformations. J. Chem. Inform. Comput. Sci. 42:64-70
45. Hartenberg RS, Denavit J. 1964. Kinematic Synthesis of Linkages. New York: McGraw-Hill
46. Chen J, Im W, Brooks CL. 2005. Application of torsion angle molecular dynamics for efficient sampling of protein conformations. J. Comput. Chem. 26:1565-78 (Pubitemid 41641686)
47. Gibson KD, ScheragaHA. 1990. Variable step molecular dynamics: an exploratory technique for peptides with fixed geometry. J. Comput. Chem. 11:468-86
48. Coutsias EA, Seok C, Jacobson MP, Dill KA. 2004. A kinematic view of loop closure. J. Comput. Chem. 25:510-28
49. Craig JJ. 1989. Introduction to Robotics: Mechanics and Control. New York: Addison-Wesley. 2nd e.d.
50. Duffy J. 1980. Analysis of Mechanisms and Robot Manipulators. New York:Wiley
51. Manocha D, Zhu Y, Wright W. 1995. Conformational analysis of molecular chains using nanokinematics. Bioinformatics 11:71-86
52. Mavroidis C, Roth B. 1994. Structural parameters which reduce the number of manipulator configurations. J. Mech. Des. 116:3-11
53. Raghavan M, Roth B. 1993. Inverse kinematics of the general 6R manipulator and related linkages. J. Mech. Des. 115:502-8
54. Go N, Scheraga HA. 1970. Ring closure and local conformational deformations of chain molecules. Macromolecules 3:178-87
55. Zhang M, White RA, Wang L, Goldman R, Kavraki L, Hassett B. 2005. Improving conformational searches by geometric screening. Bioinformatics 21:624-30 (Pubitemid 40424788)
56. Milgram RJ, Liu G, Latombe JC. 2008. On the structure of the inverse kinematics map of a fragment of protein backbone. J. Comput. Chem. 29:50-68
57. Cortés J, Siméon T, Remaud-Siméon M, Tran V. 2004. Geometric algorithms for the conformational analysis of long protein loops. J. Comput. Chem. 25:956-67
58. Wang L-CT, Chen CC. 1991. A combined optimization method for solving the inverse kinematics problems of mechanical manipulators. IEEE Trans. Robot. Automat. 7:489-99
59. Canutescu AA, Dunbrack RL. 2003. Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12:963-72 (Pubitemid 36505431)
60. Fersht A, Serrano L. 1993. Principles of protein stability derived from protein engineering experiments. Curr. Opin. Struct. Biol. 3:75-83 (Pubitemid 23058386)
61. Pace C. 2001. Polar group burial contributes more to protein stability than nonpolar group burial. Biochemistry 40:310-13 (Pubitemid 32061977)
62. Schell D, Tsai J, Scholtz J, Pace CN. 2006. Hydrogen bonding increases packing density in the protein interior. Proteins Struct. Funct. Bioinform. 63:278-82
63. Farrell D, Speranskiy K, Thorpe MF. 2010. Generating stereochemically acceptable protein pathways. Proteins Struct. Funct. Bioinform. 78:2908-21
64. Jacobs DJ, Kuhn LA, Thorpe MF. 1999. Flexible and rigid regions in proteins. Rigidity Theory Appl. 85:357-84
65. Wells S, Menor S, Hespenheide B, Thorpe MF. 2005. Constrained geometric simulation of diffusive motion in proteins. Phys. Biol. 2:S127-36 (Pubitemid 41609439)
66. Zavodszky M, Lei M, Thorpe M, Day A, Kuhn LA. 2004. Modeling correlated main-chain motions in proteins for flexible molecular recognition. Proteins Struct. Funct. Bioinform. 57:243-61 (Pubitemid 39223730)
67. Jacobs DJ. 1998. Generic rigidity in three-dimensional bond-bending networks. J. Phys. A 31:6653
68. Lee A, Streinu I, Theran L. 2008. Analyzing Rigidity with Pebble Games. New York: ACM. 226 p.p.
69. Laman G. 1970. On graphs and rigidity of plane skeletal structures. J. Eng. Math. 4:331-40
70. Liwo A, Czaplewski C, Oldziej S, Scheraga HA. 2008. Computational techniques for efficient conformational sampling of proteins. Curr. Opin. Struct. Biol. 18:134-39
71. Halperin D, Overmars MH. 1994. Spheres, molecules, and hidden surface removal. Proc. 10th Ann. ACM Symp. Comput. Geom. 1:113-22
72. WuS, LiangMP, Altman RB. 2008. The SeqFEATURElibrary of3Dfunctional site models: comparison to existing methods and applications to protein function annotation. Genome Biol. 9:R8
73. Sousa SF, Fernandes PA, RamosMJ. 2006. Protein-ligand docking: current status and future challenges. Proteins Struct. Funct. Bioinform. 65:15-26 (Pubitemid 44320611)
74. van den Bedem H, Lotan I, Latombe JC, Deacon A. 2005. Real-space protein-model completion: an inverse-kinematic approach. Acta Crystallogr. Sect. D Biol. Crystallogr. 61:2-13 (Pubitemid 41615526)
75. Enosh A, Fleishman SJ, Ben-Tal N, Halperin D. 2004. Assigning transmembrane segments to helices in intermediate-resolution structures. Bioinformatics 20(Suppl. 1):122-29
76. Xiang Z. 2006. Advances in homology protein structure modeling. Curr. Protein Pept. Sci. 7:217-27 (Pubitemid 43881004)
77. Cahill S, Cahill M, Cahill K. 2003. On the kinematics of protein folding. J. Comput. Chem. 24:1364-70
78. Singh R, Berger B. 2005. ChainTweak: sampling from the neighbourhood of a protein conformation. Proc. Pac. Symp. Biocomput. 1:54-65
79. DePristo MA, de Bakker PIW, Lovell SC, Blundell TL. 2003. Ab initio construction of polypeptide fragments: efficient generation of accurate, representative ensembles. Proteins Struct. Funct. Bioinform. 51:41-55 (Pubitemid 36293031)
80. Jacobson MP, Pincus DL, Rapp CS, Day TJF, Honig B, et al. 2004. A hierarchical approach to all-atom protein loop prediction. Proteins Struct. Funct. Bioinform. 55:351-67 (Pubitemid 38437493)
81. Yao P, Dhanik A, Marz N, Propper R, Kou C, et al. 2008. Efficient algorithms to explore conformation spaces of flexible protein loops. IEEE/ACM Trans. Comput. Biol. Bioinform. 5:534-45
82. Canutescu AA, Shelenkov AA, Dunbrack RL. 2003. A graph-theory algorithm for rapid protein sidechain prediction. Protein Sci. 12:2001-14 (Pubitemid 37022822)
83. Siciliano B, Khatib O. 2008. Handbook of Robotics. New York: Springer
84. Kolodny R, Guibas L, Levitt M, Koehl P. 2005. Inverse kinematics in biology: the protein loop closure problem. Int. J. Robot. Res. 24:151-64
85. Tosatto SCE, Bindewald E, Hesser J, Manner R. 2002. A divide and conquer approach to fast loop modeling. Protein Eng. Des. Sel. 15:279-86 (Pubitemid 34532766)
86. van Vlijmen HWT, KarplusM. 1997. PDB-based protein loop prediction: parameters for selection and methods for optimization. J. Mol. Biol. 267:975-1001 (Pubitemid 27192667)
87. Yao P, Zhang L, Latombe JC. 2012. Sampling-based exploration of folded state of a protein under kinematic and geometric constraints. Proteins Struct. Funct. Bioinform. 80:25-43
88. Hsu D, Latombe JC,Motwani R. 1999. Path planning in expansive configuration spaces. Int. J. Comput. Geom. Appl. 9:495-512
89. Silva D-A, Bowman GR, Sosa-Peinado A, Huang X. 2011. A role for both conformational selection and induced fit in ligand binding by the LAO protein. PLoS Comput. Biol. 7:e1002054
90. Shehu A, Clementi C, Kavraki LE. 2006. Modeling protein conformational ensembles: from missing loops to equilibrium fluctuations. Proteins Struct. Funct. Bioinform. 65:164-79 (Pubitemid 44320626)
91. Haspel N, Moll M, BakerML, Chiu W, Kavraki LE. 2010. Tracing conformational changes in proteins. BMC Struct. Biol. 10(Suppl. 1):1
92. Rohl CA, StraussCEM,Misura KMS, BakerD. 2004. Protein structure prediction using Rosetta. Methods Enzymol. 383:66-93 (Pubitemid 38401787)
93. Tyka MD, Keedy DA, André I, Dimaio F, Song Y, et al. 2011. Alternate states of proteins revealed by detailed energy landscape mapping. J. Mol. Biol. 405:607-18
94. Altis A,Nguyen PH,HeggerR, Stock G. 2007. Dihedral angle principal component analysis ofmolecular dynamics simulations. J. Chem. Phys. 126:244111
95. Das P,MollM, Stamati H, Kavraki LE, Clementi C. 2006. Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction. Proc. Natl. Acad. Sci. USA 103:9885-90 (Pubitemid 43993592)
96. Du R, PandeVS, Grosberg A, Tanaka T, Shakhnovich ES. 1998.On the transition coordinate for protein folding. J. Chem. Phys. 108:334-51
97. Hsu D, Latombe JC, Kurniawati H. 2006. On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robot. Res. 25:627-43 (Pubitemid 43936805)
98. Singh AP, Latombe JC, Brutlag DL. 1999. A motion planning approach to flexible ligand binding. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1999:252-61
99. Amato NM, Dill KA, Song G. 2003. Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. J. Comput. Biol. 10:239-55 (Pubitemid 37372148)
100. Thomas S, Song G, Amato NM. 2005. Protein folding by motion planning. Phys. Biol. 2:148
101. Thomas S, Tang X, Tapia L, Amato NM. 2007. Simulating protein motions with rigidity analysis. J. Comput. Biol. 14:839-55 (Pubitemid 47221820)
102. ApaydinMS, Brutlag DL,Guestrin C,Hsu D, Latombe JC, Varma C. 2003. Stochastic roadmap simulation: an efficient representation and algorithm for analyzingmolecular motion. J. Comput. Biol. 10:257-81 (Pubitemid 37372149)
103. ChiangT-H, ApaydinMS, Brutlag DL,Hsu D, Latombe JC. 2007. Using stochastic roadmap simulation to predict experimental quantities in protein folding kinetics: folding rates and?-values. J. Comput. Biol. 14:578-93 (Pubitemid 47047815)
104. Singhal N, Snow C, Pande VS. 2004. Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper βhairpin. J. Chem. Phys. 121:415-25
105. Pande VS, Baker I, Chapman J, Elmer SP, Khaliq S, et al. 2003. Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. Biopolymers 68:91-109 (Pubitemid 36098310)
106. SinghalN, Pande VS. 2005. Error analysis and efficient sampling inMarkovian statemodels formolecular dynamics. J. Chem. Phys. 123:204909-12
107. Huang X, Yao Y, Bowman GR, Sun J, Guibas LJ, et al. 2010. Constructing multi-resolution Markov state models (MSMs) to elucidate RNA hairpin folding mechanisms. Proc. Pac. Symp. Biocomput. 2010:228-39
108. Noé F, Fischer S. 2008. Transition networks for modeling the kinetics of conformational change in macromolecules. Curr. Opin. Struct. Biol. 18:154-62
109. Noé F, Krachtus D, Smith JC, Fischer S. 2006. Transition networks for the comprehensive characterization of complex conformational change in proteins. J. Chem. Theory Comput. 2:840-57
110. Ozkan S, Dill K, Bahar I. 2002. Fast-folding protein kinetics, hidden intermediates, and the sequential stabilization model. Protein Sci. 11:1958-70 (Pubitemid 34791415)
111. Chiang TH, Hsu D, Latombe JC. 2010. Markov dynamic models for long-timescale protein motion. Bioinformatics 26:269-77