Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: Strength in unity

Frontiers in Molecular Biosciences

Volumn 2, Issue MAY, 2015, Pages

  Papaleo, Elena ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Enhanced sampling; Metadynamics; Molecular dynamics; Protein dynamics; Protein NMR; Protein structure

Indexed keywords

EID: 85039779169     PISSN: None     EISSN: 2296889X     Source Type: Journal    
DOI: 10.3389/fmolb.2015.00028     Document Type: Article

References (96)

1. Abrams, C., and Bussi, G. (2013). Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16, 163-199. doi: 10.3390/e16010163
2. Allain, A., Chauvot de Beauchêne, I., Langenfeld, F., Guarracino, Y., Laine, E., and Tchertanov, L. (2014). Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs. Faraday Discuss. 169, 303-321. doi: 10.1039/C4FD00024B
3. Atilgan, A. R., Akan, P., and Baysal, C. (2004). Small-world communication of residues and significance for protein dynamics. Biophys. J. 86, 85-91. doi: 10.1016/S0006-3495(04)74086-2
4. Atilgan, C., Okan, O. B., and Atilgan, A. R. (2012). Network-based models as tools hinting at nonevident protein functionality. Annu. Rev. Biophys. 41, 205-225. doi: 10.1146/annurev-biophys-050511-102305
5. Baldwin, A. J., and Kay, L. E. (2009). NMR spectroscopy brings invisible protein states into focus. Nat. Chem. Biol. 5, 808-814. doi: 10.1038/nchembio.238
6. Banci, L. (2003). Molecular dynamics simulations of metalloproteins. Curr. Opin. Chem. Biol. 7, 143-149. doi: 10.1016/S1367-5931(02)00014-5
7. Barducci, A., Pfaendtner, J., and Bonomi, M. (2015). Tackling sampling challenges in biomolecular simulations. Methods Mol. Biol. 1215, 151-171. doi: 10.1007/978-1-4939-1465-4_8
8. Bernardi, R. C., Melo, M. C. R., and Schulten, K. (2014). Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim. Biophys. Acta 1850, 872-877. doi: 10.1016/j.bbagen.2014.10.019
9. Berteotti, A., Cavalli, A., Branduardi, D., Gervasio, F. L., Recanatini, M., and Parrinello, M. (2009). Protein conformational transitions: the closure mechanism of a kinase explored by atomistic simulations. J. Am. Chem. Soc. 131, 244-250. doi: 10.1021/ja806846q
10. Best, R. B., Zheng, W., and Mittal, J. (2014). Balanced protein-water interactions improve properties of disordered proteins and non-specific protein association. J. Chem. Theory Comput. 10, 5113-5124. doi: 10.1021/ct500569b
11. Best, R. B., Zhu, X., Shim, J., Lopes, P. E. M., Mittal, J., Feig, M., et al. (2012). Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone f, ? and side-chain ? (1) and ? (2) dihedral angles. J. Chem. Theory Comput. 8, 3257-3273. doi: 10.1021/ct300400x
12. Bhattacharyya, M., Bhat, C. R., and Vishveshwara, S. (2013). An automated approach to network features of protein structure ensembles. Protein Sci. 22, 1399-1416. doi: 10.1002/pro.2333
13. Boehr, D. D., Nussinov, R., and Wright, P. E. (2009). The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol. 5, 789-796. doi: 10.1038/nchembio.232
14. Bonomi, M., Barducci, A., and Parrinello, M. (2009). Reconstructing the equilibrium boltzmann distribution from well-tempered metadynamics. J. Comput. Chem. 30, 1615-1621. doi: 10.1002/jcc.21305
15. Camilloni, C., Cavalli, A., and Vendruscolo, M. (2013). Replica-averaged metadynamics. J. Chem. Theory Comput. 9, 5610-5617. doi: 10.1021/ct4006272
16. Camilloni, C., Robustelli, P., De Simone, A., Cavalli, A., and Vendruscolo, M. (2012). Characterization of the conformational equilibrium between the two major substates of RNase A using NMR chemical shifts. J. Am. Chem. Soc. 134, 3968-3971. doi: 10.1021/ja210951z
17. Camilloni, C., and Vendruscolo, M. (2014). Statistical mechanics of the denatured state of a protein using replica-averaged metadynamics. J. Am. Chem. Soc. 136, 8982-8991. doi: 10.1021/ja5027584
18. Collier, G., and Ortiz, V. (2013). Emerging computational approaches for the study of protein allostery. Arch. Biochem. Biophys. 538, 6-15. doi: 10.1016/j.abb.2013.07.025
19. Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., and Nussinov, R. (2013). Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol. Ther. 138, 333-408. doi: 10.1016/j.pharmthera.2013.01.016
20. Debiec, K. T., Gronenborn, A. M., Chong, L. T., and Fields, F. (2014). Evaluating the strength of salt bridges-a comparison of current biomolecular force fields. J. Phys. Chem. B, 118, 6561-6569. doi: 10.1021/jp500958r
21. Del Sol, A., Tsai, C.-J., Ma, B., and Nussinov, R. (2009). The origin of allosteric functional modulation: multiple pre-existing pathways. Structure 17, 1042-1150. doi: 10.1016/j.str.2009.06.008
22. Do, T. N., Choy, W.-Y., and Karttunen, M. (2014). Accelerating the conformational sampling of intrinsically disordered proteins. J. Chem. Theory Comput. 10, 5081-5094. doi: 10.1021/ct5004803
23. Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., and Shaw, D. E. (2012). Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429-452. doi: 10.1146/annurev-biophys-042910-155245
24. Feher, V. A., Durrant, J. D., Van Wart, A. T., and Amaro, R. E. (2014). Computational approaches to mapping allosteric pathways. Curr. Opin. Struct. Biol. 25, 98-103. doi: 10.1016/j.sbi.2014.02.004
25. Fenwick, R. B., Esteban-Martín, S., Richter, B., Lee, D., Walter, K. F. A., Milovanovic, D., et al. (2011). Weak long-range correlated motions in a surface patch of ubiquitin involved in molecular recognition. J. Am. Chem. Soc. 133, 10336-10339. doi: 10.1021/ja200461n
26. Field, M. (2015). Technical advances in molecular simulation since the 1980s. Arch. Biochem. Biophys. doi: 10.1016/j.abb.2015.03.005. [Epub ahead of print]
27. Friedrichs, M. S., Eastman, P., Vaidyanathan, V., Houston, M., Legrand, S., Beberg, A. L., et al. (2009). Accelerating molecular dynamic simulation on graphics processing units. J. Comput. Chem. 30, 864-872. doi: 10.1002/jcc.21209
28. Ghosh, A., and Vishveshwara, S. (2007). A study of communication pathways in methionyl-tRNA synthetase by molecular dynamics simulations and structure network analysis. Proc. Natl. Acad. Sci. U.S.A. 104, 15711-15716. doi: 10.1073/pnas.0704459104
29. Harvey, M. J., Giupponi, G., and Fabritiis, G. De. (2009). ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J. Chem. Theory Comput. 5, 1632-1639. doi: 10.1021/ct9000685
30. Henzler-Wildman, K., and Kern, D. (2007). Dynamic personalities of proteins. Nature 450, 964-972. doi: 10.1038/nature06522
31. Hritz, J., and Oostenbrink, C. (2008). Hamiltonian replica exchange molecular dynamics using soft-core interactions. J. Chem. Phys. 128, 144121. doi: 10.1063/1.2888998
32. Huang, J., and MacKerell, A. D. (2013). CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135-2145. doi: 10.1002/jcc.23354
33. Invernizzi, G., Lambrughi, M., Regonesi, M. E., Tortora, P., and Papaleo, E. (2013). The conformational ensemble of the disordered and aggregation-protective 182-291 region of ataxin-3. Biochim. Biophys. Acta 1830, 5236-5247. doi: 10.1016/j.bbagen.2013.07.007
34. Invernizzi, G., Tiberti, M., Lambrughi, M., Lindorff-Larsen, K., and Papaleo, E. (2014). Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops. PLoS Comput. Biol. 10:e1003744. doi: 10.1371/journal.pcbi.1003744
35. Jónsdóttir, L. B., Ellertsson, B. ö., Invernizzi, G., Magnúsdóttir, M., Thorbjarnardóttir, S. H., Papaleo, E., et al. (2014). The role of salt bridges on the temperature adaptation of aqualysin I, a thermostable subtilisin-like proteinase. Biochim. Biophys. Acta 1844, 2174-2181. doi: 10.1016/j.bbapap.2014.08.011
36. Kar, G., Keskin, O., Gursoy, A., and Nussinov, R. (2010). Allostery and population shift in drug discovery. Curr. Opin. Pharmacol. 10, 715-722. doi: 10.1016/j.coph.2010.09.002
37. Karplus, M., and Kuriyan, J. (2005). Molecular dynamics and protein function. Proc. Natl. Acad. Sci. U.S.A. 102, 6679-6685. doi: 10.1073/pnas.0408930102
38. Klepeis, J. L., Lindorff-Larsen, K., Dror, R. O., and Shaw, D. E. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Curr. Opin. Struct. Biol. 19, 120-127. doi: 10.1016/j.sbi.2009.03.004
39. Knott, M., and Best, R. (2012). A preformed binding interface in the unbound ensemble of an intrinsically disordered protein: evidence from molecular simulations. PLoS Comput. Biol. 8:e1002605. doi: 10.1371/journal.pcbi.1002605
40. Koehl, P., and Levitt, M. (1999). Theory and simulation. Can theory challenge experiments. Curr. Opin. Struct. Biol. 9, 155-156. doi: 10.1016/S0959-440X(99)80021-9
41. Kohlhoff, K. J., Robustelli, P., Cavalli, A., Salvatella, X., and Vendruscolo, M. (2009). Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J. Am. Chem. Soc. 131, 13894-13895. doi: 10.1021/ja903772t
42. Kohlhoff, K. J., Shukla, D., Lawrenz, M., Bowman, G. R., Konerding, D. E., Belov, D., et al. (2014). Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat. Chem. 6, 15-21. doi: 10.1038/nchem.1821
43. Kukol, A. (2015). Molecular Modeling of Proteins. New York, NY: Springer
44. Laio, A., and Gervasio, F. L. (2008). Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys. 71:126601. doi: 10.1088/0034-4885/71/12/126601
45. Lambrughi, M., Papaleo, E., Testa, L., Brocca, S., De Gioia, L., and Grandori, R. (2012). Intramolecular interactions stabilizing compact conformations of the intrinsically disordered kinase-inhibitor domain of Sic1: a molecular dynamics investigation. Front. Physiol. 3:435. doi: 10.3389/fphys.2012.00435
46. Lange, O. F., Lakomek, N.-A., Farès, C., Schröder, G. F., Walter, K. F. A., Becker, S., et al. (2008). Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320, 1471-1475. doi: 10.1126/science.1157092
47. Li, D.-W., and Brüschweiler, R. (2012). PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles. J. Biomol. NMR 54, 257-265. doi: 10.1007/s10858-012-9668-8
48. Lindorff-Larsen, K., Best, R. B., Depristo, M. A., Dobson, C. M., and Vendruscolo, M. (2005). Simultaneous determination of protein structure and dynamics. Nature 433, 128-132. doi: 10.1038/nature03199
49. Lindorff-Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O., and Shaw, D. E. (2012). Systematic validation of protein force fields against experimental data. PLoS ONE 7:e32131. doi: 10.1371/journal.pone.0032131
50. Manley, G., and Loria, J. P. (2012). NMR insights into protein allostery. Arch. Biochem. Biophys. 519, 223-231. doi: 10.1016/j.abb.2011.10.023
51. Mariani, S., Dell'Orco, D., Felline, A., Raimondi, F., and Fanelli, F. (2013). Network and atomistic simulations unveil the structural determinants of mutations linked to retinal diseases. PLoS Comput. Biol. 9:e1003207. doi: 10.1371/journal.pcbi.1003207
52. McCammon, J., Gelin, B., and Karplus, M. (1977). Dynamics of folded proteins. Nature 267, 585-590. doi: 10.1038/267585a0
53. Motlagh, H. N., Li, J., Thompson, E. B., and Hilser, V. J. (2012). Interplay between allostery and intrinsic disorder in an ensemble. Biochem. Soc. Trans. 40, 975-980. doi: 10.1042/BST20120163
54. Nussinov, R., and Tsai, C.-J. (2013). Allostery in disease and in drug discovery. Cell 153, 293-305. doi: 10.1016/j.cell.2013.03.034
55. Osawa, M., Takeuchi, K., Ueda, T., Nishida, N., and Shimada, I. (2012). Functional dynamics of proteins revealed by solution NMR. Curr. Opin. Struct. Biol. 22, 660-669. doi: 10.1016/j.sbi.2012.08.007
56. Palazzesi, F., Barducci, A., Tollinger, M., and Parrinello, M. (2013). The allosteric communication pathways in KIX domain of CBP. Proc. Natl. Acad. Sci. U.S.A. 110, 14237-14242. doi: 10.1073/pnas.1313548110
57. Palazzesi, F., Prakash, M. K., Bonomi, M., and Barducci, A. (2015). Accuracy of current all-atom force-fields in modeling protein disordered states. J. Chem. Theory Comput. 11, 2-7. doi: 10.1021/ct500718s
58. Pandini, A., Fornili, A., Fraternali, F., and Kleinjung, J. (2013). GSATools: analysis of allosteric communication and functional local motions using a structural alphabet. Bioinformatics 29, 2053-2055. doi: 10.1093/bioinformatics/btt326
59. Papaleo, E., Lindorff-Larsen, K., and De Gioia, L. (2012a). Paths of long-range communication in the E2 enzymes of family 3: a molecular dynamics investigation. Phys. Chem. Chem. Phys. 14, 12515-12525. doi: 10.1039/c2cp41224a
60. Papaleo, E., Renzetti, G., and Tiberti, M. (2012b). Mechanisms of intramolecular communication in a hyperthermophilic acylaminoacyl peptidase: a molecular dynamics investigation. PLoS ONE 7:e35686. doi: 10.1371/journal.pone.0035686
61. Papaleo, E., Sutto, L., Gervasio, F. L., and Lindorff-Larsen, K. (2014). Conformational changes and free energies in a proline isomerase. J. Chem. Theory Comput. 10, 4169-4174. doi: 10.1021/ct500536r
62. Pasi, M., Tiberti, M., Arrigoni, A., and Papaleo, E. (2012). xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures. J. Chem. Inf. Model. 52, 1865-1874. doi: 10.1021/ci300213c
63. Piana, S., Donchev, A., Robustelli, P., and Shaw, D. (2015). Water dispersion interactions strongly influence simulated structural properties of disordered protein states. J. Phys. Chem. B 119, 5113-5123. doi: 10.1021/jp508971m
64. Piana, S., Klepeis, J. L., and Shaw, D. E. (2014). Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr. Opin. Struct. Biol. 24C, 98-105. doi: 10.1016/j.sbi.2013.12.006
65. Piana, S., and Laio, A. (2007). A bias-exchange approach to protein folding. J. Phys. Chem. B 111, 4553-4559. doi: 10.1021/jp067873l
66. Popovych, N., Sun, S., Ebright, R. H., and Kalodimos, C. G. (2006). Dynamically driven protein allostery. Nat. Struct. Mol. Biol. 13, 831-838. doi: 10.1038/nsmb1132
67. Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., et al. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845-854. doi: 10.1093/bioinformatics/btt055
68. Rapaport, D. C. (1998). The Art of Molecular Dynamics Simulation. Cambridge, UK: Cambridge University Press
69. Reif, M. M., Winger, M., and Oostenbrink, C. (2013). Testing of the GROMOS force-field parameter set 54A8: structural properties of electrolyte solutions, lipid bilayers, and proteins. J. Chem. Theory Comput. 9, 1247-1264. doi: 10.1021/ct300874c
70. Ribeiro, A. A. S. T., and Ortiz, V. (2014). Determination of signaling pathways in proteins through network theory: importance of the topology. J. Chem. Theory Comput. 10, 1762-1769. doi: 10.1021/ct400977r
71. Sahakyan, A. B., Vranken, W. F., Cavalli, A., and Vendruscolo, M. (2011). Structure-based prediction of methyl chemical shifts in proteins. J. Biomol. NMR 50, 331-346. doi: 10.1007/s10858-011-9524-2
72. Salvatella, X. (2014). Understanding protein dynamics using conformational ensembles. Adv. Exp. Med. Biol. 805, 67-85. doi: 10.1007/978-3-319-02970-2_3
73. Saunders, M., and Voth, G. (2013). Coarse-graining methods for computational biology. Annu. Rev. Biophys. 42, 73-93. doi: 10.1146/annurev-biophys-083012-130348
74. Schlick, T. (2010). Molecular Modeling and Simulation: An Interdisciplinary Guide. New York, NY: Springer
75. Seeber, M., Felline, A., Raimondi, F., Muff, S., Friedman, R., Rao, F., et al. (2011). Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J. Comput. Chem. 32, 1183-1194. doi: 10.1002/jcc.21688
76. Shaw, D. E., Grossman, J. P., Bank, J. A., Batson, B., Butts, J. A., Chao, J. C., et al. (2014). "Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer". in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (New Orleans, LA: IEEE Press), 41-53
77. Sherwood, P., Brooks, B., and Sansom, M. (2008). Multiscale methods for macromolecular simulations. Curr. Opin. Struct. Biol. 18, 630-640. doi: 10.1016/j.sbi.2008.07.003
78. Snow, C., Sorin, E., Rhee, Y., and Pande, V. (2004). How well can simulation predict protein folding kinetics and thermodynamics? Annu. Rev. Biophys. Biomol. Struct. 34, 43-69. doi: 10.1146/annurev.biophys.34.040204.144447
79. Spiwok, V., Suæur, Z., and Hošek, P. (2014). Enhanced sampling techniques in biomolecular simulations. Biotechnol. Adv. doi: 10.1016/j.biotechadv.2014.11.011. [Epub ahead of print]
80. Stacklies, W., Seifert, C., and Graeter, F. (2011). Implementation of force distribution analysis for molecular dynamics simulations. BMC Bioinformatics 12:101. doi: 10.1186/1471-2105-12-101
81. Sutto, L., and Gervasio, F. L. (2013). Effects of oncogenic mutations on the conformational free-energy landscape of EGFR kinase. Proc. Natl. Acad. Sci. U.S.A. 110, 10616-10621. doi: 10.1073/pnas.1221953110
82. Sutto, L., Marsili, S., and Gervasio, F. L. (2012). New advances in metadynamics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2, 771-779. doi: 10.1002/wcms.1103
83. Tang, C., Schwieters, C. D., and Clore, G. M. (2007). Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR. Nature 449, 1078-1082. doi: 10.1038/nature06232
84. Tiberti, M., Invernizzi, G., Lambrughi, M., Inbar, Y., Schreiber, G., and Papaleo, E. (2014). PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins. J. Chem. Inf. Model. 54, 1537-1551. doi: 10.1021/ci400639r
85. Tomlinson, J. H., Ullah, S., Hansen, P. E., and Williamson, M. P. (2009). Characterization of salt bridges to lysines in the protein G B1 domain. J. Am. Chem. Soc. 131, 4674-4684. doi: 10.1021/ja808223p
86. Tsai, C.-J., and Nussinov, R. (2014). A unified view of "how allostery works". PLoS Comput. Biol. 10:e1003394. doi: 10.1371/journal.pcbi.1003394
87. Van den Bedem, H., Bhabha, G., Yang, K., Wright, P. E., and Fraser, J. S. (2013). Automated identification of functional dynamic contact networks from X-ray crystallography. Nat. Methods 10, 896-902. doi: 10.1038/nmeth.2592
88. Vendruscolo, M. (2007). Determination of conformationally heterogeneous states of proteins. Curr. Opin. Struct. Biol. 17, 15-20. doi: 10.1016/j.sbi.2007.01.002
89. Vendruscolo, M., Dokholyan, N., Paci, E., and Karplus, M. (2002). Small-world view of the amino acids that play a key role in protein folding. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65, 1-4. doi: 10.1103/PhysRevE.65.061910
90. Villali, J., and Kern, D. (2010). Choreographing an enzyme's dance. Curr. Opin. Chem. Biol. 14, 636-643. doi: 10.1016/j.cbpa.2010.08.007
91. Vishveshwara, S., Ghosh, A., and Hansia, P. (2009). Intra and inter-molecular communications through protein structure network. Curr. Protein Pept. Sci. 10, 146-160. doi: 10.2174/138920309787847590
92. Wand, A. J. (2012). The dark energy of proteins comes to light: conformational entropy and its role in protein function revealed by NMR relaxation. Curr. Opin. Struct. Biol. 23, 75-81. doi: 10.1016/j.sbi.2012.11.005
93. Warshel, A. (2003). Computer simulations of enzyme catalysis: methods, progress, and insights. Annu. Rev. Biophys. Biomol. Struct. 32, 425-443. doi: 10.1146/annurev.biophys.32.110601.141807
94. Whitley, M. J., and Lee, A. L. (2009). Frameworks for understanding long-range intra-protein communication. Curr. Protein Pept. Sci. 10, 116-127. doi: 10.2174/138920309787847563
95. Xia, Y., and Levitt, M. (2004). Simulating protein evolution in sequence and structure space. Curr. Opin. Struct. Biol. 14, 202-207. doi: 10.1016/j.sbi.2004.03.001
96. Zhuravlev, P. I., and Papoian, G. A. (2010). Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework. Q. Rev. Biophys. 43, 295-332. doi: 10.1017/S0033583510000119