Applications of Computational Methods to Simulations of Proteins Dynamics

  • Wieslaw Nowak
Reference work entry

Abstract

Advances in computer technology offer great opportunities for new explorations of protein structure and dynamics. Sound and well-established theoretical models may be successfully used for searching new biochemical phenomena, correlations, and protein properties. In this review the fast-growing field of computer simulations of protein dynamics is presented. The principles of currently used computational methods are outlined and representative examples of their recent advanced applications are given. In particular, protein folding studies, protein-drug interactions, transport phenomena, ion channels activity, molecular machines mechanics, origins of molecular diseases, and simulations of single molecule AFM experiments are addressed.

Experimentalists and management will not only become used to accepting the use of molecular modeling, but they will expect it. (Phillip R. Westmoreland)

WTEC Panel Report on Applications of Molecular and Materials Modeling,NIST 2002 (USA)

Keywords

Molecular Dynamic Autism Spectrum Disorder Molecular Dynamic Simulation Force Field Potential Energy Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by Polish Funds for Science (N N202 262038).

References

  1. Achary, M. S., & Nagarajaram, H. A. (2009). Effects of disease causing mutations on the essential motions in proteins. Journal of Biomolecular Structure and Dynamics, 26, 609.Google Scholar
  2. Adcock, S. A., & McCammon, J. A. (2006). Molecular dynamics: Survey of methods for simulating the activity of proteins. Chemical Reviews, 106, 1589.Google Scholar
  3. Aksimentiev, A., Balabin, I. A., Fillingame, R. H., & Schulten, K. (2004). Insights into the molecular mechanism of rotation in the Fo sector of ATP synthase. Biophysical Journal, 86, 1332.Google Scholar
  4. Aksimentiev, A., Brunner, R., Cohen, J., Comer, J., Cruz-Chu, E., Hardy, D., et al. (2008). Computer modeling in biotechnology: A partner in development. Methods in Molecular Biology, 474, 181.Google Scholar
  5. Alder, B. J., & Wainwright, T. E. (1957). Phase transition for a hard sphere system. Journal of Chemical Physics, 27, 1208.Google Scholar
  6. Aleksandrov, A., Thompson, D., & Simonson, T. (2010). Alchemical free energy simulations for biological complexes: Powerful but temperamental. Journal of Molecular Recognition, 23, 117.Google Scholar
  7. Aliev, A. E., & Courtier-Murias, D. (2010). Experimental verification of force fields for molecular dynamics simulations using Gly-Pro-Gly-Gly. The Journal of Physical Chemistry B, 114, 12358.Google Scholar
  8. Allen, M. P., & Tildesley, D. J. (1987). Computer simulation of liquids. Oxford: Clarendon Press.Google Scholar
  9. Alvarez-Paggi, D., Martin, D. F., DeBiase, P. M., Hildebrandt, P., Marti, M. A., & Murgida, D. H. (2010). Molecular basis of coupled protein and electron transfer dynamics of cytochrome c in biomimetic complexes. Journal of the American Chemical Society, 132, 5769.Google Scholar
  10. Amadei, A., Linssen, A. B., & Berendsen, H. J. (1993). Essential dynamics of proteins. Proteins, 17, 412.Google Scholar
  11. Aqvist, J., Luzhkov, V. B., & Brandsdal, B. O. (2002). Ligand binding affinities from MD simulations. Accounts of Chemical Research, 35, 358.Google Scholar
  12. Ash, W. L., Zlomislic, M. R., Oloo, E. O., & Tieleman, D. P. (2004). Computer simulations of membrane proteins. Biochimica et Biophysica Acta, 1666, 158.Google Scholar
  13. Avila, C. L., Drechsel, N. J., Alcantara, R., & Ville-Freixa, J. (2011). Multiscale molecular dynamics of protein aggregation. Current Protein & Peptide Science, 12(3), 221–234.Google Scholar
  14. Ayton, G. S., Noid, W. G., & Voth, G. A. (2007). Multiscale modeling of biomolecular systems: In serial and in parallel. Current Opinion in Structural Biology, 17, 192.Google Scholar
  15. Ayton, G. S., Lyman, E., & Voth, G. A. (2010). Hierarchical coarse-graining strategy for protein-membrane systems to access mesoscopic scales. Faraday Discuss, 144, 347.Google Scholar
  16. Bahar, I., & Rader, A. J. (2005). Coarse-grained normal mode analysis in structural biology. Current Opinion in Structural Biology, 15, 586.Google Scholar
  17. Banci, L. (2003). Molecular dynamics simulations of metalloproteins. Current Opinion in Chemical Biology, 7, 143.Google Scholar
  18. Bashford, D., & Case, D. A. (2000). Generalized born models of macromolecular solvation effects. Annual Review of Physical Chemistry, 51, 129.Google Scholar
  19. Becker, O. M., & Karplus, M. (2006). A guide to biomolecular simulations (Vol. 4). Dordrecht: Springer.Google Scholar
  20. Becker, T., Bhushan, S., Jarasch, A., Armache, J. P., Funes, S., Jossinet, F., et al. (2009). Structure of monomeric yeast and mammalian Sec61 complexes interacting with the translating ribosome. Science, 326, 1369.Google Scholar
  21. Beckstein, O., Biggin, P. C., Bond, P., Bright, J. N., Domene, C., Grottesi, A., et al. (2003). Ion channel gating: Insights via molecular simulations. FEBS Letters, 555, 85.Google Scholar
  22. Berendsen, H. J. C. E. (1976). Proceedings of the CECAM workshop on models for protein dynamics. Orsay: University of Paris.Google Scholar
  23. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., et al. (2000). The protein data bank. Nucleic Acids Research, 28, 235.Google Scholar
  24. Biarnes, X., Bongarzone, S., Vargiu, A. V., Carloni, P., & Ruggerone, P. (2011). Molecular motions in drug design: The coming age of the metadynamics method. Journal of Computer-Aided Molecular Design, 25, 395.Google Scholar
  25. Bikiel, D. E., Boechi, L., Capece, L., Crespo, A., De Biase, P. M., Di Lella, S., et al. (2006). Modeling heme proteins using atomistic simulations. Physical Chemistry Chemical Physics, 8, 5611.Google Scholar
  26. Boas, F. E., & Harbury, P. B. (2007). Potential energy functions for protein design. Current Opinion in Structural Biology, 17, 199.Google Scholar
  27. Boiteux, C., Kraszewski, S., Ramseyer, C., & Girardet, C. (2007). Ion conductance vs. pore gating and selectivity in KcsA channel: Modeling achievements and perspectives. Journal of Molecular Modeling, 13, 699.Google Scholar
  28. Borell B. (2008). Chemistry: Power play. Nature, 451, 240.Google Scholar
  29. Brooks, B. R., Brooks, C. L., 3rd, Mackerell, A. D., Jr., Nilsson, L., Petrella, R. J., Roux, B., et al. (2009). CHARMM: The biomolecular simulation program. Journal of Computational Chemistry, 30, 1545.Google Scholar
  30. Buda, F. (2009). Introduction to theory/modeling methods in photosynthesis, Photosynthesis Research, 102(2–3), 437–441.Google Scholar
  31. Carnevale, V., Raugei, S., Neri, M., Pantano, S., Micheletti, C., & Carloni, P. (2009). Multi-scale modeling of HIV-1 proteins. Journal of Molecular Structure-Theochem, 898, 97.Google Scholar
  32. Case, D. A., Cheatham, T. E., 3rd, Darden, T., Gohlke, H., Luo, R., Merz, K. M., Jr., et al. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26, 1668.Google Scholar
  33. Chen, J., & Brooks, C. L., 3rd. (2008). Implicit modeling of nonpolar solvation for simulating protein folding and conformational transitions. Physical Chemistry Chemical Physics, 10, 471.Google Scholar
  34. Chen, J., Brooks, C. L., 3rd, & Khandogin, J. (2008). Recent advances in implicit solvent-based methods for biomolecular simulations. Current Opinion in Structural Biology, 18, 140.Google Scholar
  35. Chou, K. C. (2004). Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 11, 2105.Google Scholar
  36. Christ, C. D., Mark, A. E., & van Gunsteren, W. F. (2010). Basic ingredients of free energy calculations: A review. Journal of Computational Chemistry, 31, 1569.Google Scholar
  37. Christen, M., Hunenberger, P. H., Bakowies, D., Baron, R., Burgi, R., Geerke, D. P., et al. (2005). The GROMOS software for biomolecular simulation: GROMOS05. Journal of Computational Chemistry, 26, 1719.Google Scholar
  38. Chu, J.-W., Ayton, G. S., Izvekov, S., & Voth, G. A. (2007). Emerging methods for multiscale simulation of biomolecular systems. Molecular Physics, 105, 167.Google Scholar
  39. Clementi, C. (2008). Coarse-grained models of protein folding: Toy models or predictive tools? Current Opinion in Structural Biology, 18, 10.Google Scholar
  40. Cohen, J., Olsen, K. W., & Schulten, K. (2008). Finding gas migration pathways in proteins using implicit ligand sampling. Methods in Enzymology, 437, 439.Google Scholar
  41. Cornell, W., & Nam, K. (2009). Steroid hormone binding receptors: Application of homology modeling, induced fit docking, and molecular dynamics to study structure-function relationships. Current Topics in Medicinal Chemistry, 9, 844.Google Scholar
  42. Cukier, R. I. (2004). Theory and simulation of proton-coupled electron transfer, hydrogen-atom transfer, and proton translocation in proteins. Biochimica et Biophysica Acta, 1655, 37.Google Scholar
  43. Dahl, J. P. (2001). Introduction to the quantum world of atoms and molecules. Singapore: World Scientific Publishing Co.Google Scholar
  44. Dal Peraro, M., Ruggerone, P., Raugei, S., Gervasi, F., & Elber, R. (2005). Long-timescale simulation methods. Current Opinion in Structural Biology, 15, 151.Google Scholar
  45. Dal Peraro, M., Ruggerone, P., Raugei, S., Gervasio, F. L., & Carloni, P. (2007). Investigating biological systems using first principles Car-Parrinello molecular dynamics simulations. Current Opinion in Structural Biology, 17, 149.Google Scholar
  46. DeMarco, M. L., & Daggett, V. (2009). Characterization of cell-surface prion protein relative to its recombinant analogue: Insights from molecular dynamics simulations of diglycosylated, membrane-bound human prion protein. Journal of Neurochemistry, 109, 60.Google Scholar
  47. Deng, Y., & Roux, B. (2009). Computations of standard binding free energies with molecular dynamics simulations. The Journal of Physical Chemistry B, 113, 2234.Google Scholar
  48. Dittrich, M., Freddolino, P. L., & Schulten, K. (2005). When light falls in LOV: A quantum mechanical/ molecular mechanical study of photoexcitation in Phot-LOV1 of Chlamydomonas reinhardtii. The Journal of Physical Chemistry B, 109, 13006.Google Scholar
  49. Dittrich, M., & Schulten, K. (2006). PcrA helicase, a prototype ATP-driven molecular motor. Structure, 14, 1345.Google Scholar
  50. Dodson, G. G., Lane, D. P., & Verma, C. S. (2008). Molecular simulations of protein dynamics: New windows on mechanisms in biology. EMBO Reports, 9, 144.Google Scholar
  51. Duan, Y., & Kollman, P. A. (1998). Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science, 282, 740.Google Scholar
  52. Ekonomiuk, D., Kielbasinski, M., & Kolinski, A. (2005). Protein modeling with reduced representation: Statistical potentials and protein folding mechanism. Acta Biochimica Polonica, 52, 741.Google Scholar
  53. Elber, R., Ghosh, A., & Cardenas, A. (2002). Long time dynamics of complex systems. Accounts of Chemical Research, 35, 396.Google Scholar
  54. Elcock, A. H., Sept, D., & McCammon, J. A. (2001). Computer simulation of protein–protein interactions. The Journal of Physical Chemistry B, 105, 1504.Google Scholar
  55. Ensign, D. L., Kasson, P. M., & Pande, V. S. (2007). Heterogeneity even at the speed limit of folding: Large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. Journal of Molecular Biology, 374, 806.Google Scholar
  56. Flechsig, H., & Mikhailov, A. S. (2010). Tracing entire operation cycles of molecular motor hepatitis C virus helicase in structurally resolved dynamical simulations. Proceedings of the National Academy of Sciences of the United States of America, 107, 20875.Google Scholar
  57. Frankel, D., & Smit, B. (2001). Understanding molecular simulation (2nd ed.). San Diego: Academic.Google Scholar
  58. Freddolino, P. L., Arkhipov, A. S., Larson, S. B., McPherson, A., & Schulten, K. (2006a). Molecular dynamics simulations of the complete satellite tobacco mosaic virus. Structure, 14, 437.Google Scholar
  59. Freddolino, P. L., Dittrich, M., & Schulten, K. (2006b). Dynamic switching mechanisms in LOV1 and LOV2 domains of plant phototropins. Biophysical Journal, 91, 3630.Google Scholar
  60. Freddolino, P. L., Liu, F., Gruebele, M., & Schulten, K. (2008). Ten-microsecond molecular dynamics simulation of a fast-folding WW domain. Biophysical Journal, 94, L75.Google Scholar
  61. Freddolino, P. L., Park, S., Roux, B., & Schulten, K. (2009). Force field bias in protein folding simulations. Biophysical Journal, 96, 3772.Google Scholar
  62. Freddolino, P. L., Harrison, C. B., Liu, Y., & Schulten, K. (2010). Challenges in protein folding simulations: Timescale, representation, and analysis. Nature Physics, 6, 751.Google Scholar
  63. Freddolino, P. L., & Schulten, K. (2009). Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophysical Journal, 97, 2338.Google Scholar
  64. Galeazzi, R. (2009). Molecular dynamics as a tool in rational drug design: Current status and some major applications. Current Computer-Aided Drug Design, 5, 225.Google Scholar
  65. Galera-Prat, A., Gomez-Sicilia, A., Oberhauser, A. F., Cieplak, M., & Carrion-Vazquez, M. (2010). Understanding biology by stretching proteins: Recent progress. Current Opinion in Structural Biology, 20, 63.Google Scholar
  66. Gallicchio, E., & Levy, R. M. (2011). Advances in all atom sampling methods for modeling protein-ligand binding affinities. Current Opinion in Structural Biology, 161, 161–166.Google Scholar
  67. Gao, M., Sotomayor, M., Villa, E., Lee, E. H., & Schulten, K. (2006). Molecular mechanisms of cellular mechanics. Physical Chemistry Chemical Physics, 8, 3692.Google Scholar
  68. Grubmueller, H. (2004). “Proteins as molecular machines: Force probe simulations” published in Computational soft matter: From synthetic polymers to proteins, lecture notes. In N. Attig, K. Binder, H. Grubmueller & K. Kremer (Eds.), NIC series (Vol. 23, pp. 401–422). Julich: John von Neumann Institute for Computing. ISBN 3-00-012641-4.Google Scholar
  69. Gu, J., & Bourne, P. E. (Eds.). (2009). Structural bioinformatics (2nd ed.). Hoboken: Wiley-Blackwell.Google Scholar
  70. Gumbart, J., Wang, Y., Aksimentiev, A., Tajkhorshid, E., & Schulten, K. (2005). Molecular dynamics simulations of proteins in lipid bilayers. Current Opinion in Structural Biology, 15, 423.Google Scholar
  71. Guvench, O., & MacKerell, A. D., Jr. (2008). Comparison of protein force fields for molecular dynamics simulations. Methods in Molecular Biology, 443, 63.Google Scholar
  72. Haile, M. (1992). Molecular dynamics simulation: Elementary methods. New York: Wiley.Google Scholar
  73. Hansson, T. O. C., & van Gunsteren, W. (2002). Molecular dynamics simulations. Current Opinion in Structural Biology, 12, 190.Google Scholar
  74. Hardy, D. J., Stone, J. E., & Schulten, K. (2009). Multilevel summation of electrostatic potentials using graphics processing units. Parallel Computing, 35, 164.Google Scholar
  75. Hayashi, S., Tajkhorshid, E., & Schulten, K. (2009). Photochemical reaction dynamics of the primary event of vision studied by means of a hybrid molecular simulation. Biophysical Journal, 96, 403.Google Scholar
  76. Henzler-Wildman, K., & Kern, D. (2007). Dynamic personalities of proteins. Nature, 450, 964.Google Scholar
  77. Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. (2008). GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4, 435.Google Scholar
  78. Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., & Simmerling, C. (2006). Comparison of multiple AMBER force fields and development of improved protein backbone parameters. Proteins: Structure, Function, and Bioinformatics, 65, 712.Google Scholar
  79. Hou, T., Wang, J., Li, Y., & Wang, W. (2011). Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 51, 69.Google Scholar
  80. Houriez, C., Ferre, N., Masella, M., & Siri, D. (2008). Prediction of nitroxide hyperfine coupling constants in solution from combined nanosecond scale simulations and quantum computations. Journal of Chemical Physics, 128, 244504.Google Scholar
  81. Hsin, J., Arkhipov, A., Yin, Y., Stone, J. E., & Schulten, K. (2008). Using VMD: An introductory tutorial. Current Protocols in Bioinformatics, Chapter 5, p. Unit 5 7.Google Scholar
  82. Hub, J. S., & de Groot, B. L. (2009). Detection of functional modes in protein dynamics. PLoS Computational Biology, 5, e1000480.Google Scholar
  83. Hub, J. S., Grubmuller, H., & de Groot, B. L. (2009). Dynamics and energetics of permeation through aquaporins. What do we learn from molecular dynamics simulations? Handbook of Experimental Pharmacology, 190, 57.Google Scholar
  84. Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14, 33.Google Scholar
  85. Ikeguchi, M. (2009). Water transport in aquaporins: Molecular dynamics simulations. Frontiers in Bioscience, 14, 1283.Google Scholar
  86. Jorgensen, W. L., & Tiradorives, J. (1988). The OPLS potential functions for proteins – energy minimizations for crystals of cyclic-peptides and crambin. Journal of the American Chemical Society, 110, 1657.Google Scholar
  87. Kannan, S., & Zacharias, M. (2009). Simulated annealing coupled replica exchange molecular dynamics – an efficient conformational sampling method. Journal of Structural Biology, 166, 288.Google Scholar
  88. Karplus, M. (2003). Molecular dynamics of biological macromolecules: A brief history and perspective. Biopolymers, 68, 350.Google Scholar
  89. Karplus, M., & McCammon, J. A. (2002). Molecular dynamics simulations of biomolecules. Nature Structural Biology, 9, 646.Google Scholar
  90. Kassler, K., Horn, A. H. C., & Sticht, H. (2010). Effect of pathogenic mutations on the structure and dynamics of Alzheimer’s A beta(42)-amyloid oligomers. Journal of Molecular Modeling, 16, 1011.Google Scholar
  91. Khafizov, K., Lattanzi, G., & Carloni, P. (2009). G protein inactive and active forms investigated by simulation methods. Proteins-Structure Function and Bioinformatics, 75, 919.Google Scholar
  92. Khalili-Araghi, F., Gumbart, J., Wen, P. C., Sotomayor, M., Tajkhorshid, E., & Schulten, K. (2009). Molecular dynamics simulations of membrane channels and transporters. Current Opinion in Structural Biology, 19, 128.Google Scholar
  93. Kholmurodov, K. T., Altaisky, M. V., Puzynin, I. V., Darden, T., & Filatov, F. P. (2003). Methods of molecular dynamics for simulation of physical and biological processes. Physics of Particles and Nuclei, 34, 244.Google Scholar
  94. Khurana, E., Devane, R. H., Dal Peraro, M., & Klein, M. L. (2011). Computational study of drug binding to the membrane-bound tetrameric M2 peptide bundle from influenza A virus. Biochimica et Biophysica Acta, 1808, 530.Google Scholar
  95. Klein, M. L., & Shinoda, W. (2008). Large-scale molecular dynamics simulations of self- assembling systems. Science, 321, 798.Google Scholar
  96. Klepeis, J. L., Pieja, M. J., & Floudas, C. A. (2003). Hybrid global optimization algorithms for protein structure prediction: Alternating hybrids. Biophysical Journal, 84, 869.Google Scholar
  97. Klepeis, J. L., Lindorff-Larsen, K., Dror, R. O., & Shaw, D. E. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology, 19, 120.Google Scholar
  98. Kmiecik, S., Gront, D., & Kolinski, A. (2007). Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field. BMC Structural Biology, 7, 43.Google Scholar
  99. Knapp, B., & Schreiner, W. (2009). Graphical user interfaces for molecular dynamics-quo vadis? Bioinformatics and Biology Insights, 3, 103.Google Scholar
  100. Knoll, P., & Mirzaei, S. (2003). Development of an interactive molecular dynamics simulation software package. Review of Scientific Instruments, 74, 2483.Google Scholar
  101. Kolomeisky, A. B., & Fisher, M. E. (2007). Molecular motors: A theorist’s perspective. Annual Review of Physical Chemistry, 58, 675.Google Scholar
  102. Kremer, K. (2003). Computer simulations for macromolecular science. Macromolecular Chemistry and Physics, 204, 257.Google Scholar
  103. Kubiak, K., & Nowak, W. (2008). Molecular dynamics simulations of the photoactive protein nitrile hydratase. Biophysical Journal, 94, 3824.Google Scholar
  104. Kuczera, K., Jas, G. S., & Elber, R. (2009). Kinetics of helix unfolding: Molecular dynamics simulations with milestoning. The Journal of Physical Chemistry A, 113, 7461.Google Scholar
  105. Kupfer, L., Hinrichs, W., & Groschup, M. H. (2009). Prion protein misfolding. Current Molecular Medicine, 9, 826.Google Scholar
  106. Lange, O. E., Schafer, L. V., & Grubmuller, H. (2006). Flooding in GROMACS: Accelerated barrier crossings in molecular dynamics. Journal of Computational Chemistry, 27, 1693.Google Scholar
  107. Lauria, A., Tutone, M., Ippolito, M., Pantano, L., & Almerico, A. M. (2010). Molecular modeling approaches in the discovery of new drugs for anti-cancer therapy: The investigation of p53-MDM2 interaction and its inhibition by small molecules. Current Medicinal Chemistry, 17, 3142.Google Scholar
  108. Le, L., Lee, E., Schulten, K., & Truong, T. N. (2009). Molecular modeling of swine influenza A/H1N1, Spanish H1N1, and avian H5N1 flu N1 neuraminidases bound to Tamiflu and Relenza. PLoS Currents: Influenza, 1, RRN1015.Google Scholar
  109. Leach, A. (2001). Molecular modelling: Principles and applications (2nd ed.). Harlow: Prentice Hall.Google Scholar
  110. Lee, E. H., Hsin, J., Sotomayor, M., Comellas, G., & Schulten, K. (2009). Discovery through the computational microscope. Structure, 17, 1295.Google Scholar
  111. Lee, G., Nowak, W., Jaroniec, J., Zhang, Q., & Marszalek, P. E. (2004). Nanomechanical control of glucopyranose rotamers. Journal of the American Chemical Society, 126, 6218.Google Scholar
  112. Lee, K. H., Kuczera, K., & Banaszak Holl, M. M. (2011). The severity of osteogenesis imperfecta: A comparison to the relative free energy differences of collagen model peptides. Biopolymers, 95, 182.Google Scholar
  113. Levitt, M., & Lifson, S. (1969). Refinement of protein conformation using a macromolecular energy minimization procedure. Journal of Molecular Biology, 46, 269.Google Scholar
  114. Liu, J., & Nussinov, R. (2010). Molecular dynamics reveal the essential role of Linker motions in the function of Cullin-RING E3 ligases. Journal of Molecular Biology, 396, 1508.Google Scholar
  115. Liwo, A., Czaplewski, C., Oldziej, S., & Scheraga, H. A. (2008). Computational techniques for efficient conformational sampling of proteins. Current Opinion in Structural Biology, 18, 134.Google Scholar
  116. Lonsdale, R., Ranaghan, K. E., & Mulholland, A. J. (2010). Computational enzymology. Chemical Communications, 46, 2354.Google Scholar
  117. Ma, J., Flynn, T. C., Cui, Q., Leslie, A. G., Walker, J. E., & Karplus, M. (2002). A dynamic analysis of the rotation mechanism for conformational change in F(1)-ATPase. Structure, 10, 921.Google Scholar
  118. Ma, J. P., & Karplus, M. (1997). Molecular switch in signal transduction: Reaction paths of the conformational changes in ras p21. Proceedings of the National Academy of Sciences of the United States of America, 94, 11905.Google Scholar
  119. Ma, B., & Levine, A. J. (2007). Probing potential binding modes of the p53 tetramer to DNA based on the symmetries encoded in p53 response elements. Nucleic Acids Research, 35, 7733.Google Scholar
  120. MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., et al. (1998). All-atom empirical potential for molecular modeling and dynamics studies of proteins. The Journal of Physical Chemistry B, 102, 3586.Google Scholar
  121. Mackerell, A. D., Jr., & Nilsson, L. (2008). Molecular dynamics simulations of nucleic acid-protein complexes. Current Opinion in Structural Biology, 18, 194.Google Scholar
  122. Marti, M. A., Capece, L., Bidon-Chanal, A., Crespo, A., Guallar, V., Luque, F. J., & Estrin, D. A. (2008). Nitric oxide reactivity with globins as investigated through computer simulation. Methods in Enzymology, 437, 477.Google Scholar
  123. Mayor, U., Guydosh, N. R., Johnson, C. M., Grossmann, J. G., Sato, S., Jas, G. S., et al. (2003). The complete folding pathway of a protein from nanoseconds to microseconds. Nature, 421, 863.Google Scholar
  124. McCammon, J. A., Gelin, B. R., & Karplus, M. (1977). Dynamics of folded proteins. Nature, 267, 585.Google Scholar
  125. Meirovitch, H. (2007). Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation. Current Opinion in Structural Biology, 17, 181.Google Scholar
  126. Miao, L., & Schulten, K. (2009). Transport-related structures and processes of the nuclear pore complex studied through molecular dynamics. Structure, 17, 449.Google Scholar
  127. Miller, B. T., Singh, R. P., Klauda, J. B., Hodoscek, M., Brooks, B. R., & Woodcock, H. L. (2008). CHARMMing: A new, flexible web portal for CHARMM. Journal of Chemical Information and Modeling, 48, 1920.Google Scholar
  128. Moraitakis, G., Purkiss, A. G., & Goodfellow, J. M. (2003). Simulated dynamics and biological molecules. Reports on Progress in Physics, 66, 483.Google Scholar
  129. Morra, G., Meli, M., & Colombo, G. (2008). Molecular dynamics simulations of proteins and peptides: From folding to drug design. Current Protein & Peptide Science, 9, 181.Google Scholar
  130. Morra, G., Genoni, A., Neves, M. A., Merz, K. M., Jr., & Colombo, G (2010) Molecular recognition and drug-lead identification: What can molecular simulations tell us? Current Medicinal Chemistry, 17, 25.Google Scholar
  131. Nielsen, S. O., Bulo, R. E., Moore, P. B., & Ensing, B. (2010). Recent progress in adaptive multiscale molecular dynamics simulations of soft matter. Physical Chemistry Chemical Physics, 12, 12401.Google Scholar
  132. Nowak, W., Czerminski, R., & Elber, R. (1991). Reaction path study of ligand diffusion in proteins: Application of the self penalty walk (SPW) method to calculate reaction coordinates for the motion of CO through leghemoglobin. Journal of the American Chemical Society, 113, 5627.Google Scholar
  133. Nowak, W., & Marszalek, P. (2005). Molecular dynamics simulations of single molecule atomic force microscope experiments. In J. Leszczynski (Ed.), Current trends in computational chemistry (pp. 47–83). Singapore: World Scientific.Google Scholar
  134. Nowak, W., Wasilewski, S., & Peplowski, L. (2007). Steered molecular dynamics as a virtual atomic force microscope. In H. E. Ulrich, J. M. Hansmann, S. Mohanty & O. Zimmermann (Eds.), From computational biophysics to systems biology (CBSB07), Proceedings of the NIC Workshop 2007 (p. 251). Julich: John von Neumann Institute for Computing.Google Scholar
  135. Olsen, S., Lamothe, K., & Martinez, T. J. (2010). Protonic gating of excited-state twisting and charge localization in GFP chromophores: A mechanistic hypothesis for reversible photoswitching. Journal of the American Chemical Society, 132, 1192.Google Scholar
  136. Orlowski, S., & Nowak, W. (2007). Locally enhanced sampling molecular dynamics study of the dioxygen transport in human cytoglobin. Journal of Molecular Modeling, 13, 715.Google Scholar
  137. Orlowski, S., & Nowak, W. (2008). Topology and thermodynamics of gaseous ligands diffusion paths in human neuroglobin. Biosystems, 94, 263.Google Scholar
  138. Paci, E. (2002). High pressure simulations of biomolecules. BBA-Protein Structure and Molecular Enzymology, 1595, 185.Google Scholar
  139. Paci, E., Caflisch, A., Pluckthun, A., & Karplus, M. (2001). Forces and energetics of hapten-antibody dissociation: A biased molecular dynamics simulation study. Journal of Molecular Biology, 314, 589.Google Scholar
  140. Pande, V. S., Baker, I., Chapman, J., Elmer, S. P., Khaliq, S., Larson, S. M., et al. (2003). Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. Biopolymers, 68, 91.Google Scholar
  141. Papaleo, E., & Invernizzi, G. (2011). Conformational diseases: Structural studies of aggregation of polyglutamine proteins. Current Computer-Aided Drug Design, 7, 23.Google Scholar
  142. Peplowski, L., Kubiak, K., & Nowak, W. (2008). Mechanical aspects of nitrile hydratase enzymatic activity. Steered molecular dynamics simulations of Pseudonocardia thermophila JCM 3095. Chemical Physics Letters, 467, 144.Google Scholar
  143. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., et al. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26, 1781.Google Scholar
  144. Piana, S., Sarkar, K., Lindorff-Larsen, K., Guo, M., Gruebele, M., & Shaw, D. E. (2011). Computational design and experimental testing of the fastest-folding beta-sheet protein. Journal of Molecular Biology, 405, 43.Google Scholar
  145. Pohorille, A., Jarzynski, C., & Chipot, C. (2010). Good practices in free-energy calculations. The Journal of Physical Chemistry B, 114, 10235.Google Scholar
  146. Rahman, A., & Stillinger, F. H. (1971). Molecular dynamics study of liquid water. Journal of Chemical Physics, 55, 3336.Google Scholar
  147. Rapaport, D. C. (1995). The art of molecular dynamics simulation. Cambridge, MA: Cambridge University Press.Google Scholar
  148. Rehm, S., Trodler, P., & Pleiss, J. (2010). Solvent-induced lid opening in lipases: A molecular dynamics study. Protein Science, 19, 2122.Google Scholar
  149. Rief, M., & Grubmuller, H. (2002). Force spectroscopy of single biomolecules. A EuropeanJournal of Chemical Physics and Physical Chemistry, 3, 255.Google Scholar
  150. Rodrigues, J. R., Simoes, C. J. V., Silva, C. G., & Brito, R. M. M. (2010). Potentially amyloidogenic conformational intermediates populate the unfolding landscape of transthyretin: Insights from molecular dynamics simulations. Protein Science, 19, 202.Google Scholar
  151. Romanowska, J., Setny, P., & Trylska, J. (2008). Molecular dynamics study of the ribosomal A-site. The Journal of Physical Chemistry B, 112, 15227.Google Scholar
  152. Rosales-Hernandez, M. C., Bermudez-Lugo, J., Garcia, J., Trujillo-Ferrara, J., & Correa-Basurto, J. (2009). Molecular modeling applied to anti-cancer drug development. Anti-Cancer Agents in Medicinal Chemistry, 9, 230.Google Scholar
  153. Rossle, S. C., & Frank, I. (2009). First-principles simulation of photoreactions in biological systems. Frontiers in Bioscience, 14, 4862.Google Scholar
  154. Roux, B., & Schulten, K. (2004). Computational studies of membrane channels. Structure, 12, 1343.Google Scholar
  155. Russel, D., Lasker, K., Phillips, J., Schneidman-Duhovny, D., Velazquez-Muriel, J. A., & Sali, A. (2009). The structural dynamics of macromolecular processes. Current Opinion in Cell Biology, 21, 97.Google Scholar
  156. Sakudo, A., Xue, G. A., Kawashita, N., Ano, Y., Takagi, T., Shintani, H., et al. (2010). Structure of the prion protein and its gene: An analysis using bioinformatics and computer simulation. Current Protein & Peptide Science, 11, 166.Google Scholar
  157. Sanbonmatsu, K. Y., & Tung, C. S. (2007). High performance computing in biology: Multimillion atom simulations of nanoscale systems. Journal of Structural Biology, 157, 470.Google Scholar
  158. Sansom, M. S., Scott, K. A., & Bond, P. J. (2008). Coarse-grained simulation: A high-throughput computational approach to membrane proteins. Biochemical Society Transactions, 36, 27.Google Scholar
  159. Schaeffer, R. D., Fersht, A., & Daggett, V. (2008). Combining experiment and simulation in protein folding: Closing the gap for small model systems. Current Opinion in Structural Biology, 18, 4.Google Scholar
  160. Scheraga, H. A., Khalili, M., & Liwo, A. (2007). Protein-folding dynamics: Overview of molecular simulation techniques. Annual Review of Physical Chemistry, 58, 57.Google Scholar
  161. Scheres, S. H. (2010). Visualizing molecular machines in action: Single-particle analysis with structural variability. Advances in Protein Chemistry and Structural Biology, 81, 89.Google Scholar
  162. Schlegel, H. B. (2003). Exploring potential energy surfaces for chemical reactions: An overview of some practical methods. Journal of Computational Chemistry, 24, 1514.Google Scholar
  163. Schlick, T. (2002). Molecular modeling and simulation – an interdisciplinary guide. New York: Springer.Google Scholar
  164. Schuyler, A. D., Carlson, H. A., & Feldman, E. L. (2009). Computational methods for predicting sites of functionally important dynamics. The Journal of Physical Chemistry B, 113, 6613.Google Scholar
  165. Schwede, T., & Peitsch, M. C. (2008). Computational structural biology: Methods and applications. Hackensack, NJ: World Scientific.Google Scholar
  166. Sellis, D., Vlachakis, D., & Vlassi, M. (2009). Gromita: A fully integrated graphical user interface to Gromacs 4. Bioinformatics and Biology Insights, 3, 99.Google Scholar
  167. Sen, S., Andreatta, D., Ponomarev, S. Y., Beveridge, D. L., & Berg, M. A. (2009). Dynamics of water and ions near DNA: Comparison of simulation to time-resolved stokes-shift experiments. Journal of the American Chemical Society, 131, 1724.Google Scholar
  168. Shakhnovich, E. (2006). Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chemical Reviews, 106, 1559.Google Scholar
  169. Sherwood, P., Brooks, B. R., & Sansom, M. S. (2008). Multiscale methods for macromolecular simulations. Current Opinion in Structural Biology, 18, 630.Google Scholar
  170. Shi, S., Pei, J., Sadreyev, R. I., Kinch, L. N., Majumdar, I., Tong, J., et al. (2009). Analysis of CASP8 targets, predictions and assessment methods. Database (Oxford), 2009, bap003.Google Scholar
  171. Showalter, S. A., & Bruschweiler, R. (2007). Validation of molecular dynamics simulations of biomolecules using NMR spin relaxation as benchmarks: Application to the AMBER99SB force field. Journal of Chemical Theory and Computation, 3, 961.Google Scholar
  172. Simms A. M., Toofanny R. D., Kehl C., Benson N. C., and Daggett, V. (2008). Dynameomics: Design of a computational lab workflow and scientific data repository for protein simulations. Protein Engineering, Design and Selection, 21, 369.Google Scholar
  173. Simonson, T., Archontis, G., & Karplus, M. (2002). Free energy simulations come of age: Protein-ligand recognition. Accounts of Chemical Research, 35, 430.Google Scholar
  174. Sotomayor, M., & Schulten, K. (2007). Single-molecule experiments in vitro and in silico. Science, 316, 1144.Google Scholar
  175. Spyrakis, F., BidonChanal, A., Barril, X., & Luque, F. J. (2011). Protein flexibility and ligand recognition: Challenges for molecular modeling. Current Topics in Medicinal Chemistry, 11, 192.Google Scholar
  176. Stone, J. E., Phillips, J. C., Freddolino, P. L., Hardy, D. J., Trabuco, L. G., & Schulten, K. (2007). Accelerating molecular modeling applications with graphics processors. Journal of Computational Chemistry, 28, 2618.Google Scholar
  177. Straatsma, T. P., & McCammon, J. A. (1992). Computational alchemy. Annual Review of Physical Chemistry, 43, 407.Google Scholar
  178. Straub, J. E., & Thirumalai, D. (2010). Toward a molecular theory of early and late events in monomer to amyloid fibril formation. Annual Review of Physical Chemistry, 62, 437.Google Scholar
  179. Strzelecki, J., Mikulska, K., Lekka, M., Kulik, A., Balter, A., & Nowak, W. (2009). AFM force spectroscopy and steered molecular dynamics simulation of protein contactin 4. Acta Physica Polonica A, 116, S156.Google Scholar
  180. Sugita, Y. (2009). Free-energy landscapes of proteins in solution by generalized-ensemble simulations. Frontiers in Bioscience, 14, 1292.Google Scholar
  181. Sugita, Y., & Okamoto, Y. (1999). Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 314, 141.Google Scholar
  182. Sun, Q., Doerr, M., Li, Z., Smith, S. C., & Thiel, W. (2010). QM/MM studies of structural and energetic properties of the far-red fluorescent protein HcRed. Physical Chemistry Chemical Physics, 12, 2450.Google Scholar
  183. Tajkhorshid, E., Aksimentiev, A., Balabin, I., Gao, M., Isralewitz, B., Phillips, J. C., et al. (2003). Large scale simulation of protein mechanics and function. Advances in Protein Chemistry, 66, 195.Google Scholar
  184. Tatke, S. S., Loong, C. K., D’Souza, N., Schoephoerster, R. T., & Prabhakaran, M. (2008). Large scale motions in a biosensor protein glucose oxidase: A combined approach by DENS, normal mode analysis, and molecular dynamics studies. Biopolymers, 89, 582.Google Scholar
  185. Tozzini, V. (2010). Multiscale modeling of proteins. Accounts of Chemical Research, 43, 220.Google Scholar
  186. Tozzini, V., Trylska, J., Chang, C. E., & McCammon, J. A. (2007). Flap opening dynamics in HIV-1 protease explored with a coarse-grained model. Journal of Structural Biology, 157, 606.Google Scholar
  187. Trabuco, L. G., Villa, E., Schreiner, E., Harrison, C. B., & Schulten, K. (2009). Molecular dynamics flexible fitting: A practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods, 49, 174.Google Scholar
  188. Trylska, J. (2010). Coarse-grained models to study dynamics of nanoscale biomolecules and their applications to the ribosome. Journal of Physics: Condensed Matter, 22, 453101.Google Scholar
  189. Urbanc, B., Betnel, M., Cruz, L., Bitan, G., & Teplow, D. B. (2010). Elucidation of amyloid beta-protein oligomerization mechanisms: Discrete molecular dynamics study. Journal of the American Chemical Society, 132, 4266.Google Scholar
  190. Van Der Kamp, M. W., Shaw, K. E., Woods, C. J., & Mulholland, A. J. (2008). Biomolecular simulation and modelling: Status, progress and prospects. Journal of the Royal Society Interface, 5, 173.Google Scholar
  191. Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26, 1701.Google Scholar
  192. Van Gunsteren, W. F., Bakowies, D., Baron, R., Chandrasekhar, I. C. M., Daura, X., Gee, P., et al. (2006). Biomolecular modeling: Goals, problems, perspectives. Angewandte Chemie International Edition, 45, 4064.Google Scholar
  193. van Oijen, A. M. (2007). Single-molecule studies of complex systems: The replisome. Molecular BioSystems, 3, 117.Google Scholar
  194. van Speybroeck, V., & Meier, R. J. (2003). A recent development in computational chemistry: Chemical reactions from first principles molecular dynamics simulations. Chemical Society Reviews, 32, 151.Google Scholar
  195. Vasquez, V., Sotomayor, M., Cordero-Morales, J., Schulten, K., & Perozo, E. (2008). A structural mechanism for MscS gating in lipid bilayers. Science, 321, 1210.Google Scholar
  196. Vemparala, S., Domene, C., & Klein, M. L. (2010). Computational studies on the interactions of inhalational anesthetics with proteins. Accounts of Chemical Research, 43, 103.Google Scholar
  197. Villa, E., Balaeff, A., & Schulten, K. (2005). Structural dynamics of the lac repressor-DNA complex revealed by a multiscale simulation. Proceedings of the National Academy of Sciences of the United States of America, 102, 6783.Google Scholar
  198. Vreede, J., Juraszek, J., & Bolhuis, P. G. (2010). Predicting the reaction coordinates of millisecond light-induced conformational changes in photoactive yellow protein. Proceedings of the National Academy of Sciences of the United States of America, 107, 2397.Google Scholar
  199. Wang, T., & Duan, Y. (2011). Retinal release from opsin in molecular dynamics simulations. Journal of Molecular Recognition, 24, 350.Google Scholar
  200. Wanko, M., Hoffmann, M., Frauenheim, T., & Elstner, M. (2006). Computational photochemistry of retinal proteins. Journal of Computer-Aided Molecular Design, 20, 511.Google Scholar
  201. Warshel, A. (2002). Molecular dynamics simulations of biological reactions. Accounts of Chemical Research, 35, 385.Google Scholar
  202. Warshel, A. (2003). Computer simulations of enzyme catalysis: Methods, progress, and insights. Annual Review of Biophysics and Biomolecular Structure, 32, 425.Google Scholar
  203. Warshel A., Kato M., & Pisliakov A.V. (2007). Polarizable force fields: History, test cases, and prospects. Journal of Chemical Theory and Computation, 3, 2034.Google Scholar
  204. Weiner, S. J., Kollman, P. A., Case, D. A., Singh, U. C., Ghio, C., Alagona, G., Profeta, S., & Weiner, P. (1984). A new force-field for molecular mechanical simulation of nucleic-acids and proteins. Journal of the American Chemical Society, 106, 765.Google Scholar
  205. Wong, V., & Case, D. A. (2008). Evaluating rotational diffusion from protein MD simulations. The Journal of Physical Chemistry B, 112, 6013.Google Scholar
  206. Yu, J., Ha, T., & Schulten, K. (2007). How directional translocation is regulated in a DNA helicase motor. Biophysical Journal, 93, 3783.Google Scholar
  207. Zhang, J., Li, W., Wang, J., Qin, M., Wu, L., Yan, Z., et al. (2009). Protein folding simulations: From coarse-grained model to all-atom model. IUBMB Life, 61, 627.Google Scholar
  208. Zhmurov, A., Dima, R. I., Kholodov, Y., & Barsegov, V. (2010). SOP-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors. Proteins, 78, 2984.Google Scholar
  209. Zhu, F., Tajkhorshid, E., & Schulten, K. (2004). Theory and simulation of water permeation in aquaporin-1. Biophysical Journal, 86, 50.Google Scholar
  210. Zink, M., & Grubmuller, H. (2009). Mechanical properties of the icosahedral shell of Southern bean mosaic virus: A molecular dynamics study. Biophysical Journal, 96, 1350.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Wieslaw Nowak
    • 1
  1. 1.Institute of PhysicsNicholaus Copernicus University in TorunTorunPoland

Personalised recommendations