Skip to main content

Applications of Computational Methods to Simulations of Proteins Dynamics

  • Reference work entry
  • First Online:
Handbook of Computational Chemistry

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)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 749.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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.

    CAS  Google Scholar 

  • Adcock, S. A., & McCammon, J. A. (2006). Molecular dynamics: Survey of methods for simulating the activity of proteins. Chemical Reviews, 106, 1589.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Alder, B. J., & Wainwright, T. E. (1957). Phase transition for a hard sphere system. Journal of Chemical Physics, 27, 1208.

    CAS  Google Scholar 

  • Aleksandrov, A., Thompson, D., & Simonson, T. (2010). Alchemical free energy simulations for biological complexes: Powerful but temperamental. Journal of Molecular Recognition, 23, 117.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Allen, M. P., & Tildesley, D. J. (1987). Computer simulation of liquids. Oxford: Clarendon Press.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Amadei, A., Linssen, A. B., & Berendsen, H. J. (1993). Essential dynamics of proteins. Proteins, 17, 412.

    CAS  Google Scholar 

  • Aqvist, J., Luzhkov, V. B., & Brandsdal, B. O. (2002). Ligand binding affinities from MD simulations. Accounts of Chemical Research, 35, 358.

    Google Scholar 

  • Ash, W. L., Zlomislic, M. R., Oloo, E. O., & Tieleman, D. P. (2004). Computer simulations of membrane proteins. Biochimica et Biophysica Acta, 1666, 158.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Bahar, I., & Rader, A. J. (2005). Coarse-grained normal mode analysis in structural biology. Current Opinion in Structural Biology, 15, 586.

    CAS  Google Scholar 

  • Banci, L. (2003). Molecular dynamics simulations of metalloproteins. Current Opinion in Chemical Biology, 7, 143.

    CAS  Google Scholar 

  • Bashford, D., & Case, D. A. (2000). Generalized born models of macromolecular solvation effects. Annual Review of Physical Chemistry, 51, 129.

    CAS  Google Scholar 

  • Becker, O. M., & Karplus, M. (2006). A guide to biomolecular simulations (Vol. 4). Dordrecht: Springer.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Berendsen, H. J. C. E. (1976). Proceedings of the CECAM workshop on models for protein dynamics. Orsay: University of Paris.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Boas, F. E., & Harbury, P. B. (2007). Potential energy functions for protein design. Current Opinion in Structural Biology, 17, 199.

    CAS  Google Scholar 

  • 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 

  • Borell B. (2008). Chemistry: Power play. Nature, 451, 240.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Buda, F. (2009). Introduction to theory/modeling methods in photosynthesis, Photosynthesis Research, 102(2–3), 437–441.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Chou, K. C. (2004). Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 11, 2105.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Chu, J.-W., Ayton, G. S., Izvekov, S., & Voth, G. A. (2007). Emerging methods for multiscale simulation of biomolecular systems. Molecular Physics, 105, 167.

    CAS  Google Scholar 

  • Clementi, C. (2008). Coarse-grained models of protein folding: Toy models or predictive tools? Current Opinion in Structural Biology, 18, 10.

    CAS  Google Scholar 

  • Cohen, J., Olsen, K. W., & Schulten, K. (2008). Finding gas migration pathways in proteins using implicit ligand sampling. Methods in Enzymology, 437, 439.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Dahl, J. P. (2001). Introduction to the quantum world of atoms and molecules. Singapore: World Scientific Publishing Co.

    Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Deng, Y., & Roux, B. (2009). Computations of standard binding free energies with molecular dynamics simulations. The Journal of Physical Chemistry B, 113, 2234.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Dittrich, M., & Schulten, K. (2006). PcrA helicase, a prototype ATP-driven molecular motor. Structure, 14, 1345.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Duan, Y., & Kollman, P. A. (1998). Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science, 282, 740.

    CAS  Google Scholar 

  • Ekonomiuk, D., Kielbasinski, M., & Kolinski, A. (2005). Protein modeling with reduced representation: Statistical potentials and protein folding mechanism. Acta Biochimica Polonica, 52, 741.

    CAS  Google Scholar 

  • Elber, R., Ghosh, A., & Cardenas, A. (2002). Long time dynamics of complex systems. Accounts of Chemical Research, 35, 396.

    CAS  Google Scholar 

  • Elcock, A. H., Sept, D., & McCammon, J. A. (2001). Computer simulation of protein–protein interactions. The Journal of Physical Chemistry B, 105, 1504.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Frankel, D., & Smit, B. (2001). Understanding molecular simulation (2nd ed.). San Diego: Academic.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Freddolino, P. L., Dittrich, M., & Schulten, K. (2006b). Dynamic switching mechanisms in LOV1 and LOV2 domains of plant phototropins. Biophysical Journal, 91, 3630.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Freddolino, P. L., Park, S., Roux, B., & Schulten, K. (2009). Force field bias in protein folding simulations. Biophysical Journal, 96, 3772.

    CAS  Google Scholar 

  • Freddolino, P. L., Harrison, C. B., Liu, Y., & Schulten, K. (2010). Challenges in protein folding simulations: Timescale, representation, and analysis. Nature Physics, 6, 751.

    CAS  Google Scholar 

  • Freddolino, P. L., & Schulten, K. (2009). Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophysical Journal, 97, 2338.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • Gao, M., Sotomayor, M., Villa, E., Lee, E. H., & Schulten, K. (2006). Molecular mechanisms of cellular mechanics. Physical Chemistry Chemical Physics, 8, 3692.

    CAS  Google Scholar 

  • 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 

  • Gu, J., & Bourne, P. E. (Eds.). (2009). Structural bioinformatics (2nd ed.). Hoboken: Wiley-Blackwell.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Guvench, O., & MacKerell, A. D., Jr. (2008). Comparison of protein force fields for molecular dynamics simulations. Methods in Molecular Biology, 443, 63.

    CAS  Google Scholar 

  • Haile, M. (1992). Molecular dynamics simulation: Elementary methods. New York: Wiley.

    Google Scholar 

  • Hansson, T. O. C., & van Gunsteren, W. (2002). Molecular dynamics simulations. Current Opinion in Structural Biology, 12, 190.

    CAS  Google Scholar 

  • Hardy, D. J., Stone, J. E., & Schulten, K. (2009). Multilevel summation of electrostatic potentials using graphics processing units. Parallel Computing, 35, 164.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Henzler-Wildman, K., & Kern, D. (2007). Dynamic personalities of proteins. Nature, 450, 964.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • 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 

  • 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 

  • Hub, J. S., & de Groot, B. L. (2009). Detection of functional modes in protein dynamics. PLoS Computational Biology, 5, e1000480.

    Google Scholar 

  • 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 

  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14, 33.

    CAS  Google Scholar 

  • Ikeguchi, M. (2009). Water transport in aquaporins: Molecular dynamics simulations. Frontiers in Bioscience, 14, 1283.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Kannan, S., & Zacharias, M. (2009). Simulated annealing coupled replica exchange molecular dynamics – an efficient conformational sampling method. Journal of Structural Biology, 166, 288.

    CAS  Google Scholar 

  • Karplus, M. (2003). Molecular dynamics of biological macromolecules: A brief history and perspective. Biopolymers, 68, 350.

    CAS  Google Scholar 

  • Karplus, M., & McCammon, J. A. (2002). Molecular dynamics simulations of biomolecules. Nature Structural Biology, 9, 646.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Khafizov, K., Lattanzi, G., & Carloni, P. (2009). G protein inactive and active forms investigated by simulation methods. Proteins-Structure Function and Bioinformatics, 75, 919.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Klein, M. L., & Shinoda, W. (2008). Large-scale molecular dynamics simulations of self- assembling systems. Science, 321, 798.

    CAS  Google Scholar 

  • Klepeis, J. L., Pieja, M. J., & Floudas, C. A. (2003). Hybrid global optimization algorithms for protein structure prediction: Alternating hybrids. Biophysical Journal, 84, 869.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • Knapp, B., & Schreiner, W. (2009). Graphical user interfaces for molecular dynamics-quo vadis? Bioinformatics and Biology Insights, 3, 103.

    CAS  Google Scholar 

  • Knoll, P., & Mirzaei, S. (2003). Development of an interactive molecular dynamics simulation software package. Review of Scientific Instruments, 74, 2483.

    CAS  Google Scholar 

  • Kolomeisky, A. B., & Fisher, M. E. (2007). Molecular motors: A theorist’s perspective. Annual Review of Physical Chemistry, 58, 675.

    CAS  Google Scholar 

  • Kremer, K. (2003). Computer simulations for macromolecular science. Macromolecular Chemistry and Physics, 204, 257.

    CAS  Google Scholar 

  • Kubiak, K., & Nowak, W. (2008). Molecular dynamics simulations of the photoactive protein nitrile hydratase. Biophysical Journal, 94, 3824.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Kupfer, L., Hinrichs, W., & Groschup, M. H. (2009). Prion protein misfolding. Current Molecular Medicine, 9, 826.

    CAS  Google Scholar 

  • Lange, O. E., Schafer, L. V., & Grubmuller, H. (2006). Flooding in GROMACS: Accelerated barrier crossings in molecular dynamics. Journal of Computational Chemistry, 27, 1693.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • Leach, A. (2001). Molecular modelling: Principles and applications (2nd ed.). Harlow: Prentice Hall.

    Google Scholar 

  • Lee, E. H., Hsin, J., Sotomayor, M., Comellas, G., & Schulten, K. (2009). Discovery through the computational microscope. Structure, 17, 1295.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Levitt, M., & Lifson, S. (1969). Refinement of protein conformation using a macromolecular energy minimization procedure. Journal of Molecular Biology, 46, 269.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Lonsdale, R., Ranaghan, K. E., & Mulholland, A. J. (2010). Computational enzymology. Chemical Communications, 46, 2354.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Mackerell, A. D., Jr., & Nilsson, L. (2008). Molecular dynamics simulations of nucleic acid-protein complexes. Current Opinion in Structural Biology, 18, 194.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • McCammon, J. A., Gelin, B. R., & Karplus, M. (1977). Dynamics of folded proteins. Nature, 267, 585.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Miao, L., & Schulten, K. (2009). Transport-related structures and processes of the nuclear pore complex studied through molecular dynamics. Structure, 17, 449.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Moraitakis, G., Purkiss, A. G., & Goodfellow, J. M. (2003). Simulated dynamics and biological molecules. Reports on Progress in Physics, 66, 483.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • 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 

  • 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.

    CAS  Google Scholar 

  • Orlowski, S., & Nowak, W. (2007). Locally enhanced sampling molecular dynamics study of the dioxygen transport in human cytoglobin. Journal of Molecular Modeling, 13, 715.

    CAS  Google Scholar 

  • Orlowski, S., & Nowak, W. (2008). Topology and thermodynamics of gaseous ligands diffusion paths in human neuroglobin. Biosystems, 94, 263.

    CAS  Google Scholar 

  • Paci, E. (2002). High pressure simulations of biomolecules. BBA-Protein Structure and Molecular Enzymology, 1595, 185.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Papaleo, E., & Invernizzi, G. (2011). Conformational diseases: Structural studies of aggregation of polyglutamine proteins. Current Computer-Aided Drug Design, 7, 23.

    CAS  Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Pohorille, A., Jarzynski, C., & Chipot, C. (2010). Good practices in free-energy calculations. The Journal of Physical Chemistry B, 114, 10235.

    CAS  Google Scholar 

  • Rahman, A., & Stillinger, F. H. (1971). Molecular dynamics study of liquid water. Journal of Chemical Physics, 55, 3336.

    CAS  Google Scholar 

  • Rapaport, D. C. (1995). The art of molecular dynamics simulation. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Rehm, S., Trodler, P., & Pleiss, J. (2010). Solvent-induced lid opening in lipases: A molecular dynamics study. Protein Science, 19, 2122.

    CAS  Google Scholar 

  • Rief, M., & Grubmuller, H. (2002). Force spectroscopy of single biomolecules. A EuropeanJournal of Chemical Physics and Physical Chemistry, 3, 255.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Romanowska, J., Setny, P., & Trylska, J. (2008). Molecular dynamics study of the ribosomal A-site. The Journal of Physical Chemistry B, 112, 15227.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Rossle, S. C., & Frank, I. (2009). First-principles simulation of photoreactions in biological systems. Frontiers in Bioscience, 14, 4862.

    CAS  Google Scholar 

  • Roux, B., & Schulten, K. (2004). Computational studies of membrane channels. Structure, 12, 1343.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Sanbonmatsu, K. Y., & Tung, C. S. (2007). High performance computing in biology: Multimillion atom simulations of nanoscale systems. Journal of Structural Biology, 157, 470.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Scheraga, H. A., Khalili, M., & Liwo, A. (2007). Protein-folding dynamics: Overview of molecular simulation techniques. Annual Review of Physical Chemistry, 58, 57.

    CAS  Google Scholar 

  • Scheres, S. H. (2010). Visualizing molecular machines in action: Single-particle analysis with structural variability. Advances in Protein Chemistry and Structural Biology, 81, 89.

    CAS  Google Scholar 

  • Schlegel, H. B. (2003). Exploring potential energy surfaces for chemical reactions: An overview of some practical methods. Journal of Computational Chemistry, 24, 1514.

    CAS  Google Scholar 

  • Schlick, T. (2002). Molecular modeling and simulation – an interdisciplinary guide. New York: Springer.

    Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Schwede, T., & Peitsch, M. C. (2008). Computational structural biology: Methods and applications. Hackensack, NJ: World Scientific.

    Google Scholar 

  • Sellis, D., Vlachakis, D., & Vlassi, M. (2009). Gromita: A fully integrated graphical user interface to Gromacs 4. Bioinformatics and Biology Insights, 3, 99.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Shakhnovich, E. (2006). Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chemical Reviews, 106, 1559.

    CAS  Google Scholar 

  • Sherwood, P., Brooks, B. R., & Sansom, M. S. (2008). Multiscale methods for macromolecular simulations. Current Opinion in Structural Biology, 18, 630.

    CAS  Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Simonson, T., Archontis, G., & Karplus, M. (2002). Free energy simulations come of age: Protein-ligand recognition. Accounts of Chemical Research, 35, 430.

    CAS  Google Scholar 

  • Sotomayor, M., & Schulten, K. (2007). Single-molecule experiments in vitro and in silico. Science, 316, 1144.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Straatsma, T. P., & McCammon, J. A. (1992). Computational alchemy. Annual Review of Physical Chemistry, 43, 407.

    CAS  Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • Sugita, Y. (2009). Free-energy landscapes of proteins in solution by generalized-ensemble simulations. Frontiers in Bioscience, 14, 1292.

    CAS  Google Scholar 

  • Sugita, Y., & Okamoto, Y. (1999). Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 314, 141.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Tozzini, V. (2010). Multiscale modeling of proteins. Accounts of Chemical Research, 43, 220.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • 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 

  • 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 

  • 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 

  • van Oijen, A. M. (2007). Single-molecule studies of complex systems: The replisome. Molecular BioSystems, 3, 117.

    Google Scholar 

  • 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 

  • Vasquez, V., Sotomayor, M., Cordero-Morales, J., Schulten, K., & Perozo, E. (2008). A structural mechanism for MscS gating in lipid bilayers. Science, 321, 1210.

    CAS  Google Scholar 

  • Vemparala, S., Domene, C., & Klein, M. L. (2010). Computational studies on the interactions of inhalational anesthetics with proteins. Accounts of Chemical Research, 43, 103.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Wang, T., & Duan, Y. (2011). Retinal release from opsin in molecular dynamics simulations. Journal of Molecular Recognition, 24, 350.

    CAS  Google Scholar 

  • Wanko, M., Hoffmann, M., Frauenheim, T., & Elstner, M. (2006). Computational photochemistry of retinal proteins. Journal of Computer-Aided Molecular Design, 20, 511.

    CAS  Google Scholar 

  • Warshel, A. (2002). Molecular dynamics simulations of biological reactions. Accounts of Chemical Research, 35, 385.

    CAS  Google Scholar 

  • Warshel, A. (2003). Computer simulations of enzyme catalysis: Methods, progress, and insights. Annual Review of Biophysics and Biomolecular Structure, 32, 425.

    CAS  Google Scholar 

  • Warshel A., Kato M., & Pisliakov A.V. (2007). Polarizable force fields: History, test cases, and prospects. Journal of Chemical Theory and Computation, 3, 2034.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Wong, V., & Case, D. A. (2008). Evaluating rotational diffusion from protein MD simulations. The Journal of Physical Chemistry B, 112, 6013.

    CAS  Google Scholar 

  • Yu, J., Ha, T., & Schulten, K. (2007). How directional translocation is regulated in a DNA helicase motor. Biophysical Journal, 93, 3783.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Zhu, F., Tajkhorshid, E., & Schulten, K. (2004). Theory and simulation of water permeation in aquaporin-1. Biophysical Journal, 86, 50.

    CAS  Google Scholar 

  • Zink, M., & Grubmuller, H. (2009). Mechanical properties of the icosahedral shell of Southern bean mosaic virus: A molecular dynamics study. Biophysical Journal, 96, 1350.

    CAS  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this entry

Cite this entry

Nowak, W. (2012). Applications of Computational Methods to Simulations of Proteins Dynamics. In: Leszczynski, J. (eds) Handbook of Computational Chemistry. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0711-5_31

Download citation

Publish with us

Policies and ethics