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Comparison of Protein Force Fields for Molecular Dynamics Simulations

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Molecular Modeling of Proteins

Part of the book series: Methods Molecular Biology™ ((MIMB,volume 443))

Summary

In the context of molecular dynamics simulations of proteins, the term “force field” refers to the combination of a mathematical formula and associated parameters that are used to describe the energy of the protein as a function of its atomic coordinates. In this review, we describe the functional forms and parameterization protocols of the widely used biomolecular force fields Amber, CHARMM, GROMOS, and OPLS-AA. We also summarize the ability of various readily available noncommercial molecular dynamics packages to perform simulations using these force fields, as well as to use modern methods for the generation of constant-temperature, constant-pressure ensembles and to treat long-range interactions. Finally, we finish with a discussion of the ability of these force fields to support the modeling of proteins in conjunction with nucleic acids, lipids, carbohydrates, and/or small molecules.

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References

  1. 1. Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz Jr., K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., and Kollman, P. A. (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 117, 5179–5197.

    CAS  Google Scholar 

  2. 2. MacKerell, A. D., Jr., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., Joseph-McCarthy, D., Kuchnir, L., Kuczera, K., Lau, F. T. K., Mattos, C., Michnick, S., Ngo, T., Nguyen, D. T., Prodhom, B., Reiher, W. E., Roux, B., Schlenkrich, M., Smith, J. C., Stote, R., Straub, J., Watanabe, M., Wiórkiewicz-Kuczera, J., Yin, D., and Karplus, M. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586–3616.

    CAS  Google Scholar 

  3. 3. Oostenbrink, C., Villa, A., Mark, A. E., and Van Gunsteren, W. F. (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 25, 1656–1676.

    CAS  PubMed  Google Scholar 

  4. 4. Jorgensen, W. L., Maxwell, D. S., and Tirado-Rives, J. (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118, 11225–11236.

    CAS  Google Scholar 

  5. 5. Kitson, D. H. and Hagler, A. T. (1988) Theoretical studies of the structure and molecular dynamics of a peptide crystal. Biochemistry 27, 5246–5257.

    CAS  PubMed  Google Scholar 

  6. 6. Momany, F. A., Carruthers, L. M., McGuire, R. F., and Scheraga, H. A. (1974) Intermolecular potentials from crystal data. III. Determination of empirical potentials and application to the packing configurations and lattice energies in crystals of hydrocarbons, carboxylic acids, amines, and amides. J. Phys. Chem. 78, 1595–1620.

    CAS  Google Scholar 

  7. 7. Momany, F. A., McGuire, R. F., Burgess, A. W., and Scheraga, H. A. (1975) Energy parameters in polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J. Phys. Chem 79, 2361–2381.

    CAS  Google Scholar 

  8. 8. Némethy, G., Pottle, M. S., and Scheraga, H. A. (1983) Energy parameters in polypeptides. 9. Updating of geometrical parameters, nonbonded interactions, and hydrogen bond interactions for the naturally occurring amino acids. J. Phys. Chem. 87, 1883–1887.

    Google Scholar 

  9. 9. Némethy, G., Gibson, K. D., Palmer, K. A., Yoon, C. N., Paterlini, G., Zagari, A., Rumsey, S., and Scheraga, H. A. (1992) Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J. Phys. Chem. 96, 6472–6484.

    Google Scholar 

  10. 10. Levitt, M. (1983) Molecular dynamics of native protein: I. Computer simulation of trajectories. J. Mol. Biol. 168, 595–620.

    CAS  PubMed  Google Scholar 

  11. 11. Levitt, M., Hirshberg, M., Sharon, R., and Daggett, V. (1995) Potential energy function and parameters for simulations of the molecular dynamics of proteins and nucleic acids in solution. Comput. Phys. Commun. 91, 215–231.

    CAS  Google Scholar 

  12. 12. Allinger, N. L., Chen, K. S., and Lii, J. H. (1996) An improved force field (MM4) for saturated hydrocarbons. J. Comput. Chem. 17, 642–668.

    CAS  Google Scholar 

  13. 13. Nevins, N., Chen, K. S., and Allinger, N. L. (1996) Molecular mechanics (MM4) calculations on alkenes. J. Comput. Chem. 17, 669–694.

    CAS  Google Scholar 

  14. 14. Nevins, N., Lii, J. H., and Allinger, N. L. (1996) Molecular mechanics (MM4) calculations on conjugated hydrocarbons. J. Comput. Chem. 17, 695–729.

    CAS  Google Scholar 

  15. 15. Nevins, N. and Allinger, N. L. (1996) Molecular mechanics (MM4) vibrational frequency calculations for alkenes and conjugated hydrocarbons. J. Comput. Chem. 17, 730–746.

    CAS  Google Scholar 

  16. 16. Allinger, N. L., Chen, K. S., Katzenellenbogen, J. A., Wilson, S. R., and Anstead, G. M. (1996) Hyperconjugative effects on carbon-carbon bond lengths in molecular mechanics (MM4). J. Comput. Chem. 17, 747–755.

    CAS  Google Scholar 

  17. 17. Langley, C. H. and Allinger, N. L. (2002) Molecular mechanics (MM4) calculations on amides. J. Phys. Chem. A 106, 5638–5652.

    CAS  Google Scholar 

  18. 18. Halgren, T. A. (1996) Merck molecular force field. 1. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17, 490–519.

    CAS  Google Scholar 

  19. 19. Halgren, T. A. (1996) Merck molecular force field. 2. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions. J. Comput. Chem. 17, 520–552.

    CAS  Google Scholar 

  20. 20. Halgren, T. A. (1996) Merck molecular force field. 3. Molecular geometries and vibrational frequencies for MMFF94. J. Comput. Chem. 17, 553–586.

    CAS  Google Scholar 

  21. 21. Halgren, T. A. and Nachbar, R. B. (1996) Merck molecular force field. 4. Conformational energies and geometries for MMFF94. J. Comput. Chem. 17, 587–615.

    CAS  Google Scholar 

  22. 22. Halgren, T. A. (1996) Merck molecular force field. 5. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. J. Comput. Chem. 17, 616–641.

    CAS  Google Scholar 

  23. 23. Halgren, T. A. (1999) MMFF VI. MMFF94s option for energy minimization studies. J. Comput. Chem. 20, 720–729.

    CAS  Google Scholar 

  24. 24. Halgren, T. A. (1999) MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for intermolecular-interaction energies and geometries. J. Comput. Chem. 20, 730–748.

    CAS  Google Scholar 

  25. 25. Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., and Skiff, W. M. (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114, 10024–10035.

    Google Scholar 

  26. 26. Ponder, J. W. and Case, D. A. (2003) Force fields for protein simulations, in Protein Simulations, Vol. 66, Academic Press, San Diego, pp. 27–85.

    Google Scholar 

  27. 27. MacKerell, A. D., Jr. (2004) Empirical force fields for biological macromolecules: Overview and issues. J. Comput. Chem. 25, 1584–1604.

    CAS  PubMed  Google Scholar 

  28. 28. Daggett, V. (2006) Protein folding-simulation. Chem. Rev. 106, 1898–1916.

    CAS  PubMed  Google Scholar 

  29. 29. Oldziej, S., Czaplewski, C., Liwo, A., Chinchio, M., Nanias, M., Vila, J. A., Khalili, M., Arnautova, Y. A., Jagielska, A., Makowski, M., Schafroth, H. D., Kazmierkiewicz, R., Ripoll, D. R., Pillardy, J., Saunders, J. A., Kang, Y. K., Gibson, K. D., and Scheraga, H. A. (2005) Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests. Proc. Natl. Acad. Sci. U. S. A. 102, 7547–7552.

    CAS  PubMed  Google Scholar 

  30. 30. Allen, M. P. and Tildesley, D. J. (1987) Computer simulation of liquids, Oxford University Press, Oxford.

    Google Scholar 

  31. 31. Matsuoka, O., Clementi, E., and Yoshimine, M. (1976) CI study of the water dimer potential surface. J. Chem. Phys. 64, 1351–1361.

    CAS  Google Scholar 

  32. 32. Owicki, J. C. and Scheraga, H. A. (1977) Monte Carlo calculations on the isothermal-isobaric ensemble. 1. Liquid water. J. Am. Chem. Soc. 99, 7403–7412.

    CAS  Google Scholar 

  33. Reiher, W. E. (1985), Theoretical studies of hydrogen bonding, Ph.D. thesis, Harvard University.

    Google Scholar 

  34. 34. Morozov, A. V., Kortemme, T., Tsemekhman, K., and Baker, D. (2004) Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. Proc. Natl. Acad. Sci. U. S. A. 101, 6946–6951.

    CAS  PubMed  Google Scholar 

  35. 35. Weiner, S. J., Kollman, P. A., Case, D. A., Singh, U. C., Ghio, C., Alagona, G., Profeta, S., and Weiner, P. A. (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc. 106, 765–784.

    CAS  Google Scholar 

  36. 36. Mahoney, M. W. and Jorgensen, W. L. (2000) A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. J. Chem. Phys. 112, 8910–8922.

    CAS  Google Scholar 

  37. 37. Mahoney, M. W. and Jorgensen, W. L. (2001) Diffusion constant of the TIP5P model of liquid water. J. Chem. Phys. 114, 363–366.

    CAS  Google Scholar 

  38. 38. Jorgensen, W. L. and Tirado-Rives, J. (2005) Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc. Natl. Acad. Sci. U. S. A. 102, 6665–6670.

    CAS  PubMed  Google Scholar 

  39. 39. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., and Klein, M. L. (1983) Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935.

    CAS  Google Scholar 

  40. 40. Berendsen, H. J. C., Grigera, J. R., and Straatsma, T. P. (1987) The missing term in effective pair potentials. J. Phys. Chem. 91, 6269–6271.

    CAS  Google Scholar 

  41. 41. Cornell, W. D., Cieplak, P., Bayly, C. E., and Kollman, P. A. (1993) Application of RESP charges to calculate conformational energies, hydrogen bond energies, and free energies of solvation. J. Amer.Chem.Soc. 115, 9620–9631.

    CAS  Google Scholar 

  42. 42. MacKerell, A. D., Jr., Feig, M., and Brooks, C. L., III, (2004) Improved treatment of the protein backbone in empirical force fields. J. Am. Chem. Soc. 126, 698–699.

    CAS  PubMed  Google Scholar 

  43. 43. Hu, H., Elstner, M., and Hermans, J. (2003) Comparison of a QM/MM force field and molecular mechanics force fields in simulations of alanine and glycine “dipeptides” (Ace-Ala-Nme and Ace-Gly-Nme) in water in relation to the problem of modeling the unfolded peptide backbone in solution. Proteins 50, 451–463.

    CAS  PubMed  Google Scholar 

  44. 44. Wang, J. M., Cieplak, P., and Kollman, P. A. (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem. 21, 1049–1074.

    CAS  Google Scholar 

  45. 45. Okur, A., Strockbine, B., Hornak, V., and Simmerling, C. (2003) Using PC clusters to evaluate the transferability of molecular mechanics force fields for proteins. J. Comput. Chem. 24, 21–31.

    CAS  PubMed  Google Scholar 

  46. 46. Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., and Jorgensen, W. L. (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 105, 6474–6487.

    CAS  Google Scholar 

  47. 47. Shirley, W. A. and Brooks, C. L., III (1997) Curious structure in “canonical” alanine based peptides. Proteins 28, 59–71.

    CAS  PubMed  Google Scholar 

  48. 48. Feig, M., MacKerell, A. D., Jr., and Brooks, C. L., III (2003) Force field influence on the observation of pi-helical protein structures in molecular dynamics simulations. J. Phys. Chem. B 107, 2831–2836.

    CAS  Google Scholar 

  49. 49. MacKerell, A. D., Jr., Feig, M., and Brooks, C. L., III (2004) Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J. Comput. Chem. 25, 1400–1415.

    CAS  PubMed  Google Scholar 

  50. 50. Jacob, E. J., Thompson, H. B., and Bartell, L. S. (1967) Influence of nonbonded interactions on molecular geometry and energy: Calculations for hydrocarbons based on Urey-Bradley field. J. Chem. Phys. 47, 3736–3753.

    CAS  Google Scholar 

  51. 51. Lifson, S. and Warshel, A. (1968) Consistent force field for calculations of conformations, vibrational spectra, and enthalpies of cycloalkane and n-alkane molecules. J. Chem. Phys. 49, 5116–5129.

    CAS  Google Scholar 

  52. 52. Allinger, N. L., Tribble, M. T., Miller, M. A., and Wertz, D. H. (1971) Conformational analysis. LXIX. An improved force field for the calculation of the structures and energies of hydrocarbons. J. Am. Chem. Soc. 93, 1637–1648.

    CAS  Google Scholar 

  53. 53. Jorgensen, W. L. and Tirado-Rives, J. (1988) The OPLS potential function for proteins. Energy minimizations for crystals of cyclic peptides and crambin. J. Amer. Chem. Soc. 110, 1657–1666.

    CAS  Google Scholar 

  54. 54. Bayly, C. I., Cieplak, P., Cornell, W. D., and Kollman, P. A. (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: The RESP model. J. Phys. Chem. 97, 10269–10280.

    CAS  Google Scholar 

  55. 55. Fox, T. and Kollman, P. A. (1998) Application of the RESP methodology in the parametrization of organic solvents. J. Phys. Chem. B 102, 8070–8079.

    CAS  Google Scholar 

  56. 56. Cieplak, P., Cornell, W. D., Bayly, C. I., and Kollman, P. K. (1995) Application of the multimolecule and multiconformational RESP methodology to biopolymers: Charge derivation for DNA, RNA, and proteins. J. Comput. Chem. 16, 1357–1377.

    CAS  Google Scholar 

  57. 57. Neria, E., Fischer, S., and Karplus, M. (1996) Simulation of activation free energies in molecular systems. J. Chem. Phys. 105, 1902–1919.

    CAS  Google Scholar 

  58. 58. Oostenbrink, C., Soares, T. A., van der Vegt, N. F. A., and van Gunsteren, W. F. (2005) Validation of the 53A6 GROMOS force field. Eur. Biophys. J. Biophys. Lett. 34, 273–284.

    CAS  Google Scholar 

  59. 59. Shirts, M. R., Pitera, J. W., Swope, W. C., and Pande, V. S. (2003) Extremely precise free energy calculations of amino acid side chain analogs: Comparison of common molecular mechanics force fields for proteins. 119, 5740–5761.

    CAS  Google Scholar 

  60. 60. Deng, Y. and Roux, B. (2004) Hydration of amino acid side chains: Non-polar and electrostatic contributions calculated from staged molecular dynamics free energy simulations with explicit water molecules. J. Phys. Chem. B 108, 16567–16576.

    CAS  Google Scholar 

  61. 61. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953) Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092.

    CAS  Google Scholar 

  62. 62. Wood, W. W. (1968) Monte Carlo calculations for hard disks in the isothermal-isobaric ensemble. J. Chem. Phys. 48, 415–434.

    Google Scholar 

  63. 63. McDonald, I. R. (1969) Monte Carlo calculations for one- and two-component fluids in the isothermal-isobaric ensemble. Chem. Phys. Lett. 3, 241–243.

    CAS  Google Scholar 

  64. 64. Maurits, N. M., Zvelindovsky, A. V., Sevink, G. J. A., van Vlimmeren, B. A. C., and Fraaije, J. (1998) Hydrodynamic effects in three-dimensional microphase separation of block copolymers: Dynamic mean-field density functional approach. J. Chem. Phys. 108, 9150–9154.

    CAS  Google Scholar 

  65. 65. Groot, R. D., Madden, T. J., and Tildesley, D. J. (1999) On the role of hydrodynamic interactions in block copolymer microphase separation. J. Chem. Phys. 110, 9739–9749.

    CAS  Google Scholar 

  66. 66. Shelley, J. C., Shelley, M. Y., Reeder, R. C., Bandyopadhyay, S., Moore, P. B., and Klein, M. L. (2001) Simulations of phospholipids using a coarse grain model. J. Phys. Chem. B 105, 9785–9792.

    CAS  Google Scholar 

  67. 67. Andersen, H. C. (1980) Molecular dynamics simulations at constant pressure and/or temperature. J. Chem. Phys. 72, 2384–2393.

    CAS  Google Scholar 

  68. 68. Parrinello, M. and Rahman, A. (1981) Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190.

    CAS  Google Scholar 

  69. 69. Nosé, S. (1984) A molecular dynamics method for simulations in the canonical ensemble. Mol. Phys. 52, 255–268.

    Google Scholar 

  70. 70. Hoover, W. G. (1985) Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697.

    PubMed  Google Scholar 

  71. 71. Feller, S. E., Zhang, Y. H., Pastor, R. W., and Brooks, B. R. (1995) Constant pressure molecular dynamics simulation: The Langevin piston method. J. Chem. Phys. 103, 4613–4621.

    CAS  Google Scholar 

  72. 72. Tuckerman, M. E. and Martyna, G. J. (2000) Understanding modern molecular dynamics: Techniques and applications. J. Phys. Chem. B 104, 159–178.

    CAS  Google Scholar 

  73. 73. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., Di Nola, A., and Haak, J. R. (1984) Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690.

    CAS  Google Scholar 

  74. 74. Steinbach, P. J. and Brooks, B. R. (1994) New spherical-cutoff methods for long-range forces in macromolecular simulation. J. Comput. Chem. 15, 667–683.

    CAS  Google Scholar 

  75. 75. Ewald, P. P. (1921) Die Berechnung optischer und elektrostatischer Gitterpotentiale. Ann. Phys. 64, 253–287.

    Google Scholar 

  76. 76. Hockney, R. W. and Eastwood, J. W. (1981) Computer simulations using particles, McGraw Hill, New York.

    Google Scholar 

  77. 77. Darden, T., York, D., and Pedersen, L. (1993) Particle mesh Ewald: An N•log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092.

    CAS  Google Scholar 

  78. 78. Essmann, U., Perera, L., Berkowitz, M. L., Darden, T. A., Lee, H., and Pedersen, L. G. (1995) A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593.

    CAS  Google Scholar 

  79. 79. Darden, T., Toukmaji, A., and Pedersen, L. (1997) Long-range electrostatic effects in biomolecular systems. J. Chem. Phys. 94, 1346–1364.

    CAS  Google Scholar 

  80. 80. Lagüe, P., Pastor, R. W., and Brooks, B. R. (2004) Pressure-based long-range correction for Lennard-Jones interactions in molecular dynamics simulations: Application to alkanes and interfaces. J. Phys. Chem. B 108, 363–368

    Google Scholar 

  81. 81. Brooks, C. L., III, and Karplus, M. (1983) Deformable stochastic boundaries in molecular dynamics. J. Chem. Phys. 79, 6312–6325.

    CAS  Google Scholar 

  82. 82. Brünger, A. T., Brooks, C. L., III, and Karplus, M. (1984) Stochastic boundary conditions for molecular dynamics simulations of ST2 water. Chem. Phys. Lett. 105, 495.

    Google Scholar 

  83. 83. Im, W., Bernéche, S., and Roux, B. (2001) Generalized solvent boundary potential for computer simulations. J. Chem. Phys. 114, 2924–2937.

    CAS  Google Scholar 

  84. 84. Still, W. C., Tempczyk, A., Hawley, R. C., and Hendrickson, T. (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 112, 6127–6129.

    CAS  Google Scholar 

  85. 85. Hawkins, G. D., Cramer, C. J., and Truhlar, D. G. (1995) Pairwise solute descreening of solute charges from a dielectric medium. Chem. Phys. Lett. 246, 122–129.

    CAS  Google Scholar 

  86. 86. Qui, D., Shenkin, P. S., Hollinger, F. P., and Still, W. C. (1997) The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate Born radii. J. Phys. Chem. A 101, 3005–3014.

    Google Scholar 

  87. 87. Ghosh, A., Rapp, C. S., and Friesner, R. A. (1998) Generalized born model based on a surface integral formulation. J. Phys. Chem. B 102, 10983–10990.

    CAS  Google Scholar 

  88. 88. Dominy, B. N. and Brooks, C. L., III (1999) Development of a generalized born model parametrization for proteins and nucleic acids. J. Phys. Chem. B 103, 3765–3773.

    CAS  Google Scholar 

  89. 89. Onufriev, A., Bashford, D., and Case, D. A. (2000) Modification of the generalized Born model suitable for macromolecules. J. Phys. Chem. B 104, 3712–3720.

    CAS  Google Scholar 

  90. 90. Lee, M. S., Salsbury, F. R., and Brooks, C. L., III (2002) Novel generalized Born methods. J. Chem. Phys. 116, 10606–10614.

    CAS  Google Scholar 

  91. 91. Im, W. P., Lee, M. S., and Brooks, C. L., III (2003) Generalized born model with a simple smoothing function. J. Comput. Chem. 24, 1691–1702.

    CAS  PubMed  Google Scholar 

  92. 92. Guvench, O., Shenkin, P., Kolossvary, I., and Still, W. C. (2002) Application of the frozen atom approximation to the GB/SA continuum model for solvation free energy. J. Comput. Chem. 23, 214–221.

    CAS  PubMed  Google Scholar 

  93. 93. Im, W., Feig, M., and Brooks, C. L., III (2003) An implicit membrane generalized born theory for the study of structure, stability, and interactions of membrane proteins. Biophys. J. 85, 2900–2918.

    CAS  PubMed  Google Scholar 

  94. Tanizaki, S. and Feig, M. (2005) A generalized Born formalism for heterogeneous dielectric environments: Application to the implicit modeling of biological membranes. J. Chem. Phys. 122.

    Google Scholar 

  95. 95. Gallicchio, E., Zhang, L. Y., and Levy, R. M. (2002) The SGB/NP hydration free energy model based on the surface generalized born solvent reaction field and novel nonpolar hydration free energy estimators. J. Comput. Chem. 23, 517–529.

    CAS  PubMed  Google Scholar 

  96. 96. Gallicchio, E. and Levy, R. M. (2004) AGBNP: An analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modeling. J. Comput. Chem. 25, 479–499.

    CAS  PubMed  Google Scholar 

  97. 97. Weiner, P. K. and Kollman, P. A. (1981) AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions. J. Comput. Chem. 2, 287–303.

    CAS  Google Scholar 

  98. 98. Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E., Debolt, S., Ferguson, D., Seibel, G., and Kollman, P. (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91, 1–41.

    CAS  Google Scholar 

  99. 99. Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., and Woods, R. J. (2005) The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668–1688.

    CAS  PubMed  Google Scholar 

  100. 100. Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S., and Karplus, M. (1983) CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4, 187–217.

    CAS  Google Scholar 

  101. 101. Scott, W. R. P., Hünenberger, P. H., Tironi, I. G., Mark, A. E., Billeter, S. R., Fennen, J., Torda, A. E., Huber, T., Krüger, P., and van Gunsteren, W. F. (1999) The GROMOS biomolecular simulation program package. J. Phys. Chem. A 103, 3596–3607.

    CAS  Google Scholar 

  102. 102. Christen, T., Hünenberger, P. H., Bakowies, D., Baron, R., Burgi, R., Geerke, D. P., Heinz, T. N., Kastenholz, M. A., Krautler, V., Oostenbrink, C., Peter, C., Trzesniak, D., and Van Gunsteren, W. F. (2005) The GROMOS software for biomolecular simulation: GROMOS05. J. Comput. Chem. 26, 1719–1751.

    CAS  PubMed  Google Scholar 

  103. 103. Jorgensen, W. L. (1998) BOSS — Biochemical and organic simulation system, in The Encyclopedia of Computational Chemistry (Schleyer, P. v. R., ed.), Vol. 5, John Wiley and Sons, Ltd., Athens, GA.

    Google Scholar 

  104. 104. Jorgensen, W. L. and Tirado-Rives, J. (2005) Molecular modeling of organic and biomolecular systems using BOSS and MCPRO. J. Comput. Chem. 26, 1689–1700.

    CAS  PubMed  Google Scholar 

  105. 105. Berendsen, H. J. C., van der Spoel, D., and van Drunen, R. (1995) GROMACS—A message-passing parallel molecular-dynamics implementation. Comput. Phys. Commun. 91, 43–56.

    CAS  Google Scholar 

  106. 106. Lindahl, E., Hess, B., and van der Spoel, D. (2001) GROMACS 3.0: A package for molecular simulation and trajectory analysis. J. Mol. Model. 7, 306–317.

    CAS  Google Scholar 

  107. 107. van der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., and Berendsen, H. J. C. (2005) GROMACS: Fast, flexible, and free. J. Comput. Chem. 26, 1701–1718.

    Google Scholar 

  108. 108. Nelson, M., Humphrey, W., Kufrin, R., Gursoy, A., Dalke, A., Kale, L., Skeel, R., and Schulten, K. (1995) MDScope—A visual computing environment for structural biology. Comput. Phys. Commun. 91, 111–133.

    CAS  Google Scholar 

  109. 109. Nelson, M. T., Humphrey, W., Gursoy, A., Dalke, A., Kale, L. V., Skeel, R. D., and Schulten, K. (1996) NAMD: A parallel, object oriented molecular dynamics program. Int. J. Supercomput. Appl. High Perform. Comput. 10, 251–268.

    Google Scholar 

  110. 110. Kale, L., Skeel, R., Bhandarkar, M., Brunner, R., Gursoy, A., Krawetz, N., Phillips, J., Shinozaki, A., Varadarajan, K., and Schulten, K. (1999) NAMD2: Greater scalability for parallel molecular dynamics. J. Comput. Phys. 151, 283–312.

    CAS  Google Scholar 

  111. 111. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kale, L., and Schulten, K. (2005) Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802.

    CAS  PubMed  Google Scholar 

  112. 112. Banks, J. L., Beard, H. S., Cao, Y. X., Cho, A. E., Damm, W., Farid, R., Felts, A. K., Halgren, T. A., Mainz, D. T., Maple, J. R., Murphy, R., Philipp, D. M., Repasky, M. P., Zhang, L. Y., Berne, B. J., Friesner, R. A., Gallicchio, E., and Levy, R. M. (2005) Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem. 26, 1752–1780.

    CAS  PubMed  Google Scholar 

  113. 113. Karplus, M., and McCammon, J. A. (2002) Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646–652.

    CAS  PubMed  Google Scholar 

  114. 114. MacKerell, A. D., Jr., Wiórkiewicz-Kuczera, J., and Karplus, M. (1995) An all-atom empirical energy function for the simulation of nucleic acids. J. Am. Chem. Soc. 117, 11946–11975.

    CAS  Google Scholar 

  115. 115. Ponomarev, S. Y., Thayer, K. M., and Beveridge, D. L. (2004) Ion motions in molecular dynamics simulations on DNA. Proc. Natl. Acad. Sci. U. S. A. 101, 14771–14775.

    CAS  PubMed  Google Scholar 

  116. 116. Zhang, Q. and Schlick, T. (2006) Stereochemistry and position-dependent effects of carcinogens on TATA/TBP binding. Biophys. J. 90, 1865–1877.

    CAS  PubMed  Google Scholar 

  117. 117. Spackova, N. and Sponer, J. (2006) Molecular dynamics simulations of sarcin-ricin rRNA motif. Nucleic Acids Res. 34, 697–708.

    CAS  PubMed  Google Scholar 

  118. 118. Trobro, S. and Aqvist, J. (2006) Analysis of predictions for the catalytic mechanism of ribosomal peptidyl transfer. Biochemistry 45, 7049–7056.

    CAS  PubMed  Google Scholar 

  119. 119. Pastor, N. (2005) The B- to A-DNA transition and the reorganization of solvent at the DNA surface. Biophys. J. 88, 3262–3275.

    CAS  PubMed  Google Scholar 

  120. 120. Huang, N., Banavali, N. K., and MacKerell, A. D., Jr. (2003) Protein-facilitated base flipping in DNA by cytosine-5-methyltransferase. Proc. Natl. Acad. Sci. U. S. A. 100, 68–73.

    CAS  PubMed  Google Scholar 

  121. 121. Soares, T. A., Hünenberger, P. H., Kastenholz, M. A., Krautler, V., Lenz, T., Lins, R. D., Oostenbrink, C., and Van Gunsteren, W. F. (2005) An improved nucleic acid parameter set for the GROMOS force field. J. Comput. Chem. 26, 725–737.

    CAS  PubMed  Google Scholar 

  122. 122. Schlenkrich, M., Brinkman, J., MacKerell, A. D., Jr., and Karplus, M. (1996) An empirical potential energy function for phospholipids: Criteria for parameter optimization and applications, in Membrane Structure and Dynamics (Merz, K. M., and Roux, B., eds.), Birkhauser, Boston, pp. 31–81.

    Google Scholar 

  123. 123. Feller, S. E., Yin, D. X., Pastor, R. W., and MacKerell, A. D., Jr. (1997) Molecular dynamics simulation of unsaturated lipid bilayers at low hydration: Parameterization and comparison with diffraction studies. Biophys. J. 73, 2269–2279.

    CAS  PubMed  Google Scholar 

  124. 124. Yin, D. X. and MacKerell, A. D., Jr. (1998) Combined ab initio empirical approach for optimization of Lennard-Jones parameters. J. Comput. Chem. 19, 334–348.

    CAS  Google Scholar 

  125. 125. Feller, S. E. and MacKerell, A. D., Jr. (2000) An improved empirical potential energy function for molecular simulations of phospholipids. J. Phys. Chem. B 104, 7510–7515.

    CAS  Google Scholar 

  126. 126. Feller, S. E., Gawrisch, K., and MacKerell, A. D., Jr. (2002) Polyunsaturated fatty acids in lipid bilayers: Intrinsic and environmental contributions to their unique physical properties. J. Am. Chem. Soc. 124, 318–326.

    CAS  PubMed  Google Scholar 

  127. 127. Spijker, P., Vaidehi, N., Freddolino, P. L., Hilbers, P. A. J., and Goddard, W. A. (2006) Dynamic behavior of fully solvated beta 2-adrenergic receptor, embedded in the membrane with bound agonist or antagonist. Proc. Natl. Acad. Sci. U. S. A. 103, 4882–4887.

    CAS  PubMed  Google Scholar 

  128. 128. Lagüe, P., Roux, B., and Pastor, R. W. (2005) Molecular dynamics simulations of the influenza hemagglutinin fusion peptide in micelles and bilayers: Conformational analysis of peptide and lipids. J. Mol. Biol. 354, 1129–1141.

    PubMed  Google Scholar 

  129. 129. Tajkhorshid, E., Nollert, P., Jensen, M. O., Miercke, L. J. W., O'Connell, J., Stroud, R. M., and Schulten, K. (2002) Control of the selectivity of the aquaporin water channel family by global orientational tuning. Science 296, 525–530.

    CAS  PubMed  Google Scholar 

  130. 130. Berger, O., Edholm, O., and Jahnig, F. (1997) Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys. J. 72, 2002–2013.

    CAS  PubMed  Google Scholar 

  131. 131. Chandrasekhar, I., Kastenholz, M., Lins, R. D., Oostenbrink, C., Schuler, L. D., Tieleman, D. P., and van Gunsteren, W. F. (2003) A consistent potential energy parameter set for lipids: Dipalmitoylphosphatidylcholine as a benchmark of the GROMOS96 45A3 force field. Eur. Biophys. J. Biophys. Lett. 32, 67–77.

    CAS  Google Scholar 

  132. 132. Glattli, A., Chandrasekhar, I., and van Gunsteren, W. F. (2006) A molecular dynamics study of the bee venom melittin in aqueous solution, in methanol, and inserted in a phospholipid bilayer. Eur. Biophys. J. Biophys. Lett. 35, 255–267.

    Google Scholar 

  133. 133. Leontiadou, H., Mark, A. E., and Marrink, S. J. (2006) Antimicrobial peptides in action. J. Am. Chem. Soc. 128, 12156–12161.

    CAS  PubMed  Google Scholar 

  134. 134. Grottesi, A., Domene, C., Hall, B., and Sansom, M. S. P. (2005) Conformational dynamics of M2 helices in KirBac channels: Helix flexibility in relation to gating via molecular dynamics simulations. Biochemistry 44, 14586–14594.

    CAS  PubMed  Google Scholar 

  135. 135. Moore, P. B., Lopez, C. F., and Klein, M. L. (2001) Dynamical properties of a hydrated lipid bilayer from a multinanosecond molecular dynamics simulation. Biophys. J. 81, 2484–2494.

    CAS  PubMed  Google Scholar 

  136. 136. Liepina, I., Czaplewski, C., Janmey, P., and Liwo, A. (2003) Molecular dynamics study of a gelsolin-derived peptide binding to a lipid bilayer containing phosphatidylinositol 4,5-bisphosphate. Biopolymers 71, 49–70.

    CAS  PubMed  Google Scholar 

  137. 137. Bastug, T. and Kuyucak, S. (2005) Test of molecular dynamics force fields in gramicidin A. Eur. Biophys. J. Biophys. Lett. 34, 377–382.

    CAS  Google Scholar 

  138. 138. Woods, R. J., Dwek, R. A., Edge, C. J., and Fraserreid, B. (1995) Molecular mechanical and molecular dynamical simulations of glycoproteins and oligosaccharides. 1. GLYCAM 93 parameter development. J. Phys. Chem. 99, 3832–3846.

    CAS  Google Scholar 

  139. 139. Woods, R. J. and Chappelle, R. (2000) Restrained electrostatic potential atomic partial charges for condensed-phase simulations of carbohydrates. Theochem—J. Mol. Struct. 527, 149–156.

    CAS  Google Scholar 

  140. 140. Basma, M., Sundara, S., Calgan, D., Vernali, T., and Woods, R. J. (2001) Solvated ensemble averaging in the calculation of partial atomic charges. J. Comput. Chem. 22, 1125–1137.

    CAS  PubMed  Google Scholar 

  141. 141. Kawatkar, S. P., Kuntz, D. A., Woods, R. J., Rose, D. R., and Boons, G. J. (2006) Structural basis of the inhibition of Golgi alpha-mannosidase II by mannostatin A and the role of the thiomethyl moiety in ligand-protein interactions. J. Am. Chem. Soc. 128, 8310–8319.

    CAS  PubMed  Google Scholar 

  142. 142. Lins, R. D., Pereira, C. S., and Hünenberger, P. H. (2004) Trehalose-protein interaction in aqueous solution. Proteins 55, 177–186.

    CAS  PubMed  Google Scholar 

  143. 143. Bosques, C. J., Tschampel, S. M., Woods, R. J., and Imperiali, B. (2004) Effects of glycosylation on peptide conformation: A synergistic experimental and computational study. J. Am. Chem. Soc. 126, 8421–8425.

    CAS  PubMed  Google Scholar 

  144. 144. Ford, M. G., Weimar, T., Kohli, T., and Woods, R. J. (2003) Molecular dynamics simulations of galectin-1-oligosaccharide complexes reveal the molecular basis for ligand diversity. Proteins 53, 229–240.

    CAS  PubMed  Google Scholar 

  145. 145. Kirschner, K. N. and Woods, R. J. (2001) Solvent interactions determine carbohydrate conformation. Proc. Natl. Acad. Sci. U. S. A. 98, 10541–10545.

    CAS  PubMed  Google Scholar 

  146. 146. Damm, W., Frontera, A., TiradoRives, J., and Jorgensen, W. L. (1997) OPLS all-atom force field for carbohydrates. J. Comput. Chem. 18, 1955–1970.

    CAS  Google Scholar 

  147. 147. Margulis, C. J. (2005) Computational study of the dynamics of mannose disaccharides free in solution and bound to the potent anti-HIV virucidal protein cyanovirin. J. Phys. Chem. B 109, 3639–3647.

    CAS  PubMed  Google Scholar 

  148. 148. Kony, D., Damm, W., Stoll, S., and van Gunsteren, W. F. (2002) An improved OPLS-AA force field for carbohydrates. J. Comput. Chem. 23, 1416–1429.

    CAS  PubMed  Google Scholar 

  149. 149. Kuttel, M., Brady, J. W., and Naidoo, K. J. (2002) Carbohydrate solution simulations: Producing a force field with experimentally consistent primary alcohol rotational frequencies and populations. J. Comput. Chem. 23, 1236–1243.

    CAS  PubMed  Google Scholar 

  150. 150. Lins, R. D. and Hünenberger, P. H. (2005) A new GROMOS force field for hexopyranose-based carbohydrates. J. Comput. Chem. 26, 1400–1412.

    CAS  PubMed  Google Scholar 

  151. 151. Daura, X., Mark, A. E., and van Gunsteren, W. F. (1998) Parametrization of aliphatic CHn united atoms of GROMOS96 force field. J. Comput. Chem. 19, 535–547.

    CAS  Google Scholar 

  152. 152. Schuler, L. D., Daura, X., and Van Gunsteren, W. F. (2001) An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J. Comput. Chem. 22, 1205–1218.

    CAS  Google Scholar 

  153. 153. Wang, J. M., Wolf, R. M., Caldwell, J. W., Kollman, P. A., and Case, D. A. (2004) Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174.

    CAS  PubMed  Google Scholar 

  154. 154. Ren, P. Y. and Ponder, J. W. (2002) Consistent treatment of inter- and intramolecular polarization in molecular mechanics calculations. J. Comput. Chem. 23, 1497–1506.

    CAS  PubMed  Google Scholar 

  155. 155. Patel, S., MacKerell, A. D., Jr., and Brooks, C. L. III (2004) CHARMM fluctuating charge force field for proteins: II—Protein/solvent properties from molecular dynamics simulations using a nonadditive electrostatic model. J. Comput. Chem. 25, 1504–1514.

    CAS  PubMed  Google Scholar 

  156. 156. Kim, B. C., Young, T., Harder, E., Friesner, R. A., and Berne, B. J. (2005) Structure and dynamics of the solvation of bovine pancreatic trypsin inhibitor in explicit water: A comparative study of the effects of solvent and protein polarizability. J. Phys. Chem. B 109, 16529–16538.

    CAS  PubMed  Google Scholar 

  157. 157. Anisimov, V. M., Lamoureux, G., Vorobyov, I. V., Huang, N., Roux, B., and MacKerell, A. D., Jr. (2005) Determination of electrostatic parameters for a polarizable force field based on the classical Drude oscillator. J. Chem. Theory Comput. 1, 153–168.

    Google Scholar 

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Acknowledgments

Financial support from the National Institutes of Health (NIH; R01GM051501 and R01GM070855 to ADM, and F32CA119771 to OG) and from the University of Maryland Computer-Aided Drug Design Center is acknowledged.

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  1. Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD, USA

    Olgun Guvench & Alexander D. MacKerell Jr.

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  1. Olgun Guvench
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Andreas Kukol

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Guvench, O., MacKerell, A.D. (2008). Comparison of Protein Force Fields for Molecular Dynamics Simulations. In: Kukol, A. (eds) Molecular Modeling of Proteins. Methods Molecular Biology™, vol 443. Humana Press. https://doi.org/10.1007/978-1-59745-177-2_4

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  • DOI: https://doi.org/10.1007/978-1-59745-177-2_4

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