Accelerated Molecular Dynamics in Computational Drug Design

  • Jeff Wereszczynski
  • J. Andrew McCammon
Part of the Methods in Molecular Biology book series (MIMB, volume 819)


The method of accelerated molecular dynamics (aMD) has been shown to increase the rate of phase-space sampling in biomolecular simulations. In this chapter, we discuss the theory behind aMD and describe the implementation of two versions: dual-boost and selective aMD. Each method has its practical advantages: dual-boost aMD is useful for increasing sampling of global conformational motions while selective aMD can improve the rate of convergence of free energy calculations. Special emphasis is placed on the use of these methods in computer-aided drug design, and the example of oseltamivir binding to neuraminidase is highlighted for both cases.

Key words

Molecular dynamics Conformational sampling Alchemical free energy transformations 



The work described was supported by Award Number F32GM093581 from the National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. Additional support has been provided by the NSF, NIH, HHMI, CTBP, NBCR, and the NSF Supercomputer Centers.


  1. 1.
    Ponder, J. W., Wu, C. J., Ren, P. Y., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A., Head-Gordon, M., Clark, G. N. I., Johnson, M. E., and Head-Gordon, T. (2010) Current Status of the AMOEBA Polarizable Force Field, Journal of Physical Chemistry B 114, 2549–2564.CrossRefGoogle Scholar
  2. 2.
    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, Journal of Computational Chemistry 25, 1157–1174.PubMedCrossRefGoogle Scholar
  3. 3.
    Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., and MacKerell, A. D. (2010) CHARMM General Force Field: A Force Field for Drug-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields, Journal of Computational Chemistry 31, 671–690.PubMedGoogle Scholar
  4. 4.
    Shaw, D. E., Deneroff, M. M., Dror, R. O., Kuskin, J. S., Larson, R. H., Salmon, J. K., Young, C., Batson, B., Bowers, K. J., Chao, J. C., Eastwood, M. P., Gagliardo, J., Grossman, J. P., Ho, C. R., Ierardi, D. J., Kolossvary, I., Klepeis, J. L., Layman, T., McLeavey, C., Moraes, M. A., Mueller, R., Priest, E. C., Shan, Y. B., Spengler, J., Theobald, M., Towles, B., and Wang, S. C. (2008) Anton, a special-purpose machine for molecular dynamics simulation, Communications of the Acm 51, 91–97.CrossRefGoogle Scholar
  5. 5.
    Darden, T., York, D., and Pedersen, L. (1993) Particle mesh ewald - an NLog(N) method for ewald sums in large systems, Journal of Chemical Physics 98, 10089–10092.CrossRefGoogle Scholar
  6. 6.
    Sugita, Y., and Okamoto, Y. (1999) Replica-exchange molecular dynamics method for protein folding, Chemical Physics Letters 314, 141–151.CrossRefGoogle Scholar
  7. 7.
    Christ, C. D., Mark, A. E., and van Gunsteren, W. F. (2010) Feature Article Basic Ingredients of Free Energy Calculations: A Review, Journal of Computational Chemistry 31, 1569–1582.PubMedGoogle Scholar
  8. 8.
    Hamelberg, D., Mongan, J., and McCammon, J. A. (2004) Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules, Journal of Chemical Physics 120, 11919–11929.PubMedCrossRefGoogle Scholar
  9. 9.
    Fajer, M., Hamelberg, D., and McCammon, J. A. (2008) Replica-Exchange Accelerated Molecular Dynamics (REXAMD) Applied to Thermodynamic Integration, Journal of Chemical Theory and Computation 4, 1565–1569.PubMedCrossRefGoogle Scholar
  10. 10.
    de Oliveira, C. A. F., Hamelberg, D., and McCammon, J. A. (2008) Coupling accelerated molecular dynamics methods with thermodynamic integration simulations, Journal of Chemical Theory and Computation 4, 1516–1525.PubMedCrossRefGoogle Scholar
  11. 11.
    De Oliveira, C. A. F., Hamelberg, D., and McCammon, J. A. (2007) Estimating kinetic rates from accelerated molecular dynamics simulations: Alanine dipeptide in explicit solvent as a case study, Journal of Chemical Physics 127.Google Scholar
  12. 12.
    Lin, J. H., Perryman, A. L., Schames, J. R., and McCammon, J. A. (2002) Computational drug design accommodating receptor flexibility: The relaxed complex scheme, Journal of the American Chemical Society 124, 5632–5633.PubMedCrossRefGoogle Scholar
  13. 13.
    Amaro, R. E., Baron, R., and McCammon, J. A. (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design, Journal of Computer-Aided Molecular Design 22, 693–705.PubMedCrossRefGoogle Scholar
  14. 14.
    Wereszczynski, J., and McCammon, J. A. (2010) Using Selectively Applied Accelerated Molecular Dynamics to Enhance Free Energy Calculations, Journal of Chemical Theory and Computation 6, 3285–3292.PubMedCrossRefGoogle Scholar
  15. 15.
    de Oliveira, C. A. F., Hamelberg, D., and McCammon, J. A. (2006) On the application of accelerated molecular dynamics to liquid water simulations, Journal of Physical Chemistry B 110, 22695–22701.CrossRefGoogle Scholar
  16. 16.
    Hamelberg, D., de Oliveira, C. A. F., and McCammon, J. A. (2007) Sampling of slow diffusive conformational transitions with accelerated molecular dynamics, Journal of Chemical Physics 127.Google Scholar
  17. 17.
    Russell, R. J., Haire, L. F., Stevens, D. J., Collins, P. J., Lin, Y. P., Blackburn, G. M., Hay, A. J., Gamblin, S. J., and Skehel, J. J. (2006) The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design, Nature 443, 45–49.PubMedCrossRefGoogle Scholar
  18. 18.
    Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., and Simmerling, C. (2006) Comparison of multiple amber force fields and development of improved protein backbone parameters, Proteins-Structure Function and Bioinformatics 65, 712–725.CrossRefGoogle Scholar
  19. 19.
    Amaro, R. E., Minh, D. D. L., Cheng, L. S., Lindstrom, W. M., Olson, A. J., Lin, J. H., Li, W. W., and McCammon, J. A. (2007) Remarkable loop flexibility in avian influenza N1 and its implications for antiviral drug design, Journal of the American Chemical Society 129, 7764- + .Google Scholar
  20. 20.
    Markwick, P. R. L., Cervantes, C. F., Abel, B. L., Komives, E. A., Blackledge, M., and McCammon, J. A. (2010) Enhanced Conformational Space Sampling Improves the Prediction of Chemical Shifts in Proteins, Journal of the American Chemical Society 132, 1220- + .Google Scholar
  21. 21.
    Shirts, M. R., Bair, E., Hooker, G., and Pande, V. S. (2003) Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods, Physical Review Letters 91.Google Scholar
  22. 22.
    Mobley, D. L., Graves, A. P., Chodera, J. D., McReynolds, A. C., Shoichet, B. K., and Dill, K. A. (2007) Predicting absolute ligand binding free energies to a simple model site, Journal of Molecular Biology 371, 1118–1134.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Chemistry and Biochemistry, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA
  2. 2.Howard Hughes Medical Institute, Departmentsof Chemistry and Biochemistry and Pharmacology, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA

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