Molecular dynamics (MD) simulation numerically solves Newton's equations of motion on an atomistic or similar model of a molecular system to obtain information about its time-dependent properties.

Contents

1 Applications 2 Design Constraints 3 References 4 External Links

Applications

Beginning in theoretical physics, the method of MD gained popularity in material science and since the 1970s also in biochemistry and biophysics. It serves as an important tool in protein structure determination and refinement (see also crystallography, NMR). The interaction between the objects is either described by a force field (classical MD), a quantum chemical model, or a mix between the two. Popular software packages for MD simulation of biological molecules include: AMBER, CHARMM (and the commercial version CHARMm), GROMACS, GROMOS, and NAMD.

Design Constraints

Design of a molecular dynamics simulation can often encounter limits of computational power. Simulation sizes and time duration must be selected so that the calculation can finish within a useful time period.

The simulation's time duration is dependent on the time length of each timestep, between which forces are recalculated. The timestep must be chosen small enough to avoid discretization errors, and the number of timesteps, and thus simulation time, must be chosen large enough to capture the effect being modeled without taking an extraordinary period of time.

The simulation size must be large enough to express the effect without the boundary conditions disrupting the behavior. Boundary constraints are often treated by choosing fixed conditions at the boundary, or by choosing periodic boundary conditions in which one side of the simulation loops back to the opposite side. The scalability of the simulation with respect to the number of molecules is usually a significant factor in the range of simulation sizes which can be simulated in a reasonable time period. In Big O notation, common molecular dynamics simulations usually scale by either O(nlog(n)), or with good use of neighbor tables, O(n), with n as the number of molecules.

References

• J. A. McCammon, S. C. Harvey (1987) Dynamics of Proteins and Nucleic Acids. Cambridge University Press. ISBN 0-52-135652-0 (paperback); ISBN 0-52-130750 (hardback).
• M. P. Allen, D. J. Tildesley (1989) Computer simulation of liquids. Oxford University Press. ISBN 0198556454.
• D. C. Rapaport (1996) The Art of Molecular Dynamics Simulation. ISBN 0521445612.
• Daan Frenkel, Berend Smit (2001) Understanding Molecular Simulation. Academic Press. ISBN 0122673514.
• Oren M. Becker, Alexander D. Mackerell Jr, Benoît Roux, Masakatsu Watanabe (2001) Computational Biochemistry and Biophysics. Marcel Dekker. ISBN 082470455X.
• Tamar Schlick (2002) Molecular Modeling and Simulation. Springer. ISBN 038795404X.

External Links

• The Blue Gene Project (IBM)
• BioSimGrid, a database for biomolecular simulations