A physicist, computer scientist, and biomedical engineer walk into a bar…and begin to play pool. The biomedical engineer takes aim and sends the cue ball into the rack, scattering the other fifteen balls across the table. The physicist watches as the cue ball collides and transfers momentum to the other balls and comments that the biomedical engineer put too much force into the initial hit and should have pulled back a little. Flustered, the biomedical engineer retorts that the hit was perfect and that if they slowed time and could observe every ball at any moment that all three would see that the hit was ideal. Intrigued, the computer scientist jokingly suggests that they should do just that, create a simulation where they could alter the input force and watch the system change with time as the pool balls interact with themselves as well as the sides of the table. This is the basic principle for molecular dynamics simulations.
Although the name might seem complex and long, molecular dynamics simulation (MDS) is a rather simple method for understanding how molecules interact with each other and their environment. The practice comes down to three major steps: initialize the molecules and environment, compute both molecule-molecule and molecule-environment interactions, and update the molecules within the system with new physical properties at some time interval.
The origin of molecular dynamics can be traced all the way back to Galileo’s day, where the polymath labored to discover laws governing the motion of objects . Some three hundred years later, with the advent of the computer, came the ability to construct simulations. The marriage of the two sciences was inevitable. In May of 1955, scientists Fermi, Pasta, Ulam, and Tsingou published their paper Studies of Nonlinear Problems, describing a method of using their lab computer MANIAC I to simulate a sixty-four particle, one dimensional system . This marked the first instance of true molecular dynamics simulations. Since this project, MDS has found an important place in scientific discovery.
In today’s world, many industries have made MDS an integral part of research and development . For example, researchers in the field of biochemistry use the method to replicate and predict protein folding and docking . In the pharmaceutical industry, scientists are able to investigate drug design using MDS models of protein-drug interactions . Material scientists can further explore the properties of nanocrystalline metals by simulating stress-strain deformations via environmental interactions with the material .
MDS is used widely, but how exactly is it used? Let’s look at a biochemical application, and follow an experiment observing two proteins — specifically, an antibody and its antigen — interacting in a solution. To begin, we’ll need to recall the three steps: initialize molecules and environment, compute interactions, and update the system. First, the environment and the two proteins need to be initialized and given physical properties. Position, velocity, atomic structure, energy, and other such information must be provided for each protein and environment molecule to describe the initial state. Second, we compute the molecule-molecule and molecule-environment interactions. The interaction between our antibody and antigen proteins should result in the binding and formation of a complex. In order to get to this expected result, we can’t just look at the proteins; the properties of their simulated environment must also be considered. For example, the interactions between proteins in blood versus proteins in water might differ due to unique properties of each liquid. In this second step, the energies, movement, and other physical parameters of the proteins need to be calculated so that sufficient information is available for the next step. Third: the system needs to be updated according to the information derived from the previous step’s physical, chemical, and mathematical relationships. For example, if the antibody and the antigen are too far away from one another, the system may favor moving them closer together. Alternatively, if two charged regions would repel one another, the system would likely opt to move them further apart. Steps two and three are repeated at regular time intervals until a time limit is reached. At that point, each timestep’s data may be examined and processed for further understanding and discovery, a benefit the biomedical engineer and physicist would appreciate so as to settle their debate.
Diversity and The Future
Molecular dynamic simulation is a popular and heavily-used method in many sciences. Its application is not limited to any one discipline and its ability to further scientific understanding is powerful. MDS is vital to protein, drug, and material science, and has found its way into such diverse fields as engineering and automotive design — where fluid dynamic simulations can be used to model driving conditions for vehicles with liquid cargo . From the data of these simulations, better design parameters might be considered when designing future transport vehicles . From aspirin to asphalt to audi, MDS plays a major role in the development and advancement of today’s technology.
Links and Citations:
HOOVER, W. G., LADD, A. J. C., & HOOVER, V. N. (2009). Historical Development and Recent Applications of Molecular Dynamics Simulation (pp. 29–46). https://doi.org/10.1021/ba-1983-0204.ch002
Fermi, E., Pasta, P., Ulam, S., & Tsingou, M. (1955). STUDIES OF THE NONLINEAR PROBLEMS. Los Alamos, NM (United States). https://doi.org/10.2172/4376203
Hospital, A., Goñi, J. R., Orozco, M., & Gelpí, J. L. (2015). Molecular dynamics simulations: advances and applications. Advances and applications in bioinformatics and chemistry : AABC, 8, 37–47. doi:10.2147/AABC.S70333
Sugita, Y., & Okamoto, Y. (1999). Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 314(1–2), 141–151. https://doi.org/10.1016/S0009-2614(99)01123-9
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006, September). Combining docking and molecular dynamic simulations in drug design. Medicinal Research Reviews. https://doi.org/10.1002/med.20067
Yamakov, V., Wolf, D., Phillpot, S. R., Mukherjee, A. K., & Gleiter, H. (2004). Deformation-mechanism map for nanocrystalline metals by molecular-dynamics simulation. Nature Materials, 3(1), 43–47. https://doi.org/10.1038/nmat1035
Fleissner, F., Lehnart, A., & Eberhard, P. (2010). Dynamic simulation of sloshing fluid and granular cargo in transport vehicles. In Vehicle System Dynamics (Vol. 48, pp. 3–15). https://doi.org/10.1080/00423110903042717
Rumold, W. (2001). Modeling and Simulation of Vehicles Carrying Liquid Cargo. Multibody System Dynamics, 5(4), 351–374. https://doi.org/10.1023/A:1011425305261
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