Search CTRL + K

Molecular Dynamics Studies of Proteins

Experimental techniques for elucidating information on protein structure, such as X-ray crystallography and NMR, are well established. However, useful as these techniques are, they are not without their drawbacks. Many proteins are difficult to crystallise, meaning the structure cannot be found by crystallography, and while NMR spectra are easier to measure, the information it generates is much more open to interpretation. Both techniques offer only snapshots of information on the system and lack the ability to follow the dynamics in real time.

Molecular Dynamics (MD) simulations, based on propagation of Newton’s equation of motion, provide information about a protein at the atomic level, and on time scales not currently accessible by experiment. It is possible to follow the dynamics of a protein’s function—be it as a channel protein or a catalytic enzyme—essentially in real time. The main limitation of MD is that the accuracy is greatly dependent on the quality of the force field used to describe the system. Despite this limitation, MD simulations have been used extensively to investigate proteins, and are an indispensable complement to experimental techniques.

The first MD simulation of a protein system was carried out in 1976 on the bovine pancreatic trypsin inhibitor (BPTI), a 58 residue peptide that inhibits the action of the digestive enzyme trypsin. [1] [2] Since then the rapid development of computer hardware and software has allowed longer and larger simulations to be carried out. Microseconds of simulation time is now commonplace, and the field is rapidly approaching the millisecond scale with specialist tools. The satellite tobacco mosaic virus, containing over 1 million atoms, was the first complete virus investigated via all-atom MD simulation. [3] Since then, the size and complexity of the systems MD simulations can be used to study has only grown, with simulations being carried out on the HIV-1 mature capsid [4] and, more recently, a microsecond simulation of a complete influenza A virion. [5] MD simulations of larger systems benefit greatly from the parallisabilty of many MD codes, allowing the work to be split over more cores to speed up simulation time. The Folding@home project utilised this to investigate the
Chicken Villin Headpiece using 20,000 CPU’s from participating home computers. [6] GPU acceleration of MD simulations [7] is likely to increase further the size of systems studied and the length of simulations carried out.

One of biggest barriers in chemistry (and related disciplines) is the size of systems being studied, since we cannot directly observe the behaviour of a protein in water for example. Many methods in spectroscopy provide a way of observing behaviour of molecules, but this is indirect, based on the interaction of electromagnetic radiation with the molecule in question. MD simulations give us a way to directly observe the behaviour of molecules—with the caveat that the observations are only as accurate as the methods used to model the molecules—and can help explain experimental spectroscopic observations by linking observed simulated dynamics to calculated spectra which can then be compared with experiment.

MD simulations have had a large impact in many fields, having paved the way for the vast field of computational protein folding. [8] [9] Early MD simulations led to the current understanding of proteins as dynamical entities rather than static. [10] Investigations of the dynamics of proteins and related systems, whether on an all-atom or more coarse-grained scale, have provided essential insights into the behaviour of these systems. [8:1] [4:1] [5:1] Combination of MD simulations with calculation of spectra have led to a greater understanding of a number of important protein related systems. [11] [12] [13] [14] [15] The drive to obtain MD simulations of ever larger systems at longer time scales has led to developments in computer hardware, software and various improved algorithms. [6:1] [7:1]

  1. J.A. McCammon. Molecular dynamics of the bovine pancreatic trypsin inhibitor. In H.J.C. Berendsen, editor, Report of the 1976 Workshop, Models for Protein Dynamics. 1977. ↩︎

  2. J. A. McCammon, B. R. Gelin, and M. Karplus. Dynamics of folded proteins. Nature, 267:585–590, 1977. ↩︎

  3. P. L. Freddolino, A. S. Arkhipov, S. B. Larson, A. McPherson, and K. Schulten. Molecular dynamics simulations of the complete satellite tobacco mosaic virus. Structure, 14:437–449, 2006. ↩︎

  4. G. Zhao, J. R. Perilla, E. L. Yufenyuy, X. Meng, B. Chen, J. Ning, J. Ahn, A.M. Gronenborn, K. Schulten, C. Aiken, and P. Zhang. Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics. Nature, 497:643–646, 2013. ↩︎ ↩︎

  5. T. Reddy, D. Shorthouse, D.L. Parton, E. Jefferys, P.W. Fowler, M. Chavent, M. Baaden, and M.S.P. Sansom. Nothing to sneeze at: A dynamic and integrative computational model of an influenza A virion. Structure, 23:584–597, 2015. ↩︎ ↩︎

  6. G. Jayachandran, V. Vishal, and V. S. Pande. Using massively parallel simulation and Markovian models to study protein folding: Examining the dynamics of the villin headpiece. J. Chem. Phys., 124, 2006. ↩︎ ↩︎

  7. J. A. Baker and J. D. Hirst. Molecular dynamics simulations using graphics processing units. Mol. Inform., 30:498–504, 2011. ↩︎ ↩︎

  8. K.A. Dill and J.L. MacCallum. The Protein-Folding Problem, 50 Years On. Science, 38:1042–1046, 2012. ↩︎ ↩︎

  9. M. Levitt and A. Warshel. Computer simulation of protein folding. Nature, 253:694–698, 1975. ↩︎

  10. A. E. Warshel. Bicycle-pedal model for the first step in the vision process. Nature, 260:679–683, 1976. ↩︎

  11. A. Huerta-Viga, D. J. Shaw, and S. Woutersen. pH dependence of the conformation of small peptides investigated with two-dimensional vibrational spectroscopy. J. Phys. Chem. B, 114:15212–15220, 2010. ↩︎

  12. D. P. Weliky and R. Tycko. Determination of peptide conformations by two-dimensional magic angle spinning NMR exchange spectroscopy with rotor synchronization. J. Am. Chem. Soc., 118:8487–8488, 1996. ↩︎

  13. C. Liang, J. Knoester, and T. L. C. Jansen. Proton transport in a membrane protein channel: Two-dimensional infrared spectrum modeling. J. Phys. Chem. B, 116:6336–6345, 2012. ↩︎

  14. Z. Ganim, A. Tokmakoff, and A. Vaziri. Vibrational excitons in ionophores: experimental probes for quantum coherence-assisted ion transport and selectivity in ion channels. New J. Phys., 13:113030, 2011. ↩︎

  15. C. Falvo, W. Zhuang, Y. S. Kim, P. H. Axelsen, R. M. Hochstrasser, and S. Mukamel. Frequency distribution of the amide-I vibration sorted by residues in amyloid fibrils revealed by 2D-IR measurements and simulations. J. Phys. Chem. B, 116:3322–3330, 2012. ↩︎