Biology:Adaptive sampling
Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding when coupled with molecular dynamics simulations.
Background
Proteins spend a large portion – nearly 96% in some cases[1] – of their folding time "waiting" in various thermodynamic free energy minima. Consequently, a straightforward simulation of this process would spend a great deal of computation to this state, with the transitions between the states – the aspects of protein folding of greater scientific interest – taking place only rarely.[2] Adaptive sampling exploits this property to simulate the protein's phase space in between these states. Using adaptive sampling, molecular simulations that previously would have taken decades can be performed in a matter of weeks.[3]
Theory
If a protein folds through the metastable states A -> B -> C, researchers can calculate the length of the transition time between A and C by simulating the A -> B transition and the B -> C transition. The protein may fold through alternative routes which may overlap in part with the A -> B -> C pathway. Decomposing the problem in this manner is efficient because each step can be simulated in parallel.[3]
Applications
Adaptive sampling is used by the Folding@home distributed computing project in combination with Markov state models.[2][3]
Disadvantages
While adaptive sampling is useful for short simulations, longer trajectories may be more helpful for certain types of biochemical problems.[4][5]
See also
References
- ↑ Robert B Best (2012). "Atomistic molecular simulations of protein folding". Current Opinion in Structural Biology 22 (1): 52–61. doi:10.1016/j.sbi.2011.12.001. PMID 22257762.
- ↑ 2.0 2.1 "Folding@home Simulation FAQ". Folding@home. Stanford University. September 10, 2012. http://folding.stanford.edu/English/FAQ-Simulation.
- ↑ 3.0 3.1 3.2 G. Bowman; V. Volez; V. S. Pande (2011). "Taming the complexity of protein folding". Current Opinion in Structural Biology 21 (1): 4–11. doi:10.1016/j.sbi.2010.10.006. PMID 21081274.
- ↑ David E. Shaw; Martin M. Deneroff; Ron O. Dror; Jeffrey S. Kuskin; Richard H. Larson; John K. Salmon; Cliff Young; Brannon Batson et al. (2008). "Anton, A Special-Purpose Machine for Molecular Dynamics Simulation". Communications of the ACM 51 (7): 91–97. doi:10.1145/1364782.1364802.
- ↑ Ron O. Dror; Robert M. Dirks; J.P. Grossman; Huafeng Xu; David E. Shaw (2012). "Biomolecular Simulation: A Computational Microscope for Molecular Biology". Annual Review of Biophysics 41: 429–52. doi:10.1146/annurev-biophys-042910-155245. PMID 22577825.
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