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
  1. Robert B Best (2012). "Atomistic molecular simulations of protein folding". Current Opinion in Structural Biology (review). 22 (1): 52–61. doi:10.1016/j.sbi.2011.12.001. PMID 22257762.
  2. TJ Lane; Gregory Bowman; Robert McGibbon; Christian Schwantes; Vijay Pande; Bruce Borden (September 10, 2012). "Folding@home Simulation FAQ". Folding@home. Stanford University. Archived from the original on 2012-09-13. Retrieved September 10, 2012.
  3. 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. PMC 3042729. PMID 21081274.
  4. David E. Shaw; Martin M. Deneroff; Ron O. Dror; Jeffrey S. Kuskin; Richard H. Larson; John K. Salmon; Cliff Young; Brannon Batson; Kevin J. Bowers; Jack C. Chao; Michael P. Eastwood; Joseph Gagliardo; J. P. Grossman; C. Richard Ho; Douglas J. Ierardi, Ist (2008). "Anton, A Special-Purpose Machine for Molecular Dynamics Simulation". Communications of the ACM. 51 (7): 91–97. doi:10.1145/1364782.1364802.
  5. 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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