Adaptive sampling

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding.


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]


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]


Adaptive sampling is used by the Folding@home distributed computing project in combination with Markov state models.[2][3]


While adaptive sampling is useful for short simulations, longer trajectories may be more helpful for certain types of biochemical problems.[4][5]

See also[edit]


  1. ^ Robert B Best (2012). "Atomistic molecular simulations of protein folding". Current Opinion in Structural Biology (review) 22 (1): 52–61. doi:10.1016/ PMID 22257762. 
  2. ^ a b TJ Lane, Gregory Bowman, Robert McGibbon, Christian Schwantes, Vijay Pande, and Bruce Borden (September 10, 2012). "Folding@home Simulation FAQ". Folding@home. Stanford University. Archived from the original on September 21, 2012. Retrieved September 10, 2012. 
  3. ^ a b c G. Bowman, V. Volez, and V. S. Pande (2011). "Taming the complexity of protein folding". Current Opinion in Structural Biology 21 (1): 4–11. doi:10.1016/ 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, and 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. 

External links[edit]