| 词条 | Adaptive sampling |
| 释义 |
Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding. BackgroundProteins spend a large portion – nearly 96% in some cases – 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] TheoryIf 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] ApplicationsAdaptive sampling is used by the Folding@home distributed computing project in combination with Markov state models.[2][3] DisadvantagesWhile adaptive sampling is useful for short simulations, longer trajectories may be more helpful for certain types of biochemical problems.[7][8] See also
References1. ^1 2 {{cite web |url=http://folding.stanford.edu/English/FAQ-Simulation |title=Folding@home Simulation FAQ |author1=TJ Lane |author2=Gregory Bowman |author3=Robert McGibbon |author4=Christian Schwantes |author5=Vijay Pande |author6=Bruce Borden |work=Folding@home |publisher=Stanford University |date=September 10, 2012 |accessdate=September 10, 2012 |archiveurl=https://www.webcitation.org/6AqqrNstM?url=http://folding.stanford.edu/English/FAQ-Simulation |archivedate=2012-09-21 |deadurl=yes |df= }} [1][2][3][4]2. ^1 2 3 {{cite journal |author1=G. Bowman |author2=V. Volez |author3=V. S. Pande | title = Taming the complexity of protein folding | journal = Current Opinion in Structural Biology | year = 2011 | volume = 21 | issue = 1 | pages = 4–11 | doi = 10.1016/j.sbi.2010.10.006 | pmc = 3042729 | pmid = 21081274}} 3. ^1 {{cite journal | author = David E. Shaw |author2=Martin M. Deneroff |author3=Ron O. Dror |author4=Jeffrey S. Kuskin |author5=Richard H. Larson |author6=John K. Salmon |author7=Cliff Young |author8=Brannon Batson |author9=Kevin J. Bowers |author10=Jack C. Chao |author11=Michael P. Eastwood |author12=Joseph Gagliardo |author13=J. P. Grossman |author14=C. Richard Ho |author15=Douglas J. Ierardi, Ist | title = Anton, A Special-Purpose Machine for Molecular Dynamics Simulation | journal = Communications of the ACM | volume = 51 | issue = 7 | pages = 91–97 | year = 2008 | pmid = | doi = 10.1145/1364782.1364802 | pmc = }} 4. ^1 {{cite journal | title = Biomolecular Simulation: A Computational Microscope for Molecular Biology |author1=Ron O. Dror |author2=Robert M. Dirks |author3=J.P. Grossman |author4=Huafeng Xu |author5=David E. Shaw | journal = Annual Review of Biophysics | year = 2012 | volume = 41 | issue = | pages = 429–52 | doi = 10.1146/annurev-biophys-042910-155245 |pmid=22577825 | bibcode = }} }} 7 : Molecular modelling|Simulation software|Computational biology|Mathematical and theoretical biology|Bioinformatics|Computational chemistry|Hidden Markov models |
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