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000016597 0247_ $$2DOI$$a10.1016/j.bbapap.2010.09.006
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000016597 041__ $$aeng
000016597 082__ $$a570
000016597 084__ $$2WoS$$aBiochemistry & Molecular Biology
000016597 084__ $$2WoS$$aBiophysics
000016597 1001_ $$0P:(DE-HGF)0$$aKlenin, K.$$b0
000016597 245__ $$aModelling Proteins: Conformational Sampling and Reconstruction of Folding Kinetics
000016597 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2011
000016597 300__ $$a977 - 1000
000016597 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000016597 440_0 $$019421$$aBBA - Proteins and Proteomics$$v1814$$x1570-9639$$y8
000016597 500__ $$3POF3_Assignment on 2016-02-29
000016597 500__ $$aKK and WW acknowledge the support from the DFG Center for Functional Nanostructures (C5.1) and the Landesstiftung Baden-Wurttemberg (Biomaterials program and HPC program).
000016597 520__ $$aIn the last decades biomolecular simulation has made tremendous inroads to help elucidate biomolecular processes in-silico. Despite enormous advances in molecular dynamics techniques and the available computational power, many problems involve long time scales and large-scale molecular rearrangements that are still difficult to sample adequately. In this review we therefore summarise recent efforts to fundamentally improve this situation by decoupling the sampling of the energy landscape from the description of the kinetics of the process. Recent years have seen the emergence of many advanced sampling techniques, which permit efficient characterisation of the relevant family of molecular conformations by dispensing with the details of the short-term kinetics of the process. Because these methods generate thermodynamic information at best, they must be complemented by techniques to reconstruct the kinetics of the process using the ensemble of relevant conformations. Here we review recent advances for both types of methods and discuss their perspectives to permit efficient and accurate modelling of large-scale conformational changes in biomolecules. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.
000016597 536__ $$0G:(DE-Juel1)FUEK409$$2G:(DE-HGF)$$aFunktion und Dysfunktion des Nervensystems$$cP33$$x0
000016597 536__ $$0G:(DE-Juel1)FUEK505$$2G:(DE-HGF)$$aBioSoft: Makromolekulare Systeme und biologische Informationsverarbeitung$$cP45$$x1
000016597 588__ $$aDataset connected to Web of Science, Pubmed
000016597 65320 $$2Author$$aProtein dynamics
000016597 65320 $$2Author$$aConformational ensemble
000016597 65320 $$2Author$$aAdvanced sampling
000016597 65320 $$2Author$$aMarkov models
000016597 650_2 $$2MeSH$$aAlgorithms
000016597 650_2 $$2MeSH$$aEvolution, Molecular
000016597 650_2 $$2MeSH$$aKinetics
000016597 650_2 $$2MeSH$$aModels, Molecular
000016597 650_2 $$2MeSH$$aMonte Carlo Method
000016597 650_2 $$2MeSH$$aProtein Conformation
000016597 650_2 $$2MeSH$$aProtein Folding
000016597 650_2 $$2MeSH$$aProteins: chemistry
000016597 650_7 $$00$$2NLM Chemicals$$aProteins
000016597 650_7 $$2WoSType$$aJ
000016597 7001_ $$0P:(DE-Juel1)132024$$aStrodel, B.$$b1$$uFZJ
000016597 7001_ $$0P:(DE-HGF)0$$aWales, D.J.$$b2
000016597 7001_ $$0P:(DE-HGF)0$$aWenzel, W.$$b3
000016597 773__ $$0PERI:(DE-600)2209540-8$$a10.1016/j.bbapap.2010.09.006$$gVol. 1814, p. 977 - 1000$$p977 - 1000$$q1814<977 - 1000$$tBiochimica et biophysica acta / Proteins and proteomics$$v1814$$x1570-9639$$y2011
000016597 8567_ $$uhttp://dx.doi.org/10.1016/j.bbapap.2010.09.006
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000016597 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000016597 9141_ $$y2011
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000016597 9131_ $$0G:(DE-Juel1)FUEK505$$aDE-HGF$$bSchlüsseltechnologien$$kP45$$lBiologische Informationsverarbeitung$$vBioSoft: Makromolekulare Systeme und biologische Informationsverarbeitung$$x1
000016597 9132_ $$0G:(DE-HGF)POF3-559H$$1G:(DE-HGF)POF3-550$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lBioSoft – Fundamentals for future Technologies in the fields of Soft Matter and Life Sciences$$vAddenda$$x0
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