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231225s2019 xx |||||o 00| ||eng c |
| 024 |
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|a 10.1002/jcc.26020
|2 doi
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|a pubmed24n0996.xml
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|a (NLM)31282023
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Hayami, Tomonori
|e verfasserin
|4 aut
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| 245 |
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|a Multidimensional virtual-system coupled canonical molecular dynamics to compute free-energy landscapes of peptide multimer assembly
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|c 2019
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 11.08.2020
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|a Date Revised 11.08.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2019 Wiley Periodicals, Inc.
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|a An enhanced-sampling method termed multidimensional virtual-system coupled canonical molecular dynamics (mD-VcMD) method is developed. In many cases, generalized-ensemble methods realizing enhanced sampling, for example, adaptive umbrella sampling, apply an effective potential, which is derived from temporarily assumed canonical distribution as a function of one or more arbitrarily defined reaction coordinates. However, it is not straightforward to estimate the appropriate canonical distribution, especially for cases applying multiple reaction coordinates. The current method, mD-VcMD, does not rely on the form of the canonical distribution. Therefore, it is practically useful to explore a high-dimensional reaction-coordinate space. In this article, formulation of mD-VcMD and its evaluation with the simple molecular models consisting of three or four alanine peptides are presented. We confirmed that mD-VcMD efficiently searched 2D and 3D reaction-coordinate spaces defined as interpeptide distances. Direct comparisons with results of long-term canonical MD simulations revealed that mD-VcMD produces correct canonical ensembles. © 2019 Wiley Periodicals, Inc
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a enhanced conformational sampling
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|a free-energy landscape
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| 650 |
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|a generalized-ensemble approach
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| 650 |
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|a molecular dynamics
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| 650 |
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|a protein-protein interaction
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| 650 |
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|a Peptides
|2 NLM
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| 700 |
1 |
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|a Higo, Junichi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nakamura, Haruki
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kasahara, Kota
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 40(2019), 28 vom: 30. Okt., Seite 2453-2463
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:40
|g year:2019
|g number:28
|g day:30
|g month:10
|g pages:2453-2463
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| 856 |
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|u http://dx.doi.org/10.1002/jcc.26020
|3 Volltext
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