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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.23325
|2 doi
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|a pubmed24n0759.xml
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|a eng
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| 100 |
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|a Olson, Mark A
|e verfasserin
|4 aut
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| 245 |
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|a Application of replica exchange umbrella sampling to protein structure refinement of nontemplate models
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|c 2013
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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
|b cr
|2 rdacarrier
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|a Date Completed 02.07.2014
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|a Date Revised 26.06.2013
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2013 Wiley Periodicals, Inc.
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|a We provide an assessment of a computational strategy for protein structure refinement that combines self-guided Langevin dynamics with umbrella-potential biasing replica exchange using the radius of gyration as a coordinate (Rg -ReX). Eight structurally nonredundant proteins and their decoys were examined by sampling conformational space at room temperature using the CHARMM22/GBMV2 force field to generate the ensemble of structures. Two atomic statistical potentials (RWplus and DFIRE) were analyzed for structure identification and compared to the simulation force-field potential. The results show that, while the Rg -ReX simulations were able to sample conformational basins that were more structurally similar to the X-ray crystallographic structures than the starting first-order ranked decoys, the potentials failed to detect these basins from refinement. Of the three potential functions, RWplus yielded the highest accuracy for recognition of structures that refined to an average of nearly 20% increase in native contacts relative to the starting decoys. The overall performance of Rg -ReX is compared to an earlier study of applying temperature-based replica exchange to refine the same decoy sets and highlights the general challenge of achieving consistently the sampling and detection threshold of 70% fraction of native contacts
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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|a Proteins
|2 NLM
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| 700 |
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|a Lee, Michael S
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 34(2013), 20 vom: 30. Juli, Seite 1785-93
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:34
|g year:2013
|g number:20
|g day:30
|g month:07
|g pages:1785-93
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|u http://dx.doi.org/10.1002/jcc.23325
|3 Volltext
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|h 1785-93
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