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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.25735
|2 doi
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|a pubmed25n0968.xml
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|a DE-627
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|e rakwb
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|a eng
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|a Sakae, Yoshitake
|e verfasserin
|4 aut
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|a Enhanced sampling method in molecular simulations using genetic algorithm for biomolecular systems
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|c 2019
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 01.06.2020
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|a Date Revised 01.06.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2018 Wiley Periodicals, Inc.
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|a We propose a molecular simulation method using genetic algorithm (GA) for biomolecular systems to obtain ensemble averages efficiently. In this method, we incorporate the genetic crossover, which is one of the operations of GA, to any simulation method such as conventional molecular dynamics (MD), Monte Carlo, and other simulation methods. The genetic crossover proposes candidate conformations by exchanging parts of conformations of a target molecule between a pair of conformations during the simulation. If the candidate conformations are accepted, the simulation resumes from the accepted ones. While conventional simulations are based on local update of conformations, the genetic crossover introduces global update of conformations. As an example of the present approach, we incorporated genetic crossover to MD simulations. We tested the validity of the method by calculating ensemble averages and the sampling efficiency by using two kinds of peptides, ALA3 and (AAQAA)3 . The results show that for ALA3 system, the distribution probabilities of backbone dihedral angles are in good agreement with those of the conventional MD and replica-exchange MD simulations. In the case of (AAQAA)3 system, our method showed lower structural correlation of α-helix structures than the other two methods and more flexibility in the backbone ψ angles than the conventional MD simulation. These results suggest that our method gives more efficient conformational sampling than conventional simulation methods based on local update of conformations. © 2018 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 genetic algorithm
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| 650 |
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|a molecular simulation
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| 650 |
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|a parallel computing
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| 650 |
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|a protein folding
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| 650 |
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|a sampling method
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| 650 |
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|a Peptides
|2 NLM
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|a Straub, John E
|e verfasserin
|4 aut
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| 700 |
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|a Okamoto, Yuko
|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), 2 vom: 15. Jan., Seite 475-481
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnas
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| 773 |
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|g volume:40
|g year:2019
|g number:2
|g day:15
|g month:01
|g pages:475-481
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|u http://dx.doi.org/10.1002/jcc.25735
|3 Volltext
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