myPresto/omegagene 2020: a molecular dynamics simulation engine for virtual-system coupled sampling
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- Kasahara Kota
- College of Life Sciences, Ritsumeikan University
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- Terazawa Hiroki
- Graduate School of Life Sciences, Ritsumeikan University
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- Itaya Hayato
- Graduate School of Life Sciences, Ritsumeikan University
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- Goto Satoshi
- Graduate School of Life Sciences, Ritsumeikan University
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- Nakamura Haruki
- Institute for Protein Research, Osaka University
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- Takahashi Takuya
- College of Life Sciences, Ritsumeikan University
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- Higo Junichi
- Graduate School of Simulation Studies, University of Hyogo
抄録
<p>The molecular dynamics (MD) method is a promising approach for investigating the molecular mechanisms of microscopic phenomena. In particular, generalized ensemble MD methods can efficiently explore the conformational space with a rugged free-energy surface. However, the implementation and acquisition of technical knowledge for each generalized ensemble MD method are not straightforward for end-users. Here, we present a new version of the myPresto/omegagene software, which is an MD simulation engine tailored for a series of generalized ensemble methods, which are virtual-system coupled multicanonical MD (V-McMD), virtual-system coupled adaptive umbrella sampling (V-AUS), and virtual-system coupled canonical MD (VcMD). This program has been applied in several studies analyzing free-energy landscapes of a variety of molecular systems with all-atom simulations. The updated version provides new functionality for coarse-grained simulations powered by the hydrophobicity scale method. The software package includes a step-by-step tutorial document for enhanced conformational sampling of the poly-glutamine (poly-Q) oligomer expressed as a one-bead per residue model. The myPresto/omegagene software is freely available at the following URL: https://github.com/kotakasahara/omegagene under the Apache2 license.</p>
収録刊行物
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- Biophysics and Physicobiology
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Biophysics and Physicobiology 17 (0), 140-146, 2020
一般社団法人 日本生物物理学会
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詳細情報 詳細情報について
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- CRID
- 1390849376474249600
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- NII論文ID
- 130007937194
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- ISSN
- 21894779
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可