Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules
In this paper, a multiscale virtual particle based elastic network model (MVP-ENM) is proposed for the normal mode analysis of large-sized biomolecules. The multiscale virtual particle (MVP) model is proposed for the discretization of biomolecular density data. With this model, large-sized biomolecu...
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sg-ntu-dr.10356-1421422023-06-13T03:32:51Z Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules Xia, Kelin School of Physical and Mathematical Sciences Science::Mathematics Multiscale Virtual Particle (MVP) Model Elastic Network Model (ENM) In this paper, a multiscale virtual particle based elastic network model (MVP-ENM) is proposed for the normal mode analysis of large-sized biomolecules. The multiscale virtual particle (MVP) model is proposed for the discretization of biomolecular density data. With this model, large-sized biomolecular structures can be coarse-grained into virtual particles such that a balance between model accuracy and computational cost can be achieved. An elastic network is constructed by assuming “connections” between virtual particles. The connection is described by a special harmonic potential function, which considers the influence from both the mass distributions and distance relations of the virtual particles. Two independent models, i.e., the multiscale virtual particle based Gaussian network model (MVP-GNM) and the multiscale virtual particle based anisotropic network model (MVP-ANM), are proposed. It has been found that in the Debye–Waller factor (B-factor) prediction, the results from our MVP-GNM with a high resolution are as good as the ones from GNM. Even with low resolutions, our MVP-GNM can still capture the global behavior of the B-factor very well with mismatches predominantly from the regions with large B-factor values. Further, it has been demonstrated that the low-frequency eigenmodes from our MVP-ANM are highly consistent with the ones from ANM even with very low resolutions and a coarse grid. Finally, the great advantage of MVP-ANM model for large-sized biomolecules has been demonstrated by using two poliovirus virus structures. The paper ends with a conclusion. 2020-06-16T06:23:56Z 2020-06-16T06:23:56Z 2017 Journal Article Xia, K. (2018). Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules. Physical Chemistry Chemical Physics, 20(1), 658-669. doi:10.1039/c7cp07177a 1463-9076 https://hdl.handle.net/10356/142142 10.1039/c7cp07177a 29227479 2-s2.0-85039455496 1 20 658 669 en Physical Chemistry Chemical Physics 10.21979/N9/FIFOZS © 2018 the Owner Societies (Published by Royal Society of Chemistry). All rights reserved. |
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Science::Mathematics Multiscale Virtual Particle (MVP) Model Elastic Network Model (ENM) Xia, Kelin Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
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In this paper, a multiscale virtual particle based elastic network model (MVP-ENM) is proposed for the normal mode analysis of large-sized biomolecules. The multiscale virtual particle (MVP) model is proposed for the discretization of biomolecular density data. With this model, large-sized biomolecular structures can be coarse-grained into virtual particles such that a balance between model accuracy and computational cost can be achieved. An elastic network is constructed by assuming “connections” between virtual particles. The connection is described by a special harmonic potential function, which considers the influence from both the mass distributions and distance relations of the virtual particles. Two independent models, i.e., the multiscale virtual particle based Gaussian network model (MVP-GNM) and the multiscale virtual particle based anisotropic network model (MVP-ANM), are proposed. It has been found that in the Debye–Waller factor (B-factor) prediction, the results from our MVP-GNM with a high resolution are as good as the ones from GNM. Even with low resolutions, our MVP-GNM can still capture the global behavior of the B-factor very well with mismatches predominantly from the regions with large B-factor values. Further, it has been demonstrated that the low-frequency eigenmodes from our MVP-ANM are highly consistent with the ones from ANM even with very low resolutions and a coarse grid. Finally, the great advantage of MVP-ANM model for large-sized biomolecules has been demonstrated by using two poliovirus virus structures. The paper ends with a conclusion. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Xia, Kelin |
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Article |
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Xia, Kelin |
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Xia, Kelin |
title |
Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
title_short |
Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
title_full |
Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
title_fullStr |
Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
title_full_unstemmed |
Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
title_sort |
multiscale virtual particle based elastic network model (mvp-enm) for normal mode analysis of large-sized biomolecules |
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2020 |
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https://hdl.handle.net/10356/142142 |
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