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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
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Identification Number: |
doi:10.21979/N9/FIFOZS |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2023-06-12 |
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Version: |
1 |
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Bibliographic Citation: |
Xia, Kelin, 2023, "Replication Data for: Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules", https://doi.org/10.21979/N9/FIFOZS, DR-NTU (Data), V1 |
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Citation |
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Title: |
Replication Data for: Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules |
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Identification Number: |
doi:10.21979/N9/FIFOZS |
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Authoring Entity: |
Xia, Kelin (Nanyang Technological University) |
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Software used in Production: |
MATLAB |
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Grant Number: |
M408110000 |
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Grant Number: |
M401110000 |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Xia, Kelin |
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Depositor: |
Wee, Junjie |
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Date of Deposit: |
2023-06-12 |
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Holdings Information: |
https://doi.org/10.21979/N9/FIFOZS |
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Study Scope |
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Keywords: |
Mathematical Sciences, Mathematical Sciences, Gaussian Network Model, Anisotropic Network Model, Elastic Network Model, Normal Mode Analysis, Multiscale Virtual Particle |
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Abstract: |
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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Kind of Data: |
Raw data and calculated data |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1039/C7CP07177A |
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Bibliographic Citation: |
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. |
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Citation |
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Identification Number: |
10356/142142 |
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Bibliographic Citation: |
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. |
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Label: |
VAULT_4V60_v1.rar |
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Text: |
Data and Code for generating density profile of PDBID: 4V60 |
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Notes: |
application/x-rar-compressed |