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首页> 外文期刊>Physical chemistry chemical physics: PCCP >Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules
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Multiscale virtual particle based elastic network model (MVP-ENM) for normal mode analysis of large-sized biomolecules

机译：多尺度虚拟粒子的弹性网络模型（MVP-ENM）用于大型生物分子的正常模式分析

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摘要

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.
机译：本文提出了一种多尺寸虚拟粒子基弹性网络模型（MVP-eNM），用于大尺寸生物分子的正常模式分析。提出了多尺度虚拟粒子（MVP）模型用于分离生物分子密度数据。利用这种模型，大尺寸的生物分子结构可以粗糙到虚拟粒子中，使得可以实现模型精度和计算成本之间的平衡。通过假设虚拟粒子之间的“连接”构建弹性网络。通过特殊的谐波潜在函数描述连接，其考虑了虚拟粒子的质量分布和距离关系的影响。两个独立模型，即提出了多尺度虚拟粒子的高斯网络模型（MVP-GNM）和多尺度虚拟粒子基于粒子级的各向异性网络模型（MVP-ANM）。已经发现，在Debye-Waller因子（B因子）预测中，结果从我们的MVP-GNM具有高分辨率与来自GNM的高分辨率一样好。即使是低分辨率，我们的MVP-GNM仍然可以很好地捕获B系子的全球行为，其不匹配主要来自具有大B-的区域的不匹配因子值。此外，已经证明，来自我们的MVP-ANM的低频特征模点与ANM的低频率高度一致，即使具有非常低的分辨率和粗网格。最后，伟大的通过使用两种Poliovirus病毒结构已经证明了MVP-ANM模型的大尺寸生物分子的优点。纸张以结论结束。

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