弹性网络模型的剩余网络构造与预测
Residue Network Construction and Predictions of Elastic Network Models
摘要：
在过去的十年中，我们已经见证了粗粒度蛋白质网络模型的发展和成功，用于预测与集体运动模式相关的许多平衡性质。奇怪的是，结果通常是强大的对不同的方法用于构建残差网络的知识的实验坐标。我们提出了一个系统的研究网络的建设策略，以及它们对预测性能的影响。分析是基于径向分布函数和光谱尺寸的一个大的蛋白质组，以及一个新定义的数量，角分布函数。通过将相互作用划分为基本集和剩余集，我们表明鲁棒性源于大量属于后者的长距离相互作用。这些残差对每个残差上的力矢量的影响非常小。然后，整体力平衡转化为海森函数，作为慢运动模式的小位移和相应特征向量的不变性。蛋白质的生物学相关性质的研究的影响进行了讨论。
英文摘要：
The past decade has witnessed the development and success of coarse-grained network models of proteins for predicting many equilibrium properties related to collective modes of motion. Curiously, the results are usually robust towards the different methodologies used for constructing the residue networks from knowledge of the experimental coordinates. We present a systematical study of network construction strategies, and their effect on the predicted properties. The analysis is based on the radial distribution function and the spectral dimensions of a large set of proteins as well as a newly defined quantity, the angular distribution function. By partitioning the interactions into an essential and a residual set, we show that the robustness originates from a large number of long-distance interactions belonging to the latter. These residuals have a vanishingly small effect on the force vectors on each residue. The overall force balance then translates into the Hessian as small shifts in the slow modes of motion and an invariance of the corresponding eigenvectors. Implications for the study of biologically relevant properties of proteins are discussed.
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