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使用连续和离散蛋白质模型测试相似性度量。

Testing similarity measures with continuous and discrete protein models.

作者信息

Wallin Stefan, Farwer Jochen, Bastolla Ugo

机构信息

Complex Systems Division, Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund, Sweden.

出版信息

Proteins. 2003 Jan 1;50(1):144-57. doi: 10.1002/prot.10271.

Abstract

There are many ways to define the distance between two protein structures, thus assessing their similarity. Here, we investigate and compare the properties of five different distance measures, including the standard root-mean-square deviation (cRMSD). The performance of these measures is studied from different perspectives with two different protein models, one continuous and the other discrete. Using the continuous model, we examine the correlation between energy and native distance, and the ability of the different measures to discriminate between the two possible topologies of a three-helix bundle. Using the discrete model, we perform fits to real protein structures by minimizing different distance measures. The properties of the fitted structures are found to depend strongly on the distance measure used and the scale considered. We find that the cRMSD measure very effectively describes long-range features but is less effective with short-range features, and it correlates weakly with energy. A stronger correlation with energy and a better description of short-range properties is obtained when we use measures based on intramolecular distances.

摘要

定义两个蛋白质结构之间的距离有多种方法,从而评估它们的相似性。在这里,我们研究并比较了五种不同距离度量的特性,包括标准均方根偏差(cRMSD)。使用两种不同的蛋白质模型,从不同角度研究了这些度量的性能,一种是连续模型,另一种是离散模型。使用连续模型,我们研究了能量与天然距离之间的相关性,以及不同度量区分三螺旋束两种可能拓扑结构的能力。使用离散模型,我们通过最小化不同的距离度量来拟合真实的蛋白质结构。发现拟合结构的特性在很大程度上取决于所使用的距离度量和所考虑的尺度。我们发现cRMSD度量能非常有效地描述远程特征,但对短程特征的效果较差,并且它与能量的相关性较弱。当我们使用基于分子内距离的度量时,能获得与能量更强的相关性以及对短程特性更好的描述。

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