Robust probabilistic superposition and comparison of protein structures.
Robust probabilistic superposition and comparison of protein structures.
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DOI：
10.1186/1471-2105-11-363
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发表时间：
2010-07-01
影响因子：
3
通讯作者：
Habeck M
中科院分区：
文献类型：
--
作者：
Mechelke M;Habeck M
蛋白质结构比较是结构生物信息学的核心问题。蛋白质结构的标准相异度量是具有代表性的原子位置的均方根偏差，如α-碳。为了评估RMSD，必须对被比较的结构进行最优叠加，以使RMSD最小化。如果结构包含差异很大的区域，如何评估最佳拟合就成了一个争论的问题--这种情况在核磁共振系综和经历大规模构象转变的蛋白质中遇到。我们提出了一种用于蛋白质结构稳健叠加和比较的概率方法。我们的方法旨在识别最大的结构不变核。为此，我们用异常值容忍概率分布对蛋白质结构中的非刚性位移进行建模。这些分布表现出比标准RMSD最小化下的高斯分布更重的尾部，从而适应了高度不同的结构区域。缺点是在重尾模型下，最优叠加的解析表达式不再存在。为了绕过这个问题，我们使用比例混合表示，这意味着加权的RMSD。我们开发了两个迭代程序，一个是期望最大化算法，一个是Gibbs采样器，用来估计重尾分布的局部权重、最优叠加和参数。应用表明，重尾模型捕捉了发生实质性构象变化的结构之间的差异，并可用于评估核磁共振结构的精度。通过比较贝叶斯因子，我们可以自动选择最合适的模型。因此，我们的方法是无参数的。重尾分布非常适合描述蛋白质结构中的大规模构象差异。比例混合表示便于对这些分布的拟合，并允许异常值容忍叠加。
Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as α-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely - a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions. We present a probabilistic method for robust superposition and comparison of protein structures. Our method aims to identify the largest structurally invariant core. To do so, we model non-rigid displacements in protein structures with outlier-tolerant probability distributions. These distributions exhibit heavier tails than the Gaussian distribution underlying standard RMSD minimization and thus accommodate highly divergent structural regions. The drawback is that under a heavy-tailed model analytical expressions for the optimal superposition no longer exist. To circumvent this problem we work with a scale mixture representation, which implies a weighted RMSD. We develop two iterative procedures, an Expectation Maximization algorithm and a Gibbs sampler, to estimate the local weights, the optimal superposition, and the parameters of the heavy-tailed distribution. Applications demonstrate that heavy-tailed models capture differences between structures undergoing substantial conformational changes and can be used to assess the precision of NMR structures. By comparing Bayes factors we can automatically choose the most adequate model. Therefore our method is parameter-free. Heavy-tailed distributions are well-suited to describe large-scale conformational differences in protein structures. A scale mixture representation facilitates the fitting of these distributions and enables outlier-tolerant superposition.
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影响因子：
4.4
作者：
Kudin, KN;Dymarsky, AY
通讯作者：
Dymarsky, AY
DOI：
10.1098/rspa.1977.0041
发表时间：
1977-01-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
影响因子：
--
作者：
BARNDORFFNIELSEN, O
通讯作者：
BARNDORFFNIELSEN, O
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
DOI：
10.1038/nsb0995-768
发表时间：
1995-09-01
期刊：
NATURE STRUCTURAL BIOLOGY
影响因子：
--
作者：
KUBONIWA, H;TJANDRA, N;BAX, A
通讯作者：
BAX, A
影响因子：
4.4
作者：
Kneller, Gerald R.
通讯作者：
Kneller, Gerald R.