Optimized null model for protein structure networks.
Optimized null model for protein structure networks.
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DOI：
10.1371/journal.pone.0005967
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发表时间：
2009-06-26
期刊：
影响因子：
3.7
通讯作者：
Przulj N
中科院分区：
文献类型：
--
作者：
Milenković T;Filippis I;Lappe M;Przulj N
近年来，生物网络拓扑特征的统计意义受到了广泛关注。在这里，我们认为残基相互作用图（RIGs）作为网络表示的蛋白质结构的残基作为节点和残基间的相互作用作为边缘。保度随机化模型已被广泛用于生物分子网络中。然而，这样一个网络的单一汇总统计量可能不够详细，无法捕捉蛋白质结构及其网络对应物的复杂拓扑特征。在这里，我们研究了各种拓扑性质的RIG找到一个很好的拟合网络零模型。RIG来自于不同距离截止值和不同相互作用原子组的结构多样的蛋白质数据集。我们比较了RIGs的网络结构与几种随机图模型。我们表明，三维几何随机图，对象之间的空间关系模型，提供最适合RIG。我们调查的强度之间的关系的适合和各种蛋白质的结构特征。我们表明，拟合取决于蛋白质大小、结构类别和热稳定性，但不取决于四级结构。我们将我们的模型应用于识别显著过度代表的结构构建块，即，蛋白质结构网络中的网络基序。正如预期的那样，选择几何图作为空模型会导致对图案的最具体的识别。我们的几何随机图模型可以促进蛋白质构象空间的进一步基于图的研究，并对蛋白质结构的比较和预测具有重要意义。选择一个拟合良好的空模型对于寻找在蛋白质折叠、稳定性和功能中起重要作用的结构基序至关重要。据我们所知，这是第一个研究，解决了寻找一个优化的空模型RIG的挑战，通过比较各种RIG定义对一系列的网络模型。
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.
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