Determining minimum set of driver nodes in protein-protein interaction networks.
Determining minimum set of driver nodes in protein-protein interaction networks.
复制标题
确定蛋白质-蛋白质相互作用网络中的最小驱动节点集
DOI：
10.1186/s12859-015-0591-3
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发表时间：
2015-05-07
影响因子：
3
通讯作者：
Dai DQ
中科院分区：
文献类型：
--
作者：
Zhang XF;Ou-Yang L;Zhu Y;Wu MY;Dai DQ
最近，一些研究引起了人们的注意，以确定一个最小的驱动蛋白，这是重要的基础蛋白质相互作用（PPI）网络的控制。最小支配集（MDS）模型被广泛采用。然而，因为MDS模型不生成唯一的MDS配置，所以当使用不同的优化算法时，将生成多个不同的MDS。因此，在这些MDS中，很难找到代表蛋白质的真正驱动集的MDS。为了解决这个问题，我们开发了一个中心校正的最小支配集（CC-MDS）模型，其中包括异质性的程度和介数中心的蛋白质。MDS模型和CC-MDS模型都应用于三个人类PPI网络。与MDS模型不同，当我们使用不同的优化算法实现CC-MDS模型时，CC-MDS模型生成几乎相同的驱动蛋白集合。CC-MDS模型比未校正的模型靶向更多的高度和高介数蛋白质。更中心的位置允许CC-MDS蛋白在维持整体网络连接性方面比MDS蛋白更重要。为了表明功能的重要性，我们发现，CC-MDS蛋白参与，平均而言，更多的蛋白质复合物和GO注释比MDS蛋白。我们还发现CC-MDS蛋白中的必需基因、衰老基因、疾病相关基因和病毒靶基因比MDS蛋白中的多。至于参与调节功能，CC-MDS蛋白质组显示出更强的转录因子和蛋白激酶富集。拓扑和功能显著性分析结果表明，CC-MDS模型比MDS模型能捕获更多的驱动蛋白。基于所获得的结果，CC-MDS模型是一个强大的工具，用于确定驱动蛋白，可以控制底层PPI网络。本文中描述的软件和使用的数据集可在https://github.com/Zhangxf-ccnu/CC-MDS上获得。本文的在线版本（doi：10.1186/s12859-015-0591-3）包含补充材料，可供授权用户使用。
Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins. To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model. Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software described in this paper and the datasets used are available at https://github.com/Zhangxf-ccnu/CC-MDS. The online version of this article (doi:10.1186/s12859-015-0591-3) contains supplementary material, which is available to authorized users.
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