Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
BMC Bioinformatics. 2010 Oct 12;11:505. doi: 10.1186/1471-2105-11-505.
In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).
The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters.
This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.
在许多蛋白质-蛋白质相互作用(PPI)网络中,密集连接的枢纽蛋白更有可能是必需蛋白。这被称为“中心性致死性规则”,表明蛋白质在 PPI 网络中的拓扑位置与其生物学的重要性相关。虽然在许多 PPI 网络中观察到了这种连接,但这些连接的潜在拓扑性质尚不清楚。一些被认为可能的连接是必需蛋白在维持整个网络连接中的作用,或者它们在必需蛋白簇中发挥作用。在这项工作中,我们试图通过确定蛋白质必需性和反向最近邻拓扑(RNN)之间的相关性,从不同的角度来检验必需蛋白的位置和网络拓扑。
RNN 拓扑是从 PPI 网络中衍生出的加权有向图,它是 PPI 网络中蛋白质之间拓扑依赖性的自然表示。与原始的 PPI 网络一样,我们观察到必需蛋白在 RNN 拓扑中倾向于成为枢纽蛋白。此外,在 RNN 拓扑中,必需基因在包含许多枢纽蛋白的聚类中富集(RNN 蛋白聚类)。基于 RNN 拓扑中必需基因的这两个特性,我们提出了一种新的度量方法;RNN 聚类中心性。来自各种 PPI 网络的结果表明,与其他中心性度量方法相比,RNN 聚类中心性在选择的必需蛋白比例方面表现更好。我们还研究了 RNN 聚类的生物学重要性。
本研究揭示了 RNN 聚类中心性提供了蛋白质必需性和 PPI 网络中蛋白质位置之间的最佳相关性。此外,合并的 RNN 聚类在拓扑上是重要的,因为必需蛋白在 RNN 聚类中显著富集,并且在生物学上是重要的,因为它们在许多基因本体论(GO)过程中发挥着重要作用。