School of Information Science and Engineering, Central South University, Computer Building, Changsha 410083, China.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1070-80. doi: 10.1109/TCBB.2011.147.
Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node’s essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.
鉴定必需蛋白质是理解细胞生命的基本要求的关键，对药物设计也很重要。可用的蛋白质-蛋白质相互作用 (PPI) 数据的快速增加使得在网络层面上检测蛋白质的必需性成为可能。已经提出了一系列基于网络拓扑结构的中心度度量方法来发现必需蛋白质。然而，它们大多数都倾向于只关注单个蛋白质的位置，而忽略了相互作用和蛋白质必需性之间的相关性。在本文中，提出了一种基于边缘聚类系数的识别必需蛋白质的新中心度度量方法，称为 NC。与以前的中心度度量方法不同，NC 同时考虑了节点的中心度及其与邻居之间的关系。对于网络中的每个相互作用，我们计算其边缘聚类系数。一个节点的必需性由连接它及其邻居的相互作用的边缘聚类系数之和决定。新的中心度度量 NC 考虑了蛋白质必需性的模块性质。NC 应用于三种不同类型的酵母蛋白质-蛋白质相互作用网络，这些网络分别从 DIP 数据库、MIPS 数据库和 BioGRID 数据库获得。在三个不同网络上的实验结果表明，NC 发现的必需蛋白质数量普遍超过其他六个中心度度量方法（DC、BC、CC、SC、EC 和 IC）发现的必需蛋白质数量。此外，NC 发现的必需蛋白质表现出显著的聚类效应。
