Identification of Essential Proteins Based on Edge Clustering Coefficient

Abstract

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.

Predicting essential proteins based on subcellular localization, orthology and PPI networks

“…In the previous studies, it has been shown that network centrality is an important measure for predicting essential proteins and the network centrality based on edge clustering coefficient [14] is one of the most effective measures for the identification of essential proteins. Given a PPI network G =  ( V, E ) and a protein i , its network centrality based on edge clustering coefficient NC ( i ) is defined as the sum of edge clustering coefficients of all edges directly connected with protein i in the graph G .…”

“…The topology-based methods are designed based on associations between the essentiality and the topological features of essential proteins in bio-molecular networks. Degree Centrality (DC) [8], Betweenness Centrality (BC) [9], Closeness Centrality (CC) [10], Subgragh Centrality (SC) [11], Eigenvector Centrality (EC) [12], Information Centrality (IC) [13] and Neighborhood Centrality (NC) [14] are the representatives of topology-based methods. CytoNCA [15] is a cytoscape plugin for centrality analysis and evaluation of biological networks, and ClusterViz [16] is a cytoscape APP for cluster analysis of biological network.…”

Essential Proteins Discovery Methods based on the Protein-Protein Interaction Networks

“…That is to say, the higher degree proteins have in PPIs, the more tend to be the essential proteins. The theory also becomes the basis of essential proteins discovery based on network topology (Hahn and Kern, 2005;Estrada and Rodriguez-Velazquez, 2005;Bonacich, 1987;Wang et al, 2012;Joy et al, 2005;Wuchty and Stadler, 2003;Karen and Zelen, 1989). At present, this approachs are based on the correlation between criticality and topological features of proteinprotein interaction networks.…”

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

“…For example, He and Zhang classified PPIs as essential PPIs and nonessential PPIs [41]. In recent years, many computational methods have been proposed to identify essential proteins in PPI networks [42–49]. An essential PPI is a PPI whose two proteins are both essential proteins [41].…”