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Open Access

NetEPD: A Network-Based Essential Protein Discovery Platform

Jiashuai Zhang, Wenkai Li, Min Zeng, Xiangmao Meng, Lukasz Kurgan, Fang-Xiang Wu, Min Li()
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Department of Computer Science, Virginia Common-wealth University, Richmond, VA 23284-2512, USA.
Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, Canada.
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Abstract

Proteins drive virtually all cellular-level processes. The proteins that are critical to cell proliferation and survival are defined as essential. These essential proteins are implicated in key metabolic and regulatory networks, and are important in the context of rational drug design efforts. The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets. Scientists have developed several algorithms that use these interaction datasets to predict essential proteins. However, a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing. In this study, we design, implement, and release NetEPD: a network-based essential protein discovery platform. This resource integrates data on Protein-Protein Interaction (PPI) networks, gene expression, subcellular localization, and a native set of essential proteins. It also computes a variety of node centrality measures, evaluates the predictions of essential proteins, and visualizes PPI networks. This comprehensive platform functions by implementing four activities, which include the collection of datasets, computation of centrality measures, evaluation, and visualization. The results produced by NetEPD are visualized on its website, and sent to a user-provided email, and they are available to download in a parsable format. This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.

Keywords

essential proteinscentralitydata integrationevaluationvisualization

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Tsinghua Science and Technology
Volume 25 Issue 4,
August 2020
Pages 542-552
DOI: 10.26599/TST.2019.9010056
Cite this article:
Zhang J, Li W, Zeng M, et al. NetEPD: A Network-Based Essential Protein Discovery Platform. Tsinghua Science and Technology, 2020, 25(4): 542-552. https://doi.org/10.26599/TST.2019.9010056
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10.26599/TST.2019.9010056.T1 Summary of the four PPI datasets included in NetEPD.

Dataset sourceNumber of proteinsNumber of PPIs
BioGRID63 885852 865
DIP26 60072 823
MIPS9401151
MINT32 66893 742

10.26599/TST.2019.9010056.T2List and description of centrality measures included in NetEPD.

MethodShort descriptionReference
Degree Centrality (DC)The degree of centrality of a node refers to the number of neighbor nodes directly connected to it.[12]
Network Centrality (NC)The network centrality of a node is the sum of the aggregation coefficients of all neighboring edges of the node.[13]
Betweenness Centrality (BC)The betweenness centrality of a node is the proportion of the shortest path through the node in all the shortest paths of the network.[34]
Closeness Centrality (CC)The closeness centrality of the node is inversely proportional to the sum of the shortest path from the node to all other nodes in the protein network.[35]
Eigenvector Centrality (EC)The eigenvector centrality of a node refers to the corresponding component of the main eigenvector of the network’s adjacency matrix.[35]
Semi-Local Centrality (SLC)The semi-local centrality involves the fourth-order neighbor information of the node.[36]
Local Average Connectivity (LAC)The local average connectivity of a node indicates the public node relationship of the node and its neighbours.[36]
LindexThe value of a node’s lindex is the largest integer of the neighbours that have at least k degrees.[37]
Eccentricity (ECC)The eccentricity value of a node is defined as the maximum of its shortest distance from other nodes in the network.[38]
Neighborhood Connectivity (NeiC)The neighborhood connectivity of a node is defined as the average connectivity of all neighboring nodes of the node.[39]
Mapping Entropy Centrality (MEC)This method is analogous to the concept of "information entropy" and is mainly defined by the degree centrality.[40]
Localized Bridging Centrality (LBC)The localized bridging centrality of a node is defined as the product of its own median centrality and the bridging coefficient.[41]
Local Clustering Coefficient based on Degree Centrality (LCCDC)The local clustering coefficient based on degree centrality is defined as the product of a node’s degree centrality and the local clustering coefficient.[42]
Subgraph Centrality (SC)The subgraph centrality of a node refers to the total number of closed loops that the node participates in.[43]
Weighted Index Centrality (WIC)It is a method based on the weighted index of virtual nodes to evaluate the influence of node propagation in complex networks.[44]
Maximum Neighborhood Component (MNC)The MNC is defined as the maximum neighborhood component of a subgraph that consists of a node’s neighborhoods.[45]
Density Maximum Neighborhood Component (DMNC)To better judge the criticality of nodes in biological networks, DMNC concept was proposed based on the MNC.[45]
PageRankThe PageRank algorithm sorts nodes based on the link structure of the network.[46]
LeaderRankThe LeaderRank algorithm was proposed by adding a "ground node" and the bidirectional edges with other nodes in the network.[47]
K-shell decomposition (K-shell)The k-shell decomposition method determines the influence of the nodes according to the position of the nodes in the network.[48]
Mixture Degree Decomposition (MDD)In the degree of mixture decomposition method, all nodes in the network are divided into different shells depending on their own residual degree and exhaustion degree node.[49]
Information Centrality (IC)The information centrality of a node essentially measures the average length of the harmonics of all paths with nodes as endpoints.[50]

10.26599/TST.2019.9010056.T3 Comparison of key features offered by different tools for PPI networks analysis.

ToolNumber of included topological measuresAvailable onlineInclusion of preloaded dataEvaluation of predictionsVisualization of PPI networks
CytoNCA8✗✗✓✓
CentiServer55✓✗✗✗
CentiScaPe12✗✗✗✓
Network Analyzer11✗✗✗✓
Hubba6✓✓✗✗
NetworkX10✗✗✗✓
NetEPD22✓✓✓✓