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A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

ABSTRACT: Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

SUBMITTER: Qin C

PROVIDER: S-EPMC4987049 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

Qin Chao C Sun Yongqi Y Dong Yadong Y

PloS one 20160816 8

Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is bas ...[more]

PMID: 27529423

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Project description:Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

Project description:BackgroundIdentifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.ResultsTo identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, fρ, as fρ= (density)(ρ*)(modularity)(1-ρ), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.ConclusionsBy considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

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A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

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A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

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