Abstract
BackgroundEssential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.ResultsIn order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.ConclusionsWe constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.
Highlights
Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design
Yu et al found the correlations between bottlenecks and essential proteins, where bottlenecks were defined as proteins with high degrees of centrality [12]
Based on the modular nature of a protein essentiality, Li Min et al proposed a calculation method to identify essential proteins based on local average connection [13],and they proposed a new model by adopting a new protein network recognition method based on topological potential [14], the basic idea is to treat each protein in the network as a material particle, generate a potential field around it, and calculate the topological potential of each protein to determine the importance of the protein
Summary
Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. Based on the concept of centrality, a lot of different methods, including the Degree Centrality (DC) [16], Information Centrality (IC) [17], Eigenvector Centrality (EC) [18], Subgraph Centrality (SC) [19], Betweenness Centrality (BC) [20], Closeness Centrality (CC) [21] and Neighbor Centrality (NC) [22], have been designed successively These centrality-based methods can improve the efficiency of traditional biological experiments effectively, their recognition abilities are still not very satisfactory, since there are lots of noises such as the false negatives and the false positives existing in the PPI networks [23, 24]. Zhao et al combined PPI with multiple biological data to construct a heterogeneous network to predict essential proteins [28]
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