A New Method for the Discovery of Essential Proteins

Abstract

BackgroundExperimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value.MethodIn this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality.ConclusionsWe demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

“…The protein-protein interactions (PPI) form a complex network represented as a graph, where the nodes represent proteins and undirected edges connecting these nodes represent the interactions between the proteins [32, 33]. Generally, PPI networks have shown to be a great vehicle for developing new hypotheses and designing novel laboratory experiments [34, 35]. Furthermore, essential proteins can be identified by topological analysis.…”

An ensemble framework for identifying essential proteins

“…SC counts the total number of closed walks in which a protein participates in the PIN and gives more weights to closed walks of short lengths [21]. It has been confirmed that these topological properties correlate with the essentiality of proteins to some degree [12, 18, 22, 23]. Some recently proposed methods are also based on the topological properties of PINs, such as TP [24] and LAC [25], and they outperform the mentioned commonly used centrality measures.…”

An improved interolog mapping-based computational prediction of protein–protein interactions with increased network coverage

“…8,9 Computationally analyzed PPIs help in developing new hypotheses about an organism and designing the laboratory experiments driven by the hypotheses. 10,11 In the case of infectious microorganisms, studying PPI networks offers identification of pathogenic proteins and therefore offers new opportunities for developing novel drugs and vaccines. [12][13][14] The interactions of proteins within a cell depend on several biological or physicochemical factors 15 and the PPI can be physical interactions, regulatory associations, genetic interactions, structural interactions, functional similarity associations among others.…”