A new method for predicting essential proteins based on dynamic network topology and complex information

• Dynamics is an important inherent character of protein–protein interaction (PPI) network.
• Compared with being applied to a static PPI network, topology-based methods for predicting essential proteins can achieve better effects when they are applied to a dynamic PPI network.
• We integrate the local average connectivity and the complex information and apply this integration to a dynamic PPI network to predict essential protein.
• Our method can discover more essential proteins than most of previous methods.

Predicting essential proteins is highly significant because organisms can not survive or develop even if only one of these proteins is missing. Improvements in high-throughput technologies have resulted in a large number of available protein–protein interactions. By taking advantage of these interaction data, researchers have proposed many computational methods to identify essential proteins at the network level. Most of these approaches focus on the topology of a static protein interaction network. However, the protein interaction network changes with time and condition. This important inherent dynamics of the protein interaction network is overlooked by previous methods. In this paper, we introduce a new method named CDLC to predict essential proteins by integrating dynamic local average connectivity and in-degree of proteins in complexes. CDLC is applied to the protein interaction network of Saccharomyces cerevisiae. The results show that CDLC outperforms five other methods (Degree Centrality (DC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), PeC and Co-Expression Weighted by Clustering coefficient (CoEWC)). In particular, CDLC could improve the prediction precision by more than 45% compared with DC methods. CDLC is also compared with the latest algorithm CEPPK, and a higher precision is achieved by CDLC. CDLC is available as Supplementary materials. The default settings of active threshold and alpha-parameter are 0.8 and 0.1, respectively.

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