Predicting Essential Proteins Based on Second-Order Neighborhood Information and Information Entropy

Abstract

Essential proteins are critical components of living organisms and indispensable to cellular life. Identification of essential proteins plays a critical role in the survival and development of life process and understanding the function of cell machinery. The experimental methods are usually costly and time-consuming. In order to overcome these limitations, many computational methods have been proposed to discover essential proteins based on the topological features of PPI networks and other biological information. In this paper, a new method named NIE is proposed to predict essential proteins based on second-order neighborhood information and information entropy of protein complex and subcellular localization. Firstly, a number of studies have shown that the RNA-Seq data is more advantageous than traditional gene expression data in predicting essential proteins. Meanwhile, the protein essentiality is closely related to the subcellular localization information, protein complex information and protein GO terms through data analysis. A weighted PPI network is constructed to reduce the impact of false positives and false negatives data on the identification of essential proteins, which integrates the GO terms information with Pearson correlation coefficient of RNA-Seq data. Secondly, the information entropy of protein complexes and subcellular localization is calculated to represent the biological characteristics of proteins. Furthermore, an information propagation model is constructed, which combines the biological properties of the proteins with the second-order neighborhood information in the PPI network to measure the essentiality of the proteins. In the experiments section, the proposed method is implemented on three common datasets (DIP, Krogan and MIPS) of Saccharomyces cerevisiae. A comparison study with other commonly used algorithms, including LAC, NC, PeC, WDC, UC, LIDC and LBCC is performed to evaluate the performance of NIE. The results show that the new method NIE has a better performance in predicting essential proteins.