An Iteration Method for Identifying Yeast Essential Proteins From Weighted PPI Network Based on Topological and Functional Features of Proteins

• Published in: IEEE access Vol. 8; pp. 90792 - 90804
• Main Authors: Li, Shiyuan, Chen, Zhiping, He, Xin, Zhang, Zhen, Pei, Tingrui, Tan, Yihong, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological activity; Characteristic vector; Correlation coefficients; essential proteins; Feature extraction; Gene expression; iteration method; Iterative algorithms; Iterative methods; Network topology; Networks; orthologous proteins; Prediction methods; Proteins; Topology; weighted protein-protein interaction network
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2993860

Accumulating studies have indicated that essential proteins play critical roles in numerous biological processes. With the rapid development of high-throughput technologies, a large number of Protein-Protein Interaction (PPI) data have been found in Saccharomyces cerevisiae, which facilitate the formation of PPI networks. Up to now, a series of computational methods for predicting essential proteins from PPI networks have been proposed successively. However, the prediction accuracy of these computational methods is still not quite satisfactory. In this paper, a novel prediction method called CVIM is proposed to infer potential essential proteins. In CVIM, original PPI networks will be first transferred into weighted PPI networks by implementing PCC (Pearson Correlation Coefficient) on protein gene expression data. And then, based on weighted PPI networks and information of orthologous proteins, some critical network topological features and protein functional features will be extracted for each protein in the weighted PPI network. Finally, based on these newly extracted topological and functional features of proteins, an iterative algorithm will be designed to predict essential proteins. In order to evaluate the identification performance of CVIM, we have compared CVIM with 13 kinds of state-of-the-art prediction methods. Experimental results show that CVIM can achieve prediction accuracies of 92%, 80% and 71% out of the top 1%, 5% and 10% candidate proteins separately, which significantly outperform the prediction accuracies achieved by those state-of-the-art prediction methods. We have demonstrated that the prediction accuracy of essential proteins can be effectively improved by integrating the functional and network topological characteristics of proteins, which means that the novel method CVIM may be an excellent addition to the protein researches in the future.