An Iterative Model for Identifying Essential Proteins Based on the Whole Process Network of Protein Evolution

Abstract

Introduction: Essential proteins play important roles in cell growth and regulation. However, due to the high costs and low efficiency of traditional biological experiments to identify essential proteins, in recent years, with the development of high-throughput technologies and bioinformatics, more and more computational models have been proposed to infer key proteins based on Protein-Protein Interaction (PPI) networks. background: Essential proteins play important roles in cell growth and regulation. However, due to high costs and low efficiency of traditional biological experiments to identify essential proteins, in recent years, with the development of high-throughput technologies and bioinformatics, more and more computational models have been proposed to infer key proteins based on Protein-Protein Interaction (PPI) networks. Method: In this manuscript, a novel prediction model named MWPNPE (Model based on the Whole Process Network of Protein Evolution) was proposed, in which, a whole process network of protein evolution was constructed first based on known PPI data and gene expression data downloaded from benchmark databases. And then, considering that the interaction between proteins is a kind of dynamic process, a new measure was designed to estimate the relationships between proteins, based on which, an improved iterative algorithm was put forward to evaluate the importance of proteins. objective: Experimental results show that existing methods based on the combination of biological information of proteins and topological characteristics of PPI networks can obtain much better prediction accuracy. Results: Finally, in order to verify the predictive performance of MWPNPE, we compared it with state-of-the-art representative computational methods, and experimental results demonstrated that the recognition accuracy of MWPNPE in the top 100, 200, and 300 candidate key proteins can reach 89, 166, and 233 respectively, which is significantly better than the predictive accuracies achieved by these competitive methods. Conclusion: Hence, it can be seen that MWPNPE may be a useful tool for the development of key protein recognition in the future. conclusion: Hence, it can be seen that MWPNPE may be a useful tool for the development of key protein recognition in the future. other: No