Essential protein discovery based on a combination of modularity and conservatism

• Published in: Methods (San Diego, Calif.) Vol. 110; pp. 54 - 63
• Main Authors: Zhao, Bihai, Wang, Jianxin, Li, Xueyong, Wu, Fang-Xiang
• Format: Journal Article
• Language: English
• Published: United States 01.11.2016
• Subjects: Algorithms; Computational Biology - methods; Gene Expression Regulation - genetics; Protein Interaction Mapping - methods; Protein Interaction Maps - genetics; Conservatism; Multiple network; Modularity; Essential protein
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2016.07.005

Essential proteins are indispensable for the survival of a living organism and play important roles in the emerging field of synthetic biology. Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, most of these methods ignored intrinsic biological meaning of proteins. Researches show that essentiality is tied not only to the protein or gene itself, but also to the molecular modules to which that protein belongs. The results of this study reveal the modularity of essential proteins. On the other hand, essential proteins are more evolutionarily conserved than nonessential proteins and frequently bind each other. That is to say, conservatism is another important feature of essential proteins. Multiple networks are constructed by integrating protein-protein interaction (PPI) networks, time course gene expression data and protein domain information. Based on these networks, a new essential protein identification method is proposed based on a combination of modularity and conservatism of proteins. Experimental results show that the proposed method outperforms other essential protein identification methods in terms of a number essential protein out of top ranked candidates.