Abstract
The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality of proteins. First, we give the definitions of complex average centrality (CAC) and complex hybrid centrality (CHC) which both describe the properties of protein complexes. Then we propose these new methods CDC and CIBD based on CAC and CHC definitions. In order to access these two methods, different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as experimental materials. By comparing with the competing methods including DC, BC, LAC, SC, EC, SoECC and the recent method LBCC and UC, our experimental results in networks show that the methods of CDC and CIBD can help to improve the precision of predicting essential proteins.
Highlights
Protein is one of the main components of human life
Plenty of centrality algorithms have been proposed to determine the essentiality of proteins, most of them focus on the analysis and mining of node topology characteristics
On the basis of the combination of the local features of protein complexes and topological properties, two new methods are proposed which named as complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD)
Summary
Protein is one of the main components of human life. Essential protein is defined as a protein which would result in the inability of the organism to survive when it is removed by a knockout mutation. The associate editor coordinating the review of this manuscript and approving it for publication was Jun Hu. Previous methods for identifying essential proteins mainly used some biological experiments, including conditional knockouts [5], RNA interference [6], and single gene knockouts [7], coupled with the survival ability of infected organisms being tested. Previous methods for identifying essential proteins mainly used some biological experiments, including conditional knockouts [5], RNA interference [6], and single gene knockouts [7], coupled with the survival ability of infected organisms being tested These biological experimental processes consume amounts of time and costs, and require a lot of biological resources. Yu: Two New Methods for Identifying Essential Proteins Based on the Protein Complexes and Topological Properties. In terms of the sensitivity, specificity, positive predictive value, negative predict value, F-measure, accuracy rate and the evaluation methods of ‘‘sorting-screening’’, the precision-recall curves and jackknife, the results show that our two methods are more effective in determining the essentiality of proteins than existing measures
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