关键词: 关键蛋白质, 保守蛋白质, 混合动态保守蛋白质的时序加权网络, 蛋白质结构域, 共表达复合物中心性

Abstract: The essential protein is indispensable materials in various life processes of of living organisms, so the research of essential protein not only helps to understand the mechanism of life activities in theory, as well as lays a solid foundation on drug design and disease analysis in practice. At present, most of existing computational methods for essential proteins identification are based on static PPI network, which neglect the inherent dynamics and conservative of proteins, as well as generally only consider the topology of the network, the biological nature is ignored. In addition, those methods do not solve the problem of PPI data false positive and false negative. In allusion to the problems mentioned above, the temporal weighted network with dynamic and conserved proteins is constructed and a novel method called JTBC based on temporal weighted network is proposed to predict essential proteins. Firstly, according to gene expression data, dynamic information of dynamic proteins and conserved proteins is extracted to dynamically adjust static PPI network to construct temporal PPI network, which effectively reduces false negatives data in PPI networks. Secondly, in order to reduce the negative effect of false positive, it designs node and edge cohesion coefficient（DEcc） which integrates dual topology characteristics to measure the topological importance of proteins in PPI networks, besides combines protein domain information and Pearson correlation coefficient both with biological properties to weight all the interactions in temporal networks, which accurately describe protein interactions. Finally, it comes up with co-expressed complex centrality to evaluate locally the importance score of proteins based on the aggregation and co-expressed properties of essential proteins. Considering both aspects mentioned, this method comprehensively predicts essential proteins by integrating weight and complex information. Experimental results demonstrate that the algorithm is more reasonable to identify essential proteins from a global and local perspective.

Key words: essential proteins, conserved proteins, temporal weighted network with dynamic and conserved proteins, protein domain, co-expressed complex centrality