Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks

Abstract

Hubs are generally defined as nodes with a high degree centrality, and they are important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us with a new way to analyze the essentiality of proteins. Unfortunately, most of the existing studies leverage static PPI networks that are both incomplete and noisy and ignore the temporal and spatial characteristics of PPI networks. Benefiting from the development of high-throughput technologies, abundant multi-biological datasets have been accumulated and can be used for network analysis. To reexamine the relationship between the network centrality and protein essentiality in PPI networks, in this study, we integrated PPI networks with gene expression data and subcellular localization information to construct temporal-spatial dynamic PPI networks. Based on the constructed temporal-spatial dynamic PPI networks, we introduced the maximum degree centrality (MDC) method to evaluate the essentiality of hub proteins. Our results illustrate that the integration of gene expression data or subcellular localization information can significantly reduce noise effects and improve the identification accuracy of essential proteins through the temporal-spatial analysis with disparate sources of PPI networks. Moreover, we redefined hubs and classified them into two types: temporospatial hubs and static hubs. The results show that temporospatial hub proteins are more likely to be essential.