Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States of America.
The Miami Project to Cure Paralysis, Miller School of Medicine, University of Miami, Miami, FL, United States of America.
PLoS One. 2018 May 24;13(5):e0197595. doi: 10.1371/journal.pone.0197595. eCollection 2018.
The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein's degree and betweenness centrality, we considered a protein's pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.
大规模的宿主-病毒相互作用界面筛选使对宿主病毒蛋白靶标的拓扑分析成为可能。特别是，与病毒蛋白结合的宿主蛋白通常是枢纽和具有高介数中心性的蛋白。最近，引入了其他拓扑度量方法，病毒可以利用这些方法感染宿主细胞。利用从疱疹、肝炎、艾滋病毒和流感中实验确定的人类蛋白靶集，我们汇集了来自不同途径数据库的蛋白之间的分子相互作用。除了蛋白质的度数和介数中心性之外，我们还考虑了蛋白质的途径参与度、拓扑控制网络的能力和蛋白质 PageRank 指数。特别是，我们发现，具有这些度量值增加的蛋白质往往会积累病毒靶标，并将病毒靶标与非靶标区分开来。此外，所有这些拓扑度量都与给定蛋白质在不同途径中的存在强烈相关。基于这些拓扑度量构建随机森林分类器，我们发现蛋白质 PageRank 指数对病毒（非）靶标的分类影响最大，而蛋白质拓扑控制网络的能力则起着最小的作用。
