Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets

Prajwal Devkota, Matt C. Danzi, Stefan Wuchty, Lars Kaderali
2018 PLoS ONE  
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
more » ... ctions 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. OPEN ACCESS Citation: Devkota P, Danzi MC, Wuchty S (2018) Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets. PLoS ONE 13(5): e0197595. https://doi.
doi:10.1371/journal.pone.0197595 pmid:29795705 pmcid:PMC5967884 fatcat:2dt2jiuu55ct7gx7jrtrovge5e