A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

Ackerman, Emily E.; Alcorn, John F.; Hase, Takeshi; Shoemaker, Jason E.

Lastly, though this study has used experimental data from Influenza A studies, this analysis can be used to improve the prediction of drug targets for any pathogenhost interaction given available protein interaction data because of the generality of the method. The limits of these methods lie in limited availability of large-scale, dependable databases of protein-protein interactions. Foundational maximum matching algorithms for the calculation of driver proteins must be performed with directed networks. While larger directed networks than the network from Vinayagam et al. [40] are available [70] , the network used here contains only experimentally derived data opposed to computationally predicted interactions, assuring biological confidence in the results within this study. A robust controllability analysis of the computationally predicted network presented in Uhart et al. [70] finds that 29% of proteins are categorized as indispensable where approximately 20% of proteins in the Vinayagam network are classified as the same, though there is 89% overlap in directed edges between the two networks. This suggests that methods for predicting protein interactions may over represent these key proteins within the analysis, even in combination with experimental results. However, larger networks will move towards a more complete analysis of infected cell behavior and possibly reveal further proteins of interest. Therefore, the future of this field depends on continued establishment of large, confident, directed PPI networks.