(398g) Interaction Network-Based Antiviral Protein Discovery

Drug discovery faces several challenges. Most drug attrition occurs in Phase III during human-trial clinical testing¹. While recent advances in drug design has led to a reduction in attrition due to drug safety², there has been no significant improvement in drug efficacy³. â??Whole-cellâ? networks, such as protein-protein interaction or protein phosphorylation networks, may provide reasonable models of disease, but the challenge remains in isolating the components associated with disease. Here, we demonstrate a method of combining human protein-protein interaction (PPI) data with virus-host protein interaction data to improve influenza virus antiviral protein (i.e. drug target) discovery. We show that influenza virus proteins interact with human proteins that are in network positions significant for information flow. We then isolated a subnetwork of the human PPI network enriched for virus replication processes. Selecting proteins based on their network topology, we performed an siRNA screen to determine if network position offers an advantage in drug target prioritization. We found that the subnetwork is highly enriched for antiviral proteins â?? even more enriched than the set of host proteins previously found to directly interact with virus proteins. Moreover, we demonstrate that network positions can be used to further improve candidate prioritization. Lastly, we aim to discuss the implications of this study in its relation to the current state of host response models (specifically RNA virus sensing models) and which pathways should be given priority in order to develop more accurate, physiologically relevant models of the host response.

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