Abstract 1785 Utilizing Virtual Fragment Library Screening to Identify New Inhibitor Scaffolds for DEAD-box Proteins

RNA folding pathways often depend on DEAD-box proteins to achieve an active native state. DEAD-box proteins belong to the largest helicase family, superfamily 2 (SF2). These proteins are defined as RNA-dependent ATP unwindases. For example, the human DEAD-box protein DDX52 is an essential cofactor in ribosome biogenesis. Several cancer lines have shown elevated levels of DDX52 to sustain the demands of the cell. DDX52 was recently identified as a diagnostic marker and a drug target due to its role in many cancers. To avoid common solubility issues previously faced in drug design, this study uses a faster and more efficient way of identifying possible inhibitors through virtual library screening. This structure-based design methodology allows for the identification of new inhibitor scaffolds. Many libraries were analyzed, including fragments-based, human metabolite, drug-like, and small natural compound libraries. Docking analysis was performed on both DDX52 and Rok1p. Rok1p is a yeast Homolog of DDX52 that can be readily isolated. A subset of twenty compounds was selected for further analysis based on their binding affinity and docking location. This set of compounds represents a diverse set with various ring structures and H-bonding capabilities. Binding affinities ranged from -5 to -8 kcal/mol; however, 75% of the identified compounds bind preferentially to Rok1p. Overall, one compound was found commonly among all docking locations. In vitro IC50 experiments will be presented to validate the selected inhibitor pool. Using these results, we can create a small, yet efficient library to sample the functional space of 3 unique docking locations.