High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

publication · 1 year ago
by Laura R. Ganser, Janghyun Lee, Atul Rangadurai, Dawn K. Merriman, Megan L. Kelly, Aman D. Kansal, Bharathwaj Sathyamoorthy, Hashim M. Al-Hashimi (University of Michigan, Duke University)

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sublibraries optimized for evaluating enrichment when virtually screening a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics simulations. Ensemble-based virtual screening scores molecules with an area under the receiver operator characteristic curve of ~0.85–0.94 and with ~40–75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same molecular dynamics simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits and that the degree of enrichment is dependent on the accuracy of the ensemble.

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