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

Abstract

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 sub-libraries optimized for evaluating enrichment when virtually screening (VS) a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics (MD) simulations. Ensemble-based VS 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 MD 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.