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High-throughput screening yields several small-molecule inhibitors of repeat-associated non-AUG translation.

ABSTRACT: Repeat-associated non-AUG (RAN) translation is a noncanonical translation initiation event that occurs at nucleotide-repeat expansion mutations that are associated with several neurodegenerative diseases, including fragile X-associated tremor ataxia syndrome (FXTAS), ALS, and frontotemporal dementia (FTD). Translation of expanded repeats produces toxic proteins that accumulate in human brains and contribute to disease pathogenesis. Consequently, RAN translation constitutes a potentially important therapeutic target for managing multiple neurodegenerative disorders. Here, we adapted a previously developed RAN translation assay to a high-throughput format to screen 3,253 bioactive compounds for inhibition of RAN translation of expanded CGG repeats associated with FXTAS. We identified five diverse small molecules that dose-dependently inhibited CGG RAN translation, while relatively sparing canonical translation. All five compounds also inhibited RAN translation of expanded GGGGCC repeats associated with ALS and FTD. Using CD and native gel analyses, we found evidence that three of these compounds, BIX01294, CP-31398, and propidium iodide, bind directly to the repeat RNAs. These findings provide proof-of-principle supporting the development of selective small-molecule RAN translation inhibitors that act across multiple disease-causing repeats.

SUBMITTER: Green KM

PROVIDER: S-EPMC6901296 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Project description:Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

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High-throughput screening yields several small-molecule inhibitors of repeat-associated non-AUG translation.

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