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Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening.

ABSTRACT: Bromodomains (BRDs) are epigenetic readers that recognize acetylated-lysine (KAc) on proteins and are implicated in a number of diseases. We describe a virtual screening approach to identify BRD inhibitors. Key elements of this approach are the extensive design and use of substructure queries to compile a set of commercially available compounds featuring novel putative KAc mimetics and docking this set for final compound selection. We describe the validation of this approach by applying it to the first BRD of BRD4. The selection and testing of 143 compounds lead to the discovery of six novel hits, including four unprecedented KAc mimetics. We solved the crystal structure of four hits, determined their binding mode, and improved their potency through synthesis and the purchase of derivatives. This work provides a validated virtual screening approach that is applicable to other BRDs and describes novel KAc mimetics that can be further explored to design more potent inhibitors.

SUBMITTER: Vidler LR

PROVIDER: S-EPMC3807807 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening.

Vidler Lewis R LR Filippakopoulos Panagis P Fedorov Oleg O Picaud Sarah S Martin Sarah S Tomsett Michael M Woodward Hannah H Brown Nathan N Knapp Stefan S Hoelder Swen S

Journal of medicinal chemistry 20131003 20

Bromodomains (BRDs) are epigenetic readers that recognize acetylated-lysine (KAc) on proteins and are implicated in a number of diseases. We describe a virtual screening approach to identify BRD inhibitors. Key elements of this approach are the extensive design and use of substructure queries to compile a set of commercially available compounds featuring novel putative KAc mimetics and docking this set for final compound selection. We describe the validation of this approach by applying it to th ...[more]

PMID: 24090311

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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.

Dataset Information

Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening.

Publications

Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening.

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