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Structure-based virtual screening identifies an 8-hydroxyquinoline as a small molecule GLI1 inhibitor.

ABSTRACT: The glioma-associated family of transcription factors (GLI) have emerged as a promising therapeutic target for a variety of human cancers. In particular, GLI1 plays a central role as a transcriptional regulator for multiple oncogenic signaling pathways, including the hedgehog (Hh) signaling pathway. We undertook a computational screening approach to identify small molecules that directly bind GLI1 for potential development as inhibitors of GLI-mediated transcription. Through these studies, we identified compound 1, which is an 8-hydroxyquinoline, as a high-affinity binder of GLI1. Compound 1 inhibits GLI1-mediated transcriptional activity in several Hh-dependent cellular models, including a primary model of murine medulloblastoma. We also performed a series of computational analyses to define more clearly the mechanism(s) through which 1 inhibits GLI1 function after binding. Our results strongly suggest that binding of 1 to GLI1 does not prevent GLI1/DNA binding nor disrupt the GLI1/DNA complex, but rather, it induces specific conformational changes in the overall complex that prevent proper GLI function. These results highlight the potential of this compound for further development as an anti-cancer agent that targets GLI1.

SUBMITTER: Dash RC

PROVIDER: S-EPMC7873571 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Structure-based virtual screening identifies an 8-hydroxyquinoline as a small molecule GLI1 inhibitor.

Dash Radha Charan RC Wen Jiachen J Zaino Angela M AM Morel Shana R SR Chau Lianne Q LQ Wechsler-Reya Robert J RJ Hadden M Kyle MK

Molecular therapy oncolytics 20210116

The glioma-associated family of transcription factors (GLI) have emerged as a promising therapeutic target for a variety of human cancers. In particular, GLI1 plays a central role as a transcriptional regulator for multiple oncogenic signaling pathways, including the hedgehog (Hh) signaling pathway. We undertook a computational screening approach to identify small molecules that directly bind GLI1 for potential development as inhibitors of GLI-mediated transcription. Through these studies, we id ...[more]

PMID: 33614910

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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

Structure-based virtual screening identifies an 8-hydroxyquinoline as a small molecule GLI1 inhibitor.

Publications

Structure-based virtual screening identifies an 8-hydroxyquinoline as a small molecule GLI1 inhibitor.

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