The addition of O-linked N-acetylglucosamine (O-GlcNAc) to intracellular proteins is a dynamic and ubiquitous post-translational modification in mammalian cells. This modification is installed by the enzyme O-GlcNAc transferase (OGT) and has emerged as a major regulator of cell and organismal physiology, governing central processes such as transcription. Notably, excessive OGT activity has been linked to many of the hallmark signalling pathways of cancer that can lead to metastasis and tumor growth. There is therefore a pressing need for potent and selective inhibitors of OGT to study the role of this enzyme in cancer progression and to validate OGT inhibition as a translational therapeutic strategy. Unfortunately, the development of drug-like inhibitors of OGT for use in preclinical studies remains a major challenge, as the current suite of available inhibitors suffer from liabilities relating to their selectivity and physicochemical properties. To address this need, we performed a virtual screen for novel inhibitory scaffolds of OGT using AtomNet®, a unique artificial intelligence platform that applies deep convolutional neural networks to drug discovery. We first screened a virtual library of ~3.5 million commercially available compounds for affinity within the active site of OGT. The top 30,000 scoring hits were subsequently clustered and filtered to a final subset of deliverable compounds. A set of 96 compounds were then tested for inhibition towards OGT using a series of enzyme activity and binding assays, revealing a new inhibitory scaffold with moderate potency. This scaffold was optimized with an additional SAR-by-catalog screen, leading to the discovery of improved inhibitors with low-micromolar affinity. These inhibitors will enable the development of new tool compounds to study the role of OGT in human health and disease and to validate this potential cancer target.

Discovery of novel inhibitors of O-GlcNAc transferase through structure-based virtual screening

ACS Spring Meeting 2021

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