Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1.

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ID: 84602
2020
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Abstract
Polo-like kinase 1 (PLK1) is a key regulator of mitosis and a recognized drug target for cancer therapy. Inhibiting the polo-box domain of PLK1 offers potential advantages of increased selectivity and subsequently reduced toxicity compared with targeting the kinase domain. However, many if not all existing polo-box domain inhibitors have been shown to be unsuitable for further development. In this paper, we describe a novel compound series, which inhibits the protein-protein interactions of PLK1 via the polo-box domain. We combine high throughput screening with molecular modeling and computer-aided design, synthetic chemistry, and cell biology to address some of the common problems with protein-protein interaction inhibitors, such as solubility and potency. We use molecular modeling to improve the solubility of a hit series with initially poor physicochemical properties, enabling biophysical and biochemical characterization. We isolate and characterize enantiomers to improve potency and demonstrate on-target activity in both cell-free and cell-based assays, entirely consistent with the proposed binding model. The resulting compound series represents a promising starting point for further progression along the drug discovery pipeline and a new tool compound to study kinase-independent PLK functions.
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huggins2020developmentacs Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Huggins, David J;Hardwick, Bryn S;Sharma, Pooja;Emery, Amy;Laraia, Luca;Zhang, Fengzhi;Narvaez, Ana J;Roberts-Thomson, Meredith;Crooks, Alex T;Boyle, Robert G;Boyce, Richard;Walker, David W;Mateu, Natalia;McKenzie, Grahame J;Spring, David R;Venkitaraman, Ashok R;
Journal ACS omega
Year 2020
DOI
10.1021/acsomega.9b03626
URL
Keywords
antimicrobial peptides; machine learning; support vector machine; random forest; artificial neural network

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