An Experimentally Validated Structure-Based Virtual Screening Approach to Identify Nucleotide-Binding Protein Inhibitors as a New Source of Kinase Inhibitors
Background/Objectives: Protein kinases represent major therapeutic targets because dysregulation of their phosphorylation activity is associated with several diseases, including cancer, diabetes, and inflammatory disorders. Thus, many researchers in the pharmaceutical filed are making significant effort to design potent new protein kinase inhibitors (PKIs) as potential drugs. In this context, we aimed to identify new alternatives by exploiting the chemical space defined by ligands of the nucleotide-binding protein family for the discovery of novel protein kinase inhibitors. Protein kinases bind the nucleotide adenosine triphosphate (ATP) and belong to the nucleotide-binding protein group. Methods: All ligands of the nucleotide-binding protein family, excluding known kinase inhibitors, that were identified in the ChEMBL database were used in a structure-based virtual screening approach. From this set, we aimed to identify novel nucleotide-binding protein inhibitors (NBPIs) as novel kinase inhibitors. A total of 19,709 NBPI compounds that were dissimilar to known PKIs were docked on five protein kinases, and the 200 best scoring docking poses were retained for potential purchase. Results: Only 25 compounds were commercially available in stock and were evaluated experimentally on a panel of 10 diverse protein kinases. Three NBPI compounds, one of which had originally been identified as active against the ATP-binding cassette transporter ABCG2, were identified as Haspin kinase inhibitors with micromolar activity. Conclusions: This study presents an efficient computational approach to identifying novel kinase inhibitors from a database of ligands of the nucleotide-binding protein family, and the protocol could be applied to other protein target families.