Pharmacophore modeling, docking and molecular dynamics simulation for identification of novel human protein kinase C beta (PKCβ) inhibitors

Protein kinase C beta (PKC beta) is considered as an attractive molecular target for the treatment of COVID-19-related acute respiratory distress syndrome (ARDS). Several classes of inhibitors have been already identified. In this article, we developed and validated ligand-based PKC beta pharmacophore models based on the chemical structures of the known inhibitors. The most accurate pharmacophore model, which correctly predicted more than 70% active compounds of test set, included three aromatic pharmacophore features without vectors, one hydrogen bond acceptor pharmacophore feature, one hydrophobic pharmacophore feature and 158 excluded volumes. This pharmacophore model was used for virtual screening of compound collection in order to identify novel potent PKC beta inhibitors. Also, molecular docking of compound collection was performed and 28 compounds which were selected simultaneously by two approaches as top-scored were proposed for further biological research.

Automated summary

In this article, ligand-based PKC beta pharmacophore models were developed and validated for identifying novel potent PKC beta inhibitors. Molecular docking and virtual screening were performed to select 28 top-scored compounds for further biological research.