Multi-step virtual screening to develop selective DYRK1A inhibitors.

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase-ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC values of several μM for DYRK1A and >100μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.