Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors

3D-QSAR and molecular docking study on bisarylmaleimide series as glycogen synthase kinase 3, cyclin dependent kinase 2 and cyclin dependent kinase 4 inhibitors: An insight into the criteria for selectivity

Selective glycogen synthase kinase 3 (GSK3) inhibition over cyclin dependent kinases such as cyclin dependent kinase 2 (CDK2) and cyclin dependent kinase 4 (CDK4) is an important requirement for improved therapeutic profile of GSK3 inhibitors. The concepts of selectivity and additivity fields have been employed in developing selective CoMFA models for these related kinases. Initially, sets of three individual CoMFA models were developed, using 36 compounds of bisarylmaleimide series to correlate with the GSK3, CDK2 and CDK4 inhibitory potencies. These models showed a satisfactory statistical significance: CoMFA-GSK3 (r(2)(con), r(2)(cv): 0.931, 0.519), CoMFA-CDK2 (0.937, 0.563), and CoMFA-CDK4 (0.892, 0.725). Three different selective CoMFA models were then developed using differences in pIC(50) values. These three models showed a superior statistical significance: (i) CoMFA-Selective1 (r(2)(con), r(2)(cv): 0.969, 0.768), (ii) CoMFA-Selective 2 (0.974, 0.835) and (iii) CoMFA-Selective3 (0.963, 0.776). The selective models were found to outperform the individual models in terms of the quality of correlation and were found to be more informative in pinpointing the structural basis for the observed quantitative differences of kinase inhibition. An in-depth comparative investigation was carried out between the individual and selective models to gain an insight into the selectivity criterion. To further validate this approach, a set of new compounds were designed which show selectivity and were docked into the active site of GSK3, using FlexX based incremental construction algorithm.

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

A High Throughput Luminescent Assay for Glycogen Synthase Kinase-3β Inhibitors

A high throughput luminescent assay based on the Kinase-Glo() system (Promega, Madison, WI) has been developed for screening against glycogen synthase kinase-3beta (GSK-3beta). Careful optimization of assay parameters allowed us to develop a robust, reproducible, and sensitive assay. Its usefulness has been demonstrated in a high throughput screening run when screening 55,000 compounds. This campaign yielded five chemical classes of hits, including several highly potent GSK-3beta inhibitors.

Integration of virtual and physical screening

High-throughput screening (HTS) represents the dominant technique for the identification of new lead compounds in current drug discovery. It consists of physical screening (PS) of large libraries of chemicals against one or more specific biological targets. Virtual screening (VS) is a strategy for in silico evaluation of chemical libraries for a given target, and can be integrated to focus the PS process. The present work addresses the integration of both PS and VS, respectively.