Hybrid Virtual Screening Research Articles

Identification of CK2 inhibitors with new scaffolds by a hybrid virtual screening approach based on Bayesian model; pharmacophore hypothesis and molecular docking

Protein kinase casein kinase 2 (CK2), a member of the serine/threonine kinase family, has been established as one of the most attractive targets for molecularly targeted cancer therapy. The discovery of CK2 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening approach based on Bayesian classification model, pharmacophore hypothesis and molecular docking was proposed and employed to identify CK2 inhibitors. We first established a naïve Bayes classification model of CK2 inhibitors/non-inhibitors and pharmacophore hypotheses of CK2 inhibitors. The docking parameters and scoring functions were also optimized in advance. The three virtual screening methods were sequentially used to screen two large chemical libraries, Specs and Enamine, for retrieving new CK2 inhibitors. Finally 30 compounds were selected from the final hits for in vitro CK2 kinase inhibitory assays. Five compounds with completely novel scaffolds showed a good inhibitory potency against CK2, which have good potentials for a future hit-to-lead optimization.

Pharmacophore Modeling and Hybrid Virtual Screening for the Discovery of Novel IκB Kinase 2 (IKK2) Inhibitors

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo8+docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.

Abstract 1346: Discovery of novel MSK1 inhibitors with hybrid virtual screening and structure-based drug design

Abstract Mitogen- and stress-activated protein kinase 1 (MSK1) is a serine/threonine kinase that is involved in gene transcription regulation and pro-inflammatory cytokine stimulation. MSK1 is an attractive target for the treatment of chronic inflammatory diseases. Currently, no small molecule inhibitor has been identified that specifically targets MSK1 and only H-89 is a known inhibitor. In this study, we performed hybrid virtual screening (HVS) to identify novel small molecule inhibitors that target MSK1. For the screening, we used the active conformation of the C-terminal kinase domain of the dual kinase as the protein template. Computational screening of several well-known compound databases, including the FDA, Natural Product, ZINC all purchasable and PDB ligands databases, revealed 40 potential leads. We tested 11 lead compounds and found 5 that showed substantial in vitro inhibitory activity against MSK1. These results illustrated that our computational screening processes greatly enhanced the success rate of virtual screening. The high-performing HVS protocols were especially successful to retrieve thousands of promising new leads from millions of compounds in conjunction with structure-based drug design and then to further select true hits from the majority of high-scoring decoys. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1346. doi:10.1158/1538-7445.AM2011-1346

Identification of novel antitubercular compounds through hybrid virtual screening approach

Growing resistance of prevalent antitubercular (antiTB) agents in clinical isolates of Mycobacterium tuberculosis (MTB) provoked an urgent need to discover novel antiTB agents. Enoyl acyl carrier protein (ACP) reductase (InhA) from Mtb is a well known and thoroughly studied as antitubucular therapy target. Here we have reported the discovery of potent antiTB agents through ligand and structure based approaches using computational tools. Initially compounds with more than 0.500 Tanimoto similarity coefficient index using functional class fingerprints (FCFP_4) to the reference chemotype were mined from the chemdiv database. Further, the molecular docking was performed to select the compounds on the basis of their binding energies, binding modes, and tendencies to form reasonable interactions with InhA (PDB ID=2NSD) protein. Eighty compounds were evaluated for antitubercular activity against H37RV M. tuberculosis strain, out of which one compound showed MIC of 5.70μM and another showed MIC of 13.85μM. We believe that these two new scaffolds might be the good starting point from hit to lead optimization for new antitubercular agents.