Structure-based virtual screening for the discovery of natural inhibitors for human rhinovirus coat protein.

Inhibitors of the human rhinovirus (HRV) coat protein are promising candidates to treat and prevent a number of upper respiratory diseases. The aim of this study was to find antiviral compounds from nature, focusing on the HRV coat protein. Through computational structure-based screening of an in-house 3D database containing 9676 individual plant metabolites from ancient herbal medicines, combined with knowledge from traditional use, we selected sesquiterpene coumarins from the gum resin asafetida as promising natural products. Chromatographic separation steps resulted in the isolation of microlobidene (1), farnesiferol C (2), farnesiferol B (3), and kellerin (4). Determination of the inhibition of the HRV-induced cytopathic effect for serotypes 1A, 2, 14, and 16 revealed a dose-dependent and selective antirhinoviral activity against serotype 2 for asafetida (IC50 = 11.0 microg/mL) and its virtually predicted constituents 2 (IC50 = 2.5 microM) and 3 (IC50 = 2.6 microM). Modeling studies helped to rationalize the retrieved results.

High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmaco-phores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.