Virtual Screening Procedure Research Articles

Structure-based Drug Discovery Using GPCR Homology Modeling: Successful Virtual Screening for Antagonists of the Alpha1A Adrenergic Receptor

In this paper, we describe homology modeling of the alpha1A receptor based on the X-ray structure of bovine rhodopsin. The protein model has been generated by applying ligand-supported homology modeling, using mutational and ligand SAR data to guide the protein modeling procedure. We performed a virtual screening of the company's compound collection to test how well this model is suited to identify alpha1A antagonists. We applied a hierarchical virtual screening procedure guided by 2D filters and three-dimensional pharmacophore models. The ca. 23,000 filtered compounds were docked into the alpha1A homology model with GOLD and scored with PMF. From the top-ranked compounds, 80 diverse compounds were tested in a radioligand displacement assay. 37 compounds revealed K(i) values better than 10 microM; the most active compound binds with 1.4 nM to the alpha1A receptor. Our findings suggest that rhodopsin-based homology models may be used as the structural basis for GPCR lead finding and compound optimization.

Acetylcholinesterase Inhibitory Activity of Scopolin and Scopoletin Discovered by Virtual Screening of Natural Products

For the targeting selection of acetylcholinesterase (AChE) inhibitors from natural sources we generated a structure-based pharmacophore model utilizing an in silico filtering experiment for the discovery of promising candidates out of a 3D multiconformational database consisting of more than 110,000 natural products. In our study, scopoletin (1) and its glucoside scopolin (2) emerged as potential AChE inhibitors by the virtual screening procedure. They were isolated by different chromatographic methods from the medicinal plant Scopolia carniolica Jaqc. and tested in an enzyme assay using Ellman's reagent. They showed moderate, but significant, dose-dependent and long-lasting inhibitory activities. In the in vivo experiments (icv application of 2 micromol) 1 and 2 increased the extracellular acetylcholine (ACh) concentration in rat brain to about 170% and 300% compared to basal release, respectively. At the same concentration, the positive control galanthamine increased the ACh concentration to about the same level as 1. These are the first in vivo results indicating an effect of coumarins on brain ACh.

Selection, application, and validation of a set of molecular descriptors for nuclear receptor ligands.

A methodology for the selection and validation of nuclear receptor ligand chemical descriptors is described. After descriptors for a targeted chemical space were selected, a virtual screening methodology utilizing this space was formulated for the identification of potential NR ligands from our corporate collection. Using simple descriptors and our virtual screening method, we are able to quickly identify potential NR ligands from a large collection of compounds. As validation of the virtual screening procedure, an 8, 000-membered NR targeted set and a 24, 000-membered diverse control set of compounds were selected from our in-house general screening collection and screened in parallel across a number of orphan NR FRET assays. For the two assays that provided at least one hit per set by the established minimum pEC(50) for activity, the results showed a 2-fold increase in the hit-rate of the targeted compound set over the diverse set.

Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors.

A two-step, fully automatic virtual screening procedure consisting of flexible docking followed by activity prediction by COMparative BINding Energy (COMBINE) analysis is presented. This novel approach has been successfully applied, as an example with medicinal chemistry interest, to a recently reported series of 133 factor Xa (fXa)(1) inhibitors whose activities encompass 4 orders of magnitude. The docking algorithm is linked to the COMBINE analysis program and used to derive independent regression models of the 133 inhibitors docked within three different fXa structures (PDB entries 1fjs, 1f0r, and 1xka), so as to explore the effect of receptor conformation on the overall results. Reliable docking conformations and predictive regression models requiring eight latent variables could be derived for two of the fXa structures, with the best model achieving a Q(2) of 0.63 and a standard deviation of errors of prediction (SDEP) of 0.51 (leave-one-out). The two-step procedure was then employed to screen a designed virtual library of 112 ligands, containing both active and inactive compounds. While docking energies alone could show a good performance for selecting hits, including structurally diverse ones, inclusion of COMBINE analysis regression models provided improved rankings for the identification of structurally related molecules in external sets. In our best case, a recognition rate of approximately 80% of known binders at approximately 15% false positives rate was achieved, corresponding to an enrichment factor of approximately 450% over random.

Ligand-based combinatorial design of selective purinergic receptor (A2A) antagonists using self-organizing maps.

A virtual screening procedure based on a topological pharmacophore similarity metric and self-organizing maps (SOM) was developed and applied to optimizing combinatorial products functioning as P(1) purinergic receptor antagonists. The target was the human A(2A) receptor. A SOM was developed using a set of biologically tested molecules to establish a preliminary structure-activity relationship. A combinatorial library design was performed by projecting virtually assembled new molecules onto the SOM. A small focused library of 17 selected combinatorial products was synthesized and tested. On average, the designed structures yielded a 3-fold smaller binding constant ( approximately 33 vs approximately 100 nM) and 3.5-fold higher selectivity (50 vs 14) than the initial library. The most selective compound obtained revealed a 121-fold relative selectivity for A(2A) with K(i) (A(2A)) = 2.4 nM, and K(i) (A(1)) = 292 nM. This result demonstrates that it was possible to design a small, activity-enriched focused library with an improved property profile using the SOM virtual screening approach. The strategy might be particularly useful in projects in which structure-based design cannot be applied because of a lack of receptor structure information, for example, in the many projects aiming at finding new GPCR modulators.