Abstract
The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.
Highlights
With scoring and ranking docking poses consensus approaches have been devised[14]
In the current study we address both hurdles in virtual screening simultaneously by applying a novel docking scoring approach for the identification of novel fragment-like G protein-coupled receptors (GPCRs) ligands and the prediction of their functional effect using GPCR crystal structures
In order to analyse to what extent the combined scoring approach was responsible for the high hit-rate of our previously reported virtual screening on the doxepin-bound (1) H1R crystal structure (PDB-code 3RZE23) we experimentally validated the compound selections for each of the individual scoring approaches (Fig. 1)
Summary
With scoring and ranking docking poses consensus approaches have been devised[14]. These consensus scoring approaches have been applied retrospectively[14,15] and prospectively[16,17,18] in several studies. In the current study we address both hurdles in virtual screening simultaneously by applying a novel docking scoring approach for the identification of novel fragment-like GPCR ligands and the prediction of their functional effect using GPCR crystal structures This docking scoring approach combines a conventional docking scoring function (ChemPLP) using PLANTS19 docking with the molecular interaction fingerprint (IFP) rescoring approach[20,21]. Retrospective virtual screening studies based on multiple different ß-adrenoceptor crystal structures allowed us to select an optimal combination of reference interaction fingerprint and protein conformation for the selective retrieval of novel, fragment-like ß2R agonists These results demonstrate the potential of structure-based prediction of GPCR ligand function by the integration of protein-ligand binding mode information
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