A new pseudoreceptor modeling method (PRPS) was applied to the refinement of a homology model of the human histamine H4 receptor (H4R), the prediction of a ligand binding site, and virtual screening. Retrieval of two new H4R ligands demonstrates the biological relevance of the pseudoreceptor model and provides a means for finding new hits and leads in the early phases of drug discovery.
A computer-assisted method for the generation of pseudoreceptor models is presented together with two practical applications. From a three-dimensional alignment of known histamine H4 receptor ligands, a pseudoreceptor model of the putative ligand binding site was constructed and used for virtual screening of a large collection of commercially available compounds. Two bioactive chemotypes were retrieved, demonstrating the general applicability of the approach. The pseudoreceptor model was also used to find the putative ligand binding pocket within the transmembrane receptor domain. For each frame of a molecular dynamics simulation of a homology-based H4 receptor model, we automatically extracted potential ligand binding pockets and used their compatibility with the pseudoreceptor as a selection criterion. The best-matching pocket fits perfectly with existing mutation data and previously published hypotheses suggesting Glu1825.46 as the preferred binding partner of a positively charged moiety of H4 receptor ligands. This new pseudoreceptor approach has demonstrated its suitability for both structure-based prioritization of protein receptor models, and ligand-based virtual screening with the aim to perform scaffold hopping.