Sequence-derived three-dimensional pharmacophore models for G-protein-coupled receptors and their application in virtual screening.

G-protein-coupled receptors (GPCRs) comprise a large protein family of significant past and current interest of pharmaceutical research. X-ray crystallography and molecular modeling combined with site-directed mutagenesis studies suggest that most family A GPCRs share a small-molecule binding site located in the outer part of the seven-transmembrane (7TM) bundle. Here we describe an automated method to derive sequence-derived three-dimensional (3D) pharmacophore models capturing the key elements for addressing this binding site by a small-molecule ligand. We have generated structure-based pharmacophore models from 10 homology models and 3 X-ray structures of receptor-ligand complexes. These 13 pharmacophores have been dissected into 35 different single-feature pharmacophore elements, each associated with a sequence motif or chemoprint, describing its molecular interaction partner(s) in the receptor. Subsequently, the protein sequences of 270 GPCRs have been searched for the presence of chemoprints and the appropriate single-feature pharmacophores have been assembled into three- to seven-feature 3D-pharmacophore models for each human family A GPCR. These models can be applied for virtual screening and for the design of subfamily directed libraries. A case study demonstrates the successful application of this approach for the identification of potent agonists for the complement component 3a receptor 1 (C3AR1) by virtual screening.

GPCR antitarget modeling: pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects.

G protein-coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. However, the central role that the biogenic amine binding GPCRs and their ligands play in cell signaling poses a risk in new drug candidates that reveal side affinities towards these receptor sites. These candidates have the potential to interfere with the physiological signaling processes and to cause undesired effects in preclinical or clinical studies. Here, we present 3D cross-chemotype pharmacophore models for three biogenic amine antitargets: the alpha(1A) adrenergic, the 5-HT(2A) serotonin, and the D2 dopamine receptors. These pharmacophores describe the key chemical features present within these biogenic amine antagonists and rationalize the biogenic amine side affinities found for numerous new drug candidates. First applications of the alpha(1A) adrenergic receptor model reveal that these in silico tools can be used to guide the chemical optimization towards development candidates with fewer alpha(1A)-mediated side effects (for example, orthostatic hypotension) and, thus, with an improved clinical safety profile.