In silico (or virtual) screening has become a common practice in current computer-aided drug design efforts. However, application to hit discovery in the G Protein-Coupled Receptors (GPCRs) arena was until recently hampered by the paucity of crystal structures available for this important class of pharmaceutical targets, forcing practitioners in the field to rely on GPCR models derived either ab initio or through homology modeling approaches. In this work we describe the EPIX in silico screening workflow which consists of the following stages: (1) Target modeling; (2) Preparation of screening library; (3) Docking; (4) Binding mode selection; (5) Scoring; (6) Consensus scoring and (7) Selection of virtual hits. This workflow was applied to the virtual screening of 13 GPCRs (5 biogenic amine receptors, 5 peptide receptors, 1 lipid receptor, 1 purinergic receptor and 1 cannabinoid receptor). Hit rates vary between 4% and 21% with higher hit rates usually obtained for biogenic amines and lower hits rates for peptide receptors. These data are analyzed in the context of the available experimental information (i.e., mutational data), the model (i.e., binding site location, and type of interactions) and the screening library. Specific examples are discussed in more detail as well as the future directions and challenges of this approach to in silico screening.
Keywords: GPCRs, PREDICT, In silico screening, virtual screening, structure-based drug design, docking
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