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The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed. The use of these numbering schemes has facilitated many chemogenomics studies such as consensus binding site definition, binding site comparison, ligand repurposing (e.g. for orphan receptors), sequence-based pharmacophore generation for homology modeling or virtual screening, and class-wide chemogenomics studies of GPCRs.

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

Background: G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs., Results: Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method., Conclusions: The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/.