A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization

Abstract

BackgroundG protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors.ResultsWe present a classification of GPCRs that is purely based on their ligands, complementing sequence-based phylogenetic classifications of these receptors. Targets were hierarchically classified into phylogenetic trees, for both sequence space and ligand (substructure) space. The overall organization of the sequence-based tree and substructure-based tree was similar; in particular, the adenosine receptors cluster together as well as most peptide receptor subtypes (e.g. opioid, somatostatin) and adrenoceptor subtypes. In ligand space, the prostanoid and cannabinoid receptors are more distant from the other targets, whereas the tachykinin receptors, the oxytocin receptor, and serotonin receptors are closer to the other targets, which is indicative for ligand promiscuity. In 93% of the receptors studied, de-orphanization of a simulated orphan receptor using the ligands of related receptors performed better than random (AUC > 0.5) and for 35% of receptors de-orphanization performance was good (AUC > 0.7).ConclusionsWe constructed a phylogenetic classification of GPCRs that is solely based on the ligands of these receptors. The similarities and differences with traditional sequence-based classifications were investigated: our ligand-based classification uncovers relationships among GPCRs that are not apparent from the sequence-based classification. This will shed light on potential cross-reactivity of GPCR ligands and will aid the design of new ligands with the desired activity profiles. In addition, we linked the ligand-based classification with a ligand-focused sequence-based classification described in literature and proved the potential of this method for de-orphanization of GPCRs.

RETRACTED ARTICLE: Orphan receptor ligand discovery by pickpocketing pharmacological neighbors

“…This chemogenomics approach not only explained known ‘off-target’ effects of certain ligands, but also predicted relationships unseen by phylogeny alone. These newly-predicted relationships were experimentally validated, extending previous computational efforts to organize GPCRs by their ligand similarity 12 . Although a major advance in the field, the approach cannot identify pharmacological neighbors for orphan GPCRs because it relies upon existing ligand:receptor pairings.…”

From in silico target prediction to multi-target drug design: Current databases, methods and applications

“…Frequently, protein targets are, in the spirit of chemogenomics, classified not according to sequence or fold, but according to the similarity of their ligands. Given a ligand-based classification of protein targets, one can analyze which targets are likely to be hit by a ligand, given its structure [52,53].…”

“…(As discussed above for the ligand side, also for the protein side no single descriptor exists, so sequences, binding pockets and selected subsets of residues in ligand binding pockets have all been used to capture the essential properties of a protein for binding ligands.) In this spirit, groups have employed SVMs [54] as well as substructural analysis [53] in order to relate GPCRs from the ligand-based side -and to estimate how well ligandtarget pairings could be predicted also in cases where no ligands of a particular target are known. In the latter of the above studies [53] it was found that in those cases, 93% of the targets ligands could be identified with a reliability beyond random even if no ligands were given in the training dataset (this resembles a 'receptor deorphanization' setting in practice), thereby illustrating that not only explicitly known ligand-target pairs can be predicted by computational models.…”

Cross-Pharmacology Analysis of G Protein-Coupled Receptors

“…Private initiatives, such as BioPrint [31], Wombat [32], and MDL Drug Data Report (MDDR) [33], were pioneering in this respect. Therefore, most of the cross-pharmacology analyses reported to date were based either on internal proprietary data [25] or on those commercial databases [15, 23-26]. However, recent efforts on delivering publicly available well-crated chemogenomic databases have finally opened cross-pharmacology analyses to the entire scientific community.…”

“…This information can be exploited in the design of GPCR-directed chemical libraries with optimal coverage across its members [13]. Furthermore, these data have revealed that active GPCR ligands tend to have exceptionally high levels of target promiscuity, in particular for class A aminergic GPCRs [14-17], making chemogenomic strategies especially adequate to GPCR drug discovery [18-23]. …”