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

Abstract

Background: G 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.

Results: We 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).

Conclusions: We 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.

Figure 1

Phylogenetic tree of human Class A GPCRs based on sequence information (44 residues of the GSK set). Human Class A GPCRs are clustered based on the 44 ligand-binding residues as defined in the GSK set. Subfamilies are color-coded according to ligand type whereby the broad ligand types applied by Gloriam et al. [17] were used. red - receptor with aminergic ligands; pink - peptide ligands; green - lipid ligands; dark blue - purinergic P2Y ligands; light blue - adenosine ligands; brown - melatonin ligands.

Figure 2

Phylogenetic tree of human Class A GPCRs based on ligand information (frequent substructure mining). Human Class A GPCRs are clustered based on the frequent substructure analysis. Subfamilies are color-coded according to ligand type whereby the broad ligand types applied by Gloriam et al. [17] were used. red - receptor with aminergic ligands; pink - peptide ligands; green - lipid ligands; dark blue - purinergic P2Y ligands; light blue - adenosine ligands; brown - melatonin ligands.

Figure 3

Delta-delta plot visualization of receptor distances in sequence and substructure space. The delta-delta plot visualizes how target distances differ between sequence-based classification (GSK set, y-axis) and substructure-based classification (x-axis). The average distance towards the other targets is plotted for sequence and substructure space. A few targets are highlighted in the plot to serve as examples. These are marked by a black dot and a label that denotes the gene symbol. Targets that are, on average, more distant from the rest are plotted further away from the origin; targets plotted above the diagonal are more distant in sequence space, while targets plotted below the diagonal are more distant in substructure space. For example, the FSH receptor (FSHR) is positioned relatively far from the origin and above the diagonal. This indicates that this receptor is, in general, more distant from the other receptors, most prominent in sequence space.

Figure 4

Examples of plotted scores for the leave-one-out validation. Example plots expressing the performance of the simulated receptor de-orphanization. Performance plots for the following receptors are provided (from left to right and from top to bottom): CHRM1 - muscarinic acetylcholine receptor M₁ (first category); AGTR1 - angiotensin receptor AT₁ (second category); P2RY1 - P2Y₁ purinoceptor (third category); BRS3 - bombesin receptor BB₃ (fourth category). These examples are discussed in the text. The full set of plotted scores is provided in Additional file 2 - Plotted scores for the leave-one-out validation. For each plot, receptors are ordered along the x-axis (labeled "Number of included receptors") in order of increasing distance in sequence space to the receptor under study. On the y-axis (labeled "Ligands identified"), the cumulative number of retrieved ligands is depicted, normalized linearly to the interval [0;1]. The red curve indicates the number of active ligands that are retrieved when including all (closest) receptors that are listed along the x-axis up to that point. For example, the plot of the muscarinic acetylcholine receptor M₁ (CHRM1) displays a steeply rising curve near the origin, indicating that many of its ligands are retrieved using a small number of closest receptors. The blue diagonal illustrates recovery of ligands when performance is equal to random prediction. The relative area under the curve (AUC) of the red curve is stated at the bottom of each plot. An AUC above 0.5 indicates good performance, while poor performance is indicated by an AUC of 0.5 or below.