Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

Abstract

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Keywords: GPCR; cheminformatics; drug discovery; ligand-based drug design; structure-based drug design.

Figure 1

Role of Cheminformatics in the drug discovery process. Cheminformatics is involved in almost every step of the drug discovery pipeline due to the employment and analysis of available data to translate into valuable knowledge, which can in turn be used as a data for further studies.

Figure 2

Crystal structures of representative GPCR-ligand complexes from classes A, B, C, and F presenting diverse ligand-binding sites. Class A GPCRs are classified into rhodopsin (bRho, PDB ID: 2HPY) and nonrhodopsin GPCRs. The representative structures of class A nonrhodopsin GPCRs which are further subdivided into aminergic-like (β₂AR, PDB ID: 3P0G), nucleotide-like (A_2AAR, PDB ID: 3QAK), peptide-like (μ-OR, PDB ID: 5C1M), and lipid-like receptors (CB₁R, PDB ID: 5XRA) along with their bound ligands are shown. Similarly, representative structures for class B (CRF₁ [PDB ID: 4K5Y], GCGR [PDB ID: 5EE7], full-length GLP-1R [PDB ID: 5NX2], and CTR [5UZ7]), class C (mGlu₁R [PDB ID: 4OR2]), and class F (SMO [PDB ID: 4QIN] bound to negative allosteric modulator) are shown. Receptors are shown in cartoon representation and the ligands are shown as stick models with transparent surfaces. Agonists are represented as red sticks, antagonists are shown as purple sticks, and negative allosteric modulator is shown as blue stick model.

Figure 3

Examples of β2-adrenergic receptor (β2AR) orthosteric ligands with similar structures but possess different activities. (A) BI167,107 acts an agonist (PDB ID: 4LDE) (Ring et al., 2013), (B) alprenolol acts an antagonist (PDB ID: 3NYA) (Wacker et al., 2010), and (C) carazolol acts as an inverse agonist (PDB ID: 2RH1) (Cherezov et al., 2007).

Figure 4

Representative chemical structures of various GPCR modulators.

Figure 5

Overview of the typical workflow of structure-based virtual screening (SBVS).