Characterizing clinically relevant natural variants of GPCRs using computational approaches

Molecular determinants of GPCR pharmacogenetics: Deconstructing the population variants in β2-adrenergic receptor

G protein-coupled receptors (GPCRs) are membrane proteins that play a central role in cell signaling and constitute one of the largest classes of drug targets. The molecular mechanisms underlying GPCR function have been characterized by several experimental and computational methods and provide an understanding of their role in physiology and disease. Population variants arising from nsSNPs affect the native function of GPCRs and have been implicated in differential drug response. In this chapter, we provide an overview on GPCR structure and activation, with a special focus on the β₂-adrenergic receptor (β₂-AR). First, we discuss the current understanding of the structural and dynamic features of the wildtype receptor. Subsequently, the population variants identified in this receptor from clinical and large-scale genomic studies are described. We show how computational approaches such as bioinformatics tools and molecular dynamics simulations can be used to characterize the variant receptors in comparison to the wildtype receptor. In particular, we discuss three examples of clinically important variants and discuss how the structure and function of these variants differ from the wildtype receptor at a molecular level. Overall, the chapter provides an overview of structure and function of GPCR variants and is a step towards the study of inter-individual differences and personalized medicine.

The role of ADRB2 gene polymorphisms in malignancies

Beta-2-adrenergic receptor is a member of the G protein-coupled receptor superfamily, which is highly expressed in most malignancies. There is increasing evidence showing that beta-2-adrenergic receptors are associated with carcinogenesis, proliferation, immune regulation, invasion, angiogenesis, clinical prognosis and treatment resistance in malignancies. Polymorphisms of the ADRB2 gene have been confirmed to be associated with transcriptional activity, mRNA translation, and beta-2-adrenergic receptor expression and sensitivity. This review discusses clinically relevant examples of single nucleotide polymorphisms of ADRB2 in malignancies and the effects of these polymorphisms on cancer susceptibility, prognosis and treatment response of cancer patients.

Loss of a water-mediated network results in reduced agonist affinity in a β2-adrenergic receptor clinical variant

The β₂-adrenergic receptor (β₂AR) is a member of the G protein-coupled receptor (GPCR) family that is an important drug target for asthma and COPD. Clinical studies coupled with biochemical data have identified a critical receptor variant, Thr164Ile, to have a reduced response to agonist-based therapy, although the molecular mechanism underlying this seemingly "non-deleterious" substitution is not clear. Here, we couple molecular dynamics simulations with network analysis and free-energy calculations to identify the molecular determinants underlying the differential drug response. We are able to identify hydration sites in the transmembrane domain that are essential to maintain the integrity of the binding site but are absent in the variant. The loss of these hydration sites in the variant correlates with perturbations in the intra-protein interaction network and rearrangements in the orthosteric ligand binding site. In conjunction, we observe an altered binding and reduced free energy of a series of agonists, in line with experimental trends. Our work identifies a functional allosteric pathway connected by specific hydration sites in β₂AR that has not been reported before and provides insight into water-mediated networks in GPCRs in general. Overall, the work is one of the first step towards developing variant-specific potent and selective agonists.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

Differential Dynamics Underlying the Gln27Glu Population Variant of the β2-Adrenergic Receptor

The β₂-adrenergic receptor (β₂AR) is a membrane-bound G-protein-coupled receptor and an important drug target for asthma. Clinical studies report that the population variant Gln27Glu is associated with a differential response to common asthma drugs, such as albuterol, isoproterenol and terbutaline. Interestingly, the 27th amino acid is positioned on the N-terminal region that is the most flexible and consequently the least studied part of the receptor. In this study, we probe the molecular origin of the differential drug binding by performing structural modeling and simulations of the wild-type (Gln) and variant (Glu) receptors followed by ensemble docking with the ligands, albuterol, isoproterenol and terbutaline. In line with clinical studies, the ligands were observed to interact preferentially with the Glu variant. Our results indicate that the Glu residue at the 27th position perturbs the network of electrostatic interactions that connects the N-terminal region to the binding site in the wild-type receptor. As a result, the Glu variant is observed to bind better to the three ligands tested in this study. Our study provides a structural basis to explain the variable drug response associated with the 27th position polymorphism in the β₂AR and is a starting step to identify genotype-specific therapeutics.

Structural bioinformatics analysis of variants on GPCR function

G protein-coupled receptors (GPCRs) are key membrane-embedded receptor proteins, with critical roles in cellular signal transduction. In the era of precision medicine, understanding the role of natural variants on GPCR function is critical, especially from a pharmacogenomics viewpoint. Studies involved in mapping variants to GPCR structures are briefly reviewed here. The endocannabinoid system involving the central nervous system (CNS), the human cannabinoid receptor 1 (CB1), is an important drug target and its variability has implications for disease susceptibility and altered drug and pain response. We have carried out a computational study to map deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) to CB1. CB1 mutations were computationally evaluated from neutral to deleterious, and the top twelve deleterious mutations, with structural information, were found to be either close to the ligand binding region or the G-protein binding site. We have mapped these to the active and inactive CB1 X-ray crystallographic structures to correlate variants with available phenotypic information. We have also carried out molecular dynamics simulations to functionally characterize four selected mutants.

Automated discovery of GPCR bioactive ligands

While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.