Ligand-stabilized conformational states of human beta(2) adrenergic receptor: insight into G-protein-coupled receptor activation

A comprehensive pharmacological analysis of fenoterol and its derivatives to unravel the role of β2-adrenergic receptor in zebrafish

β-adrenergic receptors (βARs) belong to a key molecular targets that regulate the most important processes occurring in the human organism. Although over the last decades a zebrafish model has been developed as a model complementary to rodents in biomedical research, the role of β₂AR in regulation of pathological and toxicological effects remains to elucidate. Therefore, the study aimed to clarify the role of β₂AR with a particular emphasis on the distinct role of subtypes A and B of zebrafish β₂AR. As model compounds selective β₂AR agonists - (R,R)-fenoterol ((R,R)-Fen) and its new derivatives: (R,R)-4'-methoxyfenoterol ((R,R)-MFen) and (R,R)-4'-methoxy-1-naphtylfenoterol ((R,R)-MNFen) - were tested. We described dose-dependent changes observed after fenoterols exposure in terms of general toxicity, cardiotoxicity and neurobehavioural responses. Subsequently, to better characterise the role of β₂-adrenergic stimulation in zebrafish, we have performed a series of molecular docking simulations. Our results indicate that (R,R)-Fen displays the highest affinity for subtype A of zebrafish β₂AR and β_2AAR might be involved in pigment depletion. (R,R)-MFen shows the lowest affinity for zebrafish β₂ARs out of the tested fenoterols and this might be associated with its cardiotoxic and anxiogenic effects. (R,R)-MNFen displays the highest affinity for subtype B of zebrafish β₂AR and modulation of this receptor might be associated with the development of malformations, increases locomotor activity and induces a negative chronotropic effect. Taken together, the presented data offer insights into the functional responses of the zebrafish β₂ARs confirming their intraspecies conservation, and support the translation of the zebrafish model in pharmacological and toxicological research.

Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

Molecular dynamics of the histamine H3 membrane receptor reveals different mechanisms of GPCR signal transduction

In this work, we studied the mechanisms of classical activation and inactivation of signal transduction by the histamine H3 receptor, a 7-helix transmembrane bundle G-Protein Coupled Receptor through long-time-scale atomistic molecular dynamics simulations of the receptor embedded in a hydrated double layer of dipalmitoyl phosphatidyl choline, a zwitterionic polysaturated ordered lipid. Three systems were prepared: the apo receptor, representing the constitutively active receptor; and two holo-receptors-the receptor coupled to the antagonist/inverse agonist ciproxifan, representing the inactive state of the receptor, and the receptor coupled to the endogenous agonist histamine and representing the active state of the receptor. An extensive analysis of the simulation showed that the three states of H3R present significant structural and dynamical differences as well as a complex behavior given that the measured properties interact in multiple and interdependent ways. In addition, the simulations described an unexpected escape of histamine from the orthosteric binding site, in agreement with the experimental modest affinities and rapid off-rates of agonists.

Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

Although the three-dimensional structures of G-protein coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence-, structure-, and dynamics-based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available data set. A blind prediction for thermostable mutations of the complement factor C5a receptor 1 retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.

Prediction of Conformation Specific Thermostabilizing Mutations for Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are highly flexible and prone to denaturation during protein extraction in detergents and purification. This poses a huge challenge to purify a conformationally homogeneous solution of GPCRs. Thermostabilizing mutations have been used widely to purify and obtain crystal structures of several GPCRs. However, identifying thermostabilizing mutations for GPCRs remains a tedious and expensive task as they are not transferable even among closely related GPCRs. Additionally, the mutations stabilizing one conformational state of a GPCR do not always stabilize other conformational state(s) of the same GPCR. Previously we developed a computational method, LiticonDesign, for rapid prediction of thermostabilizing mutations for a specific GPCR conformation. In this study, we have used LiticonDesign to predict thermostabilizing mutations for the agonist bound active-intermediate state of the human adenosine receptor (A_2AR) using the structure of the inactive state of the same GPCR and vice versa. Our study shows that the thermostable mutation predictions using LiticonDesign, for an active-intermediate state of a GPCR (A_2AR in our case), requires a homology model that is derived from an active/active-intermediate state GPCR structure as a template. Similarly, the homology models derived from inactive state GPCR conformations are better in predicting the thermostable mutations for the inactive state of A_2AR. Overall, LiticonDesign method is not only efficient in predicting thermostabilizing mutations for a given GPCR sequence but also can recover conformation specific mutations for a state of interest, if a suitable starting structure of desired conformation is chosen.

Computational design for thermostabilization of GPCRs

GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

Bioluminescence resonance energy transfer-based biosensors allow monitoring of ligand- and transducer-mediated GPCR conformational changes

G protein-coupled receptors (GPCRs) are seven-transmembrane proteins that mediate a variety of cellular response which make them a target of choice for drug development in many indications. It is now well established that GPCRs can adopt several distinct conformations that can be differentially stabilized by various ligands resulting in different biological outcomes, a concept known as functional selectivity. However, due to the highly hydrophobic nature of GPCRs, tools to monitor these conformational ensembles are limited and addressing their conformation dynamics remains a challenge with current structural biology approaches. Here we describe new bioluminescent resonance energy transfer-based biosensors that can probe the conformational rearrangement promoted by ligands with different signaling efficacies as well as the impact of transducers such as G proteins and β-arrestin on these conformational transitions. The design of such sensors for other receptors should be useful to further explore the structural determinants of GPCR functional selectivity.

Molecular pathways of oestrogen receptors and β-adrenergic receptors in cardiac cells: Recognition of their similarities, interactions and therapeutic value

Oestrogen receptors (ERs) and β-adrenergic receptors (βARs) play important roles in the cardiovascular system. Moreover, these receptors are expressed in cardiac myocytes and vascular tissues. Numerous experimental observations support the hypothesis that similarities and interactions exist between the signalling pathways of ERs (ERα, ERβ and GPR30) and βARs (β₁ AR, β₂ AR and β₃ AR). The recently discovered oestrogen receptor GPR30 shares structural features with the βARs, and this forms the basis for the interactions and functional overlap. GPR30 possesses protein kinase A (PKA) phosphorylation sites and PDZ binding motifs and interacts with A-kinase anchoring protein 5 (AKAP5), all of which enable its interaction with the βAR pathways. The interactions between ERs and βARs occur downstream of the G-protein-coupled receptor, through the G_αs and G_αi proteins. This review presents an up-to-date description of ERs and βARs and demonstrates functional synergism and interactions among these receptors in cardiac cells. We explore their signalling cascades and the mechanisms that orchestrate their interactions and propose new perspectives on the signalling patterns for the GPR30 based on its structural resemblance to the βARs. In addition, we explore the relevance of these interactions to cell physiology, drugs (especially β-blockers and calcium channel blockers) and cardioprotection. Furthermore, a receptor-independent mechanism for oestrogen and its influence on the expression of βARs and calcium-handling proteins are discussed. Finally, we highlight promising therapeutic avenues that can be derived from the shared pathways, especially the phosphatidylinositol-3-OH kinase (PI3K/Akt) pathway.

Structural Features and Ligand Selectivity for 10 Intermediates in the Activation Process of β2-Adrenergic Receptor

It has already been suggested by researchers that there should be multiple intermediate states in the activation process for G-protein-coupled receptors (GPCRs). However, the intermediate states are very short-lived and hardly captured by the experiments, leading to very limited understanding of their structural features and drug efficacies. In this work, a novel joint strategy of targeted molecular dynamics simulation, conventional molecular dynamics simulation, and virtual screening is developed to address the problems. The results from 10 intermediate conformations obtained from the work reveal that the ligand pocket is very unstable and fluctuates between the inactive state and the active one in the case of ligand-free, in particular for ECL2 as a gate-keeper of the ligand-binding. The ligand-binding site could be stable in the active state with a small volume and a completely closed ECL2, only when the G-protein-binding region is fully activated. In addition, the activations of the ligand-binding pocket and G-protein-binding site are relatively independent and exhibit a loose allosteric coupling, which contributes to the existence of multiple intermediate conformations. Interestingly, the screening performance of the agonists does not increase on increasing the overall activity of the intermediate state, but is dependent on the activated extent of the ligand pocket. The receptor is prone to bind the agonist when closing ECL2 and reducing the ligand-binding pocket volume, whereas it is more favorable for binding the antagonist when opening ECL2 and increasing the pocket volume. These observations added to previous studies could help us better understand the activation mechanism of GPCRs and provide valuable information for drug design.

Ligand-induced action of the W2866.48 rotamer toggle switch in the β2-adrenergic receptor

Studies focused on GPCRs, particularly on the β₂-adrenergic receptor (β₂-AR), have demonstrated the relationship between ligand structure, receptor conformational changes and the corresponding pharmacological outcomes. Herein, we studied the molecular details of the rotameric flip of the W286^6.48 sidechain, i.e. a presumed action switch that has not been reported in native β₂-AR thus far. It is believed that although both the 'active' and 'inactive' conformers of β₂-AR exhibit similar conformations of this switch, it may still play a substantial role in the ligand-induced activation of the receptor. By using both experimental methods (time-resolved fluorescence spectroscopy) and molecular modeling techniques (enhanced-sampling molecular dynamics), we characterized the conformational rearrangements of W286^6.48 in relation to the type of ligand present in the binding cavity and to the conformation of the receptor ('active' vs. 'inactive' β₂-AR). We found that the conformational behaviour of W286^6.48 is correlated with the pharmacological character of the ligand present in the binding cavity but not with the instantaneous conformation of the receptor. Namely, agonists promote the W286^6.48 conformations that facilitate the increase of the solvation within the inner receptor channel. In contrast, antagonists and inverse agonists act toward the decrease of the solvation in the inner channel. This creates an opportunity for using computational methodologies in determining the pharmacological properties of various ligands. The combination of the time-resolved fluorescence spectroscopy technique with the enhanced-sampling molecular dynamics simulations is shown to be a powerful tool for studying the ligand-induced conformational rearrangements in GPCRs.

Evolutionary action and structural basis of the allosteric switch controlling β2AR functional selectivity

Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the β₂-adrenergic receptor reveals three clearly distinct phenotypical clusters, showing selective impairments of either the Gi or βarrestin/endocytosis pathways with no effect on Gs activation. Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity.

Ligand-induced biased signaling is thought to result in part from ligand-specific receptor conformations that cause the engagement of distinct effectors. Here the authors trace and evaluate the impact of mutations of the β2–adrenergic receptor on multiple signaling outputs to provide structural-level insight into the determinants of GPCR functional selectivity.

Structural Elements in the Gαs and Gαq C Termini That Mediate Selective G Protein-coupled Receptor (GPCR) Signaling

Although the importance of the C terminus of the α subunit of the heterotrimeric G protein in G protein-coupled receptor (GPCR)-G protein pairing is well established, the structural basis of selective interactions remains unknown. Here, we combine live cell FRET-based measurements and molecular dynamics simulations of the interaction between the GPCR and a peptide derived from the C terminus of the Gα subunit (Gα peptide) to dissect the molecular mechanisms of G protein selectivity. We observe a direct link between Gα peptide binding and stabilization of the GPCR conformational ensemble. We find that cognate and non-cognate Gα peptides show deep and shallow binding, respectively, and in distinct orientations within the GPCR. Binding of the cognate Gα peptide stabilizes the agonist-bound GPCR conformational ensemble resulting in favorable binding energy and lower flexibility of the agonist-GPCR pair. We identify three hot spot residues (Gαs/Gαq-Gln-384/Leu-349, Gln-390/Glu-355, and Glu-392/Asn-357) that contribute to selective interactions between the β2-adrenergic receptor (β2-AR)-Gαs and V1A receptor (V1AR)-Gαq The Gαs and Gαq peptides adopt different orientations in β2-AR and V1AR, respectively. The β2-AR/Gαs peptide interface is dominated by electrostatic interactions, whereas the V1AR/Gαq peptide interactions are predominantly hydrophobic. Interestingly, our study reveals a role for both favorable and unfavorable interactions in G protein selection. Residue Glu-355 in Gαq prevents this peptide from interacting strongly with β2-AR. Mutagenesis to the Gαs counterpart (E355Q) imparts a cognate-like interaction. Overall, our study highlights the synergy in molecular dynamics and FRET-based approaches to dissect the structural basis of selective G protein interactions.

Understanding the molecular basis of agonist/antagonist mechanism of GPER1/GPR30 through structural and energetic analyses

The G-protein coupled receptors (GPCRs) represent the largest superfamily of membrane proteins in charge to pass the cell signaling after binding with their cognate ligands to the cell interior. In breast cancer, a GPCR named GPER1 plays a key role in the process of growth and the proliferation of cancer cells. In a previous study, theoretical methods were applied to construct a model of GPER1, which later was submitted to molecular dynamics (MD) simulations to perform a docking calculation. Based on this preceding work, it is known that GPER1 is sensitive to structural differences in its binding site. However, due to the nature of that past study, conformational changes linked to the ligand binding were not observed. Therefore, in this study, in order to explore the conformational changes coupled to the agonist/antagonist binding, MD simulations of about 0.25μs were performed for the free and bound states, summarizing 0.75μs of MD simulation in total. For the bound states, one agonist (G-1) and antagonist (G-15) were chosen since is widely known that these two molecules cause an impact on GPER1 mobility. Based on the conformational ensemble generated through MD simulations, we found that despite G-1 and G-15 being stabilized by similar map of residues, the structural differences between both ligands impact the hydrogen bond pattern not only at the GPER1 binding site but also along the seven-helix bundle, causing significant differences in the conformational mobility along the extracellular and cytoplasmic domain, and to a lesser degree in the curvatures of helix 2, helix 3 and helix 7 between the free and bound states, which is in agreement with reported literature, and might be linked to microscopic characteristics of the activated-inactivated transition. Furthermore, binding free energy calculations using the MM/GBSA method for the bound states, followed by an alanine scanning analysis allowed us to identify some important residues for the complex stabilization.

Molecular dynamics simulations of the free and inhibitor-bound cruzain systems in aqueous solvent: insights on the inhibition mechanism in acidic pH

The major cysteine protease of Trypanosoma cruzi, cruzain (CRZ), has been described as a therapeutic target for Chagas' disease, which affects millions of people worldwide. Thus, a series of CRZ inhibitors has been studied, including a new competitive inhibitor, Nequimed176 (NEQ176). Nevertheless, the structural and dynamic basis for CRZ inhibition remains unclear. Hoping to contribute to this ever-growing understanding of timescale dynamics in the CRZ inhibition mechanism, we have performed the first study using 100 ns of molecular dynamics (MD) simulations of two CRZ systems in an aqueous solvent under pH 5.5: CRZ in the apo form (ligand free) and CRZ complexed to NEQ176. According to the MD simulations, the enzyme adopts an open conformation in the apo form and a closed conformation in the NEQ176-CRZ complex. We also suggest that this closed conformation is related to the hydrogen-bonding interactions between NEQ176 and CRZ, which occurs through key residues, mainly Gly66, Met68, Asn69, and Leu160. In addition, the cross-correlation analysis shows evidence of the correlated motions among Ala110-Asp140, Leu160-Gly189, and Glu190-Gly215 subdomains, as well as, the movements related to Ala1-Thr59 and Asp60-Pro90 regions seem to be crucial for CRZ activity.

How Can Mutations Thermostabilize G-Protein-Coupled Receptors?

Structures of over 30 different G-protein-coupled receptors (GPCRs) have advanced our understanding of cell signaling and have provided a foundation for structure-guided drug design. This exciting progress has required the development of three complementary methods to facilitate GPCR crystallization, one of which is the thermostabilization of receptors by systematic mutagenesis. However, the reason why a particular mutation, or combination of mutations, stabilizes the receptor is not always evident from a static crystal structure. Molecular dynamics (MD) simulations have been used to identify and estimate the energetic factors that affect thermostability through comparing the dynamics of the thermostabilized receptors with structure-based models of the wild-type receptor. The data indicate that receptors are stabilized through a combination of factors, including an increase in receptor rigidity, a decrease in collective motion, reduced stress at specific residues, and the presence of ordered water molecules. Predicting thermostabilizing mutations computationally represents a major challenge for the field.

Predicted structures for kappa opioid G-protein coupled receptor bound to selective agonists

Human kappa opioid receptor (κ-OR), a G protein-coupled receptor (GPCR), has been identified as a drug target for treatment of such human disorders as pain perception, neuroendocrine physiology, affective behavior, and cognition. In order to find more selective and active agonists, one would like to do structure based drug design. Indeed, there is an X-ray structure for an antagonist bound to κ-OR, but structures for activated GPCRs are quite different from those for the inactive GPCRs. Here we predict the ensemble of 24 low-energy structures of human kappa opioid receptor (κ-OR), obtained by application of the GEnSeMBLE (GPCR Ensemble of Structures in Membrane Bilayer Environment) complete sampling method, which evaluates 13 trillion combinations of tilt and rotation angles for κ-OR to select the best 24. To validate these structures, we used the DarwinDock complete sampling method to predict the binding sites for five known agonists (ethylketocyclazocine, bremazocine, pentazocine, nalorphine, and morphine) bound to all 24 κ-OR conformations. We find that some agonists bind selectively to receptor conformations that lack the salt bridge between transmembrane domains 3 and 6 as expected for active conformations. These 3D structures for κ-OR provide a structural basis for understanding ligand binding and activation of κ-OR, which should be useful for guiding subtype specific drug design.

Residues remote from the binding pocket control the antagonist selectivity towards the corticotropin-releasing factor receptor-1

The corticotropin releasing factors receptor-1 and receptor-2 (CRF1R and CRF2R) are therapeutic targets for treating neurological diseases. Antagonists targeting CRF1R have been developed for the potential treatment of anxiety disorders and alcohol addiction. It has been found that antagonists targeting CRF1R always show high selectivity, although CRF1R and CRF2R share a very high rate of sequence identity. This has inspired us to study the origin of the selectivity of the antagonists. We have therefore built a homology model for CRF2R and carried out unbiased molecular dynamics and well-tempered metadynamics simulations for systems with the antagonist CP-376395 in CRF1R or CRF2R to address this issue. We found that the side chain of Tyr(6.63) forms a hydrogen bond with the residue remote from the binding pocket, which allows Tyr(6.63) to adopt different conformations in the two receptors and results in the presence or absence of a bottleneck controlling the antagonist binding to or dissociation from the receptors. The rotameric switch of the side chain of Tyr356(6.63) allows the breaking down of the bottleneck and is a perquisite for the dissociation of CP-376395 from CRF1R.

Deciphering the GPER/GPR30-agonist and antagonists interactions using molecular modeling studies, molecular dynamics, and docking simulations

The G-protein coupled estrogen receptor 1 GPER/GPR30 is a transmembrane seven-helix (7TM) receptor involved in the growth and proliferation of breast cancer. Due to the absence of a crystal structure of GPER/GPR30, in this work, molecular modeling studies have been carried out to build a three-dimensional structure, which was subsequently refined by molecular dynamics (MD) simulations (up to 120 ns). Furthermore, we explored GPER/GPR30's molecular recognition properties by using reported agonist ligands (G1, estradiol (E2), tamoxifen, and fulvestrant) and the antagonist ligands (G15 and G36) in subsequent docking studies. Our results identified the E2 binding site on GPER/GPR30, as well as other receptor cavities for accepting large volume ligands, through GPER/GPR30 π-π, hydrophobic, and hydrogen bond interactions. Snapshots of the MD trajectory at 14 and 70 ns showed almost identical binding motifs for G1 and G15. It was also observed that C107 interacts with the acetyl oxygen of G1 (at 14 ns) and that at 70 ns the residue E275 interacts with the acetyl group and with the oxygen from the other agonist whereas the isopropyl group of G36 is oriented toward Met141, suggesting that both C107 and E275 could be involved in the protein activation. This contribution suggest that GPER1 has great structural changes which explain its great capacity to accept diverse ligands, and also, the same ligand could be recognized in different binding pose according to GPER structural conformations.

Understanding the conformation transition in the activation pathway of β2 adrenergic receptor via a targeted molecular dynamics simulation

The conformation transition in the activation pathway of β2 adrenergic receptor was explored mainly using a target molecular dynamics simulation.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Understanding the effects on constitutive activation and drug binding of a D130N mutation in the β2 adrenergic receptor via molecular dynamics simulation

G-protein-coupled receptors (GPCRs) are currently one of the largest families of drug targets. The constitutive activation induced by mutation of key GPCR residues is associated closely with various diseases. However, the structural basis underlying such activation and its role in drug binding has remained unclear. Herein, we used all-atom molecular dynamics simulations and free energy calculations to study the effects of a D130N mutation on the structure of β2 adrenergic receptor (β2AR) and its binding of the agonist salbutamol. The results indicate that the mutation caused significant changes in some key helices. In particular, the mutation leads to the departure of transmembrane 3 (TM3) from transmembrane 6 (TM6) and marked changes in the NPxxY region as well as the complete disruption of a key ionic lock, all of which contribute to the observed constitutive activation. In addition, the D130N mutation weakens some important H-bonds, leading to structural changes in these regions. Binding free energy calculations indicate that van der Waals and electrostatic interactions are the main driving forces in binding salbutamol; however, binding strength in the mutant β2AR is significantly enhanced mainly through modifying electrostatic interactions. Further analysis revealed that the increase in binding energy upon mutation stems mainly from the H-bonds formed between the hydroxyl group of salbutamol and the serine residues of TM5. This observation suggests that modifications of the H-bond groups of this drug could significantly influence drug efficacy in the treatment of diseases associated with this mutation.

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are highly dynamic and often denature when extracted in detergents. Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious. We have developed a computational method to predict the position of the thermostabilizing mutations for a given GPCR sequence. We have validated the method against experimentally measured thermostability data for single mutants of the β₁-adrenergic receptor (β₁AR), adenosine A_2A receptor (A_2AR) and neurotensin receptor 1 (NTSR1). To make these predictions we started from homology models of these receptors of varying accuracies and generated an ensemble of conformations by sampling the rigid body degrees of freedom of transmembrane helices. Then, an all-atom force field function was used to calculate the enthalpy gain, known as the “stability score” upon mutation of every residue, in these receptor structures, to alanine. For all three receptors, β₁AR, A_2AR, and NTSR1, we observed that mutations of hydrophobic residues in the transmembrane domain to alanine that have high stability scores correlate with high experimental thermostability. The prediction using the stability score improves when using an ensemble of receptor conformations compared to a single structure, showing that receptor flexibility is important. We also find that our previously developed LITiCon method for generating conformation ensembles is similar in performance to predictions using ensembles obtained from microseconds of molecular dynamics simulations (which is computationally hundred times slower than LITiCon). We improved the thermostability prediction by including other properties such as residue-based stress and the extent of allosteric communication by each residue in the stability score. Our method is the first step toward a computational method for rapid prediction of thermostable mutants of GPCRs.

Computational method to identify druggable binding sites that target protein-protein interactions

Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.

Modeling G protein-coupled receptors in complex with biased agonists

The biological response to the activation of G protein-coupled receptors (GPCRs) typically originates from the simultaneous modulation of various signaling pathways that lead to distinct biological consequences. Hence, 'biased agonists' (i.e., compounds that selectively activate one of the pathways while blocking the others) are highly sought-after molecules to provide fine-tuned pharmacological interventions. This review describes strategies that can be deployed to model the conformation of GPCRs in complex with ligands endowed with specific signaling profiles useful for the generation of hypotheses on the structural requirements for the activation of different signaling pathways or for rational computer-aided ligand discovery campaigns. In particular, it focuses on strategies potentially applicable to model the global or local conformational states of GPCRs stabilized by specific ligands.

Structure and dynamics of G-protein coupled receptors

G-protein coupled receptors (GPCRs) are seven helical transmembrane proteins that mediate cell-to-cell communication. They also form the largest superfamily of drug targets. Hence detailed studies of the three dimensional structure and dynamics are critical to understanding the functional role of GPCRs in signal transduction pathways, and for drug design. In this chapter we compare the features of the crystal structures of various biogenic amine receptors, such as β1 and β2 adrenergic receptors, dopamine D3 receptor, M2 and M3 muscarinic acetylcholine receptors. This analysis revealed that conserved residues are located facing the inside of the transmembrane domain in these GPCRs improving the efficiency of packing of these structures. The NMR structure of the chemokine receptor CXCR1 without any ligand bound, shows significant dynamics of the transmembrane domain, especially the helical kink angle on the transmembrane helix6. The activation mechanism of the β2-adrenergic receptor has been studied using multiscale computational methods. The results of these studies showed that the receptor without any ligand bound, samples conformations that resemble some of the structural characteristics of the active state of the receptor. Ligand binding stabilizes some of the conformations already sampled by the apo receptor. This was later observed in the NMR study of the dynamics of human β2-adrenergic receptor. The dynamic nature of GPCRs leads to a challenge in obtaining purified receptors for biophysical studies. Deriving thermostable mutants of GPCRs has been a successful strategy to reduce the conformational heterogeneity and stabilize the receptors. This has lead to several crystal structures of GPCRs. However, the cause of how these mutations lead to thermostability is not clear. Computational studies are beginning to shed some insight into the possible structural basis for the thermostability. Molecular Dynamics simulations studying the conformational ensemble of thermostable mutants have shown that the stability could arise from both enthalpic and entropic factors. There are regions of high stress in the wild type GPCR that gets relieved upon mutation conferring thermostability.

Computational study on the different ligands induced conformation change of β2 adrenergic receptor-Gs protein complex

β₂ adrenergic receptor (β₂AR) regulated many key physiological processes by activation of a heterotrimeric GTP binding protein (Gs protein). This process could be modulated by different types of ligands. But the details about this modulation process were still not depicted. Here, we performed molecular dynamics (MD) simulations on the structures of β₂AR-Gs protein in complex with different types of ligands. The simulation results demonstrated that the agonist BI-167107 could form hydrogen bonds with Ser203^5.42, Ser207^5.46 and Asn293^6.55 more than the inverse agonist ICI 118,551. The different binding modes of ligands further affected the conformation of β₂AR. The energy landscape profiled the energy contour map of the stable and dissociated conformation of Gαs and Gβγ when different types of ligands bound to β₂AR. It also showed the minimum energy pathway about the conformational change of Gαs and Gβγ along the reaction coordinates. By using interactive essential dynamics analysis, we found that Gαs and Gβγ domain of Gs protein had the tendency to separate when the inverse agonist ICI 118,551 bound to β₂AR. The α5-helix had a relatively quick movement with respect to transmembrane segments of β₂AR when the inverse agonist ICI 118,551 bound to β₂AR. Besides, the analysis of the centroid distance of Gαs and Gβγ showed that the Gαs was separated from Gβγ during the MD simulations. Our results not only could provide details about the different types of ligands that induced conformational change of β₂AR and Gs protein, but also supplied more information for different efficacies of drug design of β₂AR.

Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

Critical analysis of the successes and failures of homology models of G protein-coupled receptors

We present a critical assessment of the performance of our homology model refinement method for G protein-coupled receptors (GPCRs), called LITICon that led to top ranking structures in a recent structure prediction assessment GPCRDOCK2010. GPCRs form the largest class of drug targets for which only a few crystal structures are currently available. Therefore, accurate homology models are essential for drug design in these receptors. We submitted five models each for human chemokine CXCR4 (bound to small molecule IT1t and peptide CVX15) and dopamine D3DR (bound to small molecule eticlopride) before the crystal structures were published. Our models in both CXCR4/IT1t and D3/eticlopride assessments were ranked first and second, respectively, by ligand RMSD to the crystal structures. For both receptors, we developed two types of protein models: homology models based on known GPCR crystal structures, and ab initio models based on the prediction method MembStruk. The homology-based models compared better to the crystal structures than the ab initio models. However, a robust refinement procedure for obtaining high accuracy structures is needed. We demonstrate that optimization of the helical tilt, rotation, and translation is vital for GPCR homology model refinement. As a proof of concept, our in-house refinement program LITiCon captured the distinct orientation of TM2 in CXCR4, which differs from that of adrenoreceptors. These findings would be critical for refining GPCR homology models in future.

New concepts in pharmacological efficacy at 7TM receptors: IUPHAR review 2

The present-day concept of drug efficacy has changed completely from its original description as the property of agonists that causes tissue activation. The ability to visualize the multiple behaviours of seven transmembrane receptors has shown that drugs can have many efficacies and also that the transduction of drug stimulus to various cellular stimulus-response cascades can be biased towards some but not all pathways. This latter effect leads to agonist 'functional selectivity', which can be favourable for the improvement of agonist therapeutics. However, in addition, biased agonist potency becomes cell type dependent with the loss of the monotonic behaviour of stimulus-response mechanisms, leading to potential problems in agonist quantification. This has an extremely important effect on the discovery process for new agonists since it now cannot be assumed that a given screening or lead optimization assay will correctly predict therapeutic behaviour. This review discusses these ideas and how new approaches to quantifying agonist effect may be used to circumvent the cell type dependence of agonism. This article, written by a corresponding member of the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR), reviews our current understanding of the interaction of ligands with seven transmembrane receptors. Further information on these pharmacological concepts is being incorporated into the IUPHAR/BPS database GuideToPharmacology.org.

Identifying ligand binding conformations of the β2-adrenergic receptor by using its agonists as computational probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β(2)-adrenergic receptor (β(2)AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β(2)AR. We show that the select ligands bind preferentially to different predicted conformers of β(2)AR, and identify a role of β(2)AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance.

Critical hydrogen bond formation for activation of the angiotensin II type 1 receptor

G protein-coupled receptors contain selectively important residues that play central roles in the conformational changes that occur during receptor activation. Asparagine 111 (N111(3.35)) is such a residue within the angiotensin II type 1 (AT(1)) receptor. Substitution of N111(3.35) for glycine leads to a constitutively active receptor, whereas substitution for tryptophan leads to an inactivable receptor. Here, we analyzed the AT(1) receptor and two mutants (N111G and N111W) by molecular dynamics simulations, which revealed a novel molecular switch involving the strictly conserved residue D74(2.50). Indeed, D74(2.50) forms a stable hydrogen bond (H-bond) with the residue in position 111(3.35) in the wild-type and the inactivable receptor. However, in the constitutively active mutant N111G-AT(1) receptor, residue D74 is reoriented to form a new H-bond with another strictly conserved residue, N46(1.50). When expressed in HEK293 cells, the mutant N46G-AT(1) receptor was poorly activable, although it retained a high binding affinity. Interestingly, the mutant N46G/N111G-AT(1) receptor was also inactivable. Molecular dynamics simulations also revealed the presence of a cluster of hydrophobic residues from transmembrane domains 2, 3, and 7 that appears to stabilize the inactive form of the receptor. Whereas this hydrophobic cluster and the H-bond between D74(2.50) and W111(3.35) are more stable in the inactivable N111W-AT(1) receptor, the mutant N111W/F77A-AT(1) receptor, designed to weaken the hydrophobic core, showed significant agonist-induced signaling. These results support the potential for the formation of an H-bond between residues D74(2.50) and N46(1.50) in the activation of the AT(1) receptor.

Emerging concepts and approaches for chemokine-receptor drug discovery

IMPORTANCE OF THE FIELD

Chemokine receptors are most noted for their role in cell migration. However, inappropriate utilization or regulation of these receptors is implicated in many inflammatory diseases, cancer and HIV, making them important drug targets.

AREAS COVERED IN THIS REVIEW

Allostery, oligomerization and ligand bias are presented as they pertain to chemokine receptors and their associated pathologies.Specific examples of each are described from the recent literature and their implications are discussed in terms of drug discovery efforts targeting chemokine receptors.

WHAT THE READER WILL GAIN

Insight into the expanding view of the multitude of pharmacological variables that need to be considered or that may be exploited in chemokine receptor drug discovery.

TAKE HOME MESSAGE

Since 2007, two drugs targeting chemokine receptors have been approved by the FDA, Maraviroc for preventing HIV infection and Mozobil™ for hematopoietic stem cell mobilization. While these successes permit optimism for chemokine receptors as drug targets, only recently has the complexity of this system begun to be appreciated. The concepts of allosteric inhibitors, biased ligands and functional selectivity raise the possibility that drugs with precisely-defined properties can be developed. Other complexities such as receptor oligomerization and tissue-specific functional states of receptors also offer opportunities for increased target and response specificity, although it will be more challenging to translate these ideas into approved therapeutics compared to traditional approaches.

LITiCon: a discrete conformational sampling computational method for mapping various functionally selective conformational states of transmembrane helical proteins

G-Protein-coupled receptors (GPCRs) are seven helical transmembrane proteins that mediate cell signaling thereby controlling many important physiological and pathological functions. GPCRs get activated upon ligand binding and trigger the signal transduction process. GPCRs exist in multiple inactive and active conformations, and there is a finite population of the active and inactive states even in the ligand-free condition. An understanding of the nature of the conformational ensemble sampled by GPCRs and the atomic level mechanism of the conformational transitions require a combination of computational methods and experimental techniques. We have developed a coarse grained discrete conformational sampling computational method called "LITiCon" to map the conformational ensemble sampled by GPCRs in the presence and absence of ligands. The LITiCon method can also be used to predict functional selective conformational states starting from the inactive state of the receptor. LITiCon has been applied to map the conformational ensemble of β2-adrenergic receptor, a class A GPCR. We have shown that β2-adrenergic receptor samples a larger conformational space in the ligand-free state and that different ligands select and stabilize conformations from this ensemble. In this review we describe the LITiCon method in detail and elucidate the uses and pitfalls of this method.

Action of molecular switches in GPCRs--theoretical and experimental studies

G protein coupled receptors (GPCRs), also called 7TM receptors, form a huge superfamily of membrane proteins that, upon activation by extracellular agonists, pass the signal to the cell interior. Ligands can bind either to extracellular N-terminus and loops (e.g. glutamate receptors) or to the binding site within transmembrane helices (Rhodopsin-like family). They are all activated by agonists although a spontaneous auto-activation of an empty receptor can also be observed. Biochemical and crystallographic methods together with molecular dynamics simulations and other theoretical techniques provided models of the receptor activation based on the action of so-called "molecular switches" buried in the receptor structure. They are changed by agonists but also by inverse agonists evoking an ensemble of activation states leading toward different activation pathways. Switches discovered so far include the ionic lock switch, the 3-7 lock switch, the tyrosine toggle switch linked with the nPxxy motif in TM7, and the transmission switch. The latter one was proposed instead of the tryptophan rotamer toggle switch because no change of the rotamer was observed in structures of activated receptors. The global toggle switch suggested earlier consisting of a vertical rigid motion of TM6, seems also to be implausible based on the recent crystal structures of GPCRs with agonists. Theoretical and experimental methods (crystallography, NMR, specific spectroscopic methods like FRET/BRET but also single-molecule-force-spectroscopy) are currently used to study the effect of ligands on the receptor structure, location of stable structural segments/domains of GPCRs, and to answer the still open question on how ligands are binding: either via ensemble of conformational receptor states or rather via induced fit mechanisms. On the other hand the structural investigations of homoand heterodimers and higher oligomers revealed the mechanism of allosteric signal transmission and receptor activation that could lead to design highly effective and selective allosteric or ago-allosteric drugs.

Molecular determinants of ligand binding at the human histamine H1 receptor: Site-directed mutagenesis results analyzed with ligand docking and molecular dynamics studies at H1 homology and crystal structure models

The human histamine H₁ G-protein coupled receptor (GPCR) is an important drug target for inflammatory, sleep, and other neuropsychiatric disorders. To delineate molecular determinants for ligand binding for drug discovery purposes, human H₁ receptor models were built by homology to the crystal structure of the human β₂ adrenoceptor (β₂AR) and from the recently reported crystal structure of the human H₁ receptor complex with doxepin at 3.1 Å (PDB code 3RZE). Ligand affinity of histamine and the H₁ antagonists mepyramine and (2S, 4R)-(-)-trans-4-phenyl-2-N, N-dimethylaminotetralin (PAT) at wild type and point-mutated (D3.32A, Y3.33A, W4.56A, F5.47A, W6.48A, Y6.51A, F6.52A, F6.55A, Y7.43A) human H₁ receptors were determined experimentally and results analyzed by ligand docking and molecular dynamic studies at WT and point-mutated H₁ receptor models. Differences in ligand binding affinities correlated to differences in ligand binding modes at models built according to homology or crystal structure, indicating, both models are accurate templates for predicting ligand affinity for H₁ drug design.

Receptor conformations involved in dopamine D(2L) receptor functional selectivity induced by selected transmembrane-5 serine mutations

Although functional selectivity is now widely accepted, the molecular basis is poorly understood. We have studied how aspects of transmembrane region 5 (TM5) of the dopamine D(2L) receptor interacts with three rationally selected rigid ligands (dihydrexidine, dinapsoline, and dinoxyline) and the reference compounds dopamine and quinpirole. As was expected from homology modeling, mutation of three TM5 serine residues to alanine (S5.42A, S5.43A, S5.46A) had little effect on antagonist affinity. All three mutations decreased the affinity of the agonist ligands to different degrees, S5.46A being somewhat less affected. Four functions [adenylate cyclase (AC), extracellular signal-regulated kinase 1/2 phosphorylation (MAPK), arachidonic acid release (AA), and guanosine 5'-O-(3-thio)triphosphate binding (GTPγS)] were assessed. The intrinsic activity (IA) of quinpirole was unaffected by any of the mutations, whereas S5.42A and S5.46A mutations abolished the activity of dopamine and the three rigid ligands, although dihydrexidine retained IA at MAPK function only with S5.42A. Remarkably, S5.43A did not markedly affect IA for AC and MAPK for any of the ligands and eliminated AA activity for dinapsoline and dihydrexidine but not dinoxyline. These data suggest that this mutation did not disrupt the overall conformation or signaling ability of the mutant receptors but differentially affected ligand activation. Computational studies indicate that these D(2) agonists stabilize multiple receptor conformations. This has led to models showing the stabilized conformations and interhelical and receptor-ligand contacts corresponding to the different activation pathways stabilized by various agonists. These data provide a basis for understanding D(2L) functional selectivity and rationally discovering functionally selective D(2) drugs.

Structure prediction of G protein-coupled receptors and their ensemble of functionally important conformations

G protein-coupled receptors (GPCRs) are integral membrane proteins whose "pleiotropic" nature enables transmembrane (TM) signal transduction, amplification, and diversification via G protein-coupled and β arrestin-coupled pathways. GPCRs appear to enable this by being structurally flexible and by existing in different conformational states with potentially different signaling and functional consequences. We describe a method for the prediction of the three-dimensional structures of these different conformations of GPCRs starting from their amino acid sequence. It combines a unique protocol of computational methods that first predict the TM regions of these receptors and TM helix shapes based on those regions, which is followed by a locally complete sampling of TM helix packings and their scoring that results in a few (~10-20) lowest energy conformations likely to play a role in binding to different ligands and signaling events. Prediction of the structures for multiple conformations of a GPCR is starting to enable the testing of multiple hypotheses related to GPCR activation and binding to ligands with different signaling profiles.

GPCR agonist binding revealed by modeling and crystallography

Despite recent progress in structural coverage of the G-protein-coupled receptor (GPCR) family, high plasticity of these membrane proteins poses additional challenges for crystallographic studies of their complexes with different classes of ligands, especially agonists. The ability to predict computationally the binding of natural and clinically relevant agonists and corresponding changes in the receptor pocket, starting from inactive GPCR structures, is therefore of great interest for understanding GPCR biology and drug action. Comparison of computational models published in 2009 and 2010 with recently determined agonist-bound structures of β-adrenergic and adenosine A(2A) receptors reveals high accuracy of the predicted agonist binding poses (0.8 Å and 1.7 Å respectively) and receptor interactions. In the case of the β(2)AR, energy-based models with limited backbone flexibility have also allowed characterization of side-chain rotations and a finite backbone shift in the pocket region as determinants of full, partial or inverse agonism. Development of accurate models of agonist binding for other GPCRs will be instrumental for functional and pharmacological studies, complementing biochemical and crystallographic techniques.

Homology model and docking studies on porcine β₂ adrenoceptor: description of two binding sites

The affinity of the classical β(2) adrenoceptor-selective inverse agonist ICI118,551 is notoriously lower for porcine β(2) adrenoceptors (p(2)βAR) than for human β(2) adrenoceptors (hβ(2)AR) but molecular mechanisms for this difference are still unclear. Homology 3-D models of pβ(2)AR can be useful in predicting similarities and differences, which might in turn increase the comparative understanding of ligand interactions with the hβ(2)AR. In this work, the pβ(2)AR amino acid sequence was used to carry out homology modeling. The selected pβ(2)AR 3-D structure was structurally and energetically optimized and used as a model for further theoretical study. The homology model of pβ(2)AR has a 3-D structure very similar to the crystal structures of recently studied hβ(2)AR. This was also corroborated by sequence identity, RMSD, Ramachandran map, TM-score and docking results. Upon performing molecular docking simulations with the AutoDock4.0.1 program on pβ(2)AR, it was found that a set of well-known β(2)AR ligands reach two distinct binding sites on pβ(2)AR. Whereas one of these sites is similar to that reported on the hβ(2)AR crystal structure, the other can explain some important experimental observations. Additionally, the theoretical affinity estimated for ICI118,551 closely agrees with affinities estimated from experimental in vitro data. The experimental differences between the human/porcine β(2)ARs in relation to ligand affinity can in part be elucidated by observations in this molecular modeling study.

Developing chemical genetic approaches to explore G protein-coupled receptor function: validation of the use of a receptor activated solely by synthetic ligand (RASSL)

Molecular evolution and chemical genetics have been applied to generate functional pairings of mutated G protein-coupled receptors (GPCRs) and nonendogenous ligands. These mutant receptors, referred to as receptors activated solely by synthetic ligands (RASSLs) or designer receptors exclusively activated by designer drugs (DREADDs), have huge potential to define physiological roles of GPCRs and to validate receptors in animal models as therapeutic targets to treat human disease. However, appreciation of ligand bias and functional selectivity of different ligands at the same receptor suggests that RASSLs may signal differently than wild-type receptors activated by endogenous agonists. We assessed this by generating forms of wild-type human M(3) muscarinic receptor and a RASSL variant that responds selectively to clozapine N-oxide. Although the RASSL receptor had reduced affinity for muscarinic antagonists, including atropine, stimulation with clozapine N-oxide produced effects very similar to those generated by acetylcholine at the wild-type M(3)-receptor. Such effects included the relative movement of the third intracellular loop and C-terminal tail of intramolecular fluorescence resonance energy transfer sensors and the ability of the wild type and evolved mutant to regulate extracellular signal-regulated kinase 1/2 phosphorylation. Each form interacted similarly with β-arrestin 2 and was internalized from the cell surface in response to the appropriate ligand. Furthermore, the pattern of phosphorylation of specific serine residues within the evolved receptor in response to clozapine N-oxide was very similar to that produced by acetylcholine at the wild type. Such results provide confidence that, at least for the M(3) muscarinic receptor, results obtained after transgenic expression of this RASSL are likely to mirror the actions of acetylcholine at the wild type receptor.