Molecular dynamics techniques for modeling G protein-coupled receptors

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

The investigation into the effect of the length of RGD peptides and temperature on the interaction with the αIIbβ3 integrin: a molecular dynamic study

The tripeptide Arg-Gly-Asp acid (RGD) is a protein sequence in the binding of proteins to cell surfaces, and is involved in various biological processes such as cell adhesion to the extracellular matrix, platelet activation, hemostasis, etc. The C2 domain of the Von Willebrand Factor (VWF), containing the RGD motif, plays an important role in the initial homeostasis process. It binds to the αIIbβ3 integrin and stimulates platelet aggregation. We have investigated, using the molecular Dynamic (MD) simulation method, the effect of the RGD-peptide length, and temperature variation, on the binding to the αIIbβ3 integrin receptor. We examined 10 different structural modes of the αIIbβ3 at three different temperatures; 237 K, 310 K and 318 K. Our findings show that the amino acids that form a binding pocket include Asp₂₂₄, Tyr₂₃₄, Ser₂₂₆, Tyr₁₉₀, Tyr₁₈₉, Trp₂₆₀, Trp₂₆₂, Asp_259, Lys₂₅₃, Arg₂₁₄, Asp₂₁₇, Ser₁₆₁ and Ala₂₁₈ and that the ligand-receptor interaction was increased at higher temperatures. It was also found that the increase in the number of ligands' amino acids and their types (% glycine) plays an important role in the stability, conformation, and ligand-receptor interaction.Communicated by Ramaswamy H. Sarma.

Crystal structure of a constitutive active mutant of adenosine A2A receptor

The adenosine A_2A receptor (A_2AAR) is a prototypical member of the class A subfamily of G-protein-coupled receptors (GPCRs) that is widely distributed in various tissues and organs of the human body, and participates in many important signal-regulation processes. We have previously summarized a common activation pathway of class A GPCRs in which a series of conserved residues/motifs undergo conformational change during extracellular agonist binding and finally induce the coupling of intracellular G protein. Through this mechanism we have successfully predicted several novel constitutive active or inactive mutations for A_2AAR. To reveal the molecular mechanism of mutation-induced constitutive activity, we determined the structure of a typical mutant I92N complexed with the agonist UK-432097. The mutated I92N forms a hydrophilic interaction network with nearby residues including Trp^6.48 of the CWxP motif, which is absent in wild-type A_2AAR. Although the mutant structure is similar overall to the previously determined intermediate-state A_2AAR structure (PDB ID 3qak) [Xu, Wu, Katritch, Han, Jacobson, Gao, Cherezov & Stevens (2011). Science, 332, 322-327 ▸], molecular dynamics simulations suggest that the I92N mutant stabilizes the metastable intermediate state through the hydrophilic interaction network and favors the conformational transition of the receptor towards the active state. This research provides a structural template towards the special pharmacological outcome triggered by conformational mutation and sheds light on future structural or pharmaco-logical studies among class A GPCRs.

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.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Distinct binding of cetirizine enantiomers to human serum albumin and the human histamine receptor H1

Cetirizine, a major metabolite of hydroxyzine, became a marketed second-generation H₁ antihistamine that is orally active and has a rapid onset of action, long duration of effects and a very good safety record at recommended doses. The approved drug is a racemic mixture of (S)-cetirizine and (R)-cetirizine, the latter being the levorotary enantiomer that also exists in the market as a third-generation, non-sedating and highly selective antihistamine. Both enantiomers bind tightly to the human histamine H₁ receptor (hH₁R) and behave as inverse agonists but the affinity and residence time of (R)-cetirizine are greater than those of (S)-cetirizine. In blood plasma, cetirizine exists in the zwitterionic form and more than 90% of the circulating drug is bound to human serum albumin (HSA), which acts as an inactive reservoir. Independent X-ray crystallographic work has solved the structure of the hH₁R:doxepin complex and has identified two drug-binding sites for cetirizine on equine serum albumin (ESA). Given this background, we decided to model a membrane-embedded hH₁R in complex with either (R)- or (S)-cetirizine and also the complexes of both ESA and HSA with these two enantiomeric drugs to analyze possible differences in binding modes between enantiomers and also among targets. The ensuing molecular dynamics simulations in explicit solvent and additional computational chemistry calculations provided structural and energetic information about all of these complexes that is normally beyond current experimental possibilities. Overall, we found very good agreement between our binding energy estimates and extant biochemical and pharmacological evidence. A much higher degree of solvent exposure in the cetirizine-binding site(s) of HSA and ESA relative to the more occluded orthosteric binding site in hH₁R is translated into larger positional fluctuations and considerably lower affinities for these two nonspecific targets. Whereas it is demonstrated that the two known pockets in ESA provide enough stability for cetirizine binding, only one such site does so in HSA due to a number of amino acid replacements. At the histamine-binding site in hH₁R, the distinct interactions established between the phenyl and chlorophenyl moieties of the two enantiomers with the amino acids lining up the pocket and between their free carboxylates and Lys179 in the second extracellular loop account for the improved pharmacological profile of (R)-cetirizine.

Insights Into the Explication of Potent Tyrosinase Inhibitors with Reference to Computational Studies

Background:

Pigment melanin has primarily a photo defensive role in human skin, its unnecessary production and irregular distribution can cause uneven skin tone ultimately results in hyper pigmentation. Melanin biosynthesis is initiated by tyrosine oxidation through tyrosinase, the key enzyme for melanogenesis. Not only in humans, tyrosinase is also widely distributed in plants and liable for browning of vegetables and fruits. Search for the inhibitors of tyrosinase have been an important target to facilitate development of therapies for the prevention of hyperpigmentary disorders and an undesired browning of vegetables and fruits.

Methods:

Different natural and synthetic chemical compounds have been tested as potential tyrosinase inhibitors, but the mechanism of inhibition is not known, and the quest for information regarding interaction between tyrosinase and its inhibitors is one of the recent areas of research. Computer based methods hence are useful to overcome such issues. Successful utilization of in silico tools like molecular docking simulations make it possible to interpret the tyrosinase and its inhibitor’s intermolecular interactions and helps in identification and development of new and potent tyrosinase inhibitors.

Results:

The present review has pointed out the prominent role of computer aided approaches for the explication of promising tyrosinase inhibitors with a focus on molecular docking approach. Highlighting certain examples of natural compounds whose antityrosinase effects has been evaluated using computational simulations.

Conclusion:

The investigation of new and potent inhibitors of tyrosinase using computational chemistry and bioinformatics will ultimately help millions of peoples to get rid of hyperpigmentary disorders as well as browning of fruits and vegetables.

Deciphering the Allosteric Effect of Antagonist Vismodegib on Smoothened Receptor Deactivation Using Metadynamics Simulation

The smoothened receptor (Smo) plays a key role in Hedgehog (Hh) signaling pathway and it has been regarded as an efficacious therapeutic target for basal cell carcinoma (BCC) and medulloblastoma (MB). Nevertheless, the resistance mutation and active mutants of Smo have put forward the requirement of finding more effective inhibitors. Herein, we performed metadynamics simulations on Smo bound with vismodegib (Smo-Vismod) and with cholesterol (Smo-CLR), respectively, to explore the inhibition mechanism of vismodegib. The simulation results indicated that vismodegib-induced shifts of TM5, TM6, and TM7, which permitted the extracellular extension of TM6 and extracellular loop3 (ECL3) to enter the extracellular cysteine-rich domain (CRD) groove. Therefore, an open CRD groove that has not been noticed previously was observed in Smo-Vismod complex. As a consequence, the occupied CRD groove prevents the binding of cholesterol. In addition, the HD and ECLs play crucial roles in the interaction of CRD and TMD. These results reveal that TM5, TM6, and TM7 play important roles in allosteric inhibition the activation of Smo and disrupting cholesterol binding by vismodegib binding. Our results are expected to contribute to understanding the allosteric inhibition mechanism of Smo by vismodegib. Moreover, the detailed conformational changes contribute to the development of novel Smo inhibitors against resistance mutation and active mutants of Smo.

Molecular Dynamics Simulations of Adenosine Receptors: Advances, Applications and Trends

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Adenosine receptors (ARs) are transmembrane proteins that belong to the G protein-coupled receptors (GPCRs) superfamily and mediate the biological functions of adenosine. To date, four AR subtypes are known, namely A1, A2A, A2B and A3 that exhibit different signaling pathways, tissue localization, and mechanisms of activation. Moreover, the widespread ARs and their implication in numerous physiological and pathophysiological conditions had made them pivotal therapeutic targets for developing clinically effective agents.

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The crystallographic success in identifying the 3D crystal structures of A2A and A1 ARs has dramatically enriched our understanding of their structural and functional properties such as ligand binding and signal transduction. This, in turn, has provided a structural basis for a larger contribution of computational methods, particularly molecular dynamics (MD) simulations, toward further investigation of their molecular properties and designing bioactive ligands with therapeutic potential. MD simulation has been proved to be an invaluable tool in investigating ARs and providing answers to some critical questions. For example, MD has been applied in studying ARs in terms of ligand-receptor interactions, molecular recognition, allosteric modulations, dimerization, and mechanisms of activation, collectively aiding in the design of subtype selective ligands.

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In this review, we focused on the advances and different applications of MD simulations utilized to study the structural and functional aspects of ARs that can foster the structure-based design of drug candidates. In addition, relevant literature was briefly discussed which establishes a starting point for future advances in the field of drug discovery to this pivotal group of drug targets.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

A live cell NanoBRET binding assay allows the study of ligand-binding kinetics to the adenosine A3 receptor

There is a growing interest in understanding the binding kinetics of compounds that bind to G protein-coupled receptors prior to progressing a lead compound into clinical trials. The widely expressed adenosine A₃ receptor (A₃AR) has been implicated in a range of diseases including immune conditions, and compounds that aim to selectively target this receptor are currently under development for arthritis. Kinetic studies at the A₃AR have been performed using a radiolabelled antagonist, but due to the kinetics of this probe, they have been carried out at 10 °C in membrane preparations. In this study, we have developed a live cell NanoBRET ligand binding assay using fluorescent A₃AR antagonists to measure kinetic parameters of labelled and unlabelled compounds at the A₃AR at physiological temperatures. The kinetic profiles of four fluorescent antagonists were determined in kinetic association assays, and it was found that XAC-ser-tyr-X-BY630 had the longest residence time (RT = 288 ± 62 min) at the A₃AR. The association and dissociation rate constants of three antagonists PSB-11, compound 5, and LUF7565 were also determined using two fluorescent ligands (XAC-ser-tyr-X-BY630 or AV039, RT = 6.8 ± 0.8 min) as the labelled probe and compared to those obtained using a radiolabelled antagonist ([³H]PSB-11, RT = 44.6 ± 3.9 min). There was close agreement in the kinetic parameters measured with AV039 and [³H]PSB-11 but significant differences to those obtained using XAC-S-ser-S-tyr-X-BY630. These data indicate that selecting a probe with the appropriate kinetics is important to accurately determine the kinetics of unlabelled ligands with markedly different kinetic profiles.

How Does Agonist and Antagonist Binding Lead to Different Conformational Ensemble Equilibria of the κ-Opioid Receptor: Insight from Long-Time Gaussian Accelerated Molecular Dynamics Simulation

The opioid receptors belong to the class A seven transmembrane-spanning (7TM) G protein-coupled receptors (GPCRs). The κ-opioid receptor (KOR) is a subfamily of four opioid receptors. The endogenous peptide and a variety of selective agonists and antagonists of KOR have been developed. The structurally similar ligands at the same site cause completely opposite biological functions and induce different conformational changes. To shed light on the conformation ensembles and conformational dynamics in activation and deactivation processes of KOR, we performed all-atom, long-time Gaussian accelerated molecular dynamics simulation (GaMD) on KOR binding with agonist epoxymorphinan MP1104 and antagonist JDTic, respectively. Our results revealed different conformation ensembles of KOR binding with agonist and with antagonist. Agonist binding stabilizes the active state of key motifs including DYYNM motif and CWxP motif, and biases the conformation equilibria toward the active state. Antagonist binding will not destroy inactive conformation equilibria, by keeping the stable inactive state of these crucial motifs. We found that the inactive apo form of KOR is the most stable state, while the active apo form relaxes readily to inactive state. Our results also revealed a stable intermediate (I), which is attributed to the hydrophobic interactions between Tyr246^5.58 and TM6, as well as the steric hindrance of them. Our results not only show the conformation equilibria bias of KOR by binding with agonist and antagonist, but also provide the structural information for the design and discovery of potential ligands with different functions.

Analogues of Arylamide Phenylpiperazine Ligands To Investigate the Factors Influencing D3 Dopamine Receptor Bitropic Binding and Receptor Subtype Selectivity

We have previously reported on the ability of arylamide phenylpiperazines to bind selectively to the D3 versus the D2 dopamine receptor subtype. For these studies, we used LS-3-134 as the prototypic arylamide phenylpiperazine ligand because it binds with high affinity at D3 dopamine receptor (0.17 nM) and exhibits >150-fold D3 vs D2 receptor binding selectivity. Our goal was to investigate how the composition and size of the nonaromatic ring structure at the piperazine position of substituted phenylpiperazine analogues might influence binding affinity at the human D2 and D3 dopamine receptors. Two factors were identified as being important for determining the binding affinity of bitropic arylamide phenylpiperazines at the dopamine D3 receptor subtype. One factor was the strength of the salt bridge between the highly conserved residue Asp^3.32 with the protonated nitrogen of the nonaromatic ring at the piperazine position. The second factor was the configuration of the unbound ligand in an aqueous solution. These two factors were found to be related to the logarithm of the affinities using a simple correlation model, which could be useful when designing high affinity subtype selective bitropic ligands. While this model is based upon the interaction of arylamide phenylpiperazines with the D2 and D3 D2-like dopamine receptor subtypes, it provides insights into the complexity of the factors that define a bitropic mode of the binding at GPCRs.

Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations

Metabotropic glutamate receptor 5 (mGlu₅) plays a key role in synaptic information storage and memory, which is a well-known target for a variety of psychiatric and neurodegenerative disorders. In recent years, the increasing efforts have been focused on the design of allosteric modulators, and the negative allosteric modulators (NAMs) are the front-runners. Recently, the architecture of the transmembrane (TM) domain of mGlu₅ receptor has been determined by crystallographic experiment. However, it has been not well understood how the pharmacophores of NAMs accommodated into the allosteric binding site. In this study, molecular dynamics (MD) simulations were performed on mGlu₅ receptor bound with NAMs in preclinical or clinical development to shed light on this issue. In order to identify the key residues, the binding free energies as well as per-residue contributions for NAMs binding to mGlu₅ receptor were calculated. Subsequently, the in silico site-directed mutagenesis of the key residues was performed to verify the accuracy of simulation models. As a result, the shared common features of the studied 5 clinically important NAMs (mavoglurant, dipraglurant, basimglurant, STX107, and fenobam) interacting with 11 residues in allosteric site were obtained. This comprehensive study presented a better understanding of mGlu₅ receptor NAMs binding mechanism, which would be further used as a useful framework to assess and discover novel lead scaffolds for NAMs.

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

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.

Development of novel fluorescent histamine H1-receptor antagonists to study ligand-binding kinetics in living cells

The histamine H1-receptor (H1R) is an important mediator of allergy and inflammation. H1R antagonists have particular clinical utility in allergic rhinitis and urticaria. Here we have developed six novel fluorescent probes for this receptor that are very effective for high resolution confocal imaging, alongside bioluminescence resonance energy transfer approaches to monitor H1R ligand binding kinetics in living cells. The latter technology exploits the opportunities provided by the recently described bright bioluminescent protein NanoLuc when it is fused to the N-terminus of a receptor. Two different pharmacophores (mepyramine or the fragment VUF13816) were used to generate fluorescent H1R antagonists conjugated via peptide linkers to the fluorophore BODIPY630/650. Kinetic properties of the probes showed wide variation, with the VUF13816 analogues having much longer H1R residence times relative to their mepyramine-based counterparts. The kinetics of these fluorescent ligands could also be monitored in membrane preparations providing new opportunities for future drug discovery applications.

Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time

G protein-coupled receptors (GPCRs) are integral membrane proteins and represent the largest class of drug targets. During the past decades progress in structural biology has enabled the crystallographic elucidation of the architecture of these important macromolecules. It also provided atomic-level visualization of ligand-receptor interactions, dramatically boosting the impact of structure-based approaches in drug discovery. However, knowledge obtained through crystallography is limited to static structural information. Less information is available showing how a ligand associates with or dissociates from a given receptor, whose importance is in fact increasingly recognized by the drug research community. Owing to recent advances in computer power and algorithms, molecular dynamics stimulations have become feasible that help in analyzing the kinetics of the ligand binding process. Here, we review what is currently known about the dynamics of GPCRs in the context of ligand association and dissociation, as determined through both crystallography and computer simulations. We particularly focus on the molecular basis of ligand dissociation from GPCRs and provide case studies that predict ligand dissociation pathways and residence time.

GPCRs: What Can We Learn from Molecular Dynamics Simulations?

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state.

G protein-coupled receptors (GPCRs) are a major target for drug discovery. These receptors are highly dynamic membrane proteins, and have had limited tractability using with biophysical screens that are widely adopted for globular protein targets. Thus, structure-based virtual screening (SBVS) holds great promise as a complement to physical screening for rational design of novel drugs. Indeed, the increasing number of atomic-detail GPCR X-ray crystal structures has coincided with an increase in prospective SBVS studies that have identified novel compounds. However, experimentally solved GPCR structures do not meet the full demand for SBVS, as the GPCR structural landscape is incomplete, lacking both in coverage of available GPCRs, and diversity in both receptor conformations and the chemistry of co-crystalised ligands. Here we present a novel computational GPCR binding pocket refinement method that can generate predictive GPCR/ligand complexes with improved SBVS performance. This ligand-directed modeling workflow uses parallel processing and efficient algorithms to search the GPCR/ligand conformational space faster and more efficiently than the widely used protein refinement method molecular dynamics. In this study, the resulting models are evaluated both structurally, and in retrospective SBVS. We demonstrate improved performance of refined models over their starting structures in the majority of our test cases.

Methods for the Development of In Silico GPCR Models

The Reggio group has constructed computer models of the inactive and G-protein-activated states of the cannabinoid CB1 and CB2 receptors, as well as, several orphan receptors that recognize a subset of cannabinoid compounds, including GPR55 and GPR18. These models have been used to design ligands, mutations, and covalent labeling studies. The resultant second-generation models have been used to design ligands with improved affinity, efficacy, and subtype selectivity. Herein, we provide a guide for the development of GPCR models using the most recent orphan receptor studied in our lab, GPR3. GPR3 is an orphan receptor that belongs to the Class A family of G-protein-coupled receptors. It shares high sequence similarity with GPR6, GPR12, the lysophospholipid receptors, and the cannabinoid receptors. GPR3 is predominantly expressed in mammalian brain and oocytes and it is known as a Gαs-coupled receptor activated constitutively in cells. GPR3 represents a possible target for the treatment of different pathological conditions such as Alzheimer's disease, oocyte maturation, or neuropathic pain. However, the lack of potent and selective GPR3 ligands is delaying the exploitation of this promising therapeutic target. In this context, we aim to develop a homology model that helps us to elucidate the structural determinants governing ligand-receptor interactions at GPR3. In this chapter, we detail the methods and rationale behind the construction of the GPR3 active-and inactive-state models. These homology models will enable the rational design of novel ligands, which may serve as research tools for further understanding of the biological role of GPR3.

Molecular Docking, Molecular Dynamics, and Structure-Activity Relationship Explorations of 14-Oxygenated N-Methylmorphinan-6-ones as Potent μ-Opioid Receptor Agonists

Among opioids, morphinans are of major importance as the most effective analgesic drugs acting primarily via μ-opioid receptor (μ-OR) activation. Our long-standing efforts in the field of opioid analgesics from the class of morphinans led to N-methylmorphinan-6-ones differently substituted at positions 5 and 14 as μ-OR agonists inducing potent analgesia and fewer undesirable effects. Herein we present the first thorough molecular modeling study and structure–activity relationship (SAR) explorations aided by docking and molecular dynamics (MD) simulations of 14-oxygenated N-methylmorphinan-6-ones to gain insights into their mode of binding to the μ-OR and interaction mechanisms. The structure of activated μ-OR provides an essential model for how ligand/μ-OR binding is encoded within small chemical differences in otherwise structurally similar morphinans. We reveal important molecular interactions that these μ-agonists share and distinguish them. The molecular docking outcomes indicate the crucial role of the relative orientation of the ligand in the μ-OR binding site, influencing the propensity of critical non-covalent interactions that are required to facilitate ligand/μ-OR interactions and receptor activation. The MD simulations point out minor differences in the tendency to form hydrogen bonds by the 4,5α-epoxy group, along with the tendency to affect the 3–7 lock switch. The emerged SARs reveal the subtle interplay between the substituents at positions 5 and 14 in the morphinan scaffold by enabling the identification of key structural elements that determine the distinct pharmacological profiles. This study provides a significant structural basis for understanding ligand binding and μ-OR activation by the 14-oxygenated N-methylmorphinan-6-ones, which should be useful for guiding drug design.

Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands

Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.