Modeling the 3D structure of GPCRs from sequence

Sense of Smell: Structural, Functional, Mechanistic Advancements and Challenges in Human Olfactory Research

Olfaction, the sense of smell detects and discriminate odors as well as social cues which influence our innate responses. The olfactory system in human beings is found to be weak as compared to other animals; however, it seems to be very precise. It can detect and discriminate millions of chemical moieties (odorants) even in minuscule quantities. The process initiates with the binding of odorants to specialized olfactory receptors, encoded by a large family of Olfactory Receptor (OR) genes belonging to the G-protein-coupled receptor superfamily. Stimulation of ORs converts the chemical information encoded in the odorants, into respective neuronal action-potentials which causes depolarization of olfactory sensory neurons. The olfactory bulb relays this signal to different parts of the brain for processing. Odors are encrypted using a combinatorial approach to detect a variety of chemicals and encode their unique identity. The discovery of functional OR genes and proteins provided an important information to decipher the genomic, structural and functional basis of olfaction. ORs constitute 17 gene families, out of which 4 families were reported to contain more than hundred members each. The olfactory machinery is not limited to GPCRs; a number of non- GPCRs is also employed to detect chemosensory stimuli. The article provides detailed information about such olfaction machinery, structures, transduction mechanism, theories of odor perception, and challenges in the olfaction research. It covers the structural, functional and computational studies carried out in the olfaction research in the recent past.

Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

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.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

In silico study on binding specificity of gonadotropins and their receptors: design of a novel and selective peptidomimetic for human follicle stimulating hormone receptor

Gonadotropins bind to specific receptors in spite of sharing a high level of sequence and structural similarity. This specific binding is crucial for maintaining the reproductive health of an organism. In this study, residues that dictate the receptor binding specificity of the gonadotropins (FSH and LH) have been identified using combination of in silico methods. Docking studies (ZDOCK), based on the systematic replacement of these residues, confirmed its importance in receptor binding. An interesting observation is that the relative positioning of the residues conferring binding specificity varied for the gonadotropin-receptor complexes. This spatial difference of the key residues could be exploited for design of specific modulators. Based on the identified residues, we have rationally designed a peptidomimetic (FSHP) that displays good binding affinity and specificity for hFSHR. FSHP was developed by screening 3.9 million compounds using pharmacophore-shape similarity followed by fragment-based approach. It was observed that FSHP and hFSHâ can share the same receptor binding site thereby mimicking the native hFSHR-FSH interactions. FSHP also displayed higher binding affinity to hFSHR as compared to two reported hFSHR antagonists. MD simulation studies on hFSHR-FSHP complex revealed that FSHP is conformationally rigid and the intermolecular interactions are maintained during the course of simulation.

Membrane protein native state discrimination by implicit membrane models

Four implicit membrane models [IMM1, generalized Born (GB)-surface area-implicit membrane (GBSAIM), GB with a simple switching (GBSW), and heterogeneous dielectric GB (HDGB)] were tested for their ability to discriminate the native conformation of five membrane proteins from 450 decoys generated by the Rosetta-Membrane program. The energy ranking of the native state and Z-scores were used to assess the performance of the models. The effect of membrane thickness was examined and was found to be substantial. Quite satisfactory discrimination was achieved with the all-atom IMM1 and GBSW models at 25.4 Å thickness and with the HDGB model at 28.5 Å thickness. The energy components by themselves were not discriminative. Both van der Waals and electrostatic interactions contributed to native state discrimination, to a different extent in each model. Computational efficiency of the models decreased in the order: extended-atom IMM1 > all-atom IMM1 > GBSAIM > GBSW > HDGB. These results encourage the further development and use of implicit membrane models for membrane protein structure prediction.

What has virtual screening ever done for drug discovery?

BACKGROUND

Although virtual screening is now widely applied as a hit-finding methodology within drug discovery programmes, there are relatively few reports of its contributing to compounds on the market or in the clinic.

OBJECTIVE

To assess the impact of virtual screening on drug discovery.

METHOD

Such cases as can be found in the public domain at the current time are reviewed. Additionally, some of the current challenges in structure- and ligand-based virtual screening are discussed.

CONCLUSION

It is concluded that virtual screening has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. In terms of praxis, there is 'no free lunch' in virtual screening and as many methods as possible should be applied to maximise the likelihood of success.

Modeling of mammalian olfactory receptors and docking of odorants

Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is over-expressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

New vistas in GPCR 3D structure prediction

Human G-protein coupled receptors (hGPCRs) comprise the most prominent family of validated drug targets. More than 50% of approved drugs reveal their therapeutic effects by targeting this family. Accurate models would greatly facilitate the process of drug discovery and development. However, 3-D structure prediction of GPCRs remains a challenge due to limited availability of resolved structure. The X-ray structures have been solved for only four such proteins. The identity between hGPCRs and the potential templates is mostly less than 30%, well below the level at which sequence alignment can be done regularly. In this study, we analyze a large database of human G-protein coupled receptors that are members of family A in order to optimize usage of the available crystal structures for molecular modeling of hGPCRs. On the basis of our findings in this study, we propose to regard specific parts from the trans-membrane domains of the reference receptor helices as appropriate template for constructing models of other GPCRs, while other residues require other techniques for their remodeling and refinement. The proposed hypothesis in the current study has been tested by modeling human beta2-adrenergic receptor based on crystal structures of bovine rhodopsin (1F88) and human A2A adenosine receptor (3EML). The results have shown some improvement in the quality of the predicted models compared to Modeller software.

An overview on GPCRs and drug discovery: structure-based drug design and structural biology on GPCRs

G protein-coupled receptors (GPCRs) represent 50-60% of the current drug targets. There is no doubt that this family of membrane proteins plays a crucial role in drug discovery today. Classically, a number of drugs based on GPCRs have been developed for such different indications as cardiovascular, metabolic, neurodegenerative, psychiatric, and oncologic diseases. Owing to the restricted structural information on GPCRs, only limited exploration of structure-based drug design has been possible. Much effort has been dedicated to structural biology on GPCRs and very recently an X-ray structure of the beta2-adrenergic receptor was obtained. This breakthrough will certainly increase the efforts in structural biology on GPCRs and furthermore speed up and facilitate the drug discovery process.

Signaling by G-protein-coupled receptor (GPCR): studies on the GnRH receptor

Gonadotropin-releasing hormone (GnRH) is the first key hormone of reproduction. GnRH analogs are extensively used in in vitro fertilization, and treatment of sex hormone-dependent cancers, due to their ability to bring about 'chemical castration'. The interaction of GnRH with its cognate type I receptor (GnRHR) in pituitary gonadotropes results in the activation of Gq/G(11), phospholipase Cbeta (PLCbetaI), PLA(2), and PLD. Sequential activation of the phospholipases generates the second messengers inositol 1, 4, 5-trisphosphate (IP(3)), diacylglycerol (DAG), and arachidonic acid (AA), which are required for Ca(2+) mobilization, the activation of various protein kinase C isoforms (PKCs), and the production of prostaglandin (PG) and other metabolites of AA, respectively. PKC isoforms are the major mediators of the downstream activation of a number of mitogen-activated protein kinase (MAPK) cascades by GnRH, namely: extracellular signal-regulated kinase (ERK), jun-N-terminal kinase (JNK), and p38MAPK. The activated MAPKs phosphorylate both cytosolic and nuclear proteins to initiate the transcriptional activation of the gonadotropin subunit genes and the GnRHR. While Ca(2+) mobilization has been found to initiate rapid gonadotropin secretion, Ca(2+), together with various PKC isoforms, MAPKs and AA metabolites also serve as key nodes, in the GnRH-stimulated signaling network that enables the gonadotropes to decode GnRH pulse frequencies and translating that into differential gonadotropin synthesis and release. Even though pulsatility of GnRH is recognized as a major determinant for differential gonadotropin subunit gene expression and gonadotropin secretion very little is yet known about the signaling circuits governing GnRH action at the 'Systems Biology' level. Direct apoptotic and metastatic effects of GnRH analogs in gonadal steroid-dependent cancers expressing the GnRHR also seem to be mediated by the activation of the PKC/MAPK pathways. However, the mechanisms dictating life (pituitary) vs. death (cancer) decisions made by the same GnRHR remain elusive. Understanding these molecular mechanisms triggered by the GnRHR through biochemical and 'Systems Biology' approaches would provide the basis for the construction of the dynamic connectivity maps, which operate in the various cell types (endocrine, cancer, and immune system) targeted by GnRH. The connectivity maps will open a new vista for exploring the direct effects of GnRH analogs in tumors and the design of novel combined therapies for fertility control, reproductive disorders and cancers.

Modelling the structures of G protein-coupled receptors aided by three-dimensional validation

Background

G protein-coupled receptors (GPCRs) are abundant, activate complex signalling and represent the targets for up to ~60% of pharmaceuticals but there is a paucity of structural data. Bovine rhodopsin is the first GPCR for which high-resolution structures have been completed but significant variations in structure are likely to exist among the GPCRs. Because of this, considerable effort has been expended on developing in silico tools for refining structures of individual GPCRs. We have developed REPIMPS, a modification of the inverse-folding software Profiles-3D, to assess and predict the rotational orientation and vertical position of helices within the helix bundle of individual GPCRs. We highlight the value of the method by applying it to the Baldwin GPCR template but the method can, in principle, be applied to any low- or high-resolution membrane protein template or structure.

Results

3D models were built for transmembrane helical segments of 493 GPCRs based on the Baldwin template, and the models were then scored using REPIMPS and Profiles-3D. The compatibility scores increased significantly using REPIMPS because it takes into account the physicochemical properties of the (lipid) environment surrounding the helix bundle. The arrangement of helices in the helix bundle of the 493 models was then altered systematically by rotating the individual helices. For most GPCRs in the set, changes in the rotational position of one or more helices resulted in significant improvement in the compatibility scores. In particular, for most GPCRs, a rotation of helix VII by 240–300° resulted in improved scores. Bovine rhodopsin modelled using this method showed 3.31 Å RMSD to its crystal structure for 198 C^α atom pairs, suggesting the utility of the method even when starting with idealised structures such as the Baldwin template.

Conclusion

We have developed an in silico tool which can be used to test the validity of, and refine, models of GPCRs with respect to helix rotation and vertical position based on the physicochemical properties of amino acids and the surrounding environment. The method can be applied to any multi-pass membrane protein and potentially can be used in combination with other high-throughput methodologies to generate and refine models of membrane proteins.

Analysis of the activation mechanism of the guinea-pig Histamine H1-receptor

The Histamine H(1)-receptor (H1R), belonging to the amine receptor-class of family A of the G-protein coupled receptors (GPCRs) gets activated by agonists. The consequence is a conformational change of the receptor, which may involve the binding-pocket. So, for a good prediction of the binding-mode of an agonist, it is necessary to have knowledge about these conformational changes. Meanwhile some experimental data about the structural changes of GPCRs during activation exist. Based on homology modeling of the guinea-pig H1R (gpH1R), using the crystal structure of bovine rhodopsin as template, we performed several MD simulations with distance restraints in order to get an inactive and an active structure of the gpH1R. The calculations led to a Phe6.44/Trp6.48/Phe6.52-switch and linearization of the proline kinked transmembrane helix VI during receptor activation. Our calculations showed that the Trp6.48/Phe6.52-switch induces a conformational change in Phe6.44, which slides between transmembrane helices III and VI. Additionally we observed a hydrogen bond interaction of Ser3.39 with Asn7.45 in the inactive gpH1R, but because of a counterclockwise rotation of transmembrane helix III Ser3.39 establishes a water-mediated hydrogen bond to Asp2.50 in the active gpH1R. Additionally we simulated a possible mechanism for receptor activation with a modified LigPath-algorithm.

Structure and ligand-binding site characteristics of the human P2Y11 nucleotide receptor deduced from computational modelling and mutational analysis

The P2Y11-R (P2Y11 receptor) is a less explored drug target. We computed an hP2Y11-R (human P2Y11) homology model with two templates, bovine-rhodopsin (2.6 A resolution; 1 A=0.1 nm) and a hP2Y1-ATP complex model. The hP2Y11-R model was refined using molecular dynamics calculations and validated by virtual screening methods, with an enrichment factor of 5. Furthermore, mutational analyses of Arg106, Glu186, Arg268, Arg307 and Ala313 confirmed the adequacy of our hP2Y11-R model and the computed ligand recognition mode. The E186A and R268A mutants reduced the potency of ATP by one and three orders of magnitude respectively. The R106A and R307A mutants were functionally inactive. We propose that residues Arg106, Arg268, Arg307 and Glu186 are involved in ionic interactions with the phosphate moiety of ATP. Arg307 is possibly also H-bonded to N6 of ATP via the backbone carbonyl. Activity of ATP at the F109I mutant revealed that the proposed p-stacking of Phe109 with the adenine ring is a minor interaction. The mutation A313N, which is part of a hydrophobic pocket in the vicinity of the ATP C-2 position, partially explains the high activity of 2-MeS-ATP at P2Y1-R as compared with the negligible activity at the P2Y11-R. Inactivity of ATP at the Y261A mutant implies that Tyr261 acts as a molecular switch, as in other G-protein-coupled receptors. Moreover, analysis of cAMP responses seen with the mutants showed that the efficacy of coupling of the P2Y11-R with Gs is more variable than coupling with Gq. Our model also indicates that Ser206 forms an H-bond with Pgamma (the gamma-phosphate of the triphosphate chain of ATP) and Met310 interacts with the adenine moiety.

Pharmacophore-directed homology modeling and molecular dynamics simulation of G protein-coupled receptor: study of possible binding modes of 5-HT2C receptor agonists

A new pharmacophore-based modeling procedure, including homology modeling, pharmacophore study, flexible molecular docking, and long-time molecular dynamics (MD) simulations, was employed to construct the structure of the human 5-HT(2C) receptor and determine the characteristics of binding modes of 5-HT(2C) receptor agonists. An agonist-receptor complex has been constructed based on homology modeling and a pharmacophore hypothesis model based on some high active compounds. Then MD simulations of the ligand-receptor complex in an explicit membrane environment were carried out. The conformation of the 5-HT(2C) receptor during MD simulation was explored, and the stable binding modes of the studied agonist were determined. Flexible molecular docking of several structurally diverse agonists of the human 5-HT(2C) receptor was carried out, and the general binding modes of these agonists were investigated. According to the models presented in this work and the results of Flexi-Dock, the involvement of the amino acid residues Asp134, Ser138, Asn210, Asn331, Tyr358, Ile131, Ser132, Val135,Thr139, Ile189, Val202, Val208, Leu209, Phe214, Val215, Gly218, Ser219, Phe223, Trp324, Phe327, and Phe328 in agonist recognition was studied. The obtained binding modes of the human 5-HT(2C) receptor agonists have good agreement with the site-directed mutagenesis data and other studies.

Comparative Modeling of Drug Target Proteins

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state of the art by a number of specific examples.

Construction of human ghrelin receptor (hGHS-R1a) model using a fragmental prediction approach and validation through docking analysis

The objective of this study was to investigate the reliability of a fragmental approach to build a full-length model of the human ghrelin receptor (hGHS-R1a) in its open state. The soundness of the model was verified by docking the tetrapeptide Gly-Ser-Ser(n-octanoyl)-Phe-NH2, which represents the ghrelin active core, and a dataset of 35 peptidomimetic GH secretagogues taken from literature. Docking results confirm the relevance of two distinct subpockets: a polar cavity bearing the key residues involved in receptor activation and an aromatic/apolar subpocket, which plays a crucial role in determining the high constitutive activity of hGHS-R1a. The docking scores of both subpockets are in remarkable agreement with biological data, emphasizing that the model can be used to predict the activity of novel ligands. Moreover, the subpocket selectivity of peptidomimetic GHSs suggests a cooperative role of the aromatic/apolar subpocket. Taken globally, the results highlight the potential of the fragmental approach to build improved models for any GPCR.

Multipass membrane protein structure prediction using Rosetta

We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure of surface hydrophobic residues within the membrane and minimizing hydrophobic exposure outside of the membrane. Protein conformations are built up using the Rosetta fragment assembly method and evaluated using a new membrane-specific version of the Rosetta low-resolution energy function in which residue-residue and residue-environment interactions are functions of the membrane layer in addition to amino acid identity, distance, and density. We find that lower energy and more native-like structures are achieved by sequential addition of helices to a growing chain, which may mimic some aspects of helical protein biogenesis after translocation, rather than folding the whole chain simultaneously as in the Rosetta soluble protein prediction method. In tests on 12 membrane proteins for which the structure is known, between 51 and 145 residues were predicted with root-mean-square deviation <4 A from the native structure.

Probing a model of a GPCR/ligand complex in an explicit membrane environment: the human cholecystokinin-1 receptor

A three-dimensional model structure of a complex formed by a G-protein-coupled receptor (GPCR) and an agonist ligand is probed and refined using molecular-dynamics simulations and free energy calculations in a realistic environment. The model of the human receptor of cholecystokinin associated to agonist ligand CCK9 was obtained from a synergistic procedure combining site-directed mutagenesis experiments and in silico modeling. The 31-ns molecular-dynamics simulation in an explicit membrane environment indicates that both the structure of the receptor and its interactions with the ligand are robust. Whereas the secondary structure of the alpha-helix bundle is well preserved, the region of the intracellular loops exhibits a significant flexibility likely to be ascribed to the absence of G-protein subunits in the model. New insight into the structural features of the binding pocket is gained, in particular, the interplay of the ligand with both the receptor and internal water molecules. Water-mediated interactions are shown to participate in the binding, hence, suggesting additional site-directed mutagenesis experiments. Accurate free energy calculations on mutated ligands provide differences in the receptor-ligand binding affinity, thus offering a direct, quantitative comparison to experiment. We propose that this detailed consistency-checking procedure be used as a routine refinement step of in vacuo GPCR models, before further investigation and application to structure-based drug design.

Homology modeling of opioid receptor-ligand complexes using experimental constraints

Opioid receptors interact with a variety of ligands, including endogenous peptides, opiates, and thousands of synthetic compounds with different structural scaffolds. In the absence of experimental structures of opioid receptors, theoretical modeling remains an important tool for structure-function analysis. The combination of experimental studies and modeling approaches allows development of realistic models of ligand-receptor complexes helpful for elucidation of the molecular determinants of ligand affinity and selectivity and for understanding mechanisms of functional agonism or antagonism. In this review we provide a brief critical assessment of the status of such theoretical modeling and describe some common problems and their possible solutions. Currently, there are no reliable theoretical methods to generate the models in a completely automatic fashion. Models of higher accuracy can be produced if homology modeling, based on the rhodopsin X-ray template, is supplemented by experimental structural constraints appropriate for the active or inactive receptor conformations, together with receptor-specific and ligand-specific interactions. The experimental constraints can be derived from mutagenesis and cross-linking studies, correlative replacements of ligand and receptor groups, and incorporation of metal binding sites between residues of receptors or receptors and ligands. This review focuses on the analysis of similarity and differences of the refined homology models of mu, delta, and kappa-opioid receptors in active and inactive states, emphasizing the molecular details of interaction of the receptors with some representative peptide and nonpeptide ligands, underlying the multiple modes of binding of small opiates, and the differences in binding modes of agonists and antagonists, and of peptides and alkaloids.

Antagonist and agonist binding models of the human gonadotropin-releasing hormone receptor

G-protein-coupled receptors (GPCRs) constitute one of the most important classes of drug targets. Since the first high-resolution structure of a GPCR was determined by Palczewski and co-workers [K. Palczewski, T. Kumasaka, T. Hori, C.A. Behnke, H. Motoshima, B.A. Fox, I. Le Trong, D.C. Teller, T. Okada, R.E. Stenkamp, M. Yamamoto, M. Miyano, Crystal structure of rhodopsin: a G-protein-coupled receptor, Science 289 (2000) 739-745], development of in silico models of rhodopsin-like GPCRs could be rationally founded. In this work, we present a model of the human gonadotropin-releasing hormone receptor based on the rhodopsin structure. The transmembrane helices are modeled by homology, while the extra- and intra-cellular loops are modeled in such a way that experimentally determined interactions and microdomains (e.g., hydrophobic cores) are retained. We conclude that specifically tailored models, compared to more automatic approaches, have the benefit that known interactions are easily introduced early in the homology modeling. Furthermore, tailored models, although more tedious to construct, are better suited for drug lead finding and for compound optimization. To test the stability of the receptor, we performed a 1 ns molecular dynamics simulation. Moreover, we docked two agonists (native GnRH and Triptorelin, [dTrp(6)]-GnRH) and two antagonists (Cetrorelix, dNal(1)-dCpa(2)-dPal(3)-Ser(4)-Tyr(5)-dCit(6)-Leu(7)-Arg(8)-Pro(9)-dAla(10)), and the covalently constrained dicyclic decapeptide dicyclo(1,1'-5/4-10)[Ac-Glu(1)(Gly(1)')-dCpa(2)-dTrp(3)-Asp(4)-dbu(5)-dNal(6)-Leu(7)-Arg(8)-Pro(9)-dpr(10)-NH(2)] into the putative receptor binding site. The docked ligand conformations result in ligand-receptor interactions that are generally in good agreement with site-directed mutagenesis and ligand-binding studies presented in the literature. Our results indicate that the binding conformation of the antagonists differs from that of the agonists. This difference can be linked to the activation or inhibition of the receptor.

High-throughput modeling of human G-protein coupled receptors: amino acid sequence alignment, three-dimensional model building, and receptor library screening

The current study describes the development of a computer package (GPCRmod) aimed at the high-throughput modeling of the therapeutically important family of human G-protein coupled receptors (GPCRs). GPCRmod first proposes a reliable alignment of the seven transmembrane domains (7 TMs) of most druggable human GPCRs based on pattern/motif recognition for each of the 7 TMs that are considered independently. It then converts the alignment into knowledge-based three-dimensional (3-D) models starting from a set of 3-D backbone templates and two separate rotamer libraries for side chain positioning. The 7 TMs of 277 human GPCRs have been accurately aligned, unambiguously clustered in three different classes (rhodopsin-like, secretin-like, metabotropic glutamate-like), and converted into high-quality 3-D models at a remarkable throughput (ca. 3s/model). A 3-D GPCR target library of 277 receptors has consequently been setup. Its utility for "in silico" inverse screening purpose has been demonstrated by recovering among top scorers the receptor of a selective GPCR antagonist as well as the receptors of a promiscuous antagonist. The current GPCR target library thus constitutes a 3-D database of choice to address as soon as possible the "virtual selectivity" profile of any GPCR antagonist or inverse agonist in an early hit optimization process.

4-{(2R)-[3-Aminopropionylamido]-3-(2,4-dichlorophenyl)propionyl}-1-{2-[(2-thienyl)ethylaminomethyl]phenyl}piperazine as a potent and selective melanocortin-4 receptor antagonist--design, synthesis, and characterization

Recent studies have demonstrated that melanocortin-4 receptor (MC4R) antagonists can prevent weight loss in tumor-bearing mice, which indicates clinical usage for the treatment of cachexia. In our efforts to develop potent and selective antagonists of the human MC4R, we designed piperazinebenzylamines bearing a 2,4-dichlorophenylalanine, by utilizing information derived from structure--activity relationships of MC4R agonists and mutagenesis results of the MC4R and peptide ligands. On the basis of known MC4R agonists such 6, we successfully synthesized potent MC4R antagonists exemplified by 10, which possesses a K(i) value of 1.8 nM in binding affinity. 10 does not stimulate cAMP release in HEK 293 cells expressing the human MC4 receptor at 10 microM concentration. It was demonstrated by Schild analysis that 10 was a competitive functional antagonist with a pA(2) value of 7.9 in the inhibition of alpha-MSH-stimulated cAMP accumulation. 10 also penetrated into the brain when dosed intravenously in rats.

Antagonist binding in the rat muscarinic receptor A study by docking and X-ray crystallography

A series of agonists to the rat muscarinic receptor have been docked computationally to the active site of a homology model of rat M1 muscarinic receptor. The agonists were modelled on the X-ray crystal structure of atropine, which is reported here and the docking studies are shown to reproduce correctly the order of experimental binding affinities for the agonists as well as indicate where there appear to be inconsistencies in the experimental data. The crystal and molecular structure of atropine (tropine tropate; alpha-[hydroxymethyl]benzeneacetic acid 8-methyl[3.2.1]oct-3-yl ester C17H23NO3) has been determined by X-ray crystallography using an automated Patterson search method, and refined by full-matrix least-squares to a final R of 0.0452 for 2701 independent observed reflections and 192 parameters using Mo Kalpha radiation, lambda=0.71073A at 150K. The compound crystallises in space group Fdd2 with Z=16 molecules per unit cell.

PREDICT modeling and in-silico screening for G-protein coupled receptors

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

Molecular mechanisms of ligand binding, signaling, and regulation within the superfamily of G-protein-coupled receptors: molecular modeling and mutagenesis approaches to receptor structure and function

The superfamily of G-protein-coupled receptors (GPCRs) could be subclassified into 7 families (A, B, large N-terminal family B-7 transmembrane helix, C, Frizzled/Smoothened, taste 2, and vomeronasal 1 receptors) among mammalian species. Cloning and functional studies of GPCRs have revealed that the superfamily of GPCRs comprises receptors for chemically diverse native ligands including (1) endogenous compounds like amines, peptides, and Wnt proteins (i.e., secreted proteins activating Frizzled receptors); (2) endogenous cell surface adhesion molecules; and (3) photons and exogenous compounds like odorants. The combined use of site-directed mutagenesis and molecular modeling approaches have provided detailed insight into molecular mechanisms of ligand binding, receptor folding, receptor activation, G-protein coupling, and regulation of GPCRs. The vast majority of family A, B, C, vomeronasal 1, and taste 2 receptors are able to transduce signals into cells through G-protein coupling. However, G-protein-independent signaling mechanisms have also been reported for many GPCRs. Specific interaction motifs in the intracellular parts of these receptors allow them to interact with scaffold proteins. Protein engineering techniques have provided information on molecular mechanisms of GPCR-accessory protein, GPCR-GPCR, and GPCR-scaffold protein interactions. Site-directed mutagenesis and molecular dynamics simulations have revealed that the inactive state conformations are stabilized by specific interhelical and intrahelical salt bridge interactions and hydrophobic-type interactions. Constitutively activating mutations or agonist binding disrupts such constraining interactions leading to receptor conformations that associates with and activate G-proteins.

First principles predictions of the structure and function of g-protein-coupled receptors: validation for bovine rhodopsin

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)-one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 A coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 A CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 A CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 A CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins.

G protein-coupled receptors: in silico drug discovery in 3D

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Opioid receptor random mutagenesis reveals a mechanism for G protein-coupled receptor activation

The high resolution structure of rhodopsin has greatly enhanced current understanding of G protein-coupled receptor (GPCR) structure in the off-state, but the activation process remains to be clarified. We investigated molecular mechanisms of delta-opioid receptor activation without a preconceived structural hypothesis. Using random mutagenesis of the entire receptor, we identified 30 activating point mutations. Three-dimensional modeling revealed an activation path originating from the third extracellular loop and propagating through tightly packed helices III, VI and VII down to a VI-VII cytoplasmic switch. N- and C-terminal determinants also influence receptor activity. Findings for this therapeutically important receptor may apply to other GPCRs that respond to diffusible ligands.

Conformational Changes of G Protein‐Coupled Receptors During Their Activation by Agonist Binding

The superfamily of G protein-coupled receptors (GPCRs) is the largest and most diverse group of transmembrane proteins involved in signal transduction. Many of the over 1000 human GPCRs represent important pharmaceutical targets. However, despite high interest in this receptor family, no high-resolution structure of a human GPCR has been resolved yet. This is mainly due to difficulties in obtaining large quantities of pure and active protein. Until now, only a high-resolution x-ray structure of an inactive state of bovine rhodopsin is available. Since no structure of an active state has been solved, information of the GPCR activation process can be gained only by biophysical techniques. In this review, we first describe what is known about the ground state of GPCRs to then address questions about the nature of the conformational changes taking place during receptor activation and the mechanism controlling the transition from the resting to the active state. Finally, we will also address the question to what extent information about the three-dimensional GPCR structure can be included into pharmaceutical drug design programs.

Sequence analyses of G-protein-coupled receptors: similarities to rhodopsin

G-protein-coupled receptors (GPCRs)1 constitute a large superfamily of receptor proteins responsible for signal transduction (see http://www.gpcr.org/7tm ). Throughout all higher organisms, these receptors mediate recognition of environmental stimuli like light, odor, and taste, but also hormonal and other types of communications across plasma membranes (1 ). They are also important targets for pharmacological intervention via activating or blocking their action (2 ). Three families of GPCRs were identified, with family A being by far the largest (reviewed in refs 3 -5 ). Its members are more closely related to each other within a few functional domains than those of the other families. In addition, numerous diseases have been linked to specific mutations within the genes encoding GPCRs, also making these receptors targets for specific therapeutic interventions including gene transfer (6 -9 ).

The crystallographic model of rhodopsin and its use in studies of other G protein-coupled receptors

G protein-coupled receptors (GPCRs) are integral membrane proteins that respond to environmental signals and initiate signal transduction pathways activating cellular processes. Rhodopsin is a GPCR found in rod cells in retina where it functions as a photopigment. Its molecular structure is known from cryo-electron microscopic and X-ray crystallographic studies, and this has reshaped many structure/function questions important in vision science. In addition, this first GPCR structure has provided a structural template for studies of other GPCRs, including many known drug targets. After presenting an overview of the major structural elements of rhodopsin, recent literature covering the use of the rhodopsin structure in analyzing other GPCRs will be summarized. Use of the rhodopsin structural model to understand the structure and function of other GPCRs provides strong evidence validating the structural model.

Prediction of structure and function of G protein-coupled receptors

G protein-coupled receptors (GPCRs) mediate our sense of vision, smell, taste, and pain. They are also involved in cell recognition and communication processes, and hence have emerged as a prominent superfamily for drug targets. Unfortunately, the atomic-level structure is available for only one GPCR (bovine rhodopsin), making it difficult to use structure-based methods to design drugs and mutation experiments. We have recently developed first principles methods (MembStruk and HierDock) for predicting structure of GPCRs, and for predicting the ligand binding sites and relative binding affinities. Comparing to the one case with structural data, bovine rhodopsin, we find good accuracy in both the structure of the protein and of the bound ligand. We report here the application of MembStruk and HierDock to beta1-adrenergic receptor, endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor. We find that the predicted structure of beta1-adrenergic receptor leads to a binding site for epinephrine that agrees well with the mutation experiments. Similarly the predicted binding sites and affinities for endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor are consistent with the available experimental data. These predicted structures and binding sites allow the design of mutation experiments to validate and improve the structure and function prediction methods. As these structures are validated they can be used as targets for the design of new receptor-selective antagonists or agonists for GPCRs.