Bihelix: Towards de novo structure prediction of an ensemble of G-protein coupled receptor conformations

Discovery of 3-Phenyl Indazole-Based Novel Chemokine-like Receptor 1 Antagonists for the Treatment of Psoriasis

Chemokine-like receptor 1 (CMKLR1)─a G protein-coupled receptor─has functional roles in the immune system and related diseases, including psoriasis and metabolic diseases. Psoriasis is a chronic inflammatory disease characterized by skin redness, scaliness, and itching. In this study, we sought to develop novel CMKLR1 antagonists by screening our in-house GPCR-targeting compound library. Moreover, we optimized a phenylindazole-based hit compound with antagonistic activities and evaluated its oral pharmacokinetic properties in a murine model. A structure-based design on the human CMKLR1 homology model identified S-26d as an optimized compound that serves as a potent and orally available antagonist with a pIC50 value of 7.44 in hCMKLR1-transfected CHO cells. Furthermore, in the imiquimod-induced psoriasis-like mouse model, oral administration of S-26d for 1 week significantly alleviated modified psoriasis area and severity index scores (severity of erythema, scaliness, skin thickness) compared with the control group.

Predicted structure and cell signaling of TAS2R14 reveal receptor hyper-flexibility for detecting diverse bitter tastes

The 25 human bitter taste receptors (TAS2Rs) are expressed on taste and extra-oral cells representing an integrated chemosensory system. The archetypal TAS2R14 is activated by > 150 topographically diverse agonists, raising the question of how this uncharacteristic accommodation is achieved for these GPCRs. We report the computationally derived structure of TAS2R14 with binding sites and energies for five highly diverse agonists. Remarkably, the binding pocket is the same for all five agonists. The energies derived from molecular dynamics are consistent with experiments determining signal transduction coefficients in live cells. TAS2R14 accommodates agonists through the breaking of a TMD3 H-bond instead of the prototypic strong salt bridge, a TMD1,2,7 interaction different from Class A GPCRs, and agonist-promoted TMD3 salt bridges for high affinity (which we confirmed by receptor mutagenesis). Thus, the broadly tuned TAS2Rs accommodate diverse agonists via a single (vs multiple) binding pocket through unique TM interactions for sensing disparate micro-environments.

Predicted Three-Dimensional Structure of the GCR1 Putative GPCR in Arabidopsis thaliana and Its Binding to Abscisic Acid and Gibberellin A1

GCR1 has been proposed as a plant analogue to animal G-protein-coupled receptors that can promote or regulate several physiological processes by binding different phytohormones. For instance, abscisic acid (ABA) and gibberellin A1 (GA1) have been shown to promote or regulate germination and flowering, root elongation, dormancy, and biotic and abiotic stresses, among others. They may act through binding to GCR1, which would put GCR1 at the heart of key signaling processes of agronomic importance. Unfortunately, this GPCR function has yet to be fully validated due to the lack of an X-ray or cryo-EM 3D atomistic structure for GCR1. Here, we used the primary sequence data from Arabidopsis thaliana and the GEnSeMBLE complete sampling method to examine 13 trillion possible packings of the 7 transmembrane helical domains corresponding to GCR1 to downselect an ensemble of 25 configurations likely to be accessible to the binding of ABA or GA1. We then predicted the best binding sites and energies for both phytohormones to the best GCR1 configurations. To provide the basis for the experimental validation of our predicted ligand-GCR1 structures, we identify several mutations that should improve or weaken the interactions. Such validations could help establish the physiological role of GCR1 in plants.

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.

Predicted Structure of Fully Activated Tas1R3/1R3' Homodimer Bound to G Protein and Natural Sugars: Structural Insights into G Protein Activation by a Class C Sweet Taste Homodimer with Natural Sugars

The Tas1R3 G protein-coupled receptor constitutes the main component of sweet taste sensory response in humans via forming a heterodimer with Tas1R2 or a homodimer with Tas1R3. The Tas1R3/1R3' homodimer serves as a low-affinity sweet taste receptor, stimulating gustducin G protein (GGust) signaling in the presence of a high concentration of natural sugars. This provides an additional means to detect the taste of natural sugars, thereby differentiating the flavors between natural sugars and artificial sweeteners. We report here the predicted 3D structure of active state Tas1R3/1R3' homodimer complexed with heterotrimeric GGust and sucrose. We discovered that the GGust makes ionic anchors to intracellular loops 1 and 2 of Tas1R3 while the Gα-α5 helix engages the cytoplasmic region extensively through salt bridge and hydrophobic interactions. We show that in the activation of this complex the Venus flytrap domains of the homodimer undergo a remarkable twist up to ∼100° rotation around the vertical axis to adopt a closed-closed conformation while the intracellular region relaxes to an open-open conformation. We find that binding of sucrose to the homodimer stabilizes a preactivated conformation with a largely open intracellular region that recruits and activates the GGust. Upon activation, the Gα subunit spontaneously opens up the nucleotide-binding site, making nucleotide exchange facile for signaling. This activation of GGust promotes the interdomain twist of the Venus flytrap domains. These structures and transformations could potentially be a basis for the design of new sweeteners with higher activity and less unpleasant flavors.

Structures and Agonist Binding Sites of Bitter Taste Receptor TAS2R5 Complexed with Gi Protein and Validated against Experiment

Bitter taste receptors (TAS2Rs) function in taste perception, but are also expressed in many extraoral tissues, presenting attractive therapeutic targets. TAS2R5s expressed on human airway smooth muscle cells can induce bronchodilation for treating asthma and other obstructive diseases. But TAS2R5s display low agonist affinity and the lack of a 3D structure has hindered efforts to design more active ligands. We report the structure of the activated TAS2R5 coupled to the Gi protein and bound to each of 19 agonists, using computational approaches. These agonists bind to two polar residues in TM3 that are unique for TAS2R5 among 25 TAS2R subtypes. Our predicted results correlate well with experimental results of agonist-receptor signaling coefficients, providing validation of the predicted structure. These results provide highly specific data on how agonists activate TAS2R5, how modifications of ligand structure alter receptor activation, and a guide to structure-based drug design.

Predicted structure of fully activated human bitter taste receptor TAS2R4 complexed with G protein and agonists

Bitter taste is sensed by bitter taste receptors (TAS2Rs) that belong to the G protein-coupled receptor (GPCR) superfamily. In addition to bitter taste perception, TAS2Rs have been reported recently to be expressed in many extraoral tissues and are now known to be involved in health and disease. Despite important roles of TAS2Rs in biological functions and diseases, no crystal structure is available to help understand the signal transduction mechanism or to help develop selective ligands as new therapeutic targets. We report here the three-dimensional structure of the fully activated TAS2R4 human bitter taste receptor predicted using the GEnSeMBLE complete sampling method. This TAS2R4 structure is coupled to the gustducin G protein and to each of several agonists. We find that the G protein couples to TAS2R4 by forming strong salt bridges to each of the three intracellular loops, orienting the activated Gα5 helix of the Gα subunit to interact extensively with the cytoplasmic region of the activated receptor. We find that the TAS2Rs exhibit unique motifs distinct from typical Class A GPCRs, leading to a distinct activation mechanism and a less stable inactive state. This fully activated bitter taste receptor complex structure provides insight into the signal transduction mechanism and into ligand binding to TAS2Rs.

The 3D Structure of Human DP Prostaglandin G-Protein-Coupled Receptor Bound to Cyclopentanoindole Antagonist, Predicted Using the DuplexBiHelix Modification of the GEnSeMBLE Method

Prostaglandins play a critical physiological role in both cardiovascular and immune systems, acting through their interactions with 9 prostanoid G protein-coupled receptors (GPCRs). These receptors are important therapeutic targets for a variety of diseases including arthritis, allergies, type 2 diabetes, and cancer. The DP prostaglandin receptor is of interest because it has unique structural and physiological properties. Most notably, DP does not have the 3-6 ionic lock common to Class A GPCRs. However, the lack of X-ray structures for any of the 9 prostaglandin GPCRs hampers the application of structure-based drug design methods to develop more selective and active medications to specific receptors. We predict here 3D structures for the DP prostaglandin GPCR, based on the GEnSeMBLE complete sampling with hierarchical scoring (CS-HS) methodology. This involves evaluating the energy of 13 trillion packings to finally select the best 20 that are stable enough to be relevant for binding to antagonists, agonists, and modulators. To validate the predicted structures, we predict the binding site for the Merck cyclopentanoindole (CPI) selective antagonist docked to DP. We find that the CPI binds vertically in the 1-2-7 binding pocket, interacting favorably with residues R310^7.40 and K76^2.54 with additional interactions with S313^7.43, S316^7.46, S19^1.35, etc. This binding site differs significantly from that of antagonists to known Class A GPCRs where the ligand binds in the 3-4-5-6 region. We find that the predicted binding site leads to reasonable agreement with experimental Structure-Activity Relationship (SAR). We suggest additional mutation experiments including K76^2.54, E129^3.49, L123^3.43, M270^6.40, F274^6.44 to further validate the structure, function, and activation mechanism of receptors in the prostaglandin family. Our structures and binding sites are largely consistent and improve upon the predictions by Li et al. ( J. Am. Chem. Soc. 2007 , 129 ( 35 ), 10720 ) that used our earlier MembStruk prediction methodology.

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.

Identify High-Quality Protein Structural Models by Enhanced K-Means

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.

Activation mechanism of the G protein-coupled sweet receptor heterodimer with sweeteners and allosteric agonists

The sweet taste in humans is mediated by the TAS1R2/TAS1R3 G protein-coupled receptor (GPCR), which belongs to the class C family that also includes the metabotropic glutamate and γ-aminobutyric acid receptors. We report here the predicted 3D structure of the full-length TAS1R2/TAS1R3 heterodimer, including the Venus Flytrap Domains (VFDs) [in the closed-open (co) active conformation], the cysteine-rich domains (CRDs), and the transmembrane domains (TMDs) at the TM56/TM56 interface. We observe that binding of agonists to VFD2 of TAS1R2 leads to major conformational changes to form a TM6/TM6 interface between TMDs of TAS1R2 and TAS1R3, which is consistent with the activation process observed biophysically on the metabotropic glutamate receptor 2 homodimer. We find that the initial effect of the agonist is to pull the bottom part of VFD3/TAS1R3 toward the bottom part of VFD2/TAS1R2 by ∼6 Å and that these changes get transmitted from VFD2 of TAS1R2 (where agonists bind) through the VFD3 and the CRD3 to the TMD3 of TAS1R3 (which couples to the G protein). These structural transformations provide a detailed atomistic mechanism for the activation process in GPCR, providing insights and structural details that can now be validated through mutation experiments.

Molecular-Docking-Based Drug Design and Discovery

Currently 30-50% of drug targets are G Protein-Coupled Receptors (GPCRs). However, the clinical useful drugs for targeting GPCR have been limited by the lack of subtype selectivity or efficacy, leading to undesirable side effects. To develop subtype-selective GPCR ligands with desired molecular properties, better understanding is needed of the pharmacophore elements and of the binding mechanism required for subtype selectivity. To illustrate these issues, we describe here three successful applications to understand the binding mechanism associated with subtype selectivity: 5-HT2B (5-Hydroxytryptamine, 5-HT) serotonin receptor (HT2BR), H3 histamine receptor (H3HR) and A3 adenosine receptor (A3AR). The understanding of structure-function relationships among individual types and subtypes of GPCRs gained from such computational predictions combined with experimental validation and testing is expected the development of new highly selective and effective ligands to address such diseases while minimizing side-effects.

Computational Prediction and Biochemical Analyses of New Inverse Agonists for the CB1 Receptor

Human cannabinoid type 1 (CB1) G-protein coupled receptor is a potential therapeutic target for obesity. The previously predicted and experimentally validated ensemble of ligand-free conformations of CB1 [Scott, C. E. et al. Protein Sci. 2013 , 22 , 101 - 113 ; Ahn, K. H. et al. Proteins 2013 , 81 , 1304 - 1317] are used here to predict the binding sites for known CB1-selective inverse agonists including rimonabant and its seven known derivatives. This binding pocket, which differs significantly from previously published models, is used to identify 16 novel compounds expected to be CB1 inverse agonists by exploiting potential new interactions. We show experimentally that two of these compounds exhibit inverse agonist properties including inhibition of basal and agonist-induced G-protein coupling activity, as well as an enhanced level of CB1 cell surface localization. This demonstrates the utility of using the predicted binding sites for an ensemble of CB1 receptor structures for designing new CB1 inverse agonists.

The predicted ensemble of low-energy conformations of human somatostatin receptor subtype 5 and the binding of antagonists

Human somatostatin receptor subtype 5 (hSSTR5) regulates cell proliferation and hormone secretion. However, the identification of effective therapeutic small-molecule ligands is impeded because experimental structures are not available for any SSTR subtypes. Here, we predict the ensemble of low-energy 3D structures of hSSTR5 using a modified GPCR Ensemble of Structures in Membrane BiLayer Environment (GEnSeMBLE) complete sampling computational method. We find that this conformational ensemble displays most interhelical interactions conserved in class A G protein-coupled receptors (GPCRs) plus seven additional interactions (e.g., Y2.43-D3.49, T3.38-S4.53, K5.64-Y3.51) likely conserved among SSTRs. We then predicted the binding sites for a series of five known antagonists, leading to predicted binding energies consistent with experimental results reported in the literature. Molecular dynamics (MD) simulation of 50 ns in explicit water and lipid retained the predicted ligand-bound structure and formed new interaction patterns (e.g. R3.50-T6.34) consistent with the inactive μ-opioid receptor X-ray structure. We suggest more than six mutations for experimental validation of our prediction. The final predicted receptor conformations and antagonist binding sites provide valuable insights for designing new small-molecule drugs targeting SSTRs.

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.

Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of "modeling first, refinement next". In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human β2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of "refined restraints first, modeling next", provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

Predicted 3D structures of olfactory receptors with details of odorant binding to OR1G1

Olfactory receptors (ORs) are responsible for mediating the sense of smell; they allow humans to recognize an enormous number of odors but the connection between binding and perception is not known. We predict the ensemble of low energy structures for the human OR1G1 (hOR1G1) and also for six other diverse ORs, using the G protein-coupled receptor Ensemble of Structures in Membrane BiLayer Environment complete sampling method that samples 13 trillion different rotations and tilts using four different templates to predict the 24 structures likely to be important in binding and activation. Our predicted most stable structures of hOR1G1 have a salt-bridge between the conserved D3.49 and K6.30 in the D(E)RY region, that we expect to be associated with an inactive form. The hOR1G1 structure also has specific interaction in transmembrane domains (TMD) 3-6 (E3.39 and H6.40), which is likely an important conformational feature for all hORs because of the ~94 to 98 % conservation among all hOR sequences. Of the five ligands studied (nonanal, 9-decen-1-ol, 1-nonanol, camphor, and n-butanal), we find that the 4 expected to bind lead to similar binding energies with nonanol the strongest.

Molecular basis for dramatic changes in cannabinoid CB1 G protein-coupled receptor activation upon single and double point mutations

There is considerable interest in determining the activation mechanism of G protein-coupled receptors (GPCRs), one of the most important types of proteins for intercellular signaling. Recently, it was demonstrated for the cannabinoid CB1 GPCR, that a single mutation T210A could make CB1 completely inactive whereas T210I makes it essentially constitutively active. To obtain an understanding of this dramatic dependence of activity on mutation, we used first-principles-based methods to predict the ensemble of low-energy seven-helix conformations for the wild-type (WT) and mutants (T210A and T210I). We find that the transmembrane (TM) helix packings depend markedly on these mutations, leading for T210A to both TM3+TM6 and TM2+TM6 salt-bridge couplings in the cytoplasmic face that explains the inactivity of this mutant. In contrast T210I has no such couplings across the receptor explaining the ease in activating this mutant. WT has just the TM3+TM6 coupling, known to be broken upon GPCR activation. To test this hypothesis on activity, we predicted double mutants that would convert the inactive mutant to normal activity and then confirmed this experimentally. This CB1 activation mechanism, or one similar to it, is expected to play a role in other constitutively active GPCRs as well.

Ligand- and mutation-induced conformational selection in the CCR5 chemokine G protein-coupled receptor

We predicted the structural basis for pleiotropic signaling of the C-C chemokine type 5 (CCR5) G protein-coupled receptor (GPCR) by predicting the binding of several ligands to the lower-energy conformations of the CCR5 receptor and 11 mutants. For each case, we predicted the ∼ 20 most stable conformations for the receptor along with the binding sites for four anti-HIV ligands. We found that none of the ligands bind to the lowest-energy apo-receptor conformation. The three ligands with a similar pharmacophore (Maraviroc, PF-232798, and Aplaviroc) bind to a specific higher-energy receptor conformation whereas TAK-779 (with a different pharmacophore) binds to a different high-energy conformation. This result is in agreement with the very different binding-site profiles for these ligands obtained by us and others. The predicted Maraviroc binding site agrees with the recent structure of CCR5 receptor cocrystallized with Maraviroc. We performed 11 site-directed mutagenesis experiments to validate the predicted binding sites. Here, we independently predicted the lowest 10 mutant protein conformations for each of the 11 mutants and then docked the ligands to these lowest conformations. We found the predicted binding energies to be in excellent agreement with our mutagenesis experiments. These results show that, for GPCRs, each ligand can stabilize a different protein conformation, complicating the use of cocrystallized structures for ligand screening. Moreover, these results show that a single-point mutation in a GPCR can dramatically alter the available low-energy conformations, which in turn alters the binding site, potentially altering downstream signaling events. These studies validate the conformational selection paradigm for the pleiotropic function and structural plasticity of GPCRs.

Predicted ligands for the human urotensin-II G protein-coupled receptor with some experimental validation

Human Urotensin-II (U-II) is the most potent mammalian vasoconstrictor known.1 Thus, a U-II antagonist would be of therapeutic value in a number of cardiovascular disorders.2 Here, we describe our work on the prediction of the structure of the human U-II receptor (hUT2 R) using GEnSeMBLE (GPCR Ensemble of Structures in Membrane BiLayer Environment) complete sampling Monte Carlo method. With the validation of our predicted structures, we designed a series of new potential antagonists predicted to bind more strongly than known ligands. Next, we carried out R-group screening to suggest a new ligand predicted to bind with 7 kcal mol(-1) better energy than 1-{2-[4-(2-bromobenzyl)-4-hydroxypiperidin-1-yl]ethyl}-3-(thieno[3,2-b]pyridin-7-yl)urea, the designed antagonist predicted to have the highest affinity for the receptor. Some of these predictions were tested experimentally, validating the computational results. Using the pharmacophore generated from the predicted structure for hUT2 R bound to ACT-058362, we carried out virtual screening based on this binding site. The most potent hit compounds identified contained 2-(phenoxymethyl)-1,3,4-thiadiazole core, with the best derivative exhibiting an IC50 value of 0.581 μM against hUT2 R when tested in vitro. Our efforts identified a new scaffold as a potential new lead structure for the development of novel hUT2 R antagonists, and the computational methods used could find more general applicability to other GPCRs.

SuperBiHelix method for predicting the pleiotropic ensemble of G-protein-coupled receptor conformations

There is overwhelming evidence that G-protein-coupled receptors (GPCRs) exhibit several distinct low-energy conformations, each of which might favor binding to different ligands and/or lead to different downstream functions. Understanding the function of such proteins requires knowledge of the ensemble of low-energy configurations that might play a role in this pleiotropic functionality. We earlier reported the BiHelix method for efficiently sampling the (12)(7) = 35 million conformations resulting from 30° rotations about the axis (η) of all seven transmembrane helices (TMHs), showing that the experimental structure is reliably selected as the best conformation from this ensemble. However, various GPCRs differ sufficiently in the tilts of the TMHs that this method need not predict the optimum conformation starting from any other template. In this paper, we introduce the SuperBiHelix method in which the tilt angles (θ, ϕ) are optimized simultaneously with rotations (η) efficiently enough that it is practical and sufficient to sample (5 × 3 × 5)(7) = 13 trillion configurations. This method can correctly identify the optimum structure of a GPCR starting with the template from a different GPCR. We have validated this method by predicting known crystal structure conformations starting from the template of a different protein structure. We find that the SuperBiHelix conformational ensemble includes the higher energy conformations associated with the active protein in addition to those associated with the more stable inactive protein. This methodology was then applied to design and experimentally confirm structures of three mutants of the CB1 cannabinoid receptor associated with different functions.

Molecular modelling of odorant/olfactory receptor complexes

Providing a rationale that associates a chemical structure of an odorant to its induced perception has been sought for a long time. To achieve this, a detailed atomic structure of both the odorant and the olfactory receptor must be known. State-of-the-art techniques to model the 3D structure of an olfactory receptor in complex with various odorants are presented here. These range from sequence alignment with known structures to molecular dynamics simulations in a realistic environment.

Computationally-predicted CB1 cannabinoid receptor mutants show distinct patterns of salt-bridges that correlate with their level of constitutive activity reflected in G protein coupling levels, thermal stability, and ligand binding

The cannabinoid receptor 1 (CB1), a member of the class A G-protein-coupled receptor (GPCR) family, possesses an observable level of constitutive activity. Its activation mechanism, however, has yet to be elucidated. Previously we discovered dramatic changes in CB1 activity due to single mutations; T3.46A, which made the receptor inactive, and T3.46I and L3.43A, which made it essentially fully constitutively active. Our subsequent prediction of the structures of these mutant receptors indicated that these changes in activity are explained in terms of the pattern of salt-bridges in the receptor region involving transmembrane domains 2, 3, 5, and 6. Here we identified key salt-bridges, R2.37 + D6.30 and D2.63 + K3.28, critical for CB1 inactive and active states, respectively, and generated new mutant receptors that we predicted would change CB1 activity by either precluding or promoting these interactions. We find that breaking the R2.37 + D6.30 salt-bridge resulted in substantial increase in G-protein coupling activity and reduced thermal stability relative to the wild-type reflecting the changes in constitutive activity from inactive to active. In contrast, breaking the D2.63 + K3.28 salt-bridge produced the opposite profile suggesting this interaction is critical for the receptor activation. Thus, we demonstrate an excellent correlation with the predicted pattern of key salt-bridges and experimental levels of activity and conformational flexibility. These results are also consistent with the extended ternary complex model with respect to shifts in agonist and inverse agonist affinity and provide a powerful framework for understanding the molecular basis for the multiple stages of CB1 activation and that of other GPCRs in general.

Optimized method of G-protein-coupled receptor homology modeling: its application to the discovery of novel CXCR7 ligands

Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening.

How broadly tuned olfactory receptors equally recognize their agonists. Human OR1G1 as a test case

The molecular features that dominate the binding mode of agonists by a broadly tuned olfactory receptor are analyzed through a joint approach combining cell biology, calcium imaging, and molecular modeling. The odorant/receptor affinities, estimated through statistics accrued during molecular dynamics simulations, are in accordance with the experimental ranking. Although in many systems receptors recognize their target through a network of oriented interactions, such as H-bonding, the binding by broadly tuned olfactory receptors is dominated by non-polar terms. We show how such a feature allows chemicals belonging to different chemical families to similarly activate the receptors through compensations of interactions within the binding site.

FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs

We have developed FINDSITE(X), an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER(VMT), TASSER(VMT)-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE(X) is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER(VMT)-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF(0.01)) is 22.7, whereas EF(0.01) by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF(0.01) reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html .

Characterizing and predicting the functional and conformational diversity of seven-transmembrane proteins

The activation of seven-transmembrane receptors (7TMRs) allows cells to sense their environment and convert extracellular signals (like hormone binding) into intracellular signals (through G protein-coupled and/or β arrestin-coupled pathways). A single 7TMR is capable of transducing a wide spectrum of physiological responses inside a cell by coupling to these pathways. This intracellular pleiotropic action is enabled by multiple conformations exhibited by these receptors. Developments in membrane protein structure determination technologies have led to a rapid increase in crystal structures for many 7TMRs. Majority of these receptors have been crystallized in their inactive conformation and, for some, one of the many active conformations has also been crystallized. Given the topological constraints of a lipid bilayer that results in a single fold of seven almost parallel TM helices connected by mostly unstructured loops, these structures exhibit a diversity of conformations not only across the receptors but also across the different functional forms for receptors with structures for one of the functionally active conformations. Here we present a method to characterize this conformational diversity in terms of transmembrane helix topology (TMHTOP) parameters and how to use these helix orientation parameters to predict functionally-distinct multiple conformations for these receptors. The TMHTOP parameters enable a quantification of the structural changes that underlie 7TMR activation and also sheds a unique mechanistic light on the pleiotropic nature of these receptors. It provides a common language to describe the 7TMR activation mechanisms as well as differences across many receptors in terms of visually intuitive structural parameters. Protein structure prediction methods can use these parameters to describe 7TMR conformational ensembles, which coupled to experimental data can be used to develop testable hypotheses for the structural basis of 7TMR functions.

Structure-based prediction of subtype selectivity of histamine H3 receptor selective antagonists in clinical trials

Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H(3) histamine receptor (hH(3)HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity. However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H(1), H(2), H(3), and H(4)) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol, sampling ∼35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these 10 best protein structures with the DarwinDock Monte Carlo protocol to sample ∼50 000 × 10(20) poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E206(5.46) contributes most in binding H(3) selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH(3)HR and hH(4)HR are involved in H(3)/ H(4) subtype selectivity. In addition, we find that M378(6.55) in hH(3)HR provides additional hydrophobic interactions different from hH(4)HR (the corresponding amino acid of T323(6.55) in hH(4)HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH(3)HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D114(3.32), the spacer, the aromatic ring substituted with the hydrophilic or lipophilic groups interacting with lipophilic pockets in transmembranes (TMs) 3-5-6 and the aliphatic ring located in TMs 2-3-7. These 3D structures for all four HRs should help guide the rational design of novel drugs for the subtype selective antagonists and agonists with reduced side effects.