Assessment and challenges of ligand docking into comparative models of G-protein coupled receptors

Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 μM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs

As sequencing methodologies continue to advance, the availability of protein sequences far outpaces the ability of structure determination. Homology modeling is used to bridge this gap but relies on high-identity templates for accurate model building. G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies in which homology modeling could fill the knowledge gap for structure-based drug design. To date, only about 17% of druggable GPCRs have had their structures characterized at atomic resolution. However, modeling of the remaining 83% is hindered by the low sequence identity between receptors. Here we test key inputs in the model building process using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended sequence- and structure-based alignment that accounts for structure conservation in loop regions. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target can be used expanding the conformational space sampled in a meaningful way. This optimization allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Additionally, all protocols are made available with insights into modifications that may improve accuracy at new targets.

Competitive Interactions between PIRT, the Cold Sensing Ion Channel TRPM8, and PIP2 Suggest a Mechanism for Regulation

TRPM8 is a member of the transient receptor potential ion channel family where it functions as a cold and pain sensor in humans and other higher organisms. Previous studies show that TRPM8 requires the signaling phosphoinositide lipid PIP2 to function. TRPM8 function is further regulated by other diverse mechanisms, including the small modulatory membrane protein PIRT (phosphoinositide regulator of TRP). Like TRPM8, PIRT also binds PIP2 and behavioral studies have shown that PIRT is required for normal TRPM8-mediated cold-sensing. To better understand the molecular mechanism of PIRT regulation of TRPM8, solution nuclear magnetic resonance (NMR) spectroscopy was used to assign the backbone resonances of full-length human PIRT and investigate the direct binding of PIRT to PIP2 and the human TRPM8 S1-S4 transmembrane domain. Microscale thermophoresis (MST) binding studies validate the NMR results and identify a competitive PIRT interaction between PIP2 and the TRPM8 S1-S4 domain. Computational PIP2 docking to a human TRPM8 comparative model was performed to help localize where PIRT may bind TRPM8. Taken together, our data suggest a mechanism where TRPM8, PIRT, and PIP2 form a regulatory complex and PIRT modulation of TRPM8 arises, at least in part, by regulating local concentrations of PIP2 accessible to TRPM8.

Conformational Design and Characterisation of a Truncated Diamine Oxidase from Arthrobacter globiformis

A functional mini protein can be developed by miniaturising its size. The minimisation technique provides an excellent model system for studying native enzymes, especially in creating an alternative novel biocatalyst. Miniaturised proteins may have enhanced stability, a crucial characteristic for large-scale production and industrial applications. In this study, a huge enzyme molecule, known as diamine oxidase (DAO, comprising 700 amino acids), was selected to undergo the process. By retaining the arrangement of the original functional sites of DAO in the fourth domain, a mini DAO can be designed via homology modelling. After several downsizing processes, a final configuration of 220 amino acids displayed high binding affinity towards histamine, a short-chain substrate that was catalysed by the parental DAO. The configuration also showed enhanced affinity towards a long-chain substrate known as spermidine. The gene for the designed protein was cloned and expressed in pET102/TOPO vector and overexpressed in E. coli BL21 (DE3). The new mini DAO had similar temperature tolerance and versatile substrates specificity characteristics as its parental protein. An active mini-protein with these characteristics is potentially useful for several applications such as detecting biogenic amines in the biological fluids and the environment that may give rise to health issues.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

The molecular basis of subtype selectivity of human kinin G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) are the most important signal transducers in higher eukaryotes. Despite considerable progress, the molecular basis of subtype-specific ligand selectivity, especially for peptide receptors, remains unknown. Here, by integrating DNP-enhanced solid-state NMR spectroscopy with advanced molecular modeling and docking, the mechanism of the subtype selectivity of human bradykinin receptors for their peptide agonists has been resolved. The conserved middle segments of the bound peptides show distinct conformations that result in different presentations of their N and C termini toward their receptors. Analysis of the peptide-receptor interfaces reveals that the charged N-terminal residues of the peptides are mainly selected through electrostatic interactions, whereas the C-terminal segments are recognized via both conformations and interactions. The detailed molecular picture obtained by this approach opens a new gateway for exploring the complex conformational and chemical space of peptides and peptide analogs for designing GPCR subtype-selective biochemical tools and drugs.

Assessment of the transmembrane domain structures in GPCR Dock 2013 models

The community-wide blind prediction of G-protein coupled receptor (GPCR) structures and ligand docking has been conducted three times and the quality of the models was primarily assessed by the accuracy of ligand binding modes. The seven transmembrane (TM) helices of the receptors were taken as a whole; thus the model quality within the 7TM domains has not been evaluated. Here we evaluate the 7TM domain structures in the models submitted for the last round of prediction - GPCR Dock 2013. Applying the 7 × 7 RMSD matrix analysis described in our prior work, we show that the models vary widely in prediction accuracy of the 7TM structures, exhibiting diverse structural differences from the targets. For the prediction of the 5-hydroxytryptamine receptors, the top 7TM models are rather close to the targets, which however are not ranked top by ligand-docking. On the other hand, notable deviations of the TMs are found in in the previously identified top docking models that closely resemble other receptors. We further reveal reasons of success and failure in ligand docking for the models. This current assessment not only complements the previous assessment, but also provides important insights into the current status of GPCR modeling and ligand docking.

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.

Computational design of ligand-binding membrane receptors with high selectivity

Accurate modeling and design of protein-ligand interactions have broad applications in cell, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein homology modeling-ligand docking-protein design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced fit modes of ligand binding. In structure modeling tests, we blindly predicted near-atomic accuracy ligand conformations bound to G protein-coupled receptors (GPCRs) that were rarely identified by traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally-uncharacterized GPCRs. We then applied the technique to design functional human dopamine receptors with novel ligand binding selectivity. Most blindly predicted ligand binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand binding profile of membrane receptors that remain difficult to crystallize.

Docking Methodologies and Recent Advances

Docking, a molecular modelling method, has wide applications in identification and optimization in modern drug discovery. This chapter addresses the recent advances in the docking methodologies like fragment docking, covalent docking, inverse docking, post processing, hybrid techniques, homology modeling etc. and its protocol like searching and scoring functions. Advances in scoring functions for e.g. consensus scoring, quantum mechanics methods, clustering and entropy based methods, fingerprinting, etc. are used to overcome the limitations of the commonly used force-field, empirical and knowledge based scoring functions. It will cover crucial necessities and different algorithms of docking and scoring. Further different aspects like protein flexibility, ligand sampling and flexibility, and the performance of scoring function will be discussed.

Illustrating and homology modeling the proteins of the Zika virus

The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Illustrating and homology modeling the proteins of the Zika virus

The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.

Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening

Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

Structural basis for constitutive activity and agonist-induced activation of the enteroendocrine fat sensor GPR119

BACKGROUND AND PURPOSE

GPR119 is a Gαs-coupled 7TM receptor activated by endogenous lipids such as oleoylethanolamide (OEA) and by the dietary triglyceride metabolite 2-monoacylglycerol. GPR119 stimulates enteroendocrine hormone and insulin secretion. But despite massive drug discovery efforts in the field, very little is known about the basic molecular pharmacology of GPR119.

EXPERIMENTAL APPROACH

GPR119 receptor signalling was studied in transfected cells. Mutational mapping (30 mutations in 23 positions) was performed on residues required for ligand-independent and agonist-induced GPR119 activation (AR231453 and OEA). Novel Rosetta-based receptor modelling was applied, using a composite template approach with segments from different X-ray structures and fully flexible ligand docking.

KEY RESULTS

The increased signalling induced by increasing the cell surface expression of GPR119 in the absence of agonist and the inhibitory effect of two synthetic inverse agonists demonstrated that GRP119 signals with a high degree of constitutive activity through the Gαs pathway. The mutational maps for AR231453 and OEA were very similar and, surprisingly, also similar to the mutational map for residues affecting the constitutive signalling - albeit with key differences. Surprisingly, almost all residues in extracellular loop-2b were important for the constitutive activity. The molecular modelling and docking demonstrated that AR231453 binds in a 'vertical' pocket in between mutational hits reaching from the centre of the receptor out to extracellular loop-2b.

CONCLUSIONS AND IMPLICATIONS

The high constitutive activity of GPR119 should be taken into account in future drug discovery efforts, which can now be guided by the detailed knowledge of the physiochemical properties of the extended ligand-binding pocket.

Computational fishing of new DNA methyltransferase inhibitors from natural products

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Rational design of a peptide capture agent for CXCL8 based on a model of the CXCL8:CXCR1 complex

A CXCL8-binding peptide designed from the interaction sites of CXCR1 with CXCL8 serves as a capture agent and inhibits neutrophil migration.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Opportunities and challenges in the discovery of allosteric modulators of GPCRs for treating CNS disorders

Novel allosteric modulators of G protein-coupled receptors (GPCRs) are providing fundamental advances in the development of GPCR ligands with high subtype selectivity and novel modes of efficacy that have not been possible with traditional approaches. As new allosteric modulators are advancing as drug candidates, we are developing an increased understanding of the major advantages and broad range of activities that can be achieved with these agents through selective modulation of specific signalling pathways, differential effects on GPCR homodimers versus heterodimers, and other properties. This understanding creates exciting opportunities, as well as unique challenges, in the optimization of novel therapeutic agents for disorders of the central nervous system.

Mapping substance P binding sites on the neurokinin-1 receptor using genetic incorporation of a photoreactive amino acid

Substance P (SP) is a neuropeptide that mediates numerous physiological responses, including transmission of pain and inflammation through the neurokinin-1 (NK1) receptor, a G protein-coupled receptor. Previous mutagenesis studies and photoaffinity labeling using ligand analogues suggested that the binding site for SP includes multiple domains in the N-terminal (Nt) segment and the second extracellular loop (ECLII) of NK1. To map precisely the NK1 residues that interact with SP, we applied a novel receptor-based targeted photocross-linking approach. We used amber codon suppression to introduce the photoreactive unnatural amino acid p-benzoyl-l-phenylalanine (BzF) at 11 selected individual positions in the Nt tail (residues 11-21) and 23 positions in the ECLII (residues 170(C-10)-193(C+13)) of NK1. The 34 NK1 variants were expressed in mammalian HEK293 cells and retained the ability to interact with a fluorescently labeled SP analog. Notably, 10 of the receptor variants with BzF in the Nt tail and 4 of those with BzF in ECLII cross-linked efficiently to SP, indicating that these 14 sites are juxtaposed to SP in the ligand-bound receptor. These results show that two distinct regions of the NK1 receptor possess multiple determinants for SP binding and demonstrate the utility of genetically encoded photocross-linking to map complex multitopic binding sites on G protein-coupled receptors in a cell-based assay format.

Computational analysis of negative and positive allosteric modulator binding and function in metabotropic glutamate receptor 5 (in)activation

Metabotropic glutamate receptors (mGluRs) are high-profile G-protein coupled receptors drug targets because of their involvement in several neurological disease states, and mGluR5 in particular is a subtype whose controlled allosteric modulation, both positive and negative, can potentially be useful for the treatment of schizophrenia and relief of chronic pain, respectively. Here we model mGluR5 with a collection of positive and negative allosteric modulators (PAMs and NAMs) in both active and inactive receptor states, in a manner that is consistent with experimental information, using a specialized protocol that includes homology to increase docking accuracy, and receptor relaxation to generate an individual induced fit with each allosteric modulator. Results implicate two residues in particular for NAM and PAM function: NAM interaction with W785 for receptor inactivation, and NAM/PAM H-bonding with S809 for receptor (in)activation. Models suggest the orientation of the H-bond between allosteric modulator and S809, controlled by PAM/NAM chemistry, influences the position of TM7, which in turn influences the shape of the allosteric site, and potentially the receptor state. NAM-bound and PAM-bound mGluR5 models also reveal that although PAMs and NAMs bind in the same pocket and share similar binding modes, they have distinct effects on the conformation of the receptor. Our models, together with the identification of a possible activation mechanism, may be useful in the rational design of new allosteric modulators for mGluR5.

Identification of specific ligand-receptor interactions that govern binding and cooperativity of diverse modulators to a common metabotropic glutamate receptor 5 allosteric site

A common metabotropic glutamate receptor 5 (mGlu5) allosteric site is known to accommodate diverse chemotypes. However, the structural relationship between compounds from different scaffolds and mGlu5 is not well understood. In an effort to better understand the molecular determinants that govern allosteric modulator interactions with mGlu5, we employed a combination of site-directed mutagenesis and computational modeling. With few exceptions, six residues (P654, Y658, T780, W784, S808, and A809) were identified as key affinity determinants across all seven allosteric modulator scaffolds. To improve our interpretation of how diverse allosteric modulators occupy the common allosteric site, we sampled the wealth of mGlu5 structure-activity relationship (SAR) data available by docking 60 ligands (actives and inactives) representing seven chemical scaffolds into our mGlu5 comparative model. To spatially and chemically compare binding modes of ligands from diverse scaffolds, the ChargeRMSD measure was developed. We found a common binding mode for the modulators that placed the long axes of the ligands parallel to the transmembrane helices 3 and 7. W784 in TM6 not only was identified as a key NAM cooperativity determinant across multiple scaffolds, but also caused a NAM to PAM switch for two different scaffolds. Moreover, a single point mutation in TM5, G747V, altered the architecture of the common allosteric site such that 4-nitro-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide (VU29) was noncompetitive with the common allosteric site. Our findings highlight the subtleties of allosteric modulator binding to mGlu5 and demonstrate the utility in incorporating SAR information to strengthen the interpretation and analyses of docking and mutational data.

Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator

The excitatory neurotransmitter glutamate induces modulatory actions via the metabotropic glutamate receptors (mGlus), which are class C G protein-coupled receptors (GPCRs). We determined the structure of the human mGlu1 receptor seven-transmembrane (7TM) domain bound to a negative allosteric modulator, FITM, at a resolution of 2.8 angstroms. The modulator binding site partially overlaps with the orthosteric binding sites of class A GPCRs but is more restricted than most other GPCRs. We observed a parallel 7TM dimer mediated by cholesterols, which suggests that signaling initiated by glutamate's interaction with the extracellular domain might be mediated via 7TM interactions within the full-length receptor dimer. A combination of crystallography, structure-activity relationships, mutagenesis, and full-length dimer modeling provides insights about the allosteric modulation and activation mechanism of class C GPCRs.