Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects – enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand – despite the extensive differences in sequences between mGluRs and rhodopsin

Allosteric Modulation of G Protein Coupled Receptors.

Structural coupling between the cytoplasmic (CP), transmembrane (TM) and extracellular (EC) domains of G protein coupled receptors (GPCRs) is crucial for their functioning in signal transfer from the extracellular to the intracellular side of the membrane. The focus of this thesis was to test the hypothesis that ligands can bind in each of the three domains. Depending on the location of the endogenous ligand binding site, the other two sites would become allosteric ligand binding sites. To test this hypothesis, we investigated the binding of accessory ligands to each of the three domains, CP, TM and EC. The major contributions of this thesis are as follows:I. The anthocyanin Cyanidin-3-glucoside (C3G) and the chlorophyll-derivative chlorin e6 (Ce6), were shown to physically interact with rhodopsin. These studies demonstrated the presence of a novel CP allosteric ligand binding site in rhodopsin. Biophysical evidence indicated differential effects of binding of these ligands on rhodopsin function, structure and dynamics. II. The allosteric TM ligand binding pocket in metabotropic glutamate receptors (mGluRs) was shown to be analogous in structure and function to the orthosteric TM retinal ligand binding pocket in rhodopsin. Docking of known allosteric modulators to structural models of mGluRs based on rhodopsin conformations was used to predict allosteric modulatory effects. Structural comparison of the mGluR and rhodopsin binding pockets revealed high overlap and preliminary evidence was obtained showing that an mGluR ligand can bind to rhodopsin.III. Evidence for the existence of an EC ligand binding domain was presented. Rhodopsin was shown to bind the extracellular chemokine ligand, CXCL11, an event which interfered with both rhodopsin and chemokine functions. IV. As part of the above efforts, it became necessary to develop and improve NMR spectroscopic methodology to study ligand binding of membrane proteins such as GPCRs. Thus, 1H and 19F based NMR methods to screen for novel ligands that bind to GPCRs were developed and applied to rhodopsin. Collectively, the studies presented in this thesis enhance the understanding of allosteric modulation of GPCRs in general, and of the molecular mechanism of rhodopsin and mGluR activation in the presence of allosteric ligands in particular. The results could help in the identification of new ligands to allosterically modulate receptor structures and in turn their functions at different binding pockets, thus paving new ways to selectively target this pharmacologically important class of receptors

Presynaptic release regulating metabotropic receptors: dimerization and receptor cross talk

openThe Class C subfamily of the G protein coupled receptor (GPCR) family is one of the most important in the central nervous system (CNS), and includes the glutamatergic and GABAergic metabotropic receptors (mGlu and GABAB). The mGlu receptors are fundamental in modulating the efficiency of chemical neurotransmission in the CNS and, as a consequence, they are also involved in several neurological and neurodegenerative disorders. Recently, dimerization of these receptors has become an important topic of investigation and it has been proposed to be crucial to physio-pathological processes in the CNS, as well as to the impact of therapeutics in patients. In this thesis, I will discuss the basic properties of Class C GPCRs focusing on the first and second group of mGlu receptors (Group I and II) and their possible dimerization and/or cross talk with other receptors. I will focus on recent findings concerning these processes that have been mainly obtained by using purified isolated nerve endings (here referred to as synaptosomes), a subcellular preparation of choice for studying presynaptic release regulating receptors. Starting from the pharmacological characterization of Group I and II mGlu receptors, I will then discuss different examples of cross-talk linking these receptors to other ones (i.e. the GABAB and the 5HT2A receptors). I will also show results obtained using electrophysiology to study the role of these receptor subtypes in the modulation of synaptic transmission in hippocampal slices. The resulting picture is undoubtedly complex and highlights how the cross-talk and dimerization of these receptors represent a new frontier in neuropharmacological studies.openXXXII CICLO - MEDICINA SPERIMENTALE - Farmacologia e tossicologiaSher EmanueleVergassola, Matte

Biochemical pharmacology of the positive allosteric modulation of the GABAb receptor "in Vitro" and "in Vivo"

Allosteric modulators of G-protein coupled receptors (GPCRs) interact with binding sites on the receptor molecule that are topographically distinct from the classic orthosteric site. Having only a marginal effect by themselves, they induce conformational changes of receptors that result in the modulation of agonist-induced function in either a stimulating or an inhibiting way, depending on whether they are positive or negative allosteric modulators, respectively. Their mechanism of action is, thus, in synchrony with the frequency and the magnitude of physiological signaling. This is the main reason why allosteric modulators are considered to have a better side-effect profile and to be less prone to induction of tolerance than classic orthosteric agonists. Allosteric modulators have gained significance in the scientific community in the past decade. This thesis comprises four parts and focuses on the positive allosteric modulation of the GABAB receptors. Two prototypal positive allosteric modulators CGP7930 and GS39783 have recently been discovered and characterized in Novartis Pharma (Urwyler et al. 2001 and 2003). A number of questions regarding their further characterization, namely their effects on orthosteric ligands with distinct intrinsic properties, the role allosteric modulation plays in GABAB receptor desensitization and biochemical effects of GS39783 in vivo are addressed in this thesis. Mechanisms of allosteric modulation at GABAB receptors by CGP7930 and GS39783: effects on affinities and efficacies of orthosteric ligands with distinct intrinsic properties The first part of this thesis shows that, as it is predicted by theoretical models of receptor activation, all GABAB ligand species are amenable to allosteric modulation. A number of selective GABAB receptor ligands were tested in the presence and the absence of positive allosteric modulators CGP7930 and GS39783 in in vitro assays, such as radioligand binding, GTP(γ)S and cellular cyclic AMP (cAMP) measurements. A decrease in affinity of antagonists was observed in radioligand binding experiments, without a change of the receptor number, oppositely to increases in affinity of partial agonists. In the GTP(γ)S experiment the presence of CGP7930 and GS39783 revealed intrinsic efficacies for CGP35348 and 2-OH-saclofen, two “silent” GABAB receptor antagonists. In the cAMP measurements, an even more sensitive experimental system, the two abovementioned compounds acted as partial agonists, with increased efficacies in the presence of positive allosteric modulators. Inverse agonistic tendencies were observed with the “silent” antagonist CGP52432. In this part of the thesis, the positive allosteric modulators GS39783 and CGP7930 have been shown to be useful experimental tools for elucidating intrinsic properties of orthosteric ligands. (Chapter 5, Section 5.1.) Receptor activation involving positive allosteric modulation, unlike full agonism, does not result in GABAB receptor desensitization: an in vitro study To inspect the role of the positive allosteric modulator GS39783 in GABAB receptor desensitization, receptor function and cell surface receptor density were examined in a recombinant GABAB cell line and in primary neuronal cultures upon persistent treatments with GABAB agonists, and combinations of agonists and GS39783. While the GABAB receptor desensitized after lasting pretreatments with saturating concentrations of GABAB agonists GABA or R(-)-baclofen, the combined treatment with low concentration of agonists and GS39738 did not lead to desensitization, despite activating the receptor to the same extent as desensitization-inducing agonists. These results indicate that it is the degree of occupancy of the orthosteric binding site that determines desensitization, rather than the degree of receptor activation. Desensitization experiments with the GABAB receptor and GS39783 in this study demonstrate that, according to predictions, positive allosteric modulation as a therapeutic principle may indeed be more promising than orthosteric agonism, having less propensity for developing tolerance due to receptor desensitization. (Chapter 5, Section 5.2.) Changes in behavior of allosteric and orthosteric GABAB receptor ligands after a continuous agonist pretreatment Investigating the effects of GS39783 on GABAB receptor desensitization, interesting findings revealed changes in ligand behavior upon receptor desensitization in the GABAB recombinant cell line. “Silent” antagonists such as CGP62349, CGP52432, CGP56999 and SCH50911 were found to have inverse agonistic properties, the partial agonist 2-OH-saclofen was devoid of positive intrinsic efficacy and the positive allosteric modulator GS39738 was acting in a manner of an allosteric agonist. The possibility of residual GABA present from the pretreatment and responsible for these effects was ruled out. All observed phenomena point toward an increase in constitutive activity of the receptor. Increase of constitutive receptor activity after lasting agonist pretreatments have previously been reported for the β2-adrenergic and the opioid receptors. This is, however, the first such finding for the GABAB receptor, which might be important in elucidating the valence of orthosteric ligands as well as their effects upon a chronic drug treatment. It would be interesting to see whether the same phenomena would be observed also for other members of GPCR family 3. (Chapter 5, Section 5.3.) The positive allosteric modulator GS39783 enhances GABAB receptor-mediated inhibition of cyclic AMP formation in rat striatum in vivo In the last part of this thesis, I provide the first biochemical evidence of in vivo activity of a positive allosteric modulator of GPCRs. By using in vivo microdialysis in striata of freely moving rats, changes in extracellular levels of cAMP following GABAB receptor activation were monitored. Locally applied GABAB receptor agonist R(-)-baclofen inhibited cAMP formation stimulated by 7β-forskolin in a concentration-dependent manner, which was reversed by the co-application of the selective GABAB antagonist CGP56999. Orally applied positive allosteric modulator GS39783 lacked effects on its own but, together with a threshold concentration of R(-)-baclofen, it significantly decreased cAMP formation in a dosedependent fashion. Effects of GS39783 were revoked with CGP56999, showing dependence on concomitant GABAB receptor activation by an agonist and suggesting allosteric modulation as its mechanism of action in vivo. (Chapter 5, Section 5.4.

STRUCTURE-FUNCTION STUDIES OF THE METABOTROPIC GLUTAMATE RECEPTOR TYPE 6 (mGluR6) AND COMPARISON WITH RHODOPSIN

Metabotropic glutamate receptor subtype 6 (mGluR6), a class C G protein coupled receptor (GPCR), plays a key role in visual signal transduction and is also implicated in addiction. Certain mutations in mGluR6 have been reported to cause congenital stationary night blindness. In spite of the importance of mGluR6, knowledge of the molecular basis of its function is lacking. It is imperative to improve the current understanding of its structure-function relationships, so that selective ligands that modulate its activity can be discovered. Furthermore, functional characterization of mGluR6 is also expected to lead to a better understanding of the general principles underlying the activation mechanism of GPCR family. Rhodopsin is the prototypical class A GPCR and serves as a good comparative model to establish general mechanistic patterns of activation of GPCRs. This thesis describes experimental and computational approaches to characterize the structure-function relationship of mGluR6 and its comparison with rhodopsin.Firstly, inducible stable cell lines with high levels of mGluR6 expression were established. Proper trafficking and folding of mGluR6 in these cell lines were verified. To determine mGluR6 function, existing cell-based and novel membrane-based functional assays were optimized and developed, respectively. These efforts led to the establishment of a robust system that expresses properly folded and functional mGluR6 and enabled structure-function studies to be carried out. Several transmembrane cysteine mutants were created and analyzed with the goal to study the role of the transmembrane domain of mGluR6 in activation mechanism. TM6 of mGluR6 like rhodopsin was found to play a key role in its activation supporting the hypothesis that these two GPCRs may share a general mechanism of activation despite the large sequence divergence. Additional support for this hypothesis was obtained from computational sequence analysis which showed that the highly ranking residues involved in long-range interaction in rhodopsin overlap with the allosteric binding pocket of mGluR6. Finally, with the aim to identify selective ligands for mGluR6, an integrated computational-experimental approach was undertaken. Novel allosteric ligands and possibly selective orthosteric ligands for mGluR6 were identified. Further characterization of these ligands may lead to design of selective ligands for mGluR6

Action of Molecular Switches in GPCRs - Theoretical and Experimental Studies

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

Untersuchung der Struktur und Interaktion mit allosterischen Modulatoren der Familie C GPCRs mit Hilfe von Sequenz-, Struktur- und Ligand-basierten Verfahren

This study focuses on structural features of a particular GPCR type, the family C GPCRs. Structure- and ligand-based approaches were adopted for prediction of novel mGluR5 binding ligand and their binding modes. The objectives of this study were: 1. An analysis of function and structural implication of amino acids in the TM region of family C GPCRs. 2. The prediction of the TM domain structure of mGluR5. 3. The discovery of novel selective allosteric modulators of mGluR5 by virtual screening. 4. The prediction of a ligand binding mode for the allosteric binding site in mGluR5. GPCRs are a super-family of structurally related proteins although their primary amino acid sequence can be diverse. Using sequence information a conservation analysis of family C GPCRs should be applied to reveal characteristic differences and similarities with respect function, folding and ligand binding. Using experimental data and conservation analysis the allosteric binding site of mGluR5 should be characterized regarding NAM and PAM and selective ligand binding. For further evaluation experimental knowledge about family A GPCRs as well as conservation between vertebrate rhodopsins was planned to be compared to results obtained for family C GPCRs (Section 4.1 Conservation analysis of family C GPCRs). Since no receptor structure is available for any family C GPCR, discussion of conserved sequence positions between family A and C GPCRs requires the prediction of a receptor structure for mGluR5 using a family A receptor as template. In order to predict the mGluR5 structure a sequence alignment to a GPCR template protein will have to be proposed and GPCR specific features considered in structure calculation (Section 4.1.4 Structure prediction of mGluR5). The obtained structure was intended to be involved in ligand binding mode prediction of newly discovered active molecules. For discovery of novel selective mGluR modulators several ligand-based virtual screening protocols were adapted and evaluated. Prediction models were derived for selection of possibly active molecules using a diverse collection of known mGluR binding ligands. For that purpose a data collection of known mGluR binding ligands should be established and this reference collection analyzed with respect to different ligand activity classes, NAM or PAM and selective modulators. The prediction of novel NAMs and PAMs using several combinations of 2D-, 3D-, pharmacophore or molecule shape encoding methods with machine learning techniques and similarity determining methods should be tested in a prospective manner (Section 4.2 Virtual screening for novel mGluR modulators). In collaboration with Merz Pharmaceuticals (Merz GmbH & Co. KGaA, Frankfurt am Main, Germany) the modulating effect of a few hundred molecules should be approved in a functional cell-based assay. With the objective to predict a binding mode of the discovered active molecules, molecule docking should be applied using the allosteric binding site of the modeled mGluR5 structure (Section 4.2.4 Modeling of binding modes). Predicted ligand binding modes are to be correlated to conservation profiles that had resulted from the sequence-based entropy analysis and information from mutation experiments, and shall be compared to known ligand binding poses from crystal structures of family A GPCRs.Im Rahmen dieser Arbeit wurden Konzepte zur Aufklärung struktureller und funktioneller Eigenschaften von G-Protein gekoppelten Rezeptoren (GPCR) der Familie C entwickelt und angewendet. Mit unterschiedlichen Methodiken der Bio- und Chemieinformatik orientiert an experimentellen Ergebnissen wurden Fragestellungen bezüglich des Funktionsmechanismus von GPCRs untersucht. In Verlauf wurde anhand verfügbarer experimenteller Daten aus Mutations- und Ligandenbindungsstudien ein Vergleich konservierter Bereiche der Rezeptor-Familien A und C angefertigt. Die Konserviertheitsanalyse stützte sich auf die Berechnung der Shannon-Entropie und wurde für ein multiples Sequenzalignment von Transmembrandomänen unterschiedlicher 96 Familie C GPCRs ermittelt. Konservierte Bereiche wurden mit Hilfe experimenteller Daten interpretiert und insbesondere zur Definition von Regionen in der allosterischen Bindetasche hinsichtlich Selektivität verwendet. Mit dem Ziel, neue selektive allosterische Modulatoren für den metabotropen Glutamatrezeptor des Typs fünf (mGluR5) zu finden, wurden mehrere Liganden-basierte Ansätze zur virtuellen Vorhersage der Aktivität von Molekülen entwickelt und getestet. Die dabei angewendete Strategie basierte auf der Kenntnis bereits bekannter Liganden, deren Strukturen und Aktivitätswerte für das Erstellen von Vorhersagemodelle genutzt werden konnten. Die prospektive Vorhersage stützte sich auf unterschiedliche Methoden zur Ähnlichkeitsberechnung und Arten der Molekülkodierung. Die Testung der Moleküle erfolgte hinsichtlich ihrer modulatorischen Wirkung am mGluR5. Die Art der Messung erfasste die Änderungen des Ca2+-Levels in der Zelle. mGluR5-bindende Modulatoren wurden zur Selektivitätsbestimmung einer Testung am mGluR1 unterzogen. Insgesamt konnten 8 von 228 getesteten Molekülen im Aktivitätsbereich unter 10μM ermittelt werden, darunter befand sich ein positiver allosterischer Modulator. Von den restlichen sieben negativen Modulatoren (NAM) waren fünf selektiv für mGluR5. Alle identifizierten NAMs wurden mittels molekularem Dockings auf mögliche Interaktion mit der Transmembrandomäne von mGluR5 untersucht. Die Bindungshypothese entsprach einer Überlagerung der gefundenen Moleküle und ihrer möglicher Interaktionspunkte. Exemplarisch am mGluR5 konnte somit die Eignung einer modellierten GPCR-Struktur für eine Hypothesengenerierung bezüglich Ligandenbindung und struktureller Zusammenhänge untersucht werden

Development and application of fast fuzzy pharmacophore-based virtual screening methods for scaffold hopping

The goal of this thesis was the development, evaluation and application of novel virtual screening approaches for the rational compilation of high quality pharmacological screening libraries. The criteria for a high quality were a high probability of the selected molecules to be active compared to randomly selected molecules and diversity in the retrieved chemotypes of the selected molecules to be prepared for the attrition of single lead structures. For the latter criterion the virtual screening approach had to perform “scaffold hopping”. The first molecular descriptor that was explicitly reported for that purpose was the topological pharmacophore CATS descriptor, representing a correlation vector (CV) of all pharmacophore points in a molecule. The representation is alignment-free and thus renders fast screening of large databases feasible. In a first series of experiments the CATS descriptor was conceptually extended to the three-dimensional pharmacophore-pair CATS3D descriptor and the molecular surface based SURFCATS descriptor. The scaling of the CATS3D descriptor, the combination of CATS3D with different similarity metrics and the dependence of the CATS3D descriptor on the threedimensional conformations of the molecules in the virtual screening database were evaluated in retrospective screening experiments. The “scaffold hopping” capabilities of CATS3D and SURFCATS were compared to CATS and the substructure fingerprint MACCS keys. Prospective virtual screening with CATS3D similarity searching was applied for the TAR RNA and the metabotropic glutamate receptor 5 (mGlur5). A combination of supervised and unsupervised neural networks trained on CATS3D descriptors was applied prospectively to compile a focused but still diverse library of mGluR5 modulators. In a second series of experiments the SQUID fuzzy pharmacophore model method was developed, that was aimed to provide a more general query for virtual screening than the CATS family descriptors. A prospective application of the fuzzy pharmacophore models was performed for TAR RNA ligands. In a last experiment a structure-/ligand-based pharmacophore model was developed for taspase1 based on a homology model of the enzyme. This model was applied prospectively for the screening for the first inhibitors of taspase1. The effect of different similarity metrics (Euc: Euclidean distance, Manh: Manhattan distance and Tani: Tanimoto similarity) and different scaling methods (unscaled, scaling1: scaling by the number of atoms, and scaling2: scaling by the added incidences of potential pharmacophore points of atom pairs) on CATS3D similarity searching was evaluated in retrospective virtual screening experiments. 12 target classes of the COBRA database of annotated ligands from recent scientific literature were used for that purpose. Scaling2, a new development for the CATS3D descriptor, was shown to perform best on average in combination with all three similarity metrics (enrichment factor ef (1%): Manh = 11.8 ± 4.3, Euc = 11.9 ± 4.6, Tani = 12.8 ± 5.1). The Tanimoto coefficient was found to perform best with the new scaling method. Using the other scaling methods the Manhattan distance performed best (ef (1%): unscaled: Manh = 9.6 ± 4.0, Euc = 8.1 ± 3.5, Tani = 8.3 ± 3.8; scaling1: Manh = 10.3 ± 4.1, Euc = 8.8 ± 3.6, Tani = 9.1 ± 3.8). Since CATS3D is independent of an alignment, the dependence of a “receptor relevant” conformation might also be weaker compared to other methods like docking. Using such methods might be a possibility to overcome problems like protein flexibility or the computational expensive calculation of many conformers. To test this hypothesis, co-crystal structures of 11 target classes served as queries for virtual screening of the COBRA database. Different numbers of conformations were calculated for the COBRA database. Using only a single conformation already resulted in a significant enrichment of isofunctional molecules on average (ef (1%) = 6.0 ± 6.5). This observation was also made for ligand classes with many rotatable bonds (e.g. HIV-protease: 19.3 ± 6.2 rotatable bonds in COBRA, ef (1%) = 12.2 ± 11.8). On average only an improvement from using the maximum number of conformations (on average 37 conformations / molecule) to using single conformations of 1.1 fold was found. It was found that using more conformations actives and inactives equally became more similar to the reference compounds according to the CATS3D representations. Applying the same parameters as before to calculate conformations for the crystal structure ligands resulted in an average Cartesian RMSD of the single conformations to the crystal structure conformations of 1.7 ± 0.7 Å. For the maximum number of conformations, the RMSD decreased to 1.0 ± 0.5 Å (1.8 fold improvement on average). To assess the virtual screening performance and the scaffold hopping potential of CATS3D and SURFACATS, these descriptors were compared to CATS and the MACCS keys, a fingerprint based on exact chemical substructures. Retrospective screening of ten classes of the COBRA database was performed. According to the average enrichment factors the MACCS keys performed best (ef (1%): MACCS = 17.4 ± 6.4, CATS = 14.6 ± 5.4, CATS3D = 13.9 ± 4.9, SURFCATS = 12.2 ± 5.5). The classes, where MACCS performed best, consisted of a lower average fraction of different scaffolds relative to the number of molecules (0.44 ± 0.13), than the classes, where CATS performed best (0.65 ± 0.13). CATS3D was the best performing method for only a single target class with an intermediate fraction of scaffolds (0.55). SURFCATS was not found to perform best for a single class. These results indicate that CATS and the CATS3D descriptors might be better suited to find novel scaffolds than the MACCS keys. All methods were also shown to complement each other by retrieving scaffolds that were not found by the other methods. A prospective evaluation of CATS3D similarity searching was done for metabotropic glutamate receptor 5 (mGluR5) allosteric modulators. Seven known antagonists of mGluR5 with sub-micromolar IC50 were used as reference ligands for virtual screening of the 20,000 most drug-like compounds – as predicted by an artificial neural network approach – of the Asinex vendor database (194,563 compounds). Eight of 29 virtual screening hits were found with a Ki below 50 µM in a binding assay. Most of the ligands were only moderately specific for mGluR5 (maximum of > 4.2 fold selectivity) relative to mGluR1, the most similar receptor to mGluR5. One ligand exhibited even a better Ki for mGluR1 than for mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). All hits had different scaffolds than the reference molecules. It was demonstrated that the compiled library contained molecules that were different from the reference structures – as estimated by MACCS substructure fingerprints – but were still considered isofunctional by both CATS and CATS3D pharmacophore approaches. Artificial neural networks (ANN) provide an alternative to similarity searching in virtual screening, with the advantage that they incorporate knowledge from a learning procedure. A combination of artificial neural networks for the compilation of a focused but still structurally diverse screening library was employed prospectively for mGluR5. Ensembles of neural networks were trained on CATS3D representations of the training data for the prediction of “mGluR5-likeness” and for “mGluR5/mGluR1 selectivity”, the most similar receptor to mGluR5, yielding Matthews cc between 0.88 and 0.92 as well as 0.88 and 0.91 respectively. The best 8,403 hits (the focused library: the intersection of the best hits from both prediction tasks) from virtually ranking the Enamine vendor database (ca. 1,000,000 molecules), were further analyzed by two self-organizing maps (SOMs), trained on CATS3D descriptors and on MACCS substructure fingerprints. A diverse and representative subset of the hits was obtained by selecting the most similar molecules to each SOM neuron. Binding studies of the selected compounds (16 molecules from each map) gave that three of the molecules from the CATS3D SOM and two of the molecules from the MACCS SOM showed mGluR5 binding. The best hit with a Ki of 21 µM was found in the CATS3D SOM. The selectivity of the compounds for mGluR5 over mGluR1 was low. Since the binding pockets in the two receptors are similar the general CATS3D representation might not have been appropriate for the prediction of selectivity. In both SOMs new active molecules were found in neurons that did not contain molecules from the training set, i. e. the approach was able to enter new areas of chemical space with respect to mGluR5. The combination of supervised and unsupervised neural networks and CATS3D seemed to be suited for the retrieval of dissimilar molecules with the same class of biological activity, rather than for the optimization of molecules with respect to activity or selectivity. A new virtual screening approach was developed with the SQUID (Sophisticated Quantification of Interaction Distributions) fuzzy pharmacophore method. In SQUID pairs of Gaussian probability densities are used for the construction of a CV descriptor. The Gaussians represent clusters of atoms comprising the same pharmacophoric feature within an alignment of several active reference molecules. The fuzzy representation of the molecules should enhance the performance in scaffold hopping. Pharmacophore models with different degrees of fuzziness (resolution) can be defined which might be an appropriate means to compensate for ligand and receptor flexibility. For virtual screening the 3D distribution of Gaussian densities is transformed into a two-point correlation vector representation which describes the probability density for the presence of atom-pairs, comprising defined pharmacophoric features. The fuzzy pharmacophore CV was used to rank CATS3D representations of molecules. The approach was validated by retrospective screening for cyclooxygenase 2 (COX-2) and thrombin ligands. A variety of models with different degrees of fuzziness were calculated and tested for both classes of molecules. Best performance was obtained with pharmacophore models reflecting an intermediate degree of fuzziness. Appropriately weighted fuzzy pharmacophore models performed better in retrospective screening than CATS3D similarity searching using single query molecules, for both COX-2 and thrombin (ef (1%): COX-2: SQUID = 39.2., best CATS3D result = 26.6; Thrombin: SQUID = 18.0, best CATS3D result = 16.7). The new pharmacophore method was shown to complement MOE pharmacophore models. SQUID fuzzy pharmacophore and CATS3D virtual screening were applied prospectively to retrieve novel scaffolds of RNA binding molecules, inhibiting the Tat-TAR interaction. A pharmacophore model was built up from one ligand (acetylpromazine, IC50 = 500 µM) and a fragment of another known ligand (CGP40336A), which was assumed to bind with a comparable binding mode as acetylpromazine. The fragment was flexible aligned to the TAR bound NMR conformation of acetylpromazine. Using an optimized SQUID pharmacophore model the 20,000 most druglike molecules from the SPECS database (229,658 compounds) were screened for Tat-TAR ligands. Both reference inhibitors were also applied for CATS3D similarity searching. A set of 19 molecules from the SQUID and CATS3D results was selected for experimental testing. In a fluorescence resonance energy transfer (FRET) assay the best SQUID hit showed an IC50 value of 46 µM, which represents an approximately tenfold improvement over the reference acetylpromazine. The best hit from CATS3D similarity searching showed an IC50 comparable to acetylpromazine (IC50 = 500 µM). Both hits contained different molecular scaffolds than the reference molecules. Structure-based pharmacophores provide an alternative to ligand-based approaches, with the advantage that no ligands have to be known in advance and no topological bias is introduced. The latter is e.g. favorable for hopping from peptide-like substrates to drug-like molecules. A homology model of the threonine aspartase taspase1 was calculated based on the crystal structures of a homologous isoaspartyl peptidase. Docking studies of the substrate with GOLD identified a binding mode where the cleaved bond was situated directly above the reactive N-terminal threonine. The predicted enzyme-substrate complex was used to derive a pharmacophore model for virtual screening for novel taspase1 inhibitors. 85 molecules were identified from virtual screening with the pharmacophore model as potential taspase1- inhibitors, however biochemical data was not available before the end of this thesis. In summary this thesis demonstrated the successful development, improvement and application of pharmacophore-based virtual screening methods for the compilation of molecule-libraries for early phase drug development. The highest potential of such methods seemed to be in scaffold hopping, the non-trivial task of finding different molecules with the same biological activity.Ziel dieser Arbeit war die Entwicklung, Untersuchung und Anwendung von neuen virtuellen Screening-Verfahren für den rationalen Entwurf hoch-qualitativer Molekül-Datenbanken für das pharmakologische Screening. Anforderung für eine hohe Qualität waren eine hohe a priori Wahrscheinlichkeit für das Vorhandensein aktiver Moleküle im Vergleich zu zufällig zusammengestellten Bibliotheken, sowie das Vorhandensein einer Vielfalt unterschiedlicher Grundstrukturen unter den selektierten Molekülen, um gegen den Ausfall einzelner Leitstrukturen in der weiteren Entwicklung abgesichert zu sein. Notwendig für die letztere Eigenschaft ist die Fähigkeit eines Verfahrens zum „Grundgerüst-Springen“. Der erste Molekül-Deskriptor, der explizit für das „Grundgerüst-Springen“ eingesetzt wurde war der CATS Deskriptor – ein topologischer Korrelations-Vektor („correlation vector“, CV) über alle Pharmakophor-Punkte eines Moleküls. Der Vergleich von Molekülen über den CATS Deskriptor geschieht ohne eine Überlagerung der Moleküle, was den effizienten Einsatz solcher Verfahren für sehr große Molekül-Datenbanken ermöglicht. In einer ersten Serie von Versuchen wurde der CATS Deskriptor erweitert zu dem dreidimensionalen CATS3D Deskriptor und dem auf der Molekül-Oberfläche basierten SURFCATS Deskriptor. In retrospektiven Studien wurde für diese Deskriptoren der Einfluss verschiedener Skalierungs-Methoden, die Kombination mit unterschiedlichen Ähnlichkeits- Metriken und die Auswirkung verschiedener dreidimensionaler Konformationen untersucht. Weiter wurden das Potential der entwickelten Deskriptoren CATS3D und SURFCATS im „Grundgerüst-Springen“ mit CATS und dem Substruktur-Fingerprint MACCS keys verglichen. Prospektive Anwendungen der CATS3D Ähnlichkeitssuche wurden für die TARRNA und den metabotropen Glutamat Rezeptor 5 (mGluR5) durchgeführt. Eine Kombination von überwachten und unüberwachten neuronalen Netzen wurde prospektiv für die Zusammenstellung einer fokussierten aber dennoch diversen Bibliothek von mGluR5 Modulatoren eingesetzt. In einer zweiten Reihe von Versuchen wurde der SQUID Fuzzy Pharmakophor Ansatz entwickelt, mit dem Ziel zu einer noch generelleren Molekül- Beschreibung als mit den Deskriptoren aus der CATS Familie zu gelangen. Eine prospektive Anwendung der „Fuzzy Pharmakophor“ Methode wurde für die TAR-RNA durchgeführt. In einem letzten Versuch wurde für Taspase1 ein Struktur-/Liganden-basiertes Pharmakophor- Modell auf der Grundlage eines Homologie-Modells des Enzyms entwickelt. Dieses wurde für das prospektive Screening nach Taspase1-Inhibitoren eingesetzt. Der Einfluss verschiedener Ähnlichkeits-Metriken (Euk: Euklidische Distanz, Manh: Manhattan Distanz, Tani: Tanimoto Ähnlichkeit) und verschiedener Skalierungs-Methoden (Ohne-Skalierung, Skalierung1: Skalierung aller Werte nach der Anzahl Atome, Skalierung2: Skalierung der Werte eines Paares von Pharmakophor-Punkten entsprechend der Summe aller Pharmakophor-Punkte mit denselben Pharmakophor-Typen) auf die Ähnlichkeits-Suche mit CATS3D wurde in retrospektiven virtuellen Screening Experimenten untersucht. Für diesen Zweck wurden 12 verschiedene Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank von annotierten Liganden aus der jüngeren wissenschaftlichen Literatur eingesetzt. Skalierung2, eine neue Entwicklung für CATS3D, zeigte im Durchschnitt die beste Performanz in Kombination mit allen drei Ähnlichkeits-Metriken (Anreicherungs-Faktor ef (1%): Manh = 11,8 ± 4,3; Euk = 11,9 ± 4,6; Tani = 12,8 ± 5,1). Die Kombination von Skalierung2 mit dem Tanimoto Ähnlichkeits-Koeffizienten lieferte die besten Ergebnisse. In Kombination mit den anderen Skalierungen brachte die Manhattan Distanz die besten Ergebnisse (ef (1%): Ohne-Skalierung: Manh = 9,6 ± 4,0; Euk = 8,1 ± 3,5; Tani = 8,3 ± 3,8; Skalierung1: Manh = 10,3 ± 4,1; Euk = 8,8 ± 3,6; Tani = 9,1 ± 3,8). Da die CATS3D Ähnlichkeits-Suche unabhängig von der Überlagerung einzelner Moleküle ist, könnte ebenfalls eine gewisse Unabhängigkeit von der vorhandenen 3D Konformation bestehen. Eine solche Unabhängigkeit wäre interessant um die zeitaufwendige Berechnung multipler Konformationen zu umgehen. Um diese Hypothese zu untersuchen wurden Co-Kristalle von Liganden aus 11 Klassen von Rezeptoren und Enzymen ausgewählt, um als Anfrage-Strukturen im virtuellen Screening in der COBRA Datenbank zu dienen. Verschiedene Versionen der COBRA Datenbank mit unterschiedlicher Anzahl Konformationen wurden berechnet. Bereits mit einer einzigen Konformation pro Molekül konnte im Mittel eine deutliche Anreicherung an aktiven Molekülen beobachte werden (ef (1%) = 6,0 ± 6,5). Diese Beobachtung beinhaltete auch Klassen von Molekülen mit vielen rotierbaren Bindungen. (z.B. HIV-Protease: 19,3 ± 6,2 rotierbare Bindungen in COBRA, ef (1%) = 12,2 ± 11,8). Im Mittel konnten dazu bei Verwendung der maximalen Anzahl Konformationen (durchschnittlich 37 Konformationen / Molekül) nur eine Verbesserung von 1.1 festgestellt werden. Nach der CATS3D Ähnlichkeit wurden die inaktiven Moleküle im gleichen Maß ähnlicher zu den Referenzen als die aktiven Moleküle. Zum Vergleich konnte durch Verwendung multipler statt einzelner Konformationen eine 1,8-fache Verbesserung des RMSD zu den Konformationen aus den Kristall-Struktur Konformationen erreicht werden (einzelne Konformationen: 1,7 ± 0,7 Å; max. Konformationen: 1,0 ± 0,5 Å). Um die Leistungsfähigkeit von CATS3D und SURFCATS im virtuellen Screening und im Grundgerüst-Springen zu beurteilen, wurden diese Deskriptoren mit CATS und den MACCS keys, einem Fingerprint basierend auf exakten chemischen Substrukturen, verglichen. Für die retrospektive Analyse wurden 10 Klassen von Rezeptoren und Enzymen aus der COBRA Datenbank ausgewählt. Nach den mittleren Anreicherungs-Faktoren ergaben sich für MACCS die besten Resultate (ef (1%): MACCS = 17,4 ± 6,4; CATS = 14,6 ± 5,4; CATS3D = 13,9 ± 4,9; SURFCATS = 12,2 ± 5,5). Es zeigte sich, dass die Klassen, in denen MACCS die besten Ergebnisse erzielen konnte, einen geringen gemittelten Anteil von verschiedenen Grundgerüsten aufwiesen im Verhältnis zu der Anzahl an Molekülen (0,44 ± 0,13) als die Klassen, in denen CATS am besten war (0,65 ± 0,13). CATS3D war nur in einer Klasse mit einem mittleren Anteil von Grundgerüsten (0,55) die beste Methode. SURFCATS war für keine Klasse besser als alle anderen Methoden. Diese Ergebnisse deuten darauf hin, dass Methoden wie CATS und CATS3D besser geeignet sind, um neue Grundgerüste zu finden. Es konnte weiter gezeigt werden, dass sich die Methoden einander ergänzen, dass also mit jeder Methode Grundgerüste gefunden werden konnten, die mit keiner der anderen Methoden gefunden werden konnten. Eine prospektive Anwendung wurde für CATS3D in der Suche nach neuen allosterischen Modulatoren des metabotropen Glutamat Rezeptors 5 (mGluR5) durchgeführt. Sieben bekannte allosterische mGluR5 Antagonisten mit sub-mikromolaren IC50 Werten wurde als Referenzen eingesetzt. Das virtuelle Screening wurde auf den 20.000 von einem künstlichen neuronalen Netz als am wirkstoff-artigsten vorhergesagten Molekülen der Asinex Datenbank (194.563 Moleküle) durchgeführt. Acht der 29 gefundenen Hits aus dem virtuellen Screening zeigten Ki Werte unter 50 µM in einem Bindungs-Assay. Die Mehrheit der Liganden zeigte nur eine geringe Selektivität (Maximum > 4,2-fach) gegenüber mGluR1, dem ähnlichsten Rezeptor zu mGluR5. Einer der Liganden zeigte einen besseren Ki für mGluR1 als für mGluR5 (mGluR5: Ki > 100 µM, mGluR1: Ki = 14 µM). Alle gefundenen Moleküle zeigten verschiedene Grundgerüste als die Referenz Moleküle. Es konnte gezeigt werden, dass die zusammengestellte Bibliothek von den MACCS keys als unterschiedlich zu den Referenz Strukturen betrachtet wurden, von CATS und CATS3D aber noch als isofunktional betracht wurden. Künstliche neuronal Netze („artificial neural net“, ANN) bieten eine Alternative zur Ähnlichkeits-Suche im virtuellen Screening mit dem Vorteil, dass in einer Serie von Liganden enthaltenes implizites Wissen über eine Lernprozedur in ein Modell integrierte werden kann. Eine Kombination von ANNs für die Zusammenstellung einer fokussierten aber dennoch diversen Molekül-Bibliothek wurde prospektiv für die Suche nach mGluR5 Antagonisten eingesetzt. Gruppen von ANNs wurden auf den Basis von CATS3D Repräsentationen für die Vorhersage von „mGluR5-artigkeit“ und „mGluR5/mGluR1 Selektivität“ trainiert. Dabei ergaben sich Matthews cc zwischen 0,88 und 0,92 sowie zwischen 0,88 und 0,91. Die besten 8.403 Hits (die Schnittmenge der besten Hits aus beiden Vorhersagen) aus einem virtuellen Screening der Enamine Datenbank (ca. 1.000.000 Moleküle) ergab die fokussierte Bibliothek. Diese wurde weiter mit Selbstor

Dopamine Transporter (DAT), Nicotinic Acetylcholine Receptor (nAChR), and Metabotropic Glutamate Receptor 2 (mGlu2) Irreversible Probes For Identifying Anti-Psychostimulant Therapeutics

Numerous in vitro and in vivo studies implicate that certain ligands that interact with DAT, nAChRs, and mGlu2 have tremendous potential as anti-addiction therapeutics. However, understanding how these promising anti-addiction compounds interact with their major drug targets at the molecular level is limited because of the absence of human DAT, nAChRs, and mGlu2 x-ray crystal structures. This knowledge gap is important towards rationally designing new therapeutics for psychostimulant abuse and addiction. The objective of this research was to develop irreversible chemical probes based on promising anti-addiction lead compounds (i.e., pyrovalerone, bupropion, BINA, etc) to map their binding sites and poses within the DAT, select nAChR subtypes, or mGlu2. The central hypothesis was that these compounds could be rationally derivatized, without significant alteration in their pharmacological activity, with a photoreactive group capable of forming a covalent bond to their target protein and a tag for application of a Binding Ensemble Profiling with (f)Photoaffinity Labeling (BEProFL) experimental approach. BEProFL rationally couples photoaffinity labeling, chemical proteomics, and computational molecular modeling to allow structure-function studies of the target proteins. This central hypothesis was tested by pursuing three specific aims: 1.) Identification of non-tropane photoprobes based on pyrovalerone (PV) suitable for DAT structure-function studies, 2.) Identification of bupropion (BP)-based photoprobes suitable for DAT, and nAChR structure-function studies, and 3.) Identification of irreversible mGlu2 PAM ligands as chemical probes suitable for mGlu2 structure-function studies. In the first aim, PV, a non-tropane DAT inhibitor, was structurally modified to contain a photoreactive group (i.e., an aryl azide) and a tag (i.e., 125I). These photoprobes were then pharmacologically evaluated to identify suitable candidates for DAT structure-function studies. In the second aim, BP was structurally modified to contain an aryl azide and 125I. This probe successfully identified the exact location of the bupropion-binding site within the Torpedo nAChR. Under the third aim, biphenyl-carboxylic acid indanone- and pyridone-based mGlu2 PAMs were structurally modified to contain a photoreactive group (e.g., aryl azide, acetophenone) and a tag (e.g., terminal alkyne, aliphatic azide). These compounds, at present, are being subjected to mGlu2 pharmacological evaluation to identify suitable chemical probe candidates for mGlu2 structure-function studies