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Reggie-1 (flotillin-2) and reggie-2 (flotillin-1) are membrane microdomain proteins which are associated with the membrane by means of acylation. They influence different cellular signaling processes, such as neuronal, T-cell and insulin signaling. Upon stimulation of the EGF receptor, reggie-1 becomes phosphorylated and undergoes tyrosine 163 dependent translocation from the plasma membrane to endosomal compartments. In addition, reggie-1 was shown to influence actindependent processes. Reggie-2 has been demonstrated to affect caveolin- and clathrin-independent endocytosis. Both proteins form homo- and hetero-oligomers, but the function of these oligomers has remained elusive. Moreover, it has not been clarified if functions of reggie-1 are also influenced by reggie-2 and vice versa. The first aim of the study was to further investigate the interplay and the heterooligomerization of reggie proteins and their functional effects. Both reggie proteins were individually depleted by means of siRNA. In different siRNA systems and various cell lines, reggie-1 depleted cells showed reduced protein amounts of reggie-1 and reggie-2, but reggie-2 knock down cells still expressed reggie-1 protein. The decrease of reggie-2 in reggie-1 depleted cells was only detected at protein but not at mRNA level. Furthermore, reggie-2 expression could be rescued by expression of siRNA resistant wild type reggie-1-EGFP constructs, but not by the soluble myristoylation mutant G2A. This mutant was also not able to associate with endogenous reggie-1 or reggie-2, which demonstrates that membrane association of reggie-1 is necessary for hetero-oligomerization. In addition, fluorescence microscopy studies and membrane fractionations showed that correct localization of overexpressed reggie-2 was dependent on co-overexpressed reggie-1. Thus, hetero-oligomerization is crucial for membrane association of reggie-2 and for its protein stability or protein expression. Moreover, the binding of reggie-2 to reggie-1 required tyrosine 163 of reggie-1 which was previously shown to be important for endosomal translocation of reggie-1. Since reggie-2 was implicated to function in clathrin- and caveolin-independent endocytosis pathways, the effect of reggie-2 depletion on reggie-1 endocytosis was investigated. Indeed, reggie-1 was dependent on reggie-2 for endosomal localization and EGF-induced endocytosis. By FRET-FLIM analysis it could be shown that reggie heterooligomers are dynamic in size or conformation upon EGF stimulation. Thus, it can be concluded that reggie proteins are interdependent in different aspects, such as protein stability or expression, membrane association and subcellular localization. In addition, these results demonstrate that the hetero-oligomers are dynamic and reggie proteins influence each other in terms of function. A further aim was the characterization of reggie-1 and reggie-2 function in actindependent processes, where so far only reggie-1 was known to play a role. Depletion of either of the proteins reduced cell migration, cell spreading and the number of focal adhesions in steady state cells. Thus, also reggie-2 affects actin-dependent processes. Further investigation of the focal adhesions during cell spreading revealed that depletion of reggie-1 displayed different effects as compared to reggie-2 knock down. Reggie-1 depleted cells had elongated cell-matrix-adhesions and showed reduced activation of FAK and ERK2. On the other hand, depletion of reggie-2 resulted in a restricted localization of focal adhesion at the periphery of the cell and decreased ERK2 phosphorylation, but it did not affect FAK autophosphorylation. Hence, reggie proteins influence the regulation of cell-matrix-adhesions differently. A link between reggie proteins and focal adhesions is the actin cross-linking protein -actinin. The interaction of -actinin with reggie-1 could be verified by means of co-immunoprecipitations and FRET-FLIM analysis. Reggie-1 binds -actinin especially in membrane ruffles and in other locations where actin remodeling takes place. Moreover, -actinin showed a different localization pattern during cell spreading in reggie-1 depleted cells, as compared to the control cells. These results provide further insights into the function of both reggie proteins. Their interplay and hetero-oligomerization was shown to be crucial for their role in endocytosis. In addition, both reggie proteins influence actin-dependent processes and differentially affect focal adhesion regulation.
Analysis of coding principles in the olfactory system and their application in cheminformatics
(2007)
Unser Geruchssinn vermittelt uns die Wahrnehmung der chemischen Welt. Im Laufe der Evolution haben sich in unserem olfaktorischen System Mechanismen entwickelt, die wahrscheinlich optimal auf die Erfüllung dieser Aufgabe angepasst sind. Die Analyse dieser Verarbeitungsstrategien verspricht Einblicke in effiziente Algorithmen für die Kodierung und Verarbeitung chemischer Information, deren Entwicklung und Anwendung dem Kern der Chemieinformatik entspricht. In dieser Arbeit nähern wir uns der Entschlüsselung dieser Mechanismen durch die rechnerische Modellierung von funktionellen Einheiten des olfaktorischen Systems. Hierbei verfolgten wir einen interdisziplinären Ansatz, der die Gebiete der Chemie, der Neurobiologie und des maschinellen Lernens mit einbezieht.
The aim of the thesis was to identify structure activity relationships (SAR) in the primary screening data of high-throughput screening (HTS) assays. The strategy was to perform a hierarchical clustering of the molecules, assign the primary screening data to the created clusters and derive models from the clusters. The models should serve to identify singletons, clusters enriched with actives, not confirmed hits and false-negatives. Two hierarchical clustering algorithms, NIPALSTREE and hierarchical k-means have been developed and adapted for this purpose, respectively. A graphical user interface (GUI) has been implemented to extract SAR from the clustering results. Retrospective and prospective applications of the clustering approach were performed. SAR models were created by combining the clustering results with different chemoinformatic methods. NIPALSTREE projects a data set onto one dimension using principle component analysis. The data set is sorted according to the scoring vector and split at the median position into two subsets. The algorithm is applied recursively onto the subsets. The hierarchical k-means recursively separates a data set into two clusters using the k-means algorithm. Both algorithms are capable of clustering large data sets with more than a million data points. They were validated and compared to each other on the basis of different structural classes. NIPALSTREE provided with the loading vectors first insights into SAR whereas the hierarchical k-means yielded superior results. A GUI was developed allowing the display of and the navigation in the clustering results. Functionalities were integrated to analyse the clusters in the dendrogram, molecules in a cluster, and physicochemical properties of a molecule. Measures were developed to identify clusters enriched with actives, to characterize singletons and to analyse selectivity and specificity. Different protease inhibitors of the COBRA database were examined using the hierarchical k-means algorithm. Supported by similarity searches and nearest neighbour analyses thrombin inhibitor singletons were quickly isolated and displayed in the dendrogram. By scaling enrichment factors to the logarithm of the dendrogram level, clusters enriched with different structural classes of factor Xa inhibitors were simultaneously identified. The observed co-clustering of other protease inhibitors provided a deeper insight into selectivity and specificity and shows the utility of the approach for constructing focussed screening libraries. Specificity was analyzed by extracting and clustering relative frequencies of the protease inhibitors from the clusters of dendrogram level 7. A unique ligand based point of view on the pocketome of the protease enzymes was obtained. To identify not confirmed hits and false-negatives in the primary screening data of HTS assays, three assays were retrospectively analysed with the hierarchical k-means algorithm. A rule catalogue was developed judging hits in terminal clusters based on the cluster size, the percent control values of the entries in a cluster, the overall hit rate, the hit rate in the cluster and the environment of a cluster in the dendrogram. It resulted in the identification of a high proportion of not confirmed hits and provided for each hit a rating in context of related non-hits. This allows prioritizing compounds for follow-up studies. Non-hits and hits were retrieved from terminal clusters containing hits. Molecules bearing false-negative scaffolds were co-extracted and enriched. To minimize the number of false-positives in the extracted lists, Bayesian regularized artificial neutral network classification models were trained with the data. Applying the models marked improvement of enrichment factors for the false-negatives was obtained. It proofs the scaffold-hopping potential of the approach. NIPALSTREE, the hierarchical k-means algorithm and self-organising maps were prospectively applied to identify novel lead candidates for dopamine D3 receptors. Compounds with novel scaffolds and low nanomolar binding affinity (65 nM, compound 42) were identified. To provide a deeper insight into the SAR of these molecules, different alternative computational methods were employed. Support vector-based regression and partial least squares were examined. Predictive models for dopamine D2 and D3 receptor binding affinity values were obtained. Important features explaining SAR were extracted from the models. The prospective application of the models to the diverse and novel virtual screening data was of limited success only. Docking studies were performed using a homology model of the dopamine D3 receptor. The visual inspection of the binding modes resulted in the hypothesis of two alternative binding pockets for the aryl moiety of dopamine D3 receptor antagonists. A pharmacophore model was created simultaneously requiring both aryl moieties. Virtual screening with the model identified a nanomolar hit (65 nM, compound 59) corroborating the hypothesis of the two binding pockets and providing a new lead structure for dopamine D3 receptors. The presented data shows that the combined approach of hierarchically clustering a data set in combination with the subsequent usage of the clusters for model generation is suited to extract SAR from screening data. The models are successful in identifying singletons, clusters enriched with actives, not confirmed hits and false-negative scaffolds.
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.
Das Enzym 5-Lipoxygenase (5-LO) spielt eine essentielle Rolle in der Biosynthese der Leukotriene, bioaktiver Metabolite der Arachidonsäure (AA), die an einer Vielzahl entzündlicher und allergischer Erkrankungen beteiligt sind. Die 5-LO wird bevorzugt in Zellen myeloiden Ursprungs wie Granulozyten, Monozyten oder B-Lymphozyten exprimiert. In die Regulation der zellulären 5-LO-Aktivität in der Epstein-Barr Virus-transformierten B-lymphozytären Zelllinie BL41-E95-A sind Caspasen, Aspartat-spezifische Cysteinproteasen, involviert. Das Passagieren von BL41-E95-A führt zu einer Erhöhung der Proliferationsrate der B-Lymphozyten sowie zu einem deutlichen Verlust der 5-LO-Aktivität, der mit dem Auftreten eines 62 kDa-Spaltproduktes der 5-LO und einer signifikanten Aktivitätserhöhung der Caspase-8 und -6 korreliert. Isolierte humane 5-LO wird durch rekombinante Caspase-6 zwischen Asp170 und Ser171 zu einem 58 kDa-Fragment in vitro gespalten, wobei das Tetrapeptid VEID170 innerhalb der 5-LO als Erkennungsmotiv für den Angriff der Caspase-6 dient. In einigen weiteren untersuchten Zelllinien wie Mono Mac 6 (MM6), RBL-1, PMNL oder HeLa, die nicht den B-Lymphozyten angehören, konnte die 5-LO-Spaltung weder durch das Passagieren von Zellen noch durch die Behandlung mit diversen proapoptotischen Agentien ausgelöst werden. Laut Ergebnissen aus in vitro-Untersuchungen scheinen 5-LO-positive HeLa- bzw. MM6-Zellen einen Faktor zu exprimieren, der die 5-LO direkt oder indirekt vor dem Angriff der Caspase-6 und anschließender Prozessierung schützt. Die in den BL41-E95-A-Zellen beobachtete Aktivierung der Caspasen mit anschließender Prozessierung der 5-LO lässt sich durch zwei Pflanzeninhaltsstoffe supprimieren, das Hyperforin (HP) aus Johanniskraut-Extrakten und das Myrtucommulon (MC) aus Myrte-Blättern. Beide Verbindungen scheinen in B-Lymphozyten zu einer Hemmung der Caspasen-Aktivierung zu führen. Nichtsdestotrotz führt die Behandlung der B-Lymphozyten mit HP bzw. MC zu einem apoptotischen Tod der Zellen. Offensichtlich wird dabei ein (unbekannter) einzigartiger Mechanismus der Apoptose-Induktion ausgelöst. In der vorliegenden Arbeit konnte zum ersten Mal eine potente Apoptose-induzierende Wirkung des natürlich vorkommenden Myrtucommulons auf Krebszelllinien gezeigt werden. In allen getesteten Krebszelllinien führte Myrtucommulon zum Zelltod, wobei die HL-60-Zellen mit einem IC50-Wert von 3,26 ± 0,51 µM MC am sensitivsten gegenüber MC-Einfluss waren. Zusätzlich konnte in HL-60- und MM6-Zellen nach MC-Behandlung neben einer erhöhten Caspasen-Aktivität und PARP-Spaltung ein signifikanter DNA-Abbau detektiert werden. Von besonderer Bedeutung ist die Tatsache, dass die zytotoxische MC-Wirkung eine bemerkenswerte Selektivität für entartete Zelllinien zu besitzen scheint und gegenüber nicht-transfizierten Zellen minimal ist.
The goal of this thesis was to gain further insight into the binding behavior of ligands in the heptahelical domain (HD) of group I metabotropic glutamate receptors (mGluRs). This was realized by the establishment of strategies for the detection and optimization of molecules acting as non-competitive antagonists of group I mGluRs (mGluR1/5). These strategies should guarantee high diversity in the retrieved chemotypes of the detected compounds not resembling original reference molecules (“scaffold-hopping”). The detection of new scaffolds, in turn, was divided into two approaches: First the development of pharmacological assays to screen compounds at a certain target for bioactivity (here: affinity towards the allosteric recognition site of mGluR1 and mGluR5), and second the evaluation of computer assisted methods for the identification of virtual hits to be screened afterwards on the pharmacological assays established before. Promising molecules should be optimized with respect to activity/affinity and selectivity, their binding mode investigated and, finally, compared to existing lead compounds. Initially, membrane based binding assays for the HD of mGlu1 and mGlu5 receptors with enhanced throughput (shifting from 24-well plates to 96-well plates) were set up. For the mGluR1 assay the potent antagonist EMQMCM exhibited high affinity towards the binding site (Ki ~3nM), which is in accordance with published data from Mabire et al. (functional IC50 3nM). For mGluR5 the reference antagonist MPEP binds with high affinity to the receptor (binding IC50 13.8nM), which confirmed earlier findings from Anderson et al. (binding IC50 15nM). In another series of experiments the properties of rat cerebellar (mGluR1) and corticalmembranes (mGluR5) as well as of radiotracers were investigated by means of binding saturation studies and kinetic experiments. Furthermore, the influence of the solvent DMSO, necessary for compound screening of lipophilic substances, on positive and negative controls was evaluated. As the precise architecture of the HD of mGluR1 is still not known our efforts in identifying new ligands for this receptor focused on the ligand-based approach. All computer assisted methods that were applied to virtually screen large compound collections and to retrieve potential hits (“activity-enriched subsets”) acting at the heptahelical domain of mGluR1 relied on the existence of a valid dataset of reference molecules. This was realized by an initial compilation of a mGluR reference data collection comprising in total 357 entries predominantly negative but also some positive allosteric modulators for mGluR1 and mGluR5. In the next step a pharmacophore model for non-competitive mGluR1 antagonists was constructed. It was based upon six selective, potent and structurally diverse ligands. Prospective virtual screening was performed using the CATS atom-pair descriptor. The Asinex Gold-Collection was screened for each seed compound and some of the most similar compounds (according to the CATS descriptor) were ordered and tested forbinding affinity and functional activity at mGluR1. A high hit rate of approximately 26% (IC50 < 15 micro M) was yielded confirming the applicability of this method. One compound exerted functional activity below one micro molar (IC50-value of C-07:362nM ± 0.03). Moreover, non-linear principal component analysis was employed. Again the Asinex vendor database served as test database and was filtered by the pharmacophore model for mGluR1 established before. Test molecules that were adjacently located with mGluR1 antagonist references were selected. 15 compounds were tested on mGluR1 in binding and functional assays and three of them exhibited functional activity (IC50) below 15 micro M. The most potent molecule P-06 revealed an IC50-value of 1.11 micro M (± 0.41). The COBRA database comprising 5,376 structurally diverse bioactive molecules affecting various targets was encoded with the CATS descriptor and used for training two selforganizing maps (SOM). The encoded mGluR reference data collection was projected onto this map according to the SOM algorithm. This projection allowed to clearly distinguish between antagonists of mGluR1 and mGluR5 subtype. 28 compounds were ordered and tested on activity and affinity for mGluR1. They exhibited functional activity down to the sub-micro molar range (IC50-value of S-08: 744nM ± 0.29) yielding a final hit rate of 46% (<15 micro M). Then, the Asinex collection was screened using the SOM approach. For a predicted target panel including the muscarinic mACh (M1) receptor, the histamine H1-receptor and the dopamine D2/D3 receptors, the tested mGluR ligands exhibited the calculated binding pattern. This virtual screening concept might provide a basis for early recognition of potential sideeffects in lead discovery. We superimposed a set of 39 quinoline derivatives as non-competitive mGluR1 antagonists that were recently published by Mabire and co-workers. A CoMFA model (QSAR) was established and the influence of several side chains on functional activity was investigated. The coumarine derivative C-07 was obtained as a result of similarity searching. Starting from this compound a series of chemical derivatives was synthesized. This led to the discovery of potent (B-28, IC50: 58nM ± 0.008; Ki: 293nM ± 0.022) and selective (rmGluR5 IC50: 28.6 micro M) mGluR1 antagonists. From a homology model of mGluR1 we derived a potential binding mode for coumarines within the allosteric transmembrane region. Potential interacting patterns with amino acids were proposed considering the difference of the binding pockets between rat and human receptors. The proposed binding modes for quinolines (here:EMQMCM) and coumarines (here:B-04) were compared and discussed considering in particular the influence on activity of several side chains of quinolines obtained from the QSAR studies. The present studies demonstrated the applicability of ligand-based virtual screening for non-competitive antagonists of a G-protein coupled receptor, resulting in novel, potent and selective agents.
In der vorliegenden Arbeit sollte das basolaterale Targeting des Transmembranproteins shrew-1 in polarisierten Epithelzellen analysiert werden. Es konnte gezeigt werden, dass die cytoplasmatische Domäne von shrew-1 mehrere spezifische basolaterale Sortingmotive enthält. Die Funktionalität dieser Motive wurde anhand Mutationsanalysen von Schlüsselaminosäuren untersucht. Substitution dieser Aminosäuren führt zu einer apikalen Lokalisation von shrew-1 in polarisierten MDCK Zellen. Durch Analyse der Proteinverteilung von shrew-1 Varianten in polarisierten LLC-PK1 Zellen wurde deutlich, dass das Sorting von shrew-1 in die basolaterale Plasmamembran ein AP-1B-abhängiger Prozess ist. Außerdem konnte mittels Coimmunopräzipitation eine Interaktion zwischen shrew-1 und der Untereinheit my1B aus dem Adapterproteinkomplex AP-1B nachgewiesen werden. Untersuchungen des Targetings von shrew-1 Varianten in polarisierten MDCK und LLCPK1 Zellen mit Hilfe der Transzytoseexperimente zeigten, dass die apikal lokalisierte Mutante shrew-1-NTD5 auf dem Weg zur apikalen Membranregion, trotz fehlender Sortinginformation, die basolaterale Plasmamembran durchquert. Durch Inhibition der Membranfusion mittels Tanninsäure konnte zusätzlich gezeigt werden, dass die Passage der basolateralen Plasmamembran für das Targeting von sowohl shrew-1 als auch von shrew-1-NTD5 essentiell ist. Die Beobachtungen des Turnovers von shrew-1 in der Plasmamembran von lebenden Zellen zeigten, dass shrew-1 aktiv endozytiert wird und dass nachfolgend ein Recycling des Proteins zur Plasmamembran stattfindet. Anhand der durchgeführten Untersuchungen lässt sich zusammenfassend ein Targetingmodell für shrew-1 in polarisierten Epithelzellen aufstellen, das ein postendozytotisches Sorting beschreibt: Dabei wird shrew-1 zunächst in Post-Golgi-Carriern auf unbekanntem Weg zur basolateralen Plasmamembran gebracht, wo seine unmittelbare Internalisierung und ein Weitertransport zum Recyclingendosom stattfinden. Der im Recyclingendosom lokalisierte und am Sorting beteiligte Adapterproteinkomplex AP-1B vermittelt dann den Rücktransport von shrew-1 zur basolateralen Plasmamembran.