Open Access

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.

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.

Ziel dieser Arbeit war die Identifizierung und Charakterisierung neuer potenter 5-LO-Inhibitoren unter Verwendung sowohl computergestützter als auch experimenteller Methoden. Ausgangspunkt war ein ligandenbasiertes virtuelles Screening unter Verwendung der ladungsbasierten Deskriptoren Charge 3D und TripleCharge3D. Hierbei konnten zwei neue direkte 5-LO-Inhibitoren identifiziert werden. Jede dieser beiden Substanzen diente als Startpunkt weiterer virtueller Screenings mit dem Ziel, die Potenz der Substanzen zu verbessern bzw. eine SAR der Substanzklasse zu erhalten. Dabei zeigte sich für die Klasse der Thiazolinone, dass eine hohe Toleranz gegenüber unterschiedlichen Substituenten am Grundgerüst bezüglich der Auswirkung auf die Aktivität vorliegt: insbesondere werden relativ große Substituenten toleriert. Des Weiteren scheint der 2-Phenylsubstituent für die 5-LO-inhibitorische Aktivität essentiell zu sein, da Derivate, die einen Heterozyklus an dieser Position aufweisen, inaktiv sind. Eines der aktivsten Derivate dieser Klasse, C06 (Substanz 12), konnte weiter molekular-pharmakologisch charakterisiert werden. Die Substanz zeigt keine offensichtlichen zytotoxischen Effekte, ist unabhängig vom Stimulus der 5-LO-Aktivierung und zeigt nanomolare inhibitorische Aktivität sowohl in intakten PMNL (IC50-Wert 0,65 =M) als auch in PMNL-Homogenaten (IC50-Wert 0,66 =M) sowie in zellfreiem PMNL-S100 (IC50-Wert 0,26 =M) und am gereinigten Enzym (IC50-Wert 0,3 =M). C06 ist selektiv für die 5-LO, da andere arachidonsäurebindende Proteine (PPARs, cPLA2 und 12- und 15-LO) nicht beeinflusst werden. Auch Nager-5-LO (aus der Ratte und der Maus) wird inhibiert mit IC50-Werten im nanomolaren Bereich. Allerdings zeigte sich die Substanz inaktiv in einem menschlichen Vollblutassay in Gegenwart von Serum. C06 scheint nicht an die für die Interaktion der 5-LO mit der Membran verantwortliche C2-ähnliche Domäne der 5-LO zu binden. Ebenso hat der Membranbestandteil Phosphatidylcholin keinen bzw. nur geringen Einfluss auf das inhibitorische Potential von C06. Aktivitätstests mit steigender Substratkonzentration deuten auf einen allosterischen Bindemodus von C06 hin. Diese experimentellen Daten flossen in die Identifizierung des putativen putativen Bindemodus an der 5-LO ein. Hierzu wurde zunächst ein Homologie- Modell der 5-LO unter Verwendung der kürzlich neu veröffentlichten Daten der Röntgenkristallstruktur der Kaninchen-15-LO (PDB-Code: 2p0m [Choi et al., 2008]) erstellt. Durch eine Bindetaschenvorhersage mit dem Programm PocketPicker und einer pseudorezptorbasierten Auswahl der potentiellen Bindetasche mit anschliessendem Liganden-Docking konnten zwei Bindebereiche postuliert werden, beide an einer Oberflächenbindetasche der 5-LO ausserhalb des aktiven Zentrums. .....