Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Incorporation of Molecular Nanoparticles Inside Proteins: The Trojan Horse Approach in Theranostics

CONSPECTUS: Molecular nanoparticles, MNPs, characterized by well-defined chemical formulas, structures, and sizes can interact with a variety of proteins. Fullerenes, carboranes, and gold nanoclusters well represent the diversity of MNPs properties available in nanoscience. They can have diameters smaller than 1.5 nm, be hydrophilic or hydrophobic, and can use a paraphernalia of means to establish local and global interactions with the amino acidic residues of proteins. Proteins, endowed as they are with an assortment of pockets, crevices, and gaps are natural supramolecular hosts to incorporate/hide/transport MNPs directly in water with a facile and "green" approach.This Account identifies and discusses the rules that govern the interactions and binding between MNPs and proteins. Fullerenes are composed solely by carbon atoms arranged to form hollow polyhedra. Hydrophobic interactions occur between aliphatic residues and the fullerene surface. The amino acids most effectively interacting with fullerenes are aromatic residues that establish p-p stacking interactions with the cage. Amphiphilic and charged residues produce also cation-p, anion-p, and surfactant-like interactions with the cages.Carboranes are composed of boron, carbon, and hydrogen atoms, also arranged to form cages. They are hydrophobic with unusual properties originating from the presence of boron atoms. Hydride-like hydrogens bound to the boron atoms govern carborane chemistry. These negatively charged hydrogens do not participate in classic hydrogen bonding with water and promote hydrophobic interactions with proteins. On the contrary, the electronegativity of these hydrogens drives the formation of unconventional dihydrogen bonds with the acidic hydrogen atoms of positively charged amino acid. Carboranes also establish C-H center dot center dot center dot p and B-H center dot center dot center dot p interactions with aromatic residues.Gold nanoclusters, AuNCs, are synthesizable with atomically precise stoichiometry. Amino acid residues with sulfur atoms or with nitrogen-containing heterocycles are the strongest Au binders. The proteins can act as supramolecular hosts but also as templates for the synthesis of AuNCs directly inside the protein core. Of the pristine amino acids, tryptophan, tyrosine, phenylalanine, and aspartic acid are the most efficient reducing groups. In a peptide sequence, the best Au-reducing moieties are obtained by nitrogencontaining residue such as glutamine, asparagine, arginine, and lysine. The investigation of the interactions between AuNCs and proteins therefore adds further complexity with respect to that of fullerenes and carboranes. The selection of the host proteins should consider that they will have to contain active sites for metal ion accumulation and ion reduction where AuNC can form and stabilize. This Account further discusses the hybridization of MNPs with proteins in view of creating innovative multifunctional theranostic platforms where the role of proteins is akin to that of "Trojan Horses" since they can (i) hide the MNPs, (ii) control their cellular uptake, (iii) drive their crossing of physiological barriers, and (iv) ultimately govern their biological fate

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

Development of novel ligands influencing neurotransmission in the central nervous system

The development of novel drugs targeting GPCRs is of particular interest since modulation of subfamilies of this receptor class highly influences neurotransmission in the central nervous system. This study has focused on the development of ligands for the dopamine D3 receptor. The receptor belongs to the dopamine D2-like family among the biogenic amine binding GPCRs. The dopamine D3 receptor is involved in neurological and neuropsychiatric disorders such as Parkinson’s disease, schizophrenia and drug addiction. Due to its close structural similarity to the dopamine D2 receptor subtype, it is still a challenge to identify and further optimize new leads. Therefore an in vitro screening assay, which also allows elucidating comprehensive structure-affinity relationships, is required. In this investigation the implementation and evaluation of radioligand binding assays for human dopamine D2S and dopamine D3 receptors and for the related aminergic human histamine H1 receptor stably expressed in Chinese hamster ovary (CHO) cells has been performed. Saturation binding experiments with [³H]spiperone at dopamine D2S and D3 receptors and with [³H]mepyramine at histamine H1 receptors were carried out. The determined equilibrium dissociation constant of radioligands (Kd) and the total number of specific binding sites (Bmax) of the receptor membrane preparations were in good agreement with reference data. Inhibition constants (Ki) of reference ligands obtained in radioligand competition binding experiments at dopamine hD2S, hD3 and histamine H1 receptors validated the reliability and reproducibility of the assay. In order to discriminate agonists from antagonists, a GTP shift assay has been investigated for dopamine D2S and D3 receptors. In competition binding studies at dopamine D2S receptors the high- and low affinity state in the absence of the GTP analogue Gpp(NH)p has been recognized for the agonists pramipexole and the seleno analogue 54. In the presence of Gpp(NH)p a decrease in affinity, referred to as “GTP shift”, has been revealed for agonists at dopamine D2S and D3 receptors. An effect of Gpp(NH)p on dopamine D2S receptor binding has not been observed for the antagonists ST 198 and BP 897, while a reverse “GTP shift” has been noticed at the dopamine D3 receptor. For the development of novel ligands with high affinity and selectivity for dopamine D3 receptors, investigation in refined structure-affinity relationships (SAR) of analogues of the lead BP 897 has been performed. Replacement of the naphthalen-2-carboxamide of BP 897 by aryl amide residues (1 - 4) had a clear influence on affinity binding and selectivity for dopamine D3 receptors. Introduction of the benzo[b]thiophen-2-carboxamide (1) has markedly improved binding with subnanomolar affinity and enhanced selectivity for dopamine D3 receptors. Exchanging the aryl substituted basic alkanamine residue of 1 by a 1,2,3,4-tetrahydroisoquinoline moiety (6) emphasized the benefit of the 4-(2-methoxyphenyl) piperazine residue of BP 897 regarding dopamine D2 and D3 receptor affinities. The change of particular elements of BP 897 and the rearrangement of the amide functionality resulted in inverse amide compounds with new chemical properties. Moderate affinity binding data, as obtained for the isoindol-1-carbonyl compound 11, suggest that inverse amides provide a worthwhile new lead structure with a novel structural scaffold. A hybrid approach combining privileged scaffolds of histamine H1 receptor antagonists and fragments of dopamine D3 receptor-preferring ligands, related to BP 897and analogues has been investigated. Various benzhydrylpiperazine derivatives and related structures have shown moderate to high affinities for dopamine D3 receptors with the impressive enhancement of the cinnamide substituted bamipine-related hybrid 39, exhibiting the highest affinity and selectivity for dopamine D3 receptors. Improved affinity profiles of structural modified histamine H1 receptor antagonists for dopamine D2 and D3 receptors and a refined SAR has been achieved. A SAR of derivatives of the dopamine agonist pramipexole and the related etrabamine has been studied. The propargyl substituted etrabamine derivative 61 demonstrated highest affinity and selectivity. The ligand attracts attention since neuroprotective properties have been reported for the propargyl functionality. Further development resulted in the most promising compound 64, a cinnamide derivative with 4-fluoro substitution on the phenyl ring. Subnanomolar affinity and remarkable selectivity for dopamine D3 receptors has aroused particular interest in this ligand due to its development potential as a radioligand for PET studies. Radioligand binding studies in combination with virtual screening and different classification techniques of chemoinformatic methods resulted in further elucidation of SAR. New leads with novel chemical scaffolds have been found in the bicycle[2.2.1]heptane derivative 95 and the benzhydrylidene substituted pyrrolidindione 112 and can be further optimized by chemical modifications. The outcome of the studies provides the development of various novel high affine and dopamine D3 receptor selective ligands. Modifications of lead structures or application of chemoinformatic tools in combination with radioligand competition binding assays have resulted in new leads with different chemical scaffolds. Furthermore, a comprehensive insight into structure-affinity relationships of ligands at dopamine D3 receptors has been revealed. This refined SAR is valuable to develop more affine and selective drug candidates with a designed pharmacological receptor profile.Das Ziel der Arbeit war die Entwicklung von neuen Liganden zur Beeinflussung der Neurotransmission im zentralen Nervensystem. Der Fokus lag auf dem Dopamin-D3-Rezeptor, der eine wichtige Rolle bei Morbus Parkinson, Schizophrenie und Drogenmissbrauch spielt. Aufgrund seiner Strukturähnlichkeit zum Dopamin-D2-Rezeptor ist es eine Herausforderung, neue, selektive Leitstrukturen für den Dopamin-D3-Rezeptor zu identifizieren bzw. zu optimieren. Ein in vitro Testsystem ist hierfür erforderlich und ermöglicht das Aufstellen von Struktur-Wirkungsbeziehungen (SAR) und ein rationales Wirkstoffdesign. Die Arbeit umfasste die Etablierung von Radioliganden Bindungsassays an Dopamin-D2S- und -D3-Rezeptoren, sowie am verwandten aminergen Histamin-H1-Rezeptor, die stabil in Zelllinien von Ovarien des Chinesischen Hamsters exprimiert wurden. Sättigungsstudien wurden mit [³H]Spiperon am Dopamin-D2S- und D3-Rezeptor und mit [³H]Mepyramin am Histamin-H1-Rezeptor durchgeführt. Die ermittelten Dissoziationskonstanten (Kd) und maximale Zahl der Bindungsstellen (Bmax) stimmten mit den Literaturwerten überein. Die in Verdrängungsstudien bestimmten Inhibitionskonstanten (Ki) von Referenzsubstanzen am Dopamin-D2S- und -D3-Rezeptor sowie am Histamin-H1-Rezeptor bestätigten die Zuverlässigkeit und Reproduzierbarkeit der Bindungsassays. Zur Unterscheidung der Agonisten von Antagonisten wurden „GTP-Shift“ Assays am Dopamin-D2S- und -D3-Rezeptor angewandt. Für Pramipexol und das Selenanaloga 54 wurden zwei Bindungszustände mit unterschiedlichen Affinitäten (ein so genannter „high- und low affinity state“) am Dopamin-D2S-Rezeptor in Abwesenheit von Gpp(NH)p beobachtet. Eine Affinitätsabnahme („GTP-Shift“) in Anwesenheit von Gpp(NH)p zeigte sich für die Agonisten am Dopamin-D2S- und -D3-Rezeptor. Dieser Einfluss des Gpp(NH)p konnte nicht für den Antagonisten ST 198 und den partiellen Agonisten BP 897 gezeigt werden. Für diese Verbindung wurde ein inverser „GTP-Shift“, also eine Affinitätsverbesserung am Dopamin-D3-Rezeptor beobachtet. Um neue Liganden mit hoher Affinität und Selektivität für den Dopamin-D3-Rezeptor zu entwickeln, wurden ausführliche SAR verschiedener Derivate der Leitstruktur BP 897 und ST 198 erstellt. Der Austausch des Naphthalen-2-carboxamid-Rests von BP 897 durch verschiedene Arylamid-Strukturen (1 – 4) zeigte deren deutlichen Einfluss auf die Dopamin-D3-Rezeptorbindungsaffinität und -selektivität. Die Einführung eines Benzo[b]thiophen-2-carboxamid-Rests führte in Verbindung 1 zu herausragender subnanomolarer Affinität am Dopamin-D3-Rezeptor sowie zu deutlich erhöhter Selektivität im Vergleich zu BP 897. Die Variation des lipophilen basischen Amin-Restes von 1 ergab das 1,2,3,4-Tetrahydroisochinolin-Derivat 6. Verdrängungsstudien konnten den Vorteil des 4-(2-Methoxyphenyl)piperazine-Substituenten von BP 897 bezüglich der Affinitäten am Dopamin-D2S- und -D3-Rezeptor deutlich zeigen. Modifikationen einzelner Elemente von BP 897 und ST 198 und die veränderte Integration der Amid-Funktion in dem lipophilen Aryl-Rest führten zur Substanzklasse der inversen Amide mit neuen chemischen Eigenschaften. Moderate Bindungsaffinitäten, wie für das Isoindol-1-carbonyl-Derivat 11 gezeigt, legen nahe, dass inverse Amide eine lohnenswerte neue Leitstruktur mit andersartigem strukturellem Gerüst darstellen. In einer Hybrid-Strategie wurden Strukturelemente von Histamin-H1-Rezeptorantagonisten mit Substrukturen von Liganden mit ausgeprägter Dopamin-D3-Rezeptorpräferenz kombiniert. Daraus resultierten Benzhydrylpiperazin-Derivative und verwandte Substanzen mit moderater bis hoher Affinität am Dopamin-D3-Rezeptor. Besonders hervorzuheben ist das Zimtsäureamid substituierte und zum Bamipin verwandte Hybrid 39, welches die besten Ergebnisse in dieser Serie hinsichtlich Affinität und Selektivität am Dopamin-D3-Rezeptor erbrachte. Verbesserte pharmakologische Profile der strukturell modifizierten Histamine-H1-Rezeptorantagonisten am Dopamin-D2S- und -D3-Rezeptor und eine differenzierte SAR wurden erreicht. Für Derivate des Dopaminrezeptoragonisten Pramipexol und des strukturähnlichen Etrabamin wurden SAR ausgearbeitet. Das Propargyl substituierte Etrabamin-Derivat 61 zeigte herausragende Dopamin-D3-Rezeptoraffinität und -selektivität. Der Ligand ist von Interesse, da für den Propargyl-Rest neuroprotektive Eigenschaften berichtet wurden. Die Weiterentwicklung führte zur Verbindung 64, einem Zimtsäureamid-Derivat mit 4-Fluor-Substitution am Phenylring. Subnanomolare Affinität und hohe Selektivität am Dopamin-D3-Rezeptor prädestinieren 64 zur Anwendung als potentiellen PET-Radioliganden. Radioliganden Bindungsstudien wurden auf die Ergebnisse von virtuellen Screeningstudien angewandt. Sie führten zur Identifizierung neuer Leitstrukturen und zum weiteren Verständnis der SAR. Als neue Leitstrukturen mit verschiedenartigen chemischen Gerüsten wurden unter anderem das Bicyclo[2.2.1]heptan-Derivat 95 und der Benzhydryliden substituierte Pyrrolidindion Ligand 112 gefunden. Diese können nun zur weiteren Optimierung chemisch modifiziert werden. Die in dieser Arbeit durchgeführten Radioliganden Bindungsstudien führten zur Identifizierung, Entwicklung und Optimierung von hoch affinen und selektiven Dopamin-D3-Rezeptor Liganden. Des Weiteren ermöglichten die Ergebnisse eine ausführliche Vertiefung der SAR. Die kombinierte Strategie von chemoinformatischen Methoden und Radioliganden Bindungsstudien hat das Finden neuer Leitstrukturen als potentielle Arzneistoffe erlaubt. Die Resultate ermöglichen in der Zukunft ein gezieltes Liganden-Design mit einem gerichteten pharmakologischen Rezeptorprofil

MODULATING PROTEIN FUNCTION WITH SMALL MOLECULES THROUGH COMPUTATIONAL AND EXPERIMENTAL DESIGN TECHNIQUES

The ability to modulate protein function using exogenous small molecules is a longstanding goal in chemical biology. Selective activation or inhibition of a particular protein function can help elucidate crucial molecular mechanisms and enables important advances in cell biology. Small-molecule controlled molecular systems also possess tremendous value in bioengineering and biomedical applications: activation of protein function allows the construction of protein switches and biosensor proteins, whereas inhibition of protein function contributes to the development of novel therapeutic agents. The discovery of small-molecule modulators of function is greatly aided by computational modeling methodologies. By utilizing structural information obtained through X-ray crystallography or NMR spectroscopy, these tools allow efficient and affordable examination of large small-molecule databases and provide quantitative evaluation of the likelihood that a given protein-ligand interaction occurs. Advances in computer algorithms and hardware development continue to accelerate and scale up the computation and lower the cost of this discovery process. The primary focus of this thesis is the development of structure-based computer-aided methodologies for designing small-molecule modulators of protein function. To this end I explored two parallel paths, one to study activation and one to study inhibition of protein functions. Taken together, my work aims to not only apply rational design strategies to specific proteins, but also demonstrate their general applicability. The first project, focused on activation of protein function, is built on an approach developed by our laboratory that designs a de novo allosteric binding site directly into the catalytic domain of an enzyme. This approach achieves modulation of function by a novel "chemical rescue of structure approach": a tryptophan-to-glycine mutation disrupts local structure and induces conformational changes that distort the geometry at the active site; the subsequent binding of exogenous indole then reverts this conformational change and restores the native enzyme structure. The main challenge of generalizing this approach, however, is the difficulty of rationally designing analogous conformational changes in other proteins. It is therefore important to study the possible mechanisms that can be utilized by chemical rescue of structure. Through collaborative and multidisciplinary efforts, we find that the switchable proteins built via the chemical rescue of structure are frequently controlled indirectly by modulating protein stability, rather than discrete conformational changes. Since energetic evaluation of protein stability is far more tractable than designing and/or predicting allosteric conformational changes, this finding demonstrates how chemical rescue of structure can be applied to other systems for building a variety of new protein switches. To further generalize the applicability of chemical rescue of structure, I sought to extend it to include multiple amino acids, rather than just one. I chose ChxR, a homodimeric response regulator in Chlamydia, as the model protein to examine the feasibility of this strategy. I mutated a pair of tryptophans at the dimer interface to glycine in order to disrupt the dimerization of ChxR. To enable the subsequent functional rescue, I used the removed structural elements as a template for ligand-based virtual screening and discovered a set of candidate small molecules that mimic the three-dimensional geometry and chemical properties of the removed chemical moieties. Biophysical characterization of these compounds suggests that the majority of them selectively bind to the engineered ChxR variant. This observation shows promises in extending this generalized design strategy to allow alternate activating ligands. In parallel to these efforts I carried out studies aimed at inhibition of protein function, as exemplified by my project that uses small molecules to disrupt a protein-RNA interaction. Conventional methods of inhibitor design mostly target RNA-processing enzymes and cannot be generalized to the majority of RNA-binding proteins (RBPs). I contributed to the development of a general strategy of designing competitive inhibitors targeting RBPs. This method involves identifying "hotspot pharmacophores" from the protein-RNA interaction and using it as a template in ligand-based virtual screening. To evaluate the performance of this approach, my collaborators and I applied it to Musashi-1 (Msi1), a protein that upregulates Notch and Wnt signaling pathway and promotes cell cycle progression. Our "hotspot mimicry" approach led us to discover compounds that match the hotspot pharmacophore, and thus enabled the development of novel inhibitors to the Msi1/RNA interaction that we validated in both biochemical and cell-based assays. This approach extends the "hotspot" paradigm from protein-protein complexes to protein-RNA complexes, and helps establish the "druggability" of RNA-binding interfaces. It is the first example of a rationally-designed competitive inhibitor for a non-enzymatic RNA-binding protein. Owing to the simplicity and generality, I anticipate that the hotspot mimicry approach may lead to the identification of inhibitors of other protein-RNA complexes, which in future may serve as starting points for the development of a novel class of therapeutic agents

Investigating phosphate structural replacements through computational and experimental approaches

Bioisosteric replacements are used in drug design during lead generation and optimization processes with the aim to replace one functional group of a known molecule by another while retaining biological activity. The reason to use bioisosteric replacements are typically to optimize bioavailability or reducing toxicity. Phosphate groups represent a paradigm to study bioisosteric replacements. Protein-phosphate interaction plays a critical role during molecular recognition processes, and for example kinases represent one of the largest families of drug targets. However, some challenges exclude phosphate as a promising lead-like building block: i) charged phosphates do not cross molecular membranes; ii) some widely expressed proteins such as phosphatases easily hydrolyze phosphoric acid esters, which lead phosphate-containing ligands to lose their binding affinities before reaching their biological targets; iii) introduction of phosphate groups to parent scaffold is not easy. In the first part of the thesis work, I designed and implemented a computational protocol to mine information about phosphate structural replacements deposited in the Protein Data Bank. I constructed 116, 314, 271, and 42 sets of superimposed proteins where each set contains a reference protein to either POP, AMP, ADP, or ATP as well as a certain number of non-nucleotide ligands. 929 of such ligands are under study. The chemotypes that came out as structural replacements are diverse, ranging from common phosphate isosteres such as carboxyl, amide and squaramide to more surprising moieties such as benzoxaborole and aromatic ring systems. I exemplified some novel examples and interpreted the mechanism behind them. Local structural replacements are circumstance dependent: one chemical group valid in certain set-up cannot necessarily guarantee the success of another. The data from the study is available at http://86.50.168.121/phosphates_LSR.php. In the second part, I synthesized fifteen compounds retaining the adenosine moieties and bearing bioisosteric replacements of the phosphate at the ribose 5'-oxygen to test their stability toward human macro domain protein 1. These compounds are composed of either a squaryldiamide or an amide group as the bioisosteric replacement and/or as a linker. To these groups a variety of substituents were attached: phenyl, benzyl, pyridyl, carboxyl, hydroxy and tetrazolyl. Biological evaluation using differential scanning fluorimetry showed that four compounds stabilized human MDO1 at levels comparable to ADP and one at level comparable to AMP. Virtual screening was also run to identify MDO1 binding ligands. Among 20,000 FIMM database lead-like molecules, 39 compounds were selected for testing and eleven compounds found active based on ADPr and Poly-ADPr competition binding assay. The assay is however not well validated and a second confirmatory assay was conducted using calorimetry. To the best of my knowledge, this is the first report of non-endogenous ligands of the human MDO1. Altogether, this thesis highlights the versatility of molecular recognition processes that accompanies chemical replacements in compounds; this in turns shows the limits of the concepts of molecular similarity and classical bioisosterism that are based on the conservation of molecular interactions.Bioisosteeristä korvausta käytetään lääkeainekehityksessä johtolankamolekyylien tuottamisessa ja optimoinnissa. Tarkoitus on vaihtaa molekyylin funktionaalinen ryhmä toiseksi biologisen aktiivisuuden muuttumatta. Yleensä tavoitteena on parantaa biologista hyötyosuutta tai vähentää toksisuutta. Fosfaattiryhmää on tässä työssä käytetty esimerkkiryhmänä bioisosteerisiä korvauksia tutkittaessa. Väitöskirjatyön ensimmäisessä osassa suunnittelin ja toteutin tiedonlouhintaprotokollan etsiäkseni Protein Data Bank -tietokannasta korvaavia rakenteita fosfaattiryhmälle. Kokosin 116, 314, 271 ja 42 proteiiniryhmää, joissa kussakin on vertailumolekyylinä fosfaattiryhmän sisältävä POP, AMP, ADP tai ATP, ja lisäksi ei-nukleotidisiä ligandeja. Yhteensä 929 ei-nukleotidistä ligandia tutkittiin. Niistä löydettiin monipuolisesti fosfaattiryhmän korvaavia rakenteita, muun muassa yleisesti tunnettuja fosfaatin bioisosteerejä kuten karboksyyli, amidi ja squaramidi, mutta myös erikoisempia ryhmiä kuten bentsoksaboroli ja aromaattisia rengasrakenteita. Työssäni esittelen muutamia uusia rakenteita ja tulkitsen niiden vaikutusmekanismeja. Rakenteiden korvaaminen riippuu tilanteesta; yhteen tapaukseen sopiva korvaava ryhmä ei välttämättä toimi toisessa. Työn toisessa osassa syntetisoin 15 adenosiiniyhdistettä, joiden riboosiosan 5'-hapessa oleva fosfaattiryhmä on korvattu vaihtelevalla bioisosteerisellä ryhmällä. Bioisosteerisenä ryhmänä tai linkkerinä oli joko squaramidi- tai amidiryhmä. Yhdisteiden vakaus testattiin ihmisen MDO1-makrodomeeniproteiinin kanssa.Julkaisussa virheellinen verkkoaineiston ISBN 978-951-51-0045-0

In silico screening on the herg potassium channel

Während des Arzneistoffentwicklungsprozesses scheitern fast 35% der Arzneistoffe wegen schlechter Absorption, Verteilung, Metabolismus, Ausscheidung und Toxizität (ADMET). Ein wichtiger Bestandteil dieses Scheiterns ist die Interaktion mit Anti-Target Proteinen wie Cytochrom P450, P-glycoprotein und dem hERG Kaliumkanal. Der hERG Kaliumkanal ist in vielen verschiedenen Zellen und Geweben wie dem Herz, Nerven und glatten Muskelzellen vorhanden. Im Herzen spielt der hERG Kanal während des Aktionspotentials in der dritten Phase der kardialen Repolarisierung wegen der Weiterleitung des schnellen Kalium Ausstroms (Ikr) eine wichtige Rolle. Ein Verzögern dieser Phase führt zum Long QT Syndrom (LQTs), das eine potenziell tödliche Arrhythmie verursachen kann. Viele Klassen von Medikamenten wurden wegen ihren Wechselwirkungen mit dem hERG Kanal in den letzten zehn Jahren vom Markt zurückgezogen. Wie auch andere Anti-Target Proteine, ist der hERG Kanal in der Ligandenerkennung unspezifisch, weshalb er mit vielen Klassen von Arzneistoffen wie Psychopharmaka, Antihistaminika, Antiarrhythmika und Antibiotika interagieren kann. Viele Studien zeigen, dass eine erhebliche Anzahl von Molekülen während der Schließung des Kanals nicht dissoziieren und im geschlossenen Zustand des hERG Kanals gefangen bleiben. In dieser Studie wurden Propafenon und dessen Derivate in ein Homologie-Modell des hERG Kanals im geschlossenen und geöffneten Zustand gedockt, um die hERG Hemmung und das „drug trapping“ besser verstehen zu können. Ziel war es, die Wechselwirkungen zwischen dem hERG Kanal im geschlossenen Zustand und den Liganden zu untersuchen. Aufgrund dessen wurde eine Serie von „trapped“ Propafenon- Derivaten im hERG Kanal, welcher sich im geschlossenen Zustand befand, mit Dock, einem Docking Modul des Programms MOE, und GLIDE, dem Docking-Programm von Schrödinger, gedockt. Es wurde ein svl-Skript, genannt ROTALI, verwendet, um RMSD Matrizen zu erstellen, mit welchen die Duplikate unter den Posen, die in Bezug auf die Central Cavity unterschiedlich positioniert waren, zu erkennen und zu löschen. In weiterer Forlge wurden die möglichen binding modes durch agglomeratives hierarchisches Clustering identifiziert. Die Analyse der Posen führte zur Identifizierung von zwei möglichen Binding Modes. Derselbe Prozess wurde angewandt, um eine Serie von Propafenon-Derivaten in ein Homologie- Modell des hERG Kanals im geöffneten Zustand zu docken. Drei mögliche Binding Modes wurden durch die agglomerative Cluster Analyse der RMSD Matrix identifiziert, welche durch das gemeinsame Gerüst der Propafenon Derivate und jenen Aminosäuren generiert wurde, die mit den Molekülen interagierten. Um die Flexibilität des Proteins zu berücksichtigen wurden die Propafenon Derivate zusätzlich in acht verschiedene Schnappschüsse einer Moleküldynamik des Homologie-Modelles des hERG Kanals im geöffneten Zustand gedockt. In diesem Fall wurden zwei Binding Modes selektiert. Interessanterweise war es durch das Einordnen der Posen der fünf oben genannten Cluster nach der potenziellen Energie des R1 Substituenten, geteilt durch die Anzahl an Schweratomen, möglich, zwischen den „Trapped“ und „non-Trapped“ Propafenon-Derivaten zu unterscheiden. Dieser Wert war bei den „non-Trapped“ Substanzen immer höher als bei den „Trapped“ Molekülen. Der Umstand, dass dies auch bei den Vertretern des fünften Clusters möglich ist, bei denen der R1 Substituent unterhalb der vier Phe656 zum Liegen kommt, deutet darauf hin, dass das Phänomen des Drug-Trappings mehr auf die inhärenten Eigenschaften des R1 Substituenten als auf seine Konformation zurückzuführen ist, wenn er mit dem hERG Kanal interagiert. Dies könnte bedeuten, dass die Starrheit und die Sperrigkeit der Substituenten bestimmt ob Propafenon und dessen Derivate „Trapped“ sind oder nicht, unabhängig vom Bindemodus im hERG Kanal.During the drug development process, almost 35% of the compounds fail due to poor absorption, distribution, metabolism, excretion and toxicity (ADMET). An important role on these failures is played by improper interactions with antitarget proteins, such as cytocrome P450, P-glycoprotein and the hERG potassium channel. The hERG potassium channel is expressed in various cells and tissues, such as heart, neurons and smooth muscle. In the heart, the hERG channel plays an important role in the third phase of heart repolarization, due to the conduction of the rapid delayed rectifier K+ current (Ikr). A delay of this phase of repolarization leads to a syndrome called Long QT syndrome (LQTs) which might cause a potentially fatal arrhythmia called Torsade de Pointes (TdP). Many different classes of compounds were withdrawn from the market in the past decade due to their interaction with the hERG channel. Similar to other antitarget proteins, the hERG channel is polyspecific in the ligand recognition, hence it can interact with many classes of compounds, such as psychiatric, antihistaminic, antiarrhytmic and antimicrobial drugs. Several studies show that some molecules do not dissociate during the channel gating and are trapped in the closed state of the hERG channel. In this study, propafenone and derivatives were docked into homology models of the hERG channel in the closed and open states to shed more light on hERG inhibition and on drug trapping. With the aim to investigate the interactions between the hERG channel in the closed state and the compounds investigated, a series of trapped propafenone derivatives were docked into the homology model of the hERG channel in the closed conformation using Dock, the docking tool of MOE, and Glide, the docking tool of Schrödinger. A svl script called ROTALI was used to generate RMSD matrices with which the duplicate poses lying in different directions of the central cavity were detected and deleted, thus allowing to identify possible binding modes through agglomerative hierarchical clustering. This analysis led to the identification of two possible binding modes. The same process was applied to the poses obtained by docking the propafenones into a homology model of the hERG channel in the open state. Three possible binding modes were selected through agglomerative cluster analysis of the RMSD matrix generated taking into account the propafenone derivatives’ common scaffold and the amino acids that might interact. Finally, in order to take into account protein flexibility, nine propafenone derivatives were docked into eight models of the hERG channel in the open state obtained from snapshots of molecular dynamics simulations. Clustering both according to the common scaffold RMSD and the RMSD matrix of the amino acids interacting with the poses, two binding modes were selected. Biological studies suggest that non-trapped propafenones hinder the hERG channel gating with a mechanism called “foot in the door”. In four out of the five selected clusters, it is possible to explain the “foot in the door” mechanism. Interestingly, ranking the poses of the five clusters above-mentioned according to the potential energy values of the R1 substituent, and according to this value divided by the number of heavy atoms, it is possible to distinguish between trapped and non-trapped propafenones. In the nontrapped compounds, this value is always higher than in the trapped ones. The fact that it works also in cluster five, where the R1 substituents are placed under the ring formed by the four Phe656, might indicate that drug trapping phenomena depend more on intrinsic properties of the R1 susbstituent rather than on its conformation when it interacts with the hERG channel. Hence, this might indicate that the rigidity and the bulkyness of the substituent determines whether a propafenone derivatives is trapped or not independently of the binding mode in the hERG channel

Discovery and development of novel inhibitors for the kinase Pim-1 and G-Protein Coupled Receptor Smoothened

Investigation of the cause of disease is no easy business. This is particularly so when one reflects upon the lessons taught us in antiquity. Prior to the beginning of the last century, diagnosis and treatment of diseases such as cancers was so bereft of hope that there was little physicians could offer in the way of comfort, let alone treatment. One of the major insights from investigations into cancers this century has been that those involved in research leading to treatments are not dealing with a singular malady but multiple families of diseases with different mechanisms and modes of action. Therefore, despite the end game being similar in cancers, that of uncontrolled growth and replication leading to cellular dysfunction, different diseases require different approaches in targeting them. This leads us to a particular broad treatment approach, that of drug design. A drug is, in the classical sense, a small molecule that, upon introduction into the body, interacts with biochemical targets to induce a wider biological effect, ideally with both an intended target and intended effect. The conceptual basis underpinning this `lock-and-key' paradigm was elucidated over a century ago and the primary occupation of those involved in biochemical research has been to determine as much information as possible about both of these protein locks and drug keys. And, as inferred from the paradigm, molecular shape is all-important in determining and controlling action against the most important locks with the most potent and specific keys. The two most important target classes in drug discovery for some time have been protein kinases and G Protein-Coupled Receptors (GPCRs). Both classes of proteins are large families that perform very different tasks within the body. Kinases activate and inactive many cellular processes by catalysing the transfer of a phosphate group from Adenosine Tri-Phosphate (ATP) to other targets. GPCRs perform the job of interacting with chemical signals and communicating them into a biological response. Dysfunction in both types of proteins in certain cells can lead to a loss of biological control and, ultimately, a cancer. Both of kinases and GPCRs have entirely different chemical structures so structural knowledge therefore becomes crucial in any approach targeting cells where dysfunction has occurred. Thus, for this thesis, a member from each class was investigated using a combination of structural approaches. From the kinase class, the kinase Proviral Integration site for MuLV (Pim-1) and from the GPCR class, the cell membrane-bound Smoothened receptor (SMO). The kinase \pimone\ was the target of various approaches in \autoref{chap:three}. Although a heavily studied target from the mid-2000's, there is a paucity of inhibitors targeting residues more remote from structural characteristics that define kinases. Further limiting extension possibilities is that \pimone\ is constitutively active so no inhibitors targeting an inactive state are possible. An initial project (\pone) used the known binding properties of small molecules, or, `fragments' to elucidate structural and dynamic information useful for targeting \pimone. This was followed by three projects, all with the goal of inhibitor discovery, all with different foci. In \ptwo, fragment binding modes from \pone\ provided the basis for the extension and development of drug-like inhibitors with a focus on synthetic feasibility. In contrast, inhibitors were found in \pthree\ via a large-scale public dataset of purchasable molecules that possess drug-like properties. Finally, \pfour\ took the truncated form of a particularly attractive fragment from \pone\ that was crystallised with \pimone, verified its binding mode and then generated extensions with, again, a focus on synthetic feasibility. The GPCR \smo\ has fewer molecular studies and much about its structural behaviour remains unknown. As the most `druggable' protein in the Hedgehog pathway, structural studies have primarily focussed on stabilising its inactive state to prevent signal transduction. Allied to this is that there are generally few inhibitors for \smo\ and the drugs for cancers related to its dysfunction are vulnerable to mutations that significantly reduce their effectiveness or abrogate it entirely. The elucidation of structural information in therefore of high priority. An initial study attempting to identify an unknown molecule from prior experiments led to insights regarding binding characteristics of specific moieties. This was particularly important to understand not just where favourable moieties bind but also sections of the \smo\ binding pocket with unfavourable binding. In both subsequent virtual screens performed in Chapter 4, the primary aim was to find new drug-like inhibitors of \smo\ using large public datasets of commercially-available molecules. The initial screen retrieved relatively few inhibitors so the binding pocket was modified to find a structural state more amenable to small molecule binding. These modifications led to a significant number of new, chemically novel inhibitors for \smo, some structural information useful for future inhibitors and the elucidation of structure-activity relationships useful for inhibitor design. This underpins the idea that structural information is of critical importance in the discovery and design of molecular inhibitors

Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations

A core focus in medicinal chemistry is the interpretation of structure-activity relationships (SARs) of small molecules. SAR analysis is typically carried out on a case-by-case basis for compound sets that share activity against a given target. Although SAR investigations are not a priori dependent on computational approaches, limitations imposed by steady rise in activity information have necessitated the use of such methodologies. Moreover, understanding SARs in multi-target space is extremely difficult. Conceptually different computational approaches are reported in this thesis for graphical SAR analysis in single- as well as multi-target space. Activity landscape models are often used to describe the underlying SAR characteristics of compound sets. Theoretical activity landscapes that are reminiscent of topological maps intuitively represent distributions of pair-wise similarity and potency difference information as three-dimensional surfaces. These models provide easy access to identification of various SAR features. Therefore, such landscapes for actual data sets are generated and compared with graph-based representations. Existing graphical data structures are adapted to include mechanism of action information for receptor ligands to facilitate simultaneous SAR and mechanism-related analyses with the objective of identifying structural modifications responsible for switching molecular mechanisms of action. Typically, SAR analysis focuses on systematic pair-wise relationships of compound similarity and potency differences. Therefore, an approach is reported to calculate SAR feature probabilities on the basis of these pair-wise relationships for individual compounds in a ligand set. The consequent expansion of feature categories improves the analysis of local SAR environments. Graphical representations are designed to avoid a dependence on preconceived SAR models. Such representations are suitable for systematic large-scale SAR exploration. Methods for the navigation of SARs in multi-target space using simple and interpretable data structures are introduced. In summary, multi-faceted SAR analysis aided by computational means forms the primary objective of this dissertation

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery