Average information content maximization : a new approach for fingerprint hybridization and reduction

Fingerprints, bit representations of compound chemical structure, have been widely used in cheminformatics for many years. Although fingerprints with the highest resolution display satisfactory performance in virtual screening campaigns, the presence of a relatively high number of irrelevant bits introduces noise into data and makes their application more time-consuming. In this study, we present a new method of hybrid reduced fingerprint construction, the Average Information Content Maximization algorithm (AIC-Max algorithm), which selects the most informative bits from a collection of fingerprints. This methodology, applied to the ligands of five cognate serotonin receptors (5-HT2A, 5-HT2B, 5-HT2C, 5-HT5A, 5-HT6), proved that 100 bits selected from four non-hashed fingerprints reflect almost all structural information required for a successful in silico discrimination test. A classification experiment indicated that a reduced representation is able to achieve even slightly better performance than the state-of-the-art 10-times-longer fingerprints and in a significantly shorter time

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans

Handling Location Uncertainty in Event Driven Experimentation

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ

Computational Methods for the Integration of Biological Activity and Chemical Space

One general aim of medicinal chemistry is the understanding of structure-activity relationships of ligands that bind to biological targets. Advances in combinatorial chemistry and biological screening technologies allow the analysis of ligand-target relationships on a large-scale. However, in order to extract useful information from biological activity data, computational methods are needed that link activity of ligands to their chemical structure. In this thesis, it is investigated how fragment-type descriptors of molecular structure can be used in order to create a link between activity and chemical ligand space. First, an activity class-dependent hierarchical fragmentation scheme is introduced that generates fragmentation pathways that are aligned using established methodologies for multiple alignment of biological sequences. These alignments are then used to extract consensus fragment sequences that serve as a structural signature for individual biological activity classes. It is also investigated how defined, chemically intuitive molecular fragments can be organized based on their topological environment and co-occurrence in compounds active against closely related targets. Therefore, the Topological Fragment Index is introduced that quantifies the topological environment complexity of a fragment in a given molecule, and thus goes beyond fragment frequency analysis. Fragment dependencies have been established on the basis of common topological environments, which facilitates the identification of activity class-characteristic fragment dependency pathways that describe fragment relationships beyond structural resemblance. Because fragments are often dependent on each other in an activity class-specific manner, the importance of defined fragment combinations for similarity searching is further assessed. Therefore, Feature Co-occurrence Networks are introduced that allow the identification of feature cliques characteristic of individual activity classes. Three differently designed molecular fingerprints are compared for their ability to provide such cliques and a clique-based similarity searching strategy is established. For molecule- and activity class-centric fingerprint designs, feature combinations are shown to improve similarity search performance in comparison to standard methods. Moreover, it is demonstrated that individual features can form activity-class specific combinations. Extending the analysis of feature cliques characteristic of individual activity classes, the distribution of defined fragment combinations among several compound classes acting against closely related targets is assessed. Fragment Formal Concept Analysis is introduced for flexible mining of complex structure-activity relationships. It allows the interactive assembly of fragment queries that yield fragment combinations characteristic of defined activity and potency profiles. It is shown that pairs and triplets, rather than individual fragments distinguish between different activity profiles. A classifier is built based on these fragment signatures that distinguishes between ligands of closely related targets. Going beyond activity profiles, compound selectivity is also analyzed. Therefore, Molecular Formal Concept Analysis is introduced for the systematic mining of compound selectivity profiles on a whole-molecule basis. Using this approach, structurally diverse compounds are identified that share a selectivity profile with selected template compounds. Structure-selectivity relationships of obtained compound sets are further analyzed

Graphical visualization of 2D-atom-based descriptors in PLS-QSAR-equations using MOE SVL

SAR- und QSPR-Modelle werden vor allem zur Vorhersage von Moleküleigenschaften oder von biologischer Aktivität verwendet. Oft enthalten sie zusätzliche Informationen über die zugrundeliegende Struktur-Aktivitätsbeziehung und unterstützen deren Aufklärung. Eine der vielen publizierten Methoden, die einen Einblick in substrukturelle Information aus modellbasierten QSAR-Systemen erlauben, ist RQSPR von Gombar, das im Programm “visdom” enthalten ist. Es projiziert die Beiträge einzelner Atome eines Moleküls zur QSAR-Gleichung graphisch zurück auf das jeweilige Atom. Dadurch inspiriert, stellt diese Arbeit ein neues Programm vor, das die Visualisierung von Atombeiträgen anhand einer vordefinierten PLS-QSAR Gleichung erlaubt. Dank der Implementierung in SVL, der Skriptsprache von MOE (Molecular Operating Environment), können sowohl die graphischen Möglichkeiten als auch der Window Tool Kit der Applikation genutzt werden, wodurch eine anwenderfreundliche graphische Benutzerschnittstelle (GUI, graphical user interface) ermöglicht wird. Allerdings ist der Einsatz des Skripts auf sogenannte fragmentbasierte Deskriptoren beschränkt. Deren Gesamtwert setzt sich aus mehreren Einzelwerten, die jeweils einen Teil des Moleküls, ein Fragment, beschreiben, zusammen. In unserem Spezialfall muss die Fragmentgröße genau ein Atom betragen, trotzdem können rund 57% aller in MOE verfügbaren 2D-Deskriptoren eingesetzt werden. Zusätzlich sind die E-State-Deskriptoren von Hall und Kier im Skript implementiert. Um die graphische Darstellung weiter zu verbessern, existiert ein MLCS-Modul (Multiple Largest Common Substructure), das Atome und Bindungen, die alle Moleküle im Datensatz besitzen, hervorheben kann. Weiters sind verschiedene Visualisierungsoptionen und eine spezielle Ansicht verfügbar, die bis zu vier Moleküle gleichzeitig darstellt. In einer Proof-Of-Concept-Studie mit einem Datensatz aus 79 Propafenonen, die ABCB1 inhibieren, werden Anwendungsmöglichkeiten demonstriert. Einige in Vorarbeiten aufgezeigten Eigenschaften der Propafenone, die für die Aktivität wichtig sind, finden in die Gleichung Eingang, etwa Lipophilie oder pi-pi-Interaktionen. Andere Muster, etwa die Bedeutung von Wasserstoffakzeptoren, werden hingegen nicht repräsentiert.QSAR or QSPR equations are mainly intended to predict physical properties or biological activity of chemical compounds, but they also contain further information which supports unveiling the underlying SAR of an equation, thus giving reasons for one molecule being active and a second not. Many approaches aiming at an extraction of substructural information out of model-based QSARs, created by various methods, have already been introduced, among them RQSPR by Gombar, which is included in the “visdom” tool set. It projects back contribution values to a molecule’s atoms. Inspired by this workflow, we present a new application which allows a user to visualize contributions of a molecule’s atoms to a predefined PLS-QSAR equation. Implemented in MOE (Molecular Operating Environment) SVL scripting language, it can both use MOE’s display options and MOE’s integrated window toolkit, which enables a user-friendly GUI application. The script’s backprojecting capability is restricted to so-called fragment based descriptors, thus descriptors calculated from values assigned to submolecular parts. The fragment size has to be one atom, nonetheless an amazing 57% of all MOE implemented 2D-descriptors are available, as well as the E-state descriptor set by Hall and Kier. An MLCS (multiple largest common substructure) feature highlighting atoms and bonds common to all molecules in the dataset, various display options and a special multiple-view mode additionally improve the visualization and make it highly customizable. A proof-of-concept study using a QSAR dataset of 79 propafenone-like ABCB1 inhibitors demonstrates the script’s basic appliance. It can be shown that some well-known structural features of the propafenones contributing to activity are preserved in the examplary PLS-QSAR equation, e.g. lipophilicity or pi-pi interactions. For other moieties already identified by multiple SAR studies on propafenones, e.g. hydrogen bond acceptor capabilites, results are ambiguous

Metagenomics and bio-engineering of chitin and chitosan modifying enzymes for biotechnological applications

Chitin ist nach Cellulose das am häufigsten in der Natur vorkommende Biopolymer. Chitosan ist die vollständige oder teilweise deacetylierte Version des Chitins. Chitin und Chitosan modifizierende Enzyme (CCME) finden als Werkzeuge zur Erzeugung spezieller Chitosane für die Biotechnologie und Biomedizin Anwendung. In dieser Arbeit wurde sowohl mithilfe einer metagenomischen Genbank als auch mittels mikrobieller Direktisolaten nach neuen CCMEs gesucht. Als Ausgangsmaterial für diese beiden Ansätze dienten Bodenproben, die über mehr als zehn Jahre in Kontakt mit Chitin und Chitosan waren. CCME kodierende Gene aus Bacillus spp. wurden in E. coli-Stämmen heterolog exprimiert und die entsprechenden Enzyme aufgereinigt. Eine Inkubation von Chitosanpolymeren verschiedener Acetylierungsgrade (50%, 35% und 10%) mit den aufgereinigten Enzymen generierte Mischungen aus Chitin- und Chitosan-Oligomeren, die vielversprechende Elicitor- und Priming-Effekte auf pflanzliche Zellkulturen hatten