Virtual screening for RNA-ligands : to cope with a flexible target structure

Das Ziel dieser Arbeit war es, RNA-Strukturen als potentielle Zielstrukturen für die Medikamentenentwicklung zu untersuchen. Hierbei ging es im Speziellen um die Anwendung Virtueller Screening Verfahren für die RNA-Liganden-Vorhersage. Hierzu wurde die als TAR-Motiv (transactivating response element) bekannte RNA-Struktur der mRNAs des HI-Virus ausgewählt. Diese Struktur wurde gewählt, da mit den vier PDB-Einträgen 1ANR, 1ARJ, 1LVJ und 1QD3 bereits experimentell motivierte Strukturmodelle zum Beginn der Untersuchung vorlagen. Ausschlaggebend war hierbei auch das Vorhandensein eines Tat-TAR-FRET-Assays im Rahmen des SFB 579, in welchem diese Arbeit angefertigt wurde. Die Aufmerksamkeit, welche dem HI-Virus im Rahmen der Bekämpfung der Immunschwächekrankheit bereits zukam, führte bei dem gewählten Testmodell ebenfalls zu einem, wenn auch immer noch überschaubaren Datensatz bereits getesteter Substanzen, der als Grundlage für einen Liganden-basierten Ansatz als erste Basis dienen konnte. Basierend auf diesen Voruntersuchungen ergaben sich die weiteren Schritte dieser Arbeit. Die Arbeit lässt sich zusammenfassend in vier zum Teil parallel verlaufende Phasen einteilen: Phase 1:Bestandsaufnahme bekannter Informationen über die Zielstruktur · experimentell bestimmte Zielstrukturen · experimentell bestimmte Liganden/Nichtliganden der Zielstruktur Phase 2: Ableiten eines ligandenbasierten Ansatzes zur Vorhersage von potentiellen Bindern der Zielstruktur aus Substanzbibliotheken, der nicht auf Strukturdaten der Zielstruktur beruht. Phase 3: Analyse der bekannten Konformere der Zielstruktur auf konstante Angriffspunkte für ein spezielles Liganden-Design. Phase 4: Einbinden der bekannten Strukturinformationen der Zielstruktur zur weiteren Verfeinerung der Auswahlverfahren neuer Kandidaten für die weitere experimentelle Bestimmung des Bindeverhaltens. Im Rahmen dieser Arbeit konnten mittels der Anwendung von künstlichen neuronalen Netzen in einem ligandenbasierten Ansatz durch virtuelles Screening der Chemikalien-Datenbanken verschiedener Lieferanten fünf neue potentielle TAR-RNA-Liganden identifiziert werden (drei davon mit einem Methylenaminoguanidyl-Substrukturmotiv), sowie als „Spin-Off“ durch die Anwendung der ursprünglich nur für den Tat-TAR-FRET-Assay vorgesehenen Testsubstanzen in einem Kooperationsprojekt (mittels CFivTT-Assay) zwei neue potentiell antibakterielle Verbindungen identifiziert werden. Die Beschäftigung mit der offensichtlichen Flexibilität der TAR-RNA und damit einer nicht eindeutig zu definierenden Referenz-Zielstruktur für das Liganden-Docking führte zur Erstellung eines Software-Pakets, mit dem flexible Zielstrukturen – basierend auf den Konformer-Datensätzen von MD-Simulationen – auf konstante Angriffspunkte untersucht werden können. Hierbei wurde ausgehend von der Integration eines Taschenvorhersage-Programms (PocketPicker) eine Reihe von Filtern implementiert, die auf den hierzu in einer MySQL-Datenbank abgelegten Strukturinformationen eine Einschränkung des möglichen Taschenraums für das zukünftige Liganden-Design automatisiert vornehmen können. Des Weiteren ermöglicht dieser Ansatz einen einfachen Zugriff auf die einzelnen Konformere und die Möglichkeit Annotationen zu den Konformeren und den daraus abgeleiteten Tascheninformationen hinzuzufügen, so dass diese Informationen für die Erstellung von Liganden-Docking-Versuchen verwendet werden können. Ferner wurden im Rahmen dieser Arbeit ein neuer Deskriptor für die Beschreibung von Taschenoberflächen eingeführt: der auf der „Skalierungs-Index-Methode“ basierende molekulare SIMPrint. Die Beschäftigung mit der Verteilung der potentiellen Bindetaschen auf der Oberfläche der Konformerensemble führte ferner zur Definition der Taschenoberflächenbildungswahrscheinlichkeit (Pocket Surface Generation Probability – PSGP) für einzelne Atome einer Zielstruktur, die tendenziell für die Einschätzung der Ausbildung einer potentiell langlebigen Interaktion eines Liganden mit der Zielstruktur herangezogen werden kann, um beispielsweise Docking-Posen zu bewerten.The focus of this work was the evaluation of RNA as a potential target structure for drug development, specializing on virtual screening methods for the prediction of RNA-ligands. The TAR-RNA motif of HIV was chosen as a model structure due to the prevalence of experimentally solved structure models with and without the presence of RNA-ligands. Additionally the accessibility of a Tat-TAR-FRET-assay and the availability of previously generated data (by other members of the CRC579) motivated this choice. Based upon these and extended datasets various virtual screening methods (structure- and ligand-based) were utilized to derive models applicable for the in silico screening of chemical compound libraries for potential RNA-ligands. Selected compounds identified by different virtual screening methods have thereafter been tested in the Tat-TAR-FRET-assay for their ability to displace the Tat-model peptide from its TAR-RNA target structure. As a result five potential TAR-RNA binding ligands where identified. Cooperation made it possible to test some of the compounds within an antibacterial assay system for their efficacy, identifying two novel potentially antibacterial compounds. The occupation with the inherent flexibility of the available target structure models led to the goal of identifying the “most likely” target conformer for structure based screening rounds. Therefore a database system was developed which could contain the vast information of the targets conformer space, as can be derived for example by multiple molecular dynamic simulations. A module allows now to automatically tag the location of potential binding sites (utilizing PocketPicker) on each stored conformer. Based upon this information different approaches where tested to identify the most prevalent binding site surface patch presenting atoms within a given target structure ensemble. Efforts summarizing in the definition of the pocket surface generation probability as an additional mean to weigh the interaction potential of a given target structures atom for interacting with potential ligands, enabling the definition of potentially time-stable anchor point interactions. Additionally a novel alternative descriptor system (termed SIMPrint) based upon the Scaling-index method has been tested successfully for clustering conformers and pockets, where the use of RMSD or other methods based on the alignment of defined reference points, seems inapplicable. In the future the combination of the utilized ligand-based as well as structure-based approaches could guide the establishment of an automated statistic-based selection scheme to pick the needed reference conformers out of the potential conformer space, as well as the needed ligand libraries, to improve future docking studies. Thereby eventually reducing the amount of in vitro screening needed to find RNA-ligands

Comprehensive analysis of chemical structures that have been tested as CFTR activating substances in a publicly available database CandActCFTR

Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR, which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics

Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase

The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database

Integrating Text Mining into the Curation of Disease Maps

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation