Receptor-Based Pharmacophores in Dynamic Protein Models
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1 Research Collection Doctoral Thesis Receptor-Based Pharmacophores in Dynamic Protein Models Author(s): Kunze, Jens Publication Date: 2015 Permanent Link: Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library
2 DISS. ETH NO Receptor-Based Pharmacophores in Dynamic Protein Models A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by JENS KUNZE Dipl.-Bioinf., Goethe University Frankfurt am Main born on citizen of Germany accepted on the recommendation of Prof. Dr. Gisbert Schneider, examiner Prof. Dr. Gerd Folkers, co-examiner 2015
3 V Summary The concept of pharmacophores has a long-standing history in pharmaceutical research and still remains one of most applied abstractions for receptor-ligand interactions. Beside numerous other approaches successfully applied in computer assisted drug design the pharmacophore modeling creates interpretable, illustrative representations of the results. Therefore pharmacophore models are popular among researchers of various disciplines, as critical discussions are not limited to computational experts, and refinements are often reasonable and intuitive. Recent developments in binding site detection, pharmacophore feature point definition and pharmacophoric descriptor calculation are mostly driven by computational efficiency or focused on target specific performance. Therefore it becomes harder for non-computational users to understand, interpret or influence the results. In this work a novel modular receptor-based pharmacophore workflow is presented, in order to include as much information as possible and applying the algorithms suited for the processed tasks. Furthermore uncertainties in the receptor atom positions due to crystallographic modeling errors or observed flexibility can be included and uncertaintycorrected pharmacophore models can be generated. The workflow is interactive; all individual steps are visualized in the same PyMOL session and the user is asked to provide additional information during the calculations, while the given input is processed on the fly. Structured data files for the pocket- and potential pharmacophore point description are presented to facilitate the interchange of the individual workflow segments. Potential binding pockets are calculated by software tools like PocketPicker or MDpocket and may be adjusted according to the chosen uncertainty measurement. The potential pharmacophore points are generated based on geometric interaction rules evaluated on every pocket grid point, and the receptor flexibility can be reflected at this stage as well. After a semi-automated hotspot selection the PPPs are transformed into a "virtual ligand", the corresponding LIQUID descriptor is calculated and applied as query for pharmacophore-based virtual screenings.
4 VI Summary The workflow was retrospectively evaluated with the DUD-E dataset. A parameter optimization for the pharmacophore descriptor calculation was performed revealing the difficulties of generalizing software parameters over a divers set of proteins. In a proofof-concept study, the applicability of structure-based pharmacophore searches for transient binding pockets is presented taking HIV-1 protease as an example. A follow-up study on the HIV-1 protease was conducted, including the receptor flexibility for the pharmacophore feature definitions based on MD simulations. One out of two active substances found inhibits the protease with an IC50 of 80 µm. For a known allosteric pocket of isoprenoid synthase D (IspD) all three flexibility levels (none, crystallographic temperature-factor and MD-simulation) are applied and an additional transient binding pocket is detected, predicted to be ligandable and therefore chosen as a prospective test case.
5 Zusammenfassung VII Zusammenfassung Das Konzept der Pharmacophore wird bereits seit längerer Zeit erfolgreich in den pharmazeutischen Wissenschaften zur Wirkstofffindung eingesetzt und ist auch heute eines der meistverwendeten Methoden zur Abstraktion von Rezeptor-Liganden Interaktionen. Im Gegensatz zu vielen anderen, ebenso erfolgreich angewendeten Methoden zur computergestützten Wirkstoffentwicklung sind Pharmakophormodelle leicht interpretierbar und lassen sich gut veranschaulichen. Diese Eigenschaften ermöglichen die Kommunikation von Wissenschaftlern verschiedenster Disziplinen, besprochene Veränderungen erscheinen oft intuitiv und motiviert. Viele der aktuell entwickelten computergestützten Verfahren für die Vorhersage von Bindetaschen, die Beschreibung von potentiellen Pharmakophorpunkten und die Berechnung der Deskriptoren arbeiten heute sehr effizient und sind auf spezielle Problemstellungen zurechtgeschnitten. Das Fokussieren auf bestimmte Proteine oder komplexe Algorithmen erschweren es dem Benutzer die Zusammenhänge zwischen der gewählten Methode und deren Ergebnis zu verstehen. Der Einfluss des Anwenders auf das Ergebnis und dessen Interpretierbarkeit sinken. In dieser Arbeit wird ein neuer modular strukturierter Arbeitsablauf vorgestellt, der so konzipiert ist, dass möglichst viele Informationen in die Berechnungen der Pharmakophorpunkte einfließen. Weiter soll dem Benutzer die Wahl der Algorithmen für die jeweiligen Arbeitsschritte ermöglicht werden. Ein neuer Ansatz erlaubt die Berücksichtigung von Fehlern in der Beschreibung von Atompositionen des Rezeptors. Bei diesen Fehler kann es sich um kristallographische Modellfehler oder durch bei Simulationen beobachtete Flexibilität handeln, die dazu verwendet werden fehlerkorrigierte Modelle zu erzeugen. Alle Schritte des Arbeitsablaufes werden mit PyMOL visualisiert. Die von Anwender in PyMOL durchgeführten Anpassungen und zusätzliche eingefügte Informationen werden in Echtzeit bearbeitet und in die folgenden Berechnungen integriert. Es werden übersichtliche Dateistrukturen für die Beschreibung von Bindetasche- und Pharmacophorpunkten vorgestellt. Die jeweiligen Schritte im Arbeitsablauf können
6 VIII Zusammenfassung darauf basierend ausgetauscht werden. In der Software werden die Bindetaschen mit PocketPicker oder MDpocket extrahiert und wahlweise mit dem berechneten Fehlerwerten der Kristallograühie oder der Simulationen angepasst. Geometrie basierte Regeln werden zwischen jedem Protein und jedem Taschenpunkt evaluiert und beim Einhalten der Grenzwerte werden potentielle Pharmakophorpunkte an diesen Stellen erzeugt. Diese Punkte können ebenfalls mit den Fehlerwerten der Atompositionen korrigiert werden. Daraufhin folgt eine semi-automatische Priorisierung der Punkte in dessen Anschluss ein "virtueller Ligand" der Bindetasche berechnet wird. Dieser LIQUID Deskriptor wird für die weitere pharmakophorbasierte Ähnlichkeitssuche nach potentiellen Bindern für die ausgewählte Tasche verwendet. Der Workflow wurde mit dem DUD-E Datensatz evaluiert und es wurde eine Optimierung der Deskriptor Parameter durchgeführt. Diese zeigt die Vielfältigkeit der Proteine und die damit verbundenen Probleme einer Generalisierung von Softwareparametern auf. In einer Vorstudie zur HIV-1 Protease wurde die Anwendbarkeit von pharmakophorebasierten virtuellen Ähnlichkeitssuchen für transiente Bindetaschen getestet. Die darauf aufbauende Studie zeigt, wie die durch Simulationen abgeleitete Flexibilität des Rezeptors mit Hilfe des entwickelten Verfahrens erfolgreich in die Suche integriert werden kann. Der IC50 für eine der beiden inhibierenden Substanzen liegt bei 80µM. An einer bekannten allosterischen Bindestelle der Isopreonidsynthase D (IspD) wurden die drei Möglichkeiten des Workflows zur Integration der Fehler in den Atompositionen getestet (ohne Fehlerberücksichtigung, mit kristallographischem Fehler, mit MD Flexibilität). Darüber hinaus wurde eine transiente Tasche detektiert, deren Eigenschaft zur potentiellen Ligandenbindung vorhergesagt und als weitere prospektive Anwendung bearbeitet.
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