MODELING STRUCTURE- FUNCTION RELATIONSHIPS OF MACROMOLECULAR LIGAND BINDING SITES BY FUZZY GRAPHS
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- Ferdinand Krüger
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1 Diss. ETH NO MODELING STRUCTURE- FUNCTION RELATIONSHIPS OF MACROMOLECULAR LIGAND BINDING SITES BY FUZZY GRAPHS A dissertation submitted to ETH ZURICH for the degree of DOCTOR OF SCIENCES presented by FELIX HAROLD REISEN Diplom Bioinformatiker, Goethe University Frankfurt, Germany Born November 26 th, 1982 Citizen of Switzerland and Germany Lucerne (LU), Kilchberg(ZH), Geneva (GE) Accepted on the recommendation of Prof. Dr. Gisbert Schneider, examiner, Prof. Dr. Michael Detmar, co- examiner 2012
2 Summary 1 Summary Physicochemical patterns that determine the biological function and ligand binding profiles of proteins are conserved even in the absence of sequence homology. In this work, a computational method for local feature comparison was devised for identification of target profiles of bioactive compounds and detection of potential ligand binding sites on the surface of structural protein models. We developed an algorithm for rapid pocket comparison ( PoLiMorph ) that represents binding sites by self- organizing graphs that fill the volume of the cavities. Local ligand- receptor interaction potentials are encoded in the vertices employing fuzzy set theory for property projection. For graph matching, we implemented a heuristic based on the maximum dispersion problem as a fast alternative to techniques utilizing exact clique detection or geometric hashing algorithms. For assessment of graph similarity a scoring function was applied that incorporates knowledge about the physicochemical property distributions within the binding sites. In an all- against- all virtual screening experiment with 207 binding sites extracted from the PDBbind core set, PoLiMorph correctly assigned 81% of the proteins to 69 functional classes. The method s ability to cluster pockets accommodating the same ligand chemotype in the absence of sequence similarity was demonstrated for a set of 40 proteins binding to four distinct ligand classes. Furthermore, the algorithm was used for clustering a collection of 23 kinases resulting in a hierarchical tree that corresponds well with the pharmacological profiles of these proteins. We augmented the approach to allow for modeling of druglike organic compounds and ligand- based virtual screening. In analogy to the pocket description, ligand shape was approximated by a fuzzy labeled graph that enables similarity assessment with the new matching heuristic. To assess PoLiMorph s performance, a retrospective evaluation was made on 30 target classes using a collection of bioactive reference compounds. The obtained average BEDROC score of 0.27 (σ = 0.13) confirms the method s ability to enrich active molecules in the top ranks. PoLiMorph was further improved by integrating the correlation vector method LIQUID using a consensus ranking approach, which resulted in enhanced retrospective screening performance yielding an average 4
3 Summary BEDROC score of 0.32 (σ = 0.11). Prospective application led to the discovery of novel serine protease inhibitors using a protein- protein interface of soybean trypsin inhibitor as query. Finally, graph representations of protein binding sites and small organic molecules were integrated to allow for structure- based virtual screening. Graph superimposition and comparison of pharmacophoric feature distributions were used to predict potential ligand binding. The performance of this approach was evaluated by retrospective virtual screening for selected targets, yielding BEDROC scores of 0.37, 0.11, 0.61, and 0.94 for human immunodeficiency virus protease- 1, cyclooxygenase- 2, retinoic acid receptor γ, and human vitamin D receptor, respectively. Prospective application of PoLiMorph to a predicted allosteric binding site of Helicobacter pylori High temperature requirement A (HtrA) led to the discovery of a novel chemical entity inhibiting E- cadherin cleavage. In conclusion, an algorithm for protein binding site comparison, ligand- based and receptor- based virtual screening was developed. Its applicability to early- phase drug discovery was shown in both retrospective and prospective experiments. PoLiMorph might therefore be used to provide insights into side effects of known drugs and de- orphanize proteins with known structure but unknown function. 5
4 Zusammenfassung 2 Zusammenfassung Die Funktion von Proteinen und ihre Fähigkeit mit anderen Molekülen in Wechselwirkung zu treten wird unabhängig von Sequenzähnlichkeiten durch die Anordnung lokaler physikochemischer Eigenschaften bestimmt. In dieser Arbeit wurde ein informatischer Ansatz für komparative Studien dieser Eigenschaften entworfen, um Zielproteinprofile bioaktiver Wirkstoffe zu ermitteln und potentielle Bindestellen an Proteinoberflächen zu bestimmen. Der von uns entwickelte Algorithmus für den Vergleich von Protein- Bindetaschen ( PoLiMorph ) modelliert die Volumina dieser Interaktionsstellen mit Hilfe von selbstorganisierenden Graphen. Letztere sind entsprechend lokaler physikochemischer Eigenschaften gefärbt, die unter Verwendung der Fuzzy- Mengen Theorie auf die Knoten projiziert werden. Um die Graphen miteinander zu vergleichen, wurde eine Heuristik entwickelt, die auf einem Algorithmus zur Approximation dichter Subgraphen basiert. Dieser Ansatz stellt eine schnelle Alternative zu bestehenden Techniken dar, welche nach einer optimalen Lösung des Clique Problems suchen oder das Prinzip des geometrischen Hashings verwenden. Die Qualität eines ermittelten Graphmatchings wird mit Hilfe einer Bewertungsfunktion bestimmt, welche Annahmen über physikochemische Eigenschaftsverteilungen berücksichtigt. In einem Experiment basierend auf paarweisen Ähnlichkeiten von 207 Bindetaschen des PDBbind core sets konnte PoLiMorph für 81% der Proteine die korrekte funktionelle Klasse vorhersagen. Die Fähigkeit Bindetaschen mit ähnlichen Interaktionsprofilen zu gruppieren, ohne dass Sequenzähnlichkeiten innerhalb der Proteine gegeben sind, konnte an einer Sammlung von 40 Strukturen und vier Ligandenklassen gezeigt werden. Weiterhin wurde der Algorithmus für die Analyse von 23 Kinasen eingesetzt, wobei der resultierende hierarchische Baum bekannten pharmakologischen Profilen dieser Proteine entspricht. Der Algorithmus wurde daraufhin erweitert, um die Modellierung wirkstoffähnlicher organischer Moleküle und damit Liganden- basiertes virtuelles Screening zu ermöglichen. In Analogie zur Taschenbeschreibung werden die Liganden mit Hilfe von fuzzy gefärbten Graphen approximiert, so dass molekulare Ähnlichkeiten mit Hilfe der 6
5 Zusammenfassung neu entwickelten Matchingroutine erfasst werden können. Die Güte des Algorithmus konnte in retrospektiven Experimenten anhand von 30 verschiedenen Molekülklassen in einer Sammlung von bioaktiven Referenzstrukturen gezeigt werden. Durchschnittliche BEDROC Werte von 0,27 (σ = 0,13) zeigen, dass die Methode in der Lage ist, aktive Moleküle in den oberen Rängen der nach Ähnlichkeit sortierten Moleküllisten anzureichern. Durch Integration der Korrelationsvektormethode LIQUID konnte die retrospektive Leistung auf BEDROC Werte von 0,32 (σ = 0,11) gesteigert werden. Eine prospektive Anwendung führte zur Identifizierung neuer Serinprotease- Inhibitoren, wobei eine Protein- Protein Interaktionsstelle des Sojabohnen Trypsin Inhibitors als Ausgangspunkt diente. Abschließend wurden die Graphdarstellungen der Liganden und Protein- Bindetaschen miteinander verknüpft, sodass PoLiMorph auch für Struktur- basiertes virtuelles Screening eingesetzt werden kann. Um Vorhersagen über potentielle Interaktionen von Liganden und Proteinen zu treffen werden die entsprechenden Graphen überlagert und auf ihre paarweisen physikochemischen Eigenschaftsverteilungen hin überprüft. Die Güte dieses Ansatzes wurde anhand ausgesuchter Proteinklassen evaluiert, wobei BEDROC Werte von 0,37, 0,11, 0,61 und 0,94 für die Proteine humaner Immunodefizienz Virus Protease- 1, Cyclooxygenase- 2, Retinoidsäure Rezeptor γ und humaner Vitamin D Rezeptor erreicht wurden. Eine prospektive Anwendung des Algorithmus auf eine vorhergesagte potentielle allosterische Bindestelle von Helicobacter pylori High temperature requirement A (HtrA) führte zur Entdeckung eines neuen Moleküls, welches die proteolytische Spaltung des Proteins E- Cadherin verhindert. In dieser Arbeit wurde eine Algorithmus entwickelt, der den Vergleich von Protein- Bindetaschen sowie Liganden- basiertes und Struktur- basiertes virtuelles Screening ermöglicht. Die Anwendbarkeit des Ansatzes in einer frühen Phasen der Medikamententwicklung konnte anhand von retrospektiven und prospektiven Studien gezeigt werden. PoLiMorph kann daher eingesetzt werden, um Nebenwirkungen bekannter Wirkstoffen zu erklären und um Proteine mit bekannter Struktur aber unbekannter Funktion zu charakterisieren. 7
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