Köln, Universiẗat, Diss., 2003.

Bielefeld, University, Diss., 2004.

Bielefeld, University, Diss., 2003.

Köln, Universiẗat, Diss., 2003.

University, Diss., 2003--Bielefeld.

Wassermoleküle spielen oft eine entscheidende Rolle bei der Bindung von Liganden an Proteine. Zum einen ist dies in ihrer Eigenschaft als Wasserstoffbrückendonor und -akzeptor begründet, die es ermöglicht Wechselwirkung zwischen Ligand und Rezeptor zu vermitteln. Zum anderen stellen die Desolvatisierungsenthalpie und -entropie einer Bindetasche während der Ligandbindung einen entscheidenden Anteil der Bindungsaffinität dar. Obwohl man sich dieser Einflüsse seit langem bewusst ist, sind aktuelle Methoden des computerbasierten Wirkstoffdesigns nur in sehr begrenztem Umfang in der Lage, die entsprechenden Effekte zu erfassen und vorherzusagen. Da experimentelle Daten über die Effekte von Wassermolekülen in Protein-Ligand Komplexen von Natur aus schwierig zu erhalten sind, untersucht die vorliegende Arbeit eine Modellbindetasche einer Cytochrom c Peroxidase Mutante (CCP W191G) mit Hilfe von Molecular Modeling Techniken. Diese polare und solvatisierte Kavität ist strukturell sehr gut charakterisiert und bindet kleine, kationische Heterozyklen zusammen mit unterschiedlichen Mengen an Wassermolekülen. Für die Untersuchungen wurden strukturell ähnliche Liganden mit einem unterschiedlichen Wechselwirkungsmuster ausgewählt. Davon ausgehend wurde die Möglichkeit zweier Docking-Programme, den Grad der Wasserverdrängung durch den Liganden zusammen mit dem Bindungsmodus vorherzusagen, untersucht. Die dynamischen Eigenschaften der Bindetaschenwassermoleküle wurden mittels Molekulardynamiksimulationen studiert. Schließlich wurden diese rein strukturellen Betrachtungen durch eine energetische/thermodynamische Analyse komplettiert. Die Anwendung dieser unterschiedlichen Verfahren liefert einige neue Erkenntnisse über die untersuchte Modellbindetasche. Trotz der relativen Einfachheit der kleinen Kavität der CCP W191G Mutante war die vollständige Charakterisierung und eine korrekte (retrospektive) Vorhersage des Wasser-Wechselwirkungsmuster der Ligand-Komplexe nicht trivial. Zusammenfassend kann man festhalten, dass insgesamt eine gute Übereinstimmung zwischen den durch Computersimulationen erhaltenen Ergebnissen und den kristallographischen Daten erzielt wurde. Unerwartete Befunde, die auf den ersten Blick mit den kristallographischen Beobachtungen nicht übereinstimmen, können ebenso durch Limitationen in den Kristallstrukturen bedingt sein. Darüber hinaus gaben die Ergebnisse auch eine Hilfestellung, welches Verfahren zur Beantwortung einer Fragestellung im Rahmen von Wassermolekülen im Wirkstoffdesign geeignet sind. Schließlich wurden ebenso die Begrenzungen der jeweiligen Methoden aufgezeigt. / Water molecules play an important role for the binding of small molecule ligands to proteins. One of the reasons for this is their ability to act as a hydrogen bond donor and acceptor at the same time. Additionally, the enthalpy and entropy of desolvation of the pocket is one large contribution to the overall binding affinity. Although this is long known, prediction of these effects by current methods of computer-aided drug design is rather limited. Since experimental information about water effects in protein-ligand complexes are inherently difficult to obtain, in the present work a well-suited model binding site of a mutant of the cytochrome c peroxidase (CCP W191G) is studied using molecular modeling techniques. This polar and solvated cavity is structurally very well characterized and several small, cationic heterocycles bind together with a different amount of water molecules. For this study structurally similar ligands which have a different interaction pattern where chosen. First, the ability of two docking programs to predict cavity desolvation upon ligand binding was investigated. The dynamic properties of the binding site water molecules where studied by means of molecular dynamic simulations. Ultimately, the pure structural considerations addressed in this work were complemented by an energetic/thermodynamic analysis. The application of the different methods offered some new insights into the studied model binding site. Despite the relative simplicity of the small cavity of the CCP W191G mutant, a complete characterization and a correct (retrospective) prediction of the water interaction network in ligand complexes of this model binding site is not trivial. In summary, an overall good agreement between computational results and crystallographic data is obtained. Unexpected findings, which at first sight disagree with crystallographic observations, may also be due to limitations of the crystal structures. In addition, the results help to decide which method is appropriate to address a certain question in the context of water molecules in drug design. Also, the limitations of the respective methods are exposed.

Protein-protein and protein-ligand interactions are fundamental as many proteins mediate their biological function through these interactions. Many important applications follow directly from the identification of residues in the interfaces between protein-protein and protein-ligand interactions, such as drug design, protein mimetic engineering, elucidation of molecular pathways, and understanding of disease mechanisms. The identification of interface residues can also guide the docking process to build the structural model of protein-protein complexes. This dissertation focuses on developing computational approaches for protein-ligand and protein-protein binding site prediction and applying these predictions to improve protein-protein docking. First, we develop an automated approach LIGSITEcs to predict protein-ligand binding site, based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcs performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Second, for protein-protein binding site, we develop metaPPI, a meta server for interface prediction. MetaPPI combines results from a number of tools, such as PPI_Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. After refinement, metaPPI significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. Third, for protein-protein docking, we develop a FFT-based docking algorithm and system BDOCK, which includes specific scoring functions for specific types of complexes. BDOCK uses family-based residue interface propensities as a scoring function and obtains improvement factors of 4-30 for enzyme-inhibitor and 4-11 for antibody-antigen complexes in two specific SCOP families. Furthermore, the degrees of buriedness of surface residues are integrated into BDOCK, which improves the shape discriminator for enzyme-inhibitor complexes. The predicted interfaces from metaPPI are integrated as well, either during docking or after docking. The evaluation results show that reliable interface predictions improve the discrimination between near-native solutions and false positive. Finally, we propose an implicit method to deal with the flexibility of proteins by softening the surface, to improve docking for non enzyme-inhibitor complexes.