Techniken des computergestützten Wirkstoffdesigns spielen eine wichtige Rolle bei der Entwicklung neuer Wirkstoffe. Die vorliegende Arbeit befasst sich sowohl mit der Entwicklung als auch mit der praktischen Anwendung von Methoden des strukturbasierten Wirkstoffdesigns. Die Arbeit glieder sich daher in zwei Teile. Der erste Teil beschäftigt sich mit der Entwicklung von empirischen Scoring-Funktionen, die eine Schlüsselrolle im strukturbasierten computergestützen Wirkstoffdesign einnehmen. Grundlage dieser Arbeiten sind die empirischen Deskriptoren und Scoring-Funktionen aus dem SFCscore-Programmpaket. Dabei wurde zunächst untersucht, wie sich die Zusammensetzung der Trainingsdaten auf die Vorhersagen von empirischen Scoring-Funktionen auswirkt. Durch die gezielte Zusammenstellung eines neuen Trainingsdatensatzes wurde versucht, die Spannweite der Vorhersagen zu vergrößern, um so vor allem eine bessere Erkennung von hoch- und niedrig-affinen Komplexen zu erreichen. Die resultierende Funktion erzielte vor allem im niedrig-affinen Bereich verbesserte Vorhersagen. Der zweite Themenkomplex beschäftigt sich ebenfalls mit der verbesserten Separierung von aktiven und inaktiven Verbindungen. Durch den Einsatz der Machine Learning-Methode RandomForest wurden dazu Klassifizierungsmodelle abgeleitet, die im Unterschied zu den klassischen Scoring-Funktionen keinen genauen Score liefern, sondern die Verbindungen nach ihrer potentiellen Aktivität klassifizieren. Am Beispiel des mykobakteriellen Enzyms InhA konnte gezeigt werden, dass derartige Modelle den klassischen Scoring-Funktionen im Bezug auf die Erkennung von aktiven Verbindungen deutlich überlegen sind. Der RandomForest-Algorithmus wurde im nächsten Schritt auch verwendet, um eine neue Scoring-Funktion zur Vorhersage von Bindungsaffinitäten abzuleiten. Diese Funktion wurde unter dem Namen SFCscoreRF in das SFCscore-Programmpaket implementiert. Die Funktion unterschiedet sich in einigen wesentlichen Punkten von den ursprünglichen SFCscore-Funktionen. Zum einen handelt es sich beim RF-Algorithmus um eine nicht-lineare Methode, die im Unterschied zu den klassischen Methoden, die zur Ableitung von Scoring-Funktionen eingesetzt werden, nicht von der Additivität der einzelnen Deskriptoren ausgeht. Der Algorithmus erlaubt außerdem die Verwendung aller verfügbaren SFCscore-Deskriptoren, was eine deutlich umfassendere Repräsentation von Protein-Ligand-Komplexen als Grundlage des Scorings ermöglicht. Für die Ableitung von SFCscoreRF wurden insgesamt 1005 Komplexe im Trainingsdatensatz verwendet. Dieser Datensatz ist somit einer der größten, die bisher für die Ableitung einer empirischen Scoring-Funktion verwendet wurden. Die Evaluierung gegen zwei Benchmark-Datensätze ergab deutlich bessere Vorhersagen von SFCscoreRF im Vergleich zu den ursprünglichen SFCscore-Funktionen. Auch im internationalen Vergleich mit anderen Scoring-Funktion konnten für beide Datensätze Spitzenwerte erreicht werden. Weitere ausgiebige Testungen im Rahmen einer Leave-Cluster-Out-Validierung und die Teilnahme am CSAR 2012 Benchmark Exercise ergaben, dass auch SFCscoreRF Performanceschwankungen bei der Anwendung an proteinspezifischen Datensätzen zeigt - ein Phänomen, dass bei Scoring-Funktionen immer beobachtet wird. Die Analyse der CSAR 2012-Datensätze ergab darüber hinaus wichtige Erkenntnisse im Bezug auf Vorhersage von gedockten Posen sowie über die statistische Signifikanz bei der Evaluierung von Scoring-Funktionen. Die Tatsache, dass empirische Scoring-Funktionen innerhalb eines bestimmten chemischen Raums trainiert wurden, ist ein wichtiger Faktor für die protein-abhängigen Leistungsschwankungen, die in dieser Arbeit beobachtet wurden. Verlässliche Vorhersagen sind nur innerhalb des kalibrierten chemischen Raums möglich. In dieser Arbeit wurden verschiedene Ansätze untersucht, mit denen sich diese ``Applicability Domain'' für die SFCscore-Funktionen definieren lässt. Mit Hilfe von PCA-Analysen ist es gelungen die ``Applicability Domain'' einzelner Funktionen zu visualisieren. Zusätzlich wurden eine Reihe numerischer Deskriptoren getestet, mit den die Vorhersageverlässlichkeit basierend auf der ``Applicability Domain'' abgeschätzt werden könnte. Die RF-Proximity hat sich hier als vielversprechender Ausgangspunkt für weitere Entwicklungen erwiesen. Der zweite Teil der Arbeit beschäftigt sich mit der Entwicklung neuer Inhibitoren für das Chaperon Hsp70, welches eine vielversprechende Zielstruktur für die Therapie des multiplen Myeloms darstellt. Grundlage dieser Arbeiten war eine Leitstruktur, die in einer vorhergehenden Arbeit entdeckt wurde und die vermutlich an einer neuartigen Bindestelle in der Interface-Region zwischen den beiden großen Domänen von Hsp70 angreift. Die Weiterentwicklung und Optimierung dieser Leitstruktur, eines Tetrahydroisochinolinon-Derivats, stand zunächst im Vordergrund. Anhand detaillierter Docking-Analysen wurde der potentielle Bindemodus der Leitstruktur in der Interfaceregion von Hsp70 untersucht. Basierend auf diesen Ergebnissen wurde eine Substanzbibliothek erstellt, die von Kooperationspartnern innerhalb der KFO 216 synthetisiert und biologisch getestet wurde. Die Struktur-Wirkungsbeziehungen, die sich aus diesen experimentellen Daten ableiten lassen, konnten teilweise gut mit den erstellten Docking-Modellen korreliert werden. Andere Effekte konnten anhand der Docking-Posen jedoch nicht erklärt werden. Für die Entwicklung neuer Derivate ist deswegen eine umfassendere experimentelle Charakterisierung und darauf aufbauend eine Verfeinerung der Bindungsmodelle notwendig. Strukturell handelt es sich bei Hsp70 um ein Zwei-Domänen-System, dass verschiedene allostere Zustände einnehmen kann. Um die Auswirkungen der daraus folgenden Flexibilität auf die Stabilität der Struktur und die Bindung von Inhibitoren zu untersuchen, wurden molekulardynamische Simulationen für das Protein durchgeführt. Diese zeigen, dass das Protein tatsächlich eine überdurchschnittlich hohe Flexibilität aufweist, die vor allem durch die relative Bewegung der beiden großen Domänen zueinander dominiert wird. Die Proteinkonformation die in der Kristallstruktur hscaz beobachtet wird, bleibt jedoch in ihrer Grundstruktur in allen vier durchgeführten Simulationen erhalten. Es konnten hingegen keine Hinweise dafür gefunden werden, dass die Mutationen, welche die für die strukturbasierten Arbeiten verwendete Kristallstruktur im Vergleich zum Wildtyp aufweist, einen kritischen Einfluss auf die Gesamtstabilität des Systems haben. Obwohl die Interface-Region zwischen NBD und SBD also in allen Simulationen erhalten bleibt, wird die Konformation in diesem Bereich doch wesentlich durch die Domänenbewegung beeinflusst und variiert. Da dieser Proteinbereich den wahrscheinlichsten Angriffspunkt der Tetrahydroisochinolinone darstellt, wurde der Konformationsraum detailliert untersucht. Wie erwartet weist die Region eine nicht unerhebliche Flexibilität auf, welche zudem, im Sinne eines ``Induced-Fit''-Mechanismus, durch die Gegenwart eines Liganden (Apoptozol) stark beeinflusst wird. Es ist daher als sehr wahrscheinlich anzusehen, dass die Dynamik der Interface-Region auch einen wesentlichen Einfluss auf die Bindung der Tetrahydroisochinolinone hat. Molekuardynamische Berechnungen werden deswegen auch in zukünftige Arbeiten auf diesem Gebiet eine wichtige Rolle spielen. Die Analysen zeigen zudem, dass die Konformation der Interface-Region eng mit der Konformation des gesamten Proteins - vor allem im Bezug auf die relative Stellung von SBD und NBD zueinander - verknüpft ist. Das untermauert die Hypothese, dass die Interface-Bindetasche einen Angriffspunkt für die Inhibtion des Proteins darstellt. / Methods of computational drug design play a crucial role in the development of new pharmaceutical drugs. The work presented here comprises the methodological development and the practical application of structure-based techniques in computational drug design. The first part of this dissertation focuses on the development of empirical scoring functions, which play an essential part in structure-based computer-aided drug design. The basis for this work are the empirical descriptors and scoring functions of the SFCscore software package. First, the influence of the training data composition on the prediction of empirical scoring functions was analyzed. A new training data set was created to spread the prediction range of the function and thus achieve a better separation of high and low affinity binders. The resulting function indeed yielded better predictions in the low affinity area compared to the original functions. In another approach, which also addresses the issue of discriminating active and inactive compounds, the Machine Learning method RandomForest (RF) was used to derive a classification model. Different to classical empirical scoring functions, this model no longer predicts a precise value but classifies the compounds according to their potential affinity as 'active' or 'inactive'. The example of the mycobacterial enzyme InhA showed that such models are clearly superior to different classical scoring function in terms of separating active and inactive compounds. The RandomForest algorithm was also used to derive a new scoring function for the prediction of binding affinities. This new function was implemented into the SFCscore software package under the name SFCscoreRF. This new function differs from the original SFCscore functions in several essentials points. On the one hand, the RF-algorithm is a non-linear method, which - in contrast to classical methods used for the derivation of empirical scoring functions - does not assume the additivity of the single descriptors. On the other hand, the algorithm allowes for using the whole set of available SFCscore descriptors and is therefore able to utilize a more comprehensive representation of a protein ligand complex as the basis for the prediction. Additionally, the training data set used to derive SFCscoreRF comprised 1005 complexes. This training set is one of the largest data sets used to train an empirical scoring function. The evaluation against two widely-used benchmark sets confirmed that SFCscoreRF yields superior predicitons as compared to the original functions. The comparison with other functions tested for these benchmarks shows that SFCscoreRF also achieves top results on an international level. Further analyses using a leave-cluster-out validation scheme and the participation in the CSAR 2012 Benchmark Exercise revealed that - similar to other scoring functions - SFCscoreRF shows varying performances when applied to protein-specific data sets. Additionally, by analysing the results of the CSAR 2012 data sets, valuable insight were gained regarding the prediction of docking poses and the statistical significance for the evaluation and comparison of scoring functions. The fact that empirical scoring functions are trained within a certain chemical space, is an important reason for the target-dependent performance observed in this work. Reliable predictions can only be expected within the calibrated area. Different approaches for the definition of this ``applicability domain'' are presented in this work. PCA analyses have been used to create a two dimensional representation of the ``applicability domain''. Additionally, different numerical descriptors have been tested to estimate the reliability of SFCscore predicitons. The RF-proximity has been found to be a promising starting point for future research. The development of new inhibitors for the molecular chaperone Hsp70 - a promising target in the therapy of multiple myeloma - comprises the second part of this dissertation. The basis for this work was a lead structure that was found in a previous work and attacks a novel binding pocket in the interface between the two domains of the Hsp70 protein. The optimization and development of that lead structure - a tetrahydroisochinolinone - was the primary focus of the present work. Potential binding poses in the interface were elucidated by detailed docking analyses. Based on that information, a compound library was compiled, which was synthezised and biologically analyzed by cooperation partners within the CRU 216. The resulting structure activity relationships can partially be explained on the basis of the corresponding docking poses. However, some of the effects remain unexplained. For the further development of new derivatives a comprehensive experimental characterization of the current compounds is needed. This information can be used as a basis for the refinement of the existing binding models. Hsp70 is a two-domain system, which can visit different allosteric states. To further investigate the effects of the resulting flexibility on the stability of the structure and on inhibitor binding, molecular dynamics simulations were conducted. These simulations show an above-average felxibility of the protein, which is primarily dominated by the movement of the two domains NBD and SBD relatively to each other. However, the basic conformation that is observed in the crystal structure hscaz, which was used in this work, remains stable in all simulations. Furthermore, the trajectories showed no evidence that the mutations, in which hscaz differs from the wild type protein, have a significant effect on the overall protein conformation. Although, the overall conformation of the interface between NDB and SBD remains stable, the exact conformation in this area is signficantly influenced by the domain movement. As this region includes the binding pocket of the tetrahydroisochinolinones, the conformational space of this area was analyzed in detail. The analyses expectedly reveal a high flexibility in the interface area that is dominated by the SBD-NBD movement. Furthermore, it could be shown that the conformation and dynamics can be influenced by a bound ligand (apoptozole), in terms of an induced fit mechanism. It is highly probable that the binding of the tetrahydroisochinolinones trigger similar effects, influencing the binding mechanism of this compound class. Thus, molecular dynamics simulations should play a crucial role in the future development of new compounds. The analyses also show that the dynamics of the interface region have large effects on the overall structure of the protein and vice versa. Especially, the relative orientation of NBD and SBD has a large impact on the binding pocket. This underlines the hypothesis that the interface region constitutes a promising target area for the inhibition of Hsp70.

Für die Aufklärung der absoluten Stereostruktur von chiralen Molekülen, die ein Chromophorsystem besitzen, hat sich die Kombination der experimentellen und theoretischen Spektroskopie des elektronischen Circulardichroismus (ECD) als Methode bewährt. In der vorliegenden Arbeit wurden die chiroptischen Eigenschaften von Bisbibenzyl-Makrocyclen, Mono- und Bis(cycloketo)porphyrinen, der Mohnblütenpigmente Nudicaulin I und II sowie von Bordipyrrol-Dimeren mit quantenchemischen Methoden untersucht. Zu diesem Zweck wurden verschiedene dichtefunktionaltheoretische (DFT) Ansätze und post-HF-Methoden, wie z. B. der Coupled-Cluster-Ansatz RI-CC2, bezüglich ihrer Eignung, die Grund- und angeregten Zustände (UV/vis- und ECD-Eigenschaften) der einzelnen Verbindungen korrekt wiederzugeben, evaluiert. Da bei quantenchemischen UV- und ECD-Rechnungen an ausgedehnten π-Systemen aufgrund energetisch nah beieinander liegender Anregungen die Wahrscheinlichkeit für ghost states und charge-transfer-Übergänge sowie Multireferenz-Problematiken steigt, wurden diese Aspekte genauer betrachtet. Die ersten zwei Phänomene lassen sich bereits auf TD-DFT-Niveau durch genaue Analyse der theoretischen spektroskopischen Daten ermitteln und unter Umständen durch entsprechend korrigierte Funktionale sogar sehr gut beschreiben. Im Gegensatz dazu können Doppelanregungsanteile überhaupt erst durch Rechnungen mit geeigneten Methoden, wie z. B. das hier verwendete SORCI-Verfahren, erfasst werden. Zusätzlich wurde das zur Auswertung von UV und ECD-Daten entwickelte Programm SpecDis um Funktionalitäten erweitert, welche die Berechnung des Übereinstimmungsgrades zweier UV- bzw. ECD-Kurven ermöglichen, und dadurch ein zusätzliches quantitatives Kriterium für die Verlässlichkeit des Spektrenvergleichs und folglich für die Zuordnung der absoluten Konfiguration bieten. / For the elucidation of the absolute stereostructure of chiral molecules possessing a chromophore, the combination of experimental and theoretical spectroscopy of the electronic circular dichroism (ECD) has proven a valuable tool. In the present work, the chiroptical properties of bisbibenzyl macrocycles, mono- and bis(cycloketo)porphyrins, the poppy-petal pigments nudicaulins I and II, and boron-dipyrrole dimers were investigated with quantum-chemical methods. For this purpose, different density-functional and post-HF methods, for example the coupled-cluster approach RI-CC2, were evaluated regarding their suitability to correctly describe the ground and exited states (UV/vis and ECD properties) of each compound. Since the probability of ghost states, charge-transfer transitions, and multi-reference problems increases for quantum-chemical UV and CD calculations of extended π systems due to energetically close-lying excitations, these aspects were closely examined. The first two phenomena can be detected on TD-DFT level of theory by a thorough analysis of the theoretical spectroscopic data, and can in some cases even be described quite well by specially corrected functionals. By contrast, double excitations can only be included in the calculations by using suitable methods, e.g. the herein used SORCI approach. Additionally, functionalities were added to the program SpecDis which allow for the calculation of the degree of similarity of either two UV or ECD curves, and thus, offer an additional quantitative measure for the comparison of spectra and, in consequence, for the reliability of the assignment of an absolute configuration.

Unrestricted Kohn-Sham (broken symmetry) density functional calculations have been used to determine the low-energy geometries of the chromium dihalide molecules (CrX2) and their clusters, Cr2X4, Cr3X6, and Cr4X8. The monomers are also investigated at a higher level, including coupled-cluster and state-average CASSCF computations. Our calculations show that the monomers have a 5B2 ground state arising from the Renner-Teller distorted 5IIg transition state, leading to a bent geometry. The global minima of the gas-phase clusters of CrF2 and CrCl2 consist of two-dimensional, anti-ferromagnetically coupled chains of CrX2 units forming four-membered, doubly bridged Cr2X2 rings, closely resembling their solid-state structures. The global minima of the CrBr2 and CrI2 clusters consist of the same two-dimensional chain-like structures for their dimers, but their trimers and tetramers consist of three-dimensional ’triangular’ structures which contain two capping ligands bound to three chromium atoms along with a Cr-Cr bond. Each Cr atom within these clusters has spin quantum number S=2. There is approximately a constant change in energy, between 45-55 kcal/mol, with every new CrX2 unit during cluster formation. Information about the structure of the CrCl2 clusters is used in the reanalysis of high-temperature electron diffraction data. The vapor at 1170 K contains about 77% monomeric molecules, 19% dimers, and a small amount of trimers. Monomeric CrCl2 is found to be bent with a bond angle of 149(10)degrees, in good agreement with our computations. Solid-state DFT calculations are performed on alpha-CrCl2 to determine the lattice structure and spin-coupling constants for the Cr atoms within the crystals. The GGA (PW91) method produces a structure in good agreement with the literature. In the lowest energy structure, the spins of the Cr atoms within the chains along the crystallographic c-axis are anti-ferromagnetically coupled with four parallel spins situated almost exclusively in the d-bands of Cr along these chains. This anti-ferromagnetic coupling is also seen in the CrX2 clusters.

Unrestricted Kohn-Sham (broken symmetry) density functional calculations have been used to determine the low-energy geometries of the chromium dihalide molecules (CrX2) and their clusters, Cr2X4, Cr3X6, and Cr4X8. The monomers are also investigated at a higher level, including coupled-cluster and state-average CASSCF computations. Our calculations show that the monomers have a 5B2 ground state arising from the Renner-Teller distorted 5IIg transition state, leading to a bent geometry. The global minima of the gas-phase clusters of CrF2 and CrCl2 consist of two-dimensional, anti-ferromagnetically coupled chains of CrX2 units forming four-membered, doubly bridged Cr2X2 rings, closely resembling their solid-state structures. The global minima of the CrBr2 and CrI2 clusters consist of the same two-dimensional chain-like structures for their dimers, but their trimers and tetramers consist of three-dimensional ’triangular’ structures which contain two capping ligands bound to three chromium atoms along with a Cr-Cr bond. Each Cr atom within these clusters has spin quantum number S=2. There is approximately a constant change in energy, between 45-55 kcal/mol, with every new CrX2 unit during cluster formation. Information about the structure of the CrCl2 clusters is used in the reanalysis of high-temperature electron diffraction data. The vapor at 1170 K contains about 77% monomeric molecules, 19% dimers, and a small amount of trimers. Monomeric CrCl2 is found to be bent with a bond angle of 149(10)degrees, in good agreement with our computations. Solid-state DFT calculations are performed on alpha-CrCl2 to determine the lattice structure and spin-coupling constants for the Cr atoms within the crystals. The GGA (PW91) method produces a structure in good agreement with the literature. In the lowest energy structure, the spins of the Cr atoms within the chains along the crystallographic c-axis are anti-ferromagnetically coupled with four parallel spins situated almost exclusively in the d-bands of Cr along these chains. This anti-ferromagnetic coupling is also seen in the CrX2 clusters.

Unrestricted Kohn-Sham (broken symmetry) density functional calculations have been used to determine the low-energy geometries of the chromium dihalide molecules (CrX2) and their clusters, Cr2X4, Cr3X6, and Cr4X8. The monomers are also investigated at a higher level, including coupled-cluster and state-average CASSCF computations. Our calculations show that the monomers have a 5B2 ground state arising from the Renner-Teller distorted 5IIg transition state, leading to a bent geometry. The global minima of the gas-phase clusters of CrF2 and CrCl2 consist of two-dimensional, anti-ferromagnetically coupled chains of CrX2 units forming four-membered, doubly bridged Cr2X2 rings, closely resembling their solid-state structures. The global minima of the CrBr2 and CrI2 clusters consist of the same two-dimensional chain-like structures for their dimers, but their trimers and tetramers consist of three-dimensional ’triangular’ structures which contain two capping ligands bound to three chromium atoms along with a Cr-Cr bond. Each Cr atom within these clusters has spin quantum number S=2. There is approximately a constant change in energy, between 45-55 kcal/mol, with every new CrX2 unit during cluster formation. Information about the structure of the CrCl2 clusters is used in the reanalysis of high-temperature electron diffraction data. The vapor at 1170 K contains about 77% monomeric molecules, 19% dimers, and a small amount of trimers. Monomeric CrCl2 is found to be bent with a bond angle of 149(10)degrees, in good agreement with our computations. Solid-state DFT calculations are performed on alpha-CrCl2 to determine the lattice structure and spin-coupling constants for the Cr atoms within the crystals. The GGA (PW91) method produces a structure in good agreement with the literature. In the lowest energy structure, the spins of the Cr atoms within the chains along the crystallographic c-axis are anti-ferromagnetically coupled with four parallel spins situated almost exclusively in the d-bands of Cr along these chains. This anti-ferromagnetic coupling is also seen in the CrX2 clusters.

The Diels-Alder reaction is one of the most powerful synthetic tools in organic chemistry, and asymmetric Diels-Alder catalysis allows for rapid construction of chiral carbon scaffolds. For this reason, considerable effort has been invested in developing efficient and stereoselective organo- and biocatalysts. However, Diels-Alder is a virtually unknown reaction in Nature, and to engineer an enzyme into a Diels-Alderase is therefore a challenging task. Despite several successful designs of catalytic antibodies since the 1980’s, their catalytic activities have remained low, and no true artificial ’Diels-Alderase’ enzyme was reported before 2010. In this thesis, we employ state-of-the-art computational tools to study the mechanism of organocatalyzed Diels-Alder in detail, and to redesign existing enzymes into intermolecular Diels-Alder catalysts. Papers I–IV explore the mechanistic variations when employing increasingly activated reactants and the effect of catalysis. In particular, the relation between the traditionally presumed concerted mechanism and a stepwise pathway, forming one bond at a time, is probed. Papers V–X deal with enzyme design and the computational aspects of predicting catalytic activity. Four novel, computationally designed Diels-Alderase candidates are presented in Papers VI–IX. In Paper X, a new parameterization of the Linear Interaction Energy model for predicting protein-ligand affinities is presented. A general finding in this thesis is that it is difficult to attain large transition state stabilization effects solely by hydrogen bond catalysis. In addition, water (the preferred solvent of enzymes) is well-known for catalyzing Diels- Alder by itself. Therefore, an efficient Diels-Alderase must rely on large binding affinities for the two substrates and preferential binding conformations close to the transition state geometry. In Papers VI–VIII, we co-designed the enzyme active site and substrates in order to achieve the best possible complementarity and maximize binding affinity and pre-organization. Even so, catalysis is limited by the maximum possible stabilization offered by hydrogen bonds, and by the inherently large energy barrier associated with the [4+2] cycloaddition. The stepwise Diels-Alder pathway, proceeding via a zwitterionic intermediate, may offer a productive alternative for enzyme catalysis, since an enzyme active site may be more differentiated towards stabilizing the high-energy states than for the standard mechanism. In Papers I and III, it is demonstrated that a hydrogen bond donor catalyst provides more stabilization of transition states having pronounced charge-transfer character, which shifts the preference towards a stepwise mechanism. Another alternative, explored in Paper IX, is to use an α,β -unsaturated ketone as a ’pro-diene’, and let the enzyme generate the diene in situ by general acid/base catalysis. The results show that the potential reduction in the reaction barrier with such a mechanism is much larger than for conventional Diels-Alder. Moreover, an acid/base-mediated pathway is a better mimic of how natural enzymes function, since remarkably few catalyze their reactions solely by non-covalent interactions. / <p>QC 20120903</p>

This dissertation thesis consists of a series of chapters that are interwoven by solving interesting biological problems, employing various computational methodologies. These techniques provide meaningful physical insights to promote the scientific fields of interest. Focus of chapter 1 concerns, the importance of computational tools like docking studies in advancing structure based drug design processes. This chapter also addresses the prime concerns like scoring functions, sampling algorithms and flexible docking studies that hamper the docking successes. Information about the different kinds of flexible dockings in terms of accuracy, time limitations and success studies are presented. Later the importance of Induced fit docking studies was explained in comparison to traditional MD simulations to predict the absolute binding modes. Chapter 2 and 3 focuses on understanding, how sickle cell disease progresses through the production of sickled hemoglobin and its effects on sickle cell patients. And how, hydroxyurea, the only FDA approved treatment of sickle cell disease acts to subside sickle cell effects. It is believed the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood, however, the exact details of this mechanism is still unclear. HU interacts with oxy and deoxyHb resulting in slow NO production rates. However, this did not correlate with the observed increase of NO concentrations in patients undergoing HU therapy. The discrepancy can be attributed to the interaction of HU competing with other heme based enzymes such as catalase and peroxidases. In these two chapters, we investigate the atomic level details of this process using a combination of flexible-ligand / flexible-receptor virtual screening (i.e. induced fit docking, IFD) coupled with energetic analysis that decomposes interaction energies at the atomic level. Using these tools we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to human hemoglobin, catalase and the concomitant structural changes of the enzymes. Our results are consistent with kinetic and EPR measurements of hydroxyurea-hemoglobin reactions and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity. Finally in chapter 4, we have developed a 3D bioactive structure of O6-alkylguanine-DNA alkyltransferase (AGT), a DNA repair protein using Monte Carlo conformational search process. It is known that AGT prevents DNA damage, mutations and apoptosis arising from alkylated guanines. Various Benzyl guanine analouges of O6- methylguanine were tested for activity as potential inhibitors. The nature and position of the substitutions methyl and aminomethyl profoundly affected their activity. Molecular modeling of their interactions with alkyltransferase provided a molecular explanation for these results. The square of the correlation coefficient (R2 ) obtained between E-model scores (obtained from GLIDE XP/QPLD docking calculations) vs log(ED)values via a linear regression analysis was 0.96. The models indicate that the ortho-substitution causes a steric clash interfering with binding, whereas the meta-aminomethyl substitution allows an interaction of the amino group to generate an additional hydrogen bond with the protein. Using this model for virtually screening studies resulted in identification of seven lead compounds with novel scaffolds from National Cancer Institute Diversity Set2.

←Within this dissertation the topic of virtual screening is discussed with regard to three different cancer targets and also a brief introduction of the tools used in virtual screening. In Chapter 1, the reader will be introduced to virtual screening and the programs that are used in virtual screening. In Chapter 2, the first of three projects are discussed. This project consists of the work that was done to find inhibitors of the P53 binding domain of MDMX. In this project the mobility of residues within the binding site of MDMX are discussed and the ways in which we attempted to model how drugs would bind two adjacent pockets within MDMX. In Chapter 3, the virtual screening and modeling work done for RING domain of MDM2 and MDMX is discussed. This work was done in conjunction with Moffitt Cancer Center in order to solve the 60 year old mystery of the mechanism of how thalidomide and possibly its analog lenalidomide caused children to be born limbless. Current thinking is that Cereblon through an unknown teratogenic mechanism activates an increase in FGF8. We suggest a mechanism that may happen in parallel that involves stabilization of MDM2 and the reduction of P63 levels. Chapter 4, the work that was done against the BH3 binding domain of MCL-1 is discussed in conjunction with collaboration with the Manetsch lab. In order to complete this screening the validation of IC50 values and then attempt to modify those products based upon the structure of MCL-1. Chapter 5 discusses the work done to find inhibitors of deoxycytidine kinase. All of these chapters taken together provide a brief overview of the computational work done produce inhibitors of Protein-Protein Interaction against three major cancer targets.

Computational chemistry offers one the ability to develop a better understanding of the complex physical and chemical interactions that are fundamental to macro- and mesoscopic processes that are seen in laboratory experiments, industrial processes, and ordinary, everyday life. For many systems, the physics of interest occur at the molecular or atomistic levels, and in these cases, computational modeling and two well refined simulation techniques become invaluable: Monte Carlo (MC) and molecular dynamics (MD). In this work, two well established problems were tackled. First, models and potentials for various gas molecules were produced and refined from first principles. These models, although based on work done previously by Belof et al., are novel due to the inclusion of many-body van der Waals interactions, advanced r-12 repulsion combining rules for treating unlike intra- and intermolecular interactions, and highly-efficient treatment of induction interactions. Second, a multitude of models were developed and countless MD simulations were performed in order to describe and understand the giant frictional anisotropy of d-AlCoNi, first observed by Park et al. in 2005.

In the design of novel materials, tunable properties are parameters such as composition or structure that may be adjusted in order to enhance a desired chemical or material property. Trends in tunable properties can be accurately predicted using computational and combinatorial chemistry tools in order to optimize a desired property. I present a study of tunable properties in materials and employ a variety of algorithms that ranges from simple screening to machine learning. In the case of tuning a nanocomposite membrane for olefin/paraffin separations, I demonstrate a rational design approach based on statistical modeling followed by ab initio modeling of the interaction of olefins with various nanoparticles. My simplified model of gases diffusing on a heterogeneous lattice identifies the conditions necessary for optimal selectivity of olefins over paraffins. The ab initio modeling is then applied to identify realistic nanomaterials that will produce such conditions. The second case, [alpha]-Fe₂O₃, commonly known as hematite, is potential solar cell material. I demonstrate the use of a screened search through chemical compound space in order to identify doped hematite-based materials with an ideal band gap for maximum solar absorption. The electronic structure of hematite is poorly treated by standard density functional theory and requires the application of Hartree-Fock exchange in order to reproduce the experimental band gap. Using this approach, several potential solar cell materials are identified based on the behavior of the dopants within the overall hematite structure. The final aspect of this work is a new method for identifying low-energy chemical processes in condensed phase materials. The gap between timescales that are attainable with standard molecular dynamics and the processes that evolve on a human timescale presents a challenge for modeling the behavior of materials. This problem is particularly severe in the case of condensed phase systems where the reaction mechanisms may be highly complicated or completely unknown. I demonstrate the use of support vector machines, a machine-learning technique, to create transition state theory dividing surfaces without a priori information about the reaction coordinate. This method can be applied to modeling the stability of novel materials. / text