Mathematische Verfahren zur Aufklärung der Struktur, Dynamik und biologischen Aktivität von Molekülen unter Verwendung von NMR spektroskopischen und empirischen Parametern

In der vorliegenden Arbeit werden Verfahren der Mathematik und Informatik entwickelt und eingesetzt, um Struktur, Dynamik und biologische Aktivität aus NMR spektroskopischen und empirischen Parametern zu bestimmen. Dolastatin 10 und Epothilon A sind potentielle Wirkstoffe gegen Krebs, da sie durch Wechselwirkung mit Tubulin die Zellteilung unterbinden. Die 3D Struktur beider Wirkstoffe in Lösung und die Struktur von an Tubulin gebundenem Epothilon A wird aus NMR spektroskopischen Parametern bestimmt. Dolastatin 10 liegt in einem konformationellen Gleichgewicht zwischen der cis -- und trans -- Konformation in der ungewöhnlichen Aminosäure DAP vor. Beide Konformationen des flexiblen Pentapeptids können bestimmt werden mit RMSD = 1.423 Å für das cis -- Konformer und RMSD = 1.488 Å für das trans -- Konformer. Während das trans -- Konformer gestreckt vorliegt, faltet das cis -- Konformer am DAP zurück. Epothilone A ist durch einen Makrozyklus weniger flexibel und sowohl die an Tubulin gebundene Struktur (RMSD = 0.537 Å) als auch freie Form (RMSD = 0.497 Å) kann mit geringen RMSD -- Werten bestimmt werden. Die Struktur der freien Form, welche in Lösung hauptsächlich vorliegt, ist mit der Röntgenstruktur weitgehend identisch. In der an Tubulin gebundenen Form wird eine essentielle Umorientierung der Seitenkette beobachtet, die für die Wechselwirkung mit Tubulin entscheidend ist. Dipolare Kopplungen eines Proteins sind geeignet, eine 3D Homologiesuche in der PDB durchzuführen, da die relative Orientierung von Sekundärstrukturelementen und Domänen durch sie beschrieben wird 85 . Die frühe Erkennung 3D homologer Proteinfaltungen eröffnet die Möglichkeit, die Bestimmung von Proteinstrukturen zu beschleunigen. Eine Homolgiesuche unter Nutzung dipolarer Kopplungen ist in der Lage, Proteine oder zumindest Fragmente mit ähnlicher 3D Struktur zu finden, auch wenn die Primärsequenzhomologie gering ist. Darüber hinaus wird eine Transformation für experimentelle dipolare Kopplungen entwickelt, die die indirekte Orientierungsinformation eines Vektors relativ zu einem externen Tensor in den möglichen Bereich für den Projektionswinkel zwischen zwei Vektoren und somit in eine intramolekulare Strukturinformation übersetzt. Diese Einschränkungen können in der Strukturbestimmung von Proteinen mittels Molekulardynamik genutzt werden 92 . Im Gegensatz zu allen existierenden Implementierungen wird die Konvergenz der Rechnung durch die auf diese Weise eingeführten dipolare Kopplungsinformation kaum beeinflusst. Die dipolaren Kopplungen werden trotzdem von den errechneten Strukturen erfüllt. Auch ohne die Nutzung bereits bekannter Protein­ oder Fragmentstrukturen kann so ein erheblicher Teil der NOE -- Information substituiert werden. Die Dynamik des Vektors, der die beiden wechselwirkenden Dipole verbindet, beeinflusst den Messwert der dipolaren Kopplung. Dadurch wird Information über die Dynamik von Molekülen auf der µs­Zeitskala zugänglich, die bisher nur schwer untersucht werden konnte. Die Messung dipolarer Kopplungen für einen Vektor in verschiedenen Orientierungen erlaubt die Analyse seiner Bewegung 89 . Im besonderen ist die Ableitung eines modellfreien Ordnungsparameters 2 S möglich. Weiterhin lassen sich ebenso modellfrei eine mittlere Orientierung des Vektors, axialsymmetrische Anteile und nichtaxialsymmetrische Anteile der Dynamik ableiten und auswerten. Die Anwendung der so entwickelten Protokolle auf experimentelle Daten 90 lässt Proteine deutlich dynamischer erscheinen als auf der Zeitskala der Relaxationsexperimente zu erkennen ist. Der mittlere Ordnungsparameter sinkt von 0.8 auf 0.6. Dies entspricht einer Erhöhung des Öffnungswinkels der Bewegung von ca. 22 ° auf ca. 33°. Die Bewegungen weichen teilweise bis zu 40% und im Mittel 15% von der Axialsymmetrie ab. Neuronale Netze erlauben eine schnelle (ca. 5000 chemische Verschiebungen pro Sekunde) und exakte (mittleren Abweichung von 1.6 ppm) Berechnung der 13 C NMR chemischen Verschiebung 115 . Dabei kombinieren sie die Vorteile bisher bekannter Datenbankabschätzungen (hohe Genauigkeit) und Inkrementverfahren (hohe Geschwindigkeit). Das 13 C NMR Spektrum einer organischen Verbindung stellt eine detaillierte Beschreibung seiner Struktur dar. Resultate des Strukturgenerators COCON können durch den Vergleich des experimentellen mit den berechneten 13 C NMR Spektren auf ca. 1 o/oo der vorgeschlagenen Strukturen eingeschränkt werden, die eine geringe Abweichung zum experimentellen Spektrum haben 122 . Die Kombination mit einer Substrukturanalyse erlaubt weiterhin die Erkennung wahrscheinlicher, geschlossener Ringsysteme und gibt einen Überblick über die Struktur des generierten Konstitutionssubraumes. Genetische Algorithmen können die Struktur organischer Moleküle ausgehend von derer Summenformel auf eine Übereinstimmung mit dem experimentellen 13 C NMR Spektrum optimieren. Die Konstitution von Molekülen wird dafür durch einen Vektor der Bindungszustände zwischen allen Atom -- Atom Paaren beschrieben. Selbige Vektoren sind geeignet, in einem genetischen Algorithmus als genetischer Code von Konstitutionen betrachtet zu werden. Diese Methode erlaubt die automatisierte Bestimmung der Konstitution von Molekülen mit 10 bis 20 Nichtwasserstoffatomen 123 . Symmetrische neuronale Netze können fünf bzw. sieben dimensionale, heterogene Parameterrepräsentationen der 20 proteinogenen Aminosäuren unter Erhalt der wesentlichen Information in den dreidimensionalen Raum projizieren 134 . Die niederdimensionalen Projektionen ermöglichen eine Visualisierung der Beziehungen der Aminosäuren untereinander. Die reduzierten Parameterrepräsentationen sind geeignet, als Eingabe für ein neuronales Netz zu dienen, welches die Sekundärstruktur eines Proteins mit einer Genauigkeit von 66 % im Q 3 -- Wert berechnet. Neuronale Netzte sind aufgrund ihrer flexiblen Struktur besonders geeignet, quantitative Beziehungen zwischen Struktur und Aktivität zu beschreiben, da hier hochgradig nichtlineare, komplexe Zusammenhänge vorliegen. Eine numerische Codierung der über 200 in der Literatur beschriebenen Epothilonderivate erlaubt es, Modelle zur Berechnung der Induktion der Tubulin Polymerisation (R = 0.73) und der Inhibierung des Krebszellenwachstums (R = 0.94) zu erstellen 136 . Die trainierten neuronalen Netze können in einer Sensitivitätsanalyse genutzt werden, um die Bindungsstellen des Moleküls zu identifizieren. Aus der Berechnung der Aktivität für alle Moleküle des durch die Parameter definierten Strukturraums ergeben sich Vorschläge für Epothilonderivate, die bis zu 1 000 mal aktiver als die bisher synthetisierten sein könnten

Doctor of Philosophy

dissertationSelectivity in chemical reactions is a matter of distinguishing between pathways of little energetic difference. From reactions affording no selectivity in product formation to those achieving selectivity levels of >99:1, the energy differences responsible for these disparate isomer ratios range from 0 to ~3 kcal mol-1, respectively. It is astounding that such a seemingly trivial amount of energy, on the order of the energetic barrier to carbon-carbon bond rotation in ethane (~2.9 kcal mol-1), can precipitate products in exquisitely high isomeric purity. Identifying the origin of the small energy differences that afford selectivity has, historically, been a daunting endeavor and predominantly characterized by empiricism. In recent years, the Sigman group has been developing a more efficient alternative to the typical guess-and-check approach to optimizing catalyst-substrate interactions for high site- and enantioselective outcomes. This methodology relies on the quantification and systematic modulation of various reaction features that putatively induce selectivity, ultimately enabling the identification of mathematical equations to describe these effects. Detailed herein is the process for developing reliably predictive mathematical constructs of reaction selectivity. In the context of three distinct reactions-iridium-catalyzed asymmetric hydrogenation (Chapter 2), rhodium-catalyzed site-selective C-H amination (Chapter 3), and rhodium-catalyzed asymmetric transfer hydrogenation (Chapter 4)-means for effective model development are put forth. Namely, this work describes the examination of the unconventional application of design of experiments principles, the identification of parameters capable of describing selectivity, and the process by which linear regression models are developed and validated. Through this approach, mathematical equations are developed that relate the differential free energy of selectivity to numerical depictions of steric, electronic, and hydrophobic effects. By identifying underlying predictive trends, developed models serve as a unique avenue by which mechanistic insight may be gained about selectivity engendering interactions. Consequently, these models enable the energetic optimization of substrate-catalyst interactions and the quantitative prediction of how such changes will influence reaction selectivity. Through the work of myself and my colleagues in the Sigman group, we are learning how reactions may be investigated and understood so as to make the ~3 kcal mol"1 energy range that is responsible for selectivity a vast window of opportunity for shaping reaction partners to achieve desired reaction outcomes

Diffusion of tin from TEC-8 conductive glass into mesoporous titanium dioxide in dye sensitized solar cells

The photoanode of a dye sensitized solar cell is typically a mesoporous titanium dioxide thin film adhered to a conductive glass plate. In the case of TEC-8 glass, an approximately 500 nm film of tin oxide provides the conductivity of this substrate. During the calcining step of photoanode fabrication, tin diffuses into the titanium dioxide layer. Scanning Electron Microscopy and Electron Dispersion Microscopy are used to analyze quantitatively the diffusion of tin through the photoanode. At temperatures (400 to 600 °C) and times (30 to 90 min) typically employed in the calcinations of titanium dioxide layers for dye sensitized solar cells, tin is observed to diffuse through several micrometers of the photoanode. The transport of tin is reasonably described using Fick\u27s Law of Diffusion through a semi-infinite medium with a fixed tin concentration at the interface. Numerical modeling allows for extraction of mass transport parameters that will be important in assessing the degree to which tin diffusion influences the performance of dye sensitized solar cells

Artificial neural networks for the classification of Meliaceae extractives.

Thesis (Ph.D.)-University of Natal, Durban, 1998.The goal of this project was the development of a computer-based system using artificial intelligence to classify the limonoids, protolimonoids and triterpenoids isolated from the family Meliaceae by the Natural Products Research Group of the University of Natal, Durban. A database of samples was obtained between 1991 and 1996, part of which time the author was a member of the group and isolated compounds from Turraea obtusifolia and Turraea floribunda. Over and above the problem of complexity and similarity in structures of the above mentioned natural products, are other difficulties. These include very small amounts of sample being isolated producing very weak peak signals in the C-13 NMR spectra, extraneous peaks in the NMR spectra due to different impurities and instrument noise, non-reproducible spectra due to the pulsed Fourier transform intervals and the nuclear Overhauser effect, impure samples often isolated as stereoisomeric mixtures or as mixed esters and superposition of peak signals in the NMR spectra due to carbons in the same environment within the same compound. These factors make identification by traditional computational and expert systems impossible. As a result of these shortcomings, the author has developed a novel approach using artificial neural network techniques. The artificial neural network system developed used real data from the 300 MHz NMR spectrometer in the Department of Chemistry, Durban. The system was trained to discriminate between limonoids, triterpenoids and flavonoids/coumarins from the C-13 NMR spectra of pure, impure and unseen compounds with an accuracy of better than 90%. Further differentiation of the glabretals from the rest of the protolimonoids as well as from the rest of the triterpenoids showed similarly significant results. Finally, individual limonoid discrimination within the limonoid dataset was extremely successful. Apart from its application to the extractives from Meliaceae, the methodology and techniques developed by the author can be applied to other sets of extractives to provide a robust method for the spectral classification of pre-identified natural products

ICR ANNUAL REPORT 2022 (Volume 29)[All Pages]

This Annual Report covers from 1 January to 31 December 202

The chemical synthesis of aliphatic benzo[b][1,4]dioxepin-3-one analogues related to synthetic marine odorants

Odour discrimination is a combinatorial phenomenon, as odorant molecules are known to bind to multiple olfactory receptors. It is also considered that the absolute configuration of a molecule is of crucial importance in determining the human perception of odour. Additionally, it is also recognised that a substance evokes a sense of smell provided that its molecular shape is compatible with the complementary space within the olfactory receptor. Since the benzo[b][1,4]dioxepin-3-one scaffold represents the essential structural characteristics of the synthetic marine odorant family, we were therefore interested in the effect of modulating the molecular shape via an aromatic/aliphatic ring exchange. By the substitution of the aromatic functionality with a saturated ring counterpart we endeavoured to discover the molecular interactions of the carbocyclic ring systems with olfactory receptor sites, including any potential chiral interactions occurring within the receptor(s). Our initial results revealed that an aromatic ring system was necessary for binding to the marine odorant receptor(s) and that the addition of an alkene or methyl substituent had little effect on receptor affinity. The synthesised aliphatic benzo[b][1,4]dioxepin-3-one analogues instead exhibited a plethora of odorant descriptors and consequently it was speculated that perhaps altogether new odorant families could be targeted by further chemical synthesis. It was also questioned if the fusion of 1,4-dioxepan-6-one heterocyclic rings onto naturally occurring terpenoid odorants could merge/synergise existing fragrance classes. The chemical synthesis and olfactory characterisation of a variety of aliphatic benzo[b][1,4]dioxepin-3-one analogues, as well as a series of 2-substituted and 2,3-annulated 1,4-dioxepan-6-one analogues is hereby reported

ICR ANNUAL REPORT 2020 (Volume 27)[All Pages]

This Annual Report covers from 1 January to 31 December 202

Machine Learning in Discrete Molecular Spaces

The past decade has seen an explosion of machine learning in chemistry. Whether it is in property prediction, synthesis, molecular design, or any other subdivision, machine learning seems poised to become an integral, if not a dominant, component of future research efforts. This extraordinary capacity rests on the interac- tion between machine learning models and the underlying chemical data landscape commonly referred to as chemical space. Chemical space has multiple incarnations, but is generally considered the space of all possible molecules. In this sense, it is one example of a molecular set: an arbitrary collection of molecules. This thesis is devoted to precisely these objects, and particularly how they interact with machine learning models. This work is predicated on the idea that by better understanding the relationship between molecular sets and the models trained on them we can improve models, achieve greater interpretability, and further break down the walls between data-driven and human-centric chemistry. The hope is that this enables the full predictive power of machine learning to be leveraged while continuing to build our understanding of chemistry. The first three chapters of this thesis introduce and reviews the necessary machine learning theory, particularly the tools that have been specially designed for chemical problems. This is followed by an extensive literature review in which the contributions of machine learning to multiple facets of chemistry over the last two decades are explored. Chapters 4-7 explore the research conducted throughout this PhD. Here we explore how we can meaningfully describe the properties of an arbitrary set of molecules through information theory; how we can determine the most informative data points in a set of molecules; how graph signal processing can be used to understand the relationship between the chosen molecular representation, the property, and the machine learning model; and finally how this approach can be brought to bear on protein space. Each of these sub-projects briefly explores the necessary mathematical theory before leveraging it to provide approaches that resolve the posed problems. We conclude with a summary of the contributions of this work and outline fruitful avenues for further exploration

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

High-Throughput Methods For Reaction Development Using The Mosquito® Liquid Handling Robot

High-throughput technologies have dramatically advanced processes such as drug screening and protein crystallisation. Application of related high-throughput experimentation practices have the potential to revolutionise chemical synthesis. This thesis describes the standardisation and implementation of high-throughput protocols in the Gaunt group using the Mosquito® liquid-handling robot. An automated high-throughput protocol was established for quantitative data generation. The Chan-Lam reaction of an amine and aryl boronates was used as an initial proof-of-concept reaction to standardise the high-throughput protocol. All parts of the protocol were optimised including the creation of an Excel spreadsheet and Mosquito® dosing protocols. Quantitative data was achieved using high-throughput LCMS analysis with internal standards and calibration curves. Data were tested by statistical methods integrated into the Excel Spreadsheet. Using the standardised high-throughput protocol, reaction conditions for the Chan-Lam were assessed using multi-parallel arrays. A total of 30 boronic esters were screened against eight bases, six copper catalysts and two ligands, totaling 5,888 reactions. All reaction components were parameterised using experimental and computational descriptors to generate a predictive model, in collaboration with computational chemists. To rapidly triage reaction conditions, high-throughput TLC was developed. Three transformations were investigated using this technique, reducing the total analysis time to less than 2 hours for 1536-reactions. This study demonstrates the potential of automated equipment in reaction optimisation and discovery.EPSRC GS