The Polytope Formalism: isomerism and associated unimolecular isomerisation

This thesis concerns the ontology of isomerism, this encompassing the conceptual frameworks and relationships that comprise the subject matter; the necessary formal definitions, nomenclature, and representations that have impacts reaching into unexpected areas such as drug registration and patent specifications; the requisite controlled and precise vocabulary that facilitates nuanced communication; and the digital/computational formalisms that underpin the chemistry software and database tools that empower chemists to perform much of their work. Using conceptual tools taken from Combinatorics, and Graph Theory, means are presented to provide a unified description of isomerism and associated unimolecular isomerisation spanning both constitutional isomerism and stereoisomerism called the Polytope Formalism. This includes unification of the varying approaches historically taken to describe and understand stereoisomerism in organic and inorganic compounds. Work for this Thesis began with the synthesis, isolation, and characterisation of compounds not adequately describable using existing IUPAC recommendations. Generalisation of the polytopal-rearrangements model of stereoisomerisation used for inorganic chemistry led to the prescriptions that could deal with the synthesised compounds, revealing an unrecognised fundamental form of isomerism called akamptisomerism. Following on, this Thesis describes how in attempting to place akamptisomerism within the context of existing stereoisomerism reveals significant systematic deficiencies in the IUPAC recommendations. These shortcomings have limited the conceptualisation of broad classes of compounds and hindered development of molecules for medicinal and technological applications. It is shown how the Polytope Formalism can be applied to the description of constitutional isomerism in a practical manner. Finally, a radically different medicinal chemistry design strategy with broad application, based upon the principles, is describe

Kinetic model construction using chemoinformatics

Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations. This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules. A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis

High throughput workflow for the computational design of new thermally activated delayed fluorescence emitters

This thesis explores the use of computational methods for discovering new TADF molecules, with a focus on developing a high-throughput virtual screening workflow that reduces costs and time associated with experimental screening. Using methods like STONED and SYBA, diverse molecule libraries were generated and evaluated to identify promising candidates for further investigation. The study also examines the challenges of using computational methods, such as discrepancies and limitations with computationally efficient methods. Modifications were made to parent molecules based on ΔSCF calculations and similarity map analysis. Overall, this study provides valuable insights into the use of computational methods for TADF molecule design and offers guidance for future research aimed at designing new TADF materials.Open Acces

New Approaches to Protein NMR Automation

The three-dimensional structure of a protein molecule is the key to understanding its biological and physiological properties. A major problem in bioinformatics is to efficiently determine the three-dimensional structures of query proteins. Protein NMR structure de- termination is one of the main experimental methods and is comprised of: (i) protein sample production and isotope labelling, (ii) collecting NMR spectra, and (iii) analysis of the spectra to produce the protein structure. In protein NMR, the three-dimensional struc- ture is determined by exploiting a set of distance restraints between spatially proximate atoms. Currently, no practical automated protein NMR method exists that is without human intervention. We first propose a complete automated protein NMR pipeline, which can efficiently be used to determine the structures of moderate sized proteins. Second, we propose a novel and efficient semidefinite programming-based (SDP) protein structure determination method. The proposed automated protein NMR pipeline consists of three modules: (i) an automated peak picking method, called PICKY, (ii) a backbone chemical shift assign- ment method, called IPASS, and (iii) a protein structure determination method, called FALCON-NMR. When tested on four real protein data sets, this pipeline can produce structures with reasonable accuracies, starting from NMR spectra. This general method can be applied to other macromolecule structure determination methods. For example, a promising application is RNA NMR-assisted secondary structure determination. In the second part of this thesis, due to the shortcomings of FALCON-NMR, we propose a novel SDP-based protein structure determination method from NMR data, called SPROS. Most of the existing prominent protein NMR structure determination methods are based on molecular dynamics coupled with a simulated annealing schedule. In these methods, an objective function representing the error between observed and given distance restraints is minimized; these objective functions are highly non-convex and difficult to optimize. Euclidean distance geometry methods based on SDP provide a natural formulation for realizing a three-dimensional structure from a set of given distance constraints. However, the complexity of the SDP solvers increases cubically with the input matrix size, i.e., the number of atoms in the protein, and the number of constraints. In fact, the complexity of SDP solvers is a major obstacle in their applicability to the protein NMR problem. To overcome these limitations, the SPROS method models the protein molecule as a set of intersecting two- and three-dimensional cliques. We adapt and extend a technique called semidefinite facial reduction for the SDP matrix size reduction, which makes the SDP problem size approximately one quarter of the original problem. The reduced problem is solved nearly one hundred times faster and is more robust against numerical problems. Reasonably accurate results were obtained when SPROS was applied to a set of 20 real protein data sets

Graduate School: Course Decriptions, 1972-73

Official publication of Cornell University V.64 1972/7

Molecular Simulation Studies on the Prion Protein Variants: Insights into the Intriguing Effects of Mutations

Prion diseases, or transmissible spongiform encephalopathies (TSE), are a group of rare fatal neurodegenerative maladies that affect humans and animals. The fundamental breakthrough in TSE research was the discovery of the "prion"\u23afproteinaceous infectious particle\u23af and the verification of the \u201cprotein-only\u201d hypothesis, which states that prions could self-propagate by converting the cellular prion protein (PrPC) into the scrapie form, PrPSc (or prions), and lead to neurodegeneration without using any nucleic acids. The concept of prions may unify neurodegenerative diseases under a common pathogenic mechanism. Indeed, growing evidence shows that TSE may share similar pathogenesis with common neurodegenerative syndromes such as Alzheimer\u2019s disease and Parkinson\u2019s disease, for which there are currently no cure. Today, PrP is one of the most studied models for protein misfolding mechanism and TSE serve as an excellent model for studying many other neurodegenerative diseases. Understanding the molecular mechanism of the PrP misfolding process may profoundly influence the development of diagnostics and effective therapies for neurodegenerative diseases in general. Investigating human (Hu) PrP TSE-linked mutations (more than 50 currently identified mutations, linked to ~15% of the cases) may be very instrumental in this respect, as it can provide hints on the molecular basis of the PrPC\u2192PrPSc conversion. These mutations cause spontaneous TSE, which are likely due to modifications in the native structure of PrPC. They are located all over the structure. Polymorphisms (i.e. non-pathogenic, naturally occurring mutations) in the PrP gene have been found to influence the etiology and neuropathology of the disease in both humans and sheep. In transgenic (Tg) mice, artificial mutations can determine the susceptibility to the infection of different prion strains. Intriguingly, mouse (Mo) PrP containing artificial mutations (denoted MoPrP chimera, hereinafter) have very different effects in vitro: some MoPrP chimera were found to resist PrPSc infection, whereas some others did not; some of the resistant MoPrP chimeras even exhibited a protective effect (known as the dominant-negative effect) over the co-expressed endogenous wild-type (WT) MoPrPC. Most mutations are located in the folded globular domain (GD) while fewer are located in the intrinsically disordered N-terminal domain (N-term). The N-term of PrPC has been suggested to serve multiple functions in vivo, which likely relies on the structural flexibility of this domain. Therefore, characterizing the structural features of the N-term is central for investigating not only the mutations in this domain, but also the physiological role of the N-term. Based on previous studies in our lab, in this thesis we first applied molecular dynamics simulations to studying the impact of all the known Hu TSE-linked mutations in HuPrPC GD. We next applied the same approach to study the GD structure of MoPrP chimeras which contain one or two residues from Hu or sheep PrP sequence. By studying these PrP variants, we aim to identify the structural determinants of the mutants that may play a role in the PrPC\u2192PrPSc conversion. Our calculations discovered that these mutants exhibit different structural features from those of the WT PrP GD mainly in two common regions that are likely the \u201chot spots\u201d in the protein misfolding process. These features can be classified into different types that are correlated to the types of mutants (i.e. pathogenic, resistant or dominant-negative), thus hinting to the molecular mechanisms of PrPSc formation and propagation. We have then predicted the structure of the entire PrP N-term and the impact of the Hu TSE-linked mutations in this domain using a novel Monte Carlo-based simulation approach, PROFASI. PROFASI has already shown to provide structural predictions in a disordered protein such as \u3b1-synuclein. Our results are consistent with available experimental data and therefore firmly allow us to provide the first overview on the structural determinants of all Hu TSE-linked mutations in PrP

Cheminformatics for genome-scale metabolic reconstructions

Genome-scale metabolic reconstructions are an important resource in the study of metabolism. They provide both a system and component level view of the biochemical transformations of metabolites. As more reconstructions have been created it remains a challenge to integrate and reason about their contents. This thesis focuses on the development of computational methods to allow on-demand comparison and alignment of metabolic reconstructions. A novel method is introduced that utilises chemical structure representations to identify equivalent metabolites between reconstructions. Using a graph theoretic representation allows the identification and reasoning of metabolites that have a non-exact match. A key advantage is that the method uses the contents of reconstructions directly and does not rely on the creation or use of a common reference. To annotate reconstructions with chemical structure representations an interactive desktop application is introduced. The application assists in the creation and curation of metabolic information using manual, semi-auto\-mated, and automated methods. Chemical structure representations can be retrieved, drawn, or generated to allow precise metabolite annotation. In processing chemical information, efficient and optimised algorithms are required. Several areas are addressed and implementations have been contributed to the Chemistry Development Kit. Rings are a fundamental property of chemical structures therefore multiple ring definitions and fast algorithms are explored. Conversion and standardisation between structure representations present a challenge. Efficient algorithms to determine aromaticity, assign a Kekulé form, and generate tautomers are detailed. Many enzymes are selective and specific to stereochemistry. Methods for the identification, depiction, comparison, and description of stereochemistry are described.The project was funded by Unilever, the Biotechnology and Biological Sciences Research Council [BB/I532153/1], and the European Molecular Biology Laboratory

The Origin and Early Evolution of Life

What is life? How, where, and when did life arise? These questions have remained most fascinating over the last hundred years. Systems chemistry is the way to go to better understand this problem and to try and answer the unsolved question regarding the origin of Life. Self-organization, thanks to the role of lipid boundaries, made possible the rise of protocells. The role of these boundaries is to separate and co-locate micro-environments, and make them spatially distinct; to protect and keep them at defined concentrations; and to enable a multitude of often competing and interfering biochemical reactions to occur simultaneously. The aim of this Special Issue is to summarize the latest discoveries in the field of the prebiotic chemistry of biomolecules, self-organization, protocells and the origin of life. In recent years, thousands of excellent reviews and articles have appeared in the literature and some breakthroughs have already been achieved. However, a great deal of work remains to be carried out. Beyond the borders of the traditional domains of scientific activity, the multidisciplinary character of the present Special Issue leaves space for anyone to creatively contribute to any aspect of these and related relevant topics. We hope that the presented works will be stimulating for a new generation of scientists that are taking their first steps in this fascinating field