Development of Quantum-Crystallographic Methods for Chemical and Biochemical Applications

The field of crystallography is a key branch of natural sciences, important not only for physics, geology, biology or chemistry, but it also provides crucial information for life sciences and materials science. It laid the foundations of our textbook knowledge of matter in general. In this thesis, the field of quantum crystallography – a synergistic approach of crystallography and quantum mechanics – is used as a tool to predict and understand processes of molecules and their interactions. New methods are proposed and used that provide deeper insight into the influence of local molecular environments on molecules and allows advanced predictions of the biochemical effect of drugs. Ultimately, this means that we can now understand interactions between molecules in crystal structures more completely that were long thought to be fully characterized. As part of this work, new software was developed to handle theoretical simulations as well as experimental data – and also both of them together at the same time. The introduction of non-spherical refinements in standard software for crystallography opens the field of quantum crystallography to a wide audience and will hopefully strengthen the mutual ground between experimentalists and theoreticians. Specifically, we created a new native interface between Olex2 and non-spherical refinement techniques, which we called NoSpherA2. This interface has been designed in such a way that it can be used for any kind of non-spherical atom descriptions. This will allow refinement of modern diffraction data employing modern quantum crystallographic models, leaving behind the century old Independent Atom Model (IAM). New software was also developed to provide novel models and descriptors for understanding environmental effects on the electron density and electrostatic potential of a molecule. This so-called Quantum Crystallographic Toolbox (QCrT) provides a framework for the fast and easy implementation of various methods and descriptors. File conversion tools allow the interfacing with many existing software packages and might provide useful information for future method development, experimental setups and data evaluation, as well as chemical insight into intra- and intermolecular interactions. It is fully parallelized and portable to graphic card processors (GPUs), which provide extraordinary amounts of computational power with moderate resource requirements. Especially in the context of ultra-bright X-ray sources like X-ray free electron lasers and electron diffraction these new models become crucial to have a better description of experimental findings. In applying this new framework of quantum crystallographic methods, we analyze a type of bonding at the edge of conventional organic chemistry: The push-pull systems of ethylenes. We show how X-ray constrained bonding analysis leads to the unambiguous determination of the behavior and type of bonding present in a series of compounds which are contradicting the Lewis-picture of a double-bond. This new understanding has led to the development of a new potential drug, namely a silicon analogue of ibuprofen; one of the most important drugs known to humankind. We determined its physical properties, investigated its stability and potency as a more soluble and novel alternative of ibuprofen: While retaining the same pharmaceutical activity of ibuprofen, making it a bioisoster for ibuprofen, this material shows a better applicability in aqueous media

Computational insight into the broad substrate specificity of enzymes that process nucleic acids

Many enzymes that bind DNA and RNA possess broad substrate specificity and play diverse roles in biology. Three classes of enzymes with broad substrate specificity are nucleoside hydrolases that salvage nucleic acid building blocks, and alkyladenine DNA glycosylases (AAG) and AlkB enzymes, which repair alkylated and/or deaminated DNA damage. This thesis uses advanced computational techniques to examine how enzymes process structurally diverse substrates. Specifically, structural and energetic information is provided by molecular dynamics (MD) simulations, quantum mechanics (QM) and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations, which provide insight into how each enzyme active site changes to accommodate unique substrates and quantify the impact that these changes have on catalyzed reactions. From these results, atomistic explanations for the activity of these enzymes is obtained, which can be used to develop new treatments for diseases. The computational approach presented can be applied to other enzymes that exhibit broad substrate specificity