Engineering and Characterization of Cytochrome P450 Enzymes for Nitrogen-Atom Transfer Reactions

The creation of novel enzyme activity is a great challenge to protein engineers, but nature has done so repeatedly throughout the process of natural selection. I begin by outlining the multitude of distinct reactions catalyzed by a single enzyme class, cytochrome P450 monooxygenases. I discuss the ability of cytochrome P450 to generate reactive intermediates capable of diverse reactivity, suggesting this enzyme can also be used to generate novel reactive intermediates in the form of metal-carbenoid and nitrenoid species. I then show that cytochrome P450 from Bacillus megaterium (P450_BM3) and its isolated cofactor can catalyze metal-nitrenoid transfer in the form of intramolecular C–H bond amination. Mutations to the protein sequence can enhance the reactivity and selectivity of this transformation significantly beyond that of the free cofactor. Next, I demonstrate an intermolecular nitrene transfer reaction catalyzed by P450_BM3 in the form of sulfide imidation. Understanding that sulfur heteroatoms are strong nucleophiles, I show that increasing the sulfide nucleophilicity through substituents on the aryl sulfide ring can dramatically increase reaction productivity. To explore engineering nitrenoid transfer in P450BM3, active site mutagenesis is employed to tune the regioselectivity intramolecular C–H amination catalysts. The solution of the crystal structure of a highly selective variant demonstrates that hydrophobic residues in the active site strongly modulate reactivity and regioselectivity. Finally, I use a similar strategy to develop P450-based catalysts for intermolecular olefin aziridination, demonstrating that active site mutagenesis can greatly enhance this nitrene transfer reaction. The resulting variant can catalyze intermolecular aziridination with more than 1000 total turnovers and enantioselectivity of up to 99% ee.

A variational framework for spectral discretization of the density matrix in Kohn Sham density functional theory

Kohn-Sham density functional theory (KSDFT) is currently the main work-horse of quantum mechanical calculations in physics, chemistry, and materials science. From a mechanical engineering perspective, we are interested in studying the role of defects in the mechanical properties in materials. In real materials, defects are typically found at very small concentrations e.g., vacancies occur at parts per million, dislocation density in metals ranges from $10^{10} m^{-2}$ to $10^{15} m^{-2}$, and grain sizes vary from nanometers to micrometers in polycrystalline materials, etc. In order to model materials at realistic defect concentrations using DFT, we would need to work with system sizes beyond millions of atoms. Due to the cubic-scaling computational cost with respect to the number of atoms in conventional DFT implementations, such system sizes are unreachable. Since the early 1990s, there has been a huge interest in developing DFT implementations that have linear-scaling computational cost. A promising approach to achieving linear-scaling cost is to approximate the density matrix in KSDFT. The focus of this thesis is to provide a firm mathematical framework to study the convergence of these approximations. We reformulate the Kohn-Sham density functional theory as a nested variational problem in the density matrix, the electrostatic potential, and a field dual to the electron density. The corresponding functional is linear in the density matrix and thus amenable to spectral representation. Based on this reformulation, we introduce a new approximation scheme, called spectral binning, which does not require smoothing of the occupancy function and thus applies at arbitrarily low temperatures. We proof convergence of the approximate solutions with respect to spectral binning and with respect to an additional spatial discretization of the domain. For a standard one-dimensional benchmark problem, we present numerical experiments for which spectral binning exhibits excellent convergence characteristics and outperforms other linear-scaling methods.