The development of hybrid quantum classical computational methods for carbohydrate and hypervalent phosphoric systems

Includes bibliographical references.Ab initio, density functional theory, and semi-empirical methods serve as major computational tools for quantum mechanical calculations of medium to large molecular systems. Semi-empirical methods are most effectively used in a hybrid quantum mechanics/molecular mechanics (QM/MM) dynamics framework. However, semi-empirical methods have been designed to provide accurate results for organic molecules, but often fail to treat hypervalent species accurately due to their use of an sp basis. Recently, significant breakthroughs have been made with the incorporation of d-orbitals into the semi-empirical framework, thereby allowing for accurate modeling of both hypervalent and transition metal systems. Here I consider two methods that adopt this new methodology, namely AM1/d-PhoT and AM1*. Our major focus is the simulation of chemical biological and more specifically chemical glycobiological problems of biochemical interest. When I tested the ability of both AM1/d-PhoT and AM1* to reproduce key metrics in chemical glycobiology (i.e., sugar ring pucker, phosphate participation in transferase reactions) these methods, in combination with the published parameters, performed very poorly. Using the AM1/d-PhoT and AM1* Hamiltonians I set out to re-parameterize these methods aiming to produce holistic biochemical QM/MM toolsets able to simulate fundamental problems of binding and enzyme reactivity in chemical glycobiology. We called these methods AM1/d-CB1 and AM1*-CB1. In the development of these parameter sets I focused specifically on proton transfer, carbohydrate ring puckering, bond polarization, amino acid interactions, and phosphate interactions (facets important to chemical glycobiology). Both AM1/d-CB1 and AM1*-CB1 make use of a variable property optimization parameter approach for the glycan molecular class and its chemical environment. The accuracy of these methods is evaluated for carbohydrates, amino acids and phosphates present in catalytic domains of glycoenzymes, and the are shown to be more accurate for key performance indices (puckering, etc.) and on average across all simulation derived properties (QM/MM polarization, protein performance, etc.) than all other NDDO semiempirical methods currently being used. A major objective of the newly developed AM1/d-CB1 and AM1*-CB1 is to provide a platform to accurately model reactions central to chemical glycobiology using hybrid QM/MM molecular dynamics (MD) simulations. AM1/d-CB1 is applied to a well-known reaction involving purine nucleoside phosphorylase (PNP) and results lead me to conclude that the method shows promise for modelling glycobiological QM/MM systems

Development of Semiempirical Models for Metalloproteins

Theoretical models and computational techniques are useful for gaining insight into the interactions, movements, and functions of atoms and molecules, ranging from small chemical systems with few atoms to large biological molecules with many atoms. Due to the inability of force field methods to accurately describe different properties of metalloenzymes and the prohibitive computing cost of high-level quantum methods, computationally efficient models are needed. This dissertation describes the development of new quantum semiempirical models for metalloproteins. The original AM1 (Austin Model 1) based on the neglect of diatomic differential overlap approximations was re-parameterized to describe the structural and energetic properties of biomolecules that mimic the active sites of metalloproteins. The biologically inspired genetic algorithm PIKAIA was used to optimize the parameters for each chemical element. Structures and energies of various clusters analogous to complexes found in metalloproteins were prepared as a training set using hybrid density functional theory. Models were trained to reproduce all of the properties included in the small training set. The optimized models were validated for large testing sets that incorporate bigger complexes and related reactions. Finally, the optimized models were used to study biologically-relevant processes in condensed phase using molecular dynamics simulations. All the gas- and liquid-phase results from the optimized models were compared with original semiempirical models as well as available high-level theoretical and experimental results. Metal ions play crucial roles in biological systems. They actively participate in structural, catalytic, and co-catalytic activities of a large number of enzymes. The development of semiempirical models is divided into three parts. First, new AM1 parameters for hydrogen and oxygen were developed to describe gas-phase proton transfer reactions in water and static and dynamic properties of liquid water. Gas-phase results were compared with original AM1, RM1, and PM3 models, whereas liquid results were compared with original AM1, AM1-W, and AM1PG-W models, and with available experimental results. It is found that the optimized model reproduces experimental data better than other available semiempirical models. Second, using the previously optimized model for hydrogen and oxygen, the AM1 model is re-parameterized for zinc and sulfur to describe important physical and chemical properties of zinc, water, hydrogen sulfide complexes mimicking structural motifs found in zinc enzymes. Metal-induced pKa shifts are computed for water and hydrogen sulfide, and compared with available theoretical and experimental results. Third, using previously optimized parameters for hydrogen, oxygen, and zinc, AM1 parameters for carbon and nitrogen are optimized to study proton transfer, nucleophilic attacks, and peptide hydrolysis mechanisms in zinc metalloproteases. Overall, the optimized models give promising results for the various properties of biomolecules in gas-phase clusters and in condensed phase. Particularly, the water model reproduces the proton transfer related properties in gas-phase and the structure, dielectric properties, and infrared spectra of liquid water. The zinc/sulfur model reproduces the hydration structure of zinc cation and zinc-bound hydrogen sulfide. Results for the coordination configurations of zinc solvated in water and in hydrogen sulfide confirm the versatility of the model. The optimized model for carbon and nitrogen improves the overall performance compared to AM1 and PM3. The optimized model for carbon and nitrogen reproduces structures and various energetic terms for zinc-ligands systems (representing the active sites of zinc enzymes) when compared to density functional theory results. The optimized model can be used to study metal-ligand reactivity in zinc enzymes

Automated docking of phospholipids to the phospholipase D active site: insight into the catalytic mechanism

Streptomyces sp. PMF phospholipase D (PLD) both hydrolyzes and transphosphatidylates phospholipids. The three-dimensional x-ray crystal structure of this enzyme has been recently determined to 1.4 Å resolution. To better understand the structure-function relationships of this enzyme with different potential substrates, automated docking calculations were conducted to model the structures of various PLD/phospholipid complexes. Several parameters, including the identity of the phospholipid head group and the fatty acid chain length, were varied and the effects on the docked structures were investigated. The docking results revealed a delicate balance between head group/active site interactions and hydrophobic binding of the fatty acid chains outside the active site. Specificity is therefore achieved through the use of this balance of forces. In general the docking agrees at least qualitatively well with the limited experimental information available for the Streptomyces PLD