Biomolecule structural fluctuations determine function, regulating numerous biological processes My research has shed light on several interesting cases in which structural fluctuations have been identified to assess functional differences. Chapter 2 discusses the effects of structural rearrangement of the β2-β3 loop on the DNA binding affinity of the type 6 human papillomavirus E2 protein. Chapter 3 investigates the effects of phosphorylation on the C-terminal domain of Cdc37, a protein important in the Hsp90 chaperone cycle. Chapter 4 studies the effects on cyclycization on the conformational fluctuations of a γ-AApeptide used for high-throughput libraries. Chapter 5 is a structural study on a mini-fibril of spider dragline silk, in which a native-like ensemble was generated using temperature replica exchange. Chapter 6 investigates the structural features of repetitive motifs found in spider dragline silk when subject to both dope-like and fiber-like conditions. Chapter 7 elucidates conformational differences between the RXRα and the RXRβ ligand-binding domains and seeks to understand the atomic basis for different ligand binding affinities. This body of work has contributed to the understanding of conformational fluctuations and changes that occur in protein-DNA binding systems, drug-binding, regulation of chaperones via post-translations modifications and spider dragline silk.

Metal organic frameworks (MOFs) are a class of nanoporous materials composed through self-assembly of inorganic and organic structural building units (SBUs). MOFs show great promise for many applications due to their record-breaking internal surface areas and tunable pore chemistry. This thesis work focuses on gas separation applications of MOFs in the context of carbon capture and storage (CCS) technologies. CCS technologies are expected to play a key role in the mitigation of anthropogenic CO2 emissions in the near future. In the first part of the thesis, robust machine learning quantitative structure-property relationship (QSPR) models are developed to predict CO2 working capacity and CO2/H2 selectivity for pre-combustion carbon capture using the most topologically diverse database of hypothetical MOF structures constructed to date (358,400 MOFs, 1166 network topologies). The support vector regression (SVR) models are developed on a training set of 35,840 MOFs (10% of the database) and validated on the remaining 322,560 MOFs. The most accurate models for CO2 working capacities (R2 = 0.944) and CO2/H2 selectivities (R2 = 0.876) are built from a combination of six geometric descriptors and three novel y-range normalized atomic-property-weighted radial distribution function (AP-RDF) descriptors. 309 common MOFs are identified between the grand canonical Monte Carlo (GCMC) calculated and SVR-predicted top-1000 high-performing MOFs ranked according to a normalized adsorbent performance score. This work shows that SVR models can indeed account for the topological diversity exhibited by MOFs. In the second project of this thesis, computational simulations are performed on a MOF, CALF-20, to examine its chemical and physical properties which are linked to its exceptional water-resisting ability. We predict the atomic positions in the crystal structure of the bulk phase of CALF-20, for which only a powder X-ray diffraction pattern is available, from a single crystal X-ray diffraction pattern of a metastable phase of CALF-20. Using the predicted CALF-20 structure, we simulate adsorption isotherms of CO2 and N2 under dry and humid conditions which are in excellent agreement with experiment. Snapshots of the CALF-20 undergoing water sorption simulations reveal that water molecules in a given pore adsorb and desorb together due to hydrogen bonding. Binding sites and binding energies of CO2 and water in CALF-20 show that the preferential CO2 uptake at low relative humidities is driven by the stronger binding energy of CO2 in the MOF, and the sharp increase in water uptake at higher relative humidities is driven by the strong intermolecular interactions between water. In the third project of this thesis, we use computational simulations to investigate the effects of residual solvent on Ni-BPM’s CH4 and N2 adsorption properties. Single crystal X-ray diffraction data shows that there are two sets of positions (Set 1 and 2) that can be occupied by the 10 residual DMSO molecules in the Ni-BPM framework. GCMC simulations of CH4 and N2 uptake in Ni-BPM reveal that CH4 uptake is in closest agreement with experiment when the 10 DMSO’s are placed among the two sets of positions in equal ratio (Mixed Set). Severe under-prediction and over-prediction of CH4 uptake are observed when the DMSO’s are placed in Set1 and Set 2 positions, respectively. Through binding site analysis, the CH4 binding sites within the Ni-BPM framework are found to overlap with the Set 1 DMSO positions but not with the Set 2 DMSO positions which explains the deviations in CH4 uptake observed for these cases. Binding energy calculations reveal that CH4 molecules are most stabilized when the DMSO’s are in the Mixed Set of positions.

Nitrilotriacetic Acid (NTA) is an organic ligand which has been extensively studied due to its biological significance and excellent chelating properties. Nitrilotripropionic Acid (NTPA) is a ligand that is believed to possess similar properties to NTA, but has not been as extensively studied. It has been experimentally determined that metal complexes of NTA are orders of magnitude stronger than those formed with NTPA. This is surprising, especially considering that the ligands do not differ that much from each other. NTPA contains an additional –CH2– group in each of the acid containing arms as compared to NTA. The aim of these studies were to explain, theoretically, why this is the case. Analyses were conducted with a number of software programs including, Gaussian 03, Schrödinger Maestro and AIM 2000. All Density Functional Theory (DFT) studies were conducted in solvent at the RB3LYP/6-311+G(d,p) level of theory in conjunction with a number of different solvation models. En route to explaining why the complexes differ in stability a new methodology was utilized (isodesmic reactions) in which the four stepwise protonation constants of both NTA and NTPA were successfully predicted; in fact these were the most accurate values predicted to date by DFT methods. The final step of these studies focused on predicting stability constants of metal (Zn2+ and Ni2+) complexes of NTA and NTPA. These predictions were not as accurate as those achieved for the prediction of protonation constants; however, success was achieved in predicting the trend – complexes with NTA are orders of magnitude stronger than complexes formed with NTPA. The most important observation revealed that H–clashes and C–H∙∙∙O hydrogen bonds present in M(NTPA) complexes, which are not present in M(NTA) complexes, result in the formation of additional rings which contributes to the formation of a cage. It was discovered that the H-clashes present in the M(NTPA) complexes were contributing to the overall stability of the molecule. This is completely contradictory to a previous explanation in which H-clashes, being a result of steric crowding, resulted in destabilization of a complex. If the H-clashes were not present in the M(NTPA) complexes there would not be enough stabilizing factors present in the molecule which will inevitably result in the non-existence of M(NTPA) complexes. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Chemistry / unrestricted

Computational chemistry has led to the greater understanding of the molecular world, from the interaction of molecules, to the composition of molecular species and materials. Of the families of computational chemistry approaches available, the main families of electronic structure methods that are capable of accurate and/or reliable predictions of energetic, structural, and spectroscopic properties are ab initio methods and density functional theory (DFT). The focus of this dissertation is to improve the accuracy of predictions and computational efficiency (with respect to memory, disk space, and computer processing time) of some computational chemistry methods, which, in turn, can extend the size of molecule that can be addressed, and, for other methods, DFT, in particular, gain greater insight into which DFT methods are more reliable than others. Much, though not all, of the focus of this dissertation is upon transition metal species – species for which much less method development has been targeted or insight about method performance has been well established. The ab initio approach that has been targeted in this work is the correlation consistent composite approach (ccCA), which has proven to be a robust, ab initio computational method for main group and first row transition metal-containing molecules yielding, on average, accurate thermodynamic properties, i.e., within 1 kcal/mol of experiment for main group species and within 3 kcal/mol of experiment for first row transition metal molecules. In order to make ccCA applicable to systems containing any element from the periodic table, development of the method for second row transition metals and heavier elements, including lower p-block (5p and 6p) elements was pursued. The resulting method, the relativistic pseudopotential variant of ccCA (rp-ccCA), and its application are detailed for second row transition metals and lower p-block elements. Because of the computational cost of ab initio methods, DFT is a popular choice for the study of transition metals. Despite this, the most reliable density functionals for the prediction of energetic properties (e.g. enthalpy of formation, ionization potential, electron affinity, dissociation energy) of transition metal species, have not been clearly identified. The examination of DFT performance for first and second row transition metal thermochemistry (i.e., enthalpies of formation) was conducted and density functionals for the study of these species were identified. And, finally, to address the accuracy of spectroscopic and energetic properties, improvements for a series of density functionals have been established. In both DFT and ab initio methods, the harmonic approximation is typically employed. This neglect of anharmonic effects, such as those related to vibrational properties (e.g. zero-point vibrational energies, thermal contributions to enthalpy and entropy) of molecules, generally results in computational predictions that are not in agreement with experiment. To correct for the neglect of anharmonicity, scale factors can be applied to these vibrational properties, resulting in better alignment with experimental observations. Scale factors for DFT in conjunction with both the correlation and polarization consistent basis sets have been developed in this work.

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<p>Here we describe recent technical implementations and modifications to the libefp package as well as applications of those implementations. Applications of the EFP method to biologically relevant systems are provided on a benchmark EFP-SAPT-CCSD study on the SSI dataset along with suggested basis set recommendations and a study on the pairwise EFP total energy decomposition on Factor Xa. We also report the technical overview of two computational tools we believe will lower the human barrier to utilizing the EFP method - iSpiEFP and EFPdB.</p>

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The field of organic solar cells is getting more and more attention as the need forrenewable energy sources rises. When developing new materials for organic solar cellssynthesizing the new materials, is a time consuming and costly process. Therefore acomputational model for predicting how effective a new material, is without the needfor synthesizing. In this thesis an attempt to create a model for predicting open circuitvoltage in organic solar cells. Descriptors was calculated using B3LYP/6-31G hybridfunctionals. By creating a data set of donor and acceptor molecules with known andunknown open circuit voltage, and empirically trying to find a correlation between thedata sets that can be extrapolated and modeled. The results of this thesis did notmeet the goal of creating a model for predicting the open circuit voltage. Where nosignificant correlation was found, due a to small sample size.

Enzymatic pathways have evolved over billions of years to carry out essential cellular processes and ensure the survival of their host species. These reaction pathways rely on the interconnectedness of multiple enzymes and substrate, encouraging cross-talk and, at times, competition. In many cases, enzymes require the assistance of a diffusible secondary biomolecule, known as a cofactor, to participate in catalytic reactions. This network of reactions is unfavorable when trying to optimize the production of a specific product. In order to circumvent surrounding reactions, researchers have been engineering orthogonal enzymatic pathways that operate independently from endogenous reactions within a cell. Orthogonal pathways can be created by utilizing biomimetics molecules; most enzymes have not naturally evolved affinity and activity with these are non-canonical cofactors. Nicotinamide adenine dinucleotide (NAD(H) and nicotinamide adenine dinucleotide 2’-phosphate (NADP(H)) are vital cofactors that participate in redox reactions within cells. NAD(P)(H) have been the target of enzymatic research for several decades due to their extensive involvement in reactions across species and their utility in the biotechnology industry. Creation of orthogonal pathways dependent on NAD(P)(H) analogs has massive potential in various industries, such as biofuels and biopharmaceuticals. Nicotinamide mononucleotide (NMN(H)) is a precursor molecule in the biosynthesis of NAD(H); it currently exists within cells but, in general, does not participate as a cofactor. Nicotinamide adenine dinucleotide 3’-phosphate (3’-NADP(H)) is another analog that closely resembles NAD(P)(H) for which most enzymes have not evolved natural affinity and activity. Computation and structural inspection techniques were used in an attempt to engineer formate dehydrogenase from Candida boidinii (CbFDH) for activity with the non-canonical cofactors NMN(H) and 3’-NADP(H). Amino acid positions proximal to the NAD(H) binding site were input into a PyRosetta algorithm, which then outputted a list of recommended mutations ranked by their Rosetta energy scores. Structural alignment and visual inspection were also used to design mutations. The mutations were recombinantly expressed, and the purified enzymes were assays with NAD+, NADP+, NMN+ and 3’-NADP+. None of the designed single mutations led to CbFDH activity gain with NMN+ to any meaningful degree; however, various mutations led to the removal of NAD+ activity. A strength of PyRosetta was identifying key mutations that would lead to activity removal. The single mutants D195A and D195G attained the largest specific initial rates with 3’-NADP+ under the screening assay conditions. Kinetics parameters of a simplified ordered bi bi model were calculated for these mutants. Double mutants were created in an attempt to further enhance activity. The double mutations resulted in decreased activity but enhanced the specificity for 3’-NADP+ over NAD+. To complete the 3’-NADP(H) enzymatic cycle, a non-specific cofactor oxidizer, xenobiotic reductase A (XenA), was expressed and assayed with 3’-NADPH. It was found that XenA is able to oxidize 3’-NADPH back to 3’-NADP+. The avirulence factor AvrRxo1 from a rice plant pathogen was explored since it specifically catalyzes NAD+ phosphorylation at the 3’ position. The AvrRxo1 gene was expressed in LysY/IQ competent E. coli cells and it was found that the presence of AvrRxo1 caused a longer lag phase, but the bacteria were later able to recover. Co-expressing AvrRxo1, XenA, and D195A CbFDH has the potential to create an orthogonal pathway depending on biosynthesized 3’-NADP(H) in vivo. Another in vivo non-canonical cofactor source is NAD(H)-capped RNA, which have recently captured researchers’ attention. NAD+-RNA was synthesized using the polymerase chain reaction, and it was shown that D195A and D195G CbFDH were able to reduce the NAD+ cap.