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DNA Polymerase Fidelity: A Computational Study of Pol lambda and Other X-Family Enzymes

DNA repairs and replication depend on the activity of specialized enzymes called DNA polymerases. These vary in size and complexity, but all possess a core grouping of subdomains that, like a hand, can grasp DNA and catalyze the insertion of nucleotide building blocks. Fidelity is the ability of a DNA polymerase to correctly match the original templating sequence. Any error introduced into the DNA sequence can be very deleterious and give rise to diseases such as cancer. This research centers on understanding the catalytic cycle of X-family DNA polymerases involved in oxidative DNA damage and double-strand break repairs. Specifically, it focuses on DNA polymerase lambda and its relationship to DNA polymerase beta. It investigates pol lambdas much higher single-base deletion error rate and use of large DNA shifting in contrast to pol betas large-scale thumb motions. We discover similarities in the motions of gate-keeping residues utilized by these enzymes and between the role played by Arg517 in pol lambdas fidelity and Arg283 in pol beta. Our mutation studies of Arg517 show how this residue stabilizes DNA through favorable electrostatic interactions. We relate the extent of DNA shifting in the mutants to deletion error rates, which supports a role for DNA slippage mediated deletion errors. Pol betas comparative lack of DNA motion would help to prevent deletions. Interestingly, studies of pol lambda mis-match complexes exhibit different Arg517/DNA interactions and more DNA shifting; we propose that these motions affect pol lambdas ability to insert incorrect nucleotides. Our studies of pol lambda/misaligned complexes reveal a network of positively-charged thumb residues that create strong interactions with misaligned DNA. Our energetic analyses reinforce this picture by showing that pol lambda/misaligned complexes are more stable than aligned DNA complexes. Our modeling of pol beta/misaligned DNA reveals no comparable network of thumb/DNA interactions, which agrees with its lower deletion error rate. Based on our research and other studies, we relate polymerase function and fidelity. This analysis supports the importance of intrinsic DNA polymerase motions and identifies key features of the polymerase/nucleotide interactions. We suggest a hybrid conformational selection/induced-fit model to better describe nucleotide binding. Finally, we show how this understanding may improve small molecule screening and targeted disease therapies.

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Micropolar Electromagnetic Fluids: Theory and Simulation

Molecular dynamics MD) has shown that molecular spin makes a fluid act significantly different at the micro/nano scale than at the macroscale. Experimental data also imply that the classical Navier-Stokes equations are incapable of explaining several kinds of microscale transport phenomena. Though Molecular Dynamics simulation allows the observation of molecular spin, the computation cost is unaffordable. To tackle such problems with consideration of computational efficiency, one should resort to Microcontinuum Theory. This dissertation is divided two main parts: theory and simulation. In the theoretical part, the balance laws are provided and the constitutive equations for Micropolar electromagnetic fluids are derived through two approaches: 1) Wangs representation theorem and 2) Onsagers theory. The constitutive equations of fluids are required to satisfy the axiom of objectivity. The axiom of objectivity allows the utilization of Wangs representation theorem. The constitutive equations are therefore obtained through Wangs representation theorem and later linearized for practical applications. The linear constitutive equations can be also obtained from Onsagers theory. These two sets of constitutive equations are identical. One new parameter for fluids, the curl of gyration, is found to characterize electric current. The nonlinear Onsagers theory for constitutive equations was originally derived by Edelen. It is now further extended for fluids through integration with Wangs representation. The connections to Couple Stress Theory and Navier-Stokes equations are made from Micropolar Theory. In the simulation part, a second order Finite Difference FD) method is integrated with the time-centered split method TCSM) and is successfully developed for an incompressible fluid. A higher-order Spectral Difference SD) method is further developed for compressible Micropolar fluid problems. The analytical and exact solutions, including velocity, gyration and temperature, for incompressible and compressible plane Couette flow and for incompressible Hagen-Poiseuille flow, are given. These analytical and exact solutions are used to verify the order of numerical accuracy for the aforementioned numerical solvers. Based on numerical results, the physical meanings of material coefficients are clearly described. Flows past a cylinder with and without imposed transverse uniform magnetic field are also studied. Imposition of a magnetic field is demonstrated as an effective approach for flow control.

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Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns, but less work has been done in terms of detecting anomalies in graph-based data. Also, time-series of graphs are becoming more and more common, for example, communication graphs, social networks, etc., and methods for statistical inferences are demanding. While there has been some previous work that used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains. Moreover, most anomaly-detection methods use a supervised approach, which requires some sort of baseline of information from which comparisons or training can be performed. In general, if one has an idea what is normal behavior, deviations from that behavior could constitute an anomaly. However, the issue with those approaches is that one has to train the system, and the data has to already be labeled. It is also known that no single graph feature is uniformly most powerful. This research introduced a theory of scan statistics on time series of graphs and hypergraphs to investigate an effectiveness on anomaly/change point detection problem. The primary research hypothesis of this work is that scan statistic is capable of detecting certain types of anomalies that are not apparent by using other techniques. Also, by combining multiple graph features, the performance of statistical inference can be improved compared to a method that only uses an individual feature separately. The result shows that the proposed statistics on time series of graphs and hypergraphs outperforms on certain anomalies. It is further demonstrated that a fusion statistic can provide superior inference compared to individual features alone. The major contributions of this work are the introduction of a new graph feature on detecting anomaly in time series of graphs and hypergraphs, and the confirmation of adaptive weighting as a mechanism for combination of features.

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Mixed valency and electronic structure in self-assembled monolayers, self-exchange, and hydrogen bonded assemblies

Mixed valency and electron transfer are explored in self-assembled monolayers, in intermolecular electron self-exchange reactions in solution, and in hydrogen bonded assemblies. Tetrathiafulvalene is derivatized for binding to gold in self-assembled monolayers, but the trinuclear ruthenium cluster Ru 3OOAc)6L3 where L is an ancillary ligand) is used as a building block for the majority of the work. While oxo-centered trinuclear hexaacetate clusters of many transition metals are known, the triruthenium cluster is particularly versatile because of the kinetically stable binding of a wide variety of ligands. The electronic structure can be depicted by molecular orbitals diagrams or more recently by computer generated combinations of atomic orbitals, and remains relatively unchanged for variously substituted clusters. The important differences with respect to getting an electron in or out of a cluster lie in electron delocalization onto ligand based orbitals. In combination with reorganization energies calculated from accumulated structural and vibrational data, the molecular orbital diagrams offer a great deal of explanatory power. When allowed by symmetry and energy matching, electrons in reduced clusters are delocalized onto pyridine pi* orbitals, greatly easing the transfer to an oxidized cluster in the face of a large reorganization energy. When electron delocalization is not allowed, electron self-exchange can be fast only if the reorganization energy is small. In hydrogen bonded assemblies of these ruthenium clusters, the electronic structure is still dominant in electron transfer behavior. In these cases the increase in delocalization upon dimerization appears to induce large changes in the orbital energies. This is consistent with the electronic absorptions and the thermal electron transfer behavior observed. The take-home message of this dissertation is that one must understand to electronic structure of a complex in order to understand its behavior in electron transfer reactions. This may seem obvious, but is often overlooked. With the knowledge of the electronic structure of reactants and products, one has a much greater chance of understanding the path between them. Molecular orbital diagrams seem cumbersome and outdated in this age of calculated chemistry, but many cases drawing them out is worth the investment in time. Who knows, you may even learn something.

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Postsynthetic Modification of Metal-Organic Frameworks

Metal-organic frameworks MOFs) are porous crystalline materials that are built from metal ions or metal ion clusters and organic ligands. There has been much interest in designing functionalized MOFs with enhanced host-guest interactions for potential applications in gas storage, catalysis, and separation. However, it has remained a challenge to synthesize functionalized MOFs directly through traditional MOF synthesis. This dissertation focuses on the development of postsynthetic modification PSM) as a method for functionalizing MOFs. A systematic overview of PSM will be presented to highlight PSM as a general, versatile approach for enhancing the physical and chemical properties of MOFs. In the first half of this dissertation, IRMOF-3, an amino-containing MOF, is modified with a series of alkyl anhydrides, and the effects of reagent size on modification extent are explored. In the next chapter, other amino-containing MOFs systems DMOF-1-NH2 and UMCM-1-NH2) are synthesized and modified using PSM. Through this study, PSM is shown to be a practical approach for functionalizing MOFs, and also indicates MOF topology can influence the modification outcome. The second half of this dissertation focuses on using PSM to develop MOFs for gas storage and catalysis applications. IRMOF-3, DMOF-1-NH2, and UMCM-1-NH2 are modified and tested for H2 storage. The MOFs are modified with certain functionalities e.g., alkyl vs. aromatic) to determine if H2 uptake and heat of adsorption is improved. In a separate study, UMCM-1-NH2 is modified with metal binding substituents, and is metallated with different metal ions to generate a series of potential Lewis acid MOF catalysts. The metallated UMCM MOFs are tested for the Mukaiyama aldol reaction and for epoxide ring opening catalysis, and the catalytic results are presented. Lastly, a new functionalization technique, named postsynthetic deprotection PSD), is introduced. Two new BDC ligands are synthesized with photolabile protecting groups and are incorporated into MOFs. The MOFs are then exposed to UV light, which results in the removal of the photolabile groups to produce MOFs with free, uncoordinated hydroxyl groups. This is the first example of using light to unmask functionalities in a MOF, and presents a novel route for obtaining MOFs with more complex functionalities.

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Photosensitized oxidative damage in double-stranded oligonucleotides: From hole injection to end product formation and characterization of benzo[ a]pyrene-7,8-dione modified oligonucleotide adducts

Oxidatively generated damage to DNA induced by photosensitization of the pyrene-like aromatic residue Py) derived from the covalent binding of 7,8-dihydrodiol-9,10-oxide-benzo[a]pyrene BPDE) to N2-guanine in the DNA sequence context 5-dCAT[G 1Py]CG2TCCTAC) M) in aerated solutions was monitored from the initial hole injection step to the formation of chemical end-products using HPLC, mass spectrometry, and high-resolution gel electrophoresis. Hole injection into the DNA was initiated by two-photon excitation of the Py residue with intense 355 nm laser pulses, thus producing the Py·+ radical cation and hydrated electrons e h). The dissolved molecular oxygen captures the hydrated electrons yielding the superoxide radical anion. The oxidative damage occurs mostly at the modified guanine residue G1Py and, to a lesser extent, to the more distant guanine G2. The major products are the 2-amino-5-[2-deoxy-beta-D- erythro-pentofuranosyl) amino]-4H-imidazol-4-one dIz) and M+16 lesion which has a mass larger by 16 Da than the mass of G 1Py. The structure of the M+16 product includes an unusual structure with a ruptured non-aromatic cyclohexenyl ring reported here for the first time. The formation of this product limits the efficiency of hole injection into the duplex and therefore the damage observed at G2. The formation of tandem lesions is observed even at low levels of irradiation corresponding to “single-hit” conditions when less than ∼10% of the oligonucleotide strands are damaged. It is shown that oxidative damage at G1 must occur prior to that of G2 tandem lesion). The formation of products in the unmodified complementary strand Mc in M·Mc duplexes is ∼10 times smaller than in the modified strand M. In the second part of this dissertation I successfully synthesized the 2-deoxynucleoside and oligonucleotide adducts of benzo[a]pyrene-7,8-dione BPQ) which is a potentially carcinogenic metabolite of benzo[a]pyrene different from the well known BPDE. Two novel BPQ-dC1,2 adducts were discovered in addition to the previously known four BPQ-dG, two BPQ-dA, and two other BPQ-dC adducts. The thermal stabilities of double stranded BPQ-modified DNA were substantially lower than those of the unmodified duplexes. The sites of BPQ-modification in oligonucleotide were determined by exonuclease sequencing combined with MALDI-TOF MS method.

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Development of microaffinity columns for drug analysis

This dissertation describes the use of high-performance affinity chromatography HPAC) and monolithic columns to study drug-protein interactions. The first project studied the binding of imipramine to human serum albumin HSA) by using HPAC. Frontal analysis and zonal elution competition studies determined that imipramine had one major binding site and a second group of weaker, non-specific binding regions on HSA. The overall objective of the remainder of this dissertation was to develop microaffinity columns for use in HPAC and drug-protein binding studies. The second project used silica monoliths in affinity microcolumns for the high-throughput analysis of drug-protein interactions. It was found that HSA silica monoliths in affinity microcolumns as short as 1 to 3 mm could be used to provide reliable estimates of retention factor and plate height. A comparison between silica monoliths and silica particles in microcolumns showed that silica monoliths allowed for the use of faster flow rates because of their better mass transfer properties, which decreased analysis times. The third project used silica particles in columns with lengths from 1 mm to 2 cm to compare affinity microcolumns to traditional longer affinity columns in HPAC. Zonal elution studies showed that retention factor, plate height, and peak asymmetry measurements were fairly consistent across all of the tested flow rates. However, there was a decrease in precision as shorter column lengths were used. Frontal analysis studies determined that association equilibrium constant measurements for all column lengths were consistent. In these studies, using affinity microcolumns for frontal analysis studies to decrease analysis times did not compromise the accuracy of these measurements. The last project used the noncompetitive peak decay method with HSA silica monolith affinity microcolumns to measure the dissociation rate constants of several drugs from HSA. Work with flow rates as high as 10 mL/min made it possible to provide dissociation rate constants for warfarin in less than 40 s. This method was then extended to other drugs known to bind to HSA, including diazepam, imipramine, acetohexamide, and tolbutamide.

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Background Characterization and Discrimination in the Final Analysis of the CDMS II Phase of the Cryogenic Dark Matter Search

The Cryogenic Dark Matter Search CDMS) is designed to detect Weakly-Interacting Massive Particles WIMPs) in the Milky Way halo. The phase known as CDMS II was performed in the Soudan Underground Laboratory. The final set of CDMS II data, collected in 2007-8 and referred to as Runs 125–8, represents the largest exposure to date for the experiment. We seek collisions between WIMPs and atomic nuclei in disk-shaped germanium and silicon detectors. A key design feature is to keep the rate of collisions from known particles producing WIMP-like signals very small. The largest category of such background is interactions with electrons in the detectors that occur very close to one of the faces of the detector. The next largest category is collisions between energetic neutrons that bypass the experimental shielding and nuclei in the detectors. Analytical efforts to discriminate these backgrounds and to estimate the rate at which such discrimination fails have been refined and improved throughout each phase of CDMS. Next-generation detectors for future phases of CDMS require testing at cryogenic test facilities. One such facility was developed at the University of Minnesota in 2007 and has been used continuously since then to test detectors for the next phase of the experiment, known as SuperCDMS.

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Semiparametric models for joint analysis of longitudinal data and counting processes

In this dissertation, we study statistical methodology for joint modeling that correctly controls for the interplay among longitudinal and counting processes and makes the most efficient use of data. Three types of joint modeling approaches are proposed based on three different purposes of studies. In the first topic, we develop a method for joint modeling of longitudinal data and recurrent events in the presence of an informative terminal event. We focus on data from patients who experience the same type of event at multiple times, such as multiple infection episodes or recurrent strokes, have longitudinal biomarkers, and may be subject to an event, for example death, that makes further observations impossible. To analyze such complicated data, we propose joint models based on a likelihood approach. A broad class of transformation models for the cumulative intensity of recurrent events and the cumulative hazard of the terminal event is considered. We propose to estimate all the parameters using nonparametric maximum likelihood estimators NPMLE), and we provide computationally efficient EM algorithms to implement the proposed inference procedure. Asymptotic properties of the estimators are shown to be asymptotically normal and semiparametrically efficient. Finally, we evaluate the performance of the proposed method through extensive simulations and application to real data. In the second topic, we develop a method for joint modeling of longitudinal and cure-survival data. By cure-survival data, we mean time-to-event data in which a certain proportion of patients never have any event during a sufficiently long follow-up period. These patients are believed to have been cured by treatment, such as radiation therapy or an initial surgery, and are often the source of heavy tail probabilities in survival curves. To take into account the possibility of patients being cured, we propose to model time-to-event through a transformed promotion time cure model, jointly with a linear mixed effects model for longitudinal data. Due to transformations applied to the promotion time cure model, the proposed method is able to be used in cases where the proportionality assumption does not hold. All the parameters are estimated using NPMLEs, and inference procedures are implemented via a simple EM algorithm. Asymptotic properties of the proposed NPMLEs are derived based on empirical process theory. Simulation studies are conducted and the method is applied to the ARIC data in order to demonstrate the small-sample performance of the proposed method. In the third topic, we develop a partially linear model for longitudinal data with informative censoring, where the main interest is in making inferences about the individuals trajectory of longitudinal responses, which may be informatively censored. Since a fully parameterized mean structure may be insufficient to capture the underlying patterns of longitudinal and event processes, we propose to use a partially linear model for longitudinal responses, where an unspecified underlying function is formulated along with linear covariate effects, and a transformation model is used for informative censoring times. We employ a sieve estimation for the nonparametric trajectory of longitudinal responses, where the unknown trajectory is approximated by cubic B-spline basis functions. All parameters are estimated based on a likelihood approach, and inference procedures are implemented via the EM algorithm. We also investigate a reliable way to select the number of knots and the best transformation. Through empirical process theory, asymptotic properties of the proposed estimators are shown to provide desirable properties. The validity of the proposed method is confirmed by simulated and real data examples.

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Atomistic simulations uncover microscopic details of nucleosomal electrostatics, energy landscapes of proteins and photovoltaic polymer dynamics

Molecular dynamics (MD) simulations offer researchers a high resolution window into the atomic world. Though far from perfect, they provide researchers with a direct method for probing molecular scale processes. We have used MD to address a range of questions which span three fundamentally different topics. First, we used MD to elucidate the native state energy landscape of a small globular protein. We are able to identify a subset of direct and water-mediated contacts which may be responsible for sculpting this landscape. Next, we turn to the topic of chromatin, specifically, nucleosomal electrostatics. Using a combination of all-atom simulations and Poisson-Boltzmann calculations we are able to observe and explain counterion condensation levels and distribution patterns around the nucleosome core particle. Additionally, our results reveal the significant solvent accessibility of the core particle. Finally, we use all-atom simulations to examine small, artificial, coiled-coil peptides under development for use in light harvesting antennae. Photosensitive chromophores can be tethered to these peptides at a range of sites, and we observe that the choice of these sites plays an important role in regulating energy transfer. In addition, the flexibility of the tethers imbues the chromophores with a large amount of conformational freedom, making their dynamics important for regulating energy transfer.

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