STRUCTURAL ANALYSIS OF RIBOSOME BINDING ELEMENTS OF THE 3´ UTR IN TWO POSITIVE-STRAND PLANT RNA VIRUSES

Turnip crinkle virus (TCV) and Pea enation mosaic virus (PEMV) are two positive (+)-strand RNA viruses that are used to investigate the regulation of translation and replication due to their small size and simple genomes. Both viruses contain cap-independent translation elements (CITEs) within their 3´ untranslated regions (UTRs) that fold into tRNA-shaped structures (TSS) according to nuclear magnetic resonance and small angle x-ray scattering analysis (TCV) and computational prediction (PEMV). Specifically, the TCV TSS can directly associate with ribosomes and participates in RNA-dependent RNA polymerase (RdRp) binding. The PEMV kissing-loop TSS (kl-TSS) can simultaneously bind to ribosomes and associate with the 5´ UTR of the viral genome. Mutational analysis and chemical structure probing methods provide great insight into the function and secondary structure of the two 3´ CITEs. However, lack of 3-D structural information has limited our understanding of their functional dynamics. Here, I report the folding dynamics for the TCV TSS using optical tweezers (OT), a single molecule technique. My study of the unfolding/folding pathways for the TCV TSS has provided an unexpected unfolding pathway, confirmed the presence of Ψ3 and hairpin elements, and suggested an interconnection between the hairpins and pseudoknots. In addition, this study has demonstrated the importance of the adjacent upstream adenylate-rich sequence for the formation of H4a/Ψ3 along with the contribution of magnesium to the stability of the TCV TSS. In my second project, I report on the structural analysis of the PEMV kl-TSS using NMR and SAXS. This study has re-confirmed the base-pair pattern for the PEMV kl-TSS and the proposed interaction of the PEMV kl-TSS with its interacting partner, hairpin 5H2. The molecular envelope of the kl-TSS built from SAXS analysis suggests the kl-TSS has two functional conformations, one of which has a different shape from the previously predicted tRNA-shaped form. Along with applying biophysical methods to study the structural folding dynamics of RNAs, I have also developed a technique that improves the production of large quantities of recombinant RNAs in vivo for NMR study. In this project, I report using the wild-type and mutant E.coli strains to produce cost-effective, site-specific labeled, recombinant RNAs. This technique was validated with four representative RNAs of different sizes and complexity to produce milligram amounts of RNAs. The benefit of using site-specific labeled RNAs made from E.coli was demonstrated with several NMR techniques.

Coarse Graining to Invesitigate Peptide-Lipid Interactions

Experimental characterization of molecular details is challenging, and although single molecule experiments have gained prominence, oligomer characterization remains largely unexplored. The ability to monitor the time evolution of individual molecules while they self assemble is essential in providing mechanistic insights about biological events. Molecular dynamics (MD) simulations can fill the gap in knowledge between single molecule experiments and ensemble studies like NMR, and are increasingly used to gain a better understanding of microscopic properties. Coarse-grained (CG) models aid in both exploring longer length and time scale molecular phenomena, and narrowing down the key interactions responsible for significant system characteristics. Over the past decade, CG techniques have made a significant impact in understanding physicochemical processes. However, the realm of peptide-lipid interfacial interactions, primarily binding, partitioning and folding of amphipathic peptides, remains largely unexplored compared to peptide folding in solution. The main drawback of existing CG models is the inability to capture environmentally sensitive changes in dipolar interactions, which are indigenous to protein folding, and lipid dynamics. We have used the Drude oscillator approach to incorporate structural polarization and dipolar interactions in CG beads to develop a minimalistic peptide model, WEPPROM (Water Explicit Polarizable PROtein Model), and a lipid model WEPMEM (Water Explicit Polarizable MEmbrane Model). The addition of backbone dipolar interactions in a CG model for peptides enabled us to achieve alpha-beta secondary structure content de novo, without any added bias. As a prelude to studying amphipathic peptide-lipid membrane interactions, the balance between hydrophobicity and backbone dipolar interactions in driving ordered peptide aggregation in water and at a hydrophobic-hydrophilic interface, was explored. We found that backbone dipole interactions play a crucial role in driving ordered peptide aggregation, both in water and at hydrophobic-hydrophilic interfaces; while hydrophobicity is more relevant for aggregation in water. A zwitterionic (POPC: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and an anionic lipid (POPS: 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine) are used as model lipids for WEPMEM. The addition of head group dipolar interactions in lipids significantly improved structural, dynamic and dielectric properties of the model bilayer. Using WEPMEM and WEPPROM, we studied membrane-induced peptide folding of a cationic antimicrobial peptide with anticancer activity, SVS-1. We found that membrane-induced peptide folding is driven by both (a) cooperativity in peptide self interaction and (b) cooperativity in membrane-peptide interactions. The dipolar interactions between the peptide and the lipid head-groups contribute to stabilizing folded conformations. The role of monovalent ion size and peptide concentration in driving lipid domain formation in anionic/zwitterionic lipid mixtures was also investigated. Our study suggest monovalent ion size to be a crucial determinant of interaction with lipid head groups, and hence domain formation in lipid mixtures. This study reinforces the role of dipole interactions in protein folding, lipid membrane properties, membrane induced peptide folding and lipid domain formation. Therefore, the models developed in this thesis can be used to explore a multitude of biomolecular processes, both at longer time-scales and larger system sizes.

Diversity in Catalytic Reactions of Propargylic Diazoesters

Abstract Title of Document: Diversity in Catalytic Reactions of Propargylic Diazoesters Huang Qiu, Doctor of Philosophy, 2016 Directed By: Professor Michael P. Doyle, Department of Chemistry and Biochemistry Propargylic aryldiazoesters, which possess multiple reactive functional groups in a single molecule, were expected to undergo divergent reaction pathways as a function of catalysts. A variety of transition metal complexes including rhodium(II), palladium(II), silver(I), mercury(II), copper(I and II), and cationic gold (I) complexes have been examined to be effective in the catalytic domino reactions of propargylic aryldiazoesters. An unexpected Lewis acid catalyzed pathway was also discovered by using FeCl3 as the catalyst. Under the catalysis of selected gold catalysts, propargylic aryldiazoesters exist in equilibrium with 1-aryl-1,2-dien-1-yl diazoacetate allenes that are rapidly formed at room temperature through 1,3-acyloxy migration. The newly formed allenes further undergo a metal-free rearrangement in which the terminal nitrogen of the diazo functional group adds to the central carbon of the allene initiating a sequence of bond forming reactions resulting in the production of 1,5-dihydro-4H-pyrazol-4-ones in good yields. These 1,5-dihydro-4H-pyrazol-4-ones undergo intramolecular 1,3-acyl migration to form an equilibrium mixture or quantitatively transfer the acyl group to an external nucleophile with formation of 4-hydroxypyrazoles. In the presence of a pyridine-N-oxide, both E- and Z-1,3-dienyl aryldiazoacetates are formed in high combined yields by Au(I)-catalyzed rearrangement of propargyl arylyldiazoacetates at short reaction times. Under thermal reactions the E-isomers form the products from intramolecular [4+2]-cycloaddition with H‡298 = 15.6 kcal/mol and S‡298= -27.3 cal/ (mol•degree). The Z-isomer is inert to [4+2]-cycloaddition under these conditions. The Hammett relationships from aryl-substituted diazo esters ( = +0.89) and aryl-substituted dienes ( = -1.65) are consistent with the dipolar nature of this transformation. An unexpected reaction for the synthesis of seven-membered conjugated 1,4-diketones from propargylic diazoesters with unsaturated imines was disclosed. To undergo this process vinyl gold carbene intermediates generated by 1,2-acyloxy migration of propargylic aryldiazoesters undergo a formal [4+3]-cycloaddition, and the resulting aryldiazoesters tethered dihydroazepines undergo an intricate metal-free process to form observed seven-membered conjugated 1,4-diketones with moderate to high yields.

Quantitative and Qualitative Trade-Off Analysis of Drowsy Driver Detection Methods: Single Electrode Wearable EEG Device, Multi-Electrode Wearable EEG Device, and Head-Mounted Gyroscope

Drowsy driving impairs motorists’ ability to operate vehicles safely, endangering both the drivers and other people on the road. The purpose of the project is to find the most effective wearable device to detect drowsiness. Existing research has demonstrated several options for drowsiness detection, such as electroencephalogram (EEG) brain wave measurement, eye tracking, head motions, and lane deviations. However, there are no detailed trade-off analyses for the cost, accuracy, detection time, and ergonomics of these methods. We chose to use two different EEG headsets: NeuroSky Mindwave Mobile (single-electrode) and Emotiv EPOC (14- electrode). We also tested a camera and gyroscope-accelerometer device. We can successfully determine drowsiness after five minutes of training using both single and multi-electrode EEGs. Devices were evaluated using the following criteria: time needed to achieve accurate reading, accuracy of prediction, rate of false positives vs. false negatives, and ergonomics and portability. This research will help improve detection devices, and reduce the number of future accidents due to drowsy driving.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Identification and Characterization of New Small Molecule Inhibitors of Picornavirus Replication

The Picornaviridae family consists of positive-strand RNA viruses that are the causative agents of a variety of diseases in humans and animals. Few drugs targeting picornaviruses are available, making the discovery of new antivirals a high priority. Here, we identified and characterized three compounds from a library of kinase inhibitors that block replication of poliovirus, coxsackievirus B3, and encephalomyocarditis virus. The antiviral effect of these compounds is not likely related to their known cellular targets because other inhibitors targeting the same pathways did not inhibit viral replication. Using an in vitro translation-replication system, we showed that these drugs inhibit different stages of the poliovirus life cycle. A4(1) inhibited the formation of a functional replication complex, while E5(1) and E7(2) affected replication after the replication complex had formed. A4(1) demonstrated partial protection from paralysis in a murine model of poliomyelitis. Poliovirus resistant to E7(2) had a single mutation in the 3A protein. This mutation was previously found to confer resistance to enviroxime-like compounds, which target either PI4KIIIβ (major enviroxime-like compounds) or OSBP (minor enviroxime-like compounds), cellular factors involved in lipid metabolism and shown to be important for replication of diverse positive-strand RNA viruses. We classified E7(2) as a minor enviroxime-like compound, because the localization of OSBP changed in the presence of this inhibitor. Interestingly, both E7(2) and major enviroxime-like compound GW5074 interfered with the viral polyprotein processing. Multiple attempts to isolate resistant mutants in the presence of A4(1) or E5(1) were unsuccessful, showing that effective broad-spectrum antivirals could be developed on the basis of these compounds. Studies with these compounds shed light on pathways shared by diverse picornaviruses that could be potential targets for the development of broad-spectrum antiviral drugs.