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Description
In the 1970s James Watson recognized the inability of conventional DNA replication machinery to replicate the extreme termini of chromosomes known as telomeres. This inability is due to the requirement of a building block primer and was termed the end replication problem. Telomerase is nature's answer to the end replication problem. Telomerase is a ribonucleoprotein which extends telomeres through reverse transcriptase activity by reiteratively copying a short intrinsic RNA sequence to generate 3' telomeric extensions. Telomeres protect chromosomes from erosion of coding genes during replication, as well as differentiate native chromosome ends from double stranded breaks. However, controlled erosion of telomeres functions as a naturally occurring molecular clock limiting the replicative capacity of cells. Telomerase is over activated in many cancers, while inactivation leads to multiple lifespan limiting human diseases. In order to further study the interaction between telomerase RNA (TR) and telomerase reverse transcriptase protein (TERT), vertebrate TERT fragments were screened for solubility and purity following bacterial expression. Soluble fragments of medaka TERT including the RNA binding domain (TRBD) were identified. Recombinant medaka TRBD binds specifically to telomerase RNA CR4/CR5 region. Ribonucleotide and amino acid pairs in close proximity within the medaka telomerase RNA-protein complex were identified using photo-activated cross-linking in conjunction with mass spectrometry. The identified cross-linking amino acids were mapped on known crystal structures of TERTs to reveal the RNA interaction interface of TRBD. The identification of this RNA TERT interaction interface furthers the understanding of the telomerase complex at a molecular level and could be used for the targeted interruption of the telomerase complex as a potential cancer treatment.
ContributorsBley, Christopher James (Author) / Chen, Julian (Thesis advisor) / Allen, James (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2011
Description
Cyanovirin-N (CV-N) is a naturally occurring lectin originally isolated from the cyanobacteria Nostoc ellipsosporum. This 11 kDa lectin is 101 amino acids long with two binding sites, one at each end of the protein. CV-N specifically binds to terminal Manα1-2Manα motifs on the branched, high mannose Man9 and Man8 glycosylations found on enveloped viruses including Ebola, Influenza, and HIV. wt-CVN has micromolar binding to soluble Manα1-2Manα and also inhibits HIV entry at low nanomolar concentrations. CV-N's high affinity and specificity for Manα1-2Manα makes it an excellent lectin to study for its glycan-specific properties. The long-term aim of this project is to make a variety of mutant CV-Ns to specifically bind other glycan targets. Such a set of lectins may be used as screening reagents to identify biomarkers and other glycan motifs of interest. As proof of concept, a T7 phage display library was constructed using P51G-m4-CVN genes mutated at positions 41, 44, 52, 53, 56, 74, and 76 in binding Domain B. Five CV-N mutants were selected from the library and expressed in BL21(DE3) E. coli. Two of the mutants, SSDGLQQ-P51Gm4-CVN and AAGRLSK-P51Gm4-CVN, were sufficiently stable for characterization and were examined by CD, Tm, ELISA, and glycan array. Both proteins have CD minima at approximately 213 nm, indicating largely β-sheet structure, and have Tm values greater than 40°C. ELISA against gp120 and RNase B demonstrate both proteins' ability to bind high mannose glycans. To more specifically determine the binding specificity of each protein, AAGRLSK-P51Gm4-CVN, SSDGLQQ-P51Gm4-CVN, wt-CVN, and P51G-m4-CVN were sent to the Consortium for Functional Glycomics (CFG) for glycan array analysis. AAGRLSK-P51Gm4-CVN, wt-CVN, and P51G-m4-CVN, have identical specificities for high mannose glycans containing terminal Manα1-2Manα. SSDGLQQ-P51Gm4-CVN binds to terminal GlcNAcα1-4Gal motifs and a subgroup of high mannose glycans bound by P51G-m4-CVN. SSDGLQQ-wt-CVN was produced to restore anti-HIV activity and has a high nanomolar EC50 value compared to wt-CVN's low nanomolar activity. Overall, these experiments show that CV-N Domain B can be mutated and retain specificity identical to wt-CVN or acquire new glycan specificities. This first generation information can be used to produce glycan-specific lectins for a variety of applications.
ContributorsRuben, Melissa (Author) / Ghirlanda, Giovanna (Thesis advisor) / Allen, James (Committee member) / Wachter, Rebekka (Committee member) / Arizona State University (Publisher)
Created2013
Description
DNA nanotechnology has been a rapidly growing research field in the recent decades, and there have been extensive efforts to construct various types of highly programmable and robust DNA nanostructures. Due to the advantage that DNA nanostructure can be used to organize biochemical molecules with precisely controlled spatial resolution, herein we used DNA nanostructure as a scaffold for biological applications. Targeted cell-cell interaction was reconstituted through a DNA scaffolded multivalent bispecific aptamer, which may lead to promising potentials in tumor therapeutics. In addition a synthetic vaccine was constructed using DNA nanostructure as a platform to assemble both model antigen and immunoadjuvant together, and strong antibody response was demonstrated in vivo, highlighting the potential of DNA nanostructures to serve as a new platform for vaccine construction, and therefore a DNA scaffolded hapten vaccine is further constructed and tested for its antibody response. Taken together, my research demonstrated the potential of DNA nanostructure to serve as a general platform for immunological applications.
ContributorsLiu, Xiaowei (Author) / Liu, Yan (Thesis advisor) / Chang, Yung (Thesis advisor) / Yan, Hao (Committee member) / Allen, James (Committee member) / Zhang, Peiming (Committee member) / Arizona State University (Publisher)
Created2012
Description
The metalloenzyme quercetin 2,3-dioxygenase (QueD) catalyzes the oxidative decomposition of the aromatic compound, quercetin. The most recently characterized example is a product of the bacterium Bacillus subtilis (BsQueD); all previous examples were fungal enzymes from the genus Aspergillus (AQueD). AQueD contains a single atom of Cu(II) per monomer. However, BsQueD, over expressed in Escherichia coli, contains Mn(II) and has two metal-binding sites, and therefore two possible active sites per monomer. To understand the contribution of each site to BsQueD's activity, the N-terminal and C-terminal metal-binding sites have been mutated individually in an effort to disrupt metal binding. In wild type BsQueD, each Mn(II) is ligated by three histidines (His) and one glutamate (Glu). All efforts to mutate His residues to non-ligating residues resulted in insoluble protein or completely inactive enzyme. A soluble mutant was expressed that replaced the Glu residue with a fourth His at the N-terminal domain. This mutant (E69H) has a specific activity of 0.00572 &mumol;/min/mg, which is nearly 3000-fold lower than the rate of wild type BsQueD (15.9 &mumol;/min/mg). Further analysis of E69H by inductively couple plasma mass spectrometry revealed that this mutant contains only 0.062 mol of Mn(II) per mol of enzyme. This is evidence that disabling metal-ligation at one domain influences metal-incorporation at the other. During the course of the mutagenic study, a second, faster purification method was developed. A hexahistidine tag and an enterokinase cleavage site were fused to the N-terminus of BsQueD (6xHis-BsQueD). Active enzyme was successfully expressed and purified with a nickel column in 3 hours. This is much faster than the previous multi-column purification, which took two full days to complete. However, the concentration of soluble, purified enzyme (1.8 mg/mL) was much lower than concentrations achieved with the traditional method (30 mg/mL). While the concentration of 6xHis-BsQueD is sufficient for some analyses, there are several characterization techniques that must be conducted at higher concentrations. Therefore, it will be advantageous to continue using both purification methods in the future.
ContributorsBowen, Sara (Author) / Francisco, Wilson A (Thesis advisor) / Allen, James (Committee member) / Jones, Anne K (Committee member) / Arizona State University (Publisher)
Created2010
Description
A novel small metal-binding protein (SmbP), with only 93 residues and no similarity to other known proteins, has been isolated from the periplasm of Nitrosomonas europaea. It is characterized by its high percentage (17%) of histidines, a motif of ten repeats of seven residues, a four α-helix bundle structure, and a high binding affinity to about six equivalents of Cu2+. The goal of this study is to investigate the Cu2+ binding sites in SmbP and to understand how Cu2+ stabilizes the protein. Preliminary folding experiments indicated that Cu2+ greatly stabilizes SmbP. In this study, protein folding data from circular dichroism (CD) spectroscopy was used to elucidate the role of Cu2+ in stabilizing SmbP structure against unfolding induced by decreased pH, increased temperature, and chemical denaturants. The significant stabilization effects of Cu2+ were demonstrated by the observation that Cu2+-SmbP remained fully folded under extreme environmental conditions, such as acidic pH, 96 °C, and 8 M urea. Also, it was shown that Cu2+ is able to induce the refolding of unfolded SmbP in acidic solutions. These findings imply that the coordination of Cu2+ to histidine residues is responsible for the stabilization effects. The crystal structure of SmbP without Cu2+ has been determined. However, attempts to crystallize Cu2+-SmbP have not been successful. In this study, multidimensional NMR experiments were conducted in order to gain additional information regarding the Cu2+-SmbP structure, in particular its metal binding sites. Unambiguous resonance assignments were successfully made. Cα secondary chemical shifts confirmed that SmbP has a four α-helical structure. A Cu2+-protein titration experiment monitored by NMR indicated a top-to-bottom, sequential metal binding pattern for SmbP. In addition, several bioinformatics tools were used to complement the experimental approach and identity of the ligands in Cu2+-binding sites in SmbP is proposed.
ContributorsYan, Qin (Author) / Francisco, Wilson A (Thesis advisor) / Allen, James (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2010
Description
Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.
ContributorsGreenhagen, Tanner Patrick (Author) / Yang, Yezhou (Thesis director) / Jammula, Varun C (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In the last decade, a large variety of algorithms have been developed for use in object tracking, environment mapping, and object classification. It is often difficult for beginners to fully predict the constraints that multirotors place on machine vision algorithms. The purpose of this paper is to explain some of the types of algorithms that can be applied to these aerial systems, why the constraints for these algorithms exist, and what could be done to mitigate them. This paper provides a summary of the processes involved in a popular filter-based tracking algorithm called MOSSE (Minimum Output Sum of Squared Error) and a particular implementation of SLAM (Simultaneous Localization and Mapping) called LSD SLAM.
ContributorsVan Hazel, Colton (Author) / Zhang, Wenlong (Thesis director) / Yang, Yezhou (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this framework were most effective in maximizing classification accuracy. Furthermore, the reasons why particular combinations were more effective than others is explored.
ContributorsMazboudi, Yassine Ahmad (Author) / Yang, Yezhou (Thesis director) / Ren, Yi (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
ContributorsPeterson, Austin (Author) / Yang, Yezhou (Thesis director) / Sadasivam, Aadhavan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Typical eukaryotic organelles use membranes formed by lipid bilayers in order to compartmentalize their functions within the cell. However, cells also contain membraneless organelles formed by intrinsically disordered proteins (IDPs) via liquid-liquid phase separation. The organelles form localized compartments that separate their contents from the environment.1 Here, this mechanism is used to generate artificial membraneless organelles that comprise a chemical reaction. An IDP, DEAD-box helicase (Ddx4), was bioconjugated to an enzyme, horseradish peroxidase (HRP), through the use of a bifunctional chemical linker, succinimidyl 4-(N-maleimidomethyl)cyclohexane-1-carboxylate (SMCC), in order to examine if the enzyme could be incorporated in droplets and whether its activity would be affected. The conjugation of HRP-SMCC (43.4 kDa) to Ddx4 (25.6 kDa) was successful: SDS-PAGE analysis confirmed the presence of a product that was within the range of a full conjugate.
ContributorsFavila, Saul Roberto (Author) / Ghirlanda, Giovanna (Thesis director) / Vaiana, Sara (Committee member) / Allen, James (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05