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- Creators: Yang, Yezhou
- Member of: ASU Electronic Theses and Dissertations
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
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021