ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Biochemistry
- Creators: Stephanopoulos, Nicholas
- Creators: Moore, Gary
Description
Membrane proteins act as sensors, gatekeepers and information carriers in the cell membranes. Functional engineering of these proteins is important for the development of molecular tools for biosensing, therapeutics and as components of artificial cells. However, using protein engineering to modify existing protein structures is challenging due to the limitations of structural changes and difficulty in folding polypeptides into defined protein structures. Recent studies have shown that nanoscale architectures created by DNA nanotechnology can be used to mimic various protein functions, including some membrane proteins. However, mimicking the highly sophisticated structural dynamics of membrane proteins by DNA nanostructures is still in its infancy, mainly due to lack of transmembrane DNA nanostructures that can mimic the dynamic behavior, ubiquitous to membrane proteins. Here, I demonstrate design of dynamic DNA nanostructures to mimic two important class of membrane proteins. First, I describe a DNA nanostructure that inserts through lipid membrane and dynamically reconfigures upon sensing a membrane-enclosed DNA or RNA target, thereby transducing biomolecular information across the lipid membrane similar to G-protein coupled receptors (GPCR’s). I use the non-destructive sensing property of our GPCR-mimetic nanodevice to sense cancer associated micro-RNA biomarkers inside exosomes without the need of RNA extraction and amplification. Second, I demonstrate a fully reversibly gated DNA nanopore that mimics the ligand mediated gating of ion channel proteins. The 20.4 X 20.4 nm-wide channel of the DNA nanopore allows timed delivery of folded proteins across synthetic and biological membranes. These studies represent early examples of dynamic DNA nanostructures in mimicking membrane protein functions. I envision that they will be used in synthetic biology to create artificial cells containing GPCR-like and ion channel-like receptors, in site-specific drug or vaccine delivery and highly sensitive biosensing applications.
ContributorsDey, Swarup (Author) / Yan, Hao (Thesis advisor) / Hariadi, Rizal F (Thesis advisor) / Liu, Yan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2021
Description
First evolving in cyanobacteria, the light reactions of oxygenic photosynthesis are carried out by the membrane proteins, photosystem II and photosystem I, located in the thylakoid membrane. Both utilize light captured by their core antenna systems to catalyze a charge separation event at their respective reaction centers and energizes electrons to be transferred energetically uphill, eventually to be stored as a high energy chemical bond. These protein complexes are highly conserved throughout different photosynthetic lineages and understanding the variations across species is vital for a complete understanding of how photosynthetic organisms can adapt to vastly different environmental conditions. Most knowledge about photosynthesis comes from only a handful of model organisms grown under laboratory conditions. Studying model organisms has facilitated major breakthroughs in understanding photosynthesis, however, due to the vast global diversity of environments where photosynthetic organisms are found, certain aspects of this process may be overlooked or missed by focusing on a select group of organisms optimized for studying in laboratory conditions. This dissertation describes the isolation of a new extremophile cyanobacteria, Cyanobacterium aponinum 0216, from the Arizona Sonoran Desert and its innate ability to grow in light intensities that exceed other model organisms. A structure guided approach was taken to investigate how the structure of photosystem I can influence the spectroscopic properties of chlorophylls, with a particular focus on long wavelength chlorophylls, in an attempt to uncover if photosystem I is responsible for high light tolerance in Cyanobacterium aponinum 0216. To accomplish this, the structure of photosystem I was solved by cryogenic electron microscopy to 2.7-anstrom resolution. By comparing the structure and protein sequences of Cyanobacterium aponinum to other model organisms, specific variations were identified and explored by constructing chimeric PSIs in the model organism Synechocystis sp. PCC 6803 to determine the effects that each specific variation causes. The results of this dissertation describe how the protein structure and composition affect the spectroscopic properties of chlorophyll molecules and the oligomeric structure of photosystem I, possibly providing an evolutionary advantage in the high light conditions observed in the Arizona Sonoran Desert.
ContributorsDobson, Zachary (Author) / Fromme, Petra (Thesis advisor) / Mazor, Yuval (Thesis advisor) / Redding, Kevin (Committee member) / Moore, Gary (Committee member) / Arizona State University (Publisher)
Created2022
Description
In recent years, researchers have employed DNA and protein nanotechnology to develop nanomaterials for applications in the fields of regenerative medicine, gene therapeutic, and materials science. In the current state of research, developing a biomimetic approach to fabricate an extracellular matrix (ECM)-like material has faced key challenges. The difficulty arises due to achieving spatiotemporal complexity that rivals the native ECM. Attempts to replicate the ECM using hydrogels have been limited in their ability to recapitulate its structural and functional properties. Moreover, the biological activities of the ECM, such as cell adhesion, proliferation, and differentiation, are mediated by ECM proteins and their interactions with cells, making it difficult to reproduce these activities in vitro.Thus, the work presented in my dissertation represents efforts to develop DNA and protein-based materials that mimic the biological properties of the ECM. The research involves the design, synthesis, and characterization of nanomaterials that exhibit unique physical, chemical, and mechanical properties. Two specific aspects of the biomimetic system have been to include (1) a modular protein building block to change the bioactivity of the system and (2) to temporally control the self-assembly of the protein nanofiber using different coiled coil mechanisms. The protein nanofibers were characterized using atomic force microscopy, transmission electron microscopy, and super-resolution DNA Point Accumulation for Imaging in Nanoscale Topology. The domains chosen are the fibronectin domains, Fn-III10, Fn-III9-10, and Fn-III12-14, with bioactivity such as cell adhesion and growth factor binding.
To extend this approach, these cys-nanofibers have been embedded in a hyaluronic acid scaffold to enable bioactivity and fibrous morphologies. Nanofiber integration within the HA gel has been shown to promote tunable mechanical properties and architectures, in addition to promoting a temporal display of the protein nanofibers. The hydrogels were characterized using scanning electron microscopy, mechanical compression testing, and fluorescence microscopy. The findings in this dissertation highlight the promise of biomimetic DNA and protein nanomaterials as a versatile approach for developing next-generation materials with unprecedented properties and functions. These findings continue to push the boundaries of what is possible in nanotechnology, leading to new discoveries that will have a significant impact on society.
ContributorsBernal-Chanchavac, Julio (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Jones, Anne (Committee member) / Mills, Jeremy (Committee member) / Arizona State University (Publisher)
Created2023
Description
Exoelectrogenic organisms transfer electrons from their quinone pool to extracellular acceptors over m-scale distances through appendages known as “biological nanowires”. These structures have been described as cytochrome-rich membrane extensions or pili. However, the components and mechanisms of this long-range electron transfer remain largely unknown. This dissertation describes supramolecular assembly of a tetraheme cytochrome into well-defined models of microbial nanowires and uses those structures to explore the mechanisms of ultra-long-range electron transfer. Chiral-induced-spin-selectivity through the cytochrome is also demonstrated. Nanowire extensions in Shewanella oneidensis have been hypothesized to transfer electrons via electron tunneling through proteinaceous structures that reinforce π-π stacking or through electron hopping via redox cofactors found along their lengths. To provide a model to evaluate the possibility of electron hopping along micron-scale distances, the first part of this dissertation describes the construction of a two-component, supramolecular nanostructure comprised of a small tetraheme cytochrome (STC) from Shewanella oneidensis fused to a peptide domain that self-assembles with a β-fibrillizing peptide. Structural and electrical characterization shows that the self-assembled protein fibers have dimensions relevant to understanding ultralong-range electron transfer and conduct electrons along their length via a cytochrome-mediated mechanism of electron transfer. The second part of this dissertations shows that a model three-component fiber construct based on charge complementary peptides and the redox protein can also be assembled. Structural and electrical characterization of the three-component structure also demonstrates desirable dimensions and electron conductivity along the length via a cytochrome-mediated mechanism.
In vivo, it has been hypothesized that cytochromes in the outer surface conduit are spin-selective. However, cytochromes in the periplasm of Shewanella oneidensis have not been shown to be spin selective, and the physiological impact of the chiral-induced-spin-selectivity (CISS) effect on microbial electron transport remains unclear. In the third part of this dissertation, investigations via spin polarization and a spin-dependent conduction study show that STC is spin selective, suggesting that spin selectivity may be an important factor in the electron transport efficiency of exoelectrogens.
In conclusion, this dissertation enables a better understanding of long-range electron transfer in bacterial nanowires and bioelectronic circuitry and offers suggestions for how to construct enhanced biosensors.
ContributorsNWACHUKWU, JUSTUS NMADUKA (Author) / Jones, Anne K. (Thesis advisor) / Mills, Jeremy (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.
ContributorsAbraham, Leeza (Author) / Yan, Hao (Thesis advisor) / Liu, Uan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.
ContributorsBisarad, Pritha (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.
ContributorsProcyk, Jonah (Author) / Sulc, Petr (Thesis advisor) / Stephanopoulos, Nicholas (Thesis advisor) / Hariadi, Rizal (Committee member) / Heyden, Matthias (Committee member) / Arizona State University (Publisher)
Created2022
Description
Structural-based drug discovery is becoming the essential tool for drug development withlower cost and higher efficiency compared to the conventional method. Knowledge of the
three-dimensional structure of protein targets has the potential to accelerate the process
for screening drug candidates. X-ray crystallography has proven to be the most used and
indispensable technology in structural-based drug discovery. The provided
comprehensive structural information about the interaction between the disease-related
protein target and ligand can guide the chemical modification on the ligand to improve
potency and selectivity. X-ray crystallography has been upgraded from traditional
synchrotron to the third generation, which enabled the surge of the structural
determination of macromolecular. The introduction of X-ray free electron laser further
alleviated the uncertain and time-consuming crystal size optimization process and
extenuated the radiation damage by “diffraction before destruction”. EV-D68 2A
protease was proved to be an important pharmaceutical target for acute flaccid myelitis.
This thesis reports the first atomic structure of the EV-D68 2A protease and the structuresof its two mutants, revealing it adopting N-terminal four-stranded sheets and C-terminal
six-stranded ß-barrels structure, with a tightly bound zinc atom. These structures will
guide the chemical modification on its inhibitor, Telaprevir. Integrin ⍺Mβ2 is an integrin
with the α I-domain, related to many immunological functions including cell
extravasation, phagocytosis, and immune synapse formation, so studying the molecular
ligand-binding mechanism and activation mechanism of ⍺Mβ2 is of importance. This
thesis uncovers the preliminary crystallization condition of ⍺Mβ2-I domain in complex
with its ligand Pleiotrophin and the initial structural model. The structural model shows consistency with the previous hypothesis that the primary binding sites are metal iondependent
adhesion sites on ⍺Mβ2-I domain and the thrombospondin type-1 repeat (TSR)
domains of Pleiotrophin. Drug molecules with high potency and selectivity can be
designed based on the reported structures of the EV-D68 2A protease and ⍺Mβ2-I domain
in the future.
ContributorsLiu, Chang (Author) / Liu, Wei (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Chiu, Po-Lin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Transient protein-protein and protein-molecule interactions fluctuate between associated and dissociated states. They are widespread in nature and mediate most biological processes. These interactions are complex and are strongly influenced by factors such as concentration, structure, and environment. Understanding and utilizing these types of interactions is useful from both a fundamental and design perspective. In this dissertation, transient protein interactions are used as the sensing element of a biosensor for small molecule detection. This is done by using a transcription factor-small molecule pair that mediates the activation of a CRISPR/Cas12a complex. Activation of the Cas12a enzyme results in an amplified readout mechanism that is either fluorescence or paper based. This biosensor can successfully detect 9 different small molecules including antibiotics with a tuneable detection limit ranging from low µM to low nM. By combining protein and nucleic acid-based systems, this biosensor has the potential to report on almost any protein-molecule interaction, linking this to the intrinsic amplification that is possible when working with nucleic acid-based technologies. The second part of this dissertation focuses on understanding protein-molecule interactions at a more fundamental level, and, in so doing, exploring design rules required to generalize sensors like the ones described above. This is done by training a neural network algorithm with binding data from high density peptide micro arrays incubated with specific protein targets. Because the peptide sequences were chosen simply to evenly, though sparsely, represent all sequence space, the resulting network provides a comprehensive sequence/binding relationship for a given target protein. While past work had shown that this works well on the arrays, here I have explored how well the neural networks thus trained, predict sequence-dependent binding in the context of protein-protein and peptide-protein interactions. Amino acid sequences, either free in solution or embedded in protein structure, will display somewhat different binding properties than sequences affixed to the surface of a high-density array. However, the neural network trained on array sequences was able to both identify binding regions in between proteins and predict surface plasmon resonance-based binding propensities for peptides with statistically significant levels of accuracy.
ContributorsSwingle, Kirstie Lynn (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2022
Description
Generating amorphous solid dispersions (ASDs) containing active pharmaceutical ingredients has become a favorable technique of emerging prominence to improve drug solubility and overall bioavailability. Cannabidiol (CBD) has now become a major focus in cannabinoid research due to its ability to serve as an anti-inflammatory agent, showing promising results in treating a wide array of debilitating diseases and pathologies. The following work provides evidence for generating homogenous glass phase amorphous solid dispersions containing 50% (w/w) up to 75% (w/w) CBD concentrations in the domain size of 2 – 5 nm. Concentrations up to 85% (w/w) CBD were concluded homogenous in the supercooled liquid phase in domain sizes of 20 – 30 nm. The results were obtained from polarized light microscopy (PLM), differential scanning calorimetry (DSC), as well as solution and solid-state NMR spectroscopy.
ContributorsBlass, Brandon Lewis (Author) / Yarger, Jeff L (Thesis advisor) / Holland, Greg (Committee member) / Moore, Gary (Committee member) / Arizona State University (Publisher)
Created2019