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: Woodbury, Neal W
Description
Enzymes which regulate the metabolic reactions for sustaining all living things, are the engines of life. The discovery of molecules that are able to control enzyme activity is of great interest for therapeutics and the biocatalysis industry. Peptides are promising enzyme modulators due to their large chemical diversity and the existence of well-established methods for library synthesis. Microarrays represent a powerful tool for screening thousands of molecules, on a small chip, for candidates that interact with enzymes and modulate their functions. In this work, a method is presented for screening high-density arrays to discover peptides that bind and modulate enzyme activity. A viscous polyvinyl alcohol (PVA) solution was applied to array surfaces to limit the diffusion of product molecules released from enzymatic reactions, allowing the simultaneous measurement of enzyme activity and binding at each peptide feature. For proof of concept, it was possible to identify peptides that bound to horseradish peroxidase (HRP), alkaline phosphatase (APase) and â-galactosidase (â-Gal) and substantially alter their activities by comparing the peptide-enzyme binding levels and bound enzyme activity on microarrays. Several peptides, selected from microarrays, were able to inhibit â-Gal in solution, which demonstrates that behaviors selected from surfaces often transfer to solution. A mechanistic study of inhibition revealed that some of the selected peptides inhibited enzyme activity by binding to enzymes and inducing aggregation. PVA-coated peptide slides can be rapidly analyzed, given an appropriate enzyme assay, and they may also be assayed under various conditions (such as temperature, pH and solvent). I have developed a general method to discover molecules that modulate enzyme activity at desired conditions. As demonstrations, some peptides were able to promote the thermal stability of bound enzyme, which were selected by performing the microarray-based enzyme assay at high temperature. For broad applications, selected peptide ligands were used to immobilize enzymes on solid surfaces. Compared to conventional methods, enzymes immobilized on peptide-modified surfaces exhibited higher specific activities and stabilities. Peptide-modified surfaces may prove useful for immobilizing enzymes on surfaces with optimized orientation, location and performance, which are of great interest to the biocatalysis industry.
ContributorsFu, Jinglin (Author) / Woodbury, Neal W (Thesis advisor) / Johnston, Stephen A. (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2010
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
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
Protein-surface interactions, no matter structured or unstructured, are important in both biological and man-made systems. Unstructured interactions are more difficult to study with conventional techniques due to the lack of a specific binding structure. In this dissertation, a novel approach is employed to study the unstructured interactions between proteins and heterogonous surfaces, by looking at a large number of different binding partners at surfaces and using the binding information to understand the chemistry of binding. In this regard, surface-bound peptide arrays are used as a model for the study. Specifically, in Chapter 2, the effects of charge, hydrophobicity and length of surface-bound peptides on binding affinity for specific globular proteins (&beta-galactosidase and &alpha1-antitrypsin) and relative binding of different proteins were examined with LC Sciences peptide array platform. While the general charge and hydrophobicity of the peptides are certainly important, more surprising is that &beta-galactosidase affinity for the surface does not simply increase with the length of the peptide. Another interesting observation that leads to the next part of the study is that even very short surface-bound peptides can have both strong and selective interactions with proteins. Hence, in Chapter 3, selected tetrapeptide sequences with known binding characteristics to &beta-galactosidase are used as building blocks to create longer sequences to see if the binding function can be added together. The conclusion is that while adding two component sequences together can either greatly increase or decrease overall binding and specificity, the contribution to the binding affinity and specificity of the individual binding components is strongly dependent on their position in the peptide. Finally, in Chapter 4, another array platform is utilized to overcome the limitations associated with LC Sciences. It is found that effects of peptide sequence properties on IgG binding with HealthTell array are quiet similar to what was observed with &beta-galactosidase on LC Science array surface. In summary, the approach presented in this dissertation can provide binding information for both structured and unstructured interactions taking place at complex surfaces and has the potential to help develop surfaces covered with specific short peptide sequences with relatively specific protein interaction profiles.
ContributorsWang, Wei (Author) / Woodbury, Neal W (Thesis advisor) / Liu, Yan (Committee member) / Chaput, John (Committee member) / Arizona State University (Publisher)
Created2014