Matching Items (101)
Description
Monoclonal antibody therapy focuses on engineering immune cells to target specific peptide sequences indicative of disease. An impediment in the continued advancement of this market is the lack of an efficient, inexpensive means of characterization that can be broadly applied to any antibody while still providing high-density data. Many characterization methods address an antibody's affinity for its cognate sequence but overlook other important aspects of binding behavior such as off-target binding interactions. The purpose of this study is to demonstrate how the binding intensity between an antibody and a library of random-sequence peptides, otherwise known as an immunosignature, can be evaluated to determine antibody specificity and polyreactivity. A total of 24 commercially available monoclonal antibodies were assayed on 125K and 330K peptide microarrays and analyzed using a motif clustering program to predict candidate epitopes within each antigen sequence. The results support the further development of immunosignaturing as an antibody characterization tool that is relevant to both therapeutic and non-therapeutic antibodies.
ContributorsDai, Jennifer T. (Author) / Stafford, Phillip (Thesis director) / Diehnelt, Chris (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.
ContributorsBrooks, Meilia Catherine (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Ghirlanda, Giovanna (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in the context of a high-enrollment active-learning undergraduate biology course. Using surveys and semistructured interviews, we investigated whether students perceived that instructors know their names, the importance of instructors knowing their names, and how instructors learned their names. We found that, while only 20% of students perceived their names were known in previous high-enrollment biology classes, 78% of students perceived that an instructor of this course knew their names. However, instructors only knew 53% of names, indicating that instructors do not have to know student names in order for students to perceive that their names are known. Using grounded theory, we identified nine reasons why students feel that having their names known is important. When we asked students how they perceived instructors learned their names, the most common response was instructor use of name tents during in-class discussion. These findings suggest that students can benefit from perceiving that instructors know their names and name tents could be a relatively easy way for students to think that instructors know their names. Academic self-concept is one's perception of his or her ability in an academic domain compared to other students. As college biology classrooms transition from lecturing to active learning, students interact more with each other and are likely comparing themselves more to students in the class. Student characteristics, such as gender and race/ethnicity, can impact the level of academic self-concept, however this has been unexplored in the context of undergraduate biology. In this study, we explored whether student characteristics can affect academic self-concept in the context of a college physiology course. Using a survey, students self-reported how smart they perceived themselves in the context of physiology compared to the whole class and compared to the student they worked most closely with in class. Using logistic regression, we found that males and native English speakers had significantly higher academic self-concept compared to the whole class compared with females and non-native English speakers, respectively. We also found that males and non-transfer students had significantly higher academic self-concept compared to the student they worked most closely with in class compared with females and transfer students, respectively. Using grounded theory, we identified ten distinct factors that influenced how students determined whether they are more or less smart than their groupmate. Finally, we found that students were more likely to report participating less than their groupmate if they had a lower academic self-concept. These findings suggest that student characteristics can influence students' academic self-concept, which in turn may influence their participation in small group discussion.
ContributorsKrieg, Anna Florence (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The free-base tetra-tolyl-porphyrin and the corresponding cobalt and iron porphyrin complexes were synthesized and characterized to show that this class of compound can be promising, tunable catalysts for carbon dioxide reduction. During cyclic voltammetry experiments, the iron porphyrin showed an on-set of ‘catalytic current’ at an earlier potential than the cobalt porphyrin’s in organic solutions gassed with carbon dioxide. The cobalt porphyrin yielded larger catalytic currents, but at the same potential as the electrode. This difference, along with the significant changes in the porphyrin’s electronic, optical and redox properties, showed that its capabilities for carbon dioxide reduction can be controlled by metal ions, allotting it unique opportunities for applications in solar fuels catalysis and photochemical reactions.
ContributorsSkibo, Edward Kim (Author) / Moore, Gary (Thesis director) / Woodbury, Neal (Committee member) / School of Molecular Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In this project, biochemical characteristics of peptide binding agents, synthetic antibodies or synbodies, were examined with respect to the capture efficiency and specific binding ability to norovirus. Norovirus, although generally not a deadly pathogen, is the most common cause of acute gastroenteritis and outbreaks present a large social and financial burden to the healthcare and food service industries. With Dr. Diehnelt's laboratory group, a platform has been developed that enables us to rapidly construct peptide-based affinity ligands that can be characterized for binding to norovirus. The design needed to display clear results, be simple to operate, and be inexpensive to produce and use. Four synbodies, originally engineered with a specificity to the GII.4 Minerva genotype were tested with different virus strains varying in similarity to the GII.4 Minerva between 43% and 95.4%. Initial assays utilized norovirus-like particles to qualitatively compare the capture efficiency of the different synbodies without utilizing limited resources. To quantify the amount of actual virus captured by the synbodies, western blots with RT-PCR and RT-qPCR were utilized. The results indicated the synbodies were able to enrich the dilute solutions of the different noroviruses utilizing a magnetic bead pull-down assay. The capture efficiencies of the synbodies were comparable to currently utilized binding agents such as aptamers and porcine gastric mucine magnetic beads. This thesis presents data collected over nearly two years of research at the Center for Innovations in Medicine at the Biodesign Institute located at Arizona State University.
ContributorsSlosky, Rachael Marie (Author) / Diehnelt, Chris (Thesis director) / Stafford, Phillip (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Course-Based Undergraduate Research Experiences, or CUREs have become an increasingly popular way to integrate research opportunities into the undergraduate biology curriculum. Unlike traditional cookbook labs which provide students with a set experimental design and known outcome, CUREs offer students the opportunity to participate in novel and interesting research that is of interest to the greater biology community. While CUREs have been championed as a way to provide more students with the opportunity to experience, it is unclear whether students benefit differently from participating in different CURE with different structural elements. In this study we focused in on one proposed element of a CURE, collaboration, to determine whether student's perception of this concept change over the course of a CURE and whether it differs among students enrolled in different CUREs. We analyzed pre and post open-ended surveys asking the question "Why might collaboration be important in science?" in two CUREs with different structures of collaboration. We also compared CURE student responses to the responses of senior honors thesis students who had been conducting authentic research. Five themes emerged in response to students' conceptions of collaboration. Comparing two CURE courses, we found that students' conceptions of collaboration were varied within each individual CURE, as well as what students were leaving with compared to the other CURE course. Looking at how student responses compared between 5 different themes, including "Different Perspectives", "Validate/Verify Results", "Compare Results", "Requires Different Expertise", and "Compare results", students appeared to be thinking about collaboration in distinct different ways by lack of continuity in the amount of students discussing each of these among the classes. In addition, we found that student responses in each of the CURE courses were not significantly different for any of the themes except "Different Expertise" compared to the graduating seniors. However, due to the small (n) that the graduating seniors group had, 22, compared to each of the CURE classes composing of 155 and 98 students, this comparison must be taken in a preliminary manner. Overall, students thought differently about collaboration between different CUREs. Still, a gap filling what it means to "collaborate", and whether the structures of CUREs are effective to portray collaboration are still necessary to fully elaborate on this paper's findings.
ContributorsWassef, Cyril Alexander (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022
Description
Evolution is a key feature of undergraduate biology education: the AmericanAssociation for the Advancement of Science (AAAS) has identified evolution as one of
the five core concepts of biology, and it is relevant to a wide array of biology-related
careers. If biology instructors want students to use evolution to address scientific
challenges post-graduation, students need to be able to apply evolutionary principles to
real-life situations, and accept that the theory of evolution is the best scientific
explanation for the unity and diversity of life on Earth. In order to help students progress
on both fronts, biology education researchers need surveys that measure evolution
acceptance and assessments that measure students’ ability to apply evolutionary concepts.
This dissertation improves the measurement of student understanding and acceptance of
evolution by (1) developing a novel Evolutionary Medicine Assessment that measures
students’ ability to apply the core principles of Evolutionary Medicine to a variety of
health-related scenarios, (2) reevaluating existing measures of student evolution
acceptance by using student interviews to assess response process validity, and (3)
correcting the validity issues identified on the most widely-used measure of evolution
acceptance - the Measure of Acceptance of the Theory of Evolution (MATE) - by
developing and validating a revised version of this survey: the MATE 2.0.
ContributorsMisheva, Anastasia Taya (Author) / Brownell, Sara (Thesis advisor) / Barnes, Elizabeth (Committee member) / Collins, James (Committee member) / Cooper, Katelyn (Committee member) / Sterner, Beckett (Committee member) / Arizona State University (Publisher)
Created2023
Description
People with disabilities are underrepresented in the Science, Technology, Engineering, and Math (STEM) workforce (NSF, 2016). One way to increase representation of people with disabilities in STEM fields is by supporting students with disabilities (SWDs) at the undergraduate level. In undergraduate education in the United States, SWDs represent approximately 19% of the undergraduate community (U.S. Census Bureau, 2021). However, SWDs have lower graduation and retention rates. This is particularly true for STEM majors, where SWDs make up about 9% of the STEM community in higher education. The AAC&U has defined a list of High-Impact Practices (HIPs), which are active learning practices and experiences that encourage deep learning by promoting student engagement, and could ultimately support student retention (AAC&U). To date, student-centered disability research has not explored the extent to which SWDs participate in HIPs. We hypothesized that SWDs are less likely than students without disabilities to be involved in HIPs and that students who identify as having severe disabilities would participate in HIPs at lower rates. In this study, we conducted a national survey to examine involvement in HIPs for students with disabilities in STEM. We found that disability status significantly affects the probability of participation in undergraduate research, but is not a significant factor for participation in most other HIPs. We also found that self-reported severity of disability did not significantly impact participation in HIPs, though we observed trends that students reporting higher severity generally reported lower participation in HIPs. Our open-ended responses did indicate that SWDs still faced barriers to participation in HIPs.
ContributorsPais, Danielle (Author) / Brownell, Sara (Thesis director) / Cooper, Katelyn (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2022-05