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ItemPrebiotic Formation of Plausible Proto-Nucleosides: Investigations into the Origins of Nucleotides with Ribose and Pairing Bases(Georgia Institute of Technology, 2022-12-15) Roche, Tyler PatrickThe question of the origin of life on Earth is of the most fundamental importance to modern biochemistry. Of the major molecular components of life, its genetic polymers, RNA and DNA, are perhaps the most salient targets for investigation as the products of chemical evolution. After the discovery of RNA’s ability to catalyze reactions, the RNA World Hypothesis was proposed, highlighting the potential importance of RNA in the origin of life. This hypothesis, which—in its more widely-accepted current form—states that RNA was the primary biopolymer to evolve at the outset of life’s origin, has been the guiding principle for a generation of prebiotic chemistry. Despite various challenges present in the prebiotic formation of the components required to form RNA, its synthesis under early-Earth conditions has remained a prized goal for many in the field. An alternative hypothesis—one in which RNA was not the first major prebiotic polymer—has been under recent investigation, and in this hypothesis, RNA represents the ultimate genetic polymer, one that is the product of chemical and biological evolution, through multiple stages, starting from a putative proto-RNA. In this dissertation, I describe investigations into robust pathways for the formation of the sugar components of modern nucleic acids, and the ability of noncanonical nucleobases to react directly with these sugars to form nucleosides and nucleotides, the monomers of a potential proto-RNA. Probing further, I investigate the ability of these noncanonical nucleosides to form and pair simultaneously in a one-pot solution under prebiotic conditions.
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ItemIdentification And Quantification Of Protein O-GlcNAcylation Using MS-Based Proteomics(Georgia Institute of Technology, 2022-12-14) Xu, SenhanO-GlcNAcylation is a type of protein glycosylation where a single N-acetylglucosamine (GlcNAc) is covalently bound to the serine and threonine residues. It is added by O-GlcNAc transferase (OGT) and removed by O-GlcNAcase (OGA). O-GlcNAcylated proteins are primarily located in the nucleus and the cytoplasm, but extracellular O-GlcNAcylated proteins have also been reported. This modification has been found to be extensively involved in many cellular processes, including gene transcription, protein translation, and cell cycle controls. As protein O-GlcNAcylation plays a critical role in mammalian cell survival, its dysregulation is associated with many diseases, such as cancer, diabetes, and neurodegenerative diseases. Despite its importance, O-GlcNAcylated proteins are very difficult to study because of the low abundance of many glycoproteins and the dynamic nature of this modification. Therefore, its global analysis requires effective enrichment of glycoproteins. However, the existing methods have some issues related to the efficiency and specificity. Furthermore, the knowledge of the distributions and functions of many O-GlcNAcylated proteins are not well characterized using conventional biological methods. MS-based proteomics provides a unique opportunity to systematically study protein O-GlcNAcylation at the proteome scale. My thesis focuses on developing MS-based proteomics method to systematically identify and quantify protein O-GlcNAcylation. In Chapter 2, I have developed a method integrating bioorthogonal chemistry and an enzymatic reaction to simultaneously identify and distinguish glycoproteins modified with O-GlcNAc and O-GalNAc (the Tn antigen), which are two important protein modifications with very similar structures and the same composition, but completely different functions. In Chapter 3, I have systematically quantified the nuclear-cytoplasmic distributions of O-GlcNAcylated proteins in human cells. The results demonstrate that O-GlcNAcylated proteins with different functions had distinct patterns of the distributions, and the distributions vary varied site-specifically. Moreover, the dynamics of O-GlcNAcylated proteins and non-modified ones in the nucleus and the cytoplasm was comprehensively analyzed, and the half-lives of glycoproteins in these two compartments were found to be markedly different. Additionally, glycoproteins in the nucleus were more dramatically stabilized than those in the cytoplasm under the OGA inhibition. In Chapter 4, I have comprehensively investigated the nuclear-cytoplasmic distributions of the modified (phosphorylated and O-GlcNAcylated) and non-modified forms of proteins to dissect the correlation between protein distribution and modifications. The results reveal that the different distributions between the modified and non-modified forms of proteins are associated with their functions. Moreover, bioinformatics analyses indicate that other factors are related to the impact of phosphorylation on protein distribution, including protein size and function, local structure, and adjacent amino acid residues around phosphorylation sites. In Chapter 5, I have developed a novel chemoenzymatic method based on a wild-type galactosyltransferase and uridine diphosphate galactose (UDP-Gal) for global and site-specific analysis of protein O-GlcNAcylation. This method integrates enzymatic reactions and hydrazide chemistry to enrich O-GlcNAcylated peptides. All reagents used are more easily accessible and cost-effective compared with the engineered enzyme and click chemistry reagents. In Chapter 6, I have developed a quantitative proteomics workflow to systematically determine the stoichiometries of O-GlcNAcylated proteins. The O-GlcNAcylated proteins are enriched by combining metabolic labeling and click chemistry, and the identities of O-GlcNAcylated proteins were confirmed by the site-specific mapping of glycosylation sites. The stoichiometries of glycoproteins are determined by comparing the abundance of the enriched glycoproteins versus the non-enriched flow-through using quantitative multiplexed proteomics. In Chapter 7, I designed an integrative method to site-specifically identify co-translational O-GlcNAcylation that is of very low abundance in cells but have critical functions in regulating the degradation of nascent polypeptides. In Chapter 8, I present a chemoproteomics method based on a probe that enables direct detection of the cysteine oxidation sites without the need of prior reduction. The method was first validated by reacting with insulin that contains oxidized cysteines. Next, Using Jurkat cells it was demonstrated that the probe specifically targets oxidized cysteines and does not react with other modifications and amino acid residues. Combining with multiplexed proteomics, we applied the method to study the differences of cysteine oxidation in the livers from mice fed with the high or low fat diet (HFD/HLD). It was found that the cysteine oxidation is dramatically upregulated in the HFD samples, and the upregulation is correlated with protein distribution, functions, and the flanking residues of the oxidation site.
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ItemAffordable Quantum Chemistry via Data-Driven and Local Approximations to Non-Covalent Interactions(Georgia Institute of Technology, 2022-12-14) Glick, Zachary LeeQuantum chemistry (QC) calculations can provide physically-rooted insight into intermolecular interactions. A quantitative understanding of these interactions, in turn, is of crucial importance for chemical problems like the modeling of protein-ligand interactions or molecular crystals and clusters. Unfortunately, the expensive computational cost of QC calculations prohibits their routine use in high-throughput computational workflows. The field of machine learning (ML) offers a potential workaround to this problem. Large amounts of quantum chemistry data can be generated upfront and used to parameterize models such as neural networks (NNs). The ML models can then be used to predict QC properties of new chemical systems, usually with a many order-of-magnitude reduction in computational cost. The development of such models is a rapidly evolving field, and numerous open questions exist about functional forms, dataset generation, accuracy, and generalizability. In this thesis, the development of NNs specific to the prediction of long-range, non-local intermolecular interactions--which existing models are not equipped to capture--is explored. Throughout the course of the chapters two through four, an equivariant atomic-pairwise neural network with a hybrid force field functional form referred to as AP-Net is developed. In the interest of the efficient generation of QC datasets, chapter 5 is concerned with the development and implementation of reduced-scaling dispersion algorithm. This algorithm allows for reference interactions energies to be generated at a reduced computational cost.
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ItemProbabilistic Methods For Rapid Characterization Of Complex Biological Samples(Georgia Institute of Technology, 2022-12-14) Richardson, Joseph ClaytonA descriptive set of statistics metrics and machine learning models are developed to both discriminate and classify bacteria using high throughput and information rich flow cytometry, as well as automate and improve a novel colorimetric antibiotic susceptibility testing (AST) assay, ChroMIC. Bayesian classifiers are utilized to provide a fast, flexible platform to perform rapid bacterial identification of not only unknown samples with one causative agent, but this analysis makes good on the ability to classify binary mixtures as well. Model training and validation are achieved by first constructing a library of known bacterial pathogens, then using the models to predict bacterial identity of unknown pathogens. These Bayesian methods are coupled to our previously described Probability Binning Signature Quadratic Form (PB-sQF) statistics, which enable further rigor. A fast Gram-stain support vector machine (SVM) classifier is trained with a commercially available dye kit, and was developed to pair easily with AST workflows currently in development. Once trained, the SVM classifier allows for the determination of the Gram status of unknown bacterial pathogens within minutes of bacterial isolation. To further refine and speed up the AST step of the clinical workflow, the previously developed colorimetric AST ChroMIC, was automated and refined to be a real-time assay. This simple colorimetric assay uses machine learning and computer vision to auto label bacterial growth positive and negative wells with a modified, self-correcting SVM model. ChroMIC has high categorical and essential agreements (> 90%) with the clinical gold standard technique of broth micro dilution, meaning that ChroMIC is a strong candidate for future deployment in clinical setttings. These described assays and methods constitute a complete set of clinically relevant technologies, meaning that both the identity and the antibiotic susceptibility profile of the unknown pathogen are determined rapidly post blood-culture.
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ItemProteome-Wide Analysis Of Protein-Heme Interaction And Sod1-Dependent Cysteine Oxidation(Georgia Institute of Technology, 2022-12-14) Kim, HyojungCells make important cellular decisions through signaling to regulate metabolism, cell shape and movement, expression of proteins, and so on. The cellular signaling process includes three main steps: signal reception, signal transduction, and cellular response. Due to complex and highly dynamic protein interactions in signal transduction, it is still not fully understood. One mechanism that Nature utilizes to mediate signal transduction cascades is to post-translationally modify proteins to dynamically control their structure, function, localization, and activities. Arguably, the best-studied post-translational modifications (PTMs) are regulated by enzymatic processes, such as phosphorylation, ubiquitination, and glycosylation. However, many important PTMs are regulated by non-enzymatic processes, which are comparatively less well understood. In this thesis, we investigated two non-enzymatic protein modifications to better understand their roles in cellular signaling: dynamic protein hemylation in heme signaling and cysteine oxidation in redox signaling. Heme b (iron protoporphyrin IX) plays important roles in biology as a metallocofactor and signaling molecule. However, the targets of heme signaling and the network of proteins that mediate the exchange of heme from sites of synthesis or uptake to heme-dependent or regulated proteins are poorly understood. Thus, we utilized a quantitative mass spectrometry-based chemoproteomics strategy to identify heme binding proteins as potential targets of heme signaling and heme mobilization/trafficking in Saccharomyces cerevisiae and human embryonic kidney 293 cells. The strategy involves perturbing endogenous heme availability, which leads to cellular labile heme binding proteins dynamically releasing or binding heme. Since the hemylation state of a hemoprotein will dictate how strongly it binds to hemin-agarose resin, with the apo-state binding more strongly than the holo-state due to competition between endogenous heme and hemin-agarose, targets of heme signaling, and heme trafficking factors may be identified by heme affinity chromatography and quantitative mass spectrometry. In this work, we used lead and succinylacetone, which increase and deplete labile heme in cells, respectively, as perturbants that alter in vivo protein hemylation status. By identifying only those proteins that interact with high specificity to hemin-agarose relative to control beaded agarose in an endogenous perturbant-dependent manner, we have expanded the number of proteins and ontologies that may be involved in binding and buffering labile heme or are targets of heme signaling. These include proteins involved in the regulation of translation and proteolysis, and cell growth and division in response to lead treatment in S. cerevisiae and chromatin remodeling, DNA damage response, RNA splicing, cytoskeletal organization, and vesicular trafficking in response to SA treatment in HEK 293 cells. The other non-enzymatic protein modification is cysteine oxidation. A major cellular oxidant that mediates cysteine oxidation is hydrogen peroxide (H2O2). The mechanisms by which peroxide signals are produced and transmitted to regulate cysteine oxidation are poorly understood. We propose herein that the antioxidant enzyme Cu/Zn superoxide dismutase (Sod1) may be an important source of peroxide for redox signaling. Sod1 is a highly conserved and abundant antioxidant enzyme that detoxifies superoxide by catalyzing its conversion to dioxygen and hydrogen peroxide. The Reddi lab previously found that less than 1% of Sod1 is required for cellular protection against superoxide toxicity. Thus, we are now exploring its roles as a site-specific source of peroxide to regulate redox signaling. In this thesis, we described the strategy to identify proteome-wide novel targets of Sod1 in redox signaling using the sod1Δ strain in S. cerevisiae and the combination of quantitative mass spectrometry of stable isotope labeling of amino acids (SILAC) to examine whole proteome expression and iodoTMT to examine cysteine oxidation of proteins in response to Sod1-derived H2O2. The Sod1-dependent expression of over 400 proteins and the cysteine oxidation of 99 proteins revealed a wide range of biological processes and molecular functions controlled by Sod1, including redox processes, amino acid biosynthesis, proteostasis, iron-sulfur cluster assembly, and energy metabolism, to name a few. Overall, our work probing heme and Sod1-dependent redox signaling using quantitative mass spectrometry has greatly expanded our knowledge and awareness of these signaling processes in biology.
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ItemDesigning Molecular Tools For Elucidating The Role Of Labile Copper And Zinc In Biology(Georgia Institute of Technology, 2022-12-13) Yu, JiyaoIntracellular Cu(I) and Zn(II) ions have attracted tremendous attention in present-day literature as they are suggested to play a critical role in human health and disease. However, the current understanding of the homeostatic mechanisms revolving around the transport and exchange of intracellular Cu(I) and Zn(II) remains incomplete. Full elucidation of intracellular metal regulation unlocks the potential to find novel treatment for human diseases and developmental abnormalities. However, d-block metal ions are difficult to detect and trace at the single-cell level. To this end, metal-ion selective ligands and fluorescent probes are powerful tools for investigating metal ion availabilities at physiological levels. As a contribution toward revealing Cu(I) and Zn(II) regulation in cells, this work describes an unconventional design strategy for shifting the excitation wavelength of Zn(II)-selective fluorescent probe from the UV to the visible-light spectral region. The developed Zn(II)-selective fluorescent probe offers a balanced ratiometric-emission response upon Zn(II) binding and is well suited to visualize dynamic changes of cellular Zn(II) by conventional confocal fluorescence microscopy and two-photon excitation microscopy (TPEM). In a second line of investigation, this work describes the rational design of two Cu(I)-selective ligands based on phosphine-sulfide-stabilized phosphine (PSP) donor motifs. The new PSP ligands form Cu(I) complexes with dissociation constants in the low zeptomolar regime. Extensive solution characterizations, ICP-MS, and TPEM studies revealed the potential of these compounds to serve as excellent tools for manipulating copper levels within complex biological systems.
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ItemCaCO3 Polymorphs as Mineral Catalysts for Prebiotic Phosphorylation of Uridine(Georgia Institute of Technology, 2022-12) Schaible, Micah J. ; Castañeda, Alma D. ; Menor-Salvan, Cesar ; Pasek, Matthew A. ; Burcar, Bradley T. ; Orlando, Thomas M.Establishing plausible routes for the abiotic formation of nucleotides is a challenging problem because the phosphorylation of organic molecules is thermodynamically unfavorable in water, and because common phosphorous-containing minerals such as apatite are highly insoluble. Reactions of reduced phases such as the meteoritic mineral schreibersite with ammonia containing solutions can form stable amino-derivatives of phosphates/phosphite, and carbonate-rich lakes have been suggested as environments where phosphate species and organic molecules could accumulate in significant abundances, thus promoting an ideal environment for abiotic phosphorylation. This work reports the catalytic properties of three CaCO3 polymorphs – calcite, aragonite, and vaterite – on diamidophosphate (DAP)-induced phosphorylation of the uridine nucleoside during a 24-hour dry-down reaction. It is shown that the phosphorylation reaction is accelerated in solutions containing CaCO3 compared to those with no mineral present. For un-buffered solutions with no mineral present, the primary products formed are uridine monophosphates (UMP), with yields making up 22.3 ± 3.9% of the total detected species, while solutions containing calcite and aragonite formed primarily UMP dimers (yields of 15.3 ± 1.1% and 14.8 ± 1.3%, respectively). Vaterite showed a strong preference for forming cyclic UMP (cUMP) (26.3 ± 0.3% yield), and no higher order polymers were observed using any carbonate mineral. Reactions containing CaSO4∙2H2O (gypsum) showed a preference for forming cUMP, though not as strong as vaterite, while those containing CaCl2 (calcium chloride) and CaWO4 (scheelite) did not yield any phosphorylated products other than UMPs. These results suggest that CaCO3 minerals could have played an important role in facilitating prebiotic phosphorylation in aqueous environments that undergo drying cycles.
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ItemSolution Processable Conjugated Polymers For Organic Electronic Applications(Georgia Institute of Technology, 2022-11-18) Ditullio, BrandonBroadly, this thesis work focused on the investigation and leveraging of the way in which conjugated polymers shuttle and interact with electrons/ions. This mixed ion and electron transport has led to their use in a wide range of semiconductor applications, including bio(electronics). Commercialization of these materials for such applications will be difficult without high-throughput industrial processing techniques such as roll-to-roll and additive manufacturing. Accordingly, this thesis focuses on the design, synthesis, and characterization of CPs with solution processability — a key requirement for such manufacturing technologies — to outline specific structure-property relationships that expand their viability for applications in varying design spaces (films, filaments, multi-layer architectures, etc.). For application-driven work, organic electrochemical transistors (OECTs) have been of significant contemporary interest due to their mechanical conformability, low-voltage operation, facile chemical modification, and ability to transduce cellular ion fluxes (e.g., protons, metal ions, and neurotransmitters) into exogeneous electrical signals with extremely high signal fidelity. Accordingly, this work further aimed to understand the interplay between polymer (polythiophene-based active material) chemical structure, processing, and material properties relevant to enhancing OECT performance and stability.
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ItemBuilding Blocks of Neural Network Intermolecular Interaction Potentials(Georgia Institute of Technology, 2022-10-17) Metcalf, DerekThe essence of the computational sciences is to find compressed, silicon-ready rulesets of the natural world and use them to predict all of the complexities of reality without actually observing it. Practically, no lossless variant of such a compression is compatible with the computers of today, and we instead focus on choosing a set of approximations that induce nicely-cancelling errors. One popular way of concocting approximations is to use well-established physical principles (such as the Schrödinger equation for computing properties of atomistic systems) and progressively remove complexity without introducing dependence on real-world observations. These "first principles" approaches contrast with empirical methods that often use parameters to encourage their simpler models to match experimental data at a reduced computational expense. Although less conceptually pleasant, some empiricism is a mainstay in computational chemistry as a result of the success and usefulness of molecular mechanics (MM), density functional theory (DFT), and recently, machine learning (ML). This thesis introduces developments in machine learning models, specifically neural networks, that seek to predict the strength of interactions between molecules. We further discuss neural networks imbued with physics, application domains within pharmaceutical discovery, and the all-important data upon which our models are parameterized.
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ItemLeveraging The Reactivity Of 1,3-Dipoles To Access Heterocycles And Carbocycles(Georgia Institute of Technology, 2022-09-07) Chen, DorisHeterocycles and carbocycles often exhibit remarkable structural features as well as propitious bioactivities that attract immense attention from the synthetic community. These motifs also constitute the core framework of many natural products and synthetic pharmaceuticals. For these reasons, a great need exists for the exploration and development of robust synthetic protocols that efficiently access these frameworks. In this thesis, heterocycles and carbocycles will be accessed in unprecedented ways by leveraging the reactivity of 1,3-dipoles, specifically in the form of N-alkoxyazomethine ylides and oxyallyl cations. Projects in all three realms of organic synthesis research will be discussed: (1) exploratory chemistry/methodology discovery, (2) methodology development, (3) target-oriented synthesis. First, in collaboration with Pfizer Inc., an exploratory project was initiated to study the generation and reactivity of novel N-alkoxyazomethine ylides in cycloaddition reactions to render nitrogen-containing heterocycles. In the second project, the first intramolecular, interrupted, formal homo-Nazarov cyclization methodology was developed. The method’s intramolecular oxyallyl cation trapping ability allowed facile access to complex (hetero)aryl-fused polycycles and led to a concise total synthesis of an antibacterial natural product, (±)-1-oxoferruginol. Third, in a target-oriented synthesis project, the progress made toward a key fragment in the total synthesis of an anticancer natural product, propolisbenzofuran B, using a homo-Nazarov cyclization approach, will be recounted.