Matching Items (4)
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- All Subjects: Energy transfer
- All Subjects: MATLAB
- Creators: Woodbury, Neal
Description
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.
ContributorsPoweleit, Andrew Michael (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Chiu, Po-Lin (Committee member) / School of Molecular Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
Humanity’s demand for energy is increasing exponentially and the dependence on fossil fuels is both unsustainable and detrimental to the environment. To provide a solution to the impending energy crisis, it is reasonable to look toward utilizing solar energy, which is abundant and renewable. One approach to harvesting solar irradiation for fuel purposes is through mimicking the processes of natural photosynthesis in an artificial design to use sunlight and water to store energy in chemical bonds for later use. Thus, in order to design an efficient energy conversion device, the underlying processes of the natural system must be understood. An artificial photosynthetic device has many components and each can be optimized separately. This work deals with the design, construction and study of some of those components. The first chapter provides an introduction to this work. The second chapter shows a proof of concept for a water splitting dye sensitized photoelectrochemical cell followed by the presentation of a new p-type semiconductor, the design of a modular cluster binding protein that can be used for incorporating catalysts, and a new anchoring group for semiconducting oxides with high electron injection efficiency. The third chapter investigates the role of electronic coupling and thermodynamics for photoprotection in artificial systems by triplet-triplet energy transfer from tetrapyrroles to carotenoids. The fourth chapter describes a mimic of the proton-coupled electron transfer in photosystem II and confirms that in the artificial system a concerted mechanism operates. In the fifth chapter, a microbial system is designed to work in tandem with a photovoltaic device to produce high energy fuels. A variety of quinone redox mediators have been synthesized to shuttle electrons from an electron donor to the microbial system. Lastly, the synthesis of a variety of photosensitizers is detailed for possible future use in artificial systems. The results of this work helps with the understanding of the processes of natural photosynthesis and suggests ways to design artificial photosynthetic devices that can contribute to solving the renewable energy challenge.
ContributorsBrown, Chelsea L (Author) / Moore, Ana L (Thesis advisor) / Gust, Devens (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2015
Description
Sunlight, the most abundant source of energy available, is diffuse and intermittent; therefore it needs to be stored in chemicals bonds in order to be used any time. Photosynthesis converts sunlight into useful chemical energy that organisms can use for their functions. Artificial photosynthesis aims to use the essential chemistry of natural photosynthesis to harvest solar energy and convert it into fuels such as hydrogen gas. By splitting water, tandem photoelectrochemical solar cells (PESC) can produce hydrogen gas, which can be stored and used as fuel. Understanding the mechanisms of photosynthesis, such as photoinduced electron transfer, proton-coupled electron transfer (PCET) and energy transfer (singlet-singlet and triplet-triplet) can provide a detailed knowledge of those processes which can later be applied to the design of artificial photosynthetic systems. This dissertation has three main research projects. The first part focuses on design, synthesis and characterization of suitable photosensitizers for tandem cells. Different factors that can influence the performance of the photosensitizers in PESC and the attachment and use of a biomimetic electron relay to a water oxidation catalyst are explored. The second part studies PCET, using Nuclear Magnetic Resonance and computational chemistry to elucidate the structure and stability of tautomers that comprise biomimetic electron relays, focusing on the formation of intramolecular hydrogen bonds. The third part of this dissertation uses computational calculations to understand triplet-triplet energy transfer and the mechanism of quenching of the excited singlet state of phthalocyanines in antenna models by covalently attached carotenoids.
ContributorsTejeda Ferrari, Marely (Author) / Moore, Ana (Thesis advisor) / Mujica, Vladimiro (Thesis advisor) / Gust, John (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2016