<p> Enzymes play a crucial role in modern biotechnology, industry, food processing and medical applications. Since their first discovered industrial use, man has attempted to discover new enzymes from Nature to catalyze different chemical reactions. In modern times, with the advent of computational methods, protein structure solutions, protein sequencing and DNA synthesis methods, we now have the tools to enable new approaches to rational enzyme engineering. With an enzyme structure in hand, a researcher may run an <i>in silico</i> experiment to sample different amino acids in the active site in order to identify new combinations which likely stabilize a transition-state-enzyme model. A suggested mutation can then be encoded into the desired enzyme gene, ordered, synthesized and tested. Although this truly astonishing feat of engineering and modern biotechnology allows the redesign of existing enzymes to acquire a new substrate specificity, it still requires a large amount of time, capital and technical capabilities. </p><p> Concurrently, while making strides in computational protein design, the cost of sequencing DNA plummeted after the turn of the century. With the reduced cost of sequencing, the number of sequences in public databases of naturally occurring proteins has grown exponentially. This new, large source of information can be utilized to enable rational enzyme design, as long as it can be coupled with accurate modeling of the protein sequences. </p><p> This work first describes a novel approach to reengineering enzymes (Genome Enzyme Orthologue Mining; GEO) that utilizes the vast amount of protein sequences in modern databases along with extensive computation modeling and achieves comparable results to the state-of-the-art rational enzyme design methods. Then, inspired by the success of this new method and aware of it's reliance on the accuracy of the protein models, we created a computational benchmark to both measure the accuracy of our models as well as improve it by encoding additional information about the structure, derived from mechanistic studies (Catalytic Geometry constraints; CG). Lastly, we use the improved accuracy method to automatically model hundreds of <i>putative</i> enzymes sequences and dock substrates into them to extract important features that are then used to inform experiments and design. This is used to reengineer a ribonucleotide reductase to catalyze a aldehyde deformylating oxygenase reaction. </p><p> These chapters advance the field of rational enzyme engineering, by providing a novel technique that may enable efficient routes to rationally design enzymes for reactions of interest. These chapters also advance the field of homology modeling, in the specific domain in which the researcher is modeling an enzyme with a known chemical reaction. Lastly, these chapters and techniques lead to an example which utilizes highly accurate computational models to create features which can help guide the rational design of enzyme catalysts.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10928544 |
| Date | 15 March 2019 |
| Creators | Bertolani, Steve James |
| Publisher | University of California, Davis |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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