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dc.contributor.advisorWilke, C. (Claus)
dc.creatorJackson, Eleisha Lynnette
dc.date.accessioned2017-03-09T15:16:34Z
dc.date.available2017-03-09T15:16:34Z
dc.date.issued2016-12
dc.date.submittedDecember 2016
dc.identifierdoi:10.15781/T22J6890T
dc.identifier.urihttp://hdl.handle.net/2152/45939
dc.description.abstractProteins are crucial players in the functional processes that allow for cellular life. Changes in the sequences of proteins have consequences for how these proteins function. Therefore, the study of how proteins change over time has been a central question in the field of evolutionary biology. As our understanding of how proteins function and change increases, we are not only able to test our hypotheses but we are also able to design and model new proteins, which is the ultimate test of our knowledge of how proteins function. Using the information from our protein modeling attempts, we can learn more about how natural proteins function and change over time. In this dissertation, I used protein modeling techniques to understand protein evolution. In Chapter 2, I assessed how closely designed proteins recapitulate observed patterns in natural proteins. I have found that designing proteins with a flexible-backbone protocol results in site variability that more closely mimics what is seen in natural proteins. In addition, I have also found that, in designed proteins, hydrophobic residues are often underrepresented in the core of the protein. These results suggest that our scoring functions and/or backbone sampling methods could be further improved. In Chapter 3, I used protein design to predict site-wise evolutionary rates in proteins. I found that protein design is a poor predictor of evolutionary rate, explaining only approximately 7% of the variation in rate across sites in enzymes. In Chapter 4, I used protein design and homology modeling to predict tolerance to deletions in enhanced green fluorescent protein. I also compared these predictions to predictions made using other structural properties including solvent accessibility, local packing density and secondary structure. I found that when combining computational scores from modeled structures along with other structural properties (i.e., local packing density, solvent accessibility and secondary structure) as predictors, I was largely able predict whether or not a given deletion would result in a functional protein product. Finally, in Chapter 5, I developed a computational pipeline to assess binding affinity in protein-protein interactions. I used this pipeline to recapitulate patterns of Machupo virus entry across various species. Taken together, the work presented in this dissertation has given us insight into which structural constraints affect protein evolution.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectProtein evolution
dc.subjectProtein design
dc.titleProtein design, modeling, and the evolution of proteins
dc.typeThesis
dc.date.updated2017-03-09T15:16:34Z
dc.contributor.committeeMemberMoran, Nancy
dc.contributor.committeeMemberHofmann, Hans
dc.contributor.committeeMemberSullivan, Christopher
dc.contributor.committeeMemberEllington, Andrew
dc.description.departmentEcology, Evolution and Behavior
thesis.degree.departmentEcology, Evolution and Behavior
thesis.degree.disciplineEcology, evolution, and behavior
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
dc.type.materialtext


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