<p>An abstract of a thesis devoted to using helix-coil models to study unfolded states.\\</p><p>Research on polypeptide unfolded states has received much more attention in the last decade or so than it has in the past. Unfolded states are thought to be implicated in various</p><p>misfolding diseases and likely play crucial roles in protein folding equilibria and folding rates. Structural characterization of unfolded states has proven to be</p><p>much more difficult than the now well established practice of determining the structures of folded proteins. This is largely because many core assumptions underlying</p><p>folded structure determination methods are invalid for unfolded states. This has led to a dearth of knowledge concerning the nature of unfolded state conformational</p><p>distributions. While many aspects of unfolded state structure are not well known, there does exist a significant body of work stretching back half a century that</p><p>has been focused on structural characterization of marginally stable polypeptide systems. This body of work represents an extensive collection of experimental</p><p>data and biophysical models associated with describing helix-coil equilibria in polypeptide systems. Much of the work on unfolded states in the last decade has not been devoted</p><p>specifically to the improvement of our understanding of helix-coil equilibria, which arguably is the most well characterized of the various conformational equilibria</p><p>that likely contribute to unfolded state conformational distributions. This thesis seeks to provide a deeper investigation of helix-coil equilibria using modern</p><p>statistical data analysis and biophysical modeling techniques. The studies contained within seek to provide deeper insights and new perspectives on what we presumably</p><p>know very well about protein unfolded states. \\</p><p>Chapter 1 gives an overview of recent and historical work on studying protein unfolded states. The study of helix-coil equilibria is placed in the context</p><p>of the general field of unfolded state research and the basics of helix-coil models are introduced.\\</p><p>Chapter 2 introduces the newest incarnation of a sophisticated helix-coil model. State of the art modern statistical techniques are employed to estimate the energies</p><p>of various physical interactions that serve to influence helix-coil equilibria. A new Bayesian model selection approach is utilized to test many long-standing </p><p>hypotheses concerning the physical nature of the helix-coil transition. Some assumptions made in previous models are shown to be invalid and the new model </p><p>exhibits greatly improved predictive performance relative to its predecessor. \\</p><p>Chapter 3 introduces a new statistical model that can be used to interpret amide exchange measurements. As amide exchange can serve as a probe for residue-specific</p><p>properties of helix-coil ensembles, the new model provides a novel and robust method to use these types of measurements to characterize helix-coil ensembles experimentally</p><p>and test the position-specific predictions of helix-coil models. The statistical model is shown to perform exceedingly better than the most commonly used </p><p>method for interpreting amide exchange data. The estimates of the model obtained from amide exchange measurements on an example helical peptide </p><p>also show a remarkable consistency with the predictions of the helix-coil model. \\</p><p>Chapter 4 involves a study of helix-coil ensembles through the enumeration of helix-coil configurations. Aside from providing new insights into helix-coil ensembles,</p><p>this chapter also introduces a new method by which helix-coil models can be extended to calculate new types of observables. Future work on this approach could potentially</p><p>allow helix-coil models to move into use domains that were previously inaccessible and reserved for other types of unfolded state models that were introduced in chapter 1.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/12279 |
| Date | January 2016 |
| Creators | Hughes, Roy Gene |
| Contributors | Oas, Terrence G |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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