# Statistical Characterization of Protein Ensembles

 Title: Statistical Characterization of Protein Ensembles Author: Fisher, Charles Citation: Fisher, Charles. 2012. Statistical Characterization of Protein Ensembles. Doctoral dissertation, Harvard University. Access Status: Full text of the requested work is not available in DASH at this time (“dark deposit”). For more information on dark deposits, see our FAQ. Full Text & Related Files: Fisher_gsas.harvard_0084L_10223.pdf (3.442Mb; PDF) Abstract: Conformational ensembles are models of proteins that capture variations in conformation that result from thermal fluctuations. Ensemble based models are important tools for studying Intrinsically Disordered Proteins (IDPs), which adopt a heterogeneous set of conformations in solution. In order to construct an ensemble that provides an accurate model for a protein, one must identify a set of conformations, and their relative stabilities, that agree with experimental data. Inferring the characteristics of an ensemble for an IDP is a problem plagued by degeneracy; that is, one can typically construct many different ensembles that agree with any given set of experimental measurements. In light of this problem, this thesis will introduce three tools for characterizing ensembles: (1) an algorithm for modeling ensembles that provides estimates for the uncertainty in the resulting model, (2) a fast algorithm for constructing ensembles for large or complex IDPs and (3) a measure of the degree of disorder in an ensemble. Our hypothesis is that a protein can be accurately modeled as an ensemble only when the degeneracy of the model is appropriately accounted for. We demonstrate these methods by constructing ensembles for K18 tau protein, $$\alpha$$-synuclein and amyloid beta - IDPs that are implicated in the pathogenesis of Alzheimer's and Parkinson's diseases. Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10318208 Downloads of this work: