Robert Fox2011-12-202008-06-172011-12-202008-05-132008-05-01etd-05132008-091250http://hdl.handle.net/2152.3/110Modern techniques in structural biology, like homology modeling, protein threading, protein fold classification, and homology detection have proven extremely useful. For example, they have provided us with evolutionary information about protein homology which has in some many cases lead directly to therapeutics. Due to the importance of these methods, augmenting or improving them may lead to significant advances in understanding proteins. These methods treat the high-resolution structure as a static entity upon which they operate, however we know that proteins are not static entities---they are polymers that exist in an enormous array of conformational states. Therefore, we propose to model the proteins from a statistical thermodynamic viewpoint based upon their average energetic properties. We show that this model can be used to (1) better characterize the partial unfolding process of proteins, and (2) reclassify the protein fold space from a new perspective.electronicengCopyright © is held by the author. Presentation of this material on the TDL web site by The University of Texas Medical Branch at Galveston was made possible under a limited license grant from the author who has retained all copyrights in the works.thermodynamicsSVDstructural thermodynamicsstatistical thermodynamicsremote homologyprotein foldsprotein fold classificationPCAensembleCOREXtext