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BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction

dc.creatorPutnam, Daniel Kent
dc.date.accessioned2020-08-22T00:11:34Z
dc.date.available2016-03-28
dc.date.issued2016-03-28
dc.identifier.urihttps://etd.library.vanderbilt.edu/etd-03282016-121917
dc.identifier.urihttp://hdl.handle.net/1803/11634
dc.description.abstractThe Biochemical Library (BCL) is a protein structure prediction algorithm developed in the Meiler Lab at Vanderbilt University based on the placement of secondary structure elements (SSEs). This algorithm incorporates sparse experimental data constraints from nuclear magnetic resonance (NMR), Cryo- electron microscopy (CryoEM), and electron paramagnetic resonance (EPR), to restrict the conformational sampling space but does not have the capability to use Small Angle X-Ray / Neutron data. This dissertation delineates my work to add this capability to BCL::Fold. Specifically I show and show for what type of structures SAXS/SANS experimental data improves the accuracy BCL:: Fold and importantly where it does not. Furthermore, in collaboration with Oak Ridge National Labs, I present my work on structural determination of the Cellulose Synthase Complex in Arabidopsis Thaliana.
dc.format.mimetypeapplication/pdf
dc.subjectBCL::Fold
dc.subjectSAXS
dc.subjectProtein Structure Prediction
dc.titleBCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction
dc.typedissertation
dc.contributor.committeeMemberLoukas Petridis
dc.contributor.committeeMemberDouglas P. Hardin
dc.contributor.committeeMemberMartin Egli
dc.contributor.committeeMemberThomas A. Lasko
dc.type.materialtext
thesis.degree.namePHD
thesis.degree.leveldissertation
thesis.degree.disciplineBiomedical Informatics
thesis.degree.grantorVanderbilt University
local.embargo.terms2016-03-28
local.embargo.lift2016-03-28
dc.contributor.committeeChairJens Meiler


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