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BCL::SAXS - Small Angle X-Ray Scattering Profiles to Assist Protein Structure Prediction

dc.creatorPutnam, Daniel Kent
dc.date.accessioned2020-08-21T21:13:18Z
dc.date.available2013-04-08
dc.date.issued2013-04-08
dc.identifier.urihttps://etd.library.vanderbilt.edu/etd-03182013-141442
dc.identifier.urihttp://hdl.handle.net/1803/10858
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. This algorithm can use experimental data such as nuclear magnetic resonance (NMR), Cryo-electron microscopy (CryoEM), and electron paramagnetic resonance (EPR), to assist in protein structure prediction but does not have the ability to use SAXS data. The first phase of my project was to add this capability to the BCL and create a SAXS compatibility score. GPU acceleration was used to parallelize the computations. The second phase of the project was to compute SAXS scores for protein models without side chain and loop region coordinate information, but preserve atom type information. Finally, the BCL::SAXS score was added to the minimization process in BCL::FOLD. The SAXS score can be used to filter erroneous initial protein models from further refinement, thus saving time and computation resources.
dc.format.mimetypeapplication/pdf
dc.subjectBCL::Fold
dc.subjectCRYSOL
dc.subjectde novo protein structure determination
dc.subjectDebye formula
dc.subjectGPU Acceleration
dc.subjectsmall angle X-ray scattering
dc.titleBCL::SAXS - Small Angle X-Ray Scattering Profiles to Assist Protein Structure Prediction
dc.typethesis
dc.contributor.committeeMemberTerry Lybrand
dc.contributor.committeeMemberDavid Tabb
dc.type.materialtext
thesis.degree.nameMS
thesis.degree.levelthesis
thesis.degree.disciplineBiomedical Informatics
thesis.degree.grantorVanderbilt University
local.embargo.terms2013-04-08
local.embargo.lift2013-04-08
dc.contributor.committeeChairJens Meiler


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