dc.creator | Durham, Elizabeth Ashley | |
dc.date.accessioned | 2020-08-22T17:00:12Z | |
dc.date.available | 2010-06-06 | |
dc.date.issued | 2008-06-06 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-05282008-161200 | |
dc.identifier.uri | http://hdl.handle.net/1803/12412 | |
dc.description.abstract | This Master’s Thesis project had as its objectives: (1) to optimize algorithms for solvent-accessible surface area (SASA) approximation to develop an environment free energy knowledge-based potential; and, (2) to assess the knowledge-based environment free energy potentials for de novo protein structure prediction. This project achieved its goals by developing, implementing, optimizing, and evaluating four different algorithms for approximating the SASA of a given protein model and generating knowledge-based potentials for de novo protein structure prediction. The algorithms are entitled Neighbor Count, Neighbor Vector, Artificial Neural Network, and Overlapping Spheres. | |
dc.format.mimetype | application/pdf | |
dc.subject | Proteins -- Structure | |
dc.subject | knowlege-based potential | |
dc.subject | solvent-accessible surface area | |
dc.subject | protein structure prediction | |
dc.subject | environment free energy | |
dc.subject | Computer algorithms | |
dc.subject | Proteins -- Surfaces | |
dc.title | Knowledge-based environment potentials for protein structure prediction | |
dc.type | thesis | |
dc.contributor.committeeMember | Dan Masys | |
dc.contributor.committeeMember | Dave Tabb | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Biomedical Informatics | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2010-06-06 | |
local.embargo.lift | 2010-06-06 | |
dc.contributor.committeeChair | Jens Meiler | |