This Masters 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.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-05282008-161200 |
| Date | 06 June 2008 |
| Creators | Durham, Elizabeth Ashley |
| Contributors | Dan Masys, Jens Meiler, Dave Tabb |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-05282008-161200/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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