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Knowledge-based environment potentials for protein structure prediction

dc.creatorDurham, Elizabeth Ashley
dc.description.abstractThis 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.subjectProteins -- Structure
dc.subjectknowlege-based potential
dc.subjectsolvent-accessible surface area
dc.subjectprotein structure prediction
dc.subjectenvironment free energy
dc.subjectComputer algorithms
dc.subjectProteins -- Surfaces
dc.titleKnowledge-based environment potentials for protein structure prediction
dc.contributor.committeeMemberDan Masys
dc.contributor.committeeMemberDave Tabb
dc.type.materialtext Informatics University
dc.contributor.committeeChairJens Meiler

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