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dc.contributor.authorLee, En-Shiun Annie 18:23:09 (GMT) 18:23:09 (GMT)
dc.description.abstractThreading is a protein structure prediction method that uses a library of template protein structures in the following steps: first the target sequence is matched to the template library and the best template structure is selected, secondly the predicted target structure of the target sequence is modeled by this selected template structure. The deceleration of new folds which are added to the protein data bank promises completion of the template structure library. This thesis uses a new set of template-specific weights to improve the energy function for sequence-to-structure alignment in the template selection step of the threading process. The weights are estimated using least squares methods with the quality of the modelling step in the threading process as the label. These new weights show an average 12.74% improvement in estimating the label. Further family analysis show a correlation between the performance of the new weights to the number of seeds in pFam.en
dc.publisherUniversity of Waterlooen
dc.subjectProtein Structure Predictionen
dc.subjectComparative Modellingen
dc.subjectEnergy Functionen
dc.subjectSequence-to-Structure Alignmenten
dc.subjectTemplate Selectionen
dc.subjectMachine Learningen
dc.subjectWeighted Linear Least Squaresen
dc.titleTraining of Template-Specific Weighted Energy Function for Sequence-to-Structure Alignmenten
dc.typeMaster Thesisen
dc.subject.programComputer Scienceen of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen

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