Lee, En-Shiun Annie2008-09-262008-09-262008-09-262008http://hdl.handle.net/10012/4060Threading 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.enBioinformaticsProtein Structure PredictionComparative ModellingEnergy FunctionSequence-to-Structure AlignmentTemplate SelectionThreadingMachine LearningWeighted Linear Least SquaresMaster ThesisComputer Science