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dc.contributor.authorComte, Pascal
dc.date.accessioned2010-10-26T19:21:31Z
dc.date.available2010-10-26T19:21:31Z
dc.date.issued2010-10-26
dc.identifier.urihttp://hdl.handle.net/10464/3059
dc.description.abstractThe prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectProteins -- Synthesisen_US
dc.subjectCombinatorial optimizationen_US
dc.titleBio-inspired optimization & sampling technique for side-chain packing in MCCEen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US


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