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Bio-inspired optimization & sampling technique for side-chain packing in MCCE

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Show simple item record Comte, Pascal 2010-10-26T19:21:31Z 2010-10-26T19:21:31Z 2010-10-26
dc.description.abstract The 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.iso eng en_US
dc.publisher Brock University en_US
dc.subject Proteins -- Synthesis en_US
dc.subject Combinatorial optimization en_US
dc.title Bio-inspired optimization & sampling technique for side-chain packing in MCCE en_US
dc.type Electronic Thesis or Dissertation en_US M.Sc. Computer Science en_US Masters en_US
dc.contributor.department Department of Computer Science en_US Faculty of Mathematics and Science en_US

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