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dc.contributor.advisorCheng, Jianlin, 1972-eng
dc.contributor.authorBhattacharya, Debswapnaeng
dc.date.issued2016eng
dc.date.submitted2016 Summereng
dc.descriptionDissertation supervisor: Dr. Jianlin Cheng.eng
dc.descriptionIncludes vita.eng
dc.description.abstractComputationally predicting the folded and functional three-dimensional structure of a protein molecule from its amino acid sequence with high degree of accuracy is critically important in structural bioinformatics and has huge implications in understanding and curing numerous diseases caused by protein misfolding, including CJD and type II diabetes, as well as neurodegenerative diseases like Alzheimer's, Parkinson's, Huntington's, and amyotrophic lateral sclerosis. Existing computational approaches for protein structure prediction faces two key challenges: (1) difficulty in efficiently navigating the enormous conformational space accessible to proteins and (2) difficulty in accurately capturing energetics of the complex interactions of thousand of atoms in a protein molecule in silico. This dissertation attempts to address these challenges by (1) developing novel probabilistic graphical models and experimentally motivated probabilistic sampling techniques to fully capture and efficiently explore proteins' conformational space in various granularities and (2) integrating knowledge-based information into existing energy functions in order to improve their ability to discriminate correctly folded protein structures from decoys. We show that our methods outperform many traditional and state-of-the-art protein structure prediction approaches in terms of accuracy, speed and robustness. All of these methods are freely available to the scientific community.eng
dc.description.bibrefIncludes bibliographical references (pages 78-86).eng
dc.format.extent1 online resource (xi, 87 pages) : illustrationseng
dc.identifier.merlinb118682532eng
dc.identifier.oclc987383205eng
dc.identifier.urihttps://hdl.handle.net/10355/57017
dc.identifier.urihttps://doi.org/10.32469/10355/57017eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.FASTProteins -- Structureeng
dc.subject.FASTProteins -- Structure -- Mathematical modelseng
dc.subject.FASTProteins -- Conformationeng
dc.subject.FASTMachine learningeng
dc.subject.FASTGraphical modeling (Statistics)eng
dc.titleProbabilistic graphical models for protein structure predictioneng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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