Probabilistic Algorithms for Modeling Protein Structure and Dynamics

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dc.contributor.advisorShehu, Amarda
dc.contributor.authorMolloy, Kevin Patrick
dc.creatorMolloy, Kevin Patrick
dc.date.accessioned2015-07-29T18:42:47Z
dc.date.available2015-07-29T18:42:47Z
dc.date.issued2015
dc.description.abstractSpecifically, this thesis addresses three main problems that permeate protein model- ing research. The first problem, known as “from-structure-to-function,” asks how to infer the function of a protein from knowledge of its active structure. The second problem, known as “from-sequence-to-structure,” relates to the open question of how to predict the biologically-active structure of a protein when provided information on the identities and or- der of constitutive building blocks. The third problem advances the current computational treatment of proteins to alleviate assumptions of their rigidity and instead model them as dynamic macromolecules switching between structures to tune their biological activity. The objective here is to model protein dynamics efficiently by computing the molecular motions employed in structural transitions among diverse functionally-relevant states of a protein.
dc.format.extent169 pages
dc.identifier.urihttps://hdl.handle.net/1920/9687
dc.language.isoen
dc.rightsCopyright 2015 Kevin Patrick Molloy
dc.subjectComputer science
dc.subjectRobotics
dc.subjectComputational biology
dc.subjectProtein motion
dc.subjectStochastic optimization
dc.titleProbabilistic Algorithms for Modeling Protein Structure and Dynamics
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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