Data Driven Modeling of Proteins

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dc.contributor.advisorClementi, Ceciliaen_US
dc.contributor.committeeMemberOnuchic, Joséen_US
dc.creatorChen, Justinen_US
dc.date.accessioned2019-05-17T18:31:53Zen_US
dc.date.available2019-11-01T05:01:18Zen_US
dc.date.created2019-05en_US
dc.date.issued2019-03-20en_US
dc.date.submittedMay 2019en_US
dc.date.updated2019-05-17T18:31:54Zen_US
dc.description.abstractProteins are tiny molecular machines that perform the vast majority of the functions in living cells. In order for the protein to perform its function, it has to be able to fold from a disordered coil into a specific compact structure. Two new computational methods are developed that take advantage of the large amount of data generated in both experiments and computer simulations in order to better understand how proteins work. The first method (pyODEM) improves the modeling of proteins on the global scale, while a second method (pyFrustration) probes the protein's local frustration that might impede the folding process. Use of these methods allows us to construct more dynamically accurate protein models and improves our understanding of how a protein folds and performs its function.en_US
dc.embargo.terms2019-11-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationChen, Justin. "Data Driven Modeling of Proteins." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/105931">https://hdl.handle.net/1911/105931</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/105931en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectproteinen_US
dc.subjectphysicsen_US
dc.subjectfrustrationen_US
dc.subjectprotein foldingen_US
dc.subjectmolecular dynamicsen_US
dc.subjectprotein designen_US
dc.subjectnon-linear optimizationen_US
dc.titleData Driven Modeling of Proteinsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentPhysics and Astronomyen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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