Optimization of predictive energy landscapes for membrane and globular protein structure prediction

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dc.contributor.advisorWolynes, Peter G.
dc.contributor.committeeMemberOnuchic, José N
dc.contributor.committeeMemberClementi, Cecilia
dc.creatorKim, Bobby Lee
dc.date.accessioned2016-02-03T21:37:51Z
dc.date.available2016-02-03T21:37:51Z
dc.date.created2015-12
dc.date.issued2015-10-09
dc.date.submittedDecember 2015
dc.date.updated2016-02-03T21:37:51Z
dc.description.abstractThis thesis discusses recent results using the Associative-memory, Water-mediated, Structure and Energy Model (AWSEM), an optimized, coarse-grained molecular dynamics model. AWSEM and its membrane protein extension, AWSEM-membrane, are capable of de novo protein structure prediction and through the use of statistical estimators, allow construction of free energy landscapes which can provide insight about the dynamics of protein systems. We review the origins of energy landscape theory and how one can learn energy functions using the results of spin glass-inspired statistical mechanics models. We explore the similarities and differences between the energy landscapes of proteins that have been selected by nature and those of some proteins designed by humans. We also study how robust the folding of these designs would be to the simplification of the sequences using fewer amino acid types. Using an optimized extension of AWSEM, AWSEM-membrane, we explore the hypothesis that the folding landscapes of membrane proteins are funneled once the proteins’ topology within the membrane is established. We also show that the AWSEM-membrane force field is able to sample near native binding interfaces of several oligomeric systems.
dc.format.mimetypeapplication/pdf
dc.identifier.citationKim, Bobby Lee. "Optimization of predictive energy landscapes for membrane and globular protein structure prediction." (2015) Diss., Rice University. <a href="https://hdl.handle.net/1911/88336">https://hdl.handle.net/1911/88336</a>.
dc.identifier.urihttps://hdl.handle.net/1911/88336
dc.language.isoeng
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.
dc.subjectprotein folding
dc.subjectprotein structure prediction
dc.subjectenergy landscape theory
dc.subjectmolecular dynamics
dc.titleOptimization of predictive energy landscapes for membrane and globular protein structure prediction
dc.typeThesis
dc.type.materialText
thesis.degree.departmentChemistry
thesis.degree.disciplineNatural Sciences
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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