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dc.contributor.advisorVarani, Gabrieleen_US
dc.contributor.authorBjerre, Danielen_US
dc.date.accessioned2012-09-13T17:28:14Z
dc.date.available2012-09-13T17:28:14Z
dc.date.issued2012-09-13
dc.date.submitted2012en_US
dc.identifier.otherBjerre_washington_0250E_10187.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/20660
dc.descriptionThesis (Ph.D.)--University of Washington, 2012en_US
dc.description.abstractProtein-RNA interactions play a central role in post-transcriptional regulation. By interacting with precursor and mature mRNA transcripts, RNA binding proteins (RBP) regulate the expression level and isoform of proteins within the cell in an often spatially and temporally dependent manner. I know of no existing computational method that infers base binding probabilities from structural models. Here I describe the development of a tool to infer the specificity of RNA binding interactions within the Rosetta framework. I use the well-established knowledge-based methods trained on existing x-ray models of RBP in complex or on small molecules as well as a mixture of statistical and physical parameters used in Rosetta prediction of DNA binding proteins. My computational approach infers local base and residue specificity by performing substitutions on models from x-ray crystallography or NMR. The approach explores limited local structure space through sampling of residue side chains. The structure exploration improves realism of the approach by physically accommodating base and residue substitutions. With a representative set of RBP interactions with single stranded RNA, the scoring functions are able to recover many of the interface side-chain dihedral angles and recapitulate the contacts involved in specific base recognition. I benchmark the scoring functions ability to predict the magnitude and order of base preference. I explore the application of the specificity prediction tools to design applications selected to illustrate a rational understanding of protein-RNA interactions and with potential therapeutic applications. The scoring function is able to largely recapitulate the results of experimentally investigated mutations of the pumilio-1 domain being investigated as a universal platform for binding arbitrary RNA sequences specifically. I also apply my technique to suggest changes to a RNA recognition motif aimed at re-targeting the domain to specifically bind a target involved in dysregulation in certain cancers. The results suggest that the Rosetta scoring function may be coupled with small changes in protein sequence and structure to design specificity switches on RBP domains. The computational approach to specificity promises to improve our understanding of sequence specific binding of RNA and aid the development of protein-based approaches to target RNAs involved in disease.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectcomputational design; RNA binding proteinsen_US
dc.subject.otherBiochemistryen_US
dc.subject.otherBiological chemistryen_US
dc.titleStructure-based Computational Retargeting of RNA Binding Proteinsen_US
dc.typeThesisen_US
dc.embargo.termsNo embargoen_US


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