2026-09-20T08:55:30Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/408472026-02-16T02:24:28Zcom_1773_4888col_1773_4896

DiMaio, Frank Frenz, Brandon Michael 2018-01-20T00:58:47Z 2018-01-20T00:58:47Z 2018-01-20 2017 Frenz_washington_0250E_18143.pdf http://hdl.handle.net/1773/40847 Thesis (Ph.D.)--University of Washington, 2017 Single-particle cryo-electron microscopy (cryoEM) has become a powerful tool for determining macromolecular structures. Thanks to recent advances in direct electron detectors and motion correction algorithms it can frequently deliver electron density maps in the range of 3-5Å resolution. To obtain as much atomic level detail of the structure as possible from this data an accurate atomic model must be built. This can be done manually however, it is laborious and error prone. To resolve this problem modelers have turned to computational tools which can make up for lack of experimental data. Here we describe several tools for modeling with sparse experimental data, including a novel sampling strategy for de novo model completion and a novel refinement strategy for glycans with near atomic resolution cryoEM and x-ray crystallography data. application/pdf en_US CC BY Computational Protein Modeling Cryo electron microscopy Glycans Protein Refinement Rosetta Biochemistry Biological chemistry Advances In Computer Aided Protein Structure Determination From Sparse Cryo Electron Microscopy Data Thesis Open Access open.access