DiMaio, Frankfarrell, daniel2021-08-262021-08-262021farrell_washington_0250E_22993.pdfhttp://hdl.handle.net/1773/47352Thesis (Ph.D.)--University of Washington, 2021Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Ã ), with visible secondary structure elements but poorly resolved loops, making model-building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This dissertation describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We will show how this method performs on a diverse benchmarking dataset in addition to describing how it was used to build models for three difficult protein complexes that would have been impossible to solve without this software. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.application/pdfen-USCC BYBiochemistryBiological chemistryThesis