Atomic accuracy models from 4.5 Å cryo-electron microscopy data with density-guided iterative local refinement

Direct electron detectors have made it possible to generate electron density maps at near atomic resolution using cryo-electron microscopy single particle reconstructions. Critical current questions include how best to build models into these maps, how high quality a map is required to generate an accurate model, and how to cross-validate models in a system independent way. We describe a modeling approach that integrates Monte Carlo optimization with local density guided moves, Rosetta all-atom refinement, and real space B-factor fitting, yielding accurate models from experimental maps for three different systems with resolutions 4.5 Å or higher. We characterize model accuracy as a function of data quality, and present a model validation statistic that correlates with model accuracy over the three test systems.