title: Correcting pervasive errors in RNA crystallography through enumerative structure prediction.
abstract: Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average R(free) factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models.
authors.affiliation: Department of Biochemistry, Stanford University, Stanford, California, USA.
authors.forename: Fang-Chieh
authors.initials: FC
authors.lastname: Chou
authors.forename: Parin
authors.initials: P
authors.lastname: Sripakdeevong
authors.forename: Sergey M
authors.initials: SM
authors.lastname: Dibrov
authors.forename: Thomas
authors.initials: T
authors.lastname: Hermann
authors.forename: Rhiju
authors.initials: R
authors.lastname: Das
mesh_terms: D019295:Computational Biology; D018360:Crystallography, X-Ray; D006801:Humans; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D012984:Software
chemical_list: D012313:RNA