Correcting pervasive errors in RNA crystallography through enumerative structure prediction

A Rosetta algorithm improves RNA structure by scoring each nucleotide's possible conformations based on an energy function and electron-density data. 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.