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. © 2013 Nature America, Inc. All rights reserved.
CITATION STYLE
Chou, F. C., Sripakdeevong, P., Dibrov, S. M., Hermann, T., & Das, R. (2013). Correcting pervasive errors in RNA crystallography through enumerative structure prediction. Nature Methods, 10(1), 74–76. https://doi.org/10.1038/nmeth.2262
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