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Nat Methods. 2013 Jan;10(1):74-6. doi: 10.1038/nmeth.2262. Epub 2012 Dec 2.

Correcting pervasive errors in RNA crystallography through enumerative structure prediction.

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1
Department of Biochemistry, Stanford University, Stanford, California, USA.

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.

PMID:
23202432
PMCID:
PMC3531565
DOI:
10.1038/nmeth.2262
[Indexed for MEDLINE]
Free PMC Article
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