RNA crystallographic models contain pervasive ambiguities due to the difficulty of manually fitting RNA backbones into experimental density maps . Recent advances in ab initio RNA structure prediction suggest an automated way to resolve these ambiguities. We present a protocol for Enumerative Real-space Refinement ASsisted by Electron density under Rosetta (ERRASER), coupled to PHENIX diffraction-based refinement. On 24 RNA crystallographic datasets, including a 30S ribosomal subunit and an unreleased IRES domain of hepatitis C virus, the protocol corrects the majority of steric clashes and anomalous backbone and bond geometries, as assessed by MolProbity. Furthermore, the method improves the average Rfree by 0.014 , resolves functionally important discrepancies in protein-binding kink turns and group I ribozyme active sites, and refines low-resolution structures to better match higher resolution structures. By enabling such 'super-resolution' interpretation of crystallographic data, ERRASER is a unique application of RNA structure prediction that promises routine use in experimental structural biology .
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 Rfree factor , resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models .
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RNA crystallographic models contain pervasive ambiguities due to the difficulty of manually fitting RNA backbones into experimental density maps . Recent advances in ab initio RNA structure prediction suggest an automated way to resolve these ambiguities. We present a protocol for Enumerative Real-space Refinement ASsisted by Electron
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
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PHENIX
Python-based hierarchical environment for integrated 'xtallography' (PHENIX)
R
RNA crystallographic datasets, including a 30S ribosomal subunit and an unreleased IRES domain of hepatitis C virus, the protocol
data sets, ERRASER automatically
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steric clashes and anomalous backbone and bond geometries, as assessed by MolProbity. Furthermore, the method
MolProbity-assessed errors,
R
by 0.014
factor
R
protein-binding kink turns and group I ribozyme active sites, and
noncanonical structure and
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structures
models
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higher resolution structures. By enabling such 'super-resolution' interpretation of crystallographic data, ERRASER is a unique application of RNA structure prediction that promises routine use in experimental structural biology
higher-resolution models