RNA crystallographic models , our richest sources of RNA structural information, contain pervasive errors due to ambiguities in manually fitting RNA backbones into experimental density maps. To resolve these ambiguities , we have developed a new Rosetta structure prediction tool (ERRASER: Enumerative Real-space Refinement ASsisted by Electron density under Rosetta ) and coupled it to MolProbity validation and PHENIX diffraction-based refinement. On 15 crystallographic datasetsfor ribozymes, riboswitches, and other RNA domains, ERRASER/PHENIX corrects the majority of identifiable sugar pucker errors, steric clashes , suspicious backbone rotamers, and incorrect bond lengths/angles, while, on average, improving Rfree correlation to set-aside diffraction data by 0.010. As further confirmation of improved accuracy, the refinement enhances agreement between crystals solved by independent groups and between domains related by non-crystallographic symmetry (NCS). Finally, we demonstrate successful application of ERRASER on coordinates for an entire 30S ribosomal subunit. By rapidly and systematically disambiguating RNA model fitting, ERRASER enables RNA crystallography with significantly fewer errors .
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 .
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identifiable sugar pucker errors, steric clashes , suspicious backbone rotamers, and incorrect bond lengths/angles, while, on average, improving Rfree correlation to set-aside diffraction data by 0.010. As further confirmation of improved accuracy, the refinement enhances agreement between crystals solved by independent groups and between domains related by non-crystallographic symmetry (NCS). Finally, we demonstrate successful application of ERRASER on coordinates for an entire 30S ribosomal subunit. By rapidly and systematically disambiguating RNA model fitting, ERRASER enables RNA crystallography with significantly fewer errors
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