Correcting pervasive errors in RNA crystallography through enumerative structure prediction

By Fang-Chieh Chou, Parin Sripakdeevong, S. Dibrov, T. Hermann, Rhiju Das. 2011

157 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, methodology): “(ii) ERRASER (Chou et al., 2013) improves RNA backbone conformations by combining MolProbity clash analysis, Phenix refinement and a pruned enumeration and optimization in Rosetta.”
Structures of ribosome-bound initiation factor 2 reveal the mechanism of subunit association (2016, methodology): “The single-strandedCCAendwas builtmanually into the derived density in COOT (39), and the initial fitting was globally idealized using ERRASER (46).”
An RNA-centric historical narrative around the Protein Data Bank (2021, background): “Following time, these structures are highlighted: (1) RNA viruses, 1BMV (39); (2) class I tRNA synthetase complex, 1GSG (214); (3) tRNA, a class II tRNA with a long variable loop, complexed with its specific synthetase, 1SER (215); (4) class II tRNA synthetase complex, 1ASY (216); (5) MS2 RNA coat p”
CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps (2023, methodology): “Therefore, our final protocol included an additional stage of corrections using the ERRASER2 program, an unpublished accelerated version of the ERRASER protocol for the refinement of RNA in crystallographic and cryoEM maps 24 .”
Computational Methods for RNA Structure Validation and Improvement. (2015, background): “be truly effective in automated error correction of RNA backbone, called ERRASER (Adams et al., 2013; Chou et al., 2012).”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, methodology): “The Enumerative Real-space Refinement ASsisted by Electrondensity under Rosetta (ERRASER) application is an RNA structure refinement protocol which uses electron density information to inform and guide the refinement.(37) Beta peptide design.”
Using Rosetta for RNA homology modeling (2019, background): “For this test, we select the B. subtilis guanine riboswitch (PDB ID 1Y27) (Serganov et al., 2004) and we run the structure through the ERRASER-Phenix pipeline.”
Modeling complex RNA tertiary folds with Rosetta. (2015, methodology): “For small RNA motifs, we leverage algorithms based on a “stepwise ansatz,” which enable modeling of RNA loops and motifs with near-atomic accuracy (better than 2 Å RMSD), particularly if limited NMR or crystallographic data are available (Chou et al., 2013; Sripakdeevong et al., 2014, 2011).”
RNA Structure Refinement using the ERRASER-Phenix pipeline (2013, background): “In our previous publication (12), we demonstrated that the ERRASER-Phenix pipeline resolves the majority of steric clashes and anomalous backbone and bond geometries assessed by MolProbity in a benchmark of 24 RNA crystal structures.”