Journal
NATURE METHODS
Volume 10, Issue 1, Pages 74-U105Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2262
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Funding
- US National Science Foundation [CNS-0619926, OCI-1053575]
- US National Institutes of Health [R21 GM102716, R01 AI72012]
- Burroughs-Wellcome Career Award at Scientific Interface
- Governmental Scholarship for Study Abroad of Taiwan
- Howard Hughes Medical Institute International Student Research Fellowship
- C.V. Starr Asia/Pacific Stanford Graduate Fellowship
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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.
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