Correcting pervasive errors in RNA crystallography through enumerative structure prediction
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Updated: May 16, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
Published on: December 9, 2022
Fang-Chieh Chou1, Parin Sripakdeevong, Sergey M Dibrov
1Department 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.
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