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.
@article{arxiv.1110.0276,
title = {Correcting pervasive errors in RNA crystallography through enumerative structure prediction},
author = {Fang-Chieh Chou and Parin Sripakdeevong and Sergey M. Dibrov and Thomas Hermann and Rhiju Das},
journal= {arXiv preprint arXiv:1110.0276},
year = {2012}
}