Correcting pervasive errors in RNA crystallography through enumerative structure prediction

TL;DR

This paper introduces ERRASER, a new computational method that improves RNA crystal structures by automatically correcting errors and ambiguities, leading to more accurate models that better fit experimental data.

Contribution

ERRASER combines enumerative real-space refinement with existing tools to systematically correct errors in RNA crystallography models, enhancing accuracy and resolving ambiguities.

Findings

ERRASER corrects most MolProbity-assessed errors in tested datasets.

It improves the average Rfree factor across datasets.

Refines low-resolution models to better match higher-resolution structures.

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 Rfree factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models.