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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Correcting pervasive errors in RNA crystallography through enumerative structure prediction.

Fang-Chieh Chou1, Parin Sripakdeevong, Sergey M Dibrov

  • 1Department of Biochemistry, Stanford University, Stanford, California, USA.

Nature Methods
|December 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces ERRASER, a new method that refines three-dimensional RNA models by correcting errors and improving accuracy in crystallographic density maps. It enhances RNA structure quality and model reliability.

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Last Updated: May 16, 2026

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Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biochemistry

Background:

  • Three-dimensional RNA models from crystallographic density maps often contain errors.
  • Conformational ambiguities, geometric inaccuracies, and steric clashes are common issues.

Purpose of the Study:

  • To develop and validate a novel computational method for refining RNA structures.
  • To improve the accuracy and reliability of three-dimensional RNA models.

Main Methods:

  • Introduced enumerative real-space refinement assisted by electron density under Rosetta (ERRASER).
  • Integrated ERRASER with Python-based hierarchical environment for integrated 'xtallography' (PHENIX) for diffraction-based refinement.
  • Tested on 24 diverse crystallographic data sets.

Main Results:

  • ERRASER automatically corrected the majority of errors identified by MolProbity.
  • The method improved the average R(free) factor, indicating better model fit to the data.
  • Functionally important discrepancies in noncanonical RNA structures were resolved.
  • Low-resolution models were refined to better match higher-resolution expectations.

Conclusions:

  • ERRASER effectively addresses common errors in three-dimensional RNA models.
  • The integrated approach enhances RNA structure quality and reliability.
  • This method is valuable for analyzing and interpreting RNA crystallographic data.