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Test Samples for Optimizing STORM Super-Resolution Microscopy
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Published on: September 6, 2013

How good are my data and what is the resolution?

Philip R Evans1, Garib N Murshudov

  • 1MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH, England. pre@mrc-lmb.cam.ac.uk

Acta Crystallographica. Section D, Biological Crystallography
|June 25, 2013
PubMed
Summary

This study introduces AIMLESS, a new data reduction program for crystallography. AIMLESS refines data scaling and analysis, showing that including high-resolution data improves results without harm.

Keywords:
data reductiondata scalingdata statisticssoftware

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Area of Science:

  • Crystallography
  • Structural Biology
  • Data Analysis

Background:

  • Crystallographic data reduction involves determining point-group symmetry and space group using programs like POINTLESS.
  • Scaling programs like SCALA standardize measurements and provide initial data quality statistics.
  • Determining the effective resolution of a dataset is crucial but often contentious.

Purpose of the Study:

  • To introduce and evaluate AIMLESS, a novel program for crystallographic data scaling and analysis.
  • To compare AIMLESS with existing methods and assess its impact on data quality assessment.
  • To investigate the utility of including high-resolution diffraction data in structural refinement.

Main Methods:

  • Utilized the POINTLESS program for symmetry determination.
  • Employed AIMLESS for data scaling, incorporating advanced analyses beyond SCALA.
  • Conducted refinement trials using observed and simulated data.
  • Performed automated model-building and compared electron density maps at various resolution limits.

Main Results:

  • AIMLESS offers enhanced scaling models and additional analytical capabilities.
  • Data processing statistics from AIMLESS aid in quality assessment and data rejection decisions.
  • Including weak, high-resolution data beyond conventional limits can improve structural models.
  • No detrimental effects were observed from incorporating this extended resolution data.

Conclusions:

  • AIMLESS provides a robust tool for crystallographic data reduction and quality assessment.
  • The inclusion of high-resolution data in crystallographic datasets is beneficial and should be considered.
  • This work offers guidance on evaluating dataset resolution and maximizing structural refinement outcomes.