Evidence map›Paper›PMID 31692470›Full record

ArticleActa crystallographica. Section D, Structural biology2019

SAD phasing of XFEL data depends critically on the error model.

Aaron S Brewster, Asmit Bhowmick, Robert Bolotovsky, Derek Mendez, Petrus H Zwart, Nicholas K Sauter

Abstract read
In one paragraph

Article in Acta crystallographica. Section D, Structural biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. bioRxiv : the preprint server for biology · 2025
    Article
  3. Robust error calibration for serial crystallography.Acta crystallographica. Section D, Structural biology · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Non-Canonical Amino Acids in Analyses of Protease Structure and Function.International journal of molecular sciences · 2023
    Review
  8. Article
  9. Crystal diffraction prediction and partiality estimation using Gaussian basis functions.Acta crystallographica. Section A, Foundations and advances · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Evaluating crystallographic likelihood functions using numerical quadratures.Acta crystallographica. Section D, Structural biology · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aaron S BrewsterMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Asmit BhowmickMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Robert BolotovskyMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Derek MendezMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Petrus H ZwartMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Nicholas K SauterMolecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Funding

DIALS: New Computational Methods to Enable Challenging Crystallographic ExperimentsR01GM117126 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI SAUTER, NICHOLAS K · 2016 to 2023
$6.1M
The Development of Computational Methods for Fluctuation Xray ScatteringR01GM109019 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI ZWART, PETRUS H · 2014 to 2018
$1.9M
Foundation for the National Institutes of Health GM117126Foundation for the National Institutes of Health R01GM109019NIGMS NIH HHS R01 GM109019NIGMS NIH HHS R01 GM117126U.S. Department of Energy, Office of Science 17-SC-20-SCU.S. Department of Energy, Office of Science DE-AC02-05CH11231U.S. Department of Energy, Office of Science DE-AC02-76SF00515
6 · The paper itself

Abstract

A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built. Estimating the error in the merged reflection intensities requires the understanding and propagation of all of the sources of error arising from the measurements. One type of error, which is well understood, is the counting error introduced when the detector counts X-ray photons. Thus, if other types of random errors (such as readout noise) as well as uncertainties in systematic corrections (such as from X-ray attenuation) are completely understood, they can be propagated along with the counting error, as appropriate. In practice, most software packages propagate as much error as they know how to model and then include error-adjustment terms that scale the error estimates until they explain the variance among the measurements. If this is performed carefully, then during SAD phasing likelihood-based approaches can make optimal use of these error estimates, increasing the chance of a successful structure solution. In serial crystallography, SAD phasing has remained challenging, with the few examples of de novo protein structure solution each requiring many thousands of diffraction patterns. Here, the effects of different methods of treating the error estimates are estimated and it is shown that using a parametric approach that includes terms proportional to the known experimental uncertainty, the reflection intensity and the squared reflection intensity to improve the error estimates can allow SAD phasing even from weak zinc anomalous signal.

Indexed as

Models, MolecularCrystallizationCrystallography, X-RayData Interpretation, StatisticalDatasets as TopicLikelihood FunctionsThermolysinThermolysincctbx.xfelerror modelingSAD phasingserial crystallographyXFELs

Identifiers

PMID31692470
PMCPMC6834081

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.