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IUCrJ. 2020 Feb 27;7(Pt 2):342-354. doi: 10.1107/S2052252520000895. eCollection 2020 Mar 1.

The predictive power of data-processing statistics.

Author information

1
Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0DE, England.
2
MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England.
3
Institute for Cell and Molecular Biosciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 1HH, England.
4
Science Technology and Facilities Council, Rutherford Appleton Laboratory, Didcot OX11 0FA, England.
5
Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, England.

Abstract

This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant.

KEYWORDS:

X-ray crystallography; experimental phasing; machine learning; macromolecular crystallography; phasing; structure determination

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