Journal
IUCRJ
Volume 7, Issue -, Pages 342-354Publisher
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S2052252520000895
Keywords
macromolecular crystallography; experimental phasing; machine learning; structure determination; phasing; X-ray crystallography
Funding
- Biotechnology and Biological Sciences Research Council [BB/L007398/1]
- BBSRC [BB/L007010/1, BB/L007398/1, BB/S007083/1] Funding Source: UKRI
- MRC [MC_UP_A025_1012] Funding Source: UKRI
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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.
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