The predictive power of data-processing statistics

IUCrJ
Melanie VollmarGwyndaf Evans

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.

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Citations

Aug 4, 2020·Acta Crystallographica. Section D, Structural Biology·Paul S BondKevin D Cowtan

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Methods Mentioned

BETA
X-ray
protein crystallo­graphy

Software Mentioned

AdaBoost
phenix
learn
METRIX
/ E
SHELXC
mlxtend
SQLite3
Python
ISPyB

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