The predictive power of data-processing statistics.

Melanie Vollmar,David G Waterman,James M Parkhurst,Arnaud Baslé,Gwyndaf Evans,Garib N Murshudov,Dominic Jaques

doi:10.1107/s2052252520000895

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.

Highlights

Information is held in METRIX_DB as a collection of tables, with each table relating to a stage of crystallographic data analysis, for example sequence details, data reduction, experimental phasing and the deposited Protein Data Bank (PDB) file information for reference
We have chosen to focus on particular experimental phasing approaches represented by a training database of native, single-wavelength anomalous dispersion (SAD) and multiplewavelength anomalous dispersion (MAD) data sets
A post-mortem analysis of a collection of weak S-SAD data sets is under way with the aim of including such data in METRIX_DB

Summary

Introduction

The possible factors affecting whether data lead to a structure deposition or not are manifold: (i) the crystal material comprising the purified protein and the additional chemicals used to crystallize it; (ii) the beamline hardware and capabilities, which define the experiments that can be carried out; (iii) the data-collection strategy, which is determined based on (i) and (ii); and (iv) intensity integration and assessment of the quality of the measured data as well as phase estimation, the latter determining whether a data set results in a structure or not Each of these factors can be represented by one or more metrics, in particular those describing the protein and those derived from data analysis. Use of these metrics offers a unique opportunity to predict the

Methods

Results

Conclusion