Bayesian machine learning improves single-wavelength anomalous diffraction phasing.

Maria-Jose Garcia-Bonete,Gergely Katona

doi:10.1107/s2053273319011446

Abstract

Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I(+) and I(-) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods.

Highlights

X-ray crystallography is one of the most frequently used techniques in structural biology to solve molecular structures at the atomic level
The Continuous rotation (CR) and inverse-beam geometry (IBG) data collection methods resulted in positive correlation between I(+) and I(À) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage
Using Markov chain Monte Carlo (Gilks et al, 1995) sampling, we model the joint probability of two reflection intensities in order to yield more accurate differences between the underlying structure-factor amplitudes and to determine the uncertainty of differences (Katona et al, 2016)

Summary

Introduction

X-ray crystallography is one of the most frequently used techniques in structural biology to solve molecular structures at the atomic level. It is suitable for a wide range of molecular sizes, starting from a few atoms to many thousands, and has allowed the structures of more than 135 000 macromolecules, such as proteins, to be solved (Berman et al, 2000). Accurate starting phase information is essential for the many steps leading to the final structural model. Molecular replacement is the most commonly used method to determine the structure of biomolecules by X-ray diffraction (Hendrickson, 2014). De novo structure determination requires experimental phases

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Conclusion