SAD phasing of XFEL data depends critically on the error model.

Aaron S Brewster,Robert Bolotovsky,Nicholas K Sauter,Petrus H Zwart,Derek Mendez,Asmit Bhowmick

doi:10.1107/s2059798319012877

Aaron S Brewster, Robert Bolotovsky + Show 4 more

Open Access

https://doi.org/10.1107/s2059798319012877

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Abstract

A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built. Estimating the error in the merged reflection intensities requires the understanding and propagation of all of the sources of error arising from the measurements. One type of error, which is well understood, is the counting error introduced when the detector counts X-ray photons. Thus, if other types of random errors (such as readout noise) as well as uncertainties in systematic corrections (such as from X-ray attenuation) are completely understood, they can be propagated along with the counting error, as appropriate. In practice, most software packages propagate as much error as they know how to model and then include error-adjustment terms that scale the error estimates until they explain the variance among the measurements. If this is performed carefully, then during SAD phasing likelihood-based approaches can make optimal use of these error estimates, increasing the chance of a successful structure solution. In serial crystallography, SAD phasing has remained challenging, with the few examples of de novo protein structure solution each requiring many thousands of diffraction patterns. Here, the effects of different methods of treating the error estimates are estimated and it is shown that using a parametric approach that includes terms proportional to the known experimental uncertainty, the reflection intensity and the squared reflection intensity to improve the error estimates can allow SAD phasing even from weak zinc anomalous signal.

Highlights

IntroductionSolving a novel protein structure using X-ray crystallography typically involves either a reliance on a similar structure from which molecular replacement (MR) can be used to derive phasing information, or the presence of heavy atoms that can provide anomalous differences for use in SAD (single-wavelength anomalous dispersion) or MAD (multiple-wavelength anomalous dispersion) phasing (among other methods)
Solving a novel protein structure using X-ray crystallography typically involves either a reliance on a similar structure from which molecular replacement (MR) can be used to derive phasing information, or the presence of heavy atoms that can provide anomalous differences for use in SAD or MAD phasing
We have found that how the error estimates are obtained directly affects our ability to use SAD phasing to solve an X-ray free-electron laser (XFEL) structure de novo

Summary

Introduction

Solving a novel protein structure using X-ray crystallography typically involves either a reliance on a similar structure from which molecular replacement (MR) can be used to derive phasing information, or the presence of heavy atoms that can provide anomalous differences for use in SAD (single-wavelength anomalous dispersion) or MAD (multiple-wavelength anomalous dispersion) phasing (among other methods). In SAD phasing, X-ray anomalous scattering by heavy atoms in the protein structure breaks inversion/Friedel symmetry in the diffraction pattern, with otherwise equivalent reflections typically exhibiting 3–4% differences in intensity. This information can be used to determine the heavy-atom substructure in the protein, which is used to solve the phasing problem. This approach requires highly accurately measured intensities, and the analysis of such data has been shown to benefit from maximum-likelihood methods The redundantly measured reflections are merged together using either a simple average or a weighted average (White, 2014; Kabsch, 2014; Sauter, 2015; Uervirojnangkoorn et al, 2015; Ginn et al, 2015)

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