Whole-pattern fitting technique in serial femtosecond nanocrystallography

IUCrJ

Dilanian, R.A.¹  Williams, S.R.¹  Martin, A.V.¹  Quiney, H.M.¹  Stretsov, V.A.² 

[1]ARC Centre of Excellence for Advanced Molecular Imaging, School of Physics, University of Melbourne, Parkville, Victoria 3010, Australia

[2]CSIRO Manufacturing Flagship, Parkville, Victoria 3052, Australia

关键词: PROTEIN NANOCRYSTALLOGRAPHY;    PEAK-SHAPE ANALYSIS;    WHOLE-PATTERN FITTING;    X-RAY FREE-ELECTRON LASERS;    NANOCRYSTALS;    SFX;    PROTEIN STRUCTURE;    XFEL;   

DOI  :  10.1107/S2052252516001238

学科分类：数学（综合）

来源: International Union of Crystallography

【 摘 要 】

Serial femtosecond X-ray crystallography (SFX) has created new opportunities in the field of structural analysis of protein nanocrystals. The intensity and timescale characteristics of the X-ray free-electron laser sources used in SFX experiments necessitate the analysis of a large collection of individual crystals of variable shape and quality to ultimately solve a single, average crystal structure. Ensembles of crystals are commonly encountered in powder diffraction, but serial crystallography is different because each crystal is measured individually and can be oriented via indexing and merged into a three-dimensional data set, as is done for conventional crystallography data. In this way, serial femtosecond crystallography data lie in between conventional crystallography data and powder diffraction data, sharing features of both. The extremely small sizes of nanocrystals, as well as the possible imperfections of their crystallite structure, significantly affect the diffraction pattern and raise the question of how best to extract accurate structure-factor moduli from serial crystallography data. Here it is demonstrated that whole-pattern fitting techniques established for one-dimensional powder diffraction analysis can be feasibly extended to higher dimensions for the analysis of merged SFX diffraction data. It is shown that for very small crystals, whole-pattern fitting methods are more accurate than Monte Carlo integration methods that are currently used.

【 授权许可】

CC BY