Automation of the collection and processing of X-ray diffraction data – a generic approach

Abstract

With modern detectors and synchrotron sources, it is now routine to collect complete data sets in 10–30 min. To make the most efficient use of these resources, it is desirable to automate the collection and processing of the diffraction data, ideally to a level at which multiple data sets can be acquired without any intervention. A scheme is described to allow fully automated data collection and processing. The design is modular, so that it can easily be interfaced with different beamline‐control programs and different data‐processing programs. An expert system provides a communication path between the data‐processing software and the beamline‐control software and takes decisions about the data collection based on project information provided by the user and experimental data provided by the data‐processing program.

Automatic processing of macromolecular crystallography X-ray diffraction data at the ESRF

“…The combination of highly intense focused X-ray beams, automatic sample changers, automated beam delivery, online data analysis and fast readout detectors at synchrotron macromolecular crystallography (MX) beamlines now allows for the collection of hundreds of datasets during each assigned experimental session (Arzt et al, 2005;Beteva et al, 2006;Bourenkov & Popov, 2010;Bowler et al, 2010;Cherezov et al, 2009;Cipriani et al, 2006;de Sanctis et al, 2012;Flot et al, 2010;Gabadinho et al, 2010;Incardona et al, 2009;Jacquamet et al, 2009;Leslie et al, 2002;McCarthy et al, 2009;McPhillips et al, 2002;Nurizzo et al, 2006;Soltis et al, 2008). In many cases, complete diffraction datasets can be collected in under one minute (Hü lsen et al, 2006).…”

Fully automatic macromolecular crystallography: the impact of MASSIF-1 on the optimum acquisition and quality of data

“…[13,14,17,58] However, it became clear that for MASSIF to succeed, the analysis had to be taken a step further to allow the results of analysis to be used in further experiments involving instrument control. These experiments pose a challenge for both hardware (sample changers, goniometers) and software (data acquisition and data analysis).…”

“…Despite enormous technical advances since the determination of the first structures by X-ray crystallography [1,2] the process remains a challenge and demands considerable human effort. Automation has been a cornerstone of the advances made in the last two decades, making the process of protein production, [3,4] purification, [5] crystallization [6][7][8][9] and data collection [10][11][12][13] more streamlined. Coupling the extremely bright and small beams that can be produced at modern third-generation synchrotrons with automatic sample changers and on-line data analysis [10,[12][13][14][15][16][17][18][19][20] has been one of the great advances, allowing not only large numbers of structures to be solved [21] but also incredibly challenging systems to be characterized structurally.…”

“…Automation has been a cornerstone of the advances made in the last two decades, making the process of protein production, [3,4] purification, [5] crystallization [6][7][8][9] and data collection [10][11][12][13] more streamlined. Coupling the extremely bright and small beams that can be produced at modern third-generation synchrotrons with automatic sample changers and on-line data analysis [10,[12][13][14][15][16][17][18][19][20] has been one of the great advances, allowing not only large numbers of structures to be solved [21] but also incredibly challenging systems to be characterized structurally. [22][23][24][25] As the field of macromolecular crystallography (MX) reaches maturity, efforts are now focussing on automating the steps that still require considerable human involvement: crystal mounting, [26] data collection, [7,11,27] processing [16,[28][29][30][31] and validation.…”

Recent improvements to the automatic characterization and data collection algorithms on MASSIF-1

“…Advances in robotics and software have been key in these developments and have had a particular impact on structural biology, allowing multiple constructs to be screened and purified (Camper & Viola, 2009;Hart & Waldo, 2013;Vijayachandran et al, 2011); huge numbers of crystallisation experiments to be performed (Elsliger et al, 2010;Ferrer et al, 2013;Heinemann et al, 2003;Joachimiak, 2009;Calero et al, 2014), samples to be mounted at synchrotrons (Cipriani et al, 2006;Cohen et al, 2002;Jacquamet et al, 2009;Papp et al, 2017;Snell et al, 2004), data to be analysed and processed (Bourenkov & Popov, 2010;Holton & Alber, 2004;Incardona et al, 2009;Leslie et al, 2002;Monaco et al, 2013;Winter, 2010) and the entire PDB to be validated (Joosten et al, 2012). The combination of robotic sample mounting and on-line data analysis has been particularly important in macromolecular crystallography (MX) as it allowed time to be saved, large numbers of samples to be screened and enabled the remote operation of beamlines.…”