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

Abstract

The development of automated high-intensity macromolecular crystallography (MX) beamlines at synchrotron facilities has resulted in a remarkable increase in sample throughput. Developments in X-ray detector technology now mean that complete X-ray diffraction datasets can be collected in less than one minute. Such high-speed collection, and the volumes of data that it produces, often make it difficult for even the most experienced users to cope with the deluge. However, the careful reduction of data during experimental sessions is often necessary for the success of a particular project or as an aid in decision making for subsequent experiments. Automated data reduction pipelines provide a fast and reliable alternative to user-initiated processing at the beamline. In order to provide such a pipeline for the MX user community of the European Synchrotron Radiation Facility (ESRF), a system for the rapid automatic processing of MX diffraction data from single and multiple positions on a single or multiple crystals has been developed. Standard integration and data analysis programs have been incorporated into the ESRF data collection, storage and computing environment, with the final results stored and displayed in an intuitive manner in the ISPyB (information system for protein crystallography beamlines) database, from which they are also available for download. In some cases, experimental phase information can be automatically determined from the processed data. Here, the system is described in detail.

Predicting the X-ray lifetime of protein crystals

“…By way of illustration: over the last 11 years at beamline 8.3.1 of the Advanced Light Source (ALS), more than 1,000 structures have been solved and deposited into the Protein Data Bank, but more than 25,000 datasets were collected. Similar dataset-to-deposition ratios have been reported elsewhere (2,3). A retrospective analysis of the ALS 8.3.1 data reveals that radiation damage played a dominant role in the failure to obtain phases for structure solution by anomalous dispersion methods.…”

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

“…In order to analyse the difference we looked at the average signal to noise ratios, <I/σ(I)>, for all data sets processed automatically (Monaco et al, 2013;Vonrhein et al, 2011;Sparta et al, 2016;Winter, 2010) for the year preceding and following the introduction of the protocol. This amounts to data for approximately 22,000 samples.…”

“…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.…”

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

“…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. [32] The full automation of MX data collection, where human involvement is limited to sending samples and accessing data, has been discussed within the field for many years.…”