Significant improvements to the sample-location, characterization and data-collection algorithms on the autonomous ESRF beamline MASSIF-1 are described. The workflows now include dynamic beam-diameter adjustment and multi-position and multi-crystal data collections.
The fully automatic processing of crystals of macromolecules has presented a unique opportunity to gather information on the samples that is not usually recorded. This has proved invaluable in improving sample-location, characterization and data-collection algorithms. After operating for four years, MASSIF-1 has now processed over 56 000 samples, gathering information at each stage, from the volume of the crystal to the unit-cell dimensions, the space group, the quality of the data collected and the reasoning behind the decisions made in data collection. This provides an unprecedented opportunity to analyse these data together, providing a detailed landscape of macromolecular crystals, intimate details of their contents and, importantly, how the two are related. The data show that mosaic spread is unrelated to the size or shape of crystals and demonstrate experimentally that diffraction intensities scale in proportion to crystal volume and molecular weight. It is also shown that crystal volume scales inversely with molecular weight. The results set the scene for the development of X-ray crystallography in a changing environment for structural biology.
SynopsisThe automatic processing of over 56,000 crystals by the autonomous ESRF beamline MASSIF-1 has provided a data set of crystal characteristics and properties that allows many theoretical proposals and assumptions to be evaluated experimentally. AbstractThe fully automatic processing of crystals of macromolecules has presented a unique opportunity to gather information on the samples that is not usually recorded. This has proved invaluable in improving the sample location, characterisation and data collection algorithms. After operating for four years, MASSIF-1 has now processed over 56,000 samples, gathering information at each stage, from the volume of the crystal to the unit cell dimensions, space group, quality of the data collected and the reasoning behind the decisions made in data collection. This provides an unprecedented opportunity to analyse these data together, providing a detailed landscape of macromolecular crystals and intimate details of their contents and, importantly, how the two are related. The data show that mosaic spread is unrelated to the size or shape of crystals and demonstrate experimentally that diffraction intensities scale in proportion to crystal volume and molecular weight. It is also shown that crystal volume scales inversely with molecular weight.The results set the scene for the development of X-ray crystallography in a changing environment for structural biology.
SynopsisSignificant improvements in the sample location, characterisation and data collection algorithms on the autonomous ESRF beamline MASSIF-1 are described. The workflows now include dynamic beam diameter adjustment and multi-position and multi-crystal data collections.. CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/236596 doi: bioRxiv preprint first posted online 2 Abstract Macromolecular crystallography (MX) is now a mature and widely used technique essential in the understanding of biology and medicine. Increases in computing power combined with robotics have enabled not only large numbers of samples to be screened and characterised but also for better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the world's first beamline to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014 the beamline has now processed over 39,000 samples. Improvements have been made to the speed of the sample handling robotics and error management within the software routines.The workflows initially put in place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterisation, this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal. Complex multi-position and multi-crystal data collections are now also integrated into the selection of experiments available. This has led to increased data quality and throughput allowing even the most challenging samples to be treated automatically.Key words: X-ray centring; synchrotron instrumentation; macromolecular crystallography; automation; helical data collection; multiple crystal data collection . CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/236596 doi: bioRxiv preprint first posted online 3 IntroductionAutomation is transforming the way scientific data are collected, allowing large amounts of high quality data to be gathered in a consistent manner (Quintana & Plätzer, 2015;Foster, 2005). 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., ...
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