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

Abstract

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., ...

Microfocus diffraction from different regions of a protein crystal: structural variations and unit-cell polymorphism

“…The improvement in data quality that can be gained by clustering multiple related data sets into isomorphous groups during data reduction has been explored previously in several contexts (Liu et al, 2011;Giordano et al, 2012;Foadi et al, 2013;Zander et al, 2016;Assmann et al, 2016;Diederichs, 2017;Yamamoto et al, 2017), although previous work has generally focused on non-isomorphism resulting from changes to crystal-packing interactions, rather than emphasizing the potential to use this feature of protein crystals as a means to explore conformational heterogeneity. With continuing advances in the throughput of traditional data collection (Svensson et al, 2017;Broecker et al, 2018) and in serial crystallography (Standfuss & Spence, 2017), there is a growing opportunity to parse conformational space by carefully analyzing large quantities of diffraction data. In this regard, serial crystallography experiments may hold great potential, as they typically involve measuring diffraction snapshots from thousands of unique crystals.…”