Research Article: Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1

Date Published: May 01, 2018

Publisher: International Union of Crystallography

Author(s): Olof Svensson, Maciej Gilski, Didier Nurizzo, Matthew W. Bowler.

http://doi.org/10.1107/S2059798318003728

Abstract

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.

Partial Text

Automation is transforming the way that 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 to 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 crystallization 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 ▸; Nurizzo et al., 2016 ▸; 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 online data analysis has been particularly important in macromolecular crystallography (MX) as it has allowed time to be saved, large numbers of samples to be screened, and enabled the remote operation of beamlines. However, despite these advances, a human presence is still required to sequence actions. Pioneering beamlines that have fully automated the process, such as LRL-CAT at the Advanced Photon Source (Wasserman et al., 2012 ▸) and the Stanford Synchrotron Radiation Lightsource MX beamlines (Tsai et al., 2013 ▸), removed the need for a human presence, but as they rely on optical loop centring this means that restrictions have to be placed on the size of the crystals and they tend to be robust, well diffracting samples, generally those for proprietary research in the pharmaceutical industry.

The results presented here demonstrate not only the increase in the speed and reliability of automatic data collections but also that more complex strategies can be brought into the arena of autonomous experiments. Automation is often seen as a way to deal with mundane experiments that require little human input. The autonomous system presented here is different in that in addition to automating mounting and centring, it also uses data gathered during the process to improve data-collection strategies. We have already demonstrated that MASSIF-1 collects, on average, better quality data than humans are able to (Bowler et al., 2016 ▸). The additional routines presented here add even more expert knowledge into the system that should further enhance its ability to extract the best possible data from every sample. This built-in expert knowledge means that the system is excellent not only for robust and routine data collections but also for challenging systems that diffract weakly. We have demonstrated that adapting the beam diameter can increase the number of data sets that can be processed from these types of sample. We hope that by providing more data on more samples we can improve feedback into experiment cycles and increase the amount of useful data produced.

 

Source:

http://doi.org/10.1107/S2059798318003728

 

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