Cargando…

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

Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better de...

Descripción completa

Detalles Bibliográficos
Autores principales: Svensson, Olof, Gilski, Maciej, Nurizzo, Didier, Bowler, Matthew W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930350/
https://www.ncbi.nlm.nih.gov/pubmed/29717714
http://dx.doi.org/10.1107/S2059798318003728
_version_ 1783319483833647104
author Svensson, Olof
Gilski, Maciej
Nurizzo, Didier
Bowler, Matthew W.
author_facet Svensson, Olof
Gilski, Maciej
Nurizzo, Didier
Bowler, Matthew W.
author_sort Svensson, Olof
collection PubMed
description Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the first beamline in the world to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014, the beamline has processed over 42 000 samples. Improvements have been made to the speed of the sample-handling robotics and error management within the software routines. The workflows initially put into place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterization; this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal. Complex multi-position and multi-crystal data collections have now also been 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.
format Online
Article
Text
id pubmed-5930350
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher International Union of Crystallography
record_format MEDLINE/PubMed
spelling pubmed-59303502018-05-11 Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1 Svensson, Olof Gilski, Maciej Nurizzo, Didier Bowler, Matthew W. Acta Crystallogr D Struct Biol Research Papers Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the first beamline in the world to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014, the beamline has processed over 42 000 samples. Improvements have been made to the speed of the sample-handling robotics and error management within the software routines. The workflows initially put into place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterization; this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal. Complex multi-position and multi-crystal data collections have now also been 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. International Union of Crystallography 2018-04-24 /pmc/articles/PMC5930350/ /pubmed/29717714 http://dx.doi.org/10.1107/S2059798318003728 Text en © Svensson et al. 2018 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/2.0/uk/
spellingShingle Research Papers
Svensson, Olof
Gilski, Maciej
Nurizzo, Didier
Bowler, Matthew W.
Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title_full Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title_fullStr Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title_full_unstemmed Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title_short Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1
title_sort multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on massif-1
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930350/
https://www.ncbi.nlm.nih.gov/pubmed/29717714
http://dx.doi.org/10.1107/S2059798318003728
work_keys_str_mv AT svenssonolof multipositiondatacollectionanddynamicbeamsizingrecentimprovementstotheautomaticdatacollectionalgorithmsonmassif1
AT gilskimaciej multipositiondatacollectionanddynamicbeamsizingrecentimprovementstotheautomaticdatacollectionalgorithmsonmassif1
AT nurizzodidier multipositiondatacollectionanddynamicbeamsizingrecentimprovementstotheautomaticdatacollectionalgorithmsonmassif1
AT bowlermattheww multipositiondatacollectionanddynamicbeamsizingrecentimprovementstotheautomaticdatacollectionalgorithmsonmassif1