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DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography
High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613109/ https://www.ncbi.nlm.nih.gov/pubmed/31274465 http://dx.doi.org/10.1107/S160057751900434X |
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author | Ito, Sho Ueno, Go Yamamoto, Masaki |
author_facet | Ito, Sho Ueno, Go Yamamoto, Masaki |
author_sort | Ito, Sho |
collection | PubMed |
description | High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography. |
format | Online Article Text |
id | pubmed-6613109 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-66131092019-07-17 DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography Ito, Sho Ueno, Go Yamamoto, Masaki J Synchrotron Radiat Short Communications High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography. International Union of Crystallography 2019-06-03 /pmc/articles/PMC6613109/ /pubmed/31274465 http://dx.doi.org/10.1107/S160057751900434X Text en © Ito, Ueno and Yamamoto 2019 http://creativecommons.org/licenses/by/4.0/ 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/4.0/ |
spellingShingle | Short Communications Ito, Sho Ueno, Go Yamamoto, Masaki DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title | DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title_full | DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title_fullStr | DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title_full_unstemmed | DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title_short | DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography |
title_sort | deepcentering: fully automated crystal centering using deep learning for macromolecular crystallography |
topic | Short Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613109/ https://www.ncbi.nlm.nih.gov/pubmed/31274465 http://dx.doi.org/10.1107/S160057751900434X |
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