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KAMO: towards automated data processing for microcrystals
In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930351/ https://www.ncbi.nlm.nih.gov/pubmed/29717715 http://dx.doi.org/10.1107/S2059798318004576 |
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author | Yamashita, Keitaro Hirata, Kunio Yamamoto, Masaki |
author_facet | Yamashita, Keitaro Hirata, Kunio Yamamoto, Masaki |
author_sort | Yamashita, Keitaro |
collection | PubMed |
description | In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. |
format | Online Article Text |
id | pubmed-5930351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-59303512018-05-11 KAMO: towards automated data processing for microcrystals Yamashita, Keitaro Hirata, Kunio Yamamoto, Masaki Acta Crystallogr D Struct Biol Research Papers In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. International Union of Crystallography 2018-04-24 /pmc/articles/PMC5930351/ /pubmed/29717715 http://dx.doi.org/10.1107/S2059798318004576 Text en © Yamashita 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 Yamashita, Keitaro Hirata, Kunio Yamamoto, Masaki KAMO: towards automated data processing for microcrystals |
title |
KAMO: towards automated data processing for microcrystals |
title_full |
KAMO: towards automated data processing for microcrystals |
title_fullStr |
KAMO: towards automated data processing for microcrystals |
title_full_unstemmed |
KAMO: towards automated data processing for microcrystals |
title_short |
KAMO: towards automated data processing for microcrystals |
title_sort | kamo: towards automated data processing for microcrystals |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930351/ https://www.ncbi.nlm.nih.gov/pubmed/29717715 http://dx.doi.org/10.1107/S2059798318004576 |
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