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Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography
The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly s...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3727331/ https://www.ncbi.nlm.nih.gov/pubmed/23897484 http://dx.doi.org/10.1107/S0907444913012274 |
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author | Foadi, James Aller, Pierre Alguel, Yilmaz Cameron, Alex Axford, Danny Owen, Robin L. Armour, Wes Waterman, David G. Iwata, So Evans, Gwyndaf |
author_facet | Foadi, James Aller, Pierre Alguel, Yilmaz Cameron, Alex Axford, Danny Owen, Robin L. Armour, Wes Waterman, David G. Iwata, So Evans, Gwyndaf |
author_sort | Foadi, James |
collection | PubMed |
description | The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein. |
format | Online Article Text |
id | pubmed-3727331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-37273312013-08-02 Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography Foadi, James Aller, Pierre Alguel, Yilmaz Cameron, Alex Axford, Danny Owen, Robin L. Armour, Wes Waterman, David G. Iwata, So Evans, Gwyndaf Acta Crystallogr D Biol Crystallogr Research Papers The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein. International Union of Crystallography 2013-08-01 2013-07-20 /pmc/articles/PMC3727331/ /pubmed/23897484 http://dx.doi.org/10.1107/S0907444913012274 Text en © Foadi et al. 2013 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Foadi, James Aller, Pierre Alguel, Yilmaz Cameron, Alex Axford, Danny Owen, Robin L. Armour, Wes Waterman, David G. Iwata, So Evans, Gwyndaf Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title | Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title_full | Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title_fullStr | Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title_full_unstemmed | Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title_short | Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
title_sort | clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3727331/ https://www.ncbi.nlm.nih.gov/pubmed/23897484 http://dx.doi.org/10.1107/S0907444913012274 |
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