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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...

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Autores principales: Foadi, James, Aller, Pierre, Alguel, Yilmaz, Cameron, Alex, Axford, Danny, Owen, Robin L., Armour, Wes, Waterman, David G., Iwata, So, Evans, Gwyndaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2013
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.
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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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