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PyMDA: microcrystal data assembly using Python
The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6998775/ https://www.ncbi.nlm.nih.gov/pubmed/32047415 http://dx.doi.org/10.1107/S160057671901673X |
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author | Takemaru, Lina Guo, Gongrui Zhu, Ping Hendrickson, Wayne A. McSweeney, Sean Liu, Qun |
author_facet | Takemaru, Lina Guo, Gongrui Zhu, Ping Hendrickson, Wayne A. McSweeney, Sean Liu, Qun |
author_sort | Takemaru, Lina |
collection | PubMed |
description | The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal and with unit-cell variations. A multi-stage data assembly method has previously been designed for microcrystal synchrotron crystallography. Here the strategy has been implemented as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality. |
format | Online Article Text |
id | pubmed-6998775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-69987752020-02-11 PyMDA: microcrystal data assembly using Python Takemaru, Lina Guo, Gongrui Zhu, Ping Hendrickson, Wayne A. McSweeney, Sean Liu, Qun J Appl Crystallogr Computer Programs The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal and with unit-cell variations. A multi-stage data assembly method has previously been designed for microcrystal synchrotron crystallography. Here the strategy has been implemented as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality. International Union of Crystallography 2020-02-01 /pmc/articles/PMC6998775/ /pubmed/32047415 http://dx.doi.org/10.1107/S160057671901673X Text en © Lina Takemarua et al. 2020 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 | Computer Programs Takemaru, Lina Guo, Gongrui Zhu, Ping Hendrickson, Wayne A. McSweeney, Sean Liu, Qun PyMDA: microcrystal data assembly using Python |
title |
PyMDA: microcrystal data assembly using Python |
title_full |
PyMDA: microcrystal data assembly using Python |
title_fullStr |
PyMDA: microcrystal data assembly using Python |
title_full_unstemmed |
PyMDA: microcrystal data assembly using Python |
title_short |
PyMDA: microcrystal data assembly using Python |
title_sort | pymda: microcrystal data assembly using python |
topic | Computer Programs |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6998775/ https://www.ncbi.nlm.nih.gov/pubmed/32047415 http://dx.doi.org/10.1107/S160057671901673X |
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