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Determination of crystallographic intensities from sparse data

X-ray serial microcrystallography involves the collection and merging of frames of diffraction data from randomly oriented protein microcrystals. The number of diffracted X-rays in each frame is limited by radiation damage, and this number decreases with crystal size. The data in the frame are said...

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Autores principales: Ayyer, Kartik, Philipp, Hugh T., Tate, Mark W., Wierman, Jennifer L., Elser, Veit, Gruner, Sol M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4285878/
https://www.ncbi.nlm.nih.gov/pubmed/25610625
http://dx.doi.org/10.1107/S2052252514022313
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author Ayyer, Kartik
Philipp, Hugh T.
Tate, Mark W.
Wierman, Jennifer L.
Elser, Veit
Gruner, Sol M.
author_facet Ayyer, Kartik
Philipp, Hugh T.
Tate, Mark W.
Wierman, Jennifer L.
Elser, Veit
Gruner, Sol M.
author_sort Ayyer, Kartik
collection PubMed
description X-ray serial microcrystallography involves the collection and merging of frames of diffraction data from randomly oriented protein microcrystals. The number of diffracted X-rays in each frame is limited by radiation damage, and this number decreases with crystal size. The data in the frame are said to be sparse if too few X-rays are collected to determine the orientation of the microcrystal. It is commonly assumed that sparse crystal diffraction frames cannot be merged, thereby setting a lower limit to the size of microcrystals that may be merged with a given source fluence. The EMC algorithm [Loh & Elser (2009 ▶), Phys. Rev. E, 80, 026705] has previously been applied to reconstruct structures from sparse noncrystalline data of objects with unknown orientations [Philipp et al. (2012 ▶), Opt. Express, 20, 13129–13137; Ayyer et al. (2014 ▶), Opt. Express, 22, 2403–2413]. Here, it is shown that sparse data which cannot be oriented on a per-frame basis can be used effectively as crystallographic data. As a proof-of-principle, reconstruction of the three-dimensional diffraction intensity using sparse data frames from a 1.35 kDa molecule crystal is demonstrated. The results suggest that serial microcrystallography is, in principle, not limited by the fluence of the X-ray source, and collection of complete data sets should be feasible at, for instance, storage-ring X-ray sources.
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spelling pubmed-42858782015-01-21 Determination of crystallographic intensities from sparse data Ayyer, Kartik Philipp, Hugh T. Tate, Mark W. Wierman, Jennifer L. Elser, Veit Gruner, Sol M. IUCrJ Research Papers X-ray serial microcrystallography involves the collection and merging of frames of diffraction data from randomly oriented protein microcrystals. The number of diffracted X-rays in each frame is limited by radiation damage, and this number decreases with crystal size. The data in the frame are said to be sparse if too few X-rays are collected to determine the orientation of the microcrystal. It is commonly assumed that sparse crystal diffraction frames cannot be merged, thereby setting a lower limit to the size of microcrystals that may be merged with a given source fluence. The EMC algorithm [Loh & Elser (2009 ▶), Phys. Rev. E, 80, 026705] has previously been applied to reconstruct structures from sparse noncrystalline data of objects with unknown orientations [Philipp et al. (2012 ▶), Opt. Express, 20, 13129–13137; Ayyer et al. (2014 ▶), Opt. Express, 22, 2403–2413]. Here, it is shown that sparse data which cannot be oriented on a per-frame basis can be used effectively as crystallographic data. As a proof-of-principle, reconstruction of the three-dimensional diffraction intensity using sparse data frames from a 1.35 kDa molecule crystal is demonstrated. The results suggest that serial microcrystallography is, in principle, not limited by the fluence of the X-ray source, and collection of complete data sets should be feasible at, for instance, storage-ring X-ray sources. International Union of Crystallography 2015-01-01 /pmc/articles/PMC4285878/ /pubmed/25610625 http://dx.doi.org/10.1107/S2052252514022313 Text en © Kartik Ayyer et al. 2015 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
Ayyer, Kartik
Philipp, Hugh T.
Tate, Mark W.
Wierman, Jennifer L.
Elser, Veit
Gruner, Sol M.
Determination of crystallographic intensities from sparse data
title Determination of crystallographic intensities from sparse data
title_full Determination of crystallographic intensities from sparse data
title_fullStr Determination of crystallographic intensities from sparse data
title_full_unstemmed Determination of crystallographic intensities from sparse data
title_short Determination of crystallographic intensities from sparse data
title_sort determination of crystallographic intensities from sparse data
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4285878/
https://www.ncbi.nlm.nih.gov/pubmed/25610625
http://dx.doi.org/10.1107/S2052252514022313
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