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Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser.

MPG-Autoren
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Shoeman,  R. L.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Lomb,  L.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Foucar,  L.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Rolles,  D.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Kassemeyer,  S.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Schlichting,  I.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Krasniqi,  F.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Zitation

Ekeberg, T., Svenda, M., Seibert, M. M., Abergel, C., Maia, F. R. N. C., Seltzer, V., et al. (2016). Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser. Scientific Data, 3: 160060, pp. 1-7. doi:10.1038/sdata.2016.60.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-4AF2-1
Zusammenfassung
Free-electron lasers (FEL) hold the potential to revolutionize structural biology by producing X-ray pules short enough to outrun radiation damage, thus allowing imaging of biological samples without the limitation from radiation damage. Thus, a major part of the scientific case for the first FELs was three-dimensional (3D) reconstruction of non-crystalline biological objects. In a recent publication we demonstrated the first 3D reconstruction of a biological object from an X-ray FEL using this technique. The sample was the giant Mimivirus, which is one of the largest known viruses with a diameter of 450 nm. Here we present the dataset used for this successful reconstruction. Data-analysis methods for single-particle imaging at FELs are undergoing heavy development but data collection relies on very limited time available through a highly competitive proposal process. This dataset provides experimental data to the entire community and could boost algorithm development and provide a benchmark dataset for new algorithms.