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      Macromolecular diffractive imaging using imperfect crystals

      research-article
      1 , 1 , 2 , 3 , 4 , 1 , 2 , 1 , 3 , 4 , 3 , 4 , 3 , 4 , 3 , 4 , 3 , 4 , 1 , 3 , 5 , 3 , 6 , 2 , 5 , 1 , 2 , 1 , 6 , 7 , 5 , 5 , 1 , 3 , 4 , 3 , 4 , 8 , 8 , 8 , 8 , 8 , 8 , 3 , 4 , 1 , 1 , 2 , 9
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          Abstract

          The three-dimensional structures of macromolecules and their complexes are predominantly elucidated by X-ray protein crystallography. A major limitation is access to high-quality crystals, to ensure X-ray diffraction extends to sufficiently large scattering angles and hence yields sufficiently high-resolution information that the crystal structure can be solved. The observation that crystals with shrunken unit-cell volumes and tighter macromolecular packing often produce higher-resolution Bragg peaks 1, 2 hints that crystallographic resolution for some macromolecules may be limited not by their heterogeneity but rather by a deviation of strict positional ordering of the crystalline lattice. Such displacements of molecules from the ideal lattice give rise to a continuous diffraction pattern, equal to the incoherent sum of diffraction from rigid single molecular complexes aligned along several discrete crystallographic orientations and hence with an increased information content 3 . Although such continuous diffraction patterns have long been observed—and are of interest as a source of information about the dynamics of proteins 4 —they have not been used for structure determination. Here we show for crystals of the integral membrane protein complex photosystem II that lattice disorder increases the information content and the resolution of the diffraction pattern well beyond the 4.5 Å limit of measurable Bragg peaks, which allows us to directly phase 5 the pattern. With the molecular envelope conventionally determined at 4.5 Å as a constraint, we then obtain a static image of the photosystem II dimer at 3.5 Å resolution. This result shows that continuous diffraction can be used to overcome long-supposed resolution limits of macromolecular crystallography, with a method that puts great value in commonly encountered imperfect crystals and opens up the possibility for model-free phasing 6, 7 .

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          Most cited references49

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          Linking crystallographic model and data quality.

          In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.
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            Extending the methodology of X-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens

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              Phase retrieval algorithms: a comparison

              J Fienup (1982)
              Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and the problem of phase retrieval from a single intensity measurement plus a non-negativity constraint (in astronomy) are considered, with emphasis on the latter. It is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm for the problem of two intensity measurements converge. The error-reduction algorithm is also shown to be closely related to the steepest-descent method. Other algorithms, including the input-output algorithm and the conjugate-gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                12 January 2016
                11 February 2016
                11 August 2016
                : 530
                : 7589
                : 202-206
                Affiliations
                [1 ]Center for Free-Electron Laser Science, DESY, 22607 Hamburg, Germany
                [2 ]Department of Physics, University of Hamburg, 22761 Hamburg, Germany
                [3 ]Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287, USA
                [4 ]Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
                [5 ]Department of Physics, Arizona State University, Tempe, Arizona 85287, USA
                [6 ]Physics Department, University of Wisconsin, Milwaukee, WI 53211, USA
                [7 ]Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Hellas, Crete, Greece
                [8 ]Linac Coherent Light Source, Stanford Linear Accelerator Center (SLAC) National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
                [9 ]Centre for Ultrafast Imaging, 22607 Hamburg, Germany
                Author notes
                Correspondence and requests for materials should be addressed to H.N.C at henry.chapman@ 123456desy.de
                Article
                NIHMS746269
                10.1038/nature16949
                4839592
                26863980
                76d84fb1-902f-4dbd-8870-64e11c1c9afc

                Reprints and permissions information is available at www.nature.com/reprints.

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