Tables for
Volume F
Crystallography of biological macromolecules
Edited by M. G. Rossmann and E. Arnold

International Tables for Crystallography (2006). Vol. F, ch. 19.3, pp. 428-429   | 1 | 2 |

Section 19.3.2. Small-angle single-crystal X-ray diffraction studies

H. Tsurutaa and J. E. Johnsonb*

aSSRL/SLAC & Department of Chemistry, Stanford University, PO Box 4349, MS69, Stanford, California 94309-0210, USA, and bDepartment of Molecular Biology, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, California 92037, USA
Correspondence e-mail:

19.3.2. Small-angle single-crystal X-ray diffraction studies

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We begin by briefly describing an obvious connection between small-angle solution X-ray scattering and single-crystal X-ray scattering, i.e. small-angle single-crystal diffraction (Tsuruta et al., 1998[link]; Miller et al., 1999[link]). The majority of macromolecular single-crystal studies ignore data at resolutions lower than 20 Å. Measuring these data accurately is generally difficult when collecting high-resolution data, because they are orders of magnitude stronger in intensity and frequently saturate the detector pixels or are intentionally blocked from reaching the detector by a large beam stop. It is technically challenging to record low-resolution data accurately, because they fall very close to the primary beam, where a variety of parasitic scattering effects occur. These data, however, contain information about the structure that may not be determined from only the high-resolution data. Low-resolution ([\infty]–15 Å) data are sensitive to structures that are organized over large distances and that contribute primarily to the low-frequency terms in a Fourier series. In contrast, portions of the structure that contribute to high-frequency terms must be in precisely the same position in all the unit cells within the crystal. Two examples illustrate the importance of low-resolution terms.

Assume that an exposed loop on the surface of a protein is highly mobile. The scattering-factor curves for atoms in this loop will be highly attenuated by the large Debye–Waller factor. If the temperature factors are high enough, these atoms will contribute nothing to the high-resolution data. In contrast, these atoms will contribute strongly to the low-resolution data. If the low-resolution terms are included in the Fourier series, the atom positions will not appear as individual atoms; instead, an envelope of density describing the statistically distributed positions of these atoms will be visible, and this can aid significantly in the modelling of the loop.

A second example is the nucleic acid within a spherical virus. The nucleotides generally do not display the symmetry of the icosahedral capsid, although in some cases segments of RNA or DNA clearly interact with the protein and are therefore visible at high resolution. The RNA density is not usually visible if only higher-resolution data are used, and the virus particle appears empty. The reason for this is that the RNA is best described as a uniform sphere within the particle, and the Fourier transform of such a sphere falls off rapidly if the sphere is relatively large. A typical RNA virus will have an internal RNA core of about 100 Å in radius. The scattering contribution of this region is virtually zero beyond 20 Å resolution. If the low-resolution data are measured accurately, the region occupied by RNA and its general level of interaction with the coat protein can be clearly seen (Fig.[link].


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A cross section of the electron-density map of sFHV at 14 Å resolution. The density shown in red corresponds to the protein capsid, while density in green corresponds to the bulk RNA or dynamic protein segments. The Cα backbone of the atomic model of the protein capsid seen in the high-resolution structures of sFHV is shown as yellow traces, and the model of the ordered pieces of duplex RNA is shown as a stick model. The high-resolution protein model fits the electron-density map very well. The ordered RNA model is buried in the RNA density in green.

The relation between single-crystal and solution X-ray scattering is clearly illustrated in Fig.[link], where the scattering of a single crystal and the solution scattering of a 320 Å-diameter RNA virus are compared. The figure shows the scattering expected for a uniform sphere 160 Å in radius, the observed solution X-ray scattering from this virus and the single-crystal diffraction from the virus. All three diffraction patterns are in close agreement at resolutions below 50 Å, with the only difference being the continuous sampling of the transform in the computed and solution scattering curve and the discrete sampled transform of the crystalline virus. The RNA density in Flock house virus (FHV) shown in Fig.[link] illustrates the information content of the low-resolution data. DNA density functions in polyoma virus were mapped by employing similar methods (Griffith et al., 1992[link]). Low-resolution single-crystal data are also critical for applying the methods of de novo phase determination. Geometric solids, such as a sphere, or a low-resolution electron cryo-microscopy (cryoEM) reconstruction may serve as initial phasing models for such a strategy, and the procedure of refining and extending phases is much more robust if the low-resolution data are measured accurately (Tsuruta et al., 1998[link]).


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Comparison of the absolute values of single-crystal reflection amplitudes (circles), the solution scattering intensity (thick curve) and the calculated scattering intensity from a uniform sphere (thin curve).


Griffith, J. P., Griffith, D. L., Rayment, I., Murakami, W. T. & Caspar, D. L. D. (1992). Inside polyomavirus at 25-Å resolution. Nature (London), 355, 652–654.Google Scholar
Miller, S. T., Genova, J. D. & Hogle, J. M. (1999). Collection of very low resolution protein data. J. Appl. Cryst. 32, 1183–1185.Google Scholar
Tsuruta, H., Reddy, V., Wikoff, W. & Johnson, J. (1998). Imaging RNA and dynamic protein segments with low resolution virus crystallography; experimental design, data processing and implications of electron density maps. J. Mol. Biol. 284, 1439–1452.Google Scholar

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