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

International Tables for Crystallography (2006). Vol. F, ch. 11.1, p. 209   | 1 | 2 |

Section 11.1.1. Introduction

M. G. Rossmanna*

aDepartment of Biological Sciences, Purdue University, West Lafayette, IN 47907-1392, USA
Correspondence e-mail:

11.1.1. Introduction

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Auto-indexing routines have been used extensively for initiating diffraction data collection with a single-point-detector device (Sparks, 1976,[link] 1982[link]). These methods depend upon the precise knowledge of the reciprocal-lattice vectors for a few selected reflections. Greater difficulty has been encountered for automatic indexing of oscillation images recorded on two-dimensional detectors using randomly oriented crystals, as is frequently the case for macromolecular crystal samples. In the past, the practice was to orient crystals relative to the camera axes with an accuracy of at least 1°. In this case, the indexing procedure required only refinement of the crystal orientation matrix (Wonacott, 1977[link]; Rossmann, 1979[link]). The `American method' (Rossmann & Erickson, 1983[link]), where crystals are oriented more or less randomly, is currently used because of the need for optimizing available synchrotron time and because of the deterioration in radiation-sensitive crystals during the setting process.

A variety of techniques were suggested to determine the crystal orientation, some of which required initial knowledge of the cell dimensions (Vriend & Rossmann, 1987[link]; Kabsch, 1988[link]), while more advanced techniques (Kim, 1989[link]; Higashi, 1990[link]; Kabsch, 1993[link]) determined both cell dimensions and crystal orientation. All these methods start with the determination of the reciprocal-lattice vectors assuming that the oscillation photographs are `stills'. The methods of Higashi and Kabsch, as well as, in part, Kim's, analyse the distribution of the difference vectors generated from the reciprocal-lattice vectors. The most frequent difference vectors are taken as the basis vectors defining the reciprocal-lattice unit cell and its orientation. In addition, Kim's technique requires the input of the orientation of a likely zone-axis direction onto which the reciprocal-lattice vectors are then projected. The projections will have a periodicity distribution consistent with the reciprocal-lattice planes perpendicular to the zone axis. Duisenberg (1992[link]) used a similar approach for single-point-detector data, although he did not rely on prior knowledge of the zone-axis direction. Instead, he defined possible zone axes as being perpendicular to a reciprocal-lattice plane by combining three, suitably chosen, reciprocal-lattice points.

None of the above techniques were entirely satisfactory as they sometimes failed to find a suitable crystal orientation matrix. A major advance was made in the program DENZO , a part of the HKL package (Otwinowski & Minor, 1997[link]), which not only has a robust indexing procedure but also has a useful graphical interface. Unfortunately, the indexing technique used in the procedure has never been described, except for a few hints in the manual on the use of an FFT (fast Fourier transform). Indeed, Bricogne (1986[link]) suggested that a three-dimensional Fourier transformation might be a powerful indexing tool, and Strouse (1996[link]) developed such a procedure for single-point-detector data. However, for large unit cells this procedure requires an excessive amount of memory and time (Campbell, 1997[link]).


Bricogne, G. (1986). Indexing and the Fourier transform. In Proceedings of the EEC cooperative workshop on position-sensitive detector software (phase III), p. 28. LURE, Paris, 12–19 November.
Campbell, J. W. (1997). In CCP4 Newsletter, No. 33, edited by M. Winn. Warrington: Daresbury Laboratory.
Duisenberg, A. J. M. (1992). Indexing in single-crystal diffractometry with an obstinate list of reflections. J. Appl. Cryst. 25, 92–96.
Higashi, T. (1990). Auto-indexing of oscillation images. J. Appl. Cryst. 23, 253–257.
Kabsch, W. (1988). Automatic indexing of rotation diffraction patterns. J. Appl. Cryst. 21, 67–71.
Kabsch, W. (1993). Automatic processing of rotation diffraction data from crystals of initially unknown symmetry and cell constants. J. Appl. Cryst. 26, 795–800.
Kim, S. (1989). Auto-indexing oscillation photographs. J. Appl. Cryst. 22, 53–60.
Otwinowski, Z. & Minor, W. (1997). Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326.
Rossmann, M. G. (1979). Processing oscillation diffraction data for very large unit cells with an automatic convolution technique and profile fitting. J. Appl. Cryst. 12, 225–238.
Rossmann, M. G. & Erickson, J. W. (1983). Oscillation photography of radiation-sensitive crystals using a synchrotron source. J. Appl. Cryst. 16, 629–636.
Sparks, R. A. (1976). Trends in minicomputer hardware and software. Part I. In Crystallographic computing techniques, edited by F. R. Ahmed, K. Huml & B. Sedláček, pp. 452–467. Copenhagen: Munksgaard.
Sparks, R. A. (1982). Data collection with diffractometers. In Computational crystallography, edited by D. Sayre, pp. 1–18. New York: Oxford University Press.
Strouse, C. E. (1996). Personal communication.
Vriend, G. & Rossmann, M. G. (1987). Determination of the orientation of a randomly placed crystal from a single oscillation photograph. J. Appl. Cryst. 20, 338–343.
Wonacott, A. J. (1977). Geometry of the rotation method. In The rotation method in crystallography, edited by U. W. Arndt & A. J. Wonacott, pp. 75–103. Amsterdam: North-Holland Publishing Co.

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