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Protein crystallization has gained a new strategic and commercial relevance in the next phase of the genome projects, in which X-ray crystallography will play a major role. Considerable advances have been made in the automation of protein preparation and also in the X-ray analysis and bioinformatics stages once diffraction-quality crystals are available. These advances have not yet been matched by equally good methods for the crystallization process itself. In the area of crystallization, the main effort and resources are currently being invested into the automation of screening procedures to identify potential crystallization conditions. However, in spite of the ability to generate numerous trials, so far only a small percentage of the proteins produced have led to structure determinations. This is because screening in itself is not usually enough; it has to be complemented by an equally important procedure in crystal production, namely crystal optimization. In the rush towards structural genomics, optimization techniques have been somewhat neglected, mainly because it was hoped that large-scale screening alone would produce the desired results. In addition, optimization has relied on particular individual methods that are often difficult to automate and to adapt to high throughput. This article addresses a major gap in the field of structural genomics by describing practical ways of automating individual optimization methods in order to adapt them to high-throughput techniques.

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