Chayen Naomi E, Saridakis Emmanuel
Biological Structure and Function Section, Division of Biomedical Sciences, Faculty of Medicine, Imperial College, London SW7 2AZ, England.
Acta Crystallogr D Biol Crystallogr. 2002 Jun;58(Pt 6 Pt 2):921-7. doi: 10.1107/s0907444902005322. Epub 2002 May 29.
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.
在基因组计划的下一阶段,蛋白质结晶已具有新的战略和商业意义,其中X射线晶体学将发挥主要作用。一旦获得衍射质量的晶体,蛋白质制备的自动化以及X射线分析和生物信息学阶段都取得了相当大的进展。然而,对于结晶过程本身,尚未有同样出色的方法与之匹配。在结晶领域,目前主要的努力和资源都投入到筛选程序的自动化上,以确定潜在的结晶条件。然而,尽管能够进行大量试验,但到目前为止,所产生的蛋白质中只有一小部分能够用于结构测定。这是因为筛选本身通常是不够的;它必须辅以晶体生产中同样重要的一个程序,即晶体优化。在向结构基因组学迈进的过程中,优化技术在一定程度上被忽视了,主要是因为人们希望仅通过大规模筛选就能产生预期的结果。此外,优化依赖于特定的个别方法,这些方法往往难以自动化且难以适应高通量。本文通过描述将个别优化方法自动化以使其适应高通量技术的实际方法,填补了结构基因组学领域的一个主要空白。