Merging of synchrotron serial crystallographic data by a genetic algorithm

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• 作者：Ulrich Zander ; Michele Cianci ; Nicolas Foos ; Catarina S. Silva ; Luca Mazzei ; Chloe Zubieta ; Alejandro de Maria and Max H. Nanao
• 关键词：genetic algorithms ; cluster analysis ; serial crystallography
• 刊名：Acta Crystallographica Section D
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：72
• 期：9
• 页码：1026-1035
• 全文大小：793K
• ISSN：1399-0047

文摘

Recent advances in macromolecular crystallography have made it practical to rapidly collect hundreds of sub-data sets consisting of small oscillations of incomplete data. This approach, generally referred to as serial crystallography, has many uses, including an increased effective dose per data set, the collection of data from crystals without harvesting (in situ data collection) and studies of dynamic events such as catalytic reactions. However, selecting which data sets from this type of experiment should be merged can be challenging and new methods are required. Here, it is shown that a genetic algorithm can be used for this purpose, and five case studies are presented in which the merging statistics are significantly improved compared with conventional merging of all data.