Assessing and maximizing data quality in macromolecular crystallography.
Assessing and maximizing data quality in macromolecular crystallography.
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DOI：
10.1016/j.sbi.2015.07.003
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发表时间：
2015-10
影响因子：
6.8
通讯作者：
Diederichs K
中科院分区：
文献类型：
--
作者：
Karplus PA;Diederichs K
大分子晶体结构的质量部分取决于用于产生它们的数据的质量和数量。在这里，我们回顾了最近的转变，在我们的理解如何使用数据质量指标来选择一个高分辨率的截止，导致最佳的模型，并通过合并多个测量单晶或多个晶体的多个通道的潜力，大大提高数据质量。支持这一转变的关键因素是采用了更稳健的基于相关系数的合并数据集精确度指标，以及认识到大量数据中存在的大量有用信息，这些数据曾经被认为太弱而没有价值。
The quality of macromolecular crystal structures depends, in part, on the quality and quantity of the data used to produce them. Here, we review recent shifts in our understanding of how to use data quality indicators to select a high resolution cutoff that leads to the best model, and of the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. Key factors supporting this shift are the introduction of more robust correlation coefficient based indicators of the precision of merged data sets as well as the recognition of the substantial useful information present in extensive amounts of data once considered too weak to be of value.