Takemaru Lina, Guo Gongrui, Zhu Ping, Hendrickson Wayne A, McSweeney Sean, Liu Qun
Biology Department, Brookhaven National Laboratory, Upton, NY 11973, USA.
Photon Sciences Division, NSLS-II, Brookhaven National Laboratory, Upton, NY 11973, USA.
J Appl Crystallogr. 2020 Feb 1;53(Pt 1):277-281. doi: 10.1107/S160057671901673X.
The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal and with unit-cell variations. A multi-stage data assembly method has previously been designed for microcrystal synchrotron crystallography. Here the strategy has been implemented as a Python program for microcrystal data assembly (). optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, may be applicable for assembling data sets from larger crystals for improved data quality.
微衍射X射线束线的最新进展使大分子微晶成为常规结构分析的诱人对象。在同步加速器微衍射束线收集的微晶衍射数据可能会受到辐射损伤，每个微晶的数据不完整且晶胞存在变化。此前已设计了一种多阶段数据组装方法用于微晶同步辐射晶体学。在这里，该策略已被实现为一个用于微晶数据组装的Python程序（）。它通过迭代的晶体和帧拒绝来优化微晶数据质量，包括微弱的反常信号。除了微晶，它可能适用于组装来自较大晶体的数据集以提高数据质量。
