Madhumati Sevvana, Michael Ruf, Isabel Usón, George M Sheldrick, Regine Herbst-Irmer
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引用次数: 0
摘要
与并合孪晶不同的是,非立体孪晶的不同晶域的倒易点阵并不完全重叠。这导致了三种反射:无重叠反射、完全重叠反射和部分重叠来自第二畴的反射。这使得 X 射线衍射数据的单元尺寸确定、索引、数据整合和缩放变得复杂。不过,事后看来,由于有些反射不受孪晶的影响,因此可以对数据进行分离。本文介绍了使用一种通用策略成功求解和提炼一种矿物、一种有机金属和两种蛋白质非正交孪晶的过程。单元常量和取向矩阵由 CELL_NOW 程序确定。然后用 SAINT 对数据进行整合。TWINABS 用于缩放、经验吸收校正和生成两个不同的数据文件,一个是用于结构求解和细化的去翅数据文件,另一个是用于(通常更精确的)根据总集成强度进行结构细化的数据文件。对前两个结构使用 SHELXT 进行实验相位求解,对两个蛋白质结构使用 SHELXC/D/E 进行实验相位求解;所有模型均使用 SHELXL 进行细化。
Non-merohedral twinning: from minerals to proteins.
In contrast to twinning by merohedry, the reciprocal lattices of the different domains of non-merohedral twins do not overlap exactly. This leads to three kinds of reflections: reflections with no overlap, reflections with an exact overlap and reflections with a partial overlap of a reflection from a second domain. This complicates the unit-cell determination, indexing, data integration and scaling of X-ray diffraction data. However, with hindsight it is possible to detwin the data because there are reflections that are not affected by the twinning. In this article, the successful solution and refinement of one mineral, one organometallic and two protein non-merohedral twins using a common strategy are described. The unit-cell constants and the orientation matrices were determined by the program CELL_NOW. The data were then integrated with SAINT. TWINABS was used for scaling, empirical absorption corrections and the generation of two different data files, one with detwinned data for structure solution and refinement and a second one for (usually more accurate) structure refinement against total integrated intensities. The structures were solved by experimental phasing using SHELXT for the first two structures and SHELXC/D/E for the two protein structures; all models were refined with SHELXL.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.