Autoindexing with outlier rejection and identification of superimposed lattices.
Autoindexing with outlier rejection and identification of superimposed lattices.
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DOI：
10.1107/s0021889810010782
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发表时间：
2010-06-01
影响因子：
6.1
通讯作者：
Poon BK
中科院分区：
文献类型：
--
作者：
Sauter NK;Poon BK
在自动索引之后，可以识别不适合模型晶格的布拉格点候选者，从而提供对样品质量的潜在有用的测量，并且如果存在第二晶格，则给出索引第二晶格的途径。构建一个模型晶格来拟合观察到的布拉格衍射图案对于完美的样品来说是简单的，但是当存在伪影时，索引可能具有挑战性，例如形状不佳的斑点，分裂的晶体产生多个紧密对齐的晶格以及聚集的微晶的图案的完全叠加。为了针对边缘数据优化晶格模型，可以使用已经丢弃了拟合差的点的观察结果的子集来执行细化。通过假设最佳拟合点的高斯误差分布来识别离群值，并剔除偏离该分布的点。剩余的观察结果的集合产生上级晶格模型，而被拒绝的观察结果可以用于识别第二晶格（如果存在的话）。离群值的普遍性提供了一个潜在的有用的样本质量的衡量标准。在自动索引程序labelit.index（http：//cci.lbl.gov/labelit）中，对大分子晶体学实施所述程序。
After autoindexing, Bragg spot candidates that do not fit on the model lattice can be identified, providing a potentially useful measure of sample quality and giving an avenue for indexing a second lattice, if one is present. Constructing a model lattice to fit the observed Bragg diffraction pattern is straightforward for perfect samples, but indexing can be challenging when artifacts are present, such as poorly shaped spots, split crystals giving multiple closely aligned lattices and outright superposition of patterns from aggregated microcrystals. To optimize the lattice model against marginal data, refinement can be performed using a subset of the observations from which the poorly fitting spots have been discarded. Outliers are identified by assuming a Gaussian error distribution for the best-fitting spots and points diverging from this distribution are culled. The set of remaining observations produces a superior lattice model, while the rejected observations can be used to identify a second crystal lattice, if one is present. The prevalence of outliers provides a potentially useful measure of sample quality. The described procedures are implemented for macromolecular crystallography within the autoindexing program labelit.index (http://cci.lbl.gov/labelit).