Ligand identification using electron-density map correlations.
Ligand identification using electron-density map correlations.
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DOI：
10.1107/s0907444906046233
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发表时间：
2007-01
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影响因子：
--
通讯作者：
Cohn JD
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文献类型：
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作者：
Terwilliger TC;Adams PD;Moriarty NW;Cohn JD
将自动配体拟合程序应用于（F o − F c）来自蛋白质数据库中的大分子结构的200种常见配体的exp（i c）差异密度，以从密度图中鉴定配体。本文介绍了一种识别大分子晶体结构中结合配体的方法。在识别程序中使用了对应于配体的密度的两个特征。一个是在优化配体与密度的拟合之后，配体密度与一组测试配体中的每一个的相关性。另一个是密度指纹与每个可能配体的模型密度指纹的相关性。指纹由每个测试配体与密度的相关性的有序列表组成。这两个特征使用Z分数方法进行评分，其中将相关性归一化为针对各种错配配体密度对发现的相关性的平均值和标准差，使得Z分数与偶然观察到特定相关性值的概率相关。该程序用蛋白质数据库中最常见的200种配体进行了测试，这些配体共同代表了蛋白质数据库中所有配体的57%。使用配体密度的这两个特征的组合，对代表性（F）的配体鉴定进行排序列表。 o − F c）来自蛋白质数据库中条目的exp（i c）差异密度。在200例病例中，48%的正确配体位于配体排名列表的顶部。这种方法可能是有用的，在新的大分子结构中的未知配体的识别，以及在识别的混合物中的配体结合到一个大分子。
An automated ligand-fitting procedure is applied to (F o − F c)exp(iϕc) difference density for 200 commonly found ligands from macromolecular structures in the Protein Data Bank to identify ligands from density maps. A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F o − F c)exp(iϕc) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.