On the application of the expected log-likelihood gain to decision making in molecular replacement.
On the application of the expected log-likelihood gain to decision making in molecular replacement.
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DOI：
10.1107/s2059798318004357
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发表时间：
2018-04-01
期刊：
影响因子：
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通讯作者：
McCoy AJ
中科院分区：
文献类型：
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作者：
Oeffner RD;Afonine PV;Millán C;Sammito M;Usón I;Read RJ;McCoy AJ
预期的对数似然增益可用于预测分子替换的结果并优化分子替换策略。大分子晶体结构的分子置换定相通常很快，但如果没有立即获得分子置换解决方案，晶体学家必须判断是进行分子置换还是尝试实验定相作为获得结构解决方案的最快途径。引入预期对数似然增益[eLLG；麦考伊等人。 （2017），Proc。国家科学院。科学。 USA, 114, 3637–3641] 为晶体学家提供了一个强大的新工具来帮助做出这一决定。 eLLG 是强度的对数似然增益 [LLGI; Read 和 McCoy (2016)，水晶学报。 D72, 375–387] 预计来自正确放置的模型。它被计算为函数反射的总和，该函数的反射取决于模型所考虑的散射分数、估计的模型坐标误差和数据中的测量误差。它展示了如何使用 eLLG 来回答“我可以通过分子替换来解决我的结构吗？”的问题。然而，这只是 eLLG 最明显的应用。还讨论了如何使用 eLLG 来确定搜索顺序和最小数据要求，以便使用给定模型获得分子替换解决方案，以及如何在基于片段的分子替换、单原子分子替换和似然引导模型修剪中做出决策。
The expected log-likelihood gain can be used to predict the outcome of molecular replacement and optimize molecular-replacement strategies. Molecular-replacement phasing of macromolecular crystal structures is often fast, but if a molecular-replacement solution is not immediately obtained the crystallographer must judge whether to pursue molecular replacement or to attempt experimental phasing as the quickest path to structure solution. The introduction of the expected log-likelihood gain [eLLG; McCoy et al. (2017), Proc. Natl Acad. Sci. USA, 114, 3637–3641] has given the crystallographer a powerful new tool to aid in making this decision. The eLLG is the log-likelihood gain on intensity [LLGI; Read & McCoy (2016), Acta Cryst. D72, 375–387] expected from a correctly placed model. It is calculated as a sum over the reflections of a function dependent on the fraction of the scattering for which the model accounts, the estimated model coordinate error and the measurement errors in the data. It is shown how the eLLG may be used to answer the question ‘can I solve my structure by molecular replacement?’. However, this is only the most obvious of the applications of the eLLG. It is also discussed how the eLLG may be used to determine the search order and minimal data requirements for obtaining a molecular-replacement solution using a given model, and for decision making in fragment-based molecular replacement, single-atom molecular replacement and likelihood-guided model pruning.