Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, England.
Lawrence Berkeley National Laboratory, One Cyclotron Road, BLDG 64R0121, Berkeley, CA 94720, USA.
Acta Crystallogr D Struct Biol. 2018 Apr 1;74(Pt 4):245-255. doi: 10.1107/S2059798318004357. Epub 2018 Apr 4.
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.
大分子晶体结构的分子置换相分析通常很快,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继续进行分子置换还是尝试实验相分析,以最快的速度解决结构问题。预期对数似然增益[eLLG;McCoy 等人(2017 年),美国国家科学院院刊,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个决定。eLLG 是正确放置模型后强度[LLGI;Read 和 McCoy(2016 年),晶体学报 D72,375-387]的对数似然增益。它是通过依赖于模型解释的散射部分、估计的模型坐标误差和数据测量误差的函数在反射上的总和来计算的。展示了如何使用 eLLG 回答“我能否通过分子置换解决我的结构问题?”。然而,这只是 eLLG 的最明显的应用之一。还讨论了如何使用 eLLG 来确定使用给定模型获得分子置换解决方案的搜索顺序和最小数据要求,以及用于基于片段的分子置换、单原子分子置换和可能性引导的模型修剪的决策。