On the application of the expected log‐likelihood gain to decision making in molecular replacement. Issue 4 (12th April 2018)

• Record Type: Journal Article
• Authors: Oeffner, Robert D.; Afonine, Pavel V.; Millán, Claudia; Sammito, Massimo; Usón, Isabel; Read, Randy J.; McCoy, Airlie J.
• Is Part Of: Acta crystallographica. Volume 74:Issue 4(2018)
• Page Start: 245
• Page End: 255
• Publication Date: 2018-04-12
• Subjects: maximum likelihood -- molecular replacement -- Phaser -- log‐likelihood gain -- eLLG -- LLGI; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798318004357
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : The expected log‐likelihood gain can be used to predict the outcome of molecular replacement and optimize molecular‐replacement strategies. Abstract : 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.