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On the application of the expected log‐likelihood gain to decision making in molecular replacement
Author(s) -
Oeffner Robert D.,
Afonine Pavel V.,
Millán Claudia,
Sammito Massimo,
Usón Isabel,
Read Randy J.,
McCoy Airlie J.
Publication year - 2018
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.374
H-Index - 138
ISSN - 2059-7983
DOI - 10.1107/s2059798318004357
Subject(s) - molecular replacement , pruning , function (biology) , computer science , likelihood function , algorithm , path (computing) , multiple isomorphous replacement , phaser , maximum likelihood , mathematics , chemistry , statistics , estimation theory , physics , crystal structure , crystallography , optics , x ray crystallography , programming language , evolutionary biology , diffraction , agronomy , biology
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. D 72 , 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.