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.

Highlights

  • Solving the phase problem by molecular replacement is a problem of signal to noise; the signal for the correct placement of the model must be found amongst the noise of incorrect placements

  • The signal of a placement is indicated by its translation-function Z-score (TFZ), which is the number of standard deviations over the mean (Z-score) for the loglikelihood gain on intensity (LLGI) in the translation function (TF)

  • Experienced users of Phaser may wish to see a solution with LLGI ) 64 and TFZ ) 8 to increase the certainty that the solution is correct

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Summary

Introduction

The LLGI required to be confident in a solution for the placement of the first model in molecular replacement depends on the number of parameters that have to be fixed. The dependence of A on the solvent term in square brackets in (6) is the square of the solvent term previously described (Read, 2001; McCoy et al, 2017), after studies indicated better A estimation using this functional form (data not shown). These relationships between fm, Ám, the number of reflections and the eLLG give fresh insights into molecular replacement. We discuss here the application to decisions regarding minimal data requirements, the burgeoning field of fragment-based molecular replacement, and likelihood-guided model pruning

Phaser implementation
Can I solve my structure by molecular replacement?
Implementation
Search strategies
Example
Resolution
Fragment-based molecular replacement
Single-atom molecular replacement
Twinning
10. Discussion
Findings
Funding information
Full Text
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