ArticleActa crystallographica. Section D, Structural biology2018
On the application of the expected log-likelihood gain to decision making in molecular replacement.
Article in Acta crystallographica. Section D, Structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Xtricorder: a likelihood-enhanced self-rotation function and application to a machine learning-enhanced Matthews prediction of asymmetric unit copy number.Acta crystallographica. Section D, Structural biology · 2025Article
- Slice'N'Dice: maximizing the value of predicted models for structural biologists.Acta crystallographica. Section D, Structural biology · 2025Article
- The success rate of processed predicted models in molecular replacement: implications for experimental phasing in the AlphaFold era.Acta crystallographica. Section D, Structural biology · 2024Article
- ARCIMBOLDO at low resolution: Verification for coiled coils and globular proteins.Protein science : a publication of the Protein Society · 2024Article
- Deciphering the crystal structure of a novel nanobody against the NEIL1 DNA glycosylase.Acta crystallographica. Section D, Structural biology · 2024Article
- Article
- Molecular replacement for small-molecule crystal structure determination from X-ray and electron diffraction data with reduced resolution.Acta crystallographica. Section A, Foundations and advances · 2023Article
- Assessment of three-dimensional RNA structure prediction in CASP15.bioRxiv : the preprint server for biology · 2023Article
- Article
- Predicted models and CCP4.Acta crystallographica. Section D, Structural biology · 2023Article
- Likelihood-based signal and noise analysis for docking of models into cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2023Article
- Likelihood-based docking of models into cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2023Article
- Accelerating crystal structure determination with iterative AlphaFold prediction.Acta crystallographica. Section D, Structural biology · 2023Article
- Verification: model-free phasing with enhanced predicted models in ARCIMBOLDO_SHREDDER.Acta crystallographica. Section D, Structural biology · 2022Article
- Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE.Acta crystallographica. Section D, Structural biology · 2022Article
- MrParse: finding homologues in the PDB and the EBI AlphaFold database for molecular replacement and more.Acta crystallographica. Section D, Structural biology · 2022Article
- Implications of AlphaFold2 for crystallographic phasing by molecular replacement.Acta crystallographica. Section D, Structural biology · 2022Review
- Article
- Introduction to molecular replacement: a time perspective.Acta crystallographica. Section D, Structural biology · 2021Review
- Functional elucidation of TfuA in peptide backbone thioamidation.Nature chemical biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.