ArticleActa crystallographica. Section D, Structural biology2021
Detection of translational noncrystallographic symmetry in Patterson functions.
Article in Acta crystallographica. Section D, Structural biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 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
- ARCIMBOLDO at low resolution: Verification for coiled coils and globular proteins.Protein science : a publication of the Protein Society · 2024Article
- Verification: model-free phasing with enhanced predicted models in ARCIMBOLDO_SHREDDER.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
- Phasertng: directed acyclic graphs for crystallographic phasing.Acta crystallographica. Section D, Structural biology · 2021Article
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6 authors.
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Abstract
Detection of translational noncrystallographic symmetry (TNCS) can be critical for success in crystallographic phasing, particularly when molecular-replacement models are poor or anomalous phasing information is weak. If the correct TNCS is detected then expected intensity factors for each reflection can be refined, so that the maximum-likelihood functions underlying molecular replacement and single-wavelength anomalous dispersion use appropriate structure-factor normalization and variance terms. Here, an analysis of a curated database of protein structures from the Protein Data Bank to investigate how TNCS manifests in the Patterson function is described. These studies informed an algorithm for the detection of TNCS, which includes a method for detecting the number of vectors involved in any commensurate modulation (the TNCS order). The algorithm generates a ranked list of possible TNCS associations in the asymmetric unit for exploration during structure solution.
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