Literature DB >> 31794207

High-Throughput PIXE as an Essential Quantitative Assay for Accurate Metalloprotein Structural Analysis: Development and Application.

Geoffrey W Grime1, Oliver B Zeldin2, Mary E Snell3, Edward D Lowe2, John F Hunt4, Gaetano T Montelione5, Liang Tong4, Edward H Snell3,6, Elspeth F Garman2.   

Abstract

Metalloproteins comprise over one-third of proteins, with approximately half of all enzymes requiring metal to function. Accurate identification of these metal atoms and their environment is a prerequisite to understanding biological mechanism. Using ion beam analysis through particle induced X-ray emission (PIXE), we have quantitatively identified the metal atoms in 30 previously structurally characterized proteins using minimal sample volume and a high-throughput approach. Over half of these metals had been misidentified in the deposited structural models. Some of the PIXE detected metals not seen in the models were explainable as artifacts from promiscuous crystallization reagents. For others, using the correct metal improved the structural models. For multinuclear sites, anomalous diffraction signals enabled the positioning of the correct metals to reveal previously obscured biological information. PIXE is insensitive to the chemical environment, but coupled with experimental diffraction data deposited alongside the structural model it enables validation and potential remediation of metalloprotein models, improving structural and, more importantly, mechanistic knowledge.

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Year:  2019        PMID: 31794207     DOI: 10.1021/jacs.9b09186

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  6 in total

1.  Pre- and Post-publication Verification for Reproducible Data Mining in Macromolecular Crystallography.

Authors:  John R Helliwell
Journal:  Methods Mol Biol       Date:  2022

Review 2.  Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network.

Authors:  Vincenzo Laveglia; Andrea Giachetti; Davide Sala; Claudia Andreini; Antonio Rosato
Journal:  J Chem Inf Model       Date:  2022-06-09       Impact factor: 6.162

3.  Zinc determines dynamical properties and aggregation kinetics of human insulin.

Authors:  Kevin Pounot; Geoffrey W Grime; Alessandro Longo; Michaela Zamponi; Daria Noferini; Viviana Cristiglio; Tilo Seydel; Elspeth F Garman; Martin Weik; Vito Foderà; Giorgio Schirò
Journal:  Biophys J       Date:  2021-02-03       Impact factor: 4.033

4.  Combining X-rays, neutrons and electrons, and NMR, for precision and accuracy in structure-function studies.

Authors:  John R Helliwell
Journal:  Acta Crystallogr A Found Adv       Date:  2021-05-04       Impact factor: 2.290

5.  Deflating the RNA Mg2+ bubble. Stereochemistry to the rescue!

Authors:  Pascal Auffinger; Eric Ennifar; Luigi D'Ascenzo
Journal:  RNA       Date:  2020-12-02       Impact factor: 4.942

6.  Colocation of Lipids, Drugs, and Metal Biomarkers Using Spatially Resolved Lipidomics with Elemental Mapping.

Authors:  Holly-May Lewis; Catia Costa; Véronique Dartois; Firat Kaya; Mark Chambers; Janella de Jesus; Vladimir Palitsin; Roger Webb; Melanie J Bailey
Journal:  Anal Chem       Date:  2022-08-18       Impact factor: 8.008

  6 in total

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