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Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...

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Updated: Jun 16, 2026

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
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Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy.

Edward H Snell1,2, Geoffrey W Grime3, Samuel M Webb4

  • 1The Department of Materials Design and Innovation, SUNY University at Buffalo, 120 Bonner Hall, Buffalo, New York 14260, United States.

Journal of Chemical Information and Modeling
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

Accurate metal ion representation in protein structures is crucial. This study found that metals in Protein Data Bank (PDB) entries often mismatch experimental data from protein samples, indicating widespread data integrity issues.

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Area of Science:

  • Biophysics
  • Structural Biology
  • Analytical Chemistry

Background:

  • Accurate metal ion representation in macromolecular structures is vital for chemical interpretation, computational modeling, and machine learning.
  • The elemental identity of metals in crystallographic structures is often inferred and lacks experimental validation.

Purpose of the Study:

  • To experimentally determine the elemental composition of protein samples used for metalloprotein crystal structures.
  • To assess the consistency between modeled metal ions in Protein Data Bank (PDB) entries and the actual elemental content of protein samples before crystallization.
  • To identify widespread data integrity issues in deposited macromolecular structures.

Main Methods:

  • Utilized Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to analyze the elemental composition of original protein materials.
  • Integrated spectroscopic results with automated crystallographic validation metrics like real-space Z-difference (RSZD) analysis.
  • Performed systematic rerefinement to evaluate atomic-number mismatch at metal sites.

Main Results:

  • In a majority of cases, modeled metal ions in PDB entries were inconsistent with experimentally detected elements in protein samples.
  • Additional metals were often present in protein samples but not represented in the corresponding structural models.
  • PIXE and XRFS demonstrated strong agreement and provided complementary methods for identifying suspect metal assignments.

Conclusions:

  • A widespread data integrity issue exists in deposited macromolecular structures, affecting PDB-wide entries.
  • Established an experimentally corroborated link between elemental identity and crystallographic validation metrics.
  • Developed a scalable approach for detecting chemically inconsistent annotations in structural databases used for computational modeling and machine learning.