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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis
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Identification of Novel Microcystins Using High-Resolution MS and MSn with Python Code.

David Baliu-Rodriguez1, Nicholas J Peraino2, Sanduni H Premathilaka1

  • 1Department of Chemistry and Biochemistry, University of Toledo, Toledo, Ohio 43606, United States.

Environmental Science & Technology
|January 12, 2022
PubMed
Summary

A new method using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and Python code aids in identifying toxic microcystins (MCs) in water. This approach successfully identified two new MC congeners, improving water safety analysis.

Keywords:
MSnPython softwarehigh-resolution mass spectrometrymicrocystinsstructural identification

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Toxicology

Background:

  • Microcystins (MCs) are potent cyanotoxins found in water sources, posing significant health risks.
  • Over 300 microcystin congeners exist, but many remain structurally unidentified, hindering accurate toxicity assessment.
  • Determining the precise structure of microcystins is crucial for understanding their toxicity and developing effective countermeasures.

Purpose of the Study:

  • To develop and validate a novel method for the putative identification of unknown microcystin congeners in environmental water samples.
  • To employ liquid chromatography coupled with high-resolution Orbitrap mass spectrometry (LC-HRMS) and a bottom-up sequencing strategy for MC analysis.
  • To utilize Python programming for generating potential MC structures and confirming identifications through ion fragmentation analysis.

Main Methods:

  • Water samples from the Maumee River during a harmful algal bloom were analyzed using LC-HRMS with simultaneous MS/MS.
  • Unidentified ions exhibiting characteristic microcystin fragments (135 and 213 m/z) were targeted for investigation.
  • A Python-based workflow was implemented for de novo structure elucidation and ion assignment of potential MC congeners.

Main Results:

  • The developed workflow successfully facilitated the putative identification of eight previously reported MCs lacking available standards.
  • Two novel microcystin congeners, MC-HarR and MC-E(OMe)R, were discovered and structurally characterized.
  • The method demonstrated effectiveness in identifying MCs even in complex environmental matrices like river water.

Conclusions:

  • The integrated LC-HRMS and Python-based workflow provides a powerful tool for the putative identification of unknown microcystins.
  • This approach enhances the ability to detect and characterize emerging microcystin variants, crucial for public health and water quality monitoring.
  • The discovery of new congeners underscores the complexity of microcystin diversity and the need for ongoing toxicological research.