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Peptide Identification Using Tandem Mass Spectrometry01:33

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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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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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Signature peptide selection workflow for biomarker quantification using LC-MS-based targeted proteomics.

Xiazi I Qiu1, Kenneth J Ruterbories1, Qin C Ji1

  • 1AbbVie, Inc., DMPK-BA, North Chicago, IL 60064, USA.

Bioanalysis
|April 11, 2023
PubMed
Summary

Selecting tryptic signature peptides for endogenous protein quantification via LC-MS is challenging. This study introduces a workflow to predict peptide ionization efficiency, improving method development for low-abundant protein biomarker analysis.

Keywords:
biomarkerprotein quantificationsignature peptidestargeted proteomics

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Analytical Chemistry

Background:

  • Quantifying endogenous proteins as biomarkers using LC-MS targeted proteomics is complex.
  • Tryptic signature peptide selection is critical but lacks predictive tools for ionization efficiency.
  • Current methods hinder development for low-abundant protein quantification.

Purpose of the Study:

  • To propose a novel workflow for tryptic signature peptide selection.
  • To improve method development efficiency for endogenous protein quantification.
  • To enhance success rates in identifying peptides for low-abundant protein biomarkers.

Main Methods:

  • Development of a predictive workflow for tryptic signature peptide selection.
  • Focus on predicting ionization efficiency of peptide candidates.
  • Application to endogenous target and protein biomarker quantification.

Main Results:

  • A workflow for more efficient method development is proposed.
  • Improved success rates in signature peptide selection are anticipated.
  • Addresses the challenge of blind peptide selection due to unknown ionization efficiencies.

Conclusions:

  • The proposed workflow facilitates more efficient method development.
  • Enhances success rates for low-abundant endogenous protein biomarker quantification.
  • Aids investigators in selecting optimal signature peptides for LC-MS targeted proteomics.