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Determination of the Gas-phase Acidities of Oligopeptides
Published on: June 24, 2013
Characterizing peptide neutral losses induced by negative electron-transfer dissociation (NETD)
Neil G Rumachik1, Graeme C McAlister, Jason D Russell
1Department of Chemistry, University of Wisconsin, Madison, WI 53706, USA.
Journal of the American Society for Mass Spectrometry
|February 1, 2012
Summary
Negative electron-transfer dissociation (NETD) reveals unique neutral losses from peptides, aiding amino acid identification. This fragmentation technique enhances peptide characterization and proteome analysis.
Area of Science:
- Proteomics
- Analytical Chemistry
- Mass Spectrometry
Background:
- Negative electron-transfer dissociation (NETD) is an emerging peptide fragmentation technique.
- Understanding NETD's fragmentation pathways, particularly neutral losses, is crucial for its application.
- Characterizing neutral losses can improve peptide identification in mass spectrometry.
Purpose of the Study:
- To systematically study synthetic peptides using NETD to identify common neutral losses.
- To determine the chemical composition and diagnostic utility of these neutral losses.
- To assess the specificity and sensitivity of identified neutral losses for amino acid detection.
Main Methods:
- Implementation of NETD on a hybrid ion trap/Orbitrap mass spectrometer.
- Analysis of 46 synthetic peptides using high mass accuracy and high resolution.
- Assessment of neutral loss utility using a database of 1542 identified peptides from NETD shotgun experiments.
Main Results:
- Identification of 19 unique neutral losses from 14 amino acids and three modified amino acids.
- Determination of the chemical composition of each neutral loss.
- Demonstration of the diagnostic potential of residue-specific neutral losses.
Conclusions:
- NETD generates valuable sequence-informative fragment ions and neutral losses.
- The identified neutral losses offer diagnostic utility for specific amino acids.
- Incorporating this neutral loss catalogue into database search algorithms can enhance peptide identification and acidic proteome characterization.
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