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Synthesis and Mass Spectrometry Analysis of Oligo-peptoids
Published on: February 21, 2018
Protease-dependent fractional mass and peptide properties
Harald Barsnes1, Ingvar Eidhammer, Véronique Cruciani
1Department of Informatics, University of Bergen, Norway. Harald.Barsnes@ii.uib.no
European Journal of Mass Spectrometry (Chichester, England)
|November 22, 2008
Summary
Protease specificity influences peptide properties like fractional mass. This study reveals protease-dependent characteristics in peptides, offering new insights for mass spectrometry analysis.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Mass spectrometry (MS) for peptide analysis relies on specific protease cleavage.
- Protease specificity leads to non-random amino acid distribution in peptides.
- Physicochemical effects of this distribution are well-studied for trypsin but less so for other proteases.
Purpose of the Study:
- Investigate relationships between peptide fractional mass, isoelectric point (pI), and hydrophobicity.
- Analyze protease-dependent properties of peptides generated from human proteins.
- Explore the utility of fractional mass information in peptide mass fingerprinting.
Main Methods:
- Utilized all human proteins from the Swiss-Prot database.
- Analyzed peptides generated by specific proteases: trypsin, chymotrypsin, and gluC.
- Examined distributions of fractional masses and average regression lines.
Main Results:
- Fractional mass distributions and regression lines showed similarities but also differences among proteases (trypsin, chymotrypsin, gluC).
- GluC exhibited the steepest fractional mass regression line.
- Individual protein regression lines varied up to +/-100 ppm from the average, indicating protease-dependent peptide properties.
Conclusions:
- Peptide fractional mass and other properties are significantly influenced by the protease used for digestion.
- Increasing MS instrument accuracy allows for exploitation of fractional mass data in peptide identification.
- Findings enhance understanding of protease specificity in proteomics and MS-based analyses.
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