Combinatorial Labeling Method for Improving Peptide Fragmentation in Mass Spectrometry
peer-reviewed · Journal of the American Society for Mass Spectrometry · 2017
| Date | 2017-06-01 |
| Type | peer-reviewed |
| Venue | Journal of the American Society for Mass Spectrometry |
| Publisher | American Chemical Society (ACS) |
| Contribution | adjacent |
| DOI | 10.1007/s13361-017-1606-2 |
| Citations (OpenAlex) | 16 |
| Venue 2-year citedness | 2.84 |
Abstract
Annotation of peptide sequence from tandem mass spectra constitutes the central step of mass spectrometry-based proteomics. Peptide mass spectra are obtained upon gas-phase fragmentation. Identification of the protein from a set of experimental peptide spectral matches is usually referred as protein inference. Occurrence and intensity of these fragment ions in the MS/MS spectra are dependent on many factors such as amino acid composition, peptide basicity, activation mode, protease, etc. Particularly, chemical derivatizations of peptides were known to alter their fragmentation. In this study, the influence of acetylation, guanidinylation, and their combination on peptide fragmentation was assessed initially on a lipase (LipA) from Bacillus subtilis followed by a bovine six protein mix digest. The dual modification resulted in improved fragment ion occurrence and intensity changes, and this resulted in the equivalent representation of b- and y-type fragment ions in an ion trap MS/MS spectrum. The improved representation has allowed us to accurately annotate the peptide sequences de novo. Dual labeling has significantly reduced the false positive protein identifications in standard bovine six peptide digest. Our study suggests that the combinatorial labeling of peptides is a useful method to validate protein identifications for high confidence protein inference. Graphical Abstract ᅟ.
Methods and tools
- Combinatorial acetylation/guanidinylation labeling: Combined acetylation and guanidinylation of peptides gives equivalent b- and y-ion representation in ion-trap MS/MS, allowing accurate de novo annotation.
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