Deamidation is a variable, non-enzymatic modification whereby a functional amide group is removed from glutamine and asparagine residues. This commonly occurs when protein samples are prepared under basic conditions. For example, researchers generally perform tryptic digestion protocols at a pH range of 7–8, which may cause asparagine and glutamine residues to deamidate into aspartic acid and glutamic acid, respectively. In fact, some studies indicate that between 70% and 80% of asparagine and glutamine residues deamidate after tryptic digestion.1 Because deamidation results in a minor mass shift of only 0.98 Da, wide mass tolerance parameters and/or low resolving power instrumentation may lead to the misidentification of the affected peptides.
Nepomuceno et al. (2014) used a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Scientific) for the proteomic analysis of mouse brain tissue lysate, focusing on deamidated peptides.2 Reliance upon the instrument’s high resolving power and mass accuracy allowed researchers to overcome the challenges associated with quantifying low-abundance peptides with close mass values. For this experiment, the Orbitrap mass analyzer technology provided acquisition rates of >12 Hz and 17.5 kFWHM resolving power.3,4
The researchers observed that higher precursor mass tolerance values yielded a greater number of identified proteins; however, these numbers were skewed. Of 59,055 total identified peptides, they observed two distinct distribution patterns: one with a precursor mass measurement accuracy of ±5 ppm and the second between 5 and 20 ppm; 7.2% of the peptides in the first grouping were deamidated, while a staggering 95.7% of the second group were deamidated. The researchers concluded that for the group with the wider mass tolerance, the software inaccurately identified peaks as deamidated peptides, due to the very small mass shift of 0.98 Da.
The team also confirmed that deamidated and amidated peptides possess unique elution times. They selected the peptide DISTNYYASQKK and observed that the deamidated form eluted 3 minutes before the amidated form. The mass errors for these peptides were ±3 ppm. The researchers noted that the combination of high mass measurement accuracy, MS/MS spectra, and unique elution times can assist in correctly identifying deamidated peptides.
Nepomuceno et al. searched the data, both with and without deamidation selected as a variable modification. At the ideal mass tolerance (5 ppm), the difference in identified proteins was not significant (4,933 identified proteins without deamidation selected versus 4,822 identified proteins with deamidation selected). However, with wider mass tolerance (20 ppm and 100 ppm), the selection of deamidation as a searchable variable modification resulted in a greater number of identified peptides. The researchers indicate that this is likely to be a result of the previously discussed misidentified deamidated peptides that occur within the wider window. They also note that they observed no significant differences in global proteomic data sets between normalized spectral counts among proteins searched with or without deamidation as a variable modification.
Overall, the researchers acquired more accurate identifications by excluding deamidation as a variable modification while interrogating with a narrow mass tolerance. Increasing the mass tolerance beyond 5 ppm resulted in a false increase in identified proteins, due to the same peptide being identified twice–once as animated and once as deaminated. For this reason, it is vital not only to focus on necessary instrumentation features (such as high mass accuracy and high resolving power) but also to concentrate on precise search parameters using bioinformatics software.
1. Krokhin, O.V., et al. (2006) “Deamidation of -Asn-Gly- sequences during sample preparation for proteomics: Consequences for MALDI and HPLCMALDI analysis,” Analytical Chemistry, 78(18) (pp. 6645–50).
2. Nepomuceno, A. (2014) “Accurate Identification of Deamidated Peptides in Global Proteomics Using a Quadrupole Orbitrap Mass Spectrometer,” Journal of Proteome Research, 13(2) (pp. 777–85), doi: 10.1021/pr400848n.
3. Michalski, A., et al. (2011) “Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer,” Molecular Cellular Proteomics, 10(9), M111.011015.
4. Kelstrup, C.D., et al. (2012) “Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole orbitrap mass spectrometer,” Journal of Proteome Research, 11(6) (pp. 3487–97).
Post Author: Melissa J. Mayer. Melissa is a freelance writer who specializes in science journalism. She possesses passion for and experience in the fields of proteomics, cellular/molecular biology, microbiology, biochemistry, and immunology. Melissa is also bilingual (Spanish) and holds a teaching certificate with a biology endorsement.