Targeted 18O-labeling for improved proteomic analysis of carbonylated peptides by mass spectrometry

靶向 18O 标记可提高质谱法对羰基化肽的蛋白质组学分析

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作者:Mikel R Roe, Thomas F McGowan, LaDora V Thompson, Timothy J Griffin

Abstract

Proteomic characterization of carbonylated amino acid sites currently relies on confidently matching tandem mass spectra (MS(2)) to peptides within a sequence database. Although effective to some degree, reliable proteomic characterization of carbonylated peptides using this approach remains a challenge needing new, complementary solutions. To this end, we developed a method based on partial (18)O-labeling of reactive carbonyl modifications, which produces a unique isotope signature in mass spectra of carbonylated peptides and enables their detection without reliance on matching MS(2) spectra to a peptide sequence. Key to our method were optimized measures for eliminating trypsin-catalyzed incorporation of (18)O at peptide C-termini, and for stabilizing the incorporated (18)O within the carbonyl modification to prevent its loss during liquid chromatography separation. Applying our method to a rat skeletal muscle homogenate treated with the carbonyl modification 4-hyroxynonenal (4-HNE), we demonstrated its compatibility with solid-phase hydrazide enrichment of carbonylated peptides from complex mixtures. Additionally, we demonstrated the value of (18)O isotope signatures for confirming HNE-modified peptide sequences matched via sequence database searching, and identifying modified peptides missed by MS(2) and/or sequence database searching. Combining our (18)O-labeling method with a customized automated software script, we systematically evaluated for the first time the efficiency of MS(2) and sequence database searching for identifying HNE-modified peptides. We estimated that less than half of the modified peptides selected for MS(2) were successfully identified. Collectively, our method and software should provide valuable new tools for investigators studying protein carbonylation via mass spectrometry-based proteomics.

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