Absolute two-point quantification of proteins using dimethylated proteotypic peptides.

Absolute targeted proteomics typically employs known amounts of synthetic stable isotopically labeled peptides which are mixed with the analyte and analysed by LC-MS to determine the concentration of proteins. In order to obtain more data, we evaluated the use of two different stable isotopes of the same peptide as spike-in for absolute quantification. For this purpose, peptide labeling by reductive amination was applied, which is a mild reaction for dimethylation of amine groups with very high yield. Three different forms can be generated with e.g., light and heavy labels for spike-in peptides, and medium label for endogenous peptides. The method was studied with peptides of apolipoprotein A-I, apolipoprotein B-100, and leucine-rich alpha-2-glycoprotein without and with serum. In serum, the endogenous protein concentrations were measured across four orders of magnitude by the two-point quantification method. Less than 20% of coefficient of variation (CV) values and strong correlation with R2 of 0.99 across three analytical replicates was observed. Most importantly, the two-point quantification method allows an internal quality control of the spike-in peptide as strong deviations in ratios calculated between the first and second reference indicate a methodical error. Because of the significant lower costs than synthetically stable isotopically labeled peptides, this approach might be particularly interesting for the absolute quantification of multiple proteins.

Author Correction: Large-scale intact glycopeptide identification by Mascot database search.

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Large-scale intact glycopeptide identification by Mascot database search.

Workflows capable of determining glycopeptides in large-scale are missing in the field of glycoproteomics. We present an approach for automated annotation of intact glycopeptide mass spectra. The steps in adopting the Mascot search engine for intact glycopeptide analysis included: (i) assigning one letter codes for monosaccharides, (ii) linearizing glycan sequences and (iii) preparing custom glycoprotein databases. Automated annotation of both N- and O-linked glycopeptides was proven using standard glycoproteins. In a large-scale study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in 24 serum samples. Thus, a single tool was developed that collectively allows the (i) elucidation of N- and O-linked glycopeptide spectra, (ii) matching glycopeptides to known protein sequences, and (iii) high-throughput, batch-wise analysis of large-scale glycoproteomics data sets.