Knowledge of cleavage site specificity and activity are major prerequisites for understanding protease function. On the basis of a recently presented approach for proteomic identification of cleavage sites (PICS) in proteome-derived peptide libraries, we developed an isobaric labeling quantitative LC-MALDI-TOF/TOF MS/MS approach (Q-PICS) for simultaneous determination of cleavage site specificity and robust relative quantification of proteolytic events. For GluC-protease, 737 cleavage sites were identified in a yeast proteome-derived peptide library; 94.0% showed the typical GluC specificity for peptide bonds at glutamyl and aspartyl residues. The six-plex tandem mass tagging strategy allowed for three simultaneous replicates in a single run, guaranteeing high confidence and robust statistics for quantitative measurements. Using the quantitative capacity of Q-PICS, we performed a comparison of cleavage site specificity of GluC in two different buffer systems. The results support earlier findings describing that apparent difference between the buffer systems are probably caused by the inhibitory effect of bicarbonate on the overall GluC activity and that the preference for Glu-X bonds compared to Asp-X bonds is independent of the buffer system used.
BackgroundThe robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context.ResultsWe propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns.ConclusionsWe find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The package implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html).
ARTC2.2 is a toxin-related, GPI-anchored ADP-ribosyltransferase expressed by murine T cells. In response to NAD+ released from damaged cells during inflammation, ARTC2.2 ADP-ribosylates and thereby gates the P2X7 ion channel. This induces ectodomain shedding of metalloprotease-sensitive cell surface proteins. In this study, we show that ARTC2.2 itself is a target for P2X7-triggered ectodomain shedding. We identify the metalloprotease cleavage site 3 aa upstream of the predicted GPI anchor attachment site of ARTC2.2. Intravenous injection of NAD+ increased the level of enzymatically active ARTC2.2 in serum, indicating that this mechanism is operative also under inflammatory conditions in vivo. Radio–ADP-ribosylation assays reveal that shedding refocuses the target specificity of ARTC2.2 from membrane proteins to secretory proteins. Our results uncover nucleotide-induced membrane-proximal proteolysis as a regulatory mechanism to control the substrate specificity of ARTC2.2.
Two peptide quantification strategies, the isobaric tags for relative or absolute quantitation (iTRAQ) labeling methodology and a metal-chelate labeling approach, were compared using matrix-assisted laser desorption/ionization-TOF/TOF MS and MS/MS analysis. Amino- and cysteine-directed labeling using the rare earth metal chelator 1,4,7,10-tetraazacyclododecane-N,N',N″,N″'-tetraacetic acid (DOTA) were applied for relative quantification of single peptides and a six-protein mixture. For analyte ratios close to one, iTRAQ and amino-directed DOTA labeling delivered overall comparable results regarding accuracy and reproducibility. In contrast, the MS-based quantification via amino-directed lanthanide-DOTA tags was more accurate for analyte ratios ≥5 and offered an extended dynamic range of three orders of magnitude. Our results show that the amino-directed DOTA labeling is an alternative relative quantification tool offering advantages like flexible multiplexing possibilities and, in particular, large dynamic ranges, which should be useful in sophisticated, targeted issues, where the accurate determination of extremely different protein or peptide concentration becomes relevant.
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