Abstract
Terminal amine isotopic labeling of substrates (TAILS), our recently introduced platform for quantitative N-terminome analysis, enables wide dynamic range identification of original mature protein N-termini and protease cleavage products. Modifying TAILS by use of isobaric tag for relative and absolute quantification (iTRAQ)-like labels for quantification together with a robust statistical classifier derived from experimental protease cleavage data, we report reliable and statistically valid identification of proteolytic events in complex biological systems in MS2 mode. The statistical classifier is supported by a novel parameter evaluating ion intensity-dependent quantification confidences of single peptide quantifications, the quantification confidence factor (QCF). Furthermore, the isoform assignment score (IAS) is introduced, a new scoring system for the evaluation of single peptide-to-protein assignments based on high confidence protein identifications in the same sample prior to negative selection enrichment of N-terminal peptides. By these approaches, we identified and validated, in addition to known substrates, low abundance novel bioactive MMP-2 targets including the plasminogen receptor S100A10 (p11) and the proinflammatory cytokine proEMAP/p43 that were previously undescribed.
Highlights
Terminal amine isotopic labeling of substrates (TAILS), our recently introduced platform for quantitative N-terminome analysis, enables wide dynamic range identification of original mature protein N-termini and protease cleavage products
Modifying TAILS by use of isobaric tag for relative and absolute quantification-like labels for quantification together with a robust statistical classifier derived from experimental protease cleavage data, we report reliable and statistically valid identification of proteolytic events in complex biological systems in MS2 mode
Very effective in substrate discovery, 33 new substrates and 148 other cleavage sites in previously known but not characterized substrates were discovered for matrix metalloproteinase (MMP)-2, any MS1 quantification such as isotopic acetylation, dimethylation, and stable isotope labeling with amino acids in cell culture (SILAC) leads to higher sample complexity because of the additional heavy and light labeled peptide pairs and lower proteome coverage and fewer identifications of low abundance proteins by mass spectrometrybased proteomics [30]
Summary
Cell Culture and Secretome Preparation—Mmp2Ϫ/Ϫ mouse embryonic fibroblasts used in this study were derived and cultured as described previously [32]. In-line Liquid Chromatography and Mass Spectrometry Analysis— Peptide separations by nano-LC (C18 150-mm ϫ 100 m-column at a flow rate of 100 –200 nl/min) were performed in line with tandem MS/MS analysis. Purified products were collected and dried under vacuum, and 0.5–1 mg aliquots were stored at Ϫ80 °C until further use in protein labeling reactions. Searches were evaluated by PeptideProphet (using the number of tryptic termini model for prepullout but not for pullout analyses), and subsequently, data from both experiments were combined in a single peptide list using the iProphet algorithm [43] This list was processed by ProteinProphet [44] without assembling protein groups and filtered for proteins with ProteinProphet probability Ͼ0.9. Protein sequence logos were generated using the iceLogo software package [48] with random sampling of the reference database
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