Publications by authors named "Barbara Gregorius"

Quantitative mass spectrometry is a powerful tool for the determination of enzyme activities as it does not require labeled substrates and simultaneously allows for the identification of reaction products. However, major restrictions are the limited number of samples which can be measured in parallel due to the need for isotope labeled internal standards. Here we describe the use of metal labeling of peptides for the setup of multiplexed enzyme activity assays.

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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.

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Background: The 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.

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Metal labelling of peptides and proteins using high-affinity metal-chelating compounds has found widespread applications in the medical and bioanalytical fields. In the present study we investigated the analysis of peptides derivatized either with cysteine- or amino group-directed metal-bound DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) chelators in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The metal complexes of DOTA were shown to be stable under MALDI-MS conditions.

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Motivation: Mass spectrometry (MS) is one of the most important techniques for high-throughput analysis in proteomics research. Due to the large number of different proteins and their post-translationally modified variants, the amount of data generated by a single wet-lab MS experiment can easily exceed several gigabytes. Hence, the time necessary to analyze and interpret the measured data is often significantly larger than the time spent on sample preparation and the wet-lab experiment itself.

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