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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
Published on: November 13, 2021
RAPID AND AUTOMATED PROCESSING OF MALDI-FTICR/MS DATA FOR N-METABOLIC LABELING IN A SHOTGUN PROTEOMICS ANALYSIS
1Department of Chemistry, University of Georgia, Athens, Georgia 30602, USA.
Abstract:
Offline high performance liquid chromatography combined with matrix assisted laser desorption and Fourier transform ion cyclotron resonance mass spectrometry (HPLC-MALDI-FTICR/MS) provides the means to rapidly analyze complex mixtures of peptides, such as those produced by proteolytic digestion of a proteome. This method is particularly useful for making quantitative measurements of changes in protein expression by using (15)N-metabolic labeling. Proteolytic digestion of combined labeled and unlabeled proteomes produces complex mixtures that with many mass overlaps when analyzed by HPLC-MALDI-FTICR/MS. A significant challenge to data analysis is the matching of pairs of peaks which represent an unlabeled peptide and its labeled counterpart. We have developed an algorithm and incorporated it into a compute program which significantly accelerates the interpretation of (15)N metabolic labeling data by automating the process of identifying unlabeled/labeled peak pairs. The algorithm takes advantage of the high resolution and mass accuracy of FTICR mass spectrometry. The algorithm is shown to be able to successfully identify the (15)N/(14)N peptide pairs and calculate peptide relative abundance ratios in highly complex mixtures from the proteolytic digest of a whole organism protein extract.
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