期刊
MOLECULAR & CELLULAR PROTEOMICS
卷 10, 期 12, 页码 -出版社
AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.M111.010728
关键词
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资金
- National Institutes of Health (NIH) NCRR [RR01614, RR019934]
- G. Harold and Leila Y. Mathers Charitable Foundation
- NIH [T32 AG00278]
- Larry L. Hillblom Foundation
In a recent study, in vivo metabolic labeling using N-15 traced the rate of label incorporation among more than 1700 proteins simultaneously and enabled the determination of individual protein turnover rate constants over a dynamic range of three orders of magnitude (Price, J. C., Guan, S., Burlingame, A., Prusiner, S. B., and Ghaemmaghami, S. (2010) Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 107, 1450814513). These studies of protein dynamics provide a deeper understanding of healthy development and wellbeing of complex organisms, as well as the possible causes and progression of disease. In addition to a fully labeled food source and appropriate mass spectrometry platform, an essential and enabling component of such large scale investigations is a robust data processing and analysis pipeline, which is capable of the reduction of large sets of liquid chromatography tandem MS raw data files into the desired protein turnover rate constants. The data processing pipeline described in this contribution is comprised of a suite of software modules required for the workflow that fulfills such requirements. This software platform includes established software tools such as a mass spectrometry database search engine together with several additional, novel data processing modules specifically developed for N-15 metabolic labeling. These fulfill the following functions: (1) cross-extraction of N-15-containing ion intensities from raw data files at varying biosynthetic incorporation times, (2) computation of peptide N-15 isotopic incorporation distributions, and (3) aggregation of relative isotope abundance curves for multiple peptides into single protein curves. In addition, processing parameter optimization and noise reduction procedures were found to be necessary in the processing modules in order to reduce propagation of errors in the long chain of the processing steps of the entire workflow. Molecular & Cellular Proteomics 10: 10.1074/mcp.M111.010728, 1-8, 2011.
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