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Neutron-Encoded Mass Signatures for Quantitative Top-Down Proteomics

The ability to acquire highly accurate quantitative data is an increasingly important part of any proteomics experiment, whether shotgun or top-down approaches are used. We recently developed a quantitation strategy for peptides based on neutron encoding, or NeuCode SILAC, which uses closely spaced...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2014-03, Vol.86 (5), p.2314-2319
Main Authors: Rhoads, Timothy W., Rose, Christopher M., Bailey, Derek J., Riley, Nicholas M., Molden, Rosalynn C., Nestler, Amelia J., Merrill, Anna E., Smith, Lloyd M., Hebert, Alexander S., Westphall, Michael S., Pagliarini, David J., Garcia, Benjamin A., Coon, Joshua J.
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Language:English
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Summary:The ability to acquire highly accurate quantitative data is an increasingly important part of any proteomics experiment, whether shotgun or top-down approaches are used. We recently developed a quantitation strategy for peptides based on neutron encoding, or NeuCode SILAC, which uses closely spaced heavy isotope-labeled amino acids and high-resolution mass spectrometry to provide quantitative data. We reasoned that the strategy would also be applicable to intact proteins and could enable robust, multiplexed quantitation for top-down experiments. We used yeast lysate labeled with either 13C6 15N2-lysine or 2H8-lysine, isotopologues of lysine that are spaced 36 mDa apart. Proteins having such close spacing cannot be distinguished during a medium resolution scan, but upon acquiring a high-resolution scan, the two forms of the protein with each amino acid are resolved and the quantitative information revealed. An additional benefit NeuCode SILAC provides for top down is that the spacing of the isotope peaks indicates the number of lysines present in the protein, information that aids in identification. We used NeuCode SILAC to quantify several hundred isotope distributions, manually identify and quantify proteins from 1:1, 3:1, and 5:1 mixed ratios, and demonstrate MS2-based quantitation using ETD.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac403579s