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Mol Cell Proteomics. 2007 Jan;6(1):141-9. Epub 2006 Oct 29.

Shotgun glycopeptide capture approach coupled with mass spectrometry for comprehensive glycoproteomics.

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Institute for Systems Biology, Seattle, Washington 98103, USA.


We present a robust and general shotgun glycoproteomics approach to comprehensively profile glycoproteins in complex biological mixtures. In this approach, glycopeptides derived from glycoproteins are enriched by selective capture onto a solid support using hydrazide chemistry followed by enzymatic release of the peptides and subsequent analysis by tandem mass spectrometry. The approach was validated using standard protein mixtures that resulted in a close to 100% capture efficiency. Our capture approach was then applied to microsomal fractions of the cisplatin-resistant ovarian cancer cell line IGROV-1/CP. With a Protein Prophet probability value greater than 0.9, we identified a total of 302 proteins with an average protein identification rate of 136 +/- 19 (n = 4) in a single linear quadrupole ion trap (LTQ) mass spectrometer nano-LC-MS experiment and a selectivity of 91 +/- 1.6% (n = 4) for the N-linked glycoconsensus sequence. Our method has several advantages. 1) Digestion of proteins initially into peptides improves the solubility of large membrane proteins and exposes all of the glycosylation sites to ensure equal accessibility to capture reagents. 2) Capturing glycosylated peptides can effectively reduce sample complexity and at the same time increase the confidence of MS-based protein identifications (more potential peptide identifications per protein). 3) The utility of sodium sulfite as a quencher in our capture approach to replace the solid phase extraction step in an earlier glycoprotein chemical capture approach for removing excess sodium periodate allows the overall capture procedure to be completed in a single vessel. This improvement minimizes sample loss, increases sensitivity, and makes our protocol amenable for high throughput implementation, a feature that is essential for biomarker identification and validation of a large number of clinical samples. 4) The approach is demonstrated here on the analysis of N-linked glycopeptides; however, it can be applied equally well to O-glycoprotein analysis.

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