High-throughput functional affinity purification of mannose binding proteins from Oryza sativa

We have used affinity chromatography in combination with mass spectrometry to isolate, identify, and assign a preliminary functional annotation to a large number of both known and novel proteins from rice. Rice (Oryza sativa) leaf, root, and seed tissue extracts were fractionated by column affinity...

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Veröffentlicht in:Proteomics (Weinheim) 2003-07, Vol.3 (7), p.1270-1278
Hauptverfasser: Andon, Nancy L., Eckert, Donna, Yates III, John R., Haynes, Paul A.
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Sprache:eng
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Zusammenfassung:We have used affinity chromatography in combination with mass spectrometry to isolate, identify, and assign a preliminary functional annotation to a large number of both known and novel proteins from rice. Rice (Oryza sativa) leaf, root, and seed tissue extracts were fractionated by column affinity chromatography using α‐D‐mannose as the ligand. Bound fractions were eluted and subjected to one‐dimensional electrophoresis, followed by high‐performance liquid chromatography‐tandem mass spectrometric analysis of separated proteins. This multiplexed technology resulted in the isolation and identification of 136 distinct mannose binding proteins from rice. A comparative analysis demonstrates very little overlap of identified proteins between the respective tissues, and confirms the correctly compartmentalized presence of a significant number of proteins from largely tissue‐specific biochemical pathways. Over 30% of the identified proteins with a previously annotated function are directly involved in sugar metabolism, including several highly expressed known rice lectins. Direct comparison of the peptide sequences identified in this study to those peptides identified in the most comprehensive survey of the rice proteome to date indicates that our current data represents a significant enrichment of proteins unique to this dataset. Nearly 15% of the identified proteins, identified on the basis of exact peptide matching to sequences in the rice genomic database, represent proteins without a previously known functional annotation, indicating the potential of this combined chromatographic approach to assign a preliminary function to novel proteins in a high‐throughput fashion.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.200300447