Generation of a predicted protein database from EST data and application to iTRAQ analyses in grape (Vitis vinifera cv. Cabernet Sauvignon) berries at ripening initiation
Date
2009-01-26
Authors
Lucker, Joost
Laszczak, Mario
Smith, Derek
Lund, Steven T.
Journal Title
Journal ISSN
Volume Title
Publisher
BioMed Central
Abstract
Background: iTRAQ is a proteomics technique that uses isobaric tags for relative and absolute
quantitation of tryptic peptides. In proteomics experiments, the detection and high confidence
annotation of proteins and the significance of corresponding expression differences can depend on
the quality and the species specificity of the tryptic peptide map database used for analysis of the
data. For species for which finished genome sequence data are not available, identification of
proteins relies on similarity to proteins from other species using comprehensive peptide map
databases such as the MSDB.
Results: We were interested in characterizing ripening initiation ('veraison') in grape berries at the
protein level in order to better define the molecular control of this important process for grape
growers and wine makers. We developed a bioinformatic pipeline for processing EST data in order
to produce a predicted tryptic peptide database specifically targeted to the wine grape cultivar, Vitis
vinifera cv. Cabernet Sauvignon, and lacking truncated N- and C-terminal fragments. By searching
iTRAQ MS/MS data generated from berry exocarp and mesocarp samples at ripening initiation, we
determined that implementation of the custom database afforded a large improvement in high
confidence peptide annotation in comparison to the MSDB. We used iTRAQ MS/MS in conjunction
with custom peptide db searches to quantitatively characterize several important pathway
components for berry ripening previously described at the transcriptional level and confirmed
expression patterns for these at the protein level.
Conclusion: We determined that a predicted peptide database for MS/MS applications can be
derived from EST data using advanced clustering and trimming approaches and successfully
implemented for quantitative proteome profiling. Quantitative shotgun proteome profiling holds
great promise for characterizing biological processes such as fruit ripening initiation and may be
further improved by employing preparative techniques and/or analytical equipment that increase
peptide detection sensitivity via a shotgun approach.
Description
BioMed Central
Keywords
Citation
Lucker et al. Generation of a predicted protein database from EST data and application to iTRAQ analyses in grape (Vitis vinifera cv. Cabernet Sauvignon) berries at ripening initiation. BMC Genomics 2009, 10:50