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Large-scale analysis of the yeast proteome by multi dimensional protein identification
technology
Michael P. Washburn, Dirk Wolters and John R. Yates
Nature Biotechnology
Volume 19 Pages 242-247
PRIYANK JAIN NISHANT CHORADIA
DIVYA KALRA
09 OCTOBER’08
THE DIFFERENT MINING APPROCHES
2D-PAGE & MS
Well-suited to simpler samples where the goal is to characterize major system components
Advantages• offers high throughput• takes advantage of powerful
protein-separation methodology
Unable to identify• proteins with extremes in pI and
molecular weight• low – abundance proteins• membrane associated or bound
proteins
MuDPIT
For exhaustive mining of both high- and low-abundance proteins in complex mixtures, MuDPIT is the most effective approach
Advantages• generates the most reliable protein
identification because it is based on MS-MS spectra, which directlyindicates peptide sequences
Technically challenging and stillrapidly evolving
WHY MuDPIT?
• Previous proteomic analyses of the S.cerevisiae yielded 279 proteins in a single study using 2D-PAGE coupled to MS
• Wide variety of systems coupling multidimensional chromatography to MS have been used but none identified > 200 proteins from any sample
• A fully automated high-throughput method was needed that combined resolution and identification removing all sample-handling steps once the sample has been loaded.
• A fully online 2D LC/MS/MS system like MuDPIT fulfills both of these requirements
• MuDPIT – resolution of peptides and the generation of tandem MS occur simultaneously
• MuDPIT may specifically be applied to integral membrane proteins to obtain detailed biochemical information on this unwidely class of proteins
EXPERIMENT
• S.cerevisiae was grown till mid-log phase, lysed and three different fractions were generated for analysis – soluble fraction, lightly washed insoluble fraction and heavily washed insoluble fraction
• Digestion of the soluble fraction was done using Endoproteinase Lys-C and trypsin and a complex peptide mixture was prepared for amino acid analysis on each sample
• Digestion of the insoluble fractions was done using formic acid and CNBr and a complex peptide mixture was prepared for amino acid analysis on each sample
• MuDPIT analysis was done on each sample • SEQUEST algorithm was run on each of the three data sets against the
yeast-orfs.fasta database
MuDPIT
RESULTS
• MuDPIT method is reproducible on the levels of both the chromatography and the final protein list
• The results are from the runs of three separate fractions• After combining the MS/MS data generated 5,540 peptides were assigned
to the MS spectra leading to the identification of 1,484 proteins from the S.cerevisiae proteome.
• Proteins identified in the AUTOQUEST were further analyzed using MIPS S.cerevisiae catalog
• The analysis showed that the results provide a representative sampling of the yeast proteome
• The results also proved that the MuDPIT method was largely unbiased
KNOWN SUB-CELLULAR LOCALIZATION OF PROTEINS IN S.cerevisiae
CODON ADAPTATION INDEX OF THE IDENTIFIED AND PREDICTED S.cerevisiae
PROTEOME
SENSITIVITY OF MuDPIT
IDENTIFICATION OF INTEGRAL & PERIPHERAL MEMBRANE PROTEINS
Continued…
PEPTIDE MAPPING OF THE INTEGRAL MEMBRANE PMA1
DISCUSSION
• Method used in this study provides a large-scale and global view of S.cerevisiae proteome
• Determined the proteins in a largely unbiased manner• The sensitivity level across class of proteins listed ranged from 13% of the
predicted proteins identified with pIs < 4.3 and MWs <10kDa to 43% of the predicted proteins identified with pIs >11.
• Method had slight biased against proteins with pIs < 4.3 and MWs < 10kDa
• Decreased sensitivity in these class of proteins – likely because of lack of tryptic peptides in the final mixture
CONCLUSION
• This work was a major step towards high-throughput methods because it was able to detect the low-abundance proteins, peripheral and integral membrane proteins.
• When emerging quantitative proteomic methods are combined with MuDPIT, true large-scale analysis of protein expression changes will be possible.
• Combination of MuDPIT with quantitative methods will allow for the integration of mRNA and protein expression levels needed to fully understand gene networks.