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.