data shop
Holistic therapy for proteomics experiments Computer simulation predicts whether a particular shotgun proteomics setup will be successful.
© 2007 AMERICAN CHEMICAL SOCIET Y
DAVID FENYÖ
simulations actually document what some researchers have started to suspect—that the shotgun strategy has major flaws. “We have largely moved away from the shotgun workflow precisely for the reasons that come out of the model,” he says. (a) Number of proteins
common complaint among proteomics scientists is that the dynamic ranges of many samples, which can be ≥1010, are too large for all of the proteins to be detected in shotgun experiments. To overcome this challenge, many scientists are developing instruments that can detect a larger span of protein abundances, while others are improving the peptide separation step. These parameters might not be the best ones to address when attempting to optimize proteomics experiments, however. To predict whether a particular change to the workflow will be beneficial, Jan Eriksson and David Fenyö at the Swedish University of Agricultural Sciences and Rockefeller University took a step back and reexamined the entire shotgun LC/MS/MS proteomics workflow with a holistic perspective. After speaking to colleagues and identifying important parameters, they developed a computer simulation that helps researchers optimize their experiments before they ever lift a pipette (Nat. Biotechnol. 2007, 25, 651–655). When Eriksson and Fenyö initially told other scientists about their plans to model proteomics experiments, they were met with skepticism. Fenyö says that the consensus was that the undertaking would be too hard. “It is hard, and our model is still very simple,” he says. “There are a lot of properties it doesn’t catch, but you can still get an overall idea of how the system behaves.” According to Fenyö, the goal was to have a model that simulated reallife trends. Jeff Kowalak at the U.S. National Institutes of Health applauds this aspect of the work. “The pie-inthe-sky promises of the characterization of entire proteomes in one fell swoop have not been realized,” he says. “It is time to start thinking about proteomics experiments in realistic terms.” Ruedi Aebersold at the Swiss Federal Institute of Technology Hönggerberg says the
Proteins in sample
Success rate is defined as the ratio of the areas.
Detected proteins Log (protein amount)
(b) Fraction of proteins detected
A
RDR90
RDR
RDR50
RDR10
Log (protein amount)
Researchers assessed the outcomes of proteomics workflow simulations by determining (a) the success rate and (b) the RDR.
Once the simulation was developed, the researchers tested it by varying several parameters. Outcomes were assessed by the success rate and relative dynamic range (RDR). The success rate is the ratio of the number of observed proteins to the total number of proteins in the proteome. The RDR is the ratio of the log of the dynamic range of the detected proteins to the log of the dynamic range of the whole proteome. Surprisingly, improved peptide separations (or enhanced dynamic range of the mass spectrometer) did not produce the best results. Instead, the largest gains in success rate and RDR were ob-
tained when the researchers simulated an increase in the amount of peptide loaded (or better detection sensitivity on a mass spectrometer). In addition, the order in which the improvements were implemented was important. “So if you don’t load more material or improve the detection limit of the mass spectrometer, you gain very little in overall dynamic range of your experiment by improving only the separation or the dynamic range of the mass spectrometer,” says Fenyö. He and Eriksson also validated this finding by analyzing a small amount of a yeast tryptic digest with the shotgun proteomics strategy. A better peptide separation increased the success rate but did not improve the RDR. So although more proteins were detected, they were the proteins present at high abundances. The simulation points out a few important aspects of the shotgun proteomics strategy, but experts say there is room for improvement. “Like for any simulation or model, the output is only as good as the model itself and the input. On both levels the current model is very limited,” says Aebersold. For example, the model does not consider the specific resolving power of separation methods, the ability of peptides to be ionized and to fragment in a mass spectrometer, and the redundancy of peptides in an MS analysis, he points out. Fenyö acknowledges that many refinements still are necessary but has high hopes for the use of simulations in proteomics. He says that, in the future, proteomics researchers routinely could run an improved version of the simulation before performing labor-intensive experiments. With a holistic view of their workflows, scientists could optimize and tailor the experiments to get the most bang for their buck, says Fenyö. The current version is available at http://prowl. rockefeller.edu/modeling. a —Katie Cottingham
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