IMPROVING THE SUCCESS RATE OF PROTEOME ANALYSIS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Truly comprehensive proteome analysis is highly desirable in systems biology and biomarker discovery efforts. But complete proteome characterization has been hindered by the dynamic range and detection sensitivity of experimental designs, which are not adequate to the very wide range of protein abundances. Experimental designs for comprehensive analytical efforts involve separation followed by mass spectrometry-based identification of digested proteins. Because results are generally reported as a collection of identifications with no information on the fraction of the proteome that was missed, they are difficult to evaluate and potentially misleading. Here we address this problem by taking a holistic view of the experimental design and using computer simulations to estimate the success rate for any given experiment. Our approach demonstrates that simple changes in typical experimental designs can enhance the success rate of proteome analysis by five- to tenfold. A manuscript describing this work was published in Nature Biotechnology and the more recent publications below: Modeling mass spectrometry-based protein analysis. Eriksson J, Feny¿ D. Methods Mol Biol. 2011;694:109-17. Modeling experimental design for proteomics. Eriksson J, Feny¿ D. Methods Mol Biol. 2010;673:223-30.