James Bollinger (Presenter)
Washington University School of Medicine
Bio: My studies have focused on the development and application of novel analytical techniques for the profiling of enzymes of clinical relevance. Specifically, he has utilized the triple-quadrupole ESI-MS/MS analytical platform to design reagents and assays that enable the sensitive and specific analysis of individual proteins as well as metabolite profiles derived from their enzymatic activity. In collaboration with multiple other labs, he has applied these assays to characterize relative abundance and kinetic profiles of several proteins with the greater intent of investigating in vivo roles in cellular homeostasis and disease progression.
Authorship: James Bollinger
Washington University School of Medicine, Department of Neurology, 660 S. Euclid Ave., St. Louis, MO. 63110
| Short Abstract This session discusses an empirical pipeline for the selection of peptides for a targeted proteomic experiment. Using plasma and cerebrospinal fluid as sample biological matrices, we will walk through a handful of examples and address the key considerations for assay design in bottom-up proteomics. By the conclusion of this session, participants should be able to: 1)Discuss the important physiochemical properties of a “proteotypic” peptide 2)Understand the difference between gene product and proteoform in the context of the limitations of bottom-up proteomics 3)Discuss the advantages and drawbacks of using recombinant or native protein standards 4)Select an appropriate protease to analyze their proteoform-of-interest 5)Discuss strategies for screening digested matrices for proteotypic peptides 6)Design simple experiments to optimize digestion conditions. |
Long Abstract
While the power of SRM in targeted proteomics is undeniable, the development of robust methodology has been hindered by sub-optimal approaches for the selection each target protein’s optimal or ‘proteotypic’ proxy peptides. A proteotypic peptide is defined here as a peptide that is specific to a given protein, lacks high-frequency, non-synonymous single nucleotide polymorphisms, lacks known post-translational modifications (unless specifically designing an assay for PTMs!), has physiochemical properties amenable to a robust separation by liquid chromatography and subsequent ionization in the mass spectrometer, and finally, has features that generate characteristic MS/MS fragmentation patterns via collision-induced dissociation. The identification of proteotypic peptides is crucial to the development of robust SRM assays since considerable amounts of time and resources are often spent to produce quantitative standards such as synthetic peptides(1,2), recombinant proteins from concatenated peptide sequences(3), or developing immunoaffinity reagents for the enrichment of low abundance tryptic peptides(4). Traditional strategies for selecting candidate peptides for SRM assays have relied either on the mining of DDA spectral libraries(5,6), the use of prediction algorithms trained on previous DDA experiments(7,8), or the costly synthesis of all in silico predicted tryptic peptides(9). The first two approaches are predicated on the assumption that peptides most frequently identified in DDA type experiments will produce peptides with the best signal-to-noise ratios for a targeted proteomic experiment. Unfortunately, the process of using discovery-based proteomics data is not a particularly useful approach for the selection of proteotypic peptides for targeted experiments(10). There are multiple reasons why a peptide might not be selected for MS/MS during a DDA experiment. Thus, a peptide not observed in a DDA experiment should not be excluded from consideration for an SRM experiment. Likewise, a peptide that gives an intense signal in DDA might also not be observed within a particular sample matrix due to post-translational modification or elimination from the functional protein by proteolytic cleavage. For these reasons and more, we advocate that an important initial step in the development of SRM assays should be the application of analytical standards for the empirical measurement of all a target protein’s peptides that fulfill some minimal selection criteria directly within one’s sample matrix of interest. Here we demonstrate a general strategy based on in vitro synthesized and/or native analytical standards for the systematic selection of optimal peptides for the development of an SRM assay. We apply peptide selection criteria based on a peptide’s physiochemical properties as well as the sensitivity and specificity of its SRM signal in its respective sample matrix. We also make considerations for its chromatographic properties, digestion kinetics and post-digestion stability. Using a handful of examples of proteins from plasma and cerebrospinal fluid, we will systematically walk through the empirical process of SRM peptide selection and discuss some of the key considerations for successful assay design.
References & Acknowledgements:
1)Gerber,S.A., Rush,J., Stemman,O., Kirschner,M.W., & Gygi,S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. U. S. A 100, 6940-6945 (2003).
2)Desiderio,D.M. & Kai,M. Preparation of stable isotope-incorporated peptide internal standards for field desorption mass spectrometry quantification of peptides in biologic tissue. Biomed. Mass Spectrom. 10, 471-479 (1983).
3)Beynon,R.J., Doherty,M.K., Pratt,J.M., & Gaskell,S.J. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods 2, 587-589 (2005).
4)Anderson,N.L. et al. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J. Proteome. Res. 3, 235-244 (2004).
5)Picotti, P. et al. Nat. Methods 5, 913–914 (2008).
6)Prakash, A. et al. J. Proteome Res. 8, 2733–2739 (2009).
7)Mallick, P. et al. Nat. Biotechnol. 25, 125–131 (2007).
8)Fusaro, V.A., Mani, D.R., Mesirov, J.P. & Carr, S.A. Nat. Biotechnol. 27, 190–198 (2009).
9)Picotti, P. et al. Nat. Methods 7, 43–46 (2010).
10)Stergachis, A. et al. Nat. Methods 8, 1041-1046 (2011).
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IP Royalty: no
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