Combinatorial Peptide Libraries Facilitate Development of Multiple Reaction Monitoring Assays for Low-Abundance Proteins

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• 作者：Andrei P. Drabovich ; Eleftherios P. Diamandis
• 刊名：Journal of Proteome Research
• 出版年：2010
• 出版时间：March 5, 2010
• 年：2010
• 卷：9
• 期：3
• 页码：1236-1245
• 全文大小：352K
• 年卷期：v.9,no.3(March 5, 2010)
• ISSN：1535-3907

文摘

Low-abundance proteins present in biological fluids are often considered an attractive source of new disease biomarkers. Since such proteins are poorly observed in proteome-scale discovery experiments due to an overwhelming mass of high-abundance proteins, the development of quantitative multiple reaction monitoring (MRM) assays for low-abundance proteins is a challenging task. Here, we present a strategy that facilitates the development of MRM assays for large numbers of unpurified low-abundance proteins. Our discovery strategy is based on the reduction of the dynamic range of protein concentrations in biological fluids by means of one-bead one-compound combinatorial peptide libraries (CPL). Our 2D-LC-MS/MS approach allowed us to identify a total of 484 unique proteins in ovarian cancer ascites, and 216 proteins were assigned as low-abundance ones. Interestingly, 74 of those proteins have never been previously described in ascites fluid. Treatment with CPL allowed identification of a significantly higher number of unique peptides for low-abundance proteins and provided important empirical fragmentation information for development of MRM assays. Finally, we confirmed that MRM assays worked for 30 low-abundance proteins in the unfractionated ascites digest. Using a multiplexed MRM method, relative amounts of five proteins (kallikrein 6, metalloproteinase inhibitor 1, macrophage migration inhibitory factor, follistatin-related protein, and mesothelin) were determined in a set of ovarian cancer ascites. Multiplexed MRM assays targeting large numbers of proteins can be used to develop comprehensive panels of biomarkers with high sensitivity and selectivity, and to study complex protein networks.