An Automated Method for Scanning LC-MS Data Sets for Significant Peptides and Proteins, Including Quantitative Profiling and Interactive Confirmation

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• 作者：Anders Kaplan ; Malin Sö ; derströ ; m ; David Fenyö ; Anna Nilsson ; Maria Fä ; lth ; Karl Skö ; ld ; Marcus Svensson ; Harald Pettersen ; Staffan Lindqvist ; Per Svenningsson ; Per E. Andr&eacut
• 刊名：Journal of Proteome Research
• 出版年：2007
• 出版时间：July 2007
• 年：2007
• 卷：6
• 期：7
• 页码：2888 - 2895
• 全文大小：306K
• 年卷期：v.6,no.7(July 2007)
• ISSN：1535-3907

文摘

Differential quantification of proteins and peptidesby LC-MS is a promising method to acquire knowledgeabout biological processes, and for finding drug targetsand biomarkers. However, differential protein analysisusing LC-MS has been held back by the lack of suitablesoftware tools. Large amounts of experimental data areeasily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method forscanning LC-MS/MS data for biologically significantpeptides and proteins, including support for interactiveconfirmation and further profiling. By studying peptidemixtures of known composition, we demonstrate thatpeptides present in different amounts in different groupsof samples can be automatically screened for usingstatistical tests. A linear response can be obtained overalmost 3 orders of magnitude, facilitating further profilingof peptides and proteins of interest. Furthermore, weapply the method to study the changes of endogenouspeptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducingParkinson disease symptoms.