Applying Label-Free Quantitation to Top Down Proteomics

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• 作者：Ioanna Ntai ; Kyunggon Kim ; Ryan T. Fellers ; Owen S. Skinner ; Archer D. Smith ; IV ; Bryan P. Early ; John P. Savaryn ; Richard D. LeDuc ; Paul M. Thomas ; Neil L. Kelleher
• 刊名：Analytical Chemistry
• 出版年：2014
• 出版时间：May 20, 2014
• 年：2014
• 卷：86
• 期：10
• 页码：4961-4968
• 全文大小：429K
• 年卷期：v.86,no.10(May 20, 2014)
• ISSN：1520-6882

文摘

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is 100鈥?00 渭g of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (i.e., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (rpd3) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the 螖rpd3 mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics.