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Journal ArticleDOI

Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

TLDR
MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date, identifying 131 proteins with three or more predicted transmembrane domains which allowed us to map the soluble domains of many of the integral membrane proteins.
Abstract
We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

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Journal ArticleDOI

Mass spectrometry-based proteomics

TL;DR: The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.
Journal ArticleDOI

Universal sample preparation method for proteome analysis

TL;DR: A method is described, filter-aided sample preparation (FASP), which combines the advantages of in-gel and in-solution digestion for mass spectrometry–based proteomics and allows single-run analyses of organelles and an unprecedented depth of proteome coverage.
Journal ArticleDOI

Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

TL;DR: A statistical model is presented to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST, demonstrating that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides.
Journal ArticleDOI

Andromeda: a peptide search engine integrated into the MaxQuant environment

TL;DR: A novel peptide search engine using a probabilistic scoring model that can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, and accommodates extremely large databases.
References
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Journal ArticleDOI

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Journal ArticleDOI

Proteomics to study genes and genomes

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