Journal Article10.1021/AC0341261
A statistical model for identifying proteins by tandem mass spectrometry.
TL;DR: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample, and it is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications.
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Abstract: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation−maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identif...
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References
Probability-based protein identification by searching sequence databases using mass spectrometry data.
TL;DR: A new computer program, Mascot, is presented, which integrates all three types of search for protein identification by searching a sequence database using mass spectrometry data, and the scoring algorithm is probability based.
8.8K
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.
Functional organization of the yeast proteome by systematic analysis of protein complexes
Anne-Claude Gavin,Markus Bösche,Roland Krause,Paola Grandi,Martina Marzioch,Andreas Bauer,Jörg Schultz,Jens Rick,Anne-Marie Michon,Cristina-Maria Cruciat,Marita Remor,Christian Höfert,Malgorzata Schelder,Miro Brajenovic,Heinz Ruffner,Alejandro Merino,Karin Klein,Manuela Hudak,David Dickson,Tatjana Rudi,Volker Gnau,Angela Bauch,Sonja Bastuck,Bettina Huhse,Christina Leutwein,Marie-Anne Heurtier,Richard R. Copley,Angela Edelmann,Erich Querfurth,Vladimir Rybin,Gerard Drewes,Manfred Raida,Tewis Bouwmeester,Peer Bork,Bertrand Séraphin,Bernhard Kuster,Gitte Neubauer,Giulio Superti-Furga +37 more
TL;DR: The analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions, which contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.
Large-scale analysis of the yeast proteome by multidimensional protein identification technology.
TL;DR: 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.
Proteomics to study genes and genomes
TL;DR: Proteomics can be divided into three main areas: protein micro-characterization for large-scale identification of proteins and their post-translational modifications; ‘differential display’ proteomics for comparison of protein levels with potential application in a wide range of diseases; and studies of protein–protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system.