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A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

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Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103
Cite this: Anal. Chem. 2003, 75, 17, 4646–4658
Publication Date (Web):July 15, 2003
https://doi.org/10.1021/ac0341261
Copyright © 2003 American Chemical Society
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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 identification data sets in the literature and for comparing the results obtained from different experiments.

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     Corresponding authors:  (e-mail) [email protected]; akeller@ systemsbiology.org.(fax) 206-732-1299.

    †

     Contributed equally to this work.

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     Current address:  BIATECH, North Creek Parkway, Suite 115, Bothell, WA 98011.

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    Coefficients of the derived discriminant function used to compute probabilities that SEQUEST search results of singly charged precursor ion spectra are correct, as well as data demonstrating improved discriminating power of peptide probabilities computed with adjustment for NSP and accuracy of computed peptide probabilities for the Halo_Hum and Hinf_Hum data sets. This material is available free of charge via the Internet at http://pubs.acs.org.

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