title: A statistical model for identifying proteins by tandem mass spectrometry.
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.
authors: Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington 98103, USA. nesvi@systemsbiology.org
authors: Alexey I
authors: AI
authors: Nesvizhskii
authors: Andrew
authors: A
authors: Keller
authors: Eugene
authors: E
authors: Kolker
authors: Ruedi
authors: R
authors: Aebersold
mesh_terms: D000595:Amino Acid Sequence; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010455:Peptides / Q000032:analysis / Q000737:chemistry; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*
chemical_list: D010455:Peptides; D011506:Proteins