title: A model for random sampling and estimation of relative protein abundance in shotgun proteomics.
abstract: Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.
authors.affiliation: Department of Cell Biology, The Scripps Research Institute, La Jolla, California 92037, USA.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Liu
authors.forename: Rovshan G
authors.initials: RG
authors.lastname: Sadygov
authors.forename: John R
authors.initials: JR
authors.lastname: Yates
mesh_terms: D003625:Data Collection / Q000379:methods; D015233:Models, Statistical; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D011506:Proteins