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.

Application of capillary isotachophoresis‐based multidimensional separations coupled with electrospray ionization‐tandem mass spectrometry for characterization of mouse brain mitochondrial proteome

“…Another measure of proteome data quality includes the ratio of total peptide hits to distinct protein identifications which were 22.4 (40 116 total peptide hits/1795 distinct protein) for the CITP/CZE-based proteome platform. This ratio becomes increasingly important when implementing the spectral counting-based protein quantification approach [56], as the expression levels of each protein are determined by the number of MS/ MS events. As reported by the common observation in the literature, most false peptide identifications tend to be ones in which the corresponding protein is only identified by a single peptide.…”

Functional distinctness in the exoproteomes of marine Synechococcus

“…A false‐positive rate below 0.1% for protein identification was estimated using a reverse decoy database as previously done (Christie‐Oleza et al ., 2012). Protein quantification by spectral abundance was done as previously described (Liu et al ., 2004). For normalized spectral abundance factors of each protein, spectral counts assigned to each polypeptide were divided by its molecular weight.…”

Multidimensional protein identification technology (MudPIT): Technical overview of a profiling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue

“…The cumulative spectral count was used as a semiquantitative metric for estimating relative protein abundance,°as°described°by°Liu°et°al.° [24].°Hierarchical clustering was performed using the Cluster 3.0 freeware software°package° [25]°and°the°Spearman°distance°met-ric, with average linkage selected. To improve the consistency of data grouping, a nominal low non-zero (0.01) value was substituted for blank (missing) values in cases where a protein was not detected in a particular sample.…”