A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

Abstract

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

FIG. 1

Probability versus log ratio for the 24 distinct configurations of switch ON/OFF states. Note that the scaling of the probability axes varies from plot to plot. The dominant configuration is 110–111 (log ratio ~ log 2 = 0.693), shown in red. Its nearest subordinate configuration with a distinct maximum is 001-111 (log ratio ~ log 4 = 1.386), shown in green.

FIG. 2

The multiplicity-weighted sum of the configurations shown in Figure 1. The contribution from the dominant 110-111 configuration is shown in red, and that from the 001-111 configuration is shown in green.

FIG. 3

Quantification results for all proteins showing the 95% credible intervals. The colors denote identification probabilities. Nearly all of the E. coli proteins lie just below the line corresponding to a 1:1 ratio, indicating a slight differential E. coli amount between samples.

FIG. 4

Network diagram for the quantification of homologous proteins.

FIG. 5

Qualitative and quantitative identification of LLDEGQAGENVGLLLR by means of iTRAQ isotopic labeling. The inset shows the reporter ion region of interest for quantitative analysis.