Comparison of Programmatic Approaches for Efficient Accessing to mzML Files

Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS

Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D₂O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.

Use of singular value decomposition analysis to differentiate phosphorylated precursors in strong cation exchange fractions

We studied the use of peak deviations (PDs) for application in phosphoproteomics. Due to the differences in the mass defects, the PDs of samples containing mixtures of phosphorylated and nonphosphorylated peptides show bimodal distributions. The ratios of peak heights accurately predict the phosphoproteome content of a sample. In this work, we apply a signal-processing tool, singular value decomposition, to reveal characteristic features of the phosphorylated, nonphosphorylated, and mixed samples. We show that a simple application of singular value decomposition to the PD matrix (i) detects transitions from mostly phosphorylated samples to mostly nonphosphorylated samples, (ii) reveals modes of low-abundance species in the presence of the high-abundance species (e.g., phosphorylated peptides), and (iii) simplifies the interpretation of the clustering of a covariance matrix obtained from PDs. As the eigenfunctions of the inner-product of the data matrix (made from the PDs) are Hermite functions, we observe a change of sign in the transition from samples enriched in phosphorylated peptides to samples containing fewer phosphorylated peptides. The ordering of the singular values of the data matrix points in the direction of changes to the phosphorylation content. No peptide identifications from a database were used for this study.

Use of theoretical peptide distributions in phosphoproteome analysis

The high mass accuracy and resolution of modern mass spectrometers provides new opportunities to employ theoretical peptide distributions in large-scale proteomic studies. We used theoretical distributions to study noise filtering and mass measurement errors and to examine mass-based differentiation of phosphorylated and nonphosphorylated peptides. Only the monoisotopic mass of the experimental precursor ion was necessary for this analysis. We found that peak deviations can be used to characterize the modification states of peptides in a sample. When applied to large-scale proteomic data sets, the peak deviation distribution can be used to filter chemical/electronic noise for singly charged species. Using peak deviation distributions, it is possible to separate the phosphorylated peptides from the nonphosphorylated peptides, enabling evaluation of the phosphoproteome content of a sample. Because this approach is simple, with light computational requirements, the analysis of theoretical peptide distributions has a significant potential for application to phosphoproteome analyses. For our studies we used publicly available data sets from three large-scale proteomic studies.