Improvement of phosphoproteome analyses using FAIMS and decision tree fragmentation. application to the insulin signaling pathway in Drosophila melanogaster S2 cells.

This report examines the analytical benefits of high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to liquid chromatography mass spectrometry (LC-MS) for phosphoproteomics analyses. The ability of FAIMS to separate multiply charged peptide ions from chemical interferences confers a unique advantage in phosphoproteomics by enhancing the detection of low abundance phosphopeptides. LC-FAIMS-MS experiments performed on TiO(2)-enriched tryptic digests from Drosophila melanogaster provided a 50% increase in phosphopeptide identification compared to conventional LC-MS analysis. Also, FAIMS can be used to select different population of multiply charged phosphopeptide ions prior to their activation with either collision activated dissociation (CAD) or electron transfer dissociation (ETD). Importantly, FAIMS enabled the resolution of coeluting phosphoisomers of different abundances to facilitate their unambiguous identification using conventional database search engines. The benefits of FAIMS in large-scale phosphoproteomics of D. melanogaster are further investigated using label-free quantitation to identify differentially regulated phosphoproteins in response to insulin stimulation.

Protein and gene model inference based on statistical modeling in k-partite graphs.

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.

Three machine learning algorithms as least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP) were used to model the quantitative structure-retention relationship (QSRR) for predicting and explaining the retention behavior of proteome-wide peptides in the reverse-phase liquid chromatography. Peptides were parameterized using CODESSA approach and 145 descriptors were obtained for each peptide, including diverse structural information such as constitutional, topological, geometrical and physicochemical property. Based upon that, the nonlinear LSSVM, RF and GP as well as another sophisticated linear method (partial least-squares regression (PLS)) were employed in the QSRR model development. By a series of systematic validations as internal cross-validation, external test and Monte Carlo cross-validation, the stability and predictive power of the constructed models were confirmed. Results show that regression models developed using nonlinear approaches such as LSSVM, RF and GP predict better than linear PLS models. Considering the retention times used in this work were measured in different columns and thus have a relatively large uncertainty (reproducibility within 7%), the optimal statistics obtained from GP modeling are satisfactory, with the coefficients of determination (R2) for training set and test set of 0.894 and 0.866, respectively.