Search

Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

Protein turnover rates severely decline in aging organisms, including C. elegans However, limited information is available on turnover dynamics at the individual protein level during aging. We followed changes in protein turnover at one-day resolution using a multiple-pulse 15N-labeling and accurate mass spectrometry approach. Forty percent of the proteome shows gradual slowdown in turnover with age, whereas only few proteins show increased turnover. Decrease in protein turnover was consistent for only a minority of functionally related protein subsets, including tubulins and vitellogenins, whereas randomly diverging turnover patterns with age were the norm. Our data suggests increased heterogeneity of protein turnover of the translation machinery, whereas protein turnover of ubiquitin-proteasome and antioxidant systems are well-preserved over time. Hence, we presume that maintenance of quality control mechanisms is a protective strategy in aging worms, although the ultimate proteome collapse is inescapable.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods., Graphical Abstract, Highlights • Computational method selection substantially affects phosphopeptide identification. • Deep-learning-derived metrics effectively discriminate correct and incorrect PSMs. • Novel metrics enable computational method comparison on real application data., In Brief Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying different computational pipelines to the same dataset may produce substantially different phosphopeptide identification results, underscoring a critical need for benchmarking. We present three deep-learning-derived benchmark metrics. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

Francisella tularensis is a facultative intracellular bacterium that causes the deadly disease tularemia. Most evidence suggests that Francisella is not well recognized by the innate immune system that normally leads to cytokine expression and cell death. In previous work, we identified new bacterial factors that were hyper-cytotoxic to macrophages. Four of the identified hyper-cytotoxic strains (lpcC, manB, manC, and kdtA) had an impaired lipopolysaccharide (LPS) synthesis and produced an exposed lipid A lacking the O-antigen. These mutants were not only hyper-cytotoxic but also were phagocytosed at much higher rates compared with the wild type parent strain. To elucidate the cellular signaling underlying this enhanced phagocytosis and cell death, we performed a large-scale comparative phosphoproteomic analysis of cells infected with wild-type and delta-lpcC F. novicida. Our data suggest that not only actin but also intermediate filaments and microtubules are important for F. novicida entry into the host cells. In addition, we observed differential phosphorylation of tristetraprolin, a key component of the mRNA-degrading machinery that controls the expression of a variety of genes including many cytokines. Infection with the delta-lpcC mutant induced the hyper-phosphorylation and inhibition of tristetraprolin, leading to the production of cytokines such as IL-1beta and TNF-alpha that may kill the host cells by triggering apoptosis. Together, our data provide new insights for Francisella invasion and a post-transcriptional mechanism that prevents the expression of host immune response factors that control infection by this pathogen.

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available.