The regulatory landscape of the yeast phosphoproteome.

The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data.

Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification.

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

IsobaricQuant enables cross-platform quantification, visualization, and filtering of isobarically-labeled peptides.

In mass spectrometry (MS)-based quantitative proteomics, labeling with isobaric mass tags such as iTRAQ and TMT can substantially improve sample throughput and reduce peptide missing values. Nonetheless, the quantification of labeled peptides tends to suffer from reduced accuracy due to the co-isolation of co-eluting precursors of similar mass-to-charge. Acquisition approaches such as multistage MS3 or ion mobility separation address this problem, yet are difficult to audit and limited to expensive instrumentation. Here we introduce IsobaricQuant, an open-source software tool for quantification, visualization, and filtering of peptides labeled with isobaric mass tags, with specific focus on precursor interference. IsobaricQuant is compatible with MS2 and MS3 acquisition strategies, has a viewer that allows assessing interference, and provides several scores to aid the filtering of scans with compression. We demonstrate that IsobaricQuant quantifications are accurate by comparing it with commonly used software. We further show that its QC scores can successfully filter out scans with reduced quantitative accuracy at MS2 and MS3 levels, removing inaccurate peptide quantifications and decreasing protein CVs. Finally, we apply IsobaricQuant to a PISA dataset and show that QC scores improve the sensitivity of the identification of protein targets of a kinase inhibitor. IsobaricQuant is available at https://github.com/Villen-Lab/isobaricquant.