Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.