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Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound. Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.

The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit goals of systems biology that require the knowledge of the absolute quantity of the system's components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ∼10 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution. The majority of all proteins expressed in the human osteosarcoma cell line U2OS were absolutely quantified by mass spectrometry. The quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell.

Selected reaction monitoring (SRM) is a powerful mass spectrometry technology to reliably detect selected protein targets, even those at very low abundance, but requires tedious assay development for each protein of interest. High-throughput SRM assay development is now possible by using crude synthetic peptide libraries without purification to represent each protein target. Selected reaction monitoring (SRM) uses sensitive and specific mass spectrometric assays to measure target analytes across multiple samples, but it has not been broadly applied in proteomics owing to the tedious assay development process for each protein. We describe a method based on crude synthetic peptide libraries for the high-throughput development of SRM assays. We illustrate the power of the approach by generating and applying validated SRM assays for all Saccharomyces cerevisiae kinases and phosphatases.

mProphet, a computational tool for statistically validating selected reaction monitoring (SRM) mass spectrometry data, is described. 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.

To understand the structure and function of large molecular machines, accurate knowledge of their stoichiometry is essential. In this study, we developed an integrated targeted proteomics and super‐resolution microscopy approach to determine the absolute stoichiometry of the human nuclear pore complex (NPC), possibly the largest eukaryotic protein complex. We show that the human NPC has a previously unanticipated stoichiometry that varies across cancer cell types, tissues and in disease. Using large‐scale proteomics, we provide evidence that more than one third of the known, well‐defined nuclear protein complexes display a similar cell type‐specific variation of their subunit stoichiometry. Our data point to compositional rearrangement as a widespread mechanism for adapting the functions of molecular machines toward cell type‐specific constraints and context‐dependent needs, and highlight the need of deeper investigation of such structural variants. The stoichiometry of the human nuclear pore complex is revealed by targeted mass spectrometry and super‐resolution microscopy. The analysis reveals that the composition of the nuclear pore and other nuclear protein complexes is remodeled as a function of the cell type. Synopsis The stoichiometry of the human nuclear pore complex is revealed by targeted mass spectrometry and super‐resolution microscopy. The analysis reveals that the composition of the nuclear pore and other nuclear protein complexes is remodeled as a function of the cell type. The human NPC has a previously unanticipated stoichiometry that varies across cell types. Primarily functional Nups are dynamic, while the NPC scaffold is static. Stoichiometries of many complexes are fine‐tuned toward cell type‐specific needs.

High-throughput peptide synthesis and mass spectrometry are used to generate a near-complete reference map of the Saccharomyces cerevisiae proteome; two versions of the map (supporting discovery- and hypothesis-driven proteomics) are then applied to a protein-based quantitative trait locus analysis. A global map of yeast proteins Complete 'gold standard' reference maps of the components within a system are valuable resources for a research community. This paper presents one such resource, a complete mass-spectrometric reference map of the budding yeast Saccharomyces cerevisiae . The map comes in two versions — one for discovery-driven (shotgun) and the other for hypothesis-driven (targeted) proteomic measurements — and will support most studies performed with contemporary proteomic technologies. The maps provide essentially a set of highly specific assays for the detection and quantification of every yeast protein in any sample, and their value is demonstrated here in a protein quantitative trait locus analysis. Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage 1 , 2 , 3 . Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) 3 , 4 and the other supporting hypothesis-driven (targeted) 5 , 6 proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis 7 , which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

NOTE: In the version of this Brief Communication initially published, an author name (Lukas Mueller) was incorrect. The correct author name is Lukas N Mueller. The error has been corrected in the HTML and PDF versions of the article. We describe a method to identify cross-linked peptides from complex samples and large protein sequence databases by combining isotopically tagged cross-linkers, chromatographic enrichment, targeted proteomics and a new search engine called xQuest. This software reduces the search space by an upstream candidate-peptide search before the recombination step. We showed that xQuest can identify cross-linked peptides from a total Escherichia coli lysate with an unrestricted database search.

Miscarriages affect 50-70% of all gestations and 15-20% of clinically recognized pregnancies. Recurrent pregnancy loss (RPL) occurs in 1-5% of clinical pregnancies and has an enormous demographic impact. However, the etiologies and molecular pathways of RPL are scarcely understood, and therefore, reliable diagnostic and preventive methods are not yet available. Here, we aimed to discover novel biomarkers for RPL using next-generation proteomics technology to help develop early and effective diagnostic tools. First-trimester blood samples were collected from women with RPL ( =11) and controls with elective termination of pregnancy ( =11) between 6-13 weeks of gestation. After immunodepleting 14 highly abundant proteins, plasma samples were reduced, alkylated, and trypsin digested. For the separation of peptides, nano-flow reversed-phase chromatography was applied, and then mass spectrometric analysis was performed. Differentially abundant (DA) proteins were identified using strict criteria and analyzed by protein network and Gene Ontology (GO) enrichment analyses, and two biomarker candidates (CGB and PAPPA) were validated by immunoassay. Biomarker predictive properties were demonstrated using Receiver Operating Characteristic (ROC) curves. Assessments were performed for all cases and then for two gestational age groups, before and after the start of placental circulation [\"early RPL\": gestational weeks (GW) 6-9, \"late RPL\": GW 9 13]. Altogether, 651 proteins were identified and quantified across all samples. When comparing \"early control\" and \"late control\" samples, 60 proteins [11 predominantly placenta-expressed (PPE)] were DA. When analyzing all cases, 50 DA proteins were found in RPL (top 3 down: PZP, PSG9, CGB; top 3 up: C4BPA, HBA, HBB), among which 11 PPE proteins were found, all downregulated. Enriched GO terms included 'placental function', 'oxidative processes', 'immune function', and 'blood coagulation' related biological processes. When cases were split into early and late RPL groups, 40 DA proteins were identified in early RPL (top 3 down: SHBG, CGB, CGA; top 3 up: C4BPA, SAMP, C4BPB) and 90 in late RPL (top 3 down: PZP, PAPPA, PSG9; top 3 up: THBS1, ECM1, HBB), among which only 15 were shared by both RPL groups. In early RPL, only 'placental function' and 'immune function' related biological processes were enriched, while in late RPL the top enriched GO terms included 'placental function', 'oxidative processes', 'immune function', 'blood coagulation', 'angiogenesis', 'cell migration', and 'blood circulation' related biological processes. Among GO terms, only 'placental function' related biological processes were enriched when early- and late RPL DA proteins were analyzed together. Furthermore, the areas under the ROC curves were >0.9 for two protein candidates in all RPL, for five proteins in early RPL, and for ten proteins in late RPL. Among these candidates, CGB and PAPPA were validated by immunoassay which showed a good correlation with MS data (R =0.795 and R =0.965). We discovered distinct as well as shared molecular pathways associated with RPL pathogenesis before and after the start of placental circulation and identified novel biomarkers for these pathways which have outstanding discriminative properties. Our results may facilitate a better understanding of the molecular pathways of RPL. However, larger clinical studies are needed to investigate whether the identified biomarkers also have predictive power for RPL before pregnancies fail and to test drugs for the modulation of the identified disease pathways and the prevention of RPL. Our findings highlight the importance of the maternal immune system in maintaining successful pregnancy and suggest that targeting immune pathways may offer novel therapeutic approaches for RPL.

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching ∼50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.

Biological systems are controlled by protein complexes that associate into dynamic protein interaction networks. We describe a strategy that analyzes protein complexes through the integration of label-free, quantitative mass spectrometry and computational analysis. By evaluating peptide intensity profiles throughout the sequential dilution of samples, the MasterMap system identifies specific interaction partners, detects changes in the composition of protein complexes and reveals variations in the phosphorylation states of components of protein complexes. We use the complexes containing the human forkhead transcription factor FoxO3A to demonstrate the validity and performance of this technology. Our analysis identifies previously known and unknown interactions of FoxO3A with 14-3-3 proteins, in addition to identifying FoxO3A phosphorylation sites and detecting reduced 14-3-3 binding following inhibition of phosphoinositide-3 kinase. By improving specificity and sensitivity of interaction networks, assessing post-translational modifications and providing dynamic interaction profiles, the MasterMap system addresses several limitations of current approaches for protein complexes.