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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.

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma., (© 2019 Bruderer et al.)

Proteomics is the large-scale study of proteins. The field began with protein purification and analysis by various techniques but today is largely understood to be mass spectrometry-based highly multiplexed protein quantification. This article focusses on protein expression profiling, i.e. how much of each protein is in my sample or how much does the level of each protein change upon a change in the environment?

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines., (Copyright © 2016 Elsevier Inc. All rights reserved.)

It is of highest importance to find proteins responsible for breast cancer dissemination, for use as biomarkers or treatment targets. We established and performed a combined nontargeted LC-MS/MS and a targeted LC-SRM workflow for discovery and validation of protein biomarkers. Eighty breast tumors, stratified for estrogen receptor status and development of distant recurrence (DR ± ), were collected. After enrichment of N-glycosylated peptides, label-free LC-MS/MS was performed on each individual tumor in triplicate. In total, 1515 glycopeptides from 778 proteins were identified and used to create a map of the breast cancer N-glycosylated proteome. Based on this specific proteome map, we constructed a 92-plex targeted label-free LC-SRM panel. These proteins were quantified across samples by LC-SRM, resulting in 10 proteins consistently differentially regulated between DR+/DR- tumors. Five proteins were further validated in a separate cohort as prognostic biomarkers at the gene expression level. We also compared the LC-SRM results to clinically reported HER2 status, demonstrating its clinical accuracy. In conclusion, we demonstrate a combined mass spectrometry strategy, at large scale on clinical samples, leading to the identification and validation of five proteins as potential biomarkers for breast cancer recurrence. All MS data are available via ProteomeXchange and PASSEL with identifiers PXD001685 and PASS00643.

The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics. We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics. Utilizing HRM, we profiled acetaminophen (APAP)(1)-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD). Our findings imply that DIA should be the preferred method for quantitative protein profiling., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

In bottom-up mass spectrometry-based proteomics analyses, variability at any step of the process, particularly during sample proteolysis, directly affects the sensitivity, accuracy, and precision of peptide detection and quantification. Currently, no generic internal standards are available to control the quality of sample processing steps. This makes it difficult to assess the comparability of MS proteomic data obtained under different experimental conditions. Here, we describe the design, synthesis, and validation of a universal protein standard, called DIGESTIF, that can be added to any biological sample. The DIGESTIF standard consists of a soluble recombinant protein scaffold to which a set of 11 artificial peptides (iRT peptides) with good ionization properties has been incorporated. In the protein scaffold, the amino acids flanking iRT peptide cleavage sites were selected either to favor or hinder protease cleavage. After sample processing, the retention time and relative intensity pattern of the released iRT peptides can be used to assess the quality of sample workup, the extent of digestion, and the performance of the LC-MS system. Thus, DIGESTIF can be used to standardize a broad spectrum of applications, ranging from simple replicate measurements to large-scale biomarker screening in biomedical applications.

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. 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) and the other supporting hypothesis-driven (targeted) 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, 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.

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 rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Genetic analysis in Drosophila melanogaster has been widely used to identify a system of genes that control cell growth in response to insulin and nutrients. Many of these genes encode components of the insulin receptor/target of rapamycin (InR/TOR) pathway. However, the biochemical context of this regulatory system is still poorly characterized in Drosophila. Here, we present the first quantitative study that systematically characterizes the modularity and hormone sensitivity of the interaction proteome underlying growth control by the dInR/TOR pathway. Applying quantitative affinity purification and mass spectrometry, we identified 97 high confidence protein interactions among 58 network components. In all, 22% of the detected interactions were regulated by insulin affecting membrane proximal as well as intracellular signaling complexes. Systematic functional analysis linked a subset of network components to the control of dTORC1 and dTORC2 activity. Furthermore, our data suggest the presence of three distinct dTOR kinase complexes, including the evolutionary conserved dTTT complex (Drosophila TOR, TELO2, TTI1). Subsequent genetic studies in flies suggest a role for dTTT in controlling cell growth via a dTORC1- and dTORC2-dependent mechanism.

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.

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.

Targeted mass spectrometry using selected reaction monitoring (SRM) has emerged as the method of choice for the validation in blood serum, plasma, or other clinically relevant specimens of biomarker candidates arising from comparative proteomics or other discovery strategies. Here, we describe a method in which N-glycosites are selectively enriched from biological specimens by solid phase capture and PNGase F release, and then analyzed by SRM. Focusing the highly sensitive targeted mass spectrometry method on a subproteome enriched for secreted and shed proteins reproducibly identifies and quantifies such proteins in serum and plasma at the low nanogram per milliliter (ng/mL) concentration range. This protocol is intended to give an introduction to SRM-based targeted mass spectrometry with a special focus on the validation of biomarker candidates.

The Hippo (Hpo) pathway is a central determinant of tissue size in both Drosophila and higher organisms. The core of the pathway is a kinase cascade composed of an upstream kinase Hpo (MST1/2 in mammals) and a downstream kinase Warts (Wts, Lats1/2 in mammals), as well as several scaffold proteins, Sav, dRASSF, and Mats. Activation of the core kinase cassette results in phosphorylation and inactivation of the progrowth transcriptional coactivator Yki, leading to increased apoptosis and reduced tissue growth. The mechanisms that prevent inappropriate Hpo activation remain unclear, and in particular, the identity of the phosphatase that antagonizes Hpo is unknown. Using combined proteomic and RNAi screening approaches, we identify the dSTRIPAK PP2A complex as a major regulator of Hpo signaling. dSTRIPAK depletion leads to increased Hpo activatory phosphorylation and repression of Yki target genes in vivo, suggesting this phosphatase complex prevents Hpo activation during development., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Chemical cross-linking of reactive groups in native proteins and protein complexes in combination with the identification of cross-linked sites by mass spectrometry has been in use for more than a decade. Recent advances in instrumentation, cross-linking protocols, and analysis software have led to a renewed interest in this technique, which promises to provide important information about native protein structure and the topology of protein complexes. In this article, we discuss the critical steps of chemical cross-linking and its implications for (structural) biology: reagent design and cross-linking protocols, separation and mass spectrometric analysis of cross-linked samples, dedicated software for data analysis, and the use of cross-linking data for computational modeling. Finally, the impact of protein cross-linking on various biological disciplines is highlighted.

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.

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.

An analytical strategy for the analysis of antigen epitopes by chemical cross-linking and mass spectrometry is demonstrated. The information of antigen peptides involved in the binding to an antibody can be obtained by monitoring the antigen peptides modified by a partially hydrolyzed cross-linker in the absence and in the presence of an antibody. This approach was shown to be efficient for characterization of the epitope on bovine prion protein bPrP(25-241) specifically recognized by a monoclonal antibody, 3E7 (mAb3E7), with only a small amount of sample (200 picomoles) needed. After cross-linking of the specific immuno complex, a matrix-assisted laser desorption/ionization (MALDI) mass spectrometer equipped with an ion conversion dynode (ICD) high-mass detector was used to optimize the amount of cross-linked complex formed at 202 kDa before proteolytic digestion. To identify the cross-linked peptides after proteolysis without ambiguity, isotope-labeled cross-linkers, disuccinimidyl suberate (DSS-d0/d12) and disuccinimidyl glutarate (DSG-d0/d6), together with high-resolution Fourier transform ion-cyclotron resonance mass spectrometry (FTICR-MS) were used. As a result, a complete fading of the peak intensities corresponding to the peptides representing the epitope was observed when bPrP/mAb3E7 complexes were formed., ((c) 2008 John Wiley & Sons, Ltd.)

Protein complexes have largely been studied by immunoaffinity purification and (mass spectrometric) analysis. Although this approach has been widely and successfully used it is limited because it has difficulties reliably discriminating true from false protein complex components, identifying post-translational modifications, and detecting quantitative changes in complex composition or state of modification of complex components. We have developed a protocol that enables us to determine, in a single LC-MALDI-TOF/TOF analysis, the true protein constituents of a complex, to detect changes in the complex composition, and to localize phosphorylation sites and estimate their respective stoichiometry. The method is based on the combination of fourplex iTRAQ (isobaric tags for relative and absolute quantification) isobaric labeling and protein phosphatase treatment of substrates. It was evaluated on model peptides and proteins and on the complex Ccl1-Kin28-Tfb3 isolated by tandem affinity purification from yeast cells. The two known phosphosites in Kin28 and Tfb3 could be reproducibly shown to be fully modified. The protocol was then applied to the analysis of samples immunopurified from Drosophila melanogaster cells expressing an epitope-tagged form of the insulin receptor substrate homologue Chico. These experiments allowed us to identify 14-3-3epsilon, 14-3-3zeta, and the insulin receptor as specific Chico interactors. In a further experiment, we compared the immunopurified materials obtained from tagged Chico-expressing cells that were either treated with insulin or left unstimulated. This analysis showed that hormone stimulation increases the association of 14-3-3 proteins with Chico and modulates several phosphorylation sites of the bait, some of which are located within predicted recognition motives of 14-3-3 proteins.

The trafficking of intracellular vesicles is essential for a number of cellular processes and defects in this process have been implicated in a wide range of human diseases. We identify the zebrafish mutant lbk as a novel model for such disorders. lbk displays hypopigmentation of skin melanocytes and the retinal pigment epithelium (RPE), an absence of iridophore reflections, defects in internal organs (liver, intestine) as well as functional defects in vision and the innate immune system (macrophages). Positional cloning, an allele screen, rescue experiments and morpholino knock-down reveal a mutation in the zebrafish orthologue of the vam6/vps39 gene. Vam6p is part of the HOPS complex, which is essential for vesicle tethering and fusion. Affected cells in the lbk RPE, liver, intestine and macrophages display increased numbers and enlarged intracellular vesicles. Physiological and behavioural analyses reveal severe defects in visual ability in lbk mutants. The present study provides the first phenotypic description of a lack of vam6 gene function in a multicellular organism. lbk shares many of the characteristics of human diseases and suggests a novel disease gene for pathologies associated with defective vesicle transport, including the arthrogryposis-renal dysfunction-cholestasis (ARC) syndrome, the Hermansky-Pudlak syndrome, the Chediak-Higashi syndrome and the Griscelli syndrome.

An enzyme-based cyclic pathway for trans to cis isomerization of the chromophore of visual pigments (11-cis-retinal) is intrinsic to vertebrate cone and rod vision. This process, called the visual cycle, is mostly characterized in rod-dominated retinas and essentially depends on RPE65, an all-trans to 11-cis-retinoid isomerase. Here we analysed the role of RPE65 in zebrafish, a species with a cone-dominated retina. We cloned zebrafish RPE65 and showed that its expression coincided with photoreceptor development. Targeted gene knockdown of RPE65 resulted in morphologically altered rod outer segments and overall reduced 11-cis-retinal levels. Cone vision of RPE65-deficient larvae remained functional as demonstrated by behavioural tests and by metabolite profiling for retinoids. Furthermore, all-trans retinylamine, a potent inhibitor of the rod visual cycle, reduced 11-cis-retinal levels of control larvae to a similar extent but showed no additive effects in RPE65-deficient larvae. Thus, our study of zebrafish provides in vivo evidence for the existence of an RPE65-independent pathway for the regeneration of 11-cis-retinal for cone vision.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.