Search

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor (https://bioconductor.org/packages/TPP2D). We hope that our method will facilitate prioritizing targets from thermal profiling experiments., 2D-thermal proteome profiling (2D-TPP) is a powerful assay for probing interactions of proteins with small molecules in their native context. Here the authors provide a statistical method for false discovery rate controlled analysis for 2D-TPP applications.

The phosphatases PP1 and PP2A are responsible for the majority of dephosphorylation reactions on phosphoserine (pSer) and phosphothreonine (pThr), and are involved in virtually all cellular processes and numerous diseases. The catalytic subunits exist in cells in form of holoenzymes, which impart substrate specificity. The contribution of the catalytic subunits to the recognition of substrates is unclear. By developing a phosphopeptide library approach and a phosphoproteomic assay, we demonstrate that the specificity of PP1 and PP2A holoenzymes towards pThr and of PP1 for basic motifs adjacent to the phosphorylation site are due to intrinsic properties of the catalytic subunits. Thus, we dissect this amino acid specificity of the catalytic subunits from the contribution of regulatory proteins. Furthermore, our approach enables discovering a role for PP1 as regulator of the GRB-associated-binding protein 2 (GAB2)/14-3-3 complex. Beyond this, we expect that this approach is broadly applicable to detect enzyme-substrate recognition preferences., The substrate specificity of phosphoprotein phosphatases PP1 and PP2A depends on their catalytic and regulatory subunits. Using proteomics approaches, the authors here provide insights into the sequence specificity of the catalytic subunits and their distinct contributions to PP1 and PP2A selectivity.

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor ( https://bioconductor.org/packages/TPP2D ). We hope that our method will facilitate prioritizing targets from thermal profiling experiments.

Tracking proteins' biophysical characteristics on a proteome-wide scale can provide valuable information on their functions and interactions. Thermal proteome profiling (TPP) is a multiplexed quantitative proteomics approach that measures changes in protein thermal stability-a key biophysical property-across different cellular states. Developed in 2014, as a target-deconvolution assay for drugs and other small molecules, TPP has since evolved to a system-level biochemical omics technique providing insights into context-dependent changes in protein states. In this review, we summarise key advances in the experimental and data analysis pipeline that have aided this transformation and discuss the recent developments and applications of TPP.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

The interplay between host and pathogen relies heavily on rapid protein synthesis and accurate protein targeting to ensure pathogen destruction. To gain insight into this dynamic interface, we combined Click chemistry with pulsed stable isotope labelling of amino acids in cell culture to quantify the host proteome response during macrophage infection with the intracellular bacterial pathogen Salmonella enterica Typhimurium. We monitored newly synthesized proteins across different host cell compartments and infection stages. Within this rich resource, we detected aberrant trafficking of lysosomal proteases to the extracellular space and the nucleus. We verified that active cathepsins re-traffic to the nucleus and that these are linked to cell death. Pharmacological cathepsin inhibition and nuclear targeting of a cellular cathepsin inhibitor (stefin B) suppressed S. enterica Typhimurium-induced cell death. We demonstrate that cathepsin activity is required for pyroptotic cell death via the non-canonical inflammasome, and that lipopolysaccharide transfection into the host cytoplasm is sufficient to trigger active cathepsin accumulation in the host nucleus and cathepsin-dependent cell death. Finally, cathepsin inhibition reduced gasdermin D expression, thus revealing an unexpected role for cathepsin activity in non-canonical inflammasome regulation. Overall, our study illustrates how resolution of host proteome dynamics during infection can drive the discovery of biological mechanisms at the host-microbe interface.

Recent developments in high-throughput reverse genetics1,2 have revolutionized our ability to map gene function and interactions3–6. The power of these approaches depends on their ability to identify functionally associated genes, which elicit similar phenotypic changes across several perturbations (chemical, environmental or genetic) when knocked out7–9. However, owing to the large number of perturbations, these approaches have been limited to growth or morphological readouts10. Here we use a high-content biochemical readout, thermal proteome profiling11, to measure the proteome-wide protein abundance and thermal stability in response to 121 genetic perturbations in Escherichia coli. We show that thermal stability, and therefore the state and interactions of essential proteins, is commonly modulated, raising the possibility of studying a protein group that is particularly inaccessible to genetics. We find that functionally associated proteins have coordinated changes in abundance and thermal stability across perturbations, owing to their co-regulation and physical interactions (with proteins, metabolites or cofactors). Finally, we provide mechanistic insights into previously determined growth phenotypes12 that go beyond the deleted gene. These data represent a rich resource for inferring protein functions and interactions. Thermal proteome profiling combined with a reverse genetics approach provides insights into the abundance and thermal stability of the global proteome of Escherichia coli.

Thermal proteome profiling (TPP) is based on the principle that, when subjected to heat, proteins denature and become insoluble. Proteins can change their thermal stability upon interactions with small molecules (such as drugs or metabolites), nucleic acids or other proteins, or upon post‐translational modifications. TPP uses multiplexed quantitative mass spectrometry‐based proteomics to monitor the melting profile of thousands of expressed proteins. Importantly, this approach can be performed in vitro, in situ, or in vivo. It has been successfully applied to identify targets and off‐targets of drugs, or to study protein–metabolite and protein–protein interactions. Therefore, TPP provides a unique insight into protein state and interactions in their native context and at a proteome‐wide level, allowing to study basic biological processes and their underlying mechanisms., This tutorial explains the principles of thermal proteome profiling (TPP) and analyzes the different steps of a TPP experiment. It reviews the recent developments and current applications of this methodology, and provides an outlook of possible new applications.

Current knowledge of protein localization, particularly outer membrane proteins, is highly dependent on bioinformatic predictions. To date, no systematic experimental studies have directly compared protein localization spanning the inner and outer membranes of E. coli. By combining sucrose density gradient fractionation of inner membrane (IM) and outer membrane (OM) proteins with multiplex quantitative proteomics, we systematically quantified localization patterns for >1,600 proteins, providing high-confidence localization annotations for 1,368 proteins. Of these proteins, we resolve the predominant localization of 316 proteins that currently have dual annotation (cytoplasmic and IM) in protein databases and identify new annotations for 42 additional proteins. Overall, we present a novel quantitative methodology to systematically map membrane proteins in Gram-negative bacteria and use it to unravel the biological complexity of the membrane proteome architecture in E. coli., The molecular architecture and function of the Gram-negative bacterial cell envelope are dictated by protein composition and localization. Proteins that localize to the inner membranes (IM) and outer membranes (OM) of Gram-negative bacteria play critical and distinct roles in cellular physiology; however, approaches to systematically interrogate their distribution across both membranes and the soluble cell fraction are lacking. Here, we employed multiplexed quantitative mass spectrometry using tandem mass tag (TMT) labeling to assess membrane protein localization in a proteome-wide fashion by separating IM and OM vesicles from exponentially growing Escherichia coli K-12 cells on a sucrose density gradient. The migration patterns for >1,600 proteins were classified in an unbiased manner, accurately recapitulating decades of knowledge in membrane protein localization in E. coli. For 559 proteins that are currently annotated as peripherally associated with the IM (G. Orfanoudaki and A. Economou, Mol Cell Proteomics 13:3674–3687, 2014, https://doi.org/10.1074/mcp.O114.041137) and that display potential for dual localization to either the IM or cytoplasm, we could allocate 110 proteins to the IM and 206 proteins to the soluble cell fraction based on their fractionation patterns. In addition, we uncovered 63 cases, in which our data disagreed with current localization annotation in protein databases. For 42 of these cases, we were able to find supportive evidence for our localization findings in the literature. We anticipate that our systems-level analysis of the E. coli membrane proteome will serve as a useful reference data set to query membrane protein localization, as well as to provide a novel methodology to rapidly and systematically map membrane protein localization in more poorly characterized Gram-negative species. IMPORTANCE Current knowledge of protein localization, particularly outer membrane proteins, is highly dependent on bioinformatic predictions. To date, no systematic experimental studies have directly compared protein localization spanning the inner and outer membranes of E. coli. By combining sucrose density gradient fractionation of inner membrane (IM) and outer membrane (OM) proteins with multiplex quantitative proteomics, we systematically quantified localization patterns for >1,600 proteins, providing high-confidence localization annotations for 1,368 proteins. Of these proteins, we resolve the predominant localization of 316 proteins that currently have dual annotation (cytoplasmic and IM) in protein databases and identify new annotations for 42 additional proteins. Overall, we present a novel quantitative methodology to systematically map membrane proteins in Gram-negative bacteria and use it to unravel the biological complexity of the membrane proteome architecture in E. coli.

Numerous drugs and endogenous ligands bind to cell surface receptors leading to modulation of downstream signaling cascades and frequently to adaptation of the plasma membrane proteome. In-depth analysis of dynamic processes at the cell surface is challenging due to biochemical properties and low abundances of plasma membrane proteins. Here we introduce cell surface thermal proteome profiling for the comprehensive characterization of ligand-induced changes in protein abundances and thermal stabilities at the plasma membrane. We demonstrate drug binding to extracellular receptors and transporters, discover stimulation-dependent remodeling of T cell receptor complexes and describe a competition-based approach to measure target engagement of G-protein-coupled receptor antagonists. Remodeling of the plasma membrane proteome in response to treatment with the TGFB receptor inhibitor SB431542 leads to partial internalization of the monocarboxylate transporters MCT1/3 explaining the antimetastatic effects of the drug. Cell surface thermal proteome profiling allows characterization of ligand-induced changes in protein abundances and thermal stabilities at the plasma membrane.

Protein aggregates have negative implications in disease. While reductionist experiments have increased our understanding of aggregation processes, the systemic view in biological context is still limited. To extend this understanding, we used mass spectrometry-based proteomics to characterize aggregation and disaggregation in human cells after non-lethal heat shock. Aggregation-prone proteins were enriched in nuclear proteins, high proportion of intrinsically disordered regions, high molecular mass, high isoelectric point and hydrophilic amino acids. During recovery, most aggregating proteins disaggregated with a rate proportional to the aggregation propensity: larger loss in solubility was counteracted by faster disaggregation. High amount of intrinsically disordered regions also resulted in faster disaggregation. However, other characteristics enriched in aggregating proteins did not correlate with the disaggregation rates. In addition, we analyzed changes in protein thermal stability after heat shock. Soluble remnants of aggregated proteins were more thermally stable compared to control condition. Our results provide a rich resource of heat stress-related protein solubility data, propose novel roles for intrinsically disordered regions in protein quality control and reveal a protection mechanism to repress protein aggregation in heat stress.

SummaryReversible protein phosphorylation regulates virtually every cellular process and is arguably the most well-studied post-translational modification. Still, less than 3% of the phosphorylation sites identified in humans have annotated functions. Functionally-relevant phosphorylation sites are known to trigger conformational changes to proteins and/or to regulate their interactions with other proteins, nucleic acids and small molecules - all of which can be reflected in the thermal stability of a protein. Thus, combining thermal proteome profiling (TPP) with phosphoproteomics (phospho-TPP) provides a way to assess the functional relevance of identified phosphorylation sites on a proteome-wide scale by comparing the melting behavior of a protein and its phosphorylated form(s). We performed phospho-TPP experiments in HeLa cells with an optimized protocol, and conclude that phosphorylation does affect protein thermal stability, but to a much lesser extent than previously reported.

A method for nonparametric analysis of response curves from thermal proteome profiling (TPP) experiments is proposed. The approach achieves high proteome coverage and sensitivity while controlling the false discovery-rate. Free open source software for NPARC is provided., Graphical Abstract Highlights Method for the analysis of response curves from thermal proteome profiling (TPP). NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control. Increased proteome coverage and sensitivity to identify drug-binding proteins., Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present nonparametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP data sets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. An open source software implementation of NPARC is provided.

The rise of antibiotic resistance and declining discovery of new antibiotics has created a global health crisis. Of particular concern, no new antibiotic classes have been approved for treating Gram-negative pathogens in decades. Here, we characterize a compound, SCH-79797, that kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism of action (MoA) with undetectably low resistance frequencies. To characterize its MoA, we combined quantitative imaging, proteomic, genetic, metabolomic, and cell-based assays. This pipeline demonstrates that SCH-79797 has two independent cellular targets, folate metabolism and bacterial membrane integrity, and outperforms combination treatments in killing methicillin-resistant Staphylococcus aureus (MRSA) persisters. Building on the molecular core of SCH-79797, we developed a derivative, Irresistin-16, with increased potency and showed its efficacy against Neisseria gonorrhoeae in a mouse vaginal infection model. This promising antibiotic lead suggests that combining multiple MoAs onto a single chemical scaffold may be an underappreciated approach to targeting challenging bacterial pathogens.

Adenosine triphosphate (ATP) plays fundamental roles in cellular biochemistry and was recently discovered to function as a biological hydrotrope. Here, we use mass spectrometry to interrogate ATP-mediated regulation of protein thermal stability and protein solubility on a proteome-wide scale. Thermal proteome profiling reveals high affinity interactions of ATP as a substrate and as an allosteric modulator that has widespread influence on protein complexes and their stability. Further, we develop a strategy for proteome-wide solubility profiling, and discover ATP-dependent solubilization of at least 25% of the insoluble proteome. ATP increases the solubility of positively charged, intrinsically disordered proteins, and their susceptibility for solubilization varies depending on their localization to different membrane-less organelles. Moreover, a few proteins, exhibit an ATP-dependent decrease in solubility, likely reflecting polymer formation. Our data provides a proteome-wide, quantitative insight into how ATP influences protein structure and solubility across the spectrum of physiologically relevant concentrations., ATP can function as a biological hydrotrope, but its global effects on protein solubility have not yet been characterized. Here, the authors quantify the effect of ATP on the thermal stability and solubility of the cellular proteome, providing insights into protein solubility regulation by ATP.

Increasing antibiotic resistance urges for new technologies for studying microbes and antimicrobial mechanism of action. We adapted thermal proteome profiling (TPP) to probe the thermostability of Escherichia coli proteins in vivo. E. coli had a more thermostable proteome than human cells, with protein thermostability depending on subcellular location—forming a high‐to‐low gradient from the cell surface to the cytoplasm. While subunits of protein complexes residing in one compartment melted similarly, protein complexes spanning compartments often had their subunits melting in a location‐wise manner. Monitoring the E. coli meltome and proteome at different growth phases captured changes in metabolism. Cells lacking TolC, a component of multiple efflux pumps, exhibited major physiological changes, including differential thermostability and levels of its interaction partners, signaling cascades, and periplasmic quality control. Finally, we combined in vitro and in vivo TPP to identify targets of known antimicrobial drugs and to map their downstream effects. In conclusion, we demonstrate that TPP can be used in bacteria to probe protein complex architecture, metabolic pathways, and intracellular drug target engagement.

Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target–decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target–decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb.org). The “picked” protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The “picked” target–decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used “classic” protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software.

Summary Quantitative mass spectrometry has established proteome-wide regulation of protein abundance and post-translational modifications in various biological processes. Here, we used quantitative mass spectrometry to systematically analyze the thermal stability and solubility of proteins on a proteome-wide scale during the eukaryotic cell cycle. We demonstrate pervasive variation of these biophysical parameters with most changes occurring in mitosis and G1. Various cellular pathways and components vary in thermal stability, such as cell-cycle factors, polymerases, and chromatin remodelers. We demonstrate that protein thermal stability serves as a proxy for enzyme activity, DNA binding, and complex formation in situ. Strikingly, a large cohort of intrinsically disordered and mitotically phosphorylated proteins is stabilized and solubilized in mitosis, suggesting a fundamental remodeling of the biophysical environment of the mitotic cell. Our data represent a rich resource for cell, structural, and systems biologists interested in proteome regulation during biological transitions., Graphical Abstract, Highlights • Proteome-wide variation of in situ protein thermal stability and solubility • Thermal stability of RNA Pol II varies across the cell cycle and is DNA dependent • Thermal profiling across the cell cycle delineates protein subcomplexes • Intrinsically disordered proteins are less prone to aggregation during mitosis, A proteome-wide assessment of thermal stability and solubility during the eukaryotic cell cycle demonstrates pervasive variation in mitosis and G1.

Class I histone deacetylases (HDACs) are attractive drug targets in oncology and inflammation. However, the development of selective inhibitors is complicated by the characteristic that the localization, activity, and selectivity of class I HDACs are regulated by association in megadalton repressor complexes. There is emerging evidence that isoform and protein complex selectivity can be achieved by aminobenzamide inhibitors. Here we present a chemoproteomics strategy for the determination of time-dependent inhibitor binding to endogenous HDACs and HDAC complexes. This approach enabled us to determine kinetic association and dissociation rates for endogenously expressed repressor complexes. We found that unlike hydroxamate type inhibitors, aminobenzamides exhibited slow binding kinetics dependent on association within protein complexes. These findings were in agreement with a delayed cellular response on acetylation levels of distinct histone sites and the inability of aminobenzamides to inhibit HDAC activity of a Sin3 complex isolated from K562 cells.

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

We extended thermal proteome profiling to detect transmembrane protein-small molecule interactions in cultured human cells. When we assessed the effects of detergents on ATP-binding profiles, we observed shifts in denaturation temperature for ATP-binding transmembrane proteins. We also observed cellular thermal shifts in pervanadate-induced T cell-receptor signaling, delineating the membrane target CD45 and components of the downstream pathway, and with drugs affecting the transmembrane transporters ATP1A1 and MDR1.

In recent years, phenotypic-based screens have become increasingly popular in drug discovery. A major challenge of this approach is that it does not provide information about the mechanism of action of the hits. This has led to the development of multiple strategies for target deconvolution. Thermal proteome profiling (TPP) allows for an unbiased search of drug targets and can be applied in living cells without requiring compound labeling. TPP is based on the principle that proteins become more resistant to heat-induced unfolding when complexed with a ligand, e.g., the hit compound from a phenotypic screen. The melting proteome is also sensitive to other intracellular events, such as levels of metabolites, post-translational modifications and protein-protein interactions. In this review, we describe the principles of this approach, review the method and its developments, and discuss its current and future applications. While proteomics has generally focused on measuring relative protein concentrations, TPP provides a novel approach to gather complementary information on protein stability not present in expression datasets. Therefore, this strategy has great potential not only for drug discovery, but also for answering fundamental biological questions.

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000–6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types., The proteome-wide characterization of proteostasis depends on robust approaches to determine protein half-lives. Here, the authors improve the accuracy and precision of mass spectrometry-based quantification, enabling reliable protein half-life determination in several non-dividing cell types.

Large scale phosphorylation analysis is more and more getting into focus of proteomic research. Although it is now possible to identify thousands of phosphorylated peptides in a biological system, confident site localization remains challenging. Here we validate the Mascot Delta Score (MD-score) as a simple method that achieves similar sensitivity and specificity for phosphosite localization as the published Ascore, which is mainly used in conjunction with Sequest. The MD-score was evaluated using liquid chromatography-tandem MS data of 180 individually synthesized phosphopeptides with precisely known phosphorylation sites. We tested the MD-score for a wide range of commonly available fragmentation methods and found it to be applicable throughout with high statistical significance. However, the different fragmentation techniques differ strongly in their ability to localize phosphorylation sites. At 1% false localization rate, the highest number of correctly assigned phosphopeptides was achieved by higher energy collision induced dissociation in combination with an Orbitrap mass analyzer followed very closely by low resolution ion trap spectra obtained after electron transfer dissociation. Both these methods are significantly better than low resolution spectra acquired after collision induced dissociation and multi stage activation. Score thresholds determined from simple calibration functions for each fragmentation method were stable over replicate analyses of the phosphopeptide set. The MD-score outperforms the Ascore for tyrosine phosphorylated peptides and we further show that the ability to call sites correctly increases with increasing distance of two candidate sites within a peptide sequence. The MD-score does not require complex computational steps which makes it attractive in terms of practical utility. We provide all mass spectra and the synthetic peptides to the community so that the development of present and future localization software can be benchmarked and any laboratory can determine MD-scores and localization probabilities for their individual analytical set up.

Recurrent chromosomal translocations involving the mixed lineage leukaemia (MLL) gene initiate aggressive forms of leukaemia, which are often refractory to conventional therapies(1). Many MLL-fusion partners are members of the super elongation complex (SEC), a critical regulator of transcriptional elongation, suggesting that aberrant control of this process has an important role in leukaemia induction(2,3). Here we use a global proteomic strategy to demonstrate that MLL fusions, as part of SEC(2,3) and the polymerase-associated factor complex (PAFc)(4,5), are associated with the BET family of acetyl-lysine recognizing, chromatin 'adaptor' proteins. These data provided the basis for therapeutic intervention in MLL-fusion leukaemia, via the displacement of the BET family of proteins from chromatin. We show that a novel small molecule inhibitor of the BET family, GSK1210151A (I-BET151), has profound efficacy against human and murine MLL-fusion leukaemic cell lines, through the induction of early cell cycle arrest and apoptosis. I-BET151 treatment in two human leukaemia cell lines with different MLL fusions alters the expression of a common set of genes whose function may account for these phenotypic changes. The mode of action of I-BET151 is, at least in part, due to the inhibition of transcription at key genes (BCL2, C-MYC and CDK6) through the displacement of BRD3/4, PAFc and SEC components from chromatin. In vivo studies indicate that I-BET151 has significant therapeutic value, providing survival benefit in two distinct mouse models of murine MLL-AF9 and human MLL-AF4 leukaemia. Finally, the efficacy of I-BET151 against human leukaemia stem cells is demonstrated, providing further evidence of its potent therapeutic potential. These findings establish the displacement of BET proteins from chromatin as a promising epigenetic therapy for these aggressive leukaemias.

Here we describe a set of enhanced data processing and filtering methods to improve significance and coverage of phosphopeptide identifications by mass spectrometry. We demonstrate that for samples of limited complexity, spectra-based estimation of false discovery rates will lead to overprediction of confidently identified phosphorylated peptides due to a bias caused by multiple fragmentation of highly abundant peptide species. We further provide evidence that fragmentation of abundant peptides at the tails of their chromatographic peaks is a major source for false positive peptide matches and that overall confidence in phosphopeptide identifications can be improved by a chromatographic peak-based aggregation scheme, intensity rank-based neutral loss and optimized mass error filters. When replicate runs of a standard sample were performed using different fragmentation techniques on an Orbitrap mass spectrometer we observed improvements of 7-31% in phosphopeptide coverage depending on the fragmentation method and the desired false discovery rate.

Currently, scoring algorithms of many popular search engines for tandem mass spectrometry (MS/MS) data only partially utilize the information content of high mass accuracy MS/MS data. We have developed a new rescoring scheme, H-score, that employs high mass accuracy matching of all detected fragment ions to candidate peptide sequences in an abundance independent fashion. Peptides for which b or y ions are found for all or almost all backbone fragmentation sites are rewarded. For peptide hits generated by Mascot, rescoring proved to be particularly beneficial when applied on samples containing many different potential modifications. For a histone sample acquired on an Orbitrap Velos using HCD for peptide fragmentation, the H-score identified 24% more spectra at 0.01 false positive rate than Mascot scoring of spectra processed according to state-of-the-art methods and 61% better than Mascot scoring of unprocessed MS/MS spectra. For a low-abundance sample, where many weak spectra were detected, these numbers went up to 53 and 190%, respectively. When applied on a kinase-enriched sample containing only a few modifications, a smaller but still significant gain of 5% was observed.

A recent proteomics-grade (95%+ sequence reliability) high-throughput de novo sequencing method utilizes the benefits of high resolution, high mass accuracy, and the use of two complementary fragmentation techniques collision-activated dissociation (CAD) and electron capture dissociation (ECD). With this high-fidelity sequencing approach, hundreds of peptides can be sequenced de novo in a single LC-MS/MS experiment. The high productivity of the new analysis technique has revealed a new bottleneck which occurs in data representation. Here we suggest a new method of data analysis and visualization that presents a comprehensive picture of the peptide content including relative abundances and grouping into families. The 2D mass mapping consists of putting the molecular masses onto a two-dimensional bubble plot, with the relative monoisotopic mass defect and isotopic shift being the axes and with the bubble area proportional to the peptide abundance. Peptides belonging to the same family form a compact group on such a plot, so that the family identity can in many cases be determined from the molecular mass alone. The performance of the method is demonstrated on the high-throughput analysis of skin secretion from three frogs, Rana ridibunda, Rana arvalis, and Rana temporaria. Two dimensional mass maps simplify the task of global comparison between the species and make obvious the similarities and differences in the peptide contents that are obscure in traditional data presentation methods. Even biological activity of the peptide can sometimes be inferred from its position on the plot. Two dimensional mass mapping is a general method applicable to any complex mixture, peptide and nonpeptide alike.

The availability of the results of high-throughput analyses coming from 'omic' technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a 'pathway search engine', where the input is the 'omic' data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF(beta), stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.

Many protein kinases are valid drug targets in oncology because they are key components of signal transduction pathways. The number of clinical kinase inhibitors is on the rise, but these molecules often exhibit polypharmacology, potentially eliciting desired and toxic effects. Therefore, a comprehensive assessment of a compound's target space is desirable for a better understanding of its biological effects. The enzyme ferrochelatase (FECH) catalyzes the conversion of protoporphyrin IX into heme and was recently found to be an off-target of the BRAF inhibitor Vemurafenib, likely explaining the phototoxicity associated with this drug in melanoma patients. This raises the question of whether FECH binding is a more general feature of kinase inhibitors. To address this, we applied a chemical proteomics approach using kinobeads to evaluate 226 clinical kinase inhibitors for their ability to bind FECH. Surprisingly, low or submicromolar FECH binding was detected for 29 of all compounds tested and isothermal dose response measurements confirmed target engagement in cells. We also show that Vemurafenib, Linsitinib, Neratinib, and MK-2461 reduce heme levels in K562 cells, verifying that drug binding leads to a loss of FECH activity. Further biochemical and docking experiments identified the protoporphyrin pocket in FECH as one major drug binding site. Since the genetic loss of FECH activity leads to photosensitivity in humans, our data strongly suggest that FECH inhibition by kinase inhibitors is the molecular mechanism triggering photosensitivity in patients. We therefore suggest that a FECH assay should generally be part of the preclinical molecular toxicology package for the development of kinase inhibitors.

The complexity of the human proteome, already enormous at the organism level, increases further in the course of the proteome analysis due to in vitro sample evolution. Most of in vitro alterations can also occur in vivo as post-translational modifications. These two types of modifications can only be distinguished a posteriori but not in the process of analysis, thus rendering necessary the analysis of every molecule in the sample. With the new software tool ModifiComb applied to MS/MS data, the extent of modifications was measured in tryptic mixtures representing the full proteome of human cells. The estimated level of 8-12 modified peptides per each unmodified tryptic peptide present at >or=1% level is approaching one modification per amino acid on average. This is a higher modification rate than was previously thought, posing an additional challenge to analytical techniques. The solution to the problem is seen in improving sample preparation routines, introducing dynamic range-adjusted thresholds for database searches, using more specific MS/MS analysis using high mass accuracy and complementary fragmentation techniques, and revealing peptide families with identification of additional proteins only by unfamiliar peptides. Extensive protein separation prior to analysis reduces the requirements on speed and dynamic range of a tandem mass spectrometer and can be a viable alternative to the shotgun approach.

Identification of proteins by MS/MS is performed by matching experimental mass spectra against calculated spectra of all possible peptides in a protein data base. The search engine assigns each spectrum a score indicating how well the experimental data complies with the expected one; a higher score means increased confidence in the identification. One problem is the false-positive identifications, which arise from incomplete data as well as from the presence of misleading ions in experimental mass spectra due to gas-phase reactions, stray ions, contaminants, and electronic noise. We employed a novel technique of reduction of false positives that is based on a combined use of orthogonal fragmentation techniques electron capture dissociation (ECD) and collisionally activated dissociation (CAD). Since ECD and CAD exhibit many complementary properties, their combined use greatly increased the analysis specificity, which was further strengthened by the high mass accuracy (approximately 1 ppm) afforded by Fourier transform mass spectrometry. The utility of this approach is demonstrated on a whole cell lysate from Escherichia coli. Analysis was made using the data-dependent acquisition mode. Extraction of complementary sequence information was performed prior to data base search using in-house written software. Only masses involved in complementary pairs in the MS/MS spectrum from the same or orthogonal fragmentation techniques were submitted to the data base search. ECD/CAD identified twice as many proteins at a fixed statistically significant confidence level with on average a 64% higher Mascot score. The confidence in protein identification was hereby increased by more than 1 order of magnitude. The combined ECD/CAD searches were on average 20% faster than CAD-only searches. A specially developed test with scrambled MS/MS data revealed that the amount of false-positive identifications was dramatically reduced by the combined use of CAD and ECD.

Mapping human drug targets in the cell To understand both the beneficial and the side effects of a drug, one would need to know its full binding profile to all cellular proteins. Savitski et al. take significant steps toward meeting this daunting challenge. They monitored the unfolding or “melting” of over 7000 human proteins and measured how small-molecule binding changes individual melting profiles. As a proof of principle, over 50 targets were identified for an inhibitor known to bind a broad spectrum of kinases. Two cancer drugs, vemurafib and Alectinib, are known to have a side effect of photosensitivity. The thermal profiling approach identified drug-protein interactions responsible for these side effects. Science , this issue 10.1126/science.1255784

Isobaric mass tag-based quantitative proteomics strategies such as iTRAQ and TMT utilize reporter ions in the low mass range of tandem MS spectra for relative quantification. The recent extension of TMT multiplexing to 10 conditions has been enabled by utilizing neutron encoded tags with reporter ion m/z differences of 6 mDa. The baseline resolution of these closely spaced tags is possible due to the high resolving power of current day mass spectrometers. In this work we evaluated the performance of the TMT10 isobaric mass tags on the Q Exactive Orbitrap mass spectrometers for the first time and demonstrated comparable quantification accuracy and precision to what can be achieved on the Orbitrap Elite mass spectrometers. However, we discovered, upon analysis of complex proteomics samples on the Q Exactive Orbitrap mass spectrometers, that the proximate TMT10 reporter ion pairs become prone to coalescence. The fusion of the different reporter ion signals into a single measurable entity has a detrimental effect on peptide and protein quantification. We established that the main reason for coalescence is the commonly accepted maximum ion target for MS2 spectra of 1e6 on the Q Exactive instruments. The coalescence artifact was completely removed by lowering the maximum ion target for MS2 spectra from 1e6 to 2e5 without any losses in identification depth or quantification quality of proteins.

Isobaric mass tagging (e.g., TMT and iTRAQ) is a precise and sensitive multiplexed peptide/protein quantification technique in mass spectrometry. However, accurate quantification of complex proteomic samples is impaired by cofragmentation of peptides, leading to systematic underestimation of quantitative ratios. Label-free quantification strategies do not suffer from such an accuracy bias but cannot be multiplexed and are less precise. Here, we compared protein quantification results obtained with these methods for a chemoproteomic competition binding experiment and evaluated the utility of measures of spectrum purity in survey spectra for estimating the impact of cofragmentation on measured TMT-ratios. While applying stringent interference filters enables substantially more accurate TMT quantification, this came at the expense of 30%-60% fewer proteins quantified. We devised an algorithm that corrects experimental TMT ratios on the basis of determined peptide interference levels. The quantification accuracy achieved with this correction was comparable to that obtained with stringent spectrum filters but limited the loss in coverage to10%. The generic applicability of the fold change correction algorithm was further demonstrated by spiking of chemoproteomics samples into excess amounts of E. coli tryptic digests.

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.

High-resolution and high-accuracy Fourier transform mass spectrometry (FTMS) is becoming increasingly attractive due to its specificity. However, the speed of tandem FTMS analysis severely limits the competitive advantage of this approach relative to faster low-resolution quadrupole ion trap MS/MS instruments. Here we demonstrate an entirely FTMS-based analysis method with a 2.5-3.0-fold greater throughput than a conventional FT MS/MS approach. The method consists of accumulating together the MS/MS fragments ions from multiple precursors, with subsequent high-resolution analysis of the mixture. Following acquisition, the multiplexed spectrum is deconvoluted into individual MS/MS spectra which are then combined into a single concatenated file and submitted for peptide identification to a search engine. The method is tested both in silico using a database of MS/MS spectra as well as in situ using a modified LTQ Orbitrap mass spectrometer. The performance of the method in the experiment was consistent with theoretical expectations.

The development of selective histone deacetylase (HDAC) inhibitors with anti-cancer and anti-inflammatory properties remains challenging in large part owing to the difficulty of probing the interaction of small molecules with megadalton protein complexes. A combination of affinity capture and quantitative mass spectrometry revealed the selectivity with which 16 HDAC inhibitors target multiple HDAC complexes scaffolded by ELM-SANT domain subunits, including a novel mitotic deacetylase complex (MiDAC). Inhibitors clustered according to their target profiles with stronger binding of aminobenzamides to the HDAC NCoR complex than to the HDAC Sin3 complex. We identified several non-HDAC targets for hydroxamate inhibitors. HDAC inhibitors with distinct profiles have correspondingly different effects on downstream targets. We also identified the anti-inflammatory drug bufexamac as a class IIb (HDAC6, HDAC10) HDAC inhibitor. Our approach enables the discovery of novel targets and inhibitors and suggests that the selectivity of HDAC inhibitors should be evaluated in the context of HDAC complexes and not purified catalytic subunits.

Little is known about the nature of post mortem degradation of proteins and peptides on a global level, the so-called degradome. This is especially true for nonneural tissues. Degradome properties in relation to sampling procedures on different tissues are of great importance for the studies of, for instance, post translational modifications and/or the establishment of clinical biobanks. Here, snap freezing of fresh (

A major challenge in proteomics is to fully identify and characterize posttranslational modification (PTM) patterns present at any given time in cells, tissues, and organisms. Currently, the most frequently used method for identifying PTMs is tandem mass spectrometry combined with searching a protein sequence database. Although, database searching has been highly successful for the identification of proteins, it has a number of significant drawbacks for identification of modifications. The user needs to specify all expected modifications, and the search engine needs to consider all possible combinations of these modifications for all peptide sequences. If several potential modifications are considered, the search can take much longer than the data acquisition, creating a bottleneck in high-throughput analysis. In addition, the many possible assignments that need to be tested increase the noise and require better quality data for confident identification of modifications. Here, we describe a method for identifying both known and unknown PTM using mass spectrometry that does not suffer from these problems. The method is based on the observation that, in many samples, peptides are usually present both with and without modifications. By identifying the unmodified peptide with conventional database searches, the modified species of the peptide can be identified by searching for peptides with common and similar fragments as the unmodified peptide. After identifying both the modified and unmodified peptide, the elemental composition of the modification can be deduced if the mass accuracy of the precursor ion is sufficiently high.

Quantitative mass spectrometry-based proteomic assays often suffer from a lack of robustness and reproducibility. We here describe a targeted mass spectrometric data acquisition strategy for affinity enriched subproteomes—in our case the kinome—that enables a substantially improved reproducibility of detection, and improved quantification via isobaric tags. Inclusion mass lists containing m/z, charge state, and retention time were created based on a set of 80 shotgun-type experiments performed under identical experimental conditions. For each target protein, peptides were selected according to their frequency of observation and isobaric tag for relative and absolute quantitation (iTRAQ) reporter ion quality. Retention times of selected peptides were aligned using similarity driven pairwise alignment strategy yielding 1.5-fold. We also show that a combination of higher energy collisional dissociation (HCD) with collisional induced dissociation (CID) outperformed pulsed-Q-dissociation (PQD) on the OrbitrapXL. With the CID/HCD based targeted data acquisition approach 10% more quantifiable target proteins were identified and a 2-fold increase in quantification precision was achieved. We have observed excellent reproducibility between different instruments, underlining the robustness of the approach.

Udgivelsesdato: 2007-Aug Most proteomics studies involving mapping post-translational modifications, such as the phosphorylation of serine and threonine, are performed today using the 'bottom-up' approach. This approach involves enzymatic cleavage of proteins, most often by trypsin, with subsequent nano-LC-MS/MS. The occupancy rates of phosphosites in proteins may differ by orders of magnitude, and thus the occupancy rate must be reported for each occupied phosphosite. To highlight potential pitfalls in quantifying the occupancy rates, alpha(s1)-casein from human milk was selected as a model molecule representing moderately phosphorylated proteins. For this purpose, human milk from one Caucasian woman in the eighth month of lactation was used. The phosphorylation level of caseins is believed to have major implications for the formation of micelles that are involved in delivering valuable calcium phosphate and other minerals to the new-born. Human alpha(s1)-casein has been reported to be much less phosphorylated than ruminant caseins, which may indicate a different function of caseins in humans. Revealing the phosphorylation pattern in human casein can thus shed light on its function. The current study found that the sequence region between the residues Ser70 and Ser76 in human alpha(s1)-casein is in fact phosphorylated, contrary to previous knowledge. The site of the most abundant phosphorylation is Ser75, in agreement with the known action of the mammary gland casein kinase. There is evidence for the second phosphorylation in that region, possibly at Ser73. Earlier reported positions of phosphorylations at Ser18 and Ser26 are also confirmed, but not the dominance of Ser18 phosphorylation. The occupancy rates at Ser18, Ser26 and Ser75 are estimated to be (7 +/- 2), (20 +/- 6) and (27 +/- 9)%, respectively. Owing to differences in the ionization efficiency between phosphorylated and unphosphorylated peptides a 30% error margin is added to the occupancy rates. The highlighted pitfalls of the bottom-up strategy include the sensitivity of enzymes to proximal acidic and phosphorylated residues and the presence of multiple isoforms, including unexpected ones, of the tryptic peptides. The utility of the earlier introduced PhosTS_hunter and ModifiComb approaches for evading the latter pitfall is demonstrated.

A major challenge in proteomics is to fully identify and characterize the post-translational modification (PTM) patterns present at any given time in cells, tissues, and organisms. Here we present a fast and reliable method ("ModifiComb") for mapping hundreds types of PTMs at a time, including novel and unexpected PTMs. The high mass accuracy of Fourier transform mass spectrometry provides in many cases unique elemental composition of the PTM through the difference DeltaM between the molecular masses of the modified and unmodified peptides, whereas the retention time difference DeltaRT between their elution in reversed-phase liquid chromatography provides an additional dimension for PTM identification. Abundant sequence information obtained with complementary fragmentation techniques using ion-neutral collisions and electron capture often locates the modification to a single residue. The (DeltaM, DeltaRT) maps are representative of the proteome and its overall modification state and may be used for database-independent organism identification, comparative proteomic studies, and biomarker discovery. Examples of newly found modifications include +12.000 Da (+C atom) incorporation into proline residues of peptides from proline-rich proteins found in human saliva. This modification is hypothesized to increase the known activity of the peptide.

The conventional approach in modern proteomics to identify proteins from limited information provided by molecular and fragment masses of their enzymatic degradation products carries an inherent risk of both false positive and false negative identifications. For reliable identification of even known proteins, complete de novo sequencing of their peptides is desired. The main problems of conventional sequencing based on tandem mass spectrometry are incomplete backbone fragmentation and the frequent overlap of fragment masses. In this work, the first proteomics-grade de novo approach is presented, where the above problems are alleviated by the use of complementary fragmentation techniques CAD and ECD. Implementation of a high-current, large-area dispenser cathode as a source of low-energy electrons provided efficient ECD of doubly charged peptides, the most abundant species (65-80%), in a typical trypsin-based proteomics experiment. A new linear de novo algorithm is developed combining efficiency and speed, processing on a conventional 3 GHz PC, 1000 MS/MS data sets in 60 s. More than 6% of all MS/MS data for doubly charged peptides yielded complete sequences, and another 13% gave nearly complete sequences with a maximum gap of two amino acid residues. These figures are comparable with the typical success rates (5-15%) of database identification. For peptides reliably found in the database (Mowse scoreor = 34), the agreement with de novo-derived full sequences was95%. Full sequences were derived in 67% of the cases when full sequence information was present in MS/MS spectra. Thus the new de novo sequencing approach reached the same level of efficiency and reliability as conventional database-identification strategies.