Improved Proteomics-Based Drug Mechanism-of-Action Studies Using 16-Plex Isobaric Mass Tags.

Multiplexed quantitative proteomics enabled complex workflows to study the mechanisms by which small molecule drugs interact with the proteome such as thermal proteome profiling (TPP) or multiplexed proteome dynamics profiling (mPDP). TPP measures changes in protein thermal stability in response to drug treatment and thus informs on direct targets and downstream regulation events, while the mPDP approach enables the discovery of regulated protein synthesis and degradation events caused by small molecules and other perturbations. The isobaric mass tags available for multiplexed proteomics have thus far limited the efficiency and sensitivity by which such experiments could be performed. Here we evaluate a recent generation of 16-plex isobaric mass tags and demonstrate the sensitive and time efficient identification of Staurosporine targets in HepG2 cell extracts by recording full thermal denaturation/aggregation profiles of vehicle and compound treated samples in a single mass spectrometry experiment. In 2D-TPP experiments, isothermal titration over seven concentrations per temperature enabled comprehensive selectivity profiling of Staurosporine with EC50 values for kinase targets tightly matching to the kinobeads gold standard assay. Finally, we demonstrate time and condition-based multiplexing of dynamic SILAC labeling experiments to delineate proteome-wide effects of the molecular glue Indisulam on synthesis and degradation rates.

Mass-spectrometry-based draft of the Arabidopsis proteome.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

Systematic analysis of protein turnover in primary cells.

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.

Interrogating the Druggability of the 2-Oxoglutarate-Dependent Dioxygenase Target Class by Chemical Proteomics.

The 2-oxoglutarate-dependent dioxygenase target class comprises around 60 enzymes including several subfamilies with relevance to human disease, such as the prolyl hydroxylases and the Jumonji-type lysine demethylases. Current drug discovery approaches are largely based on small molecule inhibitors targeting the iron/2-oxoglutarate cofactor binding site. We have devised a chemoproteomics approach based on a combination of unselective active-site ligands tethered to beads, enabling affinity capturing of around 40 different dioxygenase enzymes from human cells. Mass-spectrometry-based quantification of bead-bound enzymes using a free-ligand competition-binding format enabled the comprehensive determination of affinities for the cosubstrate 2-oxoglutarate and for oncometabolites such as 2-hydroxyglutarate. We also profiled a set of representative drug-like inhibitor compounds. The results indicate that intracellular competition by endogenous cofactors and high active site similarity present substantial challenges for drug discovery for this target class.

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry.

The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.

Mass-spectrometry-based draft of the human proteome.

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.

Ion coalescence of neutron encoded TMT 10-plex reporter ions.

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.

Measuring and managing ratio compression for accurate iTRAQ/TMT quantification.

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 to <10%. 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.

Chemical proteomic analysis reveals the drugability of the kinome of Trypanosoma brucei.

The protozoan parasite Trypanosoma brucei is the causative agent of African sleeping sickness, and there is an urgent unmet need for improved treatments. Parasite protein kinases are attractive drug targets, provided that the host and parasite kinomes are sufficiently divergent to allow specific inhibition to be achieved. Current drug discovery efforts are hampered by the fact that comprehensive assay panels for parasite targets have not yet been developed. Here, we employ a kinase-focused chemoproteomics strategy that enables the simultaneous profiling of kinase inhibitor potencies against more than 50 endogenously expressed T. brucei kinases in parasite cell extracts. The data reveal that T. brucei kinases are sensitive to typical kinase inhibitors with nanomolar potency and demonstrate the potential for the development of species-specific inhibitors.

A selective inhibitor reveals PI3Kγ dependence of T(H)17 cell differentiation.

We devised a high-throughput chemoproteomics method that enabled multiplexed screening of 16,000 compounds against native protein and lipid kinases in cell extracts. Optimization of one chemical series resulted in CZC24832, which is to our knowledge the first selective inhibitor of phosphoinositide 3-kinase γ (PI3Kγ) with efficacy in in vitro and in vivo models of inflammation. Extensive target- and cell-based profiling of CZC24832 revealed regulation of interleukin-17-producing T helper cell (T(H)17) differentiation by PI3Kγ, thus reinforcing selective inhibition of PI3Kγ as a potential treatment for inflammatory and autoimmune diseases.

Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes.

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.

Confident phosphorylation site localization using the Mascot Delta Score.

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.

Evaluation of data analysis strategies for improved mass spectrometry-based phosphoproteomics.

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.

H-score, a mass accuracy driven rescoring approach for improved peptide identification in modification rich samples.

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.

Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays.

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 min standard deviation for 4 h gradients. Multiple fragmentation of the same peptides resulted in better statistics and more precise reporter ion based quantification without any loss in coverage. Overall, 24% more target proteins were quantified using the targeted data acquisition approach, and precision of quantification improved by >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.

Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors.

We describe a chemical proteomics approach to profile the interaction of small molecules with hundreds of endogenously expressed protein kinases and purine-binding proteins. This subproteome is captured by immobilized nonselective kinase inhibitors (kinobeads), and the bound proteins are quantified in parallel by mass spectrometry using isobaric tags for relative and absolute quantification (iTRAQ). By measuring the competition with the affinity matrix, we assess the binding of drugs to their targets in cell lysates and in cells. By mapping drug-induced changes in the phosphorylation state of the captured proteome, we also analyze signaling pathways downstream of target kinases. Quantitative profiling of the drugs imatinib (Gleevec), dasatinib (Sprycel) and bosutinib in K562 cells confirms known targets including ABL and SRC family kinases and identifies the receptor tyrosine kinase DDR1 and the oxidoreductase NQO2 as novel targets of imatinib. The data suggest that our approach is a valuable tool for drug discovery.