Interrogating the Druggability of the 2-Oxoglutarate-Dependent

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Interrogating the Druggability of the 2‑Oxoglutarate-Dependent Dioxygenase Target Class by Chemical Proteomics Gérard Joberty,† Markus Boesche,† Jack A. Brown,‡ Dirk Eberhard,† Neil S. Garton,‡ Toby Mathieson,† Marcel Muelbaier,† Nigel G. Ramsden,†,§ Valérie Reader,†,∥ Anne Rueger,† Robert J. Sheppard,‡,⊥ Susan M. Westaway,‡ Marcus Bantscheff,† Kevin Lee,‡,# David M. Wilson,‡,⊥ Rab K. Prinjha,‡ and Gerard Drewes*,† †

Cellzome GmbH, a GlaxoSmithKline company, Meyerhofstrasse 1, Heidelberg, Germany Epinova Discovery Performance Unit, Medicines Research Centre, GlaxoSmithKline R&D, Stevenage, United Kingdom

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S Supporting Information *

ABSTRACT: 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/2oxoglutarate 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 competitionbinding format enabled the comprehensive determination of affinities for the cosubstrate 2-oxoglutarate and for oncometabolites such as 2hydroxyglutarate. 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.

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he iron- (Fe2+) and 2-oxoglutarate (2-OG) dioxygenases represent a conserved class of enzymes that catalyze the hydroxylation of proteins, nucleic acids, and metabolites.1 Members of this class are involved in diverse cellular processes including oxygen sensing,2 DNA/RNA repair,3 the posttranslational modification of collagens4 and histones,5 and metabolism.6 The enzymes share conserved sequence motifs required to catalyze the hydroxylation reaction, which proceeds via an activated Fe(IV)-oxo intermediate.7 Several members of the Fe2+/2-OG dioxygenase target class are of considerable interest in drug discovery. The prolyl hydroxylases EGLN1−3 (also known as PHD1−3) and the asparaginyl hydroxylase HIF1AN (FIH) that regulate hypoxia-inducible factor 1-alpha (HIF1A) are targets for ischemic and inflammatory conditions,8 the fat mass and obesity associated protein (FTO) for obesity,9 and the Jumonji-domain lysine demethylases (KDM) for the epigenetic therapy of cancers and inflammatory disease.5,10 Recently, we reported the development of the first small molecule inhibitor for the KDM6 subfamily comprising the histone H3K27 demethylases UTX (KDM6A) and JMJD3 (KDM6B).11 A prodrug version of the compound exhibited pronounced attenuation of cytokine responses in macrophages. Subsequent profiling in a panel of 11 biochemical KDM assays however indicated that the inhibitor also affected the KDM5 subfamily, albeit with lower potencies.12 Comprehensive © XXXX American Chemical Society

selectivity profiling across large target classes requires extensive resource for protein expression, purification, and assay development. As an alternative, chemoproteomics-based strategies have been developed, based on affinity pulldowns which require derivatization of the test compounds with linkers and additional functional groups serving as pulldown tags.13 In order to avoid the laborious synthesis of tagged analogs of each test compound, alternative approaches were devised based on combinations of unselective active-site ligands tethered to beads. This strategy was successfully applied to several classes of drug targets including protein and lipid kinases (kinobeads),14,15 histone deacetylases,16 and bromodomains.17 Incubation of the beads with cell extracts in the absence or presence of free ligand was followed by mass-spectrometrybased quantification of bead-bound enzymes, enabling the determination of apparent affinities for multiple enzyme− inhibitor interactions in parallel. In the current study, we applied this strategy to assess the affinities of Fe2+/2-OG dioxygenase enzymes for various representative drug-like inhibitor compounds, as well as for cellular cofactors, such as the cofactor 2-OG and the oncometabolite 2-hydroxyglutarate Received: January 26, 2016 Accepted: May 11, 2016

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DOI: 10.1021/acschembio.6b00080 ACS Chem. Biol. XXXX, XXX, XXX−XXX

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ACS Chemical Biology

Figure 1. Generation of a mixed affinity matrix for the Fe2+/2-OG oxygenase target class. (A) Phylogenetic tree of the Fe2+/2-OG oxygenase target class, assembled by comparison of the amino acid sequence of the catalytic domains (adapted from Johansson et al.5). The reported enzymatic activities are indicated with different colors. Several dioxygenases have more than one described enzymatic activity, and the function of some, like JMJD6, is still highly debated. The proteins that were specifically captured by the bead sets from the HUT78/HEK293 cell extract mixture are denoted in boldface. (B) Chemical ligands functionalized with amine tags for immobilization on Sepharose beads. Compound 1 and compound 2 are analogues of 2-OG. Compound 3 was based on a nicotinic acid scaffold. Compound 4 was based on a previously reported bipyridyl scaffold. Compound 5 is a regioisomer of compound 3. (C) Pattern of selective dioxygenase binding to the bead-immobilized ligands. All dioxygenases showing ≤50% residual binding in the presence of 100 μM of free competing probe were considered as specifically captured and are represented by colored circles as indicated (data from at least two independent replicate experiments). A, ALKBH; E, EGLN; FT, FTO; HI, HIF1AN; J, JMJD; K, KDM; O, OGFOD; PA, P4HA; PD, PHYHD1; PF, PHF8; PH, P3H; PL, PLOD; PY, PHYH; TM, TMLHE.

other dioxygenase subfamilies, and the different enzymatic activities are relatively well aligned with the different branches1 (Figure 1A). Initially, we synthesized two different aminetagged analogues of 2-OG, compound 1 and compound 2 (Figure 1B), for immobilization on sepharose beads. The beads were incubated with a 1:1 mixture of extract from T-cell lymphoma (HUT-78) cell nuclei and from HEK293 cells transfected with KDM6B. This mixture was chosen because KDM6B, which was of particular interest for our studies,11 is not expressed in most cell lines. Extract was subjected to gelfiltration prior to use, as described in the Methods, to eliminate free intracellular cofactors and metabolites. Aliquots of the cell extract were preincubated with either vehicle (DMSO) or with a large excess of “free” probe compound. Bead-bound proteins were eluted under denaturing conditions and processed for

(2-HG). The results indicate that, similar to protein kinase inhibitors, the development of Fe 2+/2-OG dioxygenase inhibitors is likely to pose challenges with respect to reaching sufficient potency, given the intracellular competition by excess cofactors, and to achieve selectivity across multiple enzymes which share a similar active site chemical space.

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RESULTS AND DISCUSSION A Mixed Affinity Matrix for Cellular Profiling of 2Oxoglutarate Dioxygenases. Our initial goal was to develop a mixed affinity matrix optimized for the affinity profiling of small molecule inhibitors targeting Jumonji-type KDMs. Despite their mechanistic relatedness, KDMs and other types of 2-OG dioxygenases were historically approached as different entities. The Jumonji proteins are easily distinguished from the B


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Figure 2. Dioxygenases display a broad range of affinity for 2-OG and different sensitivity to inhibition by oncometabolites. (A) The bars display pKDapp values for 41 dioxygenases for the cosubstrate 2-OG and for different oncometabolites. Gray bars: pKDapp < 3. (B) Estimated intracellular pIC50 of dioxygenase inhibition by R-2-HG in human malignant glioma. In glioma carrying wild type IDH1, R-2-HG concentration is on average around 200 μM with only few dioxygenases significantly inhibited (green line). At 10 mM R-2-HG, a concentration frequently observed in glioma carrying R132 IDH1 mutations, several dioxygenases are strongly inhibited (red line). Gray bars: pKDapp < 2.5. Error bars indicate SEM. No bar indicates that no valid data were obtained. C


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concentration of 2-OG required to competitively block 50% of the target protein from binding to the beads. These IC50 values are a measure of the affinity of the protein for 2-OG but are also affected by the affinity of the protein for the beadimmobilized ligand. The latter effect can be deduced by determining the depletion of the target proteins by the beads. Thus, apparent dissociation constants (K D app ) can be determined, which are largely independent from the bead ligands (Supporting Information Table S3).16 The dioxygenases displayed broad affinities for 2-OG, with pKDapp values ranging from 6 in the case of ALKBH3 to less than 3 for PHYH, PLOD3, or TMLHE (Figure 2A, Supporting Information Table S4). Differences in cofactor-binding affinities of the target enzymes will strongly impact the cellular potency and selectivity of 2-OG dioxygenase inhibitors. This effect has been well documented for protein kinase inhibitors and their affinities, or Km values, for ATP.15,21 Due to competition by the millimolar ATP concentrations in the cell, inhibitors for kinases with high ATP affinity typically show a more pronounced drop in potency, and concomitantly a lower selectivity versus kinases with low ATP affinities. The intracellular concentration of 2OG varies in the high micromolar range22 and is therefore expected to exert a substantial effect on the potency and selectivity of 2-OG dioxygenase inhibitors. Oncometabolite Binding Properties of 2-OG-Dependent Dioxygenases. Cancer-associated mutations in metabolic enzymes lead to abnormal accumulation of these metabolites which have been linked to oncogenic transformation.23,24 For instance, most human glioma bear somatic gain-of-function mutations in isocitrate dehydrogenase resulting in the conversion of 2-OG to high levels of the oncometabolite R-2HG.25 Germline mutations in L-2-hydroxyglutarate dehydrogenase are linked to brain tumors and cause increased levels of S-2-HG.26 Germline loss-of-function mutations in the Krebs cycle enzymes succinate dehydrogenase and fumarate hydratase cause accumulation of the respective metabolites and are linked to paragangliomas and pheochromocytoma, or leiomyomatosis and renal cell cancer, respectively.27,28 Several of these oncometabolites were shown to reach sufficient concentrations to potentially compete with 2-OG binding to dioxygenase enzymes.24 Using the same experimental conditions as above, we profiled the affinity of the dioxygenases for different oncometabolites. In general, we found that the affinities for the oncometabolites were significantly lower than for 2-OG (Figure 2A). In particular, the prolyl hydroxylase subfamilies (EGLNs, P3Hs, P4Hs, and PLODs) displayed low oncometabolite affinities and are therefore predicted to be less affected by increased oncometabolite levels. Oncometabolites have been proposed to regulate the EGLN function. At high concentrations (IC50 close to or above 1 mM) S-2-HG, succinate, and fumarate were reported to inhibit EGLNs,28−30 whereas R-2HG was reportedpossibly depending on the cellular contextas both an inhibitor31,32 and activator30 of EGLNs. Under normoxic conditions, EGLNs hydroxylate the transcription factor HIF1A, targeting it for proteasomal degradation. Moreover, the transcriptional activity of HIF1A is regulated by the Jumonji dioxygenase HIF1AN (FIH),33 which we found to have high affinity for R-2-HG. Thus, our data suggest that the increased activity of HIF1A observed in some tumors carrying IDH1 mutations may be due to inhibition of HIF1AN. In addition, cellular HIF1A hydroxylation on N803, a target for HIF1AN, was shown to be much more sensitive to R- and S- 2-HG inhibition than P402 or P564

quantitative mass spectrometry by trypsin digestion and stable isotope tagging (TMT sixplex).16 Relative protein binding to the beads was quantified by tandem mass spectrometry analysis (MS/MS) of the combined TMT-tagged peptide pools, and binding was considered specific for a given protein if we observed at least 50% reduced binding in the sample treated with excess “free” probe compound. Beads derivatized with the 2-OG analogues specifically captured five Jumonji-type proteins plus five members mostly from different subfamilies (Figure 1C). This first result already suggested that it might be difficult to predict inhibitor off-target activities based on phylogenetic relatedness. In order to broaden the target coverage of our beads, we synthesized compounds 3 and 5, which were based on a nicotinic acid derivative from an in-house screen, and compound 4, based on a published bipyridyl scaffold.18 A mixture of beads derivatized with compounds 4 and 5 captured 12 Jumonji-type proteins and 12 members from other subfamilies, covering most branches of the phylogenetic tree (Figure 1C). The result indicated that selectivity profiling of inhibitors for one class of dioxygenases, for instance the Jumonji-type enzymes, should not be restricted to related enzymes within the same subfamily but rather should be assessed against the entire dioxygenase family. In order to achieve maximal target class coverage, we resolved to employ three standardized sets of beads (M1, M2, and M3), each of which comprised a combination of three different immobilized compounds (Supporting Information Figure S1, Table S1). The M3 bead set enabled the largest coverage, comprising 41 dioxygenases from the HUT-78/HEK293 cell extract. We assessed the expression of dioxygenases in HEK293 and HUT78 cells by shotgun proteomics of cell lysates. In these samples, we identified 40 dioxygenases and performed an estimation of their abundance. From this set, 31 were captured on the beads, whereas nine were found to be expressed but not captured. Conversely, 10 dioxygenases were captured by the beads but not identified in the lysates (Supporting Information Table S2). The three TET dioxygenases are of interest because of their role in leukemogenesis.19 TET1 was identified in the HEK293 lysate used in the study but was not captured by the beads. We assume that the use of additional cell or tissue sources in the future will increase the target class coverage even further. Similarly, a four compound affinity matrix (compounds 1, 2, 3, and 5) might be useful to increase target coverage. The initial MS-based proteomics experiments designed to characterize our bead ligands were performed in shotgun mode and thus did contain many other protein identifications, potentially obfuscating the reproducible identification of the 2-OG dioxygenase members. Therefore, the systematic profiling performed in this study was based on a targeted data acquisition strategy using a predefined mass lists containing m/z, charge state, and retention time,20 for the members of the 2-OG dependent dioxygenase family only. Cofactor Binding Properties of 2-OG-Dependent Dioxygenases. We used the bead sets to profile the affinity of the dioxygenases for their natural cosubstrate 2-OG in a set of dose-dependent competition-binding experiments. As described above, the beads were incubated with cell extract aliquots after pretreatment with either vehicle or different concentrations of 2-OG (up to 4 mM). The 2-OG-dependent reduction of the reporter ion signals observed in the MS/MS spectrum for each identified protein was used to derive inhibition curves from which half-maximal inhibitory concentrations (IC50 values) were determined, referring to the D


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Figure 3. Selectivity profiles of reference and novel dioxygenase inhibitors. Circles display pKDapp for each dioxygenase; size is proportional to compound potency as indicated; no circle: no valid data. (A) Binding inhibition profiles of reference inhibitors IOX2, JNJ-42041935 (JNJ-935), JHDM-I1, and GSK-J1. IOX2 and JNJ-42041935, both reported as EGLN selective inhibitors, appear as inhibitors for many dioxygenases. JHDM-I1 appears as a nonselective pan-dioxygenase inhibitor; GSK-J1 is more selective and inhibits strongly only KDM6A/B and KDM5C/D. (B) Dioxygenase binding inhibition profiles of inhibitors for JMJD2 family demethylases. Compound 6 and compound 7 both inhibited KDM5C/D. Compound 8 is a selective KDM5C/D inhibitor and compound 9 a selective KDM4A/B/C inhibitor. A, ALKBH; E, EGLN; FT, FTO; HI, HIF1AN; J, JMJD; K, KDM; O, OGFOD; PA, P4HA; PD, PHYHD1; PF, PHF8; PH, P3H; PM, P4HTM; PL, PLOD; PY, PHYH; TM, TMLHE; TY, TYW5. E


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ACS Chemical Biology hydroxylation induced by the EGLNs.29 Our data also suggest that the histone H3 K27me3 demethylases KDM6A and KDM6B are strongly affected by R-2-HG, explaining the observation that this repressive mark was increased in astrocytes or in 3T3-L1 cells carrying IDH mutations.34,35 Furthermore, our data suggest that HIF1AN, the obesityassociated FTO, and KDM5C/KDM5D should be strongly affected by high S-2-HG concentrations. The single-strand DNA demethylase ALKBH3, which is involved in DNA repair, is predicted by our data to be sensitive to increased succinate and fumarate concentrations. Likewise P4HTM, a poorly characterized dioxygenase, was also strongly affected by fumarate (Figure 2A). To further predict the consequences of dioxygenase inhibition by high R-2-HG concentrations, we performed some estimates for the case of glioma associated with the IDH1 R132 mutation, for which oncometabolite levels were reported.36 Here, the concentration of R-2-HG is approximately 60 fold increased compared to IDH1 wild type glioma, and ∼400 fold increased compared to the cosubstrate 2-OG, which itself is not affected by the mutation. We estimated intracellular IC50 values for R-2-HG inhibition of each dioxygenase in the presence of intracellular 2-OG concentration using the equation IC50int ∼ KD,R‑2‑HGapp (1+ ([2-OG]/KD,2‑OGapp)16 based on the pKDapp values determined for 2-OG and R-2-HG and the reported 2-OG concentration of 0.05 μmol/g. This led to estimates for the intracellular pIC50 values that are significantly lower than the calculated pKDapp (Figure 2B). At the reported R-2-HG concentrations of ∼240 μM in IDH1 wild type glioma, only two dioxygenases, KDM6B and KDM7A, would be predicted to be affected. However, at the vastly increased R-2-HG concentrations of ∼10 mM in IDH1 mutant glioma,36 we predict that many additional dioxygenases should be inhibited including KDM6A, KDM2A, JMJD4, ALKBH2, and ALKBH4. Our data suggest that increased concentrations of oncometabolites exert complex effects in disease progression via deregulation of epigenetic mechanisms, DNA repair programs, and other dioxygenase controlled cellular functions. Profiling of Drug-Like Inhibitors of 2-OG-Dependent Dioxygenases. Several members of the 2-OG dioxygenase family represent potential drug targets, and considerable efforts were spent in recent years to design inhibitors, in particular for the Jumonji-type lysine demethylases in the cancer and inflammation areas5,10,11,37 and the prolyl hydroxylases for cancer, ischemia, and anemia.4,6,8 However, screening assays are currently available only for a small number of dioxygenases, and therefore the selectivity of lead compounds and drug candidates was not assessed. We decided to profile a set of representative drug-like inhibitor compounds applying the same experimental settings as for the cosubstrate and metabolite studies described above. IOX238 and JNJ-4204193539 were reported as potent (pIC50 ≥ 7) and selective (≥100 fold) EGLN1 inhibitors. Our profiling data confirm the potency of both compounds toward EGLNs but not their selectivity (Figure 3A, Supporting Information Table S5). Both compounds displayed potent affinity toward many additional oxygenases, in particular the most closely related P3Hs and OGFOD3. JNJ-42041935 was most potent for PHYHD1 (pKDapp = 8.2), a putative small molecule hydroxylase linked to Alzheimer’s disease.40 JHDM-I1 was reported as selective for a subset of Jumonji demethylases.41 In our assay, this compound was less potent for some demethylases like KDM4A/Cpossibly due to differences in experimental design (Figure 3A, Supporting Information Table

S6)but it affected a variety of dioxygenases, with the highest potency for the trimethyl lysine hydroxylase TMLHE, which has been genetically linked to autism.42 We also profiled GSKJ1, a compound from our laboratories originally proposed as a KDM6A/KDM6B selective inhibitor11 but later shown to also affect KDM5 enzymes.12 The new comprehensive profile showed that GSK-J1 was more selective than the previous three reference compounds. At low micromolar concentrations, it affects just the targets reported in the two previous studies, namely KDM6A and KDM6B, but also KDM5C and KDM5D (Figure 3A). Last, we also generated selectivity data for representative compounds from our research program focused on the discovery of KDM4 histone lysine demethylase inhibitors (Figure 3B, Supporting Information Table S7). We profiled four compounds from different chemical series displaying good potency in the KDM4 primary biochemical assays. The pyrido[3,4-d]pyrimidin-4(3H)-one derivative 643 and the 3-amino-pyridine-4-carboxylate derivative 744 are mixed KDM4 and KDM5C/KDM5D inhibitors with >1000 fold selectivity for any other oxygenase identified. The 3-cyanopyrazolo-pyrimidinone derivative 8 was shown to be a potent KDM5C and KDM5D inhibitor with >40 fold selectivity over KDM4C and >300 fold for any other oxygenase tested. The imidazopyridine-7-carboxylate 9 was shown to be a selective KDM4A, KDM4B, and KDM4C inhibitor with >50 fold selectivity for any other oxygenase tested, including KDM4D. In conclusion, we have generated a comprehensive set of data comprising cofactor, metabolite, and inhibitor binding properties for the 2-OG dependent dioxygenase target class. We find a good correlation between measured binding affinities and when availableenzymatic inhibition for small compounds (Supporting Information Table S6). Correlation is not quite as good for metabolites, maybe not too surprisingly due to the high concentrations needed to reach inhibition levels, rendering measurements highly dependent on experimental conditions. Our chemoproteomics-based methodology was proven in previous studies to generate robust and accurate data13,16,17 and now provides a means to assess potency and selectivity within this new class of promising drug targets. The combination of cosubstrate and inhibitor affinity data will aid to enable the prediction of selectivity in cellular settings, and the selection of promising chemical scaffolds as well as the identification of selective probes to support further target validation studies.

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METHODS

Chemical Synthesis. Chemical synthesis of compounds and preparation of affinity matrices are described in the Supporting Information. Cell Culture and Biochemistry. HEK-293 cells (ATCC) were grown to near confluency in DMEM medium supplemented with 10% fetal calf serum (FCS) and were transfected with pCDNA3.1 vector expressing Flag-KDM6B (1142−1642) using calcium phosphate precipitation. For experiments 1 and 2, 36−39, 49, 52, and 55, a HEK293 stable cell pool expressing KDM6B (1142−1642) was used. HUT-78 cells (ATCC) were grown in spinner flasks in IMDM medium with 20% FCS up to a cell concentration of 1 × 106 cells/ml. Cells were harvested by centrifugation and washed once with 1× PBS buffer. Low salt cell lysate (HEK-293) and nuclear lysate (HUT-78) were prepared as previously described.11 Aliquots were snap frozen in liquid nitrogen and stored at −80 °C. Cell lysates were thawed and mixed 1 to 1 at a final protein concentration of 5 mg mL−1. Buffer was exchanged via two passages through a PD-10 column (GE Healthcare). The final buffer composition was 50 mM tris (pH 7.4), 5% glycerol, 150 mM NaCl, 25 mM NaF, 1.5 mM MgCl2, 0.4% Igepal F


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ACS Chemical Biology CA-630, 200 μM sodium-L-ascorbate, 10 μM FeCl2, and one tablet of protease inhibitors (Roche) per 25 mL of lysate. The supernatant was spun for 20 min at 145 000g prior to use. Affinity profiling and peptide sample preparation were performed as described previously.16 Peptide extracts were labeled with TMT reagents in 90 mM triethylammoniumbicarbonate, at pH 8.53. After quenching of the reaction with glycine, labeled extracts were combined. Extracts from the vehicletreated sample were labeled with TMT reagent 131 and combined with extracts from compound-treated samples labeled with TMT reagents 126 to 130, fractionated with reversed-phase chromatography into nine fractions as previously described.16 Targeted LC-MS/MS Data Acquisition. Mass spectrometric analysis was performed on Orbitrap Mass Spectrometers essentially as described20 and is detailed in the Supporting Information. Peptide and Protein Identification and Quantification (Supporting Information Table S8, Figure S2). Mascot (versions 2.2, 2.3, and 2.4, Matrix Science) was used for protein identification using a 10 ppm mass tolerance for peptide precursors and 0.8 Da (CID) or 20 mDa (HCD) mass tolerance for fragment ions. The search database consisted of a customized version of the IPI protein sequence database combined with a decoy version of this database created using a script supplied by Matrix Science. Only proteins identified by at least two uniquely matching peptides were quantified. Dose−response curves were fitted using R (http://www.r-project.org/ ) and the drc package (http://www.bioassay.dk) as described.16 IC50 values were confirmed in replicate experiments using targeted data acquisition for a subset of proteins20 corresponding to all dioxygenases displayed on the phylogenetic tree of Figure 1A. Apparent dissociation constants (KDapp) were derived from the IC50 values by taking into account the amount of (off-) target sequestered by the affinity matrix using the Cheng−Prusoff relationship (IC50/KDapp, depletion factor F). Sequential binding experiments are performed to identify the respective IC50/KDapp depletion factor for each captured protein. Duplicate sequential binding experiments and a six-point dose− response are analyzed in a single 10-plexed mass spectrometric experiment. Captured proteins may bind either directly to the affinity matrix or indirectly through protein complex partners (see also the Supporting Information for more details about this chapter).

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analyzed data; M.Ba. contributed to the manuscript; and G.J. and G.D. wrote the manuscript. Notes

The authors declare the following competing financial interest(s): The authors are employees of Cellzome GmbH and GlaxoSmithKline. The company funded the work.

ACKNOWLEDGMENTS

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ABBREVIATIONS

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REFERENCES

We would like to thank C. Ester, N. Garcia-Altrieth, J. Huber, M. Jundt, K. Kammerer, M. Klös-Hudak, M. Paulmann, T. Rudi, J. Stuhlfauth, and I. Tögel for technical assistance; J. Freeman and J. Harrison for chemical synthesis; and C. Doce for the adaptation of the phylogenetic tree.

2-OG, two oxoglutarate; KDM, lysine demethylase; R-HG, Rhydroxyglutarate; S-HG, S-hydroxyglutarate; TMT, tandem mass tags

(1) Loenarz, C., and Schofield, C. J. (2008) Expanding chemical biology of 2-oxoglutarate oxygenases. Nat. Chem. Biol. 4, 152−156. (2) Bishop, T., and Ratcliffe, P. J. (2015) HIF hydroxylase pathways in cardiovascular physiology and medicine. Circ. Res. 117, 65−79. (3) Shen, L., Song, C. X., He, C., and Zhang, Y. (2014) Mechanism and function of oxidative reversal of DNA and RNA methylation. Annu. Rev. Biochem. 83, 585−614. (4) Gorres, K. L., and Raines, R. T. (2010) Prolyl 4-hydroxylase. Crit. Rev. Biochem. Mol. Biol. 45, 106−124. (5) Johansson, C., Tumber, A., Che, K., Cain, P., Nowak, R., Gileadi, C., and Oppermann, U. (2014) The roles of Jumonji-type oxygenases in human disease. Epigenomics 6, 89−120. (6) Kaelin, W. G., Jr. (2011) Cancer and altered metabolism: potential importance of hypoxia-inducible factor and 2-oxoglutaratedependent dioxygenases. Cold Spring Harbor Symp. Quant. Biol. 76, 335−345. (7) Martinez, S., and Hausinger, R. P. (2015) Catalytic Mechanisms of Fe(II)- and 2-Oxoglutarate-dependent Oxygenases. J. Biol. Chem. 290, 20702−20711. (8) Eltzschig, H. K., Bratton, D. L., and Colgan, S. P. (2014) Targeting hypoxia signalling for the treatment of ischaemic and inflammatory diseases. Nat. Rev. Drug Discovery 13, 852−869. (9) Aik, W., Demetriades, M., Hamdan, M. K., Bagg, E. A., Yeoh, K. K., Lejeune, C., Zhang, Z., McDonough, M. A., and Schofield, C. J. (2013) Structural basis for inhibition of the fat mass and obesity associated protein (FTO). J. Med. Chem. 56, 3680−3688. (10) Thinnes, C. C., England, K. S., Kawamura, A., Chowdhury, R., Schofield, C. J., and Hopkinson, R. J. (2014) Targeting histone lysine demethylases - progress, challenges, and the future. Biochim. Biophys. Acta, Gene Regul. Mech. 1839, 1416−1432. (11) Kruidenier, L., Chung, C. W., Cheng, Z., Liddle, J., Che, K., Joberty, G., Bantscheff, M., et al. (2012) A selective jumonji H3K27 demethylase inhibitor modulates the proinflammatory macrophage response. Nature 488, 404−408. (12) Heinemann, B., Nielsen, J. M., Hudlebusch, H. R., Lees, M. J., Larsen, D. V., Boesen, T., Labelle, M., Gerlach, L. O., Birk, P., and Helin, K. (2014) Inhibition of demethylases by GSK-J1/J4. Nature 514, E1−E2. (13) Bantscheff, M., and Drewes, G. (2012) Chemoproteomic approaches to drug target identification and drug profiling. Bioorg. Med. Chem. 20, 1973−1978. (14) Bantscheff, M., Eberhard, D., Abraham, Y., Bastuck, S., Boesche, M., Hobson, S., Mathieson, T., Perrin, J., Raida, M., Rau, C., Reader, V., Sweetman, G., Bauer, A., Bouwmeester, T., Hopf, C., Kruse, U., Neubauer, G., Ramsden, N., Rick, J., Kuster, B., and Drewes, G. (2007)

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschembio.6b00080. Tables S1, S4, S5, and S7 (XLSX) Table S6 (XLSX) Tables S2, S4, S5, S6, and S8 (XLSX) Methods and supplementary figures and tables (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Addresses

§ SYNthesis MedChem Ltd., Babraham Research Campus, Cambridge, U.K. ∥ VR Consulting Ltd., Haverhill, Suffolk, U.K. ⊥ Oncology iMed, AstraZeneca, Cambridge Science Park, Cambridge, U.K. # Bicycle Therapeutics Ltd., Babraham Research Campus, Cambridge, U.K.

Author Contributions

K.L., D.M.W., R.K.P., and G.D. conceived the project; G.J., M.Bo., J.A.B., D.E., N.S.G., M.M., N.G.R., V.R., A.R., R.J.S., S.M.W., and M.Ba. conducted or supervised experiments; G.J., D.E., T.M., M.Ba., and G.D. designed experiments and/or G


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