Discovery of Novel Disruptor of Silencing Telomeric 1-Like (DOT1L

Article pubs.acs.org/jmc

Discovery of Novel Disruptor of Silencing Telomeric 1‑Like (DOT1L) Inhibitors using a Target-Specific Scoring Function for the (S)‑Adenosyl‑L‑methionine (SAM)-Dependent Methyltransferase Family Yulan Wang,#,†,‡,¶ Linjuan Li,#,§,¶ Bidong Zhang,#,‡ Jing Xing,#,‡ Shijie Chen,#,‡ Wei Wan,#,‡ Yakai Song,#,II Hao Jiang,#,‡ Hualiang Jiang,#,‡,§ Cheng Luo,*,#,‡ and Mingyue Zheng*,#,‡ #

State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China † State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100191, China ‡ University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China § School of Life Science and Technology, Shanghai Tech University, Shanghai 200031, China II Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215123, China S Supporting Information *

ABSTRACT: The disruptor of telomeric silencing 1-like (DOT1L) protein is a histone H3K79 methyltransferase that plays a key role in transcriptional elongation and cell cycle regulation and is required for the development and maintenance of MLL-rearranged mixed lineage leukemia. Much effort has been dedicated toward discovering novel scaffold DOT1L inhibitors using different strategies. Here, we report the development and application of a target-specific scoring function, the SAM score, for (S)-adenosyl- Lmethionine (SAM)-dependent methyltransferases, for the discovery of novel DOT1L inhibitors. On the basis of the SAM score, we successfully identified a novel class of DOT1L inhibitors with a scaffold of [1,2,4]-triazolo-[3,4-b][1,3,4]-thiadiazole, in which compound 6 exhibits an IC50 value of 8.3 μM with selectivity versus other tested SAM-dependent methyltransferases. In cellular studies, 6 selectively targets DOT1L, blocks the proliferation of mixed lineage leukemia cell lines, and causes cell cycle arrest and apoptosis. Moreover, we analyzed the putative binding modes of 6 and its analogues obtained by molecular docking, which may assist with the future development of DOT1L inhibitors with improved potency and selectivity profiles.

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INTRODUCTION Disruptor of telomeric silencing 1-like (DOT1L) is an evolutionarily conserved histone methyltransferase (HMT) that specifically catalyzes the mono-, di-, and tri- methylation of the histone H3-lysine79 (H3K79) residue in the core domain.1 DOT1L-mediated H3K79 methylation plays an important role in transcriptional regulation, cell cycle regulation, and the DNA damage response.2,3 DOT1L is proposed to be a catalytic driver of leukemogenesis in mixed lineage leukemia (MLL).4,5 The aberrant methylation of H3K79 by DOT1L enhances the expression of leukemogenic genes, including HOXA9 and MEISI, which is an essential step in the development of MLL.6,7 Since the discovery of the critical role of DOT1L in MLL leukemia, research has focused on identifying inhibitors of DOT1L.8,9 Because almost all methyltransferases use (S)-adenosyl-L-methionine (SAM) as the methyl donor (enzyme cofactor) and because the byproducts are (S)-adenosyl-L-homocysteine (SAH), a common strategy for designing DOT1L inhibitors is to develop SAM analogues that may compete with SAM. To date, a variety © 2017 American Chemical Society

of DOT1L inhibitors have been reported, but most are SAM derivatives. Among the derivatives, the compound (2R,3R,4S,5R)-2-(6-amino-9H-purin-9-yl)-5-((((1R,3S)-3-(2(5-(tert-butyl)-1H-benzo[d]imidazol-2-yl)ethyl)cyclobutyl) (isopropyl)amino)methyl)tetrahydrofuran-3,4-diol (1, EPZ5676)10 has been initiated into phase I clinical trials for acute leukemia with rearrangement of the MLL gene. However, the pharmacokinetic properties of this compound are unsatisfactory because 1 exhibits low oral bioavailability and high clearance11 and requires continuous intravenous infusion to maintain the drug plasma concentration.12 Recently, Chen et al. discovered a novel DOT1L inhibitor 2-(2-(5-((2-chlorophenoxy)methyl)2H-tetrazol-2-yl)acetyl)-N-(4-chlorophenyl)hydrazinecarboxamide (2)13 with an IC50 of 14 μM based on highthroughput screening. Chen et al. then optimized this inhibitor to more potent inhibitors N-(1-(2-chlorophenyl)-1H-indol-6yl)-2-(2-(5-(2-chlorophenyl)-1H-tetrazol-1-yl)acetyl) Received: December 7, 2016 Published: February 6, 2017 2026

DOI: 10.1021/acs.jmedchem.6b01785 J. Med. Chem. 2017, 60, 2026−2036

Journal of Medicinal Chemistry

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hydrazinecarboxamide (3)13 with nanomolar inhibitory activity. Because the structural diversity of DOT1L inhibitors remains limited and most are SAM derivatives with an adenosine group, there remains an urgent need to develop new DOT1L inhibitors with novel scaffolds. Molecular docking-based virtual screening (VS) is a commonly used approach in drug discovery to identify novel hits with diverse structures. Although VS has been widely used and achieved many successes,14 the performance of VS may vary significantly on the basis of different protein targets.15 The algorithm for molecular docking consists of two essential parts: an algorithm for conformation searching and a scoring function for protein−ligand binding affinity evaluation. Based on the critical assessments, most existing docking programs can reproduce experimentally determined ligand binding modes with acceptable accuracy.16 However, the scoring functions are less successful, either in terms of a large number of false positives in the VS or less-accurate rank-ordering for known active ligands.15 This inaccuracy problem of the scoring functions is one of the major reasons for the poor performance of docking programs and related VS strategies. There are several strategies to improve scoring functions for protein− ligand interaction predictions, such as expanding the chemical space used to train a scoring function or integrating more explicit and accurate inclusion of the desolvation and entropic effects.17 Additionally, it is difficult for a scoring function to perform satisfactorily for every target system; therefore, customizing a scoring function suitable to only one or several targets is a pragmatic compromise.18 With increasing structural and activity data, some target-specific scoring functions for kinases have been proposed.19,20 Compared to traditional general-purpose scoring functions, these target-specific scoring functions provide more accurate affinity predictions for the targets of interest. For all types of methylation of histones and DNA, the methyl group donor is the cofactor SAM, and the binding sites for SAM are structurally conserved. However, these sites exhibit structural variances and are considered chemically tractable.21,22 In this study, we developed a scoring function for discovering novel inhibitors of HMTs, which can interpret the commonalities and differences of ligand binding to the SAM binding site. First, we collected compounds with known affinity data against SAM-dependent methyltransferases and the crystal structures of these methyltransferases. Then, protein−ligand interaction descriptors were calculated for each compound, and its corresponding target based on the putative binding poses was generated by molecular docking. Epsilon support vector regression (ε-SVR) was used to build a regression model, the SAM score, which can be used as a target-specific scoring function for the SAM-dependent methyltransferase family. By applying the SAM score to the DOT1L inhibitor virtual screening and biological evaluation, we successfully identified a novel class of DOT1L inhibitors with a scaffold of [1,2,4]triazolo-[3,4-b][1,3,4]-thiadiazole. This novel scoring approach together with the identified DOT1L inhibitors provide new insight and a starting point to develop chemical modulators of HMTs for cancer therapy.

including DNA (cytosine-5)-methyltransferase 1 (DNMT1), coactivator-associated arginine methyltransferase 1 (CARM1), protein arginine N-methyltransferase 1 (PRMT1), protein arginine N-methyltransferase 3 (PRMT3), protein arginine Nmethyltransferase 5 (PRMT5), protein arginine N-methyltransferase 6 (PRMT6), euchromatic histone-lysine N-methyltransferase 1 (EHMT1), euchromatic histone-lysine Nmethyltransferase 2 (EHMT2), SET domain containing lysine methyltransferase 7 (SETD7), SET domain containing lysine methyltransferase 8 (SETD8), suppressor of variegation 3−9 homologue 2 (SUV39H2) and DOT1L. For the 12 methyltransferases, there are 562 ligands with structure and activity data collected from BindingDB (https://www. bindingdb.org/), and these ligands were randomly grouped into a training and a test set at a ratio of 5:1. The IC50 values of all ligands spanning many orders of magnitude were normalized to pIC50 (pIC50 = 9−log10(IC50)). For each of these protein targets, one crystal structure (with a SAM or SAM derivative ligand) from the Protein Data Bank (PDB) was used to build the scoring function. Putative Binding Pose Generation. For most of these methyltransferases, the cocrystal structures with their corresponding ligands were unavailable, and AutoDock Vina was used to generate the potential binding poses of the protein− ligand complexes. To validate the Vina docking results, for each type of methyltransferase, we first performed cross docking of ligands extracted from different crystal structure. Table S1 of Supporting Information summarizes the structures used for building SAM score, the structures of their cognate proteins, and the root-mean-square deviation (RMSD) values between the native ligand poses (from crystal structures) and the docking generated poses. Most of structures have an average RMSD value less than 2.0 Å, suggesting that ligand pose generated by Vina docking is satisfactory. All the proteins and ligands were prepared with AutoDockTools, including the addition of hydrogen atoms and charges to the proteins and ligands. Then, the prepared ligands were docked into their corresponding targets, and the top-scored poses of each ligand were selected for the following studies. Intermolecular Interaction Features Calculation. To convert the poses of the receptor−ligand complexes to features suitable for machine learning, the atom pair potentials of iPMF23 were used, which is an iterative modified knowledgebased scoring function. The atom type definition of iPMF was used, which is similar to PMF0424 with a set of 17 protein and 30 ligand atom types. For an atom pair PLi, the sum of the iPMF scores at a certain distance range dj is defined as feature Fij to capture the interaction characteristics of the PL complexes. Here, the distance-dependent interaction features were calculated at the upper threshold of 12 Å (ignoring all pairs with a distance greater than 12 Å).25 The distance ranges for the iPMF features calculated are as follows: Fij was divided into Fi1, Fi2, ..., and Fi11, where Fi1, Fi2, ..., Fi11 are the sum of the potentials for 0.0 < dj ≤2.0 Å, 0.0 < dj ≤ 3.0 Å, ..., and 0.0 < dj ≤ 12.0 Å, respectively. In addition, ligand volume correction was considered in the feature calculation. Finally, for each receptor−ligand complex, we calculated 5,610 iPMF features (17 atom types of the protein × 30 atom types of the ligand × 11 distance ranges). The SAM Score Construction. Support Vector Machine (SVM) is a useful machine learning tool for classification and regression analyses and can effectively use nonlinear data by implicitly mapping the input data into high-dimensional feature

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RESULTS AND DISCUSSION Development of the SAM Score. Data Set Collection and Preprocessing. To build a specific scoring function against the SAM pocket, we collected the structures of proteins and their inhibitor for 12 SAM-dependent methyltransferases, 2027



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Figure 1. Workflow and evaluation of the SAM score. For clarity, the inactive ligands have been deleted in the comparison figure of the scoring function.

Figure 2. In vitro DOT1L activity assays for compounds. (A) AlphaLISA DOT1L assay for the inhibitory activities of all selected 171 compounds at 50 μM. The compounds with a DOT1L inhibition ratio ≥40% are labeled with a red asterisk (21 compounds), and the positive control compound 4 was depicted as a dark red column. (B) Radioactive methylation assays for DOT1L were performed, and the inhibitory activities of the above 21 compounds at 100 μM (rose red) and 50 μM (orange) were determined. (C) The structure of 5, 6, 7, and 8.

spaces with kernel functions.26 Based on the iPMF features and pIC50 values of the training set, we applied SVM to building the scoring function. Because the degree of degeneracy of iPMF features may be high and only parts of the atom pairs between the receptor and ligand have important effects on their interactions, feature selection using a recursive feature elimination (RFE)27 procedure was performed to eliminate uninformative features. In this study, the importance of each feature was estimated by the square of the Pearson’s correlation coefficient (R2) between the feature and the pIC50 value of the ligand. LibSVM28 implementation of the ε-SVR method with 5fold cross validation (CV) was used to evaluate model performance. We measured the performance with the R2

between the predicted score and pIC50 value of the ligand. In the experiment, 643 features were selected for model building, and the R2 of the 5-fold CV was 0.39. Compared with the use of Vina and Glide on the test set, the SAM score exhibited a more significant correlation with the pIC50 value of these ligands (Figure 1). The R2 of the SAM score was 0.36, whereas the R2 of Vina and Glide were near zero. The workflow for the SAM score building is presented in Figure 1, and more details are provided in the experimental section. It should be noted that the current SAM score still leaves a lot of room for improvement. The less than perfect correlation values (R2 < 0.40) are frequently observed in studies comparing different scoring functions.23,29−31 Factors account2028



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Figure 3. (A) Radioactivity assay for analogues of 5 (red), 6 (green), 7 (orange), and 8 (purple) against DOT1L. The inhibitory activity was determined at 50 μM. (B) Radioactivity assays for 6 against DOT1L and five other enzymes at 50 μM and 100 μM. (C) SPR-based binding assay of 6 with DOT1L. The compound was prepared at concentrations of 49.15 μM, 31.46 μM, 25.17 μM, 16.11 μM, 8.25 μM, and 6.60 μM.

based on the similarity of the extended connectivity fingerprint 4 (ECFP4)33 using Pipeline Pilot v7.5. For each cluster, only 1−2 compounds were selected manually. Finally, 171 compounds were selected and purchased for experimental evaluation using the following biochemical assays. DOT1L Enzymatic Assays. The 171 compounds selected by virtual screening were tested using biochemical experiments to determine their potency against DOT1L. First, the AlphaLISA assay for DOT1L histone H3 lysine-N-methyltransferase was performed to determine the inhibitory activities of the compounds at 50 μM, and 1-(3-((((2R,3S,4R,5R)-5-(4amino-7H-pyrrolo[2,3-d]pyrimidin-7-yl)-3,4-dihydroxytetrahydrofuran-2-yl)methyl) (isopropyl)amino)propyl)-3-(4-(tertbutyl)phenyl)urea (4, EPZ004777)9 was used as a reference drug. The DOT1L inhibitory activity determined using the AlphaLISA assay for these compounds and their structures are provided in Table S2 of Supporting Information. Together, 21 compounds exhibited DOT1L inhibition rates greater than 40% (asterisks in Figure 2A). The AlphaLISA assay utilizes antibodybased detection in conjunction with fluorescence and may exhibit high false-positive rates. To validate the screening results, the 3H-labeled radioactive methylation assay, a gold standard for enzyme activity detection of methyltransferases,34 was used to further evaluate the inhibitory activities of the 21 compounds at 100 μM and 50 μM. As shown in Figure 2B, four compounds, 5, 6, 7, and 8, exhibited significant inhibitory activities.

ing for the effect may involve the inaccurate protein−ligands binding conformations, inadequate consideration of protein flexibility, binding site hydration, and so on. For a data-driven approach like SAM score, because the inhibition activity data used here were collected from different sources, attention should also be paid to the data quality. To further improve its scoring ability, the experimental data used for training need to be more reliable and consistent. An ideal paradigm is to use more internal experimental data in the score optimization cycles, which allows the model to be iteratively validated and updated, and keeps a better data consistency in the meantime. Application of the SAM Score to DOT1L Inhibitor Screening. The SPECS library containing 207 163 compounds was used as the ligand database. Before ligand preparation, several chemical filters were applied on the ligand database to remove pan assay interference compounds (PAINS)32 and compounds containing inorganic atoms, unwanted functionalities, and reactive groups. The remaining 190 038 compounds and proteins were evaluated using the same protocol for the SAM score building. The structure of human DOT1L (PDB id: 3QOW) was used to generate the putative binding poses of the ligands, which were docked into the SAM pocket of the DOT1L protein using AutoDock Vina. Then, these generated poses were rescored with the scoring function of the SAM score. For each compound, only the pose with the highest rescored was considered, and the top 1000 compounds were further analyzed. To ensure structural diversity, the highly ranked compounds were further clustered into 122 clusters 2029



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Similarity-Based Analogue Searching. To identify additional compounds of interest, we applied similarity-based analogues to search for the above four active compounds using Pipeline Pilot v7.5 with the Tanimoto coefficient of the molecular fingerprint ECFP4. Finally, 122 compounds, including 19 analogues of 5, 33 analogues of 6, 27 analogues of 7, and 43 analogues of 8, were purchased from SPECS. The 3 H-labeled radioactive methylation assay for DOT1L was used to determine the inhibitory activities of these compounds at 50 μM. The structure and DOT1L inhibitory activity for these compounds are provided in Table S3 of Supporting Information. As shown in Figure 3A, the analogues of 6 exhibited more potent DOT1L inhibition. As the compounds were purchased from Specs Corp, according to the quality reports provided by Specs there are a few compounds with purity less than 95%. These compounds were further purified if they exhibited higher inhibitory activity (inhibition ratio greater than 60% at 50 μM) in the radioactive methylation assay for DOT1L. Five potential active compounds have been purified, and the HPLC analysis data of these compounds are provided in Table S4 of Supporting Information. In the end, altogether nine compounds were selected to determine their IC50 values (Table 1), and the purities of all of these compounds were assessed as greater than 95%. According to the general hypothesis that similar compounds should have similar biological activity, 6 was identified as a potential DOT1L inhibitor. Moreover, a few ADME related properties have been calculated for 6 and a SAM derived ligand (SAH) using QikProp module (version 4.4) in Schrödinger software. As shown in Table S5 of Supporting Information, 6 showed a more favorable “drug-likeness” profile than the SAM derived ligand. Therefore, 6 was selected as the representative compound for further study. Methyltransferase Enzymatic Selectivity. Ideally, the characteristics of a lead compound include potent inhibitory activity and selectivity against the target. On the basis of the above results, 6 showed potential DOT1L inhibitory activity in vitro. To determine the selectivity profile of 6 for DOT1L, we tested the inhibition ratio of the compound against five other histone and DNA methyltransferases, including DNMT1, nuclear receptor binding SET domain protein 1 (NSD1), PRMT5, PRMT1, and enhancer of zeste homologue 2 (EZH2). As shown in Figure 3B, 6 exhibited a low inhibition ratio (less than 40%) against these targets, indicating that 6 can selectively inhibit DOT1L over other tested methyltransferases. SPR-Based Binding Assay. To further understand the mechanism of action of 6 toward DOT1L, we performed surface plasmon resonance (SPR)-based binding assays in which ligand association and dissociation rate constants were determined.35 As shown in Figure 3C, 6 binds directly to DOT1L with an equilibrium dissociation constant (KD) of 17.27 μM, which equals the IC50 in enzymatic assays. Moreover, there was a positive correlation between the binding strength and dose of 6. These results suggest that 6 binds to DOT1L and blocks its activity in vitro. Structure−Activity Relationships (SAR) of 6 Analogues. Combined with the putative binding mode analysis, we investigated the SARs of 6 and its analogues. As shown in Table 1, the series of compounds have the same scaffold of [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole substituted with different groups. The putative binding modes of these compounds are shown in Figure 4. The scaffold of [1,2,4]triazolo[3,4b][1,3,4]thiadiazole aligns well with the furanoid sugar ring of

Table 1. Structure and DOT1L Inhibitory Activity of 6 and Its Analogues

SAM. In 6, triazolo and thiadiazole are substituted with 1Hbenzo[d][1,2,3]triazole-1yl (A) and 3-fluorophenyl, respectively. As shown in Figure 4A, moiety A of 6 occupies the same pocket of adenosyl in SAM and forms a pi−pi interaction with F223 and a hydrogen bond with K187. The 3-fluorophenyl moiety extends to the pocket for the tail of SAM and forms a cH−pi interaction with Y136. The analogues with small different substituted phenyls, such as 9, 15 and 16, showed comparable inhibitory activities against DOT1L and similar putative binding modes to 6 (Figure 4B). Briefly, the A moiety of these compounds also forms pi−pi interactions with F233 and a hydrogen bond with K187, and the substituted phenyl groups of 9 and 15 establish pi−pi interactions with Y136, whereas 16 makes a hydrogen bond through the epoxy of the substituted phenyl to N241. Extending the linker between the phenyl and moiety A in 21, 22, 23, and 24 weakened the 2030



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Figure 4. Putative binding modes of 6 and its analogues. (A) The putative binding mode of 6 (yellow) aligned with the crystal structure of SAM (green) and DOT1L. (B) The putative binding modes of 6 (yellow), 9 (lake blue), 15 (violet), and 16 (magenta). The hydrogen bonds are depicted as red dashed lines, and the pi−pi interactions are depicted as green lines.

Figure 5. Cell proliferation inhibition assays for 6. (A,B,C) IC50 of 6 against MLL-rearranged leukemia cell lines. (D,E,F) IC50 of 6 against non-MLLrearranged leukemia cell lines.

between 6 and these methyltransferases except DOT1L. These structural models support the selectivity experiment results of 6. Cell-Based Activity. Because DOT1L enzymatic activity is a driver of MLL-rearranged leukemia,36 small-molecule inhibitors of DOT1L may selectively affect the proliferation of MLL-rearranged leukemia cell lines. Therefore, DOT1L inhibition by 6 was further evaluated in ex vivo assays. First, the MLL-rearranged leukemia cell lines MV4-11, THP-1, and Kopn-8, and three non MLL-arranged leukemia cell lines (Kasumi-1, HL60, and K-562) were prepared for cell activity tests. Then, two human solid tumor cell lines (ACHN and Caki-1) and a human normal cell line (HK-2) were also tested. After treatment with 6 for 3 days, the proliferation ratios were measured using MTT or alamar blue assays. As shown in Figure 5, 6 selectively inhibits the proliferation of the MLL rearranged leukemia cell lines MV4-11, THP-1, and Kopn-8 at IC50 values

activity of the compounds, and replacing the phenyl group with another group, such as furan-2yl 25 or adamantyl 26, also exhibited lower inhibitory activity than 6. However, the analogues replacing moiety A with 1H-benzo[d]imidazole-1yl (B1), such as 39 and 40, exhibit comparable DOT1L-inhibitory activity as 6. The A or B1 moiety of R1 appears necessary for DOT1L inhibitory activity, and the analogues lacking the groups or substituted with other small aryl or alkyl groups lose their activity against DOLT1, like 27, 28, 30, 31, and so on. To further interpret the selectivity of 6, we aligned the SAM binding sites of DNMT1, NSD1, PRMT1, PRMT5, and EZH2 to DOT1L, and we compared the binding modes of 6 within these proteins. As shown in Figure S1 of Supporting Information, these methyltransferases share low structural similarities in their SAM-binding sites, despite their similar functions in transferring a methyl group from SAM to their targets. Moreover, few stable interactions can be observed 2031



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Figure 6. Cell cycle and apoptosis analyses. (A) MV4-11 cell lines were blocked in the G0/G1 phase after treatment with 6 for 24 h, and the effect was dose-dependent. (B) MV4-11 induced apoptosis after treatment of 6 for 72 h. The compound was prepared at 6.25, 12.5, 25, and 50 μM. Cells treated with an equal amount of DMSO were used as negative controls.

of 21.91 ± 0.34 μM, 55.78 ± 2.05 μM, and 28.08 ± 0.39 μM, respectively. The inhibition against other cell lines was much lower, as shown in Figure 5D−F and Figure S2 of Supporting Information. Thus, 6 also exhibited selective inhibition effects at the cellular level. To further verify the activities of its analogues, we tested their inhibition potencies against MV4-11, and the results are shown in Figure S3 of Supporting Information. Cell Cycle and Apoptosis. Because 6 interacts with DOT1L and effectively inhibits the proliferation of MLLrearranged leukemia cell lines, further tests were performed to determine whether the cell cycle of MV4-11 changes after treatment of 6. As shown in Figure 6A, when treated with 6 for 48 h, a portion of MV4-11 cells were arrested in the G0/G1 phase. These effects were time- and dose-dependent. Compound 6 targets DOT1L in cells and leads to cell cycle arrest. Additionally, to interpret the effect of 6 against DOT1L ex vivo, we investigated the effect of the compound on MV4-11 apoptosis.9 After treatment with 6, cells were stained with FITC and PI, and apoptotic cells were measured using flow cytometry (FCM). As shown in Figure 6B, treatment with 6 for 72 h induces apoptosis. Taken together, 6 targets DOT1L at the cellular level and causes MLL-rearranged leukemia cells to undergo cell cycle arrest and apoptosis.

scoring capability compared to Glide and Vina. Then, the SAM score was used in a practical virtual screening assay to identify DOT1L inhibitors. As verified by biochemical and cell-based assays, four potential inhibitors with novel scaffolds were identified. 6 exhibited significant binding affinity to DOT1L and moderate selectivity over other methyltransferases and non MLL-rearranged leukemia cell lines. Further mechanistic studies demonstrated that treatment with 6 blocked the cell cycle of MV4-11 at the G0/G1 phase and then induced apoptosis in the treated leukemia cells. Given the lack of novel chemotypes of methyltransferase modulators, the methyltransferase-oriented approach using the SAM score and the newly identified DOT1L inhibitors may provide new strategies for related epi-drug development.

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EXPERIMENTAL SECTION

Data Set Collection and Preprocessing. The protein structures of 12 methyltransferases were downloaded from the PDB site, and the PDB id for each methyltransferase is as follows: 2Y1X for CARM1, 3PTA for DNMT1, 3QOW for DOT1L, 1OR8 for PRMT1, 1F3L for PRMT3, 3UA3 for PRMT5, 4HC4 for PRMT6, 3FPD for EHMT1, 3K5K for EHMT2, 3CBP for SETD7, 4IJ8 for SETD8, and 2R3A for SUV39H2. For these methyltransferases, there are 562 ligands, and the IC50 values were collected from BindingDB. When converting the IC50 value to pIC50, ligands without a specific IC50 value (e.g., IC50 values greater than 109 nM) were marked as nonactive. Putative Binding Pose Generation for the SAM Score Building. The protein structures of methyltransferases were prepared using the Python script of “prepare_receptor4.py” provided by AutoDockTools in which the hydrogen atoms and Gasteiger charges were added, and all waters and ligands were removed. The ligands for each methyltransferase with a 3D structure and activity data were downloaded from BindingDB. The script of “prepare_ligand4.py” in the AutoDockTools was used to prepare ligands, including adding hydrogen atoms and Gasteiger charges to the ligands. Then, the prepared ligands were docked into their corresponding target protein with AutoDock Vina. The docking box was centered on the site of the

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CONCLUSIONS The discovery of small molecules that modulate histone and DNA methyltransferases is a viable approach for the development of new therapeutic agents. In this study, we developed a novel target-specific scoring function, the SAM score, for various methyltransferases based on the features of atom pair potentials and machine learning modeling approaches. Evaluation of a test set containing approximately one hundred known ligands revealed that the SAM score exhibited improved 2032



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cocrystallized ligand SAM or SAM derivative of each protein, and the size of the box was set to 22 Å × 22 Å × 22 Å to cover the pockets of the enzyme cofactor and substrate. The global search exhaustiveness, number of modes, and energy range were set to 50, 100, and 10, respectively, and all other parameters were kept at the default setting. Upon completion of these docking calculations, the best-docked pose was selected for each ligand according to the score predicted by Vina. Feature Selection with RFE. After obtaining the potential conformations of receptor−ligand complexes, the iPMF features of each pose were calculated using in-house Python scripts. RFE is a type of feature-ranking approach. Here, the square of the Pearson’s correlation coefficient R between each feature and the pIC50 values was used as an estimate of the importance of each feature. A grid searching strategy implemented in the LibSVM package v3.18 was recursively used to determine the best combination of parameters (i.e., C, γ, and ε) of ε-SVR with the current feature set. Additionally, an RFE procedure was followed to reduce the feature set by removing 50 less important features at a time. Finally, the subset of 643 features yielded the best 5-fold CV result, and the SVR model trained on these features was used as the final scoring function. Model Building with ε-SVR. To build the scoring function, the εSVR of the LibSVM package v3.18 was used for model training. The features of the training ligand data set were divided into five training batches for model building, and the features of the test ligand data set were used as an external test. Given a training set of n compounds, (x1, y1), ... (xn, yn), where xi denotes the feature vector of compound i and yi is the corresponding pIC50 value, the dual form of the optimization problem is

max − α , α*

type of iPMF features described above. Feature processing was the same as the SAM score, which kept the most important 643 features based on REF. Then a new score of each pose was predicted. The top 1000 compounds from the SAM score prediction were kept for structural clustering with Pipeline Pilot v7.5, in which the similarity of compounds was measured with ECFP4 and the number of each cluster was set to 10. Finally, 122 clusters were obtained, for which one or two compounds corresponding to the cluster centers were selected manually, resulting in a total of 171 compounds purchased for the followed biochemical evaluations. All compounds selected to determine IC50 values were confirmed to be ≥95% purity. Protein Expression and Purification. Human DOT1L (residues 1−416) was amplified by PCR and subcloned into a pet28a-sumo vector with an N-terminal his6 tag followed by a sumo tag. The plasmid was transformed into E. coli BL21(DE3) for overexpression of DOT1L. The cells were grown to OD600 ranging from 0.6−0.8 and then induced with 400 μM IPTG at 16 °C overnight. Cells expressing hDOT1L were first disrupted using a sonicator in 20 mM Tris-HCl pH 7.4, 200 mM NaCl, 5% glycol, and 1 mM 2-mercaptoethanol. Preliminary purification of the protein was performed with nickel affinity chromatography (HisTrap HP, GE Healthcare), and the his6 tag was cleaved off with ULP-1 at 4 °C overnight. The protein was further purified using cation-exchange (HiTrap SP, GE Healthcare) and superdex75 (10/300 column, GE Healthcare) gel-filtration chromatography. The purified protein was concentrated in buffer containing 20 mM Tris-HCl pH 7.4, 200 mM NaCl, 5% glycol, and 1 mM DTT and flash frozen at −80 °C. DOT1L Enzyme Activity Testing Assay. AlphaLISA Assay. AlphaLISA is a bead-based proximity assay used for preliminary screening of all 171 compounds and their analogues with procedures. The first is the H3 N-methylation assay in which 5 μL of compound (50 and 100 μM final) and 2.5 μL of purified DOT1L (80 nM final) were mixed in a 384 assay plate (white opaque OptiPlate-384, PerkinElmer) and incubated at 25 °C for 15 min. Then, 2.5 μL mixture of SAM and oligonuclesomes were added to final concentrations of 1 mM and 0.25 ng/μL, respectively. The negative control contained equivalent components but lacked DOT1L, and 4 was used as a positive compound. The enzymatic assay was performed in assay buffer containing 50 mM Tris-HCl pH 8.0, 150 mM NaCl, 3 mM MgCl2, and 0.1% BSA and was incubated at 25 °C for 60 min. Next, high salt buffer containing 50 mM Tris-HCl pH 7.4, 1 M NaCl, 0.1% Tween-20, 0.3% poly-L-lysine was added and incubated at 25 °C for 15 min to stop the enzymatic assay. Immediately after the enzymatic assay, we performed the AlphaLISA detection procedure. First, 5 μL of anti-Histone H3 acceptor beads and biotinylated antiH3K79me2 antibody were added into the assay plate followed by an incubation of 60 min at 25 °C. Then, 5 μL streptavidin donor beads were added and incubated in low light for 30 min at 25 °C. Finally, the plate was read with an EnVision reader (PerkinElmer Life Science). Radioactive Methylation Assay. The radioactive methylation assay is a method to further validate compound inhibition of DOT1L. At the beginning of the assay, the DOT1L protein and compounds are preincubated for 15 min at room temperature. Then, 5 μL of oligonuclesome (Active Motif) and adenosyl-L-methionine S-[methyl-3H] (3H-SAM, PerkinElmer Inc., U.S.A., lot no. 1790854) were added to start the enzymatic reaction. The reaction proceeded for 120 min at room temperature and was stopped by the addition of 5 μL of cold SAM to each well. Then, the 25 μL reaction system was transferred to a filter plate preincubated with 0.5% PEI for 15 min and vacuumed. The plate was washed 3 times with ddH2O. Finally, liquid scintillation counting was performed, and the signal was read on a MacroBeta (PerkinElmer Life Science). Enzymatic Selectivity Assay. The radioactivity inhibition assay of DNMT1 was performed in modified Tris buffer and poly(dI-DC) (Sigma, U.S.A., product no. p4929) was used as the substrate. After the enzyme solution was incubated for 15 min at room temperature, 10 μL of substrate and [3H] SAM were added to start the reaction. After incubation at 37° for 120 min, the reaction system was transferred to filter plates which were preincubated with 0.5% PEI for 15 min, and the residual solution was removed via vacuum. The counts were

n

1 2

∑ (αi − αi*)(αi − αi*)K (xi , x) i,j=1

n

n

− ε ∑ (αi + αi*) + i=1

∑ yi (αi − αi*)

(1)

i=1 n

subject to:

∑ (αi − αi*) = 0 i=1

0 ≤ αi , αi* ≤ C , i = 1, ..., n

(2)

where αi and αi* are Lagrange multipliers, and K(xi, x) is the kernel function used to map the input feature vector into higher-dimensional space. Here, the radial basis function (RBF) kernel (exp(−γ∥xi − xj∥2)) was used. After solving the optimization problem, the decision function is given by n

f (x) =

∑ (αi − αi*)K (xi , x) + b

(3)

i=1

Details of the theory can be found elsewhere. The parameters C, γ, and ε discussed above were optimized through grid searching. For each combination, a 5-fold CV was performed, and Pearson’s correlation coefficient (R2) was calculated as an estimate 37

R=

Cov(y , y ̂) σy*σ y ̂

(4)

where Cov is the covariance, and y and ŷ are actual and predicted pIC50 values, respectively. Virtual Screening. The protein structure of human DOT1L in complex with SAM (PDB code: 3QOW) was used as the receptor structure for DOT1L inhibitor virtual screening. The SPECS database was chosen for virtual screening, and it was first filtered to remove the compounds contained inorganic atoms (B, Si, Ni, Ti, Se, and so on), PAINS structures and other reactive groups. The remaining 190 038 ligands were prepared and docked into the prepared DOT1L protein with the same protocol of the SAM score building. The conformations of receptor−ligand complexes obtained by docked were rescored using our own developed scoring function, the SAM score. Briefly, the conformations of receptor−ligand complexes were converted to the 2033



Article

measured by MicroBeta (PerkinElmer Life Science). SAH (Sigma, product no. A9384) was used as the positive compound. The radioactivity inhibition assay of NSD1 was performed in modified Tris buffer. The enzyme solution, substrate solution, [3H]SAM solution, and cold SAM (Sigma, catalog no. 7007) were prepared in 1× assay buffer. Then, 10 μL enzyme solution or 1× assay buffer (low control) was transferred into a plate. After incubation for 15 min at room temperature, 10 μL each of substrate and [3H]-SAM solution were added to the plate, and the reaction began. At 120 min, 15 μL of cold SAM was added to each well to stop the reaction. A volume of 40 μL of the reaction mix was transferred to a GF/B plate (pretreated with 0.5% PEI for 15 min) using a Platemate and then washed 3 times with ddH2O via vacuum. The plate was read via MicroBeta. The radioactivity inhibition assay of PRMT5 was performed in modified Tris buffer. A volume of 15 μL of enzyme solution (prepared in 1× assay buffer) or buffer was transferred to the assay plate and incubated for 15 min at room temperature. To start the reaction, 5 μL each of substrate and [3H] SAM were added into each well and incubated for 60 min at room temperature. Then, the reaction was stopped via the addition of 5 μL of cold SAM. A 25 μL volume of reaction solution was transferred from the assay plate to a Flashplate (PerkinElmer, cat. no. SMP410A001PK) and incubated for 60 min at room temperature. The Flashplate was washed 3 times with ddH2O supplemented with 0.1%Tween-20 and was read via Microbeta. The radioactivity inhibition assay of EZH2 was performed in modified Tris buffer. The H3K27me peptide, [3H] SAM, and substrate were prepared in 1× assay buffer. At the initiation of the assay, the enzyme solution was incubated at room temperature for 15 min, and the addition of 10 μL of substrate solution was used to start the reaction. A 10 μL volume of cold SAM was added to each well to stop the reaction, and 25 μL of the mixture was transferred to a Flashplate and incubated for at least 60 min at room temperature. After washing with ddH2O+0.1% Tween-20 3 times via vacuum, the plate was read via MicroBeta. The radioactivity was determined by liquid scintillation counting. Data were fitted in Excel to obtain inhibition values. The AlphaLISA inhibition assay of PRMT1 and the enzymatic selectivity assays described above were performed by Shanghai Chempartner Co., Ltd. First, 5 μL of enzyme solution or assay buffer was added to the assay plate and centrifuged at 1000 rpm for 1 min. After an incubation of 15 min at room temperature, 5 μL of biotinylated H3R2 peptide/SAM mix (final concentrations were 50 nM and 300 nM, respectively) was added into the plate to start the reaction. At 60 min, 5 μL of acceptor beads (final concentration 10 μg/mL) was added to stop the reaction. After another 60 min, 10 μL of donor beads (final concentration 10 μg/mL) was added in low light, and the reaction system was incubated for 30 min at room temperature. The signal was read in alpha mode on an EnVision. Surface Plasmon Resonance (SPR)-Based Binding Assays. The interaction of 6 with DOT1L was measured using SPR with a Biacore T200 (GE Healthcare) and was performed at 25 °C using a CM5 sensor chip. First, the DOT1L protein was prepared in 10 mM sodium acetate (pH 5.5) for the amine-coupling procedure, and a total of 5360 response units (RU) of DOT1L protein were covalently immobilized on the chip. Ethanolamine was used to block the remaining binding sites. For kinetic measurement, 6 was diluted at concentrations ranging from 5 to 60 μM in HBS buffer (10 mM HEPES (pH 7.4), 150 mM NaCl, 3 mM EDTA, and 0.1% (v/v) DMSO). This mixture was injected over the chip for 120 s at a flow rate of 30 μL/min and then allowed to dissociate for 150 s. Finally, the state model of the T200 evaluation software was used to analyze the resulting data to obtain the KD value of 6. Cell Activity. MLL-rearranged leukemia cell lines (MV4-11, THP1, and Kopn-8), nonrearranged leukemia cell lines (Kasumi-1, HL-60, and K562), human solid tumor cell lines (ACHN and Caki-1), and a human normal cell line (HK-2) were purchased from American Type Culture Collection (ATCC) and were cultured in PRMI 1640 medium (Life technologies) supplemented with 10% fetal bovine serum (Gibco, U.S.A.) and 1% penicillin/streptomycin (Life Technologies) (except that Caki-1 was cultured in McCoy’s 5A Medium Modified (Life Technologies)). Cells were seeded in 96-well plates (Costar) and

treated with 6 of different concentrations or an equal amount of DMSO and cultured in humidified incubator at 37 °C and 5% CO2. After 72 h, an alamar blue assay was performed to measure cell viability using a microplate reader (BMG Labtech) with an excitation wavelength at 544 nm and an emission wavelength at 590 nm. The percent proliferation and IC50 curve were calculated and fitted using GraphPad Prism 5. Flow Cytometry. Cell Cycle and Apoptosis Analysis of MV4-11. The MV4-11 cell line was cultured under the conditions described above, and 2 × 106 cells were seeded into a 12-well dish. After treatment with different concentrations of 6 for 48 h, cells were harvested and processed as described in the PI/RNase staining kit (BD Pharmingen). The cells were analyzed by flow cytometry using a FACSCalibur cytometer (BD Pharmingen). For the apoptosis assay, 5 × 105 MV4-11 cells were seeded, and the time of treatment with 6 was extended to 72 h. Harvested cells were washed with cold PBS twice and then stained with annexin V-FITC and PI (Annexin V-FITC Apoptosis Detection Kit 1, BD Pharmingen). Cells were analyzed by flow cytometry as described above. Apoptotic cells were identified as annexin V-FITC or/and PI stained cells.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jmedchem.6b01785. Molecular formula strings (CSV) The crystal structures used for building SAM score, and the calculated RMSD values between the native ligand poses and docking generated poses by AutoDock Vina; structure, scores, and DOT1L inhibitory activity for 171 compounds selected from virtual screening; structure and DOT1L inhibitory activity for 122 analogues of 5, 6, 7, and 8; putative binding modes of 6 within the SAM binding sites of DOT1L, DNMT1, NSD1, PRMT1, PRMT5, and EZH2, respectively; HPLC analysis data of the five compounds that were purified, including 6, 9, 10, 15, and 21; in silico ADME parameters of compound 6 and SAH; inhibition effects of 6 toward solid tumor cells and human normal cell; analogues of 6 inhibited the cell proliferation of MV4-11 (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail for M.Z.: [email protected]. Tel: 86-21-50806600. *E-mail for C.L.: [email protected]. Tel: 86-20-50806600. ORCID

Mingyue Zheng: 0000-0002-3323-3092 Author Contributions ¶

Y.W. and L.L. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge financial support from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.), the National Basic Research Program (2015CB910304 to X.L.), the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81430084 to K.C.), the National Key Research & Development Plan (2016YF1201003 to M.Z.), the Fund of State Key Laboratory of Toxicology and Medical Countermeasures, Academy of Military Medical Science (TMC201505to C.L), 2034



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and the State Key Laboratory of Natural and Biomimetic Drugs.

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ABBREVIATIONS USED VS, virtual screening; DOT1L, disruptor of telomeric silencing1 like; MLL, mixed lineage leukemia; SAM, (S)-adenosyl-Lmethionine; SAH, (S)-adenosyl-L-homocysteine; DNMT1, DNA (cytosine-5)-methyltransferase 1; CARM1, coactivatorassociated arginine methyltransferase 1; PRMT1, proteinarginine N-methyltransferase 1; PRMT3, protein arginine Nmethyltransferase 3; PRMT5, protein arginine N-methyltransferase 5; PRMT6, protein arginine N-methyltransferase 6; SETD7, SET domain containing lysine methyltransferase 7; SETD8, SET domain containing lysine methyltransferase 8; EHMT1, euchromatic histone-lysine N-methyltransferase 1; EHMT2, euchromatic histone-lysine N-methyltransferase 2; SUV39H2, suppressor of variegation 3−9 homologue 2; NSD1, nuclear receptor binding SET domain protein 1; EZH2, enhancer of zeste homologue 2; RMSD, root-mean-square deviation; ECFP, extended-connectivity fingerprints; SPR, surface plasmon resonance; SAR, structure−activity relationships; RFE, recursive feature elimination; ε-SVR, epsilon support vector regression; SVM, support vector machine; CV, cross validation; KD, dissociation constant

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Discovery of Novel Disruptor of Silencing Telomeric 1-Like (DOT1L

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