Discovery of Novel Inhibitors Targeting the Menin-Mixed Lineage

Subscriber access provided by UNIV OF CAMBRIDGE

Article

Discovery of Novel Inhibitors Targeting Menin-Mixed Lineage Leukemia (MLL) Interface Using Pharmacophore- and Docking-Based Virtual Screening Yuan Xu, Liyan Yue, Yulan Wang, Jing Xing, Zhifeng Chen, Zhe Shi, Rongfeng Liu, YuChih Liu, Xiaomin Luo, Hualiang Jiang, Kaixian Chen, Cheng Luo, and Mingyue Zheng J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00185 • Publication Date (Web): 11 Aug 2016 Downloaded from http://pubs.acs.org on August 13, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Chemical Information and Modeling is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Chemical Information and Modeling

Discovery of Novel Inhibitors Targeting MeninMixed Lineage Leukemia (MLL) Interface Using Pharmacophore- and Docking-Based Virtual Screening Yuan Xu†,#,‡, , Liyan Yue†,#,‡, Yulan Wang†,#,, Jing Xing†,#,, Zhifeng Chen§, Zhe Shi, Rongfeng Liu, Yu-Chih Liu, Xiaomin Luo†, Hualiang Jiang†, §, Kaixian Chen†, §, Cheng Luo†,* and Mingyue Zheng†,* †

Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute

of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China #



University of Chinese Academy of Sciences，No.19A Yuquan Road, Beijing 100049, China

In Vitro Biology, Shanghai ChemPartner LifeScience Co., Ltd., #5 Building, 998 Halei Road,

Shanghai 201203, China §

School of Life Science and Technology, Shanghai Tech University, Shanghai 200031, China

KEYWORDS. Menin, Mixed lineage leukemia, small-molecule inhibitors, structure-based, pharmacophore-based, virtual screening

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 34

ABSTRACT. Disrupting the interaction between mixed lineage leukemia (MLL) fusion protein and menin provides a therapeutic approach for MLL-mediated leukemia. Here, we aim to discover novel inhibitors targeting the menin-MLL interface with virtual screening. Both structure-based molecular docking and ligand-based pharmacophore models were established, and the models used for compound screening show remarkable ability to retrieve known active ligands from decoy molecules. Verified by a fluorescence polarization assay, five hits with novel scaffolds were identified. Among them, DCZ_M123 exhibited potent inhibitory activity with an IC50 of 4.71±0.12 µM and a KD of 14.70±2.13 µM, and it can effectively inhibit the human MLL-rearranged leukemia cells MV4;11 and KOPN8 with GI50 values of 0.84 µM and 0.54 µM respectively.

INTRODUCTION The mixed lineage leukemia (MLL) protein is a large complex with multiple functional domains, including a C-terminal with histone methyl-transferase activity and an N-terminal important for the recruitment to target genes.1 Chromosomal translocations and rearrangements of MLL gene are usually identified in patients with acute leukemias, particularly common in infant.2 As a consequence of chromosomal translocations, MLL gene could be fused with one of over 70 molecularly characterized translocation partners.3 The MLL fusion protein consistently hyper-expresses its target genes such as Homeobox (Hox) family genes, induces the hyperproliferation of hematopoietic progenitors, blocks up the cell differentiation during hematopoiesis and finally results in the leukemia.4 Till now, leukemia patients with MLL


2

Page 3 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


translocations respond poorly to available treatments and are at a high risk of relapse.5 Therefore, more efficient new therapies are urgent needed. Menin is a tumor suppressor encoded by the MEN1 (Multiple Endocrine Neoplasia I) gene, and regulates the cell growth in endocrine organs.6 It is believed that menin directly binding to the N-terminus of MLL is of significance for the recruitment of MLL or MLL fusion protein to the target genes.7 Genetic ablation of menin suggested that it is required for the maintenance of taeget gene expression.8 A dominant negative inhibitor composed of amino terminal MLL sequences that blocks the menin-MLL interaction could down-regulate target genes expression and inhibit leukemic cells transformation.9 All these observations confirmed that MLL oncogenicity is significantly dependent on the interaction with menin, providing a novel and feasible approach for developing therapeutics against the MLL-mediated leukemia.10

Figure 1. Representative inhibitors targeting menin-MLL interface.11-14


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 34

MLL binds to the large hydrophobic cavity in menin with a high affinity motif (MBM1, MLL6-13) and a low affinity motif (MBM2, MLL24-40).15 Alanine scanning mutagenesis of MBM1 indicated that F9, P10, and P13 involved in hydrophobic interactions with menin are critical for this protein-protein interaction (PPI).15 The salt bridge and hydrogen bonds cloud also stabilize the menin-MLL complex.16 MBM2 could further enhance this interaction by forming electrostatic interactions. However, MBM2 alone is not strong enough to maintain the meninMLL complex, and inhibitors only targeting MBM1 interface are of more interest because they are sufficient to block menin-MLL interaction.10 The first small-molecule inhibitors of the menin-MLL interaction was identified in 2012 by a fluorescence polarization-based high-throughput screening.11 The most potent inhibitor MI-2 with the IC50 of 446 nM could disrupt the interaction of menin-MLL and reverse the leukemogenic activity of MLL fusion proteins. Based on the crystal structure of human menin in complex with MI-2,16 MI-2-2 (Figure 1) was subsequently developed, of which IC50 was improved by 10-fold times. However, MI-2-2 was not efficient in vivo because of modest cellular activity and poor metabolic stability.12 By introducing the cyano-indole ring connected to the thienopyrimidine core, a new scaffold for further modifications was developed. Two potent inhibitors MI-463 with IC50 of 15.3 nM and MI-503 with IC50 of 14.7 nM were designed,12 both of which shown favorable pharmacokinetic profile in mice. He13 et al. discovered another class of hydroxy- and aminomethylpiperidine compounds, which closely mimicked the three hydrophobic interactions of menin- MLL. Further optimization of these compounds resulted in MIV-6 (Figure 1) exhibiting an IC50 of 56 nM. In addition to the synthesis of small molecule compounds, many efforts have been focused on designing various biological agents. Zhou17 et al. designed macrocyclic peptidomimetic inhibitors (Figure 1), which bound to the menin pocket in


4

Page 5 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


a very similar way with the native MBM1. Although these macrocyclic peptidomimetic inhibitors could strongly inhibit the menin-MLL interaction with a Ki of 4.7 nM and IC50 of 18.5nM, their large molecular weight would arise the problem of druggability and confine the further development. Two aminoglycoside antibiotics, neomycin and tobramycin were also identified from existing drugs.14 Till now, no substantial progress had been made in turning these inhibitors into therapeutically useful compounds, since majority efforts were temporarily halted due to poor in vivo activity or unsatisfactory bioavailability. Limited chemo types are available for further optimization. Thus, finding menin-MLL inhibitors with novel scaffolds is still challenging research area. In this study, using pharmacophore-based and structure-based approaches, we developed a virtual screening strategy to discovery new inhibitors targeting the menin-MLL interface. The selected molecules were further evaluated by a series of biochemical analysis, and the predicted binding modes of potent inhibitors were analyzed. MATERIALS AND METHODS Ligand dataset. Seventy-four reported inhibitors targeting menin-MLL interface were collected from literatures,11, 13, 14, 16-18 and all molecules could be roughly categorized into three classes: 13 inhibitors in class I (represented by MI-2-2), 27 inhibitors class II (represented by MCP-1), and 32 inhibitors in class III (represented by MIV-6) (Figure 1).Ten inhibitors with a higher IC50 were selected to generate 5,000 decoy compounds using DecoyFinder.19 All inhibitors and decoy compounds were pre-processed by LigPrep,20 and ionization/tautomeric states were generated.


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 34

Structure-based approach: molecular docking targeting the menin-MLL interface. The structure of human menin with its inhibitor MI-2-216 (ID: 4GQ4) was downloaded from PDB database.21 Inhibitors and decoy compounds were docked into menin using Glide22 with Standard Precision (SP) mode. The protein structure was prepared using Protein preparation wizard tool in Maestro,23 which fixed the protein structure by verifying proper assignment of bonds, adding hydrogens, deleting unwanted bound water molecules and minimizing the energy. The binding pattern of MI-2-2 to menin was used as reference to define the active site and docking grids within a 14×14 ×14 Å box. As a potent inhibitor, MI-2-2 occupied the hydrophobic pockets involved in F9 and P13 and formed hydrogen bond with menin Y323. Therefore, variety constrained docking processes which required specific interactions at particular sites were further evaluated. Constraints included 1) hydrophobic interaction in the F9 pocket; 2) hydrophobic interaction in the P13 pocket; 3) hydrophobic interaction in either F9 or P13 pocket; 4) hydrophobic interaction in both F9 and P13 pockets; 5) a hydrogen bond with Y323; 6) hydrophobic interaction in F9 pocket and a hydrogen bond with Y323; 7) hydrophobic interaction in P13pocket and a hydrogen bond with Y323; 8) a hydrogen bond with Y323 and hydrophobic interaction at least in one of P9 or P13 pockets. Poses were ranked by Glide docking scores and the discerning ability of different docking settings was measured by the enrichment factor (EF). The EF is calculated as EF = (a/n)/(A/N), where N is the total number of all ligands, n is the total number of compounds in the selected fraction of the database, A is the number of known inhibitors, and a is inhibitors in the selected front fraction of the ranked list. Ligand-based approach: 3D-QSAR pharmacophores. Inhibitors with pIC50 > 5.0 were kept to generate pharmacophores with 4-6 pharmacophore features (hydrophobic group, aromatic ring, hydrogen bond acceptor or donor, and positively or negatively charged group) that are


6

Page 7 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


necessary to interact with a specific biological target. Conformations of each inhibitors were aligned based on generated common pharmacophore. Cubes with 1 Å grid were defined to cover the space occupied by these aligned conformations, and then assigned to the value of zero or one based on whether the cube was occupied. Thus, binary schemes (0/1) of active molecules were treated as independent variables in partial least-squares (PLS) regression analysis to develop the 3D-QSAR model which could qualitative predict the activity of compounds against menin. Menin Expression and Purification. The full-length of human menin were expressed and purified as described previously.14 For the further purification, a Q-Sepharose protocol (GE Healthcare) was performed after removing of the His6-SUMO tag. Fluorescence polarization (FP) assays of inhibition of menin-MLL interaction. The fluorescence polarization reaction contains N-terminal FITC-labeled MLL4-15 peptide (FITCMBM1) at 30nM and menin at 600nM in the FP buffer (50 mM HEPES, pH 7.5; 50mM NaCl; 1mM DTT; 0.1mg/ml BSA). Compounds were added immediately after the mixture and incubated for 2h in the darker at 4 °C. Change in fluorescence polarization was monitored at 535nm after excitations at 480nm using the Envison protocol and applied to determine IC50 values with the GraphPad Prism 5.0 program. Stability studies by Differential scanning fluorimetry. Differential scanning fluorimetry (DSF) were performed on a Quant Studio 6 Flex Real-Time PCR system (ABI). Each reaction were heated from 25 to 95 °C in 20µL thermal shift buffer (50mM Mops, pH6.48; 50mM NaCl; 1mM DTT) with 2.5µM menin, 5×SYPRO orange (Invitrogen) and a series of diluted compounds. All samples were tested in triplicate. ABsolute qPCR Plate Seals (Thermo Scientific) were used to limit evaporation. Change of fluorescence signal of SYPRO orange was


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 34

monitored and applied to determine the Tm of menin with Protein Thermal Shift Software Version 1.1(ABI). Surface Plasmon Resonance (SPR) Based Binding Assays. The SPR binding assays were performed on the Biacore T200 instrument (GE Healthcare) at 25 ℃ . Menin protein was covalently immobilized on a CM5 chip using a standard amine-coupling procedure in 10 mM sodium acetate (PH 5.0). The chip was first equilibrated with HBS-EP buffer (10mM HEPES PH 7.4, 150mM NaCl, 3mM EDTA, 0.05% (v/v) surfactant P20, and 0.1% (v/v) DMSO) overnight. The compound was serially diluted with HBS-EP buffer and injected for 120s (contact phase), followed by 120s dissociation. KD values of the compound to menin were determined by Biacore T200 evaluation softerware (GE Healhcare). Viability Assays. Cells were cultured in RPMI-1640 medium supplemented with 10% FBS and 1% penicillin/streptomycin (Invitrogen). For viability assay, cells were plated in 96 well plate, treated with compounds or 0.2% DMSO, and cultured at 37 °C for 24h, 48 and 72h. An Alamar Blue cell viability assay kit (Invitrogen) was employed. Plates were read for fluorescence intensity at 590nm after excitations at 544nm using the PHERAstar BMG microplate reader. RESULTS AND DISCUSSION Molecular docking targeting the menin-MLL interface. To select a docking model with a high “screening” power,25 a virtual screening test was firstly performed. Seventy-four previously reported menin-MLL inhibitors were collected from publications as true positives, which were then used to compile 5,000 decoys by DecoyFinder.19 Next, all ligands and decoys were docked into the binding site of menin (PDB ID: 4GQ4) with standard precision (SP). The area under the receiver operating characteristic curve (AUC) is 0.87 (data not show). When


8

Page 9 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


screening 1% of dataset there are thirty inhibitors in top-50 ranked compounds and about 70% collected inhibitors were successfully retrieved when screening 5% of dataset, indicating that SP docking is sufficient enough for the virtual screening of menin-MLL inhibitors. Published studies have emphasized the significance of hydrophobicity of F9, P10 and P13,15 and the hydrogen bonds involving Tyr32316 are important for the menin-MLL and menin-inhibitor interactions. Therefore, different docking models were compared including the docking process without any constraints, and the constraint docking with eight different conditions considering previously mentioned interactions and key residues. The maximum enrichment factor (EF) values of the no-constraint docking appeared when screening 1% of dataset, where thirty inhibitors were retrieved in top-50 ranked compounds, indicating that true positive compounds can be efficiently recognized among the top ranked compounds. Comparing with other docking processes with different constraints, the no-constraint docking model yielded higher EF (Figure 2A) and recovered more inhibitors (Figure 2B) during the screening, especially when only a small section (i.e., 1% or 2%) of database was covered. It is acceptable that constraint-docking yielded a poorer performance than no-constraint docking. In Glide constraint docking jobs, if a ligand does not make the required interactions with the receptor atoms, it would be simply skipped during the docking process but not to penalize on its docking score. So a constraint docking may be more specifically suited for the identification of near native poses26 rather than a task of virtual screen. Therefore, no-constraint docking was chosen for the following virtual screening.


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 34

Figure 2. The comparison between no-constraint docking and constraint docking in terms of: (A) EF and (B) the number of retrieved inhibitors. The definition of each constraint is provided in Experimental Section.

3D-QSAR models of menin-MLL inhibitors. In addition to molecular docking, a pharmacophore-based 3D-QSAR method was also used to find novel structure menin-MLL inhibitors. From the collected known inhibitors, those showing pIC50 >5.0 were used to generate common pharmacophores with 4-6 pharmacophore features. Based on the alignments of the generated pharmacophore hypotheses, 3D-QSAR models were then developed using a training set with randomly selected 2/3 of all inhibitors and a test with the rest 1/3 inhibitors. A grid was


10

Page 11 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


defined to encompass the space occupied by the training set of aligned molecules. Structural information of all compounds would be transformed into a binary scheme (0/1), based on the occupation of cubes in the grid. The binary bit values are treated as independent variables in partial least-squares (PLS) regression analysis to generate 3D-QSAR models. The final 3DQSAR models were selected based on the correlation coefficient of regression R2 and the degree of confidence p. Collected inhibitors could be divided into three classes based on their skeletons, of which 13, 27, and 32 inhibitors are included in class I (represented by MI-2-2), class II (represented by MCP-1), and class III (represented by MIV-6 Figure 1). Due to the limited numbers of inhibitors in class I and the difficulties in searching ring conformations when generate common pharmacophore for macrocyclic compounds in class II, only class III inhibitors were used to develop 3D-QSAR models. Models with reasonably predicting ability were selected (Table 1). Table 1. The predictive performance of 3D-QSAR models for class III inhibitors.

Class

Training set

Models R2

III

Test set p

Q2*

RPearson *

ADPRR

0.96

4.66E-11

0.63

0.81

APRR1

0.94

2.97E-10

0.67

0.91

APRR2

0.98

6.32E-14

0.69

0.85

DPRR

0.86

1.19 E-15

0.52

0.73

*: Q2 directly analogous to R-squared, but based on the test set predictions. RPearson value for the correlation between the predicted and observed activity for the test set.


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 34

Figure 3. The comparison between 3D-QSAR models based on class III and the docking model in test set (A, B) and in the whole data set (C, D) in terms of: EF and the number of retrieved inhibitors. The ability of these selected 3D-QSAR models for retrieving inhibitors from decoys were also investigated. 3D-QSAR models performed better than the no-constraint docking in both test set and the whole data set that majority models could retrieve all 32 class III inhibitors when only 2% of compounds were screened (Figure 3). These results indicated that pharmacophorebased models could discover inhibitors in large database and be a complementary method to the docking model. Among all four models, ADPRR which show the best ability for retrieving known inhibitors and is matched by the fewest decoys was selected.


12

Page 13 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ADPRR (Figure 4A) highlighted the role of the H-bond donor feature around hydroxyl and two ring features, which could form a hydrogen bond with D180 via a water molecule and hydrophobic interactions in P10 and P13 pockets in the crystal structure (ID: 4OG5), respectively (Figure 4B). Other two features, positive and acceptor, represented the characteristics of the skeleton structure. The ADPRR model well defined the vital interactions between Class III inhibitors and menin.

Figure 4. The selected ADPRR model for Class III inhibitors. (A). The pharmacophore features of ADPRR, matched by the reference compound. (B). The complex structure of MIV-5


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 34

and menin (ID: 4OG5). Yellow ovals are three impotant hydrophobic zones. Yellow dash line shows the hydrogen bond.

Figure 5. The virtual screen for menin-MLL inhibitors. (A). The flowchart of virtual screen. (B). The inhibition ratio of all purchased compounds. (C). The IC50 of DCZ_M123. (D). Changes in thermodynamic stability of menin upon binding of DCZ_M123. (E) SPR experiment of DCZ_M123 binding to menin.


14

Page 15 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Virtual screening of inhibitors targeting menin-MLL interface. According to previous analysis, no-constraint docking and 3D-QSAR models could well recognize positive compounds and efficiently restrain the number of false positive compounds. Therefore, these two strategies were jointly used for virtual screening of inhibitors targeting menin-MLL interface. Firstly, over 180,000 compounds in Specs database27 were kept after removing PAINS (Pan Assay Interference Compounds structures).28 Then, all these compounds were docked into menin and top 200 compounds ranked by Glide score were selected. Meanwhile, compounds were matched to the pharmacophores and predicted by the corresponding 3D-QSAR model. Only the compounds with a predicted pIC50 > 5.0 were kept. Finally, 121 compounds with structural diversity were remained and purchased for biological assay (Figure 5A). Table 2. Hit compounds in the virtual screening. Compound

Specs ID

Structure

DCZ_M27

AG-205/08592053

33.30± 7.72

DCZ_M68

AK-968/12572069

95.25± 17.55

DCZ_M71

AK-968/13027285

27.07± 12.71

DCZ_M123 AN-740/37278012

IC50 (µM)

4.71± 0.12


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

DCZ_M125 AO-022/41399554

Page 16 of 34

52.14± 8.97

A competition-based fluorescence polarization assay with the fuorescein-labeld MBM1 peptide and menin protein were performed for validation of compounds from virtual screening. For the primary screening, each compound was added with a final concentration of 200 µM to the menin-peptide mixture and incubated for 2h in the darker at 4 °C. 1% DMSO and 30 µM MBM1 were displayed as negative and positive controls respectively. Fluorescence polarization signal was recorded to evaluate inhibitory activity of compounds. Among all these compounds, twelve of them were identified with inhibition rates of over 50% (Figure 5B). A series of dilutions for each of 12 compounds were tested in the FP assay to determine the IC50 values. As shown in Table2, five compounds exhibit micro-molar inhibitory activity. Especially, DCZ_M123 disrupted the menin-MBM1 interaction with an IC50 value of 4.71±0.12 µM (Figure 5C), which represented the most potent one. The direct binding of inhibitors to menin can be detected using a differential scanning fluorimetry (DSF). Compounds were added in the mixture of menin protein and SRPRO orange dye in the thermal shift buffer. The fluorescence intensity was monitored on a Quant Studio 6 Flex Real-Time PCR system (ABI), and applied to determine the melting temperature (Tm) of


16

Page 17 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


menin protein. The presence of DCZ_M123 at 10 µM and 35 µM led to positive shifts for melting temperature (Tm) values of menin in 3.21 and 4.14°C, respectively (Figure 5D), demonstrating the direct binding mode and the dose-dependent manner. We used the Surface Plasmon Resonance (SPR) based binding assays to further validate the binding of DCZ_123 to men and quantity the binding affinity. Experiments were performed on the Biacore T200 instrument (GE Healthcare) at 25 ℃ . Menin protein was covalently immobilized on a CM5 chip using a standard amine-coupling procedure and the compound was serially diluted with HBS-EP buffer and injected for the indicated time. As shown in Figure 5E, the interactions between DCZ_M123 and menin were dose depended. The KD value was determined to be 14.70±2.13 µM.

Figure 6. AlamarBlue cell viability assay of human leukemia cells after the indicated time’s treatment with DCZ_M123. Inhibitors targeting the menin-MLL fusion protein interaction should cause growth inhibition in leukemia cells with MLL fusions.10, 11, 16 Two leukemia cells, MV4;11 harboring MLL-AF4 and KOPN8 with MLL-ENL fusions, as well as two cells without MLL fusions (K562 and NB4) were treated with potent inhibitor DCZ_M123 to determine the specific growth inhibition. As shown in Figure 6, DCZ_M123 effectively blocked the proliferation of MV4;11


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 34

and KOPN8 cells after 24h and 48h’s treatment at the indicated concentrations but showed much weaker effect in the two cells without fusions even after 72h’s treatment. All together, these results demonstrated that DCZ_M123, the novel scaffold small molecular inhibitor for menin-MLL interaction we identified from virtual screening, can specifically bind to menin cavity, disrupt the menin-MLL interaction and cause cell growth inhibition in MLL leukemia cells.

Figure 7. Predicted binding modes of DCZ_M123. The binding modes of DCZ_M123 (magentas) and MI-2-2 (yellow) in menin (Top). F9, P10, P13 and entrance hydrophobic pockets are highlighted as red, orange, blue and cyan, respectively. Residues of menin interacted with DCZ_M123 are shown as sticks with the corresponding color of their pockets (Bottom). Three hydrogen bonds are labeled as red dash lines. All of the binding-mode figures were generated using PyMOL version 1.3r1.29


18

Page 19 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


To analyze the potential binding modes and key interactions of these inhibitors with menin, molecular docking with extra-precision (XP) was performed. The binding chamber of menin is a large hydrophobic pocket which could be divided into three sub-pockets, including: F9 pocket (E179, D180 and H181), P10 pocket (F238 and C241) and P13 pocket (Y319, M322, and Y323). These sub-pockets are formerly occupied by F9, P10 and P13 of MLL. Besides, there is a hydrophobic zone composed of P245, L257, L286 and L289 located at the entrance of pocket (Figure 7, top). As a symmetric structure, one half part of the DCZ_M123 containing a quinolone and piperazine ring stretches into the bottom of the pocket, and another part turns into the hydrophobic zone at the entrance of the pocket, which acts as a cap that covers the whole interacting pocket of menin (Figure 7, bottom). Particularly, the binding mode of DCZ_M123 is quite similar to MI-2-2, where quinoline in the bottom pocket well occupies F9 and P10 subpockets, and forms a hydrogen bond with Y276. The followed piperazine occupies the P13 hydrophobic sub-pocket in a similar way to that of MI-2-2, and also forms a hydrogen bond in this pocket with Y323. Another half part of quinoline at the entrance of pocket forms a hydrogen bond with D252 (Figure 7, bottom). Notably, it was reported that MLL associates with menin in a bivalent mode using both MBM1 and MBM2 motif, and D252 is an important residue for MBM2 binding.16 Therefore, these series of compounds are promising because interactions involving both MBM1 and MBM2 could be interfered. CONCLUSION Inhibition of menin-MLL interaction is a potential therapeutic approach for acute leukemias with MLL rearrangements. In present study, docking model and 3D-QSAR models were


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 34

developed, both of which could well retrieve known active compounds from decoys. A virtual screen strategy combining these two models was used to discovery new small-molecule inhibitors for inhibiting the menin-MLL interaction from a commercial compound library. One hundred and twenty-first compounds were evaluated by fluorescence polarization assay, and five compounds with novel scaffolds were identified as menin-MLL inhibitors. Among them, DCZ_M123 showed the potent inhibitory activity with an IC50 of 4.71±0.12 µM. Moreover, a series of biophysical, biochemical and cell-based assays indicated that DCZ_M123 can specifically bind to menin cavity, disrupt the menin-MLL interaction and inhibit the growth of MLL leukemia cells. ASSOCIATED CONTENT Supporting Information Information about all collected menin-MLL inhibitors used in modeling (Table S1); Bioassay results of all selected 121 compounds (Table S2). AUTHOR INFORMATION Corresponding Author * E-mail: [email protected] (M. Z.), [email protected] (C. L.). Author Contributions The manuscript was written through contributions of all authors. / All authors have given approval to the final version of the manuscript. / ‡Y.X. and L.Y. contributed equally. Notes The authors declare no competing financial interest.


20

Page 21 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ACKNOWLEDGMENT We gratefully acknowledge financial support from the National Key Research & Development Plan (2016YF1201003 to M.Z.), the National Basic Research Program (2015CB910304 to X.L.), the Hi-Tech Research and Development Program of China (2014AA01A302 to M.Z.), the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81430084 to K.C.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.), and the Fund of State Key Laboratory of Toxicology and Medical Countermeasures, Academy of Military Medical Science (TMC201505 to C.L). We also thank Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) for providing the supercomputing service. ABBREVIATIONS MLL, mixed lineage leukemia;3D-QSAR, three dimension quantitative structure-activity relationship; PAINS, Pan Assay Interference Compounds structures; MEN1, Multiple Endocrine Neoplasia I REFERENCES (1). Thiel, A. T.; Huang, J.; Lei, M.; Hua, X., Menin as a Hub Controlling Mixed Lineage Leukemia. Bioessays 2012, 34, 771-780. (2). Driessen, E. M.; de Lorenzo, P.; Campbell, M.; Felice, M.; Ferster, A.; Hann, I.; Vora, A.; Hovi, L.; Escherich, G.; Li, C. K.; Mann, G.; Leblanc, T.; Locatelli, F.; Biondi, A.; Rubnitz, J.; Schrappe, M.; Silverman, L.; Stary, J.; Suppiah, R.; Szczepanski, T.; Valsecchi, M.; Pieters, R., Outcome of Relapsed Infant Acute Lymphoblastic Leukemia Treated on the Interfant-99 Protocol. Leukemia 2016, 30, 1184-7.


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 34

(3). He, S.; Malik, B.; Borkin, D.; Miao, H.; Shukla, S.; Kempinska, K.; Purohit, T.; Wang, J.; Chen, L.; Parkin, B.; Malek, S. N.; Danet-Desnoyers, G.; Muntean, A. G.; Cierpicki, T.; Grembecka, J., Menin-Mll Inhibitors Block Oncogenic Transformation by Mll-Fusion Proteins in a Fusion Partner-Independent Manner. Leukemia 2016, 30, 508-513. (4). Mohan, M.; Lin, C.; Guest, E.; Shilatifard, A., Licensed to Elongate: A Molecular Mechanism for Mll-Based Leukaemogenesis. Nat. Rev. Cancer 2010, 10, 721-728. (5). Slany, R. K., When Epigenetics Kills: Mll Fusion Proteins in Leukemia. Hematol. Oncol. 2005, 23, 1-9. (6). Chandrasekharappa, S. C.; Guru, S. C.; Manickam, P.; Olufemi, S. E.; Collins, F. S.; Emmert-Buck, M. R.; Debelenko, L. V.; Zhuang, Z.; Lubensky, I. A.; Liotta, L. A.; Crabtree, J. S.; Wang, Y.; Roe, B. A.; Weisemann, J.; Boguski, M. S.; Agarwal, S. K.; Kester, M. B.; Kim, Y. S.; Heppner, C.; Dong, Q.; Spiegel, A. M.; Burns, A. L.; Marx, S. J., Positional Cloning of the Gene for Multiple Endocrine Neoplasia-Type 1. Science 1997, 276, 404-407. (7). Chen, Y. X.; Yan, J.; Keeshan, K.; Tubbs, A. T.; Wang, H.; Silva, A.; Brown, E. J.; Hess, J. L.; Pear, W. S.; Hua, X., The Tumor Suppressor Menin Regulates Hematopoiesis and Myeloid Transformation by Influencing Hox Gene Expression. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 1018-1023. (8). Yokoyama, A.; Somervaille, T. C.; Smith, K. S.; Rozenblatt-Rosen, O.; Meyerson, M.; Cleary, M. L., The Menin Tumor Suppressor Protein Is an Essential Oncogenic Cofactor for MllAssociated Leukemogenesis. Cell 2005, 123, 207-218. (9). Caslini, C.; Yang, Z.; El-Osta, M.; Milne, T. A.; Slany, R. K.; Hess, J. L., Interaction of Mll Amino Terminal Sequences with Menin Is Required for Transformation. Cancer Res. 2007, 67, 7275-7283.


22

Page 23 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(10). Cierpicki, T.; Grembecka, J., Challenges and Opportunities in Targeting the Menin-Mll Interaction. Future Med. Chem. 2014, 6, 447-462. (11). Grembecka, J.; He, S.; Shi, A.; Purohit, T.; Muntean, A. G.; Sorenson, R. J.; Showalter, H. D.; Murai, M. J.; Belcher, A. M.; Hartley, T.; Hess, J. L.; Cierpicki, T., Menin-Mll Inhibitors Reverse Oncogenic Activity of Mll Fusion Proteins in Leukemia. Nat. Chem. Biol. 2012, 8, 277284. (12). Borkin, D.; He, S.; Miao, H.; Kempinska, K.; Pollock, J.; Chase, J.; Purohit, T.; Malik, B.; Zhao, T.; Wang, J.; Wen, B.; Zong, H.; Jones, M.; Danet-Desnoyers, G.; Guzman, M. L.; Talpaz, M.; Bixby, D. L.; Sun, D.; Hess, J. L.; Muntean, A. G.; Maillard, I.; Cierpicki, T.; Grembecka, J., Pharmacologic Inhibition of the Menin-Mll Interaction Blocks Progression of Mll Leukemia in Vivo. Cancer cell 2015, 17, 589-602. (13). He, S.; Senter, T. J.; Pollock, J.; Han, C.; Upadhyay, S. K.; Purohit, T.; Gogliotti, R. D.; Lindsley, C. W.; Cierpicki, T.; Stauffer, S. R.; Grembecka, J., High-Affinity Small-Molecule Inhibitors of the Menin-Mixed Lineage Leukemia (Mll) Interaction Closely Mimic a Natural Protein-Protein Interaction. J. Med. Chem. 2014, 57, 1543-1556. (14). Li, L.; Zhou, R.; Geng, H.; Yue, L.; Ye, F.; Xie, Y.; Liu, J.; Kong, X.; Jiang, H.; Huang, J.; Luo, C., Discovery of Two Aminoglycoside Antibiotics as Inhibitors Targeting the Menin-Mixed Lineage Leukaemia Interface. Bioorg. Med. Chem. Lett. 2014, 24, 2090-2093. (15). Grembecka, J.; Belcher, A. M.; Hartley, T.; Cierpicki, T., Molecular Basis of the Mixed Lineage Leukemia-Menin Interaction: Implications for Targeting Mixed Lineage Leukemias. J. Biol. Chem. 2010, 285, 40690-40698.


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 34

(16). Shi, A.; Murai, M. J.; He, S.; Lund, G.; Hartley, T.; Purohit, T.; Reddy, G.; Chruszcz, M.; Grembecka, J.; Cierpicki, T., Structural Insights into Inhibition of the Bivalent Menin-Mll Interaction by Small Molecules in Leukemia. Blood 2012, 120, 4461-4469. (17). Zhou, H.; Liu, L.; Huang, J.; Bernard, D.; Karatas, H.; Navarro, A.; Lei, M.; Wang, S., Structure-Based Design of High-Affinity Macrocyclic Peptidomimetics to Block the MeninMixed Lineage Leukemia 1 (Mll1) Protein-Protein Interaction. J. Med. Chem. 2013, 56, 11131123. (18). Manka, J.; Daniels, R. N.; Dawson, E.; Daniels, J. S.; Southall, N.; Jadhav, A.; Zheng, W.; Austin, C.; Grembecka, J.; Cierpicki, T.; Lindsley, C. W.; Stauffer, S. R. Inhibitors of the MeninMixed Lineage Leukemia (Mll) Interaction. In Probe Reports from the Nih Molecular Libraries Program; National Center for Biotechnology Information (US): Bethesda (MD), 2010; Vol. 3, Chapter 60, pp 1-25. (19). Cereto-Massague, A.; Guasch, L.; Valls, C.; Mulero, M.; Pujadas, G.; Garcia-Vallve, S., Decoyfinder: An Easy-to-Use Python Gui Application for Building Target-Specific Decoy Sets. Bioinformatics 2012, 28, 1661-1662. (20). Ligprep, version 2.3; Schrödinger, LLC: New York, 2009. (21). Protein Data Bank. http://www.rcsb.org/pdb/home/home.do (May 19, 2016), (22). Glide, version 5.6; Schrödinger, LLC: New York, 2009. (23). Maestro, version 9.0; Schrödinger, LLC: New York, 2009. (24). The Ki Calculator http://sw16.im.med.umich.edu/software/calc_ki/ (May 19, 2016), (25). Li, Y.; Han, L.; Liu, Z.; Wang, R., Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results. J. Chem. Inf. Model. 2014, 54, 1717-1736.


24

Page 25 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(26). Greenidge, P. A.; Kramer, C.; Mozziconacci, J. C.; Sherman, W., Improving Docking Results Via Reranking of Ensembles of Ligand Poses in Multiple X-Ray Protein Conformations with Mm-Gbsa. J. Chem. Inf. Model. 2014, 54, 2697-717. (27). Specs Database. http://www.specs.net/snpage.php?snpageid=home 2016), (28). Baell, J. B.; Holloway, G. A., New Substructure Filters for Removal of Pan Assay Interference Compounds (Pains) from Screening Libraries and for Their Exclusion in Bioassays. J. Med. Chem. 2010, 53, 2719-2740. (29). Pymol, version 1.3r1; Schrödinger, LLC: New York, 2010.


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 34

Table of Contents Graphic


26

Page 27 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table of Contents Graphic 114x51mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. Representative inhibitors targeting menin-MLL interface. 112x84mm (300 x 300 DPI)


Page 28 of 34

Page 29 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. The comparison between no-constraint docking and constraint docking in terms of: (A) EF and (B) the number of retrieved inhibitors. The definition of each constraint is provided in Experimental Section. 75x119mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. The comparison between 3D-QSAR models based on class III and the docking model on the test set (A, B) and on the whole data set (C, D), in terms of: EF and the number of retrieved inhibitors. 112x84mm (300 x 300 DPI)


Page 30 of 34

Page 31 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. The selected ADPRR model for Class III inhibitors. (A). The pharmacophore features of ADPRR, matched by the reference compound. (B). The complex structure of MIV-5 and menin (ID: 4OG5). Yellow ovals are three important hydrophobic zones. Yellow dash lines show the hydrogen bond. Orange dot is the water molecule. 142x136mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 5. The virtual screen for menin-MLL inhibitors. (A). The flowchart of virtual screen. (B). The inhibition ratio of all purchased compounds. (C). The IC50 of DCZ_M123. (D). Changes in thermodynamic stability of menin upon binding of DCZ_M123. (E) SPR experiment of DCZ_M123 binding to menin. 219x268mm (300 x 300 DPI)


Page 32 of 34

Page 33 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6. AlamarBlue cell viability assay of human leukemia cells after the indicated time’s treatment with DCZ_M123. 96x28mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 7. Predicted binding modes of DCZ_M123. The binding modes of DCZ_M123 (magentas) and MI-2-2 (yellow) in menin (Top). F9, P10, P13 and entrance hydrophobic pockets are highlighted as red, orange, blue and cyan, respectively. Residues of menin interacted with DCZ_M123 are shown as sticks with the corresponding color of their pockets (Bottom). Three hydrogen bonds are labeled as red dash lines. All of the binding-mode figures were generated using PyMOL version 1.3r1. 75x91mm (300 x 300 DPI)


Page 34 of 34

Discovery of Novel Inhibitors Targeting the Menin-Mixed Lineage

Recommend Documents