Virtual Screening with a Structure-Based Pharmacophore Model to

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Virtual Screening with Structure-based Pharmacophore Model to Identify Small-molecule Inhibitors of CARM1 Ting Ran, Wenjuan Li, Bingling Peng, Binglan Xie, Tao Lu, Shuai Lu, and Wen Liu J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00610 • Publication Date (Web): 04 Jan 2019 Downloaded from http://pubs.acs.org on January 4, 2019

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Journal of Chemical Information and Modeling

Virtual Screening with Structure-based Pharmacophore Model to Identify Small-molecule Inhibitors of CARM1 Ting Ran, †, ‡ , ⊥ Wenjuan Li, †, ⊥ Bingling Peng, † Binglan Xie, † Tao Lu §, Shuai Lu, §,* Wen Liu†,¶,*

†

School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative

Drug Target Research, Xiamen University, Xiamen, Fujian 361102, China

‡

Department of Chemical Biology, College of Chemistry and Chemical Engineering,

Xiamen University, Xiamen, Fujian 361105, China

¶

State Key Laboratory of Cellular Stress Biology, Xiamen University, Xiamen, Fujian

361102, China

§

Department of Organic Chemistry, School of Sciences, China Pharmaceutical

University, Nanjing, Jiangsu 210009, China

ACS Paragon Plus Environment

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*

Page 2 of 61

To whom correspondence should be addressed to:

Wen Liu Tel: +86(592)2881146 Fax: +86(592)2881146 Email: [email protected] *

Correspondence may also be addressed to:

Shuai Lu Tel: +86(025)86185186 Fax: +86(025)86185187 Email: [email protected] ⊥ These

authors contributed equally to this work


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ABSTRACT

CARM1 (coactivator-associated arginine methyltransferase 1), also known as PRMT4 (protein

arginine

N-methyltransferase

4),

belongs

to

the

protein

arginine

methyltransferase (PRMT) family, which has emerged as a potential anti-cancer drug target. To discover new CARM1 inhibitors, we performed virtual screening against the substrate-binding site in CARM1. Structure-based pharmacophore models, which were generated according to three druggable sub-pockets embedding critical residues for ligand binding, were applied for virtual screening. The importance of the solventexposed substrate-binding cavity was highlighted due to significant hydrophobicity. Aided by molecular docking, fifteen compounds structurally distinct from known CARM1 inhibitors were selected to evaluate for their inhibitory effects on CARM1 methyltransferase activity, which resulted in seven compounds exhibiting micromolar inhibition, with selectivity over other members in the PRMT protein family. Moreover, three of them exhibited potent anti-proliferation activities in breast cancer cells. Particularly, compound NO.2 exhibited potent activity both in vitro and in cultured cells,


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which will serve as a leading hit for developing CARM1 inhibitors with improved efficacy. The virtual screening strategy in this study will be applicable for the discovery of substrate-competitive inhibitors targeting other members in the PRMT protein family.


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INTRODUCTION

Protein arginine methylation has emerged as a type of post-translational modification (PTM) widely occurring in proteins, which has been acknowledged to play important roles in the regulation of protein functions

1, 2,

3,

and protein activation 6. Crosstalk

protein-protein interactions 4, protein stability

5

such as subcellular localization

between arginine methylation and other PTMs on proteins has also been extensively characterized

7.

Arginine

methylation

is

catalyzed

by

the

protein

arginine

methyltransferase (PRMT) protein family, in which eleven members, named as PRMT1 to PRMT11, have been identified so far. PRMTs are characterized to have a conserved catalytic domain, which transfers the methyl group from S-5’-adenosyl-L-methionine (SAM) to an arginine residue in both histone and non-histone substrates. Based on the methylation states they catalyzed on arginines, these enzymes are categorized into three major types 8, including type I PRMTs (PRMT1, 2, 3, 4, 6, 8) that modify both mono- and asymmetric di-methylation, type II PRMTs (PRMT5, 9) that modify both mono- and symmetric di-methylation and type III PRMTs (PRMT7) that only modify


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mono-methylation. PRMT10 and PRMT11 have no reported arginine methyltransferase activity so far.

PRMT4,

often

called

as

CARM1

(coactivator

associated

arginine

methyltransferase 1), was originally identified as a coactivator in transcriptional regulation through modifying histone H3 arginine 17 and 26 (H3R17 and R26)

9, 10.

has been reported to also modify a variety of non-histone proteins, such as SRC-3 CBP/p300

12,

NCOA2

13,

PABP1

14

and SmB

15.

It

11,

More recently, the repertoire of

CARM1’s non-histone substrates was further expanded by a study using methylarginine specific antibody to enrich methylated peptides followed by mass spectrometry analysis

16.

CARM1 was shown to be involved in many biological processes, including

RNA splicing, processing and stability, transcriptional regulation and signaling transduction. Deregulation of CARM1 has been suggested to be associated with various types of cancers

17.

For example, CARM1 was found to be overexpressed in estrogen

receptor (ER)-positive breast cancer samples and induce the expression of E2F1 by


6

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dimethylating histone H3 R17 (H3R17me2) and promoting the growth of cancer cells

13.

CARM1 has also been reported to promote tumorigenesis and metastasis of breast cancer by methylating R1064 of the chromatin remodeling factor BAF155, which drives the c-Myc-mediated oncogenic pathway cancer types, such as colon documented

21.

19,

18.

In addition, CARM1’s function in other

prostate

20

and lung cancers, has also been

Thus, CARM1 has emerged as a potential therapeutic target for cancer

treatment.

Several small-molecule inhibitors targeting the methyltransferase activity of CARM1 have been discovered (Figure S1)

22,

which function mainly in a SAM- and/or

substrate-competitive manner in terms of their binding sites. One type of CARM1 inhibitors, a series of pyrazole derivatives such as compounds 1 and 2

23, 24,

compete

with arginine for binding with CARM1, but without disrupting the binding of the cofactor, SAM. Compounds with similar binding mode include a series of benzo[d]imidazole, Nbenzylpiperidin-4-amine, pyrrole, phenoxybenzamide, phenylether derivatives such as


7


compound 3

25,

4, MS023 (5)

26,

TP-064 (6)

27,

and SGC2085 (7)

Page 8 of 61

28.

EZM2302 (8) and 9 are potent inhibitors of CARM1 reported in patents

Compounds 29-31

and are

(pyrimidin-4-yl)isoxazole and pyrazolo[3,4-b]pyridine derivatives, respectively. It is more beneficial to develop substrate-competitive than SAM-competitive inhibitor, as the former can easily achieve selectivity over other members in the PRMT protein family. In contrast, SAM-competitive inhibitors generally exhibit pan-activities against the whole PRMT family

32.

Inhibitors occupying the substrate- and SAM-binding sites

simultaneously also display selectivity over different members in the PRMT family

33.

However, they are unfavorable for drug development, especially those with high molecular weight. Some selective inhibitors targeting CARM1 enzymatic activity have also been reported without clarification of their binding modes, such as compound 10 34. Despite the availability of CARM1 inhibitors, the issue of unsatisfactory cellular toxicity towards cancer cells and poor selectivity over other PRMT members remains unresolved. It is therefore urgent that new inhibitors targeting CARM1, preferably in a substrate-competitive manner be developed.


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In this study, we employed virtual screening to discover substrate-competitive inhibitors targeting CARM1. The screening began with the analysis of the structural properties of the druggable sub-pockets at the substrate-binding site, which were then used to reveal the most important pharmacophore queries transformed from the binding modes of available potent inhibitors of CARM1. Pharmacophore models were constructed by combining pharmacophore queries, to search against a virtual smallmolecule library after rigorous validation with retrospective virtual screening. Subsequently, an extensive molecular-docking screening with high accuracy was carried out. After visual examination of the binding poses of hit compounds, 15 compounds were further chosen for evaluation of their biological activities. Finally, 7 compounds with new scaffolds were confirmed to efficiently and specifically inhibit CARM1 methyltransferase activity by in vitro bioassays. Moreover, compound NO.2 exhibited considerable toxicity to breast cancer cells, and therefore will serve as a good


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starting point for structural optimization to develop CARM1 inhibitors with improved efficacy.

METHODS AND MATERIALS

2.1 Molecular modeling

2.1.1 Analysis of CARM1 crystal structures

At present, ten crystal structures of human CARM1 catalytic domain have been reported. They were downloaded from the RCSB PDB database (http://www.rcsb.org) as shown in Table S1. The structures of co-crystalized CARM1 inhibitors were shown in Figure S2. All crystallographic waters were removed and hydrogen atoms were added on the H++ webserver (http://biophysics.cs.vt.edu) with corrected protonation states of residues at the pH value of 7.0

35.

The crystal structures were then aligned by the

backbones of amino acids using PyMOL software 36.


10

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The SiteMap suite in Schrödinger (Version 9.0) was applied to locate the binding sites on the protein for small-molecule ligands and help predicting the druggability of the binding sites

37.

A grid of points, called site points, was generated to locate these

binding sites. A site was defined by at least fifteen site points on a standard grid that were either contiguous or bridged by short gaps in solvent-exposed regions. As long as the binding site was defined, contour maps were automatically generated to represent hydrophobic, hydrophilic and hydrogen bond properties in the binding pocket. An option of “more restrictive” was selected to define the hydrophobicity of the binding sites, while other settings in the program were kept as default.

An online service called FTMAP (http://ftmap.bu.edu/login.php) was used to explore the potential space for ligand binding at the substrate-binding site

38-40.

All

crystal structures of CARM1 were submitted for online calculation. Billions of positions of sixteen standard small-molecule fragments were probed on the surface or in the buried regions of protein. Final minimized poses of probes were clustered and ranked


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on the basis of Boltzmann averaged energies. Hot spots were represented by probe clusters in the binding site, which were considered to be favorable for ligand binding. The probe clusters derived from different crystal structures were aligned to each other to determine the “real” hot spots in the binding site. Contact frequency per residue with the small-molecular fragments was given to evaluate the importance of residues for ligand binding.

2.1.2 Molecular dynamic simulation

Molecular dynamic (MD) simulations were carried out using AMBER10

41.

All

simulations started from the crystal conformation of CARM1. The protein-ligand systems were solvated in a truncated octahedral box of TIP3P water. Ions were added to neutralize the solvated system. Amber ff14SB and gaff force fields were applied to protein and ligand

42, 43,

respectively. Before MD simulations, energetic minimizations

were carried out to optimize the coordinates of solvents and ions with the protein-ligand systems constrained by a force of 50 kcal/mol. The systems were then heated from 0 to


12

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300 K using the langevin thermostat with a collision rate of 5 ps-1. 20 ps heating MD simulations with a time step of 1 fs were implemented in the constant-temperature, constant-volume ensemble (NVT) to ensure the stability of the molecular systems during the heating process. Finally, at least 20 ns equilibrium MD simulations with a time step of 2 fs were carried out in the constant-temperature, constant-pressure ensemble (NPT) to produce trajectories. The CPPTRAJ module in AMBER was used for trajectory analysis44.

2.1.3 Preparation of ligands

The 2D structures of ligands were generated by ChemBioDraw Ultra in the ChemBioOffice Suite 2014. A virtual screening library was downloaded from the ChemBridge

EXPRESS-Pick™

(http://www.chembridge.com/screening_libraries)

Collection consisting

of

100,000

stock drug-like

compounds. Additionally, 139 small-molecule inhibitors of CARM1 were retrieved from the BindingDB database (http://www.bindingdb.org) to facilitate the construction of a


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virtual screening protocol. All ligands were prepared with energy minimization using the CHARMM force field according to the Ligand Minimization protocol of Discovery Studio 3.0 (DS 3.0) provided by the Accelrys Software Inc

45, 46.

In addition, up to one hundred

low-energy conformations, were generated for each ligand within a relative energy threshold of 15 kcal/mol using the Generate Conformations module of DS 3.0 with a FAST mode. Then a multi-conformation ligand database was built by the Catalyst algorithm using the Build 3D Database module of DS 3.0. The database was automatically indexed with substructure, pharmacophore feature, and shape information to allow fast database searching by pharmacophore models using the search 3D database protocol of DS 3.0.

2.1.4 Virtual screening protocol

Initially, the pharmacophore model was applied to search the ligand database. The hit compounds were then docked into the substrate-binding site of CARM1 and ranked by the docking scoring function. The final selection was based on visual


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inspection of the docking poses of the highest-ranking compounds. The pharmacophore model and molecular docking methods were described as follows.

The pharmacophore model is an efficient virtual screening tool, that defines the spatial relationship between the pharmacophore features that represent the interaction properties between receptor and ligand. We extracted all potential pharmacophore features of ligands complementary to the substrate-binding site of CARM1 using the Interaction Generation protocol of DS 3.0 based on the docking poses of known potent inhibitors (IC50 smaller than 1 µM). Here, the substrate-binding site was defined by a sphere centered on the co-crystalized substrate-competitive inhibitors of CARM1 (PDB entry: 2Y1W, 2Y1X and 5U4X). A radius of 12 Å was set for the sphere to cover the druggable regions defined by SiteMap. Subsequently, pharmacophore models were generated with at least four features. The shortest allowed distance between features was set to 1.0 Å. The pharmacophore models were then validated by retrospective virtual screening. The DUD decoy database was built for the validation

47.

Hit rate of


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actives and enrichment factor of pharmacophore mapping were calculated to evaluate the effectiveness of the models in the identification of active compounds. In addition, in order to assess the models in balancing specificity and sensitivity of screening, the area below the receiver operating characteristic (ROC) curve (AUCroc) was calculated with the FitValue of pharmacophore mapping, which indicates the quality of mapping

48.

Higher AUCroc value means higher probability that the model will distinguish actives from inactives in the hit list from screening. A AUCroc value equal to 0.5 corresponds to random screening.

A genetic algorithm based on Cambridge Crystallographic Data Center (CCDC) GOLD 3.1 was employed for molecular docking

49.

The space around the center of the

crystalized ligands within a radius of 12.0 Å was deemed as the docking site. Ligand flexibility was allowed with all planar R-NR1R2 flipped and with carboxylic acid deprotonated. The most accurate mode was set for docking with a maximum of 100,000 genetic operations for each run of genetic algorithm. Ten conformations were generated


16

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for each compound, but only the best scored pose was reserved, which was determined by using GoldScore. The interaction annealing parameters were set to 4.0 for van der Waals (vdW) and 2.5 for hydrogen bonding.

2.2 Bioassays

2.2.1 Protein expression and purification

GST-tagged CARM1 proteins were expressed in BL21 (DE3) bacterial cells (Stratagene) and purified by using Glutathione agarose (Thermofisher) following protocols as described previously 50.

2.2.2 Methyltransferase activity assay

Methyltransferase activity was measured by monitoring the reaction product (Sadenosyl homocysteine or SAH) generated from in vitro methyltransferase assays. MTase-Glo™ Assay (Promega V7601) was performed by adding the MTase-Glo™


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Reagent to convert SAH to ADP, which was further converted to ATP by adding the MTase-Glo™ detection solution. Luminescence was then measured using a luminometer. The experiments were repeated.

2.2.3 Cytotoxicity assay

The cytotoxicity of compounds was tested by MTS (3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay in a variety of breast cancer cell lines, such as normal breast cell line MCF10A, estrogen receptor (ER)positive breast cancer cell line MCF7, and several triple negative breast cancer cell lines including HCC1806, HCC1937 and MDA-MB-231.

2.2.4 Statistical analysis

The IC50 of hit compounds were calculated by nonlinear regression using GraphPad Prism software (San Diego, CA, USA). Statistical significance of the data


18

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was calculated using a one-way analysis of variance (ANOVA) followed by Dunnett's test.

RESULTS AND DISCUSSION

3.1 Characteristics of the substrate-binding site in CARM1

CARM1 shares a conserved catalytic domain across the PRMT family, which is composed of a Rossmann fold and a β-sheet barrel required for the binding of SAM and substrate

51,

respectively. In order to determine the druggable binding pocket at the

substrate-binding site, we carried out comprehensive analysis on the structural features of the substrate-binding site based on crystal structures and MD simulations.

3.1.1 Analysis of the substrate-binding site based on crystal structures

Among all available crystal structures, three of them are co-crystal structures of CARM1 with substrate-competitive inhibitors (PDB code: 2Y1W, 2Y1X, 5U4X), which


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also bind to SAH or sinefungin (SFG, SAM analogue as a SAM-competitive inhibitor) at the SAM-binding region (Table S1 and Figure S2). As shown in Figure 1A, the substrate-binding site begins from an internal channel with two glutamic acids (Glu258 and Glu267, numbered according to the crystal structure of 2Y1W), which form the catalytic center and anchor the guanidinium group of arginine adjacent to the SAMbinding region. The sequence between these two residues forms a conserved secondary structure called double E-loop, in which tyrosine 262 (Tyr262) and asparagine 266 (Asn266) extend their sidechains toward the solvent-exposed cavity in the substrate-binding region. Another core component of the substrate-binding site is the N-terminal αX-αY helix (residues 141-166), which separates the SAM- and substrate-binding sites. The Y150F151XXY154 motif participates in the formation of the exposed cavity of the substrate-binding site. The αX-αY helix has been suggested to be stabilized upon recognition of substrate through a conserved structure motif called the THW loop, which is formed by serine 414 (Ser414), histidine 415 (His415) and tryptophan 416 (Trp416). It should be noted that this loop flanks the entrance of the catalytic center, which links the internal catalytic center and the exposed cavity.


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Correspondingly, we divided the substrate-binding site into three sub-pockets, namely catalytic center, exposed cavity and THW entrance region.

Figure 1. (A) The structure of substrate-binding region derived from the crystal structure of CARM1 (PDB code: 2Y1W). The residues are shown as stick and colored based on their locations. The green stick represents SFG located at the SAM-binding region. (B) Alignment of the crystal structure with 2Y1W. The structures are represented as cartoon with α-helices in red, β-sheets in yellow and loops in green. (C) Conformational change


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of residues at the substrate-binding region. The residues are shown as colored sticks by the secondary structure of protein.

All crystal structures were aligned to a reference structure (PDB code: 2Y1W) with the root-mean-square deviations (RMSDs) of protein backbone ranging from 0.1 Å to 1.1 Å (Table S2), suggesting a stable protein structure despite binding with different ligands. The difference only lies at the carboxyl (C)-terminal helix region known as the dimerization arm (Figure 1B), conformational change of which is important for the activation of PRMTs

52, 53.

However, the dimerization arm seems to have no impact on

the formation of the substrate-binding site. The secondary structure of the substratebinding site is highly conserved, but the sidechains exhibit subtle differences (Figure 1C). Upon binding with ligands at the substrate-binding site, these sidechains, especially those from residues located at the exposed cavity, undergo dramatic changes. For example, the sidechains of Glutamine 149 (Gln149), Arginine 446 (Arg446), Lysine 471 (Lys471) and Asparagine 472 (Asn472), which together shape a


22

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distal sub-pocket at the exposed cavity, undergo large conformational change. The distal sub-pocket is enlarged when Lys471 is turned outside the exposed cavity (Figure S3A). The folded-in conformation of Lys471 reduces the exposed surface of the exposed cavity, enabling a Cation-Pi interaction with the inhibitor in the crystal structure of 5U4X. Arg446 does not make direct contact with inhibitors, but it acts as a tail-cap of the exposed cavity, which generates more buried protein surface (Figure S3B). Its uncapped conformation contradicts the turned-out conformation of Lys471, which limits the conformational change of Lys471. In addition, Phe475 adjacent to the THW loop can also adapt a buried conformation to interact with inhibitors in the crystal structures of 2Y1X and 5U4X (Figure S3A). Other changes exemplified by the slight stretched conformation of Asparagine 162 (Asn162) induced by the inhibitor in 2Y1X may also be worthy of note.

3.1.2 Analysis based on MD simulations


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We also performed MD simulations in aqueous solution to search for potential conformational changes unrevealed by the crystal structures. Both apo- and SAMcomplexed structures were selected for simulation with the same starting conformation of protein. 20 ns equilibrium simulations were carried out (Figure S4A). The RMSDs of protein backbone converged to a narrow window from 1.0 to 3.0 Å for each structure, which was consistent with what obtained by the alignment of crystal structures (Figure S4B). Accordingly, the global stability of the protein structure was also maintained during simulations. The computed B-factor values for residue fluctuation were similar to the experimental data from crystallography (Figure 2). However, three B-factor peaks for the residues on the αX-αY helix, double E-loop and THW loop were specifically generated during stimulation, indicating enhanced flexibility of the substrate-binding site in the simulation. The holo-structure with bound-SAM exhibited decreased B-factor values for these three sequence motifs compared to the apo-structure, suggesting that binding of SAM stabilized the structure of the substrate-binding site. This might account for the co-binding mode of substrate-competitive inhibitors with SAH observed in the crystal structures. Stabilization of the C-terminal sequence (residue 471-476), which


24

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also participates in the formation of the substrate-binding site, upon binding of SAM could also be observed. In addition, although they were located far away from the substrate-binding site, the sequences covering residues 215 to 240 and 314 to 330, which form the Rossmann fold and the dimerization arm, respectively, underwent reduced residue flexibility upon SAM binding, further stabilizing the whole structure. On the other hand, the simulation confirmed the conformational change of sidechains in the substrate-binding site revealed by crystal structures. However, the pseudo dihedrals of Arg446 and Lys471 representing the conformational switch in crystal structures show biased distribution between the two windows of -180⁰ ~ -135⁰ and 135⁰ ~ 180⁰ (Figure S5). In particular, the distribution in the window from -180⁰ to -135⁰ for Phe475 almost disappeared due to Pi-Pi stacking interaction with Tyr417 (Figure S5), suggesting the conformational switch in the crystal structures was probably induced by the binding of inhibitors. In summary, despite relative flexibility occurring for the sidechain of residues, CARM1 holds a stable substrate-binding site with respect to the backbone, especially at the SAM-binding state, which might explain why a strict conformation clustering based on the simulation trajectory only generates one conformation cluster. The stable


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structure provides favorable environment for substrates or other ligands, such as smallmolecule inhibitors, to bind.

Figure 2. B-factor of residues (aa 136~478). The B-factor of residues in crystal structure is the average value on ten crystal structures, while that derived from simulations are calculated by the formula B=[(8π2)/3] * (RMSF)2 (RMSF, root mean-square fluctuation). The three conserved motifs are indicated by colored bars. C: carboxyl-terminal; N: amino-terminal.

3.2 Identification of druggable binding pocket


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Generally, a druggable binding pocket was referred to as a protein region with hydrophobicity capable of binding a drug-like molecule

54.

In this study, in order to

identify the druggable binding pocket at the substrate-binding site, SiteMap and FTMAP methods were employed to explore the distribution of hydrophobicity and affinity to bind with small-molecule fragments, respectively.

3.2.1 SiteMap analysis

The contour maps produced by SiteMap for all crystal structures were aligned, in which the white site points defined the favorable space for ligand binding (Figure 3A). Yellow contours representing hydrophobic regions were mainly located at the exposed cavity. These contours were surrounded by aromatic sidechains of hydrophobic residues, such as Tyr150, Phe153 and Tyr154, and by methylene sidechains of hydrophilic residues, such as Glu267 and Lys471. Hydrophobic contours were also observed at the THW entrance near aromatic residues, such as Tyr262 and His415. In contrast, hydrophobic contours were almost undetectable at the catalytic center except


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for the tiny yellow contour near Met269. In addition, some small hydrophobic contours were also observed outside the exposed cavity (Figure 6SA). However, these small hydrophobic contours appeared have occurred casually since they were highly dependent on the shape of the substrate-binding site. Meanwhile, the hydrogen-bond contours at the exposed cavity were dispersed along the polar sidechains of residues such as Gln149, Tyr154, Asn266 and Lys471, many of which were distributed in the region accessible to solvents (Figure 3B and Figure 6SB). Hydrogen-bond acceptor and donor contours at the THW entrance corresponded to Tyr262/Gln159 and His415, respectively. The catalytic center was enriched with hydrogen-bond donor contours corresponding to Glu258 and Glu267. A druggability score (Dscore) of 1.02 deduced by volume, enclosure and hydrophobicity of a protein cavity was obtained for the substratebinding site (Table S3), indicating high druggability for ligand binding as an experiential cutoff above 0.98 was considered as highly “druggable” small-molecule binding pockets 55.

The potential space for ligand binding occupied a volume of 338 Å3 on average,

which was large enough to accommodate drug-like small-molecules. Interestingly, although the substrate-binding site possesses a large exposed cavity, the area of buried


28

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protein surface represented by the enclosure score was larger than the solvent-exposed protein surface represented by the exposure score (Table S3). Moreover, hydrophobic regions were close to 50% of the crystal structures complexed with inhibitors. Taken together, the substrate-binding site can be considered as a druggable binding pocket. The hydrophobic exposed cavity and THW entrance largely contributed to the druggability of the substrate-binding site, whereas the catalytic center was less favorable for ligand binding due to the presence of multiple hydrogen-bond donor interaction sites.

Figure 3. SiteMap of hydrophobic (A) and hydrogen bond (B) contours. Yellow, red and blue contours represent hydrophobicity, hydrogen bond acceptor and hydrogen bond donor, respectively. Residues are shown as gray sticks, and the cofactor SAM is shown


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as green line. The white dots represent potential ligand binding sites, while the transparent gray surface represents binding pocket defined by SiteMap.

3.2.2 FTMAP analysis

The FTMAP analysis revealed that a series of small-molecule fragments have the potential to bind to the substrate-binding site (Figure 4A). The distribution of probe clusters was in good agreement with the binding of crystal ligands, which validated the FTMAP analysis. Naphthenic fragments fully occupied the druggable sub-pockets defined by SiteMap analysis. Hydrophobic naphthenic groups were enriched not only in the larger hydrophobic space at the exposed cavity, but also in the smaller hydrophobic space at the THW entrance near Tyr262 and His415. Consistent with the SiteMap analysis, the catalytic center was enriched with small polar groups possessing hydrogen-bond donors. Notably, many naphthenic groups at the exposed cavity and THW entrance also contain polar sidechains towards residues such as Tyr154, Asn266, Gln159, Tyr262 and Trp416.


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The FTMAP analysis divided the residue-fragment contacts into non-bond and hydrogen-bond interactions and provided contact frequency per residue to smallmolecule fragments. As shown in Figure 4B, Tyr150, Phe153, Tyr154, Tyr262 and Glu267 possessed higher proportion of non-bond contacts with fragments, indicating their larger contributions to the hydrophobic interactions than other residues. Among them, the sub-pocket formed by the three aromatic residues had the most populated probe clusters, further validating the central role of the exposed cavity in hydrophobic interactions. The central role of Glu267 in the non-bond interactions could be explained by its hydrophobic methylene sidechain, which participated in the formation of the exposed cavity surface as well. As for the hydrogen-bond interactions, Tyr154 at the exposed cavity was, unexpectedly, the most important residue compared to Glu258 and Glu267 at the catalytic center. Other residues, such as Asn266, Tyr262, Arg446 and Lys471, appeared to be more important in the hydrogen-bond interactions. In addition, His415 and Trp416 at the THW entrance exhibited some hydrogen-bond interactions with small-molecule fragments. Although there were small probe clusters nearby, the


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interaction mediated by Phe475, whose conformation was induced by ligand binding, was negligible in the FTMAP analysis.

Figure 4. (A) Distribution of fragment clusters in the substrate-binding region. The secondary structure of protein is shown as blue cartoons. Blue and orange sticks represent labeled amino acids and SAH, respectively. Small-molecule fragments are shown as colored lines, which are aligned with the co-crystalized ligands represented by yellow, green or gray sticks. (B) Proportion of hydrogen bond (H-bond) and non-bond contacts contributing to the interactions between fragments and residues obtained by the FTMAP calculation. The error bar indicates the root square-mean error between the proportion values derived from different crystal structures.


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3.3 Pharmacophore modeling

3.3.1 Generation of pharmacophore model

Pharmacophore models were constructed based on the docking poses of 17 substrate-competitive inhibitors with potent activities towards CARM1 from patents (Figure S7). Pharmacophore extraction revealed a series of features, 21 in total, including hydrophobicity, aromaticity, hydrogen-bond and positively-charged center features (Figure 5), which were mainly distributed along the druggable hot spots of the substrate-binding site. Moreover, the distribution of these features was highly consistent with the structural properties of the substrate-binding site revealed by the SiteMap and FTMAP analysis. Hydrophobicity and aromaticity features appeared at the exposed cavity, while hydrogen-bond features were mainly distributed along the channel from the catalytic center to the THW entrance. Positively-charged center features were enriched between Glu258 and Glu267 at the catalytic center, which made it possible for ligands to form salt bridge interactions with these two residues. Unexpectedly, positively-


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charged center features were observed at the exposed cavity, which could be related to the potential Cation-Pi interactions. Hierarchical clustering was then performed, resulting in three cluster centroids of hydrophobicity features and four cluster centroids of aromaticity features at the exposed cavity representing the interactions at specific locations. In addition, five hydrogen bond donor and four hydrogen bond acceptor features were also maintained, with only one acceptor feature located at the exposed cavity. Finally, five positively-charged centers were maintained, and two of them were located at the exposed cavity. However, to find a ligand by virtual screening, set of 21 features constituted too many structural constraints. Therefore, we attempted to reduce the number of features in the following ways. Firstly, the hydrophobic features outside the exposed cavity could be omitted as they were far away from the exposed druggable pocket. Secondly, the two positively-charged centers at the exposed cavity and the hydrophobic feature at the THW entrance could be discarded as they were significantly less enriched than the aromatic features nearby. Thirdly, the hydrogen bond features pointing to Glu258, Glu267, His415 and Trp416 at the catalytic center could also be ignored as they were less important than the feature pointing to Tyr154 at the same


34

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location according to FTMAP analysis. Fourthly, two out of the three positively-charged centers at the catalytic channel could be discarded as they were located far away from the catalytic residues Glu258 and Glu267. As such, ten features were retained for the generation of pharmacophore models, including four aromaticity, one hydrophobicity, two hydrogen bond donors, two hydrogen bond acceptors and one positively-charged center features (Figure 5). Among them, the aromaticity feature A1 and hydrophobic feature H1 were both linked to Tyr150, and so they could be considered mutually exclusive features. Similarly, two hydrogen bond features HB1 and HA2, were mutually exclusive as they both pointed to Tyr154. In addition, despite having different vectors, the aromatic features A2 and A3 were both pointing to Phe153, and therefore also defined as mutually exclusive features. One feature from the two mutually exclusive features was randomly selected to form a pharmacophore model. Moreover, the features corresponding to the most important interactions had priority during pharmacophore modeling. Twenty-seven pharmacophore models, each with at least four features, were generated by combining these features (Table S4). The


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pharmacophore models with more than six features were not further considered in the virtual screening due to extremely low hits of actives (data not shown).

Figure 5. Scheme for extraction of interaction features. Inhibitors are shown as blue and pink lines. Red and cyan balls represent positively-charged center and hydrophobic features, respectively. Green and pink vectors represent hydrogen bond donor and acceptor features, respectively. Orange vectors represent aromatic features mediating Pi-Pi interactions. Aromatic (A); Hydrophobic (H); Hydrogen bond donor (HD), Hydrogen bond acceptor (HA); Positively-charged center (POS).

3.3.2 Validation of pharmacophore model


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Next, we sought to validate the pharmacophore models by retrospective screening. To this end, a small-molecule library was generated with 128 known inhibitors (IC50 smaller than 100 µM) and 6,949 decoy compounds from the DUD library, which were labeled as actives and inactives, respectively. Within the actives, 82 compounds have IC50 smaller than 10 µM. The pharmacophore models were then mapped to each compound in the library. Due to the fact that more structural constraints were required in the mapping process, the models with more features had lower hit rates of actives (FA) (Table 1). It was found that A3 corresponding to the Pi-Pi interaction with Phe153 is a critical feature for increasing the hit rates of actives, when comparing PH2 to PH1, PH4 to PH3, PH6 to PH5, and PH8 to PH7. Nonetheless, more features led to higher mapping enrichment factor (EF) in most cases, suggesting the models with more features were more efficient for enriching active compounds. Moreover, PH2 and PH6 both had A1, A3 and A4, and POS1 exhibited the most significant enrichment. However, PH2-4, PH3-3 and PH5-3 with four features still had relatively high enrichment factors due to their ability to filter out most inactive compounds.


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Table 1. Quality parameters of pharmacophore models obtained by retrospective virtual screenings. Total number of hits (TH), number of active hits (AH), fraction of inactive hits (FI), fraction of active hits (FA), enrichment factor (EF) of pharmacophore mapping, and receiver operating characteristic (ROC) value for each pharmacophore model are listed in the table. Name

TH

AH

FI

FA

EF

ROC

PH1

2399

76

30.65%

59.38%

1.94

0.678

PH1-1

1416

60

17.89%

46.88%

2.62

0.666

PH1-2

1515

64

19.15%

50.00%

2.61

0.683

PH1-3

465

47

5.52%

36.72%

6.66

0.672

PH1-4

188

24

2.16%

18.75%

8.67

0.581

PH2

135

57

1.03%

44.53%

43.27

0.725

PH2-1

2408

70

30.85%

54.69%

1.77

0.671

PH2-3

1308

64

16.41%

50.00%

3.05

0.7

PH2-4

376

64

4.12%

50.00%

12.15

0.744

PH3

113

33

1.06%

25.78%

24.42

0.636

PH3-2

3247

70

41.92%

54.69%

1.30

0.643

PH3-3

396

65

4.37%

50.78%

11.63

0.748

PH3-4

1397

70

17.51%

54.69%

3.12

0.715

PH4

697

68

8.30%

53.13%

6.40

0.74

PH4-3

3486

74

45.02%

57.81%

1.28

0.649

PH4-4

1656

81

20.78%

63.28%

3.05

0.764


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PH5

20

2

0.24%

1.56%

6.58

0.503

PH5-1

698

72

8.26%

56.25%

6.81

0.752

PH5-2

981

65

12.09%

50.78%

4.20

0.717

PH5-3

258

63

2.57%

49.22%

19.13

0.744

PH6

117

59

0.77%

46.09%

60.23

0.731

PH6-1

1488

73

18.67%

57.03%

3.05

0.737

PH6-3

733

66

8.80%

51.56%

5.86

0.734

PH7

259

46

2.81%

35.94%

12.79

0.679

PH7-2

2185

78

27.80%

60.94%

2.19

0.711

PH7-3

939

61

11.58%

47.66%

4.11

0.702

PH8

422

71

4.63%

55.47%

3.12

0.769

PH8-3

2068

78

26.26%

60.94%

11.98

0.726

Most models had an AUCroc value larger than 0.5 that corresponds to random screening. However, the AUCroc value was not consistent with the mapping enrichment factor. The models with five features did not show obvious advantage over the models with four features, which was due to that AUCroc represents the ability of pharmacophore model to distinguish actives from inactives during screening, while mapping enrichment factor only considers the amount of actives identified from the screening library. PH8


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had the highest AUCroc value but with modest enrichment factor, suggesting that this model ranked the actives at the top of the mapping list, but it also included many inactives in the mapping list. The AUCroc values of PH6~PH8 were higher than those of PH2~PH4, implying that the hydrogen bond donor feature HD1 pointing to Tyr154 may be more favorable for the identification of actives than the hydrogen bond acceptor feature HA2 pointing to the same residue.

Taken all the above criteria into consideration, PH2, PH2-4, PH3-3, PH5-3, PH6 and PH8 were selected for virtual screening (Figure S8), which showed both significantly higher AUCroc and enrichment factor than other models. PH2-4, PH3-3, and PH8 had the possibility to identify more inactive compounds. The common positivelycharged center POS1 was about 5.22 Å from the nearest feature A4 at the THW entrance. Most models had the hydrogen bond feature pointing to Tyr154, confirming the importance of this hydrogen bond. The aromatic feature A4 seemed to be nonessential for virtual screening as it was ignored in the models of PH3-3 and PH5-3, and


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this feature was concurrent with A3. The combination of A3 and A4 could be used to divide the six models into two sub-groups, which were corresponding to two binding modes at the exposed cavity, although they had large mapping overlaps (Figure S9). Interestingly, most of the actives recovered were those having IC50 smaller than 10 µM. Thus, these pharmacophore models had the capacity to screen CARM1 inhibitors effectively.

3.4 Virtual screening

3.4.1 Virtual screening

Virtual screening was carried out hierarchically using pharmacophore model, molecular docking and visual inspection (Figure 6). The ChemBridge DIVERSet library having 100,000 compounds with structural diversity was selected for virtual screening. As the library has undergone stringent drug like and desirable chemical group filters, we directly applied the pharmacophore models to screen the library. As a result, 62, 620, 3552, 74, 16, and 371 compounds were mapped to PH2, PH2-4, PH3-3, PH5-3, PH6


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and PH8, respectively. The screening using PH2-4, PH3-3, and PH8 led to more hit compounds, which might due to their weaker enrichment ability shown in the retrospective screening. The highest hit rate of PH3-3 might due to the hydrophobic feature H1, which can accommodate various hydrophobic groups. In total, 4237 compounds were retained after removing 458 duplicate compounds in the merged list from the six models, including the compounds with relatively low FitValue of mapping in order to increase the hit rate of actives. Subsequently, these hit compounds were docked into the crystal structures of CARM1 with the most accurate GOLD docking mode using the GoldScore scoring function (Table S5). Sidechain flexibility of residues at the substrate-binding site was considered in the docking process by using the crystal structures of 2Y1W and 2Y1X, which have different local conformations of residues at the substrate-binding site. Similar to the crystal structures, SAH was included as part of the docking receptor. As a result, most compounds had a score higher than 40 predicted by GoldScore, and were then subjected to visual examination (Figure S10). 15 compounds structurally distinct with any of known inhibitors were finally purchased to assess their in vitro activities towards CARM1 methyltransferase activity. Their


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structures are shown with physicochemical properties (Figure S11), and these compounds had no structural patterns matched to the pan-assay interference compounds (PAINS) filters with potential functional groups of frequent hitters extracted from HTS assays, by comparing their structures to the PAINS library provided by the ZINC15 tools (http://zinc15.docking.org/patterns/home/). The binding modes of several representative compounds are shown in Figure 7 and Figure S12, with the hit compounds sharing similar docking poses. Most of the hit compounds had an amine- or amine-like group to imitate the salt bridge interactions of arginine in the substrate with Glu258 or Glu267 at the catalytic center. In addition to salt bridge interactions, hydrogen bonds were also formed between the amino groups and the glutamic acids. The amino groups were linked to the THW entrance mostly by an amide moiety, in which carbonyl group could make hydrogen bond interactions with Tyr154, Met260, Tyr262, and His415 at the catalytic center or the THW entrance. Unexpectedly, hydrogen bond interaction was also observed between the 1,2,4-oxadiazole group at the exposed region and Tyr154 in compound NO.14. The moieties of the hit compounds binding to the exposed cavity of the substrate-binding site can be categorized into several structural types, all of


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which were composed of aromatic groups to make Pi-Pi or hydrophobic interactions with the aromatic residues in this region. According to the LigPlot+ binding mode, many other residues were also shown to be involved in protein-ligand interactions. For example, Met260 generally made hydrogen bonds with many compounds, while Trp416 on the THW loop was always engaged in hydrophobic interactions. In addition, Ser146, Met163, Gly261, Asn266, and Gln447 were involved in the interaction dependent on the ligands bind, too.

Figure 6. Flowchart of virtual screening of CARM1 substrate-competitive inhibitors. The substrate-binding region is shown in green surface. Number of compounds (cpds)


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remained after filtering at each stage is listed. Finally, 7 compounds have the activity smaller than 100 µM.

Figure 7. The binding modes of representative hit compounds using PyMOL. The whole structure of the substrate-binding site is shown as gray cartoon. Yellow sticks represent


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ligands, while gray sticks represent amino acids. Red dash lines represent hydrogen bonds.

3.4.2 Inhibitory effects on CARM1 methyltransferase activity

In vitro methyltransferase activity of CARM1 was assessed by measuring the production of SAH. The 15 hit compounds obtained by virtual screening were evaluated for their inhibitory effects on CARM1 methyltransferase activity at concentrations ranging from 0.01 μM to 300 μM. Other members in the type I PRMT subfamily, such as PRMT1, PRMT3 and PRMT6, were also included to characterize the selectivity of these hit compounds. As shown in Table 2, 7 out of 15 compounds were active to inhibit CARM1 activity with IC50 smaller than 100 µM, and the inhibitory activities of NO.2 and NO.19 were more potent than others in the hit list (Figure S13), while AMI-1, a paninhibitor of the type I PRMTs

56,

inhibited PRMT1 with an IC50 of 73.91 µM, while was

less effective to other PRMTs, including PRMT3, CARM1 and PRMT6, which was consistent with previous report

57.

As expected, few compounds exhibited pan-activities


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against PRMTs, and most active compounds were specific for CARM1. Taken together, our virtual screening protocol was effective in identifying selective CARM1 inhibitors. Among all the active compounds targeting CARM1, the ligand efficiencies (LE) of NO.2 and NO.19, calculated using the formula of 1.4(-logIC50)/N, were about 0.25 kcal/mol, which suggested that they could serve as potential candidates as lead compounds for further structural optimization 58.

Table 2. In vitro inhibitory effects (IC50, µM) of hit compounds (cpds) from virtual screening on the methyltransferase activities of PRMT1, CARM1, PRMT3 and PRMT6 are listed. “-” represents no activity detected. AMI-1 is a known pan-inhibitor of the type I PRMT family. Cpd

CARM1

PRMT1

PRMT3

PRMT6

NO.1

-

-

-

-

NO.2

42.24

-

-

-

NO.3

163.50

54.09

-

-

NO.4

>100

-

-

-

NO.5

>100

-

-

-

NO.6

-

-

-

-

NO.12

>100

-

-

-

NO.13

-

-

-

-

NO.14

88.69

-

-

-


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NO.16

62.77

-

-

-

NO.17

-

-

-

-

NO.19

25.72

-

-

-

NO.21

52.17

-

-

-

NO.22

64.40

-

-

-

NO.23

59.60

-

-

-

AMI-1

>100

73.91

>100

>100

In respect to the structure-activity relationship, compounds such as NO.4, NO.5, and NO.19 had primary, secondary, and tertiary amino groups to imitate the binding of methylated arginine residue in the substrate. The ethyl or hydroxymethyl substitution at the α-site of the amino group attenuated the inhibitory activity, which was possibly due to undesired large volume. Interestingly, compound NO.23 still exhibited potency with ethyl directly added on the amino group. For compound NO.14, a primary amino group was directly linked to a five-membered heterocyclic moiety. All these data together suggested that there was enough space for CARM1 to accommodate proper amine-like groups at the hot spot of the catalytic center, which has never been seen in known inhibitors. This was why compound NO.21 and NO.22 with cyclic amino groups were


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active. Interestingly, compound NO.12 with 1,2,4-triazole group also inhibited the activity of CARM1 slightly, but compounds NO.6 and NO.13 with imidazole groups were inactive, which indicated that compounds with other guanidinium-like groups might be active as long as these groups can be positively charged to make salt bridge interactions with the negatively-charged glutamic acids at the catalytic center. It is worth noting that an appropriate linker group connecting the amino group and the moieties at the exposed cavity may affect the activity of such a compound. The distance between the amino group at the catalytic center and the moieties at the exposed cavity in the active compounds was nearly identical to the length of the four methylene groups, which was consistent with the distance between the positively-charged center POS1 and the aromatic feature A4 in the pharmacophore models.

3.4.2 Anti-cancer activity

CARM1 has been reported to be associated with breast cancer development 13, 18, 59-64.

12,

We therefore selected a variety of breast cancer cell lines to assess the


49


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cellular toxicity of the active compounds identified above. Specifically, MCF7 cell line was chosen to represent ER-positive breast cancers, and HCC1806, MDA-MB-231 and HCC1937 cell lines to represent triple-negative breast cancer. MCF10A normal breast epithelial cells were served as a control. Several compounds (IC50 smaller than 100 µM) were selected for cellular toxicity evaluation according to structural diversity. It was found that compound NO.2, NO.12, and NO.23 inhibited the proliferation of some of the cancer cells tested effectively, which was superior to AMI-1, a pan-inhibitor of the type I PRMT family, which was reported to have very limited toxicity to various types of cancer cells

56, 65

(Table 3). Specifically, NO.2 with moderate in vitro activity was potent at

inhibiting the proliferation of ER-positive and triple-negative breast cancer cells, while less effective in normal breast epithelial cells (Figure 8). Eventually, the compound NO.2 bearing an 2,3,4,5-tetrahydrobenzo[f][1,4]oxazepine scaffold will be structurally optimized towards finding a lead compound for CARM1 inhibition. Indeed, structural optimization has been carried out to improve the in vitro activity of NO.2 with its cellular activity maintained or improved (data not shown).


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Table 3. Cellular activities (cytotoxicity) (IC50, µM) of representative hit compounds (Cpds) against several breast cancer cells examined by MTS assay are listed. MCF10A is a normal breast epithelial cell line, MCF7 is a ER-positive breast cancer cell line, and HCC1806, MDA-MB-231 and HCC1937 are triplenegative breast cancer cell lines. “-” represents no activity detected. AMI-1 is a known pan-inhibitor of the type I PRMT family. Cpd

MCF10 A

MCF7

HCC1806

MDA-MB-231

HCC193 7

NO.2

183.20

8.26

18.03

25.58

17.99

NO.12

-

-

-

-

103.30

NO.14

-

-

-

-

-

NO.19

-

-

-

-

-

NO.22

152.60

-

-

-

-

NO.23

90.11

150.00

-

-

92.56

AMI-1

-

-

-

-

-


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Figure 8. A) Potency to inhibit CARM1 methyltransferase activity in vitro. B) Potency to inhibit MCF7, HCC1806, MDA-MB-231, HCC1937 breast cancer cell proliferation in culture for compound NO.2.

CONCLUSIONS


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In the present study, we aimed to discover new CARM1 inhibitors that specifically bind to the substrate-binding site, which was expected to endow the inhibitors with selectivity. Analysis of crystal structures and MD trajectories revealed that the substratebinding site of CARM1 has an unusually stable structure, although a few residues exhibit some sidechain flexibility. Moreover, the substrate-binding site was divided into three sub-pockets, which were confirmed to be capable of binding with small-molecule compounds by a combination of the SiteMap and FTMAP analyses. The critical residues for ligand binding were identified to be mainly hydrophobic, which greatly contributes

to

the

druggability

of

the

substrate-binding

site.

Subsequently,

pharmacophore models were generated with pharmacophore features corresponding to the interactions mediated by the critical residues. After rigorous validation, six models were applied to virtually screen the ChemBridge library consisting of 100,000 diverse drug-like small molecule compounds. The hit compounds were then subjected to a molecule docking screening followed by visual examination. Finally, 15 compounds were chosen for testing of their inhibitory effects on CARM1 as well as other PRMTS’ methyltransferase activity, which resulted in 7 active compounds with satisfactory


53


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properties (IC50 smaller than 100 µM). Moreover, a majority of the active compounds exhibit selectivity toward CARM1 over other PRMT members, as expected. Among them, 3 active compounds had inhibitory activities on the proliferation of breast cancer cells. NO.2 was the only compound that exhibited potent inhibitory effects on both CARM1’s methyltransferase activity and the growth of cancer cells, which indicates that this compound is a good starting point for structural optimization to improve efficacy. Furthermore, the virtual screening strategy used in this study is expected to be generally applicable to structure-based virtual screening targeting other PRMT members.

ASSOCIATED CONTENT

Supporting Information

Known inhibitors of CARM1, Crystalized ligands, conformation changes of residues in crystal structures, total energy and backbone RMSD of protein, pseudo dihedral of residue sidechain, SiteMap contours, structure of known inhibitors for pharmacophore


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model, pharmacophore models for virtual screening, hit compounds overlapping between pharmacophore models, GoldScore distribution for hit compounds obtained by virtual screening, hit compounds chosen for bioassays, and binding modes of hit compounds, in vitro activities of some compounds were shown in supplementary Figure S1-S13, respectively. Statistics and structural alignment of crystal structures, SiteMap properties, list of pharmacophore models, and cross-docking RMSD of crystal ligands were shown in supplementary Tables S1-S5, respectively. These materials are available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Authors

*Telephone: +86-0592-2881146. Fax: +86-0592-2881146. E-mail: [email protected] (W.L).


55


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*Telephone: +86-025-86185186. Fax: +86-025-86185187. E-mail: [email protected] (S.L.)

ORCID

Wen Liu: 0000-0003-3434-4162

Shuai Lu: 0000-0002-0169-349X

Ting Ran: 0000-0002-1387-4634

Author Contributions

⊥These

authors contributed equally.

Notes

The authors declare no competing ﬁnancial interest.


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ACKNOWLEDGEMENTS

This work was supported by National Natural Science Foundation of China (81761128015 and 8171101434), Fujian Province Health Education Joint Research Project (WKJ2016-2-09), Xiamen Science and Technology Project (2017S0091), Xiamen Science and Technology major project (3502Z20171001-20170302), the Fundamental Research Funds for the Central University (20720152009), Fujian Provincial Key Laboratory of Innovative Drug Target Research Funds, “985 project” Funds and “Thousand Young Talents Program” Funds.

REFERENCES

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Virtual Screening with Structure-based Pharmacophore Model to Identify Small-molecule Inhibitors of CARM1 Ting Ran,†, ‡,⊥ Wenjuan Li,†,⊥ Bingling Peng,† Binglan Xie, † Tao Lu§, Shuai Lu,§,* Wen Liu†,¶,*


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Virtual Screening with a Structure-Based Pharmacophore Model to

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