Discovery of New SIRT2 Inhibitors by Utilizing a Consensus Docking

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Discovery of New SIRT2 Inhibitors by Utilizing a Consensus Docking/ Scoring Strategy and Structure-Activity Relationship Analysis Shen-Zhen Huang, Chun-Li Song, Xiang Wang, Guo Zhang, Yan-Lin Wang, Xiao-Juan Jiang, Qi-Zheng Sun, Lu-Yi Huang, Rong Xiang, Yi-Guo Hu, Lin-Li Li, and Sheng-Yong Yang J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00714 • Publication Date (Web): 16 Mar 2017 Downloaded from http://pubs.acs.org on March 18, 2017

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Journal of Chemical Information and Modeling

Discovery of New SIRT2 Inhibitors by Utilizing a Consensus Docking/Scoring Strategy and Structure-Activity Relationship Analysis Shenzhen Huang†,‡, Chunli Song§,‡, Xiang Wang#, Guo Zhang§, Yanlin Wang†, Xiaojuan Jiang†, Qizheng Sun†, Luyi Huang†, Rong Xiang#, Yiguo Hu†, Linli Li§,*, and Shengyong Yang†,* †

State Key Laboratory of Biotherapy and Cancer Center, West China Hospital,

Sichuan University/Collaborative Innovation Center of Biotherapy, Chengdu, Sichuan 610041, China. §

Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of

Education, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan 610041, China. #

Department of Clinical Medicine, School of Medicine, Nankai University, Tianjin

300071, China.

ACS Paragon Plus Environment


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ABSTRACT SIRT2, which is a NAD+ (nicotinamide adenine dinucleotide)-dependent deacetylase, has been demonstrated to play an important role in the occurrence and development of a variety of diseases such as cancer, ischemia-reperfusion, and neurodegenerative diseases. Small molecule inhibitors of SIRT2 are thought as potential interfering agents for relevant diseases. Discovery of SIRT2 inhibitor has attracted much attention recently. In this investigation, we adopted a consensus docking/scoring strategy to screen for novel SIRT2 inhibitors. Structural optimization and structure-activity relationship (SAR) analysis were then carried out on highly potent compounds

with

new

scaffolds,

which

led

to

the

discovery

of

2-((5-benzyl-5H-[1,2,4]triazino[5,6-b]indol-3-yl)thio)-N-(naphthalen-1-yl)acetamide (SR86). This compound showed good activity against SIRT2 with an IC50 value of 1.3µM. SR86 did not exhibit activity against SIRT1 and SIRT3, implying a good selectivity for SIRT2. In in vitro cellular assays, SR86 displayed very good anti-viability activity against breast cancer cell line MCF-7. In western blot assays, SR86 showed considerable activity in blocking the deacetylation of α-tubulin, which is a typical substrate of SIRT2. Collectively, because of the new scaffold structure and good selectivity of SR86, it could serve as a promising lead compound, hence deserving further studies.


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INTRODUCTION Sirtuins are a family of NAD+ (nicotinamide adenine dinucleotide)-dependent protein deacetylases. In mammals, there are seven sirtuin proteins, SIRT1–SIRT71, which vary in subcellular localization and biological functions. Among all the sirtuins, SIRT1 and SIRT3 are the most widely studied ones. Recently, SIRT2 is emerging as another research focus in this field. SIRT2 is predominantly a cytoplasmic protein. To date, various substrates of SIRT2 have been identified, including histone substrates and non-histone substrates. The histone substrates of SIRT2 contain histone H4K16, H3K18, and H3K56. The non-histone substrates include various transcription factors (such as P300, FOXO3, FOXO1, HIF-1α, NF-κB, and PGC-1α), cell cycle related enzymes (such as BubR1, CDK9, and CDH1/CDC20), metabolic enzymes (such as LDH-A, PEPCK, ACLY, G6PD, and PGAM), cell signaling related substrates (such as PRLR, K-Ras, PAR-3, and TIAM1), and structural proteins (such as keratin 8 and α-tubulin)2. The multifarious substrates imply that SIRT2 plays important roles in modulating relevant biological processes, and deregulation of SIRT2 might cause various diseases. Indeed, a number of studies have demonstrated that SIRT2 is strongly associated with the occurrence and development of some cancers (e.g. glioma3, bladder cancer4, non-small cell lung cancer5, burkitt lymphoma6, colon cancer7, and breast cancer8), necrotic injuries (e.g. ischaemic stroke and myocardial infarction9), and neurodegenerative diseases (e.g. Huntington’s disease10,

11

and

Parkinson’s disease12). Here it is necessary to mention that a few studies also indicated that SIRT2 could play a tumor promotion effect in some conditions13-15, although many studies have demonstrated that SIRT2 is a tumor suppressor13, 14, 16. The disaccord implies that the effect of SIRT2 in cancer might be context-specific. Nevertheless, the controversy regarding the role of SIRT2 in pathogenesis does not



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affect the enthusiasm for the study of small molecule SIRT2 inhibitors; specific SIRT2 inhibitors could have potential applications in the treatment of diseases due to dysregulation of SIRT2 gene, or could be used a chemical probe to uncover the new biological functions and mechanism of SIRT2. To date, a number of SIRT2 inhibitors have been discovered. Some representative SIRT2 inhibitors5, 8, 10, 15, 17-19 with different scaffolds are depicted in Figure 1. Even so, no SIRT2 inhibitor has been approved to enter clinical trials or market. Some known SIRT2 inhibitors did not show satisfactory effect in preclinical studies, which might be due to their defects in pharmacodynamic or pharmacokinetic properties. Therefore, discovering more potent and selective SIRT2 inhibitors particularly with novel chemical scaffolds is still necessary at present. The aim of this investigation is to identify new types of SIRT2 inhibitors. For this purpose, we first performed a high throughput virtual screening (VS) against commercial libraries. In literature, the VS strategy either structure-based20 or pharmacophore based21 has already been applied to screen for SIRT2 inhibitors. Instead of the traditional VS strategy, a consensus docking/scoring strategy22 was adopted here. Top ranked compounds were selected to perform bioactivity evaluation. Structural optimization and structure-activity relationship (SAR) analysis were then carried out on highly potent compounds with new scaffolds. The most active compound finally obtained was subject to further investigations including selectivity, binding mode analysis with SIRT2, and bio-functional studies15, 23.


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MATERIAL AND METHODS Molecular Docking In this investigation, four molecular docking methods including LibDock, GOLD, CDOCKER, and LigandFit are involved. Program modules for LibDock, GOLD, CDOCKER, and LigandFit are those incorporated in the platform of Discovery Studio (DS) 3.1 (Accelrys Inc., San Diego, CA, USA). Before molecular docking, the receptor protein was prepared by the DS 3.1 software package with standard preparation procedures (protein preparation protocol), which include removing water molecules, adding hydrogen atoms to the protein, and assigning force field (here the CHARMm forcefield was adopted). For LibDock, the parameter “conformation method” was set to ‘BEST’ and others were set to default. For GOLD, the “pre-defined generic algorithm (GA)” setting of ‘automatic’ was employed and others were set to default. For CDOCKER and LigandFit, all parameters were set to default. Chemical Libraries Compound libraries used for virtual screening in this investigation include Specs and ChemDiv. The total number of compounds in the two libraries is approximately 550,000. All the compounds used for docking were prepared with ‘Prepare Ligands’ module in DS 3.1. Parameter values for “Change Ionization, Generate Tautomers, and Generate Isomers” were set to false. Other parameters were set to their default values. To make the retrieved compounds more drug-like, the selected hit compounds are required to meet the following conditions: (1) Lipinski Rules of Five and Veber Rule24, 25

; (2) -2.0 < AlogP < 5.024; (3) Filter by SMARTS-PAINS and Other Bad Groups in

Pipeline Pilot 8.5 (http://accelrys.com/products/pipeline-pilot/) (Figure S1). Selected compounds were purchased in milligram quantities from chemical vendors. Purity of compounds was >= 95%, as declared by the chemical vendors.



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In Vitro Enzymatic Inhibition Assays In vitro enzymatic inhibition assays were performed using Fluorescence Intensity technology provided by Shanghai Chempartner Co., Ltd. Briefly, The human SIRT1(BPS, Cat. No. 50012), human SIRT2 (BPS, Cat. No. 50013) and human SIRT3 (Cayman, Cat. No. 10011194) were mixed with assay buffer (modified Tris Buffer) and the respective inhibitor in 100% DMSO at various concentrations. And then, the mixture was incubated at room temperature for 15 minutes. The reactions were started by the addition of substrate solution containing NAD and Ac-peptide substrate. After incubation for 240 minutes at room temperature, the trypsin solution was added and incubates for 90 minutes. Then the fluorescence intensity was measured in a microplate reader (Synergy MX, λex 360 nm, λem 460 nm). The date was fitted in Excel to obtain inhibition values using equation (1). The data was fitted in GraphPad to obtain IC50 values using equation (2), Y is % inhibition and X is compound concentration.

Inh% =

Y = Bottom +

Max - Signal ∗ 100 Max - Min

Top - Bottom (1 + 10^ ((LogIC50 - X) * Hill Slope))

(1)

(2)

Cell Culture The human breast cancer cell line MCF-7 was purchased from American Type Culture Collection (ATCC, Rockville, MD, USA). It was cultured in DMEM supplemented with 10% FBS (Gibco, Eggenstein, Germany), 100 units/ml penicillin (Sigma-Aldrich) and streptomycin (Sigma), and maintained in the 37 °C incubator with a humidified 5% CO2 atmosphere.

Cell Viability Assays Cell viability was measured using MTT assays as previously described26. MCF-7


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cells (3×103 cells/well) were treated with indicated concentrations of compound for 72 h. Each assay was performed in 3 replicates.

Western Blot The MCF-7 cells were seeded in 6-well plates (Corning) at a density of 1*105 cells/well and incubated overnight, then treated with vehicle (0.5% DMSO) or test compounds as previously described6. Whole cells lysates were extracted with RIPA buffer

(Beyotime,

China)

supplemented

with

protease

inhibitor

cocktail

(Sigma-Aldrich, Merck) and PMSF (Sigma-Aldrich, Merck). Protein concentrations were determined using BCA protein assay kit (Heart, Xi’an). Protein extracts were separated by SDS-PAGE on 10% polyacrylamide Tris-Glycine gels and transferred onto a PVDF membrane (Millipore). PVDF membranes were blocked in TBS containing 5% nonfat dry milk and 0.1% Tween 20 with gentle shaking for 2 h. Antibodies were diluted in blocking buffer. The mouse monoclonal antibodies toacetylated α-tubulin (T6074) and acetylated α-tubulin (T6793) were both purchased from Sigma Aldrich.

Blots were incubated with the corresponding primary antibody

(1:1000) at 4 °C overnight. Then, blots were incubated for 1 hour with the corresponding horseradish peroxidase-linked secondary antibodies (Zhong Shan Golden Bridge Bio-technology, China) diluted 1:5,000 in blocking buffer.



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RESULTS AND DISCUSSIONS Selection of Docking Methods and Scoring Functions for the Consensus Docking/Scoring Strategy Although molecular docking–based virtual screening (DBVS) has achieved a great success, it still suffers constant criticism27, 28. The main problem that leads to the blame is the low accuracy of DBVS. As we know, DBVS involves two basic processes: docking and scoring. In fact, both of the docking and scoring are imperfect. For example, docking methods frequently fail to accurately predict the binding modes of ligands and receptors though some docking programs have shown good performance29, and scores given by scoring functions often have a poor correlation with experimental binding affinities30. To solve the problem of scoring functions, consensus scoring is often used; the consensus scoring means that if a compound is thought as a hit, it must be ranked at a top position by all or majority of scoring functions, have gotten many successful achievements in the discovery of the novel inhibitors of various target proteins31-33. To further consider the docking issue simultaneously, we adopted the consensus docking/scoring strategy (also called consensus docking in literature22), which is still a consensus scoring scheme, but the scorings could be based on different docking programs. In this section, we shall describe the selection of docking methods and scoring functions.

Selection of docking methods. Currently, four molecular docking programs, namely LibDock, GOLD, CDOCKER, and LigandFit, are available for us. We first evaluated the performance of the four docking programs in predicting the binding mode of ligand with SIRT2. The evaluation criterion is to check whether a docking program can well reproduce the binding modes of ligands in crystal structures of SIRT2-ligand complexes. We selected five co-crystal structures of SIRT2-ligand; the


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PDB codes are 4RMG, 4RMI, 4RMJ, 5D7P, and 5D7Q, and the corresponding ligands are SirReal219, SirReal119, Nicotinamide19, EX-24334, and CHIC3534, respectively. The calculated root-mean-square deviation (RMSD) values between the docked poses and those in crystal structures are shown in Table 1. Just as expected, these docking programs showed completely different performance. Among all the four docking programs, GOLD and CDOCKER displayed relatively smaller RMSD values for all the studied ligands; all the RMSD values are less than 2.0 Å. In a previous study, Sutherland et al35 indicated that the RMSD value within 2.0 Å was acceptable for DBVS. Therefore, GOLD and CDOCKER are chosen as the docking programs in the subsequent virtual screening.

Selection of scoring functions. We previously demonstrated that most of the scoring functions are sensitive to receptor28, which means that a scoring function has inconsistent performances on different receptors. To reduce the influence of some obviously poor scoring functions, we made a simple evaluation to the scoring functions available for us by a test set. The scoring functions used are those incorporated in the GOLD and CDOCKER programs. For the test set, we carefully selected 22 compounds with their bioactivities across 6 orders (IC50: 4nM ~ >100µM); the chemical structures of these compounds are given in Figure S2. Here the Spearman’ correlation coefficient (Rs) was used as the metrics to measure the performance of scoring functions; Rs measures the correlation between the order of bioactivities and that of scoring values of compounds. The calculated Rs values are listed in Table 2, and the detailed scoring function values are given in Table S1. From Table 2, we can see that, except Chemscore, the other four scoring functions showed a



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good performance. Therefore, in the following studies, the four scoring functions, namely ChemPLP, GoldScore, ASP, and -CDOCKER_ENERGY, are adopted.

Retrieving of Hit Compounds Virtual screening was then carried out to retrieve SIRT2 inhibitors by utilizing the consensus docking/scoring strategy. Chemical libraries used in the virtual screening include Specs and ChemDiv; the total number of compounds is about 550,000. The crystal structure of SIRT2 in complex with SirReal2 (PDB entry 4RMG) was used for the docking studies. Here 4RMG was selected because it has a unique conformation compared to the other known SIRT2 structures due to the induction of a 'selectivity pocket' by the ligand19, 36, 37, which may benefit to retrieve specific SIRT2 inhibitors. Both GOLD and CDOCKER were adopted with scoring functions ChemPLP, GoldScore, and ASP for GOLD and -CDOCKER_ENERGY for CDOCKER being used. We chose compounds that are all ranked at top 1% by the four scoring functions, implying a consensus score of 4. A total of 50 compounds were finally selected and purchased from compound suppliers for bioassays. Five compounds (SR13, SR17,

SR10, SR30, and SR40) showed an inhibition rate >30% against SIRT2 at the concentration of 10µM. Chemical structures and corresponding bioactivities are depicted in Figure 2. Because SR17 is the most active compound and contains a new scaffold (triazino[5,6-b]indole), further structural optimization and structure-activity relationship (SAR) analysis are carried out on this compound.

Structural Optimization and SAR Analysis of SR17 Structural optimization and SAR analysis of SR17 will focus on two positions: 5-(p-tolyl)-6H-1,3,4-thiadiazine (R1) and the allyl group at the 5-N position of the triazinoindole scaffold (R2) (Figure 3).

Optimization of the R1 group. To optimize the R1 group, the R2 position was


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fixed as its original allyl group. We searched for compounds that contain different R1 groups from commercial chemical libraries including Specs and ChemDiv. A total of 16 compounds were retrieved and then purchased from the suppliers. Chemical structures and bioactivities of these compounds are shown in Table 3. From Table 3, we can see that the bioactivities for all the new compounds do not exceed that of

SR17. Among the new compounds, S54 and S49 showed relatively higher activity with inhibition rates of 57% and 47% at the concentration of 10uM; S54 is very similar with SR17 and contains a 6-member ring in R1, and S49 contains a fused 5/6-member double ring moiety.

Optimization of the R2 group. To optimize the R2 group, the R1 group was fixed as an arbitrary 6-member ring or an arbitrary fused 5/6-member double ring structure according to the information provided above. Here it is necessary to mention that we fixed R1 as a broad 6-member ring or a fused 5/6-member double ring structures other than a specific structure, which is for increasing the chance to obtain more active compounds. Table 4 shows the chemical structures and bioactivities of compounds that contain an arbitrary 6-member ring and different R2 groups. In these series of compounds, three compounds, namely SR80 and SR86, exhibited an inhibition rate larger than 50% at the concentration of 10µM. Table 5 displays the chemical structures and bioactivities of compounds comprising a fused 5/6-member double ring structure at the R1 position and varied R2 groups. From Table 5, we can see that two compounds, namely SR83 and SR84, which have a benzo[d]thiazol group or 4,5,6,7-tetrahydrobenzo[d]thiazole group at the R1 position and a propyl group at the R2 position, displayed an inhibition rate larger than 50% at the concentration of 10µM. All compounds with inhibition rate larger than 50% at 10µM were tested for their



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half-maximal inhibitory concentrations (IC50) values, which results are shown in Table 6. Four compounds displayed an IC50 values less than 10µM. SR86, whose IC50 value is 1.3µM, is the most active one. Further studies were carried out on this compound, including binding mode analysis, isozyme selectivity assays, and bio-functional assays.

The Predicted Binding Mode of SR86 with SIRT2 Figure 4 shows the predicted binding mode of SR86 with SIRT2. For comparison,

SR86 is docked into the SIRT2 ‘selectivity pocket’ that induced by SirReal219 (PDB entry 4RMG), and then compared the docking poses with 29c38 that reported recently (PDB entry 5MAT). It is obvious that SR86 has very similar binding modes with that of 29c, rather than SirReal2, (Figure 4A, 4B, and 4C). Interestingly, these docking results suggest that the naphthyl ring can also be accommodated inside the ‘selectivity pocket’ in a manner similar to the naphthyl ring of 29c maintaining the pi–pi contacts (Figure 4C). The naphthalene group of SR86 perfectly fit in the ‘selectivity pocket’ via hydrophobic interactions with residues TYR139, LEU206, PHE143, and PHE190. The benzene ring in the scaffold triazino[5,6-b]indole forms hydrophobic interaction with three PHE residues (PHE 96, PHE119, and PHE235). Two face-to-face π-π interactions are formed: one between the naphthalene ring and the benzene ring of ‘selectivity pocket’ residues PHE190; and the other one between the benzene ring in the 5-benzyl group and imidazole ring in PHE119 (Figure 4D).

Isozyme Selectivity of SR86 The above results indicate that SR86 could potently inhibit SIRT2 with an IC50 value of 1.3µM. We then tested its activities against other SIRT family members. Here just SIRT1 and SIRT3 were tested because the bioassays against other SIRT family members are not available at this moment. In these assays, SR86 almost did not show


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activity against SIRT1 and SIRT3 (IC50 > 300µM), implying a good selectivity for SRIT2 against SIRT1 and SIRT3.

Bio-functional Assays In vitro anti-viability activity of SR86 against breast cancer cell line MCF-7. A number of studies have indicated that breast cancer cell line MCF-7 expresses a high level of SIRT2, and the survival of MCF-7 cells is sensitive to the inhibition of SIRT215, 39. We thus tested the anti-viability activity of SR86 against MCF-7 by the MTT assay. Figure 5A shows the inhibition rates of SR86 at different concentrations. Obviously, SR86 could potently and dose-dependently kill the MCF-7 cells.

Inhibition of deacetylation against typical substrates of SIRT2 in intact cells. To further examine the role of SR86 in intact cells, we tested the impact of SR86 on the acetylation status of substrate. Here α-tubulin was taken as an example since it has been demonstrated to be a typical substrate of SIRT215. As shown in Figure 5B, SR86 considerably increased the acetylation level of α-tubulin in a dose-dependent manner. As a negative control, SR95 (no activity against SIRT2) had no influence on the acetylation of α-tubulin, which confirms that SIRT2 inhibition and tubulin hyperacetylation are correlated.

CONCLUSION A consensus docking/scoring strategy was adopted to screen for new SIRT2 inhibitors. Fifty compounds retrieved were purchased and bio-assayed for their activity against SRIT2. SR17, which is the most active compound and contains a new scaffold (triazino[5,6-b]indole), was selected to perform structural optimization and SAR analysis. These studies finally led to the discovery of 4 compounds that showed an IC50 value less than 10µM against SIRT2. Further studies were performed on the most active compound, SR86, which showed an IC50 of 1.3µM against SIRT2. While



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this paper being reviewed, a study describing 29c38 was published. The predicted binding mode of SR86 with SIRT2 is obviously similar to that of 29c as observed by crystallographic analyses, thus indicating that this series of compounds also give rise to the induction of a previously described19, 36-38 ‘selectivity pocket’ in the SIRT2 active site and explain the high selectivity of SR86 for SIRT2 over other tested SIRT family

members.

SR86

shows

considerable

anti-viability

activity

against

SIRT2-overexpressed MCF-7 cells. In intact MCF-7 cells, it also exhibited considerable activity in blocking the deacetylation of substrate of SIRT2, α-tubulin. Collectively, owing to the new scaffold structure and good selectivity, SR86 could be a good lead compound and deserves further exploation.


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ASSOCIATED CONTENT Supporting Information The scores of different scoring functions for the test set compounds in the molecular docking studies. Chemical structures of compounds in the training set (compounds 1–22) together with their biological activity data (IC50 values, in parentheses). Small molecules were filtered out using Pipeline Pilot 8.5. This information is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *Phone: +86-28-85164063. Fax: +86-28-85164060. E-mail: [email protected] or

[email protected] Author Contributions ‡

These authors contributed equally to this work.

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China (973 Program, Grant No. 2013CB967204), the National Natural Science Foundation of China (Grant No: 81325021, 81473140, and 81573349), and the Program for Changjiang Scholars and Innovative Research Team in University of China (Grant No. IRT13031).

ABBREVIATIONS NAD+,

nicotinamide

adenine

dinucleotide;

VS,

virtual

screening;

SAR,

structure-activity relationship; DS 3.1, Discovery Studio 3.1; DBVS, docking–based virtual screening; RMSD, root-mean-square deviation.



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FIGURES

Figure 1. Chemical structures of representative SIRT2 inhibitors.



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. Chemical structures and bioactivities of the retrieved SIRT2 inhibitors.


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Figure 3. Regions that are the focuses in the structural optimization.



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 predicted binding mode of SR86 with SIRT2. (A) The overall deacylase domain together with SR86 and the original 4RMG ligand SirReal2; (B) The interaction mode of SR86 and the active pocket of SIRT2 (PDB entry 4RMG); (C) The interaction mode of SR86 and the active pocket of SIRT2; (D) The 2-dimensional interactions map of

SR86 with SIRT2 (PDB entry 5MAT).


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Figure 5. (A) The anti-viability activity of SR86 against breast cancer cell line MCF-7. (B) The impact of SR86 on the acetylation status of substrate of SIRT2, α-tubulin, in intact MCF-7 cells. MCF-7 cells were treated for 6 h with 40 nM Trichostatin A (a pan HDAC inhibitor) and SR86 or SR95 with indicated concentrations. AK-7 (a known SIRT2 inhibitor) and SR95 (a non-inhibitor of SIRT2) were used as positive and negative controls, respectively.



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 33

TABLES

Table 1. RMSD values (Å) between the docked poses of ligands and those in crystal structures. Program

SirReal2

SirReal1

Nicotinamide

EX-243

CHIC35

LibDock

0.7839

2.3765

3.5836

1.0162

4.9280

GOLD

0.5088

1.2822

0.8871

0.7273

0.4548

CDOCKER

0.4528

0.2720

0.6343

0.3196

0.6575

LigandFit

0.2335

7.2089

1.6221

6.4573

3.6200


Page 25 of 33

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 2. Performance of different scoring functions evaluated by a test set. -CDOCKER_ IC50

IC50

GoldScore

Chemscore

ASP

ChemPLP

(nM)

Rank

(GOLD)

(GOLD)

(GOLD)

(GOLD)

Cpd

ENERGY (CDOCKER)

Cpd1

5.4

1

17

21

8

12

2

Cpd2

48.3

2

8

5

1

7

15

Cpd3

400

3

1

11

3

2

1

Cpd4

570

4

10

3

4

3

4

Cpd5

2,400

5

2

7

12

1

5

Cpd6

4,000

6

4

8

6

4

19

Cpd7

4,500

7

14

19

19

17

3

Cpd8

7,800

8

21

1

15

10

16

Cpd9

9,800

9

6

12

16

15

12

Cpd10

10,000

10

3

9

7

8

10

Cpd11

10,800

11

12

18

13

16

18

Cpd12

11,700

12

5

10

9

9

14

Cpd13

15,600

13

18

20

21

21

17

Cpd14

16,000

14

13

14

20

13

7

Cpd15

16,900

15

9

17

11

14

11

Cpd16

32,000

16

11

6

5

6

20

Cpd17

32,300

17

20

22

22

22

13

Cpd18

56,700

18

7

4

2

5

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 26 of 33

Cpd19

57,000

19

16

2

10

11

6

Cpd20

77,500

20

15

13

14

20

9

Cpd21

100,000

21

19

15

17

18

8

Cpd22

>100000

22

22

16

18

19

22

Rs

-

-

0.476

0.138

0.418

0.555

0.397


Page 27 of 33

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Table 3. Chemical structures and bioactivities of SR17 derivatives with R1 varied and R2 fixed as an allyl group.

inhibition rate @ Cpd

ID

a

R1 10µM (%)

SR17

D026-0016

60

S38

AG-205/37047263

12

S39

AG-205/37175072

7

S40

6623-1380

43

S41

2683-0110

22

S42

8007-4289

9

S44

4249-0031

2

S45

4249-0044

-1

S46

4570-0046

-6

S47

5025-0057

11

S48

8012-9941

-2

S49

5471-0051

47

S50

2683-0125

-16

S52

8013-3506

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

a

Page 28 of 33

S54

D041-0014

57

S55

8016-6666

15

The identifier of compound in Specs or ChemDiv.


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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 4. Chemical structures and bioactivities of SR17 derivatives that contain an arbitrary 6-member ring and different R2 groups.

Inhibition rate Cpd

ID

a

R2

R1 @ 10µM (%)

S12

AG-205/10470044

2

S15

AG-690/13508002

16

SR64

AG-690/13703914

37

S13

AG-205/14194011

-8

S17

AG-690/40749354

-7

SR65

AG-690/40749355

-7 H

S18

AG-690/40749620

7

S20

AN-919/14791018

-5

SR68

AP-853/10475059

14

S23

AP-853/14367215

15

S16

AG-690/13703259

4

S29

AG-205/12900274

36

S30

3396-0065

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

a

Page 30 of 33

S31

8009-8378

0

S25

8012-9942

8

S35

8014-1380

7

S36

8015-0516

21

SR79

8012-9940

-1

SR76

AK-968/15364408

21

SR77

8013-2813

12

SR78

3698-0044

9

SR80

D026-0012

66

SR87

P160-0020

32

SR89

8008-9378

50

SR91

3698-0110

3

SR94

5471-0022

49

SR95

5471-0111

-3

SR86

AG-205/37150065

62



Page 31 of 33

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 5. Chemical structures and bioactivities of SR17 derivatives that contain an arbitrary fused 5/6-member double ring and different R2 groups.

Inhibition rate Cpd

IDa

R2

R1 @ 10µM (%)

SR61

AG-205/33678038

34 H

a

SR62

AG-205/34694051

S33

8010-5857

SR81

AG-205/12900184

SR83

3698-0126

SR84

5471-0050

SR82

AG-690/15428906

SR85

AG-205/11945035

25 48

50 66 71



42

20


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Page 32 of 33

Table 6. IC50 values of compounds with an inhibition rate larger than 50% at the concentration of 10µM.

a

Cpd

IDa

IC50 (µM)

SR80

D026-0012

5.4

SR83

3698-0126

4.6

SR84

5471-0050

2.1

SR86

AG-205/37150065

1.3



Page 33 of 33

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For Table of Contents Use Only


Discovery of New SIRT2 Inhibitors by Utilizing a Consensus Docking

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