Accepted Article
Received Date : 16-Oct-2014 Revised Date : 15-Dec-2014 Accepted Date : 17-Dec-2014 Article type : Research Article
Structure-guided discovery of a novel non-peptide inhibitor of dengue virus NS2B-NS3 protease Linfeng Li1, 2, Chandrakala Basavannacharya3, Kitti Wing Ki Chan3, Luqing Shang1, 2*, Subhash G. Vasudevan3*, Zheng Yin1, 2* 1
College of Pharmacy & State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, China 2 Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300071, China 3 Duke-NUS Graduate Medical School, Program in Emerging Infectious Diseases, 8 College Road, Singapore 169857 Corresponding author mail id: [email protected]
Abstract
Dengue fever is a fast emerging epidemic-prone viral disease caused by dengue virus (DENV) serotypes 1-4. NS2B-NS3 protease of DENV is a validated target to develop antiviral agents. A major limitation in developing DENV protease inhibitors has been the lack of, or poor cellular activity. In this work, we extracted and refined a pharmacophore model based on X-ray crystal structure and predicted binding patterns, followed by a three-dimensional flexible database filtration. These output molecules were screened according to a docking-based protocol, leading to the discovery of a compound with novel scaffold and good cell-based bioactivity that has potential to be further optimized. The discovery of this novel scaffold by combination of in silico methods suggests that structure-guided drug discovery can lead to the development of potent DENV protease inhibitors.
Keywords: dengue virus, protease inhibitor, pharmacophore, virtual screening, This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an 'Accepted Article', doi: 10.1111/cbdd.12500 This article is protected by copyright. All rights reserved.
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DENV bioassay.
1. Introduction
Dengue fever is a mosquito-borne viral infection which typically causes a mild flu-like symptom and can sporadically develop into life-threatening forms. A recent survey of dengue estimated that the global incidence was approximately 390 million, which put almost half of the world’s population at risk of infection (1). Unfortunately, there are no efficacious dengue vaccines or antiviral drugs available for prevention or treatment. Dengue fever is caused by dengue virus (DENV) serotypes 1-4, which belong to the Flavivirus genera (1-5). The virion contains a positive-sense single-stranded RNA which encodes a polyprotein that is subsequently processed into three structural proteins and seven nonstructural proteins (NS1, 2A, 2B, 3, 4A, 4B and 5) by the action of host and viral proteases (6). NS3 is a multifunctional enzyme and its N-terminal domain, associated with NS2B cofactor, is a trypsin-like serine protease that is essential for DENV replication, rendering it a prime drug target (7, 8). Most protease inhibitors are analogues of endogenous substrate (9, 10). Early in 2005, the tetrapeptide sequence Bz-nKRR was identified from functional profiling exercise as an efficient substrate, thus leading to a number of peptidomimetic inhibitors (11-15). Despite the potency of some peptide mimics, the high polarity of the basic residues had contributed to stability and cell permeability issues that prevented their further development as antivirals. One breakthrough that held promise for development of new antiviral was the determination of the crystal structure of DENV NS2B-NS3 protease and its complexed structure with the tetrapeptide aldehyde Bz-nKRR-H (PDB code: 2FOM and 3U1I, respectively) (7, 16). These structures together with other efforts shed light on the importance of the catalytic triads (His51, Asp75 and Ser135) at the active site, the unusual contribution of NS2B towards stability and formation of the pocket environment (13). These structural insights suggested the plausibility of a rational design approach. However they also This article is protected by copyright. All rights reserved.
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highlighted the challenges with the substrate analogue strategy because of the shallow pocket and the requirement to replace the basic residues. Difficulties in substrate analogue approach shifted researchers’ efforts towards an alternative method -- diverse library screening. Several traditional high throughput screenings (HTS) produced a number of compounds, most of which presented mediocre enzyme-based inhibitory activities but poor inhibition in cellular assays (17-24). In silico docking strategy has proved to be a productive supplement to exert a more reasonable screening effort than the conventional HTS. For instance, compound I was yielded in a virtual screening and showed commendable bioactivity, albeit with a low therapeutic index (CC50/EC50) of 4.0 (25). Several previous studies have obtained various non-peptide protease inhibitors with moderate to potent enzyme-based activities. Although multifaceted issues including permeability, selectivity, druggability and toxicity remains to be tackled, we select and list several recently reported lead compounds (Figure 1) herein and believe that analysis of these molecules could pave the way for development of novel DENV protease inhibitors (18-21, 23, 25).
Figure 1: Recently reported lead compounds. Their cell-based EC50 values are indicated, and ND denotes not determined (18-21, 23, 25).
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In this work we describe the discovery of a novel DENV protease inhibitor from the commercially available library by virtual screening. We carried out a hierarchy virtual screening and biological activity measurement: pharmacophore-based preliminary filtration rigid docking induced-fit docking enzyme-based inhibition bioassay cell-based prevention of infection bioassay. Finally we obtained several compounds that inhibited NS2B-NS3 protease and one hit further demonstrated antiviral activity in cell-based assay with the EC50 of 5 μM (Figure 2).
Figure 2: Schematic of the applied virtual screening cascade.
2. Experimental section
Database and chemical vendors
The library of about 5 million commercially available compounds were provided by following companies: ChemBridge Corporation, 11199 Sorrento Valley Road, Suite 206 San Diego, CA 92121, USA; Enamine Ltd., 23 Alexandra Matrosova Street, 01103 Kyiv, Ukraine; Life Chemicals Inc., 1A Dixie Avenue, Niagara-on-the-Lake
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ON L0S 1J0, Canada; Maybridge, Thermo Fisher Scientific Inc., ENA 23, Zone 1, nr 1350, Janssen Pharmaceuticalaan 3a, 2440 Geel, Belgium. The fourteen hits were purchased from the Topscience Co., Ltd. Level 8 Peninsula International Center, No 238, Jiangchang San Road, Shanghai, 200436.
Pharmacophore-based preliminary search
Four different commercial libraries were merged into an integrated database, and the molecules were converted into 3D structures by Concord (Concord 6.1.3 Manual, SYBYL-X 2.0, Copyright © 2012 Certara, L.P., http://www.certara.com/). Pharmacophore features were defined using UNITY (UNITY Manual, SYBYL-X 2.0). Constraints of all features were assigned as spheres. All hydrogen donor/acceptor atoms spheres were set to 1.3 Å, and their corresponding acceptor/donor sites spheres were set to 1.5 Å, allowing for loose orientations of the hydrogen bonds. The two positive charged centers radii were set to 2.0 Å, for ionic interactions were not so demanding. Furthermore, an aromatic feature with a 1.5 Å radius represented the π-π interaction. The pharmacophore model was used to initiate the UNITY 3D flexible search. Because of the numerous compounds, some options and parameters were optimized to save calculation time. Rings were regarded as rigid, the minimum gradient for tweak was modified to 0.005 kcal/mol, and the searching time cutoff was set to 40 seconds for each structure.
Molecular docking
In this study, molecular docking was applied in three stages. In the first stage, the selected recently-reported molecules were docked into protease to identify common pharmacophore features using MOE (MOE 2013.08 User
Manual,
Copyright
©
2014
Chemical
Computing
Group
Inc.,
http://www.chemcomp.com/). The atomic coordinate file of NS2B-NS3 protease was This article is protected by copyright. All rights reserved.
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obtained from Protein Data Bank using the code 3U1I. To prepare the raw receptor file, positive hydrogens and ionization states were assigned by the MOE Protonate 3D function, inconsequential ions and waters were removed while HOH150, HOH220 were reserved in light of their relatively buried positions and already existed interactions in the co-crystal structures. The docking process adopted an induced-fit protocol. The triangle matcher was employed as the placement method to generate initial poses. For each ligand, the timeout was set to 300 seconds and the maximum number of poses returned was set to 1000. London dG was used to rescore the placement, retaining 30 times. Ensuing refinement in an MMFF94x forcefield was evaluated by GBVI/WSA dG method, retaining 30 times as well. In the first round of virtual screening (the second stage), molecular docking was performed by AutoDock (AutoDock 4.2.5 User Guide, Copyright © 2009 The Scripps Research Institute, http://autodock.scripps.edu/). The pre-docking treatment of protein was basically the same as the first stage, except that Gasteiger partial charges were added to the protein. All the ligand files were converted into the specific type recognized by AutoDock. Referring to the position of ligand in corresponding template, the grid box was defined as a rectangular solid of 46×44×46 with an interval of 0.375 Å and set to rightly contain the whole binding pocket in order to delimit the space of ligand’s movement. Lamarckian Genetic Algorithm (LGA) was applied to execute the docking calculation. The number of GA runs was set to 20 for each molecules, and the maximum number of energy evaluations for each run was set to 250,000 to reduce the computation time. Other GA settings were remained as default. The output conformations of each docking were ranked, clustered and analyzed in AutoDock Tools. The methodology and all docking parameters of the second round of virtual screening (the third stage) were identical to those in the first stage.
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Protease inhibition assay
Peptides were assayed in 384-well microplates in protease buffer (50 mM Tris, pH 8.5, 1 mM Chaps, 20% glycerol) in a final volume of 30 µL as described elsewhere (26). Briefly, protease DENV-2 NS2B40-G4SG4-NS3185 (10 nM) was pre-incubated with test compounds (3.3% DMSO) at the concentration of 300 µM at 30 °C for 15 minutes. The reaction was initiated by the addition of fluorophore-tagged substrate Bz-nKRR-AMC at 20 µM and the incubation was continued at 30 °C for 30 minutes. The reaction was monitored by following the increase in fluorescence (λex = 380 nm, λem = 450 nm) on a Tecan Infinite®M200 microplate reader at room temperature. Blank reaction (min) was with all the components except the protein, and complete reaction (max) was with all the components of the assay. The experiments were performed in triplicate.
Cell-based flavivirus immune detection
A CFI assay was performed as previously described by Wang et al. (27). Briefly, HuH7 cells and BHK21 cells were infected with DENV-2 (MOI of 0.3) in the presence of 2-fold serial dilutions of test compounds. After incubation at 37 °C for 48 h, viral antigen production was quantified by immune detection using the 4G2 antibody and goat anti-mouse IgG conjugated with horseradish peroxidase as primary and secondary antibodies, respectively. The concentration of the compounds that decreased viral envelope protein production by 50% (EC50) was calculated using nonlinear regression analysis.
Cell viability assay
The cytotoxicities of the test compounds were determined by a Celltiter-Glo Luminescent cell viability assay, according to the manufacturer’s protocol. The BHK21 cells and HuH7 cells preparation and compound addition were performed as This article is protected by copyright. All rights reserved.
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described above in the CFI assay. After 48 h of incubation, the luminescent signals for cells treated with the test compounds were compared to those for cells treated with DMSO to measure the 50% cytotoxic concentration (CC50).
3. Results and discussion
Construction of the pharmacophore model
In our study, pharmacophore features of DENV NS2B-NS3 protease inhibitors were precisely defined based on a comprehensive parameters obtained from the X-ray crystal structure of DENV NS2B-NS3 protease in complex with the tetrapeptide aldehyde Bz-nKRR-H. On the other hand, although there was no crystal structure available for direct observation of the recently reported inhibitors from HTS, the structure diversity of these compounds enlightened us to scrutinize their similarities to extract prospective pharmacophore features (17-25). Accordingly, we launched a process to dock the inhibitors from HTS (Figure 1) into NS2B-NS3protease. The docking method and parameters for each molecule were equally set, leading to the discovery of common features. Then we ferreted out all possible pharmacophore features of both the crystal structure and the hypothetical binding patterns of protease in complex with those non-peptide inhibitors automatically by UNITY. The rational pharmacophore model demanded eliminating unimportant elements and retaining a proper amount of features, so these features were deliberately picked out one by one by visual inspection. Previous studies demonstrated that basic amino acids (Arg or Lys) in P1 and P2 positions were best units which could form ionic interactions with surrounding acidic amino acids (Asp75 and Asp129) (12). A notable cation-π or π-π interaction between inhibitors and Tyr161 also appeared to be crucial as it was preserved in most cases. In addition, the contribution of NS2B in forming the catalytic conformation was taken into account as it was considered to interact with the inhibitors. Specifically, backbone amide groups of Met84 or Ile86 were positioned as constituents of pharmacophore elements. All these features were restricted in spheres This article is protected by copyright. All rights reserved.
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with given radii, and the donor and acceptor of each hydrogen bond were designated in pairs. This model was employed as query in the following preliminary search (Figure 3).
Figure 3: Pharmacophore model. Blue sphere represents positively charged centers, interacting with Asp75 and Asp129 respectively. Yellow sphere represents aromatic moiety, forming π-stacking with Tyr161. Cyan/gray spheres linked via red lines indicate hydrogen bonds. Tetrapeptide aldehyde Bz-nKRR-H is shown as a reference ligand.
Preliminary search
Drug-like property filter described by Lipinski’s rule-of-five was applied to weed out the undesirable structures in a database which was created by combining four commercial diversity libraries (ChemBridge, Enamine, Life Chemicals and Maybridge) and contained up to 5 million compounds. Then we commenced the preliminary search dependent on the established pharmacophore model which was This article is protected by copyright. All rights reserved.
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made up of several features (Figure 3). Yet redundant features may constrain the throughput rate, thus resulting in scarce hits for next screening round. Hence in the hope of an ideal outcome, we exerted a flexible 3D search stipulating that at least one feature matched using UNITY (28, 29). Unlike classical 2D match or 2D similarity search which do not allow for a spatial scenarios, UNITY's flexible 3D search technique takes the single conformation of each structure stored in the database, and generates all feasible conformations. In this way, this method algorithmically limits the scan of all possible conformations to only those which are relevant to the query. After the search, the conformations of all hits were relaxed to reach local energy minima. Overall, the preliminary search finally produced 506 structures. No structure was omitted since this size was suitable for subsequent docking-based virtual screening.
Docking of the filtered compound database
Docking-based virtual screening became feasible in terms of computational time and hardware condition since the magnitude of compound database reduced from millions to thousands or hundreds after the pharmacophore-based filtration. In the first docking round, enzyme was postulated as a rigid body and the appraisal criterion was mainly dependent on docking scores characterized by binding energy. The docking area was circumscribed to a rectangular solid that could exactly accommodate Bz-nKRR-H as identified in the crystal structure. Important water molecules near the center of binding site were preserved since they were likely to make contribution to stabilize the interactions between compound and the enzyme. All rotational bonds of compounds were permitted except that the amide bonds were configured unrotatable because most bioactive molecules contain rigid amide plane. AutoDock was used to carry out the docking tasks. According to the docking scores, 99 candidates were generated with binding energy lower than -9.0 kcal/mol. The numerous conformations of each candidate were clustered and categorized to a group within which RMSD was less than 2 Å. A comparatively higher convergence indicated a more reliable and This article is protected by copyright. All rights reserved.
Accepted Article
2
-9.19
STOCK4S-03877
3
-9.38
STOCK5S-83467
4
-9.23
STOCK6S-06922
5
-9.37
STOCK5S-34068
6
-9.36
T0518-9424
7
-9.14
T5432819
8
-9.05
T5536598
This article is protected by copyright. All rights reserved.
Accepted Article
2
-9.19
STOCK4S-03877
3
-9.38
STOCK5S-83467
4
-9.23
STOCK6S-06922
5
-9.37
STOCK5S-34068
6
-9.36
T0518-9424
7
-9.14
T5432819
8
-9.05
T5536598
This article is protected by copyright. All rights reserved.
Accepted Article
N N O
-9.66
T6808570
10
-9.64
T6928888
11
-9.89
T5594794
12
-9.13
T5227644
13
-9.22
T5659102
14
-10.65
T5341917
9
HN O
NH
HN HN
Biological activity assay
The 14 compounds identified through virtual screening were subjected to biological evaluation. The inhibitory activity against the NS3 protease of DENV 2 was obtained
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using an in vitro assay where the reaction was initiated by adding the fluorophore-tagged substrate Bz-nKRR-AMC to the premix of protease and inhibitors (30). Covalent-coupled AMC does not emit fluorescence (λex at 380 nm and λem at 450 nm) unless it is cleaved by the active protease. Therefore, the activities of inhibitors could be determined in terms of the fluorescent response values. In vitro protease inhibitory assay reported that most of selected compounds exhibited low but measurable inhibitory activity at a concentration of 300 μM except compound 3 and 14, which rendered 73.0% and 85.3% mean inhibition, respectively. The positive control Phe-Ac-KRR-H, which was reported as a good DENV protease inhibitor (IC50 = 6.7±1.1 μM), represented 94.0% inhibition at the same concentration (Figure 4) (31). Thus, these two compounds were considered to moderately inhibit the activity of DENV NS2B-NS3 protease.
Figure 4: Biological assay of NS2B-NS3 protease inhibitor candidates. Blk refers to the blank control test with 3.3% DMSO; pc refers to positive control test with Phe-Ac-KRR-H.
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Previous studies showed that few inhibitors were effective in cell-based viral replication assay, even if they had commendable inhibitory activities in NS2B-NS3 protease inhibition assay. In order to examine whether our compounds possessed cellular inhibitory activities, an in vitro cell-based flavivirus immune detection (CFI) assay was performed. Two different mammalian host cell strains, i.e. BHK21 cell and HuH7 cell, were used in this assay. The results were expressed as EC50 values for antiviral activity (Table 2). Consistent with the in vitro protease inhibition assay, 3 and 14 were found to inhibit virus production confirming that they were able to penetrate through the cell membrane and potentially target the protease. Furthermore compounds 1, 2, 5, 7, 10 and 12 showed cellular activity in either BHK21 or HuH7 cells with selectivity index ranging from 4 to >8. These compounds did not show any in vitro protease inhibition and the activity in just one of the two cell lines tested might suggest off-target activity. Interestingly 14 was found to be active in both BHK21 and HuH7 cells with acceptable selectivity window and EC50 around 5.0 μM, whereas 3 performed slightly better in HuH7 cells than in BHK21 cells. Furthermore, the cell viability assay was also undertaken to determine the cytotoxicities of those compounds. It turned out that the CC50 values of all the molecules were higher than 300 μM, except for 14 in HuH7 strain which still denoted a good therapeutics index (> 10). Hence the results ensured their safety as drugs.
Table 2: The cell-based biological assay results of the selected compounds. BHK21 cells
HuH7 cells
Entry EC50 (μM)
CC50 (μM)
EC50 (μM)
CC50 (μM)
1
68.4 ± 4.0
>300
>100
>300
2
>100
>300
34.4 ± 1.4
>300
3
52.6 ± 2.2
>300
10.9 ± 1.9
>300
4
>100
>300
>100
>300
5
34.4 ± 4.8
>300
>100
>300
6
>100
>300
>100
>300
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7
>100
>300
40.7 ± 0.8
>300
8
>100
>300
>100
>300
9
>100
>300
>100
>300
10
30.8 ± 1.7
>300
>100
>300
11
>100
>300
>100
>300
12
31.7 ± 4.1
>300
>100
>300
13
>100
>300
>100
>300
14
5.0 ± 1.1
>300
5.0 ± 0.2
55.0
Post-docking analysis
Despite a lack of a co-crystal structures of the novel hit 14, it is still intriguing to peruse the presumptive modes of the compound bound to NS2B-NS3 protease and compare it with the known co-crystal structure (PDB code: 3U1I). The hit 14 fulfilled the pockets of the binding site, and the relatively hydrophobic region sandwiched between Pro132 and Val155 was occupied by the benzene ring of 14 (Figure 5). The lone pair on the adjacent nitrogen could accept hydrogen in the hydroxyl group of Tyr161 to form a tolerable interaction. Additionally, the benzene ring generated potent π-π stacking with Tyr161 side chain, in accordance with the pharmacophore feature. Significantly, a water molecule present in the crystal structure that was retained in the pharmacophore search could link the amino group in 14 with Met84 (NS2B) and Gly153,
thus
stabilizing
the
inhibitor-protease
complex.
Roles
of
these
pharmacophores could be corroborated by comparison between 3 and 14. The hydroxyl group of 3 could perform dual function as a hydrogen bond donor and acceptor. Pyrazol-3-one moiety also affected the binding mode through a hydrogen bond between the carbonyl group and Ile86 (NS2B) as well as another special H-π interaction between the pyrazol plane and Val155. It was consistent with our idea that both of them formed formal bonds with NS2B protein.
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Figure 5: Putative binding modes of compounds 14 (A) and 3 (B). Polar hydrogen atoms in inhibitors are indicated, and all hydrogen atoms in protease are hidden. NS2B is colored red, and NS3 is colored dark blue. Significant interactions are denoted as dashed lines.
4. Conclusions
In summary, we carried out a multi-step virtual screening campaign on DENV-NS3 and a candidate (14) was verified to be effective in both in vitro protease inhibition test and cellular infectivity assay. The proposed binding patterns coincided well with the pharmacophore model, reinforcing the validity of structure-based approach. The identification of 14 which passed Lipinski’s rule supports the application of virtual screening for drug discovery and development.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 21202087), the National Basic Research Program of China (973 program, Grant No. 2013CB911104), the Fundamental Research Funds for the Central Universities (Grant No. 65124002), the Tianjin Science and Technology Program (Grant No. 13JCYBJC24300, 13JCQNJC13100), the Specialized Research Fund for
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the Doctoral Program of Higher Education Ministry of Education of China (Grant No. 2120031120049), the "111" Project of the Ministry of Education of China (Project No. B06005) and the Open Fund of State Key Laboratory of Medicinal Chemical Biology (Nankai University). We thank Daljit Singh for technical assistance and the work in SV lab was supported by the National Medical Research Council, Singapore (http://www.nmrc.gov.sg, Grant No. NMRC/1315/2011).
Conflict of interest
There is no conflict of interest involved in this article.
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Received Date : 16-Oct-2014 Revised Date : 15-Dec-2014 Accepted Date : 17-Dec-2014 Article type : Research Article
Structure-guided discovery of a novel non-peptide inhibitor of dengue virus NS2B-NS3 protease Linfeng Li1, 2, Chandrakala Basavannacharya3, Kitti Wing Ki Chan3, Luqing Shang1, 2*, Subhash G. Vasudevan3*, Zheng Yin1, 2* 1
College of Pharmacy & State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, China 2 Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300071, China 3 Duke-NUS Graduate Medical School, Program in Emerging Infectious Diseases, 8 College Road, Singapore 169857 Corresponding author mail id: [email protected]
Abstract
Dengue fever is a fast emerging epidemic-prone viral disease caused by dengue virus (DENV) serotypes 1-4. NS2B-NS3 protease of DENV is a validated target to develop antiviral agents. A major limitation in developing DENV protease inhibitors has been the lack of, or poor cellular activity. In this work, we extracted and refined a pharmacophore model based on X-ray crystal structure and predicted binding patterns, followed by a three-dimensional flexible database filtration. These output molecules were screened according to a docking-based protocol, leading to the discovery of a compound with novel scaffold and good cell-based bioactivity that has potential to be further optimized. The discovery of this novel scaffold by combination of in silico methods suggests that structure-guided drug discovery can lead to the development of potent DENV protease inhibitors.
Keywords: dengue virus, protease inhibitor, pharmacophore, virtual screening, This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an 'Accepted Article', doi: 10.1111/cbdd.12500 This article is protected by copyright. All rights reserved.
Accepted Article
DENV bioassay.
1. Introduction
Dengue fever is a mosquito-borne viral infection which typically causes a mild flu-like symptom and can sporadically develop into life-threatening forms. A recent survey of dengue estimated that the global incidence was approximately 390 million, which put almost half of the world’s population at risk of infection (1). Unfortunately, there are no efficacious dengue vaccines or antiviral drugs available for prevention or treatment. Dengue fever is caused by dengue virus (DENV) serotypes 1-4, which belong to the Flavivirus genera (1-5). The virion contains a positive-sense single-stranded RNA which encodes a polyprotein that is subsequently processed into three structural proteins and seven nonstructural proteins (NS1, 2A, 2B, 3, 4A, 4B and 5) by the action of host and viral proteases (6). NS3 is a multifunctional enzyme and its N-terminal domain, associated with NS2B cofactor, is a trypsin-like serine protease that is essential for DENV replication, rendering it a prime drug target (7, 8). Most protease inhibitors are analogues of endogenous substrate (9, 10). Early in 2005, the tetrapeptide sequence Bz-nKRR was identified from functional profiling exercise as an efficient substrate, thus leading to a number of peptidomimetic inhibitors (11-15). Despite the potency of some peptide mimics, the high polarity of the basic residues had contributed to stability and cell permeability issues that prevented their further development as antivirals. One breakthrough that held promise for development of new antiviral was the determination of the crystal structure of DENV NS2B-NS3 protease and its complexed structure with the tetrapeptide aldehyde Bz-nKRR-H (PDB code: 2FOM and 3U1I, respectively) (7, 16). These structures together with other efforts shed light on the importance of the catalytic triads (His51, Asp75 and Ser135) at the active site, the unusual contribution of NS2B towards stability and formation of the pocket environment (13). These structural insights suggested the plausibility of a rational design approach. However they also This article is protected by copyright. All rights reserved.
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highlighted the challenges with the substrate analogue strategy because of the shallow pocket and the requirement to replace the basic residues. Difficulties in substrate analogue approach shifted researchers’ efforts towards an alternative method -- diverse library screening. Several traditional high throughput screenings (HTS) produced a number of compounds, most of which presented mediocre enzyme-based inhibitory activities but poor inhibition in cellular assays (17-24). In silico docking strategy has proved to be a productive supplement to exert a more reasonable screening effort than the conventional HTS. For instance, compound I was yielded in a virtual screening and showed commendable bioactivity, albeit with a low therapeutic index (CC50/EC50) of 4.0 (25). Several previous studies have obtained various non-peptide protease inhibitors with moderate to potent enzyme-based activities. Although multifaceted issues including permeability, selectivity, druggability and toxicity remains to be tackled, we select and list several recently reported lead compounds (Figure 1) herein and believe that analysis of these molecules could pave the way for development of novel DENV protease inhibitors (18-21, 23, 25).
Figure 1: Recently reported lead compounds. Their cell-based EC50 values are indicated, and ND denotes not determined (18-21, 23, 25).
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In this work we describe the discovery of a novel DENV protease inhibitor from the commercially available library by virtual screening. We carried out a hierarchy virtual screening and biological activity measurement: pharmacophore-based preliminary filtration rigid docking induced-fit docking enzyme-based inhibition bioassay cell-based prevention of infection bioassay. Finally we obtained several compounds that inhibited NS2B-NS3 protease and one hit further demonstrated antiviral activity in cell-based assay with the EC50 of 5 μM (Figure 2).
Figure 2: Schematic of the applied virtual screening cascade.
2. Experimental section
Database and chemical vendors
The library of about 5 million commercially available compounds were provided by following companies: ChemBridge Corporation, 11199 Sorrento Valley Road, Suite 206 San Diego, CA 92121, USA; Enamine Ltd., 23 Alexandra Matrosova Street, 01103 Kyiv, Ukraine; Life Chemicals Inc., 1A Dixie Avenue, Niagara-on-the-Lake
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ON L0S 1J0, Canada; Maybridge, Thermo Fisher Scientific Inc., ENA 23, Zone 1, nr 1350, Janssen Pharmaceuticalaan 3a, 2440 Geel, Belgium. The fourteen hits were purchased from the Topscience Co., Ltd. Level 8 Peninsula International Center, No 238, Jiangchang San Road, Shanghai, 200436.
Pharmacophore-based preliminary search
Four different commercial libraries were merged into an integrated database, and the molecules were converted into 3D structures by Concord (Concord 6.1.3 Manual, SYBYL-X 2.0, Copyright © 2012 Certara, L.P., http://www.certara.com/). Pharmacophore features were defined using UNITY (UNITY Manual, SYBYL-X 2.0). Constraints of all features were assigned as spheres. All hydrogen donor/acceptor atoms spheres were set to 1.3 Å, and their corresponding acceptor/donor sites spheres were set to 1.5 Å, allowing for loose orientations of the hydrogen bonds. The two positive charged centers radii were set to 2.0 Å, for ionic interactions were not so demanding. Furthermore, an aromatic feature with a 1.5 Å radius represented the π-π interaction. The pharmacophore model was used to initiate the UNITY 3D flexible search. Because of the numerous compounds, some options and parameters were optimized to save calculation time. Rings were regarded as rigid, the minimum gradient for tweak was modified to 0.005 kcal/mol, and the searching time cutoff was set to 40 seconds for each structure.
Molecular docking
In this study, molecular docking was applied in three stages. In the first stage, the selected recently-reported molecules were docked into protease to identify common pharmacophore features using MOE (MOE 2013.08 User
Manual,
Copyright
©
2014
Chemical
Computing
Group
Inc.,
http://www.chemcomp.com/). The atomic coordinate file of NS2B-NS3 protease was This article is protected by copyright. All rights reserved.
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obtained from Protein Data Bank using the code 3U1I. To prepare the raw receptor file, positive hydrogens and ionization states were assigned by the MOE Protonate 3D function, inconsequential ions and waters were removed while HOH150, HOH220 were reserved in light of their relatively buried positions and already existed interactions in the co-crystal structures. The docking process adopted an induced-fit protocol. The triangle matcher was employed as the placement method to generate initial poses. For each ligand, the timeout was set to 300 seconds and the maximum number of poses returned was set to 1000. London dG was used to rescore the placement, retaining 30 times. Ensuing refinement in an MMFF94x forcefield was evaluated by GBVI/WSA dG method, retaining 30 times as well. In the first round of virtual screening (the second stage), molecular docking was performed by AutoDock (AutoDock 4.2.5 User Guide, Copyright © 2009 The Scripps Research Institute, http://autodock.scripps.edu/). The pre-docking treatment of protein was basically the same as the first stage, except that Gasteiger partial charges were added to the protein. All the ligand files were converted into the specific type recognized by AutoDock. Referring to the position of ligand in corresponding template, the grid box was defined as a rectangular solid of 46×44×46 with an interval of 0.375 Å and set to rightly contain the whole binding pocket in order to delimit the space of ligand’s movement. Lamarckian Genetic Algorithm (LGA) was applied to execute the docking calculation. The number of GA runs was set to 20 for each molecules, and the maximum number of energy evaluations for each run was set to 250,000 to reduce the computation time. Other GA settings were remained as default. The output conformations of each docking were ranked, clustered and analyzed in AutoDock Tools. The methodology and all docking parameters of the second round of virtual screening (the third stage) were identical to those in the first stage.
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Protease inhibition assay
Peptides were assayed in 384-well microplates in protease buffer (50 mM Tris, pH 8.5, 1 mM Chaps, 20% glycerol) in a final volume of 30 µL as described elsewhere (26). Briefly, protease DENV-2 NS2B40-G4SG4-NS3185 (10 nM) was pre-incubated with test compounds (3.3% DMSO) at the concentration of 300 µM at 30 °C for 15 minutes. The reaction was initiated by the addition of fluorophore-tagged substrate Bz-nKRR-AMC at 20 µM and the incubation was continued at 30 °C for 30 minutes. The reaction was monitored by following the increase in fluorescence (λex = 380 nm, λem = 450 nm) on a Tecan Infinite®M200 microplate reader at room temperature. Blank reaction (min) was with all the components except the protein, and complete reaction (max) was with all the components of the assay. The experiments were performed in triplicate.
Cell-based flavivirus immune detection
A CFI assay was performed as previously described by Wang et al. (27). Briefly, HuH7 cells and BHK21 cells were infected with DENV-2 (MOI of 0.3) in the presence of 2-fold serial dilutions of test compounds. After incubation at 37 °C for 48 h, viral antigen production was quantified by immune detection using the 4G2 antibody and goat anti-mouse IgG conjugated with horseradish peroxidase as primary and secondary antibodies, respectively. The concentration of the compounds that decreased viral envelope protein production by 50% (EC50) was calculated using nonlinear regression analysis.
Cell viability assay
The cytotoxicities of the test compounds were determined by a Celltiter-Glo Luminescent cell viability assay, according to the manufacturer’s protocol. The BHK21 cells and HuH7 cells preparation and compound addition were performed as This article is protected by copyright. All rights reserved.
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described above in the CFI assay. After 48 h of incubation, the luminescent signals for cells treated with the test compounds were compared to those for cells treated with DMSO to measure the 50% cytotoxic concentration (CC50).
3. Results and discussion
Construction of the pharmacophore model
In our study, pharmacophore features of DENV NS2B-NS3 protease inhibitors were precisely defined based on a comprehensive parameters obtained from the X-ray crystal structure of DENV NS2B-NS3 protease in complex with the tetrapeptide aldehyde Bz-nKRR-H. On the other hand, although there was no crystal structure available for direct observation of the recently reported inhibitors from HTS, the structure diversity of these compounds enlightened us to scrutinize their similarities to extract prospective pharmacophore features (17-25). Accordingly, we launched a process to dock the inhibitors from HTS (Figure 1) into NS2B-NS3protease. The docking method and parameters for each molecule were equally set, leading to the discovery of common features. Then we ferreted out all possible pharmacophore features of both the crystal structure and the hypothetical binding patterns of protease in complex with those non-peptide inhibitors automatically by UNITY. The rational pharmacophore model demanded eliminating unimportant elements and retaining a proper amount of features, so these features were deliberately picked out one by one by visual inspection. Previous studies demonstrated that basic amino acids (Arg or Lys) in P1 and P2 positions were best units which could form ionic interactions with surrounding acidic amino acids (Asp75 and Asp129) (12). A notable cation-π or π-π interaction between inhibitors and Tyr161 also appeared to be crucial as it was preserved in most cases. In addition, the contribution of NS2B in forming the catalytic conformation was taken into account as it was considered to interact with the inhibitors. Specifically, backbone amide groups of Met84 or Ile86 were positioned as constituents of pharmacophore elements. All these features were restricted in spheres This article is protected by copyright. All rights reserved.
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with given radii, and the donor and acceptor of each hydrogen bond were designated in pairs. This model was employed as query in the following preliminary search (Figure 3).
Figure 3: Pharmacophore model. Blue sphere represents positively charged centers, interacting with Asp75 and Asp129 respectively. Yellow sphere represents aromatic moiety, forming π-stacking with Tyr161. Cyan/gray spheres linked via red lines indicate hydrogen bonds. Tetrapeptide aldehyde Bz-nKRR-H is shown as a reference ligand.
Preliminary search
Drug-like property filter described by Lipinski’s rule-of-five was applied to weed out the undesirable structures in a database which was created by combining four commercial diversity libraries (ChemBridge, Enamine, Life Chemicals and Maybridge) and contained up to 5 million compounds. Then we commenced the preliminary search dependent on the established pharmacophore model which was This article is protected by copyright. All rights reserved.
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made up of several features (Figure 3). Yet redundant features may constrain the throughput rate, thus resulting in scarce hits for next screening round. Hence in the hope of an ideal outcome, we exerted a flexible 3D search stipulating that at least one feature matched using UNITY (28, 29). Unlike classical 2D match or 2D similarity search which do not allow for a spatial scenarios, UNITY's flexible 3D search technique takes the single conformation of each structure stored in the database, and generates all feasible conformations. In this way, this method algorithmically limits the scan of all possible conformations to only those which are relevant to the query. After the search, the conformations of all hits were relaxed to reach local energy minima. Overall, the preliminary search finally produced 506 structures. No structure was omitted since this size was suitable for subsequent docking-based virtual screening.
Docking of the filtered compound database
Docking-based virtual screening became feasible in terms of computational time and hardware condition since the magnitude of compound database reduced from millions to thousands or hundreds after the pharmacophore-based filtration. In the first docking round, enzyme was postulated as a rigid body and the appraisal criterion was mainly dependent on docking scores characterized by binding energy. The docking area was circumscribed to a rectangular solid that could exactly accommodate Bz-nKRR-H as identified in the crystal structure. Important water molecules near the center of binding site were preserved since they were likely to make contribution to stabilize the interactions between compound and the enzyme. All rotational bonds of compounds were permitted except that the amide bonds were configured unrotatable because most bioactive molecules contain rigid amide plane. AutoDock was used to carry out the docking tasks. According to the docking scores, 99 candidates were generated with binding energy lower than -9.0 kcal/mol. The numerous conformations of each candidate were clustered and categorized to a group within which RMSD was less than 2 Å. A comparatively higher convergence indicated a more reliable and This article is protected by copyright. All rights reserved.
Accepted Article
2
-9.19
STOCK4S-03877
3
-9.38
STOCK5S-83467
4
-9.23
STOCK6S-06922
5
-9.37
STOCK5S-34068
6
-9.36
T0518-9424
7
-9.14
T5432819
8
-9.05
T5536598
This article is protected by copyright. All rights reserved.
Accepted Article
2
-9.19
STOCK4S-03877
3
-9.38
STOCK5S-83467
4
-9.23
STOCK6S-06922
5
-9.37
STOCK5S-34068
6
-9.36
T0518-9424
7
-9.14
T5432819
8
-9.05
T5536598
This article is protected by copyright. All rights reserved.
Accepted Article
N N O
-9.66
T6808570
10
-9.64
T6928888
11
-9.89
T5594794
12
-9.13
T5227644
13
-9.22
T5659102
14
-10.65
T5341917
9
HN O
NH
HN HN
Biological activity assay
The 14 compounds identified through virtual screening were subjected to biological evaluation. The inhibitory activity against the NS3 protease of DENV 2 was obtained
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Accepted Article
using an in vitro assay where the reaction was initiated by adding the fluorophore-tagged substrate Bz-nKRR-AMC to the premix of protease and inhibitors (30). Covalent-coupled AMC does not emit fluorescence (λex at 380 nm and λem at 450 nm) unless it is cleaved by the active protease. Therefore, the activities of inhibitors could be determined in terms of the fluorescent response values. In vitro protease inhibitory assay reported that most of selected compounds exhibited low but measurable inhibitory activity at a concentration of 300 μM except compound 3 and 14, which rendered 73.0% and 85.3% mean inhibition, respectively. The positive control Phe-Ac-KRR-H, which was reported as a good DENV protease inhibitor (IC50 = 6.7±1.1 μM), represented 94.0% inhibition at the same concentration (Figure 4) (31). Thus, these two compounds were considered to moderately inhibit the activity of DENV NS2B-NS3 protease.
Figure 4: Biological assay of NS2B-NS3 protease inhibitor candidates. Blk refers to the blank control test with 3.3% DMSO; pc refers to positive control test with Phe-Ac-KRR-H.
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Previous studies showed that few inhibitors were effective in cell-based viral replication assay, even if they had commendable inhibitory activities in NS2B-NS3 protease inhibition assay. In order to examine whether our compounds possessed cellular inhibitory activities, an in vitro cell-based flavivirus immune detection (CFI) assay was performed. Two different mammalian host cell strains, i.e. BHK21 cell and HuH7 cell, were used in this assay. The results were expressed as EC50 values for antiviral activity (Table 2). Consistent with the in vitro protease inhibition assay, 3 and 14 were found to inhibit virus production confirming that they were able to penetrate through the cell membrane and potentially target the protease. Furthermore compounds 1, 2, 5, 7, 10 and 12 showed cellular activity in either BHK21 or HuH7 cells with selectivity index ranging from 4 to >8. These compounds did not show any in vitro protease inhibition and the activity in just one of the two cell lines tested might suggest off-target activity. Interestingly 14 was found to be active in both BHK21 and HuH7 cells with acceptable selectivity window and EC50 around 5.0 μM, whereas 3 performed slightly better in HuH7 cells than in BHK21 cells. Furthermore, the cell viability assay was also undertaken to determine the cytotoxicities of those compounds. It turned out that the CC50 values of all the molecules were higher than 300 μM, except for 14 in HuH7 strain which still denoted a good therapeutics index (> 10). Hence the results ensured their safety as drugs.
Table 2: The cell-based biological assay results of the selected compounds. BHK21 cells
HuH7 cells
Entry EC50 (μM)
CC50 (μM)
EC50 (μM)
CC50 (μM)
1
68.4 ± 4.0
>300
>100
>300
2
>100
>300
34.4 ± 1.4
>300
3
52.6 ± 2.2
>300
10.9 ± 1.9
>300
4
>100
>300
>100
>300
5
34.4 ± 4.8
>300
>100
>300
6
>100
>300
>100
>300
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Accepted Article
7
>100
>300
40.7 ± 0.8
>300
8
>100
>300
>100
>300
9
>100
>300
>100
>300
10
30.8 ± 1.7
>300
>100
>300
11
>100
>300
>100
>300
12
31.7 ± 4.1
>300
>100
>300
13
>100
>300
>100
>300
14
5.0 ± 1.1
>300
5.0 ± 0.2
55.0
Post-docking analysis
Despite a lack of a co-crystal structures of the novel hit 14, it is still intriguing to peruse the presumptive modes of the compound bound to NS2B-NS3 protease and compare it with the known co-crystal structure (PDB code: 3U1I). The hit 14 fulfilled the pockets of the binding site, and the relatively hydrophobic region sandwiched between Pro132 and Val155 was occupied by the benzene ring of 14 (Figure 5). The lone pair on the adjacent nitrogen could accept hydrogen in the hydroxyl group of Tyr161 to form a tolerable interaction. Additionally, the benzene ring generated potent π-π stacking with Tyr161 side chain, in accordance with the pharmacophore feature. Significantly, a water molecule present in the crystal structure that was retained in the pharmacophore search could link the amino group in 14 with Met84 (NS2B) and Gly153,
thus
stabilizing
the
inhibitor-protease
complex.
Roles
of
these
pharmacophores could be corroborated by comparison between 3 and 14. The hydroxyl group of 3 could perform dual function as a hydrogen bond donor and acceptor. Pyrazol-3-one moiety also affected the binding mode through a hydrogen bond between the carbonyl group and Ile86 (NS2B) as well as another special H-π interaction between the pyrazol plane and Val155. It was consistent with our idea that both of them formed formal bonds with NS2B protein.
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Figure 5: Putative binding modes of compounds 14 (A) and 3 (B). Polar hydrogen atoms in inhibitors are indicated, and all hydrogen atoms in protease are hidden. NS2B is colored red, and NS3 is colored dark blue. Significant interactions are denoted as dashed lines.
4. Conclusions
In summary, we carried out a multi-step virtual screening campaign on DENV-NS3 and a candidate (14) was verified to be effective in both in vitro protease inhibition test and cellular infectivity assay. The proposed binding patterns coincided well with the pharmacophore model, reinforcing the validity of structure-based approach. The identification of 14 which passed Lipinski’s rule supports the application of virtual screening for drug discovery and development.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 21202087), the National Basic Research Program of China (973 program, Grant No. 2013CB911104), the Fundamental Research Funds for the Central Universities (Grant No. 65124002), the Tianjin Science and Technology Program (Grant No. 13JCYBJC24300, 13JCQNJC13100), the Specialized Research Fund for
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the Doctoral Program of Higher Education Ministry of Education of China (Grant No. 2120031120049), the "111" Project of the Ministry of Education of China (Project No. B06005) and the Open Fund of State Key Laboratory of Medicinal Chemical Biology (Nankai University). We thank Daljit Singh for technical assistance and the work in SV lab was supported by the National Medical Research Council, Singapore (http://www.nmrc.gov.sg, Grant No. NMRC/1315/2011).
Conflict of interest
There is no conflict of interest involved in this article.
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