Computational insight into dengue virus NS2B-NS3 protease inhibition: A combined ligand- and structure-based approach

Accepted Manuscript Title: Computational Insight into Dengue Virus NS2B-NS3 Protease Inhibition: A Combined Ligand- and Structure-Based Approach Authors: Junning Chen, Hailun Jiang, Fangfei Li, Baichun Hu, Ying Wang, MingXing Wang, Jian Wang, Maosheng Cheng PII: DOI: Reference:

S1476-9271(18)30500-0 https://doi.org/10.1016/j.compbiolchem.2018.09.010 CBAC 6921

To appear in:

Computational Biology and Chemistry

Received date: Revised date: Accepted date:

12-7-2018 10-9-2018 12-9-2018

Please cite this article as: Chen J, Jiang H, Li F, Hu B, Wang Y, Wang M, Wang J, Cheng M, Computational Insight into Dengue Virus NS2B-NS3 Protease Inhibition: A Combined Ligand- and Structure-Based Approach, Computational Biology and Chemistry (2018), https://doi.org/10.1016/j.compbiolchem.2018.09.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Computational Insight into Dengue Virus NS2B-NS3 Protease Inhibition: A Combined Ligand- and Structure-Based Approach Junning Chena, Hailun Jianga, Fangfei Lia, Baichun Hua, Ying Wanga, MingXing

Key Laboratory of Structure-Based Drugs Design & Discovery, Ministry of Education, Shenyang Pharmaceutical

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Wanga, Jian Wanga,*, Maosheng Cheng a

University, Shenyang 110016, China

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*Corresponding author. Prof. Wang: Tel.: +86 24 23986419; fax: +86 24 23995043; E-mail address:

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[email protected]

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Graphical abstract

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Highlights 

The relationship between the chemical features of inhibitors and their biological activities was investigated with a combined ligand- and structure-based approach firstly. Molecular docking was employed to explore the specific allosteric site for non-

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peptidic inhibitors to bind, which was proved to be located behind the catalytic triad.

This research provided an accurate binding model for the discovery and

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optimization of NS2B-NS3 protease inhibitors.

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ABSTRACT The NS2B-NS3 protease is essential for the replication process of Dengue Virus, which make it an attractive target for anti-virus drugs. Since a considerable number of NS2B-NS3 protease inhibitors have been reported so far, it is significant for the

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discovery of more effective antivirus compounds with the essential structure-activity relationship extracted from known inhibitors. In this perspective, the relationship

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between the chemical features of inhibitors and their biological activities was investigated with a combined ligand- and structure-based approach. Furthermore, 3D pharmacophore models were generated with the best selected, which consisted of five

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chemical features: one ring aromatic group, one hydrophobic group, one hydrogen bond

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donor and two hydrogen bond acceptors (RHDAA). Subsequently, molecular docking

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was employed to explore the specific allosteric site for non-peptidic inhibitors to bind,

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which was proved to be located behind the catalytic triad. Taken the results of both molecular docking and pharmacophore modeling into consideration, a model of

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receptor-ligand interaction was obtained with four essential chemical features including

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aromatic rings and hydrogen bonds. This research provided an accurate binding model

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for the discovery and optimization of NS2B-NS3 protease inhibitors.

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Keywords: Dengue; NS2B-NS3 protease; Molecular docking; Pharmacophore models.

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1. Introduction The Dengue virus (DENV) belongs to the family of flaviviridae, which is mainly distributed in tropical and subtropical areas and transmitted by mosquitoes (Aedes aegypti, mainly) (Rigau-Pérez et al., 1998). 2.5 billion people estimated are at a risk of

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infection all over the world, while there are over 100 million people suffering from DENV disease annually, including dengue hemorrhagic fever (DHF) and dengue shock

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syndrome (DSS) (Leong et al., 2007). Many factors have contributed to the spread of virus such as population growth, climate change, intensified urbanization and more frequent

international travel, leading to the trend increasing in the southern United States and

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southern China. DENV diseases have resulted in a huge public health burden due to

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their intense transmission, frequent outbreaks and the cofigurative circulation within

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the different serotypes (Bhatt et al., 2013). Although intensive efforts have been

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conducted to discover novel antivirals against DENV, there is no effective inhibitor to treat dengue infections, except that a vaccine has been licensed at present (Pitisuttithum

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and Bouckenooghe, 2016; Tatem et al., 2006). Therefore, a new strategy is urgently

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needed to the discovery of potential anti-viruses against the dengue virus to detect. Moreover, it is significant to find vital proteases that are required for the

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production of mature viruses and indispensable for their infectivity maintaining. The complex NS2B-NS3 protease is necessary for the processing at the junctions of

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NS2A/NS2B, NS2B/NS3, NS3/NS4A and NS4B/NS5, NS3, 2A, and NS4A in dengue virus, therefore it is selected as a primary target for drug design against dengue infection (Pugachev et al., 2003). In NS2B-NS3 protease, the NS3 moiety is a nonstructural protein containing the C-terminal domain and the N-terminal region (Noble et al., 2012). 4

The first C-terminal domain of NS3 protease exhibits RNA helicase, nucleoside and RNA triphosphatase activities and is associated with both of viral RNA replication and virus particle formation. The N-terminal region of NS3 wraps a peptidic cofactor NS2B, participate in the formation of the proteases that cleave viral polyprotein (Zuo et al., 2009). The NS2B cofactor is necessary for the recognition of substrates and stability

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maintenance of the complex (Noble et al., 2012; Zuo et al., 2009). The active site of NS2B-NS3 protease possesses the catalytic triad (His51, Asp75, Ser135), and

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tetrapeptide Bz-nKRR-H has been reported to bind at this position covalently (Noble et al., 2012). Whereas an analysis of the NS2B-NS3 protease surface revealed that the

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largest cavity was not the active site, while a pocket on the opposite face of the molecule formed when NS2B is wrapped around the NS3 (Noble et al., 2012), further researches

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suggested that some molecules don’t interact with residues of the catalytic triad (His51,

(Cabarcas-Montalvo et al., 2016).

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Asp75, Ser135) directly, although those involved in the interactions are close to them

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Currently, the reported inhibitors can approximately be classified as peptidic, peptide-derived and non-peptidic compounds, the last of which include benzimidazole

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derivatives, quinolone derivatives, analogue of bis-coumarine, and others. However, a

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lot of these compounds exhibit either weak activity or low compatibility due to the charged, shallow nature of the binding site and neighboring pockets (Lim et al., 2013).

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Thus, a systematic insight into NS2B-NS3 protease is performed in this paper. Subsequently, with the purpose of obtaining detailed description of NS2B-NS3

protease and its small-molecule inhibitors, bioinformatics analysis, pharmacophore modeling and molecular docking have been employed. Non-peptidic inhibitors of 5

dengue virus reported in recent years were firstly researched thoroughly. According to the common features of the inhibitors towards NS2B-NS3 pro, a common feature pharmacophore model was constructed. Herein interactions of small molecule inhibitors with the new binding pocket of NS2B-NS3 protease were systematically analyzed. The major and minor interaction points in the new binding pocket were

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demonstrated by the combination of ligand-based and structure-based approaches, providing a new strategy for designing novel and specific dengue virus

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chemotherapeutic agents.

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2. Materials and Method All calculations were carried out on a Dell PowerEdge R910 workstation. The

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Discovery Studio 3.0 (DS 3.0) software package was employed to generate pharmacophore (Biovia, D. S., 2017). Chemical structures were sketched and optimized

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with SYBYL 6.9.1(Tripos Inc) (Morris, G., 2002). Docking studies were performed

with AutoDock Tools 1.5.4 (ADT) (Morris et al., 2009; Trott & Olson, 2009). Receptorligand interaction analysis was conducted with Discovery Studio 2017 Visuliazer, a

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free graphic inter face. The crystal structure of Dengue virus NS2B-NS3 protease was

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2.1. Preparation of Proteins and Ligands

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obtained from the RCSB Protein Data Bank (https://www.rcsb.org/).

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The crystallized structures of NS2B-NS3 protease were downloaded from the RCSB Protein Date Bank (PBD codes: 2FOM (Erbel et al., 2006), 3U1I (Noble et al.,

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2012)) and DS 3.0 was applied to the preparation of protein, subsequently. Hydrogen atoms, missing amino acid residues and loop segments close the active sites were added,

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followed by ensuring that multiple bonds orders were defined correctly. Moreover, all

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of the crystallographic waters were removed from the PDB files (Liu et al., 2014; Wu et al., 2015).

The structures of ligands were extracted from the articles about dengue viral

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inhibitors, then the consistency of bonds and atom types were checked. These smallmolecule inhibitors were constructed and optimized with SYBYL 6.9.1(Tripos Inc), with all the atom types and bond order were checked. A proper protonation state for aliphatic amines was adjusted at the default pH range from 5.0 to 9.0, while generating 7

conformations of low energy ring. The geometry was optimized at the CHARMM force field using a steepest-descent algorithm, with convergence gradient values of 0.1 kcal/mol, 0.01 kcal/mol, and 0.001 kcal/mol, respectively (Li et al., 2015). Afterwards, optimized structures were imported into DS 3.0. 2.2. Generation of Common feature pharmacophore Model

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The common feature pharmacophore model was generated by DS 3.0 software

package. Training set was built in 2D/3D Visualizer within Catalyst and minimized to

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the closest local minimum based on the molecular mechanics CHARMM force field,

and the Poling algorithm was implemented to produce a maximum of 255 dissimilar

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conformations with an energy threshold of 20 kcal/mol above the calculated energy

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minimum for all compounds in the database. These diverse conformations were

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generated by conformation generation protocol using the best conformation generation

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option as available in DS 3.0.

Finally, 8 representative compounds of the training set were submitted to the

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common feature pharmacophore generation protocol HipHop module in DS 3.0. In comparing the chemical features shared between the collection of conformational

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models and the molecules in the training set, 10 hypotheses of pharmacophore were

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generated, and re-presented with three-dimension. 2.3. Validation of the Pharmacophore Model A decoy set containing active and inactive compounds was mapped onto the

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pharmacophore models by Ligand Profiler in DS 3.0. The most relevant pharmacophore model was indicated by the Heatmap generated by the results of mapping, where the active inhibitors could be differentiated from the inactive ones according to fit values.

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Finally, the selected pharmacophore hypothesis was utilized to match some chemical structures correlating to the structure-activity relationship (SAR) (Zhang et al., 2017). 2.4. Molecular Docking The receptor-ligand interactions can be fully illustrated by computer-aided docking study, which was generally applied to explore the binding site, binding affinity,

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and their correlation with the activities of native ligands cocrystallized with NS2B/NS3 pro. The related parameters of the software used in molecular modeling were listed

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below:

The DS 3.0 was used to prepare the crystal structures of NS2B/NS3 protease,

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generate common feature pharmacophore model, validate Pharmacophore model, and

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observe the three-dimensional structure of NS2B/NS3 protease. AutoDock and

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AutoDock Vina, implemented a Lamarckian Genetic Algorithm (LGA), was employed

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for molecular docking studies (Charlier et al., 2006). AutoDock Tools 1.5.4 (ADT) was used to generate a grid box with the input PDBQT files of protein, the grid box consisted

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of 80 Å× 80 Å× 80 Å points around the active site, with an available grid spacing of 0.375 Å, with other parameters were set as default. All the docking conformations were

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clustered together ground on RMSD in which differences were less than 2.0 Å, and the one with the most favorable free energy or the highest percentage frequency was

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selected as representative. Discovery Studio 2017 Visuliazer, a free software for viewing receptor-ligand interaction, was used to analyze the interaction between target

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and ligand.

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3. Results and Discussion 3.1. Pharmacophore model Due to pharmacophore model requirements, the molecules used for common feature pharmacophore generation should have a large variation in chemical

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characteristics and interact with the target protein through a similar binding mode at the same pocket. The previous researches suggested that the inhibitors had satisfactory

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consequence in both of the binding mode and the chemical structure diversity.

According to the reported inhibitors of Dengue NS2B-NS3 pro, eight typical molecules were selected as a training set (Fig. 1) (Cabarcas-Montalvo et al., 2016; Deng et al.,

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2012; Knehans et al., 2011; Raut et al., 2015; Timiri et al., 2015). Meanwhile, activity

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of the selected compounds was listed in Table1. The HipHop module of DS 3.0

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software was then used to generate a series of 3D feature-based alignments of the

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preferred diversified without considering the activity quantitatively. The better top 10 pharmacophore hypotheses including five features had scores

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between 63.098 and 65.288 kcal/mol (Table 2), which could be divided into four groups

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according to the pharmacophore features: RHHHAA (01, 02, 03, 04), RRHAA (06, 07, 09, 10), RHHDA (05) and RHDAA (08). In these four groups, the hypotheses were

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distinguished by the direction of hydrogen bond vectors, or the location of features, or

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both.

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3.2. Pharmacophore validation Before selecting the best pharmacophore modeling, we collected a total of 55 Dengue virus inhibitors from different literatures, (Cabarcas-Montalvo et al., 2016; Deng et al., 2012; Liu et al., 2014; Nitsche, Holloway, Schirmeister, & Klein, 2014; Nitsche, Steuer, & Klein, 2011; Viswanathan, Tomlinson, Fonner, Mock, & Watowich,

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2014) from which, 44 known active inhibitors and 11 inactive inhibitors were selected

to form a test set (structure see supporting information ), which was based on a wide

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coverage of activity range (the IC50 values of the test set compounds: ranging from 1.1 μM to NA)

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The compounds of test set were mapped onto all the ten hypotheses, the results of

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which were shown with Heatmap (Fig. 2b). Heatmaps are an intuitional visualization

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method for analyzing the distribution of computational data by displaying the fitted

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values as colored bars. Since the fitted values are presented in various colors: blue and black blocks represent low-fit values, green blocks represent moderate-fit values, white

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and orange blocks represent high-fit values. The result of Heatmaps suggested that the

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hypothesis 08 (RHDAA) was the most relevant one among ten pharmacophore hypotheses (Fig. 2a). Therefore, the hypothesis 08 was selected as the excellent

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pharmacophore for describing the relationship between the activity of inhibitors and their structural features. For further describe SAR accurately, six representative molecules were picked up

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among three types of NS2B-NS3 protease inhibitors and mapped into the selected pharmacophore. As shown in the Fig. 3, A1 and A2 were involved in cyano group, carboxylic acid group, and amide group. R was defined by thiazolyl and phenyl. D was involved in Hydroxyl and amino group. H consisted of aromatic group. However, 11

compound 2, 3 and 4, the guanidyl derivatives and phthalimide derivatives, were smaller in size than the other inhibitors and could not match all features, which suggested that the chemical features of A2, R, and H were essential for all the representative inhibitors in 08 pharmacophore hypothesis. 3.3. Characterization of the Binding Site of Dengue NS2B-NS3 Protease

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Dengue virus can be divided into five serotypes (DENV 1-5) according to the nature of the antigen and the sequence similarity, and all serotypes can cause dengue

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fever, four serotypes (DENV l-4) among which are the main pathogen causing dengue fever (Normile, 2013). As for the ability to respond to dengue infections, as reflected

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by the prevalence of DHF and DSS, is generally considered to be the most pronounced

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of serotype 2, followed by serotypes 1 and 3, while serotype 4 is barely associated with

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severe disease (Kyle & Harris, 2008).

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With dengue protease becoming an extremely attractive target for the medicinal chemistry, there are a number of X-ray crystallographic structures have been resolved

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in past years. The target characteristics of NS2B-NS3 protease were fully elucidated by the solved crystal structure of DENV 2 (PDB code 2FOM) and DENV 3 (PDB code

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3U1I). The NS2B-NS3 protease consists of NS3 domains and its cofactor NS2B (Figure 4). The 184-residue NS3pro is a chymotrypsin-like serine protease with a serine

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protease catalytic triad (His51-Asp75-Ser135), it contains an N-terminal serine protease and a C-terminal RNA helicase. The NS3 C-terminal domain has RNA helicase,

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nucleoside and RNA triphosphatase activities, which is involved in viral RNA replication and viral particle formation (Luo et al., 2015). The N-terminal domain of NS3 cleaves the viral polyprotein both in cis and in trans (Chambers, 1990). In order to function as an active enzyme, NS3 protease requires NS2B cofactor ("Both 12

Nonstructural Proteins NS2B and NS3 Are Required for the Proteolytic Processing of Dengue Virus Nonstructural Proteins,"), the NS2B cofactor, made up of approximately 40-amino-acids, is sometimes denoted as “closed” or “open”, with the “closed” conformation in which NS2B-NS3 protease bound to ligand or substrate, while the NS2B region forms a β hairpin that wraps around the NS3 core (Fig. 4b). In contrast,

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without any substrate, a disordered flexible NS2B domain is usually referred as “open” and the NS2B cofactor is halfway wrapped around the NS3 protease domain, and

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merely a small segment is folded in the helical conformation (Fig. 4a) (Aleshin et al., 2007; Erbel et al., 2006; Robin et al., 2009). Since the 2FOM representative structure

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(PDB code: 2FOM) of the “open” conformation of DENV-2 NS2B-NS3 protease first

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released in 2006, a large number of inhibitors against this target have been discovered,

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which provided a valuable basis for our research on ligand-based drugs (Behnam et al., 2016). Furthermore, the cocrystal structure of DENV-3 NS2B-NS3 protease bound to

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a tetrapeptide inhibitor, a representative of the “closed” conformation of NS2B, was

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reported for the first time in 2011, it described the interaction of the tetrapeptide and the NS2B-NS3 protease (Noble et al., 2012). A detailed analysis of the binding mode

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of interactions between tetrapeptide inhibitors and its active site reveals more information (Fig. 5). The P1 side chain forms a charge-charge interaction with Asp129

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of NS3, forming a hydrogen bond with the carbonyl on the backbone of Phe130 of NS3, corresponding to the S1 site of the active cavity. The P2 and P3 side chains interact with

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Met84 and Gly82 on the backbone carbonyl of NS2B, respectively, forming a single backbone interaction with Gly151 and Asn152 of NS3, corresponding to the active cavity S2 and S3. However, it is worth noting that although these interactions stabilize the β hairpin of NS2B in a “closed” conformation, none direct charge-charge 13

interactions with acidic side chains of NS2B between P2 and P3 was found (Fig. 6a). Furthermore, an observation of the structure surface revealed that the large cavity behind the polypeptide binding site may be a potential binding site for antiviral agents derived from NS2B (Val78 and Met84) and NS3 (73 to 74, 88, 89, 118, 120, 122 to 124, 147, 152) (Fig. 6b) (Chappell et al., 2008).

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In our study, after superimposing the sequences of 2FOM and 3U1I structures, we found that the NS3 pro is highly conserved and the protease sequences were ∼64.1%

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identical, and their active-site residues had ∼90% sequence identity especially (Fig. 4c-

d). The docking results of active inhibitors reported with NS2B-NS3 protease suggest

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that the peptide binding sites, binding with classical catalytic triad covalently, are not

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suitable for non-peptide inhibitors bound non-covalently. Analysis of the target surface

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further confirmed the fact that the peptide binding sites are shallower, while deeper

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pockets behind it became the focus of our study of small-molecule inhibitors. 3.4. Inhibitor binding analysis

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The ligand-based pharmacophore model only reflects the SAR of the active inhibitors without considering the spatial structure of the inhibitor binding sites. To

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further understand the structural basis for the binding affinities of the inhibitors for

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dengue viral NS2B-NS3 protease, we carefully inspected the binding poses of six represented inhibitors (compounds 1, 2, 3, 4, 7 and 8) with Autodock 4.0 and Autodock Vina 1.0 programs (Fig. 7). Meanwhile, SAR data and the sufficient analysis of the

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active site facilitated us to identify their corresponding binding modes. From our docking approach, a possible binding mode was fleetly identified for 1

binding with the dengue viral NS2B-NS3 protease. The inhibitor adopted an extended conformation that fitted perfectly into all the binding pockets of the active site. Key 14

interactions involved a firm hydrogen bond between Asn152 and the hydrogens of benziminazole. Meanwhile, two additional hydrogen bonds formed by Val155, Asn119 and the carboxyl group were observed. Finally, the hydrophobic side of the benziminazole ring directly attached to it interacted with Lys74, Leu76, Ala164 and Trp83. For compound 2, the isoindoline-1, 3-dione segment had hydrophobic

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interactions with Thr118, Ile165, Lys74, and Leu85. The phenethyl ring was projected onto the hydrophobic region containing hydrophobic residues, such as Val155 and

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Val154. Meanwhile, the nitrogen and oxygen on sulphuric acid amide formed hydrogen bond interactions with Asn152 and Asn167.

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Compounds 3 and 4 are close analogues of a small-molecule recently published

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by Tim Knehans and Andreas Schuller et al, which were shown to inhibit dengue viral

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NS2B-NS3 protease (IC50 = 7.7 µM and 37.9 µM, respectively). They were discovered

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with in silico drug design of dengue virus NS2B-NS3 protease inhibitors by fragmentbased drug designs (FBDD). Key residues of Lys73, Asn167, Leu85, Glu86, and Glu88

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participate in hydrogen bond with protonated guanidine groups. The two hydrophobic phenyl rings interact with Ala164 Trp83, Leu76 and Lys74. In addition, van der Waal

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interactions between the carboxyl esters of two compounds and Trp83, Gly148, Ile165, Val154, Leu149, Val147 play an important role in binding affinity. Compounds 7 and

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8 (IC50=11.8 µM and IC50=61.5µM, respectively) were reported with good activity for dengue viral NS2B-NS3 pro. Observing the conformation of these molecules in the

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binding site of the dengue protease, carbonyl and phenolic groups of ligand form hydrogen bond with the key amino acid such as Lys74, Ala166, and Ile165. Besides Trp69, Leu76, Thr120, Ile123, Val154 and Ala164, most of the interactions were hydrophobic and a few aromatic interactions. 15

In the docking study of small-molecules within the active sites, we found differences between the binding sites of small-molecule inhibitors and that of polypeptide inhibitors, while most small-molecule inhibitors have no direct effect on the previously proposed specific allosteric catalytic triad (His51, Asp75, and Ser135). This binding pocket locates behind the catalytic triad and is mainly formed by the amino

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acids Val155, Asn119, Thr120, Asn167, Ile165, Glu88, and Gly87 on one side and

Lue85, Leu149, Asn152, Lys74 and Lys73 on the other (Fig. 8). Compared to the

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aldehyde inhibitor Bz-nKKR-H binding pocket (PDB code 3U1I), these nonpolypeptide binding pockets are deeper and more suitable for small-molecule inhibitors

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to interact with DENV protease non-covalently.

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3.5. Comparison of the Docking Results and the Pharmacophore Model

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In an attempt to evaluate the quality of the pharmacophore model by molecular

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docking results, the small-molecular inhibitors in their “bioactive” conformation (bound to Dengue Viral NS2B-NS3 protease) were superimposed to their 3D mapping

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onto the pharmacophore hypothesis. The observed results confirmed that the proposed pharmacophore model could be adapted for the binding cavity as it is well integrated

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with the deeper pockets.

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Analysing the molecular docking results, the ligand-based pharmacophore features corresponded to highly conserved interactions with major residues in the catalytic site of NS2B-NS3 protease. For instance, one of the hydrogen bond acceptors

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(A1), mapped by groups such as carbonyl, phenolic hydroxyl and so on, lies in the upper part of the cleft and interacts with Lys74 through hydrogen bonds. While the other (A2) is located at the middle part of the binding site surrounded by Ile165, Ala166 and Asn167.The first aromatic ring (R1) represented the interaction of hydrophobic 16

interaction with Leu76, Val154 and Ala164, while the second aromatic ring (R2) stood for the hydrophobic interaction formed by ligand-based pharmacophore generation with Trp83, Gly148, Leu149, and Leu85. In addition, comparing the ligand-based pharmacophore and docking results, we can concluded that the pharmacophore of the

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hydrogen bond donor was very weak in the active site and could be ignored. To refine the pharmacophore model, both dengue NS2B-NS3 protease structural

information and docking results were taken into consideration. Subsequently, the

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docked conformations of training set compounds were imported into active sites and 10

new pharmacophore hypotheses were produced with the same parameters as described.

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The pharmacophore hypotheses mainly consisted of four or three features (RRAA,

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RDA or RRD) with scores ranging from 41.915 to 51.221. At first glance, it should be

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noticed that all the hypotheses consist of one hydrogen bond acceptor or donor. Detailed

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analysis revealed that the first hypothesis (RRAA) gives the best correlation according

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to the results of mapping and docking. (Fig. 9). In addition, according to the docked complexes, inhibitors were found to bind

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outside the catalytic triad. Some points could be proposed: (1) The new active site presented in a T-shape overall contains three sites A, B, and C, sites A and C among

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which contribute more (2) Asn119, Thr120, and Val155 of A site mainly form hydrogen bonds with hydroxyl groups, carbonyl groups, and thiol groups (3) Site B is narrow and

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it is suitable for the combination of small structural fragments, where Lys74, Asn167, Leu76 and Ala164 form a hydrophobic interaction with the aromatic ring. (4) Site C is a larger cavity, containing hydrogen bond acceptor and donor features of pharmacophore, Asn152, Asn167 of which form hydrogen bonds with sulphonamide 17

groups, cyano, thiol, etc, While hydrophobic groups of Leu85, Glu88, Gly87, Trp83 and participated hydrophobic interactions. In conclusion, the structure-based common feature pharmacophore model perfectly met the chemical features needed for A, B, and C binding site (Fig. 10).

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4. Conclusions In this perspective, with an aim of understanding the binding modes between

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Dengue NS2B-NS3 protease and its disparate small-molecular inhibitors, a ligandbased pharmacophore model was constructed, consisting of five chemical features: one

ring aromatic feature, one hydrophobic group, one hydrogen bond donor and two

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hydrogen bond acceptors (RHDAA). Furthermore, the available SAR information of

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inhibitor was obtained by six representative non-inhibitors selected from the training

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set mapping into the pharmacophore model. In order to further refining the

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pharmacophore model, the protein sequence superposition and docking study for

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NS2B-NS3 protease were carried out, the NS3 protease was highly conserved and the protease sequences were ∼64.1% identical, especially their active site residues had ∼90%

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sequence identity, these provided the possibility to develop broad-spectrum antiviral inhibitors. Meanwhile, a deeper binding pocket located behind the catalytic triad was

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found, which contained three parts and mainly formed by the amino acids Val155, Asn119, Thr120, Asn167, Ile165, Glu88, and Gly87 on one side and Lue85, Leu149,

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Asn152, Lys74 and Lys73 on the other. Merging the docking results into the new pharmacophore, the new hypothesis (RRAA) showed the high correlation between the results of inhibitors mapping and the molecular docking.

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In general, comparing the docking results and the pharmacophore model, features of aromatic ring features and hydrogen bond acceptor were crucial while the hydrogen bond donor can be ignored. The characteristics of the NS2B-NS3 protease deeper binding pocket revealed that two aromatic rings and hydrogen bond were essential anchoring points, and hydrogen bonding was important for site A, while hydrophobic

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and aromatic features were essential for both of the site B and site C. This study is a basic step in the characterization of human NS2B-NS3 protease and its interaction with

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ligands, which provides a meaningful model for NS2B-NS3 protease lead compounds discovery and optimization.

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Conflict of interest

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The authors declare that there are no conflicts of interest regarding the publication

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of this article.

Acknowledgements

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The work was financially supported by the National Natural Science Foundation

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of China (Grant No. 81628012), the Fund for long-term training of young teachers in Shenyang Pharmaceutical University (ZQN2015002), the National Natural Science Foundation of Liaoning province (Grant No. 20170540854) and Training Program

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Foundation for the Distinguished Young Scholars of University in Liaoning Province (LJQ2015109).

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Reference

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Aleshin, A.E., Shiryaev, S.A., Strongin, A.Y., Liddington, R.C., 2007. Structural evidence for regulation and specificity of flaviviral proteases and evolution of the Flaviviridae fold. Protein Sci. 16, 795-806. Behnam, M.A., Nitsche, C., Boldescu, V., Klein, C.D., 2016. The Medicinal Chemistry of Dengue Virus. J Med Chem. 59, 5622-5649. Bhatt, S.; Gething, P. W.; Brady, O. J.; Messina, J. P.; Farlow, A. W.; Moyes, C. L.; Drake, J. M.; Brownstein, J. S.; Hoen, A. G.; Sankoh, O.; Myers, M. F.; George, D. B.; Jaenisch, T.; Wint, G. R.; Simmons, C. P.; Scott, T. W.; Farrar, J. J.; Hay, S. I., 2013.The global distribution and burden of dengue. Nature. 496, 504-507. Biovia, D. S., 2017. Discovery studio modeling environment. San Diego: Dassault Systèmes. Cabarcas-Montalvo, M.; Maldonado-Rojas, W.; Montes-Grajales, D.; Bertel-Sevilla, A.; Wagner-Dobler, I.; Sztajer, H.; Reck, M.; Flechas-Alarcon, M.; Ocazionez, R.; Olivero-Verbel, J., 2016. Discovery of antiviral molecules for dengue: In silico search and biological evaluation. Eur J Med Chem. 110, 87-97. Chambers, T.J., McCourt, D.W., Rice, C.M., 1990. Production of yellow fever virus proteins in infected cells: Identification of discrete polyprotein species and analysis of cleavage kinetics using region-specific polyclonal antisera. Virology. 177, 159-174. Chappell, K.J., Stoermer, M.J., Fairlie, D.P., Young, P.R., 2008. Mutagenesis of the West Nile virus NS2B cofactor domain reveals two regions essential for protease activity. J Gen Virol. 89, 1010-1014.

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PT

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M

Charlier, C., Henichart, J.P., Durant, F., Wouters, J., 2006. Structural insights into human 5-lipoxygenase inhibition: combined ligand-based and target-based approach. J Med Chem. 49, 186-195. Deng, J.; Li, N.; Liu, H.; Zuo, Z.; Liew, O. W.; Xu, W.; Chen, G.; Tong, X.; Tang, W.; Zhu, J.; Zuo, J.; Jiang, H.; Yang, C.-G.; Li, J.; Zhu, W., 2012. Discovery of Novel Small Molecule Inhibitors of Dengue Viral NS2B-NS3 Protease Using Virtual Screening and Scaffold Hopping. J Med Chem. 55, 6278-6293. Erbel, P.; Schiering, N.; D'Arcy, A.; Renatus, M.; Kroemer, M.; Lim, S. P.; Yin, Z.; Keller, T. H.; Vasudevan, S. G.; Hommel, U., 2006. Structural basis for the activation of flaviviral NS3 proteases from dengue and West Nile virus. Nat Struct Mol Biol. 13, 372-373. Falgout, B., Pethel, M., Zhang, Y.M., Lai, C.J., 1991. Both Nonstructural Proteins NS2B and NS3 Are Required for the Proteolytic Processing of Dengue Virus Nonstructural Proteins. J Virol. 65, 2467-2475 Knehans, T.; Schuller, A.; Doan, D. N.; Nacro, K.; Hill, J.; Guntert, P.; Madhusudhan, M. S.; Weil, T.; Vasudevan, S. G., 2011. Structure-guided fragment-based in silico drug design of dengue protease inhibitors. J Comput Aid Mol De. 25, 263-274. Kyle, J.L., Harris, E., 2008. Global spread and persistence of dengue. Annu Rev Microbiol. 62, 71-92. Leong, A.S.Y., Wong, K.T., Leong, T.Y.M., Tan, P.H., Wannakrairot, P., 2007. The pathology of dengue hemorrhagic fever. Semin Diagn Pathol. 24, 227-236. 20

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ED

M

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Li, R.J., Wang, Y.L., Wang, Q.H., Wang, J., Cheng, M.S., 2015. In silico design of human IMPDH inhibitors using pharmacophore mapping and molecular docking approaches. Comput Math Metho. 2015, 418767. Lim, S. P.; Wang, Q. Y.; Noble, C. G.; Chen, Y. L.; Dong, H.; Zou, B.; Yokokawa, F.; Nilar, S.; Smith, P.; Beer, D.; Lescar, J.; Shi, P. Y., 2013. Ten years of dengue drug discovery: progress and prospects. Antivir Res. 100, 500-519. Liu, H.; Wu, R.; Sun, Y.; Ye, Y.; Chen, J.; Luo, X.; Shen, X.; Liu, H., 2014. Identification of novel thiadiazoloacrylamide analogues as inhibitors of dengue-2 virus NS2B/NS3 protease. Bioorgan Med Chem. 22, 6344-6352. Luo, D., Vasudevan, S.G., Lescar, J., 2015. The flavivirus NS2B-NS3 protease-helicase as a target for antiviral drug development. Antivir Res. 118, 148-158. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J., 2009. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 30, 2785-2791. Morris, G., 2002. SYBYL software, version 6.9. St. Louis: Tripos Associates Inc. Nitsche, C., Holloway, S., Schirmeister, T., Klein, C.D., 2014. Biochemistry and medicinal chemistry of the dengue virus protease. Chem Rev. 114, 11348-11381. Nitsche, C., Steuer, C., Klein, C.D., 2011. Arylcyanoacrylamides as inhibitors of the Dengue and West Nile virus proteases. Bioorgan Med Chem. 19, 7318-7337. Noble, C.G., Seh, C.C., Chao, A.T., Shi, P.Y., 2012. Ligand-bound structures of the dengue virus protease reveal the active conformation. J Virol. 86, 438-446. Normile, D. Tropical medicine., 2013. Surprising new dengue virus throws a spanner in disease control efforts. Science. 342, 415. Pitisuttithum, P., Bouckenooghe, A., 2016. The first licensed dengue vaccine: an important tool for integrated preventive strategies against dengue virus infection. Semin Diagn Pathol. 15, 795-798. Pugachev, K.V., Guirakhoo, F., Trent, D.W., Monath, T.P., 2003. Traditional and novel approaches to flavivirus vaccines. Int J Parasitol. 33, 567-582. Raut, R.; Beesetti, H.; Tyagi, P.; Khanna, I.; Jain, S. K.; Jeankumar, V. U.; Yogeeswari, P.; Sriram, D.; Swaminathan, S., 2015. A small molecule inhibitor of dengue virus type 2 protease inhibits the replication of all four dengue virus serotypes in cell culture. Virol J. 12, 16. Rigau-Pérez, J.G., Clark, G.G., Gubler, D.J., Reiter, P., Sanders, E.J., Vance Vorndam, A., 1998. Dengue and dengue haemorrhagic fever. Lancet. 352, 971-977. Robin, G.; Chappell, K.; Stoermer, M. J.; Hu, S.-H.; Young, P. R.; Fairlie, D. P.; Martin, J. L., 2009. Structure of West Nile Virus NS3 Protease: Ligand Stabilization of the Catalytic Conformation. J Mol Biol. 385, 1568-1577. Tatem, A.J., Hay, S.I., Rogers, D.J., 2006. Global traffic and disease vector dispersal. Semin Diagn Pathol. 103, 6242-6247. Timiri, A. K.; Selvarasu, S.; Kesherwani, M.; Vijayan, V.; Sinha, B. N.; Devadasan, V.; Jayaprakash, V., 2015. Synthesis and molecular modelling studies of novel sulphonamide derivatives as dengue virus 2 protease inhibitors. Bioorg Chem. 62, 74-82. Trott, O., Olson, A.J., 2010. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 31, 455-461.

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Viswanathan, U., Tomlinson, S.M., Fonner, J.M., Mock, S.A., Watowich, S.J., 2014. Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. J Chem Inf Model. 54, 2816-2825. Wu, H.; Bock, S.; Snitko, M.; Berger, T.; Weidner, T.; Holloway, S.; Kanitz, M.; Diederich, W. E.; Steuber, H.; Walter, C.; Hofmann, D.; Weissbrich, B.; Spannaus, R.; Acosta, E. G.; Bartenschlager, R.; Engels, B.; Schirmeister, T.; Bodem, J., 2015. Novel dengue virus NS2B/NS3 protease inhibitors. Antimicrob Agents. 59, 1100-1109. Zhang, X., Jiang, H., Li, W., Wang, J., Cheng, M., 2017. Computational Insight into Protein Tyrosine Phosphatase 1B Inhibition: A Case Study of the Combined Ligand- and Structure-Based Approach. Comput Math Metho. 2017, 4245613. Zuo, Z.; Liew, O. W.; Chen, G.; Chong, P. C.; Lee, S. H.; Chen, K.; Jiang, H.; Puah, C. M.; Zhu, W., 2009. Mechanism of NS2B-mediated activation of NS3pro in dengue virus: molecular dynamics simulations and bioassays. J Virol. 83, 1060-1070.

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Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12

23

E

CC

A

E

CC

A

EP

CC

A

T

A ED

PT

CC E A

M

N U S

EP

CC

A

EP

CC

A

TE

EP

CC

A

EP

CC

A

TE

EP

CC

A

TE

EP

CC

A TE

D

C

A

EP

CC

A

TE

Name

IC50 (μM)[a]

Ref

1

MB21

5.95

[21]

2

Compound16

48.2

[24]

3

Compound2

37.9

[22]

4

Compound1

7.7

5

CID:54692801

14.9±2.9

6

23i

9.45±0.78

7

CID:54681617

61.5±4.6

8

CID:54715399

IP T

Index

N

Table 1. Activities of inhibitors of DENV NS2B-NS3 protease.

U

SC R

[22]

A

11.8±0.2

A

CC E

PT

ED

M

Notes: [a] IC50 values are determined according to the corresponding literature.

24

[9]

[23] [9] [9]

Table 2. Summary of the pharmacophore models generated by HipHop for DENV NS2B-NS3 protease inhibitors

Features[a]

Rank[b]

Direct

Partial

Hit[c]

Hit[d]

RHHHAA

65.288

11110

00001

02

RHHHAA

65.288

11110

00001

03

RHHHAA

64.403

11110

00001

04

RHHHAA

64.025

11110

05

RHHDA

63.470

11111

06

RHHAA

63.419

07

RHHAA

63.419

08

RHDAA

63.102

09

RRHAA

10

RRHAA

5

11111

00000

5

00000

5

11101

00010

5

11111

00000

5

11111

00000

5

U

00000

N

6 6

A

M

ED

63.098

6

00001

11111

63.098

6

SC R

01

Max Fit

IP T

Hypothesis

PT

Notes: [a] R, ring aromatic group; H, hydrophobic group; D, hydrogen bond donor; A, hydrogen bond acceptor. [b] The ranking score of training set compounds fitting the hypothesis. The higher rank, the

CC E

less likely it is that the compounds in the training set fit the hypothesis by a probability correlation. The best hypothesis shows the highest value. [c] Direct Hit indicates whether (“1”) or not (“0”) a molecule in the training set mapped every feature in the hypothesis. [d] Partial Hit indicates whether (“1”) or not

A

(“0”) a molecule in the training set mapped all but one feature in the hypothesis. Numeration of molecules is from right to left in both Direct Hit and Partial Hit.

25