Benzothiazole Derivative as a Novel Mycobacterium tuberculosis

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Benzothiazole derivative as a novel Mycobacterium tuberculosis shikimate kinase inhibitor: Identification and elucidation of its allosteric mode of inhibition Rukmankesh Mehra, Vikrant Singh Rajput, Monika Gupta, Reena Chib, Amit Kumar, Priya Wazir, Inshad Ali Khan, and Amit Nargotra J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00056 • Publication Date (Web): 05 May 2016 Downloaded from http://pubs.acs.org on May 6, 2016

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Journal of Chemical Information and Modeling

Benzothiazole Derivative as a Novel Mycobacterium tuberculosis Shikimate Kinase Inhibitor: Identification and Elucidation of its Allosteric Mode of Inhibition Rukmankesh Mehra# a, Vikrant Singh Rajput# b d, Monika Gupta a, Reena Chib b, Amit Kumar a, Priya Wazir c, Inshad Ali Khan* b d and Amit Nargotra* a d #

Authors contributed equally

Affiliations a

Discovery Informatics Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India b

Clinical Microbiology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India

c

Instrumentation Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India

d

Academy of Scientific and Innovative Research, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India * Corresponding authors E-mail: [email protected], [email protected]

Address: Dr Amit Nargotra, Discovery Informatics Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India. Phone no.: 0191-2585028 EPAB Ext.: 269

ACS Paragon Plus Environment


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Abstract Mycobacterium tuberculosis shikimate kinase (Mtb-SK) is a key enzyme involved in the biosynthesis of aromatic amino acids through shikimate pathway. Since it is proven to be essential for the survival of the microbe and is absent from mammals, it is a promising target for anti-TB drug discovery. In this study, a combined approach of in silico similarity search and pharmacophore building using already reported inhibitors was used to screen a procured library of 20,000 compounds of commercially available ChemBridge database. From the in silico screening, 15 hits were identified and these hits were evaluated in vitro for Mtb-SK enzyme inhibition. Two compounds presented significant enzyme inhibition with IC50 values of 10.69 ± 0.9 µM and 46.22 ± 1.2 µM. The best hit was then evaluated for in vitro mode of inhibition where it came out to be an un-competitive and non-competitive inhibitor with respect to shikimate (SKM) and ATP respectively, suggesting its binding at an allosteric site. Potential binding sites of the Mtb-SK were identified which confirmed the presence of an allosteric binding pocket apart from the ATP and SKM binding sites. The docking simulations were performed at this pocket in order to find the mode of binding of the best hit in presence of substrates and the products of the enzymatic reaction. Molecular dynamics (MD) simulations elucidated the probability of inhibitor binding at the allosteric site in presence of the ADP and shikimate-3-phosphate (S-3-P), that is, after the formation of products of the reaction. The inhibitor binding may prevent the release of the product from the Mtb-SK, thereby inhibiting its activity. The binding stability and the key residue interactions of the inhibitor to this product complex were also revealed by the MD simulations. Residues ARG43, ILE45 and PHE57 were identified as crucial that were involved in interactions with the best hit. This is the first report of


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an allosteric binding site of Mtb-SK which could largely address the selectivity issue associated with kinase inhibitors.



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1

INTRODUCTION

Mycobacterium tuberculosis (Mtb) remains the leading infectious killers of the human race worldwide after HIV, thereby causing a health concern which affects millions of people globally. In 2014, an estimated 9.6 million new tuberculosis (TB) cases, killing 1.5 million people were reported globally (1.1 million among HIV-negative people and 0.4 million among HIV-positive people).1 The TB epidemic is becoming remarkably difficult to treat and control due to the emergence of MDR, XDR and TDR strains along with the ability of the organism to go into a dormant phase. Co-infection with HIV compromises the host defense and allows the dormant micro-organism to reactivate or render individuals more susceptible to TB which has made effective control of the disease more challenging.2 Therefore, new anti-tubercular (anti-TB) agents are urgently required that are not only efficacious against drug susceptible but also target MDR/XDR/TDR strains, can be coadministered with HIV therapeutics and act by a novel mode of action. In order to discover novel non-toxic drugs, with least chances of pre-existing resistance, that can effectively target the pathogen , it is imperative to choose such a novel target which is not only essential for the survival, but is also exclusive to Mtb.3 Shikimate pathway is one such target which utilizes phosphoenolpyruvate and erythrose-4-phosphate for the biosynthesis of chorismic acid. Chorismic acid in turn leads to the synthesis of vital metabolites including aromatic amino acids, mycobactins, ubiquinones, p-aminobenzoic acid, napthoquinones and few others. Shikimate kinase (SK), the fifth enzyme of this pathway is involved in the conversion of shikimate (SKM) to shikimate-3-phosphate (S-3-P) using ATP as co-substrate.4 Since, it is proven to be essential for the survival of Mtb5 and is absent in mammals,6 it can serve as an attractive target for anti-TB drug discovery. The novel mode of action against the pathogen


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would ensure that the compounds targeting SK will be least prone to developing resistance and the fact that expression of SK is same in drug susceptible and MDR strain7 means such compounds would be effective against both strains. In this way, two key issues of anti-TB drug discovery can be addressed, which are preventing development of drug resistance, and effectiveness against drug resistant TB. Mtb-SK is a member of nucleoside monophosphate (NMP) kinase family.8 The 3D structures of Mtb-SK have been solved by different research groups,9-15 and their coordinates have been submitted to Protein Data Bank.16 The availability of Mtb-SK crystal structures opened up a scenario to perform molecular modeling studies using these structures. In addition to the structural information, several effective inhibitors of Mtb-SK have been reported in the literature.17-21 Bandodkar and Schmitt identified pyrazolone derivatives as inhibitors of Mtb-SK and got patent of these compounds.20 The activity of the staurosporine, which is a wellrecognized ATP competitive inhibitor, was determined for Mtb-SK along with three sulfonamide analogs by Mulabagal and Calderon.19 They determined the enzyme activity of Mtb-SK by applying liquid chromatography/mass spectrometry (LC/MS) based quantification of S-3-P.19 Hsu et al. applied core site-moiety maps technique for the identification of Mtb-SK inhibitors.18 They revealed the competitive inhibitors of ATP and SKM sites of Mtb-SK. Furthermore, Simithy et al. reported 14 compounds having a 2-aminobenzothiazole or an oxadiazole-amide scaffold as Mtb-SK inhibitors.17 Recently, our research group has identified several compounds exhibiting Mtb-SK inhibitory activity and these compounds were found to be ATP competitive inhibitors.21 The present study attempts to identify novel chemotypes targeting Mtb-SK to push the targetbased anti-TB drug discovery forward with some starting point. This would further help in



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designing and synthesizing novel chemical scaffolds, ensuring pathogen clearance with a novel mechanism of action with low toxicity. In order to attain this goal, we performed in silico similarity search and pharmacophore modeling using data of Mtb-SK inhibitors from literature and screened the ChemBridge library of 20,000 drug-like commercially available compounds. This effort resulted in the identification of two Mtb-SK inhibitors with micro-molar inhibitory activities. Based on the in vitro indications from Lineweaver-Burk plots, the best hit was further elucidated for its mode of binding using docking and molecular dynamics simulations, which assisted us to propose the binding of the inhibitor at a novel allosteric site in Mtb-SK in presence of ADP and S-3-P. 2

MATERIALS AND METHODS

2.1

Chemical library for screening

A library of 20,000 compounds procured from ChemBridge was used for the screening of compounds with a potential Mtb-SK inhibitory activity. The compound library constituted a part our Institute’s drug-like repository, which was readily available for screening in vitro. These compounds were procured with the objective to identify hits that can be modified to attain structural novelty and specificity for the therapeutic targets. Therefore, this library was preferred for computational screening for the identification of Mtb-SK inhibitor candidates that could be tested in vitro. 2.2

Similarity search

Chemical similarity or molecular similarity refers to the similarity of the compounds based on their structures. Function or biological activity of a compound can be related to its chemical structure; therefore, this principle can be used to predict the properties of the compounds


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including its biological activity. The similarity search was performed on the chemical library using inhibitors reported in literature against Mtb-SK.17-20 The data of available Mtb-SK inhibitors was retrieved from literatures (Tables S1-S4), and these inhibitors were used as reference for performing similarity search on the drug-like library of 20,000 compounds of ChemBridge database. ChemAxon software was used for performing similarity search.22 2.3

Pharmacophore model building

Pharmacophore modeling is one of the important techniques used in computational chemistry that represents spatial arrangement of the essential features of one or more ligands with similar biological activity. By applying this model, a diverse chemical database can be searched to find compounds having same features with similar relative orientation. Aiming at this, pharmacophore modeling was performed using a training dataset of 14 Mtb-SK inhibitors reported by Simithy et al.17 (Table S3). The structures of the compounds were built using 2D sketcher of the Maestro.23 The 2D structures were cleaned, converted to 3D-states and prepared using LigPrep.24 The preparation of these ligands involved the generation of tautomers, steroisomers and ionization states at pH 7±2 using OPLS 2005 forcefield. Further, the various possible conformations of these ligands were generated using ConfGen.25 In the next step of pharmacophore model building, various possible pharmacophoric sites were recognized on these ligands that included hydrogen bond acceptors (A), hydrogen bond donors (D), hydrophobic (H), aromatic (R), positive ionic (P) and negative ionic (N). Pharmacophore modeling was performed using a tree-based partition algorithm as implemented in Phase.26, 27 Phase searches for spatial arrangement of the pharmacophoric features in a dataset of active compounds that are essential for biological activity.26, 27



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2.4

Computational screening of the compound library

Computational screening is an approach that involves the filtering of compounds by applying knowledge-based techniques. The rationale behind this screening process is to identify compounds from the library based on the data of available inhibitors or protein. This data provides information required for a compound to be active against a biological target of interest. Aiming at identifying new Mtb-SK inhibitors, the library of 20,000 drug-like compounds was screened in silico. Similarity search of the compound library was performed based on the MtbSK inhibitors reported in the literature (Table S1-S4). Further, the library was screened based on the developed pharmacophore model. Common hits were identified between similarity search hits and pharmacophore hits. These compounds were then used for in vitro evaluation.

2.5

In vitro Mtb-SK enzyme inhibition assay

The arok gene (531bp) was PCR amplified using gene specific primers and cloned into the pET28a vector with His tag; over-expressed in E .coli BL21 strain and purified as reported earlier.20, 21, 28 The test compounds were tested at concentrations ranging from 100 µM to 1.56 µM and the enzyme activities at these concentrations were calculated. The IC50 value for each inhibitor was determined21 by plotting the percentage inhibition against log (conc.) curve using GraphPad Prism 5 (GraphPad Software, Inc., La Jolla, CA, USA). 2.6

In vitro mode of inhibition

The inhibition mode and the inhibition constant of the best hit was evaluated by measuring activities as a function of shikimate (0.2–2.4 mM) concentration (at fixed ATP concentration of


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2.5 mM) and fixed-varied inhibitor concentrations (0,10 and 20 µM). Inhibition kinetics was also performed in the presence of fixed concentration shikimate (1.6 mM) and fixed-varied inhibitor concentrations and ATP (0.05–0.6 mM) as the variable substrate.18, 21, 29 The data from both the experiments was used to plot Lineweaver-Burk graphs and the Ki values were determined by fitting the data to the respective inhibition equation. 2.7

Evaluation of in vitro cytotoxicity against HepG2 cell line

The cytotoxicity of the hits was evaluated using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide) assay.30 The human HepG2 cell line (ATCC HB-8065) was maintained in DMEM with low glucose and plated at a density of 10,000 cells /well in 96 microwell flat bottom plate (Nunc, Thermofisher Scientific, USA) and incubated for 24 h (37°C; 5% CO2). The compounds were added at a concentration 40 µg/mL and kept for incubation at 37ºC in the CO2 incubator for 24 h. After incubation MTT was added at a concentration of 2.5 µg/mL dissolved in phosphate buffer saline (PBS) and cell viability was determined by measuring the absorbance of the reduced formazan at 570 nm using multimode reader Infinite 200 PRO (Tecan, Männedorf, Switzerland). Tamoxifen served as the drug control. 2.8

In vitro metabolism in liver microsomes

In vitro microsomal stability was accessed using a reported assay.31 Briefly, the rat liver microsomes (0.3 mg/mL) contained in phosphate buffer (pH 7.4) with a NADPH regeneration system (1 mM NADP+, 5 mM G6P, 1 U/mL G-6-PDHase) were incubated at 37ºC for 30 minutes in the presence of 5 µM compound. The control contained everything mentioned above except the NADPH regeneration system. The reactions were terminated at selected time-points (0 minutes and 30 minutes) by adding acetonitrile. Reactions were terminated and samples were



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analyzed on LC/MS under single ionization mode (SIM) and scan mode. Verapamil was used as a drug control in the assay. 2.9

In silico study to elucidate the binding mode of the best hit

2.9.1

Protein complexes preparation, binding site generation and docking

To further study the mode of inhibition of the best hit, three different protein complexes viz: Michaelis complex, complex A (proposed state in-between the Michaelis and the product complex) and product complex were studied. The Michaelis complex comprised of the shikimate kinase bound with ATP/Mg2+/SKM. Whereas, shikimate kinase bound with ADP/Mg2+/PO3/SKM and ADP/S-3-P were taken as the complex A and product complex respectively. For the preparation of the Michaelis complex, PDB structure 2IYQ, which is a closed LID structure with bound ADP and SKM, was used. The coordinates of ATP, Mg2+ and water molecules of PDB structure 2IYW were copied on to 2IYQ after superimposition, and ADP was removed from 2IYQ in order to prepare the Michaelis complex. All the water molecules forming hydrogen bonds with the bound ligands and coordinate bonds with Mg2+ were retained. For the formation of the complex A, PDB structure 2IYQ was used. Prado and coworkers have described that the reaction catalyzed by Mtb-SK proceeds through a metaphosphate intermediate,32 and therefore, the complex A was prepared accordingly. In 2IYQ, Mg2+ coordinates were incorporated from the PDB structure 1WE2, and the orientation and position of PO3- were modeled using phosphate group of S-3-P of 2IYZ and distances information as described by Prado et al.32 For product complex, the Mtb-SK structure with PDB code 2IYZ was used. This structure is co-crystallized with ADP and S-3-P that formed the products of the reaction catalyzed by the Mtb-SK. Upon transfer of the phosphoryl group in the reaction, the role of Mg2+ is not clearly defined and hence, Mg2+ was not included in the product complex. The missing coordinates of the residue


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ARG152 in 2IYZ were added using Prime as implemented in Protein Preparation Wizard of the Schrodinger software.33 All the crystallographic water molecules forming H-bonds with the ligands were maintained in all the three complexes. Further, for the preparation of these complexes for docking, proper bond orders were assigned, hydrogen atoms were added, H-bonds were optimized, and minimization of the complexes were carried out using Protein Preparation Wizard of the Schrodinger.33 The N-acetyl and N-methyl amide groups were added to the protein as N and C termini caps respectively. Potential binding sites were predicted in all the three MtbSK complexes using SiteMap.34 3D grid was generated in each complex around the predicted sites using Glide.35, 36 Docking of the best hit was performed on these grids using XP scoring function of the Glide.37 3D structure of this inhibitor was prepared using OPLS 2005 forcefield as implemented in LigPrep.24 The top ranking docking pose of the best hit in the three complexes were selected for further analysis. 2.9.2

Molecular dynamics simulations of the protein-inhibitor complexes

For MD simulations, each of the three complexes with the top-ranked docking pose of the best hit was prepared by adding simple point charge (SPC) water molecules in orthorhombic box. The docked complexes of the Michaelis, complex A and product were neutralized by adding six, four, and three Cl- ions respectively. The prepared system of Michaelis complex, complex A and product complexe contained 22872, 22765 and 24162 atoms respectively. Subsequently, each system was minimized for a maximum of 2000 iterations using a combination of steepest descent (SD) and limited memory Broydene Fletchere Goldfarbe Shanno (LBFGS) methods as implemented in Desmond.38, 39 The energy minimized system of each complex was then used for performing MD simulations using multistep protocol of the Desmond.38, 39 For each complex, 10



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ns of MD run was performed with integration time step of 2 fs in NPT ensemble. Information of energy and trajectory of the system were recorded every 1.2 ps and 4.8 ps respectively. 3

RESULTS AND DISCUSION

3.1

Similarity search

Similarity search was performed using ChemAxon22 by applying 0.6 Tanimoto coefficient as a criterion for searching the chemical library. Structure of 43 Mtb-SK inhibitors (Tables S1-S4) were used as query to search the library of 20,000 compounds that resulted in the identification of 50 compounds having ≥ 0.6 Tanimoto coefficient. 3.2

Construction of pharmacophore model

Using a tree-based partition algorithm, various possible four point pharmacophore hypotheses were derived. Pharmacophore model was built by taking the conformation of a compound in the training dataset as reference and then aligning conformations of rest of the dataset compounds on it. As the conformations of the most active compound would define the required conformation and essential features that are contributing to its activity, pharmacophore models that were built based on the most active compound in the training dataset were evaluated. The most active compound in the training dataset was compound 14 (Table S3) having IC50 1.94 ± 0.06 µM. Four hypotheses were built based on this compound 14 that matched ten active compounds of the training dataset (Table 1). These models were further evaluated based on the survival score of the actives. Survival score defines the weighed measure of site score, vector score, volume score and the number of actives matching to the hypothesis.26,

27

Based on this evaluation, ADRR

hypothesis (Figure 1, Table S5 and S6) with highest survival score of 2.856 was selected for the screening of the chemical library. ADRR hypothesis was composed of one hydrogen bond


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acceptor (A), one hydrogen bond donor (D) and two aromatic features (R). Analysis of the spatial arrangement of the pharmacophoric sites of the ADRR hypothesis on compound 14 revealed that oxygen atom of the carboxyl group was identified as acceptor site as indicated by A3 in Figure 1. Next to this carboxyl group, NH group was present which was recognized as donor site denoted as D5. Further, next to this NH group, the two aromatic groups viz thiazole ring and phenyl ring were identified as potential aromatic features denoted as R7 and R9 respectively. The arrangement of the features of ADRR hypothesis is shown in Figure 1 and ADRR features represented on compound 14 is shown in Figure S1. 3.3

In silico screening strategy

By applying similarity search technique, 50 hits were retrieved from the 20,000 compound library that followed 0.6 Tanimoto coefficient criterion. Since, similarity search cannot be the only possible criterion for the compounds to be active, pharmacophore modeling was also carried out, which included a set of common pharmacophoric features a compound should possess for being active against a biological target. By applying pharmacophore based screening, top 500 hits were selected from the library based on the fitness score. In order to find the compounds from the similarity search hits that possessed common pharmacophoric features, common hits were identified between similarity search hits and pharmacophore hits. This ensured that no active compound was missed from the compound library from similarity and pharmacophoric point of view. There were 15 compounds found to be common between similarity search hits and pharmacophore hits, which were then carried forward for in vitro screening. Earlier also, researchers have used virtual screening methods for identification of Mtb-SK inhibitors,40-41 where they have applied only structure based methods, which limits the scope of identifying any new binding pocket. Moreover, these results were not confirmed through in vitro studies.



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3.4

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In vitro Mtb-SK enzyme inhibition

The recombinant Mtb-SK enzyme was cloned; over-expressed in E. coli and purified using affinity chromatography. On carrying out the enzyme kinetics following values of steady state constants were yielded, Km SKH (mM)= 0.3994 ± 0 .003, Km ATP (mM)= 0.1306 ± 0.007 , Kcat./Km SKH

(M-1S-1) = 0.17 *105 and Kcat./Km ATP (M-1S-1) = 0.6 *105. Our results are in agreement with

the results previously reported by other groups.3, 15 The Z factor which is a measure of quality of the assay was also calculated. The above illustrated screening methodology produced a Z factor of 0.78 which proves the suitability of the assay for screening the compound library. The 15 compounds that were found to be common between similarity search hits and pharmacophore hits were tested for in vitro inhibition of Mtb-SK at single concentration of 100 µM. This resulted in the identification of 2 compounds viz. “5489375” and “5311863” which inhibited the enzyme significantly. These 2 compounds were then evaluated for the dose response effect which yielded IC50 values of 10.69±0.9 µM and 46.22±1.2 µM for compounds “5489375” and “5311863” respectively. These two in vitro hits were passed through the PAINS filter using PAINS-Remover server42 and FAFDrug 3.0 server43 to remove the possible PAINS structures. Both these compounds successfully passed through the PAINS filter of these servers, which showed that the identified inhibitors were not PAINS compounds. The identified inhibitors were also searched through SciFinder,44 which is an online scientific information retrieval system. It was revealed that these compounds were structurally novel as Mtb-SK inhibitors. The drug-like parameters of these inhibitors were calculated using QikProp.45 The calculated parameters included molecular weight, octanol-water partition coefficient, aqueous solubility, human oral absorption, binding to human serum albumin, polar surface area, number of H-bond donors and acceptors. The values of all these properties of the identified inhibitors were found to be in the


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acceptable range of the drug-like compounds.45 The structures of the identified inhibitors along with their IC50 values and the drug-like parameters are shown in Table 2. 3.5

In vitro mode of inhibition of “5489375”

The in vitro mode of inhibition was determined by plotting the Lineweaver plots by using the data collected at varied substrate (shikimate and ATP) and inhibitor concentration (Figure 2). The Lineweaver plots for the compound “5489375” suggested that the compound was an uncompetitive inhibitor with respect to SKM and non-competitive inhibitor with respect to ATP. The data was fitted to the respective inhibition equation to yield following values of Ki (dissociation constant of enzyme and inhibitor) and αKi (dissociation constant of inhibitorenzyme-substrate complex) values, Ki (µM) = 13.33 ± 1.2, αKi SKH (µM) = 8.66 ± 0.6 and αKi ATP (µM) = 11.89 ± 0.7. 3.6

In vitro cytotoxicity

The compounds “5489375” and “5311863” were evaluated for cytotoxicity against the HepG2 cell line using MTT assay where, both of them were found to be non toxic at 40 µg/mL concentration. The drug control used (tamoxifen) exhibited an IC50 of 18.24±0.018 against the HepG2 cell line. 3.7

In vitro metabolism

The compounds “5489375” and “5311863” were evaluated for in vitro metabolism using the rat liver microsomes. Both the compounds were found to be stable and no measurable metabolism was observed during 30 minutes of incubation with microsomes in the presence of a NADPH regeneration system, whereas verapamil, the drug control used, exhibited approximately 50% loss at the end of the study.



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3.8

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Evaluation of the potential binding sites, docking and MD simulations

From the in vitro experiments, it was revealed that the inhibitor “5489375” showed noncompetitive inhibition for ATP and un-competitive inhibition for SKM. The Mtb-SK enzyme comprises of three domains: the CORE domain, the LID domain and the substrate binding (SB) domain.9, 11, 14, 15, 29, 46 The CORE domain constitutes five stranded parallel β-sheets and the Ploop (Walker-A motif, residues 9-17) that forms ATP binding site. LID domain comprises the residues from 112 to 124 that show large movements during ATP and SKM binding. SB domain extends from residues 33 to 61 (α helices, α2, α3 and α4) that recognizes and binds SKM. It has been reported that ATP binds first to the SK, bringing large movement in the LID domain over the binding site,9, 11, 14, 15, 46 Subsequent to this, SKM binds to the enzyme thereby further closes the LID over the active site.9,

11, 14, 15, 46

The in vitro experiments performed for mode of

inhibition suggested that the best hit “5489375” can bind equally well to the Mtb-SK whether or not ATP is bound to the ATP site, as revealed by non-competitive inhibition for ATP. Further, the un-competitive inhibition for SKM suggested that “5489375” binds only to the enzyme-SKM complex. As ATP binds first to Mtb-SK followed by the SKM binding, it was therefore interpreted that the identified hit “5489375” binds to the enzyme in the presence of ATP and SKM, thereby indicating the presence of another binding site other than ATP and SKM sites. Hence, the closed LID structures of Mtb-SK were used for further studies in the presence of substrates and products of the reaction. Three different protein complexes were studied viz: Michaelis complex, complex A and product complex (as explained in section 2.9.1). Binding site prediction using SiteMap34 revealed the presence of only one binding site, apart from ATP and SKM binding sites, in all the three complexes (Figure 3). This revealed the presence of an allosteric site in Mtb-SK where the


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inhibitor could effectively bind with high affinity. The binding cavity of Mtb-SK was found to be L-shaped, where long arm of the L represented ATP site and the short arm represented SKM site. The predicted allosteric site in all the three complexes was similar and was present at the curve of the L. This finding is in agreement with the already published data of SK binding site of Helicobacter pylori.29 In order to further study the mode of inhibition of “5489375”, docking simulations of this inhibitor was carried out on the predicted allosteric binding site of all the three complexes. The highest-ranked pose of “5489375” identified by docking was used for MD simulation. The initial binding poses of “5489375” in the three complexes were different. This might be due to the differences in the constituting elements of the allosteric site in these complexes, though the overall location of the site was the same. The location of the phosphate group that was being transferred from ATP to SKM was different in the three complexes. This phosphate group also contributed in the formation of the allosteric site, therefore, its position is crucial in guiding the orientation of the binding ligand. The stability of “5489375” at the predicted sites of the three complexes was studied using MD simulations. For each complex, MD simulations were carried for 10 ns. During the course of this time the fluctuations of the inhibitor at the binding site was studied. It was observed that in Michaelis complex and complex A, the inhibitor showed higher fluctuations and was displaced out of the initial binding cavity to the surface of the protein (Figure 4). The inhibitor was displaced after about 4.0 ns and 1.0 ns from Michaelis complex and complex A respectively. This reflected that the inhibitor was not stable at the initial docked binding cavity of these two complexes. However, when the product complex was analyzed with respect to the movements of “5489375”, it was observed that the ligand was present in the initial binding cavity throughout the simulation, which reflected that the ligand was stable at the docked



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binding site of the product complex. Further, the RMSD analysis of the proposed allosteric inhibitor “5489375” in all the three complexes was also carried out. It was observed that in Michaelis complex and complex A the ligand RMSD was very high, that is, approximately 24 Å for the Michaelis complex and 16 Å for the complex A. The product complex showed stable trajectory of the ligand RMSD at approximately 5 Å that further revealed the stability of “5489375” at the allosteric site of the product complex. The ligand RMSD plots of the three complexes are shown in Figure 5. The stability of the inhibitor in the product complex was further proved based on the RMSF analysis of “5489375” ligand atoms, which showed a maximum of 3.5 Å fluctuation when bound to product complex. Whereas, in the Michaelis complex and complex A, the ligand atoms were highly fluctuating and showed RMSF values of up to approximately 12 Å and 6 Å respectively. The RMSF plots of the ligand atoms are shown in Figure 6. Therefore, the stability of “5489375” at the allosteric binding site of the product complex was apparent from its low RMSD and RMSF values when bound to the product complex. The higher stability of the product-“5489375” complex over the Michaelis -“5489375” and complex A-“5489375” complexes during 10 ns simulations revealed high probability of binding of “5489375” to the product complex, i.e. when the transfer of phosphate has taken place from ATP to SKM to form ADP and S-3-P. The residues that formed the allosteric binding site of Mtb-SK (product complex) and interacted with the inhibitor “5489375” were analyzed. The crucial residues identified to play role in forming interactions with “5489375” were ARG43, ILE45 and PHE57. The backbone C=O moiety of the residue ARG43 was involved in the H-bond interaction with the NH attached to benzothiazole of “5489375”. H-bond interaction with ARG43 was maintained for about 73% of the simulation time. Side chain of the residue ILE45 formed hydrophobic contacts with the


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phenyl ring of benzothiazole moiety of “5489375” and maintained this interaction for about 37.6% of the simulation time. ILE45 also formed water bridges with N-atom of thiazole ring and N-atom attached to the methylnitrobenzene moiety of the ligand. These water bridges occurred for 24.8% of the simulation time. The phenyl ring of PHE57 formed hydrophobic interaction with the phenyl ring of benzothiazole moiety of the inhibitor, and this interaction occurred for about 23% of the simulation time. The side chain of ILE37 also formed hydrophobic contacts with the benzothiazole moiety of the ligand. Apart from these residues, other residues forming contacts with “5489375” during the course of 10 ns MD run were ASP34, VAL35, GLU38, THR41, GLY42, SER44, ALA46, ASP47, PHE49, VAL116 and PRO118. It was elucidated that the inhibitor was mainly bound to the residues of SB domain during the simulation. The MD simulation results of the product complex docked with the inhibitor “5489375” are shown in Figure 7. It is therefore hypothesized that the binding of “5489375” brings about some conformational changes to the substrate binding sites of Mtb-SK that prevents the release of products and causes the inhibition of enzyme activity. It is reported that a strong salt bridge interaction between ARG117 and S-3-P is required for the release of the product.9 In order to analyze the differences in the conformations of the essential residues that were responsible for inhibition of the product release, MD simulation (for 10 ns) of the product complex was also carried out in the absence of “5489375” (allosteric inhibitor). It was revealed that in the absence of the inhibitor, ARG117 side chain moved towards the C3 phosphate group of S-3-P and formed a strong salt-bridge interaction. The distance between the N+ atom of the guanidium group of ARG117 and O- atom of C3 phosphate of S-3-P was gradually reduced from 4.93 Å to 2.65 Å, which showed that the salt bridge interaction became stronger during the simulation. However, in the presence of



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allosteric inhibitor “5489375”, the distance between these atoms participating in the salt bridge remained almost the same (changes from 5.08 Å to 4.82 Å) during the simulation, thereby preventing the formation of a strong salt bridge interaction. This phenomenon could explain the role of the proposed allosteric inhibitor of Mtb-SK in preventing the release of S-3-P from the binding site and thereby inhibiting the overall enzyme activity. 4

CONCLUSIONS

In summary, two new inhibitors of Mtb-SK were identified in this study with IC50 values of 10.69 ± 0.9 µM (5489375) and 46.22 ± 1.2 µM (5311863). The best hit “5489375” was reported to be the un-competitive and non-competitive inhibitor of SKM and ATP respectively thereby, suggesting the presence of an allosteric site. This allosteric site was identified in the Mtb-SK crystal structures, where the identified inhibitor (5489375) binds in the presence of both the substrates (ATP and SKM). Analysis of the binding mode using docking and MD simulation studies revealed that “5489375” has high probability of binding to the allosteric site of Mtb-SK after the products are formed but are not released. The inhibitor was mainly bound to the residues of the SB domain and formed crucial interactions with the residues ARG43, ILE45 and PHE57. The binding of “5489375” prevented the formation of strong salt bridge interaction between the guanidinium group of ARG117 and phosphate group of S-3-P, thus inhibiting the force required for the product release. The binding of the inhibitor to the allosteric site of MtbSK, which is the first report so far, indicates its higher selectivity for Mtb-SK as compared to other mammalian kinases. The site thus identified will help in designing more selective inhibitors for this important target.


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ASSOCIATED CONTENT Supporting Information Data of inhibitors of M. tuberculosis shikimate kinase used for computational studies (Tables S1S4); information regarding the built pharmacophore model (Tables S5, S6, Figure S1). AUTHOR INFORMATION Corresponding authors E-mail: [email protected], [email protected] AUTHOR CONTRIBUTION RM and AN: Conceived and designed the computational experiments. RM: Carried out the computational experiments. AN: Analyzed the computational data. VSR and IAK: Conceived, designed and carried out the in vitro experiments. MG: Carried out the similarity search and its analysis. AK: Made the compounds dilution in DMSO and maintained and issued the compounds for screening purpose. RC: Performed the microsomal stability assays. PW: Performed the LCMS analysis of the compounds. RM and VSR: drafted the manuscript. AN and IAK: Supervised the work, implemented resources and participated in manuscript revisions. FUNDING SOURCES Project GAP-0141: Establishment of Sub-DIC under BTIS Net programme. Project BSC-0205: Nurturing a new Pan-CSIR drug pipe line high intensity preclinical, clinical studies on lead candidates (CSIR-DPL).



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MG, AK and AN acknowledge DBT, New Delhi for their financial support through the project GAP-0141. RM, RC, PW and IAK thank CSIR, New Delhi for financial support from BSC0205. VSR thanks CSIR, New Delhi for his research fellowship (Grant no. P-81101). COMPETING INTEREST The authors declare that they have no competing interest. ABBREVIATIONS Mtb, Mycobacterium tuberculosis; SK, shikimate kinase; Mtb-SK, Mycobacterium tuberculosis shikimate kinase; SKM, shikimate; MD, molecular dynamics; S-3-P, shikimate-3-phosphate; TB, tuberculosis; HIV, human immunodeficiency virus; MDR, multidrug-resistance; XDR, extensively drug-resistant; TDR, totally drug-resistant; ATP, adenosine triphosphate; ADP, adenosine diphosphate; LDH, lactate dehydrogenase; PK, pyruvate kinase; SPC, simple point charge; PDB, Protein Data Bank; IC50, Half maximal inhibitory concentration; H-bond, hydrogen bond; MD simulation, Molecular dynamics simulations; SB domain, substrate binding domain. REFERENCES 1. WHO Global Tuberculosis Control: WHO Report 2015. Geneva, Switzerland: WHO, 2015. 2. Corbett, E. L.; Watt, C. J.; Walker, N.; Maher, D.; Williams, B. G.; Raviglione, M. C.; Dye, C. The Growing Burden of Tuberculosis: Global Trends and Interactions with the HIV Epidemic. Arch. Intern. Med. 2003, 163, 1009-1021. 3. Rosado, L. A.; Vasconcelos, I. B.; Palma, M. S.; Frappier, V.; Najmanovich, R. J.; Santos, D. S.; Basso, L. A. The Mode of Action of Recombinant Mycobacterium tuberculosis Shikimate Kinase: Kinetics and Thermodynamics Analyses. PLoS One 2013, 8, e61918. 4. Pereira, J. H.; Vasconcelos, I. B.; Oliveira, J. S.; Caceres, R. A.; de Azevedo, W. F. Jr.; Basso, L. A.; Santos, D. S. Shikimate Kinase: A Potential Target for Development of Novel Antitubercular Agents. Curr. Drug Targets 2007, 8, 459-468. 5. Parish, T.; Stoker, N. G. The Common Aromatic Amino Acid Biosynthesis Pathway is Essential in Mycobacterium tuberculosis. Microbiology 2002, 148, 3069-3077. 6. Kapnick, S. M.; Zhang, Y. New Tuberculosis Drug Development: Targeting the Shikimate Pathway. Expert Opin. Drug Discovery 2008, 3, 565-577.


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21. Rajput, V. S.; Mehra, R.; Kumar, S.; Nargotra, A.; Khan, I. A. Screening of Antitubercular Compound Library Identifies Novel Shikimate Kinase Inhibitors of Mycobacterium tuberculosis. Appl. Microbiol. Biotechnol. 2016, DOI 10.1007/s00253-0157268-8. 22. Instant JChem, version 5.9.4; ChemAxon: Budapest, Hungary, 2012. 23. Maestro, version 10.1; Schrödinger, LLC: New York, NY, 2015. 24. LigPrep, version 3.0; Schrödinger, LLC: New York, NY, 2014. 25. Watts, K. S.; Dalal, P.; Murphy, R. B.; Sherman, W.; Friesner, R. A.; Shelley, J. C. ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers. J. Chem. Inf. Model. 2010, 50, 534-546. 26. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A. PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and 3D Database Screening: 1. Methodology and Preliminary Results. J. Comput.-Aided Mol. Des. 2006, 20, 647-671. 27. Dixon, S. L.; Smondyrev, A. M.; Rao, S. N. PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching. Chem. Biol. Drug Des. 2006, 67, 370372. 28. Oliveira, J. S.; Pinto, C. A.; Basso, L. A.; Santos, D. S. Cloning and Overexpression in Soluble Form of Functional Shikimate Kinase and 5-enolpyruvylshikimate 3-phosphate Synthase Enzymes from Mycobacterium tuberculosis. Protein Expression Purif. 2001, 22, 430-435. 29. Han, C.; Zhang, J.; Chen, L.; Chen, K.; Shen, X.; Jiang, H. Discovery of Helicobacter pylori Shikimate Kinase Inhibitors: Bioassay and Molecular Modeling. Bioorg. Med. Chem. 2007, 15, 656-662. 30. Dorsey, W. C.; Tchounwou, P. B.; Sutton, D. Mitogenic and Cytotoxic Effects of Pentachlorophenol to AML 12 Mouse Hepatocytes. Int. J. Environ. Res. Public Health 2004, 1, 100-105. 31. Ling, L. L.; Schneider, T.; Peoples, A. J.; Spoering, A. L.; Engels, I.; Conlon, B. P.; Mueller, A.; Schäberle, T. F.; Hughes, D. E.; Epstein, S.; Jones, M.; Lazarides, L.; Steadman, V. A.; Cohen, D. R.; Felix, C. R.; Fetterman, K. A.; Millett, W. P.; Nitti, A. G.; Zullo, A. M.; Chen, C.; Lewis, K. A New Antibiotic Kills Pathogens Without Detectable Resistance. Nature 2015, 517, 455-459. 32. Prado, V.; Lence, E.; Vallejo, J. A.; Beceiro, A.; Thompson, P.; Hawkins, A. R.; Gonzalez-Bello, C. Study of the Phosphoryl-Transfer Mechanism of Shikimate Kinase by NMR Spectroscopy. Chem. - Eur. J. 2016, 22, 2758-2768. 33. Protein Preparation Wizard, release 2015; Schrödinger, LLC: New York, NY, , 2015. 34. Halgren, T. New Method for Fast and Accurate Binding-Site Identification and Analysis. Chem. Biol. Drug Des. 2007, 69, 146-148. 35. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening. J. Med. Chem. 2004, 47, 1750-1759. 36. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J. Med. Chem. 2004, 47, 1739-1749. 37. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra Precision Glide: Docking and Scoring


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Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes. J. Med. Chem. 2006, 49, 6177-6196. 38. Bowers, K. J.; Chow, E.; Xu, H.; Dror, R. O.; Eastwood, M. P.; Gregersen, B. A.; Klepeis, J. L.; Kolossvary, I.; Moraes, M. A.; Sacerdoti, F. D.; Salmon, J. K.; Shan, Y.; Shaw, D. E. Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing; ACM: Tampa, Florida, 2006, pp 84. 39. Desmond Molecular Dynamics System, version 3.8; D. E. Shaw Research: New York, NY, 2014. Maestro-Desmond Interoperability Tools, version 3.8; Schrödinger, LLC: New York, NY, 2014. 40. Vianna, C. P.; de Azevedo, W. F. Jr. Identification of New Potential Mycobacterium tuberculosis Shikimate Kinase Inhibitors through Molecular Docking Simulations. J. Mol. Model. 2012, 18, 755-764. 41. Segura-Cabrera, A.; Rodriguez-Perez, M. A. Structure-Based Prediction of Mycobacterium tuberculosis Shikimate Kinase Inhibitors by High-throughput Virtual Screening. Bioorg. Med. Chem. Lett. 2008, 18, 3152-3157. 42. Baell, J. B.; Holloway. G. A. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J. Med. Chem., 2010, 53, 2719–2740. 43. Lagorce, D.; Sperandio, O.; Baell, J. B.; Miteva, M. A.; Villoutreix, B. O. FAF-Drugs3: A Web Server for Compound Property Calculation and Chemical Library Design. Nucleic Acids Res. 2015, 43(W1), W200-W207. 44. SciFinder. https://scifinder.cas.org/scifinder (accessed December 5, 2015). 45. Ioakimidis, L.; Thoukydidis, L.; Mirza, A.; Naeem, S.; Reynisson, J. Benchmarking the Reliability of QikProp. Correlation between Experimental and Predicted Values. QSAR Comb. Sci. 2008, 27, 445-456. 46. Krell, T.; Maclean, J.; Boam, D. J.; Cooper, A.; Resmini, M.; Brocklehurst, K.; Kelly, S. M.; Price, N. C.; Lapthorn, A. J.; Coggins, J. R. Biochemical and X‐ray Crystallographic Studies on Shikimate Kinase: The Important Structural Role of the P‐Loop Lysine. Protein Sci. 2001, 10, 1137-1149.



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Figures

Figure 1. Four point pharmacophore hypothesis ADRR.


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Figure 2. In vitro mode of inhibition graphs of compound “5489375” against substrates SKM and ATP. (A) Lineweaver-Burk plot of specific activity-1 (µM-1 min mg) against [SKM-1] (mM-1) at 0, 10 and 20 µM of “5489375”. (B) Lineweaver-Burk plot of specific activity-1 (µM-1 min mg) against [ATP-1] (mM-1) at 0, 10 and 20 µM of “5489375”.



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Figure 3. Allosteric binding site of Mtb-SK predicted by SiteMap. This proposed allosteric site is at the curve of the L-shaped tunnel comprising of the ATP/ADP site and the SKM site formed at the long-arm and the short-arm of “L” respectively.


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


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Figure 4. Initial and final frame structures of the three complexes obtained from MD simulations. ATP/ADP is shown in tube representation with purple colored carbon atoms, Mg2+ is represented in purple colored ball, SKM/S-3-P is shown in tube representation with yellow colored carbon atoms and the inhibitor 5489375 is shown in CPK representation with green colored carbon atoms. (a) Frame structures of Michaelis complex. 5489375 moved from the initial binding site on to the surface of the Mtb-SK. (b) Frame structures of complex A. 5489375 showed movement from the initial binding site to a different position on to the surface of protein. (c) Frame structures of product complex. 5489375 spanned the same binding site throughout the simulation, thereby depicting the stability of the inhibitor in the product complex.


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Figure 5. RMSD plots of 5489375 obtained from MD simulations. (a) RMSD plot of 5489375 docked in Michaelis complex. (b) RMSD plot of 5489375 docked in complex A. (c) RMSD plot of 5489375 docked in product complex.



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Figure 6. RMSF plots of 5489375 atoms obtained from MD simulations. (a) Ligand RMSF of “5489375” docked in Michaelis complex. (b) RMSF of “5489375” atoms docked in complex A. (c) RMSF of “5489375” atoms docked in product complex.


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Figure 7. MD simulations results of the product complex docked with the inhibitor “5489375”. (a) RMSD plot of the protein backbone. Stable trajectory of RMSD was obtained below 2.8Å. (b) RMSF plot of the protein backbone. Secondary structural elements that were maintained for over 70% of the simulation time are represented in orange (α-helices) and sky-blue (β-strands) color. The protein residues forming contacts with “5489375” are also shown in green lines



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meeting the X-axis. (c) The fraction of different interactions formed between protein and ligand. (d) The crucial residues identified that were involved in interactions with “5489375”.

Tables Table 1. Pharmacophore hypothesis built based on conformations of the most active compound. S.No.

Pharmacophore hypothesis

Survival score

1

ADRR*

2.856

2

ADHR

2.742

3

AHRR

2.662

4

AADH

2.659

* Pharmacophore hypothesis ADRR was selected and used for screening compound library.


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Table 2. IC50 values of the compounds and their drug-like parameters as calculated using QikProp IC50 Compound

MW

HBD* HBA* QPlogPo/w QPlogS PHOA QPlogKhsa PSA

(µM) N NH

S N

-

N+ O O

10.69±0.9 298.319

1

5

2.566

-3.965 89.751

-0.053

80.582

46.22±1.2 312.386

1

4.75

3.763

-5.275

0.351

54.593

5489375 N NH S O

100

O

5311863 “IC50” is the fifty percent inhibitory concentration of the compound in µM. “MW” is molecular weight (acceptable range: 130 to 725), “HBD” is the number of H-bond donors (acceptable range: 0.0 to 6.0), “HBA” is the number of H-bond acceptors (acceptable range: 2.0 to 20.0), “QPlogPo/w” is the predicted octanol/water partition coefficient (acceptable range: -2.0 to 6.5), “QPlogS” is the predicted aqueous solubility mol/L (acceptable range: -6.5 to 0.5), “PHOA” is the predicted human oral absorption on 0 to 100% scale, “QPlogKhsa” is the predicted binding to human serum albumin (acceptable range: -1.5 to 1.5) and “PSA” is the Van der Walls surface area of polar nitrogen and oxygen atoms (acceptable range: 7.0 to 200.0). * QikProp calculates HBD and HBA by taking averages over a number of configurations of the ligand.



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Benzothiazole Derivative as a Novel Mycobacterium tuberculosis

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