Structural insight into Mycobacterium tuberculosis

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Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening, docking, and molecular dynamics simulations a

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Soumi Sengupta , Debjani Roy & Sanghamitra Bandyopadhyay a

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Department of Biotechnology, NTNU, 7491 Trondheim, Norway

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Department of Biophysics, Bose Institute, Acharya J.C Bose Centenary Building, P-1/12 C.I.T Road, Scheme VIIM, Kolkata 700054, India c

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Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India Published online: 11 Feb 2015.

To cite this article: Soumi Sengupta, Debjani Roy & Sanghamitra Bandyopadhyay (2015): Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening, docking, and molecular dynamics simulations, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2014.1003602 To link to this article: http://dx.doi.org/10.1080/07391102.2014.1003602

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Journal of Biomolecular Structure and Dynamics, 2015 http://dx.doi.org/10.1080/07391102.2014.1003602

Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening, docking, and molecular dynamics simulations Soumi Senguptaa, Debjani Royb and Sanghamitra Bandyopadhyayc* a

Department of Biotechnology, NTNU, 7491 Trondheim, Norway; bDepartment of Biophysics, Bose Institute, Acharya J.C Bose Centenary Building, P-1/12 C.I.T Road, Scheme VIIM, Kolkata 700054, India; cMachine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India

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Communicated by Ramaswamy H. Sarma (Received 7 August 2014; accepted 29 December 2014) Pharmacophore-based virtual screening, subsequent docking, and molecular dynamics (MD) simulations have been done to identify potential inhibitors of maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE). Ligand and structurebased pharmacophore models representing its primary binding site (pbs) and unique secondary binding site 2 (sbs2), respectively, were constructed based on the three dimensional structure of mtb GlgE. These pharmacophore models were further used for screening of ZINC and antituberculosis compounds database (ATD). Virtually screened molecules satisfying Lipinski’s rule of five were then analyzed using docking studies and have identified 23 molecules with better binding affinity than its natural substrate, maltose. Four top scoring ligands from ZINC and ATD that either binds to pbs or sbs2 have been subjected to 10 ns each MD simulations and binding free energy calculations. Results of these studies have confirmed stable protein ligand binding. Results reported in the article are likely to be helpful in antitubercular therapeutic development research. Keywords: tuberculosis; maltosyl transferase; pharmacophore modeling; virtual screening; molecular dynamics

1. Introduction Tuberculosis (TB) has arrived as a pandemic again (WHO Report, 2009) with the emergence of multi-drugresistant (MDR) and extensively drug-resistant (XDR) strains and its co-infection with human immunodeficiency virus (HIV). Streptomycin (Nunn et al., 2005) is the earliest of the antitubercular drugs. With time the therapeutic regimen of TB treatment has evolved. Presently four orally administrable drugs, viz., isoniazid (Camus, Pryor, Medigue, & Cole, 2002), rifampin, pyrazinamide, and ethambutol, are first choices for antitubercular treatment Janin (2007). The second line of treatment involve usage of injectable drugs like, streptomycin (Nunn et al., 2005), kanamycin (Ballell, Field, Duncan, & Young, 2005), amikacin (Kayukova & Praliev, 2000), and capreomycin (Newton, Lau, & Wright, 2000). With the advent of MDR and XDR strains, TB therapy has become more difficult. Though the available drugs are indispensable in the current therapeutic regimen of TB, but their certain adverse side effects and evolution of more resistant forms of the bacterium highlights the need for the discovery of novel drugs. Moreover, the synergy of HIV with TB have made simultaneous treatment of both more challenging (Nunn et al., 2005). Not only the infection with HIV assists in tubercular infection but *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

also treatment of one, intervenes the treatment of the other. For example, it has been observed that treatments of HIV with protease inhibitors are not responsive or incompatible when simultaneously used with antitubercular therapeutic, rifampin (Bonora & Di Perri, 2008). Therefore, identification of new lead molecules active against TB has become an urgent need. For discovery of novel drugs different procedures are followed. The most popular strategy in the past few decades has been modifying known successful lead molecules to increase its activity against a given target. It is a successful technique in finding new and effective drugs. The primary reason for this is the fact that if the mother lead compound is active against a given drug target then its interaction with the target can be studied and modified to make it more effective. Moreover, it consumes less time and money, and assures some meaningful outcome which encourages its popularity with the pharmaceutical companies. However, the recent difficulties with treating TB draw the attention of the scientific community to look for novel molecules with new pharmacological mode of action against new drug targets. Maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE) is a member α-amylase protein family and is a participant of a newly discovered pathway for α-glucan production from trehalose (Kalscheuer et al., 2010). The

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study experimentally proved mtb GlgE to be potential drug target. In our previous work (Sengupta, Roy, & Bandyopadhyay, 2014) we have reported homology modeled three dimensional (3D) structure of mtb GlgE. Its structure consists of five domains (A, B, C, N, and S) and four inserts (1, 2, 3, 4) out of which insert 3 and 4 are unique to the protein. Domain A is a (β/α)8 barrel fold and contains its primary binding site (pbs). Through detailed study of its binding sites discovery of a unique binding site, secondary binding site 2 (sbs2), have also been in this study. Arrangement of domain A, N and insert 3 form this small unique binding pocket. In the continued effort to study mtb GlgE, we have used it as a drug target to identify new antitubercular lead molecules through pharmacophore model-based virtual screening. In a past few years, scientists have discovered novel inhibitors for several targets using the same technique (Arooj, Sakkiah, Kim, Arulalapperumal, & Lee, 2013; John, Thangapandian, & Lee, 2012; Dror, Schneidman-Duhovny, Inbar, Nussinov, & Wolfson, 2009; Vuorinen et al., 2014). Usage of pharmacophore models for virtual screening allows an algorithm to look for molecules having properties of more than one inhibitor scaffold at a time which helps in finding novel drug candidates. In this paper, we have also developed pharmacophore models representing pbs and sbs2 of mtb GlgE. Probable inhibitors are then identified through virtual screening using the best pharmacophore and subsequently analyzed using docking studies. Four ligands with top docking scores (that either binds to pbs or sbs2) were subjected to 10 ns each molecular dynamics (MD) simulations. MD simulations ensure their stability in the dynamic environment. 2. Materials and methods 2.1. Target protein preparation Crystal structure of mtb GlgE is not solved yet. Therefore, a homology model of the protein is used for the study. The homology model of the protein was built for its structural analysis in our previous work (Sengupta et al., 2014). Two out of the three active site cavities in the protein structure identified from literature and by structural and sequence analysis in the previous work is used in this study. Among these two active sites one is the pbs, and the other is the sbs2. 2.2. Pharmacophore modeling A pharmacophore model of a given active site represents the 3D layout of chemical features and steric limitations that a small molecule must follow in order to bind to it. Discovery Studio version 3.1 (DSv 3.1) was used for construction and visualization of the pharmacophore models for the present study (Barnum, Greene, Smellie, & Sprague, 1996). Two types of pharmacophore model generation techniques, namely ligand-based pharmaco-

phore model generation and structure-based pharamcophore model generation, have been employed in the present study. The pbs (Tyr-275, Trp-305, Glu-348, Glu-375, Asn-376, Pro-377, Lys-379, Tyr-381, Asp-383, Arg-416, Asp-418, Asp- 419, His-421, Glu-447, Phe-449, Thr501, Asp-503, Ile-504, Glu-551, Lys-557, Tyr-558) is the catalytic site of mtb GlgE which belongs to α-amylase family of proteins. No known binder or inhibitor of the site available till date. However, there are a number of proteins belonging to the same family that have similar active site as pbs. Such proteins are obtained from Protein Data Bank (PDB) (Berman et al., 2000) using BLASTp (Altschul et al., 1997). Therefore to find out the chemical features of its probable inhibitors, publicly available known binders of these proteins were used. Twenty-three such binders obtained from PDB were considered for the study (see Table ST1). Their docked complexes with mtb GlgE are used for ligand-based pharmacophore model building employing the ReceptorLigand Pharmacophore Generation protocol of DSv 3.1. The protocol generates and ranks several selective pharmacophore models based on the ligand-receptor interaction observed in the docked complexes. The ranking is done according to the measure specificity and selectivity. Generated pharmacophore models constitute of predefined feature types like hydrophobic features (HY), hydrogen bond donors (HBD), and hydrogen bond acceptor (HBA). The known binders used for ligand-based pharmacophore model generation are mostly sugar or contain a sugar subunit. Maximum and minimum number of ring structures present in these molecules is 4 and 1, respectively. Therefore, small molecules with either one- or two-ringed structure were grouped together in one group (Gr-1) and the remaining with three- or four-ringed structure are grouped in a second group (Gr-2). Docked complexes of Gr-1 and Gr-2 with mtb GlgE were used to generate two sets of pharmacophore models. Best model from each group is considered and subjected to validation. Figure 1(a) and (b) shows the model generated from Gr-1 (pbs model 1) and Gr-2 (pbs model 2), respectively. sbs2 (Arg-68, Trp-223, Ala-429, Ala- 433, Lys-436, Thr-437, and Pro-440) is a unique binding site for the protein (Sengupta et al., 2014). No known binders of sbs2 are available in PDB. Therefore, the structural knowledge of this active site published recently (Sengupta et al., 2014) was used for structure-based pharmacophore generation. The binding site was marked using the “Define and Edit Binding Site” tool panel. The pharmacophore features (HY, HBD, and HBA) of the marked binding site were generated using the “Interaction Generation” protocol of “Edit and Cluster Pharmacophore Features” tool panel. Pharmacophore features

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Figure 1. Pharmacophore models for pbs. (a) pbs model 1, (b) pbs model 2 and sbs2, (c) sbs2 model. Distances between the chemical features are given in Å. Chemical features of the pharmacophore are color coded. Green signifies hydrogen bond acceptor and magenta signifies hydrogen bond donors.

were then refined and pruned manually to obtain the final pharmacophore model (shown in Figure 1(c)) complementing the key residues of the site. 2.3. Pharmacophore model validation A set of 28 molecules were fetched from ChEMBL. Out of these 28 molecules, 14 are reported to be active (with known IC50 values) against α-amylase family of proteins (shown in Table ST2). We have also considered 14 inactive molecules in order to estimate false positive rate of prediction while conducting the validation of the pharmacophore models. These 28 molecules were minimized using CHARMM forcefield of DSv 3.1. Different conformations of the molecules belonging to this test set are generated using “Generate Conformation” protocol of

“Search Small Molecule Conformations” tool panel with “Best” conformation analysis option. The default setting for “Energy Threshold” (set to 20.0 kcal/mol) and “Maximum Conformation” (set to 255) was used. Thereafter, the mapping of the molecules was done using “Ligand Pharmacophore Mapping” protocol with “Best Mapping Only” option set to true and using flexible “Fitting Method”. Three active molecules are mapped on to the pharmacophore models of pbs when “Maximum Omitted Feature” is set to 0. However, setting “Maximum Omitted Feature” to 1 resulted in mapping of all 14 inhibitors on to the pharmacophore models of pbs. The pharmacophore of sbs2 could not be validated since no inhibitors are known to bind to this site which could be mapped to the modeled pharmacophore to validate it.

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2.4. Screening of molecular databases Two databases were screened to find out probable lead molecules against mtb GlgE. These two databases are lead-like molecular library of ZINC database (Irwin, Sterling, Mysinger, Bolstad, & Coleman, 2012; Teague, Davis, Leeson, & Oprea, 1999) and antituberculosis compounds database (ATD) (Prakash & Ghosh, 2006). The molecules from the lead-like molecular library of ZINC database (3171,902) and 200 molecules from ATD were used for the study. All possible conformations of the molecules obtained from these two databases were generated using “Build 3D Database” protocol of DSv 3.1 and were saved separately. In “Build 3D Database” protocol “Number of Conformations” parameter was set to 255 and the method used for conformation generation was “BEST”. Thus, two libraries were built containing all possible conformations of molecules from ZINC database and ATD. These two libraries were then subjected to virtual screening with each pharmacophore model of pbs (pbs model 1 and pbs model 2) and sbs2 as query using “Ligand Profiler” protocol of DSv 3.1. The protocol used “flexible” fitting method and “Maximum Omitted Features” was set to 1. 2.5. Lipinski’s rule of five filters Small molecules screened from libraries were further checked for their drug likeness using Molinspiration tool. This tool uses Lipinski’s rule of five i.e. Number of HBA and HBD, molecular weight, and log P value. Small molecules that satisfied Lipinski’s rule of five, were selected for further docking studies. 2.6. Docking Small molecules obtained after application of Lipinski’s rule of five filters were subjected to energy minimization using CHARMM forcefield and were further used for docking analysis. Compounds screened using pharmacophore models for pbs and sbs2 were docked using CDOCKER and LibDock to the respective binding sites Diller and Merz (2001). For CDOCKER, the “Pose Cluster Radius” was set to 0.5 Å. “Pose Cluster Radius” parameter ensures that the root mean square deviation (RMSD) between generated poses would not be greater than the user specified limit. For LibDock “Number of Hotspots” was set to 100, and “Max Hits to Save” set to 10 using “BEST” conformation generation method. 2.7. MD simulation One ligand each from ZINC and ATD having the best docking score when docked to pbs were selected for MD simulation studies. Similarly two ligands with best

docking scores from ZINC and ATD were also selected for sbs2. Selected ligand–protein complexes were subjected to MD simulation run for 10 ns using “Standard Dynamics Cascade” protocol of DSv 3.1 which involved two stages of minimization followed by heating, equilibrium, and production run. Primarily CHARMm forcefield was applied on a protein-ligand complex and it is then immersed in water to carry out the simulation in aqueous surroundings. Explicit water model and explicit spherical boundary solvation model with harmonic restraints was used for the purpose. The radius of solvation sphere is set to 20 Å. Before MD simulation, energy minimization was done with the solvated complex using steepest descent and then conjugate gradient algorithm for 2000 steps each, with default (0.0 kcal/mol) minimization energy change cutoff, to remove steric clashes and for good solvation of water molecules into the protein-ligand complex. Lastly a 10 ns MD simulation was run for each ligand–protein complex using NPT ensemble. During the simulation SHAKE constraints were applied and Leapfrog algorithm was used as dynamics integrator. A stable environment (300 K for temperature and 1 bar for pressure) was maintained for the system throughout the simulation. The trajectories were saved every 100 ps for further evaluation. The whole process of MD simulation is repeated twice to ensure statistical significance and reproducibility of the results.

2.8. Binding free energy calculation The binding free energy (ΔGbind) was calculated using GBSA option available in DSv 3.1 with the snapshots saved from the MD simulation trajectories. The ΔGbind is calculated as: DGbind ¼ DGcomplex  ðDGprotein þ DGligand Þ:

3. Results and discussion 3.1. Pharmacophore modeling and validation 3.1.1. pbs Ligand-based pharmacophore models were built for pbs, from two groups of small molecules, which were known to bind to the catalytic site of α-amylase family proteins. One group contained smaller molecules and the other group consisted of larger molecules. The pharmacophore generated from the smaller compound group (pbs model 1) is shown in Figure 1(a) and the other pharmacophore generated from the larger compound group (pbs model 2) is shown in Figure 1(b) along with the interfeature distance in Å.

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Structural insight into mtb GlgE inhibitors The pbs model 1 consists of two HBDs and one HBA. The orientation of both the HBDs ensures that any small molecule that fits the pharmacophore will interact with the nucleophilic catalytic residues: Asp-418 and Glu-447. The pbs model 2 contains two HBDs that are oriented to interact with the key catalytic residues (Asp418 and Glu-447) of the enzyme. Other than these two HBDs, pbs model 2 contains one more HBD and HBA. Though the first two HBDs of the pbs model 2 are similar to those of the pbs model 1 but the HBAs in both the models have different orientations. The HBA of pbs model 1 is pointed towards Lys-379, whereas the HBA of model 2 is oriented towards Asp-503. Both of these pharmacophore models were validated and further used for virtual screening to identify probable mtb GlgE inhibitors. For validation of the pharmacophore models 14 known inhibitors of α-amylase family proteins and 14 inactive molecules were fetched from ChEMBL. The active molecules were successfully mapped on to either of the pharmacophore models. The accuracy of pbs model 1 is 80.7% and pbs model 2 is 84.9% (Corresponding ROC curves are shown in Figures S1(a) and (b)). 3.1.2. sbs2 Structure-based pharmacophore modeling was done for sbs2. The pharmacophore model generated for this site (sbs2 model) is shown in Figure 1(c). The sbs2 model consists of three HBDs and one HBA. The HBDs are oriented to interact with Ala-433, Lys-436, and Thr-437. The HBA is pointed towards Arg-68. In absence of known inhibitors of this site this pharmacophore model was used for virtual screening to identify probable mtb GlgE inhibitors for sbs2. 3.2. Screening of molecular library and Lipinski’s rule of five filters In the present study, pharmacophore models have been employed for preliminary virtual screening. 3.2.1. pbs Virtual screening was done with both the pharmacophore models using ZINC database. 103 small molecules were obtained having structural similarities with both the models. After checking for the drug likeness features, 31 molecules were finally selected for docking studies. Similarly ATD was also screened with pharmacophore models which yielded 36 molecules. All these 36 molecules satisfied Lipinski’s rule of five. A total of 67 molecules (31 molecules from ZINC + 36 molecules from ATD)

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were selected for subsequent docking studies to find out plausible inhibitors of mtb GlgE. 3.2.2. sbs2 Fifty-eight molecules were obtained by screening of ZINC database using the pharmacophore model of sbs2. Only 12 of them satisfied the Lipinski’s rule of five. Screening of ATD with the pharmacophore model of sbs2 yielded 21 molecules. All 21 molecules satisfied Lipinski’s rule of five. A total number of 33 molecules that were investigated for their potency against mtb GlgE which can bind to sbs2 is 33 (12 molecules from ZINC + 21 molecules from ATD) were selected for subsequent docking studies to find out plausible inhibitors of mtb GlgE which can bind to sbs2. 3.3. Docking The validated homology modeled 3D structure of mtb GlgE (Sengupta et al., 2014) was used for docking studies. CDOCKER and LibDock of DSv 3.1 were used as docking tools. CDOCKER uses simulated annealing and MD principles while LibDock is a fast feature based molecular docking tool that uses empirical scoring function. Its score gives interaction energy and binding affinity of the small molecule with the receptor. Therefore, a small molecule that binds better to the receptor has lower value of CDOCKER interaction energy and high LibDock score. Maltose is the natural substrate of mtb GlgE (Kalscheuer et al., 2010). In our previous work (Sengupta et al., 2014), we have shown that maltose binds to pbs with −97.2 kcal/mol CDOCKER interaction energy and 91.46 LibDock score. Therefore, small molecules having better docking interaction energies and scores than maltose were selected for further analysis. Since maltose does not bind to sbs2 we have considered the ligands showing good docking interaction energies and scores for this binding site. Consensus of the scores provided by the two docking tools for the selected molecules was finally obtained to find out the final set of probable inhibitors of the protein. Using the above-mentioned criteria 20 inhibitors for pbs were finally selected. Out of which 9 were from ZINC (shown in Figure S2) and 11 were from ATD (shown in Figure S3). The docking scores of these molecules obtained from ZINC and ATD are given in Table 1(i). Similarly, only three inhibitors were selected for sbs2. Out of which, 1 was from ZINC (shown in Figure S4 (a)) and the other 2 were from ATD (Figures S4(b) and (c)). Their docking scores are shown in Table 1(ii).

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Table 1.

CDOCKERc

LibDock score

(i) Ligands bound to pbs ZINC ZINC40525623 ZINC ZINC50051597 ZINC ZINC65190441 ZINC ZINC67312614 ZINC ZINC25261711 ZINC ZINC1354461 ZINC ZINC0838991 ZINC ZINC21698190 ZINC ZINC69970048 ATD AZI-OM2K2-49-6I ATD AZI-OM2K2-19-3P ATD AZI-OM2K2-29-1 ATD AZI-OM2K2-30-25 ATD AZI-OM2K2-11-2H ATD AZI-OM2K2-19-3G ATD AZI-OM2K2-49-6D ATD AZI-OM2K2-49-3 J ATD AZI-OM2K2-49-5A ATD AZI-OM2K2-49-4H ATD AZI-OM2K2-30-20

−105.51 −108.09 −98.09 −97.11 −97.77 −98.66 −98.59 −99.07 −102.83 −294.96 −206.45 −107.35 −106.40 −121.93 −124.91 −264.24 −229.39 −283.69 −221.51 −111.58

98.29 97.45 93.87 92.75 94.67 93.76 92.87 92.67 96.48 250.57 175.99 102.68 108.45 119.54 136.56 206.95 198.45 226.54 187.56 96.54

(ii) Ligands bound to sbs2 ZINC ZINC02539424 ATD AZI-OM2K2-29-5 AZI-OM2K2-30-8

−98.72 −106.48 −106.97

93.67 92.14 91.82

Database

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Docking scores of mtb GlgE-inhibitor complexes.a,b Molecule ID

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Inhibitors are obtained from ZINC and ATD. Inhibitors binding to pbs with better binding energies than maltose, and to sbs2 with high binding energies are shown. c CDOCKER interaction energy shown in kcal/mol. b

Four residues namely Tyr-381, Asp-418, Glu-447, and Asp-503 are responsible for catalytic activity of GlgE (Syson et al., 2011). Among these residues Asp418 and Glu-447 acts as nucleophile and is the main catalytic center of the protein. It is evident from Table 1(i) that all the molecules interact with both Asp-418 and Glu-447. Top scoring ligands from ZINC and ATD (ZINC40525623 and AZI-OM2K2-4) bind to the catalytic residues while forming protein-ligand complex (shown in Figure 2(a) and (b), respectively). Molecules docked to sbs2 were checked for their interaction with Arg-68, since the presence of the residue in the binding site makes the site unique for the protein. It was found that all three molecules interact with Arg-68 (Table 1(ii)). Top scoring ligands from ZINC and ATD which binds to sbs2 are ZINC02539424 and AZI-OM2K229-5, respectively. They were observed to interact with Arg-68 (shown in Figure 2(c) and (d), respectively). 3.4. MD simulation Four top scoring ligands ZINC40525623, AZI-OM2K249-6I (for pbs) and ZINC02539424, AZI-OM2K2-29-5 (for sbs2) in complex with GlgE are subjected to two 10 ns MD simulations each.

3.4.1. ZINC40525623 bound to pbs Figure 3(a) shows the temporal RMSD plot of mtb GlgE alone and mtb GlgE–ZINC40525623 complex for two 10 ns MD simulation runs. The RMSD plot shows both mtb GlgE and mtb GlgE–ZINC40525623 complex are stable throughout the simulation. Only minor fluctuations of approximately 1.5 Å are observed between 2000 and 3000 ps. Variations in RMSDs in each curve reduce after 3000 ps demonstrating stability in both the systems. Therefore, the trajectories between 3000 and 10,000 ps have been used to plot root mean square fluctuation (RMSF) and to compute an average structure of the complex. RMSFs of the amino acids constituting pbs are investigated to find out their stability (Figure 4(a)). These residues are observed to be stable in mtb GlgE– ZINC40525623 complex throughout the simulation. Figure 5(a) shows an average structure of mtb GlgE– ZINC40525623 complex. Comparison of Figure 2(a) to 5(a) shows that before MD simulations the mtb GlgE– ZINC40525623 complex exhibited three intermolecular hydrogen bonds which have increased to six after MD simulation. Previously only Lys-379, Asp-418, and Glu447 were interacting with the ligand. MD simulations reveal that three more residues (Tyr-381, Asp-503, and Asn-419) are capable of forming stable hydrogen bonds with the ligand. During the simulation, the conformation of the ligand has changed. It has moved towards Lys379 and the hydrogen bond distance between them has reduced. This has enabled Tyr-381 to form a hydrogen bond with the ligand. Hydrogen bond distances between the ligand and Asp-418 and Glu-447 have also decreased. RMSFs of two primary catalytic residues (Asp-418 and Glu-447) are substantially low in ligand bound state. 3.4.2. AZI-OM2K2-49-6I bound to pbs The temporal RMSD plot of mtb GlgE alone and mtb GlgE–AZI-OM2K2-49-6I complex for two 10 ns MD simulation runs is shown Figure 3(b). Comparison of the curves revealed that the RMSD values of both mtb GlgE and mtb GlgE–AZI-OM2K2-49-6I complex do not have much variation. In the initial stages of the MD simulation till 2000 ps the variations are more. Thereafter, both the curves stabilize till 5000 ps. Variations are again observed between 5000 and 6000 ps. After 6000 ps, the RMSD curves of both free mtb GlgE and mtb GlgE–AZI-OM2K2-49-6I complex show very minor deviation (approximately 0.5 Å) demonstrating stability. Therefore, the trajectories between 6000 and 10,000 ps have been used to plot RMSF and to compute an average structure of the mtb GlgE–AZI-OM2K2-49-6I complex.

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Structural insight into mtb GlgE inhibitors

Figure 2. Docked complexes showing hydrogen bond interaction between actives and the ligands. (a) ZINC40525623 docked to pbs. (b) AZI-OM2K2-49-6I docked to pbs. (c) ZINC02539424 docked to sbs2. (d) AZI-OM2K-29-5 docked to sbs2.

Stability of the mtb GlgE–AZI-OM2K2-49-6I complex has been further verified through RMSF analysis (see Figure 4(b)). Maximum RMSF observed in the residues constituting pbs in mtb GlgE–AZI-OM2K2-49-6I complex is 1.6 Å indicating a stable active site conformation. Figure 5(b) shows the average structure of mtb

GlgE–AZI-OM2K2-49-6I complex. Comparison of Figure 2(b) to 5(b) shows that prior to MD simulations two hydrogen bonds were present between the ligand and the protein, which have increased to five after MD simulation. Previously only Asp-418 and Glu-447 were forming hydrogen bonds with the ligand. MD simulation

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Figure 3. The temporal RMSD of the mtb GlgE (red line) and its complex with different ligands during first (black line) and second (blue line) 10 ns MD simulations. (a) ZINC40525623 bound to pbs. (b) AZI-OM2K2-49-6I bound to pbs. (c) ZINC02539424 bound to sbs2. (d) AZI-OM2K2-29-5 bound to sbs2. The X-axis shows the time in ps and Y-axis shows the RMSD in Å.

results show three more hydrogen bond formation with Asn-376, Asn-419, and Lys-557. During the simulation the hydrogen bond distances between the ligand and Asp-418 and Glu-447 have increased. This observation indicates that these two residues have moved a little far from the ligand which has enabled Asn-376, Asn-419, and Lys-557 to form more hydrogen bonds with the ligand. 3.4.3. ZINC02539424 bound to sbs2 A plot showing the comparative temporal RMSD of mtb GlgE alone and mtb GlgE– ZINC02539424 complex over two MD simulation runs is given in Figure 3(c). The curve shows that the variation in initial period of the simulation is more which reduces from 2000 ps onwards. The range of RMSD between 2000 and 6000 ps is 1–1.5 Å approximately. From 6000 ps

onwards the variation is even more minimized (between 1.25 and 1.6 Å approximately). Therefore, the trajectories between 6000 and 10,000 ps are considered to produce the most stable complexes. These trajectories are further used to plot RMSF and to compute an average structure of the complex. RMSFs of the amino acids lining sbs2 are studied to investigate their stability on ligand binding (see Figure 4(c)). It is a small active site comprising of seven residues. The highest value of the RMSF of these residues is observed to be 1.5 Å which ensures comparatively stable conformation of the site. RMSFs of four residues increase and three residues decrease on ligand binding. One of these residues, RMSF of which decrease on ligand binding is Arg-68 that lies in a loop in insert 3 of the protein. Such an observation indicates stabilization of the loop on ligand binding and formation of more stable ligand–protein complex.

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Figure 4. Comparative RMSF of the active site residues for mtb GlgE (blue bars) and its complex with various ligands (magenta bars). (a) ZINC40525623 bound to pbs. (b) AZI-OM2K2-49-6I bound to pbs. (c) ZINC02539424 bound to sbs2. (d) AZI-OM2K229-5 bound to sbs2. The X-axis shows residue numbers and Y-axis shows the RMSF in Å.

Figure 5(C) shows the average structure of mtb GlgE–ZINC02539424 complex. Prior to MD simulation mtb GlgE–ZINC02539424 complex exhibited two intermolecular hydrogen bonds which have increased to 5 after the MD simulations (see Comparison of the complex before (see Figure 2(c)) and after (see Figure 5(c)). Before MD simulation Arg-68 and Lys-436 form two hydrogen bonds with the ligand. MD simulation results show that Trp-223, Ala-429, and Ala-433 are also capable of forming stable hydrogen bonds with the ligand. Bond distances of the previously existing hydrogen bonds with Arg-68 and Lys-436 have not changed much during the simulation despite the formation of three other hydrogen bonds.

3.4.4. AZI-OM2K2-29-5 bound to sbs2 Figure 3(d) shows the temporal RMSD plot of mtb GlgE alone and mtb GlgE–AZI-OM2K2-29-5 complex for two 10 ns MD simulation runs. The curve shows that the RMSD values of both mtb GlgE and mtb GlgE–AZIOM2K2-29-5 complex have very small variation with each other. During the earlier phase of the simulation the variation is more which reduces and remains approximately same 2000 ps onwards. However, the minimum deviation is observed between 5000 and 7000 ps. Therefore, the trajectories between this time periods are used to plot RMSF and to compute an average structure of the complex.

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Figure 5. Hydrogen bond interaction between (a) ZINC40525623 and pbs, (b) AZI-OM2K2-49-6I and pbs, (c) ZINC02539424 and sbs2, (d) AZI-OM2K2-29-5 and sbs2, after 10 ns MD simulation.

The plot showing RMSFs of the amino acids of sbs2 are shown in Figure 4(d). It shows that the RMSF values of the residues are more for mtb GlgE in comparison to the mtb GlgE–AZI-OM2K2-29-5 complex. Among the seven constituent residues of the site, RMSFs of only

two residues increase on ligand binding. RMSFs of others either remain the same or decreased, thus indicating stable ligand–protein complex formation. Figure 5(d) shows an average structure of mtb GlgE– AZI-OM2K2-29-5 complex. Comparison of Figure 2(d)

Structural insight into mtb GlgE inhibitors Table 2.

Calculated binding free energy.a

Ligands ZINC40525623 AZI-OM2K2-49-6I ZINC02539424 AZI-OM2K2-29-5

Binding free energy −4467.5438 −5407.3660 −4502.40983 −5454.94883

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to help the medicinal chemists to develop new antitubercular drugs. Supplementary material The supplementary material for this paper is available online at http://dx.doi.org/10.1080/07391102.2014.1003602.

a

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Binding free energy is shown in kcal/mol.

to 5(d) shows increase in hydrogen bond during the MD simulations. Previously Arg-68 formed two hydrogen bonds and Trp-223 formed one more hydrogen bond with the ligand. MD simulation results show one more hydrogen bond formation with Lys-436. Bond distances of the hydrogen bonds formed earlier to MD simulation have changed by a maximum of 0.05 Å. 3.5. Binding free energy calculation The calculated binding free energies are shown in Table 2. The results show confirm stable complex formation on binding of all the four ligands to mtb GlgE and also highlight that the molecules from ATD are better binders to both pbs and sbs2. 4. Conclusion With the reemergence of TB with MDR and XDR strains, antitubercular drug research has become very urgent. This article aims to contribute to the urgent need of antitubercular drug research. The mtb GlgE is an enzyme that aids in glucan production from maltose phosphate. It has recently been identified as an essential gene for the survival of the bacterium. Therefore, it has been treated as a drug target in the present study. Based on the knowledge of its homology modeled 3D structure obtained in our previous work, pharmacophore models were built representing its pbs and the unique sbs2. These pharmacophore models were validated and used for screening of lead-like molecular library of ZINC and ATD. Virtually screened molecules were further subjected to docking analysis to the corresponding binding sites. Twenty-three molecules with better binding affinities than maltose were selected as probable lead-like molecules for mtb GlgE inhibitor design. Four top scoring ligands (that either binds to pbs or sbs 2) in complex with GlgE were subjected to two 10 ns MD simulation runs each. Results of the simulation studies showed increased number of hydrogen bonds and decrease in RMSF of most of the catalytic residues highlighting stable binding of the ligands to the protein. Moreover, similar curves for the trajectories of two independent MD simulation runs for each complex (shown in Figure 3(a)–(d)) ensures reproducibility and reliability of the results. Results reported in this article are likely

Abbreviation MD Mtb mtb GlgE tuberculosis pbs sbs2 ATD TB WHO MDR XDR HIV 3D DSv 3.1 PDB HY HBD HBA RMSD RMSF

Molecular dynamics Mycobacterium tuberculosis maltosyl transferase of Mycobacterium Primary binding site Secondary binding site 2 Antituberculosis compounds database Tuberculosis World health organization Mulyi-drug-resistant Extensively drug-resistant Human immunodeficiency virus Three dimensional Discovery studio version 3.1 Protein data bank Hydrophobic Hydrogen bond donors Hydrogen bond acceptor Root mean square deviation Root mean square fluctuation

References Altschul, S., Madden, T., Schffer, A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 25, 3389–3402. Arooj, M., Sakkiah, S., Kim, S., Arulalapperumal, V., & Lee, K. W. (2013). A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One, 8, e63030. doi:10.1371/journal.pone.0063030 Ballell, L., Field, R. A., Duncan, K., & Young, R. J. (2005). New small-molecule synthetic antimycobacterials. Antimicrobial Agents and Chemotherapy, 49, 2153–2163. Barnum, D., Greene, J., Smellie, A., & Sprague, P. (1996). Identification of common functional configurations among molecules. Journal of Chemical Information and Computer Science, 46, 499–511. Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., … Bourne, P. (2000). The protein data bank. Nucleic Acids Research, 28, 235–242. Bonora, S., & Di Perri, G. (2008). Interactions between antiretroviral agents and those used to treat tuberculosis: Clinical pharmacology of antiretroviral drugs. Current Opinion in HIV & AIDS, 3, 306–312. Camus, J. C., Pryor, M. J., Medigue, C., & Cole, S. T. (2002). Re-annotation of the genome sequence of mycobacterium tuberculosis H37Rv. Microbiology, 148, 2967–2973.

Downloaded by [Indian Institute of Technology - Delhi] at 00:45 24 February 2015

12

S. Sengupta et al.

Diller, D. J., & Merz, K. M., Jr. (2001). High throughput docking for library design and library prioritization. Proteins: Structure, Function, and Genetics, 43, 113–124. Dror, O., Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2009). Novel approach for efficient pharmacophore-based virtual screening: Method and applications. Journal of Chemical Information and Modeling, 49, 2333–2343. Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: A free tool to discover chemistry for biology. Journal of Chemical Information and Modeling, 52, 1757–1768. Janin, Y. L. (2007). Antituberculosis drugs: Ten years of research. Bioorganic & Medicinal Chemistry, 15, 2479–2513. John, S., Thangapandian, S., & Lee, K. W. (2012). Potential human cholesterol esterase inhibitor design: Benefits from the molecular dynamics simulations and pharmacophore modeling studies. Journal of Biomolecular Structure & Dynamics, 29, 921–936. Kalscheuer, R., Syson, K., Veeraraghavan, U., Weinrick, B., Biermann, K., Liu, Z., … Jacobs, W. R., Jr. (2010). Selfpoisoning of Mycobacterium tuberculosis by targeting GlgE in an α-glucan pathway. Nature Chemical Biology, 6, 376–384. Kayukova, L. A., & Praliev, K. D. (2000). Main directions in the search for new antituberculous drugs. Pharmaceutical Chemistry Journal, 34, 11–18. Newton, S. M., Lau, C., & Wright, C. W. (2000). A review of antimycobacterial natural products. Phytotherapy Research, 14, 303–322.

Nunn, P., Williams, B., Floyd, K., Dye, C., Elzinga, G., & Raviglione, M. (2005). Tuberculosis control in the era of HIV. Nature Reviews Immunology, 5, 819–826. Prakash, O., & Ghosh, I. (2006). Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents. Journal of Chemical Information and Modeling, 46, 17–23. Sengupta, S., Roy, D., & Bandyopadhyay, S. (2014). Modeling of a new tubercular maltosyl transferase, GlgE, study of its binding sites and virtual screening. Molecular Biology Reports, 41, 3549–3560. Syson, K., Stevenson, C. E. M., Rejzek, M., Fairhurst, S. A., Nair, A., Bruton, C. J., … Bornemann, S. (2011). Structure of streptomyces maltosyltransferase GlgE, a homologue of a genetically validated anti-tuberculosis target. Journal of Biological Chemistry, 286, 38298–38310. Teague, S. J., Davis, A. M., Leeson, P. D., & Oprea, T. (1999). The design of leadlike combinatorial libraries. Angewandte Chemie International Edition, 38, 3743–3748. Vuorinen, A., Engeli, R., Meyer, A., Bachmann, F., Griesser, U. J., Schuster, D., & Odermatt, A. (2014). Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors. Journal of Medicinal Chemistry, 57, 5995–6007. WHO Report. (2009). HIV-related TB deaths higher than past estimates. Retrieved from http://www.who.int/mediacentre/ news/releases/2009/tuberculosisreport 20090324/en/index.html