Identification of Novel Inhibitors of Leishmania donovani Î³

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Identification of novel inhibitors of Leishmania donovani #glutamylcysteine synthetase using structure based virtual screening, docking, molecular dynamics simulation and in vitro studies Pragati Agnihotri, Arjun Kumar Mishra, Shikha Mishra, Vijay Kumar Sirohi, Amogh A Sahasrabuddhe, and J Venkatesh Pratap J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00642 • Publication Date (Web): 21 Mar 2017 Downloaded from http://pubs.acs.org on March 23, 2017

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Journal of Chemical Information and Modeling

Identification of Novel Inhibitors of Leishmania donovani γ-Glutamylcysteine Synthetase using Structure Based Virtual Screening, Docking, Molecular Dynamics Simulation and In vitro Studies Pragati Agnihotri1 Arjun K Mishra1, Shikha Mishra1, Vijay Kumar Sirohi2, Amogh A. Sahasrabuddhe1, J. Venkatesh Pratap1* 1

Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow-

226031, India 2

Division of Endocrinology, CSIR-Central Drug Research Institute, Lucknow- 226031, India

*Corresponding Author: Dr. J. Venkatesh Pratap E-mail: [email protected] Fax: +91-522-2771941 Phone: +91-522-2771940 Ext: 4439 Abstract Trypansomatids maintain their redox balance by the trypanothione based redox system, enzymes of which exhibit differences from mammalian homologues. γ-glutamylcysteine synthetase (Gcs) is an essential enzyme of this pathway performing the first and rate limiting step. L-buthionineS,R-sulfoximine (BSO), a specific inhibitor of Gcs induces toxicity in hosts infected with T. brucei underlining for novel Gcs inhibitors. The present study reports identification of LdGcs inhibitors using computational approaches and their experimental validation.

Analysis of

inhibitor-LdGcs complexes shows modifications that could result in increased efficacy of these compounds.

ACS Paragon Plus Environment


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Introduction Leishmaniasis is a vector-borne parasitic Neglected Tropical Disease caused by the hemoflagellate Leishmania spp. The parasite is predominant in India, Bangladesh, Sudan, South Sudan, Ethiopia and Brazil with >10,000 cases every year in India alone1. Humans are infected by four main clinical forms of leishmaniasis:-visceral, cutaneous, mucocutaneous and post kala azar dermal leishmaniasis2, 3. As far as treatment strategies for leishmaniasis are concerned, no vaccine is available hence control entirely depends on drugs from chemical origin4. The first line anti-leishmanial drugs comprise of Pentavalent antimonials like meglumine antimoniate (PentostamR, GlaxoSmithKline) and sodium stibogluconate (GlucentimeR, Aventis pharma) while the present treatment regimen also includes Amphotericin B, deoxycholate, Paromomycin, Pentamidine isethionate and Azoles5, 6. The treatments have drawbacks like difficult dosing regimen, emerging drug resistance, emphasizing the need of new drugs. The drug discovery regimen involves the detailed characterization of pathogenic proteins or pathways that are different from human. This information can be utilized for identifying specific compounds capable of inhibiting the protein and in turn the proliferation of pathogen. One such protein in trypansomatids is γ-glutamylcysteine synthetase (Gcs, EC 6.3.2.2). Gcs is an essential protein of trypanothione biosynthesis pathway which catalyses ATPdependent ligation of L-Cysteine to L-Glutamate, forming γ-glutamylcysteine. Gcs plays an indispensable role in the survival of both prokaryotes and eukaryotes including trypanosomatids7, 8

. Null mutants of Gcs in fungi, mammals, Trypanosoma brucei (T. brucei) and also in

Leishmania infantum (L. infantum) could not survive unless rescued by exogenous glutathione915

. The high resistance of human neuroblastoma cells against oxidative damage, has been

correlated with the higher expression levels of Gcs at both mRNA and protein (catalytic subunit) levels16. L-buthionine-S, R-sulfoximine (BSO), a specific inhibitor of Gcs cures and prolongs survival of mice infected with T. brucei, implying Gcs as a potential target for therapeutics17. However, BSO induces toxicity in hosts and hence there is a need to develop more potent inhibitors of Gcs. The modern drug discovery regimen involves the identification of compounds having significant inhibition of target proteins and optimization of the identified hits to increase their affinity, selectivity,

efficacy, metabolic stability

and

oral bioavailability.


The

inhibitor

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identification require high-throughput screening (HTS) of available compounds against the potential drug target. Owing to the availability of huge amount of compounds the HTS process has been very time taking and tedious. Virtual screening is a computational, much faster equivalent of high-throughput screening. Several studies have shown the importance of virtual screening in the hunt for promising drugs18-20. Virtual screening is classified into two main categories, ligand based and structure based. The ligand based approach utilize the information from active ligands to prioritize the library compounds through pharmacophore or QSAR based screening models21, while the structure based mode is based on experimental structure of target protein active site and the compounds are screened through docking22. Here we report the discovery of novel inhibitors of L. donovani γ-glutamylcysteiene synthetase using structure based three-fold virtual screening approach. The commercially available Maybridge library was utilised for screening using BSO binding site of LdGcs as target. These inhibitors were subsequently tested for the anti-leishmanial activity and specificity. The efficient specific inhibitors of LdGcs can be a new step towards designing drugs targeting trypanosomatids.

2. Material and Methods 2.1. Ligand library preparation The Maybridge screening collection library containing 54646 compounds was used for virtual screening.as the library compounds obey Lipinski's "rule of five"23 and consist of > 87% pharmacophores of world drug index (WDI), thus making the hits obtained suitable for further development. The compounds library was downloaded (http://www.maybridge.com/) in sdf format. The ligand preparation step was done using “surflex for searching” protocol which converts the two dimensional structure of molecule to one single lowest energy 3D conformer. The outputs were saved in spreadsheet format and used for primary screening. 2.2.Docking Based Screening The LdGcs structure was modelled as described earlier24 and the putative BSO binding site was obtained by superimposition of the homologous ScGcs-BSO co-crystal structure (PDBID: 3LVV; 25). To identify more accurate and efficient inhibitors virtual screening was carried out in hierarchical manner involving several steps starting with a preliminary docking exercise to



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identify compounds that are comparatively better than BSO, and their subsequent refining and re-ranking on the basis of docking using diverse algorithms, molecular dynamics simulations and visual examination. The sequence of these steps are summarized in Figure 1. The primary screening was performed using Surflex-Dock

26

program in screen mode with first

step being receptor preparation. The ligand for screening control, BSO was extracted and stored in a separate file. Ligand (BSO) based protomol was generated with threshold 0.4 and bloat set to default. Hydrogen atoms and AMBER charges were assigned to all screening ligands and the docking was performed with default settings. Surflex virtual screening employs combined multiple screening methods like docking, 2D molecular similarity, and 3D molecular similarity. The output score represents the overall energy of ligand, protein pocket, and their non-bonded interactions computed for each jointly optimized configuration. Subsequently the high scoring hits were re-ranked using the Surflex Geom-X mode. The GeomX mode has higher spin density value (9) as compared to screen mode (3) and thus higher accuracy. Surflex uses Hammerhead procedure together with a surface-based molecular similarity algorithm set to dock flexible ligands into the receptor binding site. The docking target area is represented by a “protomol”. In this, ligand fragments are generated and aligned onto the identified probes and then remaining ligand’s fragments are docked. The scoring function is derived empirically through a weighted sum of non-linear functions of protein-ligand atomic van der Waals surface distances and includes contribution from hydrophobic, polar, entropic, solvation, repulsive and crash terms. The score reflects binding affinity in –log

10(Kd)

units. In the next step, the top 100 compounds obtained from surflex-GeomX screen were visually analysed to optimize the screening protocol. The top 20 compounds satisfying visual analysis criteria were considered for subsequent re-ranking using three independent docking programmes CDOCKER (Discovery studio 4.1), GLIDE (Schrodinger Inc.) and AUTODOCK

27

. The three

softwares utilize different scoring algorithms. CDOCKER utilizes a grid-based molecular docking method that employs CHARMM force field 28, while GLIDE uses hierarchical series of filters to search for possible location of in the active site region of receptor, with shape and properties of receptor represented by multiple sets of fields that provide accurate scoring of ligands. AUTODOCK uses rapid grid-based method for energy evaluation with a Monte Carlo


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simulated annealing search for finding optimal conformations of ligands. The AUTODOCK score is the sum of inter-molecular interaction energy, total internal energy, torsional free energy and unbound system energy and shows more negative value for more favourable interaction. GLIDE score estimates binding energy and is also negative for favourable interactions. The CDOCKER score represents CDOCKER binding energy, where a higher value indicates more favourable binding. The compounds having favourable score in all three docking scores were considered the next step. 2.3.Molecular Dynamics Simulation Molecular dynamics (MD) simulations were performed to evaluate the stability of selected hits obtained after docking. The docked protein ligand complex obtained after Geom-X dock was considered as starting model for MD simulation carried out using GROMACS 4.529 with CHARMM30 forcefield. Swissparam31 web server was used to generate ligand topologies. The protein-ligand complexes were solvated in a cubic box with TIP3P water molecules at 12 Å marginal radii. The whole system was then neutralized by adding appropriate ions and energy minimization was done using steepest descent algorithm. In the next step, these energy minimized complexes were used as input for position restrained NVT (constant number (N), volume (V), and temperature (T)) and NPT (constant number (N), pressure (P), and temperature (T)) equilibration phases of 50 ps each. Isotropic pressure coupling for 2 ps with set isothermal compressibility of 4.5 × 10−5 bar−1 was done using Parrinello-Rahman method. Particle Mesh Ewald algorithm was selected to compute Long range electrostatic interactions32. Vander Waals and Coulomb interactions were truncated at 1 nm. The systems were subjected to MD production run for 10 ns each. The g_rms and g_rmsf utilities were employed to calculate RMSD of ligands and and RMSF of protein residues and the resultant graphs plotted using GRACE

The

occupancies of the hydrogen bonds between ligand and active site residues were calculated using vmd program33. 2.4.Enzyme inhibition evaluation. Recombinant LdGcs was obtained using immobilized metal affinity chromatography and size exclusion chromatography as mentioned earlier24. The identified hit compounds were obtained commercially (Thermo Fisher scientific) with an estimated purity >90 %. Evaluation of identified Gcs inhibitors were carried out using the ADP-Glo kinase assay kit (Promega, USA)



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following the recommended protocol. It is a luminescent kinase assay that measures ADP formed from a kinase reaction, wherein ADP is converted into ATP, which is converted into light by Ultra-Glo Luciferase. Purified protein was used for enzyme inhibition studies. For the determination of primary efficacy 25, 50 and 100 μM of inhibitors (dissolved in DMSO), were incubated with 15 ug of LdGcs in 50mM Tris buffer, pH 8.0 containing 200 mM NaCl, and 5 mM MgCl2, 5 mMATP, 5 mM L-glutamate and 2.5 mM L-Cysteine. For the determination of IC50 values percentage activity of protein was monitored at in presence of increasing concentration of inhibitors. The data was subjected to nonlinear regression analysis and IC50 values were determined using prism software (GraphPad Software, Inc., La Jolla, CA, USA). 2.5. Fluorescence quenching assays The intrinsic fluorescence emission spectra of purified protein was recorded on a Cary Eclipse fluorescence spectrophotometer (Agilent Technologies) with 5 mm path length cuvettes at 25 °C. The protein was excited at 295 nm and the emission spectra recorded in the range 300 – 400 nm. Steady-state fluorescence quenching experiments were performed with increasing concentration of ligands and the change in fluorescence intensity was observed at 339 nm. Titrations with buffer alone were performed as control. The change in fluorescence was then related to binding of ligands by the following standard equation ∆F/∆Fmax = [substrate] tot / (Kd [substrate] tot) Where, ∆F is the magnitude of the difference between the observed fluorescence intensity at a given concentration of substrate and the fluorescence intensity in the absence of substrate, ∆Fmax is the difference between the observed fluorescence intensities at zero and saturating substrate concentration], [Substrate]

tot

and Kd is the apparent dissociation constant. The Kd values were

determined from non-linear least-squares regression analysis of titration data. Fluorescence spectra with all samples were corrected for the background fluorescence of the solution (buffer + substrate). Deconvolution of curves was performed using the Prism software (GraphPad Software, Inc., La Jolla, CA, USA).

2.6.Anti-leishmanial activity.


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Anti-leishmanial activity of compounds was evaluated on L. donovani DD8 promastigotes. Promastigote proliferation inhibition measurements were done by MTT [3-(4, 5-methylthiazol-2yl)-2, 5-diphenyltetrazolium bromide] assay. 2×106 cell were grown in 10% M9 medium in 96 well plates and allowed to multiply for 72 hours in the presence or absence of 25, 50 and 100 μM of compounds. 20 μL of MTT (5mg/ml) was then added to each well and cells incubated at 37ºC for 4 hours and centrifuged for 10 min. The supernatant was discarded and resuspended in 100 μL DMS. The OD was measured at 570nm. 2.7.Cell viability assay The cytotoxicity assays was determined by MTT assay 34, on human embryonic kidney cell lines HEK 293. These cells were maintained in phenol red Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) (Invitrogen , Carlsbad, CA, USA), 100 U/ml penicillin, 100 U/ml streptomycin and 100U/ml neomycin, (Sigma Chemical Co., St. Louis, MO, USA). Cells were seeded in 96-well plate at a density of 3000 cells/well and allowed to adhere for 24 h. Next day cells were washed and treated with 100µM of concentration for 72 h. At the end of incubation, 100µl of 0.5 mg/ml MTT was added to the cells and incubated for 2 h at 37°C. Following incubation, supernatants were removed, and 100 µl of dimethyl sulfoxide (DMSO) was added. The formazan crystals formed inside the viable cells were solubilized in DMSO, and the optical density was read with Microquant (Biotech, USA) at 540 nm. The values of the relative percentage of cell viability were plotted against the concentration. Experiments were carried out in triplicates. 3. Results and discussion 3.1. Virtual Screening The homology model of LdGcs as described earlier24 was used for identification of potential ligands. As the template used had the ligand phosphorylated BSO (PDB ID: 3IG5), the binding site in LdGcs homology model was identified from its structural superimposition with template. In the LdGcs structure, phosphorylated BSO, has polar interactions with Glu 92, Asn 324 and Arg 498 and hydrophobic interactions with Phe 179, Met 322, Tyr 397, and Trp 477. The binding pocket is also fenced by Glu 52, Glu 53, Glu 55, Glu 99, Gln 328, Tyr 397 and Glu 496. Almost all of these residues are conserved within trypanosomatids, yeast and human (catalytic



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subunit), with Glu 52 being the lone exception, replaced by Aspartate in human and yeast (Figure2A)24. The ideal inhibitor should retain these interactions along with specific interaction with Glu 52 and additional complementarity to the LdGcs predicted BSO binding site to have optimum specificity. These properties were considered for identification of novel inhibitor using virtual screening workflow comprised of following steps.

(a) Preliminary screening using surflex dock The reference molecule BSO was docked to the proposed site in order to obtain a cut-off score of 8.0 for screening. The first step of the virtual screening workflow involved primary screening of the 54646 molecules in the Maybridge screening library. These compounds were docked into LdGcs active site using surflex dock screen mode and resultant docking scores were compared with that of BSO. The compounds having docking score greater than or equal to BSO (8.0) were considered for the next step. (b) Geom-X dock Screening In the second step, 2041 compounds obtained as a result of Surflex dock screen were re-ranked using surflex-GeomX Dock in order to enhance accuracy and efficiency. A total of 369 compounds having Geom-X dock score more than 9.0 were analysed for the average dock score of 20 conformations. The BSO Geom-X dock score was 7.6. The top 100 compounds with high average Geom-X dock sore were considered for next screening round of visual inspection. (c) Visual analysis The parameters considered for visual inspection are: (i) Surface complementarity of ligand with protein active site. The shape complementarity of ligand in the protein active site was monitored in this step; (ii) Hydrogen bonds with Glutamates 52, 55, 92, 99, Gln 328, Tyr 397, Lys 483, Arg 494 and Arg 498 considered essential of Ld Gcs catalysis in earlier studies24; (iii) hydrophobic and ring stacking interactions with Phe 179, Met 322, Tyr 397, and Trp 477; (iv) Conservation of interactions with in all 20 docked poses. Based on this analysis, 20 compounds satisfying most of these criteria were considered for the subsequent round of screening.


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(d) Re-ranking. In this round of virtual screening, the 20 identified compounds were docked to the LdGcs active site independently using CDOCKER (Discovery studio 4.1), GLIDE (Schrodinger Inc.) and AUTODOCK27. The purpose of using three independent docking software was to improve the efficiency of virtual screening and to accurately re-rank these compounds on the basis of their affinity with LdGcs. The top five compounds having consistently high scores in all three independent docking software were taken forward for MD simulation studies. The scores of the top five compounds are summarized in Table 1, while the scores of the 20 compounds are provided in Table S1. Table 1. The top five molecules obtained as a result of virtual screening workflow along with docking scores and interaction energy score. S.

Maybridge Structure

No Code

Interacting

Sybyl

Autodock Glide C-

residues

Score

Score

score

Docker score

1

BTB13334

Glu53, Glu92, 11.9977 -10.95

-6.72

11.997

-6.27

11.029

Arg498

2

BTB13337

Glu53, Glu92, 11.0299 -10.32 Glu99,Gln328, Arg498



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3

HTS05833

Glu55, Glu92, 10.3886 -9.79

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-5.46

10.98

-5.78

10.24

-8.04

10.05

Glu99, Arg498

4

HTS01124

Glu92, Glu99, 10.2053 -8.07 Met322, Arg498

5

HTS11169

Glu92, Glu99, 10.0501 -9.48 Met322, Arg498

3.2. Molecular Docking and proposed mode of binding of active compounds The predicted binding modes of top five compounds viz. BTB13334, BTB13337, HTS05833, HTS01124 and HTS11169 are shown in Figure 3A. Arg 498 forms a hydrogen bond with all the ligands, in addition, Glu 53 and Glu 92 interact with BTB13334 and BTB13337, with the latter forming H-bonds with Glu 99 and Gln 328 as well. The interactions of Glu 99 and Gln 328 are conserved in HTS05833 and HTS01124. HTS11169. BTB13334 forms hydrogen bonds with Glu 53 and Glu 92, BTB13337 with Glu 53, Glu 92, Glu 99 and Gln 328. HTS05833, HTS01124 and HTS11169 have conserved interactions with Glu 92 and 99 with additional interactions with


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Glu55 in case of HST05833 and Met322 in HST01124 and HTS11169 respectively. The aromatic rings of ligands interact with aromatic residues Phe 179, Try 397 and Trp 477 with additional hydrophobic interaction with Met 322. The overall binding mode of all compounds is similar to that of BSO. The structural superimposition of top five compounds shows that they are broadly comprised of a central polar part surrounded by hydrophobic groups on one end and polar groups on the other end (Figure 3B). This composition is similar to BSO with the groups replaced with more hydrophobic and polar groups. The structural composition of these compounds shows they have higher potential to bind the active site and inhibit the activity of Gcs than BSO. 3.3. Molecular dynamics simulation The molecular docking procedure, while providing details of probable protein-ligand interactions in static conformations, does not give details on the stability of them. To assay the stability of docked compounds within the LdGcs active site and to get better insight into the docking results 10 ns MD simulation studies of the five protein-ligand complexes viz, the protein docked with compounds, BTB13334, BTB13337, HTS05833, HTS01124 and HTS11169 were carried out. The top scoring docked pose obtained as a result of Surflex-geomX was used as start pose for simulation studies. The predicted hydrogen bonds and hydrophobic interactions are conserved throughout the simulation. The average RMSDs of the protein backbone atoms were found to be 0.69, 0.65, 0.77, 0.64 and 0.73 nm for protein-ligand complexes of BTB13334, BTB13337, HTS 05833, HTS01124 and HTS11169 respectively (Figure 4A). The higher overall RMSD of ligand complexes is due to the presence of flexible loops in the protein. The RMSD polt of LdGcs in water is shown in Figure S1 where also the average RMSD is 0.73 nm. The root-mean-square fluctuation (RMSF) analyses highlighted possible protein movements involving residues 200300 for all the predicted complexes (Figure 4B). Higher RMSF values in this region is due to the presence of unstrcutured loop region. The RMSDs for ligands at the end of simulation were 0.12, 0.18, 0.29, 0.14 and 0.16nm for BTB13334, BTB13337, HTS05833, HTS01124 and HTS11169 respectively stating ligands are stable with in the binding site (Figure 4C). The occupancy of protein-ligand hydrogen bonds through out the MD trajectories were also analyzed and were conserved throughout the simulation. All five compounds have shown consistant hydrogen bond with Glu 92 and Arg 498 (Figure 4D). Additional residues having high occupancy values are Glu



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55 for BTB13334, Asn 324 and Arg 373 for BTB13337 and Glu 55, Gln 328 and Arg 373 for HTS11169 respectively (Figure 4D). 3.4. Enzyme inhibition evaluation After validating the identified compounds using computational approaches, experimental validation by in vitro assays were performed. In the in vitro assay, measurement of the ATPase activity was carried out in presence of inhibitors. The experiment are described in Materials and Methods, measures the amount of ADP formed using ADP-Glo assay kit (Promega, USA). The relative percentage inhibition was plotted with the 25 and 50 µM inhibitor concentration (Figure 5). BSO was taken as a positive control while FM00174, a compound available in the group was used as a negative control. The results indicate that at 50 uM concentration, all the compounds show ~ 50 % inhibition, while at 25 uM concentration, the percentage inhibition is lower. However, except for one of the compounds (HTS11169), the percentage inhibition was better than BSO, with three of the compounds (BTB13337, HTS05833 and HTS01124) showing > 40% inhibition. Protein treated with the FM00174 (negative control) show negligible inhibition, as expected. Since the compounds have shown significant inhibition of LdGcs activity, we also monitored the dose response curve of these compounds and determined half maximal inhibitory concentration (IC50). The dose response curve clearly show significant inhibition with all compounds (Figure 6 A-E). The IC50 values were found < 100 uM for all the compounds except HTS05833. (Table 2) To evaluate the relative binding affinities of compounds with LdGcs, tryptophan fluorescence quenching was monitored. The Trp 477 lies within the binding pocket enhancing the fluorescence quenching upon substrate binding. The apparent binding constants of the inhibitors lie in micromolar range comparable with BSO. The relative affinity of all compounds follow the order HTS05833 > BTB13334 > BTB13337 > HTS11169 > HTS01124 (Table 2). The saturation binding isotherms of all the inhibitors are shown in Figure 6 D. In order to rule out any possibility of false inhibition by aggregation of protein the tryptophan fluorescence spectra were recorded (Figure S2). The fluorescence spectra shows no shift in emission maxima reducing the possibility of false inhibition.


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Table 2. Apparent Kd and IC50 Values of LdGcs with different compounds. The Kd value was obtained by nonlinear regression analysis of tryptophan fluorescence quenching data using Prism software. Compound

Kdapparant (uM)

IC 50 (uM)

BSO

1.13 ± 0.27

BTB13334

1.77 ± 0.44

59 ± 1.03

BTB13337

2.2 ± 0.28

71 ± 3.1

HTS05833

1.07 ± 0.30

352 ± 0 .2

HTS01124

8.57 ± 1.31

94 ± 2.70

HTS11169

7.60 ± 1.51

79 ± 0.21

3.5. Anti-leishmanial activity and specificity The hit obtained as result of virtual screening workflow has shown significant inhibition of LdGcs activity. Next essential step is determining the effect of these inhibitors on the survival of L. donovani. For this purpose promastigote proliferation assay in the presence of 100 and 25 µM inhibitors were performed on L. donovani DD8 promastigotes. Figure 7 shows that BTB13337 is able to inhibit cell proliferation upto 60 % at 100 µM concentration while BTB13334, HTS05833 and HTS11169 shows 34, 36 and 20% inhibition. Further, the compounds BTB1334, HTS05833 and HTS11124 shows >25% inhibition even at 25 µM concentration. To validate the predicted specificity of the hit compounds we also performed cell viability assays on human HEK293 cell lines. Compounds BTB1334 treatment shows no effect on cell survival while BTB1337 and HTS11169 treatment shows >70% survival in HEK293 cells. HTS05833 and HTS01124 might be toxic as only 30 % and 20 % cell survival were observed in HEK293 cells over vehicle control (Figure 8). BTB13334 shows less than 20 % toxicity even at 8 times concentration (Data not shown). These results clearly shows that these BTB13334, BTB13337



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and HTS11169 are potential LdGcs inhibitors with novel scaffolds which can be modified to increase their efficacy and efficiency as drugs. Colloidal aggregation of the compounds also contribute to non-specific inhibition35. The possibility of compounds to behave as an aggregator can be predicted by monitoring its similarity with available aggregators using Aggregation Advisor tool

35

. Among the five

compounds only BTB13337 shows 76 % similarity with the known aggregators but has a low log P value (1). BTB13334 and HTS11169 have very low log P value (-0.1 and -0.4) with no similarity with known aggregators suggesting significantly low possibility of false inhibition by these compounds. Further HTS05833 and HTS01124 though share no similarity with known aggregators, have log P value ~3, revealing the slight possibility of aggregation. Aggregators are generally non-specific inhibitors. For this purpose we also studied the kinase activity of an unrelated kinase in presence of these compounds. No significant inhibition was observed (data not shown) suggesting these compounds might not be aggregators.

Negligible effect on

proliferation of human cell lines also justify low probability of BTB13337, BTB13334 and HTS11169 being aggregators.

4.Conclusion Implication of Gcs in L. donovani survival and pathogenesis makes it an attractive drug target for drug discovery. The known inhibitors of Gcs i.e, BSO and its derivatives have relatively higher inhibition in mammal Gcs and lack specificity towards trypanosomatids. BSO have also shown toxicity in rats infected with trypanosomatids after prolonged treatment emphasizing the need to develop more potential inhibitors of L. donovani Gcs. In the hunt of novel and specific inhibitors of LdGcs and pertaining to the availability of structural information regarding most essential catalytic residues, structure based ordered virtual screening workflow was followed. The docking studies was followed by MD simulation verification through three independent software and visual inspection to intend the discovery novel inhibitors against LdGcs. Five inhibitors were prioritized on the basis of our computational analysis that involved independent docking using different algorithms, and were taken up for experimental validation. Four of the five compounds show significant inhibition of LdGcs activity with an IC50 value

Identification of Novel Inhibitors of Leishmania donovani Î³

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