A combined spectroscopic

International Journal of Biological Macromolecules 111 (2018) 548–560

Contents lists available at ScienceDirect

International Journal of Biological Macromolecules journal homepage: https://www.journals.elsevier.com/biomac

Exploring molecular insights into the interaction mechanism of cholesterol derivatives with the Mce4A: A combined spectroscopic and molecular dynamic simulation studies Shagufta Khan a, Faez Iqbal Khan b, Taj Mohammad a, Parvez Khan a, Gulam Mustafa Hasan c, Kevin A. Lobb b, Asimul Islam a, Faizan Ahmad a, Md. Imtaiyaz Hassan a,⁎ a b c

Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India Computational Mechanistic Chemistry and Drug Discovery, Rhodes University, South Africa Department of Biochemistry, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

a r t i c l e

i n f o

Article history: Received 2 October 2017 Received in revised form 16 November 2017 Accepted 30 December 2017 Available online 10 January 2018 Keywords: Mycobacterium tuberculosis Mce4A Molecular docking Drug target Molecular dynamics simulation

a b s t r a c t Mammalian cell entry protein (Mce4A) is a member of MCE-family, and is being considered as a potential drug target of Mycobacterium tuberculosis infection because it is required for invasion and latent survival of pathogen by utilizing host's cholesterol. In the present study, we performed molecular docking followed by 100 ns MD simulation studies to understand the mechanism of interaction of Mce4A to the cholesterol derivatives and probucol. The selected ligands, cholesterol, 25-hydroxycholesterol, 5-cholesten-3β-ol-7-one and probucol bind to the predicted active site cavity of Mce4A, and complexes remain stable during entire simulation of 100 ns. In silico studies were further validated by fluorescence-binding studies to calculate actual binding affinity and number of binding site(s). The non-toxicity of all ligands was confirmed on human monocytic cell (THP1) by MTT assay. This work provides a deeper insight into the mechanism of interaction of Mce4A to cholesterol derivatives, which may be further exploited to design potential and specific inhibitors to ameliorate the Mycobacterium pathogenesis. © 2018 Elsevier B.V. All rights reserved.

1. Introduction Mammalian cell entry protein (Mce4A) plays a major role in the invasion of host cell by M. tuberculosis (Mtb) [1, 2]. Mce proteins are homologous of ATP-binding cassette transporters (ABC-transporters) [3, 4]. Mce4 is involved in the latent infection of M. tuberculosis and show a growth defect in the absence of the same operon [5, 6]. Structurally, Mce4A is cell surface associated, stationary phase protein having one trans-membrane region and also required for survival of pathogen in host cells by modulating the apoptotic response [1, 7]. The Mce4 family proteins help in the cholesterol transport in M. tuberculosis to provide energy [8, 9]. Sequence analysis of M. tuberculosis H37Rv genome suggests the presence of four operons mce1, mce2, mce3 and mce4. Each operon comprises of eight genes, two for integral membrane proteins (yrbE A and yrbE B genes), while mce A-F genes encode exported proteins which were supposed to be important for the entry and survival of the pathogen in host cells [6, 10, 11]. Many cholesterol-binding motifs which differ in length, amino acid sequence are found in trans-membrane (TM) domains. These ⁎ Corresponding author. E-mail address: [email protected] (M. Imtaiyaz Hassan).

https://doi.org/10.1016/j.ijbiomac.2017.12.160 0141-8130/© 2018 Elsevier B.V. All rights reserved.

motifs are CRAC [12], CARC [13], tilted peptides [14], cholesterol consensus motifs [15] and sterol sensing domains [16]. In the case of Mce4A, CARC and CRAC are present but not in the TM domain. The TM domain of Mce4A contain only GXXXG motif (GLMVG), which suggesting its binding to the cholesterol and helps in the long term survival of Mtb in the host by utilizing cholesterol as source of energy [17]. To understand the mechanism of host-pathogen interaction the Mce4A binding to the cholesterol must be studied because this protein helps in the entry of the Mtb into the host [18]. Better understandings of the mechanism of invasion guide us to design potent inhibitors of Mce4A which may be further exploited to regulate the bacterial pathogenesis. Design of drugs with novel modes of action has been the pursuit of the investigators to search for inhibitors. Here, we have combined both computational and experimental studies to understand the mechanism of interaction of cholesterol binding to the Mce4A. In the present study, molecular docking was performed for Mce4A with four selected ligands including cholesterol, 25-hydroxycholesterol, 5-cholesten-3β-ol-7-one and probucol. Molecular dynamics (MD) simulations were performed to evaluate the binding prototype of ligands with the Mce4A. In order to validate in silico results, fluorescence binding experiment was performed. These results confirm that all studied

S. Khan et al. / International Journal of Biological Macromolecules 111 (2018) 548–560

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Luria broth, Luria agar, kanamycin, monoclonal anti-His antibody, DNA preparation kit, dialysis tubing, 25-hydroxycholesterol, 5cholesten-3β-ol-7-one and probucol were purchased from the Sigma Chemical Co. (St. Louis, USA). Ni-HF column was purchased from GE healthcare (GE Healthcare Life Sciences, Uppsala, Sweden). Cholesterol was procured from Calbiochem (India). All chemicals and reagents were of analytical grade.

docking studies to predict the bound conformations and binding affinities. The active site of the protein was predicted by ProBis server [23]. AutoDcok4 [24] and AutoDcok Vina [25] were used for molecular docking. PyMol [26], Discovery Studio Visualizer [27] and LigPlot+ [28] were used for visualization purpose to see interactions between protein and ligands. During the molecular docking on AutoDock Vina, we predicted best ligand binding site in the protein and we repeated docking calculations several times by controlling the configuration parameter ‘exhaustiveness’ to get optimized docked poses. The best docked conformations were selected on the basis of obtained interaction energy parameters, scoring and position of docked conformer at the protein [29].

2.1. Structure predictions

2.3. MD simulations

Three-dimensional structure of Mce4A is not available in the Protein Data Bank (PDB), so we have performed structure prediction using bioinformatics tools and web-servers. For the modeling, we didn't find a good template for Mce4A because of the lack of high sequence identity and query coverage. Here, we used fold recognition approach to predict the model structure of Mce4A using online version of Modeller from SaliLab, ModWeb [19], Phyre2 [20] and I-TASSER web-servers [21].

MD simulation is a powerful computational method to describe the flexibilities of macromolecules. Many biological functions in proteins and their mechanisms can be analysed by studying their internal motions [30–33]. MD simulations were performed on free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25hydroxycholesterol and Mce4A-probucol complexes at 300 K at the molecular mechanics level implemented in the GROMACS 5.1.2 [34] using the GROMOS96 43a1 force-field. The ligands were extracted from the docked complexes using gmx grep module. The topology and forcefield parameter files of all the ligands were generated using PRODRG server [35]. The charges in the topology file were manually corrected. The generated topologies of Mce4A using pdb2gmx modules of gromacs and cholesterol, 5-cholesten-3β-ol-7-one, 25-hydroxycholesterol and probucol using PRODRG server were merged. The additional ligand

ligands interact to the protein and a detailed investigation of mechanism of interaction has been provided in this manuscript. 2. Materials and methods

2.2. Molecular docking Predicted structure of Mce4A was used for molecular docking with ligands, cholesterol, 5-cholesten-3β-ol-7-one, 25-hydroxycholesterol and probucol. Chemical structures of these ligands were downloaded from PubChem and remodelled in ChemBioDraw Ultra 12.0 [22] for

Fig. 1. Molecular docking of cholesterol with Mce4A. (A) Chemical structure of cholesterol. (B) Binding of cholesterol (stick model) with Mce4A (cartoon model). (C) 2D diagram showing interacting residues of Mce4A and cholesterol. (D) Surface representation of Mce4A complexed with cholesterol (ball and stick model).

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Fig. 2. Molecular docking of 5-cholesten-3β-ol-7-one with Mce4A. (A) Chemical structure of 5-cholesten-3β-ol-7-one. (B) Binding of 5-cholesten-3β-ol-7-one (stick model) with Mce4A (cartoon model). (C) 2D diagram showing interacting residues of Mce4A and5-cholesten-3β-ol-7-one. (D) Surface representation of Mce4A complexed with 5-cholesten-3β-ol-7-one (ball and stick model).

atoms were combined in the complex topology files, and the parameters for all the ligands were included in the system topology. The free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes were soaked in a cubic box of water (10 Å) using the gmx editconf and gmx solvate modules for creating boundary conditions and solvation, respectively. The system was then minimized using 1500 steps of steepest descent. The temperature of all the systems was subsequently raised from 0 to 300 K during their equilibration period (100 ps) at a constant volume under periodic boundary conditions. Details of MD simulation method are provided in our previous communications [36, 37]. 2.4. MMPBSA calculation A short protein-ligand snapshot of MD trajectories was extracted from the stable region of each complex for molecular mechanics Poisson Boltzmann surface area (MMPBSA) estimations. The binding energy components were calculated using MMPBSA protocols implemented in the g_mmpbsa package [38]. The binding energies were calculated using following equation: ΔGBinding ¼ GComplex − GProtein þ GLigand

where, Gcomplex signifies the total free energy of the binding complex, and GProtein and GLigand are the measure of total free energies of the individual protein and ligand, respectively.

2.5. Protein expression and purification We used our well optimized protocol for cloning, expression and purification of Mce4A, described elsewhere [18, 39]. In short, designed primers were used to amplify the Mce4A gene which was ligated into pET 28a expression vector and E. coli BL21 (DE3) competent cells for expression of the protein. IPTG (isopropyl-β-D-thiogalactopyranoside) was used for induction once absorption reached 0.6 at 600 nm and cell were further grown for 3 h at 37 °C. The cultured cells were harvested by centrifugation and pellets were dissolved in cell lysis buffer which contain 8.0 mM Na2HPO4, 286 mM NaCl, 1.4 mM KH2PO4, 2.6 mM KCl and 1% SDS (w/v) for 15 min for cell homogenization. After this the cell lysate was subjected to sonication and centrifugation at 12,000 rpm for 30 min to separate pellets and supernatant. Clear supernatant was applied to Ni-sepharose column and the bound protein was eluted with elution buffer containing imidazole. The purity of the protein was checked using SDS-PAGE. The concentration of Mce4A was determined experimentally using the molar absorption coefficient at 280 nm (16,515 M−1 cm−1) [40].

2.6. Fluorescence measurements Jasco spectrofluorimeter (Model FP-6200) was used for fluorescence measurements using 5 mm quartz cuvette. Protein was kept in the MES buffer of pH 6.0 at a concentration of 10–15 μM. Cholesterol, 25hydroxycholesterol, 5-cholesten-3β-ol-7-one and probucol were used


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Fig. 3. Molecular docking of 25-hydroxycholesterol with Mce4A. (A) Chemical structure of 25-hydroxycholesterol. (B) Binding of 25-hydroxycholesterol (stick model) with Mce4A (cartoon model). (C) 2D diagram showing interacting residues of Mce4A and 25-hydroxycholesterol. (D) Surface representation of Mce4A complexed with 25-hydroxycholesterol (ball and stick model).

in a concentration range 0–30 μM. The Mce4A was excited at 274 nm due to the presence of tyrosine residue only. Fluorescence emission spectra were recorded in the wavelength range of 300–320 nm. All ligands were dissolved in DMSO and then diluted to 1 mg/ml in the 20 mM MES buffer. The protein solution was titrated with the increasing concentration of ligands and the final spectrum was collected after subtracting fluorescence intensity of the buffer/ligands from each spectrum. Average of three independent measurements was taken for calculation of binding affinity. Fluorescence intensity decreases on increasing concentration of ligands and emission peak observed at 306 nm. The change in fluorescence intensity at 306 nm were used to estimate the binding constant (Ka) and number of binding site (n) using modified Stern–Volmer equations [41, 42]. Log ð F o − F Þ=F ¼ log K a þ n log ½L

ð1Þ

where, Fo and F are the fluorescence intensities of Mce4A at 306 nm in the absence and presence of different concentrations of ligands, respectively. Where, Ka is the binding constant and n is the number of binding site which were obtained from the intercept and slope, respectively. 2.7. MTT assay MTT assay was performed to determine cell viability as described previously [43, 44]. Briefly, THP1-cells (Human monocytic cells) were seeded in 96-wells plate at 1 × 104 viable cell per well. These cells were cultured for 48 h in presence of increasing concentration (5–

200 μM) of each ligands. Both ligands and medium were removed by centrifugation after 48 h of treatment and washed twice with phosphate buffer saline (PBS). MTT, 3-(4,5-Dimethyl-2-thiazolyl)-2,5diphenyl-2H-tetrazolium bromide, was added into each well followed by a 4–5 h incubation in the CO2 incubator at 37 °C. The remaining MTT medium was removed and 100 μl of DMSO was added to solubilise the formazan crystals by keeping on orbital shaker at 37 °C for 20 min. The absorbance of the solution was measured at 570 nm on Titerplate reader (BioRad). The obtained absorbance value was converted into percentage viability. The percent cell viability of the control (untreated) cells was taken as 100%. 3. Result and discussion Majority of active TB cases are reported not from early infection with actively growing bacilli but from recurrence of earlier implanted bacteria that have been latent or growing gradually within the host [45, 46]. This type of asymptomatic stage of the disease can last for several years during which the bacteria are resistant to available drugs and chemotherapies [47–50]. Thus, there is an urgent need for the designing of drug or inhibitor for M. tuberculosis. Here, we target Mce4A protein for designing inhibitors. Mce4A is cholesterol binding protein and helps in the survival of M. tuberculosis inside the host macrophages. In order to search for potential inhibitor against Mce4A, we choose oxy-sterol (25-hydroxycholesterol, cholest-5-en-3β-ol-7-one) which may be ineffectively metabolized and can't help for the survival of M. tuberculosis. 25-Hydroxycholesterol (25-HC, oxy sterol) contains another hydroxyl group on the 25-position along with 3-position hydroxyl

552


Fig. 4. Molecular docking of probucol with Mce4A. (A) Chemical structure of probucol. (B) Binding of probucol (stick model) with Mce4A (cartoon model). (C) 2D diagram showing interacting residues of Mce4A and probucol. (D) Surface representation of Mce4A complexed with probucol (ball and stick model).

group. Cholesterol regulatory actions can mimic by the 25-HC and related oxy sterols [51].25-HC is an effective competitor of cholesterol binding in case of luminal loop-1 of human NPC1 [52]. 7-ketocholesterol (cholest-5-en-3β-ol-7-one,7K) inhibits cholesterol efflux from macrophages to apoA-1 [53]. On the other hand, we also choose probucol drug against Mce4A, which is an inhibitor of ABCA1 [54, 55] because Mce4A is homologous to ABC transporter. Probucol has been clinically used as an anti-atherogenic compound, not only due to its lipid-lowering effect but also because its anti-oxidative nature [56, 57]. 3.1. Protein structure prediction Three-dimensional structure of Mce4A was predicted using I-Tasser server. The selection of final model was based on overall quality factor, C-score (Confidence score for estimating the quality of predicted models) and ranking. Five models were generated by I-Tasser based on several templates selected by the LOMETS meta-server on the basis

of Z-score, which were showing a good sequence identity, query coverage and Z-score to our protein. The final selected model of the Mce4A shown in Fig. S1A. Although, there is a chance of having some errors and high energy configurations in the predicted model that may lead to a physical perturbation and instability of the structures. Thus, we have optimized this model by fixing errors and energy minimization to find stable, lowest energy conformations of a system by changing the geometry. ERRAT [58] tool was used to fix the errors present in the model, and energy minimization of the model was performed using Swiss PDB viewer (SPDBV) tool [59]. The computed total internal energy of the model in force-field was −9246.558 and −14,310.680 on before and after minimization, respectively. Model evaluation and validation is an essential step in order to predict an accurate three-dimensional structure of a protein. We checked the final model to ensure whether the predicted models are following the standards or not on SAVES server (http://services.mbi.ucla.edu/ SAVES/). Ramachandran plot is used to visualize dihedral angles ψ and

Table 1 List of scoring parameters generated from molecular docking of Mce4A with ligands. Docking method

AutoDock Vina AutoDock 4

Generated parameters

Affinity (kcal/mol) Free energy of binding (kcal/mol) Intermolecular energy (kcal/mol) Vd W + H bond + de solv energy (kcal/mol) Internal energy (kcal/mol) Torsional free energy (kcal/mol) Inhibition constant, Ki (μM)

Scoring for ligands Cholesterol

5-Cholesten-3β-ol-7-one

25-Hydroxy cholesterol

Probucol

−9.9 −9.86 −11.65 −11.65 −0.71 +1.79 59.16

−9.5 −10.25 −12.04 −12.06 −0.30 +1.79 30.76

−9.0 −8.73 −10.82 −10.78 −0.41 +2.09 36.99

−7.8 −6.85 −9.84 −9.75 −2.17 +2.98 9.47


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Table 2 Parameters accepting Lipinski rule of five, predicted using SWISS-ADME web server. Ligands

M W (Da)

LogP

HBD

HBA

rBonds

Lipinski violation

Drug likeliness

Cholesterol 5-Cholesten-3β-ol-7-one 25-Hydroxycholesterol Probucol

386.65 400.64 402.65 516.84

4.89 4.49 4.55 5.79

1 1 2 2

1 2 2 2

5 5 5 8

0 0 0 2

Yes Yes Yes Yes

MW: molecular weight, LogP: lipophilicity, HBD: hydrogen bond donor, HBA: hydrogen bond acceptor, rBonds: rotatable bonds.

φ of amino acid residues in a protein structure to check whether the amino acid residues are present in a protein structure in favourable allowed region. Here, in predicted model, 92.2% (367) of residues are present in the favourable region. A very less number of residues (11 which is 2.8% of total protein) are present in outlier region, showing an acceptable quality of predicted model (Fig. S1B). Further, verification of the predicted model was also carried out using ERRAT [58] which defines the errors present in a protein structure based on the statistics of non-bonded atom-atom interactions in the previously reported reliable high-resolution structures in the PDB. Fig. S1C showing an error plot of predicted model, where the regions of the protein structure having a chance of rejection at confidence level of 95% are touched to the first line (from the below) in dark brown colour; and the regions that can be rejected at the 99% confidence level are shown in black. The overall quality factor for our model is 79.21%, which is the percentage of protein residues for which the estimated error value falls below the rejection limit that is 95%. A high resolution protein structures generally produces the overall quality factor between 95 and 100%, while for the low resolution structures, the value is around 90% or less. Finally, our predicted model is falls in the low resolution category. We further used Verify 3D [60] to determine the compatibility of three-dimensional structure. Our predicted model is passed in its assessment, 69.50% of amino acid residues had an averaged 3D-1D score ≥ 0.2. The passing criterion of its assessment is that, at least 65% of the residues should have score ≥ 0.2 in the 3D/1D profile. 3.2. Molecular docking Molecular docking studies provide an accurate and preferred orientation of the ligands at the binding site of the receptor protein. Here, in molecular docking studies, we found that all the ligands (cholesterol, 5cholesten-3β-ol-7-one, 25-hydroxycholesterol and probucol) were docked into the predicted cavities of the protein and resulted docked conformations were selected on the basis of obtained interaction energy parameters and scoring functions. Conformations of docked ligands with Mce4A are shown in Figs. 1–4. The outcomes showing free energy of binding of ligands with Mce4A are shown in Table 1. All the ligands found to interact with target protein with several hydrogen bonds and non-covalent interactions such as Alkyl, π-Alkyl and van der Waals (vdW) interactions offered by Mce4A (Figs. 1–4). The analysis of docked conformations clearly indicate that the ligands bind to the predicted active site of the protein which enters deeper into the cavity, in which the ligand interact with several active site residues of the protein which may be presumably responsible for its inhibition by decreasing the accessibility of the substrate (Figs. 1–

4C). Ala360 of Mce4A forms hydrogen bond with cholesterol along with many vdW interactions (Fig. 1C). In the case of 5-cholesten-3βol-7-one, Ala360 and Gln387 of Mce4A form hydrogen bond along with vdW, alkyl and alkyl–π interactions (Fig. 2C). It can be seen in Fig. 3C that two hydrogen bonds are formed by 25-hydroxycholesterol to the Glu257 and Glu263 of Mce4A accompanied by several vdW interactions, clearly indicating the formation of a stable complex. Fig. 4C clearly shows that the formation of hydrogen bond by Asp366 with oxygen atom of probucol. Several hydrophobic residues also play role in stabilizing the ligand–macromolecular complex by forming an alkyl, alkyl–π interaction, vdW interactions, π-π stacked, π-σ, π-sulphur interactions. All ligands were further analysed on the basis of Lipinski's Rule of five using SwissADMET web tool [61]. Interestingly, all ligands are well qualified in Lipinski's filter as described in Table 2, indicating the drug likeness of these ligands. Although, Probucol is violating some of Lipinski rules but it is avoidable because the ligand is qualified in all other filters. The physiochemical parameters of each molecule are given in Table 3, which states the ADMET properties such as BBB, HIA, HPB, PSA, which supporting the drug likeliness of the ligands and noncarcinogenic and non-mutagenic properties by PreADMET server (https://preadmet.bmdrc.kr/). 3.3. MD simulation 3.3.1. Average potential energy of system To ascertain the equilibration of the systems prior to MD analysis, the average potential energy of free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes were monitored. The constant temperature fluctuations at 300 K, for each system suggested a stable and accurate nature of the MD simulations performed. An average potential energy for free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes were found to be −1,610,583.93 kJ/mol, −1,605,266.06 kJ/mol, −1,610,287.58 kJ/mol, −1,604,854.94 kJ/mol, −1,609,497.26 kJ/mol, respectively. 3.3.2. Structural deviations and compactness Binding of a ligand in the active pocket of protein can leads to large conformational changes [62, 63]. Root mean square deviation (RMSD) is one of the fundamental properties to establish whether the protein is stable and close to the experimental structure [64]. The average RMSD values were found to be 0.79 nm, 0.82 nm, 0.84 nm, 0.74 nm, and 0.73 nm for free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-

Table 3 ADMET properties of compounds predicted using PreADMET web server.a Ligands

BBB

LogS

Log Kp

CYP2D6 inhibitor

HIA (%)

PPB (%)

Bioavailability

PSA Å2

Mutagenicity

Carcinogenicity

Cholesterol 5-Cholesten-3β-ol-7-one 25-Hydroxycholesterol Probucol

19.40 12.92 13.25 9.47

−7.40 −6.71 −6.28 −9.91

−2.47 −3.43 −3.94 −1.41

Non Non Non Non

100.0 96.35 93.95 96.68

100.0 98.25 100.0 97.67

0.55 0.55 0.55 0.55

20.23 37.30 40.46 91.06

NM NM NM NM

NC NC NC NC

BBB (Blood Brain Barrier) penetration ability: N2.0 is high absorption, LogS: water solubility, LogKp: Skin permeation, HIA (Human Intestinal Absorption): 70–100% is for well absorbed compounds PPB (Plasma Protein Binding): N95% is strongly bound, Bioavailability score: 0.55 passes the rule-of-five, PSA (Polar Surface Area): ≤90 Å2 is the optimum value. a https://preadmet.bmdrc.kr/

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Table 4 The calculated parameters for all the system obtained after 100 ns MD simulations. Complexes

Average potential energy (kJ/mol)

Radius of gyration (nm)

Average RMSD (nm)

Average SASA (backbone, nm2)

Free energy of solvation (kJ/mol/nm2)

Volume (nm3)

Density (g/l)

Mce4A Mce4A-cholesterol Mce4A-5-cholesten-3β-ol-7-one Mce4A-25-hydroxycholesterol Mce4A-probucol

−1,610,583.93 −1,605,266.06 −1,610,287.58 −1,604,854.94 −1,609,497.26

2.11 2.10 2.19 2.10 2.05

0.79 0.82 0.84 0.74 0.73

195.97 192.43 197.55 195.36 198.53

333.96 336.50 328.00 343.59 323.61

70.62 70.44 70.82 70.88 70.61

997.64 1000.33 994.79 994.02 997.91

3β-ol-7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes, respectively (Table 4). The RMSD plot suggested that the binding of cholesterol to the protein does not significantly affects the structure, while the binding of 5-cholesten-3β-ol-7-one leads to a larger structural deviation of Mce4A structure (Fig. 5A). On the other hand, the binding of 25-hydroxycholesterol and probucol to Mce4A stabilize the protein structure. The least RMSD values of 25hydroxycholesterol-Mce4A and Mce4A-probucol complexes

suggesting a strong binding. The orientations of ligands were also compared in the active pocket of protein (Fig. 5B). Probucol was highly fluctuated during the first 60 ns MD simulations; thereafter it attains a stable equilibrium throughout the 100 ns time scale. Vibrations around the equilibrium are not random, but depend on the local structure flexibility. To calculate the average fluctuation of all residues during the simulation, root mean square fluctuation (RMSF) of the Mce4A upon ligands

Fig. 5. Dynamics of ligands binding to the Mce4A. (A) RMSD plot as a function of time. Black, red, green, blue and yellow colour represent values obtained free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25-hydroxy cholesterol and Mce4A-probucol complexes, respectively. (B) Comparison of orientations of Mce4A-cholesterol (red), Mce4A-5cholesten-3β-ol-7-one (green), Mce4A-25-hydroxycholesterol (blue) and Mce4A-probucol (yellow) complexes into the active pocket of Mce4A. (C) RMSF plot for free Mce4A (black), Mce4A-cholesterol (red), Mce4A-5-cholesten-3β-ol-7-one (green), Mce4A-25-hydroxy cholesterol (blue) and Mce4A-probucol (yellow) complexes. (D) Rg values during 100,000 ps (100 ns) of MD simulation. Rg plot for free Mce4A is shown in black, red, green, blue and yellow for Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25-hydroxy cholesterol and Mce4A-probucol complexes, respectively. (E) SASA plot showing curves in black, red, green, blue and yellow for free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten3β-ol-7-one, Mce4A-25-hydroxy cholesterol and Mce4A-probucol complexes, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)


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Fig. 6. The average number of hydrogen bonds as a function of time. Curves in red, green, blue and yellow represent the number of hydrogen bonds for Mce4A-cholesterol, Mce4A-5cholesten-3β-ol-7-one, Mce4A-25-hydroxy cholesterol and Mce4A-probucol complexes, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

binding were plotted as a function of residue number (Fig. 5C). RMSF plot showed residual fluctuations are present in Mce4A at different regions of protein structure such as 80–130 aa, 150–170 aa, 250–300 aa, and 330–360 aa, respectively. These residual fluctuations found to be minimized upon ligand binding. Accordingly, the residues such as 257 aa, 263 aa, 360 aa, 366 aa, and 387 aa, that participated in interactions with ligands found to have low RMSF values.

Radius of gyration (Rg) is a parameter linked to the tertiary structural volume of a protein and has been applied to obtain insight into the stability of the protein in a biological system. A protein is supposed to have the higher Rg value due to lose packing. Average Rg values for free Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol were found to be 2.11 nm, 2.10 nm, 2.19 nm, 2.10 nm, and 2.05 nm, respectively. Rg plot

Table 5 Percentage of residues participated in average structure formation. Complexes

Mce4A Mce4A-cholesterol Mce4A-5-cholesten-3β-ol-7-one Mce4A-25-hydroxycholesterol Mce4A-probucol a

Percentage of protein secondary structure (SS %) Structurea

Coil

β-Sheet

β-Bridge

Bend

Turn

α-Helix

310-Helix

57% 57% 56% 55% 55%

26% 28% 27% 28% 28%

12% 10% 12% 14% 10%

2% 2% 2% 2% 2%

15% 14% 14% 14% 15%

12% 13% 11% 12% 13%

31% 33% 31% 27% 30%

1% 1% 1% 1% 1%

Structure = α-helix + β-sheet + β-bridge + Turn.

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Table 6 Average binding energy calculation obtained from MM-PBSA for protein-ligand complexes. Complexes

van der Waal energy (kJ/mol)

Electrostatic energy (kJ/mol)

Polar solvation energy (kJ/mol)

SASA energy (kJ/mol)

Binding energy (kJ/mol)

Mce4A-cholesterol Mce4A-5-cholesten-3β-ol-7-one Mce4A-25-hydroxycholesterol Mce4A-probucol

−241.86 ± 0.65 −212.13 ± 1.19 −247.85 ± 0.55 −268.61 ± 1.08

−23.37 ± 0.19 −84.69 ± 0.52 −10.62 ± 0.10 −14.10 ± 0.12

98.70 ± 0.50 159.84 ± 0.92 76.55 ± 0.49 60.69 ± 0.43

−19.38 ± 0.04 −18.16 ± 0.08 −20.84 ± 0.04 −20.90 ± 0.06

−185.96 ± 0.56 −155.14 ± 1.11 −202.76 ± 0.48 −214.78 ± 0.89

suggested that the protein attained a tight packing upon binding of probucol, and less tight packing upon binding of 5-cholesten-3β-ol-7one, respectively (Fig. 5D). 3.3.3. Solvent accessible surface area Solvent Accessible Surface Area (SASA) is defined as the surface area of a protein which interacts with its solvent molecules [65]. The average SASA values for Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes were also monitored during 100 ns MD simulations. The average SASA values for Mce4A, Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7one, Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes were found to be 195.97 nm2, 192.43 nm2, 197.55 nm2, 195.36 nm2, and 198.53 nm2, respectively (Fig. 5E). There was no such major change found in the SASA values due to ligand binding. The average SASA values for Mce4A-5-cholesten-3β-ol-7-one and Mce4A-probucol were slightly higher. This can be presumed as the internal residues in the protein are exposed to solvent due to denaturation or conformation change in

the protein arises due to binding of ligand. During SASA calculations, free energy of solvation of Mce4A, Mce4A-cholesterol, Mce4A-5cholesten-3β-ol-7-one, Mce4A-25-hydroxycholesterol and Mce4Aprobucol were found to be 333.96 kJ/mol/nm2, 336.50 kJ/mol/nm2, 328.00 kJ/mol/nm2, 343.59 kJ/mol/nm2, and 323.61 kJ/mol/nm2, respectively.

3.3.4. Hydrogen bonds analysis Hydrogen bonding between a protein and ligands provides a directionality and specificity of interaction that is a fundamental aspect of molecular recognition [66]. In order to validate the stability of docked complexes, hydrogen bonds paired within 0.35 nm between protein and ligands in Mce4A-cholesterol, Mce4A-5-cholesten-3β-ol-7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol were calculated in solvent environment during the 100 ns. We found that the all ligands bind to active pocket of protein with 2–4 hydrogen bonds (Fig. 6). The Mce4A-cholesterol and Mce4A-probucol complexes expected to form

Fig. 7. Fluorescence emission spectra of (A) Mce4A-cholesterol, (B) Mce4A-5-cholesten-3β-ol-7-one, (C) Mce4A-25-hydroxy cholesterol and (D) Mce4A-probucol complexes. The protein was excited at 274 nm and emission spectra were recorded in the range 300–320 nm. A progressive decrease in the fluorescence intensity was observed while successive addition of all ligands.


557

Fig. 8. Modified Stern-Volmer plot of (A) Mce4A-cholesterol, (B) Mce4A-5-cholesten-3β-ol-7-one, (C) Mce4A-25-hydroxy cholesterol, and (D) Mce4A-probucol. A linear plot for log (Fo − F) / F vs. log [ligand] were obtained according to the Eq. (1).

a maximum three hydrogen bonds, while Mce4A-5-cholesten-3β-ol-7one and Mce4A-25-hydroxycholesterol showed four hydrogen bonds.

7-one, Mce4A-25-hydroxycholesterol and Mce4A-probucol (Table 5). This may be due to decrease in α-helix and β-sheet upon ligand binding to protein (Fig. S2). In Mce4A-cholesterol complex, coil, turn and αhelix increases, while β-sheet and bend decreases. In Mce4A-5cholesten-3β-ol-7-one, complex, the decrease in average secondary structure is due to decrease in bend and turn in the conformation. In

3.3.5. Secondary structure changes upon ligand binding The average number of residues participated in secondary structure formation were slightly decreased in case of Mce4A-5-cholesten-3β-ol-

Table 7 Binding parameters of selected ligands with Mce4A obtained from fluorescence and docking studies. Binding affinity', Ka, M−1

Number of binding sites' (n)

ΔG* (kcal/mol)

Cholesterol

2.63 × 10

5

1

−9.9

5-Cholesten-3β-ol-7-one

1.99 × 103

1

−9.5

25-Hydroxycholesterol

1.90 × 105

1

−9.0

Probucol

3.16 × 103

1

−7.8

Ligands

Structure

*ΔG (free energy) calculated from docking analysis. Binding affinity' and no. of binding sites' calculated from fluorescence quenching analysis.

558


Fig. 9. Cell viability studies. (A) Effect of cholesterol, (B) 5-cholesten-3β-ol-7-one, (C) 25-hydroxy cholesterol and (D) probucol on the viability of THP1 cell line. Cells were treated with increasing concentrations of all ligands (0–200 μM) for 48 h. Cell viabilities were shown as a percentage of the number of viable cells to that of the control. Values N 100 shows the percentage increase in cell survival to that of control (untreated cells). Each data point shown is the mean ± SD from n = 3.

Mce4A-25-hydroxycholesterol and Mce4A-probucol complexes, αhelix and the β-sheet were decreased largely due to ligand binding. 3.3.6. MMPBSA analysis The extraction of binding energies from the implementation of the mm_pbsa approach using the polar and apolar solvation parameters was performed. The aim of this analysis is to establish energies associated with the binding of ligands to protein during the MD simulations. The protein-ligand vdW interaction energy, electrostatic energy, polar solvation energy, solvent accessible surface area (SASA) energy and binding energy were calculated and recorded in the Table 6. The vdW and binding energy were least for Mce4A-probucol complex, followed by Mce4A-25-hydroxycholesterol complex. The vdW energies of all protein-ligand complexes contributed more towards the total energies to a greater extent as compared to the electrostatic and SASA energies. These observations indicated that the energy components of all four ligand-protein complexes have stable binding patterns throughout the simulations. 3.4. Fluorescence binding studies In order to validate docking results, fluorescence measurements were performed to estimate actual binding affinity of ligands to the Mce4A. Mce4A was successfully cloned, expressed and purified [67]. The protein solution was excited at 274 nm and the emission spectra were recorded in the wavelength range of 300–320 nm. These fluorescence emission spectra of Mce4A with different ligands were further

corrected for baseline with the corresponding blanks. A significant decrease in the fluorescence intensity was observed with increasing concentration of ligands, indicating that Mce4A-ligands complexes were formed (Fig. 7). For the estimation of binding affinity, the value of fluorescence intensity at 306 nm was plotted as a function of [ligand] (Fig. 8). The binding affinity of Mce4A for cholesterol, 5-cholesten-3β-ol-7-one, 25hydroxycholesterol and probucol are 2.63 × 105, 1.99 × 103, 1.90 × 105 and 3.16 × 103 M−1, respectively (Table 7). Results clearly indicate that 25-hydroxycholesterol exhibited greater affinity for Mce4A. It is analysed that introduction of hydroxyl group at 25 carbon position may leads to the formation of extra hydrogen bond which contribute to the protein-ligand formation. 3.5. Cell cytotoxicity studies The primary site of infection of M. tuberculosis is human alveolar macrophages. Thus, evaluation of candidate drugs/ligands for macrophage toxicity is crucial for the development of novel inhibitors. Cell viability was determined with the help of MTT assay [68]. THP-1 cells were used to check cell viability in the presence of studied ligands; THP-1 cells are the suitable model to study the effect of these ligands because Mce4A protein helps the pathogen to invade the macrophage cells. We tested all these ligands on THP-1 cells, which are monocytic cell line that can be differentiated to macrophages. Due to this reason, THP-1 cells are widely used as an in vitro model to study infection of M. tuberculosis and other intracellular pathogens [69, 70].


Results of cell viability were shown in the Fig. 9 which clearly revealed that all ligands were non-toxic to THP-1 cells up to 200 μM concentration except probucol which was nontoxic up to 100 μM only. These ligands show very less cytotoxicity only at the higher concentration, which is insignificant. On the basis of cytotoxicity studies, it was observed that the studies compounds/ligands do not possess any cytotoxicity against THP-1 cells in the tested concentration range. 4. Conclusions Here, we have investigated the mechanism of interaction of cholesterol, cholesten-3β-ol-7-one, 25-hydroxycholesterol and probucol with the Mce4A using molecular docking followed by MD simulation and fluorescence binding studies. We observed that all four ligands bind to the predicted active site cavity of Mce4A protein and complex is stabilized by significant numbers of non-covalent interactions. We further employed MD simulations to analyse and compare the binding modes of these ligands to the Mce4A. All four complexes of Mce4A with ligands were found stable for a quite longer simulation time (100 ns) indicating functionally significant interaction. All in silico observations were further validated by fluorescence binding studies. Fluorescence-binding studies further provide an insight for interaction of ligands to Mce4A. It is interesting to note that 25-hydroxycholesterol is showing highest binding affinity to the Mce4A. Cell viability assay suggested that this ligand is nontoxic to THP-1 cell line. All these observations are clearly indicating that our findings may be implicated to design and synthesize competitive inhibitors for Mce4A to attenuate the M. tuberculosis infection. Supplementary data to this article can be found online at https://doi. org/10.1016/j.ijbiomac.2017.12.160. Acknowledgements SK is thankful to University Grants Commission (UGC), New Delhi, India, for the award of fellowship. FIK and KL thank to the Centre for High Performance Computing (CHPC), South Africa. AI, FA and MIH thank to the Department of Science and Technology (EMR/2015/ 002372), India and Indian Council of Medical Research (BIC/12(01)/ 2015), India, for the financial support. FA thanks the Indian National Science Academy for the award of Senior Scientist Position. Conflict of Interest Authors declare there is no conflict of interest. References [1] N.K. Saini, M. Sharma, A. Chandolia, R. Pasricha, V. Brahmachari, M. Bose, Characterization of Mce4A protein of Mycobacterium tuberculosis: role in invasion and survival, BMC Microbiol. 8 (2008) 200. [2] S. Arruda, G. Bomfim, R. Knights, T. Huima-Byron, L.W. Riley, Cloning of an M. tuberculosis DNA fragment associated with entry and survival inside cells, Science 261 (5127) (1993) 1454–1457. [3] N. Casali, L.W. Riley, A phylogenomic analysis of the Actinomycetales mce operons, BMC Genomics 8 (1) (2007) 60. [4] N. Rathor, A. Chandolia, N.K. Saini, R. Sinha, R. Pathak, K. Garima, et al., An insight into the regulation of mce4 operon of Mycobacterium tuberculosis, Tuberculosis 93 (4) (2013) 389–397. [5] A. Kumar, M. Bose, V. Brahmachari, Analysis of expression profile of mammalian cell entry (mce) operons of Mycobacterium tuberculosis, Infect. Immun. 71 (10) (2003) 6083–6087. [6] R.H. Senaratne, B. Sidders, P. Sequeira, G. Saunders, K. Dunphy, O. Marjanovic, et al., Mycobacterium tuberculosis strains disrupted in mce3 and mce4 operons are attenuated in mice, J. Med. Microbiol. 57 (2) (2008) 164–170. [7] N.K. Saini, R. Sinha, P. Singh, M. Sharma, R. Pathak, N. Rathor, et al., Mce4A protein of Mycobacterium tuberculosis induces pro inflammatory cytokine response leading to macrophage apoptosis in a TNF-α dependent manner, Microb. Pathog. 100 (2016) 43–50. [8] A.K. Pandey, C.M. Sassetti, Mycobacterial persistence requires the utilization of host cholesterol, Proc. Natl. Acad. Sci. 105 (11) (2008) 4376–4380.

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