Journal of Biomolecular Structure and Dynamics, 2017 https://doi.org/10.1080/07391102.2017.1341337
Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase Rohit Shukla#, Harish Shukla#, Amit Sonkar, Tripti Pandey and Timir Tripathi* Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India Communicated by Ramaswamy H. Sarma. (Received 8 April 2017; accepted 6 June 2017) Mycobacterium tuberculosis is the etiological agent of tuberculosis in humans and is responsible for more than two million deaths annually. M. tuberculosis isocitrate lyase (MtbICL) catalyzes the first step in the glyoxylate cycle, plays a pivotal role in the persistence of M. tuberculosis, which acts as a potential target for an anti-tubercular drug. To identify the potential anti-tuberculosis compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,67,748) against the MtbICL structure. The ligands were docked against MtbICL in three sequential docking modes that resulted in 340 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 27 compounds were found to fit well with re-docking studies. After refinement by molecular docking and drug-likeness analyses, three potential inhibitors (ZINC1306071, ZINC2111081, and ZINC2134917) were identified. These three ligands and the reference compounds were further subjected to molecular dynamics simulation and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the MtbICL and bound complexes. The binding free energy analyses were calculated to validate and capture the intermolecular interactions. The results suggested that the three compounds had a negative binding energy with −96.462, −143.549, and −122.526 kJ mol−1 for compounds with IDs ZINC1306071, ZINC2111081, and ZINC2134917, respectively. These lead compounds displayed substantial pharmacological and structural properties to be drug candidates. We concluded that ZINC2111081 has a great potential to inhibit MtbICL and would add to the drug discovery process against tuberculosis. Keywords: Mycobacterium tuberculosis; isocitrate lyase; virtual screening; drug target; molecular dynamic simulation; natural compounds; molecular docking; binding energy
1. Introduction Tuberculosis (TB), caused by the parasite Mycobacterium tuberculosis, has existed for millennia and remains a major global health problem. In 2015, TB was ranked higher than HIV/AIDS as one of the leading causes of death from an infectious disease worldwide (Smith, Sharma, & Sacchettini, 2004; WHO, 2015). Currently, one-third of the world’s population is infected with M. tuberculosis. In 2015, there were an estimated 10.4 million new TB cases worldwide, of which 5.9 million (~56%) were men, 3.5 million (~34%) were women and around 1.0 million (~10%) were children. People living in co-infection with HIV accounted for 1.2 million (~11%) of all new TB cases (WHO, 2015). In addition to the large number of cases, TB has become a global public health problem owing to its resistance to frontline drugs such as the InhA-inhibitor isoniazid (Gomez & McKinney, 2004; Russell, 2001; Russell, Barry, & Flynn, 2010; Smith et al., 2004). The emergence of multidrugresistant (MDR) and extensively drug-resistant (XDR) TB has intensified the need for new anti-TB drugs
(Saifullah et al., 2014) that may include innovative antimycobacterial drugs with no cross-resistance to clinically used drugs. M. tuberculosis exhibits a tendency to remain latent or persistent for decades before its activation into a symptomatic disease. To cope up with the host’s immune response, the bacterium has developed several ingenious mechanisms including the activation of glyoxylate shunt to survive inside a hostile environment and acquire essential nutrients. This metabolic process appears to provide potential targets for novel anti-TB agents (Hatzios & Bertozzi, 2011) as the mechanism is completely absent in the host (Kumar & Bhakuni, 2008; Shukla, Kumar, Singh, Rastogi, et al., 2015; Shukla, Kumar, Singh, Singh, et al., 2015; Smith et al., 2004). The ideal drug target is an enzyme with fully elucidated function and mechanism, and which is absolutely required for the survival of M. tuberculosis (Brown & Wright, 2005; Salomon & Schmidt, 2012). Isocitrate lyase (ICL; EC 4.1.3.1) is one of two enzymes comprising the glyoxylate shunt that catalyzes the cleavage of
*Corresponding author. Emails:
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[email protected] # These authors contributed equally to the work. © 2017 Informa UK Limited, trading as Taylor & Francis Group
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isocitrate to succinate and glyoxylate. It is a key ratelimiting enzyme that plays a significant role in the glyoxylate cycle, an anaplerotic pathway of the tricarboxylic acid cycle. This pathway allows the bacteria to grow on acetate or fatty acids as sole carbon sources. ICL is responsible for the persistence of M. tuberculosis inside the host (Wheeler & Ratledge, 1988). The disruption of the icl gene has been shown to attenuate the bacterial persistence and virulence in immune-competent mice (Kratky et al., 2012). MtbICL is composed of four identical subunits and requires Mg2+ or Mn2+ for its activation. Till date, a total of four MtbICL crystal structures have been solved (PDB ID: 1F61; 1F8I; 1F8 M; 5DQL) (Sharma et al., 2000). Each subunit is made up of a large α/β barrel, which consists of eight α-helices and eight βStrands. The helix α12 (residues 349–367) projects away from the barrel, and together with two consequent helices α13 (residues 370–384) and α14 (residues 399– 409), it interacts exclusively with the neighboring subunits. On top of the barrel, there is a small β-domain containing several active site residues. The ligand binding leads to conformational change, triggering the MtbICL active site to shift into a close conformation (Sharma et al., 2000). During the catalytic reaction, isocitrate is deprotonated, and an aldol condensation results in the release of succinate and glyoxylate from the active site. Several synthetic MtbICL inhibitors that are structural analogs of the reaction products, succinate, glyoxylate, and oxalate, have been developed. Some of these inhibitors are itaconate (McFadden & Purohit, 1977), 3-nitropropionate (Schloss & Cleland, 1982), and 3-bromopyruvate (Ko & McFadden, 1990). 3-nitropropionate and 3-bromopyruvate were previously shown to inhibit M. avium ICL (Ki values of 3 and 120 μM, respectively) (Schloss & Cleland, 1982; Sharma et al., 2000). However, these inhibitors were not found to be suitable as drugs, owing to their toxicity and their ability to inhibit other enzymes in vivo. Thus, 3-nitropropionate and 3-bromopyruvate may not be used directly as drug candidates but can be good starting compounds for structure-based drug design (Lee, Wahab, & Choong, 2015). In search for new anti-mycobacterial drugs, compounds of natural origin still remain a promising source. Most compounds targeting mycobacterial enzymes are bacterial metabolites, like – lassomycin, ecumicin and cyclomarin A (targeting caseinolytic protease-ATPases- ClpC1), acyldepsipeptides (targeting caseinolytic protease- ClpP), aporphine alkaloids (ATP-Dependent MurE ligase), kuwanol E, brunsvicamide B and brunsvicamide C (targeting PtpB), thiolactomycin (targeting β-ketoacyl-ACP synthase), and pyridomycin (inhibiting enoyl-ACP reductase InhA)
(Ahmad, Makaya, & Grosset, 2011; Copp & Pearce, 2007; Garcia, Bocanegra-Garcia, Palma-Nicolas, & Rivera, 2012; Sieniawska, 2015). In fact, the current drug development pipeline for TB includes only two natural product derivatives (rifampicin and rifapentinemoxifloxacin) (www.newtbdrugs.org/ pipeline/clinical). Experimental techniques used for the identification of inhibitors of M. tuberculosis growth are very expensive, time-consuming, tedious, and requires sophisticated systems for controlling the risk of infection. Nowadays, structure-based virtual screening of a series of compounds has become an initial approach to discover novel and potential drugs. This step is followed by a number of complex computational methodologies to narrow down the number of compounds for experimental analysis to a larger extent (Seeliger & de Groot, 2010). In the present study, we used 1,67,748 natural compounds from the ZINC database and performed in-depth computational analyses that included structure-based virtual screening, molecular dynamics simulations, and binding energy analyses to identify the potential inhibitors of MtbICL. The sequential representation of the methodology is shown in Figure 1.
2. Methods 2.1. Retrieval of protein and ligand structures The structure of MtbICL (PDB ID: 1F8I) (Sharma et al., 2000) was retrieved from the Protein Data Bank. The protein structure had bound its natural substrates – succinic acid and glyoxylic acid. The subset of primary and secondary metabolites were downloaded from ZINC database (Irwin & Shoichet, 2005). The library had 1,67,748 natural compounds that were retrieved in .mol2 file format.
2.2. Cavity prediction and binding site analysis The Molegro Virtual Docker (MVD) (Thomsen & Christensen, 2006) tool was used for the prediction of the binding sites. For cavity prediction, we used Molegro Molecular Visualizer (MMV). This tool was also used to visualize the hydrogen bonding, electrostatic and steric interactions of succinic acid, and glyoxylate acid with MtbICL. Cavity associated with the binding of its natural substrates (succinic acid and glyoxylate acid) was selected for virtual screening. Gly192, His193, Arg228, Asn313, Ser315, and Ser347 were taken as the reference amino acids for virtual screening as they were reported to actively participate in the stabilization with the natural substrates (Sharma et al., 2000) (Figure 2).
Novel natural compounds as potential inhibitors of MtbICL
Figure 1. lation.
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Schematic representation of the overall workflow of sequential virtual screening, ADMET and molecular dynamics simu-
2.3. Virtual screening Virtual screening, a computational technique used for drug design, plays a key role in the lead discovery process. This technology has the ability to screen ligand databases having millions of compounds using the active site residues of the protein. It facilitates screening of large data sets to identify the structures having the propensity to bind with the drug target. Prior to the virtual screening, the protein structure was prepared by removing the natural substrates and water molecules
from the PDB structure using MVD (Thomsen & Christensen, 2006). Molegro is an efficient tool for protein–ligand docking as it performs flexible docking by which the optimal geometry of ligand is determined during the docking of the ligand with protein. The complexes having the energetically favorable structure were selected. MVD calculated the interaction energies between ligands and protein from the 3D structures of the protein and ligands. The MolDock Score (an adaptation of the differential algorithm) was used for energy calculation.
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Figure 2. Schematic computed two-dimensional representations of the binding interactions between ZINC1306071, ZINC2111081, and ZINC2134917 and the active site of MtbICL structure.
In the study, 1,67,748 natural compounds were retrieved from ZINC database and screened against the structure of MtbICL. The cavity used for virtual screening had the size constraint of 15 Å. The docking parameters for virtual screening included the number of runs (1), population size (50), max iterations (2000), scaling factor (.50), and crossover rate (.90). From the first round of screening, we selected 3121 compounds along with the reference compounds 3-bromopyruvate and 3-nitropropionate (known competitive inhibitors) for the next screening step. In the second step, all the parameters were kept the same except the number of runs, which was increased from 1 to 20. From the second round of screening, we selected 340 compounds for the prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET). In this round, the number of runs was increased from 20 to 50. A higher number of runs predicted the accurate binding pose and removed the false positive binders (Mohammadi & Ghayeb, 2017).
molecule. The prediction of ADMET properties of the compounds as possible drug candidates was performed using admetSAR (Cheng et al., 2012), which is a quick, accurate, and easy-to-use prediction server. It predicts physically significant descriptors and pharmaceutically relevant properties of organic compounds. admetSAR has 95,629 compounds in its data-set that are approved by FDA and are used to predict the key features for ADMET. In this study, several features including blood brain barrier (BBB), human intestinal absorption (HIA), caco-2 permeability, P-gp substrate/inhibitor, plasma protein binding, cytochrome p450 (CYP450) substrate/inhibitor, human Ether-a-go-go-Related Gene (hERG) inhibition, AMES toxicity, carcinogenicity, biodegradability, etc. were predicted by the admetSAR server. Out of the 340 compounds and bromopyruvate and nitropropionate (reference compounds) used for ADMET prediction, we found 27 compounds that were suitable as lead compounds and followed the Lipinski and ADMET rules.
2.4. ADMET prediction
2.5. Docking analysis
Lipinski parameters were retrieved from the ZINC database. The ADMET properties are very important for approving a drug. For reducing the time, there are many servers and software that use the previously reported drug data and predict the ADMET properties of ligand
All the 27 compounds and the reference compounds were further redocked by MVD (Thomsen & Christensen, 2006), Autodock (Goodsell, Morris, & Olson, 1996) and Autodock Vina (Trott & Olson, 2010). In this step, we increased the number of runs to 100 for
Novel natural compounds as potential inhibitors of MtbICL docking in the MVD, and five poses were generated from each docking. Autodock is a freely available tool and widely usable software for protein–ligand docking. The same binding pocket was selected for docking studies. Autodock uses the semi-empirical free energy force field for the calculation of ligand binding confirmations. The receptors and ligands were prepared using AutoDock 1.5.6. Hydrogen atoms were added to the structure of MtbICL, and partial atomic charges were assigned. A three-dimensional grid box for docking was set into X = 74°, Y = 68°, Z = 126° grid points, and the grid spacing was .553 Å. The binding poses were generated by Lamarckian Genetic Algorithm. Other docking parameters were population size (150), maximum number of evaluations (2,500,000), maximum number of generations (27,000), rate of gene mutation (.02), and rate of crossover (.8). All other parameters were kept as default. Forty binding poses were generated for each ligand. The results were clustered according to the root-mean-standard deviation values and ranked by the binding free energy. Lastly, Autodock Vina was used for redocking studies. The defined grid for Autodock was considered for docking of the 27 compounds and control with Autodock Vina, which proved to be more efficient and presented accurate algorithm and was faster as compared to Autodock. The number of exhaustiveness was set to 20 for predicting the accurate result. 2.6. Molecular dynamics simulation (MDS) The Molecular dynamics simulation (MDS) study was performed in a supercomputer using GROMACS4.6.5 (Hess, Kutzner, van der Spoel, & Lindahl, 2008; Pronk et al., 2013) as earlier (Pandey et al., 2016; Shukla, Shukla, Sonkar, Pandey, & Tripathi, 2017). Six systems were created and employed for 40 ns time period simulation studies, one for predicting the stability of the apo-MtbICL structure and others for MtbICL-ligand complexes. All the systems were solvated using simple point charge model in a cubic box. Ligand topology was generated using ProDRG server (Schuttelkopf & van Aalten, 2004). Protein topology was generated using GROMOS 9653a6 force field (Oostenbrink, Villa, Mark, & Van Gunsteren, 2004). 18 Na+ ions were added for neutralization of the systems. Steepest energy minimization was performed for all the systems to give the maximum force below 1000 kJ/mol/nm for removing the steric clashes. Long range electrostatic interactions were calculated by Particle Mesh Ewald method (Darden, York, & Pedersen, 1993). For the computation of Lennard–Jones and Coulomb interactions, 1.0 nm radius cut-off was used. The LINCS algorithm (Berk Hess, Bekker, Berendsen, & Fraaije, 1997) was used to constrain the H-bond lengths. The time step was maintained at 2 fs for the simulation. For predicting the short-range
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non-bonded interaction, 10 Å cut-off distance was used. 1.6 Å Fourier grid spacing was used for the PME method for long-range electrostatics. All bonds including H-bond were fixed by Shake algorithm (Ryckaert, Ciccotti, & Berendsen, 1977). The systems were equilibrated after energy minimization. Then position restraint simulation of 1 ns was carried out under NVT and NPT conditions. Finally, all systems were submitted for 40 ns molecular dynamic simulation. The 2 fs interval was given for saving the coordinates. Then the root–meansquare deviation (RMSD), root–mean-square fluctuation (RMSF), and H-bonds were calculated by g_rms, g_rmsf, and g_hbond (Humphrey, Dalke, & Schulten, 1996). Principal component analysis (PCA) was carried out by g_covar, and g_anaeig. Free energy calculation was carried out by g_mmpbsa tool. The trajectory was analyzed by visual molecular dynamics and Chimera 1.10.2. Ultimately, Origin 6.0 was used for generating and visualizing the plots. 2.7. Binding free energy calculation The binding free energy of protein–ligand complexes was calculated using the molecular mechanics Poisson– Boltzmann surface area (MM-PBSA) method (Baker, Sept, Joseph, Holst, & McCammon, 2001; Kumari, Kumar, & Lynn, 2014). The free energy calculation analysis is useful in the later stage of drug discovery process as it provides a quantitative estimation of the binding free energy. Free energy of solvation (polar + non-polar solvation energies) and molecular mechanics potential energy (electrostatic + Van der Waals interaction) were calculated by this tool. In this study, the last 10 ns of the MD trajectory were taken for the calculation of MM-PBSA. 3. Results and discussion 3.1. Structure-based virtual screening For virtual screening, we retrieved a subset of primary and secondary metabolites from the ZINC database that had 1,67,748 compounds. All the ligands were prepared using MVD and subjected to virtual screening with apoMtbICL. The compound with the highest MolDock Score in the first step of virtual screening showed a value of −201.644. From the first step result, 3121 compounds were selected with a MolDock Score ranging between −201.644 and −135.000. In the second step, the number of docking runs was increased to 20, and all the 3121 compounds were screened. The compounds showed a MolDock Score of −210.31 to −92.990 in the second run. Increased number of runs removed the false positive binders. Ultimately, we selected 340 compounds with a MolDock Score ranging between −210.000 and −160.590 for the next step screening. These 340 compounds were screened in 50 runs and showed
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a MolDock Score ranging between −203.631 and −132.023. After that, all these 340 compounds and the reference compounds were further employed for ADMET prediction. 3.2. ADMET prediction ADMET is an important area in drug testing and designing. Lipinski parameters were used to determine the drug likeness and ADMET properties. The Lipinski parameters were taken from the ZINC database. In this study, we found 340 suitable compounds from structure-based virtual screening that were further employed for in silico ADMET prediction. Out of the selected 340 compounds, 90 compounds were approved in the Lipinski parameters (Supplementary Table 1). BBB is an important factor for drugs that have the target site in the central nervous system. BBB is a physiological barrier, which protects the compounds to cross from blood to brain. In our study, we observed that out of 340 compounds, 225 compounds had the capability to cross the BBB while 115 compounds failed. Drug absorption is a crucial factor for oral drug delivery.
Table 1.
Out of the 340 compounds, 252 compounds showed novel absorption in the intestine while 88 compounds failed to absorb in the HIA. Caco-2 is a simulated cell culture derived from the large intestine and is used for finding the absorption of the drug molecule. Seventy out of 340 compounds passed Caco-2 permeability filter while 270 compounds failed in this criteria. P-glycoprotein (P-gp) is one of the most important cell surface proteins involved in the efflux of xenobiotics. The substrate of P-gp indicates that this compound can be effluxed by these cell surface proteins. Non-substrate indicated that this compound cannot be effluxed by P-gp cell surface protein. Out of the 340 compounds, 208 compounds acted as a P-gp substrate while 132 were non-substrates. P-gp inhibitor and non-inhibitor compounds have the capability to inhibit and non-inhibit the P-gp cell surface protein, respectively. Out of the 340 compounds, 247 compounds acted as a non-inhibitor while 93 acted as an inhibitor of P-gp (Supplementary Table 2). Inside the cell, the metabolism of xenobiotics is carried out by Cytochrome P450 (CYP450) enzymes, which belong to the microsomal family of enzymes. In our study, 178 compounds showed a high inhibition while
Details of the three selected compounds and the reference compounds. Autodock (kcal mol−1)
Autodock Vina (kcal mol−1)
MVD MolDock score
Bromopyruvate
−3.88
−5.3
−64.42
Nitropropionate
−3.49
−5.7
−75.55
ZINC1306071
−6.91
−7.4
−163.162
ZINC2111081
−6.67
−8.7
−168.859
ZINC2134917
−7.41
−7.4
−167.029
ZINC ID
Compound structure
Note: ZINC database code, structure, and docking energies obtained after virtual screening, refinement by molecular docking and drug-likeness analyses are provided.
Novel natural compounds as potential inhibitors of MtbICL 162 compounds showed a low inhibition for CYP450 enzymes (Supplementary Table 3). Toxicity describes how a compound is poisonous or harmful for an organism. In our study, 69 compounds and the reference compounds – bromopyruvate and nitropropionate were found to be toxic while 271 compounds were non-toxic in nature. Carcinogenicity of a compound describes the capability of a compound to cause cancer. In our study, we found that 10 compounds and the reference compounds can act as a carcinogen while 330 compounds were noncarcinogenic. The hERG codes a channel protein that has the capability to conduct electrical current across the cell membrane and is inhibited by several compounds resulting in long QT syndrome. In our study, we found that 37 compounds acted as an inhibitor while 303 as non-inhibitor of hERG. The lethal dose of a compound describes the ability of a compound to kill 50% population of an organism. LD50 was predicted in silico in a rat model. In our study, we found that most compounds showed the LD50 value between two and three (Supplementary Table 4). 3.3. Molecular docking From the virtual screening and ADMET prediction, we selected 27 compounds as potential inhibitors of MtbICL that approved ADMET and other criteria. The re-docking of the hits was performed for predicting the accurate result. Thus, all these 27 predicted ligands were docked in the same binding cavity that was selected during the initial virtual screening using MVD, Autodock and Autodock Vina for predicting the best accurate binding pose and docking energy. The number of hydrogen bonds, docking energy, and interacting residues of all these 27 compounds are given in Supplementary Table 5. The reference compound 3-bromopyruvate showed a binding affinity of −3.88 and −5.30 kcal mol−1 from Autodock to Autodock Vina, respectively, while MVD showed −64.41 MolDock Score. The other reference compound 3-nitropropionate showed a binding affinity of −3.49, −5.7 kcal mol−1 from Autodock to Autodock Vina, respectively, while MVD showed −75.55 MolDock Score. Table 2.
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From MVD, we predicted four best compounds in terms of MolDock score as well as the number of hydrogen bonds. Compounds that have better binding affinity were ZINC2129751, ZINC2160785, ZINC2134917, and ZINC2138465 showing a MolDock Score of −179.364, −172.627, −167.029, and −165.827, respectively. From Autodock, we predicted four top hits namely ZINC2134917, ZINC2138465, ZINC2101321, and ZINC1306071 with lower binding energy of −7.41, −7.21, −6.94, and −6.91 kcal mol−1, respectively. The inhibition constant was found within a minimal range of 3.69 and 8.13 μM that supported the property of compounds in the MtbICL inhibition. In all these compounds, Trp93, Asp108, Ser191, Gly192, His193, Arg228, and His352 were found to play a key role during ligand binding. These residues were stabilized in all the 27 compounds. From Autodock Vina, we redocked all the predicted hits and predicted top compounds that have an excellent binding affinity toward MtbICL, like ZINC2111081, ZINC2134917, ZINC1306071, and ZINC639081 that showed a binding affinity of −8.7, −7.4, −7.4, and −7.0 kcal mol−1, respectively. All the compounds were docked in the same cavity where Trp93, Asp108, Ser191, Gly192, His193, Arg228, and His352 played a key role in ligand binding. From the results of MVD, Autodock and Autodock Vina, we predicted that all the compounds that interacted with MtbICL were bound to the same cavity. Ultimately, from these results, we selected three compounds – ZINC1306071, ZINC2134917, and ZINC2111081 for further analysis using molecular dynamics simulation (MDS) (Table 1).
3.3.1. Analysis of the docked complex 3-bromopyruvate was stabilized with the catalytic site by hydrogen bonds and hydrophobic interactions. It formed five hydrogen bonds with Gly192, His193, Arg228, Glu285, and Asn313. The nitrogen atom of Gly192, His193, Arg228, and Asn313 interacts with O atom of the ligand. Glu285 also interacts with O atom of the
RMSF value (nm) of catalytically important residues of apo-MtbICL and the predicted hits.
S. No.
Residue
apo-MtbICL
Bromopyruvate
Nitropropionate
ZINC1306071
ZINC2111081
ZINC2134917
1 2 4 5 6 7 8 9
Lys189 Lys190 Gly192 His193 Ser315 Phe345 Glu423 Glu424
.114 .159 .259 .301 .122 .090 .491 .498
.216 .221 .176 .164 .114 .067 .836 .809
.098 .097 .121 .088 .066 .068 .241 .223
.086 .080 .083 .094 .139 .075 .389 .475
.095 .097 .125 .156 .074 .059 .147 .216
.070 .067 .072 .077 .088 .059 .277 .411
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Table 3.
Table showing the Van der Waals, electrostatic, polar salvation, SASA, and binding energy in kJ mol−1 for each complex.
MtbICL complex with Bromopyruvate Nitropropionate ZINC1306071 ZINC2111081 ZINC2134917
Van der Waals energy −69.81 −67.98 −230.21 −189.46 −255.12
± ± ± ± ±
6.74 8.78 13.35 17.07 11.98
Electrostatic energy −19.57 −12.51 −58.87 −18.49 −32.60
± ± ± ± ±
6.53 6.89 9.58 8.03 7.97
ligand (Supplementary Figure 1). 3-nitropropionate was stabilized with the catalytic site by nine hydrogen bonds. The nitrogen atom of Gly192, His193, Arg228, and Asn313 interacts with the O atom of the ligand. The O atom of Ser315, Ser317, and Ser347 also interacts with O atom of ligand (Supplementary Figure 1). ZINC1306071 formed hydrogen bond as well as hydrophobic interaction with catalytically important residues. It formed eight hydrogen bonds with active site residues. Ser91 formed a hydrogen bond between the O atom of the ligand and its own hydroxyl group. The hydroxyl group of catalytically important polar amino acids Ser191 and Ser317 formed hydrogen bonds with the hydroxyl group and N atom of the ligand. Ser315 also formed hydrogen bonds with the O atom of the ligand by its hydroxyl group. The NH group of the nonpolar amino acid Trp93 formed a hydrogen bond with the O atom of the ligand (Figure 2). ZINC2111081 was stabilized by 11 hydrogen bonds with catalytically important amino acid and very well fit into the cavity. The hydroxyl group of Tyr89 and Ser91 interacted with O atom of the ligand. The N atom of the carboxamide group of Asn313 formed a hydrogen bond with O atom of the ligand. Ser315 and Ser317 formed three hydrogen bonds with the interaction between their hydroxyl group and two O atoms of the ligand. The hydroxyl group of Thr347 interacted with O atom of the ligand by forming a hydrogen bond. The hydroxyl group of Tyr89 formed two hydrogen bonds with H and O atoms of the ligand (Figure 2). ZINC2134917 was stabilized by five hydrogen bonds and fitted well in to cavity. The hydroxyl group of Tyr89 and Ser91 interacted with O atom of the ligand by formation of hydrogen bonding. The backbone of Asp108 interacted with O atom of the ligand. The hydroxyl group of Ser317 and N atom of His352 interacted with O atom of ligand (Figure 2). A superimposed figure of all three predicted hits with the two reference compounds are shown in Supplementary Figure 2. 3.4. MDS MDSs were carried out to understand the structural details, conformational behavior, and stability of ligand–target
Polar salvation energy 51.91 52.46 214.90 82.04 185.96
± ± ± ± ±
12.34 11.18 23.76 11.65 15.00
SASA energy −7.28 −7.67 −22.29 −17.63 −20.77
± ± ± ± ±
.54 .67 1.13 1.33 .86
Binding energy −44.74 −35.70 −96.46 −143.55 −122.53
± ± ± ± ±
12.38 7.48 19.94 17.60 16.26
complexes (Jacob, Ganguly, Kumar, Poddar, & Kumar, 2017; R. K. Pandey et al., 2017; Pathak et al., 2017; Sharma & Wakode, 2017; Sun, Zheng, & Zhang, 2017) . All atom time-dependent MDS consists of an intensive force field calculation for each of the atom in a system, followed by an integration step, which advances the positions and dynamical nature of the atoms according to the classical laws of motion. MDS allowed us to unravel the atomic-level features of the biomolecular processes including the stability analysis of protein–ligands complexes. The stability analysis of the native protein (apo-MtbICL) and the protein–ligand complexes (MtbICL-ZINC2111081, MtbICL-ZINC2134917, MtbICLZINC1306071, MtbICL-bromopyruvate and MtbICL-nitropropionate) were performed by surrounding them into a cubic box at a temperature of 300 K that was maintained computationally. Various computational analyses were carried out to evaluate the stability of the systems. 3.4.1. RMSD RMSD is used for measuring the differences between the backbones of a protein from its initial structural conformation to its final position. The stability of the protein relative to its conformation can be determined by the deviations produced during the course of its simulation. Smaller deviations indicated more stable protein structure. RMSD value for the Cα backbone was calculated for 40 ns simulation in order to evaluate the stability of all the systems. Figure 3(A) shows the plot of RMSD (nm) vs. time (ns) for apo-MtbICL and three MtbICL-ligand complexes (ZINC1306071, ZINC2111081, and ZINC2134917). The average RMSD values for apoMtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICL-ZINC2111081 an MtbICL-ZINC2134917 were found to be .46, .42, .62, .61, .38, and .50 nm, respectively. ZINC1306071 showed higher RMSD value as compared to apo-protein, reference compounds and other ligand complexes, whereas ZINC2111081 showed the least value, which confirmed its greater stability than other complexes. The entire trajectory attained the equilibration state after 20 ns; hence, RMSF, Rg, PCA and number of hydrogen bonds were calculated for the last 20 ns trajectory.
Novel natural compounds as potential inhibitors of MtbICL
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Figure 3. Molecular dynamic simulation studies. (A) RMSD. (B) RMSF. (C) Radius of gyration. (D) Evaluation of ligand–enzyme interactions by the number of hydrogen bonds as a function of time. In all panels, the colors indicate – apo-MtbICL (black), MtbICL-bromopyruvate (red), MtbICL-nitropropionate (green) MtbICL-ZINC1306071 (blue), MtbICL-ZINC2111081 (cyan), and MtbICL-ZINC2134917 (magenta).
3.4.2. RMSF Analyses of RMSF plots for the protein–ligand complexes provide information on the flexible regions of the complexes. In proteins, the loop, turns and coils showed higher RMS fluctuation as compared to helical and sheet structures. Higher RMSF value indicated the loosely organized loop or terminal ends, and low RMSF value indicated the well-structured regions. Figure 3(B) shows the RMSF plots for apo-MtbICL, and the protein complexes. The average RMSF value for apoMtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICL-ZINC2111081, and MtbICL-ZINC2134917 were recorded as .17, .16, .14, .15, .12, and .12 nm, respectively. MtbICLZINC2111081 and MtbICL-ZINC2134917 showed less RMS fluctuation as compared to the apo-MtbICL and reference compounds, which suggests that binding of the
ligand leads to decreased flexibility of the catalytically important residues, and hence, these predicted hits had the potential to inhibit the catalytic activity of MtbICL. Lys193, Lys194, Gly196, and His197 of E. coli ICL were considered as important residues for catalytic activity, and the mutation on these residues resulted in a loss of activity (Diehl & McFadden, 1993, 1994), as these were conserved residues. Therefore, approximately all the bacterial ICL are believed to exhibit a similar behavioral pattern. Ligand binding in apo-MtbICL altered the RMSF, and this fluctuation suggests that the binding of ligand altered the confirmation of these residues. Besides the conserved residues of active site, Ser319 also played an important role in catalytic activity (Rehman & McFadden, 1997), and the binding of ligand resulted in decreased RMS fluctuation. In our previous studies, we reported that Phe345, Glu423, and Glu424 were involved
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in maintaining the catalytic activity of the MtbICL (Shukla, Kumar, Singh, Singh, et al., 2015; Shukla et al., 2017). Due to ligand binding, the RMS fluctuation value decreased except in ZINC1306071 complex. The results suggested that due to binding of the ligand, the RMS fluctuation of the catalytic residues decreased, resulting in the change in conformation of protein and thereby, inhibiting the activity of MtbICL. The values of altered RMS fluctuation of catalytically important residues are showed in Table 2.
(1.20%), Tyr356 (2.00%), and Ser191 (.40%) were the key residues taking part in hydrogen bond interaction in MtbICL-ZINC2111088 while Leu194 (1.60%), Asn313 (8.19%), Thr347 (.80%), and Ser317 (.20%) were the key residues taking part in hydrogen bond interaction in MtbICL-ZINC2134917. Gln94 (5.90%), Gly192 (5.00%) and few other catalytic residues formed hydrogen bond interaction in MtbICL-bromopyruvate, while Asp108 (6.8%) and Ser110 (.10%) were the key residues taking part in hydrogen bond in MtbICL-nitropropionate.
3.4.3. Radius of gyration ( Rg)
3.4.5. PCA
The radius of gyration of the apo-MtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, and MtbICL-ligand complexes was determined to understand the level of compaction in the structure of the enzyme in the absence and presence of ligands (Figure 3(C)). The Rg is assigned as the mass weighted RMSD fit of a collection of atoms from their common center of mass (K. M. Kumar, Anbarasu, & Ramaiah, 2014). Figure 3(C) shows a clear distinction between the apo-MtbICL and the MtbICL-ligand complexes. The average Rg value for apo-MtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICLZINC2111081, and MtbICL-ZINC2134917 were found to be 2.30, 2.25, 2.28, 2.31, 2.25, and 2.33 nm, respectively. apo-MtbICL and MtbICL-ZINC1306071 showed similar Rg pattern till the end of simulation. MtbICLZINC2134917 showed a higher Rg value as compared to the other systems, suggesting the lack of stability in this complex. Overall, these results suggest that MtbICLZINC2111081 was more stable complex compared to other MtbICL-ligand complexes.
PCA was carried out for the prediction of large concerted motions during ligand binding. The top hits (ZINC1306071, ZINC2111081, and ZINC2134917), apoMtbICL, MtbICL-bromopyruvate, and MtbICL-nitropropionate, were selected for predicting the correlated motions. Figure 4(A) shows a plot of eigenvalues obtained from the diagonalization of the covariance matrix of atomic fluctuations, plotted in decreasing order vs. the corresponding eigenvector indices for apo-MtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICL-ZINC2111081, and MtbICL-ZINC2134917. It is well-known that the first few eigenvectors play an important role in protein motions. The first 15 eigenvectors accounted for 90.50, 89.58, 89.59, 88.72, 84.59, and 85.65% of the motions observed for the last 20 ns trajectory for the apoMtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICL-ZINC2111081, and MtbICL-ZINC2134917, respectively. From PCA, we concluded that the ligand binding leads to change in protein conformation and dynamics. The PCA results showed that the values for the first few eigenvectors of apo-MtbICL were higher than the MtbICL-ligand complexes, which suggests that the apoMtbICL has different correlated motions than the MtbICL-ligand complexes. The ligand binding leads to stabilization of the complex and thereby, showed less correlated motions as compared to apo-MtbICL. PCA result also showed that complexes like MtbICL-ZINC2111081 and MtbICL-ZINC2134917 were more stable as compared to the MtbICL-ZINC1306071, MtbICL-bromopyruvate, and MtbICL-nitropropionate. Figure 4(B) shows the 2D projection of the trajectories for PC1 and PC2 for apo-MtbICL and MtbICL-ligand complexes. From the figure, it can be concluded that the MtbICLZINC2111081 complex was highly stable as it occupied less space in the phase space, and the cluster was welldefined as compared to apo-MtbICL, MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, and MtbICL-ZINC2134917. All these results indicate that ZINC2111081 formed more stable complex with MtbICL. This conclusion is in appropriation with our previously predicted RMSD, RMSF, and Rg results.
3.4.4. Hydrogen bonds The number of hydrogen bonds was calculated for the last 20 ns time period trajectory for MtbICL-bromopyruvate, MtbICL-nitropropionate, and predicted complexes (ZINC1306071, ZINC2111081, and ZINC2134917). Consideration of hydrogen bonding properties in drug design is important because of their strong influence on the drug specificity, metabolization, and adsorption (Williams & Ladbury, 2008). Figure 3(D) shows that MtbICL-bromopyruvate, MtbICL-nitropropionate, MtbICL-ZINC1306071, MtbICL-ZINC2111081, and MtbICL-ZINC2134917 had an average of two, one, two, one, and three hydrogen bonds, respectively. The last 20 ns trajectory was used for the identification of the key amino acid residues that interacted with the ligands, and the percent time duration of each hydrogen bond was calculated (Supplementary Tables 6 and 7). Ser91 (59.24%), Trp93 (50.05%), and Thr347 (.10%) were the key residues taking part in hydrogen bond interaction in MtbICL-ZINC1306071. Asn131 (.30%), His352
Novel natural compounds as potential inhibitors of MtbICL
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Figure 4. Principle Component Analysis. (A) Plot of eigenvalue vs. eigenvector index. Only first 50 eigenvectors out of 1000 eigenvectors are represented in the figure. (B) PCA scatter plot along first two principal components, PC1 and PC2 showing all atom fluctuations. In both panels, the colors indicate – MtbICL-bromopyruvate (red), MtbICL-nitropropionate (green) MtbICLZINC1306071 (blue), MtbICL-ZINC2111081 (cyan), and MtbICL-ZINC2134917 (magenta).
3.4.6. Calculation of binding free energy The binding free energy was estimated using MM-PBSA method for MtbICL-ZINC1306071, MtbICLZINC2111081, and MtbICL-ZINC2134917. The snapshots were extracted from the last 10 ns of MD trajectories for the analysis of the binding free energy. The binding free energy and its interrelated constituents achieved from the MM-PBSA estimation of the predicted hits are illustrated in Table 3. All the compounds showed negative binding energy. ZINC2111081 showed the lowest binding energy of −143.549 kJ mol−1 while ZINC2134917 showed −122.526 kJ mol−1 and ZINC1306071 showed a binding value of −96.462 kJ mol−1. Furthermore, electrostatic interactions, non-polar solvation energy, and Van der Waals had negatively complimented to the overall interaction energy, although polar solvation energy has positively enriched the binding energy. From these results, we can conclude that ZINC2111081 is the best compound among the three potential inhibitors. These results are in agreement with our previous MD simulation analysis. To identify the key residues involved in ligand binding, the residue wise energy decomposition plot was created. For clear depiction of the results, only the active site residues are shown in the Figure 5. From the plot, it can be observed that Arg228 showed a positive binding affinity while all other residues showed negative binding affinity. Trp93 showed higher binding affinity as compare to other residues. The results revealed that Trp93 play important role in protein–ligand stabilization (Figure 5).
Figure 5. The contributions of individual amino acid residues of MtbICL to the total binding energies. Results are shown as the energy contribution differences between the indicated MtbICL-ligand complexes. Negative values indicate a stabilization effect for MtbICL-ligand interactions, whereas positive values indicate a destabilization effect for MtbICL-ligand interactions. The colors indicate – MtbICL-bromopyruvate (red), MtbICL-nitropropionate (green) MtbICL-ZINC1306071 (blue), MtbICL-ZINC2111081 (cyan), and MtbICLZINC2134917 (magenta).
4. Conclusion The results obtained from this work using the series of in silico approaches demonstrated that ZINC1306071, ZINC2111081, and ZINC2134917 have the potential to be developed as an antitubercular compound targeting MtbICL. Furthermore, ADMET evaluation of these three
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compounds showed their non-toxic behavior among a series of parameters that may enhance their drug likeness properties. While 3-bromopyruvate and 3-nitropropionate showed toxicity and carcinogenicity during ADMET prediction that is consistent with previous studies (Lee et al., 2015). The binding affinities of these three compounds were higher than the reference compounds. In future, in vitro and in vivo evaluation of these ligands can confirm its antitubercular activity, and they may develop a lead against TB. We conclude that out of these three compounds, ZINC2111081 has the greatest potential to inhibit MtbICL and can be used for further studies into the drug discovery process against TB. Authors’ contributions RS, HS, and AS carried out the experiments. TT, RS, TP and HS conceived the study, participated in its design and coordination and performed data analysis. TT, RS and HS drafted the manuscript. All authors read and approved the final manuscript. Disclosure statement No potential conflict of interest was reported by the authors.
List of abbreviations MtbICL ADMET RMSD RMSF PCA
Mycobacterium tuberculosis isocitrate lyase Absorption, distribution, excretion, metabolism and toxicity root-mean-square deviation root-mean-square fluctuation principal component analysis
Acknowledgements Authors thank the Sulekor supercomputing facility of NEHU. RS, HS and AS thank UGC for providing fellowship.
Supplemental data The supplementary material for this paper is available online at https://doi.10.1080/07391102.2017.1341337
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