The table shows parameters involved in the selection of lead compounds. This included pharmacophore fit score, the binding energy and the number of hydrogen and hydrophobic bond interacting residues.
Abstract
Trypanothione reductase (TR), a flavoprotein oxidoreductase is an important therapeutic target for leishmaniasis. Ligand-based pharmacophore modelling and molecular docking were used to predict selective inhibitors against TR. Homology modelling was employed to generate a three-dimensional structure of Leishmania major trypanothione reductase (LmTR). A pharmacophore model used to screen a natural compound library generated 42 hits, which were docked against the LmTR protein. Compounds with lower binding energies were evaluated via in silico pharmacological profiling and bioactivity. Four compounds emerged as potential leads comprising Karatavicinol (7-[(2E,6E,10S)-10,11-dihydroxy-3,7,11-trimethyldodeca-2,6-dienoxy]chromen-2-one), Marmin (7-[(E,6R)-6,7-dihydroxy-3,7-dimethyloct-2-enoxy]chromen-2-one), Colladonin (7-[[(4aS)-6-hydroxy-5,5,8a-trimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl]methoxy]chromen-2-one), and Pectachol (7-[(6-hydroxy-5,5,8a-trimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl)methoxy]-6,8-dimethoxychromen-2-one) with good binding energies of −9.4, −9.3, 8.8, and −8.5 kcal/mol, respectively. These compounds bound effectively to the FAD domain of the protein with some critical residues including Asp35, Thr51, Lys61, Tyr198, and Asp327. Furthermore, molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) computations corroborated their strong binding. The compounds were also predicted to possess anti-leishmanial activity. The molecules serves as templates for the design of potential drug candidates and can be evaluated in vitro with optimistic results in producing plausible attenuating infectivity in macrophages.
Keywords
- Leishmania
- trypanothione reductase
- oxidative stress
- natural product
- pharmacophore modeling
- virtual screening
- molecular dynamics
1. Introduction
Leishmaniasis is a disease caused by a single-cell eukaryotic parasite of the
Trypanothione is a major product of the trypanothione biosynthesis pathway in trypanosomes which is crucial in maintaining cellular redox potential and is essential for the parasite’s survival. This molecule is catalyzed by so many enzymes for which
Trypanothione reductase is a member of the disulphide oxidoreductase family of enzymes. It has an analogue in the human body, glutathione reductase (GR) which also carries out oxidoreductive reactions. But
Several inhibitors have been screened against this enzyme causing a reduction of infectivity and decreased capacity of the parasite to survive within intracellular macrophages. Potent compounds, such as 7-chloro-4-nitro-5-quinazolin-4-ylsulfanyl-2,1,3-benzothiadia-zole (CNQB) and 4-phenyl-5-(4-nitro-cinnamoyl)-1,3,4-thiadiazolium-2-phenylamine-chloride (PNTPC) with IC50 values 0.58 and 1.63 μM, respectively have already been tested in an
Computer-aided drug designing is an
Drugs currently used for the treatment of human leishmaniasis are toxic, having severe adverse reactions which limit their use. Aside this includes, increase in resistance by the parasite, high cost of available drugs, lack of efficacy against VL\HIV co-infections with standard chemotherapy, and the development of a single drug or formulation for all forms of leishmaniasis [14, 15, 16, 17]. Therefore, the development of novel, effective drugs with reduced side effects, is still a major priority for health researchers, in spite of many compelling research reports published on antileishmanial agents in the last 10 years [18]. In this study,
2. Materials and methods
2.1 Protein homology modeling
The protein sequence of trypanothione reductase of
2.2 Active site prediction and quality assessment
To predict the active site, the protein was submitted to CASTp 3.0 [21, 22]. The predicted active site was corroborated via a blind docking process using AutoDock Vina within PyRx version 0.9.7 [23, 24]. The active region was confirmed by the ‘Toggle selection of Spheres’ function which highlighted predicted residues from CASTp 3.0. Binding pocket was also viewed with PyMOL v2.0.0 [25]. The quality of the modeled protein was assessed by some quality measure tools. This included PROSA which determines the quality of experimentally solved structures and theoretical models in protein engineering by comparing these to that of experimentally solved protein structures in the PDB database [26]. Verify3D was used to validate the three-dimensional structure of the model [27]. PROCHECK, a quality assessment tool was also used to check the stereochemical properties of the model by generating a Ramachandran plot [28]. ProQ was also used to carry out further validation. ProQ predicted protein quality based on the LGscore and MaxSub scores [29].
2.3 Energy minimization of protein target
The modeled
2.4 Pharmacophore modeling and screening
2.4.1 Pharmacophore generation
Pharmacophore model, virtual screening, and molecular docking studies were performed to find novel
2.4.2 Library preparation and pharmacophore validation
LigandScout 4.3 was used for screening a total of 5813 compounds including 885 AfroDb entries found in ZINC database [35] and 4928 NANPDB compounds [36]. The two actives were converted to SMILES format and submitted to the Directory of useful decoys and enhanced (DUD-E) database to generate decoys for the screening [37]. A total of 100 decoys were generated and used as a decoy library. The libraries were then converted into a .ldb file format. The reliability of the pharmacophore model was validated by the area under the receiver operating characteristic (ROC) curve (AUC) [38] using two descriptors, selectivity and sensitivity.
2.4.3 Screening with pharmacophore
LigandScout 4.3 allows
2.5 Molecular docking
2.5.1 Docking of LmTR
The generated hit compounds were uploaded into PyRx (Version 0.9.6) [23]. The energy of the ligands was minimized using Universal Force Field (UFF) option in Open Babel incorporated in PyRx prior to docking. This was done to obtain 3D ligand structures which constitute atomic elements that have proper bond lengths between their atoms [39]. Ligands were converted to PDBQT format using AutoDock Vina embedded in the PyRx. Predicted active site residues were selected within a grid box of dimensions X: 42.39 Å, Y: 35.47 Å, and Z: 31.05.14 Å; and centre X: 28.58 Å, Y: 57.09 Å, and Z: −2.24 Å within the AutoDock Vina environment of PyRx for docking process.
2.5.2 Docking validation with AUC
Validation of the algorithm used for the docking process was carried out by generating an AUC plot. Decoys of five known inhibitors in complex with
2.5.3 Docking validation via superimposition and alignment
Superimposition of the crystallographic ligand and re-docking poses was used as a means of validating docking. The five crystallographic ligands in complex with
2.6 Identification of lead compounds
To identify lead compounds the binding energy, molecular bond interactions, pharmacological, and physiochemical properties were considered. This step helped to filter generalized hit compounds. These compounds in SMILES format were submitted to SwissADME [42], which calculates the corresponding ADME (absorption, distribution, metabolism, and excretion) properties of the compounds. Hydrogen bond interactions of the ligand–protein complexes were studied using LigPlot+ and PyMOL.
The hit compounds were physiochemically profiled to identify their drug-likeness and solubility in water. Lipinski’s rule of 5 was used as a metric to narrow down druggable compounds [43]. Pharmacokinetic properties of predicted compounds were determined
2.7 Prediction of activity spectra for substances (PASS) for leads
PASS assesses the probability that a compound has a suspected biological activity [44]. It has been well known that each substance has a wide spectrum of biological activities as evident from some new uses of many old drugs. The SMILES format of the leads were submitted to Way2Drug.com [45] to predict possible biological activity.
2.8 Molecular dynamics simulation and MM-PBSA calculation of protein–ligand complexes
By employing GROMACS 2018 [31], the chain A of
3. Results and discussion
3.1 Homology modeling
3.2 Active site prediction
In predicting the active site of the protein, results obtained showed 73 binding pockets. Several pockets were identified but the pocket with the largest volume and surface area of 595.278 Å3 and 924.887 Å2, respectively was selected. Larger pockets favor conformational rotation during virtual screening. A total of 80 amino acid active site residues were predicted from CASTp 3.0 (Table A1). The predicted binding site was visualized using PyMOL (Figure 1B).
The predicted active site was finally confirmed to be FAD binding site. TR has been studied to be a homodimer protein constituting FAD-binding, NADPH-binding, central, and interface domains [5]. The predicted site was concluded to be an FAD site in
3.3 Structural validation and quality prediction
PROSA was used to determine the quality of the structure by comparing it to protein structures that are experimentally solved in the PDB database. It validated the model based on the “quality score or
3.4 Pharmacophore modeling
The active ligands used as training sets to develop a pharmacophore allowed features similar to the two compounds to be identified and combined into a single geometric function as the basis for the generation of the pharmacophore (Figure 2). Pharmacophore generated utilized features that contributed regions of hydrophobicity and hydrogen bond acceptors incorporated in the model for selective screening. The oxygen from nitrogen dioxide contributed to hydrogen bond acceptors with aromatic and alkene groups contributing to the hydrophobic region (Figure A4). A number of 10 hypotheses were developed for the model. The best hypothesis with a similarity of 58.12% had a score of 0.8537 and was selected based on the AUC score of 0.99 generated by LigandScout (Figure A5-A). The AUC score was used as a metric to validate how best the pharmacophore model created could distinguish rightly between active compounds and decoys. This intends to reduce false positives and negatives during the screening process.
The screening process was successfully completed with the model which identified 42 compounds that matched the pharmacophore model with a pharmacophore fit score ranging from 55.32 to 57.98 (Table A2).
3.5 Docking validation
The docking system was validated using the five inhibitors and 250 decoys generated with DUD-E and further used their binding energies to plot an AUC with a value of 0.702 (Figure A5-B). AUC value of 1.0 verifies that the prediction of hits obtained from the hypothesis is perfect whereas values of 0.5 and less than 0.70 imply average and moderate random selection respectively [48].
Furthermore, the validation of the molecular docking was also undertaken by aligning the re-docked ligands with their respective co-crystallized complexes taken from PDB. The RMSD values of the alignment between the re-docked ligands and the co-crystalized ligands in complexes of 2YAU, 4APN, 5EBK, 6ER5, 6I7N were 1.483, 3.020, 1.920, 2.712, and 2.465 Å, respectively. Only two of the RMSD values (5EBK and 2YAU) of the alignments were below 2.0 Å, which is considered the threshold for good alignment.
Superimposition also validated the accuracy of docking at the predicted active site. The FAD molecule from the template selected for modeling, 2JK6 when extracted and docked in
3.6 Virtual screening of pharmacophore hits
When docking validation was verified, molecular docking was carried out. Molecular docking predicted various conformations of each ligand in the binding site of the
Predicted ligands | Pharmacophore fit score | Binding energy/(kcal/mol) | Hydrogen bond Residues and length (Å) | Hydrophobic bond interacting residues |
---|---|---|---|---|
ZINC95486081 | 55.95 | −9.8 | Lys60 (2.92) Ser178 (2.87) | Gly13, Gly15, Asp32, Ala46, Thr51, Cys52, Val55, Gly56, Ala159, Thr160, Tyr198, Arg287, Asp327, Met333, Leu334, Thr335 |
MTPA | 56.37 | −9.4 | Thr51 (3.12) Thr293 (2.89) Asp327 (3.13) Ser14 (3.17) | Gly11, Gly13, Gly15, Asp32, Val36, Ala46, Gly50, Cys52, Val55, Gly56, Ala159, Thr160, Tyr198, Arg287, Met333, Leu334, Thr335 |
Karatavicinol | 56.5 | −9.4 | Thr51 (3.28) Arg287 (3.00) Thr293 (3.01, 2.97) Asp327 (2.82) | Ser14, Gly11, Gly13, Val34, Asp35, Val36, Gly50, Cys52, Ala46, Phe126, Gly127, Ala159, Thr160, Gly161, Tyr198, Arg290, Met333, Ala338 |
Taccalin | 56.42 | −9.4 | Ser14 (3.21) Ala365 (3.25) | Gly13, Thr51, Cys52 Gly56, Cys57, Lys60, Gly161, Ile199, Thr198, Gly326, Met333, Leu334, Thr335, Ala338 |
Marmin | 56.18 | −9.3 | Val34 (3.17) Thr51 (2.99, 3.04) Thr160 (2.83) Thr335 (2.87) | Leu10, Gly11, Gly13, Ser14, Asp35, Ala46 Gly50, Cys52, Gly127, Ala159, Gly161, Arg290, Leu294, Ala327, Leu334, Ala338 |
13-Hydroxyfeselol | 55.62 | −9.1 | Val362 (2.79) Thr374 (2.85) Gly376 (3.20, 3.26) | Lys60, Thr198, Gly229, Phe230, Gly326, Leu334, Cys364, Ala365 |
Betaxanthin | 57.51 | −8.9 | Ser14 (2.85) Cys52 (3.03) Gly127 (3.03, 3.22, 3.30) Thr335 | Gly11, Gly13, Val34, Asp35, Val36, Gly50, Cys52, Ala46, Phe126, Ala159, Thr160, Gly161, Tyr198, Arg290, Asp327 Met333, Ala338 |
Colladonin | 55.90 | −8.8 | Asn330 (2.85) | Lys60, Gly197, Tyr198, Tyr221, Arg287, Phe230, Leu334, Ala365 |
Feselol | 55.63 | −8.8 | Asn330 (2.87) | Lys60, Gly197, Tyr198, Tyr221, Arg287, Phe230, Leu334, Ala365 |
ZINC38658035 | 55.95 | −8.7 | Tyr198 (3.31) Val362 (2.92) Tyr374 (3.30) Gly376 (2.86, 3.05) | Ile199, Phe230, Gly286 Arg287, Met333, Leu334, Cys364, Cys375 |
Pectachol | 57.18 | −8.5 | Lys60 (2.93) Gly376 (3.11) | Tyr198, Gly229, Phe230, Val332, Met333, Leu334, Ala365, Val362, Cys364, Val366, Phe367 |
FAD molecule and inhibitors from 6ER5 and 4APN | ||||
ZINC8782981 | _ | −7.2 | Lys60 (3.12) Arg287 (3.08) | Cys52, Cys57, Tyr198, Gly229, Phe230, Val332, Met333, Leu334, Ala365, Val362, Cys364 |
CHEMBL1277380 | _ | −8.2 | Lys60 (2.89) | Tyr198, Gly229, Phe230, Val332, Met333, Leu334, Ala365, Val362, Gly376 |
FAD | _ | −9.0 | Ser14 (3.11) Gly15 (2.96) Ala159 (2.97) Tyr198 (2.87) Arg287 (2.75) Met333 (2.78) Thr335 (2.88, 3.31) | Gly13, Gly50, Thr51, Cys52, Ser162, Gly197, Gly229, Phe230, Asp327, Leu334, Ala338 |
3.7 Protein–ligand interaction
Molecular interaction studies are important for understanding the mechanism of biological regulation at the molecular level and as such also provides a theoretical basis for drug design and discovery [49, 50]. Hydrogen and hydrophobic interactions are key players in stabilizing energetically favored ligands, in an open conformational environment of protein structures [29]. The intermolecular interaction and bond lengths of these 11 compounds were observed. The compound which showed the highest binding affinity, ZINC95486081 formed two hydrogen bonds with residues Lys60 and Ser178 with respective bond lengths of 2.92 Å and 2.87 Å. Five compounds including MTPA, Karatavicinol, Marmin, Betaxanthin and ZINC38658035 had four residues as the highest number of residues partaking in hydrogen bonding. MTPA formed hydrogen bonds with residues Thr51, Thr293, Asp327, and Ser14. Karatavicinol on the other hand formed hydrogen bonds with Thr51, Arg287, Thr293, and Asp327 (Figure A6-A). Marmin formed hydrogen bonds with Val34, Thr51, Thr160, and Thr335 (Figure A6-B). Betaxanthin also bonded with Ser14, Cys52, Gly127, and Thr335. Finally, ZINC38658035 formed hydrogen bonds with Tyr198, Val362, Tyr374, and Gly376. The compound 13-hydroxyfeselol was the only hit that formed three hydrogen bonds with Val362, Thr374, and Gly376. Taccalin and Pectachol formed only two hydrogen bonds with Ser14 and Ala365. On the other hand, Colladonin and Feselol formed the least hydrogen bond residues with Asn330. The shortest bond length of 2.83 Å was exhibited by Marmin with Thr160. Betaxanthin showed the highest pharmacophore fit score of 57.51 followed by Pectachol (57.18). 13-Hydroxyfeselol showed the lowest fit score of 55.62.
3.8 Pharmacological profiling
To identify lead compounds, the binding energy, molecular bond interactions, pharmacological, and physiochemical properties were considered. This step helped to filter generalized hit compounds. The top 11 compounds were profiled
3.9 Pharmacokinetic properties
Further filtering analysis subjected all 11 pharmacophore hits to pharmacokinetics profiling taking into consideration parameters such as gastrointestinal (GI) absorption, blood–brain barrier (BBB) permeation, the permeability of glycoprotein (Pgp), and cytochrome P450 (CYP). Physical parameters such as drug solubility may affect oral bioavailability but in most cases, the major determining factors are likely to be metabolism by CYP and absorption at the intestinal level [51]. CYP3A4 has been known to be responsible for the metabolism of about 50% of all drugs [52] and therefore inhibition of cytochrome can affect oxidation of substrates in cells. Absorption of drugs in the intestine if found high favors the efficacy of the compound as a drug. Multi-drug resistance transporters, such as P-glycoproteins, are essential for many cellular processes that require the transport of substrates across cell membranes [53]. Compounds that are P-gp substrates may face continual efflux which can affect the efficacy of drugs. The blood–brain barrier (BBB) prevents the brain uptake of most pharmaceuticals [54]. This is a disadvantage to neurological diseases but would be of merit since the disease of study is not related to the brain. Compounds that cross the blood–brain barrier may elucidate unwanted biological activities that could be dangerous to health. Therefore, the negative inference would be good for the compound. ZINC95486081 was predicted to show inhibition to three CYP isoenzymes. Karatavicinol, ZINC38658035, and Marmin excelled with an appreciable result (Table A4). For the purpose of narrowing down leads with potential for further computational analysis, compounds with low gastrointestinal absorption were side-lined. This included Taccalin and Betaxanthin.
3.10 Prediction activity spectra for substance (PASS)
The biological activity of the selected drug-like candidates was then evaluated using PASS. It is well known that each substance has a wide spectrum of biological activities as evident from some new uses of many old drugs. This allows the tool to utilize this information to predict biological activities based on their probable activity (Pa) and probable inactivity (Pi). When Pa is greater than Pi (Pa > Pi), the compound is likely to possess the predicted biological activity [55, 56]. PASS predicted Karatavicinol, Marmin, Colladonin and Pectachol to be potential antileishmanial agents (Table A5). Colladonin showed the highest Pa of 0.768 and Pi of 0.006 followed by Taccalin (Pa of 0.711 and Pi of 0.009) and Pectachol with a Pa of 0.694 and Pi of 0.009. Betaxanthin had no prediction as an antileishmanial agent.
3.11 Selection of lead compounds for MD and MM-PBSA analysis
The various lead compounds were considered for selection based on the criteria above. ZINC95486081 and MTPA compound although had high binding energy trailed in pharmacokinetic properties and showed Pa less than 0.500. We eliminated Taccalin and Betaxanthin because of their low GI absorption and low Pa values (Tables A3 and A4). Compounds predicted with good probable activity for antileishmanial activities included Karatavicinol, Taccalin, Marmin, 13-hydroxyfeselol, Colladonin, Feselol and Pectachol. A literature search revealed Feselol to have antiprotozoal activity against
3.12 Molecular dynamic simulation of protein–ligand complex
Molecular dynamics simulation allowed the early view of proteins as relatively rigid structures to be replaced by a dynamic model in which the internal motions and resulting conformational changes play an essential role in its function [59]. An RMSD plot generated after molecular dynamics simulation showed a deviation of about 0.25 Å (Figure A7). Further scrutinized with molecular dynamics simulations gave the protein a dynamic dimension to its 3D structural form producing a realistic environment for the ligand interactions that were carried out in the docking process.
Molecular dynamics simulations can also capture a wide variety of important biomolecular processes, including conformational change, ligand binding, and protein folding [60]. The stability of docked protein–ligand complexes was determined by their (RMSD) plots generated from the MD simulation output file. The backbones of the four complexes were observed to be stable over time (Figure 3). The fluctuations of the protein–ligand complexes were analyzed within the system to check for movement and structural stability during the course of the simulation. These movements and stability are significant for the complex functioning inside living systems. The backbone of the
The flexibility of residues contribution by the
The compactness of the complexes over simulation time is determined by the Rg. If a protein is folded well, it will likely maintain a relatively steady value of Rg, whereas its value will change over time if the protein unfolds [62]. Rg values of all complexes indicated stable complexes over 100 ns (Figure A9). The Rg graph showed most compounds experienced a fairly stable Rg. Marmin experienced the lowest Rg value around 2.33 nm compared to other complexes. This was followed by Colladonin, Pectachol and Karatavicinol with Rg values of 2.37, 2.42, and 2.45 nm, respectively. Between the known inhibitors, CHEMBL1277380 was observed to have an average Rg value of 2.46 nm whilst ZINC8782981 showed the average highest value of around 2.5 nm. Inferring from the Rg graph, the compactness of the
3.13 Evaluation of leads using MM-PBSA
MM-PBSA was employed to calculate free binding energies by per-residue decomposition of the protein complexes. At a quantitative level, simulation-based methods provide substantially more accurate estimates of ligand binding affinities (free energies) than other computational approaches such as docking [63]. Residues contributing binding free energy greater than 5 kJ/mol or less than −5 kJ/mol are considered critical for binding of a ligand to a protein [64]. MM-PBSA results showed only Asp327 amongst the hydrogen bonding residues of Karatavicinol to contribute a per residue decomposition energy of 13.65 kJ/mol. Amino acid residue Asp35 (21.89 kJ/mol) was observed with such greater contribution (Figure A10). The complex of
3.14 Other energy terms
Van der Waals forces, electrostatic and polar solvation energies, and SASA are relevant energy terms contributing to the overall free binding energy of the complex. The van der Waals energy refers to the weak attraction existing between the intermolecular forces. The van der Waals energy observed in our study showed Karatavicinol and CHEMBL1277380 to have the lowest and highest energy of −228.565 and − 171.823 kJ/mol, respectively. Colladonin, Marmin, and Pectachol also showed relatively low van der Waals energy of −189.289, −189.229, and − 209.538 kJ/mol, respectively as compared with ZINC8782981 with −222.123 kJ/mol. Electrostatic energy refers to the potential energy of a system consisting of different electric charges [65, 66, 67]. The lowest electrostatic energy was exhibited by Marmin (−386.401 kJ/mol) followed by Pectachol (−286.260 kJ/mol), and Colladonin (−249.067 kJ/mol). Karatavicinol and the other two inhibitors were observed with high electrostatic energy (Table 2). Some studies have observed that van der Waals and electrostatic forces contribute favorably to the energetics of binding along with simulations that favor the binding of complexes [66, 68].
Compound | Van der Waals energy (kJ/mol) | Electrostatic energy (kJ/mol) | Polar solvation energy (kJ/mol) | SASA energy (kJ/mol) | Binding energy (kJ/mol) |
---|---|---|---|---|---|
ZINC8782981 | −222.123 ± 14.568 | −52.495 ± 17.662 | 245.049 ± 33.654 | −24.829 ± 0.962 | −54.399 ± 20.084 |
CHEMBL1277380 | −171.823 ± 14.173 | −2.926 ± 5.485 | 82.884 ± 16.706 | −19.869 ± 1.202 | −111.732 ± 16.514 |
Karatavicinol | −228.565 ± 12.673 | −32.345 ± 21.415 | 227.483 ± 27.305 | −24.217 ± 1.100 | −57.644 ± 24.019 |
Marmin | −189.289 ± 16.726 | −386.401 ± 30.540 | 484.074 ± 28.991 | −17.498 ± 1.050 | −109.114 ± 23.461 |
Pectachol | −189.229 ± 18.203 | −286.260 ± 49.152 | 430.604 ± 71.136 | −18.602 ± 1.308 | −63.487 ± 33.289` |
Colladonin | −209.538 ± 18.908 | −249.067 ± 40.851 | 427.216 ± 49.348 | −17.548 ± 1.122 | −48.936 ± 24.773 |
Polar solvation energy on the other hand refers to the electrostatic interaction that exists between the solute and the continuum solvent [69]. The highest polar solvation energy amongst the leads was exhibited by Marmin (484.074 kJ/mol) and the lowest by Karatavicinol (227.483 kJ/mol). Solvent accessible surface area (SASA) energy was calculated after MD. This represents the non-polar solvation energy [69]. This energy measures the interactions that exist between the complex and the solvents. Amongst the leads, Karatavicinol obtained the lowest SASA energy followed by Pectachol, Colladonin, and Marmin (Table 2). Relative to these were the low SASA energies of the inhibitors ZINC8782981 and CHEMBL1277380.
The total contribution of these energies enabled the final estimation of the free binding energies in the complexes (Table 2). The lowest free binding energy contributing to more stability of the protein–ligand complex was observed by Marmin (−109.114 kJ/mol). Next amongst the four complexes was Pectachol (−63.487), followed by Karatavicinol (−57.644 kJ/mol), and Colladonin (−48.936 kJ/mol). The low binding energy of Marmin was much closer to that of CHEMBL1277380 (−111.732 kJ/mol) with that of Pectachol higher than ZINC8782981 (−54.399 kJ/mol). These energies address the potential of Marmin and Pectachol to bind most effectively at the active site of
3.15 Exploring possible implications and structure similarities of predicted leads
Karatavicinol and Marmin had lower binding energies of −9.4 and − 9.3 kcal/mol, respectively, as compared to Colladonin (−8.5 kcal/mol) and Pectachol (−8.5 kcal/mol). These binding energies are closer to that of FAD (−9.0 kcal/mol) for which can possibly compete in binding at the FAD domain. These compounds were concluded to have drug-likeness by satisfying Lipinski’s rule of 5. They also do not pass the blood–brain barrier which is good. Also, Marmin and Karatavicinol checked false for p-glycoprotein substrate. This gives the compounds an advantage to maintain their concentrations in cellular level to maximize efficacy. Pectachol and Colladonin however were implicated as P-gp substrates. These predicted preferable properties can favor their lead likeness and chances of going a long way in experimental studies. The four lead compounds were predicted as antileishmanial compounds. The four leads are confirmed not to be already existing antileishmanial drugs by structural similarity searches in www.DrugBank.ca but rather observed to be analogues of chrome 2-one. In regard to this, studies over the years have however shown some novel compounds such as 7-{[(2R*)-3,3-dimethyloxiran-2-yl]methoxy}-8-[(2R*,3R*)-3-isopropenyloxiran-2-yl]-2H-chromen-2-one and 7-methoxy-8-(4-methyl-3-furyl)-2H-chromen-2-one against
Further in this study, the interaction of the active site residues with all four lead compounds showed hydrogen bonding with Val34, Thr51, Lys60, Thr160, Ala159, Arg287, Thr293, Asp327, Asn330, Thr335, and Gly376 (Table 1). Superimposition of the docked 2JK6 and co-crystallized revealed common residues such as Ser14, Gly15, Arg287, and Thr335 (Figure A1). These residues can be observed to be unique to the FAD domain of
4. Conclusion
Trypanothione reductase has been a well-investigated target essential for trypanosomatids. Its function in controlling oxidative stress in the parasite provided an opportunity to target the trypanothione biosynthesis pathway. A total of 11 hit compounds identified by pharmacophore modeling and virtual screening were filtered to four potential leads by considering their ADME with their molecular interactions in
Acknowledgments
The authors are grateful to the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP) at the University of Ghana for making Zuputo, a Dell EMC high-performance computing cluster, available for this study.
Leu10, Gly11, Gly13, Ser14, Gly15, Gly16, Val34, Asp35, Val36, Phe44, Ala46, Ala47, Gly50, Thr51, Cys52, Val55, Gly56, Cys57, Lys60, Lys61, Gly125, Phe126, Gly127, Ala128, Arg138, Ser140, Glu141, Pro143, Ala159, Thr160, Gly161, Ser162, Trp163, Pro164, Thr165, Thr177, Ser178, Asn179, Phe182, Tyr198, Ile199, Glu202, Phe203, Met282, Leu283, Ala284, Ile285, Gly286, Arg287, Arg290, Thr293, Leu294, Gln295, Ile325, Gly326, Asp327, Val332, Met333, Leu334, Thr335, Pro336, Val337, Ala338, Ile339, Asn340, Arg355, Thr357, Asp358, His359, Thr360, Lys361,Val362, Ala363, Cys364, Ala365, Phe367, Pro435, Glu436, Ile438, Gln439, Gly442, Ile443, Lys446 |
Name | P-fit score | Binding energy (kcal/mol) | Active/decoy | Source database |
---|---|---|---|---|
ZINC95486081 | 55.95 | −9.8 | Active | AfroDB |
(S)-alpha-methoxy-alpha-trifluoromethyl-alpha-phenylacetate (MTPA) | 56.37 | −9.4 | Active | NANPDB |
Karatavicinol | 56.5 | −9.4 | Active | NANPDB |
Taccalin | 56.42 | −9.4 | Active | NANPDB |
Marmin | 56.18 | −9.3 | Active | NANPDB |
3-Hydroxyfeselol | 55.62 | −9.1 | Active | NANPDB |
ZINC95486257 | 55.9 | −9.0 | Active | AfroDB |
Betaxanthin | 56.97 | −8.9 | Active | NANPDB |
Coladonin | 56.58 | −8.8 | Active | NANPDB |
Feselol | 56.41 | −8.8 | Active | NANPDB |
ZINC38658035 | 55.9 | −8.7 | Active | AfroDB |
Pectachol | 57.18 | −8.5 | Active | NANPDB |
ZINC85967928 | 55.85 | −8.4 | Active | AfroDB |
Polyanthin | 56.39 | −8.4 | Active | NANPDB |
ZINC95486047 | 57.98 | −8.3 | Active | AfroDB |
4′-Methyl gossypetin | 56.17 | −8.2 | Active | NANPDB |
2-(nonan-8-one)-4-methoxy-quinoline | 56.41 | −8.2 | Active | NANPDB |
Orientin | 55.53 | −8.1 | Active | NANPDB |
Kaempferol-3,6-dimethylether-7-glucoside | 57.15 | −7.8 | Active | NANPDB |
ZINC95486129 | 56.43 | −7.8 | Active | AfroDB |
Ethuliaconyzophenone | 56.9 | −7.7 | Active | NANPDB |
ZINC95486209 | 56.55 | −7.5 | Active | AfroDB |
(+)-1,2-bis-(4-hydroxy-3-methoxyphenyl)-propane-1,3-diol [erythro form] | 55.9 | −7.4 | Active | NANPDB |
4-Hydroxy-2′,4′-dimethoxy-dihydrochalcone | 55.58 | −7.4 | Active | NANPDB |
Drimartol A | 56.31 | −7.4 | Active | NANPDB |
Isoarnottinin-4′- | 55.71 | −7.4 | Active | NANPDB |
4-Beta-hydroxy-6alpha-(4-hydroxy-3-methoxybenzoyl)-7-daucen-9-one | 55.93 | −7.4 | Active | NANPDB |
ZINC14686464 | 56.55 | −7.4 | Active | AfroDB |
6-(3′,4′-dimethoxybenzoyl)-jaeschkeanadiol | 57.17 | −7.3 | Active | NANPDB |
ZINC14887523 | 56.88 | −7.3 | Active | AfroDB |
Orientin-7-methoxide | 56.26 | −7.2 | Active | NANPDB |
ZINC14444870 | 56.35 | −7.2 | Active | AfroDB |
ZINC14689062 | 56.5 | −7.2 | Active | AfroDB |
1-Dehydrogingerdione | 56.05 | −7.1 | Active | NANPDB |
Onopordin | 56.27 | −7.1 | Active | NANPDB |
ZINC95486194 | 56.79 | −7.1 | Active | AfroDB |
Methyl5-(3-4-dihydroxyphenyl)-3-hydroxypenta-2,4-dienoate | 55.32 | −7 | Active | NANPDB |
Corniculatusin | 56.23 | −7 | Active | NANPDB |
3-(10-acetoxygeranyl)-4-acetoxy- | 56.14 | −7 | Active | NANPDB |
ZINC00035526 | 56.66 | −7 | Active | AfroDB |
ZINC00608186 | 57.08 | −6.8 | Active | AfroDB |
Evoxine | 57.32 | −6.2 | Active | NANPDB |
Compound ZINC ID/name | Number of Lipinski’s rules violated | MW (g/mol) | No. HA | No. HD | xLogP | Water solubility (mg/mL) | Log S | Bio. Sc |
---|---|---|---|---|---|---|---|---|
ZINC95486081 | 0 | 382.45 | 5 | 2 | 4.52 | Moderately soluble | −5.84 | 0.55 |
MTPA | 0 | 470.52 | 8 | 0 | 6.35 | Moderately soluble | −6.00 | 0.55 |
Karatavicinol | 0 | 400.51 | 5 | 2 | 4.66 | Moderately soluble | −4.85 | 0.55 |
Taccalin | 0 | 418.48 | 9 | 6 | −1.45 | Moderately soluble | 1.66 | 0.55 |
Marmin | 0 | 332.39 | 5 | 2 | 2.81 | Soluble | −3.52 | 0.55 |
13-Hydroxyfeselol | 0 | 400.51 | 5 | 2 | 1.45 | Moderately soluble | −5.93 | 0.55 |
Betaxanthin | 0 | 370.44 | 8 | 7 | −1.17 | Moderately soluble | −2.11 | 0.55 |
Colladonin | 0 | 384.51 | 4 | 1 | 5.76 | Poorly soluble | −6.50 | 0.55 |
Feselol | 0 | 384.51 | 4 | 1 | 5.76 | Poorly soluble | −6.5 | 0.55 |
ZINC38658035 | 0 | 464.63 | 6 | 3 | −4.47 | Soluble | −3.28 | 0.55 |
Pectachol | 0 | 444.56 | 6 | 1 | 5.70 | Poorly soluble | −6.70 | 0.55 |
Compound ZINC ID | GI absorption | BBB permeant | P-gp substrate | CYP1A2 inhibitor | CYP2C19 inhibitor | CYP2C9 inhibitor | CYP2D6 inhibitor | CYP3A4 inhibitor |
---|---|---|---|---|---|---|---|---|
ZINC95486081 | High | Yes | Yes | No | No | Yes | Yes | Yes |
MTPA | High | No | No | No | No | No | Yes | Yes |
Karatavicinol | High | No | No | No | No | No | No | Yes |
Taccalin | Low | No | Yes | No | No | No | No | No |
Marmin | High | No | No | No | No | No | No | No |
13-Hydroxyfeselol | High | No | Yes | No | No | No | Yes | Yes |
Betaxanthin | Low | No | No | No | No | No | No | No |
Colladonin | High | Yes | No | No | No | No | Yes | No |
Feselol | High | Yes | No | No | No | No | Yes | No |
ZINC38658035 | High | No | Yes | No | No | No | No | No |
Pectachol | High | No | Yes | No | No | No | Yes | No |
Lead compounds | Antileishmanial predicted activity | |
---|---|---|
Pa | Pi | |
ZINC95486081 | 0.224 | 0.168 |
MTPA | 0.263 | 0.130 |
Karatavicinol | 0.513 | 0.021 |
Taccalin | 0.711 | 0.009 |
Marmin | 0.557 | 0.024 |
13-Hydroxyfeselol | 0.658 | 0.030 |
Betaxanthin | — | — |
Colladonin | 0.768 | 0.006 |
Feselol | 0.768 | 0.006 |
ZINC38658035 | 0.345 | 0.074 |
Pectachol | 0.694 | 0.009 |
A.1 List of abbreviations
absorption, distribution, metabolism, excretion and toxicity
area under curve
cytochromes P450
directory of useful (docking) decoys-enhanced
GROningen MAchine for Chemical Simulations
high performance computing
identification
logarithm of the octan-1-ol/water partition coefficient
molecular dynamics
molecular mechanics Poisson Boltzmann surface area
molecular weight
permeability glycoprotein
prediction of activity spectra for substance
Protein Data Bank
radius of gyration
root mean square deviation
root mean square fluctuation
receiver operating characteristic
structure data file
simplified molecular input line entry system
universal force field
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