|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2019 | Volume
: 3
| Issue : 3 | Page : 162-170 |
|
Molecular docking studies of filarial β-tubulin protein models with antifilarial phytochemicals
Sumit T Halder1, Tehseen M Dhorajiwala1, Lalit R Samant2
1 Department of Bioinformatics, Patkar College of Arts and Science, Mumbai, Maharashtra, India
2 Molecular Genetics Research Laboratory, Bai Jerbai Wadia Hospital for Children, Mumbai, Maharashtra, India
| Date of Submission | 24-Jul-2019 |
| Date of Decision | 21-Aug-2019 |
| Date of Acceptance | 27-Aug-2019 |
| Date of Web Publication | 10-Sep-2019 |
Correspondence Address:
Mr. Lalit R Samant
Molecular Genetics Research Laboratory, Bai Jerbai Wadia Hospital for Children, Parel, Mumbai - 400 012, Maharashtra
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/bbrj.bbrj_100_19
Background: Lymphatic filariasis affects millions of people worldwide, majorily people from lower socioeconomic strata, who cannot afford proper medication and seek local treatments, mostly involving the application or administration of plant extracts. Lymphatic filariasis is caused by filarial worms, and it has been reported that tubulin beta chain protein of these worms serves as an important drug target to inhibit their growth and development. This study aims to find phytochemicals which can be used as natural inhibitors of filarial worms by targeting tubulin beta chain protein present in them. Methods: Protein structure homology modeling was carried out to model the target protein of lymphatic filariasis-causing organisms. A total of 105 phytochemicals were screened for their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, and 12 phytochemicals which passed all the filters were used for comparative docking studies along with drug albendazole which is proved to bind to tubulin beta chain of roundworms. In silico molecular docking was performed using AutoDock Vina, and several phytochemicals were found to have better binding affinity than albendazole. Results: Based on binding affinity and ADMET properties, hecogenin was selected as the best lead molecule. SwissDock was used to confirm hecogenin which has better binding affinity than albendazole against target proteins. Conclusion: This study suggests that hecogenin and other potent phytochemicals such as (-)-epicatechin, akuammicine, apigenin, boeravinone A, boeravinone B, catechin, diosgenin, rhein, and ruscogenin have promising antifilarial properties and can be used as natural inhibitors of tubulin beta chain of lymphatic filariasis-causing organisms.
Keywords: Albendazole, antifilarial phytochemicals, hecogenin, molecular docking
How to cite this article:
Halder ST, Dhorajiwala TM, Samant LR. Molecular docking studies of filarial β-tubulin protein models with antifilarial phytochemicals. Biomed Biotechnol Res J 2019;3:162-70 |
How to cite this URL:
Halder ST, Dhorajiwala TM, Samant LR. Molecular docking studies of filarial β-tubulin protein models with antifilarial phytochemicals. Biomed Biotechnol Res J [serial online] 2019 [cited 2023 Mar 29];3:162-70. Available from: https://www.bmbtrj.org/text.asp?2019/3/3/162/266558 |
| Introduction | |  |
Lymphatic filariasis belongs to a group of transmissible diseases called neglected tropical diseases (NTDs). NTDs affect more than 1 billion people worldwide and may cause fatality or lifelong disability. People belonging to lower socioeconomic strata who cannot afford basic safety measures to prevent mosquito bites and lack proper sanitation facilities have a risk of getting lymphatic filariasis. Majority of the population suffering from lymphatic filariasis are from underdeveloped or developing countries, and many of them lack resources for proper treatment and medication. Such patients seek help for local and cost-effective treatment where they use plant extracts. Use of plant extracts for the treatment of diseases has been in practice since ancient times. Phytochemicals present in such plant extracts are responsible for the treatment of various diseases, and they have very less-to-no side effects which modern synthetic drugs lack.
Lymphatic filariasis
Lymphatic filariasis (elephantiasis) is caused by parasitic nematode worms which are transmitted by various species of mosquitoes. These parasitic worms attack the host's lymphatic system where they develop and reproduce. This causes disfiguration of the body due to the enlargement of body parts, pain, and acute disability. According to the WHO, 886 million people in 52 countries are at the risk of getting infected with lymphatic filariasis; 120 million people have already been affected by lymphatic filariasis; and 15 million people suffer from lymphedema, while 25 million men suffer from hydrocele.[1]
Wuchereria bancrofti (WB), Brugia malayi (BM), and Brugia timori (BT) are three filarial worms that cause lymphatic filariasis. A larval form of these worms is introduced onto the human skin by mosquitoes acting as a vector. The larva enters the host body through bite wound and resides into a lymphatic vessel where it develops into an adult and produces microfilariae. The microfilariae migrate into the bloodstream where they get ingested by mosquitoes during their blood meal. Development from microfilariae to larvae takes place into thoracic muscles of the host mosquito and after development, they migrate into mosquito's proboscis to infect another host.[2]
Current drug treatment
The WHO recommends drug combination therapy of either albendazole with diethylcarbamazine citrate (DEC) or albendazole with ivermectin in the treatment of lymphatic filariasis. Recent findings suggest that a triple-drug therapy (albendazole + DEC + ivermectin) is more efficient than double-drug treatment.[1],[3] DEC is effective against microfilariae, larval and adult filarial worms, but its exact mechanism of action is still not known and is also not recommended for mass drug administration due to its adverse effects in onchocerciasis-endemic regions.[4] However, ivermectin is known to interact with postsynaptic glutamate-gated chloride ion channels of microfilariae and causes paralysis, but shows weaker-to-no activity against adult worms.[4] Albendazole is active against both larval and adult stages of parasitic filarial worms. It binds to the colchicine-sensitive site of tubulin (tubulin beta-2 chain) and blocks polymerization of tubulin proteins into microtubule, leading to weaker uptake of glucose and depletion of glycogen stores. Thus, adenosine triphospate(ATP) production decreases, which causes immobilization of filarial worm followed by death.[5]
The mechanism of action of drug albendazole suggests that beta-tubulin protein of nematode filarial worms can serve as a promising drug target. Because there is no three-dimensional (3D) structure available, Sharma et al. (2011) modeled beta-tubulin protein structure of BM for in silico docking analysis of benzimidazole analog for treating lymphatic filariasis.[6]
Phytochemicals
Albendazole has low aqueous solubility and therefore, it is poorly absorbed by the gastrointestinal (GI) tract. Side effects of albendazole are elevated liver enzymes, fever, itching, hair loss, headaches, and neutropenia.[5] Thus, there is a need for a substitute which is more efficient, is cost-effective to produce, and has no side effects.
Effective phytochemicals can be a better substitute for synthetic drug as they are naturally available and have no adverse effect. A list of phytoconstituents isolated from plant extracts which have been screened forin vivo andin vitro antifilarial activities was reported by Ranjini et al., and the authors suggested further molecular studies on them. The potential phytoconstituents are (-)-epicatechin, (-)-laminaribiitol, 12-hydroxy-9-octadecenoic acid; 12-methoxy-N (4)-methylakuammicine; 17-acetoxy-nor-echitamine; 19-epischolaricine; 2-(4-methoxy phenyl) imino) 4-methyl-1, 3-thiozolan-4yl); 2′-(R)-O-acetylglaucarubinone; 4-hydroxy-α-tetralone; 4-o-(b-D-glucopyranosyl-6-sulfate) gallic acid; 7,4'-dihydroxyflavon, akuammicine, akuammidine, aloe emodina, andrographiside, andrographolide, apigenin, apocynin, archidic acid, azadirachtin, beta-sitosterol, boeravinone A, boeravinone B, calctin, calotropin, carapolide A, cardenolide, catechin, chebulagic acid, chebulanin, chebulinic acid, chlorogenic acid, chlorogenin, columbin, corilagin, coumaric acid, cycloartanone, demethylwedelolactone, dihydroagnosterol, dihydrobenzofuran neolignan rel-(2alpha, 3beta)-7-O-methylcedrusin, diosgenin, dodecanoic acid, echitamidine, echitamine, epitetraphyllin B (volkenin), euphol, ferulic acid, gummiferol, hecogenin, hypaphorine, isoricinoleic acid, isoshinanolone, isoshinanolone, isovanillic acid, krisofanat acid, luteonin, mansonin, mexicanolide-methylangolensate, myricetin, neogitogenin, neohecogenin, nimbidin, nimbin, nimbolin B, nor-echitamine, Nα-formylechitamidine, palmatine, palmitate, palmitic acid, p-hydroxybenzoic acid, physcion, proanthocyanidins, protocatechuic acid, pseudoephedrine, punicalagin, quassinoid, quercetin, repenol, repenone, rhein, ricinoleic acid, robustadial A, robustadial B, ruscogenin, saponin, scopolamine, scopoline, stearic acid, stearic, stigmasterol, strebloside, tembeterine, tetracosanoic acid, tetralone-4-O-β-D-glucopyranoside, tetraphyllin B (barterin), tinosporoside, tirucallol, tubotaiwine, vellesamine, wedelolactone, zingerone, α-2-sitosterol, α-amyrin, α-zingiberol, β-sitosterol, and β-sitosterolglucoside.[7]
| Methods | | |
Protein homology modeling
Drug albendazole which is used in the treatment of lymphatic filariasis is proven to bind to tubulin beta-2 chain of organism Ascaris suum (UniProt ID: F1 L7U3).[5] The amino acid sequence of UniProt ID: F1 L7U3 was subjected to basic local alignment search tool (BLAST) in UniProt Knowledgebase (KB) database to find similar tubulin beta-2 chain of lymphatic filariasis-causing organisms. The number of hits (output sequence) was set to 1000 to achieve the maximum number of similar sequences, and the remaining filters were set to default. UniProtKB is an online protein database having information on protein, a tool for sequence alignment and BLAST.[8] The amino acid sequence of tubulin beta chain of organisms WB, BM, and BT was uploaded into advanced search option of Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) to find a suitable template protein structure for homology modeling. RCSB PDB is a huge database of 3D structures of proteins, nucleic acids, and complex assemblies.[9] The protein having the lowest resolution and best e-value against the query target protein was used as a template. The amino acid sequence of tubulin beta chain of each disease-causing organism along with template protein structure was uploaded on SWISS-MODEL web server (BIOZENTRUM, The Center for Molecular Life Sciences, University of Basel, Basel, Switzerland). SWISS-MODEL is a protein homology modeling web server where user can upload target protein sequence and template protein for user-defined automatic protein structure modeling.[10],[11]
Protein structure validation and energy minimization
Validation of the modeled protein structure helps to identify erroneous sites or residues which were not properly modeled. Heteroatoms from all the three modeled protein structures were removed, and structure validation was done using Protein Structure Analysis (ProSA, Center of Applied Molecular Engineering, Division of Bioinformatics, University of Salzburg, Salzburg, Austria), Verify3d (Molecular Biology Institute and the DOE-MBI Institute at the University of California, Los Angeles, California, United States), and RAMPAGE servers (Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom). ProSA is a diagnostic web tool based on a statistical analysis of all available protein structures used to analyze the 3D model of query protein structure for potential errors.[12] Verify3d determines the compatibility of 3D protein structure with its own one-dimensional amino acid sequence.[13],[14],[15] RAMPAGE is a web server used for Ramachandran plot analysis of a given query protein structure. Ramachandran plot is a two-dimensional (2D) graphical plot of dihedral angles psi against phi of amino acid residues present in the backbone of protein structure.[16],[17]
Energy minimization of protein structure provides stability to the protein structure and may also fix errors in protein structure. After protein structure validation, energy minimization of heteroatoms-trimmed modeled protein structures was carried out using ModRefiner web server. ModRefiner webserver by Zhang Lab (University of Michigan, MI, United States) is an algorithm for protein structure refinement. The structure refinement is performed in two steps as follows: (i) low-resolution step – main chain energy minimization and (ii) high-resolution step – fast full atomic energy minimization.[18],[19]
The energy-minimized protein structures were then again validated on RAMPAGE server to study improvements in protein structure after energy minimization.
Binding-site prediction
The SWISS-MODEL may predict binding-site residues of modeled protein structure if the template protein itself has ligand/s bound to it. Apart from binding-site residues predicted by the SWISS-MODEL, 3DLigandSite webserver (Structural Bioinformatics Group, Imperial College, London, United Kingdom) was used for predicting additional binding-site residues, if any. 3DLigandSite predicts ligand-binding sites by superimposing ligand-bound protein structures homologous to the query protein. Binding-site prediction is done in two steps as follows: (i) first, a structural scan of a query protein against the ligand-bound protein structure library is performed using MAMMOTH (matching molecular models obtained from theory) and the top 25 scoring homologous structures are retained and (ii) using single-linkage clustering, ligands are grouped, and the cluster with the highest number of ligands is selected. Amino acid residues that are within a distance threshold of the clustered ligands are predicted as part of the active site.[20]
Preparation of ligand molecules
Phytochemicals retrieved from the literature were drawn using MarvinSketch. Explicit hydrogen was added to the drawn structures; the structures were cleaned in 2D and saved in.smiles format. After saving in.smiles format, the structures were cleaned in 3D and saved in.pdb format. MarvinSketch of ChemAxon (ChemAxon Ltd., Budapest, Hungary) allows the user to quickly draw molecules through basic functions on the graphical user interface (GUI) and save them in various formats.[21]
Absorption, distribution, metabolism, excretion, and toxicity screening
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening of ligands helps to determine their absorption properties, toxicity, and drug-likeness. Ligand molecules saved in.smiles format and drug albendazole were uploaded on SWISSADME (Molecular Modeling Group of the SIB (Swiss Institute of Bioinformatics), Lausanne, Switzerland), admetSAR (Laboratory of Molecular Modeling and Design, Shanghai, China), and PROTOX webservers (Charite University of Medicine, Institute for Physiology, Structural Bioinformatics Group, Berlin, Germany) for ADMET screening. SWISSADME is a web tool used for the prediction of ADME and pharmacokinetic properties of a molecule. The predicted result consists of lipophilicity, water solubility, physicochemical properties, pharmacokinetics, drug-likeness, medicinal chemistry, and Brain Or IntestinaL EstimateD permeation method (blood–brain barrier and PGP ± prediction).[22] admetSAR provides ADMET profiles for query molecules and can predict about fifty ADMET properties. Toxicity classes are as follows: (i) Category I contains compounds with LD50 values ≤50 mg/kg, (ii) Category II contains compounds with LD50 values >50 mg/kg but <500 mg/kg, (iii) Category III includes compounds with LD50 values >500 mg/kg but <5000 mg/kg, and (iv) Category IV consists of compounds with LD50 values >5000 mg/kg. Carcinogenicity classes are as follows: (i) “Warning” is assigned to compounds with TD50>10 mg/kg body wt/day, (ii) “Danger” is assigned to compounds with TD50≤10 mg/kg body wt/day, and (iii) “Non-required” is assigned to noncarcinogenic chemicals.[23],[24] PROTOX is a Rodent oral toxicity server predicting LD50 value and toxicity class of query molecule. The toxicity classes are as follows: (i) Class 1: fatal if swallowed (LD50≤5), (ii) Class 2: fatal if swallowed (5 <LD50≤50), (iii) Class 3: toxic if swallowed (50 <LD50≤300), (iv) Class 4: harmful if swallowed (300 <LD50≤2000), (v) Class 5: may be harmful if swallowed (2000 <LD50≤5000), and (vi) Class 6: nontoxic (LD50>5000).[25]
Ligand molecules which passed all ADMET filters were further used for in silico docking against target protein molecules.
In silico docking
In silico molecular docking is an important tool in drug design and drug discovery for pharmaceutical research. Python molecular viewer (PMV) of MGLTools (Molecular Graphics Laboratory, The Scripps Research Institute, La Jolla, California, United States) is a molecule viewer having features such as atom identification, measuring tools, various structure representation styles, and support for multiple molecules and user-definable sets of atoms, residues, chains, and molecules.[26] AutoDock Tools (ADT) is a free GUI for AutoDock developed by MGL. ADT is used to set up, run, and analyze AutoDock dockings.[26],[27] AutoDock Vina (Molecular Graphics Laboratory, The Scripps Research Institute, La Jolla, California, United States) is a computer program used for docking. It is easy to use and has significantly more average accuracy than AutoDock 4.[28]
Preparing macromolecules (target protein) and configuration file for docking
Energy-minimized protein structures of all the three target proteins were uploaded into PMV, and polar hydrogens were added to the structure. After this step, protein structures were saved in.pdbqt format to preserve their Gasteiger charges. Using ADT, protein structures in.pdbqt format were loaded as macromolecule, and binding-site residues were selected. A grid box having a spacing of 1.000 Š in between the grid points was set in such a way that all the binding-site residues fit in it. The center coordinates and size of the grid box obtained after the above step were noted into in.txt file. The.txt file containing the name of the receptor file, ligand file, center coordinates, and size of the grid box in all the three dimensions is used as the configuration file required for docking by AutoDock Vina. The grid center and grid box size for all the three protein targets are mentioned in [Table 1]. | Table 1: The grid center coordinates in x, y, and z axes and grid box size in x, y, and z dimensions required by configuration file of AutoDock Vina while analyzing search space and performing docking on tubulin beta chain of Wuchereria bancrofti, Brugia malayi, and Brugiatimori
Click here to view |
Preparing ligand molecules for docking
The.pdb file of ligand molecules which passed the ADMET filters was opened into PMV. By using the “Choose Torsion” option of ADT, the number of rotatable bonds present in ligand molecules was set to maximum and then those molecules were saved in.pdbqt format for docking. The same steps were carried out for drug albendazole.
Validation of docking results using SwissDock
SwissDock (Molecular Modeling Group of the Swiss Institute of Bioinformatics, Lausanne, Switzerland) is an online protein-ligand docking web service where user can perform rigid-flexible docking by uploading protein and ligand files and can define the region of interest by entering center coordinates and size (dimensions) of the grid box.[29],[30] Drug albendazole and the phytochemical giving the best docking score using AutoDock Vina were docked against target proteins using SwissDock web service.
Output visualization
2D and 3D receptor–ligand interactions were visualized using Discovery Studio Visualizer (DSV) (Dassault Systemes Biovia Corp., San Diego, California, United States). DSV is software for visualizing small molecules and macromolecules. It provides high-performance publication-quality graphics, sequence view, chain view for multidomain protein, and 2D and 3D receptor–ligand interactions.[31]
| Results and Discussion | | |
Protein structure modeling, validation, energy minimization, and binding-site prediction
UniProt ID: J9FAY9, tubulin beta chain of WB, and UniProt ID: Q66T51, tubulin beta chain of BM, were found in BLAST result as a homologous amino acid sequence to tubulin beta-2 chain of A. suum (belongs to nematode family). UniProt ID: J9FAY9 which is the tubulin beta chain of WB was again subjected to BLAST, to find the tubulin beta chain of BT. UniProt ID: A0A0R3R1 × 9, tubulin beta chain of BT, was found in BLAST result sharing 99.8% sequence identity with tubulin-beta chain of WB. On uploading the amino acid sequences of tubulin beta chain of WB, BM, and BT individually, Chain B of PDB ID: 4I4T was found to be the best match for serving as template protein structure. The template protein PDB ID: 4I4T Chain B shares 83% identity, 88% positives, 0% gaps, and e-value of 0.0 with target protein sequences. The protein structure of 4I4T was uploaded into the SWISS-MODEL server along with the amino acid sequences of target proteins individually, and Chain B of 4I4T was selected as a template to model the protein structure of target proteins. The modeled protein structures were visualized using DSV, as shown in [Figure 1]a,[Figure 1]b,[Figure 1]c. Global Model Quality Estimation (GMQE) should be in between 0 and 1 and QMEAN (indicates the local quality of modeled structure) should be in between −4 and 1, indicating a good model. The GMQE and QMEAN scores of modeled protein structures are summarized in [Table 2]. All the three protein structures modeled by SWISS-MODEL had good structure quality and were validated on ProSA, RAMPAGE, and Verify3d webservers. The three target protein structures after validation were uploaded into ModRefiner webserver for energy minimization and were again cross-validated on RAMPAGE webserver and improvements were observed in the protein structures. Z-score from ProSA server is mentioned below which should fall between − 15 and 10, which indicates that the structure has similarities with native protein. Protein structure scoring >80% in Verify3d server is considered a good structure. In RAMPAGE server, the favored region should be >98%, the allowed region should be <2%, and outliers should be near to 0.0%, thereby indicating a stable protein structure. The protein validation scores before and after energy minimizations are summarized in [Table 3]. Binding-site residue numbers predicted by SWISS-MODEL and 3DLigandSite are comparatively summarized in [Table 4]. | Figure 1: (a) Three-dimensional structure of tubulin beta chain of organism Wuchereria bancrofti (structure was predicted by homology modeling using SWISSMODEL server, template Protein Data Bank ID: 4I4T Chain B). (b) Three-dimensional structure of tubulin beta chain of organism Brugia malayi (structure was predicted by homology modeling using SWISSMODEL server, template Protein Data Bank ID: 4I4T Chain B). (c) Three-dimensional structure of tubulin beta chain of organism Brugia timori (structure was predicted by homology modeling using SWISSMODEL server, template Protein Data Bank ID: 4I4T Chain B)
Click here to view |
 | Table 2: Global Model Quality Estimation and QMEAN (local model quality estimation) score of modeled target proteins
Click here to view |
 | Table 3: Protein structure validation scores from ProSA and Verify3d before energy minimization and comparative scores from RAMPAGE before and after energy minimization
Click here to view |
 | Table 4: Binding-site residues predicted by SWISS-MODEL and three-dimensional ligand site for target proteins
Click here to view |
Absorption, distribution, metabolism, excretion, and toxicity screening
Consensus Log Po/w value is the average of iLOGP, XLOGP3, WLOGP, MLOGP, and SILICOS-IT, which is an important ADMET factor.[22] Log Po/w value predicts the likely transport of compound and also helps in determining the compound's absorption and distribution in the body. A Log Po/w value of <5 indicates good aqueous solubility, indicating that an adequate amount of drug can reach and be maintained inside the body through oral administration. High GI absorption indicates that the compound can be easily absorbed by the GI tract.[22] Topological polar surface area (TPSA) indicates a compound's ability to permeate into cells. A TPSA value of <140 Š2 is required for good permeation of compound into the cell membrane and a value <90 Š2 is required to permeate through blood–brain barrier. Lipinski's rule of 5 helps to determine drug-likeness of the compound; an orally active drug should not violate more than the rule. However, the phytochemicals selected after ADMET screening follow all the 5 rules.
Out of 105 molecules, 12 molecules, namely (-)-epicatechin, akuammicine, apigenin, apocynin, boeravinone A, boeravinone B, catechin, diosgenin, hecogenin, rhein, ruscogenin, and scopolamine, passed the ADMET filters. The SWISS GAME results of these 12 molecules are summarized in [Table 5], and the toxicity results predicted using PROTOX and admetSAR are summarized in [Table 6]. | Table 5: Consensus Log Po/w values, water solubility, gastrointestinal absorption, drug-likeness, topological polar surface area in Å2 units, and Lipinski's rule of 5 predicted by SWISSADME server for 12 phytochemicals and drug albendazole
Click here to view |
 | Table 6: Toxicity scores of drug albendazole and phytochemicals from PROTOX and admetSAR servers
Click here to view |
In silico docking, validation, and docking output
In silico molecular docking allows studying of receptor–ligand (protein–ligand) interaction at the atomic level. AutoDock Vina predicts the binding affinity of the ligand with receptor protein for nine different ligands' conformational pose. The binding affinity of phytochemicals for their best conformational pose is mentioned in [Table 7]. Apocynin and scopolamine have high binding energy than drug albendazole, whereas the other ten phytochemicals have a greater binding affinity toward tubulin beta chain than albendazole. From [Table 7], it is clear that diosgenin, hecogenin, and ruscogenin have the lowest binding affinity scores for tubulin beta chain of WB, BM, and BT, respectively. However, diosgenin is predicted to have consensus Log Po/w value of 5.03 [Table 5], which makes it slightly less aqueous soluble, indicating a possibility of poor absorption and distribution. Furthermore, ruscogenin has been predicted to be possibly toxic [Table 6]. Therefore, the phytochemical hecogenin can be considered as the best active compound against tubulin beta chain in all the three lymphatic filariasis-causing organisms. The chemical structure of hecogenin is depicted in [Figure 2]. | Table 7: Binding affinity in kcal/mol of drug albendazole and phytochemicals for target proteins
Click here to view |
Binding affinity predicted by AutoDock Vina helps in determining which ligand has stable protein–ligand interaction with the target protein. The binding affinities of drug albendazole for tubulin beta chain of WB, BM, and BT are −5.2 kcal/mol, −5.6 kcal/mol, and −5.9 kcal/mol, respectively, whereas those of phytochemical hecogenin are −6.7 kcal/mol, −7.9 kcal/mol, and −9.2 kcal/mol, respectively. The comparative binding affinity scores of drug albendazole and phytochemical hecogenin show that hecogenin has a much higher affinity toward the target protein than albendazole. The findings from AutoDock Vina were validated by docking hecogenin and albendazole against all the three target proteins using SwissDock web service. The binding affinity scores of hecogenin and albendazole obtained using SwissDock are comparatively summarized in [Table 8] along with binding affinity scores obtained from AutoDock Vina. SwissDock predicted the binding affinities of drug albendazole for tubulin beta chain of WB, BM, and BT as −6.57 kcal/mol, −6.04 kcal/mol, and −7.33 kcal/mol, respectively, and for phytochemical hecogenin as − 6.74 kcal/mol, −6.42 kcal/mol, and −7.84 kcal/mol, respectively. From [Table 8], it is confirmed that hecogenin has a better binding affinity for all the three protein targets than drug albendazole. | Table 8: Comparison of binding affinity (kcal/mol) of drug albendazole and hecogenin for target proteins
Click here to view |
Albendazole has a TPSA of 92.31 Š2, whereas hecogenin has a TPSA of 55.76 Š2, indicating better cell membrane and blood–brain barrier permeation than albendazole. In addition, PROTOX predicted LD50 value of hecogenin as 10,000 mg/kg which is almost seven times greater than the LD50 value of albendazole, thereby making hecogenin safer to administer than albendazole.
The best-docked pose of hecogenin for tubulin beta chain and a 2D diagram showing interacting atoms and type of bond between hecogenin and tubulin between WB, BM, and BT are shown in [Figure 3]a,[Figure 3]b,[Figure 4]a,[Figure 4]b,[Figure 5]a and [Figure 5]b, respectively. | Figure 3: (a) Output of AutoDock Vina visualized using Discovery Studio Visualizer showing interacting binding-site residues of tubulin beta chain of Wuchereria bancrofti with ligand hecogenin (hecogenin is shown in green ball and stick format. The interacting binding-site residues are labeled in yellow and shown in stick format). (b) Two-dimensional diagram showing the type of interaction formed between tubulin beta chain of Wuchereria bancrofti and hecogenin (the green dotted lines indicate the conventional hydrogen bonds and light green-colored amino acid residues indicate van der Waal's forces of attraction formed between tubulin beta chain of Wuchereria bancrofti and hecogenin)
Click here to view |
 | Figure 4: (a) Output of AutoDock Vina visualized using Discovery Studio Visualizer showing interacting binding-site residues of tubulin beta chain of Brugia malayi with ligand hecogenin (hecogenin is shown in green ball and stick format. The interacting binding-site residues are labeled in white and shown in stick format). (b) Two-dimensional diagram showing the type of interaction formed between tubulin beta chain of Brugia malayi and hecogenin (the green dotted line indicates the conventional hydrogen bond, light green-colored amino acid residues indicate van der Waal's forces of attraction, purple dotted line indicates Pi-Sigma bond, pink dotted lines interacting with residue CYS12 indicate alkyl bond, and the rest of the pink dotted lines indicate Pi-Alkyl bonds formed between tubulin beta chain of Brugia malayi and hecogenin)
Click here to view |
 | Figure 5: (a) Output of AutoDock Vina visualized using Discovery Studio Visualizer showing interacting binding-site residues of tubulin beta chain of Brugia timori with ligand hecogenin (hecogenin is shown in green ball and stick format. The interacting binding-site residues are labeled in red and shown in stick format). (b) Two-dimensional diagram showing the type of interaction formed between tubulin beta chain of Brugia timori and hecogenin (light green-colored amino acid residues indicate van der Waal's forces of attraction, pink dotted lines interacting with residue TYR208 indicate Pi-Alkyl bond, and the rest of the pink dotted lines indicate alkyl bonds formed between tubulin beta chain of Brugia timori and hecogenin)
Click here to view |
Hecogenin forms van der Waal's forces of attraction with all the three target proteins; conventional hydrogen bonds can be seen between tubulin beta chain of WB and BM, whereas Pi-alkyl and alkyl bonds can be seen between tubulin beta chain of BM and BT [Figure 3]b, [Figure 4]b, and [Figure 5]b. The conventional hydrogen bonds are important bonds in protein–ligand complex formation as they provide stability to the complex. Furthermore, van der Waal's forces of attraction and alkyl bonds were observed in addition to hydrogen bonds. Thus, it can be stated that hecogenin forms a stable protein–ligand complex with target proteins. However, further experimental studies should be conducted on hecogenin to validate its antifilarial property.
| Conclusion | | |
This study focused on identifying inhibitors of organisms causing lymphatic filariasis from natural sources. Comparative in silico molecular docking analysis of phytochemicals and albendazole against target protein proved that hecogenin can be a good drug candidate against lymphatic filariasis. Furtherin vitro andin vivo studies are required to confirm these findings. Other potent phytochemicals having an antifilarial activity which are confirmed by this study and require additionalin vitro andin vivo studies are (-)-epicatechin, akuammicine, apigenin, boeravinone A, boeravinone B, catechin, diosgenin, rhein, and ruscogenin.
Acknowledgment
The authors sincerely thank Molecular Genetics Research lab staff members and B. J. Wadia Hospital for Children, for providing facility and necessary effort for this research article.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | |
| 2. | |
| 3. | Thomsen EK, Sanuku N, Baea M, Satofan S, Maki E, Lombore B, et al. Efficacy, safety, and pharmacokinetics of coadministered diethylcarbamazine, albendazole, and ivermectin for treatment of bancroftian filariasis. Clin Infect Dis 2016;62:334-41.  |
| 4. | Kwarteng A, Ahuno ST, Akoto FO. Killing filarial nematode parasites: Role of treatment options and host immune response. Infect Dis Poverty 2016;5:86. |
| 5. | |
| 6. | Sharma OP, Pan A, Hoti SL, Jadhav A, Kannan M, Mathur PP. Chemistry modeling, docking, simulation, and inhibitory activity of the benzimidazole analogue against b -tubulin protein from Brugia malayi for treating lymphatic filariasis. Med Chem Res 2011;21:2415-27. |
| 7. | Ranjini G, Selvakumari E, Gopal V. Perception of natural antifilarial drugs. Int Res J Pharm 2013;4:27-30. |
| 8. | UniProt Consortium. UniProt: A hub for protein information. Nucleic Acids Res 2015;43:D204-12. |
| 9. | Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S, et al. The RCSB protein data bank: New resources for research and education. Nucleic Acids Res 2013;41:D475-82. |
| 10. | Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res 2018;46:W296-303. |
| 11. | Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep 2017;7:10480. |
| 12. | Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:W407-10. |
| 13. | |
| 14. | Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-70. |
| 15. | Lüthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-5. |
| 16. | |
| 17. | Ramachandran GN, Sasisekharan V. Conformation of polypeptides and proteins. Adv Protein Chem 1968;23:283-438. |
| 18. | |
| 19. | Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 2011;101:2525-34. |
| 20. | Mark W. 3DLigandSite -Ligand Binding site Prediction Server. Imperial College. London: Structural Bioinformatics Group, Division of Molecular Biosciences Faculty of Natural Sciences; 2009. Available from: http://www.sbg.bio.ic.ac.uk/3dligandsite/about.html. [Last accessed on 2019 May 27]. |
| 21. | |
| 22. | Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717. |
| 23. | Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. AdmetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 2019;35:1067-9. |
| 24. | Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012;52:3099-105. |
| 25. | Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018;46:W257-63. |
| 26. | Sanner MF. Python: A programming language for software integration and development. J Mol Graph Model 1999;17:57-61. |
| 27. | Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and autoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-91. |
| 28. | Trott O, Olson AJ. Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61. |
| 29. | Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 2011;39:W270-7. |
| 30. | Szalay Z, Rohonczy J. Fast calculation of DNMR spectra on CUDA-enabled graphics card. J Comput Chem 2011;32:1262-70. |
| 31. | |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]
|
Search Pubmed for