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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 8  |  Issue : 3  |  Page : 229-236

Multiple docking analysis and In silico absorption, distribution, metabolism, excretion, and toxicity screening of anti-leprosy phytochemicals and dapsone against dihydropteroate synthase of Mycobacterium leprae


1 Department of Bioinfomatics, 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 Web Publication12-Sep-2019

Correspondence Address:



Lalit R Samant
Molecular Genetics Research Laboratory, Bai Jerbai Wadia Hospital for Children, Parel, Mumbai . 400 012, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_123_19

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  Abstract 


Background: Leprosy is a neglected tropical disease affecting millions of people. The current treatment against leprosy includes various antibacterial drugs of which dapsone is known to bind to dihydropteroate synthase of Mycobacterium leprae. Dapsone is an expensive antibacterial drug with many side effects. A natural alternative for dapsone having less to no side effects and cheaper in production is needed. The three-dimensional protein structure of dihydropteroate synthase of M. leprae is not available. Methods: Protein homology modeling of target protein was carried out, and protein structure validation and energy minimization were performed. Phytochemicals mentioned in literature having anti-leprosy properties were studied for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and that which passed ADMET filters were further carried for comparative in silico docking analysis along with dapsone. Preliminary docking analysis was carried using AutoDock Vina, and results obtained were validated using AutoDock 4.2.6 and SwissDock. Results: Neobavaisoflavone was predicted to be ten times safer for administration than dapsone. On performing in silico docking, it was found that neobavaisoflavone has better binding affinity than dapsone and forms a stable protein–ligand complex. Residues GLY.50, THR.88, and VAL.107 play an important role as binding site residues. Conclusion: Further, in vitro and in vivo experimental studies are required to confirm anti-leprosy properties of neobavaisoflavone over drug dapsone.

Keywords: Anti-leprosy, dapsone, molecular docking, neobavaisoflavone, phytochemicals


How to cite this article:
Halder ST, Dhorajiwala TM, Samant LR. Multiple docking analysis and In silico absorption, distribution, metabolism, excretion, and toxicity screening of anti-leprosy phytochemicals and dapsone against dihydropteroate synthase of Mycobacterium leprae. Int J Mycobacteriol 2019;8:229-36

How to cite this URL:
Halder ST, Dhorajiwala TM, Samant LR. Multiple docking analysis and In silico absorption, distribution, metabolism, excretion, and toxicity screening of anti-leprosy phytochemicals and dapsone against dihydropteroate synthase of Mycobacterium leprae. Int J Mycobacteriol [serial online] 2019 [cited 2019 Sep 20];8:229-36. Available from: http://www.ijmyco.org/text.asp?2019/8/3/229/266493

Sumit T. Halder and Tehseen M. Dhorajiwala are contributed equally





  Introduction Top


Neglected tropical diseases (NTDs) are a group of transmissible diseases which affect more than one billion people worldwide, mostly in tropical and subtropical regions. NTDs maybe fatal or can cause lifelong disability which has a great impact on patients' physical, social, and economic life. NTDs are common in people belonging to a lower socioeconomic community who lack proper sanitation facilities and fail to afford basic safety measures to avoid contact with parasites and infectious vectors. Since people living in poor communities can barely afford their livelihood, once infected by any of the NTDs, they seek for an uneconomical local treatment which mostly includes extracts derived from local plants. These plant extracts have phytochemicals which are the source of plant's medicinal properties. The current study is focused on leprosy which belongs to this NTD group.

Leprosy

Leprosy is a highly contagious infectious NTD caused by Mycobacterium leprae. Since this pathogenic bacterium requires an optimal temperature of 27°C–30°C, it affects the skin surface, nerves, and upper airways.[1] Globally, 215,656 new cases of leprosy were reported in the year 2013; 213,899 new cases in the year 2014; and 211,973 cases in the year 2015.[2] In 2017, 211,009 cases of leprosy occurred in tropical countries mainly underdeveloped and developing countries such as Brazil, Africa, and Southeast Asia.[3]

After a person is infected with the bacteria, the clinical manifestation of the diseases occurs in five forms that are two polar forms of tuberculoid (paucibacillary) and lepromatous (multibacillary) leprosy and three subtypes of intermediate stage leprosy – borderline tuberculoid (BT), mid-borderline (BB), borderline lepromatous (BL), and depending on the bacterial load, host immunity, and number of lesions on the screen, this classification is done. Tuberculoid form occurs when the host has a good immune response against the disease causing a milder and localized form of leprosy. There will be a single or few skin lesions in the form of macules or papules. Lepromatous leprosy occurs when the host immunity response is poor against the disease. This causes the generalized or widespread of the disease causing numerous reddish-brown lesions on the skin and nerves. The lesions will be symmetrical, and this stage can also cause edema in the feet. Indeterminate leprosy is an asymptomatic stage which is rarely identified due to the absence of any prominent symptom other than hypopigmented macules occurring on the body. This stage can last up to years and is often misdiagnosed to other skin ailments; depending on the host immunity, the disease will progress to another form of leprosy. This stage is further classified into three groups: BT leprosy, mid-borderline (BB) leprosy, and BL leprosy.[1],[3]

Current drug treatment

The drug treatment for leprosy requires a combination of antibiotics known as multidrug therapy for eliminating the bacteria. Usually, a combination of dapsone with rifampicin is recommended for patients suffering from paucibacillary leprosy, whereas patients with multibacillary leprosy are recommended a combination of rifampicin, clofazimine, and dapsone.[4]

Rifampicin acts on mycobacterial infection by inhibiting DNA-dependent RNA polymerase, suppressing the synthesis of RNA, and causing cell death.[5] Clofazimine kills the bacterium by binding to mycobacterial DNA and disrupting the cell cycle.[6] Dapsone binds to the active site of dihydropteroate competing with para-aminobenzoate and inhibits the synthesis of dihydrofolic acid. The proteins inactive dihydropteroate synthase 2 (UniProt ID: P0C0X2) and dihydropteroate synthase 1 (UniProt ID: P0C0X1) are targets of the drug dapsone.[7] However, inactive dihydropteroate synthase 2 possibly does not participate in folate metabolism and lacks dihydropteroate activity.[8]

Phytochemicals

Dapsone being an effective drug in the treatment of leprosy has few side effects such as syncope, nasal congestion, and sometimes hallucinations, which restricts its administration to lower concentration.[7] In contradiction to synthetic drugs, phytochemicals are extracted from nature and have less to no side effects, thereby making it safe for administration. Ghosh et al. reviewed the use of traditional plants against leprosy and mentioned phytochemicals such as hydnocarpic acid from Hydnocarpus species which has been used for the treatment of leprosy since the early 1920s. Hydnocarpus plant species has other active molecules such as taraktophyllin, 3,4-dihydroxybenzyl alcohol, 3-hydroxy-4-methoxybenzoic acid, and chaulmoogric acid which have exhibited antibacterial activity.[9] Psoralea corylifolia has been studied and registered in the pharmaceutical index for effective treatment of leprosy having active phytochemicals such as corylifols A-C (prenylflavonoids) in seeds; psoralen, isopsoralen, and neobavaisoflavone in dried fruits; and neobavaisoflavone, borachin, bavaislfavooz, bavachalcone, bavachromene psoralidin, corylifolinin, barachini psoralenoides, isopsoralenoside and coumarins, daidzein (4:7 dihydroxyflavone), and genistein (4'6'7 trihydroxyisoflavone) from the plant. Brombexine from leaf extract of Adhatoda vasica, allicin from Allium sativum, and asiaticoside from Centella asiatica have been studied for the treatment of leprosy.[9]

Sahoo et al. studied the anti-leprotic activity of the flavonolignans from Hydnocarpus species. Hydnocarpic, chaulmoogric, and gorlic acids were used in oils and applied for the treatment of leprosy. The compounds showed activity against the M. leprae bacteria by activating host immunity and destroying the cell wall of the bacterium or through chemotaxis.[10]


  Methods Top


Protein homology modeling

The drug dapsone used in the treatment of leprosy binds to dihydropteroate synthase (UniProt ID: P0C0X1) of M. leprae.[7] Since there is no three-dimensional (3D) protein structure present for dihydropteroate synthase of M. leprae, the amino acid sequence of UniProt ID: P0C0X1 was retrieved from UniProt Knowledgebase.[11] Template protein structure for protein homology modeling of target protein was retrieved from the Research Collaboratory for Structural Bioinformatics-Protein Data Bank (RCSB-PDB) by uploading the amino acid sequence of dihydropteroate synthase of M. leprae in advance search option of RCSB-PDB.[12] Protein structure with low resolution and good E-value is selected as a template. The target protein sequence along with template protein is uploaded to SWISS-MODEL (BIOZENTRUM, The Center for Molecular Life Sciences, University of Basel, Basel, Switzerland) server for automated protein structure homology modeling.[13],[14]

Protein structure validation and energy minimization

Heteroatoms from the modeled protein structure were deleted to achieve a clean protein structure. The structure was then validated using RAMPAGE (Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom), Verify 3D (Molecular Biology Institute and the DOE-MBI Institute at the University of California, Los Angeles, California, United States), and Protein Structure Analysis (ProSA) (Center of Applied Molecular Engineering, Division of Bioinformatics, University of Salzburg, Salzburg, Austria) webservers.

RAMPAGE is an online web server used for Ramachandran plot analysis of a given protein. It helps to visualize favored (>98%), allowed (<2%), and outlier (~0%) residues into the backbone of protein structure.[15],[16] Verify 3D determines the compatibility of 3D protein structure with its own 1D amino acid sequence. The algorithm assigns a structural class to the input protein structure based on its location and environment and compares the results to good structures.[17],[18],[19] 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. The output of this web tool consists of Z-score and plot of residue scores. Z-score determines the overall model quality of the input protein. Z-score of input protein should fall within the range of scores of native proteins of similar size. The plot of residue scores shows local model quality by plotting the average energy of 40 amino acid residue fragments. Those residues which have positive values in the plot are considered erroneous.[20],[21]

Energy minimization step ensures the stability of the modeled protein structure. After structure validation, the modeled target protein structure was energy minimized using ModRefiner webserver. ModRefiner webserver by Zhang Lab is a web tool for protein structure refinement. The structure refinement is achieved by performing “main chain energy minimization” and then performing “fast full-atomic energy minimization.”[22],[23]

To analyze the improvement in energy-minimized protein model, cross-validation was done by reusing RAMPAGE, Verify 3D, and ProSA webservers.

Binding site prediction

SWISS-MODEL predicts the binding site residues of modeled protein if template protein has ligand bonded to it. Apart from SWISS-MODEL, 3DLigandSite was used to predict the binding site of the energy-minimized target protein structure. Multiple binding site predictors ensures that no potential binding site residues are skipped. Therefore predictions from both the web servers are considered in the docking step. 3DLigandSite web server predicts ligand-binding sites by superimposing ligand-bound protein structures homologous to the query protein.[24]

Ligand phytochemicals

Standard drug dapsone and 25 phytochemicals were drawn using Marvinsketch (ChemAxon Ltd., Budapest, Hungary).[25] Explicit hydrogens were added to the drawn structures, cleaned in 2D, and saved in.smiles format. The structures were then cleaned in 3D and saved in.pdb and.mol2 format.

Absorption, distribution, metabolism, excretion, and toxicity screening

Twenty-five ligands and dapsone saved in smiles format were uploaded to SwissADME, PROTOX-II, and admetSAR webservers for ADMET screening. SwissADME (Molecular Modeling Group of the Swiss Institute of Bioinformatics [SIB], Lausanne, Switzerland) is a web tool for 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 Boiled-Egg (blood–brain barrier and PGP +/− prediction).[26] PROTOX-II (the Charite University of Medicine, Institute for Physiology, Structural Bioinformatics Group, Berlin, Germany) is a rodent oral toxicity server predicting LD50 value and toxicity class of query molecule. The toxicity classes are: (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).[27] admetSAR (Laboratory of Molecular Modeling and Design, Shanghai, China) provides ADMET profiles for query molecules and can predict about 50 ADMET properties. Toxicity classes are: (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: (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) “nonrequired” is assigned to noncarcinogenic chemicals.[28],[29]

Ligands which passed ADMET screening were used for in silico docking analysis along with drug dapsone.

In silico docking

AutoDockTools (ADT) is a free graphical user interface for AutoDock developed by Molecular Graphics Lab. ADT is used to set up, run, and analyze AutoDock dockings.[30],[31] AutoDock Vina (Molecular Graphics Laboratory, The Scripps Research Institute, La Jolla, California, United States) is a computer program used for docking.[32] It has significantly more average accuracy than AutoDock 4 and is easy to use.

Energy-minimized protein structure which is present in.pdb format was opened in Python Molecular Viewer,[30] and polar hydrogens were added to the structure and protein structure was then saved in.pdbqt format. Binding site residues were selected, and ADT was used to draw grid box with 1.0 Angstrom spacing. Center coordinates and size of the grid box for search space around the binding site residues were noted down. Best fit for center coordinates and size of the grid box for docking search space was 32.018, 4.977, 42.105 (x, y, z) and 24 × 24 × 24 (x, y, z), respectively. Using ADT's “Ligand-Torsion Tree-Choose Torsions” option, ligands saved in a.pdb file which passed the ADMET filters were set to a maximum number of rotatable bonds available and saved in.pdbqt format.

Docking was carried out using AutoDock Vina using the configuration file containing coordinates and dimensions of search space, protein structure in.pdbqt format, and ligand in.pdbqt format.

Dapsone and neobavaisoflavone were redocked to target protein using AutoDock 4.2.6[31] (Molecular Graphics Laboratory, The Scripps Research Institute, La Jolla, California, United States) and SwissDock (Molecular Modeling Group of the SIB, Lausanne, Switzerland)[33],[34] to validate results obtained from AutoDock Vina. Center coordinates and size of grid box for docking using AutoDock 4.2.6 and SwissDock were kept the same as that for AutoDock Vina. The number of genetic algorithm runs was set to 25 in AutoDock 4.2.6 to achieve 25 docked conformations, and docking was done using Lamarckian genetic algorithm at nice level = 20. Energy-minimized target protein file, dapsone in mol2 format, and neobavaisoflavone in mol2 format were uploaded into SwissDock server for accurate docking with the flexibility of side chains set to 0 Å.

Output visualization

2D and 3D interactions of receptor protein and ligand molecules 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.[35]


  Results Top


Protein structure modeling, validation, energy minimization, and binding site prediction

PDB ID: 1EYE Chain A was selected as a template for structure modeling of dihydropteroate synthase of M. leprae. Chain A of PDB ID: 1EYE has 73% identity and 76% positives with the target protein and also has a resolution of 1.7 Š. Protein structure modeled using 1EYE Chain A as a template is shown in [Figure 1].
Figure 1: Modeled protein structure of dihydropteroate synthase of Mycobacterium leprae along with Mg2+ and PMM (PTERIN-6-YL-METHYL-MONOPHOSPHATE) bounded to binding sit

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The protein structure validation scores before and after energy minimization are summarized in [Table 1]. Binding site predictions by SWISS-MODEL and 3DLigandSite are shown in [Table 2].
Table 1: Comparison of protein structure validation scores before and after energy minimization of target protein from webservers ProSA, RAMPAGE, and Verify 3D

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Table 2: Predicted binding site residue numbers of dihydropteroate synthase of Mycobacterium leprae by SWISS-MODEL and 3DLigandSite

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Absorption, distribution, metabolism, excretion, and toxicity screening

Out of 25 phytochemical molecules, only neobavaisoflavone [Figure 2] passed the ADMET filters. Consensus Log Po/w value of dapsone is 1.55 and neobavaisoflavone is 3.74. The drug dapsone has topological polar surface area (TPSA) 94.56 Š2, whereas neobavaisoflavone has 70.67 Š2. Dapsone and neobavaisoflavone both are moderately soluble, have high gastrointestinal absorption, and had 0 violations of Lipinski's rule of five. The ADME properties predicted by SwissADME of neobavaisoflavone and drug dapsone are summarized comparatively in [Table 3], and toxicity predicted by PROTOX-II and admetSAR is summarized in [Table 4].
Figure 2: Chemical structure of neobavaisoflavone

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Table 3: ADME properties of dapsone and neobavaisoflavone predicted by SwissADME

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Table 4: Toxicity prediction of dapsone and neobavaisoflavone predicted by PROTOX-II and admetSAR server

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In silico docking and docking output

Using the coordinates 32.018, 4.977, 42.105 (x, y, z) and dimensions 24 × 24 × 24 (x, y, z) for search space in the configuration file for AutoDock Vina, the docking score and interacting residues obtained for drug dapsone and neobavaisoflavone are summarized in [Table 5] for comparative study. Neobavaisoflavone has binding affinity = 8.5 kcal/mol for dihydropteroate synthase of M. leprae, whereas dapsone has binding affinity = 6.7 kcal/mol. The 2D interactions of docked dapsone and neobavaisoflavone with dihydropteroate synthase of M. leprae using AutoDock Vina are shown in [Figure 3]a and [Figure 3]b.
Table 5: Binding affinity, interacting residues, and type of interaction comparison of anti-leprosy drug dapsone with anti-leprosy phytochemical neobavaisoflavone

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Figure 3: (a) Two-dimensional interaction of dapsone with target protein showing unfavorable bond with 181st glycine residue. (b) Two-dimensional interaction of neobavaisoflavone with dihydropteroate synthase of Mycobacterium leprae showing conventional hydrogen bonds as dark green dotted lines, Alkyl, and Pi-Alkyl bonds as light pink dotted lines, Pi-Pi T-shaped as dark pink dotted lines and with TYR141, Pi-Sigma bonds as purple dotted lines, and van der Waals forces of attraction

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To confirm that neobavaisoflavone has greater binding affinity than dapsone for dihydropteroate synthase of M. leprae, both ligands were redocked using AutoDock 4.2.6 and SwissDock with the same center coordinates and grid dimensions used in AutoDock Vina. The binding affinities of neobavaisoflavone and dapsone using AutoDock 4.2.6 and SwissDock are summarized in [Table 6]. Neobavaisoflavone has binding affinities −8.5 kcal/mol, −5.36 kcal/mol, and −7.46 kcal/mol, whereas dapsone has binding affinities −6.7 kcal/mol, −4.49 kcal/mol, and −7.09 kcal/mol obtained from AutoDock Vina, AutoDock 4.2.6, and SwissDock, respectively. 3D interactions of neobavaisoflavone with protein target dihydropteroate synthase of M. leprae obtained from output file using AutoDock Vina, AutoDock 4.2.6, and SwissDock are shown in [Figure 4]a, [Figure 4]b, [Figure 4]c, respectively.
Table 6: Binding affinity of dapsone and neobavaisoflavone toward dihydropteroate synthase of Mycobacterium leprae predicted by AutoDock Vina, AutoDock 4.2.6, and SwissDock

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Figure 4: (a) Three-dimensional interactions between neobavaisoflavone (green-colored ball and stick) and amino acid residues (labeled in yellow) of dihydropteroate synthase of Mycobacterium leprae predicted by AutoDock Vina. (b) Three-dimensional interactions between neobavaisoflavone (green-colored ball and stick) and amino acid residues (labeled in yellow) dihydropteroate synthase of Mycobacterium leprae predicted by AutoDock 4.2.6. (c) Three-dimensional interactions between neobavaisoflavone (green-colored ball and stick) and amino acid residues (labeled in yellow) dihydropteroate synthase of Mycobacterium leprae predicted by SwissDock

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  Discussion Top


With millions of people suffering from leprosy in the tropical and subtropical regions, there is a necessity to find a cheaper, natural, and better alternative than chemically synthesized drugs which have side effects on administration.

Protein structure with a resolution of <2 Angstrom and identity above 70% with the query protein sequence serves as a better template for modeling. Thus, PDB ID: 1EYE Chain A was the best match to serve as template protein structure. Modeled protein structure had global model quality estimation score of 0.81, which is between 0 and 1, indicating that global model quality is good. QMEAN (Qualitative Model Energy ANalysis) score of − 0.12, which is between −4 and 1, indicating that the overall quality of the modeled structure is good. Any heteroatom present in the modeled structure was removed and was subjected to validation on webservers RAMPAGE, Verify 3D, and ProSA. After energy minimization, a noticeable amount of improvement was observed in the modeled protein structure. As shown in [Table 1], Z-score (ProSA) was improved from −9.27 to −9.14, outlier residues were reduced from 1.5% to 0.4%, and favored region residues were increased from 95.1% to 98.1%; the number of residues passing Verify 3D increased from 91.08% to 98.88%. Thus, the protein structure obtained after energy minimization was ready to be used for insilico docking studies.

Consensus Log Po/w value of < 5 indicates good aqueous solubility, which means that an adequate amount of drug can reach and be maintained inside the body through oral administration. Both ligand molecules have consensus Log Po/w value of <5 [Table 3]. TPSA indicates a compound ability to permeate into cells. A TPSA value of <140 Š2 is required for good permeation of compound into the cell membrane and value <90 Š2 is required to permeate through blood–brain barrier. Neobavaisoflavone has TPSA value <90 Š2 [Table 3], indicating that it is permeable through blood–brain barrier. Lipinski's rule of five helps to determine drug-likeness of the compound; an orally active drug should not violate more than the rule. Both ligands showed 0 violations [Table 3] for Lipinski's rule of five. Neobavaisoflavone passed all three ADMET prediction servers, whereas drug dapsone failed in both toxicity predictions [Table 4]. From [Table 4], it can be clearly stated that dapsone has ten times low LD50 value than neobavaisoflavone. As predicted by admetSAR server, dapsone may be carcinogenic and is not safe to administer it in high amounts. Neobavaisoflavone is a phytochemical, is extracted from the plant, has predicted LD50= 2500 mg/kg, and thus is safer than chemically synthesized Dapsone.

The binding affinity of dapsone predicted by AutoDock Vina toward target protein binding site is − 6.7 kcal/mol, whereas neobavaisoflavone has a better binding affinity of −8.5 kcal/mol [Table 5]. Dapsone shows unfavorable donor–donor and unfavorable acceptor–acceptor interactions with the target protein [Figure 3]a, whereas neobavaisoflavone shows conventional hydrogen bonds with residues GLY.50, ARG.89, and TRP. 132 [Figure 3]b. Conventional hydrogen bonds provide stability to the receptor-ligand docked structure. Along with conventional hydrogen bonds, Along with conventional hydrogen bonds, the Alkyl, Pi-Alkyl, Pi-Pi T-shaped, Pi-Sigma bonds, and van der Waals interactions were also formed by neobavaisoflavone.

AutoDock Vina, AutoDock 4.2.6, and SwissDock predicted that neobavaisoflavone has a better binding affinity for dihydropteroate synthase of M. leprae than dapsone [Table 6]. On comparing [Figure 4]a and c, it can be seen that residues GLY.50, ASP. 86, THR.87, THR.88, VAL.107, SER.108, ARG.111, ALA.112, TRP. 132, and LEU.134 are common, and on comparing [Figure 4]b and [Figure 4]c, it can be seen that residues GLY.50, GLU.51, SER.52, ASP. 86, THR.87, THR.88, ARG.89, and VAL.107 are common, indicating that AutoDock Vina, AutoDock 4.2.6, and SwissDock have predicted best docked posed of neobavaisoflavone in the same binding pocket. From [Figure 4]a, [Figure 4]b, [Figure 4]c, residues GLY.50, THR.88, and VAL.107 are found common, indicating that they might play an important role as binding site residues and have a greater attraction toward neobavaisoflavone than other binding site residues.

Neobavaisoflavone can be extracted from methanolic extract of seeds of P. corylifolia or its dry fruits.[36] P. corylifolia is a medicinal plant which has been successfully used in the treatment of various skin diseases such as psoriasis, leukoderma, and leprosy.[36] A study of methanolic extracts of Erythrina sigmoidea showed that neobavaisoflavone shows antibacterial properties against Gram-negative bacteria which also included multidrug-resistant phenotypes.[37] Therefore, these findings support the current insilico findings of potential anti-leprosy properties of neobavaisoflavone.


  Conclusion Top


Protein structure homology modeling of dihydropteroate synthase of M. leprae was successful, and a good quality protein structure was achieved. Comparative in silico docking analysis of neobavaisoflavone and dapsone with dihydropteroate synthase of M. leprae proved that neobavaisoflavone has better anti-leprosy properties than drug dapsone. Further, in vitro and in vivo studies are required to confirm this finding.

Acknowledgments

The authors sincerely thank Molecular Genetics Research Laboratory 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.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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