BIOTROPIKA Journal of Tropical Biology
https://biotropika.ub.ac.id/
Vol. 11 | No. 2 | 2023 | DOI: 10.21776/ub.biotropika.2023.011.02.01 IN SILICO EXPLORATION OF BIOACTIVE COMPOUNDS FROM Withania somnifera AS INHIBITOR FOR ALPHA-DELTA BUNGAROTOXIN (αδ-Btx) OF
Bungarus candidus VENOM
Kartika Prabasari1), Nia Kurnianingsih2), Nia Kurniawan1)*
ABSTRACT
The antivenom for Malayan krait (Bungarus candidus) venom has not yet been available in Indonesia, leading to many fatal snakebite cases. Alternative treatment approaches using medicinal plants are needed to be explored. This study investigated the potential of medicinal plants’ natural bioactive compounds as toxic alpha-delta bungarotoxin protein inhibitors in B. candidus venom. The approach taken is using the 3D structure of the alpha- delta protein of bungarotoxin B. candidus predicted by SWISS-MODEL. Knapsack Family Database and PubChem were used for bioactive compounds datamining. ADME analysis and drug-likeness of the compounds were carried out with SWISS-ADME. Docking between αδ-Bungarotoxin protein and bioactive compounds was carried out with Pyrx 0.9.5.
Visualization of docking results was performed with PyMOL and Discovery Studio 2016 was used to evaluate docking interactions. The docking results showed that a compound with the potential inhibitor of alpha-delta bungarotoxin came from Ashwagandha (Withania somnifera) with a binding energy ranging from -6.6 to -6.9. The compound with the best inhibitor potential, namely withanolide D, was seen from the stability of the interaction based on hydrogen bonding at three amino acid residues: THR59, SER62, and THR63. The evaluation is supported by the results of molecular dynamics simulations which show stability in almost all aspects. Our results suggest the potential for exploratory research in the field of bioinformatics related to bioactive compounds from herbal plants as an alternative to antivenom.
Keywords: alpha-delta Bungarotoxin, Bungarus candidus, inhibitor, Withania somnifera
INTRODUCTION
Envenoming snake-bite is one of the most neglected causes of mortality and morbidity in the tropics. Every year there are around 81,000- 138,000 cases of death due to snake bites, of which 421,000 out of 1.2 million cases are caused by snake venom worldwide. Likewise in Indonesia, where there were 214,883 cases of snakebites per year with 11,581 of them causing death. Snake envenoming cases are common in rural areas of Asia and Sub-Saharan Africa, where socio- economic and agricultural factors contribute to increasing human-snake interactions. Farmers are one of the groups that most often interact with snakes and become victims of snake bites when they are active on agricultural land. According to the WHO, these venomous snakes are divided into two categories based on their medical importance.
The first category consists of all highly venomous snakes with the highest medical importance and causing a high level of mortality and morbidity, while the second category consists of venomous snakes with secondary medical importance. One of the highly venomous snakes that belong to Category I in Indonesia and represent the venomous snake from the family Elapidae is the Malayan krait (Bungarus candidus) [1, 2, 3].
The Malayan Krait (Bungarus candidus) is a venomous snake which causes many cases of death in Indonesia and Southeast Asia. Cases of death by Malayan Krait bites are caused by neurotoxins in their venom. One of the proteins that make up the venom component is alpha-delta-Bungarotoxin (αδ-BgTx) which is a novel protein sub-group of alpha-Bungarotoxin. αδ-BgTx binds to α7 receptors and muscle-type nicotinic acetylcholine receptors with high affinity such as the long-chain venom protein alpha-bungarotoxin. Although there are many cases of Malayan krait bites in Indonesia, the availability of specific monovalent and polyvalent antivenom is still lacking in Indonesia.
Until now, antivenom for the B. candidus species has only been produced in Thailand and Vietnam [4, 5].
Medicinal plants such as antiophidians have been widely used to treat snakebite cases traditionally, especially in rural areas of Southeast and South Asia, Africa, and Latin America in the form of decoctions or concoctions [6]. One type of medicinal plant with the potential to inhibit snake venom activity is Ashwagandha (Withania somnifera). W. somnifera has been frequently used in exploratory studies regarding venom protein inhibitors. Previous research noted that W.
somnifera contains glycoprotein (WSG) which acts
Submitted : January, 12 2023 Accepted : June, 12 2023
Authors affiliation:
1)Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Indonesia
2)Physiology Department, Faculty of Medicine, Brawijaya University, Malang, Indonesia
Correspondence email:
*wawan@ub.ac.id
How to cite:
Prabasari, K, Kurnianingsih N, Kurniawan N. 2023. In silico exploration of bioactive compounds from Withania somnifera as inhibitor for alpha-delta bungarotoxin (Αδ- Btx) of Bungarus candidus venom.
Biotropika: Journal of Tropical Biology 11 (2): 64-73.
as a PLA2 toxin inhibitor in Indian Cobra (Naja naja) venom and various viper venom. Its pharmacological effect is to neutralize the cobra venom toxin by forming a complex between WSG and PLA2 toxin resulting in the inhibition of enzyme activity. WSG is an acidic glycoprotein similar to the alpha chain in PLI snake plasma but consists of a single subunit. In addition, it was also reported that W. somnifera has anti-edema and anti-myotoxic activity against cobra venom. Anti- hyaluronidase activity was also reported to inhibit N. naja and D. russelii venoms [7, 8].
The success rate of traditional treatment previously recorded showed a significant influence on the administration of medicinal plants as a treatment for healing the effects of snake bites.
This is what makes medicinal plants believed to be an alternative for the treatment of diseases, especially cases of snake bites other than using antivenom [6].
In-silico studies are used as an important initial screening stage in exploring compounds as alternatives in inhibiting the activity of venom toxins, one of which is alpha-delta bungarotoxin from B. candidus venom. The main objective of this study was to determine the potential of the bioactive compounds from W. somnifera as inhibitors of B. candidus venom and to observe the interaction between the alpha-delta bungarotoxin protein from B. candidus and these compounds in silico.
METHODS
Protein 3D structure collection with homology modeling. The 3D structure of the alpha-delta bungarotoxin protein from B. candidus is not yet available in the PDB or uniprot databases.
The 3D alpha-delta structure of bungarotoxin is predicted using the homology modeling method.
Available alpha-delta bungarotoxin sequences from B. candidus were obtained from NCBI (https://www.ncbi.nlm.nih.gov/) in fasta format.
Next, BLAST was performed on NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and alignment with the alpha-delta bungarotoxin sequences from other Bungarus species, namely Bungarus multicinctus and Bungarus caeruleus to see the similarity and confirm binding positions predicted site in the sequence. Alignment was performed with ClustalX 2.1 and BioEdit. Next, sequences in fasta format were input into SWISS- MODEL
(https://swissmodel.expasy.org/interactive) to obtain the predicted homology model and templates were selected based on the highest GMQE, QSQE, and identity scores. The 3D structure modeling results were then validated with
a structured assessment on SWISS-MODEL (Ramachandran plots). Then, the predicted model of alpha-delta bungarotoxin was saved in PDB format.
Data mining of Withania somnifera bioactive compounds. Bioactive compounds in W.
somnifera are identified through the KnapSack Core System in the Knapsack Family database (http://www.knapsackfamily.com/knapsack.core/t op.php) by entering the name of the plant species and a list of any bioactive compounds that have been identified in the plant will be obtained based on various studies. The data collection of natural bioactive compounds includes compound formulas, canonical smiles, PubChemID, and 3D structures obtained through the PubChem database (https://pubchem.ncbi. nlm.nih.gov/).
QSAR analysis. Quantitative structure-activity relationship (QSAR) analysis for screening bioactivity of compounds based on chemical structure was performed with the Way2Drug PASS Online website (http://www.way2drug.com/PASS Online). The selected criteria included anti- inflammatory, antioxidant, anti-neurodegenerative (neuro-degenerative diseases treatment), and neurotrophic factor. The activity was determined based on the likelihood potential score. If the activity likelihood potential (Pa) score is less than 0.5, the substance is less likely to exhibit activity in an experiment; if the score is between 0.5 and 0.7, the substance is likely to exhibit activity in an experiment; and if the score is greater than 0.7, the substance is very likely to exhibit activity in an experiment [9]. The data result were then tabulated in table form.
Molecular docking. Molecular docking between alpha-delta bungarotoxin B. candidus protein and bioactive compounds from W.
somnifera as ligands was carried out with PyRx 0.9.5 software. The 3D structure of bioactive compounds from each plant species in sdf format is entered via the Open babel menu. The energy of each compound is minimized (energy minimization) with the Open babel menu. Ligand preparation, namely all plant bioactive compounds and target macromolecules, namely alpha-delta bungarotoxin B. candidus protein, were converted to files with Autodock ligand format (*.pdbqt) on the Open babel menu. The construction of the grid on the Vina wizard menu is based on a predetermined binding site. Virtual screening of target macromolecular inhibitor candidate compounds was carried out with Autodock Vina [10].
The screening results in tabular form are then stored in .csv format and the results with a binding affinity value less than -6.5 (<-6.5) and RMSD 0 are selected and stored in .pdb format.
Visualization of the docking results was carried out with the PyMol software to obtain an overview of the protein and ligand complexes. Evaluation of the results of the docking visualization was then carried out with the Discovery Studio 2016 Client software. Evaluation is carried out by displaying ligand interactions on the tools menu of each protein/receptor complex and ligands, namely bioactive compounds, and observing what bonds are formed in the amino acid residues.
ADME and drug-likeness analysis. ADME analysis of plant bioactive compounds is carried out to determine absorption, distribution, metabolism, excretion, and pharmacological properties (drug-like) of compounds as a requirement in designing a drug. The analysis was performed using the SwissADME web server (http://www.swissadme.ch/). Canonical SMILES of each selected plant compound are inputted into the “list of smiles” column and the program is executed. Then the ADME results and drug- likeness will appear. The results of the analysis included molecular weight (MW), number of atoms (heavy and aromatic atoms), rotatable bonds, H-bond donors and acceptors, TPSA, XLOGP3, consensus log P, solubility, GI absorption, BBB permeant, CYP2D6 inhibitors, log Kp, Lipinski violations, bioavailability scores, lead likeness violations, and synthetic accessibility were then tabulated in an Excel spreadsheet.
Molecular dynamics simulation. Molecular dynamics simulations were carried out on YASARA version 21 software with the AMBER14 forcefield. The parameters used in the analysis include parameters of pH, temperature, salt, water density, and atmospheric pressure under physiological conditions set in the md_run.mcr file where parameters were selected as default (pH 7.4;
NaCl 0.9, temperature 3100 K, the density of water 0.997, and atmospheric pressure 1 atm). The setting for the duration of the simulation was 25 nanoseconds (ns) while the interval for snapshots was 25 ns. The simulation results were collected in an excel spreadsheet including RMSDLigand, RMSDBb, RMSF, and the number of H-bond.
RESULTS AND DISCUSSION
Prediction of the 3D structure of the B.
candidus alpha-delta bungarotoxin. Alpha-delta bungarotoxin sequence from B. candidus in the NCBI database was obtained with accession number CAJ77819. The sequence is 75 amino acids long. The alignment results showed that there was a difference in amino acid length between the B. candidus sequence and the other two Bungarus sequences where B. caeruleus and B. multicinctus had a sequence length of 76 amino acids. This causes a gap in the B. candidus sequence after
analysis (Figure 1). The alignment results also showed a similarity of 86% between the B.candidus and B. caeruleus sequences. This shows that the alpha-delta bungarotoxin protein is conserved. The binding sites of the alpha-delta bungarotoxin protein are Lys40, Glu58, Cys61, and Gly76 based on previous studies [11, 12]. The binding site is in a conserved site so that it can be used in molecular docking analysis.
The SWISS-MODEL results showed that the protein used as a template was the alpha- bungarotoxin isoform 31 protein with ID 1hc9.2.A, namely the alpha-bungarotoxin complex with a high-affinity peptide. The identity of the alpha- bungarotoxin A31 template sequence was 86.30%
and was the highest identity score compared to other templates. The GMQE score shown in the modeling results is 0.71, while QMEANDisco Global is 0.69. GMQE (Global Model Quality Estimate) is a quality estimate that combines the properties of the target template alignment with the template structure. The QMEANDisco global score is the per-residue average of the QMEANDisco score and provides estimates of the error and reliability of model predictions based on model size. GMQE and QMEANDisco global indicate overall model quality as measured by a range between 0 and 1, where the greater the value, the higher the expected model quality [13, 14].
In addition, structural validation is shown with Ramachandran plots (Figure 2). Based on the results of the predicted model structure assessment, the MolProbity score was 1.54 and Ramachandran favored was 92.96%. The validation results also show how likely the error is known from the Ramachandran outliers. The Ramachandran outliers shown in the predictions of this model are 4.23% at the THR8, PRO9, and SER10 amino acid residues. The smaller the percentage of outliers, the smaller the predicted model error, resulting in better model quality [15].
Potential of activity from Withania somnifera compounds. Quantitative structure-activity relationship (QSAR) analysis is a practical method for quantitatively correlating chemical structure with biological activity or chemical reactivity.
Based on the QSAR analysis with PASS Online, the selected bioactivities of compounds from Withania somnifera have various ranges of scores (Table 1). The compounds that likely exhibit anti- inflammatory activity were withanolide A and withanone (Pa > 0.5), while the other compounds show less likely activity (Pa < 0.5). The compounds that likely exhibit the activity of antioxidants were 17alpha-hydroxywithaferin A and withaferin A. For neurodegenerative disease treatment activity, only 3 compounds were likely to exhibit the activity, namely withacoagin,
Figure 1. Alignment of alpha-delta bungarotoxin B. candidus, B. caeruleus, and A31 alpha-bungarotoxin B.
multicinctus. The predicted binding sites based on research by Rajendran et al. (2018) are Lys40, Glu58, Cys61, and Gly76
Figure 2. Ramachandran plots for predicted alpha- delta bungarotoxin B. candidus based on alpha- bungarotoxin isoform A31 template
Figure 3. Three dimensional (3D) structure of alpha-delta bungarotoxin Bungarus candidus and its binding sites were highlighted in the yellow surface view
withanolide B, and withanone. Meanwhile, for the neurotrophic factor, only withacoagin, withanolide B, withanolide D, and withanone show to be active based on the potential score.
Molecular docking and analysis of toxin- ligand interactions. The docking performed was rigid docking with center coordinates x: 5.179, y:
16.097, z: 8.887 and angstroms x: 18.524, y:
28.687 and z: 21.339 according to the coverage area of the binding site, namely the amino acid residues Lys39, Glu57, Cys60, and Gly75. Based on the docking results between the alpha delta protein of bungarotoxin B.candidus and compounds from Withania somnifera, eight compounds with a binding affinity of less than -6.5 were obtained (Table 2).
The lowest binding affinity was -6.9 which was obtained for three compounds: somniferine, withanolide B, and withanone. However, if we look at the non-covalent interactions between the
alpha-delta bungarotoxin B.candidus and several compounds, there are three compounds with the most hydrogen bonds in amino acid residues, namely three amino acid residues: 17alpha- hydroxywithaferin, withanolide D, and withaferin A with the binding energy of -6.6 each; -6.8; and - 6.7 (Table 2, Figure 4). More hydrogen bonds in the amino acid residues are important to show the stability of the interaction between the protein and the ligand. Therefore, only three compounds with the most hydrogen bonds in amino acids were continued to the molecular dynamics analysis.
Furthermore, molecular docking studies are carried out to discover a single lowest-energy ligand. The lower the binding affinity value, the more stable the compound-receptor bond [16, 17].
Visualization of the docking results shows the interaction between the venom alpha-delta bungarotoxin of B. candidus and compounds as ligands in the form of 3D and 2D structures (Figure
4). Based on the results, most of the hydrogen bonds formed were located in the THR63 amino acid residue. In addition, hydrogen bonds are also formed on the THR59 and SER62 amino acid residues. The withanolide D compound had a bond length of 2.740 nm in the THR63 hydrogen bond, while the hydrogen bond with hydrophobic interactions in THR59 was 3.212 nm. Withaferin A had a slightly smaller bond length than withanolide D of 2.701 nm in the hydrogen bond of THR63 and a bond length of 3.260 nm in THR59. The 17- alpha-hydroxy withaferin A compound has the longest bond length of 2.834 nm in the hydrogen bonds of THR63 and 3.234 nm in THR59. The hydrophobic interaction formed on the THR59 amino acid residue can stabilize the complex between the toxin and the bioactive compound. In addition, the interaction of the H-bonds formed on THR63 and SER62 with a smaller bond distance causes the intermolecular bonding to become stronger.
The interactions seen include hydrogen bonds, Van der Waals bonds, alkyl bonds, carbon hydrogen bonds, and unfavorable donor-donor bonds. Hydrogen bonds and alkyl hydrophobic bonds that are formed support the stability of the binding between the receptor protein, which is alpha-delta bungarotoxin and bioactive compounds. Withanolide D, withaferin A, and 17- alpha-hydroxy withaferin A could be the most potent candidates as inhibitors for alpha-delta
bungarotoxin because they had the most formed hydrogen bonds. The formation of hydrogen bonds shows that the bond between these two molecules is fairly strong. It is because hydrogen bonds are stronger bonds than Van der Walls bonds.
Furthermore, the presence of hydrophobic bonds influences the binding affinity of the ligand and protein bonds. The hydrophobic bond makes the binding site more flexible, so this bond becomes one of the important criteria in the development of drugs and their targets [18, 19].
ADME and drug-likeness analysis results.
Based on the analysis of ADME, drug-likeness, and other pharmacological properties, it is known that all compounds have the potential as drugs (Table 3). This can be seen from Lipinski's overall compliance with only one violation in one compound namely somniferine. All compounds also have moderately soluble properties. Another pharmacological property is the high GI absorption of all compounds (Table 3). However, all components do not meet the lead-likeness criteria.
Leadlikeness indicates the starting point for a compound to be used as a drug candidate. The lead likeness criterion reduces the complexity of the best compound candidates (with a molecular weight <400). The lead-likeness violations can also be overcome by modifying the molecules after going through screening so that compounds that violate some volatility can be developed into drugs [20].
Table 1. PASS Online drug acitivty test result on 8 compounds from Withania somnifera in four categories that related for inhbit alpha-delta bungarotoxin B. candidus
Compounds Anti-inflammatory Anti-oxidant Neurodegenerative
diseases treatment Neurotrophic factor
Withanolide A 0.581 0.161 - 0.395
17alpha-hydroxywithaferin A 0.415 0.710 - 0.256
Somniferine 0.350 - - -
Withacoagin 0.429 0.186 0.706 0.750
Withanolide B 0.325 0.446 0.705 0.750
Withanolide D 0.443 0.488 0.392 0.551
Withanone 0.574 0.239 0.544 0.659
Withaferin A - 0.623 - 0.462
Table 2. List of selected compounds from docking results with binding energy less than -6.5 kcal/mol
Compounds PubChem ID Knapsack ID Binding Energy (kcal/mol)
Withanolide A 11294368 C00032513 -6.8
17alpha-hydroxy-withaferin A 12147447 C00035210 -6.6
Somniferine 14106343 C00057801 -6.9
Withacoagin 14236709 C00033489 -6.8
Withanolide B 14236711 C00033134 -6.9
Withanolide D 161671 C00003677 -6.8
Withanone 21679027 C00034339 -6.9
Withaferin A 265237 C00003676 -6.7
Figure 4. Receptor-ligand interaction between the alpha-delta bungarotoxin protein and hits compounds in 3D and 2D structures. A) Withanolide A, B) 17alpha-hydroxywithaferin A, C) Somniferine, D) Withacoagin, E) Withanolide B, F) Withanolide D, G) Withanone, and H) Withaferin A
The conditions that must be met for a compound to be used as a medicine are seen from the Lipinski rule. Lipinski’s rule of five is a requirement used in designing bioavailable drugs orally. The Lipinski Rule consists of a molecular weight < 500, has no more than five hydrogen bond donors, no more
than 10 hydrogen bond acceptors, and the calculation of the octanol-water partition (Clog P) coefficient is no more than 5. Natural bioactive compounds known to show bioavailability even though there is a violation of one of Lipinski's rule of five. Lipinski predicts that compounds with a B)
C) D)
E) F)
G) H)
A)
Table 3. ADME and drug-likeness analysis on the selected bioactive compounds
Compounds Formula MW
(g/mol)
H-Bond Acceptor/H- Bond Donor
LogP Solubility
GI Absorption log Kp (cm/s) Lipinski Bioavailability Lead likeness Synthetic Accessibility
Melianodiol C30H48O5 488.7 3/5 4.45 Moderately soluble High -5.94 Yes (0) 0.55 No (2) 6.67 Salimuzzalin C30H40O7 512.63 5/7 4.07 Moderately soluble High -6.54 Yes (1) 0.55 No (2) 6.54 Withanolide A C28H38O6 470.6 2/6 3.33 Moderately soluble High -6.86 Yes (0) 0.55 No (1) 6.39 17alpha-
hydroxywithaferin A
C28H38O7 486.6 3/7 2.69 Moderately soluble High -7.69 Yes (0) 0.55 No (1) 6.86
Somniferine C36H36N2O7 608.68 3/9 2.74 Moderately soluble High -8.16 Yes (1) 0.55 No (1) 7.32 Withacoagin C28H38O5 454.6 2/5 3.84 Moderately soluble High -6.29 Yes (0) 0.55 No (2) 6.28 Withanolide B C28H38O5 454.6 2/5 4.22 Moderately soluble High -5.76 Yes (0) 0.55 No (2) 6.34 Withanolide D C28H38O6 470.6 2/6 3.39 Moderately soluble High -6.96 Yes (0) 0.55 No (1) 6.85 Withanone C28H38O6 470.6 2/6 3.35 Moderately soluble High -7.01 Yes (0) 0.55 No (1) 6.38 Withaferin A C28H38O6 470.6 3/6 3.45 Moderately soluble High -6.45 Yes (0) 0.55 No (2) 6.83
molecular weight of more than 500 will show low permeability [21].
The conditions that must be met for a compound to be used as a medicine are seen from the Lipinski rule. Lipinski rule of five is a requirement used in designing bioavailable drugs orally. The Lipinski Rule consists of a molecular weight <500, has no more than five hydrogen bond donors, no more than 10 hydrogen bond acceptors, and the calculation of the octanol-water partition (Clog P) coefficient is no more than 5. Natural bioactive compounds are known to show bioavailability even though there is a violation of one of Lipinski's rule of five. Lipinski predicts that compounds with a molecular weight of more than 500 will show low permeability [21].
Molecular dynamics simulation analysis between the complexes of the alpha-delta bungarotoxin B. candidus and selected compounds of W. somnifera. Analysis in molecular dynamics simulations included RMSDBb, RMSDLig, RMSF, and the number of hydrogen bonds (Figure 5). RMSD, or the root mean square deviation of the protein structure with a function of time, is an important profile in monitoring the process of system equilibrium and the stability of the protein structure when bound to a ligand. In molecular dynamics simulations, RMSD is used to assess the structural deviation of the protein structure at initial conditions. The docking conformation with the lowest RMSD is determined as the ideal conformation with the lowest binding energy value. The RMSD value on protein conformation is indicated by RMSDBb, or RMSD Backbone. Based on the results obtained, withanolide D has the smallest RMSDBb value among the alpha-delta bungarotoxin complexes with other compounds, about 2.221 Å, while the other two compounds, namely withaferin A and 17-alpha-hydroxywithaferin A, have an average of 2.26 Å and 2.269 Å, respectively (Figure 5A).
In addition, the stability of the ligand structure is also determined based on the RMSD value for the position of the ligand atom, namely RMSDLig.
Based on the RMSDLig results, the smallest average value was 1.668 Å for the 17-alpha- hydroxywithaferin A ligand, while for the other two compounds, namely withaferin A, the average was 1.882 Å, as well as the largest average on withanolide D with a value of 2.13 Å (Figure 5B).
RMSD values that fluctuate around 2 Å indicate that the system has reached a stable state. However, higher or larger fluctuations indicate greater flexibility [22].
The results of RMSF, or root mean square fluctuation analysis, are used to describe the stability of the position and flexibility of amino acid residues throughout the simulation time. The largest RMSF value at the start of the simulation and quite different from the average RMSF in the other two complexes is found in the amino acid residue of the withanolide D compound, which is 2.392 Å in SER10, 2.745 Å in PRO11, 2.178 Å in ILE12, and 2.035 Å in ASN13. However, for the following amino acid residues, the average RMSF in withanolide A was stable following the control, while until the end of the simulation, the protein complex and 17 alpha-hydroxywithaferin tended to have a greater RMSF value than the other compounds. The greater RMSF value on the amino acid residue indicates that the residue is more flexible during the binding process. Most of the RMSF values are in the range of 2 Å, but closer to the last amino acid residue, the ARG73, PRO74, and GLY75 range, the fluctuations become large and the RMSF value reaches more than 3 Å (Figure 5C).
The stability of the interaction is evaluated based on the number of hydrogen bonds formed during the simulation time. The number of hydrogen bonds in the molecular dynamics simulation results between alpha-delta
Figure 5. Molecular dynamics simulations result showed on the interaction between alpha-delta bungarotoxin B. candidus and selected compounds from W. somnifera: A) RMSDBb value for MD simulation time; B) RMSDLigand value for MD simulation time; C) RMSF value for amino acid residue; D) Number of H-bond for MD simulation time.
bungarotoxin and withanolide D is higher than the fluctuations in the number of hydrogen bonds in other compounds. The average number of hydrogen bonds withanolide D is 39.966, which is also greater than the average of other compounds (Figure 5D). It is due to the large number of hydrogen bonds formed in the interaction of the alpha-delta bungarotoxin protein complex and withanolide D, which makes the interaction between the two more stable.
Potential of W. somnifera as an inhibitor of alpha-delta bungarotoxin B. candidus. This in- silico study showed that W. somnifera contains bioactive compounds that have the potential to act as alpha-delta bungarotoxin B. candidus inhibitors.
Some of these compounds include withanolide A, 17 alpha-hydroxy withaferin A, somniferine, withacoagin, withanolide B, withanolide D, withanone, and withaferin A. However, withanolide D showed the best potential as an alpha-delta bungarotoxin inhibitor based on the results of molecular docking analysis (smaller binding energy with the most hydrogen bonds).
However, other compounds from Withania somnifera also have great potential as alpha-delta bungarotoxin inhibitors. All of these compounds also have potential as drugs when viewed from the nature of ADME and drug-likeness.
Previously, W. somnifera was known as a medicinal plant with many pharmacological properties. W. somnifera or Aswagandha is one of the most widely used medicinal plants in India in
ethnomedicine due to its pharmacological properties. The most medicinally useful parts of W.
somnifera are the roots and leaves. The roots and leaves are used by the people of India as a tonic to relieve various ailments. W. somnifera has pharmacological activity as an antioxidant, antidepressant, antivenom, anti-inflammatory, anti-cancer, anti-microbial, anti-diabetic, immunostimulant, anti-parasitic, and anti-aging.
The main components of bioactive compounds found in W. somnifera are withacetroid or withanolide, alkaloids, flavonoids, phenolics, and others. The withanolide group that has the most pharmacological properties is withanolide A, withaferin A, withanone, and withanoldie D. All of these metabolites are abundant in W. somnifera roots [23, 24].
W. somnifera is one of the plants that is often explored in studies related to venom protein inhibitors. Previous research noted that W.
somnifera contains glycoprotein (WSG) which acts as a PLA2 toxin inhibitor in the venom of the Indian Cobra (Naja naja) and viper venom. The pharmacological effect that occurs is to neutralize the cobra venom toxin by forming a complex between WSG and PLA2 toxin resulting in the inhibition of enzyme activity. WSG is an acidic glycoprotein similar to the alpha chain in PLI snake plasma, but consists of a single subunit. In addition, it was also reported that Withania somnifera has anti-edema and anti-myotoxic activity in cobras.
Anti-hyaluronidase activity was also reported to
A B
C D
inhibit Naja naja venom and Daboia russelii [25, 26, 27].
CONCLUSION
There are several compounds with potential as inhibitors of alpha-delta bungarotoxin from W.
somnifera based on the results of docking with binding energies ranging from -6.6 to -6.9. These compounds include withanolide A, 17alpha- hydroxywithaferin A, somniferine, withacoagin, withanolide B, withanolide D, withanone, and withaferin A. The compound with the best potential as an inhibitor is withanolide D, judging from the stability of the interaction based on hydrogen bonding at three amino acid residues:
THR59, SER62, and THR63. The evaluation is supported by the molecular dynamics simulations, which show the stability of the alpha-delta bungarotoxin B. candidus and withanolide D complex in almost all aspects. W. somnifera has been known as a herbal plant with pharmacological properties such as anti-PLA2 for Naja naja venom and Daboia russelii, anti-myotoxic, and anti- hyaluronidase. Extracts from the roots of W.
somnifera containing compounds of the withanolide group have great potential as an alternative to antivenom. Therefore, we suggest that Withania somnifera has the potential for further study as an alternative to inhibiting Bungarus candidus venom.
ACKNOWLEDGMENT
The author would like to thank Feri Eko Hermanto (Universitas Brawijaya) for assisting and supporting this research in the Laboratory of Biocomputation, Biology Department, Brawijaya University, Malang.
REFERENCES
[1] Rifaie F, Tri M, Amir H (2017) Where did venomous snakes strike? a spatial statistical analysis of snakebite cases in Bondowoso Regency, Indonesia. HAYATI Journal of Biosciences 24(3): 142-148. doi:
https://doi.org/10.4308/hjb.24.3.142.
[2] Longbottom J, Freya MS, Maria D, Gabriel A, Francois C, Daniel JW (2018) Vulnerability to snakebite envenoming: a global mapping of hotspots. Lancet 392 (10148): 673-684. doi:
10.1016/S0140-6736(18)31224-8.
[3] Warrel, DA (2010) Guidelines for the management of snake-bites. Mahatma Gandhi Marg, New Delhi, World Health Organization.
[4] Rusmili MRA, Othman I, Abidin SAZ, Yusof FA, Ratanabanangkoon K, Chanhome L
(2019) Variations in neurotoxicity and proteome profile of Malayan Krait (Bungarus candidus) venoms. PLoS ONE 14(12):
e0227122. doi:
https://doi.org/10.1371/journal.pone.0227122 .
[5] Utkin YN, Kuch U, Kasheverov IE, Lebedev DS, Cederlund E, Molles BE, Polyak I, Ivanov IA, Prokopev NA, Ziganshin RH, Jornvall H, Alvelius G, Chanhome L, Warrell DA, Mebs D, Bergman T, Tsetlin VI (2019) Novel long-chain neurotoxins from Bungarus candidus distinguish the two binding sites in muscle-type nicotinic acetylcholine receptors.
Biochem J. 476(8):1285-1302. doi:
10.1042/BCJ20180909.
[6] Puzari U, Fernandes, Mukherjee PA (2021) Pharmacological re-assessment of traditional medicinal plants-derived inhibitors as antidotes against snakebite envenoming: A critical review. Journal of Ethnopharmacology 292: 115208. doi:
https://doi.org/10.1016/j.jep.2022.115208.
[7] Machiah DK, Girish KS, Gowda TV (2006) A glycoprotein from a folk medicinal plant, Withania somnifera, inhibits hyaluronidase activity of snake venoms. Comp. Biochem.
Physiol. C. Toxicol. Pharmacol 143: 158-161.
doi: 10.1016/j.cbpc.2006.01.006.
[8] Machiah DK, Gowda TV (2006) Purification of a post-synaptic neurotoxic phospholipase A2 from Naja naja venom and its inhibition by a glycoprotein from Withania somnifera.
Biochimie 88: 701-710. doi:
10.1016/j.biochi.2005.12.006.
[9] Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16(8):747-8. doi:
10.1093/bioinformatics/16.8.747.
[10] Trott O, Olson AJ (2010) AutoDock Vina:
improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 31(2): 455–461. doi:
10.1002/jcc.21334.
[11] Rajendran BK, Suresh MX, Bhaskaran SP, Harshitha Y, Gaur U, Kwok HF (2018) Pharmacoinformatic approach to explore the antidote potential of phytochemicals on bungarotoxin from Indian Krait, Bungarus caeruleus. Computational and Structural Biotechnology Journal 16: 450–461. doi:
10.1016/j.csbj.2018.10.005.
[12] Rajendran BK, Suresh MX, Shanmuga PB (2015) Pharmacophore modeling, in silico screening, molecular docking and molecular dynamics approaches for potential alpha-delta bungarotoxin-4 inhibitors discovery.
Pharmacognosy Magazine 11(42): 19-28.
Doi: 10.4103/0973-1296.157670.
[13] Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models.
Bioinformatics 27: 343-350. doi:
https://doi.org/10.1093/bioinformatics/btq66 2.
[14] Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T (2020) QMEANDisCo - distance constraints applied on model quality estimation. Bioinformatics
36: 1765-1771. doi:
https://doi.org/10.1093/bioinformatics/btz82 8.
[15] Oleg VS, Pavel VA, Nigel WM, Maarten LH, Robbie PJ, Anastassis P, Paul DA (2020) A Global Ramachandran score identifies protein structures with unlikely stereochemistry.
Structure 28(11): 1249-1258. doi:
https://doi.org/10.1016/j.str. 2020.08.005.
[16] Hubbard RE, Kamran HM (2010) Hydrogen bonds in proteins: role and strength. In:
Encyclopedia of Life Sciences (ELS). doi:
10.1002/9780470015902.a0003011.pub2.
[17] Guedes IA, de Magalhães CS, Dardenne LE (2014) Receptor–ligand molecular docking.
Biophys Rev 6, 75–87. https://doi.org/
10.1007/s12551-013-0130-2.
[18] Williams MA, Ladbury JE (2003) Hydrogen bonds in protein-ligand complexes, in protein ligand interactions: from molecular recognition to drug design. Weinheim. Wiley- VCH.
[19] Patil R., Das S, Stanley AY, Sudhakar A (2010) Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug- designing. PLOS ONE 5(8): 12029. doi:
10.1371/journal.pone.0012029.
[20] Hann MM, Oprea, TI (2004) Pursuing the leadlikeness concept in pharmaceutical
research. Current Opinion in Chemical Biology, 8(3): 255–263. doi:10.1016/j.cbpa .2004.04.003.
[21] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1-3): 3-26. doi: 10.1016/s0169- 409x(00)00129-0.
[22] Liu K, Watanabe E, Kokubo H (2017) Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J Comput Aided Mol Des. 31(2):
201-211. doi: 10.1007/s10822-016-0005-2.
[23] Kumar VB, Vikaskumar G, Surabhi T, Madhusudanan KP (2022) Phytochemistry of Withania somnifera. Boca Raton, CRC Press.
[24] Sangwan NS, Tripathi S, Srivastava Y, Mishra B, Pandey N (2017) Science of ashwagandha: preventive and therapeutic potentials. Springer Nature: 3-36. doi:
10.1007/978-3-319-59192-6_1.
[25] Madhusudan M, Farhan Z, Akhilender N, Nagendra PMN, Bhadrapura LD, Raghavendra H (2016) Evaluating the inhibitory potential of Withania somnifera on platelet aggregation and inflammation enzymes: An in vitro and in silico study, Pharmaceutical Biology 54 (9): 1936-1941.
doi: 10.3109/13880209.2015.1123729.
[26] Soares AM, Fábio KT, Silvana M, Miriam VL, Ana HJ, Suely VS, José RG, Bruno L, Paulo SP (2005) Medicinal plants with inhibitory properties against snake venoms.
Current Medicinal Chemistry 12(20): 1-17.
doi: 10.2174/092986705774370655.
[27] Dey A, Nath DJ (2012) Phytopharmacology of antiophidian botanicals: a review.
International Journal of Pharmacology 8: 62- 79. doi: 10.3923/ijp.2012.62.79.
