Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
ALCHEMY Jurnal Penelitian Kimia Official Website: https://jurnal.
id/alchemy Antibacterial Activity of Streptococcus mutans from Saga Herbaceous Plant (Abrus precatoriu.
: In Silico Study1 Richa Mardianingruma.
Neta Ekayanti Sugandaa.
Srie Rezeki Nur Endaha.
Ruswanto Ruswantob* Department of Pharmacy.
Universitas Perjuangan Tasikmalaya Jalan Peta 177 Tasikmalaya, 46115.
Indonesia Faculty of Pharmacy.
Universitas Bakti Tunas Husada Jalan Cilolohan 36 Tasikmalaya, 46115.
Indonesia Corresponding author: ruswanto@universitas-bth.
DOI: 10.
20961/alchemy.
Received 27 November 2022.
Revised 8 September 2023.
Accepted 8 September 2023.
Published 30 September 2023 Keywords:
Abrus precatorius.
in silico.
2HVW.
ABSTRACT.
Antibacterial is a substance that can inhibit growth or can even kill bacteria that cause One of them is infection with Streptococcus mutans bacteria that cause damage to teeth, such as dental caries.
Dental caries is a disease that affects many adults and children, permanently damaging the tooth layer and forming small holes in the teeth.
The purpose of this study was to find active compounds from the herb Saga plant (Abrus precatoriu.
, which has the potential to be antibacterial of S.
mutans in The methods used are pharmacokinetics and toxicity screening.
Lipinski's Rule of Five, as well as simulations of molecular docking and molecular dynamics.
The Abruquinone D (-6.
and Abruquinone F (-7.
were predicted to have stable interactions and be similar to amoxicillin (-7.
and native ligand (-8.
56 kcal/mo.
based on the results of screening and molecular docking simulations of active compounds from Saga herbaceous (Abrus precatoriu.
against deoxycytidylate deaminase receptors.
Molecular dynamics findings confirmed by MMGBSA methods that Abruquinone D (-41.
3876 kcal/mo.
had a lower energy value than Abruquinone F (-24.
8521 kcal/mo.
It can be inferred that Abruquinone D has a higher potential as an antibiotic (S.
than Abruquinone F.
INTRODUCTION
Antibacterial is a substance that can inhibit growth or can even kill bacteria that cause infection (Magani et , 2.
One of the causes of infection is S.
mutans, which is a Gram-positive anaerobic bacterium.
Generally, mutans are found in the human oral cavity and can cause tooth decay, such as dental caries (Kementerian Kesehatan RI, 2012.
Pramiastuti et al.
, 2.
Dental caries is a prevalent disease affecting both adults and children, causing permanent tooth damage and small holes (Norfai and Rahman, 2.
The World Health Organization reported that 90% of schoolchildren worldwide have caries, with the highest prevalence in Indonesia 8%, primarily in the 55 Ae 64 age group (Khasanah et al.
, 2019.
Kementerian Kesehatan RI, 2.
Dental caries can be reduced through treatments like fillings, root canals, and tooth extractions, as well as antibiotic drugs like amoxicillin.
However, excessive use can lead to resistance, resulting in less effective treatment, increased patient morbidity and mortality, and higher health costs (Khasanah et al.
, 2.
To overcome this problem, scientists are looking for medicinal materials that have low side effects, namely herbal plants.
There is a study that states that one of the plants that can potentially be an herbal medicine for antibacterial is Saga herbaceous plant (A.
(Andayani et al.
, 2.
Empirically, the Saga herbaceous plant (A.
is used as an alternative medicine to help treat thrush in the Banten area according to Yusransyah and Izati .
, cough in the Tasikmalaya area, sore throat in the Karawang area according to Widianto et al.
, while according to Andayani et al.
Saga leaves (A.
can treat canker sores, tonsil cough medicine, and the seeds can treat diabetes and chronic nephritis.
It turns out that the Saga plant (A.
has ingredients, including phenols, tannins, flavonoids, alkaloids, terpenoids, and saponins.
All such compounds have antibacterial activity (Widianto et al.
, 2.
Cite this as: Mardianingrum.
Suganda.
Endah.
, & Ruswanto.
2023 Antibacterial Activity of Streptococcus mutans from Saga Herbaceous Plant (Abrus precatoriu.
: In Silico Study.
ALCHEMY Jurnal Penelitian Kimia, 19.
, 177-189.
https://dx.
org/10.
20961/alchemy.
Copyright A 2023.
Universitas Sebelas Maret.
ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Research by Andayani et al.
stated that the methanol extract of Saga leaves (A.
has antibacterial activity against S.
mutans, with the smallest resistance zone diameter seen at a concentration of 10%, which is 7 mm, and the largest is seen at a concentration of 100% which is 17.
3 mm.
And there are also other researchers who state that Saga leaf ethanol extract (Abrus precatoriu.
can inhibit S.
mutans with the smallest inhibitory zone at a concentration of 1%, which is 6.
06 mm, while the largest at a concentration of 10% is 02 mm (Nisak et al.
, 2.
Several structures of compounds that were successfully isolated from A.
(Xiao et al.
, 2017.
Okoro et al.
, 2.
can be seen in Figure 1.
Figure 1.
Sample of the structures.
With the potential of Saga herbaceous plants against antibacterials, it is necessary to research more specific Saga herbaceous plants against the antibacterial S.
In silico test is a research method that uses database technology to develop further research.
The in silico method can see the interaction between ligands and receptors, know the pharmacokinetic properties, and get results faster (Makatita et al.
, 2.
Based on the background, this study aims to find candidates for medicinal materials from Saga herbaceous plant compounds as antibacterial S.
mutans through in silico study.
RESEARCH METHODS
The tools used are computer hardware and software.
The hardware used is a computer with Intel Core TM i5-6400 PC specifications @3.
90GHz .
CPU.
Nvidia Geforce GTX 970 Gigabyte OC Edition GPU, 8GB DDR4 RAM, and Laptop (ACER) DESKTOP-17KCTT8 IntelA Core TM i5-2450M CPU @2.
50GHz.
4GB RAM.
type 64-bit.
Operating system: Windows 10 Pro.
The software used Chemdraw Ultra 8.
Marvin Sketch 5.
AutoDock Tools 1.
Molegro Molecular Viewer.
PyRx 0.
AMBER 16, and BIOVIA Dyscovery Studio 2017.
Web server programs such as KNApSAcK.
PreADMET.
PDBsum.
RCSB PDB, and Lipinski's Rule of Five were The ingredients used in this study were 29 compounds of Saga herbaceous plants (A.
obtained from KNApSAcK .
ttp://w.
com/KNApSAcK/).
The receptor used is an antibacterial receptor .
eoxycytidylate deaminas.
with the code PDB ID: 2HVW (Hou et al.
, 2.
downloaded from the Protein Data Bank (PDB) in the https://w.
org/ website in the form of .
pdb format.
With the comparison drug used, namely amoxicillin.
Receptor Preparation and Validation The receptors used are downloaded from the Web Protein Data Bank.
The receptors are validated in PDBsum, which can provide details of the receptor structure, schematic diagrams of molecules in each structure, and their interaction with ligands.
Receptors are said to be good when the residual plot contained in most favored regions (A.
L) is more than 90% and disallowed regions [X.
X] is less than 0.
8% (Ruswanto et al.
, 2018.
Mardianingrum et al.
, 2021.
Docking Validation The parameter used for this docking validation is the Root Mean Square Deviation (RMSD) value.
The docking method uses natural ligands with the AutodockTools 1.
6 program and it is said to be valid if the RMSD value obtained is O 2 yI (Zubair et al.
, 2.
Copyright A 2023.
Universitas Sebelas Maret.
ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Ligand Preparation Compounds downloaded or drawn using ChemDraw Ultra 8.
0 were copied and pasted in Marvin Sketch 5.
software by protonation at pH 7.
4 ligands and saved with format *.
Then, the search results for ligand confirmation were saved with the *.
mol2 format (Ruswanto, 2.
Toxicity and Pharmacokinetic Screening In this study.
ADME predictions were carried out on Saga herbaceous plant compounds (A.
The structure of the compound was converted into a mole file (*.
and then submitted to the PreADMET website, which automatically calculates predictive absorption for Caco-2.
HIA (Human Intestinal Absorptio.
, and bound plasma binding (PPB) cells.
Toxicity tests were carried out on compounds of the Saga herbaceous (A.
in search of Ames and carcinogenicity tests of compounds (Mardianingrum et al.
, 2021a.
Ruswanto et al.
, 2.
Screening Ligand-Based Drug Likeness Drug Scans were used to observe the nature of their similarity with existing drugs (Drug Likenes.
performed using the rule of good medicine (Lipinski's rule of fiv.
and oral bioavailability of ligands.
A compound was classified to have properties similar to drugs if it meets two or more conditions with the observed parameters, namely molecular weight <500 g/mol, lipofility <5, hydrogen bond donor <5, hydrogen bond acceptor <10, and molar refractory between 40 Ae 130 (Ruswanto et al.
, 2.
Molecular Docking The molecular process of docking was carried out using the PyRx program (Dallakyan et al.
, 2015.
Pawar and Rohane, 2021.
Yuliana et al.
, 2013.
Kondapuram et al.
, 2.
Ligands that have been prepared with natural ligands were added into protein macromolecules and then forwarded.
The Gridbox data was adjusted during the docking validation step, and the AutoGrid was run until it finished.
The resulting binding energies were then selected and ordered.
Molecular Dynamics Simulation Simulation of molecular dynamics of docking complex of protease enzymes with ligands was carried out using the AMBER 16 MD program through several stages, namely: file preparation, minimization, heating, equilibration, production, and analysis of molecular dynamics simulation results with RMSD (Root Mean Square Deviatio.
RMSF (Root Mean Square Fluctuatio.
parameters and changes in interaction distance.
The latter was visualized using the Discovery Studio Visualizer (Zubair et al.
, 2020.
Mardianingrum et al.
, 2.
RESULTS AND DISCUSSION
Receptor Analysis and Preparation The receptors used are 2HVW .
igand code: DDN), and the obtained receptors have a resolution value of 67 yI, which is considered good because they meet the requirements of the most favored regions parameter with a value of 91.
2% and a disallowed region parameter of 0.
Therefore, the 2HVW receptor can be used as a target protein for molecular docking to antibacterial activity, as seen in Figure 2.
Figure 2.
The active site of the 2HVW protein with the natural ligand.
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ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
The active site of the 2HVW receptor with the number of amino acids contained in receptor 148 whose target protein interacts with 12 amino acid residues, namely Ala72.
Arg26.
Arg121.
Asn45.
Cys24.
Cys66.
Cys99.
Glu73.
His65.
His71.
Thr69.
Tyr120 forming hydrogen bonds, as well as hydrophobic bonds with 4 residues, namely Ala27.
Cys102.
Pro98 and Val29.
The results of Ramachandran plot statistics and the active site of the 2HVW receptor can be seen in Figure 3.
Figure 3.
Ramachandran plot statistics of 2HVW receptors [Source: PDBsum Databas.
Receptor Validation Docking validation was carried out with Gridbox measurements, namely x = 11.
y = 15.
z = 51.
then continued with docking analysis using AutoDockTools generated an RMSD value of 0.
336 yI with a binding energy value of -12.
20 kcal/mol.
The 2HVW receptor obtains an RMSD value that matches its requirements, which is below 2 yI (< 2yI), with a relatively small amount of energy.
The validation results of the docking method can be seen in Figure 4.
Figure 4.
Initial conformation 2HVW receptor overlay form .
and after simulated redocking validation .
[Source: Visualization of Discovery Studio Visualize.
Based on the visualization results, it can be seen that the interaction of the 2HVW receptor is the presence of hydrogen bonds with amino acid residues Ala72.
Asn45.
Cys66.
Cys99.
Glu73, and Tyr120 (Figure .
The hydrogen bonds formed are predicted to have a stable interaction with natural ligand bonds inside the most complex 2HVW receptors.
Based on these results, it can be stated that the validation of this molecular docking method meets the validation requirements and can be used for the next stage of in silico testing.
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ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Figure 5.
Visualization of natural ligand interactions after validation .
e-dockin.
of 2HVW receptors [Source:
Visualization of Discovery Studio Visualize.
Toxicity and Pharmacokinetic Screening Results Toxicity and ADME testing were carried out using PreADMET, which aims to predict the processes of absorption, distribution, metabolism, excretion, and toxicity in the human body in silico.
Toxicity testing is the ability of chemicals to cause damage as toxins.
Drugs have a therapeutic effect and side effects or toxins, so it is necessary to predict toxicity to recognize the potential of toxins from drugs as a consideration in their use.
The parameters analyzed are mutagenic and carcinogenic.
Pharmacokinetics is used to predict ADME in Saga herbaceous plant compounds, with parameters including Caco-2 (Human colon adenocarcinom.
to determine the ability to penetrate cell membranes.
HIA (Human Intestinal Absorptio.
to predict absorption in the small intestine, bound PPB (Protein Plasma Bindin.
and the ability to penetrate the brain to predict distribution in the body (Kartasasmita et al.
, 2.
The results of toxicity and pharmacokinetics screening tests can be seen in Table 1.
The results of toxicity and pharmacokinetics screening analysis (Table .
of 29 compounds in Saga herbaceous (A.
that meet the requirements there are 6 test compounds, namely Abruquinone C.
Abruquinone B.
Abruquinone D.
Abruquinone E.
Abruquinone F, and Abruquinon G.
In this study, six compounds were selected to proceed to the molecular docking stage because these six compounds met the selection parameters of pharmacokinetics and toxicity.
Based on the criteria on toxicity and pharmacokinetic prediction, a compound would ideally be Non-mutagenic.
Non-carcinogenic in both mice and rats, have medium to high Caco-2 (Human colon adenocarcinom.
permeability, have medium to good HIA, and have weak PPB .
hough this depends on the intended therapeutic use and desired pharmacokinetic profil.
Ligand-Based Drug Likeness Screening Results A screening of Lipinski's Rule of Five predictions was performed on the 6 test compounds.
Drug likeness refers to the oral administration of drugs related to the process of absorption and distribution of drugs.
The physicochemical properties of the compounds are indispensable for analyzing the similar properties of compounds with oral drugs and estimating the pharmacokinetic processes of medicinal compounds in the body.
The results of the drug scan can be seen in Table 2.
The analysis based on Table 2 indicates that all six selected compounds have met Lipinski's rules.
thus, it can be stated that the test compounds can be used orally.
However, the analysis shows that the comparing drug of amoxicillin is still better than the test compounds.
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ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Table 1.
Results of screening toxicity and pharmacokinetic studies.
Toxicity Compound Name ames_test Carsino Mouse Carsino Rat Amoxicillin Mutagen (Comparison dru.
Luteolin Mutagen Isoorientin Mutagen L-Abrien Mutagen Trigonelline Mutagen Hypaphorine Mutagen Gallic acid Mutagen Abrusoside A Non Mutagen Glycyrrhetinic acid NonMutagen Stigmasterol Non Mutagen All-trans-squalene Mutagen Abrectorin Mutagen Swertisin 8-methyl Non Mutagen Abrusin Non Mutagen Delphinidin 3Non Mutagen Abruquinone Non Mutagen Abruquinone A Mutagen Abruquinone C Non Mutagen Abruquinone B Non Mutagen 3-[.
-O-beta-DGalactopyranosylbeta-Dxylopyranosy.
-2-.
-hydroxyNon Mutagen 3,5dimethoxypheny.
7-methoxy-4H-1benzopyran-4-one Precatorin i Non Mutagen
Precatorin II
Non Mutagen
5,7-Dihydroxy-4',6dimethoxyisoflavon Non Mutagen
e 7-O-beta-Dgalactopyranoside Abruquinone D Non Mutagen
Abruquinone E
Non Mutagen
Abruquinone F
Non Mutagen
Abrussaponin I Non Mutagen
Abrussaponin II
Non Mutagen
Abruquinone G
Non Mutagen
Kaikasaponin i Non Mutagen
methyl ester
Pharmacokinetic Predictions Caco-2
HIA
PPB
Description: Caco-2 (C4 Low a.
4 Ae 70 Medium b.
E70 High.
HIA .
% Ae 20% Bada.
20% Ae 70 % Medium b.
70 Ae 100 % Good .
PPB (E90% Strongly Bound a.
C90 % Weakly Bound .
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ISSN 1412-4092, e ISSN 2443-4183 Mardianingrum et al.
ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Table 2.
Drug scan test results according to Lipinski's Rule of Five.
Lipinski's Rule of Five Compound Molecular Log P Hydrogen Hydrogen Name Weight (Lipofilit.
Bonding Donor Bond Acceptor <500 g/mol <10 Amoxicillin (Comparison Abruquinone C Abruquinone B Abruquinone D Abruquinone E Abruquinone F Abruquinone G Refractory Molar 40 Ae 130 Molecular Docking Molecular docking simulations on Saga herbaceous plant compounds as test ligands were performed on the 2HVW receptor .
igand code: DDN).
The Gridbox parameters performed are the same as at the redocking validation stage.
The reason for configuring the Gridbox is to guide the test ligands.
thus, they interact specifically within the designated region of the receptor.
The outcome of the docking process yields information on binding affinity .
G), inhibition constant (K.
, and the presence of hydrogen bonding.
Based on the data in Table 3, it can be seen that compounds that have small binding affinity values are Abruquinone D and Abruquinone F, which have values comparable to amoxicillin .
comparison dru.
and native ligand with the binding affinity of -6.
-7,08.
56 kcal/mol, respectively.
These results confirm that the native ligand, amoxicillin.
Abruquinone D, and Abruquinone F, are predicted to form stable interactions with the bacteria S.
This analysis suggests their potential as effective antibacterial drugs.
Table 3.
Results of docking of test ligand molecules with 2HVW receptor.
2HVW
Compound Name Binding Affinity .
cal/mo.
Amoxicillin (Comparison dru.
Native ligand Abruquinone C Abruquinon B Abruquinone D Abruquinone E Abruquinone F Abruquinone G Ki (AAM) The bonds that can be observed from the results of molecular docking are hydrogen and Van Der Waals These bonds greatly affect the stability of the conformation formed between the ligand and the receptor.
The improvement evidence of molecular interaction can be found in Table 4, and its visualization is available in Figure 5.
The results of the interaction of molecular enhancers can be seen in Table 4 and Figure 6.
Based on this visualization, one can observe the amino acid residues that form hydrogen bonds between the comparison compounds and the two selected test compounds.
Specifically, these interactions occur at the active sites of Ala72 (Ala.
and Glu73 (Glu.
, which are known to be located in helix-4.
It is predicted that these interactions are stable, suggesting that the test compounds may exhibit similar biological activity to the comparison compounds .
omparison drug.
This is because they bind to the same amino acid residues.
More hydrogen bonds between compounds and amino acid residues indicate a more stable and favorable interaction, as previously noted (Muchtaridi et al.
, 2.
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ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Table 4.
Interaction of molecular docking results.
Compound Name Hydrogen Bonding Amoxicillin Ala72.
Arg26.
Asn45.
Cys66, (Comparison dru.
Glu73.
Thr69 Abruquinone D Ala72.
Glu73.
Phe97 Abruquinone F Ala72.
Arg121.
Cys66.
Glu73.
His65.
Phe97 .
Hydrophobic Bonding Ala27.
His65.
Ile67.
Ile100.
Tyr120.
Pro98 Arg121.
Asn45.
Cys99.
His65.
His96.
Ile67.
Thr69.
Tyr120.
Pro98 Arg26.
Asn45.
Cys99.
Ile67.
His96.
Thr69.
Tyr120.
Pro98 .
Figure 6.
The 2D visualization of the interaction of 2HVW receptor bonds with .
Amoxicillin, .
Abruquinone D, and .
Abruquinone F.
Molecular Dynamics Simulation The RMSD result is depicted in Figure 7.
The RMSD of the Abruquinone D .
against the receptor showed a stability range at a time of 4 Ae 20 ns, with an RMSD value of A 3yI.
The Abruquinone F .
against the receptor showed a stability range at a time of 2 Ae 4 ns with a value of RMSD A 2 yI would be less stable.
Meanwhile, amoxicillin .
comparison dru.
against receptors showed a stability range at a time of 4 Ae 5 ns with an RMSD value of A 3.
5 yI.
However, the RMSD of native ligand is extremely erratic from start to end.
This shows that Abruquinone D is predicted to have a stable interaction with the 2HVW receptor if it was compared with From these results, it can be predicted that Abruquinone derivative compounds have the potential as antibacterial, which is in accordance with the results of other studies (Okoro et al.
, 2.
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ALCHEMY Jurnal Penelitian Kimia.
Vol.
2023, 177-189
Figure 7.
Graph of RMSD values from simulated molecular dynamics of compounds Abruquinone D.
Abruquinone F.
Amoxicillin and native ligand (DDN).
The analysis (RMSF) shows fluctuations in the amino acid residues that make up the receptors in the simulation process to represent the residue's flexibility (Figure 8.
High residues and fluctuations indicate high flexibility and unstable interactions due to frequent changes in their positions during the simulation.
This is consistent with the findings of Mardianingrum et al.
, which suggest that residues with low fluctuations play an active role in the binding between ligands and receptors, as they do not exhibit high flexibility.
Figure 8.
RMSF graph results of molecular dynamics simulation of compounds abruquinone D, abruquinone F, amoxicillin, and native ligand (DDN).
Based on Figure 8, the active site consists of amino acid residues, namely Asn45 (Asn.
Cys66 (Cys.
Ala72(Ala.
Glu73 (Glu.
Cys99 (Cys.
and Tyr120 (Tyr.
Abruquinone F undergoes higher fluctuations than Abruquinone D, native ligand, and amoxicillin if its active site consists of Asn45(Asn.
Ala72 (Ala.
Glu73 (Glu.
, and Cys99 (Cys.
Meanwhile, if amino acids Cys66 (Cys.
and Tyr120 (Tyr.
are present in the active site, amoxicillin experiences higher fluctuations than Abruquinone D and Abruquinone F.
Table 5 shows that Abruquinone D complex with receptors has a lower total bond-free energy value (OIGTOTAL) than amoxicillin and Abruquinone F.
The electrostatic energy (EEL) is the energy component with the greatest effect on the system.
The total value of bond-free energy (OIG) in the Abruquinone D system against receptors is lower (-41.
3876 kcal/mo.
than amoxicillin (-37.
2918 kcal/mo.
, but it was bigger than native ligand (-57.
This shows that the level of affinity of Abruquinone D to receptors is better and has more potential as an antibacterial against S.
mutans bacteria than amoxicillin .
comparison dru.
, but it was less than native ligand.
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Table 5.
Calculation of the bonding energy of the amoxicillin, abruquinone D and abruquinone F systems against receptors with the MM-GBSA Method.
System Energy Component Ligand 23 Ligand 25 cal/mo.
Amoxicillin
2HVW (DDN)
(Abruquinone D) (Abruquinone F) VDWAALS
EEL
EGB
ESURF
OIG gas (VdW EEL) OIG solv (EGB ESURF) OIG total (VdW EEL EGB ESURF) The shifting position of the selected ligand is observable in snap 1, snap 5, snap 10, snap 15, and snap 20 of each compound (Figure 9.
Figure 10, and Figure .
Figure 9 shows that the position of the ligand in the Abruquinone D compound during the simulation from snap 1 to snap 20 has no significant shift.
The position of the ligand is fixed from the beginning to 20 ns.
Figure 9.
Superimpose results of molecular dynamics simulation of Abruquinone D.
Figure 10.
Superimpose simulation results of molecular dynamics compound Abruquinone F.
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This amino acid is obtained from the homology modeling results .
nset of Figure .
Homology is the most accurate computational method of predicting the 3D structure of the target protein with a similarity of at least 30% amino acid arrangement to the structure of the molded protein.
This modeling can also explore the interaction mechanism between target proteins and ligands on an atomic scale (Saudale, 2.
The MM-GBSA calculation method produces the ligand system's free energy .
G) bonding with receptors in molecular dynamics simulations.
The smaller the free energy .
G), the greater the ability of a compound to interact with receptors.
Based on Figure 10, the position of the ligand in the Abruquinone F compound during 10 ns simulation was However, at 15 ns, the position gradually returned and completely returned to the initial position at 20 Figure 11 shows the no-shift position observed in the amoxicillin compound during the simulation.
It can be concluded from these results that Abruquinone D and Amoxicillin are in a fixed position.
Moreover, the RMSD in Figure 6 shows that Amoxicillin and Abruquinone D can be antibacterial candidates, in which Abruquinone also has a lower RMSD value than amoxicillin.
Figure 11.
Superimpose simulation results of molecular dynamics of Amoxicillin compounds.
CONCLUSION
The results of the molecular docking and molecular dynamic simulations between the active compound from the Saga herbaceous (A.
and the deoxycytidylate deaminase receptor show that the compound Abruquinone D has a better interaction than Abruquinone F.
Therefore, the compound Abruquinone D can potentially be used as an antibiotic against S.
mutans bacteria.
CONFLICT OF INTEREST
There is no conflict of interest in this article.
AUTHOR CONTRIBUTION
RM: Conceptualization.
Methodology.
NES: Data Analysis.
Manuscript Drafting.
SRNE: Manuscript Review.
RR: Manuscript Review and Editing.
ACKNOWLEDGEMENTS
The authors thank Universitas Perjuangan Tasikmalaya for the provided facilities for the research.
REFERENCES