Anti-Cancer Potential of Salaciaoblonga Wall Compounds by Targeting 5HZN and 1Q1A Receptor: A Comprehensive Molecular Docking and Simulation Approaches

 

Suraj S. Tarihalkar1*, Vipul Patil2, Sachin Kumar Patil3

1Department of Pharmaceutical Quality Assurance, Ashokrao Mane College of Pharmacy,

Shivaji University, Pethvadgaon, Kolhapur, Maharashtra, India.

2Department of Pharmaceutical Chemistry, Ashokrao Mane College of Pharmacy,

Peth Vadgaon, Maharashtra, India.

3Principal, Ashokrao Mane College of Pharmacy, PethVadgaon, Maharashtra, India.

*Corresponding Author E-mail: tarihalkarsuraj262@gmail.com, vipulpatil1230@gmail.com, sachinpatil.krd@gmail.com

 

ABSTRACT:

Plants produce phytochemicals with pharmacological properties that have been demonstrated in various conditions. In-silico techniques like molecular docking and virtual screening are being used to clarify the pharmacological aspects of bioactive chemicals of botanical origin. Aromatherapy has focused on plant secondary metabolites' anti-inflammatory, antioxidant, and anticancer capabilities. Essential oils contain monoterpenes and sesquiterpenes, which have several pharmacological activities. Salaciaoblongais a perennial plant with a long tradition of use in Ayurvedic medicine. It contains polyphenols, triterpenes of the friedelane and norfriedelane kinds, sesquiterpenes of the eudesmane type, and glycosides. The extract has positive pharmacological effects due to its interactions with molecular targets inside the human body. The primary goals of the current investigation were to investigate the bioactive compounds and isolates of Salaciaoblonga Wall extract with the receptors 5HZN (BRCA 3) and 1Q1A (IR). Bioactive compounds included 2,4- dimethylamphetamine, 19-hydroxyferruginol, Cyclotrisiloxane, Dulcitol, Epicatechin, Galactinol, Hexadecanoic acid, Kotalagenin-16-acetate, Kotalanol, Lambertic acid, Mangiferin, Neokotalanol, Neosalacinol, Quercetin, Raffinose, Salaciaoblonga Wall, Salasone A, Salasol B, Salasone C, Salasone D, Salasone E, Stachyose, and Trichloroacetic acid. Autodock 4.2.6 was used to dock these compounds to a chosen protein. Molecular docking data was used to identify the optimal binding conformation of inhibitors to enzymes, and protein-ligand complexation provided information on interactions. Salaciaoblonga had the best docking positions and free energy scores, but some had substandard ADME characteristics. This study paves the way for the development of novel medications against cancer-associated molecular targets.

 

KEYWORDS: Molecular Docking, Salaciaoblonga, Cancer, In-Silico.

 

 


 

INTRODUCTION:

Plants can produce a large variety of phytochemicals with substantial pharmacological properties. Many of these chemicals have both medical uses and beneficial health effects on people. The bark, roots, leaves, seeds, and fruits of plants can be used to extract a wide variety of substances. Salaciaoblonga Wall contains a variety of bioactive compounds, including diterpenes, eudesmane-type sesquiterpenes, friedelane-type polyphenols, alkaloids, catechins, norfriedelane-type triterpenes, glycosides, andflavonoids.

 

The results of the pharmaceutical research were demonstrated by several conditions, including diabetes, cancer, hemorrhoids, inflammation, leucorrhea, leprosy, skin conditions, amenorrhea, dysmenorrhea, hyperhidrosis, hepatopathy, dyspepsia, flatulence, colic, wounds, ulcers, andspermatorrhoea.1

 

Additionally, unlike conventional combinatorial chemistry, natural products of plant origin continue to produce unique structural diversity, highlighting a significant opportunity for the discovery of entirely novel low- molecular-weight leading molecules. In-silico techniques like molecular docking and virtual screening are being heavily used in efforts to clarify the pharmacological aspects of bioactive chemicals of botanical origin as advanced computer technologies are developed.2 Although earlier literature has not placed much emphasis on the aforementioned based viewpoints, it is still the investigation's principal goal. A rise in enthusiasm for plant secondary metabolites has been observed during the past ten years, and aromatherapy has focused mostly on these compounds' anti-inflammatory, antioxidant, and anticancercapabilities.3

 

A vast range of bioactive chemical substances obtained from plant components makes up natural goods. They may serve as therapeutic agent substitutes. The main components of essential oils are monoterpenes and sesquiterpenes, which have several pharmacological activities, including analgesic, anticancer, and antioxidant actions.4

 

It would require many years to introduce a medicine to the market using conventional approaches for the discovery of novel medications as therapeutic agents because they are expensive and time-consuming. Using a multidisciplinary approach, such as computer-based modeling and in-silico drug design, it is possible to overcome these obstacles and offer a novel medicine quickly and affordably. Various bioinformatics techniques that can simplify procedures like protein-ligand interactions, in silico ADMET prediction, and molecular docking studies have modernized drug design.5

 

Salaciaoblonga Wall (Celastraceae), Strong ties exist between perennial plants with a long tradition of use in Ayurvedic medicine and the management, prevention, and cure of numerous human illnesses, such as diabetes, cancer, and obesity. All of the compounds extracted from this plant include polyphenols, triterpenes of the friedelane and norfriedelane kinds, sesquiterpenes of the eudesmane type, and several glycosides.

 

Salaciaoblonga contains a wide range of phytoconstituents that differ from species to species as well as from origin to origin. The primary bioactive components are mangiferin, flavonoids, glucosides, xanthine, alkaloids, and two compounds with unique thiosulfonium sulfate structures: mangiferin, flavonoids, glucosides, xanthine, alkaloids, and two compounds with a unique thiosulfonium sulfate structure. The plant Salaciaoblonga extract has a variety of regional and traditional uses because of its activity on numerous molecular targets inside the human body. The chemical components of the extract interact with a wide range of molecular targets, resulting in a variety of positive pharmacological effects.6

 

In-silico analysis and comparative  molecular  docking  investigations  of  bioactive  compounds  and  isolates  of Salaciaoblonga Wall extract with the receptors 5HZN (BRCA 3) and 1Q1A (IR) were the primary goals of the current investigation. Using various bioinformatic tools, objectives also include estimating the therapeutic potential of plants and isolates and reporting on their ADMETprofiles.

 

We aimed to check the suitability of Salaciaoblonga Wall plant and active isolates from extracts by molecular docking studies. Hence targeting 5HZN nad 1Q1A receptor components in the essential oil such as The bioactive compounds isolated after the TLC, Column chromatography, FTIR, analysis of from the whole plant of Salaciaoblonga Wall were: 2,4-Dimethylamphetamine, 19-hydroxyferruginol, Cyclotrisiloxane, Dulcitol, Epicatechin, Galactinol, Hexadecanoic acid, Kotalagenin-16-acetate, Kotalanol, Lambertic acid, Mangiferin, Neokotalanol, Neosalacinol, Quercetin, Raffinose, Salacinol, Salasol A, Salasol B, Salasone A, Salasone B, Salasone C, Salasone D, Salasone E,  Stachyose,  Trichloroacetic  acid  are  isolated  from  the  different  extracts Salaciaoblonga Wall, were also selected as ligands.

 

Using Autodock 4.2.6, these compounds were docked to a chosen protein. The outstanding qualities of particular Salaciaoblonga plant components include a variety of pharmacokinetic traits. The majority follows Lipinski's rule of five. It has been outlined and summarised from docking research that a variety of parameters, including the H-bond, G score, and other factors, control the protein-ligand interaction.

 

MATERIALS AND METHODS:

1.     Protein Selection:

Two proteins, 3t6a (BRCA3) and 5hzn (IGF-1R), were chosen as targets expressed in inflammation in the current investigation and retrieved from a PDB file via the protein data bank.7 The ligands have complexes with protein 5hzn, which has a 2.20 A resolution, and protein 3t6a, which has a 2.40 A resolution, both of which utilize X-ray diffraction. BRCA (3t6a) complexes with protein 5hzn, which has a 2.20 A resolution, and protein 3t6a, which has a 2.40 A resolution, both utilizing X-ray diffraction. BRCA (3t6a) and IGF-IR (5HZN) full- sequence proteins' active site regions were predicted using the web program proteome plus (http://protiens.plus/), which predicts the binding sites of proteins. The target proteins were minimized in energy using Swiss PDB reader software.8

 

2.     Ligand Database Preparation:

25 plant chemicals were extracted from plants for the current investigation and chosen for their alleged anti- cancer properties.9 Significantly increased the underlying anticancer properties and displayed significant anti- cancer activity in earlier studies. On the other hand, the anti-cancer effect of these compounds against BRCA3 and IGF-1R was not examined.10 The pathogenesis of cancer is significantly influenced by BRCA3 and IGF-IR signaling.11 Even though these medicines have various biological activities, little is known about their pharmacokinetic and toxicokinetic properties. The pharmacodynamic effectiveness and therapeutic applicability of any substance are strongly influenced by its pharmacokinetic and toxicokinetic characteristics. The SDF structural format (SDF) was used to download the three-dimensional (3D) representation of these chemicals after they were located in the PubChem database. Utilizing the Open Babel program, the energies of the ligands were reduced before being transferred to the PDB file.12 As benchmarks, isolated substances from various whole-plant powder extracts and synthesized pharmaceuticals were chosen to dock against the molecules of proteins one at a time. ChemSketch was used to create the three-dimensional structures of each ligand molecule, which were then saved in mol format. Then PyRx software was used to convert it to PDBformat.

 

DOCKING PROTOCOL:

The PDB format of the downloaded structures of proteins was used, and then MGL Tools was utilized to transform them into the necessary PDBQT format.13 Polar hydrogen atoms were used to prepare the proteins and MGL Tools were used to handle the Gasteiger charges before docking. The docking study was carried out using the AutoDockVina program. Using co-crystallized ligand coordinates in the target protein, the grid box's size and dimensions were chosen. Discovery Studio Visualizer_16 was used to visualize the protein-ligand interaction for the two- and three-dimensional presentations.14

 

A.   Pharmacokinetics Parameters Assessment:

SwissADME and pkCSM software was used to evaluate the selected drugs' pharmacokinetic behaviors. Intestinal absorption, the blood-brain barrier (BBB), Caco-2 cells, metabolism, and P-glycoprotein substrate were some of the different factors taken into account.15

 

Structural representation of compounds isolated from Salaciaoblonga Wall plant.

 

B.    Drug-Likeness Analysis Thedrug:

SwissADME and pkCSM, two bioinformatics programmes, were used to analyse the examined compounds' similarity behaviour. Using the Lipinski rule, the drug-like behaviour gives a qualitative understanding of the pharmacokinetic aspects of the compounds following oral administration, covering their absorption, physicochemical, and bioavailability properties.16 The toxicokinetic profile for the substances under study was also computationally examined in comparison to the liver, heart, and Ames toxicity tests.

 

C.   ADMET Properties ofligands:

Checking the ADMET studies of every component is crucial for the in-silico screening of ligands. The acronym ADMET, which stands for absorption, distribution, metabolism, elimination, and toxicity, aids in the filtration of tiny compounds for investigations on molecular docking based on fundamental principles. It is employed to determine whether the Lipinski and ADMET features of ligand molecules can serve as drug molecules during the creation of pharmaceuticals. Swiss ADME, a free program, was used to analyse the properties of bioactive components in extracts of Salaciaoblonga Wall, including absorption, metabolism, excretion,  and distribution.17

 

Lipinski's "rule of five" is based on five factors: molecular weight, ten hydrogen bond acceptors, five hydrogen bond donors, and five logs of P. It is evident from the current study that a few particular biomolecules from the Salaciaoblonga plant fail in clinical trials because of subpar ADME capabilities. Because of this, Swiss ADME was used to screen the best-docked compounds. Additionally anticipated were characteristics like log BB, general CNS activity, caco-2, skin permeability, GI adsorption, etc.18,19,20

 

D.      Molecular dockingstudies:

Using the program AutoDock 4.2.6, the main bioactive components from Salaciaoblonga Wall and standard compounds have been docked against the 3t6a and 5hzn receptors. The reliable automated program AutoDock

4.2.6 can analyze interactions between proteins and their ligands. This software's docking investigations are based on the rapid-grid estimate and torsion freedom of protein-ligand molecule docking.21

 

RESULTS AND DISCUSSION:

The bioactive compounds isolated after the TLC, Column chromatography, FTIR, and analysis of the whole plant of Salaciaoblonga Wall were:1 2,4-Dimethylamphetamine,2 19-hydroxyferruginol,3 Cyclotrisiloxane,4 Dulcitol,5 Epicatechin,6 Galactinol,7 Hexadecanoic  acid,8 Kotalagenin-16-acetate,9 Kotalanol,10 Lamberticacid,11 Mangiferin,12 Neokotalanol,13 Neosalacinol,14  Quercetin,15 Raffinose,16 Salacinol,17 Salasol A,18 Salasol B,19 Salasone A,20 Salasone B,21 Salasone C,22 Salasone D,23 Salasone E,24 Stachyose,25 Trichloroacetic acid were isolated from the different extracts of Salaciaoblonga Wall powder and one standard synthetic compound, 5- fluorouracil. From biological studies of extracts, it was found that the 5-fluorouracil shows potent anticancer activity. Glycosides and flavonoids were discovered to be powerful anticancer substances. However, there have been no reports on the entire Salaciaoblonga Wall plant's capacity to exhibit anticancer action.

 

A clear image of ligand interactions with a protein's active site is provided by molecular docking. This then makes it easier to find phytomedicines while developing novel drugs. The bioactive compounds that underwent molecular docking met all five criteria set out by Lipinski, namely, 1. molecular weight (500), 2. log P (+5), and 3. The number of donors for hydrogen bonding (5). 4. There are ten or more hydrogen bond acceptors. Molar refractivity (40–130), fifth After evaluating the drug's durability, the key criterion for a ligand molecule to be evaluated for an oral drug is the rule of five, which corresponds to the druglikeliness of a compound.22,23

 

Detailed information on Lipinski’s properties of bioactive components is illustrated in Table 3. All docked  drugs show significant gastrointestinal absorption, which is evident from their ADMET profiles. Table 1 and Table 2 provide the compounds' ADMET characteristics. Based on a drug-design strategy based on structure, molecular docking data led to the development of novel medications against drug targets. By using molecular docking, the optimal binding conformation of inhibitors to enzymes was found to have the lowest energy shape. Tables 4 and Table 5 provide all the results from the molecular docking of the two proteins with theirligands.

 

Based on a drug-design strategy based on structure, molecular docking data led to the discovery of novel medications against drug targets. By using molecular docking, the optimal binding conformation of inhibitors to enzymes has been identified with the lowest energy shape. Numerous pieces of information are provided by protein-ligand complexation, such as hydrogen bonds, lipophilic interactions, and interactions in the protein- ligand interaction profile. Table 4 and Table 5 shows all the results from the molecular docking of both proteins with their ligands.


 

Table 1: The analysis of the physico-chemical properties of the selected compounds using computational analysis.

Sr.

No

Compound

Molecular weight

Log P

H-bond donor

H-bond acceptor

Molar refractivity

1

4-methyl epigallocatechin (C16H16O7)

320.29 g/mol

-8.02

5

7

320.29

2

Galactinol (C6H14O6)

182.17 g/mol

-9.61

6

6

182.17

3

Salacinol (C9H18O9S2)

334.36 g/mol

-10.43

5

9

334.36

4

Neosalacinol (C12H25O9S+)

345.39 g/mol

-11.54

9

9

345.39

5

Neokotalanol (C12H25O9S+)

345.39 g/mol

-11.54

9

9

345.39

6

Kotalanol (C12H24O12S2)

424.44 g/mol

-12.29

8

12

424.44

7

Kotalagenin-16 acetate (C32H50O5)

514.74 g/mol

-4.48

1

5

514.74

8

Salasone A (C30H48O3)

456.70 g/mol

-7.31

1

3

456.70

9

Salasone D (C30H48O3)

458.72 g/mol

-3.57

2

3

458.72

10

Epicatechin (C15H14O6)

290.27 g/mol

-7.82

5

6

290.27

11

Salasone C (C30H50O3)

458.72 g/mol

4.08

2

3

458.72


Physical and Chemical Analysis of a Few Compounds Using a Swiss AMDE, the physicochemical analysis of the chosen substances was evaluated. Molecular weight, LogP, the number of H-bond donors, the number of rotatable bonds, the number of H-bond acceptors, and surface area were among the different characteristics that were assessed.24 The compounds' physicochemical characteristics varied, as seen in Table 1. Galactinol (182.17 g/mol) had the lowest molecular weight, and kotalagenin-16-acetate (514.74 g/mol) had the highest. Kotalanol displayed the highest LogP value, while Salasone D displayed the lowest LogP value. Similar to this, Kotalagenin-16-acetate and Salasone A displayed the lowest H-bond donors, whereas Neosalacinol and Neokotalanol displayed the strongest H-bond donors. While Salasone A, Salasone C, and Salasone D displayed lower H-bond acceptor energies, Kotalanol displayed the greatest H-bond acceptor value. According to Table 1, salasone D had the highest molar refractivity, while galactinol had the lowest surface area.


 

Table 2: ADME properties of various bioactive compounds

Sl. No

Compound

Water

solubility

BBB Perme ant

(logBB)

GI absorption

Human intestinal absorption HIA (%)

Skin permeability

Toxicity Predicted

LD 50

Molar rafractivity

CACO 2

Permeability

1

4-methyl epigallocatechin

C16H16O7

No

High

60.725

-2.735

10000mg/kg

80.83

-0.199

2

Galactinol

C6H14O6

No

Low

0

-2.735

23000mg/kg (Class 6)

50.36

-0.12

3

Salacinol

C9H18O9S2

No

Low

0

-2.723

5000mg/kg

(Class 5)

67.26

-0.52

4

Neosalacinol

C12H25O9S+

No

Low

36.951

-3.303

5000mg/kg

(Class 5)

57.12

-0.352

5

Neokotalanol

C12H25O9S+

No

Low

0

-2.761

5000mg/kg

(Class 5)

76.56

-0.319

6

Kotalanol

C12H24O12S 2

No

Low

0

-2.735

2750mg/kg

(Class 5)

85.17

-0.788

7

Kotalagenin- 16 acetate

C32H50O5

No

High

98.946

-2.686

5000mg/kg

(Class 5)

146.66

0.698

8

Salasone A

C30H48O3

No

High

97.259

-2.526

4820mg/kg

(Class 5)

135.76

1.344

9

Salasone C

C30H48O3

No

High

96.294

-2.763

5100mg/kg

(Class 5)

136.72

1.208

10

Salasone D

C30H48O3

No

High

96.294

-2.763

4820mg/kg

(Class 5)

136.72

1.208

11

Epicatechin

C15H14O6

No

High

68.829

-2.735

10000

mg/kg

110.38

-0.283

 

Table 3: Lipinski score of componds isolated from Salaciaoblonga plant

Sr. No

Compounds

Lipinski

Ghose

Veber

Egan

Muegge

1

4-methyl epigallocatechin

Yes

Yes

Yes

Yes

Yes

2

Galactinol

Yes

No

Yes

Yes

No

3

Salacinol

Yes

No

No

No

No

4

Neosalacinol

Yes

No

No

No

No

5

Neokotalanol

Yes

No

No

No

No

6

Kotalanol

No

No

No

No

No

7

Kotalagenin-16 acetate

No

No

Yes

No

No

8

Salasone A

Yes

No

Yes

No

No

9

Salasone D

Yes

No

Yes

No

No

10

Epicatechin

Yes

Yes

Yes

Yes

Yes

11

Salasone C

Yes

No

Yes

No

No

 


The computational approach can be used to find drug-like qualities like the Ghose, Veber, Lipinski, Egan, and Muegge rules. Except for kotalanol and kotalagenin-16-acetate, the majority of the research substances adhered to the Lipinski rule. However, as shown in Table 3, the compounds Neosalacinol, Salasone A, Salasone C, Salacinol, Neokotalnol, and Salasone D displayed varying features in terms of the Ghose, Veber, Egan, and Muegge rules. Table 4 shows the computationally analysed drug-like behaviour of the chosen compounds.

Using AutoDockVina software, the molecular docking investigation for the chosen compounds was carried out against the 3t6a and 5HZN receptors. The compounds' affinities for the target receptors (1Q1A and 5HZN) varied.

 

1.     Molecular docking study of 5HZN with phytochemicals obtained from Salaciaoblonga plant for Anticancer activity:

Kotalagenin-16-acetate, Salasone A, Salasone D, 4-Methylepigallocatechin, Galactinol, Epicatechin, and Neokotalanol had the lowest binding scores for the 5-hzn receptors of these substances. There were numerous polar and nonpolar linkages used by these chemicals to interact. The 5HZN receptor and the epicatechin displayed two H-bonds (ALA1117 and MET1109). Similar to Kotalagenin-16-acetate, Salasone A, D, and 4 Methylepicatechin all bind to the 5HZN protein through two H-bonds (ARG1155, ARG1039), one H-bond (ARG1131), two H-bonds (ASN1097, LEU1101), and three H-bonds (ASN1137, GLN1004, GLY1003),

 

respectively. Additionally, as demonstrated in Figure 4, galactinol and neokotalanol both bind to the 5HZN receptor through four H-bonds (ASN1215, ARG1131, ARG1174, GLY1166) and four H-bonds (ASN1215, ARG1174, ARG1164, TYR1162), respectively. The molecular docking energies are shown in Table 4 as units of kcal/mol.

 

Table 4. The molecular docking score of the selected compounds against the 5HZN proteins.

Sr. No

Compound

Binding energy

H-bond

interactions No. of

hydrogen bonds

Total polar and non-polar bonding

1

Epicatechin

-8.4

ALA1117, MET1109

ALA1117, MET1109, LEU1199, PHE1195 GLY1114, MET1112

ALA1117, MET1109, LEU1199, PHE1195 GLY1114, MET1112

2

Kotalagenin-16 Acetate

-9.2

ARG1155, ARG1039

ARG1155, ARG1039

ARG1155, ARG1039

3

Salasone A

-9.2

-

ARG1131-

ARG1131

4

Salacinol

-6.5

ASN1033, GLN1004, TYR1158, GLU1043, ARG1155

ASN1033, GLN1004, TYR1158, GLU1043, ARG1155, ARG1039, GLU1040

ASN1033, GLN1004, TYR1158, GLU1043, ARG1155 ARG1039, GLU1040

5

Salasone D

-9

ASN1097, LEU1101

ASN1097, LEU1101

ASN1097, LEU1101

6

4 Methylepigallocatechin

-8.2

GLN1004, GLY1003, ASN1137

GLN1004, GLY1003, ASN1137

GLN1004, GLY1003, ASN1137 VAL1010, MET1079, MET1076, VAL1060, ALA1028, MET1139, ARG1136, GLU1077

7

Galactinol

-7.3

ASN1215, ARG1131,

ARG1174, GLY1166

ASN1215, ARG1131, ARG1174, GLY1166

ASN1215, ARG1131, ARG1174, GLY1166, SER1214, GLY1167

8

Kotalanol

-6.8

PHE1186, THR1160, ARG1174, ASN1215

PHE1186, THR1160, ARG1174, ASN1215 ARG1131, GLU1159, GLY1184

PHE1186, THR1160, ARG1174, ASN1215, ARG1131, GLU1159, GLY1184

9

Neokotalanol

-7.2

ASN1215,

ARG1174, ARG1164,

ASN1215, ARG1174, ARG1164, TYR1162

ASN1215, ARG1174, ARG1164, TYR1162, GLY1184, SER1214, TYR1163

 

 

 

TYR1162

 

 

10

Neosalacinol

-5.8

GLY1184, TYR1162, ARG1164

GLY1184, TYR1162, ARG1164 TYR1163

GLY1184, TYR1162, ARG1164 TYR1163, GLU1159

 

 

 


Through molecular docking, the chemical composition of Salaciaoblonga Wall whole plant extracts was analysedin silico. This revealed the importance of drug design for the creation of new pharmaceuticals that block targets. The majority of the molecules from the Salaciaoblonga plant had improved docking outcomes against chosen receptors (5HZN). Table 4 lists the binding energy, hydrogen bond as the number of hydrogen bonds between the enzyme and ligand grows. As binding energy of the receptor decreases the binding efficiency of the receptor will increases. Similarly, the strength of the binding rises.25

 

Fig 1. Two dimensional and three-dimensional interactions of Epicatechinagainst the enzyme 5HZN

 

Epicatechin exhibits the -8.4 binding energy against 5HZN receptors. Epicatechinis docked against 5HZN with binding energy -8.4 and has two hydrogen bond interaction with receptor which is ALA1117, MET1109. The 2D and 3D interactions of Epicatechinagainst the active site of receptor aredepicted.

 

Fig 2. Two dimensional and three-dimensional interactions of Kotalagenin-16-acetate against the enzyme 5HZN

 

Kotalagenin-16-acetate exhibits the -9.2 binding energy against 5HZN receptor. Kotalagenin-16-acetate is docked against 5HZN with binding energy -9.2 and has two hydrogen bond interaction with receptor which is ARG1155, ARG1039. The 2D and 3D interactions of Kotalagenin-16-acetate against the active site of receptors aredepicted.

 

Fig 3. Two dimensional and three-dimensional interactions of Salasone A against the enzyme 5HZN

 

Salasone A exhibits the -9.2 binding energy against 5HZN receptor. Salasone A is docked against 5HZN with binding energy -9.2 and has one hydrogen bond interaction with receptor which is ARG1131. The 2D and 3D interactions of Salasone A against the active site of receptor aredepicted.

 

Salasone D exhibits the -9 binding energy against 5HZN receptor. Salasone D is docked against 5HZN with binding energy -9 and has two hydrogen bond interaction with receptor which is ASN1097, LEU1101. The 2D and 3D interactions of Salasone D against the active site of receptor aredepicted.

 

 

Fig 4. Two dimensional and three-dimensional interactions of Salasone D against the enzyme 5HZN

 


Fig 5. Two dimensional and three-dimensional interactions of 4-Methylepigallocatechin against the enzyme 5HZN

 

 


4-Methylepigallocatechin exhibits the -8.2 binding energy against 5HZN receptor. 4- Methylepigallocatechin is docked against 5HZN with binding energy -8.2 and has three hydrogen bond interaction with receptor which is GLN1004, GLY1003, ASN1137. The 2D and 3D interactions of 4- Methylepigallocatechin against the active site of receptor aredepicted.

 

Fig 6. Two dimensional and three-dimensional interactions of Galactinol against the enzyme 5HZN

 

Galactinol exhibits the -7.3 binding energy against 5HZN receptor. Galactinol is docked against 5HZN with binding energy -7.3 and has four hydrogen bond interaction with receptor which is ASN1215, ARG1131, ARG1174, GLY1166. The 2D and 3D interactions of Galactinol against the active site of receptor are depicted.

 

The present study indicates that the isolated compounds such as Epicatechin, Kotalagenin-16 Acetate, Salasone A, Salasone D, 4 Methylepigallocatechin, and Galactinol could be docked better with 5HZN. The interaction profile of bioactive molecules reveals those two types of hydrogen bonds are involved in protein-ligand interactions, namely hydrogen bond with backbone and hydrogen bond with side chain. Structures of 5HZN complexed with ligands via hydrogen bonding play a key role in stabilizing the energetically favored interaction of proteins with ligands. Among the components isolated from plant Epicatechin, Kotalagenin-16 Acetate, Salasone A, Salasone D, 4 Methylepigallocatechin, and Galactinol could exhibit better docking results.26

 

2.              Molecular docking study of 1Q1A with phytochemicals obtained from Salaciaoblonga plant for Anticancer activity:

Epicatechin, Kotalagenin-16-acetate, Salasone C, Kotalanol, 4-Methylepigallocatechin, Galactinol, Neokotalanol displayed the lowest binding scores for the for 1Q1A receptor Epicatechin, Kotalagenin-16- acetate, Salasone C, Kotalanol, 4-Methylepigallocatechin, Galactinol, Neokotalanol have shown the lowest binding score. These compounds interacted via multiple polar and nonpolar bonds. The Epicatechin exhibited three H-bonds (CYS143, ALA180, HIS140) with the 5HZN receptor. Similarly, Kotalagenin-16-acetate binds with the 5HZN receptor via one H-bonds (ASP187), Kotalanol binds via three H-bonds (ALA180, SER137, GLU133), 4 Methylepicatechin binds via two H-bonds (ILE181, HIS142). Moreover, Galactinol binds via five H-bonds (GLU186, ALA180, ASP187, CYS143, PRO189) and Neokotalanol via four H-bonds (SER191, LYS178, HIS142, ASP187) with 5HZNreceptor.


 

Table 5. The molecular docking score of the selected compounds against the 1Q1A proteins.

Sr. No

Compound

Binding energy

No. of hydrogen bonds

H-bond interactions

Total polar and non-polar bonding

1

4 Methylepigallocatechin

-7.2

2

ILE181, HIS142

ILE181, HIS142 ALA180, PRO189, VAL182, HIS140

2

Epicatechin

-9.1

3

CYS143, ALA180, HIS140

CYS143, ALA180, HIS140 VAL182, GLU186, ASP187

3

Galactinol

-7.6

4

GLU186, ALA180, ASP187, CYS143, PRO189

GLU186, ALA180, ASP187, CYS143, PRO189

4

Kotalanol

-7.6

3

ALA180, SER137, GLU133

ALA180, SER137, GLU133, ASP128, HIS140, GLY136

5

Salasone C

-7.8

-

-

HIS142, GLY147

6

Salasone A

-7

-

-

ARG45

7

Salasone D

-7

-

-

GLY147

8

Kotalagenin-16 Acetate

-7.3

1

ASP187

ASP187, HIS142

9

Neosalacinol

-6.7

1

GLU186

GLU186, GLY136

10

Neokotalanol

-7.1

4

SER191, LYS178, HIS142, ASP187

SER191, LYS178, HIS142, ASP187

 

 

Fig 7. Two dimensional and three-dimensional interactions of Epicatechin against the enzyme 1Q1A

 

 


The in-silico analysis of chemical components of whole plant extracts of Salaciaoblonga Wall was done through molecular docking disclosed the significance of drug designing for the invention of novel drugs against the inhibition of targets. Most of the biomolecules from the Salaciaoblonga plant were shows better docking results against the active sites of both the selected receptor 1Q1A. Binding energy, hydrogen bond interactions, and Van der Waal’s interactions for the enzyme are listed in Table 5. As the binding energy lowers, binding efficiency will increase. Similarly, as the number of hydrogen bonds increases between the enzyme and ligand, then the strength of the binding is also increasing with hydrogenbonds.27

 

Epicatechin exhibits the -9.1binding energy against 1Q1A receptor. Epicatechin is docked against 1Q1A with binding energy -9.1 and has three hydrogen bond interactions with receptor which is CYS143, ALA180, and HIS140. The 2D and 3D interactions of Epicatechin against the active site of receptor are depicted.

 

 

Kotalagenin-16 Acetate exhibits the -7.3 binding energy against 1Q1A receptor. Kotalagenin-16 Acetate is docked against 1Q1A with binding energy -7.3 and has one hydrogen bond interaction with receptor which is ASP187. The 2D and 3D interactions of Kotalagenin-16 Acetate against the active site of receptor are depicted.

 

 

Fig 8. Two dimensional and three-dimensional interactions of Kotalagenin-16 Acetate against the enzyme 1Q1A

Galactinol exhibits the -7.6 binding energy against 1Q1A receptor. Galactinol is docked against 1Q1A with binding energy -7.6 and has four hydrogen bond interaction with receptor which is GLU186, ALA180, ASP187, CYS143. The 2D and 3D interactions of Galactinol against the active site of receptor aredepicted.

 

Fig 9. Two dimensional and three-dimensional interactions of Galactinol against the enzyme 1Q1A

 

Kotalanolexhibits the -7.6 binding energy against 1Q1A receptor. Kotalanolis docked against 1Q1A with binding energy -7.6 and has three hydrogen bond interaction with receptor which is ALA180, SER137, GLU133. The 2D and 3D interactions of Kotalanolagainst the active site of receptor aredepicted.

 

Fig10. Two dimensional and three-dimensional interactions of Kotalanol against the enzyme 1Q1A

4-Methylepigallocatechin exhibits the -7.2 binding energy against 1Q1A receptor. 4- Methylepigallocatechin is docked against 1Q1A with binding energy -7.6 and has two hydrogen bond interaction with receptor which is ILE181, HIS142. The 2D and 3D interactions of 4- Methylepigallocatechin against the active site of receptor are depicted.

 

 

Fig 11. Two dimensional and three-dimensional interactions of 4-Methylepigallocatechin against the enzyme 1Q1A

 

According to the current study, isolated substances such as kotalagenin-16 acetate, epicatechin, 4 methylpigallocatechin, kotalanol, and galactinol may dock with 1Q1A more effectively. The binding profile of bioactive compounds demonstrates that both types of hydrogen bonds—one with the backbone and one with the side chain are involved in interactions between proteins and their ligands. The energetically preferred binding of proteins with ligands is stabilized in part by the structures of 1Q1A complexed with ligands via hydrogen bonding. Kotalagenin-16 acetate, 4 methylepigallocatechin, epicatechin, kotalanol, and galactinol, among the key components, showed improved docking outcomes.28

 

DISCUSSION:

The process of developing plant-based drugs can benefit from the use of auxiliary techniques like molecular docking studies. A key area of focus for maximizing the therapeutic value of medicinal herbs like Salaciaoblonga Wall is elucidating and forecasting their pharmacological potential. The application of cutting-edge computational techniques such as molecular docking to analyze diverse biological activities must take into account the structural and in-vitro investigations of such phytochemical ingredients if studies on plant-based goods for pharmacology are to be successful. Here, using AutoDock 4.2.6, we examined such viewpoints. Major bioactive elements of the Salaciaoblonga Wall plant were shown to have a significant degree of inhibitory potential. According to molecular docking experiments, the inhibitory activity that was found could account for the binding of bioactive plant components.

 

The visualization programs PyRx and Discovery Studio determined the optimum fit following the release of AutoDock 4.2.6. When a medicine molecule interacts with a target, the binding energy is released, which lowers the total energy of the complex. Any conversion of the ligand from its minimal energy to its binding confirmation with the protein is also reimbursed by the release of binding energy. Therefore, the proclivity of the ligand associated with that protein will increase in proportion to the amount of energy released during the binding of the ligand to the protein. However, a negative value for the binding energy indicates that the ligand was bound naturally without requiringenergy.

 

The lowest binding energy is shown by Salasone A (-9.2 Kcal/mol) for the 5HZN receptor, Kotalagenin-16- acetate (-9.2 Kcal/mol), and Epicatechin (-Kcal/mol) for the 1Q1A receptor. The docking studies emphasise that the 1Q1A receptor has a strong affinity for isolated compounds with the best docking scores, like epicatechin, kotalagenin-16 acetate, 4 methyl epigallocatechin, galactinol, and kotalanol. The 5HZN receptor also exhibits a strong affinity for isolated compounds with the best docking scores, like epicatechin, kotalagenin-16 acetate, salasoneA, salasone D, and galactinol.

 

Salaciaoblonga Wall was used to isolate the components of 4 methyl epigallocatechin, Salasone C, Salasone D, Kotalagenin-16-acetate, Neosalacinol, Epicatechin, Galactinol, Kotalanol, Salacinol, Salasone A, and Neokotalanol. Scientific research on Salaciaoblonga Wall supports its anticancer properties. Both facts are supported by in silico analyses of both drugs, which both have strong binding energies to the 5HZN and 1Q1A receptors. Using ADMET characteristics, the pharmacokinetic evaluation of Salaciaoblonga Wall extract constituents was investigated.

 

The primary bioactive compounds' Lipinski and ADMET characteristics were summarised in Tables 2 and 3, respectively. The compounds' water solubility, the permeability of the blood-brain barrier (BBB), the permeability of the skin, GI absorption, and HIA% are all shown in Table 3. In conclusion, the findings suggest that Salaciaoblonga Wall might compete well for the therapy of anticancer activity. The findings, however, call for more research into Salaciaoblonga Wall's potential utility in ethnomedicine.

 

CONCLUSION:

The potential of Salaciaoblonga's chemical constituents was examined using molecular docking modeling methods. According to the analysis, the flavonoid separated from the powder, as well as vital components such as Epicatechin, Kotalagenin-16 Acetate, 4 Methylepigallocatechin, Kotalanol, Salasone C, Salasone D, and Galactinol. Vital components such as Epicatechin, Kotalagenin-16 Acetate, 4 Methylepigallocatechin, Kotalanol, Salasone C, Salasone D, and Galactinol, have the best docking positions among the key components. Additionally, they had the best docking free energy score against bothreceptors.

 

However, it was discovered that some of these compounds had substandard ADME characteristics. Therefore, to improve the compounds' relevant scores for various ADME features, it is necessary to structurally modify them before developing novel therapies. Even though the fact that most compound constituents have a strong chance of acting as multiple-targeted agents for the creation of anti-cancer drugs, The current study, which integrates pharmacology with bioinformatics approaches and highlights the significance of chemical elements in Salaciaoblonga Wall, paves the path for the development of novel medications against cancer-associated molecular targets.

 

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

 

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Received on 03.08.2023         Modified on 21.02.2024

Accepted on 24.06.2024   ©Asian Pharma Press All Right Reserved

Asian J. Pharm. Tech. 2024; 14(3):187-198.

DOI: 10.52711/2231-5713.2024.00032