Background: Acne vulgaris is a very common bacterial infection caused. Herbal remedies consistently give a positive response in treatment of various disease in disorders. The present study works on predicting the probable interactions between the Myristica fragrans bio-actives with the bacterial targets via network pharmacology and docking techniques. Methods: The bio-actives and disease targets were obtained from open databases and literature review. Protein and pathway enrichment analysis was done by utilizing Metascape (v.3.5.20230501, https://metascape.org/) and STRING (version 11.5, https://string-db.org/) tool. Cytoscape 3.9.1 tool was utilized for construction of network. The compounds were further assessed using Molsoft tools (v.3.7-2) and docking was done using Autodock tools (ADT 1.5.7). Results: The potential bio-actives were selected further the network construction gave insights on the highest degree values. The targets with highest interactions were docked and the complex which showed good binding was selected. Conclusion: It was found that stigmasterol and campesterol have the highest potential and can be considered as hit compounds for further studies too.

Key words: acne, network pharmacology, molecular docking


Herbal drugs are widely utilized as medicines all over the globe. Even though termed as alternative system of medicine around 80% of the population relays on herbal or traditional medicines. Traditional medicines have remedies available but lack documentation, which can be overcome by applying the modern tool such as network pharmacology.[13]

Myristica fragrans commonly known as nutmeg or Jaiphal and it belongs to family Myristicaceae. The nutmeg food is been used widely as spices in food. The nutmeg is widely grown in Asia, America and South African countries. The literature shows that nutmeg has potential activity against cancer, acts as anti-depressant, anti-diabetic, anti-obesity, anti-inflammatory, anti-oxidant and anti-microbial. Acne vulgaris is a bacterial infection caused by Propionibacterium acnes. The treatment of acne involves oral antibiotics and topical formulations. As we know that antibiotic resistance is the major issue when it comes to treatment line of antibiotics.[46] Table 1 indicating the bio-active compounds from the Myristica fragrans.

Table 1: Bio-active compounds from the Myristica fragrans
Compound name Compound type Molecular formula Molecular weight NHBA NHBD MollogP DLS
OLEIC-ACID Fatty acid C18H34O2 282.26 2 1 7.11 -0.30
STEARIC-ACID Fatty acid C18H36O2 284.27 2 1 7.65 -0.54
PENTADECANOIC-ACID Fatty acid C15H30O2 242.22 2 1 6.13 -0.54
HEXADECANOIC-ACID Fatty acid C16H32O2 256.24 2 1 6.64 -0.54
HEPTADECANOIC-ACID Fatty acid C17H34O2 270.26 2 1 7.14 -0.54
PALMITIC-ACID Fatty acid C16H32O2 256.24 2 1 6.64 -0.54
CEROTINIC-ACID Fatty acid C26H52O2 396.40 2 1 11.70 -0.54
MYRISTIC-ACID Fatty acid C14H28O2 228.21 2 1 5.63 -0.54
TRIDECANOIC-ACID Fatty acid C13H26O2 214.19 2 1 5.12 -0.54
LAURIC-ACID Fatty acid C12H24O2 200.18 2 1 4.62 -0.54
BETA-CARYOPHYLLENE Terpenes C15H24 204.19 0 0 5.35 -1.74
STIGMASTEROL Sterols C29H48O 412.37 1 1 7.39 0.62
BETA-SITOSTEROL Sterols C29H50O 414.39 1 1 8.45 0.78
CAMPESTEROL Sterols C28H48O 400.37 1 1 7.52 0.59
1,8-CINEOLE Terpenes C10H18O 154.14 1 0 2.61 -1.04
NHBD, total number of hydrogen bond donor; NHBA, the total number of hydrogen bond acceptor; MolLogP, molecular lipid/water partition coefficient (Log P); DLS, drug likeness score.

So to overcome this side effect utilization of the herbal medicines and their further formulation can help us overcome the difficulty. The promising tool which helps us in accelerating the drug development and understanding the mechanism of action of multiple target components. The network pharmacology assists us to predict the probable impact of the drugs on the disease conditions. So the current study is designed to understand the interactions of the different bio-actives of Myristica fragrans along with its targets utilizing the network pharmacology and molecular docking approach.[7,8]

Studies have been conducted to investigate the attenuation of hepatic inflammation and lipid metabolism disorders. Additionally, network pharmacology and docking studies have been performed, in which Myristica fragrans is considered an essential plant material.[911] But there was no literature found related to the bacterial network pharmacology of Myristica fragrans. So the objective of this designed study was to study the bacterial network pharmacology along with the molecular predictions of the same on acne bacteria.


Mining of phyto-compounds

The mining of the phyto-compounds is conducted by utilizing databases, namely Dr. Duke's Database (https://phytochem.nal.usda.gov/phytochem/search) and IMPPAT database (https://cb.imsc.res.in/imppat/), as well as literature.[12]

Target identification

The target of the phyto-compounds is identified utilizing Swiss target database (http://www.swisstargetprediction.ch/) and in case of the disease targets gene card database (https://www.genecards.org/) and therapeutic target database (http://db.idrblab.net/ttd/). The potential common targets are selected by utilizing the Venny 2.1 tool (https://bioinfogp.cnb.csic.es/tools/venny/).[13,14]

Network construction

The network of drug- disease- target is constructed by using the Cytoscape 3.9.1 tool (https://cytoscape.org/). The different shapes and colors are used to represent the drug, disease and target.[15,16]

Drug likeness and ADMET profile

Molsoft (v.3.7-2, htpps://molsoft.com/) database was utilised in prediction of the drug likeness of the compounds which works on the Lipinski's rule of five and the ADMET profile prediction of the compounds was done by ADMETLab 2.0.[4]

Gene ontology and path enrichment analysis

The Metascape tool (v.3.5.20230501, https://metascape.org/gp/index.html#/main/step1) is being used to study and carryout the gene ontology and path enrichment analysis.[17]

Protein-protein interaction

The protein-protein interaction analysis was carried out by the utilization of STRING database (https://string-db.org/).[18,19]

Molecular docking

In accordance with the network constructed in the previous steps the top 5 targets which have the highest degree value are considered for molecular docking. The ligands (drug) structures are retrieved from the Pubchem database (https://pubchem.ncbi.nlm.nih.gov/) and the targets (proteins) are taken from RCSB PDB (https://www.rcsb.org/). The software namely Discovery Studio Visualizer 2021 was employed for the ligand and protein structure extractions as well as helps in visualization, whereas the Autodock tools (ADT 1.5.7) was employed for docking. In molecular docking grid and docking files were created with Lamarckian genetic algorithm as output.[14,2023]


Phyto-compounds targets and potential target identification

Thirty-three phyto-compounds were identified for Myristica fragrans from various database and literature. Fourteen phyto-compounds showed to interact with the disease targets of acne bacteria. The Venny 2.1 tool helped in selecting the common potential targets from both plant and the bacteria. Nine potential targets were identified by this tool which was further studied. The Figure 1 shows the venn plot of the common targets and Table 2 comprising the binding affinity of the compounds.

Figure 1

Figure 1. Venn plot for common targets.

Table 2: Binding affinity of the compounds
Compound Protein
Binding affinity (kcal/mol) Number of Hydrogen bonds Number of hydrogen bond residue
Stigmasterol NR1H3 -11.75 1 ALA: 398, ALA: 342, PHE: 384, LEU: 507, ILE: 339, PHE: 417, VAL: 420, ILE: 416, CYS: 503, ALA: 343, ILE: 381, LEU: 380, LUE: 397
Campesterol NR1H3 -10.53 1 ALA: 398, ALA: 342, LEU: 397, ILE: 339, ALA: 343, LUE: 380, PHE: 384, LUE: 507, CYS: 503.
Oleic acid PPARA -7.99 4 ARG: 115, ARG: 435, TRP: 141, ALA: 438, MET: 303, ALA: 306, ILE: 133, PHE: 148, CYS: 437, ALA: 307, LUE: 152, ALA: 443
Heptadecanoic acid PPARA -7.66 3 ILE: 133, ARG: 435, ALA: 306, MET: 303, ILE: 132, ALA: 438, LEU: 152, ALA: 307, PHE: 148, ARG: 145, PHE: 203
Beta-sitosterol NR1H3 -7.63 1 VAL: 311, LEU: 438,
ARG: 387,
LYS: 435,
LEU: 380,
HIS: 386
ALA, alanine; PHE, phenylalanine; LEU, leucine; ILE, Isoleucine; VAL, valine; CYS, cysteine; ARG, arginine; TRP, tryptophan; MET, methionine; LYS, lysine; HIS, histidine.

Network construction

The network was constructed for drug-disease-target. The network showed highest degree value to PPARA (peroxisome proliferator activated receptor alpha) target protein followed by NR1H3 (nuclear receptor subfamily 1 group H member 3), AR (androgen receptor), CYP19A1 (cytochrome P450 family 19 sub-family A polypeptide 1), CYP17A1(cytochrome P450 family 17 sub-family A polypeptide 1), ESR1 (estrogen receptor 1), CNR2 (cannabinoid receptor 2), PPARG (peroxisome proliferator activated receptor gamma) and CA3 (carbonic anahydrase 3). The network is shown in Figure 2, where different colors and shapes are utilized for better understanding.

Figure 2

Figure 2. Network of drug-targets-disease.

Gene ontology, pathway enrichment analysis and protein interactions

The analysis was conducted using the Metascape tool (v.3.5.20230501), which revealed that the heat map in Figure 3 displays the GO:0009617 response to bacterium, and Table 3 illustrates the pathways involved. The protein-protein interactions obtained from the STRING database are analyzed using the Cytoscape 3.9.1 tool, where different color pallets and shape size are varied according to the interactions. Higher the interaction darker the color intensity and larger the size. The protein-protein interactions are shown in Figure 4.

Figure 3

Figure 3. Heat map of path enrichment analysis.

Figure 4

Figure 4. Network of protein-protein interactions. PPARG, peroxisome proliferator activated receptor gamma; PPARA, peroxisome proliferator activated receptor alpha; NR1H3, nuclear receptor subfamily 1 group H member 3; CYP19A1, cytochrome P450 family 19 sub-family A polypeptide 1; CYP17A1, cytochrome P450 family 17 sub-family A polypeptide 1; ESR1, estrogen receptor 1; CNR2, cannabinoid receptor 2; AR, androgen receptor.

Table 3: Enrichment analysis of proteins involved in acne infection
Pathway Description Gene count Matching proteins False discovery ratio
hsa03320 PPAR signaling pathway 3 PPARG, PPARA, NR1H3 0.0017
hsa00140 Steroid hormone biosynthesis 2 CYP17A1, CYP19A1 0.0414
hsa04913 Ovarian steroidogenesis 2 CYP17A1, CYP19A1 0.0414
hsa04917 Prolactin signaling pathway 2 CYP17A1, ESR1 0.0414
PPARG, peroxisome proliferator activated receptor gamma; PPARA, peroxisome proliferator activated receptor alpha; NR1H3, nuclear receptor subfamily 1 group H member 3; CYP19A1, cytochrome P450 family 19 sub-family A polypeptide 1; CYP17A1, cytochrome P450 family 17 sub-family A polypeptide 1; ESR1, estrogen receptor 1.

Molecular docking

The top 5 protein targets namely PPARA, NR1H3, AR, CYP19A1 and CYP17A1 were docked using the Autodock tools (ADT 1.5.7) and discovery studio visualizer were used. The best five conformations were shown by NR1H3 with stigmasterol, NR1H3 with campestrol, PPARA with oleic acid, PPARA with heptadecanoic acid and NR1H3 with beta-sitosterol with binding energy -11.75, -10.53, -7.99, -7.66 and -7.63 kcal/mol respectively (Figure 5).

Figure 5

Figure 5. Molecular docking interactions of protein with compounds. a: NR1H3 with stigmasterol; b: NR1H3 with campesterol; c: PPARA with oleic acid; d: PPARA with heptadecanoic acid; e: NR1H3 with beta-sitosterol. PPARA, peroxisome proliferator activated receptor alpha; NR1H3, nuclear receptor subfamily 1 group H member 3; ALA, alanine; PHE, phenylalanine; LEU, leucine; ILE, isoleucine; VAL, valine; CYS, cysteine; ARG, arginine; TRP, tryptophan; MET, methionine; LYS, lysine; HIS, histidine.


Almost 80% of the population relies on herbs for prevention, treatment, and cure.[24] The Myristica fragrans, prospective therapeutic targets, and important pathways were examined using the network pharmacology approach in order to determine how it might operate to treat acne vulgaris.[25] The 34 phyto-compounds were identified for Myristica fragrans from various database and literature out of which 14 phyto-compounds showed to interact with the disease targets of acne bacteria. Network was constructed using Cytoscape tool. Based on the bacterial network, the proteins with the highest edge count (PPARA, NR1H3, AR, CYP19A1 and CYP17A1) were selected to accelerate skin barrier repair by activating the peroxisomal proliferator activating receptor alpha (PPAR-a), which controls keratinocyte proliferation.[26] Further docking was carried out, and the highest stability was shown by NR1H3 with stigmasterol (-11.75 kcal/mol), as well as NR1H3 with campestrol (-10.53 kcal/mol). Therefore, these molecules can be further considered as potential hits for their anti-bacterial effects. TNF-a, IL-6, IL-1b, iNOS (inducible nitric oxide synthase), and COX-2 (cyclooxygenase 2) production can be inhibited by stigmasterol, whereas anti-inflammatory mediator expression can be elevated.[27]

In vitro anti-acne microbial activity study was conducted which gave promising results for stigmasterol as an anti-acne agent.[28,29] NR1H3 has an impact on lipid synthesis, inflammation, and cellular proliferation, which contribute to the etiology of acne vulgaris. There is a need for research into fresh therapeutic approaches based on their inhibition. A better understanding of the relationship between LXR (liver X-receptor) and acne lesions may enable effective inhibition, potentially targeting specific cell types or stages of the inflammatory cascade.[3032]


In the present study attempt was done to study the interactions between the phyto-compounds of the Myristica fragrans with the bacterial acne targets. It was found that the strong interaction is between the stigmasterol and campesterol with NR1H3 proteins and heat map also showed GO:0009617 response to bacterium. Stigmasterol and campesterol can be considering as the hit molecules in the anti-bacterial actions and have scope to study further. The study can be utilized as a background for formulating herbal anti-acne formulations as well as anti-bacterial studies can be conducted.



We, thank you to the KLE College of Pharmacy, Belagavi, KLE Academy of Higher Education and Research, Karnataka, India and MET's, Institute of Pharmacy, BKC, affiliated under Savitribai Phule Pune University, Nashik, India for their constant support and providing all facilities to complete this work.

Author contributions

Balekundri A: wrote and revised the first draft. Ahire ED: English editing and proofreading. Both authors are agreed the final version and submitted the article.


The study supported by the Ministry of Tribal Affairs NFST/UGC, Government of India for providing financial assistance in the form of fellowship (Award No. 202021-NFST-MAH-01235).

Ethics approval

Not applicable.

Conflict of interest

Eknath D Ahire is an Editorial Board Member of the journal. The article was subject to the journal's standard procedures, with peer review handled independently of this editor and his research groups.

Data sharing

No additional data are available.


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