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Research Article
Identification of potential molecular target of hypertension from Allium schoenoprasum by using network pharmacology and molecular docking strategies
expand article infoAditya Trias Pradana, Ginda Haro§, Novarianti Marbun|, Sofia Rahmi|, Iksen Iksen
‡ University of Surabaya, Surabaya, Indonesia
§ Universitas Sumatera Utara, Medan, Indonesia
| Institut Kesehatan Deli Husada, Deli Tua, Indonesia
¶ Department of Pharmacy, Sekolah Tinggi Ilmu Kesehatan Senior Medan, Medan, Indonesia
Open Access

Abstract

Hypertension is one of the silent killers in human life which is characterized by uncontrolled blood pressure. Although many therapeutic drugs have demonstrated success in treating hypertension, these treatments have their own drawbacks, most notably in terms of cost and side effects. Hypertension treatment with natural products has lately been proposed. This study focused on the potential of Allium schoenoprasum against hypertension based on network pharmacology and molecular docking strategies. Interested compounds and targets were identified by searching accessible databases. Protein-protein interaction (PPI), Gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) were conducted to determine the potential targets from Allium schoenoprasum. In this study, 10 potentially active compounds were obtained. PPI results showed SCR, STAT3, PIK3R1, CTNBB1, and ESR1 as the main targets of hypertension. GO and KEGG investigation confirmed the PPI targets are mainly involved in protein binding and catalytic in the membrane and cytoplasm. In the end, by using molecular docking, kaempferol, isorhamnetin, and quercetin showed the most potent compounds in Allium schoenoprasum against hypertension. In summary, Allium schoenoprasum exhibited antihypertensive activity via network pharmacology and molecular docking approaches.

Hypertension, Allium schoenoprasum L., network pharmacology, molecular docking

Introduction

One of the diseases that is one of the most dangerous to human health and has a high incidence rate all over the world is hypertension. The number of people who have been diagnosed with hypertension continues to rise on an annual basis, and it is anticipated that the number of people who have been diagnosed with hypertension will reach almost 2 billion by the year 2025 (Mills et al. 2020). A relatively small percentage of patients with hypertension can have their condition properly managed once the disease has already manifested itself. Hypertension is harmful because it increases the risk of developing cardiovascular disease, heart attack, stroke, kidney disease, intracerebral hemorrhage, end-stage organ injury, and a variety of secondary diseases (Wajngarten and Silva 2019). These diseases pose a significant risk to the lives of patients and have a significant impact on the patient’s ability to survive and live (Buonacera et al. 2019). As for the side effects of hypertension, the research and development of drugs that can cure hypertension are of the utmost significance.

Improvements in lifestyle and medication both play a role in hypertension treatment. The use of antihypertensive medication, which may also be combined with changes to one’s diet and way of life, has been shown to significantly lower blood pressure and heart rate, which in turn reduces the risk of cardiovascular disease and mortality (Al-Makki et al. 2022). These medications come with a number of drawbacks, the most notable of which is their adverse effects, high prices, and limited availability in certain regions of developing nations (Kumbhare et al. 2014). The search for novel pharmaceuticals, particularly those derived from natural products, is the significant importance for the development of treatments that are more effective and more tolerated by patients (Jung et al. 2018).

The medicinal plant Allium schoenoprasum, which was used in this study, is a member of the Amaryllidaceae family and may be found growing in large quantities throughout Asia, Europe, and North America (Haro et al. 2017). The culinary uses of this plant are its primary market (Sinaga et al. 2018). Anticancer, antioxidant, antibacterial, antilithogenic, antiviral, and vasodilator actions were among the many pharmacological effects seen in Allium schoenoprasum (Haro et al. 2017; Sinaga et al. 2018; Islamie et al. 2022). Several reports showed that organosulfur compounds from the Allium species such as diallyl disulfide downregulated intercellular adhesion molecule-1 and matrix metalloproteinase-9 and blocked the inactivation of endothelial nitric oxide synthase (eNOS), both of which have been shown to alleviate hypertension (Song et al. 2021). In addition to organosulfur, it has been shown that Allium schoenoprasum contains several flavonoids that possess antihypertensive properties. Gallic acid, for instance, reduces systolic blood pressure and suppresses oxidative stress in rats with hypertension (Jin et al. 2017). According to Yu et al (2022), p-Coumaric acid, Ferulic acid, and Sinapic acid all work to lower ACE levels, which is how they exert their antihypertensive effects. Several studies concluded that people suffering from hypertension whose diets contained quercetin and kaempferol experienced a reduction in their blood pressure (Dabeek and Marra 2019). According to Chang et al. (2020) research, another form of flavonoid called isorhamnetin has the potential to inhibit protein expression of TNF- α and IL-6 in an in vivo study.

With so many different pharmacological mechanisms at play, it can be challenging to develop and improve upon solutions that make use of natural resources because these solutions tend to have several targets and pathways (Sinaga et al. 2019). The use of network pharmacology and molecular docking has been proposed as a solution to the problem and a means of increasing the likelihood that new medications will be discovered (Iksen et al. 2022; Iksen et al. 2023). Drugs, protein targets, illnesses, genes, and other factors can all be studied in detail by using pharmacological networks. This conforms to the fundamental principle of traditional medical treatment, whereas targets acquired using network pharmacology can be validated via molecular docking (Iksen et al. 2023). This study was carried out to evaluate the potential targets and mechanisms of Allium schoenoprasum against hypertension by using network pharmacology and molecular docking techniques, which might become a promising novel treatment.

Materials and methods

Compounds screening

The information on all possible active compounds of Allium schoenoprasum was obtained by inserting the keyword Allium schoenoprasum from the online database of KNApSAcK Family Databases (http://www.knapsackfamily.com/KNApSAcK/) and our previous report (Iksen and Buana 2022). All the possible compounds were then screened according to the Lipinski rule violation = 0 and bioavailability score > 0.3.

Targets screening

Possible targets from the active compounds were obtained from the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) by inserting the SMILE code from each compound into the system. The hypertension-related targets were obtained from the GeneCards database (https://www.genecards.org/). Venny 2.1.0 was used to identify the overlapping targets obtained from the compounds and hypertension-related targets which were then defined as the potential therapeutic targets for Allium schoenoprasum to combat hypertension (https://bioinfogp.cnb.csic.es/tools/venny/).

Construction of protein-protein interaction network

Firstly, to visualize how is the interaction network between each target protein, we used the STRING database (https://string-db.org/) by inputting the targets we obtained from the Venny diagram before and downloaded the possible PPI network by choosing Homo sapiens as the model with the highest confidence score of 0.9. Then we continue to analyze the PPI network by using Cytoscape 3.9.1 (https://cytoscape.org/). In this program, from all of the PPI networks, we need to analyze which target protein is the most important by seeing several parameters provided by STRING and Cytoscape. Next, by using an additional CytoHubba plug-in, we can visualize the top 5 most important targets by using the color gradients obtained from previous results we obtained from STRING and Cytoscape.

Identification of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes

Analysis of gene ontology was conducted the in the aspect of biological process, molecular function, and cellular components. Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/) pathway enrichment analysis was conducted to obtain the possible pathway related to the treatment of Allium schoenoprasum in hypertension disease. All the data were analyzed by using RStudio with a ggplot2 plug-in as the bubble plot (Islamie et al. 2022).

Molecular docking investigation

The possible interaction between active compounds from Allium schoenoprasum and the top 5 targets was conducted by using PyRx 0.8 Virtual Screening (https://pyrx.sourceforge.io/). Each protein target was obtained from the Protein Data Bank (https://www.rcsb.org/pages/policies) with the PDB ID 3F3V, 6NJS, 2IUG, 1JDH, 7UJO respectively for Proto-oncogene c-Src (SRC), Signal transducer and activator of transcription 3 (STAT3), Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1), Catenin beta-1 (CTNBB1), and Estrogen receptor 1 (ESR1). All proteins were prepared by using PyMol 2.5 (https://pymol.org/2/) in the format of PDB by removing the water molecules and specific ligands (Iksen et al. 2022). The binding energy (kcal/mol) was obtained and recorded.

Results

Active compound and target screening

The potential active compounds from Allium schoenoprasum were obtained from our previous report (Iksen and Buana 2022) and the KNApSAcK family database led us to identify 13 compounds in Allium schoenoprasum that show promise in terms of basic pharmacokinetics (Table 1). No compounds had a bioavailability score lower than 0.3 and none of them violated Lipinski’s rule. From 13 compounds, we conducted the potential target prediction by using Swiss Target prediction and it was found that only 10 compounds (Fig. 1A) had targets namely Diallyl disulfide, 2-Methyl-2-pentenal, Tiglaldehyde, Gallic acid, p-Coumaric acid, Ferulic acid, Sinapic acid, Kaempferol, Isorhamnetin, and Quercetin. Interestingly, all of those 10 compounds had a total of 201 potential targets. Next, from the GeneCards database, we obtained around 7174 potential hypertension-related targets. As shown in Fig. 1B, 168 overlapping targets were filtered out as the potential target of Allium schoenoprasum for the treatment of hypertension.

Table 1.

The main compounds information from Allium schoenoprasum and Lipinski’s rule.

Molecule MW Rotatable bonds H-bond acceptors H-bond donors Molar refractivity TPSA Log P Lipinski violations Bioavailability score
Diallyl disulfide 146.27 5 0 0 45.19 50.6 2.49 0 0.55
2-Methyl-2-pentenal 98.14 2 1 0 30.68 17.07 1.71 0 0.55
Methyl propyl disulfide 122.25 3 0 0 36.52 50.6 2.19 0 0.55
Methyl pentyl disulfide 150.31 5 0 0 46.14 50.6 2.66 0 0.55
1-Pentanesulfenothioic acid 136.28 4 0 0 41.67 64.1 2.37 0 0.55
Tiglaldehyde 84.12 1 1 0 25.87 17.07 1.47 0 0.55
Gallic acid 170.12 1 5 4 39.47 97.99 0.21 0 0.56
p-Coumaric acid 164.16 2 3 2 45.13 57.53 0.95 0 0.85
Ferulic acid 194.18 3 4 2 51.63 66.76 1.62 0 0.85
Sinapic acid 224.21 4 5 2 58.12 75.99 1.63 0 0.56
Kaempferol 286.24 1 6 4 76.01 111.13 1.7 0 0.55
Isorhamnetin 316.26 2 7 4 82.5 120.36 2.35 0 0.55
Quercetin 302.24 1 7 5 78.03 131.36 1.63 0 0.55
Figure 1. 

The number of potential targets for each compound from Allium schoenoprasum against hypertension. A. Each compound consists of at least 3 potential targets against hypertension obtained from the Swiss target prediction database; B. Venny diagram showing 168 intercepting targets between main compounds from Allium schoenoprasum against hypertension. The yellow color represents hypertensive-related targets, and the blue represents targets from Allium schoenoprasum.

Compound-target interaction network

To visualize the interaction network between the potential targets from Allium schoenoprasum and the majority compound, we construct the compound-target network (Fig. 2). The PPI network reveals the interaction among the targets with more interacting lines between proteins representing highly connected proteins in the network which was constructed using the Cytoscape 3.9.1 software. The orange diamond and purple ellipse represent active compounds from Allium schoenoprasum and hypertension targets.

Figure 2. 

The compound-targets interaction network. The purple oval represents the targets, while the cream diamond represents the main compounds in Allium schoenoprasum.

Protein-protein interaction network

Interactions between proteins are a common mechanism by which proteins exert control of physiological processes. We used STRING 11.5 to construct a protein-protein interaction network (Fig. 3A) and search for 168 target proteins involved in the treatment of hypertension in order to better understand its function and mechanism at the protein level. Following the completion of the topology analysis on the protein-protein interaction network, it was discovered that the network has a total of 331 edges and 168 nodes. In addition to this, the average node degree is 3.94, the average local clustering coefficient is 0.441, and the PPI enrichment p-value is < 1.0e-16. Fig. 3B showed the main protein cluster and the findings from each node in the PPI network underwent additional analysis by utilizing the CytoHubba plug-in according to the number of degrees, which subsequently led to the identification of 5 top targets derived from Allium schoenoprasum. The interaction between the top 5 targets was shown in Fig. 3C, with the SRC having the highest degree of involvement, followed by STAT3, PIK3R1, CTNNB1, and ESR1 respectively.

Figure 3. 

Protein-protein interaction network. A. The main protein-protein interaction was obtained from 168 potential targets; B. The main cluster of protein-protein interaction; C. Top 5 targets from the protein-protein interaction network.

GO and KEGG enrichment analysis

Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out with the assistance of RStudio in order to further assess and acquire a better understanding of the molecular mechanism that the compounds-targets have on hypertension. These possible target genes were subjected to three different kinds of gene ontology functional annotation assessments. These analyses comprised the biological process (Fig. 4A), the molecular function (Fig. 4B), and the cellular component (Fig. 4C). The analysis results as shown in Fig. 4, the images show only the top 10 related functions. The gene ontology of biological processes was mainly involved in the metabolic process and cellular responses in several behaviors of cells. Gene ontology of molecular function showed that protein binding and catalytic activity on a protein are the main activities related to the targets. The gene ontology of the cellular component showed that the targets were mainly distributed in the cytoplasm region. Similar to gene ontology, the pathway enrichment analysis using the KEGG method only shows the top 10 pathways involved in the mechanism of Allium schoenoprasum against hypertension. The KEGG pathways are shown in Fig. 4D, which mainly focused on the pathways in cancer, metabolic pathways, nitrogen metabolism, HIF-1 signaling pathway, PI3K-Akt signaling pathway, proteoglycans in cancer, AGE-RAGE signaling pathway in diabetic complications, endocrine resistance, microRNAs in cancer, and measles.

Figure 4. 

Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. A. Biological process; B. Molecular function; C. Cellular component; D. KEGG pathway.

Molecular docking

The protein-protein interaction analysis showed that the targets SRC, STAT3, PIK3R1, CTNNB1, and ESR1 were the most important target hub in the network. We conducted further analysis by using PDB ID 3F3V, 6NJS, 2IUG, 1JDH, and 7UJO respectively for SRC, STAT3, PIK3R1, CTNNB1, and ESR1 obtained from the protein data bank and performed the molecular docking analysis to confirm the possibility of interaction between the compounds and target protein. The outcome of the docking affinity is reported in Table 2, and it is generally agreed upon that the lower the binding energy, the greater the likelihood that it will bond. In general, kaempferol, isorhamnetin, and quercetin showed the best binding energy to all 5 targets. However, some compounds showed binding energy higher than -5 kcal/mol which means they might not interact well with the targets and form less stable complexes with the targets (Syahputra et al. 2022), for example, diallyl disulfide, 2-Methyl-2-pentenal, and tiglaldehyde.

Table 2.

Binding energy (kcal/mol) of active compounds in Allium schoenoprasum and main targets.

Compounds Binding energy (kcal/mol)
SRC STAT3 PIK3R1 CTNNB1 ESR1
Diallyl disulfide -3.9 -3.2 -3.3 -3.4 -3.8
2-Methyl-2-pentenal -4.1 -4.1 -4.3 -3.8 -4.5
Tiglaldehyde -3.8 -3.8 -3.9 -3.6 -4
Gallic acid -5.6 -5.2 -5.1 -5.4 -6.4
p-Coumaric acid -6.2 -5.5 -5.3 -5.6 -6.1
Ferulic acid -6.4 -5.8 -5.1 -5.5 -6.2
Sinapic acid -6.3 -6 -4.8 -5.3 -6.2
Kaempferol -9.2 -7.2 -5.8 -7.5 -7.7
Isorhamnetin -9.5 -7.3 -5.8 -6.5 -7.6
Quercetin -9.4 -7.5 -5.9 -7.8 -7.5
Dasatinib (SRC inhibitor) -8.4 - - - -
Napabucasin (STAT3 inhibitor) - -6.6 - - -
LY294002 (PI3K inhibitor) - - -6.4 - -
MSAB (Beta catenin inhibitor) - - - -6.5 -
Elacestrant (ESR inhibitor) - - - - -6.1

Discussion

Hypertension is a condition that occurs when a person’s systolic blood pressure increases by more than 140 mmHg and the diastolic blood pressure increases by more than 90 mmHg. This condition is one of the silent killers (Mills et al. 2020). The major medical condition known as hypertension dramatically raises the likelihood of developing a variety of diseases, including those affecting the kidneys, the brain, and the heart (Wu et al. 2015). At the moment, the gold standard treatment for hypertension is a dietary modification that involves cutting back on salt consumption and alcohol intake. Additional pharmacological therapies, such as angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, diuretics, alpha-blockers, and beta-blockers, can be used singly or in combination to treat hypertension (Carey et al. 2022). However, despite the availability of several antihypertensive treatment options, there are still some issues to be concerned about. These issues include the potential adverse effects of the medications as well as their prohibitively expensive costs, both of which cause patients to be unable to purchase them, negatively impacting both their chances of surviving and the quality of their lives. As a result, the research and development of drugs that can cure hypertension are of the utmost significance.

It is indisputable that traditional medicine can be applied to prevent or cure a diverse range of complex illnesses, and it also provides a feasible source for the identification of further candidate drugs for managing hypertension (Kamyab et al. 2021). In the treatment of hypertension in particular, the rising fields of network pharmacology and molecular docking offer a cutting-edge method as well as an excellent instrument for figuring out the biological foundation of conventional medication which can be very beneficial for the discovery of new antihypertensive drugs (Zhai et al. 2021). Traditional medicine is known for having multiple components, multiple targets, and multiple pathways (Islamie et al. 2022). As a result, it would take a significant amount of time and resources to investigate the effects and mechanisms of traditional medicine and its pharmacological activities. This presents a significant barrier to the widespread acceptance and use of traditional medicine in clinical settings.

Using data mining, we first identified 13 compounds from Allium schoenoprasum that might be useful in treating hypertension in this investigation. We also integrated hypertension-related targets from the GeneCards database into the screening process, narrowing the pool of candidate targets down to only 10, from a total of 168. The investigation of protein-protein interactions provides an in-depth understanding of the network of interactions between drugs and their targets. According to the findings of network pharmacology and PPI analysis, there are five primary targets in Allium schoenoprasum that are effective against hypertension. These targets include SRC, STAT3, PIK3R1, CTNNB1, and ESR1. Inhibition of SRC has been shown in previous research to result in a reduction in blood pressure as well as improvements in cardiac and vascular function (Callera et al. 2016). In line with the findings of the previous study, blocking the SRC signaling pathway may improve the function of the vascular smooth muscle, hence reducing the risk of hypercontraction (Camargo et al. 2022). In the meantime, STAT3 is an essential component that plays a role in preventing excessive hypertension from damaging the heart. In hypertension hearts, a lack of STAT3 may decrease cardiac function due to faulty myofibrillar structure and remodeling, which may eventually result in heart failure (Zouein et al. 2013). In addition, it has been demonstrated that the gene PIK3R1, which encoded the p85 form of PI3K signaling, is closely associated with hypertension. PI3K signaling, which is one of the primary signaling regulators inside the cells, can govern apoptosis and inflammation, as well as influence the creation of nitric oxide and glucose metabolism, which can result in hypertension that is out of control (Iksen et al. 2021; Zhang et al. 2022). Meanwhile, various hypertension agents, including angiotensinogen, angiotensin-1-converting enzyme, renin, angiotensin I, and angiotensin II, are transcribed via the beta-catenin transcription factor, which is encoded by the human CTNNB1 gene (Zhou et al. 2015; Xiao et al. 2019). By facilitating nitric oxide bioavailability via the suppression of oxidative stress, ESR1 has a role in the vasodilation of blood vessels, which contributes to the reduction of blood pressure (Favre et al 2021).

A comprehensive Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis revealed that these targets are mostly involved in protein binding in the membrane and cytoplasm. The findings from the KEGG pathway enrichment study provide further credence to this interpretation. To confirm the results obtained from the network pharmacology, we conducted molecular docking against the top 5 targets from hypertension. Molecular docking analysis results corroborated the preceding findings, showing that the target proteins and the active ingredients in Allium schoenoprasum interact closely, providing proof of the binding between the active ingredients and the target protein. The results of the molecular docking study showed that the active components of Allium schoenoprasum had a strong ability to bind with these essential targets. This finding suggests that the active components of Allium schoenoprasum function regulate numerous targets at the same time, rather than a single target agent. However, our works have some limitations, especially in data mining. Additional study is required to determine the precise antihypertensive mechanisms that are at work when Allium schoenoprasum is administered. In the future, we intend to carry out additional research utilizing an in vivo model while making use of extract from Allium schoenoprasum.

Conclusions

Network pharmacology and molecular docking were used to investigate the hypertension-treating mechanism of Allium schoenoprasum. According to the protein-protein interaction network study, Allium schoenoprasum’s active components may target SCR, STAT3, PIK3R1, CTNNB1, and ESR1 to treat hypertension. Gene ontology functional enrichment analysis and KEGG pathway enrichment analysis showed that the target largely bound and catalyzed proteins in the cell’s membrane and cytoplasm. Kaempferol, isorhamnetin, and quercetin are the most potential antihypertensive components from Allium schoenoprasum, according to molecular docking.

Acknowledgments

The authors wish to express their thanks for the full support from the Faculty of Pharmacy University of Surabaya, Faculty of Pharmacy Universitas Sumatera Utara, and Sekolah Tinggi Ilmu Kesehatan Senior Medan. We also expressed our gratitude to Prof. Ginda Haro, Ph.D. for the guidance and resources for data mining.

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