Exploring the Anti-Aging Mechanisms of Queen Bee Acid Based on Network Pharmacology and Molecular Docking

Abstract

Queen bee acid (10-hydroxy-2-decenoic acid, QBA) is a biologically active compound known for its anti-aging effects, though its molecular mechanisms are not fully understood. This study employed network pharmacology and molecular docking to explore QBA’s anti-aging mechanisms. Target proteins of QBA were identified via PharmMapper, SwissTarget Prediction, and PubChem, while aging-related target genes were sourced from GeneCards, DisGeNET, and OMIM databases. Venny 2.1 identified 58 common target genes, and a protein–protein interaction (PPI) network was constructed using STRING database. Ten core target genes, including TNF, AKT1, INS, and STAT3, were analyzed for GO and KEGG pathway enrichment using DAVID. GO analysis yielded 154 entries, encompassing biological processes, molecular functions, and cellular components. KEGG pathway analysis identified 73 signaling pathways, including the FOXO signaling pathway and the lifespan regulation pathway. Molecular docking confirmed QBA’s strong binding to core target proteins via hydrogen bonds to at least three sites.

1. Introduction

Aging is an inevitable process in all organisms, marked by the progressive loss of tissue and cellular function [1]. While aging is a normal physiological phenomenon, it heightens the risk of diseases like cancer, neurological disorders, diabetes, cardiovascular diseases, stroke, and arthritis, posing a significant threat to elderly health [2]. Increasing evidence suggests that natural substances can delay aging, extend lifespan, and reduce age-related diseases, which is crucial for elderly health [3].

Royal jelly (RJ) is composed of 60–70% water, 9–18% protein, 7–18% carbohydrates, 3–8% lipids, and a minor level of minerals and vitamins [4]. It is secreted by the hypopharyngeal and mandibular glands of worker bees to feed larvae. Worker bees consume RJ for only three days, whereas the queen bee consumes it for her entire life, contributing to her longer lifespan (3–5 years) compared to worker bees (1–3 months) [5]. Additionally, RJ has shown anti-aging effects in model organisms like fruit flies, nematodes, and rats [6]. Interestingly, queen bee acid (QBA, 10-Hydroxy-2(E)-decenoic acid) is a unique fatty acid. The structure of QBA was determined by Barker et al. in 1959 using infrared spectroscopy [7]. It is a straight-chain fatty acid with 10 carbon atoms. The carboxyl and hydroxyl groups are at the ends of the carbon chain, and there is a double bond at the second carbon atom with hydrogen atoms in a cis configuration [7]. Common QBA extraction methods include solvent extraction, solution precipitation crystallization, HPLC, macroporous resin separation, and a novel method using freeze-dried royal jelly powder [8]. Research has shown that QBA prolongs lifespan and has therapeutic effects on age-related diseases [9]. Importantly, QBA promotes the differentiation of neural progenitor cells and increases the survival rate of existing neurons, a crucial factor in its benefits, which are promising methods for treating Alzheimer’s disease. It means that the therapeutic effect of Alzheimer’s disease by promoting the neurogenesis of neural progenitor cells may be achieved using QBA [10]. In addition, Kunugi et al. found that anti-tumor activity by accumulating reactive oxygen species (ROS) in cancer cells and inducing apoptosis resulted from QBA [9]. Although some evidence indicated that QBA had an anti-aging function, its mechanism for pathways is still unclear.

Network pharmacology is a cutting-edge science that integrates bioinformatics, computational chemistry, and systems biology. It acts like a “molecular detective”, delving into the complex relationships between compounds and biological targets—such as proteins, genes, and metabolic pathways—to uncover the mysteries of drug actions. It can precisely predict potential drug targets and dissect biological effects, unlocking the secrets behind drug mechanisms [11].

In this study, we embarked on a molecular journey to explore the anti-aging effects of QBA using the powerful tools of network pharmacology. We delved deep into its underlying molecular mechanisms, attempting to decode the mysterious ways in which it delays aging. Meanwhile, we employed molecular docking technology to accurately verify the binding of active components to their targets—like fitting a key into a lock—to further predict the therapeutic potential of QBA. This research not only sheds new light on the field of anti-aging but also offers fresh ideas and directions for future drug development.

2. Materials and Methods

2.1. Acquisition of Queen Bee Acid Structure and Regulatory Genes

The two-dimensional chemical structure of QBA (PubChem ID: 5312738) was downloaded from PubChem. Target proteins of QBA were identified using PharmMapper, SwissTargetPrediction, and PubChem. These targets were converted to genes using the UniProt database. After exporting the results, the target proteins were converted to genes using the UniProt database, and duplicate genes from different databases were removed.

2.2. Aging Genes Acquisition

Aging-related genes were retrieved by searching the GeneCards [12], DisGeNET [13], and OMIM [14] databases. Duplicate genes from these sources were eliminated.

2.3. Screening and Analysis of Common Target Genes

Genes regulated by QBA and aging genes were imported into Venny 2.1, an online tool for drawing Venn diagrams, to identify age-related genes affected by QBA.

2.4. Constructing an Interaction Network Between Queen Bee Acid and Aging Genes

Data on aging genes regulated by queen bee acid were imported into the STRING database [15] with the species set to Homo sapiens and the confidence level set to “medium confidence > 0.4”. The results were saved in TSV format and imported into Cytoscape software (version 3.9.1, 2024, Cytoscape Consortium, San Diego, CA, USA) for visualization and analysis. The CytoNCA (version 2.1.6, 2014, Central South University, Changsha, China) plugin was used to obtain the degree, betweenness centrality, and closeness centrality of each target gene. The top 10 genes were identified as core target genes.

2.5. Enrichment Analysis of Core Target Genes

The selected core target genes were imported into the DAVID database [16] with the species set to Homo sapiens for gene ontology (GO) biological process enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis. The species option was set to “human”, with a statistical significance threshold of p < 0.05. The top 10 enriched GO terms and top 15 enriched KEGG pathways were plotted using the Bioinformatics tool [17].

2.6. Molecular Docking

To verify the network pharmacology predictions, molecular docking of QBA with anti-aging proteins was simulated using AutoDock to confirm interactions and evaluate binding. The crystal structure of the target protein was downloaded from the RCSB Protein Data Bank [18] and saved as a PDB file. PyMOL software (version 2.5, 2000, Schrödinger, New York, NY, USA) was used to remove water molecules. Subsequently, the AutoDock Tool (version 1.5.7, 1989, The Scripps Research Institute, San Diego, CA, USA) was used to add hydrogen atoms to the target protein and save it as a receptor file in PDBQT format, while QBA was saved as a ligand file in PDBQT format. The grid box was set to cover the entire receptor protein. Molecular docking simulation was performed using AutoDock Vina (version 1.1.2, 2010) with default parameters, measuring the binding free energy and selecting the lowest docking energy as the final docking conformation. The 2D and 3D docking results were displayed using Ligplot+ (version 2.2.7, 2011, University of Dundee, Dundee, Scotland) and PyMOL.

3. Results

3.1. Aging Genes Regulated by Queen Bee Acid

From the database, it can be found that QBA regulates 273 genes, while there are 1082 aging-related genes. Among these, 58 genes overlap, indicating that 58 aging-related genes are regulated by QBA.

3.2. Interactions Among Aging-Related Genes

The network consists of 58 nodes and 766 edges, indicating multiple interactions among the 58 genes (Figure 1). To further explore the relationship between these genes and QBA, the top 10 genes with the highest degree were selected for analysis (

Table 1. Information about the core targets in the hub PPI network.

Target Name	Degree	Betweenness	Closeness
TNF	84	409.18216	0.7808219
AKT1	84	417.0469	0.7808219
INS	80	461.00653	0.7702703
STAT3	66	142.41878	0.686747
ESR1	62	217.12914	0.6785714
VEGFA	62	125.693	0.67058825
CAT	46	72.715454	0.6195652
IL10	44	55.924225	0.59375
FOXO3	44	22.538624	0.60638297
FOXO1	44	33.371983	0.60638297

). These ten genes are tumor necrosis factor (TNF), Protein Kinase B α (AKT1), insulin (INS), Signal transducer and activator of transcription 3 (STAT3), estrogen receptor 1 (ESR1), vascular endothelial growth factor (VEGFA), Catalase (CAT), Interleukin 10 (IL10), Forkhead box O3 (FOXO3), and Forkhead box O1 (FOXO1). Table 1 shows that TNF and AKT1 have the highest degree (84), indicating that they each have 84 connections with other genes. Following them in terms of degree are INS, STAT3, ESR1, VEGFA, CAT, IL10, FOXO3, and FOXO1. Furthermore, we conducted an interaction of these 10 genes (Figure 2). The optimized PPI network (Figure 2C) shows FOXO3 and CAT near the center, indicating that these two genes are central and play significant roles in gene expression.

3.3. Functional Classification of Anti-Aging Genes

To understand the functions of the 10 genes (TNF, AKT1, INS, STAT3, ESR1, VEGFA, CAT, IL10, FOXO3, FOXO1) in aging, a gene ontology (GO) enrichment analysis was conducted (Figure 3), categorized into biological process (BP), cellular component (CC), and molecular function (MF). As shown in Figure 4A, these genes are involved in BPs related to regulation, mainly focusing on transcription. According to CC classification (Figure 4B), these genes mainly affect the cytoplasm and mitochondria. For the MF classification (Figure 4C), most genes are enriched in identical protein binding function. To further understand gene function, these 10 genes were annotated for metabolic pathways. These 10 genes were enriched into 73 metabolic pathways potentially involved in QBA anti-aging, with the top 15 pathways shown in Figure 5. The pathways related to QBA anti-aging include FOXO signaling pathway, longevity regulating pathway-multiple species, prolactin signaling pathway, longevity regulating pathway, AGE-RAGE signaling pathway in diabetic complications, etc. Notably, the FOXO signaling pathway contains the most annotated genes, suggesting QBA’s anti-aging mechanism likely targets this pathway. This mirrors the PPI network, where FOXO3 is central in both protein interactions and metabolic pathways. This implies that FOXO3 plays a significant role in QBA’s anti-aging mechanism. According to the GO and KEGG analysis, the anti-aging mechanisms of QBA are diverse.

3.4. Molecular Docking Investigations on Queen Bee Acid Binding by Protein

Molecular docking is crucial for evaluating the reliability of network pharmacological predictions. In this study, six core target proteins (TNF, AKT1, INS, STAT3, ESR1, and VEGFA) were molecular-docked with QBA. The PPI network analysis identified six hub genes—TNF, AKT1, INS, STAT3, ESR1, and VEGFA—with node degrees exceeding 50, indicating their central roles in aging-related signaling pathways. To mechanistically validate this association, molecular docking simulations were performed using AutoDock Vina (version 1.1.2, 2010) to investigate potential interactions between QBA and the encoded proteins of these genes. Binding energy (kcal/mol) and interaction modes were analyzed to characterize the molecular basis of QBA’s anti-aging effects. In

Table 2. The molecular docking parameters for the energy changes of QBA and target proteins.

Name	PDB ID	Center			Size			Binding Energy (kcal/mol)
		X	Y	Z	X	Y	Z
TNF	1TNR	44.59	23.62	40.88	68	74	66	−6
AKT1	4EKL	19.78	−0.59	16.17	48	52	60	−7
INS	1OS4	5.54	10.52	20.81	40	54	52	−7.2
STAT3	6NUQ	−2.03	18.68	25.84	78	116	94	−6.3
ESR1	7UJW	0.56	30.07	49.67	78	76	94	−6.8
VEGFA	6D3O	25.03	−38.14	4.83	44	68	42	−5.7

, center X, center Y, and center Z represent the three directional coordinates of the center of the search space; size X, size Y, and size Z are the sizes of the specified search space. The binding energies of these six proteins to QBA were −6, −7, −7.2, −6.3, −6.8, and −5.7 kcal/mol (Table 2). This result indicates that these six proteins can bind to QBA, suggesting they have significant effects in treating aging. Interestingly, QBA forms hydrogen bonds with amino acid residues of TNF (Glu79, Lys78, Gln40) (Figure 6A), AKT1 (Thr195, Glu191, Thr160) (Figure 6B), INS (Glu13, Ser9, His10) (Figure 6C), STAT3 (Pro578, Val580, Asp464, Glu465) (Figure 6D), ESR1 (Arg394, Gly390, Glu353) (Figure 6E), and VEGFA (Asp34, Glu42, Tyr45) (Figure 6F). QBA is connected to at least three amino acid residues of each protein, indicating multiple binding sites. TNF, AKT1, INS, STAT3, ESR1, and VEGFA bound to QBA, with binding energies all below −5.0 kcal/mol.

4. Discussion

Network pharmacology is an interdisciplinary field that combines network analysis, systems biology, and computational chemistry to study the interactions between drugs and biological systems. It uses biological data and computational methods to build network models of biological molecules (such as proteins, genes, metabolites, etc.) to predict the interactions and biological effects of drugs with these molecules. This approach provides a better understanding of the diverse and complex effects of drug action, revealing the molecular mechanisms underlying drug treatment of diseases [19].

Further analysis of the target proteins of QBA revealed that these 10 proteins play distinct roles in aging. Some are involved in cellular aging, with their levels directly related to the aging process. In addition, some proteins are associated with aging-related complications. In organisms, tissue degeneration and age-related diseases indicate the onset of aging. Studies have found that TNF is associated with inflammatory response, and its overactivation may lead to tissue degeneration and age-related diseases [20]. This suggests that inhibiting excessive TNF activation is crucial for anti-aging. Undoubtedly, understanding how aging signals are transmitted between cells is essential for anti-aging. AKT1 is a key cellular signaling molecule, and decreased AKT1 levels can contribute to aging through various biological pathways [21]. As the organism ages, blood sugar and fat metabolism slow down. INS regulates blood sugar and fat metabolism. Studies have found that aging-related diseases, such as diabetes and cardiovascular diseases, are linked to imbalanced INS expression [22]. For elderly people, osteoporosis is an unavoidable issue. ESR1 is involved in various biological processes, including gender differentiation, reproduction, and bone metabolism. A study has found that decreased ESR1 levels may be related to osteoporosis in the elderly [23]. Cardiovascular disease also accompanies aging. Studies have found that decreased VEGFA levels may be related to age-related cardiovascular diseases [24], indicating VEGFA’s role in angiogenesis and repair. Recently, in skincare, antioxidants and anti-aging have been key selling points for consumers. CAT is a crucial antioxidant enzyme that eliminates free radicals and other harmful substances. Decreased CAT levels may be related to age-related oxidative stress and tissue damage [25], suggesting the potential of adding QBA to skincare or antioxidant health products to meet consumer needs. When mentioning health products, we also consider immune modulators. Some product advertisements now claim that they can regulate the human immune system. We found that among these 10 proteins, IL10 is a crucial immune regulatory factor that can inhibit inflammatory responses. Studies have found that decreased IL10 levels may be related to age-related immune dysfunction [26]. Notably, we found three transcription factors among these 10 proteins. FOXO3 primarily regulates cellular metabolism, apoptosis, and antioxidant responses. Studies have shown that elevated levels of FOXO3 may be related to longevity [27]. Like FOXO3, FOXO1 is also a transcription factor involved in various biological processes. Research has shown that FOXO1 is involved in cellular antioxidant defense and metabolic regulation. By activating cellular antioxidant defenses and metabolic pathways, it can reduce oxidative stress and anti-aging [28]. Another crucial transcription factor is STAT3, which is involved in cell proliferation, differentiation, and immune response. Studies have shown that the activity of STAT3 is associated with aging, particularly cardiovascular disease and cancer [29]. From the above data, it can be inferred that QBA regulates multiple target genes, exerting an anti-aging effect.

Life expectancy has always been a long-standing topic in human history. As a result, scientists are constantly exploring the causes of aging and attempting to reverse it. During this exploration, metabolic pathways like FOXO, TOR, sirtuin, and AMPK were discovered. These pathways balance various cellular metabolic processes, including energy metabolism, 2DNA repair, and cell apoptosis, thereby affecting the aging process [30]. Interestingly, the anti-aging target genes of QBA discovered in this study are annotated to these key metabolic pathways, indicating that our results are consistent with previous studies and demonstrating QBA’s significant anti-aging potential. The diversity of QBA’s anti-aging mechanisms is significant for studying multi-target synergistic mechanisms. Among these metabolic pathways, the FOXO signaling pathway is the most noteworthy. Our study found that many QBA genes annotated to the FOXO signaling pathway, suggesting a significant role in QBA’s anti-aging mechanism. Previous studies found that the FOXO protein family regulates the expression of many genes, including antioxidant enzymes, DNA repair enzymes, cell cycle regulators, and apoptosis-related genes. These genes are involved in physiological processes related to aging, such as cell proliferation, metabolism, stress response, and cell death [31]. However, the specific mechanism by which QBA participates in FOXO signaling metabolism and exerts its anti-aging function remains unclear. In addition, the relationship between QBA and the FOXO protein family has not been fully elucidated. This provides direction for future research. Our future studies will focus on specific proteins from the FOXO family to investigate QBA’s anti-aging mechanism. This will address the gap in understanding how QBA regulates aging through the FOXO signaling pathway. Our study found that QBA-regulated genes are annotated to the prolactin signaling pathway. Prolactin, a hormone secreted by the pituitary gland, plays a crucial role in breast development and lactation. Our KEGG database search revealed that the prolactin signaling pathway is related not only to milk secretion but also to aging-related proteins and pathways such as FOXO3, MAPK, and STAT (Figure S1). Decreased prolactin levels are positively correlated with aging in females. Scholars have studied the relationship between prolactin and aging and found that prolactin affects aging by regulating cell metabolism and apoptosis [31]. This suggests that QBA could be a potential supplement for breastfeeding women. An important pathway observed in this study is the AGE-RAGE signaling pathway. AGE stands for advanced glycation end products, which are molecules formed during tissue aging. RAGE, a receptor for AGE, is associated with processes such as cell apoptosis, inflammation, and oxidative stress. Current research has found that the activation of the AGE-RAGE signaling pathway is related to tissue damage, inflammation, and diseases such as Alzheimer’s, diabetes, and cardiovascular disease. Inhibiting this pathway may help anti-aging [32]. Insulin levels are also crucial during the aging process. Many studies have shown that insulin, a hormone secreted by the pancreas, plays a vital role in regulating blood sugar and energy metabolism. However, when cells become less responsive to insulin, insulin resistance occurs. Some researchers found that insulin resistance is related to the aging process. For example, insulin resistance can lead to diabetes, obesity, cardiovascular diseases, and other aging-related conditions. Therefore, QBA may help delay aging by improving insulin resistance pathways [33]. Our analysis suggests that QBA regulates aging through multiple metabolic pathways. However, further research is needed on how these metabolic pathways closely cooperate to achieve the anti-aging mechanism of QBA. Given the range of pathways involved, metabolic flow technology is included in our plan. Applying this technology may clarify how QBA regulates multiple pathways to achieve anti-aging.

Generally, lower energy indicates a more stable complex conformation and a higher likelihood of interaction under natural conditions [33]. The binding free energy of QBA to all six proteins was below zero, indicating spontaneous reactions. Thus, QBA and the predicted target could bind more effectively in vivo. In addition, there are more than three binding sites on the proteins, with hydrogen bonding and hydrophobicity as the main interaction forms. This means QBA can closely bind to proteins through multiple sites. This is also partly due to QBA’s structure, where carboxyl and hydroxyl groups promote hydrogen bond formation, and the amino groups of these proteins readily form hydrogen bonds. Thus, the binding of QBA to senescence proteins is diverse and tight. This indicates that QBA exerts its anti-aging effects through multiple targets and pathways. Network pharmacology is a widely used tool for investigating pharmacological mechanisms and actions. By analyzing disease–gene–target–drug networks, it facilitates a comprehensive understanding of disease pathology and drug effects. With its integrative and systematic approach, network pharmacology effectively identifies single or multiple targets in biological networks, accelerating drug target discovery and enhancing new drug research efficiency. This inspired us to explore QBA’s anti-aging mechanism using network pharmacology. Our study demonstrates the potential of network pharmacology in predicting the roles of novel functional factors or health foods in disease intervention. However, some aspects of network pharmacology still require refinement. Identifying targets is critical in network pharmacology, with common sources including literature-based databases, web-based servers, and molecular docking. Literature-based database targets are highly authentic but not conducive to discovering new targets. The accuracy of web server-based target predictions largely depends on the server’s predictive capabilities. For instance, PharmMapper scores based on test molecule and database molecule similarity, ignoring compound–target affinity [34]. HTDocking uses molecular docking to select optimal binding targets for the test molecule [35]; however, the scoring function precision needs improvement. Establishing evaluation criteria is essential for future molecular docking-based target prediction methods. As the number of factors in network pharmacology research increases, data complexity also rises. Processing and analyzing vast data to extract useful information is a significant challenge for network pharmacology. Meanwhile, artificial intelligence has permeated all stages of scientific research, exhibiting profound advantages in data processing. Deep learning algorithms possess an exceptional capacity to extract key features from datasets, allowing for the integration of AI with network pharmacology. This integration enables the analysis and dimensionality reduction of large-scale network pharmacology data, facilitating the extraction of critical information. Concurrently, it is imperative to acknowledge that the majority of network pharmacology data are derived from experiments, and the limited number of experimentally validated drug–target interactions does not reflect their full pharmacological actions. Given the discrepancies within existing database information, it is essential for humans to enhance database maintenance and sharing, thereby strengthening the interconnectivity among major databases. For instance, upon approval of a data review, regular users may upload their own data to the public database, or correct existing data, augmenting the database’s volume and enhancing its accuracy.

QBA exhibits multi-target and multi-pathway properties in delaying aging, potentially acting through the FOXO signaling pathway, lifespan regulation pathway, prolactin signaling pathway, AGE-RAGE signaling pathway, and insulin resistance pathway. This represents an encouraging discovery in the anti-aging field. As a functional factor derived from food, QBA is deemed safe, signifying its potential applications in food, pharmaceuticals, cosmetics, and other sectors. Employing network pharmacology, we have elucidated the molecular mechanisms behind QBA’s anti-aging effects from both genetic and metabolic perspectives, confirming the reliability of predictions through molecular docking simulation. However, our study had limitations. Although we identified QBA and its anti-aging targets through extensive database searches, these findings may be insufficient due to the exclusion of recent research not yet reflected in the databases. Additionally, these targets and key pathways require further experimental validation. This informs our future research plans, focusing on exploring QBA’s anti-aging mechanisms through in vitro, in vivo, and comparative analyses of its regulation across diverse proteins and pathways. Overall, our research offers a theoretical framework for discovering and exploiting the efficacy of novel functional factors, laying the groundwork for developing QBA-based products.

The identified targets and pathways lay a foundation for experimental validation, guiding future studies to prioritize TNF and AKT1 as potential biomarkers. The KEGG results also suggest QBA may act through hormesis-like mechanisms, balancing pro- and anti-aging pathways. This comprehensive dataset fills a critical gap in understanding QBA’s molecular actions, supporting its development as a therapeutic candidate for age-related diseases. While this study advances the understanding of QBA’s anti-aging mechanisms, several limitations exist. First, target prediction relies on incomplete pharmacological databases, potentially missing rare or tissue-specific targets. Second, PPI network analysis uses confidence thresholds, risking false positives/negatives. Third, enrichment analysis overlooks post-translational modifications critical for aging signaling. To address these, future work should validate prioritized targets experimentally, integrate single-cell transcriptomic data, and employ dynamic docking simulations. These steps would refine QBA’s anti-aging mechanisms and enhance translational potential.

5. Conclusions

This study employed network pharmacology and molecular docking to investigate QBA’s potential anti-aging mechanisms, revealing a multi-target and multi-pathway approach. The anti-aging mechanisms of QBA are believed to engage key central targets, including INS, STAT3, ESR1, VEGFA, CAT, IL10, FOXO3, and FOXO1. In addition, QBA interacts with certain proteins (TNF, AKT1, INS, STAT3, ESR1, VEGFA) via hydrogen bonding at diverse interaction sites. QBA exerts its anti-aging effects by modulating the FOXO signaling pathway, lifespan regulation pathway, prolactin signaling pathway, and AGE-RAGE signaling pathway. This research serves as a reference for further exploration into the anti-aging mechanisms of QBA. These significant findings provide substantial evidence for the application of QBA in food, cosmetics, and other sectors.