Application of ginsenoside Rg3 in non-small cell lung cancer target screening

Abstract

The invention provides an application of ginsenoside Rg3 combined with JAK1 and / or NBN, a compound generated by combining Rg3 with JAK1 and / or NBN, an application of molecules combined with JAK1 and NBN in preparation of drugs for treating non-small cell lung cancer, a method for screening target proteins combined with ginsenoside Rg3 for non-small cell lung cancer, and the like.

Description

Technical Field

[0001] The present invention belongs to the field of medicine. Specifically, the present invention relates to the application of ginsenoside Rg3 in combination with JAK1 and / or NBN, the complex generated by Rg3 in combination with JAK1 and / or NBN, the application of molecules simultaneously binding to JAK1 and NBN in the preparation of drugs for treating non-small cell lung cancer, and the method for screening the target protein of non-small cell lung cancer bound by ginsenoside Rg3. Background Art

[0002] Millions of people are diagnosed with lung cancer each year, with non-small cell lung cancer (NSCLC) accounting for 85% of newly diagnosed cases, making it a prevalent subtype. In recent years, innovations have been made in the treatment of NSCLC, including traditional modalities such as surgical resection and radiation therapy, as well as novel intervention strategies such as targeted drug therapy. Despite these advances, the five-year survival rate for NSCLC remains dishearteningly low at only 15% [1]. Significant progress has been made in the development of targeted cancer therapies, particularly in the area of ​​oncogenic mutations in genes such as EGFR, ALK, ROS1, BRAF, TRK, RET, and MET. These targeted therapies have been shown to be effective in treating specific molecular subtypes of cancer. In addition, ongoing studies and clinical trials targeting KRAS, NRG1, and HER2 have shown promising results, suggesting that they may soon become standard treatments for certain types of cancer [2]. The use of targeted chemotherapy drugs can cause adverse reactions in patients, which can have a profound impact on their quality of life, and in some cases, these side effects can even be life-threatening. Careful monitoring and management of these potential drug-related complications is essential to ensure the health and safety of patients receiving targeted chemotherapy.

[0003] Traditional herbal medicine has gained recognition for its significant therapeutic properties and inherent advantages in safety with minimal adverse effects. It has been widely used to treat various diseases, including cancer at different stages. Ginseng is a traditional Asian herbal medicine with a long history of more than 2,000 years and contains bioactive compounds such as ginsenoside Rg3 (hereinafter referred to as Rg3). This special compound exhibits biological functions and promotes cellular responses to inflammation and oxidative stress, thereby showing therapeutic effects in a range of health conditions, including neurological diseases, cardiovascular diseases, cancer, lung diseases, diabetes, and digestive system diseases [3].

[0004] Scientific studies have provided evidence that ginsenoside Rg3 exhibits antitumor activity by inhibiting the activity of matrix metalloproteinases [4]. When used in combination with radiotherapy, it exhibits radiosensitization effects by inhibiting the phosphorylation of IκB and suppressing radiation-induced NF-κB activation [5]. In addition, ginsenoside Rg3 modulates DNA damage in NSCLC cells by activating the VRK1 / P53BP1 pathway [6]. Importantly, ginsenoside Rg3 has shown great value in enhancing drug efficacy and reducing chemotherapy-induced toxicity, making it a superior treatment for first-line chemotherapy in patients with advanced NSCLC, with improved efficacy and safety compared to single-agent chemotherapy [7]. 20(S)-ginsenoside Rg3 has multiple beneficial properties in the treatment of NSCLC and can enhance the sensitivity of NSCLC cells to erlotinib, an anticancer drug, by effectively inhibiting autophagy [8].

[0005] Ginsenoside Rg3, an extract of the traditional Chinese medicine Panax ginseng, has been identified as a promising multi-target drug with efficacy in various biological pathways. Despite its recognized therapeutic potential, the exact mechanism by which it exerts its effects in the treatment of NSCLC remains uncertain. How to elucidate the specific molecular mechanisms by which ginsenoside Rg3 works in NSCLC and fully understand its mode of action in this context is unknown. Summary of the invention

[0006] The present invention provides the use of a novel ginsenoside Rg3 (preferably referred to as 20(S)-ginsenoside Rg3 hereinafter) in combination with JAK1 and / or NBN, a complex produced by the combination of Rg3 with JAK1 and / or NBN, the use of a molecule simultaneously combining with JAK1 and NBN in the preparation of a drug for treating non-small cell lung cancer, and a method for screening a target protein of non-small cell lung cancer combined with ginsenoside Rg3, etc.

[0007] Specifically, in the first aspect, the present invention provides the use of ginsenoside Rg3 in the preparation of a reagent for binding to JAK1 (Janus Kinase 1) and / or NBN (Nibrin). Preferably, the present invention provides the use of ginsenoside Rg3 in the preparation of a reagent for binding to JAK1 (Janus Kinase 1). Specific embodiments of the present invention show that Rg3 can bind to JAK1, and the binding leads to an increase in the stability of the JAK1 protein structure.

[0008] Also preferably, the present invention provides the use of ginsenoside Rg3 in the preparation of an agent for binding to NBN (Nibrin). Specific examples of the present invention show that Rg3 can bind to NBN, and the binding leads to an increase in the stability of the NBN protein structure.

[0009] Accordingly, in a second aspect, the present invention provides ginsenoside Rg3 for binding to JAK1 and / or NBN.

[0010] In a third aspect, the present invention provides a complex produced by ginsenoside Rg3 binding to JAK1 and / or NBN. Preferably, the present invention provides a complex produced by ginsenoside Rg3 binding to JAK1. Specific embodiments of the present invention show the existence of such a complex. Further, the present invention provides a method for producing the complex, which comprises the step of contacting ginsenoside Rg3 with JAK1.

[0011] Also preferably, the present invention provides a complex produced by combining ginsenoside Rg3 with NBN. The specific embodiment of the present invention shows the existence of such a complex. Further, the present invention provides a method for producing the complex, which comprises the step of contacting ginsenoside Rg3 with NBN.

[0012] In a fourth aspect, the present invention provides the use of a molecule that simultaneously binds to JAK1 and NBN in the preparation of a drug for treating non-small cell lung cancer. Preferably, the molecule is ginsenoside Rg3. This confirms that ginsenoside Rg3 may be effective in treating non-small cell lung cancer, and that JAK1 and NBN are target proteins of non-small cell lung cancer.

[0013] In a fifth aspect, the present invention provides a method for screening a non-small cell lung cancer target protein bound by ginsenoside Rg3, comprising:

[0014] (1) Obtain target protein data for non-small cell lung cancer;

[0015] (2) constructing a protein-protein interaction network using the target protein obtained in step (1);

[0016] (3) Using ginsenoside Rg3 as a ligand, rigid molecular docking was performed to predict the binding mode and affinity between the ligand and the target protein; and semi-flexible docking was performed to predict the ligand-protein interaction;

[0017] (4) optionally perform GO enrichment analysis and KEGG pathway enrichment analysis; and

[0018] (5) Perform molecular dynamics simulation on the results obtained in step (3).

[0019] Preferably, in step (1), the target protein is obtained from Genecards, OMIM, PharmGKB and Drugbank databases. In a specific embodiment of the present invention, proteins encoded by 659 candidate genes of NSCLC are obtained from Genecards, OMIM, PharmGKB and Drugbank databases.

[0020] In the preferred step (2), the candidate target (the target protein obtained in step (1)) is subjected to protein-protein interaction (PPI) network analysis using the String database. In a specific embodiment of the present invention, the PPI network analysis is performed using the String database to obtain 148 core node proteins.

[0021] Preferably, in step (3), rigid molecular docking is performed using the SailVina program; and / or, semi-flexible docking is performed using the Libdock module. In a specific embodiment of the present invention, 29 key target proteins were obtained by using rigid and semi-flexible molecular docking simulations, among which JAK1, NBN and KRAS proteins had good binding affinity with Rg3.

[0022] Preferably, in step (5), molecular dynamics simulation is performed by the RMSF (root mean square fluctuation) value of C atoms in the complex generated by ginsenoside Rg3 binding to the target protein. In a specific embodiment of the present invention, molecular dynamics simulation shows that JAK1 and NBN can be used as targets of Rg3.

[0023] The present invention cites public documents, which are used to more clearly describe the present invention, and their entire contents are incorporated herein by reference, as if their entire contents have been repeated herein.

[0024] For ease of understanding, the present invention will be described in detail below through specific embodiments and drawings. It should be particularly pointed out that these descriptions are merely exemplary descriptions and do not constitute a limitation on the scope of the present invention. Based on the discussion of this specification, many variations and changes of the present invention are obvious to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic diagram of the methodology used in the studies of the present invention.

[0026] Figure 2 These are representative images of Venn diagrams and protein-protein networks, where A: 659 intersecting genes; B: candidate protein-protein network; C: protein-protein cluster with 31 nodes.

[0027] Figure 3 The representation results of Rg3 with 4 target proteins using molecular docking are shown, among which AD: ginsenoside Rg3, targets JAK1 (PDB ID: 6N7A), EGFR (PDB ID: 1MOX) KRAS (PDB ID: 4M1Y) and NBN (PDB ID: 5WQD).

[0028] Figure 4The BP enrichment analysis of 19 key targets is shown, where A: GO bubble map; B: KEGG bubble map.

[0029] Figure 5 Shown are the comparisons of RMSF and RMSD of Rg3-protein complex and Rg3 monomer, and non-bonded interaction monitoring of Rg3-protein complex, including (A) JAK1, (B) KRAS, and (C) NBN. DETAILED DESCRIPTION

[0030] The present invention is further described below by way of examples. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art and commercially available common instruments and reagents, and references may be made to the manufacturer's instructions of the corresponding instruments and reagents.

[0031] Example 1 Methodology

[0032] In order to reveal the targets of Rg3 in the treatment of NSCLC and gain insight into its therapeutic mechanism, the interactions between the drug and the disease target can be analyzed. Disease target databases can be used to identify potential targets for NSCLC, and subsequent analysis of protein-protein interaction (PPI) networks can help select key targets. Molecular docking technology is then used to specifically identify the target proteins of ginsenoside Rg3 in NSCLC. This is followed by KEGG and GO enrichment analysis to investigate the enriched pathways associated with the identified targets, providing insight into the main pathways involved in the action of Rg3. In addition, molecular dynamics simulations can be used to analyze the interaction patterns between key target proteins and Rg3, thereby elucidating the dynamics and stability of the protein-drug complex. This comprehensive approach combines computational technology and bioinformatics tools to reveal potential targets, pathways, and molecular interactions for the therapeutic effects of Rg3 in NSCLC. The analysis aims to identify specific proteins or pathways that Rg3 interacts with, thereby better understanding how Rg3 exerts its therapeutic effects in NSCLC. By elucidating these interactions, we can elucidate the underlying molecular mechanisms and potentially discover new targets for intervention and treatment strategies for NSCLC. The methodology used in this study is as follows Figure 1 shown.

[0033] Example 2 Materials and Methods

[0034] 2.1 Data Source and Target Acquisition

[0035] Non-small cell lung cancer (NSCLC) target proteins were obtained by utilizing several databases, including GeneCards (https: / / www.genecards.org) [9], OMIM (https: / / omim.org / )

[10] , Therapeutic Target Database (https: / / db.idrblab.net / ttd / ), and PharmGKB (https: / / www.pharmgkb.org / ). Duplicate targets were eliminated, and target names were standardized to gene symbols using the UniProt database. This comprehensive data collection and standardization process ensures the accuracy and consistency of target proteins associated with NSCLC, providing a reliable basis for further analysis and research.

[0036] 2.2 Protein-protein interaction network

[0037] The target proteins screened in the previous step were used to construct a protein-protein interaction network. These screened target proteins were imported into the STRING database (http: / / string-db.org) to generate a network. To ensure the reliability of protein interactions, only targets from human sources were selected for further analysis. Specifically, targets that were experimentally validated and had a confidence score greater than 0.9 were retained, indicating that the confidence of the interaction was high. This rigorous screening process ensured that the protein-protein interaction network consisted of high-quality and trustworthy interactions between target proteins associated with non-small cell lung cancer [9].

[0038] The R programming language, specifically the igraph package, was used to calculate various parameters for each node in the protein-protein interaction network, including degree, betweenness centrality, closeness centrality, average shortest path length, and other relevant metrics. By analyzing these properties of nodes in the interaction network, we can gain insight into their structural and functional importance in the network

[10] . Using the network data generated in the previous steps, the protein-protein interaction network was visualized using Cytoscape v.3.7.1, providing an intuitive and comprehensive representation of the network structure and the relationships between target proteins associated with non-small cell lung cancer

[11] .

[0039] 2.3 Active site analysis and molecular docking

[0040] The target genes obtained from the previous step were entered into the UniProt database and the Homo sapiens proteins were selected using the screening tool. Further screening was performed to identify experimentally validated targets with high-resolution X-ray crystal structures. From the screening results, the corresponding proteins were selected for further analysis.

[0041] To analyze the active sites of the selected target proteins, the active site analysis module in the Discovery Studio 2022 software was used. For molecular docking studies, a two-step approach was used. First, rigid molecular docking was performed using the SailVina program, which enabled the ligand to dock efficiently and accurately with the target protein, and the docking simulation was designed to predict the binding mode and affinity of the ligand to the target protein [12, 13]. In the second step, semi-flexible docking was performed using the Libdock module, which takes into account the flexibility of the ligand and protein receptor, allowing for more accurate predictions of ligand-protein interactions. The docking study provided valuable insights into the potential binding mode and affinity of the selected target protein with ginsenoside Rg3, which helps to understand their interactions and potential therapeutic significance in non-small cell lung cancer.

[0042] Ginsenoside Rg3 was hydrogenated and subjected to MMFF force field using SailVina, and the energy of the constructed molecular structure was minimized using Chem3D 19.0 until a minimum root mean square (RMS) gradient of 0.010 was reached. This process helped to optimize the conformation and energy of Rg3 for subsequent molecular docking simulations. For protein crystal structures, SailVina was used for hydrogenation, force field addition, and dehydration of molecules. In the case of the lack of certain amino acid residues in the protein crystal structure, the Biopython library was used to repair and improve the structure, which was suitable for subsequent docking studies.

[0043] After protein processing, a docking area of ​​30*30 was defined for molecular docking based on the previously obtained binding site information, and the docking parameters were set to the default values ​​to ensure a comprehensive exploration of ligand-protein interactions. These steps provide a reliable and optimized setting for molecular docking studies between ginsenoside Rg3 and target proteins, which is helpful for studying their binding modes and potential therapeutic significance in non-small cell lung cancer.

[0044] Proteins with a SailVina docking score lower than -9 were selected for further analysis. These protein crystal structures were imported into Discovery Studio (DS) software for processing, and hydrogenation, dehydration, and clean protein steps were performed to ensure that the protein structure was in a state suitable for docking simulation. The CHARMM36 force field was applied to the protein structure to improve its energy and conformation. For the ligand ginsenoside Rg3, its three-dimensional structure was constructed, and a hydrogenation reaction was performed while applying the CHARMM36 force field to optimize its conformation. The processing ligand module in DS was used with default settings to further optimize the small molecule. In order to perform semi-flexible molecular docking between the ligand and the selected target protein, the DS Libdock module was used, and the docking preference was set to "high quality and fast". By utilizing these methods and tools, ligand-protein interactions can be explored, and the potential binding mode between ginsenoside Rg3 and non-small cell lung cancer-related target proteins can be elucidated, which helps to understand its therapeutic significance.

[0045] 2.4GO and KEGG pathway enrichment analysis

[0046] Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the potential mechanism of ginsenoside Rg3 in NSCLC.

[0047] By comparing the identified target genes with the GO database, significant enrichment of specific GO terms was identified, indicating the functional relevance of Rg3 in NSCLC. The R package org.hs.eg.dbwas was used to convert gene symbols to gene IDs. The R packages DOSE, clusterProfiler, enrichplot, colorspace, stringi, BiocManager, and ggplot2

[14] were used for GO analysis and bubble plots of modules including molecular function (MF), cellular component (CC), and biological process (BP) were obtained

[15] .

[0048] The R packages DOSE, clusterProfiler, enrichplot, colorspace, stringi, ggplot2, and pathview were used to perform KEGG enrichment analysis, visualize the results, and generate bar graphs.

[0049] By integrating the results of GO enrichment and KEGG pathway analysis, we can obtain the biological processes and metabolic pathways affected by ginsenoside Rg3 in NSCLC, and this knowledge will help to better understand the therapeutic potential of Rg3 and aid in the development of targeted therapeutic strategies for NSCLC.

[0050] 2.5 Molecular dynamics simulation

[0051] The protein or protein complex was treated with the CHARMM force field

[16] and subsequently positioned in a rhombic box, which was solvated using an explicit periodic boundary solvation water model

[17] . The conformational optimization of the protein began with an initial extreme descent method with a maximum step size of 100,000 and an RMS gradient of 1, followed by further optimization using the base NR algorithm with a maximum step size of 100,000 and an RMS gradient of 0.1

[18] . The simulation was started at an initial temperature of 50 K and gradually increased to 300 K over 40 ps to mitigate rapid conformational disruption. Once the temperature reached 300 K, the simulation was extended for another 100 ps to allow the protein conformation to stabilize and reach equilibrium, applying periodic boundary conditions (PBC) to prevent boundary effects. Using NVT ensembles, simulations were performed for 10,000 ps at stabilization conditions at 300 K to reach dynamic equilibrium of the protein conformation. Calculations included the determination and analysis of root mean square deviation (RMSD), root mean square fluctuation (RMSF), total energy, and hydrogen bond interactions, and all calculations were performed using the BIOVIA Discovery Studio 2022 software package.

[0052] Example 3 Results and Discussion

[0053] 3.1 Non-small cell lung cancer targets

[0054] Many target genes are related to non-small cell lung cancer. The GeneCards database provides 6934 genes related to non-small cell lung cancer, among which 200 target genes with correlation scores above 77.6 were selected for further analysis. In addition, 345 genes, 170 genes and 164 genes were obtained from the OMIM database, the therapeutic target database and the PharmGKB database, respectively. By merging the target genes in the four databases and eliminating duplicate genes, a total of 659 candidate genes were identified for studying the mechanism of ginsenoside Rg3 in treating non-small cell lung cancer. Venn diagram ( Figure 2 a) showed that NRAS was a common target in all four databases, targets MDM2, EGFR and ABCB1 were shared among GeneCards, Therapeutic Target and PharmGKB databases, PIK3CA and IL1B targets could be retrieved from GeneCards, Therapeutic Target and PharmGKB databases, in addition, targets MUC1, TERT and KIT could be retrieved from GeneCards, OMIM and Therapeutic Target databases, and these eight targets appeared in three databases.

[0055] 3.2 PPI network analysis

[0056] The initial 659 candidate genes were analyzed on the STRING platform to construct a protein-protein interaction (PPI) network specifically for human non-small cell lung cancer target proteins. To improve clarity, isolated nodes were excluded from the network, and then 242 genes were obtained. The visualization of the network was achieved using Cytoscape software. In the protein interaction network ( Figure 2 b), Nodes located closer to the center and exhibiting darker colors indicate a greater number of connections to other targets, thus reflecting higher degree values ​​and more central locations in the PPI network.

[0057] To further refine the target selection, target proteins with a centrality of more than 2 were specifically selected, resulting in 148 core nodes and 652 edges being retained. Among these target proteins, TP53 had the largest number of connected edges, with a total of 48 edges, establishing itself as a key core target protein, CTNNB1, BRCA1, and HSP90AA1 exhibited more than 30 edges, while EGFR, STAT3, EP300, ESR1, HDAC1, and MYC had more than 20 edges. JAK1 and NBN had 4 and 5 edges, respectively, and the results highlight the importance of these target proteins in non-small cell lung cancer.

[0058] 3.3 Active site and molecular docking

[0059] X-ray crystal structures of 148 core target proteins were obtained from the UniProt database. The binding sites of each protein were identified by analysis using Discovery Studio software. In order to study the interaction between ginsenoside Rg3 as a ligand drug and the target protein, reverse molecular docking was performed using the rigid molecular docking method SailVina. The binding energy between the ligand and the receptor was low, indicating that the conformation was more stable and the possibility of interaction was higher. In order to identify potential target proteins, a value of -9 was used as the Autodock Vina score threshold, and 32 protein groups with scores below the threshold after screening were selected for flexible molecular docking with Libdock, and target proteins with Libdock scores greater than 130 were selected for further analysis. According to the docking results shown in Table 1, 19 proteins with flexible docking scores exceeding 130, including JAK1, NBN, KRAS, EGFR, BARD1, etc. Figure 3Results of the molecular docking of Rg3 with four target proteins Among the screened target proteins, JAK1 emerged as an important protein node in the JAK1 / STAT3 signaling pathway associated with non-small cell lung cancer [19, 20], while NBN is overexpressed in non-small cell lung cancer and other tumors

[21] . In addition, the remaining screened protein targets play a vital role in non-small cell lung cancer [22, 23]. The docking results showed that Rg3 had good binding affinity with the above-mentioned multiple target proteins, thus confirming its effective activity as a representative multi-target drug. However, it is important to note that Rg3 exhibits limited selectivity because it can interact with protein receptors of various biological pathways in the body. Therefore, this feature enables Rg3 to show therapeutic effects on multiple diseases to a certain extent.

[0060] Table 1 Scores of 19 core targets of LibDock and Autodock vina

[0061]

[0062] 3.4GO enrichment analysis

[0063] GO enrichment analysis provides an understanding of the biological processes, molecular functions, and cellular components associated with ginsenoside Rg3 target genes. To further explore the effects of ginsenoside Rg3 on non-small cell lung cancer target genes, GO enrichment analysis was performed using R language, and the analysis included three key aspects: biological processes, cellular components, and molecular functions.

[0064] Figure 4The bubble plot shown shows the enrichment of the effects of ginsenoside Rg3 on NSCLC in various biological processes. Notably, the prominent biological processes include peptidyl-tyrosine phosphorylation (GO: 0018108), peptidyl-tyrosine modification (GO: 0018212), and positive regulation of kinase activity (GO: 0033674), and the three processes involve the targets JAK1, EGFR, and NBN. In particular, the first two processes are closely related to JAK1 because they regulate the phosphorylation and modification of tyrosine residues, thereby affecting JAK1 activity [24, 25]. Regarding cellular components, enrichment analysis showed that Rg3 mainly targets membrane raft (GO: 0045121), membrane microdomain (GO: 0098857), focal adhesion (GO: 0005925), and cell-substrate junction (GO: 0030055), which are mainly located on the cell membrane and are closely related to EGFR. Regarding molecular functions, enrichment analysis showed significant enrichment of protein serine / threonine / tyrosine kinase activity (GO:0004712) and protein tyrosine kinase activity (GO:0004713), which are regulated by targets such as JAK1 and EGFR

[26] .

[0065] 3.5 KEGG pathway enrichment analysis

[0066] KEGG pathway enrichment analysis helped identify signaling pathways that were significantly enriched in Rg3 target genes. Molecular docking was used to identify 19 core target genes, and then signaling pathways with a p-value less than 0.01 were selected for further analysis. The p-value is a statistical measure of enrichment significance, and the lower the p-value, the higher the significance. Figure 4 In b, the signaling pathways were arranged according to the p-values, and the top 20 pathways were selected to generate the KEGG pathway diagram. KEGG pathway analysis found that ginsenoside Rg3 may exert its anti-NSCLC effects through multiple pathways, including EGFR tyrosine kinase inhibitor resistance (hsa01521), proteoglycans in cancer (hsa05205), and PI3K-Akt signaling pathway (hsa04151). KEGG pathway enrichment analysis showed that Rg3 may regulate NSCLC by affecting these pathways.

[0067] This analysis provided a comprehensive understanding of the molecular pathways and networks involved in NSCLC and elucidated the potential mechanisms by which Rg3 exerts its therapeutic effects.

[0068] 3.6 Molecular dynamics simulation results and analysis

[0069] Although molecular docking is widely used to predict the binding mode and affinity between molecules, it only provides a static view of ligand binding within the receptor active site. Molecular dynamics simulations are able to examine the dynamic processes involved in protein-ligand interactions within a given time frame. In this study, molecular dynamics simulations were performed to investigate the dynamic changes occurring in three proteins complexed with ginsenoside Rg3, enabling the observation of energetic and conformational changes to assess the stability of proteins when bound to Rg3

[27] .

[0070] Molecular dynamics was used to further simulate the dynamic binding of protein-Rg3 interaction models with flexible docking scores over 170. Four protein-Rg3 binding models, namely EGFR-Rg3, JAK1-Rg3, NBN-Rg3 and KRAS-Rg3, were analyzed in detail. The RMSF (root mean square fluctuation) values ​​of protein C atoms were calculated to provide information about the displacement of individual residues and to study the behavior of amino acid residues in the protein when interacting with the ligand. RMSD (root mean square deviation) analysis was performed on the molecular dynamics trajectory to evaluate the stability of the protein-Rg3 binding system, and a RMSD plot of the protein backbone atoms changing over time was generated, which enabled the analysis of the conformational changes of the protein structure during the entire simulation process.

[0071] like Figure 5 As shown in a, the RMSD fluctuation range of the JAK1-Rg3 complex is 2.23-6.78nm, with an average of 4.92nm. In contrast, the RMSD fluctuation range of JAK1 alone is 2.61-9.33nm, with an average of 6.17nm. During the simulation, the RMSD of the JAK1 protein monomer showed significant fluctuations over time, while the RMSD of the JAK1-Rg3 complex was stable at around 7.50ns, verifying a smaller fluctuation range compared with the JAK1 monomer. Throughout the molecular dynamics simulation, the hydrogen bond connection between JAK1 and ginsenoside Rg3 was analyzed to understand the rapid decomposition and reconstruction process of hydrogen bonds in protein molecules or between proteins and drugs. The amino acid residues with the highest connection frequency are Gly1023, Asp1021, Ser043, Gly887, and Arg1002, with connection frequencies of 0.81, 0.77, 0.61, 0.56, and 0.56, respectively. Initially, Gly1023, Gly887, and Ser1043 were linked to Rg3 at relatively low frequencies (e.g. Figure 5As shown). However, as the dynamics simulation proceeded, the connection frequency gradually increased. The connection frequency of Asp1021 fluctuated significantly, while Arg1002 did not initially form a hydrogen bond during the molecular dynamics simulation, but the connection frequency increased and eventually formed a relatively stable connection. The RMSF (root mean square fluctuation) analysis of JAK1-Rg3 and JAK1 showed that their ranges were 0.52-13.40nm and 0.62-14.41nm, respectively, with average values ​​of 1.76nm and 2.14nm. During the molecular dynamics simulation, there was not much difference in the RMSF between the JAK1-Rg3 complex and the JAK1 monomer. However, compared with the JAK1 monomer, the RMSF values ​​of the four amino acid residues (Gly1023, Asp1021, Ser1043 and Gly887) were lower in the complex. These results indicate that in the JAK1-Rg3 complex, amino acid residues are more likely to reach equilibrium during the dynamic process. The stable binding of Rg3 to JAK1 resulted in increased stability of the JAK1 protein structure, which highlighted JAK1 as a promising target for Rg3.

[0072] During the molecular dynamics simulation, Figure 5 b shows that the RMSD of the KRAS-Rg3 complex shows a significant trend of change over time. In contrast, the RMSD of the KRAS monomer tends to be stable, with lower fluctuation values ​​compared to the complex, and the instability of the KRAS-Rg3 complex is mainly attributed to the non-bonded position of the two amino acid chains at the end of the complex. Removal of the terminal amino acid chain does not cause significant changes in the region where Rg3 and KRAS bind, thereby maintaining the stability of the complex. In addition, RMSF analysis shows that the RMSF of the KRAS-Rg3 complex fluctuates in the range of 2-4nm, which is significantly lower than the RMSF fluctuation range of KRAS (6-10nm). Comparative analysis shows that the binding of Rg3 to KRAS reduces the fluctuation of amino acid residues and contributes to the overall stability of the protein. In summary, the binding of Rg3 to KRAS enhances the overall stability of the protein, as evidenced by the lower RMSD values ​​and reduced RMSF fluctuations in the complex compared to the KRAS monomer.

[0073] like Figure 5 As shown, the RMSD of the NBN-Rg3 complex stabilized in the range of 4-5 nm after an initial range of 6.5 nm, in contrast, the RMSD of the NBN monomer continued to increase, reaching 9.5 nm at 10 ns. The difference in the data suggests that Rg3 contributes to the stability of the protein NBN, as the RMSD values ​​of the complex remain relatively stable compared to the monomer. The RMSF of the NBN-Rg3 complex is generally smaller than that of the NBN monomer, which confirms the role of Rg3 in reducing the fluctuation of amino acid residues in NBN and enhancing the stability of the protein structure.

[0074] Example 4 Conclusion

[0075] By applying the GeneCards database, OMIM, therapeutic target database and PharmGKB, a complete list of 659 candidate genes associated with non-small cell lung cancer was obtained, and then protein-protein interaction (PPI) analysis was performed on these genes, resulting in the analysis of 148 core candidate target proteins. The reverse molecular docking method was used to improve the accuracy of protein-Rg3 docking, so that specific target proteins that showed strong affinity and interacted with Rg3 in the context of non-small cell lung cancer could be identified. 19 target proteins with good interactions with Rg3 were selected according to the molecular docking score; KEGG pathway analysis using R language scripts identified 20 potential pathways regulated by ginsenoside Rg3 in the treatment of non-small cell lung cancer. Among them, the PI3K-Akt pathway involves multiple genes, including Akt, AKT1, PIK3CA, MAPK3 and TP53, which plays a vital role in cell survival, proliferation and growth, and its dysregulation is associated with the development and progression of cancer. GO enrichment analysis showed that regulating protein phosphorylation and tyrosine kinase activity are important pathways for Rg3 to inhibit the growth of non-small cell lung cancer cells.

[0076] Molecular dynamics simulations showed that the binding of Rg3 to JAK1 and NBN contributed to the overall stability of these proteins, indicating strong interactions between Rg3 and these target proteins. The stable binding of Rg3 to multiple proteins demonstrated that Rg3 could be a favorable drug for the treatment of complex diseases such as non-small cell lung cancer, which involve multiple signaling pathways and molecular targets. The results of molecular dynamics simulations support ginsenoside Rg3 as a potential drug for further research, with the potential to develop highly selective Rg3 derivatives to improve its therapeutic characteristics. Enrichment analysis and protion-Rg3 binding information provide a basis for the design and development of novel ginsenoside derivatives, confirming that Rg3 can enhance the selectivity and potency for specific targets while maintaining the therapeutic effect of Rg3 on non-small cell lung cancer.

[0077] Example 5 Spectroscopic study on the interaction between ginsenoside Rg3 and JAK1 protein

[0078] Fluorescence spectrum experiment was conducted using FP-6500 fluorescence spectrophotometer: 2 mL human serum albumin solution was transferred into a 1 cm quartz colorimetric cell, and appropriate amount of ginsenoside Rg3 solution was added in sequence using a microinjector for fluorescence measurement, wherein 280 nm was used as the excitation wavelength, the excitation and emission slit passband widths were both 5 nm, the scanning wavelength range was 280-460 nm, and the synchronous fluorescence spectrum of JAK1 and Rg3 was measured at Δλ=20 nm and Δλ=60 nm. 2 mL JAK1 solution was transferred into a 1 cm quartz colorimetric cell, and ginsenoside Rg3 solution was added using a microinjector to form a solution with a molar concentration ratio of 1:1 between Rg3 and JAK1, and fluorescence measurement was performed, and the measurement method was the same as above. The fluorescence results of Rg3 on JAK1 showed that as the amount of Rg3 substance in the JAK1 solution increased, the fluorescence peak intensity of tryptophan near 336nm changed, indicating that the binding site of Rg3 to JAK1 may be close to the Trp residue in JAK1.

[0079] The circular dichroism experiment was conducted using a J-810 circular dichroism spectrometer: 500 μL of JAK1 solution was transferred to a 0.1 cm quartz colorimetric cell, and ginsenoside Rg3 solution was added in sequence using a microinjector for circular dichroism determination, wherein the slit bandwidth was 1 nm, the scanning wavelength range was 200-260 nm, the scanning speed was 50 nm / min, and the response time was 2 s. The circular dichroism results showed that after the addition of Rg3, the two groups of JAK1 structures changed slightly, indicating that Rg3 could bind to JAK1 and change the secondary structure of the JAK1 protein and the microenvironment of the amino acids.

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Claims

1. Application of ginsenoside Rg3 in the preparation of reagents for binding to JAK1 (Janus Kinase 1).

2. Application of ginsenoside Rg3 in the preparation of reagents for binding NBN (Nibrin).

3. Complex of ginsenoside Rg3 and JAK1.

4. A method for producing the complex according to claim 3, comprising the step of contacting ginsenoside Rg3 with JAK1.

5. Complex of ginsenoside Rg3 and NBN.

6. A method for producing the complex according to claim 5, comprising the step of contacting ginsenoside Rg3 with NBN.

7. Ginsenoside Rg3, which binds to JAK1 and / or NBN.

8. Use of molecules that simultaneously bind to JAK1 and NBN in the preparation of drugs for the treatment of non-small cell lung cancer.

9. The use according to claim 8, wherein the molecule is ginsenoside Rg3.

10. A method for screening a non-small cell lung cancer target protein bound by ginsenoside Rg3, comprising: (1) Obtain target protein data for non-small cell lung cancer; (2) constructing a protein-protein interaction network using the target protein obtained in step (1); (3) Using ginsenoside Rg3 as a ligand, rigid molecular docking was performed to predict the binding mode and affinity between the ligand and the target protein; and semi-flexible docking was performed to predict the ligand-protein interaction; (4) Optional GO enrichment analysis and KEGG pathway enrichment analysis; as well as (5) Perform molecular dynamics simulation on the results obtained in step (3).

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