Exploring the potential mechanisms of Danshen against COVID-19 via network pharmacology analysis and molecular docking

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein–Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

www.nature.com/scientificreports/database 27 .The results were visualized using the Bioinformatics platform ((bioinformatics.com.cn)).The statistical significance threshold for the enrichment analysis was set at p ≤ 0.05.

Molecular dynamics simulations
The molecular dynamics (MD) simulations for the TNFLuteolin and TP53-Luteolin systems were performed using Gromacs software package (2020) 31 .The protein and ligand were described by the ff14SB 32 and GAFF 33 force fields, respectively.Then, two systems were neutralized with an appropriate amount of counterion (Cl − or Na +), followed by solvation with TIP3P 34 water molecules in an orthogonal box, leaving at least 10 Å between the solute atoms and the box boundary.Moreover, the particle mesh Ewald (PME) 35 summation method was implemented to deal with Coulombic interactions, and the Shake 36 algorithm was applied to restrain the bond vibrations involving hydrogen atoms.The MD simulations mainly included four steps: energy minimization, heating, equilibration, and production runs.Firstly, four minimizations were performed by utilizing the steepest descent and the conjugate gradient algorithm through 25,000 steps with the force constants of 5.0, 2.0, 0.1, and 0 kcal/mol/Å2 to all heavy atoms, protein backbone, and Cα atoms, respectively.Then, each system was heated from 0 to 310 K with a force constant of 5.0 kcal/mol/Å2 for all heavy atoms.Next, all systems were equilibrated through 3000 steps with harmonic restraints of 1.0, 0.5, and 0.1 kcal/mol/Å2 on all heavy atoms, protein backbone, and Cα atoms, respectively.Finally, a 100 ns MD simulations was simulated with randomized initial atomic velocities for each system.

Binding free energy calculation and enrichment calculations
To evaluate the binding affinity of ligands on protein, the binding free energy was calculated using the MM/ GBSA 37 method for each system.Firstly, water molecules and counter ions were extracted from the MD trajectory, and then 250 snapshots at time intervals of 20 ps were taken from the last 20-ns MD simulation.For each snapshot, the binding free energy ("∆" "G" _"bind") was calculated utilizing the following formula: To assess the validity of the docking protocol, we conducted enrichment calculations using PyRx 38 and SPSS Statistics 39 software.Specifically, we utilized 500 decoy compounds from the DUD.E 40 database and 10 validated compounds along with Luteolin.Autodocking was performed on these compounds, and subsequently, we generated receiver operating characteristic (ROC) curves based on the obtained docking scores.The ROC curves provide a visual representation of the trade-off between true-positive (TP) and false-positive (FP) results.To quantify the performance of the docking protocol, we computed the area under the curve (AUC) of the ROC curves.The AUC value ranges between 0 and 1, with a value close to 0.5 indicating random selection of TP and FP results, while a value close to 1 suggests a higher likelihood of correctly identifying TP results before encountering FP results.By analyzing the ROC curve and examining the computed AUC, we can gain valuable insights into the predictive capability and effectiveness of our docking protocol in distinguishing true positives from false positives 41 .

Targets prediction and network construction
In the TCMSP database, a total of 202 initial components of Danshen were identified (Supplementary Table 1).After removing duplicates and applying the aforementioned filtering criteria, 6 components did not have corresponding targets, resulting in the final selection of 59 candidate components of Danshen (Supplementary Table 2).Utilizing TCMSP predictions, a total of 930 targets for Danshen candidate components were collected.Following the removal of duplicates, 123 targets and their respective symbols were obtained (Supplementary Table 3).We introduced 59 candidate components and 123 targets into Cytoscape and constructed a Danshencomponents-targets network (Fig. 2A), containing 183 nodes and 989 edges.To further investigate the relationship between Danshen and COVID-19, we utilized the Genecards database and identified 1355 potential targets by inputting "novel coronavirus pneumonia" as a query.After applying the filtering standards mentioned above, 497 targets were eliminated, and 858 COVID-19-related targets were retained (Supplementary Table 4).By employing a Venn diagram (Fig. 2B), an intersection was generated between the 123 targets of Danshen candidate components and the 858 COVID-19-related targets, resulting in 31 common targets (Supplementary Table 5).These common targets were considered potential targets for Danshen in treating COVID-19 and were utilized for subsequent network construction and pathway enrichment analysis.The 31 common targets and corresponding to 45 components were used to construct the Danshen-45 components-31 common targets-COVID-19 network (Fig. 2C), which consisted of 183 nodes and 989 edges.Nodes with higher degrees, as indicated by a greater number of edges, exhibited larger sizes within the network, signifying their greater importance.Network analysis revealed a threshold degree value of5.6 for the 45 candidate components.Table 1 displays the top 10 compound nodes with the largest degree sizes.Among them, 6 components exhibited degrees higher than 5.6: (MOL000006, degree = 19), (MOL007154, degree = 12), (MOL007111, degree = 8), (MOL007050, degree = 8), (MOL007100, degree = 7), (MOL007088, degree = 6).Thus, these compounds are deemed critical nodes within the network and hold significant potential for treating COVID-19, suggesting that Danshen exerts its effects on COVID-19 through multiple components.

PPI network construction
The PPI network was used to understand the correlation between various targets in Danshen and COVID-19.
In this study, we constructed 123 targets PPI network of 59 candidate components (Fig. 3A), then introduced data from STRING to Cytoscape to acquire a visualization result based on the degree value (Fig. 3B).The result showed that the candidate components targets PPI network had 115 nodes and 687 edges.In addition, we constructed the COVID-19-related targets network with PPI data (Fig. 4A) and developed a Cytoscape-processed result (Fig. 4B).The result indicated that the COVID-19 targets PPI network had 838 nodes and 28,307 edges.Then the common targets PPI network (Fig. 5A) and Cytoscape-processed result (Fig. 5B) was established, which consists of 31 nodes and 180 edges.A bigger node and deeper color indicate a greater degree of value.The edge  thickness stands for the edge betweenness, and a thicker line indicates a stronger relationship between the two targets.The degree value of each target was shown in (Fig. 6A), and the degree value threshold of common targets' PPI network was 11.61, calculated from Cytoscape-Centiscape 2.2 Menu.Topological analysis of the common targets PPI networks identified 12 genes with scores higher than the threshold as the core targets, following TP53, TNF, VEGFA, APP, MAPK14, IL4, CDK1, FOS, BCL2L1, MCL1, IL2, CXCL8.Then the PPI network of all core targets was produced (Fig. 6B).These dates suggested that candidate components probably acted on COVID-19 mainly through the targets of TP53, TNF, VEGFA, APP, MAPK14, IL4, CDK1, FOS, BCL2L1, MCL1, IL2, CXCL8.

KEGG enrichment analyses
To investigate the underlying biological processes associated with Danshen treatment for COVID-19, a KEGG analysis was performed on 31 common targets.The analysis identified 96 statistically significant pathways (Supplementary Table 6), from which the top 30 pathways with the highest count value were selected for further analysis (Fig. 7A).The observed prominence of Pathways in cancer and the PI3K-Akt signaling pathway suggests that Danshen may exert its therapeutic effects on the target disease primarily through its interactions with Pathways in cancer or the PI3K-Akt signaling pathway.Furthermore, a circle diagram was constructed to examine the enrichment analysis of the top 10 KEGG pathways among the 12 core genes (Fig. 7B).The results indicated a significant enrichment of the core genes in pathways related to cancer.In summary, the KEGG analysis of Danshen treatment on COVID-19 identified several key pathways, with a notable emphasis on Pathways in cancer and the PI3K-Akt signaling pathway.Additionally, the enrichment analysis of core genes further supported their involvement of Pathways in cancer.

GO enrichment analysis
The GO analysis encompassed the exploration of biological processes (BP), cellular components (CC), and molecular functions (MF) in this study.764 statistically significant GO terms were derived (Supplementary Table 7), comprising 696 BP terms, 31 CC terms, and 37 MF terms.The visualization of the top 10 highly abundant enrichment terms for BP, CC, and MF (Fig. 8A) facilitated the interpretation of the results.The analysis of biological processes (BP) unveiled the prominent involvement of the common genes in various cellular activities, including cell activation, regulation of kinase activity, leukocyte activation, regulation of protein kinase activity, positive regulation of protein phosphorylation, regulation of cellular catabolic process, positive regulation of kinase activity, positive regulation of transferase activity, leukocyte differentiation, and inflammatory response.The analysis of cellular components (CC) identified the centrosome, axon, transferase complex, and phosphorus-containing www.nature.com/scientificreports/groups as the primary cellular locations where the targets were concentrated.In terms of molecular functions (MF), the main functional roles encompassed signaling receptor activator activity, signaling receptor regulator activity, and receptor-ligand activity.Subsequently, a circular diagram (Fig. 8B) was constructed to elucidate the enrichment analysis of biological process pathways among the 12 core genes, revealing a notable enrichment of leukocyte differentiation among the core genes.To further investigate the intricate relationship between Danshen, its compounds, core targets, pathways, and COVID-19, two network diagrams were devised.The "Core targets-BP pathways" network (Fig. 9) and the "Danshen-compounds-core targets-KEGG pathways-COVID-19" network (Fig. 10) provided a comprehensive visualization of the interconnections, underscoring the multi-faceted nature of Danshen's actions, involving multiple targets and diverse components.

Molecular docking
The PPI network analysis identified the top 3 targets among the core targets, namely TP53, VEGFA, and TNF.These targets were selected for molecular docking with their corresponding compounds.Specifically, TP53 corresponded to Luteolin and Tanshinone IIA, VEGFA corresponded to Luteolin, and TNF corresponded to Luteolin.The 2D structures of Tanshinone IIA and Luteolin can be found in Fig. 11.The binding affinities of these complexes were evaluated and presented in Table 2.The results of the matching analysis demonstrated that top 3 targets and compound molecules exhibited docking scores below − 5. Notably, three targets structures were utilized: TP53 (PDB ID: 6RZ3), VEGFA (PDB ID: 4zff), and TNF (PDB ID: 1A8M).Luteolin ligand formed five hydrogen bonds with a docking score of − 6.55 kcal/mol against 1A8M (Fig. 12A), three hydrogen bonds with a docking score of − 7.19 kcal/mol against 6RZ3 (Fig. 12B), and four hydrogen bonds with a docking score of − 5.1 kcal/2 mol against 4ZFF (Fig. 12C).On the other hand, Tanshinone IIA ligand formed three hydrogen bonds with a docking score of − 5.29 kcal/mol against 6RZ3 (Fig. 12D).

Root-mean-square deviations
We selected the TNF-Luteolin and TP53-Luteolin to perform molecular dynamics simulations.The root-meansquare deviations (RMSDs) of the Cα atoms of TNF-Luteolin and TP53-Luteolin were calculated by aligning to their first frame to evaluate the conformational stability of each simulated system (All raw data can be found from Supplementary_Raw data_Molecular Dynamics Simulations).As illustrated in Fig. 13, two simulations converged and displayed RMSD values smaller than 4 Å over the last 50 ns, indicating the high stability of TNF-Luteolin and TP53-Luteolin systems.In addition, TNF-Luteolin exhibited a smaller RMSD value (~ 2 Å) than that of TP53-Luteolin (~ 3.5 Å), indicating TNF-Luteolin should more stable than TP53-Luteolin during the MD simulations.To further evaluate the binding affinity between two proteins and luteolin, the MM-GBSA method was utilized to calculate the binding free energy for the TNF-Luteolin and TP53-Luteolin over the last 20 ns MD trajectories.As listed in Table 3, the binding free energies (ΔGMM/GBSA) were − 11.31, and − 19.84 kcal/ mol for TNF-Luteolin and TP53-Luteolin, respectively.Hence, luteolin exhibited a higher binding affinity to TNF compared to TP53.In detail, the van der Waals (ΔEvdw) interaction and the direct electrostatic interaction (ΔEele) were the crucial driving force for the combination between protein and luteolin.In contrast, the polar component (ΔGpol,sol) was unconducive to the ligand binding.

Enrichment calculations
To ensure reliable results and avoid false-positive hits during the docking analysis, enrichment calculations were performed using a set of 500 decoy compounds obtained from the DUD.E database.For the calculation of the enrichment factor, a total of 10 compounds from each enumeration along with Luteolin were selected.These 10 compounds were mixed with the 500 decoy sets and re-docked using the Autodock protocol against the TP53 and TNF receptors.To assess the performance of the docking protocol, a validation factor, such as the receiver operating characteristic (ROC) curve, was calculated.The results obtained from this analysis are summarized in Supplementary Tables 8 and 9.For the TP53 receptor, the results showed that AUC = 0.899 (Fig. 14A).For the TNF receptor, the results showed that AUC = 0.688 (Fig. 14B).The ROC curve plot confirms the specificity and sensitivity of the employed method in identifying active compounds.

Discussion
COVID-19, a novel respiratory infectious disease, has rapidly spread worldwide, resulting in significant public health consequences 42 .Clinical observations have indicated that common manifestations in COVID-19 patients include fever, cough, fatigue, and myalgia 43 .Currently, vaccination have reduced the risk of death related to COVID-19 to some extent, but the significant sequelae, such as lung damage, pulmonary fibrosis, myocarditis arrhythmias and stroke are still exist 44 .Traditional Chinese medicine (TCM) has demonstrated potential antiviral properties against various viruses 45 , such as herpes simplex virus, influenza virus, and human immunodeficiency    Network pharmacological analysis offers a promising approach to explore the impact of TCM on diseases by systematically elucidating the complex components, targets, and mechanisms involved.Therefore, in this study, our aim was to explore the potential therapeutic effects of Danshen in the treatment and mitigation of COVID-19 using network pharmacology.We aimed to identify the core targets, biological pathways, and potential mechanisms of Danshen in the treatment of COVID-19.Additionally, in order to assess the reliability of the binding, we calculated the docking score between Danshen components and their corresponding targets, which will provide new insights for the treatment of COVID-19 and vaccine development.
Specifically, our investigation identified 59 candidate components of Danshen that may be effective in treating COVID-19, with Luteolin emerging as the most promising compound due to it's the largest number of targets interactions in target prediction and network construction.Luteolin is a naturally occurring flavonoid found in various plants 50 , and it possesses various pharmacological activities, including anti-inflammatory 51 , anti-tumor 52 , and anti-allergy effects 53 .Existing studies have demonstrated that Luteolin can attenuate LPSinduced myocardial injury by activating AMPK-mediated autophagy 54 , suppress THP-1 macrophage pyroptosis by inhibiting ROS production through Nrf2 activation and NF-κB inactivation 55 , and mitigate breast cancer hallmarks by downregulating Nrf2-mediated gene expressions 56 .Notably, Luteolin has also shown potential in alleviating long-COVID syndrome symptoms, such as cognitive dysfunction and fatigue, likely through its antiinflammatory effects 57 .These findings, along with our results underscore the significance of Luteolin in the context of Danshen's efficacy against COVID-19.In addition to Luteolin, other candidate components derived from Danshen exhibited pharmacological activities relevant to COVID-19.Among them, Tanshinone IIA displayed a higher degree value in target prediction and network construction.Tanshinone IIA is a fat-soluble quinone compound widely used in the treatment and adjuvant therapy of cardiovascular diseases 58,59 , non-alcoholic fatty liver disease 60 , and cancers 61 .Notably, Tanshinone IIA has been shown to suppress gastric cancer proliferation by inducing ferroptosis, inhibit glucose metabolism leading to apoptosis in cervical cancer, and promote cell apoptosis in ovarian carcinoma cells through direct upregulation of miR-205 62 .Molecular docking studies further revealed that Tanshinone IIA exhibited favorable interactions with spike and main protease receptors, indicating its intrinsic anti-SARS-CoV-2 potency 63 .Moreover, our screening process identified 31 common targets shared by Danshen components and COVID-19, highlighting the different targets of Danshen relevant to COVID-19.Through topological analysis, we identified 12 core targets among these 31 common targets for further investigation.Analysis of protein-protein interaction networks indicated that TP53, VEGFA, and TNF were the top three core targets.These targets are primarily associated with inflammation, suggesting that Danshen may exert an anti-inflammatory effect.Molecular docking results showed that all the docking scores were below -5.Then we selected the two lowest docking scores results to perform molecular dynamics, the results showed high stability of www.nature.com/scientificreports/TNF-Luteolin and TP53-Luteolin systems, and TNF-Luteolin showed more stable than TP53-Luteolin during the molecular dynamics simulations.To further evaluate the binding affinity between two proteins and luteolin, we use the MM-GBSA method to calculate the binding free energy for the TNFLuteolin and TP53-Luteolin over the last 20 ns molecular dynamics trajectories.luteolin exhibited a higher binding affinity to TNF compared to TP53.Importantly, TP53, which is crucial in regulating tumor immune responses, plays a multifaceted role in cancer development and therapy 63 .Notably, TP53 has also been identified as a hub protein in the context of Yinqiao powder, another TCM formulation with potential effects against COVID-19 64 .Additionally, TP53 has been implicated as a pivotal target of puerarin, a TCM compound with potential COVID-19 treatment effects 65 .These findings further support the importance of TP53 in the context of Danshen's efficacy against COVID-19.
Furthermore, TNF and VEGFA, which exhibited higher degree values in target prediction and network construction, may also play significant roles in Danshen's therapeutic effects against COVID-19.TNF is a crucial mediator and regulator of immune responses in both healthy and diseased conditions 66 .It can induce programmed cell death (PCD) through apoptosis and/or necroptosis 67 .TNF has been identified as a core gene target in Lithospermum erythrorhizon against COVID-19, and Tormentic acid, a primary component of Lithospermum erythrorhizon, has shown strong binding affinity with TNF 68 .Similarly, VEGFA is a key regulator of angiogenesis and vascular permeability 69 .Interestingly, VEGFA has also been identified as a core target in Scutellaria baicalensis, another TCM herb with potential therapeutic effects against COVID-19 70 .Overall, the network pharmacology analysis and molecular docking results highlight the potential of Danshen and its components, particularly Luteolin and Tanshinone IIA, in the treatment of COVID-19.These components exhibit interactions with core targets involved in inflammation, immune response, and viral infection.
The docking results demonstrated that Luteolin exhibited binding affinity towards TP53, VEGFA, and TNF, while Tanshinone IIA showed specific binding to TP53.Regarding Luteolin, we observed that it formed five hydrogen bonds with TNF, as shown in Fig. 12A, resulting in a docking score of -6.55 kcal/mol.For TP53, Luteolin formed three hydrogen bonds, as depicted in Fig. 12B, with a docking score of -7.19 kcal/mol.Additionally, Luteolin formed four hydrogen bonds with VEGFA, as illustrated in Fig. 12C, with a docking score of -5.1 kcal/mol.These hydrogen bonding interactions contribute to the stability and affinity of Luteolin binding to the respective target proteins.In the case of Tanshinone IIA, we observed that it formed three hydrogen bonds with TP53, as shown in Fig. 12D, resulting in a docking score of -5.29 kcal/mol.These hydrogen bonding interactions play a crucial role in stabilizing the Tanshinone IIA-TP53 complex.The docking scores obtained for the top three targets and compound molecules were below -5, indicating strong binding affinity.Additiconally, The findings from this docking validation protocol demonstrate the satisfactory performance of the autodock docking protocol.The ROC curve plot further validates the specificity and sensitivity of the employed method in identifying active compounds.
In comparison to the published paper by Simon J. L. Petitjean 49 , which demonstrated the effects of Danshen extracts against COVID-19 by blocking the binding of SARS-CoV-2 to the ACE2 receptor and alleviating the inflammatory response from leukocytes by interfering with NF-κB signaling, our study fills the gap by investigating multiple targets and pathways in a comprehensive and systematic manner.Additionally, a study conducted by Shiling Hu 17 only revealed that three Salvianolic acids can inhibit the entry of 2019-nCoV spike pseudovirus into ACE2 high expression cells by binding to the RBD of the 2019-nCoV spike protein and ACE2 protein, which prompted us to explore the potential of Danshen's ingredients treating COVID-19 in a comprehensive and systemic way.Based on this, network pharmacology holds great potential in elucidating the intricate connections among multiple ingredients from Danshen, multiple targets, and COVID-19, which also offers a novel research approach for studying diseases related to traditional Chinese medicine.
However, it is worth mentioning that the potential active components, possible targets, and important biological pathways of Danshen in the treatment of COVID-19 is based on computational technologies and still requires further validation through pharmacological and preclinical studies.
Due to the spike protein of the 2019-nCoV virus facilitates the entry of the virus into cells, particularly respiratory epithelial cells, by binding to the angiotensin-converting enzyme 2 (ACE2) receptor, leading to infection and the development of the disease [71][72][73] , we want to explore whether ingredients from Danshen could play inhibition potential in binding to ACE2 and spike protein.We set out to evaluate the toxicity of Tanshinone IIA on ACE2 high-expressing HEK293T cells.Moreover, we further need to evaluate the binding capacity of Tanshinone IIA to ACE2 and the spike protein of 2019-nCoV using molecular docking and surface plasmon resonance.Additionally, many researchers have shown that 2019-nCoV can induce acute inflammatory response like acute lung inflammation 42,74,75 , we set out to establish a mouse model of acute lung inflammation infected with SARS-CoV-2 S according to the previous study 76 , and determine the pathological alterations in mice with oral and intravenous pretreatment with Tanshinone IIA dose-dependently.We hope this work will contribute to developing novel therapeutic strategies for the treatment of COVID-19 from ingredients of Danshen. https://doi.org/10.1038/s41598-024-62363-x

Figure 1 .
Figure 1.Workflow for Danshen treatment on COVID-19.(A) The first step was to predict targets.(B) The second step was to construct network.(C) The third step was to make enrichment analysis.(D) The fourth step was to develop molecular docking.

Figure 2 .
Figure 2. Targets prediction and network construction.(A) Danshen-components-targets network.The red octagon represents Danshen.Yellow Ellipse represents targets.The Blue octagon represents candidate components.(B) Venn diagram of 31 potential common targets.(C) Danshen-components-candidate targets-COVID-19 network.Yellow Ellipse represents common targets.Blue Ellipse represents candidate components.Purple V represents COVID-19.Green Triangle represents Danshen.Nodes from small to large represent the degree value from low to high.

Figure 3 .
Figure 3. Protein-Protein interaction (PPI) network construction of the candidate components of Danshen.(A) Targets PPI network of candidate components in STRING.(B) Cytoscape-processed of Targets' PPI network of candidate components.Blue Ellipse represents targets.

Figure 6 .
Figure 6.Degree value of core targets and Protein-Protein interaction (PPI) network construction of core targets.(A) Degree value of core targets.(B) PPI network of core targets.

Figure 7 .
Figure 7. Top 30 most meaningful pathways in KEGG enrichment analysis of common targets with the basis set as P < 0.05.(A) Dot plot of KEGG enrichment.The color changes from red to green, indicating that the − log10 (p value) changes from large to small.The larger the surface area, the greater the enrichment degree.(B) Circle diagram.The genes are linked to their assigned terms via different colored ribbons, the larger ribbons, the more targets were enriched in pathways.

Figure 8 .
Figure 8. Top thirty most meaningful pathways in GO enrichment analysis of common targets with the basis set as P < 0.05.(A) Dot plot of GO enrichment.The color changes from red to green, indicating that the − log10 (p value) changes from large to small.The larger the surface area, the greater the enrichment degree.(B) Circle diagram.The genes are linked to their assigned terms via different colored ribbons, the larger ribbons, the more targets were enriched in BP pathways.

Figure 12 .
Figure 12.Molecular docking verification.(A) Luteolin bound to the candidate pocket of TNF.(B) Luteolin bound to the candidate pocket of TP53.(C) Luteolin bound to the candidate pocket of VEGFA.(D) Tanshinone IIA bound to the candidate pocket of TP53.
www.nature.com/scientificreports/virus[46][47][48] .Previous studies have also suggested the therapeutic potential of Danshen, a TCM herb, in the context of COVID-1949 .Moreover, as stated in the Protocol (National Health Commission of the People's Republic of China, 2020.Diagnosis and Treatment Protocol for COVID-19 Trial Version 7), Danshen has been actively employed in clinical settings for managing COVID-19.Specially, during the critical phase of clinical treatment, the administration of Xuebijing injection, comprising Safflower, Red Peony Root, Szechwan Lovage Rhizome, Danshen, and Angelica sinensis, was advised for the management of the disease.However, a comprehensive understanding of the detailed mechanisms underlying Danshen's effectiveness against COVID-19 is still lacking.

Figure 14 .
Figure 14.The plot of receiver operator characteristic (ROC) area under the curve: (A) Enrichment result for TP53.(B) Enrichment result for TNF.

Table 1 .
The top10 candidate components in degree value.

Table 2 .
Docking results of top3 core targets with corresponding components.

Table 3 .
Components of the binding free energy between two proteins and luteolin averaged in the Last 20 ns (kcal/mol).