Prediction Targets of a Novel Sesquiterpene Lactone From Eupatorium lindleyanum DC. Against Viral Pneumonia Using Computational Pharmacology

Abstract

Objective: This study employed computational pharmacology to explore the possible therapeutic targets and molecular mechanisms of F1012-2, a novel sesquiterpene lactone from Eupatorium lindleyanum DC. (EL), against viral pneumonia. Methods: With network pharmacology, we first looked into various databases for genes and proteins related to viral pneumonia, as well as the potential targets of F1012-2. By overlapping these 2 groups of genes, we acquired the candidate targets of F1012-2 against viral pneumonia. Afterward, enrichment analysis was performed to elucidate the interactive targets in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. The protein–protein interaction (PPI) network was constructed through the STRING database, and the results were imported into Cytoscape software to search for the key genes by Network Analyzer. Finally, molecular docking analysis was used to further validate the candidate targets of F1012-2 against viral pneumonia. Results: A total of 110 target genes were found for the 3 compounds within F1012-2 (Eupalinolide G, [EG] Eupalinolide I, [EI] and Eupalinolide J [EJ]), while 4322 potential therapeutic targets were uncovered related to viral pneumonia, and the intersection of the 2 groups generated 78 target genes. Among the candidate genes, epidermal growth factor receptor (EGFR), interleukin-1beta (IL1B), tyrosine-protein kinase SRC, Caspase-3 (CASP3), and transcription factor and oncoprotein JUN have the highest degree, betweenness, and closeness, which could fit into binding with EG, EI, and EJ. The enrichment analysis indicated that the major pathways involved were sphingolipid, neurotrophin, tumor necrosis factor (TNF), and Toll-like receptor signaling pathways. Conclusion: These findings showed that by the combination of network pharmacology and molecular docking, we could speculate on the possible mechanism of F1012-2's effect on viral pneumonia, with EGFR, IL1B, SRC, and CASP3 as the promising targets.

Keywords

sesquiterpene lactone F1012-2 viral pneumonia network pharmacology molecular docking

Introduction

Viral pneumonia is a prevalent cause of respiratory infection that may result in the death of very young children and the elderly above the age of 75 years.¹ The emergences of avian influenza A (H5N1), severe acute respiratory syndrome (SARS), and the ongoing coronavirus infectious disease 2019 (COVID-19) have emphasized the important role of viral infection as causes of pneumonia.²

The real-time reverse transcription polymerase chain reaction (RT-PCR) and other new rapid molecular assays for pneumonia detection are contributing to viral pneumonia diagnosis.³ However, its treatment is undesirable on account of less active targets against viruses. Antiviral medications, such as oseltamivir, ribavirin, and acyclovir, are currently limited in clinical practice because of their side effects and virus resistance. Despite the use of immunotherapy, it has played a limited role in the treatment of viral pneumonia.²

Traditional Chinese medicines (TCMs), such as Lianhua Qingwen (LHQW) capsule, Jinhua Qinggan (JHQG), and Lung Cleansing and Detoxifying Decoction (LCDD), have long been used for the treatment of respiratory diseases.^4–6 Eupatorium lindleyanum DC. (EL), also called “Yemazhui,” has the effects of “clearing heat to remove toxicity,” and is widely used to treat cough and chronic bronchitis for various ages in China.⁷ A great number of studies have proved that EL has many pharmacological activities, including anticancer,^8,9 antiviral,¹⁰ and anti-inflammatory.¹¹ Importantly, EL preparations, such as Eupatorium Kechuan powder and Yemazhui capsule, are clinically applied to treat respiratory diseases.⁷ F1012-2 is a novel, active, sesquiterpene lactone fraction isolated from EL. In our previous study, we reported that F1012-2 had significant anticancer activities.^9,12 However, its effect on viral pneumonia remains unknown.

In this study, we explored the molecular mechanism of F1012-2 against viral pneumonia using computational pharmacology methods. Network pharmacology is a novel approach that mixes computer science and medicine, establishing a “compound-protein/gene-disease” network. This method is perfect for research of TCMs that have multi-components.¹³ Molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets, which are used in modern drug design. These techniques provide powerful tools for understanding the effect and mechanism of F1012-2 against viral pneumonia.

Materials and Methods

Obtaining the Targets of F1012-2 and Viral Pneumonia

F1012-2 consists of 3 compounds, namely Eupalinolide G (EG), Eupalinolide I (EI), and Eupalinolide J (EJ). The ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) chromatogram of F1012-2 and the structures of its compounds can be seen in the Supplemental material. The ratio of EG, EI, and EJ in F1012-2 was 2:3:1, based on peak areas. First, the active compounds of F1012-2 and their targets were obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcm-sp.php),¹⁴ SwissTargetPrediction (https://swisstargetprediction.ch/),¹⁵ similarity ensemble approach (SEA, http://sea.bkslab.org/),¹⁶ and Bioinformatics Analysis Tool for the molecular mechanism of TCM (BATMAN-TCM, http://bionet.ncpsb.org.cn/batm-tcm/).¹⁷ A compound–target network was constructed by means of Cytoscape_v3.8.2. Then, several databases were used to search for viral pneumonia-related genes: Online Mendelian Inheritance in Man (OMIM, https://omim.org/),¹⁸ GeneCards 5.9 (https://www.genecards.org/),¹⁹ DrugBank 5.0 (https://www.drugbank.ca/),²⁰ DisGeNET 7.0 (https://www.disgenet.org/),²¹ and Therapeutic Target Database (TTD, http://db.idrblab.net/ttd/).²² The target gene set was acquired with the help of Uniprot (https://www.uniprot.org/).²³ Subsequently, we took the target genes of F1012-2 and the therapeutic targets for viral pneumonia to obtain the overlapping genes.

Enrichment Analysis

Based on the overlapping genes of F1012-2 and viral pneumonia, gene ontology (GO), including biological processes (BP), cellular components (CC), and molecular functions (MF), was performed to illustrate the underlying mechanism, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to find the key signaling pathways.

Protein–Protein Interaction (PPI) Network and Core Subnetwork

The targets of F1012-2 and viral pneumonia were used to build a PPI network through STRING database. We set a combined score of 0.400. Afterward, the PPI network was imported into Cytoscape. We filtered genes according to each score of degree, betweenness, and closeness to acquire the critical subnetwork.

Molecular Docking Analysis

Molecular docking analysis was carried out as described previously.²⁴ The 2D ligand structures of EG, EI, and EJ were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov).²⁵ We downloaded the 3D structure of the proteins in RCSB Protein Data Bank (PDB) as .pdb files.²⁶ All chains of the structure were used to prepare the receptor, and all water molecules around the protein were removed. PyMOL v2.3.2 software was performed to dehydrate the receptor protein, and Autodock software was used to carry out hydrogenation and charge calculation of proteins.²⁷ Finally, several docking poses were obtained for the molecule by Autodock Vina,²⁸ and the one with the optimal binding energy was chosen.

Results

Screening the Potential Targets of F1012-2 and Viral Pneumonia

F1012-2 consists of 3 compounds, namely EG, EI, and EJ. Their structures are shown in Figure 1A. From TCMSP, SwissTargetPrediction, SEA, and BATMAN-TCM databases, we acquired 110 compound-target genes of these 3 compounds, and we visualized the compound–target interaction network by Cytoscape_v3.8.2 (Figure 1B). Then, we gained 696, 4016, 42, 13, and 5 viral pneumonia-related genes from OMIM, GeneCards, DrugBank, DisGeNET, and TTD databases, respectively (Figure 1C). After removing the duplication, 4322 genes were acquired. By overlapping the targets of the compounds and the disease, we finally obtained 78 candidate genes (Figure 1D).

Figure 1.

Identification of compound–target gene interactions. (A) Structures of EG, EI, and EJ. (B) Construction of the 3 compounds–target interaction network. (C) Identification of the viral pneumonia-related genes by taking a union of all the results from 5 databases. (D) Identification of an intersection of compound-target genes and viral pneumonia-related genes.

GO Enrichment Analysis

To find the potential BP, CC, and MF of the 78 genes, GO enrichment analysis was carried out with Database for Annotation, Visualization and Integrated Discovery (DAVID) database. According to the Q value from small to large, the top 20 terms are shown in Figure 2. The GO analysis indicated that these genes played a vital role in various BP, such as drug response, signal transduction, apoptosis, and inflammatory response. The CC analysis indicated that plasma membrane and cytosol were the major locations where F1012-2 exerted its antiviral pneumonia activity.

Figure 2.

Gene Ontology (GO) enrichment analysis of 78 target genes. (A) Top 20 significantly enriched biological processes (BP). (B) Top 20 significantly enriched cellular components (CC). (C) Top 20 significantly enriched molecular functions (MF).

KEGG Enrichment Analysis

To understand further how F1012-2 affects viral pneumonia through these target genes, KEGG enrichment analysis was performed by importing the 78 target genes into the DAVID database. We selected the top 20 terms based on the Q value from small to large, as shown in Figure 3. The analysis indicated that these target genes mainly affected the pathways related to cancer, sphingolipid, neurotrophin, tumor necrosis factor (TNF), and Toll-like signal pathways. In addition, we found other virus-related signaling pathways, as shown in Table 1, suggesting that these play an important role in virus infection.

Figure 3.

Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the top 20 significantly enriched pathways.

Table 1.

Virus-Related Pathway Enriched by 78 Target Genes With Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis.

Term	Description	Gene ratio	Count	P value	Q-value
Hsa05161	Hepatitis B	0.0211	15	2.81 × 10⁻¹⁰	2.60 × 10⁻⁹
Hsa05164	Influenza A	0.0253	16	3.02 × 10⁻¹⁰	2.60 × 10⁻⁹
Hsa05169	Epstein–Barr virus infection	0.0177	13	4.87 × 10⁻⁹	2.99 × 10⁻⁸
Hsa05203	Viral carcinogenesis	0.0298	14	2.11 × 10⁻⁷	8.64 × 10⁻⁷
Hsa05160	Hepatitis C	0.0193	11	1.31 × 10⁻⁶	3.51 × 10⁻⁶
Hsa05168	Herpes simplex infection	0.0266	11	2.28 × 10⁻⁵	4.56 × 10⁻⁵
Hsa05416	Viral myocarditis	0.0083	5	.00317	0.00354
Hsa05166	HTLV-I infection	0.0369	8	.0189	0.0189

PPI Network and key Network Analysis

A PPI network of 78 targets was first obtained from STRING database, and then imported into Cytoscape for further analysis. The filters were set as an adjusted average of degree, betweenness, and closeness. Finally, 5 key target genes epidermal growth factor receptor (EGFR), interleukin-1beta (IL1B), tyrosine-protein kinase SRC, Caspase-3 (CASP3), and transcription factor and oncoprotein JUN were acquired (Figure 4).

Figure 4.

Identification of key target genes by Cytoscape.

Molecular Docking of Active Compounds and the key Proteins

Five key target genes, EGFR, IL1B, SRC, CASP3, and JUN, were selected to dock with EG, EI, and EJ. The docking scores were shown in Figure 5A. We set the docking score at −6.0 kcal/mol. The blue color, with a high docking score, indicates a poor connection needing high binding energy. Conversely, the red color, with a low docking score, indicates a better interaction needing low binding energy. As shown in Figure 5A, the docking scores of JUN with EG, EI, and EJ were all higher than −6.0 kcal/mol indicating that EG, EI, and EJ presented lower binding activities with JUN. The docking scores of EGFR, IL1B, SRC, and CASP3 with EG, EI, and EJ were all lower than −6.0 kcal/mol indicating that the 3 compounds could easily bind in the pockets of EGFR, IL1B, SRC, and CASP3 proteins. Therefore, we exhibited the molecular docking of EGFR, IL1B, SRC, and CASP3 with EG, EI, and EJ in Figure 5B. In order to investigate further the molecular docking of the active compounds with EGFR, IL1B, SRC, and CASP3, the inhibitors of these 4 key proteins (namely Dasatinib, Alflutinib mesylate, Ivachtin, and terminolic acid) were used as positive controls. The results showed that the binding energies of the positive controls with the target proteins were all lower than −6.0 kcal/mol (Table 2).

Figure 5.

Molecular docking of active compounds and key proteins. (A) Molecular docking score. (B) Molecular docking between Eupalinolide G (EG), Eupalinolide I (EI), and Eupalinolide J (EJ) ligands and SRC, CASP3, EGFR, and IL1B receptors.

Table 2.

The Binding Energy of the Target Protein With Their Positive Controls by Molecular Docking Analysis.

Target protein	Molecule name	Binding energy (kcal/mol)
SRC	Dasatinib	−8.8
EGFR	Aiflutinib mesylate	−7.4
CASP3	Ivachtin	−8.5
IL1B	Terminolic acid	−6.9

Discussion

Viral pneumonia is extremely infectious and pathogenic. For example, COVID-19 has rapidly spread around the world over the past 2 years, and there is still a lack of targeted drugs. TCM has a long history of treating epidemic disease, known as “Wen Yi” or “Wen Bing” in ancient China. TCMs consist of various herbs or natural products with many active compounds that target multiple pathways and are applied for the different symptoms of a patient.²⁹ However, it is difficult to elucidate the exact mechanism of a TCM due to its diverse compounds and their targets.

Network pharmacology is a new subject that is based on the theory of computer science, medicine, and systems biology, which has been widely used for TCM.³⁰ As an important and assistant method, network pharmacology can better understand the interaction mechanism of the multicomponents and multi-targets of TCM. EL is traditionally used to treat respiratory disease, and made a great contribution in the fight against SARS-CoV in 2003.³¹ In this present study, we conducted an EL target and viral pneumonia-related gene intersection that acquired a total of 78 target genes by analyzing the active fraction F1012-2 of EL. GO and KEGG enrichment analysis indicated that EL can regulate pathways related to virus pneumonia. Among the 78 target genes, PPI network and network analyses found 5 key targets. Molecule docking was performed to verify further the interaction between the compounds of EL and the key targets.

The enrichment analysis of the target genes of EL's interaction with viral pneumonia by GO and KEGG is interesting. First, the 8 significant KEGG terms indicated that EL could regulate viral infection. This result was consistent with a previous study¹⁰ which indicated that EL had antiviral effects. Moreover, the GO (BP) terms indicated that EL might be involved in the process of apoptosis and anti-inflammation during the treatment of viral pneumonia.

The active fraction F1012-2 is composed of 3 distinct compounds, EG, EI, and EJ, which have been reported to possess anti-inflammatory,³² and antitumor^33,34 activities. However, the antiviral pneumonia effect has not been investigated. Molecular docking can provide further information of the interaction between the compounds of EL and the key targets. In our study, we found that EG, EI, and EJ could easily bind into the pocket of EGFR, IL1B, SRC, and CASP3, and EJ had the best binding activity with lower binding energy (−8.3, −7.4, −9.1, and −7.4 kcal/mol) than EG (−7.0, −7.1, −7.8, and −7.5 kcal/mol) and EI (−7.2, −7.5, −7.4, and −7.0 kcal/mol), as shown in Figure 5A. In order to investigate the reliability of molecular docking, we examined the molecular docking of the four target proteins with their positive controls. The binding energies of the positive controls with the target proteins were all lower than −6.0 kcal/mol (Table 2). Furthermore, the binding energy of EJ with SRC (−9.1 kcal/mol), EGFR (−8.3 kcal/mol), and IL1B (−7.4 kcal/mol) were all lower than the positive control (−8.8, −7.4, and −6.9 kcal/mol, respectively), as shown in Figure 5A and Table 2, thus suggesting that EJ has a better connection with SRC, EGFR, and IL1B. Therefore, based on the above results, we proposed that EJ might be the main active component of F1012-2. In addition, among the key targets of SRC, EGFR, and IL1B, EJ and SRC have the best connectivity. The proteases play a key role in viral replication. SRC, a known regulator of cellular tyrosine kinase, has an important role in cell mobility, proliferation, and survival,³⁵ and is the main target in cellular cleavage events. Some SRC inhibitors are in current clinical use.³⁶ These results showed that EJ might be one of the SRC inhibitors.

In this network-based study, we explored the potential therapeutic targets and signal pathways of EL against viral pneumonia. The major pathways involved were sphingolipid, neurotrophin, TNF, and Toll-like receptor signal pathways. EGFR, IL1B, SRC, and CASP3 were the key targets. These multi-target and multi-pathways can be applied to illustrate the potential mechanisms of EL's effect on viral pneumonia, which is constructive to the development of new therapeutic strategies against viral pneumonia using the Chinese herb EL. Nevertheless, further experimental studies are needed to validate this assumption.

Conclusion

We have suggested the potential mechanism of EL against viral pneumonia using computational pharmacology. GO and KEGG enrichment analysis showed that the major pathways involved were sphingolipid, neurotrophin, TNF, and Toll-like receptor signaling pathways. Molecular docking verified EGFR, IL1B, SRC, and CASP3 as the key targets, and EJ might be the main active component of F1012-2. We believe that this discovery may contribute to the development of new therapeutic strategies against viral pneumonia.

Supplemental Material

sj-docx-1-npx-10.1177_1934578X221131410 - Supplemental material for Prediction Targets of a Novel Sesquiterpene Lactone From Eupatorium lindleyanum DC. Against Viral Pneumonia Using Computational Pharmacology

Supplemental material, sj-docx-1-npx-10.1177_1934578X221131410 for Prediction Targets of a Novel Sesquiterpene Lactone From Eupatorium lindleyanum DC. Against Viral Pneumonia Using Computational Pharmacology by Xintong Shen, Hongtao Hu, Jiajin Xu and Shasha Tian in Natural Product Communications

Footnotes

Acknowledgements

The authors are extremely thankful to Dr Siyue Lou from Zhejiang Chinese Medical University for kindly revising the language of the article.

Author’s Contribution

Xintong Shen and Hongtao Hu performed the statistical analysis. Shasha Tian and Jiajin Xu drafted the article and Xintong Shen and Hongtao Hu were co-first authors. All authors contributed to the article and approved the submitted version.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are accessible once requested from the corresponding author.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Key Research Project of Traditional Chinese Medicine in Zhejiang Province, Zhejiang Chinese Medical University Research Fund Project, College Students’ innovation project, and Natural Science Foundation of Zhejiang Province (grant numbers 2022ZZ008, KC201912, S202210344108, and LQ19H160006).

ORCID iD

Shasha Tian

Supplemental Material

Supplemental material for this article is available online.

References

Ruuskanen

Lahti

Jennings

Murdoch

. Viral pneumonia. Lancet (London, England). 2011;377(9773):1264-1275. doi:10.1016/s0140-6736(10)61459-6

Pagliano

Sellitto

Conti

Ascione

Esposito

. Characteristics of viral pneumonia in the COVID-19 era: an update. Infection. 2021;49(4):607-616. doi:10.1007/s15010-021-01603-y

Kerneis

Visseaux

Armand-Lefevre

Timsit

. Molecular diagnostic methods for pneumonia: how can they be applied in practice? Curr Opin Infect Dis. 2021;34(2):118-125. doi:10.1097/qco.0000000000000713

Shen

Yin

. The mechanisms and clinical application of traditional Chinese medicine Lianhua-Qingwen capsule. Biomed Pharmacother. 2021;142:111998. doi:10.1016/j.biopha.2021.111998

Xiao

Tian

Zhou

, et al. Efficacy of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in treatment of COVID-19: a randomized controlled trial. Pharmacol Res. 2020;161:105126. doi:10.1016/j.phrs.2020.105126

Huang

Zhang

, et al. Traditional Chinese medicine (TCM) in the treatment of COVID-19 and other viral infections: efficacies and mechanisms. Pharmacol Ther. 2021;221:107843. doi:10.1016/j.pharmthera.2021.107843

Wang

Lai

Wang

Lou

. Traditional applications, phytochemistry, and pharmacological activities of Eupatorium lindleyanum DC.: a comprehensive review. Front Pharmacol. 2020;8:577124. doi:10.3389/fphar.2020.577124

Yang

Zhao

Lou

Zhao

. Eupalinolide O, a novel sesquiterpene lactone from Eupatorium lindleyanum DC., induces cell cycle arrest and apoptosis in human MDA-MB-468 breast cancer cells. Oncol Rep. 2016;36(5):2807-2813. doi:10.3892/or.2016.5115

Tian

Chen

Yang

, et al. F1012-2 inhibits the growth of triple negative breast cancer through induction of cell cycle arrest, apoptosis, and autophagy. Phytother Res. 2018;32(5):908-922. doi:10.1002/ptr.6030

10.

Peng

Dou

Huang

, et al. Antiviral effects of Fufang Yemazhui capsule in vitro and in vivo. Chin Tradit Pat Med. 2008(05):650-654. doi:1001-1528 (2008) 05-0650-04

11.

Chu

Ren

Xia

Chen

Zhang

. Eupatorium lindleyanum DC. sesquiterpenes fraction attenuates lipopolysaccharide-induced acute lung injury in mice. J Ethnopharmacol. 2016;185:263-271. doi:10.1016/j.jep.2016.03.022

12.

Dai

Tian

Zhang

Zhu

Zhao

. F1012-2 induced ROS-mediated DNA damage response through activation of MAPK pathway in triple-negative breast cancer. Biomed Res Int. 1012;2021(2021):6650045. doi:10.1155/2021/6650045

13.

Zhang

Zhu

Bai

Ning

. Network pharmacology databases for traditional Chinese medicine: review and assessment. Front Pharmacol. 2019;10:123. doi:10.3389/fphar.2019.00123

14.

Wang

, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. doi:10.1186/1758-2946-6-13

15.

Daina

Michielin

Zoete

. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357-W364. doi:10.1093/nar/gkz382

16.

Keiser

Roth

Armbruster

Ernsberger

Irwin

Shoichet

. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25(2):197-206. doi:10.1038/nbt1284

17.

Liu

Guo

Wang

, et al. BATMAN-TCM: a bioinformatics analysis tool for Molecular mechANism of Traditional Chinese Medicine. Sci Rep. 2016;6:21146. doi:10.1038/srep21146

18.

Amberger

Bocchini

Schiettecatte

Scott

Hamosh

. OMIM.Org: online Mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43(Database issue):D789-D798. doi:10.1093/nar/gku1205

19.

Rebhan

Chalifa-Caspi

Prilusky

Lancet

. Genecards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics (Oxford, England). 1998;14(8):656-664. doi:10.1093/bioinformatics/14.8.656

20.

Wishart

Feunang

Guo

, et al. Drugbank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074-D1082. doi:10.1093/nar/gkx1037

21.

Piñero

Queralt-Rosinach

Bravo

, et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford). 2015;2015:bav028. doi:10.1093/database/bav028

22.

Zhou

Zhang

Lian

Wang

Zhu

. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res. 2022;50(D1):D1398-D1407. doi:10.1093/nar/gkab953

23.

UniProt Consortium. Uniprot: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480-D489. doi:10.1093/nar/gkaa1100

24.

Xia

Xun

. Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19. Cell Prolif. 2020;53(12):e12949. doi:10.1111/cpr.12949

25.

Kim

Chen

Cheng

, et al. Pubchem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388-D1395. doi:10.1093/nar/gkaa971

26.

Burley

Bhikadiya

, et al. RCSB Protein data bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021;49(D1):D437-D451. doi:10.1093/nar/gkaa1038

27.

Morris

Huey

Lindstrom

, et al. Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785-2791. doi:10.1002/jcc.21256

28.

Trott

Olson

. Autodock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461. doi:10.1002/jcc.21334

29.

Zhao

Zhou

, et al. Prevention and treatment of COVID-19 using traditional Chinese medicine: a review. Phytomedicine. 2021;85:153308. doi:10.1016/j.phymed.2020.153308

30.

Hao da

Xiao

. Network pharmacology: a Rosetta stone for traditional Chinese medicine. Drug Dev Res. 2014;75(5):299-312. doi:10.1002/ddr.21214

31.

Lin

. Cout the genuine medicinal herbs in Jiangsu province. Healthy Times. 2021(006). doi:10.28434/n.cnki.njksb.2021.000230

32.

Wang

Zhong

Fang

, et al. Potential anti-inflammatory sesquiterpene lactones from Eupatorium lindleyanum. Planta Med. 2018;84(2):123-128. doi:10.1055/s-0043-117742

33.

Lou

Chen

Zhang

Yang

Zhao

. Eupalinolide J suppresses the growth of triple-negative breast cancer cells via targeting STAT3 signaling pathway. Front Pharmacol. 2019;10:1071. doi:10.3389/fphar.2019.01071

34.

Yang

Shen

Zhou

, et al. Precise discovery of a STAT3 inhibitor from Eupatorium lindleyanum and evaluation of its activity of anti-triple-negative breast cancer. Nat Prod Res. 2019;33(4):477-485. doi:10.1080/14786419.2017.1396596

35.

Roskoski

Jr . Src protein-tyrosine kinase structure and regulation. Biochem Biophys Res Commun. 2004;324(4):1155-1164. doi:10.1016/j.bbrc.2004.09.171

36.

Meyer

Chiaravalli

Gellenoncourt

. Characterising proteolysis during SARS-CoV-2 infection identifies viral cleavage sites and cellular targets with therapeutic potential. Nat Commun. 2021;12(1):5553. doi:10.1038/s41467-021-25796-w

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.37 MB