Graphical abstract graphic file with name ga1.jpg [37]Open in a new tab List of Abbreviations: CHMs, Chinese herb medicines; TCM, Traditional Chinese medicine; ACE2, Angiotensin-converting enzyme II; RBD, Receptor-binding domain; SPR, Surface plasmon resonance; PPI, Protein-protein interaction; KD, Equilibrium dissociation constants Keywords: COVID-19, SARS-CoV-2, Chinese herb medicine (CHM), ACE2, Network pharmacology, Surface plasmon resonance (SPR) Highlights * • Three CHM components with the potential against COVID-19 are identified using TCMSP. * • SPR assay result shows good binding affinity of puerarin and quercetin to ACE2. * • Puerarin and quercetin impairs the binding of viral S-protein to ACE2 receptor. * • Quercetin can also directly bind to S-protein to exert an viral neutralizing effect. * • Results from this study propose a prompt application of puerarin on COVID-19 patients. Abstract The outbreak of COVID-19 raises an urgent need for the therapeutics to contain the emerging pandemic. However, no effective treatment has been found for SARS-CoV-2 infection to date. Here, we identified puerarin (PubChem CID: 5281807), quercetin (PubChem CID: 5280343) and kaempferol (PubChem CID: 5280863) as potential compounds with binding activity to ACE2 by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Molecular docking analysis showed that puerarin and quercetin exhibit good binding affinity to ACE2, which was validated by surface plasmon resonance (SPR) assay. Furthermore, SPR-based competition assay revealed that puerarin and quercetin could significantly affect the binding of viral S-protein to ACE2 receptor. Notably, quercetin could also bind to the RBD domain of S-protein, suggesting not only a receptor blocking, but also a virus neutralizing effect of quercetin on SARS-CoV-2. The results from network pharmacology and bioinformatics analysis support a view that quercetin is involved in host immunomodulation, which further renders it a promising candidate against COVID-19. Moreover, given that puerarin is already an existing drug, results from this study not only provide insight into its action mechanism, but also propose a prompt application of it on COVID-19 patients for assessing its clinical feasibility. 1. Introduction The emergence of a novel and highly pathogenic coronavirus SARS-CoV-2 caused an outbreak of acute infectious pneumonia in Wuhan, China, in late 2019. At the early stage of infection, patients generally present with flu-like clinical manifestations, such as fatigue, fever and dry cough, with a characteristic ground-glass opacity lesion of lung in CT findings. Thereafter, as the disease progresses, in severe cases, patients may show dyspnea, respiratory distress syndrome, septic shock and even death [38][1], [39][2]. Recent phylogenetic analysis result showed that SARS-CoV-2 virus belongs to Beta coronavirus, which is an enveloped, single-stranded RNA virus with the ability to infect animals and humans, causing sporadic zoonotic outbreaks and occasional epidemics [40][3]. SARS-CoV-2 shares a common bat coronavirus ancestor with the human-infecting SARS-CoV, and its S-protein shows high structural homology to that of SARS-CoV in the receptor-binding domain (RBD) that mediates the interaction with the host receptor [41][3], [42][4]. So far, the known human receptor of beta coronaviruses includes angiotensin-converting enzyme 2 (ACE2) for SARS-CoV and dipeptidyl peptidase-4 (DPP4) for MERS-CoV [43][5], [44][6]. Recent studies have demonstrated that ACE2 is the human receptor for SARS-CoV-2, and the cell entry of SARS-CoV-2 depends on ACE2, suggesting that ACE2-targeting strategy holds great promise for the drug discovery against COVID-19 [45][7], [46][8], [47][9], [48][10]. Traditional Chinese medicine (TCM) has been applied on protection against plagues in China since ancient times. As an integral part of TCM, Chinese herb medicine (CHM) shows a unique therapeutic effect on various infectious diseases, including the SARS epidemic of 2003, owing to its holistic treatment concept and multi-component, multi-target pharmacological characteristics [49][11]. More recently, the combined use of TCM and modern Western medicine has benefited the COVID-19 patients with shorter hospitalization and improved symptoms [50][12]. However, due to the vast diversity of components in CHM and the complexity of the interaction between those components and the disease, it is still a considerable challenge to uncover the mysteries of CHMs at the molecular level, which to a great extent hampers the general acceptance of TCM worldwide. Recently, a series of computational methods under the umbrella of TCM network pharmacology have been devised, such as the network platform-based prediction of the active compounds from CHMs, which opens up a new path for unraveling the action mechanism of CHM and thereby greatly accelerate the process of CHM-based new drug discovery [51][13], [52][14], [53][15], [54][16], [55][17]. In this study, we first screened out puerarin as the candidate compound targeting human ACE2 from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and identified the potential effective herbs containing puerarin through TCMSP analysis platform. Next, according to the TCM theory that the compounds existing in the same CHM may have related and synergistic pharmacological activities [56][18], we screened all the active compounds contained in the above-obtained herbs and identified two other compounds with great anti-SARS-CoV-2 potential. The possible interaction of these compounds with ACE2 was further investigated by molecular docking and surface plasmon resonance assays, and the results support the view that puerarin and quercetin could significantly impair the binding of viral S-protein to its human ACE2 receptor, shedding light on CHM-based new drug discovery against COVID-19 (See workflow scheme in [57]Fig. 1). Fig. 1. Fig. 1 [58]Open in a new tab Workflow scheme. This work was composed of four main parts, including natural compound selection, molecular docking, SPR verification, and PPI network construction & enrichment analysis. 2. Material and methods 2.1. Screening of potential herbs and their active compounds targeting ACE2 To screen the potential CHMs and their compounds targeting ACE2, we input “angiotensin-converting enzyme 2″ to the search window of ”target name“ in TCMSP online platform. The parameters for selection of active compounds were set as oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 as standard. 2.2. Selection of the key active compounds against COVID-19 Statistical analysis of the overlapped compounds existing in the five individual herbs was presented in a Venn diagram. Prediction of potential targets for the selected compounds was performed using TCMSP database. Based on the selected candidate compounds and putative targets, a compound-target network was constructed by using Cytoscape. Then, the topological parameters of each candidate compounds in the network, including Degree, Betweenness Centrality and Clossness Centrality, were calculated by using a Cytoscape plugin CytoNCA. 2.3. Molecular docking analysis Flexible docking process between chemical compounds and target proteins were conducted by the software AutoDock 4.2. A total of 30 docking conformations were extracted and ranked according to the docking energy value. The detailed docking process was performed as follows: (a) amino acids within 14 Å distance of the binding region on the receptor ACE2 were placed into the grid box for docking, and the different types of atoms were then used as probes to scan and calculate the grid energy, which was performed by AutoGrid program; (b) conformational searching for ligands within the box was performed by using Autodock program. The ultimately rankings were resulted according to the scorings based on the conformation, orientation, position and energy of the ligands. The top 1 conformation with the lowest binding energy was selected for further binding modes analysis. 2.4. Surface plasmon resonance assay Surface plasmon resonance (SPR) analysis was conducted with Open SPR instrument (Nicoyalife, Canada). The COOH sensor chip was firstly installed on the OpenSPR instrument in accordance with the standard procedure. 1) Run the buffer at the maximum flow rate and exhaust the bubble after reaching the signal baseline. 2) Inject HCl (10 mM) to clean the chip surface and run for 1 min. 3) Slow down the flow rate of buffer solution (PBS) to 20 µL/min, then load 200 µL EDC (400 mM)/NHS (100 mM) (1:1) solution to activate COOH sensor chips and run for 4 min. 4) The ACE2 (40 µg/ml) and the S-protein (100 nM) were diluted with activation buffer (total 200 µL). 5) The injection port was rinsed with buffer solution and emptied with air. 6) Fill with 200 µL blocking solution (20 µL/min, 4 min), wash the sample ring with buffer solution and empty it with air. 7) Observe baseline for 5 min to ensure stability. Next, the selected compounds were diluted into a series of solutions with different concentration, which were then injected into the chip with the concentration from low to high. In each cycle, the sample (200 µL) was flowed through the chip for 7 mins at a constant flow rate of 20 µL/min (The binding time of the compounds and ACE2 was 240 s, and naturally dissociate for 180 s). After detection, 0.05% SDS was added as the regeneration buffer to dissociate the compounds from the target protein. The kinetic parameters of the binding reactions were calculated and analyzed by using Trace Drawer software (Ridgeview Instruments AB, The Kingdom of Sweden). 2.5. PPI network and module analysis The PPI systematic network was constructed and visualized using Search Tool of Retrieval of Interacting Genes (STRING; version 11.0; [59]https://stringdb.org) database and Cytoscape software 3.2.1, respectively. The proteins with a combined score > 0.7 were selected for PPI analysis. Molecular Complex Detection (MCODE) was used to screen the modules of the PPI network. The top modules were defined as having Degree cutoff > 5 and K-core > 5. The core subnetwork extraction from each of the parent PPI network was performed using the MCODE plugin with the same parameter settings as the above. 2.6. Screening of disease targets The GeneCards database ([60]http://www.genecards.org/) was used to acquire COVID-19 disease targeted genes. “Novel Coronavirus” was input as the keywords for searching, and the result was exported to an excel document. All the obtained targets were further confirmed by using Uniprot database. 2.7. Enrichment analysis The functional and pathway enrichment analyses of the obtained putative and core targets were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 ([61]https://david.ncifcrf.gov/). The analyses were mainly divided into two types, GO biological functional and KEGG signaling pathways. A p < 0.05 was considered as statistically significant. Meanwhile, WebGestalt ([62]http://www.webgestalt.org) was also used as the enrichment method for quercetin and COVID-19 co-targeted over-representation analysis (ORA). 3. Results 3.1. Screening of potential CHMs and their active compounds targeting ACE2 To screen the potential CHM compounds targeting human ACE2 receptor, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was employed. From the above database, puerarin (PubChem CID: 5281807) was identified as the compound with potential binding activity to ACE2. Next, we screened out a total of five CHMs containing puerarin from the TCMSP database, namely Radix Bupleuri (Chinese name: Chaihu), Radix Puerariae (Chinese name: Gegen), Puerariae flower (Chinese name: Gehua), Radix Cyathulae (Chinese name: Chuanniuxi), and Radix Hemerocallis (Chinese name: Xuancaogen). According to the TCM theory, the compounds present in the same CHM generally have related and synergic pharmacological activities. We therefore expanded the screening range to all compounds contained in the five CHMs, with a screening criteria of oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 ([63]Table 1). As such, a total of 41 compounds were obtained after removal of overlapped ones ([64]Supplementary Table 1). Table 1. Specific information on the five chosen herbs and their potential active ingredients obtained from a virtual screening (TCMSP database). Name of herbs MOL Number Name of potential active ingredients OB/% DL Radix Bupleuri(Chaihu) __________________________________________________________________ MOL004718 α-spinasterol 42.98 0.76 MOL000354 isorhamnetin 49.60 0.31 MOL004644 sainfuran 79.91 0.23 MOL000490 petunidin 30.05 0.31 MOL000449 stigmasterol 43.83 0.76 MOL001645 linoleyl acetate 42.10 0.20 MOL013187 cubebin 57.13 0.64 MOL004624 longikaurin A 47.72 0.53 MOL000098 quercetin 46.43 0.28 MOL000422 kaempferol 41.88 0.24 MOL004628 octalupine 47.82 0.28 MOL004598 3, 5, 6,7-tetramethoxy-2-(3, 4,5-trimethoxyphenyl) chromone 31.97 0.59 MOL004648 troxerutin 31.60 0.28 MOL004653 (+)-anomalin 46.06 0.66 MOL004609 areapillin 48.96 0.41 MOL002776 baicalin 40.12 0.75 MOL004702 saikosaponin c_qt 30.50 0.63 MOL012297 puerarin 24.03 0.69 __________________________________________________________________ Radix Puerariae (Gegen) __________________________________________________________________ MOL000392 formononetin 69.67 0.21 MOL000358 beta-sitosterol 36.91 0.75 MOL003629 daidzein-4,7-diglucoside 47.27 0.67 MOL002959 3′-methoxydaidzein 48.57 0.24 MOL012297 puerarin 24.03 0.69 __________________________________________________________________ Puerariae flower (Gehua) __________________________________________________________________ MOL011791 kakkalide 46.91 0.67 MOL011793 kakkatin 55.25 0.24 MOL001749 ZINC03860434 43.59 0.35 MOL000392 formononetin 69.67 0.21 MOL001792 DFV 32.76 0.18 MOL000449 stigmasterol 43.83 0.76 MOL003629 daidzein-4,7-diglucoside 47.27 0.67 MOL002959 3′-methoxydaidzein 48.57 0.24 MOL005916 irisolidone 37.78 0.3 MOL000422 kaempferol 41.88 0.24 MOL000098 quercetin 46.43 0.28 MOL000468 8-o-methylreyusi 70.32 0.27 MOL012976 coumestrol 32.49 0.34 MOL004957 HMO 38.37 0.21 MOL000358 beta-sitosterol 36.91 0.75 MOL000359 sitosterol 36.91 0.75 MOL008400 glycitein 50.48 0.24 MOL013305 garbanzol 83.67 0.21 MOL012297 puerarin 24.03 0.69 __________________________________________________________________ Radix Cyathulae (Chuanniuxi) __________________________________________________________________ MOL000098 quercetin 46.43 0.28 MOL000358 beta-sitosterol 36.91 0.75 MOL012286 betavulgarin 68.75 0.39 MOL012298 rubrosterone 32.69 0.47 MOL012297 puerarin 24.03 0.69 __________________________________________________________________ Radix Hemerocallis (Xuancaogen) MOL001255 boswellic acid 39.55 0.75 MOL000422 kaempferol 41.88 0.24 MOL001243 3alpha-hydroxy-olean-12-en-24-oic-acid 39.32 0.75 MOL002268 rhein 47.07 0.28 MOL013343 hemerocallone 63.01 0.54 MOL000471 aloe-emodin 83.38 0.24 MOL001771 poriferast-5-en-3beta-ol 36.91 0.75 MOL013345 picraquassioside C 53.99 0.69 MOL012297 puerarin 24.03 0.69 [65]Open in a new tab 3.2. Puerarin, quercetin and kaempferol were selected as the key compounds with anti-SARS-CoV-2 potential Of the selected compounds from the five herbs, quercetin or kaempferol ranked the second in frequency (3 times), while puerarin ranked the highest, existing in all of the five herbs. Statistical analysis of the overlapped compounds existing in each of the five herbs was shown in a Venn diagram ([66]Fig. 2A). Then the potential drug targets of the selected compounds from the five herbs were predicted using the online TCMSP analysis platform, which led to identification of 240 putative targets and subsequent construction of a compound-target network. Next, the topological parameters of each selected compound in the constructed network above, including Degree, Betweenness Centrality and Clossness Centrality, were calculated and scored by using a Cytoscape plugin CytoNCA. The higher the score, the higher the core degree and importance of a compound in the network. The top 10 compounds with the scores from high to low were listed in [67]Table 2 (the rest compounds were listed in [68]Supplementary Table 2). Among these, the top 2 compounds were quercetin and kaempferol. According to the above results, in addition to puerarin, quercetin and kaempferol were also selected as the key compounds for further analysis. The molecular formulas and chemical structures of puerarin, quercetin and kaempferol are shown in [69]Fig. 2B. Fig. 2. [70]Fig. 2 [71]Open in a new tab Selection of the key compounds from CHMs. (A) The Venn diagram of 3 candidate core compounds from 5 herbs. Red box: puerarin; Blue box: quercetin; Green: kaempferol. (B) The 2D-chemical structure of puerarin, quercetin and kaempferol downloaded from the TCMSP database. (For interpretation of the references to colour in this figure legend,