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Publications > Journals > Journal of Exploratory Research in Pharmacology > Article Full Text

  • Original Article
  • OPEN ACCESS

Virtual Screening of Shuanghuanglian Components for the Binding to the Proteinases of SARS-CoV-2

  • Yu-Shun Yang1,2,3,* ,
  • Dilimulati Ainiwaer2,
  • Xiao Wang1,
  • Chao-Yue Wang1 and
  • Jie Yang2,* 
 Author information  Cite
Journal of Exploratory Research in Pharmacology   2023;8(2):95-106

doi: 10.14218/JERP.2022.00061

Abstract

Background and objectives

Coronavirus disease 2019 (COVID-19), a global pandemic disease caused by SARS-CoV2 infection, has existed for nearly three years. However, there are currently only a few therapeutic drugs available. The objective of this study attempted to explore the potential therapeutic actions of Shuanghuanglian, a traditional Chinese medicine, using molecular docking simulation technology.

Methods

The ingredients of Shuanghuanglian and the approved drugs were structurally evaluated. The potential bindings of the individual ingredients in Shuanghuanglian to the PLPro and Mpro of the SARS-CoV2 were evaluated by molecular docking simulation according to the energy parameters. The corresponding binding patterns into each defined site were analyzed. The pharmacokinetics of the individual ingredients were predicted to preliminarily evaluate their oral bioavailability.

Results

There were 482 unique natural products in the categories of fatty acids, aromatic compounds, glycosides, and sterols. The successfully docked rates of the Shuanghuanglian components binding to the PLPro and Mpro were all higher than those of the compounds in the Food and Drug Administration-approved Drug Library. In general, Shuang and Lian took the primary status in providing the top hits via the hydrogen bonds, while Huang acted as an important supplement to the global activity. Though the selected hits faced the common difficulty of polarity, the deglycosylation and the package by the carriers could also be practical to overcome the pharmacokinetic violation.

Conclusions

Shuang and Lian retain the potential ability to interact with the PLPro and Mpro of SARS-CoV2, and other herbs seem to have the potential to be involved.

Keywords

Coronavirus disease 2019, Natural products, Virtual screening, Potential interactions, Traditional Chinese medicine

Introduction

It has been nearly three years since the Coronavirus (CoV) disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, Hubei Province, China.1 COVID-19 was caused by an infection with a new CoV named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),2 which was the third highly pathogenic virus in addition to the SARS-CoV in 2003,3 and the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012.4 COVID-19 spread to all countries around the world and was declared a pandemic disease by the World Health Organization.5,6 The SARS-CoV-2, particularly for the recent dominant Omicron strains, seems to be less fatal (∼5% vs. ∼10% and ∼40%, respectively) but is more contagious (R0 = 2.0–6.5) than SARS and MERS.7–10 Currently, there are a few drugs that have been approved for the treatment of COVID-19 although different types of vaccines have demonstrated to limit the severity of COVID-19 around the world. Hence, understanding the molecular pathogenesis of COVID-19 and the discovery of new therapies are urgently needed (Fig. 1).11–13 During the life cycle of SARS-CoV-2, four major proteins have been regarded as the potential targets, including the Spike protein for the recognition onto the host, the RNA-dependent RNA polymerase (RdRP) for the replication of RNA, the papain-like protease (PLPro), and main protease (Mpro) for preparing the biosynthesis of the next generation of the virus.

The life cycle of the SARS-CoV-2 and current potential drugs for COVID-19.
Fig. 1  The life cycle of the SARS-CoV-2 and current potential drugs for COVID-19.

(a) The illustration of the SARS-CoV-2 life-cycle following its infection. (b) The potential drugs for treating COVID-19.

At present, the highly restricted policies, immediate isolation of individuals with a positive SARS-CoV2 infection and extensive screening, as well as effective therapies have controlled the spreading of COVID-19 in China.14,15 In 2020, potential drug candidates, such as Remdesivir, Favipiravir and Chloroquine/Hydroxychloroquine, were chosen for the treatment of COVID-19 patients.16–19 Recently, Paxlovid and Molnupiravir were approved by the Food and Drug Administration (FDA) of the USA for treatment of patients with early stages of COVID-19 and other biological drugs, such as specific neutralization antibodies that had also been approved for the treatment of patients with severe COVID-19.20–22 Furthermore, Chinese medicinal herbs, such as Shuanghuanglian, Lianhua Qingwen capsules, Qingfei Paidu decoction, and other modified prescriptions have been recommended for the treatment of patients with mild COVID-19.16,23,24 However, the pharmacological actions of these Chinese medicinal herbs, particularly for Shuanghuanglian, the first recommended Chinese medicinal herb, have not yet been clarified.25

This study explored the potential bindings of the ingredients in Shuanghuanglian to the PLPro and Mpro of SARS-CoV2 according to their crystal structures available in the literature.26 Shuanghuanglian contains three major ingredients: Lonicerae Japonicae Flos (Shuang means Shuanghua also called honeysuckle), Scutellariae Radix (Huang means Huangqin), and Forsythiae Fructus (Lian means Lianqiao).27–29 A previous study has reported that miR2911 in honeysuckle inhibits the SARS-CoV-2 replication and accelerates the negative conversion of infected patients.30–32 Accordingly, we attempted to explore the therapeutic potential of abundant natural products in Chinese medicinal herbs.33–35 Based on its therapeutic efficiency, the relatively clear compositions, available reference value of other prescriptions, and our previous experience, Shuanghuanglian was chosen for the in silico analysis. We scanned the database of the traditional Chinese herbs, analyzed the natural products of each ingredient, conducted the molecular docking simulation, and gave further discussion on the practical potential. Our findings could aid in the design of new medical herbs and help in internationally standardized research on traditional Chinese medicines.36–38

Materials and methods

General system and software information

The investigation was mainly conducted on a workstation with the following system configurations: Intel CORE i7-9800X CPU @3.80 GHz processor, system memory: 32 GB RAM, system type: 64-bit operating system, and Windows 10 as the operating system. The data library was mainly summed up and analyzed by the in silico simulation in the Windows system using Python and Cygwin. The two-dimensional structure of the compounds was drawn using Chemdraw 14.0 software (ChemBioOffice, CambridgeSoft). The preparation and molecular docking simulation were performed using Discovery Studio 3.5 and Discovery Studio Visualizer 2016 (Accelrys Software, Inc).

Selection of the natural products

The natural products of the corresponding herbs were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) Version 2.3 launched by the Lab of Systems Pharmacology at Northwest University, Xi’an, Shaanxi Province, China.39 All the compounds were collected with the Mol ID in this database. The structures in the mol2 format were identified using Chemdraw 14.0 software (ChemBioOffice, CambridgeSoft). The comparison on the percentage of the pass was performed using the FDA-approved Drug Library (2,747 compounds) supported by Selleck (China) (Shanghai, China).

Ligand preparation

All the compounds were prepared with the “Prepare Ligands” modules in Discovery Studio 3.5 (Accelrys Software, Inc). Each prepared ligand was identified to confirm that the conformation agreed with the one in the original database.

Receptor preparation

Based on the topic of the specific treatment instead of the prevention or broad-spectrum therapy, we mainly investigated PLPro and Mpro for preparing the biosynthesis of the next generation virus in the life cycle of SARS-CoV-2. To avoid the same nationality of the initial report,26 the recent released protein structures by the National Institutes of Health, Bethesda, Maryland, USA or National Science Foundation, Alexandria, Virginia USA were used. Accordingly, the crystal structures of the wild type SARS-CoV-2 PLPro (PDB code: 7JRN),40 and SARS-CoV-2 Mpro (PDB code: 7CWC) were chosen.41 Subsequently, we prepared the protein structures with the “Prepare Protein” modules in Discovery Studio 3.5 (Accelrys Software, Inc). During the automatic procedure, the water molecules were eliminated if they did not participate in the interactions, the polar hydrogen was added, and the incomplete loops were repaired. The preparation was performed under a CHARMm minimization.42

Afterwards, the hierarchy view of the prepared proteins was unfolded. For PLPro, the binding site was defined as one of GRL0617 (a selective inhibitor of SARS-CoV PLPro).43 For Mpro, the definition of the binding sites was more complex according to our findings. Basically, the most important site was the N3-binding site as reported.26 When we studied the receptor cavities more carefully, we defined five binding sites which were more suitable for small molecules. All the binding sites were investigated in this work. Among them, Sites I and II were separated from the N3-binding site, which was near the α-helix-rich region of Mpro; Sites III and IV were independent on the beta (β)-sheet-rich region; while Site V was at the linking position of the two regions.

Molecular docking simulation protocol

Molecular docking simulation was carried out using Discovery Studio 3.5 (Accelrys Software, Inc), and visualization was performed using Discovery Studio Visualizer 2016 (Accelrys Software, Inc). Since the involved ligands bore structural diversity, we chose the graphical user interface CDOCKER protocol, a CHARMm force field-based molecular docking tool with a half-flexible receptor, to implement the simulation.44 The major steps were as follows:

  • Initially, the conformations of the ligands were generated through high temperature molecular dynamics with different random seeds.

  • In each defined binding site, the original ligands were removed. Subsequently, translating the center of each ligand to a specified position within the receptor active site and performing a series of random rotations led to random orientations of the conformations. During the generation of the random orientations, each time when the calculated softened energy was lower than the set limit, the orientation was recorded. This step was repeated until the desired number (here we set three to improve the accuracy as well as to avoid unnecessary crosses in order) of the low-energy orientations was achieved, or the test times of the bad orientations had reached the maximum number.

  • Afterwards, each recorded orientation was subjected to simulated annealing molecular dynamics. The simulation experienced a heated process and was cooled down to the target temperature. The final energy minimization of each ligand in the rigid receptor using non-softened potential was conducted. The heating steps were 2,000 with 700 of the heating target temperature; while the cooling steps were 5,000 with 300 cooling target temperature.

  • Finally, the CHARMm energy (interaction energy plus ligand strain) and the interaction energy alone of each recorded pose were calculated. Three (as we set) poses saved for each ligand were ranked according to the CHARMm energy and the dock scores (more negative, thus more favorable for binding).

Pharmacokinetic properties and Lipinski’s rule of five

The pharmacokinetic properties comprising absorption, distribution, metabolism, and excretion (ADME) were predicted using the Swiss ADME protocol (http://www.swissadme.ch/ ).45,46 The predicted data from this source were checked with the data from the TCMSP Version 2.3.39 We also checked the top hits with the Lipinski’s oral drug likeliness properties consisting of: 1) the molecular weight (<500 Daltons), 2) number of hydrogen bond donors (<5), 3) number of hydrogen bond acceptors (<10), 4) log P (<5), and 5) molar refractivity (<140).47

Results and discussion

Preliminary analysis of the natural products

Shuanghuanglian contained three major ingredients: Lonicerae Japonicae Flos (Shuang), Scutellariae Radix (Huang), and Forsythiae Fructus (Lian). The natural products of the corresponding herbs were obtained from the TCMSP system Version 2.3.39 From this database, the structures were downloaded and named with their Mol ID. In Table S1, the Mol ID of the compounds from all three herbs were listed. A general analysis of these natural products led to some hints. First, the reported number of natural products in Shuang, Huang, and Lian were 236, 143 and 150, respectively. They were all rich in diversity because a random sampling of 10 herbs in the database indicated that all 10 herbs had fewer than 50 natural products. Secondly, some of the components of the herbs were repeated. As colored in Table S1 and shown in Figure 2, 20 compounds in Shuang and Huang, 21 compounds in Shuang and Lian, 10 compounds in Huang and Lian, were the same. There were four compounds (MOL000069, MOL000254, MOL000357, and MOL000358) that appeared in all three herbs. Interestingly, they covered the categories of fatty acids, aromatic compounds, glycosides, and sterols. Moreover, there were 482 distinguished natural products in Shuanghuanglian.

The number of ingredients in Lonicerae Japonicae Flos (Shuang), Scutellariae Radix (Huang), and Forsythiae Fructus (Lian), and four compounds in all three herbs.
Fig. 2  The number of ingredients in Lonicerae Japonicae Flos (Shuang), Scutellariae Radix (Huang), and Forsythiae Fructus (Lian), and four compounds in all three herbs.

(a) The numbers of natural products in the three herbs. (b) The compounds that appeared in all three herbs.

Molecular docking simulation

Focusing on specific treatment, we chose the crystal structures of the wild type SARS-CoV-2 PLPro (PDB code: 7JRN),40 and SARS-CoV-2 Mpro (PDB code: 7CWC),41 respectively. For the other two enzymes, because the Spike protein might be for prevention and RdRP for the broad-spectrum treatment, which could also be helped by miR2911,32 the binding sites of the PLPro and Mpro were identified. As shown in Figure 3, the binding site in PLPpro was that of the original ligand GRL0617, while the binding sites in Mpro were complex. The basic site was that of ligand N3 together with another five binding sites. Sites I and II were separated from the N3-binding site, which was near the alpha (α)-helix-rich region of the Mpro; Sites III and IV were independent in the β-sheet-rich region, while Site V was at the linking position of the two regions. All of the natural products could have binding sites according to the steric and electronic surroundings. We chose the CDOCKER protocol considering the structural diversity of the ligands. Accordingly, the “-CDOCKER Interaction Energy” was selected as the basic index to evaluate the possibility of the interaction between each ligand and the binding site, while the “-CDOCKER Energy” was chosen as the reference index to evaluate the possible steadiness of the ligand-receptor complex.

The defined binding sites in SARS-CoV-2 PLPro (a) and Mpro (b).
Fig. 3  The defined binding sites in SARS-CoV-2 PLPro (a) and Mpro (b).

Mpro, main protease; PLPro, papain-like protease.

From the simple object to the complex one, we chose to analyze the results from SARS-CoV-2 PLPro. Among the 482 natural products, 438 were docked into this site while 255 reached the basic requirement of potential interactions (we set as -CDOCKER Interaction Energy >30.0 kcal/mol). We listed the MOL IDs of the 255 compounds in Table S2 and illustrated the 2D binding patterns of the top 15 hits in Figure S1. The top 15 hits were also compared in Table 1. First, Shuanghuanglian seemed to have potential for SARS-CoV-2 PLPro because 90.87% (438 out of 482) of its investigated components were docked into the PLPro, which was higher than that of the compounds in the FDA-approved Drug Library supported by Selleck (China) (Shanghai, China) (81.14%; 2,229 out of 2,747). Simultaneously, the “-CDOCKER Interaction Energy” of the top hits were almost in the rational range of 40.0–60.0 kcal/mol, which was almost the most potential situation of CDOCKER before further modification and evaluation. Secondly, the flavones, glycosides, and polyphenols were preferred in the top hits, while long chain fatty acid esters also appeared. Specially, among the top hits, long chain fatty acid esters were all from Huang. Thirdly, Shuang and Lian were more important than Huang for binding to the PLPro. Additionally, the repeated compounds did not appear frequently (two out of 15; 13.33%). In particular, as shown in the 3D binding pattern of MOL003130 (Fig. 4a) and MOL002037 (Fig. 4b), the major key residues for the hydrogen bonds were Lys157, Leu162, Asp164, Arg166, Glu167, Gly266, Asn267, Tyr268, Gln269, Tyr273, and Thr301, respectively. We then chose the top three to evaluate their possible druggability.

Table 1

The comparison of the top 15 hits in SARS-CoV-2 PLPro

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL003130−6.2903563.3313Shuang10Madreselvin A
MOL00203741.015160.9759Lian7Amentoflavone
MOL000010−3.9846558.1761Shuang7Rhoifolin
MOL00187544.339657.8812Shuang4Isochlorogenic acid
MOL003051−1.6869756.8953Shuang1Scolymoside
MOL00330938.374556.5246Lian6Plantainoside A
MOL00307635.893256.4097Shuang23,5-Di-O-caffeoylquinic acid methyl ester
MOL00328431.500356.2757Lian5Caleolarioside A
MOL00307738.436956.182Shuang84,5-Di-O-caffeoylquinic acid methyl ester
MOL003334−3.9374755.5806Lian7Forsythoside D
MOL000415−2.9552555.0801Shuang & Lian3Rutin
MOL01316151.964754.8761Huang1Methyl Hexacosanoate
MOL0077928.6579954.3395Huang4Isomartynoside
MOL00973449.45553.8073Huang1Methyl lignocerate
MOL00000714.481653.6873Shuang & Huang4Cosmetin
The 3D binding patterns of the top hits in the detailed binding sites with the H-bond surface comprising MOL003130 in PLPro (a), MOL002037 in PLPro (b), MOL003008 in Mpro (c), and MOL003337 in Mpro (d).
Fig. 4  The 3D binding patterns of the top hits in the detailed binding sites with the H-bond surface comprising MOL003130 in PLPro (a), MOL002037 in PLPro (b), MOL003008 in Mpro (c), and MOL003337 in Mpro (d).

Actually, with higher homology with SARS-CoV, Mpro has been widely investigated.26 Here, we also analyzed the results in the N3-binding site before that of the further defined ones. Among the 482 natural products, 451 were docked into this site, while 246 could be potential for interactions with the Mpro. We listed the MOL ID of the 246 compounds in Table S3 and illustrated the 2D binding patterns of the top 15 hits in Figure S2. The hints from the docking results in SARS-CoV-2 Mpro were different (Table 2). Initially, compared with that in PLPro, the success rate of docking was even as high as 93.57% (451 out of 482), which was also higher than that of the compounds in the FDA-approved Drug Library (83.25%; 2,287 out of 2,747). However, one questionable point was that the “-CDOCKER Interaction Energy” of the top hits was all beyond 60.0 kcal/mol, thus suggesting that the N3-binding site could be too large for the investigated ligands. Accordingly, we subsequently divided it into smaller sites. Secondly, flavones, glycosides, and polyphenols were preferred. Simultaneously, the long chain species were alkanes. The long chain compounds were all from Huang, and they were not favorable for generating hydrogen bonds. Thirdly, though Shuang and Lian still covered a majority of the top hits, their compounds were quite different from those docked in the PLPro because only two out of 15 (MOL003130 and MOL007792) were the same. All the top hits were unique herbs. We illustrated the 3D patterns of MOL003008 (Fig. 4c) and MOL003337 (Fig. 4d) to infer the key residues for the hydrogen bonds as Phe3, Arg4, Lys5, Arg131, Thr199, Ser284, Leu287, Glu288, and Glu290, respectively.

Table 2

The comparison of the top 15 hits in SARS-CoV-2 Mpro at the N3-binding site

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL00300817.065374.3945Shuang9Madreselvin B
MOL00333713.661274.0027Lian6Forsythoside F
MOL00333229.180871.6769Lian7Forsythoside C
MOL003110−52.602171.2342Shuang5Centauroside
MOL0031300.35592268.1028Shuang7Madreselvin A
MOL00087054.446764.6227Huang0Hexatriacontane
MOL00779220.824663.9904Huang5Isomartynoside
MOL00333124.355463.6139Lian4Forsythiaside
MOL003285−5.439963.3833Lian9N/A
MOL00333327.183962.5565Lian7Acteoside
MOL00331628.79961.8015Lian5β-Hydroxyacteoside
MOL00301312.677661.1352Shuang4Secologanic dibutylacetal
MOL00331313.632960.7986Lian4Suspensaside A
MOL00052214.555460.2791Lian4Arctiin
MOL00522457.022260.1095Huang0Tetratetracintane

In addition, it could be possible that smaller binding sites could be more rational than the N3-binding site for the prepared ligands. Instead of simply reducing the radius, we identified the new sites according to the real cavities. Preliminarily, we compared the feasibility of the five divided sites. The successful docked and interaction possible ligands were Site I: 339 and 21; Site II: 436 and 257; Site III: 390 and 173; Site IV: 367 and 169; Site V: 412 and 221. Because the top hit (MOL003283) in Site I indicated the “-CDOCKER Interaction Energy” of merely 36.0175 kcal/mol, we reasonably believed that Site I did not have the potential for the prepared ligands from Shuanghuanglian. The top 50 hits in each site were listed in Table S4, and the 2D binding patterns of the top 15 hits were illustrated in Figures S3-S6. Site II was similar too, but smaller than the N3-binding site. From the energy index in Table 3, Site II was also more rational than the N3-binding site, which realized our purpose of defining the divided sites. The top hits were all formed from flavones, glycosides, and polyphenols. Shuang and Lian contributed to 14 out of 15 top hits, except the one that appeared in both herbs. Since Site II was similar to the N3-binding site, a high percentage of ligands (33.3%, five out of 15, were marked in Table 3) appeared in the top hits in both sites. We compared the detailed binding patterns of MOL003130 in both sites (Fig. 5a, b, and d), and found that these two conformations interacted with the different residues in the Mpro. Coincidently, MOL003130 also appeared in the top hits in PLPro. Other repeated ligands in both the Mpro Site II and PLPro included MOL000415, MOL001875, MOL003051, MOL000010, and MOL003334. For each repeat, we picked the top three from the previously unpicked ones for further evaluation. They were MOL003316, MOL003313, MOL003013, MOL000415, MOL001875, and MOL003051.

Table 3

The comparison of the top 15 hits in the SARS-CoV-2 Mpro Site II

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL0004156.8356864.3218Shuang & Lian11Rutin
MOL003130−8.2377763.2077Shuang8Madreselvin A
MOL00187534.274161.9011Shuang7Isochlorogenic acid
MOL0030511.7975761.1418Shuang7Scolymoside
MOL00292111.63559.2781Huang9N/A
MOL00331621.751659.1990Lian7β-Hydroxyacteoside
MOL0000105.5397659.0876Shuang6Rhoifolin
MOL00311837.225858.1035Shuang9Isochlorogenic acid C
MOL00333215.597158.0757Lian5Forsythoside C
MOL00333412.918356.2545Lian8Forsythoside D
MOL00637030.544655.3136Huang75-O-Caffeoylquinic acid
MOL0033134.3565555.2171Lian4Suspensaside A
MOL003013−17.449855.1203Shuang3Secologanic dibutylacetal
MOL00301022.110754.5034Shuang6Quercetin-3-O-β-D-glu
MOL00333615.819453.9310Lian8Forsythoside E
The 3D binding patterns of MOL003130 to SARS-CoV-2 detailed in the N3-binding site (a), Sites II (b) and V (c), and in the relative positions of the above sites (d).
Fig. 5  The 3D binding patterns of MOL003130 to SARS-CoV-2 detailed in the N3-binding site (a), Sites II (b) and V (c), and in the relative positions of the above sites (d).

When we divided the sites, we thought that Sites III and IV were not typical for the small molecular ligands because they were surrounded mainly by the β-sheet and their successfully docked ligands were relatively fewer (<400). Though the energy index was also less favorable, these two sites could have potential because the “-CDOCKER Interaction Energy” of the top hits was almost in the rational range of 40.0–60.0 kcal/mol (Tables 4 and 5). Interestingly, the compounds from Huang, regardless of their chain lengths, appeared more frequently in the top hits. Accordingly, we could not ignore the possibility that Huang might interact at these binding sites and act as an important supplement to the overall activity of Shuanghuanglian.

Table 4

The comparison of the top 15 hits in the SARS-CoV-2 Mpro Site III

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL00087042.114748.0156Huang0Hexatriacontane
MOL00859542.850546.4620Huang1Methyl henicosanoate
MOL00536841.454345.0270Huang2Methyl tricosanoate
MOL00328427.350544.9463Lian3Caleolarioside A
MOL00329019.047944.4435Lian3N/A
MOL00301016.586943.5525Shuang6Quercetin-3-O-β-D-glu
MOL00070210.246843.2031Lian5Guajavarin
MOL00973029.874442.6731Huang1Methyl icos-11-enoate
MOL00066343.344742.6685Shuang1Lignoceric acid
MOL00287941.021942.5431Huang1Diop
MOL003322−1.6940542.5174Lian4Forsythinol
MOL00522431.317742.3766Huang0Tetratetracintane
MOL00202738.083942.3339Huang0Methyl behenate
MOL00303033.753242.3317Shuang0Ginnol
MOL00293416.954242.2364Huang5Neobaicalein
Table 5

The comparison of the top 15 hits in the SARS-CoV-2 Mpro Site IV

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL00330932.947755.5423Lian6Plantainoside A
MOL00000918.763751.3359Shuang5Luteolin-7-O-glucoside
MOL00334513.080850.3323Lian5N/A
MOL00056115.256750.2364Shuang & Lian4Astragalin
MOL00043715.408849.6075Lian5Hirsutrin
MOL00000715.116749.0453Shuang & Huang5Cosmetin
MOL00293515.784748.0702Huang4Baicalin
MOL00291222.840247.9820Huang3Dihydrobaicalin
MOL00293125.893047.8252Huang4Scutellarin
MOL00328430.390646.1796Lian4Caleolarioside A
MOL003297−5.3517246.0809Lian3N/A
MOL00301018.580345.6709Shuang6Quercetin-3-O-β-D-glu
MOL0007029.1169344.8240Lian3Guajavarin
MOL0031282.871744.7907Shuang3Dinethylsecologanoside
MOL003119−8.5863244.5271Shuang6Loniceracetalide A

Although the surrounding of Site V contained both the α-helix and β-sheet, the top hits seemed more similar to the ones in Site II, which were in an α-helix-rich region. As shown in Table 6, four out of the top five hits (in total five out of 15 as marked) repeated the top hits in Site II or the N3-binding site. We noticed that MOL003130 appeared again in the leading position with the potential energy values. Given that Site V was close to the N3-binding site, we illustrated the detailed binding patterns of MOL003130 in Site V (Fig. 5c) and compared its relative position in Mpro (Fig. 5d). Because these two conformations were both bound near the center of the corresponding sites, there seemed no key residues to interact with both of the conformations of MOL003130. Alternatively, MOL003130 could bind to the different sites of the Mpro simultaneously. We also selected the top three unpicked ligands from this site (MOL003336, MOL003284, and MOL003334).

Table 6

The comparison of the top 15 hits in the SARS-CoV-2 Mpro Site V

MOL ID-CDOCKER interaction energy (kcal/mol)-CDOCKER energy (kcal/mol)SourceNumber of H-bondsGeneral name
MOL003130−3.1717762.6809Shuang6Madreselvin A
MOL003336−3.4928357.1383Lian7Forsythoside E
MOL00328434.453056.6149Lian8Caleolarioside A
MOL0033342.7598955.4900Lian8Forsythoside D
MOL003285−13.227554.0015Lian4N/A
MOL00053613.039053.3301Lian3Matairesinoside
MOL00330931.510552.9643Lian6Plantainoside A
MOL00270249.009452.6923Shuang1Nonacosanol
MOL00303048.179352.3531Shuang2Ginnol
MOL0030207.0185351.5526Shuang3Secologanoside 7-methylester
MOL00311831.198151.4023Shuang6Isochlorogenic acid C
MOL00973450.123351.1693Huang1Methyl lignocerate
MOL00332717.751851.0608Lian5Rengyoside C
MOL000357−50.561950.5453All2Sitogluside
MOL003292−11.987850.4217Lian3(+)-Epipinoresinol-4′-O-D-glucoside

Pharmacokinetic potential

We picked 15 hits to predict their pharmacokinetic potential by checking the ADME properties and the violation with Lipinski’s rule of five. The major properties were listed in Table S5 with the corresponding simplified molecular input line entry system (SMILES) codes in Table S6, and the oral bioavailability radar maps were presented in Figure 6. For treating COVID-19, we analyzed the oral bioavailability. For all of the selected hits, all the natural products had the most common difficulty of polarity. Given the major structural moieties were flavones, glycosides, and polyphenols, their topological polar surface area (TPSA) went beyond the limit of 130 Å2.48 Moreover, a majority of the hits faced the problem of size (11 out of 15), which could result from the fact that these compounds contained one or more glycosides. Several hits needed improvement on flexibility (five out of 15). Actually, two possible approaches could be applied to improve the oral bioavailability. One was modifying the deglycosylated metabolites of these natural products. As shown in Figure 6, deglycosylated MOL003130 exhibited no violations of Lipinski’s rules. Further modification could improve the potential of the interaction with its targets in the binding sites. The others were packaging the drug-like compounds in certain carriers, such as human serum albumin (HSA), metal-organic framework (MOF), and aptamers. Consequently, it could be practical to overcome one or two violations.

The oral bioavailability radar maps of the selected hits and the metabolite of MOL003130.
Fig. 6  The oral bioavailability radar maps of the selected hits and the metabolite of MOL003130.

The colored zone was the suitable physiochemical space for oral bioavailability limited by LIPO (lipophility), SIZE, POLAR (polarity), INSO (insolubility), INSA (insaturation) and FLEX (flexibility).

Future directions

Similarity in the compounds and ingredients

There were some common compounds among the top hits in the different binding sites, including MOL003130, MOL000415, MOL001875, MOL003051, MOL000010, and MOL003334. They shared some similar moieties, such as phenylpropanoids, flavones, and glycosides. Their ADME properties had advantages in insaturation and flexibility, as well as disadvantages in size and polarity.

Among the relevant efficacious Chinese medicines, there was also similarity in the ingredients. Lianhua Qingwen capsules, supplied during the COVID-19 pandemic in Shanghai in 2022, shared the major ingredients of Shuang and Lian, while Qingfei Paidu decoction shared the major ingredient of Huang.49,50 Moreover, the involved ingredients, such as Atractylodes macrocephala Koidz, Belamcanda chinensis (L.) Redouté, and Citrus reticulata Blanco, contained similar components as those found in Shuanghuanglian.

Significance of the components and substitutability

Subsequently, we intended to define the sites in the Mpro, especially the separation of Sites I and II. According to the experience of the structural analysis, the analysis of the released structure of Mpro revealed the reported binding site was bulky. The so-called N3-binding site was bound with a small peptide, which was consistent with our recognition.26 However, we believed that the evolution of the species obeyed the natural rules, thus inferring that the binding site could be composed of two or more known sites. Although the major topic of this work was analyzing the natural products, we also conducted virtual screening during the beginning months of the pandemic and reported the separation of the binding site as public information instead of a research paper. Sites I and II, respectively had the unique features of the previous CoV site and angiotensin receptor. This result was consistent with the clinical characteristics of SARS-CoV2.11–13 Furthermore, some of the main components of Shuang and Lian were originally applied for inhibiting the similar sites.

Subsequently, the analysis of the prepared ligands in each binding site unveiled the substitutability of the herbs. Initially, Huang could be the supplement to the overall activity of Shuanghuanglian, and Huang could be de-emphasized considering the main bioactivity of the prescription. The herb resources of the selected hits are listed in Table S7. Among the 15 natural products, three were unique for Shuang and seven in Lian, hence inferring that these two herbs were irreplaceable. Several herbs also had five other hits and included Chrysanthemi Flos (359 compounds), Aurantii Fructus Immaturus (65 compounds), Ginkgo Semen (80 compounds), Herbahypericiperforati (37 compounds), Canarii Fructus (56 compounds), and Saussureae Involucratae Herba (55 compounds). These herbs could provide possible substitutability in urgent situations.

Conclusions

This study screened the potential binding of natural products in Shuanghuanglian to the the PLPro and Mpro of SARS-CoV2 by molecular docking simulation. There were 482 distinguished natural products in Shuanghuanglian that were categorized in the fatty acids, aromatic compounds, glycosides, and sterols. These compounds in these three herbs had certain repetition. The N3-binding site of the Mpro was further divided into five detailed sites. The successfully docked rates of the Shuanghuanglian components to these proteins were all higher than that of the compounds in the FDA-approved Drug Library. A majority of the top hits might interact with the binding sites via hydrogen bonds. In general, Shuang and Lian took the primary status in providing the top hits, while Huang could act as an important supplement to their overall activity. Moreover, we checked the pharmacokinetic potential of the top hits. Though the selected hits faced the common difficulty of polarity, the deglycosylation and the package by the carriers could also be practical to overcome the violation. The substitutability of these herbs indicated that Shuang and Lian were irreplaceable, while other herbs seemed to have potential to be involved after further evaluation and analysis of the diversity and risks in the future. The findings from this work could open an avenue for international and standardized research on traditional Chinese medicines, thus contributing to global public health.

Supporting information

Supplementary material for this article is available at https://doi.org/10.14218/JERP.2022.00061 .

Table S1

The natural products from the three major ingredients of Shuanghuanglian.

(DOCX)

Click here for additional data file.

Table S2

The docked natural products which reached the basic requirement of potential interactions with SARS-CoV-2 PLPro (Ranked from left to right in each line).

(DOCX)

Click here for additional data file.

Table S3

The docked natural products which reached the basic requirement of potential interactions with SARS-CoV-2 Mpro N3-binding site (Ranked from left to right in each line).

(DOCX)

Click here for additional data file.

Table S4

The top 50 hits in each binding site in SARS-CoV-2 Mpro.

(DOCX)

Click here for additional data file.

Table S5

The predicted ADME properties of the selected hits and the Metabolite.

(DOCX)

Click here for additional data file.

Table S6

The SMILE codes of the selected hits and the Metabolite.

(DOCX)

Click here for additional data file.

Table S7

The herb resources of the selected hits.

(DOCX)

Click here for additional data file.

Figure S1

The 2D binding patterns of the top 15 hits in SARS-CoV-2 PLPro.

(DOCX)

Click here for additional data file.

Figure S2

The 2D binding patterns of the top 15 hits in SARS-CoV-2 Mpro at N3-binding site.

(DOCX)

Click here for additional data file.

Figure S3

The 2D binding patterns of the top 15 hits in SARS-CoV-2 Mpro Site II.

(DOCX)

Click here for additional data file.

Figure S4

The 2D binding patterns of the top 15 hits in SARS-CoV-2 Mpro Site III.

(DOCX)

Click here for additional data file.

Figure S5

The 2D binding patterns of the top 15 hits in SARS-CoV-2 Mpro Site IV.

(DOCX)

Click here for additional data file.

Figure S6

The 2D binding patterns of the top 15 hits in SARS-CoV-2 Mpro Site V.

(DOCX)

Click here for additional data file.

Abbreviations

ADME: 

absorption, distribution, metabolism, and excretion

CoV: 

coronavirus

FDA: 

Food and Drug Administration, USA

HSA: 

human serum albumin

MERS: 

Middle East respiratory syndrome

MOF: 

metal-organic framework

Mpro: 

main protease

PLPro: 

papain-like protease

RdRP: 

RNA-dependent RNA polymerase

SARS: 

severe acute respiratory syndrome

SMILE: 

simplified molecular input line entry system

TCMSP: 

traditional Chinese medicine systems pharmacology database and analysis platform

TPSA: 

topological polar surface area

Declarations

Acknowledgement

We thank Prof. Yang Zhou and Dr. Peng-Fei Qi from Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences for their help in checking the parameters of the virtual screening.

Data sharing statement

The data from this study are available within the article and its supplementary files. Other corresponding details can be shared by requesting from the corresponding authors.

Funding

This work was supported by the grants from the Innovation Fund for Technology of Nanjing University (2021), Research Project of Jinhua Traditional Chinese Medicine Science and Technology (2022KY33), and Scientific Research Project of Jinhua Advanced Research Institute (GYY202108 and GYY202104).

Conflict of interest

Yu-Shun Yang is a part-time academic consultant of Hi-Techjig Co. Ltd. (Zhenjiang, China). The authors have no other conflicts of interest to declare.

Authors’ contributions

Conceptualization: YSY; formal analysis: YSY, DA, XW, and CYW; methodology: YSY and JY; writing the original draft: YSY; reviewing and editing: XW, CYW, and JY. All authors read and agreed to the published version of the manuscript.

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Virtual Screening of Shuanghuanglian Components for the Binding to the Proteinases of SARS-CoV-2

Yu-Shun Yang, Dilimulati Ainiwaer, Xiao Wang, Chao-Yue Wang, Jie Yang
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