Abstract
Purpose
The Corona Virus Disease 2019 (COVID-19) pandemic has become a
challenge of world. The latest research has proved that Xuanfei Baidu
granule (XFBD) significantly improved patient’s clinical symptoms, the
compound drug improves immunity by increasing the number of white blood
cells and lymphocytes, and exerts anti-inflammatory effects. However,
the analysis of the effective monomer components of XFBD and its
mechanism of action in the treatment of COVID-19 is currently lacking.
Therefore, this study used computer simulation to study the effective
monomer components of XFBD and its therapeutic mechanism.
Methods
We screened out the key active ingredients in XFBD through TCMSP
database. Besides GeneCards database was used to search disease gene
targets and screen intersection gene targets. The intersection gene
targets were analyzed by GO and KEGG. The disease-core gene target-drug
network was analyzed and molecular docking was used for verification.
Molecular dynamics simulation verification was carried out to combine
the active ingredient and the target with a stable combination. The
supercomputer platform was used to measure and analyze the number of
hydrogen bonds, the binding free energy, the stability of protein
target at the residue level, the solvent accessible surface area, and
the radius of gyration.
Results
XFBD had 1308 gene targets, COVID-19 had 4600 gene targets, the
intersection gene targets were 548. GO and KEGG analysis showed that
XFBD played a vital role by the signaling pathways of immune response
and inflammation. Molecular docking showed that I-SPD, Pachypodol and
Vestitol in XFBD played a role in treating COVID-19 by acting on NLRP3,
CSF2, and relieve the clinical symptoms of SARS-CoV-2 infection.
Molecular dynamics was used to prove the binding stability of active
ingredients and protein targets, CSF2/I-SPD combination has the
strongest binding energy.
Conclusion
For the first time, it was found that the important active chemical
components in XFBD, such as I-SPD, Pachypodol and Vestitol, reduce
inflammatory response and apoptosis by inhibiting the activation of
NLRP3, and reduce the production of inflammatory factors and chemotaxis
of inflammatory cells by inhibiting the activation of CSF2. Therefore,
XFBD can effectively alleviate the clinical symptoms of COVID-19
through NLRP3 and CSF2.
Keywords: COVID-19, Xuanfei Baidu granule, bioinformatics analysis,
molecular docking, molecular dynamics
Graphical Abstract
The mechanisms analysis of Xuanfei Baidu Granules (XFBD) in the
treatment of COVID-19.
[55]graphic file with name fcimb-12-965273-g012.jpg
Introduction
Coronavirus disease 2019 (COVID-19) is a highly infectious disease
caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
([56]Lai et al., 2020). Since the outbreak of COVID-19, it has been
characterized by strong infectivity, long treatment time after
infection, and high mortality of patients with severe illness
([57]Fogarty H, et al., 2020; [58]Rowan NJ, et al., 2020; [59]Goldman
DT, et al., 2021). The latest clinical research findings that the main
physiological and pathological feature of severe COVID-19 is “cytokine
storm”, also known as inflammatory storm ([60]Ouédraogo et al., 2020).
It is an immune response produced by a positive feedback loop between
cytokines and immune cells, and it is also the state which the body’s
immune system has evolved from “self-protection” to “over-protection”
([61]Bellanti and Settipane, 2020). Therefore, the outbreak of
inflammation is an important pathological factor leading to the
aggravation and even death of patients with respiratory damage caused
by COVID-19 ([62]Liu et al., 2021), however, there is no clear
antiviral therapy for COVID-19 in the clinic ([63]Panyod et al., 2020).
The latest clinical trials show that traditional Chinese medicine has a
significant effect on viral pneumonia. Clinical studies have shown that
XFBD combined with conventional drugs can significantly improve
clinical symptoms such as fever, cough, fatigue, loss of appetite, etc.
XFBD treatment can increase the number of white blood cells and
lymphocytes to improve immunity, while significantly reducing
C-reactive protein and erythrocyte sedimentation rate to play an
anti-inflammatory effect ([64]Xiong et al., 2020; [65]Zhao et al.,
2021a). Meta-analysis demonstrated that XFBD alleviated clinical
symptoms in most patients with mild or moderate COVID-19, and reduced
the transition of mild patients to severe disease ([66]Runfeng et al.,
2020; [67]Wang et al., 2022).At present, the symptomatic treatment of
COVID-19 with integrated traditional Chinese and Western medicine has
been clinically applied in China, and good therapeutic effects have
been achieved.
Currently, the National Health Commission of China recommends the
traditional Chinese medicine compound Xuanfei Baidu Granule (XFBD) for
the clinical treatment of COVID-19 ([68]Xie, 2020).
Xuanfei Baidu granule (XFBD) consists of 13 Chinese materia herbs:
bitter almond, atractylodes, artemisia annua, patchouli, polygonum
cuspidatum, verbena, reed root, ephedra, coix seed, exocarpium,
licorice, semen lepidii, and gypsum ([69]Zhao et al., 2021a). XFBD is a
traditional Chinese medicine compound for the treatment of
anti-epidemic, which is designed for the pathological characteristics
of wet toxin ([70]Xie, 2020). XFBD has the effects of inhibiting viral
infections, promoting the absorption of lung inflammation, and reducing
inflammatory factors.
A large number of clinical studies have shown that Xuanfei Baidu
granule (XFBD) can effectively relieve the clinical symptoms of
COVID-19 patients ([71]Li et al., 2021a). The latest clinical study
found that XFBD combined with conventional drugs significantly improved
the clinical symptoms of COVID-19 patients, increased the number of
white blood cells and lymphocytes, and decreased C-reactive protein and
erythrocyte sedimentation rate. This result suggested that XFBD had a
potential immunomodulatory role in the treatment of COVID-19 ([72]Xiong
et al., 2020).
However, there is currently a lack of more in-depth and systematic
research on Xuanfei Baidu granule (XFBD) in the treatment of COVID-19.
And XFBD is a traditional Chinese medicine compound, its complex
components also hinder the related research in the treatment of
COVID-19. Molecular dynamics can comprehensively and systematically
simulate the interaction and binding stability between small molecule
monomers and protein targets with the help of powerful computing power.
Molecular dynamics (MD) is an interdisciplinary subject based on the
knowledge of physics, chemistry, life science, materials and other
disciplines. It uses large computer clusters (or even supercomputers)
as the carrier, it aims to obtain data such as microstructure, physical
and chemical properties, and performance characterization parameters of
materials by calculation ([73]Santos et al., 2019). It is a supplement
and in-depth excavation of the traditional materials discipline mainly
based on experiments. Through the data obtained by calculation, the
mechanism behind the experiment is researched and analyzed at multiple
levels from the microscopic, mesoscopic and macroscopic scales. So that
it is not only limited to “qualitative”, but can rise to the
theoretical height of “quantitative” ([74]Anuar et al., 2021). It
analyzes the behavioral law of molecular motion by solving the
potential function of intermolecular interaction and the equation of
motion, simulates the dynamic evolution process of the system, and
provides microscopic quantities (such as: the coordinates and velocity
of molecules, etc.) and macroscopic observable quantities (such as: the
relationship between the temperature, pressure, heat capacity of the
system, etc.) ([75]Sivakumar et al., 2020), so as to study the
equilibrium properties and mechanical properties of the composite
system, it is an effective research method to study the properties of
drugs and protein stability. Firstly, molecular dynamics solves the
equation of motion for a many body system composed of atomic nuclei and
electrons. Secondly, molecular dynamics can not only directly simulate
the macroscopic evolution characteristics of matter, but also obtain
calculation results that are consistent with or similar to the
experimental results. Finally, molecular dynamics can give the
microscopic evolution process of the system from the atomic level, and
intuitively show the mechanism and law of the experimental phenomenon.
Therefore, molecular dynamics can provide a clear picture of the
microstructure, particle motion and their relationship with macroscopic
properties. Molecular dynamics can also make our research more
efficient, more economical, and more predictable.
This study used bioinformatics to screen out potential effective
monomers from Xuanfei Baidu granule (XFBD). The core intersection
targets of XFBD and COVID-19 were screened by GeneCards database. PPI,
GO and KEGG were used to analyze the potential associations between
gene targets to explore the mechanisms of action and potential
pathways. Molecular system movement was used to their simulate the
result of calculating interrelationships from the cellular level to the
chemical group level. Molecular docking was used to determine the
affinity of monomeric compounds and protein targets, molecular dynamics
was used to simulate the stability of bound complexes. The research on
the mechanism of XFBD in the treatment of COVID-19 will promote its
clinical application, lay a solid foundation for related research and
promote further research.
Material and methods
Identification and screening of active compounds
Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,
[76]http://tcmspw.com/) was used to screen and analyse all compounds of
the thirteen Chinese medicinal herbs in Xuanfei Baidu granule (XFBD)
([77]Daina et al., 2019). Compounds of XFBD are screened according to
two key parameters, namely oral bioavailability (OB) and drug
similarity (DL), in the assessment categories of absorption,
distribution, metabolism and excretion. OB was defined as the degree to
which active ingredients are used by the body ([78]Ru et al., 2014). OB
largely determines the effect of the compound on the disease, DL is
used to screen and refine candidate compounds early in drug
development. In this study, the active compounds in XFBD were selected
according to the criterion of OB≥30% and DL≥0.18 ([79]Xu et al., 2012).
The intersection of disease and drug gene targets
We used the GeneCards ([80]https://genecards.weizmann.ac.il/v3/),
“COVID-19” and “SAR-Cov-2” were uesd to be the key words to obtain the
disease gene targets, and COVID-19-related genes were screened from
genecard with relevance score≥5 as the threshold, relevance score is a
comprehensive evaluation of the association between genes and research
diseases. We also imported the 13 Chinese materia herbs of Xuanfei
Baidu granule (XFBD) into genecards to obtain drug gene targets. The
drug gene targets and the disease gene targets were combined through
the venny website to obtain intersection gene targets.
Xuanfei Baidu granule treatment of COVID-19 interaction protein targets
(Protein-Protein Interaction) network building
The STRING database was used to analyze the protein-protein interaction
(PPI) of Xuanfei Baidu granule (XFBD) in the treatment of COVID-19.
STRING database covers the majority of known human protein–protein
interaction information ([81]Szklarczyk et al., 2019). In order to
further clarify the interaction between potential protein targets, all
potential therapeutic protein targets of XFBD on COVID-19 were imported
into Cytoscape 3.7.1 to analyze ([82]Shannon et al., 2003), we defined
the protein type as “Homo sapiens”, and obtained relevant information
on protein interactions by STRING database. Finally, the network
topology parameters were analyzed by Cytoscape 3.7.1, and the hub
protein targets were screened out according to the criterion that the
node degree value and the betweenness center value were greater than
the average value.
The gene target enrichment analysis
The interaction gene targets were used in DAVID database for gene
ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and
Genomes (KEGG) enrichment analysis. We obtained the molecular function
(MF), cellular component (CC) and related biological process (BP) of
the gene targets through GO enrichment. The disease-related targets
obtained from screening were input into the DAVID database by entering
the list of target gene names and selecting the species as “homo
sapiens” ([83]Huang Da et al., 2009). In this study, KEGG pathway
enrichment analysis was performed on the relevant signaling pathways
involved in the target, and gene target screening was performed under
the condition of p<0.05. The main biological processes and signaling
pathways of Xuanfei Baidu Granules (XFBD) on COVID-19 were analyzed.
This study visualized the results of GO enrichment and KEGG enrichment
by the Omicshare Tools platform ([84]Cao et al., 2022).
Network diagram of “ Disease-core target gene-drug “
Cytoscape 3. 7. 1 network map software was used to construct a
disease-core target gene-drug network and conduct topological analysis.
The core gene targets can be screened based on the node degree value
greater than two times the median ([85]Cao et al., 2022).
Component target molecular docking and validation of the docking protocol
Molecular docking was used to study the molecular affinity of Xuanfei
Baidu granule (XFBD) small-molecule potent antiviral compounds with
COVID-19 protein targets. The protein crystal structure used for
docking was downloaded from the PDB database, and the 3D structure of
the small molecule was downloaded from the PUBCHEM database, and energy
minimization was performed under the MMFF94 force field. In this study,
AutoDock Vina 1.1.2 software was used for molecular docking work.
Before docking, PyMol 2.5 was used to process all receptor proteins,
including removal of water molecules, salt ions and small molecules
([86]Kim et al., 2016). Then set up the docking box, use the PyMol
plugin center of mass.py to define the center of the docking box based
on the position of the crystal ligand, and set the box side length to
22.5 angstroms. In addition, ADFRsuite 1.0 was used to convert all
processed small molecules and receptor proteins into the PDBQT format
necessary for docking with AutoDock Vina 1.1.2. When docking, the
exhaustiveness of the global search is set to 32, and the rest of the
parameters remain the default settings. The output highest scoring
docked conformation was considered to be the binding conformation for
subsequent molecular dynamics simulations ([87]Kim et al., 2016). The
study used the original crystal ligand of the protein target as a
positive reference, and we analyzed and compared the binding posture of
the original crystal ligand and protein, the chemical bond length and
the chemical bond angle by re-docking the original crystal ligand and
protein. Finally, the consistency of the binding mode can indicate the
correctness of the molecular docking protocol ([88]Cao et al., 2022).
Molecule dynamics
The highest scoring conformations determined by molecular docking
analysis were further validated by running 50ns molecular dynamics
simulations. Molecular dynamics (MD) simulation is a comprehensive set
of molecular simulation methods combining physics, mathematics and
chemistry. This method mainly relies on Newtonian mechanics to simulate
the motion of molecular systems, we calculate macroscopic properties
such as thermodynamic quantities of a system by taking samples from an
ensemble of different states of a molecular system.
In this study, all-atom molecular dynamics simulations were performed
based on the small molecule and protein complexes obtained from the
molecular docking results as the initial structure, and the simulations
were performed using AMBER 18 software ([89]Maier et al., 2015). The
charge of the small molecule was calculated in advance by the
antechamber module and the Hartree–Fock (HF) SCF/6-31G* of the gaussian
09 software before the simulation. Afterwards, small molecules and
proteins were described using the GAFF2 small molecule force field and
the ff14SB protein force field, respectively. Each system used the LEaP
module to add hydrogen atoms to the system, added a truncated
octahedral TIP3P solvent box at a distance of 10 Å, and added Na+/Cl-
to the system to balance the system charge ([90]Harrach and Drossel,
2014). Finally, the simulated topology and parameter files were
exported.
Ligands were parameterized using a generic amber force field (GAFF)
using a combination of AmberTools18 and ACPYPE 51 protocols ([91]Wang
et al., 2006). After the initial addition of hydrogen atoms to each
system, the system uses the steepest descent algorithm for vacuum
minimization. Solvent was then added and the system ions were
equilibrated using counter ions (Na+/Cl-). The proteins were all energy
minimized using the steepest descent method and the conjugate gradient
method. This was followed by an NVT and NPT ensemble (1000 ps, dt of 2
fs) and an MD run (100 ns, dt of 2 fs) at 298 K temperature and 1 bar
pressure using the skip integrator algorithm. The coordinates and
energy of the system are saved every 10 ps. Finally, 50ns production
simulations were carried out for each system under periodic boundary
conditions. For all simulations, the van der Waals force (vdw) cutoff
and short-range electrostatic interactions were set to 10 Å. The
Particle-Mesh-Ewald (PME) method is used to evaluate long-range
electrostatic interactions. Molecular dynamics simulation trajectories
include protein-ligand complex root mean square deviation (RMSD), root
mean square fluctuation (RMSF), radius of gyration and solvent
accessible surface area (SASA).
MMGBSA binding free energy calculation
The binding free energy was investigated using the MM-PBSA method, and
the conformational stability was studied in detail. The binding free
energies between proteins and ligands for all systems were calculated
by the MM/GBSA method ([92]Hou et al., 2011). The molecule dynamics
trajectory of 50 ns was used for calculation, and the specific formula
is as follows:
[MATH:
ΔGbind=ΔGcomplex−
(ΔGrece
ptor+ΔG
ligand)=ΔE
internal+ΔEVDW+ΔEelec+ΔGGB+ΔGSA
:MATH]
In the formula, Einternal represents internal energy, EVDW represents
van der Waals interaction and Eelec represents electrostatic
interaction. The internal energy includes bond energy (Ebond), angular
energy (Eangle) and torsional energy (Etorsion); GGB and GGA are
collectively referred to as solvation free energy, where GGB is the
polar solvation free energy and GGA is the non-polar solvation free
energy. For this paper, the GB model developed by Nguyen was used for
calculation (igb = 2). The non-polar solvation free energy (GSA) was
calculated based on the product of surface tension (γ) and solvent
accessible surface area (SA), GSA = 0.0072 × SASA15. The entropy change
is ignored in this study due to high computational resource consumption
and low precision ([93]Cao et al., 2022).
Results
Identification of potentially active compounds in Xuanfei Baidu granule
In total, 178 potential compounds in Xuanfei Baidu granule (XFBD) were
retrieved from the TCMSP database with the criteria of DL≥0.18 and
OB≥30%, by further improving the OB score (OB≥74%), five core active
compounds in XFBD were screened out, shown in [94]Table 1 .
Table 1.
The core active compounds in Xuanfei Baidu Granules (XFBD) Binding free
energies and energy components.
MOL_ID Molecule Name OB MW Alogp Caco2 BBB DL
MOL013287 Physovenine 106.219 262.34 2.08 0.50 0.20 0.18
MOL012922 I-SPD 87.34 327.41 3.09 0.75 0.20 0.54
MOL007207 Machiline 79.64 285.37 2.82 0.78 0.08 0.23
MOL005890 pachypodol 75.06 356.40 2.99 0.83 0.11 0.39
MOL000500 Vestitol 74.65 272.32 3.14 0.85 0.29 0.20
[95]Open in a new tab
OB, oral bioavailability.
MW, molecular weight.
BBB, blood brain barrier.
DL, drug similarity.
Obtained common gene targets by intersection
We obtained 1308 Xuanfei Baidu granule (XFBD) gene targets and 4600
COVID-19 gene targets. A total of 548 intersection gene targets were
processed by Venny, shown in [96]Figure 1 .
Figure 1.
Figure 1
[97]Open in a new tab
Intersection targets-active ingredient networks. Targets of the
intersection of Xuanfei Baidu granule (XFBD) and COVID-19.
Core intersection target screening and PPI network diagram
We obtained intersection genes targets of relevance score through
GeneCards, relevance score≥5 which were considered as a core
intersection gene target, through STRING database analysis of 33
mapping of the core intersection gene targets of COVID-19 and XFBD, the
study constructed the PPI network interaction map of the target protein
of XFBD in the treatment of COVID-19, shown in [98]Figure 2A . 11 core
genes (such as CSF2, IFNG, NLRP3, etc.) were obtained by setting the
interaction score (confidence degree>0.95), and the study used the 11
core gene targets to reconstruct the core PPI network, shown in
[99]Figure 2B .
Figure 2.
[100]Figure 2
[101]Open in a new tab
Protein-protein interaction (PPI) network. (A) PPI network of protein
target, (B) PPI network of core protein target (confidence>0.95).
GO and KEGG enrichment analysis
The 33 intersection gene targets were imported into the DAVID database
for enrichment analysis. Under the condition of p<0.05, the GO
enrichment analysis yielded a total of 277 GO entries, including 239 BP
entries, 23 CC entries, and 15 MF entries. According to the number of
targets contained, the top 10 BP, CC and MF compressions were screened.
The results showed that in biological processes, biological processes
were highly correlated with inflammation and viral replication, mainly
involving the cytokine-mediated signaling pathway, inflammatory
response, and immune response. Among cell components, extracellular
space, extracellular region and cell surface account for a relatively
large amount. In molecular functions, cytokine activity, protein
binding and receptor binding are relatively high, shown in
[102]Figures 3A–F . KEGG pathway analysis yielded 72 pathways with
p<0.05. According to the number of targets contained, the first 15
pathways were screened. The results showed that the enriched pathways
involved multiple pathways related to inflammation and immune response,
mainly coronavirus disease COVID-19, influenza A, cytokine-cytokine
receptor interaction and other signaling pathways, shown in
[103]Figures 3G, H .
Figure 3.
[104]Figure 3
[105]Open in a new tab
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
analysis of related genes. (A) The top 10 terms in biological processes
(BP) were greatly enriched. (B) The subnetwork displayed the first 10
BP terms and related genes. (C) The top 10 terms in cellular components
(CC) were greatly enriched. (D) The subnetwork displayed the first 10
CC terms and related genes. (E) The top 10 terms in molecular function
(MF) were greatly enriched. (F) The subnetwork displayed the first 10
MF terms and related genes. (G) The first 15 KEGG pathways were showed.
(H) the subnetworks displayed the first 15 KEGG pathways and related.
Disease-core gene target-drug network
The disease-core gene target-drug network was constructed to show the
main signal pathway and biological process of Xuanfei Baidu granule
(XFBD) in the treatment of COVID-19, shown in [106]Figure 4 .
Figure 4.
[107]Figure 4
[108]Open in a new tab
Disease-core gene target-drug network. Square nodes represent gene
targets, triangular nodes represent signaling pathways (KEGG), and
octagonal nodes represent gene ontology (GO) of related genes.
Molecular docking
The 11 core intersection gene targets were selected for molecular
docking. The stability of receptor-ligand binding depends on the
binding energy. The lower the binding energy of the complex, the more
stable the receptor-ligand binding conformation. The results show that
the binding of CSF2/I-SPD complex is mainly maintained by hydrogen
bonding and hydrophobic interaction. For example, I-SPD can form
hydrogen bonding with GLN-43 on CSF2 protein, and also with TYR-71,
LEU-42, ILE-104, PRO-105 forms a hydrophobic interaction, shown in
[109]Figure 5A . The binding of CSF2/Vestitol complex is mainly through
hydrophobic interaction, for example, the small molecule Vestitol and
PRO-76, LEU-42, TYR-71, ILE-104, PRO-105 on the protein form
hydrophobic interaction, shown in [110]Figure 5B . In the NLRP3/I-SPD
binding complex, the small molecule I-SPD forms hydrogen bonds with
GLN-468, SER-470, ALA-72, and also with VAL-197, GLU-473, LEU-472,
TYR-476, PHE -419 forms a hydrophobic interaction. In addition, we also
observed that I-SPD and ARG-422 form cationic pi conjugation, shown in
[111]Figure 5C . The binding of NLRP3/Pachypodol suggested that the
small molecule Pachypodol forms hydrogen bonds with VAL-197, GLU-200
and GLU-213, and also forms hydrophobic interactions with LEU-199 and
PRO-196 on the protein, shown in [112]Figure 5D , molecular docking
result scores are shown in [113]Figure 6 .
Figure 5.
[114]Figure 5
[115]Open in a new tab
Molecular docking of active ingredients and core targets. (A)
CSF2/I-SPD, (B) CSF2/Vestitol, (C) NLRP3/I-SPD, (D) NLRP3/Pachypodol.
Figure 6.
[116]Figure 6
[117]Open in a new tab
Screening docking results between ligands and receptors.
Molecular dynamics results
The root mean square partiality of molecular dynamics simulation is
used to reflect the movement process of the complex. The larger the
RMSD value of the complex, the more severe the fluctuation and the more
intense the movement. On the contrary, the movement is stable. The RMSD
of the four systems gradually converged in the first 5 ns of the
simulation, and kept stable fluctuations in the subsequent simulations.
It is suggested that the motion of the four complexes is stabilized
after the combination of the kinetics. In comparison, CSF2/Vestitol
(red line) has the lowest RMSD, followed by NLRP3/I-SPD, then
CSF2/I-SPD, and finally NLRP3_Pachypodol, indicating that the stability
of these complexes is CSF2/I-SPD, CSF2/Vestitol, NLRP3/I-SPD,
NLRP3/Pachypodol. However, it is worth emphasizing that the RMSD
results of all complexes suggest that small molecules can bind to
proteins and maintain a relatively stable state. The results are shown
in [118]Figure 7 .
Figure 7.
[119]Figure 7
[120]Open in a new tab
Complex root mean square deviation (RMSD) difference over time. ns,
nanosecond.
Combined free energy calculation results
Based on the trajectory of the molecular dynamics simulation, we
calculated the binding energy using the MMGBSA method, which can more
accurately reflect the binding mode of small molecules and target
proteins. The binding energies of CSF2/I-SPD, CSF2/Vestitol,
NLRP3/I-SPD, and NLRP3_Pachypodol complexes were -20.89 ± 1.32
kcal/mol, 27.57 ± 2.78 kcal/mol, -30.52 ± 1.17 kcal/mol, and -21.65 ±
3.36 kcal/mol. The negative values indicate that both molecules have
binding affinity for the target protein, and lower value indicate
stronger binding. Obviously, our calculations show that these molecules
and the corresponding proteins have a certain binding affinity and are
very strong. Among them, NLRP3/I-SPD and CSF2/Vestitol have higher
binding energies. For the binding energy of the NLRP3/I-SPD complex,
the energy decomposition shows that the van der Waals energy is the
main contribution energy. For the binding energy of the CSF2/Vestitol
complex, the energy decomposition shows that the electrostatic energy
is the main contribution energy. The experimental results are shown in
[121]Table 2 .
Table 2.
Binding free energies and energy components predicted by MM/GBSA
(kcal/mol).
System name CSF2/I-SPD CSF2/Vestitol NLRP3/I-SPD NLRP3/Pachypodol
ΔE [vdw] -31.85 ± 0.83 -35.21 ± 1.70 -39.13 ± 4.72 -26.90 ± 1.87
ΔE [elec] -74.07 ± 6.98 1.43 ± 2.49 -77.18 ± 10.66 -15.70 ± 5.59
ΔG[GB] 88.70 ± 7.47 10.83 ± 2.40 90.77 ± 6.69 24.61 ± 4.35
ΔG[SA] -3.67 ± 0.11 -4.63 ± 0.15 -4.97 ± 0.18 -3.65 ± 0.23
ΔG[bind] -20.89 ± 1.32 27.57 ± 2.78 -30.52 ± 1.17 -21.65 ± 3.36
[122]Open in a new tab
ΔE[vdW]: van der Waals energy.
ΔE[elec]: electrostatic energy.
ΔG[GB]: electrostatic contribution to solvation.
ΔG[SA]: non-polar contribution to solvation.
ΔG[bind]: binding free energy.
Hydrogen bond analysis
Hydrogen bonds are one of the strongest non-covalent binding
interactions. The more the number, the better the binding. The results
suggest that the number of hydrogen bonds between small molecules and
NLRP3 is significantly more than the number of hydrogen bonds with
CSF2. Combining the above binding modes, we can see that the number of
hydrogen bonds is small. The interaction of molecules and NLRP3 may be
dominated by hydrogen bonding, especially the NLRP3/I-SPD complex with
the strongest binding energy. The interaction of small molecules with
CSF2 may not mainly occur through hydrogen bonding, but through
hydrophobic interaction. The results are shown in [123]Figure 8 .
Figure 8.
[124]Figure 8
[125]Open in a new tab
Changes in the number of hydrogen bonds between small molecule ligands
and protein receptors in complex system simulations (A) CSF2/I-SPD, (B)
CSF2/Vestitol, (C) NLRP3/I-SPD, (D) NLRP3/Pachypodol.
The stability of the target protein at the residue level
To explore the local fluctuations of macromolecular proteins at the
residue level, the vibrations of each residue after compound binding
were explored as root mean square fluctuations (RMSF). RMSF can reflect
the flexibility of proteins during molecular dynamics simulations.
Usually, after the drug binds to the protein, the flexibility of the
protein decreases, thereby achieving the effect of stabilizing the
protein and exerting the effect of enzymatic activity. The RMSF of the
CSF2 and NLRP3 proteins after binding different small molecules is
generally low, indicating that the protein as a whole has good
rigidity, shown in [126]Figure 9 . It is worth noting that for CSF2,
the decrease in RMSF after the binding of Vestitol small molecule
indicates a significant decrease in protein rigidity; however, for
NLRP3, the effect of I-SPD and Pachypodol on protein RMSF was not
different.
Figure 9.
[127]Figure 9
[128]Open in a new tab
Changes in the stability of protein targets at the residue level (A)
CSF2/I-SPD and CSF2/Vestitol. (B) NLRP3/I-SPD and NLRP3/Pachypodol.
Analysis of the radius of gyration
The radius of gyration (Rg) reflects the compactness of the embodiment
and can reflect the degree of binding of the system. For the CSF2
protein, the Rg after combining two small molecules acts at 13.7
angstroms; for the NLRP3 protein, the compactness after combining the
small molecules is about 23.8 angstroms. The overall values are low,
implying that the system is denser and more closely combined. It is
worth mentioning that the CSF2 protein is smaller, and the Rg of CSF2
is smaller than that of NLRP3, the results are shown in [129]Figure 10
.
Figure 10.
[130]Figure 10
[131]Open in a new tab
Analysis of protein folding state and overall conformation (A)
CSF2/I-SPD and CSF2/Vestitol. (B) NLRP3/I-SPD and NLRP3/Pachypodol. ns,
nanosecond.
Analysis of solvent accessible surface area
Solvent accessible surface area is calculated as the interface
surrounded by solvent. This solvent behaves differently under different
conditions and is therefore a useful parameter for studying protein
conformational dynamics in a solvent environment. The contact area
between the four complexes and water is similar, and the small molecule
has little effect on the effect of protein and water, the results are
shown in [132]Figure 11 .
Figure 11.
[133]Figure 11
[134]Open in a new tab
Analysis of Solvent Accessible Surface Area (SASA) (A) CSF2/I-SPD and
CSF2/Vestitol. (B) NLRP3/I-SPD and NLRP3/Pachypodol. ns, nanosecond.
Discussion
This study explored the pharmacological mechanism of Xuanfei Baidu
granule (XFBD) in the treatment of COVID-19 by molecular docking and
molecular dynamics simulation based on molecular system movement. For
the first time, it was found that the important active chemical
components I-SPD and Pachypodol in XFBD could reduce the inflammatory
response and apoptosis by inhibiting the activation of NLRP3, and
reduce the production of inflammatory response. And I-SPD and Vestitol
could inhibit the activation and chemotaxis of inflammatory cells
through CSF2, prevent the generation of inflammatory storm. Therefore,
Vestitol, Pachypodol and I-SPD in XFBD could effectively treat COVID-19
through NLRP3 and CSF2 and reduce the clinical symptoms of patients.
Bioinformatics analysis of XFBD
Pachypodol, I-SPD and Vestitol in XFBD play a role in treating COVID-19
by acting on NLRP3, CSF2, and relieve the clinical symptoms of
SAR-Cov-2 infection.
Pachypodol and I-SPD reduce inflammation and apoptosis by inhibiting
the activation of NLRP3, thereby exerting protective effects on the
respiratory and nervous systems of patients. Analysis of protein
interaction network PPI suggested that NLRP3 was closely related to
viral infections and inflammatory responses targets, GO analysis
results suggest that NLRP3 is mainly located in extracellular space,
KEGG pathway analysis found that NLRP played a role in coronavirus
disease COVID-19, influenza A and other pathways. The analysis results
suggest that the SARS-CoV 3a protein, as a transmembrane pore-forming
viral protein, can activate the NLRP3 inflammasome by forming ion
channels in macrophages. At the same time, NLRP3 is found to play a
role in pathways such as influenza A, and the inflammasome NLRPS can
induce the production of the inflammatory cytokine IL-10 in host cells,
resulting in an inflammatory cytokine storm. Inflammatory cytokine
storms can cause acute respiratory distress syndrome (ARDS) and acute
lung injury (ALI).
Vestitol and I-SPD mainly act on CSF2 to suppress cytokine storm and
infiltration of immune cells. CSF2 was closely related to inflammatory
targets in PPI. GO analysis results suggest that CSF2 is mainly located
in extracellular region. KEGG pathway analysis found that CSF2 played a
role in cytokine-cytokine receptor interaction and other pathways. CSF2
can be seen as an attractive mediator. CSF2 is produced as a
pro-inflammatory cytokine by many cells, including macrophages, T
cells, endothelial cells, and epithelial cells. CSF2 can control the
production and differentiation of granulocytes and macrophages, and
CSF2 has the effect of promoting tissue inflammation
However, the current bioinformatic analysis results can only predict
potential relationships between drugs and gene targets and proteins.
Therefore, the use of molecular docking and molecular dynamics in this
study can verify the potential relationship of XFBD in the treatment of
COVID-19.
Analysis of molecular docking and molecular dynamics
There is a strong affinity between active ingredient of medicine (such
as Pachypodol, I-SPD and Vestitol) and the protein targets (such as
NLRP3 and CSF2) through molecular docking tests. Molecular dynamics
suggest that they can maintain a very stable binding state, and then
play a pharmacological role in the treatment of COVID-19.
I-SPD could stably act on NLRP3 and CSF2, especially NLRP3/I-SPD showed
strong stability. Molecular docking showed that the binding energies of
small molecules to NLRP3 and CSF2 reached -7.9 and -8.0. Based on the
trajectory of the molecular dynamics simulation, we calculated the
binding energy using the MMGBSA method, which could more accurately
reflect the binding mode of small molecules and target proteins. The
binding free energy results showed NLRP3/I-SPD and CSF2/I-SPD were
-39.13 ± 4.72 kcal/mol and -31.85 ± 0.83 kcal/mol, for the binding
energy of the NLRP3/I-SPD complex, the energy decomposition showed that
the van der Waals energy was the main contributing energy. In the
molecular dynamics simulation, the RMSDs of NLRP3/I-SPD and CSF2/I-SPD
both converged gradually in the first 5 ns of the simulation and
preserved stable fluctuations in subsequent simulations, implying that
the kinetics of the four complexes are stabilized after binding, and
CSF2/I-SPD binding was more stable than NLRP3/I-SPD. NLRP3/I-SPD
binding results suggested that small molecule I-SPD forms hydrogen
bonds with GLN-468, SER-470, ALA-72, and also formed with VAL-197,
GLU-473, LEU-472, TYR-476, PHE-419 Hydrophobic interaction.
The binding of Pachypodol to NLRP3 is relatively stable, molecular
docking showed that the binding energies of small molecules to NLRP3
reached -8.2. The binding free energy results show NLRP3/Pachypodol was
-26.90 ± 1.87kcal/mol. The number of hydrogen bonds of NLRP3/Pachypodol
is relatively stable. The high fluctuation of residues in
NLRP3/Pachypodol may be due to the influence of its own multiple
peptide chains. NLRP3/Pachypodol binding results suggested that small
molecule Pachypodol formed hydrogen bonds with VAL-197, GLU-200,
GLU-213, and also formed with LEU-199 and PRO-196 Hydrophobic
interaction.
Vestitol combined with CSF2 can form stable complexe, but there were
some abnormal fluctuations, which may be due to the influence of the
number and angle of binding bonds. molecular docking showed that the
binding energies of small molecules to CSF2 reached -7.9. The binding
free energy results showed CSF2/Vestitol was -35.21 ± 1.70 kcal/mol,
for the binding energy of the CSF2/Vestitol complex, the energy
decomposition showed that electrostatic energy was the main
contributing energy. We found that RMSF decreased after CSF2 bound to
the small molecule Vestitol, suggesting that protein rigidity was
significantly decreased. CSF2/Vestitol binding results suggested that
PRO-76, LEU-42, TYR-71, ILE-104, PRO-105 on small molecules and
proteins form hydrophobic interactions.
We presented the microscopic evolution process of the complex system
from the level of small molecules and protein residues through
molecular docking and molecular dynamics. Computer simulations
visualized the binding states of NLRP3/I-SPD, CSF2/I-SPD,
NLRP3/Pachypodol and CSF2/Vestitol. The simulation results showed that
the combination of the four complexes can remain relatively stable in
the kinetic simulation, thus providing theoretical support for the role
of small molecule drugs.
There are certain differences between Xuanfei Baidu granule (XFBD) and
traditional single small molecule drugs in the treatment of COVID-19
([135]Choudhury et al., 2021; [136]Yan et al., 2021). Because Xuanfei
Baidu granule (XFBD) as a traditional Chinese medicine compound
contains thousands of active small molecules, XFBD can treat diseases
through multiple small molecular components acting on multiple
disease-related target proteins, while reducing the adverse drug
reactions. Therefore, molecular docking and molecular dynamics can be
used to more deeply and objectively study the mechanism of action of
small molecules in XFBD that coordinate and interact with each other to
treat COVID-19. Some studies have used network pharmacology methods to
enrich the targets and pathways of traditional Chinese medicines (such
as: Lung Cleansing and Detoxifying Decoction (LCDD)) and explore their
therapeutic effects on COVID-19 ([137]Xu et al., 2021). This study not
only analyzed and drawed on relevant network pharmacology research
results, but also used the supercomputer platform to simulate the
relationship between small molecule drugs and protein targets through
molecular dynamics. For example, molecular dynamics can show the
moverment stable between small molecule drugs and protein targets. The
root mean square deviation partiality (RMSD)of molecular dynamics
simulation can reflect the movement process of the complex.
Therefore, the results of this study could further explain the
mechanism of action and related signaling pathways of XFBD in the
treatment of COVID-19.
Pachypodol and I-SPD can reduce inflammation and apoptosis through NLRP3
As an essential component of the innate immune system, the NLRP3
inflammasome is important for antiviral host defense, and its abnormal
activation can lead to pathological tissue damage during infection.
The NLRP3 inflammasome is a high molecular weight protein complex
composed of the upstream sensor protein NLRP3 and the downstream
effector protein caspase-1 ([138]Lamkanfi and Dixit, 2012). When
caspase-1 is activated, it promotes the activation of cytokines IL-1β
and IL-18 ([139]Mangan et al., 2018), which eventually leads to cell
rupture and apoptosis ([140]Liu et al., 2016; [141]Orning et al., 2019;
[142]Liu et al., 2020). During COVID-19, the NLRP3 inflammasome is
overactivated ([143]Ratajczak et al., 2021), leading to the production
of IL-1β/18 and promoting cytokine storm ([144]Lin et al., 2019).
Viruses are stimulators of cytokine release syndrome development
([145]Tisoncik et al., 2012). Cytokine storm usually causes patients to
express clinical symptoms such as fever, hypotension, and hypoxemia
([146]Shimabukuro-Vornhagen et al., 2018). Elevated levels of IL-1β
produced by the NLRP3 inflammasome further activate neutrophils,
resulting in increased levels of the neutrophil extracellular traps
(NETs) production. High levels of NETs lead to increased clot formation
associated with COVID-19 and damage to endothelial and alveolar cells
([147]Zhao et al., 2021b). Activation of NLRP3 requires at least two
steps: initiation and activation ([148]Xue et al., 2019). The first
step of initiation is activation of the nuclear factor kappa B (NF-κB)
signaling pathway ([149]Gritsenko et al., 2020). NF-κB can enhance the
transcription of pro-IL-1β, pro-IL-18 and NLRP3 ([150]Afonina et al.,
2017). Moreover, the oligomerization of NLRP3 and the assembly of NLRP3
and pro-caspase-1 into the NLRP3 inflammasome ([151]Strowig et al.,
2012), which is mainly composed of adenosine triphosphate (ATP)
([152]Karmakar et al., 2016), oxidized mitochondrial DNA (ox-mtDNA))
([153]Jia et al., 2020), and mitochondrial reactive oxygen species
(mtROS) ([154]Zhong et al., 2016) participated in the completion.
SARS-CoV-2 can cross the BBB into the central nervous system, directly
infect brain tissue, and affect human neural progenitor cells and brain
organoids ([155]Zhang et al., 2020).
I-SPD and Pachypodol have the ability to penetrate the blood-brain
barrier and inhibit NLRPS3-mediated inflammatory responses in the
central nervous system. SARS-CoV-2 invades brain tissue in two ways:
the hematogenous pathway and the neuronal retrograde pathway. BBB
permeability is increased in patients with neurodegenerative diseases,
promoting SARS-CoV-2 neuroinvasion ([156]Zubair et al., 2020). NLRP3 is
activated by SARS-CoV-2 in the central nervous system, and high levels
of peripheral cytokines (such as IL-1β and IL-6) can directly pass
through the BBB or reduce BBB integrity ([157]Mohammadi et al., 2020),
inducing peripheral leukocytes and monocytes penetration, impairs
immune homeostasis in the brain ([158]Heneka et al., 2013; [159]Yan
et al., 2020). At the same time, NLRP3 promotes the aggregation of
peptides into pathogenic fibrils and the production of inflammatory
cytokines, promotes mitochondrial dysfunction and apoptosis
([160]Freeman and Swartz, 2020), and evolves into neurological lesions.
Therefore, we believe that I-SPD and Pachypodol can reduce the
inflammatory response and apoptosis caused by the new coronavirus by
acting on NLRP3, thereby exerting a protective effect on the
respiratory and nervous systems of patients.
Vestitol and I-SPD prevent the generation of inflammatory storm and the
infiltration of immune cells by inhibiting the overexpression of CSF2
Colony-stimulating factor 2 (CSF2), also known as
granulocyte-macrophage colony-stimulating factor (GM-CSF) ([161]Damiani
et al., 2020). CSF2 is produced and secreted by many different types of
cells, mainly monocytes, macrophages and eosinophils ([162]Hamilton and
Anderson, 2004), and normally regulates inflammatory responses and
immune activation ([163]Shi et al., 2006).
CSF2 can induce the survival and activation of macrophages and
neutrophils, promote the maturation of alveolar macrophages, and play
the functions of phagocytosis and killing of viruses ([164]Mehta
et al., 2015). The transcription factor PU.1 potentiates the promoting
effect of CSF2 on the maturation of alveolar macrophages ([165]Berclaz
et al., 2007). Elevated levels of CSF2 in alveolar macrophages
stimulate the production of reactive oxygen species (ROS). CSF2 affects
the activation and proliferation of immune cells ([166]Hamilton, 2008),
and plays an important role in maintaining immune homeostasis in lung
tissue ([167]Rösler and Herold, 2016).
CSF2 regulates the Th1 immune response by inducing the production of
dendritic cells ([168]Wang et al., 2000; [169]Miller et al., 2002).
Interestingly, CSF2 can exert protective effects in humans. CSF2 can
regulate the metabolism of vascular collagen ([170]Ponomarev et al.,
2007; [171]Li et al., 2015; [172]Shiomi and Usui, 2015), promote the
proliferation and migration of vascular endothelial cells, thereby
contributing to the process of angiogenesis ([173]Tisato et al., 2013),
and induce keratinocyte proliferation and migration, which in turn
stimulates wound healing ([174]Szabowski et al., 2000; [175]Barrientos
et al., 2008). CSF2 has been shown to protect the lung by restoring
barrier function and stimulating epithelial cell proliferation
([176]Huang et al., 2011), and the alveolar epithelium exerts a
protective effect against oxidative stress-induced mitochondrial damage
([177]Sturrock et al., 2012). However, when SARS-CoV-2 infected lung
tissue, CSF2 was one of the most up-regulated genes in the cells. A
cohort study demonstrated a positive correlation between CSF2 and
disease severity in COVID-19 patients ([178]Zhao et al., 2021c). High
levels of CSF2 are found in the blood of severe COVID-19 patients
([179]Wu and Yang, 2020), so CSF2 is a proxy for excessive inflammation
in severe COVID-19 patients ([180]Kluge et al., 2020). When CSF2 is
overexpressed in the body, activated monocytes induce T cell death,
resulting in lymphopenia, pathological hyperinflammatory immune
response, pulmonary fibrosis and severe immune cell infiltration
([181]Xing et al., 1996).
The crucial downstream signaling of CSF2R has been shown to involve
JAK2/STAT5 ([182]Lehtonen et al., 2002), ERK ([183]Hansen et al., 2008;
[184]Achuthan et al., 2018), NF-κB and the phosphoinositide
3-kinase-AKT pathway ([185]Perugini et al., 2010; [186]Van De Laar
et al., 2012). CSF2 is regulated by JAK2, and when activated by
phosphorylation, regulates the proper differentiation and maturation of
macrophages ([187]Notarangelo and Pessach, 2008), and participates in
various intracellular signaling pathways such as STAT5 and MAPK
([188]Hansen et al., 2008). Janus kinase (JAK) activates tyrosine
kinase, which then phosphorylates STAT3. Phosphorylated STAT3 activates
NF-κB and upregulates the expression of inflammatory cytokines, thereby
enhancing inflammation, cell damage and fibrosis ([189]Cao et al.,
2022). Macrophages repolarize through the CSF2/CSF2R axis to acquire
the M1 phenotype ([190]Ao et al., 2017). Mouse experiments confirmed
that CSF2-IRF4 signaling can upregulate MHC class II expression
([191]Van Der Borght et al., 2018). CSF2 enhances the
antigen-presenting capacity of macrophages by increasing the expression
of MHC-II ([192]Ushach and Zlotnik, 2016). CSF2 upregulates IRF4
expression by enhancing JMJD3 demethylase activity ([193]Yashiro
et al., 2018), and activated IRF4 can upregulate CCL17 expression in
monocytes/macrophages, mediating the production of inflammation
([194]Achuthan et al., 2016). CSF2 produces airway inflammation by
activating airway eosinophils after segmental allergen challenge
([195]Liu et al., 2002). CSF2 induces infiltration and activation of
eosinophils in the Th2 network ([196]Nakagome and Nagata, 2011),
producing and releasing specific granule proteins in vitro ([197]Nagata
et al., 1998), ultimately leading to airway pathology. The use of
anti-CSF2 receptor monoclonal antibodies to target patients with severe
pulmonary disease in COVID-19 can significantly improve clinical
symptoms ([198]De Luca et al., 2020; [199]Temesgen et al., 2020).
Therefore, we believe that I-SPD and Vestitol inhibit the
overexpression of CSF2 and prevent the generation of inflammatory storm
and infiltration of immune cells, preventing mild and common COVID-19
patients from turning into severe ones.
The mechanisms analysis of Xuanfei Baidu in the treatment of COVID-19
The summary of the mechanisms analysis of Xuanfei Baidu granule (XFBD)
in the treatment of COVID-19 is shown in [200]Graphical Abstract .
Conclusion
This study revealed the pharmacological mechanism of Xuanfei Baidu
Granule (XFBD) in the treatment of COVID-19 through molecular docking
and molecular dynamics simulation. The results showed that the
important active chemical components I-SPD and Pachypodol in Xuanfei
Baidu Granules (XFBD) can reduce the inflammatory response and
apoptosis by inhibiting the activation of NLRP3, and reduce the
production of inflammatory response. I-SPD and Vestitol can inhibit the
activation and chemotaxis of inflammatory cells through CSF2,
preventing the generation of inflammatory storm.
Therefore, Vestitol, Pachypodol and I-SPD in Xuanfei Baidu Granules
(XFBD) can effectively alleviate the clinical symptoms of COVID-19
patients through NLRP3 and CSF2.
Current molecular docking and molecular dynamics analyses are difficult
to quantify. Since the research based on molecular dynamics is still in
the stage of simulation analysis, the body function is a continuous and
dynamic process. The process of disease occurrence, drug development
and efficacy are also dynamic. This study will verify the
pharmacological mechanism of Xuanfei Baidu Granules (XFBD) in the
treatment of COVID-19, as well as the target and related signaling
pathways of active ingredients through cell experiments in the future.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[201]Supplementary Material.
Author contributions
LX, JC, XZ, GX, ZY contributed to the conception of the study; JC, XY,
SC, MW, CW, HX, YC, DL contributed significantly to analysis and
manuscript preparation; JC, HX, YC, RZ, XH, TC, JT, QD performed the
data analyses and wrote the manuscript; XZ, GX, JC, ZY helped perform
the analysis with constructive discussions. All authors contributed to
the article and approved the submitted version.
Funding
This study was supported by “Sichuan College Students’ innovation and
entrepreneurship training program (S202113705049)”.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[202]https://www.frontiersin.org/articles/10.3389/fcimb.2022.965273/ful
l#supplementary-material
[203]Click here for additional data file.^ (458.6KB, xlsx)
[204]Click here for additional data file.^ (8.8KB, xlsx)
[205]Click here for additional data file.^ (72.1KB, xlsx)
References