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Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

BiochemistryNetwork pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus


Target screening of ellagic acid against RV

935 ellagic acid targets were collected through 11 databases such as HIT, TCMSP, and BATMAN, and 295 anti-RV targets were collected through the Gene Cards database, and the target species information was human. The targets collected above were intersected, and the obtained protein was the potential target for ellagic acid to exert RV resistance. The target for ellagic acid to exert RV resistance was visualized by the Venn diagram, and the results are shown in Fig. 1. There were a total of 35 intersection proteins, and the results are shown in Table 1.

Figure 1
figure 1

Venn diagram of potential targets of ellagic acid anti-RV.

Table 1 The target of ellagic acid anti-RV.

PPI network

35 intersection targets were imported into the STRING database to obtain the protein–protein interaction network diagram (Fig. 2A). The tsv files of intersection targets were imported into Cytoscape for visualization, analysis, and draw the Hithubs network (Fig. 2B). The values of degree centralities, betweenness centralities, and closeness centralities among targets were obtained by CytoNCA plug-in. 9 core targets higher than the three average values (18.41, 0.67, 32.82) were screened based on the average values of betweenness centralities, closeness centralities and degree centralities. Results showed that the core targets of ellagic acid anti-RV were IL-1β, ALB, NF-Κb, TLR4, TNF-α, TP53, SAMD3, EGF, and IL-4. The screening process and results were shown in Supplementary File S1.

Figure 2
figure 2

Ellagic acid potential RV targets the PPI network (A) and Hithubs network (B).

Enrichment analysis

A total of 35 ellagic acid anti-rotavirus targets were input into the DAVID database for GO and KEGG enrichment analysis, the results showed that 16 GO terms (including 9 BP, 3 CC, and 4 MF) and 122 KEGG pathways were enriched, the results are shown in Supplementary File S2. The enriched GO terms and KEGG pathways was sorted in descending order according to P value, a smaller P value represents a higher reliability of enrichment, the top 20 terms and pathways in P values were visualized in Fig. 3. In this study, the results showed that the targets of EA anti-RV were mainly enriched in biological processes such as host-virus interaction, inflammatory response, stress, and immunity; cellular components such as secretion, cytoplasm, and extracellular matrix; molecular functions such as cytokines, growth factors, proteinases, and metalloproteinases (Fig. 3A). Host-virus interaction (P value = 1.22E−4), Secreted (P value = 3.12E−7), and Cytokine (P value = 1.15E−5) are the most relevant enriched terms of biological processes, cellular components, and molecular functions, respectively. The targets of EA anti-RV were significantly enriched in KEGG pathways such as inflammatory bowel disease, IL-17 signaling pathway, and MAPK signaling pathway (Fig. 3B). The results showed that the anti-RV effect of ellagic acid may be related to the regulation of cellular inflammatory response.

Figure 3
figure 3

Construction of ellagic acid–target–pathway network. (A) The target that was enriched by the Gene Ontology of ellagic acid anti-RV. (B) The target that was enriched by the KEGG pathway of ellagic acid anti-RV.

Construction of ellagic acid-target-pathway network

The 9 core targets of EA anti-RV were integrated with the functions and pathways enriched in the top 20 P values, and the ellagic acid–target–pathway–function network was constructed by Cyotoscape (Fig. 4). Nodes in the network diagram represent EA, target protein, enrichment function, and pathway respectively. The side represents the interaction between the EA and the corresponding functions and pathways of target proteins, and there were 87 pairs of pathway–protein–function relationships, including 8 pairs of protein–function relationships and 79 pairs of pathway–protein relationships. The results were shown in Supplementary File S3. The network diagram showed that EA can play antiviral, anti-inflammatory, and immunomodulatory roles by regulating pathway-related target proteins.

Figure 4
figure 4

Ellagic acid–target–pathway network model.

Molecular docking analysis

EA was paired to the surface of TP53, TLR4, TNF-α, ALB, IL-1β, NF-κB1, SAMD3, EGF, and IL-4 active pockets, respectively. EA formed hydrophobic forces with residues such as CYS229, LEU145, TRP146, VAL147 and PRO151 of TP53, and formed one hydrogen bond with residue CYS220 and two hydrogen bonds with residue ASP228 (Fig. 5A). EA formed hydrophobic forces with residues ALA139, LEU138, ILE114, LEU117, ALA118 of TLR4, and formed two hydrogen bonds with residues ASN137 (Fig. 5B). EA formed hydrophobic forces with residues such as PRO20, ALA18, VAL17, TYR151, and VAL150 of TNF-α, and formed hydrogen bonds with residues such as ALA33, VAL150, and ALA18 (Fig. 5C). EA formed hydrophobic forces with residues LEU24, LEU155, ALA158, LEU139, LEU135 of ALB, and formed a hydrogen bond with residues GLU132 and two hydrogen bonds with residues LYS20 (Fig. 5D). EA formed a hydrogen bond with GLN81 and GLU25 residues of IL1β, and a π-Cation (π-cation) bond with residues LYS74 (Fig. 5E). EA formed hydrophobic forces with residues MET208 and LEU210 of NFKB1, formed one hydrogen bond with residues ASN247 and LEU210, two hydrogen bonds with residues MET208, and two π-Cation (π-cation) bonds with residues LYS147 (Fig. 5F). EA formed hydrophobic forces with residues ILE32, PHE33 and ALA35 of IL4, formed a hydrogen bond with residues ILE32, THR44 and LYS123, formed two hydrogen bonds with residues ARG115, and forms a π-Cation (π-cation) bond with residues LYS37. Form a π–π bond with residue PHE33 (Fig. 5G). EA formed hydrophobic forces with residues such as VAL388, ALA381, and ALA328 of SMAD3, forming two hydrogen bonds and a π-Cation bond with residue ARG367, and a hydrogen bond with residue GLU382 (Fig. 5H). EA formed hydrophobic forces with residues such as ALA691, VAL690, ALA689, ILE603, and ALA604 of EGF, forming a hydrogen bond with residues ILE603 and two hydrogen bonds with residues VAL692 (Fig. 5I).

Figure 5
figure 5

Molecular docking complexes of ellagic acid with TP53 (A), TLR4 (B), TNF (C), IL-1β (D), NSP5 (E), NF-κB (F), SAMD3C (G), EGFC (H) and IL-4C (I) (yellow represents hydrogen bonds, and green represents π-Cation bonds).

As shown in the results of XP (Table 2) and MM-GBSA energy (Table 3, Fig. 6), the score of EA with TP53 and TLR4 were − 9.115 and − 6.285, respectively, and the results of MM-GBSA energy were − 50.22 kcal/mol and − 39.58 kcal/mol, respectively. The low binding free energy and docking scores indicate that EA has strong binding stability with TP53 and TLR4. The binding free energy of EA with TNF-α, IL-4, and SAMD3 was lower than − 30 kcal/mol, but the docking score was higher than − 6, indicating that the binding stability of EA with TNF-α, IL-4, and SAMD3 was good. In addition, EA had higher docking scores or binding free energies with IL1-β, NFKB1, ALB, and EGF, indicating that the binding of EA to these four proteins was unstable.

Table 2 XP docking score of the core target with ellagic acid.
Table 3 Statistical analysis of MM/GBSA results.
Figure 6
figure 6

Statistical diagram of the MM/GBSA calculation for the complexes.

MD simulations

EA and TP53, TLR4, and TNF-α proteins were simulated by 100 ns MD, and their molecular dynamics trajectories were analyzed. The results showed that EA and TP53, TLR4, and TNF proteins were stable after 60, 20, and 20 ns, respectively, and the system was in a state of equilibrium (Fig. 7A–C). When EA bound to TP53, the protein showed high structural flexibility in the 125–130 AA residue region (Fig. 7D). After binding to TLR4 protein, EA showed high structural flexibility in the 20–30 AA, 60–80 AA, and 90–100 AA residue regions (Fig. 7E). When EA bound to TNF-α protein, the protein exhibited high structural flexibility in the 10–20 AA, 75–85 AA, and 90–105 AA residue regions (Fig. 7F).

Figure 7
figure 7

RMSD and RMSF plots throughout the 100 ns MD simulation. (AC) The molecular dynamics simulation -RMSD value (the blue line represents the proteins, and the red line represents ellagic acid). (DF) The molecular dynamics simulation -RMSF value (α-helical and β-strand regions are highlighted in red and blue backgrounds, respectively. Protein residues that interact with the ligand are marked with green-colored vertical bars).

Protein–ligand interactions can be monitored throughout the simulation. The main amino acids that play an important role in the binding of EA and TP53 protein are LEU145, VAL147, and CYS220, and their interactions are mainly water bridge, hydrogen bond, and hydrophobic action (Fig. 8A). Amino acids that play an important role in the binding of EA to TLR4 protein are ILE114, LEU117, and THR136, and their interactions are mainly water bridge, hydrogen bond, and hydrophobic action (Fig. 8B). The amino acids that play an important role in the binding of EA to TNF-α protein are mainly ALA18, ARG32, ALA33, and VAL150, and their interactions are mainly water bridge, hydrogen bond, and hydrophobic action (Fig. 8C).

Figure 8
figure 8

The interaction and binding mode of ellagic acid with TP53, TLR4, and TNF protein in molecular dynamics simulation. (AC) The contribution of amino acids at TP53, TLR4, and TNF binding sites to EA-protein binding, respectively. (DF) How interactions between EA and specific amino acids of the TP53, TLR4, and TNF proteins have changed over time, respectively (shown in orange with varying depths, according to the proportions on the right side of the figure). (GI) A detailed diagram of EA’s interactions with TP53, TLR4, and TNF protein residues.

During the entire trajectory, the interaction between EA and specific amino acids of TP53, TLR4, and TNF-α proteins, respectively changed over time as shown in Fig. 8D–F. The results showed that TP53 amino acid residues LEU145, VAL147, PRO151, THR155, CYS220, PRO223, and ASP228 had multiple contacts with EA (Fig. 8D). TLR4 amino acid residues ILE114, GLN115, LEU117, THR136, ASN137, LEU138, ALA139, ASN143, and PHE144 had multiple contacts with EA (Fig. 8E). TNF-α amino acid residues VAL17, ALA18, ARG32, ALA33, SAN34, PHE144, ALA145, GLU146, GLY148, GLN149, and VAL150 had multiple contacts with EA (Fig. 8F). The diagram is shown in Fig. 8G–I. The conformational evolution of each RB in ellagic acid within the entire simulated locus (0–100 ns) is shown in Supplementary Figure S1.

Key binding sites saturated mutation affinity results

After saturation mutation of key binding sites, the Δaffinity results of TP53 were 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The corresponding values were 349.066 kcal/mol, 58 kcal/mol and 44.884 kcal/mol, respectively (Fig. 9A). The highest Δaffinity results of TLR4 were 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP), corresponding to 23.015 kcal/mol, 20.665 kcal/mol, and 14.626 kcal/mol (Fig. 9B). The highest Δaffinity results for TNF-α were 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY), corresponding to 21.868 kcal/mol, 5.812 kcal/mol, and 4.338 kcal/mol (Fig. 9C).

Figure 9
figure 9

Trend diagram of saturation mutagenesis results at key binding sites.

Among the top10 Δaffinity results of TP53, TLR4, and TNF-α (Table 4), TP53 occurred more frequently at 220 sites, TLR4 occurred more frequently at 136 and 144 sites, and TNF-α occurred more frequently at 32 and 144 sites, respectively (Fig. 10). The MM-GBSA binding free energy results of affinity mutations at the top 10 Δaffinity results of TP53, TLR4, and TNF-α are shown in Table 5. These mutations inhibit binding by weakening the ligand’s van der Waals force on the protein. It is difficult to find significant changes in the two-dimensional structure of the top3 sites with Δaffinity of TP53, TLR4, and TNF-α, which indicates that the mutation mainly affects non-covalent bonds such as hydrogen bonds and salt bridges (Supplementary Fig. S2).

Table 4 TOP10 saturation mutagenesis affinity of key binding sites.
Figure 10
figure 10

TOP10 results of saturation mutagenesis affinity of key binding sites.

Table 5 The change of MM-GBSA binding free energy in saturation mutagenesis affinity TOP3 at key binding sites.

The affinity between ellagic acid and TLR4 protein

SPR biosensor was used to detect the affinity between ellagic acid and resatorvid with TLR4 protein. The results showed that the dissociation constant Kd value of ellagic acid and resatorvid was low, and the affinity parameters are shown in Table 6. Both ellagic acid (Fig. 11A) and resatorvid (Fig. 11B) had a good affinity for the fitting curves produced by reacting with TLR4 protein.

Table 6 Affinity of TLR4 protein with ellagic acid and resatorvid.
Figure 11
figure 11

Surface plasmon resonance was used to test the affinity of TLR4 protein with different concentrations of ellagic acid (A) and resatorvid (B).

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