Network of active compound-candidate targets
After filtering was operated for Lipinski rules, thirty-two in sixty-eight compounds were screened as active compounds in SCR. and listed in Supplementary Table 1. Chemical formats of smile and sdf were generated to predict the potential targets. Then 718 potential targets (some were duplicates) associated with compounds were screened from the SwissTargetPrediction, PharmMapper, and SEA server, respectively. Fig. 1 elucidated the relationship between compounds and candidate targets. Three main clustering huddles consisted of 32 active compounds indicated that different compounds were apt to interact different targets. Interestingly, the candidate targets of organic acids, such as vanillic acid, syringic acid, oleanolic acid and protocatechuic acid, and flavonoids both had apparently characteristic targets and common targets between each other. This kind of phenomenon was based on a lot of complicated compounds in traditional herbs such as SCR.
Enriched analysis of candidate targets
To further explore the crucial mechanism of compound-related sophisticated targets, 718 targets were uploaded David database to obtain KEGG pathways to clarify the potential functions of compounds. Due to the defect of database, only 715 targets were identified and associated with 91 effective KEGG pathways (Supplementary Table 2). For sake of better interpretating the principal function of pathways, Functional Annotation Clustering was carried out. As Fig. 2 shown, seven annotation clustering huddles comprised of 91 KEGG pathways respectively were related to metabolism, nervous system, nervous and humoral regulation, hormone production and release, infection, immunoregulation, acid and lipid metabolism and comprehensive regulation. The result manifested effect of the multiple and potential regulations of SCR. All above were corresponded to the existing researches [42-45].
Compound-target-pathway network and analysis of GO and KEGG
In the above results, we especially concerned the immunoregulation and the inflammatory pathway in comprehensive regulation, therefore, we further researched the essential mechanisms on PID. Firstly, eighty-six PID-related targets were retrieved from databases. Then, a total of 17 Intersection targets were obtained and uploaded to STRING platform to enrich functions. Next, the top five results of GO enrichment (A) and the top 10 KEGG pathways (B) were selected as shown in Fig. 3. The biological processes, including response to chemical, cellular response to chemical stimulus, response to toxic substance, female pregnancy, etc., indicated that the predicted proteins commonly respond to exogenous substances and participate in female pregnancy. Furthermore, the molecular function and cellular component indicated that compounds may affect the cytokine receptor, antioxidant activity, and peroxidase activity by binding to the proteins. To clarify the relationship between herb and disease, a network of the compounds, targets, and pathways were visualized by Cytoscape. We found that a total of 28 compounds could act on 17 key targets and associate with 20 effective pathways (Fig. 4), a bigger node implying the degree of multiple regulations.
PPI network analysis
17 intersection targets were analyzed using the PPI network in STRING platform (Fig. 6). Relevant parameters of network were as follows, 1. number of nodes and edges: 17 and 76, 2. average node degree and expected number of edges: 8.94 and 19, 3. PPI enrichment p-value: <1.0e-16. Otherwise, Red nodes (MMP9, TNF, IL6, PTGS2, LCN2) and purple nodes (IL2, STAT3, IL6) were used to highlight the IL-17 signaling pathway and Th17 cell differentiation, respectively. MPO mentioned in “3.3” and all highlighted targets except inflammatory factor (IL2 and IL6) were selected as core targets to illuminate the SCR interfering mechanism against PID utilizing MMGBSA-docking.
Molecular docking with binding free energy based MMGBSA
The heat map was employed to stick out the features of 32 active compounds as shown in Fig. 7 (Original data is presented in Supplementary Table 1). According to the distance metric of Average Dot Product in Mev, compounds that had a high activity clustered together excellently, with a high affinity to PTGS2, LCN2, TNF, MPO, STAT3, and MMP-9. Rutin (10; -10.758), moracin M (44; -9.326) and oxyresveratrol (30; -8.098) were found to occupy the top score, which exceeded or neared the original ligands in verified docking (Supplementary Table 3). Furthermore, compounds demonstrated a binding affinity to one or several targets. According to the cutoff of 0.5 (Fig.7), 19 capital protein-ligand molecular interactions were analyzed and are illustrated in Table 1.
Further analysis demonstrated that rutin possessed a strong binding ability to LCN2, TNF (Fig.8 D, D1: rutin and LCN2; Fig.8 E, E1: rutin and TNF), especially with PTGS2. As shown in Fig.8 A, A1, based on the geometric matching, it was observed that rutin and the active cavity of PTGS2 protein matched well, completely wrapping in the loop region formed by amino acids. In terms of interaction, the electrostatic potential ((purplish-blue: positive region, such as -OH)) of rutin adapted to surface of the protein (A), and the two glycosides on rutin formed three hydrogen bonds (B) with GLN 434, HIE 214, and TYR 385, indicating that the compound was stable in the pocket of PTGS2. As shown in Fig.8 B, B1, oxyresveratrol (-38.84 kcal/mol) bound to MPO by forming six H-bonds with ARG C 504, NAG D 641, TRP A 32, ARG A 31, ARG B 31, TRP B 32. The aglycon with a big volume only demonstrated two -OH, with a lower electrostatic potential compared with glycosyl, indicating that compound was situated at a stable region. Alternatively, Fig.8 C, C1 demonstrated that moracin M (-37.01 kcal/mol) separately formed one interaction of pi-cation and three H-bonds with LYS A 45, GLU A 62, GLN A 96, GLU B 360, affecting the spatial configuration of STAT3. Finally, Fig. F, F1 indicated that oxyresveratrol formed two Pi-pi stacking with TYR 179 and PHE 192, three H-bonds with ALA 191, HIS 210, and GLY 233, and the distribution of electrostatic potential focus on the benzene ring on both sides, thus approaching the surface of MMP-9, which illustrated the formation process of a stable configuration. Based on the above results, the H-bond and electrostatic potential were the main factors regulating the function of targets.