DataSheet1_Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model.docx

Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification.Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10 ns using RMSD, RMSF, Rg, and hydrogen bonding metrics.Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol.Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.</p

Agreement comparison with respect to data with good and poor sleep efficiency.

Agreement comparison with respect to data with good and poor sleep efficiency.</p

Hypnograms of the subject no. 4.

The hypnograms scored by fully manual scoring (scorer 2) (A), fully automatic staging (B) and the HCSS system (C).</p

Evaluation of the HCSS system.

(A) The agreement in overall, high-reliability and low-reliability epochs, along with the kappa coefficient between the manual scorings and the HCSS system collaborated scorings. (B) The average of the scoring time for one subject spent in manual and HCSS groups. Percentage of reduced manual scoring time with the assistance of the HCSS system; OA: overall, HR: high-reliability, LR: low-reliability.</p

The hypnogram and SCF values of subject no. 5.

The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCF (C). The red lines indicate disagreement between the expert and the automatic scoring system.</p

The hypnogram and SCD value of PSG from subject no. 3.

The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCD (C). The red lines indicate disagreement between the expert and the automatic scoring system.</p

Reliability analysis examples.

Disagreements can be detected by using the SWR (A), SCD (B) and SCF (C) features.</p

Histogram of 12 sleep features in Wake, N1, N2, N3, and REM stages of 30 PSG data.

The X-axis represents the normalized feature values and the Y-axis represents the number of epochs. Features are power: (A) 0–30 Hz EEG, (B) 0–30 Hz EMG; power ratio: (C) 0–4 Hz / 0–30 Hz EEG, (D) 8–13 Hz / 0–30 Hz EEG, (E) 22–30 Hz / 0–30 Hz EEG; power (F) 0–4 Hz EOG; spectral frequency: (G) 0–30 Hz mean frequency EEG, (H) 0–30 Hz mean frequency EMG; duration ratio: (I) alpha ratio EEG, (J) Spindle ratio EEG, (K) SWS ratio EEG; amplitude: (L) mean amplitude EMG.</p

Percentage of low-reliability epochs across sleep stages where sleep experts have to examine.

Percentage of low-reliability epochs across sleep stages where sleep experts have to examine.</p

The architecture of the voting process.

According to the value of the SWR, SCD, and SCF features, the voting process determines a scored epoch as a high reliability or low reliability.</p