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Piperine is the main active component of Piper longum L., which is also the main component of anti-sciatica Mongolian medicine Naru Sanwei pill. It has many pharmacological activities such as anti-inflammatory and immune regulation. This paper aims to preliminarily explore the potential mechanism of piperine in the treatment of sciatica through network pharmacology and molecular docking. TCMSP, ETCM database and literature mining were used to collect the active compounds of Piper longum L. Swiss TargetPrediction and SuperPred server were used to find the targets of compounds. At the same time, CTD database was used to collect the targets of sciatica. Then the above targets were compared and analyzed to select the targets of anti-sciatica in Piper longum L. The Go (gene ontology) annotation and KEGG pathway of the targets were enriched and analyzed by Metascape database platform. The molecular docking between the effective components and the targets was verified by Autodock. After that, the sciatica model of rats was established and treated with piperine. The expression level of inflammatory factors and proteins in the serum and tissues of rat sciatic nerve were detected by ELISA and Western blot. HE staining and immunohistochemistry were carried out on the sciatica tissues of rats. The results showed that Piper longum L. can regulate the development of sciatica and affect the expressions of PPARG and NF-kB1 through its active ingredient piperine, and there is endogenous interaction between PPARG and NF-kB1.Jiu-wang Yu and Hong-wei Yuan have contributed equally to this work.

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The purpose of this paper is to explore the possible mechanisms of anti-inflammatory and scar repair by Mongolian horse oil. We used TCM database and literature mining to collect active compounds of horse oil and used Swiss TargetPrediction and SuperPred server to find targets of compounds. Anti-inflammatory drug targets were collected through the CTD database. Go annotation of targets and KEGG pathway were enriched and analyzed through Metascape database platform. Molecular docking between active ingredients and targets was verified by AutoDock software. Metascape analysis revealed that the key candidate targets were significantly enriched in a number of pathways associated with inflammatory pathology. The results of molecular docking showed that oleic acid, a major component of animals oil, could influence the regulatory functions of TNF, NGF, IL6, IL1B, Jun, and CDK1. This suggests that animals oil can regulate the development of inflammation through its active ingredient, oleic acid, and can influence the expression of multiple signaling pathways, with theoretical endogenous interactions with TNF, NGF, IL6, IL1B, JUN, and CDK1 proteins.

<p>In this study, a research strategy combining network pharmacological analysis, protein docking and molecular docking virtual computation was adopted. It was found that phillyrin and chlorogenic acid could block the combination of 2019-nCoV S-protein and ACE2 at the molecular level. Both can be used as potential inhibitors of 2019-nCoV for further research and development. </p>

Objective: This article intends to use molecular docking technology

BACKGROUND: Omicron VOC (BF.7) is a variant of SARS-CoV-2 that is currently spreading globally as a dominant strain. BF.7 is more infectious than existing Omicron variants, and to date there are no specific therapeutic agents for this variant. METHODS: The active compounds were collected by TCMSP, ETCM database and literature mining method, and the targets of the compounds were searched by Swiss Target Prediction and SUPERPRED database, while the targets of Omiron virus were collected by DisGeNET and GEO database, and then the intersecting targets were compared and analyzed. In this study, Swiss-Model was applied to construct the Spike RBD structure of Omicron variant BF.7 by replacing mutant amino acids into the Native Spike (S) structure, and the structural changes of Native S were compared. The four active compounds screened were docked with Omicron S protein and Omicron S-hACE2 complexes. To evaluate the structural stability of the complexes in a physiological environment, we also performed molecular dynamics simulations of the docked complexes and compared them to the control drug, chloroquine. The affinity of ligands and protein complexes was determined by free energy analysis using the MM-PBSA algorithm, and the structural changes of S proteins in combination with ligands were evaluated. RESULTS: A total of 12 mongolic medicines were screened and 310 active ingredient predictions were made, with a total of 55 genes overlapping with Omicron variants and 14 targets with the largest differences being conserved. Once these 14 targets were mapped to the active ingredients of 12 mongolic herbs, four more precise active ingredients were filtered out. Of these four phytochemicals, Berberine was the most potent inhibitor of Omicron S protein. In addition, molecular docking simulations revealed that Berberine can bind stably to Omicron S protein and the Omicron S-hACE2 complex. Using molecular dynamics simulations, Berberine was shown to be able to form a stable complex with Omicron S in a physiological environment with better results than the control drug chloroquine. Free energy analysis by the MM-PBSA algorithm and evaluation of S protein structural changes following ligand binding also demonstrated a higher affinity of Berberine for Omicron S compared to the other small molecule compounds. CONCLUSION: Berberine was found to have the most substantial inhibitory potential against Omicron VOC (BF.7) S protein and could be further investigated and developed as a potential inhibitor of Omicron.