Kadsura coccinea, (Lem.) A. C. Sm. (Lem.) A. C. Sm.

Yang, Yupei, Liu, Yongbei, Yu, Huanghe, Xie, Qingling, Wang, Bin, Jiang, Sai, Su, Wei, Mao, Yu, Li, Bin, Peng, Caiyun, Jian, Yuqing & Wang, Wei, 2022, Sesquiterpenes from Kadsura coccinea attenuate rheumatoid arthritis-related inflammation by inhibiting the NF-κB and JAK 2 / STAT 3 signal pathways, Phytochemistry (113018) 194, pp. 1-9 : 8

5.9. Target prediction analysis of the active compounds from K.coccinea

Active compounds 2, 6, 11, 13–14, 16, and 18–20 were selected for the target prediction analysis based on the in vitro anti-RA and anti-inflammatory activities screening results. The Swiss Target Prediction (https://www.swisstargetprediction.ch/) database (Gfeller et al., 2014) was used to retrieve the predicted gene targets for the selected nine active compounds. The sdf format of the optimized structure of each compound was uploaded to the Swiss Target Prediction online platform for screening, with the screening condition limited to “ Homo sapiens ”. High probability targets were selected after the elimination of duplicate contents.

5.10. Prediction of RA targets

The potential therapeutic targets for RA were searched in online disease databases, GeneCards (http://www.genecards.org/) (Stelzer et al., 2016) and MalaCards (https://www.malacards.org/) (Rappaport et al., 2017). The search term was “rheumatoid arthritis”, followed by further filtering using the conditions “gene” and “ Homo sapiens ”. The prediction targets from the two databases were combined as the final RA targets.

5.11. Construction of protein-protein interactions (PPI) network

The common RA active compounds prediction targets were uploaded into STRING (https://string-db.org/cgi/input.pl) to build the PPI network interaction (Szklarczyk et al., 2016). The PPI network was then constructed and visualized using the Cytoscape V3.6.0 software.

5.12. Pathway and functional enrichment analysis

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the active compounds were done to investigate the potential signaling pathways and biological processes regulated by these compounds. The enrichment results were then visualized using Bioinformatics (http://www.bioinformatics.com. cn/). KEGG pathway analysis is a prominent analysis tool in network pharmacology that aids in the understanding of drug action mechanisms in disease. On the other hand, GO enrichment analysis identifies the biological processes (BP), molecular functions (MF), and cellular components (CC) of the target compounds. The top 20 GO enrichments and KEGG pathways with higher counts based on a significance threshold of P <0.05 were then further analyzed.