Network pharmacological evaluation for identifying novel drug-like molecules from ginger (Zingiber officinale Rosc.) against multiple disease targets, a computational biotechnology approach

Ginger (Zingiber officinale Rosc.) is a popular spice used globally in ethnic cuisines and witnessed its extensive use in traditional medicine. In this study, we identified 12 phytochemicals from the ginger rhizome extract (hexane) through GC/MS analysis. After evaluating drug-likeliness, these phytochemicals were docked with 16 target proteins in silico, and docking scores were compared with their respective control drugs. Furthermore, multivariate statistical analysis (principal component analysis-PCA) was performed, and three different chemical clusters were identified. Pharmacophore analysis further identified common functional descriptors in the compounds under study. Finally, we developed a unique three-level network taking phytochemicals, target proteins and associated diseases based on the optimum docking scores. Overall, Oleic acid, Palmitic acid and Shogaol showed the highest coverage to the target proteins (12, 10 and 9 targets, respectively) and Oleic Acid scored the highest (5956) in PatchDock when docked against Peroxisome proliferator-activated receptor gamma (PDB id 1KNU, UniProt id P37231). This work provided significant insight into developing the protocol for rapid identification of potential drug likeliness of the identified phytochemicals. [GRAPHICS] .

Automated summary

In this study, 12 phytochemicals from ginger rhizome extract were identified and evaluated for drug likeliness through in silico docking with target proteins. Multivariate statistical analysis and pharmacophore analysis helped identify common functional descriptors and chemical clusters in the compounds. A unique three-level network was developed to integrate phytochemicals, target proteins, and associated diseases based on optimal docking scores. Oleic acid, Palmitic acid, and Shogaol showed the highest coverage to target proteins, with Oleic acid scoring the highest against Peroxisome proliferator-activated receptor gamma in PatchDock. This research provides important insights into developing a protocol for rapid identification of potential drug likeliness of phytochemicals.