Molecular modelling, docking and network analysis of phytochemicals from Haritaki churna: role of protein cross-talks for their action.

Phytochemicals are bioactive agents present in medicinal plants with therapeutic values. Phytochemicals isolated from plants target multiple cellular processes. In the current work, we have used fractionation techniques to identify 13 bioactive polyphenols in ayurvedic medicine Haritaki Churna. Employing the advanced spectroscopic and fractionation, structure of bioactive polyphenols was determined. Blasting the phytochemical structure allow us to identify a total of 469 protein targets from Drug bank and Binding DB. Phytochemicals with their protein targets from Drug bank was used to create a phytochemical-protein network comprising of 394 nodes and 1023 edges. It highlights the extensive cross-talk between protein target corresponding to different phytochemicals. Analysis of protein targets from Binding data bank gives a network comprised of 143 nodes and 275 edges. Taking the data together from Drug bank and binding data, seven most prominent drug targets (HSP90AA1, c-Src kinase, EGFR, Akt1, EGFR, AR, and ESR-α) were found to be target of the phytochemicals. Molecular modelling and docking experiment indicate that phytochemicals are fitting nicely into active site of the target proteins. The binding energy of the phytochemicals were better than the inhibitors of these protein targets. The strength and stability of the protein ligand complexes were further confirmed using molecular dynamic simulation studies. Further, the ADMET profiles of phytochemicals extracted from HCAE suggests that they can be potential drug targets. The phytochemical cross-talk was further proven by choosing c-Src as a model. HCAE down regulated c-Src and its downstream protein targets such as Akt1, cyclin D1 and vimentin. Hence, network analysis followed by molecular docking, molecular dynamics simulation and in-vitro studies clearly highlight the role of protein network and subsequent selection of drug candidate based on network pharmacology.Communicated by Ramaswamy H. Sarma.