Detection of probable phytochemical inhibitors targeting kallikrein related peptidase 7 (KLK7) in ovarian cancer through molecular dynamics and virtual screening approaches.

High-grade serous ovarian carcinoma (HGSOC) is the predominant and most lethal form of ovarian cancer, originating from the epithelium of the fallopian tubes. It has been shown that HGSOC subtype II epithelial ovarian cancer accounts for 50-70% of all ovarian malignancies. It is the most common cause of mortality for women with ovarian cancer. By using in-silico techniques to find out potential drugs against his disease, this study seeks to report the need for efficient treatments. The protein kallikrein-related peptidase 7 (KLK7), whose overexpression leads to HGSOC, was chosen as a drug target. Based on their Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) characteristics, the ligands were carefully picked from the IMPATT library that contained 17,967 phytochemicals. Auto Dock Vina was then used to dock the 88 finalized compounds against the protein. Density Functional Theory (DFT) and molecular dynamic simulation analyses were used to assess the compound that was the most stable with the protein. The protein-ligand combination was stable during the MD simulation. Post simulation analysis, such as RMSF (Root Mean Square Deviation), RMSD (Root Mean Square Deviation), Rg (Radius of Gyration), and HB (Hydrogen Bonding), revealed the stability of the proposed compound with the KLK7 protein.